Merge branch 'main' into dev

1058 files changed, 122601 insertions, 102058 deletions

diff --git a/.Rbuildignore b/.Rbuildignore
index e7cbe786..52d228ed 100644
--- a/.Rbuildignore
+++ b/.Rbuildignore
@@ -30,8 +30,11 @@
 ^vignettes/.*.toc$
 ^vignettes/figure
 ^vignettes/prebuilt/.*_dlls
+^vignettes/prebuilt/.*.html$
 ^vignettes/FOCUS_Z.tex$
 ^vignettes/mkin.tex$
 ^vignettes/web_only/.*$
 ^.*\.Rproj$
 ^\.Rproj\.user$
+^codecov\.yml$
+^\.github$
diff --git a/.github/.gitignore b/.github/.gitignore
new file mode 100644
index 00000000..2d19fc76
--- /dev/null
+++ b/.github/.gitignore
@@ -0,0 +1 @@
+*.html
diff --git a/.gitignore b/.gitignore
index 4a79508d..ce03692c 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,5 +1,4 @@
 docs/articles/*_cache/
-coverage/
 install.log
 inst/rmarkdown/templates/hierarchical_kinetics/skeleton/skeleton_cache
 inst/rmarkdown/templates/hierarchical_kinetics/skeleton/skeleton_files
diff --git a/.travis.yml b/.travis.yml
index 3aec6949..006ba521 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -24,6 +24,3 @@ script:
 
 after_failure:
   - ./run.sh dump_logs
-
-after_success:
-  - travis_wait 30 ./run.sh coverage
diff --git a/DESCRIPTION b/DESCRIPTION
index f623e963..dcaf8f48 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,8 +1,8 @@
 Package: mkin
 Type: Package
 Title: Kinetic Evaluation of Chemical Degradation Data
-Version: 1.2.7
-Date: 2023-10-13
+Version: 1.2.9
+Date: 2025-02-13
 Authors@R: c(
   person("Johannes", "Ranke", role = c("aut", "cre", "cph"),
     email = "johannes.ranke@jrwb.de",
@@ -15,14 +15,13 @@ Description: Calculation routines based on the FOCUS Kinetics Report (2006,
   models, model solution based on eigenvalues if possible or using numerical
   solvers.  If a C compiler (on windows: 'Rtools') is installed, differential
   equation models are solved using automatically generated C functions.
-  Heteroscedasticity can be taken into account using variance by variable or
-  two-component error models as described by Ranke and Meinecke (2018)
-  <doi:10.3390/environments6120124>.  Hierarchical degradation models can
-  be fitted using nonlinear mixed-effects model packages as a back end as
-  described by Ranke et al. (2021) <doi:10.3390/environments8080071>.  Please
+  Non-constant errors can be taken into account using variance by variable or
+  two-component error models <doi:10.3390/environments6120124>.  Hierarchical
+  degradation models can be fitted using nonlinear mixed-effects model packages
+  as a back end <doi:10.3390/environments8080071>.  Please
   note that no warranty is implied for correctness of results or fitness for a
   particular purpose.
-Depends: R (>= 2.15.1),
+Depends: R (>=  4.1.0)
 Imports: stats, graphics, methods, parallel, deSolve (>= 1.35), R6, inline (>= 0.3.19),
   numDeriv, lmtest, pkgbuild, nlme (>= 3.1-151), saemix (>= 3.2), rlang, vctrs
 Suggests: knitr, rbenchmark, tikzDevice, testthat, rmarkdown, covr, vdiffr,
@@ -36,4 +35,4 @@ VignetteBuilder: knitr
 BugReports: https://github.com/jranke/mkin/issues/
 URL: https://pkgdown.jrwb.de/mkin/
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.2.3
+RoxygenNote: 7.3.2.9000
diff --git a/GNUmakefile b/GNUmakefile
index 76163ab6..23f994a1 100644
--- a/GNUmakefile
+++ b/GNUmakefile
@@ -125,8 +125,7 @@ pd_all: roxygen
 	git add -A
 
 coverage:
-	mkdir -p docs/dev/coverage
-	"$(RBIN)/Rscript" -e "covr::report(file = 'coverage/coverage.html')"
+	"$(RBIN)/Rscript" -e "covr::report(file = 'docs/coverage/coverage.html')"
 
 r-forge:
 	git archive main > $(HOME)/git/mkin/mkin.tar;\
diff --git a/NAMESPACE b/NAMESPACE
index bcea2b1b..5b9a1c85 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -158,6 +158,7 @@ importFrom(lmtest,lrtest)
 importFrom(methods,is)
 importFrom(methods,signature)
 importFrom(nlme,intervals)
+importFrom(nlme,nlme)
 importFrom(parallel,detectCores)
 importFrom(parallel,mclapply)
 importFrom(parallel,parLapply)
diff --git a/NEWS.md b/NEWS.md
index 9ddd4128..f8ba9a87 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,8 +1,22 @@
-# mkin 1.2.7
+# mkin 1.2.9
 
-- 
+- 'R/plot.mixed.R': Support more than 25 datasets
 
-# mkin 1.2.6
+- 'R/mkinfit.R': Support passing the observed data as a 'tibble'
+
+- 'R/parplot.R': Support multistart objects with covariate models and filter negative values of scaled parameters (with a warning) for plotting.
+
+- 'R/create_deg_func.R: Make sure that no reversible reactions are specified in the case of two observed variables, as this is not supported
+
+# mkin 1.2.8 (unreleased)
+
+- 'R/{mhmkin,status}.R': Deal with 'saem' fits that fail when updating an 'mhmkin' object
+
+# mkin 1.2.7 (unreleased)
+
+- 'R/illparms.R': Fix a bug that prevented an ill-defined random effect to be found if there was only one random effect in the model. Also add a test for this.
+
+# mkin 1.2.6 (2023-10-14)
 
 - 'inst/rmarkdown/templates/hierarchical_kinetics/skeleton/skeleton.Rmd': Fix an erroneous call to the 'endpoints()' function
 
diff --git a/R/create_deg_func.R b/R/create_deg_func.R
index 5794c65c..d3e11f78 100644
--- a/R/create_deg_func.R
+++ b/R/create_deg_func.R
@@ -61,16 +61,21 @@ create_deg_func <- function(spec, use_of_ff = c("min", "max")) {
     ),
   ")")
 
-  if (length(obs_vars) >= 2) {
-    supported <- FALSE # except for the following cases
+  if (length(obs_vars) >= 2) supported <- FALSE
+  # Except for the following cases:
+
+  if (length(obs_vars) == 2) {
     n1 <- obs_vars[1]
     n2 <- obs_vars[2]
     n10 <- paste0("odeini['", parent, "']")
     n20 <- paste0("odeini['", n2, "']")
 
     # sfo_sfo
-    if (all(spec[[1]]$sink == FALSE, length(obs_vars) == 2,
-        spec[[1]]$type == "SFO", spec[[2]]$type == "SFO")) {
+    if (all(
+        spec[[1]]$sink == FALSE,
+        spec[[1]]$type == "SFO",
+        spec[[2]]$type == "SFO",
+        is.null(spec[[2]]$to))) {
       supported <- TRUE
       k1 <- k_parent
       k2 <- paste0("k_", n2, if(min_ff) "_sink" else "")
@@ -80,8 +85,12 @@ create_deg_func <- function(spec, use_of_ff = c("min", "max")) {
     }
 
     # sfo_f12_sfo
-    if (all(use_of_ff == "max", spec[[1]]$sink == TRUE, length(obs_vars) == 2,
-        spec[[1]]$type == "SFO", spec[[2]]$type == "SFO")) {
+    if (all(
+        use_of_ff == "max",
+        spec[[1]]$sink == TRUE,
+        spec[[1]]$type == "SFO",
+        spec[[2]]$type == "SFO",
+        is.null(spec[[2]]$to))) {
       supported <- TRUE
       k1 <- k_parent
       k2 <- paste0("k_", n2)
@@ -92,8 +101,12 @@ create_deg_func <- function(spec, use_of_ff = c("min", "max")) {
     }
 
     # sfo_k120_sfo
-    if (all(use_of_ff == "min", spec[[1]]$sink == TRUE, length(obs_vars) == 2,
-        spec[[1]]$type == "SFO", spec[[2]]$type == "SFO")) {
+    if (all(
+        use_of_ff == "min",
+        spec[[1]]$sink == TRUE,
+        spec[[1]]$type == "SFO",
+        spec[[2]]$type == "SFO",
+        is.null(spec[[2]]$to))) {
       supported <- TRUE
       k12 <- paste0("k_", n1, "_", n2)
       k10 <- paste0("k_", n1, "_sink")
@@ -104,8 +117,12 @@ create_deg_func <- function(spec, use_of_ff = c("min", "max")) {
     }
 
     # dfop_f12_sfo
-    if (all(use_of_ff == "max", spec[[1]]$sink == TRUE, length(obs_vars) == 2,
-        spec[[1]]$type == "DFOP", spec[[2]]$type == "SFO")) {
+    if (all(
+        use_of_ff == "max",
+        spec[[1]]$sink == TRUE,
+        spec[[1]]$type == "DFOP",
+        spec[[2]]$type == "SFO",
+        is.null(spec[[2]]$to))) {
       supported <- TRUE
       f12 <- paste0("f_", n1, "_to_", n2)
       k2 <- paste0("k_", n2)
@@ -119,7 +136,6 @@ create_deg_func <- function(spec, use_of_ff = c("min", "max")) {
 
   }
 
-
   if (supported) {
     deg_func <- function(observed, odeini, odeparms) {}
 
diff --git a/R/illparms.R b/R/illparms.R
index 68a6bff6..b4b37fbb 100644
--- a/R/illparms.R
+++ b/R/illparms.R
@@ -102,12 +102,14 @@ illparms.saem.mmkin <- function(object, conf.level = 0.95, random = TRUE, errmod
     ints <- intervals(object, conf.level = conf.level)
     ill_parms <- character(0)
     if (random) {
-      ill_parms_random <- ints$random[, "lower"] < 0
-      ill_parms <- c(ill_parms, names(which(ill_parms_random)))
+      ill_parms_random_i <- which(ints$random[, "lower"] < 0)
+      ill_parms_random <- rownames(ints$random)[ill_parms_random_i]
+      ill_parms <- c(ill_parms, ill_parms_random)
     }
     if (errmod) {
-      ill_parms_errmod <- ints$errmod[, "lower"] < 0 & ints$errmod[, "upper"] > 0
-      ill_parms <- c(ill_parms, names(which(ill_parms_errmod)))
+      ill_parms_errmod_i <- which(ints$errmod[, "lower"] < 0 & ints$errmod[, "upper"] > 0)
+      ill_parms_errmod <- rownames(ints$errmod)[ill_parms_errmod_i]
+      ill_parms <- c(ill_parms, ill_parms_errmod)
     }
     if (slopes) {
       if (is.null(object$so)) stop("Slope testing is only implemented for the saemix backend")
diff --git a/R/mhmkin.R b/R/mhmkin.R
index 6265a59e..14a7ac29 100644
--- a/R/mhmkin.R
+++ b/R/mhmkin.R
@@ -219,11 +219,22 @@ print.mhmkin <- function(x, ...) {
   print(status(x))
 }
 
+#' Check if fit within an mhmkin object failed
+#' @param x The object to be checked
+check_failed <- function(x) {
+  if (inherits(x, "try-error")) {
+    return(TRUE)
+  } else {
+    if (inherits(x$so, "try-error")) {
+      return(TRUE)
+    } else {
+      return(FALSE)
+    }
+  }
+}
+
 #' @export
 AIC.mhmkin <- function(object, ..., k = 2) {
-  if (inherits(object[[1]], "saem.mmkin")) {
-    check_failed <- function(x) if (inherits(x$so, "try-error")) TRUE else FALSE
-  }
   res <- sapply(object, function(x) {
     if (check_failed(x)) return(NA)
     else return(AIC(x$so, k = k))
@@ -235,9 +246,6 @@ AIC.mhmkin <- function(object, ..., k = 2) {
 
 #' @export
 BIC.mhmkin <- function(object, ...) {
-  if (inherits(object[[1]], "saem.mmkin")) {
-    check_failed <- function(x) if (inherits(x$so, "try-error")) TRUE else FALSE
-  }
   res <- sapply(object, function(x) {
     if (check_failed(x)) return(NA)
     else return(BIC(x$so))
@@ -280,7 +288,13 @@ anova.mhmkin <- function(object, ...,
   if (identical(model.names, "auto")) {
     model.names <- outer(rownames(object), colnames(object), paste)
   }
-  rlang::inject(anova(!!!(object), method = method, test = test,
+  failed_index <- which(sapply(object, check_failed), arr.ind = TRUE)
+  if (length(failed_index > 0)) {
+    rlang::inject(anova(!!!(object[-failed_index]), method = method, test = test,
+      model.names = model.names[-failed_index]))
+  } else {
+    rlang::inject(anova(!!!(object), method = method, test = test,
       model.names = model.names))
+  }
 }
 
diff --git a/R/mkinfit.R b/R/mkinfit.R
index c851fddb..52053685 100644
--- a/R/mkinfit.R
+++ b/R/mkinfit.R
@@ -21,7 +21,8 @@ utils::globalVariables(c("name", "time", "value"))
 #'   "FOMC", "DFOP", "HS", "SFORB", "IORE"). If a shorthand name is given, a
 #'   parent only degradation model is generated for the variable with the
 #'   highest value in \code{observed}.
-#' @param observed A dataframe with the observed data.  The first column called
+#' @param observed A dataframe or an object coercible to a dataframe
+#'   (e.g. a \code{tibble}) with the observed data.  The first column called
 #'   "name" must contain the name of the observed variable for each data point.
 #'   The second column must contain the times of observation, named "time".
 #'   The third column must be named "value" and contain the observed values.
@@ -292,6 +293,9 @@ mkinfit <- function(mkinmod, observed,
   # Get the names of observed variables
   obs_vars <- names(mkinmod$spec)
 
+  # Coerce observed data to a dataframe
+  observed <- as.data.frame(observed)
+
   # Subset observed data with names of observed data in the model and remove NA values
   observed <- subset(observed, name %in% obs_vars)
   observed <- subset(observed, !is.na(value))
diff --git a/R/mkinpredict.R b/R/mkinpredict.R
index 60456fb2..4c6d7862 100644
--- a/R/mkinpredict.R
+++ b/R/mkinpredict.R
@@ -22,7 +22,7 @@
 #' variables. The third possibility "eigen" is fast in comparison to uncompiled
 #' ODE models, but not applicable to some models, e.g. using FOMC for the
 #' parent compound.
-#' @param method.ode The solution method passed via [mkinpredict] to [ode]] in
+#' @param method.ode The solution method passed via [mkinpredict] to `deSolve::ode()` in
 #' case the solution type is "deSolve" and we are not using compiled code.
 #' When using compiled code, only lsoda is supported.
 #' @param use_compiled If set to \code{FALSE}, no compiled version of the
@@ -36,7 +36,7 @@
 #'   the observed variables (default) or for all state variables (if set to
 #'   FALSE). Setting this to FALSE has no effect for analytical solutions,
 #'   as these always return mapped output.
-#' @param na_stop Should it be an error if [ode] returns NaN values
+#' @param na_stop Should it be an error if `deSolve::ode()` returns NaN values
 #' @param \dots Further arguments passed to the ode solver in case such a
 #'   solver is used.
 #' @return A matrix with the numeric solution in wide format
diff --git a/R/multistart.R b/R/multistart.R
index aeea2d81..4ba82a43 100644
--- a/R/multistart.R
+++ b/R/multistart.R
@@ -40,7 +40,7 @@
 #' f_mmkin <- mmkin("DFOP", dmta_ds, error_model = "tc", cores = 7, quiet = TRUE)
 #' f_saem_full <- saem(f_mmkin)
 #' f_saem_full_multi <- multistart(f_saem_full, n = 16, cores = 16)
-#' parplot(f_saem_full_multi, lpos = "topleft")
+#' parplot(f_saem_full_multi, lpos = "topleft", las = 2)
 #' illparms(f_saem_full)
 #'
 #' f_saem_reduced <- update(f_saem_full, no_random_effect = "log_k2")
@@ -50,7 +50,7 @@
 #' library(parallel)
 #' cl <- makePSOCKcluster(12)
 #' f_saem_reduced_multi <- multistart(f_saem_reduced, n = 16, cluster = cl)
-#' parplot(f_saem_reduced_multi, lpos = "topright", ylim = c(0.5, 2))
+#' parplot(f_saem_reduced_multi, lpos = "topright", ylim = c(0.5, 2), las = 2)
 #' stopCluster(cl)
 #' }
 multistart <- function(object, n = 50,
diff --git a/R/nlme.R b/R/nlme.R
index 6b2d06d0..de82d99c 100644
--- a/R/nlme.R
+++ b/R/nlme.R
@@ -126,7 +126,7 @@ nlme_function <- function(object) {
 
 #' @rdname nlme
 #' @importFrom rlang !!!
-#' @return A \code{\link{groupedData}} object
+#' @return A `nlme::groupedData` object
 #' @export
 nlme_data <- function(object) {
   if (nrow(object) > 1) stop("Only row objects allowed")
diff --git a/R/parplot.R b/R/parplot.R
index 3da4b51a..a33112a5 100644
--- a/R/parplot.R
+++ b/R/parplot.R
@@ -35,9 +35,6 @@ parplot.multistart.saem.mmkin <- function(object, llmin = -Inf, llquant = NA,
   scale = c("best", "median"),
   lpos = "bottomleft", main = "", ...)
 {
-  oldpar <- par(no.readonly = TRUE)
-  on.exit(par(oldpar, no.readonly = TRUE))
-
   orig <- attr(object, "orig")
   orig_parms <- parms(orig)
   start_degparms <- orig$mean_dp_start
@@ -59,11 +56,10 @@ parplot.multistart.saem.mmkin <- function(object, llmin = -Inf, llquant = NA,
   selected <- which(ll > llmin)
   selected_parms <- all_parms[selected, ]
 
-  par(las = 1)
   if (orig$transformations == "mkin") {
     degparm_names_transformed <- names(start_degparms)
     degparm_index <- which(names(orig_parms) %in% degparm_names_transformed)
-    orig_parms[degparm_names_transformed] <- backtransform_odeparms(
+    orig_degparms <- backtransform_odeparms(
       orig_parms[degparm_names_transformed],
       orig$mmkin[[1]]$mkinmod,
       transform_rates = orig$mmkin[[1]]$transform_rates,
@@ -74,14 +70,17 @@ parplot.multistart.saem.mmkin <- function(object, llmin = -Inf, llquant = NA,
       transform_fractions = orig$mmkin[[1]]$transform_fractions)
     degparm_names <- names(start_degparms)
 
-    names(orig_parms) <- c(degparm_names, names(orig_parms[-degparm_index]))
+    orig_parms_back <- orig_parms
+    orig_parms_back[degparm_index] <- orig_degparms
+    names(orig_parms_back)[degparm_index] <- degparm_names
+    orig_parms <- orig_parms_back
 
     selected_parms[, degparm_names_transformed] <-
       t(apply(selected_parms[, degparm_names_transformed], 1, backtransform_odeparms,
       orig$mmkin[[1]]$mkinmod,
       transform_rates = orig$mmkin[[1]]$transform_rates,
       transform_fractions = orig$mmkin[[1]]$transform_fractions))
-    colnames(selected_parms)[1:length(degparm_names)] <- degparm_names
+    colnames(selected_parms)[degparm_index] <- degparm_names
   }
 
   start_errparms <- orig$so@model@error.init
@@ -99,6 +98,12 @@ parplot.multistart.saem.mmkin <- function(object, llmin = -Inf, llquant = NA,
 
   # Boxplots of all scaled parameters
   selected_scaled_parms <- t(apply(selected_parms, 1, function(x) x / parm_scale))
+  i_negative <- selected_scaled_parms <= 0
+  parms_with_negative_scaled_values <- paste(names(which(apply(i_negative, 2, any))), collapse = ", ")
+  if (any(i_negative)) {
+    warning("There are negative values for ", parms_with_negative_scaled_values, " which are set to NA for plotting")
+  }
+  selected_scaled_parms[i_negative] <- NA
   boxplot(selected_scaled_parms, log = "y", main = main, ,
     ylab = "Normalised parameters", ...)
 
diff --git a/R/plot.mixed.mmkin.R b/R/plot.mixed.mmkin.R
index d6c3d0de..f05f1110 100644
--- a/R/plot.mixed.mmkin.R
+++ b/R/plot.mixed.mmkin.R
@@ -93,7 +93,7 @@ plot.mixed.mmkin <- function(x,
   nrow.legend = ceiling((length(i) + 1) / ncol.legend),
   rel.height.legend = 0.02 + 0.07 * nrow.legend,
   rel.height.bottom = 1.1,
-  pch_ds = 1:length(i),
+  pch_ds = c(1:25, 33, 35:38, 40:41, 47:57, 60:90)[1:length(i)],
   col_ds = pch_ds + 1,
   lty_ds = col_ds,
   frame = TRUE, ...
diff --git a/R/status.R b/R/status.R
index 8bcd3a16..f9d79e7d 100644
--- a/R/status.R
+++ b/R/status.R
@@ -74,15 +74,19 @@ print.status.mmkin <- function(x, ...) {
 status.mhmkin <- function(object, ...) {
   if (inherits(object[[1]], "saem.mmkin")) {
     test_func <- function(fit) {
-      if (inherits(fit$so, "try-error")) {
-        return("E")
+      if (inherits(fit, "try-error")) {
+          return("E")
       } else {
-        if (!is.null(fit$FIM_failed)) {
-          return_values <- c("fixed effects" = "Fth",
-            "random effects and error model parameters" = "FO")
-          return(paste(return_values[fit$FIM_failed], collapse = ", "))
+        if (inherits(fit$so, "try-error")) {
+          return("E")
         } else {
-          return("OK")
+          if (!is.null(fit$FIM_failed)) {
+            return_values <- c("fixed effects" = "Fth",
+              "random effects and error model parameters" = "FO")
+            return(paste(return_values[fit$FIM_failed], collapse = ", "))
+          } else {
+            return("OK")
+          }
         }
       }
     }
diff --git a/README.md b/README.md
index 53ea77c1..2d3720ca 100644
--- a/README.md
+++ b/README.md
@@ -1,9 +1,10 @@
 # mkin
 
+<!-- badges: start -->
 [![](https://www.r-pkg.org/badges/version/mkin)](https://cran.r-project.org/package=mkin)
-[![mkin status badge](https://jranke.r-universe.dev/badges/mkin)](https://jranke.r-universe.dev/ui/#package:mkin)
-[![Build Status](https://travis-ci.com/jranke/mkin.svg?branch=main)](https://app.travis-ci.com/github/jranke/mkin)
-[![codecov](https://codecov.io/github/jranke/mkin/branch/main/graphs/badge.svg)](https://app.codecov.io/gh/jranke/mkin)
+[![mkin status badge](https://jranke.r-universe.dev/badges/mkin)](https://jranke.r-universe.dev/#package:mkin)
+[![Build Status](https://app.travis-ci.com/jranke/mkin.svg?token=Sq9VuYWyRz2FbBLxu6DK&branch=main)](https://app.travis-ci.com/jranke/mkin)
+<!-- badges: end -->
 
 The [R](https://www.r-project.org) package **mkin** provides calculation routines for the analysis of
 chemical degradation data, including <b>m</b>ulticompartment <b>kin</b>etics as
diff --git a/_pkgdown.yml b/_pkgdown.yml
index 996b8d3b..bc073ac0 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -5,6 +5,7 @@ development:
   version_label: info
 
 template:
+  bootstrap: 5
   bootswatch: spacelab
 
 news:
@@ -66,6 +67,7 @@ reference:
       - multistart
       - llhist
       - parplot
+      - check_failed
   - title: Datasets and known results
     contents:
       - ds_mixed
@@ -177,5 +179,7 @@ navbar:
         href: articles/twa.html
       - text: Example evaluation of NAFTA SOP Attachment examples
         href: articles/web_only/NAFTA_examples.html
+    - text: Test coverage
+      href: coverage/coverage.html
     - text: News
       href: news/index.html
diff --git a/codecov.yml b/codecov.yml
new file mode 100644
index 00000000..04c55859
--- /dev/null
+++ b/codecov.yml
@@ -0,0 +1,14 @@
+comment: false
+
+coverage:
+  status:
+    project:
+      default:
+        target: auto
+        threshold: 1%
+        informational: true
+    patch:
+      default:
+        target: auto
+        threshold: 1%
+        informational: true
diff --git a/docs/404.html b/docs/404.html
index eadf13f5..582d67b2 100644
--- a/docs/404.html
+++ b/docs/404.html
@@ -4,166 +4,101 @@
 <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
 <meta charset="utf-8">
 <meta http-equiv="X-UA-Compatible" content="IE=edge">
-<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
 <title>Page not found (404) • mkin</title>
-<!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous">
-<script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="https://pkgdown.jrwb.de/mkin/bootstrap-toc.css">
-<script src="https://pkgdown.jrwb.de/mkin/bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous">
-<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous">
-<!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="https://pkgdown.jrwb.de/mkin/pkgdown.css" rel="stylesheet">
-<script src="https://pkgdown.jrwb.de/mkin/pkgdown.js"></script><meta property="og:title" content="Page not found (404)">
-<!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]-->
+<script src="https://pkgdown.jrwb.de/mkin/deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
+<link href="https://pkgdown.jrwb.de/mkin/deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet">
+<script src="https://pkgdown.jrwb.de/mkin/deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="https://pkgdown.jrwb.de/mkin/deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet">
+<link href="https://pkgdown.jrwb.de/mkin/deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet">
+<script src="https://pkgdown.jrwb.de/mkin/deps/headroom-0.11.0/headroom.min.js"></script><script src="https://pkgdown.jrwb.de/mkin/deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="https://pkgdown.jrwb.de/mkin/deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="https://pkgdown.jrwb.de/mkin/deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="https://pkgdown.jrwb.de/mkin/deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="https://pkgdown.jrwb.de/mkin/deps/search-1.0.0/fuse.min.js"></script><script src="https://pkgdown.jrwb.de/mkin/deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="https://pkgdown.jrwb.de/mkin/pkgdown.js"></script><meta property="og:title" content="Page not found (404)">
 </head>
-<body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-title-body">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="https://pkgdown.jrwb.de/mkin/index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav">
-<li>
-  <a href="https://pkgdown.jrwb.de/mkin/reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="https://pkgdown.jrwb.de/mkin/#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu">
-<li>
-      <a href="https://pkgdown.jrwb.de/mkin/articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="https://pkgdown.jrwb.de/mkin/articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="https://pkgdown.jrwb.de/mkin/articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="https://pkgdown.jrwb.de/mkin/articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="https://pkgdown.jrwb.de/mkin/articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="https://pkgdown.jrwb.de/mkin/articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="https://pkgdown.jrwb.de/mkin/articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Performance</li>
-    <li>
-      <a href="https://pkgdown.jrwb.de/mkin/articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="https://pkgdown.jrwb.de/mkin/articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="https://pkgdown.jrwb.de/mkin/articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="https://pkgdown.jrwb.de/mkin/articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="https://pkgdown.jrwb.de/mkin/articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+<body>
+    <a href="https://pkgdown.jrwb.de/mkin/#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="https://pkgdown.jrwb.de/mkin/index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto">
+<li class="nav-item"><a class="nav-link" href="https://pkgdown.jrwb.de/mkin/reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles">
+<li><a class="dropdown-item" href="https://pkgdown.jrwb.de/mkin/articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="https://pkgdown.jrwb.de/mkin/articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="https://pkgdown.jrwb.de/mkin/articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="https://pkgdown.jrwb.de/mkin/articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="https://pkgdown.jrwb.de/mkin/articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="https://pkgdown.jrwb.de/mkin/articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="https://pkgdown.jrwb.de/mkin/articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="https://pkgdown.jrwb.de/mkin/articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="https://pkgdown.jrwb.de/mkin/articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="https://pkgdown.jrwb.de/mkin/articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="https://pkgdown.jrwb.de/mkin/articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="https://pkgdown.jrwb.de/mkin/articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul>
 </li>
-<li>
-  <a href="https://pkgdown.jrwb.de/mkin/news/index.html">News</a>
-</li>
+<li class="nav-item"><a class="nav-link" href="https://pkgdown.jrwb.de/mkin/coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="https://pkgdown.jrwb.de/mkin/news/index.html">News</a></li>
       </ul>
-<ul class="nav navbar-nav navbar-right">
-<li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
+<ul class="navbar-nav">
+<li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="search.json">
+</form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul>
 </div>
-<!--/.nav-collapse -->
-  </div>
-<!--/.container -->
-</div>
-<!--/.navbar -->
 
-      
 
-      </header><div class="row">
-  <div class="contents col-md-9">
-    <div class="page-header">
+  </div>
+</nav><div class="container template-title-body">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
       <h1>Page not found (404)</h1>
+
     </div>
 
 Content not found. Please use links in the navbar.
 
-  </div>
-
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav>
-</div>
-
+  </main>
 </div>
 
 
-
-      <footer><div class="copyright">
-  <p></p>
-<p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p>
-<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer>
+    </footer>
 </div>
 
-  
 
 
-  
+
 
   </body>
 </html>
diff --git a/docs/articles/FOCUS_D.html b/docs/articles/FOCUS_D.html
index 303ddb6f..dab5f1ee 100644
--- a/docs/articles/FOCUS_D.html
+++ b/docs/articles/FOCUS_D.html
@@ -4,145 +4,90 @@
 <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
 <meta charset="utf-8">
 <meta http-equiv="X-UA-Compatible" content="IE=edge">
-<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
 <title>Example evaluation of FOCUS Example Dataset D • mkin</title>
-<!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous">
-<script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css">
-<script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous">
-<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous">
-<!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet">
-<script src="../pkgdown.js"></script><meta property="og:title" content="Example evaluation of FOCUS Example Dataset D">
-<meta property="og:description" content="mkin">
-<!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]-->
+<script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
+<link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet">
+<script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet">
+<link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet">
+<script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Example evaluation of FOCUS Example Dataset D">
 </head>
-<body data-spy="scroll" data-target="#toc">
-    
+<body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
 
-    <div class="container template-article">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
 
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav">
-<li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu">
-<li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto">
+<li class="nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="active nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles">
+<li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul>
 </li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
       </ul>
-<ul class="nav navbar-nav navbar-right">
-<li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
+<ul class="navbar-nav">
+<li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json">
+</form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul>
 </div>
-<!--/.nav-collapse -->
+
+
   </div>
-<!--/.container -->
-</div>
-<!--/.navbar -->
+</nav><div class="container template-article">
+
 
-      
 
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header toc-ignore">
-      <h1 data-toc-skip>Example evaluation of FOCUS Example Dataset
-D</h1>
+
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Example evaluation of FOCUS Example Dataset D</h1>
                         <h4 data-toc-skip class="author">Johannes
 Ranke</h4>
             
             <h4 data-toc-skip class="date">Last change 31 January 2019
-(rebuilt 2023-10-30)</h4>
+(rebuilt 2025-02-13)</h4>
       
       <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/FOCUS_D.rmd" class="external-link"><code>vignettes/FOCUS_D.rmd</code></a></small>
-      <div class="hidden name"><code>FOCUS_D.rmd</code></div>
-
+      <div class="d-none name"><code>FOCUS_D.rmd</code></div>
     </div>
 
     
@@ -240,10 +185,10 @@ the <code>mkinparplot</code> function.</p>
 <code>summary</code> method for <code>mkinfit</code> objects.</p>
 <div class="sourceCode" id="cb11"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## mkin version used for fitting:    1.2.6 </span></span>
-<span><span class="co">## R version used for fitting:       4.3.1 </span></span>
-<span><span class="co">## Date of fit:     Mon Oct 30 09:40:58 2023 </span></span>
-<span><span class="co">## Date of summary: Mon Oct 30 09:40:58 2023 </span></span>
+<pre><code><span><span class="co">## mkin version used for fitting:    1.2.9 </span></span>
+<span><span class="co">## R version used for fitting:       4.4.2 </span></span>
+<span><span class="co">## Date of fit:     Thu Feb 13 15:49:53 2025 </span></span>
+<span><span class="co">## Date of summary: Thu Feb 13 15:49:53 2025 </span></span>
 <span><span class="co">## </span></span>
 <span><span class="co">## Equations:</span></span>
 <span><span class="co">## d_parent/dt = - k_parent * parent</span></span>
@@ -251,7 +196,7 @@ the <code>mkinparplot</code> function.</p>
 <span><span class="co">## </span></span>
 <span><span class="co">## Model predictions using solution type analytical </span></span>
 <span><span class="co">## </span></span>
-<span><span class="co">## Fitted using 401 model solutions performed in 0.123 s</span></span>
+<span><span class="co">## Fitted using 401 model solutions performed in 0.053 s</span></span>
 <span><span class="co">## </span></span>
 <span><span class="co">## Error model: Constant variance </span></span>
 <span><span class="co">## </span></span>
@@ -367,33 +312,26 @@ the <code>mkinparplot</code> function.</p>
 <span><span class="co">##   100       m1    33.13  31.98162  1.148e+00</span></span>
 <span><span class="co">##   120       m1    25.15  28.78984 -3.640e+00</span></span>
 <span><span class="co">##   120       m1    33.31  28.78984  4.520e+00</span></span></code></pre>
-  </div>
-
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-
-      </div>
-
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside>
 </div>
 
 
 
-      <footer><div class="copyright">
-  <p></p>
-<p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p>
-<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer>
+    </footer>
 </div>
 
-  
 
 
-  
+
 
   </body>
 </html>
diff --git a/docs/articles/FOCUS_D_files/accessible-code-block-0.0.1/empty-anchor.js b/docs/articles/FOCUS_D_files/accessible-code-block-0.0.1/empty-anchor.js
deleted file mode 100644
index ca349fd6..00000000
--- a/docs/articles/FOCUS_D_files/accessible-code-block-0.0.1/empty-anchor.js
+++ /dev/null
@@ -1,15 +0,0 @@
-// Hide empty <a> tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) -->
-// v0.0.1
-// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020.
-
-document.addEventListener('DOMContentLoaded', function() {
-  const codeList = document.getElementsByClassName("sourceCode");
-  for (var i = 0; i < codeList.length; i++) {
-    var linkList = codeList[i].getElementsByTagName('a');
-    for (var j = 0; j < linkList.length; j++) {
-      if (linkList[j].innerHTML === "") {
-        linkList[j].setAttribute('aria-hidden', 'true');
-      }
-    }
-  }
-});
diff --git a/docs/articles/FOCUS_D_files/figure-html/plot-1.png b/docs/articles/FOCUS_D_files/figure-html/plot-1.png
index f0b51c1f..c0832a1a 100644
--- a/docs/articles/FOCUS_D_files/figure-html/plot-1.png
+++ b/docs/articles/FOCUS_D_files/figure-html/plot-1.png
diff --git a/docs/articles/FOCUS_D_files/figure-html/plot_2-1.png b/docs/articles/FOCUS_D_files/figure-html/plot_2-1.png
index f6180470..02cfcfb4 100644
--- a/docs/articles/FOCUS_D_files/figure-html/plot_2-1.png
+++ b/docs/articles/FOCUS_D_files/figure-html/plot_2-1.png
diff --git a/docs/articles/FOCUS_D_files/header-attrs-2.6/header-attrs.js b/docs/articles/FOCUS_D_files/header-attrs-2.6/header-attrs.js
deleted file mode 100644
index dd57d92e..00000000
--- a/docs/articles/FOCUS_D_files/header-attrs-2.6/header-attrs.js
+++ /dev/null
@@ -1,12 +0,0 @@
-// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
-// be compatible with the behavior of Pandoc < 2.8).
-document.addEventListener('DOMContentLoaded', function(e) {
-  var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
-  var i, h, a;
-  for (i = 0; i < hs.length; i++) {
-    h = hs[i];
-    if (!/^h[1-6]$/i.test(h.tagName)) continue;  // it should be a header h1-h6
-    a = h.attributes;
-    while (a.length > 0) h.removeAttribute(a[0].name);
-  }
-});
diff --git a/docs/articles/FOCUS_D_files/header-attrs-2.7/header-attrs.js b/docs/articles/FOCUS_D_files/header-attrs-2.7/header-attrs.js
deleted file mode 100644
index dd57d92e..00000000
--- a/docs/articles/FOCUS_D_files/header-attrs-2.7/header-attrs.js
+++ /dev/null
@@ -1,12 +0,0 @@
-// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
-// be compatible with the behavior of Pandoc < 2.8).
-document.addEventListener('DOMContentLoaded', function(e) {
-  var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
-  var i, h, a;
-  for (i = 0; i < hs.length; i++) {
-    h = hs[i];
-    if (!/^h[1-6]$/i.test(h.tagName)) continue;  // it should be a header h1-h6
-    a = h.attributes;
-    while (a.length > 0) h.removeAttribute(a[0].name);
-  }
-});
diff --git a/docs/articles/FOCUS_L.html b/docs/articles/FOCUS_L.html
index 7b5acf17..30092709 100644
--- a/docs/articles/FOCUS_L.html
+++ b/docs/articles/FOCUS_L.html
@@ -4,142 +4,90 @@
 <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
 <meta charset="utf-8">
 <meta http-equiv="X-UA-Compatible" content="IE=edge">
-<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
 <title>Example evaluation of FOCUS Laboratory Data L1 to L3 • mkin</title>
-<!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous">
-<script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css">
-<script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous">
-<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous">
-<!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet">
-<script src="../pkgdown.js"></script><meta property="og:title" content="Example evaluation of FOCUS Laboratory Data L1 to L3">
-<meta property="og:description" content="mkin">
-<!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]-->
+<script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
+<link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet">
+<script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet">
+<link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet">
+<script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Example evaluation of FOCUS Laboratory Data L1 to L3">
 </head>
-<body data-spy="scroll" data-target="#toc">
-    
+<body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
 
-    <div class="container template-article">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.5</span>
-      </span>
-    </div>
 
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav">
-<li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu">
-<li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto">
+<li class="nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="active nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles">
+<li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul>
 </li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
       </ul>
-<ul class="nav navbar-nav navbar-right">
-<li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
+<ul class="navbar-nav">
+<li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json">
+</form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul>
 </div>
-<!--/.nav-collapse -->
+
+
   </div>
-<!--/.container -->
-</div>
-<!--/.navbar -->
+</nav><div class="container template-article">
+
+
 
-      
 
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header toc-ignore">
-      <h1 data-toc-skip>Example evaluation of FOCUS Laboratory Data L1
-to L3</h1>
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Example evaluation of FOCUS Laboratory Data L1 to L3</h1>
                         <h4 data-toc-skip class="author">Johannes
 Ranke</h4>
             
             <h4 data-toc-skip class="date">Last change 18 May 2023
-(rebuilt 2023-08-09)</h4>
+(rebuilt 2025-02-13)</h4>
       
       <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/FOCUS_L.rmd" class="external-link"><code>vignettes/FOCUS_L.rmd</code></a></small>
-      <div class="hidden name"><code>FOCUS_L.rmd</code></div>
-
+      <div class="d-none name"><code>FOCUS_L.rmd</code></div>
     </div>
 
     
@@ -168,18 +116,18 @@ model fit. This covers the numerical analysis given in the FOCUS
 report.</p>
 <div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">m.L1.SFO</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="va">FOCUS_2006_L1_mkin</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">m.L1.SFO</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## mkin version used for fitting:    1.2.5 </span></span>
-<span><span class="co">## R version used for fitting:       4.3.1 </span></span>
-<span><span class="co">## Date of fit:     Wed Aug  9 17:55:39 2023 </span></span>
-<span><span class="co">## Date of summary: Wed Aug  9 17:55:39 2023 </span></span>
+<span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">m.L1.SFO</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## mkin version used for fitting:    1.2.9 </span></span>
+<span><span class="co">## R version used for fitting:       4.4.2 </span></span>
+<span><span class="co">## Date of fit:     Thu Feb 13 15:49:54 2025 </span></span>
+<span><span class="co">## Date of summary: Thu Feb 13 15:49:54 2025 </span></span>
 <span><span class="co">## </span></span>
 <span><span class="co">## Equations:</span></span>
 <span><span class="co">## d_parent/dt = - k_parent * parent</span></span>
 <span><span class="co">## </span></span>
 <span><span class="co">## Model predictions using solution type analytical </span></span>
 <span><span class="co">## </span></span>
-<span><span class="co">## Fitted using 133 model solutions performed in 0.031 s</span></span>
+<span><span class="co">## Fitted using 133 model solutions performed in 0.011 s</span></span>
 <span><span class="co">## </span></span>
 <span><span class="co">## Error model: Constant variance </span></span>
 <span><span class="co">## </span></span>
@@ -256,37 +204,38 @@ report.</p>
 <p>A plot of the fit is obtained with the plot function for mkinfit
 objects.</p>
 <div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">m.L1.SFO</span>, show_errmin <span class="op">=</span> <span class="cn">TRUE</span>, main <span class="op">=</span> <span class="st">"FOCUS L1 - SFO"</span><span class="op">)</span></span></code></pre></div>
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">m.L1.SFO</span>, show_errmin <span class="op">=</span> <span class="cn">TRUE</span>, main <span class="op">=</span> <span class="st">"FOCUS L1 - SFO"</span><span class="op">)</span></span></code></pre></div>
 <p><img src="FOCUS_L_files/figure-html/unnamed-chunk-4-1.png" width="576"></p>
 <p>The residual plot can be easily obtained by</p>
 <div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="../reference/mkinresplot.html">mkinresplot</a></span><span class="op">(</span><span class="va">m.L1.SFO</span>, ylab <span class="op">=</span> <span class="st">"Observed"</span>, xlab <span class="op">=</span> <span class="st">"Time"</span><span class="op">)</span></span></code></pre></div>
 <p><img src="FOCUS_L_files/figure-html/unnamed-chunk-5-1.png" width="576"></p>
-<p>For comparison, the FOMC model is fitted as well, and the <span class="math inline">\(\chi^2\)</span> error level is checked.</p>
+<p>For comparison, the FOMC model is fitted as well, and the
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><msup><mi>χ</mi><mn>2</mn></msup><annotation encoding="application/x-tex">\chi^2</annotation></semantics></math>
+error level is checked.</p>
 <div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">m.L1.FOMC</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"FOMC"</span>, <span class="va">FOCUS_2006_L1_mkin</span>, quiet<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## Warning in mkinfit("FOMC", FOCUS_2006_L1_mkin, quiet = TRUE): Optimisation did not converge:</span></span>
 <span><span class="co">## false convergence (8)</span></span></code></pre>
 <div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">m.L1.FOMC</span>, show_errmin <span class="op">=</span> <span class="cn">TRUE</span>, main <span class="op">=</span> <span class="st">"FOCUS L1 - FOMC"</span><span class="op">)</span></span></code></pre></div>
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">m.L1.FOMC</span>, show_errmin <span class="op">=</span> <span class="cn">TRUE</span>, main <span class="op">=</span> <span class="st">"FOCUS L1 - FOMC"</span><span class="op">)</span></span></code></pre></div>
 <p><img src="FOCUS_L_files/figure-html/unnamed-chunk-6-1.png" width="576"></p>
 <div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">m.L1.FOMC</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div>
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">m.L1.FOMC</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## Warning in sqrt(diag(covar)): NaNs produced</span></span></code></pre>
-<pre><code><span><span class="co">## Warning in sqrt(1/diag(V)): NaNs produced</span></span></code></pre>
-<pre><code><span><span class="co">## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result</span></span>
-<span><span class="co">## is doubtful</span></span></code></pre>
-<pre><code><span><span class="co">## mkin version used for fitting:    1.2.5 </span></span>
-<span><span class="co">## R version used for fitting:       4.3.1 </span></span>
-<span><span class="co">## Date of fit:     Wed Aug  9 17:55:39 2023 </span></span>
-<span><span class="co">## Date of summary: Wed Aug  9 17:55:39 2023 </span></span>
+<pre><code><span><span class="co">## Warning in cov2cor(ans$covar): diag(V) had non-positive or NA entries; the</span></span>
+<span><span class="co">## non-finite result may be dubious</span></span></code></pre>
+<pre><code><span><span class="co">## mkin version used for fitting:    1.2.9 </span></span>
+<span><span class="co">## R version used for fitting:       4.4.2 </span></span>
+<span><span class="co">## Date of fit:     Thu Feb 13 15:49:55 2025 </span></span>
+<span><span class="co">## Date of summary: Thu Feb 13 15:49:55 2025 </span></span>
 <span><span class="co">## </span></span>
 <span><span class="co">## Equations:</span></span>
 <span><span class="co">## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent</span></span>
 <span><span class="co">## </span></span>
 <span><span class="co">## Model predictions using solution type analytical </span></span>
 <span><span class="co">## </span></span>
-<span><span class="co">## Fitted using 342 model solutions performed in 0.07 s</span></span>
+<span><span class="co">## Fitted using 342 model solutions performed in 0.023 s</span></span>
 <span><span class="co">## </span></span>
 <span><span class="co">## Error model: Constant variance </span></span>
 <span><span class="co">## </span></span>
@@ -354,36 +303,40 @@ objects.</p>
 model is overparameterised, <em>i.e.</em> contains too many parameters
 that are ill-defined as a consequence.</p>
 <p>And in fact, due to the higher number of parameters, and the lower
-number of degrees of freedom of the fit, the <span class="math inline">\(\chi^2\)</span> error level is actually higher for
-the FOMC model (3.6%) than for the SFO model (3.4%). Additionally, the
-parameters <code>log_alpha</code> and <code>log_beta</code> internally
-fitted in the model have excessive confidence intervals, that span more
-than 25 orders of magnitude (!) when backtransformed to the scale of
-<code>alpha</code> and <code>beta</code>. Also, the t-test for
-significant difference from zero does not indicate such a significant
-difference, with p-values greater than 0.1, and finally, the parameter
-correlation of <code>log_alpha</code> and <code>log_beta</code> is
-1.000, clearly indicating that the model is overparameterised.</p>
-<p>The <span class="math inline">\(\chi^2\)</span> error levels reported
-in Appendix 3 and Appendix 7 to the FOCUS kinetics report are rounded to
-integer percentages and partly deviate by one percentage point from the
-results calculated by mkin. The reason for this is not known. However,
-mkin gives the same <span class="math inline">\(\chi^2\)</span> error
-levels as the kinfit package and the calculation routines of the kinfit
-package have been extensively compared to the results obtained by the
-KinGUI software, as documented in the kinfit package vignette. KinGUI
-was the first widely used standard package in this field. Also, the
-calculation of <span class="math inline">\(\chi^2\)</span> error levels
-was compared with KinGUII, CAKE and DegKin manager in a project
-sponsored by the German Umweltbundesamt <span class="citation">(Ranke
-2014)</span>.</p>
+number of degrees of freedom of the fit, the
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><msup><mi>χ</mi><mn>2</mn></msup><annotation encoding="application/x-tex">\chi^2</annotation></semantics></math>
+error level is actually higher for the FOMC model (3.6%) than for the
+SFO model (3.4%). Additionally, the parameters <code>log_alpha</code>
+and <code>log_beta</code> internally fitted in the model have excessive
+confidence intervals, that span more than 25 orders of magnitude (!)
+when backtransformed to the scale of <code>alpha</code> and
+<code>beta</code>. Also, the t-test for significant difference from zero
+does not indicate such a significant difference, with p-values greater
+than 0.1, and finally, the parameter correlation of
+<code>log_alpha</code> and <code>log_beta</code> is 1.000, clearly
+indicating that the model is overparameterised.</p>
+<p>The
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><msup><mi>χ</mi><mn>2</mn></msup><annotation encoding="application/x-tex">\chi^2</annotation></semantics></math>
+error levels reported in Appendix 3 and Appendix 7 to the FOCUS kinetics
+report are rounded to integer percentages and partly deviate by one
+percentage point from the results calculated by mkin. The reason for
+this is not known. However, mkin gives the same
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><msup><mi>χ</mi><mn>2</mn></msup><annotation encoding="application/x-tex">\chi^2</annotation></semantics></math>
+error levels as the kinfit package and the calculation routines of the
+kinfit package have been extensively compared to the results obtained by
+the KinGUI software, as documented in the kinfit package vignette.
+KinGUI was the first widely used standard package in this field. Also,
+the calculation of
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><msup><mi>χ</mi><mn>2</mn></msup><annotation encoding="application/x-tex">\chi^2</annotation></semantics></math>
+error levels was compared with KinGUII, CAKE and DegKin manager in a
+project sponsored by the German Umweltbundesamt <span class="citation">(Ranke 2014)</span>.</p>
 </div>
 <div class="section level2">
 <h2 id="laboratory-data-l2">Laboratory Data L2<a class="anchor" aria-label="anchor" href="#laboratory-data-l2"></a>
 </h2>
 <p>The following code defines example dataset L2 from the FOCUS kinetics
 report, p. 287:</p>
-<div class="sourceCode" id="cb14"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb13"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">FOCUS_2006_L2</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span></span>
 <span>  t <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/rep.html" class="external-link">rep</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span>, <span class="fl">3</span>, <span class="fl">7</span>, <span class="fl">14</span>, <span class="fl">28</span><span class="op">)</span>, each <span class="op">=</span> <span class="fl">2</span><span class="op">)</span>,</span>
 <span>  parent <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">96.1</span>, <span class="fl">91.8</span>, <span class="fl">41.4</span>, <span class="fl">38.7</span>,</span>
@@ -396,15 +349,16 @@ report, p. 287:</p>
 <p>Again, the SFO model is fitted and the result is plotted. The
 residual plot can be obtained simply by adding the argument
 <code>show_residuals</code> to the plot command.</p>
-<div class="sourceCode" id="cb15"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb14"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">m.L2.SFO</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="va">FOCUS_2006_L2_mkin</span>, quiet<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">m.L2.SFO</span>, show_residuals <span class="op">=</span> <span class="cn">TRUE</span>, show_errmin <span class="op">=</span> <span class="cn">TRUE</span>,</span>
+<span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">m.L2.SFO</span>, show_residuals <span class="op">=</span> <span class="cn">TRUE</span>, show_errmin <span class="op">=</span> <span class="cn">TRUE</span>,</span>
 <span>     main <span class="op">=</span> <span class="st">"FOCUS L2 - SFO"</span><span class="op">)</span></span></code></pre></div>
 <p><img src="FOCUS_L_files/figure-html/unnamed-chunk-8-1.png" width="672"></p>
-<p>The <span class="math inline">\(\chi^2\)</span> error level of 14%
-suggests that the model does not fit very well. This is also obvious
-from the plots of the fit, in which we have included the residual
-plot.</p>
+<p>The
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><msup><mi>χ</mi><mn>2</mn></msup><annotation encoding="application/x-tex">\chi^2</annotation></semantics></math>
+error level of 14% suggests that the model does not fit very well. This
+is also obvious from the plots of the fit, in which we have included the
+residual plot.</p>
 <p>In the FOCUS kinetics report, it is stated that there is no apparent
 systematic error observed from the residual plot up to the measured DT90
 (approximately at day 5), and there is an underestimation beyond that
@@ -419,25 +373,27 @@ kinetics.</p>
 <div class="section level3">
 <h3 id="fomc-fit-for-l2">FOMC fit for L2<a class="anchor" aria-label="anchor" href="#fomc-fit-for-l2"></a>
 </h3>
-<p>For comparison, the FOMC model is fitted as well, and the <span class="math inline">\(\chi^2\)</span> error level is checked.</p>
-<div class="sourceCode" id="cb16"><pre class="downlit sourceCode r">
+<p>For comparison, the FOMC model is fitted as well, and the
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><msup><mi>χ</mi><mn>2</mn></msup><annotation encoding="application/x-tex">\chi^2</annotation></semantics></math>
+error level is checked.</p>
+<div class="sourceCode" id="cb15"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">m.L2.FOMC</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"FOMC"</span>, <span class="va">FOCUS_2006_L2_mkin</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">m.L2.FOMC</span>, show_residuals <span class="op">=</span> <span class="cn">TRUE</span>,</span>
+<span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">m.L2.FOMC</span>, show_residuals <span class="op">=</span> <span class="cn">TRUE</span>,</span>
 <span>     main <span class="op">=</span> <span class="st">"FOCUS L2 - FOMC"</span><span class="op">)</span></span></code></pre></div>
 <p><img src="FOCUS_L_files/figure-html/unnamed-chunk-9-1.png" width="672"></p>
-<div class="sourceCode" id="cb17"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">m.L2.FOMC</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## mkin version used for fitting:    1.2.5 </span></span>
-<span><span class="co">## R version used for fitting:       4.3.1 </span></span>
-<span><span class="co">## Date of fit:     Wed Aug  9 17:55:40 2023 </span></span>
-<span><span class="co">## Date of summary: Wed Aug  9 17:55:40 2023 </span></span>
+<div class="sourceCode" id="cb16"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">m.L2.FOMC</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## mkin version used for fitting:    1.2.9 </span></span>
+<span><span class="co">## R version used for fitting:       4.4.2 </span></span>
+<span><span class="co">## Date of fit:     Thu Feb 13 15:49:55 2025 </span></span>
+<span><span class="co">## Date of summary: Thu Feb 13 15:49:55 2025 </span></span>
 <span><span class="co">## </span></span>
 <span><span class="co">## Equations:</span></span>
 <span><span class="co">## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent</span></span>
 <span><span class="co">## </span></span>
 <span><span class="co">## Model predictions using solution type analytical </span></span>
 <span><span class="co">## </span></span>
-<span><span class="co">## Fitted using 239 model solutions performed in 0.044 s</span></span>
+<span><span class="co">## Fitted using 239 model solutions performed in 0.015 s</span></span>
 <span><span class="co">## </span></span>
 <span><span class="co">## Error model: Constant variance </span></span>
 <span><span class="co">## </span></span>
@@ -495,26 +451,29 @@ kinetics.</p>
 <span><span class="co">## Estimated disappearance times:</span></span>
 <span><span class="co">##          DT50  DT90 DT50back</span></span>
 <span><span class="co">## parent 0.8092 5.356    1.612</span></span></code></pre>
-<p>The error level at which the <span class="math inline">\(\chi^2\)</span> test passes is much lower in this
-case. Therefore, the FOMC model provides a better description of the
-data, as less experimental error has to be assumed in order to explain
-the data.</p>
+<p>The error level at which the
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><msup><mi>χ</mi><mn>2</mn></msup><annotation encoding="application/x-tex">\chi^2</annotation></semantics></math>
+test passes is much lower in this case. Therefore, the FOMC model
+provides a better description of the data, as less experimental error
+has to be assumed in order to explain the data.</p>
 </div>
 <div class="section level3">
 <h3 id="dfop-fit-for-l2">DFOP fit for L2<a class="anchor" aria-label="anchor" href="#dfop-fit-for-l2"></a>
 </h3>
-<p>Fitting the four parameter DFOP model further reduces the <span class="math inline">\(\chi^2\)</span> error level.</p>
-<div class="sourceCode" id="cb19"><pre class="downlit sourceCode r">
+<p>Fitting the four parameter DFOP model further reduces the
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><msup><mi>χ</mi><mn>2</mn></msup><annotation encoding="application/x-tex">\chi^2</annotation></semantics></math>
+error level.</p>
+<div class="sourceCode" id="cb18"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">m.L2.DFOP</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="va">FOCUS_2006_L2_mkin</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">m.L2.DFOP</span>, show_residuals <span class="op">=</span> <span class="cn">TRUE</span>, show_errmin <span class="op">=</span> <span class="cn">TRUE</span>,</span>
+<span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">m.L2.DFOP</span>, show_residuals <span class="op">=</span> <span class="cn">TRUE</span>, show_errmin <span class="op">=</span> <span class="cn">TRUE</span>,</span>
 <span>     main <span class="op">=</span> <span class="st">"FOCUS L2 - DFOP"</span><span class="op">)</span></span></code></pre></div>
 <p><img src="FOCUS_L_files/figure-html/unnamed-chunk-10-1.png" width="672"></p>
-<div class="sourceCode" id="cb20"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">m.L2.DFOP</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## mkin version used for fitting:    1.2.5 </span></span>
-<span><span class="co">## R version used for fitting:       4.3.1 </span></span>
-<span><span class="co">## Date of fit:     Wed Aug  9 17:55:40 2023 </span></span>
-<span><span class="co">## Date of summary: Wed Aug  9 17:55:40 2023 </span></span>
+<div class="sourceCode" id="cb19"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">m.L2.DFOP</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## mkin version used for fitting:    1.2.9 </span></span>
+<span><span class="co">## R version used for fitting:       4.4.2 </span></span>
+<span><span class="co">## Date of fit:     Thu Feb 13 15:49:55 2025 </span></span>
+<span><span class="co">## Date of summary: Thu Feb 13 15:49:55 2025 </span></span>
 <span><span class="co">## </span></span>
 <span><span class="co">## Equations:</span></span>
 <span><span class="co">## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *</span></span>
@@ -523,7 +482,7 @@ the data.</p>
 <span><span class="co">## </span></span>
 <span><span class="co">## Model predictions using solution type analytical </span></span>
 <span><span class="co">## </span></span>
-<span><span class="co">## Fitted using 581 model solutions performed in 0.119 s</span></span>
+<span><span class="co">## Fitted using 581 model solutions performed in 0.043 s</span></span>
 <span><span class="co">## </span></span>
 <span><span class="co">## Error model: Constant variance </span></span>
 <span><span class="co">## </span></span>
@@ -595,7 +554,7 @@ based on the chi^2 error level criterion.</p>
 </h2>
 <p>The following code defines example dataset L3 from the FOCUS kinetics
 report, p. 290.</p>
-<div class="sourceCode" id="cb22"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb21"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">FOCUS_2006_L3</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span></span>
 <span>  t <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">3</span>, <span class="fl">7</span>, <span class="fl">14</span>, <span class="fl">30</span>, <span class="fl">60</span>, <span class="fl">91</span>, <span class="fl">120</span><span class="op">)</span>,</span>
 <span>  parent <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">97.8</span>, <span class="fl">60</span>, <span class="fl">51</span>, <span class="fl">43</span>, <span class="fl">35</span>, <span class="fl">22</span>, <span class="fl">15</span>, <span class="fl">12</span><span class="op">)</span><span class="op">)</span></span>
@@ -607,16 +566,22 @@ report, p. 290.</p>
 one or more datasets in one call to the function <code>mmkin</code>. The
 datasets have to be passed in a list, in this case a named list holding
 only the L3 dataset prepared above.</p>
-<div class="sourceCode" id="cb23"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb22"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># Only use one core here, not to offend the CRAN checks</span></span>
 <span><span class="va">mm.L3</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"FOMC"</span>, <span class="st">"DFOP"</span><span class="op">)</span>, cores <span class="op">=</span> <span class="fl">1</span>,</span>
 <span>               <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="st">"FOCUS L3"</span> <span class="op">=</span> <span class="va">FOCUS_2006_L3_mkin</span><span class="op">)</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">mm.L3</span><span class="op">)</span></span></code></pre></div>
+<span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">mm.L3</span><span class="op">)</span></span></code></pre></div>
 <p><img src="FOCUS_L_files/figure-html/unnamed-chunk-12-1.png" width="700"></p>
-<p>The <span class="math inline">\(\chi^2\)</span> error level of 21% as
-well as the plot suggest that the SFO model does not fit very well. The
-FOMC model performs better, with an error level at which the <span class="math inline">\(\chi^2\)</span> test passes of 7%. Fitting the
-four parameter DFOP model further reduces the <span class="math inline">\(\chi^2\)</span> error level considerably.</p>
+<p>The
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><msup><mi>χ</mi><mn>2</mn></msup><annotation encoding="application/x-tex">\chi^2</annotation></semantics></math>
+error level of 21% as well as the plot suggest that the SFO model does
+not fit very well. The FOMC model performs better, with an error level
+at which the
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><msup><mi>χ</mi><mn>2</mn></msup><annotation encoding="application/x-tex">\chi^2</annotation></semantics></math>
+test passes of 7%. Fitting the four parameter DFOP model further reduces
+the
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><msup><mi>χ</mi><mn>2</mn></msup><annotation encoding="application/x-tex">\chi^2</annotation></semantics></math>
+error level considerably.</p>
 </div>
 <div class="section level3">
 <h3 id="accessing-mmkin-objects">Accessing mmkin objects<a class="anchor" aria-label="anchor" href="#accessing-mmkin-objects"></a>
@@ -626,12 +591,12 @@ as a row index and datasets as a column index.</p>
 <p>We can extract the summary and plot for <em>e.g.</em> the DFOP fit,
 using square brackets for indexing which will result in the use of the
 summary and plot functions working on mkinfit objects.</p>
-<div class="sourceCode" id="cb24"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">mm.L3</span><span class="op">[[</span><span class="st">"DFOP"</span>, <span class="fl">1</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## mkin version used for fitting:    1.2.5 </span></span>
-<span><span class="co">## R version used for fitting:       4.3.1 </span></span>
-<span><span class="co">## Date of fit:     Wed Aug  9 17:55:41 2023 </span></span>
-<span><span class="co">## Date of summary: Wed Aug  9 17:55:41 2023 </span></span>
+<div class="sourceCode" id="cb23"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">mm.L3</span><span class="op">[[</span><span class="st">"DFOP"</span>, <span class="fl">1</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## mkin version used for fitting:    1.2.9 </span></span>
+<span><span class="co">## R version used for fitting:       4.4.2 </span></span>
+<span><span class="co">## Date of fit:     Thu Feb 13 15:49:56 2025 </span></span>
+<span><span class="co">## Date of summary: Thu Feb 13 15:49:56 2025 </span></span>
 <span><span class="co">## </span></span>
 <span><span class="co">## Equations:</span></span>
 <span><span class="co">## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *</span></span>
@@ -640,7 +605,7 @@ summary and plot functions working on mkinfit objects.</p>
 <span><span class="co">## </span></span>
 <span><span class="co">## Model predictions using solution type analytical </span></span>
 <span><span class="co">## </span></span>
-<span><span class="co">## Fitted using 376 model solutions performed in 0.075 s</span></span>
+<span><span class="co">## Fitted using 376 model solutions performed in 0.024 s</span></span>
 <span><span class="co">## </span></span>
 <span><span class="co">## Error model: Constant variance </span></span>
 <span><span class="co">## </span></span>
@@ -714,14 +679,15 @@ summary and plot functions working on mkinfit objects.</p>
 <span><span class="co">##    60   parent     22.0     23.26 -1.25919</span></span>
 <span><span class="co">##    91   parent     15.0     15.18 -0.18181</span></span>
 <span><span class="co">##   120   parent     12.0     10.19  1.81395</span></span></code></pre>
-<div class="sourceCode" id="cb26"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">mm.L3</span><span class="op">[[</span><span class="st">"DFOP"</span>, <span class="fl">1</span><span class="op">]</span><span class="op">]</span>, show_errmin <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
+<div class="sourceCode" id="cb25"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">mm.L3</span><span class="op">[[</span><span class="st">"DFOP"</span>, <span class="fl">1</span><span class="op">]</span><span class="op">]</span>, show_errmin <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
 <p><img src="FOCUS_L_files/figure-html/unnamed-chunk-13-1.png" width="700"></p>
 <p>Here, a look to the model plot, the confidence intervals of the
 parameters and the correlation matrix suggest that the parameter
 estimates are reliable, and the DFOP model can be used as the best-fit
-model based on the <span class="math inline">\(\chi^2\)</span> error
-level criterion for laboratory data L3.</p>
+model based on the
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><msup><mi>χ</mi><mn>2</mn></msup><annotation encoding="application/x-tex">\chi^2</annotation></semantics></math>
+error level criterion for laboratory data L3.</p>
 <p>This is also an example where the standard t-test for the parameter
 <code>g_ilr</code> is misleading, as it tests for a significant
 difference from zero. In this case, zero appears to be the correct value
@@ -734,38 +700,40 @@ parameter <code>g</code> is quite narrow.</p>
 </h2>
 <p>The following code defines example dataset L4 from the FOCUS kinetics
 report, p. 293:</p>
-<div class="sourceCode" id="cb27"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb26"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">FOCUS_2006_L4</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span></span>
 <span>  t <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">3</span>, <span class="fl">7</span>, <span class="fl">14</span>, <span class="fl">30</span>, <span class="fl">60</span>, <span class="fl">91</span>, <span class="fl">120</span><span class="op">)</span>,</span>
 <span>  parent <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">96.6</span>, <span class="fl">96.3</span>, <span class="fl">94.3</span>, <span class="fl">88.8</span>, <span class="fl">74.9</span>, <span class="fl">59.9</span>, <span class="fl">53.5</span>, <span class="fl">49.0</span><span class="op">)</span><span class="op">)</span></span>
 <span><span class="va">FOCUS_2006_L4_mkin</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/mkin_wide_to_long.html">mkin_wide_to_long</a></span><span class="op">(</span><span class="va">FOCUS_2006_L4</span><span class="op">)</span></span></code></pre></div>
 <p>Fits of the SFO and FOMC models, plots and summaries are produced
 below:</p>
-<div class="sourceCode" id="cb28"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb27"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># Only use one core here, not to offend the CRAN checks</span></span>
 <span><span class="va">mm.L4</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"FOMC"</span><span class="op">)</span>, cores <span class="op">=</span> <span class="fl">1</span>,</span>
 <span>               <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="st">"FOCUS L4"</span> <span class="op">=</span> <span class="va">FOCUS_2006_L4_mkin</span><span class="op">)</span>,</span>
 <span>               quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">mm.L4</span><span class="op">)</span></span></code></pre></div>
+<span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">mm.L4</span><span class="op">)</span></span></code></pre></div>
 <p><img src="FOCUS_L_files/figure-html/unnamed-chunk-15-1.png" width="700"></p>
-<p>The <span class="math inline">\(\chi^2\)</span> error level of 3.3%
-as well as the plot suggest that the SFO model fits very well. The error
-level at which the <span class="math inline">\(\chi^2\)</span> test
-passes is slightly lower for the FOMC model. However, the difference
-appears negligible.</p>
-<div class="sourceCode" id="cb29"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">mm.L4</span><span class="op">[[</span><span class="st">"SFO"</span>, <span class="fl">1</span><span class="op">]</span><span class="op">]</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## mkin version used for fitting:    1.2.5 </span></span>
-<span><span class="co">## R version used for fitting:       4.3.1 </span></span>
-<span><span class="co">## Date of fit:     Wed Aug  9 17:55:42 2023 </span></span>
-<span><span class="co">## Date of summary: Wed Aug  9 17:55:42 2023 </span></span>
+<p>The
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><msup><mi>χ</mi><mn>2</mn></msup><annotation encoding="application/x-tex">\chi^2</annotation></semantics></math>
+error level of 3.3% as well as the plot suggest that the SFO model fits
+very well. The error level at which the
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><msup><mi>χ</mi><mn>2</mn></msup><annotation encoding="application/x-tex">\chi^2</annotation></semantics></math>
+test passes is slightly lower for the FOMC model. However, the
+difference appears negligible.</p>
+<div class="sourceCode" id="cb28"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">mm.L4</span><span class="op">[[</span><span class="st">"SFO"</span>, <span class="fl">1</span><span class="op">]</span><span class="op">]</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## mkin version used for fitting:    1.2.9 </span></span>
+<span><span class="co">## R version used for fitting:       4.4.2 </span></span>
+<span><span class="co">## Date of fit:     Thu Feb 13 15:49:56 2025 </span></span>
+<span><span class="co">## Date of summary: Thu Feb 13 15:49:56 2025 </span></span>
 <span><span class="co">## </span></span>
 <span><span class="co">## Equations:</span></span>
 <span><span class="co">## d_parent/dt = - k_parent * parent</span></span>
 <span><span class="co">## </span></span>
 <span><span class="co">## Model predictions using solution type analytical </span></span>
 <span><span class="co">## </span></span>
-<span><span class="co">## Fitted using 142 model solutions performed in 0.027 s</span></span>
+<span><span class="co">## Fitted using 142 model solutions performed in 0.009 s</span></span>
 <span><span class="co">## </span></span>
 <span><span class="co">## Error model: Constant variance </span></span>
 <span><span class="co">## </span></span>
@@ -818,19 +786,19 @@ appears negligible.</p>
 <span><span class="co">## Estimated disappearance times:</span></span>
 <span><span class="co">##        DT50 DT90</span></span>
 <span><span class="co">## parent  106  352</span></span></code></pre>
-<div class="sourceCode" id="cb31"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">mm.L4</span><span class="op">[[</span><span class="st">"FOMC"</span>, <span class="fl">1</span><span class="op">]</span><span class="op">]</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## mkin version used for fitting:    1.2.5 </span></span>
-<span><span class="co">## R version used for fitting:       4.3.1 </span></span>
-<span><span class="co">## Date of fit:     Wed Aug  9 17:55:42 2023 </span></span>
-<span><span class="co">## Date of summary: Wed Aug  9 17:55:42 2023 </span></span>
+<div class="sourceCode" id="cb30"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">mm.L4</span><span class="op">[[</span><span class="st">"FOMC"</span>, <span class="fl">1</span><span class="op">]</span><span class="op">]</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## mkin version used for fitting:    1.2.9 </span></span>
+<span><span class="co">## R version used for fitting:       4.4.2 </span></span>
+<span><span class="co">## Date of fit:     Thu Feb 13 15:49:56 2025 </span></span>
+<span><span class="co">## Date of summary: Thu Feb 13 15:49:56 2025 </span></span>
 <span><span class="co">## </span></span>
 <span><span class="co">## Equations:</span></span>
 <span><span class="co">## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent</span></span>
 <span><span class="co">## </span></span>
 <span><span class="co">## Model predictions using solution type analytical </span></span>
 <span><span class="co">## </span></span>
-<span><span class="co">## Fitted using 224 model solutions performed in 0.04 s</span></span>
+<span><span class="co">## Fitted using 224 model solutions performed in 0.013 s</span></span>
 <span><span class="co">## </span></span>
 <span><span class="co">## Error model: Constant variance </span></span>
 <span><span class="co">## </span></span>
@@ -900,35 +868,26 @@ Validierung von Modellierungssoftware als Alternative zu ModelMaker
 </div>
 </div>
 </div>
-  </div>
-
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-
-        <nav id="toc" data-toggle="toc"><h2 data-toc-skip>Contents</h2>
-    </nav>
-</div>
-
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside>
 </div>
 
 
 
-      <footer><div class="copyright">
-  <p></p>
-<p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p>
-<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer>
+    </footer>
 </div>
 
-  
 
 
-  
+
 
   </body>
 </html>
diff --git a/docs/articles/FOCUS_L_files/accessible-code-block-0.0.1/empty-anchor.js b/docs/articles/FOCUS_L_files/accessible-code-block-0.0.1/empty-anchor.js
deleted file mode 100644
index ca349fd6..00000000
--- a/docs/articles/FOCUS_L_files/accessible-code-block-0.0.1/empty-anchor.js
+++ /dev/null
@@ -1,15 +0,0 @@
-// Hide empty <a> tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) -->
-// v0.0.1
-// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020.
-
-document.addEventListener('DOMContentLoaded', function() {
-  const codeList = document.getElementsByClassName("sourceCode");
-  for (var i = 0; i < codeList.length; i++) {
-    var linkList = codeList[i].getElementsByTagName('a');
-    for (var j = 0; j < linkList.length; j++) {
-      if (linkList[j].innerHTML === "") {
-        linkList[j].setAttribute('aria-hidden', 'true');
-      }
-    }
-  }
-});
diff --git a/docs/articles/FOCUS_L_files/header-attrs-2.6/header-attrs.js b/docs/articles/FOCUS_L_files/header-attrs-2.6/header-attrs.js
deleted file mode 100644
index dd57d92e..00000000
--- a/docs/articles/FOCUS_L_files/header-attrs-2.6/header-attrs.js
+++ /dev/null
@@ -1,12 +0,0 @@
-// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
-// be compatible with the behavior of Pandoc < 2.8).
-document.addEventListener('DOMContentLoaded', function(e) {
-  var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
-  var i, h, a;
-  for (i = 0; i < hs.length; i++) {
-    h = hs[i];
-    if (!/^h[1-6]$/i.test(h.tagName)) continue;  // it should be a header h1-h6
-    a = h.attributes;
-    while (a.length > 0) h.removeAttribute(a[0].name);
-  }
-});
diff --git a/docs/articles/FOCUS_L_files/header-attrs-2.7/header-attrs.js b/docs/articles/FOCUS_L_files/header-attrs-2.7/header-attrs.js
deleted file mode 100644
index dd57d92e..00000000
--- a/docs/articles/FOCUS_L_files/header-attrs-2.7/header-attrs.js
+++ /dev/null
@@ -1,12 +0,0 @@
-// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
-// be compatible with the behavior of Pandoc < 2.8).
-document.addEventListener('DOMContentLoaded', function(e) {
-  var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
-  var i, h, a;
-  for (i = 0; i < hs.length; i++) {
-    h = hs[i];
-    if (!/^h[1-6]$/i.test(h.tagName)) continue;  // it should be a header h1-h6
-    a = h.attributes;
-    while (a.length > 0) h.removeAttribute(a[0].name);
-  }
-});
diff --git a/docs/articles/index.html b/docs/articles/index.html
index f6ad43c2..b1b9f39a 100644
--- a/docs/articles/index.html
+++ b/docs/articles/index.html
@@ -1,122 +1,68 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Articles • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Articles"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-article-index">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Articles • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Articles"></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
 
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-article-index">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
       <h1>Articles</h1>
     </div>
 
     <div class="section ">
       <h3>All vignettes</h3>
-      <p class="section-desc"></p>
+      <div class="section-desc"></div>
 
-      <dl><dt><a href="FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></dt>
-        <dd>
-        </dd><dt><a href="FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></dt>
-        <dd>
-        </dd><dt><a href="mkin.html">Short introduction to mkin</a></dt>
-        <dd>
-        </dd><dt><a href="prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></dt>
+      <dl><dt><a href="prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></dt>
         <dd>
         </dd><dt><a href="prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></dt>
         <dd>
@@ -124,41 +70,45 @@
         <dd>
         </dd><dt><a href="prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></dt>
         <dd>
-        </dd><dt><a href="twa.html">Calculation of time weighted average concentrations with mkin</a></dt>
-        <dd>
-        </dd><dt><a href="web_only/FOCUS_Z.html">Example evaluation of FOCUS dataset Z</a></dt>
-        <dd>
-        </dd><dt><a href="web_only/NAFTA_examples.html">Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance</a></dt>
-        <dd>
         </dd><dt><a href="web_only/benchmarks.html">Benchmark timings for mkin</a></dt>
         <dd>
         </dd><dt><a href="web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></dt>
         <dd>
         </dd><dt><a href="web_only/dimethenamid_2018.html">Example evaluations of the dimethenamid data from 2018</a></dt>
         <dd>
+        </dd><dt><a href="FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></dt>
+        <dd>
+        </dd><dt><a href="FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></dt>
+        <dd>
+        </dd><dt><a href="web_only/FOCUS_Z.html">Example evaluation of FOCUS dataset Z</a></dt>
+        <dd>
+        </dd><dt><a href="mkin.html">Short introduction to mkin</a></dt>
+        <dd>
         </dd><dt><a href="web_only/multistart.html">Short demo of the multistart method</a></dt>
         <dd>
+        </dd><dt><a href="web_only/NAFTA_examples.html">Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance</a></dt>
+        <dd>
         </dd><dt><a href="web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></dt>
         <dd>
+        </dd><dt><a href="twa.html">Calculation of time weighted average concentrations with mkin</a></dt>
+        <dd>
       </dd></dl></div>
-  </div>
-</div>
+  </main></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/articles/mkin.html b/docs/articles/mkin.html
index 3bd0d4d3..fcf37ee4 100644
--- a/docs/articles/mkin.html
+++ b/docs/articles/mkin.html
@@ -1,154 +1,82 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en">
-<head>
-<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
-<meta charset="utf-8">
-<meta http-equiv="X-UA-Compatible" content="IE=edge">
-<meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>Short introduction to mkin • mkin</title>
-<!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous">
-<script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css">
-<script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous">
-<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous">
-<!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet">
-<script src="../pkgdown.js"></script><meta property="og:title" content="Short introduction to mkin">
-<meta property="og:description" content="mkin">
-<!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]-->
-</head>
-<body data-spy="scroll" data-target="#toc">
-    
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Short introduction to mkin • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Short introduction to mkin"></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
 
-    <div class="container template-article">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.5</span>
-      </span>
-    </div>
 
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav">
-<li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu">
-<li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul>
-</li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul>
-<ul class="nav navbar-nav navbar-right">
-<li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul>
-</div>
-<!--/.nav-collapse -->
   </div>
-<!--/.container -->
-</div>
-<!--/.navbar -->
+</nav><div class="container template-article">
+
+
 
-      
 
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header toc-ignore">
-      <h1 data-toc-skip>Short introduction to mkin</h1>
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Short introduction to mkin</h1>
                         <h4 data-toc-skip class="author">Johannes
 Ranke</h4>
             
             <h4 data-toc-skip class="date">Last change 18 May 2023
-(rebuilt 2023-08-09)</h4>
+(rebuilt 2025-02-13)</h4>
       
       <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/mkin.rmd" class="external-link"><code>vignettes/mkin.rmd</code></a></small>
-      <div class="hidden name"><code>mkin.rmd</code></div>
-
+      <div class="d-none name"><code>mkin.rmd</code></div>
     </div>
 
     
     
-<p><a href="https://www.jrwb.de" class="external-link">Wissenschaftlicher Berater, Kronacher
-Str. 12, 79639 Grenzach-Wyhlen, Germany</a><br> Privatdozent at the
+<p><a href="https://www.jrwb.de">Wissenschaftlicher Berater, Kronacher
+Str. 12, 79639 Grenzach-Wyhlen, Germany</a><br /> Privatdozent at the
 University of Freiburg</p>
-<div class="section level2">
-<h2 id="abstract">Abstract<a class="anchor" aria-label="anchor" href="#abstract"></a>
-</h2>
+<div id="abstract" class="section level1">
+<h1>Abstract</h1>
 <p>In the regulatory evaluation of chemical substances like plant
 protection products (pesticides), biocides and other chemicals,
 degradation data play an important role. For the evaluation of pesticide
@@ -158,40 +86,38 @@ nonlinear optimisation. The <code>R</code> add-on package
 this guidance from within R and calculates some statistical measures for
 data series within one or more compartments, for parent and
 metabolites.</p>
-<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="st"><a href="https://pkgdown.jrwb.de/mkin/">"mkin"</a></span>, quietly <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
-<span><span class="co"># Define the kinetic model</span></span>
-<span><span class="va">m_SFO_SFO_SFO</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"M1"</span><span class="op">)</span>,</span>
-<span>                         M1 <span class="op">=</span> <span class="fu"><a href="../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"M2"</span><span class="op">)</span>,</span>
-<span>                         M2 <span class="op">=</span> <span class="fu"><a href="../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span>
-<span>                         use_of_ff <span class="op">=</span> <span class="st">"max"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
-<span></span>
-<span></span>
-<span><span class="co"># Produce model predictions using some arbitrary parameters</span></span>
-<span><span class="va">sampling_times</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span>, <span class="fl">3</span>, <span class="fl">7</span>, <span class="fl">14</span>, <span class="fl">28</span>, <span class="fl">60</span>, <span class="fl">90</span>, <span class="fl">120</span><span class="op">)</span></span>
-<span><span class="va">d_SFO_SFO_SFO</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/mkinpredict.html">mkinpredict</a></span><span class="op">(</span><span class="va">m_SFO_SFO_SFO</span>,</span>
-<span>  <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_parent <span class="op">=</span> <span class="fl">0.03</span>,</span>
-<span>    f_parent_to_M1 <span class="op">=</span> <span class="fl">0.5</span>, k_M1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Log.html" class="external-link">log</a></span><span class="op">(</span><span class="fl">2</span><span class="op">)</span><span class="op">/</span><span class="fl">100</span>,</span>
-<span>    f_M1_to_M2 <span class="op">=</span> <span class="fl">0.9</span>, k_M2 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Log.html" class="external-link">log</a></span><span class="op">(</span><span class="fl">2</span><span class="op">)</span><span class="op">/</span><span class="fl">50</span><span class="op">)</span>,</span>
-<span>  <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fl">100</span>, M1 <span class="op">=</span> <span class="fl">0</span>, M2 <span class="op">=</span> <span class="fl">0</span><span class="op">)</span>,</span>
-<span>  <span class="va">sampling_times</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># Generate a dataset by adding normally distributed errors with</span></span>
-<span><span class="co"># standard deviation 3, for two replicates at each sampling time</span></span>
-<span><span class="va">d_SFO_SFO_SFO_err</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/add_err.html">add_err</a></span><span class="op">(</span><span class="va">d_SFO_SFO_SFO</span>, reps <span class="op">=</span> <span class="fl">2</span>,</span>
-<span>                             sdfunc <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fl">3</span>,</span>
-<span>                             n <span class="op">=</span> <span class="fl">1</span>, seed <span class="op">=</span> <span class="fl">123456789</span> <span class="op">)</span></span>
-<span></span>
-<span><span class="co"># Fit the model to the dataset</span></span>
-<span><span class="va">f_SFO_SFO_SFO</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">m_SFO_SFO_SFO</span>, <span class="va">d_SFO_SFO_SFO_err</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># Plot the results separately for parent and metabolites</span></span>
-<span><span class="fu"><a href="../reference/plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">f_SFO_SFO_SFO</span>, lpos <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"topright"</span>, <span class="st">"bottomright"</span>, <span class="st">"bottomright"</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
-<p><img src="mkin_files/figure-html/unnamed-chunk-2-1.png" width="768"></p>
+<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(<span class="st">&quot;mkin&quot;</span>, <span class="at">quietly =</span> <span class="cn">TRUE</span>)</span>
+<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="co"># Define the kinetic model</span></span>
+<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a>m_SFO_SFO_SFO <span class="ot">&lt;-</span> <span class="fu">mkinmod</span>(<span class="at">parent =</span> <span class="fu">mkinsub</span>(<span class="st">&quot;SFO&quot;</span>, <span class="st">&quot;M1&quot;</span>),</span>
+<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a>                         <span class="at">M1 =</span> <span class="fu">mkinsub</span>(<span class="st">&quot;SFO&quot;</span>, <span class="st">&quot;M2&quot;</span>),</span>
+<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a>                         <span class="at">M2 =</span> <span class="fu">mkinsub</span>(<span class="st">&quot;SFO&quot;</span>),</span>
+<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a>                         <span class="at">use_of_ff =</span> <span class="st">&quot;max&quot;</span>, <span class="at">quiet =</span> <span class="cn">TRUE</span>)</span>
+<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb1-8"><a href="#cb1-8" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb1-9"><a href="#cb1-9" aria-hidden="true" tabindex="-1"></a><span class="co"># Produce model predictions using some arbitrary parameters</span></span>
+<span id="cb1-10"><a href="#cb1-10" aria-hidden="true" tabindex="-1"></a>sampling_times <span class="ot">=</span> <span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">1</span>, <span class="dv">3</span>, <span class="dv">7</span>, <span class="dv">14</span>, <span class="dv">28</span>, <span class="dv">60</span>, <span class="dv">90</span>, <span class="dv">120</span>)</span>
+<span id="cb1-11"><a href="#cb1-11" aria-hidden="true" tabindex="-1"></a>d_SFO_SFO_SFO <span class="ot">&lt;-</span> <span class="fu">mkinpredict</span>(m_SFO_SFO_SFO,</span>
+<span id="cb1-12"><a href="#cb1-12" aria-hidden="true" tabindex="-1"></a>  <span class="fu">c</span>(<span class="at">k_parent =</span> <span class="fl">0.03</span>,</span>
+<span id="cb1-13"><a href="#cb1-13" aria-hidden="true" tabindex="-1"></a>    <span class="at">f_parent_to_M1 =</span> <span class="fl">0.5</span>, <span class="at">k_M1 =</span> <span class="fu">log</span>(<span class="dv">2</span>)<span class="sc">/</span><span class="dv">100</span>,</span>
+<span id="cb1-14"><a href="#cb1-14" aria-hidden="true" tabindex="-1"></a>    <span class="at">f_M1_to_M2 =</span> <span class="fl">0.9</span>, <span class="at">k_M2 =</span> <span class="fu">log</span>(<span class="dv">2</span>)<span class="sc">/</span><span class="dv">50</span>),</span>
+<span id="cb1-15"><a href="#cb1-15" aria-hidden="true" tabindex="-1"></a>  <span class="fu">c</span>(<span class="at">parent =</span> <span class="dv">100</span>, <span class="at">M1 =</span> <span class="dv">0</span>, <span class="at">M2 =</span> <span class="dv">0</span>),</span>
+<span id="cb1-16"><a href="#cb1-16" aria-hidden="true" tabindex="-1"></a>  sampling_times)</span>
+<span id="cb1-17"><a href="#cb1-17" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb1-18"><a href="#cb1-18" aria-hidden="true" tabindex="-1"></a><span class="co"># Generate a dataset by adding normally distributed errors with</span></span>
+<span id="cb1-19"><a href="#cb1-19" aria-hidden="true" tabindex="-1"></a><span class="co"># standard deviation 3, for two replicates at each sampling time</span></span>
+<span id="cb1-20"><a href="#cb1-20" aria-hidden="true" tabindex="-1"></a>d_SFO_SFO_SFO_err <span class="ot">&lt;-</span> <span class="fu">add_err</span>(d_SFO_SFO_SFO, <span class="at">reps =</span> <span class="dv">2</span>,</span>
+<span id="cb1-21"><a href="#cb1-21" aria-hidden="true" tabindex="-1"></a>                             <span class="at">sdfunc =</span> <span class="cf">function</span>(x) <span class="dv">3</span>,</span>
+<span id="cb1-22"><a href="#cb1-22" aria-hidden="true" tabindex="-1"></a>                             <span class="at">n =</span> <span class="dv">1</span>, <span class="at">seed =</span> <span class="dv">123456789</span> )</span>
+<span id="cb1-23"><a href="#cb1-23" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb1-24"><a href="#cb1-24" aria-hidden="true" tabindex="-1"></a><span class="co"># Fit the model to the dataset</span></span>
+<span id="cb1-25"><a href="#cb1-25" aria-hidden="true" tabindex="-1"></a>f_SFO_SFO_SFO <span class="ot">&lt;-</span> <span class="fu">mkinfit</span>(m_SFO_SFO_SFO, d_SFO_SFO_SFO_err[[<span class="dv">1</span>]], <span class="at">quiet =</span> <span class="cn">TRUE</span>)</span>
+<span id="cb1-26"><a href="#cb1-26" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb1-27"><a href="#cb1-27" aria-hidden="true" tabindex="-1"></a><span class="co"># Plot the results separately for parent and metabolites</span></span>
+<span id="cb1-28"><a href="#cb1-28" aria-hidden="true" tabindex="-1"></a><span class="fu">plot_sep</span>(f_SFO_SFO_SFO, <span class="at">lpos =</span> <span class="fu">c</span>(<span class="st">&quot;topright&quot;</span>, <span class="st">&quot;bottomright&quot;</span>, <span class="st">&quot;bottomright&quot;</span>))</span></code></pre></div>
+<p><img src="/home/jranke/git/mkin/docs/articles/mkin_files/figure-html/unnamed-chunk-2-1.png" width="768" /></p>
 </div>
-<div class="section level2">
-<h2 id="background">Background<a class="anchor" aria-label="anchor" href="#background"></a>
-</h2>
+<div id="background" class="section level1">
+<h1>Background</h1>
 <p>The <code>mkin</code> package <span class="citation">(J. Ranke
 2021)</span> implements the approach to degradation kinetics recommended
 in the kinetics report provided by the FOrum for Co-ordination of
@@ -212,7 +138,8 @@ purpose compartment based tool providing infrastructure for fitting
 dynamic simulation models based on differential equations to data.</p>
 <p>The ‘mkin’ code was first uploaded to the BerliOS development
 platform. When this was taken down, the version control history was
-imported into the R-Forge site (see <em>e.g.</em> <a href="https://cgit.jrwb.de/mkin/commit/?id=30cbb4092f6d2d3beff5800603374a0d009ad770" class="external-link">the
+imported into the R-Forge site (see <em>e.g.</em> <a
+href="https://cgit.jrwb.de/mkin/commit/?id=30cbb4092f6d2d3beff5800603374a0d009ad770">the
 initial commit on 11 May 2010</a>), where the code is still being
 updated.</p>
 <p>At that time, the R package <code>FME</code> (Flexible Modelling
@@ -221,8 +148,9 @@ was already available, and provided a good basis for developing a
 package specifically tailored to the task. The remaining challenge was
 to make it as easy as possible for the users (including the author of
 this vignette) to specify the system of differential equations and to
-include the output requested by the FOCUS guidance, such as the <span class="math inline">\(\chi^2\)</span> error level as defined in this
-guidance.</p>
+include the output requested by the FOCUS guidance, such as the
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><msup><mi>χ</mi><mn>2</mn></msup><annotation encoding="application/x-tex">\chi^2</annotation></semantics></math>
+error level as defined in this guidance.</p>
 <p>Also, <code>mkin</code> introduced using analytical solutions for
 parent only kinetics for improved optimization speed. Later, Eigenvalue
 based solutions were introduced to <code>mkin</code> for the case of
@@ -234,9 +162,8 @@ times.</p>
 <p>The possibility to specify back-reactions and a biphasic model
 (SFORB) for metabolites were present in <code>mkin</code> from the very
 beginning.</p>
-<div class="section level3">
-<h3 id="derived-software-tools">Derived software tools<a class="anchor" aria-label="anchor" href="#derived-software-tools"></a>
-</h3>
+<div id="derived-software-tools" class="section level2">
+<h2>Derived software tools</h2>
 <p>Soon after the publication of <code>mkin</code>, two derived tools
 were published, namely KinGUII (developed at Bayer Crop Science) and
 CAKE (commissioned to Tessella by Syngenta), which added a graphical
@@ -257,44 +184,50 @@ be specified for transformation products. Starting with KinGUII version
 KinGUII.</p>
 <p>A further graphical user interface (GUI) that has recently been
 brought to a decent degree of maturity is the browser based GUI named
-<code>gmkin</code>. Please see its <a href="https://pkgdown.jrwb.de/gmkin/" class="external-link">documentation page</a> and <a href="https://pkgdown.jrwb.de/gmkin/articles/gmkin_manual.html" class="external-link">manual</a>
+<code>gmkin</code>. Please see its <a
+href="https://pkgdown.jrwb.de/gmkin/">documentation page</a> and <a
+href="https://pkgdown.jrwb.de/gmkin/articles/gmkin_manual.html">manual</a>
 for further information.</p>
 <p>A comparison of scope, usability and numerical results obtained with
-these tools has been recently been published by <span class="citation">Johannes Ranke, Wöltjen, and Meinecke
+these tools has been recently been published by <span
+class="citation">Johannes Ranke, Wöltjen, and Meinecke
 (2018)</span>.</p>
 </div>
 </div>
-<div class="section level2">
-<h2 id="unique-features">Unique features<a class="anchor" aria-label="anchor" href="#unique-features"></a>
-</h2>
+<div id="unique-features" class="section level1">
+<h1>Unique features</h1>
 <p>Currently, the main unique features available in <code>mkin</code>
 are</p>
 <ul>
-<li>the <a href="https://pkgdown.jrwb.de/mkin/articles/web_only/compiled_models.html">speed
+<li>the <a
+href="https://pkgdown.jrwb.de/mkin/articles/web_only/compiled_models.html">speed
 increase</a> by using compiled code when a compiler is present,</li>
-<li>parallel model fitting on multicore machines using the <a href="https://pkgdown.jrwb.de/mkin/reference/mmkin.html"><code>mmkin</code>
+<li>parallel model fitting on multicore machines using the <a
+href="https://pkgdown.jrwb.de/mkin/reference/mmkin.html"><code>mmkin</code>
 function</a>,</li>
 <li>the estimation of parameter confidence intervals based on
 transformed parameters (see below) and</li>
-<li>the possibility to use the <a href="https://pkgdown.jrwb.de/mkin/reference/sigma_twocomp.html">two-component
-error model</a>
-</li>
+<li>the possibility to use the <a
+href="https://pkgdown.jrwb.de/mkin/reference/sigma_twocomp.html">two-component
+error model</a></li>
 </ul>
 <p>The iteratively reweighted least squares fitting of different
 variances for each variable as introduced by <span class="citation">Gao
-et al. (2011)</span> has been available in mkin since <a href="https://pkgdown.jrwb.de/mkin/news/index.html#mkin-0-9-22-2013-10-26">version
-0.9-22</a>. With <a href="https://pkgdown.jrwb.de/mkin/news/index.html#mkin-0-9-49-5-2019-07-04">release
+et al. (2011)</span> has been available in mkin since <a
+href="https://pkgdown.jrwb.de/mkin/news/index.html#mkin-0-9-22-2013-10-26">version
+0.9-22</a>. With <a
+href="https://pkgdown.jrwb.de/mkin/news/index.html#mkin-0-9-49-5-2019-07-04">release
 0.9.49.5</a>, the IRLS algorithm has been complemented by direct or
 step-wise maximisation of the likelihood function, which makes it
 possible not only to fit the variance by variable error model but also a
-<a href="https://pkgdown.jrwb.de/mkin/reference/sigma_twocomp.html">two-component
+<a
+href="https://pkgdown.jrwb.de/mkin/reference/sigma_twocomp.html">two-component
 error model</a> inspired by error models developed in analytical
 chemistry <span class="citation">(Johannes Ranke and Meinecke
 2019)</span>.</p>
 </div>
-<div class="section level2">
-<h2 id="internal-parameter-transformations">Internal parameter transformations<a class="anchor" aria-label="anchor" href="#internal-parameter-transformations"></a>
-</h2>
+<div id="internal-parameter-transformations" class="section level1">
+<h1>Internal parameter transformations</h1>
 <p>For rate constants, the log transformation is used, as proposed by
 Bates and Watts <span class="citation">(1988, 77, 149)</span>.
 Approximate intervals are constructed for the transformed rate constants
@@ -319,9 +252,9 @@ well as in the subsequent calculation of parameter confidence intervals.
 In the current version of mkin, a logit transformation is used for
 parameters that are bound between 0 and 1, such as the g parameter of
 the DFOP model.</p>
-<div class="section level3">
-<h3 id="confidence-intervals-based-on-transformed-parameters">Confidence intervals based on transformed parameters<a class="anchor" aria-label="anchor" href="#confidence-intervals-based-on-transformed-parameters"></a>
-</h3>
+<div id="confidence-intervals-based-on-transformed-parameters"
+class="section level2">
+<h2>Confidence intervals based on transformed parameters</h2>
 <p>In the first attempt at providing improved parameter confidence
 intervals introduced to <code>mkin</code> in 2013, confidence intervals
 obtained from FME on the transformed parameters were simply all
@@ -343,13 +276,14 @@ are considered by the author of this vignette to be more accurate than
 those obtained using a re-estimation of the Hessian matrix after
 backtransformation, as implemented in the FME package.</p>
 </div>
-<div class="section level3">
-<h3 id="parameter-t-test-based-on-untransformed-parameters">Parameter t-test based on untransformed parameters<a class="anchor" aria-label="anchor" href="#parameter-t-test-based-on-untransformed-parameters"></a>
-</h3>
+<div id="parameter-t-test-based-on-untransformed-parameters"
+class="section level2">
+<h2>Parameter t-test based on untransformed parameters</h2>
 <p>The standard output of many nonlinear regression software packages
 includes the results from a test for significant difference from zero
 for all parameters. Such a test is also recommended to check the
-validity of rate constants in the FOCUS guidance <span class="citation">(FOCUS Work Group on Degradation Kinetics 2014,
+validity of rate constants in the FOCUS guidance <span
+class="citation">(FOCUS Work Group on Degradation Kinetics 2014,
 96ff)</span>.</p>
 <p>It has been argued that the precondition for this test, <em>i.e.</em>
 normal distribution of the estimator for the parameters, is not
@@ -360,18 +294,18 @@ reliability of parameter estimates, based on the FOCUS guidance
 mentioned above. Therefore, the results of this one-sided t-test are
 included in the summary output from <code>mkin</code>.</p>
 <p>As it is not reasonable to test for significant difference of the
-transformed parameters (<em>e.g.</em> <span class="math inline">\(log(k)\)</span>) from zero, the t-test is
-calculated based on the model definition before parameter
-transformation, <em>i.e.</em> in a similar way as in packages that do
-not apply such an internal parameter transformation. A note is included
-in the <code>mkin</code> output, pointing to the fact that the t-test is
-based on the unjustified assumption of normal distribution of the
-parameter estimators.</p>
+transformed parameters (<em>e.g.</em>
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>l</mi><mi>o</mi><mi>g</mi><mrow><mo stretchy="true" form="prefix">(</mo><mi>k</mi><mo stretchy="true" form="postfix">)</mo></mrow></mrow><annotation encoding="application/x-tex">log(k)</annotation></semantics></math>)
+from zero, the t-test is calculated based on the model definition before
+parameter transformation, <em>i.e.</em> in a similar way as in packages
+that do not apply such an internal parameter transformation. A note is
+included in the <code>mkin</code> output, pointing to the fact that the
+t-test is based on the unjustified assumption of normal distribution of
+the parameter estimators.</p>
 </div>
 </div>
-<div class="section level2">
-<h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a>
-</h2>
+<div id="references" class="section level1">
+<h1>References</h1>
 <!-- vim: set foldmethod=syntax: -->
 <div id="refs" class="references csl-bib-body hanging-indent">
 <div id="ref-bates1988" class="csl-entry">
@@ -382,12 +316,14 @@ Applications</em>. Wiley-Interscience.
 FOCUS Work Group on Degradation Kinetics. 2006. <em>Guidance Document on
 Estimating Persistence and Degradation Kinetics from Environmental Fate
 Studies on Pesticides in EU Registration. Report of the FOCUS Work Group
-on Degradation Kinetics</em>. <a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics" class="external-link">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a>.
+on Degradation Kinetics</em>. <a
+href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a>.
 </div>
 <div id="ref-FOCUSkinetics2014" class="csl-entry">
 ———. 2014. <em>Generic Guidance for Estimating Persistence and
 Degradation Kinetics from Environmental Fate Studies on Pesticides in EU
-Registration</em>. 1.1 ed. <a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics" class="external-link">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a>.
+Registration</em>. 1.1 ed. <a
+href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a>.
 </div>
 <div id="ref-gao11" class="csl-entry">
 Gao, Z., J. W. Green, J. Vanderborght, and W. Schmitt. 2011.
@@ -397,29 +333,34 @@ Science and Technology</em> 45: 4429–37.
 </div>
 <div id="ref-pkg:mkin" class="csl-entry">
 Ranke, J. 2021. <em>‘<span class="nocase">mkin</span>‘:
-<span>K</span>inetic Evaluation of Chemical Degradation Data</em>. <a href="https://CRAN.R-project.org/package=mkin" class="external-link">https://CRAN.R-project.org/package=mkin</a>.
+<span>K</span>inetic Evaluation of Chemical Degradation Data</em>. <a
+href="https://CRAN.R-project.org/package=mkin">https://CRAN.R-project.org/package=mkin</a>.
 </div>
 <div id="ref-ranke2012" class="csl-entry">
 Ranke, J., and R. Lehmann. 2012. <span>“Parameter Reliability in Kinetic
 Evaluation of Environmental Metabolism Data - Assessment and the
 Influence of Model Specification.”</span> In <em>SETAC World 20-24
-May</em>. Berlin. <a href="https://jrwb.de/posters/Poster_SETAC_2012_Kinetic_parameter_uncertainty_model_parameterization_Lehmann_Ranke.pdf" class="external-link">https://jrwb.de/posters/Poster_SETAC_2012_Kinetic_parameter_uncertainty_model_parameterization_Lehmann_Ranke.pdf</a>.
+May</em>. Berlin. <a
+href="https://jrwb.de/posters/Poster_SETAC_2012_Kinetic_parameter_uncertainty_model_parameterization_Lehmann_Ranke.pdf">https://jrwb.de/posters/Poster_SETAC_2012_Kinetic_parameter_uncertainty_model_parameterization_Lehmann_Ranke.pdf</a>.
 </div>
 <div id="ref-ranke2015" class="csl-entry">
 ———. 2015. <span>“To t-Test or Not to t-Test, That Is the
 Question.”</span> In <em>XV Symposium on Pesticide Chemistry 2-4
-September 2015</em>. Piacenza. <a href="https://jrwb.de/posters/piacenza_2015.pdf" class="external-link">https://jrwb.de/posters/piacenza_2015.pdf</a>.
+September 2015</em>. Piacenza. <a
+href="https://jrwb.de/posters/piacenza_2015.pdf">https://jrwb.de/posters/piacenza_2015.pdf</a>.
 </div>
 <div id="ref-ranke2019" class="csl-entry">
 Ranke, Johannes, and Stefan Meinecke. 2019. <span>“Error Models for the
 Kinetic Evaluation of Chemical Degradation Data.”</span>
-<em>Environments</em> 6 (12). <a href="https://doi.org/10.3390/environments6120124" class="external-link">https://doi.org/10.3390/environments6120124</a>.
+<em>Environments</em> 6 (12). <a
+href="https://doi.org/10.3390/environments6120124">https://doi.org/10.3390/environments6120124</a>.
 </div>
 <div id="ref-ranke2018" class="csl-entry">
 Ranke, Johannes, Janina Wöltjen, and Stefan Meinecke. 2018.
 <span>“Comparison of Software Tools for Kinetic Evaluation of Chemical
 Degradation Data.”</span> <em>Environmental Sciences Europe</em> 30 (1):
-17. <a href="https://doi.org/10.1186/s12302-018-0145-1" class="external-link">https://doi.org/10.1186/s12302-018-0145-1</a>.
+17. <a
+href="https://doi.org/10.1186/s12302-018-0145-1">https://doi.org/10.1186/s12302-018-0145-1</a>.
 </div>
 <div id="ref-schaefer2007" class="csl-entry">
 Schäfer, D., B. Mikolasch, P. Rainbird, and B. Harvey. 2007.
@@ -433,39 +374,28 @@ Piacenza.
 Soetaert, Karline, and Thomas Petzoldt. 2010. <span>“Inverse Modelling,
 Sensitivity and Monte Carlo Analysis in <span>R</span> Using Package
 <span>FME</span>.”</span> <em>Journal of Statistical Software</em> 33
-(3): 1–28. <a href="https://doi.org/10.18637/jss.v033.i03" class="external-link">https://doi.org/10.18637/jss.v033.i03</a>.
+(3): 1–28. <a
+href="https://doi.org/10.18637/jss.v033.i03">https://doi.org/10.18637/jss.v033.i03</a>.
 </div>
 </div>
 </div>
-  </div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
 
-        <nav id="toc" data-toggle="toc"><h2 data-toc-skip>Contents</h2>
-    </nav>
-</div>
 
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-
-
-      <footer><div class="copyright">
-  <p></p>
-<p>Developed by Johannes Ranke.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p>
-<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
+    </footer></div>
 
-      </footer>
-</div>
 
-  
 
 
-  
 
-  </body>
-</html>
+  </body></html>
diff --git a/docs/articles/mkin_files/accessible-code-block-0.0.1/empty-anchor.js b/docs/articles/mkin_files/accessible-code-block-0.0.1/empty-anchor.js
deleted file mode 100644
index ca349fd6..00000000
--- a/docs/articles/mkin_files/accessible-code-block-0.0.1/empty-anchor.js
+++ /dev/null
@@ -1,15 +0,0 @@
-// Hide empty <a> tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) -->
-// v0.0.1
-// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020.
-
-document.addEventListener('DOMContentLoaded', function() {
-  const codeList = document.getElementsByClassName("sourceCode");
-  for (var i = 0; i < codeList.length; i++) {
-    var linkList = codeList[i].getElementsByTagName('a');
-    for (var j = 0; j < linkList.length; j++) {
-      if (linkList[j].innerHTML === "") {
-        linkList[j].setAttribute('aria-hidden', 'true');
-      }
-    }
-  }
-});
diff --git a/docs/articles/mkin_files/figure-html/unnamed-chunk-2-1.png b/docs/articles/mkin_files/figure-html/unnamed-chunk-2-1.png
deleted file mode 100644
index 65c1a613..00000000
--- a/docs/articles/mkin_files/figure-html/unnamed-chunk-2-1.png
+++ /dev/null
diff --git a/docs/articles/mkin_files/header-attrs-2.6/header-attrs.js b/docs/articles/mkin_files/header-attrs-2.6/header-attrs.js
deleted file mode 100644
index dd57d92e..00000000
--- a/docs/articles/mkin_files/header-attrs-2.6/header-attrs.js
+++ /dev/null
@@ -1,12 +0,0 @@
-// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
-// be compatible with the behavior of Pandoc < 2.8).
-document.addEventListener('DOMContentLoaded', function(e) {
-  var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
-  var i, h, a;
-  for (i = 0; i < hs.length; i++) {
-    h = hs[i];
-    if (!/^h[1-6]$/i.test(h.tagName)) continue;  // it should be a header h1-h6
-    a = h.attributes;
-    while (a.length > 0) h.removeAttribute(a[0].name);
-  }
-});
diff --git a/docs/articles/mkin_files/header-attrs-2.7/header-attrs.js b/docs/articles/mkin_files/header-attrs-2.7/header-attrs.js
deleted file mode 100644
index dd57d92e..00000000
--- a/docs/articles/mkin_files/header-attrs-2.7/header-attrs.js
+++ /dev/null
@@ -1,12 +0,0 @@
-// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
-// be compatible with the behavior of Pandoc < 2.8).
-document.addEventListener('DOMContentLoaded', function(e) {
-  var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
-  var i, h, a;
-  for (i = 0; i < hs.length; i++) {
-    h = hs[i];
-    if (!/^h[1-6]$/i.test(h.tagName)) continue;  // it should be a header h1-h6
-    a = h.attributes;
-    while (a.length > 0) h.removeAttribute(a[0].name);
-  }
-});
diff --git a/docs/articles/prebuilt/2022_cyan_pathway.html b/docs/articles/prebuilt/2022_cyan_pathway.html
index c22b07e4..ea4dd035 100644
--- a/docs/articles/prebuilt/2022_cyan_pathway.html
+++ b/docs/articles/prebuilt/2022_cyan_pathway.html
@@ -1,155 +1,374 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en">
+
+<html>
+
 <head>
-<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
-<meta charset="utf-8">
-<meta http-equiv="X-UA-Compatible" content="IE=edge">
-<meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>Testing hierarchical pathway kinetics with residue data on cyantraniliprole • mkin</title>
-<!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous">
-<script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../../bootstrap-toc.css">
-<script src="../../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous">
-<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous">
-<!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../../pkgdown.css" rel="stylesheet">
-<script src="../../pkgdown.js"></script><meta property="og:title" content="Testing hierarchical pathway kinetics with residue data on cyantraniliprole">
-<meta property="og:description" content="mkin">
-<!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]-->
+
+<meta charset="utf-8" />
+<meta name="generator" content="pandoc" />
+<meta http-equiv="X-UA-Compatible" content="IE=EDGE" />
+
+
+<meta name="author" content="Johannes Ranke" />
+
+
+<title>Testing hierarchical pathway kinetics with residue data on cyantraniliprole</title>
+
+<script>// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
+// be compatible with the behavior of Pandoc < 2.8).
+document.addEventListener('DOMContentLoaded', function(e) {
+  var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
+  var i, h, a;
+  for (i = 0; i < hs.length; i++) {
+    h = hs[i];
+    if (!/^h[1-6]$/i.test(h.tagName)) continue;  // it should be a header h1-h6
+    a = h.attributes;
+    while (a.length > 0) h.removeAttribute(a[0].name);
+  }
+});
+</script>
+<script>/*! jQuery v3.6.0 | (c) OpenJS Foundation and other contributors | jquery.org/license */
+!function(e,t){"use strict";"object"==typeof module&&"object"==typeof module.exports?module.exports=e.document?t(e,!0):function(e){if(!e.document)throw new Error("jQuery requires a window with a document");return t(e)}:t(e)}("undefined"!=typeof window?window:this,function(C,e){"use strict";var t=[],r=Object.getPrototypeOf,s=t.slice,g=t.flat?function(e){return t.flat.call(e)}:function(e){return t.concat.apply([],e)},u=t.push,i=t.indexOf,n={},o=n.toString,v=n.hasOwnProperty,a=v.toString,l=a.call(Object),y={},m=function(e){return"function"==typeof e&&"number"!=typeof e.nodeType&&"function"!=typeof e.item},x=function(e){return null!=e&&e===e.window},E=C.document,c={type:!0,src:!0,nonce:!0,noModule:!0};function b(e,t,n){var r,i,o=(n=n||E).createElement("script");if(o.text=e,t)for(r in c)(i=t[r]||t.getAttribute&&t.getAttribute(r))&&o.setAttribute(r,i);n.head.appendChild(o).parentNode.removeChild(o)}function w(e){return null==e?e+"":"object"==typeof e||"function"==typeof e?n[o.call(e)]||"object":typeof e}var f="3.6.0",S=function(e,t){return new S.fn.init(e,t)};function p(e){var t=!!e&&"length"in e&&e.length,n=w(e);return!m(e)&&!x(e)&&("array"===n||0===t||"number"==typeof t&&0<t&&t-1 in e)}S.fn=S.prototype={jquery:f,constructor:S,length:0,toArray:function(){return s.call(this)},get:function(e){return null==e?s.call(this):e<0?this[e+this.length]:this[e]},pushStack:function(e){var t=S.merge(this.constructor(),e);return t.prevObject=this,t},each:function(e){return S.each(this,e)},map:function(n){return this.pushStack(S.map(this,function(e,t){return n.call(e,t,e)}))},slice:function(){return this.pushStack(s.apply(this,arguments))},first:function(){return this.eq(0)},last:function(){return this.eq(-1)},even:function(){return this.pushStack(S.grep(this,function(e,t){return(t+1)%2}))},odd:function(){return this.pushStack(S.grep(this,function(e,t){return t%2}))},eq:function(e){var t=this.length,n=+e+(e<0?t:0);return this.pushStack(0<=n&&n<t?[this[n]]:[])},end:function(){return this.prevObject||this.constructor()},push:u,sort:t.sort,splice:t.splice},S.extend=S.fn.extend=function(){var e,t,n,r,i,o,a=arguments[0]||{},s=1,u=arguments.length,l=!1;for("boolean"==typeof a&&(l=a,a=arguments[s]||{},s++),"object"==typeof a||m(a)||(a={}),s===u&&(a=this,s--);s<u;s++)if(null!=(e=arguments[s]))for(t in e)r=e[t],"__proto__"!==t&&a!==r&&(l&&r&&(S.isPlainObject(r)||(i=Array.isArray(r)))?(n=a[t],o=i&&!Array.isArray(n)?[]:i||S.isPlainObject(n)?n:{},i=!1,a[t]=S.extend(l,o,r)):void 0!==r&&(a[t]=r));return a},S.extend({expando:"jQuery"+(f+Math.random()).replace(/\D/g,""),isReady:!0,error:function(e){throw new Error(e)},noop:function(){},isPlainObject:function(e){var t,n;return!(!e||"[object Object]"!==o.call(e))&&(!(t=r(e))||"function"==typeof(n=v.call(t,"constructor")&&t.constructor)&&a.call(n)===l)},isEmptyObject:function(e){var t;for(t in e)return!1;return!0},globalEval:function(e,t,n){b(e,{nonce:t&&t.nonce},n)},each:function(e,t){var n,r=0;if(p(e)){for(n=e.length;r<n;r++)if(!1===t.call(e[r],r,e[r]))break}else for(r in e)if(!1===t.call(e[r],r,e[r]))break;return e},makeArray:function(e,t){var n=t||[];return null!=e&&(p(Object(e))?S.merge(n,"string"==typeof e?[e]:e):u.call(n,e)),n},inArray:function(e,t,n){return null==t?-1:i.call(t,e,n)},merge:function(e,t){for(var n=+t.length,r=0,i=e.length;r<n;r++)e[i++]=t[r];return e.length=i,e},grep:function(e,t,n){for(var r=[],i=0,o=e.length,a=!n;i<o;i++)!t(e[i],i)!==a&&r.push(e[i]);return r},map:function(e,t,n){var r,i,o=0,a=[];if(p(e))for(r=e.length;o<r;o++)null!=(i=t(e[o],o,n))&&a.push(i);else for(o in e)null!=(i=t(e[o],o,n))&&a.push(i);return g(a)},guid:1,support:y}),"function"==typeof Symbol&&(S.fn[Symbol.iterator]=t[Symbol.iterator]),S.each("Boolean Number String Function Array Date RegExp Object Error Symbol".split(" "),function(e,t){n["[object "+t+"]"]=t.toLowerCase()});var d=function(n){var e,d,b,o,i,h,f,g,w,u,l,T,C,a,E,v,s,c,y,S="sizzle"+1*new Date,p=n.document,k=0,r=0,m=ue(),x=ue(),A=ue(),N=ue(),j=function(e,t){return e===t&&(l=!0),0},D={}.hasOwnProperty,t=[],q=t.pop,L=t.push,H=t.push,O=t.slice,P=function(e,t){for(var n=0,r=e.length;n<r;n++)if(e[n]===t)return n;return-1},R="checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|ismap|loop|multiple|open|readonly|required|scoped",M="[\\x20\\t\\r\\n\\f]",I="(?:\\\\[\\da-fA-F]{1,6}"+M+"?|\\\\[^\\r\\n\\f]|[\\w-]|[^\0-\\x7f])+",W="\\["+M+"*("+I+")(?:"+M+"*([*^$|!~]?=)"+M+"*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|("+I+"))|)"+M+"*\\]",F=":("+I+")(?:\\((('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|((?:\\\\.|[^\\\\()[\\]]|"+W+")*)|.*)\\)|)",B=new RegExp(M+"+","g"),$=new RegExp("^"+M+"+|((?:^|[^\\\\])(?:\\\\.)*)"+M+"+$","g"),_=new RegExp("^"+M+"*,"+M+"*"),z=new RegExp("^"+M+"*([>+~]|"+M+")"+M+"*"),U=new RegExp(M+"|>"),X=new RegExp(F),V=new RegExp("^"+I+"$"),G={ID:new RegExp("^#("+I+")"),CLASS:new RegExp("^\\.("+I+")"),TAG:new RegExp("^("+I+"|[*])"),ATTR:new RegExp("^"+W),PSEUDO:new RegExp("^"+F),CHILD:new RegExp("^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\("+M+"*(even|odd|(([+-]|)(\\d*)n|)"+M+"*(?:([+-]|)"+M+"*(\\d+)|))"+M+"*\\)|)","i"),bool:new RegExp("^(?:"+R+")$","i"),needsContext:new RegExp("^"+M+"*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\("+M+"*((?:-\\d)?\\d*)"+M+"*\\)|)(?=[^-]|$)","i")},Y=/HTML$/i,Q=/^(?:input|select|textarea|button)$/i,J=/^h\d$/i,K=/^[^{]+\{\s*\[native \w/,Z=/^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/,ee=/[+~]/,te=new RegExp("\\\\[\\da-fA-F]{1,6}"+M+"?|\\\\([^\\r\\n\\f])","g"),ne=function(e,t){var n="0x"+e.slice(1)-65536;return t||(n<0?String.fromCharCode(n+65536):String.fromCharCode(n>>10|55296,1023&n|56320))},re=/([\0-\x1f\x7f]|^-?\d)|^-$|[^\0-\x1f\x7f-\uFFFF\w-]/g,ie=function(e,t){return t?"\0"===e?"\ufffd":e.slice(0,-1)+"\\"+e.charCodeAt(e.length-1).toString(16)+" ":"\\"+e},oe=function(){T()},ae=be(function(e){return!0===e.disabled&&"fieldset"===e.nodeName.toLowerCase()},{dir:"parentNode",next:"legend"});try{H.apply(t=O.call(p.childNodes),p.childNodes),t[p.childNodes.length].nodeType}catch(e){H={apply:t.length?function(e,t){L.apply(e,O.call(t))}:function(e,t){var n=e.length,r=0;while(e[n++]=t[r++]);e.length=n-1}}}function se(t,e,n,r){var i,o,a,s,u,l,c,f=e&&e.ownerDocument,p=e?e.nodeType:9;if(n=n||[],"string"!=typeof t||!t||1!==p&&9!==p&&11!==p)return n;if(!r&&(T(e),e=e||C,E)){if(11!==p&&(u=Z.exec(t)))if(i=u[1]){if(9===p){if(!(a=e.getElementById(i)))return n;if(a.id===i)return n.push(a),n}else if(f&&(a=f.getElementById(i))&&y(e,a)&&a.id===i)return n.push(a),n}else{if(u[2])return H.apply(n,e.getElementsByTagName(t)),n;if((i=u[3])&&d.getElementsByClassName&&e.getElementsByClassName)return H.apply(n,e.getElementsByClassName(i)),n}if(d.qsa&&!N[t+" "]&&(!v||!v.test(t))&&(1!==p||"object"!==e.nodeName.toLowerCase())){if(c=t,f=e,1===p&&(U.test(t)||z.test(t))){(f=ee.test(t)&&ye(e.parentNode)||e)===e&&d.scope||((s=e.getAttribute("id"))?s=s.replace(re,ie):e.setAttribute("id",s=S)),o=(l=h(t)).length;while(o--)l[o]=(s?"#"+s:":scope")+" "+xe(l[o]);c=l.join(",")}try{return H.apply(n,f.querySelectorAll(c)),n}catch(e){N(t,!0)}finally{s===S&&e.removeAttribute("id")}}}return g(t.replace($,"$1"),e,n,r)}function ue(){var r=[];return function e(t,n){return r.push(t+" ")>b.cacheLength&&delete e[r.shift()],e[t+" "]=n}}function le(e){return e[S]=!0,e}function ce(e){var t=C.createElement("fieldset");try{return!!e(t)}catch(e){return!1}finally{t.parentNode&&t.parentNode.removeChild(t),t=null}}function fe(e,t){var n=e.split("|"),r=n.length;while(r--)b.attrHandle[n[r]]=t}function pe(e,t){var n=t&&e,r=n&&1===e.nodeType&&1===t.nodeType&&e.sourceIndex-t.sourceIndex;if(r)return r;if(n)while(n=n.nextSibling)if(n===t)return-1;return e?1:-1}function de(t){return function(e){return"input"===e.nodeName.toLowerCase()&&e.type===t}}function he(n){return function(e){var t=e.nodeName.toLowerCase();return("input"===t||"button"===t)&&e.type===n}}function ge(t){return function(e){return"form"in e?e.parentNode&&!1===e.disabled?"label"in e?"label"in e.parentNode?e.parentNode.disabled===t:e.disabled===t:e.isDisabled===t||e.isDisabled!==!t&&ae(e)===t:e.disabled===t:"label"in e&&e.disabled===t}}function ve(a){return le(function(o){return o=+o,le(function(e,t){var n,r=a([],e.length,o),i=r.length;while(i--)e[n=r[i]]&&(e[n]=!(t[n]=e[n]))})})}function ye(e){return e&&"undefined"!=typeof e.getElementsByTagName&&e}for(e in d=se.support={},i=se.isXML=function(e){var t=e&&e.namespaceURI,n=e&&(e.ownerDocument||e).documentElement;return!Y.test(t||n&&n.nodeName||"HTML")},T=se.setDocument=function(e){var t,n,r=e?e.ownerDocument||e:p;return r!=C&&9===r.nodeType&&r.documentElement&&(a=(C=r).documentElement,E=!i(C),p!=C&&(n=C.defaultView)&&n.top!==n&&(n.addEventListener?n.addEventListener("unload",oe,!1):n.attachEvent&&n.attachEvent("onunload",oe)),d.scope=ce(function(e){return a.appendChild(e).appendChild(C.createElement("div")),"undefined"!=typeof e.querySelectorAll&&!e.querySelectorAll(":scope fieldset div").length}),d.attributes=ce(function(e){return e.className="i",!e.getAttribute("className")}),d.getElementsByTagName=ce(function(e){return e.appendChild(C.createComment("")),!e.getElementsByTagName("*").length}),d.getElementsByClassName=K.test(C.getElementsByClassName),d.getById=ce(function(e){return a.appendChild(e).id=S,!C.getElementsByName||!C.getElementsByName(S).length}),d.getById?(b.filter.ID=function(e){var t=e.replace(te,ne);return function(e){return e.getAttribute("id")===t}},b.find.ID=function(e,t){if("undefined"!=typeof t.getElementById&&E){var n=t.getElementById(e);return n?[n]:[]}}):(b.filter.ID=function(e){var n=e.replace(te,ne);return function(e){var t="undefined"!=typeof e.getAttributeNode&&e.getAttributeNode("id");return t&&t.value===n}},b.find.ID=function(e,t){if("undefined"!=typeof t.getElementById&&E){var n,r,i,o=t.getElementById(e);if(o){if((n=o.getAttributeNode("id"))&&n.value===e)return[o];i=t.getElementsByName(e),r=0;while(o=i[r++])if((n=o.getAttributeNode("id"))&&n.value===e)return[o]}return[]}}),b.find.TAG=d.getElementsByTagName?function(e,t){return"undefined"!=typeof t.getElementsByTagName?t.getElementsByTagName(e):d.qsa?t.querySelectorAll(e):void 0}:function(e,t){var n,r=[],i=0,o=t.getElementsByTagName(e);if("*"===e){while(n=o[i++])1===n.nodeType&&r.push(n);return r}return o},b.find.CLASS=d.getElementsByClassName&&function(e,t){if("undefined"!=typeof t.getElementsByClassName&&E)return t.getElementsByClassName(e)},s=[],v=[],(d.qsa=K.test(C.querySelectorAll))&&(ce(function(e){var t;a.appendChild(e).innerHTML="<a id='"+S+"'></a><select id='"+S+"-\r\\' msallowcapture=''><option selected=''></option></select>",e.querySelectorAll("[msallowcapture^='']").length&&v.push("[*^$]="+M+"*(?:''|\"\")"),e.querySelectorAll("[selected]").length||v.push("\\["+M+"*(?:value|"+R+")"),e.querySelectorAll("[id~="+S+"-]").length||v.push("~="),(t=C.createElement("input")).setAttribute("name",""),e.appendChild(t),e.querySelectorAll("[name='']").length||v.push("\\["+M+"*name"+M+"*="+M+"*(?:''|\"\")"),e.querySelectorAll(":checked").length||v.push(":checked"),e.querySelectorAll("a#"+S+"+*").length||v.push(".#.+[+~]"),e.querySelectorAll("\\\f"),v.push("[\\r\\n\\f]")}),ce(function(e){e.innerHTML="<a href='' disabled='disabled'></a><select disabled='disabled'><option/></select>";var t=C.createElement("input");t.setAttribute("type","hidden"),e.appendChild(t).setAttribute("name","D"),e.querySelectorAll("[name=d]").length&&v.push("name"+M+"*[*^$|!~]?="),2!==e.querySelectorAll(":enabled").length&&v.push(":enabled",":disabled"),a.appendChild(e).disabled=!0,2!==e.querySelectorAll(":disabled").length&&v.push(":enabled",":disabled"),e.querySelectorAll("*,:x"),v.push(",.*:")})),(d.matchesSelector=K.test(c=a.matches||a.webkitMatchesSelector||a.mozMatchesSelector||a.oMatchesSelector||a.msMatchesSelector))&&ce(function(e){d.disconnectedMatch=c.call(e,"*"),c.call(e,"[s!='']:x"),s.push("!=",F)}),v=v.length&&new RegExp(v.join("|")),s=s.length&&new RegExp(s.join("|")),t=K.test(a.compareDocumentPosition),y=t||K.test(a.contains)?function(e,t){var n=9===e.nodeType?e.documentElement:e,r=t&&t.parentNode;return e===r||!(!r||1!==r.nodeType||!(n.contains?n.contains(r):e.compareDocumentPosition&&16&e.compareDocumentPosition(r)))}:function(e,t){if(t)while(t=t.parentNode)if(t===e)return!0;return!1},j=t?function(e,t){if(e===t)return l=!0,0;var n=!e.compareDocumentPosition-!t.compareDocumentPosition;return n||(1&(n=(e.ownerDocument||e)==(t.ownerDocument||t)?e.compareDocumentPosition(t):1)||!d.sortDetached&&t.compareDocumentPosition(e)===n?e==C||e.ownerDocument==p&&y(p,e)?-1:t==C||t.ownerDocument==p&&y(p,t)?1:u?P(u,e)-P(u,t):0:4&n?-1:1)}:function(e,t){if(e===t)return l=!0,0;var n,r=0,i=e.parentNode,o=t.parentNode,a=[e],s=[t];if(!i||!o)return e==C?-1:t==C?1:i?-1:o?1:u?P(u,e)-P(u,t):0;if(i===o)return pe(e,t);n=e;while(n=n.parentNode)a.unshift(n);n=t;while(n=n.parentNode)s.unshift(n);while(a[r]===s[r])r++;return r?pe(a[r],s[r]):a[r]==p?-1:s[r]==p?1:0}),C},se.matches=function(e,t){return se(e,null,null,t)},se.matchesSelector=function(e,t){if(T(e),d.matchesSelector&&E&&!N[t+" "]&&(!s||!s.test(t))&&(!v||!v.test(t)))try{var n=c.call(e,t);if(n||d.disconnectedMatch||e.document&&11!==e.document.nodeType)return n}catch(e){N(t,!0)}return 0<se(t,C,null,[e]).length},se.contains=function(e,t){return(e.ownerDocument||e)!=C&&T(e),y(e,t)},se.attr=function(e,t){(e.ownerDocument||e)!=C&&T(e);var n=b.attrHandle[t.toLowerCase()],r=n&&D.call(b.attrHandle,t.toLowerCase())?n(e,t,!E):void 0;return void 0!==r?r:d.attributes||!E?e.getAttribute(t):(r=e.getAttributeNode(t))&&r.specified?r.value:null},se.escape=function(e){return(e+"").replace(re,ie)},se.error=function(e){throw new Error("Syntax error, unrecognized expression: "+e)},se.uniqueSort=function(e){var t,n=[],r=0,i=0;if(l=!d.detectDuplicates,u=!d.sortStable&&e.slice(0),e.sort(j),l){while(t=e[i++])t===e[i]&&(r=n.push(i));while(r--)e.splice(n[r],1)}return u=null,e},o=se.getText=function(e){var t,n="",r=0,i=e.nodeType;if(i){if(1===i||9===i||11===i){if("string"==typeof e.textContent)return e.textContent;for(e=e.firstChild;e;e=e.nextSibling)n+=o(e)}else if(3===i||4===i)return e.nodeValue}else while(t=e[r++])n+=o(t);return n},(b=se.selectors={cacheLength:50,createPseudo:le,match:G,attrHandle:{},find:{},relative:{">":{dir:"parentNode",first:!0}," ":{dir:"parentNode"},"+":{dir:"previousSibling",first:!0},"~":{dir:"previousSibling"}},preFilter:{ATTR:function(e){return e[1]=e[1].replace(te,ne),e[3]=(e[3]||e[4]||e[5]||"").replace(te,ne),"~="===e[2]&&(e[3]=" "+e[3]+" "),e.slice(0,4)},CHILD:function(e){return e[1]=e[1].toLowerCase(),"nth"===e[1].slice(0,3)?(e[3]||se.error(e[0]),e[4]=+(e[4]?e[5]+(e[6]||1):2*("even"===e[3]||"odd"===e[3])),e[5]=+(e[7]+e[8]||"odd"===e[3])):e[3]&&se.error(e[0]),e},PSEUDO:function(e){var t,n=!e[6]&&e[2];return G.CHILD.test(e[0])?null:(e[3]?e[2]=e[4]||e[5]||"":n&&X.test(n)&&(t=h(n,!0))&&(t=n.indexOf(")",n.length-t)-n.length)&&(e[0]=e[0].slice(0,t),e[2]=n.slice(0,t)),e.slice(0,3))}},filter:{TAG:function(e){var t=e.replace(te,ne).toLowerCase();return"*"===e?function(){return!0}:function(e){return e.nodeName&&e.nodeName.toLowerCase()===t}},CLASS:function(e){var t=m[e+" "];return t||(t=new RegExp("(^|"+M+")"+e+"("+M+"|$)"))&&m(e,function(e){return t.test("string"==typeof e.className&&e.className||"undefined"!=typeof e.getAttribute&&e.getAttribute("class")||"")})},ATTR:function(n,r,i){return function(e){var t=se.attr(e,n);return null==t?"!="===r:!r||(t+="","="===r?t===i:"!="===r?t!==i:"^="===r?i&&0===t.indexOf(i):"*="===r?i&&-1<t.indexOf(i):"$="===r?i&&t.slice(-i.length)===i:"~="===r?-1<(" "+t.replace(B," ")+" ").indexOf(i):"|="===r&&(t===i||t.slice(0,i.length+1)===i+"-"))}},CHILD:function(h,e,t,g,v){var y="nth"!==h.slice(0,3),m="last"!==h.slice(-4),x="of-type"===e;return 1===g&&0===v?function(e){return!!e.parentNode}:function(e,t,n){var r,i,o,a,s,u,l=y!==m?"nextSibling":"previousSibling",c=e.parentNode,f=x&&e.nodeName.toLowerCase(),p=!n&&!x,d=!1;if(c){if(y){while(l){a=e;while(a=a[l])if(x?a.nodeName.toLowerCase()===f:1===a.nodeType)return!1;u=l="only"===h&&!u&&"nextSibling"}return!0}if(u=[m?c.firstChild:c.lastChild],m&&p){d=(s=(r=(i=(o=(a=c)[S]||(a[S]={}))[a.uniqueID]||(o[a.uniqueID]={}))[h]||[])[0]===k&&r[1])&&r[2],a=s&&c.childNodes[s];while(a=++s&&a&&a[l]||(d=s=0)||u.pop())if(1===a.nodeType&&++d&&a===e){i[h]=[k,s,d];break}}else if(p&&(d=s=(r=(i=(o=(a=e)[S]||(a[S]={}))[a.uniqueID]||(o[a.uniqueID]={}))[h]||[])[0]===k&&r[1]),!1===d)while(a=++s&&a&&a[l]||(d=s=0)||u.pop())if((x?a.nodeName.toLowerCase()===f:1===a.nodeType)&&++d&&(p&&((i=(o=a[S]||(a[S]={}))[a.uniqueID]||(o[a.uniqueID]={}))[h]=[k,d]),a===e))break;return(d-=v)===g||d%g==0&&0<=d/g}}},PSEUDO:function(e,o){var t,a=b.pseudos[e]||b.setFilters[e.toLowerCase()]||se.error("unsupported pseudo: "+e);return a[S]?a(o):1<a.length?(t=[e,e,"",o],b.setFilters.hasOwnProperty(e.toLowerCase())?le(function(e,t){var n,r=a(e,o),i=r.length;while(i--)e[n=P(e,r[i])]=!(t[n]=r[i])}):function(e){return a(e,0,t)}):a}},pseudos:{not:le(function(e){var r=[],i=[],s=f(e.replace($,"$1"));return s[S]?le(function(e,t,n,r){var i,o=s(e,null,r,[]),a=e.length;while(a--)(i=o[a])&&(e[a]=!(t[a]=i))}):function(e,t,n){return r[0]=e,s(r,null,n,i),r[0]=null,!i.pop()}}),has:le(function(t){return function(e){return 0<se(t,e).length}}),contains:le(function(t){return t=t.replace(te,ne),function(e){return-1<(e.textContent||o(e)).indexOf(t)}}),lang:le(function(n){return V.test(n||"")||se.error("unsupported lang: "+n),n=n.replace(te,ne).toLowerCase(),function(e){var t;do{if(t=E?e.lang:e.getAttribute("xml:lang")||e.getAttribute("lang"))return(t=t.toLowerCase())===n||0===t.indexOf(n+"-")}while((e=e.parentNode)&&1===e.nodeType);return!1}}),target:function(e){var t=n.location&&n.location.hash;return t&&t.slice(1)===e.id},root:function(e){return e===a},focus:function(e){return e===C.activeElement&&(!C.hasFocus||C.hasFocus())&&!!(e.type||e.href||~e.tabIndex)},enabled:ge(!1),disabled:ge(!0),checked:function(e){var t=e.nodeName.toLowerCase();return"input"===t&&!!e.checked||"option"===t&&!!e.selected},selected:function(e){return e.parentNode&&e.parentNode.selectedIndex,!0===e.selected},empty:function(e){for(e=e.firstChild;e;e=e.nextSibling)if(e.nodeType<6)return!1;return!0},parent:function(e){return!b.pseudos.empty(e)},header:function(e){return J.test(e.nodeName)},input:function(e){return Q.test(e.nodeName)},button:function(e){var t=e.nodeName.toLowerCase();return"input"===t&&"button"===e.type||"button"===t},text:function(e){var t;return"input"===e.nodeName.toLowerCase()&&"text"===e.type&&(null==(t=e.getAttribute("type"))||"text"===t.toLowerCase())},first:ve(function(){return[0]}),last:ve(function(e,t){return[t-1]}),eq:ve(function(e,t,n){return[n<0?n+t:n]}),even:ve(function(e,t){for(var n=0;n<t;n+=2)e.push(n);return e}),odd:ve(function(e,t){for(var n=1;n<t;n+=2)e.push(n);return e}),lt:ve(function(e,t,n){for(var r=n<0?n+t:t<n?t:n;0<=--r;)e.push(r);return e}),gt:ve(function(e,t,n){for(var r=n<0?n+t:n;++r<t;)e.push(r);return e})}}).pseudos.nth=b.pseudos.eq,{radio:!0,checkbox:!0,file:!0,password:!0,image:!0})b.pseudos[e]=de(e);for(e in{submit:!0,reset:!0})b.pseudos[e]=he(e);function me(){}function xe(e){for(var t=0,n=e.length,r="";t<n;t++)r+=e[t].value;return r}function be(s,e,t){var u=e.dir,l=e.next,c=l||u,f=t&&"parentNode"===c,p=r++;return e.first?function(e,t,n){while(e=e[u])if(1===e.nodeType||f)return s(e,t,n);return!1}:function(e,t,n){var r,i,o,a=[k,p];if(n){while(e=e[u])if((1===e.nodeType||f)&&s(e,t,n))return!0}else while(e=e[u])if(1===e.nodeType||f)if(i=(o=e[S]||(e[S]={}))[e.uniqueID]||(o[e.uniqueID]={}),l&&l===e.nodeName.toLowerCase())e=e[u]||e;else{if((r=i[c])&&r[0]===k&&r[1]===p)return a[2]=r[2];if((i[c]=a)[2]=s(e,t,n))return!0}return!1}}function we(i){return 1<i.length?function(e,t,n){var r=i.length;while(r--)if(!i[r](e,t,n))return!1;return!0}:i[0]}function Te(e,t,n,r,i){for(var o,a=[],s=0,u=e.length,l=null!=t;s<u;s++)(o=e[s])&&(n&&!n(o,r,i)||(a.push(o),l&&t.push(s)));return a}function Ce(d,h,g,v,y,e){return v&&!v[S]&&(v=Ce(v)),y&&!y[S]&&(y=Ce(y,e)),le(function(e,t,n,r){var i,o,a,s=[],u=[],l=t.length,c=e||function(e,t,n){for(var r=0,i=t.length;r<i;r++)se(e,t[r],n);return n}(h||"*",n.nodeType?[n]:n,[]),f=!d||!e&&h?c:Te(c,s,d,n,r),p=g?y||(e?d:l||v)?[]:t:f;if(g&&g(f,p,n,r),v){i=Te(p,u),v(i,[],n,r),o=i.length;while(o--)(a=i[o])&&(p[u[o]]=!(f[u[o]]=a))}if(e){if(y||d){if(y){i=[],o=p.length;while(o--)(a=p[o])&&i.push(f[o]=a);y(null,p=[],i,r)}o=p.length;while(o--)(a=p[o])&&-1<(i=y?P(e,a):s[o])&&(e[i]=!(t[i]=a))}}else p=Te(p===t?p.splice(l,p.length):p),y?y(null,t,p,r):H.apply(t,p)})}function Ee(e){for(var i,t,n,r=e.length,o=b.relative[e[0].type],a=o||b.relative[" "],s=o?1:0,u=be(function(e){return e===i},a,!0),l=be(function(e){return-1<P(i,e)},a,!0),c=[function(e,t,n){var r=!o&&(n||t!==w)||((i=t).nodeType?u(e,t,n):l(e,t,n));return i=null,r}];s<r;s++)if(t=b.relative[e[s].type])c=[be(we(c),t)];else{if((t=b.filter[e[s].type].apply(null,e[s].matches))[S]){for(n=++s;n<r;n++)if(b.relative[e[n].type])break;return Ce(1<s&&we(c),1<s&&xe(e.slice(0,s-1).concat({value:" "===e[s-2].type?"*":""})).replace($,"$1"),t,s<n&&Ee(e.slice(s,n)),n<r&&Ee(e=e.slice(n)),n<r&&xe(e))}c.push(t)}return we(c)}return me.prototype=b.filters=b.pseudos,b.setFilters=new me,h=se.tokenize=function(e,t){var n,r,i,o,a,s,u,l=x[e+" "];if(l)return t?0:l.slice(0);a=e,s=[],u=b.preFilter;while(a){for(o in n&&!(r=_.exec(a))||(r&&(a=a.slice(r[0].length)||a),s.push(i=[])),n=!1,(r=z.exec(a))&&(n=r.shift(),i.push({value:n,type:r[0].replace($," ")}),a=a.slice(n.length)),b.filter)!(r=G[o].exec(a))||u[o]&&!(r=u[o](r))||(n=r.shift(),i.push({value:n,type:o,matches:r}),a=a.slice(n.length));if(!n)break}return t?a.length:a?se.error(e):x(e,s).slice(0)},f=se.compile=function(e,t){var n,v,y,m,x,r,i=[],o=[],a=A[e+" "];if(!a){t||(t=h(e)),n=t.length;while(n--)(a=Ee(t[n]))[S]?i.push(a):o.push(a);(a=A(e,(v=o,m=0<(y=i).length,x=0<v.length,r=function(e,t,n,r,i){var o,a,s,u=0,l="0",c=e&&[],f=[],p=w,d=e||x&&b.find.TAG("*",i),h=k+=null==p?1:Math.random()||.1,g=d.length;for(i&&(w=t==C||t||i);l!==g&&null!=(o=d[l]);l++){if(x&&o){a=0,t||o.ownerDocument==C||(T(o),n=!E);while(s=v[a++])if(s(o,t||C,n)){r.push(o);break}i&&(k=h)}m&&((o=!s&&o)&&u--,e&&c.push(o))}if(u+=l,m&&l!==u){a=0;while(s=y[a++])s(c,f,t,n);if(e){if(0<u)while(l--)c[l]||f[l]||(f[l]=q.call(r));f=Te(f)}H.apply(r,f),i&&!e&&0<f.length&&1<u+y.length&&se.uniqueSort(r)}return i&&(k=h,w=p),c},m?le(r):r))).selector=e}return a},g=se.select=function(e,t,n,r){var i,o,a,s,u,l="function"==typeof e&&e,c=!r&&h(e=l.selector||e);if(n=n||[],1===c.length){if(2<(o=c[0]=c[0].slice(0)).length&&"ID"===(a=o[0]).type&&9===t.nodeType&&E&&b.relative[o[1].type]){if(!(t=(b.find.ID(a.matches[0].replace(te,ne),t)||[])[0]))return n;l&&(t=t.parentNode),e=e.slice(o.shift().value.length)}i=G.needsContext.test(e)?0:o.length;while(i--){if(a=o[i],b.relative[s=a.type])break;if((u=b.find[s])&&(r=u(a.matches[0].replace(te,ne),ee.test(o[0].type)&&ye(t.parentNode)||t))){if(o.splice(i,1),!(e=r.length&&xe(o)))return H.apply(n,r),n;break}}}return(l||f(e,c))(r,t,!E,n,!t||ee.test(e)&&ye(t.parentNode)||t),n},d.sortStable=S.split("").sort(j).join("")===S,d.detectDuplicates=!!l,T(),d.sortDetached=ce(function(e){return 1&e.compareDocumentPosition(C.createElement("fieldset"))}),ce(function(e){return e.innerHTML="<a href='#'></a>","#"===e.firstChild.getAttribute("href")})||fe("type|href|height|width",function(e,t,n){if(!n)return e.getAttribute(t,"type"===t.toLowerCase()?1:2)}),d.attributes&&ce(function(e){return e.innerHTML="<input/>",e.firstChild.setAttribute("value",""),""===e.firstChild.getAttribute("value")})||fe("value",function(e,t,n){if(!n&&"input"===e.nodeName.toLowerCase())return e.defaultValue}),ce(function(e){return null==e.getAttribute("disabled")})||fe(R,function(e,t,n){var r;if(!n)return!0===e[t]?t.toLowerCase():(r=e.getAttributeNode(t))&&r.specified?r.value:null}),se}(C);S.find=d,S.expr=d.selectors,S.expr[":"]=S.expr.pseudos,S.uniqueSort=S.unique=d.uniqueSort,S.text=d.getText,S.isXMLDoc=d.isXML,S.contains=d.contains,S.escapeSelector=d.escape;var h=function(e,t,n){var r=[],i=void 0!==n;while((e=e[t])&&9!==e.nodeType)if(1===e.nodeType){if(i&&S(e).is(n))break;r.push(e)}return r},T=function(e,t){for(var n=[];e;e=e.nextSibling)1===e.nodeType&&e!==t&&n.push(e);return n},k=S.expr.match.needsContext;function A(e,t){return e.nodeName&&e.nodeName.toLowerCase()===t.toLowerCase()}var N=/^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i;function j(e,n,r){return m(n)?S.grep(e,function(e,t){return!!n.call(e,t,e)!==r}):n.nodeType?S.grep(e,function(e){return e===n!==r}):"string"!=typeof n?S.grep(e,function(e){return-1<i.call(n,e)!==r}):S.filter(n,e,r)}S.filter=function(e,t,n){var r=t[0];return n&&(e=":not("+e+")"),1===t.length&&1===r.nodeType?S.find.matchesSelector(r,e)?[r]:[]:S.find.matches(e,S.grep(t,function(e){return 1===e.nodeType}))},S.fn.extend({find:function(e){var t,n,r=this.length,i=this;if("string"!=typeof e)return this.pushStack(S(e).filter(function(){for(t=0;t<r;t++)if(S.contains(i[t],this))return!0}));for(n=this.pushStack([]),t=0;t<r;t++)S.find(e,i[t],n);return 1<r?S.uniqueSort(n):n},filter:function(e){return this.pushStack(j(this,e||[],!1))},not:function(e){return this.pushStack(j(this,e||[],!0))},is:function(e){return!!j(this,"string"==typeof e&&k.test(e)?S(e):e||[],!1).length}});var D,q=/^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]+))$/;(S.fn.init=function(e,t,n){var r,i;if(!e)return this;if(n=n||D,"string"==typeof e){if(!(r="<"===e[0]&&">"===e[e.length-1]&&3<=e.length?[null,e,null]:q.exec(e))||!r[1]&&t)return!t||t.jquery?(t||n).find(e):this.constructor(t).find(e);if(r[1]){if(t=t instanceof S?t[0]:t,S.merge(this,S.parseHTML(r[1],t&&t.nodeType?t.ownerDocument||t:E,!0)),N.test(r[1])&&S.isPlainObject(t))for(r in t)m(this[r])?this[r](t[r]):this.attr(r,t[r]);return this}return(i=E.getElementById(r[2]))&&(this[0]=i,this.length=1),this}return e.nodeType?(this[0]=e,this.length=1,this):m(e)?void 0!==n.ready?n.ready(e):e(S):S.makeArray(e,this)}).prototype=S.fn,D=S(E);var L=/^(?:parents|prev(?:Until|All))/,H={children:!0,contents:!0,next:!0,prev:!0};function O(e,t){while((e=e[t])&&1!==e.nodeType);return e}S.fn.extend({has:function(e){var t=S(e,this),n=t.length;return this.filter(function(){for(var e=0;e<n;e++)if(S.contains(this,t[e]))return!0})},closest:function(e,t){var n,r=0,i=this.length,o=[],a="string"!=typeof e&&S(e);if(!k.test(e))for(;r<i;r++)for(n=this[r];n&&n!==t;n=n.parentNode)if(n.nodeType<11&&(a?-1<a.index(n):1===n.nodeType&&S.find.matchesSelector(n,e))){o.push(n);break}return this.pushStack(1<o.length?S.uniqueSort(o):o)},index:function(e){return e?"string"==typeof e?i.call(S(e),this[0]):i.call(this,e.jquery?e[0]:e):this[0]&&this[0].parentNode?this.first().prevAll().length:-1},add:function(e,t){return this.pushStack(S.uniqueSort(S.merge(this.get(),S(e,t))))},addBack:function(e){return this.add(null==e?this.prevObject:this.prevObject.filter(e))}}),S.each({parent:function(e){var t=e.parentNode;return t&&11!==t.nodeType?t:null},parents:function(e){return h(e,"parentNode")},parentsUntil:function(e,t,n){return h(e,"parentNode",n)},next:function(e){return O(e,"nextSibling")},prev:function(e){return O(e,"previousSibling")},nextAll:function(e){return h(e,"nextSibling")},prevAll:function(e){return h(e,"previousSibling")},nextUntil:function(e,t,n){return h(e,"nextSibling",n)},prevUntil:function(e,t,n){return h(e,"previousSibling",n)},siblings:function(e){return T((e.parentNode||{}).firstChild,e)},children:function(e){return T(e.firstChild)},contents:function(e){return null!=e.contentDocument&&r(e.contentDocument)?e.contentDocument:(A(e,"template")&&(e=e.content||e),S.merge([],e.childNodes))}},function(r,i){S.fn[r]=function(e,t){var n=S.map(this,i,e);return"Until"!==r.slice(-5)&&(t=e),t&&"string"==typeof t&&(n=S.filter(t,n)),1<this.length&&(H[r]||S.uniqueSort(n),L.test(r)&&n.reverse()),this.pushStack(n)}});var P=/[^\x20\t\r\n\f]+/g;function R(e){return e}function M(e){throw e}function I(e,t,n,r){var i;try{e&&m(i=e.promise)?i.call(e).done(t).fail(n):e&&m(i=e.then)?i.call(e,t,n):t.apply(void 0,[e].slice(r))}catch(e){n.apply(void 0,[e])}}S.Callbacks=function(r){var e,n;r="string"==typeof r?(e=r,n={},S.each(e.match(P)||[],function(e,t){n[t]=!0}),n):S.extend({},r);var i,t,o,a,s=[],u=[],l=-1,c=function(){for(a=a||r.once,o=i=!0;u.length;l=-1){t=u.shift();while(++l<s.length)!1===s[l].apply(t[0],t[1])&&r.stopOnFalse&&(l=s.length,t=!1)}r.memory||(t=!1),i=!1,a&&(s=t?[]:"")},f={add:function(){return s&&(t&&!i&&(l=s.length-1,u.push(t)),function n(e){S.each(e,function(e,t){m(t)?r.unique&&f.has(t)||s.push(t):t&&t.length&&"string"!==w(t)&&n(t)})}(arguments),t&&!i&&c()),this},remove:function(){return S.each(arguments,function(e,t){var n;while(-1<(n=S.inArray(t,s,n)))s.splice(n,1),n<=l&&l--}),this},has:function(e){return e?-1<S.inArray(e,s):0<s.length},empty:function(){return s&&(s=[]),this},disable:function(){return a=u=[],s=t="",this},disabled:function(){return!s},lock:function(){return a=u=[],t||i||(s=t=""),this},locked:function(){return!!a},fireWith:function(e,t){return a||(t=[e,(t=t||[]).slice?t.slice():t],u.push(t),i||c()),this},fire:function(){return f.fireWith(this,arguments),this},fired:function(){return!!o}};return f},S.extend({Deferred:function(e){var o=[["notify","progress",S.Callbacks("memory"),S.Callbacks("memory"),2],["resolve","done",S.Callbacks("once memory"),S.Callbacks("once memory"),0,"resolved"],["reject","fail",S.Callbacks("once memory"),S.Callbacks("once memory"),1,"rejected"]],i="pending",a={state:function(){return i},always:function(){return s.done(arguments).fail(arguments),this},"catch":function(e){return a.then(null,e)},pipe:function(){var i=arguments;return S.Deferred(function(r){S.each(o,function(e,t){var n=m(i[t[4]])&&i[t[4]];s[t[1]](function(){var e=n&&n.apply(this,arguments);e&&m(e.promise)?e.promise().progress(r.notify).done(r.resolve).fail(r.reject):r[t[0]+"With"](this,n?[e]:arguments)})}),i=null}).promise()},then:function(t,n,r){var u=0;function l(i,o,a,s){return function(){var n=this,r=arguments,e=function(){var e,t;if(!(i<u)){if((e=a.apply(n,r))===o.promise())throw new TypeError("Thenable self-resolution");t=e&&("object"==typeof e||"function"==typeof e)&&e.then,m(t)?s?t.call(e,l(u,o,R,s),l(u,o,M,s)):(u++,t.call(e,l(u,o,R,s),l(u,o,M,s),l(u,o,R,o.notifyWith))):(a!==R&&(n=void 0,r=[e]),(s||o.resolveWith)(n,r))}},t=s?e:function(){try{e()}catch(e){S.Deferred.exceptionHook&&S.Deferred.exceptionHook(e,t.stackTrace),u<=i+1&&(a!==M&&(n=void 0,r=[e]),o.rejectWith(n,r))}};i?t():(S.Deferred.getStackHook&&(t.stackTrace=S.Deferred.getStackHook()),C.setTimeout(t))}}return S.Deferred(function(e){o[0][3].add(l(0,e,m(r)?r:R,e.notifyWith)),o[1][3].add(l(0,e,m(t)?t:R)),o[2][3].add(l(0,e,m(n)?n:M))}).promise()},promise:function(e){return null!=e?S.extend(e,a):a}},s={};return S.each(o,function(e,t){var n=t[2],r=t[5];a[t[1]]=n.add,r&&n.add(function(){i=r},o[3-e][2].disable,o[3-e][3].disable,o[0][2].lock,o[0][3].lock),n.add(t[3].fire),s[t[0]]=function(){return s[t[0]+"With"](this===s?void 0:this,arguments),this},s[t[0]+"With"]=n.fireWith}),a.promise(s),e&&e.call(s,s),s},when:function(e){var n=arguments.length,t=n,r=Array(t),i=s.call(arguments),o=S.Deferred(),a=function(t){return function(e){r[t]=this,i[t]=1<arguments.length?s.call(arguments):e,--n||o.resolveWith(r,i)}};if(n<=1&&(I(e,o.done(a(t)).resolve,o.reject,!n),"pending"===o.state()||m(i[t]&&i[t].then)))return o.then();while(t--)I(i[t],a(t),o.reject);return o.promise()}});var W=/^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/;S.Deferred.exceptionHook=function(e,t){C.console&&C.console.warn&&e&&W.test(e.name)&&C.console.warn("jQuery.Deferred exception: "+e.message,e.stack,t)},S.readyException=function(e){C.setTimeout(function(){throw e})};var F=S.Deferred();function B(){E.removeEventListener("DOMContentLoaded",B),C.removeEventListener("load",B),S.ready()}S.fn.ready=function(e){return F.then(e)["catch"](function(e){S.readyException(e)}),this},S.extend({isReady:!1,readyWait:1,ready:function(e){(!0===e?--S.readyWait:S.isReady)||(S.isReady=!0)!==e&&0<--S.readyWait||F.resolveWith(E,[S])}}),S.ready.then=F.then,"complete"===E.readyState||"loading"!==E.readyState&&!E.documentElement.doScroll?C.setTimeout(S.ready):(E.addEventListener("DOMContentLoaded",B),C.addEventListener("load",B));var $=function(e,t,n,r,i,o,a){var s=0,u=e.length,l=null==n;if("object"===w(n))for(s in i=!0,n)$(e,t,s,n[s],!0,o,a);else if(void 0!==r&&(i=!0,m(r)||(a=!0),l&&(a?(t.call(e,r),t=null):(l=t,t=function(e,t,n){return l.call(S(e),n)})),t))for(;s<u;s++)t(e[s],n,a?r:r.call(e[s],s,t(e[s],n)));return i?e:l?t.call(e):u?t(e[0],n):o},_=/^-ms-/,z=/-([a-z])/g;function U(e,t){return t.toUpperCase()}function X(e){return e.replace(_,"ms-").replace(z,U)}var V=function(e){return 1===e.nodeType||9===e.nodeType||!+e.nodeType};function G(){this.expando=S.expando+G.uid++}G.uid=1,G.prototype={cache:function(e){var t=e[this.expando];return t||(t={},V(e)&&(e.nodeType?e[this.expando]=t:Object.defineProperty(e,this.expando,{value:t,configurable:!0}))),t},set:function(e,t,n){var r,i=this.cache(e);if("string"==typeof t)i[X(t)]=n;else for(r in t)i[X(r)]=t[r];return i},get:function(e,t){return void 0===t?this.cache(e):e[this.expando]&&e[this.expando][X(t)]},access:function(e,t,n){return void 0===t||t&&"string"==typeof t&&void 0===n?this.get(e,t):(this.set(e,t,n),void 0!==n?n:t)},remove:function(e,t){var n,r=e[this.expando];if(void 0!==r){if(void 0!==t){n=(t=Array.isArray(t)?t.map(X):(t=X(t))in r?[t]:t.match(P)||[]).length;while(n--)delete r[t[n]]}(void 0===t||S.isEmptyObject(r))&&(e.nodeType?e[this.expando]=void 0:delete e[this.expando])}},hasData:function(e){var t=e[this.expando];return void 0!==t&&!S.isEmptyObject(t)}};var Y=new G,Q=new G,J=/^(?:\{[\w\W]*\}|\[[\w\W]*\])$/,K=/[A-Z]/g;function Z(e,t,n){var r,i;if(void 0===n&&1===e.nodeType)if(r="data-"+t.replace(K,"-$&").toLowerCase(),"string"==typeof(n=e.getAttribute(r))){try{n="true"===(i=n)||"false"!==i&&("null"===i?null:i===+i+""?+i:J.test(i)?JSON.parse(i):i)}catch(e){}Q.set(e,t,n)}else n=void 0;return n}S.extend({hasData:function(e){return Q.hasData(e)||Y.hasData(e)},data:function(e,t,n){return Q.access(e,t,n)},removeData:function(e,t){Q.remove(e,t)},_data:function(e,t,n){return Y.access(e,t,n)},_removeData:function(e,t){Y.remove(e,t)}}),S.fn.extend({data:function(n,e){var t,r,i,o=this[0],a=o&&o.attributes;if(void 0===n){if(this.length&&(i=Q.get(o),1===o.nodeType&&!Y.get(o,"hasDataAttrs"))){t=a.length;while(t--)a[t]&&0===(r=a[t].name).indexOf("data-")&&(r=X(r.slice(5)),Z(o,r,i[r]));Y.set(o,"hasDataAttrs",!0)}return i}return"object"==typeof n?this.each(function(){Q.set(this,n)}):$(this,function(e){var t;if(o&&void 0===e)return void 0!==(t=Q.get(o,n))?t:void 0!==(t=Z(o,n))?t:void 0;this.each(function(){Q.set(this,n,e)})},null,e,1<arguments.length,null,!0)},removeData:function(e){return this.each(function(){Q.remove(this,e)})}}),S.extend({queue:function(e,t,n){var r;if(e)return t=(t||"fx")+"queue",r=Y.get(e,t),n&&(!r||Array.isArray(n)?r=Y.access(e,t,S.makeArray(n)):r.push(n)),r||[]},dequeue:function(e,t){t=t||"fx";var n=S.queue(e,t),r=n.length,i=n.shift(),o=S._queueHooks(e,t);"inprogress"===i&&(i=n.shift(),r--),i&&("fx"===t&&n.unshift("inprogress"),delete o.stop,i.call(e,function(){S.dequeue(e,t)},o)),!r&&o&&o.empty.fire()},_queueHooks:function(e,t){var n=t+"queueHooks";return Y.get(e,n)||Y.access(e,n,{empty:S.Callbacks("once memory").add(function(){Y.remove(e,[t+"queue",n])})})}}),S.fn.extend({queue:function(t,n){var e=2;return"string"!=typeof t&&(n=t,t="fx",e--),arguments.length<e?S.queue(this[0],t):void 0===n?this:this.each(function(){var e=S.queue(this,t,n);S._queueHooks(this,t),"fx"===t&&"inprogress"!==e[0]&&S.dequeue(this,t)})},dequeue:function(e){return this.each(function(){S.dequeue(this,e)})},clearQueue:function(e){return this.queue(e||"fx",[])},promise:function(e,t){var n,r=1,i=S.Deferred(),o=this,a=this.length,s=function(){--r||i.resolveWith(o,[o])};"string"!=typeof e&&(t=e,e=void 0),e=e||"fx";while(a--)(n=Y.get(o[a],e+"queueHooks"))&&n.empty&&(r++,n.empty.add(s));return s(),i.promise(t)}});var ee=/[+-]?(?:\d*\.|)\d+(?:[eE][+-]?\d+|)/.source,te=new RegExp("^(?:([+-])=|)("+ee+")([a-z%]*)$","i"),ne=["Top","Right","Bottom","Left"],re=E.documentElement,ie=function(e){return S.contains(e.ownerDocument,e)},oe={composed:!0};re.getRootNode&&(ie=function(e){return S.contains(e.ownerDocument,e)||e.getRootNode(oe)===e.ownerDocument});var ae=function(e,t){return"none"===(e=t||e).style.display||""===e.style.display&&ie(e)&&"none"===S.css(e,"display")};function se(e,t,n,r){var i,o,a=20,s=r?function(){return r.cur()}:function(){return S.css(e,t,"")},u=s(),l=n&&n[3]||(S.cssNumber[t]?"":"px"),c=e.nodeType&&(S.cssNumber[t]||"px"!==l&&+u)&&te.exec(S.css(e,t));if(c&&c[3]!==l){u/=2,l=l||c[3],c=+u||1;while(a--)S.style(e,t,c+l),(1-o)*(1-(o=s()/u||.5))<=0&&(a=0),c/=o;c*=2,S.style(e,t,c+l),n=n||[]}return n&&(c=+c||+u||0,i=n[1]?c+(n[1]+1)*n[2]:+n[2],r&&(r.unit=l,r.start=c,r.end=i)),i}var ue={};function le(e,t){for(var n,r,i,o,a,s,u,l=[],c=0,f=e.length;c<f;c++)(r=e[c]).style&&(n=r.style.display,t?("none"===n&&(l[c]=Y.get(r,"display")||null,l[c]||(r.style.display="")),""===r.style.display&&ae(r)&&(l[c]=(u=a=o=void 0,a=(i=r).ownerDocument,s=i.nodeName,(u=ue[s])||(o=a.body.appendChild(a.createElement(s)),u=S.css(o,"display"),o.parentNode.removeChild(o),"none"===u&&(u="block"),ue[s]=u)))):"none"!==n&&(l[c]="none",Y.set(r,"display",n)));for(c=0;c<f;c++)null!=l[c]&&(e[c].style.display=l[c]);return e}S.fn.extend({show:function(){return le(this,!0)},hide:function(){return le(this)},toggle:function(e){return"boolean"==typeof e?e?this.show():this.hide():this.each(function(){ae(this)?S(this).show():S(this).hide()})}});var ce,fe,pe=/^(?:checkbox|radio)$/i,de=/<([a-z][^\/\0>\x20\t\r\n\f]*)/i,he=/^$|^module$|\/(?:java|ecma)script/i;ce=E.createDocumentFragment().appendChild(E.createElement("div")),(fe=E.createElement("input")).setAttribute("type","radio"),fe.setAttribute("checked","checked"),fe.setAttribute("name","t"),ce.appendChild(fe),y.checkClone=ce.cloneNode(!0).cloneNode(!0).lastChild.checked,ce.innerHTML="<textarea>x</textarea>",y.noCloneChecked=!!ce.cloneNode(!0).lastChild.defaultValue,ce.innerHTML="<option></option>",y.option=!!ce.lastChild;var ge={thead:[1,"<table>","</table>"],col:[2,"<table><colgroup>","</colgroup></table>"],tr:[2,"<table><tbody>","</tbody></table>"],td:[3,"<table><tbody><tr>","</tr></tbody></table>"],_default:[0,"",""]};function ve(e,t){var n;return n="undefined"!=typeof e.getElementsByTagName?e.getElementsByTagName(t||"*"):"undefined"!=typeof e.querySelectorAll?e.querySelectorAll(t||"*"):[],void 0===t||t&&A(e,t)?S.merge([e],n):n}function ye(e,t){for(var n=0,r=e.length;n<r;n++)Y.set(e[n],"globalEval",!t||Y.get(t[n],"globalEval"))}ge.tbody=ge.tfoot=ge.colgroup=ge.caption=ge.thead,ge.th=ge.td,y.option||(ge.optgroup=ge.option=[1,"<select multiple='multiple'>","</select>"]);var me=/<|&#?\w+;/;function xe(e,t,n,r,i){for(var o,a,s,u,l,c,f=t.createDocumentFragment(),p=[],d=0,h=e.length;d<h;d++)if((o=e[d])||0===o)if("object"===w(o))S.merge(p,o.nodeType?[o]:o);else if(me.test(o)){a=a||f.appendChild(t.createElement("div")),s=(de.exec(o)||["",""])[1].toLowerCase(),u=ge[s]||ge._default,a.innerHTML=u[1]+S.htmlPrefilter(o)+u[2],c=u[0];while(c--)a=a.lastChild;S.merge(p,a.childNodes),(a=f.firstChild).textContent=""}else p.push(t.createTextNode(o));f.textContent="",d=0;while(o=p[d++])if(r&&-1<S.inArray(o,r))i&&i.push(o);else if(l=ie(o),a=ve(f.appendChild(o),"script"),l&&ye(a),n){c=0;while(o=a[c++])he.test(o.type||"")&&n.push(o)}return f}var be=/^([^.]*)(?:\.(.+)|)/;function we(){return!0}function Te(){return!1}function Ce(e,t){return e===function(){try{return E.activeElement}catch(e){}}()==("focus"===t)}function Ee(e,t,n,r,i,o){var a,s;if("object"==typeof t){for(s in"string"!=typeof n&&(r=r||n,n=void 0),t)Ee(e,s,n,r,t[s],o);return e}if(null==r&&null==i?(i=n,r=n=void 0):null==i&&("string"==typeof n?(i=r,r=void 0):(i=r,r=n,n=void 0)),!1===i)i=Te;else if(!i)return e;return 1===o&&(a=i,(i=function(e){return S().off(e),a.apply(this,arguments)}).guid=a.guid||(a.guid=S.guid++)),e.each(function(){S.event.add(this,t,i,r,n)})}function Se(e,i,o){o?(Y.set(e,i,!1),S.event.add(e,i,{namespace:!1,handler:function(e){var t,n,r=Y.get(this,i);if(1&e.isTrigger&&this[i]){if(r.length)(S.event.special[i]||{}).delegateType&&e.stopPropagation();else if(r=s.call(arguments),Y.set(this,i,r),t=o(this,i),this[i](),r!==(n=Y.get(this,i))||t?Y.set(this,i,!1):n={},r!==n)return e.stopImmediatePropagation(),e.preventDefault(),n&&n.value}else r.length&&(Y.set(this,i,{value:S.event.trigger(S.extend(r[0],S.Event.prototype),r.slice(1),this)}),e.stopImmediatePropagation())}})):void 0===Y.get(e,i)&&S.event.add(e,i,we)}S.event={global:{},add:function(t,e,n,r,i){var o,a,s,u,l,c,f,p,d,h,g,v=Y.get(t);if(V(t)){n.handler&&(n=(o=n).handler,i=o.selector),i&&S.find.matchesSelector(re,i),n.guid||(n.guid=S.guid++),(u=v.events)||(u=v.events=Object.create(null)),(a=v.handle)||(a=v.handle=function(e){return"undefined"!=typeof S&&S.event.triggered!==e.type?S.event.dispatch.apply(t,arguments):void 0}),l=(e=(e||"").match(P)||[""]).length;while(l--)d=g=(s=be.exec(e[l])||[])[1],h=(s[2]||"").split(".").sort(),d&&(f=S.event.special[d]||{},d=(i?f.delegateType:f.bindType)||d,f=S.event.special[d]||{},c=S.extend({type:d,origType:g,data:r,handler:n,guid:n.guid,selector:i,needsContext:i&&S.expr.match.needsContext.test(i),namespace:h.join(".")},o),(p=u[d])||((p=u[d]=[]).delegateCount=0,f.setup&&!1!==f.setup.call(t,r,h,a)||t.addEventListener&&t.addEventListener(d,a)),f.add&&(f.add.call(t,c),c.handler.guid||(c.handler.guid=n.guid)),i?p.splice(p.delegateCount++,0,c):p.push(c),S.event.global[d]=!0)}},remove:function(e,t,n,r,i){var o,a,s,u,l,c,f,p,d,h,g,v=Y.hasData(e)&&Y.get(e);if(v&&(u=v.events)){l=(t=(t||"").match(P)||[""]).length;while(l--)if(d=g=(s=be.exec(t[l])||[])[1],h=(s[2]||"").split(".").sort(),d){f=S.event.special[d]||{},p=u[d=(r?f.delegateType:f.bindType)||d]||[],s=s[2]&&new RegExp("(^|\\.)"+h.join("\\.(?:.*\\.|)")+"(\\.|$)"),a=o=p.length;while(o--)c=p[o],!i&&g!==c.origType||n&&n.guid!==c.guid||s&&!s.test(c.namespace)||r&&r!==c.selector&&("**"!==r||!c.selector)||(p.splice(o,1),c.selector&&p.delegateCount--,f.remove&&f.remove.call(e,c));a&&!p.length&&(f.teardown&&!1!==f.teardown.call(e,h,v.handle)||S.removeEvent(e,d,v.handle),delete u[d])}else for(d in u)S.event.remove(e,d+t[l],n,r,!0);S.isEmptyObject(u)&&Y.remove(e,"handle events")}},dispatch:function(e){var t,n,r,i,o,a,s=new Array(arguments.length),u=S.event.fix(e),l=(Y.get(this,"events")||Object.create(null))[u.type]||[],c=S.event.special[u.type]||{};for(s[0]=u,t=1;t<arguments.length;t++)s[t]=arguments[t];if(u.delegateTarget=this,!c.preDispatch||!1!==c.preDispatch.call(this,u)){a=S.event.handlers.call(this,u,l),t=0;while((i=a[t++])&&!u.isPropagationStopped()){u.currentTarget=i.elem,n=0;while((o=i.handlers[n++])&&!u.isImmediatePropagationStopped())u.rnamespace&&!1!==o.namespace&&!u.rnamespace.test(o.namespace)||(u.handleObj=o,u.data=o.data,void 0!==(r=((S.event.special[o.origType]||{}).handle||o.handler).apply(i.elem,s))&&!1===(u.result=r)&&(u.preventDefault(),u.stopPropagation()))}return c.postDispatch&&c.postDispatch.call(this,u),u.result}},handlers:function(e,t){var n,r,i,o,a,s=[],u=t.delegateCount,l=e.target;if(u&&l.nodeType&&!("click"===e.type&&1<=e.button))for(;l!==this;l=l.parentNode||this)if(1===l.nodeType&&("click"!==e.type||!0!==l.disabled)){for(o=[],a={},n=0;n<u;n++)void 0===a[i=(r=t[n]).selector+" "]&&(a[i]=r.needsContext?-1<S(i,this).index(l):S.find(i,this,null,[l]).length),a[i]&&o.push(r);o.length&&s.push({elem:l,handlers:o})}return l=this,u<t.length&&s.push({elem:l,handlers:t.slice(u)}),s},addProp:function(t,e){Object.defineProperty(S.Event.prototype,t,{enumerable:!0,configurable:!0,get:m(e)?function(){if(this.originalEvent)return e(this.originalEvent)}:function(){if(this.originalEvent)return this.originalEvent[t]},set:function(e){Object.defineProperty(this,t,{enumerable:!0,configurable:!0,writable:!0,value:e})}})},fix:function(e){return e[S.expando]?e:new S.Event(e)},special:{load:{noBubble:!0},click:{setup:function(e){var t=this||e;return pe.test(t.type)&&t.click&&A(t,"input")&&Se(t,"click",we),!1},trigger:function(e){var t=this||e;return pe.test(t.type)&&t.click&&A(t,"input")&&Se(t,"click"),!0},_default:function(e){var t=e.target;return pe.test(t.type)&&t.click&&A(t,"input")&&Y.get(t,"click")||A(t,"a")}},beforeunload:{postDispatch:function(e){void 0!==e.result&&e.originalEvent&&(e.originalEvent.returnValue=e.result)}}}},S.removeEvent=function(e,t,n){e.removeEventListener&&e.removeEventListener(t,n)},S.Event=function(e,t){if(!(this instanceof S.Event))return new S.Event(e,t);e&&e.type?(this.originalEvent=e,this.type=e.type,this.isDefaultPrevented=e.defaultPrevented||void 0===e.defaultPrevented&&!1===e.returnValue?we:Te,this.target=e.target&&3===e.target.nodeType?e.target.parentNode:e.target,this.currentTarget=e.currentTarget,this.relatedTarget=e.relatedTarget):this.type=e,t&&S.extend(this,t),this.timeStamp=e&&e.timeStamp||Date.now(),this[S.expando]=!0},S.Event.prototype={constructor:S.Event,isDefaultPrevented:Te,isPropagationStopped:Te,isImmediatePropagationStopped:Te,isSimulated:!1,preventDefault:function(){var e=this.originalEvent;this.isDefaultPrevented=we,e&&!this.isSimulated&&e.preventDefault()},stopPropagation:function(){var e=this.originalEvent;this.isPropagationStopped=we,e&&!this.isSimulated&&e.stopPropagation()},stopImmediatePropagation:function(){var e=this.originalEvent;this.isImmediatePropagationStopped=we,e&&!this.isSimulated&&e.stopImmediatePropagation(),this.stopPropagation()}},S.each({altKey:!0,bubbles:!0,cancelable:!0,changedTouches:!0,ctrlKey:!0,detail:!0,eventPhase:!0,metaKey:!0,pageX:!0,pageY:!0,shiftKey:!0,view:!0,"char":!0,code:!0,charCode:!0,key:!0,keyCode:!0,button:!0,buttons:!0,clientX:!0,clientY:!0,offsetX:!0,offsetY:!0,pointerId:!0,pointerType:!0,screenX:!0,screenY:!0,targetTouches:!0,toElement:!0,touches:!0,which:!0},S.event.addProp),S.each({focus:"focusin",blur:"focusout"},function(e,t){S.event.special[e]={setup:function(){return Se(this,e,Ce),!1},trigger:function(){return Se(this,e),!0},_default:function(){return!0},delegateType:t}}),S.each({mouseenter:"mouseover",mouseleave:"mouseout",pointerenter:"pointerover",pointerleave:"pointerout"},function(e,i){S.event.special[e]={delegateType:i,bindType:i,handle:function(e){var t,n=e.relatedTarget,r=e.handleObj;return n&&(n===this||S.contains(this,n))||(e.type=r.origType,t=r.handler.apply(this,arguments),e.type=i),t}}}),S.fn.extend({on:function(e,t,n,r){return Ee(this,e,t,n,r)},one:function(e,t,n,r){return Ee(this,e,t,n,r,1)},off:function(e,t,n){var r,i;if(e&&e.preventDefault&&e.handleObj)return r=e.handleObj,S(e.delegateTarget).off(r.namespace?r.origType+"."+r.namespace:r.origType,r.selector,r.handler),this;if("object"==typeof e){for(i in e)this.off(i,t,e[i]);return this}return!1!==t&&"function"!=typeof t||(n=t,t=void 0),!1===n&&(n=Te),this.each(function(){S.event.remove(this,e,n,t)})}});var ke=/<script|<style|<link/i,Ae=/checked\s*(?:[^=]|=\s*.checked.)/i,Ne=/^\s*<!(?:\[CDATA\[|--)|(?:\]\]|--)>\s*$/g;function je(e,t){return A(e,"table")&&A(11!==t.nodeType?t:t.firstChild,"tr")&&S(e).children("tbody")[0]||e}function De(e){return e.type=(null!==e.getAttribute("type"))+"/"+e.type,e}function qe(e){return"true/"===(e.type||"").slice(0,5)?e.type=e.type.slice(5):e.removeAttribute("type"),e}function Le(e,t){var n,r,i,o,a,s;if(1===t.nodeType){if(Y.hasData(e)&&(s=Y.get(e).events))for(i in Y.remove(t,"handle events"),s)for(n=0,r=s[i].length;n<r;n++)S.event.add(t,i,s[i][n]);Q.hasData(e)&&(o=Q.access(e),a=S.extend({},o),Q.set(t,a))}}function He(n,r,i,o){r=g(r);var e,t,a,s,u,l,c=0,f=n.length,p=f-1,d=r[0],h=m(d);if(h||1<f&&"string"==typeof d&&!y.checkClone&&Ae.test(d))return n.each(function(e){var t=n.eq(e);h&&(r[0]=d.call(this,e,t.html())),He(t,r,i,o)});if(f&&(t=(e=xe(r,n[0].ownerDocument,!1,n,o)).firstChild,1===e.childNodes.length&&(e=t),t||o)){for(s=(a=S.map(ve(e,"script"),De)).length;c<f;c++)u=e,c!==p&&(u=S.clone(u,!0,!0),s&&S.merge(a,ve(u,"script"))),i.call(n[c],u,c);if(s)for(l=a[a.length-1].ownerDocument,S.map(a,qe),c=0;c<s;c++)u=a[c],he.test(u.type||"")&&!Y.access(u,"globalEval")&&S.contains(l,u)&&(u.src&&"module"!==(u.type||"").toLowerCase()?S._evalUrl&&!u.noModule&&S._evalUrl(u.src,{nonce:u.nonce||u.getAttribute("nonce")},l):b(u.textContent.replace(Ne,""),u,l))}return n}function Oe(e,t,n){for(var r,i=t?S.filter(t,e):e,o=0;null!=(r=i[o]);o++)n||1!==r.nodeType||S.cleanData(ve(r)),r.parentNode&&(n&&ie(r)&&ye(ve(r,"script")),r.parentNode.removeChild(r));return e}S.extend({htmlPrefilter:function(e){return e},clone:function(e,t,n){var r,i,o,a,s,u,l,c=e.cloneNode(!0),f=ie(e);if(!(y.noCloneChecked||1!==e.nodeType&&11!==e.nodeType||S.isXMLDoc(e)))for(a=ve(c),r=0,i=(o=ve(e)).length;r<i;r++)s=o[r],u=a[r],void 0,"input"===(l=u.nodeName.toLowerCase())&&pe.test(s.type)?u.checked=s.checked:"input"!==l&&"textarea"!==l||(u.defaultValue=s.defaultValue);if(t)if(n)for(o=o||ve(e),a=a||ve(c),r=0,i=o.length;r<i;r++)Le(o[r],a[r]);else Le(e,c);return 0<(a=ve(c,"script")).length&&ye(a,!f&&ve(e,"script")),c},cleanData:function(e){for(var t,n,r,i=S.event.special,o=0;void 0!==(n=e[o]);o++)if(V(n)){if(t=n[Y.expando]){if(t.events)for(r in t.events)i[r]?S.event.remove(n,r):S.removeEvent(n,r,t.handle);n[Y.expando]=void 0}n[Q.expando]&&(n[Q.expando]=void 0)}}}),S.fn.extend({detach:function(e){return Oe(this,e,!0)},remove:function(e){return Oe(this,e)},text:function(e){return $(this,function(e){return void 0===e?S.text(this):this.empty().each(function(){1!==this.nodeType&&11!==this.nodeType&&9!==this.nodeType||(this.textContent=e)})},null,e,arguments.length)},append:function(){return He(this,arguments,function(e){1!==this.nodeType&&11!==this.nodeType&&9!==this.nodeType||je(this,e).appendChild(e)})},prepend:function(){return He(this,arguments,function(e){if(1===this.nodeType||11===this.nodeType||9===this.nodeType){var t=je(this,e);t.insertBefore(e,t.firstChild)}})},before:function(){return He(this,arguments,function(e){this.parentNode&&this.parentNode.insertBefore(e,this)})},after:function(){return He(this,arguments,function(e){this.parentNode&&this.parentNode.insertBefore(e,this.nextSibling)})},empty:function(){for(var e,t=0;null!=(e=this[t]);t++)1===e.nodeType&&(S.cleanData(ve(e,!1)),e.textContent="");return this},clone:function(e,t){return e=null!=e&&e,t=null==t?e:t,this.map(function(){return S.clone(this,e,t)})},html:function(e){return $(this,function(e){var t=this[0]||{},n=0,r=this.length;if(void 0===e&&1===t.nodeType)return t.innerHTML;if("string"==typeof e&&!ke.test(e)&&!ge[(de.exec(e)||["",""])[1].toLowerCase()]){e=S.htmlPrefilter(e);try{for(;n<r;n++)1===(t=this[n]||{}).nodeType&&(S.cleanData(ve(t,!1)),t.innerHTML=e);t=0}catch(e){}}t&&this.empty().append(e)},null,e,arguments.length)},replaceWith:function(){var n=[];return He(this,arguments,function(e){var t=this.parentNode;S.inArray(this,n)<0&&(S.cleanData(ve(this)),t&&t.replaceChild(e,this))},n)}}),S.each({appendTo:"append",prependTo:"prepend",insertBefore:"before",insertAfter:"after",replaceAll:"replaceWith"},function(e,a){S.fn[e]=function(e){for(var t,n=[],r=S(e),i=r.length-1,o=0;o<=i;o++)t=o===i?this:this.clone(!0),S(r[o])[a](t),u.apply(n,t.get());return this.pushStack(n)}});var Pe=new RegExp("^("+ee+")(?!px)[a-z%]+$","i"),Re=function(e){var t=e.ownerDocument.defaultView;return t&&t.opener||(t=C),t.getComputedStyle(e)},Me=function(e,t,n){var r,i,o={};for(i in t)o[i]=e.style[i],e.style[i]=t[i];for(i in r=n.call(e),t)e.style[i]=o[i];return r},Ie=new RegExp(ne.join("|"),"i");function We(e,t,n){var r,i,o,a,s=e.style;return(n=n||Re(e))&&(""!==(a=n.getPropertyValue(t)||n[t])||ie(e)||(a=S.style(e,t)),!y.pixelBoxStyles()&&Pe.test(a)&&Ie.test(t)&&(r=s.width,i=s.minWidth,o=s.maxWidth,s.minWidth=s.maxWidth=s.width=a,a=n.width,s.width=r,s.minWidth=i,s.maxWidth=o)),void 0!==a?a+"":a}function Fe(e,t){return{get:function(){if(!e())return(this.get=t).apply(this,arguments);delete this.get}}}!function(){function e(){if(l){u.style.cssText="position:absolute;left:-11111px;width:60px;margin-top:1px;padding:0;border:0",l.style.cssText="position:relative;display:block;box-sizing:border-box;overflow:scroll;margin:auto;border:1px;padding:1px;width:60%;top:1%",re.appendChild(u).appendChild(l);var e=C.getComputedStyle(l);n="1%"!==e.top,s=12===t(e.marginLeft),l.style.right="60%",o=36===t(e.right),r=36===t(e.width),l.style.position="absolute",i=12===t(l.offsetWidth/3),re.removeChild(u),l=null}}function t(e){return Math.round(parseFloat(e))}var n,r,i,o,a,s,u=E.createElement("div"),l=E.createElement("div");l.style&&(l.style.backgroundClip="content-box",l.cloneNode(!0).style.backgroundClip="",y.clearCloneStyle="content-box"===l.style.backgroundClip,S.extend(y,{boxSizingReliable:function(){return e(),r},pixelBoxStyles:function(){return e(),o},pixelPosition:function(){return e(),n},reliableMarginLeft:function(){return e(),s},scrollboxSize:function(){return e(),i},reliableTrDimensions:function(){var e,t,n,r;return null==a&&(e=E.createElement("table"),t=E.createElement("tr"),n=E.createElement("div"),e.style.cssText="position:absolute;left:-11111px;border-collapse:separate",t.style.cssText="border:1px solid",t.style.height="1px",n.style.height="9px",n.style.display="block",re.appendChild(e).appendChild(t).appendChild(n),r=C.getComputedStyle(t),a=parseInt(r.height,10)+parseInt(r.borderTopWidth,10)+parseInt(r.borderBottomWidth,10)===t.offsetHeight,re.removeChild(e)),a}}))}();var Be=["Webkit","Moz","ms"],$e=E.createElement("div").style,_e={};function ze(e){var t=S.cssProps[e]||_e[e];return t||(e in $e?e:_e[e]=function(e){var t=e[0].toUpperCase()+e.slice(1),n=Be.length;while(n--)if((e=Be[n]+t)in $e)return e}(e)||e)}var Ue=/^(none|table(?!-c[ea]).+)/,Xe=/^--/,Ve={position:"absolute",visibility:"hidden",display:"block"},Ge={letterSpacing:"0",fontWeight:"400"};function Ye(e,t,n){var r=te.exec(t);return r?Math.max(0,r[2]-(n||0))+(r[3]||"px"):t}function Qe(e,t,n,r,i,o){var a="width"===t?1:0,s=0,u=0;if(n===(r?"border":"content"))return 0;for(;a<4;a+=2)"margin"===n&&(u+=S.css(e,n+ne[a],!0,i)),r?("content"===n&&(u-=S.css(e,"padding"+ne[a],!0,i)),"margin"!==n&&(u-=S.css(e,"border"+ne[a]+"Width",!0,i))):(u+=S.css(e,"padding"+ne[a],!0,i),"padding"!==n?u+=S.css(e,"border"+ne[a]+"Width",!0,i):s+=S.css(e,"border"+ne[a]+"Width",!0,i));return!r&&0<=o&&(u+=Math.max(0,Math.ceil(e["offset"+t[0].toUpperCase()+t.slice(1)]-o-u-s-.5))||0),u}function Je(e,t,n){var r=Re(e),i=(!y.boxSizingReliable()||n)&&"border-box"===S.css(e,"boxSizing",!1,r),o=i,a=We(e,t,r),s="offset"+t[0].toUpperCase()+t.slice(1);if(Pe.test(a)){if(!n)return a;a="auto"}return(!y.boxSizingReliable()&&i||!y.reliableTrDimensions()&&A(e,"tr")||"auto"===a||!parseFloat(a)&&"inline"===S.css(e,"display",!1,r))&&e.getClientRects().length&&(i="border-box"===S.css(e,"boxSizing",!1,r),(o=s in e)&&(a=e[s])),(a=parseFloat(a)||0)+Qe(e,t,n||(i?"border":"content"),o,r,a)+"px"}function Ke(e,t,n,r,i){return new Ke.prototype.init(e,t,n,r,i)}S.extend({cssHooks:{opacity:{get:function(e,t){if(t){var n=We(e,"opacity");return""===n?"1":n}}}},cssNumber:{animationIterationCount:!0,columnCount:!0,fillOpacity:!0,flexGrow:!0,flexShrink:!0,fontWeight:!0,gridArea:!0,gridColumn:!0,gridColumnEnd:!0,gridColumnStart:!0,gridRow:!0,gridRowEnd:!0,gridRowStart:!0,lineHeight:!0,opacity:!0,order:!0,orphans:!0,widows:!0,zIndex:!0,zoom:!0},cssProps:{},style:function(e,t,n,r){if(e&&3!==e.nodeType&&8!==e.nodeType&&e.style){var i,o,a,s=X(t),u=Xe.test(t),l=e.style;if(u||(t=ze(s)),a=S.cssHooks[t]||S.cssHooks[s],void 0===n)return a&&"get"in a&&void 0!==(i=a.get(e,!1,r))?i:l[t];"string"===(o=typeof n)&&(i=te.exec(n))&&i[1]&&(n=se(e,t,i),o="number"),null!=n&&n==n&&("number"!==o||u||(n+=i&&i[3]||(S.cssNumber[s]?"":"px")),y.clearCloneStyle||""!==n||0!==t.indexOf("background")||(l[t]="inherit"),a&&"set"in a&&void 0===(n=a.set(e,n,r))||(u?l.setProperty(t,n):l[t]=n))}},css:function(e,t,n,r){var i,o,a,s=X(t);return Xe.test(t)||(t=ze(s)),(a=S.cssHooks[t]||S.cssHooks[s])&&"get"in a&&(i=a.get(e,!0,n)),void 0===i&&(i=We(e,t,r)),"normal"===i&&t in Ge&&(i=Ge[t]),""===n||n?(o=parseFloat(i),!0===n||isFinite(o)?o||0:i):i}}),S.each(["height","width"],function(e,u){S.cssHooks[u]={get:function(e,t,n){if(t)return!Ue.test(S.css(e,"display"))||e.getClientRects().length&&e.getBoundingClientRect().width?Je(e,u,n):Me(e,Ve,function(){return Je(e,u,n)})},set:function(e,t,n){var r,i=Re(e),o=!y.scrollboxSize()&&"absolute"===i.position,a=(o||n)&&"border-box"===S.css(e,"boxSizing",!1,i),s=n?Qe(e,u,n,a,i):0;return a&&o&&(s-=Math.ceil(e["offset"+u[0].toUpperCase()+u.slice(1)]-parseFloat(i[u])-Qe(e,u,"border",!1,i)-.5)),s&&(r=te.exec(t))&&"px"!==(r[3]||"px")&&(e.style[u]=t,t=S.css(e,u)),Ye(0,t,s)}}}),S.cssHooks.marginLeft=Fe(y.reliableMarginLeft,function(e,t){if(t)return(parseFloat(We(e,"marginLeft"))||e.getBoundingClientRect().left-Me(e,{marginLeft:0},function(){return e.getBoundingClientRect().left}))+"px"}),S.each({margin:"",padding:"",border:"Width"},function(i,o){S.cssHooks[i+o]={expand:function(e){for(var t=0,n={},r="string"==typeof e?e.split(" "):[e];t<4;t++)n[i+ne[t]+o]=r[t]||r[t-2]||r[0];return n}},"margin"!==i&&(S.cssHooks[i+o].set=Ye)}),S.fn.extend({css:function(e,t){return $(this,function(e,t,n){var r,i,o={},a=0;if(Array.isArray(t)){for(r=Re(e),i=t.length;a<i;a++)o[t[a]]=S.css(e,t[a],!1,r);return o}return void 0!==n?S.style(e,t,n):S.css(e,t)},e,t,1<arguments.length)}}),((S.Tween=Ke).prototype={constructor:Ke,init:function(e,t,n,r,i,o){this.elem=e,this.prop=n,this.easing=i||S.easing._default,this.options=t,this.start=this.now=this.cur(),this.end=r,this.unit=o||(S.cssNumber[n]?"":"px")},cur:function(){var e=Ke.propHooks[this.prop];return e&&e.get?e.get(this):Ke.propHooks._default.get(this)},run:function(e){var t,n=Ke.propHooks[this.prop];return this.options.duration?this.pos=t=S.easing[this.easing](e,this.options.duration*e,0,1,this.options.duration):this.pos=t=e,this.now=(this.end-this.start)*t+this.start,this.options.step&&this.options.step.call(this.elem,this.now,this),n&&n.set?n.set(this):Ke.propHooks._default.set(this),this}}).init.prototype=Ke.prototype,(Ke.propHooks={_default:{get:function(e){var t;return 1!==e.elem.nodeType||null!=e.elem[e.prop]&&null==e.elem.style[e.prop]?e.elem[e.prop]:(t=S.css(e.elem,e.prop,""))&&"auto"!==t?t:0},set:function(e){S.fx.step[e.prop]?S.fx.step[e.prop](e):1!==e.elem.nodeType||!S.cssHooks[e.prop]&&null==e.elem.style[ze(e.prop)]?e.elem[e.prop]=e.now:S.style(e.elem,e.prop,e.now+e.unit)}}}).scrollTop=Ke.propHooks.scrollLeft={set:function(e){e.elem.nodeType&&e.elem.parentNode&&(e.elem[e.prop]=e.now)}},S.easing={linear:function(e){return e},swing:function(e){return.5-Math.cos(e*Math.PI)/2},_default:"swing"},S.fx=Ke.prototype.init,S.fx.step={};var Ze,et,tt,nt,rt=/^(?:toggle|show|hide)$/,it=/queueHooks$/;function ot(){et&&(!1===E.hidden&&C.requestAnimationFrame?C.requestAnimationFrame(ot):C.setTimeout(ot,S.fx.interval),S.fx.tick())}function at(){return C.setTimeout(function(){Ze=void 0}),Ze=Date.now()}function st(e,t){var n,r=0,i={height:e};for(t=t?1:0;r<4;r+=2-t)i["margin"+(n=ne[r])]=i["padding"+n]=e;return t&&(i.opacity=i.width=e),i}function ut(e,t,n){for(var r,i=(lt.tweeners[t]||[]).concat(lt.tweeners["*"]),o=0,a=i.length;o<a;o++)if(r=i[o].call(n,t,e))return r}function lt(o,e,t){var n,a,r=0,i=lt.prefilters.length,s=S.Deferred().always(function(){delete u.elem}),u=function(){if(a)return!1;for(var e=Ze||at(),t=Math.max(0,l.startTime+l.duration-e),n=1-(t/l.duration||0),r=0,i=l.tweens.length;r<i;r++)l.tweens[r].run(n);return s.notifyWith(o,[l,n,t]),n<1&&i?t:(i||s.notifyWith(o,[l,1,0]),s.resolveWith(o,[l]),!1)},l=s.promise({elem:o,props:S.extend({},e),opts:S.extend(!0,{specialEasing:{},easing:S.easing._default},t),originalProperties:e,originalOptions:t,startTime:Ze||at(),duration:t.duration,tweens:[],createTween:function(e,t){var n=S.Tween(o,l.opts,e,t,l.opts.specialEasing[e]||l.opts.easing);return l.tweens.push(n),n},stop:function(e){var t=0,n=e?l.tweens.length:0;if(a)return this;for(a=!0;t<n;t++)l.tweens[t].run(1);return e?(s.notifyWith(o,[l,1,0]),s.resolveWith(o,[l,e])):s.rejectWith(o,[l,e]),this}}),c=l.props;for(!function(e,t){var n,r,i,o,a;for(n in e)if(i=t[r=X(n)],o=e[n],Array.isArray(o)&&(i=o[1],o=e[n]=o[0]),n!==r&&(e[r]=o,delete e[n]),(a=S.cssHooks[r])&&"expand"in a)for(n in o=a.expand(o),delete e[r],o)n in e||(e[n]=o[n],t[n]=i);else t[r]=i}(c,l.opts.specialEasing);r<i;r++)if(n=lt.prefilters[r].call(l,o,c,l.opts))return m(n.stop)&&(S._queueHooks(l.elem,l.opts.queue).stop=n.stop.bind(n)),n;return S.map(c,ut,l),m(l.opts.start)&&l.opts.start.call(o,l),l.progress(l.opts.progress).done(l.opts.done,l.opts.complete).fail(l.opts.fail).always(l.opts.always),S.fx.timer(S.extend(u,{elem:o,anim:l,queue:l.opts.queue})),l}S.Animation=S.extend(lt,{tweeners:{"*":[function(e,t){var n=this.createTween(e,t);return se(n.elem,e,te.exec(t),n),n}]},tweener:function(e,t){m(e)?(t=e,e=["*"]):e=e.match(P);for(var n,r=0,i=e.length;r<i;r++)n=e[r],lt.tweeners[n]=lt.tweeners[n]||[],lt.tweeners[n].unshift(t)},prefilters:[function(e,t,n){var r,i,o,a,s,u,l,c,f="width"in t||"height"in t,p=this,d={},h=e.style,g=e.nodeType&&ae(e),v=Y.get(e,"fxshow");for(r in n.queue||(null==(a=S._queueHooks(e,"fx")).unqueued&&(a.unqueued=0,s=a.empty.fire,a.empty.fire=function(){a.unqueued||s()}),a.unqueued++,p.always(function(){p.always(function(){a.unqueued--,S.queue(e,"fx").length||a.empty.fire()})})),t)if(i=t[r],rt.test(i)){if(delete t[r],o=o||"toggle"===i,i===(g?"hide":"show")){if("show"!==i||!v||void 0===v[r])continue;g=!0}d[r]=v&&v[r]||S.style(e,r)}if((u=!S.isEmptyObject(t))||!S.isEmptyObject(d))for(r in f&&1===e.nodeType&&(n.overflow=[h.overflow,h.overflowX,h.overflowY],null==(l=v&&v.display)&&(l=Y.get(e,"display")),"none"===(c=S.css(e,"display"))&&(l?c=l:(le([e],!0),l=e.style.display||l,c=S.css(e,"display"),le([e]))),("inline"===c||"inline-block"===c&&null!=l)&&"none"===S.css(e,"float")&&(u||(p.done(function(){h.display=l}),null==l&&(c=h.display,l="none"===c?"":c)),h.display="inline-block")),n.overflow&&(h.overflow="hidden",p.always(function(){h.overflow=n.overflow[0],h.overflowX=n.overflow[1],h.overflowY=n.overflow[2]})),u=!1,d)u||(v?"hidden"in v&&(g=v.hidden):v=Y.access(e,"fxshow",{display:l}),o&&(v.hidden=!g),g&&le([e],!0),p.done(function(){for(r in g||le([e]),Y.remove(e,"fxshow"),d)S.style(e,r,d[r])})),u=ut(g?v[r]:0,r,p),r in v||(v[r]=u.start,g&&(u.end=u.start,u.start=0))}],prefilter:function(e,t){t?lt.prefilters.unshift(e):lt.prefilters.push(e)}}),S.speed=function(e,t,n){var r=e&&"object"==typeof e?S.extend({},e):{complete:n||!n&&t||m(e)&&e,duration:e,easing:n&&t||t&&!m(t)&&t};return S.fx.off?r.duration=0:"number"!=typeof r.duration&&(r.duration in S.fx.speeds?r.duration=S.fx.speeds[r.duration]:r.duration=S.fx.speeds._default),null!=r.queue&&!0!==r.queue||(r.queue="fx"),r.old=r.complete,r.complete=function(){m(r.old)&&r.old.call(this),r.queue&&S.dequeue(this,r.queue)},r},S.fn.extend({fadeTo:function(e,t,n,r){return this.filter(ae).css("opacity",0).show().end().animate({opacity:t},e,n,r)},animate:function(t,e,n,r){var i=S.isEmptyObject(t),o=S.speed(e,n,r),a=function(){var e=lt(this,S.extend({},t),o);(i||Y.get(this,"finish"))&&e.stop(!0)};return a.finish=a,i||!1===o.queue?this.each(a):this.queue(o.queue,a)},stop:function(i,e,o){var a=function(e){var t=e.stop;delete e.stop,t(o)};return"string"!=typeof i&&(o=e,e=i,i=void 0),e&&this.queue(i||"fx",[]),this.each(function(){var e=!0,t=null!=i&&i+"queueHooks",n=S.timers,r=Y.get(this);if(t)r[t]&&r[t].stop&&a(r[t]);else for(t in r)r[t]&&r[t].stop&&it.test(t)&&a(r[t]);for(t=n.length;t--;)n[t].elem!==this||null!=i&&n[t].queue!==i||(n[t].anim.stop(o),e=!1,n.splice(t,1));!e&&o||S.dequeue(this,i)})},finish:function(a){return!1!==a&&(a=a||"fx"),this.each(function(){var e,t=Y.get(this),n=t[a+"queue"],r=t[a+"queueHooks"],i=S.timers,o=n?n.length:0;for(t.finish=!0,S.queue(this,a,[]),r&&r.stop&&r.stop.call(this,!0),e=i.length;e--;)i[e].elem===this&&i[e].queue===a&&(i[e].anim.stop(!0),i.splice(e,1));for(e=0;e<o;e++)n[e]&&n[e].finish&&n[e].finish.call(this);delete t.finish})}}),S.each(["toggle","show","hide"],function(e,r){var i=S.fn[r];S.fn[r]=function(e,t,n){return null==e||"boolean"==typeof e?i.apply(this,arguments):this.animate(st(r,!0),e,t,n)}}),S.each({slideDown:st("show"),slideUp:st("hide"),slideToggle:st("toggle"),fadeIn:{opacity:"show"},fadeOut:{opacity:"hide"},fadeToggle:{opacity:"toggle"}},function(e,r){S.fn[e]=function(e,t,n){return this.animate(r,e,t,n)}}),S.timers=[],S.fx.tick=function(){var e,t=0,n=S.timers;for(Ze=Date.now();t<n.length;t++)(e=n[t])()||n[t]!==e||n.splice(t--,1);n.length||S.fx.stop(),Ze=void 0},S.fx.timer=function(e){S.timers.push(e),S.fx.start()},S.fx.interval=13,S.fx.start=function(){et||(et=!0,ot())},S.fx.stop=function(){et=null},S.fx.speeds={slow:600,fast:200,_default:400},S.fn.delay=function(r,e){return r=S.fx&&S.fx.speeds[r]||r,e=e||"fx",this.queue(e,function(e,t){var n=C.setTimeout(e,r);t.stop=function(){C.clearTimeout(n)}})},tt=E.createElement("input"),nt=E.createElement("select").appendChild(E.createElement("option")),tt.type="checkbox",y.checkOn=""!==tt.value,y.optSelected=nt.selected,(tt=E.createElement("input")).value="t",tt.type="radio",y.radioValue="t"===tt.value;var ct,ft=S.expr.attrHandle;S.fn.extend({attr:function(e,t){return $(this,S.attr,e,t,1<arguments.length)},removeAttr:function(e){return this.each(function(){S.removeAttr(this,e)})}}),S.extend({attr:function(e,t,n){var r,i,o=e.nodeType;if(3!==o&&8!==o&&2!==o)return"undefined"==typeof e.getAttribute?S.prop(e,t,n):(1===o&&S.isXMLDoc(e)||(i=S.attrHooks[t.toLowerCase()]||(S.expr.match.bool.test(t)?ct:void 0)),void 0!==n?null===n?void S.removeAttr(e,t):i&&"set"in i&&void 0!==(r=i.set(e,n,t))?r:(e.setAttribute(t,n+""),n):i&&"get"in i&&null!==(r=i.get(e,t))?r:null==(r=S.find.attr(e,t))?void 0:r)},attrHooks:{type:{set:function(e,t){if(!y.radioValue&&"radio"===t&&A(e,"input")){var n=e.value;return e.setAttribute("type",t),n&&(e.value=n),t}}}},removeAttr:function(e,t){var n,r=0,i=t&&t.match(P);if(i&&1===e.nodeType)while(n=i[r++])e.removeAttribute(n)}}),ct={set:function(e,t,n){return!1===t?S.removeAttr(e,n):e.setAttribute(n,n),n}},S.each(S.expr.match.bool.source.match(/\w+/g),function(e,t){var a=ft[t]||S.find.attr;ft[t]=function(e,t,n){var r,i,o=t.toLowerCase();return n||(i=ft[o],ft[o]=r,r=null!=a(e,t,n)?o:null,ft[o]=i),r}});var pt=/^(?:input|select|textarea|button)$/i,dt=/^(?:a|area)$/i;function ht(e){return(e.match(P)||[]).join(" ")}function gt(e){return e.getAttribute&&e.getAttribute("class")||""}function vt(e){return Array.isArray(e)?e:"string"==typeof e&&e.match(P)||[]}S.fn.extend({prop:function(e,t){return $(this,S.prop,e,t,1<arguments.length)},removeProp:function(e){return this.each(function(){delete this[S.propFix[e]||e]})}}),S.extend({prop:function(e,t,n){var r,i,o=e.nodeType;if(3!==o&&8!==o&&2!==o)return 1===o&&S.isXMLDoc(e)||(t=S.propFix[t]||t,i=S.propHooks[t]),void 0!==n?i&&"set"in i&&void 0!==(r=i.set(e,n,t))?r:e[t]=n:i&&"get"in i&&null!==(r=i.get(e,t))?r:e[t]},propHooks:{tabIndex:{get:function(e){var t=S.find.attr(e,"tabindex");return t?parseInt(t,10):pt.test(e.nodeName)||dt.test(e.nodeName)&&e.href?0:-1}}},propFix:{"for":"htmlFor","class":"className"}}),y.optSelected||(S.propHooks.selected={get:function(e){var t=e.parentNode;return t&&t.parentNode&&t.parentNode.selectedIndex,null},set:function(e){var t=e.parentNode;t&&(t.selectedIndex,t.parentNode&&t.parentNode.selectedIndex)}}),S.each(["tabIndex","readOnly","maxLength","cellSpacing","cellPadding","rowSpan","colSpan","useMap","frameBorder","contentEditable"],function(){S.propFix[this.toLowerCase()]=this}),S.fn.extend({addClass:function(t){var e,n,r,i,o,a,s,u=0;if(m(t))return this.each(function(e){S(this).addClass(t.call(this,e,gt(this)))});if((e=vt(t)).length)while(n=this[u++])if(i=gt(n),r=1===n.nodeType&&" "+ht(i)+" "){a=0;while(o=e[a++])r.indexOf(" "+o+" ")<0&&(r+=o+" ");i!==(s=ht(r))&&n.setAttribute("class",s)}return this},removeClass:function(t){var e,n,r,i,o,a,s,u=0;if(m(t))return this.each(function(e){S(this).removeClass(t.call(this,e,gt(this)))});if(!arguments.length)return this.attr("class","");if((e=vt(t)).length)while(n=this[u++])if(i=gt(n),r=1===n.nodeType&&" "+ht(i)+" "){a=0;while(o=e[a++])while(-1<r.indexOf(" "+o+" "))r=r.replace(" "+o+" "," ");i!==(s=ht(r))&&n.setAttribute("class",s)}return this},toggleClass:function(i,t){var o=typeof i,a="string"===o||Array.isArray(i);return"boolean"==typeof t&&a?t?this.addClass(i):this.removeClass(i):m(i)?this.each(function(e){S(this).toggleClass(i.call(this,e,gt(this),t),t)}):this.each(function(){var e,t,n,r;if(a){t=0,n=S(this),r=vt(i);while(e=r[t++])n.hasClass(e)?n.removeClass(e):n.addClass(e)}else void 0!==i&&"boolean"!==o||((e=gt(this))&&Y.set(this,"__className__",e),this.setAttribute&&this.setAttribute("class",e||!1===i?"":Y.get(this,"__className__")||""))})},hasClass:function(e){var t,n,r=0;t=" "+e+" ";while(n=this[r++])if(1===n.nodeType&&-1<(" "+ht(gt(n))+" ").indexOf(t))return!0;return!1}});var yt=/\r/g;S.fn.extend({val:function(n){var r,e,i,t=this[0];return arguments.length?(i=m(n),this.each(function(e){var t;1===this.nodeType&&(null==(t=i?n.call(this,e,S(this).val()):n)?t="":"number"==typeof t?t+="":Array.isArray(t)&&(t=S.map(t,function(e){return null==e?"":e+""})),(r=S.valHooks[this.type]||S.valHooks[this.nodeName.toLowerCase()])&&"set"in r&&void 0!==r.set(this,t,"value")||(this.value=t))})):t?(r=S.valHooks[t.type]||S.valHooks[t.nodeName.toLowerCase()])&&"get"in r&&void 0!==(e=r.get(t,"value"))?e:"string"==typeof(e=t.value)?e.replace(yt,""):null==e?"":e:void 0}}),S.extend({valHooks:{option:{get:function(e){var t=S.find.attr(e,"value");return null!=t?t:ht(S.text(e))}},select:{get:function(e){var t,n,r,i=e.options,o=e.selectedIndex,a="select-one"===e.type,s=a?null:[],u=a?o+1:i.length;for(r=o<0?u:a?o:0;r<u;r++)if(((n=i[r]).selected||r===o)&&!n.disabled&&(!n.parentNode.disabled||!A(n.parentNode,"optgroup"))){if(t=S(n).val(),a)return t;s.push(t)}return s},set:function(e,t){var n,r,i=e.options,o=S.makeArray(t),a=i.length;while(a--)((r=i[a]).selected=-1<S.inArray(S.valHooks.option.get(r),o))&&(n=!0);return n||(e.selectedIndex=-1),o}}}}),S.each(["radio","checkbox"],function(){S.valHooks[this]={set:function(e,t){if(Array.isArray(t))return e.checked=-1<S.inArray(S(e).val(),t)}},y.checkOn||(S.valHooks[this].get=function(e){return null===e.getAttribute("value")?"on":e.value})}),y.focusin="onfocusin"in C;var mt=/^(?:focusinfocus|focusoutblur)$/,xt=function(e){e.stopPropagation()};S.extend(S.event,{trigger:function(e,t,n,r){var i,o,a,s,u,l,c,f,p=[n||E],d=v.call(e,"type")?e.type:e,h=v.call(e,"namespace")?e.namespace.split("."):[];if(o=f=a=n=n||E,3!==n.nodeType&&8!==n.nodeType&&!mt.test(d+S.event.triggered)&&(-1<d.indexOf(".")&&(d=(h=d.split(".")).shift(),h.sort()),u=d.indexOf(":")<0&&"on"+d,(e=e[S.expando]?e:new S.Event(d,"object"==typeof e&&e)).isTrigger=r?2:3,e.namespace=h.join("."),e.rnamespace=e.namespace?new RegExp("(^|\\.)"+h.join("\\.(?:.*\\.|)")+"(\\.|$)"):null,e.result=void 0,e.target||(e.target=n),t=null==t?[e]:S.makeArray(t,[e]),c=S.event.special[d]||{},r||!c.trigger||!1!==c.trigger.apply(n,t))){if(!r&&!c.noBubble&&!x(n)){for(s=c.delegateType||d,mt.test(s+d)||(o=o.parentNode);o;o=o.parentNode)p.push(o),a=o;a===(n.ownerDocument||E)&&p.push(a.defaultView||a.parentWindow||C)}i=0;while((o=p[i++])&&!e.isPropagationStopped())f=o,e.type=1<i?s:c.bindType||d,(l=(Y.get(o,"events")||Object.create(null))[e.type]&&Y.get(o,"handle"))&&l.apply(o,t),(l=u&&o[u])&&l.apply&&V(o)&&(e.result=l.apply(o,t),!1===e.result&&e.preventDefault());return e.type=d,r||e.isDefaultPrevented()||c._default&&!1!==c._default.apply(p.pop(),t)||!V(n)||u&&m(n[d])&&!x(n)&&((a=n[u])&&(n[u]=null),S.event.triggered=d,e.isPropagationStopped()&&f.addEventListener(d,xt),n[d](),e.isPropagationStopped()&&f.removeEventListener(d,xt),S.event.triggered=void 0,a&&(n[u]=a)),e.result}},simulate:function(e,t,n){var r=S.extend(new S.Event,n,{type:e,isSimulated:!0});S.event.trigger(r,null,t)}}),S.fn.extend({trigger:function(e,t){return this.each(function(){S.event.trigger(e,t,this)})},triggerHandler:function(e,t){var n=this[0];if(n)return S.event.trigger(e,t,n,!0)}}),y.focusin||S.each({focus:"focusin",blur:"focusout"},function(n,r){var i=function(e){S.event.simulate(r,e.target,S.event.fix(e))};S.event.special[r]={setup:function(){var e=this.ownerDocument||this.document||this,t=Y.access(e,r);t||e.addEventListener(n,i,!0),Y.access(e,r,(t||0)+1)},teardown:function(){var e=this.ownerDocument||this.document||this,t=Y.access(e,r)-1;t?Y.access(e,r,t):(e.removeEventListener(n,i,!0),Y.remove(e,r))}}});var bt=C.location,wt={guid:Date.now()},Tt=/\?/;S.parseXML=function(e){var t,n;if(!e||"string"!=typeof e)return null;try{t=(new C.DOMParser).parseFromString(e,"text/xml")}catch(e){}return n=t&&t.getElementsByTagName("parsererror")[0],t&&!n||S.error("Invalid XML: "+(n?S.map(n.childNodes,function(e){return e.textContent}).join("\n"):e)),t};var Ct=/\[\]$/,Et=/\r?\n/g,St=/^(?:submit|button|image|reset|file)$/i,kt=/^(?:input|select|textarea|keygen)/i;function At(n,e,r,i){var t;if(Array.isArray(e))S.each(e,function(e,t){r||Ct.test(n)?i(n,t):At(n+"["+("object"==typeof t&&null!=t?e:"")+"]",t,r,i)});else if(r||"object"!==w(e))i(n,e);else for(t in e)At(n+"["+t+"]",e[t],r,i)}S.param=function(e,t){var n,r=[],i=function(e,t){var n=m(t)?t():t;r[r.length]=encodeURIComponent(e)+"="+encodeURIComponent(null==n?"":n)};if(null==e)return"";if(Array.isArray(e)||e.jquery&&!S.isPlainObject(e))S.each(e,function(){i(this.name,this.value)});else for(n in e)At(n,e[n],t,i);return r.join("&")},S.fn.extend({serialize:function(){return S.param(this.serializeArray())},serializeArray:function(){return this.map(function(){var e=S.prop(this,"elements");return e?S.makeArray(e):this}).filter(function(){var e=this.type;return this.name&&!S(this).is(":disabled")&&kt.test(this.nodeName)&&!St.test(e)&&(this.checked||!pe.test(e))}).map(function(e,t){var n=S(this).val();return null==n?null:Array.isArray(n)?S.map(n,function(e){return{name:t.name,value:e.replace(Et,"\r\n")}}):{name:t.name,value:n.replace(Et,"\r\n")}}).get()}});var Nt=/%20/g,jt=/#.*$/,Dt=/([?&])_=[^&]*/,qt=/^(.*?):[ \t]*([^\r\n]*)$/gm,Lt=/^(?:GET|HEAD)$/,Ht=/^\/\//,Ot={},Pt={},Rt="*/".concat("*"),Mt=E.createElement("a");function It(o){return function(e,t){"string"!=typeof e&&(t=e,e="*");var n,r=0,i=e.toLowerCase().match(P)||[];if(m(t))while(n=i[r++])"+"===n[0]?(n=n.slice(1)||"*",(o[n]=o[n]||[]).unshift(t)):(o[n]=o[n]||[]).push(t)}}function Wt(t,i,o,a){var s={},u=t===Pt;function l(e){var r;return s[e]=!0,S.each(t[e]||[],function(e,t){var n=t(i,o,a);return"string"!=typeof n||u||s[n]?u?!(r=n):void 0:(i.dataTypes.unshift(n),l(n),!1)}),r}return l(i.dataTypes[0])||!s["*"]&&l("*")}function Ft(e,t){var n,r,i=S.ajaxSettings.flatOptions||{};for(n in t)void 0!==t[n]&&((i[n]?e:r||(r={}))[n]=t[n]);return r&&S.extend(!0,e,r),e}Mt.href=bt.href,S.extend({active:0,lastModified:{},etag:{},ajaxSettings:{url:bt.href,type:"GET",isLocal:/^(?:about|app|app-storage|.+-extension|file|res|widget):$/.test(bt.protocol),global:!0,processData:!0,async:!0,contentType:"application/x-www-form-urlencoded; charset=UTF-8",accepts:{"*":Rt,text:"text/plain",html:"text/html",xml:"application/xml, text/xml",json:"application/json, text/javascript"},contents:{xml:/\bxml\b/,html:/\bhtml/,json:/\bjson\b/},responseFields:{xml:"responseXML",text:"responseText",json:"responseJSON"},converters:{"* text":String,"text html":!0,"text json":JSON.parse,"text xml":S.parseXML},flatOptions:{url:!0,context:!0}},ajaxSetup:function(e,t){return t?Ft(Ft(e,S.ajaxSettings),t):Ft(S.ajaxSettings,e)},ajaxPrefilter:It(Ot),ajaxTransport:It(Pt),ajax:function(e,t){"object"==typeof e&&(t=e,e=void 0),t=t||{};var c,f,p,n,d,r,h,g,i,o,v=S.ajaxSetup({},t),y=v.context||v,m=v.context&&(y.nodeType||y.jquery)?S(y):S.event,x=S.Deferred(),b=S.Callbacks("once memory"),w=v.statusCode||{},a={},s={},u="canceled",T={readyState:0,getResponseHeader:function(e){var t;if(h){if(!n){n={};while(t=qt.exec(p))n[t[1].toLowerCase()+" "]=(n[t[1].toLowerCase()+" "]||[]).concat(t[2])}t=n[e.toLowerCase()+" "]}return null==t?null:t.join(", ")},getAllResponseHeaders:function(){return h?p:null},setRequestHeader:function(e,t){return null==h&&(e=s[e.toLowerCase()]=s[e.toLowerCase()]||e,a[e]=t),this},overrideMimeType:function(e){return null==h&&(v.mimeType=e),this},statusCode:function(e){var t;if(e)if(h)T.always(e[T.status]);else for(t in e)w[t]=[w[t],e[t]];return this},abort:function(e){var t=e||u;return c&&c.abort(t),l(0,t),this}};if(x.promise(T),v.url=((e||v.url||bt.href)+"").replace(Ht,bt.protocol+"//"),v.type=t.method||t.type||v.method||v.type,v.dataTypes=(v.dataType||"*").toLowerCase().match(P)||[""],null==v.crossDomain){r=E.createElement("a");try{r.href=v.url,r.href=r.href,v.crossDomain=Mt.protocol+"//"+Mt.host!=r.protocol+"//"+r.host}catch(e){v.crossDomain=!0}}if(v.data&&v.processData&&"string"!=typeof v.data&&(v.data=S.param(v.data,v.traditional)),Wt(Ot,v,t,T),h)return T;for(i in(g=S.event&&v.global)&&0==S.active++&&S.event.trigger("ajaxStart"),v.type=v.type.toUpperCase(),v.hasContent=!Lt.test(v.type),f=v.url.replace(jt,""),v.hasContent?v.data&&v.processData&&0===(v.contentType||"").indexOf("application/x-www-form-urlencoded")&&(v.data=v.data.replace(Nt,"+")):(o=v.url.slice(f.length),v.data&&(v.processData||"string"==typeof v.data)&&(f+=(Tt.test(f)?"&":"?")+v.data,delete v.data),!1===v.cache&&(f=f.replace(Dt,"$1"),o=(Tt.test(f)?"&":"?")+"_="+wt.guid+++o),v.url=f+o),v.ifModified&&(S.lastModified[f]&&T.setRequestHeader("If-Modified-Since",S.lastModified[f]),S.etag[f]&&T.setRequestHeader("If-None-Match",S.etag[f])),(v.data&&v.hasContent&&!1!==v.contentType||t.contentType)&&T.setRequestHeader("Content-Type",v.contentType),T.setRequestHeader("Accept",v.dataTypes[0]&&v.accepts[v.dataTypes[0]]?v.accepts[v.dataTypes[0]]+("*"!==v.dataTypes[0]?", "+Rt+"; q=0.01":""):v.accepts["*"]),v.headers)T.setRequestHeader(i,v.headers[i]);if(v.beforeSend&&(!1===v.beforeSend.call(y,T,v)||h))return T.abort();if(u="abort",b.add(v.complete),T.done(v.success),T.fail(v.error),c=Wt(Pt,v,t,T)){if(T.readyState=1,g&&m.trigger("ajaxSend",[T,v]),h)return T;v.async&&0<v.timeout&&(d=C.setTimeout(function(){T.abort("timeout")},v.timeout));try{h=!1,c.send(a,l)}catch(e){if(h)throw e;l(-1,e)}}else l(-1,"No Transport");function l(e,t,n,r){var i,o,a,s,u,l=t;h||(h=!0,d&&C.clearTimeout(d),c=void 0,p=r||"",T.readyState=0<e?4:0,i=200<=e&&e<300||304===e,n&&(s=function(e,t,n){var r,i,o,a,s=e.contents,u=e.dataTypes;while("*"===u[0])u.shift(),void 0===r&&(r=e.mimeType||t.getResponseHeader("Content-Type"));if(r)for(i in s)if(s[i]&&s[i].test(r)){u.unshift(i);break}if(u[0]in n)o=u[0];else{for(i in n){if(!u[0]||e.converters[i+" "+u[0]]){o=i;break}a||(a=i)}o=o||a}if(o)return o!==u[0]&&u.unshift(o),n[o]}(v,T,n)),!i&&-1<S.inArray("script",v.dataTypes)&&S.inArray("json",v.dataTypes)<0&&(v.converters["text script"]=function(){}),s=function(e,t,n,r){var i,o,a,s,u,l={},c=e.dataTypes.slice();if(c[1])for(a in e.converters)l[a.toLowerCase()]=e.converters[a];o=c.shift();while(o)if(e.responseFields[o]&&(n[e.responseFields[o]]=t),!u&&r&&e.dataFilter&&(t=e.dataFilter(t,e.dataType)),u=o,o=c.shift())if("*"===o)o=u;else if("*"!==u&&u!==o){if(!(a=l[u+" "+o]||l["* "+o]))for(i in l)if((s=i.split(" "))[1]===o&&(a=l[u+" "+s[0]]||l["* "+s[0]])){!0===a?a=l[i]:!0!==l[i]&&(o=s[0],c.unshift(s[1]));break}if(!0!==a)if(a&&e["throws"])t=a(t);else try{t=a(t)}catch(e){return{state:"parsererror",error:a?e:"No conversion from "+u+" to "+o}}}return{state:"success",data:t}}(v,s,T,i),i?(v.ifModified&&((u=T.getResponseHeader("Last-Modified"))&&(S.lastModified[f]=u),(u=T.getResponseHeader("etag"))&&(S.etag[f]=u)),204===e||"HEAD"===v.type?l="nocontent":304===e?l="notmodified":(l=s.state,o=s.data,i=!(a=s.error))):(a=l,!e&&l||(l="error",e<0&&(e=0))),T.status=e,T.statusText=(t||l)+"",i?x.resolveWith(y,[o,l,T]):x.rejectWith(y,[T,l,a]),T.statusCode(w),w=void 0,g&&m.trigger(i?"ajaxSuccess":"ajaxError",[T,v,i?o:a]),b.fireWith(y,[T,l]),g&&(m.trigger("ajaxComplete",[T,v]),--S.active||S.event.trigger("ajaxStop")))}return T},getJSON:function(e,t,n){return S.get(e,t,n,"json")},getScript:function(e,t){return S.get(e,void 0,t,"script")}}),S.each(["get","post"],function(e,i){S[i]=function(e,t,n,r){return m(t)&&(r=r||n,n=t,t=void 0),S.ajax(S.extend({url:e,type:i,dataType:r,data:t,success:n},S.isPlainObject(e)&&e))}}),S.ajaxPrefilter(function(e){var t;for(t in e.headers)"content-type"===t.toLowerCase()&&(e.contentType=e.headers[t]||"")}),S._evalUrl=function(e,t,n){return S.ajax({url:e,type:"GET",dataType:"script",cache:!0,async:!1,global:!1,converters:{"text script":function(){}},dataFilter:function(e){S.globalEval(e,t,n)}})},S.fn.extend({wrapAll:function(e){var t;return this[0]&&(m(e)&&(e=e.call(this[0])),t=S(e,this[0].ownerDocument).eq(0).clone(!0),this[0].parentNode&&t.insertBefore(this[0]),t.map(function(){var e=this;while(e.firstElementChild)e=e.firstElementChild;return e}).append(this)),this},wrapInner:function(n){return m(n)?this.each(function(e){S(this).wrapInner(n.call(this,e))}):this.each(function(){var e=S(this),t=e.contents();t.length?t.wrapAll(n):e.append(n)})},wrap:function(t){var n=m(t);return this.each(function(e){S(this).wrapAll(n?t.call(this,e):t)})},unwrap:function(e){return this.parent(e).not("body").each(function(){S(this).replaceWith(this.childNodes)}),this}}),S.expr.pseudos.hidden=function(e){return!S.expr.pseudos.visible(e)},S.expr.pseudos.visible=function(e){return!!(e.offsetWidth||e.offsetHeight||e.getClientRects().length)},S.ajaxSettings.xhr=function(){try{return new C.XMLHttpRequest}catch(e){}};var Bt={0:200,1223:204},$t=S.ajaxSettings.xhr();y.cors=!!$t&&"withCredentials"in $t,y.ajax=$t=!!$t,S.ajaxTransport(function(i){var o,a;if(y.cors||$t&&!i.crossDomain)return{send:function(e,t){var n,r=i.xhr();if(r.open(i.type,i.url,i.async,i.username,i.password),i.xhrFields)for(n in i.xhrFields)r[n]=i.xhrFields[n];for(n in i.mimeType&&r.overrideMimeType&&r.overrideMimeType(i.mimeType),i.crossDomain||e["X-Requested-With"]||(e["X-Requested-With"]="XMLHttpRequest"),e)r.setRequestHeader(n,e[n]);o=function(e){return function(){o&&(o=a=r.onload=r.onerror=r.onabort=r.ontimeout=r.onreadystatechange=null,"abort"===e?r.abort():"error"===e?"number"!=typeof r.status?t(0,"error"):t(r.status,r.statusText):t(Bt[r.status]||r.status,r.statusText,"text"!==(r.responseType||"text")||"string"!=typeof r.responseText?{binary:r.response}:{text:r.responseText},r.getAllResponseHeaders()))}},r.onload=o(),a=r.onerror=r.ontimeout=o("error"),void 0!==r.onabort?r.onabort=a:r.onreadystatechange=function(){4===r.readyState&&C.setTimeout(function(){o&&a()})},o=o("abort");try{r.send(i.hasContent&&i.data||null)}catch(e){if(o)throw e}},abort:function(){o&&o()}}}),S.ajaxPrefilter(function(e){e.crossDomain&&(e.contents.script=!1)}),S.ajaxSetup({accepts:{script:"text/javascript, application/javascript, application/ecmascript, application/x-ecmascript"},contents:{script:/\b(?:java|ecma)script\b/},converters:{"text script":function(e){return S.globalEval(e),e}}}),S.ajaxPrefilter("script",function(e){void 0===e.cache&&(e.cache=!1),e.crossDomain&&(e.type="GET")}),S.ajaxTransport("script",function(n){var r,i;if(n.crossDomain||n.scriptAttrs)return{send:function(e,t){r=S("<script>").attr(n.scriptAttrs||{}).prop({charset:n.scriptCharset,src:n.url}).on("load error",i=function(e){r.remove(),i=null,e&&t("error"===e.type?404:200,e.type)}),E.head.appendChild(r[0])},abort:function(){i&&i()}}});var _t,zt=[],Ut=/(=)\?(?=&|$)|\?\?/;S.ajaxSetup({jsonp:"callback",jsonpCallback:function(){var e=zt.pop()||S.expando+"_"+wt.guid++;return this[e]=!0,e}}),S.ajaxPrefilter("json jsonp",function(e,t,n){var r,i,o,a=!1!==e.jsonp&&(Ut.test(e.url)?"url":"string"==typeof e.data&&0===(e.contentType||"").indexOf("application/x-www-form-urlencoded")&&Ut.test(e.data)&&"data");if(a||"jsonp"===e.dataTypes[0])return r=e.jsonpCallback=m(e.jsonpCallback)?e.jsonpCallback():e.jsonpCallback,a?e[a]=e[a].replace(Ut,"$1"+r):!1!==e.jsonp&&(e.url+=(Tt.test(e.url)?"&":"?")+e.jsonp+"="+r),e.converters["script json"]=function(){return o||S.error(r+" was not called"),o[0]},e.dataTypes[0]="json",i=C[r],C[r]=function(){o=arguments},n.always(function(){void 0===i?S(C).removeProp(r):C[r]=i,e[r]&&(e.jsonpCallback=t.jsonpCallback,zt.push(r)),o&&m(i)&&i(o[0]),o=i=void 0}),"script"}),y.createHTMLDocument=((_t=E.implementation.createHTMLDocument("").body).innerHTML="<form></form><form></form>",2===_t.childNodes.length),S.parseHTML=function(e,t,n){return"string"!=typeof e?[]:("boolean"==typeof t&&(n=t,t=!1),t||(y.createHTMLDocument?((r=(t=E.implementation.createHTMLDocument("")).createElement("base")).href=E.location.href,t.head.appendChild(r)):t=E),o=!n&&[],(i=N.exec(e))?[t.createElement(i[1])]:(i=xe([e],t,o),o&&o.length&&S(o).remove(),S.merge([],i.childNodes)));var r,i,o},S.fn.load=function(e,t,n){var r,i,o,a=this,s=e.indexOf(" ");return-1<s&&(r=ht(e.slice(s)),e=e.slice(0,s)),m(t)?(n=t,t=void 0):t&&"object"==typeof t&&(i="POST"),0<a.length&&S.ajax({url:e,type:i||"GET",dataType:"html",data:t}).done(function(e){o=arguments,a.html(r?S("<div>").append(S.parseHTML(e)).find(r):e)}).always(n&&function(e,t){a.each(function(){n.apply(this,o||[e.responseText,t,e])})}),this},S.expr.pseudos.animated=function(t){return S.grep(S.timers,function(e){return t===e.elem}).length},S.offset={setOffset:function(e,t,n){var r,i,o,a,s,u,l=S.css(e,"position"),c=S(e),f={};"static"===l&&(e.style.position="relative"),s=c.offset(),o=S.css(e,"top"),u=S.css(e,"left"),("absolute"===l||"fixed"===l)&&-1<(o+u).indexOf("auto")?(a=(r=c.position()).top,i=r.left):(a=parseFloat(o)||0,i=parseFloat(u)||0),m(t)&&(t=t.call(e,n,S.extend({},s))),null!=t.top&&(f.top=t.top-s.top+a),null!=t.left&&(f.left=t.left-s.left+i),"using"in t?t.using.call(e,f):c.css(f)}},S.fn.extend({offset:function(t){if(arguments.length)return void 0===t?this:this.each(function(e){S.offset.setOffset(this,t,e)});var e,n,r=this[0];return r?r.getClientRects().length?(e=r.getBoundingClientRect(),n=r.ownerDocument.defaultView,{top:e.top+n.pageYOffset,left:e.left+n.pageXOffset}):{top:0,left:0}:void 0},position:function(){if(this[0]){var e,t,n,r=this[0],i={top:0,left:0};if("fixed"===S.css(r,"position"))t=r.getBoundingClientRect();else{t=this.offset(),n=r.ownerDocument,e=r.offsetParent||n.documentElement;while(e&&(e===n.body||e===n.documentElement)&&"static"===S.css(e,"position"))e=e.parentNode;e&&e!==r&&1===e.nodeType&&((i=S(e).offset()).top+=S.css(e,"borderTopWidth",!0),i.left+=S.css(e,"borderLeftWidth",!0))}return{top:t.top-i.top-S.css(r,"marginTop",!0),left:t.left-i.left-S.css(r,"marginLeft",!0)}}},offsetParent:function(){return this.map(function(){var e=this.offsetParent;while(e&&"static"===S.css(e,"position"))e=e.offsetParent;return e||re})}}),S.each({scrollLeft:"pageXOffset",scrollTop:"pageYOffset"},function(t,i){var o="pageYOffset"===i;S.fn[t]=function(e){return $(this,function(e,t,n){var r;if(x(e)?r=e:9===e.nodeType&&(r=e.defaultView),void 0===n)return r?r[i]:e[t];r?r.scrollTo(o?r.pageXOffset:n,o?n:r.pageYOffset):e[t]=n},t,e,arguments.length)}}),S.each(["top","left"],function(e,n){S.cssHooks[n]=Fe(y.pixelPosition,function(e,t){if(t)return t=We(e,n),Pe.test(t)?S(e).position()[n]+"px":t})}),S.each({Height:"height",Width:"width"},function(a,s){S.each({padding:"inner"+a,content:s,"":"outer"+a},function(r,o){S.fn[o]=function(e,t){var n=arguments.length&&(r||"boolean"!=typeof e),i=r||(!0===e||!0===t?"margin":"border");return $(this,function(e,t,n){var r;return x(e)?0===o.indexOf("outer")?e["inner"+a]:e.document.documentElement["client"+a]:9===e.nodeType?(r=e.documentElement,Math.max(e.body["scroll"+a],r["scroll"+a],e.body["offset"+a],r["offset"+a],r["client"+a])):void 0===n?S.css(e,t,i):S.style(e,t,n,i)},s,n?e:void 0,n)}})}),S.each(["ajaxStart","ajaxStop","ajaxComplete","ajaxError","ajaxSuccess","ajaxSend"],function(e,t){S.fn[t]=function(e){return this.on(t,e)}}),S.fn.extend({bind:function(e,t,n){return this.on(e,null,t,n)},unbind:function(e,t){return this.off(e,null,t)},delegate:function(e,t,n,r){return this.on(t,e,n,r)},undelegate:function(e,t,n){return 1===arguments.length?this.off(e,"**"):this.off(t,e||"**",n)},hover:function(e,t){return this.mouseenter(e).mouseleave(t||e)}}),S.each("blur focus focusin focusout resize scroll click dblclick mousedown mouseup mousemove mouseover mouseout mouseenter mouseleave change select submit keydown keypress keyup contextmenu".split(" "),function(e,n){S.fn[n]=function(e,t){return 0<arguments.length?this.on(n,null,e,t):this.trigger(n)}});var Xt=/^[\s\uFEFF\xA0]+|[\s\uFEFF\xA0]+$/g;S.proxy=function(e,t){var n,r,i;if("string"==typeof t&&(n=e[t],t=e,e=n),m(e))return r=s.call(arguments,2),(i=function(){return e.apply(t||this,r.concat(s.call(arguments)))}).guid=e.guid=e.guid||S.guid++,i},S.holdReady=function(e){e?S.readyWait++:S.ready(!0)},S.isArray=Array.isArray,S.parseJSON=JSON.parse,S.nodeName=A,S.isFunction=m,S.isWindow=x,S.camelCase=X,S.type=w,S.now=Date.now,S.isNumeric=function(e){var t=S.type(e);return("number"===t||"string"===t)&&!isNaN(e-parseFloat(e))},S.trim=function(e){return null==e?"":(e+"").replace(Xt,"")},"function"==typeof define&&define.amd&&define("jquery",[],function(){return S});var Vt=C.jQuery,Gt=C.$;return S.noConflict=function(e){return C.$===S&&(C.$=Gt),e&&C.jQuery===S&&(C.jQuery=Vt),S},"undefined"==typeof e&&(C.jQuery=C.$=S),S});
+</script>
+<meta name="viewport" content="width=device-width, initial-scale=1" />
+<style type="text/css">html{font-family:sans-serif;-webkit-text-size-adjust:100%;-ms-text-size-adjust:100%}body{margin:0}article,aside,details,figcaption,figure,footer,header,hgroup,main,menu,nav,section,summary{display:block}audio,canvas,progress,video{display:inline-block;vertical-align:baseline}audio:not([controls]){display:none;height:0}[hidden],template{display:none}a{background-color:transparent}a:active,a:hover{outline:0}abbr[title]{border-bottom:1px dotted}b,strong{font-weight:700}dfn{font-style:italic}h1{margin:.67em 0;font-size:2em}mark{color:#000;background:#ff0}small{font-size:80%}sub,sup{position:relative;font-size:75%;line-height:0;vertical-align:baseline}sup{top:-.5em}sub{bottom:-.25em}img{border:0}svg:not(:root){overflow:hidden}figure{margin:1em 40px}hr{height:0;-webkit-box-sizing:content-box;-moz-box-sizing:content-box;box-sizing:content-box}pre{overflow:auto}code,kbd,pre,samp{font-family:monospace,monospace;font-size:1em}button,input,optgroup,select,textarea{margin:0;font:inherit;color:inherit}button{overflow:visible}button,select{text-transform:none}button,html input[type=button],input[type=reset],input[type=submit]{-webkit-appearance:button;cursor:pointer}button[disabled],html input[disabled]{cursor:default}button::-moz-focus-inner,input::-moz-focus-inner{padding:0;border:0}input{line-height:normal}input[type=checkbox],input[type=radio]{-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;padding:0}input[type=number]::-webkit-inner-spin-button,input[type=number]::-webkit-outer-spin-button{height:auto}input[type=search]{-webkit-box-sizing:content-box;-moz-box-sizing:content-box;box-sizing:content-box;-webkit-appearance:textfield}input[type=search]::-webkit-search-cancel-button,input[type=search]::-webkit-search-decoration{-webkit-appearance:none}fieldset{padding:.35em .625em .75em;margin:0 2px;border:1px solid silver}legend{padding:0;border:0}textarea{overflow:auto}optgroup{font-weight:700}table{border-spacing:0;border-collapse:collapse}td,th{padding:0}@media print{*,:after,:before{color:#000!important;text-shadow:none!important;background:0 0!important;-webkit-box-shadow:none!important;box-shadow:none!important}a,a:visited{text-decoration:underline}a[href]:after{content:" (" attr(href) ")"}abbr[title]:after{content:" (" attr(title) ")"}a[href^="javascript:"]:after,a[href^="#"]:after{content:""}blockquote,pre{border:1px solid #999;page-break-inside:avoid}thead{display:table-header-group}img,tr{page-break-inside:avoid}img{max-width:100%!important}h2,h3,p{orphans:3;widows:3}h2,h3{page-break-after:avoid}.navbar{display:none}.btn>.caret,.dropup>.btn>.caret{border-top-color:#000!important}.label{border:1px solid #000}.table{border-collapse:collapse!important}.table td,.table th{background-color:#fff!important}.table-bordered td,.table-bordered th{border:1px solid #ddd!important}}@font-face{font-family:'Glyphicons Halflings';src:url(data:application/vnd.ms-fontobject;base64,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);src:url(data:application/vnd.ms-fontobject;base64,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) format('embedded-opentype'),url(data:application/font-woff;base64,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) format('woff'),url(data:application/font-sfnt;base64,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) format('truetype'),url(data:image/svg+xml;base64,<?xml version="1.0" standalone="no"?>
<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN" "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd" >
<svg xmlns="http://www.w3.org/2000/svg">
<metadata></metadata>
<defs>
<font id="glyphicons_halflingsregular" horiz-adv-x="1200" >
<font-face units-per-em="1200" ascent="960" descent="-240" />
<missing-glyph horiz-adv-x="500" />
<glyph horiz-adv-x="0" />
<glyph horiz-adv-x="400" />
<glyph unicode=" " />
<glyph unicode="*" d="M600 1100q15 0 34 -1.5t30 -3.5l11 -1q10 -2 17.5 -10.5t7.5 -18.5v-224l158 158q7 7 18 8t19 -6l106 -106q7 -8 6 -19t-8 -18l-158 -158h224q10 0 18.5 -7.5t10.5 -17.5q6 -41 6 -75q0 -15 -1.5 -34t-3.5 -30l-1 -11q-2 -10 -10.5 -17.5t-18.5 -7.5h-224l158 -158 q7 -7 8 -18t-6 -19l-106 -106q-8 -7 -19 -6t-18 8l-158 158v-224q0 -10 -7.5 -18.5t-17.5 -10.5q-41 -6 -75 -6q-15 0 -34 1.5t-30 3.5l-11 1q-10 2 -17.5 10.5t-7.5 18.5v224l-158 -158q-7 -7 -18 -8t-19 6l-106 106q-7 8 -6 19t8 18l158 158h-224q-10 0 -18.5 7.5 t-10.5 17.5q-6 41 -6 75q0 15 1.5 34t3.5 30l1 11q2 10 10.5 17.5t18.5 7.5h224l-158 158q-7 7 -8 18t6 19l106 106q8 7 19 6t18 -8l158 -158v224q0 10 7.5 18.5t17.5 10.5q41 6 75 6z" />
<glyph unicode="+" d="M450 1100h200q21 0 35.5 -14.5t14.5 -35.5v-350h350q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-350v-350q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v350h-350q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5 h350v350q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xa0;" />
<glyph unicode="&#xa5;" d="M825 1100h250q10 0 12.5 -5t-5.5 -13l-364 -364q-6 -6 -11 -18h268q10 0 13 -6t-3 -14l-120 -160q-6 -8 -18 -14t-22 -6h-125v-100h275q10 0 13 -6t-3 -14l-120 -160q-6 -8 -18 -14t-22 -6h-125v-174q0 -11 -7.5 -18.5t-18.5 -7.5h-148q-11 0 -18.5 7.5t-7.5 18.5v174 h-275q-10 0 -13 6t3 14l120 160q6 8 18 14t22 6h125v100h-275q-10 0 -13 6t3 14l120 160q6 8 18 14t22 6h118q-5 12 -11 18l-364 364q-8 8 -5.5 13t12.5 5h250q25 0 43 -18l164 -164q8 -8 18 -8t18 8l164 164q18 18 43 18z" />
<glyph unicode="&#x2000;" horiz-adv-x="650" />
<glyph unicode="&#x2001;" horiz-adv-x="1300" />
<glyph unicode="&#x2002;" horiz-adv-x="650" />
<glyph unicode="&#x2003;" horiz-adv-x="1300" />
<glyph unicode="&#x2004;" horiz-adv-x="433" />
<glyph unicode="&#x2005;" horiz-adv-x="325" />
<glyph unicode="&#x2006;" horiz-adv-x="216" />
<glyph unicode="&#x2007;" horiz-adv-x="216" />
<glyph unicode="&#x2008;" horiz-adv-x="162" />
<glyph unicode="&#x2009;" horiz-adv-x="260" />
<glyph unicode="&#x200a;" horiz-adv-x="72" />
<glyph unicode="&#x202f;" horiz-adv-x="260" />
<glyph unicode="&#x205f;" horiz-adv-x="325" />
<glyph unicode="&#x20ac;" d="M744 1198q242 0 354 -189q60 -104 66 -209h-181q0 45 -17.5 82.5t-43.5 61.5t-58 40.5t-60.5 24t-51.5 7.5q-19 0 -40.5 -5.5t-49.5 -20.5t-53 -38t-49 -62.5t-39 -89.5h379l-100 -100h-300q-6 -50 -6 -100h406l-100 -100h-300q9 -74 33 -132t52.5 -91t61.5 -54.5t59 -29 t47 -7.5q22 0 50.5 7.5t60.5 24.5t58 41t43.5 61t17.5 80h174q-30 -171 -128 -278q-107 -117 -274 -117q-206 0 -324 158q-36 48 -69 133t-45 204h-217l100 100h112q1 47 6 100h-218l100 100h134q20 87 51 153.5t62 103.5q117 141 297 141z" />
<glyph unicode="&#x20bd;" d="M428 1200h350q67 0 120 -13t86 -31t57 -49.5t35 -56.5t17 -64.5t6.5 -60.5t0.5 -57v-16.5v-16.5q0 -36 -0.5 -57t-6.5 -61t-17 -65t-35 -57t-57 -50.5t-86 -31.5t-120 -13h-178l-2 -100h288q10 0 13 -6t-3 -14l-120 -160q-6 -8 -18 -14t-22 -6h-138v-175q0 -11 -5.5 -18 t-15.5 -7h-149q-10 0 -17.5 7.5t-7.5 17.5v175h-267q-10 0 -13 6t3 14l120 160q6 8 18 14t22 6h117v100h-267q-10 0 -13 6t3 14l120 160q6 8 18 14t22 6h117v475q0 10 7.5 17.5t17.5 7.5zM600 1000v-300h203q64 0 86.5 33t22.5 119q0 84 -22.5 116t-86.5 32h-203z" />
<glyph unicode="&#x2212;" d="M250 700h800q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#x231b;" d="M1000 1200v-150q0 -21 -14.5 -35.5t-35.5 -14.5h-50v-100q0 -91 -49.5 -165.5t-130.5 -109.5q81 -35 130.5 -109.5t49.5 -165.5v-150h50q21 0 35.5 -14.5t14.5 -35.5v-150h-800v150q0 21 14.5 35.5t35.5 14.5h50v150q0 91 49.5 165.5t130.5 109.5q-81 35 -130.5 109.5 t-49.5 165.5v100h-50q-21 0 -35.5 14.5t-14.5 35.5v150h800zM400 1000v-100q0 -60 32.5 -109.5t87.5 -73.5q28 -12 44 -37t16 -55t-16 -55t-44 -37q-55 -24 -87.5 -73.5t-32.5 -109.5v-150h400v150q0 60 -32.5 109.5t-87.5 73.5q-28 12 -44 37t-16 55t16 55t44 37 q55 24 87.5 73.5t32.5 109.5v100h-400z" />
<glyph unicode="&#x25fc;" horiz-adv-x="500" d="M0 0z" />
<glyph unicode="&#x2601;" d="M503 1089q110 0 200.5 -59.5t134.5 -156.5q44 14 90 14q120 0 205 -86.5t85 -206.5q0 -121 -85 -207.5t-205 -86.5h-750q-79 0 -135.5 57t-56.5 137q0 69 42.5 122.5t108.5 67.5q-2 12 -2 37q0 153 108 260.5t260 107.5z" />
<glyph unicode="&#x26fa;" d="M774 1193.5q16 -9.5 20.5 -27t-5.5 -33.5l-136 -187l467 -746h30q20 0 35 -18.5t15 -39.5v-42h-1200v42q0 21 15 39.5t35 18.5h30l468 746l-135 183q-10 16 -5.5 34t20.5 28t34 5.5t28 -20.5l111 -148l112 150q9 16 27 20.5t34 -5zM600 200h377l-182 112l-195 534v-646z " />
<glyph unicode="&#x2709;" d="M25 1100h1150q10 0 12.5 -5t-5.5 -13l-564 -567q-8 -8 -18 -8t-18 8l-564 567q-8 8 -5.5 13t12.5 5zM18 882l264 -264q8 -8 8 -18t-8 -18l-264 -264q-8 -8 -13 -5.5t-5 12.5v550q0 10 5 12.5t13 -5.5zM918 618l264 264q8 8 13 5.5t5 -12.5v-550q0 -10 -5 -12.5t-13 5.5 l-264 264q-8 8 -8 18t8 18zM818 482l364 -364q8 -8 5.5 -13t-12.5 -5h-1150q-10 0 -12.5 5t5.5 13l364 364q8 8 18 8t18 -8l164 -164q8 -8 18 -8t18 8l164 164q8 8 18 8t18 -8z" />
<glyph unicode="&#x270f;" d="M1011 1210q19 0 33 -13l153 -153q13 -14 13 -33t-13 -33l-99 -92l-214 214l95 96q13 14 32 14zM1013 800l-615 -614l-214 214l614 614zM317 96l-333 -112l110 335z" />
<glyph unicode="&#xe001;" d="M700 650v-550h250q21 0 35.5 -14.5t14.5 -35.5v-50h-800v50q0 21 14.5 35.5t35.5 14.5h250v550l-500 550h1200z" />
<glyph unicode="&#xe002;" d="M368 1017l645 163q39 15 63 0t24 -49v-831q0 -55 -41.5 -95.5t-111.5 -63.5q-79 -25 -147 -4.5t-86 75t25.5 111.5t122.5 82q72 24 138 8v521l-600 -155v-606q0 -42 -44 -90t-109 -69q-79 -26 -147 -5.5t-86 75.5t25.5 111.5t122.5 82.5q72 24 138 7v639q0 38 14.5 59 t53.5 34z" />
<glyph unicode="&#xe003;" d="M500 1191q100 0 191 -39t156.5 -104.5t104.5 -156.5t39 -191l-1 -2l1 -5q0 -141 -78 -262l275 -274q23 -26 22.5 -44.5t-22.5 -42.5l-59 -58q-26 -20 -46.5 -20t-39.5 20l-275 274q-119 -77 -261 -77l-5 1l-2 -1q-100 0 -191 39t-156.5 104.5t-104.5 156.5t-39 191 t39 191t104.5 156.5t156.5 104.5t191 39zM500 1022q-88 0 -162 -43t-117 -117t-43 -162t43 -162t117 -117t162 -43t162 43t117 117t43 162t-43 162t-117 117t-162 43z" />
<glyph unicode="&#xe005;" d="M649 949q48 68 109.5 104t121.5 38.5t118.5 -20t102.5 -64t71 -100.5t27 -123q0 -57 -33.5 -117.5t-94 -124.5t-126.5 -127.5t-150 -152.5t-146 -174q-62 85 -145.5 174t-150 152.5t-126.5 127.5t-93.5 124.5t-33.5 117.5q0 64 28 123t73 100.5t104 64t119 20 t120.5 -38.5t104.5 -104z" />
<glyph unicode="&#xe006;" d="M407 800l131 353q7 19 17.5 19t17.5 -19l129 -353h421q21 0 24 -8.5t-14 -20.5l-342 -249l130 -401q7 -20 -0.5 -25.5t-24.5 6.5l-343 246l-342 -247q-17 -12 -24.5 -6.5t-0.5 25.5l130 400l-347 251q-17 12 -14 20.5t23 8.5h429z" />
<glyph unicode="&#xe007;" d="M407 800l131 353q7 19 17.5 19t17.5 -19l129 -353h421q21 0 24 -8.5t-14 -20.5l-342 -249l130 -401q7 -20 -0.5 -25.5t-24.5 6.5l-343 246l-342 -247q-17 -12 -24.5 -6.5t-0.5 25.5l130 400l-347 251q-17 12 -14 20.5t23 8.5h429zM477 700h-240l197 -142l-74 -226 l193 139l195 -140l-74 229l192 140h-234l-78 211z" />
<glyph unicode="&#xe008;" d="M600 1200q124 0 212 -88t88 -212v-250q0 -46 -31 -98t-69 -52v-75q0 -10 6 -21.5t15 -17.5l358 -230q9 -5 15 -16.5t6 -21.5v-93q0 -10 -7.5 -17.5t-17.5 -7.5h-1150q-10 0 -17.5 7.5t-7.5 17.5v93q0 10 6 21.5t15 16.5l358 230q9 6 15 17.5t6 21.5v75q-38 0 -69 52 t-31 98v250q0 124 88 212t212 88z" />
<glyph unicode="&#xe009;" d="M25 1100h1150q10 0 17.5 -7.5t7.5 -17.5v-1050q0 -10 -7.5 -17.5t-17.5 -7.5h-1150q-10 0 -17.5 7.5t-7.5 17.5v1050q0 10 7.5 17.5t17.5 7.5zM100 1000v-100h100v100h-100zM875 1000h-550q-10 0 -17.5 -7.5t-7.5 -17.5v-350q0 -10 7.5 -17.5t17.5 -7.5h550 q10 0 17.5 7.5t7.5 17.5v350q0 10 -7.5 17.5t-17.5 7.5zM1000 1000v-100h100v100h-100zM100 800v-100h100v100h-100zM1000 800v-100h100v100h-100zM100 600v-100h100v100h-100zM1000 600v-100h100v100h-100zM875 500h-550q-10 0 -17.5 -7.5t-7.5 -17.5v-350q0 -10 7.5 -17.5 t17.5 -7.5h550q10 0 17.5 7.5t7.5 17.5v350q0 10 -7.5 17.5t-17.5 7.5zM100 400v-100h100v100h-100zM1000 400v-100h100v100h-100zM100 200v-100h100v100h-100zM1000 200v-100h100v100h-100z" />
<glyph unicode="&#xe010;" d="M50 1100h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5zM650 1100h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v400 q0 21 14.5 35.5t35.5 14.5zM50 500h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5zM650 500h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400 q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe011;" d="M50 1100h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM450 1100h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200 q0 21 14.5 35.5t35.5 14.5zM850 1100h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM50 700h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200 q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM450 700h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM850 700h200q21 0 35.5 -14.5t14.5 -35.5v-200 q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM50 300h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM450 300h200 q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM850 300h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5 t35.5 14.5z" />
<glyph unicode="&#xe012;" d="M50 1100h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM450 1100h700q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-700q-21 0 -35.5 14.5t-14.5 35.5v200 q0 21 14.5 35.5t35.5 14.5zM50 700h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM450 700h700q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-700 q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM50 300h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM450 300h700q21 0 35.5 -14.5t14.5 -35.5v-200 q0 -21 -14.5 -35.5t-35.5 -14.5h-700q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe013;" d="M465 477l571 571q8 8 18 8t17 -8l177 -177q8 -7 8 -17t-8 -18l-783 -784q-7 -8 -17.5 -8t-17.5 8l-384 384q-8 8 -8 18t8 17l177 177q7 8 17 8t18 -8l171 -171q7 -7 18 -7t18 7z" />
<glyph unicode="&#xe014;" d="M904 1083l178 -179q8 -8 8 -18.5t-8 -17.5l-267 -268l267 -268q8 -7 8 -17.5t-8 -18.5l-178 -178q-8 -8 -18.5 -8t-17.5 8l-268 267l-268 -267q-7 -8 -17.5 -8t-18.5 8l-178 178q-8 8 -8 18.5t8 17.5l267 268l-267 268q-8 7 -8 17.5t8 18.5l178 178q8 8 18.5 8t17.5 -8 l268 -267l268 268q7 7 17.5 7t18.5 -7z" />
<glyph unicode="&#xe015;" d="M507 1177q98 0 187.5 -38.5t154.5 -103.5t103.5 -154.5t38.5 -187.5q0 -141 -78 -262l300 -299q8 -8 8 -18.5t-8 -18.5l-109 -108q-7 -8 -17.5 -8t-18.5 8l-300 299q-119 -77 -261 -77q-98 0 -188 38.5t-154.5 103t-103 154.5t-38.5 188t38.5 187.5t103 154.5 t154.5 103.5t188 38.5zM506.5 1023q-89.5 0 -165.5 -44t-120 -120.5t-44 -166t44 -165.5t120 -120t165.5 -44t166 44t120.5 120t44 165.5t-44 166t-120.5 120.5t-166 44zM425 900h150q10 0 17.5 -7.5t7.5 -17.5v-75h75q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5 t-17.5 -7.5h-75v-75q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v75h-75q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5h75v75q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe016;" d="M507 1177q98 0 187.5 -38.5t154.5 -103.5t103.5 -154.5t38.5 -187.5q0 -141 -78 -262l300 -299q8 -8 8 -18.5t-8 -18.5l-109 -108q-7 -8 -17.5 -8t-18.5 8l-300 299q-119 -77 -261 -77q-98 0 -188 38.5t-154.5 103t-103 154.5t-38.5 188t38.5 187.5t103 154.5 t154.5 103.5t188 38.5zM506.5 1023q-89.5 0 -165.5 -44t-120 -120.5t-44 -166t44 -165.5t120 -120t165.5 -44t166 44t120.5 120t44 165.5t-44 166t-120.5 120.5t-166 44zM325 800h350q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-350q-10 0 -17.5 7.5 t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe017;" d="M550 1200h100q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5zM800 975v166q167 -62 272 -209.5t105 -331.5q0 -117 -45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5 t-184.5 123t-123 184.5t-45.5 224q0 184 105 331.5t272 209.5v-166q-103 -55 -165 -155t-62 -220q0 -116 57 -214.5t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5q0 120 -62 220t-165 155z" />
<glyph unicode="&#xe018;" d="M1025 1200h150q10 0 17.5 -7.5t7.5 -17.5v-1150q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v1150q0 10 7.5 17.5t17.5 7.5zM725 800h150q10 0 17.5 -7.5t7.5 -17.5v-750q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v750 q0 10 7.5 17.5t17.5 7.5zM425 500h150q10 0 17.5 -7.5t7.5 -17.5v-450q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v450q0 10 7.5 17.5t17.5 7.5zM125 300h150q10 0 17.5 -7.5t7.5 -17.5v-250q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5 v250q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe019;" d="M600 1174q33 0 74 -5l38 -152l5 -1q49 -14 94 -39l5 -2l134 80q61 -48 104 -105l-80 -134l3 -5q25 -44 39 -93l1 -6l152 -38q5 -43 5 -73q0 -34 -5 -74l-152 -38l-1 -6q-15 -49 -39 -93l-3 -5l80 -134q-48 -61 -104 -105l-134 81l-5 -3q-44 -25 -94 -39l-5 -2l-38 -151 q-43 -5 -74 -5q-33 0 -74 5l-38 151l-5 2q-49 14 -94 39l-5 3l-134 -81q-60 48 -104 105l80 134l-3 5q-25 45 -38 93l-2 6l-151 38q-6 42 -6 74q0 33 6 73l151 38l2 6q13 48 38 93l3 5l-80 134q47 61 105 105l133 -80l5 2q45 25 94 39l5 1l38 152q43 5 74 5zM600 815 q-89 0 -152 -63t-63 -151.5t63 -151.5t152 -63t152 63t63 151.5t-63 151.5t-152 63z" />
<glyph unicode="&#xe020;" d="M500 1300h300q41 0 70.5 -29.5t29.5 -70.5v-100h275q10 0 17.5 -7.5t7.5 -17.5v-75h-1100v75q0 10 7.5 17.5t17.5 7.5h275v100q0 41 29.5 70.5t70.5 29.5zM500 1200v-100h300v100h-300zM1100 900v-800q0 -41 -29.5 -70.5t-70.5 -29.5h-700q-41 0 -70.5 29.5t-29.5 70.5 v800h900zM300 800v-700h100v700h-100zM500 800v-700h100v700h-100zM700 800v-700h100v700h-100zM900 800v-700h100v700h-100z" />
<glyph unicode="&#xe021;" d="M18 618l620 608q8 7 18.5 7t17.5 -7l608 -608q8 -8 5.5 -13t-12.5 -5h-175v-575q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v375h-300v-375q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v575h-175q-10 0 -12.5 5t5.5 13z" />
<glyph unicode="&#xe022;" d="M600 1200v-400q0 -41 29.5 -70.5t70.5 -29.5h300v-650q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5v1100q0 21 14.5 35.5t35.5 14.5h450zM1000 800h-250q-21 0 -35.5 14.5t-14.5 35.5v250z" />
<glyph unicode="&#xe023;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 1027q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5 t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5t-57 214.5t-155.5 155.5t-214.5 57zM525 900h50q10 0 17.5 -7.5t7.5 -17.5v-275h175q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v350q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe024;" d="M1300 0h-538l-41 400h-242l-41 -400h-538l431 1200h209l-21 -300h162l-20 300h208zM515 800l-27 -300h224l-27 300h-170z" />
<glyph unicode="&#xe025;" d="M550 1200h200q21 0 35.5 -14.5t14.5 -35.5v-450h191q20 0 25.5 -11.5t-7.5 -27.5l-327 -400q-13 -16 -32 -16t-32 16l-327 400q-13 16 -7.5 27.5t25.5 11.5h191v450q0 21 14.5 35.5t35.5 14.5zM1125 400h50q10 0 17.5 -7.5t7.5 -17.5v-350q0 -10 -7.5 -17.5t-17.5 -7.5 h-1050q-10 0 -17.5 7.5t-7.5 17.5v350q0 10 7.5 17.5t17.5 7.5h50q10 0 17.5 -7.5t7.5 -17.5v-175h900v175q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe026;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 1027q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5 t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5t-57 214.5t-155.5 155.5t-214.5 57zM525 900h150q10 0 17.5 -7.5t7.5 -17.5v-275h137q21 0 26 -11.5t-8 -27.5l-223 -275q-13 -16 -32 -16t-32 16l-223 275q-13 16 -8 27.5t26 11.5h137v275q0 10 7.5 17.5t17.5 7.5z " />
<glyph unicode="&#xe027;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 1027q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5 t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5t-57 214.5t-155.5 155.5t-214.5 57zM632 914l223 -275q13 -16 8 -27.5t-26 -11.5h-137v-275q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v275h-137q-21 0 -26 11.5t8 27.5l223 275q13 16 32 16 t32 -16z" />
<glyph unicode="&#xe028;" d="M225 1200h750q10 0 19.5 -7t12.5 -17l186 -652q7 -24 7 -49v-425q0 -12 -4 -27t-9 -17q-12 -6 -37 -6h-1100q-12 0 -27 4t-17 8q-6 13 -6 38l1 425q0 25 7 49l185 652q3 10 12.5 17t19.5 7zM878 1000h-556q-10 0 -19 -7t-11 -18l-87 -450q-2 -11 4 -18t16 -7h150 q10 0 19.5 -7t11.5 -17l38 -152q2 -10 11.5 -17t19.5 -7h250q10 0 19.5 7t11.5 17l38 152q2 10 11.5 17t19.5 7h150q10 0 16 7t4 18l-87 450q-2 11 -11 18t-19 7z" />
<glyph unicode="&#xe029;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 1027q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5 t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5t-57 214.5t-155.5 155.5t-214.5 57zM540 820l253 -190q17 -12 17 -30t-17 -30l-253 -190q-16 -12 -28 -6.5t-12 26.5v400q0 21 12 26.5t28 -6.5z" />
<glyph unicode="&#xe030;" d="M947 1060l135 135q7 7 12.5 5t5.5 -13v-362q0 -10 -7.5 -17.5t-17.5 -7.5h-362q-11 0 -13 5.5t5 12.5l133 133q-109 76 -238 76q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5h150q0 -117 -45.5 -224 t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5q192 0 347 -117z" />
<glyph unicode="&#xe031;" d="M947 1060l135 135q7 7 12.5 5t5.5 -13v-361q0 -11 -7.5 -18.5t-18.5 -7.5h-361q-11 0 -13 5.5t5 12.5l134 134q-110 75 -239 75q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5h-150q0 117 45.5 224t123 184.5t184.5 123t224 45.5q192 0 347 -117zM1027 600h150 q0 -117 -45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5q-192 0 -348 118l-134 -134q-7 -8 -12.5 -5.5t-5.5 12.5v360q0 11 7.5 18.5t18.5 7.5h360q10 0 12.5 -5.5t-5.5 -12.5l-133 -133q110 -76 240 -76q116 0 214.5 57t155.5 155.5t57 214.5z" />
<glyph unicode="&#xe032;" d="M125 1200h1050q10 0 17.5 -7.5t7.5 -17.5v-1150q0 -10 -7.5 -17.5t-17.5 -7.5h-1050q-10 0 -17.5 7.5t-7.5 17.5v1150q0 10 7.5 17.5t17.5 7.5zM1075 1000h-850q-10 0 -17.5 -7.5t-7.5 -17.5v-850q0 -10 7.5 -17.5t17.5 -7.5h850q10 0 17.5 7.5t7.5 17.5v850 q0 10 -7.5 17.5t-17.5 7.5zM325 900h50q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-50q-10 0 -17.5 7.5t-7.5 17.5v50q0 10 7.5 17.5t17.5 7.5zM525 900h450q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-450q-10 0 -17.5 7.5t-7.5 17.5v50 q0 10 7.5 17.5t17.5 7.5zM325 700h50q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-50q-10 0 -17.5 7.5t-7.5 17.5v50q0 10 7.5 17.5t17.5 7.5zM525 700h450q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-450q-10 0 -17.5 7.5t-7.5 17.5v50 q0 10 7.5 17.5t17.5 7.5zM325 500h50q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-50q-10 0 -17.5 7.5t-7.5 17.5v50q0 10 7.5 17.5t17.5 7.5zM525 500h450q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-450q-10 0 -17.5 7.5t-7.5 17.5v50 q0 10 7.5 17.5t17.5 7.5zM325 300h50q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-50q-10 0 -17.5 7.5t-7.5 17.5v50q0 10 7.5 17.5t17.5 7.5zM525 300h450q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-450q-10 0 -17.5 7.5t-7.5 17.5v50 q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe033;" d="M900 800v200q0 83 -58.5 141.5t-141.5 58.5h-300q-82 0 -141 -59t-59 -141v-200h-100q-41 0 -70.5 -29.5t-29.5 -70.5v-600q0 -41 29.5 -70.5t70.5 -29.5h900q41 0 70.5 29.5t29.5 70.5v600q0 41 -29.5 70.5t-70.5 29.5h-100zM400 800v150q0 21 15 35.5t35 14.5h200 q20 0 35 -14.5t15 -35.5v-150h-300z" />
<glyph unicode="&#xe034;" d="M125 1100h50q10 0 17.5 -7.5t7.5 -17.5v-1075h-100v1075q0 10 7.5 17.5t17.5 7.5zM1075 1052q4 0 9 -2q16 -6 16 -23v-421q0 -6 -3 -12q-33 -59 -66.5 -99t-65.5 -58t-56.5 -24.5t-52.5 -6.5q-26 0 -57.5 6.5t-52.5 13.5t-60 21q-41 15 -63 22.5t-57.5 15t-65.5 7.5 q-85 0 -160 -57q-7 -5 -15 -5q-6 0 -11 3q-14 7 -14 22v438q22 55 82 98.5t119 46.5q23 2 43 0.5t43 -7t32.5 -8.5t38 -13t32.5 -11q41 -14 63.5 -21t57 -14t63.5 -7q103 0 183 87q7 8 18 8z" />
<glyph unicode="&#xe035;" d="M600 1175q116 0 227 -49.5t192.5 -131t131 -192.5t49.5 -227v-300q0 -10 -7.5 -17.5t-17.5 -7.5h-50q-10 0 -17.5 7.5t-7.5 17.5v300q0 127 -70.5 231.5t-184.5 161.5t-245 57t-245 -57t-184.5 -161.5t-70.5 -231.5v-300q0 -10 -7.5 -17.5t-17.5 -7.5h-50 q-10 0 -17.5 7.5t-7.5 17.5v300q0 116 49.5 227t131 192.5t192.5 131t227 49.5zM220 500h160q8 0 14 -6t6 -14v-460q0 -8 -6 -14t-14 -6h-160q-8 0 -14 6t-6 14v460q0 8 6 14t14 6zM820 500h160q8 0 14 -6t6 -14v-460q0 -8 -6 -14t-14 -6h-160q-8 0 -14 6t-6 14v460 q0 8 6 14t14 6z" />
<glyph unicode="&#xe036;" d="M321 814l258 172q9 6 15 2.5t6 -13.5v-750q0 -10 -6 -13.5t-15 2.5l-258 172q-21 14 -46 14h-250q-10 0 -17.5 7.5t-7.5 17.5v350q0 10 7.5 17.5t17.5 7.5h250q25 0 46 14zM900 668l120 120q7 7 17 7t17 -7l34 -34q7 -7 7 -17t-7 -17l-120 -120l120 -120q7 -7 7 -17 t-7 -17l-34 -34q-7 -7 -17 -7t-17 7l-120 119l-120 -119q-7 -7 -17 -7t-17 7l-34 34q-7 7 -7 17t7 17l119 120l-119 120q-7 7 -7 17t7 17l34 34q7 8 17 8t17 -8z" />
<glyph unicode="&#xe037;" d="M321 814l258 172q9 6 15 2.5t6 -13.5v-750q0 -10 -6 -13.5t-15 2.5l-258 172q-21 14 -46 14h-250q-10 0 -17.5 7.5t-7.5 17.5v350q0 10 7.5 17.5t17.5 7.5h250q25 0 46 14zM766 900h4q10 -1 16 -10q96 -129 96 -290q0 -154 -90 -281q-6 -9 -17 -10l-3 -1q-9 0 -16 6 l-29 23q-7 7 -8.5 16.5t4.5 17.5q72 103 72 229q0 132 -78 238q-6 8 -4.5 18t9.5 17l29 22q7 5 15 5z" />
<glyph unicode="&#xe038;" d="M967 1004h3q11 -1 17 -10q135 -179 135 -396q0 -105 -34 -206.5t-98 -185.5q-7 -9 -17 -10h-3q-9 0 -16 6l-42 34q-8 6 -9 16t5 18q111 150 111 328q0 90 -29.5 176t-84.5 157q-6 9 -5 19t10 16l42 33q7 5 15 5zM321 814l258 172q9 6 15 2.5t6 -13.5v-750q0 -10 -6 -13.5 t-15 2.5l-258 172q-21 14 -46 14h-250q-10 0 -17.5 7.5t-7.5 17.5v350q0 10 7.5 17.5t17.5 7.5h250q25 0 46 14zM766 900h4q10 -1 16 -10q96 -129 96 -290q0 -154 -90 -281q-6 -9 -17 -10l-3 -1q-9 0 -16 6l-29 23q-7 7 -8.5 16.5t4.5 17.5q72 103 72 229q0 132 -78 238 q-6 8 -4.5 18.5t9.5 16.5l29 22q7 5 15 5z" />
<glyph unicode="&#xe039;" d="M500 900h100v-100h-100v-100h-400v-100h-100v600h500v-300zM1200 700h-200v-100h200v-200h-300v300h-200v300h-100v200h600v-500zM100 1100v-300h300v300h-300zM800 1100v-300h300v300h-300zM300 900h-100v100h100v-100zM1000 900h-100v100h100v-100zM300 500h200v-500 h-500v500h200v100h100v-100zM800 300h200v-100h-100v-100h-200v100h-100v100h100v200h-200v100h300v-300zM100 400v-300h300v300h-300zM300 200h-100v100h100v-100zM1200 200h-100v100h100v-100zM700 0h-100v100h100v-100zM1200 0h-300v100h300v-100z" />
<glyph unicode="&#xe040;" d="M100 200h-100v1000h100v-1000zM300 200h-100v1000h100v-1000zM700 200h-200v1000h200v-1000zM900 200h-100v1000h100v-1000zM1200 200h-200v1000h200v-1000zM400 0h-300v100h300v-100zM600 0h-100v91h100v-91zM800 0h-100v91h100v-91zM1100 0h-200v91h200v-91z" />
<glyph unicode="&#xe041;" d="M500 1200l682 -682q8 -8 8 -18t-8 -18l-464 -464q-8 -8 -18 -8t-18 8l-682 682l1 475q0 10 7.5 17.5t17.5 7.5h474zM319.5 1024.5q-29.5 29.5 -71 29.5t-71 -29.5t-29.5 -71.5t29.5 -71.5t71 -29.5t71 29.5t29.5 71.5t-29.5 71.5z" />
<glyph unicode="&#xe042;" d="M500 1200l682 -682q8 -8 8 -18t-8 -18l-464 -464q-8 -8 -18 -8t-18 8l-682 682l1 475q0 10 7.5 17.5t17.5 7.5h474zM800 1200l682 -682q8 -8 8 -18t-8 -18l-464 -464q-8 -8 -18 -8t-18 8l-56 56l424 426l-700 700h150zM319.5 1024.5q-29.5 29.5 -71 29.5t-71 -29.5 t-29.5 -71.5t29.5 -71.5t71 -29.5t71 29.5t29.5 71.5t-29.5 71.5z" />
<glyph unicode="&#xe043;" d="M300 1200h825q75 0 75 -75v-900q0 -25 -18 -43l-64 -64q-8 -8 -13 -5.5t-5 12.5v950q0 10 -7.5 17.5t-17.5 7.5h-700q-25 0 -43 -18l-64 -64q-8 -8 -5.5 -13t12.5 -5h700q10 0 17.5 -7.5t7.5 -17.5v-950q0 -10 -7.5 -17.5t-17.5 -7.5h-850q-10 0 -17.5 7.5t-7.5 17.5v975 q0 25 18 43l139 139q18 18 43 18z" />
<glyph unicode="&#xe044;" d="M250 1200h800q21 0 35.5 -14.5t14.5 -35.5v-1150l-450 444l-450 -445v1151q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe045;" d="M822 1200h-444q-11 0 -19 -7.5t-9 -17.5l-78 -301q-7 -24 7 -45l57 -108q6 -9 17.5 -15t21.5 -6h450q10 0 21.5 6t17.5 15l62 108q14 21 7 45l-83 301q-1 10 -9 17.5t-19 7.5zM1175 800h-150q-10 0 -21 -6.5t-15 -15.5l-78 -156q-4 -9 -15 -15.5t-21 -6.5h-550 q-10 0 -21 6.5t-15 15.5l-78 156q-4 9 -15 15.5t-21 6.5h-150q-10 0 -17.5 -7.5t-7.5 -17.5v-650q0 -10 7.5 -17.5t17.5 -7.5h150q10 0 17.5 7.5t7.5 17.5v150q0 10 7.5 17.5t17.5 7.5h750q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 7.5 -17.5t17.5 -7.5h150q10 0 17.5 7.5 t7.5 17.5v650q0 10 -7.5 17.5t-17.5 7.5zM850 200h-500q-10 0 -19.5 -7t-11.5 -17l-38 -152q-2 -10 3.5 -17t15.5 -7h600q10 0 15.5 7t3.5 17l-38 152q-2 10 -11.5 17t-19.5 7z" />
<glyph unicode="&#xe046;" d="M500 1100h200q56 0 102.5 -20.5t72.5 -50t44 -59t25 -50.5l6 -20h150q41 0 70.5 -29.5t29.5 -70.5v-600q0 -41 -29.5 -70.5t-70.5 -29.5h-1000q-41 0 -70.5 29.5t-29.5 70.5v600q0 41 29.5 70.5t70.5 29.5h150q2 8 6.5 21.5t24 48t45 61t72 48t102.5 21.5zM900 800v-100 h100v100h-100zM600 730q-95 0 -162.5 -67.5t-67.5 -162.5t67.5 -162.5t162.5 -67.5t162.5 67.5t67.5 162.5t-67.5 162.5t-162.5 67.5zM600 603q43 0 73 -30t30 -73t-30 -73t-73 -30t-73 30t-30 73t30 73t73 30z" />
<glyph unicode="&#xe047;" d="M681 1199l385 -998q20 -50 60 -92q18 -19 36.5 -29.5t27.5 -11.5l10 -2v-66h-417v66q53 0 75 43.5t5 88.5l-82 222h-391q-58 -145 -92 -234q-11 -34 -6.5 -57t25.5 -37t46 -20t55 -6v-66h-365v66q56 24 84 52q12 12 25 30.5t20 31.5l7 13l399 1006h93zM416 521h340 l-162 457z" />
<glyph unicode="&#xe048;" d="M753 641q5 -1 14.5 -4.5t36 -15.5t50.5 -26.5t53.5 -40t50.5 -54.5t35.5 -70t14.5 -87q0 -67 -27.5 -125.5t-71.5 -97.5t-98.5 -66.5t-108.5 -40.5t-102 -13h-500v89q41 7 70.5 32.5t29.5 65.5v827q0 24 -0.5 34t-3.5 24t-8.5 19.5t-17 13.5t-28 12.5t-42.5 11.5v71 l471 -1q57 0 115.5 -20.5t108 -57t80.5 -94t31 -124.5q0 -51 -15.5 -96.5t-38 -74.5t-45 -50.5t-38.5 -30.5zM400 700h139q78 0 130.5 48.5t52.5 122.5q0 41 -8.5 70.5t-29.5 55.5t-62.5 39.5t-103.5 13.5h-118v-350zM400 200h216q80 0 121 50.5t41 130.5q0 90 -62.5 154.5 t-156.5 64.5h-159v-400z" />
<glyph unicode="&#xe049;" d="M877 1200l2 -57q-83 -19 -116 -45.5t-40 -66.5l-132 -839q-9 -49 13 -69t96 -26v-97h-500v97q186 16 200 98l173 832q3 17 3 30t-1.5 22.5t-9 17.5t-13.5 12.5t-21.5 10t-26 8.5t-33.5 10q-13 3 -19 5v57h425z" />
<glyph unicode="&#xe050;" d="M1300 900h-50q0 21 -4 37t-9.5 26.5t-18 17.5t-22 11t-28.5 5.5t-31 2t-37 0.5h-200v-850q0 -22 25 -34.5t50 -13.5l25 -2v-100h-400v100q4 0 11 0.5t24 3t30 7t24 15t11 24.5v850h-200q-25 0 -37 -0.5t-31 -2t-28.5 -5.5t-22 -11t-18 -17.5t-9.5 -26.5t-4 -37h-50v300 h1000v-300zM175 1000h-75v-800h75l-125 -167l-125 167h75v800h-75l125 167z" />
<glyph unicode="&#xe051;" d="M1100 900h-50q0 21 -4 37t-9.5 26.5t-18 17.5t-22 11t-28.5 5.5t-31 2t-37 0.5h-200v-650q0 -22 25 -34.5t50 -13.5l25 -2v-100h-400v100q4 0 11 0.5t24 3t30 7t24 15t11 24.5v650h-200q-25 0 -37 -0.5t-31 -2t-28.5 -5.5t-22 -11t-18 -17.5t-9.5 -26.5t-4 -37h-50v300 h1000v-300zM1167 50l-167 -125v75h-800v-75l-167 125l167 125v-75h800v75z" />
<glyph unicode="&#xe052;" d="M50 1100h600q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-600q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 800h1000q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1000q-21 0 -35.5 14.5t-14.5 35.5v100 q0 21 14.5 35.5t35.5 14.5zM50 500h800q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 200h1100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100 q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe053;" d="M250 1100h700q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-700q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 800h1100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5v100 q0 21 14.5 35.5t35.5 14.5zM250 500h700q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-700q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 200h1100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100 q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe054;" d="M500 950v100q0 21 14.5 35.5t35.5 14.5h600q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-600q-21 0 -35.5 14.5t-14.5 35.5zM100 650v100q0 21 14.5 35.5t35.5 14.5h1000q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1000 q-21 0 -35.5 14.5t-14.5 35.5zM300 350v100q0 21 14.5 35.5t35.5 14.5h800q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5zM0 50v100q0 21 14.5 35.5t35.5 14.5h1100q21 0 35.5 -14.5t14.5 -35.5v-100 q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5z" />
<glyph unicode="&#xe055;" d="M50 1100h1100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 800h1100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5v100 q0 21 14.5 35.5t35.5 14.5zM50 500h1100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 200h1100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100 q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe056;" d="M50 1100h100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM350 1100h800q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5v100 q0 21 14.5 35.5t35.5 14.5zM50 800h100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM350 800h800q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-800 q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 500h100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM350 500h800q21 0 35.5 -14.5t14.5 -35.5v-100 q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 200h100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM350 200h800 q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe057;" d="M400 0h-100v1100h100v-1100zM550 1100h100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM550 800h500q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-500 q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM267 550l-167 -125v75h-200v100h200v75zM550 500h300q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-300q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM550 200h600 q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-600q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe058;" d="M50 1100h100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM900 0h-100v1100h100v-1100zM50 800h500q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-500 q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM1100 600h200v-100h-200v-75l-167 125l167 125v-75zM50 500h300q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-300q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 200h600 q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-600q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe059;" d="M75 1000h750q31 0 53 -22t22 -53v-650q0 -31 -22 -53t-53 -22h-750q-31 0 -53 22t-22 53v650q0 31 22 53t53 22zM1200 300l-300 300l300 300v-600z" />
<glyph unicode="&#xe060;" d="M44 1100h1112q18 0 31 -13t13 -31v-1012q0 -18 -13 -31t-31 -13h-1112q-18 0 -31 13t-13 31v1012q0 18 13 31t31 13zM100 1000v-737l247 182l298 -131l-74 156l293 318l236 -288v500h-1000zM342 884q56 0 95 -39t39 -94.5t-39 -95t-95 -39.5t-95 39.5t-39 95t39 94.5 t95 39z" />
<glyph unicode="&#xe062;" d="M648 1169q117 0 216 -60t156.5 -161t57.5 -218q0 -115 -70 -258q-69 -109 -158 -225.5t-143 -179.5l-54 -62q-9 8 -25.5 24.5t-63.5 67.5t-91 103t-98.5 128t-95.5 148q-60 132 -60 249q0 88 34 169.5t91.5 142t137 96.5t166.5 36zM652.5 974q-91.5 0 -156.5 -65 t-65 -157t65 -156.5t156.5 -64.5t156.5 64.5t65 156.5t-65 157t-156.5 65z" />
<glyph unicode="&#xe063;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 173v854q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5 t155.5 -155.5t214.5 -57z" />
<glyph unicode="&#xe064;" d="M554 1295q21 -72 57.5 -143.5t76 -130t83 -118t82.5 -117t70 -116t49.5 -126t18.5 -136.5q0 -71 -25.5 -135t-68.5 -111t-99 -82t-118.5 -54t-125.5 -23q-84 5 -161.5 34t-139.5 78.5t-99 125t-37 164.5q0 69 18 136.5t49.5 126.5t69.5 116.5t81.5 117.5t83.5 119 t76.5 131t58.5 143zM344 710q-23 -33 -43.5 -70.5t-40.5 -102.5t-17 -123q1 -37 14.5 -69.5t30 -52t41 -37t38.5 -24.5t33 -15q21 -7 32 -1t13 22l6 34q2 10 -2.5 22t-13.5 19q-5 4 -14 12t-29.5 40.5t-32.5 73.5q-26 89 6 271q2 11 -6 11q-8 1 -15 -10z" />
<glyph unicode="&#xe065;" d="M1000 1013l108 115q2 1 5 2t13 2t20.5 -1t25 -9.5t28.5 -21.5q22 -22 27 -43t0 -32l-6 -10l-108 -115zM350 1100h400q50 0 105 -13l-187 -187h-368q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5v182l200 200v-332 q0 -165 -93.5 -257.5t-256.5 -92.5h-400q-165 0 -257.5 92.5t-92.5 257.5v400q0 165 92.5 257.5t257.5 92.5zM1009 803l-362 -362l-161 -50l55 170l355 355z" />
<glyph unicode="&#xe066;" d="M350 1100h361q-164 -146 -216 -200h-195q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5l200 153v-103q0 -165 -92.5 -257.5t-257.5 -92.5h-400q-165 0 -257.5 92.5t-92.5 257.5v400q0 165 92.5 257.5t257.5 92.5z M824 1073l339 -301q8 -7 8 -17.5t-8 -17.5l-340 -306q-7 -6 -12.5 -4t-6.5 11v203q-26 1 -54.5 0t-78.5 -7.5t-92 -17.5t-86 -35t-70 -57q10 59 33 108t51.5 81.5t65 58.5t68.5 40.5t67 24.5t56 13.5t40 4.5v210q1 10 6.5 12.5t13.5 -4.5z" />
<glyph unicode="&#xe067;" d="M350 1100h350q60 0 127 -23l-178 -177h-349q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5v69l200 200v-219q0 -165 -92.5 -257.5t-257.5 -92.5h-400q-165 0 -257.5 92.5t-92.5 257.5v400q0 165 92.5 257.5t257.5 92.5z M643 639l395 395q7 7 17.5 7t17.5 -7l101 -101q7 -7 7 -17.5t-7 -17.5l-531 -532q-7 -7 -17.5 -7t-17.5 7l-248 248q-7 7 -7 17.5t7 17.5l101 101q7 7 17.5 7t17.5 -7l111 -111q8 -7 18 -7t18 7z" />
<glyph unicode="&#xe068;" d="M318 918l264 264q8 8 18 8t18 -8l260 -264q7 -8 4.5 -13t-12.5 -5h-170v-200h200v173q0 10 5 12t13 -5l264 -260q8 -7 8 -17.5t-8 -17.5l-264 -265q-8 -7 -13 -5t-5 12v173h-200v-200h170q10 0 12.5 -5t-4.5 -13l-260 -264q-8 -8 -18 -8t-18 8l-264 264q-8 8 -5.5 13 t12.5 5h175v200h-200v-173q0 -10 -5 -12t-13 5l-264 265q-8 7 -8 17.5t8 17.5l264 260q8 7 13 5t5 -12v-173h200v200h-175q-10 0 -12.5 5t5.5 13z" />
<glyph unicode="&#xe069;" d="M250 1100h100q21 0 35.5 -14.5t14.5 -35.5v-438l464 453q15 14 25.5 10t10.5 -25v-1000q0 -21 -10.5 -25t-25.5 10l-464 453v-438q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v1000q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe070;" d="M50 1100h100q21 0 35.5 -14.5t14.5 -35.5v-438l464 453q15 14 25.5 10t10.5 -25v-438l464 453q15 14 25.5 10t10.5 -25v-1000q0 -21 -10.5 -25t-25.5 10l-464 453v-438q0 -21 -10.5 -25t-25.5 10l-464 453v-438q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5 t-14.5 35.5v1000q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe071;" d="M1200 1050v-1000q0 -21 -10.5 -25t-25.5 10l-464 453v-438q0 -21 -10.5 -25t-25.5 10l-492 480q-15 14 -15 35t15 35l492 480q15 14 25.5 10t10.5 -25v-438l464 453q15 14 25.5 10t10.5 -25z" />
<glyph unicode="&#xe072;" d="M243 1074l814 -498q18 -11 18 -26t-18 -26l-814 -498q-18 -11 -30.5 -4t-12.5 28v1000q0 21 12.5 28t30.5 -4z" />
<glyph unicode="&#xe073;" d="M250 1000h200q21 0 35.5 -14.5t14.5 -35.5v-800q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v800q0 21 14.5 35.5t35.5 14.5zM650 1000h200q21 0 35.5 -14.5t14.5 -35.5v-800q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v800 q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe074;" d="M1100 950v-800q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5v800q0 21 14.5 35.5t35.5 14.5h800q21 0 35.5 -14.5t14.5 -35.5z" />
<glyph unicode="&#xe075;" d="M500 612v438q0 21 10.5 25t25.5 -10l492 -480q15 -14 15 -35t-15 -35l-492 -480q-15 -14 -25.5 -10t-10.5 25v438l-464 -453q-15 -14 -25.5 -10t-10.5 25v1000q0 21 10.5 25t25.5 -10z" />
<glyph unicode="&#xe076;" d="M1048 1102l100 1q20 0 35 -14.5t15 -35.5l5 -1000q0 -21 -14.5 -35.5t-35.5 -14.5l-100 -1q-21 0 -35.5 14.5t-14.5 35.5l-2 437l-463 -454q-14 -15 -24.5 -10.5t-10.5 25.5l-2 437l-462 -455q-15 -14 -25.5 -9.5t-10.5 24.5l-5 1000q0 21 10.5 25.5t25.5 -10.5l466 -450 l-2 438q0 20 10.5 24.5t25.5 -9.5l466 -451l-2 438q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe077;" d="M850 1100h100q21 0 35.5 -14.5t14.5 -35.5v-1000q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v438l-464 -453q-15 -14 -25.5 -10t-10.5 25v1000q0 21 10.5 25t25.5 -10l464 -453v438q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe078;" d="M686 1081l501 -540q15 -15 10.5 -26t-26.5 -11h-1042q-22 0 -26.5 11t10.5 26l501 540q15 15 36 15t36 -15zM150 400h1000q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1000q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe079;" d="M885 900l-352 -353l352 -353l-197 -198l-552 552l552 550z" />
<glyph unicode="&#xe080;" d="M1064 547l-551 -551l-198 198l353 353l-353 353l198 198z" />
<glyph unicode="&#xe081;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM650 900h-100q-21 0 -35.5 -14.5t-14.5 -35.5v-150h-150 q-21 0 -35.5 -14.5t-14.5 -35.5v-100q0 -21 14.5 -35.5t35.5 -14.5h150v-150q0 -21 14.5 -35.5t35.5 -14.5h100q21 0 35.5 14.5t14.5 35.5v150h150q21 0 35.5 14.5t14.5 35.5v100q0 21 -14.5 35.5t-35.5 14.5h-150v150q0 21 -14.5 35.5t-35.5 14.5z" />
<glyph unicode="&#xe082;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM850 700h-500q-21 0 -35.5 -14.5t-14.5 -35.5v-100q0 -21 14.5 -35.5 t35.5 -14.5h500q21 0 35.5 14.5t14.5 35.5v100q0 21 -14.5 35.5t-35.5 14.5z" />
<glyph unicode="&#xe083;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM741.5 913q-12.5 0 -21.5 -9l-120 -120l-120 120q-9 9 -21.5 9 t-21.5 -9l-141 -141q-9 -9 -9 -21.5t9 -21.5l120 -120l-120 -120q-9 -9 -9 -21.5t9 -21.5l141 -141q9 -9 21.5 -9t21.5 9l120 120l120 -120q9 -9 21.5 -9t21.5 9l141 141q9 9 9 21.5t-9 21.5l-120 120l120 120q9 9 9 21.5t-9 21.5l-141 141q-9 9 -21.5 9z" />
<glyph unicode="&#xe084;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM546 623l-84 85q-7 7 -17.5 7t-18.5 -7l-139 -139q-7 -8 -7 -18t7 -18 l242 -241q7 -8 17.5 -8t17.5 8l375 375q7 7 7 17.5t-7 18.5l-139 139q-7 7 -17.5 7t-17.5 -7z" />
<glyph unicode="&#xe085;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM588 941q-29 0 -59 -5.5t-63 -20.5t-58 -38.5t-41.5 -63t-16.5 -89.5 q0 -25 20 -25h131q30 -5 35 11q6 20 20.5 28t45.5 8q20 0 31.5 -10.5t11.5 -28.5q0 -23 -7 -34t-26 -18q-1 0 -13.5 -4t-19.5 -7.5t-20 -10.5t-22 -17t-18.5 -24t-15.5 -35t-8 -46q-1 -8 5.5 -16.5t20.5 -8.5h173q7 0 22 8t35 28t37.5 48t29.5 74t12 100q0 47 -17 83 t-42.5 57t-59.5 34.5t-64 18t-59 4.5zM675 400h-150q-10 0 -17.5 -7.5t-7.5 -17.5v-150q0 -10 7.5 -17.5t17.5 -7.5h150q10 0 17.5 7.5t7.5 17.5v150q0 10 -7.5 17.5t-17.5 7.5z" />
<glyph unicode="&#xe086;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM675 1000h-150q-10 0 -17.5 -7.5t-7.5 -17.5v-150q0 -10 7.5 -17.5 t17.5 -7.5h150q10 0 17.5 7.5t7.5 17.5v150q0 10 -7.5 17.5t-17.5 7.5zM675 700h-250q-10 0 -17.5 -7.5t-7.5 -17.5v-50q0 -10 7.5 -17.5t17.5 -7.5h75v-200h-75q-10 0 -17.5 -7.5t-7.5 -17.5v-50q0 -10 7.5 -17.5t17.5 -7.5h350q10 0 17.5 7.5t7.5 17.5v50q0 10 -7.5 17.5 t-17.5 7.5h-75v275q0 10 -7.5 17.5t-17.5 7.5z" />
<glyph unicode="&#xe087;" d="M525 1200h150q10 0 17.5 -7.5t7.5 -17.5v-194q103 -27 178.5 -102.5t102.5 -178.5h194q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-194q-27 -103 -102.5 -178.5t-178.5 -102.5v-194q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v194 q-103 27 -178.5 102.5t-102.5 178.5h-194q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5h194q27 103 102.5 178.5t178.5 102.5v194q0 10 7.5 17.5t17.5 7.5zM700 893v-168q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v168q-68 -23 -119 -74 t-74 -119h168q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-168q23 -68 74 -119t119 -74v168q0 10 7.5 17.5t17.5 7.5h150q10 0 17.5 -7.5t7.5 -17.5v-168q68 23 119 74t74 119h-168q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5h168 q-23 68 -74 119t-119 74z" />
<glyph unicode="&#xe088;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 1027q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5 t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5t-57 214.5t-155.5 155.5t-214.5 57zM759 823l64 -64q7 -7 7 -17.5t-7 -17.5l-124 -124l124 -124q7 -7 7 -17.5t-7 -17.5l-64 -64q-7 -7 -17.5 -7t-17.5 7l-124 124l-124 -124q-7 -7 -17.5 -7t-17.5 7l-64 64 q-7 7 -7 17.5t7 17.5l124 124l-124 124q-7 7 -7 17.5t7 17.5l64 64q7 7 17.5 7t17.5 -7l124 -124l124 124q7 7 17.5 7t17.5 -7z" />
<glyph unicode="&#xe089;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 1027q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5 t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5t-57 214.5t-155.5 155.5t-214.5 57zM782 788l106 -106q7 -7 7 -17.5t-7 -17.5l-320 -321q-8 -7 -18 -7t-18 7l-202 203q-8 7 -8 17.5t8 17.5l106 106q7 8 17.5 8t17.5 -8l79 -79l197 197q7 7 17.5 7t17.5 -7z" />
<glyph unicode="&#xe090;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 1027q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5q0 -120 65 -225 l587 587q-105 65 -225 65zM965 819l-584 -584q104 -62 219 -62q116 0 214.5 57t155.5 155.5t57 214.5q0 115 -62 219z" />
<glyph unicode="&#xe091;" d="M39 582l522 427q16 13 27.5 8t11.5 -26v-291h550q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-550v-291q0 -21 -11.5 -26t-27.5 8l-522 427q-16 13 -16 32t16 32z" />
<glyph unicode="&#xe092;" d="M639 1009l522 -427q16 -13 16 -32t-16 -32l-522 -427q-16 -13 -27.5 -8t-11.5 26v291h-550q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5h550v291q0 21 11.5 26t27.5 -8z" />
<glyph unicode="&#xe093;" d="M682 1161l427 -522q13 -16 8 -27.5t-26 -11.5h-291v-550q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v550h-291q-21 0 -26 11.5t8 27.5l427 522q13 16 32 16t32 -16z" />
<glyph unicode="&#xe094;" d="M550 1200h200q21 0 35.5 -14.5t14.5 -35.5v-550h291q21 0 26 -11.5t-8 -27.5l-427 -522q-13 -16 -32 -16t-32 16l-427 522q-13 16 -8 27.5t26 11.5h291v550q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe095;" d="M639 1109l522 -427q16 -13 16 -32t-16 -32l-522 -427q-16 -13 -27.5 -8t-11.5 26v291q-94 -2 -182 -20t-170.5 -52t-147 -92.5t-100.5 -135.5q5 105 27 193.5t67.5 167t113 135t167 91.5t225.5 42v262q0 21 11.5 26t27.5 -8z" />
<glyph unicode="&#xe096;" d="M850 1200h300q21 0 35.5 -14.5t14.5 -35.5v-300q0 -21 -10.5 -25t-24.5 10l-94 94l-249 -249q-8 -7 -18 -7t-18 7l-106 106q-7 8 -7 18t7 18l249 249l-94 94q-14 14 -10 24.5t25 10.5zM350 0h-300q-21 0 -35.5 14.5t-14.5 35.5v300q0 21 10.5 25t24.5 -10l94 -94l249 249 q8 7 18 7t18 -7l106 -106q7 -8 7 -18t-7 -18l-249 -249l94 -94q14 -14 10 -24.5t-25 -10.5z" />
<glyph unicode="&#xe097;" d="M1014 1120l106 -106q7 -8 7 -18t-7 -18l-249 -249l94 -94q14 -14 10 -24.5t-25 -10.5h-300q-21 0 -35.5 14.5t-14.5 35.5v300q0 21 10.5 25t24.5 -10l94 -94l249 249q8 7 18 7t18 -7zM250 600h300q21 0 35.5 -14.5t14.5 -35.5v-300q0 -21 -10.5 -25t-24.5 10l-94 94 l-249 -249q-8 -7 -18 -7t-18 7l-106 106q-7 8 -7 18t7 18l249 249l-94 94q-14 14 -10 24.5t25 10.5z" />
<glyph unicode="&#xe101;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM704 900h-208q-20 0 -32 -14.5t-8 -34.5l58 -302q4 -20 21.5 -34.5 t37.5 -14.5h54q20 0 37.5 14.5t21.5 34.5l58 302q4 20 -8 34.5t-32 14.5zM675 400h-150q-10 0 -17.5 -7.5t-7.5 -17.5v-150q0 -10 7.5 -17.5t17.5 -7.5h150q10 0 17.5 7.5t7.5 17.5v150q0 10 -7.5 17.5t-17.5 7.5z" />
<glyph unicode="&#xe102;" d="M260 1200q9 0 19 -2t15 -4l5 -2q22 -10 44 -23l196 -118q21 -13 36 -24q29 -21 37 -12q11 13 49 35l196 118q22 13 45 23q17 7 38 7q23 0 47 -16.5t37 -33.5l13 -16q14 -21 18 -45l25 -123l8 -44q1 -9 8.5 -14.5t17.5 -5.5h61q10 0 17.5 -7.5t7.5 -17.5v-50 q0 -10 -7.5 -17.5t-17.5 -7.5h-50q-10 0 -17.5 -7.5t-7.5 -17.5v-175h-400v300h-200v-300h-400v175q0 10 -7.5 17.5t-17.5 7.5h-50q-10 0 -17.5 7.5t-7.5 17.5v50q0 10 7.5 17.5t17.5 7.5h61q11 0 18 3t7 8q0 4 9 52l25 128q5 25 19 45q2 3 5 7t13.5 15t21.5 19.5t26.5 15.5 t29.5 7zM915 1079l-166 -162q-7 -7 -5 -12t12 -5h219q10 0 15 7t2 17l-51 149q-3 10 -11 12t-15 -6zM463 917l-177 157q-8 7 -16 5t-11 -12l-51 -143q-3 -10 2 -17t15 -7h231q11 0 12.5 5t-5.5 12zM500 0h-375q-10 0 -17.5 7.5t-7.5 17.5v375h400v-400zM1100 400v-375 q0 -10 -7.5 -17.5t-17.5 -7.5h-375v400h400z" />
<glyph unicode="&#xe103;" d="M1165 1190q8 3 21 -6.5t13 -17.5q-2 -178 -24.5 -323.5t-55.5 -245.5t-87 -174.5t-102.5 -118.5t-118 -68.5t-118.5 -33t-120 -4.5t-105 9.5t-90 16.5q-61 12 -78 11q-4 1 -12.5 0t-34 -14.5t-52.5 -40.5l-153 -153q-26 -24 -37 -14.5t-11 43.5q0 64 42 102q8 8 50.5 45 t66.5 58q19 17 35 47t13 61q-9 55 -10 102.5t7 111t37 130t78 129.5q39 51 80 88t89.5 63.5t94.5 45t113.5 36t129 31t157.5 37t182 47.5zM1116 1098q-8 9 -22.5 -3t-45.5 -50q-38 -47 -119 -103.5t-142 -89.5l-62 -33q-56 -30 -102 -57t-104 -68t-102.5 -80.5t-85.5 -91 t-64 -104.5q-24 -56 -31 -86t2 -32t31.5 17.5t55.5 59.5q25 30 94 75.5t125.5 77.5t147.5 81q70 37 118.5 69t102 79.5t99 111t86.5 148.5q22 50 24 60t-6 19z" />
<glyph unicode="&#xe104;" d="M653 1231q-39 -67 -54.5 -131t-10.5 -114.5t24.5 -96.5t47.5 -80t63.5 -62.5t68.5 -46.5t65 -30q-4 7 -17.5 35t-18.5 39.5t-17 39.5t-17 43t-13 42t-9.5 44.5t-2 42t4 43t13.5 39t23 38.5q96 -42 165 -107.5t105 -138t52 -156t13 -159t-19 -149.5q-13 -55 -44 -106.5 t-68 -87t-78.5 -64.5t-72.5 -45t-53 -22q-72 -22 -127 -11q-31 6 -13 19q6 3 17 7q13 5 32.5 21t41 44t38.5 63.5t21.5 81.5t-6.5 94.5t-50 107t-104 115.5q10 -104 -0.5 -189t-37 -140.5t-65 -93t-84 -52t-93.5 -11t-95 24.5q-80 36 -131.5 114t-53.5 171q-2 23 0 49.5 t4.5 52.5t13.5 56t27.5 60t46 64.5t69.5 68.5q-8 -53 -5 -102.5t17.5 -90t34 -68.5t44.5 -39t49 -2q31 13 38.5 36t-4.5 55t-29 64.5t-36 75t-26 75.5q-15 85 2 161.5t53.5 128.5t85.5 92.5t93.5 61t81.5 25.5z" />
<glyph unicode="&#xe105;" d="M600 1094q82 0 160.5 -22.5t140 -59t116.5 -82.5t94.5 -95t68 -95t42.5 -82.5t14 -57.5t-14 -57.5t-43 -82.5t-68.5 -95t-94.5 -95t-116.5 -82.5t-140 -59t-159.5 -22.5t-159.5 22.5t-140 59t-116.5 82.5t-94.5 95t-68.5 95t-43 82.5t-14 57.5t14 57.5t42.5 82.5t68 95 t94.5 95t116.5 82.5t140 59t160.5 22.5zM888 829q-15 15 -18 12t5 -22q25 -57 25 -119q0 -124 -88 -212t-212 -88t-212 88t-88 212q0 59 23 114q8 19 4.5 22t-17.5 -12q-70 -69 -160 -184q-13 -16 -15 -40.5t9 -42.5q22 -36 47 -71t70 -82t92.5 -81t113 -58.5t133.5 -24.5 t133.5 24t113 58.5t92.5 81.5t70 81.5t47 70.5q11 18 9 42.5t-14 41.5q-90 117 -163 189zM448 727l-35 -36q-15 -15 -19.5 -38.5t4.5 -41.5q37 -68 93 -116q16 -13 38.5 -11t36.5 17l35 34q14 15 12.5 33.5t-16.5 33.5q-44 44 -89 117q-11 18 -28 20t-32 -12z" />
<glyph unicode="&#xe106;" d="M592 0h-148l31 120q-91 20 -175.5 68.5t-143.5 106.5t-103.5 119t-66.5 110t-22 76q0 21 14 57.5t42.5 82.5t68 95t94.5 95t116.5 82.5t140 59t160.5 22.5q61 0 126 -15l32 121h148zM944 770l47 181q108 -85 176.5 -192t68.5 -159q0 -26 -19.5 -71t-59.5 -102t-93 -112 t-129 -104.5t-158 -75.5l46 173q77 49 136 117t97 131q11 18 9 42.5t-14 41.5q-54 70 -107 130zM310 824q-70 -69 -160 -184q-13 -16 -15 -40.5t9 -42.5q18 -30 39 -60t57 -70.5t74 -73t90 -61t105 -41.5l41 154q-107 18 -178.5 101.5t-71.5 193.5q0 59 23 114q8 19 4.5 22 t-17.5 -12zM448 727l-35 -36q-15 -15 -19.5 -38.5t4.5 -41.5q37 -68 93 -116q16 -13 38.5 -11t36.5 17l12 11l22 86l-3 4q-44 44 -89 117q-11 18 -28 20t-32 -12z" />
<glyph unicode="&#xe107;" d="M-90 100l642 1066q20 31 48 28.5t48 -35.5l642 -1056q21 -32 7.5 -67.5t-50.5 -35.5h-1294q-37 0 -50.5 34t7.5 66zM155 200h345v75q0 10 7.5 17.5t17.5 7.5h150q10 0 17.5 -7.5t7.5 -17.5v-75h345l-445 723zM496 700h208q20 0 32 -14.5t8 -34.5l-58 -252 q-4 -20 -21.5 -34.5t-37.5 -14.5h-54q-20 0 -37.5 14.5t-21.5 34.5l-58 252q-4 20 8 34.5t32 14.5z" />
<glyph unicode="&#xe108;" d="M650 1200q62 0 106 -44t44 -106v-339l363 -325q15 -14 26 -38.5t11 -44.5v-41q0 -20 -12 -26.5t-29 5.5l-359 249v-263q100 -93 100 -113v-64q0 -21 -13 -29t-32 1l-205 128l-205 -128q-19 -9 -32 -1t-13 29v64q0 20 100 113v263l-359 -249q-17 -12 -29 -5.5t-12 26.5v41 q0 20 11 44.5t26 38.5l363 325v339q0 62 44 106t106 44z" />
<glyph unicode="&#xe109;" d="M850 1200h100q21 0 35.5 -14.5t14.5 -35.5v-50h50q21 0 35.5 -14.5t14.5 -35.5v-150h-1100v150q0 21 14.5 35.5t35.5 14.5h50v50q0 21 14.5 35.5t35.5 14.5h100q21 0 35.5 -14.5t14.5 -35.5v-50h500v50q0 21 14.5 35.5t35.5 14.5zM1100 800v-750q0 -21 -14.5 -35.5 t-35.5 -14.5h-1000q-21 0 -35.5 14.5t-14.5 35.5v750h1100zM100 600v-100h100v100h-100zM300 600v-100h100v100h-100zM500 600v-100h100v100h-100zM700 600v-100h100v100h-100zM900 600v-100h100v100h-100zM100 400v-100h100v100h-100zM300 400v-100h100v100h-100zM500 400 v-100h100v100h-100zM700 400v-100h100v100h-100zM900 400v-100h100v100h-100zM100 200v-100h100v100h-100zM300 200v-100h100v100h-100zM500 200v-100h100v100h-100zM700 200v-100h100v100h-100zM900 200v-100h100v100h-100z" />
<glyph unicode="&#xe110;" d="M1135 1165l249 -230q15 -14 15 -35t-15 -35l-249 -230q-14 -14 -24.5 -10t-10.5 25v150h-159l-600 -600h-291q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5h209l600 600h241v150q0 21 10.5 25t24.5 -10zM522 819l-141 -141l-122 122h-209q-21 0 -35.5 14.5 t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5h291zM1135 565l249 -230q15 -14 15 -35t-15 -35l-249 -230q-14 -14 -24.5 -10t-10.5 25v150h-241l-181 181l141 141l122 -122h159v150q0 21 10.5 25t24.5 -10z" />
<glyph unicode="&#xe111;" d="M100 1100h1000q41 0 70.5 -29.5t29.5 -70.5v-600q0 -41 -29.5 -70.5t-70.5 -29.5h-596l-304 -300v300h-100q-41 0 -70.5 29.5t-29.5 70.5v600q0 41 29.5 70.5t70.5 29.5z" />
<glyph unicode="&#xe112;" d="M150 1200h200q21 0 35.5 -14.5t14.5 -35.5v-250h-300v250q0 21 14.5 35.5t35.5 14.5zM850 1200h200q21 0 35.5 -14.5t14.5 -35.5v-250h-300v250q0 21 14.5 35.5t35.5 14.5zM1100 800v-300q0 -41 -3 -77.5t-15 -89.5t-32 -96t-58 -89t-89 -77t-129 -51t-174 -20t-174 20 t-129 51t-89 77t-58 89t-32 96t-15 89.5t-3 77.5v300h300v-250v-27v-42.5t1.5 -41t5 -38t10 -35t16.5 -30t25.5 -24.5t35 -19t46.5 -12t60 -4t60 4.5t46.5 12.5t35 19.5t25 25.5t17 30.5t10 35t5 38t2 40.5t-0.5 42v25v250h300z" />
<glyph unicode="&#xe113;" d="M1100 411l-198 -199l-353 353l-353 -353l-197 199l551 551z" />
<glyph unicode="&#xe114;" d="M1101 789l-550 -551l-551 551l198 199l353 -353l353 353z" />
<glyph unicode="&#xe115;" d="M404 1000h746q21 0 35.5 -14.5t14.5 -35.5v-551h150q21 0 25 -10.5t-10 -24.5l-230 -249q-14 -15 -35 -15t-35 15l-230 249q-14 14 -10 24.5t25 10.5h150v401h-381zM135 984l230 -249q14 -14 10 -24.5t-25 -10.5h-150v-400h385l215 -200h-750q-21 0 -35.5 14.5 t-14.5 35.5v550h-150q-21 0 -25 10.5t10 24.5l230 249q14 15 35 15t35 -15z" />
<glyph unicode="&#xe116;" d="M56 1200h94q17 0 31 -11t18 -27l38 -162h896q24 0 39 -18.5t10 -42.5l-100 -475q-5 -21 -27 -42.5t-55 -21.5h-633l48 -200h535q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-50v-50q0 -21 -14.5 -35.5t-35.5 -14.5t-35.5 14.5t-14.5 35.5v50h-300v-50 q0 -21 -14.5 -35.5t-35.5 -14.5t-35.5 14.5t-14.5 35.5v50h-31q-18 0 -32.5 10t-20.5 19l-5 10l-201 961h-54q-20 0 -35 14.5t-15 35.5t15 35.5t35 14.5z" />
<glyph unicode="&#xe117;" d="M1200 1000v-100h-1200v100h200q0 41 29.5 70.5t70.5 29.5h300q41 0 70.5 -29.5t29.5 -70.5h500zM0 800h1200v-800h-1200v800z" />
<glyph unicode="&#xe118;" d="M200 800l-200 -400v600h200q0 41 29.5 70.5t70.5 29.5h300q42 0 71 -29.5t29 -70.5h500v-200h-1000zM1500 700l-300 -700h-1200l300 700h1200z" />
<glyph unicode="&#xe119;" d="M635 1184l230 -249q14 -14 10 -24.5t-25 -10.5h-150v-601h150q21 0 25 -10.5t-10 -24.5l-230 -249q-14 -15 -35 -15t-35 15l-230 249q-14 14 -10 24.5t25 10.5h150v601h-150q-21 0 -25 10.5t10 24.5l230 249q14 15 35 15t35 -15z" />
<glyph unicode="&#xe120;" d="M936 864l249 -229q14 -15 14 -35.5t-14 -35.5l-249 -229q-15 -15 -25.5 -10.5t-10.5 24.5v151h-600v-151q0 -20 -10.5 -24.5t-25.5 10.5l-249 229q-14 15 -14 35.5t14 35.5l249 229q15 15 25.5 10.5t10.5 -25.5v-149h600v149q0 21 10.5 25.5t25.5 -10.5z" />
<glyph unicode="&#xe121;" d="M1169 400l-172 732q-5 23 -23 45.5t-38 22.5h-672q-20 0 -38 -20t-23 -41l-172 -739h1138zM1100 300h-1000q-41 0 -70.5 -29.5t-29.5 -70.5v-100q0 -41 29.5 -70.5t70.5 -29.5h1000q41 0 70.5 29.5t29.5 70.5v100q0 41 -29.5 70.5t-70.5 29.5zM800 100v100h100v-100h-100 zM1000 100v100h100v-100h-100z" />
<glyph unicode="&#xe122;" d="M1150 1100q21 0 35.5 -14.5t14.5 -35.5v-850q0 -21 -14.5 -35.5t-35.5 -14.5t-35.5 14.5t-14.5 35.5v850q0 21 14.5 35.5t35.5 14.5zM1000 200l-675 200h-38l47 -276q3 -16 -5.5 -20t-29.5 -4h-7h-84q-20 0 -34.5 14t-18.5 35q-55 337 -55 351v250v6q0 16 1 23.5t6.5 14 t17.5 6.5h200l675 250v-850zM0 750v-250q-4 0 -11 0.5t-24 6t-30 15t-24 30t-11 48.5v50q0 26 10.5 46t25 30t29 16t25.5 7z" />
<glyph unicode="&#xe123;" d="M553 1200h94q20 0 29 -10.5t3 -29.5l-18 -37q83 -19 144 -82.5t76 -140.5l63 -327l118 -173h17q19 0 33 -14.5t14 -35t-13 -40.5t-31 -27q-8 -4 -23 -9.5t-65 -19.5t-103 -25t-132.5 -20t-158.5 -9q-57 0 -115 5t-104 12t-88.5 15.5t-73.5 17.5t-54.5 16t-35.5 12l-11 4 q-18 8 -31 28t-13 40.5t14 35t33 14.5h17l118 173l63 327q15 77 76 140t144 83l-18 32q-6 19 3.5 32t28.5 13zM498 110q50 -6 102 -6q53 0 102 6q-12 -49 -39.5 -79.5t-62.5 -30.5t-63 30.5t-39 79.5z" />
<glyph unicode="&#xe124;" d="M800 946l224 78l-78 -224l234 -45l-180 -155l180 -155l-234 -45l78 -224l-224 78l-45 -234l-155 180l-155 -180l-45 234l-224 -78l78 224l-234 45l180 155l-180 155l234 45l-78 224l224 -78l45 234l155 -180l155 180z" />
<glyph unicode="&#xe125;" d="M650 1200h50q40 0 70 -40.5t30 -84.5v-150l-28 -125h328q40 0 70 -40.5t30 -84.5v-100q0 -45 -29 -74l-238 -344q-16 -24 -38 -40.5t-45 -16.5h-250q-7 0 -42 25t-66 50l-31 25h-61q-45 0 -72.5 18t-27.5 57v400q0 36 20 63l145 196l96 198q13 28 37.5 48t51.5 20z M650 1100l-100 -212l-150 -213v-375h100l136 -100h214l250 375v125h-450l50 225v175h-50zM50 800h100q21 0 35.5 -14.5t14.5 -35.5v-500q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v500q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe126;" d="M600 1100h250q23 0 45 -16.5t38 -40.5l238 -344q29 -29 29 -74v-100q0 -44 -30 -84.5t-70 -40.5h-328q28 -118 28 -125v-150q0 -44 -30 -84.5t-70 -40.5h-50q-27 0 -51.5 20t-37.5 48l-96 198l-145 196q-20 27 -20 63v400q0 39 27.5 57t72.5 18h61q124 100 139 100z M50 1000h100q21 0 35.5 -14.5t14.5 -35.5v-500q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v500q0 21 14.5 35.5t35.5 14.5zM636 1000l-136 -100h-100v-375l150 -213l100 -212h50v175l-50 225h450v125l-250 375h-214z" />
<glyph unicode="&#xe127;" d="M356 873l363 230q31 16 53 -6l110 -112q13 -13 13.5 -32t-11.5 -34l-84 -121h302q84 0 138 -38t54 -110t-55 -111t-139 -39h-106l-131 -339q-6 -21 -19.5 -41t-28.5 -20h-342q-7 0 -90 81t-83 94v525q0 17 14 35.5t28 28.5zM400 792v-503l100 -89h293l131 339 q6 21 19.5 41t28.5 20h203q21 0 30.5 25t0.5 50t-31 25h-456h-7h-6h-5.5t-6 0.5t-5 1.5t-5 2t-4 2.5t-4 4t-2.5 4.5q-12 25 5 47l146 183l-86 83zM50 800h100q21 0 35.5 -14.5t14.5 -35.5v-500q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v500 q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe128;" d="M475 1103l366 -230q2 -1 6 -3.5t14 -10.5t18 -16.5t14.5 -20t6.5 -22.5v-525q0 -13 -86 -94t-93 -81h-342q-15 0 -28.5 20t-19.5 41l-131 339h-106q-85 0 -139.5 39t-54.5 111t54 110t138 38h302l-85 121q-11 15 -10.5 34t13.5 32l110 112q22 22 53 6zM370 945l146 -183 q17 -22 5 -47q-2 -2 -3.5 -4.5t-4 -4t-4 -2.5t-5 -2t-5 -1.5t-6 -0.5h-6h-6.5h-6h-475v-100h221q15 0 29 -20t20 -41l130 -339h294l106 89v503l-342 236zM1050 800h100q21 0 35.5 -14.5t14.5 -35.5v-500q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5 v500q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe129;" d="M550 1294q72 0 111 -55t39 -139v-106l339 -131q21 -6 41 -19.5t20 -28.5v-342q0 -7 -81 -90t-94 -83h-525q-17 0 -35.5 14t-28.5 28l-9 14l-230 363q-16 31 6 53l112 110q13 13 32 13.5t34 -11.5l121 -84v302q0 84 38 138t110 54zM600 972v203q0 21 -25 30.5t-50 0.5 t-25 -31v-456v-7v-6v-5.5t-0.5 -6t-1.5 -5t-2 -5t-2.5 -4t-4 -4t-4.5 -2.5q-25 -12 -47 5l-183 146l-83 -86l236 -339h503l89 100v293l-339 131q-21 6 -41 19.5t-20 28.5zM450 200h500q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-500 q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe130;" d="M350 1100h500q21 0 35.5 14.5t14.5 35.5v100q0 21 -14.5 35.5t-35.5 14.5h-500q-21 0 -35.5 -14.5t-14.5 -35.5v-100q0 -21 14.5 -35.5t35.5 -14.5zM600 306v-106q0 -84 -39 -139t-111 -55t-110 54t-38 138v302l-121 -84q-15 -12 -34 -11.5t-32 13.5l-112 110 q-22 22 -6 53l230 363q1 2 3.5 6t10.5 13.5t16.5 17t20 13.5t22.5 6h525q13 0 94 -83t81 -90v-342q0 -15 -20 -28.5t-41 -19.5zM308 900l-236 -339l83 -86l183 146q22 17 47 5q2 -1 4.5 -2.5t4 -4t2.5 -4t2 -5t1.5 -5t0.5 -6v-5.5v-6v-7v-456q0 -22 25 -31t50 0.5t25 30.5 v203q0 15 20 28.5t41 19.5l339 131v293l-89 100h-503z" />
<glyph unicode="&#xe131;" d="M600 1178q118 0 225 -45.5t184.5 -123t123 -184.5t45.5 -225t-45.5 -225t-123 -184.5t-184.5 -123t-225 -45.5t-225 45.5t-184.5 123t-123 184.5t-45.5 225t45.5 225t123 184.5t184.5 123t225 45.5zM914 632l-275 223q-16 13 -27.5 8t-11.5 -26v-137h-275 q-10 0 -17.5 -7.5t-7.5 -17.5v-150q0 -10 7.5 -17.5t17.5 -7.5h275v-137q0 -21 11.5 -26t27.5 8l275 223q16 13 16 32t-16 32z" />
<glyph unicode="&#xe132;" d="M600 1178q118 0 225 -45.5t184.5 -123t123 -184.5t45.5 -225t-45.5 -225t-123 -184.5t-184.5 -123t-225 -45.5t-225 45.5t-184.5 123t-123 184.5t-45.5 225t45.5 225t123 184.5t184.5 123t225 45.5zM561 855l-275 -223q-16 -13 -16 -32t16 -32l275 -223q16 -13 27.5 -8 t11.5 26v137h275q10 0 17.5 7.5t7.5 17.5v150q0 10 -7.5 17.5t-17.5 7.5h-275v137q0 21 -11.5 26t-27.5 -8z" />
<glyph unicode="&#xe133;" d="M600 1178q118 0 225 -45.5t184.5 -123t123 -184.5t45.5 -225t-45.5 -225t-123 -184.5t-184.5 -123t-225 -45.5t-225 45.5t-184.5 123t-123 184.5t-45.5 225t45.5 225t123 184.5t184.5 123t225 45.5zM855 639l-223 275q-13 16 -32 16t-32 -16l-223 -275q-13 -16 -8 -27.5 t26 -11.5h137v-275q0 -10 7.5 -17.5t17.5 -7.5h150q10 0 17.5 7.5t7.5 17.5v275h137q21 0 26 11.5t-8 27.5z" />
<glyph unicode="&#xe134;" d="M600 1178q118 0 225 -45.5t184.5 -123t123 -184.5t45.5 -225t-45.5 -225t-123 -184.5t-184.5 -123t-225 -45.5t-225 45.5t-184.5 123t-123 184.5t-45.5 225t45.5 225t123 184.5t184.5 123t225 45.5zM675 900h-150q-10 0 -17.5 -7.5t-7.5 -17.5v-275h-137q-21 0 -26 -11.5 t8 -27.5l223 -275q13 -16 32 -16t32 16l223 275q13 16 8 27.5t-26 11.5h-137v275q0 10 -7.5 17.5t-17.5 7.5z" />
<glyph unicode="&#xe135;" d="M600 1176q116 0 222.5 -46t184 -123.5t123.5 -184t46 -222.5t-46 -222.5t-123.5 -184t-184 -123.5t-222.5 -46t-222.5 46t-184 123.5t-123.5 184t-46 222.5t46 222.5t123.5 184t184 123.5t222.5 46zM627 1101q-15 -12 -36.5 -20.5t-35.5 -12t-43 -8t-39 -6.5 q-15 -3 -45.5 0t-45.5 -2q-20 -7 -51.5 -26.5t-34.5 -34.5q-3 -11 6.5 -22.5t8.5 -18.5q-3 -34 -27.5 -91t-29.5 -79q-9 -34 5 -93t8 -87q0 -9 17 -44.5t16 -59.5q12 0 23 -5t23.5 -15t19.5 -14q16 -8 33 -15t40.5 -15t34.5 -12q21 -9 52.5 -32t60 -38t57.5 -11 q7 -15 -3 -34t-22.5 -40t-9.5 -38q13 -21 23 -34.5t27.5 -27.5t36.5 -18q0 -7 -3.5 -16t-3.5 -14t5 -17q104 -2 221 112q30 29 46.5 47t34.5 49t21 63q-13 8 -37 8.5t-36 7.5q-15 7 -49.5 15t-51.5 19q-18 0 -41 -0.5t-43 -1.5t-42 -6.5t-38 -16.5q-51 -35 -66 -12 q-4 1 -3.5 25.5t0.5 25.5q-6 13 -26.5 17.5t-24.5 6.5q1 15 -0.5 30.5t-7 28t-18.5 11.5t-31 -21q-23 -25 -42 4q-19 28 -8 58q6 16 22 22q6 -1 26 -1.5t33.5 -4t19.5 -13.5q7 -12 18 -24t21.5 -20.5t20 -15t15.5 -10.5l5 -3q2 12 7.5 30.5t8 34.5t-0.5 32q-3 18 3.5 29 t18 22.5t15.5 24.5q6 14 10.5 35t8 31t15.5 22.5t34 22.5q-6 18 10 36q8 0 24 -1.5t24.5 -1.5t20 4.5t20.5 15.5q-10 23 -31 42.5t-37.5 29.5t-49 27t-43.5 23q0 1 2 8t3 11.5t1.5 10.5t-1 9.5t-4.5 4.5q31 -13 58.5 -14.5t38.5 2.5l12 5q5 28 -9.5 46t-36.5 24t-50 15 t-41 20q-18 -4 -37 0zM613 994q0 -17 8 -42t17 -45t9 -23q-8 1 -39.5 5.5t-52.5 10t-37 16.5q3 11 16 29.5t16 25.5q10 -10 19 -10t14 6t13.5 14.5t16.5 12.5z" />
<glyph unicode="&#xe136;" d="M756 1157q164 92 306 -9l-259 -138l145 -232l251 126q6 -89 -34 -156.5t-117 -110.5q-60 -34 -127 -39.5t-126 16.5l-596 -596q-15 -16 -36.5 -16t-36.5 16l-111 110q-15 15 -15 36.5t15 37.5l600 599q-34 101 5.5 201.5t135.5 154.5z" />
<glyph unicode="&#xe137;" horiz-adv-x="1220" d="M100 1196h1000q41 0 70.5 -29.5t29.5 -70.5v-100q0 -41 -29.5 -70.5t-70.5 -29.5h-1000q-41 0 -70.5 29.5t-29.5 70.5v100q0 41 29.5 70.5t70.5 29.5zM1100 1096h-200v-100h200v100zM100 796h1000q41 0 70.5 -29.5t29.5 -70.5v-100q0 -41 -29.5 -70.5t-70.5 -29.5h-1000 q-41 0 -70.5 29.5t-29.5 70.5v100q0 41 29.5 70.5t70.5 29.5zM1100 696h-500v-100h500v100zM100 396h1000q41 0 70.5 -29.5t29.5 -70.5v-100q0 -41 -29.5 -70.5t-70.5 -29.5h-1000q-41 0 -70.5 29.5t-29.5 70.5v100q0 41 29.5 70.5t70.5 29.5zM1100 296h-300v-100h300v100z " />
<glyph unicode="&#xe138;" d="M150 1200h900q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-900q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5zM700 500v-300l-200 -200v500l-350 500h900z" />
<glyph unicode="&#xe139;" d="M500 1200h200q41 0 70.5 -29.5t29.5 -70.5v-100h300q41 0 70.5 -29.5t29.5 -70.5v-400h-500v100h-200v-100h-500v400q0 41 29.5 70.5t70.5 29.5h300v100q0 41 29.5 70.5t70.5 29.5zM500 1100v-100h200v100h-200zM1200 400v-200q0 -41 -29.5 -70.5t-70.5 -29.5h-1000 q-41 0 -70.5 29.5t-29.5 70.5v200h1200z" />
<glyph unicode="&#xe140;" d="M50 1200h300q21 0 25 -10.5t-10 -24.5l-94 -94l199 -199q7 -8 7 -18t-7 -18l-106 -106q-8 -7 -18 -7t-18 7l-199 199l-94 -94q-14 -14 -24.5 -10t-10.5 25v300q0 21 14.5 35.5t35.5 14.5zM850 1200h300q21 0 35.5 -14.5t14.5 -35.5v-300q0 -21 -10.5 -25t-24.5 10l-94 94 l-199 -199q-8 -7 -18 -7t-18 7l-106 106q-7 8 -7 18t7 18l199 199l-94 94q-14 14 -10 24.5t25 10.5zM364 470l106 -106q7 -8 7 -18t-7 -18l-199 -199l94 -94q14 -14 10 -24.5t-25 -10.5h-300q-21 0 -35.5 14.5t-14.5 35.5v300q0 21 10.5 25t24.5 -10l94 -94l199 199 q8 7 18 7t18 -7zM1071 271l94 94q14 14 24.5 10t10.5 -25v-300q0 -21 -14.5 -35.5t-35.5 -14.5h-300q-21 0 -25 10.5t10 24.5l94 94l-199 199q-7 8 -7 18t7 18l106 106q8 7 18 7t18 -7z" />
<glyph unicode="&#xe141;" d="M596 1192q121 0 231.5 -47.5t190 -127t127 -190t47.5 -231.5t-47.5 -231.5t-127 -190.5t-190 -127t-231.5 -47t-231.5 47t-190.5 127t-127 190.5t-47 231.5t47 231.5t127 190t190.5 127t231.5 47.5zM596 1010q-112 0 -207.5 -55.5t-151 -151t-55.5 -207.5t55.5 -207.5 t151 -151t207.5 -55.5t207.5 55.5t151 151t55.5 207.5t-55.5 207.5t-151 151t-207.5 55.5zM454.5 905q22.5 0 38.5 -16t16 -38.5t-16 -39t-38.5 -16.5t-38.5 16.5t-16 39t16 38.5t38.5 16zM754.5 905q22.5 0 38.5 -16t16 -38.5t-16 -39t-38 -16.5q-14 0 -29 10l-55 -145 q17 -23 17 -51q0 -36 -25.5 -61.5t-61.5 -25.5t-61.5 25.5t-25.5 61.5q0 32 20.5 56.5t51.5 29.5l122 126l1 1q-9 14 -9 28q0 23 16 39t38.5 16zM345.5 709q22.5 0 38.5 -16t16 -38.5t-16 -38.5t-38.5 -16t-38.5 16t-16 38.5t16 38.5t38.5 16zM854.5 709q22.5 0 38.5 -16 t16 -38.5t-16 -38.5t-38.5 -16t-38.5 16t-16 38.5t16 38.5t38.5 16z" />
<glyph unicode="&#xe142;" d="M546 173l469 470q91 91 99 192q7 98 -52 175.5t-154 94.5q-22 4 -47 4q-34 0 -66.5 -10t-56.5 -23t-55.5 -38t-48 -41.5t-48.5 -47.5q-376 -375 -391 -390q-30 -27 -45 -41.5t-37.5 -41t-32 -46.5t-16 -47.5t-1.5 -56.5q9 -62 53.5 -95t99.5 -33q74 0 125 51l548 548 q36 36 20 75q-7 16 -21.5 26t-32.5 10q-26 0 -50 -23q-13 -12 -39 -38l-341 -338q-15 -15 -35.5 -15.5t-34.5 13.5t-14 34.5t14 34.5q327 333 361 367q35 35 67.5 51.5t78.5 16.5q14 0 29 -1q44 -8 74.5 -35.5t43.5 -68.5q14 -47 2 -96.5t-47 -84.5q-12 -11 -32 -32 t-79.5 -81t-114.5 -115t-124.5 -123.5t-123 -119.5t-96.5 -89t-57 -45q-56 -27 -120 -27q-70 0 -129 32t-93 89q-48 78 -35 173t81 163l511 511q71 72 111 96q91 55 198 55q80 0 152 -33q78 -36 129.5 -103t66.5 -154q17 -93 -11 -183.5t-94 -156.5l-482 -476 q-15 -15 -36 -16t-37 14t-17.5 34t14.5 35z" />
<glyph unicode="&#xe143;" d="M649 949q48 68 109.5 104t121.5 38.5t118.5 -20t102.5 -64t71 -100.5t27 -123q0 -57 -33.5 -117.5t-94 -124.5t-126.5 -127.5t-150 -152.5t-146 -174q-62 85 -145.5 174t-150 152.5t-126.5 127.5t-93.5 124.5t-33.5 117.5q0 64 28 123t73 100.5t104 64t119 20 t120.5 -38.5t104.5 -104zM896 972q-33 0 -64.5 -19t-56.5 -46t-47.5 -53.5t-43.5 -45.5t-37.5 -19t-36 19t-40 45.5t-43 53.5t-54 46t-65.5 19q-67 0 -122.5 -55.5t-55.5 -132.5q0 -23 13.5 -51t46 -65t57.5 -63t76 -75l22 -22q15 -14 44 -44t50.5 -51t46 -44t41 -35t23 -12 t23.5 12t42.5 36t46 44t52.5 52t44 43q4 4 12 13q43 41 63.5 62t52 55t46 55t26 46t11.5 44q0 79 -53 133.5t-120 54.5z" />
<glyph unicode="&#xe144;" d="M776.5 1214q93.5 0 159.5 -66l141 -141q66 -66 66 -160q0 -42 -28 -95.5t-62 -87.5l-29 -29q-31 53 -77 99l-18 18l95 95l-247 248l-389 -389l212 -212l-105 -106l-19 18l-141 141q-66 66 -66 159t66 159l283 283q65 66 158.5 66zM600 706l105 105q10 -8 19 -17l141 -141 q66 -66 66 -159t-66 -159l-283 -283q-66 -66 -159 -66t-159 66l-141 141q-66 66 -66 159.5t66 159.5l55 55q29 -55 75 -102l18 -17l-95 -95l247 -248l389 389z" />
<glyph unicode="&#xe145;" d="M603 1200q85 0 162 -15t127 -38t79 -48t29 -46v-953q0 -41 -29.5 -70.5t-70.5 -29.5h-600q-41 0 -70.5 29.5t-29.5 70.5v953q0 21 30 46.5t81 48t129 37.5t163 15zM300 1000v-700h600v700h-600zM600 254q-43 0 -73.5 -30.5t-30.5 -73.5t30.5 -73.5t73.5 -30.5t73.5 30.5 t30.5 73.5t-30.5 73.5t-73.5 30.5z" />
<glyph unicode="&#xe146;" d="M902 1185l283 -282q15 -15 15 -36t-14.5 -35.5t-35.5 -14.5t-35 15l-36 35l-279 -267v-300l-212 210l-308 -307l-280 -203l203 280l307 308l-210 212h300l267 279l-35 36q-15 14 -15 35t14.5 35.5t35.5 14.5t35 -15z" />
<glyph unicode="&#xe148;" d="M700 1248v-78q38 -5 72.5 -14.5t75.5 -31.5t71 -53.5t52 -84t24 -118.5h-159q-4 36 -10.5 59t-21 45t-40 35.5t-64.5 20.5v-307l64 -13q34 -7 64 -16.5t70 -32t67.5 -52.5t47.5 -80t20 -112q0 -139 -89 -224t-244 -97v-77h-100v79q-150 16 -237 103q-40 40 -52.5 93.5 t-15.5 139.5h139q5 -77 48.5 -126t117.5 -65v335l-27 8q-46 14 -79 26.5t-72 36t-63 52t-40 72.5t-16 98q0 70 25 126t67.5 92t94.5 57t110 27v77h100zM600 754v274q-29 -4 -50 -11t-42 -21.5t-31.5 -41.5t-10.5 -65q0 -29 7 -50.5t16.5 -34t28.5 -22.5t31.5 -14t37.5 -10 q9 -3 13 -4zM700 547v-310q22 2 42.5 6.5t45 15.5t41.5 27t29 42t12 59.5t-12.5 59.5t-38 44.5t-53 31t-66.5 24.5z" />
<glyph unicode="&#xe149;" d="M561 1197q84 0 160.5 -40t123.5 -109.5t47 -147.5h-153q0 40 -19.5 71.5t-49.5 48.5t-59.5 26t-55.5 9q-37 0 -79 -14.5t-62 -35.5q-41 -44 -41 -101q0 -26 13.5 -63t26.5 -61t37 -66q6 -9 9 -14h241v-100h-197q8 -50 -2.5 -115t-31.5 -95q-45 -62 -99 -112 q34 10 83 17.5t71 7.5q32 1 102 -16t104 -17q83 0 136 30l50 -147q-31 -19 -58 -30.5t-55 -15.5t-42 -4.5t-46 -0.5q-23 0 -76 17t-111 32.5t-96 11.5q-39 -3 -82 -16t-67 -25l-23 -11l-55 145q4 3 16 11t15.5 10.5t13 9t15.5 12t14.5 14t17.5 18.5q48 55 54 126.5 t-30 142.5h-221v100h166q-23 47 -44 104q-7 20 -12 41.5t-6 55.5t6 66.5t29.5 70.5t58.5 71q97 88 263 88z" />
<glyph unicode="&#xe150;" d="M400 300h150q21 0 25 -11t-10 -25l-230 -250q-14 -15 -35 -15t-35 15l-230 250q-14 14 -10 25t25 11h150v900h200v-900zM935 1184l230 -249q14 -14 10 -24.5t-25 -10.5h-150v-900h-200v900h-150q-21 0 -25 10.5t10 24.5l230 249q14 15 35 15t35 -15z" />
<glyph unicode="&#xe151;" d="M1000 700h-100v100h-100v-100h-100v500h300v-500zM400 300h150q21 0 25 -11t-10 -25l-230 -250q-14 -15 -35 -15t-35 15l-230 250q-14 14 -10 25t25 11h150v900h200v-900zM801 1100v-200h100v200h-100zM1000 350l-200 -250h200v-100h-300v150l200 250h-200v100h300v-150z " />
<glyph unicode="&#xe152;" d="M400 300h150q21 0 25 -11t-10 -25l-230 -250q-14 -15 -35 -15t-35 15l-230 250q-14 14 -10 25t25 11h150v900h200v-900zM1000 1050l-200 -250h200v-100h-300v150l200 250h-200v100h300v-150zM1000 0h-100v100h-100v-100h-100v500h300v-500zM801 400v-200h100v200h-100z " />
<glyph unicode="&#xe153;" d="M400 300h150q21 0 25 -11t-10 -25l-230 -250q-14 -15 -35 -15t-35 15l-230 250q-14 14 -10 25t25 11h150v900h200v-900zM1000 700h-100v400h-100v100h200v-500zM1100 0h-100v100h-200v400h300v-500zM901 400v-200h100v200h-100z" />
<glyph unicode="&#xe154;" d="M400 300h150q21 0 25 -11t-10 -25l-230 -250q-14 -15 -35 -15t-35 15l-230 250q-14 14 -10 25t25 11h150v900h200v-900zM1100 700h-100v100h-200v400h300v-500zM901 1100v-200h100v200h-100zM1000 0h-100v400h-100v100h200v-500z" />
<glyph unicode="&#xe155;" d="M400 300h150q21 0 25 -11t-10 -25l-230 -250q-14 -15 -35 -15t-35 15l-230 250q-14 14 -10 25t25 11h150v900h200v-900zM900 1000h-200v200h200v-200zM1000 700h-300v200h300v-200zM1100 400h-400v200h400v-200zM1200 100h-500v200h500v-200z" />
<glyph unicode="&#xe156;" d="M400 300h150q21 0 25 -11t-10 -25l-230 -250q-14 -15 -35 -15t-35 15l-230 250q-14 14 -10 25t25 11h150v900h200v-900zM1200 1000h-500v200h500v-200zM1100 700h-400v200h400v-200zM1000 400h-300v200h300v-200zM900 100h-200v200h200v-200z" />
<glyph unicode="&#xe157;" d="M350 1100h400q162 0 256 -93.5t94 -256.5v-400q0 -165 -93.5 -257.5t-256.5 -92.5h-400q-165 0 -257.5 92.5t-92.5 257.5v400q0 165 92.5 257.5t257.5 92.5zM800 900h-500q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5 v500q0 41 -29.5 70.5t-70.5 29.5z" />
<glyph unicode="&#xe158;" d="M350 1100h400q165 0 257.5 -92.5t92.5 -257.5v-400q0 -165 -92.5 -257.5t-257.5 -92.5h-400q-163 0 -256.5 92.5t-93.5 257.5v400q0 163 94 256.5t256 93.5zM800 900h-500q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5 v500q0 41 -29.5 70.5t-70.5 29.5zM440 770l253 -190q17 -12 17 -30t-17 -30l-253 -190q-16 -12 -28 -6.5t-12 26.5v400q0 21 12 26.5t28 -6.5z" />
<glyph unicode="&#xe159;" d="M350 1100h400q163 0 256.5 -94t93.5 -256v-400q0 -165 -92.5 -257.5t-257.5 -92.5h-400q-165 0 -257.5 92.5t-92.5 257.5v400q0 163 92.5 256.5t257.5 93.5zM800 900h-500q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5 v500q0 41 -29.5 70.5t-70.5 29.5zM350 700h400q21 0 26.5 -12t-6.5 -28l-190 -253q-12 -17 -30 -17t-30 17l-190 253q-12 16 -6.5 28t26.5 12z" />
<glyph unicode="&#xe160;" d="M350 1100h400q165 0 257.5 -92.5t92.5 -257.5v-400q0 -163 -92.5 -256.5t-257.5 -93.5h-400q-163 0 -256.5 94t-93.5 256v400q0 165 92.5 257.5t257.5 92.5zM800 900h-500q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5 v500q0 41 -29.5 70.5t-70.5 29.5zM580 693l190 -253q12 -16 6.5 -28t-26.5 -12h-400q-21 0 -26.5 12t6.5 28l190 253q12 17 30 17t30 -17z" />
<glyph unicode="&#xe161;" d="M550 1100h400q165 0 257.5 -92.5t92.5 -257.5v-400q0 -165 -92.5 -257.5t-257.5 -92.5h-400q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5h450q41 0 70.5 29.5t29.5 70.5v500q0 41 -29.5 70.5t-70.5 29.5h-450q-21 0 -35.5 14.5t-14.5 35.5v100 q0 21 14.5 35.5t35.5 14.5zM338 867l324 -284q16 -14 16 -33t-16 -33l-324 -284q-16 -14 -27 -9t-11 26v150h-250q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5h250v150q0 21 11 26t27 -9z" />
<glyph unicode="&#xe162;" d="M793 1182l9 -9q8 -10 5 -27q-3 -11 -79 -225.5t-78 -221.5l300 1q24 0 32.5 -17.5t-5.5 -35.5q-1 0 -133.5 -155t-267 -312.5t-138.5 -162.5q-12 -15 -26 -15h-9l-9 8q-9 11 -4 32q2 9 42 123.5t79 224.5l39 110h-302q-23 0 -31 19q-10 21 6 41q75 86 209.5 237.5 t228 257t98.5 111.5q9 16 25 16h9z" />
<glyph unicode="&#xe163;" d="M350 1100h400q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-450q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h450q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-165 0 -257.5 92.5t-92.5 257.5v400 q0 165 92.5 257.5t257.5 92.5zM938 867l324 -284q16 -14 16 -33t-16 -33l-324 -284q-16 -14 -27 -9t-11 26v150h-250q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5h250v150q0 21 11 26t27 -9z" />
<glyph unicode="&#xe164;" d="M750 1200h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -10.5 -25t-24.5 10l-109 109l-312 -312q-15 -15 -35.5 -15t-35.5 15l-141 141q-15 15 -15 35.5t15 35.5l312 312l-109 109q-14 14 -10 24.5t25 10.5zM456 900h-156q-41 0 -70.5 -29.5t-29.5 -70.5v-500 q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5v148l200 200v-298q0 -165 -93.5 -257.5t-256.5 -92.5h-400q-165 0 -257.5 92.5t-92.5 257.5v400q0 165 92.5 257.5t257.5 92.5h300z" />
<glyph unicode="&#xe165;" d="M600 1186q119 0 227.5 -46.5t187 -125t125 -187t46.5 -227.5t-46.5 -227.5t-125 -187t-187 -125t-227.5 -46.5t-227.5 46.5t-187 125t-125 187t-46.5 227.5t46.5 227.5t125 187t187 125t227.5 46.5zM600 1022q-115 0 -212 -56.5t-153.5 -153.5t-56.5 -212t56.5 -212 t153.5 -153.5t212 -56.5t212 56.5t153.5 153.5t56.5 212t-56.5 212t-153.5 153.5t-212 56.5zM600 794q80 0 137 -57t57 -137t-57 -137t-137 -57t-137 57t-57 137t57 137t137 57z" />
<glyph unicode="&#xe166;" d="M450 1200h200q21 0 35.5 -14.5t14.5 -35.5v-350h245q20 0 25 -11t-9 -26l-383 -426q-14 -15 -33.5 -15t-32.5 15l-379 426q-13 15 -8.5 26t25.5 11h250v350q0 21 14.5 35.5t35.5 14.5zM50 300h1000q21 0 35.5 -14.5t14.5 -35.5v-250h-1100v250q0 21 14.5 35.5t35.5 14.5z M900 200v-50h100v50h-100z" />
<glyph unicode="&#xe167;" d="M583 1182l378 -435q14 -15 9 -31t-26 -16h-244v-250q0 -20 -17 -35t-39 -15h-200q-20 0 -32 14.5t-12 35.5v250h-250q-20 0 -25.5 16.5t8.5 31.5l383 431q14 16 33.5 17t33.5 -14zM50 300h1000q21 0 35.5 -14.5t14.5 -35.5v-250h-1100v250q0 21 14.5 35.5t35.5 14.5z M900 200v-50h100v50h-100z" />
<glyph unicode="&#xe168;" d="M396 723l369 369q7 7 17.5 7t17.5 -7l139 -139q7 -8 7 -18.5t-7 -17.5l-525 -525q-7 -8 -17.5 -8t-17.5 8l-292 291q-7 8 -7 18t7 18l139 139q8 7 18.5 7t17.5 -7zM50 300h1000q21 0 35.5 -14.5t14.5 -35.5v-250h-1100v250q0 21 14.5 35.5t35.5 14.5zM900 200v-50h100v50 h-100z" />
<glyph unicode="&#xe169;" d="M135 1023l142 142q14 14 35 14t35 -14l77 -77l-212 -212l-77 76q-14 15 -14 36t14 35zM655 855l210 210q14 14 24.5 10t10.5 -25l-2 -599q-1 -20 -15.5 -35t-35.5 -15l-597 -1q-21 0 -25 10.5t10 24.5l208 208l-154 155l212 212zM50 300h1000q21 0 35.5 -14.5t14.5 -35.5 v-250h-1100v250q0 21 14.5 35.5t35.5 14.5zM900 200v-50h100v50h-100z" />
<glyph unicode="&#xe170;" d="M350 1200l599 -2q20 -1 35 -15.5t15 -35.5l1 -597q0 -21 -10.5 -25t-24.5 10l-208 208l-155 -154l-212 212l155 154l-210 210q-14 14 -10 24.5t25 10.5zM524 512l-76 -77q-15 -14 -36 -14t-35 14l-142 142q-14 14 -14 35t14 35l77 77zM50 300h1000q21 0 35.5 -14.5 t14.5 -35.5v-250h-1100v250q0 21 14.5 35.5t35.5 14.5zM900 200v-50h100v50h-100z" />
<glyph unicode="&#xe171;" d="M1200 103l-483 276l-314 -399v423h-399l1196 796v-1096zM483 424v-230l683 953z" />
<glyph unicode="&#xe172;" d="M1100 1000v-850q0 -21 -14.5 -35.5t-35.5 -14.5h-150v400h-700v-400h-150q-21 0 -35.5 14.5t-14.5 35.5v1000q0 20 14.5 35t35.5 15h250v-300h500v300h100zM700 1000h-100v200h100v-200z" />
<glyph unicode="&#xe173;" d="M1100 1000l-2 -149l-299 -299l-95 95q-9 9 -21.5 9t-21.5 -9l-149 -147h-312v-400h-150q-21 0 -35.5 14.5t-14.5 35.5v1000q0 20 14.5 35t35.5 15h250v-300h500v300h100zM700 1000h-100v200h100v-200zM1132 638l106 -106q7 -7 7 -17.5t-7 -17.5l-420 -421q-8 -7 -18 -7 t-18 7l-202 203q-8 7 -8 17.5t8 17.5l106 106q7 8 17.5 8t17.5 -8l79 -79l297 297q7 7 17.5 7t17.5 -7z" />
<glyph unicode="&#xe174;" d="M1100 1000v-269l-103 -103l-134 134q-15 15 -33.5 16.5t-34.5 -12.5l-266 -266h-329v-400h-150q-21 0 -35.5 14.5t-14.5 35.5v1000q0 20 14.5 35t35.5 15h250v-300h500v300h100zM700 1000h-100v200h100v-200zM1202 572l70 -70q15 -15 15 -35.5t-15 -35.5l-131 -131 l131 -131q15 -15 15 -35.5t-15 -35.5l-70 -70q-15 -15 -35.5 -15t-35.5 15l-131 131l-131 -131q-15 -15 -35.5 -15t-35.5 15l-70 70q-15 15 -15 35.5t15 35.5l131 131l-131 131q-15 15 -15 35.5t15 35.5l70 70q15 15 35.5 15t35.5 -15l131 -131l131 131q15 15 35.5 15 t35.5 -15z" />
<glyph unicode="&#xe175;" d="M1100 1000v-300h-350q-21 0 -35.5 -14.5t-14.5 -35.5v-150h-500v-400h-150q-21 0 -35.5 14.5t-14.5 35.5v1000q0 20 14.5 35t35.5 15h250v-300h500v300h100zM700 1000h-100v200h100v-200zM850 600h100q21 0 35.5 -14.5t14.5 -35.5v-250h150q21 0 25 -10.5t-10 -24.5 l-230 -230q-14 -14 -35 -14t-35 14l-230 230q-14 14 -10 24.5t25 10.5h150v250q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe176;" d="M1100 1000v-400l-165 165q-14 15 -35 15t-35 -15l-263 -265h-402v-400h-150q-21 0 -35.5 14.5t-14.5 35.5v1000q0 20 14.5 35t35.5 15h250v-300h500v300h100zM700 1000h-100v200h100v-200zM935 565l230 -229q14 -15 10 -25.5t-25 -10.5h-150v-250q0 -20 -14.5 -35 t-35.5 -15h-100q-21 0 -35.5 15t-14.5 35v250h-150q-21 0 -25 10.5t10 25.5l230 229q14 15 35 15t35 -15z" />
<glyph unicode="&#xe177;" d="M50 1100h1100q21 0 35.5 -14.5t14.5 -35.5v-150h-1200v150q0 21 14.5 35.5t35.5 14.5zM1200 800v-550q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5v550h1200zM100 500v-200h400v200h-400z" />
<glyph unicode="&#xe178;" d="M935 1165l248 -230q14 -14 14 -35t-14 -35l-248 -230q-14 -14 -24.5 -10t-10.5 25v150h-400v200h400v150q0 21 10.5 25t24.5 -10zM200 800h-50q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5h50v-200zM400 800h-100v200h100v-200zM18 435l247 230 q14 14 24.5 10t10.5 -25v-150h400v-200h-400v-150q0 -21 -10.5 -25t-24.5 10l-247 230q-15 14 -15 35t15 35zM900 300h-100v200h100v-200zM1000 500h51q20 0 34.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-34.5 -14.5h-51v200z" />
<glyph unicode="&#xe179;" d="M862 1073l276 116q25 18 43.5 8t18.5 -41v-1106q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v397q-4 1 -11 5t-24 17.5t-30 29t-24 42t-11 56.5v359q0 31 18.5 65t43.5 52zM550 1200q22 0 34.5 -12.5t14.5 -24.5l1 -13v-450q0 -28 -10.5 -59.5 t-25 -56t-29 -45t-25.5 -31.5l-10 -11v-447q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v447q-4 4 -11 11.5t-24 30.5t-30 46t-24 55t-11 60v450q0 2 0.5 5.5t4 12t8.5 15t14.5 12t22.5 5.5q20 0 32.5 -12.5t14.5 -24.5l3 -13v-350h100v350v5.5t2.5 12 t7 15t15 12t25.5 5.5q23 0 35.5 -12.5t13.5 -24.5l1 -13v-350h100v350q0 2 0.5 5.5t3 12t7 15t15 12t24.5 5.5z" />
<glyph unicode="&#xe180;" d="M1200 1100v-56q-4 0 -11 -0.5t-24 -3t-30 -7.5t-24 -15t-11 -24v-888q0 -22 25 -34.5t50 -13.5l25 -2v-56h-400v56q75 0 87.5 6.5t12.5 43.5v394h-500v-394q0 -37 12.5 -43.5t87.5 -6.5v-56h-400v56q4 0 11 0.5t24 3t30 7.5t24 15t11 24v888q0 22 -25 34.5t-50 13.5 l-25 2v56h400v-56q-75 0 -87.5 -6.5t-12.5 -43.5v-394h500v394q0 37 -12.5 43.5t-87.5 6.5v56h400z" />
<glyph unicode="&#xe181;" d="M675 1000h375q21 0 35.5 -14.5t14.5 -35.5v-150h-105l-295 -98v98l-200 200h-400l100 100h375zM100 900h300q41 0 70.5 -29.5t29.5 -70.5v-500q0 -41 -29.5 -70.5t-70.5 -29.5h-300q-41 0 -70.5 29.5t-29.5 70.5v500q0 41 29.5 70.5t70.5 29.5zM100 800v-200h300v200 h-300zM1100 535l-400 -133v163l400 133v-163zM100 500v-200h300v200h-300zM1100 398v-248q0 -21 -14.5 -35.5t-35.5 -14.5h-375l-100 -100h-375l-100 100h400l200 200h105z" />
<glyph unicode="&#xe182;" d="M17 1007l162 162q17 17 40 14t37 -22l139 -194q14 -20 11 -44.5t-20 -41.5l-119 -118q102 -142 228 -268t267 -227l119 118q17 17 42.5 19t44.5 -12l192 -136q19 -14 22.5 -37.5t-13.5 -40.5l-163 -162q-3 -1 -9.5 -1t-29.5 2t-47.5 6t-62.5 14.5t-77.5 26.5t-90 42.5 t-101.5 60t-111 83t-119 108.5q-74 74 -133.5 150.5t-94.5 138.5t-60 119.5t-34.5 100t-15 74.5t-4.5 48z" />
<glyph unicode="&#xe183;" d="M600 1100q92 0 175 -10.5t141.5 -27t108.5 -36.5t81.5 -40t53.5 -37t31 -27l9 -10v-200q0 -21 -14.5 -33t-34.5 -9l-202 34q-20 3 -34.5 20t-14.5 38v146q-141 24 -300 24t-300 -24v-146q0 -21 -14.5 -38t-34.5 -20l-202 -34q-20 -3 -34.5 9t-14.5 33v200q3 4 9.5 10.5 t31 26t54 37.5t80.5 39.5t109 37.5t141 26.5t175 10.5zM600 795q56 0 97 -9.5t60 -23.5t30 -28t12 -24l1 -10v-50l365 -303q14 -15 24.5 -40t10.5 -45v-212q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5v212q0 20 10.5 45t24.5 40l365 303v50 q0 4 1 10.5t12 23t30 29t60 22.5t97 10z" />
<glyph unicode="&#xe184;" d="M1100 700l-200 -200h-600l-200 200v500h200v-200h200v200h200v-200h200v200h200v-500zM250 400h700q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-12l137 -100h-950l137 100h-12q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5zM50 100h1100q21 0 35.5 -14.5 t14.5 -35.5v-50h-1200v50q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe185;" d="M700 1100h-100q-41 0 -70.5 -29.5t-29.5 -70.5v-1000h300v1000q0 41 -29.5 70.5t-70.5 29.5zM1100 800h-100q-41 0 -70.5 -29.5t-29.5 -70.5v-700h300v700q0 41 -29.5 70.5t-70.5 29.5zM400 0h-300v400q0 41 29.5 70.5t70.5 29.5h100q41 0 70.5 -29.5t29.5 -70.5v-400z " />
<glyph unicode="&#xe186;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM500 700h-200v-100h200v-300h-300v100h200v100h-200v300h300v-100zM900 700v-300l-100 -100h-200v500h200z M700 700v-300h100v300h-100z" />
<glyph unicode="&#xe187;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM500 300h-100v200h-100v-200h-100v500h100v-200h100v200h100v-500zM900 700v-300l-100 -100h-200v500h200z M700 700v-300h100v300h-100z" />
<glyph unicode="&#xe188;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM500 700h-200v-300h200v-100h-300v500h300v-100zM900 700h-200v-300h200v-100h-300v500h300v-100z" />
<glyph unicode="&#xe189;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM500 400l-300 150l300 150v-300zM900 550l-300 -150v300z" />
<glyph unicode="&#xe190;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM900 300h-700v500h700v-500zM800 700h-130q-38 0 -66.5 -43t-28.5 -108t27 -107t68 -42h130v300zM300 700v-300 h130q41 0 68 42t27 107t-28.5 108t-66.5 43h-130z" />
<glyph unicode="&#xe191;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM500 700h-200v-100h200v-300h-300v100h200v100h-200v300h300v-100zM900 300h-100v400h-100v100h200v-500z M700 300h-100v100h100v-100z" />
<glyph unicode="&#xe192;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM300 700h200v-400h-300v500h100v-100zM900 300h-100v400h-100v100h200v-500zM300 600v-200h100v200h-100z M700 300h-100v100h100v-100z" />
<glyph unicode="&#xe193;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM500 500l-199 -200h-100v50l199 200v150h-200v100h300v-300zM900 300h-100v400h-100v100h200v-500zM701 300h-100 v100h100v-100z" />
<glyph unicode="&#xe194;" d="M600 1191q120 0 229.5 -47t188.5 -126t126 -188.5t47 -229.5t-47 -229.5t-126 -188.5t-188.5 -126t-229.5 -47t-229.5 47t-188.5 126t-126 188.5t-47 229.5t47 229.5t126 188.5t188.5 126t229.5 47zM600 1021q-114 0 -211 -56.5t-153.5 -153.5t-56.5 -211t56.5 -211 t153.5 -153.5t211 -56.5t211 56.5t153.5 153.5t56.5 211t-56.5 211t-153.5 153.5t-211 56.5zM800 700h-300v-200h300v-100h-300l-100 100v200l100 100h300v-100z" />
<glyph unicode="&#xe195;" d="M600 1191q120 0 229.5 -47t188.5 -126t126 -188.5t47 -229.5t-47 -229.5t-126 -188.5t-188.5 -126t-229.5 -47t-229.5 47t-188.5 126t-126 188.5t-47 229.5t47 229.5t126 188.5t188.5 126t229.5 47zM600 1021q-114 0 -211 -56.5t-153.5 -153.5t-56.5 -211t56.5 -211 t153.5 -153.5t211 -56.5t211 56.5t153.5 153.5t56.5 211t-56.5 211t-153.5 153.5t-211 56.5zM800 700v-100l-50 -50l100 -100v-50h-100l-100 100h-150v-100h-100v400h300zM500 700v-100h200v100h-200z" />
<glyph unicode="&#xe197;" d="M503 1089q110 0 200.5 -59.5t134.5 -156.5q44 14 90 14q120 0 205 -86.5t85 -207t-85 -207t-205 -86.5h-128v250q0 21 -14.5 35.5t-35.5 14.5h-300q-21 0 -35.5 -14.5t-14.5 -35.5v-250h-222q-80 0 -136 57.5t-56 136.5q0 69 43 122.5t108 67.5q-2 19 -2 37q0 100 49 185 t134 134t185 49zM525 500h150q10 0 17.5 -7.5t7.5 -17.5v-275h137q21 0 26 -11.5t-8 -27.5l-223 -244q-13 -16 -32 -16t-32 16l-223 244q-13 16 -8 27.5t26 11.5h137v275q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe198;" d="M502 1089q110 0 201 -59.5t135 -156.5q43 15 89 15q121 0 206 -86.5t86 -206.5q0 -99 -60 -181t-150 -110l-378 360q-13 16 -31.5 16t-31.5 -16l-381 -365h-9q-79 0 -135.5 57.5t-56.5 136.5q0 69 43 122.5t108 67.5q-2 19 -2 38q0 100 49 184.5t133.5 134t184.5 49.5z M632 467l223 -228q13 -16 8 -27.5t-26 -11.5h-137v-275q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v275h-137q-21 0 -26 11.5t8 27.5q199 204 223 228q19 19 31.5 19t32.5 -19z" />
<glyph unicode="&#xe199;" d="M700 100v100h400l-270 300h170l-270 300h170l-300 333l-300 -333h170l-270 -300h170l-270 -300h400v-100h-50q-21 0 -35.5 -14.5t-14.5 -35.5v-50h400v50q0 21 -14.5 35.5t-35.5 14.5h-50z" />
<glyph unicode="&#xe200;" d="M600 1179q94 0 167.5 -56.5t99.5 -145.5q89 -6 150.5 -71.5t61.5 -155.5q0 -61 -29.5 -112.5t-79.5 -82.5q9 -29 9 -55q0 -74 -52.5 -126.5t-126.5 -52.5q-55 0 -100 30v-251q21 0 35.5 -14.5t14.5 -35.5v-50h-300v50q0 21 14.5 35.5t35.5 14.5v251q-45 -30 -100 -30 q-74 0 -126.5 52.5t-52.5 126.5q0 18 4 38q-47 21 -75.5 65t-28.5 97q0 74 52.5 126.5t126.5 52.5q5 0 23 -2q0 2 -1 10t-1 13q0 116 81.5 197.5t197.5 81.5z" />
<glyph unicode="&#xe201;" d="M1010 1010q111 -111 150.5 -260.5t0 -299t-150.5 -260.5q-83 -83 -191.5 -126.5t-218.5 -43.5t-218.5 43.5t-191.5 126.5q-111 111 -150.5 260.5t0 299t150.5 260.5q83 83 191.5 126.5t218.5 43.5t218.5 -43.5t191.5 -126.5zM476 1065q-4 0 -8 -1q-121 -34 -209.5 -122.5 t-122.5 -209.5q-4 -12 2.5 -23t18.5 -14l36 -9q3 -1 7 -1q23 0 29 22q27 96 98 166q70 71 166 98q11 3 17.5 13.5t3.5 22.5l-9 35q-3 13 -14 19q-7 4 -15 4zM512 920q-4 0 -9 -2q-80 -24 -138.5 -82.5t-82.5 -138.5q-4 -13 2 -24t19 -14l34 -9q4 -1 8 -1q22 0 28 21 q18 58 58.5 98.5t97.5 58.5q12 3 18 13.5t3 21.5l-9 35q-3 12 -14 19q-7 4 -15 4zM719.5 719.5q-49.5 49.5 -119.5 49.5t-119.5 -49.5t-49.5 -119.5t49.5 -119.5t119.5 -49.5t119.5 49.5t49.5 119.5t-49.5 119.5zM855 551q-22 0 -28 -21q-18 -58 -58.5 -98.5t-98.5 -57.5 q-11 -4 -17 -14.5t-3 -21.5l9 -35q3 -12 14 -19q7 -4 15 -4q4 0 9 2q80 24 138.5 82.5t82.5 138.5q4 13 -2.5 24t-18.5 14l-34 9q-4 1 -8 1zM1000 515q-23 0 -29 -22q-27 -96 -98 -166q-70 -71 -166 -98q-11 -3 -17.5 -13.5t-3.5 -22.5l9 -35q3 -13 14 -19q7 -4 15 -4 q4 0 8 1q121 34 209.5 122.5t122.5 209.5q4 12 -2.5 23t-18.5 14l-36 9q-3 1 -7 1z" />
<glyph unicode="&#xe202;" d="M700 800h300v-380h-180v200h-340v-200h-380v755q0 10 7.5 17.5t17.5 7.5h575v-400zM1000 900h-200v200zM700 300h162l-212 -212l-212 212h162v200h100v-200zM520 0h-395q-10 0 -17.5 7.5t-7.5 17.5v395zM1000 220v-195q0 -10 -7.5 -17.5t-17.5 -7.5h-195z" />
<glyph unicode="&#xe203;" d="M700 800h300v-520l-350 350l-550 -550v1095q0 10 7.5 17.5t17.5 7.5h575v-400zM1000 900h-200v200zM862 200h-162v-200h-100v200h-162l212 212zM480 0h-355q-10 0 -17.5 7.5t-7.5 17.5v55h380v-80zM1000 80v-55q0 -10 -7.5 -17.5t-17.5 -7.5h-155v80h180z" />
<glyph unicode="&#xe204;" d="M1162 800h-162v-200h100l100 -100h-300v300h-162l212 212zM200 800h200q27 0 40 -2t29.5 -10.5t23.5 -30t7 -57.5h300v-100h-600l-200 -350v450h100q0 36 7 57.5t23.5 30t29.5 10.5t40 2zM800 400h240l-240 -400h-800l300 500h500v-100z" />
<glyph unicode="&#xe205;" d="M650 1100h100q21 0 35.5 -14.5t14.5 -35.5v-50h50q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-300q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5h50v50q0 21 14.5 35.5t35.5 14.5zM1000 850v150q41 0 70.5 -29.5t29.5 -70.5v-800 q0 -41 -29.5 -70.5t-70.5 -29.5h-600q-1 0 -20 4l246 246l-326 326v324q0 41 29.5 70.5t70.5 29.5v-150q0 -62 44 -106t106 -44h300q62 0 106 44t44 106zM412 250l-212 -212v162h-200v100h200v162z" />
<glyph unicode="&#xe206;" d="M450 1100h100q21 0 35.5 -14.5t14.5 -35.5v-50h50q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-300q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5h50v50q0 21 14.5 35.5t35.5 14.5zM800 850v150q41 0 70.5 -29.5t29.5 -70.5v-500 h-200v-300h200q0 -36 -7 -57.5t-23.5 -30t-29.5 -10.5t-40 -2h-600q-41 0 -70.5 29.5t-29.5 70.5v800q0 41 29.5 70.5t70.5 29.5v-150q0 -62 44 -106t106 -44h300q62 0 106 44t44 106zM1212 250l-212 -212v162h-200v100h200v162z" />
<glyph unicode="&#xe209;" d="M658 1197l637 -1104q23 -38 7 -65.5t-60 -27.5h-1276q-44 0 -60 27.5t7 65.5l637 1104q22 39 54 39t54 -39zM704 800h-208q-20 0 -32 -14.5t-8 -34.5l58 -302q4 -20 21.5 -34.5t37.5 -14.5h54q20 0 37.5 14.5t21.5 34.5l58 302q4 20 -8 34.5t-32 14.5zM500 300v-100h200 v100h-200z" />
<glyph unicode="&#xe210;" d="M425 1100h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5zM425 800h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5 t17.5 7.5zM825 800h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5zM25 500h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v150 q0 10 7.5 17.5t17.5 7.5zM425 500h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5zM825 500h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5 v150q0 10 7.5 17.5t17.5 7.5zM25 200h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5zM425 200h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5 t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5zM825 200h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe211;" d="M700 1200h100v-200h-100v-100h350q62 0 86.5 -39.5t-3.5 -94.5l-66 -132q-41 -83 -81 -134h-772q-40 51 -81 134l-66 132q-28 55 -3.5 94.5t86.5 39.5h350v100h-100v200h100v100h200v-100zM250 400h700q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-12l137 -100 h-950l138 100h-13q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5zM50 100h1100q21 0 35.5 -14.5t14.5 -35.5v-50h-1200v50q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe212;" d="M600 1300q40 0 68.5 -29.5t28.5 -70.5h-194q0 41 28.5 70.5t68.5 29.5zM443 1100h314q18 -37 18 -75q0 -8 -3 -25h328q41 0 44.5 -16.5t-30.5 -38.5l-175 -145h-678l-178 145q-34 22 -29 38.5t46 16.5h328q-3 17 -3 25q0 38 18 75zM250 700h700q21 0 35.5 -14.5 t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-150v-200l275 -200h-950l275 200v200h-150q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5zM50 100h1100q21 0 35.5 -14.5t14.5 -35.5v-50h-1200v50q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe213;" d="M600 1181q75 0 128 -53t53 -128t-53 -128t-128 -53t-128 53t-53 128t53 128t128 53zM602 798h46q34 0 55.5 -28.5t21.5 -86.5q0 -76 39 -183h-324q39 107 39 183q0 58 21.5 86.5t56.5 28.5h45zM250 400h700q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-13 l138 -100h-950l137 100h-12q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5zM50 100h1100q21 0 35.5 -14.5t14.5 -35.5v-50h-1200v50q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe214;" d="M600 1300q47 0 92.5 -53.5t71 -123t25.5 -123.5q0 -78 -55.5 -133.5t-133.5 -55.5t-133.5 55.5t-55.5 133.5q0 62 34 143l144 -143l111 111l-163 163q34 26 63 26zM602 798h46q34 0 55.5 -28.5t21.5 -86.5q0 -76 39 -183h-324q39 107 39 183q0 58 21.5 86.5t56.5 28.5h45 zM250 400h700q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-13l138 -100h-950l137 100h-12q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5zM50 100h1100q21 0 35.5 -14.5t14.5 -35.5v-50h-1200v50q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe215;" d="M600 1200l300 -161v-139h-300q0 -57 18.5 -108t50 -91.5t63 -72t70 -67.5t57.5 -61h-530q-60 83 -90.5 177.5t-30.5 178.5t33 164.5t87.5 139.5t126 96.5t145.5 41.5v-98zM250 400h700q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-13l138 -100h-950l137 100 h-12q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5zM50 100h1100q21 0 35.5 -14.5t14.5 -35.5v-50h-1200v50q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe216;" d="M600 1300q41 0 70.5 -29.5t29.5 -70.5v-78q46 -26 73 -72t27 -100v-50h-400v50q0 54 27 100t73 72v78q0 41 29.5 70.5t70.5 29.5zM400 800h400q54 0 100 -27t72 -73h-172v-100h200v-100h-200v-100h200v-100h-200v-100h200q0 -83 -58.5 -141.5t-141.5 -58.5h-400 q-83 0 -141.5 58.5t-58.5 141.5v400q0 83 58.5 141.5t141.5 58.5z" />
<glyph unicode="&#xe218;" d="M150 1100h900q21 0 35.5 -14.5t14.5 -35.5v-500q0 -21 -14.5 -35.5t-35.5 -14.5h-900q-21 0 -35.5 14.5t-14.5 35.5v500q0 21 14.5 35.5t35.5 14.5zM125 400h950q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-283l224 -224q13 -13 13 -31.5t-13 -32 t-31.5 -13.5t-31.5 13l-88 88h-524l-87 -88q-13 -13 -32 -13t-32 13.5t-13 32t13 31.5l224 224h-289q-10 0 -17.5 7.5t-7.5 17.5v50q0 10 7.5 17.5t17.5 7.5zM541 300l-100 -100h324l-100 100h-124z" />
<glyph unicode="&#xe219;" d="M200 1100h800q83 0 141.5 -58.5t58.5 -141.5v-200h-100q0 41 -29.5 70.5t-70.5 29.5h-250q-41 0 -70.5 -29.5t-29.5 -70.5h-100q0 41 -29.5 70.5t-70.5 29.5h-250q-41 0 -70.5 -29.5t-29.5 -70.5h-100v200q0 83 58.5 141.5t141.5 58.5zM100 600h1000q41 0 70.5 -29.5 t29.5 -70.5v-300h-1200v300q0 41 29.5 70.5t70.5 29.5zM300 100v-50q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v50h200zM1100 100v-50q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v50h200z" />
<glyph unicode="&#xe221;" d="M480 1165l682 -683q31 -31 31 -75.5t-31 -75.5l-131 -131h-481l-517 518q-32 31 -32 75.5t32 75.5l295 296q31 31 75.5 31t76.5 -31zM108 794l342 -342l303 304l-341 341zM250 100h800q21 0 35.5 -14.5t14.5 -35.5v-50h-900v50q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe223;" d="M1057 647l-189 506q-8 19 -27.5 33t-40.5 14h-400q-21 0 -40.5 -14t-27.5 -33l-189 -506q-8 -19 1.5 -33t30.5 -14h625v-150q0 -21 14.5 -35.5t35.5 -14.5t35.5 14.5t14.5 35.5v150h125q21 0 30.5 14t1.5 33zM897 0h-595v50q0 21 14.5 35.5t35.5 14.5h50v50 q0 21 14.5 35.5t35.5 14.5h48v300h200v-300h47q21 0 35.5 -14.5t14.5 -35.5v-50h50q21 0 35.5 -14.5t14.5 -35.5v-50z" />
<glyph unicode="&#xe224;" d="M900 800h300v-575q0 -10 -7.5 -17.5t-17.5 -7.5h-375v591l-300 300v84q0 10 7.5 17.5t17.5 7.5h375v-400zM1200 900h-200v200zM400 600h300v-575q0 -10 -7.5 -17.5t-17.5 -7.5h-650q-10 0 -17.5 7.5t-7.5 17.5v950q0 10 7.5 17.5t17.5 7.5h375v-400zM700 700h-200v200z " />
<glyph unicode="&#xe225;" d="M484 1095h195q75 0 146 -32.5t124 -86t89.5 -122.5t48.5 -142q18 -14 35 -20q31 -10 64.5 6.5t43.5 48.5q10 34 -15 71q-19 27 -9 43q5 8 12.5 11t19 -1t23.5 -16q41 -44 39 -105q-3 -63 -46 -106.5t-104 -43.5h-62q-7 -55 -35 -117t-56 -100l-39 -234q-3 -20 -20 -34.5 t-38 -14.5h-100q-21 0 -33 14.5t-9 34.5l12 70q-49 -14 -91 -14h-195q-24 0 -65 8l-11 -64q-3 -20 -20 -34.5t-38 -14.5h-100q-21 0 -33 14.5t-9 34.5l26 157q-84 74 -128 175l-159 53q-19 7 -33 26t-14 40v50q0 21 14.5 35.5t35.5 14.5h124q11 87 56 166l-111 95 q-16 14 -12.5 23.5t24.5 9.5h203q116 101 250 101zM675 1000h-250q-10 0 -17.5 -7.5t-7.5 -17.5v-50q0 -10 7.5 -17.5t17.5 -7.5h250q10 0 17.5 7.5t7.5 17.5v50q0 10 -7.5 17.5t-17.5 7.5z" />
<glyph unicode="&#xe226;" d="M641 900l423 247q19 8 42 2.5t37 -21.5l32 -38q14 -15 12.5 -36t-17.5 -34l-139 -120h-390zM50 1100h106q67 0 103 -17t66 -71l102 -212h823q21 0 35.5 -14.5t14.5 -35.5v-50q0 -21 -14 -40t-33 -26l-737 -132q-23 -4 -40 6t-26 25q-42 67 -100 67h-300q-62 0 -106 44 t-44 106v200q0 62 44 106t106 44zM173 928h-80q-19 0 -28 -14t-9 -35v-56q0 -51 42 -51h134q16 0 21.5 8t5.5 24q0 11 -16 45t-27 51q-18 28 -43 28zM550 727q-32 0 -54.5 -22.5t-22.5 -54.5t22.5 -54.5t54.5 -22.5t54.5 22.5t22.5 54.5t-22.5 54.5t-54.5 22.5zM130 389 l152 130q18 19 34 24t31 -3.5t24.5 -17.5t25.5 -28q28 -35 50.5 -51t48.5 -13l63 5l48 -179q13 -61 -3.5 -97.5t-67.5 -79.5l-80 -69q-47 -40 -109 -35.5t-103 51.5l-130 151q-40 47 -35.5 109.5t51.5 102.5zM380 377l-102 -88q-31 -27 2 -65l37 -43q13 -15 27.5 -19.5 t31.5 6.5l61 53q19 16 14 49q-2 20 -12 56t-17 45q-11 12 -19 14t-23 -8z" />
<glyph unicode="&#xe227;" d="M625 1200h150q10 0 17.5 -7.5t7.5 -17.5v-109q79 -33 131 -87.5t53 -128.5q1 -46 -15 -84.5t-39 -61t-46 -38t-39 -21.5l-17 -6q6 0 15 -1.5t35 -9t50 -17.5t53 -30t50 -45t35.5 -64t14.5 -84q0 -59 -11.5 -105.5t-28.5 -76.5t-44 -51t-49.5 -31.5t-54.5 -16t-49.5 -6.5 t-43.5 -1v-75q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v75h-100v-75q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v75h-175q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5h75v600h-75q-10 0 -17.5 7.5t-7.5 17.5v150 q0 10 7.5 17.5t17.5 7.5h175v75q0 10 7.5 17.5t17.5 7.5h150q10 0 17.5 -7.5t7.5 -17.5v-75h100v75q0 10 7.5 17.5t17.5 7.5zM400 900v-200h263q28 0 48.5 10.5t30 25t15 29t5.5 25.5l1 10q0 4 -0.5 11t-6 24t-15 30t-30 24t-48.5 11h-263zM400 500v-200h363q28 0 48.5 10.5 t30 25t15 29t5.5 25.5l1 10q0 4 -0.5 11t-6 24t-15 30t-30 24t-48.5 11h-363z" />
<glyph unicode="&#xe230;" d="M212 1198h780q86 0 147 -61t61 -147v-416q0 -51 -18 -142.5t-36 -157.5l-18 -66q-29 -87 -93.5 -146.5t-146.5 -59.5h-572q-82 0 -147 59t-93 147q-8 28 -20 73t-32 143.5t-20 149.5v416q0 86 61 147t147 61zM600 1045q-70 0 -132.5 -11.5t-105.5 -30.5t-78.5 -41.5 t-57 -45t-36 -41t-20.5 -30.5l-6 -12l156 -243h560l156 243q-2 5 -6 12.5t-20 29.5t-36.5 42t-57 44.5t-79 42t-105 29.5t-132.5 12zM762 703h-157l195 261z" />
<glyph unicode="&#xe231;" d="M475 1300h150q103 0 189 -86t86 -189v-500q0 -41 -42 -83t-83 -42h-450q-41 0 -83 42t-42 83v500q0 103 86 189t189 86zM700 300v-225q0 -21 -27 -48t-48 -27h-150q-21 0 -48 27t-27 48v225h300z" />
<glyph unicode="&#xe232;" d="M475 1300h96q0 -150 89.5 -239.5t239.5 -89.5v-446q0 -41 -42 -83t-83 -42h-450q-41 0 -83 42t-42 83v500q0 103 86 189t189 86zM700 300v-225q0 -21 -27 -48t-48 -27h-150q-21 0 -48 27t-27 48v225h300z" />
<glyph unicode="&#xe233;" d="M1294 767l-638 -283l-378 170l-78 -60v-224l100 -150v-199l-150 148l-150 -149v200l100 150v250q0 4 -0.5 10.5t0 9.5t1 8t3 8t6.5 6l47 40l-147 65l642 283zM1000 380l-350 -166l-350 166v147l350 -165l350 165v-147z" />
<glyph unicode="&#xe234;" d="M250 800q62 0 106 -44t44 -106t-44 -106t-106 -44t-106 44t-44 106t44 106t106 44zM650 800q62 0 106 -44t44 -106t-44 -106t-106 -44t-106 44t-44 106t44 106t106 44zM1050 800q62 0 106 -44t44 -106t-44 -106t-106 -44t-106 44t-44 106t44 106t106 44z" />
<glyph unicode="&#xe235;" d="M550 1100q62 0 106 -44t44 -106t-44 -106t-106 -44t-106 44t-44 106t44 106t106 44zM550 700q62 0 106 -44t44 -106t-44 -106t-106 -44t-106 44t-44 106t44 106t106 44zM550 300q62 0 106 -44t44 -106t-44 -106t-106 -44t-106 44t-44 106t44 106t106 44z" />
<glyph unicode="&#xe236;" d="M125 1100h950q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-950q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5zM125 700h950q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-950q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5 t17.5 7.5zM125 300h950q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-950q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe237;" d="M350 1200h500q162 0 256 -93.5t94 -256.5v-500q0 -165 -93.5 -257.5t-256.5 -92.5h-500q-165 0 -257.5 92.5t-92.5 257.5v500q0 165 92.5 257.5t257.5 92.5zM900 1000h-600q-41 0 -70.5 -29.5t-29.5 -70.5v-600q0 -41 29.5 -70.5t70.5 -29.5h600q41 0 70.5 29.5 t29.5 70.5v600q0 41 -29.5 70.5t-70.5 29.5zM350 900h500q21 0 35.5 -14.5t14.5 -35.5v-300q0 -21 -14.5 -35.5t-35.5 -14.5h-500q-21 0 -35.5 14.5t-14.5 35.5v300q0 21 14.5 35.5t35.5 14.5zM400 800v-200h400v200h-400z" />
<glyph unicode="&#xe238;" d="M150 1100h1000q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-50v-200h50q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-50v-200h50q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-50v-200h50q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5 t-35.5 -14.5h-1000q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5h50v200h-50q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5h50v200h-50q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5h50v200h-50q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe239;" d="M650 1187q87 -67 118.5 -156t0 -178t-118.5 -155q-87 66 -118.5 155t0 178t118.5 156zM300 800q124 0 212 -88t88 -212q-124 0 -212 88t-88 212zM1000 800q0 -124 -88 -212t-212 -88q0 124 88 212t212 88zM300 500q124 0 212 -88t88 -212q-124 0 -212 88t-88 212z M1000 500q0 -124 -88 -212t-212 -88q0 124 88 212t212 88zM700 199v-144q0 -21 -14.5 -35.5t-35.5 -14.5t-35.5 14.5t-14.5 35.5v142q40 -4 43 -4q17 0 57 6z" />
<glyph unicode="&#xe240;" d="M745 878l69 19q25 6 45 -12l298 -295q11 -11 15 -26.5t-2 -30.5q-5 -14 -18 -23.5t-28 -9.5h-8q1 0 1 -13q0 -29 -2 -56t-8.5 -62t-20 -63t-33 -53t-51 -39t-72.5 -14h-146q-184 0 -184 288q0 24 10 47q-20 4 -62 4t-63 -4q11 -24 11 -47q0 -288 -184 -288h-142 q-48 0 -84.5 21t-56 51t-32 71.5t-16 75t-3.5 68.5q0 13 2 13h-7q-15 0 -27.5 9.5t-18.5 23.5q-6 15 -2 30.5t15 25.5l298 296q20 18 46 11l76 -19q20 -5 30.5 -22.5t5.5 -37.5t-22.5 -31t-37.5 -5l-51 12l-182 -193h891l-182 193l-44 -12q-20 -5 -37.5 6t-22.5 31t6 37.5 t31 22.5z" />
<glyph unicode="&#xe241;" d="M1200 900h-50q0 21 -4 37t-9.5 26.5t-18 17.5t-22 11t-28.5 5.5t-31 2t-37 0.5h-200v-850q0 -22 25 -34.5t50 -13.5l25 -2v-100h-400v100q4 0 11 0.5t24 3t30 7t24 15t11 24.5v850h-200q-25 0 -37 -0.5t-31 -2t-28.5 -5.5t-22 -11t-18 -17.5t-9.5 -26.5t-4 -37h-50v300 h1000v-300zM500 450h-25q0 15 -4 24.5t-9 14.5t-17 7.5t-20 3t-25 0.5h-100v-425q0 -11 12.5 -17.5t25.5 -7.5h12v-50h-200v50q50 0 50 25v425h-100q-17 0 -25 -0.5t-20 -3t-17 -7.5t-9 -14.5t-4 -24.5h-25v150h500v-150z" />
<glyph unicode="&#xe242;" d="M1000 300v50q-25 0 -55 32q-14 14 -25 31t-16 27l-4 11l-289 747h-69l-300 -754q-18 -35 -39 -56q-9 -9 -24.5 -18.5t-26.5 -14.5l-11 -5v-50h273v50q-49 0 -78.5 21.5t-11.5 67.5l69 176h293l61 -166q13 -34 -3.5 -66.5t-55.5 -32.5v-50h312zM412 691l134 342l121 -342 h-255zM1100 150v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1000q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5h1000q21 0 35.5 -14.5t14.5 -35.5z" />
<glyph unicode="&#xe243;" d="M50 1200h1100q21 0 35.5 -14.5t14.5 -35.5v-1100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5v1100q0 21 14.5 35.5t35.5 14.5zM611 1118h-70q-13 0 -18 -12l-299 -753q-17 -32 -35 -51q-18 -18 -56 -34q-12 -5 -12 -18v-50q0 -8 5.5 -14t14.5 -6 h273q8 0 14 6t6 14v50q0 8 -6 14t-14 6q-55 0 -71 23q-10 14 0 39l63 163h266l57 -153q11 -31 -6 -55q-12 -17 -36 -17q-8 0 -14 -6t-6 -14v-50q0 -8 6 -14t14 -6h313q8 0 14 6t6 14v50q0 7 -5.5 13t-13.5 7q-17 0 -42 25q-25 27 -40 63h-1l-288 748q-5 12 -19 12zM639 611 h-197l103 264z" />
<glyph unicode="&#xe244;" d="M1200 1100h-1200v100h1200v-100zM50 1000h400q21 0 35.5 -14.5t14.5 -35.5v-900q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v900q0 21 14.5 35.5t35.5 14.5zM650 1000h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400 q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5zM700 900v-300h300v300h-300z" />
<glyph unicode="&#xe245;" d="M50 1200h400q21 0 35.5 -14.5t14.5 -35.5v-900q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v900q0 21 14.5 35.5t35.5 14.5zM650 700h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v400 q0 21 14.5 35.5t35.5 14.5zM700 600v-300h300v300h-300zM1200 0h-1200v100h1200v-100z" />
<glyph unicode="&#xe246;" d="M50 1000h400q21 0 35.5 -14.5t14.5 -35.5v-350h100v150q0 21 14.5 35.5t35.5 14.5h400q21 0 35.5 -14.5t14.5 -35.5v-150h100v-100h-100v-150q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v150h-100v-350q0 -21 -14.5 -35.5t-35.5 -14.5h-400 q-21 0 -35.5 14.5t-14.5 35.5v800q0 21 14.5 35.5t35.5 14.5zM700 700v-300h300v300h-300z" />
<glyph unicode="&#xe247;" d="M100 0h-100v1200h100v-1200zM250 1100h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5zM300 1000v-300h300v300h-300zM250 500h900q21 0 35.5 -14.5t14.5 -35.5v-400 q0 -21 -14.5 -35.5t-35.5 -14.5h-900q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe248;" d="M600 1100h150q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-150v-100h450q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-900q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5h350v100h-150q-21 0 -35.5 14.5 t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5h150v100h100v-100zM400 1000v-300h300v300h-300z" />
<glyph unicode="&#xe249;" d="M1200 0h-100v1200h100v-1200zM550 1100h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5zM600 1000v-300h300v300h-300zM50 500h900q21 0 35.5 -14.5t14.5 -35.5v-400 q0 -21 -14.5 -35.5t-35.5 -14.5h-900q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe250;" d="M865 565l-494 -494q-23 -23 -41 -23q-14 0 -22 13.5t-8 38.5v1000q0 25 8 38.5t22 13.5q18 0 41 -23l494 -494q14 -14 14 -35t-14 -35z" />
<glyph unicode="&#xe251;" d="M335 635l494 494q29 29 50 20.5t21 -49.5v-1000q0 -41 -21 -49.5t-50 20.5l-494 494q-14 14 -14 35t14 35z" />
<glyph unicode="&#xe252;" d="M100 900h1000q41 0 49.5 -21t-20.5 -50l-494 -494q-14 -14 -35 -14t-35 14l-494 494q-29 29 -20.5 50t49.5 21z" />
<glyph unicode="&#xe253;" d="M635 865l494 -494q29 -29 20.5 -50t-49.5 -21h-1000q-41 0 -49.5 21t20.5 50l494 494q14 14 35 14t35 -14z" />
<glyph unicode="&#xe254;" d="M700 741v-182l-692 -323v221l413 193l-413 193v221zM1200 0h-800v200h800v-200z" />
<glyph unicode="&#xe255;" d="M1200 900h-200v-100h200v-100h-300v300h200v100h-200v100h300v-300zM0 700h50q0 21 4 37t9.5 26.5t18 17.5t22 11t28.5 5.5t31 2t37 0.5h100v-550q0 -22 -25 -34.5t-50 -13.5l-25 -2v-100h400v100q-4 0 -11 0.5t-24 3t-30 7t-24 15t-11 24.5v550h100q25 0 37 -0.5t31 -2 t28.5 -5.5t22 -11t18 -17.5t9.5 -26.5t4 -37h50v300h-800v-300z" />
<glyph unicode="&#xe256;" d="M800 700h-50q0 21 -4 37t-9.5 26.5t-18 17.5t-22 11t-28.5 5.5t-31 2t-37 0.5h-100v-550q0 -22 25 -34.5t50 -14.5l25 -1v-100h-400v100q4 0 11 0.5t24 3t30 7t24 15t11 24.5v550h-100q-25 0 -37 -0.5t-31 -2t-28.5 -5.5t-22 -11t-18 -17.5t-9.5 -26.5t-4 -37h-50v300 h800v-300zM1100 200h-200v-100h200v-100h-300v300h200v100h-200v100h300v-300z" />
<glyph unicode="&#xe257;" d="M701 1098h160q16 0 21 -11t-7 -23l-464 -464l464 -464q12 -12 7 -23t-21 -11h-160q-13 0 -23 9l-471 471q-7 8 -7 18t7 18l471 471q10 9 23 9z" />
<glyph unicode="&#xe258;" d="M339 1098h160q13 0 23 -9l471 -471q7 -8 7 -18t-7 -18l-471 -471q-10 -9 -23 -9h-160q-16 0 -21 11t7 23l464 464l-464 464q-12 12 -7 23t21 11z" />
<glyph unicode="&#xe259;" d="M1087 882q11 -5 11 -21v-160q0 -13 -9 -23l-471 -471q-8 -7 -18 -7t-18 7l-471 471q-9 10 -9 23v160q0 16 11 21t23 -7l464 -464l464 464q12 12 23 7z" />
<glyph unicode="&#xe260;" d="M618 993l471 -471q9 -10 9 -23v-160q0 -16 -11 -21t-23 7l-464 464l-464 -464q-12 -12 -23 -7t-11 21v160q0 13 9 23l471 471q8 7 18 7t18 -7z" />
<glyph unicode="&#xf8ff;" d="M1000 1200q0 -124 -88 -212t-212 -88q0 124 88 212t212 88zM450 1000h100q21 0 40 -14t26 -33l79 -194q5 1 16 3q34 6 54 9.5t60 7t65.5 1t61 -10t56.5 -23t42.5 -42t29 -64t5 -92t-19.5 -121.5q-1 -7 -3 -19.5t-11 -50t-20.5 -73t-32.5 -81.5t-46.5 -83t-64 -70 t-82.5 -50q-13 -5 -42 -5t-65.5 2.5t-47.5 2.5q-14 0 -49.5 -3.5t-63 -3.5t-43.5 7q-57 25 -104.5 78.5t-75 111.5t-46.5 112t-26 90l-7 35q-15 63 -18 115t4.5 88.5t26 64t39.5 43.5t52 25.5t58.5 13t62.5 2t59.5 -4.5t55.5 -8l-147 192q-12 18 -5.5 30t27.5 12z" />
<glyph unicode="&#x1f511;" d="M250 1200h600q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-150v-500l-255 -178q-19 -9 -32 -1t-13 29v650h-150q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5zM400 1100v-100h300v100h-300z" />
<glyph unicode="&#x1f6aa;" d="M250 1200h750q39 0 69.5 -40.5t30.5 -84.5v-933l-700 -117v950l600 125h-700v-1000h-100v1025q0 23 15.5 49t34.5 26zM500 525v-100l100 20v100z" />
</font>
</defs></svg> ) format('svg')}.glyphicon{position:relative;top:1px;display:inline-block;font-family:'Glyphicons Halflings';font-style:normal;font-weight:400;line-height:1;-webkit-font-smoothing:antialiased;-moz-osx-font-smoothing:grayscale}.glyphicon-asterisk:before{content:"\2a"}.glyphicon-plus:before{content:"\2b"}.glyphicon-eur:before,.glyphicon-euro:before{content:"\20ac"}.glyphicon-minus:before{content:"\2212"}.glyphicon-cloud:before{content:"\2601"}.glyphicon-envelope:before{content:"\2709"}.glyphicon-pencil:before{content:"\270f"}.glyphicon-glass:before{content:"\e001"}.glyphicon-music:before{content:"\e002"}.glyphicon-search:before{content:"\e003"}.glyphicon-heart:before{content:"\e005"}.glyphicon-star:before{content:"\e006"}.glyphicon-star-empty:before{content:"\e007"}.glyphicon-user:before{content:"\e008"}.glyphicon-film:before{content:"\e009"}.glyphicon-th-large:before{content:"\e010"}.glyphicon-th:before{content:"\e011"}.glyphicon-th-list:before{content:"\e012"}.glyphicon-ok:before{content:"\e013"}.glyphicon-remove:before{content:"\e014"}.glyphicon-zoom-in:before{content:"\e015"}.glyphicon-zoom-out:before{content:"\e016"}.glyphicon-off:before{content:"\e017"}.glyphicon-signal:before{content:"\e018"}.glyphicon-cog:before{content:"\e019"}.glyphicon-trash:before{content:"\e020"}.glyphicon-home:before{content:"\e021"}.glyphicon-file:before{content:"\e022"}.glyphicon-time:before{content:"\e023"}.glyphicon-road:before{content:"\e024"}.glyphicon-download-alt:before{content:"\e025"}.glyphicon-download:before{content:"\e026"}.glyphicon-upload:before{content:"\e027"}.glyphicon-inbox:before{content:"\e028"}.glyphicon-play-circle:before{content:"\e029"}.glyphicon-repeat:before{content:"\e030"}.glyphicon-refresh:before{content:"\e031"}.glyphicon-list-alt:before{content:"\e032"}.glyphicon-lock:before{content:"\e033"}.glyphicon-flag:before{content:"\e034"}.glyphicon-headphones:before{content:"\e035"}.glyphicon-volume-off:before{content:"\e036"}.glyphicon-volume-down:before{content:"\e037"}.glyphicon-volume-up:before{content:"\e038"}.glyphicon-qrcode:before{content:"\e039"}.glyphicon-barcode:before{content:"\e040"}.glyphicon-tag:before{content:"\e041"}.glyphicon-tags:before{content:"\e042"}.glyphicon-book:before{content:"\e043"}.glyphicon-bookmark:before{content:"\e044"}.glyphicon-print:before{content:"\e045"}.glyphicon-camera:before{content:"\e046"}.glyphicon-font:before{content:"\e047"}.glyphicon-bold:before{content:"\e048"}.glyphicon-italic:before{content:"\e049"}.glyphicon-text-height:before{content:"\e050"}.glyphicon-text-width:before{content:"\e051"}.glyphicon-align-left:before{content:"\e052"}.glyphicon-align-center:before{content:"\e053"}.glyphicon-align-right:before{content:"\e054"}.glyphicon-align-justify:before{content:"\e055"}.glyphicon-list:before{content:"\e056"}.glyphicon-indent-left:before{content:"\e057"}.glyphicon-indent-right:before{content:"\e058"}.glyphicon-facetime-video:before{content:"\e059"}.glyphicon-picture:before{content:"\e060"}.glyphicon-map-marker:before{content:"\e062"}.glyphicon-adjust:before{content:"\e063"}.glyphicon-tint:before{content:"\e064"}.glyphicon-edit:before{content:"\e065"}.glyphicon-share:before{content:"\e066"}.glyphicon-check:before{content:"\e067"}.glyphicon-move:before{content:"\e068"}.glyphicon-step-backward:before{content:"\e069"}.glyphicon-fast-backward:before{content:"\e070"}.glyphicon-backward:before{content:"\e071"}.glyphicon-play:before{content:"\e072"}.glyphicon-pause:before{content:"\e073"}.glyphicon-stop:before{content:"\e074"}.glyphicon-forward:before{content:"\e075"}.glyphicon-fast-forward:before{content:"\e076"}.glyphicon-step-forward:before{content:"\e077"}.glyphicon-eject:before{content:"\e078"}.glyphicon-chevron-left:before{content:"\e079"}.glyphicon-chevron-right:before{content:"\e080"}.glyphicon-plus-sign:before{content:"\e081"}.glyphicon-minus-sign:before{content:"\e082"}.glyphicon-remove-sign:before{content:"\e083"}.glyphicon-ok-sign:before{content:"\e084"}.glyphicon-question-sign:before{content:"\e085"}.glyphicon-info-sign:before{content:"\e086"}.glyphicon-screenshot:before{content:"\e087"}.glyphicon-remove-circle:before{content:"\e088"}.glyphicon-ok-circle:before{content:"\e089"}.glyphicon-ban-circle:before{content:"\e090"}.glyphicon-arrow-left:before{content:"\e091"}.glyphicon-arrow-right:before{content:"\e092"}.glyphicon-arrow-up:before{content:"\e093"}.glyphicon-arrow-down:before{content:"\e094"}.glyphicon-share-alt:before{content:"\e095"}.glyphicon-resize-full:before{content:"\e096"}.glyphicon-resize-small:before{content:"\e097"}.glyphicon-exclamation-sign:before{content:"\e101"}.glyphicon-gift:before{content:"\e102"}.glyphicon-leaf:before{content:"\e103"}.glyphicon-fire:before{content:"\e104"}.glyphicon-eye-open:before{content:"\e105"}.glyphicon-eye-close:before{content:"\e106"}.glyphicon-warning-sign:before{content:"\e107"}.glyphicon-plane:before{content:"\e108"}.glyphicon-calendar:before{content:"\e109"}.glyphicon-random:before{content:"\e110"}.glyphicon-comment:before{content:"\e111"}.glyphicon-magnet:before{content:"\e112"}.glyphicon-chevron-up:before{content:"\e113"}.glyphicon-chevron-down:before{content:"\e114"}.glyphicon-retweet:before{content:"\e115"}.glyphicon-shopping-cart:before{content:"\e116"}.glyphicon-folder-close:before{content:"\e117"}.glyphicon-folder-open:before{content:"\e118"}.glyphicon-resize-vertical:before{content:"\e119"}.glyphicon-resize-horizontal:before{content:"\e120"}.glyphicon-hdd:before{content:"\e121"}.glyphicon-bullhorn:before{content:"\e122"}.glyphicon-bell:before{content:"\e123"}.glyphicon-certificate:before{content:"\e124"}.glyphicon-thumbs-up:before{content:"\e125"}.glyphicon-thumbs-down:before{content:"\e126"}.glyphicon-hand-right:before{content:"\e127"}.glyphicon-hand-left:before{content:"\e128"}.glyphicon-hand-up:before{content:"\e129"}.glyphicon-hand-down:before{content:"\e130"}.glyphicon-circle-arrow-right:before{content:"\e131"}.glyphicon-circle-arrow-left:before{content:"\e132"}.glyphicon-circle-arrow-up:before{content:"\e133"}.glyphicon-circle-arrow-down:before{content:"\e134"}.glyphicon-globe:before{content:"\e135"}.glyphicon-wrench:before{content:"\e136"}.glyphicon-tasks:before{content:"\e137"}.glyphicon-filter:before{content:"\e138"}.glyphicon-briefcase:before{content:"\e139"}.glyphicon-fullscreen:before{content:"\e140"}.glyphicon-dashboard:before{content:"\e141"}.glyphicon-paperclip:before{content:"\e142"}.glyphicon-heart-empty:before{content:"\e143"}.glyphicon-link:before{content:"\e144"}.glyphicon-phone:before{content:"\e145"}.glyphicon-pushpin:before{content:"\e146"}.glyphicon-usd:before{content:"\e148"}.glyphicon-gbp:before{content:"\e149"}.glyphicon-sort:before{content:"\e150"}.glyphicon-sort-by-alphabet:before{content:"\e151"}.glyphicon-sort-by-alphabet-alt:before{content:"\e152"}.glyphicon-sort-by-order:before{content:"\e153"}.glyphicon-sort-by-order-alt:before{content:"\e154"}.glyphicon-sort-by-attributes:before{content:"\e155"}.glyphicon-sort-by-attributes-alt:before{content:"\e156"}.glyphicon-unchecked:before{content:"\e157"}.glyphicon-expand:before{content:"\e158"}.glyphicon-collapse-down:before{content:"\e159"}.glyphicon-collapse-up:before{content:"\e160"}.glyphicon-log-in:before{content:"\e161"}.glyphicon-flash:before{content:"\e162"}.glyphicon-log-out:before{content:"\e163"}.glyphicon-new-window:before{content:"\e164"}.glyphicon-record:before{content:"\e165"}.glyphicon-save:before{content:"\e166"}.glyphicon-open:before{content:"\e167"}.glyphicon-saved:before{content:"\e168"}.glyphicon-import:before{content:"\e169"}.glyphicon-export:before{content:"\e170"}.glyphicon-send:before{content:"\e171"}.glyphicon-floppy-disk:before{content:"\e172"}.glyphicon-floppy-saved:before{content:"\e173"}.glyphicon-floppy-remove:before{content:"\e174"}.glyphicon-floppy-save:before{content:"\e175"}.glyphicon-floppy-open:before{content:"\e176"}.glyphicon-credit-card:before{content:"\e177"}.glyphicon-transfer:before{content:"\e178"}.glyphicon-cutlery:before{content:"\e179"}.glyphicon-header:before{content:"\e180"}.glyphicon-compressed:before{content:"\e181"}.glyphicon-earphone:before{content:"\e182"}.glyphicon-phone-alt:before{content:"\e183"}.glyphicon-tower:before{content:"\e184"}.glyphicon-stats:before{content:"\e185"}.glyphicon-sd-video:before{content:"\e186"}.glyphicon-hd-video:before{content:"\e187"}.glyphicon-subtitles:before{content:"\e188"}.glyphicon-sound-stereo:before{content:"\e189"}.glyphicon-sound-dolby:before{content:"\e190"}.glyphicon-sound-5-1:before{content:"\e191"}.glyphicon-sound-6-1:before{content:"\e192"}.glyphicon-sound-7-1:before{content:"\e193"}.glyphicon-copyright-mark:before{content:"\e194"}.glyphicon-registration-mark:before{content:"\e195"}.glyphicon-cloud-download:before{content:"\e197"}.glyphicon-cloud-upload:before{content:"\e198"}.glyphicon-tree-conifer:before{content:"\e199"}.glyphicon-tree-deciduous:before{content:"\e200"}.glyphicon-cd:before{content:"\e201"}.glyphicon-save-file:before{content:"\e202"}.glyphicon-open-file:before{content:"\e203"}.glyphicon-level-up:before{content:"\e204"}.glyphicon-copy:before{content:"\e205"}.glyphicon-paste:before{content:"\e206"}.glyphicon-alert:before{content:"\e209"}.glyphicon-equalizer:before{content:"\e210"}.glyphicon-king:before{content:"\e211"}.glyphicon-queen:before{content:"\e212"}.glyphicon-pawn:before{content:"\e213"}.glyphicon-bishop:before{content:"\e214"}.glyphicon-knight:before{content:"\e215"}.glyphicon-baby-formula:before{content:"\e216"}.glyphicon-tent:before{content:"\26fa"}.glyphicon-blackboard:before{content:"\e218"}.glyphicon-bed:before{content:"\e219"}.glyphicon-apple:before{content:"\f8ff"}.glyphicon-erase:before{content:"\e221"}.glyphicon-hourglass:before{content:"\231b"}.glyphicon-lamp:before{content:"\e223"}.glyphicon-duplicate:before{content:"\e224"}.glyphicon-piggy-bank:before{content:"\e225"}.glyphicon-scissors:before{content:"\e226"}.glyphicon-bitcoin:before{content:"\e227"}.glyphicon-btc:before{content:"\e227"}.glyphicon-xbt:before{content:"\e227"}.glyphicon-yen:before{content:"\00a5"}.glyphicon-jpy:before{content:"\00a5"}.glyphicon-ruble:before{content:"\20bd"}.glyphicon-rub:before{content:"\20bd"}.glyphicon-scale:before{content:"\e230"}.glyphicon-ice-lolly:before{content:"\e231"}.glyphicon-ice-lolly-tasted:before{content:"\e232"}.glyphicon-education:before{content:"\e233"}.glyphicon-option-horizontal:before{content:"\e234"}.glyphicon-option-vertical:before{content:"\e235"}.glyphicon-menu-hamburger:before{content:"\e236"}.glyphicon-modal-window:before{content:"\e237"}.glyphicon-oil:before{content:"\e238"}.glyphicon-grain:before{content:"\e239"}.glyphicon-sunglasses:before{content:"\e240"}.glyphicon-text-size:before{content:"\e241"}.glyphicon-text-color:before{content:"\e242"}.glyphicon-text-background:before{content:"\e243"}.glyphicon-object-align-top:before{content:"\e244"}.glyphicon-object-align-bottom:before{content:"\e245"}.glyphicon-object-align-horizontal:before{content:"\e246"}.glyphicon-object-align-left:before{content:"\e247"}.glyphicon-object-align-vertical:before{content:"\e248"}.glyphicon-object-align-right:before{content:"\e249"}.glyphicon-triangle-right:before{content:"\e250"}.glyphicon-triangle-left:before{content:"\e251"}.glyphicon-triangle-bottom:before{content:"\e252"}.glyphicon-triangle-top:before{content:"\e253"}.glyphicon-console:before{content:"\e254"}.glyphicon-superscript:before{content:"\e255"}.glyphicon-subscript:before{content:"\e256"}.glyphicon-menu-left:before{content:"\e257"}.glyphicon-menu-right:before{content:"\e258"}.glyphicon-menu-down:before{content:"\e259"}.glyphicon-menu-up:before{content:"\e260"}*{-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}:after,:before{-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}html{font-size:10px;-webkit-tap-highlight-color:rgba(0,0,0,0)}body{font-family:"Helvetica Neue",Helvetica,Arial,sans-serif;font-size:14px;line-height:1.42857143;color:#333;background-color:#fff}button,input,select,textarea{font-family:inherit;font-size:inherit;line-height:inherit}a{color:#337ab7;text-decoration:none}a:focus,a:hover{color:#23527c;text-decoration:underline}a:focus{outline:thin dotted;outline:5px auto -webkit-focus-ring-color;outline-offset:-2px}figure{margin:0}img{vertical-align:middle}.carousel-inner>.item>a>img,.carousel-inner>.item>img,.img-responsive,.thumbnail a>img,.thumbnail>img{display:block;max-width:100%;height:auto}.img-rounded{border-radius:6px}.img-thumbnail{display:inline-block;max-width:100%;height:auto;padding:4px;line-height:1.42857143;background-color:#fff;border:1px solid #ddd;border-radius:4px;-webkit-transition:all .2s ease-in-out;-o-transition:all .2s ease-in-out;transition:all .2s ease-in-out}.img-circle{border-radius:50%}hr{margin-top:20px;margin-bottom:20px;border:0;border-top:1px solid #eee}.sr-only{position:absolute;width:1px;height:1px;padding:0;margin:-1px;overflow:hidden;clip:rect(0,0,0,0);border:0}.sr-only-focusable:active,.sr-only-focusable:focus{position:static;width:auto;height:auto;margin:0;overflow:visible;clip:auto}[role=button]{cursor:pointer}.h1,.h2,.h3,.h4,.h5,.h6,h1,h2,h3,h4,h5,h6{font-family:inherit;font-weight:500;line-height:1.1;color:inherit}.h1 .small,.h1 small,.h2 .small,.h2 small,.h3 .small,.h3 small,.h4 .small,.h4 small,.h5 .small,.h5 small,.h6 .small,.h6 small,h1 .small,h1 small,h2 .small,h2 small,h3 .small,h3 small,h4 .small,h4 small,h5 .small,h5 small,h6 .small,h6 small{font-weight:400;line-height:1;color:#777}.h1,.h2,.h3,h1,h2,h3{margin-top:20px;margin-bottom:10px}.h1 .small,.h1 small,.h2 .small,.h2 small,.h3 .small,.h3 small,h1 .small,h1 small,h2 .small,h2 small,h3 .small,h3 small{font-size:65%}.h4,.h5,.h6,h4,h5,h6{margin-top:10px;margin-bottom:10px}.h4 .small,.h4 small,.h5 .small,.h5 small,.h6 .small,.h6 small,h4 .small,h4 small,h5 .small,h5 small,h6 .small,h6 small{font-size:75%}.h1,h1{font-size:36px}.h2,h2{font-size:30px}.h3,h3{font-size:24px}.h4,h4{font-size:18px}.h5,h5{font-size:14px}.h6,h6{font-size:12px}p{margin:0 0 10px}.lead{margin-bottom:20px;font-size:16px;font-weight:300;line-height:1.4}@media (min-width:768px){.lead{font-size:21px}}.small,small{font-size:85%}.mark,mark{padding:.2em;background-color:#fcf8e3}.text-left{text-align:left}.text-right{text-align:right}.text-center{text-align:center}.text-justify{text-align:justify}.text-nowrap{white-space:nowrap}.text-lowercase{text-transform:lowercase}.text-uppercase{text-transform:uppercase}.text-capitalize{text-transform:capitalize}.text-muted{color:#777}.text-primary{color:#337ab7}a.text-primary:focus,a.text-primary:hover{color:#286090}.text-success{color:#3c763d}a.text-success:focus,a.text-success:hover{color:#2b542c}.text-info{color:#31708f}a.text-info:focus,a.text-info:hover{color:#245269}.text-warning{color:#8a6d3b}a.text-warning:focus,a.text-warning:hover{color:#66512c}.text-danger{color:#a94442}a.text-danger:focus,a.text-danger:hover{color:#843534}.bg-primary{color:#fff;background-color:#337ab7}a.bg-primary:focus,a.bg-primary:hover{background-color:#286090}.bg-success{background-color:#dff0d8}a.bg-success:focus,a.bg-success:hover{background-color:#c1e2b3}.bg-info{background-color:#d9edf7}a.bg-info:focus,a.bg-info:hover{background-color:#afd9ee}.bg-warning{background-color:#fcf8e3}a.bg-warning:focus,a.bg-warning:hover{background-color:#f7ecb5}.bg-danger{background-color:#f2dede}a.bg-danger:focus,a.bg-danger:hover{background-color:#e4b9b9}.page-header{padding-bottom:9px;margin:40px 0 20px;border-bottom:1px solid #eee}ol,ul{margin-top:0;margin-bottom:10px}ol ol,ol ul,ul ol,ul ul{margin-bottom:0}.list-unstyled{padding-left:0;list-style:none}.list-inline{padding-left:0;margin-left:-5px;list-style:none}.list-inline>li{display:inline-block;padding-right:5px;padding-left:5px}dl{margin-top:0;margin-bottom:20px}dd,dt{line-height:1.42857143}dt{font-weight:700}dd{margin-left:0}@media (min-width:768px){.dl-horizontal dt{float:left;width:160px;overflow:hidden;clear:left;text-align:right;text-overflow:ellipsis;white-space:nowrap}.dl-horizontal dd{margin-left:180px}}abbr[data-original-title],abbr[title]{cursor:help;border-bottom:1px dotted #777}.initialism{font-size:90%;text-transform:uppercase}blockquote{padding:10px 20px;margin:0 0 20px;font-size:17.5px;border-left:5px solid #eee}blockquote ol:last-child,blockquote p:last-child,blockquote ul:last-child{margin-bottom:0}blockquote .small,blockquote footer,blockquote small{display:block;font-size:80%;line-height:1.42857143;color:#777}blockquote .small:before,blockquote footer:before,blockquote small:before{content:'\2014 \00A0'}.blockquote-reverse,blockquote.pull-right{padding-right:15px;padding-left:0;text-align:right;border-right:5px solid #eee;border-left:0}.blockquote-reverse .small:before,.blockquote-reverse footer:before,.blockquote-reverse small:before,blockquote.pull-right .small:before,blockquote.pull-right footer:before,blockquote.pull-right small:before{content:''}.blockquote-reverse .small:after,.blockquote-reverse footer:after,.blockquote-reverse small:after,blockquote.pull-right .small:after,blockquote.pull-right footer:after,blockquote.pull-right small:after{content:'\00A0 \2014'}address{margin-bottom:20px;font-style:normal;line-height:1.42857143}code,kbd,pre,samp{font-family:monospace}code{padding:2px 4px;font-size:90%;color:#c7254e;background-color:#f9f2f4;border-radius:4px}kbd{padding:2px 4px;font-size:90%;color:#fff;background-color:#333;border-radius:3px;-webkit-box-shadow:inset 0 -1px 0 rgba(0,0,0,.25);box-shadow:inset 0 -1px 0 rgba(0,0,0,.25)}kbd kbd{padding:0;font-size:100%;font-weight:700;-webkit-box-shadow:none;box-shadow:none}pre{display:block;padding:9.5px;margin:0 0 10px;font-size:13px;line-height:1.42857143;color:#333;word-break:break-all;word-wrap:break-word;background-color:#f5f5f5;border:1px solid #ccc;border-radius:4px}pre code{padding:0;font-size:inherit;color:inherit;white-space:pre-wrap;background-color:transparent;border-radius:0}.pre-scrollable{max-height:340px;overflow-y:scroll}.container{padding-right:15px;padding-left:15px;margin-right:auto;margin-left:auto}@media (min-width:768px){.container{width:750px}}@media (min-width:992px){.container{width:970px}}@media (min-width:1200px){.container{width:1170px}}.container-fluid{padding-right:15px;padding-left:15px;margin-right:auto;margin-left:auto}.row{margin-right:-15px;margin-left:-15px}.col-lg-1,.col-lg-10,.col-lg-11,.col-lg-12,.col-lg-2,.col-lg-3,.col-lg-4,.col-lg-5,.col-lg-6,.col-lg-7,.col-lg-8,.col-lg-9,.col-md-1,.col-md-10,.col-md-11,.col-md-12,.col-md-2,.col-md-3,.col-md-4,.col-md-5,.col-md-6,.col-md-7,.col-md-8,.col-md-9,.col-sm-1,.col-sm-10,.col-sm-11,.col-sm-12,.col-sm-2,.col-sm-3,.col-sm-4,.col-sm-5,.col-sm-6,.col-sm-7,.col-sm-8,.col-sm-9,.col-xs-1,.col-xs-10,.col-xs-11,.col-xs-12,.col-xs-2,.col-xs-3,.col-xs-4,.col-xs-5,.col-xs-6,.col-xs-7,.col-xs-8,.col-xs-9{position:relative;min-height:1px;padding-right:15px;padding-left:15px}.col-xs-1,.col-xs-10,.col-xs-11,.col-xs-12,.col-xs-2,.col-xs-3,.col-xs-4,.col-xs-5,.col-xs-6,.col-xs-7,.col-xs-8,.col-xs-9{float:left}.col-xs-12{width:100%}.col-xs-11{width:91.66666667%}.col-xs-10{width:83.33333333%}.col-xs-9{width:75%}.col-xs-8{width:66.66666667%}.col-xs-7{width:58.33333333%}.col-xs-6{width:50%}.col-xs-5{width:41.66666667%}.col-xs-4{width:33.33333333%}.col-xs-3{width:25%}.col-xs-2{width:16.66666667%}.col-xs-1{width:8.33333333%}.col-xs-pull-12{right:100%}.col-xs-pull-11{right:91.66666667%}.col-xs-pull-10{right:83.33333333%}.col-xs-pull-9{right:75%}.col-xs-pull-8{right:66.66666667%}.col-xs-pull-7{right:58.33333333%}.col-xs-pull-6{right:50%}.col-xs-pull-5{right:41.66666667%}.col-xs-pull-4{right:33.33333333%}.col-xs-pull-3{right:25%}.col-xs-pull-2{right:16.66666667%}.col-xs-pull-1{right:8.33333333%}.col-xs-pull-0{right:auto}.col-xs-push-12{left:100%}.col-xs-push-11{left:91.66666667%}.col-xs-push-10{left:83.33333333%}.col-xs-push-9{left:75%}.col-xs-push-8{left:66.66666667%}.col-xs-push-7{left:58.33333333%}.col-xs-push-6{left:50%}.col-xs-push-5{left:41.66666667%}.col-xs-push-4{left:33.33333333%}.col-xs-push-3{left:25%}.col-xs-push-2{left:16.66666667%}.col-xs-push-1{left:8.33333333%}.col-xs-push-0{left:auto}.col-xs-offset-12{margin-left:100%}.col-xs-offset-11{margin-left:91.66666667%}.col-xs-offset-10{margin-left:83.33333333%}.col-xs-offset-9{margin-left:75%}.col-xs-offset-8{margin-left:66.66666667%}.col-xs-offset-7{margin-left:58.33333333%}.col-xs-offset-6{margin-left:50%}.col-xs-offset-5{margin-left:41.66666667%}.col-xs-offset-4{margin-left:33.33333333%}.col-xs-offset-3{margin-left:25%}.col-xs-offset-2{margin-left:16.66666667%}.col-xs-offset-1{margin-left:8.33333333%}.col-xs-offset-0{margin-left:0}@media (min-width:768px){.col-sm-1,.col-sm-10,.col-sm-11,.col-sm-12,.col-sm-2,.col-sm-3,.col-sm-4,.col-sm-5,.col-sm-6,.col-sm-7,.col-sm-8,.col-sm-9{float:left}.col-sm-12{width:100%}.col-sm-11{width:91.66666667%}.col-sm-10{width:83.33333333%}.col-sm-9{width:75%}.col-sm-8{width:66.66666667%}.col-sm-7{width:58.33333333%}.col-sm-6{width:50%}.col-sm-5{width:41.66666667%}.col-sm-4{width:33.33333333%}.col-sm-3{width:25%}.col-sm-2{width:16.66666667%}.col-sm-1{width:8.33333333%}.col-sm-pull-12{right:100%}.col-sm-pull-11{right:91.66666667%}.col-sm-pull-10{right:83.33333333%}.col-sm-pull-9{right:75%}.col-sm-pull-8{right:66.66666667%}.col-sm-pull-7{right:58.33333333%}.col-sm-pull-6{right:50%}.col-sm-pull-5{right:41.66666667%}.col-sm-pull-4{right:33.33333333%}.col-sm-pull-3{right:25%}.col-sm-pull-2{right:16.66666667%}.col-sm-pull-1{right:8.33333333%}.col-sm-pull-0{right:auto}.col-sm-push-12{left:100%}.col-sm-push-11{left:91.66666667%}.col-sm-push-10{left:83.33333333%}.col-sm-push-9{left:75%}.col-sm-push-8{left:66.66666667%}.col-sm-push-7{left:58.33333333%}.col-sm-push-6{left:50%}.col-sm-push-5{left:41.66666667%}.col-sm-push-4{left:33.33333333%}.col-sm-push-3{left:25%}.col-sm-push-2{left:16.66666667%}.col-sm-push-1{left:8.33333333%}.col-sm-push-0{left:auto}.col-sm-offset-12{margin-left:100%}.col-sm-offset-11{margin-left:91.66666667%}.col-sm-offset-10{margin-left:83.33333333%}.col-sm-offset-9{margin-left:75%}.col-sm-offset-8{margin-left:66.66666667%}.col-sm-offset-7{margin-left:58.33333333%}.col-sm-offset-6{margin-left:50%}.col-sm-offset-5{margin-left:41.66666667%}.col-sm-offset-4{margin-left:33.33333333%}.col-sm-offset-3{margin-left:25%}.col-sm-offset-2{margin-left:16.66666667%}.col-sm-offset-1{margin-left:8.33333333%}.col-sm-offset-0{margin-left:0}}@media (min-width:992px){.col-md-1,.col-md-10,.col-md-11,.col-md-12,.col-md-2,.col-md-3,.col-md-4,.col-md-5,.col-md-6,.col-md-7,.col-md-8,.col-md-9{float:left}.col-md-12{width:100%}.col-md-11{width:91.66666667%}.col-md-10{width:83.33333333%}.col-md-9{width:75%}.col-md-8{width:66.66666667%}.col-md-7{width:58.33333333%}.col-md-6{width:50%}.col-md-5{width:41.66666667%}.col-md-4{width:33.33333333%}.col-md-3{width:25%}.col-md-2{width:16.66666667%}.col-md-1{width:8.33333333%}.col-md-pull-12{right:100%}.col-md-pull-11{right:91.66666667%}.col-md-pull-10{right:83.33333333%}.col-md-pull-9{right:75%}.col-md-pull-8{right:66.66666667%}.col-md-pull-7{right:58.33333333%}.col-md-pull-6{right:50%}.col-md-pull-5{right:41.66666667%}.col-md-pull-4{right:33.33333333%}.col-md-pull-3{right:25%}.col-md-pull-2{right:16.66666667%}.col-md-pull-1{right:8.33333333%}.col-md-pull-0{right:auto}.col-md-push-12{left:100%}.col-md-push-11{left:91.66666667%}.col-md-push-10{left:83.33333333%}.col-md-push-9{left:75%}.col-md-push-8{left:66.66666667%}.col-md-push-7{left:58.33333333%}.col-md-push-6{left:50%}.col-md-push-5{left:41.66666667%}.col-md-push-4{left:33.33333333%}.col-md-push-3{left:25%}.col-md-push-2{left:16.66666667%}.col-md-push-1{left:8.33333333%}.col-md-push-0{left:auto}.col-md-offset-12{margin-left:100%}.col-md-offset-11{margin-left:91.66666667%}.col-md-offset-10{margin-left:83.33333333%}.col-md-offset-9{margin-left:75%}.col-md-offset-8{margin-left:66.66666667%}.col-md-offset-7{margin-left:58.33333333%}.col-md-offset-6{margin-left:50%}.col-md-offset-5{margin-left:41.66666667%}.col-md-offset-4{margin-left:33.33333333%}.col-md-offset-3{margin-left:25%}.col-md-offset-2{margin-left:16.66666667%}.col-md-offset-1{margin-left:8.33333333%}.col-md-offset-0{margin-left:0}}@media (min-width:1200px){.col-lg-1,.col-lg-10,.col-lg-11,.col-lg-12,.col-lg-2,.col-lg-3,.col-lg-4,.col-lg-5,.col-lg-6,.col-lg-7,.col-lg-8,.col-lg-9{float:left}.col-lg-12{width:100%}.col-lg-11{width:91.66666667%}.col-lg-10{width:83.33333333%}.col-lg-9{width:75%}.col-lg-8{width:66.66666667%}.col-lg-7{width:58.33333333%}.col-lg-6{width:50%}.col-lg-5{width:41.66666667%}.col-lg-4{width:33.33333333%}.col-lg-3{width:25%}.col-lg-2{width:16.66666667%}.col-lg-1{width:8.33333333%}.col-lg-pull-12{right:100%}.col-lg-pull-11{right:91.66666667%}.col-lg-pull-10{right:83.33333333%}.col-lg-pull-9{right:75%}.col-lg-pull-8{right:66.66666667%}.col-lg-pull-7{right:58.33333333%}.col-lg-pull-6{right:50%}.col-lg-pull-5{right:41.66666667%}.col-lg-pull-4{right:33.33333333%}.col-lg-pull-3{right:25%}.col-lg-pull-2{right:16.66666667%}.col-lg-pull-1{right:8.33333333%}.col-lg-pull-0{right:auto}.col-lg-push-12{left:100%}.col-lg-push-11{left:91.66666667%}.col-lg-push-10{left:83.33333333%}.col-lg-push-9{left:75%}.col-lg-push-8{left:66.66666667%}.col-lg-push-7{left:58.33333333%}.col-lg-push-6{left:50%}.col-lg-push-5{left:41.66666667%}.col-lg-push-4{left:33.33333333%}.col-lg-push-3{left:25%}.col-lg-push-2{left:16.66666667%}.col-lg-push-1{left:8.33333333%}.col-lg-push-0{left:auto}.col-lg-offset-12{margin-left:100%}.col-lg-offset-11{margin-left:91.66666667%}.col-lg-offset-10{margin-left:83.33333333%}.col-lg-offset-9{margin-left:75%}.col-lg-offset-8{margin-left:66.66666667%}.col-lg-offset-7{margin-left:58.33333333%}.col-lg-offset-6{margin-left:50%}.col-lg-offset-5{margin-left:41.66666667%}.col-lg-offset-4{margin-left:33.33333333%}.col-lg-offset-3{margin-left:25%}.col-lg-offset-2{margin-left:16.66666667%}.col-lg-offset-1{margin-left:8.33333333%}.col-lg-offset-0{margin-left:0}}table{background-color:transparent}caption{padding-top:8px;padding-bottom:8px;color:#777;text-align:left}th{}.table{width:100%;max-width:100%;margin-bottom:20px}.table>tbody>tr>td,.table>tbody>tr>th,.table>tfoot>tr>td,.table>tfoot>tr>th,.table>thead>tr>td,.table>thead>tr>th{padding:8px;line-height:1.42857143;vertical-align:top;border-top:1px solid #ddd}.table>thead>tr>th{vertical-align:bottom;border-bottom:2px solid #ddd}.table>caption+thead>tr:first-child>td,.table>caption+thead>tr:first-child>th,.table>colgroup+thead>tr:first-child>td,.table>colgroup+thead>tr:first-child>th,.table>thead:first-child>tr:first-child>td,.table>thead:first-child>tr:first-child>th{border-top:0}.table>tbody+tbody{border-top:2px solid #ddd}.table .table{background-color:#fff}.table-condensed>tbody>tr>td,.table-condensed>tbody>tr>th,.table-condensed>tfoot>tr>td,.table-condensed>tfoot>tr>th,.table-condensed>thead>tr>td,.table-condensed>thead>tr>th{padding:5px}.table-bordered{border:1px solid #ddd}.table-bordered>tbody>tr>td,.table-bordered>tbody>tr>th,.table-bordered>tfoot>tr>td,.table-bordered>tfoot>tr>th,.table-bordered>thead>tr>td,.table-bordered>thead>tr>th{border:1px solid #ddd}.table-bordered>thead>tr>td,.table-bordered>thead>tr>th{border-bottom-width:2px}.table-striped>tbody>tr:nth-of-type(odd){background-color:#f9f9f9}.table-hover>tbody>tr:hover{background-color:#f5f5f5}table col[class*=col-]{position:static;display:table-column;float:none}table td[class*=col-],table th[class*=col-]{position:static;display:table-cell;float:none}.table>tbody>tr.active>td,.table>tbody>tr.active>th,.table>tbody>tr>td.active,.table>tbody>tr>th.active,.table>tfoot>tr.active>td,.table>tfoot>tr.active>th,.table>tfoot>tr>td.active,.table>tfoot>tr>th.active,.table>thead>tr.active>td,.table>thead>tr.active>th,.table>thead>tr>td.active,.table>thead>tr>th.active{background-color:#f5f5f5}.table-hover>tbody>tr.active:hover>td,.table-hover>tbody>tr.active:hover>th,.table-hover>tbody>tr:hover>.active,.table-hover>tbody>tr>td.active:hover,.table-hover>tbody>tr>th.active:hover{background-color:#e8e8e8}.table>tbody>tr.success>td,.table>tbody>tr.success>th,.table>tbody>tr>td.success,.table>tbody>tr>th.success,.table>tfoot>tr.success>td,.table>tfoot>tr.success>th,.table>tfoot>tr>td.success,.table>tfoot>tr>th.success,.table>thead>tr.success>td,.table>thead>tr.success>th,.table>thead>tr>td.success,.table>thead>tr>th.success{background-color:#dff0d8}.table-hover>tbody>tr.success:hover>td,.table-hover>tbody>tr.success:hover>th,.table-hover>tbody>tr:hover>.success,.table-hover>tbody>tr>td.success:hover,.table-hover>tbody>tr>th.success:hover{background-color:#d0e9c6}.table>tbody>tr.info>td,.table>tbody>tr.info>th,.table>tbody>tr>td.info,.table>tbody>tr>th.info,.table>tfoot>tr.info>td,.table>tfoot>tr.info>th,.table>tfoot>tr>td.info,.table>tfoot>tr>th.info,.table>thead>tr.info>td,.table>thead>tr.info>th,.table>thead>tr>td.info,.table>thead>tr>th.info{background-color:#d9edf7}.table-hover>tbody>tr.info:hover>td,.table-hover>tbody>tr.info:hover>th,.table-hover>tbody>tr:hover>.info,.table-hover>tbody>tr>td.info:hover,.table-hover>tbody>tr>th.info:hover{background-color:#c4e3f3}.table>tbody>tr.warning>td,.table>tbody>tr.warning>th,.table>tbody>tr>td.warning,.table>tbody>tr>th.warning,.table>tfoot>tr.warning>td,.table>tfoot>tr.warning>th,.table>tfoot>tr>td.warning,.table>tfoot>tr>th.warning,.table>thead>tr.warning>td,.table>thead>tr.warning>th,.table>thead>tr>td.warning,.table>thead>tr>th.warning{background-color:#fcf8e3}.table-hover>tbody>tr.warning:hover>td,.table-hover>tbody>tr.warning:hover>th,.table-hover>tbody>tr:hover>.warning,.table-hover>tbody>tr>td.warning:hover,.table-hover>tbody>tr>th.warning:hover{background-color:#faf2cc}.table>tbody>tr.danger>td,.table>tbody>tr.danger>th,.table>tbody>tr>td.danger,.table>tbody>tr>th.danger,.table>tfoot>tr.danger>td,.table>tfoot>tr.danger>th,.table>tfoot>tr>td.danger,.table>tfoot>tr>th.danger,.table>thead>tr.danger>td,.table>thead>tr.danger>th,.table>thead>tr>td.danger,.table>thead>tr>th.danger{background-color:#f2dede}.table-hover>tbody>tr.danger:hover>td,.table-hover>tbody>tr.danger:hover>th,.table-hover>tbody>tr:hover>.danger,.table-hover>tbody>tr>td.danger:hover,.table-hover>tbody>tr>th.danger:hover{background-color:#ebcccc}.table-responsive{min-height:.01%;overflow-x:auto}@media screen and (max-width:767px){.table-responsive{width:100%;margin-bottom:15px;overflow-y:hidden;-ms-overflow-style:-ms-autohiding-scrollbar;border:1px solid #ddd}.table-responsive>.table{margin-bottom:0}.table-responsive>.table>tbody>tr>td,.table-responsive>.table>tbody>tr>th,.table-responsive>.table>tfoot>tr>td,.table-responsive>.table>tfoot>tr>th,.table-responsive>.table>thead>tr>td,.table-responsive>.table>thead>tr>th{white-space:nowrap}.table-responsive>.table-bordered{border:0}.table-responsive>.table-bordered>tbody>tr>td:first-child,.table-responsive>.table-bordered>tbody>tr>th:first-child,.table-responsive>.table-bordered>tfoot>tr>td:first-child,.table-responsive>.table-bordered>tfoot>tr>th:first-child,.table-responsive>.table-bordered>thead>tr>td:first-child,.table-responsive>.table-bordered>thead>tr>th:first-child{border-left:0}.table-responsive>.table-bordered>tbody>tr>td:last-child,.table-responsive>.table-bordered>tbody>tr>th:last-child,.table-responsive>.table-bordered>tfoot>tr>td:last-child,.table-responsive>.table-bordered>tfoot>tr>th:last-child,.table-responsive>.table-bordered>thead>tr>td:last-child,.table-responsive>.table-bordered>thead>tr>th:last-child{border-right:0}.table-responsive>.table-bordered>tbody>tr:last-child>td,.table-responsive>.table-bordered>tbody>tr:last-child>th,.table-responsive>.table-bordered>tfoot>tr:last-child>td,.table-responsive>.table-bordered>tfoot>tr:last-child>th{border-bottom:0}}fieldset{min-width:0;padding:0;margin:0;border:0}legend{display:block;width:100%;padding:0;margin-bottom:20px;font-size:21px;line-height:inherit;color:#333;border:0;border-bottom:1px solid #e5e5e5}label{display:inline-block;max-width:100%;margin-bottom:5px;font-weight:700}input[type=search]{-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}input[type=checkbox],input[type=radio]{margin:4px 0 0;margin-top:1px\9;line-height:normal}input[type=file]{display:block}input[type=range]{display:block;width:100%}select[multiple],select[size]{height:auto}input[type=file]:focus,input[type=checkbox]:focus,input[type=radio]:focus{outline:thin dotted;outline:5px auto -webkit-focus-ring-color;outline-offset:-2px}output{display:block;padding-top:7px;font-size:14px;line-height:1.42857143;color:#555}.form-control{display:block;width:100%;height:34px;padding:6px 12px;font-size:14px;line-height:1.42857143;color:#555;background-color:#fff;background-image:none;border:1px solid #ccc;border-radius:4px;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075);box-shadow:inset 0 1px 1px rgba(0,0,0,.075);-webkit-transition:border-color ease-in-out .15s,-webkit-box-shadow ease-in-out .15s;-o-transition:border-color ease-in-out .15s,box-shadow ease-in-out .15s;transition:border-color ease-in-out .15s,box-shadow ease-in-out .15s}.form-control:focus{border-color:#66afe9;outline:0;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 8px rgba(102,175,233,.6);box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 8px rgba(102,175,233,.6)}.form-control::-moz-placeholder{color:#999;opacity:1}.form-control:-ms-input-placeholder{color:#999}.form-control::-webkit-input-placeholder{color:#999}.form-control[disabled],.form-control[readonly],fieldset[disabled] .form-control{background-color:#eee;opacity:1}.form-control[disabled],fieldset[disabled] .form-control{cursor:not-allowed}textarea.form-control{height:auto}input[type=search]{-webkit-appearance:none}@media screen and (-webkit-min-device-pixel-ratio:0){input[type=date].form-control,input[type=time].form-control,input[type=datetime-local].form-control,input[type=month].form-control{line-height:34px}.input-group-sm input[type=date],.input-group-sm input[type=time],.input-group-sm input[type=datetime-local],.input-group-sm input[type=month],input[type=date].input-sm,input[type=time].input-sm,input[type=datetime-local].input-sm,input[type=month].input-sm{line-height:30px}.input-group-lg input[type=date],.input-group-lg input[type=time],.input-group-lg input[type=datetime-local],.input-group-lg input[type=month],input[type=date].input-lg,input[type=time].input-lg,input[type=datetime-local].input-lg,input[type=month].input-lg{line-height:46px}}.form-group{margin-bottom:15px}.checkbox,.radio{position:relative;display:block;margin-top:10px;margin-bottom:10px}.checkbox label,.radio label{min-height:20px;padding-left:20px;margin-bottom:0;font-weight:400;cursor:pointer}.checkbox input[type=checkbox],.checkbox-inline input[type=checkbox],.radio input[type=radio],.radio-inline input[type=radio]{position:absolute;margin-top:4px\9;margin-left:-20px}.checkbox+.checkbox,.radio+.radio{margin-top:-5px}.checkbox-inline,.radio-inline{position:relative;display:inline-block;padding-left:20px;margin-bottom:0;font-weight:400;vertical-align:middle;cursor:pointer}.checkbox-inline+.checkbox-inline,.radio-inline+.radio-inline{margin-top:0;margin-left:10px}fieldset[disabled] input[type=checkbox],fieldset[disabled] input[type=radio],input[type=checkbox].disabled,input[type=checkbox][disabled],input[type=radio].disabled,input[type=radio][disabled]{cursor:not-allowed}.checkbox-inline.disabled,.radio-inline.disabled,fieldset[disabled] .checkbox-inline,fieldset[disabled] .radio-inline{cursor:not-allowed}.checkbox.disabled label,.radio.disabled label,fieldset[disabled] .checkbox label,fieldset[disabled] .radio label{cursor:not-allowed}.form-control-static{min-height:34px;padding-top:7px;padding-bottom:7px;margin-bottom:0}.form-control-static.input-lg,.form-control-static.input-sm{padding-right:0;padding-left:0}.input-sm{height:30px;padding:5px 10px;font-size:12px;line-height:1.5;border-radius:3px}select.input-sm{height:30px;line-height:30px}select[multiple].input-sm,textarea.input-sm{height:auto}.form-group-sm .form-control{height:30px;padding:5px 10px;font-size:12px;line-height:1.5;border-radius:3px}.form-group-sm select.form-control{height:30px;line-height:30px}.form-group-sm select[multiple].form-control,.form-group-sm textarea.form-control{height:auto}.form-group-sm .form-control-static{height:30px;min-height:32px;padding:6px 10px;font-size:12px;line-height:1.5}.input-lg{height:46px;padding:10px 16px;font-size:18px;line-height:1.3333333;border-radius:6px}select.input-lg{height:46px;line-height:46px}select[multiple].input-lg,textarea.input-lg{height:auto}.form-group-lg .form-control{height:46px;padding:10px 16px;font-size:18px;line-height:1.3333333;border-radius:6px}.form-group-lg select.form-control{height:46px;line-height:46px}.form-group-lg select[multiple].form-control,.form-group-lg textarea.form-control{height:auto}.form-group-lg .form-control-static{height:46px;min-height:38px;padding:11px 16px;font-size:18px;line-height:1.3333333}.has-feedback{position:relative}.has-feedback .form-control{padding-right:42.5px}.form-control-feedback{position:absolute;top:0;right:0;z-index:2;display:block;width:34px;height:34px;line-height:34px;text-align:center;pointer-events:none}.form-group-lg .form-control+.form-control-feedback,.input-group-lg+.form-control-feedback,.input-lg+.form-control-feedback{width:46px;height:46px;line-height:46px}.form-group-sm .form-control+.form-control-feedback,.input-group-sm+.form-control-feedback,.input-sm+.form-control-feedback{width:30px;height:30px;line-height:30px}.has-success .checkbox,.has-success .checkbox-inline,.has-success .control-label,.has-success .help-block,.has-success .radio,.has-success .radio-inline,.has-success.checkbox label,.has-success.checkbox-inline label,.has-success.radio label,.has-success.radio-inline label{color:#3c763d}.has-success .form-control{border-color:#3c763d;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075);box-shadow:inset 0 1px 1px rgba(0,0,0,.075)}.has-success .form-control:focus{border-color:#2b542c;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 6px #67b168;box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 6px #67b168}.has-success .input-group-addon{color:#3c763d;background-color:#dff0d8;border-color:#3c763d}.has-success .form-control-feedback{color:#3c763d}.has-warning .checkbox,.has-warning .checkbox-inline,.has-warning .control-label,.has-warning .help-block,.has-warning .radio,.has-warning .radio-inline,.has-warning.checkbox label,.has-warning.checkbox-inline label,.has-warning.radio label,.has-warning.radio-inline label{color:#8a6d3b}.has-warning .form-control{border-color:#8a6d3b;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075);box-shadow:inset 0 1px 1px rgba(0,0,0,.075)}.has-warning .form-control:focus{border-color:#66512c;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 6px #c0a16b;box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 6px #c0a16b}.has-warning .input-group-addon{color:#8a6d3b;background-color:#fcf8e3;border-color:#8a6d3b}.has-warning .form-control-feedback{color:#8a6d3b}.has-error .checkbox,.has-error .checkbox-inline,.has-error .control-label,.has-error .help-block,.has-error .radio,.has-error .radio-inline,.has-error.checkbox label,.has-error.checkbox-inline label,.has-error.radio label,.has-error.radio-inline label{color:#a94442}.has-error .form-control{border-color:#a94442;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075);box-shadow:inset 0 1px 1px rgba(0,0,0,.075)}.has-error .form-control:focus{border-color:#843534;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 6px #ce8483;box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 6px #ce8483}.has-error .input-group-addon{color:#a94442;background-color:#f2dede;border-color:#a94442}.has-error .form-control-feedback{color:#a94442}.has-feedback label~.form-control-feedback{top:25px}.has-feedback label.sr-only~.form-control-feedback{top:0}.help-block{display:block;margin-top:5px;margin-bottom:10px;color:#737373}@media (min-width:768px){.form-inline .form-group{display:inline-block;margin-bottom:0;vertical-align:middle}.form-inline .form-control{display:inline-block;width:auto;vertical-align:middle}.form-inline .form-control-static{display:inline-block}.form-inline .input-group{display:inline-table;vertical-align:middle}.form-inline .input-group .form-control,.form-inline .input-group .input-group-addon,.form-inline .input-group .input-group-btn{width:auto}.form-inline .input-group>.form-control{width:100%}.form-inline .control-label{margin-bottom:0;vertical-align:middle}.form-inline .checkbox,.form-inline .radio{display:inline-block;margin-top:0;margin-bottom:0;vertical-align:middle}.form-inline .checkbox label,.form-inline .radio label{padding-left:0}.form-inline .checkbox input[type=checkbox],.form-inline .radio input[type=radio]{position:relative;margin-left:0}.form-inline .has-feedback .form-control-feedback{top:0}}.form-horizontal .checkbox,.form-horizontal .checkbox-inline,.form-horizontal .radio,.form-horizontal .radio-inline{padding-top:7px;margin-top:0;margin-bottom:0}.form-horizontal .checkbox,.form-horizontal .radio{min-height:27px}.form-horizontal .form-group{margin-right:-15px;margin-left:-15px}@media (min-width:768px){.form-horizontal .control-label{padding-top:7px;margin-bottom:0;text-align:right}}.form-horizontal .has-feedback .form-control-feedback{right:15px}@media (min-width:768px){.form-horizontal .form-group-lg .control-label{padding-top:14.33px;font-size:18px}}@media (min-width:768px){.form-horizontal .form-group-sm .control-label{padding-top:6px;font-size:12px}}.btn{display:inline-block;padding:6px 12px;margin-bottom:0;font-size:14px;font-weight:400;line-height:1.42857143;text-align:center;white-space:nowrap;vertical-align:middle;-ms-touch-action:manipulation;touch-action:manipulation;cursor:pointer;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;background-image:none;border:1px solid transparent;border-radius:4px}.btn.active.focus,.btn.active:focus,.btn.focus,.btn:active.focus,.btn:active:focus,.btn:focus{outline:thin dotted;outline:5px auto -webkit-focus-ring-color;outline-offset:-2px}.btn.focus,.btn:focus,.btn:hover{color:#333;text-decoration:none}.btn.active,.btn:active{background-image:none;outline:0;-webkit-box-shadow:inset 0 3px 5px rgba(0,0,0,.125);box-shadow:inset 0 3px 5px rgba(0,0,0,.125)}.btn.disabled,.btn[disabled],fieldset[disabled] .btn{cursor:not-allowed;filter:alpha(opacity=65);-webkit-box-shadow:none;box-shadow:none;opacity:.65}a.btn.disabled,fieldset[disabled] a.btn{pointer-events:none}.btn-default{color:#333;background-color:#fff;border-color:#ccc}.btn-default.focus,.btn-default:focus{color:#333;background-color:#e6e6e6;border-color:#8c8c8c}.btn-default:hover{color:#333;background-color:#e6e6e6;border-color:#adadad}.btn-default.active,.btn-default:active,.open>.dropdown-toggle.btn-default{color:#333;background-color:#e6e6e6;border-color:#adadad}.btn-default.active.focus,.btn-default.active:focus,.btn-default.active:hover,.btn-default:active.focus,.btn-default:active:focus,.btn-default:active:hover,.open>.dropdown-toggle.btn-default.focus,.open>.dropdown-toggle.btn-default:focus,.open>.dropdown-toggle.btn-default:hover{color:#333;background-color:#d4d4d4;border-color:#8c8c8c}.btn-default.active,.btn-default:active,.open>.dropdown-toggle.btn-default{background-image:none}.btn-default.disabled,.btn-default.disabled.active,.btn-default.disabled.focus,.btn-default.disabled:active,.btn-default.disabled:focus,.btn-default.disabled:hover,.btn-default[disabled],.btn-default[disabled].active,.btn-default[disabled].focus,.btn-default[disabled]:active,.btn-default[disabled]:focus,.btn-default[disabled]:hover,fieldset[disabled] .btn-default,fieldset[disabled] .btn-default.active,fieldset[disabled] .btn-default.focus,fieldset[disabled] .btn-default:active,fieldset[disabled] .btn-default:focus,fieldset[disabled] .btn-default:hover{background-color:#fff;border-color:#ccc}.btn-default .badge{color:#fff;background-color:#333}.btn-primary{color:#fff;background-color:#337ab7;border-color:#2e6da4}.btn-primary.focus,.btn-primary:focus{color:#fff;background-color:#286090;border-color:#122b40}.btn-primary:hover{color:#fff;background-color:#286090;border-color:#204d74}.btn-primary.active,.btn-primary:active,.open>.dropdown-toggle.btn-primary{color:#fff;background-color:#286090;border-color:#204d74}.btn-primary.active.focus,.btn-primary.active:focus,.btn-primary.active:hover,.btn-primary:active.focus,.btn-primary:active:focus,.btn-primary:active:hover,.open>.dropdown-toggle.btn-primary.focus,.open>.dropdown-toggle.btn-primary:focus,.open>.dropdown-toggle.btn-primary:hover{color:#fff;background-color:#204d74;border-color:#122b40}.btn-primary.active,.btn-primary:active,.open>.dropdown-toggle.btn-primary{background-image:none}.btn-primary.disabled,.btn-primary.disabled.active,.btn-primary.disabled.focus,.btn-primary.disabled:active,.btn-primary.disabled:focus,.btn-primary.disabled:hover,.btn-primary[disabled],.btn-primary[disabled].active,.btn-primary[disabled].focus,.btn-primary[disabled]:active,.btn-primary[disabled]:focus,.btn-primary[disabled]:hover,fieldset[disabled] .btn-primary,fieldset[disabled] .btn-primary.active,fieldset[disabled] .btn-primary.focus,fieldset[disabled] .btn-primary:active,fieldset[disabled] .btn-primary:focus,fieldset[disabled] .btn-primary:hover{background-color:#337ab7;border-color:#2e6da4}.btn-primary .badge{color:#337ab7;background-color:#fff}.btn-success{color:#fff;background-color:#5cb85c;border-color:#4cae4c}.btn-success.focus,.btn-success:focus{color:#fff;background-color:#449d44;border-color:#255625}.btn-success:hover{color:#fff;background-color:#449d44;border-color:#398439}.btn-success.active,.btn-success:active,.open>.dropdown-toggle.btn-success{color:#fff;background-color:#449d44;border-color:#398439}.btn-success.active.focus,.btn-success.active:focus,.btn-success.active:hover,.btn-success:active.focus,.btn-success:active:focus,.btn-success:active:hover,.open>.dropdown-toggle.btn-success.focus,.open>.dropdown-toggle.btn-success:focus,.open>.dropdown-toggle.btn-success:hover{color:#fff;background-color:#398439;border-color:#255625}.btn-success.active,.btn-success:active,.open>.dropdown-toggle.btn-success{background-image:none}.btn-success.disabled,.btn-success.disabled.active,.btn-success.disabled.focus,.btn-success.disabled:active,.btn-success.disabled:focus,.btn-success.disabled:hover,.btn-success[disabled],.btn-success[disabled].active,.btn-success[disabled].focus,.btn-success[disabled]:active,.btn-success[disabled]:focus,.btn-success[disabled]:hover,fieldset[disabled] .btn-success,fieldset[disabled] .btn-success.active,fieldset[disabled] .btn-success.focus,fieldset[disabled] .btn-success:active,fieldset[disabled] .btn-success:focus,fieldset[disabled] .btn-success:hover{background-color:#5cb85c;border-color:#4cae4c}.btn-success .badge{color:#5cb85c;background-color:#fff}.btn-info{color:#fff;background-color:#5bc0de;border-color:#46b8da}.btn-info.focus,.btn-info:focus{color:#fff;background-color:#31b0d5;border-color:#1b6d85}.btn-info:hover{color:#fff;background-color:#31b0d5;border-color:#269abc}.btn-info.active,.btn-info:active,.open>.dropdown-toggle.btn-info{color:#fff;background-color:#31b0d5;border-color:#269abc}.btn-info.active.focus,.btn-info.active:focus,.btn-info.active:hover,.btn-info:active.focus,.btn-info:active:focus,.btn-info:active:hover,.open>.dropdown-toggle.btn-info.focus,.open>.dropdown-toggle.btn-info:focus,.open>.dropdown-toggle.btn-info:hover{color:#fff;background-color:#269abc;border-color:#1b6d85}.btn-info.active,.btn-info:active,.open>.dropdown-toggle.btn-info{background-image:none}.btn-info.disabled,.btn-info.disabled.active,.btn-info.disabled.focus,.btn-info.disabled:active,.btn-info.disabled:focus,.btn-info.disabled:hover,.btn-info[disabled],.btn-info[disabled].active,.btn-info[disabled].focus,.btn-info[disabled]:active,.btn-info[disabled]:focus,.btn-info[disabled]:hover,fieldset[disabled] .btn-info,fieldset[disabled] .btn-info.active,fieldset[disabled] .btn-info.focus,fieldset[disabled] .btn-info:active,fieldset[disabled] .btn-info:focus,fieldset[disabled] .btn-info:hover{background-color:#5bc0de;border-color:#46b8da}.btn-info .badge{color:#5bc0de;background-color:#fff}.btn-warning{color:#fff;background-color:#f0ad4e;border-color:#eea236}.btn-warning.focus,.btn-warning:focus{color:#fff;background-color:#ec971f;border-color:#985f0d}.btn-warning:hover{color:#fff;background-color:#ec971f;border-color:#d58512}.btn-warning.active,.btn-warning:active,.open>.dropdown-toggle.btn-warning{color:#fff;background-color:#ec971f;border-color:#d58512}.btn-warning.active.focus,.btn-warning.active:focus,.btn-warning.active:hover,.btn-warning:active.focus,.btn-warning:active:focus,.btn-warning:active:hover,.open>.dropdown-toggle.btn-warning.focus,.open>.dropdown-toggle.btn-warning:focus,.open>.dropdown-toggle.btn-warning:hover{color:#fff;background-color:#d58512;border-color:#985f0d}.btn-warning.active,.btn-warning:active,.open>.dropdown-toggle.btn-warning{background-image:none}.btn-warning.disabled,.btn-warning.disabled.active,.btn-warning.disabled.focus,.btn-warning.disabled:active,.btn-warning.disabled:focus,.btn-warning.disabled:hover,.btn-warning[disabled],.btn-warning[disabled].active,.btn-warning[disabled].focus,.btn-warning[disabled]:active,.btn-warning[disabled]:focus,.btn-warning[disabled]:hover,fieldset[disabled] .btn-warning,fieldset[disabled] .btn-warning.active,fieldset[disabled] .btn-warning.focus,fieldset[disabled] .btn-warning:active,fieldset[disabled] .btn-warning:focus,fieldset[disabled] .btn-warning:hover{background-color:#f0ad4e;border-color:#eea236}.btn-warning .badge{color:#f0ad4e;background-color:#fff}.btn-danger{color:#fff;background-color:#d9534f;border-color:#d43f3a}.btn-danger.focus,.btn-danger:focus{color:#fff;background-color:#c9302c;border-color:#761c19}.btn-danger:hover{color:#fff;background-color:#c9302c;border-color:#ac2925}.btn-danger.active,.btn-danger:active,.open>.dropdown-toggle.btn-danger{color:#fff;background-color:#c9302c;border-color:#ac2925}.btn-danger.active.focus,.btn-danger.active:focus,.btn-danger.active:hover,.btn-danger:active.focus,.btn-danger:active:focus,.btn-danger:active:hover,.open>.dropdown-toggle.btn-danger.focus,.open>.dropdown-toggle.btn-danger:focus,.open>.dropdown-toggle.btn-danger:hover{color:#fff;background-color:#ac2925;border-color:#761c19}.btn-danger.active,.btn-danger:active,.open>.dropdown-toggle.btn-danger{background-image:none}.btn-danger.disabled,.btn-danger.disabled.active,.btn-danger.disabled.focus,.btn-danger.disabled:active,.btn-danger.disabled:focus,.btn-danger.disabled:hover,.btn-danger[disabled],.btn-danger[disabled].active,.btn-danger[disabled].focus,.btn-danger[disabled]:active,.btn-danger[disabled]:focus,.btn-danger[disabled]:hover,fieldset[disabled] .btn-danger,fieldset[disabled] .btn-danger.active,fieldset[disabled] .btn-danger.focus,fieldset[disabled] .btn-danger:active,fieldset[disabled] .btn-danger:focus,fieldset[disabled] .btn-danger:hover{background-color:#d9534f;border-color:#d43f3a}.btn-danger .badge{color:#d9534f;background-color:#fff}.btn-link{font-weight:400;color:#337ab7;border-radius:0}.btn-link,.btn-link.active,.btn-link:active,.btn-link[disabled],fieldset[disabled] .btn-link{background-color:transparent;-webkit-box-shadow:none;box-shadow:none}.btn-link,.btn-link:active,.btn-link:focus,.btn-link:hover{border-color:transparent}.btn-link:focus,.btn-link:hover{color:#23527c;text-decoration:underline;background-color:transparent}.btn-link[disabled]:focus,.btn-link[disabled]:hover,fieldset[disabled] .btn-link:focus,fieldset[disabled] .btn-link:hover{color:#777;text-decoration:none}.btn-group-lg>.btn,.btn-lg{padding:10px 16px;font-size:18px;line-height:1.3333333;border-radius:6px}.btn-group-sm>.btn,.btn-sm{padding:5px 10px;font-size:12px;line-height:1.5;border-radius:3px}.btn-group-xs>.btn,.btn-xs{padding:1px 5px;font-size:12px;line-height:1.5;border-radius:3px}.btn-block{display:block;width:100%}.btn-block+.btn-block{margin-top:5px}input[type=button].btn-block,input[type=reset].btn-block,input[type=submit].btn-block{width:100%}.fade{opacity:0;-webkit-transition:opacity .15s linear;-o-transition:opacity .15s linear;transition:opacity .15s linear}.fade.in{opacity:1}.collapse{display:none}.collapse.in{display:block}tr.collapse.in{display:table-row}tbody.collapse.in{display:table-row-group}.collapsing{position:relative;height:0;overflow:hidden;-webkit-transition-timing-function:ease;-o-transition-timing-function:ease;transition-timing-function:ease;-webkit-transition-duration:.35s;-o-transition-duration:.35s;transition-duration:.35s;-webkit-transition-property:height,visibility;-o-transition-property:height,visibility;transition-property:height,visibility}.caret{display:inline-block;width:0;height:0;margin-left:2px;vertical-align:middle;border-top:4px dashed;border-top:4px solid\9;border-right:4px solid transparent;border-left:4px solid transparent}.dropdown,.dropup{position:relative}.dropdown-toggle:focus{outline:0}.dropdown-menu{position:absolute;top:100%;left:0;z-index:1000;display:none;float:left;min-width:160px;padding:5px 0;margin:2px 0 0;font-size:14px;text-align:left;list-style:none;background-color:#fff;-webkit-background-clip:padding-box;background-clip:padding-box;border:1px solid #ccc;border:1px solid rgba(0,0,0,.15);border-radius:4px;-webkit-box-shadow:0 6px 12px rgba(0,0,0,.175);box-shadow:0 6px 12px rgba(0,0,0,.175)}.dropdown-menu.pull-right{right:0;left:auto}.dropdown-menu .divider{height:1px;margin:9px 0;overflow:hidden;background-color:#e5e5e5}.dropdown-menu>li>a{display:block;padding:3px 20px;clear:both;font-weight:400;line-height:1.42857143;color:#333;white-space:nowrap}.dropdown-menu>li>a:focus,.dropdown-menu>li>a:hover{color:#262626;text-decoration:none;background-color:#f5f5f5}.dropdown-menu>.active>a,.dropdown-menu>.active>a:focus,.dropdown-menu>.active>a:hover{color:#fff;text-decoration:none;background-color:#337ab7;outline:0}.dropdown-menu>.disabled>a,.dropdown-menu>.disabled>a:focus,.dropdown-menu>.disabled>a:hover{color:#777}.dropdown-menu>.disabled>a:focus,.dropdown-menu>.disabled>a:hover{text-decoration:none;cursor:not-allowed;background-color:transparent;background-image:none;filter:progid:DXImageTransform.Microsoft.gradient(enabled=false)}.open>.dropdown-menu{display:block}.open>a{outline:0}.dropdown-menu-right{right:0;left:auto}.dropdown-menu-left{right:auto;left:0}.dropdown-header{display:block;padding:3px 20px;font-size:12px;line-height:1.42857143;color:#777;white-space:nowrap}.dropdown-backdrop{position:fixed;top:0;right:0;bottom:0;left:0;z-index:990}.pull-right>.dropdown-menu{right:0;left:auto}.dropup .caret,.navbar-fixed-bottom .dropdown .caret{content:"";border-top:0;border-bottom:4px dashed;border-bottom:4px solid\9}.dropup .dropdown-menu,.navbar-fixed-bottom .dropdown .dropdown-menu{top:auto;bottom:100%;margin-bottom:2px}@media (min-width:768px){.navbar-right .dropdown-menu{right:0;left:auto}.navbar-right .dropdown-menu-left{right:auto;left:0}}.btn-group,.btn-group-vertical{position:relative;display:inline-block;vertical-align:middle}.btn-group-vertical>.btn,.btn-group>.btn{position:relative;float:left}.btn-group-vertical>.btn.active,.btn-group-vertical>.btn:active,.btn-group-vertical>.btn:focus,.btn-group-vertical>.btn:hover,.btn-group>.btn.active,.btn-group>.btn:active,.btn-group>.btn:focus,.btn-group>.btn:hover{z-index:2}.btn-group .btn+.btn,.btn-group .btn+.btn-group,.btn-group .btn-group+.btn,.btn-group .btn-group+.btn-group{margin-left:-1px}.btn-toolbar{margin-left:-5px}.btn-toolbar .btn,.btn-toolbar .btn-group,.btn-toolbar .input-group{float:left}.btn-toolbar>.btn,.btn-toolbar>.btn-group,.btn-toolbar>.input-group{margin-left:5px}.btn-group>.btn:not(:first-child):not(:last-child):not(.dropdown-toggle){border-radius:0}.btn-group>.btn:first-child{margin-left:0}.btn-group>.btn:first-child:not(:last-child):not(.dropdown-toggle){border-top-right-radius:0;border-bottom-right-radius:0}.btn-group>.btn:last-child:not(:first-child),.btn-group>.dropdown-toggle:not(:first-child){border-top-left-radius:0;border-bottom-left-radius:0}.btn-group>.btn-group{float:left}.btn-group>.btn-group:not(:first-child):not(:last-child)>.btn{border-radius:0}.btn-group>.btn-group:first-child:not(:last-child)>.btn:last-child,.btn-group>.btn-group:first-child:not(:last-child)>.dropdown-toggle{border-top-right-radius:0;border-bottom-right-radius:0}.btn-group>.btn-group:last-child:not(:first-child)>.btn:first-child{border-top-left-radius:0;border-bottom-left-radius:0}.btn-group .dropdown-toggle:active,.btn-group.open .dropdown-toggle{outline:0}.btn-group>.btn+.dropdown-toggle{padding-right:8px;padding-left:8px}.btn-group>.btn-lg+.dropdown-toggle{padding-right:12px;padding-left:12px}.btn-group.open .dropdown-toggle{-webkit-box-shadow:inset 0 3px 5px rgba(0,0,0,.125);box-shadow:inset 0 3px 5px rgba(0,0,0,.125)}.btn-group.open .dropdown-toggle.btn-link{-webkit-box-shadow:none;box-shadow:none}.btn .caret{margin-left:0}.btn-lg .caret{border-width:5px 5px 0;border-bottom-width:0}.dropup .btn-lg .caret{border-width:0 5px 5px}.btn-group-vertical>.btn,.btn-group-vertical>.btn-group,.btn-group-vertical>.btn-group>.btn{display:block;float:none;width:100%;max-width:100%}.btn-group-vertical>.btn-group>.btn{float:none}.btn-group-vertical>.btn+.btn,.btn-group-vertical>.btn+.btn-group,.btn-group-vertical>.btn-group+.btn,.btn-group-vertical>.btn-group+.btn-group{margin-top:-1px;margin-left:0}.btn-group-vertical>.btn:not(:first-child):not(:last-child){border-radius:0}.btn-group-vertical>.btn:first-child:not(:last-child){border-top-right-radius:4px;border-bottom-right-radius:0;border-bottom-left-radius:0}.btn-group-vertical>.btn:last-child:not(:first-child){border-top-left-radius:0;border-top-right-radius:0;border-bottom-left-radius:4px}.btn-group-vertical>.btn-group:not(:first-child):not(:last-child)>.btn{border-radius:0}.btn-group-vertical>.btn-group:first-child:not(:last-child)>.btn:last-child,.btn-group-vertical>.btn-group:first-child:not(:last-child)>.dropdown-toggle{border-bottom-right-radius:0;border-bottom-left-radius:0}.btn-group-vertical>.btn-group:last-child:not(:first-child)>.btn:first-child{border-top-left-radius:0;border-top-right-radius:0}.btn-group-justified{display:table;width:100%;table-layout:fixed;border-collapse:separate}.btn-group-justified>.btn,.btn-group-justified>.btn-group{display:table-cell;float:none;width:1%}.btn-group-justified>.btn-group .btn{width:100%}.btn-group-justified>.btn-group .dropdown-menu{left:auto}[data-toggle=buttons]>.btn input[type=checkbox],[data-toggle=buttons]>.btn input[type=radio],[data-toggle=buttons]>.btn-group>.btn input[type=checkbox],[data-toggle=buttons]>.btn-group>.btn input[type=radio]{position:absolute;clip:rect(0,0,0,0);pointer-events:none}.input-group{position:relative;display:table;border-collapse:separate}.input-group[class*=col-]{float:none;padding-right:0;padding-left:0}.input-group .form-control{position:relative;z-index:2;float:left;width:100%;margin-bottom:0}.input-group-lg>.form-control,.input-group-lg>.input-group-addon,.input-group-lg>.input-group-btn>.btn{height:46px;padding:10px 16px;font-size:18px;line-height:1.3333333;border-radius:6px}select.input-group-lg>.form-control,select.input-group-lg>.input-group-addon,select.input-group-lg>.input-group-btn>.btn{height:46px;line-height:46px}select[multiple].input-group-lg>.form-control,select[multiple].input-group-lg>.input-group-addon,select[multiple].input-group-lg>.input-group-btn>.btn,textarea.input-group-lg>.form-control,textarea.input-group-lg>.input-group-addon,textarea.input-group-lg>.input-group-btn>.btn{height:auto}.input-group-sm>.form-control,.input-group-sm>.input-group-addon,.input-group-sm>.input-group-btn>.btn{height:30px;padding:5px 10px;font-size:12px;line-height:1.5;border-radius:3px}select.input-group-sm>.form-control,select.input-group-sm>.input-group-addon,select.input-group-sm>.input-group-btn>.btn{height:30px;line-height:30px}select[multiple].input-group-sm>.form-control,select[multiple].input-group-sm>.input-group-addon,select[multiple].input-group-sm>.input-group-btn>.btn,textarea.input-group-sm>.form-control,textarea.input-group-sm>.input-group-addon,textarea.input-group-sm>.input-group-btn>.btn{height:auto}.input-group .form-control,.input-group-addon,.input-group-btn{display:table-cell}.input-group .form-control:not(:first-child):not(:last-child),.input-group-addon:not(:first-child):not(:last-child),.input-group-btn:not(:first-child):not(:last-child){border-radius:0}.input-group-addon,.input-group-btn{width:1%;white-space:nowrap;vertical-align:middle}.input-group-addon{padding:6px 12px;font-size:14px;font-weight:400;line-height:1;color:#555;text-align:center;background-color:#eee;border:1px solid #ccc;border-radius:4px}.input-group-addon.input-sm{padding:5px 10px;font-size:12px;border-radius:3px}.input-group-addon.input-lg{padding:10px 16px;font-size:18px;border-radius:6px}.input-group-addon input[type=checkbox],.input-group-addon input[type=radio]{margin-top:0}.input-group .form-control:first-child,.input-group-addon:first-child,.input-group-btn:first-child>.btn,.input-group-btn:first-child>.btn-group>.btn,.input-group-btn:first-child>.dropdown-toggle,.input-group-btn:last-child>.btn-group:not(:last-child)>.btn,.input-group-btn:last-child>.btn:not(:last-child):not(.dropdown-toggle){border-top-right-radius:0;border-bottom-right-radius:0}.input-group-addon:first-child{border-right:0}.input-group .form-control:last-child,.input-group-addon:last-child,.input-group-btn:first-child>.btn-group:not(:first-child)>.btn,.input-group-btn:first-child>.btn:not(:first-child),.input-group-btn:last-child>.btn,.input-group-btn:last-child>.btn-group>.btn,.input-group-btn:last-child>.dropdown-toggle{border-top-left-radius:0;border-bottom-left-radius:0}.input-group-addon:last-child{border-left:0}.input-group-btn{position:relative;font-size:0;white-space:nowrap}.input-group-btn>.btn{position:relative}.input-group-btn>.btn+.btn{margin-left:-1px}.input-group-btn>.btn:active,.input-group-btn>.btn:focus,.input-group-btn>.btn:hover{z-index:2}.input-group-btn:first-child>.btn,.input-group-btn:first-child>.btn-group{margin-right:-1px}.input-group-btn:last-child>.btn,.input-group-btn:last-child>.btn-group{z-index:2;margin-left:-1px}.nav{padding-left:0;margin-bottom:0;list-style:none}.nav>li{position:relative;display:block}.nav>li>a{position:relative;display:block;padding:10px 15px}.nav>li>a:focus,.nav>li>a:hover{text-decoration:none;background-color:#eee}.nav>li.disabled>a{color:#777}.nav>li.disabled>a:focus,.nav>li.disabled>a:hover{color:#777;text-decoration:none;cursor:not-allowed;background-color:transparent}.nav .open>a,.nav .open>a:focus,.nav .open>a:hover{background-color:#eee;border-color:#337ab7}.nav .nav-divider{height:1px;margin:9px 0;overflow:hidden;background-color:#e5e5e5}.nav>li>a>img{max-width:none}.nav-tabs{border-bottom:1px solid #ddd}.nav-tabs>li{float:left;margin-bottom:-1px}.nav-tabs>li>a{margin-right:2px;line-height:1.42857143;border:1px solid transparent;border-radius:4px 4px 0 0}.nav-tabs>li>a:hover{border-color:#eee #eee #ddd}.nav-tabs>li.active>a,.nav-tabs>li.active>a:focus,.nav-tabs>li.active>a:hover{color:#555;cursor:default;background-color:#fff;border:1px solid #ddd;border-bottom-color:transparent}.nav-tabs.nav-justified{width:100%;border-bottom:0}.nav-tabs.nav-justified>li{float:none}.nav-tabs.nav-justified>li>a{margin-bottom:5px;text-align:center}.nav-tabs.nav-justified>.dropdown .dropdown-menu{top:auto;left:auto}@media (min-width:768px){.nav-tabs.nav-justified>li{display:table-cell;width:1%}.nav-tabs.nav-justified>li>a{margin-bottom:0}}.nav-tabs.nav-justified>li>a{margin-right:0;border-radius:4px}.nav-tabs.nav-justified>.active>a,.nav-tabs.nav-justified>.active>a:focus,.nav-tabs.nav-justified>.active>a:hover{border:1px solid #ddd}@media (min-width:768px){.nav-tabs.nav-justified>li>a{border-bottom:1px solid #ddd;border-radius:4px 4px 0 0}.nav-tabs.nav-justified>.active>a,.nav-tabs.nav-justified>.active>a:focus,.nav-tabs.nav-justified>.active>a:hover{border-bottom-color:#fff}}.nav-pills>li{float:left}.nav-pills>li>a{border-radius:4px}.nav-pills>li+li{margin-left:2px}.nav-pills>li.active>a,.nav-pills>li.active>a:focus,.nav-pills>li.active>a:hover{color:#fff;background-color:#337ab7}.nav-stacked>li{float:none}.nav-stacked>li+li{margin-top:2px;margin-left:0}.nav-justified{width:100%}.nav-justified>li{float:none}.nav-justified>li>a{margin-bottom:5px;text-align:center}.nav-justified>.dropdown .dropdown-menu{top:auto;left:auto}@media (min-width:768px){.nav-justified>li{display:table-cell;width:1%}.nav-justified>li>a{margin-bottom:0}}.nav-tabs-justified{border-bottom:0}.nav-tabs-justified>li>a{margin-right:0;border-radius:4px}.nav-tabs-justified>.active>a,.nav-tabs-justified>.active>a:focus,.nav-tabs-justified>.active>a:hover{border:1px solid #ddd}@media (min-width:768px){.nav-tabs-justified>li>a{border-bottom:1px solid #ddd;border-radius:4px 4px 0 0}.nav-tabs-justified>.active>a,.nav-tabs-justified>.active>a:focus,.nav-tabs-justified>.active>a:hover{border-bottom-color:#fff}}.tab-content>.tab-pane{display:none}.tab-content>.active{display:block}.nav-tabs .dropdown-menu{margin-top:-1px;border-top-left-radius:0;border-top-right-radius:0}.navbar{position:relative;min-height:50px;margin-bottom:20px;border:1px solid transparent}@media (min-width:768px){.navbar{border-radius:4px}}@media (min-width:768px){.navbar-header{float:left}}.navbar-collapse{padding-right:15px;padding-left:15px;overflow-x:visible;-webkit-overflow-scrolling:touch;border-top:1px solid transparent;-webkit-box-shadow:inset 0 1px 0 rgba(255,255,255,.1);box-shadow:inset 0 1px 0 rgba(255,255,255,.1)}.navbar-collapse.in{overflow-y:auto}@media (min-width:768px){.navbar-collapse{width:auto;border-top:0;-webkit-box-shadow:none;box-shadow:none}.navbar-collapse.collapse{display:block!important;height:auto!important;padding-bottom:0;overflow:visible!important}.navbar-collapse.in{overflow-y:visible}.navbar-fixed-bottom .navbar-collapse,.navbar-fixed-top .navbar-collapse,.navbar-static-top .navbar-collapse{padding-right:0;padding-left:0}}.navbar-fixed-bottom .navbar-collapse,.navbar-fixed-top .navbar-collapse{max-height:340px}@media (max-device-width:480px) and (orientation:landscape){.navbar-fixed-bottom .navbar-collapse,.navbar-fixed-top .navbar-collapse{max-height:200px}}.container-fluid>.navbar-collapse,.container-fluid>.navbar-header,.container>.navbar-collapse,.container>.navbar-header{margin-right:-15px;margin-left:-15px}@media (min-width:768px){.container-fluid>.navbar-collapse,.container-fluid>.navbar-header,.container>.navbar-collapse,.container>.navbar-header{margin-right:0;margin-left:0}}.navbar-static-top{z-index:1000;border-width:0 0 1px}@media (min-width:768px){.navbar-static-top{border-radius:0}}.navbar-fixed-bottom,.navbar-fixed-top{position:fixed;right:0;left:0;z-index:1030}@media (min-width:768px){.navbar-fixed-bottom,.navbar-fixed-top{border-radius:0}}.navbar-fixed-top{top:0;border-width:0 0 1px}.navbar-fixed-bottom{bottom:0;margin-bottom:0;border-width:1px 0 0}.navbar-brand{float:left;height:50px;padding:15px 15px;font-size:18px;line-height:20px}.navbar-brand:focus,.navbar-brand:hover{text-decoration:none}.navbar-brand>img{display:block}@media (min-width:768px){.navbar>.container .navbar-brand,.navbar>.container-fluid .navbar-brand{margin-left:-15px}}.navbar-toggle{position:relative;float:right;padding:9px 10px;margin-top:8px;margin-right:15px;margin-bottom:8px;background-color:transparent;background-image:none;border:1px solid transparent;border-radius:4px}.navbar-toggle:focus{outline:0}.navbar-toggle .icon-bar{display:block;width:22px;height:2px;border-radius:1px}.navbar-toggle .icon-bar+.icon-bar{margin-top:4px}@media (min-width:768px){.navbar-toggle{display:none}}.navbar-nav{margin:7.5px -15px}.navbar-nav>li>a{padding-top:10px;padding-bottom:10px;line-height:20px}@media (max-width:767px){.navbar-nav .open .dropdown-menu{position:static;float:none;width:auto;margin-top:0;background-color:transparent;border:0;-webkit-box-shadow:none;box-shadow:none}.navbar-nav .open .dropdown-menu .dropdown-header,.navbar-nav .open .dropdown-menu>li>a{padding:5px 15px 5px 25px}.navbar-nav .open .dropdown-menu>li>a{line-height:20px}.navbar-nav .open .dropdown-menu>li>a:focus,.navbar-nav .open .dropdown-menu>li>a:hover{background-image:none}}@media (min-width:768px){.navbar-nav{float:left;margin:0}.navbar-nav>li{float:left}.navbar-nav>li>a{padding-top:15px;padding-bottom:15px}}.navbar-form{padding:10px 15px;margin-top:8px;margin-right:-15px;margin-bottom:8px;margin-left:-15px;border-top:1px solid transparent;border-bottom:1px solid transparent;-webkit-box-shadow:inset 0 1px 0 rgba(255,255,255,.1),0 1px 0 rgba(255,255,255,.1);box-shadow:inset 0 1px 0 rgba(255,255,255,.1),0 1px 0 rgba(255,255,255,.1)}@media (min-width:768px){.navbar-form .form-group{display:inline-block;margin-bottom:0;vertical-align:middle}.navbar-form .form-control{display:inline-block;width:auto;vertical-align:middle}.navbar-form .form-control-static{display:inline-block}.navbar-form .input-group{display:inline-table;vertical-align:middle}.navbar-form .input-group .form-control,.navbar-form .input-group .input-group-addon,.navbar-form .input-group .input-group-btn{width:auto}.navbar-form .input-group>.form-control{width:100%}.navbar-form .control-label{margin-bottom:0;vertical-align:middle}.navbar-form .checkbox,.navbar-form .radio{display:inline-block;margin-top:0;margin-bottom:0;vertical-align:middle}.navbar-form .checkbox label,.navbar-form .radio label{padding-left:0}.navbar-form .checkbox input[type=checkbox],.navbar-form .radio input[type=radio]{position:relative;margin-left:0}.navbar-form .has-feedback .form-control-feedback{top:0}}@media (max-width:767px){.navbar-form .form-group{margin-bottom:5px}.navbar-form .form-group:last-child{margin-bottom:0}}@media (min-width:768px){.navbar-form{width:auto;padding-top:0;padding-bottom:0;margin-right:0;margin-left:0;border:0;-webkit-box-shadow:none;box-shadow:none}}.navbar-nav>li>.dropdown-menu{margin-top:0;border-top-left-radius:0;border-top-right-radius:0}.navbar-fixed-bottom .navbar-nav>li>.dropdown-menu{margin-bottom:0;border-top-left-radius:4px;border-top-right-radius:4px;border-bottom-right-radius:0;border-bottom-left-radius:0}.navbar-btn{margin-top:8px;margin-bottom:8px}.navbar-btn.btn-sm{margin-top:10px;margin-bottom:10px}.navbar-btn.btn-xs{margin-top:14px;margin-bottom:14px}.navbar-text{margin-top:15px;margin-bottom:15px}@media (min-width:768px){.navbar-text{float:left;margin-right:15px;margin-left:15px}}@media (min-width:768px){.navbar-left{float:left!important}.navbar-right{float:right!important;margin-right:-15px}.navbar-right~.navbar-right{margin-right:0}}.navbar-default{background-color:#f8f8f8;border-color:#e7e7e7}.navbar-default .navbar-brand{color:#777}.navbar-default .navbar-brand:focus,.navbar-default .navbar-brand:hover{color:#5e5e5e;background-color:transparent}.navbar-default .navbar-text{color:#777}.navbar-default .navbar-nav>li>a{color:#777}.navbar-default .navbar-nav>li>a:focus,.navbar-default .navbar-nav>li>a:hover{color:#333;background-color:transparent}.navbar-default .navbar-nav>.active>a,.navbar-default .navbar-nav>.active>a:focus,.navbar-default .navbar-nav>.active>a:hover{color:#555;background-color:#e7e7e7}.navbar-default .navbar-nav>.disabled>a,.navbar-default .navbar-nav>.disabled>a:focus,.navbar-default .navbar-nav>.disabled>a:hover{color:#ccc;background-color:transparent}.navbar-default .navbar-toggle{border-color:#ddd}.navbar-default .navbar-toggle:focus,.navbar-default .navbar-toggle:hover{background-color:#ddd}.navbar-default .navbar-toggle .icon-bar{background-color:#888}.navbar-default .navbar-collapse,.navbar-default .navbar-form{border-color:#e7e7e7}.navbar-default .navbar-nav>.open>a,.navbar-default .navbar-nav>.open>a:focus,.navbar-default .navbar-nav>.open>a:hover{color:#555;background-color:#e7e7e7}@media (max-width:767px){.navbar-default .navbar-nav .open .dropdown-menu>li>a{color:#777}.navbar-default .navbar-nav .open .dropdown-menu>li>a:focus,.navbar-default .navbar-nav .open .dropdown-menu>li>a:hover{color:#333;background-color:transparent}.navbar-default .navbar-nav .open .dropdown-menu>.active>a,.navbar-default .navbar-nav .open .dropdown-menu>.active>a:focus,.navbar-default .navbar-nav .open .dropdown-menu>.active>a:hover{color:#555;background-color:#e7e7e7}.navbar-default .navbar-nav .open .dropdown-menu>.disabled>a,.navbar-default .navbar-nav .open .dropdown-menu>.disabled>a:focus,.navbar-default .navbar-nav .open .dropdown-menu>.disabled>a:hover{color:#ccc;background-color:transparent}}.navbar-default .navbar-link{color:#777}.navbar-default .navbar-link:hover{color:#333}.navbar-default .btn-link{color:#777}.navbar-default .btn-link:focus,.navbar-default .btn-link:hover{color:#333}.navbar-default .btn-link[disabled]:focus,.navbar-default .btn-link[disabled]:hover,fieldset[disabled] .navbar-default .btn-link:focus,fieldset[disabled] .navbar-default .btn-link:hover{color:#ccc}.navbar-inverse{background-color:#222;border-color:#080808}.navbar-inverse .navbar-brand{color:#9d9d9d}.navbar-inverse .navbar-brand:focus,.navbar-inverse .navbar-brand:hover{color:#fff;background-color:transparent}.navbar-inverse .navbar-text{color:#9d9d9d}.navbar-inverse .navbar-nav>li>a{color:#9d9d9d}.navbar-inverse .navbar-nav>li>a:focus,.navbar-inverse .navbar-nav>li>a:hover{color:#fff;background-color:transparent}.navbar-inverse .navbar-nav>.active>a,.navbar-inverse .navbar-nav>.active>a:focus,.navbar-inverse .navbar-nav>.active>a:hover{color:#fff;background-color:#080808}.navbar-inverse .navbar-nav>.disabled>a,.navbar-inverse .navbar-nav>.disabled>a:focus,.navbar-inverse .navbar-nav>.disabled>a:hover{color:#444;background-color:transparent}.navbar-inverse .navbar-toggle{border-color:#333}.navbar-inverse .navbar-toggle:focus,.navbar-inverse .navbar-toggle:hover{background-color:#333}.navbar-inverse .navbar-toggle .icon-bar{background-color:#fff}.navbar-inverse .navbar-collapse,.navbar-inverse .navbar-form{border-color:#101010}.navbar-inverse .navbar-nav>.open>a,.navbar-inverse .navbar-nav>.open>a:focus,.navbar-inverse .navbar-nav>.open>a:hover{color:#fff;background-color:#080808}@media (max-width:767px){.navbar-inverse .navbar-nav .open .dropdown-menu>.dropdown-header{border-color:#080808}.navbar-inverse .navbar-nav .open .dropdown-menu .divider{background-color:#080808}.navbar-inverse .navbar-nav .open .dropdown-menu>li>a{color:#9d9d9d}.navbar-inverse .navbar-nav .open .dropdown-menu>li>a:focus,.navbar-inverse .navbar-nav .open .dropdown-menu>li>a:hover{color:#fff;background-color:transparent}.navbar-inverse .navbar-nav .open .dropdown-menu>.active>a,.navbar-inverse .navbar-nav .open .dropdown-menu>.active>a:focus,.navbar-inverse .navbar-nav .open .dropdown-menu>.active>a:hover{color:#fff;background-color:#080808}.navbar-inverse .navbar-nav .open .dropdown-menu>.disabled>a,.navbar-inverse .navbar-nav .open .dropdown-menu>.disabled>a:focus,.navbar-inverse .navbar-nav .open .dropdown-menu>.disabled>a:hover{color:#444;background-color:transparent}}.navbar-inverse .navbar-link{color:#9d9d9d}.navbar-inverse .navbar-link:hover{color:#fff}.navbar-inverse .btn-link{color:#9d9d9d}.navbar-inverse .btn-link:focus,.navbar-inverse .btn-link:hover{color:#fff}.navbar-inverse .btn-link[disabled]:focus,.navbar-inverse .btn-link[disabled]:hover,fieldset[disabled] .navbar-inverse .btn-link:focus,fieldset[disabled] .navbar-inverse .btn-link:hover{color:#444}.breadcrumb{padding:8px 15px;margin-bottom:20px;list-style:none;background-color:#f5f5f5;border-radius:4px}.breadcrumb>li{display:inline-block}.breadcrumb>li+li:before{padding:0 5px;color:#ccc;content:"/\00a0"}.breadcrumb>.active{color:#777}.pagination{display:inline-block;padding-left:0;margin:20px 0;border-radius:4px}.pagination>li{display:inline}.pagination>li>a,.pagination>li>span{position:relative;float:left;padding:6px 12px;margin-left:-1px;line-height:1.42857143;color:#337ab7;text-decoration:none;background-color:#fff;border:1px solid #ddd}.pagination>li:first-child>a,.pagination>li:first-child>span{margin-left:0;border-top-left-radius:4px;border-bottom-left-radius:4px}.pagination>li:last-child>a,.pagination>li:last-child>span{border-top-right-radius:4px;border-bottom-right-radius:4px}.pagination>li>a:focus,.pagination>li>a:hover,.pagination>li>span:focus,.pagination>li>span:hover{z-index:3;color:#23527c;background-color:#eee;border-color:#ddd}.pagination>.active>a,.pagination>.active>a:focus,.pagination>.active>a:hover,.pagination>.active>span,.pagination>.active>span:focus,.pagination>.active>span:hover{z-index:2;color:#fff;cursor:default;background-color:#337ab7;border-color:#337ab7}.pagination>.disabled>a,.pagination>.disabled>a:focus,.pagination>.disabled>a:hover,.pagination>.disabled>span,.pagination>.disabled>span:focus,.pagination>.disabled>span:hover{color:#777;cursor:not-allowed;background-color:#fff;border-color:#ddd}.pagination-lg>li>a,.pagination-lg>li>span{padding:10px 16px;font-size:18px;line-height:1.3333333}.pagination-lg>li:first-child>a,.pagination-lg>li:first-child>span{border-top-left-radius:6px;border-bottom-left-radius:6px}.pagination-lg>li:last-child>a,.pagination-lg>li:last-child>span{border-top-right-radius:6px;border-bottom-right-radius:6px}.pagination-sm>li>a,.pagination-sm>li>span{padding:5px 10px;font-size:12px;line-height:1.5}.pagination-sm>li:first-child>a,.pagination-sm>li:first-child>span{border-top-left-radius:3px;border-bottom-left-radius:3px}.pagination-sm>li:last-child>a,.pagination-sm>li:last-child>span{border-top-right-radius:3px;border-bottom-right-radius:3px}.pager{padding-left:0;margin:20px 0;text-align:center;list-style:none}.pager li{display:inline}.pager li>a,.pager li>span{display:inline-block;padding:5px 14px;background-color:#fff;border:1px solid #ddd;border-radius:15px}.pager li>a:focus,.pager li>a:hover{text-decoration:none;background-color:#eee}.pager .next>a,.pager .next>span{float:right}.pager .previous>a,.pager .previous>span{float:left}.pager .disabled>a,.pager .disabled>a:focus,.pager .disabled>a:hover,.pager .disabled>span{color:#777;cursor:not-allowed;background-color:#fff}.label{display:inline;padding:.2em .6em .3em;font-size:75%;font-weight:700;line-height:1;color:#fff;text-align:center;white-space:nowrap;vertical-align:baseline;border-radius:.25em}a.label:focus,a.label:hover{color:#fff;text-decoration:none;cursor:pointer}.label:empty{display:none}.btn .label{position:relative;top:-1px}.label-default{background-color:#777}.label-default[href]:focus,.label-default[href]:hover{background-color:#5e5e5e}.label-primary{background-color:#337ab7}.label-primary[href]:focus,.label-primary[href]:hover{background-color:#286090}.label-success{background-color:#5cb85c}.label-success[href]:focus,.label-success[href]:hover{background-color:#449d44}.label-info{background-color:#5bc0de}.label-info[href]:focus,.label-info[href]:hover{background-color:#31b0d5}.label-warning{background-color:#f0ad4e}.label-warning[href]:focus,.label-warning[href]:hover{background-color:#ec971f}.label-danger{background-color:#d9534f}.label-danger[href]:focus,.label-danger[href]:hover{background-color:#c9302c}.badge{display:inline-block;min-width:10px;padding:3px 7px;font-size:12px;font-weight:700;line-height:1;color:#fff;text-align:center;white-space:nowrap;vertical-align:middle;background-color:#777;border-radius:10px}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.btn-group-xs>.btn .badge,.btn-xs .badge{top:0;padding:1px 5px}a.badge:focus,a.badge:hover{color:#fff;text-decoration:none;cursor:pointer}.list-group-item.active>.badge,.nav-pills>.active>a>.badge{color:#337ab7;background-color:#fff}.list-group-item>.badge{float:right}.list-group-item>.badge+.badge{margin-right:5px}.nav-pills>li>a>.badge{margin-left:3px}.jumbotron{padding-top:30px;padding-bottom:30px;margin-bottom:30px;color:inherit;background-color:#eee}.jumbotron .h1,.jumbotron h1{color:inherit}.jumbotron p{margin-bottom:15px;font-size:21px;font-weight:200}.jumbotron>hr{border-top-color:#d5d5d5}.container .jumbotron,.container-fluid .jumbotron{border-radius:6px}.jumbotron .container{max-width:100%}@media screen and (min-width:768px){.jumbotron{padding-top:48px;padding-bottom:48px}.container .jumbotron,.container-fluid .jumbotron{padding-right:60px;padding-left:60px}.jumbotron .h1,.jumbotron h1{font-size:63px}}.thumbnail{display:block;padding:4px;margin-bottom:20px;line-height:1.42857143;background-color:#fff;border:1px solid #ddd;border-radius:4px;-webkit-transition:border .2s ease-in-out;-o-transition:border .2s ease-in-out;transition:border .2s ease-in-out}.thumbnail a>img,.thumbnail>img{margin-right:auto;margin-left:auto}a.thumbnail.active,a.thumbnail:focus,a.thumbnail:hover{border-color:#337ab7}.thumbnail .caption{padding:9px;color:#333}.alert{padding:15px;margin-bottom:20px;border:1px solid transparent;border-radius:4px}.alert h4{margin-top:0;color:inherit}.alert .alert-link{font-weight:700}.alert>p,.alert>ul{margin-bottom:0}.alert>p+p{margin-top:5px}.alert-dismissable,.alert-dismissible{padding-right:35px}.alert-dismissable .close,.alert-dismissible .close{position:relative;top:-2px;right:-21px;color:inherit}.alert-success{color:#3c763d;background-color:#dff0d8;border-color:#d6e9c6}.alert-success hr{border-top-color:#c9e2b3}.alert-success .alert-link{color:#2b542c}.alert-info{color:#31708f;background-color:#d9edf7;border-color:#bce8f1}.alert-info hr{border-top-color:#a6e1ec}.alert-info .alert-link{color:#245269}.alert-warning{color:#8a6d3b;background-color:#fcf8e3;border-color:#faebcc}.alert-warning hr{border-top-color:#f7e1b5}.alert-warning .alert-link{color:#66512c}.alert-danger{color:#a94442;background-color:#f2dede;border-color:#ebccd1}.alert-danger hr{border-top-color:#e4b9c0}.alert-danger .alert-link{color:#843534}@-webkit-keyframes progress-bar-stripes{from{background-position:40px 0}to{background-position:0 0}}@-o-keyframes progress-bar-stripes{from{background-position:40px 0}to{background-position:0 0}}@keyframes progress-bar-stripes{from{background-position:40px 0}to{background-position:0 0}}.progress{height:20px;margin-bottom:20px;overflow:hidden;background-color:#f5f5f5;border-radius:4px;-webkit-box-shadow:inset 0 1px 2px rgba(0,0,0,.1);box-shadow:inset 0 1px 2px rgba(0,0,0,.1)}.progress-bar{float:left;width:0;height:100%;font-size:12px;line-height:20px;color:#fff;text-align:center;background-color:#337ab7;-webkit-box-shadow:inset 0 -1px 0 rgba(0,0,0,.15);box-shadow:inset 0 -1px 0 rgba(0,0,0,.15);-webkit-transition:width .6s ease;-o-transition:width .6s ease;transition:width .6s ease}.progress-bar-striped,.progress-striped .progress-bar{background-image:-webkit-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:-o-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);-webkit-background-size:40px 40px;background-size:40px 40px}.progress-bar.active,.progress.active .progress-bar{-webkit-animation:progress-bar-stripes 2s linear infinite;-o-animation:progress-bar-stripes 2s linear infinite;animation:progress-bar-stripes 2s linear infinite}.progress-bar-success{background-color:#5cb85c}.progress-striped .progress-bar-success{background-image:-webkit-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:-o-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent)}.progress-bar-info{background-color:#5bc0de}.progress-striped .progress-bar-info{background-image:-webkit-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:-o-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent)}.progress-bar-warning{background-color:#f0ad4e}.progress-striped .progress-bar-warning{background-image:-webkit-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:-o-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent)}.progress-bar-danger{background-color:#d9534f}.progress-striped .progress-bar-danger{background-image:-webkit-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:-o-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent)}.media{margin-top:15px}.media:first-child{margin-top:0}.media,.media-body{overflow:hidden;zoom:1}.media-body{width:10000px}.media-object{display:block}.media-object.img-thumbnail{max-width:none}.media-right,.media>.pull-right{padding-left:10px}.media-left,.media>.pull-left{padding-right:10px}.media-body,.media-left,.media-right{display:table-cell;vertical-align:top}.media-middle{vertical-align:middle}.media-bottom{vertical-align:bottom}.media-heading{margin-top:0;margin-bottom:5px}.media-list{padding-left:0;list-style:none}.list-group{padding-left:0;margin-bottom:20px}.list-group-item{position:relative;display:block;padding:10px 15px;margin-bottom:-1px;background-color:#fff;border:1px solid #ddd}.list-group-item:first-child{border-top-left-radius:4px;border-top-right-radius:4px}.list-group-item:last-child{margin-bottom:0;border-bottom-right-radius:4px;border-bottom-left-radius:4px}a.list-group-item,button.list-group-item{color:#555}a.list-group-item .list-group-item-heading,button.list-group-item .list-group-item-heading{color:#333}a.list-group-item:focus,a.list-group-item:hover,button.list-group-item:focus,button.list-group-item:hover{color:#555;text-decoration:none;background-color:#f5f5f5}button.list-group-item{width:100%;text-align:left}.list-group-item.disabled,.list-group-item.disabled:focus,.list-group-item.disabled:hover{color:#777;cursor:not-allowed;background-color:#eee}.list-group-item.disabled .list-group-item-heading,.list-group-item.disabled:focus .list-group-item-heading,.list-group-item.disabled:hover .list-group-item-heading{color:inherit}.list-group-item.disabled .list-group-item-text,.list-group-item.disabled:focus .list-group-item-text,.list-group-item.disabled:hover .list-group-item-text{color:#777}.list-group-item.active,.list-group-item.active:focus,.list-group-item.active:hover{z-index:2;color:#fff;background-color:#337ab7;border-color:#337ab7}.list-group-item.active .list-group-item-heading,.list-group-item.active .list-group-item-heading>.small,.list-group-item.active .list-group-item-heading>small,.list-group-item.active:focus .list-group-item-heading,.list-group-item.active:focus .list-group-item-heading>.small,.list-group-item.active:focus .list-group-item-heading>small,.list-group-item.active:hover .list-group-item-heading,.list-group-item.active:hover .list-group-item-heading>.small,.list-group-item.active:hover .list-group-item-heading>small{color:inherit}.list-group-item.active .list-group-item-text,.list-group-item.active:focus .list-group-item-text,.list-group-item.active:hover .list-group-item-text{color:#c7ddef}.list-group-item-success{color:#3c763d;background-color:#dff0d8}a.list-group-item-success,button.list-group-item-success{color:#3c763d}a.list-group-item-success .list-group-item-heading,button.list-group-item-success .list-group-item-heading{color:inherit}a.list-group-item-success:focus,a.list-group-item-success:hover,button.list-group-item-success:focus,button.list-group-item-success:hover{color:#3c763d;background-color:#d0e9c6}a.list-group-item-success.active,a.list-group-item-success.active:focus,a.list-group-item-success.active:hover,button.list-group-item-success.active,button.list-group-item-success.active:focus,button.list-group-item-success.active:hover{color:#fff;background-color:#3c763d;border-color:#3c763d}.list-group-item-info{color:#31708f;background-color:#d9edf7}a.list-group-item-info,button.list-group-item-info{color:#31708f}a.list-group-item-info .list-group-item-heading,button.list-group-item-info .list-group-item-heading{color:inherit}a.list-group-item-info:focus,a.list-group-item-info:hover,button.list-group-item-info:focus,button.list-group-item-info:hover{color:#31708f;background-color:#c4e3f3}a.list-group-item-info.active,a.list-group-item-info.active:focus,a.list-group-item-info.active:hover,button.list-group-item-info.active,button.list-group-item-info.active:focus,button.list-group-item-info.active:hover{color:#fff;background-color:#31708f;border-color:#31708f}.list-group-item-warning{color:#8a6d3b;background-color:#fcf8e3}a.list-group-item-warning,button.list-group-item-warning{color:#8a6d3b}a.list-group-item-warning .list-group-item-heading,button.list-group-item-warning .list-group-item-heading{color:inherit}a.list-group-item-warning:focus,a.list-group-item-warning:hover,button.list-group-item-warning:focus,button.list-group-item-warning:hover{color:#8a6d3b;background-color:#faf2cc}a.list-group-item-warning.active,a.list-group-item-warning.active:focus,a.list-group-item-warning.active:hover,button.list-group-item-warning.active,button.list-group-item-warning.active:focus,button.list-group-item-warning.active:hover{color:#fff;background-color:#8a6d3b;border-color:#8a6d3b}.list-group-item-danger{color:#a94442;background-color:#f2dede}a.list-group-item-danger,button.list-group-item-danger{color:#a94442}a.list-group-item-danger .list-group-item-heading,button.list-group-item-danger .list-group-item-heading{color:inherit}a.list-group-item-danger:focus,a.list-group-item-danger:hover,button.list-group-item-danger:focus,button.list-group-item-danger:hover{color:#a94442;background-color:#ebcccc}a.list-group-item-danger.active,a.list-group-item-danger.active:focus,a.list-group-item-danger.active:hover,button.list-group-item-danger.active,button.list-group-item-danger.active:focus,button.list-group-item-danger.active:hover{color:#fff;background-color:#a94442;border-color:#a94442}.list-group-item-heading{margin-top:0;margin-bottom:5px}.list-group-item-text{margin-bottom:0;line-height:1.3}.panel{margin-bottom:20px;background-color:#fff;border:1px solid transparent;border-radius:4px;-webkit-box-shadow:0 1px 1px rgba(0,0,0,.05);box-shadow:0 1px 1px rgba(0,0,0,.05)}.panel-body{padding:15px}.panel-heading{padding:10px 15px;border-bottom:1px solid transparent;border-top-left-radius:3px;border-top-right-radius:3px}.panel-heading>.dropdown .dropdown-toggle{color:inherit}.panel-title{margin-top:0;margin-bottom:0;font-size:16px;color:inherit}.panel-title>.small,.panel-title>.small>a,.panel-title>a,.panel-title>small,.panel-title>small>a{color:inherit}.panel-footer{padding:10px 15px;background-color:#f5f5f5;border-top:1px solid #ddd;border-bottom-right-radius:3px;border-bottom-left-radius:3px}.panel>.list-group,.panel>.panel-collapse>.list-group{margin-bottom:0}.panel>.list-group .list-group-item,.panel>.panel-collapse>.list-group .list-group-item{border-width:1px 0;border-radius:0}.panel>.list-group:first-child .list-group-item:first-child,.panel>.panel-collapse>.list-group:first-child .list-group-item:first-child{border-top:0;border-top-left-radius:3px;border-top-right-radius:3px}.panel>.list-group:last-child .list-group-item:last-child,.panel>.panel-collapse>.list-group:last-child .list-group-item:last-child{border-bottom:0;border-bottom-right-radius:3px;border-bottom-left-radius:3px}.panel>.panel-heading+.panel-collapse>.list-group .list-group-item:first-child{border-top-left-radius:0;border-top-right-radius:0}.panel-heading+.list-group .list-group-item:first-child{border-top-width:0}.list-group+.panel-footer{border-top-width:0}.panel>.panel-collapse>.table,.panel>.table,.panel>.table-responsive>.table{margin-bottom:0}.panel>.panel-collapse>.table caption,.panel>.table caption,.panel>.table-responsive>.table caption{padding-right:15px;padding-left:15px}.panel>.table-responsive:first-child>.table:first-child,.panel>.table:first-child{border-top-left-radius:3px;border-top-right-radius:3px}.panel>.table-responsive:first-child>.table:first-child>tbody:first-child>tr:first-child,.panel>.table-responsive:first-child>.table:first-child>thead:first-child>tr:first-child,.panel>.table:first-child>tbody:first-child>tr:first-child,.panel>.table:first-child>thead:first-child>tr:first-child{border-top-left-radius:3px;border-top-right-radius:3px}.panel>.table-responsive:first-child>.table:first-child>tbody:first-child>tr:first-child td:first-child,.panel>.table-responsive:first-child>.table:first-child>tbody:first-child>tr:first-child th:first-child,.panel>.table-responsive:first-child>.table:first-child>thead:first-child>tr:first-child td:first-child,.panel>.table-responsive:first-child>.table:first-child>thead:first-child>tr:first-child th:first-child,.panel>.table:first-child>tbody:first-child>tr:first-child td:first-child,.panel>.table:first-child>tbody:first-child>tr:first-child th:first-child,.panel>.table:first-child>thead:first-child>tr:first-child td:first-child,.panel>.table:first-child>thead:first-child>tr:first-child th:first-child{border-top-left-radius:3px}.panel>.table-responsive:first-child>.table:first-child>tbody:first-child>tr:first-child td:last-child,.panel>.table-responsive:first-child>.table:first-child>tbody:first-child>tr:first-child th:last-child,.panel>.table-responsive:first-child>.table:first-child>thead:first-child>tr:first-child td:last-child,.panel>.table-responsive:first-child>.table:first-child>thead:first-child>tr:first-child th:last-child,.panel>.table:first-child>tbody:first-child>tr:first-child td:last-child,.panel>.table:first-child>tbody:first-child>tr:first-child th:last-child,.panel>.table:first-child>thead:first-child>tr:first-child td:last-child,.panel>.table:first-child>thead:first-child>tr:first-child th:last-child{border-top-right-radius:3px}.panel>.table-responsive:last-child>.table:last-child,.panel>.table:last-child{border-bottom-right-radius:3px;border-bottom-left-radius:3px}.panel>.table-responsive:last-child>.table:last-child>tbody:last-child>tr:last-child,.panel>.table-responsive:last-child>.table:last-child>tfoot:last-child>tr:last-child,.panel>.table:last-child>tbody:last-child>tr:last-child,.panel>.table:last-child>tfoot:last-child>tr:last-child{border-bottom-right-radius:3px;border-bottom-left-radius:3px}.panel>.table-responsive:last-child>.table:last-child>tbody:last-child>tr:last-child td:first-child,.panel>.table-responsive:last-child>.table:last-child>tbody:last-child>tr:last-child th:first-child,.panel>.table-responsive:last-child>.table:last-child>tfoot:last-child>tr:last-child td:first-child,.panel>.table-responsive:last-child>.table:last-child>tfoot:last-child>tr:last-child th:first-child,.panel>.table:last-child>tbody:last-child>tr:last-child td:first-child,.panel>.table:last-child>tbody:last-child>tr:last-child th:first-child,.panel>.table:last-child>tfoot:last-child>tr:last-child td:first-child,.panel>.table:last-child>tfoot:last-child>tr:last-child th:first-child{border-bottom-left-radius:3px}.panel>.table-responsive:last-child>.table:last-child>tbody:last-child>tr:last-child td:last-child,.panel>.table-responsive:last-child>.table:last-child>tbody:last-child>tr:last-child th:last-child,.panel>.table-responsive:last-child>.table:last-child>tfoot:last-child>tr:last-child td:last-child,.panel>.table-responsive:last-child>.table:last-child>tfoot:last-child>tr:last-child th:last-child,.panel>.table:last-child>tbody:last-child>tr:last-child td:last-child,.panel>.table:last-child>tbody:last-child>tr:last-child th:last-child,.panel>.table:last-child>tfoot:last-child>tr:last-child td:last-child,.panel>.table:last-child>tfoot:last-child>tr:last-child th:last-child{border-bottom-right-radius:3px}.panel>.panel-body+.table,.panel>.panel-body+.table-responsive,.panel>.table+.panel-body,.panel>.table-responsive+.panel-body{border-top:1px solid #ddd}.panel>.table>tbody:first-child>tr:first-child td,.panel>.table>tbody:first-child>tr:first-child th{border-top:0}.panel>.table-bordered,.panel>.table-responsive>.table-bordered{border:0}.panel>.table-bordered>tbody>tr>td:first-child,.panel>.table-bordered>tbody>tr>th:first-child,.panel>.table-bordered>tfoot>tr>td:first-child,.panel>.table-bordered>tfoot>tr>th:first-child,.panel>.table-bordered>thead>tr>td:first-child,.panel>.table-bordered>thead>tr>th:first-child,.panel>.table-responsive>.table-bordered>tbody>tr>td:first-child,.panel>.table-responsive>.table-bordered>tbody>tr>th:first-child,.panel>.table-responsive>.table-bordered>tfoot>tr>td:first-child,.panel>.table-responsive>.table-bordered>tfoot>tr>th:first-child,.panel>.table-responsive>.table-bordered>thead>tr>td:first-child,.panel>.table-responsive>.table-bordered>thead>tr>th:first-child{border-left:0}.panel>.table-bordered>tbody>tr>td:last-child,.panel>.table-bordered>tbody>tr>th:last-child,.panel>.table-bordered>tfoot>tr>td:last-child,.panel>.table-bordered>tfoot>tr>th:last-child,.panel>.table-bordered>thead>tr>td:last-child,.panel>.table-bordered>thead>tr>th:last-child,.panel>.table-responsive>.table-bordered>tbody>tr>td:last-child,.panel>.table-responsive>.table-bordered>tbody>tr>th:last-child,.panel>.table-responsive>.table-bordered>tfoot>tr>td:last-child,.panel>.table-responsive>.table-bordered>tfoot>tr>th:last-child,.panel>.table-responsive>.table-bordered>thead>tr>td:last-child,.panel>.table-responsive>.table-bordered>thead>tr>th:last-child{border-right:0}.panel>.table-bordered>tbody>tr:first-child>td,.panel>.table-bordered>tbody>tr:first-child>th,.panel>.table-bordered>thead>tr:first-child>td,.panel>.table-bordered>thead>tr:first-child>th,.panel>.table-responsive>.table-bordered>tbody>tr:first-child>td,.panel>.table-responsive>.table-bordered>tbody>tr:first-child>th,.panel>.table-responsive>.table-bordered>thead>tr:first-child>td,.panel>.table-responsive>.table-bordered>thead>tr:first-child>th{border-bottom:0}.panel>.table-bordered>tbody>tr:last-child>td,.panel>.table-bordered>tbody>tr:last-child>th,.panel>.table-bordered>tfoot>tr:last-child>td,.panel>.table-bordered>tfoot>tr:last-child>th,.panel>.table-responsive>.table-bordered>tbody>tr:last-child>td,.panel>.table-responsive>.table-bordered>tbody>tr:last-child>th,.panel>.table-responsive>.table-bordered>tfoot>tr:last-child>td,.panel>.table-responsive>.table-bordered>tfoot>tr:last-child>th{border-bottom:0}.panel>.table-responsive{margin-bottom:0;border:0}.panel-group{margin-bottom:20px}.panel-group .panel{margin-bottom:0;border-radius:4px}.panel-group .panel+.panel{margin-top:5px}.panel-group .panel-heading{border-bottom:0}.panel-group .panel-heading+.panel-collapse>.list-group,.panel-group .panel-heading+.panel-collapse>.panel-body{border-top:1px solid #ddd}.panel-group .panel-footer{border-top:0}.panel-group .panel-footer+.panel-collapse .panel-body{border-bottom:1px solid #ddd}.panel-default{border-color:#ddd}.panel-default>.panel-heading{color:#333;background-color:#f5f5f5;border-color:#ddd}.panel-default>.panel-heading+.panel-collapse>.panel-body{border-top-color:#ddd}.panel-default>.panel-heading .badge{color:#f5f5f5;background-color:#333}.panel-default>.panel-footer+.panel-collapse>.panel-body{border-bottom-color:#ddd}.panel-primary{border-color:#337ab7}.panel-primary>.panel-heading{color:#fff;background-color:#337ab7;border-color:#337ab7}.panel-primary>.panel-heading+.panel-collapse>.panel-body{border-top-color:#337ab7}.panel-primary>.panel-heading .badge{color:#337ab7;background-color:#fff}.panel-primary>.panel-footer+.panel-collapse>.panel-body{border-bottom-color:#337ab7}.panel-success{border-color:#d6e9c6}.panel-success>.panel-heading{color:#3c763d;background-color:#dff0d8;border-color:#d6e9c6}.panel-success>.panel-heading+.panel-collapse>.panel-body{border-top-color:#d6e9c6}.panel-success>.panel-heading .badge{color:#dff0d8;background-color:#3c763d}.panel-success>.panel-footer+.panel-collapse>.panel-body{border-bottom-color:#d6e9c6}.panel-info{border-color:#bce8f1}.panel-info>.panel-heading{color:#31708f;background-color:#d9edf7;border-color:#bce8f1}.panel-info>.panel-heading+.panel-collapse>.panel-body{border-top-color:#bce8f1}.panel-info>.panel-heading .badge{color:#d9edf7;background-color:#31708f}.panel-info>.panel-footer+.panel-collapse>.panel-body{border-bottom-color:#bce8f1}.panel-warning{border-color:#faebcc}.panel-warning>.panel-heading{color:#8a6d3b;background-color:#fcf8e3;border-color:#faebcc}.panel-warning>.panel-heading+.panel-collapse>.panel-body{border-top-color:#faebcc}.panel-warning>.panel-heading .badge{color:#fcf8e3;background-color:#8a6d3b}.panel-warning>.panel-footer+.panel-collapse>.panel-body{border-bottom-color:#faebcc}.panel-danger{border-color:#ebccd1}.panel-danger>.panel-heading{color:#a94442;background-color:#f2dede;border-color:#ebccd1}.panel-danger>.panel-heading+.panel-collapse>.panel-body{border-top-color:#ebccd1}.panel-danger>.panel-heading .badge{color:#f2dede;background-color:#a94442}.panel-danger>.panel-footer+.panel-collapse>.panel-body{border-bottom-color:#ebccd1}.embed-responsive{position:relative;display:block;height:0;padding:0;overflow:hidden}.embed-responsive .embed-responsive-item,.embed-responsive embed,.embed-responsive iframe,.embed-responsive object,.embed-responsive video{position:absolute;top:0;bottom:0;left:0;width:100%;height:100%;border:0}.embed-responsive-16by9{padding-bottom:56.25%}.embed-responsive-4by3{padding-bottom:75%}.well{min-height:20px;padding:19px;margin-bottom:20px;background-color:#f5f5f5;border:1px solid #e3e3e3;border-radius:4px;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.05);box-shadow:inset 0 1px 1px rgba(0,0,0,.05)}.well blockquote{border-color:#ddd;border-color:rgba(0,0,0,.15)}.well-lg{padding:24px;border-radius:6px}.well-sm{padding:9px;border-radius:3px}.close{float:right;font-size:21px;font-weight:700;line-height:1;color:#000;text-shadow:0 1px 0 #fff;filter:alpha(opacity=20);opacity:.2}.close:focus,.close:hover{color:#000;text-decoration:none;cursor:pointer;filter:alpha(opacity=50);opacity:.5}button.close{-webkit-appearance:none;padding:0;cursor:pointer;background:0 0;border:0}.modal-open{overflow:hidden}.modal{position:fixed;top:0;right:0;bottom:0;left:0;z-index:1050;display:none;overflow:hidden;-webkit-overflow-scrolling:touch;outline:0}.modal.fade .modal-dialog{-webkit-transition:-webkit-transform .3s ease-out;-o-transition:-o-transform .3s ease-out;transition:transform .3s ease-out;-webkit-transform:translate(0,-25%);-ms-transform:translate(0,-25%);-o-transform:translate(0,-25%);transform:translate(0,-25%)}.modal.in .modal-dialog{-webkit-transform:translate(0,0);-ms-transform:translate(0,0);-o-transform:translate(0,0);transform:translate(0,0)}.modal-open .modal{overflow-x:hidden;overflow-y:auto}.modal-dialog{position:relative;width:auto;margin:10px}.modal-content{position:relative;background-color:#fff;-webkit-background-clip:padding-box;background-clip:padding-box;border:1px solid #999;border:1px solid rgba(0,0,0,.2);border-radius:6px;outline:0;-webkit-box-shadow:0 3px 9px rgba(0,0,0,.5);box-shadow:0 3px 9px rgba(0,0,0,.5)}.modal-backdrop{position:fixed;top:0;right:0;bottom:0;left:0;z-index:1040;background-color:#000}.modal-backdrop.fade{filter:alpha(opacity=0);opacity:0}.modal-backdrop.in{filter:alpha(opacity=50);opacity:.5}.modal-header{min-height:16.43px;padding:15px;border-bottom:1px solid #e5e5e5}.modal-header .close{margin-top:-2px}.modal-title{margin:0;line-height:1.42857143}.modal-body{position:relative;padding:15px}.modal-footer{padding:15px;text-align:right;border-top:1px solid #e5e5e5}.modal-footer .btn+.btn{margin-bottom:0;margin-left:5px}.modal-footer .btn-group .btn+.btn{margin-left:-1px}.modal-footer .btn-block+.btn-block{margin-left:0}.modal-scrollbar-measure{position:absolute;top:-9999px;width:50px;height:50px;overflow:scroll}@media (min-width:768px){.modal-dialog{width:600px;margin:30px auto}.modal-content{-webkit-box-shadow:0 5px 15px rgba(0,0,0,.5);box-shadow:0 5px 15px rgba(0,0,0,.5)}.modal-sm{width:300px}}@media (min-width:992px){.modal-lg{width:900px}}.tooltip{position:absolute;z-index:1070;display:block;font-family:"Helvetica Neue",Helvetica,Arial,sans-serif;font-size:12px;font-style:normal;font-weight:400;line-height:1.42857143;text-align:left;text-align:start;text-decoration:none;text-shadow:none;text-transform:none;letter-spacing:normal;word-break:normal;word-spacing:normal;word-wrap:normal;white-space:normal;filter:alpha(opacity=0);opacity:0;line-break:auto}.tooltip.in{filter:alpha(opacity=90);opacity:.9}.tooltip.top{padding:5px 0;margin-top:-3px}.tooltip.right{padding:0 5px;margin-left:3px}.tooltip.bottom{padding:5px 0;margin-top:3px}.tooltip.left{padding:0 5px;margin-left:-3px}.tooltip-inner{max-width:200px;padding:3px 8px;color:#fff;text-align:center;background-color:#000;border-radius:4px}.tooltip-arrow{position:absolute;width:0;height:0;border-color:transparent;border-style:solid}.tooltip.top .tooltip-arrow{bottom:0;left:50%;margin-left:-5px;border-width:5px 5px 0;border-top-color:#000}.tooltip.top-left .tooltip-arrow{right:5px;bottom:0;margin-bottom:-5px;border-width:5px 5px 0;border-top-color:#000}.tooltip.top-right .tooltip-arrow{bottom:0;left:5px;margin-bottom:-5px;border-width:5px 5px 0;border-top-color:#000}.tooltip.right .tooltip-arrow{top:50%;left:0;margin-top:-5px;border-width:5px 5px 5px 0;border-right-color:#000}.tooltip.left .tooltip-arrow{top:50%;right:0;margin-top:-5px;border-width:5px 0 5px 5px;border-left-color:#000}.tooltip.bottom .tooltip-arrow{top:0;left:50%;margin-left:-5px;border-width:0 5px 5px;border-bottom-color:#000}.tooltip.bottom-left .tooltip-arrow{top:0;right:5px;margin-top:-5px;border-width:0 5px 5px;border-bottom-color:#000}.tooltip.bottom-right .tooltip-arrow{top:0;left:5px;margin-top:-5px;border-width:0 5px 5px;border-bottom-color:#000}.popover{position:absolute;top:0;left:0;z-index:1060;display:none;max-width:276px;padding:1px;font-family:"Helvetica Neue",Helvetica,Arial,sans-serif;font-size:14px;font-style:normal;font-weight:400;line-height:1.42857143;text-align:left;text-align:start;text-decoration:none;text-shadow:none;text-transform:none;letter-spacing:normal;word-break:normal;word-spacing:normal;word-wrap:normal;white-space:normal;background-color:#fff;-webkit-background-clip:padding-box;background-clip:padding-box;border:1px solid #ccc;border:1px solid rgba(0,0,0,.2);border-radius:6px;-webkit-box-shadow:0 5px 10px rgba(0,0,0,.2);box-shadow:0 5px 10px rgba(0,0,0,.2);line-break:auto}.popover.top{margin-top:-10px}.popover.right{margin-left:10px}.popover.bottom{margin-top:10px}.popover.left{margin-left:-10px}.popover-title{padding:8px 14px;margin:0;font-size:14px;background-color:#f7f7f7;border-bottom:1px solid #ebebeb;border-radius:5px 5px 0 0}.popover-content{padding:9px 14px}.popover>.arrow,.popover>.arrow:after{position:absolute;display:block;width:0;height:0;border-color:transparent;border-style:solid}.popover>.arrow{border-width:11px}.popover>.arrow:after{content:"";border-width:10px}.popover.top>.arrow{bottom:-11px;left:50%;margin-left:-11px;border-top-color:#999;border-top-color:rgba(0,0,0,.25);border-bottom-width:0}.popover.top>.arrow:after{bottom:1px;margin-left:-10px;content:" ";border-top-color:#fff;border-bottom-width:0}.popover.right>.arrow{top:50%;left:-11px;margin-top:-11px;border-right-color:#999;border-right-color:rgba(0,0,0,.25);border-left-width:0}.popover.right>.arrow:after{bottom:-10px;left:1px;content:" ";border-right-color:#fff;border-left-width:0}.popover.bottom>.arrow{top:-11px;left:50%;margin-left:-11px;border-top-width:0;border-bottom-color:#999;border-bottom-color:rgba(0,0,0,.25)}.popover.bottom>.arrow:after{top:1px;margin-left:-10px;content:" ";border-top-width:0;border-bottom-color:#fff}.popover.left>.arrow{top:50%;right:-11px;margin-top:-11px;border-right-width:0;border-left-color:#999;border-left-color:rgba(0,0,0,.25)}.popover.left>.arrow:after{right:1px;bottom:-10px;content:" ";border-right-width:0;border-left-color:#fff}.carousel{position:relative}.carousel-inner{position:relative;width:100%;overflow:hidden}.carousel-inner>.item{position:relative;display:none;-webkit-transition:.6s ease-in-out left;-o-transition:.6s ease-in-out left;transition:.6s ease-in-out left}.carousel-inner>.item>a>img,.carousel-inner>.item>img{line-height:1}@media all and (transform-3d),(-webkit-transform-3d){.carousel-inner>.item{-webkit-transition:-webkit-transform .6s ease-in-out;-o-transition:-o-transform .6s ease-in-out;transition:transform .6s ease-in-out;-webkit-backface-visibility:hidden;backface-visibility:hidden;-webkit-perspective:1000px;perspective:1000px}.carousel-inner>.item.active.right,.carousel-inner>.item.next{left:0;-webkit-transform:translate3d(100%,0,0);transform:translate3d(100%,0,0)}.carousel-inner>.item.active.left,.carousel-inner>.item.prev{left:0;-webkit-transform:translate3d(-100%,0,0);transform:translate3d(-100%,0,0)}.carousel-inner>.item.active,.carousel-inner>.item.next.left,.carousel-inner>.item.prev.right{left:0;-webkit-transform:translate3d(0,0,0);transform:translate3d(0,0,0)}}.carousel-inner>.active,.carousel-inner>.next,.carousel-inner>.prev{display:block}.carousel-inner>.active{left:0}.carousel-inner>.next,.carousel-inner>.prev{position:absolute;top:0;width:100%}.carousel-inner>.next{left:100%}.carousel-inner>.prev{left:-100%}.carousel-inner>.next.left,.carousel-inner>.prev.right{left:0}.carousel-inner>.active.left{left:-100%}.carousel-inner>.active.right{left:100%}.carousel-control{position:absolute;top:0;bottom:0;left:0;width:15%;font-size:20px;color:#fff;text-align:center;text-shadow:0 1px 2px rgba(0,0,0,.6);filter:alpha(opacity=50);opacity:.5}.carousel-control.left{background-image:-webkit-linear-gradient(left,rgba(0,0,0,.5) 0,rgba(0,0,0,.0001) 100%);background-image:-o-linear-gradient(left,rgba(0,0,0,.5) 0,rgba(0,0,0,.0001) 100%);background-image:-webkit-gradient(linear,left top,right top,from(rgba(0,0,0,.5)),to(rgba(0,0,0,.0001)));background-image:linear-gradient(to right,rgba(0,0,0,.5) 0,rgba(0,0,0,.0001) 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1);background-repeat:repeat-x}.carousel-control.right{right:0;left:auto;background-image:-webkit-linear-gradient(left,rgba(0,0,0,.0001) 0,rgba(0,0,0,.5) 100%);background-image:-o-linear-gradient(left,rgba(0,0,0,.0001) 0,rgba(0,0,0,.5) 100%);background-image:-webkit-gradient(linear,left top,right top,from(rgba(0,0,0,.0001)),to(rgba(0,0,0,.5)));background-image:linear-gradient(to right,rgba(0,0,0,.0001) 0,rgba(0,0,0,.5) 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1);background-repeat:repeat-x}.carousel-control:focus,.carousel-control:hover{color:#fff;text-decoration:none;filter:alpha(opacity=90);outline:0;opacity:.9}.carousel-control .glyphicon-chevron-left,.carousel-control .glyphicon-chevron-right,.carousel-control .icon-next,.carousel-control .icon-prev{position:absolute;top:50%;z-index:5;display:inline-block;margin-top:-10px}.carousel-control .glyphicon-chevron-left,.carousel-control .icon-prev{left:50%;margin-left:-10px}.carousel-control .glyphicon-chevron-right,.carousel-control .icon-next{right:50%;margin-right:-10px}.carousel-control .icon-next,.carousel-control .icon-prev{width:20px;height:20px;font-family:serif;line-height:1}.carousel-control .icon-prev:before{content:'\2039'}.carousel-control .icon-next:before{content:'\203a'}.carousel-indicators{position:absolute;bottom:10px;left:50%;z-index:15;width:60%;padding-left:0;margin-left:-30%;text-align:center;list-style:none}.carousel-indicators li{display:inline-block;width:10px;height:10px;margin:1px;text-indent:-999px;cursor:pointer;background-color:#000\9;background-color:rgba(0,0,0,0);border:1px solid #fff;border-radius:10px}.carousel-indicators .active{width:12px;height:12px;margin:0;background-color:#fff}.carousel-caption{position:absolute;right:15%;bottom:20px;left:15%;z-index:10;padding-top:20px;padding-bottom:20px;color:#fff;text-align:center;text-shadow:0 1px 2px rgba(0,0,0,.6)}.carousel-caption .btn{text-shadow:none}@media screen and (min-width:768px){.carousel-control .glyphicon-chevron-left,.carousel-control .glyphicon-chevron-right,.carousel-control .icon-next,.carousel-control .icon-prev{width:30px;height:30px;margin-top:-15px;font-size:30px}.carousel-control .glyphicon-chevron-left,.carousel-control .icon-prev{margin-left:-15px}.carousel-control .glyphicon-chevron-right,.carousel-control .icon-next{margin-right:-15px}.carousel-caption{right:20%;left:20%;padding-bottom:30px}.carousel-indicators{bottom:20px}}.btn-group-vertical>.btn-group:after,.btn-group-vertical>.btn-group:before,.btn-toolbar:after,.btn-toolbar:before,.clearfix:after,.clearfix:before,.container-fluid:after,.container-fluid:before,.container:after,.container:before,.dl-horizontal dd:after,.dl-horizontal dd:before,.form-horizontal .form-group:after,.form-horizontal .form-group:before,.modal-footer:after,.modal-footer:before,.nav:after,.nav:before,.navbar-collapse:after,.navbar-collapse:before,.navbar-header:after,.navbar-header:before,.navbar:after,.navbar:before,.pager:after,.pager:before,.panel-body:after,.panel-body:before,.row:after,.row:before{display:table;content:" "}.btn-group-vertical>.btn-group:after,.btn-toolbar:after,.clearfix:after,.container-fluid:after,.container:after,.dl-horizontal dd:after,.form-horizontal .form-group:after,.modal-footer:after,.nav:after,.navbar-collapse:after,.navbar-header:after,.navbar:after,.pager:after,.panel-body:after,.row:after{clear:both}.center-block{display:block;margin-right:auto;margin-left:auto}.pull-right{float:right!important}.pull-left{float:left!important}.hide{display:none!important}.show{display:block!important}.invisible{visibility:hidden}.text-hide{font:0/0 a;color:transparent;text-shadow:none;background-color:transparent;border:0}.hidden{display:none!important}.affix{position:fixed}@-ms-viewport{width:device-width}.visible-lg,.visible-md,.visible-sm,.visible-xs{display:none!important}.visible-lg-block,.visible-lg-inline,.visible-lg-inline-block,.visible-md-block,.visible-md-inline,.visible-md-inline-block,.visible-sm-block,.visible-sm-inline,.visible-sm-inline-block,.visible-xs-block,.visible-xs-inline,.visible-xs-inline-block{display:none!important}@media (max-width:767px){.visible-xs{display:block!important}table.visible-xs{display:table!important}tr.visible-xs{display:table-row!important}td.visible-xs,th.visible-xs{display:table-cell!important}}@media (max-width:767px){.visible-xs-block{display:block!important}}@media (max-width:767px){.visible-xs-inline{display:inline!important}}@media (max-width:767px){.visible-xs-inline-block{display:inline-block!important}}@media (min-width:768px) and (max-width:991px){.visible-sm{display:block!important}table.visible-sm{display:table!important}tr.visible-sm{display:table-row!important}td.visible-sm,th.visible-sm{display:table-cell!important}}@media (min-width:768px) and (max-width:991px){.visible-sm-block{display:block!important}}@media (min-width:768px) and (max-width:991px){.visible-sm-inline{display:inline!important}}@media (min-width:768px) and (max-width:991px){.visible-sm-inline-block{display:inline-block!important}}@media (min-width:992px) and (max-width:1199px){.visible-md{display:block!important}table.visible-md{display:table!important}tr.visible-md{display:table-row!important}td.visible-md,th.visible-md{display:table-cell!important}}@media (min-width:992px) and (max-width:1199px){.visible-md-block{display:block!important}}@media (min-width:992px) and (max-width:1199px){.visible-md-inline{display:inline!important}}@media (min-width:992px) and (max-width:1199px){.visible-md-inline-block{display:inline-block!important}}@media (min-width:1200px){.visible-lg{display:block!important}table.visible-lg{display:table!important}tr.visible-lg{display:table-row!important}td.visible-lg,th.visible-lg{display:table-cell!important}}@media (min-width:1200px){.visible-lg-block{display:block!important}}@media (min-width:1200px){.visible-lg-inline{display:inline!important}}@media (min-width:1200px){.visible-lg-inline-block{display:inline-block!important}}@media (max-width:767px){.hidden-xs{display:none!important}}@media (min-width:768px) and (max-width:991px){.hidden-sm{display:none!important}}@media (min-width:992px) and (max-width:1199px){.hidden-md{display:none!important}}@media (min-width:1200px){.hidden-lg{display:none!important}}.visible-print{display:none!important}@media print{.visible-print{display:block!important}table.visible-print{display:table!important}tr.visible-print{display:table-row!important}td.visible-print,th.visible-print{display:table-cell!important}}.visible-print-block{display:none!important}@media print{.visible-print-block{display:block!important}}.visible-print-inline{display:none!important}@media print{.visible-print-inline{display:inline!important}}.visible-print-inline-block{display:none!important}@media print{.visible-print-inline-block{display:inline-block!important}}@media print{.hidden-print{display:none!important}}
+</style>
+<script>/*!
+ * Bootstrap v3.3.5 (http://getbootstrap.com)
+ * Copyright 2011-2015 Twitter, Inc.
+ * Licensed under the MIT license
+ */
+if("undefined"==typeof jQuery)throw new Error("Bootstrap's JavaScript requires jQuery");+function(a){"use strict";var b=a.fn.jquery.split(" ")[0].split(".");if(b[0]<2&&b[1]<9||1==b[0]&&9==b[1]&&b[2]<1)throw new Error("Bootstrap's JavaScript requires jQuery version 1.9.1 or higher")}(jQuery),+function(a){"use strict";function b(){var a=document.createElement("bootstrap"),b={WebkitTransition:"webkitTransitionEnd",MozTransition:"transitionend",OTransition:"oTransitionEnd otransitionend",transition:"transitionend"};for(var c in b)if(void 0!==a.style[c])return{end:b[c]};return!1}a.fn.emulateTransitionEnd=function(b){var c=!1,d=this;a(this).one("bsTransitionEnd",function(){c=!0});var e=function(){c||a(d).trigger(a.support.transition.end)};return setTimeout(e,b),this},a(function(){a.support.transition=b(),a.support.transition&&(a.event.special.bsTransitionEnd={bindType:a.support.transition.end,delegateType:a.support.transition.end,handle:function(b){return a(b.target).is(this)?b.handleObj.handler.apply(this,arguments):void 0}})})}(jQuery),+function(a){"use strict";function b(b){return this.each(function(){var c=a(this),e=c.data("bs.alert");e||c.data("bs.alert",e=new d(this)),"string"==typeof b&&e[b].call(c)})}var c='[data-dismiss="alert"]',d=function(b){a(b).on("click",c,this.close)};d.VERSION="3.3.5",d.TRANSITION_DURATION=150,d.prototype.close=function(b){function c(){g.detach().trigger("closed.bs.alert").remove()}var e=a(this),f=e.attr("data-target");f||(f=e.attr("href"),f=f&&f.replace(/.*(?=#[^\s]*$)/,""));var g=a(f);b&&b.preventDefault(),g.length||(g=e.closest(".alert")),g.trigger(b=a.Event("close.bs.alert")),b.isDefaultPrevented()||(g.removeClass("in"),a.support.transition&&g.hasClass("fade")?g.one("bsTransitionEnd",c).emulateTransitionEnd(d.TRANSITION_DURATION):c())};var e=a.fn.alert;a.fn.alert=b,a.fn.alert.Constructor=d,a.fn.alert.noConflict=function(){return a.fn.alert=e,this},a(document).on("click.bs.alert.data-api",c,d.prototype.close)}(jQuery),+function(a){"use strict";function b(b){return this.each(function(){var d=a(this),e=d.data("bs.button"),f="object"==typeof b&&b;e||d.data("bs.button",e=new c(this,f)),"toggle"==b?e.toggle():b&&e.setState(b)})}var c=function(b,d){this.$element=a(b),this.options=a.extend({},c.DEFAULTS,d),this.isLoading=!1};c.VERSION="3.3.5",c.DEFAULTS={loadingText:"loading..."},c.prototype.setState=function(b){var c="disabled",d=this.$element,e=d.is("input")?"val":"html",f=d.data();b+="Text",null==f.resetText&&d.data("resetText",d[e]()),setTimeout(a.proxy(function(){d[e](null==f[b]?this.options[b]:f[b]),"loadingText"==b?(this.isLoading=!0,d.addClass(c).attr(c,c)):this.isLoading&&(this.isLoading=!1,d.removeClass(c).removeAttr(c))},this),0)},c.prototype.toggle=function(){var a=!0,b=this.$element.closest('[data-toggle="buttons"]');if(b.length){var c=this.$element.find("input");"radio"==c.prop("type")?(c.prop("checked")&&(a=!1),b.find(".active").removeClass("active"),this.$element.addClass("active")):"checkbox"==c.prop("type")&&(c.prop("checked")!==this.$element.hasClass("active")&&(a=!1),this.$element.toggleClass("active")),c.prop("checked",this.$element.hasClass("active")),a&&c.trigger("change")}else this.$element.attr("aria-pressed",!this.$element.hasClass("active")),this.$element.toggleClass("active")};var d=a.fn.button;a.fn.button=b,a.fn.button.Constructor=c,a.fn.button.noConflict=function(){return a.fn.button=d,this},a(document).on("click.bs.button.data-api",'[data-toggle^="button"]',function(c){var d=a(c.target);d.hasClass("btn")||(d=d.closest(".btn")),b.call(d,"toggle"),a(c.target).is('input[type="radio"]')||a(c.target).is('input[type="checkbox"]')||c.preventDefault()}).on("focus.bs.button.data-api blur.bs.button.data-api",'[data-toggle^="button"]',function(b){a(b.target).closest(".btn").toggleClass("focus",/^focus(in)?$/.test(b.type))})}(jQuery),+function(a){"use strict";function b(b){return this.each(function(){var d=a(this),e=d.data("bs.carousel"),f=a.extend({},c.DEFAULTS,d.data(),"object"==typeof b&&b),g="string"==typeof b?b:f.slide;e||d.data("bs.carousel",e=new c(this,f)),"number"==typeof b?e.to(b):g?e[g]():f.interval&&e.pause().cycle()})}var c=function(b,c){this.$element=a(b),this.$indicators=this.$element.find(".carousel-indicators"),this.options=c,this.paused=null,this.sliding=null,this.interval=null,this.$active=null,this.$items=null,this.options.keyboard&&this.$element.on("keydown.bs.carousel",a.proxy(this.keydown,this)),"hover"==this.options.pause&&!("ontouchstart"in document.documentElement)&&this.$element.on("mouseenter.bs.carousel",a.proxy(this.pause,this)).on("mouseleave.bs.carousel",a.proxy(this.cycle,this))};c.VERSION="3.3.5",c.TRANSITION_DURATION=600,c.DEFAULTS={interval:5e3,pause:"hover",wrap:!0,keyboard:!0},c.prototype.keydown=function(a){if(!/input|textarea/i.test(a.target.tagName)){switch(a.which){case 37:this.prev();break;case 39:this.next();break;default:return}a.preventDefault()}},c.prototype.cycle=function(b){return b||(this.paused=!1),this.interval&&clearInterval(this.interval),this.options.interval&&!this.paused&&(this.interval=setInterval(a.proxy(this.next,this),this.options.interval)),this},c.prototype.getItemIndex=function(a){return this.$items=a.parent().children(".item"),this.$items.index(a||this.$active)},c.prototype.getItemForDirection=function(a,b){var c=this.getItemIndex(b),d="prev"==a&&0===c||"next"==a&&c==this.$items.length-1;if(d&&!this.options.wrap)return b;var e="prev"==a?-1:1,f=(c+e)%this.$items.length;return this.$items.eq(f)},c.prototype.to=function(a){var b=this,c=this.getItemIndex(this.$active=this.$element.find(".item.active"));return a>this.$items.length-1||0>a?void 0:this.sliding?this.$element.one("slid.bs.carousel",function(){b.to(a)}):c==a?this.pause().cycle():this.slide(a>c?"next":"prev",this.$items.eq(a))},c.prototype.pause=function(b){return b||(this.paused=!0),this.$element.find(".next, .prev").length&&a.support.transition&&(this.$element.trigger(a.support.transition.end),this.cycle(!0)),this.interval=clearInterval(this.interval),this},c.prototype.next=function(){return this.sliding?void 0:this.slide("next")},c.prototype.prev=function(){return this.sliding?void 0:this.slide("prev")},c.prototype.slide=function(b,d){var e=this.$element.find(".item.active"),f=d||this.getItemForDirection(b,e),g=this.interval,h="next"==b?"left":"right",i=this;if(f.hasClass("active"))return this.sliding=!1;var j=f[0],k=a.Event("slide.bs.carousel",{relatedTarget:j,direction:h});if(this.$element.trigger(k),!k.isDefaultPrevented()){if(this.sliding=!0,g&&this.pause(),this.$indicators.length){this.$indicators.find(".active").removeClass("active");var l=a(this.$indicators.children()[this.getItemIndex(f)]);l&&l.addClass("active")}var m=a.Event("slid.bs.carousel",{relatedTarget:j,direction:h});return a.support.transition&&this.$element.hasClass("slide")?(f.addClass(b),f[0].offsetWidth,e.addClass(h),f.addClass(h),e.one("bsTransitionEnd",function(){f.removeClass([b,h].join(" ")).addClass("active"),e.removeClass(["active",h].join(" ")),i.sliding=!1,setTimeout(function(){i.$element.trigger(m)},0)}).emulateTransitionEnd(c.TRANSITION_DURATION)):(e.removeClass("active"),f.addClass("active"),this.sliding=!1,this.$element.trigger(m)),g&&this.cycle(),this}};var d=a.fn.carousel;a.fn.carousel=b,a.fn.carousel.Constructor=c,a.fn.carousel.noConflict=function(){return a.fn.carousel=d,this};var e=function(c){var d,e=a(this),f=a(e.attr("data-target")||(d=e.attr("href"))&&d.replace(/.*(?=#[^\s]+$)/,""));if(f.hasClass("carousel")){var g=a.extend({},f.data(),e.data()),h=e.attr("data-slide-to");h&&(g.interval=!1),b.call(f,g),h&&f.data("bs.carousel").to(h),c.preventDefault()}};a(document).on("click.bs.carousel.data-api","[data-slide]",e).on("click.bs.carousel.data-api","[data-slide-to]",e),a(window).on("load",function(){a('[data-ride="carousel"]').each(function(){var c=a(this);b.call(c,c.data())})})}(jQuery),+function(a){"use strict";function b(b){var c,d=b.attr("data-target")||(c=b.attr("href"))&&c.replace(/.*(?=#[^\s]+$)/,"");return a(d)}function c(b){return this.each(function(){var c=a(this),e=c.data("bs.collapse"),f=a.extend({},d.DEFAULTS,c.data(),"object"==typeof b&&b);!e&&f.toggle&&/show|hide/.test(b)&&(f.toggle=!1),e||c.data("bs.collapse",e=new d(this,f)),"string"==typeof b&&e[b]()})}var d=function(b,c){this.$element=a(b),this.options=a.extend({},d.DEFAULTS,c),this.$trigger=a('[data-toggle="collapse"][href="#'+b.id+'"],[data-toggle="collapse"][data-target="#'+b.id+'"]'),this.transitioning=null,this.options.parent?this.$parent=this.getParent():this.addAriaAndCollapsedClass(this.$element,this.$trigger),this.options.toggle&&this.toggle()};d.VERSION="3.3.5",d.TRANSITION_DURATION=350,d.DEFAULTS={toggle:!0},d.prototype.dimension=function(){var a=this.$element.hasClass("width");return a?"width":"height"},d.prototype.show=function(){if(!this.transitioning&&!this.$element.hasClass("in")){var b,e=this.$parent&&this.$parent.children(".panel").children(".in, .collapsing");if(!(e&&e.length&&(b=e.data("bs.collapse"),b&&b.transitioning))){var f=a.Event("show.bs.collapse");if(this.$element.trigger(f),!f.isDefaultPrevented()){e&&e.length&&(c.call(e,"hide"),b||e.data("bs.collapse",null));var g=this.dimension();this.$element.removeClass("collapse").addClass("collapsing")[g](0).attr("aria-expanded",!0),this.$trigger.removeClass("collapsed").attr("aria-expanded",!0),this.transitioning=1;var h=function(){this.$element.removeClass("collapsing").addClass("collapse in")[g](""),this.transitioning=0,this.$element.trigger("shown.bs.collapse")};if(!a.support.transition)return h.call(this);var i=a.camelCase(["scroll",g].join("-"));this.$element.one("bsTransitionEnd",a.proxy(h,this)).emulateTransitionEnd(d.TRANSITION_DURATION)[g](this.$element[0][i])}}}},d.prototype.hide=function(){if(!this.transitioning&&this.$element.hasClass("in")){var b=a.Event("hide.bs.collapse");if(this.$element.trigger(b),!b.isDefaultPrevented()){var c=this.dimension();this.$element[c](this.$element[c]())[0].offsetHeight,this.$element.addClass("collapsing").removeClass("collapse in").attr("aria-expanded",!1),this.$trigger.addClass("collapsed").attr("aria-expanded",!1),this.transitioning=1;var e=function(){this.transitioning=0,this.$element.removeClass("collapsing").addClass("collapse").trigger("hidden.bs.collapse")};return a.support.transition?void this.$element[c](0).one("bsTransitionEnd",a.proxy(e,this)).emulateTransitionEnd(d.TRANSITION_DURATION):e.call(this)}}},d.prototype.toggle=function(){this[this.$element.hasClass("in")?"hide":"show"]()},d.prototype.getParent=function(){return a(this.options.parent).find('[data-toggle="collapse"][data-parent="'+this.options.parent+'"]').each(a.proxy(function(c,d){var e=a(d);this.addAriaAndCollapsedClass(b(e),e)},this)).end()},d.prototype.addAriaAndCollapsedClass=function(a,b){var c=a.hasClass("in");a.attr("aria-expanded",c),b.toggleClass("collapsed",!c).attr("aria-expanded",c)};var e=a.fn.collapse;a.fn.collapse=c,a.fn.collapse.Constructor=d,a.fn.collapse.noConflict=function(){return a.fn.collapse=e,this},a(document).on("click.bs.collapse.data-api",'[data-toggle="collapse"]',function(d){var e=a(this);e.attr("data-target")||d.preventDefault();var f=b(e),g=f.data("bs.collapse"),h=g?"toggle":e.data();c.call(f,h)})}(jQuery),+function(a){"use strict";function b(b){var c=b.attr("data-target");c||(c=b.attr("href"),c=c&&/#[A-Za-z]/.test(c)&&c.replace(/.*(?=#[^\s]*$)/,""));var d=c&&a(c);return d&&d.length?d:b.parent()}function c(c){c&&3===c.which||(a(e).remove(),a(f).each(function(){var d=a(this),e=b(d),f={relatedTarget:this};e.hasClass("open")&&(c&&"click"==c.type&&/input|textarea/i.test(c.target.tagName)&&a.contains(e[0],c.target)||(e.trigger(c=a.Event("hide.bs.dropdown",f)),c.isDefaultPrevented()||(d.attr("aria-expanded","false"),e.removeClass("open").trigger("hidden.bs.dropdown",f))))}))}function d(b){return this.each(function(){var c=a(this),d=c.data("bs.dropdown");d||c.data("bs.dropdown",d=new g(this)),"string"==typeof b&&d[b].call(c)})}var e=".dropdown-backdrop",f='[data-toggle="dropdown"]',g=function(b){a(b).on("click.bs.dropdown",this.toggle)};g.VERSION="3.3.5",g.prototype.toggle=function(d){var e=a(this);if(!e.is(".disabled, :disabled")){var f=b(e),g=f.hasClass("open");if(c(),!g){"ontouchstart"in document.documentElement&&!f.closest(".navbar-nav").length&&a(document.createElement("div")).addClass("dropdown-backdrop").insertAfter(a(this)).on("click",c);var h={relatedTarget:this};if(f.trigger(d=a.Event("show.bs.dropdown",h)),d.isDefaultPrevented())return;e.trigger("focus").attr("aria-expanded","true"),f.toggleClass("open").trigger("shown.bs.dropdown",h)}return!1}},g.prototype.keydown=function(c){if(/(38|40|27|32)/.test(c.which)&&!/input|textarea/i.test(c.target.tagName)){var d=a(this);if(c.preventDefault(),c.stopPropagation(),!d.is(".disabled, :disabled")){var e=b(d),g=e.hasClass("open");if(!g&&27!=c.which||g&&27==c.which)return 27==c.which&&e.find(f).trigger("focus"),d.trigger("click");var h=" li:not(.disabled):visible a",i=e.find(".dropdown-menu"+h);if(i.length){var j=i.index(c.target);38==c.which&&j>0&&j--,40==c.which&&j<i.length-1&&j++,~j||(j=0),i.eq(j).trigger("focus")}}}};var h=a.fn.dropdown;a.fn.dropdown=d,a.fn.dropdown.Constructor=g,a.fn.dropdown.noConflict=function(){return a.fn.dropdown=h,this},a(document).on("click.bs.dropdown.data-api",c).on("click.bs.dropdown.data-api",".dropdown form",function(a){a.stopPropagation()}).on("click.bs.dropdown.data-api",f,g.prototype.toggle).on("keydown.bs.dropdown.data-api",f,g.prototype.keydown).on("keydown.bs.dropdown.data-api",".dropdown-menu",g.prototype.keydown)}(jQuery),+function(a){"use strict";function b(b,d){return this.each(function(){var e=a(this),f=e.data("bs.modal"),g=a.extend({},c.DEFAULTS,e.data(),"object"==typeof b&&b);f||e.data("bs.modal",f=new c(this,g)),"string"==typeof b?f[b](d):g.show&&f.show(d)})}var c=function(b,c){this.options=c,this.$body=a(document.body),this.$element=a(b),this.$dialog=this.$element.find(".modal-dialog"),this.$backdrop=null,this.isShown=null,this.originalBodyPad=null,this.scrollbarWidth=0,this.ignoreBackdropClick=!1,this.options.remote&&this.$element.find(".modal-content").load(this.options.remote,a.proxy(function(){this.$element.trigger("loaded.bs.modal")},this))};c.VERSION="3.3.5",c.TRANSITION_DURATION=300,c.BACKDROP_TRANSITION_DURATION=150,c.DEFAULTS={backdrop:!0,keyboard:!0,show:!0},c.prototype.toggle=function(a){return this.isShown?this.hide():this.show(a)},c.prototype.show=function(b){var d=this,e=a.Event("show.bs.modal",{relatedTarget:b});this.$element.trigger(e),this.isShown||e.isDefaultPrevented()||(this.isShown=!0,this.checkScrollbar(),this.setScrollbar(),this.$body.addClass("modal-open"),this.escape(),this.resize(),this.$element.on("click.dismiss.bs.modal",'[data-dismiss="modal"]',a.proxy(this.hide,this)),this.$dialog.on("mousedown.dismiss.bs.modal",function(){d.$element.one("mouseup.dismiss.bs.modal",function(b){a(b.target).is(d.$element)&&(d.ignoreBackdropClick=!0)})}),this.backdrop(function(){var e=a.support.transition&&d.$element.hasClass("fade");d.$element.parent().length||d.$element.appendTo(d.$body),d.$element.show().scrollTop(0),d.adjustDialog(),e&&d.$element[0].offsetWidth,d.$element.addClass("in"),d.enforceFocus();var f=a.Event("shown.bs.modal",{relatedTarget:b});e?d.$dialog.one("bsTransitionEnd",function(){d.$element.trigger("focus").trigger(f)}).emulateTransitionEnd(c.TRANSITION_DURATION):d.$element.trigger("focus").trigger(f)}))},c.prototype.hide=function(b){b&&b.preventDefault(),b=a.Event("hide.bs.modal"),this.$element.trigger(b),this.isShown&&!b.isDefaultPrevented()&&(this.isShown=!1,this.escape(),this.resize(),a(document).off("focusin.bs.modal"),this.$element.removeClass("in").off("click.dismiss.bs.modal").off("mouseup.dismiss.bs.modal"),this.$dialog.off("mousedown.dismiss.bs.modal"),a.support.transition&&this.$element.hasClass("fade")?this.$element.one("bsTransitionEnd",a.proxy(this.hideModal,this)).emulateTransitionEnd(c.TRANSITION_DURATION):this.hideModal())},c.prototype.enforceFocus=function(){a(document).off("focusin.bs.modal").on("focusin.bs.modal",a.proxy(function(a){this.$element[0]===a.target||this.$element.has(a.target).length||this.$element.trigger("focus")},this))},c.prototype.escape=function(){this.isShown&&this.options.keyboard?this.$element.on("keydown.dismiss.bs.modal",a.proxy(function(a){27==a.which&&this.hide()},this)):this.isShown||this.$element.off("keydown.dismiss.bs.modal")},c.prototype.resize=function(){this.isShown?a(window).on("resize.bs.modal",a.proxy(this.handleUpdate,this)):a(window).off("resize.bs.modal")},c.prototype.hideModal=function(){var a=this;this.$element.hide(),this.backdrop(function(){a.$body.removeClass("modal-open"),a.resetAdjustments(),a.resetScrollbar(),a.$element.trigger("hidden.bs.modal")})},c.prototype.removeBackdrop=function(){this.$backdrop&&this.$backdrop.remove(),this.$backdrop=null},c.prototype.backdrop=function(b){var d=this,e=this.$element.hasClass("fade")?"fade":"";if(this.isShown&&this.options.backdrop){var f=a.support.transition&&e;if(this.$backdrop=a(document.createElement("div")).addClass("modal-backdrop "+e).appendTo(this.$body),this.$element.on("click.dismiss.bs.modal",a.proxy(function(a){return this.ignoreBackdropClick?void(this.ignoreBackdropClick=!1):void(a.target===a.currentTarget&&("static"==this.options.backdrop?this.$element[0].focus():this.hide()))},this)),f&&this.$backdrop[0].offsetWidth,this.$backdrop.addClass("in"),!b)return;f?this.$backdrop.one("bsTransitionEnd",b).emulateTransitionEnd(c.BACKDROP_TRANSITION_DURATION):b()}else if(!this.isShown&&this.$backdrop){this.$backdrop.removeClass("in");var g=function(){d.removeBackdrop(),b&&b()};a.support.transition&&this.$element.hasClass("fade")?this.$backdrop.one("bsTransitionEnd",g).emulateTransitionEnd(c.BACKDROP_TRANSITION_DURATION):g()}else b&&b()},c.prototype.handleUpdate=function(){this.adjustDialog()},c.prototype.adjustDialog=function(){var a=this.$element[0].scrollHeight>document.documentElement.clientHeight;this.$element.css({paddingLeft:!this.bodyIsOverflowing&&a?this.scrollbarWidth:"",paddingRight:this.bodyIsOverflowing&&!a?this.scrollbarWidth:""})},c.prototype.resetAdjustments=function(){this.$element.css({paddingLeft:"",paddingRight:""})},c.prototype.checkScrollbar=function(){var a=window.innerWidth;if(!a){var b=document.documentElement.getBoundingClientRect();a=b.right-Math.abs(b.left)}this.bodyIsOverflowing=document.body.clientWidth<a,this.scrollbarWidth=this.measureScrollbar()},c.prototype.setScrollbar=function(){var a=parseInt(this.$body.css("padding-right")||0,10);this.originalBodyPad=document.body.style.paddingRight||"",this.bodyIsOverflowing&&this.$body.css("padding-right",a+this.scrollbarWidth)},c.prototype.resetScrollbar=function(){this.$body.css("padding-right",this.originalBodyPad)},c.prototype.measureScrollbar=function(){var a=document.createElement("div");a.className="modal-scrollbar-measure",this.$body.append(a);var b=a.offsetWidth-a.clientWidth;return this.$body[0].removeChild(a),b};var d=a.fn.modal;a.fn.modal=b,a.fn.modal.Constructor=c,a.fn.modal.noConflict=function(){return a.fn.modal=d,this},a(document).on("click.bs.modal.data-api",'[data-toggle="modal"]',function(c){var d=a(this),e=d.attr("href"),f=a(d.attr("data-target")||e&&e.replace(/.*(?=#[^\s]+$)/,"")),g=f.data("bs.modal")?"toggle":a.extend({remote:!/#/.test(e)&&e},f.data(),d.data());d.is("a")&&c.preventDefault(),f.one("show.bs.modal",function(a){a.isDefaultPrevented()||f.one("hidden.bs.modal",function(){d.is(":visible")&&d.trigger("focus")})}),b.call(f,g,this)})}(jQuery),+function(a){"use strict";function b(b){return this.each(function(){var d=a(this),e=d.data("bs.tooltip"),f="object"==typeof b&&b;(e||!/destroy|hide/.test(b))&&(e||d.data("bs.tooltip",e=new c(this,f)),"string"==typeof b&&e[b]())})}var c=function(a,b){this.type=null,this.options=null,this.enabled=null,this.timeout=null,this.hoverState=null,this.$element=null,this.inState=null,this.init("tooltip",a,b)};c.VERSION="3.3.5",c.TRANSITION_DURATION=150,c.DEFAULTS={animation:!0,placement:"top",selector:!1,template:'<div class="tooltip" role="tooltip"><div class="tooltip-arrow"></div><div class="tooltip-inner"></div></div>',trigger:"hover focus",title:"",delay:0,html:!1,container:!1,viewport:{selector:"body",padding:0}},c.prototype.init=function(b,c,d){if(this.enabled=!0,this.type=b,this.$element=a(c),this.options=this.getOptions(d),this.$viewport=this.options.viewport&&a(a.isFunction(this.options.viewport)?this.options.viewport.call(this,this.$element):this.options.viewport.selector||this.options.viewport),this.inState={click:!1,hover:!1,focus:!1},this.$element[0]instanceof document.constructor&&!this.options.selector)throw new Error("`selector` option must be specified when initializing "+this.type+" on the window.document object!");for(var e=this.options.trigger.split(" "),f=e.length;f--;){var g=e[f];if("click"==g)this.$element.on("click."+this.type,this.options.selector,a.proxy(this.toggle,this));else if("manual"!=g){var h="hover"==g?"mouseenter":"focusin",i="hover"==g?"mouseleave":"focusout";this.$element.on(h+"."+this.type,this.options.selector,a.proxy(this.enter,this)),this.$element.on(i+"."+this.type,this.options.selector,a.proxy(this.leave,this))}}this.options.selector?this._options=a.extend({},this.options,{trigger:"manual",selector:""}):this.fixTitle()},c.prototype.getDefaults=function(){return c.DEFAULTS},c.prototype.getOptions=function(b){return b=a.extend({},this.getDefaults(),this.$element.data(),b),b.delay&&"number"==typeof b.delay&&(b.delay={show:b.delay,hide:b.delay}),b},c.prototype.getDelegateOptions=function(){var b={},c=this.getDefaults();return this._options&&a.each(this._options,function(a,d){c[a]!=d&&(b[a]=d)}),b},c.prototype.enter=function(b){var c=b instanceof this.constructor?b:a(b.currentTarget).data("bs."+this.type);return c||(c=new this.constructor(b.currentTarget,this.getDelegateOptions()),a(b.currentTarget).data("bs."+this.type,c)),b instanceof a.Event&&(c.inState["focusin"==b.type?"focus":"hover"]=!0),c.tip().hasClass("in")||"in"==c.hoverState?void(c.hoverState="in"):(clearTimeout(c.timeout),c.hoverState="in",c.options.delay&&c.options.delay.show?void(c.timeout=setTimeout(function(){"in"==c.hoverState&&c.show()},c.options.delay.show)):c.show())},c.prototype.isInStateTrue=function(){for(var a in this.inState)if(this.inState[a])return!0;return!1},c.prototype.leave=function(b){var c=b instanceof this.constructor?b:a(b.currentTarget).data("bs."+this.type);return c||(c=new this.constructor(b.currentTarget,this.getDelegateOptions()),a(b.currentTarget).data("bs."+this.type,c)),b instanceof a.Event&&(c.inState["focusout"==b.type?"focus":"hover"]=!1),c.isInStateTrue()?void 0:(clearTimeout(c.timeout),c.hoverState="out",c.options.delay&&c.options.delay.hide?void(c.timeout=setTimeout(function(){"out"==c.hoverState&&c.hide()},c.options.delay.hide)):c.hide())},c.prototype.show=function(){var b=a.Event("show.bs."+this.type);if(this.hasContent()&&this.enabled){this.$element.trigger(b);var d=a.contains(this.$element[0].ownerDocument.documentElement,this.$element[0]);if(b.isDefaultPrevented()||!d)return;var e=this,f=this.tip(),g=this.getUID(this.type);this.setContent(),f.attr("id",g),this.$element.attr("aria-describedby",g),this.options.animation&&f.addClass("fade");var h="function"==typeof this.options.placement?this.options.placement.call(this,f[0],this.$element[0]):this.options.placement,i=/\s?auto?\s?/i,j=i.test(h);j&&(h=h.replace(i,"")||"top"),f.detach().css({top:0,left:0,display:"block"}).addClass(h).data("bs."+this.type,this),this.options.container?f.appendTo(this.options.container):f.insertAfter(this.$element),this.$element.trigger("inserted.bs."+this.type);var k=this.getPosition(),l=f[0].offsetWidth,m=f[0].offsetHeight;if(j){var n=h,o=this.getPosition(this.$viewport);h="bottom"==h&&k.bottom+m>o.bottom?"top":"top"==h&&k.top-m<o.top?"bottom":"right"==h&&k.right+l>o.width?"left":"left"==h&&k.left-l<o.left?"right":h,f.removeClass(n).addClass(h)}var p=this.getCalculatedOffset(h,k,l,m);this.applyPlacement(p,h);var q=function(){var a=e.hoverState;e.$element.trigger("shown.bs."+e.type),e.hoverState=null,"out"==a&&e.leave(e)};a.support.transition&&this.$tip.hasClass("fade")?f.one("bsTransitionEnd",q).emulateTransitionEnd(c.TRANSITION_DURATION):q()}},c.prototype.applyPlacement=function(b,c){var d=this.tip(),e=d[0].offsetWidth,f=d[0].offsetHeight,g=parseInt(d.css("margin-top"),10),h=parseInt(d.css("margin-left"),10);isNaN(g)&&(g=0),isNaN(h)&&(h=0),b.top+=g,b.left+=h,a.offset.setOffset(d[0],a.extend({using:function(a){d.css({top:Math.round(a.top),left:Math.round(a.left)})}},b),0),d.addClass("in");var i=d[0].offsetWidth,j=d[0].offsetHeight;"top"==c&&j!=f&&(b.top=b.top+f-j);var k=this.getViewportAdjustedDelta(c,b,i,j);k.left?b.left+=k.left:b.top+=k.top;var l=/top|bottom/.test(c),m=l?2*k.left-e+i:2*k.top-f+j,n=l?"offsetWidth":"offsetHeight";d.offset(b),this.replaceArrow(m,d[0][n],l)},c.prototype.replaceArrow=function(a,b,c){this.arrow().css(c?"left":"top",50*(1-a/b)+"%").css(c?"top":"left","")},c.prototype.setContent=function(){var a=this.tip(),b=this.getTitle();a.find(".tooltip-inner")[this.options.html?"html":"text"](b),a.removeClass("fade in top bottom left right")},c.prototype.hide=function(b){function d(){"in"!=e.hoverState&&f.detach(),e.$element.removeAttr("aria-describedby").trigger("hidden.bs."+e.type),b&&b()}var e=this,f=a(this.$tip),g=a.Event("hide.bs."+this.type);return this.$element.trigger(g),g.isDefaultPrevented()?void 0:(f.removeClass("in"),a.support.transition&&f.hasClass("fade")?f.one("bsTransitionEnd",d).emulateTransitionEnd(c.TRANSITION_DURATION):d(),this.hoverState=null,this)},c.prototype.fixTitle=function(){var a=this.$element;(a.attr("title")||"string"!=typeof a.attr("data-original-title"))&&a.attr("data-original-title",a.attr("title")||"").attr("title","")},c.prototype.hasContent=function(){return this.getTitle()},c.prototype.getPosition=function(b){b=b||this.$element;var c=b[0],d="BODY"==c.tagName,e=c.getBoundingClientRect();null==e.width&&(e=a.extend({},e,{width:e.right-e.left,height:e.bottom-e.top}));var f=d?{top:0,left:0}:b.offset(),g={scroll:d?document.documentElement.scrollTop||document.body.scrollTop:b.scrollTop()},h=d?{width:a(window).width(),height:a(window).height()}:null;return a.extend({},e,g,h,f)},c.prototype.getCalculatedOffset=function(a,b,c,d){return"bottom"==a?{top:b.top+b.height,left:b.left+b.width/2-c/2}:"top"==a?{top:b.top-d,left:b.left+b.width/2-c/2}:"left"==a?{top:b.top+b.height/2-d/2,left:b.left-c}:{top:b.top+b.height/2-d/2,left:b.left+b.width}},c.prototype.getViewportAdjustedDelta=function(a,b,c,d){var e={top:0,left:0};if(!this.$viewport)return e;var f=this.options.viewport&&this.options.viewport.padding||0,g=this.getPosition(this.$viewport);if(/right|left/.test(a)){var h=b.top-f-g.scroll,i=b.top+f-g.scroll+d;h<g.top?e.top=g.top-h:i>g.top+g.height&&(e.top=g.top+g.height-i)}else{var j=b.left-f,k=b.left+f+c;j<g.left?e.left=g.left-j:k>g.right&&(e.left=g.left+g.width-k)}return e},c.prototype.getTitle=function(){var a,b=this.$element,c=this.options;return a=b.attr("data-original-title")||("function"==typeof c.title?c.title.call(b[0]):c.title)},c.prototype.getUID=function(a){do a+=~~(1e6*Math.random());while(document.getElementById(a));return a},c.prototype.tip=function(){if(!this.$tip&&(this.$tip=a(this.options.template),1!=this.$tip.length))throw new Error(this.type+" `template` option must consist of exactly 1 top-level element!");return this.$tip},c.prototype.arrow=function(){return this.$arrow=this.$arrow||this.tip().find(".tooltip-arrow")},c.prototype.enable=function(){this.enabled=!0},c.prototype.disable=function(){this.enabled=!1},c.prototype.toggleEnabled=function(){this.enabled=!this.enabled},c.prototype.toggle=function(b){var c=this;b&&(c=a(b.currentTarget).data("bs."+this.type),c||(c=new this.constructor(b.currentTarget,this.getDelegateOptions()),a(b.currentTarget).data("bs."+this.type,c))),b?(c.inState.click=!c.inState.click,c.isInStateTrue()?c.enter(c):c.leave(c)):c.tip().hasClass("in")?c.leave(c):c.enter(c)},c.prototype.destroy=function(){var a=this;clearTimeout(this.timeout),this.hide(function(){a.$element.off("."+a.type).removeData("bs."+a.type),a.$tip&&a.$tip.detach(),a.$tip=null,a.$arrow=null,a.$viewport=null})};var d=a.fn.tooltip;a.fn.tooltip=b,a.fn.tooltip.Constructor=c,a.fn.tooltip.noConflict=function(){return a.fn.tooltip=d,this}}(jQuery),+function(a){"use strict";function b(b){return this.each(function(){var d=a(this),e=d.data("bs.popover"),f="object"==typeof b&&b;(e||!/destroy|hide/.test(b))&&(e||d.data("bs.popover",e=new c(this,f)),"string"==typeof b&&e[b]())})}var c=function(a,b){this.init("popover",a,b)};if(!a.fn.tooltip)throw new Error("Popover requires tooltip.js");c.VERSION="3.3.5",c.DEFAULTS=a.extend({},a.fn.tooltip.Constructor.DEFAULTS,{placement:"right",trigger:"click",content:"",template:'<div class="popover" role="tooltip"><div class="arrow"></div><h3 class="popover-title"></h3><div class="popover-content"></div></div>'}),c.prototype=a.extend({},a.fn.tooltip.Constructor.prototype),c.prototype.constructor=c,c.prototype.getDefaults=function(){return c.DEFAULTS},c.prototype.setContent=function(){var a=this.tip(),b=this.getTitle(),c=this.getContent();a.find(".popover-title")[this.options.html?"html":"text"](b),a.find(".popover-content").children().detach().end()[this.options.html?"string"==typeof c?"html":"append":"text"](c),a.removeClass("fade top bottom left right in"),a.find(".popover-title").html()||a.find(".popover-title").hide()},c.prototype.hasContent=function(){return this.getTitle()||this.getContent()},c.prototype.getContent=function(){var a=this.$element,b=this.options;return a.attr("data-content")||("function"==typeof b.content?b.content.call(a[0]):b.content)},c.prototype.arrow=function(){return this.$arrow=this.$arrow||this.tip().find(".arrow")};var d=a.fn.popover;a.fn.popover=b,a.fn.popover.Constructor=c,a.fn.popover.noConflict=function(){return a.fn.popover=d,this}}(jQuery),+function(a){"use strict";function b(c,d){this.$body=a(document.body),this.$scrollElement=a(a(c).is(document.body)?window:c),this.options=a.extend({},b.DEFAULTS,d),this.selector=(this.options.target||"")+" .nav li > a",this.offsets=[],this.targets=[],this.activeTarget=null,this.scrollHeight=0,this.$scrollElement.on("scroll.bs.scrollspy",a.proxy(this.process,this)),this.refresh(),this.process()}function c(c){return this.each(function(){var d=a(this),e=d.data("bs.scrollspy"),f="object"==typeof c&&c;e||d.data("bs.scrollspy",e=new b(this,f)),"string"==typeof c&&e[c]()})}b.VERSION="3.3.5",b.DEFAULTS={offset:10},b.prototype.getScrollHeight=function(){return this.$scrollElement[0].scrollHeight||Math.max(this.$body[0].scrollHeight,document.documentElement.scrollHeight)},b.prototype.refresh=function(){var b=this,c="offset",d=0;this.offsets=[],this.targets=[],this.scrollHeight=this.getScrollHeight(),a.isWindow(this.$scrollElement[0])||(c="position",d=this.$scrollElement.scrollTop()),this.$body.find(this.selector).map(function(){var b=a(this),e=b.data("target")||b.attr("href"),f=/^#./.test(e)&&a(e);return f&&f.length&&f.is(":visible")&&[[f[c]().top+d,e]]||null}).sort(function(a,b){return a[0]-b[0]}).each(function(){b.offsets.push(this[0]),b.targets.push(this[1])})},b.prototype.process=function(){var a,b=this.$scrollElement.scrollTop()+this.options.offset,c=this.getScrollHeight(),d=this.options.offset+c-this.$scrollElement.height(),e=this.offsets,f=this.targets,g=this.activeTarget;if(this.scrollHeight!=c&&this.refresh(),b>=d)return g!=(a=f[f.length-1])&&this.activate(a);if(g&&b<e[0])return this.activeTarget=null,this.clear();for(a=e.length;a--;)g!=f[a]&&b>=e[a]&&(void 0===e[a+1]||b<e[a+1])&&this.activate(f[a])},b.prototype.activate=function(b){this.activeTarget=b,this.clear();var c=this.selector+'[data-target="'+b+'"],'+this.selector+'[href="'+b+'"]',d=a(c).parents("li").addClass("active");d.parent(".dropdown-menu").length&&(d=d.closest("li.dropdown").addClass("active")),
+d.trigger("activate.bs.scrollspy")},b.prototype.clear=function(){a(this.selector).parentsUntil(this.options.target,".active").removeClass("active")};var d=a.fn.scrollspy;a.fn.scrollspy=c,a.fn.scrollspy.Constructor=b,a.fn.scrollspy.noConflict=function(){return a.fn.scrollspy=d,this},a(window).on("load.bs.scrollspy.data-api",function(){a('[data-spy="scroll"]').each(function(){var b=a(this);c.call(b,b.data())})})}(jQuery),+function(a){"use strict";function b(b){return this.each(function(){var d=a(this),e=d.data("bs.tab");e||d.data("bs.tab",e=new c(this)),"string"==typeof b&&e[b]()})}var c=function(b){this.element=a(b)};c.VERSION="3.3.5",c.TRANSITION_DURATION=150,c.prototype.show=function(){var b=this.element,c=b.closest("ul:not(.dropdown-menu)"),d=b.data("target");if(d||(d=b.attr("href"),d=d&&d.replace(/.*(?=#[^\s]*$)/,"")),!b.parent("li").hasClass("active")){var e=c.find(".active:last a"),f=a.Event("hide.bs.tab",{relatedTarget:b[0]}),g=a.Event("show.bs.tab",{relatedTarget:e[0]});if(e.trigger(f),b.trigger(g),!g.isDefaultPrevented()&&!f.isDefaultPrevented()){var h=a(d);this.activate(b.closest("li"),c),this.activate(h,h.parent(),function(){e.trigger({type:"hidden.bs.tab",relatedTarget:b[0]}),b.trigger({type:"shown.bs.tab",relatedTarget:e[0]})})}}},c.prototype.activate=function(b,d,e){function f(){g.removeClass("active").find("> .dropdown-menu > .active").removeClass("active").end().find('[data-toggle="tab"]').attr("aria-expanded",!1),b.addClass("active").find('[data-toggle="tab"]').attr("aria-expanded",!0),h?(b[0].offsetWidth,b.addClass("in")):b.removeClass("fade"),b.parent(".dropdown-menu").length&&b.closest("li.dropdown").addClass("active").end().find('[data-toggle="tab"]').attr("aria-expanded",!0),e&&e()}var g=d.find("> .active"),h=e&&a.support.transition&&(g.length&&g.hasClass("fade")||!!d.find("> .fade").length);g.length&&h?g.one("bsTransitionEnd",f).emulateTransitionEnd(c.TRANSITION_DURATION):f(),g.removeClass("in")};var d=a.fn.tab;a.fn.tab=b,a.fn.tab.Constructor=c,a.fn.tab.noConflict=function(){return a.fn.tab=d,this};var e=function(c){c.preventDefault(),b.call(a(this),"show")};a(document).on("click.bs.tab.data-api",'[data-toggle="tab"]',e).on("click.bs.tab.data-api",'[data-toggle="pill"]',e)}(jQuery),+function(a){"use strict";function b(b){return this.each(function(){var d=a(this),e=d.data("bs.affix"),f="object"==typeof b&&b;e||d.data("bs.affix",e=new c(this,f)),"string"==typeof b&&e[b]()})}var c=function(b,d){this.options=a.extend({},c.DEFAULTS,d),this.$target=a(this.options.target).on("scroll.bs.affix.data-api",a.proxy(this.checkPosition,this)).on("click.bs.affix.data-api",a.proxy(this.checkPositionWithEventLoop,this)),this.$element=a(b),this.affixed=null,this.unpin=null,this.pinnedOffset=null,this.checkPosition()};c.VERSION="3.3.5",c.RESET="affix affix-top affix-bottom",c.DEFAULTS={offset:0,target:window},c.prototype.getState=function(a,b,c,d){var e=this.$target.scrollTop(),f=this.$element.offset(),g=this.$target.height();if(null!=c&&"top"==this.affixed)return c>e?"top":!1;if("bottom"==this.affixed)return null!=c?e+this.unpin<=f.top?!1:"bottom":a-d>=e+g?!1:"bottom";var h=null==this.affixed,i=h?e:f.top,j=h?g:b;return null!=c&&c>=e?"top":null!=d&&i+j>=a-d?"bottom":!1},c.prototype.getPinnedOffset=function(){if(this.pinnedOffset)return this.pinnedOffset;this.$element.removeClass(c.RESET).addClass("affix");var a=this.$target.scrollTop(),b=this.$element.offset();return this.pinnedOffset=b.top-a},c.prototype.checkPositionWithEventLoop=function(){setTimeout(a.proxy(this.checkPosition,this),1)},c.prototype.checkPosition=function(){if(this.$element.is(":visible")){var b=this.$element.height(),d=this.options.offset,e=d.top,f=d.bottom,g=Math.max(a(document).height(),a(document.body).height());"object"!=typeof d&&(f=e=d),"function"==typeof e&&(e=d.top(this.$element)),"function"==typeof f&&(f=d.bottom(this.$element));var h=this.getState(g,b,e,f);if(this.affixed!=h){null!=this.unpin&&this.$element.css("top","");var i="affix"+(h?"-"+h:""),j=a.Event(i+".bs.affix");if(this.$element.trigger(j),j.isDefaultPrevented())return;this.affixed=h,this.unpin="bottom"==h?this.getPinnedOffset():null,this.$element.removeClass(c.RESET).addClass(i).trigger(i.replace("affix","affixed")+".bs.affix")}"bottom"==h&&this.$element.offset({top:g-b-f})}};var d=a.fn.affix;a.fn.affix=b,a.fn.affix.Constructor=c,a.fn.affix.noConflict=function(){return a.fn.affix=d,this},a(window).on("load",function(){a('[data-spy="affix"]').each(function(){var c=a(this),d=c.data();d.offset=d.offset||{},null!=d.offsetBottom&&(d.offset.bottom=d.offsetBottom),null!=d.offsetTop&&(d.offset.top=d.offsetTop),b.call(c,d)})})}(jQuery);</script>
+<script>/**
+* @preserve HTML5 Shiv 3.7.2 | @afarkas @jdalton @jon_neal @rem | MIT/GPL2 Licensed
+*/
+// Only run this code in IE 8
+if (!!window.navigator.userAgent.match("MSIE 8")) {
+!function(a,b){function c(a,b){var c=a.createElement("p"),d=a.getElementsByTagName("head")[0]||a.documentElement;return c.innerHTML="x<style>"+b+"</style>",d.insertBefore(c.lastChild,d.firstChild)}function d(){var a=t.elements;return"string"==typeof a?a.split(" "):a}function e(a,b){var c=t.elements;"string"!=typeof c&&(c=c.join(" ")),"string"!=typeof a&&(a=a.join(" ")),t.elements=c+" "+a,j(b)}function f(a){var b=s[a[q]];return b||(b={},r++,a[q]=r,s[r]=b),b}function g(a,c,d){if(c||(c=b),l)return c.createElement(a);d||(d=f(c));var e;return e=d.cache[a]?d.cache[a].cloneNode():p.test(a)?(d.cache[a]=d.createElem(a)).cloneNode():d.createElem(a),!e.canHaveChildren||o.test(a)||e.tagUrn?e:d.frag.appendChild(e)}function h(a,c){if(a||(a=b),l)return a.createDocumentFragment();c=c||f(a);for(var e=c.frag.cloneNode(),g=0,h=d(),i=h.length;i>g;g++)e.createElement(h[g]);return e}function i(a,b){b.cache||(b.cache={},b.createElem=a.createElement,b.createFrag=a.createDocumentFragment,b.frag=b.createFrag()),a.createElement=function(c){return t.shivMethods?g(c,a,b):b.createElem(c)},a.createDocumentFragment=Function("h,f","return function(){var n=f.cloneNode(),c=n.createElement;h.shivMethods&&("+d().join().replace(/[\w\-:]+/g,function(a){return b.createElem(a),b.frag.createElement(a),'c("'+a+'")'})+");return n}")(t,b.frag)}function j(a){a||(a=b);var d=f(a);return!t.shivCSS||k||d.hasCSS||(d.hasCSS=!!c(a,"article,aside,dialog,figcaption,figure,footer,header,hgroup,main,nav,section{display:block}mark{background:#FF0;color:#000}template{display:none}")),l||i(a,d),a}var k,l,m="3.7.2",n=a.html5||{},o=/^<|^(?:button|map|select|textarea|object|iframe|option|optgroup)$/i,p=/^(?:a|b|code|div|fieldset|h1|h2|h3|h4|h5|h6|i|label|li|ol|p|q|span|strong|style|table|tbody|td|th|tr|ul)$/i,q="_html5shiv",r=0,s={};!function(){try{var a=b.createElement("a");a.innerHTML="<xyz></xyz>",k="hidden"in a,l=1==a.childNodes.length||function(){b.createElement("a");var a=b.createDocumentFragment();return"undefined"==typeof a.cloneNode||"undefined"==typeof a.createDocumentFragment||"undefined"==typeof a.createElement}()}catch(c){k=!0,l=!0}}();var t={elements:n.elements||"abbr article aside audio bdi canvas data datalist details dialog figcaption figure footer header hgroup main mark meter nav output picture progress section summary template time video",version:m,shivCSS:n.shivCSS!==!1,supportsUnknownElements:l,shivMethods:n.shivMethods!==!1,type:"default",shivDocument:j,createElement:g,createDocumentFragment:h,addElements:e};a.html5=t,j(b)}(this,document);
+};
+</script>
+<script>/*! Respond.js v1.4.2: min/max-width media query polyfill * Copyright 2013 Scott Jehl
+ * Licensed under https://github.com/scottjehl/Respond/blob/master/LICENSE-MIT
+ *  */
+
+// Only run this code in IE 8
+if (!!window.navigator.userAgent.match("MSIE 8")) {
+!function(a){"use strict";a.matchMedia=a.matchMedia||function(a){var b,c=a.documentElement,d=c.firstElementChild||c.firstChild,e=a.createElement("body"),f=a.createElement("div");return f.id="mq-test-1",f.style.cssText="position:absolute;top:-100em",e.style.background="none",e.appendChild(f),function(a){return f.innerHTML='&shy;<style media="'+a+'"> #mq-test-1 { width: 42px; }</style>',c.insertBefore(e,d),b=42===f.offsetWidth,c.removeChild(e),{matches:b,media:a}}}(a.document)}(this),function(a){"use strict";function b(){u(!0)}var c={};a.respond=c,c.update=function(){};var d=[],e=function(){var b=!1;try{b=new a.XMLHttpRequest}catch(c){b=new a.ActiveXObject("Microsoft.XMLHTTP")}return function(){return b}}(),f=function(a,b){var c=e();c&&(c.open("GET",a,!0),c.onreadystatechange=function(){4!==c.readyState||200!==c.status&&304!==c.status||b(c.responseText)},4!==c.readyState&&c.send(null))};if(c.ajax=f,c.queue=d,c.regex={media:/@media[^\{]+\{([^\{\}]*\{[^\}\{]*\})+/gi,keyframes:/@(?:\-(?:o|moz|webkit)\-)?keyframes[^\{]+\{(?:[^\{\}]*\{[^\}\{]*\})+[^\}]*\}/gi,urls:/(url\()['"]?([^\/\)'"][^:\)'"]+)['"]?(\))/g,findStyles:/@media *([^\{]+)\{([\S\s]+?)$/,only:/(only\s+)?([a-zA-Z]+)\s?/,minw:/\([\s]*min\-width\s*:[\s]*([\s]*[0-9\.]+)(px|em)[\s]*\)/,maxw:/\([\s]*max\-width\s*:[\s]*([\s]*[0-9\.]+)(px|em)[\s]*\)/},c.mediaQueriesSupported=a.matchMedia&&null!==a.matchMedia("only all")&&a.matchMedia("only all").matches,!c.mediaQueriesSupported){var g,h,i,j=a.document,k=j.documentElement,l=[],m=[],n=[],o={},p=30,q=j.getElementsByTagName("head")[0]||k,r=j.getElementsByTagName("base")[0],s=q.getElementsByTagName("link"),t=function(){var a,b=j.createElement("div"),c=j.body,d=k.style.fontSize,e=c&&c.style.fontSize,f=!1;return b.style.cssText="position:absolute;font-size:1em;width:1em",c||(c=f=j.createElement("body"),c.style.background="none"),k.style.fontSize="100%",c.style.fontSize="100%",c.appendChild(b),f&&k.insertBefore(c,k.firstChild),a=b.offsetWidth,f?k.removeChild(c):c.removeChild(b),k.style.fontSize=d,e&&(c.style.fontSize=e),a=i=parseFloat(a)},u=function(b){var c="clientWidth",d=k[c],e="CSS1Compat"===j.compatMode&&d||j.body[c]||d,f={},o=s[s.length-1],r=(new Date).getTime();if(b&&g&&p>r-g)return a.clearTimeout(h),h=a.setTimeout(u,p),void 0;g=r;for(var v in l)if(l.hasOwnProperty(v)){var w=l[v],x=w.minw,y=w.maxw,z=null===x,A=null===y,B="em";x&&(x=parseFloat(x)*(x.indexOf(B)>-1?i||t():1)),y&&(y=parseFloat(y)*(y.indexOf(B)>-1?i||t():1)),w.hasquery&&(z&&A||!(z||e>=x)||!(A||y>=e))||(f[w.media]||(f[w.media]=[]),f[w.media].push(m[w.rules]))}for(var C in n)n.hasOwnProperty(C)&&n[C]&&n[C].parentNode===q&&q.removeChild(n[C]);n.length=0;for(var D in f)if(f.hasOwnProperty(D)){var E=j.createElement("style"),F=f[D].join("\n");E.type="text/css",E.media=D,q.insertBefore(E,o.nextSibling),E.styleSheet?E.styleSheet.cssText=F:E.appendChild(j.createTextNode(F)),n.push(E)}},v=function(a,b,d){var e=a.replace(c.regex.keyframes,"").match(c.regex.media),f=e&&e.length||0;b=b.substring(0,b.lastIndexOf("/"));var g=function(a){return a.replace(c.regex.urls,"$1"+b+"$2$3")},h=!f&&d;b.length&&(b+="/"),h&&(f=1);for(var i=0;f>i;i++){var j,k,n,o;h?(j=d,m.push(g(a))):(j=e[i].match(c.regex.findStyles)&&RegExp.$1,m.push(RegExp.$2&&g(RegExp.$2))),n=j.split(","),o=n.length;for(var p=0;o>p;p++)k=n[p],l.push({media:k.split("(")[0].match(c.regex.only)&&RegExp.$2||"all",rules:m.length-1,hasquery:k.indexOf("(")>-1,minw:k.match(c.regex.minw)&&parseFloat(RegExp.$1)+(RegExp.$2||""),maxw:k.match(c.regex.maxw)&&parseFloat(RegExp.$1)+(RegExp.$2||"")})}u()},w=function(){if(d.length){var b=d.shift();f(b.href,function(c){v(c,b.href,b.media),o[b.href]=!0,a.setTimeout(function(){w()},0)})}},x=function(){for(var b=0;b<s.length;b++){var c=s[b],e=c.href,f=c.media,g=c.rel&&"stylesheet"===c.rel.toLowerCase();e&&g&&!o[e]&&(c.styleSheet&&c.styleSheet.rawCssText?(v(c.styleSheet.rawCssText,e,f),o[e]=!0):(!/^([a-zA-Z:]*\/\/)/.test(e)&&!r||e.replace(RegExp.$1,"").split("/")[0]===a.location.host)&&("//"===e.substring(0,2)&&(e=a.location.protocol+e),d.push({href:e,media:f})))}w()};x(),c.update=x,c.getEmValue=t,a.addEventListener?a.addEventListener("resize",b,!1):a.attachEvent&&a.attachEvent("onresize",b)}}(this);
+};
+</script>
+<style>h1 {font-size: 34px;}
+       h1.title {font-size: 38px;}
+       h2 {font-size: 30px;}
+       h3 {font-size: 24px;}
+       h4 {font-size: 18px;}
+       h5 {font-size: 16px;}
+       h6 {font-size: 12px;}
+       code {color: inherit; background-color: rgba(0, 0, 0, 0.04);}
+       pre:not([class]) { background-color: white }</style>
+<script>
+
+/**
+ * jQuery Plugin: Sticky Tabs
+ *
+ * @author Aidan Lister <aidan@php.net>
+ * adapted by Ruben Arslan to activate parent tabs too
+ * http://www.aidanlister.com/2014/03/persisting-the-tab-state-in-bootstrap/
+ */
+(function($) {
+  "use strict";
+  $.fn.rmarkdownStickyTabs = function() {
+    var context = this;
+    // Show the tab corresponding with the hash in the URL, or the first tab
+    var showStuffFromHash = function() {
+      var hash = window.location.hash;
+      var selector = hash ? 'a[href="' + hash + '"]' : 'li.active > a';
+      var $selector = $(selector, context);
+      if($selector.data('toggle') === "tab") {
+        $selector.tab('show');
+        // walk up the ancestors of this element, show any hidden tabs
+        $selector.parents('.section.tabset').each(function(i, elm) {
+          var link = $('a[href="#' + $(elm).attr('id') + '"]');
+          if(link.data('toggle') === "tab") {
+            link.tab("show");
+          }
+        });
+      }
+    };
+
+
+    // Set the correct tab when the page loads
+    showStuffFromHash(context);
+
+    // Set the correct tab when a user uses their back/forward button
+    $(window).on('hashchange', function() {
+      showStuffFromHash(context);
+    });
+
+    // Change the URL when tabs are clicked
+    $('a', context).on('click', function(e) {
+      history.pushState(null, null, this.href);
+      showStuffFromHash(context);
+    });
+
+    return this;
+  };
+}(jQuery));
+
+window.buildTabsets = function(tocID) {
+
+  // build a tabset from a section div with the .tabset class
+  function buildTabset(tabset) {
+
+    // check for fade and pills options
+    var fade = tabset.hasClass("tabset-fade");
+    var pills = tabset.hasClass("tabset-pills");
+    var navClass = pills ? "nav-pills" : "nav-tabs";
+
+    // determine the heading level of the tabset and tabs
+    var match = tabset.attr('class').match(/level(\d) /);
+    if (match === null)
+      return;
+    var tabsetLevel = Number(match[1]);
+    var tabLevel = tabsetLevel + 1;
+
+    // find all subheadings immediately below
+    var tabs = tabset.find("div.section.level" + tabLevel);
+    if (!tabs.length)
+      return;
+
+    // create tablist and tab-content elements
+    var tabList = $('<ul class="nav ' + navClass + '" role="tablist"></ul>');
+    $(tabs[0]).before(tabList);
+    var tabContent = $('<div class="tab-content"></div>');
+    $(tabs[0]).before(tabContent);
+
+    // build the tabset
+    var activeTab = 0;
+    tabs.each(function(i) {
+
+      // get the tab div
+      var tab = $(tabs[i]);
+
+      // get the id then sanitize it for use with bootstrap tabs
+      var id = tab.attr('id');
+
+      // see if this is marked as the active tab
+      if (tab.hasClass('active'))
+        activeTab = i;
+
+      // remove any table of contents entries associated with
+      // this ID (since we'll be removing the heading element)
+      $("div#" + tocID + " li a[href='#" + id + "']").parent().remove();
+
+      // sanitize the id for use with bootstrap tabs
+      id = id.replace(/[.\/?&!#<>]/g, '').replace(/\s/g, '_');
+      tab.attr('id', id);
+
+      // get the heading element within it, grab it's text, then remove it
+      var heading = tab.find('h' + tabLevel + ':first');
+      var headingText = heading.html();
+      heading.remove();
+
+      // build and append the tab list item
+      var a = $('<a role="tab" data-toggle="tab">' + headingText + '</a>');
+      a.attr('href', '#' + id);
+      a.attr('aria-controls', id);
+      var li = $('<li role="presentation"></li>');
+      li.append(a);
+      tabList.append(li);
+
+      // set it's attributes
+      tab.attr('role', 'tabpanel');
+      tab.addClass('tab-pane');
+      tab.addClass('tabbed-pane');
+      if (fade)
+        tab.addClass('fade');
+
+      // move it into the tab content div
+      tab.detach().appendTo(tabContent);
+    });
+
+    // set active tab
+    $(tabList.children('li')[activeTab]).addClass('active');
+    var active = $(tabContent.children('div.section')[activeTab]);
+    active.addClass('active');
+    if (fade)
+      active.addClass('in');
+
+    if (tabset.hasClass("tabset-sticky"))
+      tabset.rmarkdownStickyTabs();
+  }
+
+  // convert section divs with the .tabset class to tabsets
+  var tabsets = $("div.section.tabset");
+  tabsets.each(function(i) {
+    buildTabset($(tabsets[i]));
+  });
+};
+
+</script>
+<style type="text/css">.hljs-literal {
+color: #990073;
+}
+.hljs-number {
+color: #099;
+}
+.hljs-comment {
+color: #998;
+font-style: italic;
+}
+.hljs-keyword {
+color: #900;
+font-weight: bold;
+}
+.hljs-string {
+color: #d14;
+}
+</style>
+<script src="data:application/javascript;base64,/*! highlight.js v9.12.0 | BSD3 License | git.io/hljslicense */
!function(e){var n="object"==typeof window&&window||"object"==typeof self&&self;"undefined"!=typeof exports?e(exports):n&&(n.hljs=e({}),"function"==typeof define&&define.amd&&define([],function(){return n.hljs}))}(function(e){function n(e){return e.replace(/&/g,"&amp;").replace(/</g,"&lt;").replace(/>/g,"&gt;")}function t(e){return e.nodeName.toLowerCase()}function r(e,n){var t=e&&e.exec(n);return t&&0===t.index}function a(e){return k.test(e)}function i(e){var n,t,r,i,o=e.className+" ";if(o+=e.parentNode?e.parentNode.className:"",t=B.exec(o))return w(t[1])?t[1]:"no-highlight";for(o=o.split(/\s+/),n=0,r=o.length;r>n;n++)if(i=o[n],a(i)||w(i))return i}function o(e){var n,t={},r=Array.prototype.slice.call(arguments,1);for(n in e)t[n]=e[n];return r.forEach(function(e){for(n in e)t[n]=e[n]}),t}function u(e){var n=[];return function r(e,a){for(var i=e.firstChild;i;i=i.nextSibling)3===i.nodeType?a+=i.nodeValue.length:1===i.nodeType&&(n.push({event:"start",offset:a,node:i}),a=r(i,a),t(i).match(/br|hr|img|input/)||n.push({event:"stop",offset:a,node:i}));return a}(e,0),n}function c(e,r,a){function i(){return e.length&&r.length?e[0].offset!==r[0].offset?e[0].offset<r[0].offset?e:r:"start"===r[0].event?e:r:e.length?e:r}function o(e){function r(e){return" "+e.nodeName+'="'+n(e.value).replace('"',"&quot;")+'"'}s+="<"+t(e)+E.map.call(e.attributes,r).join("")+">"}function u(e){s+="</"+t(e)+">"}function c(e){("start"===e.event?o:u)(e.node)}for(var l=0,s="",f=[];e.length||r.length;){var g=i();if(s+=n(a.substring(l,g[0].offset)),l=g[0].offset,g===e){f.reverse().forEach(u);do c(g.splice(0,1)[0]),g=i();while(g===e&&g.length&&g[0].offset===l);f.reverse().forEach(o)}else"start"===g[0].event?f.push(g[0].node):f.pop(),c(g.splice(0,1)[0])}return s+n(a.substr(l))}function l(e){return e.v&&!e.cached_variants&&(e.cached_variants=e.v.map(function(n){return o(e,{v:null},n)})),e.cached_variants||e.eW&&[o(e)]||[e]}function s(e){function n(e){return e&&e.source||e}function t(t,r){return new RegExp(n(t),"m"+(e.cI?"i":"")+(r?"g":""))}function r(a,i){if(!a.compiled){if(a.compiled=!0,a.k=a.k||a.bK,a.k){var o={},u=function(n,t){e.cI&&(t=t.toLowerCase()),t.split(" ").forEach(function(e){var t=e.split("|");o[t[0]]=[n,t[1]?Number(t[1]):1]})};"string"==typeof a.k?u("keyword",a.k):x(a.k).forEach(function(e){u(e,a.k[e])}),a.k=o}a.lR=t(a.l||/\w+/,!0),i&&(a.bK&&(a.b="\\b("+a.bK.split(" ").join("|")+")\\b"),a.b||(a.b=/\B|\b/),a.bR=t(a.b),a.e||a.eW||(a.e=/\B|\b/),a.e&&(a.eR=t(a.e)),a.tE=n(a.e)||"",a.eW&&i.tE&&(a.tE+=(a.e?"|":"")+i.tE)),a.i&&(a.iR=t(a.i)),null==a.r&&(a.r=1),a.c||(a.c=[]),a.c=Array.prototype.concat.apply([],a.c.map(function(e){return l("self"===e?a:e)})),a.c.forEach(function(e){r(e,a)}),a.starts&&r(a.starts,i);var c=a.c.map(function(e){return e.bK?"\\.?("+e.b+")\\.?":e.b}).concat([a.tE,a.i]).map(n).filter(Boolean);a.t=c.length?t(c.join("|"),!0):{exec:function(){return null}}}}r(e)}function f(e,t,a,i){function o(e,n){var t,a;for(t=0,a=n.c.length;a>t;t++)if(r(n.c[t].bR,e))return n.c[t]}function u(e,n){if(r(e.eR,n)){for(;e.endsParent&&e.parent;)e=e.parent;return e}return e.eW?u(e.parent,n):void 0}function c(e,n){return!a&&r(n.iR,e)}function l(e,n){var t=N.cI?n[0].toLowerCase():n[0];return e.k.hasOwnProperty(t)&&e.k[t]}function p(e,n,t,r){var a=r?"":I.classPrefix,i='<span class="'+a,o=t?"":C;return i+=e+'">',i+n+o}function h(){var e,t,r,a;if(!E.k)return n(k);for(a="",t=0,E.lR.lastIndex=0,r=E.lR.exec(k);r;)a+=n(k.substring(t,r.index)),e=l(E,r),e?(B+=e[1],a+=p(e[0],n(r[0]))):a+=n(r[0]),t=E.lR.lastIndex,r=E.lR.exec(k);return a+n(k.substr(t))}function d(){var e="string"==typeof E.sL;if(e&&!y[E.sL])return n(k);var t=e?f(E.sL,k,!0,x[E.sL]):g(k,E.sL.length?E.sL:void 0);return E.r>0&&(B+=t.r),e&&(x[E.sL]=t.top),p(t.language,t.value,!1,!0)}function b(){L+=null!=E.sL?d():h(),k=""}function v(e){L+=e.cN?p(e.cN,"",!0):"",E=Object.create(e,{parent:{value:E}})}function m(e,n){if(k+=e,null==n)return b(),0;var t=o(n,E);if(t)return t.skip?k+=n:(t.eB&&(k+=n),b(),t.rB||t.eB||(k=n)),v(t,n),t.rB?0:n.length;var r=u(E,n);if(r){var a=E;a.skip?k+=n:(a.rE||a.eE||(k+=n),b(),a.eE&&(k=n));do E.cN&&(L+=C),E.skip||(B+=E.r),E=E.parent;while(E!==r.parent);return r.starts&&v(r.starts,""),a.rE?0:n.length}if(c(n,E))throw new Error('Illegal lexeme "'+n+'" for mode "'+(E.cN||"<unnamed>")+'"');return k+=n,n.length||1}var N=w(e);if(!N)throw new Error('Unknown language: "'+e+'"');s(N);var R,E=i||N,x={},L="";for(R=E;R!==N;R=R.parent)R.cN&&(L=p(R.cN,"",!0)+L);var k="",B=0;try{for(var M,j,O=0;;){if(E.t.lastIndex=O,M=E.t.exec(t),!M)break;j=m(t.substring(O,M.index),M[0]),O=M.index+j}for(m(t.substr(O)),R=E;R.parent;R=R.parent)R.cN&&(L+=C);return{r:B,value:L,language:e,top:E}}catch(T){if(T.message&&-1!==T.message.indexOf("Illegal"))return{r:0,value:n(t)};throw T}}function g(e,t){t=t||I.languages||x(y);var r={r:0,value:n(e)},a=r;return t.filter(w).forEach(function(n){var t=f(n,e,!1);t.language=n,t.r>a.r&&(a=t),t.r>r.r&&(a=r,r=t)}),a.language&&(r.second_best=a),r}function p(e){return I.tabReplace||I.useBR?e.replace(M,function(e,n){return I.useBR&&"\n"===e?"<br>":I.tabReplace?n.replace(/\t/g,I.tabReplace):""}):e}function h(e,n,t){var r=n?L[n]:t,a=[e.trim()];return e.match(/\bhljs\b/)||a.push("hljs"),-1===e.indexOf(r)&&a.push(r),a.join(" ").trim()}function d(e){var n,t,r,o,l,s=i(e);a(s)||(I.useBR?(n=document.createElementNS("http://www.w3.org/1999/xhtml","div"),n.innerHTML=e.innerHTML.replace(/\n/g,"").replace(/<br[ \/]*>/g,"\n")):n=e,l=n.textContent,r=s?f(s,l,!0):g(l),t=u(n),t.length&&(o=document.createElementNS("http://www.w3.org/1999/xhtml","div"),o.innerHTML=r.value,r.value=c(t,u(o),l)),r.value=p(r.value),e.innerHTML=r.value,e.className=h(e.className,s,r.language),e.result={language:r.language,re:r.r},r.second_best&&(e.second_best={language:r.second_best.language,re:r.second_best.r}))}function b(e){I=o(I,e)}function v(){if(!v.called){v.called=!0;var e=document.querySelectorAll("pre code");E.forEach.call(e,d)}}function m(){addEventListener("DOMContentLoaded",v,!1),addEventListener("load",v,!1)}function N(n,t){var r=y[n]=t(e);r.aliases&&r.aliases.forEach(function(e){L[e]=n})}function R(){return x(y)}function w(e){return e=(e||"").toLowerCase(),y[e]||y[L[e]]}var E=[],x=Object.keys,y={},L={},k=/^(no-?highlight|plain|text)$/i,B=/\blang(?:uage)?-([\w-]+)\b/i,M=/((^(<[^>]+>|\t|)+|(?:\n)))/gm,C="</span>",I={classPrefix:"hljs-",tabReplace:null,useBR:!1,languages:void 0};return e.highlight=f,e.highlightAuto=g,e.fixMarkup=p,e.highlightBlock=d,e.configure=b,e.initHighlighting=v,e.initHighlightingOnLoad=m,e.registerLanguage=N,e.listLanguages=R,e.getLanguage=w,e.inherit=o,e.IR="[a-zA-Z]\\w*",e.UIR="[a-zA-Z_]\\w*",e.NR="\\b\\d+(\\.\\d+)?",e.CNR="(-?)(\\b0[xX][a-fA-F0-9]+|(\\b\\d+(\\.\\d*)?|\\.\\d+)([eE][-+]?\\d+)?)",e.BNR="\\b(0b[01]+)",e.RSR="!|!=|!==|%|%=|&|&&|&=|\\*|\\*=|\\+|\\+=|,|-|-=|/=|/|:|;|<<|<<=|<=|<|===|==|=|>>>=|>>=|>=|>>>|>>|>|\\?|\\[|\\{|\\(|\\^|\\^=|\\||\\|=|\\|\\||~",e.BE={b:"\\\\[\\s\\S]",r:0},e.ASM={cN:"string",b:"'",e:"'",i:"\\n",c:[e.BE]},e.QSM={cN:"string",b:'"',e:'"',i:"\\n",c:[e.BE]},e.PWM={b:/\b(a|an|the|are|I'm|isn't|don't|doesn't|won't|but|just|should|pretty|simply|enough|gonna|going|wtf|so|such|will|you|your|they|like|more)\b/},e.C=function(n,t,r){var a=e.inherit({cN:"comment",b:n,e:t,c:[]},r||{});return a.c.push(e.PWM),a.c.push({cN:"doctag",b:"(?:TODO|FIXME|NOTE|BUG|XXX):",r:0}),a},e.CLCM=e.C("//","$"),e.CBCM=e.C("/\\*","\\*/"),e.HCM=e.C("#","$"),e.NM={cN:"number",b:e.NR,r:0},e.CNM={cN:"number",b:e.CNR,r:0},e.BNM={cN:"number",b:e.BNR,r:0},e.CSSNM={cN:"number",b:e.NR+"(%|em|ex|ch|rem|vw|vh|vmin|vmax|cm|mm|in|pt|pc|px|deg|grad|rad|turn|s|ms|Hz|kHz|dpi|dpcm|dppx)?",r:0},e.RM={cN:"regexp",b:/\//,e:/\/[gimuy]*/,i:/\n/,c:[e.BE,{b:/\[/,e:/\]/,r:0,c:[e.BE]}]},e.TM={cN:"title",b:e.IR,r:0},e.UTM={cN:"title",b:e.UIR,r:0},e.METHOD_GUARD={b:"\\.\\s*"+e.UIR,r:0},e});hljs.registerLanguage("sql",function(e){var t=e.C("--","$");return{cI:!0,i:/[<>{}*#]/,c:[{bK:"begin end start commit rollback savepoint lock alter create drop rename call delete do handler insert load replace select truncate update set show pragma grant merge describe use explain help declare prepare execute deallocate release unlock purge reset change stop analyze cache flush optimize repair kill install uninstall checksum restore check backup revoke comment",e:/;/,eW:!0,l:/[\w\.]+/,k:{keyword:"abort abs absolute acc acce accep accept access accessed accessible account acos action activate add addtime admin administer advanced advise aes_decrypt aes_encrypt after agent aggregate ali alia alias allocate allow alter always analyze ancillary and any anydata anydataset anyschema anytype apply archive archived archivelog are as asc ascii asin assembly assertion associate asynchronous at atan atn2 attr attri attrib attribu attribut attribute attributes audit authenticated authentication authid authors auto autoallocate autodblink autoextend automatic availability avg backup badfile basicfile before begin beginning benchmark between bfile bfile_base big bigfile bin binary_double binary_float binlog bit_and bit_count bit_length bit_or bit_xor bitmap blob_base block blocksize body both bound buffer_cache buffer_pool build bulk by byte byteordermark bytes cache caching call calling cancel capacity cascade cascaded case cast catalog category ceil ceiling chain change changed char_base char_length character_length characters characterset charindex charset charsetform charsetid check checksum checksum_agg child choose chr chunk class cleanup clear client clob clob_base clone close cluster_id cluster_probability cluster_set clustering coalesce coercibility col collate collation collect colu colum column column_value columns columns_updated comment commit compact compatibility compiled complete composite_limit compound compress compute concat concat_ws concurrent confirm conn connec connect connect_by_iscycle connect_by_isleaf connect_by_root connect_time connection consider consistent constant constraint constraints constructor container content contents context contributors controlfile conv convert convert_tz corr corr_k corr_s corresponding corruption cos cost count count_big counted covar_pop covar_samp cpu_per_call cpu_per_session crc32 create creation critical cross cube cume_dist curdate current current_date current_time current_timestamp current_user cursor curtime customdatum cycle data database databases datafile datafiles datalength date_add date_cache date_format date_sub dateadd datediff datefromparts datename datepart datetime2fromparts day day_to_second dayname dayofmonth dayofweek dayofyear days db_role_change dbtimezone ddl deallocate declare decode decompose decrement decrypt deduplicate def defa defau defaul default defaults deferred defi defin define degrees delayed delegate delete delete_all delimited demand dense_rank depth dequeue des_decrypt des_encrypt des_key_file desc descr descri describ describe descriptor deterministic diagnostics difference dimension direct_load directory disable disable_all disallow disassociate discardfile disconnect diskgroup distinct distinctrow distribute distributed div do document domain dotnet double downgrade drop dumpfile duplicate duration each edition editionable editions element ellipsis else elsif elt empty enable enable_all enclosed encode encoding encrypt end end-exec endian enforced engine engines enqueue enterprise entityescaping eomonth error errors escaped evalname evaluate event eventdata events except exception exceptions exchange exclude excluding execu execut execute exempt exists exit exp expire explain export export_set extended extent external external_1 external_2 externally extract failed failed_login_attempts failover failure far fast feature_set feature_value fetch field fields file file_name_convert filesystem_like_logging final finish first first_value fixed flash_cache flashback floor flush following follows for forall force form forma format found found_rows freelist freelists freepools fresh from from_base64 from_days ftp full function general generated get get_format get_lock getdate getutcdate global global_name globally go goto grant grants greatest group group_concat group_id grouping grouping_id groups gtid_subtract guarantee guard handler hash hashkeys having hea head headi headin heading heap help hex hierarchy high high_priority hosts hour http id ident_current ident_incr ident_seed identified identity idle_time if ifnull ignore iif ilike ilm immediate import in include including increment index indexes indexing indextype indicator indices inet6_aton inet6_ntoa inet_aton inet_ntoa infile initial initialized initially initrans inmemory inner innodb input insert install instance instantiable instr interface interleaved intersect into invalidate invisible is is_free_lock is_ipv4 is_ipv4_compat is_not is_not_null is_used_lock isdate isnull isolation iterate java join json json_exists keep keep_duplicates key keys kill language large last last_day last_insert_id last_value lax lcase lead leading least leaves left len lenght length less level levels library like like2 like4 likec limit lines link list listagg little ln load load_file lob lobs local localtime localtimestamp locate locator lock locked log log10 log2 logfile logfiles logging logical logical_reads_per_call logoff logon logs long loop low low_priority lower lpad lrtrim ltrim main make_set makedate maketime managed management manual map mapping mask master master_pos_wait match matched materialized max maxextents maximize maxinstances maxlen maxlogfiles maxloghistory maxlogmembers maxsize maxtrans md5 measures median medium member memcompress memory merge microsecond mid migration min minextents minimum mining minus minute minvalue missing mod mode model modification modify module monitoring month months mount move movement multiset mutex name name_const names nan national native natural nav nchar nclob nested never new newline next nextval no no_write_to_binlog noarchivelog noaudit nobadfile nocheck nocompress nocopy nocycle nodelay nodiscardfile noentityescaping noguarantee nokeep nologfile nomapping nomaxvalue nominimize nominvalue nomonitoring none noneditionable nonschema noorder nopr nopro noprom nopromp noprompt norely noresetlogs noreverse normal norowdependencies noschemacheck noswitch not nothing notice notrim novalidate now nowait nth_value nullif nulls num numb numbe nvarchar nvarchar2 object ocicoll ocidate ocidatetime ociduration ociinterval ociloblocator ocinumber ociref ocirefcursor ocirowid ocistring ocitype oct octet_length of off offline offset oid oidindex old on online only opaque open operations operator optimal optimize option optionally or oracle oracle_date oradata ord ordaudio orddicom orddoc order ordimage ordinality ordvideo organization orlany orlvary out outer outfile outline output over overflow overriding package pad parallel parallel_enable parameters parent parse partial partition partitions pascal passing password password_grace_time password_lock_time password_reuse_max password_reuse_time password_verify_function patch path patindex pctincrease pctthreshold pctused pctversion percent percent_rank percentile_cont percentile_disc performance period period_add period_diff permanent physical pi pipe pipelined pivot pluggable plugin policy position post_transaction pow power pragma prebuilt precedes preceding precision prediction prediction_cost prediction_details prediction_probability prediction_set prepare present preserve prior priority private private_sga privileges procedural procedure procedure_analyze processlist profiles project prompt protection public publishingservername purge quarter query quick quiesce quota quotename radians raise rand range rank raw read reads readsize rebuild record records recover recovery recursive recycle redo reduced ref reference referenced references referencing refresh regexp_like register regr_avgx regr_avgy regr_count regr_intercept regr_r2 regr_slope regr_sxx regr_sxy reject rekey relational relative relaylog release release_lock relies_on relocate rely rem remainder rename repair repeat replace replicate replication required reset resetlogs resize resource respect restore restricted result result_cache resumable resume retention return returning returns reuse reverse revoke right rlike role roles rollback rolling rollup round row row_count rowdependencies rowid rownum rows rtrim rules safe salt sample save savepoint sb1 sb2 sb4 scan schema schemacheck scn scope scroll sdo_georaster sdo_topo_geometry search sec_to_time second section securefile security seed segment select self sequence sequential serializable server servererror session session_user sessions_per_user set sets settings sha sha1 sha2 share shared shared_pool short show shrink shutdown si_averagecolor si_colorhistogram si_featurelist si_positionalcolor si_stillimage si_texture siblings sid sign sin size size_t sizes skip slave sleep smalldatetimefromparts smallfile snapshot some soname sort soundex source space sparse spfile split sql sql_big_result sql_buffer_result sql_cache sql_calc_found_rows sql_small_result sql_variant_property sqlcode sqldata sqlerror sqlname sqlstate sqrt square standalone standby start starting startup statement static statistics stats_binomial_test stats_crosstab stats_ks_test stats_mode stats_mw_test stats_one_way_anova stats_t_test_ stats_t_test_indep stats_t_test_one stats_t_test_paired stats_wsr_test status std stddev stddev_pop stddev_samp stdev stop storage store stored str str_to_date straight_join strcmp strict string struct stuff style subdate subpartition subpartitions substitutable substr substring subtime subtring_index subtype success sum suspend switch switchoffset switchover sync synchronous synonym sys sys_xmlagg sysasm sysaux sysdate sysdatetimeoffset sysdba sysoper system system_user sysutcdatetime table tables tablespace tan tdo template temporary terminated tertiary_weights test than then thread through tier ties time time_format time_zone timediff timefromparts timeout timestamp timestampadd timestampdiff timezone_abbr timezone_minute timezone_region to to_base64 to_date to_days to_seconds todatetimeoffset trace tracking transaction transactional translate translation treat trigger trigger_nestlevel triggers trim truncate try_cast try_convert try_parse type ub1 ub2 ub4 ucase unarchived unbounded uncompress under undo unhex unicode uniform uninstall union unique unix_timestamp unknown unlimited unlock unpivot unrecoverable unsafe unsigned until untrusted unusable unused update updated upgrade upped upper upsert url urowid usable usage use use_stored_outlines user user_data user_resources users using utc_date utc_timestamp uuid uuid_short validate validate_password_strength validation valist value values var var_samp varcharc vari varia variab variabl variable variables variance varp varraw varrawc varray verify version versions view virtual visible void wait wallet warning warnings week weekday weekofyear wellformed when whene whenev wheneve whenever where while whitespace with within without work wrapped xdb xml xmlagg xmlattributes xmlcast xmlcolattval xmlelement xmlexists xmlforest xmlindex xmlnamespaces xmlpi xmlquery xmlroot xmlschema xmlserialize xmltable xmltype xor year year_to_month years yearweek",literal:"true false null",built_in:"array bigint binary bit blob boolean char character date dec decimal float int int8 integer interval number numeric real record serial serial8 smallint text varchar varying void"},c:[{cN:"string",b:"'",e:"'",c:[e.BE,{b:"''"}]},{cN:"string",b:'"',e:'"',c:[e.BE,{b:'""'}]},{cN:"string",b:"`",e:"`",c:[e.BE]},e.CNM,e.CBCM,t]},e.CBCM,t]}});hljs.registerLanguage("r",function(e){var r="([a-zA-Z]|\\.[a-zA-Z.])[a-zA-Z0-9._]*";return{c:[e.HCM,{b:r,l:r,k:{keyword:"function if in break next repeat else for return switch while try tryCatch stop warning require library attach detach source setMethod setGeneric setGroupGeneric setClass ...",literal:"NULL NA TRUE FALSE T F Inf NaN NA_integer_|10 NA_real_|10 NA_character_|10 NA_complex_|10"},r:0},{cN:"number",b:"0[xX][0-9a-fA-F]+[Li]?\\b",r:0},{cN:"number",b:"\\d+(?:[eE][+\\-]?\\d*)?L\\b",r:0},{cN:"number",b:"\\d+\\.(?!\\d)(?:i\\b)?",r:0},{cN:"number",b:"\\d+(?:\\.\\d*)?(?:[eE][+\\-]?\\d*)?i?\\b",r:0},{cN:"number",b:"\\.\\d+(?:[eE][+\\-]?\\d*)?i?\\b",r:0},{b:"`",e:"`",r:0},{cN:"string",c:[e.BE],v:[{b:'"',e:'"'},{b:"'",e:"'"}]}]}});hljs.registerLanguage("perl",function(e){var t="getpwent getservent quotemeta msgrcv scalar kill dbmclose undef lc ma syswrite tr send umask sysopen shmwrite vec qx utime local oct semctl localtime readpipe do return format read sprintf dbmopen pop getpgrp not getpwnam rewinddir qqfileno qw endprotoent wait sethostent bless s|0 opendir continue each sleep endgrent shutdown dump chomp connect getsockname die socketpair close flock exists index shmgetsub for endpwent redo lstat msgctl setpgrp abs exit select print ref gethostbyaddr unshift fcntl syscall goto getnetbyaddr join gmtime symlink semget splice x|0 getpeername recv log setsockopt cos last reverse gethostbyname getgrnam study formline endhostent times chop length gethostent getnetent pack getprotoent getservbyname rand mkdir pos chmod y|0 substr endnetent printf next open msgsnd readdir use unlink getsockopt getpriority rindex wantarray hex system getservbyport endservent int chr untie rmdir prototype tell listen fork shmread ucfirst setprotoent else sysseek link getgrgid shmctl waitpid unpack getnetbyname reset chdir grep split require caller lcfirst until warn while values shift telldir getpwuid my getprotobynumber delete and sort uc defined srand accept package seekdir getprotobyname semop our rename seek if q|0 chroot sysread setpwent no crypt getc chown sqrt write setnetent setpriority foreach tie sin msgget map stat getlogin unless elsif truncate exec keys glob tied closedirioctl socket readlink eval xor readline binmode setservent eof ord bind alarm pipe atan2 getgrent exp time push setgrent gt lt or ne m|0 break given say state when",r={cN:"subst",b:"[$@]\\{",e:"\\}",k:t},s={b:"->{",e:"}"},n={v:[{b:/\$\d/},{b:/[\$%@](\^\w\b|#\w+(::\w+)*|{\w+}|\w+(::\w*)*)/},{b:/[\$%@][^\s\w{]/,r:0}]},i=[e.BE,r,n],o=[n,e.HCM,e.C("^\\=\\w","\\=cut",{eW:!0}),s,{cN:"string",c:i,v:[{b:"q[qwxr]?\\s*\\(",e:"\\)",r:5},{b:"q[qwxr]?\\s*\\[",e:"\\]",r:5},{b:"q[qwxr]?\\s*\\{",e:"\\}",r:5},{b:"q[qwxr]?\\s*\\|",e:"\\|",r:5},{b:"q[qwxr]?\\s*\\<",e:"\\>",r:5},{b:"qw\\s+q",e:"q",r:5},{b:"'",e:"'",c:[e.BE]},{b:'"',e:'"'},{b:"`",e:"`",c:[e.BE]},{b:"{\\w+}",c:[],r:0},{b:"-?\\w+\\s*\\=\\>",c:[],r:0}]},{cN:"number",b:"(\\b0[0-7_]+)|(\\b0x[0-9a-fA-F_]+)|(\\b[1-9][0-9_]*(\\.[0-9_]+)?)|[0_]\\b",r:0},{b:"(\\/\\/|"+e.RSR+"|\\b(split|return|print|reverse|grep)\\b)\\s*",k:"split return print reverse grep",r:0,c:[e.HCM,{cN:"regexp",b:"(s|tr|y)/(\\\\.|[^/])*/(\\\\.|[^/])*/[a-z]*",r:10},{cN:"regexp",b:"(m|qr)?/",e:"/[a-z]*",c:[e.BE],r:0}]},{cN:"function",bK:"sub",e:"(\\s*\\(.*?\\))?[;{]",eE:!0,r:5,c:[e.TM]},{b:"-\\w\\b",r:0},{b:"^__DATA__$",e:"^__END__$",sL:"mojolicious",c:[{b:"^@@.*",e:"$",cN:"comment"}]}];return r.c=o,s.c=o,{aliases:["pl","pm"],l:/[\w\.]+/,k:t,c:o}});hljs.registerLanguage("ini",function(e){var b={cN:"string",c:[e.BE],v:[{b:"'''",e:"'''",r:10},{b:'"""',e:'"""',r:10},{b:'"',e:'"'},{b:"'",e:"'"}]};return{aliases:["toml"],cI:!0,i:/\S/,c:[e.C(";","$"),e.HCM,{cN:"section",b:/^\s*\[+/,e:/\]+/},{b:/^[a-z0-9\[\]_-]+\s*=\s*/,e:"$",rB:!0,c:[{cN:"attr",b:/[a-z0-9\[\]_-]+/},{b:/=/,eW:!0,r:0,c:[{cN:"literal",b:/\bon|off|true|false|yes|no\b/},{cN:"variable",v:[{b:/\$[\w\d"][\w\d_]*/},{b:/\$\{(.*?)}/}]},b,{cN:"number",b:/([\+\-]+)?[\d]+_[\d_]+/},e.NM]}]}]}});hljs.registerLanguage("diff",function(e){return{aliases:["patch"],c:[{cN:"meta",r:10,v:[{b:/^@@ +\-\d+,\d+ +\+\d+,\d+ +@@$/},{b:/^\*\*\* +\d+,\d+ +\*\*\*\*$/},{b:/^\-\-\- +\d+,\d+ +\-\-\-\-$/}]},{cN:"comment",v:[{b:/Index: /,e:/$/},{b:/={3,}/,e:/$/},{b:/^\-{3}/,e:/$/},{b:/^\*{3} /,e:/$/},{b:/^\+{3}/,e:/$/},{b:/\*{5}/,e:/\*{5}$/}]},{cN:"addition",b:"^\\+",e:"$"},{cN:"deletion",b:"^\\-",e:"$"},{cN:"addition",b:"^\\!",e:"$"}]}});hljs.registerLanguage("go",function(e){var t={keyword:"break default func interface select case map struct chan else goto package switch const fallthrough if range type continue for import return var go defer bool byte complex64 complex128 float32 float64 int8 int16 int32 int64 string uint8 uint16 uint32 uint64 int uint uintptr rune",literal:"true false iota nil",built_in:"append cap close complex copy imag len make new panic print println real recover delete"};return{aliases:["golang"],k:t,i:"</",c:[e.CLCM,e.CBCM,{cN:"string",v:[e.QSM,{b:"'",e:"[^\\\\]'"},{b:"`",e:"`"}]},{cN:"number",v:[{b:e.CNR+"[dflsi]",r:1},e.CNM]},{b:/:=/},{cN:"function",bK:"func",e:/\s*\{/,eE:!0,c:[e.TM,{cN:"params",b:/\(/,e:/\)/,k:t,i:/["']/}]}]}});hljs.registerLanguage("bash",function(e){var t={cN:"variable",v:[{b:/\$[\w\d#@][\w\d_]*/},{b:/\$\{(.*?)}/}]},s={cN:"string",b:/"/,e:/"/,c:[e.BE,t,{cN:"variable",b:/\$\(/,e:/\)/,c:[e.BE]}]},a={cN:"string",b:/'/,e:/'/};return{aliases:["sh","zsh"],l:/\b-?[a-z\._]+\b/,k:{keyword:"if then else elif fi for while in do done case esac function",literal:"true false",built_in:"break cd continue eval exec exit export getopts hash pwd readonly return shift test times trap umask unset alias bind builtin caller command declare echo enable help let local logout mapfile printf read readarray source type typeset ulimit unalias set shopt autoload bg bindkey bye cap chdir clone comparguments compcall compctl compdescribe compfiles compgroups compquote comptags comptry compvalues dirs disable disown echotc echoti emulate fc fg float functions getcap getln history integer jobs kill limit log noglob popd print pushd pushln rehash sched setcap setopt stat suspend ttyctl unfunction unhash unlimit unsetopt vared wait whence where which zcompile zformat zftp zle zmodload zparseopts zprof zpty zregexparse zsocket zstyle ztcp",_:"-ne -eq -lt -gt -f -d -e -s -l -a"},c:[{cN:"meta",b:/^#![^\n]+sh\s*$/,r:10},{cN:"function",b:/\w[\w\d_]*\s*\(\s*\)\s*\{/,rB:!0,c:[e.inherit(e.TM,{b:/\w[\w\d_]*/})],r:0},e.HCM,s,a,t]}});hljs.registerLanguage("python",function(e){var r={keyword:"and elif is global as in if from raise for except finally print import pass return exec else break not with class assert yield try while continue del or def lambda async await nonlocal|10 None True False",built_in:"Ellipsis NotImplemented"},b={cN:"meta",b:/^(>>>|\.\.\.) /},c={cN:"subst",b:/\{/,e:/\}/,k:r,i:/#/},a={cN:"string",c:[e.BE],v:[{b:/(u|b)?r?'''/,e:/'''/,c:[b],r:10},{b:/(u|b)?r?"""/,e:/"""/,c:[b],r:10},{b:/(fr|rf|f)'''/,e:/'''/,c:[b,c]},{b:/(fr|rf|f)"""/,e:/"""/,c:[b,c]},{b:/(u|r|ur)'/,e:/'/,r:10},{b:/(u|r|ur)"/,e:/"/,r:10},{b:/(b|br)'/,e:/'/},{b:/(b|br)"/,e:/"/},{b:/(fr|rf|f)'/,e:/'/,c:[c]},{b:/(fr|rf|f)"/,e:/"/,c:[c]},e.ASM,e.QSM]},s={cN:"number",r:0,v:[{b:e.BNR+"[lLjJ]?"},{b:"\\b(0o[0-7]+)[lLjJ]?"},{b:e.CNR+"[lLjJ]?"}]},i={cN:"params",b:/\(/,e:/\)/,c:["self",b,s,a]};return c.c=[a,s,b],{aliases:["py","gyp"],k:r,i:/(<\/|->|\?)|=>/,c:[b,s,a,e.HCM,{v:[{cN:"function",bK:"def"},{cN:"class",bK:"class"}],e:/:/,i:/[${=;\n,]/,c:[e.UTM,i,{b:/->/,eW:!0,k:"None"}]},{cN:"meta",b:/^[\t ]*@/,e:/$/},{b:/\b(print|exec)\(/}]}});hljs.registerLanguage("julia",function(e){var r={keyword:"in isa where baremodule begin break catch ccall const continue do else elseif end export false finally for function global if import importall let local macro module quote return true try using while type immutable abstract bitstype typealias ",literal:"true false ARGS C_NULL DevNull ENDIAN_BOM ENV I Inf Inf16 Inf32 Inf64 InsertionSort JULIA_HOME LOAD_PATH MergeSort NaN NaN16 NaN32 NaN64 PROGRAM_FILE QuickSort RoundDown RoundFromZero RoundNearest RoundNearestTiesAway RoundNearestTiesUp RoundToZero RoundUp STDERR STDIN STDOUT VERSION catalan e|0 eu|0 eulergamma golden im nothing pi γ π φ ",built_in:"ANY AbstractArray AbstractChannel AbstractFloat AbstractMatrix AbstractRNG AbstractSerializer AbstractSet AbstractSparseArray AbstractSparseMatrix AbstractSparseVector AbstractString AbstractUnitRange AbstractVecOrMat AbstractVector Any ArgumentError Array AssertionError Associative Base64DecodePipe Base64EncodePipe Bidiagonal BigFloat BigInt BitArray BitMatrix BitVector Bool BoundsError BufferStream CachingPool CapturedException CartesianIndex CartesianRange Cchar Cdouble Cfloat Channel Char Cint Cintmax_t Clong Clonglong ClusterManager Cmd CodeInfo Colon Complex Complex128 Complex32 Complex64 CompositeException Condition ConjArray ConjMatrix ConjVector Cptrdiff_t Cshort Csize_t Cssize_t Cstring Cuchar Cuint Cuintmax_t Culong Culonglong Cushort Cwchar_t Cwstring DataType Date DateFormat DateTime DenseArray DenseMatrix DenseVecOrMat DenseVector Diagonal Dict DimensionMismatch Dims DirectIndexString Display DivideError DomainError EOFError EachLine Enum Enumerate ErrorException Exception ExponentialBackOff Expr Factorization FileMonitor Float16 Float32 Float64 Function Future GlobalRef GotoNode HTML Hermitian IO IOBuffer IOContext IOStream IPAddr IPv4 IPv6 IndexCartesian IndexLinear IndexStyle InexactError InitError Int Int128 Int16 Int32 Int64 Int8 IntSet Integer InterruptException InvalidStateException Irrational KeyError LabelNode LinSpace LineNumberNode LoadError LowerTriangular MIME Matrix MersenneTwister Method MethodError MethodTable Module NTuple NewvarNode NullException Nullable Number ObjectIdDict OrdinalRange OutOfMemoryError OverflowError Pair ParseError PartialQuickSort PermutedDimsArray Pipe PollingFileWatcher ProcessExitedException Ptr QuoteNode RandomDevice Range RangeIndex Rational RawFD ReadOnlyMemoryError Real ReentrantLock Ref Regex RegexMatch RemoteChannel RemoteException RevString RoundingMode RowVector SSAValue SegmentationFault SerializationState Set SharedArray SharedMatrix SharedVector Signed SimpleVector Slot SlotNumber SparseMatrixCSC SparseVector StackFrame StackOverflowError StackTrace StepRange StepRangeLen StridedArray StridedMatrix StridedVecOrMat StridedVector String SubArray SubString SymTridiagonal Symbol Symmetric SystemError TCPSocket Task Text TextDisplay Timer Tridiagonal Tuple Type TypeError TypeMapEntry TypeMapLevel TypeName TypeVar TypedSlot UDPSocket UInt UInt128 UInt16 UInt32 UInt64 UInt8 UndefRefError UndefVarError UnicodeError UniformScaling Union UnionAll UnitRange Unsigned UpperTriangular Val Vararg VecElement VecOrMat Vector VersionNumber Void WeakKeyDict WeakRef WorkerConfig WorkerPool "},t="[A-Za-z_\\u00A1-\\uFFFF][A-Za-z_0-9\\u00A1-\\uFFFF]*",a={l:t,k:r,i:/<\//},n={cN:"number",b:/(\b0x[\d_]*(\.[\d_]*)?|0x\.\d[\d_]*)p[-+]?\d+|\b0[box][a-fA-F0-9][a-fA-F0-9_]*|(\b\d[\d_]*(\.[\d_]*)?|\.\d[\d_]*)([eEfF][-+]?\d+)?/,r:0},o={cN:"string",b:/'(.|\\[xXuU][a-zA-Z0-9]+)'/},i={cN:"subst",b:/\$\(/,e:/\)/,k:r},l={cN:"variable",b:"\\$"+t},c={cN:"string",c:[e.BE,i,l],v:[{b:/\w*"""/,e:/"""\w*/,r:10},{b:/\w*"/,e:/"\w*/}]},s={cN:"string",c:[e.BE,i,l],b:"`",e:"`"},d={cN:"meta",b:"@"+t},u={cN:"comment",v:[{b:"#=",e:"=#",r:10},{b:"#",e:"$"}]};return a.c=[n,o,c,s,d,u,e.HCM,{cN:"keyword",b:"\\b(((abstract|primitive)\\s+)type|(mutable\\s+)?struct)\\b"},{b:/<:/}],i.c=a.c,a});hljs.registerLanguage("coffeescript",function(e){var c={keyword:"in if for while finally new do return else break catch instanceof throw try this switch continue typeof delete debugger super yield import export from as default await then unless until loop of by when and or is isnt not",literal:"true false null undefined yes no on off",built_in:"npm require console print module global window document"},n="[A-Za-z$_][0-9A-Za-z$_]*",r={cN:"subst",b:/#\{/,e:/}/,k:c},i=[e.BNM,e.inherit(e.CNM,{starts:{e:"(\\s*/)?",r:0}}),{cN:"string",v:[{b:/'''/,e:/'''/,c:[e.BE]},{b:/'/,e:/'/,c:[e.BE]},{b:/"""/,e:/"""/,c:[e.BE,r]},{b:/"/,e:/"/,c:[e.BE,r]}]},{cN:"regexp",v:[{b:"///",e:"///",c:[r,e.HCM]},{b:"//[gim]*",r:0},{b:/\/(?![ *])(\\\/|.)*?\/[gim]*(?=\W|$)/}]},{b:"@"+n},{sL:"javascript",eB:!0,eE:!0,v:[{b:"```",e:"```"},{b:"`",e:"`"}]}];r.c=i;var s=e.inherit(e.TM,{b:n}),t="(\\(.*\\))?\\s*\\B[-=]>",o={cN:"params",b:"\\([^\\(]",rB:!0,c:[{b:/\(/,e:/\)/,k:c,c:["self"].concat(i)}]};return{aliases:["coffee","cson","iced"],k:c,i:/\/\*/,c:i.concat([e.C("###","###"),e.HCM,{cN:"function",b:"^\\s*"+n+"\\s*=\\s*"+t,e:"[-=]>",rB:!0,c:[s,o]},{b:/[:\(,=]\s*/,r:0,c:[{cN:"function",b:t,e:"[-=]>",rB:!0,c:[o]}]},{cN:"class",bK:"class",e:"$",i:/[:="\[\]]/,c:[{bK:"extends",eW:!0,i:/[:="\[\]]/,c:[s]},s]},{b:n+":",e:":",rB:!0,rE:!0,r:0}])}});hljs.registerLanguage("cpp",function(t){var e={cN:"keyword",b:"\\b[a-z\\d_]*_t\\b"},r={cN:"string",v:[{b:'(u8?|U)?L?"',e:'"',i:"\\n",c:[t.BE]},{b:'(u8?|U)?R"',e:'"',c:[t.BE]},{b:"'\\\\?.",e:"'",i:"."}]},s={cN:"number",v:[{b:"\\b(0b[01']+)"},{b:"(-?)\\b([\\d']+(\\.[\\d']*)?|\\.[\\d']+)(u|U|l|L|ul|UL|f|F|b|B)"},{b:"(-?)(\\b0[xX][a-fA-F0-9']+|(\\b[\\d']+(\\.[\\d']*)?|\\.[\\d']+)([eE][-+]?[\\d']+)?)"}],r:0},i={cN:"meta",b:/#\s*[a-z]+\b/,e:/$/,k:{"meta-keyword":"if else elif endif define undef warning error line pragma ifdef ifndef include"},c:[{b:/\\\n/,r:0},t.inherit(r,{cN:"meta-string"}),{cN:"meta-string",b:/<[^\n>]*>/,e:/$/,i:"\\n"},t.CLCM,t.CBCM]},a=t.IR+"\\s*\\(",c={keyword:"int float while private char catch import module export virtual operator sizeof dynamic_cast|10 typedef const_cast|10 const for static_cast|10 union namespace unsigned long volatile static protected bool template mutable if public friend do goto auto void enum else break extern using asm case typeid short reinterpret_cast|10 default double register explicit signed typename try this switch continue inline delete alignof constexpr decltype noexcept static_assert thread_local restrict _Bool complex _Complex _Imaginary atomic_bool atomic_char atomic_schar atomic_uchar atomic_short atomic_ushort atomic_int atomic_uint atomic_long atomic_ulong atomic_llong atomic_ullong new throw return and or not",built_in:"std string cin cout cerr clog stdin stdout stderr stringstream istringstream ostringstream auto_ptr deque list queue stack vector map set bitset multiset multimap unordered_set unordered_map unordered_multiset unordered_multimap array shared_ptr abort abs acos asin atan2 atan calloc ceil cosh cos exit exp fabs floor fmod fprintf fputs free frexp fscanf isalnum isalpha iscntrl isdigit isgraph islower isprint ispunct isspace isupper isxdigit tolower toupper labs ldexp log10 log malloc realloc memchr memcmp memcpy memset modf pow printf putchar puts scanf sinh sin snprintf sprintf sqrt sscanf strcat strchr strcmp strcpy strcspn strlen strncat strncmp strncpy strpbrk strrchr strspn strstr tanh tan vfprintf vprintf vsprintf endl initializer_list unique_ptr",literal:"true false nullptr NULL"},n=[e,t.CLCM,t.CBCM,s,r];return{aliases:["c","cc","h","c++","h++","hpp"],k:c,i:"</",c:n.concat([i,{b:"\\b(deque|list|queue|stack|vector|map|set|bitset|multiset|multimap|unordered_map|unordered_set|unordered_multiset|unordered_multimap|array)\\s*<",e:">",k:c,c:["self",e]},{b:t.IR+"::",k:c},{v:[{b:/=/,e:/;/},{b:/\(/,e:/\)/},{bK:"new throw return else",e:/;/}],k:c,c:n.concat([{b:/\(/,e:/\)/,k:c,c:n.concat(["self"]),r:0}]),r:0},{cN:"function",b:"("+t.IR+"[\\*&\\s]+)+"+a,rB:!0,e:/[{;=]/,eE:!0,k:c,i:/[^\w\s\*&]/,c:[{b:a,rB:!0,c:[t.TM],r:0},{cN:"params",b:/\(/,e:/\)/,k:c,r:0,c:[t.CLCM,t.CBCM,r,s,e]},t.CLCM,t.CBCM,i]},{cN:"class",bK:"class struct",e:/[{;:]/,c:[{b:/</,e:/>/,c:["self"]},t.TM]}]),exports:{preprocessor:i,strings:r,k:c}}});hljs.registerLanguage("ruby",function(e){var b="[a-zA-Z_]\\w*[!?=]?|[-+~]\\@|<<|>>|=~|===?|<=>|[<>]=?|\\*\\*|[-/+%^&*~`|]|\\[\\]=?",r={keyword:"and then defined module in return redo if BEGIN retry end for self when next until do begin unless END rescue else break undef not super class case require yield alias while ensure elsif or include attr_reader attr_writer attr_accessor",literal:"true false nil"},c={cN:"doctag",b:"@[A-Za-z]+"},a={b:"#<",e:">"},s=[e.C("#","$",{c:[c]}),e.C("^\\=begin","^\\=end",{c:[c],r:10}),e.C("^__END__","\\n$")],n={cN:"subst",b:"#\\{",e:"}",k:r},t={cN:"string",c:[e.BE,n],v:[{b:/'/,e:/'/},{b:/"/,e:/"/},{b:/`/,e:/`/},{b:"%[qQwWx]?\\(",e:"\\)"},{b:"%[qQwWx]?\\[",e:"\\]"},{b:"%[qQwWx]?{",e:"}"},{b:"%[qQwWx]?<",e:">"},{b:"%[qQwWx]?/",e:"/"},{b:"%[qQwWx]?%",e:"%"},{b:"%[qQwWx]?-",e:"-"},{b:"%[qQwWx]?\\|",e:"\\|"},{b:/\B\?(\\\d{1,3}|\\x[A-Fa-f0-9]{1,2}|\\u[A-Fa-f0-9]{4}|\\?\S)\b/},{b:/<<(-?)\w+$/,e:/^\s*\w+$/}]},i={cN:"params",b:"\\(",e:"\\)",endsParent:!0,k:r},d=[t,a,{cN:"class",bK:"class module",e:"$|;",i:/=/,c:[e.inherit(e.TM,{b:"[A-Za-z_]\\w*(::\\w+)*(\\?|\\!)?"}),{b:"<\\s*",c:[{b:"("+e.IR+"::)?"+e.IR}]}].concat(s)},{cN:"function",bK:"def",e:"$|;",c:[e.inherit(e.TM,{b:b}),i].concat(s)},{b:e.IR+"::"},{cN:"symbol",b:e.UIR+"(\\!|\\?)?:",r:0},{cN:"symbol",b:":(?!\\s)",c:[t,{b:b}],r:0},{cN:"number",b:"(\\b0[0-7_]+)|(\\b0x[0-9a-fA-F_]+)|(\\b[1-9][0-9_]*(\\.[0-9_]+)?)|[0_]\\b",r:0},{b:"(\\$\\W)|((\\$|\\@\\@?)(\\w+))"},{cN:"params",b:/\|/,e:/\|/,k:r},{b:"("+e.RSR+"|unless)\\s*",k:"unless",c:[a,{cN:"regexp",c:[e.BE,n],i:/\n/,v:[{b:"/",e:"/[a-z]*"},{b:"%r{",e:"}[a-z]*"},{b:"%r\\(",e:"\\)[a-z]*"},{b:"%r!",e:"![a-z]*"},{b:"%r\\[",e:"\\][a-z]*"}]}].concat(s),r:0}].concat(s);n.c=d,i.c=d;var l="[>?]>",o="[\\w#]+\\(\\w+\\):\\d+:\\d+>",u="(\\w+-)?\\d+\\.\\d+\\.\\d(p\\d+)?[^>]+>",w=[{b:/^\s*=>/,starts:{e:"$",c:d}},{cN:"meta",b:"^("+l+"|"+o+"|"+u+")",starts:{e:"$",c:d}}];return{aliases:["rb","gemspec","podspec","thor","irb"],k:r,i:/\/\*/,c:s.concat(w).concat(d)}});hljs.registerLanguage("yaml",function(e){var b="true false yes no null",a="^[ \\-]*",r="[a-zA-Z_][\\w\\-]*",t={cN:"attr",v:[{b:a+r+":"},{b:a+'"'+r+'":'},{b:a+"'"+r+"':"}]},c={cN:"template-variable",v:[{b:"{{",e:"}}"},{b:"%{",e:"}"}]},l={cN:"string",r:0,v:[{b:/'/,e:/'/},{b:/"/,e:/"/},{b:/\S+/}],c:[e.BE,c]};return{cI:!0,aliases:["yml","YAML","yaml"],c:[t,{cN:"meta",b:"^---s*$",r:10},{cN:"string",b:"[\\|>] *$",rE:!0,c:l.c,e:t.v[0].b},{b:"<%[%=-]?",e:"[%-]?%>",sL:"ruby",eB:!0,eE:!0,r:0},{cN:"type",b:"!!"+e.UIR},{cN:"meta",b:"&"+e.UIR+"$"},{cN:"meta",b:"\\*"+e.UIR+"$"},{cN:"bullet",b:"^ *-",r:0},e.HCM,{bK:b,k:{literal:b}},e.CNM,l]}});hljs.registerLanguage("css",function(e){var c="[a-zA-Z-][a-zA-Z0-9_-]*",t={b:/[A-Z\_\.\-]+\s*:/,rB:!0,e:";",eW:!0,c:[{cN:"attribute",b:/\S/,e:":",eE:!0,starts:{eW:!0,eE:!0,c:[{b:/[\w-]+\(/,rB:!0,c:[{cN:"built_in",b:/[\w-]+/},{b:/\(/,e:/\)/,c:[e.ASM,e.QSM]}]},e.CSSNM,e.QSM,e.ASM,e.CBCM,{cN:"number",b:"#[0-9A-Fa-f]+"},{cN:"meta",b:"!important"}]}}]};return{cI:!0,i:/[=\/|'\$]/,c:[e.CBCM,{cN:"selector-id",b:/#[A-Za-z0-9_-]+/},{cN:"selector-class",b:/\.[A-Za-z0-9_-]+/},{cN:"selector-attr",b:/\[/,e:/\]/,i:"$"},{cN:"selector-pseudo",b:/:(:)?[a-zA-Z0-9\_\-\+\(\)"'.]+/},{b:"@(font-face|page)",l:"[a-z-]+",k:"font-face page"},{b:"@",e:"[{;]",i:/:/,c:[{cN:"keyword",b:/\w+/},{b:/\s/,eW:!0,eE:!0,r:0,c:[e.ASM,e.QSM,e.CSSNM]}]},{cN:"selector-tag",b:c,r:0},{b:"{",e:"}",i:/\S/,c:[e.CBCM,t]}]}});hljs.registerLanguage("fortran",function(e){var t={cN:"params",b:"\\(",e:"\\)"},n={literal:".False. .True.",keyword:"kind do while private call intrinsic where elsewhere type endtype endmodule endselect endinterface end enddo endif if forall endforall only contains default return stop then public subroutine|10 function program .and. .or. .not. .le. .eq. .ge. .gt. .lt. goto save else use module select case access blank direct exist file fmt form formatted iostat name named nextrec number opened rec recl sequential status unformatted unit continue format pause cycle exit c_null_char c_alert c_backspace c_form_feed flush wait decimal round iomsg synchronous nopass non_overridable pass protected volatile abstract extends import non_intrinsic value deferred generic final enumerator class associate bind enum c_int c_short c_long c_long_long c_signed_char c_size_t c_int8_t c_int16_t c_int32_t c_int64_t c_int_least8_t c_int_least16_t c_int_least32_t c_int_least64_t c_int_fast8_t c_int_fast16_t c_int_fast32_t c_int_fast64_t c_intmax_t C_intptr_t c_float c_double c_long_double c_float_complex c_double_complex c_long_double_complex c_bool c_char c_null_ptr c_null_funptr c_new_line c_carriage_return c_horizontal_tab c_vertical_tab iso_c_binding c_loc c_funloc c_associated  c_f_pointer c_ptr c_funptr iso_fortran_env character_storage_size error_unit file_storage_size input_unit iostat_end iostat_eor numeric_storage_size output_unit c_f_procpointer ieee_arithmetic ieee_support_underflow_control ieee_get_underflow_mode ieee_set_underflow_mode newunit contiguous recursive pad position action delim readwrite eor advance nml interface procedure namelist include sequence elemental pure integer real character complex logical dimension allocatable|10 parameter external implicit|10 none double precision assign intent optional pointer target in out common equivalence data",built_in:"alog alog10 amax0 amax1 amin0 amin1 amod cabs ccos cexp clog csin csqrt dabs dacos dasin datan datan2 dcos dcosh ddim dexp dint dlog dlog10 dmax1 dmin1 dmod dnint dsign dsin dsinh dsqrt dtan dtanh float iabs idim idint idnint ifix isign max0 max1 min0 min1 sngl algama cdabs cdcos cdexp cdlog cdsin cdsqrt cqabs cqcos cqexp cqlog cqsin cqsqrt dcmplx dconjg derf derfc dfloat dgamma dimag dlgama iqint qabs qacos qasin qatan qatan2 qcmplx qconjg qcos qcosh qdim qerf qerfc qexp qgamma qimag qlgama qlog qlog10 qmax1 qmin1 qmod qnint qsign qsin qsinh qsqrt qtan qtanh abs acos aimag aint anint asin atan atan2 char cmplx conjg cos cosh exp ichar index int log log10 max min nint sign sin sinh sqrt tan tanh print write dim lge lgt lle llt mod nullify allocate deallocate adjustl adjustr all allocated any associated bit_size btest ceiling count cshift date_and_time digits dot_product eoshift epsilon exponent floor fraction huge iand ibclr ibits ibset ieor ior ishft ishftc lbound len_trim matmul maxexponent maxloc maxval merge minexponent minloc minval modulo mvbits nearest pack present product radix random_number random_seed range repeat reshape rrspacing scale scan selected_int_kind selected_real_kind set_exponent shape size spacing spread sum system_clock tiny transpose trim ubound unpack verify achar iachar transfer dble entry dprod cpu_time command_argument_count get_command get_command_argument get_environment_variable is_iostat_end ieee_arithmetic ieee_support_underflow_control ieee_get_underflow_mode ieee_set_underflow_mode is_iostat_eor move_alloc new_line selected_char_kind same_type_as extends_type_ofacosh asinh atanh bessel_j0 bessel_j1 bessel_jn bessel_y0 bessel_y1 bessel_yn erf erfc erfc_scaled gamma log_gamma hypot norm2 atomic_define atomic_ref execute_command_line leadz trailz storage_size merge_bits bge bgt ble blt dshiftl dshiftr findloc iall iany iparity image_index lcobound ucobound maskl maskr num_images parity popcnt poppar shifta shiftl shiftr this_image"};return{cI:!0,aliases:["f90","f95"],k:n,i:/\/\*/,c:[e.inherit(e.ASM,{cN:"string",r:0}),e.inherit(e.QSM,{cN:"string",r:0}),{cN:"function",bK:"subroutine function program",i:"[${=\\n]",c:[e.UTM,t]},e.C("!","$",{r:0}),{cN:"number",b:"(?=\\b|\\+|\\-|\\.)(?=\\.\\d|\\d)(?:\\d+)?(?:\\.?\\d*)(?:[de][+-]?\\d+)?\\b\\.?",r:0}]}});hljs.registerLanguage("awk",function(e){var r={cN:"variable",v:[{b:/\$[\w\d#@][\w\d_]*/},{b:/\$\{(.*?)}/}]},b="BEGIN END if else while do for in break continue delete next nextfile function func exit|10",n={cN:"string",c:[e.BE],v:[{b:/(u|b)?r?'''/,e:/'''/,r:10},{b:/(u|b)?r?"""/,e:/"""/,r:10},{b:/(u|r|ur)'/,e:/'/,r:10},{b:/(u|r|ur)"/,e:/"/,r:10},{b:/(b|br)'/,e:/'/},{b:/(b|br)"/,e:/"/},e.ASM,e.QSM]};return{k:{keyword:b},c:[r,n,e.RM,e.HCM,e.NM]}});hljs.registerLanguage("makefile",function(e){var i={cN:"variable",v:[{b:"\\$\\("+e.UIR+"\\)",c:[e.BE]},{b:/\$[@%<?\^\+\*]/}]},r={cN:"string",b:/"/,e:/"/,c:[e.BE,i]},a={cN:"variable",b:/\$\([\w-]+\s/,e:/\)/,k:{built_in:"subst patsubst strip findstring filter filter-out sort word wordlist firstword lastword dir notdir suffix basename addsuffix addprefix join wildcard realpath abspath error warning shell origin flavor foreach if or and call eval file value"},c:[i]},n={b:"^"+e.UIR+"\\s*[:+?]?=",i:"\\n",rB:!0,c:[{b:"^"+e.UIR,e:"[:+?]?=",eE:!0}]},t={cN:"meta",b:/^\.PHONY:/,e:/$/,k:{"meta-keyword":".PHONY"},l:/[\.\w]+/},l={cN:"section",b:/^[^\s]+:/,e:/$/,c:[i]};return{aliases:["mk","mak"],k:"define endef undefine ifdef ifndef ifeq ifneq else endif include -include sinclude override export unexport private vpath",l:/[\w-]+/,c:[e.HCM,i,r,a,n,t,l]}});hljs.registerLanguage("java",function(e){var a="[À-ʸa-zA-Z_$][À-ʸa-zA-Z_$0-9]*",t=a+"(<"+a+"(\\s*,\\s*"+a+")*>)?",r="false synchronized int abstract float private char boolean static null if const for true while long strictfp finally protected import native final void enum else break transient catch instanceof byte super volatile case assert short package default double public try this switch continue throws protected public private module requires exports do",s="\\b(0[bB]([01]+[01_]+[01]+|[01]+)|0[xX]([a-fA-F0-9]+[a-fA-F0-9_]+[a-fA-F0-9]+|[a-fA-F0-9]+)|(([\\d]+[\\d_]+[\\d]+|[\\d]+)(\\.([\\d]+[\\d_]+[\\d]+|[\\d]+))?|\\.([\\d]+[\\d_]+[\\d]+|[\\d]+))([eE][-+]?\\d+)?)[lLfF]?",c={cN:"number",b:s,r:0};return{aliases:["jsp"],k:r,i:/<\/|#/,c:[e.C("/\\*\\*","\\*/",{r:0,c:[{b:/\w+@/,r:0},{cN:"doctag",b:"@[A-Za-z]+"}]}),e.CLCM,e.CBCM,e.ASM,e.QSM,{cN:"class",bK:"class interface",e:/[{;=]/,eE:!0,k:"class interface",i:/[:"\[\]]/,c:[{bK:"extends implements"},e.UTM]},{bK:"new throw return else",r:0},{cN:"function",b:"("+t+"\\s+)+"+e.UIR+"\\s*\\(",rB:!0,e:/[{;=]/,eE:!0,k:r,c:[{b:e.UIR+"\\s*\\(",rB:!0,r:0,c:[e.UTM]},{cN:"params",b:/\(/,e:/\)/,k:r,r:0,c:[e.ASM,e.QSM,e.CNM,e.CBCM]},e.CLCM,e.CBCM]},c,{cN:"meta",b:"@[A-Za-z]+"}]}});hljs.registerLanguage("stan",function(e){return{c:[e.HCM,e.CLCM,e.CBCM,{b:e.UIR,l:e.UIR,k:{name:"for in while repeat until if then else",symbol:"bernoulli bernoulli_logit binomial binomial_logit beta_binomial hypergeometric categorical categorical_logit ordered_logistic neg_binomial neg_binomial_2 neg_binomial_2_log poisson poisson_log multinomial normal exp_mod_normal skew_normal student_t cauchy double_exponential logistic gumbel lognormal chi_square inv_chi_square scaled_inv_chi_square exponential inv_gamma weibull frechet rayleigh wiener pareto pareto_type_2 von_mises uniform multi_normal multi_normal_prec multi_normal_cholesky multi_gp multi_gp_cholesky multi_student_t gaussian_dlm_obs dirichlet lkj_corr lkj_corr_cholesky wishart inv_wishart","selector-tag":"int real vector simplex unit_vector ordered positive_ordered row_vector matrix cholesky_factor_corr cholesky_factor_cov corr_matrix cov_matrix",title:"functions model data parameters quantities transformed generated",literal:"true false"},r:0},{cN:"number",b:"0[xX][0-9a-fA-F]+[Li]?\\b",r:0},{cN:"number",b:"0[xX][0-9a-fA-F]+[Li]?\\b",r:0},{cN:"number",b:"\\d+(?:[eE][+\\-]?\\d*)?L\\b",r:0},{cN:"number",b:"\\d+\\.(?!\\d)(?:i\\b)?",r:0},{cN:"number",b:"\\d+(?:\\.\\d*)?(?:[eE][+\\-]?\\d*)?i?\\b",r:0},{cN:"number",b:"\\.\\d+(?:[eE][+\\-]?\\d*)?i?\\b",r:0}]}});hljs.registerLanguage("javascript",function(e){var r="[A-Za-z$_][0-9A-Za-z$_]*",t={keyword:"in of if for while finally var new function do return void else break catch instanceof with throw case default try this switch continue typeof delete let yield const export super debugger as async await static import from as",literal:"true false null undefined NaN Infinity",built_in:"eval isFinite isNaN parseFloat parseInt decodeURI decodeURIComponent encodeURI encodeURIComponent escape unescape Object Function Boolean Error EvalError InternalError RangeError ReferenceError StopIteration SyntaxError TypeError URIError Number Math Date String RegExp Array Float32Array Float64Array Int16Array Int32Array Int8Array Uint16Array Uint32Array Uint8Array Uint8ClampedArray ArrayBuffer DataView JSON Intl arguments require module console window document Symbol Set Map WeakSet WeakMap Proxy Reflect Promise"},a={cN:"number",v:[{b:"\\b(0[bB][01]+)"},{b:"\\b(0[oO][0-7]+)"},{b:e.CNR}],r:0},n={cN:"subst",b:"\\$\\{",e:"\\}",k:t,c:[]},c={cN:"string",b:"`",e:"`",c:[e.BE,n]};n.c=[e.ASM,e.QSM,c,a,e.RM];var s=n.c.concat([e.CBCM,e.CLCM]);return{aliases:["js","jsx"],k:t,c:[{cN:"meta",r:10,b:/^\s*['"]use (strict|asm)['"]/},{cN:"meta",b:/^#!/,e:/$/},e.ASM,e.QSM,c,e.CLCM,e.CBCM,a,{b:/[{,]\s*/,r:0,c:[{b:r+"\\s*:",rB:!0,r:0,c:[{cN:"attr",b:r,r:0}]}]},{b:"("+e.RSR+"|\\b(case|return|throw)\\b)\\s*",k:"return throw case",c:[e.CLCM,e.CBCM,e.RM,{cN:"function",b:"(\\(.*?\\)|"+r+")\\s*=>",rB:!0,e:"\\s*=>",c:[{cN:"params",v:[{b:r},{b:/\(\s*\)/},{b:/\(/,e:/\)/,eB:!0,eE:!0,k:t,c:s}]}]},{b:/</,e:/(\/\w+|\w+\/)>/,sL:"xml",c:[{b:/<\w+\s*\/>/,skip:!0},{b:/<\w+/,e:/(\/\w+|\w+\/)>/,skip:!0,c:[{b:/<\w+\s*\/>/,skip:!0},"self"]}]}],r:0},{cN:"function",bK:"function",e:/\{/,eE:!0,c:[e.inherit(e.TM,{b:r}),{cN:"params",b:/\(/,e:/\)/,eB:!0,eE:!0,c:s}],i:/\[|%/},{b:/\$[(.]/},e.METHOD_GUARD,{cN:"class",bK:"class",e:/[{;=]/,eE:!0,i:/[:"\[\]]/,c:[{bK:"extends"},e.UTM]},{bK:"constructor",e:/\{/,eE:!0}],i:/#(?!!)/}});hljs.registerLanguage("tex",function(c){var e={cN:"tag",b:/\\/,r:0,c:[{cN:"name",v:[{b:/[a-zA-Zа-яА-я]+[*]?/},{b:/[^a-zA-Zа-яА-я0-9]/}],starts:{eW:!0,r:0,c:[{cN:"string",v:[{b:/\[/,e:/\]/},{b:/\{/,e:/\}/}]},{b:/\s*=\s*/,eW:!0,r:0,c:[{cN:"number",b:/-?\d*\.?\d+(pt|pc|mm|cm|in|dd|cc|ex|em)?/}]}]}}]};return{c:[e,{cN:"formula",c:[e],r:0,v:[{b:/\$\$/,e:/\$\$/},{b:/\$/,e:/\$/}]},c.C("%","$",{r:0})]}});hljs.registerLanguage("xml",function(s){var e="[A-Za-z0-9\\._:-]+",t={eW:!0,i:/</,r:0,c:[{cN:"attr",b:e,r:0},{b:/=\s*/,r:0,c:[{cN:"string",endsParent:!0,v:[{b:/"/,e:/"/},{b:/'/,e:/'/},{b:/[^\s"'=<>`]+/}]}]}]};return{aliases:["html","xhtml","rss","atom","xjb","xsd","xsl","plist"],cI:!0,c:[{cN:"meta",b:"<!DOCTYPE",e:">",r:10,c:[{b:"\\[",e:"\\]"}]},s.C("<!--","-->",{r:10}),{b:"<\\!\\[CDATA\\[",e:"\\]\\]>",r:10},{b:/<\?(php)?/,e:/\?>/,sL:"php",c:[{b:"/\\*",e:"\\*/",skip:!0}]},{cN:"tag",b:"<style(?=\\s|>|$)",e:">",k:{name:"style"},c:[t],starts:{e:"</style>",rE:!0,sL:["css","xml"]}},{cN:"tag",b:"<script(?=\\s|>|$)",e:">",k:{name:"script"},c:[t],starts:{e:"</script>",rE:!0,sL:["actionscript","javascript","handlebars","xml"]}},{cN:"meta",v:[{b:/<\?xml/,e:/\?>/,r:10},{b:/<\?\w+/,e:/\?>/}]},{cN:"tag",b:"</?",e:"/?>",c:[{cN:"name",b:/[^\/><\s]+/,r:0},t]}]}});hljs.registerLanguage("markdown",function(e){return{aliases:["md","mkdown","mkd"],c:[{cN:"section",v:[{b:"^#{1,6}",e:"$"},{b:"^.+?\\n[=-]{2,}$"}]},{b:"<",e:">",sL:"xml",r:0},{cN:"bullet",b:"^([*+-]|(\\d+\\.))\\s+"},{cN:"strong",b:"[*_]{2}.+?[*_]{2}"},{cN:"emphasis",v:[{b:"\\*.+?\\*"},{b:"_.+?_",r:0}]},{cN:"quote",b:"^>\\s+",e:"$"},{cN:"code",v:[{b:"^```w*s*$",e:"^```s*$"},{b:"`.+?`"},{b:"^( {4}|	)",e:"$",r:0}]},{b:"^[-\\*]{3,}",e:"$"},{b:"\\[.+?\\][\\(\\[].*?[\\)\\]]",rB:!0,c:[{cN:"string",b:"\\[",e:"\\]",eB:!0,rE:!0,r:0},{cN:"link",b:"\\]\\(",e:"\\)",eB:!0,eE:!0},{cN:"symbol",b:"\\]\\[",e:"\\]",eB:!0,eE:!0}],r:10},{b:/^\[[^\n]+\]:/,rB:!0,c:[{cN:"symbol",b:/\[/,e:/\]/,eB:!0,eE:!0},{cN:"link",b:/:\s*/,e:/$/,eB:!0}]}]}});hljs.registerLanguage("json",function(e){var i={literal:"true false null"},n=[e.QSM,e.CNM],r={e:",",eW:!0,eE:!0,c:n,k:i},t={b:"{",e:"}",c:[{cN:"attr",b:/"/,e:/"/,c:[e.BE],i:"\\n"},e.inherit(r,{b:/:/})],i:"\\S"},c={b:"\\[",e:"\\]",c:[e.inherit(r)],i:"\\S"};return n.splice(n.length,0,t,c),{c:n,k:i,i:"\\S"}});"></script>
+
+<style type="text/css">
+  code{white-space: pre-wrap;}
+  span.smallcaps{font-variant: small-caps;}
+  span.underline{text-decoration: underline;}
+  div.column{display: inline-block; vertical-align: top; width: 50%;}
+  div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;}
+  ul.task-list{list-style: none;}
+    </style>
+
+<style type="text/css">code{white-space: pre;}</style>
+<script type="text/javascript">
+if (window.hljs) {
+  hljs.configure({languages: []});
+  hljs.initHighlightingOnLoad();
+  if (document.readyState && document.readyState === "complete") {
+    window.setTimeout(function() { hljs.initHighlighting(); }, 0);
+  }
+}
+</script>
+
+
+
+
+
+
+
+
+
+<style type="text/css">
+.main-container {
+  max-width: 940px;
+  margin-left: auto;
+  margin-right: auto;
+}
+img {
+  max-width:100%;
+}
+.tabbed-pane {
+  padding-top: 12px;
+}
+.html-widget {
+  margin-bottom: 20px;
+}
+button.code-folding-btn:focus {
+  outline: none;
+}
+summary {
+  display: list-item;
+}
+details > summary > p:only-child {
+  display: inline;
+}
+pre code {
+  padding: 0;
+}
+</style>
+
+
+
+<!-- tabsets -->
+
+<style type="text/css">
+.tabset-dropdown > .nav-tabs {
+  display: inline-table;
+  max-height: 500px;
+  min-height: 44px;
+  overflow-y: auto;
+  border: 1px solid #ddd;
+  border-radius: 4px;
+}
+
+.tabset-dropdown > .nav-tabs > li.active:before, .tabset-dropdown > .nav-tabs.nav-tabs-open:before {
+  content: "\e259";
+  font-family: 'Glyphicons Halflings';
+  display: inline-block;
+  padding: 10px;
+  border-right: 1px solid #ddd;
+}
+
+.tabset-dropdown > .nav-tabs.nav-tabs-open > li.active:before {
+  content: "\e258";
+  font-family: 'Glyphicons Halflings';
+  border: none;
+}
+
+.tabset-dropdown > .nav-tabs > li.active {
+  display: block;
+}
+
+.tabset-dropdown > .nav-tabs > li > a,
+.tabset-dropdown > .nav-tabs > li > a:focus,
+.tabset-dropdown > .nav-tabs > li > a:hover {
+  border: none;
+  display: inline-block;
+  border-radius: 4px;
+  background-color: transparent;
+}
+
+.tabset-dropdown > .nav-tabs.nav-tabs-open > li {
+  display: block;
+  float: none;
+}
+
+.tabset-dropdown > .nav-tabs > li {
+  display: none;
+}
+</style>
+
+<!-- code folding -->
+
+
+
+
 </head>
-<body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-article">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav">
-<li>
-  <a href="../../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu">
-<li>
-      <a href="../../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul>
-</li>
-<li>
-  <a href="../../news/index.html">News</a>
-</li>
-      </ul>
-<ul class="nav navbar-nav navbar-right">
-<li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul>
-</div>
-<!--/.nav-collapse -->
-  </div>
-<!--/.container -->
-</div>
-<!--/.navbar -->
 
-      
+<body>
+
+
+<div class="container-fluid main-container">
+
+
+
+
+<div id="header">
 
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header toc-ignore">
-      <h1 data-toc-skip>Testing hierarchical pathway kinetics with
+
+
+<h1 class="title toc-ignore">Testing hierarchical pathway kinetics with
 residue data on cyantraniliprole</h1>
-                        <h4 data-toc-skip class="author">Johannes
-Ranke</h4>
-            
-            <h4 data-toc-skip class="date">Last change on 20 April 2023,
-last compiled on 30 October 2023</h4>
-      
-      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/prebuilt/2022_cyan_pathway.rmd" class="external-link"><code>vignettes/prebuilt/2022_cyan_pathway.rmd</code></a></small>
-      <div class="hidden name"><code>2022_cyan_pathway.rmd</code></div>
-
-    </div>
-
-    
-    
-<div class="section level2">
-<h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a>
-</h2>
+<h4 class="author">Johannes Ranke</h4>
+<h4 class="date">Last change on 13 February 2023, last compiled on 13
+Februar 2025</h4>
+
+</div>
+
+<div id="TOC">
+true
+</div>
+
+<div id="introduction" class="section level1">
+<h1>Introduction</h1>
 <p>The purpose of this document is to test demonstrate how nonlinear
 hierarchical models (NLHM) based on the parent degradation models SFO,
 FOMC, DFOP and HS, with serial formation of two or more metabolites can
@@ -158,7 +377,7 @@ be fitted with the mkin package.</p>
 173340 (Application of nonlinear hierarchical models to the kinetic
 evaluation of chemical degradation data) of the German Environment
 Agency carried out in 2022 and 2023.</p>
-<p>The mkin package is used in version 1.2.6 which is currently under
+<p>The mkin package is used in version 1.2.9 which is currently under
 development. The newly introduced functionality that is used here is a
 simplification of excluding random effects for a set of fits based on a
 related set of fits with a reduced model, and the documentation of the
@@ -170,49 +389,47 @@ but is also loaded to make the convergence plot function available.</p>
 also provides the <code>kable</code> function that is used to improve
 the display of tabular data in R markdown documents. For parallel
 processing, the <code>parallel</code> package is used.</p>
-<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://pkgdown.jrwb.de/mkin/">mkin</a></span><span class="op">)</span></span>
-<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://yihui.org/knitr/" class="external-link">knitr</a></span><span class="op">)</span></span>
-<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va">saemix</span><span class="op">)</span></span>
-<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va">parallel</span><span class="op">)</span></span>
-<span><span class="va">n_cores</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/parallel/detectCores.html" class="external-link">detectCores</a></span><span class="op">(</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># We need to start a new cluster after defining a compiled model that is</span></span>
-<span><span class="co"># saved as a DLL to the user directory, therefore we define a function</span></span>
-<span><span class="co"># This is used again after defining the pathway model</span></span>
-<span><span class="va">start_cluster</span> <span class="op">&lt;-</span> <span class="kw">function</span><span class="op">(</span><span class="va">n_cores</span><span class="op">)</span> <span class="op">{</span></span>
-<span>  <span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/Sys.info.html" class="external-link">Sys.info</a></span><span class="op">(</span><span class="op">)</span><span class="op">[</span><span class="st">"sysname"</span><span class="op">]</span> <span class="op">==</span> <span class="st">"Windows"</span><span class="op">)</span> <span class="op">{</span></span>
-<span>    <span class="va">ret</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/parallel/makeCluster.html" class="external-link">makePSOCKcluster</a></span><span class="op">(</span><span class="va">n_cores</span><span class="op">)</span></span>
-<span>  <span class="op">}</span> <span class="kw">else</span> <span class="op">{</span></span>
-<span>    <span class="va">ret</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/parallel/makeCluster.html" class="external-link">makeForkCluster</a></span><span class="op">(</span><span class="va">n_cores</span><span class="op">)</span></span>
-<span>  <span class="op">}</span></span>
-<span>  <span class="kw"><a href="https://rdrr.io/r/base/function.html" class="external-link">return</a></span><span class="op">(</span><span class="va">ret</span><span class="op">)</span></span>
-<span><span class="op">}</span></span>
-<span><span class="va">cl</span> <span class="op">&lt;-</span> <span class="fu">start_cluster</span><span class="op">(</span><span class="va">n_cores</span><span class="op">)</span></span></code></pre></div>
-<div class="section level3">
-<h3 id="test-data">Test data<a class="anchor" aria-label="anchor" href="#test-data"></a>
-</h3>
+<pre class="r"><code>library(mkin)
+library(knitr)
+library(saemix)
+library(parallel)
+n_cores &lt;- detectCores()
+
+# We need to start a new cluster after defining a compiled model that is
+# saved as a DLL to the user directory, therefore we define a function
+# This is used again after defining the pathway model
+start_cluster &lt;- function(n_cores) {
+  if (Sys.info()[&quot;sysname&quot;] == &quot;Windows&quot;) {
+    ret &lt;- makePSOCKcluster(n_cores)
+  } else {
+    ret &lt;- makeForkCluster(n_cores)
+  }
+  return(ret)
+}
+cl &lt;- start_cluster(n_cores)</code></pre>
+<div id="test-data" class="section level2">
+<h2>Test data</h2>
 <p>The example data are taken from the final addendum to the DAR from
 2014 and are distributed with the mkin package. Residue data and time
 step normalisation factors are read in using the function
 <code>read_spreadsheet</code> from the mkin package. This function also
 performs the time step normalisation.</p>
-<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">data_file</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/system.file.html" class="external-link">system.file</a></span><span class="op">(</span></span>
-<span>  <span class="st">"testdata"</span>, <span class="st">"cyantraniliprole_soil_efsa_2014.xlsx"</span>,</span>
-<span>  package <span class="op">=</span> <span class="st">"mkin"</span><span class="op">)</span></span>
-<span><span class="va">cyan_ds</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/read_spreadsheet.html">read_spreadsheet</a></span><span class="op">(</span><span class="va">data_file</span>, parent_only <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div>
+<pre class="r"><code>data_file &lt;- system.file(
+  &quot;testdata&quot;, &quot;cyantraniliprole_soil_efsa_2014.xlsx&quot;,
+  package = &quot;mkin&quot;)
+cyan_ds &lt;- read_spreadsheet(data_file, parent_only = FALSE)</code></pre>
 <p>The following tables show the covariate data and the 5 datasets that
 were read in from the spreadsheet file.</p>
-<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">pH</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/attr.html" class="external-link">attr</a></span><span class="op">(</span><span class="va">cyan_ds</span>, <span class="st">"covariates"</span><span class="op">)</span></span>
-<span><span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="va">pH</span>, caption <span class="op">=</span> <span class="st">"Covariate data"</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
+<pre class="r"><code>pH &lt;- attr(cyan_ds, &quot;covariates&quot;)
+kable(pH, caption = &quot;Covariate data&quot;)</code></pre>
+<table>
 <caption>Covariate data</caption>
-<thead><tr class="header">
+<thead>
+<tr class="header">
 <th align="left"></th>
 <th align="right">pH</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="left">Nambsheim</td>
@@ -236,24 +453,25 @@ were read in from the spreadsheet file.</p>
 </tr>
 </tbody>
 </table>
-<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="kw">for</span> <span class="op">(</span><span class="va">ds_name</span> <span class="kw">in</span> <span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">cyan_ds</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
-<span>  <span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span></span>
-<span>    <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="fu"><a href="../../reference/mkin_long_to_wide.html">mkin_long_to_wide</a></span><span class="op">(</span><span class="va">cyan_ds</span><span class="op">[[</span><span class="va">ds_name</span><span class="op">]</span><span class="op">]</span><span class="op">)</span>,</span>
-<span>      caption <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste</a></span><span class="op">(</span><span class="st">"Dataset"</span>, <span class="va">ds_name</span><span class="op">)</span>,</span>
-<span>      booktabs <span class="op">=</span> <span class="cn">TRUE</span>, row.names <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span><span class="op">)</span></span>
-<span>    <span class="fu"><a href="https://rdrr.io/r/base/cat.html" class="external-link">cat</a></span><span class="op">(</span><span class="st">"\n\\clearpage\n"</span><span class="op">)</span></span>
-<span><span class="op">}</span></span></code></pre></div>
-<table class="table">
+<pre class="r"><code>for (ds_name in names(cyan_ds)) {
+  print(
+    kable(mkin_long_to_wide(cyan_ds[[ds_name]]),
+      caption = paste(&quot;Dataset&quot;, ds_name),
+      booktabs = TRUE, row.names = FALSE))
+    cat(&quot;\n\\clearpage\n&quot;)
+}</code></pre>
+<table>
 <caption>Dataset Nambsheim</caption>
-<thead><tr class="header">
+<thead>
+<tr class="header">
 <th align="right">time</th>
 <th align="right">cyan</th>
 <th align="right">JCZ38</th>
 <th align="right">J9C38</th>
 <th align="right">JSE76</th>
 <th align="right">J9Z38</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="right">0.000000</td>
@@ -465,15 +683,17 @@ were read in from the spreadsheet file.</p>
 </tr>
 </tbody>
 </table>
-<table class="table">
+<table>
 <caption>Dataset Tama</caption>
-<thead><tr class="header">
+<thead>
+<tr class="header">
 <th align="right">time</th>
 <th align="right">cyan</th>
 <th align="right">JCZ38</th>
 <th align="right">J9Z38</th>
 <th align="right">JSE76</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="right">0.000000</td>
@@ -659,15 +879,17 @@ were read in from the spreadsheet file.</p>
 </tr>
 </tbody>
 </table>
-<table class="table">
+<table>
 <caption>Dataset Gross-Umstadt</caption>
-<thead><tr class="header">
+<thead>
+<tr class="header">
 <th align="right">time</th>
 <th align="right">cyan</th>
 <th align="right">JCZ38</th>
 <th align="right">J9Z38</th>
 <th align="right">JSE76</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="right">0.0000000</td>
@@ -923,15 +1145,17 @@ were read in from the spreadsheet file.</p>
 </tr>
 </tbody>
 </table>
-<table class="table">
+<table>
 <caption>Dataset Sassafras</caption>
-<thead><tr class="header">
+<thead>
+<tr class="header">
 <th align="right">time</th>
 <th align="right">cyan</th>
 <th align="right">JCZ38</th>
 <th align="right">J9Z38</th>
 <th align="right">JSE76</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="right">0.000000</td>
@@ -1061,15 +1285,17 @@ were read in from the spreadsheet file.</p>
 </tr>
 </tbody>
 </table>
-<table class="table">
+<table>
 <caption>Dataset Lleida</caption>
-<thead><tr class="header">
+<thead>
+<tr class="header">
 <th align="right">time</th>
 <th align="right">cyan</th>
 <th align="right">JCZ38</th>
 <th align="right">J9Z38</th>
 <th align="right">JSE76</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="right">0.000000</td>
@@ -1201,25 +1427,25 @@ were read in from the spreadsheet file.</p>
 </table>
 </div>
 </div>
-<div class="section level2">
-<h2 id="parent-only-evaluations">Parent only evaluations<a class="anchor" aria-label="anchor" href="#parent-only-evaluations"></a>
-</h2>
+<div id="parent-only-evaluations" class="section level1">
+<h1>Parent only evaluations</h1>
 <p>As the pathway fits have very long run times, evaluations of the
 parent data are performed first, in order to determine for each
 hierarchical parent degradation model which random effects on the
 degradation model parameters are ill-defined.</p>
-<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">cyan_sep_const</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"FOMC"</span>, <span class="st">"DFOP"</span>, <span class="st">"SFORB"</span>, <span class="st">"HS"</span><span class="op">)</span>,</span>
-<span>  <span class="va">cyan_ds</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, cores <span class="op">=</span> <span class="va">n_cores</span><span class="op">)</span></span>
-<span><span class="va">cyan_sep_tc</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">cyan_sep_const</span>, error_model <span class="op">=</span> <span class="st">"tc"</span><span class="op">)</span></span>
-<span><span class="va">cyan_saem_full</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/mhmkin.html">mhmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">cyan_sep_const</span>, <span class="va">cyan_sep_tc</span><span class="op">)</span><span class="op">)</span></span>
-<span><span class="fu"><a href="../../reference/status.html">status</a></span><span class="op">(</span><span class="va">cyan_saem_full</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
-<thead><tr class="header">
+<pre class="r"><code>cyan_sep_const &lt;- mmkin(c(&quot;SFO&quot;, &quot;FOMC&quot;, &quot;DFOP&quot;, &quot;SFORB&quot;, &quot;HS&quot;),
+  cyan_ds, quiet = TRUE, cores = n_cores)
+cyan_sep_tc &lt;- update(cyan_sep_const, error_model = &quot;tc&quot;)
+cyan_saem_full &lt;- mhmkin(list(cyan_sep_const, cyan_sep_tc))
+status(cyan_saem_full) |&gt; kable()</code></pre>
+<table>
+<thead>
+<tr class="header">
 <th align="left"></th>
 <th align="left">const</th>
 <th align="left">tc</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="left">SFO</td>
@@ -1249,14 +1475,15 @@ degradation model parameters are ill-defined.</p>
 </tbody>
 </table>
 <p>All fits converged successfully.</p>
-<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="../../reference/illparms.html">illparms</a></span><span class="op">(</span><span class="va">cyan_saem_full</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
-<thead><tr class="header">
+<pre class="r"><code>illparms(cyan_saem_full) |&gt; kable()</code></pre>
+<table>
+<thead>
+<tr class="header">
 <th align="left"></th>
 <th align="left">const</th>
 <th align="left">tc</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="left">SFO</td>
@@ -1289,16 +1516,17 @@ degradation model parameters are ill-defined.</p>
 of the parent compound is ill-defined. For the biexponential models DFOP
 and SFORB, the random effect of one additional parameter is ill-defined
 when the two-component error model is used.</p>
-<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">cyan_saem_full</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span>digits <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
-<thead><tr class="header">
+<pre class="r"><code>anova(cyan_saem_full) |&gt; kable(digits = 1)</code></pre>
+<table>
+<thead>
+<tr class="header">
 <th align="left"></th>
 <th align="right">npar</th>
 <th align="right">AIC</th>
 <th align="right">BIC</th>
 <th align="right">Lik</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="left">SFO const</td>
@@ -1376,74 +1604,71 @@ when the two-component error model is used.</p>
 two-component error model is preferable for all parent models with the
 exception of DFOP. The lowest AIC and BIC values are are obtained with
 the FOMC model, followed by SFORB and DFOP.</p>
-<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/parallel/makeCluster.html" class="external-link">stopCluster</a></span><span class="op">(</span><span class="va">cl</span><span class="op">)</span></span></code></pre></div>
+<pre class="r"><code>stopCluster(cl)</code></pre>
 </div>
-<div class="section level2">
-<h2 id="pathway-fits">Pathway fits<a class="anchor" aria-label="anchor" href="#pathway-fits"></a>
-</h2>
-<div class="section level3">
-<h3 id="evaluations-with-pathway-established-previously">Evaluations with pathway established previously<a class="anchor" aria-label="anchor" href="#evaluations-with-pathway-established-previously"></a>
-</h3>
+<div id="pathway-fits" class="section level1">
+<h1>Pathway fits</h1>
+<div id="evaluations-with-pathway-established-previously" class="section level2">
+<h2>Evaluations with pathway established previously</h2>
 <p>To test the technical feasibility of coupling the relevant parent
 degradation models with different transformation pathway models, a list
 of <code>mkinmod</code> models is set up below. As in the EU evaluation,
 parallel formation of metabolites JCZ38 and J9Z38 and secondary
 formation of metabolite JSE76 from JCZ38 is used.</p>
-<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="kw">if</span> <span class="op">(</span><span class="op">!</span><span class="fu"><a href="https://rdrr.io/r/base/files2.html" class="external-link">dir.exists</a></span><span class="op">(</span><span class="st">"cyan_dlls"</span><span class="op">)</span><span class="op">)</span> <span class="fu"><a href="https://rdrr.io/r/base/files2.html" class="external-link">dir.create</a></span><span class="op">(</span><span class="st">"cyan_dlls"</span><span class="op">)</span></span>
-<span><span class="va">cyan_path_1</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span></span>
-<span>  sfo_path_1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span></span>
-<span>    cyan <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"JCZ38"</span>, <span class="st">"J9Z38"</span><span class="op">)</span><span class="op">)</span>,</span>
-<span>    JCZ38 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"JSE76"</span><span class="op">)</span>,</span>
-<span>    J9Z38 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span>
-<span>    JSE76 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>,</span>
-<span>    name <span class="op">=</span> <span class="st">"sfo_path_1"</span>, dll_dir <span class="op">=</span> <span class="st">"cyan_dlls"</span>, overwrite <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>,</span>
-<span>  fomc_path_1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span></span>
-<span>    cyan <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"FOMC"</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"JCZ38"</span>, <span class="st">"J9Z38"</span><span class="op">)</span><span class="op">)</span>,</span>
-<span>    JCZ38 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"JSE76"</span><span class="op">)</span>,</span>
-<span>    J9Z38 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span>
-<span>    JSE76 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>,</span>
-<span>    name <span class="op">=</span> <span class="st">"fomc_path_1"</span>, dll_dir <span class="op">=</span> <span class="st">"cyan_dlls"</span>, overwrite <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>,</span>
-<span>  dfop_path_1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span></span>
-<span>    cyan <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"JCZ38"</span>, <span class="st">"J9Z38"</span><span class="op">)</span><span class="op">)</span>,</span>
-<span>    JCZ38 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"JSE76"</span><span class="op">)</span>,</span>
-<span>    J9Z38 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span>
-<span>    JSE76 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>,</span>
-<span>    name <span class="op">=</span> <span class="st">"dfop_path_1"</span>, dll_dir <span class="op">=</span> <span class="st">"cyan_dlls"</span>, overwrite <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>,</span>
-<span>  sforb_path_1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span></span>
-<span>    cyan <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFORB"</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"JCZ38"</span>, <span class="st">"J9Z38"</span><span class="op">)</span><span class="op">)</span>,</span>
-<span>    JCZ38 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"JSE76"</span><span class="op">)</span>,</span>
-<span>    J9Z38 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span>
-<span>    JSE76 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>,</span>
-<span>    name <span class="op">=</span> <span class="st">"sforb_path_1"</span>, dll_dir <span class="op">=</span> <span class="st">"cyan_dlls"</span>, overwrite <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>,</span>
-<span>  hs_path_1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span></span>
-<span>    cyan <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"HS"</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"JCZ38"</span>, <span class="st">"J9Z38"</span><span class="op">)</span><span class="op">)</span>,</span>
-<span>    JCZ38 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"JSE76"</span><span class="op">)</span>,</span>
-<span>    J9Z38 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span>
-<span>    JSE76 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>,</span>
-<span>    name <span class="op">=</span> <span class="st">"hs_path_1"</span>, dll_dir <span class="op">=</span> <span class="st">"cyan_dlls"</span>, overwrite <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
-<span><span class="op">)</span></span>
-<span><span class="va">cl_path_1</span> <span class="op">&lt;-</span> <span class="fu">start_cluster</span><span class="op">(</span><span class="va">n_cores</span><span class="op">)</span></span></code></pre></div>
+<pre class="r"><code>if (!dir.exists(&quot;cyan_dlls&quot;)) dir.create(&quot;cyan_dlls&quot;)
+cyan_path_1 &lt;- list(
+  sfo_path_1 = mkinmod(
+    cyan = mkinsub(&quot;SFO&quot;, c(&quot;JCZ38&quot;, &quot;J9Z38&quot;)),
+    JCZ38 = mkinsub(&quot;SFO&quot;, &quot;JSE76&quot;),
+    J9Z38 = mkinsub(&quot;SFO&quot;),
+    JSE76 = mkinsub(&quot;SFO&quot;), quiet = TRUE,
+    name = &quot;sfo_path_1&quot;, dll_dir = &quot;cyan_dlls&quot;, overwrite = TRUE),
+  fomc_path_1 = mkinmod(
+    cyan = mkinsub(&quot;FOMC&quot;, c(&quot;JCZ38&quot;, &quot;J9Z38&quot;)),
+    JCZ38 = mkinsub(&quot;SFO&quot;, &quot;JSE76&quot;),
+    J9Z38 = mkinsub(&quot;SFO&quot;),
+    JSE76 = mkinsub(&quot;SFO&quot;), quiet = TRUE,
+    name = &quot;fomc_path_1&quot;, dll_dir = &quot;cyan_dlls&quot;, overwrite = TRUE),
+  dfop_path_1 = mkinmod(
+    cyan = mkinsub(&quot;DFOP&quot;, c(&quot;JCZ38&quot;, &quot;J9Z38&quot;)),
+    JCZ38 = mkinsub(&quot;SFO&quot;, &quot;JSE76&quot;),
+    J9Z38 = mkinsub(&quot;SFO&quot;),
+    JSE76 = mkinsub(&quot;SFO&quot;), quiet = TRUE,
+    name = &quot;dfop_path_1&quot;, dll_dir = &quot;cyan_dlls&quot;, overwrite = TRUE),
+  sforb_path_1 = mkinmod(
+    cyan = mkinsub(&quot;SFORB&quot;, c(&quot;JCZ38&quot;, &quot;J9Z38&quot;)),
+    JCZ38 = mkinsub(&quot;SFO&quot;, &quot;JSE76&quot;),
+    J9Z38 = mkinsub(&quot;SFO&quot;),
+    JSE76 = mkinsub(&quot;SFO&quot;), quiet = TRUE,
+    name = &quot;sforb_path_1&quot;, dll_dir = &quot;cyan_dlls&quot;, overwrite = TRUE),
+  hs_path_1 = mkinmod(
+    cyan = mkinsub(&quot;HS&quot;, c(&quot;JCZ38&quot;, &quot;J9Z38&quot;)),
+    JCZ38 = mkinsub(&quot;SFO&quot;, &quot;JSE76&quot;),
+    J9Z38 = mkinsub(&quot;SFO&quot;),
+    JSE76 = mkinsub(&quot;SFO&quot;), quiet = TRUE,
+    name = &quot;hs_path_1&quot;, dll_dir = &quot;cyan_dlls&quot;, overwrite = TRUE)
+)
+cl_path_1 &lt;- start_cluster(n_cores)</code></pre>
 <p>To obtain suitable starting values for the NLHM fits, separate
 pathway fits are performed for all datasets.</p>
-<div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">f_sep_1_const</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/mmkin.html">mmkin</a></span><span class="op">(</span></span>
-<span>  <span class="va">cyan_path_1</span>,</span>
-<span>  <span class="va">cyan_ds</span>,</span>
-<span>  error_model <span class="op">=</span> <span class="st">"const"</span>,</span>
-<span>  cluster <span class="op">=</span> <span class="va">cl_path_1</span>,</span>
-<span>  quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
-<span><span class="fu"><a href="../../reference/status.html">status</a></span><span class="op">(</span><span class="va">f_sep_1_const</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
-<thead><tr class="header">
+<pre class="r"><code>f_sep_1_const &lt;- mmkin(
+  cyan_path_1,
+  cyan_ds,
+  error_model = &quot;const&quot;,
+  cluster = cl_path_1,
+  quiet = TRUE)
+status(f_sep_1_const) |&gt; kable()</code></pre>
+<table>
+<thead>
+<tr class="header">
 <th align="left"></th>
 <th align="left">Nambsheim</th>
 <th align="left">Tama</th>
 <th align="left">Gross-Umstadt</th>
 <th align="left">Sassafras</th>
 <th align="left">Lleida</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="left">sfo_path_1</td>
@@ -1487,18 +1712,19 @@ pathway fits are performed for all datasets.</p>
 </tr>
 </tbody>
 </table>
-<div class="sourceCode" id="cb11"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">f_sep_1_tc</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_sep_1_const</span>, error_model <span class="op">=</span> <span class="st">"tc"</span><span class="op">)</span></span>
-<span><span class="fu"><a href="../../reference/status.html">status</a></span><span class="op">(</span><span class="va">f_sep_1_tc</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
-<thead><tr class="header">
+<pre class="r"><code>f_sep_1_tc &lt;- update(f_sep_1_const, error_model = &quot;tc&quot;)
+status(f_sep_1_tc) |&gt; kable()</code></pre>
+<table>
+<thead>
+<tr class="header">
 <th align="left"></th>
 <th align="left">Nambsheim</th>
 <th align="left">Tama</th>
 <th align="left">Gross-Umstadt</th>
 <th align="left">Sassafras</th>
 <th align="left">Lleida</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="left">sfo_path_1</td>
@@ -1552,18 +1778,18 @@ for the parent only fits is used as an argument
 <code>no_random_effect</code> to the <code>mhmkin</code> function. The
 possibility to do so was introduced in mkin version <code>1.2.2</code>
 which is currently under development.</p>
-<div class="sourceCode" id="cb12"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">f_saem_1</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/mhmkin.html">mhmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">f_sep_1_const</span>, <span class="va">f_sep_1_tc</span><span class="op">)</span>,</span>
-<span>  no_random_effect <span class="op">=</span> <span class="fu"><a href="../../reference/illparms.html">illparms</a></span><span class="op">(</span><span class="va">cyan_saem_full</span><span class="op">)</span>,</span>
-<span>  cluster <span class="op">=</span> <span class="va">cl_path_1</span><span class="op">)</span></span></code></pre></div>
-<div class="sourceCode" id="cb13"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="../../reference/status.html">status</a></span><span class="op">(</span><span class="va">f_saem_1</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
-<thead><tr class="header">
+<pre class="r"><code>f_saem_1 &lt;- mhmkin(list(f_sep_1_const, f_sep_1_tc),
+  no_random_effect = illparms(cyan_saem_full),
+  cluster = cl_path_1)</code></pre>
+<pre class="r"><code>status(f_saem_1) |&gt; kable()</code></pre>
+<table>
+<thead>
+<tr class="header">
 <th align="left"></th>
 <th align="left">const</th>
 <th align="left">tc</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="left">sfo_path_1</td>
@@ -1599,19 +1825,20 @@ Fisher Information Matrix could not be inverted for the fixed effects
 fits, ill-defined parameters cannot be determined using the
 <code>illparms</code> function, because it relies on the Fisher
 Information Matrix.</p>
-<div class="sourceCode" id="cb14"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="../../reference/illparms.html">illparms</a></span><span class="op">(</span><span class="va">f_saem_1</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
+<pre class="r"><code>illparms(f_saem_1) |&gt; kable()</code></pre>
+<table>
 <colgroup>
-<col width="18%">
-<col width="77%">
-<col width="4%">
+<col width="18%" />
+<col width="77%" />
+<col width="4%" />
 </colgroup>
-<thead><tr class="header">
+<thead>
+<tr class="header">
 <th align="left"></th>
 <th align="left">const</th>
 <th align="left">tc</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="left">sfo_path_1</td>
@@ -1641,18 +1868,68 @@ sd(f_JCZ38_qlogis)</td>
 </tr>
 </tbody>
 </table>
-<p>The model comparison below suggests that the pathway fits using DFOP
+<p>The model comparisons below suggest that the pathway fits using DFOP
 or SFORB for the parent compound provide the best fit.</p>
-<div class="sourceCode" id="cb15"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_saem_1</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span>digits <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
-<thead><tr class="header">
+<pre class="r"><code>anova(f_saem_1[, &quot;const&quot;]) |&gt; kable(digits = 1)</code></pre>
+<table>
+<thead>
+<tr class="header">
 <th align="left"></th>
 <th align="right">npar</th>
 <th align="right">AIC</th>
 <th align="right">BIC</th>
 <th align="right">Lik</th>
-</tr></thead>
+</tr>
+</thead>
+<tbody>
+<tr class="odd">
+<td align="left">sfo_path_1 const</td>
+<td align="right">16</td>
+<td align="right">2693.0</td>
+<td align="right">2686.8</td>
+<td align="right">-1330.5</td>
+</tr>
+<tr class="even">
+<td align="left">fomc_path_1 const</td>
+<td align="right">18</td>
+<td align="right">2427.9</td>
+<td align="right">2420.9</td>
+<td align="right">-1196.0</td>
+</tr>
+<tr class="odd">
+<td align="left">dfop_path_1 const</td>
+<td align="right">20</td>
+<td align="right">2403.2</td>
+<td align="right">2395.4</td>
+<td align="right">-1181.6</td>
+</tr>
+<tr class="even">
+<td align="left">sforb_path_1 const</td>
+<td align="right">20</td>
+<td align="right">2401.4</td>
+<td align="right">2393.6</td>
+<td align="right">-1180.7</td>
+</tr>
+<tr class="odd">
+<td align="left">hs_path_1 const</td>
+<td align="right">20</td>
+<td align="right">2427.2</td>
+<td align="right">2419.4</td>
+<td align="right">-1193.6</td>
+</tr>
+</tbody>
+</table>
+<pre class="r"><code>anova(f_saem_1[1:4, ]) |&gt; kable(digits = 1)</code></pre>
+<table>
+<thead>
+<tr class="header">
+<th align="left"></th>
+<th align="right">npar</th>
+<th align="right">AIC</th>
+<th align="right">BIC</th>
+<th align="right">Lik</th>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="left">sfo_path_1 const</td>
@@ -1697,20 +1974,13 @@ or SFORB for the parent compound provide the best fit.</p>
 <td align="right">-1180.7</td>
 </tr>
 <tr class="odd">
-<td align="left">hs_path_1 const</td>
-<td align="right">20</td>
-<td align="right">2427.2</td>
-<td align="right">2419.4</td>
-<td align="right">-1193.6</td>
-</tr>
-<tr class="even">
 <td align="left">dfop_path_1 tc</td>
 <td align="right">20</td>
 <td align="right">2398.0</td>
 <td align="right">2390.1</td>
 <td align="right">-1179.0</td>
 </tr>
-<tr class="odd">
+<tr class="even">
 <td align="left">sforb_path_1 tc</td>
 <td align="right">20</td>
 <td align="right">2399.9</td>
@@ -1721,29 +1991,27 @@ or SFORB for the parent compound provide the best fit.</p>
 </table>
 <p>For these two parent model, successful fits are shown below. Plots of
 the fits with the other parent models are shown in the Appendix.</p>
-<div class="sourceCode" id="cb16"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_saem_1</span><span class="op">[[</span><span class="st">"dfop_path_1"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div>
+<pre class="r"><code>plot(f_saem_1[[&quot;dfop_path_1&quot;, &quot;tc&quot;]])</code></pre>
 <div class="figure" style="text-align: center">
-<img src="2022_cyan_pathway_files/figure-html/unnamed-chunk-7-1.png" alt="DFOP pathway fit with two-component error" width="700"><p class="caption">
+<img src="data:image/png;base64,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" alt="DFOP pathway fit with two-component error" width="672" />
+<p class="caption">
 DFOP pathway fit with two-component error
 </p>
 </div>
-<div class="sourceCode" id="cb17"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_saem_1</span><span class="op">[[</span><span class="st">"sforb_path_1"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div>
+<pre class="r"><code>plot(f_saem_1[[&quot;sforb_path_1&quot;, &quot;tc&quot;]])</code></pre>
 <div class="figure" style="text-align: center">
-<img src="2022_cyan_pathway_files/figure-html/unnamed-chunk-8-1.png" alt="SFORB pathway fit with two-component error" width="700"><p class="caption">
+<img src="data:image/png;base64,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" alt="SFORB pathway fit with two-component error" width="672" />
+<p class="caption">
 SFORB pathway fit with two-component error
 </p>
 </div>
 <p>A closer graphical analysis of these Figures shows that the residues
 of transformation product JCZ38 in the soils Tama and Nambsheim observed
 at later time points are strongly and systematically underestimated.</p>
-<div class="sourceCode" id="cb18"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/parallel/makeCluster.html" class="external-link">stopCluster</a></span><span class="op">(</span><span class="va">cl_path_1</span><span class="op">)</span></span></code></pre></div>
+<pre class="r"><code>stopCluster(cl_path_1)</code></pre>
 </div>
-<div class="section level3">
-<h3 id="alternative-pathway-fits">Alternative pathway fits<a class="anchor" aria-label="anchor" href="#alternative-pathway-fits"></a>
-</h3>
+<div id="alternative-pathway-fits" class="section level2">
+<h2>Alternative pathway fits</h2>
 <p>To improve the fit for JCZ38, a back-reaction from JSE76 to JCZ38 was
 introduced in an alternative version of the transformation pathway, in
 analogy to the back-reaction from K5A78 to K5A77. Both pairs of
@@ -1752,55 +2020,56 @@ corresponding amide (Addendum 2014, p. 109). As FOMC provided the best
 fit for the parent, and the biexponential models DFOP and SFORB provided
 the best initial pathway fits, these three parent models are used in the
 alternative pathway fits.</p>
-<div class="sourceCode" id="cb19"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">cyan_path_2</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span></span>
-<span>  fomc_path_2 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span></span>
-<span>    cyan <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"FOMC"</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"JCZ38"</span>, <span class="st">"J9Z38"</span><span class="op">)</span><span class="op">)</span>,</span>
-<span>    JCZ38 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"JSE76"</span><span class="op">)</span>,</span>
-<span>    J9Z38 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span>
-<span>    JSE76 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"JCZ38"</span><span class="op">)</span>,</span>
-<span>    name <span class="op">=</span> <span class="st">"fomc_path_2"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>,</span>
-<span>    dll_dir <span class="op">=</span> <span class="st">"cyan_dlls"</span>,</span>
-<span>    overwrite <span class="op">=</span> <span class="cn">TRUE</span></span>
-<span>  <span class="op">)</span>,</span>
-<span>  dfop_path_2 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span></span>
-<span>    cyan <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"JCZ38"</span>, <span class="st">"J9Z38"</span><span class="op">)</span><span class="op">)</span>,</span>
-<span>    JCZ38 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"JSE76"</span><span class="op">)</span>,</span>
-<span>    J9Z38 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span>
-<span>    JSE76 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"JCZ38"</span><span class="op">)</span>,</span>
-<span>    name <span class="op">=</span> <span class="st">"dfop_path_2"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>,</span>
-<span>    dll_dir <span class="op">=</span> <span class="st">"cyan_dlls"</span>,</span>
-<span>    overwrite <span class="op">=</span> <span class="cn">TRUE</span></span>
-<span>  <span class="op">)</span>,</span>
-<span>  sforb_path_2 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span></span>
-<span>    cyan <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFORB"</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"JCZ38"</span>, <span class="st">"J9Z38"</span><span class="op">)</span><span class="op">)</span>,</span>
-<span>    JCZ38 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"JSE76"</span><span class="op">)</span>,</span>
-<span>    J9Z38 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span>
-<span>    JSE76 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"JCZ38"</span><span class="op">)</span>,</span>
-<span>    name <span class="op">=</span> <span class="st">"sforb_path_2"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>,</span>
-<span>    dll_dir <span class="op">=</span> <span class="st">"cyan_dlls"</span>,</span>
-<span>    overwrite <span class="op">=</span> <span class="cn">TRUE</span></span>
-<span>  <span class="op">)</span></span>
-<span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">cl_path_2</span> <span class="op">&lt;-</span> <span class="fu">start_cluster</span><span class="op">(</span><span class="va">n_cores</span><span class="op">)</span></span>
-<span><span class="va">f_sep_2_const</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/mmkin.html">mmkin</a></span><span class="op">(</span></span>
-<span>  <span class="va">cyan_path_2</span>,</span>
-<span>  <span class="va">cyan_ds</span>,</span>
-<span>  error_model <span class="op">=</span> <span class="st">"const"</span>,</span>
-<span>  cluster <span class="op">=</span> <span class="va">cl_path_2</span>,</span>
-<span>  quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu"><a href="../../reference/status.html">status</a></span><span class="op">(</span><span class="va">f_sep_2_const</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
-<thead><tr class="header">
+<pre class="r"><code>cyan_path_2 &lt;- list(
+  fomc_path_2 = mkinmod(
+    cyan = mkinsub(&quot;FOMC&quot;, c(&quot;JCZ38&quot;, &quot;J9Z38&quot;)),
+    JCZ38 = mkinsub(&quot;SFO&quot;, &quot;JSE76&quot;),
+    J9Z38 = mkinsub(&quot;SFO&quot;),
+    JSE76 = mkinsub(&quot;SFO&quot;, &quot;JCZ38&quot;),
+    name = &quot;fomc_path_2&quot;, quiet = TRUE,
+    dll_dir = &quot;cyan_dlls&quot;,
+    overwrite = TRUE
+  ),
+  dfop_path_2 = mkinmod(
+    cyan = mkinsub(&quot;DFOP&quot;, c(&quot;JCZ38&quot;, &quot;J9Z38&quot;)),
+    JCZ38 = mkinsub(&quot;SFO&quot;, &quot;JSE76&quot;),
+    J9Z38 = mkinsub(&quot;SFO&quot;),
+    JSE76 = mkinsub(&quot;SFO&quot;, &quot;JCZ38&quot;),
+    name = &quot;dfop_path_2&quot;, quiet = TRUE,
+    dll_dir = &quot;cyan_dlls&quot;,
+    overwrite = TRUE
+  ),
+  sforb_path_2 = mkinmod(
+    cyan = mkinsub(&quot;SFORB&quot;, c(&quot;JCZ38&quot;, &quot;J9Z38&quot;)),
+    JCZ38 = mkinsub(&quot;SFO&quot;, &quot;JSE76&quot;),
+    J9Z38 = mkinsub(&quot;SFO&quot;),
+    JSE76 = mkinsub(&quot;SFO&quot;, &quot;JCZ38&quot;),
+    name = &quot;sforb_path_2&quot;, quiet = TRUE,
+    dll_dir = &quot;cyan_dlls&quot;,
+    overwrite = TRUE
+  )
+)
+
+cl_path_2 &lt;- start_cluster(n_cores)
+f_sep_2_const &lt;- mmkin(
+  cyan_path_2,
+  cyan_ds,
+  error_model = &quot;const&quot;,
+  cluster = cl_path_2,
+  quiet = TRUE)
+
+status(f_sep_2_const) |&gt; kable()</code></pre>
+<table>
+<thead>
+<tr class="header">
 <th align="left"></th>
 <th align="left">Nambsheim</th>
 <th align="left">Tama</th>
 <th align="left">Gross-Umstadt</th>
 <th align="left">Sassafras</th>
 <th align="left">Lleida</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="left">fomc_path_2</td>
@@ -1830,18 +2099,19 @@ alternative pathway fits.</p>
 </table>
 <p>Using constant variance, separate fits converge with the exception of
 the fits to the Sassafras soil data.</p>
-<div class="sourceCode" id="cb20"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">f_sep_2_tc</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_sep_2_const</span>, error_model <span class="op">=</span> <span class="st">"tc"</span><span class="op">)</span></span>
-<span><span class="fu"><a href="../../reference/status.html">status</a></span><span class="op">(</span><span class="va">f_sep_2_tc</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
-<thead><tr class="header">
+<pre class="r"><code>f_sep_2_tc &lt;- update(f_sep_2_const, error_model = &quot;tc&quot;)
+status(f_sep_2_tc) |&gt; kable()</code></pre>
+<table>
+<thead>
+<tr class="header">
 <th align="left"></th>
 <th align="left">Nambsheim</th>
 <th align="left">Tama</th>
 <th align="left">Gross-Umstadt</th>
 <th align="left">Sassafras</th>
 <th align="left">Lleida</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="left">fomc_path_2</td>
@@ -1872,18 +2142,18 @@ the fits to the Sassafras soil data.</p>
 <p>Using the two-component error model, all separate fits converge with
 the exception of the alternative pathway fit with DFOP used for the
 parent and the Sassafras dataset.</p>
-<div class="sourceCode" id="cb21"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">f_saem_2</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/mhmkin.html">mhmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">f_sep_2_const</span>, <span class="va">f_sep_2_tc</span><span class="op">)</span>,</span>
-<span>  no_random_effect <span class="op">=</span> <span class="fu"><a href="../../reference/illparms.html">illparms</a></span><span class="op">(</span><span class="va">cyan_saem_full</span><span class="op">[</span><span class="fl">2</span><span class="op">:</span><span class="fl">4</span>, <span class="op">]</span><span class="op">)</span>,</span>
-<span>  cluster <span class="op">=</span> <span class="va">cl_path_2</span><span class="op">)</span></span></code></pre></div>
-<div class="sourceCode" id="cb22"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="../../reference/status.html">status</a></span><span class="op">(</span><span class="va">f_saem_2</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
-<thead><tr class="header">
+<pre class="r"><code>f_saem_2 &lt;- mhmkin(list(f_sep_2_const, f_sep_2_tc),
+  no_random_effect = illparms(cyan_saem_full[2:4, ]),
+  cluster = cl_path_2)</code></pre>
+<pre class="r"><code>status(f_saem_2) |&gt; kable()</code></pre>
+<table>
+<thead>
+<tr class="header">
 <th align="left"></th>
 <th align="left">const</th>
 <th align="left">tc</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="left">fomc_path_2</td>
@@ -1903,20 +2173,22 @@ parent and the Sassafras dataset.</p>
 </tbody>
 </table>
 <p>The hierarchical fits for the alternative pathway completed
-successfully.</p>
-<div class="sourceCode" id="cb23"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="../../reference/illparms.html">illparms</a></span><span class="op">(</span><span class="va">f_saem_2</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
+successfully, with the exception of the model using FOMC for the parent
+compound and constant variance as the error model.</p>
+<pre class="r"><code>illparms(f_saem_2) |&gt; kable()</code></pre>
+<table>
 <colgroup>
-<col width="14%">
-<col width="42%">
-<col width="42%">
+<col width="14%" />
+<col width="42%" />
+<col width="42%" />
 </colgroup>
-<thead><tr class="header">
+<thead>
+<tr class="header">
 <th align="left"></th>
 <th align="left">const</th>
 <th align="left">tc</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="left">fomc_path_2</td>
@@ -1935,19 +2207,21 @@ successfully.</p>
 </tr>
 </tbody>
 </table>
-<p>In both fits, the random effects for the formation fractions for the
-pathways from JCZ38 to JSE76, and for the reverse pathway from JSE76 to
-JCZ38 are ill-defined.</p>
-<div class="sourceCode" id="cb24"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_saem_2</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span>digits <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
-<thead><tr class="header">
+<p>In all biphasic fits (DFOP or SFORB for the parent compound), the
+random effects for the formation fractions for the pathways from JCZ38
+to JSE76, and for the reverse pathway from JSE76 to JCZ38 are
+ill-defined.</p>
+<pre class="r"><code>anova(f_saem_2[, &quot;tc&quot;]) |&gt; kable(digits = 1)</code></pre>
+<table>
+<thead>
+<tr class="header">
 <th align="left"></th>
 <th align="right">npar</th>
 <th align="right">AIC</th>
 <th align="right">BIC</th>
 <th align="right">Lik</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="left">fomc_path_2 tc</td>
@@ -1957,27 +2231,55 @@ JCZ38 are ill-defined.</p>
 <td align="right">-1103.5</td>
 </tr>
 <tr class="even">
+<td align="left">dfop_path_2 tc</td>
+<td align="right">22</td>
+<td align="right">2234.4</td>
+<td align="right">2225.8</td>
+<td align="right">-1095.2</td>
+</tr>
+<tr class="odd">
+<td align="left">sforb_path_2 tc</td>
+<td align="right">22</td>
+<td align="right">2239.7</td>
+<td align="right">2231.1</td>
+<td align="right">-1097.9</td>
+</tr>
+</tbody>
+</table>
+<pre class="r"><code>anova(f_saem_2[2:3,]) |&gt; kable(digits = 1)</code></pre>
+<table>
+<thead>
+<tr class="header">
+<th align="left"></th>
+<th align="right">npar</th>
+<th align="right">AIC</th>
+<th align="right">BIC</th>
+<th align="right">Lik</th>
+</tr>
+</thead>
+<tbody>
+<tr class="odd">
 <td align="left">dfop_path_2 const</td>
 <td align="right">22</td>
 <td align="right">2288.4</td>
 <td align="right">2279.8</td>
 <td align="right">-1122.2</td>
 </tr>
-<tr class="odd">
+<tr class="even">
 <td align="left">sforb_path_2 const</td>
 <td align="right">22</td>
 <td align="right">2283.3</td>
 <td align="right">2274.7</td>
 <td align="right">-1119.7</td>
 </tr>
-<tr class="even">
+<tr class="odd">
 <td align="left">dfop_path_2 tc</td>
 <td align="right">22</td>
 <td align="right">2234.4</td>
 <td align="right">2225.8</td>
 <td align="right">-1095.2</td>
 </tr>
-<tr class="odd">
+<tr class="even">
 <td align="left">sforb_path_2 tc</td>
 <td align="right">22</td>
 <td align="right">2239.7</td>
@@ -1992,31 +2294,30 @@ and BIC values and are plotted below. Compared with the original
 pathway, the AIC and BIC values indicate a large improvement. This is
 confirmed by the plots, which show that the metabolite JCZ38 is fitted
 much better with this model.</p>
-<div class="sourceCode" id="cb25"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_saem_2</span><span class="op">[[</span><span class="st">"fomc_path_2"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div>
+<pre class="r"><code>plot(f_saem_2[[&quot;fomc_path_2&quot;, &quot;tc&quot;]])</code></pre>
 <div class="figure" style="text-align: center">
-<img src="2022_cyan_pathway_files/figure-html/unnamed-chunk-13-1.png" alt="FOMC pathway fit with two-component error, alternative pathway" width="700"><p class="caption">
+<img src="data:image/png;base64,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" alt="FOMC pathway fit with two-component error, alternative pathway" width="672" />
+<p class="caption">
 FOMC pathway fit with two-component error, alternative pathway
 </p>
 </div>
-<div class="sourceCode" id="cb26"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_saem_2</span><span class="op">[[</span><span class="st">"dfop_path_2"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div>
+<pre class="r"><code>plot(f_saem_2[[&quot;dfop_path_2&quot;, &quot;tc&quot;]])</code></pre>
 <div class="figure" style="text-align: center">
-<img src="2022_cyan_pathway_files/figure-html/unnamed-chunk-14-1.png" alt="DFOP pathway fit with two-component error, alternative pathway" width="700"><p class="caption">
+<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABUAAAAYACAIAAAA41HWdAAAACXBIWXMAAB2HAAAdhwGP5fFlAAAgAElEQVR4nOzdd1hTZ/sH8PuEMMIIG9kgW0RRUdCKWnetbdHS6mvrtm59La+7/tyrVm2to85WXG3B2qrUvVcVxQqoDJGhsvcIhMzz+yM2RVSIjATw+7l69Uqe8+TJN8nJkTvnnOcwLMsSAAAAAAAAADRtHE0HAAAAAAAAAIDaoYAHAAAAAAAAaAZQwAMAAAAAAAA0AyjgAQAAAAAAAJoBFPAAAAAAAAAAzQAKeAAAAAAAAIBmAAU8AAC0ZFmidDkr13QKAAAAgAaAAh4A3jo5OTnMSwwMDDp27Dh79uySkhKNpJo5cybDMHfv3q3PIBMnTmQYJjExsaFSNXfXCs8vSpjxS+aPan7ekJCQl9exahYvXqzmVNAiZWRkzJw5s3PnziYmJsbGxt7e3rNnz05OTtZ0rte6fv16hw4deDzerl27NJ0FAKD54Wo6AACAZujp6QUGBipusyybn58fFxcXHR0dHh4eGxtramqq2XiqYFlWLBZraWlxudiYv0KFrPy3rP1EdKngVC/zAfZ6Tmp7ag8Pj379+invpqSkpKSkuLm5OTs7KxtdXV3VlgdaqtOnTw8dOrSystLZ2bl79+5isTg+Pv7bb7/dvXt3WFjYoEGDNB2wOrlcPnz48JycnKCgIE9PT03HAQBofhiWZTWdAQBArXJycqytrZ2dnVNTU6u2P336dMiQIffu3Zs+ffrWrVvVnGrmzJlbt26Niory8/NT8SGRkZFdu3adM2fO+vXrFS0pKSl5eXm+vr56enqNlrTZCMvceybvGJfRlrKSNobt57qu0FSS5cuXL1u27Ouvv54/f76mMkADKpHL95eVf2TAc3rVb2dnKoRlLPuJgX5jx8jLy/Py8iopKQkNDf38888ZhiEiuVweGho6ceJEExOT+Ph4Kyurxo7xRp4+ferk5NS/f/+zZ89qOgsAQLOEQ+gBAJ5zdHT8/vvviejixYuazlJHLi4uAQEBqN6JKFeUdSH/BEPMl63/z5DLjxfE3i25qelQ0EJcEYp+KC0bmZufIpFWW/SzoDykoGhZYbGk8XeQXL16tbCwcMyYMSNHjlRU70TE4XDGjx8/ZcqUwsLCQ4cONXaGNyUUConIyUl9h8MAALQwKOABAP7l7e1NRCkpKXV7eGVlZYPGUTeJRCKTyTSdomH8nLlHykp7mvf3NvId0moEEYVlhkpYsaZz1Z1QKJTLMRtfkzBQX+8dPd18mXxM3gs1/M+C8lVFJUT0f6bG2v9U1I0nPj6eXlMMDx069N1339XV1W3sDA2urKxM0xEAAJo0FPAAAP9KSEggInt7e2WLVCpds2ZNYGCgsbGxu7t7cHDwvXv3qj6kdevWPXr0uHnzZrt27Xg8Hp/P79279549e6r2GTVqFMMwT548qdqomLXu4cOHrwtTVlY2b948f39/Pp9vaWkZEBCwY8cO5XlPgwYN6tq1KxFt2LCBYRjFrrapU6dWm8Su1vwLFy5kGCYtLW3y5Ml8Pl9bW9vBwWHcuHFZWVlv+OY1IQ/LomNL7/K09IdYjyCid80H2us554tzzuYd13S0F9T8EdM/n050dPSAAQMMDAz09fUDAwPDw8OJ6OzZs4MGDbK0tLSxsRk7dmx+fv4bjQz1ocsw2y3MevH08mXy0bn5jyVSIgoXVCiq90Wmxv8xNFBDDAcHByIKDQ1NS0urtqhfv36XLl2aNm2askWVVeL8+fPvvfeenZ2doaGhj4/PmjVrKioq3qhDzc8ybtw4Ly8vItqzZw/DMIojnubOncswTEFBwf79+x0dHd9///0GDAwA0PJg3iMAqJfz58+vWrVKIBCo+XkdHR2/++67hj0OMyMj48svvyQi5eR2FRUVvXv3vn37dqtWrXr06JGbm3vs2LGIiIjdu3ePGTNG+cBnz5699957YrFY8cBbt25dvnz51q1b1cr4N5Kfn+/n5/f06dO2bdsOHDhQIBBcu3Zt6tSpWVlZy5cvJ6LJkyd7eXlt2rSpd+/eQUFBrzxzXsX8RDR16tSLFy8OGDDA1tb24sWLoaGhMTExd+7c0dLSqvNL0BQ5K/s18yci+qjVcGOuKRFxGM5ndhO+SV78Z85v3U37mGibaTojkQofsdInn3xSWlo6ZsyYsrKyo0ePRkZGXrt2bfv27X369Bk6dOipU6f27dtXXFx89OjRNx0Z6kyHYTabm/63oOiKsHJsbv5/DA1/KC0lokWmxp+rpXonok8//XTp0qWpqalt2rQZPHjwRx991LdvXzs7u5d7qrJKHDx4cNSoUVwu19fX19/fPzIyctGiRTExMWFhYSp2qPVZPvvsMxsbm7Vr13bt2vU///lPr169lAnDw8NnzZrVv3//3r17N1RgAICWiQUAqKuKigo+n6+pzdegQYPqFjs7O5uIeDzee/8YOHBg586dFaeO29ra5ufnK3ouXbqUiIYNG1ZRUaFouXLlCp/PNzExUfZRzCvu5OQUHx+vaElKSvLw8CCiCxcuKFpGjhxJRGlpaVVjzJgxg4gePHhQ9W5UVJTi7urVq4lo4cKFyv6ZmZl8Pt/FxUXZcuvWLSKaM2eOsmXKlClElJCQoHr+BQsWEJG+vv6dO3cULSKRyNfXl4hiYmLq9g43uKzK9DVJC5Yn/m/FozkPS6Nrbj+Te2xcdNCk2E+XJYZUbd+SunZcdNCsh2NUHKcBLVu2jIi+/vrrqo2qfMSKT8fd3T0vL+/5q9iyRbH+79mzR9GSl5dnYmKio6MjlUpVHxledrK8Ijg7V8X/Rufk50llIrl8cl6B19OMNk8yvJ5m9M7MVv2xDZI5IyPj008/1dHRUW4Y27Rp89///vf27dtVu6mySnh4ePB4vNTUVMXdiooKxXYsNzdXxQ6qPIviKKcvvvhC2TJnzhwisrKyqrrBaZDAAAAtEvbAA0DdCQQCDZ6vWM9jvIVC4enTp5V3GYZxcnLq06fP2rVrzc3NFY2bNm3i8/k7duzg8XiKlp49e4aEhCxfvjw0NHT27NnKh69fv15xaCgRubm5bdu2rX///t9++22fPn3qFq979+47duwIDg5WttjY2LRq1eqNXrXq+adPn965c2fFbR0dnY8++igmJiYrK6t9+/Z1y9+w8sQ5SeXxittPhSneRr6vay+XCSJywolIIhc/EaZU7T/cdlxsaVSppLhUUlzrOGp4Uap/xAsWLLCwsFDcHjhwIBG1adNmwoQJihYLCwt/f/+zZ88WFxcrVt0GWXneQtFi8UOxRMXOHKI8mcxCS7unru4VYSXLELGUJZVlUe2zSPzz2AY4jdHW1jY8PLykpOTSpUsXLly4cOFCfHx8fHz85s2bP/744/379xsYGJBqq0RGRoa+vr5y68fj8SIiIvLy8pRbj1o71GfFGz9+fNWtTYMEBgBokVDAA0DdWVpafvnll5s2bWLVfm6tnp7e4sWL6zPCy5eRqyYzM7OkpGTAgAHVrgk/aNCg5cuXK/YjKSnP21To16+fkZFRXFxcneP16tWrV69eLMumpaWlpaWlpqZevnw5KSnJ0NBQxRHeKH+3bt2q3lX80d90tDPq9HWbHRWycm1G21bPoYb2I1kHymUCbyPf0fZTq/W31Gk10HLIidzf7PWcJjn9z07PsdbxG5XqH7GLi4vytr6+PhEpfy2q2liHkaGqeSbGQw30ZaptzMy0ODZaWuGCilXFJQyRq7b2Y4nEmOEsMzNx4NZy4onisQ2Q+B/GxsZDhgwZMmQIET19+vTAgQPr1q37/fffra2tt23bRqqtEsOGDdu7d6+3t/eIESN69OgREBDg4eGh2KetYof6rHhdunSperdBAgMAtEgo4AGgXr799tvZs2crDkpXGw6H4+LiYmxs3KjPkp6eTkQ2NjbV2hWnmFadkc7Y2PjlitfBwSExMZFlWaZOk1FLJJIVK1Zs3769oKCAYRgbG5tOnTpZW1urPt2A6vmJyMysSZwWXgMrHeta258J064WnmOIGWDxIUOMgZYhEeWLc5Ud/E0DLxacTK988lSYaq/n9Lpx1EP1j/jlVajmuQnqv/K8nbSIvLS1Ve8fLqhYVlRMRItMjT810FecD7+qqDjUysJNWx1/X4WHh/N4vA8//LBqo6Oj46JFiwYMGODv7793794tW7ZwOBxVVomdO3f6+fnt3bt3w4YN69evZxjGz89v7ty5w4YNU7FDfVa8aluqBgkMANAioYAHgPqys7N75bRJzZ1iLvqXf5tQHMNZdab6kpKSioqKantBMzIyrK2ta6jeaz77YOTIkeHh4WPGjJk0aVKHDh0Ug3fp0qXanvMGyd9iHMv5Vc7KiWhT6qqaex7N/vkd03fVken16v8Rq39kUKpavStmras6p516avj//e9/+fn5FRUVHE71o/G7dOliaWmZn59fXl5uZGSkyiqhra09ffr06dOn5+fn37p168qVKz/99NPw4cOJSFES19qhPitetZfQIIEBAFokXEYOAODVbG1t+Xz+rVu3iouLq7afOXOGiNq0aVO18eTJk1XvXrlypaSkRHFVeaWqV4lnWfbOnTuve2qhUBgREeHr6xsaGvrOO+8ofxooKSlppPwtg4u+h6VOK1X+8zBoq9moDfIRq3lkULpZKVJU74urzDmvwzCbzE3f0dMtkMsn5hXUfip8vXXo0EEkEu3fv//lRSkpKXl5ed7e3kZGRqqsEikpKSEhIT///DMRWVhYfPDBB+vXr//xxx+J6MSJE6p0aMAVr0ECAwC0VNgDDwDwWrNmzVq5cuW0adP27t2rq6tLRDdu3NiwYYOJicnYsWOr9pw3b56vr6+7uzsRpaSkTJ06lYgUF6UjIisrKyI6evTo/PnzFS0bN26s4Qx5DocjlUqLioqEQqFiQiapVLpu3bqkpCQej1ftsPyqvwvUOX/L8L7Vx+9bfazpFCp5o4+4iYwMSkYcji1Xa6KR4fAXrxinxzDbLMzmFhQVy+VqeJfnzZt34sSJadOmlZaWzpgxQ7kT+9GjRyNGjCAixUyHqqwSfD5/06ZN9vb2vXv3Vh7NHh8fT0SKzVqtHRpwxWuQwAAALRUKeACA15o/f/6pU6d++eWXK1eu+Pv75+bm3r59m8Ph7Nmzx9LSUtmNz+cbGxv7+vp27dqViCIjIysqKkaNGqWc2W7YsGGbN29esGDB1atXXVxc7t69e/PmTScnp2onoivp6uqOHDly7969rq6u/fr143A4169fJ6KAgIDIyMhx48YtWrTI3d1dMZ/T4cOHGYYZOXKkv79/3fKD+qn4ETepkUHJR0f7vE2rVy7SY5gtFmqaUaJnz55btmz58ssvZ82atWDBAjc3NzMzs+zs7EePHrEsO3z48JCQEFJ5lZg6der27dvd3d0DAwNNTEzi4+NjY2NdXFwmTZpERBYWFjV3aMAVr0ECAwC0WOq8Zh0AQFOgOC3c2dlZlc5isXjlypXdunVTXII4ODj43r17VTs4OzsrplaaMWOGt7c3n8/v2bPnzp07q41z5syZ7t278/l8ImIYZsqUKYrZoV93HXihULh06VJ3d3d9ff2OHTvOmzdPIBDcunXLx8dHX1//0qVLLMvK5fI5c+Y4OjoaGRn98ccf7EvXgVclv+JK45cvX67a+PXXXxPR6dOnVXmLoFavvA68Kh/xy5+OYm7CTz75pOpQiunH8/PzVR8ZWowHDx5MmTLFw8PD0NDQ0NDQx8cnODj4woULVfuoskqIxeJt27b5+fmZmZkpxlm4cGFOTo5ykFo7qPIsr7sO/K1btxo8MABAi8Swar/4EwBAS9K6devKykrVr7CdlZWlq6vb9Gd9BwAAAICmBofQAwCo1cvXdQMAAAAAUAVmoQcAAAAAAABoBlDAAwAAAAAAADQDOAceAKBeMjIyWJa1t7fXdBAAAAAAaOFQwAMAAAAAAAA0AziEHgAAAAAAAKAZQAEPAAAAAAAA0AyggAcAAAAAAABoBlDAAwBAS5ZWIpVjshdoTKyUFaUKNZ0CAADeCijgAeCts3PnToZhjI2Ns7KyXl4aGRnJMMyoUaMaO0br1q0dHBzqOcjChQsZhomMjKznOBMnTmQYJjExsZ7jNDUXn4j6/pK3/W+BBjOUl5evXbu2f//+zs7OBgYG3t7eQ4YMOXXqlAYj1UrxLejdu/crl4aFhTEMM3HiRDWnqptjx44xDLNx48ZGGl9eLnv84b2H7f/K3faskZ7idRQvreaN1cyZMxmGuXv3rurDqrI1aOx3FQAAXgcFPABohlxQIXr8RJyWwYolGglQWlr6v//9TyNPDWqTXS5T/l8jjh8/7ubm9tVXX50/f14mk/n6+paWlh47duz999/v2bOnQKDJXxaaMpZlRSKRVCpt4iPLy2WPg2PKrhURS+nzH6m/hgcAgLcNCngAUDfRo9Tspd8/HTc/a8H6zDlrn46Zl7/lgKyoRP1Jfv311/Pnz6v/eZughQsX3rp1y8nJSdNBWpSTJ09+8skn2dnZ06ZNy8nJefbs2V9//ZWenh4ZGdm1a9dr1659/PHHcrlc0zGbotu3b+vp6S1cuLApj6yo3gXXi7TtdG0WuRCHaRk1PLYGAABNGQp4AFCr8ht3sxZ/V/kwidHR1nV11HawYWUywZXIzLlfSzJz1Zlk8ODBRDRt2jSRSKTO522aXFxcAgIC9PT0NB2k5SgrK/viiy8kEsm+ffu2bdtmZWWlXOTv73/u3Lm2bdueO3fu4sWLGgwJdSavkCV/+rx69zjlZ7OwtdNWL0UNn70xTdPp6gVbAwCApgwFPACojzSvMH/bQZLJjYP6Ofy41mbdPLvvFtlvW6bXzlNWXJb33U/Eqm+2sd69e48cOTIpKenrr79uwGGFQmEL26fa8l6ReuzevTsrK6tfv36jR49+eamhoeGSJUs6d+58+/btVz68rKyskQM2irdkbZFXyJI/iSm7+rx613XhEZH5aFtFDZ+5NLm51/AAANBkoYAHAPUpO32VFUsMe3YxHTWEo6eraORamrVaOIVrZS5OTRfGqnUStY0bN5qYmKxdu/bx48c1dCsrK5s3b56/vz+fz7e0tAwICNixYwdb5bcGxUxy0dHRAwYMMDAw0NfXDwwMDA8PJ6KzZ88OGjTI0tLSxsZm7Nix+fn51QYXiUSzZ8/u0KGDsbFxjx49tm7dWq3D+fPn33vvPTs7O0NDQx8fnzVr1lRUVFTrw7LswYMHBwwYYGZm5uDgMG7cuJfn5zt06NDAgQMtLS2trKwGDhx45syZqkunTp1addqq+rwiUPjjjz+IqIZDtYcNG3bnzp2vvvpKcXfu3LkMwxQUFOzfv9/R0fH9999XtEul0jVr1gQGBhobG7u7uwcHB9+7d6/aULWuJKqsRXVW57XldakGDRrUtWtXItqwYQPDMIcOHVL0r/WbSET5+fmTJ09u27atqanpgAEDDh8+XHXp60Z+I6+s3hWaSw3/RlsDqu1dVVDl0wEAgPrjajoAADR75TfuSvMKX7lIz8dD1+3fEykr4x8TEbeVRcnRc9V6cq3MpbkFFTfu8ny9VBlfz9tN16N1PZNbWVmtWbNm2rRp06dPr/YnrFJ+fr6fn9/Tp0/btm07cOBAgUBw7dq1qVOnZmVlLV++vGrPTz75pLS0dMyYMWVlZUePHo2MjLx27dr27dv79OkzdOjQU6dO7du3r7i4+OjRo8qHiESiwYMH37hxo0uXLu3atYuKirp+/frly5d/++03RYeDBw+OGjWKy+X6+vr6+/tHRkYuWrQoJiYmLCys6lOvW7fu1KlTAwYMGDRo0JkzZ0JDQ+/fv3/79m0O5/mvtGPGjNm/f7+5ubm/v39FRcXVq1fPnj27evVqZfX4SnV4RY1EKGXD4ysqpSyHYYZ48Cz1OdXaFXcTCqUllc93/zJEbS21k4qkRBSXL9l6tyw2VyKVP29vZaD1nzb6WpzqI1RV7bneVHR0NBG1a9fujR4VHh4+a9as/v37K2aAr6io6N279+3bt1u1atWjR4/c3Nxjx45FRETs3r17zJgxiofUupKouBbV05uuLTWkmjx5speX16ZNm3r37h0UFOTn50eqfRMfPXrUt2/f9PR0W1vbbt26JSYmDh8+vH///sqQrxxZSZQqLD6aS/+sCxy+lsVYO4bLVG1nxfL8g1niNCGHzzX71Lr46L8n/ij6m4+2leRKMpcnZy5NLr9VatjN+JXj/PsoIy2LcXYMl2nAz6Jmb7o1qPVdpTfZTgIAQD2hgAeAepGVCvK+2/u6pToujrbfzFPelZeWE1Hx4ddeQKv8zn1z1cbXcbaz3dAA01BNnjw5NDT07NmzYWFhw4cPf7nDrl27nj59unDhwjVr1ihasrKyvLy8Dh48WO0PUw6HExcXZ2FhQURbt26dOXPm1q1b9+zZM2HCBCLKz893d3c/deqUTCbT0tJSPCQvLy8lJSU2Ntbd3Z2IHj9+PHjw4CNHjvz+++8ff/wxEa1cuZLH48XFxTk7OxORUCjs0KFDeHj41q1bLS0tlU999uzZ69evd+7cmYgqKysDAgLu3r2blJTk6elJRIcPH96/f/+QIUP2799vZGRERMnJyQMHDly8eHG/fv38/f1f9+bU4RU1krOplStulCpul4jkcwKMXm5/2eVnz2c3iM6VROdKqrW7mHC72enUPELV53ojFRUVAoHA2Ni46sdEREeOHNmzZ0/VFm1t7ePHjyvvLlu2LCoqqn379oq733zzze3bt4cNGxYaGsrj8Yjo6tWrH3744ZdffvnBBx+Ym5uTCiuJimtRPb3p2lJDqiFDhtjY2GzatMnPz2/WrFmK8VX5JoaEhKSnp4eEhKxfv175LEuWLFGGfOXIStlfpxYceuHQFT0PA6Oepi+3E5G8VJqz6Um1RkV/UVK54mygklN5Jafyah6HiHRd9fl9zFR7m+urDluDWt9VepPtJAAA1BMKeACoFy2+ocW0z183/5x+5xd2P3KMDCg7z3hIP6Lqu5tE8cmViSn6fm1VHJ/n51O/4P9E4nB27NjRpUuXkJCQQYMG8fn8ah26d+++Y8eO4OBgZYuNjU2rVq1ePkZ9wYIFiuqFiAYOHEhEbdq0UVQvRGRhYeHv73/27Nni4mJF3aWwceNGRfVORG5ubtu2bevfv/+2bdsUBXxGRoa+vr6yP4/Hi4iIyMvLU9RyStOnT1dU70Skp6f33nvvxcbGZmdnKwr41atX83i8ffv2Kf5eJyJXV9dvvvkmODj4wIEDNRTwdXtFjaG/s97//I3KJaw2h0Z467/crrj7qEBaJHq+B57DkLeF9pMS6bVnIm8L7a52uvfzxBLZ8/bWxlpdbHReHqGqas/1RhTHPpSXl0skEm1tbWV7Wlra6dOnq/asNlXY+PHjldU7EW3atInP5+/YsUP5iffs2TMkJGT58uWhoaGzZ88mFVYSFdeienrTteVNU9X6TXz06NHJkyd9fHw2bNigPPZk8eLFf/zxx8snHbxSqznO2ra6rOz5ysA11zHsZlKtnZWwRUdyJJkiLWMt009stIz//elK2V/Py5DhMKycNephqt+F/8px/n2UmbZRoIkq8RrEm24NVHxXVd9OAgBAPaGAB4D6MuzTTcWeem1cRUlpsuIyixmjqrazEknGl6uJyCCwc33Gr5uOHTtOnz598+bNixcv/v7776st7dWrV69evViWTUtLS0tLS01NvXz5clJSkqGhYbWeLi4uytv6+vpE5OX1wukAisaqGIYZNGhQ1ZZ+/foZGRklJCQo7g4bNmzv3r3e3t4jRozo0aNHQECAh4eHh4dHtXECAwNf90QymSw+Pt7GxubAgQNV+yjORlYc5v06dXhFjURfm5neqfobXkO70s9xFdeeiTpYaS/q9uod6bWOUDd6enp8Pr+0tDQ5ObnqmzZ79mxF1a3QpUuXBw8eVH1gly5dlLczMzNLSkoGDBhgampatc+gQYOWL1+u+kpSQwepVFp1BymHw6nz/tI3XVtUXLeVav0mxsXFEVFQUJCyzlR45awBr6Tnrm+71LXWdtv/c1FcPa70bL77yU66rV/4xaHot5zM5Y9ZOWu72MV6fusaxlG/OmwNVHxXVd9OAgBAPaGABwD1MRrYo/TUVcHlSC0zE5PggYyuDhHJCovzt/8szcnXcbLjtffUSLCVK1cePnx427ZtyvOKlSQSyYoVK7Zv315QUMAwjI2NTadOnaytrQUCQbWeDFP9sIJaDyw3MzN7+VpNDg4OiYmJLMsyDLNz504/P7+9e/du2LBh/fr1DMP4+fnNnTt32LBhVR9S9RJl1WRkZIjF4idPnsyYMePlpTVPdV6HVwRKAQEB586di4qKqlbHKsnl8vj4+GqNNjY2ytvp6enVWhTs7OyI6MmT58dv17qS1NBBJpOtWrVKOTKXy61awL9uBrJXtr/p2qLiuq1U6zcxMzOTiOzt7as9UHGIfgPiGGi5HfFV1PBJ7/9dtYYv+i0n7YuHrPQV1XtTUIetgYrvqurbSQAAqCfMQg8A6sNtZWE+9TPicEp+P/Psi6+y/u/bzHnrnk1dIrwXp8U3tAwZRxzNbJT4fP53330nk8mmTJkik8mqLho5cuSqVas++OCDGzduCASCjIyMiIiIl/+crZvCwsKXr0KfkZHh7OysKIe0tbWnT58eFRWVm5sbERExe/bslJSU4cOHK6b4Vnq5dlKytrbW0tLq378/+yo174GH+vj888+JaNWqVVKp9JUdbty4UV5eXq2x6n5OxWqWnZ1drY/isGTlSljrSlJDB11d3arrg0QiqfrUGRkZr0z+7NkzInJ0dFT93XiZiuu2Uq3fREUexa8eVRUXF9cn5yspanjD7ibiZ5VJ7/8tShVSk6/eqU5bAxXf1UbdTgIAQFUo4AFArQx7drFeMUvXy0UurBQlpIhTnjEMY9Cji+2GBdr21hoMpphX+c6dOzt37lQ2CoXCiIgIX1/f0NDQd955R/VjOQQAACAASURBVHkMcElJSYM8Kcuyp069MKXf5cuXS0pKfHx8iCglJSUkJOTnn38mIgsLiw8++GD9+vU//vgjEZ04cULFp9DR0XFxcbl79261WvH69eszZ8583dz7UH+jRo3q2LFjYmLixo0bX14qEommTJlS8wi2trZ8Pv/WrVvViiXFp9amTRtSYSWp21pkZ2dnaWmZnJxc9VpiCnK5/NixY0RUbQr3N/KmqVT5JirekOPHj1e7EH0jreQcAy3X33wNAozFzyqTPvg7Z9OTtAkPWSlru8y1aVbvVKetgSrvamNvJwEAoCoU8ACgbnperjar/uewZ43Nqv/ZfD3Xcd96y1ljtMzUN43T6/zwww+6urr79+9XtnA4HKlUWlRUJBQKFS1SqXT16tVJSUkymaxBLnE8Z86c5ORkxe3k5OSpU6cS0dy5c4mIz+dv2rRp/vz5VSeCUhx0rZz3ThUhISGFhYWfffaZ8q/29PT0Tz/9dOvWrYqDsaExcDicTZs2aWlpLViwYPTo0VV3pN++fbtLly7Jycm2trY1DzJr1qySkpJp06Ypj9S4cePGhg0bTExMxo4dSyqsJHVei0JCQliW/eijj27evKlszMvLGz9+/I0bNzp16tS3b19V34uXqJiqsrJScUOVb6Krq+tHH310//79BQsWKKvNXbt2RUREvBxAOXJ9aBlx3Y52MAgwFj+pzPi/x6yMtV3maj3Huf4jN5433Rqo8q6qYTsJAABKOAceADRDy4SvZVJ9ynfNcnNzW7BgQdVzgHV1dUeOHLl3715XV9d+/fpxOJzr168TUUBAQGRk5Lhx4xYtWvRGtXQ1NjY22dnZ7du379q1q1wuj4yMFAqFs2bN6t69OxFZWFhMnTp1+/bt7u7ugYGBJiYm8fHxsbGxLi4ukyZNUv1ZJk2adOzYsePHjzs6OgYEBBQWFt65c0cul69Zs0axq78Fs9LnEJGlvmZO3e/Zs2dYWNi4ceMOHDhw4MABe3t7JyenhISEgoICS0vLkydPnj59esuWLTWMMH/+/FOnTv3yyy9Xrlzx9/fPzc29ffs2h8PZs2eP4gpwta4kdV6L5s+fHx0dHR4e/s477zg5OTk5ORUUFDx+/FgkErVu3TosLExXV7fO70ytqRSTnx0+fJhhmJEjR/r7+6vyTVy/fv3du3fXr18fFhbWoUOHx48fx8XFffbZZ4pd/Qovj1znV0H/1PCPh0SXR5bYLne1nu1cn9Hq5sKFC9XmwlT45ptv2rVrV62xDluDWt/Vxt5OAgBAVdgDDwDwr4ULF7q5uVVt+eGHH5YuXWpoaHjkyJHY2Njg4OCYmJjvv//ex8fn8OHDrztJWEW+vr7R0dEff/zxs2fPYmJi3nnnnUOHDm3atEnZ4fvvv9+2bZuXl9edO3dOnDghl8sXLlx48+bNGmate5mWltapU6e2bNni4+Nz69attLS0Pn36nDp1auHChfUJ3yz0c9Y7PcyyMeaZV1FwcHBiYuL8+fPbtm1bVFQUExNjZ2e3ZMmSxMTEPn36BAcHK3akv46BgcFff/21cuVKJyenixcvZmdnBwUFRUZGjhr173Ucal1J6rYWcTicsLCwCxcuDB061MDAICoqqqysLDAw8LvvvktISKj2NamDmlN5e3vPmTNHV1c3NDRUMY+aKt9EDw+P6OjoiRMnGhkZXblyxdraeseOHevWrav6vC+PXE9aRlyPU53aRnfTSPVORFlZWadfpbCw8OXOddgaqPKuNup2EgAAqmJwaBMAAAAAAABA04c98AAAAAAAAADNAAp4AAAAAAAAgGYABTwAAAAAAABAM4ACHgAAAAAAAKAZQAEPAAAAAAAA0AyggAcAAAAAAABoBlDAAwAAAAAAADQDKOABAAAAAAAAmgEU8AAAAAAAAADNAAp4AAAAAAAAgGYABTwAAAAAAABAM4ACHgAAAAAAAKAZQAEPAAAAAAAA0AyggAcAAAAAAABoBlDAAwAAAAAAADQDKOABAAAAAAAAmgEU8AAAAAAAAADNAAp4AAAAAAAAgGYABTwAAAAAAABAM4ACHgAAAAAAAKAZQAEPAAAAAAAA0AyggAcAAAAAAABoBlDAAwAAAAAAADQDKOABAAAAAAAAmgEU8AAAAAAAAADNAAp4AAAAAAAAgGYABTwAAAAAAABAM4ACHgAAAAAAAKAZQAEPAAAAAAAA0AyggAcAAAAAAABoBlDAAwAAAAAAADQDKOABAAAAAAAAmgEU8AAAAAAAAADNAFfTAZql8+fPp6SkaDoFAABoUp8+fdzc3DSdAhoS/n0HAIAm/u87w7KspjM0M6WlpaampnK5XNNBAABAk/r27Xv+/HlNp4AGg3/fAQCAmvy/79gD/8aEQqFcLufxeKNGjXrTx7ImMmKJKdFqjGAAAKAeOTk5x44dq6io0HQQaEj1+fcdAABagGbx7zsK+Dri8/k7d+58o4cUSQq+SpiuxWit8fqBzzVupGAAANDYbt68eezYMU2ngEZRh3/fAQCgZWgW/75jEjv1OZy1TySvrJCV/551UNNZAAAAAAAAoJlBAa8mj8sTIouu6XB0uAz3WuH5p0LMkQMAAAAAAABvAAW8OrDE/pL5I0vse5ZD+1oMZok9mLGLJUwfCAAAAAAAAKpCAa8O1wsvpFYkmWqbD7IaGmT9H2Ou6ePyhDvFNzSdCwAAAAAAAJoNFPCNrlIu/D37EBF9ajNGl6Onx+ENsR5BRGGZe8VykabTAQAAAAAAQPOAAr7RReSEl0iKXA08/U0D9zz9PvTZtp7m/Z313YokBafzjmo6HQAAAAAAADQPKOAbV644+1zenwwxn9l+cbPw8l9Fl64Wnosq/muE7QSGmJO5RwrEeZrOCAAAAAAAAM0ACvjG9WvGT1JWEmjW10bP/rfsA4rGsMy9TjyXzibviOXiI/80AgAAAAAAANQABXwjihfERpfe1uPwPrb+/M+swyWSImeJjb3UqlCSfzrn6HDbcToc3ciia4/K4zSdFAAAAAAAAJo6FPCNRc7Kfs7YQ0SdTd6Ju3PqdO4fRHT/+pBH14OI6M+sX5OiL3Xk+7PE/pr5Ey4pBwAAAAAAADXjajpAi3Wv9HZG5VMiul544boBERHLav2t154heU8iqRa7U+sQFRMRpVU8flB2r51RJ43mBQAAAAAAgCYNBXxjceS5eBu2L68sEeRkFJhLOXJqxdoQEUuMrdwqi8klIvNCroGNvaGuib2ek6bzAgAAaF5aWppAIPDy8uJyuUQUGxu7f//+vLy8tm3bDh48uG3btpoOCAAAoEk4hL6xWOq0muO6YvKZ9loyhoiGWo5Y3H4rERExqzruGmw6hGVIW8yZfjVgtssyU21zzaYFAADQrIiICFtb29atW7dr187Dw+PBgweXLl3q3Lnzxo0b9+/fP3/+/E6dOm3evFnTMQEAADQJBXzjuqETlWslMas0GGA3VCZlh53NCz6fTyx94PCZaSUvy0Z8S3xL0xkBAAA07O7du0FBQdnZ2V27du3evXtGRsaIESNmz55tYmKydevW8+fPb9682cTE5Msvv4yKitJ0WAAAAI3BIfSNqFwmuNQ5l4iGlPbmyrWfzIj/9GweET39MsFxk9f7BQGH7C6f6ZzRV1bB09LXdFgAAACNWbFiBRGdOHFi0KBBRHTt2rXevXvLZLKLFy/27t2biPr27du3b19fX9+1a9ceOXJEw3EBAAA0BAV8Y5GxtDflWCFj6vBY1zjPKu7nONHh7EodDodl83/MEIhZc08bU7Ftlo34QOrJL1w/4TCaTgwAAKAhMTEx3bp1U1TvRNSjR48uXbrExcUpqncFb2/vgICA2NjYOox/+PDhXbt21dxHLBYTUXFxcR3GBwAAUA8U8I1lzqXM44/6MKZ97prIrbdm9bqTXanLWTvegRhm4Z6ndCDzQoDbifk9pClM5D16kpW9uoe1piMDAABoRl5eXvv27au22NvbFxYWVutmZWV19+7dOox/+PDh8+fPq9JTJBLVYXwAAAD1QAHfWPIqhUR62i40ZVNWjzsllbqctRMc4lwMiGjtRIeFu5/1jSzW+pl2T7WWRFOeEH8uAADA28vNzS0qKkoul3M4z2fnmTNnTkFBQbVuiYmJlpaWdRh/z549kyZNqrlPWlraxIkTlQEAAACaIBTwjcWdbxkpF0z6KqvHzRKRPueb1Q5xcn1iiYjiXA3Wb3SYO//Zu78Vy7No5xAbN+O6/DkCAADQMvTr1+/bb7+dOHHi1q1beTweEQUEBFTrs2vXrri4uLFjx9ZhfD6f369fv5r7PHjwoA4jQ8sjYcVXCs768rtY6rTSdBYAgOrwM3Nj6WWns/JkTq+bxRI9zrdb7RPf07doX0EcIob4HUQJ/fQ3bXMQ63H63Chefjq3h62OpvMCAABozNKlS1u3bv3TTz+ZmZmNHDmy2tItW7YEBARMnjzZwMBg4cKFGkkIb4+I7PCfM/Zsf7KeVex4AQBoSlDAN5YnS5M9LhdW6nFWf+MYzTEkMZXa69vaiq3tJZU2OiSiv8lg/Wp7sQ7jdaHg0aoUTecFAADQGD6fHxMTM2/ePE9Pz5KSkmpLT548efv2bT8/v6tXr3p4eGgkIbwl8sU5Z/KOE1FaxeMbhRc1HQcAoDoU8I3FvxWXiIjLyK0Yi05Mv2Jex+sV68cmrx+X7Hmrol+JvllnhrUk0mKIqJsN9sADAMBbzcjIaN26ddHR0REREdUWrV69Ojk5OSoqqlOnThrJBm+PsMxQCSu21rUloiNZByvlQk0nAgB4AQr4xrKjn9XVTsZ6AtnCCU8tHpZnPcgfNPXi7OIF8wsXfjr52rOkfNtYwZzJ6TpC+aUuxnt7mms6LwAAQBPVqVMnFxcXTaeAli9BcP9uyU1djt5c15UeBt4l0qI/cw5rOhQAwAswiV1jGdpG//dFLnabn7heKVw07ilHzn5atuKpPIOIckvy903fRcToiORJfc2fTHf8jydP03kBAKDle9NrsPn5+TVSEoAGxMrY/N3p+n7GBl34dR5Ezsp/zviRiAa3+sRU23yE3YQVj+aczTseaNZPsUMeAKApQAHfWN511H3XUZftZ/ooKJouF7LEZlCOYlGSPEUmEvJIz7C/+fAjvsM5jGajAgDAW6Jz585v1J9lMYkXNHWsjH0yKa4wLJvD47iE+fL7mNVtnCuFZ9Mr0yx0Wg20/IiInHiu3c36XC+8cDhr30xnTJ0IAE0FCvgXsCxbXFxccx/F5Doq/k1TdLmw9FaRFhFDTCvGMpOyiIglNkue48JxKr5eVHStyLRXHf+lAQAAeCMzZszQdASAhqSs3hktRi6UpwyPqVsNXy4T/JH9MxENtx2rzTyfmegTm1F3S27eK4l8UHbPx6hjA0cHAKgTFPAvmD59+vbt21XpmZ+fX2ufX3dntl6QoC1iL39swstk7S5bKwp4IjrTttizrW+f34oTg2M2T3JKcDeY7mc4ob1BvdIDAADUaMuWLZqOANBgWBn7ZEpcYVg2x0DL7Yhv0R+5eTvTkz+NcTnUzvg9izca6nh2mEBa6mXYzs+4m7KRzzUZbPXJb1n7f8n8cYXH91qMVkO/AgCAN4YC/gXOzs7m5uZyubyGPjKZrLS0tNY98GVXitwWJHJE7Plg0z2DbbjecpOPHCj1nmLpnwMrrvpac0Vsz4iSGTuerJrkWNJWv8FeBgAAQP0kJSVlZGS8++67mg4C8GrPq/dfsjkGWm6/dzDsbmLY3ZSI8namp3x+/41q+KzK9IsFJzkMZ4TthGqLBlh+dK3wfFZl+uWC030tBjfwawAAeHOYhf4F8+bNy8/PL6zRjRs3iIhhajlxPW9nOkck59roBn/n49ZDizVmij5pq1wqzn1m6F9wb0Yoa8boieVb8iv/52/UuK8NAABANSzLrl69evBglCvQRLEy9snkF6p3IiKGHDZ4Wk62Z0XylM/vl5yu/WBJhV8zf5Kxsl5mAxx4ztUWcRnupzZjiOho9i8CaWlDvgYAgDrBHvjGYrvUpfxWsSRLFPV5dPoWO28jnUHt205ULn72RFZp9t6iT5lCKddOx34Rro4DAADqxrLssmXLDh48mJubW7VdJpMJhcJ27dppKhhADZ5X77++WL0rMOSwwZPeZD98bGnU/bK/ichU2/xKwdlX9uFzTUqlxUdzfh1pN6nBXgYAQJ2ggG8sep4G7mf87g6Mcr9WtnRmet/f/BLcXJVLpSnpi6ent46SFreSiw7ktm+Ny8gBAIC67dixY8WKFXw+38bGJikpydra2t7evqSkJCkpKSAgYPPmzZoOCPAKz2YlFP6arWXEdTvawSDAuPpiRQ0vo7w96amj7ruf9Kv52nKX/ynaf88+VPPzXis4/7ndRIZw8SAA0CQU8I1Iz12//JgPE/Sg9TXB6eC725brKRdJUtJaZ5QLrLmHtt98Zmo3Nz1zualJsAFOgwcAAPX56aefDA0NExISbGxsRo8eXVRUFBERQUSrVq0KDQ1t27ZtrSMAqF95VCkR6Xnp83wMX92DIZMhlnk/ZciFcuFDQc0F/GCrYFNtMznVNP+Rgqu+J6p3ANA4FPCNaHOU4Psoke04p2Xbn3hcF0xYIr+px5NVColIKC1PsRJ8N65jvo4HR48llsLSK4I9UcADAID6pKSkBAYG2tjYENG77767ZMkSRfuCBQv27t27cuXKr7/+WqMBAV7BNcz30ft3y++UPh4S7Xa0A8eg+uTwgr+Kk4fHkpw1H2NrMdqmltEMPF0NPBstLABAA8Mkdo2IwxBDlGmps2KyY4kxt/2NCluunXLpqvGUH6jNcWGJiC2jtkVlmksKAABvI7FYbGj4fB+ms7NzZmZmRUUFEXG53MDAwHPnzmk0HcCr6TjpeZz003HSE9wsfjwkWiaQVV0q+Kv48dBoebnMfIyt0xYv4mCfOQC0KCjgG9EMP8PHU2ySp9hcWuIQtedsuZnEvdJaubTQ9JGiem9F5V0l/yWzrSzVcmk6AACABuTp6ZmQkKC43bp1a5ZlY2JiFHflcnliYqLmogHUpGoNnzz03xoe1TsAtHgo4NXhfP6JeNuH13Zesjd0VDYK07OJyFmL/rR1sNQxfSJM/qvwkuYyAgDAW6d3794PHjz46quv8vPznZ2dzc3Nd+3aRURCofDChQv29vaaDgjwWi/X8KjeAeBtgAK+0ZVJS4/nhBER10OHN/vfeezET57oyEvalqw8nRtuqm1ORL9lH6iUCzUWFAAA3jJLlixxdHRcu3btkSNHGIaZOnVqaGhox44dvby8srKyRowYoemAADXRcdJzj+ikbacruFmc9P7fj4dEy8tlFuPsnLa2QfUOAC0VJrFrXMnF0s//LCqqXEFEN4gqim8S/aRYJElLFRM/4s7cCDFL1J0hMjJMi7e/35Hvr9HIAADwtjAyMrp79+6uXbu8vLyIaMmSJY8ePTp+/LhMJhs/fvz8+fM1HRCgFrouPI9Tfo8G3a34u5SILMbZOW72wlTxANCCoYBvXKUieWGFjkyuo7jL9f93mlPxkyfEYZhAPekVhhUpmty8DWw1ERMAAN5SFhYWX331leK2trZ2WFiYUCjkcDi6urqaDQagIkUNnzbhgUFXE/s17qjeAaBlQwHfuDq20okeby2RsUQUknv3ptyGy9WVSkVEJCsoYEtLGT5fr0/FPO7jIIv++tqMNo74AgAAjeLxeJqOAPBmdF14npe6aDoFAIA6oIBvdPpchrjM6qKcm2RHHDIytyvKSVEsml5ctJ3Pl3F466WuvZhiY46ZZqMCAMBb5eHDhzV3aNu2rXqSAAAAgCpQwKvJIYGMiCyp2N7I7Pw/BbxV+tONHTvOLiiUcvRn5yT95hCg0YwAAPB28fHxqbkDy+L6pgAAAE0ICnh1SCqPN5GkM8QJt2u3wsH+/OMoRfudxETXQZJFxsyGojxGcCRNaOHMc9VsVAAAeHvMmDGjWktZWdmdO3fi4uKcnJzmzZunkVQAAADwOijgG52clR/K2OUlTO1m2qtS7mzpY0n/XO791P1oeUnZBP2KMfJjsdJHYZk/zXddrdGwAADwFtmyZcvLjSzLbtiwYd68ebUeYA8AAABqhuvAN7p7pZFPhalEdLPoyopHs2+3j1Muyky8Q0ThpVmxpXeJKFHwMEFwX1M5AQAAiIhhmLlz53bt2nX79u2FhYWajgMAAAD/QgHf6Gx07V0NPJ15ror/vJ3dlIvKkks4rEzAdRFzTEy4Zh4G3hY6rTQYFQAAQMHPz49lWYlEoukgAAAA8C8cQt/obPUcFrmtU94V62Vu1j0sFVUQkayirPJhvo5PqwLtToaSGxMdQ8x1LDWXFAAA4LmUlBQ+n9+qFX5WBgAAaEJQwKublinfjO+Ym5eguCu6l6Lj06pSv7e46OK+J+FDLCe7mOBDAQAAdXjy5MnLjZWVlUePHj116lRgYKD6IwEAAEANUCs2rkop+1NsuUDywmV4rHguufS8gBfHJ3Pl3XI45pQ7MjLdc7c07/YYayMdRhNhAQDg7eLs7Py6RVwud8mSJWrMQkR08uTJiIiIpKQkV1fXKVOmdOzYsVqHuXPnpqWlHT58WM3BAAAAmggU8I3r6jPRxttl1RqNW3nQ05OK2+LMJHEWw7FjM7TekYkYIioVSY10tNUdFAAA3j4jR458ZbuVldWwYcMCAgLUGWbKlCk7d+5U3L5w4cLu3bu//fbbL7/8smqf8+fPR0dHqzMVAABAk4ICvnG966i7NJAvfHEP/M+X3OnO89vi7Mc9tSuvk66ZC6cktVws0b9TcsPO6F31RwUAgLfNgQMHNB3hubCwsJ07d7q4uKxbt65du3Z3796dM2dOSEhI69atg4KCNJ0OABrM3tjy0ymVP75vZogDTgHqBAV849LRYkb7GFRrPOrlqbwtyXpUSSt15XNLtIz1zXXF2XQq9/eBNv48LX31JgUAANCYrVu36unpnTt3zsXFhYg8PT29vb179OgxZcqUPn36GBkZ1XN8kUj04MGDmvskJyfX81kAoFYnkoX3ciSPi6UdrHDAKUBdoIBvXCzRznuCZ2Wy7HJZQoGUWCKiPEtXLUZLxsqISFqcdSdyJtNOj5yp3IpD2XQn/tPV8qurOr+n4egAANDibNy48Y36z549u5GSVJOQkPDOO+8oqneFDh06bNmyZdy4cevXr1+xYkU9x58wYcKhQ4dU6SmXy+v5XABQK5atvQ8AvBIK+MZVJpJvvF0mr7aR4upyWrWWZT8mImJZwZNMPSNrbWeWsSGKo5Iytwup0TPaZVrr2moiMgAAtFhz5sx5o/5qK+CFQuHLlfOYMWN++OGHjRs3fvHFF46OjvUZv0+fPgkJCbVmiIuLYxgc1gsAAE0XCvjGxdflHPzQPLVYmlQsvfqkkiUqkOQLhHwdG0+JooAnkmQnmni2kxVzyITDsWZ5+UIZy5l15YGf8fMjBns56AbY6mjuRQAAQAtx9OjRai3btm07d+5cx44dg4ODnZ2di4qKzp49GxER8dlnn61bt05twdzd3W/dupWTk1P1yvMMw2zfvj0gIGDChAlnzpzhcDh1Hn/8+PHjx4+vuc+DBw/atWuHAh7gWals+Y2SL9obdrXD358ATQ4K+EYXYKsTYKuz+GpJSomMiBg9U64/uR10ivmngyjzkVCszXlKXBNWy5PE1rzyv9sXFFIUCRQdbmeKfxtqrqH4AADQclSbEC48PPz8+fMrVqxYvHixsnHGjBk7d+6cMmVKr169Jk2apJ5gX3zxxYwZM3r16rVv374uXbooa3U/P7+5c+d+/fXXo0eP3r59u3rCALzlbmSILj0R2RhooYAHaIJQwKvJ/K78QAfdbGH2McFf8Rbvupo6Kwt4cWaipUmqtqQsX96O0SNGh/V0vskj0TtmvQ21jLhaTKC9riajAwBAC7Vr167WrVtXrd4VJk+e/P3334eHh6utgJ8+fXpsbOyuXbu6du2qra19584dX19fxaIVK1akpqYeOnTojz/+wAnqAGqgOEG9Qc5Szy6XSWQvtIhlLBHllMuflb6wwFKfo8dt3ONfBGL2cELFB248S/26H84DoHGqFvAymezBgwfZ2dmvXDpw4MCGi9QyGeowA1vrETkPZ50GPMzU9vSgiOeLJFmJw1zbsR7snlIBEflQbtiAYBzABwAAjS0qKqp79+6vXOTm5nb16lV1hvnhhx+6det28ODBlJQUtsoMV9ra2j///HP37t03b978+PFjdUYCgPo4liT834XiVy6afrawWouPpfaxYItGzXM0Sbjqr9ICoXxOQH0vbAGgQSoV8FlZWe+//350dPTrOrCYSvI1xDI2+I+CjLJ/f2KUm7OijoyvoZM2cSUkJSJpUdYPOena1sbEErF0n9PqoTDTh4cZ7AAAoHHZ2trGxMTIZDItLa2q7TKZLDo62sHBQZ1htLS0xo4dO3bs2JcXcTicmTNnzpw5Mzs7Gxd7A2guHPlabqZckeyFMiG3XCaSkZWBlu4LWx3q2KrRryqnSCKWoWyB5k2lAn7hwoXR0dG+vr5BQUH6+k3i+uTJycnnzp2Li4srKCioqKgwNze3s7Ozs7P78MMPbWxsNJ3uXzKWiirlJaIqh/xlkXd+xWfbCk9zHB7LUxVtksp4bbYrsUQcsim7eEn2zMd5oWYSAwDAWyMwMHD37t3z5s1bv3698rRzuVw+f/78Z8+evf/++5qN9zJra2tra2tNpwAAlXRspXNmuGW1xk/+yL+XI/lhgKkaKnaAFkmlAv7cuXNubm63b9/W0dH8VBapqanTpk07ffr0K5dOnz49KChow4YNzs7O6s31ajwuc+UzK4Hk3wL+7wtX9UZLdSsZd46LsoAXxccbBHRlGZIlU2ujv+5JMh+U3fMx6qih1AAA8FZYs2bNyZMnv/3227NnzwYFBdnb26enpx8/fvz+/fv29varm5TZDgAAIABJREFUV6/WdEAAaHQSOVsheWGntFD6fE/1C7ugiHS0GF4jn6YOALWqvYCXSqWZmZmTJ09uCtV7QUFB//79k5OT27ZtGxQU5OPjY25uzufzS0tLCwsLExIS/vzzzyNHjsTExFy/fr3qpWg0SItDxrrPd2sU/5VnMLaSW6n9Vwe+Q5wbFV1QtOtev8+OJe2nrDiBY9Qn4KqOz+acyzsM23MYrdcPDAAAUC8WFhYXL14MCQk5efLkgwcPlO1BQUHr1683N8cFUABaOKGU7ftLXk657OVFRxKFRxKFVVt0tZijwRYeZpgDG0CTav8GyuVyXV3dzMxMNaSp1VdffZWcnLx27doFCxa8ssOyZcv27t07adKkJUuW7Ny5U83xalZ+qyTl41huhXZs9+Lvg9p8stWFip4vMrwU6xItfKStTcQkioqFPJt7bECcIBY74QEAoFF5eHicOHHi0aNHcXFxmZmZjo6O3t7eLi4ums4FAOrA5ZCNAadS+sIeeLGMFUrZl/e383UYfW3sgQfQsNoLeB0dndGjR+/bty8qKqpz585qyFSDq1evenp6vq56Vxg3btyhQ4euX7+utlSqKL9V8nhoNAmoeFBp5Awuw5KedzuKf740VZK6feKT9budEp24PXR0c0gk4DoXcQ01GhkAAN4WHh4eHh4emk4BAOqmzWGOfFx97vdf4ir+72pJsCdvVU9jjaSqP4mcDTlf/KzshSML8oVyIjqWJIzMEldttzHU+uZdY74uri0HzYNKx8B89dVXz54969Wr14wZM7p27WpmZlatQ69evRoh2yvk5eW1a9eu1m4ODg4xMTG1dlMbRfUuK5OaBrfq+GOffU8LtZlKIwNLE8a4mC0hIiFVFpamz5/IrNvtaGkxcoI2u7Wk7IBA0oOn6egAANCCJCYmEpGTk5Oenp7ybg08PT3VEQugflipTJqVy8rkXCszjj7+eGrSuByGiLQbuVguFbEXnoheOeF8vlCuqOSV4vIlOeVGKOChuVCpgG/durXixjfffPPKDmq7jFy3bt3OnTv3+PFjNze31/XJzc09ffp0t27d1BOpVpJs0eMh92QCmdl/rJ12et+Wiu+xlbNnpne4IgjnuEbK/lZ0+9M3c9p9u/kTn847rX2grc3eUsG1StF9saSdDqboBACAhuHl5UVEt27dCggIUN6tAS4TC02crLis6Ofj5TfusiIxERGHw2vnafr5hzoujpqOBq8229/oXo64jUXj/n1rzuNcHGFZ8GKhfixJ+FNseZA7b3x7g6rtxrocBz6mnYJmQ6UCvuZD1tXpv//976lTp7p27bpkyZKgoCAnJ6eqS7Oysk6cOLFy5crc3Nzx48drKmQ1DJdhdDhEMlmZlGSsl1xryZeZLlcERKx7lQI+PzeBqItUm9NVX89ZW3uEkcGeUsGXaYVn3Fpx8YMgAAA0hC+++IKILC2fX9hpypQpGo0DUC+SrLzsJZtkRSXEMNp2rRhtbUlGtjAmvvLhI8uQcfoBHTQdEF6hi41OFxt1TIxtY6hlY/hCWa44ct6Cx/GxxO4xaMZUKuDXrl3b2DlU1L9//y1btsz6h7GxsZmZGZ/PFwgEhYWFRUVFRMTlcn/44YchQ4ZoOuxzXAsd9xOdkj74u+REfvKwmHJiXS6Vlppx0weLvPb+exxB7pOHpdbcNaGORfrycrl8nJHhj0WCTJ7sXGHlIAs9DeYHAIAWY/fu3VXvbt++XVNJAOqLZfO+/VFWVKLX1t1i+kiulTkRyYWVxb/8WXryct7m/XabnbnmJppOCdAAIjPFF59UhnQx0sNl/ICovvt2k5KSLl++3BBJVDV16tSHDx/OmzevY8eOIpEoNTU1JiYmLS1NV1e3c+fOa9asefbs2eTJk9UZqVa8dobuf3bimmuXni+UnS8qN+bcmFx23Yoxtfu3gE9gk1fvcHxiqMsKacGlkqUXSrSeMUS0Irdk2ZOSjulZZyuEr38GAAAAgLeIMDZRnJrOtTJv9dVURfVORByentn4Twy6d2JF4rIzVzWbsPnS4hARaaFUbDJ2RQv2xJT/nSOuvSu8Bep1IUeWZVevXn348OHy8vKGCqQKd3f3devWrVu3jmXZioqKyspKU1NTDqcBDjS/d+9eWFhYzX3y8vLqMLKep76et6HgWhERmegUkr3HOaHFpVleuiE8kVxIREXyonTjPI6ZffF1+RlRJRGRDunYU7GRLIzKiaWrlaIBmJcFAAAaWl5eHo/HMzQ0JKLw8PATJ064u7tPnjxZeaQ9QBNUGZdERAY9OjO61Y/HNurXvfzG35UPH2siV0vQ10lvtI90eBv82dlUyFkiov9n77zDmrreOP7ee7MnZLCXExBRcZTiHnXUal11V61a21q1v1atttpaW2u1VTvtsHW3KuKsoiLujQsU3MiQkUBCErLXHb8/oohILSpJgN7Pw+MTzjm55xsh4X7Pec/74uS/jaP5b1AjA09R1MKFC//66y+VSlW5nSAIq9Vak7TwbgJBED6fz+fz/31ozViwYEFycnJNRpLkU7yHKAeZOy7LdEqHMJ2AgFPtMzTJ1updk2RF2QykaQZkuYb1ev/oyaRxwp7YTEIkotClaUaV1on4AQBwEGSikK4qR0NDQ0NTm1gslokTJyYlJZ0+fbpTp06JiYmjR492da1bty4tLY328DR1FtJgBgCGzPfxLkwuAQDSYPS0poaClIt+1lnkbRW1Dx1SQNMwqJGB/+2337744guRSBQYGJidnR0QEBASEqLX67Ozs+Pj43/88Ud3q6yC3W7PyMhgMpnt2rVztRw8ePDXX38tLS2NjY0dOXJkr169nu3K33zzTefOnZ88prS09Lvvvqv5hj+FU7ljs/QHylA+JWxzkp/QtmSl0HrCEHq2jHSyWqDNM4j7Bt6SlTljVvH33wf/yjFu85chxSTiB0ABIDBDLGrCfK5wCRoaGhoamip8++23SUlJkZGRLqO+fPlyqVS6evXqGzduzJ8///vvv1+8eLG3NdLQVA8q5AEAoTM83kVoywEAFdbaBg9Nw6BtACtIgMUHeyKFHg2N+6iRJ1y7dq1AILh161ZgYOD48eN1Ot3evXsB4Msvv1y/fn1MTIybRT7C1q1bp0yZYjQaAaB379579uxZu3bttGnTXL1paWmrV6/++OOPn+2eIzo6Ojo6+sljrl279t1339X8mvZsi/5AGQAI41UYy2C/cVLYVmi81IV0sBAMb4E+LLF7g7wz/bDRv9CpjEAGKNXGRiQAYAhCALXNZB4n4DEReumQhoaGhqbW2LJlS1BQUEZGBpfLValU6enps2bNGjx48ODBgxMTE5OTk2kDT1Nn4UQ10QOYT10UD+mDPLrJYTqa5hrgJWk0dZQ2fsxTr/vV7jUXntZLONh77ek4WRrPUaNt5Nzc3M6dOwcGBgJA9+7dMzIyXO0fffQRRVGLFi1yo8BHuXjx4ujRowmCGDRoUOfOnQ8dOjRhwoSZM2fGxMTs378/Ly9v165djRs3/uqrrzycWu8JcKL5ftPDAEB/ws+hDqBI0ndMu+ZHXxTE23ktMqPR5hUjb5J3/u4hUaAsADBSJABQBNjOAmWEfByPSVU2+U3Z5Dfle4d03notNDQ0NDQNifz8/Pj4eC6XCwBpaWkURXXv3t3VFR0dnZeX501xNDRPhNM6mhkS4FSq1ctXE+X3o+Upp7N86z7T8fMIkyns28W7CmmeTKbDcc5mr7bLSlG7zRbD05xX9QpmJ/XnNcvaTJO3hdBUg520UUB5W4VbqNEOvMPhcOW2AYCIiAiFQmGxWHg8HoPBcLnopUuXulPkQxYtWoSi6KlTp9q2bQsAH3/88dKlS4VC4eHDhwMCAlzyWrZsGRMTs3z58oq7EK8TsrQZykZLVuSbMl706X5T8sZQAPB/j6X5XRWGxvERvpkyA4CBMq57wcbRQ0XNSgQHsAJ+C2F2oNAwIPIAHuSxoKGhoaGheU7EYnFhYaHr8alTpxAEiY+Pd32r0+lYLDrQtD5BUuRJ7aEoQWwAO8jbWjwBgqHymZNKFnxvuXzN+s4nrMahCIPpuFdEmq2AINJ3RjH8ZW4V4MgvsqRddZaoERRhhgfzO7Wr9kA+zT/xfpmuhCA+9RWPFjxy2MFKUW+rNRftjuli4TSR0FvyagJFAUAte8SrKufUgzob/shVLU4KAKal6pjoI9G4HQJZq/rRv3XVkGO+/XXO/ATf7hNDp3tbS+1TIwMfGRl569Yt1+NGjRpRFHX16tWEhAQAIEny9u3bbhT4KBkZGR07dnS5dwCYPn360qVLe/bs6XLvLpo2bfrCCy9cu3bNY6pqQtDnTa6n3ZaeYZediPlu1p0b7XxIWyTVM3DZ6dIYtPkF4n5Qg9NwmR8bDgAIBRQCwAZpT9gb7HfCZqd4gPVAhkZy6TB6GhoaGppaITY29ujRo/fu3fP19U1MTGzfvr1MJgMApVJ55swZDx+Ro3lOjmr2by5eHcgO+SLyBwzB/v0J9R9WWFDQso90G3eZL1y138l3NbKbRfiOG8Rp0cx981IOp2ZVounE+UptF8s37/UZ2V88tK/75m1gvCUSfKHTL9LpAaDCw1e49wAMG8zjeVWgdzDYyTILQVS3KmBxUlWWC4qMhIdk1SsooDYp/sAp/LT2SFdJ7yb8yH9/Tr2iRga+R48ey5cvnzdv3syZMyMiIqRS6e+//56QkGC1Wo8cORISEuJulRXo9XqxWFzxretx5RYXvr6+mZmZHlNVQ74dENGB7Rx8tGzcmqJvHcjFGEHbAiZCQSzaosLA28vSAYZSBHDSUXsARYVSBoQaXlq2zV/20Qn9iQJ7E19GGz+md18IDQ0NDU3DYPbs2ampqdHR0TweT6PRzJs3DwA2b948d+5ci8UyadIkbwukqSlmwrSnZCsAKO1FR8v295YP9LYiD8GQS+SzJkutNuc9BUUSzAA5JvFx96Tq79dZLmSiHLagZwK7eQRFENYrN82nL+s27wUA2sPXkFECPoYgn2nLF+n0JMBYAd9GUVPVWpd73+AnDWb8J9ahqtAllH1lUoDz0YDb6am6s8WOlX18Oz6agU/ArIUq2g2P09oj+Za7CCAUUJsVqz9p9g3SsEoQ1OinvmDBgrCwsCVLluzYsQNBkKlTp65fvz4uLi4qKkqpVFaUnPEAUVFRly5dstlsrm9PnjwJAJcuXaKoh7/lTqczPT29RYsWHlNVQxYRmSVxqsxObAZOzfqzYN62G3PWF6IkZW7zQsUYa3o6RQB1HvnxBV/yGkkWAgCoCGKkqswIFADYcTqAnoaGhoamdujdu/fGjRtDQ0NtNtukSZOmTJkCABcvXiwqKpo6derkyZO9LZCmpuxU/mUijHKWPwD8XZpowqvJzd6AQbkcdlRjTotmHnDv1vTrlguZqJAfuPRDyaTX+J3bC7rFy//3ht/ctwBByrcdwMvoXEU1ZTif97nEBwAW6/TrjaZ31NrzdrvLvYcx/rvVl3hMRMxGK38xUAQA+I+1Y7R/fwwbad1ZsgkA3gidJmXJ8yzZZ7XHvC2qlqnRj10oFF6+fHnx4sVRUVEAsGDBghEjRty6dUupVE6aNGnu3LluFvmQsWPHlpSUDBs2LDU1ddOmTW+99ZZEIrlx48YXX3zh8vAEQcyePbu4uPiVV17xmKoaEhkVNO/63307XJBNlGE4xJ0HjIC/e0jze3WtWBay37guvKDBdaCyKwgg8ExEaLgNACU4URqAA4AKiDwc9+bLoKGhoaFpQIwbN+727dsmk2nNmjUMBgMAZsyYoVarf/nlFyaTDviqHxTbCk5oD6EI9l6j+bHCthbCvLNks7dFNVhMpy4BgHhIH2ZIQOV2XvtYfkIc5cQt5696SVq9pMLDf11uqPvuXW8nK38Z7CQAUI+126uNgKfxCHtKk/ROXRN+ZGdJr6EBYwFgm3KjlbB4W1dtUqO3B0EQMpnMFVkHAEwmc+vWrVarFUVRNpvtTnlVeffdd/fu3bt///79+/cDgEAgOHPmzAcffLBw4cKNGzc2b978+vXrhYWFzZo1mz17tieF1QR+txfKt6fYb+XyE8R+b8Wr/ijlReVdaBnx80/5g1h+RY5SAKBwnMxPBNH0DbfuAEQDQIjqvIatVrE7s5wIACwl9JYSameAvFFd/WijoaGhoanXNG7c2NsSaJ6ORMVakiJ6ywYGc8JGBU+6cfvqSW1qD2m/UG6Et6U1QHBFKQBwYqo5Y89p2dx8Nt1ZXOJxUfWbgTzuOqM5z+kEgGECXp1175+d0v91vRofaHZQbdeVVmn8MF74ThxdW87TqBwlh9XJCCBjgt5EAHnRt9txzcFs8819qh2vBY7ztrpao0Y78GFhYXPmzMnKyqrcyOVyPezeAYDJZB48eHD16tXjx4+fPHny6dOnW7VqtWvXrmHDhuXl5aWkpJSUlIwYMSItLY3D4XhY27+Ccjl+H76Jcjnmcxl44Sr56Av86NsYSQFALPNh8Xl73lkAyFHdzyxgJ5AIw55Wmq/5xQ4AiKSYNor6SFNO56ygoaGhoakVLly4MGPGjC5dusTFxQHAwYMH61oiWJoncFl/7rrxCh8TDPQfAQCB7JCe0v4kRW5RrPG2tIYJRZIAgFQbu+xqJJ6l+FmOE99htlT7TAJgu9mS62yYAZg2inpHrc1zOsUoigD8ojduMpm9Lap6JFy0SgS7iIUCAIJAlXYfNurDpqPbvUBi8VqccnaW9GrEawYACCBjgt9EAElV/11qV3hbXa1RoyUug8GwbNmyZcuWtWnTZvz48aNHj66c9d3DYBg2efLkygfzRCLR9u3bVSqVSqVq3rx5XS57w45sHLT8I93mvZaLmYS2GAAUfiwAaIW2OQDHXWPyz7H9W4PNLnF9e+vu+MpXOJHqEPZEMsHxu8E4tW6X1qChoaGhqft8+eWXCxYsqJxKJjk5eeXKlV999dXHH3/sRWE0NQGnnNuUGwFgaODrAsb9u4JXA0aeKz9xy5R1WX+unTjBqwIbIMwAuSOvyH4nnxVRNYuz/U4eADAC5c9w2RV6wzGr7ZLdsVjiU9n5EQDzNLo9FmsvLmelTPLsuuskLvdeETl/3u74TFu+WKcHgLGP1pbzGBqH+qf8JV0lL/WU9a/S9b/2wv+1f+Te2+SgWq8t4TOR9In+HtDWoPKwuYEbpswrhgsclOuKnHcRzm3SUdLjjPboNuWG6REN5I9ajRaHSktLt2zZMnDgwOvXr8+cOTMkJKR///6JiYlWq9Xd+mqOn59fy5Yt67J7d8Hwl8k/mBi24ZvgHz4N/vFTg4Bp5GNx+MOUe7aciyjgAPffpghCIQiJIKSrbgRGIqxrKELBj+XGYUc145O1Ky+bvPNKaGhoaGjqOSkpKZ9++mnjxo137NixcOFCV+PIkSMDAgLmzZt38OBBr6qj+XdS1H+r7MpgTlg3Se+KRj4mGBIwBgC2KtY7KYf31HkEijKfuVy6+JfCt+YXTv5YOW+F/u/DpM3uvgl58a0BQL/zIFH+SKZA+9175hMXAEV5L7R+hstOEQp4CLLbbJmvLa/Yh69w73wEmSJqaPHYVdx7GINROaedt/bhExVrC6y5Scr1GofaKwKqZVgkt2sou9XTFKKyEOYbxv9QOgaSIhKL1wDAQP8RYqZv5a7hgeO5GC9df/668YqX1NUyNTLwPB5v1KhRe/bsUSqVv/32W8eOHVNSUkaPHu3v7z958uQTJ05UXrmnqQkIk8kM9mcG+QuYiMKP3QRtJMTuL+kRJq0f/Axwv9AjRSEUhVIU6jL0BElpFCRxDwCB64H2Mwp70q0GlZWBhoaGhsZjfPvttxwOJzU1dejQoa4K8ADQuXPnCxcuoCi6YsUK78qjeTIGvPyAaicAjAqahD5a+L2bpE8oN6LMUXpQvcdL6jwBZXeUfvWr+rt11owbhFZP6I32O3m6P3crZi9xKlRumpTfqR07qjFeplN8+LVh71H7nTzbjWzd5j0ln/1A4YSoX1dmkN8zXDaOzfpDLuUjyG6z5UONjgAgAOZry/dYrFwE+VUubV3n96ielg80usez1g3n8z7xFQPAYp3+sNXmYUmuuBUAcJCOJOV6D8/+BAY05a57RSJ+mrD8X+8tW5772SntYfepqlMc1aQU2e75sQJ6ywdU6RIxfF7xGwYAWxRrSKohnEJ+uuMZUqn07bffPnnyZH5+/tKlS+Vy+dq1a7t37x4REfHJJ59kZ2e7SWUD5uhIeQwGKCBtIOZha/6hNi2Xy/kOAPiyq+iNF9NatfiBxykDgP6tCmSvQGwEMxAwRACvvsxJGix1PYkE+ERb3rZImdNAT0nR0NDQ0NQuGRkZCQkJj2etCw0Nbd++PX0Svo6TpNhgJSztxAkxwjZVulAEHR00GQCSS7dpnWXVPh2nnGd1x81EPY7jK/ttszXjBiYWSqeOCfltUejapX4fv8NqHIaXlJV+9QvlcLplVgTxm/sWp2VzQqfXbtipnLeiZMEP+p2plN0h7N3Jd8LQZ75wWzbrd7mUjyD7LdYPNbp52vK/zRYugqySSzuwG5p7BwATSQYzqsk5P0bA/8RXjCGIkXyWbALPTEXmiJ6yl9ko52L5mTvm654UUItkPNht3qH8q4ElYK8WE278u2QLAAz0H2En7WbCVOWrk6SnL1OqsBUe1zSEyLJnSfOoVCqTk5MPHjx47949AMAwTKfTLV68eMmSJVOmTFm5ciWjrmaPrHOQlPHDO/hdCwB0wOJOEWmu5qI0RY837HYlX212tvFnverT++Nbu27c4QHAXUsGoGHXSWdLFlPlIFLBOhLjBQBGArg+6FkIgiH0GRkaGhoamhrB5XKrbZdIJPS6fF0mz5J9TnccAPzZgSc0qdWOkbH8yhyqncq/3gx7//He3SVb9qt2xonjZ9TPc6GOAoX59GWUww74cibzwbFzXruW3NhI5fwVjrwi4+Gzov7d3DE1JhQEfDbDcumaJS3DqVQjKMqMCBZ0j2c3DX/OK7s8/FtqzQGLFQA4DdG939XhWWrnkObc9X4yiqIY1d21jhHwh/F5bM/e0J7QphZa82Us/5FBE0UMn90lWzYXr17QbAWK1LN0dDjl3KpcDwA8jG/Ay/eqto0InOBtUe4lRb3LtRa5pvBHKHzSyN0lW7pKezOQ+l0k9SmcdmFh4c6dO7dv337mzBmKojAM6969+/Dhw4cOHSoUCl3H51atWhUVFfX++9X8naCpCgUFM29rNioAAaAgnhEHD86pKc4b9qSNC+A6ABAraQHAXpYPSaEAABwIy0BSCMA1hzOYgRXjxIda3U5/v2/1hr/NFgTAQVGlBB7BwJ4wMw0NDQ0NDQC0adPmwoULRqNRKHw0M5PJdP78+datn+UoL41nOKZJoYACgP2qnU8eeU534vWQtznoIys1KkdJqnovPNipe3wPv+5jvXwNKIrf7QXmo0njEBZTPLSvesUa6+VrbjLwAAAIwusQy+sQW+sXbs1mxbHZp202AGjDZrVtWO4dAD47rU8rdkRJGdFSJvyzRfewezcTpl0lmwFgZNAbTITVTz7ktPZIgTXvlPZwN2mff3oWigIA1LWds1T1HpVdGcQJnRz63pfZcw6r93aV9A5gB3lblxsRM335WI2SREhZfg0gG2CNDPyyZct27Nhx/vx5AMAwrEePHiNGjBg6dKhc/vATc+zYsZ06dWrUqNGJEydoA//vUFDwwa2y1cUu9w4AjX0aS2y+WlIHALjNQp0vUcRzAeQLr6+Q+WYCAMAKAGA4zXLHeTUrHgEoxgkRipYR5LAStZokXFd6kcOOa3CnpGhoaGho3MH48eMPHz48YcKE9evXVzSazeaxY8fqdLrXXnvNMzLUavXZs2drPn7QoEHuE1Nf6Cnrz0AYLg//ZALYwVXcOwBsVazDKacPU1Lu1CYq1n7e/Lsqp+jrPniZDgBYYdXYElZ4EADgaq2nNT03rnPvp202NiAIAmk2+xyN7hupbz372TwRO04BgK2OHffcU7LVhBuiBLGuwg0slDU8cMKv95btKtnUwacTD6s+JT6Pgcx6QejDqUNb9Aa8fJ9qBwCMCprUiNess6TXKe3hJMX69xrN87Y0N9JbNrC3bKC3VXiOGhn4OXPmVPbtfn7VJ+fw9fWNiIhwVZGleRKPundOxF2nujnoyZC2/tpLOteQ9lv25TT6X7EI1Kqu4bJcAEAeLO91sKeQ4oQUK4kAGEgSBURNEgBAAbRns36WSVh1bCGQhoaGhqZuMm7cuEOHDv35558pKSmuP+69e/dOT0/XarX9+vV79913PSPj+vXrgwcPrvn4Z0idm5qaunbtWvKJR2r1ev2zXdwrRHCbRIRMfbbn3jRlZujPc1Du/KZfr8j9rNhWcEyT0kv2Su0qdDcIiwkApL2aNPuUzVExoB7hcu8V594xgLfUmv0WKwA0MA9f11Daio5q9ldkjnDRwafTUc3+26bre0uTRgZN/Kfnvtu2blUH2K7800pY4sTxLYVxADAscNwl/dkrhgtZxvRYYVtvq6OpHWpk4H/55Zdhw4ZV8e0URSGPGkWxWJyXl1eb6hokj7p3ftsytl+Wld/Cet0RExeVeemWa9QN06UVv6vmTBIaGDGfNf2Ny4RTF4xGAABoIYx5QxbA1pb/bbYAAPlg9b0Jk7FKLuXR7p2GhoaGpsZs3LhxwIABX3311c2bNwHg2LFjTZo0Wbx48ZQpUxBP/UHp3r17VlbWwoULd+zYAQBTp04Vi8W1O8Wvv/66e/fumoysLwb+mSEpckvxGgAY4D9cypIPD5zwU/6S3SVb4n26CBgib6t7CljhwQBgTb8ufrVXlS5L+jUAYEUEe0HWs1LFvbvOvbvOw3vXw2usZJmVjJQ0hPxWF5SOKQe0X3XzeaUJp3J7omItQRE9pP1CuRGV20cHvflF9qzDZcndpH0C2PXg1+meNeeM9igiRgEoAAAgAElEQVQDYQx/cOhdxBAP8Bu+TbkhUbG2RfPWWH0LtKGplhq9G6dOnVpQUPDGG2/o9fpdu3YBAEEQ/v7+/fr1W758eUBAgJtFNijUqwrLVhcDAFDgNz2ME1RkOgnMINJ6HSI4D8PArhDXsRLrB0nZ86c1mnltNoNpdiBfAkhZbO1Z7ZX+8qGLJCHpdkch/jACSYETZpLkYQ/fmRqClGLVR/U4KMpOUUK0DsX80NDQ0NB4hREjRowYMYIgiOLi4oCAAJY3zmG1bNly+/bt7dq1S09Pnzt3bnj48yYDq8Kvv/46ZsyYJ48pLCycNWsWWm//MjpIx57SrXGiF5rwI58w7KjmQJHtnpzl30c+EABcO3XXjBl/l24dGzzFU2JrAV6HViifZ7t2R//3YfGglyrabTey9bsOAYCg54veU/fUfFdu+Nts4SPI73Jpxbn3tmzWqgcePoSBfSD2wgrLjEO6S0rHqdf9/Pn13vtlqZ0mB3W11FHZwF81XMoypvMxwZCAqh8RYdxGXSQvndCkJirWvd/oE8+KfWoooLYUr6GA6iN/tfKJ997ygae0h5S2omOalJfqW6DNM+Mg7btLE+NELzTjR3tbS+1TIwNvMBj69Olz+/btbt3u5wJBEEQkEm3atOnQoUOZmZn+/v7uFNmgoPD78Xv89qKQpc10W24BAFNmA2BEOyJFQXyDwgwADnBkETf9rO2ZDGv6tY8BwOkUAUBO3qj8gtdGXMdMzUts/o+EAuIUZau0b5Bms09SawbwuEseW7JVE8Qbao2GII8H+XPoHXsaGhqa/yRGo7F///6DBw+eNWsWAGAYFhYW5l1Jo0aNSk9Pd8eVAwIChg8f/uQx9b1s3n7Vjv2qHed0x7+K+pmNcqodYyZMe0oSAWBU0KSKPMyjgiZ9duf9Y5oD3aR9Qji1vHTiPlA+VzplhPqHDbo/d1vOpnPjWiBMpj0733L5GlCUsHdnTotm3tb4dPig6M8ySZWsde3YrN/l0mllWm9FhmhtJEGBwU75V38MvH6DU/hWxToAeDVgZEUEihE3CBhCBBAAGBIw9mL5mUzDpbofgn5ed/KO+YaI4fOK3yPpS1wb8j/lL/m7ZMuLng20OVq2/6T28IyIj6Us+b+PrlWSS7elqHad1518wudh/aVGBn7BggW3b99esWJFRXY6FEVzc3NXrlw5Y8aMhQsX/vrrr+4U2bB4UIvCfMlQPD+b1V2aLongNSMlbwQ3Hd62i+7yvqSNrgEXiIyXGAk2u6Tys+0OESDgaA6oPwkUAAKoEyGZFALgBJinLf9dLuUiCAD4MzA+iuy1WCmApZU8vJogJqg0eTgew2IyafdOQ0ND819FKBTm5+cfO3bMZeDrAh06dGjatCldjPYZ0DrLUtS7AUDn1OxX7Xx8L9HF7pItJsIYLWgVJ46vaAzihHaX9jtStm9L8ZoPm3zx+LMoJ24+c9l6+Rqu1iIsFqtpmLBnAjPE+wGY/M7tEQZDs3qrPafAnlPgakSYTPHgl3yGv+xdbU/LbB/RbJ/qnVU7Nist2Pv/289AthZfmmZwPpp64q4OB4AvzhiErEfuQjsGs96J8/R58mOaAyX2YgA4pTlyTnscAGykrdRezMcEMtYj25OJirUtI+OQuprA3EE6dpT8BQCvBY7jYrwqvRWBNrtLE18PfsszkjQOdZJyvYN0JCrWTouY65lJXZQ5Sg+q98C/fR7WX2r0N/LkyZOtW7eeOXNmlfbp06dv2LDh2LFjbhDWYJG/E2K5YtBuKQGA0h8Ksu/6zOsxFgCgJcBNm9DysGbPWeTKjTFVz9t0idmnCWl1lxGCAFAIxDKYPwVIvzO6asghl+yOaWXaVTIJE0EaMRjr5bJJak2yxUoALJP6YgAagpyo1uTheBSTuVourfexUDQ0NDQ0z8GXX345ZcqUzMzMVq1aeVsLAED37t3rb/F5vExnOnzGdj2bNFtRkYAbGyl4qSMmFv77M2uDJMV6B2lvxGuWb7l7UL27q6T341teClvhcU0KiqCjgydX6RocMPp8+ambpsx0/fm2lbw9ADiVatXXq5xFJRUtthvZhuRjPiNe9nnN+yaZ92IbbrsY65WbjrwiiiCYAXJu2xiP/bfTPJlLJY7jBfZquzJVVbMPlpiIpzXwZ3XHDLi+n/wpUmBWQWW//4tdZMuv3G4iTCarqXKLzqFxkg4Wyn7mudzKftUOjUMtZvj6MqU3jFcfHxAranvNmHFcc7CHtF8wxxPBVlsV6xykAwAu6895uFblVsV6J+VowovMtdw5qN7dRfKSjFV9CvZ6So0M/N27d3v1qpogxEV4eHhKSkqtSmrgIBgS/lsLAHB5+Gb7yueUGY++LJBLQ6JPaiPOhA14MPI2frtVQKaUL27Ca2HFoVCPl9spDaP0vnsHaM9mubLWLZb4AIDLw5+z2fdYLH4YowuHHcNirpVLJ6k1ByxWAPjIRzxJXZbjxKOYzHV+Up96e8yPhoaGhqZWGDBgwPfff9+7d+/Jkye3b98+MDCwygnw+Pj4f3ouTWXMpy6V/baZqpQR3Xbtjn7PEfn7b3DjWrh79mzzzYvlZ1go693wOTtK/kzTnUxSrp8a/mGVYa5MXb1krzweJ8/HBIP8R24q/mOrYm0rUduK6HrSaitdtBJXaZghAeKBvZjhQZTVZk67ajx0ujxxH8rjivp3d/er+1cQJpPXoRWvQ51YhKKpzMhoXlNfhoN4JPz/izP6uzpiQSdx00ePeDaTPF3VgCLbvbWFK0mKCGAHtxF1eDaFo4ImdZb0rNCXrk9LLt2GIChFkQGckClh71dsufsyJXXWveMU7grA0eO6FbkLnzCSpIgDqp1vhrm94Pdt0/VL+rNslNPRt/sxTUqScv1ngm9RxBPW45Yp67L+HBvlvBsye5v6rzTdiW3KDY9/HtZramTgo6OjMzMzH087T1HUlStXoqMbYG4At1LFw3c4T3RRX2aGocbjOgDfJmhEDpkPACSJR1vWctpTPN2iA7dk4WIotwNG2BiUBUd48RzWbzKp6wQ7BhBawKCsACEUG0E2Gs13nPjnvj4jBLzKHv6UzW4iSdq909DQ0NC4kMlkrgdLliypdkCDT8leK9iy7qh/2ggkye/YVtArAfMV42qtMeWkNeOG6ps/ApfMYkWEuG92CqgtijUUUP39hrmyymfoL1wsP9NT2j9SEFMx7IrhwjVjBgJIMCf0YvmZx68jYIi4GE/tKE1V7+3vN9TVaEg+hqs07KbhAZ//D3lwNpsTG8mNba5avrp8S7Kgx4sot6GdL6WpLVAEOgRWTYopZKEARCs/Zpz/c9X526pYR1IEACQWr2kpbMNAmGdtdj1JvszjugZka3GVhXA9tgB5hrIbTQAACiNxpqhyXECIDweNkTGthOWk5hAAvBn63s6STSW2okJrXldJ7+cR6RkYCKOL5CWlvagmg+PEbl+WpYDaqlgLAP39hvbzG5xlzCi05p/UpnaX9nP31CRFbr63CgA6nxHqP/88wYdKn4pdLD/TQ9IvShjr7tk9Ro0MfN++fRctWvThhx8uXbq04mQaQRCffvppTk7O3LkePdXQMLjv4RFEu1kJALbcpvb8cldXV5+OOdp812NTmpPblekvNAPIlGYnABNQBKFIH/zGELaJgzwMGSoqJ5y3kfgA5k2m8xUe947e+IWuXIqhvbicGBbzO6nvFLXWRJIiFF1Nu3caGhoaGgAAmD17trclNAS0f+4CkvQZ/rLPyPsZnllhQbx2LTW/JxpTT+s27fGf/677Zj+pOZRvuevLlLoCiX2Z0pf9huwu2bJFsXpBsxUVW16p6r0AQAG1sei3J1/wUCUDb0m7AgC+Ywchj2ZW473YhtOiqe3GXVvmLV685yJjadxKsZFYk2m2P7ph7vLAP1wyiTkPt/EwBHqEcXqEe21HOl2fdt14RYAJBQxRib34sDq5n9+QT3XlCpzIxfFpIqHaQr6yXU2QAAAIExjxFCIGwoYAwIE824E8W5ULJg+XXbUnGvDyZvzoF327oQi26t6Kncq/Oog7PX6kvA5Sp0pInNCk5ltzJExZX/kgJsIaETThl/xvdij/6uDTmY+5N83BofS1RYwin3JG/HEWACIsh06nBce66/+8uuyLF9dgjOdaM6o71MjAz58/f9++fStWrNi+fXuvXr2CgoJUKtWxY8eys7OjoqIWLFjgbpUNEgRDwn+NBoLSbi0BAIqkAADlY2N+mrBu5GbXmPOHL95ee4uD+ezIVBrtTADACFv78jkAkGLmdvftJmb6PrwiBYPtvM2NeADgoOBng3GmRrdKJmnGZH5VrieBQgExkORinX6Zl0qJ0tDQ0NDUKZYtW+ZtCfUeXK115BaiAp54WNXNJd+xg0xHz9kyb5M2O8pxi9WxkdbdJVsAYGTQxIr43n7yIae1Rwqseae0h7tJ+7gae8n6C2uWfboZ/2HMP15aBgDs5hGPD2M3j7DduOtUlj3fK6jPUJTjngIvUQOGssKCGP4ybwt6XnbfsW7IMlfbdSDXWqXlotLRI9zTqcVd4BS+TbkRAAYHjAngBC/PWbCnNCnBt/tssehDjW6l3ggA7wiEwyN5hUaCwKjbTRxmLrDtiG85VgBEsACL8HnEAQUKMBa79EjxPgSQ0cFvIoDE+3Q5VpZyx3x9b2nSiKA3vPIy6ylWwvJ3SSJU+lBqL+7YQtj6hvHq3tKkUUGT3Dd1eVHeHucBYEDvw2IG7jpwDAnnBBlxJqXYkHrwu5dfmeO+2T1JjQw8m80+cuTIokWLVq5cuXbtWlcjiqKTJ09evHgxj1cP1qXqJgiGhP/eAgBcHh4AmAHsHsO6YHxfwqwDAJVKdf3SjYSOCSF+F28WvgAArUTtES6Rb82xk9YdJX9NCp1R7ZWni4VmilpvNL1TppVhaDFORDGZc31E/9PoXOfhK3v4mw7nJpN5ulgYgFX19QTAT3pDOIMxhE//lGloaGhoaKrisrisiBCEUfVvKMrnMgL9nIVKQq1FQwPdMfvfJYl6XNeUH9XBp1NFIwtlDQ+c8Ou9ZbtKNnXw6cTD+ADQTpzQTpzw1BOgKABQBPF46m2KIAEAQetoUm53Y7mYqdu4y6lUV7SwoxpL3xzh1uMS7mZ8LF/OR8lHk8b/cNmoMpP/ayf04z8Sv9n6+QLgn4cU9e5SuyKIE9pd2gdFsDaiDlcMF3eWbJoYOh0BmP3Awy/uJjaS5GS1xuygwhmMDUHSZIvtK5WhX2POvI5VF7O+y/2BoIju0r4R3CauljHBk7/Inn24LLmrtE/lsuo0T+bv0kQ9rmvGj27v07GicXTQ5M/ufHC4bF8XyUvuS6G388pP1lAiIp/d2tpMNKMHKzSQNFvNZ9NfOnx4+zDNPklaF2OZQFjvF9oAoKah1D4+PitWrNBqtenp6Tt37kxLS9NqtatXr6YrwD8nLg8vfzOE10GMsFBc5cAwjNeyZ8WAv//++6jmgEi+BwEKAAYH93EtDSIAp7VH8yz/mK13jo9oAI9rp6hinGjEZKzzk77IYa+VS0UoesBi/VCjIx6MTLFad5gtE1SaEoKofAUCYLZGt8pg2mCsfjmWhoaGhobmvw4DAwAKx6vvdbU/tj5eK6jsyiNl+xFAxgS9WaW6VQefTpGCGAOu31ua9DxTMIP9AcB2rZqbDdu1OwBQF4rJeR5jyknV1787lWqGzJcX34bXPhYV8Oy3cpXzVthu3PW2umdHyEJGRPFGtXjkS8xGAeDlJpwq7dFS7xh4A15+QLUTAEYHTUYRDABGBU9mIMzT2iN5lux+PO5yqS8GsFJv/FZvmKzWZDmc4QzGBj+p/z+/DV1l3rkYb1DAqIrGMG7jTr49cQpPUqx7gp6Vl01/XrPU3uur36jsyqNl+ysCGSragzlhXSUvkRSxRbHGTVMr7UVng3NRCgbeahG4ZDa3S3ySVVYc2kT69uiuvd4OL2BZeOTu26vdNLuHebqz0Hw+Py4ubsiQIfHx8WKx2E2a/msgGBL6fWTUsfZNtrZqtKElAPBa9ano3bFrx56SRA5bI5NeAYDDuZhrTYsCAKA2K1ZTUH2GoXKSvO5wuh4bSKqcJAEghsVcJZcIUOSAxTpPq3P1vikUxLKYBThe2cO73HuKxSpE0S8lPm567TQ0NDQ0NPUaZpA/IIgjt5A0V40xxlUaZ0kZwmYx5BJ3TL1ZsQannF2lvSN4TR/vHR30Joqgh8uSXWWunw1+5/YAoPtzF1FuqNxuSD7qyCvCfESc2ObPfPF6ilOh0q7bAQgieWNoyK9f+H34pt9Hb4eu+lLQK4FyOMt+3EA9uPuiqZYXgthhIixC/IyrWkmKDVbC0k6cECNs4yAdVsLixwp4SfZKRTbHCg//h8FUE/dOUESiYh0ADPIfJWb4Vu4aFjiOi/GuGC5mGdOrfa4Fp767aPzuovHZXkvDY1PxHziFd5P2qQhkqGBY4Dg+JrhhvJppuOSOqROL1pAoxKULWgydgDAZaQr7Z6f0K84bAUDQ7YWXTwejFBxnXCi2Fbhjdg9ToxB6Gveht5Nnix33s/w25gMA5NjEsT1UDBaFOwAg925u1qVXBGHNhfxCtSZu03XL220EPrYJahULp5xKgCXlmY15zXLLcQC4oHBUBD7dZDrzOHgIMALZ6EW74w2VZoOfNJzBaMNi/SGXTlFr9pmtC3x9+AgiRNE1cumbam2mwzFWVbZRLgtgYB89cO+r5ZKWrAaS8oGGhoaGhqZ2wUQCbptoa8YNzaotoiF9HLdzSYsVFfLZzRvrNuwAiuInxCHM2r/dumbMcN0HExSxXbmx2jE+DInWWZaoWPd+o0+ebRZh3y6mExccuQWKD74S9O7ICg8hLVbL+SvWKzcBQSSThyPM/9wdgunwGYoghL07iQY8jJdE2CzZO2MceUWO3ELLxSx+p7ZeVPj8KE3EF2cMU1rz2wZUzSH//MyJF86JFz7bc/OtOed0xxkI47XAcTiFf5E9y4jrv4xc+WrAyHPlJ+6ab10oPx3v06UThx3MwApwAgB6cNlPcO8A4Frk8mMH9pT1r9IlYogH+g1PUm5IVKxt0bw1hlS9juuum6CrdQAAwHXjlccDGSrgY4KB/iMSFWs3K9a0ELauqFVZK2QaLmWZMgCAxKg97KOIknFLLQdoc89auF25FwCMLa0CA2YQE9uUG95v9GktTu0VaAPvZZafN26+cT/wBqWoASe0uSEcvKmAG9nJcv2Yq/3qTo5v/zcBgM8i9HZYccG48RoBMMLVeycXAPSux7vuWHfdebADgAAqh3wdvnVcwMxy3Xm7/Q2VZp2fNILBaMNibfeX60mS/6AuoMuouzz8eHVZFIN51GZzNbZi1f5nNw0NDQ0NTYNBMmGo8lau+Wy6+WzVbTrUR+Q7ZqA7Jj1ffsr14LT2yJNHZhku20grB+U+wywIA/P/5N2yHzdYr9zU70ytaEe5HMmUkfyEuGe4Zn3HficfAPid2lXtQBB+p3aO3EJ7dl59N/AnCu2peTY5D3WHgX9mKKC2FK+mgOorH+zPDkpR7VLYCgFgZ8lfE0LeHRIwZn3hz0mK9U0E7d7VmAtwQoaiOpJcbzQLUHSaqPolAyNucB0zGew/yk7a7I8N6CjpcUSzX2krOqY58JJsgHtfYX2GAmpz8WoAQAD5IffLasfgFAEAKrvyuOZg7f5nXtSfdT3IaGMG3R4A0OhjAdqU2tT7VTsBAFrdH5llSLeTNjZav4tf1lcDn5OTc+jQoRs3bmg0GovFIpVKg4ODg4ODBw4cGBjoljwxbuK1KJ7RQZmu3qKsNr/Q5sOTSw1B7LdnN+HHvVJh4O2Z28Lf6G8j9c2k2KWc9lESxtQ4gcaGX9CfthM2AAjnNlbowgr0eIdAVhPfR36mLSKZEhb6q1zytlpz0e54XVW2Ri6NZDLDGVV/9C67PlmtzXI4FDjBR2j3TkNDQ0ND8++gPC7CZoK1amEqAEA5bMQ9+ecH+Y8MYof+0zG6yvizA5/NvbvARAL/T6bZbuVYL13D1RqExWI3Ded3bof+V7PbkhYrAKCiaqphYWIhAJCmen8i2rWjTNaxfeVzuhPZ5psihk9/v6EGvHyvahsAoAh6Snu4h/TlLpKXjmsO5liLx5QUFlJCV+R8ht1RkdNumkjIZSAAwGE8PJu9X7XDQpgB4PeC7548+98lid2lfWt339itpOlOHtccnBL2vpTliWIBDtKuc2oAwEKY8605Tx6ssitrd/ZB/qOC2KHlOw+SFiswGcJeCScYLa8BkPamw+RjzGfTnQVKABD26RwS2qq+u3eojwY+Ly/v3XffTUlJqbZ32rRpgwYNWr58eUREhGd1PSOt/Zjfv+SjSDnmyC8OHDHnJhcVKe1SJ2Vv3RdLnEvgBABY7uWHM38SNeYzEeaAgL3pACACEEFLmdWIGwCAiTAZ5T8BQGMfxuKu1eQm4CLI73LpjDLtaZt9vErzxz84cx6KBmFoluspKEhRutgcDQ0NDQ3Nv6D9cxdRbuRENRH26WzPLSCNZlQsZEeE6PccduQXlycdkLwxtNYnlbH8X/YbUuuX/Sc4UU04UVUPtf43QcVCAMBLNaywqpnJ8RI1AGC+DSpLlB/fWWREpDVYKnIrdtK2Q/knAMhZ/vtU27MM6VbC4scK5DP4eZa7P+Z9Ge/blY3JbgiHmihhCIa4zr3343GhUl76Sc0FHAzpGfFwTU3G8qthZXJ/dhDylLnDvIgRN/xVvMpCmBMVa6dFzPXAjGyUsyTqFx2u/deRCEAoJ6J2Z5ex/F72G6KXc3VbkgEATl7Nim8MAkCtVKv56YTRBCDEJD6h7092Vdao79QzA6/RaHr37p2TkxMTEzNo0KCWLVtKpVKRSGQwGLRa7a1bt5KTk3fs2HH16tXTp0/Xowz5jAC5I78YV5dxWwnN5/URBZbSRtK2XdpdPHbBNeBK8vW4d6P+6ekEReAkBQDOfz6Ew0GQn2WS2RrdIavtM61+V0DV1TgC4CON7qDVJkCRAIxx1+kcry7bKJcFP1YXh4aGhoamnrJixYqnGj9r1iw3KWkwkBar5VwGgmHymRMxiQ+/a4eKLmZ4kGLWEtPxNN9xgxHM+3eNmk1KhoQpfrkhVFHyItzYSFvWbWPKCV77loA83MslrTbTsTQA4MRGek9dLaNxqPkhc+Zs63hvZhS+uKnfdHcVAPtXcsy3XRu8OZbbOZbbrkaVQwkOAACtU3NAteuG6H8mRgSHUE3jav2x+wG5/XhcEmCORrdSbwzBsKGRj0SO9JK90kv2ikdfiUfYWfKXK7Lgsv7cdeOVGGEbD0wqZvqKmb7/Ps5tiAb0NJ++7ChUAgCu0oAAKCdOGE0AACgqmzqmYbh3qHcGft68eTk5OUuWLPnoo4+qHbBw4cJ169a99dZbCxYsWLVqlYflPTPMQD8AcCpV/LaNzef1je5Zzzfi9x32coWBzztQ3Gl6W3is2KqTcjpJJ4pgCCDwb4ujLAT5TiZZYzA1eiybjsu9Jz/IWteIwag4D097eBoaGpoGw+zZs59qPG3g/xVnUQmFE+ym4dhjFVtY4cEMPymu0hBlWoa/l22zYmFOyfJ8QJGwH6NkbzTAotb7c2wUUK80efbDAjVE2Kezfu8R69Vb6m/X+o4bzPCTAoA9p0D7x1ZcU85uHsFtQJn5tyk2vLi8fctdkRRQRR9lAwV+M7zj4aMEsRNC3jUTJgqoE5qDZQ5VpCAmVtgOAHItd9L1aTyUH8qSCsD8Dkf1sqRr5ef253EBYEm5noU8dif9NHx70fjXoxXjXBtnZifVdl1p5XYEYFa8cEwL7xwzKbDmndIexhAswbf7ae2RrYr1C5t/iyINxLtWy+enDemlDgCgEt7Em2goq62MJQQAI4M7NX4SoBhDLkFzuZBbxmUgS7qJG/nUMwtchadQr1aruVyuQCAAgKSkpH379jVr1uztt9+Wyz1xssLFyZMnIyMj/8m9u5g4ceKmTZtOnz7tMVXPDzNQDgC4Us1r1wYAGhVYAaBj/84YGyPsBADoC00FWQpZzP2bA4OxcZmudURIMoo6AQBFkJqcggMADOCtx05tVXHvruj6yjntaA9PQ0ND0zDYvXt3lZaff/750KFDcXFxw4YNi4iI0Ol0qampe/fuHTNmzNdff+0VkfULyu4AAIRb/aFKlMsBgOpyY3kUl3tHGAiFUwXv3QKAhufhZx8tJyiqfxPuc1m0GoAKeH4fTlEt+c18LsN8LgOTiCknThrNAMDwl8lnTobnc4l1B4NTx/uU0XJXLMEmrr96vdWO1kUfZwN4x8OjCNpN2gcA0nQnyxwqEUP8XsR8LsYDAAqoRXdm51tzxjNuDfQfAdDs8af353FdNv55MNopvZ18vJ2i4PF2k6OakZ5hi2I1SZF95YOGBb5+x3yjyJZ/QpvaQ9rPW3o8wME8W6n5fiVsYPjCg5SFBILeEQYCAFgALPfrO2br8P+EgbdYLBMnTkxKSjp9+nSnTp0SExNHjx7t6lq3bl1aWprHPLxarY6Njf3XYaGhoVevXvWAnlqBIghcbwQA69Wb3BeCAKBJvg0AugT3GvjKwN07799sBZ+LXj5y2Xndqb+KV1G6CYXFfq8FD5zYkgUATIT5yjUDAP60U5tIqpjAj1vtLve+Ri6NfVAx7kFOO02Ww/mhVrfZj464o6Ghoan3DBo0qPK3SUlJhw8f/uKLLz799GFZnenTp69ateqdd97p1q3bW2+95XGN9QxM6gMAzuISoKgqzo1yOJ0lakAQhsybYaWKRbkly/MRJtJoQ6yj0FY0907BjJuUk5RPCfGiqlrHSVIk9fgPwS1wWjQNWv5R+bYDlvNXCa0eADBfsaDbC+KhfdDndokexoZT/ZLUhQaiSjtCgf832pZnYp0MZPnrEenRsT2R8re3KYs+ziYdZMCsCG+IBQfp2FHyJzyo0H5fKiCjgx7enboAACAASURBVN9cenfePtWOjr493Jez7bPOolkvCCsXjTM7qC6bVHwWcmqsX+WRLAzhMryzjnOx/Mxt03UhQzTQfwQDYQ4PHP9z/tc7lX91EHcSMJ6xel/dJ/k1mcL0yO/wn1mW7XcsfBayeaC0cjuHgTT1rd/uHaBmyRi+/fbbpKSkyMhIl1Ffvny5VCrdtWvX4sWLc3Nzv//+ezeLfEhCQsK5c+fu3r37hDEqlSolJSUhIcFjqp4H243s4hlflG/aAwBEucGUuhthOsQGZ4CJYGEwbuy4ipFJm5MYODOM2wgBBPXZAQDrM50oxedjAhbKZmEIAPArfVg4KWqPxaohql//u+lwnrHZF+jKh5Soz9jsbAR5TyyMfbTeuxBFl0p8xChqr2t5SGloaGhoaoPff/+9UaNGld27i7fffjs6OjopKckrquoXzCB/RoCM0OqNR85W6dLvOULZHezmjbyYsF2xKLfk6zyXe/d5Ve43LTTk6+ZAQeHM2+rVRd5S1QBg+Mtk08eFrv8m9PfFoauXhP6x2Pf1QfXOvQMAigAbq2o1EQom7la+fEbnZCDLJ4SkRwsB4OgLPn+O8QMUUXyWU7Ii3wtaAfardmgc6jBu486SXpXbm/Gj24kTHKR9Z8lfbhUgYCFiNlrxJWKjAIAAVG4Us1FvuXcH6dim3AAAQwNe52F8AGgnTogRtjETJle1vIaBBa9qTA7k2rbctFT+uljiAAArTlVp33TdUmb1WnBEbVGjFYgtW7YEBQVlZGRwuVyVSpWenj5r1qzBgwcPHjw4MTExOTl58eLF7hbq4r333jtw4MCLL764YMGCQYMGhYeHV+5VKpX79u1btGiRSqWaNGmSZyQ9D7ZbOaWLfqGcTlZYkFOpopy4oPsLxssGp1r2dcZpJjV8wIABcrlcrVYDgFar3b1798iRI1vyeiXebsJjEno7TNynY2EAAFobCQDnFI7xyffTP2rFRE6EU+hE94bL/bFHAuDP2uzTyrR2ivrc1+egxXrRbgeAZeWGRgxGp0rVblQEMb1MpyfJGFa9qZlBQ0NDQ1NzLl261KlTp2q7mjZtevLkSQ/rqaf4jh6o/m6d5o+tuEor7JmAyXzx0jLjwZOGAycBQXxHea12dBX37mr0mxYKAEVz7xR+cBsA5G82qH14D4NgKCap3znnWRhycOSjW9YUHJqUJT2jw5mArmZsG9YcAE5qD60v/JnRVRrccUHxjBzFZzkA4OF9eJ1Tk6LejQAyJvhN5LG8UKOCJ2UaL6fpTnaX9mvGj/aksLpDinpXmUMVxm3URfJSReOooEmf3fngiGZ/V2nvYI7X0hDWFjfKnEN2lr0TJ/igw8OAgmXnDUbHI66+kcK2bKsiraUwkawakdHajzm4ef1ba6tMjQx8fn5+3759uVwuAKSlpVEU1b17d1dXdHT0gQMH3KevCr179/7pp5/+9wCxWCyRSEQikclk0mq1Op0OABgMxi+//DJ48GCPqXpGKErz2xbK6RT26yqd9Jrio2WO3ELRq71shVrVz6XSa1pjyknRgB5jx46tiHFYt27dyJEjO/BeX6m1ulouKh85VndXi9/VPgikLwGmBIwickKpZinfZ0uG9e02/MY+DJd7t1HUGAF/uIAnwdDZGp2NohwU9W6Z9gepb3cuBwA0BDlJrcnD8Sgmc4XUm7F/NDQ0NDRuIigo6OrVqwRBYI+u8xIEceXKldDQUG8Jq1/wO7XDS8p0icn6nQf1Ow9WtCMoyu/WAWEyKIL0fBb6at27i8oenqRI/yn1/p6e5qnQkeRktSaBzZ7tI6rigykKkqdlBW9TOZnI5tnE98O6uNq7SF46oUnNs2Sn9T3X6ceuBe/dUnyWg7JRT+al36pY5yDtbUQvBHPCzISpSi8b5XSV9D5clpyoWPtJs28ed/j1mpRc29671qXdfYSsf3xdOqfmgGoXAIwOerNyyrpgTlhXSe/jmpQtijWzG3/uCbkPyLNkXzNm9PMbzESqKV/9bNwzEDgJObpHDg5vflWaqXJWfMu5Y5YvLOCa8IhiW98Alnraw99SERvt0+i/UQdeLBYXFha6Hp86dQpBkPj4eNe3Op2OVV1FcfcxderUl156afXq1YcOHbp582ZeXh4AYBgml8vbt28/dOjQiRMnBgQEeFLSs2HPKXAWlTDkEsnEYYCiPiP6W6/cZAb6CTozVT+XOsslphPnRQN6TJ48ucLAHzp0qKCgoHNY2N7X+DobOetoudpCrujpI+eha66aTxTaJ7fmdwt9uIVuBfJnzHgLx6fqtKp8KoCPxseyXO59OJ8331cMAL24nHV+0nfVWh1JOijqfxrdD1LfWBZrgrosx4lHMZnr/KQ+DaXoAg0NDQ1NZTp37vzHH3/MmTNn2bJl6IOPepIk586dW1hY2L9/f+/Kq0eIh/XltIo0JB+z3bxL6o2AoBSOUyRpOnbedOw8Q+rjM3aQoFKFOXdTsiL/gXtvWcW9u/CbFnrHcI23mFU0K5sl4/gO8Xt8jMcwpZWX71L5z4pg+nn0lvKZUa8u0u9Rh/4QxW5UL/fxbBSV58RvOpxWivrUV1zhCCmAX44UJ2xUAcCakYHcBH6FDUYAGR00ecndjw+od3Ye0ysgL7xkRX7Rx9mSkQEMuSd+agXWvIvlZwDgiuHCjGuvP2FkniU7XZ/WTlw/ztLWkK03LScL7aNbODqHsP9pzDblBjtp6+DTKVIQU6VrWODrl8rP3DBevWq41FrU3s1i7+MgHb/c+0bjUJMUOShglFvnaiFjtpDdjxe2Zhqz37uJm/A74bwmRVbfDcXRMmbQ503cKsDD1MjAx8bGHj169N69e76+vomJie3bt5fJZACgVCrPnDkTE1P1t8TdNGvW7Ouvv/76668pirJYLDabzdfXF60Nk7lw4cIff/zxyWMIggAA6p8rrtcQZ4ECADgtmyMYBgC89rG89rEAwG8vAgBC7+MsKAGKatmyZYcOHS5evAgAJEmuXbt24cKFrt9RDoYAQLtAVqgQS75rA4CmvoxOj76x40m2KxEd80XIduIby0wu975Q4lPx/9WGxdriL3tLrS3AcQdFzdBo5SimJAjavdPQ0NA0bL766qv9+/d/++23qampgwYNCgkJKSoq2rNnT1ZWVkhIiMfOxzUM2M0i5B9MtF27U7r4F8qJIwwMACiKQlAU15SX/biB0OjEQ/p4Rowl3QgAzCA2/4XqA7wNuD4lJnkAbwDLwiq9VPgvBp4kLRcyLZev4WU6hMVkNwkTdI93lU97fowndDnDr5IWwnBE22x/25p4+F13rOuzzFUaXfdlg3eWVdmgHN+SPyyyNm126Q8FxfOzASC7f3qz/W3ro4cPxLCfZZJpZdotJjNOUZ9LfFy1iJeU67eGUbI23GZXrEP3qzN6ySigEovXMlHma4Hjm/KjOvh0ulB++kDq9tjVLQDAd4g/Q+ahNRcOxpWy5FbC8q8jGQhTxKha09FNoIjrXw/t9j8hJ1WO+fZ53SkWyhoeOOHxXj4mGOg/YotizZbi1THC1gzEE2djD6h3ahxq14POkl7uSy5YGWuWKXtgBq51FrcTLxweFJdjmrOxuGRFPoWTwYurKU9QT6mRgZ89e3Zqamp0dDSPx9NoNPPmzQOAzZs3z50712KxePG0OYIgfD6fz+fX1gUNBoMrDt8DUA4nACAsFgA48ovtd/Mpqx2T+XJaNmOFchyFgBt4FEEiDGzy5MkuAw8Af/zxx/z585nMh2+84wUm5j+/D1255QfeU5fyiWNgBQqquHcX4QzGFn/Z9DJtht2BU6AkiBAMo907DQ0NTcNGJpMdPXr0gw8+2L9//7Vr1yraBw0atGzZMqm0dhzafwfK4Sz7aSPlxAGAwu+nRKYeZJPVbUnmto1hhQd7QEn4L9GOQpsl3XCnX3rzA22ZgVV37faf3dZvRl+WhVUQX3BnzPEoaPdPl8LVWtWy1Y7cgooW6+Vr+h0HfccNEg3o+Zw6K9w7JmLYbpldlvhfPXxGqeOa2llt1/XH2tNLHbVo4O+7dxThNOXZ7pjrr4fvyGG7PPw2swUAFkp8lpbr/zSaGTx05w+pI99tF3Gd12XurYvN4RDsBYDm/BatRO1HBk0svpjf5L1wwoD7DPaLWBPjsUB1P1bAN9G/e2iyGsNjIm/E8iUc798tb1aspoBiIez1RT9XO4CgcABQOUqOlh3oI3/V3Xp0Tk2KajcAhHOb3LPmbFdufDt8lrsntWaZsgek4xqnuK903/Rw521bVoyw8abY3LFZpT8UAAXBXzUQD18jA9+7d++NGzd++eWXxcXFkyZNmjJlCgBcvHixqKjo/+ydd3gUVReHz7TtfTebHkglkAQIRUCQIr0XaVIFEWmC2D5FRZEiFpAmSpUuRRAIHRQEqQJJSCGkkp5s731mvj82xDQgQBKK+z48D5k7d+6c3Z3dmd+9p0yfPv3NN9+sZyOrkpube/v27T59yuoZ5ufnr1q1yh3F17Jly8mTJz+2U8Dy5csXLFjgcj2oHtvt27c7duyIPPFkG+4lAQB7ek7xvGX29JzydoQgGIGvOgrYmIDvnr8fM2bMRx99ZDAYAKCoqOjAgQOjRo0CABtpB8CXX1cYrLzO9/eo4aNoGwvjKN8KCPAQdLqQX+PPjARFl0nEg0oVJooGgGKKTLA7ut6nsK0HDx48eHgxiIiIOHr0aHp6empqalFRUVBQULNmzUJCQp62Xc8llhvJLrUOADCJSDxuMLtlU4RBODLuan89Yk/PAYoynbwgmVq/rqRuMCEeHhebMTDectOQ3reqhs+5dSdgvDdHzSG6sS8suKh1qf7RXWwrqiGdIW13lC5c4yxS4HKpoH83RpAvZbVbriaazl/TbDmAsJj8HjUnQawNpku6rJGJlIWUTvTz+yI0c2C8NcWU0ftG+PFWhM99n2oA4ItOwjHNOK7KmaSHHlBRNPw+TIZWeEDDUQgX19lio2J1XuGnGYBA0PImktd9soYnGi9oM/reDD/+3Gv4BIcjw+kiEKStbS+NJ5xb3KLte9yobLP5NbNglcDgb9hSsHZSwEwkGQbOGYgb8KLuJS1/6YYQL1Sc+ePxeUfB0zYBXLSr2FYAACbSmGp8SCHtfNvdBjBpb9EWO2V7SdRplN+keWkzr+oudJP1ieDWl9f2hCMafYLxvZ9y+HoyKZq3to+PPsMOAFYXdCsmm08KnL4pr3RV3rYM86GBvj/3kbSUP9/5uWtbB2/8+PHjx4+v2PLOO+98+umnbl/6hmTx4sXz58/v16+fW8DHxcWNHTvWaDS69/75559r1qz59ttv58yZ83jj8/kPqZEoENTNF5UVHYEymY6cfADAhHx2bDNUwHPmF1sTbjP4J8XdmPwe7cpNGj9+/I8/ls2orVq1yi3gDXYcAATiKwZrj/jSmqeiAeCSzX6SbwMEWBRiQqlJCvVWubRKXnoAUJPUWyq1iaJlGKoiKZKGmWrtComo53NYE8WDBw8ePDwSERERERERT9uK5x7r9SQAQAjCd9Hccg9zVkwTn8jQ4g+XOgpKLDdTJA1lzP00vC3dXDooi6PmWF6xvbyv2yCzaWvB2j1Fv7QQtGGgVWWz4eg5Z5GC0TjAZ+G76L05fc5LzVnNm6hWb9NuP8R7pS3CfBwnatMlXebQBMpMSif6NVodCSgSfjQ2o3+8NcXklsQP0PAYApHSmp+/o70ItH5EpWJ1XsEnGYBA0A+Rsin+ABD6W4sXQMOvkUmmKdUZThcKMItdekV9FgDUooObPx4yZ7UzKEk69J1hv68+oPPX7Dixbsi7Q5gGZma3zFPzTzayNY0hWj3J2a/Y7Eaa7lnTWpGRon43W/tz2NIGz/74PIIj+MImq0rtRQ/tiSJoCKfef+qzzHeu6f5moIzhvhPEhLSP15BDpbt3FW76ImLZoyYXjC913lFX0jjJKicA5BvI3an/xlM4kk3vrc7hm8nELtxlXwZYE2m4VxVeb6cuvcq1twx4d3ZB7+Nq0gr2Hs99cu7HL2T/VKbn9+zZ89lnn/H5/KFDhwJAaWnppEmTnE7nV199NXjwYA6Hc+HChU8//fS9995r27btyy+/3PAW1h6EyUAFXEppBxThdWvH7dQWE3AdecWkweTIykMYdsL338mRWbNmrV271h14f+nSpbizV1q2but2yuNzC8Rco9bMBwCNlco3kL48DL/3c3fJZp+h1LgQmsyDEQxuQpA9yeGcWE3Dq0mqYta6f+yO91QaF03PUeu+punBT6+ArQcPHjx4qG+uXbu2ffv2hIQEk8kUHx9/8uRJf3//6Ojop23X84ezsBQAmE2Cq8SHIwTO6/WKZvM+ylg1crteqa7hSaMrtfcVhopZ1L6o629DUBbamdXzL82pu5bME8qDg7xHVRnBfPkmAIjHDUYrqyxel5eMp/6238m23krjtG3+qIZVV+8AgMsY4UdjMwbEW5MfruEbmOrqHQBQLhb6W4usEYnG809Zw9/VuxoLH+eRngb4y2ZzASAAFEAm7d1c0MZFOwEgpt0/p0Ky+787wDfZd8SckX/NPNdt6atME1PRS5n/TXFzvE0Aq9FDx38wn2h0JST5nlDwloBXsd1IUe78TUaamil4yLraC4nVRTvISvHuLooGALOT1tv/9TxBEaQ8Kb2U4dUwQeYPhQba7c/f12uYjCEHgL7yYX9r/8izZv+t+aNifbuHQtEw9rDaTtYQ+p+scn56Xl++uW5VDt9MGjsIf10bQKJU20AiMIM4kGJhEfDJcMHXBn0C8LJXhjaZntnvnDrkhh78non36rF5hG/7s3CDX758OY/HS05ODgoKAoCDBw+q1eo1a9bMnDnT3SEsLKxt27atW7deunTp4cOHG9K2R8WRV+RSalAmg3I49QfP6A+eKd+FcljmW41zptmiEp24lACAS45AdrOulpSz7g6vf/i195Sf3X/fznij/MDvrhq/u2rkEgiOIgBASWhHS4pGgcwDMhnZgVlYaYC2glyBq1umgnkDkSDY3iFSmgETFKocl6spg/jFSypE0Z5s1j4fr3EKlZmi52l0Xhj2MutZuYl68ODBg4c6ZNGiRfPnz6+YmfXIkSNr1qxZsmTJJ5988hQNey6haQCouWIcgd3b36BgQjzsYMuMAfHWW8b0vjddeicoIbdDrnRbAI/Lh3vZxZdmzjum2N9R/GoVDeAqUQEAs0lw9ZFZkSH2O9nOIsWjmlSjeneDyxjhR545DV+jendTpuGHP00N/2uq5bPz+u+6iYY9Yqi/O2vddqOZQJDpfN56oynOSo8QznLntFuXuyydnVq0WRH2TiRchT6f9QUAfR9j792j+uB14+QwVyT4RK1drjcAQLmGL1fvQTg+4j+5gHSp0D7pqKZKhIibWaeqJuqKkhGHhze0N/SD+VvzR44lQ0xI+8jLSnq7l+LX5S7bX7y9jfBlNlbbjxVF4OMO/DuaSqHN+QbyYoE9UIBVzNtti+bBJZ0o3brBwJkisyY6HaX+JNwBRhDytUFPAXzA5bc/k6sFwAQ4K+K5v65q65eyaNGi9u3br1mz5u+//05ISACAI0eOxMTEfP311/VpXlVSU1NfeeUVt3oHgKSkJAAYMWJExT7R0dHt27ePj49vSMMeA0dWHgBw2rXwXfwer1t7wt8b4bABgNWiqf8Pn5JGIalj6o8q3Z2DhXjEwGnlx5pvHpEYMt0/n15cLFCAcQgEAJgY4s/DSBr0dsrAIO0tKBoFKhchkxCgwe6i9WbafgWhDUBzaGsspbGTTor+xWiqqN7dp4gkiFO+3kE4TgEs0enBgwcPHjy8cJw4ceLzzz8PCQnZv3//l19+6W4cNWqUj4/PvHnzTp48+cCjPVQF85EBgP1ODmmsXKSapk1/XAYAlPcUVmhxCRF+JJbdnG/PtJBKZ26H3OQfUjr5/LsOFs5t2kb0soNy7C/Z8Qjj0jQA6Anz3NRJm/IfUsGnHFuaOXNIAmUmZZP8G61pCtX83d0anh3Fs2VYMgcnPCjvdoOgP6ku+CQDABr92LRcvetsVPmqIMrBQve14LUXOgpsmUMSGt7CYjMJAMUm8qE9K1JRva+UiqcL+T/KJCwE2We2fKHRZd5zgR4aNi7sUCy3nRAAsl/N2jlve64jq64sH8RhfycVYwDL9YafDEYAMFLUFKUmyeH0x7HNXlJ5tXjP/wJsHBWzUCGz0j93ZAiHQKq0yzjPVoiBjbIeKNkJACN8JzLRf9122oleieBGGVz6OMW+KodcLXKMPKhO19ScgGxCNHdxZ2HFf6834wBAtIyo2Nj/SKywv4xUO/QDEzcp2QE4VkKQjFdoWyRFAbzP43f/ME+7vxQT4GGHYllN6iz9+dOiVp/6s3OD53K5JSUl5Zv3q/dOEMSDE9E9C9B2BwAgHDYzIlg2c5z/ys+lk4cDAMZjY1Ix7qUBANMFrbPUUbwkpyNC31wyomXLlvcOpoKvr8YQAIBVPUXnxsgvjpN7czE7SU9uwU2Y7H1zkvfmwRIcR0awORElZX4Wo6Kom5O8b4zzPh0ib0oQXgLs4gRvHy42kseZJeRXVO9uJCh6wlc+Xyz8UPj083N48ODBg4c6Z/ny5SwW69SpU8OGDStPatOpU6dr166hKLps2bKna95zB6dVFABQNnvJ/JW21Ay3xHUWlSq+2+DIzAUARnhjR3aeS6FuYMPcGp7VVZDVI+vYkqMjG09GkUp3/FF+kxgo86r2fLo5tWI74ScHAHtqZvUxbamZAHDE96beqb2o+TPVdKs2lpAmkrZTAMAIYN4vGBblYG73Q5fOSde0DtmQEHIGykIBwJJoBBoAQG2lOmwvff2QpryPS+lwFNoBgBH43KT+dat3JoL8KJN0Y7PgXjw8E0H2mS3vK7LLXaAxHhZ+pFXYwZbkjwSFkb8WbaKhzmZV+nHY30rFGMAqvXGF3jBFqbnlcPjj2FYvmT/+X1TvABDrTVyZ4H1zUqV/HfwZAPBjL3GV9s39GiyrRq2IK92rd2pDuU3aiV+psmuM/5sIIGeUcSWVY/XP3LXdKHFcLLA/yXkRBhqyPUbYX+ZSOXSDEjcp2SwKAQ4AwDs8fo8P8jR7Stzqndv2RRA1tRLwz84NvmfPnomJiX/99Ve5DQBw6NChin1yc3OvXLnSrl27BrPq8cAkIrgXL+eGEeQHAI7cIleJihAqAMBwTpve+0bxkuz03jccd21ffPFFeefffvvNXvDvXVbARMe3KgSAb6/qlRZKyEQ781j/+Pt8JRNh9xLmJxgvsAiHkIkGsvF9Pl5/+sk5KAIAjXB8poAvrKliHALwOo/bhc0CADtN7zSZC1yPNsXrwYMHDx6eWeLj4zt06FA9qU1gYGCbNm0qFpZrMLKysn7++efZs2ePHTt26NChU6ZM+eKLL9avX19cXNzwxjwqnLbNMS4XAJz5xSXzV+aOez9vwoeFsxdarpWJW+vVxKKPvi2Y8UXBtM8NR881pEu9s8BmuqWVpknbU6804VXNBS0hZL29BtNA/1pYSZ5xOrYGAM32g1Wi942n/7Zn5hZFYDfQspe2u3ATRT/8CYHbRtD4lygER4oWZhd/nVO9A2Uhs0beMp7XEnJG+KFYpNbe2gPC2P1D2XWewY4Tyw/Z0wJlocp1BXlz0oCGFKXTQULavcRajjxb+oB4R76N204YsvuR0wE8Fe44nW71vlomeaVCjGRHFvNHmYQAOh1vgTGjy12gUTYq6CEd5DdKiIszzLev6y7VoTHlGn6dweRR7881CkfJaeURBJAxflOqJ6sLYod0knR30a69RVuqH/uEP4XTTmpb71S+3sM7MYbvUjly+sR7ZdgAACVpasodzZ4SKwv76q1GryRbu+5SpKjum/n7eaFWMfDPzg1+yZIl586d69+////+97+JEyd27dr1nXfe+eCDDzgczvDhw3Ecv3Tp0rRp06xW65QpUxrMqseDFRWOELgtOd2ensOMCAYAItAHUNRZrND9dhzjG1A25Sy2AwDCQB35tvgeN7QrOzWOan035QYA0DStPvqDfOqGQ+m2+GIHAGSZXXxurtHcKC6n5O0YfwBgIQgAlN+JVSbJUcX+YT5jAQADwB6xEt55m32RVr8SNS6RiHp4yst58ODBwwsBm12zU7dEIsnIyGhIS3JycmbMmHHixIka986cOXPw4MHff/9948aNG9KqRwLlsCXTRiuXbwaaRnCctjtoAATDaIp034oxkQCTiEid3qXSan75zZaa4fXemzXHzNcp1lvGtP7XcS0m0ohiJ4uzD91B/as+AUbxW5xWxuVasy5pznaUlBV4F/TpbD531ZFXVPj+EkGfzozGAZTZYr6aaLmSQCNwarjT/YyBAlpgyz2nPvWqrO9DjREP8waAu5NTihdnAwK+H/8bYF+m3s9pCDkj/FgrVuQjeLr+0F1U+86PhKC7JGRPi+xRiarNhQDgei8Y7ukNR54tvd9Nx10rt50w7FAsxns+ZGcYQcwVCloxGW2qVRBozaBaWTYWg/cb8l4VXaABgI1xhvi8/oCaBY/NKyymP47nuVwA0JPN8qj355Q9Rb+4aKeYkN7QX76hv1y9AwUUACQYrqUYE6L4Lat3eGyytS53er+lYwM+2Jff+oZp3uS8JV8EDTyhfvmk3sJFF00NyvBlgp0yOkBre9qOPU9MbZPYPSM3+MDAwBMnTvTp02f+/Pnz58/n8Xje3t4Gg2HcuHFvvPEGjuM2mw3H8bVr1w4YMKDBrHo8UC5bMPBV/YFTpYt/Eo8bzO3YCuWwcZnYpVCbzl2lKR7KJigryYrmhe1r8ffoJFGiIWBaKqvbdEgpm5swJRwTFaTshvJ59AD3f9tTtG4B70bv0gHwAMDl4p5UbOws6enOCfmovMJi9mKzTllts1WaCXzeB0I+/ohTAB48ePDg4ZmiZcuW165dMxqNVUqomkymq1evtmjRosEsUavVGSQrjQAAIABJREFUPXv2zMrKioqKGjx4cHR0tFQqFQgEBoNBo9GkpaUdOXJk//79iYmJf//9t7e3d4MZ9qhwO8QiH7+t2bDHpSpLN0WTJAAgDMJr9kROuxaAIABguZGsXrPDcjXREPencMgjpGV+DKy3jBkD4mktlfNKNkfF9b7tnds/5ffVB0zephr7HyrdXS7gEQbh/dlM5fLNtrQs7a648j4Ig8iYEZGHnUQRjKJJ93P57yU7XxJ14uEPTxv+r4ZflA1QpuGfRL3XNxU1PMdEIm0l8NyqdwDAAKZWTvxeTlzpXtQW35nb5GXJ29X3dpaW1Sw4qTw00HtknRjjjnvPc7kkGKonqS1GswBFpz/byedpJ02ZSUz0+MW8XjxslDVBfw0AtE71McWBB3e+ojtftwL+2Egvs5MCgDib9fu+9Ox3ClqeNy2Zm4NStI2HLd0YJGoj+Fsk5iAIgSLuxGHPNbW68p6dGzwAREVFZWdn7969e926dbdu3crKKsulQVGUv7//8OHDZ82aFRAQ0JAmPTaiUQNcpSrzxZvqdb+q1/0KCOL2pqMoruVOH5eGZDfnhx+JxSVE099bpA1LlCYYNl9s8np4q9yMmwAANK09smzK8t1+3LLJe5XVdaFAJ8DFE46UhWa5aGexoexTNlt8b6RMH3mnMICNowh0b8QaH/0IaRhZCLJSJtlrsizS6bcaTTfs9h+kkgDPLKkHDx48PLdMmDDhzJkzEydO3LJlS3mj2WweO3asVqsdPnx4g1kyb968rKysr7/++uOPP66xw5dffvnLL79MnTp1/vz569ate9TxaZrOy8sjyQf5eBcUFLj/yM7OrrILQZDAwEAcr/rUZLVaa/DtF3OQD9/w0lnJ/BLa6TAc+4symr3ef5PTOvrf/mIOjO6DbvhNe+iUOiYYQdFHGP9R7CHT7Ka3Smg95ejmuv11OupA2898SZQkGjVz9OUNV00+JqezqjdpIB2Sk5NTPj4mEfosfNeacNtyPdleonDQFB3oY2sVEoeuAgoomhQjUh2tAQAzaTpcumeM/5QH2POv/YMCG2+OKtfw3rOD3OodlWLs9fIiRilkV+5fP+/PI/QPBvYKuWVOKWNvydQ8e1xPWXX1Xt/2WKzWrdcVOkcl+XFdjQJg14rseGVPDjkX6xMIjzR+viH3lCIOACw2y7LUBRV3cTgcBEEAwB0ocUxxoJOkO8vFecLXawb4HGfeQRA/DN0m90p0OD5Sa1fpjQAwXcBv0M+31v1pPWV6u5i66+L8ICc6sBvGHpquIXK7gd+fXBO6OwebGOby5UD1/iyUPdl3do6m5hyHIpEIrRCr20bYoXx8vZ4AwNVqdXZ2aW3ssWhQACbiMGdnayv2FzLxDQbTDwYDzUAY25o5x2cQF7R2Noqu89U3Z2Y5HW8qi79zOtgPG7+oqAieeRC6FvFX27dvnzBhwtChQ7ds2bJ9+/ZZs2bRNG02m8eMGXP48OGKVdwaHovFYrFYcBzn8XjVP4n6IDk5OSYmBkXRBz8H1B7z5XjjyQv2jLu03YFyWC4dbU7p5SzF2c35oftb0DbKXZKEMpPuWqNXxfEzCj4sOxhBth3/e3zvsor3W5PMX1001PK8YWL85KjHqYKY6HDMVWmLSVKMokskoq4ed3oPHjz8x7h8+fLLL7/coUOHS5fqMhb0qTBhwoTt27ez2Wy5XJ6bm9ujR4+bN29qNJo+ffocO3YMaShPq6ZNm9I0nZaW9uBuPXr0KC4uTklJedTxp06dumHDhse1DgCga9euZ8+erdhit9uDg4PvF5zv7u9SqAtmfIGJhYHrF9kdjir9z/cZH8gV9Dr9a4ZB89DxG/GEE0NjOngFiBhMncOez4Q3Nq4ifLzu1x8AmqChP7G+EyKCi+TVwy/9efqvMwBAGlyZg+PN/xiIQOYE7cyE0pqTzz3YnjZzmsVMCqcpGkGQY2/+HTYgMGJYIwQAQbAvI5a7K4TX5v3R7CnJnZpKkzQzmG3PsWpQ3RTz3Fwq/1HtabD+L2Ntl7EXMmjcxkBZDorXXhh6sEy9N4A9Ie16sqbtqbF/jdjWDClKuVb78V+e2KbFJ6G1HHxmwP8Gxrz2JK8X5fODdu5mtWzpLMj3W77s/L69AHDMYv1IrSUBZnDZX0dHNfDn+9D+lhLzT6zvmqChAGAH+1zb/KvkjQawJ2r+Matvy20DJOW10xr++pcMmSfqPUu9/yv9mZ8fe3y9nTLYaQBwOBxdunRRKBSivnP4L482Z/7mMmUaDhx2r2W2a9fu9MHdLBwBAAtN7zSau+Fo57Cw4uJihGDKXl9qPL/DdvdGxfFPnz3bPL+IBniDz/ufSHA61XDsq7wrWNKVXWMYoWGNT5xCmUz93j3FH7xXm9f7jN/fa6V4x48ff/r06e3bt584cUIulwNAz549y2/wM2bMqGcjHwSHw+Fwnu9qftwOsdwOsQAANG04lZgz8S5pwt1r74WfZKh3lRA+hP+CMMkYX3et0XbnY5uzmt2ypboP2bby6/G9y7zaxkZxI6WEi6JNDnrZNUMzb3setjY9e5TV5g0AYfJSjmw3AMgI+cSA6aHiqoFPtaQFg3HAx+t/at15m22GSjOez31fKGB43Ok9ePDg4Tlk27ZtAwYMWLJkye3btwHg7NmzoaGhixcvfuuttxpMvQOAUqmMiYl5aLfAwMDExMTHGD8yMrJ6Kp8qOBwO9yJ8jT2jo6OrtBAEERMTc78YQ3d/0mACAFwqAgSp3l9DuQIBooNDSKPoweP3kvjPDYxm3FvC8mHzIgGK5i6RTh3F69a+RnuCXYHzte/xKG48M+lH4daezXu5292pmN0afhnry08bLVVgqkd6vSxvRtS4MABAUKT0vI6rE6riLBEDEJpB0zS5u3DTB6Ff1fL9kYzyARpy306151gJX+Yvvr9hhUQI1Nn7Xwf9G7eERm3KG0sAvrNrPjrrzXJQphjejQURZzOs7l1sHIlq3YGdWnMluTqxJ8qbk/XnSuD9W/cb52IQ2MIlaCox54/o1LRif28OsuugmGWt+bKvcXyZzi/pq7tYTZXJYmNjJ0+eXL7JxXhNuNFP+P7jy1egLVtCURE9d06Ltm3djf04bCdNz9Po1pqtoVPeYu+subThU7ke2jV9aZxlSLArsAgrucPI6mbtuIK98BvR2luM1Pq2J8aeGtbs5VgfRi3714s9YgkASGUyaUjI442frXP136dy3CvByHznUOC9DqKpryFskIz9mEwBACgC6LBdcW6MF8FAJinUtxyOZDarbPywTtBhFF8khSNfVhwfB2jJIOIdzv1mSz8OG+MwDneVQjYjpEkTfOFilMkEm43z5xn3z/sD7LfZbM/+InytVuDd7N27132DdzgcGIaFhobOnTv3rbfewv5jRRrrfAW+HMpC3m5/2Z5tJ7zMTW/2xcVE1rBE/SkVAACKNFoTKZ3gR5nJzKEJpy+cmWX718nw3LlzXbp0qThUvoHs/quCAjqm6eqc3MFGcyMAeLMlUcSbYyHNADAhYHpXae8nsZYG2GI0/aA3Omk6kiC+l4pDCU8skAcPHv4TvEgr8OWQJFlYWOjj48NgPOb07pMwcODA06dPJycnh4WF3a+PQqFo0aJF27ZtDx8+XB821Mf93VWqKpj5JSYRBa5fVH1v4awFzhKl3/J57jI098OWlF6ycA1QFK9be37PjphUTKq1xtMXTeeuAoL4fD6LFRNR5RB33LtL4xQO8ArZFo0wqkoyUu/KGBRvuWFgBrMjTrQm/B8hIdnqu1/H668igBAosbjJj1KGFwCcUPy+t3griqAUTc0O/rSloG3tB9QdVKh/LQ5YHM4Me+aWZF7aWqq2Vk151SzbEptm2t9dZmNWemNfi+B8+6qwwWxz0c7P7sy+mtkmr6Dfe235M1vXHNZeHSdNrzEYX2IyO7Jq+Nwv2+yX7fZZAn59r828rVQXkuRGL6lPNSlxyGxZqNV/JRH1dpG0xYbyuSi3ZmXYYJA6V8ageMtNgy2EZTwY3TtYmP/BHeW6AoSJhuyMEfaRAUCa03nIbJ0m4NVY2ul555srxvUJpv+1509tWdsrrQoqKzX5qMbgqPSF0tkoo4NmdQWKSwMAuwgVZKEA4MfD1vQVT9dobjkcADCMy1ksEQHA8WzbrFPaviGsNb3EVcYnAT5Ra+MsVj6KTif5i44ZujZmsNogf1ptEhT9RS6NIIiHGvlc3N8fQXGNHDly5MiRT/cG/2JDmkhnkRMA+F39cBGhO6gw/KEGAIyPkUYyd1YaAEgn+HHbCztcahOLx8S7ktwHvvvuu9evXy+fSTmXZy8xkTG+uoQiUVrGRBQpexDJ0qB+yKws/UUAWKHMyPV5yZ/H7tH4MR3gEYBJfF4bJvNDtTbN6RxeqvxIJBjN47p/7E9arCiC9KzJu15JkgfM1lE8juhF/HXz4MGDh+eIPXv2yGSy7t27AwCGYUFBQRV3HTlyZPv27Q1jyezZs48fP96+ffv58+cPHjy4UaNGFfcWFxcfPXp04cKFCoWi4jLgsw8ul2ISEanRWRNvs1tUWiC13c5yligxPo8R4PPgQbS7DgNFiUb2E43sVzasVMSMCMblUt2eo9qdh3yXfljlkMLPMl0aJzOUU6N6BwBMiIcdaJkSfcmeYy35/m7gD01q+Ypum27F66+igFJA9ZcPd6t3AOjhNfC85rS7wvPuwk3R/JY48vBnZTeiIXLRkMdJr9sAeHHQ6gI+NYSTGlLDXEOQqEEfbE4rjyjsZR6/ZC1q+JWT43KtN5g2I+YVUnH3yo9qf1pt76q1Tprux2FH1kLtPAnrvKT32zWYw3714k3D0bP57nLLCMIMDRIO7clpV5eZz2pPuXonwjhzNvkL0tXO87oB3zUBAOW6guyxSSE7Y/JfFU5Wqo0U1YrJqPEB2IOMjR4eLqvSuPiSYfMt83iKux8xm2ja6kf1C2MulIrtNP2moky9h+D4h6KH12/HAL6WigEgzmJdjRhRMX0nyKm2UrVX788Lj7xkWuUG76EOIeQMd45TzT69Sx1vvKClSRohUNJE+s4LLl6Skzvztu6wQn9CjWDI0i+/7v35QHegSEJCwvr166dPnw4A+QbyzWPuDHYiAHA4/50JPpdnh7zGAI3dm0mZNgDbsZFeTSSPv3IewyAO+ngt1xu2G81fafV/WG1fS0ReGPaxRmen6U/FwrG8SllkFSQ5QaHOdbm4KDKO9wwlmPXgwYOH/yCjR48GgDlz5ixbtqyKP11CQsKOHTsaTMD37Nlz9erVc+4hFAolEolAIDCZTBqNRqvVAoC7ysyQIUMaxqS6AUEEA7ppt/2uWrlVNnsiu2WZhrclpytXbgUAfv8u8MDpbFJntGfmomyWcGivKruEQ3oa4v60Z+WROgNW+enW6+1A4wWtPctStCjb/6sanBpoF50/9w5pdGECXDrhQev/FaFo6tfCTQBAAeUuHV++C0fwkX6TVuUsRgFVOErOKI/0kQ+t5bB1wo4UC0XTE6Lr+NFi/1CZ0lJJwF8udHzyl45AkdOjKyUSQhHw5zecU6repT2i2AcATIwNABnm2wDta3lsBEFMEfA2GkzvqrUVNXy5ep/M51VU75SNMl/W8buIAW2IsBqapJTLN1uuJgAAymWjfC6pNdgzcxXfbRQM6i6Z0KDXFVRQ78wwTsTxVh8kqL3HZzJs9B83jd1XRAOAcl1B1tikNasCjJ25vTnsVz3q/dGRu7Dt3rLxpSoTTe+3WEmAPBd102EHgBAc3+Etq+WyX7mGz/lDNXtuwbU+wt+/9N38Yql3qKWAf2i2mKioqAd38FBL3HVKskYkGP7UAID33Ea2NLP+uIoRyA5YGl7wcYb+hBpBkUYbmsWO9Gl0ZkTuub3uA+fNmzd06FAfHx8/PjanDf+K6k6eNRsAGCijVNXG5kQBoJmMaC4n1A5lsjEeABAE6SHvEPrENTBYCDJPJGzDZM7X6C7a7O+otLu9ZR+LBAu0+sVaPQCUa/hy9d6MQQzmPGVXKA8ePHjwAABsNnvlypVpaWl79uwRChvO+7c606dP79Gjx8aNG0+fPn379u2cnBwAwDDMy8urTZs2w4YNmzRpko/PQxarn0EE/bvZb2da/kkqXfQjLpcSPjKnQu0qUQEAu2VT4ZCqsrwKLpUGaJrw90aqxakhBE4E+NjTc1wKdRUBL+wvC/m1efbYpNLlueCi/ZeEV9xLk3Tu26naA6XueHhObG1Ldv2t/aPAluv+20Ka3k99s0oHDMFJ2gUAcYp9naW9OFgt5DQNBZ9kqLcWNd7QTDjgcXLrull4UU/TMD6aW7cSk4UjgYJKsjxDiwIAgkCV9gZmf/EOK2mJFbbzM3fIAEg3p6gdoeUOEQ/lfaEAB+Rng/FdtfYHqbgHm3XeZp+r1jpp+g0+r+Jqpzt403RJJx7u3XhTFILVu4bX/37KcjUB5XNlb7/urrxIu0jTH5c0Ww4YDv/BDG3E7diqvm0ox6V1ZvSPt94ysiK44cdibWnmRhPuUDaaRkC8ufQMgvT4IUrrpFybi6bOyW+yMfT9wb4vamhxiZkEAIWljiOIy4kkiHINf9BsBQTgnnoXP4rTLgbwv0Qqc0Y+YaO77dW2o7Hwjc/fjePB1Eq8VQ/0r0LtA+k9PBTK5KLvTfWSepegl1R/XKU/pmRHlwWc0DRNGlwAcGLz922bHzeZjACg0+nef//9nTt3YgiMaW5JTlsaQTsBoIuk1zcnypKvhHgV9YoqAYAzqguFtjwAEApu4+jndWJ2LzaruY/XUp0hgsABYBSPiyHIFxrdYq2eomE8n6siqclKda7LFUkQm7ykfI//vAcPHjw8A0ybNg1BkOXLl7dv3z4uLu4BIegNQHh4+DfffPPNN9/QNG2xWGw2m1gsRp/z+wWCofIP39IfOWs4/IdLoXYp1ACACfmC/t0Eg3sg2ENeHYLjAEA7qhZ7c0M7HACA1LS4JOwjC9kZkz02qXRVHgCUa3iapHOnpmr2lLjVO7ftwx1Ty6nop22jbA/u7KJrtrkSNOS/d0e5oQAAsickh2yLfmwNT9FA0UDT8F9Iqptrzbqo+RNH8BG+E3cp+QAmkiYPlOx4K2hu7QeZI+QDwM8G41y19i0Bb5PB5KBpdwbv8j7l6h0AtL+VAkB9a3iaJA1xfwKA/MMprGZlFy2CY/zerwCOqX/apT94qiEFvGp9gfWWESGQ0N9a2LOsWaNuUVZK9oZfUm+h7I00yaaSI3Zyyafy2fHaiHjrgMXF+JBGDx/0mUdhoYbuV7kVexV+uWX55ZalYkuwEP/9NRmfUQdXRSRBbJRLx5aqSAQAQICg2x9RvQOA9k919shEwkbf6CmIumTi7FMloMkt10c3wNxTg1ErAT9r1qwqLUaj8Z9//klNTW3UqNFHH31UD4b9R9EdUuS8kQwuWjTM23BMqdpcKHndF1BEf0Kli1MiGCIe4a3ZXZI/9w4ARE4JXLjwq7lzy36sd+3aNW7cuL59+55Xnyq/a/6lOUWjPYDkA8BNXbKxYG/F090y3FA4SuSMupmX8sGwFdKyfBJTlWoaYJ5YtESr+1qnN1P0Easly+mKJIhRPO6wEuU3UnFbpieNggcPHjw8ZTAM++6775o2bTpjxoyXXnpp37597pD4pwuCIFwul8t9USKtUFQ4qLtw4KvOwlKXRo+LBYS/94M958shfL0QBuEoKHGptLisUtIml0rryCtGCILwqzmAvLqGfxL1DgBNeTE4grto1yvSnv7MmgMq/9acKbDlNmKHCHDRQ4a7p95RFirs56U9UJo9ITl4a7Ro4OOvw/8XoIH+tXATDXQvr0E+TD8Z2wwAbKb5ivZ8V2mfcG7Th45QTrmG/0lvArivemcEsPwXheXNTmsADe/IKaDMFiLQt1y9l8Pr0k675YC7A8ptoHyH4pE+yk2FziJ7zrgkW6aFspDCib7ylU36Y+iRbYjXhNt+O5TvJpvCEqyAIt5zGilIUl6f6b2dRaWGuD+tiWmkzoByOazIEH6/rqymtS3+V0tIira6ars6a3ZSFE0D1MElYaPpb7XG8mkDA02NSlU5K1e6tLhoAPgrz951l6Jiu4iJruwhEiYZM0bfwm305RHibj9HHzmn6DQhg7VHkeBIavlLDIK/IBq+VgJ+9erV1Rtpmv7+++8/+uijxyjH6qFGnMX2nDeSaSctnxkY8E2E4bQ6+/Vbml+LUS5GmUkEQxpviRYPlTNDOcWLs/PfS+d3kcyaNWvbtm3x8fHuEd5+++2kpKQwbqQ7u4w305eD8VJYFpWTDwBqVbuQwDgct4pxqY2yWUizlCGXEfVymyxwkTkuV4rT1ZnNOm+1rTQYACCSIEbwOAu1OgrAQFVNCePBgwcPHp4WU6ZMCQsLe+211/r06bNq1Sp3UhUPdYg9PccQ96c1OZ0ymlEuhxUdLhjwam0euxEmg9u+pen8P6o12+X/m4reC6+lrDbVjzuAoridWiP3nxAX9pEFb4nOmZBUuioPUMSRZyvznD8cy23zaOodAP7W/OmiXQBwQX36wT3TTMkKe7Gc6XvfHjTkf3BHuaEAYaLBO2KEfWTMMHbJt3dzJiQFb40RDfJo+PtyVXs+3ZwqwEX95cMBYFwUt50fM8Xuc0RB7yrcOD/ie+RRpFQsk4EBQgKNIkjrChcSZSazhie61Xv48VbMYDYzlJMx8Kb2t1Ig6ca/RNeTFiL1RgAg5DXkt0NwDJMIqUIbqTM2mIBnBrObnG6T1v0fyy0jAIjG+b7+sUikUG2VS0N7ypasbTT77bthCVYA8F8W/lUf5umi0s1e0g41pfd/cswX/lGt3UU7y1bpSIfefDnefCVBOLSneMygOjyRLw+7Ocm7SuOwA+pEhaNrEHNTP0kdnsvthISjYKPpNxUad9y7P47pSMpM0wVCF9kYyFtVLzaLi7YYKjkI5AOZf1ZTOjUFt1CXR4h7/BzdhMlo0jtg2TboNCGD9bsyAV4cDf/48c8Ignz44YcHDhz46aefFi5cKJHU5Wf53wQTE6ymPOsto/5ICcM70Wvm4JBfm2e/fosykwDAfUkoHiondS79USUAMIPZhDcDw/ENGza0b9/e5XIBQH5+/tSpU0euGEgBBQCl9mIAsN9zcrM7OSl3psY0XaNxqt0taodC5VA86P76uGySS8cplEUu8pyVxBGgaACAEAJfotVTAHOE/O6eDB8ePHjw8CzRtWvXq1evDhgwYMaMGcnJyfcr5+vhMdAfPKPdeQjuxRtSZovlaqLl2i3RyH6iEX0ferh43GBrcoYtOb1w9kJup9a4l8Sl0povXCe1ekwiEo8b/ODDRQO9grfF5ExIKl2RC+468I+l3gGgs6SHg7I5a+Eb78Xw9mLe37/Prd4rl+Dymx8KACXf3s2Z+ExreB4DAYCn5Y3roBz7S3YAwHDf8WyMAwAoAk0keDA17LLuz1xr1iXN2Y6SV2s52nmb/R2VhgS6BYOR6HDMvRcP71bvxgvacvUOAJxYfnhcq4yBN7W/KwCS60nDY1wO3JPxVaFpd3uDqXc3jgIbpS/Tis4saxOX7B9wjSlVmWi6Y6YVc5V1i7umPtMbE2CoP14vK/D2zFzlmu1AUrzuHQT9uxE+XqTOYPrrmv7ASf2BU7hcyu/RsT7OW9+MacalaegRwipX7+6491KSdMfDY4HQP4Q5lyN0X21/5du/uKDvEshc8ErlpC3/6ItfT8Qt1KUR4p4/RzdhMgAAA3i/d8C326DzhEz278pkSI6ut7mnhuRJE5i1bt36ypUrTmctwpw8PAyUhUYca+VOdFm0lMGMSBIOaB2yu3nBx5m2NJM1yehSODJHJFoSjMxQTsTxVpgAB4DWrVt/8MEHS5cudQ+yd+/edl1f6j9kOA1lS9xZhNj9K8jEXVp9pDr//ZeaxjXhRCIIwseFD7q/PgG+GLZD7jWyVKUiSRddNhV8zGIFgDlC/jRBbfPlePDgwYOHBiMsLOzKlSsjRoxYu3Ytk1kvK0j/QSz/3NLuOAgoKhzWS9D7FUwiIrV64+mL+v0ndXuOEn7eDw3oxSQi36/eVa7eZr+T7Q4PdsOMCJbNnoBJHuapXkHDo2zssdU7AMiZvmP833q8Y/+lJvXu5rnQ8M29iOZyor3f0wkDPKbYr3Yog9ghVVQ6A2UO8xm7IW/FvuJtrYTt3dr+wbjVe3nc+0q90R0Pv4IlCBp/p4p6d9MAGp4RHIAwCHtWnrOghKhcYdFyM4UyWQgfL0zUcI+R5qv6zCHxlJWSjPQxXdWZL+s+mQqf/xyQwiD7bNOM/7oUENBN8ubuVLT6VT2GB4O/iQrCn1Re1Yhuz1EgKeGg7uJ7efhxuVQ0oi/hJ1f+8ItuzzH+qx1qGZjzTBEkwD7pIJiuqqTexSgqRtHynHYnnLYoIKYI+AAgZaMAwCEqZZc0XdRljrlVRb27wQA+uqfh4XfFXTSl8aao513DP+kVlp2dLRAIvL2rell4eDwwER5+ODat0zn7XV7ubE3T1nZBD2mz69LbHa5ak0x3et2wZ1rc6p3w+/fR6ssvvzx16tTNmzfdm5++99kfbS9HxTR3b27FNQBOABgRKTyYbkkpDO4g+6RXKx6Pgdbr/DEBCBdFVCQA0HQFby4u8vz9uHjw4MHDC4mPjw+fX+lRWCQSHT9+fPbs2T/99NPTsuoFQ7fnKABI3hgm6NfV3YKJhaKR/TCJUP3zr7o9R2uTkQv3kfkufs+elm1NTqcMJlTAY0WHsyIfIfBVNNCr6bX2KBtlBDxVD7j7q3c3tdHwW5LMq6+bqkToun392mwprdiIAMxszZvcvC6TKbBw5PdhVc1uGDRO1QnlQQSQMf5TqvvJtxd3Oac+mWG+fUxx4DXfcQ8eqop6h3vx8JsVBuWYW+KbFkYgK/xYJfXuhhPLD/s9NnNwvPZ3BeApwZuj6yL2+V8QJoP3agfjifOKb9cEJlaDAAAgAElEQVR7fTCFEVRW49CWkqH+cQcA8Pt1qcvzPQzzNT1lpRACkc8K9P0sJL3PDetl3Yi3nCGdOKOXK2kU4f0Qsb0Xq9NVfXCKLfKyJdHhiGHUfcUy2umyJd0BDBW+1rvKLm7H1ro9R51FCntOATO0zkp9G+yU3l7pS+YgAQBsLjq/su+6jIOyn1gPpzqcUC3nfMW89Ncdzin3Pzx79C3KTLIHyMZujBFXywyKAfyvd0D6dqZl2C3t/lLRAC/xiOdbutZKwOfm5lZvtNlsBw8ePH78eKdOneraqv80mAhvtNore1yGSyHO6Hcz/Fgrwpcp6C2zJplqVO8AwGQy9+zZ07p1a4PBAAA2m63boNH+Hx9DGJV+dnckm91/bEw0b0w0t/dn7BwojSvd66SdA+QjGGhdziWrSOoNpSrX6QojcA1Fach/I96X6PQUTU8U8OrwdB48ePDg4TEoLi6u3ugutz5w4ECNRtPwJr1gkFq9424hymXze79SZRe/+8u63UecRaUuhRqvKeK3OszIEGZkyGMbwwpvUMfjGjGcVivXFQBA441R1dW7G7/5oc5Cu3pn8d3JyS2KuiCMqo/jCguls9ecSUdfrV1RUybt55S9RVsclN2fFaR3av/RXazeIYwbmWG+fVJ5qLO0pxfjvhKlwEW61fvkyhXj5gj53H+METctAMD4uHF19e6G21bgNTWg5Pu72t9KA7+NwL3q2BlBPHaQPT3HkZ1f9P7XzNAgTCp2FSsceUUAwGkbw+/duW5P92C8ZgSabxq0+0ozBsaHHYptdLzV9V7/hF8zh18z0yiy8Uufm12QWW9kBKfYHF7Ez9/4Fmn1TQiizvM0k3oj7SJxmbjG8AEiyM9ZpCBVGqgjAZ9vIHvvUdrJGvLYXSlyVMkex2MgZ1+XS9hPtD43isdJcjiXSERVcs67NfwirX74A+MmRMPkqk2F9rNa/JIeXhHX0MNCYj/kAwDhx+S2f5oFU+uEWgn4xo0b3/d4HJ8/f36dmeMBAADYLRvz22w13uxky4CMfjdDfm2uP6ECAJSDVVfvbsLCwtavXz969Gj3pqP4jvH3BYETvgUAk4MiaShfb3eQZYkl3XO3p5VxJtJ4Q395tPcnGoOsS1Dd+Ey+qVRnOV3NGMQYHvcLjc7dKMMwNUnSAN/oDSiCjOe/KOmFPXjw4OE54c6dOwDQqFEjFotVvlkjISEhISGPrxU9uCG1egAgfLyQ6ompEYTw8yZ1RpdKW0sB/wLAieUzg9n2HKtiZa7gVQkmrOFB1HLDoDuiBABhf6/q6h0APmrHn9aSW0VbtNlSStFw/Q1vtPJaoJBZ935/pIm0Z1g4sXXpyH3Wajtvs78n5Fevs2ukqGV6w0u40y3aC215P+V+94ChXLTzrOr4SL837teBjyKRBNGJxXxHWPUlTOnud3y82nu7wvxBurERm9+1hhRX6u3FJctzAcB/UXidq3cAQNks34VztbsOG09ftGfmQmYuAKA8jnBQd8Hgng+tvFi3IBjSeGMUAGj3lWYOjg85FHtkT8RrozPYCpfXiojkzuist3OjrlpoOdHyeOu3A5AdRrOkHvzYESYBAJTNXmOlRNpqAwCEUWefBY+BREjwKtNkpWbKQdJsHJFxKr1AOQdjEU+6Aj/j/tG1kQSxQ/4Qn5egFZEIgig3FmS9lhi6rwW/SyUNT1nIrBGJxgtawpsRHhfLCHzu83DVSsCPG1ezH45cLh85cmS7du3q1CQPgImFhC+XH3vRWjTMlmZJfekqUDQziBV6qGWN6t3NqFGjjh07tm3bNvem6ty2tTMGjBgxYsh+VZLSuWOgJMarzKVn1XXTyutGs5MCgA9Cv9qQ90OhLe+z86XJRejeIdLWPnXw/RehaBsmYwiX84VGRwJMFvBOW6z5LjKMwNUkpaWopTp9AemaIxRwalew1UbTB82Wnmy2tKYf7ht2h4miungS43nw4MHDA4mMjASAK1euuO/d7s0HQNO1rSTkoUYQJhMAKLO1xr3udrR+ElY/m+BejIgTrdP73jBfN2T0vxkWF4uLK/kbWxKMmUMSSL1LNETuFk41IriPLBcyUbSeg1udCkdGv5u2NLPvZyG+HwfX1bAHzZZTVluqw7nRS1JRwxsparJSnexw6jmsXl6DNE7VQ4dCAHlJXNXjoyJCFN3jfR9FhEDftTH57DvK9QVZIxJD97bgd6uk4dXbi3Nn3gaK9vsi1HtOnTlsV7WCyZBMGi4eM8ienU8ZjJhYyAgNqmEWrEGoqOGzB8UvPBzLvNrBVmL7XGCf+kZG1FWLXoav39b4+1DWCBwbUT8J9jAeF5eJXSqt/U5OFTccymi2pWUDgjAa+9fV6cQs9OBrVa+QD8/qD9yxjIvifNzhMZNo1Ak1f78RCPyhCQAoNxZkjaik4cvU+19awpsRfqwVq8mLsHxYKwG/ffv2+rbDQxWY4Y1dqnj/+VjR1zxrkokZyok40Yrwfcg9fu3atdeuXUtLS3NvvvnmmxEREQBVv8+z2/ACBViQAAOAIHbw/Ijvr+surc0PACDNzpqf1SiaPK06EsVvGcBqVBv7t8qlxyzWj9Ra8l7WunE87kSFKtPpasFgDGAwdplM24zmUxbb52Lhq7UQ3sct1gVa/XaTeauXTFZZw5+y2t5Xa2maTgjwxWs3HeDBgwcP/02mTJkCAF5eZXHF06ZNe6rmvPjgPjKUy3aWKB15ReXRvG6cxUpHXhHCZFTJ1PXCQ/gzI463Tu97w5JgzBwYX1HDu1tcWqdoiDz4l2jkiZf16pxy9Q4AxYuyAaCuNPwnYuFtp/OWwzFFqSnX8OXqPQjH/ycS+mCT6uRcDwGBwGVNAEC5viBrZCUNr97xr3r3+bBxvRvCZNR5hfPHo9I6/KD44EMtv/BxtnojI+qqBeTEvh0hCYHoBKVqm5esnlLQA4LwurXX7TuuWrvT+/OZuFfZJ0JZbcpVW2m7g9M6GhPXr2e4jI0CwBO6yj85zeVEMxnRvXE17YBA4A9NaKBVGwvLNfwLqd7hyZPYeagnGGGNzJfjXaW54ceGaX4tEQ/3JuSM/Hfv6E+pIi++VGW6uhwul7tr164OHTrY7XYAMBqNAwcObDb/OEBVD6ihEf/GNREIo4O463pEA1AWJ1Zoy/Nm+uLIv2c5pYrbW7RFzvBZFLm6Yvv9OGS2zNPoKID3hYIpAh4A+GLYL16yiUpVosOBAOzwli3R6pMczpkqzVKJaPDDJix7sFnbCCLN6ZyoVFXU8G717qLpGQK+R7178ODBw4PZsGFDxU1Pprr6BsEwXtd2hqPnVCu3yj+ZhsvKFoVIrV614hegaV7ntkg9pLx6xinT8H0qafjnSL2zo3iyN/0LPkyvQw3vg2Hb5LKJCvUth+MNpXqzlxQFmKLUJDuc/ji22UvqU9P6c5op+Y4peaD3CBSpU91Yk4ZX7yjOndFw6v1ZA8GQxuujaAetO6RIGRLfPoQRHm8FH0bUydbfBLOmKDW3HI561fDCIT0tN5Id2fmF7y7ivNSc8JWTWr3l2i1Sb8REfMmbI+rjpM8gfjwsbvh9/UeCljehHbR6W1HWyMSQ7TGlP+QaL2gJP2bE8VbM0KefBKSuqFnAL1u27JFGef/99+vCGA//wgxvDAD2jFxcTMhnBLobHcU2R55Ns7tEPj3wfgfGxsauWLFi+vTp7s38/Hzj0nHi2ftrf+pkY/zy7AVypu94/7ej+C0BwODSx5XuBQCFo+SU8nA/+WsPHWSBVl9Rvbvxx7GtXrKJSlWCw5HicP7q7bXLaN5jNstr4RPFR9GtcumbSnWywzleodoql8oxrFy9v8nnVY/j8uDBgwcPHp46opH9rbfuOHILC+cs5LSKwr1lLqXacj2ZtjsIP7l4zKCnbWBtUW4sQACRTakbN13Cnxl+vFVG35uWBGPW0AS/L0KzxyWRepd4qLyeCow/IS6lI3NAvC3NzGrCDTscS3gzcBlxd3JK8aJsoMB3Xt1o+K1y6USFOtXhnKhU4wApDqf72alGTWgmTWtzvzW5DFyc10M24MkNqAQCgcua0CSt2lSYNTJRNiVA8WM+ULT/ojDvd2vljPnigRBI4y3Rh8bcbHRcFx5vRXwYzU62ZoZymAAbvSRvKtVJDudkpXqXXFZjvOeTnp3J8PniHfXGvea/b5gvXC9vZzUNlc4Y99/Jo/EQUKTRmkgAUG8ryhyWAAAvnnqH+wn4Dz744JFG8Qj4OocZGoTyuQhe6fsvfd1Xf1Sl3lr0AAEPANOmTUtLS1u5cqV7U5eV4NjwNjUyDqCGOX47SffcrfTjYYx7QWOBrMYBrEYFttzl2Qs+C/82mBN+oHiHlbQICbHeqT1S+ltH8atCoqYEjxX4UiJCAQZwquYv9cex7XLZfpOlD5uNAYznc2ufyk6Aopu8yjT8RIV6soD3lVbvVu8fiJ5mNI4HDx48PC94JugbHpTL9vlqjmbjPvOlm+bL8WWtCMJp31I6ZRT6nORzLfwss3RFLgA48m1+C+rGq9ldZjyj703zdUPGwHgAeBL1jtwvOLYucCkdGf3jrakmVhNu+LFWhDcDAMTDvAHg7uSU4iXZAHWp4ccr1HccTgDwvb96B4BDJbtNLoP7j/aizjy8rp+FEAhaEQkAqk2FitV5AOC/8L+r3t1YMFjwne9UFryc5my6u3m5LOTfe0ZNcjgTHI7u9ZOVCeVyRK/1QUjKciuNsthQJoMR2kg0sg/hW0O1xf8uFTT8C6ne4X4C/uDBg1Vafvzxx9OnT8fGxr722muNGzfWarWnTp2Ki4sbM2bMN998U/92/udAmIyAH7+skq5D2E9GeDOsySbjeS2/84Mk9LJly7Kzs+Pi4tybluQzy7/839afV1bviSKAo8g/xY7yfDBCQvxFxPJTysMppgQpwyvPmn1BcwZFML1Ty0LZNsq6v2TH5MB3Hmz/oGrSvRxfDJt1n9XyLKdrjlrTlcWaKeSza/KHr6jhv9DoaACPevfgwYOH2uOZoH8qYHye19xJ4rGDbLczSa0BEwlYTUPx+2URe/Zwq3eEgQJNlyy7CwB1ruHtOdYnXHuf3IJLUVAfGexqVO9uxMO8AUHuTkquQw3PRRD+vZfBQxDefV5Ssa3grPo4iqABrEZ51pzfS3aND6iHlBYIBK2IRFBEuanQf2FY/WWte17gochOPy/RL97VZ1X4KLpFLou3OzrUW1pK0/l/1D/tpJ0u9yZltdmS75SkpAsH9xCPG1xPJy3HvaqI13eWyDoBRRqtiRT2knJfEj4g//fzS80CfvDgShfB3r17z5w589VXX33++efljbNmzVq3bt20adO6dOkyderU+jXzPwlaTQMjDFQ2JaB4cbZmd8mDBTyGYbt27ercuXN8fNlk/7Z1q0L9pO6af6Vm8rurxvLqjiFCTGEhDXYKAH66adqXZgEAgK4AXT/NojXYDaaUJgBzAGmjrChgFzV/dpP2CeaE1+3rBQAjTd11ujY5Tccs1k/Ewp41zV8KUHQcn/eJWksDCFF0wnOyduHBgwcPzwKeCfqnCC6X8p5DN9eiBVmlK3IRAgneGo0QSPbYpJJld2kn5b+kbh4DGAGsJn+2MZ7XigbLn8Rz/uP29TKb71I70/uWxb2HH43FZVUr9YiHysEVdXdKSvGSbARHfD5q/CSnM1LUFKXmtsPpg2MojWQ4XZOV6s1eUmG1ymS7izaTNNlN2qeHbMD89Dl/aU51lfUJZD3R2WvkXL49ZZz/2wvDcN5TSALvIOlRh9TNvYgFrzwrtbuj7p+0goMgHetNvdsz7qp+3A4kxe/RUdC/K+4rJ7V601/X9PtP6A+exr1l/J4d6+nUbkZGcmwuekDYfZfoni1QRDRE/rSNqC+Q2lSI6dGjR05OTlZWVvVdzZo18/PzO3PmTD3Y9oySnJwcExODoihJkg1/dlLvKvg4XTLKp8bKnFUoLCxs165dYWFhecuyZcvee++939OtH/ypq+UZCdzUtd2nDsrh3sQRwkU7Q7lN5oUtRerBWy3Z4Vyg1SU7nADQicWcJxYG45Wmmcrj3mUYqiKpxjjujoevc0s8ePDg4X5cvnz55Zdf7tChw6VLl562LU/E3r17R48evWDBgooT9ADgnqBft27df2qC/une359NihZklXx3FyGQ4G0xooFeAKA/ocoem0TbKe/ZQXWl4Z9l1DuKc6elAkDEmda89qL7dcsZn6T9XeHkuUzJru6y/o93Lrd6v+VwuOPeMQQmKtR5LlczBlFFwycY/lmVs5iL8b6OXMvDBbsKN55RHYnkxXwUuvDxTv0ABuxT3VY7z4z2ChY9hdTXBUayy05FAB/7a+wLK8ZqSenitdb4VOGQHuJxQyq2my/eVP6wGRMJAtcvgsctQe/It2WPvkX4MYO3RqOc//RD9XNxf6/VV/H69esdO9Y8qRMWFnb+/Pk6NalWZGVlnT59OjU1Va1WWywWqVTq7+/v7+8/cOBAX1/fhrenwcCEeKOfmtWys7+///Hjx7t27arRaNwt77//vsVi+WTeZ0Imavs/e+cdFsX19fEzM9sr7C69dxsq9horGrFg/6mJMbYYEzVFo4kxxlclpmpiehE1JhbsBey9YAVURKkinS0sy/bdKe8fi+uCgKi06HyefXyWM3funFl3Z+Z777nn4FXGbj4+U663UiOCuUMDKue9cQrfVrSBYt2zUjgKGAtlmUgjTlmZKCtbn35Ffb6H8ysNeGo22rGYO9xc4nT67zXaCyZzdIniDQF/rljIRxBwUO8zhYK3RAL7evhqGp6kiPvGrEBeaGMMMdDQ0NC8MPzxxx8BAQHV1DsAzJkz54cffoiLi3upBDxNNSrVOwsN2NzOpt4BQPyqLPDf8JzXbpeuzwOAF1LDqyyKfwv/6C99tb2os/M4t7Jtxdqz6vtTU0MSOnFCalhMW/LVffVeOWBwetHJ3OLcjqJuUtZTr0mupt5tEdr2nHaO8/A4hccVbQSAUe7/s617j3afdLn83D3d7STN5U7iHs97/lXBKQoAcLJhe6V5OigrbrqVDhgqHjOk2iZ+706xWQ9UJLE4t4AXWH2NQ44V/71CO10kaMWsNXDAkm/KjEoy3zfCTW32uJtBuzu85Bq+5VOvcRpPT8+bN28+PiBNEERKSoqPT10J1Rqc+/fvDxs2LDg4eO7cuT/++OPWrVv37du3YcOGlStXzpkzx9fXd/z48bm5uU3pUtOjSywv/DSTND75ahoeHh4fHy8QPEoF/9lnn3206MMBvqyoII7ji4UhABAmYQz2ZxMU1deHTQkOccWnJYJyiiIHyl79uvXvvZwHACA4aQGAncWbzaSpMc4OBZgk4B/zcJ0q5JMUtUGrG1Ys3683HHVQ74ucRLb18O1YzFwcnyZXyR2+n7tK/onJXGLLnE9DQ0NDUxvXr19v1apVjZuCg4OvX79e4yaal4Ea1bsNm4ZH2Gjp+rzCpZnN5WHjsb0oNqXi2sb8n4yEAeWiQbs6CgdIrMXmzKE3bBXgHSld96BoVQ6CwYmlxzMiMyykJa540zMcdLVac8ti8WUw/nF9lLXOltPOh4GlWaxfllfYjCeUh0rMRR5s74HSKJuFjwlGu00GgB1FG62U5dnOmqYlQ2i0FEEwnMVoTXWX93cL3zG41yd4damWbcWnKZQHDMYLRnNtPdvVO6+TiOnJ1p5XZ4+7SRroKKQWTb0EfJ8+fQoLCxcvXkySjxQjSZJLlizJz8+vbXK+MVCpVJGRkUeOHGnbtu3SpUu3bt169OjRxMTEo0ePbtu27fPPP+/QocPu3bsjIyNLS0ubzKumR/FHQekPecrYwic3BejRo8fBgwf5/EdrxdetWzdjxgwcx2tsfyjL9P6J8nF7S/cXngIAE2HkY4JRbpMEDNEs3/ciXUZQAByUo7aqEkp3Z+rv4lTN/TwnIhRd6iSOc3OJYLMUBPFxWfkHyjKcot4WCe1Z60Qo+qeLtBWTmYvjsxQq27ezxFx0QnEQABLke1QWRWP4RkNDQ/Ni0KIG6GlaDqXrHtjUe+Df1dW7DfGrMlup9tL1ebap+BeGe7rbNzSJAKDB1fHyXQCAclFbIXR7HXh749J1Dwo/y0Iw5PrnSfdevTdYNoKFsq+VX8zQ33na43bjsPtw2Jtdq9d7t9WH781hd2OzwKGy7ySvGZhD7ff+0qHeHH+FpfSY4sCznjpNywVhMwGANJmhprXPw9Nz2Fb8GJf9kUptv5pnW/E3FUolQfowsNqy4lsKHqr3CGHIgYjQw51sGj4rOoXUN52GN+JPXtBN40i9BPwXX3zh5eW1du3aDh06LFu27Lffflu2bFnHjh2/++47b2/vmJiYxvbSztKlS7Ozs9esWZOamhoTEzN58uQhQ4b06NFjyJAhkyZNWrFixfXr12NjY3Nzc23Z2v7rEGXledM+KovdVc0uGecGAPJf8iiiXt/4/v37nz59Wip9lDtn06ZNY8eONRqNjzfu58sOdmZkq6krt+eRVm8AGOM+RcCozBsf7TZJyBCZSBMCyGHl3jVZnyy99+4l9ZlnOr8n04bF/NdV9qXEyQlFKQAEoJggSh2eNZ1QdKOrtB2LaaXA9llsL4rFKZyNciykeVfx343kGA0NDc0LQMsZoKdpUVgKTACACTBWYK0Jq9iBXEzAAABrQaOE4zULJEVuLdwAAN2d+iKAHFMclJuLoRYNb1fvum8siYMuurLcJ3pOi3IdCwBbC/8iqacLOh/H5/3pUl2923DHsL9cpGP4PACwVfbtIOoSLuzk2AZF0MleMwDgUOmucmvZM54/TUsFE/AZMmdSZzCl51TbRGh1e4L8zEwGG5DDBuNClZoAuI/jMxQqJUFyUSQfJ47U9MBvKTDZCkDwIoQhBzthTgx2EM+m4XWJ5Vmjm0jD77hraL+h5Fx+rTECNI9TLwEvk8lOnToVFRVlk81z586NiYm5fft2dHT0qVOnHGVhY3Pu3LmwsLCPP/64jjbTp0/v16/fhQsXmsyrxoMiSFJv1J2/Vm28TRwlYwdwLQ9MmnhlPbvq2rXruXPnvLy87JaDBw8OGzasoqKiWkspF10TqRIKHphMLldvzUPMYUbCkCDfY3udUR0N4bcGABRBcRLnYjylpfSvvO9zDVnPd661ggBE83lfSpwwBFAE2a83DCuWHzc+elygAEgA230yTXvzVsV1DspdErSajXKulJ9/hlFwGhoampeEljNAT9Oi8F4TIh4uw8usmVHJxjs6m1GToNQcrnzqMKbqMocn4WqraIjUc2Vw83nawJxRHS0w5cpYbjN85/eWDMQp646H8fAoFw3a0V7Yz9kqt2SOTC74KMOm3t1/D9zZ618AmOQ1g4EwX3UZI2O55hnvny9r+ATPecb7F9QnGQjjf57TH9/aWtC+k7iHmTTtLvmnwQ/dNOitVKrC6vi690ADABacrGZPU1rJl2rWFkEEA3sCgOqXf3G5ym4mjSblD5vn7zzMICkzUGwEOWowvq0se1OukhMEF0WMJNWKyZwiqF6z6XH1brM3vYbPUuMkBVnqRonnfVGpbz7J0NDQ+Pj4jIyMtLS0oqIiX1/fNm3aBAYGNqpzj6NQKMLDw5/YzMfH5+bNm03gT2PDcJEwXCS4osySV4QwGObMXMpixaROnDbBLnN9ChZnyH/JdxpV30Qpbdq0uXDhQmRkZFZWpdg+e/ZseI/+83/aI3SW2XLaXS22YMm665o8mXOuFeeZTC7nb85Vm351EqdX642gCAAwEcbpPvNKzIVe3MYtDeqEoQggOEUFMhk5Vvy62WwrMqcmyelyVbrVGspkUhSxrWgDAES7T/LnBb/qMnp/6fathRuWh3yLIs+YlpOGhobmBcY2QP/BBx8kJCSkpqba7dHR0d98801TDtDTtCgQFhq4JTxn6m1NvDJzeHJIfIT2rLpgSQYA+HwTKujjnDk8CVdZRUOkgVvbo5wX5A6rJ3T7SrcBwP8832QirPEeU29oEpM1V1K1ye2EEQCA8rCgnR2yJ9zUnlXLf81HMMTvzzYJvffrlNo2gvYdRd0AgIWyxnu88duDb/eW/NvVqTcPa8hit7aJfRnL9ULZSbuRJJECjcjbqQJFKCbCBIBLZacHSoc9Q7nf8/nmFRcqiKqTRiV6AgDejC9jVg0OiPTnfNqrgUv3vX1EfamwhplYuZGK3l19yurDbsJ3Owkeb/yiIo4ebLyRas7OK3x/Na9re6aHC6GuMFy9RWh1PZ1E69ms93HcTFFMBLlgNAGAXb3HulYvQ1iberdh0/AZw5JsGj54X0eUT+e0a1k8XUGI0NDQ0NDQRnKlPvTs2fP48eNZWVnBwbUO98rl8iNHjvTs2bMpHWs8OG1DdGeuyL/5Ey95dOVCOWzBkMGYkKe7oNZfq+B3re8F1N/f/+zZsz37R+ZlptkseXeTl742xOPdLQyZLwCczzefzzcDhANUDpQQBDv17nsTO6V29C6x96OyKK6Un8cQbLTb5L6SwY6H0OHaXcV/txa27+bUpwGTwHdgsX6SOi9QqXOs+Hg+b7GTGADKSHKGXJVutQYwGH+5SE4p4wtNea5sj0GyKAAY5jr2gvpknjHnovpUNSdpaGhoaGy0kAF6mpaGo4ZPH3SD1FfOj+V/lIHxUEJPvmDqHQD2l2zX4RWtBOGdxT0BQMRwGu46flfx39uKNqwM/cG24Nym4XMm3dKeV/v93sY0ynom4xiKYJO8Ztr76ebU57TqcLruzsHSuBqnyp+NMqvSFlQot5QkyPfY7YXFA7JyB4cG/uvhVhl8SgF1pfz8Mwj4Ih2Rq6l5IrTksZnY9LKGnzId5M+usJBAUXipkjQYAcCKse7zpEySCNDLAQAVCRkyZwDgMZAu7qwGd6Alg7BZbsvnl22I052/rr94w27ntAmRvfOaj7vsR5N5nrLM+nD8xa7enR+rLZf37l3zfSMnhBcS3wkT1Re4LkcAACAASURBVCAGbRr+bq+rusTykm9yPVcENd550TwDNQv49PR0APDz8+NwOPY/6yAsLKzBPauRBQsWHD58uEePHsuXL4+Ojvbz83PcWlxcHB8fv2rVKrlcPmPGjKZxqbFherkCAF6iRAU8bsc2KI9jLSgx3c2uOBDP7zSw4qy4eHV28P6I+nfo6el57uzZ/kOjcm9fs1msJZmq74YHv/On1qdHVDDXR4hprOpL6tMUQAAvVKOT3iwWbr8R7sYTtnfX2nZxYblnG9KVFnmxuaBa/zmGjHNlx8+VHT9UuvP9gM+eoZJKbfTjctZLnReo1Lv0BhGKzhQJbOqdhyDuDKwA19nSukzxnMlAmPBwFPz3B9/tLt7SRdyLi9WQt5OGhobmpUWr1UZFRY0ePXrhwoXNPkBP0wKxafi7fa6Z0nQA4L7YH8HQ4jX3CT3JacVvaerdkm9iyFgo9xldKjYVnFYdRhF0soMUH+Iy6kLZiWJTwWnVkcEPq7ujPCx4X0dcjTOkzO9yVpAUMVg23JtT5Yl0sueslZkLTygP9ZMOcWd7QUMgYcpm+b7/+Pr2Q2qfLIBQzitDPSrH3RBAnq3Q7/9a8/r7si1VpfqbCarccmJTlNTf6dE0LIqAeyPMyr4Zzn8znF8el1B+OgETC6Vzp5SFtuq3VeEmZu0MM6tid1Jmi8t7b/L7dmnwQ/8nQPlc2YJpTpNGmG5n4GoNyudyWgex/L1tW30YGB9Byh0CKDwYmKimyvCiSGnFyTJzjlGToJRMcq/xWMrYQlJPICxU2M+5Mc6F5nmoWcDbispcvny5e/fu9j/rgKopI2JjEBkZ+eOPP773ELFYLJFIRCKRTqcrKytTq9UAwGAwfvnll9GjRzeNS42N/nIKAACKeq1fjokqI4VM97Lla37HrBdQ3oiKk2W6xHJBT6f69+nnIUtNPD1u3LijR49WHqVcdfuriU6jlkwY/tkrPuyN+ZuLhZVrt5xkEMwemFc05LRqXzJefSgnUX32Na+3HLVxe1Hn6T7zDpbGFZryHhhzGlDAg4OGj9Xq9ukNZSQZwGAYKCrRZL5sMrlwxg7EstuLHl3Wuzv1Pa08kqG/c1C+c6LHtAb0hIaGhua/jlAozM3NPX369MKFC5vbF5oWiuLPQtPdyjXwytgioCgAChDElK5Xbix0ndtS6hSod5bmzr7DCeWHxEcwXJ5lYnZ7USxBEQOkr/pw/O1GBsKY4DHtx9w1+0u29XDqayu6DgCAIgwpM0lz+Y42xVapp1pvvtyAvpLBZ1XHthdtfD9g2TOe1WP0cu7/uPHeA+1R0IXy20S5dnv+Q7g9JsuZKAIA7gLUR9QUcdQUTlTEnwYA149ms1sFlmkrhxMEg3oCiih//kez//hLK+BtMFylgkHVA43v4/ibclU5SbIRxExRAMBCkNNG00KV+jupc7X/Odd5vhROFS7LejAnDUhKMsWjWm9FK7JLf8iz1aEQDpA04snQPBM1C/hZs2YBgItLpfR6++23m86jJzF37tzBgwf/9ddfx48fv3v37v379wEAwzAXF5cuXbqMHTt2+vTp7u41DyY9kQcPHtg1bW0UFtareFuDYMkttGTnA4YCQZpzCphSMWW1YhInTqsgybQxyl/+5Xco1Sa6Fn2WFXq8y1OFq/P5/P3797/55pvbt2+3WUgCL9sb8zW7sNvGXwfIhmEI9jCtO4AUoN0pAD8Av2r9uLE9H5/Z7isZ3Mt5QIm50IvzaGG8Dq9goRwW+rzxTv24nBiJ02KVuowkxSi62VXKQpBvy4r3GAk5u8cepAe3vOItkcDp4YjjFK+Z/5ex8ITi4CuSSHe253MenYaGhuZFYvXq1bNnz75161b79u2b2xeaFof85/yCJRmAgPc3odqTZZojSgAQD5OJhsryP0wvWJwBAC1Bw6t3lubOukMRlDFNZ1ux/7QaPqXi2m1tEh8TjHGfUm1ThLh7O2FEqjZ5X+n2173esttxCt9Z/DdUrdTjyBj3166VX7xVcf22NqlaxniaOrDkFpB6I8vXk92q+kIe/itdy2J3WXILSZ0BFdBhlY+wqXd71jpvBkNOEBaqMqcdADyu4d3e9wOAwmVZD+beBQBHDV+0Irvk28oqkuIRDTkPBwCnHpjlVZdjpKtwAEgutWxPq6JkAp0Z3TxerlUS9admAf/nn386/vnrr782iTP1JSQk5Kuvvvrqq68oijIYDCaTydnZGa0pRORpWbBgwYED9aqf6Vhxp/GwZOcBANPT1ZpfovjyN+ph5XZWoI84ejAAsGQpTLcRusua8gNyp2jXp+qczWZv3bq1Y8eOS5cutZ9O/PZNvdJv7ty5842gubXtaMIpkgIes64BAwzBHNW7Fq/46O4sJsIaKIsa6hL9PDldykjyrwqdraSchiQ3afUfOYmEml87GEqsznPTKI+NWt0uvWG6kD9NKOAhiC83sI9k0PmyE3FFGxcEfPrMx6WhoaF58RgxYsT3338fGRk5c+bMLl26eHh4VLuZ2gLxGhuFQnHp0qX6t4+Ojm48Z2hs2NW7z9owl9neLjO88j5MR1DE57tQhIUiDCRvwb2WoOHt6t3tA7+Ko6pn0PA4hccVbQSAUe7/ezTH7sBkz5nLM947ozrSTzrEPj9/Uhlfai4CgLNlxxxTyj3O9qLYdmERDZgV6MWG0GgBgOFSw6wvgmGYREwWmgiNlhbwjsxXljnmnI91ld62WOcry8wUxUKQowZjBIs5TVg94V+NGr7o/xpRvd+UW2cfrrnGYUK2KSG7SkFKBCDxDTcXXgtap9NyeLokdi0NBEH4fD6f32AZPhctWvTE2Xu1Wr1z504EaYoLMWk0AQCu0gAAheNMT1eEw8FLFZacfMW6jYAiFG5wXxKQ/2F64fJs8TAZwnq6bzmCIEuWLGnTps3rr79uryeXnJzcpUuXDRs2jB07tsa9JuxTFeuIn4c4d/es792Ri3GDea3SdLcOlsYZCf0Ur9lP5acdx6x1b4sEn6k1sVpdqbmoTJcqRFjviRkliHmTjnHNCus12s0VFf/j4VEcvLO450X16ZSKa3e0KW2FHZ/t0DQ0NDQvHjKZzPZmzZo1NTZomiVyd+7ceaqFb022cO+lRbmpqODjDEDAd30r2XQvAEDYqN/Pre0NZG96Ak7lfXCvYHEGJmBIp1YPwW0a1HtKc2ffoQjK49NAj08C3N7zzRyebLyjyxiaFHK4E9OtXk8pp5QJJeYiAEjXpWbp79XYhovxdXhFXNGmhYErbBaFpdT2Jt+YW3f/5dYyK2l9/gjElwRMwAMAXF29yDEAAEUR5VoAoNV7NXpy2HKC1JKkPWvdKxz2jzLJfGWZhaLcMSycVfPXr5qGN2UaSr7JRVhowOaGV+8A0ErKmN2Rr7VQAJBZZr1RYg2VMBGESlfhEW6sMGkVWerOx6TPmtLihecpBLxCoeByuQKBAADi4uLi4+NDQkLmzJljj7RvXt54442dO3cajcbn6aRv3759+/atu01qamqTCXhMIgYAymDERALRqEHi0ZEAQOGE9uj5sr/3AEGiQoFshpfi9wJTul4ZW+jyduUouOaIEldY63lDHTlyZGJiYs8hoyoKs22W8vLycePGzZkzZ+3atTxe9atkqISRprROO1T226vO/X3Z9TkEA2EuClqZpb93pfx8z5pWcNUHR/W+2VXqgmFiFF2gUsdbRJ68sX6GPd/mfA4ADIC2zJA87igNI+gPPXOTVu9lPONGAQJwoHQHLeBpaGho7CxatKi5XQAA6N+//+3bt1esWLF7924AmDt3rlgsbm6nXmr0VzVAAdOVJRpcaylB0RApQ8bCFRb9VU2zCHj1ntLcGXcovFK9AwBDxgqJj7Bp+Mxh9dXwdtGepLlSd8tsfToFlG0ufYrnzFckkSQ8isdM16XuKNoYKmg7ybNKHmUp06Wh1DtBQexNvdpcJQj0WrEFAM4XmPV4lYEtGRd9M5yPPvfjqq2DJnnsBQBg+XsjbJblfr4lr4jl68nCEACw/Wu4fpvUG5geLpi4hjULLZx8LRGfZZwWzucyGv6jvGW22tT7RlepfQ3pKxz2Oqnz+yp1CUHcx/FO7Fo1PGWhilZm585JA6oydaV4uKzBnQQANoZ83KMyyGXzbf2NEmtPLxaGQLoKjwrizGjfkDUXX2zqJeANBsP06dPj4uIuXLjQu3fv7du3T5482bZp48aNly9fbgka3mq1mkymJ7f7T8HwrPxgXRa8yelYmUoQYWCi4f31Z6+Yc/JRNhNhID7fhmaNTSEe5vlQ/V30YN49IClTht5rVa319hxp06bNtN9Ob/z8HV3SIbvx999/P3/+/NatWzt06ODY+JsBTq487M8UXXY5Xk8BbyOY3yqYXyUhYlzx5hx9+ivSyG5OfRlIXd/GiofqPYjJ2Owik2IoAPTjcr6VOr+vUhVxBksYgnDrxYfNyfbWfcVkwC1G7zLU7T5/soIX1Q6/FC6gnwhpaGhoHvHNN980twuVtGvXbteuXZ07d05KSlqyZEm1KjMvG6RWb7yVjitUCIPBCvThtA56pJ9IkjSaUC4HGmLlYG14fxliuqfXX9VkDL0RmtCJ5c+t1sCSb8ocnoQrLPzuYq8v6vWk0bA8rt5tPIOGn+bzTg/nVwiqepm0x/HhBtgj4VEE8+U+Oi5OWX/N/RoAMnR3tLimkRa9Z6nxLy/XNDUNcK3YYlPyjgzyY/uJnzfYdmwo71qxpWky2AEAwmYJB/eqiD8j//oP14WzXAO8vx/s5CnAjDfvKn/5FwBEwwc0jScNy2/Juu1pBi8hNjK4+q/p+QlgMngosk7q7FT1sjCQy/lB6vytpkJW5+XCfbE/ABStzH4q9Z6H439r9VOFfD9G9e8YBbBJq+MgyGQBrckbhXr9qteuXRsXFxcWFmYT6t9++61UKv3rr7/S0tI+/fTT77//PiYmppH9BAD4+eefd+3aVdvWtLQ0ABgw4NGv+vTp003gVaNifVBse6NYv0kyfRyvWweEzcJLleV7jppz8gHAViRTOEDSobAfysMAQLWl2KbeEQZSuu4BANRTw3851PvtHnuO//vL4sWLLZbKe0BaWlr37t1XrFjx0UcfYVjltRtFYEkP4cwOfFntkS3fXdX+maL/aoBTdAinjoPmG+9n6NMy9GlnVEeXBn9pt5MUcVub3ErQjo1W7n7SaKqm3m1EcjnfS6UfqtR3GT22+Y9hVB0ipgBOGE3rNRVZVvFl5rAF4kYZUKShoaF58cjMzCwsLOzfv39THnTSpElJSUmN0fOVK1f27t1bdxuFQgHNHZ9PEaRmZ4LmwEnKYrUbmZ6u0rmvUSZzxcGTprQsCicQBsZuHSweNYgb0aYx3MBEjOB9HbNGp+ivajKikqppeEu+KTMqyXzfyO8uDt7XERM29XpM9a7S3JmPIuerbWXIWCGHIjKikkx39Vkjk0MOd2JImXX0xscEncQ9ntOlY4oDCkspC2VZSMv2wtg2YR1sdeMbljAJY90gp2JdlbGG8wXmxEJLX292T68qQxV+Ysbzq3cAmN2RP7tjk8owp8kjzen3zVkPihZ/xfLz6uYswktVpUWlAMDr0VE4pE9TOtNQWAkKACxPHiZ6Fr6U1FqLagCXM4Bb16O4DffF/pzWfKYri9+9vnNdp42mf3X640bTZlepv4OGpwBWqzVbdXophtICvpGo1w9727Ztnp6eycnJXC5XLpcnJSUtXLhw9OjRo0eP3r59+6FDh5pGwBuNxjNnztTd5okN/luQWj0AMF1lVrlS8cNmQBCExaTMFgBAWEwKx0mDiSJIBENRHgYU5M66U7azBEjwXBHEDRfmTLlVuu4BhVPea0KeeCxWBS5elzt7/Iy+iX2nTJmSnl5ZMc5sNn/yyScHDhzYtGmTY5VgR/VOUlBmIh0tp/PMVpI6ft9Yt4B/L+DTy+pzZ1RHXVlVUg/sL91xsDSul3P/Wb7v2yzDeFwA6MflSB4bRIzkcra4Sg0kxXgswAsBiORyBnE58QbjHYu1FauuWzgNDQ0NjQ2KomJiYnbu3KnX65vyuF27dg0ODmY8Np/z/KxevfrQoUNPbtfcAl71yz+6s1cBQbgdW7P8vSiz1ZB8x1okL/38B8qWbhZFUT6PNJpMt9NNt9PFowc7v94odXOraPhhSaGHKzV8E6h3E2nclP9zuLBTb8nAGhsUfp5FEZRooORx9W6D4cIK3BKe1u2KMU2n+rfYbYFvjc0aigq8PF6+GwDe8Vu8vWhjsbnglDIh0mVkYxxrVEj1+Vs9TiUWWrp6sOZEVM9S9h8F5bDdV75fviNee/S8JbcAcgEAMLFQFD1YPGJAo8aevMw4jXy6eOr/CfhnTObLJvM0ucqu4e3qnY0gX0noAvKNRb0uu7m5uUOHDuVyuQBw+fJliqLsQ/KtW7c+fPhw4/nnyKJFi9zc3ObNm0dR1Pfffz906FDHre+8886BAwcKCgqaxpmmARXyAIAV5COeOEx77Lw5K48yW1Ahn9e5nWBQz5LPvkf5XOThdLT8x/yyHSUA4Pa+n/sifwAI3No+Z8ot+Y95AFC3hsdV1szhScZUnTK2KGxfxxs3bixYsCA2NtbeIDExMSIiYs2aNfPmzXs84f+axIpNt/XvdhLM7yLEHET0E5+CGAizj2RQH8kgR+Md7c340l0AkKg+O0A2LIgXBgAcBBnDrzVnScdaknPYQAFG8rgjeY/ueVqS/Lq8oiObFc3jPi77aWhoaF4SKIpasWLFP//8I5fLHe0EQRiNxvDw8Cb2p3///pmZmY3R87p16/r160cQdc1/lZaWrlu3rkGK2jwbhhupurNXUS5HMLiXNb/YcPUWwmSyWwVhAoE5KxcAnF8fLRzSG+VxSYNRdzJRvfWgZt8Jpo+noF8DFAB/nMc1PGBIE8y9HyiNu1p+IaXiaitBuJRVg6jwXBb04O20ilNlJd/lui/0f7wBrrLen54KJMUJ40snP2Np4fqzs/hvI2HoJO7RXtQFAL6/v3p/6fYezv2ENeW0p6kPCIvpPHW006QRxpz8z1KRUGfmnP4etHRvUXAQ5FeZZK6y7LLJ/LpcudlFFshkfKHWbNXpWQjyvdS5N+fRMtsSPaE0VMndYAskURlJm3Ao0RGpCisAiNiob1Ot1/jvUq8rr1gszs/Pt70/f/48giD2ujJqtZpVp3ZqWKZOndqnT5+pU6fOmjVr3rx5X331lW1YAQBsuda8vLyazJkmgNM6GACMKXclMycI+ncHiqIsVoTNAgDNnqMAwGlTGR6v2lJcsKzyoceQVFHwcaa11Oz3U+v6aHi7ekf5GKHFs0anBO/ruGHDhqioqLlz59pCCgHAYDC89957O3bs+OOPP9q2bevYQxsZEwH48Ybueonl+8HOdYTW14cdRRtsWWEooLYU/PZ56NoGL76SYcV36Q279IbfKrQzhYKxfB6LlvE0NDQvH7/99tvKlStFIpGHh0dmZqa7u7u3t7dGo8nMzOzevfv69eub28EGIzg4+IkZ+1JTU9etW9c0/tSI7uQlAEBYzIqDp+xGy4NC+3uWnyfK4wIAyuOKRg5EhXzlT1s0uw43koAHu4aPTtZfq8gYkQwUZXlgalT1LreUnFAcAgALadlZvPltvxr+1yST3REmkjvrTtHn2YBT7kuqzMMT5XjW6GTjbR0nhBdy6Klrwj8tucbsS2WnGQhjgscbANBe1CVc2Om2NmlvydY3vN9u1EO/8CBMRrmX777Tch8TNodW7y0PRw0/TaHsxWYfNBhZCPKD1Lm/Q9x+uZkctE1hwmuY1juUVZl9fMMt/YZbegBAAA6Ml7WR0TGzdVGvH0N4eHhKSsqDBw8qKiq2b9/epUsXW+GZ4uLiixcvBgYGNrKTVQgICDh79uz//d///frrr506dbpx40ZTHr2JYbhKed3ak0ZT6epfrAUlhis31f8eIC249uj58h0JACAaMRBs697fvQsk5f5RANOVpT2nlv+Up95ZmjU6RdjHKXBre4SNyn/MK/ikhmkNXGmxqXdOCK9tUk/JZHdCi2eOStZdUI8bNy41NbVaaZ9Lly516tRp+fLlZrPZbhwTyt0yUurKQxMLLVFxigsF5seOU18y9XcLTfn2bD15xvuXyho+l0FnNmut1DmYySjAif9Ta4YUyzdrdUa6LhENDc1LRmxsrEAguHfvXkZGxtSpU7t06XLt2rWMjIxVq1YplcpqY7U0jY0lJx8ACI2W5e/l8sEMr/XLPb/52DFll+V+vmN7Qb9umJPQWqzA5arG8woTMYL3R/C7iiy5xsZW7wCwvXADTlk7irqyUPa18osZ+rQamzmPd/P/qy3CQIpW5ZR8dd9uJ8rxzJFJhmQtJ4QXktCJ6fEUqXafAQqobYV/UUANdRntxva0GSd5zcAQ7KzqaK4xu1GP/jJAUgAA9Xw+W6nWzFGUaUny8U2XTebRJYoLpmd/QH2xMaXrU1tdTB94nSjHn2pHm4bvwWGrCPKgwch8TL0DgICJDvJjt3NhOr48BBgASLloGxnTT8xo+9A+yJ/jKaBn4J9AvQT8okWLcBxv3bp1YGBgQUHB9OnTAWDr1q3dunUzGAwzZsx4Yg8NC4Zhn3322YULF6xWa8+ePWNiYuoOivtPI50zmenpasktKPwgRvHT3xUJZwpmfqz6cwdFEE4TozhtQ8r3y23q3Wt1sMeyAHYYDwAQAKYnW5dYXqnhN7dDWKj8x7ySr3MdO8eVlswRyTb1HnK4E9OL7fdbG8lkd1JPZI27qbugdnV13bt3b1xcnEQise9lsVhWrVrVrl27kydP2o3dPVnxE1z6+rBVRnJGfJnC8Cz/I/YboWPwvS0yjQLqqGL/+bITFrJhLr7DeNz97q7rpM6tmMxSgviyvGJgUenPFVpNTdd9GhoamheSnJycPn36eHh4AED//v2Tk5Nt9o8//piiqFWrVjWrdwAAn3/+OZP5skzF2BLfsAK9Pb5YxO/dienpygrw5vd+lM/cVgT7EQjCcHcBAEKtaVTHbBpe/KpMPEzWqOo9TXcrpeIaB+VO835nmMsYCqithX+RVM335cc1fBOrdwC4rD6bqb8rYjhFuY61Gz3Y3gNlUY8eaWiaitsWyzmTaaZCVU3DXzaZ5yrL0q3WB/jTqdNno1RPvHZQFb1b6fg6+cAEAOuv66rZPzhZTjb3d8SUrs+MSrIUmPRXNZkjkuqp4c0UdcZoslAUG0ECHiYu4SDgw2AAwD2rNdNa2Q8DhfWRzvvHyRxfszvwAWBEMPfgeNmpyS4HHtp/f9XZiUNHWzyBen1AkZGRf//9t4+Pj8lkmjFjxuzZswHg2rVrBQUFc+fOnTlzZiM7WTPdu3dPSUl5/fXXly1b1rdvX3uQ/wsGJhZ6fPmRaFg/hMWkTBYAII0mpo+H60eznCZGAYDhphZIChNgwkGSB3Pv6s6XI0xEPMol7EQXlj9Xl1ieOSqZ3UbA9GQDgCH5UfURXGnJHO6g3t3ZAIBgSDUNDwATJky4detWtan4rKysyMjImTNnKhSKxELzvgzjuXzzqGBupD+HBLAtdMmrIPZlGO2v/ZnGuyor1M5Z1bFcY7YtYH66zzwGwkQAqcDLD8v3qK2qHUUbN+b/9GHajKvlFxrks0UBXuVx97i7/CqTRLBZ5ST5k0Y7sKj0q/KKkhd3SIiGhobGjsViEQgqE1/5+/sXFRUZDAYAYDAYffr0OX78eLN6BwBAkiTeJM/cLQHbYzy/d2fEIeWqLWYeMBQelp5xhKzQAwBSjyzTzwkmYgTt6hC0s0PjqXeSIrYV/gUAo9wmipnOw1zHSlkuecacC+qTte3iPN7N75fWCIYUrcopWpmdMeyGIVnLCeOHHOncBOrdQpr3lPwLABM83uBiVdL0RLtNEjJEmfq7NzSJje2GtxADAG962TDAjzKJL4Nx22J9U6Gyz8dcN1veVZaZKGocn9c0SdELtcTlQkuqwur4KjdRAFCgxavZTz0wWYjmVPA29W4ttQj7S9hBPEOKtp4afptOP1dZ9q6ybJVas02nZyFIGIupJalpCuW/Wv3EUuV0ubIJ/H85qe9VeOrUqVOnTnW0zJ8//9NPP7XF0jcXAoEgNjY2Kipqzpw5ZWVlzehJo4LyuJKZE5zfGGO6nV665jcEwzxWfYAKKm8VHh8HGO/oNPHK9P43KDOB8rHg3R0EfZwBIDShU0ZUkv6K5m73y5SR5LYT+P7U2rZXpXq/U0W927BpeAAo21aSNe6mrTcvL6+9e/fu2bNn/vz5RUVFtpYURcXGxu7cs5f96kfCvq8jaPWvU5rKuvBUeTXjmckuPjXVNTEShv0l2wGAAqqrU+++ksHF5sIj8r0AcESxr7dk4Fy/j44rD2bp7903ZHZzarAiIghAfy6nP5dzzWz5s0J73mTepNX9o9NH8TgzhIKwl2bmh4aG5iUkLCzs3r17tvcBAQEURd28ebNnz54AQJKkvRwJTVNiTEoTjxxkz9fF8HBBuWzSaAYAtKpQtzwotBaVonwu09Ptid1ai0ptqXAZLhJO6yBbPp0WxUlVQqEpz5XlPthlBACwUNZ4jzd+f/DdnuJ/uop7V1PIdiRTPADgwdy7thhDThg/JOHJtd8bhHj5bpVF4ccN6iWpXpmch/HHuE/5u+C37YWx7YWdWWgjjiZMbMXr78tx5dGTluCOYZtdpdPkqjSLdYZCFesizbTicxQqA0WN4/NWSpya5jPq5M46NdlFa6kiy3+4rj31wPxeF+FAvypfBjc+xmE0WxomU6Yhc0SytdQiGigJ3NGB0OKZUUmGFG1GVFLIoQiGpK5n4IFczl9a3QWTGcBsW/feg8O2rYdfXa4BgAnCF6QsQgvk2b/JgYGBzave7YwfP/7WrVs//fTTTz/91Ny+NCIIk8Ht1JbbsTWF47ozlx/ZWWjApnZMbzZlJgBBfL4Ntal39e7SOxGJ0knu2HT2ewAAIABJREFUCBOhjCTKwwLjOlSWQiWpzFEpxjs6Tit+yNHOjuq9sk/bPPz/3Ek9kT3hljnbYLOPHTs2LS1twYIFjkl6teVq5falhasGWu+eYjMQ28u2hh1FwG5hMxAOA/EQYDJezYPE+0u3a3A1AggTYU3wmAYPx+ABAKfwXcVbujr1Xhr85do2sRM937TvlaG/s6v478eXmZlJ09N+wl3ZrD9cpHvcXKJ4XIqiDuiNY0oUcxSqCjqonoaG5gVlwIABqampS5cuVSqV/v7+Uqn0jz/+AACj0Xjy5Elvb+/mdvDlAuVzAcB0J7P0i1/NGfcpnCC0Ov25a9TDEFsKffSgby1WKNbGAoBgYE+EUdfsq7WotGT5D4ULVinXb1b9vq109c/5s5Zq9hyFlnR30xO6gyVxADDJayYDqZQN3Z36hvLbVuCag/KddewrmeLh91sbBEOaUr2rLIqjin0AMMRlpNIiV1hKq71aC9rLWK5lVuUxxYHGduaFUe8UgMZMOr60FhIASKq6XWepedbapuF9GYw0i3WSXPlWk6t3G35iRrUl384cFAC8hFg1u0vz/d+ZMg2Zw5KsxWabeke5KNOVFZLQidOKb7ylzRyRjJfVFTbrw2D0ZlcqiCAGoweHzUGQ1wQ8mwTgoMgIXvWShzQNxVPEQV29enXLli0pKSk6nS45Ofno0aNeXl7t2rVrPOfqj5eX17vvvtvcXjQFwiF9jMlp2mMXRcMHAIJQBGG4kVa4rNhagAEGQFCFn2XzIkTcdgKUg1Fmsvir+0ABwkJJA5E7PTV4fwQmwCicwkvMAMDy5TBqmgwHAARDOG34AEDoCVyN2yW+WCz+4YcfJk6cOGfOnDt37tjbW0qy8te/PmrUqG+//TYkJGTELuVdpXWIP+fnofUqAik3F59SHgYACqhXXUfLWK4AwEG5Y91f25j/E4IgNzSJd7QpbYUdnZgSxx2PKw7d0CQmyPeE8tt8FLQKQzAAOKVM+Lfwzxk+82srIVsHrVnM76TOH4pFm3W6XTrDOZM5w4p3aXkzFTQ0NDTPz/Lly+Pi4tasWePn5zdnzpy5c+euXr06JSWlrKysuLh4zpw5ze3gywXL19NYpkG5bGPKXWPK3SrbUBRIUnf4HF5QynCV4PIyU2oGRRCsAG/berrasOQVlXz2Pak3oAIet0NrlMexPCgyZ9xXbz1oLZTL5k+tY9+mZE/xPzpC20bQvqOoq6N9itfM/8tYeEJx8BVJpPvDLHGPI5nsLujlxHBhorVMEjQ4CfLdFtICAH/mfV93y3j57qGu0UyEfpB4Mu8dL4/Prr5OBACKdESnjaWOFgRg5SviKW1qiMuwafhJpcpcKw4AI3ncJlbv/wkq1XuJWTRIEri9A/qwgJRNw2dGJdk0fB3z8Hv0hgMGIwtBuAhy12qdpyybJOAtVJVTFHgwsGKcmKcsO+zh2oTn9BJR3+/z6tWre/To8dNPP124cCElJQUADh06FB4evmbNmsZ0j6Y6vM7tGDJna1Gp6W6W6XZG4bsr8uac0V3CEAYh7HyO6VZszyrP7yZiSJlAAYIhAVvCmV5s/RVN3vy7AICw0JDDnZhurIpjquxJt0hTDWPwpevzij7PBhTx+7U1v0v1Qqa9e/dOTk7+9ttvhUKho/3AgQPt2rVbtGiRVV8loU6WGn81TrH2mtZaS6aOrUV/4ZQVAJyZUsdMMH0kgwJ4IRRFAcC2og0kVX1p+hSvWUNdooUMUZG5wNZDBa7ZXfIPBVRc8SYDoa/f51odLwa21El8xtNtu5vMUb0/wPGyljRlQUNDQ/M8CIXCGzduxMTEtGrVCgCWL18+ceLEe/fuFRcXz5gxY8mSJc3tIHz++ecm01NHVP1HsVWDQ5gs4eBeTC83hIGhPC4nPJTftwuQJMJiAoIYb97VHr9ovHkXAISDe7uvfB+tYwE8Ral++ZfUG3jdO3j/utLlg+nSOZM9vljo/vkClMvRnb1iuJLSZGdXB0Wm/LNlx1EEm+w1q9omX25gH8kgnMLjijbW3QnLj9Nk6h0Agvmt3NleLiy3J77aCSPsMQU0dePKR8XsKi8hCwUAFIFqdicO6sSuVcUU4IQ9fDIbx2vMS/8yY0rXZw698bh6t8F0ZYUcjGAH8Yy3tFmjkmtbD+/PYHRksX6RSba5yVww7KLJ/J5SbaGo6UJBgrvrcB63O6euQSsWhgAAi07d8EwgVD1KZx05cmTYsGFBQUFff/317du3V6xYQVHUhQsXJkyYUFJScuTIkaFDhzaBry2E1NTU8PBwFEWbK/V9eVxCeVwCJzzMfDeLwgnC2EFzNhAQkI7XgOmS9nKEReHOkLEYzgxTpgETYoSWYPtzrQoLqSfc3vP1iqmsBm/PWiGKlAZua486pHwsXZ9XuDTTpt6lr3nU4UxxcfGnn366efNmsurFkSWSioZ/NP71Gb9GuQBAUoll0n4VQUFrKXPrKImo6jX3tjZpXc5KQAAoGO46rrWgvePWIlP+tqINtjyuU73fHiB99XE3CIoggbANb28u+OWs6hgCQAH0lgyc6bPgqT7eOpATxMCiUgxBhvO4bwj5rejl8TQ0LyuJiYm9evXq2bPnpUuXmtuXhsdoNKIoymY3eg6wlkaz39+BokpX/2K8eRdhswR9u7ICfSiT2XDtluluNiCIy4czOO1CzPdyiHIt5iRkhwVioicsMbXk5BUt/hpzFnv/vMIxMR4AaI+eV/25g9uhtdtnzR/A+F3OijvalMGyEVMeE/AAUIFrPrk310gYPghcHi7s9HgDmheYAi3R71+5txA7+1p953Kvmy22de/DeNw7FmsejrdhMWNdpOJmrSS/+HT57nTj1wOcxoU1f2B5ZlSS9pyaE8ZvdaFbNfVux1JgSut8uZp2qI04vX5FWQUFlBcDO+TuykGevKq/REd8clbzfhdBhyZZ81J//hP393qF0K9du5bD4Rw7diwwMLC4uNhm7NOnz9WrV/39/b/77ruXSsA3O4JBvcp3HzGlZgBFCfp1Q4V8iiyqOO+p2iV2eWOI06CL5SfBonDHlRZuuCBwW/vM4UnmXCNQIJni7rUq2N6PbalYZlRSxXFVzuRbdg1ff/UOAB4eHrGxse++++4HH3xw/vx5u91SoVJu+3jHxdgh3385ZsyYTu6sbdHSJac16WXWEj1ZTcAflu8BqEy/Gy/fHS/fXdvhEuS7axTwGIJhgAFAnjHnfNkJDBgkEADUxbJTUqbLaPfJT/5Y64Ezig7hcY8ajHv1hr16Q3c2e6qQP4BLF7ugoaF5oeBym//58iUFQVwXz1b9uUN39qr2xEW7GRXypbP/x+8ZAQC8ru1r37865uw8AOB2aoOwmBRBWgtLKIuVIXXCnMW8nhGqP3eYsx40+Ek8LUmaK3e0KQBQbC749cE3NbbhYXwjYdhRtLFtaEcUoe+6NLViV++2de9ygnDMade8Gh4Ami1bXVUkkz2059WmDL1yU6HrXJ8aWpBUcUwOqSdQPuY06glDJ+dMphh1BQUUF0ELcWKesuwnmeSJGv5UnvlcvjlUwmhpAv4/Qb0EfHJycs+ePQMDA6vZfXx8unTpkpqa2giO0dQKQ+okHNRLe+wCKuTpzl8HkmTwgdcq2HAvXLEZFXZxE0RcMeb1ZXoG+29sZ8rQ4worAAACeKmFtFAo59Ev6nENr/ijoP7q3U7nzp3Pnj27c+fOJUuW5Obm2u3qvIyxY8f26NHjyy+/7NevX8JEWame9Hms0km4sHO6Po2iyABeSI1pZq2UNVt/j6TIdsKOdXuytXADSZGvuozqJO4Rm/9jibkwqeJKQwl4JoKslTrniYX/6vR79IYrZvMVs9mXwZgi4I3l84TNfVegoaGheSLffffdU7VfuHBhI3lCUyMImyWbN1U8OtJwIxUvVSIsJivQl9etPcp5loAI0mACAJTDLtu4S3fqMmmsXIzACvR1/l8UIAhpNAFFQT2myxqPTH2a7Y1NxtdBiblQS2jEjHrl1qF5CbHVezdR1EQBb4WzE1I1L/1shWqDi7S5ntb6+rBvyq0d3FpE8KZ0qgeQ1IP59woWZwAFru9U1fAU5H2QrtpSjPKwoJ0d+N3FdXR1zmSa/zByfoKAN02uumgy10fDWwkKAKz04oZnor5J7Gobj5dIJJmZmQ3nD029YPl5AQCpNQCKiob14/XoiHI5pesfKP4ya6+H8tsYeWGJfv9O0CWWZ41OIQ2E82hX3aXyilNlWaOSg3Z2wBwS13HC+MEHIjKHJ1ccV93rfdWUrgcU8fvlKdS7DQRBJk6cOHLkyLVr13755Zc6nc6+6fLly/379x82bFhMTExERITdnqXGj+eaprTh5RlzKIpEAJGbi2vsHCe4uYVRrrJLBaa8Ony4Wn4hQ39HxBCPdJvIxXifBK/55N7cAmPurYob7UWdAWBP8T8X1acjxN37Sgb5cYOe6gTt+DIYnziJ54tEe/SGf3S6PBz/srzie412JI87hY6rp6GhadksWrToqdrTAr5ZYHq7i73dn78fhkQMALqTiaTJDAjC9HRDeRxrkdySk1f65e9AUQypU/OqdwAY7T45hN+aeCzHzePIWK60eqepg+Xqckf1bsOu4W9brFt0+ndEwrq6aDRGBnNHBreg4CbpNE8AsGl4ykS6fehXuYGCvPfvKTcUojwsaFcH4St1/eIc1ftiJxEA2D7qemp4mmemXgK+Y8eOV69e1Wq11TKW6XS6K1eudOjQoXF8o6kV6uEgussH020xdQDg870Pyz+3cFm2Pq0DIIjukjprzE1ST0jf8PT7qZUpw5AZnay7VJ4x5Ebw3o5Mz0dj+dy2gpD4iMzhyY/U++tPp94fdcXlfvrpp9OnT5+/eNnebX9T5KP78eHDh48cOTJmzJgVK1aEh4cDwJ839bvuGTbd0vcKCwTueQooPaGrsdvi0o7384ZbrZz2krs1NgAAC2nZVfw3AIz1eN02jS9kiEa6TdxRtHFr0V9thO0ZCNNMmdVW1SllwjnVsfXttnDQZ7+SClDkDSH/dSH/jNG0Tae/aDLv1Bt26g0RbNZkAX8ol8Oir1k0NDQtj3379lWz/Pzzz8ePH4+IiBg3bpy/v79arT527NjBgwenTJny1VdfNYuTNA0Fp10oAEKazExPV5dFs1i+ngBA4YT28Nmyv/cCANPfq7l9BDbK6STu0dxe0LwIzBOJFCQxQyio9gRm0/AbtbphdGEzB+wavnB5FgC4fej3VOr9utkyT6m2UtRMoWCRU2W66wAGY6OLdJpCedFkXqRS/yST1NEDzTNTLwH/xhtvnDhxYtq0aZs2bbIb9Xr9a6+9plarx48f31je0dQCYTAAAMrj2tW7Dbf3/S3ZeYqNVn1ah6zoFNJI2tQ7oAinFT/sZJes0SnGO7r0wdeD90dwQh4Fq3PbCkISIgoWZcimezlPcHtO9zw9PXf/E3v3048+/fTTvXv32u0URe3Zs2ffvn0TJkz4/PPPF3QOza/ArxRZDqT06O3d59PeHG9RzaJ3G2JZk2PuKxn2cfDrtR30sGKP0iL35Qb0cR5kNw6WjTirOlZiLjypTBjqEj3Zc2YfyaDL6rMIIGz0UdreB8ZsEcPJmSl92jNFAQZyOQO5nAc4vk1n2Ks3JJstyWbLGhRd7ix+lb5P0NDQtDCio6Md/4yLiztx4sTKlSs/++wzu3HevHm///7722+/3a9fv7feeqvJfaRpMEizxZZghtQZrQUlTA8XhMkkysot+cVgy2FM0AGsNC0aDAEAQOs3JzKKX+tzlzuGfeJUVyj4y4l0midFUHnv3Sv8PAthIcY7OtWWYpSPBe/pKOjtVPe+eThupahZIsFCcZViVUFMxmYX2ZsKZY615vT1NM9PvQT81KlTjx8/vmXLliNHjri6ugJAZGRkUlJSWVnZq6+++s477zSykzTVQbk8ACCNRuPNe9wOrR5tIEmm221eGG5Ib+eo3m0bWd6c0OOds8ff1F/RFC7LDNpRJXSC20YQktCQyV1bt269Z8+ey5cvf/zxx2fPnnXwkdyxY8fOnTvHjRv3ydKlua3CvkisuFiAj92tf6sj/51OAuZj12kWqgcwM2uvwqK2qo7I9wHAZM9ZjultMAR7zWv2dzkrDpTu6OH8ipjh7MPx9/Hwd9y3xFy4MmMRAITw20z0nBbIC32Gk/VjMD52Er0vFsYbjNt0+jsW61WzhRbwNDQ0LZw//vgjICDAUb3bmDNnzg8//BAXF0cL+P805rQsAMCcxES5RrE2FjAUZbFsK+ERJoOy4pb7+c3tIw1NXbgLsHc7CYKc67vml+Zpkc3wAoC89+4VfJwJABY2ytnQ7onqHQDG8nn9OBwpVkNOgSAm46iHGx2J2njU9/fw999/jxgx4osvvrh79y4AnD59OigoKCYmZvbs2QgdKtzkoFw2AAAF8q9+F48ZwuveARMJLLmFmj3HTGmZnABAebh4/AS3+T7VBi0ZzsyQgxGlax8IBzVRTEuPHj3OnDlz/PjxZcuWXb161W4nSXLnzp27du2KiopatfDjE1S7vRnG9dd1R3NMMf2cIqom+SjREQCQX1HrSF5c0SYzaerm1CdM0LbaprbCjuHCTre1SftLtr/hPffxfaUslx7Or9zQJGbo71xSn3k2AW+DgyDj+LxxfF4BTrg5XNFuW6xnjaZIHieMXiFPQ0PTkrh+/Xrv3r1r3BQcHHzu3Lkm9oemYSG0egAQ9O/K8HDVHbtgzsknjSZUwON1DhePjSx8P4ao0Dd7EjsamjpAAD7s1jyr1l8e7BreykZjZvi24TOekDL6ITWqdxs8h6uKlaTePqLOrlpPXmchAWB3uvHUA5Oj3Y2H/TLUWVpLcTsaG08xoDVx4sSJEycSBFFYWOju7s5i0Un/mw2mlzsAoAIeqTOU74gv3xFv34QK+KROL+iJu73nW+O+KA/zWBYIAKSeyP8oQzRI4jzueWPmn0hkZGRkZOSJEyc++eST69ev2+0URcXHx8fHx/fu3fvtWR8eZvdNL8NfP6i6/qYbl/Hol59XQQJAsb7mSL9sffrV8gsslDXe440aG7zmNXtZ+vyzqmOvSIf4P5a7jomwZvt+8AY5N12XGsxvbbff1d06pzreShDeWdxTwHi6m4c3o0qm/Y1a3WGD8ecKbTiLOY7Pi+Jx6ZT1NDQ0LQFPT8+bN28SBIFhVa5aBEGkpKT4+NRUXojmvwMm4AMArtI4vz5aOKgXRZCUxYJyOQCAK9VAUZiQT6t3Ghoa2QwvbjvBB9f1aSgiMzTwyhqDlbpabDFYqcc36SykzlLFUqQlVEaSFvB189QRKRiG+fpWUYYXLlzo06dPw7lE82TYbYIYMmdcqWZ6uFiLFSifhzAZDFcJp22oITGZ1OkFfbs+sRNzjlH1d5FqS5EpXe/xcUB9Fxg9B4MHDx40aNC+fftWr16dlJTkuOnixYsXL17s2q175KSFfj2GcBhVnUFq+M3b2Vm8mQLKQloW331CqOe+kq3vB1SPFLXBRjntRV0cLcmaq1fKz18pP7+9KHZ12I9SlkvdndfBR04iCYoeNBhvW6y3LZovyyte5XHH83md2Cz6uYmGhqYZ6dOnz59//rl48eJvvvkGfTiwSJLkkiVL8vPzo6Kimtc9mueE3ToIAIw3UgmNFhMLEQxFuJX5X3SnEgGA3Sa4Of2joaFpMfC7icsKcSi1PLnpUyJmo+dfd9WaqzzM70k3rL+hGxvGW9BZ4GjnMRFavT+RWgU8juMbNmy4du2aXC4PCQl56623wsLCAMBoNF66dKmkpESj0eh0uqtXr+7evZui6tJXNA0OgmGSWRPlX/9pLVYAAGUyO/0viigr1564SGr1rEAf4auvPLETbrjA59vQgiWZxV/c11/W+Me2ZcgaPaoCQZAxY8aMGTPm8OHDMTExFy9edNx67eqVa1cnSgLaboma59J9FIIxKAqsJFRYCADIUeP9t8od27vysF+GOosYT16oY6P+LQFgvMdUX27AtfKLWqLCMeOd2qoiKfKp9LwHhi1zFn/kJDpmNO3WGa6azfv0hn16gz+DEc3nRvN5HlXnvmhoaGiahi+++CIhIWHt2rXHjh2Ljo729vYuKCg4cODA7du3vb29Y2JimttBmueC6enK69zOcCO1NOYXl/feZHq5AQBFENqjF8p3HwEEEY8Y2Nw+0tDQvPg4sVEndlULBwUAIQvxEdHPwE8NUqP2NhgM/fr1cwx15vP5CQkJTCZz5MiRKpWqWvuXSsCnpqaGh4ejKEoQT65Z2qgYLqcof9tK6gyORm5EG9n8NzCRoLa9qqE9XXZ/xh1cYeF3FzNkTOlrnk6jnn2q+Wk5e/ZsTEzM8ePHH9/EkHiLB84S9p6Ccmo9FwyBA+NlraRNt7CcpMj5d143EgZfbuBw13FdnWpeO1o3eTi+R2/YqzfKCQIAUICubHY0nzuEx+XToYw0NP8FEhMTe/Xq1bNnz0uXLjW3L89LRkbGBx98kJCQ4GiMjo7+5ptvQkJCmsurZqHl3N8bEKK8omTZOmuJAhCE6e2OCfmW/GJSqwcA5ykjxWOHNreDNaC2qr7J/qyzuNc4j1pLz9DQ0DQ44/eqkkstr/iwNw5v9FRZm2/rV16smBbOX95b9OTWTch/4v5e8wz8d999d/36dalU+v777wcFBeXm5q5fv37atGlsNlulUg0ZMiQiIoLP5yMIIpPJunZ9crQ2TWPA69HRu0Mr3ZHz6q0HKADBoF7CAd3ZYYFP1YlwgKT1pW4P5t/VX9IQFXjFEZX/hrbO4xt9VbyNfv369evX7+rVq1988cWBAwccR4LwsgLVrhWGI2sFvadw+05nSH2CnJgboqpUpOQyEVnThtmgCNrbeeBF9ak8Y84xxYFnE/C+DMb7YtF8seiSyXxAbzhhNF0xm6+YzavUmiE87qdOov9n777Do6rSBoC/t09PZjIz6Y1UAqEjvYM0BbuoCKKslV3Lqousn66uiuu64oLKioCiIoIiCIIggnSkk4SE9J5MZiaZSSbT7tz2/TEhhJDQTIXze3h8bs49986ZmMnNe9qLVsgjCNJhEhMTt23blpubm5WVVVlZGRUVlZKS0qPHtT1KkC6LCNSE/utl+zdbnXt/58pMHAAA0NHhgbNuUwxO7eTGteI705oqtnK7ZePgwOFRcvSjiCDtwuYRXRevS2cFCQC8vFTmuKgTU01j/gFzpItoOYDftGkTQRD79u3r1athT+8777yzd+/egiAsWbLkueee68AWIpeDy2WaOyfx9jrH9r282Xqt0bufUM97zjgFB8/EytkiT/G8s5zFZ3y64/YuuuWWWzZv3pyRkbF48eINGzY0HfrwOB2enf/Ddn2m6DdNO+PJugmTLr4Uhw7P1PZg+Pz7wubmODODmdDGwkpv2eflH4UzUf0DhvTRDMTgygPpBMAoGTNKxjhFaafHs9nlPsn6fnS5J8tl4+SyK16OIAjShhITExMTrz8HB9KV4Up50J/u0z1yF1deJXq8pDGI1GuvfFknKXDlHLUfAAAJpK8rVrwSv/hqHqkIglyTkjp+0nqr0NJ2dcdMvmZLVmkC9j8UbFCgGL6raDmALywsTElJaYzeASA5OTklJSUjI+Phhx/uqLYhVyvw/mmuAye8GbmuAyeUowZd+YIm2Hx33vTTXBWrGhEY/0O/rMG/+0q95X/LdZ9wRH+WghFXempKILIi3hbdcqmpqd98883Lr/5jwtNv2w9vkLgLWSUkUXCd2nry1NZBn/fVjH1UNWgmRtIAgAFsuUefou/o3GwkRvVSX5Rio4azFrhyClw5+227Hol8ZrRuUmvXXkqFX0g+l8dxY5pE7z+43FWCMFkuj6NQBlQEQdqFIAhnz56tqqpq8ezkyV1xijVyHTCKpGMjOrsVVyCB9E3lSgmkifrbjtUeyHdln6g9fH2T3RAEuYxqj9hi9N4inwBOn9i2ATxaM/pHtBwV1NXVGQzNF0KHhoZmZGQEBQW1f6uQa4MrFdrZM6uXr61ZuUE+qDd+1eO3bL47d+opztQQveNKoueRIXnTTrnT6m0bqlzH63p820feq9Ul6KJLKLgvzZPu7PFdH9XQa9gf7jJ6Jyc+sOjDgopFxb98UbRjNVt7URcgW5JmXfOs/Yc3VSMeDBo7Jz4m2iDvEltfpKoH/DNp6em6o4XuvATFhVx0JZ6Cs/WnU1R9YxTxVxxDiCCJZvnnltQ5qgVxWV19EkVNVcinKeSRZJd4vwiC3BhMJtO0adPOnDnTWoWbao8bpNMdtO0ucudpqaC7Q2eHy6LWlH/ybeXqvppBNM5c+WIEQa5abwP1cG9FjeeiIP5wha/WKxoU+ODQi7a1DlUSUZo2HkkaHEon6cgxkeijfT1a/Z+BX7IK99ISpOtQjR/qPp3pPZsr+Ti4ugD+0ugdAIgAMvnQLSVPnqv5upIt8mQPP2b8c1TIwlhC1TxuFF1C/l1nnIdqAaDgjjNxm/u1SQxP4vDRrVoALcxbzLL/WLdu3ZIlS9LT05vW4etrancsq/3lk6rUiZNOPfL6vNtnJinbPwveFYTLosJlUc0KN1etS3Oc2AhfG+mQN5I+bLqb/dX4VB/0rdP1i8ebw3E5ddyHdY4+ND1NIZuikAejjesRBPnDXnnllTNnzvTt23fmzJkKhaKzm4Pc1Lyi54eqtQBwb+hcBpeNDpq0z/ZLsTt/h3XzjOD7O7t1CHJDYQjsHyMDmhXes6nmtNfXM4haNqndV9n0DKK239dx22bfYNC83BsFhhlfnC9xPHZ1c61bjN4bRf+vJx0jN71VIAmS+cMS2/qq8LfjdfeGNA4hN0bvVDBDRTDuk442jOEbMQzzyCOPPPLII3e8t3nX2k88GbslqUlPoSi40nampe28b1Vk7KQ5r/x5/pxhMVec8t/B7gt7JIg2pDtO8RIvNmm8yVvOAx8pi7n85SnjljCCAAAgAElEQVQ09aYu8DVJOuRlt7s9uz3edJ8v3ed7r9bRn6FvlcsnKWQoBR2CINdt165d8fHxx44do+l2TyOKIJe31byhjrPHKZOGaEcBAAbYA2GPvZu/aLtl4wjt+GvK3oogXc132e4lx52rp2k7MncScqPqrgF8QUHBrl27srKyampq3G53UFBQeHh4eHj47bffHhoaeuXrb1BXGb1LnJQ77RRnYtVjdXHf9cVb2sg9dGEMiKLpnSLAMM7EFj+aWb2qIvLfifI+atEtFNyb5jxUSxlpwcFzJ1lZktKb48qfeSZ+Y1/VyLbvtBsxZnyGZsgg2hSRufbzzz+32+1Nz/I1ZXnfvv3Yd+/9/ZapS/++4O5pE7Eus7AmlImYHf4EhF9UyEvcP3Jf4CRfIKW7zXjveP3Uy9+ExLAxctkYucwrSfu97Ha3Z5/He5L1nWR979bW9aapW+XyyWh2PYIg14jn+crKyieeeAJF70ins/rMu6w/YYA9GDa/ccVZgrLnoMDhx2sPbaz66vGoFzq3hQjyRxw3+cwuIbOaRwE88sd1v1nxRUVFU6dOjY+Pf+qpp5YtW/bNN99s3rx51apVb7755hNPPBEVFXXPPfcUFxd3djO7NhwIDQkAvI0TPa1kuxUlXwULALgcD3s9jjTQzkO150YeL56fmX9XWv1+O2mghHpB9AiAgTfHJUtSii4h/+4050F7yzf8A4KVBACkJMb/5z//KS8v/+yzz/r169esjiRwpiNb7r3t1vj4+LfffruioqLNm9FWSIy61TBDSwXVcraTdUeanvrdtu+7yjW8xLd4oQzDbpXLPgzSHg4PWRKknaqQyzEsw8f9p84x1WTO5bgOaT6CIDcIURQZhqmsrOzshiAIfFu5mpe4EbrxsYqEpuX3h82jceao/UCuK6uz2oYgCNKltDpgu2/fvmb72NXV1QHApZvbAYDVam3zlrWopqZm0qRJBQUFvXr1mjlzZu/evYOCgjQajcPhsNls2dnZP/3008aNG9PS0g4ePBgc3EHJzLsdjMAStg/Im37Kk16fN/1Uwk8DyGbdgRKUvZBbs6YSVxBx3/VVj9EaHo8wvVtkXVFu+7YKAAgDJblE0SNo7wnWzQopmp3hj+G9Oa78u9PaaRzeT6FQzJ8/f/78+UeOHPn00083bNjg8XiaVigsLHz11Vdff/31SZOnPDJ37h0zZzBMl9sh4+7Q2XeHzjax5YGkrrHwjOPYirIlAJBef/KZmL+FMOGtXS7HsCkK+RSFnJWkg172F4/HxAtB+IUR+AKOd0tSL5rqfl10CIJ0FJqm58yZs2bNmhMnTgwadG0ZTBCkDZ1zpp+uOyrD5XeHzG52SkfpJxtmbjVvWFex6rXE91FKOQRpP/79pAj0t2OX12oAz3FcdXX1peUtFnaYRYsWFRQULF68eOHChS1W+Mc//vH5558//vjjr7322qefftrBzetSPCfPEgYdHRXW4lnKSCdsG5A3/ZQnw5l328UxvARlz+dYV5Y3Ru8AQASQYa/2cB2udZ10kEZacgqCW5CnqqOWJhMasse3fQpnpXtzXLJEhTfXXXBPWvzm/sqhzffGuG40gQFAs2B02LBhw4YN++9//7t+/fqPPvooIyOj6VlBEHZs37Zj+zaFOmDW/ffPm/vwyJEj26o9bSWUuSijz9HaA/6DCm/pgZpf7w2b23jKK3pkeAsp7xkMmyCXTbhk28K51uoaQQzC8bFy2Ti5bISMkXWZZQUIgnQdixYtKisrGzNmzIIFC4YOHarT6ZpVGDNmTKc0DLl5iJK4rmIVANwWfG8A1ULv/3TjPYftv5V4Cg7Z9ozUTejwBiLIzWJQCHWyyndLaJcb90KawVrMEONyua7pLkqlso3acwU9e/aUJCk7O/vy1SZOnGgymTIzM9ujDWfPnk1NTcVxXBBamXzeBQj1zrJHX8FlTMgbz9I9Ilurxll8edNPec+55Kmqhhi+pejdz/xhScWr+UACQeOCWwQMQAJSTwc/H61/LNz1e23hrHTRK1KhDGdiZcnKlBND2+rtuDjpq7OuOxLlIcpWl3nv27fvs88+27hxo9frbbFCSkrKnDlzHnzwwcjIVr8hnajUU/Rm3l8BQEvpanzVw7Xj5kc96z/1k/m7H6rWhjDhqZoB94Q+TGFXXq26pt75jdNdyjdMxZdh2FAZM14uGyeT6VHPKoL8YUeOHBk+fPiwYcMOHz7c2W35Q664Y8hNlUauWzzfbzz7an5ZU/4JAOgoPYG1/JSv5x1e0RNAat9L+fRqHoII0tW8/FvtxhzPe+MC705qYTymi3Dz0uFydlQkw3S1TaE7ULd4vrc8At9hAfm1slqtqampV6wWGRmZlpbWAe3psgi1SjX6Fue+o+bF/wt9+wXSGNRitebj8Fv7m94qajF6B4CAafqq94uFWl7gRe1dRv388Mo3Cl1H6yr+nmd+vzjh5wE91vUpuC+NM7EAoJ/T8sj/9VFS2JP9W01H7zdmzJgxY8b4t0VYtWrV6dOnm1XIyspauHDhokWLRo0aNXv27LvvvlurbfckGVdvXeVKURIZXFbjqyaAOGLfO0E/zb8U0MCEyAlFFVtRZa0YFDA8QdmQZ16UBF7iW8yOO1etmqtW5XP8Ho93t8d71ufb6/Hu9XhxgN40PVrOjJbJ0AR7BEFam86GIB3Gwdf6D2zcFeZ4sqKXFViKRAE80tVlWDkHe1GKdYtLAIA8O3+onG0sxDEsOoAIuyRVc2dRkNjEmGtLeIx0ipZH4Lus22+/fdeuXWfPno2Pj2+tjsVi6du37+DBg7ds2dIebeguPfSSIFje+Z8n7RwVYgh556+EptUAmKti86ad9ua6CA0pOHhcQcRt7Kse1Ty4dZ+uz731hOgRAUAzQddjfV9chjt226r+VeQ6VpfwU3+uylf06FkQAQBCXowJ+0dce76/K0hLS/viiy/Wrl3b2gYNDMNMmzZt9uzZ06ZNk8k6+bfV8dpDy0v+zeAyVvQCAIMzrMjGKZMWxb/rX+8nSkKRO9/B1/YLuKVxBeD7Ba9lu85GyWKHaEdPNsy8zP2rBXGv17vH4z3iZb3nP/JBOD5SLhsrYyYp5F3l0YEg3US36KFHrlV3eb7feKp9Fgmu/OeoglAqiSv05iNIpzta6XtwS81VVpaT2PFHguXkzTvi3dV0i+d7NxuB+8tf/sLz/NChQ5cuXVpSUtLsrMlkWrly5eDBgy0Wy6OPPtopLew6MIIwvPgYHRPOVVkt/1ohelqeWA4AVAiT8PMAWaLyMtE7ADgP14oeESMwUkc6dtv8E+Y1E3SJvwzsZx3HVfmKH8sEEfzRZe0WS/u9tavRt2/fJUuWVFRUbN68+Y477qCo5kk7WJbdtGnT3XffHRwcPHfu3O3bt3OdtIu7T/R9Z1oDALzIY4AFM2GsyDIEU+DKOVZ70F8Hx4g4ZVL/gCFN9++JlMfiQBR7Cn4wrW2aZL6Oszt5R9OX0BP4PUrFJ3rdkfCQ5XrdAyplOEnUiOKPLvfzNfad7ot2AbyiOs5ex7d9rgEEQbqUvLy8vXv3dnYrkJuCnjYa6OAr/kPRO9ItxGvJW2NlIyKYpv+MChwAEnRUs/K7k+QyFL0j16ibjcADwPLly5999ll/rBUQEKDT6TQajdPptNls/vTgJEl+9NFHTzzxRDs1oHv10Au2WtPfP+CtNiYhJvjVZ3BlqwtvOLPP9E5h0AOhrW0+J3FS0dyM2i1WXE3gFM7bOM3EoB7f9sFluP17c/FjmZIgEVpKsHMAADgWMDXI+EyUenSXmKZutVrXr1+/du3a33//vbU6Op3ujjvuuO+++8aPH39pwN9+tpjXb65apyCUbsE1XDtukuG2N3NfxDFMkEQtFfRO8scM3uoEAZ/oK3Tnygl5tLxhvoNHcL+Q9ahPZENlEVMNd47QjW/t2gKO3+/15nH8cwFqI9EwBr/T7dnvZW9h6OEyxkC0MDBfx9W+eG4+jmH/SVmtItR/7K0jSHfVLXro/whJkubNm/fdd99d6544XdZLL730/vvvX01NDMNEUbxyPQRBkKvWLdbAI9BNnu/dbAQeAJ566qnMzMyXX365f//+LMsWFRWlpaUVFxczDDNo0KB33nmnrKys/aL3bofQBYa88SxpDGLziqte/1B0uVurSQXTke/1IJRVrkOnPKezLq2JUVjsmtTAGQaxXhB9IhlIOX6tKXoovfrziqJHMyVBkiUpBTtH6ijt3cE4jdVtq86bdurckKPVn1f4J953IoPBsGDBgiNHjuTn57/xxhuJiYmX1rHZbKtXr54yZUpoaOhjjz22bds2lmUvrda27FzNz5ZNAOAWXAwuuyf04Wh53AjdeEESFaTKztXssGy+zOU0TierejdG7wDA4LI+moEUTld6y36v3d9YLoGUUX+q1FPUOFYfR5Hz1Kp3dIHGJoH69y73Dy73Qlvt6ErzjCrLv2odB5rMugeA5SXvCRLPidz/iq/qT2EEQboySZJef/31uLg49cWUSuWaNWvi4jpzJVTbkslkON79/uZBEARBkGa63wh8U5Ikud1ur9er1Wrb5MG8fPnyDz744PJd7z6fr7y8vHv10PM1teY3l3EV5uBXnpQP7H1pBYkXajdsq9++T/Q2hKwYSajGDNHOvRNXXNRTeGEcXkXgJM7XNkw79yeBJw10wrb+8hQVb/VZV1VUr6rw72lH6qigh0P18yOY2K7S73j8+PG1a9euW7/BUmVqrY5Go5k+ffpdd901derUdtrZcUXpB7/b98txpUd03RU6+zbjPQDg4GtfyX7aI7gxwCicejvp4yDacE23FSSh1FNoZEIbJxxm1ae9X/g6AMgJxbzIBYMChrd4YY0gbnd7DrPsMS/rPv/LgcawAQw9nGHCRdN3pQsxqWHuyd/j34tTttAVgiA3vG7RQ381li9f/vTTT2s0muDg4Ly8vJCQkIiIiLq6ury8vCFDhixduvSWW27p7DZ2nO41ww5BkG4EjcB3F93i+d69A/g298gjj6xZs+YqK3evb53o8rC5RfJ+PeGSpEESL1gWL/ekZQOGMQnRpEEn1DnZcwWSINBRYSH/fA5XKi6qf0kM3yx6b1qzdrPFsrzMdawOAADHNOO0xmejNeObpxruLKIo7t+//8u16zZt+qG2ptX9b+Vy+YQJE2bMmHHbbbeFhoa21asXuHLeyV+IY4Qg8Xo6+O3kZY3ZcbZbfvje9KWMUHgF95DAUU9E//UPvpZHcK+v/PycM93qM88Ke/RWwwx/ebXP8mv1T+GyqERlSjBzIXcAL0mnfdxhL3vYy2b6fI1/zBKSWy8UB/EFNJsWQfD/7rniDzYMQbqjbvGAvxqDBw/Ozs7Ozc0NDQ2dM2eO3W7funUrALz11ltffPFFWlpal81K0x5QAI8gSDtBAXx30S2e790ygN++ffvWrVvz8vLi4uKefPLJ/v37N6vw0ksvFRcXf/fdd9d6Z57nS0tLL18nNzd36tSpN9IDvm7LbvuXmwhtQNDj94v1LsHhxBVy0qCzf7XZV1qpnjQi6IkHml3SGMOTASQVJfNkOCkjnbB9gCy55T/13Ccd1pXl9o0W0S3gCqKfeSx0sQ07BEH47bffNmzYsGnTpurqViN5DMMGDx48Y8aM22+/vU+fPn/kFSWQ3sp7ucidh2O4KIkDAoaEMOGNZ0UQD9T86hKcAIABtjD+ncbscX8QK3qbLqrfZvl+o+lr//HzPV5LVQ+49JI6UTziZb+rzTvFgRfXN5aHeXf/n84wNmhKmzQMQbqRbvGAvxpBQUG33HLLzz//DACrV69+7bXXysvLAYDn+aSkpHvvvffdd9/t7DZ2HBTAIwjSTlAA3110i+d7y3ngu7Inn3zy008/9R/v3r37s88+++CDD5577rmmdX799dczZ85cx81JkuzRo8fl67jdrS4j76bqd+wHACos2PLeZ9CkQ4dJjgUcd+49pp17Fy67KNm4fz28P4bnrxS9A4BioCZ6YErEu4n278y4mgAMOLMvZ9xxKoQJejBUe08wEdjJP4oEQUycOHHixImffPLJvn373l+94beft7D2qmbVJEk6duzYsWPHXn311ZiYmBkzZsyYMWP06NHXsemdzVdd5M4DAP+i9FN1R1urKYF0qu73tgrgm22JN0E/ncFlea5zNq5aTxsby/fW7NhV/VOULLanus8o3cQJcmJzwVv9RV+Yamj/kL8c87J7nZW0YFtfuW2kbgKJUVZB+KzeGU2Sgxk6nkIZ5hGke/D5fCpVw7SpmJiYyspKt9utUChIkhw5cuSuXbtuqgAeQRCknTAEBgCy7hd4IV1RN/s5Wr9+/aefftqjR49//etfqampJ0+efPHFF59//vnY2NiZMy+XBxu5lOjyWD9YRcVG8JYaIHBvZi5GU4rBfUiDTqirdx9PZ7OLMJKUOM5XXC5Lbr6VkT+GL3kyy33K0WNdn8tE742IAFI///wgsySJLsF1rM51rK58YW7AdEPE4gQqjLnsDToCSZITJkyYMGGCKC7/dufBQzt+/HnrpqKioktrFhcXL126dOnSpRqNZtKkSVOnTp06dWpYWNilNVsURBseCvvTOtNKCWCC/rZAsoXt+g/YfjWzlYmqlKnGu/7Qu2qdDJdP1N82UX9bs3ITW2Hylpu85UdrD/RQJP5i+dEn+nAMfyr8QYDqO3XhDo344rl9rCR+Xb7ikchn9nvZr+obdqsOwPEBDD2IoQcxdC+a7iJJ5iu8pe/kLxyhHfdg+J86uy0I0lUkJSVlZ2f7j2NjYyVJSktLGzZsGACIopiTk9OprUMQBLlBLBioSg6ibo1tNa8Qgly9bhbAf/TRRzKZbNeuXf5x8qSkpJSUlFGjRj355JPjx49Xq1FSq2sgOJyes7metGwAAEGkIkKC//40aWhYnS663JZ/r/SezQUA0dnypAOMwmJW9bq+V6dCmN7ZI2u3WGxrTY69dvtGs/KWAOMzkZJP5Gs4KrR5JG9eWlr7gzn6fylX01PQJnAcf3Dq6Aenjob//icjI2Pr1q2rvt1cePYkSM03L3Q4HBs3bty4cSOGYX379p06derkyZOHDx9+xWH5tPoToiRFyKLDmcgWK/RRD9zFVua7cur5Og3Zcnq/djIr7NFRuolF7jyv6JHhssP2vQAwRjd5ddmyIncejdN91IOG68YdrNl9wP7rzOD7b1cEAcAxL3uc9ZkE4TeP9zePFwDkGNaPoQcydH+a7svQyku2YOgw6ypWegT37urtw7XjYhTxndUMBOlSxo0b9/777y9atOiFF16IiYkJCgpasWLFsGHDPB7P7t27IyIiOruBCIIgN4JgJfFQL8WV6yHIVehmAXx2dvbw4cObznLv16/fsmXL5s2b9+9///vNN9/sxLZ1O1SoIeT/FlQv+5KvqQUA0qDDqAs/D7hSYXh2btnjr4IkYUy7DIzjclx3f4ju/hBfudd1pC5gmh4AShZk274xyfuodfcFa+8KpqNkAGB6t8j0ViEA5E49mfjzwA6L4Rulpqampqa+8PIry/cXfbHhx/wjP3vOHZA4b7NqkiSdOXPmzJkzixcvVqvV48aNmzx58sSJE1vMWlfjs56tPw0A5d6SNeWfXObVRUk4aNt9f9i8NnxHV4QBFiGLjpBFA8AbuX8VQWQI2QPhj+6p/tklOK1sVa4r872eK47bD7Gi95OS9/sGDPJwttvkPRYa+ntx/XGv7wTLnvT5Cjn+iJc94mUBgACIp6gBDD1axoyVd2gn9Km6o1nOdACQQPqmcuUr8YuxrrYNA4J0htdee23Dhg2LFy+Ojo5+4oknnnrqqbfeeuvMmTM2m81kMqGcrAiCIAjS1XSzTexUKtXgwYN/++23poWSJA0ZMiQzM/PcuXNRUVEA0L9//zNnzrTTW7vBNrnhq6rLF/zDf4zLZZqZEzW3jfOvePeknTP/82MACH79L/LUDsoWVvNlZfmiPKGWBwDAQDk4gAggHbtqGiuQBqpTYvim8mz8dxm2rzfvKD++w52xW3BYLl8/MjJywnmNm9hLIO20/mhmK6/4chRGjddPD2GudnJ+2zrrPPNBwT8A4NHIBSN1E/2FbsGFASYnFHtqfv66/FMAIDGKlzgACJdF/TNpaePlpawjQ4AMH5xhfZkcx5//VO4NCw4+n3+elyQRgG63wXle4v8v5y9mtnJQ4PAc59l63vF41AtDtaPb6eWQm0G32OTmKlVXV69YsWLEiBFjxozhOG727NlbtmwRBOHhhx/++OOPZbKbaMLnDfZ8RxAEQa5Vt3i+d7MR+ISEhN9//91sNgcHBzcWYhi2fPnyIUOGPPbYYzt37myThPA3j8ZuDgzHRI+39tuf6rfv1f/lEaGuzrb6+4Y6Pl+HtSdoTpju/hDHrhrb9+a6n6sb8s8BAEgYQ0isyFu53CknE7YPaJqvroMl6MhFY4wLR885bpr1Y657074T/Lk9xrJ9R48ebfHPvrKysi+++OKLL74AgJSUFH8kP3bs2CmGOzq66ddIAmllyRIACKR0SapUq8/c9KxTqE9VDwiktLWcXU4ophjvqPCU9lBc6Oup4+3v5D7BiZyBCVlgvGeQcXymjzvj8xEARuLC0vh7zdV5HJdEU31pug9NpdJ0LEW24cd4p3Wzma0MpLQnag8HkFoA+M60pn/ALc3280OQm5Ner1+0aJH/mKKo9evXezweHMeZ9pl7hSAIgiDIH9HNAvj58+cvWLBgzJgxa9asGTx4cGOsPnDgwJdeeundd9+dM2fO8uXLO7eR3Quh1QCOY4BJogAAGGCCw2le/AkIIgDgMlr0+kh9C/urtR+MwQNuMwTcZqh6t6jyrcLGYoltWHzOV3N5U08n/Ny/nWJ40SPi8ivHjzgGQ8LoIWH0O2MnCuJEAgebzfbLL79s2rZz0087uNrmO9j7ZWVlZWVlLVu2jCCIQYMGjRkzZsyYMSNHjtRoNG39PtrAvuqdDr4OAGo529/OXW4ybT1fpyI086OebVooxxXxyp65ziwLa8qoPzVSN2EgQw9kaJfgXF22lMGZKHmPwQEj4ikyj+OyfFyWj1sHAABqHO9NU31pOpWmetNU02j/Wjn42u2WHwCAFVkAqOPtOlpv81X/bNl0R0jz/IgIcrNZu3btwIEDk5OTmxbK5XIAOHbsWGVl5R13dPV+RgRBEAS5qXSzAP6ZZ55JT09fsWLF0KFDKYo6fvx43759/afefPPNoqKitWvXbtq0SRSbbzOGtAaXMbKecd7MPOWowYKt1ptdAIIEokT3iKLjo52/HCCDAumoTpi8bf6gpDF6D5oTphmvq/3RUrezRnQLAMDX+PKmnU7Y1h+XE3S0DCPaaPa1BGUv5lR/Xhm1JClo7jW8awIHANDpdLNmzRp3+72lG6vL8zI95/Zpyw9VZhxpMfWgIAhHjx49evToe++9RxBE//79R48ePXbs2JEjR2q1HdpjchkYdg0D4QQ0r0zjzIs93hAloYqt1NMXZs1UessO2fb4j8s9xf8O/9ObusAsH3eGZQ95agp4yiKKjSvnAcBIEL1oqjdN9aKooTKGuZbJ9t+ZvvQIbiMTYmGr9LSx2mdx8y4MsB3WTSN1E5pmzutScpyZ35u+vC/skbZKH4ggLZo9e/aHH37YLID3++qrr7766qva2tqObxWCIAiCIK3pZgE8AHzyySfDhg37+uuvCwsLm65ypyjqm2++GTFixNKlS/Pz8zuxhd1O4P3Tq17/r+vgCfXkUbpH78EVconjXYdO1v2wEwACZ90GGObcf1ziOOWw/rhC3gFNMn9QUvFaw/9E/aPhUf9NBgy09wSLHtG2vqpiUZ7g4PlqX86EE6JTIPV0wOSggGl69YQgQvUHcpZJUPp8dvXKCgAo+XO2JIH+kevpuQhWEgcfDj5Wqf29cvAdCf8XrhCPHDmye/fuLTt+zTh1XBT4Sy8RBOHEiRMnTpz44IMPcBzv3bv36NGjR4wYMWLEiMjIljeo7xhjgm5NUvXyis2367uUDJe3tkofx4gw2UXvIkHZ85X4xQXuHAtbNVw7DgDkGDaQoVn34UNVH8YBxBH6EaHPOciodB+X6eMsgmDxCP5t7e9SKt7WBTbeipWky8TzJZ6Cw7bfCIyo8VlwDP9zzKKNVV+lO0764/nvTV8+Gf3iVX4rOhIv8WvKP65iK78sX/5G4hIc6yKZ+JAbBM/zr7zySuOXP/74Y3l5ebM6Xq93w4YNAQEdmvwCQRAEQZAr6mab2F2lqqqqgoKCESNGtMfNb8hNbup3H7Z9tl7iL35HGBZw163aB24HSSp58HmJ4zGKUgzqrRw9WN6/F0a2V1BRt7264L40/3Fj9N6Ur8ybN/UUW+wBCTAKk/iGn2FCRSQfGcLEXlcXQ2P0TmIgAQgS4FjU0uTri+FbtPiIY8VRszfviDfnoJh/sL40+2o+fREREcOHD/dvp9G/f/8rpqbr1mxc9frKz0vcBTau+onovw4MGAYAEsB2+7E1lv0+Ot5HRs0PjLpLrffXP+PzzbHUBOBYCkUl01RPikqhqUiS9P+8SCC9m78oz3XOQAdbfeaxQVPmRDxpYU2v5vxZkAQSpzmR/Vvc20mq60yF2H52WDdvqPzCf/xg+J8m6qd3anOQFnSLTW5aw7LsVW5N99JLL7333nvt3Z6u44Z8viMIgiBXr1s837vfCPzVCAkJCQkJ6exWdCfqCcOZhBjH1j3e9GzeVkcEqJjkOM1t42TJcQAAGGZ8+fG6Lbu9Z3NdR067jpzGVQrlsAHKUQNlyXHQ1rsGih4BMAAJZEmKqCVJl2b7oiNlUR/3zJt5Cngg1GTirwPrtlfX/Vztq2RxGgeAsudz6vfa1GN06gk69Vgtob7Sz7kEZX/NqV5ZgRHnuwMwDESp9M/nJE40/KltMiE/O1gdG0juSbr9UP9bvbykdVi9eUeg+Ciff/gcor0AACAASURBVMRWktNaMF9eXr5hw4YNGzYAgEKhGDRokD+eHzJkiNHYRad/XzcdpX8q+qVmhRhAEHg17GHRexAAItUvATQE8FZvESVR1YJsv8DuPz/fXolh/mCe4otOsx49qbX6zApCeWfIgwBgZELH66f9Yt2iITU1Puu6ypWvJfwHv5aVAu3NwddtNW/wH0sAP1atGxo4WkWqO7dVyI2Epunff//dfzx06NDnnntu1qxZl1ZTqVQpKSkd2zQEQRAEQa7gxgzgketAR4Xpn5nd2ll5/xR5/xTBVus8eNK1/7ivuLx+18H6XQeJQLXx5ceZxNg2bAkRQIIEAODNcRc9lhmzqlezJe5svrv4sUzgJQCMCCBlSUpZkjL4+ejGCr5KrzfP7c1zW1eWYySmHBygnqDTjNcpBmpaWC0vQdlfc6wryjECkwQJl+O6B0KrV1cAhoEklb2QAwBtEsMrSGxWT8WsngovLx2qYPcUK/aGhlQNnAEAOrdtcXTO/v379u3bl5GR0domDm63e//+/fv37/d/GRMTc8t5AwYMUCo7M7VeuxqqHd0vYLDJW+7g61LVAxrLzfV7+9p+9uJ6NxnR1zDHCoHnfFyVIJxkfSdZH4ARNC9jkigTrck0vcGDz1ZJDIbNDJ511H6gxmdVkZpST9FB++7Rukmd+O6a+cH0tUdo2DQBA3AJzs3mdbPDH+/cViE3EgzDhgwZ4j+eMmXKmDFjGr9EEARBEKSLuzGn0LcrNMUOALgyk/PAcffh01yV1fD8POWIgW17f8vHZeULc/1hvPae4KYxPJvvzp18ijOzAECHyxJ2DGhhzrwouU7V1++ucey2uY7VXZhjryFVIwPVY3TqMVp5L5V/nL9p9I4xeI+1qQFT9JaPy8r/luuvABhEfpDUVuPwzeTZ+APlrFaG35nY8C7MNfaHP9ppyTrqKThedu60p6UN8C5FEESvXr0GDx7sj+d79+5Nkjd+95xX9KQ5TlR6yzjRNzNklj8tnF0U3yldk+Fzu/AINxnuIUKk87vr9XGv0fsyMAzjJd4B6lo6VS6YjOBa1nMJiXWJb1epp+jNvL9KkiSBNFo3ab9tFwBgGP6PxA8iZTGd3Trkgm4xxe46iKLocrnU6pt0xgd6viMIgtzkusXzvUv8zYp0O1RkqPbBGdoHZ4geLy5vvpbScyrTm5UnS02WJffAGPo67m98JhIA/DG8/XszAPhj+KuK3gEAx5SDNMpBmpC/xQpOwbnX5thtc/xmY/Pdddur67ZXAwCppyPejncdd1hXlmMENI3eLzTgfAxf9tdcjMLbcD18owQdmaC76GNIKwNM4WPdwWNgHDwUST4XYzl06NDBgwcPHTpUWFjY2n0EQUhPT09PT1+1ahUAUBSVkJAwsAl/XqgbjAyXDwkc1axQi+NPG0d+Xf5pkWsXAEhAeMhQFx7OY0o5e8oDHABIAKWqh2roQQCQBTC80hpH4kk0k0BRcSSZQFFBROdMql9XuVKURABIUfd9JPKZGs6aWX9GksRvK1a/FPdmpzQJuSFZLJa1a9cyDPP000/7S1wu18svv/z55597vd6UlJTHHnvs+eef79xGIgiCIAhyKTQCf81QD/0VmV79gM0uBACMIpmEWFnvBFmfJCYhBrvGbN7NxuFDX4nNm3b6ytF767gKtn6frX6fvX6f3VfuDZxuqN1mbTiHY/p5YYY/RchTlIBjFxrwt1z/MUZgfcpGE5qO6POq8Yj7y9g0i298tGx0JOMvzLHxT64/S5aexMpOVmUdz8lM9/l8V3lDiqJ69eo1YMCAAQMG9O/fPzU19WYYYVtVtvSQbU8vdb/GreYP2vYcsu2x+EwqUj065Okl1bkeMlyi4hyX/BYMwPE4iowjyR4UGUdRvWlK29Z7PVzqWO3B/5W8DwA4RryRuCRcFlXhLX0993lREgBgQcwrAwLQPOeuolv00Ldm+/btDz/8sM1mGzt27G+//eYvvOuuuzZt2gQABoPBarUCwKxZs9atW9eZDe1w6PmOIAhyk+sWz3cUwF8z9IC/Is5kde454k3PZovK4fxyboyhmcRY9cQRyhEDLn95U9UrK0qfz/bH8BiFS5wI1xu9N29kJYvJ8ax+v/M1F4XBpJbqsb6PanhDorLyl3Mtn5QBgHKgJmnv4Et31OswB8vZedts4vnPq4oU4rhSlfVsfe7x7FOHc3OyW1s536LQ0NCBAwf26tUrJSVl4MCBPXv2xNs/QO1I/rnooiRONswMYcJbrLPNsrHaZx6mHSMR+r31+XbQCXS0QTUyj+PrL/5myjD+SHik7Hy+OrMg1AhiNEUqryUj/eX5RN+rOQuqfRYAmKS//YHwx/zlX5X/77eaHQBgoIPfSl5GYdczpQVpc93iAd+igoKCfv36ud3uJ5988s4775w4cSIAHDt2bMiQIRqNZvfu3YMGDSosLLz33ntPnTq1Z8+ecePGdXaTOw56viMIgtzkusXzHU2hR9oeFWrQPjQDHpohutzezHzv2VzPmSzOZPVm5AjVtmsK4PXzw0WfWP63XJCgDaN3AKDCmPoD9sbonTTQqpGB7tP1vlIvX8MBQPWqirIXcxt7uNyn64V6vmNG4Fs0MoLZ95DxSIXvSAV7zOSrqIc0LBaMsWC8XT4WOzyerC88c+zYsWPHjp08efLSrM7NmEymn3766aeffvJ/qVarU1NT+/Tp06dPn5SUlN69ewcFBbX/e2pHv9X87J+LvtP64+Vr/m7fv6z314+HKx18HYVRckIBABZBKOT4FVWbi3jwEEYlcDQW5a8vSuL0ijwXpgGAAIxLplXRFBFDkrEkGUuR4QRBXldUv8O6yR+9KwnV7cH3NZbfFTr7eO0hp1Bv9Zl3WbdOM959HTdHkEYfffSR0+lcvXr1vHnzGgu//vprAFi4cOGgQYMAoEePHuvWrUtOTv7kk09uqgAeQRAEQbo+FMAj7QhXKjCK9JzJ4iot/hLOVG3+58e6P91HhRj8JUJdveXdT3G1kkmI8f/DlRcF58anzy9Hl9osevdT9FPL+6g96fW4jOCtPk+GM2nXQCKAxBUEAHBVPn+XgZ8kSln9jigGaZQDNYpBAcpBGiKgoz8+YSri7iT53UlyAKhyCSerfCeruFNVPotb1AYGDpk40T+YtqPQuzXDrLLl4ab0iuwzJ0+ePHfu3OXn2tTX1x8+fLhpX6NWq01JSfEP0ffq1atPnz7dK2vdmKDJGGASXHmGkYEO9gftGjKgsdBIEEaCGBpzv0dwW33mQErbZH6CFM2fKcbjvISxTqKOsuxR9sI5TBJVUB9Nkn3kxmiSjCaJAQytvtLsBjtX87Nlk//4rtDZTZPGKQnV7SH3ratYBQBbzd8N144LpHRX8Q1AkJbt2LEjIiJi7ty5TQt37doFALNnX0hEkpiYmJCQkJOT09HtQxAEQRDkstAU+muGpthdPceOfbaV38MlQRRG06Fvv0DHRgAAV15V+dfFUuM3E8OoMCMTF0XHRTPxUUxiLGAYAFR/XlG72RL5YXJbRe9+Qh2fd/tp9ykHxuASKzLxisSfB1ChjPt0fd7tp4RaHicxkZeoEEYSJN7aZLI9BrJ4hWKQJmCqXntXcBs26Y97Yof912IvABAYHJhtDFYSdrv91KlTJ06eOn3qVHp6Wm5u7nX89EZERPTs2TMlJaVnz57Jyck9e/bsXiF9G5JAquVsFp9FIoJtoCzm+UKOL+R8aV6LBwKki7PK96GICNsiTuL0lGGi4XZONtQjSZEkGUkSMuApnMIAW1n638P23wCAxukwJgKaL9WQKrzlnOQDgFG6ifMiF3TcW0Va0S2m2LVIo9EMHTr0l19+aSypqKiIiIhISUnJzMxsWnPs2LGnTp1yOBwd3sZOg57vCIIgN7lu8XxHI/BIe+EqLbZV3wNIQBDqMbfQCTESx7lPnvWmZUs+n/mtjyNWvIURBBUREvG/N73nCti8Yjav2FdQxlWYuQoz7D8OALpH79FMGwsA+nnh+nktr2T+I4gAMmFr/4YYnsbZfHfetFPh/4wvfjxLcPA4jYs+MfAOY+wXvTES8xV7XCccrhMO94k6d1q9P9W87dsqza16QkVYlpV6zrkUfVTyfmpFqhpXXtuOfW3oX2MDfi1m0q0cgWF6OQEAWq12woQJK+r7ZWgfTrmfvEMtqOx5XEWWtTAzMyM9LS3Nbrdf8bbl5eXl5eX+kTo/rVabmJjYs2fPpKSkpKSkxMTE+Ph4hmHa8b11DRhgWipISzWsMhgODW9ZlHQWX805n43FjBaJKeH5cl4YREs5gDl5h5N37HdkrHEmNt6HlDwysVot1cvFEGBGMqJNJlZ7PaU48K29dKmnqF3fGnLDw3Gcpi/aSeHXX38FgAkTJjSrWVNT03HNQhAEQRDk6qAAHmkv9q82gyRhFBn67kt0dEPsrZk2tn7PkZpP1gp19a7Dp1WjBgEAoQ1QDh+gHD4AACRB4Eoq2IJSX0EZZ7Iw8dHNbsvmFtVt2kWFG+nocCoylIoIxchrDpW9WfnOvUd9xRXA82SIPmxh38rFavfpeozGvXnuglnpANAsegcAOkZOx8i19wQDgMRLnkyn+4SD0JCEigAAyydlvjJvwx+8OKa90xi7pjcAcCZW4iU6snmyvfYTKMPvSVbck9y8PFiJn6ySTpu502YAiANZHNH79viR5KN6ariqhrFmp6ennz17Nisr69y5cyzLtnDri9nt9qNHjx49erSxBMfxqKio+Pj4hISEhIQE/yzc2NhYiqLa9C12UThGhDDGEOaSiQkBK+t4ex1nNzJheid/xucr54VSnvOA3ElEOv11LoRUEi3WMWKNTKxOFNKjcZe/tNRdIEiChgqo9pn1dNea9IF0I3FxcVlZWZIkYec3a9iyZQtcEsCLolhQUBAVFdUJTUQQBEEQpHUogEfai/dcAQCoJ4xojN791OOH1W3axZssrgPH/QF8UxhB0D2i6B5RMKnl23pOZ7mPp8Px818TOBVqpCNDqagwJiFG3q/n5VslCWLNp+uce440lvhKK93H0lX9e4i+Yd5MN84QIitgFCb6RO1dwTGre/mj9+btJDFFX7Wi74W1yok/D6j7tcZzpt6dVu/JcnEVXgAQXUJmvyOiSyACSHlvlbyXSt5LJe+tkvVS+cP+jrR0kvat0WJWNZ9Vw2VVc5nVXEEtn2Pjc2x8hlaz8/7p06dP99fcnO2sKivkTTn2kpzMzLOZmZm5ublXk7VOFMXi4uLi4mL/mJ4fSZIxMTFxcXE9evRo/G9cXJxSqWyvt9r1BJDaAFILAPM1FwprBLGUZ3NZx8bqvWaRYIkgFg9icZ0PD/ThgfUQByyIrjX+yiwelKN+MgO478tOpMiNo9QpoSQRShB6TMyo+wUHPIg29lL3RdvUI5c3bNiwjz/+eN26dQ8++CAAFBcXb9u2jabpsWPHNq22bNkyj8czfvz4zmklgiAIgiCtQAE80l4kLwsAspYiaiYmgjdZhJorT9u+VMDdU+geUb7icq600ldayVVZufIqrrwKjpwGgNB3X2o+aC9JEs9j50eA7Wt+cO45gjF0wMyJ8n49MRnD5hXXbfqFryzUDBYwfLQnwwkAEiddJnpvER0jN8yPaHhNTgIcAACX44G3Gxy/2vhqn/NQrfNQbUNtDOhoubyXUpaklCcrZT1Vir6qxvzz7UfD4EPD6aHhDTEeK0g5NXxWNZesvzBCXlLH/3VvPYABwHDvqFu//b8AAOA4Lj8//0xGZmFe7rlzWefOncvNzXU6nVfzojzP5+fn5+fnNysPDg72x/M9evSIOS8iIuImGa4HgCACDyLk/Rn5VMXkck8JAABIAthsIm4RCYuA9dPEa4Pf8FfO5oRXnUYJwwFiDgpwsLau8T449KGFWlqs7VF/dogqPpggwkkyEavbXPUlhdM6Sj8uaAra+g7xW7hw4apVq+bOnbt79+7o6OjPP/+cZdn77rsvIKBhE0dRFL/66qtXXnmFJMkFC9CGCwiCIAjStaAAHmk3BA48CI4WYjzR7QYAIK4nTsNIQjE4VTE41f+lxPFceZWvzMSVVopelo4MbVa/etmXzgMnSIOOCjMSgRrnvmOA44bn5ikG9fZvj0dHhSmH9je98j5XWRL6Emd6X+1Jr7/W6L15I6nzF+JYzMpeAMCZfZ5Mp+es05vp9GQ6PedcvmKPr9hTt63aX9HweETkB0mST6xeXYHLCVmSkklUkLr2DWUZAutjpPoYL3qVqADy1eGaE1W+fDsfp234FUFRFBGc8OZBLR48Mi6JHD2PfFRLqj1VkqWgriIvNzs7Nzc3Ly+vtLT06tPRm81ms9ncbI8QgiAiIiIa4/nY2NioqKjIyMjIyMgbeHW9hgxMUQdevk4KwPhAsYTnK3mhShBMglDBCyZBqOIFu0h5CYOXMJwR4cz5T9zdjK2y9qD/uB7khcw4GYaHEjjPm/ab16rAE0YyD4XPjZHH+etIIJV7Siic0lJBDN5xKz6QDhYREbFp06aHHnpo9erV/pLo6OglS5b4j7du3XrvvfeyLIth2NKlS1NSUjqvpQiCIAiCtAAF8Eh7IbQBfJW1fucB9fihTctFj9ebXQgAsvjIP/4qGEXSsRH+De1bboZeixE4b6nhLec3ZJIky78+xUiSigkPvOtWMsRAR4YG3jvF+t817Ln0xF+ecB6q1UzUYURbjodTwTQVrNOMbxgFlXiJLXB7slzecy5vtost9igGaACgfp+97MXcxqtIPR31YVLgHUYQJefhWiKAYuLk/ix37QcDmNdHOa9P8/ntKhoLUxHFdXxmNZdZzQEAgBKgD0H3iR5JvvGKZng4w7Jsfn5+3sUqKiqu/tUFQSgpKSkpKdm3b1+zU6GhoZGRkf54Pjo62n8QERERHByMXVfq9W4nEMcDabrvJXPkvZJkEgQzL5gFoVIQzIJQJ0oPqBMJxT+rfRaX4LTKRq6vdZ+vrgTl4/6jzdV8KGUxEoSeIFhfSXbtXlqslUu1L0XMGhgwxF+njrfvqf5ZQSgDKd3AgGEkhp4a3d6UKVOys7P3799/7ty5yMjIe++9V6FQ+E95PB69Xt+vX7+XX3559OjRndK84uJip9OZnJxMkiQApKenf/nll1artVevXtOnT+/Vq1entApBEARBugiURu6aoTQzV6nux1/tX20GANXYodq5dxBqFQBw5VXWJZ/7SioAsPCl/0eFdUQeMkkQeXM1V2Gu2/Ire66ANOiEWofEXdjomwo1qG+bYPvsWyrMqBjWnyuuIIP1pDGIDNaTwUFUsB5jOmhdscRJ1k/L3GfqvbluNs8t1PPhbycEPxtl/85cNO8sAAAGdLiMiZMzcQomRs70kNOxciZWTmg6KKxysGJBrZBv5wpq+UK7kF/Llzt4QYKFwzR/6tsQ8x+t9D37qz1ITsRryRdvUQeRbEFBQWFhYUFBQeNBSUnJ1Syqvxo0Tftj+4iIiLCwsKioqPDwcP9BcHDwzTMh/zJ4SfrV460QBLMgmHnBIvIVHG+TQGjl1/+zSueTuobd8lebd3zu9FCSixKc4wIHDA/oY8CJIAI3u8+uq1ihIJRaKmhW2KNBtMFfX5TEcm8xjcu0lO4GHsnvFmlmup2tW7c+8cQTJpMJAGJjY7ds2WK1WidPnsxx/h5DoGn63//+91/+8pd2agB6viMIgtzkusXzHY2lIO1Fc9u4+p0HeEuNc+/vzv3HSIMOfD7e3pBSWDX2lo6J3gEAI3AqzEiFGb1Zeey5ArHWIXE8LmPohGhcLvMVlnEmq+2zbwEAcNy17xhf3XxxPhGgJg06xbD+ATMntm9TKcy44MK2z0I9T6hJAFAND9TeHezJdLKFHl+511furd93USPJIIrpIZclKsNej6PCGADgqlhST1/3KoDWaBi8fzDeP/hCVMyJktklhqsvzAtwcZLdK1rdYnYNNzCEmtNbmZqampqa+nuF78BRR9h0fKiGjFCBzGniq0tc5uKK0hL/1ndFRUX+v92vic/n8w/aX3oKw7Dg4OCQkJCIiIjQ0NCwsLCQkJDw8PCQkJCwsDCDwdAsn9aNisSwKQp5s0IJwCaI1aJgFoQaQaw6/19OkiapL3w2nczgal9DSoI1PlhjtZ0/Y6Dkz5JSPSU6j9fYE+W0FsejSVJwbt9k+goASIz8a483klQN46UV3tLd1dtlhNxIB4/UTSAx1LGCXOTkyZMzZ84EgKFDhxIEcfz48QceeICiqMDAwNdffz05OTkrK+utt9567rnnhg8fPmhQ8w1QEQRBEOQmgQJ4pL1gBBHyz+fN//yIK68CUeTN1Y2nlCMGBj31YMc3iQoLBgCR45UjBgQ98QDuD2lE0fHzftvnGwEkMkir//PDbG4xb67mzNW8pYY3V/MWm1BXL9TVi25P8wBekmpWfCvUOkiDjtBpSX0gqdcRei2hDcAI/I832B+9AwAVzviT0kmC5Cv1svlutsjDFnrYYo+v0MMWufkajq/hXMcdqhGBQXPCLMvLyl/KxUiMCmPoSJksWRn2ehypo0ACX7mXMtIY0wbNa2gbjkWoL5rVPz6aOflISHEdb3GLoyIvRMi5di7d4h9J8weEGoBUgFR9Ih43hNwwLjBcTXi9Xn8wn1tYVFlaUlJSUlpaWlJSYjKZrn51fSNJkqqqqqqqqs6cOdNiBYPBYDQaQ0JCQkNDjUZjeHi4/8uQkBCDwWAwGHC8zb5RXQ3WsIsennTZSQqPB+gSaa9FEC2CUC2KNkGwCGKNKNgFkcNVHKg8BBzl4Wh9Q7q7v6v6JihPOPg6TvT9zqv/YrIoMMxA4A7WavGoKNFJSRk5EDJIlRKI41oCP2Xbsa7iMxpn9LRxQczCYCbMfx87V3Os9qCCUAbRxp6qVAxuilUSN7M333wTALZt2zZ16lQAOHDgwLhx4wRB2LNnz7hx4wBgwoQJEyZM6Nu37+LFizdu3NjJzUUQBEGQToICeKQdkUGBYf95xXXwhPvwKV+5GaMIJj5GNX6oLCWhU9qDyRuCSVnfFLxxQBLH5X2SMJqUfBxG4kSgRnFLn2YXCvY63moj9dpm5SLrc+47Jvm4S18LV8g1MyYE3jOljd8CgTGxcia2+WgqZ2LZYo9Qw2km6wFAFqego2W+MtZX6vWVep2HajUTggJnGEyLi0zvFIJ/TX64jA5n6AgZFSGjwxkqgqEjZFQwc2EHvj9ARWO9Dc3Dwjm9lcPDmeI6vsQhlDn4UodQ5hDK64Vqj1jt8ZU6hHA1IZPJkpOTd7ki/lvcV5OEJwwlN92qNSpwjuMqKipKSsrO5BbWmctNFeVlZWUlJSWVlZU2m63FNlwNq9VqtVozMzNbPIvjuNFoNBgMwcHB/gP/scFg0Ov1er3eYDDodDf47u6ylkbv4fwAvk0UbaJgFUSbINpFkZWkmZoQtXaxv84Kh7OUdwBANgcAoSBr2GPyQzeAu6FHD4N+ZOB7JNTLhJo7OWfw+Z0Kvzdv3lZfRkouUnQ9E/nkLZpUBsMAYL9t1w+mtQpCqaeN86Oe1ZANm//V8fY0xwk5rjAyIdHnd+ZDupG0tLRhw4b5o3cAGDVq1ODBg7OysvzRu19KSsqQIUPS09M7qY0IgiAI0vlQAI+0L4wgVGOGqMYM6eyGAABwpVX+g5pPvnYfPSPv1xOXMWxuUWMQztvrW7yQ0AYQ2oBLy3EZE/7hq2xBqf3LTbz1ojBSdHvqNu5Ujx1CNAn7hXpn5fNvix6W1GuJQA0ZFIgHqEldIB6gJrUBTHIsdr0LtqlQhgq9sEm75tag3pkjJJ/oq2B9pV7RLWhuDQIAeW8VEyv3VbCc2ceZfe5Tl9wIAyqYkSUpYr9MJYMoX7m3/jc7aaTpCIYKYciglpsn1NYLNjsmk1GhBrjsfnLxWjJee9GvHQnA4hLcnBQbeKHcqMCVFOZgxdNmn9UtGBU4RVExMTErS7XrhHjQgzYCDxlPDFHhRgWhI1m81qTyWuTuqrKyssrKyrKysoqKisrKSrPZfB3j9o1EUfQP4GdkZLRWhyRJ/XlGozHoEv5TjTm6bhiNA/iXeY48rlHNVMqrBbFGFGsF0S4KNaJoE8RaUbSLYm1DocjhSg6UXjyEOr+QHgCymGk557doedwB4DDJMSwAx3ExximfR0pOUvTY7LXRDKnBsR4UlWH5+qBtt7/+3xP+FadI8h/vrdm5zfI9g8v0tHF+5HMqUu0vt3HVGY5TNE7r6eAEZQvZLpEOZrVa+/S5qPM0IiLi0u45o9F48uTJDmwXgiAIgnQtKIBHbiKiywMAyhED3ScyPCfPek6ebTiBYYrh/d2HT4su9+WubwlpDLIu/dIfvRN6nWJACkZTnoxcrqRC4rjKl/8V8dk7jdPpMQzHaFqqrecqzFyFudmtlKMGGZ595OIWi/W/HpZ4ntCoCG0AoVHhGhWhUV0+SG6E0Xiz4frAGYbAGQaQgDOzvjKWq/T6I3yuwuurZH2lXt7i46pYzuLjqlgyiCpfmFe72dJ4ORXCJB8cTIUw9fvstZstpIGWXBY2+7RQW4nLvBjNkoFy9eRRAXffevU9ERhAsLL5vvr391Tc31NR6xU5EQyKC5PYB4bQhyt8VU7B7hXtXvFcTeMZIwbGnbPGxZ3vBfgp37M136ujxTDMfovSVmUylZeXV1VVlZeXWywW/38tFssfCe/9eJ73B/mXr0aSZFBQkK4J7f+zd9/hUVR7H8B/U7a3VJIQSgIJLYBIkSJSFF5sGBTUF/UVQQTs1ytYeL2ogCIX1HvFgrxcK167IkXgRmyoSCcYQksIRUhI2WR7m5nz/rEkLgklwSSbzX4/D0+e5MzJ5JezbGa/OzPnxMbW+jy2miA07UIDzSlJEJLO++swokpFqZIVgaOO4h+HpNvN8Yx3VciyTVGqFGZTFA9jHlkmMpDq9CU8n3uJvKen1Xgj9nqBE5ySg+f4tf6EDypLvcFKrQAAIABJREFUzDxn4nmf1M6mHicwjyh5PbbKdmrexHMmnv+hfP1B+6+84lUrVc90WZiGk/bhlpGRsX37dkVRau5bmTlzZkVFRa1uBw4cSExMrPPdAAAA0QIBHqKIYDESkRgf037pPNfmXf4jv7OApEppo7+st2S1uX/ZJVhMDd2nd1+Bb38hEVlu/K/Y22+oafds++3U39+U7U7bJ2tjJo4NNvJGfbvXn1XcHtlqk6w2ubJKrnTIVTa5yqHYHfq+PWvt3FdwtGLZR7V/JM8LZqNgMVkmXG0YfGmtjXKVnRjjjQZOde5nN0eqZI0qWUNkrrWFSUwq9TOZqdtriSjpoQ6CSQic8PlP+gLFPiYzYkREpUuO2dbXTGqQSVR9T4QgWdf4+Ke+1l3aUZ2s1V1iTLy7HXGkuGTfEY8YrxLjVJy6vneVx2hr97yxi+7GLjoiKvcop1xysVM+5VLK3HKJS9GK1D7kVvyVhzzfHQ3eaW/47rb0gZed3vTsT/bdhzyJWr6rjo/VMJ23QnSXM/sp2VGucpUFbGUlJSXFxcVlZWWlpaV1w8NFkyQpuO59fTqbzeZgko85k6Va8PPY2Njg540b+P1Hfrev/d6795Di8ghmg7ZnF/N1I1XtkhvxR4TiiOJ4Pq7OXAP9Ner+Z67+4GbMrihVimJTFLvCbIpS809N3GBDygjjfcGei6rsdsVnV4hIJool9emrYN5zE7lt1fu7ksxXEpGGAqKIQBh+o0aNeumll+65555XX31Vp9MR0cCBtS/dWrZsWX5+/l133XUR+9+3b9+qVavO36eez1AAAIAwwjJyDYZlZiKXd19hyd9eFmItqf94ijeccVtv6cJl7m17YiZcHfPf1zdon6WLlrm37BETYtstnVd70wtvurf/JibGtXtj7kVWrCj29ZukklK5yiFXOWS7Q7E5ZYczuNF87fC4KTeHdvcVHC1+cjExRkS8VsMb9bzJIJgMvMkgWEym/7qisWJY4ISvbNlvtq+2KD6d4lMzv0bx6lhAzeTaSbJn/uXqDtqCcbvt35wOw4JJ1Pc1df68D6/l7Rutjh+sgkUU49VinCjEqsQ4lRirEmJVvO5PzR5n9ylbi/2nXIpBxY3r8sdj/eT3tk/2n/06C5GnrZOSLNXT+/3j37nfHvGoKk+knDo0oOgXq89jJdnTI72U2IlTZY7KikpreempU3a7/c/U2SgMBoO5WjDSm81mk8kU2mg0Gk0mk9FojImJMZlMJpNJqz3LGm/2Nd9Z3/uClDOPC4IQP+1W01VDmun3aQxOhVUpij34j7HgJ06F2RXFwRSHwhyK4lCYgykGjlvRJsHcwNkKI2KZmchit9v79OlTVFSk1WrHjx+/YsWK0K1LlixZsWLF1q1bDQbDzp07u3Tp0tD9jx07ds2aNfXsjJdGAADRKSKO7zgDD1FE262Ttltn7/7CkmeXJMyYqO7UnojkSlvlv1e7t+3hdVrT1cMauk/pRBkRqbt1qrtJP+RS9/bfZJvz4ivmefO1w2u1MVlR7A7Z4VKl1k7jYpxFk5kmlVkVh1Px+hSvj0KXxGMUd/cZgd9/+Hjx314mIt6gF4x63qjnDXreoOMNOt5kMF05RIg7+53bqlQNTz/pMgqISExKMI25Qp2aJNudju92ebYflf1q5tfF3HKrKsmg7qAlIsvV8YGTPqnCL1kDskNy5zqYTyEtf/LZQvfOswdgXscLwTAfozL0NaU+l0kcuXc5XNttgkUUY1RCjKjJ1IuxZ79W36zhR6WdJaAuGGGZPcRc6pYrvYrVo5S5FatXsXqUCo8crxNM1VcHlC/98LuqjL1xHSmu42+dBj3AO7QuGzEiJz18/cwj3tPTDXRUcRZR1vortb5K0V2RzCp76J0V1U6VlReXltutFVWVFS6X66x1NgqXy+VyuRq6Ap8oiiaTyWKxGAyG4FsAeknhD5/Qi6JZpdELKq0oGkWVQVRrBcEw9+8dS+8wZ3UxGo1ms1mr1RqNxib6dRqFkeeMvEDUem5GaPXMZnNubu78+fM3bNhgs9lqbf3666+3bt3ar1+/ZcuWXUR6J6K5c+dmZWWdv4/L5Xr11Vdb+P9tAACIcjgD32A4Ax/RpIqqkqf/IZWUE5EQY+JUKqm8khjjNOo2M6fqLu3R0B2eeHh+4ESJ8YoBCQ9PqrXJ+dOO8n+8zatVHf79cuNU3xCK16c4XIrDJTucitOteLz6/r2FmDPuEfAfPVHyzCuK4+zZ0nLDVbF33hja4jtYVPLsEo7jOL1OtlYREa/Tant15U16wWgwXTNcTIit+mxd1UdriajNY/foL7tErrSRIPAaNVd9ObRskziR4w0CEXn3u2wbKqQKv2wNSFZJsgbkyoBUGZCtAcX7x93pnIa/5Ngw3iDk9/vVe+CPagWL2KvwCl7LH52eb9tQIVjEmn9ijCiYRd4oCmZBMIm8WRRjRG1Xg6qthi7Eu/9wyVMveQTVsZ79uAnXJ8YZLk1SBUrKTj37ilRW+X7G8E29h1cFWJVPkc+8g17kKW9qsoo/PUPB1Z+UHbJKRJRqEtbdZLZVWoM+3H5y7/Fy8lQprirZXRVwVPqcVR5Hpdte5bZX2aoq65bUMhkMBo1GExMTo9VqdTqdxWLRaDRGo9FoNKpUqtjYWJVKZTQa9Xq9RqOxWCyiKAb76PX6YGOwp8Viibjl+iLiHfrWZOfOnTExMZ06neWt0kZ06tSp5OTkpKSkC85qAQAArVJEHN9xBh6iixgf03bRE7bP1jt/2CJXOYiI06j1/XrG3HqdKjXpInaoSooPnCjxHiyqu8mzJZeIeFN4TubwWg2v1VDi+RY5U3dM7fD2Qubzy0634nIrwY8uj+LysIBkuKJ/7W/gOI7nFY+XvMHby0nxeN1bc09vFYXYiWNjbhpj/2qj4vH6Co5ygnDqhTeDl/QTx/F6Ha9VcxoNp9UIFmPMf1+v7dZB281Qs3smye5te4jUvFbDFIH8KtkjyC5SdzByKoVIaPf3Lra1ZXKVJFUF5CpJ3VHLa3giCpT4pHK/VO4//5gIZrH3sWGcyBVk77JvtApGgTeKvEEwXh7T8fXuRGT9d7HzV5t3zy65qrtgVvcbM5L/zcvrAg6jwJs0cdMfLXn677fv23x/VyFu8k1E5PSzKp9i8ymVXsXmU9oaxZr0TkRjM3T/KfLafErnGFGv0xp0bdu2bUtEz5woO6GX6panI0rW8DsmJ1VVVlZVVVVWVj6Tc3zv7xUqn72dyn15gq+qqspms9lstsMl1gprlc9l9ziq3A5buN6HDZ75/zPL+IUKzt5nNpuDlwaEfgy+ERD8GOzDcVxMTAwRxcTEcBwX7Bl8UyDYrWaTwWBQq9UX+uHQ0vXt2zfcJQAAALQICPAQdXidNvZ/xsXekS1X2pksC3EW7k/MAWa+bqR7517pVLlt1TeWG0bVtHvzC9y/7iIifZ155loaTqMWNWqKj7lgT01mWof3Fyser2xznHjgWSLW5rF7mMwUt4f5A4ah/YiIeJ7TacnjZV6fkBCr7thWrrApXh8LBBSXO3Sef21WF03nDqH7d23aVv7aCjobThASHrrTfFU/81V/vCUhlZSXLl7O8ZxlpGi+QssUDfOJjKkVr0CKRkhIYV5OtkuyQ5LtsmyXtF0Nit1GxHE6johkpyw7ZSKSbRKTGSdwJ+ce9v/uJWpD1IaInDv31ynkKiJy7vTE3UXEUcVD+5w/V/E6IUHLJ1lE42ALze5ERFUrS1077Teq+AkGgdfznDdQ9fkpTuQEs0gi936ausApOEWuKlZlZ+TwM7tPcfiZK6B0jVNxRMEZ7NLT09VHO5AlECDyGoVn72hTU0S/d05pvErNJISK16l4nczrULzOOM79t77MXu2rvPISq01x283kTlZ5nU6nzWaz2+02uyPg913wEW9OlZWVRFReXn7BnhehJtWbzWZBENRqtcFgICKLxaJSqQYMGPC3v/0NOb9levrpp59//vlAIBDuQgAAAFqESA3whYWFOTk5+fn5FRUVbrc7Pj4+NTU1NTV17NixKSkp4a4OIgHHnesG7wbRXtJNndbOf+T3yvdWur7fqh/Yh1OLnt353vwCYsTrtaFT07cOvE7Lnb7gmZOt9loTB0hlVrnKTkRicht1x9S2i588vYExxe1RPD7m8zOfT/H6NF1rL9ylu7SHacwwxe5QPF7mlxSvj3k8TJIVt5c44tS1b3f37itwb9l9rjqNg7It40aHtrg27zo+7SkiEojiriYmqYjUJOh5nXTy0R/ip/13xpd9nL9UlS9dyQKk69OH1EbvvhMsQMzPK36B/KS4JMXNcSqP88ethkF9PL85fUWemv27d9njb9cSsWMPF0gVZznHXsNEZCJK66TL2jOEiArG7Xb9WkUiJ5jEvSJnGhHXYUk3IlpyyFm1zaYInMYgHNtq5QSONwlEtJTnTjgUj5p3E/MKXHEn47GMeI/EVOX+/pJ8XYaFOBIsorqt5peVFbZKiYhSjfx3d/xxjcklb5U4PH7mcytum+J3M79H8ToVj4P53UbON62r7HQ6PR6Pw+FYs7fc5nQrPpfeWZ6skTxMttvtbrfb7W7wmothFAgEgm8QBD/WsmHDBp7nn3322WavCy5MURRJOt+zCQAAIKpEXoAvKiq677771q9ff9at999/f3Z29uLFi9PS0pq3LoheyfMfKX7874ETp/zHTvqPnQw2MiLBqE95/tHzLecWsTiNmjcZFIfL+s4XnE5rHDYguDS9/9jJ8n+8Q4pCRJrO7c/8Ho436HmD/jy7FWLM8ffcUv8yjCMGiknxss3J/AHmDzCfj0my4vaQrDB/wDC09i0AYmKcukNb2eEK9icK/nMxLwV+J6m0wjgiQ9vd4P7pmOJwxYzpKMQpFf/39Vl/dPkSUmyOrj9c6T/uVVwy8ymyXZJOHTj56PNEpM+MleITmcIzRWCSQEwgWaPOSCdBzwJMcUnMxxSPrM0Ui2cvZpLi3dtVdhqJSK6SiEiqPKrvviNmwtXly34PlPiIqNa5coEo9NIFwcRnrknUXdI9r9vmQImv5rIBTWf9l6vSSpySbfJxlu/ZOSOPEznewBPRW1rBL3BE5NeLCqdVSOtVxRyL8X47Qjfkiozb93hdeVVCjIo0dHmCb2Nv7U+9zb2qjr/VPiZwSkNEpOb+nuuyuh2K7HfwXr/skRW/z+/w8AFZ8jpNvgevMNjtdk+V21Hi+PRAhU+SZI+N1MqlcQGPx+P1eB0OxzGbL+CyM6Yo7tozljW/AwcOhLsEAAAAgAuLsGhRUVExevTowsLCrKys7Ozsnj17xsfHm81mu91utVr379+/Zs2azz//PDc396effkpKuphbmgEaitdqUv/xlHPjZvvX3wdKK0hhQozJOGyA5cb/4jSt9qJc47AB9rXfM0kqX/Je5QdfqVKTFJvTf7w4eMe7GB+jyUxr8iI4Ttsj88LdqmkyOrZ9aXZoCwtIzO9nkswkWay+iUCT3t6zZ7/jm5/avT6XNxoUt4dkWfH6FJ/f9tkGYoomI02T2VE/pC8ncpr0P9ao8x9p79vbWXF5xCQ/kZX5/CwgEZHi9nACnzirj75fz9Cf7vxhS/mSI0RkvOQYy1IR45gkMsbxGr/z24A6LbXr94PcuxxMYswjK17Fs/ug66ffiEiRVEREksgYzxReZakqXVAYd/fNCXe1t39nZX6FFJLtkqajv/TR53gisWxIgNoQcUxisk0mIs4mB2fz04S8OZCq9V5j/oJ28ye/uVbxn77eoSdRb1Xg2dI1nCgc/8eNwZsOiOj2kIeBSE+kJzo9gLKZdbqi1DLl1n2X5wdO+mp6etK0WXdXMkk+MV/nPyn88d0G8qqpkndJTPYwT4CXXBq/WatQFq+53OFyuv6zkoxljgDzS8wbow0EEnyaXiqXWtz2bSVvq2REXtmpMOZT3BLJEvP7ZS9HJMkO4sgVcEnKBSYcFdXaGTNmnL8PAAAAQEsQYQF+9uzZhYWFCxYseOKJJ87a4Zlnnnn77benTZs2Z86cN998s6H7r6qq2r59+/n7HDlypKG7hdaP44yjhhhHRdJC2X+SZfzVrs27ZKuNREG22mSrjYg4gWcyI46Lm3IzRcK84pxKrHuJROyUCZ5H5ss258lHn098dKo6vR0x5tmVX/bPd4gpnEqVPPfhutfzE5E6rV3yvEfq/9ONwwdqunRiHg9TmOL2EhExJfgJJwi6S3twKlHd7o/F8BRHjCPHH+zA/KffHSBGisvMqQbo+2aZr0lIeeqPabr9h49b3+6s+ALxHY4ovoNERArP/Jzi8RJpYidN0HRJl+0SKUREsi1g/+z9QAmRopgGfi87LCxw+ncUjA4iUmekp93V0/lLFRExn6J4lMDvJd59hUwSiXFERIxXJJGIdLEVjo37VGmpMTekuLZUyXY5uLC8qZ2n6rP1RES+wZwqjklqqp59T+unFApOZ2gmIvIS2UgtcEb1F0TUs/ga8laPg5t4tyem0/r4224t+Jox79lujeZDPorEq/xrJp06nNX7tid+93gdRMzH/D7y8Xru42usx/QJU7onjRgxov4PHAAAAEC4RNgyct27d2eM7d9fd2apM4waNaq4uHjv3r0N3f/NN9/82Wef1bNzZA0dQKMLHC8uXbQ8cPJUaCOnVsVPvcV45eBwVdUoqj7fUPXh6tNfCDzHiCkKEXEcl/S/92r7NHi5wYgglVcWP/H34OoMtQhxMW0XPyGYz1xSgTHfoSOK109EzOMNDlEQx/O6vj041Rlvcyger+uHrUr1EgYkK4rHW/25aLhyqGAyEBGTFNkhExEnup3ffMf8AanSJ5Wdfp+F+UlMkIQYirnlOhYwuXc7mHz6T7FUZnV+t1NxeP/428x45udVKS5Ne1fc3bf4jhjd222y6/QJeXWyx3/wKxaQLONGm0Zf3qCxiohlZloHSZJkWdZoLrwA5J+HZeQAAKJcRBzfI+wMfFlZWa9evS7YrX379rm5uRfsVtfEiRPtdruiKOfp4/f7f/zxR61We54+ANFA1T6l7UuzXT9v9+zaJ1dUcnqdtku68cpBQmwjzA4YXjHjx6jbp1Qs+0iuspOsBPOgqm1S4l+nqNNSw1xckxETYlPmP1r26nu+/YdD23W9u8bfd0ft9E5EHKfpkl7//fM6ba0pD89dyemfHDd5wvl7qjuE/ilukzit23k6azpT6CoGRER04QMKhJcoiqIYYa9VAAAAmk6EHRQHDx6ck5NTUFCQkZFxrj6lpaXr168fPPhiTgDedNNNN9100/n7BN+ht1giPqIA/HmcKBiHDzQOHxjuQhqf/rLe+st6y5U2f+FxUomajI68QXfhb4twYnJCyvy/+g4d8e49pDjdvNmo69lF3an9hb8TAAAAAJpehAX4hx56aN26dYMGDZozZ052dnbHjh1DtxYXF69du3bevHmlpaVTpkwJV5EA0GoIsRZd/6h7t06TmdYccxACAAAAQANFWIAfPXr0kiVLHq5msVji4uLMZrPT6bRarcEFfkVRfP3118eNGxfuYgEAAAAAAAAaTYQFeCK69957R40atXz58pycnH379hUVFRGRIAiJiYn9+/e/6aabJk+enJycHO4yAQAAAAAAABpT5AV4IsrMzFy4cOHChQsZY2632+v1xsbG8pGwZhUAAAAAAADAxYnIAF+D4ziDwWAwGMJdCAAAAAAAAEDTwllrAAAAAAAAgAgQ2Wfgw8hqtfbv37+h37Vv375AIKBSqZqiJDgrRVECgYBareY4Lty1RBG/38/zPFZvbk6yLCuK0rVrV41GE+5aWj+XyxXuEqCp4PjeuCRJUhRFrVaHu5AWR5ZlSZLw+qSu4Cs3lUqF22NrYYz5/X5RFAVBCHctLY7f77dYLGlpaX9yPxFxfMfL6wYzmUwmk8nhcOzYsePi9hAIBBq3JLggj8cT7hKikd/vD3cJUScvLy/cJUSRtm3bhrsEaEw4vjcdSZLCXUILhdcn5yLLcrhLaKEwMudSUVFRUVHRKLtq4cd3jjEW7hoiz8mTJ4uLixv6XZIkDRo0SBCEd955pwmKgrN79913v/nmm0mTJo0aNSrctUSLkpKSWbNmJScnL1q0KNy1RJHFixfn5ub+85//vPzyy8NdS7To3r27Xq8PdxXQmHB8b3SzZs0qKSlZtGgRVgiq5eWXX965c+cjjzzSt2/fcNfSsnz66aerVq265ZZbxo4dG+5aWpaff/556dKll19++YwZM8JdS8tSVFQ0Z86crl27fvDBB42ywxZ+fMcZ+IvRtm3bi3hjJvjGPM/zd9xxRxMUBWe3efPmb7755rLLLsOwN5uDBw/OmjXLbDZjzJvThx9+mJubm5GR0a9fv3DXAhCpcHxvdHPnzi0pKcnOzs7MzAx3LS3LZ599tnPnzuHDh2dnZ4e7lpYlPz9/1apVffr0wROqFsbY0qVLO3XqhJGpZceOHXPmzDEajVHyEgj3lgAAAAAAAABEAAR4AAAAAAAAgAiAAA8AAAAAAAAQARDgAQAAAAAAACIAAjwAAAAAAABABECABwAAAAAAAIgACPAAAAAAAAAAEQABHgAAAAAAACACIMADAAAAAAAARAAEeIgKHMeFuwSA5oD/6gAAEQR/tM8FI1MXxgSCxHAXEEVUKtWECRO0Wm24C4kuY8aM+eGHH4YOHRruQqJIhw4dhg8fPmTIkHAXEl1uuOGG4uLiPn36hLsQgKiD4/t5jB8/fvPmzR06dAh3IS3O2LFjjx492rdv33AX0uKMGjVq1apVI0aMCHchLc7AgQN79ep17bXXhruQFqdLly6DBw++7rrrwl1IM+EYY+GuAQAAAAAAAAAuAJfQAwAAAAAAAEQABHgAAAAAAACACIAADwAAAAAAABABEOABAAAAAAAAIgACPAAAAAAAAEAEQIAHAAAAAAAAiAAI8AAAAAAAAAARAAEeAAAAAAAAIAIgwAMAAAAAAABEAAR4AAAAAAAAgAiAAA8AAAAAAAAQARDgAQAAAAAAACIAAjwAAAAAAABABECABwAAAAAAAIgACPAAAAAAAAAAEQABHiLb999/X1ZWFu4qAAAAAC5g8eLFr732WrirAIg8eMEfCgG+meTk5Nxwww2JiYk9evS49957rVZruCtqDfLy8kaOHPnLL7+cdWt9xhyPS4O88cYb/fr1M5lMbdq0GTZs2CeffFK3D4a9ETkcjpkzZ1566aVGozE9PX3cuHE7d+6s2w1jDhBGeHIFNdYBohV7//33Z82a9dlnn9XdFJ0jc/z48UmTJqWkpJjN5kGDBuH/TA1Jkl555ZVBgwZZLJaOHTuOGzdu27ZtdbtFz+DgBX9tDJreG2+8IQiCWq0ePnx4ly5diKhz586HDx8Od10Rb8KECUS0cuXKupvqM+Z4XOpPkqTp06cTkUajGT58+MiRI7VaLRFNnz49tBuGvRE5HI709HQiSk5Ovv766wcPHkxEHMetXr06tBvGHCCM8ORijXqAaMWKiorMZjMRjRgxotam6ByZvLy8du3a8Tzfp0+fq6++2mAwENH8+fND+0TnyMiyPGrUKCJKSkq67rrrhg4dKggCx3EfffRRaLeoGhy84K8FAb7JHTx4UKVSxcfHHzx4MNjy/PPPE9HVV18d3sIi13fffff3v/+9X79+wTeh6j6f6zPmeFwa5O233yairl27FhcXB1sKCgo6depERGvXrg22YNgb1+OPP05EU6ZMkWU52LJ27VqO41JSUmr6YMwBwghPrqDGOkC0YpIkXX755SaTqW6Aj86R8fv97dq1MxgM3333XbDlyJEjJpOJ5/kjR44EW6JzZBhjy5cvJ6Jrr73W7XYHW7Zv367X600mk9frDbZEyeDgBf+5IMA3uccee4yI/vnPf4Y2ZmVlEVFBQUG4qopoGRkZoVeR1H0+12fM8bg0yFVXXUVEv/76a2jjp59+SkQzZswIfolhb1yXXHKJVqutOX4HDRo0iIiKioqCX2LMAcIIT66gxjpAtGJz587lOO5f//pX3QAfnSPz3nvvEdHcuXNDG//1r39df/3169evD34ZnSPDGLvzzjuJaMOGDaGN2dnZRLR58+bgl1EyOHjBfy64B77J5eTkEFHwiVcj+GVwEzTU999/f+TIkSNHjtx7771n7VCfMcfj0iCHDx9WqVQDBgwIbezVqxcRHTp0KPglhr1xtW/ffvz48TqdLrRREAQicjqdwS8x5gBhhCdXUGMdIFqrLVu2zJ0794EHHhgzZkzdrdE5MsuWLSOiSZMmhTZOmTJl9erVNaMUnSNDRCqViohqTdgWvGc7uImiZnDwgv9cxHAX0Moxxvbt22c2mzt27Bja3rNnTyLKz88PU12RLTU1NfiJxWKpu7U+Y47HpaG++OILjuN4/oy3/Hbs2EFEnTt3Jgx7E1i9enWtlk2bNm3bti09Pb1bt26EMQcIKzy5ajTKAaK1cjqdd9xxR0ZGxsKFC+vOmxW1I5Obm2s0Gjt06PD999///PPPxcXFvXv3HjduXJs2bYIdonZkiOjBBx/8+OOPH3/88fj4+CuuuMJms7366qubNm0aPXp08GLy6BkcvOA/FwT4puV2u71eb0pKSq32+Ph4IqqoqAhHUa1cfcYcj0tD9enTp1bL7t27Z86cyXFccO4iDHvT2bJly4svvnj8+PFt27Z169btww8/FEWRMOYAYYUnV41GOUC0Vg899NDRo0c3b95c63KqoOgcGbfb7XA4Onfu/PDDD7/yyis17bNnz37nnXeuv/56itaRCbrkkkt+/vnnkSNHXnPNNTWNt9xyy7vvvhv8PJoHJ1Q0vwrCJfRNy+v1ElFw3tFQwRa32x2Gmlq7+ow5Hpc/gzH27rvvDhs2rLi4+MUXX+zbty9h2JtSRUVFbm7u3r17ZVnWaDQ1xxuMOUAY4cl1Vhd9gGiVPv/887fffvvpp5+umYV8RtKbAAAgAElEQVSrlugcmeDvVVhY+M477yxdurSkpKSkpOSVV15xuVy33Xbb8ePHKVpHJsjhcPz1r3+1Wq3du3f/n//5n7Fjx5pMpq+++io4WyRF9+CEiuZXQQjwTSs2NlYQhJobVmvY7XaqfvsHGld9xhyPy0XbsWPHoEGD7rrrLoPB8OWXXz7yyCPBdgx707n22msPHDhgt9s3btx47NixMWPG7NmzhzDmAGGFJ1ddf+YA0fqcOHFi2rRpgwcPfuKJJ87VJzpHJngRGRG99tpr06dPT0pKSkpKevDBB2fPnu1wOJYsWULROjJBkydP3rhx4zPPPLN379733ntv1apVBw4cyMjIuO+++zZs2EDRPTihovlVEAJ80+J5PjExse5dT8GWmls7oBHVZ8zxuFyEQCAwe/bsgQMH5uXlPfXUUwcPHhw3blzNVgx7M7jyyiufeeYZv98fnL8XYw4QRnhyhfrzB4jWZ+XKlVarlef5O+64Y+LEiRMnTrzvvvuIKD8/f+LEicGZxqNzZIxGI8/zoijefPPNoe3BecV2795N0ToyRFRaWvrFF1+kp6fPmTOH47hgY0pKyqJFi4ho6dKlFMWDU0s0vwpCgG9y7dq1q6qqKikpCW3cv38/RfL/mxauPmOOx6VBFEW58847FyxYMGLEiH379s2bNy+4nm0oDHsj2rlz5zXXXBM8EREqOH1dzeS0GHOAMMKTK6ixDhCt0s8///xRtVWrVhFRaWnpRx99FFxmj6JyZERRTE9PF0Wx5lR8kF6vJyJZloNfRuHIEFFpaSljrGvXrjXpPah79+5EVDMa0Tk4dUXtqyAE+CZ34403MsbWrFkT2rhmzRpRFMeOHRuuqlq3+ow5HpcGef311z/66KPbb799w4YNHTp0OGsfDHsjslgs69evX7FiRa324IypwfVLCWMOEFZ4cgU11gGilbn//vtrLd38+++/U/U68B6PJ9gtCkeGiCZMmOD1ejdv3hzauGnTJgqZEzE6RyYzM1OlUgVnvQltz83Npeq50ylaB6eu6H0V1CSry0OIkydPiqLYsWPHU6dOBVuCs1CMHz8+vIW1AsH7ylauXFmrvT5jjselQTIzM/V6vd1uP08fDHvjCk56tGzZspqW/Pz8pKQktVqdl5cXbMGYA4QRnlxBjXWAaPVCA3yN6ByZY8eOiaKYlZV19OjRYEteXl5KSoparf7tt9+CLdE5Moyx4J0FDz/8sN/vD7YUFBRkZGRwHJeTkxNsibbBwQv+WhDgm8PSpUt5nk9JSZk8efLo0aNFUezcufPhw4fDXVfEO9fzmdVvzPG41FNxcTERabXaPmczc+bMmp4Y9ka0fft2g8FARFlZWTfeeOPQoUNVKhXHcf/4xz9Cu2HMAcIIT67GPUC0bmcN8CxaRyb4WxsMhlGjRo0YMUKj0fA8//LLL9ftE20jU15enpGRQUTt2rW74YYbhg0bptFoiOjJJ58M7RZVg4MX/LUgwDeTL7/88vrrr4+Pj+/WrdvUqVOLi4vDXVFrcJ7nM6vfmONxqY+ff/75PFfxTJgwIbQzhr0RHThwYNKkSampqRqNpnPnzuPGjdu6dWvdbhhzgDCK8idXox8gWrFzBXgWrSOzatWqG264ISkpKTU19frrr//pp5/q9onOkXG73c8+++xll11mMpnat29/7bXXbty4sW636BkcvOCvhWOMNfy6ewAAAAAAAABoVpjEDgAAAAAAACACIMADAAAAAAAARAAEeAAAAAAAAIAIgAAPAAAAAAAAEAEQ4AEAAAAAAAAiAAI8AAAAAAAAQARAgAcAAAAAAACIAAjwAAAAAAAAABEAAR4AAAAAAAAgAiDAAwAAAAAAAEQABHgAAAAAAACACIAADwAAAAAAABABEOABAAAAAAAAIgACPAAAAAAAAEAEQIAHAAAAAAAAiAAI8AAAAAAAAAARAAEeAAAAAAAAIAIgwAMAAAAAAABEAAR4AAAAAAAAgAiAAA8AAAAAAAAQARDgAQAAAAAAACIAAjwAAAAAAABABECABwAAAAAAAIgACPAAAAAAAAAAEQABHgAAAAAAACACIMADAAAAAAAARAAEeAAAAAAAAIAIgAAPAAAAAAAAEAEQ4AEAAAAAAAAiAAI8AAAAAAAAQARAgAcAAAAAAACIAAjwAAAAAAAAABEAAR4AAAAAAAAgAiDAAwAAAAAAAEQABHgAAAAAAACACIAADwAAAAAAABABEOABAAAAAAAAIgACPAAAAAAAAEAEQIAHAAAAAAAAiAAI8AAAAAAAAAARAAEeAAAAAAAAIAIgwAMAAAAAAABEAAR4AAAAAAAAgAiAAA8AAAAAAAAQARDgAQAAAAAAACIAAjwAAAAAAABABECABwAAAAAAAIgACPAAAAAAAAAAEQABHgAAAAAAACACIMADAAAAAAAARAAEeAAAAAAAAIAIgAAPAAAAAAAAEAEQ4AEAAAAAAAAigBjuAi5SYWFhTk5Ofn5+RUWF2+2Oj49PTU1NTU0dO3ZsSkpKuKsDAAAAAAAAaGQcYyzcNTRMUVHRfffdt379+rNuFUUxOzt78eLFaWlpzVsXAAAAAAAAQBOKsABfUVExcODAwsLCrKys7Ozsnj17xsfHm81mu91utVr379+/Zs2aHTt2ZGRk/PTTT0lJSeGuFwAAAAAAAKBxRFiAnz59+rJlyxYsWPDEE0+cq8/bb789bdq0KVOmvPnmm01Rg6IoN9xww759+5pi5wAAECkeeOCBRx55JNxVQKPB8R0AAKjFH98jLMB3796dMbZ///7zdxs1alRxcfHevXuboobS0lKc2wcAgMGDB//yyy/hrgIaDY7vAABALf74HmGT2JWVlfXq1euC3dq3b5+bm9tENQTf8khISNiyZUsT/QgAAGjJdu3aNWHChHBXAY0Mx3cAgCgXEcf3CAvwgwcPzsnJKSgoyMjIOFef0tLS9evXDx48uEkrEQShU6dOTfojAACgZTp16lS4S4CmguM7AEDUiojje4StA//QQw9JkjRo0KBXXnnl6NGjtbYWFxcvX758wIABpaWlU6ZMCUuFAAAAAAAAAE0hws7Ajx49esmSJQ9Xs1gscXFxZrPZ6XRardbKykoiEkXx9ddfHzduXLiLBQAAAAAAAGg0ERbgiejee+8dNWrU8uXLc3Jy9u3bV1RURESCICQmJvbv3/+mm26aPHlycnLyxe3c7/fn5eWdf2I/q9VKRIqiXNyPAAAAAAAAALgIkRfgiSgzM3PhwoULFy5kjLndbq/XGxsby/ONcDvA3XffvWLFivr0LC8v//M/DgAAAAAAAKCeIjLA1+A4zmAwGAyGxtrh8OHDL7gArMfjyc/Pb6yfCAAAAAAAAFAfERbgXS4Xx3F6vT608Zdffvnggw/27NkTExPTu3fvGTNmtG/f/uL2P3Xq1KlTp56/T15eXq9evTiOu7gfAQDQKjkVphAjIhdjUvV9SBIxt/LHTUkJAp8kCDVflsvKoUDAzVjgvDcuBaWKYi+1qubLo5K0zx84V2ctx2lC/kp3FMW24h8/16EoxySZiMw8LxCliAL+oAMAAEBEiLAAbzQau3XrFnqS/H//938XLFhQc9f6mjVrlixZ8vrrr99xxx1hqhEAoCViRA5F8TDmY8zM8zEhtx0VBqStPp9LYQFibsb8jHkZ8zLmZyQz5mSMiFwKk4n5GEsTxVcS4moS76MVlV+7PfWswcBxv6Qmq6uj9eSy8oKAVM/vFTlue2pyTSy/r9x6uN7fq+W4LSE/966yivyQ8H+nyfBkjKWeuwIAAAAIowgL8LWsW7fu+eefj4+Pnzt37vDhw0VR3LRp01NPPTV16tR+/fp179493AUCADSyYA63KczCc+aQEL7L5//O63UrzMWYW1EcjDkVxc2YlzGHEkzjf5zlNvH8j22TtNWB9uEKa2G9w3CFrAQYqwnD3urdGnmOJ46I9Bynqs73AnEG/o/T273ValXIifH/Nho2erx6jlPV45qm3mpV6En1GWbTdx7vuU7cexnzhfy+GSpRHfK9V+q0wS/sisIR102lqrMDAAAAgJYosgP8yy+/zPP82rVrBw4cGGzp2rVr3759L7vssnnz5v373/8Ob3kAAA3iUJTf/IFKRbGd/sfs1Z87FWZXFCdjjuolMEw8/33bJH11Ln26surQhUK4mec1HKflqLdaHRpoH7aYf/H6jDwnEmfgODXHaTlOw5GG43iOM3EcERl4TiBOzXGpohD6va8lxF3073u70XC78SInMRmr143V6y7ue+83m+43my7uewEAAADCKLID/J49e/r27VuT3oP69u3bv3//nTt3hqsqAIBQToWVynKlolQqSnn1J5WyUqkoZp5fGBdTc/75gfLKrT7fBXdo4nkzz3VXnXFG+unYmF0+v5bn9Bxn4Dgzzxt4zsDxao6z8Kcz+bl2OFqnHa3T/vnfFAAAAACaVGQH+EAg0KZNm7rtbdu2veBk8gAATWG33/+ly10uKxWyUqbIFbLiO/cMbWqOsyssvnoOtav1WpGjGJ638HwMzwcvkg/+M/GcieNNZ142H6qfRt1Po26SXwkAAAAAWobIDvADBgzYs2cPYyx0TnjG2N69e3v37h3GwgCglXExVizJxbJcKsslslwmy6dk5ZQsl8mySNwnSQmJ1ZOrv2Fz/Og94yy6nuMSBSFB4GN4Po7n4wUhludjBD6W5zNVYrzwRyCfaDRMvNhLygFahyNHjjidzm7duomiSER79ux57733ysrKsrKyrrvuuqysrHAXCAAAEE6RF+CLioquvfbazMzMzMzMgQMHbtiwYd68eXPmzKnp8MILLxw8ePCmm24KY5EAENFKZXmVy3NSlk/K8glJKpFlp3LOs+hxPC+HfPm/sZZRPn8cz8fxfIIgJAr8ea5dB4Aaq1evnj59enFxMRGlp6evWrWqrKxszJgxgcDpJQP+9re/LVq06KGHHgprmQAAAOEUYQF+2LBhBQUF69atW7duXU1jTYCXJGnAgAG7d+/u0aPHU089Fb4yAaClsyrKCUn+XZJOSPLvsuRlNMtirjkZ/qrN8anLHdpfy3GpopAkCG0EIUUQEgQ+ufpjvBCysjlRB1HsIEbYn1aAsNuxY0d2djYRDRo0SBCEbdu2TZw4UaVSxcTEPP300926dcvPz58/f/5f/vKXIUOG9O/fP9z1AgAAhEeEvcr84YcfiMjtdh8+fLigoKCwsLCgoODIkSPBrbIs7969e8iQIe+++67BgMtQAYCIqEyWj0jyMUkqCkjHJemYJB+XJFed+9Kv1mlHVE/kNslkjBP4REFoKwhtRSFFEM515zkANIq5c+cS0dq1a6+55hoi2rRp08iRI2VZ/vbbb0eOHElEV1111VVXXXXJJZcsWLDg888/b+j+A4HA3r17ZVk+Tx+r1UpESvVCDwAAAC1QhAX4IL1e37Nnz549e9ZqV6lUR48e7dChQ1iqAoAWZX8g8LTVVhAIuM82h5yJ59uJQqogtBPFdqLQWRQHajU1WzurxL9YzM1YLEC0y83NHTx4cDC9E9EVV1wxYMCA/Pz8YHoP6tGjx8CBA/fs2XMR+7/77rvff//9+vQsLy+/iP0DAAA0j4gM8OfC8zzSO0D0OCXLRQGpSJIKA9JhSSqW5MdjzDVn0ff7A3v8fiKK5fmOopimEjuKQgdRbC+KHUTBgjPqAC1JWVlZrdln27VrFzwlHqpNmzY7duy4iP0PHTo0Pz///H08Hs8F+wAAAIRXqwrwANCK2RTlYEAqCAQOBqRDgcDBgOSoc6XriZDrY8cZ9L3U6kSBx9XvAC1fRkbG9u3bFUXhq5+wM2fOrKioqNXtwIEDiYmJF7H/adOmTZs27fx98vLyevXqxWHWSQAAaMEQ4AGg5ZKJ3rQ7dvj8BQGptM7NqzE831klpotiJ5XYWSV2FlWpYuh0ctRZhT9xAJFh1KhRL7300j333PPqq6/qdDoiGjhwYK0+y5Yty8/Pv+uuu8JQHwAAQMuAV7cA0CI4FGV/QDoQCPRUq/qo1cHGIwFpic0R/FzPcRkqVReVmFn9MXQFdQCIaE8//fSXX3751ltv/fvf/x4/fvyKFStCty5ZsmTFihVbt241GAxPPvlkuIoEAAAIOwR4AAiPKkX5zR/I8/v3+QP7A4Hj0ukT7N1Uqi+TT18i21klvpoQxxNlqlSpooALWwFaK7PZnJubO3/+/A0bNthstlpbv/76661bt/br12/ZsmVdunQJS4UAAAAtAQI8ADQTN2P5/sBvfn8wt9ck9iANx3VRid1UqmyDPrT9qupJ6QCgdTOZTAsXLly4cGHdTc8999xrr73WqVOn5q8KAACgRUGAB4Am96nL/Z7DWRQ4I7LrOK6HWtVTre6hVnVXqTqpzrx/HQCgWt++fcNdAgAAQIuAAA8AjcmhKLv9AYeiXKPX1VzxvsrlLghIIsdlqcReanVPtaqnWpWhUiGxAzSzhq7B1q9fvyaqBAAAAC4CAjwA/FnHJGmHz7/L59/l9x8OSMG13ZIFoa/m9Fx0ryTEHZekbiqVGuszAYRV//79G9SfMdZElQAAAMBFQIAHgAZjRIUBaZvPt93n3+7zhy7wpua4LLVqkEbTS62qaYzl+djqieUBIIweeOCBcJcAAAAAFw8BHgDqq0iSfvb6tnh9O3z+SkWpaY/j+X4adV+Nuo9G3QOn2QFasCVLloS7BAAAALh4CPAAUC+/+QO3niqruZo2WRAGaNT9NOr+Gk0nlYjIDtDKHDp06MSJEyNGjAh3IQAAAPAHBHgAqM2uKFt8/u0+X2+1+jq9LtiYIggDtZo2Aj9Qoxmg0bTHnPEArRdj7Lnnnvv0009dLle4awEAAIA/IMADABGRxNgOv3+z17fZ69/r9wdvas9S+2sCfILAv50YH8YKAaDRMcaeeeaZFStWlJaWhrbLsuzxeHr16hWuwgAAAOCsEOABolqpLG/y+n7weDf7fE7l9AXyao7rr1YP1mpq0jsAtEpLly6dO3eu2WxOSUk5dOhQcnJyu3btbDbboUOHBg4c+Morr4S7QAAAADgDAjxA1JGJ9vj8P3p9P3i9+/2Bmtvau6hUQ7WawVpNf41ai4noAKLAW2+9ZTQa9+/fn5KScuedd1ZWVq5evZqI5s+f/84772RlZYW7QAAAADgDH+4CAKBZfefxDj1Rcltp+VK7Y58/oOW4K3XaZ2It37ZN+io5cVaMeahWg/QOECUOHz48dOjQlJQUIhoxYsSuXbuC7U888QRjbN68eWGtDgAAAGrDGXiAVs7NmD4kkBdJUpWidBTF4TrNMK12gEaNVd8Aopbf7zcajcHP09LSTp486Xa79Xq9KIpDhw7Nycl54YUXwlshAAAAhEKAB2idDgekjR7vNx7vb37/fWbTAxZTsH2KyXijQR/L4+obAKCuXbvu378/+Hl6ejpjLDc3d/DgwUSkKMqBAwfCWh0AAADUhhfxAK1Knj/wks1+XUnpdSWlL9nse/x+DcfVWvIN6R0AgkaOHJmXlzd79uzy8vK0tLT4+Phly5YRkcfj2bhxY7t27cJdIAAAAJwBZ+ABWoP9gcA6t2e923tMkoItMTw/Uqe9Sqe9HPe0A8A5zJkz55NPPlmwYEHHjh2nT59+7733zp8/f/fu3Vartbi4ePr06eEuEAAAAM6AAA8QwQoC0tduz3q3p6g6t7cRhDF67Sidtp9GI5z/mwEg6plMph07dixbtqxbt25ENGfOnIMHD65atUqW5SlTpjz++OPhLhAAAADOgAAPEJG2+nzzKm0FgdO5PV7gx+h01+h1fTVqXB8PAPWXkJAwe/bs4Ocqlerjjz/2eDw8z2s0mvAWBgAAAHUhwANEpB88voKAFMvzo/Xaa3S6AVqcbweAxqHT6cJdQtRhsuL+dbd72x6ptJwTVepO7Y1XDlJ3aBvuugAAoMVBgAdo6fyMfefxfuv1XqfXD9OePif2kMV0jV7XXa1CbgeAi7Z3797zd8jKymqeSqKZVFFV9vdlvsJjNS3e/EP2td9Zxo2OvW0sYRITAAAIEakBvrCwMCcnJz8/v6Kiwu12x8fHp6ampqamjh07NiUlJdzVATQCRrTL51/ldq9ze+2KQkQ8cTUBXsNxPdWqsBYIABGvZ8+e5+/AGGueSqIWCwROzXs18HuJmJRgGXululN75vO7t//mWL/J9uV/eK3GMn5MuGsEAIAWJPICfFFR0X333bd+/fqzbr3//vuzs7MXL16clpbWvHUBNJoTkrzS7V7l8tRMKd9TrbpBrx9nwHWtANCYHnjggVotDodj27Zt+fn5HTt2fOyxx8JSVVRx5Pwc+L1E1T4lZf5f+eo/8tpeXXWXZp167vWqz9YbRw0RLKbwFgkAAC1HhAX4ioqK0aNHFxYWZmVlZWdn9+zZMz4+3mw22+12q9W6f//+NWvWfP7557m5uT/99FNSUlK46wVoAD9j33i8n7ncW7w+hYiIkgVhrEGXrdd3VkXYUxUAIsKSJUvqNjLGFi9e/Nhjj13wAnv489y/7iai2P++jj/zLVpdn+76vlnuHXmenXuNIweFqToAAGhxIiwVzJ49u7CwcMGCBU888cRZOzzzzDNvv/32tGnT5syZ8+abbzZzeQAXp0iSPnW6V7rclYpCRFqO+y+d9kaD/jKtBlPKA0Az4zhu1qxZX3zxxRtvvDFv3ry4uLhwV9SaBUrKiUiTmVZ3k6ZruntHXqC4rLlrAgCAFizCAvyPP/7YtWvXc6X3oMmTJ3/wwQc//fRTs1UFcNEqZOXRisqtPl/wNtPuatXNBv31ep2JR3IHgHDq16/fr7/+GggEwl1IK8fxHBEx5SxzDTBZISIOhwMAAAgRYQG+rKysV69eF+zWvn373NzcZqgH4E/aHwhs8fn0HHetXneL0dAL89IBQMtw+PBhs9mMm9Gamio1SSqv9O49aBw+sNYmb95BIlK1a8BDoHh9ru+3eHL3SRU2XqfRdE03jbpcbBPfmBVHAo/EPsp3j07XtjNhqRYAaG0uEOB9Pt+uXbtUKlW/fv2CLRs2bHjjjTdOnTrVq1evW2+99aqrrmr6Iv8wePDgnJycgoKCjIyMc/UpLS1dv3794MGDm7MwgPpgRMH724dUTyZ/uVbzRVJiB5VowEJBANDsjh49WrfR6/WuXLly3bp1Q4cObeZ6vv7669WrVx86dKhz584zZsy49NJLa3WYNWvWkSNHPv3002YurOkYhvb35O6v+nCNNquLmBBb0+787lfv3kO8Qae7tL4r+fkKj5Ut+j+pvLKmxbv3kH31t3F3jTeNuaKR627Zcoq883+xF1ZJ84dZwl0LAEAjO1+A//jjj++55x6Hw0FEo0ePXrVq1VtvvXX//fcHt/7666/Lly9/8sknn3vuueaolIiIHnrooXXr1g0aNGjOnDnZ2dkdO3YM3VpcXLx27dp58+aVlpZOmTKl2aqCZvCbP/CEtXKyyTjBoK+1ycvY4xWVTsaWJ8a32BDsVNhXbvcHDleRJPFEO9ulaKoTe3ecdQeAMDnPii2iKM6ZM6cZa6EZM2bUTF6zcePG//u//3vppZf+8pe/hPb55ptvdu/e3ZxVNTXj8Muc32727is8+egC0+gh6vQOzO93b9vj3rqHiGLvvJGv3/ojUkXVqfmvKQ6XJqOj+fqRqrZtZIfLtWm784etFcs/4U0Gw5C+TfyrtCB+mRFRQAl3HRBRvvN4rYoyvs7rTCKqVJQPna4bDfoUAdd0QPidM8Bv27Zt4sSJOp0uOzu7oqIiJydn0qRJX331VVZW1qJFi7p377579+6ZM2c+//zzo0ePHjFiRPOUO3r06CVLljxczWKxxMXFmc1mp9NptVorKyuJSBTF119/fdy4cc1TEjQPu6IUBaQ51iqZsVuNhpp2L2P3lVs3e33JgqAQtcA/q0WS9IHD9ZXb7VQYESULwlSzUYPz7QDQAtxxxx1nbW/Tps0tt9wycGDti7qbzscff/zmm2926tRp4cKFvXr12rFjx8yZMx955JH09PTs7OxmKyMMeL7NEzPKX33fvW2PbeU3Nc2cRh13542mq4bUcze2z9YpDpeuX882j03jhNO3zesu6a7J6Fix/JPKd78wDOpDuJ0e4Nyer7L9LslHJOlRizm0vVJRJpdWHAgEJEYPYU1HaAHOGeDnzZvH8/ymTZv69u1LRE8++eQLL7xgMpm++eab5ORkIkpLS+vZs2dWVtbixYubLcAT0b333jtq1Kjly5fn5OTs27evqKiIiARBSExM7N+//0033TR58uRghReBMVZVVXX+Pna7/eJ2Dn/G5VrN07GWZyttz1baZKLbjAYKSe/xAr88Mb5FpXdGtMnre8/h/MV7eoK6yzSa202Gq3TaFlUnAESz999/P9wlnPbqq69qtdqcnJxOnToRUdeuXXv06HHFFVfMmDHjyiuvNJla84tm3qBr8/g038Ei9/bfpFPlnCiqO3UwDO3XoOXf3b/mElHcnTfWpPcg05gr7F9/HzhZ6is4qumS3qDCGLHXjrzglBwzOz8rcrhYDFq5J2MsD1dULrc7iagmw9ek984q8faQE0gAYXTOAL9r164hQ4YE0zsRPfDAAy+88MKVV14Zmo0zMjIuu+yyvLy8Ji/zTJmZmQsXLly4cCFjzO12e73e2NhYvjHeV7733nvrufgcY2eZMBaaVPDE+7OVtvmVNiK6yaCvSe/vJia0nJXSfYytdnvecTgLAxIRaTnuBoPudqOhiwqvfgAAzm7//v1DhgwJpvegPn36LFmyZPLkyYsWLZo7d+6f3P/GjRs/+eST8/cJXscXruO7pkt6QwN2DcXjlR1OXq9TpdaZ8Y7jNJlpgZOlgZLyhu7/18ofdtq2EFFO2Zpr2tx4cbUBRIorddrXEmIfLK9cbnfKjB6LMVsVZUppxYFAIF0U306MjxdwDQu0COfMPCPnFCkAACAASURBVDabzWL5Y+aP4OehLUGxsbF79uxpouIuiOM4g8FgMDTa+2GdO3dOSEiQZfk8fWRZxkn4cAnN8CucrqKA1KLSu01R/u10feBwVSgKESULwh0mw80GvRlXLQJAy/Diiy82qP+jjz7aRJXU4vF4FKX2LcuTJk16/fXXX3zxxalTp3bo0OHP7H/JkiVfffVVfXpG4hv0waXmmCwTY1TnFi0myVS9Xl39+RXf58Urgp+vPvXJkNgRFlXs+b+l0Unlla5fdgaOniSmiCltDIMvVbW7yEssAepjmFa7JCH2wfLKtx1OL2M7ff5gen+3TXwi7n6HFuOcsadbt27bt2/3er1arZaIfvzxRyLavn07Y4yrPjYEAoGdO3f26NGjeWo9vzvvvPPTTz/1eDx/ZiezZs2aNWvW+fvk5eX16tWLwz3MYXKr0RBg7Lkqe1FAMvBcy0nvX7rc8yttbsaIqIdadZfJeI1OK+L/CQC0JDNnzmxQ/2YL8JmZmb/++uupU6dCF67jOO6NN94YOHDg3XffvWHDhj9zqd1rr702duzY879Bf+LEiblz5zbKBX3NjNOoxYRYqbzSV3BUk5kWuonJsndfIRGp2qU0aJ9rSj+zBsrT9ZkWMWa3fdvnJSumtH+wEWu+AMaqPltv+2w9C3nIqj752jRqSNyUm7mQ477CaOa3VYVVUuh3V3oVItp4xJv9eSC03ajmXroyJsmAJAbnVJPhP3S6iAjpHVqgcyaf22+//S9/+cv48eMffvjhsrKyxx9/PC4uLj8/f+7cuXPmzOE4TpblmTNnnjhxYsaMGc1Z8bkEAgGv1xvuKqDJeRn71usjYkScW2FbfL4WEuB3+PwexoZpNXebjZdpNOEuBwDgLFauXFmr5bXXXsvJybn00kvHjx+flpZWWVn5n//8Z/Xq1bfddtvChQubrbCpU6c+8MADw4cPf/fddwcMGFCTovv16zdr1qwXXnjhzjvvfOONNy56/6mpqXfffff5++Tl5f35a/XDxTC0v21ljnX5J0lzHvxj4nrGKj9YJVurVO1T1Gmp9d9bhb/sP2X/z959xkVxfX0APzOzvcGyLF1ERIoNRBSxBVuMLda/JcauMdbER2MSE1tiibFEo8ZojMYSY7BGxS42VERpSlERQaXussv2PjPPizUEERWUInq/H1/AzOzsIVF2fjP3nnsEA2y4x3gHpjhVm3xFGdNF8kEjXpMaqf4Zqn0nVFHHAcf5ndpwWwZgDMKUkaW7cF175gplskg/G1N6pIWkzz006SwVjJsoMVH2JF9WrpZEAR55seYslguB59pIAAjnsFF6R9402POGilmt1l69ep07d87+rUAguHLlyqxZs2JiYnx9ff39/dPS0h4/ftykSZNbt27Zn9LXtI0bN+7fv/95e9PT02UyWdl2eufPn6+JMuxP4HEcf/GNfKQmlO1aN5zP/0WjBYBvxQ4f1UVbkUc2mxjHhf9eZdpoWkXRzmh+FIK8A65du9a+ffuIiIirV6/WdS2vJSoqavjw4YsXL54/f37Z7Zs3b7Yv6vbJJ5/UWjGTJ0/esmULADCZzBs3bgQHB9u3W63WUaNG/f333zwej6Iok8lUQ6Pc6/XnO6U3Fnz5o7VQTjg5Cru3Z3q6kWqNPjbBfC8bYxCuC2ZwmvpV/mwbc1YkqK+1F0dO9P4cAPYV7DghO9SYHzDP7wcManxkmU2myJvxHQ2065eTuaHNSrdbcnILF6yjDEa3hTM5LfxLt8sNVJH+qf9l53JMPyfouvlwZrYWlN0uYGE+Dm/ETX/kjVU6792NwBUUbaXpcULBXEfRy1+JvBXqxef7c3+LMZnMU6dO/fHHH5cuXWIymTNmzGjZsuWhQ4fGjx9/8ODBBw8eMJnMoUOHbtq0qXbSOwAYjcYLFy68+JiXHoDUX2XTu33kvJTAS3va1WaGv2WxbNLoLhpNbdnsP1wk9o0MDHMm0IB5BEHqky1btjRq1KhcegeAyZMnr1u3LioqqjYD/C+//BIREbF79+4HDx6UjehMJnPPnj0dOnT4+eef79+/X2v11C84n+u6aKb8p+3muw9UUcdLtxOOQudpo6qU3jP1GYnqOBbOHug2EgBoku4rHnK15EKW/m68KjbcsVP1V/80/dUkmiQF77Utm94BgOXjJfqwm2rvMV3sjbIBXsrDpbyn7p7fUVgBQMzBm0tR+1ikCsp2rdvhIsmwWu3z4QEAZXjkzfGi25AEQUyYMKHskDORSLR//36ZTCaTyfz9/VksVs1X+J85c+a4urpOnz6dpum1a9f27Nmz7N6pU6ceOXIkNze3NktCao353/QuJYg/pBJfJgMAhgn4FhqWq9RLStRMDPsfn1fTZSSZLZs02ssmMwBwMawXr5buXiEIgtSEmzdvdujQocJdfn5+9vY3tYYgiLFjx44dO/bZXTiOz5gxY8aMGYWFhVlZWbVZVT3CcBa7L5llvH3PlJxuU5TgPB47sBG/XSuMXYWrNYqmdudtpoHu6zJEwpLa5JbM/slkiXXIzpG/szZE5f8RImrDxmv2s8+aVwgAnOb+z+7itvBX7QVrblGNFoC8m8qldylBSAmitKcdoAyPvDFeZRyRi4uLi4tLtZdSGaNGjerYseOoUaPsk+VWrFjB5T6Z6MXj8QDA07MKU7yQeuSEwVguvduNEvIBYLlKvVipGsjj1lzTuASzZatWd8FoAgAehg0W8D4RCtGAeQRB6jUPD4+UlBSSJImnJ3mSJJmcnNygQYO6Kux53Nzcyi5ni5SHYdyWAdyWAa98gkvK04+NOU5M5/elH9rklnu9E00ZegAQjeC02BRy2yX5pOxwf7fh1VdxRezrEVTYTdD+F7UeznFA3nyfFSvt673vkDqXrhjXmcNZIxF/rijZrtX5MhlDav5ZEYK81MvjR05OTmpqqs32pL3nrVu35syZM2bMmB9//DEtLa2Gy6tAo0aNLl68uHjx4k2bNoWGhiYkJNR+DUjte4/LmSoS7nJ5Kr3bjRLyV0vEi5wcayi9XzebR8uKP5YVXzCahDg+RSSM8XCd5+iA0juCIPVdx44d8/Ly5s6dW3YJN4qivvzyy8ePHz/v4TzytjKQ+kOFewBguOd4XIHZ0zu3qUAY6WQtNHf5NNLpofiE/GCxRVajZTBcnQHAcv/hs7vM97IBgOEurdECkHeTK0G0ZLHKpne7blzOWonYjSAc6uESFchb6UVP4I8ePTp58uSCggIAaNSo0ZEjR+Ryec+ePa3WJ2tyzJ8/f+XKlTNnzqyNSssgCGL+/Pnvv//+yJEjIyIiFi5c+NVXX9VyDUgtE+P4DAfh8/b24nGft+t1JFss69TaOJMZAEQ4PkbIHyXgC9GvbwRB3hbLli07fvz4mjVrTp8+3b9/fy8vr9zc3CNHjty+fdvLy2vp0qV1XeBbi9TqSKUa5/MYzlVbXJ0ymXFOTS108k/RXq1N04QfFGxpndknyZSh5/jz/Y62IkSMrKEp2vPKoTOG71sXtV+089OGVVuPsEp44cGqfSe0MdcE3dqXbZ5PKtXqw2cAgB8e8uIzoLV+kVewSvLcf4zduJxuXDRrEnlTPDfAJyQk9O/fHwDatWtHEMSNGzdGjBjBZDIdHR0XLlwYGBiYnp6+ZMmSzz//vH379mFhYbVY8xPh4eHJyckzZ8789ttvo6Oj6+PCrcgb647Vuk6ttQ+Yd8DxcULBSAFfgKMLAgRB3irOzs4xMTGzZs06fvx4ampq6fb+/fuvXLlSIpHUYW1vK0P8LdX+k5YHj+zfMiSOwl7vifp1xV64VJX5/kP14TOmlDuU0YQxCHaAr6h3JC88uBoLk1uKYoqPA4CqoDh28HGHLJHWR/vPz0dMyu2gBOI7ouPsjm7xrgNmDjj88z9Z0ruNea8+UP/FWA09BV3b6c5dK5z/k6hfV27LQCBw850H6n/OkioNp1mTl/7gwS5MPzGjk1ettmpCEASpHc8N8PalUKOjo3v16gUAly9f7tKlC0mSMTExXbp0AYBu3bp169YtODh4+fLlBw4cqLWKyxIIBNu2bevdu/fkyZOVSmWd1IC8ff7S6b8vUdMAfAwbLRSME6Kn7giCvLX8/f2jo6Pv3buXnp6en5/v7e3dtGlTX1/fuq7r7VSy56j64CkAwLkchouEVGlsClXJ7n+MyRmu86ZgrIpbpmtPXlJs22+fGY5zOZTRZErLNKVlCru1l3w6AqrpabPWpqZoilfC7THzfYcHopKGJYd+PmQQ6sECAAA4HP7hYJ+5fRvcbDBgZn/NASW0qZa3rZhk0nCw2nSXbqiijpdtqs9pEeAyZ8JLf2Q/MePUMDTMHkGQt9NzA3xKSkpERIQ9vQNAp06d2rRpk56ebk/vdk2bNg0PD79161aNl/lCQ4YMiYiIOHz4cN2Wgbw1DDTNxrDhAv4kkcAJRXcEQd4B/v7+/v4VNP1GqpEhIVV98BTGIJzGDhZ074AxCAAw3rqr2LjLlHqvZM8Rp7GDn32VKS1T8fs+AHAY0F3UpwshdqCMJv3F+JI/j2jPXWV6uoo+7FYt5fny/Fe5bMkadwt/AMwgTvN/Ilo6d6JokgJgYE9GB9D/0PKPHsJFYIwgTScMnCY11dALYxDOM8cIenTUX4q3PMwHmmZ6uPAiWvFaN6+uGxYIgiD11HMDvFwub9myZdktXl5ezz7ldnFxeRPayHl6ek6bNq2uq0DqKxNNUwC8f68JJggFE4SCui0JQRCkJty9excAGjZsyOFwSr99gYCAmhom/Q7SHD4DAOLRA4UfdC7dyG0Z4DJvSv4XK7SnLjsO64M/M89Wtf8E0LTj8D6OQ548U8G5HOEHnRmuzkVLf1EdPCXsE/ni4feVV/BDDn4PrHyr9+Hmzh6uJE1+nzlHT+q+81/HJXgAACxw3u+SER5nfmDMnXvP79BL5qK/Jk5QY05Q4xp9CwRBkHrnuQHez8/v5s2bFEWVzi2fM2eOQqEod9jdu3elUjRICamvbDQdpTdsVGuZGBbj4YqetiMI8nYLDAwEgLi4uPDw8NJvX4Cm6doo6x1A20jT3WyMIARdI8rtYjX05AQ0MmVkme9lc4ODnnqV1WpKv48RhKhPl3Kv4rZqyvL2sDzKt2Q9Yvs3qpYiL3c+H7KnJVPPTBsb996pD88qjj0yZgPAkaK/h3mMsx8j2/DI/MCIEZjkY/dqeVMEQRCkSp4b4Lt3775mzZpJkyZt2LDBvta6/cO+rC1btqSnp48dO7ZGS0SQGnLOaFqt0mTbbADQhctB6R1BkLfexIkTAaD0zvunn35ap+W8QyidHigKdxRV2EOe4SKBjCxSpS23nVTrgKQIZ/GzT+YBgOnpanmUb1OoqqUr/Q3Vlet+VwV9+EH7A4XXBEmRl45tiAIADLBzxdHvSXq6sT2K1j7MX5yFEVjDX5uKB7tWx9siCIIgVfPcAL9w4cJDhw5t27Ztz549gwcP3r17d9m969ev3717d3x8PJ/P//rrr2u+TgSpTves1uUqjX19uEYMxmxHEVodBEGQd8Fvv/1W9ttNmzbVVSXvGpzPAwyj9AbaasOY5a++bEo1ABCC8lPKcS4bACiDCWj62bnfpE4PABVm+6qyUJZ9BTtC/moVtD+QxgCjgU609Z7U+96ebAeW+JLyzN/524b/Myrv2/v29O40wu313xRBEAR5Bc996CgSiVJSUubOnRsQEKBWq8vtPX78eHx8fOvWrS9duoTa3iD1SAlFLS5RDyqUx5nMjjj+rdjhiJsUpXcEQRCkRmFMBruxN2216a+W7xxkKyo2Z9wHAn92JDzO5zHcnCmD0XjrTrldZInafCcbcJzl2+D1yzspP+S1y6Pjho6Agecav7TRaQDgmubWbUzXQa4juQQP/5VC6R1BEORN8KJRw0KhcMWKFcnJyUePHi23a+nSpVlZWTdv3gwNDa3J8hCk2thoOkpn6FMg26vTA8BQAS/a3WWkgM9A/WwRBHm3yeVynU5n/zoqKmrMmDFLliyRy+V1W9XbR9g7EgCUW/cZ4v9bvsf6uKDoh820jRR0bosL+RW8qkdHAFBs2mN5lF+6kVRp5au30VYrPzyYEL1u19USqyL358yOP3cCDLx/CnSb1PDOZ3eTPkoEAEuyMb/7veH7RnXY2IEm6Aa/Br456Z0mKVthsTVfRtvIuq4FQRCk9lQ8hP7cuXMMBqNz587Yc7JNaW6Pj4+Xy+V9+vSpqQIRpDpcMpmXl6hzbDYA6Mxhf+no4PvMCEYEQZB3jcFgGDduXFRUVGxsbIcOHfbu3TtixAj7ru3bt8fFxaE+tdVI0CnMdPuu7nyc7MctDFdnpruU1Ogs2blA06yGnhWuIQcAot6RxsQ0U1pm/pwfuC0CGG7OpFprSs6gTGaG1Mlp3JDXL+zSD8fbrYuwp3fniZ43VFcKTHmy6UUA0GpPqCFRy09k0QR99pszoT2MPcDj9d/xNZElatXeaP3VRMpoAgCMxeS1aeE4oh/TDf11fRvEmczNWExhRev4ykgynyRDWKzarwpB3hwVP4Hv3r17ZGTkBx98UFRU9OLXL1y4sG/fvjVQGIJUjzwbOb1YOVmuyLHZGjMZm6WSzVIJSu8IgiAAsGbNmqioqICAAHtQX7VqlUQiOXTo0NKlSx88eLB27dq6LvDtgmHOU0dKJg4lnBxsRcXG5AzLg8cYiynqHem2ZBbO51b8IibD9Zupot6RGIEbUzK0py4b4pIps4XXtqX70tmEk8NrFpWx5maDFR40Rjut9HSe6GmfDA8AvVwGXp12NfnjpCdl9GHe7Xn3SOHfOpvmNd/xNVkfF+TP+UF77iplNDFcJAw3Z9pq019JLPhihelOVt3Whry+BLNlnFwxQlasIKlyu7JttiFFxSOLigtJNOYCeae9KMacPn06ODh49+7d3bt3r7WCEKQamWh6SJFcRVECHJsuEqIB8wiCIGX99ddfHh4eSUlJXC5XJpMlJibOnj17wIABAwYM2Lt377Fjx5YuXVrXNb5dMEz4QWdhz06WR/lkiQbncVg+XhiL+ZIXsZhO44c4/q+XKf0+qdLgPC470JchdXr9cuTbco0L1DQOyqXa1p8GAsBJ+aFii8yb22iA6wilpTh2ynknRxfvjZ5w1PZ+aK/TPU4cKtwzyqvOFi+gSVK2ciup1nKDAyWThjPcnAGAVKqVOw7oryTKV271XL8A51V8KwSpFwKYzAAm867VOkZevEPqLCGePGvMttnGyBRykmzPYUsJom6LRJC69dw58K1aterevXtRUVHPnj2/+eYbm81Wm2UhSLVgYVg7DnsQn3fczWWMUIDSO4IgSFk5OTnh4eH2xWLj4uJomo6MjLTvCgoKys7Orsvi3mIYxmroyQ0JYvs3eml6L4UL+bzwYGHPTvxOYdWS3gEg++pdADA7miL6dQWAEqvihOwQAIzwmIhj+FCPMVyCd+G9s+CEAw0h2WEERlxUnn5syqmWd38Fhvhb1vwippeby1ef2tM7ABBODqd7DNsTPoBUa3UxcXVVG1ItBDi2zUUSwGRmWW2jZMVykgSAHJttrEwhJ8kwNmuDsxOK78g77rkBXiKRnD59etmyZTiOL1u2LDIy8vHjx7VZGYK8PhzgJ4l4qZMjulmLIAjyLAcHh9IP98uXL2MYFh4ebv+2pKSEhSaavtVMlHHflL35IXkcJedez4QbKbG/PlxtpkyNeQEam+qG6spdXVozdfCAzwaCguKHOzRZEdJF0ouiqb/yfq+zmlMzAUDYNaLcUnw/3dT9LmxmINimtMw6Kg2pNk44bs/w9qfuiWbzGJlCRpJhbNYWqYSLHsYg77wXdaHHMOzrr7++ePGit7f3lStXQkJC/vnnn1qrDEGqigTYrdO3zSv8vqT8wocIgiDIs1q0aJGcnPzw4UONRrN3796wsDBnZ2cAKCgouHLliq+vb10XiNSgG6orCob86Kqj+SF5kEcbBhUX3csFgCzD3U0PV256uHL39V+bjvMX5YsKmhesWfLjpKzBZ4uPAcAd3e3b2sQ6qZnSaAGAeGYAAkUDAJAYRqrqeIo+Ui3KZvjRKL0jyNNeFODt2rdvn5ycPGDAAKVSOWDAgJkzZ5rN5lqoDEGqJMNiHVYkX1qi1lKUQ0WdSxEEQZBy5syZY7PZgoKCfH19c3Nzx40bBwB79uxp27atwWAYP358XReI1KDmwlYdnLqGuLfN/02uaa0TFgkHzRwsyhPZ9woLhQNnDLKn9yM/HbHyLKUvZOFsFsauk5pxPg8AXpDScQGvFstBapATji92cmAARgJwMGyZkyNK7whiV6le3GKx+NChQxs2bJgzZ8769etjY2P//vvvJk2a1HRxCFIZJpr+RaPdptGRAB4MYr6jQySXU9dFIQiC1AM9evTYuXPnkiVL8vLyxo8fP2nSJAC4ceNGbm7ulClTJkyYUNcFIjVIzJRMaDDT/jV1nLw/KBmuwMTZk5scD8UI7N7IREu+kR/u8ME/kX0EH9dtqaXYAY20Z6/oL98UfdAZKopznMDGtV8VUhNybLaZxSU2oHkYZqDpyXLlDhcJmhGJIFCZJ/Clpk+fHhcX5+/vn5SUFBoa+ueff9ZcWUh992GhrG+hTFbROh+bNboWuQUxRlO1vNENs2Vgofw3jQ4ARgv5x9xcUHpHEASpvFGjRt29e1en0/3+++8MBgMAZsyYIZfLf/nlFyazsv3VkPoO5xON9wfzwx0sj02ZfRPv9Uyw5BgF7Rz8/mlFCN6gyMSLaEWIHcz3sos37aH+vZCgrVbaYgUAnMMSdG1XpwUi1SPHZiud937U3aV0PrwcLSCHIJV8Al8qJCQkISFh6tSpu3bt+vjjj8+dO6fX62uoMqRe42F4isUyVqb4w0XiUuZ26SaN9me1lgDg4687DkpLUes12j+1egqgCZPxvZNjMOq3hCAI8trQ1Pd3EyFk+B0OuT8gWX9dDQCCdg6ND1dzercVFusuxVtycmkbyXRz5rUN5jT3r9IZcA5b+vnYoiW/6GKuGa4lsZs0BIIw339It/4UGITTxKGE2KEaC0bqRNn0bp/3vs1FMl6muGu1jpEp0HN4BKlagAcAgUCwc+fObt26TZs2bfv27TVRE/IW+E3qNEGuuG2xjpYpdrhIXAkCAH7X6uzpfZmTOJxd5elzJprm/Dte7rzRtLhEXUSSLAybLBKMF75JDwgQBEHqlfj4+F27diUnJ+t0uqSkpFOnTnl6ejZv3ryu60Jqmz3D50xIAxzz2dqMEBC0mXo4PcOmsDba1pxwrPJFY1mqfSdU+04ARdm/NQJojl/ktmoq/XysfWZ7JR3CPFd1n0uarUD9+zC2NegYHADofc8dyywqe/DIZrzZbYWvUzZSy4pJarRMISfJdhz2Jmcn+4WfvafdOJnintU6Xq7Y5yrloPnwyDus4iH0zZs39/HxecHLxowZk5CQEBwcXCNFIfWfEMd/l0pasJgPbbYxMkURSf6u1a1Saezp/UM+t6onvGoyh+UWrFJpNBT1pbJkarGyiCRbsVkHXaXD+PzhRcW9CmVoWBWCIEhVLVmypF27dhs2bIiNjU1OTgaAY8eOtWjRYvny5XVdGlIHCCGjcVRw470t7en9wcjbyr8KNacVmX0TSZXtlU+rPnRa9Xc0AAi6RkhnT3CdN8VxaG9CKDAmpct+2Aw0XflT6ay0ygpanKllcEr/2F+vsVBq81N/tBbqlWtG6oSMJBUkGVEmvds54fh2F0kAk/nYRuqoKvyFQZC3T8U3U2/fvv3SVwYEBMTFxe3evRs1pUcqZM/w9ufwAwvlJRT1yundfjYcw37X6vboDEaa4mDYLAfRx0J+CUmNkRdnWW1BLCZqPY8gCFIlJ0+enD9/fuPGjX/88cfbt28vWrQIAIYNG7Z///558+aFhob27NmzrmtE6gZtph58fFt9spjhzCJEhCFZm9k3scmx0Bc/hzdlGqx5JmHkU8u8kSqNat8JwDCXuZN4YS3sG7mhzYTd2xfMW23KyNJdvino3KaShU0K5o9syrM+HeEi98g1ZurCR1IR+79rASaO8ZjoOW0905TFvOjhJibwZ0dWOuF4lKuznqbFaLEh5N1W5X8AJEneuXPH/jWHw5k4ceK0adOquyrkLWHP8G4EUUJRAPClo8OrpXcAaMFibnB2wgCMNOVCEP+4SUcL+SUkNU5enGW1NWYytjhL0Kc0giBIlaxZs4bD4Zw+fXrQoEH2FeABoGPHjvHx8TiOr169um7LQ+oKbaEejL6tPlHMcGY1iW7lfzaME8g3JGvv9U60Ka3Pe5X+uvpu5xuZfZOSVl4xU/+1qjXcuE1brLw2LUrTux3h5Oj4v94AYLiSUKXyeEzMgY2X/WO/ABA9vRGl93rKuaL0bsfCMJTeEeRF/wYKCgqWLl3aunXrshtNJlNQUJCbm9vYsWMLCgpquDyk3ovSGwpJ0v4R+qdOX/Qa7UM7c9iTRAImhslIco/OoCCpcfLiTKutMZPxh9TZmUC/0BEEQaomKSkpIiLi2a51DRo0CAsLS01NrZOqkDp0SXnmhiz2wajb6ugn6Z3bTMB0YTU5HsoJ5BtvaTP7JlWY4fXX1fcHJJNaGwDQi02nlxwo3WUtkMFzFnhjN/UDAGt+0bO7EARBkAo9N/NcuHAhJCTk22+/TUxMLLudIAg/P7+ioqIdO3Y0b948Ojq65otE6qvSee+LxI5l58O/8glnOYg2ODuxMWyHVte3UIbSO4IgyGviciseGOXk5GQyVc96n0h9ka67tevBpkdj7pRN7/ZdTzJ8QMUZvjS98wY4XPzyIo2D549u6ctvPtltn+L+gq5jaEYzgiBIpVUce4qKivr27SuTyaZMmZKUlFR2F4fDyczMzM7OHjlypFKpHDNmjEKhqJVSkXqmbNe6oQJeuZ52lTnDPat1YKG8Z4HMVqa9TWcOe4mTGANQUZQDjqP0oINTwAAAIABJREFUjiAI8spCQkLi4+O1Wm257Tqd7vr166hV7TuFoqmonG0fzO/V6HIji9ja5Nh/6d3ueRm+NL2LB7meW3T29oe3rs67QuNgWqouWJ0NAEwPFwAw3Xnw7Jua72QBAMPTtcZ/PARBkLdFxZ1Ili1bptfr582bt3Tp0goP8PHx2bVrl0Ag2Lx587Jly9A0OaSc3zS6NWoNAbBcIu7H4wKAEMd/k0rGyRUZFus4mWKXi7Pk+cGbAtih1a1Vay007c9kYmVu2ytIaotGQwNgAGqK2qrVfeUoqo0fCUEQ5K0zevTos2fPjhkz5o8//ijdqNfrR44cWVJSMmTIkNopQy6XX716tfLH9+/fv6pvQdO0SqV68TEajaaqp32bXFCc9PzFvdFlXyvfemDtfo63JATaAACl1RtT79nkSpzDZvt5Nzna6m63m8Zb2sef3Wm0q0XZ9G76mUp4GMfGOf+bNfEgbA9f1q5g4QMMMOm4FkrmAUN8ijEpnduqaek7kiVqVdRxAOBHtKpSqTYKvr+iDnFlDfR/Mn7EfpmAo6XFEAR5B1Qc4GNjY9ls9tdff/2CV2IYtmbNmj179sTHx9dMbUg9tkmjLZve7RxwfPu/Gf6k0ThSwK/wtTKS/EqpumYyYwDDBfy5jqLSXiZl571/KhJ9qyzZodUBAMrwCIIgr2DUqFFnzpzZtWvXyZMnXVxcAKBHjx6JiYlKpfKDDz6YOnVq7ZSRlpY2YMCAyh9PV2XVMbtp06Zt2rSphk7+FtCTusNFez0auwEGTAPTM8lzb9Pfm3Fb6P4+pYm+QFvLDJjHWloL/QCA21JYNr17/x60+MH/AUA/1/+5sT2CP4k4T57tsqJb/sIsgMYOg3uq9h4r+uFXYdf23NBmGIthuf9Ic+ICqdKyA3wr34Le7oHKtjvNkFhkLQ3wE1ry5QZKyEIBHkGQt1/FAf7Bgwd+fn4CgaDCvaV4PF6zZs3u3r1bA4W9RFZW1pkzZ9LT0xUKhcFgkEgknp6enp6e/fr1c3d3r/16kHJ+lToRgLVms8ptd8DxHVLJWaPpfV7Fsy7PG03fKFUlFCUh8KVix/e4nNJdz3atE+FOM4uVKMMjCIK8sp07d/bt23fZsmUZGRkAcP78+caNGy9dunTSpElYbT3PjIyMtC9id+DAAQCYMmWKg4ND9b6Fj4+PRCKhqBetCk6S5Dv7EP5w4V86m4YY4NPQOejhjDud1nWKpWP/CVkcdlgLGMYNCWJ5e1B6Y8mxIs3lRkDRzmPEom5OmX2SSK3NaZhbwy1Nz5ecyDU9lLJce0j7AUB7py7nh548T8V0Wdk1f2FWg9XNHIfYVAdPa8/Eas/Elr4vNzhQ+vk4qGJfcYqmAaDsQnJTQ19yyYogCPLWqDjAs9lsFqt89KqQwWB48cdhtcvOzp46derJkycr3Dtt2rT+/fuvWrXKx8enNqtCymnLZj9vlxDHB/J5z2630fQatfYPrY4G6MRhL3cSlx1jr6KerPfuz2Rud5E44TgAdOawf5KIP1OU7NDqGABzUIZHEASpuqFDhw4dOpQkyby8PDc3t0peAFSv5s2b79+/v3Xr1omJiV9++WXDhg2r9/xz586dO3fui49JTU1t0aJFrd22eHPkmx5fUJzEMXyE5wTJGA8AeDjjTsefO16bFmtwszT+fCbbryEAqE8ptHG3gKK4PpkMnkp93JHU2jA27v6trwH0h4v2AsAwj/G5xofbH2/8n8foEZ4TVnzwTcuDwc6ZEuXfRQExffmd2+ouXrfk5IGNZLg589q25AYH1fEPjyAIUt9UHOBbtGhx8eJFk8nE4XAqPMBOr9enpaW1b9++ZmqrgEKh6NGjR1ZWVrNmzfr379+8eXOJRCISiTQajVKpvHPnzrFjxw4cOJCSkhIbG+vqinqi1BsFJPl/xSXJFgsDw2Y5CMcJBeUuoM4bTeXSu10XLmedRPyZouQPre5zByHj3bvwQhAEeTVarbZ3794DBgyYPXs2ABAE4e3tXbclDR8+vNzaN0gt2Ju/jaTJbs59vDgNAcCe4XOmZ0Rs7Jg57UHTf9P7g49u0WbKdboXXXLekq1ymY9pzjvor6sz+yZmbMvUcTRBgpYhojbfZc7JNeXszN20xOfnjxZ9LMoUWsU27/WBAMD0cBGP6PcKFT7SkHH55iEBPBx9yCMI8s6reMxSq1atrFbr77///uIXr1u3jiTJli1b1kBhFZs3b15WVtby5ctTU1OXLl06YsSI999/v127du+///7w4cMXLVp08+bNbdu25eTkLFiwoNaqQl7TBaNpYKE82WJxI4gdUsn4Z9I7APTmcVc4iXc+nd7tunA5u10kW6USlN4RBEEqTygU5uTknD9/vq4L+U+bNm38/PwYjIqfLiA1IUl9PVWbxCcE/V2HlW6UjPEg2icDhnlt9M1Ym/hfep/p7flDADekKQCQsrwmR1oJOootD02uI8WO+Y7DPcdfVp59ZHwAACq94uaI86KLQpOjaf+6qAfe91+nyOXXNF9fUN8osDy7647COuG4Mq34ySx92kYakzPU/5xV7T+pv5pI6Q2v874IgiBvoIo/I7/44osdO3bMmjXLx8enT58+FR6zb9+++fPni0Sir776qiYrfMqlS5cCAgJe/I7jxo37888/Y2NjX3AM8oYgATaptZs0WgogkstZ7uTo+JyJcGwM+5Bf8bR5AGhZFwM+EQRB6rslS5ZMmjTp1q1btXkv/gUiIyMzMzPruoo31P0Sm42iAyXMajynjbbtK9gBAAPcRggYZaah0bSjw8PkgcYGB9sZ5ysfECraRrvOauj5vR8A4HwuAFBGM84n/A4EX+hzxPGmaNhnI0THRYfMf+oNns76/3VZlcWNZRPODM1Os8JBsTd/22L/n3CMeE4hL2Gy0QBgJivoL3juofnCI3MLKbOZM9Nw87byt79tiv+WG8A5bIchHzj07/6iVegRBEHqlYoDvFQq3bp164ABA/r27du1a9fp06cHBQU1bNgQw7Ds7OyMjIy1a9devnwZANasWePl5VVr5crl8hYtWrz0sAYNGqSkpNRCPcjrMNP0eLki0WxhYNj/OQgrfPCOIAiC1Jy+ffuuXbu2R48eEyZMCAsLc3d3x5++ixoeHl5XtSHlfHxUobfQyRPciOr7sDwtP1JoznfneEVKPijSkxwG5sDGAQAwjOHk0FRbcO6L621XhkOZ9A4AltxCAGBIxABwi0z8+4c9fb/40DPJI6PXdVhHK7RDuq7h+qb5GB1Nt3693T6kO/shJ8/06JLyTKTkg1cocsaZkkuPzQAwLlpZdvsdhbXxrwX2r9cn6NYn6Aha/C3Dvbs3j9MyEGMxzZk5ptR7Jbv/IVUap7GDX/0/E4IgyJvkuaPU+vXrl5CQMGPGjJiYmJiYGPtGDMNKl1dxdnbeuHHj0KFDa6PMf0VERJw5c+b+/ft+fn7PO0Ymk508eTIiIqI2C0NegZKiUswWN4JYIxG3eqZfPYIgCFLTnJ2d7V8sX768wgPezTXV3kwaM20maRtFE9WU4NW2kmjZfgBwZ3sdKzz6dXSYVGD+suuT5x+GPrg5U6dsnX3cSeFskE6Y08m+3Zz1yJicgREEp6U/AJyQHbRyrdE/Hu0360P3VPeBMwe1kdiC0nR6B+uRnw8q3BU3suPsLzwhO/RqAR6DKvy8/Pah0s4+lpw8miJ5rZuRuq7y1Vs10Rf47UPZ/o1e4d0RBEHeNC+aZhYSEnL58uWoqKgDBw7cuHFDJpORJCmVSkNCQnr16jV69Gg+v+J1vGvOzJkzT5w40a5duwULFvTv379cl9qCgoLo6Ojvv/9eJpONHz++lmtDqsqdIE64uzoROB8NbEMQBKkLc+bMqesSkDqTrL5hJA0AkKiOu25NM9vCi/Tk/oKdT3Z7A9h7GnYqzoYHnXSpTYkgfVxSya7DQFGiD7sRQgEAdHbqwcRZIIRz6893m9HFPdU9qBC0PMbZ1QV4EBMswMG5vnx/AGgubPVqdf7cw1FroS49Nm/v49S5ARsA7iisffYVB0qY0f9z3pCg++mG9hNn9bC9G1iNGmA4lnfoz9LXMpzFvJCm+vgU3blrKMAjCPJ2eHmfGPvqMrVQSmX06NFj/fr1n/3LwcHByclJJBLpdDqlUllSUgIADAbjl19+GTBgQF0Xi1TARNM0APffxN6A8YrT4RAEQZDXt3LlyrouAakzbR076kiNPcPrzKyrAGyc3dtlUOkBtqJiw43btM3G0+PcG78/Ip98dvMjWjmO/ND+dQenrh2cuqZrU1arFxncjZAKAGDg4pFN+ncKGPrN3WkKizzcsVMnp+41+rOQKi0AWB/l0SRFOAg5zZpgTKY5M9uaL7MVlwCAOTu3RgtAEASpNfWv0euUKVO6d+++devWM2fOZGRkZGdnAwBBEFKpNCwsbNCgQePGjXNzc6vrMpEKJFsss4pLGBicdndFz9wRBEEQpA5xCV4flyH2rxVG6nsoYuOcIe6j/zvCHWweCvU/54wP0myUEmMz2U18hO935Ee0KtsTjqLJvY+3dV/SvfEZX0JAKByYrnkmcnwqdqb1EPfRmx+uPlCwK8yhPZfgVaaqxxryuytqM/nURnuT+VXXtVuSdRQNBisFAA+V5m/XxbKABrafUW/SMjgAIOjRTjiyH8ZkchiYMw7amGuKzXuBoii9/jX/cyEIgrwh6l+AB4AmTZqsWLFixYoVNE0bDAaTySQWi/HndC+vkq+//vqHH36ozJFoWmBV7dXpl6k0VpruxGGj9I4gCIIgbz6Gi0Qy6SXDMM8WRQcu9A84FYjzicb7g7fnWyO/yfTOMmT2TAg90daf3+yePu2obN9Q9zGVecdbckvMQ3OFu0rXirMz0vhf7Mb2r/ew/fZEzgYAoAF2KwFAxMZPDnV27dbeEJdiTEoDiqrMuyMIgrz5Kg7wTOZLFinBcdzX17djx45LlixxdXWtgcIqBcMwPp9f+1PxkSqx0PR3JeoDegMAjBby5zo61HVFCIIgCPLmSpFZo+6UX8DcRgEALIxVE083jvnQjxvuUTeNYB+obJ+dUry/WdIhnm9m41uneGc9shYbqIufen+/+ZFHpiGmw41Lsz95yNPEA33ZK399d4+XnrN3Y66HgDBYn3pMsvK69rbcOqO14MBdo0pnoUjKRDBxAD5BW2jMTAGLsrEpGwDgPK59dIAzF2cRGG21kbJiAAAazuaYVsVr13UXBzjVy8dXCIIgdhX/CmMwXvSrjaZpi8Vy586dO3fuHD58+Pbt27U/ZD0nJ0en0wUGBtpLvXXr1s6dO+VyebNmzfr06dOsWbNXO+3y5cuf14m3VGpqaosWLTDUd61yiklqpkKZZLZwMOx7J8e+vOeu5Y4gCIIgCADsSTfsfybA2+3LMJbbojBSVQ3wacXW2MdPPeW2B2ajjd6cpCu7HcOgrx/XQ1Bxw5pcla3rL487JJhMbPyHCQ3S3LigIQHAyGfMn+y98NdH3gWmqSsfLZrSsETEvJRroOHlDeUxgFau5X+crSl6AAh1Y01z1+fNWpHj4DYhbHygM/PokCdN7D4mc0Ze/BsACJHAadxgTkgQzmKZ7mYW7j1mySsCAIzJvPzYnKm03SywoACPIEi9VvGvMKOx/MdDOTabLScnZ8mSJTt27Jg3b962bdtqoLaKHT16dPLkyQUFBQDQqFGjI0eOyOXynj17Wq1PBlbNnz9/5cqVM2fOrLWSkOdJt1inFSsLSdKDQWx0dgp82cgOBEEQBEG+DBe2dmOWG/S9MFZjo+jFHUUM/L8UzCSwjl5VSO82Cvak6w/fM6bIrM/uNVjpH69ry20s0FELO4qePZgmae+lWYIEtZVr+2fZRUFLWTgAANx7MLJEHejW4u9jG7OHzu7qke24YnvC3tXnnHzYGKyufKkV0l9LApoOatPk46Y8e85XmSgAUPOfDO4jNTr5uh1lX4I7CCm1lpA4Pin7NStAEASpa694D5LBYPj5+W3btu3atWuxsbHVW9MLJCQk9O/fHwDatWtHEMSNGzdGjBjBZDIdHR0XLlwYGBiYnp6+ZMmSzz//vH379mFhYbVWGPKskwbj10qViaZbsVnrJU4SohqaFCAIgiDIW8+Jiw8NLN/y7bsrGhvA/4J47NdYBz4217w4VtOpAXtqqIAsk2WNNnrnbT2XgY1u8dS0RDYBQwIqbj4nW/9I+VchxsYl+xtMaTvBlJGl2nOEMlt/aoCViKDPRTxUY8PaXQRdT/Fj0cxNI3wPB79y2aWs+UUAIGjaaHGnJ4n9ntIGAA95TvZvmW5SwsnR8jCXtpFMdxdeuxDjrbtmtZbbMuD13x1BEORN8FqDiHAcDwgIiImJqa5qXuq7774DgOjo6F69egHA5cuXu3TpQpJkTExMly5dAKBbt27dunULDg5evnz5gQMHaq0wpCwaYL1a+6tGSwMM4fMWiB2YaMYBgiAIgtQ1C0kDAJeBzW4rLLtdYaR23tbzmNjccOFzXloeJ4CPERhtpgTXec4BgrxVJzgW4HeKcPQOhCI6YMzElrlpRWsuapRMGkDc3IVPCKrhB7DfdChzUfFkA5fLatTAkv2YLFFLpo7kBDUGDLPmF5X8ecScnkmIBMKenSCxgkEHCIIg9c5rBXiFQhEXF+fn51dd1bxUSkpKRESEPb0DQKdOndq0aZOenm5P73ZNmzYNDw+/detWrVWFlGWm6XlK1XGDkQD4SuzwsQC1GEQQBHlTrF5dtTHMs2fPrqFKkDqRpk0GaFgtp3Lo5eyzvVnO+LT877L0cToCtwrea+s8YzR+sgTAhLFYwAnSXjfRJMVu8Mj9q7bV8qZMdykAmO9m8zuWH2XJDW1qyX5MmS2FC9ZibBbGYFB6AwDgfK7L3E9wPg9AXS01IAiC1K1XCfAZGRnx8fGPHj3avn27XC6fPn16tZf1PHK5vGXLlmW3eHl5KZXKcoe5uLgkJCTUWlVIqRKKml6sTDRbWBj04PI+qii937ZYf9NoZzmKGr2wVyKCIAhS7ebMmVOl41GAf5ukaG6eV5wEmGyhKl6nrarEg1wBIGd8mvq0gOsX6Ln2/dJdjETN/SnplJHiBWs4bgmmtGB++1AAMFMmDHAWXrWue+08WFkqWxMxg9cuRLX/pPbcVX5kOLuxN5Q+jLdadaevAAD/vbbmuw9shcVFGEfr5stp4S/oGqFxFIHcqjRSAJCvI1PlTz2K9xISjhw0yw9BkHrjVRLU8ePHS68AIiMj586dW60lvYifn9/Nmzcpiipd9X3OnDkKhaLcYXfv3pVKpbVWFVJqgVKVaLa4E4SOpqINBg4G3zk5lv1UvGWxTJQrtRTVmctBAR5BEKSWHT58uNyWjRs3njlzplWrVoMHD/bx8SkpKTl9+vTRo0c/+uijFStW1EmRSE2w0ba/87cBuABAnukRQPWsHyQe5Ao0nT021Xg/qHinwX0eAEBQtkE4/zFlJCWjPYThGs1RIItLAMBA6uffncnBuYsC1jCxKmT4ya0Ek1sJAOAOKNfOK+hylt9u/k+iDzpzWgQ0pvhXgOWZmkRqdbw2LaQzRgNAar5+5FENRQPQAOcsAMWlp9qcpCvXaZ/PxK6OchWw0Fw/BEHqh1dJUAEBAcOHD/fw8Gjbtu3QoUNrc0G17t27r1mzZtKkSRs2bOByuQAQHh5e7pgtW7akp6ePHTu21qpCSnXlclgY9pWjKMdGTpYr7Gu/l2b40vTei8cdyK+4KQ6CIAhSc+yNYEtFRUWdPXv2u+++mz9/funG6dOnb968+dNPP33vvfc++eSTWq8RqZifmKG1UKxX7WB3rji60JzvwPAHgBKrIlOf0YQfVLrXPpPcaH2VHu3iwW7FG3drb4QULMsGDOvXiOu89RFmpiSjPRpuCCz+NREAMC4HAP4p2ltiVQDAafmRPi5DqvpGFE3uzttiwW3nu+uD0nn0kXPqI+eIRp2gcWeBUcsLD5HOHG0/0k3MbeNu0j/94+RrSaWJ8hAQTtynnrd7CgguA6V3BEHqDYym69OCGhqNJiQkJDs7m8PhDB48ePfu3WX3rl+/fvfu3fHx8Xw+PzEx0d/fvyZqsK8Dj+M4SZI1cf63RoLZ8olcYaDpQXze906OGRbreLlCQ1Ef8LirJOKKl5RFEASpD65du9a+ffuIiIirV6/WdS2vpXv37tnZ2VlZWc/uatq0qYeHx9mzZ2u/qrryZn6+G6x0kZ5s5MiwUUADzcSrEDUXXFbbl4sjaTLP9IgCSgCehTqCwTCKuBoPjlfpkTwGFl9gYWBwd7L7KxRZuGCd+qzJkBpGU4CxcNpCScZ4NFwfSNtsudMXkUq1x49zFZ74grufUTRFA83GOcsDf3FkOlXpXf7JO/X16QZms7gyBzcQEgcGOUvKZPWFl9W70wyLOzl83Aw9QkAQpGL14vO9ns35EYlEKSkpc+fODQgIUKvLNyM5fvx4fHx869atL126VEPpHXlWssWir+g2UGs2a4tUwsOwg3rDjGIlSu8IgiBvmps3bwYGBla4yz5nrZbrQZ719UV1j73yTKWNgUOV0jsNcOKBKVVuTZVbM4opjc5Lp/Mu1BEAYLNxlVpX+y77n/gCCwCI2K94WSjs0Z7t8VgQdgsjsP/Su9VavHEXqVSzfL1ZjRrszdtG0uR7kp6hDu3MlOlA4e6Xn7cMPak7ITtE05Wt0GCjqfr0iApBEKSy6t8kZKFQuGLFigon5i1dunTjxo2+vr61X9W7iQRYqFQd0BtGCPgLxA7PHmDP8BPlihijCQB6ovSOIAjyJvHw8EhJSSFJkiCe+t1MkmRycnKDBg3qqjCkVLGRpAEURqpJFV+IAZwcKi3QkYXmvN8ercUB+7ThnMxix+XXNE1djFzXtXyGYLrP12ycAwAmGz3sH8Urz4nkdwzTX08xxCULWukJcSNhG3XxpgRjUhqp0uJcjvO0kSnahNvaRB7BH+j2kYky3tYkXlWe7yL5wJfnH2cyZ1ito4WCZy8P9DS9Q6vryuUEMpmHCv+04IUT3tszp/F3Bwv/PFa0z4fbeL7/qo0J+p9uaKe3FsxqU9kF8BAEQeq1evYE/sVCQ0NReq81RpqeVqw8oDdwMex9Lud5h3EwDIcnVwS82uyXgCAIgrxMx44d8/Ly5s6dS1FU6UaKor788svHjx936NChDmtDXp+EizeXMm+afuPzcwb6BL/n6ektIgDAiy8OdRXy9TlFx040c2I0lzIbOb7eEx0Mk84a5zCoJ8tTiRPXNMdidOfjSJWW7e/jtvT/cG/Xv/O3A0B/1+FChkjKcn1f+iEN9F95v9NAb9Bof1RpvlCUlJu3oKfpT+SK9Wrtbq0+3/T4guI0jhHDPScAQF+XIRKWNMeYdVV5/rXKRhAEqYfq3xN45E2gpqjJcmWKxSLG8V+lTi1ZFfeSTbNYx8sVBpoKZ7NvWyyH9AYM4Pun+9IjCIIgdWXZsmXHjx9fs2bN6dOn+/fv7+XllZube+TIkdu3b3t5eS1durSuC0ReV7wq9p4+TcRw6Oc6tOz24bbRjyZlCIqEd2ITAre2fv03wghC/FE/hw+7mVLv2eRKjMNmN2nI8vECgJPyw4XmPHe2V1fn3vaDw/iDjhqzsiApruTiXMf2E+XKEwYjAKz8d5iePb3b17WZIhL+9WgtRZPdnft6cRoCAAtnD3QbufXR2n0FOxtSrV6/eARBkHoEBXikymQkOUGuuG+1eTGI36QSn+esBpf2dNe6ZLPlE7nioN4AKMMjCIK8GZydnWNiYmbNmnX8+PHU1NTS7f3791+5cqVEIqnD2pDXZ6Es+wt2AsBAt5Fc4r/ObcIis3pmsaBICADGfeoMKs32gz8AkDQ81jz1IJxFgCu/ClPfcAGP1y6k7BaNTX20KAoAhnuOJ7Anp/oixphUNKlt6Lf7C3YtC2z3h1QyXq44YTBSAKslYgtNT5UrE80WN4L4w0VSpL+Rpk3mE4IPXYeVnjZC/N5FxalMfUamPh2gUqMvGTgGAEx0/YEgSD2HAjxSNbk2coJc8chma8xkbJVK3IiKP9fTnuk5b58PjzI8giDIG8Xf3z86OvrevXvp6en5+fne3t5NmzZF89HeDifkB4stMm9uo05O3Us3SkusPX54SBZb7jbk7n9f+n878+BA0dX7Jnykh8pERe6RlTvJF+HCT1sJXrmGgwW7jaQhWBTWQhhaulFroSgac2MElFivHZcdHOj20TapZLxcccpgJAE0JBVvNrsRxA4XiQcBm57cg/hIwPhvljsG2AjPid/fm5NpyKhkgB/fku/Ewfs05r7yz4IgCPImqEKAl8vlXC5XIBAAQFRUVHR0dJMmTSZPniyVSmusPOTNct9qmyhXFJFkCxZzs1Qixp+bwScXKzQU1YvHXVmma11rNmuT1OlTufKg3hDGZqGl4BEEQd4Q/v7+aPWWOqcyUztu681PzwV/pCEBYG+G4VKuuez2UFdmd5/nNqABgBKr4qTsMAC4sj1Oyg/bN2bedVr4KyFRWLJ82b/MdNMwLSuFoi9+UbdPUQPA+o893B2eujLEALyEr9589pHxQWzJOQbGGOYx7tm9vV0H75HHnZQd7uTUrSnLdZtUMk6uOGswAoA9vXszGNGy/UXmfA9Og0hJz3Iv9+E2bu/U5WGuDQAq057fU0hMa/3qdyIQBEHeEJUK8AaDYdy4cVFRUbGxsR06dNi7d++IESPsu7Zv3x4XF4cy/LvglsUyWa5UUVQ4m71R6sR/YUO6fjweDfCFo6jcx35bNnuLVLJVq2vGYtZotQiCIEglxcfH79q1Kzk5WafTJSUlnTp1ytPTs3nz5nVd1zsn+r7p55u6CncdvW8st0XCxV8c4ONVsWbKBAA3VFduqK4AgLBQOGDG/xwU/IeB+rPrNzfhWQAAQiA6wG3I7GHtU9RsAibGtMYY1dZwdk/e7xRNdZJ0FzK1IbVVAAAgAElEQVQc9OR/PxpFUwDgzHJp5RCeqI6Lyt8x1WduIybDm2CkUhYAaMJiejIYGpvquOwgAIzwmIBjFdxH+J/76EsFi7X65CauDQDQ39gqy7LavBkEs6IrOg1F6Sjag4HWDkKQN06lAvyaNWuioqICAgLsQX3VqlUSiWTr1q3p6enffPPN2rVrUZ+bt95Vk3lasdJE0+9zOSslYtbL2sl/6Sh63q4wNiuM7VTdBSIIgiCvYsmSJQsWLKDp/5bMPnbs2IYNG5YtW/b111/XYWFvK5tcqYm+YLp1x6ZQ4XwuO6CR6IPO7ABfABjgz7VSdBphTeSYu+g5UhsBAHvvGB6pyeGBPG8HwojTpwVGdxvR1sAOdqm4fWypCHGkgdSTtM3+LSOPcP9cyiwgzKFmapumB79v6ZGCHkLXv1vmDLrdOlGdMz7NZ1uzasnwtzQJ9/RpAHBRcfqi4nTZXQXm+QAeyzK/4vPyAeCm+uodfdZyvWOq1SLBCQvQl42m2YqSEOM+I2nAAPv14Sr7CymgTKSJhbMY2JMrWCbH2Cxgc7zRtzesef2a3ylxJvM4uaItm/2r1In79HXdYxs5RlasoKjLHq6i5w+3RBCkTlQqwP/1118eHh5JSUlcLlcmkyUmJs6ePXvAgAEDBgzYu3fvsWPHUIB/u100mj5TlJhpejCft9jJEd2MRRAEeTucPHly/vz5jRs3/vHHH2/fvr1o0SIAGDZs2P79++fNmxcaGtqzZ/lxy8jrMNy4XfzzDsposn9L6Q02mUIfm+Aw8H3xR/34TGxsC/5ateaYhjzuZNzmIglkMi/lmh+pyX5NuAHujHEyRbbV5iEiJge8fCi4iOEw0O0j+9fWfPPdUTctj02CCMfgQyHEM2uuK4TU0kne8397WHKwCOfhDX9t+vo/LI/gOzDENtr67C4ccADgEjw+IQAAghAu1nKTrU/mveso2j4fPgdCxdgpoMmyT+8BwEyZzE+fkKLLLUJXI0gAiqYrfF4NAGaaZter1XIbMxmuBBFvNn8qV5bN8Pb0XkCSYWwWH6V3BHnzVCrA5+Tk9OzZk8vlAkBcXBxN05GRkfZdQUFBJ06cqLn6kFpwRG9crdYsEjt0eWY59wKSHFukyKVIiqZHCvjfiB3q00cTgiAI8kJr1qzhcDinT5/29fUtKCiwb+zYsWN8fLyPj8/q1atrP8BnZWWdOXMmPT1doVAYDAaJROLp6enp6dmvXz93d/daLqZ6WR7ly9f8Tltt/Pahon5dmR4upFqnv3RDffi0+uAphlQs7NERAKaJhPettnNG0xiZYpv0yUIAWqDGyRR3rdZGDMZyJ8eqvrX+ptry2AQArrMaEgLitNHUlMn0enp09L2G3NvNhWEJ6uLDsoa/BFVqWvkL+fEDf2q2/aHa9vstPUk/tQsnjQC0QP2tyISROFx1McmtJNeGjbUKvBkMALD3tLtLeXTz+HWZmGsv9Kbq6o7cX+xniHT+YLDbx6Un5OK10ZpuklyRbbX9LpX4MstfP0fpDN+r1N86ioYJ+LVQSbWQEsROF8kYmSLebJ4kV2yRSngYlm8jx8uLC0gylM36VSpBz2wQ5A1UqQDv4ODw+PFj+9eXL1/GMCw8PNz+bUlJCes5a4Aj9YWepmQk+ZmiZK1E3LVMhi8gydGy4lySBIDxQsEcRxFK7wiCIG+TpKSkiIiIZ3vON2jQICwsrOzCcrUgOzt76tSpJ0+erHDvtGnT+vfvv2rVKh8fn9qsqhqp952grTZhjw6SyU8aCeF8nuPwPkxvd/mabaq/jgm6RmAEwcSwnyTiWYqSc0bTeLnCg8sANqykNI8pWyMGY4eLRPqc9V9ewLGfi3Sil3xrbvbYVMZfTT/zt0lw/A8XZ7/SIErDuH8KwxLUNia27keP+aQtEK+eVjXHH5j+TDNUuOtElhEAGKE0zgXaCJprEMUzjvHlA4DHiZJfptw9Pkr853TnP5icmQ5CC2W2L0fXw7nfOUX0ZcWZHs593dle1VJkJT202QpJcpS8eJfUuWyGj9IZFpWoaIAHVltt1vP6vBmMHS6SMTJFgtnyiVyxWOz4abEi10aGsllbpJIXdztCEKSuVCrAt2jRIiYm5uHDh2KxeO/evWFhYc7OzgBQUFBw5cqVZs2a1XCRSM0aIeAXkeRmje5zRclPEnE3LgcACklyrEyRayObspiLxQ7N0W0aBEGQt5F9eN2znJycMjMza60MhULRo0ePrKysZs2a9e/fv3nz5hKJRCQSaTQapVJ5586dY8eOHThwICUlJTY21tXVtdYKqzY0bUhMAwxzHNq73B5++1D1/pOWR/mWzIfsQF8AKJvh9X5Wphf9GF49vQMAYNDgpwALZVZvkxuHpwzf6rc3lD1WVvy9UKfQX/3QZbjq2+xesUobE1u3zkvQy7kJs9oazY5pwXfjE2bbU4/gf07QFumpma2Frnw8hm18QJKDSZ4kHG/jxgIA1WFZ9rhUsNK9N8pFJOa2yBEAjssOKq3F3lzf4Z7jrbTlguLUn3m/zfFdXPlKbhRYtiTrFnRwaFC+wW5lNWAw8m2kkqQ+lhXv/Pf2xwH9k/QOAI2r779brSmb4QcXyc00jdI7grzhKhXg58yZc/r06aCgIB6Pp1Ao5s2bBwB79uz58ssvDQbD+PHja7hIpMZ97iACgM0a3SxFyU8ScTMWc4xM8chma8ZibpNKUP8SBEGQt1JISEh8fLxWqxUKhWW363S669evBwcH11ol8+bNy8rKWr58+VdffVXhAYsWLdq+ffsnn3yyYMGCzZs311ph1YXU6GizhXAQEmKHZ/cyG3paHuXb5Ap7gId/M/y0YuVlkxnjgwSIV0/vdhhcmHOBWUI3P9S878QHxFa/P0OZn6vopurr0iVi4Q6ePb3zekl+dhZX48BpHgMb6F/+JtGOVH2RnurVmOPvxBgOT60pa0/vtJV2HOCijpZ3/FXmxucpv3U4KT+MAfaR50QMsMHuo26orqRrU07nJ3d0CeZVruVedJYx5qG5S0PzR01fcRXb9RLxeLki1WItoajRsuKdLs4plv9n77zDo6jaPvxM2d53s+mNJCS00FsogggIUgVFfBXpCuKL2MDygYgUlfIqIAhYaBYQBJEeEAsl9EACBEJIgNTtfXZnp3x/TBJCEiCENGDuK1eu2TNnzj6z2czM75ynkLMsxep9rEI+Qv5Q1seNxPGFOvUYg9nHsnIU+SrgHpWGeHh46pcqCfg+ffqsX79+7ty5eXl548aNmzhxIgCcPHkyNzd38uTJ48ePr2UjeeqCUg0/zWyVIYidYXj1zsPDw/No88orrxw4cGD06NFr164tbXS73S+99JLVan3uuefqzJJ//vknISHhTuqdY+zYsT/++OPhw4erMf6pU6e2bNly9z5GoxEAWJat1IyePXv269evXOPatWszMjIqHa1cf0QoAADS5f7g/ffZCp37FXijAZDbnd2cLHvZZAa5HADspHfml1+pjYZq29OyT7OjAYeQdxAW2MRtiQMmZltXKPZ0DGn99WjFRien3nNjzR2/XPcxTT/4+d69f1HYyyDQVWzfNm1TxPdalEEz2uacD08OeTqgy55WhYtz0jLSyQ99nTVPxMuaAYAMkw8KGvHDtZ2v79CHeI9SrKCxMzXUc+3u9nCVFrZt//38+nPVtj9KJModPNQWGGRlmJFFRoJlGQAAGKuQT1cra+rzqeP++RT9gcVGA2AM6wIYeupM5z9+x/3+O/VvaPbz/fn+Ndg/Ly+v0kMaFmx1ycrKMhqN1T784SUtLQ0AUBStb0NqhblWW5MbedxPNumvb3N4eHh4GiJHjx4FgKSkpPo2pAYYNWoUAEgkkqioKADo3bu3VqsFgH79+jEMU2dm6HS6nj173rPbmDFjAgICqjH+wIED7/o0dG/i4+PLjenxeNA7z3FX7H9j8qzs4VM668PK9ZThgtRBE7KHT/EX3nqsMtP0oLzCJjfyYv76N/KXrU1u5DVOuyhObFlNexAY/kufsalDfs1f/8WVjza8+NVp2YHjur3fDUk+LTuQoj34wo+XIzb8iIhENXi+d+kfPvPPmJX5l823PWYUbb55XLb3tOzAVOHE0p7dsE4p8j2nZQe+HfuFyWfgerpJZthWQ6/f9sWszI9dfCJmZX7wm5vuac+Hf5piVuYre4x5QPsxpTJ65+7Sh6UmN/I+t9pr9vOpy/55fqp3fmGTG3kvFBRJEhPjTpxuciMvaus2VCarkfH5/nz/h7F/A7+/V2kFvlIq5rzhedgppOm9nuLaNhgCWRQVXSHPKg8PDw/Po8T69esHDhw4f/78S5cuAcChQ4diY2PnzZs3ceJEpA7daJOSkpKTk69evRoXF3enPgaDYe/evUlJSdUYf/Hixd26dbt7n6Kiov/9738IgixYsKDi3h49epRrkUgk27dvv3jxYqWjVeyv6NXZ9suu1X2f+ysh0CMs9lLHGLZztkVl8ZDhQXhQANdoYZhxBnMmTeu93l7nzoh8vr8iw26ER8Rv+/3pPw/oLOb7tccRaixoclWJqwcEDjepDXOmvw0AzXY0b30A/CLkf8vC07qJhxwzhH9yK6T8wc/3Lv1/lQdZb2+x/W7Ie/UKDnhOUmFo79jP4LPiHQj8if3Ve16vNpvbeoNtMF8PADccVKqBClY0BwCBRk76IDC+7bTPPis7YEV7cBwHIIcMGdK8f5MHtD+D9KWUbIsQZJhMevf+9zt+nfXPp+jRRlMuRbcRCdfodX/Om5dyMmXvU32hQ8fu/xzu89efpevwDdN+vj/fv8b737hxY8WKFZUe1XBAWLaiJ1flnDhxYsOGDampqS6X6+zZs/v27QsLC2vRokWt2tcASU9PT0xMRFGUpuui6GidUUjTIwqNRoYBgFZC4TmS5ALwnqpQW46Hh4fnMefYsWNdunRJSkriluIfDWiazsvLCw4OrpfiMsnJyf3791er1bNmzRoyZAjnDlBKQUHBrl27Pv3009zc3K1btw4dOrQ2bKjt+zvrIwtmfkleu4GIhLKkNoKwINru9KSkUiYrKpOGzHtbEB4MAFamuGJcrABfpw/QYSgA+Fn2TbP1EOHVoOgPgbqE+0mWRjK+jy6/YSaN4yOmdtX2AoBl2fNTbSd6fzw05nDk14tCL/WVuFiBiPX8Ehxhch1W4uqWyna18QkAwJ+mPRJMMie5+RULtWeEPl6LA4Dtd0P2mHTWzwZNiwqbe9sMzt/m/etyVzQ/ntjrg16sjwmaGhk2v3GG2T/gV1OUCr9upwRCu59UBcvgyKh7VBn8+F/7xgueT7qrXm7+QJHqW92eWRYbU6ZFg6Hr9WVS+j8kmGhmRJGxgKbbiYSrymStu05Row3mIpruLBZ9y1eS43nMeCju71UNb547d27nzp2XL19++PDh1NRUANi5c2diYmKls9Q8DxenfeQEo/m5EvU+Si77JSjgNaXcz7LTzNYJRvM6p6u+beTh4eHhqXk2bdp08OBBbhvDsMjIyFL1vmnTJs67vm7o06fPsmXLHA7Hm2++GR0drVarY2JiWrduHRcXp9VqQ0NDJ06cmJ+fv2LFilpS73UAIhIGz3pD1rUtS/pdfx23/rjDsfMQZbIKG4UHfzqNU+8AsMLuLKfeAUCAIF/pNE9KxFaGmW2x39f77jJsNZPGKElsF+2TAMACa/abNBdbRKVECkh25Frbd7hb60/zIdJ52685+9h2rvvZRBbV7LlzXHKd35i36rubS8OUPpUIDZSiAMC46eyxF1g/GzAurJx69zLE9sKfAaD7iKcjv2oCAEVLb7gOFy/e+xiytKebLv+g4qNZu48p+0PSLAAQ1G3tDh8D90NZ9f6SXNZCKAAAK828YjRdfdhqyF3x+7l676tuzzkfhePrAnVBGHbaR7qY+/t8eHh46oAqTRbu3bt35syZsbGxX3zxRVpa2uzZswHghRde2LJly4cffti2bdunn366ds3kqU32E94jXh+3/bxM+oFGBQDTVEqCZdc73Ue8vpsUNVohr1cbeXh4eHhqnpEjRwLAm2++uXjxYuz2DOepqakbN27csGFDnRkzefLk3r17f/vtt8nJyZcuXcrOzgYADMP0en379u2HDRs2duzY4ODgOrOnNkDlUv1b49QvGLznMyizDZVJRE1ixAkxUEY+DZJJfCz7pkpZqt45OA3/ld3ZTHgfy+8Wv2mf8ffS/O0A8I852XVSOuytXmIPS4kg+IzbNDy/xaLN/mvjXn0TF3rVfWc+vVu4+ZXX/ltTZ83BsMzPed9zG3GxK5b3mSvGUABApZjyKa19j8m2w6ifFC5pdut5Y2fRFjtlRQA5kLXT/VVHFajcEZ5loi8M15UAYxyUBSCA++D8DJnjuRotLdb/WTZq6BaTh6rEyfSzY47PjjnKtgTJsAMv6quSyn6r2zOzTM756Wqlk2HGGs0XSL+VZsYYzT8F6iLxh2YdvotYtDlIHy/ARRWCZaJw/LdgvY1mVHwmYx6ehkeVrjJLliwRi8X79++PiYkpKCjgGrt163bixIno6OjFixfzAv6hxs4U+woKURilkHNXcS/LZpbMJbPAVxPh4eHheTSRSCRfffVVRkbGpk2bVKpKKpzVJY0bN/78888///xzlmU9Ho/X69VoNHdJRPSQIggNFIQG3mlvS6GwpbaSKAaKAQB4V628r/falP8Dyfg6a3o0ljUFAIL2/JV8hFPvZ/uj58etH/n2CPU51fOvPqfKZzEfm9NMHH3R22xGk3T1qRYvtL+/E7srf1v253pzAoRBXoY4WcB+br00q2NzBAAQiPkx8dqoNPsuU+YzZxvvaiNpXqzhi3z5ACB0CdtMa6m6pDIGUyved1jzW/j9cgDwUTIAoGgJAFC0dOpflzqoAwFALkC6hgs1YlTgv03AExRD0iDBUeHtTuEBEhSr2mPOV3YnN+J4hZz7QyhQ9Hu9brTRnEH6zTS9weX+SF3P/0T3ReKdJ4O0KKp95P71eHgeDaok4M+ePZuUlFQxa11ERET79u3T09NrwTCeOqKQpo95SQBQo6iNYcYaTWv1AeE49rrJcszr02HoS3J56/uZ7Ofh4eHheYiYNGkSgiBLlizp3LnzH3/8cZcccnUJgiAymUx2ex7sx5xhv5lcJLOvD44pZKhYdO8DADLdl07ZjgpR4fDgl7mW/Xu29ZuaJPCwWYM07VagR25Yf/nq5+defV6brQGAS89cXrEgrvc3AUNWWn2v2ixIgXbEPQLLq4ibdm0r/AkARoaOdVC2N89oz7u1I+M8CVopACBC1LE4ISOfbHLWkTngloafGPnWM8ZhtuFF7CVfkV44a2K8hcCBKB6T9ksAgKGFAEDR4gs32ly44eZ2LT8NGAo/D9a1C741FcLFwL+fpKh2DDwn/EvVO4cSRdeVaHhe7/Lw8NQBVb3USCSSStu1Wq3X6605e3jqFAvDjDWYDTTdQSTcExKYJBaZaWa00TTWaObU+1p9wGSlPKlqDwo8PDw8PA8dGIYtXrx4zZo1WVlZHTt2LA2J52lQEOcyMo2+6w46e8qcG6PezX9ngevgMbhrHmKGZX7MW8MC20v3jBiTuGnX1b8uBI/TCjwC7xDxk2vjGivjAoSB6htqsbP4Lq+5oWpuXGuZ+PupsScRGsmZeNH6a80Ew/9e+IuLcjSRJ7ZVdX5C2xcHMQD8Y/6rtMORIuqTF8LMXdWUicwccJa44AIAzIn6RtjYVF9BgHDWpGiL6t7LTgj3gwACiNVb1TzNVeQTrXquVl3RCYLT8FNVitHymo83TCa8mXeIrs+j6B0egg9S5+F53KiSgG/duvWJEyecTme5dpfLdfz48VatWtWCYTy1jpdlXzdaciiqmVCwIkCnRtEVAdpOYpGFZlJ9pBpF1z6EKVV5eHh4eKrBhAkT9u/fjyBIv379Vq5cWd/m8NyGbcveorlfA8sAAK7XIAKcvJ5nWvmjYfF3LH1H+XbSfuQGcQ0A9hq3/zf95fk/v2cecUPgEV7qffPVeVFP52dMvvCa5Ih4wIwBmA/L7HPFEeYITg/u8+6rR5nxx8fmnhp7EmjImXDhwTV8gTf3kHkPiqAvho0HABRBVQINAPxt2W8mjVwfFoDCkUuz4lRP6zgN7zpiyxxwxnPaQYSLf/m/uNA4SXO9QHivlOgs98MCzbB2Xw1XE3hCLBouq3z1Xomik5WKULyGU7bnUvRUk+XFIuNZH1luVzZFvWgwzTBbT/l8Nfumjy0pXt9eD1HpLifDrHe6jY9W/Smeh5cqCfhXXnnFZDKNHj3a4biV9sPtdr/00ktWq/W5556rNfN4agsG4H2L7RxJhuHY6gCdHK0Q/sWHvfPw8PA8TvTs2fP48eOxsbGvv/76lClT/CX1n3nqF+LMBdsvOwFFEBQDgLClsyI3LNa/NRZVyDwpqfbf9t3pQBkmV+BKGSaXYfLw6xGD3xoi8Agz+l9Knr1XiBg8eDiT/saAGQNwH35h8IX9H+/fsnSfKQyJTaXfey072OBMmZByauxJlmYfXMP/kv89zdI9dU9HiKO5FhEqBgA/Q24r/LFsT0aIxvzUktPwV54+7Ul1imKlHf9sv2FCyO/DA3YMD0gbHzL+ybWtmi8CgAglBgAqMQUAwTJ06wvOrh3e7dnp/eSX0DlPqFiA04UP/Xc4DMcGyyRulp1oNJfV8NkUNdpgNtJ0Z7GoTX3UfXwk+T+r7S2zdaWj/IKlk2HGG80LbPafXZ56MYyHpxxVEvCjRo0aNWrUtm3bgoODFy5cCAB9+vSJjIzcsWNHv379Xn/99Vo2kqfm+Zvw7vMQChRdFaDj8tx6WfZ1k+W416fF0FYioY1mxjyENVF4eHh4eKpNXFxcSkpK7969V6xYsXz58vo2hwcAwP7bfgDQjhoKJUnpEQyVdW0XOP1VQBDHjoPsHe7ULRRtvmq+flmLjctabHxP8YmAEABAaNfoEaEj3hfnNTLb3phK4D48/znLX9MPUajkfNi4wkgpAITecOB6SWyeImVCypmXzrA0m/PqBd+1ylcm70mq40Sa84wMkw8NerHcLhwRHLP+nem+VLYRERVreAAQxUrj97QVhN6K4zvvTLnqPa4UogCAIggAeBg3AKAItNbEdw1oy6L2/ZYNIhQAgK5hD/p6AAGYr9UMkUndLDveaD7pIwEgh6LGGMxGmm4vEq4I0Aoq5JDnqR7vqJQYwFK7s6yG59R7GumPxPGR8mpmT+DhqVmqGgO/fv36TZs2xcfHc1noDx06FBAQsHLlyp07dyL8heMhpKVQOEwmXaPXxgpwKFHvXNz7On3AWr2Oi4fnNTwPDw/PI0xwcLBCoSjbolar9+zZM3nyZB/vl9sAYEm/9/I1RIAr+nQrt0vcNFbYKJwhvL6s6/ccR/GkNvzzeABQzpR0+L3j80ED26NbLEEIAFAX9UCEXhVNHf0+2+KY26Px7liy7SZe2KXVcyK3MPhCEADggUJMU52QOoqlNuevBYAhwSPluNJzKq1o3oobo6eTNwsAoH1RJAvsz3nfsnCb1OY0fPR3zRMOtiur3imW+rVgPQD0CugPACRDAwBNSQHAS/tMBDMidIwIFZ+wHS705VU0hhP8VagW17DAAOZp1UNkUoJlXzOa//AQow1mA023FwlX63US/iG85ugvlSzUaTgNv8LhBAAnw0wwWtJIfyiOfa/XBWI1HCLBw1M97uNyPGLEiBEjRtA0nZeXFxwcLKxXj52srKzk5OSLFy+azWaPx6PT6cLCwsLCwgYNGhQSUjMZUx9tdBg6T6vmtsuq99K49xUBWq5xjNHEB8Pz8PDwPJKUloYtC47jK1asGDRokMViqXuTeADAQjAHr3tpBhiXxxraGpXLNFkUt5786yUPXhL15gxry3i1fQ2uRk3uPWbglAgAyJ1x5ebbl6+6M3KfPHNotevJ1wdGXvQPeuPF7gHQ8ojLo/FuX7rVGmMBgN2FO/7z3guyc2pXoEv1qwLXVKcezQHjHxkGRXbO3GsX9J84rrEEBtKe0BqMIiUA/HR9MGHqn4LCzpSbJCUCgD+v+4YnlCxy9te7AeReRiMuXm06YNrJFZY7ZT8G0NRK2gGULIsCgNWLdv/x+gvdV6AICgDHrYcB+pUzZkRTiZdin4oWV+NE6hdOwwPA727PDLOVBeDVey3RXyoBgPfM1mV2p49lU7zkeZIMxbH1+oCwmk5wwMNTbe5blWEYFhkZWRumVJHs7OzXX3997969le6dMmXKkCFDFi1aFB0dXbd2PcSscbiOeX0BGLpOHxBTItTFCFKq4d+3WLcE6evXSB4eHh6eB+fy5csAEBUVJRaLS19WSkxMTMXysTx1wxfHnb9mlETbNn0GAOAfO/fq48O3shGBqAU0a3G60L+6asOWanjhTEh8J7HJlDa/Lds+5I1nIy/7Ii/7PGpy+9KtdALL+gH34k+89wSn3rct3wY4sohthyH3J2AclO0Pw6+Et53Do3MAAyCB20saWYRy8AMA+AAAGAC4YPL3/MlQto8QQw6M1IcpMAAo8OZyjYXefABgwQ8ACMqwNAaAkpT4misPxz0A4KRtFe1pqhMs6PkwFWkvCwYwUSnf5/F6WQYFeE2p4NV7LVGq4Vc7XADAq3eeBkiVBPyFCxfu3qF58+Y1Ycy9MZvNffr0ycrKat68+ZAhQ1q0aKHT6ZRKpcPhsFgsGRkZO3fu3Lp167lz5w4fPhwUFFQ3Vj0sGGl6vs0xUCp5SnLb9HMXseiy3/+2Shlz+zI7p+HnW+0x/PI7Dw8PzyNBkyZNACAlJaVTp06lL+8Ce9dCZTy1xMhm0pKAd3AfPcO4CUnrpttMEppln28qwRAEABjC6zl6BlgYWcG7/i4ETolId55Vz5U9sbjHX3As7aXnuwUjoddIALBrxHY9oshiBXrBwOkDw86Ec+rdHmYDP1x0nUtUtL2vszjvOE3QnpCgfzdMdNcAACAASURBVLXqdJa9pX8YRng6bQbDCIUCO4IWp/XGWI2HRJQiRCW6LbozQoErS1pGhb/2ZEB/ALhpR8acBwwjASBEhuc5AIAFQCZETouU6wDgX1yZlvlIBQDmUNQ4g9nLMjoMM9P0VJNllV7XQcSnr6sVuolFYTh+g6IAoI9EzKt3noZGlYRZixYt7t6hzm7wH374YVZW1oIFC95///1KO8yePfuHH3549dVXZ82atWrVqrqxqmFyg6I+stj+I5dxU4kEy042WS6QfgWCPCkRL7Q5Cml6kU6DAbQTCduJtJUOIkaQOSWe9jw8PDw8DzsTJkwAAL2+2Klq0qRJ9WoOT+W0DhS0DixeK3a4Uct3u7G8f/7o/CbNwifdVCIMoYpMhoU/kDm58p6dAmIVdx+tHIJXpSecJzp+1bHHkq6xf9CRl30eFelWikOymVEjX5LbWELjkVilt9Q7AADIsPuucN5B3ZWgPX7W7883uP48hgdqFX27c7tys6kCh/DdHjlBmNm+bb8YEeV2X7T0lHt0C9m0Dnc8HRwRREtiAcDoO5IQe0SIytIyhwWIRC6R3+FDAWDSjiAxKgYAkqYBYEcmkZztLTuCXIisH6iNrkI9+QZFDkWVxr2v1OvmWu2/uz2vGc28hq8NuLj3GxSlQVEHw6xzupUo+rry/v7LeHhqlSpdwt54441yLU6n8+TJkxcvXoyKipo+fXotGFY5//zzT0JCwp3UO8fYsWN//PHHw4cP15lVDZNcij7lI8/6SApgoFTyntl6gfRH4vibauVMi+03t0eOIiTL8i5YPDw8PI8Pa9asKfuSL/ne8FH07e49l+E5lcZSFKC4adk6zObwXcpiaVoQFqQdPex+B+yu7d3q017fyDN6zyuIvOzzavy/L9viVgheeukFuY0BAIlVSomp7cu22cNsMdfEvVu/otKHx0jj7/eNRKi4j34QADjTjpqPpiue6qALLLZ2GW4sAKq7tne8Fr9x9ixDePOSbitoz/pZ2w6Dope2Yuw9yZC/FqwLDjR0Eb+blgk4CjIBypV89/mFPrg1Dkmz5O2Z6L0U4vE/ZE4lZdU7F/deGg/Pa/gah1PvpXHv50mSi4cHAF7D8zQcqiTgly1bVrGRZdlFixZNnz79ng72NYjRaExMTLxnt4iIiHPnztWBPQ2ZLmLRu2rlIpvjA7P1gIc4SHiVKPqNXrvM7vjN7REjyPIALa/eeXh4eHh4GjIIhurfm2Dfug9MCAC4j6YKGQrBMHmvJO3oZ1HZfde1Ilh2tMGc8bKaEKED9hi3TfnJEm156vOeEtct6Yt78Y5rOiV/vD8nxhsW0qy0fnv1QEVCAGAIb8VdLEUzJAkoCmXye7Mkc+2VdPtOozhB1nhPW0GgEABSDf7jeT4AuOg6f9HZWi3Q5ohaABBFbvrFZpLlZ9w+io0I3acWSgcE90cRZP4x5+DGktndlGXfToghkocqDb2TYTj13lksWhmgFSMIlOS0Y1l2h4eYbDRvDw4Mr5aPdxFNz7ban5VJ+0oqSey30uG8TtHztOrHx328nHoPw7Ew/FZOO+A1PE+DofpORAiCvPfee7/99tvKlSs//fRTrbZyH+yaJSkpKTk5+erVq3FxcXfqYzAY9u7dm5SUVAf2NHDGK+QAsMjm2E94UYBFOs06p2uTyyNGkG/02k4i0T1H4OHh4eF5lFi8ePF99X/nnXdqyRKeqoNgmHrEM8iaQqDZgKmjJTKRKD66GtKdI5+iL/v9MQJ88tTYpQO+thCWJ5Y80eSP4mDJ/Hg8KJvG/Gz8gXiURvZ9sv/nvO+mx376IPYLG0UAgDftCuP1oeLbnj2I0+lbenZMS0xoVvJAWqreAcB72f13n5PUjsR+Ucop+6yFbi5gvhFAIwA4AwQA5LroxSdc3LE385++CZCW42qmwwEg20YVupkE7UPmMF8WCoBi2W5i0bIS9c6BAczXaTAE2eshfNWNY82hqL8I77+E9wud5hnpbfkFP7c51jpdYgT5UK1UolWtOf2wM81sPU+SkTi+LlAXXDKj1F8qoQHeN1uX2Z3hGD5YJrn7IDw8dcCDXtTatWuXkpLi9/trxJp7MnXq1D179nTu3HnWrFlDhgyJiooqu7egoGDXrl2ffvqpwWAYN25c3ZjUwOkjEXOVMADge4crxefj1TsPDw/PY8u77757X/15Ad/QkCW1FmEPtIYcK8C3BukjcDzFsquAyH1iyROJv7XkdgWMkwQ+YS+8IkIWAuuHuEONAWDfJ/tP24+1U1V/XUQQFiRuEuvNyDItW6+fOhopcfmmCgzm7zZfeLbviaiwTNqNiACh2OzR6fadRkyFi35odnnWlYh0r2dwmn9v+4+7KVMN/lTHyfRChdsdfvd3vGimACDN6F9wzLF2QF2sMNUSGhQ9EhZc6S4MYL5WPf8BEhV1EoneUSkX2x3TzVYKYHCJhl9id6x1ugQIslineXzUOwCIESRWgH+rv6XeOQZKJQDwidX2YP95PDw1xoMK+GvXrimVyjrL996nT59ly5a9WYJKpdJqtUql0uVyWSwWq9UKJQVshw4dWjcmNWR8LDvNbPWxbGMBnumnUnw+AfDqnYeHh+fxZfv27eVavv766+Tk5DZt2gwfPjw6Otpqte7fv/+PP/74z3/+8/nnn9eLkTyVohQhbhJKi8A/CE2FAhfl/L3gl7LqPf/torMTTQAAnUARI499PRIhEU7D/7pgXUtlOwFS/Vhr3aQXCz5a4jl+LnfKx9L2LfsKY3WUCJ+7nvb7/u/cpRltml0Av6g9NJlx1faPFVPh0m0tx4eTnu+i5k24oU8jMp858+TutrEt8i9fXdZcGaD1vCbEZBYPfjhHrpPSXaOdey6r/DTSP8F+1XPeyxASf+srhsCmAYJ3O/I+z3djglIOAIvtjg/NVgAYLJUssTvWOFwCBPlSp+lVmWv9I8zXAXec6xkolQyU8mvvPA2FKgn469evV2z0er3bt2/fs2dPt273Ub/kwZk8eXLv3r2//fbb5OTkS5cuZWdnAwCGYXq9vn379sOGDRs7dmxwcOWzlY8bC2z2S6Q/EscThYJMPwUANLBFFAO8fufh4eF5LBkyZEjZl5s3bz5w4MCcOXNmzpxZ2vjGG2+sWrVq0qRJPXr0ePXVV+vcxscXxk1QRgsixPEgPYKVX/ncOEjnZ9iaWgM8ZN7T4YsOLbYVe84fnXT0zPDTYCnZ3RQiPo8cNH0g6sfiDjVmZrJZ6y43Ud47CdGdEIQHh8x727Rio+9KjvPAkWfhyLMACI4p+vfQvDzkewE+JtfUf2aO/h8nWqLejTTdNVDSfXeHG4PPes46M585k7L6JCtkJWIjIZ5LALjQWIB3KSzHrlrEIosBpA7lnFCtBwAMBgMYXm4eIGihL58Dj6ccZTX8Hg/xF+F9PNU7D89DBFKVCnDInVOd4Ti+e/fuPn361KhVVYVlWY/H4/V6NRoNWhNOPgsXLvziiy9omr5LH5qmHQ4HgiAMw9ylW72zx0O8bbaKEKSHWLSf8IoRZLBMstnlwQDmazV8DA8PDw9PtTl27FiXLl2SkpKOHj1a37Y8EL17987Ozs7Kyqq4q1mzZqGhoQcOHKh7q+qL9PT0xMREFEXv/hhQG/guX7P+vNN7IRNYFgBQiVjes5N65IBqB7rfk8uX0twdDNz26bfOZLyYUbFP6LGQnu/0QCgUAOL/aS9vq3rw9yWv3fRdyWa8PlynFicmYGolALB+NnPUeddOk0eBfre2UWai2EwzXcWirwO0IgShbVTmoDOes040Fjvz7TlfIMkNlWcN/Dmlb5jG8GLn/csPjPD6hW/03iwWkABQYOj04+nGzzeRftazBmx+HPjW4VpsdwAAhiBLefXO8xjzUNzfq7QC//LLL1faHhgYOGLEiE6dOtWoSfcBgiAymUwmk9XUgFlZWSaTqaZGq0euU9RMqw0A4gU4p945z/lIHF9kc3xosV72+xsJ8Odq7cmAh4eHh6fhc+rUqa5du1a6Ky4u7p9//qljex5PXH8fN634EWgGEQgEYYGsl/QXGh17/vacuRDy6TTsAYKcK+Wmgw6VY3GhTc8jRpZlERxGPjVe0awS52G325EpOM1QDACAu2YWLYQxEcKYiNuaWMgene7aacI0gnXrolIaC4BmOoiEnHoHAEyNx21vkzngDJHu6jypU8LB9pgaB4BTIvJnMIeIwidHvbcGLfICMy7ivyoRCgAFOrrAZB8Ux6vQquJgi/++LMu6qpsVr6a45qfCcExU2fKhk2FsDBtRraz7PDyPDFUS8Bs2bKhtO+6L3bt3//HHH5mZmbGxsZMmTWrTpk25Du+9915OTs6vv/56vyOvXLlywYIFd+9z6dKlrl273sUrod4hWfZNk9XNsACQRvpFgKzR69qLhAAwXiEnWXap3fmD0wUA/aUSWe2cyO4s76dH7Cue1rYJ4r3XeHh4eBoooaGh586do2kauz1pE03TqampERERdzqQp6bw5xvMK38CmlE921c1/GkuSbs/t9C0fIPv6nXj0vXBs6fW4NudN/iH/WYa1UL2cVeleqjeus3AUpA1/FzsllaKJ2/T8J6zzqtDzjAEAwCSRIWkjfIOQz4otJu27zECAP6sPqOJGGgaAIw0Y2eYwJKvJa4TBL0ZlTPxgvey23PeqXhCc/cxQ+TYmv4Pce66OqY07n2ARLLd4ymNh68XY86T5AtFppZC4bd6reJ279pCmh5tMOdR1MHQoCCM1/A8jy8PX27JSZMmDRgw4Jtvvjl48ODq1avbtWv35Zdflutz4MCBLVu2VGNwBEE090KprK17WE2xzOG87PcHosWXNhEKmjJXwDAMRwFYAARqcQ7iVCFp8DDnDWTtvQUPDw8PzwPSrVu3vLy86dOnlw0KYxhmxowZN2/evNPiPE8N4tj1F0vRij5dNS8NLi2xJggPDpo5BVPKvelXfFcryUNUbQrdNAtQ6KYBgei1LTTPBwEA42OuPnfOeag0Ah48Z51XBp6hHTQASBIVjXe1weS1pZcwORb9XXPAEd/3+d2XFnUSCZsJBTkUNcZgNpTEMjiSzTf+ewkAAqdElKp3jRhFADSSh+9RtkFRNmvdAp36HZWSBvjQbN3hIerFnkgcj8Cx8yQ5wWhxlrkucer9BkU1Fwo1j1NufB6eilS+At9g68Ru2rRp1apVMTExn3/+eWJi4unTp99999233nqrUaNG5RLzPM6c8JI4gszTqXe5Pddp+qyPHG0w/RAY0FiA7/YQH1qsDMCTElFjgUDagP0IeHh4eHhqm/nz5+/evXvJkiX79+8fMmRIeHh4bm7ujh070tLSwsPD582bV98GPvp4L2YCgLx3+bkSVCaVdmnr3PuP9+JVUVxUZYc+KAiGRH/bHACsvxaxPubqc+fitrRSPKnl1Dtjp6BEvePa2nWmsw7SrV0c/so7uc8tN+qVctX70eOM5gukf4zBvDZQJ/7Tdu3F84yXCZwSEf55fOlRsWp8x3MB4QoMAPRSlGZZCc4/1dwfXL13AYIs1Wl6SsQAMEEp9wG73O780GzFAZ6p83V4NYquDwwYbTCfJ8kxRvP3ep0KRUvVezOhYLVeK+QfX3kebyoX8A22Tuzy5cvFYnFycnJMTAwAJCQkNGvWrHv37pMmTerVq5dCwRcLAQBYHqBxMGysAO8mFpEs+6bZ+hfhHWswjVMqltjsNMBUlWKykv+seHh4eB53AgIC/vzzz7feemv37t3p6eml7UOGDFm4cKFOp6tH2x4TGIcLAPCASnzCcb0WABi7874HZVnGQyBCISK4R6RkRQ0f9kls/rxsxkkBgKSFvA7Ue5afGm00mfvKw76M7DvthnF+No4ga2ZEjTOYM/z+BVtzXnn9BltBvXM0Cyi27ZchOpJmhXyd7vvhqNdXTr1zTFEqAGC53fm+xdZVLFLV+XJ3MIatC9SNNpgvkv5xRvNnWvUbJiun3jk9X8f28PA0NCq/sjfYOrEZGRldunTh1DtH69atly1bNnbs2IULF86ZM6fOLGnI6DFMX+LpJkSQr3QaTsMvstpZhFfvPDw8PDy3iI+P37Vr15UrVy5evJifnx8ZGdmsWbOy91meWgWVS2m7k7Y6MFX5WzNtsQEAqriPTL2+zBz7b/uJ8xmsjwQEEcVGKvo9Ie/REe68YolgSPTq5izJ2n43sD4m9/1Mrr1u1t4BYI7VbqaZ7mLRtNEhHqUiZ9yFgnnXQgC+nxE1b0vO85Ovsz62UvVeFo2YF3X3TYJAMFgqGSSTdhOXLy88RalQoegNipLXk1ouq+GfKzKRLMurdx6eUioX8A22TixBEBWLt40ePXrFihWLFy+eMGFCZGRk3VjyECFEkP4S8d+El0VAiiC9JXwBOR4eHh4ecDqdzzzzzNChQ9955534+Pj4+LsJJJ5aQtwk1p9X5PrruHbMsLLtrI90H0vlOlRxKOf+w+bvNgPNAIKgMilLkr6r133LN3hOpenfGlexsHwpiABptLZF9ph02+/FheXqTL0DwMsKWTOhYJpKIUIQ0bAglmKvT7xYMO+a9qrnpe0G1sfC5LC7q3ee6qHD0M91d0wH+LK8xmo8VY9gDFukU79kMJMsK0aQL3UaXr3z8HBUKQv96tWrGzVqVFa9c7z22mtfffXV5s2b60zAN27cOCUlpaioKCgoqLQRQZCVK1d26tRp/Pjx+/btq5GC8A0WBmCZ3dFCKHzq9hKdJpqZbbUlCoVmmn5HrSxbe2O3h/jQYmMBGgnwbD81tiQevgatumT2W4jbJlYKXDQAXLNRR3J9ZdujVDgXrsbDw8PDU78oFIqcnJxDhw7VWRwcT0UU/Xs4D6U4dh3CtCrlgJ4IhqV4fT9ZbON+3qW22ESNo0UJjUo7Eyw732pvIRS8UEFc+a5km9dsAgDV0D7KQb0wlYL1+/cdyDj3bwZbQIo3nJa0agoAmVYKALJt1KqzrrKHSwRI26/wLHtW7F+x4pbyxjvrSL0DQB+JuE+ZRxrtiGAAuD7xomVTIQAEvhEZ/lnjurGEp0FRSNPvmm1+lhUiiJdlp5mt/Ao8Dw9HlVRcw6kTO2HChDfeeKNHjx7r1q3r0KFDqVZv167de++999lnn73yyisrV66sM3vqnnQf+Y3DBQgyR6N6vkwV91lW2yHC+yfhZQFihPhIWfGtfbeHmG62cnHv4xXy0nj4GtTwF0z+IVtMldYM3XjBs/GCp2yLEEOOjQpU865uPDw8PA2AuXPnTpw48fz58y1btqxvWx5ThNFh2tHDLGu3Wtdvc2xLFkSH7m7XIrlVk/O9Oy8qKGr15uhS73eCZScZLSd8vmxKWFHA27buA5ZVDX9a8+Kg4iaB4O1cvS8yAADAC3D8Vix9ppX64nj50PruzQ9jc/ZGHo/sltQ7vl5jybUjghEEuf7GJf2r4WGfxtWnKTz1RNmsdVwMPBcPz2t4Hh6oooBvOHVip0yZcv78+dWrV3fu3FkgEJw8ebJVq1bcrjlz5mRnZ//444/btm2r6Gb/yNBEKAjEMANNz7LaAYDT8Ls9xCHCCwAsgAxB+pY4yZdV71zce2k8fA1q+AgF1i9G7CBvk/DXbFSBi45R4yG3F54Jk2MKIX/l5eHh4WkQDBw48Msvv+zTp8/48ePbt28fEhJSzoutU6dOdWCG0Wg8evRo1fs/YnVnlAN6CkL01h93kNfz6LQrI69cT1cpL0WHvv/WmHUB2jAAKKPegzBsvlZdfgiW9Z6/DAiiHPhkaRsCsLCX+oLJ7/rnJG2xybq0xQN12TZqf7a3kRrv2+h2Pz7/tSLpAbFQ7NF5pAPQC/jR+J3tJIny2j73O6F5PogrccfzGFJWvXOKvWxOO17D8/BUSb9169ZtzZo106dPX7hwYemtvbRO7DPPPFObFpZnxYoVSUlJGzduvHbtGsveEo0CgeCnn37q2rXr0qVLr169Wpcm1SVCBNkZrB9YaDTQ9McWm5dhB8skn1hs3F4JgmwNDtSiKACkkiSn3t9SKV9VyksP/1KnmWKyHPH6JhjN+0MCRQ9cikMpQpf3LR9DNeeIY12a++Xm0tGJ9RxD9QjjdrtJkgQAlmVttuLvgNfrJYhbtVt9Pp/Hc8sJomzPipAk6Xa778sGhUKB43e7jGg05b8bUqlUJLotX45arUZKvodisVhSJk1DxcN5eHhqkICAAG5jwYIFlXYoe5+tPS5cuDB06NCq968bq+oSSdvmkrbNKYOZKjIjAnxDZMhkl/ukjxxlMK0LDNBj6Osmywmfj8vsFVnhqsu4CdbvR+VSTHGb5B4QKx4QKzadyncdTwnoGyrvFL0/27s/29tYg0/vdCtnnpchPrj0JUb5xljfYd/y4HacBipzwJnGu9rWo4bnAQA/y8622lsJhSPk0op7DxLeXR5itkalfIQErZ1hOPWeKBR8p9cpUBQAgjFsbaButMF0kfS/ZrRsCNQJ+EpyPI8xVRLwDapOLIZhY8aMGTNmTMVdKIr+97///e9//1tYWJiVlVWXVtUlChQt1fALbPZfXG4HywKABEG2BQdG4cUr3mIE0WPYKIVs3O23cxGCfB2gfddsLaTpstd7gmK5NfO6O5NHHZqmHQ4HJ7OtVqvf73e5XNxLm81GkqTL5eIaud8URTmdTu4oALDb7QzDlL4kCMLr9QKAw+Ggabqez63OEQqFspLAEKVSyXkDlTYKBAK5XA4AGIYplUoAQFFUpVKV7SORSMRiMQCoVCoURUvbuZelEwfclIFMJhMKhWXflIfnkeR+q8bWEj179kxLS5s9e/bWrVsBYPLkydz/7+MGHqjDA3UAIAJYJRG/ZjSf9JGvGEzBOJ56Z/UOAIhYBAjCen0sRSN4+UQzjNMNAKhUXPFAjh2Fm+yUte2NjqLXWcqOX+txDWGQRv824jV8vWNmmN/dnt/cHjfLjL39cW6Ph3jPbKUBRitkrYTC+rKwxsmhqHLqnSMEw9YFBow2mNJJ0sIwQRifUInn8aVKau2hqxMbHBwcHBxc31bUImU1/DWKAgDx7eodAJoIBIdCK3c/EyHIsgBtucZZ/9q3XSZ2PBdQWlWVh4OiKIfDYbPZ7Ha743asVqvD4eBkOdficrk8Ho/dbi9dHuepEUiSLP08rVZrXb516VwAJ/XlcrlAIOD8CDjZLxKJpFIpN4mA47hCoeC2Of3P7eV+c5MI3OzAPZ0XeHjqgIULF9a3CcW0aNFiy5Yt7dq1O3PmzIwZM6KiourbonpGgiCr9LqJRvNpH1lI0wHoHdU7ACA4JowOI7NzPSfPy5LalN1FO1ze9CuAIMK46EqPNZCFB0y7AjP13d5Koqz+611yDs45SLHkc3Ne0P8ZcKX/6cY72kjbKmv8BHmqQjCGfaJVz7LYvrA5AKBUw5eq9zdUikdJvQNAK6HwtyB9IwEurrDGHoJhW4L0RppX7zyPO1V9fOTrxNY7jJsgr91kvF48QCuMDlOg6Edq5ZvmYiUzIvWS7PpBT7sW0o4toVqeVCYPwwKYiIaYPsDoYVaccb3YTBqvrTHB43Q6TSaTyWQym82WEqxWK/fbZrNxv202m8vluvdwPI8uDMNwUwa1MXGgVqtRFFWr1ZzvAKfzOcHPKXyNRsNNB3DiX6lUCgQClUrF9eRmEzQaDe8swFPjZGZm5uXl9ezZsy7fdOTIkWfOnKmNkT/44IPPPvusKj0bjn8+AoCVaBgEAQTu5jOs6NvdvOpny+pNuE4tii9OXE/bnMYl3zNen7R9Iq6rEDkPAAA/532ruaIaPu15xsYUdS/aPWc3jdMCVLhl1qbJwqn0Xipz8Nma0vCs30+cuUjm5LI0LQjSS9o0w7SPo6vFfTFcJkUAZlpsX9gcfhZeVcr3llHvU5SKew/xsNFUeMeVJCWKPkrxAjw81eP+5BBfJ7ZeoG1Oy7qt7qNngC5W17hOnTFm2FvhQVByO9/Qson02s0hi74VxkQEvjuB88F7ZEjO8a5Pd6MIzOxapQcIk8lkLKGwsLDsS7PZzIl2n89374EaNmWDyUtjxcsJuYq67i5R5aWO6FXH6XRSFHWnvZWG3Ff0TSgrjEsjBe50+CMGd4IWi6VGRuOW/dVqNSfvFQqFUChUqVSc+OfaZTKZQqEQiURKpZL7Cmk0Gq4/Ny9wv98BnkcSlmXnzZv366+/3m9ejAekQ4cOcXFxvHMKAHhZdrLJcsLrC8KwIAw9T/pHG0zrAgMiKnjIcyieSiLOpHtOphV8tESU0EgYFkw7nMT5y6yPxHVq7YQRlR510XU+/3TO0DeHYXaM6sX8NnOrUqZ2UDaKpVgBs2nWzxPxyY6d5hrR8J7jqeY1m2jbrez3CI4pBz6pfnEQwi+o3pVhMikAzLTY/md3XPH793qIR1i98/Dw3JPK75GXL18GgKioKC5klHt5FxISEmrcMh4OymQt/L8llMmK4JiwSQymkJM3808oFTPDAhlAAGBaasaPifFGDF31bB9MIRu466+iOctDFs5AJXeMdqsDRBgCACK8ZlKMUAwLAEzJoojb7c7LyysqKiooKCgsLCwqKsrPzzcYDNxvo9Ho9/tr5H0fEG5NtXThVCgUKpVKzum6dB2V68M5aXO/EQRRq9VQkh+u9CUnsQCAW3St53OrUTx+1kOxABAguW1a/aqV8lLFf3WS9Blsxa4Qfo+zqRZDkeIUfRTDnsrz2B0uAQbNNIjH7QIAiqIsdsclE+UmvD4vAQA+j5vykwDgdtpRYANFFOklAMBqtdIM5FtdJOkDlgHCiSDFExPlMgI2fDhvkQefDpDL5dyXViaTcV9XmUwmFotVKhUXCKBSqUr7lN2QSCQKheLR+4o+wrAsO3v27I0bNxoMhrLtNE0TBJGYmFjH9vTs2TMzM7M2Rl6wYMGdEvWVkp6enpiYiDSA5Ficek/xFse96zGMi4e/m4ZHUf27E+y/7nXs/NOXcc2XcQ0AZkRn6QAAIABJREFUAEGknVrpxj+PlSSu10tRKLnYMiy958/NQ6cOEzlEioGarz9YSgP9Utirl13pyaY/RJjYSBddXpTZhI237zJdHZraeHdbSYtqTvC5j54x/u8HYFlhRCzDNJV3RsncG55T5+3bD9BWZ8B/R1Vv2McHTsP/n8W2y0MAr955eB5vKhfwTZo0AYCUlBSufgz38i40HH+zhxF/vsF18KgvM4fxkniAWtKmubxHR6TEfcj8zU+UySpqEqN/axzn/3aE8M4ymTn13sbieG1Qr/8wDBcPv6J3FxAKB27b79h5SP18/3o8qQmtZGEKbEhjyb27VgZJkoWFhbm5ufn5+Xl5efvP5Rgu3FzrL9rkNd+8ebOOfdo5Xa1Wq5W3o1arVSqVUqlUKBQymYzblslkUqlUrVZzcc51aWf1sBCM28/iKJQt+EfS7OFc0u5jfBTLAjh8DAC4KZZiQIDC2ESZWlystK9aqWWnXVYvI8TgoyRlo5IkiCcKyI//tRMUSzPg9hdfH2iWdZGsGEe+66/tHFb84Wy7Qkw/ZGNYQAC+6afpHV088bQh3TP7sP0OVssX9VI/G1/87VqT6l5S6OC2P2uner5JcareVWddeyoUOi5lXA/VyKbFPVeedS067uSe2ec9oRrZrLh9xRnX4hNOlqFYrxsAaI8NAFivi2Vo1uce00zUSU/7fD6CIPZl2vddcbB+gvX7ugczMXLG4/F4vd70AmdGoYv1e1mSYCmSIT1A+Rmvi2UohnBAQ71yulwul8tlMpmqPQKK4QqFQq1SSiQSmUwmlisNpBAXSaRKlUQixUUSiUIlFMtkUolGrezdWKtRyuRyuVKpBKHkaBEqkquVIrRPI5EALZZSFAOnC0mqZBpPKUJLNZZKhEYob9MzLpKlWVaMI9xMIs9d+Oabb+bMmaNUKkNCQjIzM4ODg8PDw+12e2ZmZqdOnZYuXVrfBj6OlFPvXNz7Kr3unhoewTD1yAHKob19l7Ioiw2VSkTxjcp5zrcJEu4fqQ9XYABw0Lw7Yk24yCESBItOzjvrcNmaylu2VXVKkDc/ZvvbRTkA4A/rlk6rlnraOvwGsnBRTqO1LapxRgzhtazZDCyrHDy4aKWKSHO501Vx23upCm4Wffq16+/jsu7tJa2bVufDepyQIggCwF0EBXeNp+Dh4Xm0qVzAT5gwAQD0ej33ctKkSXVn0WOGfcdB68btpYvL5LXrnhPnbdv3Bc2YJIwM9ecbiNRLqEwS9P4kVC6laTrl0qVRmdcoL8ESBHi9iWcv/fvKc5GRkb+Hhg42mo008/UT7WPPXmx17Gz9CnidBH25eSUlT8pCEMTNmzfz8/Nv3ryZm5ubl5fHbeTn5xcVFVWcFapZ1S6TyXQ6XUBAgFar1el0Wq1Wq9VqNBpuQ12CRqPhspo3QBgWUotIM8EgCPSIvCV18l30d+fcFi9DMazDx1Is6yJZkmYJisUQZOGTqrbBt8Tze3/auA96aR/1gNhiSbwu3fPZMced3jdYhr1YInH/uuHbebV4jXpArL9UwF+zUVcslbvWowAM3Prj6iRogARlWNBI0EDprUfS5gF460ABVeZboBSi3MKYTIC0Cbq1wPtklOiCSUKzrEyAdg2/VaCuX4w4z0XTlWlkhQApnSkAgCGNJUYP46NZlQgtWxt5WIKEoFiaBReppFkACCndJRcgr7WWa0tcBnq76Obn3W6KFWPI5LbyUleCm076h/NuXzkjWHCQjEyATmsvE/mL6w5cNRMbThsJgkBoX78wRonTNpuNoqisQuvf2U6CICjCxdAU6bIxlJ8k3H6vByhSwbqAphwOR9nQg4YAQ1N2m9Vuq1LWgK8ra0SEErlMqteouAkyKy264ZejQgkilKBiOSqWI0IJIpJiEiUilEzrEti7iV4ul8tksj8LsDmnWBZBRBiyur+mW8lX4kCO94O/7VwckhADcYl/kBBFprSTV3u28WHn+++/l8vlGRkZISEhr7zyitVq/eOPPwBg7ty5a9eubd68eX0b+DiyzO5M8fqCbs85L0GQFQG6iUZzKkn+n8W27s6BcqhYJGnT7C7jx6pxAHBRzh2Fm2T/kcWcivMX+rB3/OhsrKejT/rIFNE4xRMt++wu2oojAr/ff27CEY1BhanwoKmR1Tsj4lQ67XQJoxI49Q4A7uP2q0NT47a3Vg3vZ9243fXXcV7A3529HuJds5UBeEoiPkR4/2d3AEBpkWAeHp7HCoRfPL9fOBc7FEUfvJSX68BR0zc/VboLU8hDl/4fcSrd9PVGWbf25xMj1qxZs2/fvjuFBAuFwuiYGEtsLMQnvJRvG+Kium/7FhcIAGDJCee+bO+vQ3VKUbGocJLs2jQ3Qd32p999lbjppJ+Jk0QobokoHIFWQYKnoqrvje9wOHJzc3NzcwsKCm7cuJGXl5eXl8dtmM3mag97d7jVpICAAL1eHxgYGBgYqC+DTqfT6XRli43XF0VumtONrYOEpSuFRW7623NuE8F4/KzHzzrI4g2CYr0U+00/zRMRxYJk4wXPx/8Wr1F/0l1VOmOy5pz7TvIbReDrvppSjXo0z/fR33YWQCFEP+upal5SgOCajVp+2oUgxSJHJUIBQIojAhQUInRovERaIn78DHsklxSgIBOiLfUCtMySQL6LphlAEZALi1sxBCnd5qkN7HY7SZJOp9Pj8fh8vtJqhS6Xy+fz2e12TufbbDafz+d2u51Op8/nczgcnL+AzWZ76KIG7gQqlKAiSZBWpdeopFKpTCZzY4p0hwAEUlQkRaUqRChFhRJUIkclypeeardwcOP7Gv/YsWNdunRJSko6evRoLZ1C3aDT6Tp27Lhnzx4A+P7772fNmpWbmwsAFEUlJCQ8//zzVUz8Vnt8/PHH8+fPr5vAqBq8vz8Ih72+n13uGWplxZzzLoada7O3EApelj9o0so/ijZvK/wJAAIzAgdPGyJ2iq93uq6/qpeapbSI2j1/9/XO1xEG6TOnb3xyPKrEGv/eVtahmtPZ1o2/237923PtGV82Im4sjVrdPGdcui+bkLZRRK0MLZr9mSA8OOzL/3vAM3qE4dR7adz7b27PTIuNAXhLpeQ1PA9PzfJQ3N/5PDH1BuunLD9sAQAEQ1XPPi1Nao3KZf7cAvu2/d70TNrpsv60UxgWlG4zfrF6yb+X0u4+GkmSVzIyICMDdu36CuArAJFiU9OmTZs0aXIOibEoGu0PavN0u3iFQgEAe68RX56s3LV499Xyz+4KIZI67m41+cxmc2FhYWFhYUFBQVFRUV5eHheOXlBQcPPmzdpIgCQSiYKCgsLDw/V6fVhYWGBgYEhISHBwcFBQUGhoqF6v53I31AssQJrBb/DQDh9r8zE2H+P0sQ6ScfgYioF3Oipa6ItF8q4s79Tk4vXJ2d1Uo1oUy++917zfn6/8QxNhCF2mSkCHEGHPSBGOIhox2jPy1srzS82lWjGKoyBAEYUQwYt/g0yAKoRIqfc7AHQJEx36T2DFN4pR40ueqjxfcTkEKFL2rcsSKueTEtU1XLm7UuepamO3271eL1cckSAIbsPr9bpcrtINl8vl9Xq50omlGwRBOJ1Ol8tV73koGJJgSCLPacm7fu/Oy1eLB+zZU8fp1hsIJEmWpi2Mjo7Oz8/3eDxSqRTH8W7duiUnJ9e7gGcY5i6ZMh9JuolF3cSVX1flKPKZtkoX53uSIG/RWNbUz5Celu4dX/4+dOrQqONRAEDoPBKzdMAHA47M/ScoOSw+OZ6S0/HbWldbvQMAbaedp7pQdkTcWNp4d1tBiKjx7raZz5zxnHXmTLwhDhSyZIPIXNMwKafe4facdsCvw/PwPH7ch4A3Go0SiYS702/evHnXrl2NGzd+7bXXHvxh8fGEOHeJ8ZEAoJ/+qrRdcVAZrlNLWjYp/Hip92Km+/CpHwPxdw9toZjqlHbz+Xypqampqancy+dXAwDIZLLg4GB9YJBaolMEBCs0eoUmQKJQYbjgpFVURMATEcJgWZlYaMITxPhXr/a43W6CIMoWWrNarVwZtlqpdo6gAqVeoA0RqIKE2lBWEYioQ0TqIEVAKK7S43ItABAANhGyYrCutoUiw4LVyxAUG6a4FVBr9DD/O+kscNFOkp3RWdEhpNgpfedVYtqBOyZOfzJKVCrgIxRYnAYXYYhOgnYIueUQPqKpVC5EcRSkOCIVIEoRKsERqQCRCRCFEC27xJ2gxb97RlvxXaQ4Mjyh/v0LeB5eVCoVNxdQbSiKcjqdpfqfW/z3eDxWq5UgCIIgbDYbd1VxOBxOp5MgCJfLZbfbPR6Px+Op4+oDXq/3m2++eTwFfEJCQkZGBrfdqFEjlmXPnTuXlJQEAAzD3DOFLc/DS7ys2QdxCyiWmnV5qk/mLa1Xp8JtaGSh90ZM9xlPAgukjMxek9uxU99qvxFto4pWqSg7CPQUp94BQBgh5jQ8kUb4VV30TQtr5qweOYpo+j2LjQaYorwta90wmZQFmGWx/c/uSBKLEu9cd42Hh+fRo0oC3uPxjB07dvPmzYcPH+7atesvv/zy4osvcrt++OGHlJQUXsNXA2/qJQDA1MpS9V4MgmjHP5f/zoI5KQe+v3qu3FEBAQFxcXGoVCq5nHMuoDEBkCC05d3IKSoqqsqbut3urKysrKysO3XYdN/nUX0EAkFISEhERER4eHhYWBi3oQkMee+szCbQIWglX04fgA8AfMUzGiSNeKmajAHx0exPFzw3nbSFYAwe2uJlLARjIYpTFMzorHi1dfE899E836ZLHm77vNFfKuBbBAh6RoowBFGJEbUIVYlQpQhRClGlCNVK0Jb6W7fYloGCfS9U8o8j4eU3z8MPV8H+LjUL74nb7fZ4PNwsAKfqrVYrt8Fpfo/H43a7bTYbt8E1ckeVLUxYRR7bWipPPvnkokWLPvzww7fffjs6Olqn061evTopKYkgiIMHD4aHh9e3gTy1y37jDt9Vz7ApwzGXENc4Ga+SLApVDVAxfgdZwALAqTEnz0ae7eTpESOtThVh2kZlDjrjuwaYzCVvcwzoVgDFPn3CCHHcjpaXu/1D2TWW3ZrA9ylMzbuFlkeNok9LxC2EgjGK8svsw2VSIYLs8xDhd6gsyMPD86hSpWvlkiVLNm/enJCQwAn1RYsW6XS6b7/99uLFix999NGXX345b968WrbzEYR2ef6fvfsMbKp6GwD+3Huz92qadO9FWQWEAgKCyBRwoajIKypDURH0j6IgIuBGxQkKiigyBaHMsmUItKVQOujeaZI2afa64/2QWgoUKFKo4vl9MTn35N4nCeb2ufec5wAALmxlGhs7OHBZ/unLsve7IhLu7jV2Om3niISESuaV1z7Z73kdX7bSeTD2rjg7xuSeOJmZtrugprrIYy/y2uvaltLfanw+PyQkJCgoKDQ0NCgoKDg4OCwszP80MDAQx/ErX3KyP+P0XZKW/+9g475yT5ycvW7cJTecuQTGa8NKdSQNDS5KLbx4/7zBRc//w1LaSJrd9P96Sx78K2E+WOFZdPzy2eMYgIKPq/h4kupi+j06mi9kY2wcUwvxBOXF9khZ63fFEQS5IUKhUCgU/u2rw/6s3p//OxwO/ygAu93uf+y/FuDvYLfbU1JS3nzzzfaN/99i/vz5GzZseO+998LDw6dOnTp9+vRFixZlZ2ebTCadTjd16tSODhC5haxk49HM9HEzHuQ3CDwRRusmHqua4k/ELTssAMCwAfNBn+WppnDTr4KVc2Pfx26w8jntoAqHZ7rO23nxQuV4o+u0XTfvU/mjo/gpnTAOx1Nc3rhhl6hbvTVzoKeUV/xQdtyuFIzTyl8F/2VcDPtYedUrofcL+PcL0BV/BPnPaVMC/+uvvwYFBZ05c4bP5xsMhqysrNmzZ48bN27cuHHr1q1LS0tDCfzfwAqQAwBlMgPDwKWrzm77dtXn+aebnwrZnI97DDk0au4WVfSQjNWJjdWU3QEAGJsFAK4zecbjxwEgCiBKFcFNHRQw+xmWSm4ymXJzc/Pz8+duynTVFkntVWZ99a2oUy0SifwT0YOCgjQajUaj0Wq1/jnqQUFBCsUNZ7NsHJNyL/lA2AQGADjeVE3tGvLqfRdMZJ2DqrNTOjutd1J6B1XvpBmAKd1Ec/o0DT8rMpG7S5s+igbXxRkKg8K4b6RKWDgoeLhKgCv5hIKHK/j4lWtRETi0LGOOIMg/ikAgEAgEaHTYdYnF4szMzBUrVvjXi50/f35hYeG2bdsoipo8efKcOXM6OkB4++2333oLlTe7JU5lHRkxfYSwXljbrWb7x9t9Hh8EQOAnmrGvjGUwZvun22L3xHfd1GXEm6N2wY6K4JIIQcwN7d9b5Xbl2gFA+aRWPaOHcanVefpc7fwMZ6FTkJjDDa4AAE6EShUfalhhdGZYyXofO6j1yf8IgiBIszYl8OXl5cOGDfNX7f7zzz8ZhmmeK5iYmOivXovcKEGvLpYt6bTLbV6fJn/s/ub22pLSya+9Qv+1OoBSJP65z6gktXYPl4eHMZVVkhgnxhNKKLOF8ZHAAum4+wRlhYzHy1LJuSnJVZHxDQwGRh+AWJ7Qp29CHzU2xkMxM3uJ7wnj2iyN9Qa9qd5Qb9CTVoPdXG8wGCwWC03Tx4oMZjedpGLLWxQ54/F4fD5fJpPx+XyBQOAfE9u83JpSqeyoinEnarx5Db5aGzU8itc8fL2gwTdmU/2VQ+oJDNRCIl5x8V97n2DObw+qOARohMQl75eFPdv1Zkv7IgiC/IuoVKq5c+f6H7PZ7PXr17tcLhzHudx/RCrFYrFYVxRjR24e7aIDnpBR9T5rsrFy4v67eF1ZSgUAwAAo2VsJAPHKLmRio86Qpz2SNGre6IB71XCDg+h5CcKQ9+OqXy+smV+Miwj1/56rnnPKtNsOAI7zKezAAOXkUHdJZM28MsAgdGk8yt4RBEHaok0nRalUWlVV5X/8xx9/YBjWu3dv/1Oz2czhcG5VdHc0bmwESyUn682WjXs8eSXCvim4WOitrH1x3hsmd9PkahaL9WXPoQmKgKpZs4sacFYi83nA8O8LU4N4IaTRXE8LAGA2ncSJbFrx1XSBrs1sfW22z07b/qo8rwRQAiQmhLJ3zFI1d3h6h+lIlWfpKEXzKmUdy+ikq21UjY2stlF59ZfUpy1rJJ/c3vQ2K6xkL23TTf4wCWt0DJ/AIVBIaIS4RkiohYRWiKsExJW30LuqUcUXBEGQVvwTVtlEbjkMCBZBgS/AKh65Wx7QZZQwpHvTpr9KHzhrzpWVnfYAEASLRfydyyjqF0IBoPr1wqpZF2yHzY1b7YCBbIy68XdD44EwTKY2byoDDMI+S+BNDPol1zkmli9ubbXRH845fs51rh2jCBSi+d4IgvzXtennuHPnzgcOHKioqJDL5evWrevZs6dKpQIAnU537NixTp063eIg71AYFjDz6bq3lzEU6c4rducVA0CF3fJ7aV5zl7eendbHyPC7JGRhEjLChgH4avgmG99kIwHEgAMAFDZcvviKkINFSi9+s3n1PpqBEAkhazH+HMcuHwGuFRE4Bu11amxw0TtKXA8nCARtmKPebEW2/WStt9BEGl20j7r8VrrFTa/LcwIAzcDAUK6PBo2IeLnnxbIuAjb22b3ts74OgiDIHemTTz65of6zZ8++RZEgHQvn4XF7ehSOyPRUAG3tKzxVKEztfkkPBmrmVXmqIjAORG/syo0WtHHPWwtd6/Odnw+VqwU4tMjhG7ca/HfaA54LMXxZWf16kXmT3p+9q54J/u6s4/0TVpuXnta9lUXRjlR5yi3kBROJEngEQZA2JfCvvvrq3r17ExMTBQJBQ0ODf6zd2rVr58yZ43Q6J0+efIuDvGNxE6IC355R/8VPpNHkb/muKJv6a/B8ly5dpo4e5ViznR0cqI0jMDMwNogRrRN3LuMRghnhr7+4wVDPFS8fJtOImr5Hmoa8Bl+AAG9Z2u25XWYPxVA080gCv2ViHy2/5NtfeLf0+RRRiLh9To3fn3WsyLZzCOyxxFZO+evznUerPTU2qoeG82bfpqVlHT7m45O2K9L2i3QO6s0jlssaH03kt1fMCIIgd7xXX331hvqjBP4Oxgnjxe3qceG+U75qZd3HDfzu50QDujRtY6DsyRO24zyMoDQvU67j623bGzEOmxsXIRqcygkLusZud5W6T+m85wzeS+4TXHZyZ1o8YAAA/GvKtO/KMgiCIHekNiXwQ4cO/emnnxYtWlRTUzN58uTnnnsOAE6fPl1dXT19+vRnnnnmFgd5J+MlxQR/+bYrO99bVF6ddXbz7xcX3X3xheeWBK0WTiZfKnb+YLcDAFWCqVUOldRh9lUWen9l04MAIF7FCRUTu0vdn2fY7o3gfZ1lb/VAOjv99h+X1FePkbNaLmPGwqFdMmEGwOCgqmwkAOwrc5/R+6qsZJyCtaB/07LSNAPvHLV6KAYAfC1WuBeysV/HKk1u+mSt1+ZlWC3K1eUYvblGMlTC6hdyyXwNKRdPVqGR8AiCIG21devWy1q++uqr9PT07t27P/TQQxEREWazee/evdu3b3/88cc/+OCDDgkSuW04Ybz4vXcVDDjqq1eWPV4QMOWkoHs0ANR9bLFnSjCCkvQ748mpau7vKSyz7jwse2SE7JERbTyE4auq6jmFgIFsnLpxq6Fq1gXbQXPjNgPgmPxBtXmTvvKVAgCA7tJb8P4QBEHuQG2d0TRx4sSJEye2bHnxxRfffPNN/1h6pO0YinYez3Qcy/LV6gHD2CEaYf+ewt5dHUdOL9++1U02jYcXyjRreT2tBZ0A4JhWZvSRjJuha+Fs41AWy4lh2DmGsfFEAPBDjmN+X8nhSk+hiRwdjd0bwXNdegH7RI3Hv465hIt1DmhKgHEMBoff1Fx3kgYGGDZ+8Vb/5xm2bUXuGhvla1o3HQ5WevwPKizUgv7QfOjVoxU6OxUqIRKVl6TfPTQcABh6RXX3n847co3We8K4b/eX3EzMCIIg/3Fjx45t+XTDhg379u1buHDhvHnzmhtnzJixfPnyadOmDRw4cMqUKbc9RuS24oTxEg73K7jnuM+gNK4AUc9trgvJ7qpIjKAkqZmEoIYTESIdO4QdqqWdLuef2dY9fzSu34GLBJIRA6+78+bsvWnk/NdV1XMK/dl7+FeJyolaYQ9J9RtFla8UaF6JhCBUfAFBEOT6/n5l16ioqHaM4z+Ctjn0Hyz3FJQ2t/hq9M6TZ81qhbVW/0tZbnM7774ZFc5w/2N7JwYDoEpwYMDmCLu4OwwAYFeJa/5fo9ADhPjyHpevF5r8fZ0/pY+Vs38afdVF3fZXuLcVuRcPkIpa1I9pcNH1LlrIxlrenP/stG19vrPeRQcIiP0TAvh/DddPL/OUW0gACBDgwIDRRfcL4Y6M4oVIiOSASxL15rrxCIIgSAdasWJFZGRky+zdb+rUqZ9//vmGDRtQAv9fwAnnx+/vWzQs01urtP45mrJjGBdTT3H6imp4STGB82b4l60FAF5SLC8p1vDx942/pokHp2Lca53N3QWO6tcLASB4YUzAcyEAoH4+FCOwug/LghbGKJ/QAoD6xTCy3lf3SXn0Z+WKN2Nv/XtFEAT517uBBP7UqVNr1qzJzs622+1nzpzZs2dPcHBwcnLyrQvuTsMwho++8xSUslRy6SMjeIkxwDDunAuNG3aSBtO68nyzx+XvqFAoJszgVjOfA4Cbpy2XP4LTHmF9gw2CBwQcAGFRo8DNIrEzhTMYwBcOkAGAv+Qb3ba5Y0YnbffSfDamaVEM5pss+xm9z+CgcZwxu2iTmza5aP+MdDaOHZ+oVvCbBrVn6X0GJ03gECwmWg50XztGYXTRIWKCS2ALj1lX5ziGhHMfS2pr2RsEQRDkNsvIyOjXr1+rm2JiYo4cOXKb40E6CjeSH7unR9GILG+1GxcQ0Zu6Wjd9CQDyJ8Y0Z+9+gj7duAlRnoJSV84FQc/OV9shQ9GcUB6/k8h13t7ws07xuJYdyAGAgKkhAVNDmrt5K92mTXUAYEsSWv/6m6TIRHourYhj9zIAUG4hFS1WfgWAIBHR/McJgiDIf0RbE/hFixbNnz+fYS7+nqalpX355ZdLlix54403bk1sdxpnxnl3XjGhkGnff42QNd0zZwcHekoqbQf/XFOS09xz3DP3OyRHlYDTQBeK+gGA1nvQhEcDwH1HqjWc6jVP1rNoAgOGARByKADIrScB4Ize2yWAXWunamyU0Umb3bTZTXsvTetfP2TZWOAEADaOHX4ioLmga2kjCQCndJ6WneU8XCXAk1VsSYsK9t+PkJvd9JVrs0m4eMtuCIIgyD9cUFDQ2bNnKYoiiEtqoFAUlZ2dHRoa2lGBIbcfN5Ifuyul7v0y5f8FifpITV/XA4ZxosNb6RkT4SkoJevqAcDopJsrz/lq9bY9R82ucJCFFX6xTi5wi2b3ZH0qcp+z59+XGZHWXRLKAwAPxbx52GJw0uJ678PvFkv1Hl2c8Iup4aSH2Vro2lvuuXDF8jp+7xy1XtaiERLHJqrb81NAEAT5x2tTAr979+558+ZFR0d/+OGHOTk5CxYsAIBHH31006ZNc+fOTUlJGTZs2K0N847gPH0OAKSj72nO3v24cRF/7t1Xbm/0P+XxeOwxXi8ADbSPkLP0yU+trst6SNnAZ4MVhG5WcmT/zvzyHFc2QfhoirjgOtQXRja4KQDYVODaVOC67LjNWTYDDAAEiQgVHxdy8Fg5q2W+HStnZ9R5p3UX9gvhKni4gk8oeHjzDXYv7Vlfu7arpGeCqDOHwFqu42Lx0G8csth9l1wm8F8O+DnXub/ikisCWhGxZICUQGk+giDIP0D//v2/++67//3vfx999BGON/000zQ9Z86cqqqqkSNHdmx4yG3GjeSHL09qeoLjQFJAUcC6vMAtQ5IAAAR+tNrzf2mmFqd/DLh3AxcAYGnCCACABhB+ipIzAAAgAElEQVQ9Qs8zlUeVOI8Pyhh8tBdHyzU66W1FLrnJ9863FdIGb2EEf9GkEJeHAYAqGwVAXXa4OAWr1k7ZvUyElNVylh+BwV1oRh6CIP89bUrgly5dyuPx9u7dGxUVpdPp/I39+/c/depURETEJ598ghL4tiCNDQDAibz8hoZ4aP8Da1Y1P+02pItX7PQ/Fpkffuv/KhV6MnWX7MN3uVaA8lBXl6MZSQ0hBwb0xXE3ULwdVWcS+b09/pMpBloRobNTIhvJ89B6JQdarNWSVeeL/rbp66t30RUWstfqur3j1UFiAgAEbAwAegdx+wa3UtwuTb9xj/H3P81HliR8xScuGRVfY6P2lrlbHbxf2kj6M/lmHAJ7o49YxruxDN6/gr2EewNLyiMIgiDXtWTJkp07dy5dunTv3r1jx44NCQmprq7etm1bTk5OSEjI4sWLOzpApMOwQzTe0irX+UJBj0snSzKM+3whAHBCtIECIlbBcpEMQ1KUqREYBuPzTITATTEqPsajfJTNAUB/N03zwnf6kApX8cis2J0pIVrutr5ix/izWIOX7i4O/b7TciFra5HrtwuucbH8B+MvKWWnEhDxCtbTO0xHqjxv95cMCL2p+rsIgiB3gDYl8GfOnElNTb2yal1oaGjPnj3Pnz9/CwK7A2EsFgAwvssHhlW5yrf/sb/5qW1oX4ByAKavZUjEZKFCT7qEhMREznndsmCK4tAQx97Qkfmu7lDS1P9M/uQn8r3+xxQD1TYqtM694JtKvpf6cFJodoLoGiG5fFBlo/wJvKzW878fq1gcBiZfvr6r0avfY9wGABbSnGbY+Ih2UsutSSr2zvEBRucll8x/znXuLXM/2UlwX+QlJeXDpKwbzd4BYFQMX87DUek7BEGQ9qVSqQ4cOPDKK6/s3Lmz5dl87NixH330kVKp7MDYkI4luruXqbTKvPo3blQoIb+4xlvj5t2+6jqWSs5Nio4liF3jAwCg/ss19qMnxUP7KadOmLrbvK/cvXig7N4InreyVvfaBwwwQTsXlj5Z6DxjKxqZFb48iXomF6tyC3tLY7Z2I8QsAMjS+wAgVEL0C0EpOoIgyLW0dQ48n9/62h4KhaKoqKj94rmTcUK1rjN5rrP5/JROzY0Gj+5/B18oLq/0P8VYLN/QaSXss72L9yXO6MTXUxdSBMuXeae+Vh9/QvX2t5XvsycUqQIAADD/iHgMx32AMQzFYQAIHKL0nteXV0ocJADMWV21eWakaLhrXR7t9igFfH2niEOPBz/Dwppqwiv5eO8gDgC4i5wPLiwSmn0wq8AsJeQPBbaMfEPtjz7GGytMLHYUpBvTBiruU3O1LTvEKVhxikv+LflHzkfJWO1yJiYwuBtddEcQBLkF4uLiduzYUVhYmJeXV1tbGxYWlpSUhBaaQcTDB9j/OO0traqZtUQ8OJUTEUxZHc6T2e68YsBx5XOPYi3qJrjOFQCA5P4hl+2EExbE757kzMjxVRXHbO1eNDrLlWO/MDgDAFpm7wiCIEjbtel3s1u3bqdOnbLZbGKxuGW73W4/efJk165db01s11JSUpKenp6Xl9fQ0OB0OpVKZXBwcHBw8P3336/Vaq//+o4gHNDLsv2Abe9Rfkoyv2uCv3G97seyw1XNfcSdu7JEPKyq97DnA/lG8kKKYNlyfpzvszUv3TPWGtEr1/76F+aF0yQVWi4wTePJafriCm1Bte4531ZK7KR0mJITwTcur57wRfnh0CM8/t1uj5LNdghkhxiRRkk/+PQOU3OJ1yCjd8E3FXKrT6/kBDZ4S57O/Ty98Xi3pon6iQGkOuYEB+cRGBErTCx05K3X/fhiBKpciCAIcueIi4uLi4vr6CiQfxCMzQp864X6L35yncmz/L6vuR0XC1XTJvBbjqtnGMpsBQxjawOu3A8rSA0AlMnCUrJj01L8OTzK3hEEQf62Nv10PvXUU/v27Zs0adKPP/7Y3OhwOJ544gmz2fzwww/fquhaU1ZW9vzzz+/evbvVrS+88MLYsWM//vjjiIiI2xlVW3AiQiT3D7Zu269f/LWgVxdeUkwhp+KM+mTtH4bmPuyo0b3Ob3ripZ5yI1kVy903Qda76JvYTkNGDbnvbflnsnd6xp7QLl5ZvGN+UmUQN7PO6yZBKinR8BSVJoWi1r1gRaXETlZ3l+zqp2S7qeBR6sQdhr6z+56bCocimg6x0/jbI9J78L+mkwcZvW9/Uy63knlRgveeCRt+3PzEDv3La2sImvkjRQoADb46NYCWG1xgP8/FeXycf8Zy8rztTLK4+23+ABEEQZB2R1HU+fPn6+rqWt2Katz8lxESUeCbz3sKy5yZ5ymjCeNxubERgtTuOO/SMXEYhvO4tMtN2524WHjZTmibAwAwAQ8AWEp23K4elp1G2Vg1Lry8Nh6CIAjSFm1K4CdOnJienr5mzZrdu3er1WoAGDp0aFZWlslkGj58+PPPP3+Lg7yooaFh6NChJSUlnTp1Gjt2bHJyslKplEgkVqvVZDIVFBSkpaVt3rz57NmzR48eDQwMvP4eby/FxHE4j2vZstd5Mtt+KnvTFD1N0obMhuYOYSEDn37BKTaSABBa5HnhtVqAsT4WtqtvrmhO3b73fmPNGxH5R9S984oWTgt3a7kAYLFGW6wQbHAv+KZCYiOz40X7kyUvLyxikczPowPL75aP+MM89VvGPtFe018EAD7aW8ysOf/sqwDgKXYWjsjyWUlRP9mRKeFuo2/sx7HBvcU184tfXl+7dIgse8iZn2uWy9mKSlcpAHhod7ggusJZ8mvtyoVxnxPYrTr7MsAUOwoiBTHNo/0RBEGQdqfT6UaOHJmdnX21Di2Xj0X+m7hxkdy4yGv34cSEu3MuOP48Ix7av2U77fa4zuQCADcmwt9CyFiKx294pCSHwACAg6NatgiCIG2eA//TTz+NHj16yZIl+fn5AHDw4MHo6OjFixc/99xzGHb7fk/nzp1bUlLy3nvvvf766612WLBgwQ8//DBlypT58+cvX778tgXWVhgmGz9SPLSf89S5A97DBnW195jN422q086WBny4CRM38gDAy8NLO3FpFpZ42skmmd5HeD3/nJz1ZGb6W+nD3hkWfjzine8vvPpqWD1fFhO5PkhPTvq2l8RGlneVRDypnfVGIUYyAPBkmv7Ac7nnNGSXjV1nr6naLAs1J7JIhjzVeHSwakS4LrJwRJZP5xH1k8X81s13yOIPI3BWOO2mdEvKKqbknZt/Eu4FHi5gwHSXrH+W5WSls1TBVunc1Ycadg9RjdK9X+bMtIZ9lchWX6vCHO1yk0YTxiJYgQFYGxaR22nYvFn38yDlsKdCpt/sZ44gCIJcxRtvvJGdnd21a9exY8cKBILrvwBBWiO+r78754L5p61sTQAbVwMAC8doh6v+i5+oRhs3LpITEXztPXRTs1V8vHtg639LvNZbPCCU2ysI1bJFEARpcwIPAOPHjx8/fjxFUTU1NRqNhsPpgJ/RI0eOxMfHXy1793v66ad/+eWXo0eP3raobhQhl+L3dt+f+y0wINhxcZU1SXwvWQEFAF4e/r9XIhUS16wP9BgNdhkuaqQJL9Fr1V0x++IsCY0AIPKyNbiwHiDJXvvQmwMEVvJMgsh2vzryrSKGZMS9zWZOCXGs5+DvOv055cTp/rxeR90Pf1lV1/nhLfHrAOD3E+uGTx/WnL3jQgJjoEuRA3NKAUA7NwoAdEvK7n6nP58QnLznuIQlnRTyvIoTuNOwmYNzAWBr3a/RH0eYvqwDgKKRWbE7U1rm8L20nN2l7q6BHG9ZtXntNte5AqBoAMD5PGH/nrIJownJVcvjN/pMafpNAHDElD5IOSyMj2opIQiC3BLp6ekxMTGnTp3qkHM6cscQpnZ3DbzLfvhU3TtfPNIpJVSbELtpd/XZPNrhwsVC1QtPXncPd4dyT0666sDJGDkrRo4mzCMIggAA3PCCXgRBhIWFtTzT385U2Wg0tqVGXWhoqMFguG63DrSlbq2dcUSW8fIrjc2N7DEDcnuLGBw4bnryZv2sd/WiRursAOGLB2JPjGEBAIMx8kpZxN4I4GJRa7pYVDwAmLD/IYGZ7+Hhh3rI+n1QynjowJfCYvY/tP9T04HX9zM49FmRqnbV1QZwCB8TsVAgIWQ4ifd+rqdP5xEPlMds7Y4LCWDgnu+q5i2v4D2ZQ5p8AACzOBmTMzAa6/l2SsAF9UPaiXxCcH/gI1K2vM5TE8oL7/55N9OXdRgH50bx3QWOopFZPoO3+b2MiOIdn6iOK8vTvfGR60wehmGcsCC2JoB2e2zpR2tffZ/U11/tw9mkW+Oh3TycTzP02pqVt+5bQBAE+S8jSbK2tnbIkCEoe0dunuqFJ+VPjMEF/PDcrEf3rfUdz6QdLn7XBO17r7KD/3FTGhEEQf69rno5kyTJlStXnj592mAwxMbGTpkyJT4+HgBcLtfx48fr6uosFovdbj916tTmzZtv2xy51NTU9PT04uLimJiYq/UxGAy7d+9OTU29PSH9DbXuqkMNe2hKkLpftai0rLld0C/1/UfC+u498Ow7QV0K7QCQnSA6OE/s4+NfL4nhMdXdt9sBANgQ9UtnYS8pa7sJAAJf6GQ7eBaq3DPXVmMMBL4UFrwk9k/zkSLPBRgFHJx795K7wzNlAF6MIAVBZ8ZKxv3csNon8AGA1+SlXRTOwytfKui0vx4AiHx78f1nYrZ3X29apaqXAQDFptRSTX/FEADg4ryHNE+uqvoi+ouo5F+TKTat/F4bNiCmaPQZ13l70bDM2F0pbE1TbRvK1GhctpohKfHwAfIJ9+NCPgD4dMaGr39255cYl67Svv8aXDH/osR54YT5EAtjz4lZ9FnpokJH7unGY71k/W75t4IgCPIfQ9M0l8utra3t6ECQOwKOSx+4TzJykLuglKw34VwONyaCpVF1dFgIgiB3mtYTeKfTOXDgwIyMjOaW5cuX79y5k81m33///Q0NDa2+6jZ46aWXdu3a1adPn/nz548dOzY8PLzlVp1Ot2PHjnfffddgMEyePLmjgryudbWrqmoHFJeP3y/L9zq/8DeKuYrRJxR7n2Sqlak0UeFvdPPwTImUAQAaMzqayrlhBA4+5nzC0Xdk7E0vRgQlCq1vRpVPz8cYhlSx7TO9pY7Cjbqf/J1jJnXipYvcJ20AIH4sMOC9qYFS4WHX4d++/O2BFx9Q5EDRqCycZ3Kc5pAc/KtHgl46Vu88a8sZeSwgTJG4M5HkkWkfpoUkRm7WrWmOf9B3g5N/7ESx6T3v7uJ3k8xWLYhN696Uw4/Ias7hrbuPMB6vsG+K8tnxza9lawMC33qh5uV3PSWV7vOFvM7xLT8ZBphfa1YywAxXjwvnR4/TTFhd/fX62h+6Snr6x+23F8rUaD+a6SuvZiiaFagS9O7KjQ5rx/0jCIL883E4nKeeemr16tUZGRk9e/bs6HCQjpRryw7ihcrZypvcD8blNC+UiyAIgtwKrSfwn3zySUZGhlKpnDlzZnR0dHl5+bJlyyZNmsTlchsaGu67777u3bsLhUIMw1QqVa9evW5buEOHDv3iiy9e/otUKlUoFBKJxG63m0wms9kMACwW6+uvvx43btxti+qGZFlOnredEfP6SSlnbfnx5vYeZGLvTbYLKYK5r1Xy7XRRHD+kwtsn2wpvw7fzg6fNN/Q5YKbYuKOLWJJpKX8mjxPGoy84n3qnSF/jNH5XgzGMg08I630lY7O3f7rdw3cDAEZjlpf07pNyikPhJGH9pSEtcMu5h84CAChgyxdbHnzxQcgBAA5GUPoBpeJADz/uiK/+Luq8OPF8U/Ze3aOq2nxxpfrU5ak9furpz95L7y7FbJiLcvJVgti07kWjzrhyL+bw7txiABAPvfzmOcblCAfcZfltjzu3+LIE/oT5UKmzUMKSDVIM22HYlCofFCGIKXcW7zZuHRP4aHt9BZZt+xvXbmNI6mLLb3uEfVOUzz9x+dI4CIIgd7S5c+dWVVUNHDhwxowZffr0USgUl3UYOHBghwSG3E4ZluNfl38YwgtfELcUv2XrywCAp7jCtucPT2EZ4/YQcik/pZN4+IBr1MRBEARBrtR6Ar9lyxaCIA4fPtypUyd/ywMPPJCcnExR1Keffjpz5szbGOHlpk+ffu+9937//ffp6en5+fllZWUAQBBEQEBAz549H3zwwaefflqj0XRghNdAMr4Nuh8BYFgYbq2dt8JxXP/XpmQi4Uhn6dxJlSI7lTFE/Nns4Gid+/WXqvrstkZkuDT1Pi8b++Dp0PMxgjkcLOVEo6/ao5ygbfhVZ/iqCgBoAf7J5LD5v+s0OZrRM0dv+3QbyfMNXXJf3K44kk/uX7S/z6ep0mpJ5InIwkcKAcDLeNxylzG5QV4uBwBCjiUThZ3zzgKOOaRmyi4GALvKaY6pH6l+SEA0Lesq/pgn/InLsCHrg8zS3qUh/PCHNRP5hAAAWCpO7I5LcnjaagMAQnX5n4MAwApQAABlsbZs9NDuTbo1ADA+aNI2/fojpvR8e86EoGfeL56707C5n3ywkhNw81+BdfsB809bAMOE/XrwuydhbLanpMKefsxxPIt2uQPnTr9yVD9ybYzX57lQRpotOJ/LjY0gZJKOjghBkLaKjGxaHuzDDz9stQNaRu6O56W9G2p/BIBqd8Whhr2DVSNu0YHMa7dbtuyFv/5FkQ2NnuIK264jAa89w0uKvUUHRRAEufO0nsCXlpYmJSU1Z+8AkJCQkJSUlJOTM3HixNsV21XFxsZ+8MEHH3zwAcMwTqfT7XbL5XIcv+GCfFdauXLlN998c+0+LpcL/u7fNBmNxw0eHQCcsP0BnaB+YWPzpihR7C/duI+mYwDA8jKsYCiO5L//WehbL1dp6n00F/9uWvi5UP64KF53ORsAgMCCFkaL75FXzrzAkAzupN/dqotYkVQxNU97Xjtm9hibxhq3N84n8O5cvLPP932k1RKbxnZw9gE37QIAjMEGfDIwLi0G+DhbTvhqfdbjqepnG1zFnTw1LowNDrldVi157JmHuqyIE9/TBQAa1ugqvs7DWFjgs84RIUnZkFXtquDi/Oa34M/hC0dkufMdZZPOi1OEAEbKbGFrL0+8KbMFAHCxsGXjDv2mRp8pgh8dzAtbWbkMAPJsZwcrR/aU9T3deGxz3c9Twl75G5/5JcdttJnXpQGGBcyaLEzt7m8U9ksR39e/7q1PXWfyHH9mN7cj10fTlq3plt/30Q5XUwuGCft0U0x+mJBLOzQyBEHa5NqruiD/BXuMW+u9BjFLYiOtW+vW9pbfLSTa/5a4dddhy297MIKQjB0i7NsDFwt9NXrr7/tcZ/MN7y0P+uQNlvpmR+8jCIL8R7SewFssloCAy5MurVabk5OjVP6DfmExDBMKhUKh8Ppd2+bgwYOZmZlt6fn3EvgoQVwXSU+S8VXZSiw+q6XU5m/HACP6xdlExMKvw+bNrOz2h33WC9VLvwy5EMI/vDC226a6ikc0F4Rs3EpO+LEa22kkJKyY37uztVzF41rR3XLaRVW9fMH2h7ls0vnI1cklU85qz2m157SUgMz99MI9Xw2W5ErcQe7zK/NDgptutsS/Gxu8RUvzmeiNye59vxu+C6BsUsMqJWVx4QLC+QO1nrfhgRcfkFXKM1+6kP3L76SIVIC8G5HMkPCHMbfIewZ3kcCH1X+++9KJ/pzwYE6olhMRQtllpN4LAJxQHjchylNUbj98kpd0ScVBhqQcRzMAgJcQ3dxY79XvMW7DAHss+Bn/NHgtL0Tnrt5Q+8Os6AVnrRknzUcGKYfFCZP+xsfezHn6HOPxCnp1vixLZ2sCZA8Pb/h+g+NoJkrg24phjJ//6DiWBQDcmHB2kJqyOdy5RY4TZzxF5ZrFs1lKWUeHiCDIdbz33nsdHQLSkcy+hp2G3wDg+fA5aYaNubbs3+vWPR78bPsehfGRjet2AEDArKcFvbv5G1lKGb9znHHpKseJM42bd6umP9G+B0UQBLlTXbUK/ZU3tNvlFvc/3KpVq2bNmnXt5LykpOTRRx/9e5+GmqudGflWvv3cR7b59mI35aOb2lmBv6V0I3FuXTdm8eqwNydVdjlqn/VC9dKvQr6nMOrhIGAAt5Iz1tXiWRZ/9i7s1TRQmRPKA4DoTV3PJxwjjd6Sh85G/Nip+rVCykzG/NxNskjhOGeFECxl9z19I0b5X0K76bO/HWKACXwiVNZXVvltjjiV4y5/wF3swgVE9KauW6LWOxocx6edGDl3pKRMYTneUJFaDl2gbkH1sAXD4jb1NEupssmnAMAo91gL8jlnCwCAskntOUMoKyO5Txn+VSJpVtt2HbbvP8EKUEjHDsXYLACgrPaGb9b6ag3sEA2vRZ2b9bU/+hhvX/kgs6+h0JEnYUnnxrz/QfGb1e6KjMZjwwLGbtdv+LVm5fy4jzH4+0PcfTV6AGh1qB4vOQ4AfDV1f3vn/zX2I6ccx7JwIV/92nP+Tw8AKLPFuHSVO7/EtGKd+o1pHRshgiA3o6ioqKamZtCgQR0dSPsoLCzcsmXLtfvo9fprd7jzbNT95KHdvWT94kWdxCzJ/MKZBxp2DVAODeGFX//FbebOL6EdTk5UWHP23gTDZI+PcZw44zqdA9Pb8YAIgiB3sqsm8P9NHA4nJSXl2n243Juqc0Yz9K81qwAg1BAHkO5v1PTvOWFswBcmG4VDbST33S/D3nquqsuxphy+axCnv43Leq2we5YFRJdk781wIRH2aXzZM7m0iyp7PCf0kzjJcFXZxBzHaas7yL3hs/Vqcu+znpfVXC0A4Dw89LP4ypcvGL+vZmsYhqQ4IcLQb3volpQpH9cK+0gfpf8vpbiXb4mFAcYXWzGm12BuVDgAQBRQao/vBVvvlb37ye9hzeJL7IRiJviqdI5T9YaVStrFSO5TRv/aBePibE2A8rnH6r9d27huh2XTHkIuARZBGc0MSeJCQcArT2NEU6WcAvv5TMsJHMO7Se/6peY7ABigGmb06u9RjVhT/e3v+g1Tw2bycH6Fq+S46WA/xeCb+PRpAACitYsvOHaxA9IGtt1/AIDimfHN2TsAEHJpwGvP1bywwJmVS9abWSp5xwWIIMjfxzDM4sWLN27c6HA4OjqW9jF79uy0tLS29KT/MyeCEseFk+YjbIzziHYSAATxQgcp7ztQv2tdzcpXoxe244EoowkAOJHBV25iawNwHpey2hmPF+Ny2vGgCIIgdyqUwN9uhxr2VLvLAziB7rKLaeSwfikPdBZ8VmMFAA5pr9SL3pkW9s63lV2O2WfPqP708+DHP6sOPNXo5BGcH5KvzN79ZA8HJnQSFQ7JoKxk5cwLrIAy0ujlRvKFmwMJkl3sKFheuXRe7Ef+zqqngzECq5hRULuohh8XJ1YZ2Rpu2LKmW+LkaQ81wc7YGX68VR5xRsOewRf/VS7+CTDz9eWTc8mPnQFcjfaNSAgCFy+y4pUs2uVrzt79fUVDUj2Fpbb9JxiSJI2m5lBxDtv80xZOdJjskZEYm5Wm3wAANEN/Xd5URSmtbkNa3Qb/Yx/t+bL8A//jbfr1N5PAszQBAOApLIeRl2/yFJYDAEur/ts7/2+haU9JJeC4sE+3y7YQEhGvc5zz1DlPUTlK4BHkH45hmAULFvz8888Gg6FlO0VRLperc+fOHRVYu3v77bdbVvZpldFoXLVq1X9hvCEAMMCsrf3ev26ritN07ntA88Qp89E8+7ls6+luknZbYwjjsACA8fpa2UbTjI8EDAPWLax+jyAIcif5NyXwRqPx+PHj1+/3l7Fjx966YP4eB2Xfqv8VANRO0Y8Htza3y37H8n4+ErE8rDyJN4rcvJF8rEbNWzwt9M2vKzsftX82tERiIl1C4t1nwsYH8XzVnuYXYhjEK9hKftNfG/xEYXJBv/zUU94KF2n0AoEpnw7SxIS/yyzbrt8Qxo9sGYzyqSAAqJhR4CrsBJDrq9GzgwMBwHHSUjwum7KRsrEqAnYxXuCEXlLVX/5gIACUT87VLS4FANnogKLRWWTD5dl701GmPyG5f4grr9hbVE7Wm2mLzaczkmYLaba4zhYIenXhxkZ0k/Z2Ug6KIWvclTQwgYyKxxZiLBZgQDK+GncVAKPhhnBxbpL48nTxhgh6dTav/s1xIks87G5e4sUZ+JTN3rhxFwBcmY4iraI9XqBpXMDHOOwrtxISMQDQDudtjwtBkBvz7bffLly4UCKRaLXaoqIijUYTEhJisViKiop69+69bNmyjg6w3fTs2fO6a92fP39+1apVtyeeDnfcdLDMWSRnK0eqH2xuFBKiMZpH19Z8v65mZbK4Gwtr5Rf+b2CHBgGA+3wR4yP98+mauc5dYCiKHaJpHpSHIAiCXNtVE/jDhw9fVsfOYrEAwJXF7QDAaDS2e2RXys3NvaHV3f+Bi99kNZ6wk1YAyGWVFJVXN7d3KomW4NTcyZUffymuizoJ8DgAlKl570wLe/vbSqmJdIjxxbPCisX8JSesl+0zVEwceuLifWNCwko82bvkgWxXjo2yU7XzSxq3GkM+jH20z9MtX1XlLm/wGro+1QsAKl7IdxV2Kp+cHr3lIVeO15+9yx9QC7tlOE+4+V0TrqwoLn8wkPExFVPydItL9Z9W0E5KOkoVtaYzxmnlxgU7RMMO0cB9/Zue0zRpMPlq6mivjxsbAQD3qkbdqxq1vOKTKndF1+rAsT+wASiMAJZGxQ4O3JsiPRCQwyNZc2MWEzze3/vk/VgBCsn9gy1b0/ULv5CMGMhP6YRx2J7Ccuv2/WRDIzcmXDjgrpvZ/38HzuNiXA7tclNW+5VL+Pp0BgBAhegR5J9v1apVIpGooKBAq9U+9dRTZrN5+/btALBo0aIff/zxureskX8pD+3eXPczADysncjFLzmxDlaOONywt8Zduc+YNlz9QLscjhMRzIkI8ZZXm77foCS2LsgAACAASURBVJjyGPbXRDay3mxauQEARIP6tMuBEARB/guumsD7fL76+vor21ttvD0GDRqUk5OzYMGCzZs3A8D06dOl0n9ZhhB1wHq3XkwT4OTwf7B4/Y184AXy1G4uCC3UGzMaDasfPY4TNA0CNlal4a17K+bunYbsEWq9iAAP0zmAI+FeUsWte+DlF8gJERGX3gMAzFsMNXOLnFnWwnszpcNV2rmRgpSm4fffVXxW7S4P50c/9fC0EEdU9f9Kbce0F/pv8FSG0G4Q9aHZsi3OEwZcwFc8/XCr70XxqAYAKqbkXTt7bwWOszQqlkbVsq3EceFU41EOznlI/SS72zlfrYE0mnw1el+N/q6zzKnn8TJR+e5ls0a+suyyi/c3Sv7EGMbrs+46bNm237Jtf3M7LyE64NVnsVanxyNXwjB+53hnRo417aD88ftbbvGWVrrzSzA2q+UYBwRB/plKS0v79++v1WoBYNCgQfPnz/e3v/766z/88MO77777/vvvd2iAyC2xXb+x0WeKFsT3kQ+8bBOOEROCn/24ZP42/YZU+SApu31mQimnTah7+3Pb/uPughJBn26EVOyrqnMczaBdbm5MuGTU5WEgCIIgV9N6LmS3229zHG2UnJy8adOmHj16ZGVlzZkzJzy8Pauk3mq+Kp1v4+HBmEI18+kDaTua28PxULaXxfaCl49xLEz4pKCEZ305WiJUzLpg8s2coE6cEfwkwOPbGk7Wet9IFfcOamuVF/kDaukwpf6zCsOySsvuesue+ugNXaUjVADwsPbJn6q/rXCVrK7+esH0T4H0Vc+tchWGAABXW82WZpA6hh0cGPDyJHaI5mr7VzyqIaQsZ5ZN82p4W7P31jTPxBsR8GCI5m7odTcAMF6fr9bgq9X7ag0jqk9tTMjaN9B0L+7jXvqP1vD+cm+Vjh2kZgep2Vo1KyiQrQ1gqeSAXaVYPYYpJj8suqeP/dCf3rJqhqTYGpWgT3dBr85XfQnSGumD9zkzz1u27AVgpPcPwcVChqKcp86ZVm4AmpaMvgcX8Ds6RgRBrsPr9YpETYNoIiIiamtrnU6nQCBgsVj9+/dPT09HCfydx+jVpxu3A0A3aa9827lW+wTxQmvdVb/V/fJ06Ix2OSg3Jlwzf0b9F2t8NXrL5j1NrRgmTO2unDoBY7fPWH0EQZD/gtYT+HZcWf1WeOyxx7Kysjo6ihtWcejM7yE98YQYtkmy72Rpc3skHubjYdXR3MhcN83DwUK+9nXFpzMiXMp2OJ/hAkI7NypgaqhhWaV1fwMnmAsAjb8blCel8x76MCP8RDAvDADUL8bhYlHVzHzOXZ7ABziEeAgvMYbfPQmuV8tHOlwlHa66dp/rOmraX+YswgBjgPEvSNtEABADEAMMJHH0uRaee6dxywOax1u+lqw3kfp6Ul/vOpPX3Iix2WxtgHBQb+mYIa0ekRMZoohsfWQB0kbcuEjls+MbVm60/LbXsiWdkElou5Px+QCA3yNZ9viYjg4QuSk+nZFxuXChgBV4s/+DI/9k8fHxBQUF/seRkZEMw5w9ezY1NRUAaJq+cOFCh0aH3BL76tN8jBcANut+vnbPo6b9j2ifErFaL517o7jxUUGfz3OfzfcUltNOF6GS87slcsKC2mXnCIIg/x3/piJ2zXr16hUTE8Ni/cuC/65BtimuO9DALaGjMy5OZQ9hh7/zenjt/dxXJ1UlnXd6ORjfRb3yZfnXL0VCAGd3qfus3gcABgcFAIcqPSoBHi27sffOUrKD3okOeqdpSLPhyyr7iUb9ssrgbgHK/5NT40lCwlL9X1DGoJMbLKu1vJDesrtHqRMBu03jyf8w7QMABpht+vXX7nnElH5ZAq/9YA6pN/pqDD6dgaw1+HQGn87QQJtOx+UNOE1ensAzjOW3vbTXy9YEsIPULK36yvnbHcvsa9B7dAmi5I4OpE3Ew+7mRIY0bt7jPneBMlsAwzjhweLhA8T39r0zhjO484qcf2b7dEbAcU5YkGhAL3aotqODusVo2rrjkGX7AcrU6G9gqZXSMUPEw+6+M75T5DL33HPPxx9/PHfu3FmzZkVERCiVyhUrVqSmprpcrv3794eEhHR0gEj76yFNrXPXUEBdt6eWGyIg2vMsiRE4P6UTPwXVVkAQBPn7/mU5sN+gQYOKioo6Ooob9qD9ArtOJ+6V0uON4rfsZc3tGcNTnGIBmQHvPR72xsqqpBKHP4ef9nlp/dTwLy/dyYps+8FK9+7xrZQSbLuIlZ0M31SZ1uqc2TbnzAumX+vi9/cEgCA8TAoynbt6a92vCaLkOOFtOsU+qHnyvO06QypEu/mC3XzRu4rL2jECZwcFsoMCWzauK3or36lzCfH4SztTZot5XRq0qG6IC/gsTQBbq2Jr1cIBd7GDOnIZOYqhPildUOuumhk5r4ukRwdG0nbcuMjAN6YxJEXb7LiAf8es4ku73PXLfnKevji41JV53rI1XTLqHsWkB25DKssAo/fUaritLJt8Cw9KUcYPv3NmngcAllKGSyWU2UIaGhq+3+AuKA14eRLK4e888+fP37Bhw3vvvRceHj516tTp06cvWrQoOzvbZDLpdLqpU6d2dIBI+4sTJsVFze/oKBAEQZC/6V+ZwP9LRcvZz2YfbMwS0rXscrqyuT2MiMhhoGtleaSKn7k0VvZ2cVC2nWZjQjc9ZaPuwJdJMh4OAAfK3QYnPTiCNy72piqxAwAnjBfyXmzwgujG7UbzZr3wLikAeEpdVE/rJNb/QTfc3dWjukvp7ubkRfMBx74q/yDPfjZKENdD2meQcvhNHv1KCaLka99zNq3Vlb+cDzTDK8R8uzxsDfcanTMtJ/Kd5wEg25Fx3nYmWdy9eROhkAW+9bznQplPZyB1Rp/OSDuc3tJKb2klAHgratVzplyyL4ZxZedjLBZLo2IpZdedUHCTDjTsrHVXAcC62lVJ4q4s7F/zvyfGIv75NecbvMZl5Yt7ywa0XDOpdQxj/Hil62w+LhJIRgzkxkUyFOU+W2BLP2pNO4ARuHziDSyH8fek6TduqVv7iHbSiHaqAt0W1q37nJnnCalYNWMiv3sSAADDOE+dq//6F8fRDG58pGQEKjR1pxGLxZmZmStWrEhISACA+fPnFxYWbtu2jaKoyZMnz5kzp6MDRBAEQRDkEv+aDOEOkBefcuSPsBEnSDe4DExTMX8CiDl72N9LGxtiWAekaleU5+DK4I/GViir3QBwNl44o4coUckGgKdtlMHpeb676Mqy838PxsXlDwfKH266d01IWfxOQme2DY5TnON4+Te5AIALCG40P14bw9FAY4jlsHZ37wH9+dFNA+oO1O+q89QoOQGJos5h/Kh2iepKprW68mn5QDPsIK67yFk0Iit2V8rVcniS8W2sXd38dH3tD0lxXXDs4uqy/K6J/K6JzU9pm8NXZyTrjD6dUXBXl8v25s4t0i/+2v8YYxEstZIVqGJrAliBKnaQmtclAWNdsm4tQ9Gkvh5omhWguNF70XbSuq1uPQCICHGdp2Z//Y5hAWNvaA+3h5f2vlv0Ko7h82I/aq8lgm+PDbofq1zlOnd1irSPhnutWZfOjBzX2XxCJtEumc1SK/2Ngp6dBX1T9AuWWdIOiIb2Y2tuahTMtZl89TsMmwFgm359qnygjH35wJNbgqataQcBIGD2ZF5SbFMjhgl6d1XhuOGD5dbtB1ACf0dSqVRz5871P2az2evXr3e5XDiOc7nXulSKIAiCIEiHQAn87VN+lDd8B4kxTBVdw0DTKG45P4hLE9M36L75P23BeMBY0HWjXV7r8W9NKnU1v/zNvpLhdd5ul2bvDEW5s/M9xRWM10coZfyUTn87r2ApWOGfCex/1rjOecgGEWmReKsIb7XblWNX5MgU0NPfLR9Ohn4cFzAt1FPstL2g57Boo7SiXlg9JGak6plglorDUMza3781e0xsMTdBltwneCAAsORsAChxFug4tQKWMFIQq+JcHKxuIc1mnwkDCOKFsrGLeS/JkPUba3TTS4Bm1NND2WE88wa984y1cHhWSFo8S8vh4wK8xUR9mqG26zcavHXNLTXuyoMNuwcqh5l9DQCgZKtaJvM+xtvItUM4XxbTVfjXcRmKsRc1uqO8ACCO1IiHD/BV1Pj09ZTJYq+vc7p0UA4iG84mMdkjI2SPjgIAF+W0NNbZ0g6wDxSAww0AGIvgd0/iTRjq0QivPK6X9ljIRgxAyVFj0DQm+be6tQ7KHi9KHqoa/WX5+7/XresjHyBltc/6Pe1oj3FrjbsSANKNabfz5vBNKnLkZzQeBwCSIdfVrpoZ+dY1OjuOZwGAdNzQ5uzdj5cYLRzQy37wT+efZ6Xj7r110W6sXe2lPSyM5aHdm3Rrng17+dYdq5m3uo6y2dmagIvZ+18EPZMJmZg0NJD1ZpbqH/dvErkZv/zyS48ePfy335vx+XwAOHXqVG1t7bhxt3y8CYIgCIIgbYcS+NvEuEaXvLQcGOb0UFPjnurm9lAieN009WPfGJ5frSPiwOPEXlpci9EMsDAgmQsR/K5/9YyRs2Lkl3xf7tyi+q9+Jg0NF5t+2CwaeJfi2fE478bunPhq9calq7zlNf6nOABHCoIYheKZpwBXe0pc7mKnp9TprXRTZpIbJwQAR6Y19Fho8x5qoRRwTPNaRMMvusTn45rbc+F4ywOJgQBwV0JOtZCI/CFZOlJlOdqQ+/gJ8GEAUIcVCSXisK8TJYMVtj/N5x8+wW3kAGCUiDKuqGYoBhcQOBf3FDuLkzK9Ah+O4wKRKGJ5kvgehe2E+cz//RFolT4LzwEAzsN3vbmrqlfl8b37OW9ThJ0AgFKM4OEX1zZzUg7/lRQCIwQ8YfiKJFF/eeGITOdpK8khSS5JYIRAHBT+9b2SIQrLH4acp07ibhwAPAzOIwnLMXbF7MMAYGNbSQ4JoGSR/cQOTJB0lq0wWvZXn1+dhVMEALCAUIiUYV90EvWV2bMtx6fvpn00APBwnn+Ss4/2Bnqk4+FRADBA2WPEBIfUvvvV3x697xlXke3AS9tIlw8AxCxphDomaH4UL0HoLnemz9nsdXkwABlbESmIJWRs/9WAHGuWk3YAgJIdEK9NVr8QytZyfSbvng82Oh0OAAgUBSWGdlVM0LAUbNpB7f1ys81mJUVUmDAyvkXhg3NUlgnqWRgrhBcRG5AkuUfeiJl31f0WdDaI8BGn4UhgoDoxqis/UeQ/bm52lslZz/Ahgh8TyG0q9kZIWXW+mnqvkYOxg3hhUomsecVBG2lt8BmFhEjOVracL0AzlJt2C4h2WwuDZuhfalYwwNyrGnXMfPCcNSPHltVZnHK1/qTOCADchFYGlfASou0H//Tp9O0V25WKHQWnGo9ycM7MyHmflr57wnxosGpElCDu+q+8ObTVDgBEq/k5hhEKOdVoo602QAn8neXJJ5/87LPPLkvg/dasWbNmzZrGxsbbHxWCIAiCIFeDEvjboeFnXeUL+RjN/DpCXRUZptq9t3lTvEeT8qd9/Sz1o0sNU+fWAgBGA8XCCJLJHKj4ebR6/FX26c4r0i/6ivGR7FCtsHdXjM/zVekcx7Psh06S9ebAeTMwoq0TtimzpW7BMspkYamVosGpbK2abDA7jpzyltfUf/aNZtEsQUorlbQU4zXcaIGv1k02+Gg7xdCgfEIDAOK75ZIxSqfRTjsoFskCC0nZnbQTAIAkGMLDAQYDAMZDMz4aAMDOcG08zNd0I9prc5NGLwBYfq/nNjZdhiDshD/Tpp1NVXMxGufauQDgs3t9ei8AUA0+oU6I0X/F58CC7aFVUAkmEOgvtlNANr8FLly8zOGzeX16b+mEc87TVgBgeVksLwsAfDYP2eAFAMaKCUyC5qq9NAC4af9/BSC4+GECMIwMwMi4+bIqmf/NAoAb3Nbd+aK+qd5ilyrn4rpcTrD5HwTAJfXzVKA6cvRIef9i0QmB9g9Nc3sjGET9ZLwEoe1AQ/COi+PAzWBofqyFiyX99FDBUrIDXwk3bawL+urifqqhkPHQgbPCjb9Uq99VqqHpVnMlFDT3kYFABmH+r6UEskPej906ZnNUWtTg95pr+7vz4WTo0viAKSHG9TWeZ8xCIACgHsrq4WKZxr8+ASiFBoyLR67qJBurbjxoKBx/BqOwWq6PwAiBSBT+bZJksMJ2qjFr8iHGxwAAm81Ryi+OKNEx1Q6OEwdcwpIGqUKD5kfzEoSeStfRubt9Tg8pIZUcdag8Qv1iGCeERzb4/ly6z+G0AwAjhACPIowIHRQwOMQdfJo6dgTf2SCuDeaFqZSB0lEBOB9nSKYs/cL/s3ef8VFUawPAnynbezab3kgj9C6EjoBU6aCIDVFR7F5F9KrY+7WDvl4VFASuUqSJiIiKdOkQIL23zfZeZub9sGEJm0LAJMsmz/8Dv+yZs5MnZ3c5+8ycYmFNFFC8SjFlDAO2dpiM8azO7rKSUlLJU3sqOcYuYW0kY6x9I5EikhC02OIIHHBry7/igJugmZEh7XGTZsr26vVry756Lu0t/2CNVkLKJADgX3z+8rA4Xzkpu752bUDXxuv1Pvvss/6HmzdvLi0tDajjdDq///57heJ6X9sCIYQQ6mgwgW91rgJH0aJzBMttmySo7GR58v+0b3nL/UfjiJj043YguNNDpD32WQFqs/fwu2O2DA9n9d6GT8qyus/Xch6vfOLIsLtn+BdXU8wcX/XSR84z2dbf9svGDm1mhMZ12xm9SdgjPXLJA/6Z24rJo2o+W2Pdc1D/9fqoVxoawUuApL8cIHB7WEEnUeqa3r6frb8fqln+P2BZUiziJ8Vybo+7sJTzMqRMEvXyY/yECABQjA/vU34j57qYYVNAyWjD+irt8hIACL8r1rC5ijF65WPU4ffFFS08yxi9kqFKzsTaT5sFyeLUTb0EKWIAMI62/HfnFyRDAMDjyS+mKjqLuPCDefsKhuV/+ct/n+v0ZowgHhrHermi+86ad+l4EfyIh+PLXyvg3GzEovjoF1MoKQUAykma3hUjOTcX8ETnqQvV76wkw9SR/15E8HmkkORFj2YdTm9Vjfb9H5y5lbzEWEoq5pyO8AUzACBsVpSkn8KX/ln3HDTv+LMw2fXrjUaBl7rzzCCJPJwOU5AyxZ/CY6c7nXSUu5fc/rooQ8LaLl45oAhpphIAwu+IE8SLHWY7BxyfFBBAMAZP7d/CsTbGCgASSkrRlHJGBACEz4tlOK/ZZAQAMSPlETz1ndEAoJ4ZY62yWG1mwgIiQiwgLy2RaDGbHA4bC6yUkomFUuMw8yHDn+G9w5kJ4DK5dG4tAZBEp/gWQZR2kbMDwGN0E06CT/L952FMXrfXxQLLAUcRNI+oTXcJhiQ5knSTlJsCAI/Vzeg9AOApdcmKpP4LH/ZCiz8eBSgUUJtOGEErHaYSZkiMv1Srfrz0JqyGEl6MMPKxhJr15eKP+GKonT0eAYMBoBIKpSAYBTfW/oGgtYA27q20iIcTqr4pNj5WBgAMgBsyADKql+XFf5SkXVNWcn/tRY2Ln9ubTH9C6Ut/1H0bkGKq08ruionhlr/0WbcfBhcAAZSSEktkCR9nSDOV9pOWU4/sZ1xeABBLpFJJbcwEj7DwTU7WQRG0lJJZwqy6yVWqBPVYYlL2SyeTHYkjLCPdrPtw0u/9Fg6lVTzWxug2VLjdbgKATwpICeUf0UCKSPLipQRKTov7yn0pv7vYyTG171tKRtHhF2eLuFn/cwGAFxdNyiSe8mrX+fyA0QeO41mM0UyHq3D8fPvAMMx7773nf7hnz549e/Y0WPPpp59uq6AQQggh1CyYwLc6XgRf2FnsPGcbcUw7cZeM9BBZEQX+VCCWjAaA9GMX57oT4MveEz7OkG3TEwAyfgM395zZBZ7yajoqXHXXjLpLo/OiNaq7Zmjf/9q651AzE3iOYWwHjgNBqBfOvWzdNZIMWzDbfvCE81wuozdSYcoGn247ZLKfsmgWxAIZeHvQW1Wj+7+1wLLKWRMUM28ieDwAYIzmms/WOI6eqflwZfS7S3zDBEgRCaJLf4X1L0PBPWd989716yoZo1c5NaLTyu4EjxD83C9n0jHbX0bZqDBRd5njjCV/3ukuBwdywK0t+9ItdgHAYNXI9JiuANAFevZRDDxuOuQSOddZVzwd+UqjjeBmC+edNu/S0Rp+6rY+oq5SYTdZ/m2nqpeXAEXEvVk7JZgUU3XutddyF+cQQodick9h50tpJCkS8pPiwu6+qeqNz/iREPXSvXWfIuhUO4yf4GKcReTP0wv0Ks+kn5RUzhHnxTpdBNzvz8hzbOf+Nh8YcMMQ/4vlrdByjJvgCQgeIR+rlsNlk7SbQEmpmAeS6y/dRqt5KS9cedt5DrjXchZzdm5Qv1H0UCmfUFTYtDuqN6VIOj+XOgQARD1l/feMvuJ5/BRjwvvXjOVcLOtgAQBownehJGxGpHyYirEyAMB5OdZ6aadir9PjsNk8nFtKySgez3chI+KuBCaK05m0YGKV/DCxUKqaHgEAmnlxRo/eYNSVOgoNRp2aDB+kGgEAwEJOZVaJo5AgyAxJt0hxjHKyBgBUoyJyppxxV7s44CQegbTI7C0t9Fb2EneTWfpYGauX5+DxOFpkYACA4Ms5AAdjZ4HlOfmUh+S8tetauI1uykiRXhIAwAxOsLkLnZAJ1jNG/jHa918uB14L6Ou2hgB4AOAEKw8gWZ48cPFo01qt9b0aAOgBPQAAgK0SlsQ+nlz9bUn503nNaeGEjzLCF8Tq1lYU3Zd1qZQkOn3VTTU70vRTTd5tJ/3jUQiKIGUU5x7LeTz63y9w6jyS5tEETcv5kQ/yrL+tYywKW/EI0+TDJEHSBE3J6JiXkoXpEneRs+KtfIJ38fNLAKWo7VYIgvD/TMqpsFujKSnFOlnj5mr/hQNKRslGhBE8AljOdsRc94ICpbzUPfkW0QAAQkCSotbdDKIj4PP5Bw8e9P08aNCgxx9//NZbb61fTSqVdu3atW1DQwghhNAVYALf6kgJJd4Yph1VKauQA8CZqWfL99T4j0YKYvzfoTkAgoOamzU5DyTsP+8YmSDoFcE7WOa6IUaQIL9stXNPUTkAiHpk1B8nL+rbDQDcRWXNDI/Rm1i7gw5X1V/9jhQKBGmJjlMX3KVVooYSePMuXf7cU6yTtR8zJy7rEpDDW3bu5Txe6ahBylsn+QsppTziqXvLn3jdXVzuPHW+dquqADRBkMCxULOyjHWw/uwdAETdpWnb++ZMOmbZoyclFAD4vvEfMPyRYzsHADyCPz1qnv9Mt8bcc8p8lGG956ynjpsO9VEMrP/bODebP++0aUcNreGnbe8j6ioFAMU4dfKanvm3nar+pBgA/Dl8Aw1osgIArWlgnXDfEmiM0dzYc0V9u52K7a6vPBnDi50w9TF2sOFv+5FsIn/ikXihG6YqO60yrPxf+Ype8v58UgAA+i/+Z9m9HwBIqZgOD6M1KlqjpsJVdLiKnxjLi41s7Bf9c/v1ewrsOSqeuoesz1t5/wYgnkt9a7/h9zzbhcPGvwYqh13baQkBSdUbf05r+HQjSzHKIbCdCR4RMzkpBpICyikplfFwn2pXxYoL/8dw3hfS34sVpfgOxULqB/mvnLYc49R0/7hRvkJBsmjYmotvVI6reuMzx/Hy8mfelo0ffsNHqZyXcZw8Z/11P8cwiuk3qebVLsbu4dxu1i2mJP7x7erJUbxikc5ezTGcyqGWiqW8GAEARMyLt/ayldeUAEAMxGu42hkTrJs7pz2ld9e4WaeDsReb8tmJ1A3KoeydzEn7MYvB6BtP4fDYY8dXx0KyakbkgbO/O412AFDzI9Khi28qCsuxWTWnWCcDAGJK2kme6hsZQaXxtBlagUUAADRBq4ThvGg+AHhFHrvMITbUXkviGI4xegEIAD54gCgDDhgPMB5waZcfFcTa2PAhrn0EgNXfwrJRKmG6xLCjWreqovFX+BLOxUU8FF+zoqz06ey65fEfdNbcF1ezsrz40fONPdeP4JOdvumuvFlj3qXLn3ea4NU2PKWgk77oJh2itB0xFz96jqAv/V9EKS8t/Ekr/RcU6Kh/JXorz1v/OG/6lZL0ckiHRktHDqLCOsSIcYIgBg6s/c9w/PjxI0aM8D9ECCGE0HUOE/hWxwG3a/eWPvraLcqij0fZqi/efCOIn1/pm/5vHcEBABAAJ7pK3xiu4f66LOXrF8X/ftplN1o5jwcAGtyojODxgCA4rxc4DohmTJplfPc/qYaPUhQAAMPUP+LP3gmK8H2DD8jhnefyAEA6KvB7IcGjJcP6G3/Y4TyX12ACLx2kjHkppez5XNbBCtLE/uzdR9Rdmry6R87k46yNISVUp9U93KxrU+V3vqM3R85W8y8lfxp+5DjNlJ+qNwLAuvKve8j7Bux81mD27tPMHJ6UigGAMTSQpXv1JgCgZI0uxmbyGnZoNwHA3Pj7xLJ0o0f/v/MfuFhn9P2Dbo6cEwncXufeQkfeTu3mmyPnAICwe5rzQr63Wsda7W6r3V1YZ9oqScZ9ujRg1XRGb3QXlFKaMFqtIiUiuFYu1rmhcjUAzIq+Y0PlapZjAWBDxaoZUfNWlHz6ffnK3vIBdcfeXz/WlH/p5Twj1DclXczefW6NuScr++Qf+l9GqsfHi5ICn0YQmifvqfl0tf3QCdOGnaY65Yopo1W33ewv4BF8HhX4MZTLFXJ5A3lgcveMZGhgqbBBMAoAqt2Vz59/hOG8zye/QwBBSejx/5rlq5Bnv/BGzpKT7pODXSMjIqOnf3p3g39sEtfd4NFxwCl5Kv9uDsr+4X32jahwlblZVydxaji/9iqPelS05IymzFnsZB1dZb27iHqwFgYAWIbZvO/DqrJiF+lOzhf2PCPkxfIVU2dJxw/9evfHlgqjm3VlSHvcGDXJNwJCeVfEWviaqfYAQIwwfmzYzYzZCwAe1rO1ZDxOdgAAIABJREFU6nvaQgOAglYOjRzj27FSMEFy6vApcY0IAMSUpHtYX/moMACgBvCLBheJ9CIAEJCCaH4cY/ICAAeczq3lmXkAQBO0UCD2XbnzurxexkPaa6/+MCavp9oNAI5sq+P0pasMTfDkHaClx5xFqfZzPTwlJZ7ircYffg67Z5Zs7JDmPL3d2LFjR92HLMvabDaZTBaseBBCCCHUNEzgW92prCO9n+hFesisKWejzkQ78zzsxXxYoNLMXGEjOOAI8OXw3fJsUyMpYfhla8iPTgxMjXz3e91FgcsOAYCnqAw4jg5XNyt7B6DUCoLH81brGZOFUlz2pY1jWHd+CQDQ9W7Om3+tzd7DF8SG3RKVN+OEblUFcJC4/FIOz1ptAECpGrh17xuQz1oa/p7tzLZVvlsIAECAK8de/mpe7Cup/qNerbvkX9kcwxEUwdqYsmdzTv7ntM6tBQAhJbIx1vUV39Y9m5fzCkiBi3Vp3VV1dz5jLFZ3blnxU1rbIRet4af/1FfYRQIAnjKXcbtWPS+alFCKcerkb7rn33mm+pNigkfUDcNPmJFs3vqb7a+/5VPHBAyIsP1xCAAEGSn1n+XzQ/k3DsYeJ0wigMiynNxRvdHFOgFgW9X3Gn6UnFb0kg8odOT9VL1xaNhoFU8tGTZAMmwAADAmi7fGwNQYvDUGr1bn1RoIPo9SBq5HoP1wpTMr1/czKRRQ4SpaE0aHKSm1UtA5WdSrgWSyQVurfjB69CnizhRBX7CeldMKhmPOWU/dGD6xkzitwJ7zc/WPU6MaGIIbXGctJ06Zj4oo8bSouQGHooVxN6on7qrZurb8q8Upr9Z/LikSRjx9r+t8vu3AMU+FliBJXmKMdPgNrTfMYV3ZV17OMyxsTCdx4KWiFHHnQarhBwx/rK9YtShpcWNnoAiq7u6MfhGC6IiLmwLU1UcxsO6YFN+QdQroWVOeAY7zlFUxRjOlUvBiInz/mdx705P1TyIQCR9d+IKDdbhZl4JW1t0ucbZ7QbWr0sU6k8SpKl7tpSVFUljvj4aWO0vcrCtd1itJUnsJT9kjnPhWVOAqdTKOvopBGeobfOUMx3xS+EaFs9TB2AepRtwWWzsbRTJRsXbvOrvBSnBEF2mPRSnPUDIaAES3yldHrOLr+QAQI0y4N+ExxugBAAdjX1b0tsAiBIBwWjP6ZyHFz+VFaVS39XbkyEVdlO5c1rb/mO6LdaRYKBnSr7FGbjeqq6u/++47gUCwaNEiX4nNZlu8ePGKFSucTmfXrl0XLFjwxBNPBDdIhBBCCNWHCXyrU6hVpeoqcaVYVaDa/va22IdTIb/2UCdnRFyOsyxZsOERzW3vl4SXEAYN73w3ZkO6XNhk+i3s0ZkQ8J1nchynLoh6dr50gOMM/9sOAOL+V57V7EPweKK+Xe2HThq+3RT+8B11037Txp2MycJPiOFFX5bAm3fr82+tzd4TPswAAlJ+7J037YRudQXApRyelEmhQsvUGAKeDgDeGj0AkPKGV7R2nrf5br5FPpZQ/WlJ1ftFwEHsq6kA4NW6cyYdd2RZhekSOkpg/VNvO2Q6aPi99omMY6d2cxN/7EHj7xMipnsra/QrN9iPnvHWhNuODAUAaa+znFMN0NVV4MiZeMxd4jSsr0rd1JuUUIrJmogH46o+Kq56vyjqqSRKHviREfXtTkeFu4vLaz5coV4413dDnvN4TZt+sf5xmODxGruhZ/ToDxj+AIBSZ+F7+UvrHvJw3i+K3/c/dLHO3TXbZ0Xf6S+hFDJKIYOUhCb+WACQTRhBCPherZ7RGVmHky2t9JRW1h4jiIRv3yVFl10bsh89484rpsIUtEpBhauoMAUlk2rdVbu0WwkgZkff9WXJhwAwPWqel/N8V/bf78tXLIh/9O2853doNw4Ju7HB7DFYWI5ZV/41AEyJvEVBN7D02pSoWw4Y/zhvPX3UdKCfIrPBkwgykhvcTK7FnbWcOGE+QhP0sLAxWncDe9SNUI87bPzrb9P+89YzGdLmfrqvHUHw4qJ4cVFXrglAEpSEkkqowI9zOD/Sf8O/rt7yAb3lAwJ/IRD1r7MAAEVQj3d6oX65gBS+2+ULB+uwM7YwXjh18cKBhJIuGf6m1l1lZ2wpks5iXu1FSRmETTHdVu4scbHOxMNOSnhKkJYU9dKjhIB/cbBEP2HXVN2X3+tXbBAP7E00NiipXfjpp5/uuOMOvV4/cuRIfwJ/xx13bNq0CQA0Gs3Zs2effPLJw4cPr127NqiRIoQQQigQJvCtLikyLeK36L/H/hZ9OmbuW3f8cm8OPHfxkDuipIfg6DJq4lM14SWEPpL3+kcJDgnh4rimE3hSLFJMv8m4blv1W58rZoyTDOpNSiXuojLTpl+cZ7JJqVgxbWzzI1TNvdlx4pz1j8NenVE+YTitUTMmi/W3A7YDx4EkVXfPrFvZvFuff8tJ1smG31ObvQOAdJDyUg5P1I6lF3ZLc2UXWH7dJ+xx2RbWnMtt2/s3AAi7NjwoXTklIurppMp3C6uXl0Y8nFC9rLjqgyIAiHw0wZ+98zsJzTt1lIJO+V/PJHGa1l1NApUobjTXKrTnccAmidLchaWVSz9ibQ6Cz5MMVrgNNmeuxPRnIvfsd6p548vfIN0lTiAJ635j7vQTqZt6m3/VVS8vAYCoxQ1k7wBA8GjN4/OrXv7EduC4/fBJQiQkCGBdHs7tAYJQPzA3YFi7n4xWDA0brfNofQ8L7DkOxh4uiFTx1Lm28xzHJolTfXuhk0D2qpfwNIcks48ks4/vZ9bu8NYYGK3eqzcxeiOllAdk7wCg//J7r/ayxdUIHu/7uXpPonuAJeO87XSNuzpB1GlY2BgA+EP3S6mzKNd+4Qbl0EPGvesrvn0g8alrCLKV/K7bWeYsBoB9+j2HDH82WIfjWAD4vnxlH/lAkgjm0mibKtcAgJfzvpG75Eo1v3s29c02Cep619iFg8ZGHPRTZPqu1JTtfsUDoLpresAsJNn44ZZd+9xFZa5zeQH/a7UneXl5t9xyi91uX7Ro0fTptSOSDh8+vGnTJrlcvnv37v79++fn58+ePXvdunX333//qFGjghswQgghhOrCBL4t/CbYseuTLbMemSM5Kq4s+NtfLlXHfvB+3PLntM6TZl00/dq3CdUcb4ciXEFeOZdQzhzHGM2Wn/80rttmXLfNX04p5RGL76NUV7EUEy8uKvLZB7QfrHCeyXaeubS+FCHghz8wt+4d/suy948y6u5LLR2kTNnUO2/6Cf98eNm4YeaffrftO0qp5MpbJvnSRW+1rmb5d95qHT85/rKxA5eLWZoCAJXvFlYvL9EsitcuK6n6oEi3qsJb4xZ1lfITBKafdZSCTtvSR9xPXnQ+HwBYYArsOU3/pUX2fO1nK1mbQ3xDz/AH55EySdRSNv/2U6afdOZDwyx/m1mnUDpIEf9xRt7sk9b9xvPDDrsKHJyHi3w8MebFRkfCC1ISxIN6W38/xDEsZ7VfKk9NEA/o0dizKIKaH/+w7+d9+t+yLCcVtOrltA9ElHhT5ZqtVd9zHPev5JdaavdvUiziJ4ggof469JdonpjvOJ7lrTEwBpNXZ2T0xiKV6VyiCQCOyM4fqTwPAMWOgntPzfA/xT9h4Yhx3/DcxCR5BqWS02EKSqVo5iSOVlJz8cpIqbOw6ZoWr9nFOkVUvQ0G2lCKuLPFa7pyPYBkcbtNLNsIy3qqaoAkBWmd6h8UZCS7i8o85VXtOIH/9NNPrVbr119/PX/+fH/h6tWrAWDJkiX9+/cHgOTk5LVr12ZkZCxfvhwTeBRMHOcuKnMXlQPH8aI1grQkaMYXJIQQat8wgW9dLAeLf6/+syqC5e4peFz06DvOktJLm8CfuLvLa4vLnVkOfTjx7r/5VdF8koLPHZZ3ZM3YbJkg1PfOkQzuY/llnyu3kHO66PAwUf/u8nHDycZXTWuMsHt67McvWHYfcJ6+wJgspEQk7JIqGzO47u5x1oNGX/auWRgX/17n+nmlNFOZsr5X7syTulUVpISOfy89/OE7aj5cad62x7LzL15sJOf1esqqgOMolULzxD1NJ3j+HF67vETzULx2WUmD2TsALEpcfMXU3Se+mOcp3cCLidA8ucA3RJbgk8mre+bOOmn5Tc95eII4JuXHPpSUSt/R7/yIw85sOwBELEqIfa2B2e9+htWbrXsOEjxaOnIgHaHmOGAtNtvvh1w5RdXv/jfqxUea/kv9S8TNjrnLl0ZOipi137CnyJG3T//b0LCr2JvtHxKkdxKkX5bVOI2n6KJXvP6dEhpHe8G4eVd1ee3u6MIenaOWPhJQx1NezVqslEpBqeS+bQVbz+zoOwerRnq5K0ceztMEN3sHgLmxC+bGLghuDB2F78PIcQ0f9ZUH9dpTa/v555/j4uLuuuuuuoW7du0CgNtvv91fkp6enpaWduHChbaOD6GLnFm5+i+/dxdf+tZEa8JUd073jyxDCKGOCRP41mXzcNtyvB42HQAMBPz7fo/n1Rp/NjRrKz+tyKFV8V6+L7HKySeOcPQA2OpyPMnIo6hmzcAUdk1rbCD61SIlYsWU0YopjaaLjlNW35rzqpmRjd0VFveRC9PF9uMW22ETAEgy+9ARauN3mx1ncnxLphMCvmRoP9XcKZTyyqsc183hI59I9Fa5PdWugOwdABJEnRJEDdxMq8/wy1YTgGRwv7oTXD2Vblde7W1zTw24Cx2i7lL7cTNjqH2d7MdMjJXx7VJen7eyxrTtN4KiIl98RNjl0l16+aSRFc+95zydbTt4oulvG9ur1hs9+iRRSqaqdnMyPsmfGXX7F8UfrK9Y1U+RGcT0Ml3Z8wvl+jzbhTdyl/BI3mudP6070b3aWvp83uMMME8a5sVWi5muJiba5DWYGIO5/op6nMtd/q83fRsoAAApEVEqBaWUUyoFpZBJMnsLOrfkbHMCiDhhYgueELUTBMGL1HjKq1wX8oVdL78wx3G+vTN4MdfRag4trqysbNCgQWSd25hlZWXnz5/v2rVrfHx83ZrR0dHHjh1r8wARAgCw/326+t3/AsPSaqWgaxpBU67zeZ4KrfY/XzH3zJZPHBHsABFCKGgwgW9dMj7x8y3qX8pPMFztyvMvf1AKjtqjfQoU1RH8ov/Sd8SWAcDqY7E1B/mvTlA0M3tvY5p7Y+1HzbrvKnKnn0hZ30s2LHCYAGtj8madtB+38OOEnb6pXWdLkJIQ+eIjrN3hrdYBSfKiIwjeVbzrYpamcG626qPiqo+KJf3k1gNGSkmnbesr7n0tuxyxVgcA1L124C52Zk885i5ySvpI3KUFHm1EzuTjkY8llL+Sx3k5zcI4006d9aApb9px3535+ue0HT4JDCsZObBu9g4AdLhKMf0m/Vc/2A8cbyKBr3FX7dRuIYC4PW5h3dHyA1XDf9ftzLZlba9eX3f5urbHAbem/EsOuPGa6QHL1EVI426KmPJT9cYf4448N/Ktpkf7E3yebNxQV3YBozcxRjNrc7A2h39RPWdWbsw7l62vznmZmmWrWJuDkksppZxSyiiFjFLKSYWMUsqpRlZAROiKJEP6Gn/YoV+5MerlR+uuBGHevsdTUkGFKQRdGp0v0w6QJMnnXzb5/9dffwWA0aMDr97qdLq2CwuhOli7Q7f8O2BYxZTRynlTCN+XIo6z7Nyr++oHw7cbRX261l8fFyGEOghM4FtdkkJwv2Lg/VW6Chs7ziwymuvsKh0bvXRBQma0/IjYNcYqmpJKOr2suYgrTPQmKa6/l4YkEj/rAgC67yryZp0MyOF92btlr4EfJ0zb0VfQ6bJdx0mxiJ8Ud22/Nvb1NCCIqg+L/mH2DgCUQgoX18AHAE+1O3vcUXeJU5qpjHtdWv3WGnvOKGeBvOyFXACIejopZmlKxMOOnAnHrAdNeTNPpO/oW3ejex9vRTUANHj32JfSeyqqmwjpf+UrPZx7sGpUwNxmAoi5sQteyX7qF+2WYWFjIgVNzV1vVfv0vxXYc1Q8tX8HvromR87eZ9iTZ7uwv2hHpnqkbxH+hhFEWJ0FERmLlTVavAYzYzCxJouga+AkBdZqsx84znmZBk8mGztUvTBw4zrn6WzW7aZkEkoppxSygCXKEPKRTxlt3XvEnV9c/uQbsgkj+PHRrNVm23/cfuQUEIT6ntnEdXkJtaWkpKRkZWVxHEdcnCmwZcsWqJfAsyybl5eXkHCFrS4Qag32AycYs1XYNU11Z51+hyBk44e7C0otu/dbd+9X3T41eAEihFAwXX9ZYjt1zOax8djzVp3LVXv/nUcLP1jeQ+/g7ZQ7gIA1uTa2svbr1IEywZcTwoIXbOMayeGbzt7/udjXUkkRadymTfys6zVn7wAg7JYGANY//1bMHE+KhO4Ch7vECQCqOZG2vTuBZKP+RRm2h5t21EQtTvKtWidIEslGqXSrKuzHzIydbegmvO9Va2hKLdvIPNuLfHuYCUjhrOg76h9NFKUMDhu1T//bDxXfPJz07NX+sS3CyTp88/NjhPG7a36qX8FbVRNeRZg08H3xVxFLtos1kbJxw+UThl9xnSFKJqVkUl58A6uF11ZQymM/ftFdUsGarYzBxJgsjNnK6E2MycKaraQ8cKEHT2ll5csf1y0hBHzffnuUQiabMFzUq0tz/+yLOIZxXSjwVmqBJPkJMfxOce17dnQHQYqEUS8+ov3PV668YsO3m/zlhICvvu8W8aDeQYytDWRmZi5btmzt2rW33XYbABQWFm7fvp3P548cObJutU8++cThcNx4443BiRJ1bK7cIgAQZzbwYRQP7mvZvd9XASGEOiZM4NvIR3LVE1Z9jfPSWixEdIyOpOleHBDQx8LvHS2Ai7nM2KTA/b2uI/VyeElfeatm7z7R/06O/vc/nSMt7JoqSEty5RRWvbosfNE8ycCouDfTSp/NKXnygqSLUZjGV0wZHnanwlXgEKSIAQA4KF2crVtVQfDJTt/0aHAIPS82AgCcWbmysUMDDjmzcgGAF9vwZtosx64p+woAFLRyc9W6Bus4GBsAHDMdyrKe6irtec1/+DU7af7b5DEAwFnLibOWEw1X0gAAWORMfhem86lq/Yr1jmNnIpYs/OfL1NER6sY24WugcpRGMWW0u6yKNVkYo5kxWzmX21ut81brAIAQ8A2dpW/kLhkSduPcmAUA4K2sKV/8Nuf1UjIJKZdSChkpk1JyCSmTUkqZ+IZejhPnDKs3M3qj/1fwE2LC7rtF2K7HV3cQdIQ6+q2n7X+fdhw7660xkEKBIC1JMuIGSnHt1wdDxZIlS7766qu77rpr9+7diYmJK1ascLlcc+bMUShq9y5hWXbVqlXPPvssTdMPP/xwcKNFHRPrcAIA1dCKvL5C1u6ofwghhDoITOBb1zmdZ+5mncXNAQAhAg9T6T9EKaPIVAASmGw4lOM5Qni6a3gbpofXG6N9/SGJxM+6cBzo11TkzTrpW7WOHy9M+6m1svcWQxCaJ+ZXvvihK7ug7PHX6Ag1weeJu8rsWd1tWb1k44b5Vt2vm71Xf1ZC8Mnk1T0UE8MbPKX4hl6GVZtt+49JRw4S9crwl3sra0ybfgEAyZC+DT7R6NWXOYsAoNpdWa2rbLCO3xnzsaAk8D1l/aZG3urh3PUPeXVG294jACDMSOGnJvD54hH/nsKeyNf931rHyfOG1ZvD5s9qy1AJmrpssCUA63SxJgtjsjAWmzC90wfl79oZ26/abZmqkUmiFKBIQshn9Q6vzgg6Y8DZLL/ud+cWAQAvLkqQ3gkY1nnqvLu4vPLFD0Xd03iJsaRUTEkltTm/QsaPj8ab8yGGIMQDeooHBOFjFVxxcXGbNm2aN2/e119/7StJTEz84IMPfD9v3bp19uzZLpeLIIiPP/64a9euwYsUdVyUSg4Angpt/UO+WWn01eyVixBC7Qwm8K2L5YBha3/mHOA6cilP46fF+LJ3Jofw1bR7Gtva6PpDEkmfdwEA/ZqKkMneAQCAjlDHvPes8X/brX8e9t2bFXXS8mOjjLvCKz+08JPKwhfEAjQ3e/edUDH9JuP6HVVvLJeNHCTs3YXg0a4LBZade1m7Q9S3m7h/w1vBh/HCn0xeWuNuaoa8D0VQfRWDrvEP/mdElHhqVOBUc5+qlZ87jiqUcyYqR028VNqvO/3vReWL37bs3KucM4mUBPMtQQoFpFBAR4YDwAnzYd8IAg64tWVfLkl9g9aExX/xOudyMxYba7YwJitjsbFmK2Oxeiu1tgMngCTVC2+VjR7sO5sjK6fqxY+A4xynsx2nswN+l3LWeOWtk+uWsDaHcf0OgiRJqZiUSSiphJSKSamElIopmQTn56MgGj9+/Pnz5//8889z587Fx8fPnj1bLK5dvcLhcISHh/fu3Xvx4sXDhw8PSniFhYVWqzUjI4OmaQA4derUt99+q9Vqu3XrNmnSpG7dugUlKtSWRD0zzFt/s+7eL580khRf6kc4hjFv3wMAwjqXyxFCqKPBBL51dQvnnb730gjql4p1L1/8mY6OJgsIdw6cXhAl5oXgvTuSSPq8CyWl7CcsSV91C4ns3YeUisMWzA6bP9NTrQMvQ2vCCAG/+pPi0mdzip+4QAhJ9W3RzczefZS3TORY1vTjLsvu/Zbd+/3lksw+6odub+LGbDdZyM625Tjn6QtAELIJgV/x+Ymxwq6pztPZzvN54n7dgxJdAC/n/b58JQDMjL7915rtObZzh41/DVQOAwBCwKcFfAi/bEsF/TcbgWVl44b7s3cAEHVNi/3w+er3vvSUVkqG9edFhjMWG2uxMRYrZ3fyUwO3rHOeyTZv/a3hgEhSff8tsjFD6pZxLrdl1z4AIKViUiImpWJSIiIlYkoqxmwftTiNRjNz5sz65XPmzJkzZ07bx+OzdevWhQsXVlRUAECnTp22bNmi1WrHjRvnubj95AsvvPDuu+8++uijwYoQtQ1R7y6++W6VSz8KWzBb2DkZCMJdVGZY9aMru5BWK6U3ZgY7RoQQChpM4NtUec2l8WB0VBSn4oAOwdTdjyTi3+8c7CCuFUnyoi5tQhPxSALn5cpeyC168Jzhf5Xm3/SkkExe21M+thlzsAlCddvN0hsH2f484i4qA4alozWSzD6C9GbtTh+KWIeTc3tIsYiSNbCdGy9S4zydzRhM9Q8FxS/aLZWu8mhh3HjNdBmtWFmy7Pvylb3lAwRkw4tN+AbPS+qtn8SLi5KNHapfsZ6SSgLut9cn6t9D88Q93ho968vzrXbWamNtdtZqZ13uuruX+dgOntCv3NDgqQgBX/PInQGLq7FOl3X3AYKmSImIFIt8/xK+H4SCpmNDfozRwugNhEDAi9ZcceVF1KqOHj06depUABg0aBBFUUeOHJk7dy6Px1MqlUuXLs3IyMjKynrttdcef/zxwYMH9+/fP9jxotZEEJqn7q166SN3QWnl8x8QAj5BkrUT4xWyiCUP4P9yCKGODBP41sUB/PeEtchcuxXWT2cL/Id4EdGcEng3cC8cMPu+y8v4xGP9ZaKQTulDWeQTiQBQ9kLu1WXvF/GiNMo5E69cr10ghQKgSNbp4lzu+veHGZMZAEhJ41vKtSGz17i9ej0AzI1ZQBHUsLAxf+h+KbDn/Fz9Y2OzA1iHCwAa3BLPV9ic9ZMIimxs+YMGSQb1ZoxmxmBirfbaPN/mYKx21mbnPF7O4w2ob9v7t37F+oZ/tYCveWJ+wNwN1ma3/LofCIIUC0mJmBQLSZGQFIt8DzvgTX7736eN3+9w5xf7HpISsWzsYOXsiR2wKa4Tr7zyCgBs3759woQJALB3795Ro0YxDPPbb7+NGjUKAEaPHj169OhevXq9+eabGzY0fLULtRu0Whn9zjPmLbutfx7xVtVwAJRKIcnso5hxE6WUBzs6hBAKJkzgW5fZxb57yOLbTYyM5mr0Zf5DhCOacwChgu1g8xwmwAsAMCpBODAGvz4GTeQTiQSPqF5ekvBRxlVl7x0OSQpSEl3ZBbb9x6SjLpufzxgtjlMXgCCukwEI6ytWORh7H8XA7rI+AEAAMTdmwZu5z+7QbhwaNlrN19R/CqWSQyF4KrT8pLiAQ57yKgCgWmH9JELAV0wd0/z6ksF9GJOlNuG3O1ibnbU7WZuDtTs4rxe8TEB96x9HDKt+bPhXU5TmXwvEN1y2nJu3xmDe/CvBowmRkBQJCF+2LxKSIgEpl/GiG2i3EGLauNOwZisAkGIRHaXh7A5Ppdb046+O4+eiXnnsOrn21NGcPHkyMzPTl70DwLBhwwYMGJCVleXL3n26du06cODAU6dOBSlG1KZIkVB5yyTlLZM4l5tj2foDlxBCqGPCBL51KQTkt5PVRSbvGdq9SWT3Vlb4DyXK4292yn7g26wqNnUMdbtDouZTN2D2HmwRDydEPJwQ7ChCgHzCCG12gf7r9ZRSLupTu1S1t8agff8rzuUWD+xFq5XBjRAAih35+/S/0QQ9O/ouf2GqJGOAcshh41/rK75dmPiv+s8S9eriOJ5l3rZHfENPgrq0dyBrtVt/O+ir0AbBN42UiJWzxje/vnTEDZzLxZitrN3B2Z21Cb/dyTqdwLBEvfGo9gPHzTv+aOxsmsfvlgy9bAyzO7/EsGYLEAQpFJBSMSEQkEI+IRSQEhEplQS0ZHA5s3INa7YCRYbdOUM2bhhBUwDgzi/RfvyNu6hM99/vNY/fHewYOyKtVtuz52VXkeLi4vR6fUC1iIiIo0ePtmFcKPgIAR+HJiKEkB8m8K0uM5afGcvvWWJkXC7GWLtbFUHTSeGRi3vK5nrFt1Zpi8FbHcPcoWhgy1OErk+Sof0cJ7Ksfxyuen05PyGGFxvJWG2uc3mcl6Ej1Op7g7YOlh8H3JqyLzngxmpujhLE1D10S8z8k+a/Dxn3jgofny4JXNRaOjrTtOVX14X86jc/V90+lZ8UByzrzMrVf72eMZiEXVOF3dPa8O/WiUjxAAAgAElEQVRoGaREpJh+U/Pry24aSsqljMHMOZ2s08U6nJzdydodvmmovNiogPrO8/mOE+caO1v4onkBi07ZD52o+XQ1kCQpERF8HingkxIRIRAQAj4VplBMG0vJL19egWVdBaUEAYRYRPB4pJBPioTXNmvdvGU3AChnT5RPGukv5CfHRz7/UNljr9r2HVXdMe16uPzU0aSmpv79998sy5IXX9annnpKp9MFVLtw4YJGE9oDQBBCCKF/AhP4NiIiSbv20oZhVFh4tIQGgBKv18ZxABCB6yeh0EIQ4Q/fwU+KM238xV1c7i4uBwCgSOmIG1R3zQjMvoLhkGFvti1LTismR8wOOKTiqcdrpm2uWrem7Kul6f8h4LK7O6RIGLlkYdVryx0nzjlOnCMEfGA5zuMBAF5clObJe9r3lu+u7ALz9j3OrDzWbCUVMlGPdPnkG/mdAmcTBJBPGC5ITWAtNtbpZu0OzuFkXW7O5WJtDiBJUe/AMQucl+E8Hs7LsDZ7/bPxE2KkIwfWLTFt2W1YvTmgGkFRhFBAKWWaJ+/hJ8bWPeSt1ll+3QcApFhE8HkEj0eKRQRNEiKR80wOAMjGDA44Gx2uEvXuYj900nUulx6Ka6S1tTFjxrz//vv33Xffp59+KhKJAGDgwIEBdb744ousrKy77777Gs7/zjvvvPPOOyzLNlGHYRgA4EJnR1eEEEIdECbwbeSbCPXsY5dG/VGqCAFN7He6HqrRuzhutkR8e0OreSN0XSMI+c03yiaOcOcVe3VGUiQUpCY2uPZb23Oz7g2VqwBgZvQdIqqBkCZEzPjLsLvYkf+XfvewsMDJ5/zkhJj3nzNt+sV24DijNwEALyZSOuIG+eRR7XuRM9PGXwxrt8LFBIbRG61/HLb+9XfY3TPlE0Y09cyrXPVAMqSf+IZenNvtW5afc3tYu5NzODm3h2NZyZB+AfWFXVOF3dJYh4u12zm3l3O5WbuDYxjOZmftDsZogcs38rP8ut+08ZdGfz1JNrgOFh2uAgDGZG3+H4JaytKlSzdt2vT111+vWbNm5syZq1evrnv0k08+Wb169eHDhyUSybPPPnsN5y8sLKx/P79BRLu+QocQQijUYQLfRjJ4vAfczocvPqTlmioR81CN3slxsyXil8OU+H0BhSiCogTpna63LX1+rdmqc2sB4H/lK3ybwNfnYl0AsLHiu0zVSJoI/M+QUsrD5s8Kmz+Lc7mBJAger5VDDj7bgeOGNVuAIhVTb5KNzqTCw7zVOsvOvebte/Rfr+dFR9S/kf5PEDya4NHNXDFOkN4p6uXHAgo5L8O5XMA1sGWA4uYbKaWMc7hYh5NzuzmPl7U5OK+Xc7ocZ3OBYRiztf44Ed/FGlKGs5mCQC6Xnzx58rXXXtu5c6fJFLgJ5U8//XT48OF+/fp98cUX6enp13D+ZcuWvf76603X0Wq1nTt3Dg8Pv4bzI4QQQm0DE/i2Q9fUXPo5IWJ/pJPhALN3hFoDC7X3kO2MremaBEEwnLd+An+pQru+5V6Xce02AFAvmCO7aaivhBetCbt7BqWUGVZvNq7b1rIJ/D9H0BRBN5z/kzKJfOLIBg9VvfGZ49hZ628HFdMuG3nBGEyO41kAIOyS0tKRomaRyWRvv/3222+/Xf/Q66+/vmzZsuTk5Gs+OUEQKpWq6TputxvwDjxCCKHrGybwbaeystL/Mx2tYQiYKRZh9o5Qa5gcMWu0eiILTc139RGQwiay947DW1njKa+ilDLZ2CEBh+STRpk27HTlFTd41zrkyCeNchw7a1y3lZSKZDdm+lbC85RWaj9cyTpd4oG9aU1YsGNEgfr27RvsEBBCCKHrQqh+bc3Ly9u1a1dWVpZOp7Pb7Wq1OjY2NjY29uabb46Ojg52dA2rqqry/0xrwgFAy3IejuPjxX6EWkGDU99RY7x6AwDwYqLqL9FH8Gg6WuPOL2F0xnaQwIt6ZShmjDNt3Kn7fK1x3TZeXDRrtrpLKoDjeDGR6oW3BjtAdJmlS5e+8cYbHo8n2IEghBBC14XQS+ALCgoWLVr0888/N3j0oYcemjp16nvvvZeUlNS2cV1Z3TvwfWJjTST5p9P5SI3hk3AV5vAIoeAi+HwAYJ3OBo+ydge0o9kEqttu5ifFGv+33VNWxRgtAEAI+LIbM5VzJ5NiUbCjQ5dhWdbr9QY7CoQQQuh6EWIJvE6nGzt2bF5eXrdu3aZOndq9e3e1Wi2Xy81ms16vP3/+/LZt2zZs2HDy5Mm//vorMjIy2PFeJqe83P/zkrTUyAj1PdU6zOERQtcDXlwUweO5C8u8Wn3AGHJPSYW3soaUiOhIdbDCa3GSwX0lg/t6q2q8NQZCKOAnxBC8EOsQEUIIIdQBhdj3leeeey4vL+/NN99csmRJgxVeeumlFStW3H///S+++OL//d//tXF4TdjvdGVXXLoDHx0Vlc7jfR2hnl+t+9PpfFJn+FCtojGHRwgFCSkUSIb0tf5+SPvhyogl91MXN7Zk9CbtR98AgHTEQIKighpjy6Mjw+lIXHIcIYQQQiEjxBL4P//8s3Pnzo1l7z7z58//7rvv/vrrr2s4/4YNGz7//POm61itVgDgLu6T3ExP6gzeGq3/oW90QAaPtyJCPb9at9vh3GCz3yLFvYsQQkGjun2aMyvXdSG/7KGXxQN7USoFU6O3Hz7FOl28uCjlLZOCHSBCCCGEUEcXYgm8Vqvt0aPHFavFx8efPHnyGs6/bt26X3/99RqeeEXzpZJjDOtbEVuhUCgUCl+5L4dfabFlCq+3jbQRQh0LpZRFv/ZEzedrHcfOWvccrC0lCPGg3ur7biElODkcBcHSpUuff/75YEeBEEIIXS9CLIHPzMzctWtXbm5uampqY3Wqq6t//vnnzMzMazj/l19+uXDhwqbrGAyGOXPmhIVd3T5DCxWygscf821v++STT9Y9lMHjvRWmvNpQEUKoxVFhysjnHvSUVjqzclmrjZRJhT3SeVGaYMeFOi6apmk6xL6rIIQQQq0nxDrFRx99dMeOHYMGDXrxxRenTp2amJhY92hFRcX27dtfffXV6urqe+655xrOr1AoxowZ03Qd325w1/B94q233po3bx5Jkt26dbuG2BBCqG3w4qJ4cVHBjgIhhBBCCAUKsQR+7Nixn3zyyWMXKRSKsLAwuVxutVr1er3BYAAAmqaXL18+bdq0YAfbgOaM/0cIIYQQQgghhOoLsQQeAB588MExY8Z8+eWXu3btOnfuXEFBAQBQFKXRaPr37z9jxoz58+dHReG9I4QQQgghhBBC7UroJfAAkJaW9vbbb7/99tscx9ntdqfTqVKpSJIMdlwIIYQQQgghhFBrCckE3o8gCIlEIpHg7msIIYQQQgghhNo5vGuNEEIIIYQQQgiFgNC+Ax9EHo/n6NGjV/us/Pz8mpoamUzWGiGhBnEcV1lZGR0dHexAOhaDwSASiYRCYbAD6UC8Xq/T6RwwYECwA+kQLly4EOwQUGvB/r01YF98RSaTiaZpHFXaGHwLXZHBYBAKhSKRKNiBXKdYljWZTIMHD266Wkj075jAXzWCIABAr9f3798/2LEghBAKGl93gNoN7N8RQgjBdd+/YwJ/1TQazd1333369OmrfSLHcceOHQMAsVjcCnGhhrndbq/Xy+fzaRrf7W2E4ziHw0GSJN6Bb0sul4thmOTkZJVKFexYOgSBQHDvvfcGOwrUkrB/bz0ej8fj8WBf3AS73U4QBN4+bQx+nWua76sXvoWa4HsLxcXFRUZGNl3z+u/fCY7jgh1DR+Hrung8ntvtDnYsHchDDz20fPnyZcuWLVq0KNixdBTZ2dmdO3dOT08PiWFI7cakSZN++umn7du3T5w4MdixINSxYP9+RYsXL3733Xffeeedp59+OtixXI+MRqNKpVIqlQaDIdixXKfw61zTSktL4+Pj4+LiSkpKgh3Lderuu+/+5ptvVq5ceddddwU7ln8KF7FDCCGEEEIIIYRCACbwCCGEEEIIIYRQCMAEHiGEEEIIIYQQCgGYwCOEEEIIIYQQQiEAE3iEEEIIIYQQQigEYAKPEEIIIYQQQgiFAEzgEUIIIYQQQgihEIAJPEIIIYQQQgghFAIwgUcIIYQQQgghhEIAJvAIIYQQQgghhFAIoIMdQAdCkqRAIBAIBMEOpGMRiUQAIBaLgx1IB+Jrc9+/qM3gWx2hYMH+/YrwP6im8fl8iqKw32wCvoWaJhKJCILAt1AT2tNbCBP4tkNR1NatW3k8XrAD6VieeeaZtLS0W2+9NdiBdCDx8fE//PBDSkpKsAPpWN59992JEycOHz482IEg1OFg/35Fjz32WHR09B133BHsQK5TYrF4y5YtCoUi2IFcv/DrXNPUavXGjRtjYmKCHcj168UXX+zdu/e0adOCHUgLIDiOC3YMCCGEEEIIIYQQugKcA48QQgghhBBCCIUATOARQgghhBBCCKEQgAk8QgghhBBCCCEUAjCBRwghhBBCCCGEQgAm8AghhBBCCCGEUAjABB4hhBBCCCGEEAoBmMAjhBBCCCGEEEIhABN4hBBCCCGEEEIoBGACjxBCCCGEEEIIhQBM4BFCCCGEEEIIoRCACTxCCCGEEEIIIRQCMIFHCCGEEEIIIYRCACbwCCGEEEIIIYRQCMAEHiGEEEIIIYQQCgGYwKPQ9vvvv2u12mBHgRBCCKFLsHdGqA289957y5YtC3YUqK1hAt9Gdu3aNWXKFI1G07Vr1wcffFCv1wc7ovbgzJkzo0aN2r9/f4NHm9Pm+Lpclc8++6xfv34ymSwiImL48OHff/99/TrY7C3IYrE89dRTffr0kUqlnTp1mjZt2rFjx+pXwzZHKIjww1XfP++d27GW6knbpRbs9TqCVatWPf300+vXr69/qMM20fDhw4mG2Gy2utVCvn041Po+++wziqL4fP6IESPS09MBICUlJT8/P9hxhbxZs2YBwI8//lj/UHPaHF+X5vN6vQsXLgQAgUAwYsSIUaNGCYVCAFi4cGHdatjsLchisXTq1AkAoqKiJk+enJmZCQAEQWzdurVuNWxzhIIIP1wN+oe9c3vVgj1pu9SCvV5HUFBQIJfLAWDkyJEBhzpyE8XExAgEgox6bDabv047aB9M4FtddnY2j8dTq9XZ2dm+kjfeeAMAxo8fH9zAQteePXveeeedfv36+S5C1f+K0Jw2x9flqqxYsQIAOnfuXFFR4SvJzc1NTk4GgO3bt/tKsNlb1jPPPAMA99xzD8MwvpLt27cTBBEdHe2vg22OUBDhhytAi/TO7VhL9aTtVUv1eh2B1+sdMmSITCarn8B35Cay2WwEQUyYMKGJOu2jfTCBb3WLFy8GgI8++qhuYbdu3QAgNzc3WFGFtNTU1LqjSOp/RWhOm+PrclVGjx4NAAcPHqxb+MMPPwDAAw884HuIzd6yevXqJRQK7XZ73cJBgwYBQEFBge8htjlCQYQfrgAt0ju3Yy3Vk7ZXLdXrdQSvvPIKQRBfffVV/QS+IzfR6dOnAeDRRx9tok77aB+cA9/qdu3aBQBTp06tW+h76DuErtbvv/9eWFhYWFj44IMPNlihOW2Or8tVyc/P5/F4AwYMqFvYo0cPAMjJyfE9xGZvWfHx8TNnzhSJRHULKYoCAKvV6nuIbY5QEOGHK0CL9M7tWEv1pO1VS/V67d6hQ4deeeWVhx9+eNy4cfWPduQmys3NBYC0tLQm6rSP9qGDHUA7x3HcuXPn5HJ5YmJi3fLu3bsDQFZWVpDiCm2xsbG+HxQKRf2jzWlzfF2u1saNGwmCIMnLLvkdPXoUAFJSUgCbvRVs3bo1oGTv3r1Hjhzp1KlTRkYGYJsjFFT44arvn/fO7VuL9KTtWIv0eu2e1Wq9/fbbU1NT33777fpLr3XwJsrLywOAmpqaCRMmHDlyhMfj9erV6/HHHx8/fryvQrtpH7wD37rsdrvT6VSr1QHlvhKdTheMoNq55rQ5vi5Xq3fv3r169apbcuLEiaeeeoogCN+SPNjsrefQoUNz5szJzMwcNWpUWlra5s2baZoGbHOEggo/XFcLW6xFetKO4J/0eu3eo48+WlRUtHr16oChCj4dvIl8d+Bffvnl06dPDxw4MCYmZteuXRMmTHj++ed9FdpN+2AC37qcTicA+FaJrMtXYrfbgxBTe9ecNsfX5Z/gOO6bb74ZPnx4RUXFf/7zn759+wI2e2vS6XQnT548e/YswzACgcDfwWCbIxRE+OG6WthidV1zT9oR/JNer33bsGHDihUrli5d6l8qMkAHb6LCwkKapl966aWSkpLt27cfPXr0r7/+UqvVb7755oEDB6AdtQ8m8K1LpVJRFOWfuuNnNpvh4vUe1LKa0+b4ulyzo0ePDho06O6775ZIJJs2bXriiSd85djsrWfixIkXLlwwm827d+8uLi4eN27cqVOnANscoaDCD9fVwhbz+yc9aUfwT3q9dqysrOz+++/PzMxcsmRJY3U6eBPt2LHD4/EsXbqUIAhfSWZm5ssvv8yy7OrVq6EdtQ8m8K2LJEmNRlN/joqvxD9bDLWg5rQ5vi7XwOPxPPfccwMHDjxz5szzzz+fnZ09bdo0/1Fs9jZw4403vvTSS263+9tvvwVsc4SCCj9cVwtbDFqiJ+1QrqHXa8d+/PFHvV5PkuTtt98+d+7cuXPnLlq0CACysrLmzp175513QodvogaNHDkSAHzXgNpN+2AC3+ri4uKMRmNlZWXdwvPnz0NIvVFCS3PaHF+Xq8Ky7J133vnmm2+OHDny3Llzr776qm/30bqw2VvQsWPHJkyY8MknnwSU+xby0Wq1vofY5ggFEX64rlYHb7GW6knbpRbs9dq3ffv2rbtoy5YtAFBdXb1u3TrffoTQgZuIZVmXy+X1egPKfQsoKJVK38P20T6YwLe66dOncxy3bdu2uoXbtm2jafrmm28OVlTtW3PaHF+Xq7J8+fJ169bNmzdv586dCQkJDdbBZm9BCoXi559/9o34qsu3RKpvw1LANkcoqPDDdbU6eIu1VE/aLrVgr9dePfTQQwGbgZeWlsLFfeAdDoevWodtotLSUqFQGLBNIwDs3bsXAHr27Ol72E7ap7U2mEcXlZeX0zSdmJhYVVXlK1mxYgUAzJw5M7iBtQO+WUA//vhjQHlz2hxfl6uSlpYmFovNZnMTdbDZW5ZviZovvvjCX5KVlRUZGcnn88+cOeMrwTZHKIjww9WYf9I7t2Mt1ZO2Vy3V63UcdRN4v47cRIMHDwaAt956i2VZX8nx48cjIyOlUmlRUZGvpH20DybwbeHzzz8nSTI6Onr+/Pljx46laTolJSU/Pz/YcYW8xr4icM1rc3xdmqmiogIAhEJh74Y89dRT/prY7C3o77//lkgkANCtW7fp06cPHTqUx+MRBPHhhx/WrYZtjlAQ4YerQf+wd26XWrYnbZdasNfrIBpM4LkO3EQ5OTldunQBgM6dO8+YMWPQoEE0TQsEgm+//bZutXbQPpjAt5FNmzZNnjxZrVZnZGTce++9FRUVwY6oPWjiKwLXvDbH16U59u3b18QonlmzZtWtjM3egi5cuHDXXXfFxsYKBIKUlJRp06YdPny4fjVsc4SCCD9c9f3z3rn9afGetF1qwV6vI2gsgec6cBNZLJZnnnlmyJAhMpksLS3t1ltvPXv2bP1qod4+BMdxVz/uHiGEEEIIIYQQQm0KF7FDCCGEEEIIIYRCACbwCCGEEEIIIYRQCMAEHiGEEEIIIYQQCgGYwCOEEEIIIYQQQiEAE3iEEEIIIYQQQigEYAKPEEIIIYQQQgiFAEzgEUIIIYQQQgihEIAJPEIIIYQQQgghFAIwgUcIIYQQQgghhEIAJvAIIYQQQgghhFAIwAQeIYQQQgghhBAKAZjAI4QQQgghhBBCIQATeIQQQgghhBBCKARgAo8QQgghhBBCCIUATOARQgghhBBCCKEQgAk8QgghhBBCCCEUAjCBRwghhBBCCCGEQgAm8AghhBBCCCGEUAjABB4hhBBCCCGEEAoBmMAjhBBCCCGEEEIhABN4hBBCCCGEEEIoBGACjxBCCCGEEEIIhQBM4BFCCCGEEEIIoRCACTxCCCGEEEIIIRQCMIFHCCGEEEIIIYRCACbwCCGEEEIIIYRQCMAEHiGEEEIIIYQQCgGYwCOEEEIIIYQQQiEAE3iEEEIIIYQQQigEYAKPEEIIIYQQQgiFAEzgEUIIIYQQQgihEIAJPEIIIYQQQgghFAIwgUcIIYQQQgghhP6fvfsMjKJa+wD+zGxvySab3ntCEghJwFAFBERABQsgiIAUAQEVRRG4gFIELkWltwuIiAIKSO8KqEQgFVJI722zSTbZXmbeD8sb0ggtyWbD8/tyyZnJ7pMLZvY/c85zLAAGeIQQQgghhBBCyAJggEcIIYQQQgghhCwABniEEEIIIYQQQsgCYIBHCCGEEEIIIYQsAAZ4hBBCCCGEEELIAmCARwghhBBCCCGELAAGeIQQQgghhBBCyAJggEcIIYQQQgghhCwABniEEEIIIYQQQsgCYIBHCCGEEEIIIYQsAAZ4hBBCCCGEEELIAmCARwghhBBCCCGELAAGeIQQQgghhBBCyAJggEcIIYQQQgghhCwABniEEEIIIYQQQsgCYIBHCCGEEEIIIYQsAAZ4hBBCCCGEEELIAmCARwghhBBCCCGELAAGeIQQQgghhBBCyAJggEcIIYQQQgghhCwABniEEEIIIYQQQsgCYIBHCCGEEEIIIYQsANPcBTylzMzMixcvJicny2QylUolkUhcXV1dXV1fe+01Z2dnc1eHEEIIIYQQQgi1MIKmaXPX8GSys7M//PDDc+fONXmUyWSOGDFi3bp1Xl5erVdDUlJScnJy670+Qgihdo7FYvXp08fOzs7chaCWhNd3hBB6zrX/67uFBXiZTBYVFZWZmRkSEjJixIjQ0FCJRGJlZVVdXV1RUZGamnrq1KmYmBg/P7+//vrL0dGxNWpQKBT29vYajaY1XhwhhJClePXVV0+ePGnuKlCLwes7QgghaPfXdwubQr9w4cLMzMxVq1Z9+eWXTZ7w1Vdf7d2794MPPliyZMmOHTtaowalUqnRaLhc7muvvdYar48QQqidk8lkV65ckclk5i4EtSS8viOE0HPOIq7vFvYEvlOnTjRNp6amNn/aoEGDiouLk5KSWqOG0tJSJycnR0fHkpKS1nj91lClpY7eU4/w50l42LYQIYSe1Y0bN3r16tWzZ89//vnH3LWgFmOJ13eEEEItyCKu7xYW56RS6eP0qHN3dy8rK2uDeizFr6nqlf9U/5SkMnchCCGEEEIIIYSekoUF+J49e964cSMjI6OZc8rKys6dO9ezZ882q6r90xlpANBTljTbAiGEEEIIIYRQXRYW4D/66CODwdCjR4+NGzfm5uY2OFpcXLx79+7u3buXlZVNnjzZLBUihBBCCCGEEEKtwcKa2A0ePHjTpk0f/z9ra2tbW1srKyuFQlFRUVFZWQkATCZz69atI0eONHexCCGEEEIIIYRQi7GwAA8AM2fOHDRo0O7duy9evJiSkpKdnQ0ADAbD3t6+W7dub7755vvvv+/k5GTuMhFCCCGEEEIIoZZkeQEeAPz9/desWbNmzRqaplUqlUajsbGxIckWWA5w+fLlw4cPN3+OWq0GgJqammd/O4QQQgghhBBC6DFZZICvRRCEQCAQCAQt9YKbNm36/fffH+dMlaqddnTXGOiPL1WVKI11B6UqCgCOpKqu5WvrjtvxyO8H2QjZRJuWiBBCCCGEEELoyVlkgD9z5szJkyfT09N9fX1nzJgRHh7e4ITPP/88JyfnyJEjT/rKW7duff311w0GQzPnFBYWLlu2rEUe+LcGuZa6mqdtsuG8VEWZknwtBgkytVHItsh/BgghhBBCCCH0XLG85DZjxowdO3aY/nz58uVdu3Zt2LDhk08+qXvOpUuX4uPjn+LFXVxcHtm+/u7du8uWLXuKF28bjgLGH+PsZep6Qf1IqupAkmpUEH98CL/uuJhLuokYbVsgQgghhBBCCKGnYWEB/tChQzt27PDx8VmzZk3nzp1jYmLmzZs3d+5cb2/vESNGmLu69sJZyHAW1ovl1/IZAGDPJ0PtWWYqCiGEEEIIIYTQM7GwAL9582Yul3vx4kUfHx8ACAwMDA4O7tu374wZM1566SWRSGTuAhFCCCGEEEIIoVZhYQE+NTW1V69epvRu0rVr102bNr3//vtr165tzzPbEUIIIfRIOTk5CoUiKCiIyWQCQGJi4v79+6VSaUhIyPDhw0NCQsxdIKpHl1ekio7XF5cRJMlydxH0iWTa2Zi7KIQQ6sgsLMCr1WqKohoMTpw4cevWrevXr586daqHh4dZCkMIIYTQszh58uT06dOLi4sBwNvb+8SJE1KpdMiQIXq93nTC4sWL165d+9FHH5m1THQfrdfLdh1W/BEN9IO+uVU/n7R++xXx268AgRvcIIRQq7CwAO/v7x8dHV1aWuro6Fg7SBDEtm3boqKipkyZcv78+XbbH96M8DKKEEKoPYuJiTH1sunRoweDwbh169bYsWNZLJZYLF66dGlQUFBycvKKFSs++eSTXr16devWzdz1Iijf9KPyn1iCwxYO6MEN9KEpoyYhVfFXTNWh00DT4tHDzF0gQgh1TBaWdadOnarRaPr16/fvv//WfRQfGRn5+eefX7p0acKECTU1NWassH3q7sz2ETOjXNjmLgQhhBBqgmkR3OnTp2/cuPHXX39dunQpJSUlLi7u0KFDs2bNGjhw4Jw5c/744w8Gg7Fq1SpzF4tAnXhP+U8sKeA7f/OZZOpoQd9uwn5Rdh9NdFwwA0hSfvS8oUxm7hoRQqhjsrAn8LNmzUpMTNy5c2ePHj1YLNatW7fCwsJMh5YtW5adnf3TTz8dO3as8TT751w3J/bFd+zNXQVCCCHUtFUBeMkAACAASURBVISEhJ49ew4dOtT0Zd++fbt3756cnDxgwIDac4KDg6OiohITE5/i9a9evXrw4MHmz1Gr1QCAjwEeh/Kv2wBgPWIg29O17jgvPFjYJ1Jx7ZYyOt769YFmqq4VGeU1qluJ+oISoGmWmxO/exhDjB2UEUJtysICPABs3bq1Z8+eBw4cyMrKoussu2KxWAcPHuzdu/fGjRszMjLMWCFCCCGEnohUKu3SpUvdETc3t4qKiganOTg4xMTEPMXrb9iw4cSJE49zpkqleorXf97oC0sBgBvi3/gQNzRAce2WvqCkzYtqZTQt//1S1eEztE5fO1ax5zfxqFes33gZFysihNqM5QV4BoMxadKkSZMmNT5EkuScOXPmzJlTUlKSmZnZ5qUhhBBC6Gn4+fndvn2boqjaRjbz5s2TyRpOw7537569/dNMKNu8efPw4cObP6ewsHDZsmXYSeexGI0AAAxGE4cYJACAsaPNhaw6fKbqyFkgCH73ztxgfyBAk5yhunWn8uBJSqO1Gfe6uQtECD0vLC/APw4nJycnJydzV9GmZGrqWr72VT8ui2zbe8A0UFqK5OLHHYQQQk9v0KBBGzZsmDZt2ubNm3k8HgBERUU1OGfnzp3JyclN3sF/JHd39w8++KD5c+7evYv70T4mprO9NiNXm5bD8fNscEiblgMATGc7M5TVavRFpVVHzxMMhv28qfzunU2DVq++pI5LLluzQ378krBvd5a7s3mLRAg9JzB3dRCbYxTzrlSdy9K05ZtSSmP68Ng7ftcV0VVt+b4IIYQ6mKVLl3p7e+/Zs8fW1nb8+PENjm7atCkqKmr69OkCgWDBggVmqRDVJYjqCgDy4xeNFfU+AOiyCxR/RANB8F8IM1NprUJ5/TYYKeGAHrXp3YQXHiwc2BsoSnH9lrlqQwg9bzDAdxAaAw0AagP9yDNbCqU0ZrwRX3Ot0lhlyBwZjxkeIYTQU7OyskpISPjiiy8CAwPlcnmDo2fOnLl582ZkZOS1a9cCAgLMUiGqix8Vxg32N1ZUFX3x3+oTl7Vp2ZqUzKpfThX/ZwOt04sG92Z7uJi7xpakyy8GAG5YUONDvK6dAECfVxyt0VY+pImy1Gi8rdW1aoUIoedHx5xCj1qbKb0r/qliu3H53ayqjpdljoz3Pd5V2ENs7tIQQghZJJFItGbNmjVr1jQ+tHLlyi1btvj4+LR9VahpBOEwf1rZuv9p7tyr2H+s7hHhgB62U0aZq67WojcAAMFmNT5CsJkAkGolnC6VeTCZPzhInOq3BsgzGCaUyUqNxrPODl5M/OCNEHpW+HsEPTFKacx8O8GU3v3PRnA8ubkzU2Q/FWeMiPf9NUzU18bcBSKEEOpQIiIizF0CaogU8J2WzFbFJqn+idMXlwJJsj1dhS925wR2wPssTAcJAOiy8/mRoQ0O6bLyAcCPzerCZifqdBPLZHUzfG16j+JwXJvs+YcQQk8IAzy6z0BBidLoJnrE1cWU3muuV95P7948APDc1gkAZD8VZ76dgBkeIYQQei4QBD8ytHGm7Xh43TpXn71ac/aacEBPpuTBZENjpbz69J8AYBMRstvedqq0IlGne7esfL+9nSuTkWswTCyTlRqNkRz2VntbFm41hxBqCRjg0X1LrsuPpKpOjbIPtH3ov4om0zsAAElghkcIofbvSTdRj4yMbKVKELIgvLAgXlgndUJK8YJ14lGvcEP8AQhNcrr813PGqmpu5wBeeDAQRG2GnyAtX2Uj/qKiypTed9pL+JjeEUItBAO85anSUIuvy+Xaev3qMioMAPC/ROWpjAeN6EkCAmyYC3pZPc5Fo1hhpGgoVRofFuDrrnuvl97//808tnaiKbri55KsUQm4Hh4hhNqhbt26PdH5NN12vVERas/sP51c9t+dmqR02Y5f6o5zO/k6fDYVCAIARCRZm+Enl8uMNGB6Rwi1OAzwlie90nAms+nt4jIqDKYkX+tmke6T7iI+qwWuHNKdBYp/qggm4Xe8a8P0DgAABIPw3B6sy9EoblTlzU4Nvt3j2d8UIYRQC5o9e7a5S0DIIpECntNXHyn/iVP+dVtfWAI0zXJ1EvSJFPSKAPLBpk4iklxqY/VOqUxP02yC+MpGjOkdIdSyMMBbnu7O7KNv2il09bYq+V+C8mq+dkqYoJ87p+64s5DRIukdAKxftS/dmGeQ6goWpPv83IXkNrEHYcXBEsW/cgCwe9+1Rd4UIYRQC9q0aZO5S0DIYhGEoHeEoHdzLRVzDYYPyyv1QAsIUklT08tlpvXwbVYjQqjDwwBvkcIcGm5kYpo572fD7O3Gaeo7WgDXnx9wPiJ9WFz1RVnWmASfQ2ENMrzsx+LcWSlA0S5LfR1mubdSGQghhNpAenp6YWFh//79zV0IQhajbte6DRKbOeWVpvXwmOERQi2oiYeoCD0MN0Dgfyac5cSpvlyRNSaB0jyYBVA3vTt97mW+GhFCCD0rmqZXrlw5fPhwcxeCkMWom9532kscGIzd9rZd2Owig3GCtLzQYDR3gQihDgKfwD+PVAb6cIpKa6jXmii/2ggAZzLVKeX6uuMh9qw+dZ7qmzJ8+rA4U4Y3PYfH9I4QQpaIpumvvvrqwIEDZWVldceNRqNare7cubO5CkPIslRS1KQyWanR+AKHs93ellenp90UqeyOTj9ZKjvqZC/A9fAIoWeGAf55dDpDvfzv6iYPHUlVNxgRsYn4yU51R7gBAv+T4WnDYqsvV2SNTRS/ap839x5QtOsKP8dPPFuraIQQQi1t+/bty5Yts7KycnZ2Tk9Pd3JycnNzk8vl6enpUVFRGzduNHeBCFmGCiMlrZ/eTUQk+T97yVRpRapeX01RAgZOpEcIPSsM8M+jV3x4JUpKXf8J/JkMdX6NcZgfz11U7+rStdF6ewDgdhIEnIlIGxZbfVFWfVEGAJjeEULI4uzZs0coFKampjo7O0+YMKGysvLkyZMAsGLFin379oWEhJi7QIQsgy+LedXFScwgGwd0EUn+5CCpoWkbEheuIoRaAAb4DiLciXUmiwiWNBG2GxOxiTmRwgaDKeX6/BrjqEDei+6P1QavNsMbpDpM7wghZImysrL69Onj7OwMAP3791+yZIlp/Msvv9y7d+/y5ctXr15t1gIRshgSxkPzOZMgbHDyPEKoheC9wA5idBA/YbJTqP1jBfiWwu0k6PTXCwEXIzG9I4SQJdLpdELh/fu5Xl5eRUVFKpUKAJhMZp8+fS5evGjW6hBCCCHUEAZ49ExYrhxhT7G5q0AIIfQ0AgMDU1NTTX/29vamaTohIcH0JUVR9+7dM19pCCGEEGoCBniEEELoOTVgwIC7d+8uXLiwvLzcy8tLIpHs3LkTANRq9eXLl93c3MxdIEIIIYTqwQDf8RlpkGupR5+HEELoObNkyRIPD49Vq1b99ttvBEHMnDlz37594eHhQUFBxcXFY8eONXeBCCGEEKoHm9h1fJ9drrqco7k8zsGB39z9Gjs+SQDY8fCeDkIIPS9EIlFMTMzOnTuDgoIAYMmSJWlpaSdOnDAajZMnT54/f765C0QIIYRQPRjgO74cuUFloEuVxuYD/LK+1jPDhT5i/CeBEELPETs7u4ULF5r+zGKxDh06pFarSZLkcB5rRxL0PFPo6OgibX8PLhNv/iOEUFvB37joPh6TwPSOEEKIx+NhekePY0e8Yvq5yhPpanMX8vQWXpWPPi4z0uauAyGEHhsGNvSALkfN9uACiVuVIoTQcyEpKan5E0JCQtqmEmSJFDoaABR6C+6zcyVXI1VRFWrKvtlZiggh1H5ggEf3lW7ILVyaYf2yxOdgF4LT7i5j0l0FVb9LPb4P5PjyzV0LQgh1EKGhoc2fQNP4aBIhhBBqRzDAIwCAkvU5RUszAUB+XpY5NtH35/aV4Uu/zytclA4AaUNjA85GYIZHCKEWMXv27AYjNTU1t27dSk5O9vT0/OKLL8xSFUIIIYQeBgN8h6Iy0Gcy1Ib6c9kq1BQAXMjWJEn1dce7OLCC7VhQm95Jwnmht3RHQfWF9pXh76d3kuAG8DWpSszwCCHUUjZt2tR4kKbpdevWffHFF4+cYI8QQgihNoYBvkM5mKRadaO6yUNbYxUNRjytmVfG2pd+n2dK757bOknedbZ50yF9WFz1BVnmmESfX7qQXDNn+LJNeYWL0oEAjw2BtuOcMkcl1FytTHs5xv9MBDdQYN7aEEKoQyII4vPPPz969Oi2bduWL19ua2tr7ooQQgghdB8G+A5luC+3sMaoo+otWbyQpanQUC/7cG3/P43zy3S2qYrQeM3dlem6HDWQhMfGoLIt+QXz01j2bLYrxyjXV1+SZb1j5gxftimvYEE6EODxbZDdVFcA8D0SZsrw6cNiMcMjhFDriYyMjI6O1uv1jz4VPQdUBvqbf6rl2npz/JLKDQDwa6rqVrGu7ribiPl5lKi9tcQ10nBPpq//EQlMkxZTZfpSZb1POz42TD6znf0ACCEEABjgOxhnIWNpH6sGg0lSfYWGmmnNcLhWobwtV92q1pfdv9Ca/sdzWyebNxxK1uUYqwzGKoPpEMEiqi/JMkclMO1YTAlb9KJY2MeGactqs5+lcXoHAJLPwAyPEEJtICsry8rKytHR0dyFoHYhrcLwc7KqyUNJ5QZTkq/FIGBamMCW1y4W4tX6/lbNlkazEU0mna5oMPKSJ2fXUJx7ghBqjzDAd3z2BerPfikxfF5d9P93nZl2bKaEpbmnBILw3BQkedcZAEITexqqDIYynb5Yq8lQMe1Y+Z+m1fxx/5Im3Z4PJCHobiUe4WAzwoHtyW3+TU9lqJf/Xb1tiE2EE/span6Q3r8LspviWvcQZniEEGopubm5jQc1Gs3x48fPnj3bp0+fNq7nzJkzJ0+eTE9P9/X1nTFjRnh4eIMTPv/885ycnCNHjrRxYairA+vH12yrNPWeXx9JVV/L14wK4r3oXu9TgauI0cLpnaKlOws4PnyrlyVP/RrdndlhDqwGW76nyvQGCoIkLGadetkMor/HIz7nIISQuTwiwGu12ri4OBaLFRkZaRo5f/78tm3bSktLO3fuPGbMmIEDB7Z+kehpUXTJhtzpy7NIIw1cUjLGSdTPRtDdmuXMibf/AwDsp7lKJrncP5kkmLYspi2LGyQQDbAFAJJFZr6TCAA2Y5wMJVrlTbnyX7nyX3nhwnR+hJXNSAfxGw4cb16T7xxbqi9XU3ek+qcI8NUXZAUL0gHAc0snyQSXxieYMnzGyHjFP1WZbyeE3On1pG+BEEIIALy8vB52iMlkLlmypA1rgRkzZuzYscP058uXL+/atWvDhg2ffPJJ3XMuXboUHx/fllWhWr1cOQ1GbhXrruVDsB1rmG9rxl2Kzv0wRXagmGAQnjuCbd9xerqX6evO6eve8Efosb9UqqL2Dbd9xn3g86uNRQpjlMvTPLRACKEn0lyAP3To0LRp02pqagBg8ODBJ06c2LNnz6xZs0xHo6Ojd+/evWDBgpUrV7ZFpegJ0Voq462Emj8rCALO97Id/l2AZ/CDJ9VO87xK1uaU7y0U9bcVv27f+Nu12eq8z+4BgLC32HNzJ5JHUipj9UVZ5bGy6nPlqthqVWx14dIMYZS17XgX21GOpIDRUpUzHdkkj6TUlDK2WvKeCzS1Bk1fqtPlawCA7dn0HQSEEEKPNH78+CbHHRwcRo8eHRUV1WaVHDp0aMeOHT4+PmvWrOncuXNMTMy8efPmzp3r7e09YsSINisDtTu16Z1D0loqd3oyADx1hm89n16piivR/fmug5uoxT4OIYRQkx4a4G/dujV27FgejzdixAiZTHbx4sWJEyf+/vvvISEha9eu7dSpU3x8/Lx587755pvBgwf379+/DWtGj0VfqlNcr2Q5cfa+5/q7I/dV+3p3hV2W+gJJlKzJzhx/5+SHntfDrMqURpWBBgAGCQFK42cbs62lOmFPse9vXUkeCQAknyEe4SAe4UBpqJrLFZXHS6tOSBXRckW0vHhZZnBcT4Z1y6zI4IeJ/I6HZ7wZX767EAy0x8YgqN8JR5enSX8tTpevEfaw9vmlS4u8KUIIPYd+/PFHc5dw3+bNm7lc7sWLF318fAAgMDAwODi4b9++M2bMeOmll0Qi0bO/RWVlZfMnVFc3vY0LMhuKzp2ZIvupmOQzfH8NU8XVFC5Kz52eDBRtO87Z3MXVo9RRNIBSTz/6VIQQejYPnS+0fPlykiSvX79+/Pjx69evf/nll4cPH2az2ZcuXRo6dKiXl9fIkSPPnTvHZrPXrVvXlhWjR6I0FFA024MbdOOF4JgeFd2sCQDrRpu6uyz2MX7oQVD0q1tzXa9WmNI7AEjK9bO/zbKW6qpCRb7HujKEDAAoVRqX/119LE1do6NJLmk93M5rV0iXrL42IxwYtizSmkkwCQCoOFRSdUJKG571AibsLfY72pUUMMr3FeV9lAp1msbq8jRpw2J1OWphD2vf4+Gm8hBCCFm01NTUXr16mdK7SdeuXTdt2lRSUrJ27dpnf/05c+bYPkrv3r0BgKYxg7UP9dO76EUbx489XFf600Y6d2ZKxcFic9eHEELm8dBHpnFxcb169YqIiDB9OXv27NWrV7/00ktOTg+mLfn5+b3wwgt3795t9TLRY1PF1WS8HscQM7lBAq/dIQxr5vqXxCVKo4cVI65UfypDbccnZ4YLTSd3/6//LQDG1ry5Pxeu6W/t/I6TLleTNixGV6Enu1n3OtG1Nh5fztXuu6MEAA6D2P+abTcnNgAUr8qu/L0MAFiObEplpNTGnClJAMBy4fgPshN7P1NvOVOGz3gzvnxfEQCYnsPXpndBFKZ3hBB6YuvXr3+i8z/77LNWqqQBtVpNUVSDwYkTJ27dunX9+vVTp0718PB4ltd3dna2sbFp/hyj0YgP4duL2vQuYPgeCRO9eP/vzvFjDwAoXJSeOzMFANrbc3iEUAtSGehzmZrB3lwRG/d0rOehAV4ul1tbW9d+afpz3RETGxubxMTEVioOPSlttjrzrXhDpd5Qqddmq9Nfi/M/GS6xZkp45II/5YdTVQAg5j4I8ADQ/b/+RTyyZH1OycxkRoW+9PtcfaG29gF47WlvBfJoGs5mqVNkBtN/Q4X/ySj9Lpdgk2xXjiZFGT84RvdDZ8ZKf+b/CvVZqsD9hVuZRCz4/W1T79+Yo4DhZ/O4M+0bZHinz73Tht9P736/Y3pHCKEnNm/evCc6v80CvL+/f3R0dGlpad2N6wiC2LZtW1RU1JQpU86fP0+ST99mbOHChQsXLmz+nLt373bu3Jkg8JPi4wq2Y7IZRGCLbzFbN73/GibqW+/OS8tmeDaDAAA2fqBAAB+crazQUEfekOCvgHbiSIpq2d/VnypEsyKFjz77efLQKBUUFHT79m2NRsPlcgHg2rVrAHD79m2apmuvbXq9PjY2Njg4uG1qBQClUkkQBJ/Przv4zz///PTTT4mJiWKxuEuXLjNmzHB3d2+zktoPg0yfMTLetMc7w5rJtGWpYqtNGZ5hzdRTtB2PfCOAN6oTv8E3unztCwAl63MKvkiDOtPX657DYRDvhvDfDbn/vUVfZ5rS+8W5Xpft2DM35HhkqGRvxH89w6NqpldwlvKVvyq7pdT8m65ad6pCqDKKawwFjhwAYJLw7wRHMfdxP4QJe4t9f+mSOTqhfF9RxeFSSmUU9hTXTuxHCCH0RI4fP95gZMuWLRcvXgwPD3/rrbe8vLwqKysvXLhw8uTJcePGrVmzps0Kmzp16uzZs/v16/fDDz907969NqtHRkZ+/vnnq1evnjBhwrZt29qsHvQ4RgXxRwU1/FDx7Armp5vSu9+xrsJe4sYnOH7sQeuooq8zc2emMGxY1kPtnvq91g4Ql6upxssM0XPoRqFWZaDVBprPxAjfLpiW96qfeWVux/PQAP/uu+9+8sknb7311scffyyVSufPn29ra5ucnLxs2bIlS5YQBGE0GufNm1dYWDhjxow2K1coFAYFBaWkpNSOLFq0aNWqVbUr1k6dOrVp06atW7c+rLNuB5Y3K0WbqQIA0prpfyKc5cRJGxqjiq1OHxbrdyp83UtNXAJruXztCwSUrMtpMr03UPR1ZsnaHIJNev8QmsliZuRovp7h8dX2PPdizerd+b8t9lP2sz0YZrVeYfQRM3sLGW+uzPC4U1MUKLz5hqPxRRvRE14mRQNsfQ+HZY5OoFRGYQ9rTO8IIfTUGnR0P3z48KVLl5YtW7Z48eLawdmzZ+/YsWPGjBn9+vX74IMP2qawWbNmJSYm7ty5s0ePHiwW69atW2FhYaZDy5Yty87O/umnn44dO9Z4mj3qeDT3lADAduFwAx56d0DYW2zqS6/NUD3Lez3Fxm8ZlYYtsQoDVS9UFCmMALDqRnWDub79PbhvBeJ2OQihlkQ8rFmLXq8fOnTo5cuXTV8KhcK///577ty5V65c8fHxCQgISEpKys/P9/f3T0xMND2lb4tyCaJugD979uywYcMkEsmyZcv69evHZDKvX7/+n//8p6qqKi4urlOnTq1Rg2mKHUmSRqOxNV6/FkWDnqI5jMe6C0gb6dSe/6qTlaQVM+BkOD/SSmek79xRcickarPU/DCR36lwps0jJrlp7ik5PnyC1dw71k3v4tfsaYA/crXb4xTpmaql2/M8ijVKd57v6a67iw0/3FEu6W01sbNAdqC4cGG6oUIPAPxwkdMX3uJX7ZvcHK4Zymi5/Fy54zwvTO8IIbO7ceNGr169evbs+c8//5i7lmcyaNCg7OzszMzMxoeCg4NdXFwuXbrUZsUYjcYff/zxwIEDWVlZR48e7dq1a+0hiqK2bNmycePGjIwMaLU+c212fUfNM5Tr0ofHqZMU3ACB/9kIlmPDjK2IrsocGW9UGCXjnT23dmqwVU1r2x6nWPtvzWOe3EnCOjXq6ScIoLbUeXeJykDfmeqET+DbiW1xinX/1swMF86LaoGNSB6TRVzfH/oEnsVinT9/ft++fdeuXWOxWHPmzOnSpcuxY8cmT5589OjRrKwsFos1evTobdu2tVl6b+zbb78lSfL06dO1e9UGBgZGRES88MILy5cvP3jwoLkKaxGfXq66mq+9Mtbe5lETzmkjnTM1SZ2sZFgz/U+E8yOtKjXUtLMVcaX6zdtCPGYmqRJqMl6Ne2SG5wY+ou1cg/QOAATAS56clzw5N4tFP7iyBy/O8MhXxw6KvfulD/x/RpeMd7Z5w0H6v8KyjXmquJqssYm8EKHzAm/xCIfHj/GCHtaCHg1bMCCEEHoWt2/fNrVeb8zPz8+0eq7NMBiMSZMmTZo0qfEhkiTnzJkzZ86ckpKSJm83oI6Eacf2Px1uyvDpQ2MbZHjzpncAeL+LwFfM1NefC7LqRnWRwrigp5VL/ccMnR1aukEAQui511w7MQaDMWXKlClTptSOWFlZ/frrr2VlZWVlZQEBAWz2E887almJiYkRERG16d0kIiKiW7dusbGx5qqqpWRUGqq1VInS2HyAN8gNKV1v6KU6hjXT7/dwfqSVykCPP1mRKtO7iRhhYUL7s5FpQ2MeM8M3Q/ZjccnaHIJJ+PzY2Xp4w9vJLzizX3jHMSlCWDoi3iVf89rmvJgPPWuPkgKG40ce9h+4yX4oKv02V52kyBp/h9dZ6PIfX+thdo+M8WUq6rtbNRM7CwJtW2areYQQQgDg4uKSkJBgNBoZjHqpw2g0xsfHt8OGMk5OTnV3w0Ed1cMyvNnTOwBwGMRg74bPrjbH1ABAX3cOflBBCLW2p/kt4+Dg4ODg0OKlPAW9Xt9kJS4uLnXXyVs06qY87dtcp3leVi9LGhzKrzHK1BR/XIJeqgMG4X8qgh8uAoAl1+TiP2XvGunZ6wMcBCQIGQFnI+8Nvq1KqMmbk+pzoPPTVcJyZhMMgjbQ6mRF4wBv4qOljSqjAYDlyml8lOSS9tPd7N53ke0vLl6brb6jyByTwI+0clnk0/inq+tSjuZQiorHJBb3tnq64hFCCDXWp0+fXbt2ffHFF2vXrq3tG0dR1Pz58/Pz84cNG2be8tDzrHGG12arzJ7eUcegMdCxpboGa3GMNABAdKG2wfLVMAe2ELcxa2V6il58rdrUS6JWQY0RAE5mqBOl+rrjrkLGkj5WvOd4pcOjA3xOTo5CoQgKCmIymQCQmJi4f/9+qVQaEhIyfPjwkJCQ1i/yobp3756YmFi3MT4A0DSdlJTUpUsXMxbWUoIzldolBVqVMfOdRJ+fOjfos/rh+cqaFMWG29UAABStvCXnh4t+SVZp9hZ9eryYoEHLI2BDIBCgTlYYynQAwAt6+r3ZrQZJvPaG5ExOKvo6kzbSzl96NzhBfUeR/mqsQaa3elmS8ZEXpKpN48UK4/K/q1/25o4I4BEABJu0m+oqec9ZuqewdF2OKqY6481423ecvHY/9N+SgaJDspRB0ZXGzgEMK7y3jRBCLeObb745c+bMhg0bLly4MGLECDc3t4KCghMnTty5c8fNzW3lypXmLhA915h2bP9T4WnDYjUpyvRXYnRFWkpplLzbuumdNhrBYCQ4Zp5kilrVqhvVB5Kabn847Wxlg5FhvtxNg22aPBm1FJmaOnpPZWyqvUlBjdGU5GsRAFPCBI+/L3XH09xPfvLkyenTpxcXFwOAt7f3iRMnpFLpkCFD9Pr7d0EWL168du3ajz76qC0q/X/Z2dnDhg3z9/f39/ePioo6f/788uXLlyxZUnvC6tWr09LS3nzzzbasqjV4pSrG7ckHLcXtJNCkKLPevdMgw9doqTFnpGCghX3Ein/k+Z/dK6kxRCeq3j9eTAAQHFK6qwAArIfbZ41NpDSU3WRX50U+z1KSzZuOAJAzOal4RRYA1M3w6rsP0rvvz13Y8UoA4DIJAEgq15/P1pzP1hxMVu0aamParIXgkA4z3e0mukh3F5ZuyNWX6EyvQymNjXvgXzfaLAAAIABJREFUi25ULdqZxzLQGblKv9/DMcMjhFCLsLOzu3Llyty5c8+cOXP37t3a8REjRqxdu1YiaW5uFEJtgGnPDjgTYcrwACB519lzW6ukd9pgrDl7VfFHtC6/GGiaIbbiR3W1fmMw0w6TWwc00IubI2+YFqMLtUYaerg27B/9ig/uI9DqnASMU6Pspap6Qf1khvpIqvo1P96ooHp/BTZc8nlO79BMF/qYmJju3bsDQFRUFIPBuHXrVkBAAIvFKigoWLp0aVBQUHJy8ooVK6RS6c2bN7t169Y25fbr1y8jI6OoqKjuIJPJNN1TMBgM3bt3j4+PDw4OvnnzpkDw9E+bm9E2XWoVN6ruvhrH1lKMMU5hu4KLlmWVrMsh2KTP/lDrV+1N54z/JnfuqgySywi920t+Rpr38T2aogkCaACP9YEcP37WmARKTREMgjbSdpNdPb4PetLe702qPFqaMzmJNtDO//ExZXj1XUX68AfpneCQlRrqSq52uC/XlOGPpanX/ltTqjQeGSmJcGp4V5vW00ACwSAqfinJmZpkNdDWZakvP+L+bPnqC7K0dxJJHWXgkkwNJehuhRkeIWReFtGl9omkpaUlJycXFRV5eHgEBwf7+DzT3V4LhV3o2y2DVJc9OYnXSeC22r810julVJUu36LNyAUAYJAEk0lrdQBACvgOC6Zzg3wf+QrDDkvvVRjOjLa3uDXwlNIoO1BsPcyO7W62ptTtBHahb2+wC/3DPPS3zLJlywDg9OnTQ4cOBYDr168PGDDAaDReuXJlwIABADBw4MCBAweGhYWtWrXqt99+a5tyr169CgAqlSorKysjIyMzMzMjIyMnJ8d01NR0p1evXj/88EMrpfdWojbQ57M0GsP9mym8hGrXj1LZWurKC2LmB+4pqWoY7WxXrrfdV5jx3l3R9k4BY5wAYOjJMoIG3mRXliPb7n3X6ONSl8syoEE80t7+AzcAcJrvbZruzg0SeHzXMukdTM/haciZcv85vPhV+9r07nOwC8EhAcCGS9bd+PSNAN4Qb25mlaGzfRMt9Go3rmN7cRlWzOrLFdVXKsQjHFy/9tVmq7PGJZI66mJPG2Kmx/Clacpb1enDYv1OPn03PoQQQg0EBAQEBASYuwqEmsa0Z/ufDG+91y/f/KM2I5fpZCeZPIobFkQwGPr84sqDJ1W3EsvW7HTbuIQUPeJTZbAdq1xNOQkesW1Qe0MpjRlvxiv+ripZnxNwNoLjyzd3RciyGSlYeaM60ok13BenLbSihwb4hISEnj17mtI7APTt27d79+7Jycmm9G4SHBwcFRWVmJjY6mXWx+fzQ0NDQ0NDG4yzWKzc3FwPD4+nfmVTI5/HObNlN6Hdf0f53//fUzQoW7VoVx6po668IN4xypmKU94/KdR63EDtG5fL5dNT5AIGN1gYdLdayyYls9wBoHxPocsVGQ1AEIT8d6l0ez7Hj1+yOhtoIJiEJlWZ93Gqx8aWy/BvOdJ6Ond6cvGKrNJvcyml0Xqonc9PnQn2Qy9dfBZRN70X1Bh/u6ca04nvVGfCvLCHOCSpV+m3udLtBVXHy2quVNA6itJQFW847uolec+B7X82In1orCq+JuO1OP9TEQyxhd3nRggh87p37x4AeHp6mraANX3ZjMDAwLYoCyHz0WUXqG7dIQU852VzGbb3N6xluTs7fDGtdMVWdUJK9dmr4tGPaOi47iVx61fawmrTO5CEvkibNjQWMzx6RplVhh/uKG8VY4BvXQ/NP1KptEEfODc3t4qKiganOTg4xMTEtEppT44kyWdJ7wDAZrMJ4qHLClrPKz7cQoXRSIMkWdHnf3lMHZXzst2h1xwpLf2KD1fMJQHgj1zNwaEOXtbM8KMlWRPuWg+REDT83dVqjA2rfE9h3sepAOCxPhBIyP/0Xv7naQSLpHWU/Qx366F2We8klO8tBIAWzPC27zgBQO705MdJ740dTlFtiVXsSVR+2cPqnWB+bVFGK+bMLmLFQu74Q0VdkmpICq71to2x5fC11KEU1e/papv3Pb7YmG0fX3Oi17+75vrsHuvQYM9VhBBCDxMUFAQA0dHRph1YTV82o+0viAi1MXVCCgAI+navTe/3EYTV6wPVCSnqhNRHBniLQ6mMmaMSFH9XsVw5fr91LZifVnO1Mu3lGP8zEdxAS5rEitoVI00DAF43WttDA7yfn9/t27cpiqrdV2bevHkymazBaffu3bO3t2/FAtvWihUrVqxY0fw5pjVydfvePztPa+ayvtYGqe7uqDhKQ0nGO0ds7bTnN1mFVj87UthJwsqoNFzJ0QBA9DgXkZHy+72s6qQUAC72tGGuy+27IRsA4md5/NZJBACvLPIRrMwypXf3tQFAgM8vYVljEsr3FjLtWS5LHr2U6zHZvuPEsGKq4qud5nk9UXoHgMldBGmVhovZmv9ck5/MUK/qZ+1pzQQAIwVSldElVx2SqiApuNjTJt2NO/d/eQo+4+hAu3O9bOV8xuIZnl9vy/XM00zdkKV+2QYwwCOE0OOZOnUqANReuGfMmGHWchAyP2OFHABYLk1sS8xydQQAo6xhW3JLR6mMmW8n1FyrZLlyAs5Gcnx4vkfCMkcl1FytTB8WixkeoXbuoQF+0KBBGzZsmDZt2ubNm3k8HgCY7tbXtXPnzuTk5EmTJrVqic8Pks9g2rN1SrUmXWVU1uugcyJdXaaiAOCvdHXvlPuT6nOdORnuvNILUoIGDZs8zGBkJKsA4F6gaPdPXQxSnd1kV9Pzdm4gn2nP1uVrNGlN75nx1KyH2VkPa2JPeCMFWVUGP9uHtgERc8ntQ2zOZGq+/kv+b5Fu+JHyT7qL3u8i4LOI88HczLnJtJ4Wf+A24xt/Q7nubrpCFFs94WTp5Hi57SIf0URXwyi7wtfjvXLVxg+S4Hxky/5QCCHUUe3atavul9u2bTNXJehJKXT0J5crh/vy3gjAuaktieBxAIBSNPEBiapRAgDJ71D/hzdO7wBA8hntLcNTGi0lryF4XIaVsG3ekcMktBTNsrA+Bh1ZF3uWgEWEOWDTq4Ye+o906dKl3t7ee/bssbW1HT9+fIOjmzZtioqKmj59ukAgWLBgQSsX+bwgBYyAsxEcb57yX3n6sFhena0UPugq9LVhsvX0xiOFXdKUNJMAgGv9JQAQO8opOlLM1VEr/5e33paxqr/1twNtxK/b2025n951BZr0obG6fA2/q8hj0yNmS7aUnQmKVw5Lz2Zqmj9tmC/3/Bj7NwJ4agO96kb1qGPlaRUGkBtpHQUAVsFCay4pceOmbeq0cqpHlRfPkK8pm5Fc/HIMM13NcWIDQO3+cwghhFAHlizT/5GrPZLawjfiEcfPEwBU/yY0nvirjI4DALbvMy3PbFeaTO8mpgwv6mejL9WlD4vV3FM28zqtSh2fUrL427wJnxfM+ip/8peFc76uPv0nUFRrv++2ITa7h9qyWmGbA/R0ertxEqc4DfZ+3vdHaOyhT+CtrKwSEhJWrFhx/vx5uVze4OiZM2du3rwZGRm5c+fONutbK5VKn6ih/4gRI1qvmFbCduf6n4lIHxariquZuDYzbYqnaVzIJpwYxLg9eZJ0JVPCMsj0DBEzuacN6OgMufF/E1xf9uZW/1ri/UmK3+/hAqsHDUhM6V2breZ3FbVl53apigKABts5NknMJde9JH7Vj/efa/KEMv3bx8r/ec/R47ugvE9S8+am0hRt/4HbXak+Pkj4XS/xXp2heEWWKq4m8+14AGA5cvyOd231HwYhhDo0qVTK4/GEQiEAHD58+PTp0/7+/tOnT+9IS+Q6AFxW2kp4XTsx7W11uYWyXYds33+bYN3/bKy8EVf9+yUgCNHg3uatsMVQdOZbCTXXK1munIBzkRzvhjMLSD7D93BY5tsJNdcr01+LC7rWneXEaeMa5UfPV/58CmiaYLEY9raUQqUvllbs/VUdl+Qwf3rt305r6O7ccJ9jhNqn5v4zEIlEa9asWbNmTeNDK1eu3LJlSxvvE5uUlDRy5MjHP99CW+/UZniXbPXiHTkwwhYkLEpNvbEuyzNdCXYsn32dc2Yl273vquWQoDMCwLQIoe8kx1wSKg6XZIyI8/s9XNDdCsyX3p9Cfw/OudH2G27VlKsoHosQTnEFgLxPUvM/uwcARm8BABgJQjLe2foVu9T+t3Q5agAwlOtKN+Q6/8eH5Yi/cxFC6ImpVKr333//8OHDf/31V+/evX/55ZexY8eaDu3duzc6OhozPDKhaNBTNIfRAR9OEiyW3ez3Sldsqbnwl+rWHV6XQILN0mbk6rILAMD6zZc5Ad7mrrFlUBpKGVsNAKK+NhyvptcFkAKGzVuONdcr9UVabba6jQO8OiG18uBJYJA240ZYvfIiwWEDTatik2Tbf1bHp1T9fNJmwhttWQ9q3qeXq06kqxvHrRSZ3nd7cd0RPpP4dpB4kBc+S28ZTQf4y5cvM5nMF1988WGt2iIiIkx/uHnzplQqHT58eGsVWEf//v3v3Lnz1VdfmbadnzlzprW19SO/yxLFkuSXE9y/3JzjU6BJGhY7YbLHrJ8Lu6Qr5SLmV+97FKRqYI4PAECNEQAcBOSEzgKCQXjuCgZ4kOFZzmxLSe8mQjaxpLdV7Zd2dTJ8p2nuZwNEAEApjdnv3dHlqFkuXFE/ceWvpeV7CysOl7gs8XWY5W620hFCyDJt2LDh8OHDgYGBpqC+bt06iUSye/fu5OTkRYsWfffddytXrjR3jahd+PRy1Z95mitjHWx5HXCJMDfE32n5XNmOn3XZBYqrN02DDCuh+J1XO87jdwCSz/A72jXjzfiKX0pILumxMQgaTRev/LXU9OzEZYmvsGdb740nP34BAGzGvW49YtD9IYLgR4YyF8wo+nJt9dlr1qOHkdy2nhSAHkapf9yHpVojrX30rFz0uJoO8IMGDQKAl19+ef/+/Y6Ojs18/9KlS8+dO9dmz7pDQ0N//fXXyMjI2NjY+fPne3p6ts37tjE9RZdYs76a4fnVthyfAs3G1RkcHVVpxfp6hoeCxwjKVqV6P5gk7yxk8JkEABAMwnNnMK2nKo+VZYyMY9iwdDlqQTcrv9/DGdYWuV/6D52t2B949N6Z12NX/itvOBUNtjdN62K7cf3PRnC8ec7zvQsXZ1SdlBYuSref5vqknfARQug59/PPP7u4uMTFxfF4vLKystjY2M8++2zkyJEjR4785ZdfTp06hQEemWRUGmp0dKnK2CEDPABw/Dxd1n6pyynQZRfQBgPLyYET5E2w2vvDjycl7C02ZfjyfUVg2l24Toav/LU0Z0oSbaRdlvg6feHVxrXRRkqTnAEkKXq5T4NDbB93boC3JjVTey+LF9apjQtDD7PjFZsGIyky/atHyjtJWKdGNdHiGrWU5nLdhQsXwsLCDhw4YMrz7cc777wTGxtr7ipaUV93TuoHTgCge9chbWgM5GpYTpxj87wLgTz0UwEZXx0S35Pjy+//U1l+jfH7QQ/+4yGYhNfeUIC7lcfKjHKDoJuV34lwhtVjpff0CkN0kW5cCL/9zI/7q0Cb6C/Me8v5nd+KJx8rqY6urCnSst25AWcj2F48AOD48X1+7qL8V07pKIJN6ku0Jetyxa/Zi/o1/IWCEEKosZycnCFDhpj2momOjqZpun///qZDnTp1Onv2rDmLe74ZKVDo63XtUuopADBQINfWG2eRBJ/Vbq7cFo7t5cb2cjN3Fa3rYRnevOkdACiFEowUw1pE8pqYaM10tIPUTGNVddsXhlB789BoFx4eLpFILl26NGTIkC+//PLrr79mMtvLU9zu3bv7+fm1n3paD9uDG3A+smxLvv1U14oktXtcDRlXzbRhsVzv/2pjkSCqf9k2ZXimJE0v1Xlu6/SY6R0A1t2suZSj8bdh9nB9svXkRgr2JCor63+eiCnRAcDlXG2p6sE4AeArZr4Z+LjbsfxvqO2Cq/KjYGPLI18+UGhdP73XEkTdX0lR82eldHu+dHu+9VA71xV+T7kDCkVrczR1+7IihFBHZW1tnZ+fb/rz9evXCYKo3TK2srKSzcb2ImYz6nh5Qpm+8XhMiS5ib2ndEZKA7a/YDPTExaXocTXO8JVHy8yb3sG0XR9BUEoVrTc0blZniu6kgN/UtyL0fHloupNIJBcuXFi9evWSJUu++eabq1ev/vzzz+7u7WKZcf/+/dPT081dRRthu3HdVvkDACSpeyZUA4B4pANwyEXX5NVaWk9BjZ4W179qE0zC/bvAJ30jnZEGAB31xKsh4sp0q6ObviH6d4H27wJtg8EXPTh2D5+AZ6Dgk0uV+TUPFsp4WzNP9bLJ1tAvJtVcmuxWGaOEmPtbm1hzyO8Gimun89mMctQXaUvW5sjPlldflDl+7Onyte8T/Sy0jsoaf0d+ptxpvrfL4jbt0YgQQm2vc+fOV65cyc3NtbGx+eWXX7p162ZnZwcAxcXFf//9d0hIiLkLfH65ihg58nprRg0UKPUUkwRB/Y2qOQyw5nTMae0WZFeC8t8i7ZaXbSylz5+wt9j3ly6ZoxPK9xVps9U116uAol2X+Tl+arbVqQSLyfFx12bmqaLjBH271z1kKK80za7n+HuZqTqE2pHmHs8SBLFgwYJ+/fqNHTv277//7tq16549eyxxb7YOI+pODQCIRzpEF2l/SVax2sf1OsKRvfJF6wYz+i7namNKdAM9OZFO9R7guFkxmknvAKDSU3/madWGhvcRSqLEl6PEAADSB08kCIAS5YP1eASDcPzUUzLBuXhFdvm+wrJt+c5LfIjHvpTSOirr3Tvys+UAULImG2jaZcmT5X+EELIs8+bNu3DhQqdOnfh8vkwmW7hwIQAcPHhw/vz5KpVq8uTJ5i7w+bVpcMO1YP8W6cadkEU6sQ++Lmmpd0nV62eVV7wvEo4XNpyzpqXpL2SVZUbqgKMdo6Xer+M6nqZOlelz5cYAW4uZHyoaYOt7OCxzdELN1UoAMG96v1/S0H7azT/Kdh0mhQJeeLBp0FBSXrZ+N63XC/p2Y1iLzFshQu3Bo3/L9OrVKz4+fvLkycePHx85cuScOXPWrl3L4WAHyLbG1NNuJVoQs0Qv2hy5JgcAPousjc1GCnKqDb5iM1w2SALeCW44o6lURcWU6Hq7cSZ2frJ57FYc8vJYe6mq3u2A05nqnfHK4b68bs6sr/+qDrFjffaCSMIjrTmku1XDzxVMO7b7d4EOH3vQBppgENosdfGKLPEIe/EIh2betza9MyUsx489i5Znlvw3BwAwwyOEOrDBgwfv379/xYoVhYWFkydPnjZtGgDcunWroKBg5syZU6ZMMXeBqHUpKLrEYFxZKTfS9ESR0DSoMtCns9T7WYoMpkFIkz8nK5lAVGkoADiXpUkofXAbnUFChBPbLJ892htTO2eL275YNMDW90hY7uxUhxluDrM9zF0OCPu9oE5IUV6/XbpyK8vNieXmZKyq1qbngJFiuTjaTn7b3AWiRyAJAgAesokZajGP9TvXxsbm2LFjmzdvnjdv3qZNm/76669Dhw75+/u3dnGoLopNfDPN48uXbaop+nyWhiRAwCLk/z8//fuYmi0xij3DbPt5POW9Fc+46h1bc9mZLrDWzH+zjgKGo6BeLI8t1QGAhEf29+BuFyiTyvWfXq5a3d861P6h7WE53vcXsSujqyoOl1QcLhH2FrutDuCHN3HvltZRWe/dkZ8tZ9qx/U+F80KFHF9e9qS7Jf/NoXW06wq/Fv35EEKoHXnvvffee++9uiNz5sxZtGiRaS496ti6cdjLbMVLKqpWV1XraJhmJQSAfXcU31MK0oEGLVT+Sy+tebBKbnOMosEr+Nkwz4+xb9OiUYsS9bcNvdvL3FX8P4Kw/2gix8dDfuyCvqBEX1ACAASTIRzYy2bCSFwA3/75iBlvBvKinLF/Sut6gpums2fP7tOnz5gxY+Li4iIiIrZv3/7uu++2XmWogXdDBOd5jND+1r+lqrVGuq87J6fKUHtUqqQAoFT1lHssys+VD1+fzdBTsC2vkEu4Lm+nkdXDinHqbbsv/qz6I1c743zlhFDBgp4idrOT5G3HOhtVxuIV2Yq/q1L73ZKMc3JZ6styfnCb4356P/0gvQOAeISD977Q7El3S7/LBQDM8Aih54ePD3YAeY68JeADwJKKqg3yagCYJBLcdNaRepprJPqU86zc719hz2VpqjTUKz5cMbfeOrheT9j4FqFHIAir114SDeuvy8ozVlSRfD7bz6PJvvSoHWKRxNoBYnNX0fE92aynrl27xsTEfPjhhz/++OP48eMvX76sVCpbqTJUl1FDuazOmtzXJqtSsP+uEgB6uLDTZHoASKswyDVUpYYCgMIa412p3kfMbH5Hmcwqw8I/5Rrj/ale/vHVozfnMPX0rRBhxD1V6be5R9PUl0Y7m45GOrGX9LZq3R/vSdjyyF1DbX+4o1wTXbP/rjKmRLdxsNirmY3uCbCf6mY72qnkvzll2/JlB4orj5U5zvV0/MiD5DOaTO8m4hEO3j+EZk/EDI8Q6uBu3rz5448/xsfHKxSKuLi48+fPu7q6hoaGmrsu1EbqZvjjSlWWwSAhyb1Odv5eD66tCaX6Kg01O1LYSdLRtkZH7RDBwH51CD3UEy9bEgqF+/fvHzhw4KxZs/bu3dsaNaHGft2a73+wODZOser/b5is/bfG9IcPzlbUnrY5RrE5RtHLlfPja7Z1v91IQ3ypLsyBzSQBANIqDLdLdKZDXVMVo37IZ+rp031t941wCk9RfP5Dfu/TZRUK4/7XHAFApqbaVYAHAAJgUmdBNyf2R5eqksr1r/9avrq/eJhvc3dnGVZM1xV+dlNcC/+TUfV7WfGKLNm+IuclPlXHykzr3hukdxPx6w7ee0Oz379b+l0uwSGxLz1CqONZsWLFkiVLTCt4TU6dOrV58+ZvvvlmwYIFZiwMNWC6gpOts7j0LQHfSNNfVcqzDAY+Qex1sPNvtI8XqlWqNOrqT3nUUzQAlCgoPvPBAQYJjnwGo320HEYIdRhN/3YODQ318vJq5tsmTpzYo0ePMWPGJCQktEpdqD6fRAUAyMJEEh5DpjZKeKSzkJEm0+soCJCw2CQU1hgrNZSriGHDJRsvgz+Solp0Tb6wl9WULgIAGOrDPT/GXmOgqasVxh8LaD3NmOyaOMAeSnWvT3Ph9bYyzEh+7apsZACPsdDHVfQ03WedBQwAcBa2TOdaApr4wBJqzzrxlt2ia/JTGepPL1cN8XF6ZL95jjfP56fOir+rCr5MU8XV5H6QDABAg/u3gY3Tu4l4pIPDLffS7/NK1mQ7fuzBsMIPNAihjuPcuXOLFy/29fX973//e+fOna+++goAxowZ8+uvvy5cuDAiImLIkCHmrhHd19me9UFXYW+3VpmyrqfpPzRa010cFU3/qdb4s5q+LKJDKaqFV+VNHpp8RtZgpPEzFdRSfklWOQgYL3liX2303Gk6jdy5c+eR3xkYGBgdHX3gwAGttuFG36jF8W5W6QDmzHH7PVENAD+8attJwur/U1l+jXHnKzbuIsaCP+WHU1WzI4Wjg5ro8FGhoQDA1ELWxM+GWX1RljUjmdZSDrPc3dYECE9XAICXNbPLaEe5NTNrXKJxV4Edj7T55ml62k3tKhjszWluZvuTiHRi+dkwG39qEbKJ7weJB3lxDBQ8/sarwt7ioKvdZQdLir7K0JfogIDc6Sm8YCE3qImG+fLT5WXbCgDAca4npneEUAezYcMGLpd74cIFHx+f4uJi02CfPn1u3rzp5eW1fv16DPDtB5tBzO/RKnto6Wn6I1nln2qNhCTHiYRb5NWm9fCmnnYmRQojANTU3zL2+eQqYnhZM410vZbzJQqjngInIaPBFr9BEvzk0CoqNdSia3IXIeMlz+b2GEKoQ3qmXytcLnfq1KktVQp6GG22WpenYUpY/M7CGQyyWkv/U6g7maGp0tEAsCNeacUmEqV6ADiXpcmR15vUFSxhvurHa/ya1RdlWWMTKc399N7gqPUQic/BLlnjEks35gGA65NneAKgpdI7AATbsZppcvtaUz9gM2idvvrUH5qb/wpDy9X8IE22P6U2pg2J8T8bwQuu98BBfl6WNeEOraMc5ni028Z+CCH01OLi4nr27Nm4a527u3u3bt3u3r1rlqpQW6qb3k0z5x0ZZG1Pu9oMrzXQAFCuxgAPfdw4l8c2/Ewy7LD0XoVhzzDbQMvZB96i6SkAAANlcTv3oZZRc70ya2yi6EUb772hBOe5W6by6N8yycnJv/32W1paWlVVlV6vFwqFjo6OXbt2HTNmjJVV+1oa3VHV/FkBAMK+NkASE0IF1/O1k04/WPf+c9KDPoJX87RX8+pNiCAABng2XBxOaaiscYmUhrId59w4vZtYD5F4bu6UMy2pdGOe1csSUX/LmACmp+gPz1c6CxkLelrxmE08lDdWK0qXbdLlFAIAyWcJwzJ5Hpk1SREGqeO9Qbe4XnzxG47Gar3jJ17K29VZ4xJpLeUwx8NtFW6aiBDqmHi8pu+B2trapqent3ExqI3paXpWecV1jVbCIH+wt/NlMQHgLQGfhv9j7yzjo7i6OHxm3TdrcScGBE1wCxIcEqRYCy1W+rbQ0kIFWqTQUqPF2lLBCgWCNUgIEpzgIUpChHiykXXX2Xk/bFhihARiwD4f+G3u3LlzVpi5/3uPwBqp/BeFkowgc5h0AMDhAFCgEV+7ibKd1keDqokIiYSzFzgAY7Fec0/BiXRsqewXLyeq67LcqSkWDSo/Kcp7M813f5fXTcM3JOCzsrKWLFkSFxdX79Fly5YtWLBgw4YNFIq9tEPLoroqAwBmGMf6Zz838tqBbK3J8leSWm7EFnWns8m4s3n6VJFptC+la83S6B4sAr1ORnocBcccylXEipXnJbp0NbVzPXFuqBoV7SgBAJInhdLxpQmE05mwu2VGtRFmUUnpAAAgAElEQVS7W2b8Zxy3Vj15ABBv+cdYUEp0c+ItmEbp7A84nKm0QrbvZMVf5aZKZ22qWpuqBgDpgXKz3IQZMLt6t2PHzitM9+7d7969q1KpmMwavtlqtfrOnTvdunVrHTNEItHNmzcb3z8iIqLljHmtiNPpa6l3K1PpNABYI5X/IFe8waBREcSNgc+RmZ3or9os+atrinyFee94XuMD8ey0KGJjxaqsj5zILqv9N+KQ5kml9JKiz9bkjEk0VRiVb7l4/d7RruGtqOOr1LtDpKM6Xq44K857K83339dLwz9VwJeVlY0aNaq4uHju3LnDhg0zGo2HDh06f/78mjVrOnXqlJGRsW/fvk2bNhUWFh45cgSHe40+stYGeyzgh1TtgRNwMDuYBgAHM7RyIzqzM92DiS9QoKki0xBPcr0x8HXx/beLtXxazrgk/9M9aml4iwbNnZqiuaMgeVD8T/ckOr00i6AsMu5oJH9xnCxHao4vMU4JrLGzZMgp0KU8xDMZzuuW4tlVs1Wim5Pj5wssuu2VO0lmedWHbKowAoBgkbtdvduxY+cVZs6cORcuXHj77bf37Nlja9RoNG+++aZMJps6dWrrmJGenh4ZGdn4/hjWZL/Z5OTkQ4cONXyiSCRq6rAvO4Mo5A9YzHF0qg+h9oRwKp3GweEkqIWKIABAeEXFw4UCvUhrkeosApp9KtsuOCTcY7Doi3T5VyTnh/HHtLU5bYZNvQOA5N8yALBreABQ35I/mpJi0aC8Oa5evwbpc7Q5YxMVZ8S5M1J9D3bFUV6X/8VPFfBr1qwpLCyMioqaPn26tWXevHlz5szZtm1bUlLStGnT1qxZ89FHH23btu3HH3/84osvWsvg1w5dhtosMhLdyA+YhOv3VB+GMG31SEh4BABINX+rFgx2pKgLlTUi4TPEJgC4UmSQVstjh3vXM0xh5sTLc8Yl+cf0CPMkV2otgVyCRYM+mpKijpeRPCj+sT3JPk2LMG9z/LmE6Cn8u0LjIPfaiUl1KZkAQB/Sy6beq0AQSvBAVFNaq7/qisxUYXyJ1i/s2LFjp0nMnj07Li5u3759Z8+edXR0BIDw8PDExESpVDp69Oj333+/dcwICwuz5sA/duwYAPzvf/9js9nNe4lVq1bFxMQ0pqfF8hqFeTNxuMXspybGG061u1jaaVUy1Wn3FbdwCN6CocfLD/ThDKLjXxon0GZEn6PNGZdkqjCyhnGdPvbKm5lq1/BgVe+RyTb1DjiEEkj3j+2ZMzbRmtvr9dHwTxXwt2/f7tatm029W1mxYsW+fftOnTr1wQcfIAiydevW6Ojoffv22QV887I1QS3Vo2sHsgFAda1q+/3Ta4ocqXm0L6Ujr8pJ/tsh7FIVWstLvESF/nBbVe+wGWKTVcnbODze5TAVZ46T5oxPmhbT4+03+C+7erdCIyBhdWrpAQAqUwIA0bl27hldmrroEylmIpHdpORAP+UlKf8dV/VtpT5TnTn4Lp5JcHzfg/e2K2J3sLNjx84rx969e8ePH79hw4aHDx8CwOXLlzt06PDtt98uXLgQQVrvphccHHz06NGQkJDExMTPP//cy8urecf/4YcfBg4c2HCfioqKTZs22Z0K9WbsWrHBXHMdQ2nEACC+2JAvr7FD0JlP8Gq+nLUvL3ic1U+hre14mbFglgOlOwEg0nlmlvpBuir5RHnULLcFAPBnkvrgQ231zigGACDRWcIOVFZvpxKQZb2ZI7xf4rUn666yqczAGsb1PdQNR8V1ON49NzL5NdfwT9T7bBevX4P0FrBK9ddTwz/1nltYWBgWFlar0cPDAwCq137v2bPn04Lk7Tw3u1LVKiP2aR8WnYhobikAAA1l5UjNTBLiz3kS4t7LhdTLpfa5niz8n6M5Ym2NB++VIkNcgT7Mkxxe847mQMF1fde5ypd+fFKHQ11L1+S+7Oq9LhtuKo9l6b4dwu5DowAAqtZUP6pLU+eMTzTLUJKgnDO2xGXjVEOulhJAN4uMOeOSdBlqExiKPswU/VXi/r3/y5LMz44dO3Yaz7Rp06ZNm4aiaGlpqbOzM4nUZm5HM2bMSExMbImRO3Xq1KlTp4b7PHjwYNOmTS1x9ZeLfQ+0399W1nvo+zo7BP5cwtlptZfFK43le4t/H+UY2YXZs0mXzjaZpKilL6WeJXijHr1oNgygUVjtcoXl0z7MtEqTz2u/loEZjLrkDGOhECwYwYVP6xmMY9ZTo7derkrPl+gLCAixPyesJ6vP6uyllyRnBvPC3SleyZWm4pq+pVZQDOq2Z0nNI7xf8H20GXXVOwAw+jq85hpeHS+r7jlfrLaMOSya1pG2egALACiBdL8TPXLGJSrjJPmz03z3d0FI7fEu0Yw89UbTuXPnhIQEg8FAJj+5jVofq7Z1cQzDsrKy3N3dW9rK1w1riJ41Uk9zVwEA8k4MeKAb6kVpzOJu3XVHqd4SV6DvxCfO6FRPhLzv3i55b6YpzoqzhicAAMmTEnAmhFQnd31ZWZlIJKqsrKysrBSLxSKRSCKRGAwGlar+DX82m02j0RgMBovFYrPZTCaTx+M5OjoKBAKBQNCauQ+JeERusHxwXvaWoPNs3AXt7RSHKaMBQcCm3iUmWrCJ4nqHHNQfwSOUADoAEAQk/9M9rBoeIeJ0D9Q545PYY/nuG/zJfo1KNGDHjh077RmVSjV27NjIyMhly5YBAB6P9/T0bFuTevXq5efnR6gTj22nNRnhTc6UUo1ojXwB8cVGpdEy0IPEqjYzJuCQwR71iO0DpX9nqFPLDKUbgn4j45rwuP9EIsszmVdx2DMZNVSfMkmZFpmk8CTuOtBxqQeniW+oNRjsQa73o3it0Fy/J911DFWpbS0ImeQwZRRrUni5QehMcUPgqbJTg6qPlf0LAGbMdKri8Nvu74fxRl4Sn4kq3bm8w7qt4Q7l6hpbUxKdZUq0mEfFHZvEr96OIODGfFlT39Wr3q20Ww1vlpkIDsSnf7HNQ8HCDIsGZQ3nWj3ni1VmnRnLkZptHajBjA5Hu2cNu6c4I5bsK+PPd2tZg9qapz4jJ02a9Nlnn82fP3/79u3W5LR5eXkffPABAAwdOhQASkpKli1blpWV9fHHH7eaua8bJqHBWKLHOxC69Wf/60PrzH/q9+VIxwGAc52k6wCAPatOJkLG+e6v0vA29Z6Xl3fv3r3U1NScx2g0mgYGaSo8Hs/Dw8PDw8Pb29v6wtPT08vLy8XFpdndFz/tw3Si4zbcVP0roiX1nbsq8Qj5ryjuvKmmcjRnXKJZaqL3xZM5JwEwZviA6icSBCS/mB45YxP1mRqCEwnTWhSxYmWcRLDQ3XmFD4FDfNoV7dixY6f9w2QyCwoKLl++bBXw7YGwsDB77bo2x8eB8PMwh1qN44+IlRLLF31Ztji+p5GqTEhV3gcAmUkSW/nfJOdZjb/0HAZjrUy+XqYAAJuGVyYp08cnUhVogMTc8a0c9FRPPOt5lnhQDLIkploTImukQKbEVKGpMffowCHUW4zWztNQXbwp2X4AAMgB3tSuHYGAN2Tl6ZIfyg6ciqPeiXVNnOg0PdJ55tNOP1l+SItWKf9rkrgh3JGTnN+8K4vPUKcmK+91Z/XyYNWY4pIJCADgEajV/vJiLNDljL5vjXuvpd6tMPo6dDjW/dHkZMm/ZQgJ57k1qE3srI78pKhg3gNGfwffqK44Wgt+EdyZzuU/FqiuyxWxYvb42i4/AGDRWcrW5wIGRCcSc9ir7y371DvgJ598Eh0dvX///piYmC5dumi12gcPHhiNxoULF/bv3x8APv7446NHjwYHB69evboVDX69wLMJ9N5s1nAu4JB+bg35NH4Yyozwp/o61POFfn5ZHvNI3/CFEDLOe1/n65sv39Jevf6/b+/cuSOVShs+5QWRSCQSiSQ5OblWO5FIdHd39/Pz8/PzCwoK6tixY2Bg4ItvCs0Jpvd0Ii0+L0sH53f7LliReKLfvdU4bhezzAkALJI0cEAdpo8j+XrUOpHAJVIC6fpMDWawBN3qXfFzoWSvsPL3YmlUuctKH/58d6ROlT47duzYeVn45ptvFi5cmJqa2rVr17a2xc6Lck9+Q2gonug0vYF9zpYGxdAo4W4A6MsZckd27Wzl8UHcEXySYyNPn8agIQiskcrXyxQowFsMujJRmT4hkaxAU4cxu+WZ0PuqRxOT/E72eA4NvyVB9dt9db2H3jlde84z3Ivy15j2uNXfPkEVKtme/wCAt2Aac/RgW7su+WHub9vj+EkAcKYyeiB3eL0/hjJ9yUXJaQCg4mghDv3ipRejhLu/8Pt2ovP0A6U7okp3BjO7E5BXfNdEfVdhzTnvuNSrunq/UKA/maP/LoxNJyKMfmzmEI4iViw/KWpzAS8/Kcp/Ow0zYcpL0tw3Ujoc6dZyGt51dQcAKP+xIG92ms/eLtCTVf2oRWfJnZaiuiwlOpL8Y16dEOAGeOrtD4/HX7p0ad26dX/88Ud8fDwA8Pn8FStWfPTRR9YO/v7+a9as+fTTT+n0xga32GkqODo+8FJoY3riEahXvQNAttRsQJ+6A2+xWG7evHn48OFjx44JhcLnNLT5MJlM+fn5+fn51XMrMJnMwMDA4ODg4ODgLl26dOvWzcnJqakjBwuIx6fwl12SXymCld1nTC+6Ne/RFUZXV01qqC6nE71Pd4c3BtU6BUOxwkUZ8hOVeBbBL7oH2ZvquS3IcbFH6cocxTlJ8fLsyj9KXNd04Exq7NTEjh07dtoV48eP37x5c3h4+Pz580NDQ+v6QPXp06etbLPTJMTGih1FW0yYkUcUDOQObyszLohjyg2ljmSXeR5LEIBbsquHhXve9/6s8SO88bgE/QaZgpiq9pyeSVagSeGsPv92C5BZcsYkahKUOWMT/U71aKofXKgzqasjsdYOfKbEZLZAEI9YK0Rx0GvvD98ktLeSLDo9LSS4unoHAGr3jtfeJJlIGN6CN+GMT/sxHBTutGAWAJjk8uYAztA0ZWK2Jv2e/MYw3pirkvOl+qILopjRjpNa6c20EdypTqqLUsn+sryZqR2OdmMOqlo/2p+uvVZsmN6ROsCNXPRJliJWjKPhff4JbltrbeqdP99NEStWXZW1mobPn5OG2xQEj5cpa6j30z0pHV8LWdrQ+iWFQtmwYcOGDRuKiorweLybW41wgg0bNrSwba8LKiNmqVmZ1vqH0mAxyVGEgCCPHePZ5Ob0LZfJZFu2bNm5c2dJSUlj+rPZbA8PDz6f7+TkJBAI+Hw+j8cjk8kODrUd7Wzj63Q6tVqtUqkUCoVcLheLxdb4ebFYbDQaG2+qSqVKSEhISEiwtTg5OXXr1i3kMd7e3o0Zx4GC2zGWuz1RvfmeKsqrn2OvTvM5cm0KtXSNRPyvkeRb4PzZk3EwFCt8N0N6qBzPIvid6EHvVbXaRwmkdzjWXXFaXPJljuGRNn92mvo9D4+NAY1/O3bs2LHTTuDzq8JHv/vuu3o7PEfFdTttwiHhHhNmBICjZftC2P2o+DbI1aIyK09VHAaAN90WEhDCVJc5iYo7CYqbmeoHQYwm6A2rht91o9xlXhFZXqXeg+kkoIP/mZ45YxK1yapHE5KaquHrDVPvu7dCpLXsGce114F/EYyFQgCg9uxcq71Al3ufk4tHkbnXg/cNy6r3x5CsvPdAlQQATmTXobzReAQf6Tzzn5LfDwl3d2OFznRbsDF39cmKw/04YWziK+0TgUO8tncEAMn+stypKR2OdGMO5sBjUWCxQNHHmeIdpTgavsORbswhbflRKM6K8+c+wEyY00eebt/6Gz7UZo9JVF2VPYpM9ovujqsvnrdZsGl40tLMXm+5gxv59VTv0LCAt9HmWW1eYbYnqTfeqT8J3PA9Fb9vyJEz8MuXdbC2vNWZ9vWgJpTGLTCbPaulArKFCclksh9++WX7tm1KheJp57LZ7JCQkF69egUGBvr7+wcEBFhLBDcLFoulvLy8sLCwuLi4uLi4qKjI+rqoqEgsFjdmhIqKivPnz58/f976J4/HCwkJCQ0N7dWrV+/evV1dXZ92IgLwfk9GTyfSlgRVSIgPw53MGApE94qC+enCdbkAYNXwT1PvNtjj+KyRPNFfJeXf56NSU90L2WmHGC0GIo7Uhs6lduy0N5YvX97WJthpBqzVs8k4ihPZtUiXd7ry6FSXOa1vxn/l/2pRTWdmd2vyeQ6RN9ZxcnT5gYPCHWv8f8EhTVDIo3LMrvOKqHL0XjhL9XdAML0qipDkTnkRDW+nhcCMJgBAKDWCPTHADpbuwADrf4vpXUqz/hj2l/71dcAmHFI1IzVj5qjSndbXU1ze0lt0ABDi0O+S5EyxLv9U5ZHRgshOjK4Z6tT/yvfP9VhsG/zVfJDX0vBHqzQ8ggFp3SNxVHk7Ue95b6ZhBotVvQMAuQMt4EzP7DGJ6pvyR5OaX8NnSc1J5Y+3/aa68MuN3L3CT/4tOUJCbm7Oo91RoDxiwW+d0jGEmasb6UMhto/0fi2KPdFrG8Oh4BzIuFobHEqjBcMguMLAVpmVLAKCAIYBk4TjN8Uv5bre8K5IEkalYAgGAHvGcwe5k6VS6aZNmzZv3apW1lMhhsVihYeHDx8+PCwsLCgoqOUqAONwOFdXV1dX1379+tU6pNPp8vLycnJysrOzMzMzMzIyMjMzFU9faLAikUiq63kPD4++ffv269evX79+ISEhRGLtp3tfN1JfN57tT85UJwCwaXinZV4Nq3crCBFx/MDD8YOqsPnK7cUVGwsE73k4Lvasm3rETptTqMvdkLOiN2fgfI8P29oWO3baCz/99FNbm2DnRbFVzx7nNLULs8e67OXnRScHckc4k5+6lv0cMEgIAsAgPvXpVqTLvy69gEPwM1zn2RpHO0Zel14o1hVck8aF8UY18lrWuHeqAk0MZ/36ixuq0zirCW89zmlXXcPnRiZ1ON6NwLG7u7cxBAEHAEyFNSIxb8mu5GgeMi30/jeYhB5c64+hVF90TXrB9mO4Ib1UaSy3vv694Mdaw56uOHq64qj1dbz04jjHKY7kqvrJXCpuiCfZ7ynRoy8xdTQ8gsGC6DLiTVn7VO9WWlTDzz4lkeiq1SDo6vDWUEPEZcnMv4oAQM4krJ3vWVpohEIjAHwfxn4j6NWvFVX/776u4KkFDofz9fUdOHDgN9988xzRyHZszOhIm0QUqc7HG3IKMKOJwGVTe3QeLOuiNsEvTngRgMcABwwDXwdC3Ix6ki42gA+BwMHhruj0DH8ciAC0sq+++nXbtm3KOtKdyWROmDBh2rRpo0aNas3qbvVCpVI7d+7cuXMNLyyhUPjw4cMHDx48ePAgOTk5PT1dp9M1MIh1Y//IkSPWAUNDQwc8hsOp58ZXpERPd6BP3BJU8WGmcF2uLLpCl6ZuWL3XA4qZKozCr3PFu0pdv+7AfcP5FV0ffinBADtQusOEGW9KLw/mhvvTO7a1RXbs2LHTPFirZ/NJTqMEE4kIaQB3WLz04pGyf5Z4r2jGq2wc5lCqQhvI+H1QuMOCWUYJItwoT9w2iQjpDZe3txf+FF2+v7fDQBr+2Q6uJgy7tSRdoECLgil9/+3GBrM1Hh4AbBoezzDy364oXc/Q3FflDN3BCpMxBvdmjR+KkOy78W0DtWdn+dGzqou3mGOGEARcADBaDP+V7QeAkTccyUYTLSTY9mM4Vravl8MAOp4BAChmBgAEEAqeioMay0MGi96MmQkIwVqJkE3kUPBPkpPhEdg19hVNNl5dw7+RMq4j3TdBidHwHY49CYxvE+SnRPlz0jAT5rzM2/XrDrWOkjvQAmKrNHzutOaMh/+kNzNNVMPXtdTL8wRAxGWJnkO8+0PAIPeqHwbpKYUtXz3qF/ANl2DFMMxoNGZmZmZmZh4/fjwtLc3Z2bllzHv1ke0/qTgeB4+DDM0VYv3DXMvQQMCTDEkqAMj3pAJAwynobejN2LVig/nxKtVChLmdqFQ5WLSlB0eFrtepau9jCwSC5cuXv//++wwGo7neUUtg3a4fPrwqKw+KollZWcnJyffv309ISEhKSnpaLXoA0Ol0169fv379OgDgcLguXboMGTJk8ODBgwYNsgUF7E7V7H2gOcwibP0xwPRZ9vOodwDHxZ7UrszSFTnaFFXBvHT1dbnntrav8GHHyl15fI7mIQKIVcmvDthod6S3Y8fOK4AGVUeXHwCA6a7vEBESAEx1mX1fcStJceeBKimY2aO5LuTOxLs/vbz2PfmNLHU6k8Ca4DSt1qFeDgMuSWKz1OmnKg5Pd53b8FVMGPaxRIZEst9J1Xqm650OVgYvcIfHOe0A4C0G3ZhXVLH+T8W1IEBZCNFIdq0wlUhlB05q4hOc1izBs5kv/F7tNBlygA+tV1ftvdSyL3/hzBxP7RZ0ShUtNYndJPROV01EDxf64N4A0MthwGXJmUz1g1MVh2e4zsMAuyuPB4AxjpPqBn1ITeKVmR+YLMYlPisD6J3a4F21IdU0vG+CUk/GYds7ta16B4CiDzMxE8Yex6+r3q2Q/Wi+B7tmhd1TXZVJD5Xz5zZPMfYZHWkzam673Cw1zB7nZO7PWTvHub/ba6HYa1G/UG94exMAzGZzQUHBN998888//6xcuXLXrl0tYNurjzLmsiL6PELAsyYOp/friaNRTKUViuMXrBkrDOlKALgvIANAqHOjBPyBDO23N2tssFswifjoJ+pLcbV64pl8nwmLk/9a3j6LCJgxU4L8VjdWaL1pePB4fKdOnTp16jRr1iwAsFgs2dnZ9+/fv3///p07dxITE/X6+svmWSyWlJSUlJSUrVu3IgjSsWPHwYMHh4WFTekzOKmCmCYyLWGS/tvRSfx3qdt3/vTQJqh3K8zBnKDrvSQHyst/yMc9Todj0VnsHvVti9FiPFq2FwDedHv3jOi/Ql1uvPTiIO6ItrbLjh07dl6Uk+WH1GZlEKNLCLsqJI1FcBjnOPVo2d4o4a6vAzbjkRavkm3CjEfK/gGASc5v1rvHPtN1wbqcZRfEMUN4I53JDc3pV8kUF3V6h1ncRQ4M4yc5xR9nAQpvLHI3Y9h6mWKDTMEzo52/+0sZ72+scMUzcX6nBtB6jNKn50h3HTEWCUWbdjuvbVqQFAmPAADpFSkl3pbwP5xT+f2fOYr06OJtvY4xzr9TiRBg5Ck6ydXJacV7CKHqI57puuDrnE8uiE8P4o4o1uVnazKsv1jbOCqzcm32x+4Ur6W+q0YJIk5VHD5YuvM1WXa/XWqcGys1Pi4dhevusCBP3zdVuXGOe4YIhT/Kqnfu4UQ6OolX3zAtheNiD+GaXOVZifRQOXd6PXu3qMJc/EkWAJB9qewx/Ja2pyiYSXwt1Ts8dww8gUDw8/PbtWvXrVu3rEXm7DQVzGCUHzoNAIJl8zUp3LKfVO4/ulB78qk9OiF/FAMAsaOE2dfvPBUPRqxn4wR8uDclU2LSmav+50vyM2JWz1RXFFfvQ+QLQqd+2G3svJGBHDq9nVZKjC4/cKYyupfDgP95ffrMzjgcLigoKCgo6M033wQAk8mUkpJy69atmzdv3rhxo7i4uN6zMAzLyMjIyMj4448/EATp1KmzW5dBPQYMJcwZFTAt5PlNxyG8t1x4b1XFaAnX51VsKhS86+78ubc90U5bEVt5TGIUeVJ9h/JH0wmMPwt/Pla2L5Tdv02yNNuxY8dOc1FmKLkkicUhuJmu86u3jxRMvC69INQXX5GcHc4f19JmnBOdFBsrAeCuPD5BcbPePiSErLfoDgl3f+TzVQNDVaAoF4fb6cgLWkgU4/FFH2UWL88CgJmL3AHgW5miOCPP44qfscwDzyb4nehhXWqndg10Xr9U+PG3+gfZ+owcSif/Bi5Rix/DHMQ6tHmr/Lye4KgU3upFP6e+J8Fp8n2NJiLWo9Cx24hZzBH9EfKTSawH1XswN/yK5NwB4Y4KvRAALGD5KXeVrYPEJFKZlTKT5IuH75FwZAAo1OXekl3tzwmz9SnTlzxUp4XxRuJafn2qNcHjgIADI1r1pwVB/pri8tcUl7o9cQjQiK29ouG8zBvBI6VfPSp8NwMswJ1ZQ8OjSvOjiCRtopLkRfE/1ZPo/JpK69bhhXI/4HC4wMDAS5cuNZc1rxX69ByLTk8O8FFeYQm/zgIAQ7amw/EeeAYeIRLBDEBLMq4dLTskcqI35LdWHQ8W/sehVUXdYmNjZyyfUd23HEenuy39+NYXn7u1b4f5SkNZnOgUANyT3xjOHxtAr12VpGGIRGJoaGhoaOiSJUsAoLi4+MaNGzdv3rx+/XpqaqrFYql7CoZh6ekPIP3B9ajtv35M6NWrV3h4+MiRI/v06dNwOMkzIbmSMTNW+WuR5F+hy+c+gkXuCMk+S2hVpCbxWdFxBJBZbgsQQPo4DLosPputST9VeWSay9ttbZ0dO3bsPD9RpbtQDA3jjfageldvJyCEN1ze/rXgu+PlB/s4DGIQmuxQ1iTyNFnWF5nqtIZ75mqzG+6wQ8BDMYyEIADAn+cGANU1fASFWjjljq7MA0dHbOrdCp7JYAzrp/jvvC45s0kCvm/jQhTtNIZzkhgJTg4ACqYJh+BGDV9qIPIMIIOahYMHcoffkF5+qErFAwEA1Gal2lxPZmWRscL2usLwJD2eGTNvK9hQbhCaMOMoQUQLvZc2oZcLKW1+7Z3td05Lrxcb9ozjDmoH0d1OS70AoPSrR4XvZQA80fCo0vxoYpImQUnyogTEhpC82jij1ivPC4kTiURy+/ZtPz+/5rLmtcIskgKA7lGAPDYXwSMEHlF9W5EbmdTheA88HkfRmo1ZhIelOgDo6dTkndutW7d+8sknKIraWqihvTy2bMF5eH2tM2yj08ktlmH+xYkS7jZjZjaBozDLDpTuXO2/sUm1Z2rh4eExY8aMGTNmAIBcLr9+/fq1a9euXbuWmJhoNpvr9jebzbdu3bp169a6devoTGUrLDMAACAASURBVPbIEcPCw8NHjRrl6+v7HFfnz3ej92aXfpmjvCQtWZEj+qvE9esOnElOL68jmAWz/F74Ax4hvOe1/KXwZzsk3G20GPpyBtsi6Ga5zf86e9kF0anB3PDmzdJsx44dO61GijIhTZVIw9MnOc+qe7Qnu08ws8cDVdKJikNvui1sUUvmeXxYpMtrTE8B+Rkpk/AA+GrzE/48N8yEFS/PKl6eBRimipfrMlgI0eizK6BumBvR1QkAUKm8iebbaR4UJpk1YzwNz9Ciagtm+f7RyoZPcaW4zfWsEfLwT8lvhdq8vpzBhbr8Mn3xEN7IIbxROEA8qD62PhfEMeUGIQCcKI/qyxnMJrzSxeHbH3U1fEuo96iH2q/jlfsmcBsZRPy68TwC/uHDh3fv3i0qKtq9e7dIJFq8ePGzz7FTB4RA0Of7a7NoCB7x2t6RMYiTPSZRfVvxKCLpsw3+pKXXjWm9K2JlwCI20n/exsqVK7/77rvqLZx35g5a++23Lvz/iSXX9YYlYuk2Prd9avgMdWqy8i4FR/3S//sfc1cV6fKuSy8M4Y1slsEdHBwmTJgwYcIEAFCr1fHx8VeuXLl8+fL9+/erL3bY0KgU0dHR0dHRABAQEDB27NgxY8YMHjy4Sbn6qV0Yfid7KM9LSr7M0T/U5M95UNm72H2DP70vu1neVCtzXXohUXEHALoyQwZwh7W1Oc/gkSYzQX6ThCNNcZ5ta/Sk+g7kDr8uvXBYuOdDn2dML+zYsWOnHYJi6CHhbgDgkxzPiY7X28eau/uy5GwYb1T1zPDNDoPA7MTs1kKDCxa5A0Dx8qzi5dkAgJBRVs8bZJ96to4sag0A4Gj2rb/nQWaS3JPfGMgd3phKARcK9AYUG9ehRhjmkbK9Bos+hN1PQHI6LzppAQuTwKLgGgrVDGJ09aY+SYd2T36jUJvHJLDeclsk1Bd/92jFLdmV8Y5v8EhPyjApzYpTFYcBwJnsWm4QHi8/+Lb7+01+t3ZqcjJHF/VQuzWcw29czianpV6YwSJcn1f4XgaqMUv2lmkTlWRfasCZkOYKSs8Qm4woliUx1yvgWSQcADBJ7VHItA7PI+BjY2OXL19ufR0WFvbZZ581q0mvC4rLVG1WMIJgnr8FcWe5AIC1gqLmjiJ4SYpRaAKgersTAwmEMb5NeBpt27atunpHCATHNV87THznSxovmETcI+C/IxK3Ww1vwdCo0p0AMMFpGp/kZC038l/5v70cBjTmidIkGAzG6NGjR48eDQAKheLq1asXL168cOFCRkZGvf2zs7Ozs7M3b95Mo9GGDh1qFfM+Pj71dq4LaySv43CuZK+w7Js8zV1FVniCQ4Sj2zo/sm87TUNQLzpUe7z8oPX1kbK9Pdl923MYOQbYAeEODLAxjpOrP/4BYIrL7ATFzWTl3TRVYhdmz7ay0I6d1ufnn39uUv9ly5a1kCV2XoRSfWG5oRQAinT5Rbr8BnpaMDRJebdFBXxLY9PweBbB6V2lPk2uvZ1c109ecysZAEgdvNrAxJefXcXb0lXJpfqiuR7P3pZbdkmuM2GjfKiEx3KvQJd7S3aFgBCmusx2Iru6UTx3Fm8lIMR1gVusC0nPxGipSoU42fktGp7uRw/q5TDgrjz+aNneRV5P7kLR5ft1qLYrK3SW6/yvsj68Jokbwh3pTbP7Ar8Qp3P1d4TGlErj8EZvnjt/7gMAwvV5xUurstY1o3p/JsEC4r4J3EDu65tb6nkEfGBg4IwZM1xdXXv37j1t2jSknYnAl4KKzYUVmyUIAvTgRMQoB2wKIAjJgxJwpmfWyARjhgFDWAgCY8YJxrOb8B0dPXp06dKltj8JTJbL79v5IUMVN4AagQBAByJhh4A3t1JyXW9YJpFt43Pb1Zd3SXKmRF/oSHIOF4yHJtaeeRHYbPbEiRMnTpwIAGVlZXFxcefPn79w4UJFRUXdzlqt9vTp06dPnwaAwMBAq5IfMmQIifQMRwkEj/DnunGnOVdsLqzYWiQ/XqmIFQsWuLms9MU7vFAwS6txsuKQwiwLoHdGwZyryTpdeWyqy+xnn9ZGXJPEFWgfcYi80YJIa4vIWMEhcgkIkUVgj3d840jZP1HCXZ0CurVClmY7dtoJtvX3RmIX8O0TD6rPQs+lMpP0mT1JOFJ/ztBWMKlFESxyp3VnEp1IgJMJl19Wnr1O8vFgDO1rPYqhqHz/SUNWHt6BSevdtW1NfRlJUtxJVyUDQLz0YhhvlA/tGUkETCigGKAYRgAEADDADpbuwAAbJYh0IrsCQH/u0EuSM/nanNjK/+oN8ajLWVG02FjpSfWx1YiZ7jo3RZlwR359KH+MNQiuSJd/XXoBj+BnuM7lEl3S0jYAKesAfccKv+9eipi+ds7jktaNxabhW1m9W3k9q8fZeB7ZMH78+PHjxze7Ka8PFZsLS796hOAR11U8/X2hMrZIn55D69sdz6Qbi4TMTg9yleEchRkh4wAHirNi4epc58+9OVOcGh722rVrs2fPtiVpozGZjlGH+oeGYgm4u2YD43GyykAicbdjlYbXYRit3ay/aFD1yfJDADDDbR4BqVpUa3ztmebCxcVlzpw5c+bMwTAsJSXl1Jlzfx0+U/rgJmY21e2clZWVlZW1adMmJpM5cuTIcePGjRs3zlZevl5wdLzLl778eW7C9XnS/WWVvxcb8nUdjrSU82EzUmkouyg+jQAyw20eArAue/l50YlB3OFO7TKMXG/RWZ0FprvOtS7/Z6of/JS7qgsrZKnPVwAQLphwXRpXpi+5LDk7ouWzNDcVtVl1W36tH2cIHd+uU07aeek4fry2u/Vvv/0WFxfXo0ePKVOmeHt7y2Sy8+fPnzp1atasWT/88EObGGnnmSCA9KuWlNuK0ixXm1WuFI+2sKjFofexxp1ROTPGyw6cFP/2r/LUJXKAD2Yy69OzzWIZ4HH899/CUe0u9E3DjJkPle0BABeye5mhJEq46wu/DU3Sw7dkV3M0D1kEh7GOk60t1sSxG3K+OFt5fBB3OJ/0jBmszCQ5UxkNADNdF9jSHnGIvFGCiJMVhw6U7lgT8DMCyEHhDgtmGSWIcCa7VWotlSoGheT/SLMjQX6zl8OA53jvLwXWbwLfLjMgO3/uwwrnkX1pL8tG1CuD/eNubaSHyq3q3evPTtwZzvq098W/7TMWlhoLS6t6IBAbxn3zRCVmsDwan6RNU2NGS8G8dAzFuNOemv0lPT09MjLSVv+cSCQeO3LEKWxoRxJRPsRSqEB9qv3XCiQSo50Faks7Uu8AEF1+QI2qOjK6dmf1tjVa12KvSs5HCXcvbbD2TLODIEj37t27d+++asXnf96pXL83VpV2yZx5WSsqqdtZpVIdO3bs2LFjOByuV69eEydOnDBhQpcuXZ42ONGF7PV7R8cPPMo3FrDDeQCAys2a+0rWUA7g2tGXUp0Dwh1mzDyEN9IasdafO/SG9NKRsr2Lvb9oa9Pq4VzlCYVZBgB/FG78o3AjAGAACECqMmFeSmT1nifKDw7mhpNw7StLyr+lf96Vx+drsxd6ftzWtth5pYiIqJG0+fDhwxcuXFi3bt2qVU/KOC1evPjPP/987733hgwZ8u6777a6jXaeBzNm+u7RSrGxYrX/z7WS0r9isCePxHPZsv0njUVCY1FVcnKihwtv4XRKJ7srdZOJE52qNJS5UNxX+n3/VdaSHM3De/IbvR0GNvJ0o8UQXb4fAN5wmVM9qq4DLbAvZ/At2dXDwn/e935GsO2Rsn8MFn0vhwGBjBqFh8Y6Trkhu1Sky4uXXqTgqFnqdBaBPcFpmq2DNcb+kHB3N1aotebcq8fbXeguDHx3x/Y1S7FB69myRS7s1ItdwLc2CAEBAAwDzGABAEqXALdf1+oS0w05BRatniDgUHt2dllUbu2sSVKBBWP0ZatvKwoXZpiKy5iD8AQ+h+TtBrgna3FCoXDMmDEymazqEgiyY8eO0aNGWf90puOd6bU9hJ3weKf25DUs1BdfkZzDIbiZbvNrHZrk/OY9+Y1UZUIbhisv6uPY1+fND+PGqk3Y3z1Fl86fPXPmTHx8vNForNXTYrHcuXPnzp07X375pY+Pz4QJEyZOnDhkyJB6y9FROzN8dgdbX5d8mSP5R0jtwnD7xp81nNvib6mJpKuSU5X3qXhapPNMa8sbLnMSFbcTFbfTVcmdmd3b1ry60AlMHIKzYE+qBj5tXYRJaHfZBK3zJwC4LbsWxhvtT+/Y1hbZeWX566+/fHx8qqt3K4sWLdqyZcvhw4ftAv5l4bzolLXU1kHhjs86fNPIs1KNxnmVkrksxgcsZq1DGgxbLJZWougJJwGhPS33AwAjrA99YKghO89UWokQ8ERPV7KvB7QzIxvGkJ2vvnbPVFiKYRjRWUDv14Pas3PrvwWlWRFTeQQAZrrOp+MZk5xn7Sn+zaqHGxm7HlN5VGIUeVM79OfWjtSY6jInUXEnQXEzQ5XSQJrDXE3WHdl1Eo70Rp3artYctH8V/XK0bC8JIQPAJOc3qydFIuHI3jS/Au2js6LjE52mN/Jdv1yEeZLDPFt7bUJlxKKztRP9qQ7k1tj6v1lqkOtrePAXKVEAeCAyxebqq7d7sPBdBK9v6LsNu4BvbThTnExlhpIvcgoXP7SYLIIF7ggBT+vd1Ra1ZcjVDrqdCQAYAogFEyz2dP3CuXD+efk5B+FaCb1LIsm1CM91cJg2hjliAACgKDpr1qzi4mLbJTZs2DBnzpw2eXfPzUHhTguGjuCPc6fUTj9jXW09JNwdVbqrU2CbhSt3cySenyEwosAkOfXsGrx8+XKVSnXx4kVrPHxZWVndU/Lz87du3bp161Yulztu3LiIiIjRo0fT6fVn4+PNdlFdlurS1I8ikljDuO4bAygBzZy377mxYGiUcBcATHSabqvXYnWWO1b270HhznUBm3HtLIx8BH+czTFeg6pXZL6vNiv/5/XpyYpDpfqi6a5z223xWAyw/aV/Y4A5kpwrjeVRwl1f+f9oj+6z00IkJCQMGFC/66mfn9+1a9da2R47z4fCJIupOAIAZBwlU/0gQXEzlN2/kefqMexXhQrF4EP2Ew2vwbB3RZJEg9GD0NC9vUBhPpihfa8Hg0NpbQdfhICndPJvUsn3dgKGopK/otQXb9laDJl56it3qF0DBcvm4+itmhr2WNk+HartzuodzOwBAIO4I65Izln1cITTjGeeLjWJz4tOIoDMdFtgfU6ZMbPIUO5CcQcADpE31nFydPmBg8KdXwdsqneeYEs3O0oQySfVE4HYhzPoiuRstiYDHntl1uow03X+949WxlYeG8AZVith7SsJqjAb8nW07rVX3BqPRGfRmmqoZZ0ZAwCx1lKsrCrJdOihdnuSWmGwLAl5/gs1ksRy4+xT9afzOJypPZyprd6Cx8Hdt51aZ1mhPWMX8G2A42JPACj5Iqf44ywAECxwtx0y5GqzxyQSTRgAIBicG8pbvpxf9sWPOERG6xikfdhR8yAEz2YCpEv+OGgqKuPOm7phw4arV6/aRvjggw+++KI9ujQ3gDV7CgKIK8XTuvFYCzbBgYyjlBlKLoljwwUTWt9CK2Q8Qq729FEjtLDREyMjIzEMS0xMjImJiYmJuX//PlYnDYhUKt23b9++ffsoFMqIESMiIyMnTJhQK1Se0dehU2I/0Z8l5T/lKy9Jiz7MCjjbXrKjXxCfLtUXOZJdhvPHVm8fJYiMl14S6ouvSM4P449pK/OeyYnyKLVZGcTo0sthAB3P2Ji3pj0Xj70miSvS5XGJ/K/8f/w6Z1m+Nuem9HL7r9hn5yXF1dU1JSUFRVE8vsbcGkXR5ORkD49XM5r61eNo+T69RdeT3TeY2X1vyR9Rpbu6MkMa41HclUT6hc9dJpFtV6qMGLbcgQUAOgz7n0iSaDC64PE7BfwGtt/3p2t3pWq82IRZndpvRZL2hnTXUfXFWwiZxBo3lNotCCHg9Zl5ylMXdalZlT/+7bz2w1bbh7e6phMQwjTXd6wtCCCzXBd892jFmcr/BnKG80iC2Fz9X8nqWtMaI4oBwBvHJWJjhQb9BA+4uRkA8LCXdx6VG1uuLw1iBFvz45gsRgAo1Rddl16styTwPfmNfG0OAMSJTl0Sx9ZrpxEzWF+Md5xmi5C34U/vGOrQ/578xrHyfe96fvK8H8bLgUloyB5935Cnc13bwXm593OMEF9imHtaaqkvX92X1xS1WnalahaHMFvu56gxYXH5+oEe5BkdaUqjpfqhB2JTkQIN5hM92TWeTR4sgrWG3GuOXcC3DdU1fHyJoTLCCQAoJfrADx+SRFVe2acH8S7OcCXtSTDRgwg+PPrHIYOvqA3f5SlvBjov62HMPKSMvZJDRdavX28bduTIkVu2bGmSJeZKiVkiRyUyVK5E5SocjcKODK/unw8AhpwC7Z0UhEDAMel4Fh3HZODZTByTjmczEGIz+LGcE50EAAywvSXbn9XzRBsK+OpozVh4lIhCQDYOcwjzJIeEhISEhKxZs6asrCwmJubkyZMXL17U6XS1ztLr9Vadj8fj+/XrFxERMXnyZF9fX+tRHAXn9JEnb46LeFcpPYQFAIY8neKMmDvNiSBos9gnDaq21lyd5brAllzQCgEhvOEy59eC76PL9/d2GMggtPgy7XNQpi+5LDlji87oxOzWlRWSqrzfPovHVs+9xyCwJjm/uaNoc/uv2Gfn5WXgwIF///33Z5999tNPP+Ee3/ktFsvnn39eXFw8duzYhk+30x4o1OXelF623pAFJOfLkrPFuoLzopPjnd5ozOkjqZSfeZxlEtlOlRoAPmAzF4kk9wxGFzz+H0d+wzvwZov13yZmr36NMZWUq87HI0Siy/qlJN+q2n7kQF/GoFDhFxv16TnaOym0vq0RlYYBdqB0BwZYuGCCc7VktLXqt90vN6aJ6kniCwDpIhMAF4ALAFbld0doDKKVAkCm+kGm+kH1zinKe/UKeAQQa7yb3lJ7ylQds4mu1npeK9bcE960tqiNBAAPrdm4PeOm0txZppCcVYg4xkfuVM8QZyIJ/wq6rZmEhuwxiYY8HQAI1+YCAs7LvJs6iICG93UgGNAa/2fFWovOjPFpOCqh6nNTGiwKAyagtezn+E+a5ue7qtUDWN8OqR3MuPq6Yr9CO60j7c3O9slPPdgFfJth0/CePxecz9en+TPWbi8gKcwYAggGahp+T4QTqNDfGcFgdRBLM9/rwf5uvV/pqkflP2sd35uozTk8/5OPTKaqG6uzs/PevXtr7aLUi0WtVV+7Z3j4SJ+Zh8pqr7fRenUlerhUb5EfjtUl1V8dneju7PLtMhz9hYqZD+WPZhMdGtOz/QQDUwnIQA9yXL5+Qaz0zzEcW+VMFxeXhQsXLly4UKvVxsXFnThxIiYmRiQS1TodRdH4+Pj4+PhPP/20W7dukydPnjRpkjXpHYFDtN2RK7YUineWlq3PdfzQy+kjT1ydXAatwLGyfRpUbdW9dY/2ZPftzOyerko+WXFoltuC1jfvmUQJd6EYOpQ32oPibW2Z5bogQ5XaPovHniiPUphlfvSgUIf+ANCPM+Sq5FyO5mFs5X9TXN5qa+vsvIJs2LAhNjb2l19+OX/+fEREhLu7e0lJycmTJ9PS0tzd3b/99tu2NtDOM7DJsJGCCOue51tui75/tDKm8mg/TlgjPYqra/jTWl05ijZGvdt5DrR3UgDDGGF9bOrdCp7rwI4Ml+46ormd3DoC/q48PluTYS2qWutQ9fptK/t1mhJIrbVE80a0xGjBjkziVRgKRcaqtE14nCVJHyc1AR4IKJi7MEN6svvYTrG66Nell8OAbqxQE1b/GgEAGCyG5RkLMnLmyxQdU+ocVRsJG6/5AABADwBIyQAAyXs9GJ/2aY87Ci9ClXrP1dJ6MHmzXUs+zRauyQVosoYP5BLOTa99W1h0VnahQP/tYPYI76rZ7G/31b/cU432bdmaDlbXfY3JvgLYZJog4EUiEZVKZTAYAHD48OHTp0/7+/svWrRIIGiDgJPc3Ny4uLiMjAyJRKLVank8npubm5ub24QJE1xcXJ59fvvAcbGnTGfRfJ27ILrCzBATlWYMhyAWDAAe+tLmdmVAqVCXmE50d6aFdiHiYJQPxWksF1WZy38sqPwDDjhnpJeX2kb7888/nZyeUajDiuzgKdW569bXeDaT4CIgcNh4LhvPZhHdnGqpdwDgzn9DezcVMxgtKg2qUluUGlShsqjUqEJlUagxFK3VX/L3Ye2dZIITn+jqSHR1JLo6Ed2cCM4C5ClTgT4Og/o4DGrkh9ZOQAC2j+L8nazek6al1LdASaPRIiIiIiIiUBS9cePGiRMnjh8/npeXV7dnSkpKSkrKmjVr/P39J0+ePHny5F69eiEIAgDOn3qbhAbFGXHZhjzR3yUuX/jw57oireg7VKovuiaNA4BKQ9m67PrLQatQJQBckpwZxh/r3M5KyiUr76WpEq2JeWyN1liAc6ITB4U7m1osp0WpNJRdFMdaPRitVlkDC9dnLz8nOj6QO6x9Vuyz81LD5/MvXbr08ccfx8bGPnjwZMcsIiLip59+4vF4bWibncZw+3EFr3GOU6wt/vSOPdl97ytuRZfvX+C5tJHjjKRSvuM6fCaVl6MoHUHs6r2FMJWLAIAc4F33EDnQBwDM5bVX/FsCo8V4tGwvAExxmV3XvatW/bZO/Nq+llYf/858Yk+8P1TtMsEN6aWLxUIOkbfI65MfHn31UJ0yy21+Yx5bJByZBE8N96DjGbPcFnB02tTSGp+M2YIvknAJONST9yR8mkPk0fHUoV6vWjp6U4UxZ0KSIVdL68b0O9mDwCESeMSC+enCNblgxqz12O28VjRKwGu12rlz5x4+fDg+Pn7AgAFRUVEzZ1Zlot69e/ft27dbU8Pn5+e///77Z8+erffoBx98EBERsXHjRm9v71Yz6UUI/NS7koIrWZFDVJoZ/TlmsVGfrQEAkQNxaSjDIhVKcy6xAody+1Rlo0FVZtVVGQCInGS/5T7JgDJ79uyJEyc28qKMoX0BQcg+HuSOvkTXZ2t+orOAPXF4PQcwDLNYkDp7/haVGpUrUbnSkFVNr+JxRCcBrVcXzuxIeCVAAN7tzni3+zPKdOPx+MGDBw8ePPjnn39OTU09ceLEiRMn7t+/X7dnTk7ODz/88MMPP3h4eFiV/MCBAzsc6aa5qyhd9Uh9Q168LKtic6Hzp968t12RVnEPkxhF1kTuYmOlGCob6GnBUKlR1K4EvBkzHxbuBoCJztMZhBplTiY6Tb8tu5ajediuisceEO40Y6YhvBp+AdbUvu25Yp+dl52AgIDTp09nZ2dnZGQIhUJPT89OnTrZQnvstGeMFuN/9VXwmuE6L02VeEt2dQhvVCM913QYdlitwTAMANFg2CG1xhoPb6eZwSEAgFksAPBPye8Gi2Gh59KqdWTrdkirBMCfEf0nMYpIOJLSLI+t/K/ePgggRbq8G9JLA7n1zQBrYrDoj5X/CwBTXWYH0Dv344TdlF0+WrbvA+/PX9zaEfxxI/i1Gyu1ln57K7gUYtyUri9+ifaMqcKYMzZRn6WhdWP6xfQgcIgAwJniBAAF89OF6/MAwK7hXzcaJeB/+eWXw4cPBwYGWoX6xo0beTzejh07MjIyvvzyy82bN7eal51EIgkPD8/Nze3cuXNERERwcDCPx2OxWEqlUiqVZmZmxsTEHDt2LCUlJT4+vpHb0W2O4xJPvANRl6py/bqDRYOmh9xGpaa+qUpzpZHIZgKAuVJi7YmqzI8ikjV3FSQPyp8e/+hyqjyO+Hz+pk2b6o6sS8lUHI+jhXZhjQur3k728yL71U723jAohp4TnQhm9vCkVrtHIEhd9Q4Agk/mcd+ZbCoTmYSVJmGFqbTCJKwwV0pNwgpVnJLz5sTaMfa5RRa1luTlin/JpwslKvRGiWFyIJX4lFruXbt27dq166pVq4qKik6cOBEdHX3t2jW0jgtDcXHxli1btmzZ4uzsPGnSpKlTpw6JHaKKlQrX5uqzNEUfZop3lbqu7cAa0eKbY11ZIRuCfm84Ms0KFUdtb/vDF8Qx5QahC8V9GK92HK+1Ht4/Jb+3n+KxGaqUVGUCBUe1Feqz0c4r9tl5NQgICAgICGhrK+w0jdOVRyVGkVedCl48kmCkYGJMxZGDpTtWBWx8pp+RDsNsce8LWMzv5AprPLxdwzc7RDdnADCk52SFwlXJeQAIYgQP5oYDgD49BwCI7s6tYMY1SRwAGC3GY2X/NtzzqvR8YwR8TMVRuUnagRbYlzMEAKa5vp2kvHNfccv+2HpBaqj3U1Xq3QpnihNgULDgRTW83ow9EJsAYPM99d4HVSnfS5QoAJzI0SVX1IhucCDjvh/KphHai+via0ujBPzBgwddXV2TkpKoVGplZWViYuKyZcsiIyMjIyOjoqJiYmJaTcCvXLkyNzf3u+++e1qi9bVr1+7evfvdd99dvXr1n3/+2TpWvTi82S4ALgCAo+H9T/W4MzaJqzAVT0jyj/IDBNElPTSXixG6g029GzaRosfG2E7//vvvazk6GouEsn+idSkPAQDPYkBNAf8cnBedPFq294rk3LdB24jIsxOq4bkOeK4DpfOT+i6YyWwqLcfRqLXUO2ZGy7/8GTOjVlNJ3m5ETzeSjxvJy53o4VzvAkG7ZfM9VXS2LuqhdusIjgerIcs9PT2XLFmyZMkSsVhsVfIXLlwwGAy1upWXl2/fvn379u0CgSAyMnLKt1O6CTuKfijSJqseRSYzBnLc1nag923ZMuZ1N9UfqJJwgGugpmt7QGlWWHPvjeCPl5rEdTsEMYL5JCexseKc6MQEp2mtbmANLBh6ULgTACKcZ9TNjc8iOIxxnPxfe63YZ+cV4O7du/v27UtOTlar1UlJSefOnXNzcwsODm5ru+w0hNQkPic6gQAy021+XYk+3q7KEAAAIABJREFU3nHqLdmVAl3uM8tYVFfvVs95Ph5ny2ln0/CVWsv8WKmqZqZomR4DgK0Jql2pmurtrgz87nFc8quYSOwFofftLt9/Un474dCQK9aW/8r+7cUeQBAqFMcvAAB9YD3pZpqdWW4LrLnfn0lj5LfIWPG4mFzVT9H+2GoWTBXG7JH3rXHv/qd64h1qqzbOVCfMaCn830Ph+jyEjHNa2rTNOStSvaVCgwLAQ0ntTAQlSrREWWOTCY/Ast4ML3aL51CjEREAoBPt95D6adQXUFBQMGrUKCqVCgC3b9/GMCwsLMx6qGPHjmfOnGk5+2px7dq1wMDAhsukzZ07d//+/fHx8a1m1Yuw6Z6qVIX+NMzB9guldWOu+NR3xa+FHjmavFmPeFP76e7fLFu9XZ06VPfAQPKg+EV3nv7OeFutspCQkLlz51YfU335tuSvQ5jJhKNT2ZNGscYNeUEjlWZ5TOURABAbK86JTo53nPocgyBEAsnbvZ52At5hxnhdYrqxsBRVqnWpWbrULNsp7CmjHKa23/pktVjQjZ5QZkytNI0/KtowhD2uw7Nz+/H5/Pnz58+fP1+pVJ4+fTo6Ojo2Nlaj0dTqJhKJ/v7777///pvL5U4cP3EEeXDQGU91vCxrRIJgobvHpsCWeUP1UKov2pz/DQ6QdYFb25XDfC3Oio7rUC0A7Cv5o+GesZX/jRRMJONaNlNLw1yUxJbqiwgIkYAQrHsytaDgKASE0P4r9tl5Gfnmm29Wr15dvf5lTEzMr7/+umHDhhUrVrShYXYa5pBwt9Fi8Kd3JOHIBbrcuh16Oww8Uxl9rPzfUIf+T7vF6TFsgUiSaDC6EvD/CPjuBDwAjKRSfuI6LJfIdqrUFARZzGYCgNJgyZGaTfUlnJfpMZm+xixfacCMKJCbrtpUFosewwRPWbsvMJs9CYS2LSGVazK/K5bMYtDnM2sH0JkwbKVUnm8273fkk5/iCU9w5LGnjDoljJKA0kXDppKZeVBy5ML6IXsNmMFI79eD2q01kvWGsPuFsPs112iHhLtNmHEAd5gv7YkXz2hB5A3pJaG++Ko0bihvdHNd67VCcVpkyNUCAp6bg+qqdyvcWS6y45WKWHHl1qLnE/CuDLwvm5ArN7/bjTHQo2qL7mSO7miWbqI/dWpgjamsCwNfXb1rbityZ6ayx/A9twU1HNqpN2Nr45VCdY0bRaHSDABHM3W3hcbq7V4s/Me9mF0ExFE+L5Qk+xWmUQKezWYXFxdbX1+/fh1BkD59qhJLymQyEqn1ClyJRCJrpu6G8fDwSEmpm6uyPXIwQyvRWb7sz+JQcAAAFqzyt2J+pfnr9zx/3VsCWRrJER9WH4n0lLNZbsDT9Kze964u/T3m7k3bCGvXrrXV/sFQVLbnP+WZqwDAHDGA81YEjtEM1ReOlu3ToVo3imepvuh0xdGBnGEORO6LD2uDHRnOjgwHALNYZiosNRaWGvNLjAUlpnKxuUJSuzeGaRPT8QwaydsdIbdZcbV6CeIRY94QfHVNceqR7sM4+eVCw7rBz3A00qIaDDASjsRisWbOnDlz5kydTnfm7Jl9h/dejL2kUqpq9ZdKpXv27tkDexzYDiMDhg3M7zU0keqGWaQmMRVPo+Co+BZe5z4k3G3BUAtAlHDXUp+vWvRaL4IHxcuR5IzBs1ObCsjOtcrjtT5XxOcAwIyZ9pf+/YyekrN2AW+nGTl79uyqVas6dOjw448/pqWlrV27FgCmT59+9OjRlStX9uzZc9SoUW1to516KDOUJMhvAkCO5uHTMoxakZuk1yRxT6vAelGnr6XerYymUQFguUT2u1I1n8WgIogfh3D7bUeVocZNdWuC+r9s7YehjMkBNSYbHCqaoDzXg9WHTaztUtQwC0TSbJNpO5/bl1I7smmLQvWHUvUhm/k/VlsmGNdiWIUZ3ShXGjGsuiUmDFsqkV3S6bk4XO24uJpgkb1vpO8GgPBjJIrBuGMBXBdkdmY6efUfyHt3egubXw9mianilwLuTBdq8DNy+tgY24GiMmJWD4tMdVqi4jYZR5niXKNUCgEhTHWZ/VvBD1YXg/ZZaLadw53uLD1coY6X5c5KDTgTQvatR80Kv85VxIoRIuKx+fn3cnhUfK7c3MWRMMC96v+d1XPeg4W3tdRFc1vxKDIJVaOSvUJMb/H6u1MDGr5EhR7N1NY7JytUmq1K3sYtBP7Xk9GYbbDXlkYJ+C5duly6dKmwsJDD4URFRYWGhvL5fAAoKyu7ceNG586dW9jIJ/Tr1y8uLu7Ro0d+fk8t/lRZWXn27Nl+/ZptZbFFsf6UbTsf6juKkhU5bwTS1y30QvZ1pcxL02dpDPmdMROGY+iZIVctGu2W9LuWxyeEhoaOGzfO+hpVqEQ/79RnPPo/e+cZF8XVhfEzM9sbW6nSq9h7ib13jQi2NKMm0SSWmGhiemJMU99ojKZojCaxgL33gooFBVRQ6b0u23uZmffD4LouiChFNPx/fFju3r17Z1l273PvOc9B6DTJrMm8wQ3zCuSbsi8qT9MQ2jsBH+4s/fu65tLO0r9n+c1vkMFdoElFNKmI3aUqbpO0WBGGq7Iypdyp+PZXAAAUZbTyZAT7MYP9GEG+jMBWDVKUvi4QJF5sKTThBi7G92HdrwRzR38zUZ3QNsxC57Y7nBa5J8N0S25bM1SUadtzQXnKRtpEdMni4GUMtGrf4df8FVfVFwCAh/G/b/0bZUHEZrNzO9wUBcGL7w2QX1GLE/0OHzisUqlc5qDWqGOTd8fCbuENYVh0ULjE743dM4tGFo7+Zirft6om3z/Fv11RnQcAL1arD4K/cuQ+nFEcva65hAHmyw6M8nrJEXuZZ8y6rb/JwbjerFZh3Pv/1wSJ6+xaGkrPNNxO1SVzMR4BxE3ttVu6pHb8zo3xCtefXqIBvUQDnvYs6spEr+mpuuS69GzHb4royhb+O6xatYrFYh0/fjwoKKi0tJRq7NOnz9WrVwMCAlauXNn0Av75qDLT2Ahp4rb8Tjq7ayHY6mAIzflc1IXBbNZSodtQDsuz2qH3CA5bjKFKnGDfO0wWMlHhg4t5HgMBABELdcka21267WDFzqu8C4uDl9XtgqroxGTctFrnVCpdNDyl3mkI0r4JD41qpB2D/qNE9IFCtUajIwHmCvgAYCPJhQrVaZNZgKLrZZLVGm0bBmMcpwb5ccFsWV1xl41hPZgdOvXtYc0v6VqadNU7P36edGHkU6gVaq+0Zo5ONqXpK/8qCT3QidO5Tq4HKwZVLTMIkthavBEARntMqn6008WtF1Vo9kB57FSfmQ07cxYGCADruU7GRrlYyO4O2dE3dOdUGSOvV9fwJV/nlP2Yh2BIwB9thOPcn/iJBEwEABiPk/PiUO9uI6T6i2plbBkA1KLhQ0S0fZOkavMDOTg70037M02TwtnjQh+4LjEb9ebV9ziKtBDq/XLBMAnW+AH/TU+dLun9998/fvx469atORyOQqFYunQpAGzdunXJkiVGo/H1119v5EneZ968eUeOHOnZs+dnn302fvx4f/8HYkVKS0sPHTr09ddfV1RUNOWsnhg74dpiTjcCgMaNDgCREWzOkc6ZI5PM6QZGADvs8AsYv8/NlBvH9qx39P/ss8+Qe9+syg2x5ttZmFjo/sEsZmhAg8yQBHJb8UYSyGGycR5M78ner93UXr+kOttfMqwJSrLXeMDOjAjiD+9nSc+xFZZaC0qsBSX6M5cBAMEwur+P9O3pDH+fJ3guA67X2tUMhOlcNTdZc+Wi6rTeruPR+G/6L3II4LV536VoEwEARbBVkRsFtKpvst2l/2Yb0wEA4ZzdMv7PpaftWSr7i7sqB7a1mjhlAICTdpy0A1SN40YXcTEeAHiz/Gjo/X/G1rx2BrsO2NBtjOytt98n7eSpU6d27twZtydOq9S6zFytUV/dlXQVkvYiJ/tv6y3fpx47Z3yrD0JpEroZNxlwPQCUW0rshJ2OVT3vFdX5DEMaAKTpb4yQTXDsi28pWk/FYSKArG6zxdG+OvebW7okAKAhGABM8JxqJ+07SjZtKvw5iBPOxjj+7KAh0jHOL6bcWs5BOUK6xLFb0UItNGw0Ywst1J3k5ORevXpV95z39fXt2rWrc2G5JuA5qzLTqLAxzsKgz+o/DgtBXuZzH3Zvd+aTGHxS2XYAcFefel1z6bE+3JYIBVaS3KY3OGv4NRrdr1odBvCdWPhCtZP5pmckhw0AHyhUP2t0ADCbz1uoUJ0ymQUoulEmkWLoPzoDCQYDQUzlPfDanjaZ5yuUdiSoAy0gJnDWZsaf1nbmmX6f30p/55Ytten3xB3qHeVguMaeOTa57hqeIl55vMicx6cJOgt6yK3l1TsMlIxM06WcVhweIBnuxaohj/KJETDRP0eLxaynm1HR6KAcLDiuZg1f8nVO2fe5CIYEbGwjmtSkvt2Gy5qsF1NwPS6K8gjY2MZ0S585NkkZW0bayYA/2yAP2VVpU60eIRU57+9Gq+Wc/8kgzETO1JvaEwp2G17ooU406fO2Fq2TgB86dOiWLVuWLVtWXFz8+uuvz549GwASExOLiormzJkzc2YDb6rVPpOff/55/j3c3NzEYrFAINDr9UqlkjqlpNFo69atmzDhGahVNmlPpctelCXTAAAFEoY7B3VjouDOCDvaufLPYslL3nQfJgBr+bqfCaLqIV26dBkz5r5q4vbrhnI5wimjG9DL/YoqPsNwW0ATjnafBABShsdw2biDFTvraGzbGKBslmR2DACQNps1r9iaXWDJLrBmF1iLyqw5BdbcIlcBT5J2hZomEQKCmHDjHf0tla2SgTL7iAc75v9P8W+nK48AAIZgP7T+XUSvcgQ8VXnotv4mANAQugk30mlV//8h3NaVVjkbY7di+fOw+1FhM3zfyTTcoSE0b5ZvIEe0N4pclqDdftuI6Ed+32UIDaHxaW405P4/3VTvmVO9a/j3GeUeNepeRV8AADqMGDFixIgRv/7669mzZ3fu3Llnz56KCteibjpSf9B+/KD2+Mc/LB/8e783/n3n1SFvT/d5gwCCiTKdo8TnB36ca8qyE1YBXeQc1TbVZ9YNbaIB10sZ7lza/VA6P3ZQgSnXSOhthM2T5TNAMoIE8pzieJmlOElzGQAS4MwLokGOIkarcr6k3HHc6KIfWv/mfPJ/V3+LjXJCuBHOrrY20mqw63k0/lMPZW+hhf8glMFNdcRicWZmnWyuGoTnssrMf5MdJX/ZSKuEIVNY5TtK/mov6FIX71sKBOBTkRsAODT8VYt1vVaHAXwvEY2u6Uy7iZHj+NcqzSQehzqH/1mjO2Q05djslHq/arHk2ewfidyWqzRfqzQA4NDwp03mBQqVnQRv88nJ4vYZ+tsp2qsAcE19cax7dGzp5u0lf0aGdWiMPDh7pUp39Jwp5S6u1qBcDjMiSDCiP8p3p9Q7K4wbeqBT8adZytiyzDHJIfs7cbvWaRlJArm3bDsA6OzaT9LfraUnTuIHKmLf8HuvYa4HYH/5jvPKk4uCvvBkPsmZzbNFjRr+6at3nZ1S7wgNoTz2MscmqXaXA0AtGr5pcKh3ADCl6TNHJz9/Gr6uQQUvv/zyyy+/7Nzy7rvvfvzxx1QsfVMyZ86cIUOGbNiw4cSJE3fu3MnNzQUADMNkMlnXrl0nTpw4Y8YMT88nLMJRUFDwsL1/B8XFxU82eHWy1XZnO5j4Qgsk6XgApe6MIBHtSI45qcz6YU+BozJEWlra7t33y3V+/vnniJNLCqdbe063hiyGaSWsjqqebIyTb8r2ZPqM9ph0QXU6z5R9SXWu91MNUUbodGZoADM0gBKgpMVqlyup+isqmyJRfVFhlaMIOuAkz7T/HMrlMEP9t/fPuiHIpR4eyAltxaqK4GCjXC7G49MEPix/rpMgn+k3P9eYxaPxPBjejmN2ABjlPnGU+8TqU/Jm+XqzfB2/smnIN/3cpkdyfAU0PqMBPstoNNqQIUOGDBmybt26hISEuLi4uLg4R9SrAx2p36s6vHfUYaFQOKLrsClvTBs5fiQ4fXCxMU4kr4a3Sii3dY2BFVFeLw2Vjfno7lwb2KZ5z6KWF5O9Z6zOXcZEWVFeL3kzfZ1LEHcX9iFJwoDrZUxPDO6vRc5UHiky5wPABdWpHqK+jiXd91mf5BgzACCQE/pJ6A+OjZVbuqQ8Y5aAJgzmhjv+WC200EID0rFjx6tXr+p0Oj7/gQxVvV5/5cqVDh2artjE81pl5r/GXf2t65pLTJT1Uci3q3OXFZryjlXsG+MRXfcRnDX8bLnCDtB81DsAZNrsJ0zms2bL/ySi78TCxUp1js3ORJCNMskpk/lXrY6BIOeFAiaCfK5Uf63SEADTedx4s2WhQmUjSS/zqY62c4OkK77IqFKz+8q3fx3+c7zyRKm56LTi8FBpzW4FT4wx8Vblms2EyUz9iqt1tuJy3bFkQ/pIayGwwrihhzvRPZn+f0QCgDK2LGtccsi+Ttxuj9bwCCCR/A7ZhruP7omgIZyIel6Ig1Jz0YHyWJzEtxVvbJA4lOYPysGCYztkRd3QX1BljLwuHCmTbyhC6Ejg5rb1iZyvjYcsWqurd6q9+Wh40krkvnxLe0JBkzECN7ctej/DlKbPGJYUeqQz3eM50vDkswxBEHq9vrKyEsfxBhlw3LhxjfrSacy42umnzYbSoPUlQetLctU2tRnv+0/53+0SrnNP9vkq79N49dg4edD6knSFzfHwSZPu27936dKFIIgGueqHsbv03xkp479If48giWTN1Rkp43/M+pQkyQuKUzNSxi9Ifc1oNzTqBB6JzqY9JT+8tXjDX4W/GOx6R/tv+atmpIynfm4n7C+YtTQ36u3cqLePLH39h+1T1m6cvmvt3PJVG3VnLzf9nLNVNnvDvFtJkiTtdvupU6feeustd/faPsFFXNGsGbOOHz9ut9uf7Ik2FqyZkTJ+Te5y58ZV2V/OSBm/uXB9HQdRW5XX1AlnK4+lapOd27cWbViQ9trsG1Ef330HJ+6/OgvTZlB/xLduTibI++/2zYXrPkufvyr7y6MVe53HMeMmtU3pPEILLTQSCQkJANCrV6+nPZH6smXLFgB48cUXNRrN2rVrqa82vV5PfRuuXbu2yWYSERERHh7+yG6DBw+OjIx8gvE//fRTFK1XtO3UqVNdxrTZbB07PrTI1n+hvyRmWdD6EsGAGVR/nMA/vTt/Rsr4A+VxJEmmqpNnpIx/+fIYrmcN2vsR4yOI/8EjEQXFEQXFsg+WNJPrpZB99HFEQXHr3IKYsgpqhhEFxVPL5REFxRF5hfxRVc5EwmnTI/KLIvKLPL75Njy3IKKgeHj2rhkp4xOUZ+JKtsxIGf91xgdRfw+bkTK+18cdfPt7zkgZPz1+FEvIaMD5j+/eO/3Ft3Kj3l7Xc0RXqZeUyYkUSr9qMzTFY/d17slb4WespWZHf6vZus7zx+vck+c4+9qirrv59ZmPxqbaW7qtwljWIK//sF96OdZ4NzSJT/390GT9cb09ffj169yT17knk4SnKneXPNn4DK9wpl975x/v93YHrS/ZkKK/VWGlframGrr9VdZm0gOOVx3QNvGc/de5J79lfjJ9yvQa3m8RY85w9lznnvyO+anz+c3D5tP6jf8FrS8RjpjXUK8nYcGzJqVc5568ERCvu6nu2LGjGBHGsjdc557cxd4kQcR1H7+Zf78/Rlp/M6wTiyAIl8vlch+avvW4LFq06JGn9yqVKi4uDnlIgZBa+C1Z/8MVV1NxisHb5NSNX2O8PSutJTJGamVVrRb8nl/dxRvpux5+/N7gOBeYxUn79pI/AeC2/uZ1zaXe4oFnFEdzjBlH5Hsmek5vvDk4IEgi3ZBaai7S4dph0nGOw9595dtPVR6ibncT9nGcKg+TjXOjCUV0iQ/LrzW/I/Qaa1eorVn5bln5He/kWbMLCJPZCEnGi0nsjpGYW9M5o14osrx6UNlORv9piDCgIUw1MAwbNGjQoEGD1q5dGx8fHxcXt2vXrurR9SqDasOmDRs2bZBJZROjJsbExPTv3x97SJ2e6hSYcigjw2ivV53bp/i8fjv9Rrzy+EDJCF92wCPHcaOLakyGnOozs0Z7mzf9FqXpUzQ2lTfL1zlf464+tcxSXAh5OcaM4bLxjvZP0+dXWssRQDq5dX8n4H75q0zDHYW1QkgX+7ODnSMFWmihhZdffvnEiRN///330aNHqX3AoUOHJiUlKZXKESNGzJ07t8lm0thVZkwmkyMB7cmoXuCTJMnqjf+p/sabR5l+7czpF6n+ZxXHisx5VLYdAIRx2lRe1kp7Crq80zr+k6THGl/63vvs9u2BJAFBRLNmGxMuPsXrZUuYEdGBGXvyDeUmAJB/+w1CEOK337lptXEQ5E0B/yeNNtliBQBAEJpnldWieuu/AOD57feil1+hhpMYDgVzwoO5EX8VrkMAmewxI25jdKcVQeFR/ulxecUXK3xecO80N+LS8psNNf8YNx8Giv2bk/pJ8jmqEbfSwy0xOCrAuHpBj5t0D6dKwyis5m94RRU1gjboF9Z3b5s/TCXuNMjr+XfRb9c1l7IN6fX/e/n28/R5wd2qs+HJdHY/2Fq8IZLfgcq/a/7/L/Xsj3KxkF0dMoZdM6YZAv5owxsjNix57PG5HYZ7vLWpxocsv+TqsgS93oBdaxxW29+yPuEinEv4tY8ty8cYx7j0JUnyNp7+vuWL31grhtL6X8SvHrAfq30+Nptr5flHzr+W/qSVyJl+S3OkkiZjhB7qRAtlGgwGJal+y/z+r6wVwWjA76yVb5gXKUjlE4zf3EBI8tFllqBanViSJN99993/Zp3Y1NTUdu3aoSiK47UXCnFlb4ZpWYLWOWZea6n6TcBAEQR0VoIggYYidoJ8MZx1txK/o7AdjJa2ltABoN3Euan37Os6d+587do1W14xwqTTvRsl9WV9/o+J6os9Rf3f8Ft4pGJPXOlmJsqyEGYZw2NZxM8FptzlmR9iCG1Z+Bp3ZgPbAittlQqrPJgTht7LBDtasSe2dDN1+w2/hT1FVd83hea884oTYrrUh+3flt+prjn5JGkrKrNk5SM0jNu3m8udivVb7Uo1FZzPDAtAuQ2p90r1+JR9iiIdzmMgxyfLPLgNn+qG43h8fHxsbOyuXbvkcvnDunl4eEyaNCkmJqZPnz61n0qRQH6f9XGG4fZI9xddBDwAbCveeKLyQASv3eLgrxvmAuqAlbCWWgq1do2U7u5sirOh4KdUXYrOrgnihn0c8j3VSJDEW7cm20kbAPiw/L4OX+Pof0y+T2GVC+ni9oIuLSH6LTwWly5d6t27d69evaij+Ged2NjY5cuX37lzx2q1YhgWHBy8cOHC2bNn132br/6MHTv2xIkTqamptVeZ6dChQ7du3fbv398Yc3ji7/cWAMCA6z+6O1dv174dsMSxV1tpLf/47rt20vZhyPK6e9+u0eiovPfvJKIki3Wb3sBCkBpryzUN6/J+uKZJaMPvuCjoC7jnOX/KZAYADEGGsVlHjCaqJwJAAixyE8wS8AAg3myZU6mkKgd52BKDdJtEdImFMBtxA4/Gl9LdAUBpVWhxNQtl82j8SmsFiqBfhv3kXODmiSFxvOCl90kc992wHBPwAIDEybt9rppu6VkRXF6H04SmxPuHJYwgXwBIUJ2NK9k8y29+JLtD3oxU1Z4KzI3W+mJ3RkB9Mxfu6m/9kP0pdXtB4CftBV2feCg7af8sfX6ZpXiq98zB0lGfZywsNhdM9p7hvJX/fKM5Wpkz/RZpIfgDxMFxHVD2Y0cVFWjxj86p9VbSpVFrIfwEmIB5f0AEoJsX4+Pe95MpChely38ronLy+f1rqBBJWoicaTc1xxQ0KSP8VBdm8COW0MdyzYtOqdcNF/Xzre+/tot6Z0c+UBPRLrdmjk423dazwriPjKV/Jr7f63QG2FIntkGYEMaeEPbA52C7jWVGGwkAp6bKxGx05LqSPvvLBywJSJcwXmvHHbez0tHTZDJlnNju+HXhwoX24vLSj37EpKJWa79o8KlmGNKoArNcjLen7N+j8r0A0NXthVRdktxavibv29bcdmKGVGGVx5VueTtgSUM9b44xY03ucq1dDQADpCOmeM1goEwACOe1bS/oIqJLWrH8uwjvH+H6sgKm+cx+7KdBELqvF923pn0HkjRev4Wrdabk21U9vd2ZoQHMsEBmWCDD3xvqF/XgxcMORsu+uKBJKbfR0EYJoMAwbODAgQMHDly7du2ZM2diY2P37NlTWVnp0q28vPyXX3755ZdfvL29J02aNHny5F69etUY03FFdT7DcFtAcxvjXkMS4zjPyZfU56ikxybzUWegDH92cPX2WX4LAIAgCRS5/w2EIug0n1np+lSVTRnOu18bz0Za40q3ECQOABdVp78JX0u1EyT+W8EqjU0tZkh6CvvVZ6nRQgvPCjExMTExMTiOFxcXe3p6Mp5Gpa7nrMrMf5D9ZTv0dm0Er53zd8ETeN861DuV906lvrv40jclmYY71zWXACBNl5KiTWwn6EbVexei6FAOK05vPGI0YQCT+dwdOgMOgACs1GgBIIxBn1upJEiSuuZyelcOKxfMZ6lh9Xad3n4/KtNMmMxWEwAQJFFqKWoQAY+rdaTNRpMIKfUOAKSNtJVYAIAZzGaGeZgSS+xyBSPI14Drtxdv1OO6f4t//yp8DbsDX7WnAtfhdqWtngKeIIltJRsBwJcdUGjK216yKZLf0dnN97E4WXmwzFLsxWw1SDoKRbCp3jNX5Hy+r2x7T1E/N1oNevI5w6HeUTaqO6vMjr7xBBreT4D9O1bi0vjmUdXJPPPHvQVDAli1PNZ3RTgQIP+jKHtSSnBsB/7ABwoHklYi5+VblHoPPdTpkeodAIYHsobPekLbsgcgyJypNzXHFDQZI+xwZ1Zr19BsmowRcrBT5qiQCrELAAAgAElEQVQk811D1rjksJNdMP6zXVuuTrNvhnVinzmUJuJEntkl3MFRRm5PuonLQDpcUo28oKwIZI/8MdRF3O3cudOqr6oELpVKoydNUnz3O2nH2e0bzBTEmQvK0ySQAOAIUAeAi6pT1I00bXKatqpm9XXNJRNufNywZBLIS6qzWYa7xeaCke4TOwqqjsENuF6Pa/k0ARvjnK08aiOsM33nAUAgJ3RB4Kf1v65HgyA+//vEnJZpzsi1ZuRZcgpsxeW24nL92SsAwBvUSzq3vikDfAaycpDw0f3qDYZhDse706dPU0peqVS6dCspKVmzZs2aNWt8fX2jo6MnT57crVs3h5K3EpadpVsAIJATSpWsr04gO+SWLulxrYYbD2f1TjFAMnyAxPUzio4wPgpenmPMUNoqnYW9hbDc1F63EGYwQLml1CHgzYTp64wPjLhBxJAMlIzoKx7ieIiVsKII0mKh38KzyI4dO6RS6eDBgwEAwzA/Pz/nuw4ePPj33383zUyesyoz/zUoAzYUQauXVhnjEZ2gOptnyr6kOttbNLD2cdZrdeu1OhqCrJSIhrFZcM/Tzk6ScQbjnErlnzJJp5pKzDYSBEn8W/w7tSICgB0lm+S0SEq9b3KXHDdWOcMBgvRmMjsyGB8pVAQACrBSo0UBIYBEAL6TiCwk+blSnceJ4WMCrmH/YOmoF5xKsVzTJBwu3+VGF831X8zBuA2i3gEAYdAAgLDej1JGWWjogU6ZY5I1hyot6TJOEIrQ6QCwr2y7HtcBQJmlJOHbE7zlDEAR//WtH6ukXI2cUx4vNOVJGR5LQ75blrm42FxwqvJQLQfmVsKitauljBpiS7V2zYHyWACY4vM6ZaYbye/QXtD1pvba3rJtr7Zqunyfp4L2hCL3pVukhXB/21c2u1XGyCTdWWXWhOSQ3R3RRgjnrBkEfFeFA4D8j6LsmBvBcR34A6o0PGklcl66pTlcSal3dhterQPVC1xjV+4oE77orjkg53QRcDrw7Wq79pQSAMSTPaurdwq6O8N9rm/BvLumNL35joHb3a3xZtgE1EnAN5M6sXK5/LGCGcaPb0YRNSuv6rbfMT7s3uWXtQDwVokZAPYb8F3nNX+MfGBba8OGDY7br732mj3xljk1AxPwRNPq5LqHk3hs6V9h3Mg6npGOkE0Q0IQApMauuqg8gwLaVzKEqlh+XXO53FLSiu3fnt8FADyY3nVR7xWWUiFdTB2nA0CG/vaGgtXU7WDDbYeAb8fvvK7tNp1d+3H6OwCQoDwzSDIykBNalzk3FCify+nZkdOzIwCQOG7NK7Zk5Foz8yw5hXRvV6M4EidMSamYWMgIaIVgT2iPFF9oyVbZX2rLoTfOmTyNRhs2bNiwYcPWr19/8uTJHdt37N21V2PQuHQrLCxctWrVqlWrAgICYmJiJk+e3Llz5xRtotJWCQA3tNduaK/V8iyV1vJb2qTObj0b4xIaiWBueDA33KWRjXG+b/1bsTlfZVMGOb33CJIwEyaNXaWxq65hCQ4Br7Iplt5920KYBTThaPeoobL7BsKV1nIEUBFdjDZCWaAWWmgQpkyZAgDz589fuXKlS8B8SkrKP//802QCHhq5ykwLjcr2kj9xEq/RD4WBMid6Td9QsDq2ZHMnQY/a1wzHjWZn9U6BAHwpFgJAnMF43mxpSgEfrzxRYMpFACWBQAApt5QY9afedRs4jM0+bDRRkQID2eyTJtMCheoniWidTFJotx8wmm5YrA71Pu6ef/7nSvVN5nAZFr7aqysfvR9K4McKvKW9XmjKS9enPpZjf+1gPC4mdsOVGktWPjPkXtmd9vzQg50yRyeZs7iEtqePp2epueiM4giKoNFer6avTOGtrVLvkun1TZA04Po9ZVsBYLL3a0yUNcX79ZU5X+wv3/GwA3MSyP/lfpVpuPNRyLfBHNdv5z1l/5pwY3tB13b8zo7Gad4zb+tuxCtO9BcPC+A8NPvmWUd7QpEz9SZhJtzf9m31fRgAhB3pnDEySX9RnTUxpTE0fJbK/uMV3YJuPCqN9z4PaPibwbHt+QPEVWfvTaLe7Upb5phk001dyRfZuNaOcrGQPR15vYUBG9rkzUyrWFtAE9M9FwdUf6B6f0XhonQA8Fjg/6yrd6h7GbnmUCc2LS3tsfbd65je3zRMjeQgCJAkacnMtxaUyBn8224+ejqTKtTARHAriXnJrQBQKmOmFlhCfyujpj92ZyWiyMs6f75qIARRRkYrN+8BANHLE1BenY6+T1UeOiE/EK84ERQR5ihyXgveLN9JXi9Tyc8AMMJ9wiSvV6i7+kuGf3L33WJTwWut3g7ihNU+DkESm4vW3dAmau2aDoKu8wM/odqDuKEvek5jYWx/dlAI54GkOAbK3Fm6xUpYuBjPgOu3lmxYGvLdUyk4DwAIhjGD/ZjBfjCyf40djJdT5P/7EwAQJoMZ5MsMD2SGBTHDAjDhY2xar7yqS5XbdmeY/jdYGCJqxJAeOp0+cuTIkSNHWn63HD927J+Vfx++eESPuzp25OXl/fDDDz/88ENISMiESRPCh3TyDJc9cnAmygrnPU1LywZEQHMTVCuzx8G4P7b+Q2tXa+wqd8b9ZQ0TZXmxWhWZ8rR2daE5z9FeYi78NH0eCSSKoFGeL490f9FxV44xA0MwCd2dR3uogWKxueBQxa6x7tHOSf4ttNAYsNns1atX3717d8eOHW5uT3lZExoa+v3333///fckSRqNRrPZLBKJ6uke30Jjc0N77ZYuCQBEDOk5xfHqHUggORhXa1cfqdg90eulWob6VSbWE2Qw3fWrkNLwL3I5kYymi3Uy4cZ9ZdsBgFLv1Dn8sfJt30b03XxPvVNx/qs1tF+1OkrDd6AzllmqtshJgAp7lZ/CKBaxwXqhgNFHTgs8asSjnTQOFbnwQ/anByt29hINkDAe/Z1bJxCE17+HZs9xxfqtHp++gwmrvnFYrVnSKQUVG6TWCo+CeUUHl+3HSXyQdGTHbZ1ka3kkCkXflHSePqj+z++SVdGG37G9oMtN7fWHHZhfVV9I16cBwNbiDc41ZQGgwJR7XnkSQ7Ap3jOcH+LO9BosHXVMvm9ryYaPQr59WsvFRqW6egcAZginUTX88VzzyTxzhITmKuChSsOTdrJyU3F2zM2gf9rJfy/SHG1S9Q4AuNaOIEAY8KwXU0L2dBRN8gCAvJlpJV9lA4CLhlfvq8h9LZW0kR4L/H2WPQ8bPXVSC82kTuyAAQOoDPxdu3YBwJw5c576UqPutJXRl8ncFH/s0B07j2BY3IDoq8j93WULiQGAt9wKAMUyBkECgMMvEJQJOx0OkKzg7nYrB1drWa2DeQN61OWpdXbt/vIdAGAhzDtLt8z2W1jHOTuSn0e73y9fJ2N4DJONO1Sxc1vxxqWhD0hrgiQKzLkslOXJ9Km6LsJ8WRVvI61udFEb3v33CR1hjPWIqfFJswx3r6ovMFDGhyHLV+Z8kW1Iv6w61+upFpyvBVb7MP7g3ua72baSCvOdbPOdbKqd5i5hRYaIXnnRkXhWC9/0c5t/Un270jZ+V+XszuaO/hn9JUMb9UuIyWSOHTdu7LhxZqPp8P7Dcft2Hth/wGB0VfJZWVkrvlsB30FERASVItumTZsaB/yPgCKokC4W0h+IjuFg3M9CV5BAau1qAe1+coSQLm4n6FxoytPYVVbS4mjPNNz5NqvK+HOS18uj3KOo2ySQt7TXGShLynCXMGSbCtfmGDMU1ooPQ5Y/lyuSFpoPb731FoIgq1at6tmz54EDB2rxkGtKGrzKTAuNx+nKw9SN3aX/1N7zlOJw7QLeA8M8HiJDEICmPHsHgH3l2zV2FQIIgiBz/Rf/mr8SJ21mwrSwLPkSRDhXp5/vxgcASsOPvBc7QH1wU/nwswS8NWXHiujdAUCGYgPYrpnGlMq9rrm0p+xfys+lQXCbOMx47ZY1v7h4/lfcPl3pHlJcozNcSrZXKNz6eeiu99UcrfTTeud+l9kvbmDxJ5mAImeWnr7TL62DsWc9D7RLLTVkVUzznnVbd7PGA3NHyh4NoecaMxOUZ14Q399E2FaygSCJ4bLxjhWmg/GeUy6r4rMMd6+pE7oJX6jPnJshNap3ikbV8JTseOhBKAJ+ayIAoHJTcdbEFABomsj5rAkppps6jE/DdXYERUiCxIQ0XG3PmpASvLPDwzT886feoY4C/pVXXjl58uSrr776119/ORoNBsP06dNVKpVzcfLGpm3btjt37uzSpUtSUtKSJUtcTG6aOZasfN3xCwiT4fnpO4sigqbo8BE75CY7CQC/X9uEWUm+vqeNhqjcaO915yeVWRNLrQYb2cmDfjJln2OQtoNjsPKKrztEsdqHoyfVANDJg/F6+9qWOLvL/jHihmBueIEx97IqfpB0VPXApOpYCeuusr8BIMrrZZeAtzEeky6qTmcbH5DW+8t3HJfvN+IGJspa124bJTnYGOcr7+/1J8+zLuTayo7lYyfp/j68AT34w/sgNZkbk0BuK9lIAjnSfaIPyy/K86WNhWt2lv7d2a0nE63NV+NpgfF5kjnTAIDQGy0ZuZaMPEtGjiUz316h0Fco2F3acnt1euQgbWX0A5Ok3yRot98x/nyVKc7APu17bpzPgEafPQCLw544JWrilCi9Ur+hz9pDmUfO269YwOLS7e7du1999dVXX33Vtm1bSsmHhz/6LfSfAgHEJSCQg3FrNG7wZvn2EPYtsRRqbCoudn9L9IY2cU3ucup2D2HfHGMGAGQa7lxSncMQjINx3RmeHkzvxryIFv6jYBj2448/tm7deu7cud27d4+Li6NS4ltooY4Ml40XM6R16Vmj/2jzpMJSSm1MkEAOEA/v7NZzqGzMkYo9WnrrNIjAEFglETvH+c9349tIcqNOf8BowgCGc9mHDSb0nqddlkV7EO9MIAwf67XO1sMrtDUoIwthBoBLqnNDZGMDGuiFQtksz8/nKdb/a7yeqjt23tHOCPCRvvuKRcm9PfqSf0LA9Nn+lemFVOS878D82/LU+h9oby/+EyfxAQ9mVTgOzLeVbHTZnj5SsUdhlfuxg4bJxm4oWB1XuqWzW09q/ZmovpiuTxPQ3Go8+2Gh7AmeUzcXrdtRsqmDoCsDfTqlChoDQ6I2e8pNKu/dRb1TMEM4oYc7Z45M0l9U585IDY59wiNVBgYAwMQe58/tpOGbRr1njk02Jmkp9Y4JaIGb2xZ/nGW6rac0fPakGzVq+OdSvUMdBXzzqRNLMWXKlKQk14KizR/DhWtAkoIR/ZgRQQDQio9RTmGLuvE7IoHqPbc0JMhFdAJBAOBsQZWIupyYpCmuOtdFaIzK8NHn2AIAgFIAMANAUpmtFgHvCDqa6TsvQXX2YHncv0W/18UJ9nDFLuqTtI/YdSXHRFnjPSZvLlrvLK1TdclG3ODO9OotGuAY3JpTYF3+O6bW2QAAQUgct+YUKHMKDOcTPT55G+W65mWcV57MNWaK6JIRsgkA0Fs88LTiSK4x83DF7hc9pz3i9X2qoDwOu3Mbduc2AAAEYS0qs8uVnE6RLt00u4/p4xOZwX7M0ABmRDDDzwtQFAA4dOSb/m5MwaWtScFKdeSSIya8v/rF0KbwuqPgiXkLUpe8umdGzrd3jqWeOGE/dwm9ZrG5KvnU1NTU1NTPPvusY8eOlJIPDn5mVmPNBC7Ge9N/UfX2EE7r/pJhJeZCrV2dqksGAMqbh3IGpvq83OqtgZIR1G0rYUlUX+TTBB5M7xZh30L9mTVrVkhISFRU1IgRI9asWTNnzpynPaMWnhki+R0i+U0UjNlk/Fv8h520AwAH41IrkLEeMQmqs1a8RGy90R4pHMp2LYLDRREAQAG+lYjGctiRdPoKtZbS8PvMAAjT3ZLga/i38uHnmgBAAqmxqaC+tdvugwn57h+9Zc0rNt28i6u1KIfFighmtQkFBDnF3nP2pyMvzn+RkX4/7308UXWgnai+2F3Y58melMqqcLx0zozzmHxZFZ9puON8YK6yKaiCR9N8ZoZyI88pjmca7hyu2B3l9RJBEnGlmwHAQli+zHivxqejshuUtsqTlQcdcW3PAbZSM2khAAFu14dGHNM9GYxWTFuZxZpnflifR7KoO7+HN7O3z2PufSDgtyaCP0jM7cSvf7nBWqiu3kP2deJ2E3A68jPHJJvSXDU8YSHy59wp+SrbnG5Q7S4nbaTnBwHenz9X69W6Jtxu2bJlzJgxVJ1YADhz5kxwcPA333wze/bsGktPNSrdunULCQmh0Z6xAgC2ojIAYLV13UJ7rT2XtIQT/+YAQLmEwaQhM9tz20rpn8Zri/X2NsWHi+/15LQdPKP0RiBiFM+IooxDUQTayGrLB3MOOhrjPumS6myeKdslMKk6Cqv8qHwPAPQRD8o35TjfJbeUn6w8mGvMBACVTeGQ1ouCvjATJudDSMJkLl/+K67WsTtECKeOZQb7kXa7KeWO8q/dlsy8yp+3uH/4pvPIZsJEmZ3EeL9GbQoggEzzmbU888OjFXv7iodIGa4ecs0UFGX4eTP8atBUtuIKW1GZrahMf+4qAKAsJiPUnxUexAwL1Ibwi7CNXdtz0nNeUqravn/KVKGnvdmpEbczq00bEUV5dJnoEXywzfSjLwnf84y/fWHjt38cvnrUhttc+qakpKSkpCxdujQyMjI6Onr69OmhoU3qNfj8waPxqZzAXaX/HKrYGcwJnxew9JusJbnGzEBOKJU+6uUUOhivPLG1uMrb8g2/hT1FVTYNJtx4XXNJQHPzZLVyZzy53Reu1RNGE8bj1tFoo4XngAEDBly5cmXMmDFz585NTU19mPdNCy0896TpUm7pklAEJUhivMcUHk0AACyUPdFz+qbCtRGGPywkcVXduoewr+Mht622NRqdc1z9TD4PAFaotdQqGQH4n08fX6xvjc/ogIbQ6uJV9LgwAnwYAQ8En2vtmoMVcaZwI2eXlPUVJpvpI4r2gAcPtDsKuj3BgTZO4jtKNgHAeI8pfJqrJRAb41Q/MN9RsslCmHsI+4Zx2wDAVJ9ZX2e8f0y+t494kIgupWITLIRZbn2ERjXg+sedbXNGOM7d5+uQ4k+z8malkTgpnuL6nU4Y8OxJNwzXtAxfVtAOV++euhPgRgtweyJVhYDoxcZdnD9MvQMAVfI9c7Srhqf8F/Pn3FHuKAOA50+9Q90FPDSPOrEUAwYMaDLnvAaExAkAQGg1hI4jGIabOABQIWb8NFjIoiH9/ZgCJlKsI2+d3u3oxu0yrl//sN5tZIyAh3pfOUMFHfFpAiroiIEyX/ScvqHgJ+fApBo5Kt9rJawA4FAID+1ZsXeU+0QmyqJ+nO/SHTtP5eq7L51LObQjdDqnW3tmsF/xwuXGa7cs2QXM4Pu1UvaX7dDYVCHcCOcd32BOeA9Rv8uqc3Glm+f4f1CXq27OSN95STBmoCU9x5KRa87ItZdVmm9lmG9lAEDcy1p7gI3B0LSLWFda3jcnf4LGTgI0oYCnQEA4ViYcKwOAscFjA94TLWTOjGdePiO7dCE3wWZzVfK3b9/+8ssvv/rqq+7du8fExERHR/v6+jb1nJ8j5Nby4/L9CCBTfWZSqYPfZn1UbM7/JvwXF1ujrm69yy2lFZZSA653zgk8UXlgb9k26vb8wE863CuDp7frbmgThXSxN8u3ttUhSerPXNYcOG0rrKoYygho5TZ+CLdv14a90haaJyEhIZcvX46Ojl63bh2T+fxEorbQQt3BSfzf4j8AgCAJquS4464+4sFnFceoM4zYkr86Cro5Vj4BdFoUlzOQzRrsFFffkcFgIIiVJBGAHkxmW5aY0eSHXg9jV+nfJtzYUdCtfYducPSBu/pJhp5THs8zZh2V7x3nMflxR6aqtWMIZiZMhyt2V+9AAEFHGEpb5TH5vrEeMdRpPwNlOPySA9jBvcUDLypPx5VueSfgwx9a/661u5bOqQ4KaB1TOZ4hPBb6A0Dxp1n5b94GAGcNTxjwrIkp+otqhi8r9HBnZuCTb7niBOhthHOL2U4CgAUnNZYH2nl09ElrLj0JhInIHJVkvKHDBDRc+4B6p6BJGaEHO2WMSjLfMdCENLvanh19I+x4F8l0LwSBgoXpHvP9vJa6llF7Dnjs7RaXOrEt1B26h9R8K92Slc9qV5U/7MZE7TixPln/elY+YeQCQIWY3s7pRN2cc620qJC6TWNxuO2Gsjv5MrzrtHViI61U0NFEz5c4WFWMfS9R/3OKY47ApIc9NpzXJtuYjpN2o91gIgw8TOAYgQDCjJuYKIsqwiljej6s9Lcp5Q4ACMYNcamvhomFvAHdtYfOmlPuOAR8hbXsZOUhBJCp3jNdwvujvV5J1lxJVF8cKBkZUTef8zxT9v6yHVFeLzVUMdUGA0EYga0Yga34I/oBAK7WWTJzLek5t9UpdwKKEAQhSRJFEC+PeC+PeJaguyl5AqAIMywQrWZ40zQEbWvP/CRr9AXe6OIhOpExsUfqUc3JsxfP4Tju3I0kyStXrly5cuX999/v1atXdHT0pEmTWrVqsU9/bHaUbLKR1hfEg6gSD9R+1hX1+Z2lW1wC74V08XQf1wBOAOgtGqi1qyssZWbC5HwCf7Ai7rh8PwAggHwYsjyUW1X9QWvX3NaliBkyb2YrHsaXr9lsOH8NAFAOGxPwcI3OmlckX/2X+U6WZPZkaDZLzxYaCk9PTxd7WqFQeOTIkXnz5q1fv/5pzaoFACjD8WSLdTiHXeNq+ZTJHEKn+T9roYjPBKcrD5dZqmIfg7nhx+T7nO8NYIfkGjORe1Hf4z2mUO0cBFkmfiDx7brF+oZcYSXJdgz6Last0WI5ajSPq5Y8+FQoMOVcUJ6iIbSYB03dKajF2HdZSw9X7HpBNOhxXfETVGcAACdxKqyyFi6qzozxiK4yP5JNdH6iaK9XkjSXkzSX03QpbfgdZYzmaITUNNSo4QkDnhV1o0HUu9JEDI+VK01E9bs2pBg2pDzgcCxkoodjpB5NVXneLreaUvUAgGAIALDb8NjtXE+2aDKG6EWP0js5do0dECAMuDlNz+nAF0/zEk+rbynEZkudPvrT0tJq7/Af96auI5weHXQnL2oPnuH27UaTigDg7zHi6L2KdUn64VcTSXM4AFRI6I4VMoKAIeWw4+E+3YYjzMeIZT1asbfSWuHLDnAUrAbqQ9kpMOlhqbNd3Xq35rX/8M5bVDBSF7feM3zfedzrxZVqAKD7eFS/i97KEwDslSpHy/bijXbS1lc8pHrVdxFdMsp94p6yrdtKNnweugpFHrH1R5DEpsKfC015OlzzFEvQ1QVMyOd0a8/q2vZYxhkwA0mSXqxWAyUjthZvQABN0V69uCMnJItlojO3tB/ZV4z3CxcywwLo3jW8pI0Et6sg7GhnfYK67PtcOA2DTncfwulFvrHsou+1Xcd3nz9/vrqST0hISEhIWLRoUe/evWNiYqKiory9WzK068Rd/a0kzWUmyoryvL+5FuP9Woo28Yr6/EDpCCq2sHakDPeXfN6s3t5fPMxCmMstpTbCKnIy0t9ZuuWC8hQAYAj2fukU5vlrKJctmT3Z3j0w25QloondEsv0G/bqjl9ghvjzBvVqiAttoRlRWlpavZFGo61bt27s2LFKpbLpp9QCxRKF+qrFMsZk/l4icvnaW67S/q3Xh9Hp+zwbqN5YC05cVJ1x3KY+HqtDZbEnKM84BLwLlHo3kmQUl/OVWLhJp1+h1i5VqgCgOWj4HSV/kUACIP/L+fJhfRAEsRLWveXbZvrOe6zBp3nPvqW7XpeerXnt75sfuT9QKFpAE45yn7ir9J9tJRu/CvsJRZpIMTZPXDS8cKwsK+qG/oKq/uodAFAUhEwUf1C/W+ykGSeZNIT1oLOdGwulo023rmb4sQI2tsmblWZX2TA+TX9JnRNzI2hHB9RpY7Pil8LS5TmAAEJDSBspneFTPdfg+aNOAr5t20ccezariuvNFnanSHaH1qYbd0o//MFtwlBWZIg3QYIVBaDhJguKSgHAO5wnY1d9SL3UhnPhzgnHw317jyup83NR2ekAMNV7loviDWAH9xINSFCd2Vn699sBSx42Ag2hebFasVB2T1G/rm69H+9SAQAAYTEBgDCaqt9F6I0AgLKr4jPTdCkp2kQEkAhe29u6G9X7+7IDmSir0JR3Xnmyv2RY7c8brzxeaMoDgGxD+hVVvCM3uNlyVnG0yJxPHb9P9Z4ZyesQrzhZZM4DgJNR9oh9AemVWKyoTRxBjj6VPO+XrQwemxkeKJw8mhHYREfcvN7CkH2djNe1pT/maQ7JYYvxBXqbsZMHwzLWgeTDsbGxFy9eJIgHPvsJgrhw4cKFCxcWLFjQp0+fmJiYiRMnenk9t1uh9Ycgia3FGwFgtMck50p1lKfjvvLtW4s3fha64pEbWA/Di9Wqxrq7gyQjcdJeYS3DCTtxKBEAZAtmsDtF/pL3/XXNJQBgSdgfz32DWLVLs+c4b1AvrV1dYi4U0SVShgf2315RPaOkp6cDgL+/P4vFcvxaI0FBQUFBz2Hk4bMCC0EA4KDRBADOGp5S7wDAbL670882g6Wj/ir6BQB6CftzaTUksplwU4LqDEkSztH1zriod9QpH76ZaHgTYQQAO2mTW8tr72nGa1jF1U44r004r04He2bCtOHuanAyP3JmuGzCBeXpEnPhWcXxQdKRjzuN5wyPhf6EmSj9Jif/zdtlKzjmuwaGHyvsSBeGf31jE4RM9MQU163AX67rVyXqZnXgvtetTkm7jUeVsfysNFxnx/g07Wll5qjrNHem1+IAThdBxdqCog8zndW73+pwaMIthqdFnQT8O++4nr7qdLrExMTbt2/7+/svXry4ESb2fCJb9Lp85UbTjbvKv3aTOIZgONF/IdBp7M6RpvNsAHtkB54j3rwdmacurjKQYzKZnp0GlqgeOrILcaVbLIS5m0zDxtEAACAASURBVPCFGmPOY7xfTdZeua65RAUmAUChOe9Yxb4Ka+m8gI95ND4AMFHW0pDv6nOxzCA/a06h8eoNZsiD1f5I0njlBgAw7sXPH6rYBQAkkH8U/FT7mIcqdtYu4I24wTlkK650Sye3Hs2zBB2FAdfvLd8OACRJdnLr0ZbfCQCm+cz8IftTBJAKhvrugohxbsNLz5b+nEM76NN5oKGgY0Ga8dotmpdM3FQCnoLTRRC8vb3ptr58Zb5qV7nin1LYiowa02/mr68p3TQ7d+6Mi4tLSEhw2c4jCCI+Pj4+Pn7evHl9+vSJjo6OiopqUfLVOas4VmTOkzI8hsvGudw10n3iBdWpAlPORdVp54CaBiGAEzLbbyEA2ApLi0u+oblL2J0iAWCgdCQJRIWlnI7Sha27KvnHbKVyXKn+TbXqjv4mALjRRcvDf3FYaejs2kpruYzhwatmWdRCsyIiIgIALl++3KNHD8evtdCyQf+0WC4WTqmQF9nxg0YTCfCDRIQC/KCuUu8SDP1JIn7kIC08AdfUCfmsCVbUDVdvRUmry706emABe4Ifls+3Z13XXBouG+/SQYETLuqdYiafZyXJNRrdUqUqnEELp9fmQNzYfBzyvdJW+chuCCBieg1Z5V9c0Oit5IpB9a2Vs788VmNT+bD8AjmhNW4lDJSM2F7y596yrT1EfblYk7sCNTO8PgoEgNJvchpQvT9dDDbyRK55eBCLTatNcjtreJSDGRK1AKA/p5K84lWxrvA/qN6hjgL+559/rt5IkuSKFSsWL178yAD7FhygHLbHJ28br6WWfJmlOyfgdi1B6DQAEI6NNv10XeVGX3bHOK2PgKrEuH//fscDB77wQld/cY7R6O/26COvbGP6FVU8AFgIy+aidTX24dPcTLhxR8mmL8L+d0l19s/Cn0kgaQjdRBh50DCbbbzBvXSnErT7TzGD/Tk97hWYIQjVv/stWfmYyI3TtR3V1kPYt47niuGPCiHeV75dZ9c6fnX2yW+e7CvbprdrAYCG0KK9XqUaI3jtOrv1TNJcBoB9Zdt7CvvNGeo3VGW/UmId+8YcslJhzS+uXs7AlJSm/Gs3vZUHMzyIGR7IDPJDGA2/PmBH8gI2tvH6JKhidYHinxL1frm12BJxrtv8+fPnz59fWFi4c+fO2NjYK1euPEzJz58/v0XJu2DA9XvLtwHAZO/XqptKUNY+v+Wv3FX6d1e33rXYT9YHXKMDAJp7lb9dJK99JO++pa1GJsZ1elyt7S8ZRgJZaS1noRznf9v/5X6VZ8wCgFYs/y/CVjnCHQ243oDrJXRZy3F9M2HWrFkAIJNVnbe89dZbT3U6LTwUCYbu8JBNKa8stNsPGU0kgDuG/aXTA4AYRbe7y7xr8sRtof6IGVIN2k6PeeI0SUfDnyh5371Vjfnf5c6yI0wrPYRLltUobtkoEsGgt2XQF7gJtuoNA1ksn3t/qTkCPgNBDhpMbih63WItw3HKr77pwRBMxnjydLy4uyaznVze343xWJXDH0Rv156UHwCAYnPBh3dq+yDS47qT8oPjPWvOVvhP4fVRIMrFdCcVfj+3ftbVOwBsvmVYeVWnsQhebffQetgUjgLvhLEqcxPX2x9Q7695/3fUOzyBiZ0DBEE++OCD3bt3r1+//uuvvxaLW3aC6waCqA9xtGcFAKBP9J4g054L45UMziA09jODpEIWyrz3aegs4F9Q4XPVyUtm9KvLM9zQJFIlMW9qr9Xes8icX2ktZ2FsMUPaWdBzqGxsA5ZqY4YGuI0fotl7ouLHP5hhAcywQNJiM924Y69QAIZK505HmFVCpb9k2CMD4+tCqaXodOUh6nYgJ5SymWnOJehKzUVnFEcRABJgmGycp5MlwWTvGbe0SXbSSh3Rv+TzRoiIFiKiAQC4S2juEpyAcgPu7CNiV6hsJeW2knLj1ZsAgGAYI7AVMyyQGRbAigzFxA8tIvoEMAPZvj+Fey4NVGwpYbfjA4D5rqHsxzy3EdIF7y5YuHBhQUFBXFxcbGzs1atXXR7bouSrs798h96upSOMVF0yVQS+OjSErrVrDlTExdzb6GlYqHJxuFpb471UO8rldBf2qbEycC9Rf4Ik5NYyO2l33rn5JnNJmaUYRbC2/E4LAj9xtFsJC07ijbQZ0UIt/PHHH86/tjjVNWfEKLrdQ0pp+MP38tHEKLrDQ9aqRb03Gq+2mjvAbn+1QlEOwRrZj7/LJBwEAYBki/WNSoWdIEdy2D/6voVBzZqTgyD/uksB4KTJ/I1KswHTb3aXOOwGZ/J5M/m80ybzAoXKRpJ9WEw3tAlNvRsIan++nsE5DJQZwo1QWOWP7IkgqC87oH7P9vzgMc/PY14zc2h+Ukx2EgAMtjq9lZihHJSF4gYcAFAGSlirMjdJGymd5eP3v4hm7HnV8NTXv7RLly6XL1+uXlyqhYdR8kV22Yo8hI7IZreS/140+nDF0ONywk4CwMTTlSIJDV7zAAC5XH7lyhXqIQggQ7wCMFFdA1OHu0+QMj1qiXs04gYbaXWjidzoQnemlzvTq4tbo3hTiaaPw8RCdewhS0aeJSOPaqR7e0jenMJq0/Blw7cX/4mTBAB4s3yXBC/7ImNhmaWEcuNvniXotpZswEkcAFgoi44wXaqtBHPD7+pvAcBZxbGBkhEujvrfXtb+ddMwKYKztBdfwEQBgD+0D7tdhDk925KRZ0nPsRaUWrLyLVn5cBgQGuaz+lOaRwOXV6G7MzzfD6Bu684qlTvKlDvKGF+w3N/xa/WKz6JFixYtWpSXlxcbGxsXF3ftmut2krOSf+GFF6KjoydOnOjj4+P6NP8B0nQpAGAjrecUxx/VMxkaR8DTW3mhHLatqMyaU8AIeuDNZk7NwJVqTOzmOJ+vzlDp2KHSsdXbO7p1T1RfUFor5dYyRyMJ5Mfp7yisch7G7ynqN83JTp8gCQRBmrP3ZAstNCWUhh9RWqEjCABgIMi2FvXe+Hyt0jAQkGIYlc3+u0ySbrW9UanQE2Q7BuOsyXzMaBr1qMPzvixmdybzqsXyaoXCWcM71PssAe9ZVO8NBQNlLg5e9rRn0cKzgfGGLmtMMm7AuZ0FhhSdQ70DCQgDkUz1+q+tGuor4HNycgQCgYdH09liP7vE3jXe/SR7zNEKHEN+fdU3ubVgxBjbpL1lDDtZKWKYOGirUsvAuLLPjMTBEe7hhQcdrmBthFIfLy/2vYDzR8LFeP3EQ2u8y0KY95XvOCU/RAL5c9u/Gz05HEEEo/rzB/cypWbYS+UIrepMuDGKUd3UXrulS3JYwTFQZrTXqz/nfYsAkqi+OEgyqo6WKk2GwiqnZBsAmAnzvvJtD+tJkPYLylOTH6z10sWTsTXNGHfXeK7A/GVft2GBLACgeUp5nlJe/x4AQJgt1sx8S0auJSOXtNlQnmt4kiW7wF5eyQwLpGoi1BPprFYIC6tYnW/ONBZ9kFH+U36blN4oGw0ICFi8ePHixYtzc3Pj4uIepuTPnz9//vz5BQsW9OrVa9KkSVFRUf+pevLvBHyYrq9TLlJ43SopPgEIDeMP66PZe6JixUb392c6NLzlbo58zWYAEIzo/wT/uTFer8Z4vWon7c6aHAEkiBOms2v1uC7DcMfRbiOtS+++rbGpZUyP/uJhw6rZAbRQf1auXPlY/RctWvToTi00Jhu0et299YCVJFdrtD9W86VvoWEhAArtuBcNpTT8tPLKYtxOqfc0q5WAOokFJoL8JhPPqVReNlteqqj8SyYNptPizeaFCpWNJF/j8xa5tTiG1BcLYc435YRyW7ds+zYs1M5SM/mcodS7XWUTjncP/Kutald58edZspmt1PsqSAthumvImpASsq8jt3tDhpo2c+ok4PPz86s3ms3mvXv3HjlypE+fGsIpW6gO/5dCSr2veqnV1QheQI5xxHE5AJCASFVWUEF8F2GfZM2EQxVmnDyiu+8/P8DTnzegB4I9+Y67lbD8VbQunNum1FJ0XL4fAaSfZCgDZTbAVdUBhMngdGks1UGBk/j2kk0AQJJkF7delDMf5QlHBSRvK9nwWejKJ3bwbgzEDCk1PT5N0EPYr0ZlVGwuvK27wUI5vUUDXO4aGcRqI5V9dE59udg655hqkD9zWT8354h6lMVktQtjtXNNlXcg//EPqpIfJnZjhgaywgOZYYGMYF/kiZx1EBoifc1b+oqX+lBlxS+FhBGnUp61p5XMADYziB0YGFgXJX/x4sWLFy++9957PXv2pJS8v79/Dc/3fOHJ9PFkPv3QA2H0SPOdbEt6TsmSHxmBrWhSkb1CYc0rBgB2h9aCcYOfeGQa4vpdQwXFaOwqNnp/awkFTEATKqzyUnPRdc0lh4A34cZlWYstuNmd6TlIOurJ6mK0QPH+++8/Vv8WAf90+UGt3aTTA4AQRdkIUorjVCx9i4ZvVFZLRLPkyptWqzsNdUPRdJsNANrcU+8L3QQj65a7zkKQ9dIqDf+avPIdAX+5Wmslydf4vCXCFvXeAPxZ+HOi+uI0n9lDpKOf9lyeK0YHs3PU9nGhT7/koYt6R+iIeIonVSjOc3EAiZP5b95Wbi/LGv/f0vBIXQxmkYefutBotMOHDw8dWvN573NJampqu3btUBR1qYBdOyVf55R9n4vQEY8NbbljpJabutKJN3CVLamDW3w7/oJ/i4CE65F8z+merT7PIuzEKHRaha4qLyiu/8Rxm9cw/J98fb+vbPu+8u00hPaG/8Kb2qRB0lEB7OAnHq0Zcky+b0fJJgCgIbSvw9c46tuXmos+y5iPAw4kvNpqboNk2jcUBly/6PbrVsLV4bZGJnm9Msp9YvV2goR/04w/XtEabKSQiX7cWzAxvK6ftoaEJP25q5aMXEJncDRSmfP8kf15/bvXcZxaMKXp7/S4AigiHCN1f9eP1+sBx1pKye/cuTMxMfFhIyAI0rVr16ioqKioqJCQkPpPqYXaIW029Y5DuqPnCbOFakE5bMGYgW5Rw+uzh/hYWAhzpbVCTJc6MuT/z955B0ZRbm38zMzO9prdTSc9kITQQu8IoXexi6CAAvez670oCiKgXgFRsaDgBUURpRk6hC69pUBIIL237b1M+/7YGNKAECCN+f21O/PuzNlkd3ae9z3nOTbKuujm60ZCDwBdJHFvhS32bLeS5i/zlgGAN9d3hGp8hOguhuoPkHPnzg0YMKB///5nz55ttpM+EHbt2lVny3fffXf48OEePXpMmzYtJCTEYDAkJibu2bPnueee+/zzzwMDm7XbRcvStN/3h0e1evfUvQtRxFMPDwDjhAJWwz9ULDTt0fAoAnSNO+W3ZNJXpPdmh+5kGI+G9zxtW+qdASi2UHXq3Uf9qXFTTOLT6jomdgES7GGYiOXYbu7X7Hza70VvXi27nBvWtBU5HwKACBN/FrXW00SJpWWxupknErTDOvDe63/3D/kXFy3fJ1nf6SP5V1zD3ylS677e/RxlJKvVe/0xDMXkz7lu2FaBSTgxyf1w3/tdnmwTv++NWoGfPn16g9u9vb2feuopTysaljtQXfce+kusfJK3PdVSMC2VMhDySd4/xas1bubfZwREnqPndQtTrgj5X+ejL+6rVu9inNu7a7cmq3cGmBJH4UFNAgCQDHlWf/L10IUP7I21DsykaU/FVs/jMeopPjWs4Pz4gcOUY45q9wHAX+Wbe8sHCrG7GF02Gyigaq6vR5PcGQQQBd6wSSSKwAuxwhEhvA//Np0sdP37uPFYgfPbUY1KiRcNiBMNiAMAorTClZnvysxz3cxzF5W5sgvg0KnGC3h3bpEjJZ3UGVGhgNcxRBDXuVrp8SOFqjkBul/LjLs1xt0aYU+pz2tB8ineCAcBgOo1+fz8/O3bt2/fvv3ixYt1phQZhrl06dKlS5fee++9bt26eZR8TExMI2NjuVcQHFdMnyJ/erw7p4gyWzG5hBsWhDRvwS0P5ddxfBBh4lXR67WERu/WBPBvZWQ4aEeJs8BNu/PsWQww1QLeRBrWF3zJRfk+PL/HlGPq3PM94kyeXKvr1datW48cObJ06dJFixZVb3z11Vd//PHHefPmDR069JVXXmn2GFkAANaaLR71rqjhWlfT006GoosVj8pyU/MjQdHXpeK5Oj3FAAdBSIYBgAAONl1yz3cRfAR5Xiy86HLTDCNAkCdEbcm88/Pz5vUptgZ3jfqzrv/c09HCT4c+4M8kxVD/K1pT7ipx0653wpZUb6cZekvpTwAgwIQ2yppQ8fv0gLn3ea4yK0XQECRlPSaaTpGFzNKTnHpLv06SWX7WbHLRNTdm6kkA2JPtyNDVMlNTCbAPBkg5KACKIBgCAAxBA8M0XLxCA+NiAAAweHRc6Bu1As9Sk3udoXfesKX3Og8AgZ9Fer8W5LhuzRx9hTKS8kneob/E9vm9Uu+gd67MoSqqpmY7p/b/cunq9zdV3UsN9wvZsWat/IkxTQjVQOh+KFiVbbvBANNFEpdtv+Gg7O+ELfFkmLcbNhWvPaE7BAA4gneRxmG1k3VJhrxmTiIZAgDGqKc85f9iiwTZDPyV6Vh+1szDkDMveDf5AkY7Xe78YtxbVce1nijTVCz5GpWIeZ1CeZEhvMgQ3N+bsth03/1mv5JWcyTHW6l6bQY/+laKB1Hp1q4r1vxUQmrdAMAN5KvnBapeCsBkdScQi4qKdu7cuW3btnPnzlV7QNQnLCxswoQJTz755MCBA++QH8TyKGCnbKXOIq27IkrcRf7PPFeuPfOTrAWeZhzDlGNmBFaZRRsJ/bayX3go34fnP8hrxP10FW4TM/SNIT4+Pi8vLycnp/6umJgYf3//I0eONH9ULUWrWoGfUq65SRBKDN1au2OcjqI9/eH9MeyoP+tA9LDweM5baUaAIA6GAQARgtoYuiePW+1L30j+djpf0xrcDOPHwcpISoWhnnr4hxb7g2TnTcc3Vyx1pEKxmWIAAqV1m8jN6iqaEfuAl0mqUywB4PXQhd2lVasLJ3QHNxX/IOZIOAhuJo0A8FHH1R34IfdzriGbK00uOulFX4xNbmkqGTpiwjZttBLf+2Qt4+RsAznmT00jNSeKwMnnvf3FGAA40qxZ45NIHSEdpQz7vSvKr/W/YSgmf/Z1w/YKTMaJ3N1D2PMB5La0id/3tnH5aNPwwoXSEV7mo/qKrwqko5SmA1rKSCI44vdR2K1UEC7CIIAwkBwtDvbmndPfyige6B0oGtSzCefVE9qPM9/2NEXnoviMwPkXjae3lf3ye+lPSzt+3Z56MmdYr3keEAyRZLpwp5G2q80SUcswtaNgdCifZu7LyAXl8/hRDZVXUBRtc5A6ozu/2HLoFACgIiHQNO1wInyeZHg/PMCXMlvt55LdhaUVy771XfI6r2Oo56W4N9fvwzCfd0L0f5RVflvkvGkr+TC77LM81Qx/9b868EJv5fx36NDB00++uLh4x44d27dvP3v2bH0ln5ubu2bNmjVr1oSHh0+bNm3q1Kl9+/ZllfyjiRATRYii6iTPhwk7fhL1XYmzQOfW9JLfKpjPtWedM5z0PLZR1sd9n2/WWFslly9fHjhwYIO7IiIi/v7772aOh6WauVLxNpt9uUJep9+7EkP/8FZ/YDA+xm/zXaBbLcku98sanY1hunPxq+6qtUERBnwGu+Jy/59Wv1blxW/cj061en9RIn5DJqmuh28rGv7xToL6pXkx68tdFJP4tJp3H33gG0N1iqUXV613a/4s/TlWEsdBOHbK9lf57wDgpt1W2iLD5SbC+EfJhn+HL72f0xkctJ1kXDQjfGQWcpuNCAVn++OqUkutudG92Y5Dec4JEYLRobWuZkFSzKPeAUAQK47cF5c1PsmcqMt97mpNDf8w1HtboeFrB+tS+wBBcCR8a7fc56+ZDmozxyRF7OhWtE+LXTKljk3qcaQXAARUusBJIwxkxEhWvhA4ykWeSDxe/fJhvfvhvuomnNdFOZ2UU4AKHbR9rPc0JVc9Uj3xlP6wp/d4e3L7iJF0q3CVYsDpIotr8IpLM/RVSxLD0J3FPZo7uOZFWLs6aE+2I8dAzu4mlnDv96cID/TtsOG/rtzCqmT7zHxKb/TsYpwuPDhAMmIAAMinjdb9tNWSeFq37g//Ve/XPAIqQFUvBaheDDAf1lV+W2g+rq9cW1T5Y7F8gtp/SRi/Y605+8DAQI+S12q1+/fv37Zt26FDh+q3q8zJyVmxYsWKFSvUavWYMWOefPLJMWPG4E0y4WNpZ/jy/H1rVNN46CHr8+/wpSXOIjNhuF2rjkcNf3//1NRUiqKw2h4HFEWlpKQ8Us0gWhtjhYLbOaUpMfQHVcN1VSwPhAV6Q7V6pwH+JZWcdro8nnZKDDvvdP1qsb3ciEr4murdU/de09OurWj4FuSv8s12ysZHhXq3RoAJK1ylRzR7xnhP3VXxh4U0y3CFiTAAgIkw8jF+hvVqkul8nKxfnYMwFIWg6MPof8RyT3T3xrt717pDy9ARkAedvDjjwu80Hdmghn+U1TvcTsCzLrUPFoSHhm3u4tHw2dNSi1ZFWZflxGbbMsdeiX3c74WfiykLKY1Xrp/sRzmoszN2mt0WzwvVStWQFYvufPDb4ccPfDrgpd+Kf1TgyjHqKQDAQTiezmq7yrf0kw8Wc9rJZ/26ORkAKCBTTBfvPDLNkvSE3wvNElSr4MdkW4aO2HrDsfIx2cDA+3X1QLg4Pyq8en2++P+WkBVa4YA4IAh+p7CqQSjq9dIT9kvX3Pkluh+38LtG8SJDarWpQ0A6SikdpXRct1Z+W6jfWmHcXYlwkdCfG+5ToFKpZsyYMWPGDI1Gk5CQsGPHjmPHjtVX8hqN5tdff/31119VKtWkSZMef/zx+Ph4Hq+Z+iywtBUQQKLFXaPFXVs6kFbEoEGD1q9f/5///GflypXoPy2paZpesGBBUVHRuHHjWjY8FpYWYYZEfNbpOulw0v+41r0oEVX70o8SCoYK7v77coMgXtUaCIaZLRG/+49rnceXfq5Gf9HlmqPRHfDzbuRK/iNIoSPvlP4IAqiTtgOAg7IDwO6KrWGiTse0+xFALKQJQ7Ax6qn7KrejgAHAn6Ubu0jjcIQLAJTRYtp9xH4+hazUAYJwg/3FQ/tKxgxB2EmTNkgdDR+6qUvBK9eNuzWPpnqH2wn4hISEOlvu7FL78ONs89TU8IFvZnw4M/Cd41p1quXV7/IRGjhy3PtfHahy8rUtJecvnqh+1fD4ERxFEz+UTtrhyTt6yv/F6n7v1Z3VEir+mB7QTqyJZgW9nm270ZiRUQ+th3br5OuR8vdPmK6Uu79Ptt6/gK8JQ1JkpQ7hYN5vvVRnVhvBOfzYSNupy5bDZyyHzwAAppDxIoN5ESG8yBBuRBAq4AOAoLM4eG2M/8cRxp0Vkse8AMDwV2XZslz5JLX65UA8oG60arX65Zdffvnllw0Gw+7du3fs2HH48GGn01lnmFar3bBhw4YNG6RS6fjx46dOnTp27FixuOmlziws7ZtPP/10//79q1evTkxMnDx5cmBgYHFx8e7du69duxYYGPjJJ5+0dIAsLC2AGEE86v3fcuksiRgAJCj6k9rLo+EzwC1D714kzTDAQ+Alifit2v3e+Qjyo9rrNa2+kGx5q4XWzJbSn2iGxlEuQbtjJN3SLal8VOCkHT8WfEExlBxXGAnDaPXkx/2eT7em5tmzpBy5xl1xWLNnnPc0d15xxSffUUYLACAYxtC0O79En7/TeuqS76JX0Xt3ImRpEKOLTtcSNe3UCs0kANgI5kyxq+ZIIY508+beT2mCIFYcuTcua0KSOVF3PfYsqXVjck7knjhhj0ex+0DDAp51qX0YIDw09NcuuU+nmo/p3/tfUfb8Dt7pViAY4CCkkbD8bZhxxtgz2bwDuVb9kqFDhzb++C7a+WPB6mJn/tJOX/NRwe7yP02EIUIU1Uc+qOawZ/1nL85844Tu4DDl6EB+e+iw3VEU01HE2pI3QLic8+cU5dF8Z4TiAc83MyQJDINwOA3mpHk6yQv7dGVIypWVTxlM9otX7RevAgAgCD86wmfxqx5jc9ybq55XlaNLlLqcmbbyVbaKrwrkE9XebwSLejUwe6VQKGbOnDlz5ky73X706NFt27bt2rXLbDbXGWY2m7ds2bJlyxY+nz9o0KAJEyY89dRTfn737EZO6Y1EmQYwlBvoh4rbknUwC0tjUKlUx44de+utt/bv35+WdsuQcvLkyStXrlQqlS0YGwtLS/G1yVJTvXuoqeF32Oz/kt5FNkRz8UsBDf/o8BFkvZr9ct2JS8YzN63XcYRL0O4ocZc3Qxctvvl6uasUBcRA6LgI10gYpBzZRJ+nEECeC5jzadZ7dsoGAHsqtvUTDLB9tpYyWgRdO8mfn8wL68BQtPPqDf3PO9y5RZqvfvZZ9H8t/f4aS9mneaZ9muAfYgRdWuNSxIw9+uvaukmRAFBoJmfsrdtlafFA6cwu9zV1IuhSpeGr1PveOGH3R1G9QyNN7NatWxcaGlpTvXuYO3fu119/vXXrVlbANxJUgIb92e3shCT5BVOvFXkMAKbAKQMBADd2VvYsdFgESKorA/6ZtBoyZEgjj+yinV/kLsm23ZBxFAzDVLrLj2j3IYA86z8bqW1qVt1ZbUvJ/+7T7YOl9YMAxIfUqiw6Xezic5Bevtz7OSzK52ESMWWxEmUa3K+uR4M7txAApOMf43eOBACiTOPOzndlFbiy8915xURRGUOSdTqTUQaT15NCUe9elWuLjAmVhr8qTQe03cqGNdjz04NQKJw4ceLEiROdTmdiYuLOnTv37Nmj19f9wXA6nUeOHDly5Mg777wzcODAKVOmTJkyJTQ09K7v0Xkjx7ApwZWZV/UcQYQ9YxUvPt40TwoWllZLx44d9+3bl5mZmZ6eXlpaGhQUFBMTExYWdvdXsrC0Uz7zktsYZoSgbl2uR8PvsjvGChq2J3hE4KBAaplpdQAAIABJREFU0IA9tOR/N+3eVvYLAFBAogj6bMDs6iJQz1ovBRQATPV93tMbOFzYqa9iyHnDSQWuNBC6rdfWjNFbeFFh3h/8y9PXFuFggrjOvmFBpW994kjNcN3MLVAHJWQ56pyXoBkA+PKSFa+dYDEqlF+neLt5KFmUXfFlAQBkjU+K3BfXCjX8hAi+vLYtvI2gUyoIEY5296n1F8MQ6Hl/d54eBF3EkQfiKr4q9Hm1g6DrI6reoZECnnWpfYCgArRoZZRrTlrXTNvVGPF3zwd+9Xm2wEwqCh02AbpwGmX6scobjCuWf57rC3n6cWH8Z2LusvRX5MjPtt1QctXvhn0swITrC78kGWKwV3yoMLL+4Cm+z14wnsqwXk0yXYiT9X3wb/JRhXa6iOJyoCiOrxqTtcbLiotiXtqnpxkYF85f2F/qJ256MwJBny7Wo+cMm3d5vzO75jq87fRld34JJhHz/imMx/3UuJ9aNLg3ADAkBQyN1PGZo+mStz6hrXZMKhZGBks/D7ZdV3G85QiOUFaqYF46L0Sgmh1Q06++Jnw+f9KkSZMmTSJJ8sSJEzt37kxISCgrK6szjKKov//++++//3777be7d+8+efLkyZMn9+jRsK+h7cwVzde/AE2jQgE3JJChKXdOkf3yNef1LJ8lr/PCgxp8FQtL28JisYwbN27KlCnvvPNOx44dO3bs2NIRsbC0Cvrxb1t0JkHR6eJHPQH706EyFwWch9Zr7aDmL6270mPDPFw11tMcroesrx8voMxVAgAUQ8k4ciXunW5J9bykm6TXFeM5A6EDgEvCjK7+3t0nDkdqe3Nicol4eD/TriOOlIz/+Si337A3ePYNqdY6W7L0xPqxzW0b6VHvCBcV9pDYLpiyJiRF7m11Gv6V7uJXanem9rSRC5Jimybc/S8Wq8YFHCRWfW+TI4IYcci6Rz3xtlECnnWpvR+OFjiz9WTNLSka4tisoNgc27UIEYUh5RJOqJm0CrDlrwRlleytHoZ16HamxA0ADAN3FfARoqi3wz4KEYSLOdJ0S2qK+RIAaNwVawtWNjiejwqsYN5aurGrtCcHYf087hdSozf8mmC/kMp4ugcjCD86XDF9cnUrtVYCD0M+HCBddcGyP8d5osA1P048p5uI26Q2MPJpY+xnk+3nU8qXfiObFI/7e1NWu+30ZfP+EwAgf35inTV2DwgHA6i3HUVFg3rZzydTRosj6TrAdQBwl0Dx/6lQeYRxtz/QTMWaQmm8l/rlQNloJdymiIrD4cTHx8fHx3/77bfnz59PSEj466+/srOz649MSUlJSUn5+OOPg4ODJ02aNHny5KFDh3I4VV8EUmvQfr8ZaFo2dZT8ybEIFwcAymLVr/vTdi5Z8+WGgC8/ZF1wWNoBEokkPz//+PHjrBMtCwtL45kQ8RATEAyE7kDlXwDgoO0iTDzF59nqXSbSVOOxcXXekgaPwCBweqClt79P/V14gA8AkDrj2xPEHb04JFWrMflXlyxuGt7sLeHWmJvgYEh8cHN74pZ+nFPxZQGCI6G/xMpGK/Nmphn3aDLHXmlnJd+jQ/mj5/i2dBRtkkbdg7IutU3G5KLnHjAw9XdwkJROVbNoCjMJAO/8O1wv5bjOJFcPmRYXPW+CF44hUV63/k0HNQlqrk9PWf/6h4yVVK0lZtrSPQ9uWK/VH1YTjbvCSOhU3AaucSyNx11QUv7R17TVjmAYN6wDwuEQhaXO9OyyRV+q33xJ1L919a6b2UU0Ooz/6VnLvhzHFxct2286Fg2QPnbvP04cb6X3wvmaVT85r2U6r2Xe2oEg8mfGS+Ibztm5Hco5TynnPEVqDa6sfHdWviu70J1bSFZooULrPTOOovobtpWbE3XmRB03mK+aFaCa6c9R3TYXC0XRAQMGDBgwYMWKFWlpaX/99VdCQkJSUlL9kQUFBd98880333yjUCjGjRs3efLkMWPGUEfOMi63aGBPxfOTqkdiErHqzZeIkgp3YanjSpqwX/f6R2NhaXMsX7785Zdfvnr1ateuLWnOr9Fozp492/jxdZx6WFhY2g3byza5aCcCCAOMkqv+teTH6l1SjsxJ22mG9jxV83zVNe5gGaCzbTcJ2q02C6Nu8uludTPkAYC22gEAFfBUImx217qZFN9esbppZnY3kZDTkq0BSj/OKV+Zj+BI6KYu8olqAAj9Jdaj4bMmJrUzDc/SNBol4FmX2iYj46FLh8iKLbWMRtO1xKkiV4wKz9QTiJuR2SiGg0zqL92R5SzJvyXgJ0YE1XEOv2w6u7X0ZxzhLo8K81yzrKRZxJHUqXIf5/14kCCMYmot+zeIF65i1ft9wlCUZvUG2moX9oxVznsWU8gAgHG5jX/uM+0+qv32V17HUI5S3tJh1sJXhK0ZKX8uRrjkjClLT845oB8RzF8yWOp/jxn1/OjwgK8+tBw+7UhOJ/UmlM/jdQyVjB7MDQloWmAclYKjUlRNedC0u7jcnVfE6xSG+6oDPonQ/1am+anElWMv/SindGmWIMIkH4tJRvrzwoPxAJ/btXiNjY2NjY1dtGhRfn5+QkJCQkLC6dOnKaqu96/BYNi8efPmzZt5PN6ADmEjJN5Pz5lWp9gdwVDRkN7u33Y5b+SwAp6lfTBhwoSvvvpq5MiRs2fP7tWrl5+fH1rbXrtv3+Yos7p+/fqUKVMaP55hGpgYZ2FhaQfcsKYBAAMMABQ68godebcbqcRV74Z9XHPLJeOZtQUr7UKqYybfdiGFF1XXy8N2IRUAuOGt18K5vnoHAISLhv4SmzsjzbSX1fBVFJopHxHKa1IOaTugUQKedam9H56rl/3+e7r9VJGruzeebyIlJjdCM1x//qLBsgPZZndxevWwQVMm1nwVwbi3lv5c/eD/QhZcsyR9k/fZEGX89IC5NUdyUR5b2d5sOJLSiZIKPMBH/e+Xq5PGER5XMWMqqTPYziRZD5+WPzOhZYNskH4B3H1PqDel2b6+bDla4CRoZuP4ey7xQiUi2eOjZY+PfvDxoSg3yJ8b5O95xlHg3q8Feb8aZD6mr/gy03rS6ripcNyEyp9K+UFn+OGVvEg/bkQQv2OooHdXBGugMi8kJOTNN9988803tVrt3r17d+3alZiYaLfXLYFzuVzHszOOQ8aicSPi4uImTpw4adKk6lJ5zEsOALS5boEcC0sbRaVSeR589tlnDQ5oHqk8bNiwa9euLVmyZMeOHQAwf/58mUzWDOdlYWFpbcwLfveL3CVu2jXe54kgfq06xJ1lmyvcpShgNFAooJWu8gUZc+sfwcZxnR5kGbn/JC88SDSol2cjQ9HGLbtdN3MxuUTYu0tzvJN7p0H17gHhomGbWA1fRZaeHLdNMzlSsGp461ohazYaW8bJutQ+JDz587g/DwBsBdcYqqoZQ5BE7t+tc82Rhyp3ad2VfvxAvVt7xXTusunMpuIfSYZQc9nqkZbElZENAKLBveuXfIuH97edSXKm57REXI0CQ+GlrqIJEYINV219/R+AO+hDBwHpCC/piH7OTGPl15nGBDNpktrSu7krNBLitDM9ywygevUF8bA7TWCpVKoXX3zxxRdfdDgchw8f3r179549eyorK+sMYxjmypUrV65cWbJkSYcOHSZMmDBp0qQeRgIA0FbpUMjC0gTefffdlg6hitjY2O3bt/fs2TMpKWnBggXBwa13iYyFheXhcUZ/zE27+KjASprTrak1dxlJPQDQQAEADbSe0N7uIM7OvswRh+arn027j/I6hjIE4byWSVbqAENV859H6/UXaA0Yd1eWr8wHgNCfY+uodw8eDZ89LdVyXJ/zTGqXm4Pqj2lxPEvivKZbJDcKjYOiGaiw0Q/3NK2Ye/NhYl1qm0yFjdqX4yQoBgBSKgkASNcSBM14mQkAKBJgJ5Kt2ptXqsfHhUfWTAk2ELp9lTsAoIs4zk7bTuuPbi7+yUqau0jiRqkn1T1Z02AYR0qG49pN2mhGxSJedLiwd9cGfchYakJZbADQYJI8R6kAAMpsae6Y7hG1EF3Qr5YizdARJA1d7tEXtDnhd5QHfdenw1eMaZ9Gv7Vc2CtYNqyL7Xyh9nfKelku6E5i8lsXN/uVNOuRM9zgQG54B154kGcVHQAEAoHHvp6m6XPnzu3atSshISErK6v+6YqKitauXbt27VoRzh2kDpwcrZ5aPsDXl507Y2nzrFzZsNFpS/HMM880aFdx/+Tk5CQkJJDknYrLKioqHsapWVhYAMCdW2i7kEqWaxEOxg0OEA3qhXk1kGiTarkMAE7acVKXeOcDhgo7zgtu2IDTC1fZneeNW/a4c4vcuUWejXigr/Llpz0Nblsh/EgRJuFQFtKQUCmboEYaSg53Ztkd16wAIOrZSnOUwuScVcPlHb1Yo9+HS8N/35s3bwJAcHAwn8+vfnoHOnXq9MAja2d8fdn6Z0atZF2PjPcykQBwgYINFyzm7FsCvm+v3jUHeyw9IkXRidrdPJSv4vpo3RV9FUOmB7xSpwC+aRBlGs2XG6qvcQAA+09wvJXqN19sbT7qrQ1MIgIAUmeov4vU6AEAk7a91drpu/VGFz02jL9ooNRH1HoncRAckU/xlk/x9jx15qrsKWn2lOLyz0vlU33UcwJEfWQAYL+Qar90zX6pytMRk0u54UG8sA7csCBeVCgmEaMoOnDgwIEDB65YsSIjIyPhz207vv8xWVtG10sethHuQ6W5h5YvefXTpT179pwwYcL48ePj4uKQh9YOl4WlpcjKyiopKRk2bFhznrR3794RERHVLSEeIG+99daePXsaM5KmH91VHRaWhwHtdOnWbradqTU3Z9iyR/HsROmkEXUGvxHyQYEjtzGHjRLHqm9v5CQZOVA8tI8zI5sorfRMGfAiQ25nl+MhUIoZnXRLlVXzo0WR++OyJiYZtlUAyYRsjEVqe+k5b9qyJyWTWrd0pDJkQ+fbHafFmdrxITYpYPHQ8G9kVFQUAJw/f97jXuN5egdYO5m78mIXkZxfVZdb08TOk0Kvl+GTOwp+LEqpHj9gVHz14xz7zfOGv3GEayT0AOCincHcMK27Is2cxPg/gL88ZTSXf/Q1pTdy1F7i4f05PipKb7L9fdFdWFqx7Dvf5W9xg5voSfYowIuJgN1HbX9fkk0eWae1mPXYOQDgx0S0UGhN5/Ve4lUXLAdynQDw7ShFS4fTWBTTfBAc0W4oMR836H8v028piz7XVxArVs5+UhjX2ZVT6M4pcOUUUUaz40qa40oaAKACfuC65TWz6aKjo6OXLH5z0rSMZWuOFmUfKS84XVlkJ9x1zkXT9KVLly5duvTRRx/5+/uPHz9+/Pjx8fHxItGj3h+YpX3AMMwnn3yybds2m83WnOcdNmxYg1kw98/ChQtjYu7SOlij0WzYsIGdj2NheZAwjOaL/zmS01EBXxw/gBcRzBCkI+m67VyyftNfgCLSCcNrDg8RRoQIH8yNE8LFBd2iBd2iGzl+x+MqmoYWtEUT9pBE7onLmphk+KsSIK2mhnfetGWNSyIq3NKRyrAtXVF+A14/LI8ODQv4OXPmAIBaXVWAMW/evOaLqJ3S0Yvzn75VK7EHc52nilyPBfPyTaRnBd4g5bwe6vyyosAzAEex3uNGeR4zwPxe8hMDTEdxzHVLijfX10jqs6wZocLIPHvWnoqtzwbMvs/YjH/uo/RGfudI7/fnofwq33vpxOG67zdbT17Qb9zhu+T1+zxFO0bQozMe6EsUl1euWKec95wnl552OI1/7LWdS0b5PMmo1likdGdmdhGNCeP/fM0+vNl7n94XCMgne8sne7vyHLpNpa4cBzeIDwAli/OJco7XMwO8n5mIYAhZoXXlFrlzCt25hQif52nzXhPLkTOkRh8x5+mAS2lPZmS73MRZTfHRysLjmuJSo77+aUtLS9evX79+/Xo+nz9s2LDx48ePHTs2PDy8Od4yC8v9wTDMkiVLfvvttzo2EBRFORyOLl1aqdVTE+jXr1+/fv3uPCYtLY0V8CwsDxb7hVRHcjoml/guexv3q1IW4mF9heeSNas3GLbsFQ3qjclbRa5iy3aPq4qhIQ3PqneWOjQs4NevX1/z6dq1a5slmEeF0WH87VNVnVWcjVdtWjmH4CAFgfyCtMvVA2J8AwT/rOOd1R/Ps2fJcK9ceyYADFQOz7JmpFmSnZQDRbCjuv1DlCMD+EFNDoahKE9Sk3Lec9XqHQAQDPWa86T9YqrzehalN1aXDTeS780WBmC+VFL/GpPmJjZZrPNlktCHkCTZ/CAY6v3O7PLFXzuS04vnL+YG+iJc3F1YyrgJBMNUr81osMSr9eMjwuoUxl8odRM0MyiwDUh6XqjA/6Nb+tm4R+MudBp2VOD+POXzfsrpfqL+Paqa1TWE4eedtNPleYyKBNLwoEmjBj/ZuSM3IiitrGjf/v179+69fPly/Txbp9N58ODBgwcPAkCnTp3GjRs3bty4IUOGcLltwSCQ5ZHkhx9+WLp0qVQq9fPzy8rK8vX1DQwMNJlMWVlZffv2XbNmTUsHyMLC0raxnroMAPInxlardw+i/j1sPWPtl6/ZL12VjBzYQtG1RupoeN/3QrMnJT+a6r3CRv0v1dZJiecYa9mXlFhIACgwUysu1PKZknCR6Z1FEm7LT8Q8bNqDgmpzIAA9fKoW/TaP99k+Ui1X4FcuXawe0H/IYM8DF+3cUf4bAPhy/W/a0qLFXfZV7HDTLgWuKnMVR4m73LBe21Ly07vhS5scDGUw03YHR6Woc2EFAFTA50UGO67edBdXCO5RwG+y2Ew0XU5SH3vJa15prrmJ2RqdhaZ783ntQ8ADAN7Bz2/Ve4bfdtnPJ7sLSwEAEITfpZNi+mReeNPnVloVDMCs/XonyfT15y7oJ+3m3Xr97eoTdbK37rcy3aZSZ5a9fGV++ap8cX+5crqffKo3JmngQ+iz+FX7lTR3bpE7r4gyWlyZ+a7MfMvRswDQYXDvRYsWLVq0qKKiYt++ffv27UtMTLRaG+gqd/PmzZs3b3755ZdisTg+Pn7cuHFjx44NDAx86O+WheVe2LBhg1gsvnHjhp+f34wZMwwGg6dQfPny5T///HPnzi1fZvnRRx99+umnBEG0dCAsLCxNgSitAIAGreP4sZH2y9eIkvJmD6q1I+whidjZPXtKiuGvSuNuDUMxsjGqsM1dEN4jpN4BYHe2839XbXwO4iQbKBkusZA/Jte9AVPw0GfqNfBuf9yDgtJoNAKBQCwWA8DWrVv37dsXGRk5d+7c6kx7lqbh4qL+YvTChQvVW/qPrcqf31ux3UjoO/CDs+zpHITDQXE37eqvGNpF0nNd4eoSZ6EYk6Rbr6aaL3eT9mri6UkKAOA2bvMMhlkFfCFNEzUWGwUIgt8tw/BrlWKeRr/dZgeAag1frd7HCgWPi9rVt4ujlKvfmMnMe5Yoq2QIEvf3RtvXG0QAlg2WfXrOfKHUPW2ndmw4/+3eklB525iC4ai5Pm8F+7wVbD1n1P1aZtxZYT1rtJ41Fr2bKZ+o9nrOTzJMUdPuldcxtNq7kdQZPUrenVfkzivmKKSe7T4+PrNmzZo1a5bTaju4cs2h1CuHki7lFRXWP7vVak1ISEhISACArl27jhkzZsyYMYMGDcLxtjQJwtJeyc3NHTRokJ+fHwAMGzZs8eLFnu3vvffexo0bly1b9t///rdFAwSapu9sHc/CwtKq8dxAog0pTxS7NYClNqI+soiE7tlTUigz+WiqdwAgaQYARgTzO6tq3XAWmMk/MxzBUs7T0bUM83AMmRDxSFjoNer+2263v/TSS1u3bj19+vTAgQP/+OOPZ5991rNr48aN58+fZzV800BpZsnaghIfbsm/Q09cqWFB37cvAGjdFYma3QCgJ/Q0Q4cIw0udxVyEK0RFRc48L1ylJ7RSjhwA/izd0FnSnYM0RU1hShmCc8hKvcVs/ZamCkhKR1EmmjbTjI1hyBlTqsbVnh/lIMgCuXS6uCrP30LTK41mDoIoUFSBoQoU9ULRjxXyJQbjdpudAVjqJb/hJl7W6Cw0PVooWKlU3KuzuY6i11usT4qE4XgDb/Mvm91CMzMkLewfhvC43JB2u8T6eCfByFD+uhTrxqu2/TnOQ3nOJzsJX+8lbs029XUQ95eL+8s7rOxoSKjU/1ZmOWPU/1mu/7Mc9+epZvj7vh9av2sLRynnKOXC3revBC4o7X69pDvHd0Gfidkxxr9t2hMVhRfystwNSY6rV69evXp1xYoVEolkxIgRHjHP9rtmaUHcbrdnXh4AQkJCSktL7Xa7UCjkcDiDBg06fPhwiwt4FhaWNg3HV02UVLiy8vGAuo7xrqw8z4CWiKsNIOoj63i4p/mo3ntu4COo3qsJkmJze4hrbjlb4vozwxEgqbv90aFRkm/16tVbt27t1KmTR6ivWrVKqVT+9NNP6enpH3zwwVdfffXJJ5885DjbJxE2qnOOTW0kdNYynU7n2SiRSCIjIwHgiuk8wbgBwEZZACDXXmXMe1S3v/oIZtIIAOWu0nx7doSoVrOAEpJabjQ9KRIOr+Gw7SGXJFcZzYUEuUAhG8znCXrE2C9ePb//+KaBPeuMRBhG5CJQYa0j2BmGZmp1HrjmJrbZ7NAQCCA7bPYTTpeVpl0M053HmysVN0HwnXY6f7FY99jsG72VHWsvXf5ktn5hMuMI8pxYyGHNhx4mEi7yTh/J9M7CNVes227Y/8iwJ2Q5XuspntemLqCoCFM+76d83s9d6NT9XqbfUu7KsZf9N0/+uLcgRkxZSKABkzV2OowfE6Gc/5zrRq47rziyCIsQy2f5RNhiBp+qKDpRUfC3pbJMq63/KovFUr0sHx0dPXbs2DFjxgwePNjTuZOFpdno1KnTjRs3PI9DQ0MZhklNTe3fvz8A0DR91yayLCwsLHdG1Leb40qacfsBQVxnTHrrbsF5I8d2NgnBMGHvri0YXitH0Fks6NyWbrFYmodG3aRu2bLF398/OTlZIBBUVlYmJSW98847U6ZMmTJlyh9//LF3715WwN8rzkybZl3Jyh5iI4BRgrmK0qp3devWDUVRABjkNYJiqAOVO22Utae8f6igbvnQZdPZfHu2Pz8oXjU+XNSpzt4ckjzhcJ52ulYrFSMF/CyCPOV0JrncyS63/p9spd+ttsF8nuK5SY7UGxE7Ev9rd/J6dVHLpVKjBTl9GU5e4NCMz4f/EkSG3Pnt9Ofz1quVhSRpoGkDRRtoWkfRWpoqISknwwCAjqI8I1NcrsfLNe/JpTMlVdcjGiDN7fbGMF/sTrp+nFBwwOE86XC+VKmrqeE96h0FWKaQseq9efARYZ8Mkb3cTbT6omV/jvOHZGvbEvDVcIP4fu+F+r0XajtvIipcghgxAKT3Ok9qCOkopfIZX+kY1d3dYhBEMmKAZMQAAGBIiigsdecVS/OLp+QVj6/Qes19JhMjDxw4sG/fvgsXLlD/fBFqkpGRkZGRsXr1aqFQOHTo0FGjRo0ePTo6urFtb1hY7ofHHnts1apVCxcufPvtt0NCQpRK5bp16/r37+9wOI4ePcq6NrCwsNwnoqF9LYdOuXIKy/79X+mkeF5kVRs58/6TQNHSKSM5aq+WjpGFpY3RKAGfn58/evRogUAAAOfPn2cYZtiwYZ5d0dHRBw4ceHjxtUucN2xZ45KISjcmxwHAKMHdxenVe3v0qDLHFmFignHbKGuQIHR+0L9RpK6QGOQ1/P0b/yp1Fqq43gjU1a5D+Lz5Uslas+VNrV6NYRW1lQOKINOEgtdlUgDAA3193purWb2x2/6TsP9k9RiEx1W+8kxj+mciAIP4PIBb/uTHHE4UYJiAf9Hpmq/V2xkGACJwTgcOp4KkC0jSRNMyFAWArVbbxwYTAIhRJJTDCcPxcA6nI86JxHH/GpX5ZRSFMEwPHjfZ5a7W8BstVep9mICf5iYmsx24m5EQGWfNSMX/9STbwayJqN+tTgHSEUr95jLTXo1prwaTcQI/i1TO8G/kcRAOxg3rwA3rUHNjD4AePXosXLhQr9cfOXJk9/frDl84V+lsIGPFbrcfOHDAc0UNCgoaPXr0yJEjR4wY4eXF3tywPCwWL168devWzz77LDg4eO7cufPnz1++fHlKSopery8rK5s7d25LBwgfffTRhx9+2NJRsLCwNBEEQ73fn1+5ar3rRq5+4/YaOxDpuKGK5ye1XGgs7RnbRVPJh9ner3aQT/Ju6VgePI0S8DKZrKioyPP41KlTCIJ4irQBwGAwsB2S7olb6l3CoYwEAAzvKDiSW2sF3vPAQOgOViYAAMbxe6Fw3zg+rUDqLt/58gLy7Fl/lm6M6dgVRWqtYN8giEqK4iAIyTAVFCVCkLFCQSlFnXW6uAiyRqkYWiO1nt+lU8A3iy1HzjqvZlAmKyoS8mPCxfEDOSpF097mOzqDi2HmSMRbbXY7w/Th89Jc7myC7ITjDobeYrWH4binhL4nj9ePz7vpJgw0fc1NXHPfshqWoGgUzoni4p1wXE/RJ5wuPoJ053FTXO6XKnWTRYKNFhsK0IvHPeZwilFkgVzKLsI3M528al1D/sp0/JBsfSpa+HyMkN8K+qk2geDvo/2XhBu2V+j/KLcnma1njcoZ/pSZ1P9RLuorE3Zreq9aLy+vp5566onxEyxHzyWfOZt49vSxm9ev6MrIhvx7CgsLPb3lMQzr1avXqFGjRo4c2a9fP9b67sFCW2ykzoDy+RwfJTySVw+JRHLlypV169ZFRUUBwOLFizMzM3fv3k1R1KxZsxYsWNDSAQKHw+G0l5YlLCyPJphc4rfsLfvFVPuFVKJcg2AYN7SDeFjfOvPdLI8ydoKZuU+vsddSOmY3DQCbr9v35jhqbsfvVotru2DKnpJCWUjbRVPI/zorptX1X2jrILVKmW/D6NGjjx07lp2drVAoOnfu7Ofnd/HiRQAoKyuLiIjo3Lmz5+kjQlqP8OUgAAAgAElEQVRaWpcuXVAUbTAb9s44M21Z45KJcpd0hFfIz7FZ45McV60cKecJ3pys/GzPmIsXL/bu3RsADml2/Vm60Y3KrkvecmLewY6//B2Hb3fkhRH/ra6BzybI/xpNZ5wuAEABgjicfJLEAPrzeacbUu8Pgy1W2zKDyfPZGi0UfKFUpLjcL2u0DgYAIArHN3krJbUtSY00nUOQuSSZQ5CZBJHpJnS1hQ2KIDTD4AgSg+Opbrfn3fXh8c67XHwEWavy6sdvAy3K2zc/Jls9PTmDZZx9T6oEbVPDV0OUuThKHOGimh+Kit7NBAB+J5HXUz6KJ3x44Q+gywBDUrr0zMSEXYeOHT2WklRs0t95vFgkGvbYY/Hx8SNHjoyJibn/AB5lHMnpxq37XdkFwDAAgEnF4pED5dPGINxGTZGcO3duwIAB/fv3P3v27EOOtAVwOBwoivJ4j9wV9X5+31lYWFhYmozOQQ/fUml1312WAgAPQwiamRwpWDW8gS7X1epd0FnsuG5FOMg9afg28fveqFntd999NzExMTo6WigU6nS6hQsXAsDvv/++YMECu90+a9ashxxkO8GZZfeod+FjipJVUeEKvNPR3tlPpGpPluXYcqsGoejpoODeAADQXzGsnCR+IGKdiFRClTwvlgskjzd4ZBEmCRNWVchfdbufr9SRDCNB0akiwfNiURCHs8ZkWWu2nHa6OM2i3gGgG5fLR1EHTQNATx4XAwjhcLwwrISkACAS54jqNRSRo2hPHrcn71ZCh4aibhLkDTdxgyBuEEQ+QaIABMOkEW7PABTgvMuFA7wnl/bisZkgLc/cHuIoJf71ZYvRxbRt7Q4AALhflYBRPOXrzLIbdlQ6b9pKl+WWLssV9pAopvkoHvfhBjX924RwMFXX6Oe6Rj+3+D0AuHHjxqFDhxITE0+cOGG3N5Bjb7XZ9u7du3fvXgDwU6pGjogfOXF8fHy8r69vk2N4NDElHDFs3gUMg/J5HF81bbWRWoNpxyFHcrrvktdR4SPRhOYOeCrmWFhYWFhYmgelAD0z3cfgrLV0tzndtj7F9nyM6OXutapkvQSozkH7iBpwKapW74rHfUI2dK5cU1iyODt/1nWGYLyeaT83S40S8CNHjty0adPy5ctLSkpmzZr18ssvA8ClS5eKi4vnz58/e/bshxxke8CZZc8am+RZe//ltZDfjxo38NChQbyI7d2uj0qnT1d9XrnBwevcJNds+T+phEAk25AhRoQUksVLpbYxyucbcyIBgnpj6GN8/msyiafInAEw/7OUTTOMrRE5F/fPK1qdg6ZjedzrLvdnBpODZnbb7SUkFcThVFDUHrujP5839W6d0tUYpsawQf+sqzsZxkozb+j0SS43AIhRxEozAEAALDGYPjOao3C8Kw/vyuV25eJBbMplCzE0iDc0qNbCHUlDaqU7Vo3z6jVpaytwvPAOX3QK/Lyj5ZjesKPCuEdjT7bYky0li7JFfWSKJ3wUU71x3/tdroyKioqKinrjjTdcLtepU6cSExMTExOvXr3aYJ5UmU67aesfm7b+gSBI586dR4wYMWLEiKFDh0ql0vsMo93jvJFj2LwLEEQxfbJ03DAE5wCAKytf+82v7twi/U9bVa/PbOkYHyJffPHFPY1/5513HlIkLCztGwbgrNPVnccVNVSeU0pSOpru0riUHxaWRwExFxFzayXHy3goAEh5SAdp3aR5UUNp9HXUO8JBfN4OBoCSxdkFc9MBoN1o+Eal0DdIbm6uVCpVqVQPNqDWTxNS7Gqq97A/ur11xrw32/F1vLzjV/nZx/Q7p6T+77P/84wc2D/e+OcvFMAsifik05lDkP6I7XuloJOgiVbADMAnBtNmq42LIPEC/n67AwNYoVSMe8hLTJ8ZTQDwH7lsp83+kd7o+ZBF4fhGb2UWQf5ktr4tl3S692rejRbrCqMZAfAcEAWgAXgAKgwrpaiaH2UFinbjcbtx8e5cbpfb/HyyNA+b0mwfnzZ7C9HZ3cTPxQiFeJv/XzAu2nxYp99eYTqgpW0UAACKSAbJfd4OlsYrH+y5ysvLjxw5cuTIkcOHD5eWlt55MIfD6eYbODAgdGivPgOHDpaGh+Ad/FiD3zpUfr7Ofumq/Klx8qfG1dxOVupK3lzOEGSHH5dhXg1k5dWkTaTYNQhyjxfDJt8ktEXYFHqWB8gJh3O+Vh/DxTeqldLaWYc3COKlSp2Fps8G+ErrJSSysLB4WJtsXXXBMr+H+N2+d3cgqq/eq3dVrC4oWZyNYEjwjzF31fBt4ve96auUYWFhDzCOdgypJ7JGXyEq3dJ4ZdgfXWt2pbLs0fjqiKTUa9VbumYETuFI3ybNGyxWABCSxYGWNRzFojsc/6TD+aPF+rpUUr8CvKZ692TOB3M4a82W/+gMAFBTwzsZhn+buzqCYVAEudfO7e/Lq2y9h/P533AwDUkBwHiRQI6ivXnc3k1SFB71jgL05vEuuFwAgCJIFxxPdbsdDLPZW+liINVNXHO7U91uLUWfcDhPOJwAgAFE4PgLEtG0u635szwMBgXyYlR4upb47Jx5bbL1xS6iGbFCz6xqGwXhobIJatkENW2nTAe0hh0V5kSd5W8DZaU8At523sQNE+DeD6Csw9fXd/r06dOnTweA9PT0w4cPHzly5MSJE1artf5gkiSvFOdfKc5fc+E474dVvZR+/dWBAwNDe3bvIQwJFMcP4EUE339IbR1nehYASEYOrLOd460UdI+2X7zqTM8WDerVEqE1BwkJCXW2fPfdd4cPH+7Ro8e0adNCQkIMBkNiYuKePXuee+65zz//vEWCZGFpB3TjcYM5nHQ38ZJGV1PDe9S7kaZHCvgSVr2zsDwI7qDeAaD9rcPfg4C/ePHir7/+mpKSYrVak5OTDx06FBAQEBsb+/CCax8wTpqyUgCAB/BQbo3PE8UgBpJGEFPFzeptYUxId4ajwtAKigYABZmOMdbfStYt7riqfqM4APjJbP3SZKYBskmyH9QS8AzAMoNpi9XGQ5DvVF4D+TwAeF0moYBZZ7Yu0BtxBBkp4APAb1bb50bzRwrZE/X0bSZBzNboonB8vbop64o6ip6p0WpIypeDVZDUaqNZgCDPi5vS7e1vp9Oj3qtd6wbweccczgyCiOVy09zuN3TG4/4+1bMYpSSV4nanut0pLiKdIG4SxF82e00Bf8ThVKBoLBfnsYvzD5kwOWfPE6rL5e6vL1nPlri+umRZl2J9Mko4t7vIR3SvU0OtC1SIKab5KKb5UBbSfFjH7yQCAMtJQ9b4JARDxAPk8ine8knq6lr6+yQmJiYmJuaNN94gCOL8+fMeMX/p0iWSJOsPdlHUmcriM5XFq66fFx3d3lvlP+SvHhM/+aBHjx4YduvPTpZrUbEQFT8qc1sMRdE2B4JhmEJWf68nW4EyNTA50m6YPHlyzadbt249cuTI0qVLFy26NVP86quv/vjjj/PmzRs6dOgrr7zS7DGysLQHFCi62Vs1U6NNdxMzK3UbvZVyFK1W70P4/FVKBXv/wcJy/1AWMntqCmUhFdN8QjZ0Rhoq2PR5O5iyUeWf5xXMSxd2k/Cj23br6cYK+OXLly9evLhmKt3evXu//fbbTz/99P333384sbUTcH9eREL37Kkpul9KgYHgb6u84lEjCTRjE2HGohvVg/t8M3YWaq4gaDk4jAy3hD8KZQhw7DurPz7Qa3jNw1brcxTgLZn0uXrN/E44nHXUu4e3ZFIAWGe2/ltnOBvgK0QQOYqSDPOR3ggANTV8JkG8VKnT07SiSYulHvWeQ5CezPnDDudHeuMnBhMANEHDq1EsEucoMey808VHkO9VXv34vOUG0+9WWzZB9OdxeWiteWx/DubPEXiyDFwMc4MgArFbn/Zkl/s1rR4AuAjSmYvHcblxPG4PHlfBzoU/NHr5cn+d6HWh1L022XqqyPXLNdufGfalg2XTOrUHuyxMwlE8XmVwKugqlk9SmxN1llMGyylD8X8yw7d3k458kKn1OI4PHjx48ODBS5cuNZvNJ0+ePHr06NGjR69fv95gwrONJE6UF5woL1iamCCTyYYMGTJ8+PBhw4Z14ksqFn0FDIPJpXgHP24HP7yDr+cB2k7TVRAMQwV82uGkLFZMIq6zl9KbAACVtO3f9Xti3bp1oaGhNdW7h7lz53799ddbt25lBTwLS5NRYugvatVMjdaj2xcqpK9rDR71/o1KwWXXD1gawfECV2Kec9FAaTuoQGw86VrilzS7Wtio23KUi+J+PMpMOrPslInkeDVQosu4aMdVCwBgMg4ma/M+WY16AwcPHly0aFF4ePiKFSuuXbu2ZMkSAHj66ae3b9++cOHCuLi40aNHP9ww/8FmsyEIIhTWurM8e/bs5s2br169KpfLu3btOm/evA4dWldjSfEAecRf3bOnpug2lQIAMtUXAFA9AQB8f1KfUuYZhmOczx8LzCXICA6otEv1nPAs0UtFgvEAsLXslzhZPwF2643/12jaYrXxGWZh4uneh88UUjQmFQt6xsqmjsL9vQEghotPEgqmioT1U+vfkkkVKFpB0QIEAYAJQoGeoj8zmhbrjQTDPCsWAUAOQc7W6PQ0PZjPW3a3ctD61FHvchR9UiQEgCZr+Cgu3ovH22K1edR7fz4PAD5UyADgd6st2U18r/K63YWNhyDdak9wxHDxmRLxeacrmyCSXe5kl/t/FkAAwnBODy63J48bx+OyNngPg77+3L7+Xtc0xHdJ1qP5zjQN0T4EfE04Cjzs966UhTQd0BoTKm1XzKgEAwDdb2W6X0ulo5TyiWp+xwcmEaVS6cSJEydOnAgAFRUVx44dO3bs2NGjR/Py8hocbzKZ9uzZs2fPHgBQenn1Vgf0kSj7GL2jDCbntVvZQBylXPmv5wXdoh9UnK0HXlSYIznddvyCdNKImtspo8WRkgEA/KhHqEDs8uXLAwfWrSbwEBER8ffffzdzPCws7Yw6Gp4CYNU7yz2xKc32d5FrYiR/QMAj1Npz2w3H9hv2V+PEPX25w4Pv8sYRHhp5IC5rfJLjqiVrfFLk3jiOspaGZ9x07oxrpgNajoobubcH7t/m/5KNkiirV6/m8/mJiYlhYWFlZVVqc9CgQRcvXgwJCfniiy+aTcCLxeKoqKiMjIzqLR988MFnn31Wvei0d+/eb7755vvvv/eUjLY4Jhe96oLF6KIBwOeD8JFLs3WbSjvmONEpPkevmJ8FSC3Jrh6Mh4fnMhCF430dP2fSJqU7iYfy0wTPeTT8vsodT/i94Bm5xmTZZLHhNL1o3da4m7kAgHBxymy1Hj9vO5ukfvNFYe+uPhj2uVJxu8BerL30NEMiAoDPjKZlBhMA9OHxXtRotRQ9mM/7RuXVhCTz2RpdTfXu2fikSEgwzHKD6RODqQMHG8K/h/5bB+0Oj3qv2e8dAfhQIaMB/rDaXtPqzwf4choXKg9B3pNLAcBKMylud5LLneRyX3W7cwgyhyC32+wA4I1hHiXfj8eLwFkx/yDposZ/GK0wumgp99bcapmVqrDT3b3biSUvJuF4PeXr9dStOivrGYP1jNF6xlj6UQ6/o0g+US2bqBb1lDZUHNNEfHx8nn322WeffRYA8vLyjh49euzYsRMnTlRft+ug0+sP6vUHAQBAJpH0j+rcLzC0j0TVicRInZGs0NUZT2r0lsOnOV5yPMAXD/RpMAu99SMd/5gjOd2wZQ8qEYmH9QUEAQCiTKP9aiPtcAr7dOV4P2AnwtaMv79/amoqRVE1CysAgKKolJSU1jYbzsLSFlFi6CK5bJZWRzEgQtBlXjJWvbM0Ho/Coei7DGtnUAwDAN4ibOuURv0i497cyH1xWeOTHNesWRNqaXjGTee+cM20r0q9C2LrJt+1RRolS5KTk/v371/fta5Dhw69evVKS0t7CIE1igMHDnz66adKpXLp0qVDhw7lcDinTp368MMP58yZ07Nnz+joll87ulzu/j39n37OXM752UEL1xf2O6OfC3RakBAASsw51YOxyMhgDqcPZsw0n/FseUEeauIzn5roIsH430w/D/Yq9eH5rzdb15otGM28//PO3uUarzdmCvt0Q3hcolxj2nHIevy8ZvVG/9ULcT/1PYVaU8OLUMRKM01W7wAgRpE4Hvc7lZe8dlL6c2IRAvCdyYLdo2rpzMVHCPgzJKI+vFrTZgjAYoXMC0UtDN1I9V4nzkF8nqdTHQVw3e1Odrkvu9zJLnclRR2wOw7YHQDwu7eqB9tn/kEjr12a8eYR4+Vyd7QSf7O3OD6k6c3VWy1BX0fJx6uNezWm/Vpnpq38C1v5F/m4P08+QS2fqBYPUiAPNDsuNDR0zpw5c+bMAYCMjIwTJ04cP378xIkTGo2mwfEmi+XgpfMHL50HAIlEMrBf/6FJpweLkN69e3P/SWCx/X3JtDOx+iWoSIAH+OAd/HB/H15YEL9LxwcY/8ND0D1aNjnetOuI9rvfDFv2cgN9aJvDlVcMNI37qpWvPNvSATYrgwYNWr9+/X/+85+VK1dW1yHRNL1gwYKioqJx48bd+eUsLCx35QZBvKkz0AwIUcRG03M1+pprGywsLA+EBjV8u1Tv0PgaeIGg4TRXLy+vrKysBxfPvfHll1+iKLpv376+fft6tnTq1CkuLq5Pnz7Lli37/fffWyqwah4L4q8f6+Ukq6tSFTfj+NGvZg4/Y+x/zQIAuUxB9WB1x04lFLWJFPVE5Vza2FnSfbR6EhflpVt276PCUdq2rWyTQvXaapMZBXh3y54B6Tk+n/+HGxLgeTnuq1b933QAsB4/b0pIVM1vVN/4msyQiLQUvd5isdJMBM5psnoHgN+8b9tf8Fmx6Nl7r4EP4nC+VTXsXY8AvCa7e3uJu4IBdOVyu3K5MyUAALkEmeR2X3a5KygqgHNrbepXi+2owxnH4/bkcXvwuEJ2Hv0BMT1WWGShMnTE+ydN7VLAI9wq73qGYqxnjKY9GuNejbvIqVlXrFlXjMk5slEq2XiVbJwaFTzgG7vo6Ojo6Oj58+cDQG5urqcv3bFjx3S6umvsHiwWy8HDiQcPJwIAjuNdu3aNj48fOHDggP69vAQ8oqDUXVxOlJTTVrsrM9+Vme95lc/i1wRdO9U9FsNA6/uOKF6YggcHGLfuI8u1Dr0RABAcF48YoJg+qb0W/9+OTz/9dP/+/atXr05MTJw8eXJgYGBxcfHu3buvXbsWGBj4ySeftHSALCxtm5qudR8rZHO0Os8WVsOzsDxw6mj4iJ3dC9+8UaXe9/UQdG4n6h0aKeC7d+9+8eJFi8UikdSSSVar9cKFC926dft/9u48rqkrewD4eWv2BAgBwo4IIqKAgIi1WmsXbe1o93baapepjp1Ol5lOxzozbX/d1+lM9+lMdewytavWrYttrfsGrqiIIKDskEBC1rf+/ggERFwrCYHz/fTTT959L/HwQsg77957bv/Edmb79u0bO3asP3v3GTt2bH5+/q5du87jBT/88MM33nhDkk43TsXtdsNZL41LEtBr5oaYGvdrh/vhBcdVdhEAdofVQHPnLi4tlZXlOM93rNQeq4x/OOVvJEEBwF/Ml3Yc+q1TdPzgGnOkzUYALLTaLiouVeVl+bN3P8Osyx3rtnn2HIJzV8kLy1xO/+OvnK7zyLQHjWEMPYyhT67Mv8nj3e71+taxowAyWWasQlGgYMdiDbxf5prhqmnDlN9XeQ2KE/I9GS7gGPMBgaAI3aRw3aTw+JfTXbs72le1tK9o9hxyWj9rtH7WGHlnbOKbIwFA5iSCvfC/UcOGDZs7d+7cuXMlSdq/f//69et//vnnjRs3tra29nk8z/MlJSUlJSUAQNN0Tk7OxIkTJ02adNGDtxtZFV/bwNc18XWNkturSInv9VzrB8vsq9bRpggmLpqJj2FiozrH3p9UPS7wtJMKtJMK+PpmobWNVLJsUhwxJEfZREZG/vTTTw8//PCaNWt6jqebOXPmyy+/bDQOodkECF1wPbN337z3nvPhMYdH6IJjoti0lblHrtrt3u84MGaL5JY6+94HUfYOZ5nAz549+4cffpgzZ85///tff6PT6bztttva2tpuuOGG/oruTHiej4qKOrk9Nja25zz5s7dmzZqdO3eezZFnmcCfjAKoV+leeCHh/ufrIhqF4546/y5m+IhE7/o419cAcGvsb8iuxdc1lPZXMTcvblxRqb1TAnhYr71mT5kVgI6LPvn1mdgoIAihzX6uHV+VvOCf9z5eoXjZZvfNhx/KOXyf/hkZvsXjLfFyxV7uAMft5/j9HL+kAwiAVIYuUCjyFGy+go2mQnuBtKBgSOLq1BP63peVu/+8rn1SomJ2lubiBMUgy+QBQJ2rU+fqYv82zFvltq1u6Vjfpr8iEgBs31mO3rRXmaExTIs0TI9UF+j7XBbllyBJMjs7Ozs7+4EHHoCunvlNmzb9/PPPx48f7/MpgiAUFxcXFxf/4x//AACz2Txx4sSLLrpo4sSJubm55ElXopRWAwQITa1CU6t714Huf1qrVmYON/3hHoIO8seEiY3yVf0cytLT01evXl1eXn7w4MH6+vrExMTMzMyTJ80hhM5JrSD6svepKuVrxnCGIADASJGLTMbZzZYynp/bYvkk2oTXCghdWEyMIm1N7pGrdnvKnYNs5LzfWSXwd9xxx9q1az/88MNvv/3WlzBffvnlu3btslqt06ZNu++++/o5yFMqKCjYt2+fLMtEj0xVluUDBw6MGTPmPF7w/ffff+SRR05/TGVl5c0333zypeo5kIn9grpuGK1udHZ0dBWXoqgkc3WccwUA5BmKRulyej7j4ojpj7viBUJp5HfrOaVTrQYAyeHq9cIeWT5udzKyTCoV5529+0bO0wThr2mHOXxPSoK4VKW8VKUEALcs7/VyxV6u2Mvt5bgKXqjghU8cTgBIpOk8BftrrSaLHSRV2YLCoCBpklhX411X400x0LNHq69LV2vZwZfIgyJFFXV/YtT9ib5N2kCTOsp9wOE+4Gh8tZqJYtPWjFVm9OMn0d8zDwCHDh3asGHDxo0bN2zYcKpkHgAaGho+//zzzz//HAAiIyMnTJhw0UUXXXTRRfn5+QqFAgAM112hnzlVaGjh6xr5umZfXz1f3yw5XO69ZbLbQ5y4YJtzU7H36HHGHOXLq0O0SF6ISk9PT08PjSoGCIWEelHolb37RFHUB1HGOc2Wo7zglmQtOQi/0dD52dHA3b3a6hb66CO8c7W1V8s4M/vJzMEwSKq2Q/z1CouDO+Gn9p2E57faX93R0bM9Tkd9ca1RcaYuDSZGkfbN2ObXa4yzY5UjBmEWc7Zz4D/44IMZM2Y899xzvp7tdevWpaamPvvss/feey8R2PmNVVVVV111VVpaWlpaWmFh4Xfffff0008//vjj/gNeeOGF8vLy66677jxeXK1W5+Xlnf4YheJCrD0gE/p6zzGpVpY7h+uHJ8aniCsAgACiwnnojernaaL73dlE5LYTmUqxJcG58in6L7FpxAvhemr3QZnjia78sEOSftNi3cdxryXH5pzLCNVKXpjd3GqVpMkq5evGzqVNZus0MsgvtNufabMpCeLaITYv9CypCGK8UuGris/LcinHl3i5nV5uF+c9JgjHBKFZFP9j6v7z6pRlFUHggLmzd2mSYtvsqK/K3Yv3Oatswv9tsr+yveOaNNWcLE16xGBeGkAz3jCmapJjS7vtm1bbN63ccY/YIQJAy39q275o1l0SbrgyUp2j66fZBb458/PmzQOAmpqajV3KyspONfiotbV1xYoVK1asAACFQpGXl1dUVDRhwoSioiJzvJmJj+l5sNhuB4I4ebn1tk9WCU3dI/lJlZKJjaLNUUxctCo3UzE86QL/nAgAAERRLC0tbWxs7HNvwFaZQWiQGadQbIiNjqT6SDWiKGpZjMkrY/aOTiDLIJ31AF/xPIcCDzicKLd5JBffx8/jFuRetzNUNHGWPzgTzcY9m3ZBIhyAiHMdCi6KYl1dXUxMDMsGYbrg5MmTKyoq6uvrezbSNM3zPAAIglBQULBnz57MzMwdO3ZoNP1yx6W0tHT06NEkSYqieH6vMPGjpgaH9N5zh4qbf17gfdrXmH35+LEv9zEk3me34UkvaczqeFUjHN9rWOimzLH2jhf/sSRxxLDI++8glYoOSbqnxbKf440dzrdffC913q2aCWPPMp5X2u3vdzh6Zu9+SzocL7TbUxl6VcxQH2V6TkSAwxy/h+MKFIq0riXoSrzc7OZWHUmOVbD5CjZfwY5iWRw7d5ZEGX6o9nxQ6txWxwEAATA3V/to4QUoXhgSJI9EKkkAqJpd2vZVk6+RiWb1Vxj1V0Tqp0RQYYG4ndHS0rJ58+YNGzZs3rx5165dgiCczbOSk5MnTJgwfvz4CRMmZGdn0/QpQ+WO1btLSvn6Zr6+ma9vkjqc/l2UQZfw/vO9jhda22SPl46OJAK+0OPWrVt9tye2bNkS4H/6wmpoaLjqqqv27NlzqgPOe75YKPrl3+8IIXRh3bnauvG4979XR1ycEPKrl58KJ/ZO1J/fav+8zP1Ykf7GjBPKqOsV/X7TKyS+38/5ooeiqMTExJ4tmzZtmjhx4oUL6XTWr18PAC6X6+jRoxUVFZWVlRUVFdXV1b69vnVrJ0yYsGTJkn7K3s+VW5AX7XM6e9xVOtImWL08AHV0uGqfUA9dI+hrE0dHOvITtWv6fJ0RjndFQqEVajgqwk3FUAD1et0fH5j94hsfeeY/LhaMfmTSuIMGLSVJFp2m+fKJY4pyzz7Ie/TadJaZplKevDDpHJ12OMMYKewzPje+4naZJw6ej6DIeJo+Jgjr3J51bg8AqAgiR8HmK9g8BZvNssqBV6x74KAIuDJFeWWKstwqLCl1rih3NzuH0BW2L3sHgOR/Z0bcEmP7vtX+nYU77rF82GD5sIGgCHW+Xj/VqHQ7hrAAACAASURBVL8sQp134WfL+5lMplmzZs2aNQsAnE7njh07Nm3atGXLlq1bt9pstlM9q7q6urq62rcsiEajyc/PLyoqKiwsLCwsNJvNPY9kE2PZxFj/puRw8fVNfF0T39DMJsT2elnJ46174CmZ44EgaFMEY46iY0yMOZI2RzFmE2OOGoDV7wegxx57bM+ePdnZ2TNnzlSrcaQVQgihQGMpgj3x0sW3qaIJgwJzkD6cMoEXBOH999/fuXNnc3NzWlra3LlzR4wYAQBut3vLli2NjY02m83hcOzYsePLL78M8B16tVqdlZWVlZXVq51hmJqaml73F4Jr43Hv30+cvAEAABQZJf99cVL9w22wrLOJGZ5aa54R7irTiUdPfh212Dm4kZFsRqHcQo+ggWgO0y94eM4Tb3/yzzEZhw1aSpJEkkxzusffMuOcrlzDSfJX6r6XCQSAi5SD9oZfgKXQ9HfmqAZR3Onhir3eEo47ygtbPd6tHi8A0ASRxTB5Ctb3nx4r055CegT97CTD0xcbev6O72vmPz3kujRZcUmist+y1wGBUJCGqyINV0UCgOeQ0/Zdq/17i2Obzbnd5txua3juKB3O6C4Jj3owSZOv79dINBrNlClTpkyZAgCSJJWWlvqS+U2bNtXU1JzqWU6nc/369b5bsQCQmJjoT+bHjh2rVJ5QwpDUqhXpKYr0lD5filSw2smFntLDQrNVaLYIzRbY2127VHNxgenBORfg5xzs1q5dO3z48B07dgRlVB1CCCGEzlXfCbzL5Zo8eXJxcbG/5V//+teaNWsYhrnmmmtOtXRw0JEkOaCydwCYkqR4cqLBxXdOdJdleHlHBxkl03kAJHBlVf4jzQnD3QzUhD96ifeL1o4fLo2cfnvcPN8uuyStcLlnqFW+5UZ4WX7A0vaz20MD0aTVPPjob3gAGmSBJDMoanF6ihZzvwHMTFG/0qh+pVEBgEWUdnFcsddb4uXKOH4Px+3huPc7gAQYzbL/NkXo8K08hV4jqNZUupceci095ErQU8uviwxTDonzphypUY7URD+UJDnFjvVt9h8s9h8s3qPutmXNkkdK/Txb8kiWJfWKFJX2ojBS04/TNUiSHDNmzJgxY3w1Tevq6rZu3bply5Zt27aVlJRwHHeqJx47duzYsWOffvopADAMk5OTU1hYWFBQMG7cuPT09DPUCiUI47xbAEAWRaHZKjQ08w0tQmML39AsNFtPri0vWNob//YaANDRkUyMiY6JZGJMtNnEREcOzQXkAEAQhPr6+nnz5mH2jhBCCIWKvhP4V199tbi42Gg0PvTQQ6mpqdXV1a+//vqcOXMUCoXFYrniiityc3M1Gg1BEJGRkQUFBQEOOoQwJHFHVvegxMOOQ68cCqPzicgG7rlZVVdYKv273KkmAOiQpG/pK3OJzT9bvrsscsY2IVxNkv+ydxzkeK8s36PTAgBDEK8bw305PADBgwwECDKRwTC4pmhoMVLk5Srl5SolADhlebeX2+Xldnq9+zm+jOc7JFnX9WYWe7lKnh+rUKQyNL7BJ/t9vs6opj496Gp1i95BU9flrJEayt8t761yO7a0a8cbAMD+XevxPx4GAIIlNQV6/aURpvsSKF2/TxePi4u74YYbfCuMejyekpKSbdu2bd68edu2bQ0NDad6Fs/zO3fu9C/kaTAY8vLy/Pl8XFzcqZ5IUBRjNjFm0ynHEfmIouR0S06X0Gzx7D/c4/lE+K+vMVx7xTn9jIODJEkKhaJXWRmEEEIIDWR9X8ktW7aMoqj169ePGjXK13LttddmZWWJovjaa6899NBDAYxwUHGLLpDCZI+c8KNXtLd7eLuvnVZSwwzbj0IqAEHIAoBEALHTwz1mb2cJgpPlZJqe1WN2IkMQz4Ybpnm5DkkCAJCBIeDZiDDM3kOXhiAmKhUTlQoAHSfLLlnu+W4utLYdF0QA0JPkWAWbx7K5CjaLZRQ4yxcAADQMcW+25t5sjSiDfwg9L8lPbLTrFcT16eq0QV2yvidFikqR0pnJ6q+INC8cZv/R4iqxOza3Oza3U2GMaV483+i1r7Vq8vXKkf1eK0SpVPrWlvvjH/8IANXV1du2bdu+ffv27dt37drl9XpP9USbzfbTTz/99NNPvs3Y2NiCgoKCgoL8/Py8vLzIyMhzjYSOMiYsel5otgqNLZ199Y0tQmOr0GKBoVSnrSeWZWfPnr1kyZLi4uL8/Pxgh4MQQqg330XNUFuvYGj+1Gev74vao0ePZmZm+rN3AMjIyMjMzNy/f/8dd9wRqNgGoTH6PNnZwP9E6MqFY3Ktvz09efhn6fO/cLlfaud50jA5+Z3btOrbWxwAHCfLSTT93yhjz0pyHZI0v7WtQ5IIovPKk5fh9xbrElNkPI11zUMeSxC9Cgo+GR623Okq9nINoviz2/Oz2+M7bBTLjGXZXAWbq2Aj8PYNQM8J8Fa39MVhlyjBv/c4s6OY60eorxmu1A+laiikijQvTDEvTBE7BMeGdtf+jvAbogGg8YXqlv/UAgATzWovDtddHK6dFK5MC0QBs+Tk5OTk5FtuuQUABEE4fPjw5s2bN23aVFJScujQodOUU6mvr//666+//vpr36bZbM7rUlRUdJb5fHdf/TlU+RzkFi5cePz48cmTJ99///3jx4+PiIjodcDkyZODEhhCCPUTyemSHC4qTB8S86duG6U2KMic6BAI9QK6Jk3V6JQGceH9X6jvBN5ms5lMpl6NZrN5//79RqOxz6egc6J3irVS91DS9BEZNEHvOf5ooWrqdvrSV2yeoyJRynEAkEjTS6KM0VR3Wu5f750mQJBBS5IOSaKBqBfEOS2tmMMPShOUiglKBQA0imKxl9vt5Uq8XAXP7/Zyu70cdAAApNB0roIdq2CvUClx8jwARGuo5ddFLj3kWlnh2dvM7222PbvFfnmKclaaalKCYkgtrUDpaMPVkYarO7Nc033xklu0r7Py9d62L5ravmgCAMas0E0K114crpsUrhh2htHoFwRN06NGjRo1atTcuXMBoKWlZfv27Tt37tyxY8fOnTtPX2yloaFh1apVq1atAgCCIIYPH+7rmR87dmxubm5YWFgA4h8cUlI6awS+9NJLfR4wpJaRQwgNZrLc8cNm+5r1/PEGAACSVKQlh113hSqvd1XsAeWyZOVlycozHze45Mew+dOG1j2Lc3LKYaUnVw86Qz0hdJLPylzv7nZKfV396Jxizx74PVL8W3srbWTbcLZ0etR1T1rbv3S4ASCCJD84MXvnZdmXvTMEwctyBsO8Z4p4vM3mq2lXL4h3t7R+Fm3CsfSDVQxFzVCrZqhVAOCQ5D0ct9vL7eK4vV6uShCqBOErp6tYo34+AhMYAIDMSOapiw1/maD/vsrz5WH35lrvqgr3qgp3tIb6fJYxTjdEb3Up0zVJ/8oEAM8Rl2NDW8eGNsfGNr7Ba/200fppIwCw8UrtxWERt5r1l/buj+0/JpNpxowZM2bM8G3W19eXlJSUlJRs3rx5y5YtLpfrVE+UZfnIkSNHjhz55JNPfC09++fHjRsXHR0diB8gNC1YsCDYISCEUL+TRanl7++7tu8FAFKlJPVa0WrzHj7a9Py7YTdMC7tlRrADROgcDJV5oUFR2sLX2IQ+d1UmqOqpRv+mQ5dY0tyhiIEsXe4UlfKfFGURRQC4Ravumb0DgFWSDvTI3n1V6/w17WggjgtijSCEYUnhIUBL+qfNgwhwiON3e7lSnuu5KGCJl3uw1RpNU7ksm6Ngx7Js7NAboKGgiGuGq64Zrmp0isvL3cvK3bUdolvAfkVQpqmVaerIe+IAwFPm7PAl85vauVqP9ZNG+/eWMTWTAKB1UZ0sytqLwlQjtRCoCWmxsbGxsbHXXHMNAAiCUFpaunPnzuLi4uLi4v379/M8f5rn9uyfB4DExMSxY8fm5OTk5ubm5OQMtMVKguv5558PdggIIdTvbF9959q+l9RpjHNv0RRmA0nKHN/x/aa2j5a3f/Etm5qoLhgT7BgROluYwPejJyca7s3W9myRZLj0k2aSgNbLjJt/sELXiu9v3JI7soj9vmVMbtjUe1osvuydAHjH7ginqNu13YWmDCQZR9PHBKFnzXmGIP5hDL+/1brJ4w0jyVhqyGVoiALIYpkslgE4oSyZDOCQZQvHH+T4jx1OAIiiqDEsk6Ngc1h2FMsoh1IZvBgN9dtc7W9ztT1r3QHA7FVWBydNT1Vdl64yqobo6BVlhkaZoTHNjQcZ3Iccjs3tbKIKAAQrf+yBMt8xdASjmRAW/UCidkJAh3jQNJ2Tk5OTk3PvvfcCAM/z5eXlJV127tx5mpXqoGuxuuXLl/s2DQZDVlaWr39+1KhRWVlZCgXOsuvDkSNH6urqLrnkkmAHcmH8/e9/f+utt05/jO8XCWcNIDTIyLxgX/kTEETUo3OVI1N9jQTL6GdMIWjK8p/PbF99jwk8CiGYwPcjkoAE/Qm5tOS7KpChzMJzzd2LwBeNSU/SJw3T5tzTYtnP8QCgJcmHwnTPWW3PtdkAwJ/DOyW5WRRHsswi0wkrxikI4s3IiN+3Wjd7vK2iZMIcHgEAQL6C3RoXU8rxu7zcHo7b4+WaRfEHt/iD2wMAFEAGy2SzbLaCzWGZRHqo/EHomb3LAA0O8Wi7sLeZ/7Has3TmkC/zQYAqU6vK7Lz5SEcwKR+Otq1qcWxu52o9tlUtAKCdECa08XV/qWDjFJpCg6bQEIDV6fwYhvFNnp89ezYAuFyu3bt37+py8OBBQeh76JOPzWbbvHnz5s2bfZtKpfKyyy5btGjRyZVfhjJZlp999tnPP//c6XQGO5YLo6ys7OjRo2c+DiE06HBHj0kuN5sc58/e/bRTi6wfLPNW1EgeL6nEm7koNJzykmv9+vW9rmZsNhsA9HmJ09LScsEjG8RkAv74TtnvHJ0lmliWDTeHdUiSL3vXEoRDlq/XqG/TaFggnrS298zhjRS5LjZaQxDMSR2nCoJ412RsEyXjkCrPhc5ERRAFCragq9RqtSDs9XJ7OX4Px5Vz/AGOP8Dx/3M4ASCepj6LNoUPsQIKBMCamyLXH/OurfaOjWb87aIE2+q9mZFMuHJonZCThV8bFX5tFABwNR7H9nZtURgAuHZ3WD7oXD+coAjlSI12Qpim0KAtCmMTA1puR61W+1aq8216PJ59+/b58/n9+/efvn/e4/GsWrXq6aeffv311wMS78Aiy/KTTz750UcfNTc392wXRdHtdo8ePTpYgV1w77777l/+8hdRFE9zTHl5+fTp04mhNC4JoaFAtDkAgI7q4wY9wTBUhEFobJXaO8gYTOBRaDhlAs/zfGtr68ntfTaic6Wr6u4HUJsVT1c+Umd8dj8nJNJUkygRALdq1QBwo0YtyPLTbbbn2mxmipqqUgLAaQrUkQDnkb3v47iPHc7f6/Unl6/nZfk1W0ciTd2i7ff1olFgJNN0Mk3P1AAAuGW5lOP3erk9HLeX4zokWegxdvRTh3Mvx49imdEsM5JhTr5nNGgwJHFyldcvDrsWrrdRJBTFKqYPU16eohyyo+v92CRlRFKM77F+SkTqlzmOjW2Ore2uPR3uUoe71NHyXi0AMLEKTYFBU2jQjNOrc/RkYO+AKJXKcePGjRs3zrfJ83xpaenu3bv37Nmze/fuffv22e32k5/VZ+NQ8O677z711FN6vd5sNh85ciQmJiY+Pt5msx05cqSwsHAw3dQgSTIpKen0x5ymViJCKHSRGhV0pfG9SZJkdwIAqQ3EWqoIXRB9J/AOR1+/4ugCUXBSC1fv39TEqixcSyvXlMZET1Op3rB3TFQqkroGM9+q1QDAS+12qd/iWef2rnC6d3q4JVGRCfQJFe8ftLStc3syGAYT+EGpV+d8L0sdrjKeX+YEAGAJIoNhRrPMGJbNYplkhh70uezEeMUliYrNtdymWu+mWu/jG235ZvbKFOUVKUqzFqeoABBguNJouNIIAJJHcu22O7fZHFvbndttfL23/evm9q+bAYDUUCN+yleN0sqCLDRzTGyg+zcYhsnNzc3N7Vz5XZblyspKXz7vS+kbGhqSkpKGbDH2RYsWabXasrIys9k8e/bstra2lStXAsAzzzzz3//+d9SoUcEOECGEfil2WAJBU96Kar6xhYk5YSixq6RUcrmZ2GhM4FEI6TuB12gwW+tHeqdY12MReF28BgASLI+/OPK9Oyxu6Era/W7Vam7tz/z5Xr12p9db4uXmNLf6c3h/9h5Oki8YcU2yoehdU8TPbs8+jt/HcUd5YR/H7eO4j8EJADqSzGSYLJYZzTJZLBs3GCvbx+mo96+KsHmltVWeb6s8m2u57fXc9nru6c32cbHskhkRDDlohyScK1JJaovCtEVh0Q8ngQyeI07nTrtzh8253SZYeIIiAODYg2WWJfVMFKsu0GvG6nWXGjUF+sCH6ls0fvjw4TfeeKOvxeFwqNXqIbtO6tGjRydOnGg2mwHgkksuefzxx33tCxYsWLx48dNPP/3CCy8ENUCEEPqlSJVSO7mw48ctzS++F/Wn3zCxnWuLeg4esbzzPwDQTZ8U1AAROjdDpWbVAEESMDNNVXOkqV7uTuBTh6UCgAzyq43fVROXRlLkZFVgZ5ASxL9Nxt+2WHd4vbObW5dEGc0U9bClbZ3boyfJf5uMIxjmzK+CBp1oirpZq7kZAAAcklzKcfs5fh/HlXJ8oyhu93q3e72+IyNIcp5eN1s3CG/8GRTkDRnqGzLUDk5ed8zz3VHP+uPeQxbBIwDTNXDBK8o0QWDpiU4EKNM1ynSN8TZzz2ZNgcG2ppVv5myrW22rW+HZqtHlFzExCsemNvdBpzpbp8rWBXiwvY9Wqz3zQYMXx3H+M5CcnFxfX+9yudRqNU3TEydOXLt2LSbwCKFBIHz2LO+Rau5Yfd1DzyrSk+lwA9/QzFXXAYC6YIz+youDHSBC5wAT+P7lEeQrP22p7eiumkNGQm6YprZHD3yx9SbN1hkAwBSJRARYRMkiilGBLSOvIoh3TRFdObxlGENt9XB6klxkMo5iMXtHoCWJ8UrF+K4CrS2iWMrxpRxfynGlHG+VpF0cN7trBTunLL/cbgsnqVEsM4plzINiTQQt27mYPC/JnAgaprP73SXIkz9uFiSYnKD4TbYmy4Sfl75F3hkbeWes96jbWWxzFduBImgTCwA19x3yHnUDAMEQqlFadZ5enaNTj9WrMrUEg2Mc+t2IESPKyjqXCUxJSZFlee/evUVFRQAgSdLhw4eDGh1CCF0YpEYd8+wf2j762vHTVm/ZUW9Xo+FXU/WzLoehOggLhShM4PsXQYD6xGtQqR20dqFebvS30JGJAEAagAgnAWCiShEZjITHl8PPa7Hu9HqbRFFLEpi9o1MxUdQUFTWla6hIsyj2rF1fxvGfOrprQUWQZCbLjGSZkQyTyTKJNB3SaRlDEgzZcxNSDHRJI7eywt3mkZbMiAheaCFAMUylGKaKuCnG35L45kjr0kZXid1T5nTt6XDt6fC1EwpSlaVV5+rUOXrNWJ0qSws4Z6EfTJky5ZVXXlm4cOEf/vCH5ORko9H43nvvFRUVud3uH3/8MT4+PtgBIoTQhUGqlMZ7bw6/fSZXcUx0OGljGJuSQDCYCqHQg7+1/UtBEd/c1F0t46vGj1c1fZ66+DefSN0J/D/ubM1Kibyr2dIuyQbu8M1qgYTLgxEs0ACarktkBRA6vFxGZ6fXgJE8BfsvU8QuL+dbo84qSZs83k2ezvH2WpIYwTCZLDOSYUayzHCapkO5vj1DEp/NMh6zi+uPeQvM3Te8yiz83zba08PpyYmKi+IVGuxMPgXdpHDdpHAAkJyia0+Ha7fdtbvDtbvDU+FyldhdJXaAOgAI+1XUsP+NBgDH5nZCQapGackhvyjABfH4449/9tlnzz//fFJS0rx58+bPn//MM8/s2bPHarU2NDTMmzcv2AEihBCIbTZXcSlf30RQJJMYpy4YTZ7vbFNSpVSOTr+w4SEUYJjAB46Fa/m+ZQUBhM7CeKAzmaF02i/E8leaW22SnE25lY63Vno0RWET1FSgpxPzsvywpe1nt0dPkgkUfYDn5jRblkQZE2n8JUHnbJJSOUnZ+eVaL4iHeP4gxx/i+UMc3yiKJV6uxNu5NLeaIP5jMuaeohJ+qEjUU3dknVDAtsYu7mrkdjVySw+5GJLINzOTE5UXx7MjjJjK943UUNqLwrQXdZbMFB2ie09nMu/a36HMUAOAp9xZfmUJABA0oUhTq7N1UfcnqnN0wYw7xOl0upKSkvfeey8jIwMAHn/88fLy8hUrVoiiePfdd//5z38OdoAIoaFNkto/+8b29VqZF/xtpFoVPuda3dQJQYwLoSDC3CxwltYv4iTvhPBLKmsr/Y10QuJezVyQ5ElK5RuR5tfcIw47Dqxs+uzm2LsCGZsve//R7fHNex/G0L758JjDo18ulqZiaWpq181yqySVcfxBjj/Ylc/3XCJxudP1nt2RzNAZDDOCoUeyTDwdkkvWXZmiXHVj5Loa7/pj3t1N3NY6bmsd9wJApIqcmqx8YqJeQWEifzqUltJODNdODO/ZqEhSmebFOza3ew47PYecnkNOIInk9zK9Ve6aew9QEYwqS6sarVOP1rIpKgLP8NmJjIxcuHCh7zHDMJ9++qnb7SZJUqEI9Jp/CCHUi2XRFx3fbgCSVBdmK0YMA0F07yvzlJZb3vkf8IJuGlaPR0MRJmYBcsR5aJdtG0sqxuqLdrR87W9n4xOAIEiZu0XhZomIW2N/89SRP/7Qumqy8YoYRVxgYuuVvfvmvftr2mEOjy6sCJKcoFRMUPadGxwTxCpBqBKEdW6Pr0VFEGkMk87Q6QyTztIZDGMIkWIzI43MSCNz31itzSttquU2HPduOu5tdIqfHnLdOVqTHtH5mRIkECRZGdqVAQKEUJAJr44AANkruQ863Qcd+ksjAICv8zi220AG25pW35GkmlJlalSjdaosre8/yoB/xPrw8ccf5+Xl+brf/VQqFQDs2LGjvr5+1qxZQQoNITTUecurOr7bSCjY6Md+q8zqHPduuO4Kx7ptrW9/bP1wubowmwo3BDdIhAIPL2gCQZKlj+r+JYM8I+qGVS2fO+x2/y46Lp6W3QKh+p1N/phpiKfU2fqC3bbtS+sXP5Ty18CE9+8Ox49uTzhJLooyZnStGKciiHdMEXNbLCVe7k+W9k+jIwMTDBriHjDortGoyji+jOfLOKGc5xtF0bcEvf+YKIpKZ+gRDHOaGwEDikFBXp2qvDpVCQCV7UIHJ/uzdwC47qvWMiufE8XemKG6MUN96pdB3QgFqc7VqXM7B89rJ4Zn7Z/g3GV373e4Sx3uUgd33OMstjuLu//YsvFKVbY24eURbGJA1+kc4G6//fZ//OMfvRJ4nw8//PDDDz9sb28PfFQIIQQAjvU7QJb1V0/xZ+8+2injXcX7Xdv3Orfu0V81OVjhIRQsmMAHwgbr98fd1RFMpEkRXe2qaHd2d7XpzfRI+xv7DY/KQNzd1DSq4zVWsgHAPntxpfNwqmZEAMIby7LjlYoFYfpe672rCeI9k/Fv1vbhWKITBVAKTafQ9HRQ+TbtknSYF8p5vpznD3PCEZ5vFsVmUdzk8S7ucOyIN2u6auDt47g6QUxl6BSaZgZqYbzUsN6fptEmpszKlzRy1TahZwLf7pVUNIEj7c8Sm6xik1Xh10X7NsV2wV3a4UvmXfscnjInV+vhaj0RN8awicqWf9fWPlquSFYpR2lVmVrj7eYhldULgvDYY4/5N7/++uva2tpex3g8ns8++8xgwK4thFDQ8DX1AKDKGXnyLlVOpmv7Xr6mLuBBIRR8mJj1O5foXNb4PwCIUyZWOA/raMNW9RGwdu5NidjrYGcDACM5PFRUmf6Ri9xLlHKHglTpmbDARNhzfe9e1ATxqjG8z10IBYaeJAsUbEFXlTsZoFYQffl8GElqeiTq81usVkkCAJogkmhqOMOk0vRwhh7G0MMGcEr/7GTDwgn6kkbOpO6eGtDkFC/9pEWSITuKGRfLjjOzudEslrI/e1QYfcIUekn2Vnv4Jq+20AAApIIEgvAccXmOuNqXN3srXMmLRgUz3MASRfGVV17xb65bt27dunV9HvmnP/0pUEEhhFBvMs8DANFXmVtfo+8AhIYaTOD73R77jg7BDgD7O3ZBB3BURJW2e0SizqxopMcAQJL7q3rFpU46vjLs0S+iTcqBmmwgFFwEQAJNJfSoiue3INyw1uUu54VaQajkhcoeFWspgISuZD6VYYbR9EiWoWCg0DDEpIQTbqLpWDIrktnVxO1s4HY2cG8BUARkGJl8M5sfw16WrGCxZ/6ckIRvCXrflnF2bMQtMZ7DLvchh7fcFTYrKrjRBRjLstu2bfM9Hj9+/EMPPXTLLbecfJhWq83MzAxsaAgh1I2OMnorj3HVtYrUxF67uKrjvgOCERdCQYYJfL/L0Y/7VfTNgtx5j3CTq1mo/9K/N334+AommQHp14YEkqz9FJKO8aJHljGBR+hcXaNWXaNWAYBXlo/yQoUgVPB8JS9U8EKtIFQLQrUggLvz4EtVyrciI/zPrRVEmoAYaqAk9WqG+HSW0eaVihu5HfVcSSO3v4U/0MofaOWX7Hc+VKD7fZ422DGGNoIlVaO1qtFD8TQSBFFYWOh7PG3atMmTJ/s3EUJo4FDlZzm37rYvX6spyiXVKn+70NTq+HErAKjyRwcvOoSCBhP4fqemNLNibvU9bvDWfnvoAbGriB2loCr0JhmIiSrVrZG3AcD1suySZX2IFNlGaGBSEMRIlhnJMtA1kZ6T5SpBOMoLFbxwVBCqeCGL7a74cEwQrmpoFgHUBJHC0Ck0PYyhk2k6maaTGVoVvLtpBgU5NUk5NUkJAB5B3tfC72zgjliFKYnd3fXfVXme2GhLC2cKzOy9ORoViaPfhAAAIABJREFUVrNH5+Kbb77puSlJktPp1Ol0wYoHIYT8NBML7Ct/4qrrGha8EnbzVf5l5No/Wy253JqiXMXwpGDHiFAQYAIfUEvrFgmlsn/TaA5vZjIB4BJl52BgmiD02PeO0IXGEsQIhulVptEvkqImq5R7vZxFkg5w/AHuhDl1MRSVzNDJNJ1EU8k0ncLQScFYVVFJE+PM7Dhz76mANq/U6pJaXN4tdd70CHrasM4/Ji5errELqWE0DrZHJ2tubv74448VCsV9993na3E6nY8++ujixYs9Hk9mZuY999zz8MMPBzdIhNAQR1Bk1GPzm597h6upa3ltcc9dqpyRkfffEazAEAouTOADZ5+9eH/HLvF4d4verLMzIwAgXj4G0McqPgihAFAThG84vV2SjgrCUV6oFoRqXqgShGOC2CiKjaK4Dbz+4x8J09+j6xx6LQHs9nJhJJlAU2ww7r7dlKGekqjc28w1OKRLk7p75h9bb1tV4aZJGBHBjI5iRkcyo6OYEREMjeN7hrw1a9bccccdVqv1kksu8Sfwd9xxx7JlywDAZDIdOHDgD3/4w44dOz755JOgRooQGupoY5j5hT851m1zbt0t1DcBSbJJcZrJ4zTjcwB7vNBQhQl8gIiyuLR+MQBEWeP9jXy0hiaUKrF+bcMnRbpXCMC/RAgFk54kc1g2h+3u5RYB6gWxRhBqBKGKF2oEoUEUU3r0wK9yuv9sbQMAEiCGphIoOoGmEujO/yfSVABmxJjU5GXJvUv6XZmiPGLlK9oE38z5pQAAwJBEhpGemqzEKfRDVmVl5c033+xyue67775rr73W17hjx45ly5bp9foff/wxPz//6NGjN95449KlS+fOnTtlypTgBowQGuIIhtZdMVF3xcRgB4LQQIEJfID80Lqq0VsXpTDbmrqzdDJ+GACYpZoad+Vm608TI6YGL0CEUB+orqL3E6HvpRbHKtipKmUFL9SJYr0g1gvidu8JB+hJMrErpU+lmavUSjognQZXpSqvSlW6ePlAK1/awu9v4Utb+CqbsL+FL7MIc3M0/hXmf6j21DvEDCMzxsQocQr9YPfmm286HI5Fixbddddd/saPPvoIABYsWJCfnw8Aw4YN++STTzIyMt5++21M4BFCCKEBBRP4QOgQ7CubPgOA2+LufZR/uXtH3GgAuFafuKUDvmj4MM9QpKLUwQoSIXQe4mnqzcgIABAB6gThmCAeF4TjPf5vl6RSTirtmldPEuEzukrplvH8pw6XiSLjKTqOpuJoKoqiLmx/vZohCsxsQdfMeScvl7bwGobwZ+8ywIM/tHsEGQCyTMzX10f6nysDDgoahL799tv4+Pg5c+b0bFy7di0A3H777f6W9PT0tLS0w4cPBzo+hBBCCJ0WJvCB8FXjRy7ROUafN1o3tqGjyd8uxmYoCOI2Y15re2a58+Dq5i9uMM8OYpwIofNGASTSdCJNw4l99RZROi4KxwSxVhDskjxR2b13udO91OHseTBDEGaKiqWpWIqKpal4ijLTlJmiYyiSuRD99hqGKIw9oQweAfDm5eE/VHsOtPK50d27Ojj58qXNbkFOC6dHRDDpEXR6BDMigo5Q4Rz60FZXVzd+/Hiyx8yOurq6srKyzMzMhISEnkeazeZdu3YFPECEEEIInQ4m8P2u1lOz0foDANR7jj9V/sdjDfv9u6jY2DDx2MtHXrELNgD4vmXlJcZpkWxU0GJFCF1oRoo0UmxO7+LxAAC/02vTGPqYINQJYp0g1oliiygeE4RjgtDrSBIghaGXmCKNXT30HlmuFoQoior4xXPspyQppiT1niBAkRChJA9bhd1N/O6m7rL8kSpyhJGZm6OZGN/3nAI0wJEkybIn/Dr+8MMPADB1au85XBaLJXBhIYQQQujsYALf75q89ZIsAUAr19wKze66Dv8uOsbMetdXuyt9m4LMt3JNmMAjNEToSPJ6zQmzZryyXO+bS985o75zan2zKNYLokuWjV1HLrC2f+dyA4CSIMwUFU1T0RRlpqgoijRTVAxNRVNU+C/I7dU0seYmU7tHKrMI5W38YatQbhWOWPlWt9Ra6zWpSX8Cb3VL/7fZrmOJ9Aj6V2mqMAV20Q9oqampBw8elGWZ6BrTsWLFCjgpgZckqbKyMjExMQghIoQQQujUMIHvd3mGoqdHvM7LPAB02DsW81/72mmVijYY/hx2ZSp9ua9FQSrMivhTvhBCaLBTEEQKTaectM68CMDLsrLHKPopSkUNLzSIok2SqgSh6qROewBQEkQ0RZkoMkfBPmzQ+xNrGUCQ5bMZkx+mJMfHsePjujts6zrEY3YxM7I7wkMWflWF2/e41i4unKD3PXby8meHXJFqMtlApxhoLYsT6geEoqKit95665NPPvn1r38NANXV1atXr2ZZ9pJLLul52BtvvOF2uy+99NLgRIkQQgihU8AEPhDilJ2dGAerDvobjTGGz6NNo1gmSEEhhEIGBUCdmG/P1KhnatQA4JZlXxd9kyjWi2KTKDaJUr0gNImSXZJqBKFGgN1e7h6dNqyrQ/6+VuvPbo+RIk0kFU1TkSQZTVGRVPf/jSR5qlL5cToqTkf1bLkoXrF0pvFgK19jF28a2T2gYGWF+5ktdv+mSU0OC6NTDHSSgUoNpy9JUF7gen3o7CxYsOD999+fM2fOjz/+mJSUtHjxYq/Xe9NNNxkMBt8BkiR9+OGHjz32GE3T999/f3CjRQghhFAvmMAHVG1trf+xKorB7B0h9AupCCKVoVOZPv6Ye2S5ThBbRFFHkmE9htObSJICsIiSRZTKeP7kJxIARoqMJKkoiipUsnfruheNF2TZC6A5Mb3vWeje7+pUVbNTOtLGV7WLVTahxSW1uLjt9Zxv74uXGG7I6Mz2m5ziigpPtJpMj6AzjPhXsX/Fx8cvW7bstttuW7Roka8lKSnptdde8z1euXLljTfe6PV6CYJ4/fXXMzMzAx9hdXW1w+HIyMigaRoA9u3b98EHH7S0tIwaNerqq68eNWpU4ENCCCGEBg5M4AOqrq7O/zgmLjqIkSCEBj3lKXL7pyLCnowIs4hikyi1iGKLKDWLYoskNotSiyg2i5JVFFtFqVWUyni+2Ou9S6f15+vXN7WW87ySIHwZvpEijSQZSVERFBlJkiaKCidJE0XqSFLHEg/kd2b+MkCDQ6xqF6ptYpVNaPNIRXHdNfCWlLr+tdvhe7z46ohJCZ27Gp3i91Ues4aK11MJOhyEf8FMmzatrKxsw4YNhw4dSkhIuPHGG9Xqzpspbrc7MjIyJyfn0UcfnTRpUoADW7ly5bx58xoaGgAgJSVlxYoVLS0tV155Jd91m+lvf/vbyy+//MADDwQ4MIQQQmjgwAQ+oGqPdffApyeOCGIkCKGhjAQwUZSJogD66PEWASyi2CJKraIYQ1M98+Z0hj4uCG5ZrhPEOhBP9foMQUSQ5BVq5cIwAwAQALFaysZKnFHOopThJBnWYwD9nCw1RUC1TXBw8vDw7m+lN4odSw+5/JthCjJWR8VqqTgdNSKCvjFDTWJGf75MJtP1119/cvtNN9100003BT4eACgpKZk5cyYAjB8/nqKonTt33nrrrQzDhIWFPfHEExkZGQcPHnzmmWceeuihCRMm5OfnByVIhBBCKOgwgQ+o2qruBN4TmxPESBBC6FQogCiKiuorvX/ZGA4AblluEUWLKFklqUUULZJkFaUWUbRKkkWUWkTRKctNorjTw/mfKMjyLc2tnCz7W1iCCCfJcJIMp8iIVDKGJMMpcj14wlxkOEmOYpm7szUKmjhuF453iLV2sd0rtXulg62dnbHpEUxudGd4Xx9xLyt3R6rIUZHMnNEaTOxD0VNPPQUAq1evnj59OgBs3LhxypQpoij+9NNPU6ZMAYCpU6dOnTo1Ozv7+eef//LLL4McLkIIIRQkmMAHVE3Ncf/jxsjIIEaCEELnTUUQiTSdeOovEK8st0lSz4n3NEHcr9eVcFybKFkl0SpKLlluEsUmUYQ+puHDGJb9NDry8Yv0ACAC/M3aXs8JtEQQPCF5gZWIGo1gd4thFGkgyW+Ouzce9wLAsnL3xQkKfzf+myWO5eVuk5o0a6lIFRmjpUxqMkZDmdRknI5iMNEfSPbu3VtUVOTL3gHg4osvLigoOHjwoC9798nMzCwsLNy3b1+QYkQIIYSCDxP4gDreYw58dmJSECNBCKH+oyCIGIrq1XivXntvj01fkt8uSb6e/DZRapekdklqkySbJE1Udk+Sd0nSNy63x9d7TwKoAAB+bnN3v1YasGmgBEItEwu5tusc6lu0GgCoaBOqbEINBSCDbJWBJ4AHEEDmwaQmf7w1SsNgDj9QtLS0jBkzpmdLfHy81WrtdVhUVFRJSUkA40IIIYQGFkzgA6qxud7/uCgZE3iE0NDlS/JjKKqvafgn0JHkt+aoI7xgk6R2SbJJkk2S2kWpXZJ7tnhA9hCylZN0pMeXwP99athteao5DkvXwP3uAfwdojSztVlPkmEkeadOO6nH/QIUFMOHDy8uLpYkiewauPHII49YLJZehx0+fNhkMgU8OoQQQmigwAQ+cDiOa7d3dSZQ1KT4uKCGgxBCISOaoqJP6tLvxS5J7ZLUIclJdOdXG0nA2HDFfFJ3hOc7ZNkuSXZJskuyXZIkqrsOXyxFYQIfdJdddtnf//73e++9980331SpVABQWFjY65j33nvv4MGDd95553m8fmNj48aNG09/zPHjx09/AEIIIRR0mMAHTmNjoyRLvsdMREScAq8XEULogtGTpL7HrHsfCuD3Bt3JB3dIkk2SOyTJLktj2N6L2KPAe+KJJ5YtW7Zo0aL//e9/119//UcffdRz7xtvvPHRRx/t2LFDo9E89thj5/H68+fPX758+dkcKUnSebw+QgghFBiYwAeOb21bH100jgBECKGg0ZGkjgSAM/Tqo4DR6/V79+595plnvvvuO5vN1mvvmjVrduzYkZeX995776Wnp5/H6//2t79lWVbusQ7CyTwez8qVK339/wghhNDAhAl8gCxzurZXVfs3zebu8fP1gvip03WHVhNJ9e47QgghhIYInU734osvvvjiiyfvevbZZ996661hw4ad94tfeeWVV1555emPaWpqiomJ0ev15/2vIIQQQv0NE/gA+au13Xq0yr+ZGh/ve1AniLOamh2SbBHFZyLCghQdQgghNHCNHTs22CEghBBCAwJ2+QZICsMIzU3+zZFx8dAjeycImK7GMXsIIYTQCZ544gmGOdNCBQghhNCQEao98JWVlWvXrj148KDFYnG5XEajMS4uLi4u7pprrjGbzcGOrg+fR0eOaG3xbybFmhtE8bqu7P2l8PCLsAYyQgghdCJJkgRBCHYUCCGE0EARegl8VVXVfffd9+233/a593e/+93MmTNfeeWV5OTkwMZ1BiqCyGxr8y9Qs06n/29js70re5+hwe53hBBCCCGEEEKnE2IJvMViufzyyysrK0eNGjVz5sysrCyj0ajX6+12u9VqLSsrW7Vq1Zdffrl3795NmzZFR0cHO94TNPeoQr9DH6bC7B0hhBBCCCGE0FkLsQR+4cKFlZWVzz///IIFC/o84Mknn1y8ePHcuXMff/zxf/3rX+f6+oIgHDt27PTH1NbWnuvL+vRcRo6OigKAixVKzN4RQgghhBBCCJ2NEEvgN2zYMGLEiFNl7z533XXXxx9/vGnTpvN4/d/85jdLliw5myMlSTqnV5ZluaWlaw48QdAmEwBs8HheaLctCDOcY5gIIYTQkPDEE0/89a9/DXYUCCGE0EARYgl8S0vL6NGjz3hYQkLC3r17z+P1CwsLN2/efPrkXBTFmpqacy2KSxBEQnJydWUlACjSRzwXE7Wow1HJC0s6nACAOTxCCCF0MpqmaTrErlUQQgih/hNiX4pFRUVr166tqKgYPnz4qY5pbm7+9ttvi4qKzuP158+fP3/+/NMf09TUFBMTExERcU6v3CCKmjfeVD/zNEFRz7/0ynUa9XS16samFszhEUIIIYQQQgidjRBbB/6BBx4QBGH8+PGvv/56TU1Nr70NDQ3/+c9/CgoKmpub77777qBEeCrXNbaIWWOSPv186bff/X78OABQEcTSKFMiTQPAkg7n505XsGNECCGEEEIIITRwhVgCf/nll7/xxht2u/3BBx9MTk4OCwsbNmxYTk7O8OHDIyIiYmNj77333vr6+rfffnvWrFnBDvYEHZJMALwUET5D3V21TksSX0abkmgaAI7wuM4tQgghhBBCCKFTCrEh9AAwf/78yy677D//+c/atWsPHTpUVVUFABRFmUym/Pz866677q677oqJiQl2mL19FG0kAcawbK92LUl8FWP6ye25TKUMSmAIIYQQQgghhEJC6CXwAJCWlvbiiy+++OKLsiy7XC6PxxMeHk6SA3o0Qc5JqbufmiB6dssjhBBCCCGEEEInC8kE3o8gCI1Go9Fogh0IQgghhBBCCCHUvwZ0rzVCCCGEEEIIIYR8QrsHPohsNttNN910rs/asmWLy+XCIQOBxPO80+nU6/UDfJLFIGO32xmGUalwbkjgeL1eQRAuvvhihUIR7FgGP4vFEuwQUH/B7/dBT5Zlm82mVqvZU09vRAMHx3Eul8tgMBAEEexY0Jk5nU6tVjt+/PhgB3KeQuL7nZBlOdgxhJiOjg6j0cjzfLADQQghFEzTpk375ptvgh0FumDw+x0hhBAM+O937IE/ZzqdbsuWLb7q9+dEFMVbb72VJMkHH3ywPwJDffrpp5/27t176aWXZmdnBzuWoaK9vX3x4sXh4eF33nlnsGMZQpYvX15VVfXYY4/l5uYGO5ahYuLEicEOAV1I+P0+RJSVlX3zzTcZGRnTp08PdizozFauXFlRUXHNNdcMHz482LGgM5Ak6Z///CdJkkuXLg12LL/IAP9+xx74wOF5nmVZhmE4jgt2LEPI7373u7fffvutt9667777gh3LUFFeXj5ixIj09PTDhw8HO5Yh5Oqrr16zZs3q1auvuuqqYMeC0NCC3++h5X//+99tt93261//+uOPPw52LOjMrrvuumXLln311VfXXnttsGNBZyAIAsMwNE3jUKZ+hbOCEUIIIYQQQgihEIAJPEIIIYQQQgghFAIwgUcIIYQQQgghhEIAJvAIIYQQQgghhFAIwAQeIYQQQgghhBAKAZjAI4QQQgghhBBCIQATeIQQQgghhBBCKARgAo8QQgghhBBCCIUATOARQgghhBBCCKEQgAk8QgghhBBCCCEUAuhgBzCEMAyTl5enVquDHcjQUlBQoNFocnJygh3IEBIbG5uYmDh+/PhgBzK0FBYWbtmyJSMjI9iBIDTk4Pd7aBk9erROpxs3blywA0FnpbCw8Mcff8zMzAx2IOjMaJrOz89XKpXBDmSQI2RZDnYMCCGEEEIIIYQQOgMcQo8QQgghhBBCCIUATOARQgghhBBCCKEQgAk8QgghhBBCCCEUAjCBRwghhBBCCCGEQgAm8AghhBBCCCGEUAjABB4hhBBCCCGEEAoBmMAjhBBCCCGEEEIhABN4hBBCCCGEEEIoBGACjxBCCCGEEEIIhQBM4BFCCCGEEEIIoRCACTxCCCGEEEIIIRQCMIFHCCGEEEIIIYRCACbwCCGEEEIIIYRQCMAEHiGEEEIIIYQQCgGYwCOEEEIIIYQQQiEAE3gU2n7++eeWlpZgR4EQQgghhC4AvLQLLfh+BR4m8AGydu3aX/3qVyaTKTMzc/78+VarNdgRDQalpaVTpkzZsmVLn3vP5pzj+3JO3nnnnby8PJ1OFxUVNWnSpM8+++zkY/C0X0AdHR2PPPJIbm6uVqtNSUmZNWvWrl27Tj4MzzlCQYQfrhAyadIkoi9OpzPYoaFOv/zSDgXSad4v/Lj1Ixn1v3feeYeiKJZlJ0+enJ6eDgCpqalHjx4Ndlwh74YbbgCA5cuXn7zrbM45vi9nTxCEefPmAYBCoZg8efKUKVOUSiUAzJs3r+dheNovoI6OjpSUFACIiYmZMWNGUVERABAEsXLlyp6H4TlHKIjwwxVaYmNjFQpFxkmcTmewQ0OdfuGlHQqw07xf+HHrP5jA97vy8nKGYYxGY3l5ua/lueeeA4Bp06YFN7DQtW7dupdeeikvL893E+rkvxpnc87xfTknixcvBoARI0Y0NDT4WioqKoYNGwYAq1ev9rXgab+w/vznPwPA3XffLYqir2X16tUEQZjNZv8xeM4RCiL8cIUWp9NJEMT06dODHQjqwwW5tEMBc8b3Cz9u/QoT+H736KOPAsA///nPno2jRo0CgIqKimBFFdKGDx/ecxTJyX81zuac4/tyTqZOnQoA27Zt69n4+eefA8Bvf/tb3yae9gsrOztbqVS6XK6ejePHjweAqqoq3yaec4SCCD9coWX//v0A8MADDwQ7ENSHC3JphwLmjO8Xftz6Fc6B73dr164FgJkzZ/Zs9G36dqFz9fPPP1dXV1dXV8+fP7/PA87mnOP7ck6OHj3KMExBQUHPxtGjRwPAkSNHfJt42i+shISE66+/XqVS9WykKAoAHA6HbxPPOUJBhB+u0FJRUQEAaWlpwQ4E9eGCXNqhgDnj+4Uft35FBzuAQU6W5UOHDun1+qSkpJ7tWVlZAHDw4MEgxRXa4uLifA8MBsPJe8/mnOP7cq6++uorgiBI8oRbfiUlJQCQmpoKeNr7wcqVK3u1bNy4cefOnSkpKRkZGYDnHKGgwg9XyKmsrASA1tbW6dOn79y5k2GY7Ozshx56aNq0acEODV2ASzsUSKd/vwA/bv0Me+D7l8vl8ng8RqOxV7uvxWKxBCOoQe5szjm+L+cqJycnOzu7Z8uePXseeeQRgiB8xe3wtPef7du333TTTUVFRVOmTElLS/v6669pmgY85wgFFX64Qo6vS/D//u//9u/fX1hYGBsbu3bt2unTp//1r38NdmjoDPDjFnLw49avMIHvXx6PBwD0en2vdl+Ly+UKQkyD3dmcc3xffglZlpcsWTJp0qSGhoZXX3117NixgKe9P1kslr179x44cEAURYVC4b9SwXOOUBDhhyvkVFdX0zT95JP/z959h0dRrX8Af2d7L9n0QgghhF7FUBUQREAEARUVRBGvqFyvem3XgoqKei2/qygKWJEiKIIgiKKogAIiJQGSQEgCgdTtvc/8/hhcQghVks3C9/Pw5Jk9czL7ZpfJ5p055z3PHz16dO3atTt37tyyZYvBYHjllVe2bt0a7ejgTHC6xRycbk0KCXzT0uv1QqEwMmE1wuFw0F8XDuHiOpfXHO/LBdu5c2efPn3uvPNOpVK5cuXKhx9+mG/Hy950Ro4ceeDAAYfD8dNPP1VUVAwfPrygoIDwmgNEFU6umPPdd98Fg8HnnnuOYRi+pW/fvi+88ALLsosWLYpubHBmON1iDk63JoUEvmkJBIKEhASLxdKgnW+JTCCBi+hcXnO8LxcgGAw+9dRTeXl5+/bte+aZZw4ePDh27NjIXrzszWDIkCHPP/98IBBYuHAh4TUHiCqcXJeGQYMGERF/VRRaLJxulwacbhcLEvgml56ebrPZampq6jcWFxcTfuM0mXN5zfG+nBeWZe+4445XXnll0KBBRUVFL774olqtbtAHL/tFtGvXrhEjRsyZM6dBO1++zmg08g/xmgNEEU6uGMKyrN/vD4VCDdr5kiI6nS4aQcF5wOkWQ3C6NTUk8E3uxhtv5Dju22+/rd/47bffikSi0aNHRyuqS9u5vOZ4X87L3Llzv/jii9tvv/37779v1apVo33wsl9EWq12/fr1pw4z42vt8ivfEl5zgKjCyRVDjh07JpPJGiyGSkSbN28moq5du0YjKDgPON1iCE63JtfM685fhqqqqkQiUWZmZm1tLd/yySefENH48eOjG9gl4MknnySiVatWNWg/l9cc78t5ycnJUSgUDofjDH3wsl9cvXr1IqL58+dHWgoLC5OSkiQSyb59+/gWvOYAUYSTK7b069ePiF599VWWZfmW3bt3JyUlqVSqI0eORDc2iPg7f9pB8zvd+4XTrUkhgW8OH3zwgUAgSElJueuuu4YNGyYSibKzs8vKyqIdV8w73W8N7txec7wv56i6upqIZDJZ98Y8+uijkZ542S+iP//8U6lUElGnTp1uvPHGAQMGiMVihmH+97//1e+G1xwginByxZCSkpIOHToQUW5u7rhx4/r06SMSiaRS6cKFC6MdGpzwN/+0g2Z2uvcLp1uTQgLfTFauXHn99dcbDIb27dtPmzaturo62hFdCs7wW547t9cc78u5+O23384wimfChAn1O+Nlv4gOHDgwZcqUtLQ0qVSanZ09duzYP/7449RueM0BoggnVwxxOp1PPPFE//791Wp1Tk7OxIkT9+/fH+2g4CR//087aE5neL9wujUdhuO4iz0qHwAAAAAAAAAuMhSxAwAAAAAAAIgBSOABAAAAAAAAYgASeAAAAAAAAIAYgAQeAAAAAAAAIAYggQcAAAAAAACIAUjgAQAAAAAAAGIAEngAAAAAAACAGIAEHgAAAAAAACAGIIEHAAAAAAAAiAFI4AEAAAAAAABiABJ4AAAAAAAAgBiABB4AAAAAAAAgBiCBBwAAAAAAAIgBSOABAAAAAAAAYgASeAAAAAAAAIAYgAQeAAAAAAAAIAYggQcAAAAAAACIAUjgAQAAAAAAAGIAEngAAAAAAACAGIAEHgAAAAAAACAGIIEHAAAAAAAAiAFI4AEAAAAAAABiABJ4AAAAAAAAgBiABB4AAAAAAAAgBiCBBwAAAAAAAIgBSOABAAAAAAAAYgASeAAAAAAAAIAYgAQeAAAAAAAAIAYggQcAAAAAAACIAUjgAQAAAAAAAGIAEngAAAAAAACAGIAEHgAAAAAAACAGIIEHAAAAAAAAiAFI4AEAAAAAAABiABJ4AAAAAAAAgBgh2Q0oAAAgAElEQVSABB4AAAAAAAAgBiCBBwAAAAAAAIgBSOABAAAAAAAAYgASeAAAAAAAAIAYgAQeAAAAAAAAIAYggQcAAAAAAACIAUjgAQAAAAAAAGIAEngAAAAAAACAGIAEHgAAAAAAACAGIIEHAAAAAAAAiAFI4AEAAAAAAABiABJ4AAAAAAAAgBiABB4AAAAAAAAgBiCBBwAAAAAAAIgBSOABAAAAAAAAYgASeAAAAAAAAIAYgAQeAAAAAAAAIAYggQcAAAAAAACIAUjgAQAAAAAAAGIAEngAAAAAAACAGIAEHgAAAAAAACAGiKIdQOzhOO7BBx8sLi6OdiAAABBNU6dOvfXWW6MdBVw0+HwHAABq8Z/vDMdx0Y4hxhiNxsTExGhHAQCXMkbAxHfSmfbbOBa/oluu/v37b9myJdpRwEWDz3cAAKAW//mOO/DnjWVZItLr9cuXL492LABwacpXbN2v3NnFc2UX95XRjgUaUVhY+K9//Yv/OIBLBj7fAQAuczHx+Y4E/gJJJJKhQ4dGOwoAuATVBWqWFX9AHBWrdt/R6x6DJCHaEUFDSqUy2iFAU8HnOwDAZSsmPt9RxA4AoGVZXvVpmAsRUZANLq/+NNrhAAAAAEBLgQQeAKAFKXIV7LJvizzcYfvtgGt/FOMBAAAAgJYDCTwAQEvBcuySyg/57SxFDr+xpGoBy7XouVgAAAAA0DyQwMMJwao65/pNtmVrHWt/CZRVRDscgMvOL+b1lb4KIkqWpT2R/VKSJJWIjnoPb7b8GO3QAAAAACD6UMQOiIjCTpf5gy88f+RTvWUFZR2y4x+YLEqOj2JgAJcPd9i1snYpv31b6jSJQDoxberb5S8R0dc1i3rr+iuEMVBYBQAAAACaDu7AA7FeX+1z73i27xFIJapBebpbRqmHDRBq1b6i0upn/y9ktEQ7QIDLwjc1S90hJxH10OZ1Vvcgom6aK7qoexKRM+RYU4t1rQAAAAAud0jggewr1gcqqiStUtPeeTZ+xmTdTSMM905Me/c5ebf2Yavd8umKaAcIcOmr9h3baP6OiISM8KaUKZH2iWlTBYyQiDaY1tT4K6MWHwAAAAC0AEjgL3sc5/p5OxHFz5gkjNNFmgVyWfyDUxix2LNjb9jpil580OKYArWmQF20o7jULK36iK9Ud23CDcnS1Eh7ijT9mviRRMRy7LKqT6IWHwAAAAC0AEjgL3dhqz1sdwr1WkmbVg12CbVqaU4msWywojoqsUELZA9Znzv48AsHH3GGHNGO5dKxx7Fjn3M3ESmEyq6aKw57S+v/66HNUwgVRJTv+HOvc1e0gwUAAACAqEERu8sd6w8SkUAmaXQvI5cREefzN2tM0IKtqF7kDXuIaGXN4jvS74t2OOfBz/qWVX3SQ5vHzypvUb6pOV67zhN2v3bo6TP3bIHxAwAAAEDziNUEvrS0dMOGDYWFhWaz2ePxGAyGtLS0tLS00aNHp6SkRDu6WCKK05JQEDJZWa9PIJedtI/jghVVRCRMiItOcNDCHPYc+s2yUcSIOWI3WTYMNozIkLeOdlDnak3tl7+Yv//T9vsrHd5XClXRDuckUoGciBhiNCLt6fo4Qjbur54Al7DDhw+7XK727duLRCIiKigoWLhwodFo7NSp06hRozp16hTtAAEAAKIp9hL48vLy+++/f/369Y3ufeCBB8aMGfPGG2+0bt26eeOKVYxUIuuY49t7wL7yB/1tN9Tf5fple8hoESXESTJwTQSII25p1Ucccdcm3BDigj8YVy+qnPdk29kMMdEO7eyMgdofjKuJyBV2flPzxW1p06Id0UkcIRsRccTZQ7Zz6QlwSVqzZs29995bXV1NRFlZWatXrzYajcOHDw8Gg3yHZ5999vXXX3/wwQejGiYAAEA0xVgCbzabhw0bVlpa2qlTpzFjxnTu3NlgMGg0GofDYbFYiouLv/322xUrVuTn52/ZsiUpKSna8cYG/S2jqveX2FduYO0u9ahBokRD2Opw/bzN8c2PRKS/bTQxMZChQVPbbt1U4i7SiHSjEsdzxG21/lLiLtpp33qFtl+0Qzu7ZVUfh7igQqjysu6N5u8GGYanyjKiHdQJd2XMKHEXnUvPXCVuP8KlaefOnWPGjCGiPn36CIXCHTt23HrrrWKxWKfTPffcc+3bty8sLHzppZceeuihfv36XXHFFdGOFwAAIDpiLIF/6qmnSktLX3nllSeffLLRDs8///wnn3zyj3/8Y+bMmfPmzWvm8GKUtH2b+AduN3/whfOn350//X5iB8PoJo5SDuwdvdCgpQiwgRU1i4hoQspkuVBBRDcm37bw2AdfVH7cVd1LIpBGO8AzKXIV7LJvFzBCT9glIAFL4SWVCx7NnhXtuE7IUXbIUXaIdhQA0TRr1iwiWrt27YgRI4ho8+bNgwcPDofDGzduHDx4MBFdc80111xzTbdu3V555ZUVK857fVOr1bpz586z9iGiyA1/AACAFijGEvhNmzbl5uaeLnvn3XXXXYsXL96yZUuzRXUJkHVqJ8lM8R+qqN8oSjQorugarZCgRVlb95U5YMyUZ/ePG8K3XG0Yvsm84bC39HvjN6OTbo5ueGfAcuzSyo+IiOPCRMQSK2JEha6CPY4d3TW4OAXQUuTn5/ft25fP3olo4MCBvXv3Liws5LN3XseOHfPy8goKCi7g+Pfcc885pv18Gg8AANAyxVgCbzQau3TpctZuGRkZ+fn5zRDPpSHsdNU893ao1iTUqZUDe4tTEkNmq/u3naEaU81zb6e88m9xKiYjXNYsQdP3xm8YYm5Nuzsy450h5ta0aa8eempt3Yp++sEGSUJ0gzydn83fHfMdETPiIBfspx/0p31rgPUT0ReVH3VWdxcx4mgHCNCsznoXuoFevXo1USQNGI3Grl1PumScnp5usVgadEtMTDzfH4E3adIkl8sVDofP0Mflcm3btu0CDg4AANBsYiyB79u374YNGw4dOtS2bdvT9amrq1u/fn3fvn2bM7CYZl/+XajWJG3XOunp+wVKBd+om3Cd6Z2F7q27LZ+sSHr6/uhGCNG1rOqTAOvvo7+qnbJj/fYcZYee2j477VtX1iye1uqhaIV3Bu6w65vaZUQU5IIake72tH8kSJO/qflCzIjrAjU/mtZelzA22jECNKvznT3OcVwTRdJA27Zt//zzT5ZlBQIB3/Loo4+azeYG3Q4cOJCQcCGXC8eOHTt27FnO93379nXp0oVB2RcAAGjBYiyBf/DBB7/77rs+ffrMnDlzzJgxmZmZ9fdWV1evXbv2xRdfrKurmzp1arSCjC1cmHVt3kFE8ffdHsneiYgRiw3Tb/Ps2u/dUxS2OYQ6TfRihGgqcRf9aftdIpCMT5586t6JqVP3Ondttf56tWF4C5zF/U3NUlfIIWYkQS7Az94fkTBui+Unc8BIRKtrlvXVXa0V66MdJkDzmTFjRrRDaNzQoUPfeuute+65591335XL5USUl5fXoM/8+fMLCwvvvPPOKMQHAADQMsRYAj9s2LA5c+b86y9arTYuLk6j0bhcLovFws9bE4lEc+fOPeuFduCFrXbW5REZdOJT1ooTKOXSdq19ew8Gj9Yggb88ccQtrlzAEddT29cZdji9jlP7dNNcscP227KqT57Oea1FLSlX7Tv2s3k9wzABLtD6r9n7EoFkQsod8468KWJEPta7smbJnRkPRDtSgOYzZ86caIfQuOeee27lypUff/zxkiVLxo8fv2jRovp758yZs2jRoj/++EOpVP7nP/+JVpAAAABRF2MJPBHdd999Q4cO/fDDDzds2FBUVFReXk5EQqEwISHhiiuuGDdu3F133ZWcnBztMGMGFwoRESNufCYw386hJO/lKt+xo8JbRkTbrL9us/56hp5lnoP7nLu7qHs2V2hnt7TqozAXFjBCAbH1Z+/n6Qb+bFp/0L1fQILNlh+vNlybpciJbqgALVBJSUllZeWgQYOa5+k0Gk1+fv5LL730/fff2+32BnvXrVv3xx9/9OrVa/78+e3atWuekAAAAFqg2EvgiSgnJ+e111577bXXOI7zeDw+n0+v10dmzf0d//vf/856d4IvgXPqxLwYJYrTMSJhyGhhXR6BSnHSPo4LHD5GRKJEQ3SCg2hLlWa0U3YKsL6z9pQKZSnS9GYI6RztcezY59xNRCwXTpQmb7X+stX6S2SvWqQmIpZYIlpW9cmTbWdHKUyAForjuJdffvnLL790u93N9qRqtZr/cD9118svv/zee++1adOm2YIBAABomWIygY9gGEapVCqVyot1wP3795eVlZ1LzzNXso0hjEQs69reu2u/dem3hntOWgzMsfaXsMUuTk0Up2NEw2UqUZryZNuXox3Fhch37Ihs1/lr6vw1p+tZ4i5yh11KoapZ4gJoWTiOe/755xctWlRXV1e/PRwOe73ec1n2pXn07NmCRvcAAABEUWwn8PXdcccdX375pdfr/TsHmTdv3lNPPXXmorsHDx4cMWLEpVSlVn/raN/eA87vN4Vtds3IQaKEuLDd6dq4zfnjb8Qw+smoJgCxZ2zyrfucu80BY3fNld00jZTd9rHelTWLA2xgXPLtyN7hsvXBBx/MmjVLo9GkpKSUlJQkJyenp6fb7faSkpK8vLx33nkn2gECAADASS6dBD4YDPp8Zx/oe2YCgSArK+vMfTwez998lpZGkpWe8MhU0zsLPdvzPdvzI+2MSBh353hF765n+N4mwhF32HOolbyNkBE2/7NDU3CHXZ6wmyEySBIj09E54g649jlDDgEj6KzuIRXI+HaWC++0b/OE3QJG0EOTpxKpI+1/2Lb4Wb+IEXXXXhlJvDni9jp2+lm/WCDuqOomEUgLHDv5UvPWoHmT9UcBMZGDR8gEigAb2Gr7dWTS+BZVfg+g2Xz88ccqlaq4uDglJeWOO+6wWq1r1qwhopdeeunTTz/t1KlTtAMEAACAk8RSAv/ee+999dVXp9tbWFhIRIMHD460/Pzzz80R1iVB0btr2tvPONZv9hUUhx0ugUIu65itvu4qcWpSVOL5wbh6WdUnQ+NH3ZZ2T1QCgPo44rxhj0J4Yq4Ky4V/s/5sC1oCbGBA3JAkaSrfbgrUfXT0bUvAxBAzPmVyb11/vr3EXfRa6TMsFyaiG5NvG510fLJGgWPn2+Uv8dvXJYy9OfVOfnuPY8f7R17nt6sSjt6SelekfX7F//Hbw31jTrTb/5hz+BV+e0TijTelTDnqPcw/POItPfNPV+uv9oW9cqGCiGxBy6LKeSzHyYXy6xJuzJC3jvy8f9q3MsQohMpcVScR03jRR4CYU1ZWNmDAgJSUFCIaNGjQzJkz+fYnn3zyk08+efHFF1999dWoBggAAAAniaUE3uv1/vLLL2fuc9YOcDrCOJ3+ttF02+hoB0KOkG117TIi2mhef7VheJqsVbQjuvRxxO117KoLVHvDnjzdwETp8TUFa/xVb5W9YArUEtG4lEnXJ07g2wucuz45+i6/7We9kesspkDdAdd+ftscODGlVi3SpEjTAqxfLJBkyk+UocpS5PSPGxJg/UJGlKcfGGnvoOo6PGGMj/UKGdHVhmvrt49KnOAKOwQkuCpuWKQ9R9lxkOE6d9jJENNb25+Ibk6d0lPbxxa0fG/8xh/2cgz11w9p81e1+dpA1ZdVC32sl4hGJo3ns3ciOuIt22Xfzm/rxYZIAr/Lsf2DI2/w26OTbr4x+TZ++7C39O3yl8JcWC3STEydGinC72d9m8wbiEglUvfU9jn15j9ACxEIBFSq4yNZWrduXVVV5fF4FAqFSCQaMGDAhg0bkMADAAC0KLGUwD/66KNJSUkzZszgOO5///vf8OHD6++9//77V69efezYsWiFBxfLiupF3rBHIpAGWP/Syg8fzZ4V7YguHQHW/1X1wlp/tTNkH544Nk93PGc+4Nr/v/IX+W1zwBhZGj3A+u1BK0OMWqSNE8dHjtNB1WVs8q0hNqgSaeon3u1VnV/t8AFDjJgR68RxkfZkadqLuY1MptWItHdnPHhqu1yoiNxdb9A+PmXSqe0qkfqO9On1W0SMuL2qMxH10V91av+O1G2wYQTLhf2sP5K9E1FXTa9nc163BS0ssZ3U3ev9vF2vTbjBGjQH2WD9GfVBNuANuwNswBVyVPmORhL4nfZtS6s+4rfHJE0ckzyR3y5xF80pny1ghBqR9va0f+Sqjo9PDnKBP6xbJAKpThyXrcgVMBdhTQ2Ac5Gbm1tcXMxvZ2VlcRyXn5/ft29fImJZ9sCBA1GNDgAAABqKpQSeiCZPnjxgwIDJkydPmzZtxowZr732mlwu53cpFAoiSktLi2qA8HdVeMu2WH4SMaLHsme9Xf5yoatgl317T21etONq6XysVyqQRSZye8OeeRVvHvUetodsIxPHjUu+nW8/6jv8o2ktv13qLo4k8K0V2UPjRxGRWqTtpz8xD6WVPGtO588lAmmDp5MKZDck3dJoJImSmFmzQMAI62fvRMQQ0+ia8EqhamLq1FPbc5Qd5nRe5A17g2zAIEmItPfS9rGl3GEPWYNs8Er9gEh7mAsHOH8gHHCEbMd8hyMJ/O+WXz47NpffvjXt7mHxxwfCHPMdWVr5kUQg0YsNoxIn1H8Kb9gjFkhETIz9DoeWZvDgwW+88cZTTz31yCOPtG7d2mAwzJ8/v2/fvl6v96effkpPb0FrQwIAAADFXAJPRFlZWb/++uvs2bNnzZq1YcOGRYsW9erVK9pBwUWzpPIjjrihCaOzFbljkm5ZXLlgWdXHXTQ9xIwk2qE1B5YLe8KeSM22U4W4UP2cLcD63zk8+7DnkCfs7h83JHI32xGy7XPuZjmWvxke6Z+tyP13m+fDXFgn1mfIT9RrlAnkpys3cGr2DvWJGYlY1PA/p1QgG5k47tTO7VWd3+u81B12BthAvCQx0t5De2WNv9IUqAuw/nbKjpH2I57SIlcBv50iTR+WcDyx32bdNL/iLSLSivT3tX60nfL4hQA/69tp36YUquIliZh7Audi5syZy5cvf+WVVzIzM++999777rvvpZde2rNnj8Viqa6uvvfee6MdIAAAAJwk9hJ4IhIKhc8+++y11157++239+3b97nnnnvyySejHRRcBNttmw+692tE2tGJNxHRYMOIX80/HPMd2WBcMzJxfLSjaw7vHJ5d5Nz7TLv/Zsha12/niPv06HsHXPtMwbpe2r73ZT7Gt3tZzyF3UYANSAWySCU5IkqSpr7a/gOGGK1Y3+Ambf2R4dD8hIxQI9I1aNSIdI1OGegfNyRD3toSNPtZXw/NiXEoerEhXpJoDZodIZsn7I60bzB9+3X1In77vszHIkUEq3xHfzStlQvlCZLkAXFDUIQPItRq9c6dO+fPn9++fXsimjlz5sGDB1evXh0Oh6dOnfrEE09EO0AAAAA4SUwm8Ly8vLw9e/Y8+OCDzzzzzNq1awUCzBqNbQE28FX1QiIanzKZH9gsYAS3pt39eunMNbVf9tMPrj+n+pK02769wLGTiN4sfU4j0mUrc6ek38/v8rO+7bZNATYgYITaeumfVqR/o8NHRJxKpGlwtPo3eCF2tZK3aVWv7B8vV9Xpvx3mE5Gf9dWvkJenG2gO1FmDFl/Ymy7LjLRvt23+xbye35YLFZGpE3scO1bVLFWJ1EmSlHEpkyLL8sFlJT4+/qmnnuK3xWLxsmXLvF6vQCCQSjH6BgAAoMWJ4QSeiFQq1ccffzxy5Mh7773XYrFEOxz4W76r+9ocMLaSt+mvHxJp7KDq2lObt8u+fUXNokarncUuT9hd4i6q9FXoxPp++sEhLris+lN+lyNkd4TsYsGJgdkygXx2+7kB1h8vSWpwR/0M4+3hktegvn2CJCly0ae+kYnjkqWp5qApzIa6qk/MOTrqLa/wlhFRIeV301zR9a/6fN8bv/my+jONSJcqy/hHq4cjQwY44iwB06nDOuASEykuAwAAAC3NpfBH2IQJE/r27btq1apoBwIXzho0rzeuIqLb0u5uUIL7ltSpex27f7f8PMQwotECYzGBIy7EBevP5H+9dCa/RLmAEfbRXfWDcXWdvzpVltFJ3W2D8Vud2PBo9gv1j1C/CDzAeZEKZH31g05tH5108xXafqZgHcexXTQnEnsRIyJibEGLPWg1B4yRBH5p5Yd8EcRkadrj2S9GBsX4WG+lryJOHK8V6VFCP7bs37//zB06derUPJEAAADAubgUEngiSktLe+CBB6IdBVy45VWf+llfnm5gpBxXRIIkaVjC9evqvl5S9eFTbV+NFFqPFXscf3xXt/Ko97CQEc5uP1f911j3nto8pUiVLsvsrO7hCjvX1q0goompUzuquhU59x3zHf7JtC6y7jpAE0mRpafIGlYavyZ+1GDDdfaQLcgGEqUpkfZMebZBkmALWqxBc4ALRNo/OfruDttvRKQR6V7I/T+tSM+320PWI54ygyQhQZIsEVwWdShjTufOnc/cgeO45okEAAAAzsUlksBDTCtxF/1h28IQc4Wu32Fv6akdOqt7/mhaW+o+sN26qY/+6uaP8BzZg9ZDnmJ70HqV4drIGOMtlo0l7iIiSpdl1h94PDrp5tF/bX909B1v2NNTm9dZ3YOIbku7+7+lz66t/WqAfsglP/MfWiYBI9SLDQ0a+8cN6R83hCMuyAbrJ+RXaPtZAiZz0ChmxAISRtoXHPm/QlcBEamE6lc6vB+ZY18XqDnmPRInNqTI0hvMAoBmNmPGjAYtTqdzx44dhYWFmZmZjz/+eFSiAgAAgNNBAg/Rt7p2GUccEb13+LUz9/ymdlmLTeB/Ma///Ng8/gcxSBK7/TWd+M70B4bFj06TZZxaZ453xFv6u+VnESO6KWUK39Je1aWXtu9O+9avqj+f1upfzRM/wDliiGlwO723rn+k4n19g+NHCBihOWiUCeT1r159cPh1/lKdSqR5o8OCyFKFx3xHqnxH+TXwkNg3jzlz5pzayHHcG2+88fjjj591gD0AAAA0MyTwMS94rMZfcpgLBIUGnaxTjkAee3/1dlJ3d4ec9Vvq/NUe1iNhJKmyjPrtOaqO1AKUe0oKXfmVvooRCeMy5K35RrlQqRHp0uWZucpOHdVdI51VInWu6rSTSDnillZ+xBF3bcIN9deBuyX1zgLHzq3WX4bEj2ijaNdkPwpAE+ql7dtL2/fU9lFJE7ZafzEF6tQibf3Efk75bGOgloh04rg3OnwYmU5f4S03BmriJYmpsoz6hSSgiTAM89hjj3399dfvv//+iy++GBeHcUAAAAAtBRL4GBY8VmP+YKmv+MSYc4FMqrnhGu346xhhLNWRui5h7HUJYyMPC535b5Q9R0QBLnCV4dpBhuHRC60Re527/q9sFr+dJmsVSeDzdAMjq3Odu+3WTQfdhRqRbtTJ093jJUnDE274tu6rRcfmPdvujZib+Q9wBqdL7MelTPrT9rs5UGeQJNYvhvdG6UxX2ElEKbL0l3PfjbRXeMvsIVuCJClRkoLieRddr169tm3bFgwGox0IAAAAnIAEPlYFKqpqnv0/1u0VqlWy7u0FCkXwaJWvqNS2fF2wqi7hX1OIicmUj+XCS6o+JKKO6m6FzvyVNYuv1A1QCJVRCeao7/Ae+x8H3YVXaPtdbbiWb0yVZnTTXBEnjs9Vde6pzfs7xw+wgRU1i4hoQspkuVDRYO+opAlbrBsPe0u3Wn/t11gJcYBLzOmugt2UOmW3/Q9ToK6VPCvSyHLhl0ueDHIBImqv6vx49kuRXZW+Cl/YmyBNitTPhwtQVlam0WiSkpKiHQgAAACccJYE/siRI0VFRddddx3/8OjRo++8805+fn44HO7evfvUqVOxwEx0cJx57mLW7VXkdY//52SB7PgMUl9Rad2rH7i3/Km4squyX88zHyNksvqLDklz24gSG5aqiqKfzOuqfEcTJckPZT3zVtkLxa59q2uXTUyd2vyRuELOWQf/HebCRCQTyCMJvEGS8K+sZy7KU6yrW2EOGNNkrbprrnSHXad2GJk4bknlhyuqP++l7YMpwXDZGhg3dGDc0AaNAkY4JnlioSvfHDDWn37iDrueP/gwf+b21V99T6uHI7tMgVqWYw2SRCEjJCAioiNHjpza6PP5Vq1a9d133w0YMKD5QwIAAIAzOFMC//LLL8+cOXPkyJF8Ar9mzZrbb7/d6Tw+V3njxo3vvvvuf//733/9C0W2mlug/Jj/0BGhXpvwrymMRBxpl3XI1k8ea/5gqXPDb40m8KzL49t30Lv3gK+gOFhtJCJF766JT/yjfp9gVV31f94Q6jWyDm1lHbPlPToJVA1vDjcRd9i1pmY5EU1Mu1vEiG9NnfZCySM/mtYOjBuaJmvVdM8b5AJFzoJ9zt1yofLG5Nv4RoVQOdgwgiOug6orXxz+4nKHXeuNK4mo0lfx4P7JZ+hpDZp/Mq0dmTj+oscAENNGJo4bmTiuQaNCqBxiGFniKTIH6mQCeaTdEjQ9WXw/y4UFjHB04k1jkic2b7AtVOvWrU+3SyQSzZw5sxljAQAAgLM7bQK/bNmyZ555Rq1W33jjjURUW1t71113BYPBWbNmjRkzRqFQbN68+emnn37kkUd69+7dr1+/ZowZyF9aQUTyHh3rZ+88ZZ8e5g+WBg6dfFOFZZ0/bXX9+Ju/7Cj9taivQKmQdc7R3nBNY8/ABY/VBI/VODdsYYRCWeccRZ/uit7dhDp1E/w0J3xdvcgVdnZUde2u6U1EGfLWA+OG/mr+4Yuqj//d5vmme953ymfvd+4hIolAekPSLfzdOQEjuC1tWtM9qZARJkpSrEHzWXsyxMSJ45suEoBLCUPMrWl3n9quEmp6avLKvSWWgMkZdjR/YC3TpEmTGm1PTEy8+eab8/L+1iwhAAAAuOhOm8C/9dZbKpVq3759rVq1IqJVq1aZzeZ33333gQce4Du0bdu2d+8WG9gAACAASURBVO/evXr1evXVV1evXt1M8QIREXFeHxHxN8Y5fyBw+BjrD4ri9eLURIFSTgzD+vzEcZFp8Kb3Frl+/YOIGLFY2r6NvEuurGuutE0GCRop+yROTcz4+NVAaYWvuMy3p9C7v8SbX+zNLzYvWC7v3iHhwSkXcEOedXmCNUZGJBKnJp560YFX5Tv6q2WDgBHeWi9tHpc8aYftt/3OPQWOnV01vc73eU/FEXfEW1blO3qlbkCk/HU7ZccQF+yg6tpL27fZxtbKBPJZuW83z3MBgEQgub/140TEciwq3kV8/vnn0Q4BAAAucWGHK2yyMBKJODWx0ewDzstpE/jCwsKBAwfy2TsR7d27l4huuumm+n06d+7cp0+f3bt3N2mIcCqhQUdEwSNVprmL3Zt3cMEQ3y5OTlBfO4A4TmTQ1S9iJ1DKxalJultGKXp3OV3+XB8jFErbZUnbZWlvuIZ1uj1/7vVs2+MtKPbu2h+oqJR1zDn3UP0Hy62LV/sKD/F3/hmxWNmvp37SDUK9tkHPpVUfsVx4aPz19UfLq0Wa0Uk3L6v6ZEnVhx3VXUXM2YM//rys79RJ46XuA+8fed0SNBGRmBFHVq4enXTz6KSbz/2HAoDYhewd4O9g3Z6wzSmQS4VxKBIJAGfh3V1oW7bW/9fQYIFSrhrSV3fzyFhc97rlOG0Cr1Qqa2pqIg+Tk5Mb7SYWi0Oh0MWPC85I1rkdCQXe/CIiIoFA2jZToJAFjtUEa4yWhSuJSNatQ/3+cVNvavQ450KgVqoG91EN7sO6PcFqo7Rt5rl/r/u3XcZ3PqUwy0glkvRkNhAMVta6ft3uzS9MnvWQOPVEceOd9q37nXuUQtUNSbc0OMjQ+Ot/Nf9Q46/8ybRueMKYc3nejaZ1iysXTM34Z/+4IfXbq/3HLEGTXmzoqe3TFNPaAQBauDfffPO8+v/73/9uokgatW7dujVr1pSUlGRnZ0+fPr1Hj4a/qB977LHDhw9/+eWXzRkV8Ly7C21frfcfLOevyIvi9errrtKMHsIIURUSABrhWP2T5fNVxHECuUyUksh5vcFqo2PNRu/uwuQXHxKqVdEOMFadNoEfNmzYkiVLfv3116uvvpqI+FK033zzzT333BPpc+TIkW3btg0d2rA4MDQ1oVYtitOFjBZGKNRPHqMeNoCRSoLVdab3FvuLS4lI1qndRX9SgVJxavbOBYO2L7+TZmcqruzaYOG6kNFieu9zCrPaG67R3jySL5UfMlnNcxd5Cw4Y3/ok9b+P86NoQlzoq+rPiejG5NtVoobT7IWM8Pa0e94se3517bI++qu0Iv2Z43SE7CtqFnHELax8/8vqhf3jBt+UMoXfNSDumk7q7jpxHJZVB4DL06OPPnpe/ZszgZ8+ffq8efP47Z9++mnBggVvvfXWQw89VL/Pjz/+uGfPnmYLCSLsX/9gXbqGOI6RiEUJcazDHTJZrYu+8e7an/T0/YxUEu0AAaBl8Zcctny+ioj0k8dqRg5ixCIiChyuNM1ZGDhSaf5gaeJj95ztGNC40ybws2fP/uWXX0aNGvXEE09MmTJl0KBB//znPx999FGFQjFhwgSRSPT7779Pnz7d6/VOm9aEhb6gUcGqupDRQiIhFwpbPv3a8tlKRiLm/AEiIpGQQmHPtl2qq3s3QySBsqP2r38gIkmrVO3465T9ekTSeOf3m7hAUDmwt/6OGyP9RfH6xCenVz70UuDwMe/eA/JuHYjoB+M3tf4qhpgj3tLPjs1t9ImkApk37FlVs3RK+v1nDmllzWJv2ENEQTYYZG0sx9bfqxe3oDXzAACa2apVqxq0vPfeexs2bOjRo8f48eNbt25ttVp/+OGHNWvW3Hbbba+99lqzBbZs2bJ58+a1adPmtdde69Kly86dOx999NGHH344KytrzJhzGnsFTce396B1yWoSCvS3j9WMuIoRi4nIW3DAPHeRr/CQ5fNVhmmYhgYAJ3Gs/ok4Tjt+uHbMiXu9ktZpSc/cX/ngi57t+aEakygZRZovxGkT+IyMjPXr11933XUzZ86cOXOmSqVKSkpyOByTJk268847RSKRz+cTiURz5869/vrrmzNiICL+NruyT3d5tw7ODVv8pRWcPyDUqOS9Oquu6VvzzP95Cw6wXl8zTC+R5rYx3HurfcX6QEWV8f8+ti1P0o4brhxwBSMU+ApLiUh9Td8G38JIxKqretu+Wu8rPMQn8PmOP4mII26z5cczP12BY2ej7dX+Y/agrb2qc4W3bLPlRxEj6qXt84ftN2KYAXGNltkHALgcNUiGly9f/uOPP86aNevZZ5+NNM6YMWPevHnTp0+/+uqr//GPf5xyjCbx7rvvymSyDRs2tGnThohyc3M7duw4cODA6dOnDxkyRK3+u2ug+P3+ffv2nblPaWnp33yWS5X9mw1EpJ94ff2Va+Rdc5Oevr/q0VddG37T33q9QNlMK87CZavQFMzQiNQSDKKMDb6iQ0SkvqbhUmVCvVbeq7N7y5++okMqJPAX5EzrwHfq1KmsrOyLL76YN29eQUFB5IONZdm0tLQJEybMmDEjPT29WeKEk7AuDxEJ43T87HTiOC4Q5AewccEQEcP5A749RYq+zTHNWz2sv2pwH9ev2+1f/xCsrDXNWWj7cp1u3HDW6SQioaGREe98I+tw8Q/vyvjnAddZ/q7iZSqyG7SUug8sqfqw3FNCRLPbv7ek8iOWY69NuOHm1DtlQsUFLEFnXlxtfP9oxpu5yryGZfYAAC4x8+fPz8rKqp+98+6999633357+fLlzZbAFxcX9+vXj8/eed27d58zZ85dd931+uuvz5o1628ef+rUqUuWLDmXnizLnr3TZYXjfPtLiGHU1w5osEeckSLr1NZbcMBXVKq4oktUooPLxAFL6IavTCOyZXOGnWUqJbQQYaebiETxjbxffGPY7mzumC4VZ0rgiUgsFk+ePHny5MlE5PF4PB6PSCRSqVQi0Vm+EZqUQKMiorDRcvwxw0Smn1k/X0nEMQKBvFfnZouHEQnV1/RTDerj3rzD/vX3wao609zFQqWciMIWmzgloUH/sNlGRIK/alckS1OTpakX9tTbbZvKPSVyoaKvftBhT+lB936NSMuXlL+AJehMn1ZVPFhMLHdozO62q3oo+7TcHJ4LsKyHZf0s6wlzfpb1sgKlQNZOSURhZ8j5syVkDhJHRMRIBHG3JjNCxl/utSyuZgMsEXE+lvWd9EcqF2RZV5iIhHHitBfbCjUizy5H3dyjnP94N6FenPZSW6FG5Ml3mj6u5A/OE8gEjOx4ZW9GxAhVIiISxYvjbkthREzIErSvMwmUQiISSATiFImip4aIiOW8hW6+XagWMiKBQClkxLiyDtB8/vzzz/79+ze6q23btps2bWq2SLxe76mZ85QpU+bOnfvmm29OmzYtsizOhbnmmmsOHDhw1hgKCwsZBr+FTsJ6fFwwJFDKG73HLkowEP4Qh6Zn9bIckcWH62sxQ6hUhB2ukMV+ag4ftvCJgDIacV0KziMPVygUCgXGR7UIso5tiWE8u/aHTNb6Z0Wg7Khj/Sa+w7ksF3dxMUKBalCe6qre7t93Ob79mVg2XHbUtXGbrNNJy85xwaBryw4iknVoeDv9rEyB2j/tWzuqurWSZ/EtNybf3kOb11bZnuPomQMziGhcyiS5UEHnvwRdJHtX9NR4djkOjW2OHJ4Lc5yP5ZNY1h12bLSEjIGwPRR2hhixIOnBVgKl0LnZWvPa4bA9GLaHWC/LusNhR+NLP7Rb31M1QF/1Qpnxg6P128WJEs21htq3j5g+rDyXqHQ3JGquiTMvrrZ8UXNS+5hEzTVx5k+rTB81chw35wlTmIic5CKiEBc2ODOUfbQVrx2sWXMs0s1HPsN/MuQdlJZlNfZvjQEK+shf/ziMRMAIiZEIGBHDiBmhTmyYlGJoFR8o9Tp+NDNSASMSMBJGFCfWDo3XxelYT9iT7xQpxbo4HSMVCFRCsUKsjdcJ1UKGYXQ6HRGJxWKVCsVOARpKTU3Nz88Ph8PCkwuJh8PhPXv2ZGRkNFskOTk527Ztq62tTUo6sUAJwzDvv/9+Xl7e3Xff/f333wv+xurBU6dOnTp16pn77Nu3r0uXLkjgGxDIpSQUsF4/5w+cWqwuZHMQkVCFvw8B4CTSDm092/e4ft6mu2lE/faww+XZuY8uKBEA3mkTeLlcrtVq33nnnZtvRmGSFkeUaFD27eH+fVftS+/Fz5jMF4dnvf7aVz8gliOG0U2KXskfgUA54ArlgCtCdebKf73k+nW70KDVjb+O/9QPW2ym95eEakySzDR5t/bnflSWY98/8vou+zaOuF7avg+0foJvlwsVHVRdieibui9MgbpW8qwB+hMz9M59CTrzwuPZe+oL2UkPZR65r8iypLpkzO62K7qpBlz4YC3/IU+wNhAyB0PGQMgcDJkDIXMwZAmGTMGQKRC2BMOuMCMRtPuupzJPWz27rPbtivrfruiq1o6Kd/xgdv5iaXBkRswIlEKBhL9rLRAoBaIEibStgogMtyaHzAGhSsTX2henSlUDdESU/O/W0kz58dvyUoFAfuJP4WA45Aw6neR2eBwexlvl+d31hcvWymacUOP2ud0+j9PrdAXdoTlh96tup83pS/Ta3XaO4+xuO0ec3eNo/OefcZrX5bkzv2onPzxClH+angvOeJzGKBQKiUgiCgmVUoVIJFIp1GK5WJeuFwqFGrWG8ZNGr5XIxWqdRiaTyeVylUolFov1er1UKlUoFGq1WqFQKJVKrVarUCjkcvl5RwDQkgwYMGDBggWPP/7466+/HkmPWZZ94oknjh49OnLkyGaLZNq0aTNmzLj66qs/++yz3r17R4Lp1avXY4899uqrr95xxx3vv/9+s8UDJwgE0pzW/uIy15Y/G0xnDVtsvn0HiWGk7bKiFR0AtEya6wd7/si3ffWdUK1UDRvACAVEFKyqM73zGev2ynt1rr+eNJyX0ybwPp/P5/PdcsstX3/99XvvvWcwoHZ3y2L4x8RgZW3gSGX1k6+L4vUClSJ4rIYLhYlIP3nsea3W3kREiQbDfbeZ3v3c/vUPzu82iTNTWZc7WFVHLCdUqxIevovO515KgPPvc+4WC8S9dQNGJU5osNcaNK+vW0VEt6ZOEzAnDnuOS9CZF1YdmXE8exfHS4oH/JH+ejsisiypPjQ+/6w5POsOW7+qDVT6glX+YE0g7Aq3mtNelqOom1Nx7D8lZ/65GCEjTpUKNSIi0o9LChzzC7UioVooUIskKVLNtQYiSn2mjfa6eIFCKFQLBQqhQCEU6s40dkbRS5P1SWciYlnWZDKZTKaSP38zGo11dXVGp9FisdhsNqvVWv+ry+U6c5yXDI/H4yEPEZk85hOtxRd4NIZhdBqtQqFQqJQajUatViuVSpVKpdVqNRqNSqVSq9UqlUqv1/PbGo1Go9FotVq9HlP4oEWYPXv2unXr3nrrrR9++GHMmDHp6enHjh1bvXr13r1709PTX3755WaL5IEHHigoKJg/f36fPn3EYvGOHTu6devG75o1a1Z5efnixYtXrlyJCepRoRk5yFhcZvn4K6FKqcg7/r4Ea4zGtz7m/AFl/55CfcudcQYAUSHrkK27ZaTti7XmD5fbvvxOnJ7Muj2BimpiWVFyfPz0W6MdYAw7UxqQm5v7+OOPP/LII506dXr55ZfvvPPOBkPsIIoEKkXK7H/bVnzv2vBbyGQlk5Vvj5s6XjNycHRji1Bd1VuUGGdbtNpXXOovLuMbRQmG+IfuFKcnn/l7D3sOVfjK++uHCBkhEVn/WzNj9b/S5rbVZTScUU9Ey6s+9bO+K3UDclWdGuzqpO7eRd1zr3PXNzVf3JF+36nfWz97F2pFR2YUEUel4/Ozv+pGfA4/Lj/z3VxRojRwzB+o8AWO+YLHfIGjvrA91PqzzuqB+rq5R6teOKl2sa/IJctRyDqqFN3UAqVQZBCL4iWieLHIIBYZJMI4scggFidIhHEiofrEOajopcn6rJHKBYxUoOqva/RVqq2traurO3bsWF1dXWVlZU1NTVVVVV1dnekvHMc1+o1wUXAcZ7XbrHbbBXwvn8nzXyN0Op1er+c3IvR6vU6nk0qlFz1+gPj4+I0bNz788MPr1q2rX6R9zJgxr7/+ejNfu587d27fvn0XLVpUVlZW/3eXWCxesmRJ//7933nnnUOHDjVnSMBT9u3hu6af86ff615fIE5OEKcnh+1Of2kFsaw4NTHubgzVBIBG6CaMkGSkWL9YGzxazVfKYCRi1aB++ttvwLoVfwdzur/vGYZp3759UVFRVVXV9OnT16xZk5ub+/LLL48fP76ZQ2xp+DlyAoEgHA5HOxYiIuK4UK257u1PAyWHNaMGxd3V8O50SxC2OUI1JuemP9w/b+eCQUYsUo+4Wjd+eKNnb12g5qOKt0vcRUT0UNazXTW9qp4vrXnjMBGJ4sQ53/aQdz1pPaFS94HZh54UC8Qv5b4bL0ls5ID+6mcO/DPMhZ9t90Zr+UnzbRpk70cfPkBEyp4a904HI2Jk7ZW+Mg/nafyGDyMRtFnaVTvc4C/31r1bIYoTi5Ok4lSpNEsu63Axy3LU1NRUVFQcPXr06NGjR44cqaioqKysrKysrK2tDQaDF/GJ/iZ+tDkR6XQ6hmGEQqFGoyEifvB5pJtcLpfJTixwKJFIlMqzv1xWq/XUxnA47HAcH70fCoWcTmeD7UiHQCDgdrsv+EdrCRQKhV6vj4uL09cTeWgwGOLi4uLi4gwGAz/tH5rU1q1b+/Xr17dv399//z3asVwcBw8eLCwsrKqqatWqVceOHetXg29RampqSktLT1d4729qcZ/vLQrHOX/YYluxPmyx8w2MWKQalKefNFagxGQiuMjWl/kKjCf9hVPlDK855E1VC0e3Pen/m1hAd3ZR6mUXXiADmkGozhwyWhipRJKZyoibu0rXeYmJz/ezF7FLTU1dvXr1V1999dRTT02YMKFdu3Z33XXX5MmT09LSmiE+ODuGESXHUygk1Gt1t4yKdjSNE+o0Qp1G2r6N7sZrbV9869q0w7H6J9fGrboJ16mvu5oRnTSyo9C5p8RdpBSqBhqGdVR35bN3Rswoemjcf9hLrt/dIIf/svozjrgAG3i86CzLHa2qWfJQ1onVkjy7nEceKCKOFD3V5k+r/OVeYijj9XYCpci9s5ALcd59LiIiARFLJGT045OkWXJJulSSLpNkyCQZMr74nDRLnvFm7t9/lWpraw8dOlRWVlZeXl5eXl5RUcHn7X6//+zf/PcIBAI+D9Rqtfyob36ad/1tjUYjkUg2+34oCRUKxYK81AETM6ZqNBqhUMhn7E0d5EXhcrmCwaDf7/d4PPxXPrfnvwaDQZfLxW/zjXa7PRgMOhwOr9fr8/lsNpvf73e73Tabzev1er3eZoucXweksvLsZQiFQmHcX/jE3mAwGAyG+Pj4+Ph4Qz24qw8R7dq1a9euXbSjOLvk5OTk5LMM4IImwTDq4QPVw/oHjlSFzFaBQi5pkyGQ4XcINInnNttN3kZun1Q5w/N2N5z0l6QU3tYRt3NbNFGiQZSI6dgXzblWoZ8wYcLYsWMXLFgwa9as//znP08//fTgwYOHDBkycODA3r1717+fBlGR8vK/uXBYIG/pb4QoIS7+n3dorh9iWbjSt/eA5dOvHes36e+eIO2eG6kSPzBuWLI0PUvRViqQVb1YxmfvWZ910Y6IL79jr22N8eCIXTlrehxfioxIIzr7/UaGJU21LmNXRs2Kw76DHsOkFPVVen+ph6/o5tl1/M6tfkxCwvSMsCscsgTt64yu32wCqUCUIAkc8yk6q1rP78iILk6aWltbW1RUVFJSUlpaeujQIf5rU0xEj68nMTExISGBv2fLD8yObPC3ys/qkLt47aHP0wXJLMeVcvuZ5HC8Iv6ix9ykLm4teo7jbDab2+32er0Oh8PpdLpcLpfL5XA47HZ75KHNZnM6nU6n026387vsdvtFDKOBcDhsNBqNRuNZe6pUKv5/BZ/YR/6f1H8YHx9jbzGcGb+UWmZmJv/BfdaV1XJzL8LVSbh0CASSrHRJVnq044BL3Nzh+j+rA/VbKuzhL4o9rbTCie1PytXVUsHYdhgDApeXsw+hb9AeCARWrly5YMGCjRs38t8rkUia4Q5hy4EhdheLd9f+mqUrtiWXbe3nEmrUr3ec32Clt6oXy2peK+ezd90NCUTEBTk+hxfqRDmrT+Twp2LdYe9+l3evy1Pg9O51efe7WPeJ98twe0rmvI5hW+jIA0X274xcgCOilKfbpPynXh1djg5PL7QsriYiSRt5+597iwwXMuYnHA6Xl5cXFxcXFRUdOHCgsLCwuLi40QHhF0Cr1aampiYnJ6elpSUmJqanp/NfI6nXRaxbwRH3Usnj5Z6SMUkTg1xwXd2KbEXuUzmvMhQb995bGpvNFsnnHQ4H/9B2KqvNarFabdZQuPG1A5uaUCjkM3w+t+fTe347KSkpPj4+ISEhIaGRyhSXtpgYYtcofrDMtm3b8vLyIg/P4LKqo4HPd4AWa1tl4PY15j5pksWjcSP3InOE7J8dm5unG3ilbkC0Y4m+mPh8P4914HkSieSWW2655ZZbDh8+vG7dug0bNmzcuLEpIoOWJsgFxEzDBWD/DnnPTpsSfvnVsouIcqTJAjop1Tw1eyciRsxkLezC5/AlN+w+NYe3fl1r/87k2e30l3i48El/d0oyZLIOSnkHpayDSnd9AhEJdSLN0DjbmuM3Kt1/2FkfK/hrGlXQGPDs/Gt+dXXAu9+lvurslcM5jjt8+PC+ffv27dtXUFBQWFh44MCBv3mFS6fTZWRkZGZmtmrVKiMjg99OTU1NSUlpzmXMtlh+KveU6MWG6xLHEtFv1o2lngPbrZv66K9uthguJXyBunPv73Q6rX+xWCzWeiwWi5lXZ7ZYzS7PxZztHw6Ha2pqampqztCHT/ITEhL4rJ7fTk5O5m/v8xv16yBAFE2bNo2IItdcpk+fHtVwAAAgyr6qXrjbvr3Ytbe9qotGhBUlYsB5J/ARrVu3vv/++++///5QKDr3haA5VXjLXzn0n/5xQyalnWWe+XnpqrnCHXYPNAztou5Zv/3U7J11h30HPYoeakbMZH3WuXzKPtsaY8n1u5L+mRk0BqTZisQHMlhPuHzKvuPrnIsZeUeVvIta0VUl76KWd1WJ9A3vn3t2OioeLCaOkh/JNC+udmwwl91a0GZpV4FMEKwLlIzc5St2y7uqZFkK6zd1ZTfndykdyE96r8/n8+3fv3/37t35+fl79uwpKCiIlFU7XxqNJjs7Ozs7OysrKysrKzMzMzMzMyMj4xzHtzcpH+v9umYxEd2UMkUqkBHR+ORJHx+d82X1wh7aPL4FmpRarVar1a1atTprz0AgYPmL+S9Go/GvJN9kMprNFrPFZgmzF+c247kk+UqlMjk5mU/vIxspKSmRjZbw//xysGDBgvoPsbI6AMDl7Ii39DfLRiLyhj0raxZPSb8/2hHB2V14An/iEKKLcBBoyTjillQu8LO+n03f9dcPzlLkXNhx/Kxvs+XHeElid82VfEsPbV4PbV6DbrY1xprXyokoc0FHPnsPmQIlI3d7C10pT7VJeSqLkQjk7ZX29aawI1z1chkRiRIkiQ9kCBTCrIVdwpagooda3lnFSM5SklScKhUnSIJ1AU++M3tV99Kxe/gcvtXb7Q+N2+Mrdsu7qlt/2KlsYj4RSXOVjExARKFQaN++fTv+sm/fvgu4hqVUKtu3b5+bm9v2L9nZ2YmJjZTQbyHW1C63B63Zytw8/UC+pX/ckJ/N68s9Jevqvr4x+bbohgf1SSSScyz0ZbPZjEYjv+Kg2Ww2mUwn1iCsMZmMxlpTncN1gRekGnC73aWlpaWlpafrIJPJEhMTU1NT+Qyfv2/PZ/h8+8WtXwAAAHCZ44hbUvkhR1xvXf/d9u2bLT8ONlzXSt5CVyGBiNPm3j6fL1bKSl/OnBu2iNOSZR3bNumz7LD9dtBdyBDDEbe06qP/tH3lAqY977ZvX1j5gT1oTZSmRBL4RsnayhlRiAuJal74RZraQdKm/YFhOwPlXiKqnl3KhbjUmW2s35q4IEdE4mRp4v0Z2lHH62zpbzyPHFicIs35vlfJyF2OnyzEHWrzZbfScfmODeb93bdyAVbeVd16Qaeyifm+Mk9dO6vx9soP/r14x44du3fvPt/a44mJiR07dszNze3QoUOHDh1yc3NbtWoVQ+dXXaBmg/FbhpjbUqdF3nr+4exDT66vWzUwbmijC/hBIMzVulmW45yBExM6vCEuEOba6kVJ9QZ0lNtCReYQEbWLE7XVn/jNXGgKFtQ1vlhgrkHcI+nEuJKdNYESS4iIOiWIuyScaN9VEzhoafwaU69kVU6OLifn+CW5EkuoxBoioq6J4nT18dgCgUDhkdqCw7Vuq8lpMVotJqvFbDUZTXU1DrPJbjTWmerszotQk8/n8/ErL5yug0KhSE5OTklJ4fN5/mtSUlKkUSK5mHN8LitGo1Eul/OXSJYvX7527dqcnJx77733MqxucGFWe7zZIlEnSSN1UipCod99/ptUpwzfgr/njm8tWikzZ9jZp7YBwOlst24qcRdpRLo70x+IE8d/b/xmceWCJ9vORnmjFu60CXwLX16otLR0w4YNhYWFZrPZ4/EYDIa0tLS0tLTRo0enpKREO7pm4isuNc/7Qto2M+XVx5ruWQJs4Mvqz4hoYurUdcavD7mLt1s399Ffdb7H+c640h60Zilybk+758w9ZR1UWQvSy/9R6StPKBldyfpN9XYyNf8tDzuCIbOfiDRDDW2+6Cr4G4t/ynIUOet6lozc5dhoIaLsr7uVjssPmQJcJ9Hhaaa51zy021RQwBQ5dzvp3nM9ZlxcXJcuXTp37sx/7dChQ1xc3AVHS92zgAAAIABJREFU2BJ8UflxiAsOjBvaYPBFtjI3T3/VNuuvX1Z/dl9mE/4nvIj8Yc4X4pRigaje/5rD9lChKeQIsO0N4u6JJ/4EX3HA+2d1wBVkicju54jIE+SCLEdE3RPFLww8MU9s0hrz1soAEfVKliwfe6K+zrXLjEcdjQ9TT1EJt0w6cdVj3Eqzw88SUbxcsH3K/7N33mFRXF8fPzPbd9nGLh1EpCo2EKNYIvZeYo0p1miipidvikk0iaYYo4kxyS/Fkthr7IhibygqKCCiVGkL23ud8v6xBCmrFKlmPo+PD9w5c/cMLDvzvfcUr8rxmYdVRrvrKmJiNnpjzkPLucfUJgdZe3zOv+O1kXDQ5CqvNf1QhQ+eXDRpVsU4k8lccIWptvgB+AEAuAO4A/y7ZthJRL//vIfdbnfu5M/bnVVcpsANKimu6ivQKxSKcllZWVl5mUKBY3Z4Msxmc15eXl5e3qMMPDw8vLy8pF7efHcvX1+f0A6+Pt5ezvqOvr6+QiGV1+cCs9k8d+7cPXv2XLp0qX///rt27Zo5c6bz0ObNm69evUpp+DopxfAPVRo2gvzm4d6n+rNTrgObrVCqcCKQTo99Kjqu4RqdNeM+ptKiHDYrLKi1ytFbMfJysY3dRH1hKNoF3m4oHYUAPhX222TYCfv+sm0AMNXnZQ6NO8FrxlXNhWzT3RvaK71F/Vvbu/pCAplnvt+RE0JD/kPLpK7/DN55552wsLBFixZVHbx8+bJQKOzatWvVwalTp54+fbqpSmrXh/z8/MWLFyckJLg8umTJkokTJ37//fcdO3ZsMZdaC+PpJABg94ho1lc5Lv9HZVd04HQaKh3LoXE3Fa3fK/s7SvhM/dOesTKl+XraNE2YmuvfRdKL5xvwKEvChOtPqXTHlfpTGtJBAwDCxgIAQAlelEW6oBsJ7KLXMxW/FUNTqHcnFRp+dMqD0/kny85kDsi7eO5C5s0sbH69AuMRBAkKCur5Lz169KhPlnI74q4x7ZY+mY1yJnu/WPvoNJ9Zqbpr17WXh0jGhLtFtrx7AFBswNPkDoOdCBXTo70f7sH+ctN4ucRmcpBGO2nBSCtG6m2EU8iGiOknZjyUJVMOqLRWAgBELPTm3IeCdsVlneER4rnGeOW3fGa1B8ooLyaAHUWQquMcOsKkIc/4VNsuXhzllqawA0CsX7Wn/I/6CjKUrnfg+/lVm2Hls8JrMjsA9Kk+8xcDhTfKXIvn7h7VNgzf6OWWWm4HgB6e1WaY2Zl7S+6wYqStemFIo53s68sEACaT6VxCjZF1IGV2AIjxZv4x+uHO2Li9yjvFKlwnx40q3KCs+EKvwPVymlklsavK5eVmi9mlk/WnonleRobLozQmGxV4McVeDIEn292LI/Ziir1EEi++1PfN0d3HR/xH5f3atWv37NkTHh7uFOrff/+9RCLZsGFDZmbmJ5988uOPP3711Vet7WNbx5dOm+7G3WM0v6ZQV9Xwleq9P5sVw2r34SGkza7ecsB46gpZpUQ/KyxIuuQlhp/XY06koGgSOgrpSS97CVlP+tRHUUm8fL/zCb+/+xAA4NC4k7xn/l386+7SzT0EMUy0faw5Hpcf2CfbMlQ6ts4NwqcJ123kEAQZNmxYYmJinYOjR49OSEhosTYzKpWqT58+ubm5kZGREydO7Nq1q0QiEQgEer1erVZnZWUdPXr05s2bISEhly5d8vJqljtKG2kzQ+J40dyPCLPF7+flDO/m2iHROFRLs5bYCOtHIV+F8SIrG4lN8JoxyXsmiZGFb2WZr+s77ejGCqlWYtpGWBOVR0WIqPMhteHERajyDkE5bPc5U9yGxla1x5T2wjey9Ikqwko4RxheTMJM4AYMABie5W5RSSibwRs8oWQ5guswAPD+IMh32ZNm6SgUinNOEs/ezc4ioV7vZD8/v97/EhMTIxY/tSF8BIkvv/9OibWwt6j/IPcRLm3OqI6n6K524AQtC12DIk12Z01XOC4X23Q2Um8n9DZCbyP1dkJnI/U2IlxCr9pFpt9WebkJBwAeA0mb7117vCoMFOEykAH+rJ+GP6wA/+dtU5rczmeivX2Yz1VpJ3tL7rincvCZKAAIWAjyr/YGgGAxnUNt/tQbggSDnQAAjADzv+EARgeBE+AvoIlYKACYTCaZTHY7t/RWXplKXkozqRx6hUwmUygUpaWlZWVym61hqSv1hyf2uHTmZM+ePet/SrtoM1MfIiMjtVptTk4Oh8ORy+Xe3t7vvffe6tWrAaB79+4Igty+fbu1fWw5Gn1/JwFWaHQ7jSY2gvxP6t6XzcrHsNlylQLH+7NZP0vd2e0nZ8olJI6Xr/jFmnEfodE40ZEMPy/CYDLfTMe1BpTP81n5bgtreCtGRm4oY9ORO6/UXeyDgoKiNmqHcmnWEgdh/zDkqzBeF+cgCeSK7P8rMOdM8p45wWtGU72WNTPHkpKBKTQIi8kKCeT174XymqaJks6h+ThrsZWwoAjti7Af/NhNsIXWLu7v7SwQZenSpbm5ud98881HH33k0uDzzz/fvHnzwoULly1b9vvvv7ewey2JLTOXMFuYHXybT70DwJ7Sv2yE9RnRgDBeJFRNe1Yc6C8YYlyk0OwvB4D7o1PCjkdXavhc073fCr9X2RViG8c/QYIw6Lz+vVghgSSGWVIzLbezlP/bTpIEKyRal6ji9RZye/LNKQbtEQWgiFtfoXCcB6+3oOjd+5Y7RmYHDqHDHHIva/EIpntS8ccWwsLkdONb7xrLvstH6IjP0qDHXYArzGbzhQsXEhMTExMTMzIy6rP8JBaLe1fB19e3oS/aTrmlv15iLQSA69rL17WXH2NZaMnPMKR0F8S4PGrFSLWVUJoJtZXw4KKR0ocbv99e1cfnWrVWIlBIPzRFiv77lPtagqaslvx2orEQJDxMz5rXnXdbbucx0D6+1fa49j0nKdThbkyEy0A4dITLQPhMFHX1FL2gBw+AV3u8p2e1iHqKRoMiULltInl4164W7cbj8ZwFHac8YhK9Xl9aWiqXy0tLS8vLy8vKymQymVwuLykpkcvlcrmcIIjGuWfSKNasWbN169bGnd6uKSgoGDlypLMh5dWrV0mSjIuLcx7q3Lnz8ePHW9O59gMC8JlYCAA7jaZFSvVysXCtzvDUqHcAMJy4ZM24T5eIvD5dwgioSFQUW23Kn/42J6epft/l/eVbreshBQVFg9hdutlO2PqKn61U7wCAADLTd/63OUvj5fv7i4dImE8qMXCdQbnuL0vavcoR45kkzbZDkoUzeANcPzE2iH1lW62EhYWybYR1Z8mG94O/fPI52wXtTMBfuHAhPDz8Uerdydy5c7dv337p0qUW86pVMN/MAABOTNc6LRtNrulesvYSE2VO9ZkFAPnmbC+Wr7MO+TXlpVuzLkoSxDQBnd2ZZ7qmq6rhExQHVXZFB9Jv5FYHyuN6L3+D2akibF4wbohmx+Wy727lzVbguisA4DZAHJYQLRguCUvsxQrmMjyZmNqRPSrFkmlkd+aFxUfbCqw5E1Mtd7hW+jASIwUjJME7u2sPKwpeuSP7Og+hI94fdKzzWgiCSE1NdYr2y5cv19mYHQEkPDSs38D+/fv3j42NjYiIaEc155qQQE5wT0FvO1l39jITYQVwHi6mnCqwHs2xlptwlYWQmXBzlRxsBorcmudVmbt4tcReYsABgKi+kLJsgOC23CFiIQImKmChgoovECELFbKqyfBXHiG/fd1ovm7/oYSopx6BQCAQCCIiXCcN4Tgul8vLysqcIt+p6ktLS5397WQymdn8uBD9ttwDolkRCoVFRUXOry9evIggSJ8+FZ1BNBoNVRew/lTV8B+rtQDw1Kh3ADCevgIA7q/MqFTvAICyWdI3ZhUvWmbNzHbIFAwfqlwCBUX7wJnozkSZU7xfrnEolNc5RtTvuvby/rJtCzu88ySvQjoc5St+sRcU0wRubsP6MwN9CbPFfPWW5XaWYt3fCIPB7dPjSeYvsOReUZ+lITQvpq/CUZZpTEvRXYuu1dzqqaSdCXiFQtGtW7c6zQICAp76qD/LzXQA4MbU/dNoHCSQO0o3kECO9pgsZXre1CX9UrAqjBf5YcjKqZ6zRO/wJCfECB8JORTFieTlTk8znFPfH3EzND6aHcGb4Tv3GdGADn/dt8huCmeNrFTvToqXkY6yCABAOYhwrKfXGx0AABBwi60IadYnKC2ZRgDosC6C7sGkezBDDkXlTEzF9ZhTvSMsVDzNS3tModlXXr624DECXqfTnTx5Mj4+Pj4+Xi6XP/6S6XR6VFRU/x79orBuo5aN8wyikvpAwvR4M+iTym81VgIBEFWpO7ApzbT9jrnMhItY6PznH9bqW3fDmFklc5tFQ8RsVMJBpVw02otZtfLQzokSlYUQs1Eeo9pj7sgg9sggqr08RX2h0Wg+Pj4+Pj5RUVEuDfR6vVPVFxcXOxV+eXl5SUmJUqns0aPHsmXLWtjhNkK3bt3OnDnz4MEDsVi8a9eumJgYqVQKADKZ7PLly5GRrVPYop2CALzE5x0wma0kiQLMcuM9HeqdxHF7YSlCo3Giu9Q4hHLYnG7hpqRUe35R8wl4G05mV2+lYcdJACBIyFBUKxGCIBDmTme4DLWioKAAAABnSykSyDGeU1zusc/wnXtbf+Oa5kKcZGTV/fmGYki4aC8oZvh5eX/xNk3Edw7yhw/QHTyl2XZQtXEPp1dXhN7IjRYSyJ0lG0ggBTRRoTXPjcYHgN2lm7oJohjI07/03M4EfGxsbGJiYk5OTkjIIxunyeXyhISE2NjYRxk8BThKyx0yBY3vxgoJbKo57UVW2Vd57jN9+IPEAHBJfTrfnC1mSEZ5TnKQ9t2lfwHAfdOd66rLnh+IQ06E2Hn2az9f7xEThyKo/66wvOnptgum7DEpofHR0ghPKdOztOgCALAjw3XHlMq/ShwyW8ihKLqE4bHAz3g1j9RccX+pi+SVIbU9ET3nxd9RZjinzp+dERofzQ7l8noLwhJ76U+pPV/zR1goAJStKdDsKwcUEU/QqjftFU4ZRRPyK2e4d+/ekSNH4uPjL1265HC4LgDmhEajRUdHDx48OC4ubsCAAXw+/zHG/x1wEu4oHMUGvMSAFxuwYgPu/GfFSDYdOfuCpye3QsNfLLIV6DAA4LlVi1FYN0yUJnd482hSLurJRQWPrjrDoSOVHcsoKJoP5wZ+586dW9uRtsX7779/8uTJzp07c7lclUq1dOlSANixY8eHH35oNpvnzZvX2g62J/IxbI5cZSVJbxqtDMffUmmc+fCt7deTQtodQJIIi4nQXHxWIxw2AJCWOuLanoSPz+kOZbsogWHHyYn7lTUGZ3XlLR8gaD5nKCjaO5fUpwvMOQBwx3DrvinTpQ0TYdrBtrt082ehqxv9QsaLNwBAPOu5SvXuRDhxqOlCsr2w1Honm9PYUtxJmvPZprsclKvBVABgxA1CukhhL09UHBnj+ahUvKeHdibg33zzzePHj/ft23fZsmUTJ04MDKwmX2Uy2bFjx1asWCGXy5/Wxw7SgSEMuuXmHQDgRHcBtGlqhtmLrNljUmz5FvXe8uCd3ZnDuP+UbQeA6b5zWCj7aPlepb2cQ+NaHZbiV+/TEwJpAvr5dadyA7JvTDrDSKMlrEkoWJ73xhfv2i+YnRqeHcHDtKglp/O9EXlYuQMAUB6NMOMgYXh/GGRIKFJtKAcy3KUzKAcN3tsjd/ptw1l19siKXX1OpBsn0s1pUL6usHR5LqBIh5/DzKfW6eMtxrPX+JOG3/XgHo4/dujQoaysrMdfb3Bw8IgRI4YNGzZkyBCRSPR446ebYgNeoMMwAgZ1YFUK8E/P6/ZkuYg3FrLQSCnDrUpB9f+NFBcbcF83Grf6/nknEb2TqJ19vFBQ/AcZPnz4li1bVq5cWVJSMm/evAULFgDA9evXi4uLFy1aNH/+/NZ2sN1QtWrdeqn7aq3emQ//FGh4lMNGOWzCbMHVWpp7zTumo1gGADRpM95JBwaw8rRY1SwrgoRMpQNFoIu0WpkSFg2J9Xv6N98oKJ6EXFNFRnq26e7jLYssBTbCWv+2UzVwlJQBAKdrWM0DCMLuGmYvLHWUlDVOwNsJ24Gy7QCAIggADHQfdlF9ykKYAeBI+d5+4sEiRvvu31wn7ewJe/jw4evXr3/rX4RCobu7u0AgMBqNarXa2c2OTqf/+uuvkyZNam1nmw6SNF5INpy8bMspAJygCdzoPp5AQ93imibNo1K90z2YmMKeOzNN8atGF6EBgN8frPn9wRqnmdVhGb5ieMeTgXae/dD3e1WByrEfjqVfR0ggBi151u03QdDurqUzcwzn1PeH3+D0FBjPx5AEADjYoVzpPD/3F3zokoobrS2vCADoXpJHeAQoBw3e86+G/3dFwHmofF1hySfZgCKB/+ssedHHNuTdEyvX/nPq5In9v5VZTI+5TJFINGzYsOHDhw8fPjwoqMGl754CSo240U6GuT/8q193w/DTDaPz650TJZWNzaK9GffUDE8eLYBP8+fT/AU0fz7dn0+rEeUOAGw6EiJuZx8jFBQUVXn55ZdffrlaGuQbb7zxySefOGPpKepD7ZrzVWvaPQUanhPVxXQlRbsvQbLw+arjlrR7tuwHKIfN7vzIuMgn57kwTtX+IPBvFXomDTk0hXqXUlA0jOf95j0jGlAfSzFD0mj1DvBvcSOXKS00FAAAb2Tp2WPy/Sq7QkgX6zBNR07wnIAlJtyQorsmYUpVduX+sm3zA95stNftgvb35L1o0aJhw4Zt2LAhMTHx7t27+fn5AECj0Tw8PGJiYiZPnjx37lxv70a2FbHZbBmP6CFcSW5ubuMmbxwkhivWbDRfT3N+izAYuN6I640Ik0GYrU8+v724Qr1zo/ghh6PKvsmX/1okeU0YtDIof0B+Vctu+7uHnQwnacThtYdUIcqxH44NuN7BKrHoO+s9L3k9u2QAcgQ8lwSYb+owDWY4q0YYwPQq4kXrAzcvRLkP//7tBSWmizcAQR6fwO9Sw1eq94BfwzP87+974+v9+/fLZLLHzBMZGTl27NgxY8b079+fTm9/b/jGgRNQqMdyNFiutuL/XA1mcpAAsGG0++DAikfJAD49UEj3cUO7SBjdqrQEnxbBnRbBdT01BQXF006nTk/aofM/hZEgK9X7L1J3FoIAAALwqVjoIMl9JvMipfqgt0dge74BCaeOMiffNpy8RNodwonDGP7euM5ounRDu+sokKRw8giESTXsoKBoH7BRThf+E1WPqyd0L4mjuMyW/YDdpeYCny27AADojWqkpXYoTygOIYCYCRMCyEy/VxBAZvjOS9enqu0qOsK4oj47RDI6iBv65JfQZnnk7eTy5cuhoTWvvPZgaWlps/j1WEJDQ1etWrVq1SqSJM1ms9VqFYvFaFMEk8+fP3/79u31sWx0v6KGotl2yHw9jcZ3E8+ZzOvbE2ExHWUK3f4TxrNXFT/+5bvmI4Zv42ut2Yut2aMr1HvokWiaiO6/KgwA5L8WjftsQqdt3ZRxqq+zP6IjjK8i1nMmcrL/SCHMEHkk0tfgL7wuoHswo+P7ssK4BfMzNPvkWXHXAa+IceP1FgRt76pYvdZRJJN9tFo4ZSQrtCPpcFhSM3X7T5AOB3/EAIZvHWWfUQ4avLu7s0Je9rhU8TQv+frCO+S9K/1vHfrg6GN0O8pAu3QPdBvBD4vr9MuQLW70/1Zm+4Ui2+ITGgtWsz2eOweNlDIiJA//6ieHcyaHN00rTgoKivZLcnLy1q1bb926ZTQaU1NTT5w44efn17VrM3Y5eZoggEQA4jjsHyViVpVaICjAF+4iBoIcNplrfR63M5gdfKVvzVH+vNV47prx3LWqh/jD+gsnDW8txygoKNosvL5R2n3HNdsOeX/+ZtU1PtOVFFtWHsrjcLq7zqV9PLtKNtkJmztDqnYo+4njQnmdAcCD6TXcY3y8fL8bXaB1qHaUbFga+i0CT209y0cKeIvFkpOTU5/BVgRBEB6Px+O56CDVOIYMGVJn+rTFYsnMzGyZpmK4wWg4fh5oqNey15lB/s5BhreHdMlLgCDGM0m6A4nSJS81bvLa6h0AAIFKDZ/3Uvr1726QvclRnpOkTC/oDSEHe2ZPSu18tAsAIFI0LD6a3ZkHAHQREwAq1bvkJZ/AXzsDinh9slj+7W/2ghLl+i1VX5o3IMZ93rT6OIlyacF7uqdOvJV/OfOXtX8ex04XEiWQ4NqYz+dHxIXwBtCGeActyOm/Y442w5B6sHznS34LG/cjauMY7eS6G4Z0haNAhy0fIBzdqSLMwY6TGAH+fFonET1UTO8kpoeI6SEietXq8RQUFBROVq5cuWzZMpJ8KDGPHj36888/f/311x9//HErOtZeEKDo+UespKMAy8TCZWJhC7vUHPBio5gd/fWHT1tuZWJKDcrlsMKDBKOe5URTrQooKChcIBg/xHjuqu1+fumH3wknDGV08CXNVtPVVEPiZQAQvzABYTW4YsU9450buisIIBqHko4ye4sGFFgqIqO7C2IuqE9pHSo6Qs8137umudBXPKiJL6nN4FrAFxcXt7AfDaWgoMBoNEZERDiDotPS0rZs2aJQKJzx0o3ufDNv3rw6q99lZGR069atZQS8NSObxHFOVJdK9V6JcNJw45kky6066k88Ctfq3UkVDR/1fz1N3xrGLJjsPMKMZhvDjLxbXAAo7V4aorExDCwan84K47KCObZ8CxCAMFHpPD9nxgtdKvb59gPT+WTTtVtYmRJoKLOjv9vgvvVfcjOZTHv37v1W9ed9SxIJrrcwBALB+PHjp02bFvFs6HeFn9AQ2oLA77y4/jPxsmX33zqnShgkGRHA7ti4H1QbweQg7ygdWSrHoABWoLDil3WlxLYpzQQACIDO+jAkZFhHdtbCRmaRUFBQ/KdISEj47LPPgoODv/vuu/T09M8//xwAZsyYsW/fvqVLl0ZHR48cObK1faRoKzB8PCSvPl+3HQUFBQUAyuN4ffa6/Ls/HEUy5S/bqhxAxTPH80cObMScR8p3A4BTEWCEfV3+yto2GIkBwOHy3f85Ae/n59fCftSfI0eOvPrqq87w6aCgoMOHDysUipEjR1Z2C/vss89Wr1795ptPQ/UCXK0DAIafi6V9ho8H0FBcqydxAqE1bGeVxEineuf1FoQciqIJar0NEPD4psNF1anw3eHPLh1EDMAgEkgbcW3qKd4tro1vp1tRnzPe2WdSpXP9OqyPYEfwHKU2IIDViWPLs+RMuhVyOIoXIwAAhE5zGxrrNrTBXf0uX768efPmPXv2GAwGlwYCgWDixIlTp04dMWIEm80mgfw2ZykJ5CiPSd7CjgDgw/AfLBl1SnlsV8mm/wv+sqEOtC5mjMxUOjIUjnSFI0PhyNNizlIgY4PZPw0XO22GBrLXDhVJOGhnCUPCoXbXKSgoGszatWvZbPbJkyc7depUmZc0YMCA5OTkjh07rlmzhhLwFG0QOorQUGDSntr4WAqKpwOGn5fv9x+bLl43p9zBFWqExWSFBLoN7edS2tSHboJe5TaZyqGgI3Q/dofaQfIyW7GNsIno4p7Cpin13TZpTEkVgiBMJlOrdMy+efPmxIkTAaBv3740Gu369eszZ85kMBgikWj58uURERGZmZkrV658++23+/XrFxMT0/IeNi0omwUAhPFhQy/CaCYsVrqHO2GxAk4gLGZD1bsTkiABAKEhrotDAhyV7zPTTACAkigQAADy34t55zhAIwVD3W2HK0qXW0tNhouavJlphIWQzvENWBN+f0yK6ZqucPHdzsmN+ctRq9Vbtmz5448/7t51HVzAZrPHjBkzc+bMcePGsdkPa+Mlac5lm+4K6KLRnpMrByd6z7yqvXjXmHZTk+S9/BTCZIhfmsjp2aYbQeMEzItXJ5XY8CoBBwwUiZTSu3kwXur6MGGEhsLEUCqDnYKCovGkpqbGxsbWrloXEBAQExNTZ1VXirZGPoZ1oNNdtGsHMBCEhSQ9XfVyb3fQUfh1hJhFCXgKijYPwqC7DYl1G9LgnTyXPOs+/Lj8AAAsDHw3RtivtkGu+d7X2R+ZcNMQyegmecW2yeMEvFwu3759O4vFWrx4sXPEZDJ98MEHmzdvtlqtXbp0mT9//jvvvNMiflbw5ZdfAsCxY8dGjx4NABcvXhw8eDCO42fOnBk8eDAADB06dOjQoT169Pjmm2/279/fkr41B8zgAACw3LpL2uzORJHyFT/bC2UBG74yJ6cBACs4sBHTInQkLKHX/dEpxqu6nImpIYeiaG7V7ugKe7l2RVnUjmiSCfwN0vKQcrCU4wPtiBAldYTtoBEYUDS0yPeij/GENjsxFQhS+opfhx8iNAfKzTf1ACCe1uCltczMzHXr1m3dutVisbhyGgnsHvvJkjnTpk2r3bndRlj3ybYCwHTf2Rzaw/LpPJrbJK/nt5X8safsr8VMP7KguHzlL5zu4eKXn6udldDy2HHyltyRWmb3dqNVSnEbQWYoHQgCkRJGNw9GNw9GVw9GuITOeMRSCwUFBcWTwOG4Xgd0d3fPzs5uYWconoTzFutrSnUch/2TRMyonuhXiGGz5So9QVz282a3SA5gczOs4xN0t6KgoGifHC7frce0KIKeU504pzrh0oaBMu2EbZ9sy2uB77ewey3GIwV8fHz8yy+/rFar4+LiKgX8yy+/fODAAQDw8PC4c+fOu+++m5ycvHPnzhZyFuD27duxsbFO9Q4AAwcO7N27d2ZmplO9O+nSpUufPn3S0tJazKvmg9nRnxXa0ZZdoPjpb+kbswiDyZZbiPI49gcy9V//AAB/eP9GzhzADjsefX90iulahYYHnJT/r0g0wYPTxS3144tRW6JwBnHiy+N5nfLgPtBsdIJOSNe5P7d4KtPMKOpZdOTTw763/cZ9MI5upTvVu/aQvGDeHRJt75F6AAAgAElEQVQjvd4N9P6/jvX0hCTJEydO/PDDD4mJiVWrKFXi5+cXPeaFWwHPzR0cuWCAwOUkx8r3aR3qjpzgWHFcjUNxklHnVCeKrQ8y3hs6ILmf7p+TlrR7lg9WuQ2MEc0cT/dwb8BPral58Yg6pcwOABw6MiGU43yk4tKR8y940lFg05+GZywKCoq2TM+ePZOTkw0GQ42oOqPReO3atR49WqLVEEVTEcxgiFH0nMX6pkpTVcM71XsZjvdhsZhPhXpvLUi7A9foUC4H5TdZ+WQKipYHU6htWXmE1UYTuLG6BNP4bq3tUX15YMkFAIIkMg23H29ZYG5DZdebHNcCPjc3d8aMGWazefHixc8995xzMDk5+cCBAwKB4PTp0zExMXl5edOmTdu1a9fChQur6udmRaFQdO/eveqIv7+/Wq2uYebp6Xnz5s2Wcam5kS5+UfbpD+Zrt4szlzF8PQAAQWlly38EkuT1i+YN6NXomatq+OxxKSROWlIN8p8LRRM8pVvEBINMXZVqHGhBHUiXk10HfD9A3V2T/PP1axuv9X3tmYDrAc99MTXl29ScrQ9GqMdJX/DVHpLnz8lwqne/L2v2e3SJ2WzesmXLunXrXFb+pzOYfYeOHT5tdvSAYRdLHXeyLA90eHyutaqNkIX092cp7eUnFIcrW0HWmAdF0Jl+81fnLjuq/Kf/mF/9h/bT/nPCcPy88cJ1U1Iqf/Qg0ZSRKK95e56bMfKmzH6t1K6zEZ/0E1Qq865Shg0jo70ZI4M4Vf12Y1IPWBQUFC3BrFmzTp06NXv27L/++qty0GQyvfjiixqNZurUqa3nGkWD8afTtnpK5yiU5yzWJUr1eqk7C0FKMHyeQlWG471YzF883NtjuZSrmgu7SjfNC3iju6DxzzxPiDX9nnZfgjUrF3ACABi+nvyRzwpGPwtN0cOYgqLFwLV61R+7zNfT4d89M4RB5w/rL35pYiNqwrc8iwM/KLTk18fSh936kbbNB+Jyz/Odd9758ccfN23aNHfu3MrBN998c/369VX7yty/fz8iImLKlCl79+5tGXd79OihUCiKi4sru75fu3ZNpVKNGTOmqllkZKTBYCgsLGwOH5xV6FEUxXG8OeavjaOkXPXbDuvd3MoRhMUUThgqnDoKeeJkNnuR9f7IFHuhBQAQFkraCABAGGinrV0fPFv4e+bqgd89G3YyHAA8FwX4rw4DAEuaIXtcKqZ2iCZ4Bv3dFWEg2oMNU+/FxcW//PLLH3/8UXvxBQDo7v6CZ2fxY2fQBB51TrXvOck160/J2kt0hOHP7vAos0JrAUHiz7oPnxOwBAAwhVqz44jp0g0gSVZIoM+3/1fnCzUUO07eLHNcKbFdLbWnye3Yv0Xij0/3CHNvTO0JCgqKtkNSUlK/fv1iY2OvXLnS2r48KbNmzdq6dSuHw/H09Hzw4MGwYcNSUlLUavWoUaPi4+NbpuVKG6Hl7+/NQa4Dm6NQKnFiIJv1sUi4QKkqwfBeLObvHhJeO/xtmnHTx1mLDJjeg+m1MmI9A2kFjaE7eEqz/RCQJEKj0aRiwmgiTBYA4PTo7PnRqwiDuqdTtA9wjU72yVpMrkJYTE7PLjQBD1OoLen3ACfYXUK8PnudejNDO7m/u/49JSQk+Pv7z549u+pgYmIiALz00sOu42FhYaGhoffu3WtWF6sybNiwtWvXLliw4Oeff3am7fXpU7NS2h9//JGZmTlnzpwW86q5Yfh5ea94x1FcVvb5T7hWL579HH9oP5TbNNXLaEI6XUiz//s1JrcDAMJAGD4s2dXCaa/PEJQKUB4tYHWYZJav8xROd37Ioajs8Snaw/L82RmiiR4PXs0kMdLn4yCfT2pWQqpBZmbm119/vWfPnsquAVXp37//W2+9nec3tNhUbbxAh2UqsY5CehdptXeskIWGuzMO5ZcAAEY6KltBPooyW4nzC7qHu8dbs4UThmr3xDe6EuajeKDDvrisTy61W7CK1TEaCj09GX38WIMCWJR6p6CgaFNs2bJl3LhxX3/9tbN06NmzZ4ODg7/66qsFCxa0mHpXKBQNelRylrNtECRJarXax9vo9fqGTtsGCWbQ//KQzlEoL1pt18sVVpJsd+qdtDvsBcWE1U6Xio5AvAHTA4DCXn5ScXisZ0tHhVgz7mu2HwIEEc8czx8zCGWzgCTNKXdUv++03L6r2XHYffbkumehaLfoE1Wln+f6fNJJOEba2r48Keq//sHkKlZYkOcHC2miirQpe2Gp/JvfrJk5+qNnhM+NaF0PKeqJ6x14gUDQt2/fkydPVo6UlJT4+/t36dLlzp07VS3j4uJSUlJa7J6n1+t79uyZn5/PZrOnTJmybdu2qkfXr1+/bdu25ORkHo+XkpISFhbWHD601go9bjAWzfsYYTI6bPm+cZXnXcypx3ImpJpu6Bl+LIQAu8yGMFFejMB4RYtyaYCRhJ3gRguCNkeygmsGmZtTDdnjU3At5vy2TvWenp6+evXqHTt21P65MRiMSZMmOXsHuDx3S4bpi0v6WV15y13lwJtxk9xeVp/r9WL6VC1x11SQUC1wf/sd87KLOgSgs5TR358Z68uK8WHyGO3myYmCgqJO2sUKfUPBcbykpMTb25vJbOlNznPnzjUoF8/lo8vjee21137//ff6WCIIQhBE3XYtDgnkftk2H5Zff/chdRpfsdoWKFUECUIUiffxcm8nkd6E2aLZfth49ippdwCARoz9uqicQGGgdNgFVSILZX8T8auI0aLFa8pX/GK5fVf8wnjh5GotFe35xaUffofQ0IBN36IcqqLe04nuhCrvhTTSRiAMJGhLN9H4uiND2yyEwVT4yscIivr/8gXNXVj1kDX9XtkX6+meEv9fv2gt99oO7eL+7nozEEXRGvfvU6dOAcDQoUNrWKpUqmbyzCUCgeD27dsrV648ceKETqercTQ+Pj45OblXr15//PFHM6n3VsSWlQ8kyQrr2OTqnRnIDovvBSRZ8lmOZJav2yBxVmyy9Z4JAMSTPDtu7oq4Ep/cKH7okWinhn+8er9169by5cuPHDlS+5FLIpEsXLhwyZIlfn5+jb4QLo3XkRPc6NNrY88rIkmSFfzIgPxKdmaaj+Zabsjsc7vzPupbsbgwozM3REwPEdOp3uwUFBRtGYPBMGbMmEmTJr333nsAQKPROnSo+3OvOYiLi0tPT//888+d7WMWLVokFArrPKtBhISEeHh4YBj2GBscx9vyJvwV9dl4+X4aQuvEC/NhPS69swTDl2m0BAkMBHQE+ZFK48yHbzFXGweuN5Z99oOjpBwQhNkpgObG3dnzGk4jPRSMC0iiN8uvzFayv2zb/IA3W84nkrTeuQ8Iwh85sMYRZpA/OyLYmplty8rjRHVpOZcoWopK9c6N4ptTDfmz0tu1hrcXFANOsMKDa6h3AEC7dDwzyhyRZvcxGNtRQbv/Mq4FfHBwcGZmJkmSleFzhw8fhloCniCI3NzcFr7f8/n8VatWrVq1qvahr7766pdffqndz/bpwHYvFwDY4U2jVGuod2YgGwCCtnZTaxWp088j9whAAQjQn1Vb0gzcXq5rv3Oj+BEXnrFmm4UjJS4NcnJyli1btnv37tq7GZ07d3777bdfeuklLrd5C8g1GJIsW/YjYbVxY7qJXhjP7OD7KMMiA/7pBR0A0FCQcB4WI6Cj0Me3kftXKbqrmca0Gb5zWiXNj4KC4j8Fn88vKCg4e/asU8C3Ll27dt23b1+vXr1SUlI+/PDDwMDGNEl9DO+///7779fRUsgZYdc20/6thGVf2VYAwEl8e8mf73d65EZZCYbPlitKcCKKID4SCpeYTRettjf+rWnXgi43GPWGPY6ScmZHP4935zF8vdL0N7PzT7OAqfKwA4DSKqejjCvqs4MlozpxW2iThjCaSQxH+TyXxW7pXhLIzMa1bXfRh6LR6BNV+S+mkTbCc0mA/7dhpStyy74ryJ+VHvR3N9GEdqnhCYsNAFA3Fxm4xxT7L/VWp4fTe5oMXErAtwdc7xDGxsbm5+dX9ocrKCg4duwYk8mMi4urarZ+/XqLxTJkSN2hXC1DdHT006reAcBRVAYArC71KvBeBwSZMzHVdEPPCuKEn4xxqncA0CrVN8acRU4RiDsaejhKNMED12E5k27ZC1w1ZgcAAFYnjkv1LpPJFi9e3KVLl507d9ZQ79HR0fv378/IyFi4cGGbU+8AgCCimeNRDtt8I730vW+U67dgcpWDIC8W2T6/pFt11VBpGMCnrYoT/jRcdGO214IeTdBRRodpNhSuO6OMPyE/9OSzUVBQUNTJypUrT5482XYarz7//POt7UJTYiOsZ5THnSncT8jR8r06h6YjJ5hL42UabqfpXbfaeVCufCm3oAQnIvOLPvvkB8GCpd/vOSEB0qnhbQ1PPWgxcLXWlJSKsJheSxczfL1wEt9VugkAhCx3AoBtRTHE4cH0IoHcWbKRhBa6EITLBgQhzVbSVeEeXGcAgOZuZNNm0ViJbXfMZqxhvwvdcWX2+FRLmqFu09ZDn6jKm5lGWCvUOyDguyzY+4OOpIPMn52uPaxoxJzXtBcXpT9/U5fU5N7WE+fGu6NUXmNc7VCeUBwCAJ0AO4mfaQXPKBqOawH/0Ucfsdns2bNnz58//8svvxw8eLDNZps0aVJlSBtBEH///ffHH39Mp9Nff/31FnT4v4to5nj3uVM53Zpg1Zl0kLYcCwBwurrRvSp2ejGVI234JWmaxOxrCTsdzY9zdxsgBgBM63CU2es/udVq/fLLL0NDQ//3v//VqFTXt2/fo0eP3rhxY/LkyWgbzscTjI3z++VzwZg4O4OZcFf32m+3e/1ZPOeYemuGeUuGyY4/vFdNjeCODeYIWE1zLftkW62EBQCOyvep7I25PVBQUFA0iHHjxv3444/Dhw9funTpP//8k5SUdK06LexP7969Q0JC6PSnpNjnftnWbSW/byz66QnnUdjLTyqOIIC87P/aBK8ZALCjdANG1pSUxflFLyvUZVxO17zCb/af4PN5CJ3mden6yrWbRQRx0Wp7T6VpswrempUHJMnpHu6UGaeUR8tsJUKGWG4r46G8OVs8mXZEZit2o/Nzzfeuas63jFcIjcYK7kDiuOlKao1DuFpnzbgPCMIK69gyzrQ1NqWZll/UHbj3yD2e2mgPK/JeSDOcVWePa7CGJ0jYkWnOUrlYSWlaaqt3J0+i4S24eWfJRhth3VGywUZY6z6hGWAGBdD4bo7iMvPNjKrju0s32wmbj4yJkEiC+rDaoWwV9ygahGvh4e/v72z5vmnTpuXLlxcUFAQGBv7www/Oo0eOHOFyuXPmzLFarT/88EOXLlTmT/NDkoTZTGKYdne84fQVTKl5kskQFhpyNIouZmiPKPJnZ5AOEgB08Uq3bJ4+SN/lVB9eqFC5uaT4w/uAQMD34by+9c1FjI+P79q16/Lly02manXko6Kijh07lpSUNHbs2EZEJ/KZKAC0WB04nISzavqKwGFTh7y/vPvUs56dTSS9k0mxwE1xaIqUSWsWNwrMOVfUZ+kII4wXaSdsB8q2N8erUFBQUFRFKpUuWbJELpd/8803U6ZM6devX9/qtLA/cXFx2dnZT1IVpe0gsxafVSUAQJr+Rroh5Umm2lW6CSMd/d2HBHFDh0nH+rE7yG2y08p459EcDfbmKa3K6Lhy+IxC6NYtv3jF77sZJXJMoSYdGMJidSwq++bnbWIEuWS1WdvqJjxhNAMATSwEAAOmP1K+B/6tVjjFd5a3jtf/Ch8AGAgDAPbJtraYCuKPHgQA6o17LamZlYOYXCVf9Ttpd/Bio2gi12mGTz02jAQAK17fd5T2sCJ/djrpIFmdOJja0VANf6vc/tmFaoGQzcGj1LuTRmv4I/K9ekwLABqH6ph8f9P6XE8QGiqcMhIAlD9s1h8/T1isAHCv/MYN7RUGhkzfK4lGu9sJ297Sv1vFPYoG8chF7lGjRmVlZV24cOHu3bsBAQHTpk2rjHa2WCxSqbRnz54ffPDBs88+21Ku/nex5TxQ/rzVUVyl0DoN5Q/t5z57MsJqZKY0twc/5GhUzrhU7SF5/pyMoL+6iqd5kXRSMFrCFLGUm0sK38wCgIDvwz1efVylnEoKCgrefvvtQ4dqxn6HhISsWLFi+vTpT7Ll7sZEAYDDaKFN++13TF9cqoh47O7JGOnu6Hv9tCj5GsJkBL64pjlekQRyZ+lGEsgRHhPiJCM+yXojSXN+kGRkKK9zc7wcBQUFhZM608IpGs2O0g04iUuZnkq7fFfppi5hPWgIre7TanHXmJaqu8ZGOVO8XwIAFKE97ztvTd7nh8t39xU/K6SL/++sNk3uQFXqty7dWFskCy4u47oL2ZGhCJ1uu5dnLywFgI75xVtTMpHRgzhtNQ2eJnADAOf+xD9l28y4yZPlI7fJAjgdBzD6FVuP9EtxTx/BVtrlEqanyi6Pl//znPcLLeCY27O9Lbfumi5eL//qV0aAD9PfG9cZbPfySRxneHu4z5/WAj48BTgbD5MO0uutDr7Lg/NnZ2iPKO6PTgk9EsWNrtcKiB0HALATzbgC5ZDZnOrdY4F/bfXuxHdZMGEm5D8X5s/JiEyNrcxCfQxye9kpxVEEkJl+r+ws2ZAgPzhAPMST5dP0F1AXgrFxjpIyQ+Jl9ca96o17ES576wsPSB9ywGVR0JQZHTp3S89anKy9NFg6OozXjndnbYTVgOmlTM/WdqQZeVyUmoeHx5QpU2qPT58+ffr06c3mEkU1bDkPypavI212upeU26srKuA5HpSar6cZTl7CylVenyyCxgrjCg0/NlV7SH5/5I2wEzHSmb4A0FD1brPZVq9e/c0335jN5qrjXl5en3/++fz58xkMRuM8rERmxABAaW6uvn25WsyDg1ZGwsd4MwcHsnp7M8eEcAL4NACAuJfs+XEAAM3z6HNNcyHbdFdAF431nMKhcUd6TDgq37ezZMNnYd8jLm8gFBQUFE3B6tWrW9uFp5NU3bU7hls8mtvSkG+/y/1MZi0+o4wf7jG+ofMQJLGzZCMAjPOaJmSInYOR/J7dBb3S9DcPlu2c7b/YjgEAmLUGhCQ7FxQLp4wUzxhb+WxgPJOk/N8OkiR51257T6jZS6jVKTfhV0vt40I4rM7BgKLW9Ht5xSkX1adQhKZxqABgpu8rppNXAMAtLGyaT4//PVhtwg0IIAnygwPdh0qZXs3uIoJ4vDmL1SlA989JR5HMUSQDAIRGcxvc133Wcyi/CSrgPPVUVe9+X4UCQNCWbvmz0rVHFNnjU+uv4ZsbmoDO8GXZ8iymFD2ux2hCFyoJN+Km6zoAYHgzaaJ6JfvsKtmIkY6B7sOGScfmm+8nac7vk21d3PGDJva+PiCI5NWZnOhI/bFztqzcGxGqUh+H0MqZNPoTfmgoAIzymHS4fPeOkg3Lw9a00+dPEsg1eZ/nmbM/DVnVkdsUhcPaJE9JmtlTC0mqft1O2uxuQ2Ilrz6P0CoW7x1FsrIvf7bcvms4e5U/1HXv9PrAieAxQzjYDYcpWe/ch1dtK22Qek9ISHjzzTezs7OrDtJotMWLF3/55ZcikajRvrUMu+6a/0o3ZauxZwNYm8dWtJbtImVsGF2zzSwzqF6RCI3ATtj3l20DgGk+s5xt6sd6Tb2kOVNgyU3SnOsnbkBvZAoKCgqKVgcjsb2yvwFgovdMEcN9hu/cdfkrD5XvihUPcqM3TKicVR0vtj7wYHqNqC7+X/B9JdOQdkGVOMh9BIAIAAizFQAYAT7imdUs3YbEWlIzTUmpjrKaxavaAmuvG/dlmflMdEgg323QM8azV3fd/oHwJxAEcRA4HaH/nL2S9LPD/wHCViHFV2gIzYpbAMBB2v8p27Gwwzst4SWCCMYP4Y8ZZM8rwpQalMthhQSiPBfVvClqU1u9A4CzrXpb0/AojxZ2vNf90TfNN/XZY1Kc2aZVDQgTnjv1tumajhnADj0W7VLh1yDTmHZLf52NciZ7vwgAU31mpeiu3dBduWO4Fcnv2VxX8li4vbtze3e34OZLdxcDDi+Ev84XVfxexnhOuaw5U2jJu6Q+PdB9WKu494Qkac7lmLIAYGfpxo9Cvm6nyxB10nYLiVEAgC230F5YSpeKJQsfqncAYAT4uM+ZDADG009UzbLow/vmG3qr0Iq4odpD8nuDr1eo97V1q3etVvvqq6+OHj26hnqPiYm5cuXKTz/91PbVO0bAsou6bDUm5aCjg+sOgqqNetM+2Yffma6kwBNkFR6T71PZFYGc4H7uFVqdhbKn+rwMAHtK/7bg5seeTUFBQUHRtjipOFxmK/Vh+w+WjAKAHoKYbvxoM246ULajQfOYcOOh8t0AMMN3Hh1hECS+uehnZ71oT5bPUOkYZ/pVhTVOAADDx0XUqNm/AwEIidfs59oWsDhIALBgBAC4z5vKCgm0Ijb4N/sdIzEzarWwCQubMIPFhBtx8mEsnlPJtxgIjcYK7ciLjeL0iKDUez1xqd6dODW8aLwHrsOyx6eaU9pENz6GHyvseC9WJ475tiFnXCqmeVgzjzDhOVNuGy9pmAHs0PhoVlDd7wGCxHeVbASACV7TnRE0YoZkjOdkANhVuokgmyuwtD4cLt+tw7WhvM4xoocbgUyUOcX7ZQDYL9vaHp8/bYR1n2wrANARRrbp7jXNxdb2qLlwvXRUZ8wziqKdOnUaMGDAypUrvbyaP37pv4q9oBgAWF1DNdsO8Qb0YoU87IvLjelWadA4tAflyg0lOAs7uT7x9YAPSybdN98yAAIBa8M9FtSh3k+cODF37lyZTFZ10MPDY9WqVXPmzGmbTXQBIF+L5WiwoR3ZKAIAQEfhr7HudhwG+rNojVrLwhRqW26hYu0mXaCf6Plx3JiuDY2xd3bvQACZ6Te/6jJhrDjujPJ4nvn+ccUB56otBQUFBUXbR49pj8n3AcBM3/mVSe/P+87LvH/7vPpknGRUAKdjPac6WLbTiOk7u3WPFvYBgDOq4xfVpxBAIty6BnKCJ3jNuKq5kG26a8ZNAEyg0wDAnldI4njVFf98LTZK3WVMhO1deav1r6onKIftveLttw4krFVsk3nY+ibxB14WsIL8hZOGs3tEVJrtLN14RX02wq3b6x0/akVv/1MY7eTS81qtrdpeRZ4GA4Dtd8znC21Vx7156NeDRHQUrHdNFer93UC/LyuCmR2lNu1huftLvjQ3GsJAgv7umvdyuu6YMue5W10z+6O8xtSJaFqcGv7+6JtODe/ch2+EegeAM6rjxdYHnkzvYR7jKgdHeU66qD5dYi28oE6Mk4xqtut4HLKSzNOKowggkxT9CQ8jTcivPNRHPPCcKuG+KfOofO80n9mt4l6jOVq+T+tQB3PDn5UM31z0817Z31HCZ1hoY7bo2jiuBfzjO7iQJGm327OysrKysg4ePJienu7t7d087v3XIW0OACA0etO5ZNJqqyrgESYDaCjpwEicQBqlPtV7ygDg4huXJg17QSrw4R7hFb6VJZ3vL53j+5izMAxbuXLlihUrqnZ3R1H0xRdfXLt2rVQqbYQnzY2DIBPyrNvvmG/I7CTAX2PdBwawnIf6+bGeZGaP9+cbz1zV7U+wPyiRr/qdFRIoen4cp2cDKs85u3f0FQ+qUS/EKem/zv6oFYudUFBQUFA0lH2yrRbcHCXs05UfVTnow/YfIhmTqDyys3TjB8Er6jNPqbXIWcQeQZC/i3/FSEey5hIAkED+kL8iSvAMALjR+TpMo8U0AF6oGxcAMKVG8f0GyasznaXRCYv17t4LGNKthOPO7PC4m3sbAWEwsobxZEU2BjB6DpmJP+du5XKsAGDOrbTpxo++qjmfZUzPMKR2F/RqPWcfckufLGV6+bMD6zZtn5QY8fhcq8tQwwc67IEOqzpCR+GDvgIpB0W5NJRDwx2YvcBKYiRCR+wPrPdH37QXWtV7y0MORdHcaISFcMjsAEATMRD6w22Mb5P0SaXVehib7CQApCscE/dX63PGoiErnxWGuTdlUnANDd9pd/eCVzIbqt5NuPFw2W4AeN5vHh15uDPKQJjTfWf/WvDdftm23qIBPJpbE3peJ7haq/pj91+hZ7EQPDqFxzmWUExLdBsaW1kYGwHkBb9Xvrj/XqLiyED34d6sdvC54URhLz+pOOx8fg7ihp5Wxhda8hLkByd6P9/arjU9rt/uFksdUUkYhhUUFKxcufLvv/9eunTppk2bmsE3CqBLxQDgKC4HALqPR9VD9iIZ4ARdKm6cegcA/+/CYBp9yNAJPQW9AYAbLYi4+MzjT8nLy5s5c2ZycnLVwR49emzcuLFXrya7iZ7Mt+Zrq90MUsrtAJChcPyeaqw67smjTQzloI/e85YZ8Z2Z5t13zUoLAQBcBjIhhNPLu5Gl+2uD0Gj84f3d4voYEi/r/jlhy3lQvvIXdkSw6IXx7C51V87INt29ob3CRJnO2sI1COaGx4oHXdGc2yvbsqTjh03lMwUFBQVFM1FoybusPkNH6LV3riZ4z0jSns8ypt/UJfUSxtY5VbohxRlhm2m4XTlotYpv3fk/gmAlVAyMBAAHxgGAi4Rk0qD3AAEgAbapEFQjwm1/XvvTwvOGqG7wb+Be28fZZMsBjt8sf8Bjn0aPyfe1BQGfbkj5Kf9rAV30TcSvzkI2Tx/h7vSj06QqS7UsjOUXdPl6PMqb+U5MNQnaQUiXclAAYAayQ+Ojc8anav4pJwnS74uQ7PEp9kIroIjpmi5nYmrQtm75M9PMKXpmIDv0cBTCevhAe7LAVmNdwInJTmYoaraCv6tyNK2Ah0oNP+qm+bbhTvck0k4wO7DDjveqT9l5JwfKthtxQxe37j0FNZ+uY4T9Ity6ZhkzjpTved53XtN6/hhwtU72ydosYWl2iBUAaIHe518k8HIViR+jH7zMG9CrsviliOGucaj2lGJqQ58AACAASURBVP71ZtDSFnPvCdldutlB2vu7D+nEDTNiBoW9HACOK/7p7z7k6atI38i3O51ODwkJ2bRpU1JS0qVLl5rWJ4pK2N3CECYDU2kAgNUp4OEBktTtPQ4AnOhIlyfieoywEAwv10pVf1LF7Mhmh/E6+TegS8TOnTtfe+01vf5hkhKdTl+6dOmnn3765HXmK1FbiEUnXHe5Tym3O5V8Vfz5tN4+Li7zRpl9w23TmYKKBqVdpIwXI7njQzjN0UweYdAFYwbxh8bqEy7oD56yZuWWff6T34+fMnwf93lBArm95E8SSCFdfFS+16WNGTcDwE1dUpYxI8Kta5N7TkFBQUHRVJBA7ijZQAI53GN87W0rHs3tOe8Xthb/trv0r+6CXgykjqXkOMlIHs3NmfKtdWgOl+8GgFD6y9fsYpf2dgLsjGrSQo8yy1EOwmYBAMJkuA2OafSlOVFZiNXXDC9Fcrt6NNlNvzb9xHFZpoz6WEYL+jSfG/UEJ/FdJZsAQI9pj8j3Tm9vIcf1J0JS85fOY6IAuDsH7e//yGBGbk9+yJGonPGp2oNyfaKKMOG8PsIO6yPyZqSZrukyo5III84MZIfF1xTG/zwnKTZUSxFPlzs+vajr5slYOVBYdZxJQ5pcvTth+LHCEnrdH3XTlm9pqHovtRadU51EEfR5v/kuDV70W7j8/junlMcGug/zY3doOq8fh/qv/ZhCfXdURZ7CdUkuSAA6Ob8zgLKohv1t/XUbYW0XIehZxvQU3VUWynZuiR0s32HBTQBgJ+z7ZFteC3zaGqY+0TseRdHw8PAzZ840lTcUNUC5HOGk4do98QBgy8qj+3iiPI6jsFR3INF8Ix3lsIWTR9Y+y5Zrvj86Bddhwft68AfWvNNr/inPn5Xh1lcYdqq+93K73f7666//+eefVQc7dOiwY8eO/v37N+rKHok7B/1yoLDEWO1TO0PhuFxs6+rBqHGT4DORnl4uHiNwAl4+orbjJJOGjOvEfimSG910u+6PAmExhROH8UcMMBy/YC+S0SV11PBT25WFljwAUNjLz6tOPt74tv46JeApKCgo2jLJ2kv3TZkIIHy68FGf6iyUrbSXn1AcHuc59fGzsVD2APeKrm9r8j4ngRgmHfeC35BnROa/849oHMoubj0i3XoCwKY0s8KCh0voE/xRQ+JlQqcHAECQSEOpn1ldxhECADPI3xkf+yScfWDdm2Wmo7DSQ1i3dS0UZmL5RZ3RUS0Q+54KA4BfUoy771butg9FkaFdpYz3+/BrzdHmOKOMl9mKxQyJ1qE+pTgyyH24V/sJOW4ZuD35gRsic6ffJkw4zZ0RvK8HXczotKf7vQHXCSOOsNDgvT1qC2MRGxWxq0WYGu0kAPAYSLOuH9WA4ccKO9lLublUMsuH6d8AHbtH9jdB4iiCrsr55FE2JEmQQB4o2/56x4+bwtk6IAwm07VbCIM+td9bwWR61UNYudKQeBl14wonDa867snybhfqnSCJHSUbAWCs11QRw73EWnhOdRJFaAgATuLJ2ktDpKPDeK63PNspTyTgVSrV1atXQ0Ke2iZ7bQFebJRTwGt2H9PsPlY5jrpxPd97xRljXxWneneU2gAgd+rtGhrelmcpXJIFAPbJ9a1Gq1Kppk6deu7cuaqDkyZN2rhxo7t7zV5rTcKLkTUj0LZkmC4X26K9mB/U73ZOQ+H/+vBxAiaHcyScFm21gHLYwskj6mMpYXq8HfSZs8/t40EQpD7xlhQUFBR1smbNmgbZv/fee83kydPHGWU8AJBA7in9q07LOgV8JTd1Sc6W8hO8ZgDA2GBuuHePb3I+tqHnp4T/ImF6HMq2Kix4JxH9tVgx2Wu0/thZ44XrznbldE8J75losALKfqKCL06cEW1EY5uu3FE6TuRbXR66p8LuQbV46Zsy+zu9+Y3NEWwhTLjRGRkx23/xTV3SRfWp3e0q5LhlsBdZi967BziJ0BBc7Sh8I6vDuojCRXcJGwF0hLQRhW9khRzsSeO30c7WDB+Wz9Kghp6FkxgAECRhwo2Pt8RIF5kCzYG9oBhwghUe7O0R6gPVOgKAB1m4JocwmgOeH0ETtGhOfpNwTnWi2FogZXqN9JgA/1b4HyYdhyLoScVhANhesnF56Pco0rY/UBpCY/5a7t69m5ycXFhYuHnzZoVC8frrrze5WxSV2AtKAIAV3okmcLPlPCCtNppUzO0VKRg7mCauuQReqd65Pfk0EcNwTl2p4ZM05/zRQNMsBW7AcgfnJA++sRrqyHgHgPT09IkTJ+bn51eOsNnsb7/99q233mray3wSCnTYn7dNl4tsPwwTRf2bNTCvO691vaqBPa9If/w8f1g/VninysG2kLxHQUHxn+L99xsWSUgJ+MfwAMPMBNmZWbEfONZzSqq+okYMAWgBeHQAJR1cdIoK4UbUHnQJRmLOrkjPeb/oRq9Ywg7hRfQW9U/WXton2/JqYLVf0G0tZIUPgPABJEECSSI0NEeDQbqpzITvyqzWFIpJQ/6fvbOMj+LqwviZmXXfbNyIIyGGu1PcpVSwUopDqVKB0pbSIqWU0valUKBIcXeCFw0QwUkIcdtk3Xdn5r4fNoQYIcTT7v+XD9k7d++cTXZ35rn33OcMCuKwibqrGtPDl713hMxYcgX+19uGGzmWWa357T1KTDF4C4kGrt4BYF/OVgOlbyGMCBe19uMF3dJcjdfG3NXFhglb1XdoDQVrhjlxQKw11cRvL/b8KvDpuDvqg3LdWSWlI9kBXP8/Wz6dfM9wQ/NkeHxD1vCVgVLr9BdvWBJTkdlMyKTvRQ3FWn9QGZsqLl5Hvgm0yQIAuKA8Bz4Mw/k8Wm9EJjM0NgFvoPQH83YAwOuek5gYK1ZzvWjGk8CI66pLWlKdYXp6WXW2m1Pfl47WWKjKR+X48eNFdwA9evT45JNPajQkByWwpmUBADeyuWTMgIp7Fql3TjOe6b4e2ZCgrVh/U5M8KsGwkfrT55deP/ZuEd/C4G04+9nZftLhLz314cOH33rrLb3++dxhSEjI3r17w8IakBFOgtw25mABRQOBgc5a9WLstY3hWpz+/HX9+evciGaSMQPZzQJe/hwHDhw4qGkOHjxYquXXX3+Njo6OiooaNWqUn5+fSqU6ffr0kSNH3nzzzWXLltVLkI2FafnKTJJcIZMO4HEBIFzUJlzUBgAogI8VqhNG0yyRcLa4Wnngp/IP5lmyPTk+PWQlcrte95ycoL11Q/1PT+f+AM+r+U45rlSby8mwe6Iiv7ikKdVIIvHYZnVquhZVxprHnjnf1Kn0FrmGT4Yp9ZIyGseINzynAICIIR7sOmZPzl87sze2CIkoqiD478BgQ19c1ChLvrVSNSQAxOZaJxxVFm/3FhLfdhUTOJAF1sR+t63pZn57sd1zPuhw1JOhcZSWZPtzQ060ZnqxQ45GJQ6MNdzQJI9MCD7ZCqvDGaUaRH/xhuKPXcjy3KdJf+4ay9fT5eOpzJIW1PUI4SQGAFu2vOwh2myhFCrAcXv1isbF4dxdelLry/X35QbkWLJ3Zm8CgL7OQ1NMSQhQb+dBB3K3A8D+nG1txZ3/NTaTVRHwTZs2HTdunKenZ7t27caOHdtgi37/O7BnwbF8X1JFrLh6tySbkA0BBvqbGruGZ04i273VvsX+FjQLnV5yWubkOtjtJcl7v/7669y5c4vXiuvTp8/u3bul0vLtc+oLMRsLljIjXZnvRvD9JfU5d0uptYarsdbULKBphrsLv0Mk0/t5eUXJ6P4Yg9Aeu2BKeGRKeOSQ8Q4cOKgXhg0bVvzh7t27z5w588033yxcuLCocfbs2evWrZs+fXr37t3fe++9Oo+x0TCCz1ut0X6sUAGAXcNDMfUuxPE+vGptH9WS6uPy/QDwhucUvKQglDJl/V2GH8rb+XfWnwDPc7a/7CS6lVvC7VWbbbmQaxXJWD2alFDIYjbeu9KOXA7KsiN7A43o11yGFjmQ9XUZ8o8yOseceV5xso/zoPoNr2ZJ15JHn5jKXSRRmegrmSXqwBM4zG8rdOHhllSzNd0MAE5j3AgBAQD8NqLgE62U23Pc5jVherEBgOXHFfVyKticbYzT0gaKEDW+RXhjzJ2CtdsAIV67cH7n1rhQYMvI0Z28aE3Pzvt6jefKz+wlHusdlr8PIRLYMnONN+/w2oYXP6Q9cg6RFCc0uPpOGXWMhlSdUxwHgHRTyqcPpxW1H8z7u1RPLak5q3iFvUsNnKp8TgYPHjx48OAaD8VB+WAYRhCsgIoMKsuqd7d5vrwoUeqU+/qbGnOYhXOX3XZjOwC4POefSQNne7J9K/a//frrrxcvXly8ZdasWatXr2Yw6v+L9VKGJVtPvd6cZ5838hMzjo159eLzCFkep1hTMxFJMdyduWFNq/mdpTl8Vr3jCLI938ik3nVM2Kuj07tjMCYTADA2S/L6INHgXtpj57VHzxfK+PCm4tEDKlNwzoEDBw5qgz/++MPf37+4erczbdq0n3/+effu3Q4BXwHTRAIbQr9qdR8rVCTAEB73sYqcla3IElFCHN/g4tSsZImW7feNzWSMypcy3ZOzxUQZW4k7hAojyx4d4DrysupsuumpgdIDFIrzESHcESHPU2T119WJ8x4OY8DRxSHfdauK85yDcrmpvvJIf49PCIa4jS1qtJcP/CX1+0O5OzpIugoYjW8x80U0lzEPjXYuldyx477xRIr5jVDeAP8SM0FufMKFhwMAv43I+4fgzAVJGR8nAgNzedcbAHgRQl7E87SUrEVPCjZnYyzc/6+witV7sBMjUMLo4dPAZp1oWrlxDyAknTBCPLTQdZIb3lTYp1Put2stj55qDp6Wvl1+0iupsBESRp0lHWAELh7dX7lxb/7qzdI3hgh6tsf5PFKh1h07rzlyDjBMPKS3etcxw/V4MjcfcJzl5y3o0U7Qq1OV61XXARycG8hraveTooFW2hQI0RKG1ESbLLQZANg4h4tz1aQKwzAx4yX20o2I+tdjDirGed5EWmdguLzQLs7y1GRX79wWfHOS0a7evb4rdKdInXKfc5dtkpi4ai7CkcJfsT1zw6KQlS8aDSH00UcfrVq1qqiFwWD8+OOPc+fOrcEX9arIuAQA6Kz0yP0FCXIbALRxZwVJq/juNd9PUqzbUTyJCOdzJWMGigb1gCqlk2gOnVFtPQgYxusQyY1ojjEZlscp+os3dGev0iazywfPK3zifK5k7EDRoJ7aY+e1x86b7jw23XnsPOttQc8OVXstDhw4cFAdbt269aJiIkFBQZcuXarjeBod9gz5X7W6zxQqGuAPuS5LRLEQtsHFKZxVQqinashF/2haODOPjK7UpHOq8clV5XkA0JGa39NWlNvHPhfPENzCtZ27+ZYWNvrr6uTh8WCgnABGfZNk6SlmB1V2JdBGo9EHFBnaEhv4rRQCgAOJppNPS3jR8ZnYtiFOTcRVuSjnG2mAMjvgaNqWLUdWKyEVl7X7qXdsyLon5y8AGOUxnk+U2DAcJW7fUhh1Txd3MG/n217/qsmvUOfS3u8X0y0A4C9mVLD9wXW2LwBkLkjKmP8YAOwavoisRU/yVqVhLDxga5h40Es+FzIufnpcQ0lHL8KSmEoWqJieruIhvYq3Y2yW06RROQtWGK7GlSvgVQfkqVPu81uLAg9E2tMT6gDRgO62LLnu1CXl5n3KzfswJhPZbAAABC4e1lexfiepUBd1tjx+ann8VH8xxu3zGTivvJ3zDQA2zlkQtNT++58Za64oz7USdxjgMmLpkwUsnAWAWWnL/IBFZ/KP3tJcfaS/29WpT/0GXFM4BHxDB+dycG5F043yX9Nt2Raci5dV79LRbodyd4Z93oKr5gKGYTS029j2QNSBOM2NKHE51VNpmp4yZcrmzZuLWvh8/r59+/r1K6dYXcVQNCSryWAnRo3MKzpx8BAn5oFEEwA4c/FZrQVVVu+muAfyH9YhimK4OXPDm2FspiUpzfL4qXLzPkqplk4Y8aoDUkq1eucxwDCXD97hd4yyNwp6tBf275q76GfD1VhBr47cyObFn/Jcxh+/YLhyuwHenThw4OA/gqenZ0JCAkVRBFHiDpKiqPj4eB8fn/oKrBFRXMMjHgCJBaUww31LL7PbKAAAI1nZEjBJxocIEAAkGR5W3NPXd9uGPlF+XA8bQidN5i4cthTH7eqd0lPUEJeHD40tnxge9L9tORTWIbRSa1A0ApWZ1ljKidZKIbuSL9aCWcux6qsUmToSAJ6oCvPXkMWq3ntSF32Z1hda7rH8vCRjBvLaR1TxBDUHAkhSkoFSxkn5wQKr3Ivj2608MfCG55RFifMuKE72kPXz5jSp+zgbGi/S8K+k3hssthw5ALCbBpRdAWIH+mJMJpmvRDYSYzIoRCUbHwfxmuIYoTogT518D5FIf039ZFic3SCgLsLFMNnUsbxWLbTHL5gfJiOrDedzuRHNRQO756/eTCrUnGaBkjcHs4P9EUma4h6oth60PHpa8MsW10+nvXzweiXNlHxVeZ6BMUZ7jF+fvhoB6ucyHMfwQ7k7/85aP9vvszu629dVl3rI+gfzm798uAaPQ8A3elym+yh35VJqEgBcZ/kUqXcAeKS/90+XC36ePqJMMSAEDEh89wkAbM78NULUptSGOpqmp06dWly9S6XSY8eOdexYlQJm6+L1P8bo1vSVDgqsVrLTvXzbyhjdPxkWAJBx8fciBW+F8rhVnRZAFmvB79sRRYmH9pa+PQzwwqQg4807+as2ao6c43WIZIe8WrEQw/UEZLPxOkQWqXc7rCZe4hF9VdsOGf65WUrA28H5XMmYAS/1JnTgwIGD2qNLly7r16//5JNPVqxYgT/7SqRp+tNPP83IyBg4cGD9htfA0dK0BSEXgpghFkabzIk2GwZApiCuAQeApzbSj8koyj2VG2kAyNVXVsD3lA1QaX0FbLML/yVVpoQMsR83EADOmMyfKFSBTMYfT5n5IxMoPSUd5Zb1ddAPhxXf7sryT9CZByaoT7aWNH+5yzSbwM6/6aq3loj2QKLp2yvaESHchZ1LJIcLmHiVc2wlHDxbT3kIcACgDabcxT9bUzIBgOHujPN4ZF6BNTVLvmK9ZOxAydh6fjceTzbNjVbPaUM8Zu8HgBaCiNua6+X29OMGJRsf78ze+FHA13UbYwOlrIb/d6j3SoMAYG/OllP5hwa6jup9vZ9dvbtM99Eczzfc0NSphgfgtm7Jbd0SAJDNZt/mqT12gSxQsYOauH01B2MyAABjMvidWrGbBmR/8J3x5l3LkzR2UMOdjUKAtmetR4AiRG2vqy+lGJOEDHGoMAoD7CLzdLop5ZLqTKSobYz68s7sjV8GL8eg0du3OQR8o8eaaqL1hVPfxngdbaBwPgEACNAf6auCzwaLMp8t8JIwUDX8PtzVkdoj8j3D3MYVDYIQmjFjxsaNG4taPDw8Tp8+3bJly6pFVWCiAaDAWNU5eQAAWH5D90ecHgGI2PjUCP6kcD6veiv6xtv3KKWGHdREOn548blSXttw0dDemn2ndGeuvqqAt2XlAgC3ZUjZQ5ywpgBgzch9pQERSWn2n2J6uvI7tSqaYnDgwIGD2mDp0qXHjx9ftWrV6dOnhw0b5u3tnZmZefjw4bt373p7e3/33Xf1HWCDZoJckU6S/3OR7dQbEm02NoZZECKCQJFBLVdrN+n0H4pF74oEJoS4GGayIQCgUWWrpahM+JfRrqEuzAMjKytvunDYzZhMOkab9l4GW09JR7n5/RmalWu1eeJL/vT8YHZm6HVDxqA4zvFWnGYvr7RKYCBml7gG2WfPWQRWqr06+IsZDwps7nwCAJQb91pTMpmebs7zJrIDfQEAUZQ++opy0z717uPsZoHc8KY1dV4AMCboNCcKXGf5VLJ6mdxAA8AddTq4mAEguuBIdMGRCvo/0CVkmzM8OY40FgC7hkeQ+VlSxgeJmsP52nNKjI0HbA8T92/c6p3p4QoAlsdPAaFSi/CW5HRkszFcZRiTmWPJPFNwFACSd98LXOyFSOQ2v4nXt0Fu83yTBsYWavgaKqRH6UhKTbIqYRaAPTPpMMXeBwDRsD529V4EQyYR9OygPXreFPegIQv4+7r4J4ZHAHBbc+225hoA6EjND08+K+pwNHe3/ZcUY9Id7e0IUZt6ibMGcQj4Ro/uogqRiODhmIChv6J+MjI+aH8kziei849orKphvw8BAJpBmSQmfoGAdYloNjjskf7usby9vWWDiorKLliw4I8//iga08fH5/z584GBgVWOyu50orNWdqmhXOLzrGwGNqElf1oUX1ITtwvWpxkAwGsXXjbTid8+UrPvlL3Dq0HRAACMcuZNMXtKKv1qsxi2jBz17uMAoN55TDS8j6B7+1Lfpw4cOHBQUzg7O587d27+/PnHjx+/d+9eUfuwYcNWrFghk8nqMbaGT2s267HN9o68gAIgMOx/ztIv4rXZXmSqj22TzsbEsFAWY75CdcZk3uNWWZViQciKkBDHDTaaQqC1lBD8BRQtI/AXzWQLcXzdU8bT9zJYeuruQHH/P5phDCyDSTLaIDOBP/0rpP20NP0FZdLA2ODKafi6hFJr9ZdiMCbT7cuZDNfCNx5GEML+3WizVbXtoPbwmRoU8Pba45SO1EYrKimcaJMZAFwJtyBpDxuyvbS/E9PZlf2SEkL/KVzn+AJA5mdJjUu9mx7o81anu8324YaXUxKSHeLHcJbasuWaw2fFw55vqUAWq3LTPgDgd4oCgF3ZmyhEtbgY2nNRD0Qhtw+aeH0TBAAsH07w8VaFGn54fPU1vDXdnDjgti3b4r+5pWSYayWfRSpUAMDyKeftyvLxBACqQFWdqGobV5Z7ML+5jbaqbAoNqWbjHA+2V9HRXEuWmTaLGRIpU8bAmW7/ik+lQxg0ejw+9zfGanWXVAwhMFxZdg3vs7fZ/uzt3X7sLswVYkxM56kXp4lZTTg+q5pN9/rwwwdTSERuzvx1tt8CAFi7du3y5cuLBvTy8jp37lx11DsAJKlIAHisfEniX8VsGuRkIZGo5mb6abMFAMq14rA30mZz2UMVw3CTAYD1STr0KW0EZXmSCgAMt1fzXGH5ezvPeluz/5QtJ1/xvx3q3cfFg3sJXuuCcxpZjVwHDhw0CkJCQo4dO5aYmPjgwYPs7GxfX98WLVoEBDiKXL6cz6TiK2ZLGkkCAIXQTxq9rADP4gAmAwB4nc/boTdGm8wiHBfjeCaiXz+dLzZSaIIb9uLr2tR8xQOr7Q8XmROU7rNRp1+p1s4VC6eLyq8tb7ihyRmZwNJT94ZLli9x36VUzhYLl9u0QMAwDnehixR2i5PHJOguqpKGxIWcbMUObBDVreyYHyYDQpzwpkXqvQhhn06q7YfsHarmNVuKIvWOsfGXCidE0dpj53UnL6k5gRDSlz4TN/gcEo8ezI34N+ykrQ6RbiwhyxjmUtrcrgJc5/hiTCxvTbrPT83E/RrB/KDpnj5pUCypsGmO5wcfacWLKvPRw3Gnd8bIV6y3bxfnd26FCwW2zFzdiYu23HyGi5N4xGt3tLfuaG+3uBjae1EvRMHtt293/NipSF/WoIa3q3drmhkAUibdq7yGt99h0kZT2UO0wQgAGKdBl5dzZXt8FvR9vjXvi0ezMcA+CfzWn/d8Q3G66ek3SR8ZKP2nQd+5FxP2jZpXkEb5+fl6vd7+++7duydOnLhkyZL8/PzaCcwBIKst96ufVTsqStACAJxHBO6NEHaTknlWwIDhzNJfUceMOtN5WeewA2EYF2f7c+3qPeR4a1YTjogh6e08EABiNTcyTKn79++fN29e0Whubm5nz54NCqpuYTP7kgH9Kgvw/2RYBu0p+PqytqiFTWA1qN4BgOEkAQBbVl7ZQ9asXABgOL1ylXte23DAMP3FGPu2vSIonV6z/zQAVMF6R9Czg9fPC13mv8Py86aUGuWWA5nTF6p3H6d1hlcdyoEDBw4qQ0hIyPDhw2fOnDl48GCHeq8MChPdJiYnjSTh2ZXujtV6p5kVkxVeArfpDNEmM7KB4h/Uc0P+o9kPR5/O73tZubv37VE784ftK7D/jNpfcDPneeX2YCbTgNB7+Yr7ZIk13o06/Qq1FgMIYr5QL+UsfUrpKbY/d9jGyOZcVgpJfqRQmREaw+ctdZHiADiPCNgeTogYthxL3pr02vizVIyZRBoLXfzHRiMAMNqQWmvSMThGJ2eNhS5lSo8LeDibhSxW2mJ9wcCvQJF6l450a3GrA9ufa7ihSRoYa/cSKgWy2vK+XavacoCUKwrLzeKY+WFy3pLfNAejqx9Mo2ZgICf+Hfd2Hq8m7Vym+7R80LlxqXeGM4tSk0lDYo1xurLdeO3CnWePxzls4807+as35327Vrlpry03n9XEy23RHMRj78zeFHQ+uNeiXogEwxzztRlXd2ZvpNDz9EyWDyf4aCuWL6fwzamvyv5Ta1qheue3F7vN80U2lDLpnvqQ/OXPBGD5ewOA8dbdsofsjayARrATZFf2RhLZOjn1LK7eAcCXG9BF2ptE5K7sTfUVW41TqTkeo9E4efLk3bt3X758uXPnzjt37nzjjTfshzZt2nT9+nUXlwZX1+FfgDU103w/CVms8MaQinvaNXzy6ATdJRXDjUXIGMKrglAIBQzRrmBONNI+SLXdeE1wCRQAAK4sLwbGJJFt0ZFPdk8+Rj/T2Twe79ChQ02b1uQes0qy7Lruj3g9APiIatHDgxvVQvX3Yf3FGNGwPgzn51odUbT2YDQAcKNeeUKd6e0u7NtZd/py7sKfeJ1bEwIuYDgymY237pIKNTvYT9CldVVixXF+51b8TlGm+Iea/afMD5PVu48brtz2+rl0uWYHDhw4qCYxMTFbt26Nj4/X6/VxcXGnTp3y8vKqsgfKfwS9DVn4CNmAjAHCB8N9EQBgbAAAMgEjAhEmBEQDGYOBCibvHSiT4wAAIABJREFUz+50TWVjYhYGHhyvHfBjysqJ3rZnri73C2xtn6mgL6RiI0IHDcYvjWpMhoAEANj8TL0vlIr7PCtMc9lsYWLQnv08OcvtQz/9NY0lxZT/YWLLxR4PrTYaQIjhM8VC+1w4stCp796ntCTDmeU6/ZXvyO25+1VeAU+Q28YdUpRysLez4IIGwB96fAgIYFMeAPiJGafHudgrZNMGE22xYkwmzq7uMmBx9e63MRRjYPbFT2OcLmlIbPCRVoSkxI2xattB871EhkwiHtWflUQCAFA0EATQlGrbYXZQE055DjgO/gUUqXfRa7KArWFpsx+q9uQlDY4NOhTFbyMq1VnQvR03srn+YowlMQWZzIRMyo1qwW8fATh+Kv9QriVr6qqpQIJ4oHP40tDjj49lmzMuKE72dh5UNAKrCSfg7/BHXWMMNzTKnTml6u29FGuGOWlwrF2929fwcS6R80NKyoR7fhtCpWPcKnqld/Xc1u10Z65qj5xjh/jz2oQVHkBIveeE+cETQijgtQ1/pXjqnof6O7GaG2ycM8r97bJHR3q8fVNzJUF7664uNkzYqu7Dq3EqJeBXrVq1e/fupk2b2oX6ypUrZTLZhg0bHjx48MUXX6xevdrhc1Mb2BeKGR6VmhwpruGtzlZKirgqFiAMSwOth/bgTwe0mBZKrBCDPtt4+L0TFmPhfDaTydy3b1/79uWUl6sYioaNdwyqksVm5AYKABJV5PIbJWYrfYXEuBbl5OylaUg+E5vTRjg5rBZ35bH8vXntwo0xd3IXrXaaOIIbFYoxGZYnaeodR8wPkwmpWNivaxWGdZoyhsxVmO481J+9Wryd4ebsumBatYzoMIwb1YIb1cL8KFl37ALh5Cg458CBgxpmyZIlixYtQsXM1Y4ePbp27dqlS5d+9tlnFTzxPw6BAbqO2xANJEaqgSkB7NldPSMUARMQCeQNDFQw5UBu32sqkolROMaxURY+0eqhbunOrCMf+FMsjE1gxcto4wDfOUkA4KDByGgLlgf0Zp1+mVqLASySiscJCi+RCGB2gZJE6AeZdPCzfWHCbtKgg5FJI+K1m7JFOiN87S5lECqafkeu2Owqc6GwpxPuak4WMJxZwceiOM1f+Wrb2Zvdx48zNLiKFaG5DMyJg5vIEgLeaEM2GvGYGBMrzODFeVzAMGcuXrTXX3/+GiDEaV5Opa5Xoqx6h2IJzKU0/M6HxjSFRZfMQCG9Oc0DLRefPuK7gRPck/qsD+huHxDblyQyeLjz8bdC+USjt7V28JwS6v3vcJyD+20IBQDVnjy7Y3xZDU+IheKhvUs16kjtkbzdAMCb70QtNGpOKjRb5GOGT1yb+v3B3B3tJV0FjMJxSJUtfdZDQMAJ5kmGVnbvuh1rhjlpYKwlxVSk3gHA48sAAMj5ISX13fsA8CINn/9HZsaHj1neHLepvQ1Xz8h/WMdpEcQOaoJIypTw0JaVBzgum/5Gg60Db4dG1I6sPwEgTNgq25yRbS7H0Cpc2PqG+p+d2RtbhEQQWB0Z/tceGKqEIWpoaKharX7y5AmXy5XL5e7u7h9++OGKFSsAIDw8HMOwhISE2g+1oXDv3r2wsDAcxymqWhbrL0W17aDm4BnJuEGS0ZWtNEYbqCcj4/VX1FaejWVkAiCNl+bA2gN6V33pnhQ6Mfmy/I7S/hDDsI0bN06aNKkKcd7Ktb5+UFH5/jcmujlzS2taGoGNRuzavwDSRlPekt8siSmFjzEMEAIAQiJ0+3wGK8C3CmMab96Vr1gPNI1x2TiTCQgQou01bBtC5RsHDhzUONeuXevUqVPHjh2vXr368t4NmJMnTw4YMCAwMHD58uV3795dvHgxQujy5ctjxozJzc09efJkv3796jvGuuNVr+9yI22frd5OGg6SRhzDnpvMIyBjMJQPUw7k9ruqtDEwisA4FhoASAZYmATfRMU3Eyyf6GNjYh+2EwZJGRczLEUjI4AbMku2iAS6cLNjZAErQMcEABxgUjg/UML4Q6v/SaMlAIpreCVNLziaNundVI6RfjpO1ud/Ye8qlPettiBELPsox3xcYVfv3NCXV5KrG+ZGq48lm9b0lQwK5Ob/tMlw5TbL19P5/UksX08AAIT0568r1u9CNtLti5ncqBZVPlG56r2IIgnEixIGH2lVwMQ6ba1U7rGdvSNkUW4NepOwg8pTVr3b2xGFUt+9r9qTR4gZ5Wr4svyV+dtFxelwUev3/RfKf83I/DQRMPD5qenWXn/e08X1dh70ltdUAKA0ZNKQOGOslh3ECznRiunxCp5H5ar3InKWPM35IQUjsHLX4e3q3b7lh+XD8fzYor9wovhOeIarTDb19ep87uqGeG3MmpSllew8y+/T1uKKKmQ3iut7pVbgU1NT+/Xrx+VyAeD69esIoR49etgPNW/e/MSJE7UX338Z+wo808u98k/B+UTQ/sgnw+P01zQAYHQ1HVh7YEbHT5xZpT+03y1cKr9zuOjh119/XTX1DgCt3FjfdRNrSq7Ab75rlBupECljeEiJSTtvEeHMxTN01IrrOjEb+7Zb4ZIyjkEdqHcAwHlc92/n66Mv6y/csKZmIopmerjw2keIhvYhRFW5oaFNZsXv24GmxSNfk44bXLTebrhyu2DNFvWeE7x24Sy/V0uFqjz6s9eMt+4IenXitWlZI9Y+Dhw4+E+xatUqDodz+vTpgICAnJwce2OXLl1iYmL8/Px+/PHHuhfwycnJ0dHRDx48UCgURqNRJpN5eXl5eXkNGTLEw6NhuQe78nBXHr5Koz1oNjIx7EeZ9OMslYWNAAAwkLaFT77JD7iqBBbGYOJMA3WzpVAlZr52RYnjtEVERD7Sbzqaq/u5Wbcg7huHFXfkJY3NMWB2BkwMAEClQMwDWwwUdnDi4vPbCt8TCQgMVqq1CxQqGsFQPldJ06/nFmRGseV/+Hz+XkbAToWGnbjx55CpuQV9Z6aaz+vxBqbeSyF7d6w1Pduanp394fdMb3dCJLBl5VFqLQCIh/epjoowJxrs6t3pdfcmf7TAytxvsHw4wcdaJQ64bYzTJY9JCIluvbqPJDU+xXAtDudzaYOJ3TzwgVfIuTRLG3dWryZsAFDvOoZsJMZkuI3rtVf1Ubw1ZLLP7Oq8fAcNgRepdwCwy2CocB2+OOmmlH+UZwiMGOf5DgC4zvIBgMxPEzPmPx66fOTDznfOK050l73mYfGqjnpP7H+7eOZ8qQ4eXwYgGuUuT0197z7GxiVDn2f1Fqp3AK+lwZrDcv11TfYKTtCBL2htui1HjhE4q4k3u0UQRjSCesZ+3KBW4vZm+uVG1GycU2qHfCOlUgJeLBZnZBRmI/zzzz8YhhUlWqtUKharHiYdG9EFvsrYsuQAwPSqaONKWXA+4TzJS39NAzzYvW6X2NepmSAMK7ln7cKFC2t//LXo4cCBA7/88ssqx5lnoP4Xry9V6sbuQJOiIdfFl/Bd8xIQKWry9ziDmUQ+wvrJYMEIXNi/m7B/NwCovqWtMSaB0uo5zQOlbw4t3s7v3NqSlKY9ek5/9prTlDHVOUUFmOIfGG/eNd68y/R2Fw/pze/W1lFzzoEDB5UnLi6uY8eOZV3rfHx82rRpU7ywXB2QkpIyc+bMkydPlnt01qxZw4YNW7lypZ+fX11GVTGrNNr1Wj0Tw1bJpCeMJgsbIRowHHCEjV6SE7BLhdgYg0VQOtLWVfrTIHechXVvwWGvz2bQNEgY7Csql88SWTvCV/eWXMu2QrEL6VnMdFlQuCbP8sfeduf7kQwA4DCwvv4cALAh9MRGBjIZyTbyc6XKgNBfOn0mRQJA827OzfbIno5JUPyVjeHY11km43m91omx7a8m6xqqegcAXMj3WPqhetcx/Zmrtowc+3QF09NVMnYgv0u16jbbcq2UjgQAQUdJWfVuh+nG4gTzrWlmy1MjotCQIK5RYZPvvAY4Dgj5LBy6JR0/l2YJc2VOixIgizUt5QpQCACdx1JyLOk5lvT2kq4thK9sW+vgpaQYk35LWz7c7Y3OTr1q9USkymZX7+IBzgHbwzBWae2KEZjfH6HIgtSH5U+Gx7WI7ch0faEI2pG9gUZ0P5dhRc7nRRpe+4l8+FdjY7nXj+Tt7LmklzFWyw58ZfUOAIot2dY0M8bC/Te1fJF3veeiQN05peGWNuf7p0UCvmBTll29+6xs6jLN22WKV/LoBN0/qifD7wefaMVr19B3vJdCwnSa7fff2vBVqXv9sLCwc+fOpaWlSaXSnTt3tmnTxtnZGQBycnKuXLkSGhpay0GWoDFe4KsAomhSXgAYxnR/ZYNAYU8npzc9Ykff1rvq33R9r5R6l8vlb775ZlF+oJeX119//YVVQ8RaKKQw0kaynL0YNhpKrcwbbPSDmzYMYEQI95MOL88+qnWqvWptTbbXli/nms3rEKE9es6SXItOv86zx7ObBmiPnrNl5hb8vl2186hoUA/ha10a+G4lBw4cNBzs6XVlcXJySkpKqrMwFApF3759k5OTQ0NDhw0b1rJlS5lMJhKJtFqtUql89OjR0aNH9+3bl5CQcPnyZTe3V5variVWP1Pvq2XSY0bTcaOJoDDzdfBojnfcl9dnh4pkYzYmxtWR4v7Oqd+F2M6rWe7w3gDJmyZzv21KMFCEiKGNVmR+kthkTbMm4ue3ZJt1+stqCwDwcnCjB00B2sHVr3eVtXtmWWdDaL5CddZkluH4fLHwJ43uW5XafhkeyectcZJg3SFwT0TymISCTVkAQDizdm71SwpkkQgRDThdC+dynCaNkr493JaeTZvMDJmU4V4DpcKF3aTey0IyFySmz3+EEHKZWjotDlnppxPuas8oGM6soMNRdpHPDvEDDAOaxvlcQiKE9OcLEoYrt4GiCbEwm6O8AjfsjTuy//w65Ce88W+vbVAgQFsy/6ew5m/PXh8uaiNk1OatIw32ohIYE3/h/SEOGBsDAKAA6BduQ76lufpYf5+JscJErVNNyc8PvAM8i5NxkdLja5dBaDDNoI1kVdbe7Ti/46XcnWdJNj59605ZC0Y7mQuSDLe0GBv3XFxYYapgU1b63EfwTL0DAM5/ZqT1jyppQGzwiVZsf8dtZIOmUgL+o48+On36dPPmzXk8nkKh+PzzzwHg77///vTTT41G4zvvvFPLQT6nMV7gqwaVr0QkxXBxwlivUGDTDtOT7fdHCw86IMLULpBfwlIeITRlypSiPEkcx7ds2WKfjqkyfmLGrclu5pICftwhZaLS1rsJe0UvCQDYaLQ+wbD5joGkwUdIfNddXNyzp1FTWFueX843HcHnAQBteuXa8pUHY7NEg3sKB3QzXo3VHDpjTc1SbTuk2XdK0LuTZOwAh4x34MBBxURGRsbExOh0OqGwRH1jvV5/48aNiIi6W078/PPPk5OTv//++wULFpTbYfHixZs2bXrvvfcWLVq0bt26OgusAqJNZrt6P2w0nTKaRDju8YhxV21rkslsLuIAAMOKGBYU312A/+7vrcdxVwQRQAJs/dzNmcBb/1VA2UgAKJ6mCwBFrnW2uyDRELOaC1aotVaAqXKFXcOTz9S7EMf/5yLzZBDb9MZ8isIAPhGLJj3bDibsLrVreJxPBB+L+rWFgEaI2YDVexEYg6jxslWFi58LEjM+eAwAxTU8stJPx9/VHCsoZRBASMX8dhGGG/G00WzLygMobDfevKvctA8AGM7S6A4pNKBezgPu6xKyzOnnS1qLO6g+V5Tn0kzJAGCmTAdy/57gPb32zsWQMYOPRSUNjlMflqdMvOf/V0uMWeLzgiiUNu2Bak8eIWQEHYpkur/wVvZo3h4AsCHryuRFpY/1hpEHRnrGeQEATuKIQE1+bVYF9Q4ATA92yOnWdgvGxIGxwUejGE4lVEP2V8nytekYCw/YEmYv3VdWvdtxaPjGRaU2NvTt23fLli0+Pj5ms/mdd96ZOnUqANy8eTMzM3PGjBlTpkyp5SCfU3SBv3fv3nfffffGG2+89tprHTp0eO2118aNG7d48eJbt25t3LgxNTV10aIyH5hGhS1HDgDMylnQFyd/fWbBn1kAwMY5pdQ7AKxYseLo0aNFDxcuXNirVw3kI7EJTMzGi//Yt8wwcUzMxgtM9MSjqg3xBhrBxDD+8dddGoJ6J3MLjDfiDZdvWZLTX61gfUkYTmIAsGWXV1s+MxcAGDJJlQevJBhB8Lu29VyxwO3LWZywprTJrD16Tn/+Rm2f14EDB42dCRMmFBQUTJw4UavVFjUaDIa33npLpVKNHj26ziK5dOlS06ZNX6Te7UyePLl79+6XL1+uwviff/459jLCwsIAgKbpco+OGVN6M9Q6qZiaMql/UOApo4nSaO4M6Hdl4wYAOLnltx8WDCIZFCBAOFwcIV5u1h9iGRitC2+7TPfvrtn+rhVZAYAGeuyqt4vG36ozLFNrcYB5XBGdjqWlpU0RCeVLlwCAFWBCehavdRu/PzedNZlxk2mji8yTQUyWK/IpSkbgCGCZUiUZNaYoZlEPp9cKRndLG8wLFY4bM6aUeidJMjQ09EV/jbKvtzb62+8W7EkBtR2P0zSP7dIDCKH0+Y/GsobZu7Ew5mrxd5pjBUaWuZRBAEmSw7autdEUIJQ+7+uEXzcDQMaBk/Jl62iTOQ5Md4QpTwPMfIw/3O3NMR4TAWBL4jqulF2Pf89/Wf/wNmG/3f2xqOV8wQlZU0mtxsOLEI3NekeDtHYNj2zPV6dsFttaz2XKnbkGZBwvnyFoL6lg/HaSLk04AYanZsUDdamfyO9aecZ5IRwBAMmhMApLGX/P/NBQtfhZ7uxucYNS6HTTHd0e700SXFzUP/ur5NwfUzEWHrA1TDzImSTJub7T0uY8RAj9YFnjOt2n1Pg4nwjYEyHoILZmmpMGxhpT9A3t/VA3/Tt16gQNnspulx0/fvz48eOLt8yZM+eLL76o5uLtq1LJC/z27durdoFvOBAyCc5hc1u92vYE8yNDxgePGVKm8xSvskdv3ry5cOHzKuJdu3Yt/rD2+OWW/pHC5i9mLO8pbuVe/zat1rQs5frd5kfPM5oYrjLpW0P5natSsJ0T2Rz2ntSfuy4a3JuQPF/CQhSlPXIWALiRdeXeiWHcyObcyObW1ExT/CN+12rtGHTgwMF/gfHjx0dHR2/duvXkyZOurq4A0Ldv39jYWKVS2b9//5kzZ9ZZJPn5+Xb9XDE+Pj5VK3xTmZo7FUOXmep1w3GUkWHLzs6YON6WmmJNSQEzjxPc3nD78DTGYAZJYGwcLPTsj7LW4NiZvoXq3XzvLv+NL36xfcnCWCZk4mLct5mjrlE37ePvNxhxgG+cJK1J1nIorMOq/N/vAOD6+ZcYh+O1/k+Giwul1fqvX+e56sfJckWizRbAZGx2kR3Q6X/SGTx+XAUAmv177c/NR4oXxY8QquDPUjf9x4fyeAyskxerbuI5ITiXm5v7AWv6p6w5AHDQdnwZZ1F3opMKafa2PdkldFCp/mrSuur+zU/DOuCA+/CFACBmsvPNxj+TEoZGRZ3ukg8AI73GCxjCVuL2oYLI+xAfOb3Z9R/uVDN+TkBrpXPpfcgN4f9Vx/09h0k5EhYAKB5qZM3FGIZ1+jL86MR/ANViPI/p5Bnmj/8U/QzF1uERhTJmPOpsaGtAxlnmT+/SDysef6DrqL7SoREjIh4+LNFzNmtKF2ZnG5CHIqIX/voVw52dOuW+7rwyaVCs/+HwqsWvQMpp5o/WcVY2xQN/56yYYf5Yg7Q9HrfPPfFcvdv7D9D2xAC7RcXvtR0pd3xCQPj+2vxhuxvWDLP6QH5Dez/UY/+GRqXKyDUcnJ2dw8LCzp8/X3G3yZMnHz16ND8/vzZiqLMyclUgfe6jgo1Zkndd3X705xMljGo0Gk1UVFRKSmH5NKlUGh8f7+tblapplWHsIcXtHOvYZtzve0ieqMirWZYxzXhcRv1n7lkeP839Zi2yWHEBj9M8EGMxLcnpZG4BAEjfHi4e3qcKY+Z9u9aU8Ijp6SqdOJIb3gxjEJYnaaq/D5vvJjKcpZ6rv8Q59Z9xAACUWotzORi7/udQHDho7DSKMjOVZ/fu3UuXLn348KHVaiUIIjAwcP78+VOnTiWIutvNO2TIkOjo6Hv37gUFBb2oj1wuj4iIaNu27eHDh1/UpzrU4PXdmmpK7B9rzTQzvdm2TAvFgDWrvG/1FXJI7JSSlzMsnlKTLC+2NctCiBnBh6N4rQt39maQlIqmwlmsVA3Ze0e+n5hx9o3CXLz1Wt0qjQ4AOBi21dXZvvZepN5dCAIA/tTpV6q1BMBSJ+nQZ3u7MnSUOx9n4vV/CW4gFBX04oYJTXd0DBkz+Fgrbsvy7f0QRcuX/mZKeHTJrcXXYSPmkIlvkU9NcQ8vRuWe76n1ZHh902KNfd97tjnjq8T3EaDFIT95c5pUJ8JeO/LTNeTNSW5STiPwAK8l8q15nz2cSQPFI/jLm/+x+um3T4yPoBJlwGoE0x1d0uA4UmmTDHX12xiaPvuhcmeuPXOe305ctTGzFyfnrkzFWHjAlpbiwYWfa9pEJ49N0J1XMl1ZwcdbcZrxqza4LduSOCDWkmzkRQn57cT56zIxNh6wLUw84PlSqzZakTzuDrLQrjN8vJeH2D2yTA/0LC8OIWYAgDXTnDQg1pJi4rUWBR+JIkT/RV/kRnF9f4XvhZiYmDlz5nTt2jUqKgoATp06Vcf+tADQsWPHa9euPXnypII+crn85MmTHTvW+me7oUGpSeWuXMDgr9f++C7p01JHZ86cWaTeMQzbtGlT7al3AAh1ZgJAMxkTAIKkjAkt+Q1BvSMbmb9mC7JYBd3b+6xb4vrpNJf573j/8pVs2huA46q/D1vTsqowrPP7k1gBPrZsufz7/6W9OT913Lycz1aa7yYSUrHrgukNRL2TCnXm9IUZ732p2naQLFDVdzgOHDhoQIwdOzY+Pt5oNKalpRmNxsePH0+fPr0u1TsAzJ07lyTJDh06rFmzJi0trdTRnJycDRs2tG3bVi6X16XzTpVh+XFDTrdm+XFtmRalO4MgYe4HmW2idZ6PTWlD4yg1yfLmWLMshIgRdPC5egcAHwYRzmIBgIxLSDh4kLTwBppEKMFaWEbOitATm+29fEWizRbIZGxxcXZ59s+aIhTMFQspgM+VqpsWKwAkKclef8u/vKQFB89wneXjvSwEELxUvU89oZwerXH9dJpocM/uysQtV38bdnGv4fJtLaa71tUIAG/4Ti1yrfPk+HSX9aMRvSPrz2pGaCERArBQjWmNrcb5O3sDDRQAjPaYwCP4b3u/h2EYAGzPWm+lLbV9dm64MOhQFCFhqA/L74ddrSX1DgA4Fw/cHSHs6WSTW5MGxpofGSoYpAKYnuyQE63YgTxjnK5c9Q4Aor6ywF3hOAeX/56R/v4jQFCwMeth+xsPO8dYM8y2LIu9pDwvUhh0MPK/qd4bC5UV8EuWLOnQocPatWsvX74cHx8PAEePHg0LC/v+++9rM7zS/Msu8DVL/p+ZtIEie6AMrwwRo8T3y+bNm//++++ih3PmzBk2bFgthWGl0OLLmr/vG6AGLN5rGFP8AzKvgOXr6TzrrecL0Rgm7NtZNLA70LTu9JUqDEsIBR7ffeg0aSTLzxswDCia6e4iHt7Hc9VnLL9y9jLUC4SAx24eSBuMmoNnMmd9lf/jn8U3EThw4OC/ya5du86ePWv/nSAIX1/fotKwu3btKrV1rlbp27fvL7/8otVq582b5+fnJ5FIAgICIiMjg4KCnJycPD09p06dmp2d/dtvvw0fPrzOoqoOLF+Oan9ovjfLKZdUujEIEuZ8mPXFxDRCQ9m82dZMMyFiBB2K4rct31VbyMKujnf9rZ8UAEiE3n/mWjdByKcBvlCqCyiqGZP5l4uzrGSh5hki4ftiEYFhJoQAIM9I0Qhy9Q0uZ7B+cZ3l47u6maCzpKK1d4BzaZazqWaMxXSaNMp73ZLI6SNkE0Y4z3r76oImZgbZWtwxVBhZ/Ckj3N8UEMKH+jtxmmrZ0OhtCCryOP/381B/J0FzEwA8OT7dnPoCgC83oIu0NwCobcrT+bWSg1MKXpTQbu1uy7FUV71/9Uy9bwsrrt7tFGr4Hk42uTVpcJwlxVS1sxRq+CBeuerdjqiPLGBnOM7BC/7MSux/O33eI0BgTTU97nv7cd9blqcmXqQw6EgUQ1pZC225JYdGdfH1YqD0d3WxCP7Dn4piVErAnzx5cuHChQEBAfv27Vu8eLG98fXXX3d3d//8889PnTpViwGW5N93ga8pEIkK1mcBwIOxDwCgm+y1okNyufzDDz8sehgVFbV8+fJaCsNCoTcPK7beM2KAtfVgdfNpEIvPRViSUgGA1zEK8NLvfPsGeEtSStVGxpgM0eBenisX+O36ucmuNV5rv5K+PZwQNqByuxib5f7VXI8fPuZ3bYNhmOFaXO6XP2V/slx/8QaykfUdnQMHDuqHcePG9enT5/333y+bNB4fH79t27a6DGbGjBn379//5JNPoqKiLBZLSkpKQkJCamoqm81u06bN0qVLMzIypk2bVpchVYeLJvMsnnHJX75yb5ZTHkl4shg2xDHQ+Z5MZqaFFhIVqHc7bAIjsBLqfaOL7DOJ+BOJiAbIp+iJQn4p9W5nmkiQ4O3RrWHkfzVYnN/1CjnV+kXqvSyESMDv1Eo0pFdBB5cblhsMjDnGY0KpPnxCMNR9HADszN5IIluVYzPaEAAoTf/RaRca0dsyCytNvO01DccK3+SjPMazcTYAHJbvVlhrZZ9sKXhRwuBjrSRDXYOPRlVZvQOAYkcOAAi7S8X9ZeV2wLm458IAwMCWa9FdUFb5RAwXFi9SyPbnVpCKb9fwGAPTX1EDAs+vAnkRAlum2Zpu5rbgv5J6j9XcWPBoxsaMtVUOuPL8L23lT0+/uaCoO9XZkKmUgF+1ahWHwzl9+vTIkSOLXOt6Xw0DAAAgAElEQVS6dOkSExOD4/iPP/5Y8dNrln/ZBb6mUB/Jt2aaOSH8VkM793Ue0l7StejQRx99pFQWfhfw+fydO3ey2bV1UVeb6Xv5pI+Q2DdStnOYzE/csNJvaIMZAAhhOV9qhEgAALShirOez8EwrLzbqQYCO6iJy7xJ3r99IxndnxALrU/TC37ZmjXvW2Sx1ndoDhw4qB+4XO7PP/88aNAgjUZT37FAcHDwsmXLYmNjjUajXq8vKCiwWq05OTk3b9787LPP3N3d6zvAypJPUXMUKhtCBZ7MPVsDGN4cKtvK8ueqgzgu2TajEF+8wffvpngmWb5CyyCp0yYzFFt7l+D4FldZSxYTACYLBR+KRfZ1+EMGY52+sP8kyEYarsYqN+9TrNuh2nV0y5M1CFB/l2GubI+ynXvJBnhzmuRb86Lzj5Y9+mrnrebzGy3nFSdyLFkA0EbSsZmgZVG7iCEe5v4GAJC0bX9uHU0v8iKEAX+HFd/nUgX8NoTiPEIbrUif+6jczArTXX3ymARAIB7kLHurnPdVZUA2lDLhrmpvnvmRwb6V/UU9bVmWolVza5qJ1BQu5FAaitZUdlHHhqw7szcCwDXVhSeGR1WLuZLEa2Pu6+IB4EDOdgOlr9VzNQoqpa/i4uI6duwYEBBQqt3Hx6dNmzZ1vxPefoFftmwZQshoNJrNZqlUipdZU60Cd+7c2bVrV8XuNbXkjVcKZLNhzFeoAF+wIRMAXGZ4u4i9w8XP3dQvXLhQfAnl66+/DgkJqcE4S+HGJ8696SLh4LwGsOO9LIRUCAC2vIKyh2y5+QBASKv1Bd1YIJzEknGDxaP6GS7f1p64hKzWBrfbwYEDB3XF9OnTMQxbtWpVhw4djhw5UoGHXF2CYRifz+fzq+jnVO8wMYwJYAMIYzFXt5axTjglDYi1ppgkAFYh8cMG37Rw7iqN9i+9/i8X50BmiZuxJBs5SV6gpOmD7i6JNtKu3je5ypoVuyt4VyQAgB812i9VmgE8LsvxHV6bZM1eTCrU9t/vhxpTmykB4Iri7A31P+X2twuMo/I93WR9SzkKO3gpBkp/IPdvACAwxliPyaWO9nEefL7gRL4175rqYg9Z/2B+8/qI8ZURdpMGHYx8MiK+YHM2oqHJ2mZQzFTSdFefNDiWVNpEr8kCtoRhrKooGrt6Vx/JZ0iZ7CCu4aY2aWBs8PFyyrkr/spOm/MIEHIe76nYnVuwORsAuFFCDMeMt7WJA2JDTrRi+b28CPyp/MMF1jwGxiARuSP7zy+Dl2NQK19EJCJ3Zm8CAD4h0FO6Q7k73vSaWhsnakRUdoGUyy3/H+nk5JSUlFRz8bwaNX6B/+KLL4qXSa+AsuUHahDjjfj8VZtcPniH1z6iMv2taWbdP2qcizuNLbE6QZLk7NmziwoNREREzJs3r8ajtVCIooHHLPzQegrq1PfoleCGNVXvPGa4dFMysh9efB0eIe3xi/YO9RZcnYMxmYKeHQQ9O9R3IA4cOKhPCIJYsWJF8+bNZ86c2a5duz179vTu3bu+g2r02N3HwlmsDS5OQhwHf27wiVZJg2IpLdn8QKSvP5VqMrdis2It1on5BcU1fJF6787lBDGZzjgxRsB9WyAIKTOn/65IIMIxJU071HvtghCpULMCfPidWuF8Hma7DHANAFSUCirMcKcQaaZMLxXwN7Ktv8XqS63J0ggBwFeXtQJmCS03MIAzrgWvKq+i8XAkb7eRMgAAG2evSC6n2rHlmYPdruxNXwbX1p7QGkfQSRJ0IPLJiHjFlmyA5xq+UL0rbKLXZIE7wjF2tdQ7IWYEHYrkNOMnj07QXVIlDYgNPlFCwxeqdxp5fRPk9kETyQjXp2/f5bYUBB2IBByeDIs3xGgqo+FVNsWxvL0AMNPvk+1Z61OMSVeV5zs79apC8C/ldP5huSXHg+M93fejr5M+PKc42U32WjULPTR2KiXgIyMjY2JidDqdUCgs3q7X62/cuBERUSmRWYMcP378yJEjSUlJgYGB06dPt7viF+fjjz9OTU3ds2fPq478/fffd+nSpeI+eXl5P/30U40s+L8I091ERFFkfmX3wCi2ZQONJMPdiJIp6xs3brx//779dxzHf//9dwajhnPa78hts6NVNgpdftutAWeOF8JuGsBpGWK+l5i7eI1s6uvspv6AYWS+UrX9sOn2PZzPFfbvVt8xNggsj54qN+3lhDcTvtaF4eJU3+E4cOCg1nn33XeDgoJGjRrVv3//NWvWzJgxo74jatzICPy8pxsfx4umtNn+3Ba3OiASESLGrwBqmhZg2FyF6oLJPFFesMnVOZjJeGoj38kvUNJ0Vw77Z5mUALhnte7RmywIljpJS82OZ5PUep3eQKMpQgHzmYa/kmm5l19i67XuurpJLpkpZqyLK5F3yiKw15vziibfHVgplKgskTz8TFRj2f0HiYb2VmIYAPhCu3e0uYJtm1Bmjmhgd9Ggni8akE8IeMTLV5iuZlkuZ5bvqZ6QV3oXPY7Bv17AFyVjGymDXcm/iGxzRp1EVGOU1fCm+4aaVe/BR6J4rUQAELg3oqyGV/yVnTb7IQB4LwtxneUDAKLXZC0fdyaEDIyBAUDQwcgnw+IMN7WJg+OanmrN9Hrhltt9OVsttLm1uGOkqJ2JMq5PX70nZ0srcQcuUcPvTy2pOSbfCwBveE7x4fr1kL12ruDEzqw/Pwr8pmZP1LiolJybMGHCmTNnJk6cuHnz5qJGg8Hw1ltvqVSq0aNH11Z05TF9+vR16wqdLc6ePbt+/fpVq1a9//77xfucOXPGbpX/qrRs2bJly5YV97l3795PP/1UhcErD5lXAAAMt3LcI8sBgWJ7DgDcHXT3Vk7saI9C02C9Xv/VV18V9ZowYUKNl9Y7nGRacEFjoVB7T1ZtTmjUJC7vT8r96mdrWlbOl6twHhdjsyiVBgBwDtv1o3cJsfClI/wXsOUVWJLTLcnpmkNneG1aCvt354aFONLsHTj4d9OjR48bN24MHjx45syZ9+7de1HmnYNKIipzXcR5hRocA5DiOACskUntGn6yvOBbJ+kilaqAorty2L84O7ExDAC8GQw+hh02mADBUtlzDZ9NUhPyC7JIqjWbxSj25Tw3Wq22PM8Q7HlTM313dhcGLJvsu7zM1lYuExvX/F+uBivPon+0ex6VYyiAMBhPRsJ+RbE2xpjIydPvLmOcfeQyYlw1zzuntbCVG6tUxbiZp1QIYEFHQRPR88wLBg6t3f/9xoQcorLfPFgjvC0RdJIE7olIHp2g2JJNaUn9JRWptIkHOQdsrW7mfHH1DgA4jwjcG5E8KkH3z3MNn7syFRAIu0vt6t1Occs6QsRosq7Fw7Y3rKkm5Z5ct/fLX+VONj6+prpY5OPYQdr9guJUkuHhMfm+IhlSU+zL2WqijJGidi2FUQAwwv2tGNXlB/o78dqYSFG7mj1XI6JSAn78+PHR0dFbt249efKkq6srAPTt2zc2NlapVPbv33/mzJm1HORzdu3atW7duoCAgGXLloWFhd2+ffujjz6aP3++v79/7dVFq3vI3HwAYLpXSsDrr6is6Wbcm3GwyU6BUlT0yVm5cmVubq79dy6X+803NTlThQBW39T9eluPAN5swVvURdRYvkQJichz+aeaA9H6C9fJAhUYTTiPy2sbLhkzgFG5P/h/AUH3dkwPF93JS4ZrccaYO8aYO0wvN2G/boIe7XCe457egYN/LUFBQdevXx8zZsxvv/1We3anDopgYliRhp9doKABiqt3AAhkMta7yN4rUBw2mmwAK2RSAiCHoiblF2SRVCSL9T9nWfHr77Ke4thny7aeJwua78nGEGLZYMHm9LtLmiqLGXFxGdjAAE7dvdQGTxdv1iOFrbiMpvXGByYmALR0KbF/gU1gXUMlGItJ5hYgqw1jvYJjUVm23jMsuaot99AP10qbdQ0O4v7cR1Kd0zV8xnpMuieIq0zPRppELewmDdwXkTwqQX1QDgC1od7tFGr40c81vPeykKdv39VdVGV9keT1XXDZAW05lqev30EU4kUInSeVXwgZAdqR9ScC1N91uN3HEQPsTa93v0n86HT+oa5Ovd3YnlV4LeWSbnp6WXmWgTHGek6yt/AJwVD31//O2rAza2NLYRQDq9anr/FS2YTqLVu2DB48eOnSpQ8fPgSA8+fPBwYGfvfdd1OnTq3LCbC1a9dyOJzo6Gi7o17Tpk1btGjRtWvX6dOn9+rVq1SGf2MFITJfCRhWyRV4/XUNAMiH5CMcOki62xvlcvmqVauK+nz44Yc+Pj7lP//VIWn47KJ6/2MTgcOXnUQTWjYykyGMzZKMGyQZN4jWGRBJEmJh2apyDtgh/uwQf+nEkfqzV3WnL9uy8pQb96i2H5KMHSge1qe+o3PgwEHN4O7uXurSKZFITpw4MXfu3N9//72+ovpPwcSw+SLhVbPFihALYJ5YxC55WxXFZv3hLHuvQHHCaAKADySid+QFGSQVyWKtd5EJ8BKd+/hx+vhxAECxNSftxxSgke39JmduagdcUbVemBiwPUzc3zFVXT6Dg7iDg0rMUOsu3oh86IsBHBpVzh8tnSAQ2BBJVlPAu/JxJy5OlTRW0lhoABCyS9ydMHHwETZcm6Gawpfr78v1r+8oahdhV2ngvoin4+4Iezr5bwytmnoHAPkv6eoj+RgLDz7WihdZjgjC+UTg3ojHfW6Z7upT373f9Gyb/7N33/FNVW8DwJ97s9MkHUn33qUUSilQCmjLElmCoiAqICggguPnAlGQFwEH4kBFGYKCICAIsqHsPTsoLaWb7jar2euO94/U0JYChbZJW873Dz/NuSf3PkVI7nPPeEK3di+YeL36hxKaBL8vG+Twlhpz3ug0Y56e110YtjeO4dJ0knhBebJQnytiugx3f9bWGMgL7ec28Jzi+N+VG+cEzXu0X6cRGugt5etooIe6j/aq91BgkHj4aXlKmfH2Uem+pz2evc8ZOrGHWBE9fvz48ePHkyRZXl7u5eXFZrPbLqx7ycnJ6devX/398Hv06PHjjz9OnTp1+fLlrTvI7CiEQkUTJMNF1MyvBMlrvgwRM3fgYbaRM1DytLXx008/1Wg01p/d3d0//PDD1gpPb6FnH1GeLjXxWdjPT7m2t0rvDwVvqp4cUh/DWej83DDRmKGGq9fVh84Yb+QarmaiBB5BOo3Kysq7G5lM5qpVq0aPHm0rQYq0hUyzJYDJkJPUazK5mabdGLiCpGZK5Rs8JO4MvIwgY/67Daifw58yGPU03WT2biP/s/L27JtA0T6fheZP9N7gq/ARMGIPywpfzmxJDn9bRXx0QjWrpyA5oAN/9Tcf00MCN5su5mapqKEMRoZQ0PJZaSNDeSNDG58kfHUVRdObRrl2c3fAzTZiB8InXGPLk1p4Eqe+zjgXp4yU/M9KfqywyT3gNWeUxlw9AAiTXAHAuti+YOL1mp9KAO7k8JYac96IVGOOjtddGL4vjunWdA5ioow7KjcBwHifKY2Wu7/gPTlVdTFVdTFLk95V2KOFvxoAXK49m6vLFjGdR3m8UL8dxxgv+r72TcHCPdXbE12TnVmuLb9Wh/PQj3wYDEZAQIBDsncAMBgMd2//PmXKlN69e69YsaKkpMQhUbUuQioHAKZHc3cOY7qy3Gf4TQiftrLrRi+OLwDk5ORs2LDB1mHRokUiUatVR5uyX3G61CTh4VueEXfo7B1pPoyB8xN6eH32lt+vn3t8/Iajw0EQ5NHdunXr1q1bRqOx/ssmhYSE9OrVy7HRdmK5Fsv4aumzVdJXpDLruvfDXh7JPK6coqbUyJ6tqnmhWlpgubNqPY7D/sLNFcdAT9MuOP7r/bP3N+uyd68PgwCAxuDUq/7uM/1oE1X4cqbqUBO1VJvjUqX5apX5SJHx0d7e4XAirOPAtPbExQYHaFr5524A4Pfp7oCwEOQ/gn4uIdticS4u/bW05J2cux82qVPkRa9k0ibKY7a/z8JQa6Ntw7yan0rK5uVBs7N3ANhfvaPWogjihSa6Jjc6JGK6jPB4DgD+qviNou9boaEZzJR5R+VGABjnPenujfGiBd17iHobKcM/VZtbeKEOqlkj8LadzO+la9eurRHMg4WHh1+8eLG6utrT09PWiGHYL7/8kpCQ8Nprrx0+fLhN94e3A1KqAIBmbv0tXVumT9cErIzCGBgbr0un582bRxB13/oRERHTp7dasUQaQG2iwlyZ64a7+Ys6/zwupBGmuImld/qL6dpTl5z69eT37YHdVegIQZB2JSoqCgAuXryYkJBge3kftkKkSOsKYDIjWMxcCwEAfThs67r3lWLXsVXSQoIAgAgWy49553u2kiSXKVUUDTgGtRT1f8ra5eLG+9JDU9m7FY2B/zeRACBdXfbI4/CP298F7L/iOrJVm035JU4D4hnOQktZlXrvMePNAtyJ7/zCcMdGiCCiwW4h22ILJ2TI1pcDQMAPUbZxeHWKvHDidcpIecz29/sqosG7nhKHbOxWOCmz5qcS2kipTypM+Xp+D2HY3rj6e9o1IjNXH5buAYCn3MfIzDV3d+gp6psi3VthLD0pPzJI0qJ/HQdr/pGbpSKmixtLkq3JuLtDN1F8uvrKWcWxgZLhQbzQllyrI2pWAv/Ajdnt9gX/+uuvz5kzJykp6Y8//ujdu7ctV4+Pj//www+//PLLyZMnd/Rle4RMCQBMd/EDe1I6svyTfMpI+S4Js/17u3Hjxp49e2x9vvzyS1br5VQYwIEX3AGg/VeMQ+xGn5alv5Kpv5KJr/tbkNRHMKQfO6DV9i9BEKR1vf766wDg7u5uffnGG2hOjWPoKFpG1g1SlZGkjKR8mQw5RZX91yilSD1NW9fDV5Lky9WySpIEgGE83hmj0boevlEOr9heZc3efZeENbF9tDWHp0C6tqxoUmboPz2ETzyOU08fGoYBjmkOn9YcPm1rY7g6e3z4OlPSVn+AHCZmsNCuXHSzhTxYkzn8fbJ3K+vmeYWTMqXrygDggdk7AOyt/ttCmwFgTcm39+kGAP9W/5UsHoZjj/gXmKKpQ9LdAKAmalcULrpPTxroQzW73gj84NEu1HE1K4GfM2dOoxaNRnPlypXs7OzAwMCPPvqoDQJr2uzZs69fv75mzZq+ffuyWKwrV67YqtAvXry4qKho8+bNu3btunuafQfCiQxhB/o2Z16W6qCM0pP8fiIZX+oFdSnT119/bXuekpCQ8Oyzrby7A0rdkUbEr43nRARrU86Z8m+rD5xUHzjJCQ8SDOnn1D8e56JFFgjSvqxdu7b+y47+yLvjMtK0gYYnuJxais40m6dIZd+4uc5V1JppmglAAKhJSkNRrjheP3uPZbOXuLncMltse9rVz+GV26qAojnBPPfpfrYLSXg4BuDOxwEAMPCaF6zYVkWqidpdNSiBfyA2A8MAfFfM16ScNd0qovQGhtiVFxctHNKvTWuyOLMxgwWY91glgSCNNMrhXUa5F750v+zdyprDF03J5HZxCtvzgOwdAAJ5ITlsz/v3sfLlBrZkj3McwweKny4xFjWnc2+X/o98oY6rWQn8jz/+eHcjTdPffPPNRx999MAJ9q1r1apViYmJf/75Z2FhYf2RfxaLtWXLlv79+69cuTI/P9+eIbUubnSYz4qPm9NT+U81AGQn39ifs+z/Ir/34waWlpZu3brV1uHjj5t1nvu7Umn++pLmrXgBWvGONAljs4SD+wkH9zMXl2uPndeevmzKKzblFSt//8dt6jjBoERHB4ggCNLu+DIZ5329uBimoajXpYrrZvMkqYygIZrNWuji/JZcKSXJz5WqRa7Or9TIq0gSALqz2b95iPkYFsdhr5aIZ0jlB/UGLoYtc6tb3OS3PFJ/45qpyJA/LiNsZyzuxACAKDEr5UV3LwEDrCtdR6aSaoIXI/Ce38k3+m45DGDVU64YBiw/jtvU5+15aSYOAMDogHXOEUcRDXYL+at74cTrsvXlst8rgKLvn71bOY+UxOQOYAiZGOvBf9kGSUYMkoxopXgfwFY3DmkS1sLZ74mJiZcuXZLJZG5uzd10zQ6qqqoKCgr692+TRzI3btzo1q0bjuMk2dIdGlqCMlDXA05RJnrjv79rxbrvojcImaK33nrrp59+snaIiorKyspq4Y4AF8rN0w8qDAQ9v5/ote5oz3bkwWizRXchTXvsvPFmgXBIf/HMFx0dEYK0vgsXLvTr1y8xMfH8+fOOjuXhrFix4qH6v//++20USTvkqO/3fIvl+WqZiaZZGGz3cI9iswotxKtSuZQkQ1lM61Z23dns9R5ip3oZXarJPEMqNwNc9fVi/9duvm3MHXHNfNsoSHQJ3dWDIbgzxZ6QmvNGphmytbwYQfj+nkzxA0bb9BZabmgwn3FfvuGby5pRYbwP+jQsPcjFhWyUaramffmGEjX5Zk+BowNBOpgHzpxHHqhDfL8/RBm5JsXHx1+8eNFisbRKNK3Fy8vLy8vL0VG0OaaEbY6xqN3UXQU9hEyRXC6vv/n8xx9/3PL9/L66qDYQ9IQu/KndUPaONAvGZgmS+giS+pC1GoYI/bVBkPblgw8ebq3gY5XAO0QVSc6WKU00zcNwA03NkSv+cJeEsJi/u4snS+XW7L0bh73evUH2DgA9OeydXu46imbXa2cHciMOxOeOuKa9UFvwbLothyek5twRqcabumZm70aCTtpSozA0sSBxX75hX76hfgsDh93PSaIlaBPTVtOoIj2CNJNoqDj8ULypQO82ofPnQY+zlibwhYWFIpGo/p7wiH3gPDzmZn8dqR1SNeoJtyEA8N133+l0OutRf3//F19shZHPjxNFhbXEi9F89GgduT9KZ8C4bIxxZ7SH4SK8u5ty027dxXSnfj0FA/uyfDzsGCCCIAAAu3fvbtTy888/p6SkxMXFjRs3LigoSKlUHjlyZO/evS+99NJXX33lkCAfH1UkOaVGXkIQ0WzWSrHre/La62bzFKnsD3cJD8dst2gCDGvydi2Q2UTz3Tk8bSAfKnsHABaORYtZt9VE/UatmVYaKQEba7S5moCFu6Dt1hCkfXDqJXLq1WrVo5H2qVkJ/O3bt+9uNBqNu3fvPnjw4IABA1o7KuTBCIWF6cJ0Yghe8n0dADQazapVq2xHP/jgAzab3fKrJPiwE3xa4TxIZ2UuLFH8vsuUW0QTBGDA9JC4PDtUMLgf3GPlHqnVEdUy1a4jql1HOFEhwoGJ/H5xOI9r57AR5LE1ZsyY+i+3b99+9OjRxYsXL1iwwNY4Z86c1atXv/HGG0lJSTNmzLB7jI+L+tn7enexM46vc3ezrod/RSrDaKgmyS4sVg1FXjCa5sgUP0ncOM1bFM0O5Ibv7Zk7/Jr2Qm3BuHRCZjHeeojsHQAYOPwxqvHSyG039fNPqUaG8pYlOT/0b9sx5SsJAAhzbelwF4IgSCtq1kdSUFDQPd/PZC5cuLDVwkEACJnygYVJjDd1N/tflkzz9f+mbn3Lr7/+qlQqrT+LxeJp06Y97HVXXNb8nqk78IK7Gw/HMOAz0aA78gCqPcdqN+6m4b99NGggqmWyX//SnLjktfid+qPxNpJZLwsHJmpOXNCfTzPlFJpyCrH1fzv1jXNK6sPrFnGvtB9BkDayZs2a4ODg+tm71cyZM3/44Yft27ejBL6NaCjKmr13Z7PXubsJcRwAhDi+zt1tklR2y0wAQHcOe727uJogp0hlZx8yh+eE8CIOxucOv6Y9VwsAD5W9IzYv7JbjANemonmmHRlN685d0x6/YCoqo80WpoeY36e7aNRAhhBtMYB0VM1K4F955ZUm2z08PMaPH5+QkNCqIT3WNCnn5Kv/krw1SZB0vz9Vxd9VtJmyfYmbTKbvv//edvSdd94RCB76U2l3rkFvoXfnGTbd0Lly8cMT3B/2DMhjxZCdr9y0CwAwLkc0pD87LIBQqDQp54jKGtOtQtlPm9zfebXJN3KiQjhRIfS0F3QX07THLxqz87WnLmlPXXIeO9T1lTFNvgVBkDZy9erVe234GhYWdvr06SYPIS0no6jyhtm7lZamNRQNABjAYldnJwwLYTH/cJc8cg6fPyaN4coK29UDZe8PiwZQmyj0XLlDoy0W6Te/6a/dsLVYSitVpZXaYxc8Pn6DExrgwNgQ5JE1K4HftGlTW8eBWJkLSwCANpju3021XwYAmX0zFGpNd1H8pk2bKioqrIcEAsHs2bMf4dIGggaA9Rk6tZlC0+aRB1Ks3gI0MJyFvj8swAV8a6Pz6EGyHzdqT1/Rnb0mnj7hPmVyMQ5bkJQgSEogauTaU5cN126wA33sFftDoEmSVKoxHGe4itAEAaTz8fHxycjIIEmS0XDKDEmS6enp/v7+jgqs0wtmMo94e7ozcFbDD5b3ZMoKgozjsBe7uoSx6m7SQljM9e6SV2tkZ42mNWrtW85N7DDSJE4Ir2tmv1YOvbOoXlmi+Ksy8Kcu/PjOvGCYNpmJGjlgGNNTjLEeu4c4inV/66/dYIgEri+P4cV3xXlcU2Fp7fb9xszcmmW/1L+BaSH9lUzNkTOm3GLKYGSKXXhx0c7PDGF6SVrl5AjSCFrV074QNQoAYHqImzyqJTSLcv8XrAhLzOoNAuyfwL8iZd26i+J//PFHW58ZM2Y8Wkk/az1BtZmK92IvH+jyKNEjjxNLhRQAJLNebvDlh2Hi2ZN0F9JoC6E7eUk4IvmB52F6iF1eGO7ywvC7DxE1ckt5NTcm3CH3HJYqae22/YYrmZTRBAAMocDpyV4uL4xorS97BGkPBgwYsHbt2o8++mj58uW2wiUURc2dO7e0tHTECDuV/H08+TCbWGc0kMf1YTIWu7k02nM+nMX83UOyrFYVz0FP2FtB1VdFFZ8XAkDeM2nhe+I6ZQ5vqahWbt5ruJZJESQGgLFZ/IQeri8/88B1mp0GUS3THL+AsVhei99l+dXtys7tEnv0c+cAACAASURBVOr56ZzqJT8ZM3PVB066jG/xpxxNy1Zt1p64eOe6UoXmyFntqcvu77zK79O9pedHkLs0ncCjOrGOQkjvl8Dvrt6isMj8D/sAgOoJNcmiIp26njp16vr169YOTCbz3XfffZTrUqC1UAAg5uNrhrty0QJ45L5ItRZoGgDjxXdtdAhj4Eyxq6VKarpd0dxBonuQ/bLZmJmLO/H4CT0ET/TmxoTbbQzcmJVX8+VqymAEDGNKXGmSIpUq9f6T+ksZXovfvde/UATpcJYtW3bgwIFvv/32yJEjY8aM8fPzKysr27NnT2Zmpp+f39KlSx0d4GNnhuieK+DCWcwN7o788GHhGAAwOv4NgjV7xxgYv5dId0nVKXN4Y3Ze9dJVtMkCABgAAEabLbozVwxpWV6L3mYH+Tk4PrswpGUDTTsl9rBl71YYA3d57umqzFxDalbLE/janYe0Jy7iPK7LxNFO/XoynAXm0kr17hTt6SvS79b7LJ/X6OoI0nJNJ/CoTqxj0HRdAu/exBB6mfH2SfkRAAg6GwwAVUnVOIb3dO77v5/v/M8aO3bso814XHhGRVAAAOMieC4cVAwGeQC8bmInTRPE3cPjNEECANbU4NJDcR49mNYbTQUl2uMXtMcvMNycnfrHC57ozQ5p22m9lFZf8806ymB06h/vOvlZptgFAMwlFfLVW023Cmu+Wefz1UdoOj3SOUgkkuPHj//vf/87cODAjRt3lomOGTNm+fLlYjF6VoXcMTiI83qs0wtRHXsWUvV3t63Ze+Cv0a7jPW/PyFZsq8obnRb2b5xT706Sw1N6Q83Xa63ZOwAAAweqbsNZSquv+WqN78qFGKvzT8Il5LUAwPL3vvsQK8Ab/hs2awnaZFbtSgEM85g7gxtTt7E0O8BH8vYUjM3WHD1Xu/PQvbYEQpBH1vS/XlQn1iHIWjVtsTBEAqypCXLbKjZQNPkUd7Rvuj+N06zB/M+Cv2XUsuv/z5ozZ06zLkTD6RKTddE7ABwrNu3O02MY0DRUaKkDBcb6nSPcmKiACtIIxuNiDAZNkpqDp0XPDK5/iNLqCbkSALhdI1p4FV7PrryeXS0V1bozV3VnrlqqpOq9x9V7j7N8Pd0mP8uLj2nh+e9Fc/QcpdHxYqPc333VlqizA3w8F8yueG+ZubDUkH6TFxfdRldHEDuLiIjYv39/bm5udnZ2RUVFQEBAdHR0SEiIo+NC2h1nDv5xYsdOcau/u12+IN+avbtN9AKAwDXRAKDYVnXrmbQT/xeuiHJq9BYa4JPTqvotGMC4SH6cZ/tdT645eoHS6gGAExniNmksJzKYpmhTdp7i953m2xWEVKG/kOb0ZG9Hh9nmMA4LAKimNpaiDEYAaPJ++6EYs/Npk5kbFWrL3m2cxw3THD1nSMtu4SUQ5G5NJ2aoTqxD3Gf4/ZrqQpYm3YkhSLo+uNJSUN6jIsWyNxkftnbDbxZL3RPW6OjopKSk5lxo1y393JOqRo3WNfD78g378g3124VsPH0aKqCCNMaJCjVm5Sq37GVHBHOj6u71SY226tPvgaYxFoufENsqF2L5eLpMGOkyYaQpr1h35qru/DVLebXu3LW2S+CNmbkAIBz2ZKNhdpzLEQzsW7ttv/FGLkrgkU5Ao9GMGDFi7Nix77//fkRERERESx+6PTKpVHr+/Pnm9290l4IgzXF39g4AGAMLXBMNNCi2VyXMz10yPSAvsPH2q1uz9Y1adBY6zrP97hakO3cFAJhiV6/P3sLYLADAGBi3W6TX5/8rm7WQ0hm0Z648Dgm8daWAIfWG68RRjb7QDVczAYAd3NIJfdYRiyYnyTPd3XAuh9LqKaMJ53JaeCEEqa9ZI6uoTqx93GsBPEETOyo3AcCzXi+bvtcBAPUUZqHN2yt+/+2332zdZs6c2cwLqc30gzv9x0I9RGfk8eH+9uSy2Ytogqj69FuWjwfb35tQ6Ux5RUCSAOD6yhiM0cprMTjhQZzwILdXnzPlFTc5I442mVv+NB0ASJUa7rEVBdNTAgBkrbrlV0EQhxMKhcXFxSdOnHD4OrisrKyxY8c2vz9Noy8m5OE0mb1bYQwscG20kaDhn+olG0rKVkYbYwQAQAN8elqFASx50rnBuTAYGNCu8zFSqgAA/pO9rNm7Dc7n8Xt10566TFTUOCg0u+LFRjElrubicsWm3W6vjIH/9uk05hTUbjsAAMJBfVt4CZzDAQDKYLj7EE2QlNkCOI6z2+9kjUYImVJ/OYOokgEDZwf58nt3v085IcSBmpXAozqx9kEqVNDUCPwh6e5qU4UP13+A86D0QyeZwEx8YdAhYt+/B3cXFBRY+/B4vEmTJjXzQmPCeeUa0kg2uAH655bBTNLdPVnRDUvF9vDoMJ87iD0xxC5eS96tXvwzpTdYKmostrsBDHMZP1w0MrmtLozjnMgmJvcqN+5S7T/Bi4l06t+TnxCLOz36Kk2czwcAUqW5+5A1dced0PcZ0kksWbJk+vTp169f797dkVslJycnZ2ZmLlq0aOfOnQAwa9YsZ2fnB77rMbdVq2Nj2HNNfdaVEMRWrX6aUCBp7QepHZT6mKJ8QT4ABPzcpVH2boUxsMj1XQuNpOqALOTDnG6FT2AMzJrAA8CL0R1t2T9Nw3+5ZSPWlJ6mSHuH5AgYiyWe9VLNsl/Ve44Zrmby4qJxPs+Uf9uQfhNoWpCU0PKpfOxgPwAwXs+9e5jdcC0TKIod4m97cNCe0SSp3PSv+uBJIClbI87juk0dJxiU6MDAkCY1K4FHdWLtg9ejCz+7m1NSn/qNaqL2YM0/ADDR57Xzx46Jajm1/rW/k3Pjnfse2nXG1m38+PGurvesC1KtI3/L0E3p5uQrZACAmIcv6N94GdvhQqOZpIcGct/sec8tcBGkPk5YkP/apZojZ3Tn0wilCudyuNFhjip8ypC4YRhmyLhpyLiJrdnKi+3C79eT37vbIzw85kQGG7PzdGev8mKjGhygad3ZawDQ5BMEBOmIRo0a9f333w8dOvS1117r1auXt7c33vBeMyEhwT6RxMTE7NixIz4+PjU1de7cuYGBgfa5bgdFA3xVqzbStJSkZjbcuL6YIKbUyGtIMpDJmCBovKL78cQJ5jFdWYTSojogdZvghbGa2IXUfNugT9MAAD9WiHXwrfZxoZDU6PVXr7s8/3SDAzRtvH4LAJhuj0slOV5sF88Fs2W/bqk/0oCxWc7PDHZ+oRXKZLL8vDhRIaacQtmPm9zfnmybBmguqZD/9jcACAf3a/lV7EC+aov21CWMweAP6MWJCKIthCH9pjHzlmzVZpqkhEObHsdFHKVZCTyqE2sfLH9vj7mNp8H/XbnRQOp7OvcN4oddOnisB/S4nVCiJTVio3fpqWpbt+nTp9/nzHvyjL9d1/FZ2Lu96wp7pddYaJqO80TlZJEWwThs0ejBotGDH9y1jYlGJAmSeusvZejOpRoyb+mv3dBfu4GxmLzuUaJnBnO7hjf/VMIh/dT7jmtPXmL5eIieGWJdC0AZTYrf/jYXljAlrvxe3drs90AQu5JI6h63ffHFF012sPNk9RdffDE1NbUtzrxixYpVq1bdv4/ZbIYOMj8fA/jczWWeXPm9Sg0Athzelr335XLGtGAiUifDCeGF74/LG5VWu0daOPF6yOZuWMOCO6YCfe7wVEulSdDPJeSvDl+4mx/fVVVRbc4vUWzY4frSM9asktIZFBt2WKplAMB7nIqTc2Mi/FYuNGblm4pKaZOZ5SXh9ujCELbaYJVk5sTKT7/TX0ovm13A79UdF/At5VX61CwgKV5slGBIB0h9jZm3tKcu4Tyu58I5nPAga6PzmCGaY+flv2xRbtzFT4hl3LvCJWJ/zUrgUZ1YOyDktbTJzHAV4TyurbHYUHBecYKJMV/wnryz8k9+KQ8AigcUAUDKjmOkuW4GVFhEWL9+93vCZ13HbpsynyWzTPxXzmViaVPR7nRI54E78QWDEgWDEkm1Vn8hTXcxzZiVr792g1CqfL6e2/zzMD0lbq+9IF+9Vbl5j/rAKU54IE2QpluFlM6AsVmSd15tlZX2CNIePGzV2LbWu3fvsLAwJrP1S5/k5uYWFha2+mkdaBSfxwD4UK78XqWmgJ4lEt4miFdr5DUkGc9h/yxx46Jql/XwugvD98XljUpTHZIVvpxZP4evy94rTIJ+LmG7euBOLa2B6nDCYU+qD5ykSUq9/6T2+EV2iD9QlKmwlDaZAQDnsAXJdppZ017gOLdbBLdbm+zTyfL39l76nmzVn6bcYs3Rc9ZGjMkQDk9ynTS21fcDagvak5cBwPnZobbs3Uo4uJ/+UoYhNUt/KQMNwrcrzfqORHVi2w5tIVT/HtWmnLUWqwQc50aFuowfzo2JoIH+q3wdDfQw97EETZxWpPDe4fV5faA5hAATvXPzP7aT9H6+x9abhmXn1ZufEXd/0JJ1C0XPOaI0k/TEDremC0GahyESCIc9IRz2BKnSGFKzmtzx7v6EQ/oz+HzZuq2kUqW/fN3ayPJyl7w3jdPGVegRxJ6WL1/u6BAaSE5OzsvLa4sz//rrr5988glBEPfpk5ubO3z4cKzj5L3D+TwA+FCuXKnS1FLUYb2xmiTjOew17mJ+x/kt7KZBDv/S9ZAt3TEO3vmydwBgeklcpzynWL8DACiD0ZhV798UhonfeInhLHRYcJ0Ry8/Le9kH5sJSU24RZTQxxa7cbhEMlw5Tc9FcWgkA3Ngudx/i9ehiSM2ylFbYPSjkfpr7kBvViW0LlMFY/flPptxiAGCIBLiAT0iVxuy8qv/Ld3t1XN4ATp7uJgAck+0/JN1F0aTZ1fSdeCkQoCk21GTLrSfBcAwbpL9SpdETdI7C8sAEXmmkq3RUdw/WvL4NPr6ZOAAAh4m+8pHOg+EsFAxsYo9Z3flU2co/OBHB/D6x/ITYu3eOtJRWKjb+Q6l1DRqrpIrVf3l+OhsXomWlSOeXl5dXXl6enJzs6EBaB4ZhAQEB9++j1zeuFtb+2XL4jRodAKDs/f7u5PCH5YUvXff5PCx/bPq9sncMINiZiXfMP0vRiGSGSKDYuMu6QbIV01MinvZ82xVhfcyxQ/zZHfQRP0EAAMZqIius2/XQfL9Hn4j9PdwsNcfWie18FOt3mHKLmZ4SyZsvW9fo0iazau/x2m37Fb/vJEKG4BiDokkjZQCA8GPhoSdCj80/buGb8w+U2E7ik+Du4ulM1TR3zZ4HHz/3iocTG2M33KOlhwc7pdjYywvNDUY6P4zDBgw3Zucbs/MVv+9khwQ4JcTyE2KtpVxps6X6i18JmZIbFery8jOciGAgSUPGTeUfu0wFJdLvf/dcMNvRvwGCtC2appcuXfr333/rdLoH90YcKprNEuC4iqIAoA+HjbL3++N1F4btjcsflaY6LFcfU9AELXzSNXRHLM5vYuz94AQH7MnaWpwG9OL3jTPlFFjKqgDDWIE+3IjgDrEjOmJnTHc3c0mFuaiMHeDT6JC5sBTuUVgXcaCmE/hbt24BQGBgIJfLtb28j8jIyFaPrNMja9Xak5cwFtNzwWyWlzvQtPFWISc00OX5p4EganccCt5Xs+r9vwyUbmn+PJmpesj6YYxibMjnz3JinHqcjLOdZ8Hri1/v+vqnMi1Ac4cO3HhNfHx/OdB5qtwpFhWNQx4D/PgY//VfGFKz9ZfSDWnZ5sISc2GJ8q+9LB9PweBEnMMmauSc0ADPz96qeyDNwPm9u3PCgyveW2rIuGnMKeBGhTr6l0CQVkDT9KJFi/7888+amgZ1oUmSNBgM3bo5fr/Gzz77bNmyZRaLxdGBtFO3CWJKjVxFUSEs5m0L8Ytay8KwWSI0Qfp++LHCsH1x+aPSCKXlPtk7ALA66Pj7fzAmgxsTwY1BY2/I/fB6d9Nfu6H65zC/T/f6W3FZyqq0py4DhvF7O/67AKmv6QQ+KioKAC5evGitH2N9eR8dYsvW9saYlQ80zYvtwvJyBwDt6cuyHze5ThztPG6YcNgTtTsOGW/ksnH2Eem/qlpFmCmSUYzhIoYqTCNLLcnPzbeehMHBewzrysDufPHoLPSr+xVS/Z0Kn2aSVploAPgzS7c331A/Bjcuvn6kmwsHBwAXDp7gg4bfkccFzuM69e/p1L8nbbEYrt/SX8owXMm0VFQrN+/hx3YBANHoQY2mkzFchILB/VS7jhjSslECj3QOv/766+LFi0Uikbe3d15enpeXl5+fn0qlysvLS0hIWLlypaMDBIqi7r9w/XFmzd5t695PGYzW9fAAgHL4++PHCiOOxqsOyNxn+t0re0eQx4Qgua/mwClzSUXlvOUuzz/NDg8CgjSkZ9f+fYg2mQWDEh9hLyGkTTWdwL/++usA4O7ubn35xhtv2C+ixwap1gAA08PNcP2W5sgZY0YOAGgOnaIMRtHIZIzFpHSGWoO08quiGeveYDzJosAi6yVfWfA9c9OdCV3+T3jt027vTSfaWvQWOkdu0VuaeKSiNtFqE1m/RaqndGbahdNWvyOCtH8Yi8WPj+HHxwBFGXMKMSZDvmYbAFin01tZKmoYQidc6GRtJGW1DgsXQVrV+vXrBQJBTk6Ot7f35MmTlUrl3r17AWDJkiW///57165dHR0gck+Nsnc+htXf0w5QDv8g3EgnbiTa0ARBAGMyPObPqvniV/PtcukPf9Q/xO/bQzx9gqMCQ+6l6QR+7dq19V/+8ssvdgnm8YI78QHAcC1LfeCUrZFQqlW7UzQp52gLgXHYZ/8vJX5NPABQJy0AkNMjCyjY/88BW3//J59OL3dfYbhSqo4AgEypRcDCFw1wtiXwQSLmFxfVtxQWAGDhsOs5iYB9Z/68mIfzWR17ehiCtBoc50aHAQDO5QAApa+brmIprSx/bxlgGDcqBBM4AQDGRQ+9kE6isLBwwIAB3t7eAJCcnLxw4UJr+7x58zZs2PD5559/+eWXDg0QaRoN8JpUXk2SCRzOr+53KsYN5/MsAPPlypUqTQSLNbjebFgEQZB7YUpcvb/8UHvqkv5ShqWyBmMw2IG+guQEXk/0GLc9av1Sq0gzcSODAcBSLcM4bJcXhusvZZjyit2mPW/MyNFfuwEAMnU3n588aZwueKko9K9gjMRccl0VqeqK8krrGXCusMzl0/JcbjYAgAkAtmTpt2Q1WAnPZmDBznVzw57053aRoCXuCPIA7BA/Y06B/nImNzocABgSV37ProaMm8bsuqUr+otpGBPn9Yzhdg3HmGjuJdKBmc1mgUBg/TkoKKiiokKv1/P5fCaTOWDAgJSUFJTAt08YQCSLFc1ifS12bVTv/Rk+Dwf4Va0Rd4QC1AiCtBMYiykc0l84BNV77wAeIoGXSqU8Hs/6Tb99+/b9+/eHh4fPnDnTNtMeeSi4sxDDcZqimO6uTv16ao6eAwBej2iGSKhPyzbmh2G5ATROH/005dawW4E9gkbMHxG9L1qZttl2BreEPh5eWdafmaaelVqsmwfLX9ggnfATMrdk1+0hHClGz2sQ5MEEgxLVB09rDp7iRAQ59euJ87geH79B6Q2yX//Sn0/FACNVGvX+k+r9J3Ennteid9jBfo4OGUEeUWRkZE5OjvXn4OBgmqYzMjISExMBgKKoB25hawefffbZp59+6ugo2qOfJY3rX9qM4vNG8Xn2DAZBEASxm2ZldHq9furUqdu3bz979mz//v23bt06ceJE66ENGzZcvHgR5fCPwHgjl6YojM2ylFWXv7WYpigAqF6yiqiRGQsj9Lld4b/sfYL3VP+Xgygfs36aMiPvpu0My94d13+wPwDwGPw1l5225+hfiuaPj+LXv8quXIPWjLYYRJCHwA7yc3n+6dq/D0q/Xa/ee5wTGQIkacjIsVRUA4ZJ/jeVKXbRX7thSM2ylFfTaHNspCMbOHDgN998M3/+/Pfeey8oKEgsFq9ZsyYxMdFgMBw7dszPz/EPp5hMJpOJnj4jCIIgSJ1mfSl+++2327dvj4yMtCbq33zzjVgsXrduXXZ29ieffPL9998vXbq0jePshEiZEgD4A3phFKU7cxVoGgCIGpm5KlafG4Jh9PF5x24NuwUA8i/KvHe4Rp3v88/bO1Wfqa1vd3d3nzpqer3bGtXdl6g1UssuqO3y2yBIp+IyYSTDRaTcus+UV2zKK7Y2Mj3E4ukTeHHRAMCJDHF96Zkm36s+cNJcVMbrHsmN7cIQCewWM4I8goULF27fvv2LL74IDAycOXPmrFmzlixZkp6erlAoKisrZ86c6egAEQRBWooyGPWXMsyFpTRBMD0k/F4x9fepRZAOp1kJ/F9//eXj45OWlsbj8WpqalJTU99///2xY8eOHTt269at+/btQwn8I8A4bADAKEoyZ5JgSP+qT79leohB9JzikAxjYPmvnMoaWTfY7rfbj1BaKD357819treP8H/q7kGJMjX5/C65haJtL2tNFIZZHw7A3zn606UmW+ekAM57vdEWtQjSNOGwJwTJCYYbuURlDeA4O9CXExWKNWNNqfboeXNJhfbERcAwTmgAr0c0r2c0JywQcLQeFWl3hELhtWvX1qxZY60Xu3Dhwtzc3D179pAkOW3atLlz5zo6QARpWqmGZGLgLUC7kCAPoDtzVb5uG6W7U0dZuflfQVIf8fQJ1ltxBOlwmpXAFxcXDxs2jMfjAcDFixdpmk5OTrYe6tKly8GDB9suvk6MHegDAMbMW7SFoE0mAGA4B1b+KAMA7mTq0GvXAUDM9mAUYQKpwOBq3Mfaba3uY/Vkdm/DdQ2ve4MMvEJLplWbG12I/m8GvVRPSfWUrZ2iASXwCHIfGIfNj4952Hd5fjpbdyHNkJ5tzMoz5d825d+u3XEQF/B5sV1EowdxwgLbItTWRRlN5rxiUqtniAScsEB0i9O5SSSS+fPnW39msVjbtm0zGAw4jnM4qNoC0k6RNDyzQyZgYWde8bDbRXUW+u8c/chQnjsfPY3tMHRnr0pX/gE0zY2J4PeKwThsc2Gp9vQV7clLpErjOX8WYKgYE9LxNCuBd3Z2Li0ttf585swZDMMSEhKsL5VKJZvtgHu7goKClJSU7OxsuVyu1+vFYrGvr6+vr+/o0aOt5XDaP3awPzvQ13y7XL52m+uUZ0WjBjol9DAU62r3StV/myVJEmm4bIrfLPmRSgAo61m6+p/1Ol3ddnTemOeAwU9woxrXL+3jw57eQ2AhaQCgKNhwQ9dVwvr2ssZE0u/2Fg4MaHA3FuiMVhUiSOtjuDmLRiaLRibTZosxK8+Qlm1Iy7JUSnXnrlF6g+cnbzo6wPuhTWbl5j2ao+doc93afpzLET79pMuEERgL1bB4XFif1yNIu2UhabWJMpN2Tb3+zTN8fk5do6c+SkCDHx0DZTQpftsBNO326nOiUYNs7aLRg6oW/mBIy9adu+Y0oJcDI0SQR9OsFK5bt27Hjx+/ffu2q6vr1q1be/XqJZFIAKCysvLcuXNdu9q1QmBRUdGbb7556NChJo/Onj17zJgx33zzTVBQkD2jehQYJp71UtVnP2iPXzBm5zn1iTXeLBD0rqy5ibHzfcfOeS57bU5MbFzRdZYSqsvjyoqPVdje+nTAkLBtsVi9iu48FgYATiws0u3O/9PvPF0A4PsrGgDwEzJi3NH9N4LYD8Zm8eKieXHRAM8TVTLjzXxORHCjPpTBqL+Yzg70YQf7O3wcgDKaqhetNOXfBgzjRIUwXZ0JudKUd1u1O8WUV+z56WyMhZ76dXgrVqx4qP7vv/9+G0WCIB2LiaABwEyijYE7DENaNqnRciJD6mfvAMDy8XR5caT817+0p6+gBB7piJp1N/bBBx8cOXKkS5cufD5fLpdb59pt2bJl7ty5er1+2rRpbRzkHXK5fOjQoQUFBV27dh0zZkxMTIxYLBaJRGq1WqFQ5OTk7Nu3b+fOnRkZGWfPnvX09LRbYI+GExboteht2c9/WsqqVHuOAQDJoHd8W9P3m6dCTof0fCNO76vWnVcBQGl0afnyGtsbp/32Bu7UYN3XW/GCHh6sYcFo2ARB2iOml0TgJbm7XXvsvOL3fwCAIRRwu0Vwu0fyukUyPZvoaQe12w+Y8m+zvNzdP5rODvCxNpoKSqTL1xqz8lT/prg8P9whgbUFSm/QHj2vT8smZQqMx+WEBQqHDngcKgJ+8MEHD9UfJfAIgnRQltJKAOB1j7z7EC+2CwBYSiruPoQg7V+zEvihQ4du3LhxyZIl5eXl06ZNmz59OgBcuXKlrKxs1qxZr732WhsHecf8+fMLCgq++OKLefPmNdlh0aJFGzZsmDFjxsKFC1evXm23wB4ZJzzI97tPjDfyTHnFlNF4MjBPISg/9PnBKUumOaXw8kamkhqSIWSqb5rM+roZrV6eXv0G9mt0Hlcu/kx4XfZerCL8hEwmWqKFIO2e04BelooaQ/pNokauO5+qO58KAEwPMa97FK9XDL9XN7tFQlsIbco5wDD3D1+3Ze8AwAkNkLzzatWC7zSHzriMe9rh0wRahamgRPr1GkJea2sxF5ZqUs65jHva5cWRDgzMDnbv3t2o5eeff05JSYmLixs3blxQUJBSqTxy5MjevXtfeumlr776yiFBIgiCtBxNkADQ5Nwxa6O1A4J0OM2dDzlp0qRJkybVb3nrrbc++eQT61x6uzl9+nRkZOS9snerqVOnbt68+ezZs3aLqqUwjNstgtstQk3UHr75J1DA5fDjtyRXTi1U7ZMCAKkhFF9obN3HPjcWx3EA2J6jz1cQ8xJF+H931BQNa9O1X1/SPBXMfaUr39YIAFerTB71tl1hYFikmOnKRVk+gjgSw0UknvEiAFiqpMbMW8brtww3cokaueboOc3Rc74/LGD52mkmkaWsijIYWf7e7EDfRoe4XUKZEldCpiRq5I6aHdCKyFp1zZJVpEbLiQh2HjuU5e9NaXW6C2ma/SdrdxzEnQWi4UmOjrENjRkzaX8zkQAAIABJREFUpv7L7du3Hz16dPHixQsWLLA1zpkzZ/Xq1W+88UZSUtKMGTPsHiOCIEgrYHqKAcBUWHr3IVNBCQB0gm805PH06AsaQ0JCWjGOZpJKpd26PXhIyt/fPyMjww7xtK7tFX+YKTMAvOgzlc8TBG/oej3oDKUjKaBOm8/bugUMqqtd+UuqtkRNvtrdyee/Miobb+i+vqQBgCNFxiNFxvon35pt2JptqN8S6cY8MN69TX8jBEGaieXlzvJyFw4dADRtLiozXL9FGQzMu2bdm3KLSI2OGx2G87itGwClNwDAvQrX4yIhyJSUTt+6F3UI1a4jpEbLi4v2mPfGf3UB3TnhQdzI4Jrl62q37hMO7oexH5cdQ9asWRMcHFw/e7eaOXPmDz/8sH37dpTAIw53vcZyqOEtDUHRAEBQYL3nqW9sOC/CDe3WgQAA8OO6KpgM/eXrxpwCblSorZ02mWu37QcAfp/ujosOQR7dQ3zGXb58edOmTenp6VqtNi0t7fDhw76+vjExD11jqSUSExNTUlLy8/PDwsLu1aempubQoUOJiYn2DKyFDKlZVR6m88oTAMDC2BdrT1+Un+n6SaSnzgMAMsgsBV031ZMtZBWH3tASGgFTaK0PV387FTYDAMCJhcd6smzzXK9Umq17rviJGIGiO//H+/mi0lAI0v5gGDvEnx3i38Qhmq76vx9pkxlwnBPiz+0azu0azukS2irJPMNZCABEjRwAzIWlxpsFpErNcHHmxoSz/bwIqRwAGC6ill/I4fSXrwOA60vP/Je91+En9OBEBJlyi41Zeby4aAdFZ29Xr17t379/k4fCwsJOnz5t53gQ5G4/p2qPFhvvbicoenWatlGjVE8uH+jSwisSFLx3rPa2mqjfKNdTALAn33ClskG9Xi8nxtcDnZ05aEpju8NwcxaNHqzadaT685+dxwzh94rBuBxzQalq1xFzSQXTSyJ6+klHx4ggj6K5CfySJUsWLlxI03eSxX379v3000/Lli37+OOP2ya2Jrz99tsHDx7s27fvwoULx4wZExjYoKJyZWXl/v37P//885qaGnturddClvLq6mW/XB7Lhm4AABbafFN1fciSIZ6HPQCAYBPre22ClLrOfZ3iKQtdbrodyWzi0ckLUXyCgiR/Tv0ScWN3yjKlFgB4Joz3fh9U+wRBOiwMc3t1nO7UZVN+sbXCvOrfo8DAOcH+nOgw4dABLO9Hn1PD8vVkuDkTUkX5e8sa7evDDvShNDqWryfDraW3xQ5HkxQhUwIDZwc1XikAAJzQQFNuMVEts39gjuLj45ORkUGSJIPRYGNUkiTT09P9/Zt6kIQg9jU/UdjLi03VuwUlKPrbK1omjr3Xu8GkITYDezqkFR5oasxUSrGxyQ3n5XrKmsnbZEktVTohSuDbJ9eJoyitXpNytnb7gdrtB2ztLD8vj3kzMQ4aykI6pGYl8IcOHVqwYEFoaOjXX3+dmZm5aNEiAJgwYcKOHTvmz5/fs2fPYcOGtW2Y/xk6dOiPP/74zn+cnZ3d3NxEIpFWq1UoFEqlEgCYTOaqVavGjh1rn5BajqiSAgAbq5uxiVHY4KVDIg9HkWyCYWZW9qjIysu1dR6pHBr+SRD1Fw1OoCdoAKj/9cLCsckxjYvDIwjSaQiH9hcO7U+bzMacQmNWnjErz5bME9Vyjw9ff/RTY5jwqSdqt+6zlFTgXK5T/3imp5iorNGeu2a+XQEAohHJrfVbOBCGYxgDp0mKkNdqj503pGYRciXO5bLDA4VPPUGbLQAATMaDTtN5DBgwYO3atR999NHy5cutu6sAAEVRc+fOLS0tHTFihGPDQxAACHRmTu/R4H7VSFgTeJgZ1/SqnxZy5eLHJ7rLDQ0S9T15ht+u654J573WvcGNlpCN1R81QdoXHBfPfNEpqbf22AVzUSltJpieYn6f7oLkBIz1uCyVQjqfZn3ifPvtt1wu98iRIyEhIZWVldbGAQMGXL58OSgoaMWKFXZL4AFg1qxZQ4YMWbduXUpKys2bN4uKigCAwWC4u7v36tXrueeemzp1qpeXl93iaTlCoQKA3touTj79CYJw/ojHO8Sm+bR5GMXbBWl+maoTdTPEWCxWX0kvwQUn5gyC2kKpTDQASHVkgPAxut1EEATjsHmxUbzYKACwJvOmvKK7t6wna9Xqg6dY3h6cqBCW14MH5y3l1dYfKKNRe+Yyw9WZVNTSlrpJpOaSylb9JRwEw1h+Xubi8or3llL6ukm5JGgsVVLdmas4nwsAbH9vh4ZoV8uWLTtw4MC333575MiRMWPG+Pn5lZWV7dmzJzMz08/Pb+nSpY4OEEEcw1vA8BY0uLmyzpwX8/AYd5T4dTDcqND6a+ARpKNrVgKflpaWmJh49651/v7+vXr1unHjRhsEdj/h4eFfffXVV199RdO0Xq83Go2urq62oYOW+O2333755Zf79zEYDABQfzVBC5HyWgAQuLg/JRlZ/HqWYlcVQ8gM292D4cIskuQcNZ609Rw0aFDv5YPyRqVqjiiKJmfiA90Bw6wVMDJqLOFuTD6zM1R4QhCk+eon843oL6ardh62/sxwEXEiQ7hdQjhRoexg/0bLvwGAMhj1F9OAgbu9+rz2xEVzUSlRLQMM40QECZ7oI1//t+7MFbep47COPzrNi+9mLi6n9EZudJjLCyPqdqE/n1q74xClN+JCASci2NEx2o9EIjl+/Pj//ve/AwcO1P82HzNmzPLly8VisQNjQxAEQRDkbs2d88Pj8Zpsd3Nzy8vLa714Hg6GYU5OTk5OrTZp/MSJE9euXWtOz1ZM4AlFLQAw3FzMJUbFtioA8Hwv0CnBGQC6fBcPT/JtPa+5PhlzTvNUsnj6zirVAZlPpKjYlztxt7z+2Vg4Fu/FetKfY2uR6uuqXKZVmxtt9yLm4eOi+CjpR5BOSTCwL01Rxqx8061Cslatv5Suv5QOABiHzY0Oc397Ci688+FpKa+mLQQ7JEA0/EnR8Ccpg5FSaxkuIusSQfWBE5ZKKVEjY/nYqaxd26HUddtWm2+X669ksKqklE5vuHYDKAoAwGSiTGacy7nfKTqXiIiI/fv35+bmZmdnV1RUBAQEREdHO6TQDIIgSEdHVMuIGjmwmOxA31avF9PekDR5RpHSTRgvZqOyVnbVrAS+R48ely9f1mg0QmGDLdC0Wu2lS5diY2PbJjYHWL9+/XvvvXf/5LygoGDChAmtMuBvZR2BZ4pd2IFcz3cCqn8oqfyiiBvCtyjNMJh96cIlW09+zJCuBbrJe2sA4FiCy22fJj4XLBR9scJ8scJ896EL5eYL5Y3bu7qzuojRZDAE6YQwDls0Itm6dt1SJTXlFJpuFhhvFVrKqw3pNy01ck69BJ42mwHAlrjiPG79Ow+MywEA2mSxZ/xtxJCaBQDcmAjjjVz1gVO2doabM0PAN5dUGrPy+PF2LbDSHkRERERERDg6CgRBkI5KfyVTuWWPpfS/5WYM3Cmxp9vkZxluzg6Nqw0drPnnn6rNQfywBeHLMUADgvbTrAR+8uTJR48enTJlyu+//25r1Ol0L7/8slKpfP7559sqOrtjs9k9e/a8fx8Op5VHZmwj8ADguzQcY+NVy4sLp90Akr7wTCZB1C1AjY2NPTswIv/ZdMpMiSf7fPhT1Lq1VQQNr3Z3+v26LtyNuf8FdwYGNMCmG7oq3Z2dV+QGKrXKXFhL9PRi9/ZusN+mhIdHuqHsHUE6P2udeUFyAgBQGh2p0TYaS2dK3ADAeDOvesnPnIhgTmQwJzwI5/MAgLZYiEopYBhD0il2oVeogIF7ffaWuajMkJZNyBQYl8MJD+L37q7ctMtcUmmtpff4IEnyxo0bVVVVTR615x43CNJMbAbmwcdF9t34HUPpCXJv6r3HFX/8AwAMZyErwIc2mc2FJbqzV403cr2W/K8529B0OEqLfH/NTgAo1uefUxwf4DbY0RE9RpqVwE+aNCklJWXTpk2HDh3y8PAAgKFDh6ampioUiqeffvrNN99s4yA7OdsIvPWlz2ehAFC1vBgAjmQctXUbFjc0/9l0SkeKJ/sE/hQFeN03ya5begD4KEHEwAAAMIC7N6JflapdcVnT14eNysghCIILnepPnrdieohxPo/SGwzpNw3pNwEAMIzl68kJD6I0Ospo4kQEM4RtsuGzPdl2oacJgh3izw5pUCaNMpoBAGM/Ro81KysrR4wYkZ6efq8OrbheDEFaC45ByoseuH0z6jhPto+A0dcHFR5DGjMXlSk27gIMc5vynHB4knWXGVJRK/1xkzHzluyHP7yXvd/5ngDtrNxkoowStqfMXL2z8s9eLv24eNMLrpFW19w18Bs3bhw1atSyZctu3rwJACdOnAgNDV26dOn06dMxe/2NlEql58+fb37/MWPGtF0wrYUmKcpgxLkcXHBnrbvPgpDqlSWkiTh587StMeKfEIokzyS6burlSv1RAwDWAqUqE83EsA+O11q74RiM78L/KAEl6giCPBzJnEk1y9cCADvID2jaUl5lKauylFUBAGDgOnG0g+NrFRjGCvA1F5YY0m7y+3Svf4QmSOP1HABgBzZRIr6z+vjjj9PT02NjY8eMGcPn8x/8BgRpHwRse6dDsR6sM6942PmiSIegPnASaNr5mcGiUQNtjQw3F895M8ve+j9TXrEpr7iTbY9aoL91QXmKhbE/Cl28ruSHXF32vuq/n/ee7Oi4HhcPUbhy/Pjx48ePJ0myvLzcy8uLzbb3M8isrKyHqu7eIcYNMAYumTMJ53HrP5kzZOloE3WVnaHW1W225Iq7xJLhR/u6rn3OizLTDau/A0HT1pJyVoW1hH2CRxCkM+H36e425VnFpt3motIGB3DMqV88t1uDBdK0hajdth9jMdlhgZzQAIaLyK6xtoAgOUFRWKLYsIMd4MP0klgbaZJSbNhByJQsf29OaIBjI7SnlJSUsLCwy5cv2/87HUEQpHMw3SoEAKfkhEbtGIft1DdOfeCk8WZBZ0rgaaC3lK+jgX7aY6yE7TnR97XFuR8cke4Z4DbEi+Pj6OgeCw+RwFsxGIyAgAY3N2fPnh0wYEDrhXRPycnJmZmZixYt2rlzJwDMmjXL2bkzbAshSOrTqMVUZACA/Z6HQVnX0h/v4zrFb+Y34TPrzRjrtaGaAohxZ20c5dbghGy7rgpDEKTTEI0axI2JVO8/YczKo3R6hlDA7RYhGjmQ5efVqKelska1O8X2kilxZYcGcEIDrfk87tR+59EJhw3Qn0815hSUv7/MqV9Plr83pdXrL6VbyqsxNksy66XON9HxXgiCqKiomDlzJsreEQRBHhml0QMAw7WJB9nWHewojc7eMbWl84oTRfo8V5Z4hMdzABDIC+3vNuis4tjflX+8FfSxo6N7LNwzgScI4rfffrty5UpNTU14ePiMGTMiIyMBwGAwnD9/vqqqSqVSabXay5cv79y5025j3TExMTt27IiPj09NTZ07d25gYKB9rmtnzsMl3mtCryzIsLWMGDr88Ku+O3Yrto8VS3g4AFgo2rpP3csxfGf77uOCIEgnxg7ylcx+5cHdAnw8P3nTeCPXVFBiLighZEpCptRfygAAwDDJmy8LBvZt81gfCcZgeHwyS7Fuu/b0Fe2Ji7Z2lpe7eM4rnWmQ5IEoiuJwOBUVFY4OBEEQpAPDnQWkRktKlXfvFENIFQDAcO48K1tNlHFn1Z8A8Lz3JA5eV63mee9J11QX0lSXbmjSYoRxDg3wsdB0Aq/X65OSkq5evWprWb169YEDB1gs1ujRo+VyB+/Q++KLL6ampjo2hjaFsTBzf6y6UGp9yWQyJ2559c3T5tsqokBJSHhsAGDhGAfHTBSd4P3gXfHd+TgAWDN/BEGQVsGLi+bFRQMA0LSlvNqayZvybxOVUozZ+MuFVGt1Z66yfDzYwX4On2+P87iStyY7Pz/ckJZFypTWXeh5sVHQevVBOwQ2mz158uQ//vjj6tWrvXr1cnQ4bWvDhg0///zz/fsYDAboIOvvEARpP7jRYZayKk3KWfHMifXbKY1Ofz4NALhdwx0UWuvbV72j1qII5Uf2dU2yNYqYLiM9xu2o3LS1Yv3/RXzPwBgOjPBy7dlcXfaLPlOZWKfdkrbpBH7FihVXr14Vi8XvvvtuaGhocXHxypUrp0yZwuFw5HL5U089FRcX5+TkhGGYRCLp3bu3nYPu3bt3WFgY864bxM6BNlNl8/J20ftsLf3793dxcQFo/NzEwwkv1ZDWOfVlGnJfvmFyNyc+s4nJn+Mi+d092GGunfNPDEEQB8Mwlp8Xy88L7loQZKM5dLp2+wHrzwxXZ3aQLzvYjx3szw7yY3lJHDJrneXtzvJOtv9125X58+eXlpYmJSXNmTOnb9++bm5ujTokJSU1+cYO5+jRo9euXWtOT5TAIwjyUESjBmqPXdCknMMFTs7PPYXzuABgLqmQ//wnqdHyukc2qnjScUnN1UekezDAJvq+1qjw+1PuY84ojlUYS0/KDw2WjHRUhEqLfEPpTybK6MpyG+nReSqdN9J0Rrdr1y4Gg3Hq1KmuXbtaW5599tmYmBiSJL/77rt3333XjhE2ITk5OS8vz7ExtB3VQZl0Tdle5722luHDhz/wXWvStZuz9F5OjLERTSw9xTGIdEPZO4IgDiMc0p8ymsz5JebiMlKpMihVhrRs6yGGs9D7yw+Z7o1TR8QOgoPrlgx8/fXXTXboNNns+vXr33vvvfv3KSgomDBhAv6YTcRAEKSFWD6e4lkvyX7ZrNp1RL3vBMvHgzKaiGoZALC83CVzOs/e7NsqNlhoc3+3QSH8iEaHmBjzBe8pPxV/sbvqrwSXJwRMx0y1+7vyDxNlBIB91Tv6uw5yYXXOW4umk7rCwsLo6Ghb9g4AUVFR0dHRmZmZkyZNsldsjwXjzQKyVu2UeGe5iPaCygyWC7LLtpYRI0Y0/WbM9h+wkAAAZqqT3GkhCNLJMNyc3SY/CwBA00SN3FxUZi4uMxeVmYrKaKMJSKpRf1NukTG7gO3vxQr0ZUpcHRDx42HevHmODsFOOBxOfHz8A/vYJxgEQToZQXICy8dD+ddeY1a++XY5AOBOPMHAvi7jR+D89rur60PJ0Wamqi4CgITlcUp+pMk+IqaLmqj9t3rby77T7RsdAEC+LueS8gwbZwfzw29ps3ZW/fma/9v2D8MOmk7gVSqVu7t7o0Zvb+/MzEyxWNz2UT1GZN9vIBQq3sbl1vk2AKC7VJtGXjeYDdaXfn5+/9/enYc3Ua1/AH/PzGRv0iVdWcpSNgsoiAooCojI4gJcBH8gVwQRBFxAUYGrgIooitcFcEEUN8QLIgqyXUTUC8giq1qgbAVaCm2TNmn2ZGZ+fwwNoS3Q1rYh5ft5fHySyUny9pT09NuZc06bNm1Cn7Jov3PtMc/CPrGDW+kzCvwphnDOMwEAqBzGhKR4ISle36ndJVpZP13uzcxSbnNRenWj+upG9VWN6qlT66kb1WfqOjuxrZa9+uqr4S4BAKAu0LRokjz9CcnlDuRbmUqlSo6vY+uq/FSwVrnx/dmvL93yF8v6IfVGcrU7E14mecnpj2WSeycMuCXu9ucPPr7Vuul2c58m+rqzAEHQRS+rLnsJGS4qq3ZyQAxYbcQxTnNuCx/JLbn2OTZL50+/X3/bnVtzfERk80pE9FuO98O9Tr8o/3TSe12iql2SqsgrmbE6HQDULeZRgx2//u47kePLypaKnZ6/Dnv+OjdzSkg0N5g/4+rZ7C1cDh8+nJOT061bt3AXAgAQMTi9Tt2ofrirqBE9E+4xCKWX2S9XA22jWk7vRLTZulHZ3K5P4gANp+2ZcM+avOVf5Syc2vy1UtP16wDMiw4n0VpEsizExgT/ROfabZd90q9se7DNduMtD/5gDd6du8uh3Hh6Y5Fyo6GR//mBxNoqOWxOBAKvFdkfjDJ01pa+xFEiesdmd0jyC7HRYakNAKqdumlqXNNU5bZotflO5PhO5PhPnvadyhUSzWXTu/XjZWKRXdUwRdUwRd0wRUhJYDyuTqo6WZZfeeWVZcuWOZ11avtiAAComuaGa5obrgl3FeXzSO5vzywmokEpw5XN7e5JGrS1cNNR16Fthb90ju0W5vqqGwJ8OAWsRUTEm89P73RsKzotn8n2Zit3OUHV/fYeKr2GiP7I99m9cn0jX+CW0uNVwdXmr0+6Ki4lPeDz/+z2/ObxzouP6xKS4SWiadai5U6XkeOmxJgEnJQDqHP4uGhdXPS5LevKI/sDxT/9Jnt99Nse5QjjeaFeorphiqpBsqFLB1W9pNoqNsLIsjxjxowvv/wyLy8v9Lgoim63u23btuEqDAAAoIJWnV1q8xemGVp2jL1VOaLhtAOTh3186t1vcr+4PrpTcMv6ugEBPpxESxERCXHnzxs7t9u2ijuDd6+9sfOSwY2U20NXWraf9r3RPaZJjDBxY6FyRT0RbTrp3XTSm1MsEtG8XY7Ff7lC36JJtPDvHjFc5KfaXnrdMK/vS4dzXIH1XXNsN52WiGSimYW25U6XlrF3zbFI7wBXJ6YS6r/1L8+BI/5TZ/yncn3ZZwJ5Fv+pXP+pXCLyZBxJnlF6GRvRVszUquDiI1etDz744KWXXjKZTCkpKYcPH05OTm7QoIHNZjt8+HDHjh3ffffdcBcIAABwKfm+sxvyf2DEhtYbFXq1/M1x3X+yrD3uOrwm79sByUPDWGG1u2iA/+WXX0qtY2ez2Yio7OJ2RJSfn1/tldVhvuPZjs27fFnZgQIrETH1uQnwJJNzh31L4PwE+JMpt520i6kmnoiCC8yfdojbc3zlLjefUywqST7oeFHAK8q68jaHjyyMaGpsNBF96XA+YSl81xzbVad9udC2xOHUMvZ+fFynMpfWA8DVQ0g0RyWeX2NV9vr8OWd8p3IDufnaa1uWauw7efr0pNdIkoT4WFWD5Oh+PbVtS++Ic5X45JNPoqKiDh48mJKS8uCDDxYWFq5atYqIZs6c+emnn4ZuRgMAAHAF+vr0JwHZ3yWuR6n16hixofVHzTo8eV3ed7fG3RGvrjszji8a4P1+f0FBQdnj5R6ECpL9gfy5X7i27go96PhlOxM485ghnqMeV4Hzd3lv8CF9enenXyLiiehYUYCIch1i/xa6n4Ym2r0X7Lo0b5djQ5bnsQ5RPRtfcEIpycDXgfSuKJXhb9aof/F4kd4BoCymUaubpqpLZtGXwkcbNc0b+46dDBQUBgoK+RjTVRvgjx071qVLl5SUFCLq1q3btGnTlOOTJ09etGjRyy+//Nprr4W1QAAAgIs64Ni/x7Zdy+kGJg8r+2iavmXH2Nu2Ff6yLPezsY2eqf3yakj5Ad7hcNRyHVeJvNcXuPdkKLd5o0Fye+VAgIjs//3df1ZWpfXYI/7hks5tIKc111PXP7dWhMsvK4m9yCsTUaqJV1J9UKyWI6L6Rr5NQl2eEh+a4X/xeNVESO9whbNLkk264IoZlywFQg6IJDslmYjieb656vzP5EJJ2un1SfK5pj4ij1T6ypvgc5MF/u6QnWYP+f1rXW6RyifLVCyf/wtgMs+PNhmDP1C2erzr3W6HJJfemb088Rz3XMjaE+td7m+crou9bykJPPdSbIym5Lkrne7vXa5yry0qy8xxM+POP/c7p2uJwyWSTEQ8McMlZw0l8fyM2OiUV54iSQrkWf25eZoWjSv2tnWQz+eLijq3qnDjxo1Pnz7tcrn0er0gCF26dNmwYQMCPAAAXJkkWfoqZyERRativzu7pNw2HtFNRDuLtvSI79vCUEcuKys/wBsMhlqu42rg/uOQkt616c3jHxsmJJrPzpzv3nvAk1zPs6xN4Y96w01ZW8Tz688ntL8jeHvRH05/RX6bvjoESm5IjHnkCv7CD3WHR5Z9skxETllWkrBTlkSZiMghS0rIjea49JCtwk8FxE1uj/KsAMnOkn82LkkOkExEkkyOkoNxHDc5xqQqCYfLna7/OJwSkVeWvSVtAjI5Q/7tOSVJSa0JPP99ckJsyb4Sz1uLljsvWJbiEnii/6Yk1RPORenJlsJfPd6Kd8u1alWqcO5H+mtF9m2Vee5del3wuZ8UO7ZU+LmM6CFjVP2Smr91ujdX+Lk80YRoU0rJWvHr3e6tlXnu0zGm5JLn/uj27Pf5Kv7cJ6KNKTxPHCckxwvJ8RV8Yp3UsmXLgwcPKrebNGkiy/K+ffs6d+5MRJIkHTp0KKzVAQAAXJTVn5/jOUlEZ72nz3pPX7rxfvvuOh7goSbYlq8jIt4cnTT9ccZzRBQ34r6z2/46O9VudBqIyLndtUU8PwE+/roeNiIicgXkT/84t5GPcHXv+C4TvVxo+9rh1DLWRav50e1R5sN3u+pXoroSuGQ5IMtuWfbJ57KuV5aNHBd6VvlUQPzR7RZl8siyl+SATC5ZIqJiSZaJvLLskWWZqFiSiCiO496Oj9OXBOl3bMUf2YsreHaXEa1PSWpYEixfLCyqbCgNPvcXt+cPn7+Cz1VfuNlofYGP4TjjhSeEdYxThRzgiKI4jojq8byZP/8JHxxl0HPn76qJtGVOLAfPNqfwfEPhfD8/G23aorno18sYGdn5V04R+NSQ574cF7PV49UxVpEfNg0FIZjeiWi2OeZAhfuqnsCnhOz09ro59g9v+at7lPu+yRc+97j/3F/2glclXEzyhe97levevfucOXOmTp361FNPNW7c2Gw2L1iwoHPnzm63e+PGjQ0aNAh3gQAAAOWLVydNbDrN6rv8/G6OcR2iO9dCSbUDAb72+LPPEpHhlg6s5Hd0WYrOft5nzDGebX2GJRT4N8VnSaeUhxivimvdxeYlIvo6w2V1S3qBuQJys9ir9FsmEm32eH9ye5aWrFrXUauZVWhT5sM/H2Nqq1Zfo67L0weqnVuW/bJsk2S/LLtl2SlLfpmKJclP5JZk5Sy3U5YDJBsYeyQ5U+1uAAAgAElEQVTkEuvvna7FDqdLln2y7JJlv3wuupf7LhzR2pTEYD6cWWj71eOpYIUqxmySpC/JWi753FluDWPKtdMGxpQVHgyM4y+80VgQkkLC8FiTsYVKpTwkEDOU/FFAxzEVMSLiGEWVHEwVhIbCBeHwqD8Q+r5EJDAyhOx6oGes3E0QxpqMY03GCn69pfTQaXtU9S9T16hVVf44pPD8QIO+as+N4bjOVZ3SYmCsytNh9Iy1xse/SqZNm7Z06dJXX321UaNGY8aMGTt27MyZM/fu3Wu1WnNzc8eMGRPuAgEAAC6qrfH6cJcQBldpGgwL2R8gIj42Rrnry/Yc6LNdfUqV3yrvP6/sOGy9o/6pH2n/ucbaZh3zZQOR9Opv9t1n/ETEcYxInvt78akWukGtyv/1uo6sVleeH1zuyZZCItIy9mFC3E0aDRFNjY2WiL5yOGcU2njG9tRPvqp2kpOJTgfEIknyyLJblh2S5JRltyx7JNkuS8rZ7GJJ9siyR5b1jM02xwYz579t9oV2R8WnH9yu07ZQnQtIv3q85Z6R1jGmYkzLmIaRmjEtY2rGUgUhMeRs56OmqBZqgYg0xLSM8YwMjCMiI8cYkZoxHWNEZOI4Ikq+8Iz0lJjoKTHRpd+1Yjpo1B006su3K48W4RDqLqPRuGvXrgULFrRq1YqIpk2blpmZuXLlSlEUR44c+dxzz4W7QAAAALgAAnzt4Qw6yenyHs4iIl+253Cf3WJWIK9V3sp3Vubabj1ruWGf+E6wceuYzlafRERbss9N7HT4JCLadtp3tDBgdZ+fEN8mQXVLA02XhprdZ33tk6oYUa58Huncl9xQxbcu2XiPEfXR65Y6XAGSKWInw1slqViSHJJslyRHSQ5X/l8sSS5JdsmyctvIcW+ZY2NKLqueWWj7yuGs4LtwRGcDYtOSq9k1jOkZ4xkzcUxFTM8xJX5HcxxPZOCYcrZZz5iKWGOVEEzvRDQrLmakMUrJ5zrG1OxcdK9IGe016vZVDdIAUBPi4+OnTp2q3FapVP/5z3/cbjfHcRoN1gcFAAC44iDA1x7dta2Kf9zi3r7PtSv7+EMnvcfdea3yVr/zgyfKnaL/afDcqMcztgYbP/Nnq7W7bb9eX875xny39Pr24uBds47bMTzprjTtXWl1eR74zVptF63nkD9w2Bd4JN+yIN4cxbHdXt+j+ZYAyS1UQietNuyn32UiuyTZJdkuScWSZJflYkmyS1KxJDtkqViSiyVJxdiLsdGmkhD+ts3+ob2imz7wRBZRCgb4FiqhocBHcZyOMR1jRo7TM6ZlTM8xI+M0jHSMM3JMzZiesfoXznMebzKOr+ql3RqckQaoKxYvXtyhQwfl9HuQTqcjoh07dpw+fbp///5hKg0AAADKgQBfe2L+7y7Hpm2SU53ZZ4/k0lta53/31opT7utPHe4liHL7zRvc8rkN5Ey65GbUaPzXp/0C++1aE8fIpOEcPikg0b3NdSlR5y9I5hndkHxVnM9sKPAfJZhzRfHBvII9Xt/oAst4o/FJi9Upy331utfNsTW3JpVTlm2iZJOC/8kSyYOiDMF3XOtyv20rLpSkYqlCWwWMMBquLbmIIJHnzTxnYJyRY0aOMzAWde7/zMg4A8cMjNNzzMCYkeMSeS70cvT7owz3R2HDCACoumHDhr399tulArziiy+++OKLL4qKimq/KgAAALgYBPjaw8eYzOMeOPXkTsmll3lp9fQ1otZ/8vCdLk/yrbuLtnvPrz9PN955qEDX8oR70Pr83641STIVec4lw/+7Rt+xXgQk9sUOJxE9UF68POYPLHE4R5qiqrAQdArPf54Yr2T4MT6LKFN1pXeLKK12ufNEsUiSgv/ZJKlIKn95tnqCcFvJgluH/P6TgQARMSIjx0VzzMRxRo4zMWbkuCiOM3LMyLgojhk5rqHAtwq5HH1olGEoQjgA1KJAIDBlypTg3e+//z47O7tUG4/Hs3Tp0ujoKq46AQAAADUEAb5WRXW9qeE80+Ehh6lQ3+dfd33/zoquiQuT1gzqvVQaFLKBXF9X+5Yn3AGBfdU3qaGRvz5ZzYi25HjzXZGxF7xM9LbN7pDkM6L4dLQp9KGj/sDw/AKLKDVVCUOqFFxTeP7ZmOgJBVZRpliemxYbUza9i0SFolQoSVZJtIiSVZKUuwWiWChJhZJUJEp+oo8TzG1KLgVfWOz4tLj8S9n1jMXwnIlxMTwXzXEmxhoKwk0hE7mfjDYNNhiUU+hV+IoAAGqTKIpz5swJ3t20adOmTZvKbfnMM8/UVlEAAABQIQjwtc3aqdmzq+SnhmclHY7v/8SA+Ef0/DJ23JdzQjp3AkTgVeMzmgVUbO6DDX5Pj5rbydg3TUdEQ1da8l2+sNZeUYzotbjYCZbChXYHEQUzfDC936LV/KOq+1Tt9vqmWKwSkZaxQlF6tMDyUbw5imNEtMvre6XIlhcQrdIlt4EmIiI9YxKdbzUkSq9mZGRcLM/FcFwMx0VzXDTHYjjussuzMaJ6AraVBoDIoFart23bptzu1KnThAkT/u///q9ss6ioqPT09NotDQAAAC4DAb62HXIdy4mLmvFZ6+nD/0o+HM+mkCiJ21L20LFzDW6g69Qq/b+HNdjf1kRipK6s3kOnnR8f+3hB4UK7Q5Tp2RjT8UBgRL5FSe/z4uM0l0zFHlk+I4oWUcoTxXzl/5JYIEonA4GcwLlO8cgyEe31+kYXnFvT7qDff8DnJyJGZOa4WJ6L5bh4no/juFiei+O4WI4z83wMx8VyXCzPhWbuVEGYeOHFAgAAdRJjrGPHjsrt3r17d+3aNXgXAAAArnAI8LVKJvm3Mx82F02HzQ9/OSlt0riTssS4aH5z8s5ggL+V77inlWFnG6OBY15RLvZJf+b7icjpl4noeFGgdbwqSh0Bu53fptXOjY99vKBwUbHDJkn/83jzRVFJ71rGJKICUTwrSnZJukGjDub53V7fEwVWyyVXg2NE8Txv5jgzzx0N+PeUZPgHogy3ajV6VjqcAwBAudauXRt6V5Ikp9NpNFZxiwoAAACoaQjwtWq3bVuW+6iZqOGWZUMntSOZeQyyr6ho+2/bg21uUXVq+Kfjnz/kfXF3IhFN/cUe+gr/+tW29KDr23/EX/a9HJL8abGjj16Xpirnu7zR7ckTRWUW+hqX+02b/cXYmC7a0rv+5oniYwXWlirVy3ExVfh6b9VqZ8ZFT7UWfet0EVE9QTBw3Mh8S25AzBdFsaTZE9HGsSVbmjlkuUiSNIwl8lwCzyfwfALHxfM8kfy+3eGV5T56/evmmOCXlB0Qh+cX7PH6xhVYPkuMD90pDQAAypWXl7d48WKNRjNu3DjliNPpfPbZZxctWuTxeNLT0x9++OGJEyeGt0gAAAAoC2mnVsWrE2NUcbrdmnufaqVySdv6GBc/m9Sz54KAK6A0iDa3+P7BtLEfu+/9uYAx+vyuxGaxglZgRCTJFJBkNc+6N6rQfu+bPZ759uIlDueiRHML1QW7di93uqZZi4joXoPewJhFlE4HxMcKrPPi40IzfJ4oDs+zZAUCpkquzWYRpecLi476/WdEyR+yhPvpQOB0IBC8a+a5BI6vL/Dddee/otu0mt8bpGjLXGD/k9vjleUBBv3MuJjQahoI/GcJ8cPzC476A6Ish30reACAK9yaNWv++c9/Wq3Wbt26BQP8P//5zxUrVhBRQkLCX3/99dRTT+3YsWPJkiVhrRQAAABKQ4CvVSmaBvEH4ns81V3lUh/qdfD4U2pbUt/N3m3BBuzGXvHD/njb23XClzn3bCpwqtmIBddcY1Zd4jUv5g6dtqtO+4vbMyLPEprhlfQuEU2OMRkYI6JhRsPJQOBLh3NcgfVdc2w3nZaILKI0Mt+SFQi0UqnmmGOV58pEeaKYHRBPBQKnAmK2GMgJiDkBMU8Ux0cbx5WcRc8KBH52e859RYxkmYwcc8myKFM3nfYRY1QyzyfwF10crmx6J6Lbddpf6yUllLfzXAOBX52c6Ed6BwC4nKNHj95///0ul2vcuHEDBgxQDu7YsWPFihUmk2njxo033HDDsWPHBg0a9PXXX48ePbp79+7hLRgAAABCIcDXqp1FW0zbo1QutSRIu4bt3Z5wd/39xVtCAry+7Z2nfCuzu6TkbpUbHDZ0/Ku4yu8lMDbXHDvBUvhTSIYPpveJ0abhxiilZUCW7zboZKLFDucTlsJ3zbFt1erh+QVHfYGGKuEWneZtW/FZUTwVCGQHRG95m6LzRPqQ8NxBo/4hOTFXEqdYigpK5r3v8HofLyj82e1pIgjPxlRlubhy07tCy1i5sR8AAELNmzfP4XB88sknI0aMCB788ssviWjy5Mk33HADETVt2nTJkiWtWrV67733aj/AZ2VlORyOVq1aCYJARPv37//888/z8/Nbt2591113tW7dupbrAQAAuKIgwNeqa4zXrhm2PGtfVuMtje998v59nzY1LlnjlF3Ko0JcgrZJ+w5RJ0d95Y89HO00CvPur/fe33g7FWNvh2T4B4yG+bZiJb2PNkUFm71vd7xvL+6t1w406Jc7XU9YCuM5LlcUGaNTgcDH9gt2RzdzXANBaCjwDQWhgcA3EPj6vJAslM7WHKOpIeldy1jomnZEVLUMDwAAf8e6desaNGgwfPjw0IMbNmwgomHDhgWPtGjRonnz5ocOHarN2latWjVmzJjc3FwiatKkycqVK/Pz83v16uX3+5UGL7zwwhtvvPHEE0/UZlUAAABXFAT4WuUI2M9S7n9f3dBr+oRGm5z/eujki851wUeN99ylaU7XTuwY+1tRIEb19mONT8aovjnoGthSnx5flavo6cIMP9dWTOfOvRsyfP5Dfn+mP5Dp9x/0+YloncsTx3P3GfTfOF25olif5008d9AXkEnurNX8X5QhVeBTBUFfsRPdo/Ityprz80N2jLtNq/23OXaCpXBRsaONWtVXr6vaFwUAAFWTk5PTqVMnLmRlk5ycnIMHD6anpzds2DC0ZUpKyu7du2utsF27dvXr14+IOnXqxPP8zp07hwwZolKpYmJipk+f3qpVq4yMjJkzZ06YMOHmm29WrhQAALiiiFabe0+GP6+AqVSatFTttS3Zxa8eBagyBPha9VXOxyKRLfG5+U/FTDp8ypzt2evaHHw0+vZeT72THf+bw24QXny44ckYFRF9+odrX17gmwHmKr+pirHr1apNbo9MJDD2jdP5rs0ulmkTkGWrKP3X7VaOnJGks5Ikk/yQMeq5yp8tv16tbqemWXExpfZ776HTvmOOnWsrbiDgJxoAQG3jOE6tVoce+fHHH4moR48epVpaLJbaK4vopZdeIqLVq1f36dOHiP73v/91795dFMWffvpJuYy/R48ePXr0uO6661599dXly5fXZm0AAJcmB8TCxd8Xr/1FDpz/FVuIjzWPHaq77powFgZ1EgJ87dle9L9M518B7Q3WYzEvDjuht4m/0d4i2aY8qo+Om/JR6vV7HPY4Ydecln0aGb7NdOU7pfvT9QOaV/1M9Ud2x8Jih71kW/WALJ8KiBxRmkpooVK1Uqmaq4RmKlV9gV/v8jxtsdolOYbj2qjVmz0eIuqh01YhvRPR4CgDR6Qp73T9jRr1cKOhmaqK1xQAAECVpaWlZWRkyLLMSn4+r1y5ksoEeEmSjh49mpqaWmuF7du3r3Pnzkp6J6Jbb731xhtvzMjICJ2En56e3rFjx/3799daVQAAFVHwzqfO3/YQz+k7tlM3aSB7fe5df/pOnj476/2kyY/q2qeHu0CoUxDga4lP8n2T+zkRjbR3Mgw9KdhEItrg3RRscIfh1hv2uO1G4ciSVk/dksSIejbWZhT4H2itL/VS3zpdaSrhugtPoRBRgSitcbn2+/2zYmPUjGX6/eMLrC5ZVtJ7qiB012l3erwZfn80x6XwfG5AfD0uRlm53SJK8+12mYgjKpKkrd5za8j/6vH+7PZ001Vo47ogmWhMvsUny7PNsXddeJ28XZJG5FsyfH6nLD8QZajUywIAwN/UuXPn+fPnL1myZOjQoUSUlZW1evVqtVrdrVu30GZz5851u9233357rRWWn59/7bXXhh5p0KCB1Wot1SwxMXHXrl21VhUAwGW5tu9z/raHi9InPT9e06yRcjB26D2FS1bZvv2v5YOv6s+bznDiCqoPAnwtWZG3Ms9vb5vfJm6EWizyE1FOQ7bx0P+CDXpbb7ZHCzPGpC65Ll45LdI+SdU+6dyn3SvLytns7ID4L2uRlrH58XE3azUOSd7m9W71eLd7vcf857ZY76BWD4kyFEtyTkBUloyfEG0aY4oiIr8sP2Ep/Nnt2ezxxvAsQCQQ5Yvig3mWrEAgXa26Vav90F4sydRCLXRQa5Y4nBMshe/Fx90csj/8ZTGiMSbj2zb7c5ZCIgpm+GB6byQIvXWYAA8AUNsmT5788ccfDx8+fOPGjY0aNVq0aJHX6x08eHB0dLTSQJKkL774YsqUKYIgPPbYY7VWWLNmzX7//XdJkoLz8ydNmlT2Mv5Dhw4lJCTUWlUAAJfl2LSNiGLuvyuY3omIGIsdco97d4YvK9u976D+hrZhqw/qHAT42lDot7zqTU8sev6JEQVioZ+I1nSJW5+0xXHQqTQwc3HNkm6YuaBh9hmtr8w+bW/Z7AvtjudiTA8aoxoIfF+9bo3L/WiBpamgOuL3B6faMCKZqKHAD4wyKHcp5KEQcqnDnxY7lfQ+ymScbCkkIp5Ypi/QTq0eEmVY4nDOKrL9kJxYqS95jClKYDSnyP6cpVAkulevK5akUSXp/bNEs5nnLv8qAABQrRo0aLBixYoHHnjgk08+UY40atTorbfeUm6vWrVq0KBBXq+XMfbuu++mp9feZZ933HHHv//970ceeWTevHk6nY6IOnbsWKrNggULMjIyHnrooSq8/vfff7948eJLt7HZbEQkl7dbKgDAxfhO5BCRvkOb0g8wpru+tS8r25eVgwAP1QgBvjYsPf1po2Ptnv4nz4okIlo/NPbzkUnigK+DDfrwPXQ+Rhqm6ibfY8nvvU93dyPdbQ01RPSmzb7Q7iCiI4HAAZ9/ucu1xuUmIr9Mh/x+nrEb1armKmGVy10syQbGTgXEv7y+9hp1FMfqCfx1avU6l/stm52IRhgNEyyFP7u9sRzXWq0qlmTl239/lF7PsaYq1WRLoU+WHzJGddFqxhdYlzpcg6P0L8RG16/SEpoPG6OIaE6Rfaql0CPL3zicf5Sk9ySsyQkAECa9e/c+ePDgr7/+euDAgYYNGw4aNEivPzdXy+12x8fHt2vX7tlnn73ttttqs6rp06evWLHik08++eqrrwYOHKhsTR80d+7cL7/8cseOHQaDYcqUKVV4/c8///zbb7+tpmIBAM6TfX4iYprSk1uJiNNqiEj2+Wq7JqjTEOBr3HHX4cw//nz60eujikQiWj8k9vOpyVL2ieNnfgu2uUe4U2+XZgzJmrGkcU4TzbdZ7h0nfD8/kPhWSXqP47ntHt8yhyv4FIEoQMSR3EunnW93KOndKctNVUIzlUBELVSqH1OSiOhmrWaategtm32l03004I/juEWJ5hYhU3FSBaGtWv14gVVJ78qqdfPj44IZfkhVJ6sHM/wMa5FMhPQOAHAlSEhIGDhwYNnjgwcPHjx4cO3XQ0Qmk2nfvn0zZ85cv369ciY81Jo1a3bs2NGhQ4cFCxa0aNGiCq//0UcfDRs2LBAIXKKNzWZ75JFHYmNjq/D6AHDVEuJjRVux/+Rpvm3LUg8pJ+eFhLhw1AV1FgJ8jbMX2O4bPUhjl4ho3TDzF/9KJB9ZP/1UpnMrw2vbtcvs1KHZF1a9XZr2wImJ/23mvIVrcVL1ls2+wO4govoClxOQrCTF81wvne5WreaTYucOr1fPmEuWXymyy0RGjhVLcmNBWJRgNnIXXJ0+0KAXZXlGoe1owK9jrFR6J6JtHu9jBVa/LI80Rj1Tsub8LVrN3Pi4xwusSx2uaI57Kroqa9ET0WCDfrHDmRsQGdEDRgPSOwAAlMtoNM6ePXv27NllH3rllVfmz5/ftGnTKr94XFzcgAEDLt3m7NmzRKTCWlMAUBm6Dm28R08WLVuXlN4sdON3X1a2a/te4jhd+9ZhLA/qHgT4Gtf8TIuDxTYiKmqp+XJqIk/k9xbbl52/fj72wZFr+ia22+Kod8wXZROTT/mOpWu3NnX/z05ElCrwK5ITt3m8esZu1GqUnwo3aTWP5lt3eL1EJBMxIiW9f5ZoTiyTkP2y/IvHq0zpc8vyz25vqQD/u9dXKr0rbi3J8L95vBRdla+9WJIezrfkBsRYnisUpdmFtmiOu1eP5esAAKASrr/++nCXAABQPtNd3Yr/u9mTcfjsjHej7+ujadpQ8vpcO/cXfb1aDoimPl2FeFzXA9UpUgP80aNHN2zYkJGRYbFYXC6X2WyuX79+/fr177nnnpSUlHBXd55M9PJZf1+TymDzxxzyPv507rw5ydaPF4p2u9KAN8dH975n0Kwz9Y75iCiQqObTdESysjRdPYFfkZyoZ+z2Czdy0zH2VIxx2FlfgGQqyfCPmqLKTe8TLIU/uT1xHPeA0TDfVqzMhx9tigq2edQU1UevS1OV84/hVq1mXUqirrzt3C9LSe/Bee8/uNzKfHgiQoYHAICKmD59+qxZs/x+f7gLAQAoH2fQJ/1r3NlXP/AcOOp5eV7oQ4bO7WOH/yNchUFdFXkB/vjx4+PGjVu3bl25j44fP75fv35z5sxp3Lhx7dZVvtP2wNIc30+PNZ41LyvW5u+4tijPe+aVDe8HG8SNfHj0a5auy4uIyGYWZnzZME9zfv3bQlEqEKVUoXQsP+j3j823BkjmiYkkK+vPTyu0xXBc15CoH5relSvnk3hemQ9PIRleYKzc9K5IrtJF76XSexLPh65pR8jwAABQAZIkXXriOgBA2KmbNKj/1lT72l/cO//w51mYStA0TY3q0Vl/47XhLg3qoAgL8BaLpWfPnkePHm3dunW/fv3atGljNptNJpPdbrdarQcPHvzhhx+WL1++b9++zZs3JyUlhbteqmcSBrXSHSlUL36x+UMvHnFbcxevnuKXzu0ex8fFPZV9V9eVJen960ZSqo4kkYgSeM4iSm5ZHnAmb0VyYmiGP+j3j8yzFEqSMu9dy5hHlpX58E9YCt81xyoZPiDLj1sKfwlJ70Q00KAnIiXDaxgNN0aVrblajM63Kun985Cr+h82Rnllea6teKqlMJnnbtJUYm95AAAAAIArE2fQx9zXJ+a+PuEuBOq+CAvwU6dOPXr06Kuvvjp58uRyG8yYMWPRokWjR4+eNm3ahx9+WNnXlyQpKyvr0m2ys7Mr/oKM6LVuMUQkE913cvHKmVMD8vnrAAc0H33nSh+VpHdVI12OKFLIvPfHC6yuCzO8XZIezrMUSpKJY3ZJbiwIHybEvWC1Bde0m2ApXJGc0FgQNro9pdK7IpjhXy+y3xdlMFTp8vjLcshSmkr4JKH0nPxxJiMjes/ucEjYaBcAAAAAAKASIizA//rrry1btrxYeleMGDFi8eLFmzdvrsLrjx49+uOPP65IS0mSKvXKswqLvnvrJSkkvXeKuuW5P3sQkS1eeOPrJkUNVD5RJKJGgvBtcoIy7/2d+LgnC6wuWf7HmbxVKYkpPM+I1IxFc5xNkoKr1n2QEKesaadnjGekJPKbtZrxJmO5k9sHGvQxHJcnijWU3onou+REIir34vuxJuNIY5Smxt4aAAAAAACgToqwAJ+fn9+2bdvLNmvYsOG+ffuq8Ppt27a97C41oiieOHFCECrXdd+5PHxMjOQoVu5ez1/7b3kqR6wwUXhxSZP8egKTz52Rbq1WBReNu0OnfTs+bkKB1SnLs4vsb5tj1YzF8twZn7+pSvg0wZzA80SkY+z9hLjR+ZZdXl8KxyvbyBk57rFo48Xq6XHhqnjV7tLz5pHeAQCgIqZPn/7888+HuwoAAIArRYQF+M6dO2/YsOHIkSPNmjW7WJu8vLx169Z17ty5Cq//5JNPPvnkk5duc/bs2eTkZLPZXKlXvlGtzp87zzLz5WSOkzvf+cqKWzVWXpWi+Xl58/xYUcdxbVWCU5ZPBAJrXO5nY0zB/dJ76rTvxsfNLrIP1OuIyCZJR/yB0PSu0DO2IME8Ot+y2+vLCYhxaq78OgAAACKHIAiV/Ys5AABAHRZhg+ITTzyxdu3aTp06TZs2rV+/fo0aNQp9NDc3d/Xq1S+//HJeXt7IkSPDVWS59vt8iTfetGrr1tZq1dAdlhmNnAN32Se83nxGfY3TUrjW5b7XoB9o0B/1B7IDgaQL54330GmDJ8wTeX59SmIMx5Xd2k3P2KIEc64opuJ3HQAAAAAAgDonwpJez549586d+2SJ6OjouLg4k8nkcDisVmthYSERCYLw3nvv9e/fP9zFXuDb5ASOWDzPEVFnpt6p9/01JEVVX0NEc8yxj5qimqlURJSmEi6xo5si5eL7uqkYQ3oHAAAAAACokyIv7I0dO/aOO+5YuHDhhg0bDhw4cPz4cSLieT4hIeGGG274xz/+MWLEiOTk5HCXWVroYuzjOxibxalubaBW7nJEoavEAwAAAAAAAJQVeQGeiJo3bz579uzZs2fLsuxyuTweT2xsLMdFzKxvgaO70mp2DTkAAAAAAACoYyIywAcxxgwGg8FgCHchAAAAAAAAADUrsgN8GPl8vh9//LGyzzp06NCZM2eMxovu7gbVThTFgoKCxMREhr3ralFhYaFWq9XpdOEu5Cri9/uJqGPHjuEu5KqQkZER7hKgpmB8v8J5PB6n01nZzYCgCpTfoJKSksJdyFXh7Nmz8fHx/MUXuoLqYrPZ0tPTL/YPOyLGdwT4SlOu1S8sLOzZs2e4awEAgLCJoKlbUBEY3wEAgK748R0BvtLi4+PHjx9/6NChyj5RluWNGzcSUVMR6+IAABRGSURBVFxcXA3UBeVzOp1er9dgMGg0mnDXcrUQRdFms/E8Hx0dHe5ariLFxcV+v79t27Y4W1JrRowYEe4SoDphfI8INptNFMXo6Gicq6xp+A2q1ni9XqfTqdFoMC+4pim/o2q12i5dulyi2RU+vjNZlsNdw9XC7/er1WqVSuXz+cJdy1Vk/Pjx77333vz588eNGxfuWq4WmZmZLVu2bNGiRRV+D4Yqu+uuu9asWbN69eq+ffuGuxaAqwvG99rUvn37vXv37tmzp127duGupY4bM2bMggULPvzww9GjR4e7ljpu4cKFjzzyyKhRoz766KNw11LH7du3r127dtddd93evXvDXUvVXdGXBwAAAAAAAACAAgEeAAAAAAAAIAIgwAMAAAAAAABEAAR4AAAAAAAAgAiAAA8AAAAAAAAQARDgAQAAAAAAACIAAjwAAAAAAABABECABwAAAAAAAIgACPAAAAAAAAAAEQABHgAAAAAAACACCOEu4CrC87xOp9NoNOEu5OpiMBiIKCoqKtyFXEUMBgNjTOl5qDX4pw4QLhjfa5Pysw5DTC3AsFJrlE7Gv+paUDd+gDBZlsNdw1Vky5YtgiB07Ngx3IVcRQoLC9esWTNo0CC1Wh3uWq4i69atS01NTU9PD3chV5Hs7OytW7cOGjSIMRbuWgCuOhjfa01mZmZmZubdd98d7kLqPovFsm7dusGDB6tUqnDXUsf5/f5ly5b16tXLbDaHu5a674cffmjevHnLli3DXUjVIcADAAAAAAAARADMgQcAAAAAAACIAAjwAAAAAAAAABEAAR4AAAAAAAAgAiDAAwAAAAAAAEQABHgAAAAAAACACIAADwAAAAAAABABEOABAAAAAAAAIgACPAAAAAAAAEAEQIAHAAAAAAAAiAAI8AAAAAAAAAARAAEeAAAAAAAAIAIgwAMAAAAAAABEAAR4AAAAAAAAgAiAAA8AAAAAAAAQARDgIbL9/PPP+fn54a4CAAAAagQGegD4m+bMmTN//vxwV1FtEOBryYYNG+69996EhIT09PSxY8dardZwV1QX/Pnnn927d9+6dWu5j1akz/F9qZT333+/Q4cORqMxMTHxtttuW7p0adk26PZqVFxcPGnSpPbt20dFRTVp0qR///67d+8u2wx9DhBG+HDVqL8/0MNlVdfgDpdVjcM6VNwXX3zxzDPPfPPNN2UfitSulqHmvf/++zzPq9Xqrl27tmjRgojS0tKOHTsW7roi3n333UdE3333XdmHKtLn+L5UXCAQGDNmDBFpNJquXbt2795dq9US0ZgxY0KbodurUXFxcZMmTYgoOTn57rvv7ty5MxExxlatWhXaDH0OEEb4cNW0vznQw6VV4+AOl1WNwzpU3PHjx00mExF169at1EOR29UI8DUuMzNTpVKZzebMzEzlyKxZs4iod+/e4S0scm3atOn111/v0KGD8keosuN6Rfoc35dKWbRoERG1bNkyNzdXOXLkyJGmTZsS0erVq5Uj6Pbq9dxzzxHRyJEjRVFUjqxevZoxlpKSEmyDPgcII3y4ak61DPRwWdU1uENFVNewDhUXCARuueUWo9FYNsBHdFcjwNe4Z599lojeeeed0IOtW7cmoiNHjoSrqojWrFmz0KtIyo7rFelzfF8qpUePHkS0bdu20IPLli0jokcffVS5i26vXtddd51Wq3W5XKEHO3XqRETHjx9X7qLPAcIIH66aUy0DPVxWdQ3uUBHVNaxDxb300kuMsY8//rhsgI/orsYc+Bq3YcMGIurXr1/oQeWu8hBU1s8//5yVlZWVlTV27NhyG1Skz/F9qZRjx46pVKobb7wx9GDbtm2J6PDhw8pddHv1atiw4cCBA3U6XehBnueJyOFwKHfR5wBhhA9XzamWgR4uq7oGd6iI6hrWoYK2b9/+0ksvPfbYY7169Sr7aER3tRDuAuo4WZYPHDhgMpkaNWoUerxNmzZElJGREaa6Ilv9+vWVG9HR0WUfrUif4/tSWd9++y1jjOMu+JPfrl27iCgtLY3Q7TVg1apVpY7873//27lzZ5MmTVq1akXoc4CwwoerRv39gR4qoloGd6igahnWoYIcDsewYcOaNWs2e/bsskvTRXpXI8DXLJfL5fF4UlJSSh03m81EZLFYwlFUHVeRPsf3pbLatWtX6sjevXsnTZrEGFPWv0G315zt27e/+eabp06d2rlzZ6tWrZYsWSIIAqHPAcIKH64wQudXl2oZ3KGy/s6wDhX0xBNPnDhx4rfffit1yYMi0rsal9DXLI/HQ0TK4oehlCMulysMNdV1FelzfF/+DlmWP/vss9tuuy03N/fNN9+8/vrrCd1ekywWy759+/766y9RFDUaTXBcQZ8DhBE+XGGEzq8JVR7cobL+zrAOFbF8+fJFixZNnz49uBBmKZHe1QjwNSs2Npbn+eDMliC73U4lf+aB6lWRPsf3pcp27drVqVOnhx56yGAwrFixYuLEicpxdHvN6du376FDh+x2+8aNG0+ePNmrV6/9+/cT+hwgrPDhCiN0frX7O4M7VNbfGdbhsnJyckaPHt25c+fJkydfrE2kdzUCfM3iOC4hIaHs1AvlSHCKF1SjivQ5vi9V4Pf7p06d2rFjxz///PP555/PzMzs379/8FF0ey24/fbbZ8yY4fP5Pv/8c0KfA4QVPlxhhM6vRn9/cIcqq8KwDpf13XffWa1WjuOGDRs2ZMiQIUOGjBs3jogyMjKGDBny4IMPUuR3NQJ8jWvQoEFRUdGZM2dCDx48eJAi4d9HhKpIn+P7UimSJD344IOvvvpqt27dDhw48PLLLyubaoZCt1ej3bt39+nTZ+7cuaWOK+vc5OfnK3fR5wBhhA9XGKHzq0V1De5wWdU4rENFbNmy5esSK1euJKK8vLyvv/5a2SWRIryrEeBr3IABA2RZ/uGHH0IP/vDDD4Ig3HPPPeGqqm6rSJ/j+1Ip77333tdff/3AAw+sX78+NTW13Dbo9moUHR29bt26L7/8stRxZWVUZZ9SQp8DhBU+XGGEzq8W1TW4w2VV47AOlzZ+/PhSu6ZnZ2dTyT7wbrdbaRbZXV1zW8yD4vTp04IgNGrU6OzZs8qRRYsWEdHAgQPDW1gdoExu+e6770odr0if4/tSKc2bN9fr9Xa7/RJt0O3VS1l5ZcGCBcEjGRkZSUlJarX6zz//VI6gzwHCCB+uWvB3Bnq4rOoa3KEiqmtYh8oKDfBBEd3VCPC14YMPPuA4LiUlZcSIET179hQEIS0t7dixY+GuK+JdbFyXK9bn+L5UUG5uLhFptdp25Zk0aVKwJbq9Gv3+++8Gg4GIWrduPWDAgC5duqhUKsbY22+/HdoMfQ4QRvhw1bS/OdDDJVTv4A6XVY3DOlRKuQFejuSuRoCvJStWrLj77rvNZnOrVq1GjRqVm5sb7orqgkuM63LF+hzfl4rYsmXLJa7iue+++0Ibo9ur0aFDh4YPH16/fn2NRpOWlta/f/8dO3aUbYY+BwgjfLhq1N8f6OFiqn1wh8uqxmEdKu5iAV6O2K5msixX6dJ7AAAAAAAAAKg9WMQOAAAAAAAAIAIgwAMAAAAAAABEAAR4AAAAAAAAgAiAAA8AAAAAAAAQARDgAQAAAAAAACIAAjwAAAAAAABABECABwAAAAAAAIgACPAAAAAAAAAAEQABHgAAAAAAACACIMADAAAAAAAARAAEeAAAAAAAAIAIgAAPAAAAAAAAEAEQ4AEAAAAAAAAiAAI8AAAAAAAAQARAgAcAAAAAAACIAAjwAAAAAAAAABEAAR4AAAAAAAAgAiDAAwAAAAAAAEQABHgAAAAAAACACIAADwAAAAAAABABEOABAAAAAAAAIgACPAAAAAAAAEAEQIAHAAAAAAAAiAAI8AAAAAAAAAARAAEeAAAAAAAAIAIgwAMAAAAAAABEAAR4AAAAAAAAgAiAAA8AAAAAAAAQARDgAQAAAAAAACIAAjwAAAAAAABABECABwAAAAAAAIgACPAAAAAAAAAAEQABHgAAAAAAACACIMADAAAAAAAARAAEeICry8SJE9nlvPDCC0T0yCOPMMYOHToU7pIBAACuCmfPni07KBsMhvbt2z/99NM2m62G3vf7779njL355pvK3TD+AlCqEgAoSwh3AQBQq1q0aHHHHXcE7x47duzYsWPNmjVr3Lhx8GBaWloYKgMAAAAirVbbpUsX5bYsywUFBRkZGXv37l26dOn+/ftjY2PDW15Zsiz7fD6e5wUByQKgxuFjBnB1GTt27NixY4N3X3zxxRkzZowaNeq5554r1XLKlCmjRo1q1KhR7RYIAABwVUtOTt6wYUPokZMnT/bv33/Pnj0vvPDCvHnzarqAyv4CsGPHjk6dOk2aNOmNN96o0cIAgHAJPQBcTNOmTTt27KjVasNdCAAAwFUtNTX1nXfeIaKffvqpFt4OvwAAXMkQ4AGg0txutyRJ4a4CAADgapGenk5Ex44dK/fR4uLi2i0HAMIGAR4Ayjd27NjQNWymTJnCGNu7d++dd95pMBj0en2XLl2WLl1KRP/973/79OmTkJCQkpLy0EMPFRQUlHqpxYsX9+rVKyEhITExsVevXuvXr6/tLwYAACCSHTx4kIgaNGig3H3mmWcYYxaL5fPPP09NTe3bt2+w5WXH3IKCgjFjxrRu3To2NvbOO+9ctmxZqQalfgEgIp/PN3369M6dO5tMpg4dOkyYMMFutysP9enTp1OnTkQ0Z84cxtjixYursRIAKAsBHgAq4b777tu7d+/w4cPvvvvubdu2PfDAA48//njfvn1FURwwYIAgCJ999tmoUaNCnzJ8+PBhw4bt2rXrxhtvTE9P//XXX3v37j1r1qxwfQkAAACRJScnZ8KECUQUXNxOsXTp0lGjRrVt27Zfv37KkcuOuZmZme3bt1+wYEFRUVHnzp2PHj16//33v/fee5d496Kioptvvvmll146e/bs7bffHggE3nnnnZtuuunkyZNENGbMGKW27t27v/322x06dKi5SgCAiEgGgKvYjBkziOi1114r+9Cjjz5KRAcPHlTuTp48mYiaN2+en5+vHJk7d67yY2ThwoXKkfz8/JiYGLVaHQgElCPKKfr+/fvb7XblyJEjR9LS0jiO2759e81+bQAAABHlzJkzRKTT6XqX6NWr1w033KBMR69Xr15BQYHSctKkSUSUmJi4b9++4NMrMuYq5+onTpwYHKlfeuklZTSfM2eOcqTULwBPP/00ET311FOiKCpH5syZQ0TDhw9X7m7bto2IJk2aVO2VAEBZOAMPAJUwefLk+Ph45XavXr2I6Jprrnn44YeVI/Hx8TfddJPP5ysqKlKOvPLKKzqd7rPPPjMajcqRtLS0119/XZKkL774otbLBwAAuNK53e51JdavX79r167k5OSRI0fu2bPHbDaHthw5cuS1114bvHvZMTczM3PNmjVt2rSZM2cOz/NKmxdeeKF9+/YXK8bpdM6dOzc1NfW1117juHPBYeLEiV27dr3EvvQ1UQkAKLCNHABUQtOmTYO39Xo9EbVq1Sq0gXJQIYrigQMHUlJSSmV1ZZL83r17a7ZWAACACNS4cePjx49XpOWNN94YvF2RMTcjI4OI+vXrF4ziioEDB+7Zs6fct8jMzPT5fHfccYdKpQoe5Dju559/vlhVNVQJACgQ4AGgEhhjpY4E/3BeVk5Ojs/nO3HixGOPPVb2USyZCwAA8HekpKQEb1dkzD19+jSFrIQX1Lhx44u9RVZWFhHVq1ev4lXVUCUAoMAl9ABQU5KTk3me79mzZ7kTeHAGHgAA4O8IPX1dkTE3NTWViLKzs0u9TnDiW1lKdM/Lyyt1XJIkURTLfUoNVQIACgR4AKgparW6adOmu3btcjqdocc3b978+OOPYzM5AACA6lKRMfeaa64hopUrV0qSFNrmEiNyq1atlAvmS8X1Zs2aJSUlybJca5UAgAIBHgBq0MSJE61W69ChQ4OjeHZ29qBBg+bNm1e/fv3w1gYAAFCXXHbMTUtLu/fee//444/JkycHk/OCBQtWrVp1sdeMjo4eMWJEZmbmtGnTgnF9/vz5x48f7927d+jEOo/HU6OVAIACc+ABoAaNHj36+++/X7lyZWpqaseOHa1W686dOyVJmjVrVps2bcJdHQAAQN1RkTH3jTfe2LVr1xtvvPGf//ynXbt2R44cycjIGDp06FdffXWxl33llVc2b948a9asZcuWXXfddSdOnNi5c2diYuIbb7yhNIiKiiKiZcuWMcaGDRt200031VAlAEA4Aw8ANYrn+bVr186dO7dNmzbbtm3Lysq6/fbb165dO2XKlHCXBgAAUKdUZMxt0aLF3r17H3nkEaPR+MsvvyQnJ3/wwQezZ8++xMsmJSXt3r37mWeeMZlMa9eutdlso0eP3r9/f3AJvfT09EmTJmk0mk8//VRZna6GKgEAImLlzl0BAAAAAAAAgCsKzsADAAAAAAAARAAEeAAAAAAAAIAIgAAPAAAAAAAAEAEQ4AEAAAAAAAAiAAI8AAAAAAAAQARAgAcAAAAAAACIAAjwAAAAAAAAABEAAR4AAAAAAAAgAiDAAwAAAAAAAEQABHgAAAAAAACACIAADwAAAAAAABABEOABAAAAAAAAIgACPAAAAAAAAEAEQIAHAAAAAAAAiAAI8AAAAAAAAAARAAEeAAAAAAAAIAIgwAMAAAAAAABEAAR4AAAAAAAAgAiAAA8AAAAAAAAQARDgAQAAAAAAACIAAjwAAAAAAABABECABwAAAAAAAIgACPAAAAAAAAAAEQABHgAAAAAAACACIMADAAAAAAAARAAEeAAAAAAAAIAIgAAPAAAAAAAAEAEQ4AEAAAAAAAAiAAI8AAAAAAAAQARAgAcAAAAAAACIAAjwAAAAAAAAABEAAR4AAAAAAAAgAvw/iRFJ2Coixp4AAAAASUVORK5CYII=" alt="DFOP pathway fit with two-component error, alternative pathway" width="672" />
+<p class="caption">
 DFOP pathway fit with two-component error, alternative pathway
 </p>
 </div>
-<div class="sourceCode" id="cb27"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_saem_2</span><span class="op">[[</span><span class="st">"sforb_path_2"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div>
+<pre class="r"><code>plot(f_saem_2[[&quot;sforb_path_2&quot;, &quot;tc&quot;]])</code></pre>
 <div class="figure" style="text-align: center">
-<img src="2022_cyan_pathway_files/figure-html/unnamed-chunk-15-1.png" alt="SFORB pathway fit with two-component error, alternative pathway" width="700"><p class="caption">
+<img src="data:image/png;base64,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" alt="SFORB pathway fit with two-component error, alternative pathway" width="672" />
+<p class="caption">
 SFORB pathway fit with two-component error, alternative pathway
 </p>
 </div>
 </div>
-<div class="section level3">
-<h3 id="refinement-of-alternative-pathway-fits">Refinement of alternative pathway fits<a class="anchor" aria-label="anchor" href="#refinement-of-alternative-pathway-fits"></a>
-</h3>
+<div id="refinement-of-alternative-pathway-fits" class="section level2">
+<h2>Refinement of alternative pathway fits</h2>
 <p>All ill-defined random effects that were identified in the parent
 only fits and in the above pathway fits, are excluded for the final
 evaluations below. For this purpose, a list of character vectors is
@@ -2024,29 +2325,29 @@ created below that can be indexed by row and column indices, and which
 contains the degradation parameter names for which random effects should
 be excluded for each of the hierarchical fits contained in
 <code>f_saem_2</code>.</p>
-<div class="sourceCode" id="cb28"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">no_ranef</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/matrix.html" class="external-link">matrix</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="op">)</span>, nrow <span class="op">=</span> <span class="fl">3</span>, ncol <span class="op">=</span> <span class="fl">2</span>, dimnames <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/dimnames.html" class="external-link">dimnames</a></span><span class="op">(</span><span class="va">f_saem_2</span><span class="op">)</span><span class="op">)</span></span>
-<span><span class="va">no_ranef</span><span class="op">[[</span><span class="st">"fomc_path_2"</span>, <span class="st">"const"</span><span class="op">]</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"log_beta"</span>, <span class="st">"f_JCZ38_qlogis"</span>, <span class="st">"f_JSE76_qlogis"</span><span class="op">)</span></span>
-<span><span class="va">no_ranef</span><span class="op">[[</span><span class="st">"fomc_path_2"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"cyan_0"</span>, <span class="st">"f_JCZ38_qlogis"</span>, <span class="st">"f_JSE76_qlogis"</span><span class="op">)</span></span>
-<span><span class="va">no_ranef</span><span class="op">[[</span><span class="st">"dfop_path_2"</span>, <span class="st">"const"</span><span class="op">]</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"cyan_0"</span>, <span class="st">"f_JCZ38_qlogis"</span>, <span class="st">"f_JSE76_qlogis"</span><span class="op">)</span></span>
-<span><span class="va">no_ranef</span><span class="op">[[</span><span class="st">"dfop_path_2"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"cyan_0"</span>, <span class="st">"log_k1"</span>, <span class="st">"f_JCZ38_qlogis"</span>, <span class="st">"f_JSE76_qlogis"</span><span class="op">)</span></span>
-<span><span class="va">no_ranef</span><span class="op">[[</span><span class="st">"sforb_path_2"</span>, <span class="st">"const"</span><span class="op">]</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"cyan_free_0"</span>,</span>
-<span>  <span class="st">"f_JCZ38_qlogis"</span>, <span class="st">"f_JSE76_qlogis"</span><span class="op">)</span></span>
-<span><span class="va">no_ranef</span><span class="op">[[</span><span class="st">"sforb_path_2"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"cyan_free_0"</span>, <span class="st">"log_k_cyan_free_bound"</span>,</span>
-<span>  <span class="st">"f_JCZ38_qlogis"</span>, <span class="st">"f_JSE76_qlogis"</span><span class="op">)</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/parallel/clusterApply.html" class="external-link">clusterExport</a></span><span class="op">(</span><span class="va">cl_path_2</span>, <span class="st">"no_ranef"</span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">f_saem_3</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_saem_2</span>,</span>
-<span>  no_random_effect <span class="op">=</span> <span class="va">no_ranef</span>,</span>
-<span>  cluster <span class="op">=</span> <span class="va">cl_path_2</span><span class="op">)</span></span></code></pre></div>
-<div class="sourceCode" id="cb29"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="../../reference/status.html">status</a></span><span class="op">(</span><span class="va">f_saem_3</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
-<thead><tr class="header">
+<pre class="r"><code>no_ranef &lt;- matrix(list(), nrow = 3, ncol = 2, dimnames = dimnames(f_saem_2))
+no_ranef[[&quot;fomc_path_2&quot;, &quot;const&quot;]] &lt;- c(&quot;log_beta&quot;, &quot;f_JCZ38_qlogis&quot;, &quot;f_JSE76_qlogis&quot;)
+no_ranef[[&quot;fomc_path_2&quot;, &quot;tc&quot;]] &lt;- c(&quot;cyan_0&quot;, &quot;f_JCZ38_qlogis&quot;, &quot;f_JSE76_qlogis&quot;)
+no_ranef[[&quot;dfop_path_2&quot;, &quot;const&quot;]] &lt;- c(&quot;cyan_0&quot;, &quot;f_JCZ38_qlogis&quot;, &quot;f_JSE76_qlogis&quot;)
+no_ranef[[&quot;dfop_path_2&quot;, &quot;tc&quot;]] &lt;- c(&quot;cyan_0&quot;, &quot;log_k1&quot;, &quot;f_JCZ38_qlogis&quot;, &quot;f_JSE76_qlogis&quot;)
+no_ranef[[&quot;sforb_path_2&quot;, &quot;const&quot;]] &lt;- c(&quot;cyan_free_0&quot;,
+  &quot;f_JCZ38_qlogis&quot;, &quot;f_JSE76_qlogis&quot;)
+no_ranef[[&quot;sforb_path_2&quot;, &quot;tc&quot;]] &lt;- c(&quot;cyan_free_0&quot;, &quot;log_k_cyan_free_bound&quot;,
+  &quot;f_JCZ38_qlogis&quot;, &quot;f_JSE76_qlogis&quot;)
+clusterExport(cl_path_2, &quot;no_ranef&quot;)
+
+f_saem_3 &lt;- update(f_saem_2,
+  no_random_effect = no_ranef,
+  cluster = cl_path_2)</code></pre>
+<pre class="r"><code>status(f_saem_3) |&gt; kable()</code></pre>
+<table>
+<thead>
+<tr class="header">
 <th align="left"></th>
 <th align="left">const</th>
 <th align="left">tc</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="left">fomc_path_2</td>
@@ -2069,14 +2370,15 @@ be excluded for each of the hierarchical fits contained in
 all updated fits completed successfully. However, the Fisher Information
 Matrix for the fixed effects (Fth) could not be inverted, so no
 confidence intervals for the optimised parameters are available.</p>
-<div class="sourceCode" id="cb30"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="../../reference/illparms.html">illparms</a></span><span class="op">(</span><span class="va">f_saem_3</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
-<thead><tr class="header">
+<pre class="r"><code>illparms(f_saem_3) |&gt; kable()</code></pre>
+<table>
+<thead>
+<tr class="header">
 <th align="left"></th>
 <th align="left">const</th>
 <th align="left">tc</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="left">fomc_path_2</td>
@@ -2095,16 +2397,17 @@ confidence intervals for the optimised parameters are available.</p>
 </tr>
 </tbody>
 </table>
-<div class="sourceCode" id="cb31"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_saem_3</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span>digits <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
-<thead><tr class="header">
+<pre class="r"><code>anova(f_saem_3[, &quot;tc&quot;]) |&gt; kable(digits = 1)</code></pre>
+<table>
+<thead>
+<tr class="header">
 <th align="left"></th>
 <th align="right">npar</th>
 <th align="right">AIC</th>
 <th align="right">BIC</th>
 <th align="right">Lik</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="left">fomc_path_2 tc</td>
@@ -2114,27 +2417,55 @@ confidence intervals for the optimised parameters are available.</p>
 <td align="right">-1105.5</td>
 </tr>
 <tr class="even">
+<td align="left">dfop_path_2 tc</td>
+<td align="right">20</td>
+<td align="right">2237.3</td>
+<td align="right">2229.5</td>
+<td align="right">-1098.6</td>
+</tr>
+<tr class="odd">
+<td align="left">sforb_path_2 tc</td>
+<td align="right">20</td>
+<td align="right">2241.3</td>
+<td align="right">2233.5</td>
+<td align="right">-1100.7</td>
+</tr>
+</tbody>
+</table>
+<pre class="r"><code>anova(f_saem_3[2:3,]) |&gt; kable(digits = 1)</code></pre>
+<table>
+<thead>
+<tr class="header">
+<th align="left"></th>
+<th align="right">npar</th>
+<th align="right">AIC</th>
+<th align="right">BIC</th>
+<th align="right">Lik</th>
+</tr>
+</thead>
+<tbody>
+<tr class="odd">
 <td align="left">dfop_path_2 const</td>
 <td align="right">20</td>
 <td align="right">2282.2</td>
 <td align="right">2274.4</td>
 <td align="right">-1121.1</td>
 </tr>
-<tr class="odd">
+<tr class="even">
 <td align="left">sforb_path_2 const</td>
 <td align="right">20</td>
 <td align="right">2279.7</td>
 <td align="right">2271.9</td>
 <td align="right">-1119.9</td>
 </tr>
-<tr class="even">
+<tr class="odd">
 <td align="left">dfop_path_2 tc</td>
 <td align="right">20</td>
 <td align="right">2237.3</td>
 <td align="right">2229.5</td>
 <td align="right">-1098.6</td>
 </tr>
-<tr class="odd">
+<tr class="even">
 <td align="left">sforb_path_2 tc</td>
 <td align="right">20</td>
 <td align="right">2241.3</td>
@@ -2147,13 +2478,11 @@ confidence intervals for the optimised parameters are available.</p>
 two-component error) are lower than in the previous fits with the
 alternative pathway, the practical value of these refined evaluations is
 limited as no confidence intervals are obtained.</p>
-<div class="sourceCode" id="cb32"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/parallel/makeCluster.html" class="external-link">stopCluster</a></span><span class="op">(</span><span class="va">cl_path_2</span><span class="op">)</span></span></code></pre></div>
+<pre class="r"><code>stopCluster(cl_path_2)</code></pre>
 </div>
 </div>
-<div class="section level2">
-<h2 id="conclusion">Conclusion<a class="anchor" aria-label="anchor" href="#conclusion"></a>
-</h2>
+<div id="conclusion" class="section level1">
+<h1>Conclusion</h1>
 <p>It was demonstrated that a relatively complex transformation pathway
 with parallel formation of two primary metabolites and one secondary
 metabolite can be fitted even if the data in the individual datasets are
@@ -2163,55 +2492,50 @@ practical feasibility of iterative refinements based on ill-defined
 parameters and of alternative checks of parameter identifiability based
 on multistart runs.</p>
 </div>
-<div class="section level2">
-<h2 id="acknowledgements">Acknowledgements<a class="anchor" aria-label="anchor" href="#acknowledgements"></a>
-</h2>
+<div id="acknowledgements" class="section level1">
+<h1>Acknowledgements</h1>
 <p>The helpful comments by Janina Wöltjen of the German Environment
 Agency are gratefully acknowledged.</p>
 </div>
-<div class="section level2">
-<h2 id="appendix">Appendix<a class="anchor" aria-label="anchor" href="#appendix"></a>
-</h2>
-<div class="section level3">
-<h3 id="plots-of-fits-that-were-not-refined-further">Plots of fits that were not refined further<a class="anchor" aria-label="anchor" href="#plots-of-fits-that-were-not-refined-further"></a>
-</h3>
-<div class="sourceCode" id="cb33"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_saem_1</span><span class="op">[[</span><span class="st">"sfo_path_1"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div>
+<div id="appendix" class="section level1">
+<h1>Appendix</h1>
+<div id="plots-of-fits-that-were-not-refined-further" class="section level2">
+<h2>Plots of fits that were not refined further</h2>
+<pre class="r"><code>plot(f_saem_1[[&quot;sfo_path_1&quot;, &quot;tc&quot;]])</code></pre>
 <div class="figure" style="text-align: center">
-<img src="2022_cyan_pathway_files/figure-html/unnamed-chunk-20-1.png" alt="SFO pathway fit with two-component error" width="700"><p class="caption">
+<img src="data:image/png;base64,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" alt="SFO pathway fit with two-component error" width="672" />
+<p class="caption">
 SFO pathway fit with two-component error
 </p>
 </div>
-<div class="sourceCode" id="cb34"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_saem_1</span><span class="op">[[</span><span class="st">"fomc_path_1"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div>
+<pre class="r"><code>plot(f_saem_1[[&quot;fomc_path_1&quot;, &quot;tc&quot;]])</code></pre>
 <div class="figure" style="text-align: center">
-<img src="2022_cyan_pathway_files/figure-html/unnamed-chunk-21-1.png" alt="FOMC pathway fit with two-component error" width="700"><p class="caption">
+<img src="data:image/png;base64,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" alt="FOMC pathway fit with two-component error" width="672" />
+<p class="caption">
 FOMC pathway fit with two-component error
 </p>
 </div>
-<div class="sourceCode" id="cb35"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_saem_1</span><span class="op">[[</span><span class="st">"sforb_path_1"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div>
+<pre class="r"><code>plot(f_saem_1[[&quot;sforb_path_1&quot;, &quot;tc&quot;]])</code></pre>
 <div class="figure" style="text-align: center">
-<img src="2022_cyan_pathway_files/figure-html/unnamed-chunk-22-1.png" alt="HS pathway fit with two-component error" width="700"><p class="caption">
+<img src="data:image/png;base64,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" alt="HS pathway fit with two-component error" width="672" />
+<p class="caption">
 HS pathway fit with two-component error
 </p>
 </div>
 </div>
-<div class="section level3">
-<h3 id="hierarchical-fit-listings">Hierarchical fit listings<a class="anchor" aria-label="anchor" href="#hierarchical-fit-listings"></a>
-</h3>
-<div class="section level4">
-<h4 id="pathway-1">Pathway 1<a class="anchor" aria-label="anchor" href="#pathway-1"></a>
-</h4>
+<div id="hierarchical-fit-listings" class="section level2">
+<h2>Hierarchical fit listings</h2>
+<div id="pathway-1" class="section level3">
+<h3>Pathway 1</h3>
 <caption>
 Hierarchical SFO path 1 fit with constant variance
 </caption>
 <pre><code>
-saemix version used for fitting:      3.2 
-mkin version used for pre-fitting:  1.2.6 
-R version used for fitting:         4.3.1 
-Date of fit:     Mon Oct 30 10:03:13 2023 
-Date of summary: Mon Oct 30 11:18:26 2023 
+saemix version used for fitting:      3.3 
+mkin version used for pre-fitting:  1.2.9 
+R version used for fitting:         4.4.2 
+Date of fit:     Thu Feb 13 18:31:33 2025 
+Date of summary: Thu Feb 13 19:05:53 2025 
 
 Equations:
 d_cyan/dt = - k_cyan * cyan
@@ -2224,7 +2548,7 @@ Data:
 
 Model predictions using solution type deSolve 
 
-Fitted in 1273.632 s
+Fitted in 480.873 s
 Using 300, 100 iterations and 10 chains
 
 Variance model: Constant variance 
@@ -2329,17 +2653,17 @@ JCZ38  14.46  48.05
 J9Z38 103.86 345.00
 JSE76 143.91 478.04
 
-</code></pre>
-<p></p>
+</pre>
+<p></code></p>
 <caption>
 Hierarchical SFO path 1 fit with two-component error
 </caption>
 <pre><code>
-saemix version used for fitting:      3.2 
-mkin version used for pre-fitting:  1.2.6 
-R version used for fitting:         4.3.1 
-Date of fit:     Mon Oct 30 09:58:51 2023 
-Date of summary: Mon Oct 30 11:18:26 2023 
+saemix version used for fitting:      3.3 
+mkin version used for pre-fitting:  1.2.9 
+R version used for fitting:         4.4.2 
+Date of fit:     Thu Feb 13 18:32:28 2025 
+Date of summary: Thu Feb 13 19:05:53 2025 
 
 Equations:
 d_cyan/dt = - k_cyan * cyan
@@ -2352,7 +2676,7 @@ Data:
 
 Model predictions using solution type deSolve 
 
-Fitted in 1011.299 s
+Fitted in 534.75 s
 Using 300, 100 iterations and 10 chains
 
 Variance model: Two-component variance function 
@@ -2459,17 +2783,17 @@ JCZ38  15.81  52.52
 J9Z38 107.97 358.68
 JSE76 114.20 379.35
 
-</code></pre>
-<p></p>
+</pre>
+<p></code></p>
 <caption>
 Hierarchical FOMC path 1 fit with constant variance
 </caption>
 <pre><code>
-saemix version used for fitting:      3.2 
-mkin version used for pre-fitting:  1.2.6 
-R version used for fitting:         4.3.1 
-Date of fit:     Mon Oct 30 10:04:48 2023 
-Date of summary: Mon Oct 30 11:18:26 2023 
+saemix version used for fitting:      3.3 
+mkin version used for pre-fitting:  1.2.9 
+R version used for fitting:         4.4.2 
+Date of fit:     Thu Feb 13 18:33:51 2025 
+Date of summary: Thu Feb 13 19:05:53 2025 
 
 Equations:
 d_cyan/dt = - (alpha/beta) * 1/((time/beta) + 1) * cyan
@@ -2484,7 +2808,7 @@ Data:
 
 Model predictions using solution type deSolve 
 
-Fitted in 1368.338 s
+Fitted in 618.676 s
 Using 300, 100 iterations and 10 chains
 
 Variance model: Constant variance 
@@ -2604,17 +2928,17 @@ JCZ38  20.53   68.19       NA
 J9Z38 140.07  465.32       NA
 JSE76 318.86 1059.22       NA
 
-</code></pre>
-<p></p>
+</pre>
+<p></code></p>
 <caption>
 Hierarchical FOMC path 1 fit with two-component error
 </caption>
 <pre><code>
-saemix version used for fitting:      3.2 
-mkin version used for pre-fitting:  1.2.6 
-R version used for fitting:         4.3.1 
-Date of fit:     Mon Oct 30 10:00:40 2023 
-Date of summary: Mon Oct 30 11:18:26 2023 
+saemix version used for fitting:      3.3 
+mkin version used for pre-fitting:  1.2.9 
+R version used for fitting:         4.4.2 
+Date of fit:     Thu Feb 13 18:34:01 2025 
+Date of summary: Thu Feb 13 19:05:53 2025 
 
 Equations:
 d_cyan/dt = - (alpha/beta) * 1/((time/beta) + 1) * cyan
@@ -2629,7 +2953,7 @@ Data:
 
 Model predictions using solution type deSolve 
 
-Fitted in 1120.168 s
+Fitted in 627.822 s
 Using 300, 100 iterations and 10 chains
 
 Variance model: Two-component variance function 
@@ -2742,17 +3066,17 @@ JCZ38  22.01  73.1       NA
 J9Z38 130.09 432.2       NA
 JSE76 210.98 700.9       NA
 
-</code></pre>
-<p></p>
+</pre>
+<p></code></p>
 <caption>
 Hierarchical DFOP path 1 fit with constant variance
 </caption>
 <pre><code>
-saemix version used for fitting:      3.2 
-mkin version used for pre-fitting:  1.2.6 
-R version used for fitting:         4.3.1 
-Date of fit:     Mon Oct 30 10:02:52 2023 
-Date of summary: Mon Oct 30 11:18:26 2023 
+saemix version used for fitting:      3.3 
+mkin version used for pre-fitting:  1.2.9 
+R version used for fitting:         4.4.2 
+Date of fit:     Thu Feb 13 18:33:18 2025 
+Date of summary: Thu Feb 13 19:05:53 2025 
 
 Equations:
 d_cyan/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
@@ -2771,7 +3095,7 @@ Data:
 
 Model predictions using solution type deSolve 
 
-Fitted in 1252.502 s
+Fitted in 584.724 s
 Using 300, 100 iterations and 10 chains
 
 Variance model: Constant variance 
@@ -2888,17 +3212,17 @@ JCZ38  22.34  74.22       NA      NA      NA
 J9Z38 119.92 398.36       NA      NA      NA
 JSE76 200.41 665.76       NA      NA      NA
 
-</code></pre>
-<p></p>
+</pre>
+<p></code></p>
 <caption>
 Hierarchical DFOP path 1 fit with two-component error
 </caption>
 <pre><code>
-saemix version used for fitting:      3.2 
-mkin version used for pre-fitting:  1.2.6 
-R version used for fitting:         4.3.1 
-Date of fit:     Mon Oct 30 10:12:10 2023 
-Date of summary: Mon Oct 30 11:18:26 2023 
+saemix version used for fitting:      3.3 
+mkin version used for pre-fitting:  1.2.9 
+R version used for fitting:         4.4.2 
+Date of fit:     Thu Feb 13 18:35:43 2025 
+Date of summary: Thu Feb 13 19:05:53 2025 
 
 Equations:
 d_cyan/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
@@ -2917,7 +3241,7 @@ Data:
 
 Model predictions using solution type deSolve 
 
-Fitted in 1809.832 s
+Fitted in 729.575 s
 Using 300, 100 iterations and 10 chains
 
 Variance model: Two-component variance function 
@@ -3034,17 +3358,17 @@ JCZ38  22.20  73.75       NA      NA      NA
 J9Z38 108.23 359.53       NA      NA      NA
 JSE76 179.30 595.62       NA      NA      NA
 
-</code></pre>
-<p></p>
+</pre>
+<p></code></p>
 <caption>
 Hierarchical SFORB path 1 fit with constant variance
 </caption>
 <pre><code>
-saemix version used for fitting:      3.2 
-mkin version used for pre-fitting:  1.2.6 
-R version used for fitting:         4.3.1 
-Date of fit:     Mon Oct 30 10:02:30 2023 
-Date of summary: Mon Oct 30 11:18:26 2023 
+saemix version used for fitting:      3.3 
+mkin version used for pre-fitting:  1.2.9 
+R version used for fitting:         4.4.2 
+Date of fit:     Thu Feb 13 18:34:05 2025 
+Date of summary: Thu Feb 13 19:05:53 2025 
 
 Equations:
 d_cyan_free/dt = - k_cyan_free * cyan_free - k_cyan_free_bound *
@@ -3062,7 +3386,7 @@ Data:
 
 Model predictions using solution type deSolve 
 
-Fitted in 1230.946 s
+Fitted in 632.71 s
 Using 300, 100 iterations and 10 chains
 
 Variance model: Constant variance 
@@ -3199,17 +3523,17 @@ JCZ38  22.98  76.33       NA           NA           NA
 J9Z38 116.28 386.29       NA           NA           NA
 JSE76 193.42 642.53       NA           NA           NA
 
-</code></pre>
-<p></p>
+</pre>
+<p></code></p>
 <caption>
 Hierarchical SFORB path 1 fit with two-component error
 </caption>
 <pre><code>
-saemix version used for fitting:      3.2 
-mkin version used for pre-fitting:  1.2.6 
-R version used for fitting:         4.3.1 
-Date of fit:     Mon Oct 30 10:09:13 2023 
-Date of summary: Mon Oct 30 11:18:26 2023 
+saemix version used for fitting:      3.3 
+mkin version used for pre-fitting:  1.2.9 
+R version used for fitting:         4.4.2 
+Date of fit:     Thu Feb 13 18:37:01 2025 
+Date of summary: Thu Feb 13 19:05:53 2025 
 
 Equations:
 d_cyan_free/dt = - k_cyan_free * cyan_free - k_cyan_free_bound *
@@ -3227,7 +3551,7 @@ Data:
 
 Model predictions using solution type deSolve 
 
-Fitted in 1633.433 s
+Fitted in 807.852 s
 Using 300, 100 iterations and 10 chains
 
 Variance model: Two-component variance function 
@@ -3364,35 +3688,162 @@ JCZ38  22.34  74.2       NA           NA           NA
 J9Z38 110.14 365.9       NA           NA           NA
 JSE76 177.11 588.3       NA           NA           NA
 
-</code></pre>
-<p></p>
+</pre>
+<p></code></p>
 <caption>
 Hierarchical HS path 1 fit with constant variance
 </caption>
 <pre><code>
-saemix version used for fitting:      3.2 
-mkin version used for pre-fitting:  1.2.6 
-R version used for fitting:         4.3.1 
-Date of fit:     Mon Oct 30 10:02:52 2023 
-Date of summary: Mon Oct 30 11:18:27 2023 
+saemix version used for fitting:      3.3 
+mkin version used for pre-fitting:  1.2.9 
+R version used for fitting:         4.4.2 
+Date of fit:     Thu Feb 13 18:33:29 2025 
+Date of summary: Thu Feb 13 19:05:53 2025 
 
 Equations:
-d_cyan/dt = - ifelse(time 
-<p></p></code>
+d_cyan/dt = - ifelse(time &lt;= tb, k1, k2) * cyan
+d_JCZ38/dt = + f_cyan_to_JCZ38 * ifelse(time &lt;= tb, k1, k2) * cyan -
+           k_JCZ38 * JCZ38
+d_J9Z38/dt = + f_cyan_to_J9Z38 * ifelse(time &lt;= tb, k1, k2) * cyan -
+           k_J9Z38 * J9Z38
+d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76
+
+Data:
+433 observations of 4 variable(s) grouped in 5 datasets
+
+Model predictions using solution type deSolve 
+
+Fitted in 596.235 s
+Using 300, 100 iterations and 10 chains
+
+Variance model: Constant variance 
+
+Starting values for degradation parameters:
+        cyan_0    log_k_JCZ38    log_k_J9Z38    log_k_JSE76   f_cyan_ilr_1 
+      102.8845        -3.4495        -4.9355        -5.6040         0.6468 
+  f_cyan_ilr_2 f_JCZ38_qlogis         log_k1         log_k2         log_tb 
+        1.2396         9.7220        -2.9079        -4.1810         1.7813 
+
+Fixed degradation parameter values:
+None
+
+Starting values for random effects (square root of initial entries in omega):
+               cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1
+cyan_0          5.406        0.00        0.00       0.000       0.0000
+log_k_JCZ38     0.000        2.33        0.00       0.000       0.0000
+log_k_J9Z38     0.000        0.00        1.59       0.000       0.0000
+log_k_JSE76     0.000        0.00        0.00       1.013       0.0000
+f_cyan_ilr_1    0.000        0.00        0.00       0.000       0.6367
+f_cyan_ilr_2    0.000        0.00        0.00       0.000       0.0000
+f_JCZ38_qlogis  0.000        0.00        0.00       0.000       0.0000
+log_k1          0.000        0.00        0.00       0.000       0.0000
+log_k2          0.000        0.00        0.00       0.000       0.0000
+log_tb          0.000        0.00        0.00       0.000       0.0000
+               f_cyan_ilr_2 f_JCZ38_qlogis log_k1 log_k2 log_tb
+cyan_0                0.000           0.00 0.0000 0.0000 0.0000
+log_k_JCZ38           0.000           0.00 0.0000 0.0000 0.0000
+log_k_J9Z38           0.000           0.00 0.0000 0.0000 0.0000
+log_k_JSE76           0.000           0.00 0.0000 0.0000 0.0000
+f_cyan_ilr_1          0.000           0.00 0.0000 0.0000 0.0000
+f_cyan_ilr_2          2.038           0.00 0.0000 0.0000 0.0000
+f_JCZ38_qlogis        0.000          10.33 0.0000 0.0000 0.0000
+log_k1                0.000           0.00 0.7006 0.0000 0.0000
+log_k2                0.000           0.00 0.0000 0.8928 0.0000
+log_tb                0.000           0.00 0.0000 0.0000 0.6773
+
+Starting values for error model parameters:
+a.1 
+  1 
+
+Results:
+
+Likelihood computed by importance sampling
+   AIC  BIC logLik
+  2427 2419  -1194
+
+Optimised parameters:
+                      est.      lower      upper
+cyan_0            101.9660  1.005e+02  1.035e+02
+log_k_JCZ38        -3.4698 -4.716e+00 -2.224e+00
+log_k_J9Z38        -5.0947 -5.740e+00 -4.450e+00
+log_k_JSE76        -5.5977 -6.321e+00 -4.875e+00
+f_cyan_ilr_1        0.6595  3.734e-01  9.456e-01
+f_cyan_ilr_2        0.5905  1.664e-01  1.015e+00
+f_JCZ38_qlogis     25.8627 -4.224e+05  4.225e+05
+log_k1             -3.0884 -3.453e+00 -2.723e+00
+log_k2             -4.3877 -4.778e+00 -3.998e+00
+log_tb              2.3057  1.715e+00  2.896e+00
+a.1                 3.3228         NA         NA
+SD.log_k_JCZ38      1.4071         NA         NA
+SD.log_k_J9Z38      0.5774         NA         NA
+SD.log_k_JSE76      0.6214         NA         NA
+SD.f_cyan_ilr_1     0.3058         NA         NA
+SD.f_cyan_ilr_2     0.3470         NA         NA
+SD.f_JCZ38_qlogis   0.0644         NA         NA
+SD.log_k1           0.3994         NA         NA
+SD.log_k2           0.4373         NA         NA
+SD.log_tb           0.6419         NA         NA
+
+Correlation is not available
+
+Random effects:
+                    est. lower upper
+SD.log_k_JCZ38    1.4071    NA    NA
+SD.log_k_J9Z38    0.5774    NA    NA
+SD.log_k_JSE76    0.6214    NA    NA
+SD.f_cyan_ilr_1   0.3058    NA    NA
+SD.f_cyan_ilr_2   0.3470    NA    NA
+SD.f_JCZ38_qlogis 0.0644    NA    NA
+SD.log_k1         0.3994    NA    NA
+SD.log_k2         0.4373    NA    NA
+SD.log_tb         0.6419    NA    NA
+
+Variance model:
+     est. lower upper
+a.1 3.323    NA    NA
+
+Backtransformed parameters:
+                      est.     lower     upper
+cyan_0           1.020e+02 1.005e+02 1.035e+02
+k_JCZ38          3.112e-02 8.951e-03 1.082e-01
+k_J9Z38          6.129e-03 3.216e-03 1.168e-02
+k_JSE76          3.706e-03 1.798e-03 7.639e-03
+f_cyan_to_JCZ38  5.890e-01        NA        NA
+f_cyan_to_J9Z38  2.318e-01        NA        NA
+f_JCZ38_to_JSE76 1.000e+00 0.000e+00 1.000e+00
+k1               4.558e-02 3.164e-02 6.565e-02
+k2               1.243e-02 8.417e-03 1.835e-02
+tb               1.003e+01 5.557e+00 1.811e+01
+
+Resulting formation fractions:
+                   ff
+cyan_JCZ38  5.890e-01
+cyan_J9Z38  2.318e-01
+cyan_sink   1.793e-01
+JCZ38_JSE76 1.000e+00
+JCZ38_sink  5.861e-12
+
+Estimated disappearance times:
+        DT50   DT90 DT50back DT50_k1 DT50_k2
+cyan   29.02 158.51    47.72   15.21   55.77
+JCZ38  22.27  73.98       NA      NA      NA
+J9Z38 113.09 375.69       NA      NA      NA
+JSE76 187.01 621.23       NA      NA      NA
+
 </pre>
+<p></code></p>
 </div>
-<div class="section level4">
-<h4 id="pathway-2">Pathway 2<a class="anchor" aria-label="anchor" href="#pathway-2"></a>
-</h4>
+<div id="pathway-2" class="section level3">
+<h3>Pathway 2</h3>
 <caption>
 Hierarchical FOMC path 2 fit with two-component error
 </caption>
 <pre><code>
-saemix version used for fitting:      3.2 
-mkin version used for pre-fitting:  1.2.6 
-R version used for fitting:         4.3.1 
-Date of fit:     Mon Oct 30 10:32:26 2023 
-Date of summary: Mon Oct 30 11:18:27 2023 
+saemix version used for fitting:      3.3 
+mkin version used for pre-fitting:  1.2.9 
+R version used for fitting:         4.4.2 
+Date of fit:     Thu Feb 13 18:46:08 2025 
+Date of summary: Thu Feb 13 19:05:53 2025 
 
 Equations:
 d_cyan/dt = - (alpha/beta) * 1/((time/beta) + 1) * cyan
@@ -3407,7 +3858,7 @@ Data:
 
 Model predictions using solution type deSolve 
 
-Fitted in 1185.728 s
+Fitted in 536.687 s
 Using 300, 100 iterations and 10 chains
 
 Variance model: Two-component variance function 
@@ -3456,28 +3907,28 @@ Likelihood computed by importance sampling
   2249 2241  -1104
 
 Optimised parameters:
-                       est.      lower      upper
-cyan_0            101.55265  9.920e+01  103.90593
-log_k_JCZ38        -2.32302 -2.832e+00   -1.81416
-log_k_J9Z38        -5.13082 -5.942e+00   -4.31990
-log_k_JSE76        -3.01756 -4.262e+00   -1.77360
-f_cyan_ilr_1        0.70850  3.657e-01    1.05135
-f_cyan_ilr_2        0.95775  2.612e-01    1.65432
-f_JCZ38_qlogis      3.86105  9.248e-01    6.79733
-f_JSE76_qlogis      7.51583 -1.120e+02  127.03921
-log_alpha          -0.15308 -4.508e-01    0.14462
-log_beta            2.99165  2.711e+00    3.27202
-a.1                 2.04034  1.811e+00    2.26968
-b.1                 0.06924  5.745e-02    0.08104
-SD.log_k_JCZ38      0.50818  1.390e-01    0.87736
-SD.log_k_J9Z38      0.86597  2.652e-01    1.46671
-SD.log_k_JSE76      1.38092  4.864e-01    2.27541
-SD.f_cyan_ilr_1     0.38204  1.354e-01    0.62864
-SD.f_cyan_ilr_2     0.55129  7.198e-02    1.03060
-SD.f_JCZ38_qlogis   1.88457  1.710e-02    3.75205
-SD.f_JSE76_qlogis   2.64018 -2.450e+03 2455.27887
-SD.log_alpha        0.31860  1.047e-01    0.53249
-SD.log_beta         0.24195  1.273e-02    0.47117
+                       est.      lower     upper
+cyan_0            101.55265  9.920e+01  103.9059
+log_k_JCZ38        -2.32302 -2.832e+00   -1.8142
+log_k_J9Z38        -5.13082 -5.942e+00   -4.3199
+log_k_JSE76        -3.01756 -4.262e+00   -1.7736
+f_cyan_ilr_1        0.70850  3.657e-01    1.0513
+f_cyan_ilr_2        0.95775  2.612e-01    1.6543
+f_JCZ38_qlogis      3.86105  9.248e-01    6.7973
+f_JSE76_qlogis      7.51583 -1.120e+02  127.0392
+log_alpha          -0.15308 -4.508e-01    0.1446
+log_beta            2.99165  2.711e+00    3.2720
+a.1                 2.04034  1.843e+00    2.2382
+b.1                 0.06924  5.749e-02    0.0810
+SD.log_k_JCZ38      0.50818  1.390e-01    0.8774
+SD.log_k_J9Z38      0.86597  2.652e-01    1.4667
+SD.log_k_JSE76      1.38092  4.864e-01    2.2754
+SD.f_cyan_ilr_1     0.38204  1.354e-01    0.6286
+SD.f_cyan_ilr_2     0.55129  7.198e-02    1.0306
+SD.f_JCZ38_qlogis   1.88457  1.711e-02    3.7520
+SD.f_JSE76_qlogis   2.64018 -2.450e+03 2454.9447
+SD.log_alpha        0.31860  1.047e-01    0.5325
+SD.log_beta         0.24195  1.273e-02    0.4712
 
 Correlation: 
                cyan_0  l__JCZ3 l__J9Z3 l__JSE7 f_cy__1 f_cy__2 f_JCZ38 f_JSE76
@@ -3508,15 +3959,15 @@ SD.log_k_J9Z38    0.8660  2.652e-01    1.4667
 SD.log_k_JSE76    1.3809  4.864e-01    2.2754
 SD.f_cyan_ilr_1   0.3820  1.354e-01    0.6286
 SD.f_cyan_ilr_2   0.5513  7.198e-02    1.0306
-SD.f_JCZ38_qlogis 1.8846  1.710e-02    3.7520
-SD.f_JSE76_qlogis 2.6402 -2.450e+03 2455.2789
+SD.f_JCZ38_qlogis 1.8846  1.711e-02    3.7520
+SD.f_JSE76_qlogis 2.6402 -2.450e+03 2454.9447
 SD.log_alpha      0.3186  1.047e-01    0.5325
 SD.log_beta       0.2420  1.273e-02    0.4712
 
 Variance model:
-       est.   lower   upper
-a.1 2.04034 1.81101 2.26968
-b.1 0.06924 0.05745 0.08104
+       est.   lower upper
+a.1 2.04034 1.84252 2.238
+b.1 0.06924 0.05749 0.081
 
 Backtransformed parameters:
                       est.     lower    upper
@@ -3548,17 +3999,17 @@ JCZ38   7.075  23.50       NA
 J9Z38 117.249 389.49       NA
 JSE76  14.169  47.07       NA
 
-</code></pre>
-<p></p>
+</pre>
+<p></code></p>
 <caption>
 Hierarchical DFOP path 2 fit with constant variance
 </caption>
 <pre><code>
-saemix version used for fitting:      3.2 
-mkin version used for pre-fitting:  1.2.6 
-R version used for fitting:         4.3.1 
-Date of fit:     Mon Oct 30 10:34:49 2023 
-Date of summary: Mon Oct 30 11:18:27 2023 
+saemix version used for fitting:      3.3 
+mkin version used for pre-fitting:  1.2.9 
+R version used for fitting:         4.4.2 
+Date of fit:     Thu Feb 13 18:47:06 2025 
+Date of summary: Thu Feb 13 19:05:53 2025 
 
 Equations:
 d_cyan/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
@@ -3578,7 +4029,7 @@ Data:
 
 Model predictions using solution type deSolve 
 
-Fitted in 1329.843 s
+Fitted in 594.209 s
 Using 300, 100 iterations and 10 chains
 
 Variance model: Constant variance 
@@ -3739,17 +4190,17 @@ JCZ38  12.50  41.53       NA      NA      NA
 J9Z38 118.69 394.27       NA      NA      NA
 JSE76  24.32  80.78       NA      NA      NA
 
-</code></pre>
-<p></p>
+</pre>
+<p></code></p>
 <caption>
 Hierarchical DFOP path 2 fit with two-component error
 </caption>
 <pre><code>
-saemix version used for fitting:      3.2 
-mkin version used for pre-fitting:  1.2.6 
-R version used for fitting:         4.3.1 
-Date of fit:     Mon Oct 30 10:41:05 2023 
-Date of summary: Mon Oct 30 11:18:27 2023 
+saemix version used for fitting:      3.3 
+mkin version used for pre-fitting:  1.2.9 
+R version used for fitting:         4.4.2 
+Date of fit:     Thu Feb 13 18:49:43 2025 
+Date of summary: Thu Feb 13 19:05:53 2025 
 
 Equations:
 d_cyan/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
@@ -3769,7 +4220,7 @@ Data:
 
 Model predictions using solution type deSolve 
 
-Fitted in 1705.043 s
+Fitted in 751.883 s
 Using 300, 100 iterations and 10 chains
 
 Variance model: Two-component variance function 
@@ -3846,15 +4297,15 @@ f_JSE76_qlogis      4.54884  -7.25628  16.35395
 log_k1             -2.38201  -2.51639  -2.24763
 log_k2             -4.66741  -4.91865  -4.41617
 g_qlogis           -0.28446  -1.14192   0.57300
-a.1                 2.05925   1.83267   2.28582
-b.1                 0.06172   0.05076   0.07268
+a.1                 2.05925   1.86481   2.25369
+b.1                 0.06172   0.05062   0.07282
 SD.log_k_JCZ38      0.81137   0.25296   1.36977
-SD.log_k_J9Z38      0.83542   0.25396   1.41689
+SD.log_k_J9Z38      0.83542   0.25395   1.41689
 SD.log_k_JSE76      0.97903   0.30100   1.65707
 SD.f_cyan_ilr_1     0.37878   0.13374   0.62382
 SD.f_cyan_ilr_2     0.67274   0.10102   1.24446
-SD.f_JCZ38_qlogis   1.35327  -0.42361   3.13015
-SD.f_JSE76_qlogis   1.43956 -19.15140  22.03052
+SD.f_JCZ38_qlogis   1.35327  -0.42359   3.13012
+SD.f_JSE76_qlogis   1.43956 -19.14972  22.02884
 SD.log_k2           0.25329   0.07521   0.43138
 SD.g_qlogis         0.95167   0.35149   1.55184
 
@@ -3885,19 +4336,19 @@ g_qlogis       -0.1656 -0.0928
 Random effects:
                     est.     lower   upper
 SD.log_k_JCZ38    0.8114   0.25296  1.3698
-SD.log_k_J9Z38    0.8354   0.25396  1.4169
+SD.log_k_J9Z38    0.8354   0.25395  1.4169
 SD.log_k_JSE76    0.9790   0.30100  1.6571
 SD.f_cyan_ilr_1   0.3788   0.13374  0.6238
 SD.f_cyan_ilr_2   0.6727   0.10102  1.2445
-SD.f_JCZ38_qlogis 1.3533  -0.42361  3.1301
-SD.f_JSE76_qlogis 1.4396 -19.15140 22.0305
+SD.f_JCZ38_qlogis 1.3533  -0.42359  3.1301
+SD.f_JSE76_qlogis 1.4396 -19.14972 22.0288
 SD.log_k2         0.2533   0.07521  0.4314
 SD.g_qlogis       0.9517   0.35149  1.5518
 
 Variance model:
        est.   lower   upper
-a.1 2.05925 1.83267 2.28582
-b.1 0.06172 0.05076 0.07268
+a.1 2.05925 1.86481 2.25369
+b.1 0.06172 0.05062 0.07282
 
 Backtransformed parameters:
                       est.     lower     upper
@@ -3930,17 +4381,17 @@ JCZ38   8.93  29.66       NA      NA      NA
 J9Z38 110.45 366.89       NA      NA      NA
 JSE76  17.96  59.66       NA      NA      NA
 
-</code></pre>
-<p></p>
+</pre>
+<p></code></p>
 <caption>
 Hierarchical SFORB path 2 fit with constant variance
 </caption>
 <pre><code>
-saemix version used for fitting:      3.2 
-mkin version used for pre-fitting:  1.2.6 
-R version used for fitting:         4.3.1 
-Date of fit:     Mon Oct 30 10:35:39 2023 
-Date of summary: Mon Oct 30 11:18:27 2023 
+saemix version used for fitting:      3.3 
+mkin version used for pre-fitting:  1.2.9 
+R version used for fitting:         4.4.2 
+Date of fit:     Thu Feb 13 18:46:57 2025 
+Date of summary: Thu Feb 13 19:05:53 2025 
 
 Equations:
 d_cyan_free/dt = - k_cyan_free * cyan_free - k_cyan_free_bound *
@@ -3958,7 +4409,7 @@ Data:
 
 Model predictions using solution type deSolve 
 
-Fitted in 1379.466 s
+Fitted in 585.771 s
 Using 300, 100 iterations and 10 chains
 
 Variance model: Constant variance 
@@ -4126,17 +4577,17 @@ JCZ38   7.203  23.93       NA           NA           NA
 J9Z38 131.918 438.22       NA           NA           NA
 JSE76  14.287  47.46       NA           NA           NA
 
-</code></pre>
-<p></p>
+</pre>
+<p></code></p>
 <caption>
 Hierarchical SFORB path 2 fit with two-component error
 </caption>
 <pre><code>
-saemix version used for fitting:      3.2 
-mkin version used for pre-fitting:  1.2.6 
-R version used for fitting:         4.3.1 
-Date of fit:     Mon Oct 30 10:41:39 2023 
-Date of summary: Mon Oct 30 11:18:27 2023 
+saemix version used for fitting:      3.3 
+mkin version used for pre-fitting:  1.2.9 
+R version used for fitting:         4.4.2 
+Date of fit:     Thu Feb 13 18:50:00 2025 
+Date of summary: Thu Feb 13 19:05:53 2025 
 
 Equations:
 d_cyan_free/dt = - k_cyan_free * cyan_free - k_cyan_free_bound *
@@ -4154,7 +4605,7 @@ Data:
 
 Model predictions using solution type deSolve 
 
-Fitted in 1739.402 s
+Fitted in 767.874 s
 Using 300, 100 iterations and 10 chains
 
 Variance model: Two-component variance function 
@@ -4233,17 +4684,17 @@ f_cyan_ilr_1               0.70640  3.562e-01  1.057e+00
 f_cyan_ilr_2               1.42704  3.170e-01  2.537e+00
 f_JCZ38_qlogis             2.84779  1.042e+00  4.654e+00
 f_JSE76_qlogis             8.63674 -6.407e+02  6.580e+02
-a.1                        2.07082  1.846e+00  2.296e+00
-b.1                        0.06227  5.120e-02  7.334e-02
+a.1                        2.07082  1.877e+00  2.265e+00
+b.1                        0.06227  5.098e-02  7.355e-02
 SD.log_k_cyan_free         0.49674  1.865e-01  8.069e-01
-SD.log_k_cyan_bound_free   0.28537  6.808e-02  5.027e-01
+SD.log_k_cyan_bound_free   0.28537  6.809e-02  5.027e-01
 SD.log_k_JCZ38             0.74846  2.305e-01  1.266e+00
 SD.log_k_J9Z38             0.86077  2.713e-01  1.450e+00
 SD.log_k_JSE76             0.97613  3.030e-01  1.649e+00
 SD.f_cyan_ilr_1            0.38994  1.382e-01  6.417e-01
 SD.f_cyan_ilr_2            0.82869  3.917e-02  1.618e+00
-SD.f_JCZ38_qlogis          1.05000 -2.809e-02  2.128e+00
-SD.f_JSE76_qlogis          0.44681 -3.986e+05  3.986e+05
+SD.f_JCZ38_qlogis          1.05000 -2.808e-02  2.128e+00
+SD.f_JSE76_qlogis          0.44681 -3.985e+05  3.985e+05
 
 Correlation: 
                       cyn_f_0 lg_k_c_ lg_k_cyn_f_ lg_k_cyn_b_ l__JCZ3 l__J9Z3
@@ -4272,19 +4723,19 @@ f_JSE76_qlogis         0.0064  0.0345  0.2015 -0.7058
 Random effects:
                            est.      lower     upper
 SD.log_k_cyan_free       0.4967  1.865e-01 8.069e-01
-SD.log_k_cyan_bound_free 0.2854  6.808e-02 5.027e-01
+SD.log_k_cyan_bound_free 0.2854  6.809e-02 5.027e-01
 SD.log_k_JCZ38           0.7485  2.305e-01 1.266e+00
 SD.log_k_J9Z38           0.8608  2.713e-01 1.450e+00
 SD.log_k_JSE76           0.9761  3.030e-01 1.649e+00
 SD.f_cyan_ilr_1          0.3899  1.382e-01 6.417e-01
 SD.f_cyan_ilr_2          0.8287  3.917e-02 1.618e+00
-SD.f_JCZ38_qlogis        1.0500 -2.809e-02 2.128e+00
-SD.f_JSE76_qlogis        0.4468 -3.986e+05 3.986e+05
+SD.f_JCZ38_qlogis        1.0500 -2.808e-02 2.128e+00
+SD.f_JSE76_qlogis        0.4468 -3.985e+05 3.985e+05
 
 Variance model:
-       est.  lower   upper
-a.1 2.07082 1.8458 2.29588
-b.1 0.06227 0.0512 0.07334
+       est.   lower   upper
+a.1 2.07082 1.87680 2.26483
+b.1 0.06227 0.05098 0.07355
 
 Backtransformed parameters:
                           est.      lower     upper
@@ -4322,22 +4773,21 @@ JCZ38   8.535  28.35       NA           NA           NA
 J9Z38 105.517 350.52       NA           NA           NA
 JSE76  17.837  59.25       NA           NA           NA
 
-</code></pre>
-<p></p>
+</pre>
+<p></code></p>
 </div>
-<div class="section level4">
-<h4 id="pathway-2-refined-fits">Pathway 2, refined fits<a class="anchor" aria-label="anchor" href="#pathway-2-refined-fits"></a>
-</h4>
+<div id="pathway-2-refined-fits" class="section level3">
+<h3>Pathway 2, refined fits</h3>
 <caption>
 Hierarchical FOMC path 2 fit with reduced random effects, two-component
 error
 </caption>
 <pre><code>
-saemix version used for fitting:      3.2 
-mkin version used for pre-fitting:  1.2.6 
-R version used for fitting:         4.3.1 
-Date of fit:     Mon Oct 30 11:12:56 2023 
-Date of summary: Mon Oct 30 11:18:27 2023 
+saemix version used for fitting:      3.3 
+mkin version used for pre-fitting:  1.2.9 
+R version used for fitting:         4.4.2 
+Date of fit:     Thu Feb 13 19:03:52 2025 
+Date of summary: Thu Feb 13 19:05:53 2025 
 
 Equations:
 d_cyan/dt = - (alpha/beta) * 1/((time/beta) + 1) * cyan
@@ -4352,7 +4802,7 @@ Data:
 
 Model predictions using solution type deSolve 
 
-Fitted in 1872.856 s
+Fitted in 830.375 s
 Using 300, 100 iterations and 10 chains
 
 Variance model: Two-component variance function 
@@ -4401,26 +4851,26 @@ Likelihood computed by importance sampling
   2249 2242  -1106
 
 Optimised parameters:
-                     est.   lower   upper
-cyan_0          101.24524      NA      NA
-log_k_JCZ38      -2.85375      NA      NA
-log_k_J9Z38      -5.07729      NA      NA
-log_k_JSE76      -3.53511      NA      NA
-f_cyan_ilr_1      0.67478      NA      NA
-f_cyan_ilr_2      0.97152      NA      NA
-f_JCZ38_qlogis  213.48001      NA      NA
-f_JSE76_qlogis    2.02040      NA      NA
-log_alpha        -0.11041      NA      NA
-log_beta          3.06575      NA      NA
-a.1               2.05279 1.82393 2.28166
-b.1               0.07116 0.05910 0.08322
-SD.log_k_JCZ38    1.21713 0.44160 1.99266
-SD.log_k_J9Z38    0.88268 0.27541 1.48995
-SD.log_k_JSE76    0.59452 0.15005 1.03898
-SD.f_cyan_ilr_1   0.35370 0.12409 0.58331
-SD.f_cyan_ilr_2   0.78186 0.18547 1.37824
-SD.log_alpha      0.27781 0.08168 0.47394
-SD.log_beta       0.32608 0.06490 0.58726
+                     est.   lower  upper
+cyan_0          101.24524      NA     NA
+log_k_JCZ38      -2.85375      NA     NA
+log_k_J9Z38      -5.07729      NA     NA
+log_k_JSE76      -3.53511      NA     NA
+f_cyan_ilr_1      0.67478      NA     NA
+f_cyan_ilr_2      0.97152      NA     NA
+f_JCZ38_qlogis  213.48001      NA     NA
+f_JSE76_qlogis    2.02040      NA     NA
+log_alpha        -0.11041      NA     NA
+log_beta          3.06575      NA     NA
+a.1               2.05279 1.85495 2.2506
+b.1               0.07116 0.05912 0.0832
+SD.log_k_JCZ38    1.21713 0.44160 1.9927
+SD.log_k_J9Z38    0.88268 0.27541 1.4900
+SD.log_k_JSE76    0.59452 0.15005 1.0390
+SD.f_cyan_ilr_1   0.35370 0.12409 0.5833
+SD.f_cyan_ilr_2   0.78186 0.18547 1.3782
+SD.log_alpha      0.27781 0.08168 0.4739
+SD.log_beta       0.32608 0.06490 0.5873
 
 Correlation is not available
 
@@ -4435,9 +4885,9 @@ SD.log_alpha    0.2778 0.08168 0.4739
 SD.log_beta     0.3261 0.06490 0.5873
 
 Variance model:
-       est.  lower   upper
-a.1 2.05279 1.8239 2.28166
-b.1 0.07116 0.0591 0.08322
+       est.   lower  upper
+a.1 2.05279 1.85495 2.2506
+b.1 0.07116 0.05912 0.0832
 
 Backtransformed parameters:
                       est. lower upper
@@ -4469,18 +4919,18 @@ JCZ38  12.03  39.96       NA
 J9Z38 111.14 369.19       NA
 JSE76  23.77  78.98       NA
 
-</code></pre>
-<p></p>
+</pre>
+<p></code></p>
 <caption>
 Hierarchical DFOP path 2 fit with reduced random effects, constant
 variance
 </caption>
 <pre><code>
-saemix version used for fitting:      3.2 
-mkin version used for pre-fitting:  1.2.6 
-R version used for fitting:         4.3.1 
-Date of fit:     Mon Oct 30 11:17:06 2023 
-Date of summary: Mon Oct 30 11:18:27 2023 
+saemix version used for fitting:      3.3 
+mkin version used for pre-fitting:  1.2.9 
+R version used for fitting:         4.4.2 
+Date of fit:     Thu Feb 13 19:05:47 2025 
+Date of summary: Thu Feb 13 19:05:53 2025 
 
 Equations:
 d_cyan/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
@@ -4500,7 +4950,7 @@ Data:
 
 Model predictions using solution type deSolve 
 
-Fitted in 2122.961 s
+Fitted in 945.728 s
 Using 300, 100 iterations and 10 chains
 
 Variance model: Constant variance 
@@ -4577,12 +5027,12 @@ f_JSE76_qlogis     1.9658      NA     NA
 log_k1            -1.9503      NA     NA
 log_k2            -4.4745      NA     NA
 g_qlogis          -0.4967      NA     NA
-a.1                2.7461 2.59886 2.8932
+a.1                2.7461 2.59274 2.8994
 SD.log_k_JCZ38     1.3178 0.47602 2.1596
 SD.log_k_J9Z38     0.7022 0.15061 1.2538
-SD.log_k_JSE76     0.6566 0.15614 1.1570
+SD.log_k_JSE76     0.6566 0.15613 1.1570
 SD.f_cyan_ilr_1    0.3409 0.11666 0.5652
-SD.f_cyan_ilr_2    0.4385 0.09483 0.7821
+SD.f_cyan_ilr_2    0.4385 0.09482 0.7821
 SD.log_k1          0.7381 0.25599 1.2202
 SD.log_k2          0.5133 0.18152 0.8450
 SD.g_qlogis        0.9866 0.35681 1.6164
@@ -4593,16 +5043,16 @@ Random effects:
                   est.   lower  upper
 SD.log_k_JCZ38  1.3178 0.47602 2.1596
 SD.log_k_J9Z38  0.7022 0.15061 1.2538
-SD.log_k_JSE76  0.6566 0.15614 1.1570
+SD.log_k_JSE76  0.6566 0.15613 1.1570
 SD.f_cyan_ilr_1 0.3409 0.11666 0.5652
-SD.f_cyan_ilr_2 0.4385 0.09483 0.7821
+SD.f_cyan_ilr_2 0.4385 0.09482 0.7821
 SD.log_k1       0.7381 0.25599 1.2202
 SD.log_k2       0.5133 0.18152 0.8450
 SD.g_qlogis     0.9866 0.35681 1.6164
 
 Variance model:
      est. lower upper
-a.1 2.746 2.599 2.893
+a.1 2.746 2.593 2.899
 
 Backtransformed parameters:
                       est. lower upper
@@ -4635,18 +5085,18 @@ JCZ38  13.04  43.33       NA      NA      NA
 J9Z38 120.93 401.73       NA      NA      NA
 JSE76  26.39  87.68       NA      NA      NA
 
-</code></pre>
-<p></p>
+</pre>
+<p></code></p>
 <caption>
 Hierarchical DFOP path 2 fit with reduced random effects, two-component
 error
 </caption>
 <pre><code>
-saemix version used for fitting:      3.2 
-mkin version used for pre-fitting:  1.2.6 
-R version used for fitting:         4.3.1 
-Date of fit:     Mon Oct 30 11:17:59 2023 
-Date of summary: Mon Oct 30 11:18:27 2023 
+saemix version used for fitting:      3.3 
+mkin version used for pre-fitting:  1.2.9 
+R version used for fitting:         4.4.2 
+Date of fit:     Thu Feb 13 19:05:49 2025 
+Date of summary: Thu Feb 13 19:05:53 2025 
 
 Equations:
 d_cyan/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
@@ -4666,7 +5116,7 @@ Data:
 
 Model predictions using solution type deSolve 
 
-Fitted in 2175.807 s
+Fitted in 947.743 s
 Using 300, 100 iterations and 10 chains
 
 Variance model: Two-component variance function 
@@ -4743,13 +5193,13 @@ f_JSE76_qlogis  414.80884      NA      NA
 log_k1           -2.38601      NA      NA
 log_k2           -4.63632      NA      NA
 g_qlogis         -0.33920      NA      NA
-a.1               2.10837 1.88051 2.33623
-b.1               0.06223 0.05108 0.07338
+a.1               2.10837 1.91261 2.30413
+b.1               0.06223 0.05085 0.07361
 SD.log_k_JCZ38    1.30902 0.48128 2.13675
 SD.log_k_J9Z38    0.83882 0.25790 1.41974
 SD.log_k_JSE76    0.58104 0.14201 1.02008
 SD.f_cyan_ilr_1   0.35421 0.12398 0.58443
-SD.f_cyan_ilr_2   0.79373 0.12007 1.46740
+SD.f_cyan_ilr_2   0.79373 0.12007 1.46739
 SD.log_k2         0.27476 0.08557 0.46394
 SD.g_qlogis       0.96170 0.35463 1.56878
 
@@ -4767,8 +5217,8 @@ SD.g_qlogis     0.9617 0.35463 1.5688
 
 Variance model:
        est.   lower   upper
-a.1 2.10837 1.88051 2.33623
-b.1 0.06223 0.05108 0.07338
+a.1 2.10837 1.91261 2.30413
+b.1 0.06223 0.05085 0.07361
 
 Backtransformed parameters:
                       est. lower upper
@@ -4801,18 +5251,18 @@ JCZ38  11.49  38.18       NA      NA      NA
 J9Z38 107.55 357.28       NA      NA      NA
 JSE76  27.20  90.36       NA      NA      NA
 
-</code></pre>
-<p></p>
+</pre>
+<p></code></p>
 <caption>
 Hierarchical SFORB path 2 fit with reduced random effects, constant
 variance
 </caption>
 <pre><code>
-saemix version used for fitting:      3.2 
-mkin version used for pre-fitting:  1.2.6 
-R version used for fitting:         4.3.1 
-Date of fit:     Mon Oct 30 11:17:04 2023 
-Date of summary: Mon Oct 30 11:18:27 2023 
+saemix version used for fitting:      3.3 
+mkin version used for pre-fitting:  1.2.9 
+R version used for fitting:         4.4.2 
+Date of fit:     Thu Feb 13 19:05:38 2025 
+Date of summary: Thu Feb 13 19:05:53 2025 
 
 Equations:
 d_cyan_free/dt = - k_cyan_free * cyan_free - k_cyan_free_bound *
@@ -4830,7 +5280,7 @@ Data:
 
 Model predictions using solution type deSolve 
 
-Fitted in 2121.218 s
+Fitted in 936.368 s
 Using 300, 100 iterations and 10 chains
 
 Variance model: Constant variance 
@@ -4972,18 +5422,18 @@ JCZ38  12.92  42.93       NA           NA           NA
 J9Z38 114.71 381.07       NA           NA           NA
 JSE76  26.04  86.51       NA           NA           NA
 
-</code></pre>
-<p></p>
+</pre>
+<p></code></p>
 <caption>
 Hierarchical SFORB path 2 fit with reduced random effects, two-component
 error
 </caption>
 <pre><code>
-saemix version used for fitting:      3.2 
-mkin version used for pre-fitting:  1.2.6 
-R version used for fitting:         4.3.1 
-Date of fit:     Mon Oct 30 11:18:24 2023 
-Date of summary: Mon Oct 30 11:18:27 2023 
+saemix version used for fitting:      3.3 
+mkin version used for pre-fitting:  1.2.9 
+R version used for fitting:         4.4.2 
+Date of fit:     Thu Feb 13 19:05:52 2025 
+Date of summary: Thu Feb 13 19:05:53 2025 
 
 Equations:
 d_cyan_free/dt = - k_cyan_free * cyan_free - k_cyan_free_bound *
@@ -5001,7 +5451,7 @@ Data:
 
 Model predictions using solution type deSolve 
 
-Fitted in 2200.603 s
+Fitted in 950.661 s
 Using 300, 100 iterations and 10 chains
 
 Variance model: Two-component variance function 
@@ -5080,8 +5530,8 @@ f_cyan_ilr_1               0.72263      NA      NA
 f_cyan_ilr_2               1.45352      NA      NA
 f_JCZ38_qlogis             2.00778      NA      NA
 f_JSE76_qlogis           941.58570      NA      NA
-a.1                        2.11130 1.88299 2.33960
-b.1                        0.06299 0.05176 0.07421
+a.1                        2.11130 1.91479 2.30780
+b.1                        0.06299 0.05152 0.07445
 SD.log_k_cyan_free         0.50098 0.18805 0.81390
 SD.log_k_cyan_bound_free   0.31671 0.08467 0.54875
 SD.log_k_JCZ38             1.25865 0.45932 2.05798
@@ -5095,7 +5545,7 @@ Correlation is not available
 Random effects:
                            est.   lower  upper
 SD.log_k_cyan_free       0.5010 0.18805 0.8139
-SD.log_k_cyan_bound_free 0.3167 0.08467 0.5488
+SD.log_k_cyan_bound_free 0.3167 0.08467 0.5487
 SD.log_k_JCZ38           1.2587 0.45932 2.0580
 SD.log_k_J9Z38           0.8683 0.27222 1.4644
 SD.log_k_JSE76           0.5933 0.14711 1.0394
@@ -5104,8 +5554,8 @@ SD.f_cyan_ilr_2          0.8854 0.13797 1.6329
 
 Variance model:
        est.   lower   upper
-a.1 2.11130 1.88299 2.33960
-b.1 0.06299 0.05176 0.07421
+a.1 2.11130 1.91479 2.30780
+b.1 0.06299 0.05152 0.07445
 
 Backtransformed parameters:
                           est. lower upper
@@ -5143,30 +5593,29 @@ JCZ38  11.06  36.75       NA           NA           NA
 J9Z38 106.71 354.49       NA           NA           NA
 JSE76  25.44  84.51       NA           NA           NA
 
-</code></pre>
-<p></p>
+</pre>
+<p></code></p>
 </div>
 </div>
-<div class="section level3">
-<h3 id="session-info">Session info<a class="anchor" aria-label="anchor" href="#session-info"></a>
-</h3>
-<pre><code>R version 4.3.1 (2023-06-16)
-Platform: x86_64-pc-linux-gnu (64-bit)
-Running under: Ubuntu 22.04.3 LTS
+<div id="session-info" class="section level2">
+<h2>Session info</h2>
+<pre><code>R version 4.4.2 (2024-10-31)
+Platform: x86_64-pc-linux-gnu
+Running under: Debian GNU/Linux 12 (bookworm)
 
 Matrix products: default
-BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0 
-LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
+BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.11.0 
+LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.11.0
 
 locale:
- [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
- [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
- [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
- [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
+ [1] LC_CTYPE=de_DE.UTF-8       LC_NUMERIC=C              
+ [3] LC_TIME=de_DE.UTF-8        LC_COLLATE=de_DE.UTF-8    
+ [5] LC_MONETARY=de_DE.UTF-8    LC_MESSAGES=de_DE.UTF-8   
+ [7] LC_PAPER=de_DE.UTF-8       LC_NAME=C                 
  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
-[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
+[11] LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=C       
 
-time zone: Europe/Zurich
+time zone: Europe/Berlin
 tzcode source: system (glibc)
 
 attached base packages:
@@ -5174,63 +5623,75 @@ attached base packages:
 [8] base     
 
 other attached packages:
-[1] saemix_3.2 npde_3.3   knitr_1.44 mkin_1.2.6
+[1] saemix_3.3      npde_3.5        knitr_1.49      mkin_1.2.9     
+[5] rmarkdown_2.29  nvimcom_0.9-167
 
 loaded via a namespace (and not attached):
- [1] sass_0.4.7        utf8_1.2.3        generics_0.1.3    stringi_1.7.12   
- [5] lattice_0.21-9    digest_0.6.33     magrittr_2.0.3    evaluate_0.22    
- [9] grid_4.3.1        fastmap_1.1.1     cellranger_1.1.0  rprojroot_2.0.3  
-[13] jsonlite_1.8.7    processx_3.8.2    pkgbuild_1.4.2    deSolve_1.35     
-[17] mclust_6.0.0      ps_1.7.5          gridExtra_2.3     purrr_1.0.1      
-[21] fansi_1.0.4       scales_1.2.1      codetools_0.2-19  textshaping_0.3.6
-[25] jquerylib_0.1.4   cli_3.6.1         crayon_1.5.2      rlang_1.1.1      
-[29] munsell_0.5.0     cachem_1.0.8      yaml_2.3.7        inline_0.3.19    
-[33] tools_4.3.1       memoise_2.0.1     dplyr_1.1.2       colorspace_2.1-0 
-[37] ggplot2_3.4.2     vctrs_0.6.3       R6_2.5.1          zoo_1.8-12       
-[41] lifecycle_1.0.3   stringr_1.5.0     fs_1.6.3          MASS_7.3-60      
-[45] ragg_1.2.5        callr_3.7.3       pkgconfig_2.0.3   desc_1.4.2       
-[49] pkgdown_2.0.7     bslib_0.5.1       pillar_1.9.0      gtable_0.3.3     
-[53] glue_1.6.2        systemfonts_1.0.4 xfun_0.40         tibble_3.2.1     
-[57] lmtest_0.9-40     tidyselect_1.2.0  rstudioapi_0.15.0 htmltools_0.5.6.1
-[61] nlme_3.1-163      rmarkdown_2.23    compiler_4.3.1    prettyunits_1.2.0
-[65] readxl_1.4.2     </code></pre>
+ [1] sass_0.4.9        utf8_1.2.4        generics_0.1.3    lattice_0.22-6   
+ [5] digest_0.6.37     magrittr_2.0.3    evaluate_1.0.1    grid_4.4.2       
+ [9] fastmap_1.2.0     cellranger_1.1.0  jsonlite_1.8.9    processx_3.8.4   
+[13] pkgbuild_1.4.5    deSolve_1.40      mclust_6.1.1      ps_1.8.1         
+[17] gridExtra_2.3     fansi_1.0.6       scales_1.3.0      codetools_0.2-20 
+[21] jquerylib_0.1.4   cli_3.6.3         rlang_1.1.4       munsell_0.5.1    
+[25] cachem_1.1.0      yaml_2.3.10       inline_0.3.20     tools_4.4.2      
+[29] colorout_1.3-2    dplyr_1.1.4       colorspace_2.1-1  ggplot2_3.5.1    
+[33] vctrs_0.6.5       R6_2.5.1          zoo_1.8-12        lifecycle_1.0.4  
+[37] MASS_7.3-61       pkgconfig_2.0.3   callr_3.7.6       pillar_1.9.0     
+[41] bslib_0.8.0       gtable_0.3.6      glue_1.8.0        xfun_0.49        
+[45] tibble_3.2.1      lmtest_0.9-40     tidyselect_1.2.1  htmltools_0.5.8.1
+[49] nlme_3.1-166      compiler_4.4.2    readxl_1.4.3     </code></pre>
 </div>
-<div class="section level3">
-<h3 id="hardware-info">Hardware info<a class="anchor" aria-label="anchor" href="#hardware-info"></a>
-</h3>
-<pre><code>CPU model: Intel(R) Xeon(R) Gold 6134 CPU @ 3.20GHz</code></pre>
-<pre><code>MemTotal:       247605564 kB</code></pre>
+<div id="hardware-info" class="section level2">
+<h2>Hardware info</h2>
+<pre><code>CPU model: AMD Ryzen 9 7950X 16-Core Processor</code></pre>
+<pre><code>MemTotal:       64927788 kB</code></pre>
 </div>
 </div>
-  </div>
 
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
 
-        <nav id="toc" data-toggle="toc"><h2 data-toc-skip>Contents</h2>
-    </nav>
-</div>
+
 
 </div>
 
+<script>
 
+// add bootstrap table styles to pandoc tables
+function bootstrapStylePandocTables() {
+  $('tr.odd').parent('tbody').parent('table').addClass('table table-condensed');
+}
+$(document).ready(function () {
+  bootstrapStylePandocTables();
+});
 
-      <footer><div class="copyright">
-  <p></p>
-<p>Developed by Johannes Ranke.</p>
-</div>
 
-<div class="pkgdown">
-  <p></p>
-<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
+</script>
 
-      </footer>
-</div>
+<!-- tabsets -->
+
+<script>
+$(document).ready(function () {
+  window.buildTabsets("TOC");
+});
+
+$(document).ready(function () {
+  $('.tabset-dropdown > .nav-tabs > li').click(function () {
+    $(this).parent().toggleClass('nav-tabs-open');
+  });
+});
+</script>
 
-  
+<!-- code folding -->
 
 
-  
+<!-- dynamically load mathjax for compatibility with self-contained -->
+<script>
+  (function () {
+    var script = document.createElement("script");
+    script.type = "text/javascript";
+    script.src  = "https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML";
+    document.getElementsByTagName("head")[0].appendChild(script);
+  })();
+</script>
 
-  </body>
+</body>
 </html>
diff --git a/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-13-1.png b/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-13-1.png
index 50f17cf4..d2201974 100644
--- a/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-13-1.png
+++ b/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-13-1.png
diff --git a/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-14-1.png b/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-14-1.png
index 31e46086..7380ba4c 100644
--- a/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-14-1.png
+++ b/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-14-1.png
diff --git a/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-15-1.png b/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-15-1.png
index daefad97..4de15105 100644
--- a/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-15-1.png
+++ b/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-15-1.png
diff --git a/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-20-1.png b/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-20-1.png
index 063e8615..d57badf1 100644
--- a/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-20-1.png
+++ b/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-20-1.png
diff --git a/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-21-1.png b/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-21-1.png
index d666672d..eb629c4d 100644
--- a/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-21-1.png
+++ b/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-21-1.png
diff --git a/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-22-1.png b/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-22-1.png
index 55a517f0..a2abb2f7 100644
--- a/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-22-1.png
+++ b/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-22-1.png
diff --git a/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-7-1.png b/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-7-1.png
index 2693b983..28ec82ba 100644
--- a/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-7-1.png
+++ b/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-7-1.png
diff --git a/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-8-1.png b/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-8-1.png
index 55a517f0..a2abb2f7 100644
--- a/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-8-1.png
+++ b/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-8-1.png
diff --git a/docs/articles/prebuilt/2022_cyan_pathway_files/figure-latex/unnamed-chunk-11-1.pdf b/docs/articles/prebuilt/2022_cyan_pathway_files/figure-latex/unnamed-chunk-11-1.pdf
new file mode 100644
index 00000000..e38951ad
--- /dev/null
+++ b/docs/articles/prebuilt/2022_cyan_pathway_files/figure-latex/unnamed-chunk-11-1.pdf
diff --git a/docs/articles/prebuilt/2022_cyan_pathway_files/figure-latex/unnamed-chunk-12-1.pdf b/docs/articles/prebuilt/2022_cyan_pathway_files/figure-latex/unnamed-chunk-12-1.pdf
new file mode 100644
index 00000000..3d43286e
--- /dev/null
+++ b/docs/articles/prebuilt/2022_cyan_pathway_files/figure-latex/unnamed-chunk-12-1.pdf
diff --git a/docs/articles/prebuilt/2022_cyan_pathway_files/figure-latex/unnamed-chunk-13-1.pdf b/docs/articles/prebuilt/2022_cyan_pathway_files/figure-latex/unnamed-chunk-13-1.pdf
new file mode 100644
index 00000000..71c3a6a7
--- /dev/null
+++ b/docs/articles/prebuilt/2022_cyan_pathway_files/figure-latex/unnamed-chunk-13-1.pdf
diff --git a/docs/articles/prebuilt/2022_cyan_pathway_files/figure-latex/unnamed-chunk-14-1.pdf b/docs/articles/prebuilt/2022_cyan_pathway_files/figure-latex/unnamed-chunk-14-1.pdf
new file mode 100644
index 00000000..c7bc3667
--- /dev/null
+++ b/docs/articles/prebuilt/2022_cyan_pathway_files/figure-latex/unnamed-chunk-14-1.pdf
diff --git a/docs/articles/prebuilt/2022_cyan_pathway_files/figure-latex/unnamed-chunk-15-1.pdf b/docs/articles/prebuilt/2022_cyan_pathway_files/figure-latex/unnamed-chunk-15-1.pdf
new file mode 100644
index 00000000..8317cdd6
--- /dev/null
+++ b/docs/articles/prebuilt/2022_cyan_pathway_files/figure-latex/unnamed-chunk-15-1.pdf
diff --git a/docs/articles/prebuilt/2022_cyan_pathway_files/figure-latex/unnamed-chunk-17-1.pdf b/docs/articles/prebuilt/2022_cyan_pathway_files/figure-latex/unnamed-chunk-17-1.pdf
new file mode 100644
index 00000000..d270c72e
--- /dev/null
+++ b/docs/articles/prebuilt/2022_cyan_pathway_files/figure-latex/unnamed-chunk-17-1.pdf
diff --git a/docs/articles/prebuilt/2022_cyan_pathway_files/figure-latex/unnamed-chunk-18-1.pdf b/docs/articles/prebuilt/2022_cyan_pathway_files/figure-latex/unnamed-chunk-18-1.pdf
new file mode 100644
index 00000000..75e8718c
--- /dev/null
+++ b/docs/articles/prebuilt/2022_cyan_pathway_files/figure-latex/unnamed-chunk-18-1.pdf
diff --git a/docs/articles/prebuilt/2022_cyan_pathway_files/figure-latex/unnamed-chunk-19-1.pdf b/docs/articles/prebuilt/2022_cyan_pathway_files/figure-latex/unnamed-chunk-19-1.pdf
new file mode 100644
index 00000000..b6e1bf16
--- /dev/null
+++ b/docs/articles/prebuilt/2022_cyan_pathway_files/figure-latex/unnamed-chunk-19-1.pdf
diff --git a/docs/articles/prebuilt/2022_cyan_pathway_files/figure-latex/unnamed-chunk-20-1.pdf b/docs/articles/prebuilt/2022_cyan_pathway_files/figure-latex/unnamed-chunk-20-1.pdf
new file mode 100644
index 00000000..970cac53
--- /dev/null
+++ b/docs/articles/prebuilt/2022_cyan_pathway_files/figure-latex/unnamed-chunk-20-1.pdf
diff --git a/docs/articles/prebuilt/2022_cyan_pathway_files/figure-latex/unnamed-chunk-21-1.pdf b/docs/articles/prebuilt/2022_cyan_pathway_files/figure-latex/unnamed-chunk-21-1.pdf
new file mode 100644
index 00000000..9b9f59d0
--- /dev/null
+++ b/docs/articles/prebuilt/2022_cyan_pathway_files/figure-latex/unnamed-chunk-21-1.pdf
diff --git a/docs/articles/prebuilt/2022_cyan_pathway_files/figure-latex/unnamed-chunk-22-1.pdf b/docs/articles/prebuilt/2022_cyan_pathway_files/figure-latex/unnamed-chunk-22-1.pdf
new file mode 100644
index 00000000..df4c103f
--- /dev/null
+++ b/docs/articles/prebuilt/2022_cyan_pathway_files/figure-latex/unnamed-chunk-22-1.pdf
diff --git a/docs/articles/prebuilt/2022_cyan_pathway_files/figure-latex/unnamed-chunk-6-1.pdf b/docs/articles/prebuilt/2022_cyan_pathway_files/figure-latex/unnamed-chunk-6-1.pdf
new file mode 100644
index 00000000..b0c7f51a
--- /dev/null
+++ b/docs/articles/prebuilt/2022_cyan_pathway_files/figure-latex/unnamed-chunk-6-1.pdf
diff --git a/docs/articles/prebuilt/2022_cyan_pathway_files/figure-latex/unnamed-chunk-7-1.pdf b/docs/articles/prebuilt/2022_cyan_pathway_files/figure-latex/unnamed-chunk-7-1.pdf
new file mode 100644
index 00000000..01c707e3
--- /dev/null
+++ b/docs/articles/prebuilt/2022_cyan_pathway_files/figure-latex/unnamed-chunk-7-1.pdf
diff --git a/docs/articles/prebuilt/2022_cyan_pathway_files/figure-latex/unnamed-chunk-8-1.pdf b/docs/articles/prebuilt/2022_cyan_pathway_files/figure-latex/unnamed-chunk-8-1.pdf
new file mode 100644
index 00000000..e2f0820d
--- /dev/null
+++ b/docs/articles/prebuilt/2022_cyan_pathway_files/figure-latex/unnamed-chunk-8-1.pdf
diff --git a/docs/articles/prebuilt/2022_dmta_parent.html b/docs/articles/prebuilt/2022_dmta_parent.html
index 9fdf75f7..14f12c0e 100644
--- a/docs/articles/prebuilt/2022_dmta_parent.html
+++ b/docs/articles/prebuilt/2022_dmta_parent.html
@@ -1,155 +1,396 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en">
+
+<html>
+
 <head>
-<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
-<meta charset="utf-8">
-<meta http-equiv="X-UA-Compatible" content="IE=edge">
-<meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P • mkin</title>
-<!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous">
-<script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../../bootstrap-toc.css">
-<script src="../../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous">
-<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous">
-<!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../../pkgdown.css" rel="stylesheet">
-<script src="../../pkgdown.js"></script><meta property="og:title" content="Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P">
-<meta property="og:description" content="mkin">
-<!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]-->
+
+<meta charset="utf-8" />
+<meta name="generator" content="pandoc" />
+<meta http-equiv="X-UA-Compatible" content="IE=EDGE" />
+
+
+<meta name="author" content="Johannes Ranke" />
+
+
+<title>Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</title>
+
+<script>// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
+// be compatible with the behavior of Pandoc < 2.8).
+document.addEventListener('DOMContentLoaded', function(e) {
+  var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
+  var i, h, a;
+  for (i = 0; i < hs.length; i++) {
+    h = hs[i];
+    if (!/^h[1-6]$/i.test(h.tagName)) continue;  // it should be a header h1-h6
+    a = h.attributes;
+    while (a.length > 0) h.removeAttribute(a[0].name);
+  }
+});
+</script>
+<script>/*! jQuery v3.6.0 | (c) OpenJS Foundation and other contributors | jquery.org/license */
+!function(e,t){"use strict";"object"==typeof module&&"object"==typeof module.exports?module.exports=e.document?t(e,!0):function(e){if(!e.document)throw new Error("jQuery requires a window with a document");return t(e)}:t(e)}("undefined"!=typeof window?window:this,function(C,e){"use strict";var t=[],r=Object.getPrototypeOf,s=t.slice,g=t.flat?function(e){return t.flat.call(e)}:function(e){return t.concat.apply([],e)},u=t.push,i=t.indexOf,n={},o=n.toString,v=n.hasOwnProperty,a=v.toString,l=a.call(Object),y={},m=function(e){return"function"==typeof e&&"number"!=typeof e.nodeType&&"function"!=typeof e.item},x=function(e){return null!=e&&e===e.window},E=C.document,c={type:!0,src:!0,nonce:!0,noModule:!0};function b(e,t,n){var r,i,o=(n=n||E).createElement("script");if(o.text=e,t)for(r in c)(i=t[r]||t.getAttribute&&t.getAttribute(r))&&o.setAttribute(r,i);n.head.appendChild(o).parentNode.removeChild(o)}function w(e){return null==e?e+"":"object"==typeof e||"function"==typeof e?n[o.call(e)]||"object":typeof e}var f="3.6.0",S=function(e,t){return new S.fn.init(e,t)};function p(e){var t=!!e&&"length"in e&&e.length,n=w(e);return!m(e)&&!x(e)&&("array"===n||0===t||"number"==typeof t&&0<t&&t-1 in e)}S.fn=S.prototype={jquery:f,constructor:S,length:0,toArray:function(){return s.call(this)},get:function(e){return null==e?s.call(this):e<0?this[e+this.length]:this[e]},pushStack:function(e){var t=S.merge(this.constructor(),e);return t.prevObject=this,t},each:function(e){return S.each(this,e)},map:function(n){return this.pushStack(S.map(this,function(e,t){return n.call(e,t,e)}))},slice:function(){return this.pushStack(s.apply(this,arguments))},first:function(){return this.eq(0)},last:function(){return this.eq(-1)},even:function(){return this.pushStack(S.grep(this,function(e,t){return(t+1)%2}))},odd:function(){return this.pushStack(S.grep(this,function(e,t){return t%2}))},eq:function(e){var t=this.length,n=+e+(e<0?t:0);return this.pushStack(0<=n&&n<t?[this[n]]:[])},end:function(){return this.prevObject||this.constructor()},push:u,sort:t.sort,splice:t.splice},S.extend=S.fn.extend=function(){var e,t,n,r,i,o,a=arguments[0]||{},s=1,u=arguments.length,l=!1;for("boolean"==typeof a&&(l=a,a=arguments[s]||{},s++),"object"==typeof a||m(a)||(a={}),s===u&&(a=this,s--);s<u;s++)if(null!=(e=arguments[s]))for(t in e)r=e[t],"__proto__"!==t&&a!==r&&(l&&r&&(S.isPlainObject(r)||(i=Array.isArray(r)))?(n=a[t],o=i&&!Array.isArray(n)?[]:i||S.isPlainObject(n)?n:{},i=!1,a[t]=S.extend(l,o,r)):void 0!==r&&(a[t]=r));return a},S.extend({expando:"jQuery"+(f+Math.random()).replace(/\D/g,""),isReady:!0,error:function(e){throw new Error(e)},noop:function(){},isPlainObject:function(e){var t,n;return!(!e||"[object Object]"!==o.call(e))&&(!(t=r(e))||"function"==typeof(n=v.call(t,"constructor")&&t.constructor)&&a.call(n)===l)},isEmptyObject:function(e){var t;for(t in e)return!1;return!0},globalEval:function(e,t,n){b(e,{nonce:t&&t.nonce},n)},each:function(e,t){var n,r=0;if(p(e)){for(n=e.length;r<n;r++)if(!1===t.call(e[r],r,e[r]))break}else for(r in e)if(!1===t.call(e[r],r,e[r]))break;return e},makeArray:function(e,t){var n=t||[];return null!=e&&(p(Object(e))?S.merge(n,"string"==typeof e?[e]:e):u.call(n,e)),n},inArray:function(e,t,n){return null==t?-1:i.call(t,e,n)},merge:function(e,t){for(var n=+t.length,r=0,i=e.length;r<n;r++)e[i++]=t[r];return e.length=i,e},grep:function(e,t,n){for(var r=[],i=0,o=e.length,a=!n;i<o;i++)!t(e[i],i)!==a&&r.push(e[i]);return r},map:function(e,t,n){var r,i,o=0,a=[];if(p(e))for(r=e.length;o<r;o++)null!=(i=t(e[o],o,n))&&a.push(i);else for(o in e)null!=(i=t(e[o],o,n))&&a.push(i);return g(a)},guid:1,support:y}),"function"==typeof Symbol&&(S.fn[Symbol.iterator]=t[Symbol.iterator]),S.each("Boolean Number String Function Array Date RegExp Object Error Symbol".split(" "),function(e,t){n["[object "+t+"]"]=t.toLowerCase()});var d=function(n){var e,d,b,o,i,h,f,g,w,u,l,T,C,a,E,v,s,c,y,S="sizzle"+1*new Date,p=n.document,k=0,r=0,m=ue(),x=ue(),A=ue(),N=ue(),j=function(e,t){return e===t&&(l=!0),0},D={}.hasOwnProperty,t=[],q=t.pop,L=t.push,H=t.push,O=t.slice,P=function(e,t){for(var n=0,r=e.length;n<r;n++)if(e[n]===t)return n;return-1},R="checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|ismap|loop|multiple|open|readonly|required|scoped",M="[\\x20\\t\\r\\n\\f]",I="(?:\\\\[\\da-fA-F]{1,6}"+M+"?|\\\\[^\\r\\n\\f]|[\\w-]|[^\0-\\x7f])+",W="\\["+M+"*("+I+")(?:"+M+"*([*^$|!~]?=)"+M+"*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|("+I+"))|)"+M+"*\\]",F=":("+I+")(?:\\((('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|((?:\\\\.|[^\\\\()[\\]]|"+W+")*)|.*)\\)|)",B=new RegExp(M+"+","g"),$=new RegExp("^"+M+"+|((?:^|[^\\\\])(?:\\\\.)*)"+M+"+$","g"),_=new RegExp("^"+M+"*,"+M+"*"),z=new RegExp("^"+M+"*([>+~]|"+M+")"+M+"*"),U=new RegExp(M+"|>"),X=new RegExp(F),V=new RegExp("^"+I+"$"),G={ID:new RegExp("^#("+I+")"),CLASS:new RegExp("^\\.("+I+")"),TAG:new RegExp("^("+I+"|[*])"),ATTR:new RegExp("^"+W),PSEUDO:new RegExp("^"+F),CHILD:new RegExp("^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\("+M+"*(even|odd|(([+-]|)(\\d*)n|)"+M+"*(?:([+-]|)"+M+"*(\\d+)|))"+M+"*\\)|)","i"),bool:new RegExp("^(?:"+R+")$","i"),needsContext:new RegExp("^"+M+"*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\("+M+"*((?:-\\d)?\\d*)"+M+"*\\)|)(?=[^-]|$)","i")},Y=/HTML$/i,Q=/^(?:input|select|textarea|button)$/i,J=/^h\d$/i,K=/^[^{]+\{\s*\[native \w/,Z=/^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/,ee=/[+~]/,te=new RegExp("\\\\[\\da-fA-F]{1,6}"+M+"?|\\\\([^\\r\\n\\f])","g"),ne=function(e,t){var n="0x"+e.slice(1)-65536;return t||(n<0?String.fromCharCode(n+65536):String.fromCharCode(n>>10|55296,1023&n|56320))},re=/([\0-\x1f\x7f]|^-?\d)|^-$|[^\0-\x1f\x7f-\uFFFF\w-]/g,ie=function(e,t){return t?"\0"===e?"\ufffd":e.slice(0,-1)+"\\"+e.charCodeAt(e.length-1).toString(16)+" ":"\\"+e},oe=function(){T()},ae=be(function(e){return!0===e.disabled&&"fieldset"===e.nodeName.toLowerCase()},{dir:"parentNode",next:"legend"});try{H.apply(t=O.call(p.childNodes),p.childNodes),t[p.childNodes.length].nodeType}catch(e){H={apply:t.length?function(e,t){L.apply(e,O.call(t))}:function(e,t){var n=e.length,r=0;while(e[n++]=t[r++]);e.length=n-1}}}function se(t,e,n,r){var i,o,a,s,u,l,c,f=e&&e.ownerDocument,p=e?e.nodeType:9;if(n=n||[],"string"!=typeof t||!t||1!==p&&9!==p&&11!==p)return n;if(!r&&(T(e),e=e||C,E)){if(11!==p&&(u=Z.exec(t)))if(i=u[1]){if(9===p){if(!(a=e.getElementById(i)))return n;if(a.id===i)return n.push(a),n}else if(f&&(a=f.getElementById(i))&&y(e,a)&&a.id===i)return n.push(a),n}else{if(u[2])return H.apply(n,e.getElementsByTagName(t)),n;if((i=u[3])&&d.getElementsByClassName&&e.getElementsByClassName)return H.apply(n,e.getElementsByClassName(i)),n}if(d.qsa&&!N[t+" "]&&(!v||!v.test(t))&&(1!==p||"object"!==e.nodeName.toLowerCase())){if(c=t,f=e,1===p&&(U.test(t)||z.test(t))){(f=ee.test(t)&&ye(e.parentNode)||e)===e&&d.scope||((s=e.getAttribute("id"))?s=s.replace(re,ie):e.setAttribute("id",s=S)),o=(l=h(t)).length;while(o--)l[o]=(s?"#"+s:":scope")+" "+xe(l[o]);c=l.join(",")}try{return H.apply(n,f.querySelectorAll(c)),n}catch(e){N(t,!0)}finally{s===S&&e.removeAttribute("id")}}}return g(t.replace($,"$1"),e,n,r)}function ue(){var r=[];return function e(t,n){return r.push(t+" ")>b.cacheLength&&delete e[r.shift()],e[t+" "]=n}}function le(e){return e[S]=!0,e}function ce(e){var t=C.createElement("fieldset");try{return!!e(t)}catch(e){return!1}finally{t.parentNode&&t.parentNode.removeChild(t),t=null}}function fe(e,t){var n=e.split("|"),r=n.length;while(r--)b.attrHandle[n[r]]=t}function pe(e,t){var n=t&&e,r=n&&1===e.nodeType&&1===t.nodeType&&e.sourceIndex-t.sourceIndex;if(r)return r;if(n)while(n=n.nextSibling)if(n===t)return-1;return e?1:-1}function de(t){return function(e){return"input"===e.nodeName.toLowerCase()&&e.type===t}}function he(n){return function(e){var t=e.nodeName.toLowerCase();return("input"===t||"button"===t)&&e.type===n}}function ge(t){return function(e){return"form"in e?e.parentNode&&!1===e.disabled?"label"in e?"label"in e.parentNode?e.parentNode.disabled===t:e.disabled===t:e.isDisabled===t||e.isDisabled!==!t&&ae(e)===t:e.disabled===t:"label"in e&&e.disabled===t}}function ve(a){return le(function(o){return o=+o,le(function(e,t){var n,r=a([],e.length,o),i=r.length;while(i--)e[n=r[i]]&&(e[n]=!(t[n]=e[n]))})})}function ye(e){return e&&"undefined"!=typeof e.getElementsByTagName&&e}for(e in d=se.support={},i=se.isXML=function(e){var t=e&&e.namespaceURI,n=e&&(e.ownerDocument||e).documentElement;return!Y.test(t||n&&n.nodeName||"HTML")},T=se.setDocument=function(e){var t,n,r=e?e.ownerDocument||e:p;return r!=C&&9===r.nodeType&&r.documentElement&&(a=(C=r).documentElement,E=!i(C),p!=C&&(n=C.defaultView)&&n.top!==n&&(n.addEventListener?n.addEventListener("unload",oe,!1):n.attachEvent&&n.attachEvent("onunload",oe)),d.scope=ce(function(e){return a.appendChild(e).appendChild(C.createElement("div")),"undefined"!=typeof e.querySelectorAll&&!e.querySelectorAll(":scope fieldset div").length}),d.attributes=ce(function(e){return e.className="i",!e.getAttribute("className")}),d.getElementsByTagName=ce(function(e){return e.appendChild(C.createComment("")),!e.getElementsByTagName("*").length}),d.getElementsByClassName=K.test(C.getElementsByClassName),d.getById=ce(function(e){return a.appendChild(e).id=S,!C.getElementsByName||!C.getElementsByName(S).length}),d.getById?(b.filter.ID=function(e){var t=e.replace(te,ne);return function(e){return e.getAttribute("id")===t}},b.find.ID=function(e,t){if("undefined"!=typeof t.getElementById&&E){var n=t.getElementById(e);return n?[n]:[]}}):(b.filter.ID=function(e){var n=e.replace(te,ne);return function(e){var t="undefined"!=typeof e.getAttributeNode&&e.getAttributeNode("id");return t&&t.value===n}},b.find.ID=function(e,t){if("undefined"!=typeof t.getElementById&&E){var n,r,i,o=t.getElementById(e);if(o){if((n=o.getAttributeNode("id"))&&n.value===e)return[o];i=t.getElementsByName(e),r=0;while(o=i[r++])if((n=o.getAttributeNode("id"))&&n.value===e)return[o]}return[]}}),b.find.TAG=d.getElementsByTagName?function(e,t){return"undefined"!=typeof t.getElementsByTagName?t.getElementsByTagName(e):d.qsa?t.querySelectorAll(e):void 0}:function(e,t){var n,r=[],i=0,o=t.getElementsByTagName(e);if("*"===e){while(n=o[i++])1===n.nodeType&&r.push(n);return r}return o},b.find.CLASS=d.getElementsByClassName&&function(e,t){if("undefined"!=typeof t.getElementsByClassName&&E)return t.getElementsByClassName(e)},s=[],v=[],(d.qsa=K.test(C.querySelectorAll))&&(ce(function(e){var t;a.appendChild(e).innerHTML="<a id='"+S+"'></a><select id='"+S+"-\r\\' msallowcapture=''><option selected=''></option></select>",e.querySelectorAll("[msallowcapture^='']").length&&v.push("[*^$]="+M+"*(?:''|\"\")"),e.querySelectorAll("[selected]").length||v.push("\\["+M+"*(?:value|"+R+")"),e.querySelectorAll("[id~="+S+"-]").length||v.push("~="),(t=C.createElement("input")).setAttribute("name",""),e.appendChild(t),e.querySelectorAll("[name='']").length||v.push("\\["+M+"*name"+M+"*="+M+"*(?:''|\"\")"),e.querySelectorAll(":checked").length||v.push(":checked"),e.querySelectorAll("a#"+S+"+*").length||v.push(".#.+[+~]"),e.querySelectorAll("\\\f"),v.push("[\\r\\n\\f]")}),ce(function(e){e.innerHTML="<a href='' disabled='disabled'></a><select disabled='disabled'><option/></select>";var t=C.createElement("input");t.setAttribute("type","hidden"),e.appendChild(t).setAttribute("name","D"),e.querySelectorAll("[name=d]").length&&v.push("name"+M+"*[*^$|!~]?="),2!==e.querySelectorAll(":enabled").length&&v.push(":enabled",":disabled"),a.appendChild(e).disabled=!0,2!==e.querySelectorAll(":disabled").length&&v.push(":enabled",":disabled"),e.querySelectorAll("*,:x"),v.push(",.*:")})),(d.matchesSelector=K.test(c=a.matches||a.webkitMatchesSelector||a.mozMatchesSelector||a.oMatchesSelector||a.msMatchesSelector))&&ce(function(e){d.disconnectedMatch=c.call(e,"*"),c.call(e,"[s!='']:x"),s.push("!=",F)}),v=v.length&&new RegExp(v.join("|")),s=s.length&&new RegExp(s.join("|")),t=K.test(a.compareDocumentPosition),y=t||K.test(a.contains)?function(e,t){var n=9===e.nodeType?e.documentElement:e,r=t&&t.parentNode;return e===r||!(!r||1!==r.nodeType||!(n.contains?n.contains(r):e.compareDocumentPosition&&16&e.compareDocumentPosition(r)))}:function(e,t){if(t)while(t=t.parentNode)if(t===e)return!0;return!1},j=t?function(e,t){if(e===t)return l=!0,0;var n=!e.compareDocumentPosition-!t.compareDocumentPosition;return n||(1&(n=(e.ownerDocument||e)==(t.ownerDocument||t)?e.compareDocumentPosition(t):1)||!d.sortDetached&&t.compareDocumentPosition(e)===n?e==C||e.ownerDocument==p&&y(p,e)?-1:t==C||t.ownerDocument==p&&y(p,t)?1:u?P(u,e)-P(u,t):0:4&n?-1:1)}:function(e,t){if(e===t)return l=!0,0;var n,r=0,i=e.parentNode,o=t.parentNode,a=[e],s=[t];if(!i||!o)return e==C?-1:t==C?1:i?-1:o?1:u?P(u,e)-P(u,t):0;if(i===o)return pe(e,t);n=e;while(n=n.parentNode)a.unshift(n);n=t;while(n=n.parentNode)s.unshift(n);while(a[r]===s[r])r++;return r?pe(a[r],s[r]):a[r]==p?-1:s[r]==p?1:0}),C},se.matches=function(e,t){return se(e,null,null,t)},se.matchesSelector=function(e,t){if(T(e),d.matchesSelector&&E&&!N[t+" "]&&(!s||!s.test(t))&&(!v||!v.test(t)))try{var n=c.call(e,t);if(n||d.disconnectedMatch||e.document&&11!==e.document.nodeType)return n}catch(e){N(t,!0)}return 0<se(t,C,null,[e]).length},se.contains=function(e,t){return(e.ownerDocument||e)!=C&&T(e),y(e,t)},se.attr=function(e,t){(e.ownerDocument||e)!=C&&T(e);var n=b.attrHandle[t.toLowerCase()],r=n&&D.call(b.attrHandle,t.toLowerCase())?n(e,t,!E):void 0;return void 0!==r?r:d.attributes||!E?e.getAttribute(t):(r=e.getAttributeNode(t))&&r.specified?r.value:null},se.escape=function(e){return(e+"").replace(re,ie)},se.error=function(e){throw new Error("Syntax error, unrecognized expression: "+e)},se.uniqueSort=function(e){var t,n=[],r=0,i=0;if(l=!d.detectDuplicates,u=!d.sortStable&&e.slice(0),e.sort(j),l){while(t=e[i++])t===e[i]&&(r=n.push(i));while(r--)e.splice(n[r],1)}return u=null,e},o=se.getText=function(e){var t,n="",r=0,i=e.nodeType;if(i){if(1===i||9===i||11===i){if("string"==typeof e.textContent)return e.textContent;for(e=e.firstChild;e;e=e.nextSibling)n+=o(e)}else if(3===i||4===i)return e.nodeValue}else while(t=e[r++])n+=o(t);return n},(b=se.selectors={cacheLength:50,createPseudo:le,match:G,attrHandle:{},find:{},relative:{">":{dir:"parentNode",first:!0}," ":{dir:"parentNode"},"+":{dir:"previousSibling",first:!0},"~":{dir:"previousSibling"}},preFilter:{ATTR:function(e){return e[1]=e[1].replace(te,ne),e[3]=(e[3]||e[4]||e[5]||"").replace(te,ne),"~="===e[2]&&(e[3]=" "+e[3]+" "),e.slice(0,4)},CHILD:function(e){return e[1]=e[1].toLowerCase(),"nth"===e[1].slice(0,3)?(e[3]||se.error(e[0]),e[4]=+(e[4]?e[5]+(e[6]||1):2*("even"===e[3]||"odd"===e[3])),e[5]=+(e[7]+e[8]||"odd"===e[3])):e[3]&&se.error(e[0]),e},PSEUDO:function(e){var t,n=!e[6]&&e[2];return G.CHILD.test(e[0])?null:(e[3]?e[2]=e[4]||e[5]||"":n&&X.test(n)&&(t=h(n,!0))&&(t=n.indexOf(")",n.length-t)-n.length)&&(e[0]=e[0].slice(0,t),e[2]=n.slice(0,t)),e.slice(0,3))}},filter:{TAG:function(e){var t=e.replace(te,ne).toLowerCase();return"*"===e?function(){return!0}:function(e){return e.nodeName&&e.nodeName.toLowerCase()===t}},CLASS:function(e){var t=m[e+" "];return t||(t=new RegExp("(^|"+M+")"+e+"("+M+"|$)"))&&m(e,function(e){return t.test("string"==typeof e.className&&e.className||"undefined"!=typeof e.getAttribute&&e.getAttribute("class")||"")})},ATTR:function(n,r,i){return function(e){var t=se.attr(e,n);return null==t?"!="===r:!r||(t+="","="===r?t===i:"!="===r?t!==i:"^="===r?i&&0===t.indexOf(i):"*="===r?i&&-1<t.indexOf(i):"$="===r?i&&t.slice(-i.length)===i:"~="===r?-1<(" "+t.replace(B," ")+" ").indexOf(i):"|="===r&&(t===i||t.slice(0,i.length+1)===i+"-"))}},CHILD:function(h,e,t,g,v){var y="nth"!==h.slice(0,3),m="last"!==h.slice(-4),x="of-type"===e;return 1===g&&0===v?function(e){return!!e.parentNode}:function(e,t,n){var r,i,o,a,s,u,l=y!==m?"nextSibling":"previousSibling",c=e.parentNode,f=x&&e.nodeName.toLowerCase(),p=!n&&!x,d=!1;if(c){if(y){while(l){a=e;while(a=a[l])if(x?a.nodeName.toLowerCase()===f:1===a.nodeType)return!1;u=l="only"===h&&!u&&"nextSibling"}return!0}if(u=[m?c.firstChild:c.lastChild],m&&p){d=(s=(r=(i=(o=(a=c)[S]||(a[S]={}))[a.uniqueID]||(o[a.uniqueID]={}))[h]||[])[0]===k&&r[1])&&r[2],a=s&&c.childNodes[s];while(a=++s&&a&&a[l]||(d=s=0)||u.pop())if(1===a.nodeType&&++d&&a===e){i[h]=[k,s,d];break}}else if(p&&(d=s=(r=(i=(o=(a=e)[S]||(a[S]={}))[a.uniqueID]||(o[a.uniqueID]={}))[h]||[])[0]===k&&r[1]),!1===d)while(a=++s&&a&&a[l]||(d=s=0)||u.pop())if((x?a.nodeName.toLowerCase()===f:1===a.nodeType)&&++d&&(p&&((i=(o=a[S]||(a[S]={}))[a.uniqueID]||(o[a.uniqueID]={}))[h]=[k,d]),a===e))break;return(d-=v)===g||d%g==0&&0<=d/g}}},PSEUDO:function(e,o){var t,a=b.pseudos[e]||b.setFilters[e.toLowerCase()]||se.error("unsupported pseudo: "+e);return a[S]?a(o):1<a.length?(t=[e,e,"",o],b.setFilters.hasOwnProperty(e.toLowerCase())?le(function(e,t){var n,r=a(e,o),i=r.length;while(i--)e[n=P(e,r[i])]=!(t[n]=r[i])}):function(e){return a(e,0,t)}):a}},pseudos:{not:le(function(e){var r=[],i=[],s=f(e.replace($,"$1"));return s[S]?le(function(e,t,n,r){var i,o=s(e,null,r,[]),a=e.length;while(a--)(i=o[a])&&(e[a]=!(t[a]=i))}):function(e,t,n){return r[0]=e,s(r,null,n,i),r[0]=null,!i.pop()}}),has:le(function(t){return function(e){return 0<se(t,e).length}}),contains:le(function(t){return t=t.replace(te,ne),function(e){return-1<(e.textContent||o(e)).indexOf(t)}}),lang:le(function(n){return V.test(n||"")||se.error("unsupported lang: "+n),n=n.replace(te,ne).toLowerCase(),function(e){var t;do{if(t=E?e.lang:e.getAttribute("xml:lang")||e.getAttribute("lang"))return(t=t.toLowerCase())===n||0===t.indexOf(n+"-")}while((e=e.parentNode)&&1===e.nodeType);return!1}}),target:function(e){var t=n.location&&n.location.hash;return t&&t.slice(1)===e.id},root:function(e){return e===a},focus:function(e){return e===C.activeElement&&(!C.hasFocus||C.hasFocus())&&!!(e.type||e.href||~e.tabIndex)},enabled:ge(!1),disabled:ge(!0),checked:function(e){var t=e.nodeName.toLowerCase();return"input"===t&&!!e.checked||"option"===t&&!!e.selected},selected:function(e){return e.parentNode&&e.parentNode.selectedIndex,!0===e.selected},empty:function(e){for(e=e.firstChild;e;e=e.nextSibling)if(e.nodeType<6)return!1;return!0},parent:function(e){return!b.pseudos.empty(e)},header:function(e){return J.test(e.nodeName)},input:function(e){return Q.test(e.nodeName)},button:function(e){var t=e.nodeName.toLowerCase();return"input"===t&&"button"===e.type||"button"===t},text:function(e){var t;return"input"===e.nodeName.toLowerCase()&&"text"===e.type&&(null==(t=e.getAttribute("type"))||"text"===t.toLowerCase())},first:ve(function(){return[0]}),last:ve(function(e,t){return[t-1]}),eq:ve(function(e,t,n){return[n<0?n+t:n]}),even:ve(function(e,t){for(var n=0;n<t;n+=2)e.push(n);return e}),odd:ve(function(e,t){for(var n=1;n<t;n+=2)e.push(n);return e}),lt:ve(function(e,t,n){for(var r=n<0?n+t:t<n?t:n;0<=--r;)e.push(r);return e}),gt:ve(function(e,t,n){for(var r=n<0?n+t:n;++r<t;)e.push(r);return e})}}).pseudos.nth=b.pseudos.eq,{radio:!0,checkbox:!0,file:!0,password:!0,image:!0})b.pseudos[e]=de(e);for(e in{submit:!0,reset:!0})b.pseudos[e]=he(e);function me(){}function xe(e){for(var t=0,n=e.length,r="";t<n;t++)r+=e[t].value;return r}function be(s,e,t){var u=e.dir,l=e.next,c=l||u,f=t&&"parentNode"===c,p=r++;return e.first?function(e,t,n){while(e=e[u])if(1===e.nodeType||f)return s(e,t,n);return!1}:function(e,t,n){var r,i,o,a=[k,p];if(n){while(e=e[u])if((1===e.nodeType||f)&&s(e,t,n))return!0}else while(e=e[u])if(1===e.nodeType||f)if(i=(o=e[S]||(e[S]={}))[e.uniqueID]||(o[e.uniqueID]={}),l&&l===e.nodeName.toLowerCase())e=e[u]||e;else{if((r=i[c])&&r[0]===k&&r[1]===p)return a[2]=r[2];if((i[c]=a)[2]=s(e,t,n))return!0}return!1}}function we(i){return 1<i.length?function(e,t,n){var r=i.length;while(r--)if(!i[r](e,t,n))return!1;return!0}:i[0]}function Te(e,t,n,r,i){for(var o,a=[],s=0,u=e.length,l=null!=t;s<u;s++)(o=e[s])&&(n&&!n(o,r,i)||(a.push(o),l&&t.push(s)));return a}function Ce(d,h,g,v,y,e){return v&&!v[S]&&(v=Ce(v)),y&&!y[S]&&(y=Ce(y,e)),le(function(e,t,n,r){var i,o,a,s=[],u=[],l=t.length,c=e||function(e,t,n){for(var r=0,i=t.length;r<i;r++)se(e,t[r],n);return n}(h||"*",n.nodeType?[n]:n,[]),f=!d||!e&&h?c:Te(c,s,d,n,r),p=g?y||(e?d:l||v)?[]:t:f;if(g&&g(f,p,n,r),v){i=Te(p,u),v(i,[],n,r),o=i.length;while(o--)(a=i[o])&&(p[u[o]]=!(f[u[o]]=a))}if(e){if(y||d){if(y){i=[],o=p.length;while(o--)(a=p[o])&&i.push(f[o]=a);y(null,p=[],i,r)}o=p.length;while(o--)(a=p[o])&&-1<(i=y?P(e,a):s[o])&&(e[i]=!(t[i]=a))}}else p=Te(p===t?p.splice(l,p.length):p),y?y(null,t,p,r):H.apply(t,p)})}function Ee(e){for(var i,t,n,r=e.length,o=b.relative[e[0].type],a=o||b.relative[" "],s=o?1:0,u=be(function(e){return e===i},a,!0),l=be(function(e){return-1<P(i,e)},a,!0),c=[function(e,t,n){var r=!o&&(n||t!==w)||((i=t).nodeType?u(e,t,n):l(e,t,n));return i=null,r}];s<r;s++)if(t=b.relative[e[s].type])c=[be(we(c),t)];else{if((t=b.filter[e[s].type].apply(null,e[s].matches))[S]){for(n=++s;n<r;n++)if(b.relative[e[n].type])break;return Ce(1<s&&we(c),1<s&&xe(e.slice(0,s-1).concat({value:" "===e[s-2].type?"*":""})).replace($,"$1"),t,s<n&&Ee(e.slice(s,n)),n<r&&Ee(e=e.slice(n)),n<r&&xe(e))}c.push(t)}return we(c)}return me.prototype=b.filters=b.pseudos,b.setFilters=new me,h=se.tokenize=function(e,t){var n,r,i,o,a,s,u,l=x[e+" "];if(l)return t?0:l.slice(0);a=e,s=[],u=b.preFilter;while(a){for(o in n&&!(r=_.exec(a))||(r&&(a=a.slice(r[0].length)||a),s.push(i=[])),n=!1,(r=z.exec(a))&&(n=r.shift(),i.push({value:n,type:r[0].replace($," ")}),a=a.slice(n.length)),b.filter)!(r=G[o].exec(a))||u[o]&&!(r=u[o](r))||(n=r.shift(),i.push({value:n,type:o,matches:r}),a=a.slice(n.length));if(!n)break}return t?a.length:a?se.error(e):x(e,s).slice(0)},f=se.compile=function(e,t){var n,v,y,m,x,r,i=[],o=[],a=A[e+" "];if(!a){t||(t=h(e)),n=t.length;while(n--)(a=Ee(t[n]))[S]?i.push(a):o.push(a);(a=A(e,(v=o,m=0<(y=i).length,x=0<v.length,r=function(e,t,n,r,i){var o,a,s,u=0,l="0",c=e&&[],f=[],p=w,d=e||x&&b.find.TAG("*",i),h=k+=null==p?1:Math.random()||.1,g=d.length;for(i&&(w=t==C||t||i);l!==g&&null!=(o=d[l]);l++){if(x&&o){a=0,t||o.ownerDocument==C||(T(o),n=!E);while(s=v[a++])if(s(o,t||C,n)){r.push(o);break}i&&(k=h)}m&&((o=!s&&o)&&u--,e&&c.push(o))}if(u+=l,m&&l!==u){a=0;while(s=y[a++])s(c,f,t,n);if(e){if(0<u)while(l--)c[l]||f[l]||(f[l]=q.call(r));f=Te(f)}H.apply(r,f),i&&!e&&0<f.length&&1<u+y.length&&se.uniqueSort(r)}return i&&(k=h,w=p),c},m?le(r):r))).selector=e}return a},g=se.select=function(e,t,n,r){var i,o,a,s,u,l="function"==typeof e&&e,c=!r&&h(e=l.selector||e);if(n=n||[],1===c.length){if(2<(o=c[0]=c[0].slice(0)).length&&"ID"===(a=o[0]).type&&9===t.nodeType&&E&&b.relative[o[1].type]){if(!(t=(b.find.ID(a.matches[0].replace(te,ne),t)||[])[0]))return n;l&&(t=t.parentNode),e=e.slice(o.shift().value.length)}i=G.needsContext.test(e)?0:o.length;while(i--){if(a=o[i],b.relative[s=a.type])break;if((u=b.find[s])&&(r=u(a.matches[0].replace(te,ne),ee.test(o[0].type)&&ye(t.parentNode)||t))){if(o.splice(i,1),!(e=r.length&&xe(o)))return H.apply(n,r),n;break}}}return(l||f(e,c))(r,t,!E,n,!t||ee.test(e)&&ye(t.parentNode)||t),n},d.sortStable=S.split("").sort(j).join("")===S,d.detectDuplicates=!!l,T(),d.sortDetached=ce(function(e){return 1&e.compareDocumentPosition(C.createElement("fieldset"))}),ce(function(e){return e.innerHTML="<a href='#'></a>","#"===e.firstChild.getAttribute("href")})||fe("type|href|height|width",function(e,t,n){if(!n)return e.getAttribute(t,"type"===t.toLowerCase()?1:2)}),d.attributes&&ce(function(e){return e.innerHTML="<input/>",e.firstChild.setAttribute("value",""),""===e.firstChild.getAttribute("value")})||fe("value",function(e,t,n){if(!n&&"input"===e.nodeName.toLowerCase())return e.defaultValue}),ce(function(e){return null==e.getAttribute("disabled")})||fe(R,function(e,t,n){var r;if(!n)return!0===e[t]?t.toLowerCase():(r=e.getAttributeNode(t))&&r.specified?r.value:null}),se}(C);S.find=d,S.expr=d.selectors,S.expr[":"]=S.expr.pseudos,S.uniqueSort=S.unique=d.uniqueSort,S.text=d.getText,S.isXMLDoc=d.isXML,S.contains=d.contains,S.escapeSelector=d.escape;var h=function(e,t,n){var r=[],i=void 0!==n;while((e=e[t])&&9!==e.nodeType)if(1===e.nodeType){if(i&&S(e).is(n))break;r.push(e)}return r},T=function(e,t){for(var n=[];e;e=e.nextSibling)1===e.nodeType&&e!==t&&n.push(e);return n},k=S.expr.match.needsContext;function A(e,t){return e.nodeName&&e.nodeName.toLowerCase()===t.toLowerCase()}var N=/^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i;function j(e,n,r){return m(n)?S.grep(e,function(e,t){return!!n.call(e,t,e)!==r}):n.nodeType?S.grep(e,function(e){return e===n!==r}):"string"!=typeof n?S.grep(e,function(e){return-1<i.call(n,e)!==r}):S.filter(n,e,r)}S.filter=function(e,t,n){var r=t[0];return n&&(e=":not("+e+")"),1===t.length&&1===r.nodeType?S.find.matchesSelector(r,e)?[r]:[]:S.find.matches(e,S.grep(t,function(e){return 1===e.nodeType}))},S.fn.extend({find:function(e){var t,n,r=this.length,i=this;if("string"!=typeof e)return this.pushStack(S(e).filter(function(){for(t=0;t<r;t++)if(S.contains(i[t],this))return!0}));for(n=this.pushStack([]),t=0;t<r;t++)S.find(e,i[t],n);return 1<r?S.uniqueSort(n):n},filter:function(e){return this.pushStack(j(this,e||[],!1))},not:function(e){return this.pushStack(j(this,e||[],!0))},is:function(e){return!!j(this,"string"==typeof e&&k.test(e)?S(e):e||[],!1).length}});var D,q=/^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]+))$/;(S.fn.init=function(e,t,n){var r,i;if(!e)return this;if(n=n||D,"string"==typeof e){if(!(r="<"===e[0]&&">"===e[e.length-1]&&3<=e.length?[null,e,null]:q.exec(e))||!r[1]&&t)return!t||t.jquery?(t||n).find(e):this.constructor(t).find(e);if(r[1]){if(t=t instanceof S?t[0]:t,S.merge(this,S.parseHTML(r[1],t&&t.nodeType?t.ownerDocument||t:E,!0)),N.test(r[1])&&S.isPlainObject(t))for(r in t)m(this[r])?this[r](t[r]):this.attr(r,t[r]);return this}return(i=E.getElementById(r[2]))&&(this[0]=i,this.length=1),this}return e.nodeType?(this[0]=e,this.length=1,this):m(e)?void 0!==n.ready?n.ready(e):e(S):S.makeArray(e,this)}).prototype=S.fn,D=S(E);var L=/^(?:parents|prev(?:Until|All))/,H={children:!0,contents:!0,next:!0,prev:!0};function O(e,t){while((e=e[t])&&1!==e.nodeType);return e}S.fn.extend({has:function(e){var t=S(e,this),n=t.length;return this.filter(function(){for(var e=0;e<n;e++)if(S.contains(this,t[e]))return!0})},closest:function(e,t){var n,r=0,i=this.length,o=[],a="string"!=typeof e&&S(e);if(!k.test(e))for(;r<i;r++)for(n=this[r];n&&n!==t;n=n.parentNode)if(n.nodeType<11&&(a?-1<a.index(n):1===n.nodeType&&S.find.matchesSelector(n,e))){o.push(n);break}return this.pushStack(1<o.length?S.uniqueSort(o):o)},index:function(e){return e?"string"==typeof e?i.call(S(e),this[0]):i.call(this,e.jquery?e[0]:e):this[0]&&this[0].parentNode?this.first().prevAll().length:-1},add:function(e,t){return this.pushStack(S.uniqueSort(S.merge(this.get(),S(e,t))))},addBack:function(e){return this.add(null==e?this.prevObject:this.prevObject.filter(e))}}),S.each({parent:function(e){var t=e.parentNode;return t&&11!==t.nodeType?t:null},parents:function(e){return h(e,"parentNode")},parentsUntil:function(e,t,n){return h(e,"parentNode",n)},next:function(e){return O(e,"nextSibling")},prev:function(e){return O(e,"previousSibling")},nextAll:function(e){return h(e,"nextSibling")},prevAll:function(e){return h(e,"previousSibling")},nextUntil:function(e,t,n){return h(e,"nextSibling",n)},prevUntil:function(e,t,n){return h(e,"previousSibling",n)},siblings:function(e){return T((e.parentNode||{}).firstChild,e)},children:function(e){return T(e.firstChild)},contents:function(e){return null!=e.contentDocument&&r(e.contentDocument)?e.contentDocument:(A(e,"template")&&(e=e.content||e),S.merge([],e.childNodes))}},function(r,i){S.fn[r]=function(e,t){var n=S.map(this,i,e);return"Until"!==r.slice(-5)&&(t=e),t&&"string"==typeof t&&(n=S.filter(t,n)),1<this.length&&(H[r]||S.uniqueSort(n),L.test(r)&&n.reverse()),this.pushStack(n)}});var P=/[^\x20\t\r\n\f]+/g;function R(e){return e}function M(e){throw e}function I(e,t,n,r){var i;try{e&&m(i=e.promise)?i.call(e).done(t).fail(n):e&&m(i=e.then)?i.call(e,t,n):t.apply(void 0,[e].slice(r))}catch(e){n.apply(void 0,[e])}}S.Callbacks=function(r){var e,n;r="string"==typeof r?(e=r,n={},S.each(e.match(P)||[],function(e,t){n[t]=!0}),n):S.extend({},r);var i,t,o,a,s=[],u=[],l=-1,c=function(){for(a=a||r.once,o=i=!0;u.length;l=-1){t=u.shift();while(++l<s.length)!1===s[l].apply(t[0],t[1])&&r.stopOnFalse&&(l=s.length,t=!1)}r.memory||(t=!1),i=!1,a&&(s=t?[]:"")},f={add:function(){return s&&(t&&!i&&(l=s.length-1,u.push(t)),function n(e){S.each(e,function(e,t){m(t)?r.unique&&f.has(t)||s.push(t):t&&t.length&&"string"!==w(t)&&n(t)})}(arguments),t&&!i&&c()),this},remove:function(){return S.each(arguments,function(e,t){var n;while(-1<(n=S.inArray(t,s,n)))s.splice(n,1),n<=l&&l--}),this},has:function(e){return e?-1<S.inArray(e,s):0<s.length},empty:function(){return s&&(s=[]),this},disable:function(){return a=u=[],s=t="",this},disabled:function(){return!s},lock:function(){return a=u=[],t||i||(s=t=""),this},locked:function(){return!!a},fireWith:function(e,t){return a||(t=[e,(t=t||[]).slice?t.slice():t],u.push(t),i||c()),this},fire:function(){return f.fireWith(this,arguments),this},fired:function(){return!!o}};return f},S.extend({Deferred:function(e){var o=[["notify","progress",S.Callbacks("memory"),S.Callbacks("memory"),2],["resolve","done",S.Callbacks("once memory"),S.Callbacks("once memory"),0,"resolved"],["reject","fail",S.Callbacks("once memory"),S.Callbacks("once memory"),1,"rejected"]],i="pending",a={state:function(){return i},always:function(){return s.done(arguments).fail(arguments),this},"catch":function(e){return a.then(null,e)},pipe:function(){var i=arguments;return S.Deferred(function(r){S.each(o,function(e,t){var n=m(i[t[4]])&&i[t[4]];s[t[1]](function(){var e=n&&n.apply(this,arguments);e&&m(e.promise)?e.promise().progress(r.notify).done(r.resolve).fail(r.reject):r[t[0]+"With"](this,n?[e]:arguments)})}),i=null}).promise()},then:function(t,n,r){var u=0;function l(i,o,a,s){return function(){var n=this,r=arguments,e=function(){var e,t;if(!(i<u)){if((e=a.apply(n,r))===o.promise())throw new TypeError("Thenable self-resolution");t=e&&("object"==typeof e||"function"==typeof e)&&e.then,m(t)?s?t.call(e,l(u,o,R,s),l(u,o,M,s)):(u++,t.call(e,l(u,o,R,s),l(u,o,M,s),l(u,o,R,o.notifyWith))):(a!==R&&(n=void 0,r=[e]),(s||o.resolveWith)(n,r))}},t=s?e:function(){try{e()}catch(e){S.Deferred.exceptionHook&&S.Deferred.exceptionHook(e,t.stackTrace),u<=i+1&&(a!==M&&(n=void 0,r=[e]),o.rejectWith(n,r))}};i?t():(S.Deferred.getStackHook&&(t.stackTrace=S.Deferred.getStackHook()),C.setTimeout(t))}}return S.Deferred(function(e){o[0][3].add(l(0,e,m(r)?r:R,e.notifyWith)),o[1][3].add(l(0,e,m(t)?t:R)),o[2][3].add(l(0,e,m(n)?n:M))}).promise()},promise:function(e){return null!=e?S.extend(e,a):a}},s={};return S.each(o,function(e,t){var n=t[2],r=t[5];a[t[1]]=n.add,r&&n.add(function(){i=r},o[3-e][2].disable,o[3-e][3].disable,o[0][2].lock,o[0][3].lock),n.add(t[3].fire),s[t[0]]=function(){return s[t[0]+"With"](this===s?void 0:this,arguments),this},s[t[0]+"With"]=n.fireWith}),a.promise(s),e&&e.call(s,s),s},when:function(e){var n=arguments.length,t=n,r=Array(t),i=s.call(arguments),o=S.Deferred(),a=function(t){return function(e){r[t]=this,i[t]=1<arguments.length?s.call(arguments):e,--n||o.resolveWith(r,i)}};if(n<=1&&(I(e,o.done(a(t)).resolve,o.reject,!n),"pending"===o.state()||m(i[t]&&i[t].then)))return o.then();while(t--)I(i[t],a(t),o.reject);return o.promise()}});var W=/^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/;S.Deferred.exceptionHook=function(e,t){C.console&&C.console.warn&&e&&W.test(e.name)&&C.console.warn("jQuery.Deferred exception: "+e.message,e.stack,t)},S.readyException=function(e){C.setTimeout(function(){throw e})};var F=S.Deferred();function B(){E.removeEventListener("DOMContentLoaded",B),C.removeEventListener("load",B),S.ready()}S.fn.ready=function(e){return F.then(e)["catch"](function(e){S.readyException(e)}),this},S.extend({isReady:!1,readyWait:1,ready:function(e){(!0===e?--S.readyWait:S.isReady)||(S.isReady=!0)!==e&&0<--S.readyWait||F.resolveWith(E,[S])}}),S.ready.then=F.then,"complete"===E.readyState||"loading"!==E.readyState&&!E.documentElement.doScroll?C.setTimeout(S.ready):(E.addEventListener("DOMContentLoaded",B),C.addEventListener("load",B));var $=function(e,t,n,r,i,o,a){var s=0,u=e.length,l=null==n;if("object"===w(n))for(s in i=!0,n)$(e,t,s,n[s],!0,o,a);else if(void 0!==r&&(i=!0,m(r)||(a=!0),l&&(a?(t.call(e,r),t=null):(l=t,t=function(e,t,n){return l.call(S(e),n)})),t))for(;s<u;s++)t(e[s],n,a?r:r.call(e[s],s,t(e[s],n)));return i?e:l?t.call(e):u?t(e[0],n):o},_=/^-ms-/,z=/-([a-z])/g;function U(e,t){return t.toUpperCase()}function X(e){return e.replace(_,"ms-").replace(z,U)}var V=function(e){return 1===e.nodeType||9===e.nodeType||!+e.nodeType};function G(){this.expando=S.expando+G.uid++}G.uid=1,G.prototype={cache:function(e){var t=e[this.expando];return t||(t={},V(e)&&(e.nodeType?e[this.expando]=t:Object.defineProperty(e,this.expando,{value:t,configurable:!0}))),t},set:function(e,t,n){var r,i=this.cache(e);if("string"==typeof t)i[X(t)]=n;else for(r in t)i[X(r)]=t[r];return i},get:function(e,t){return void 0===t?this.cache(e):e[this.expando]&&e[this.expando][X(t)]},access:function(e,t,n){return void 0===t||t&&"string"==typeof t&&void 0===n?this.get(e,t):(this.set(e,t,n),void 0!==n?n:t)},remove:function(e,t){var n,r=e[this.expando];if(void 0!==r){if(void 0!==t){n=(t=Array.isArray(t)?t.map(X):(t=X(t))in r?[t]:t.match(P)||[]).length;while(n--)delete r[t[n]]}(void 0===t||S.isEmptyObject(r))&&(e.nodeType?e[this.expando]=void 0:delete e[this.expando])}},hasData:function(e){var t=e[this.expando];return void 0!==t&&!S.isEmptyObject(t)}};var Y=new G,Q=new G,J=/^(?:\{[\w\W]*\}|\[[\w\W]*\])$/,K=/[A-Z]/g;function Z(e,t,n){var r,i;if(void 0===n&&1===e.nodeType)if(r="data-"+t.replace(K,"-$&").toLowerCase(),"string"==typeof(n=e.getAttribute(r))){try{n="true"===(i=n)||"false"!==i&&("null"===i?null:i===+i+""?+i:J.test(i)?JSON.parse(i):i)}catch(e){}Q.set(e,t,n)}else n=void 0;return n}S.extend({hasData:function(e){return Q.hasData(e)||Y.hasData(e)},data:function(e,t,n){return Q.access(e,t,n)},removeData:function(e,t){Q.remove(e,t)},_data:function(e,t,n){return Y.access(e,t,n)},_removeData:function(e,t){Y.remove(e,t)}}),S.fn.extend({data:function(n,e){var t,r,i,o=this[0],a=o&&o.attributes;if(void 0===n){if(this.length&&(i=Q.get(o),1===o.nodeType&&!Y.get(o,"hasDataAttrs"))){t=a.length;while(t--)a[t]&&0===(r=a[t].name).indexOf("data-")&&(r=X(r.slice(5)),Z(o,r,i[r]));Y.set(o,"hasDataAttrs",!0)}return i}return"object"==typeof n?this.each(function(){Q.set(this,n)}):$(this,function(e){var t;if(o&&void 0===e)return void 0!==(t=Q.get(o,n))?t:void 0!==(t=Z(o,n))?t:void 0;this.each(function(){Q.set(this,n,e)})},null,e,1<arguments.length,null,!0)},removeData:function(e){return this.each(function(){Q.remove(this,e)})}}),S.extend({queue:function(e,t,n){var r;if(e)return t=(t||"fx")+"queue",r=Y.get(e,t),n&&(!r||Array.isArray(n)?r=Y.access(e,t,S.makeArray(n)):r.push(n)),r||[]},dequeue:function(e,t){t=t||"fx";var n=S.queue(e,t),r=n.length,i=n.shift(),o=S._queueHooks(e,t);"inprogress"===i&&(i=n.shift(),r--),i&&("fx"===t&&n.unshift("inprogress"),delete o.stop,i.call(e,function(){S.dequeue(e,t)},o)),!r&&o&&o.empty.fire()},_queueHooks:function(e,t){var n=t+"queueHooks";return Y.get(e,n)||Y.access(e,n,{empty:S.Callbacks("once memory").add(function(){Y.remove(e,[t+"queue",n])})})}}),S.fn.extend({queue:function(t,n){var e=2;return"string"!=typeof t&&(n=t,t="fx",e--),arguments.length<e?S.queue(this[0],t):void 0===n?this:this.each(function(){var e=S.queue(this,t,n);S._queueHooks(this,t),"fx"===t&&"inprogress"!==e[0]&&S.dequeue(this,t)})},dequeue:function(e){return this.each(function(){S.dequeue(this,e)})},clearQueue:function(e){return this.queue(e||"fx",[])},promise:function(e,t){var n,r=1,i=S.Deferred(),o=this,a=this.length,s=function(){--r||i.resolveWith(o,[o])};"string"!=typeof e&&(t=e,e=void 0),e=e||"fx";while(a--)(n=Y.get(o[a],e+"queueHooks"))&&n.empty&&(r++,n.empty.add(s));return s(),i.promise(t)}});var ee=/[+-]?(?:\d*\.|)\d+(?:[eE][+-]?\d+|)/.source,te=new RegExp("^(?:([+-])=|)("+ee+")([a-z%]*)$","i"),ne=["Top","Right","Bottom","Left"],re=E.documentElement,ie=function(e){return S.contains(e.ownerDocument,e)},oe={composed:!0};re.getRootNode&&(ie=function(e){return S.contains(e.ownerDocument,e)||e.getRootNode(oe)===e.ownerDocument});var ae=function(e,t){return"none"===(e=t||e).style.display||""===e.style.display&&ie(e)&&"none"===S.css(e,"display")};function se(e,t,n,r){var i,o,a=20,s=r?function(){return r.cur()}:function(){return S.css(e,t,"")},u=s(),l=n&&n[3]||(S.cssNumber[t]?"":"px"),c=e.nodeType&&(S.cssNumber[t]||"px"!==l&&+u)&&te.exec(S.css(e,t));if(c&&c[3]!==l){u/=2,l=l||c[3],c=+u||1;while(a--)S.style(e,t,c+l),(1-o)*(1-(o=s()/u||.5))<=0&&(a=0),c/=o;c*=2,S.style(e,t,c+l),n=n||[]}return n&&(c=+c||+u||0,i=n[1]?c+(n[1]+1)*n[2]:+n[2],r&&(r.unit=l,r.start=c,r.end=i)),i}var ue={};function le(e,t){for(var n,r,i,o,a,s,u,l=[],c=0,f=e.length;c<f;c++)(r=e[c]).style&&(n=r.style.display,t?("none"===n&&(l[c]=Y.get(r,"display")||null,l[c]||(r.style.display="")),""===r.style.display&&ae(r)&&(l[c]=(u=a=o=void 0,a=(i=r).ownerDocument,s=i.nodeName,(u=ue[s])||(o=a.body.appendChild(a.createElement(s)),u=S.css(o,"display"),o.parentNode.removeChild(o),"none"===u&&(u="block"),ue[s]=u)))):"none"!==n&&(l[c]="none",Y.set(r,"display",n)));for(c=0;c<f;c++)null!=l[c]&&(e[c].style.display=l[c]);return e}S.fn.extend({show:function(){return le(this,!0)},hide:function(){return le(this)},toggle:function(e){return"boolean"==typeof e?e?this.show():this.hide():this.each(function(){ae(this)?S(this).show():S(this).hide()})}});var ce,fe,pe=/^(?:checkbox|radio)$/i,de=/<([a-z][^\/\0>\x20\t\r\n\f]*)/i,he=/^$|^module$|\/(?:java|ecma)script/i;ce=E.createDocumentFragment().appendChild(E.createElement("div")),(fe=E.createElement("input")).setAttribute("type","radio"),fe.setAttribute("checked","checked"),fe.setAttribute("name","t"),ce.appendChild(fe),y.checkClone=ce.cloneNode(!0).cloneNode(!0).lastChild.checked,ce.innerHTML="<textarea>x</textarea>",y.noCloneChecked=!!ce.cloneNode(!0).lastChild.defaultValue,ce.innerHTML="<option></option>",y.option=!!ce.lastChild;var ge={thead:[1,"<table>","</table>"],col:[2,"<table><colgroup>","</colgroup></table>"],tr:[2,"<table><tbody>","</tbody></table>"],td:[3,"<table><tbody><tr>","</tr></tbody></table>"],_default:[0,"",""]};function ve(e,t){var n;return n="undefined"!=typeof e.getElementsByTagName?e.getElementsByTagName(t||"*"):"undefined"!=typeof e.querySelectorAll?e.querySelectorAll(t||"*"):[],void 0===t||t&&A(e,t)?S.merge([e],n):n}function ye(e,t){for(var n=0,r=e.length;n<r;n++)Y.set(e[n],"globalEval",!t||Y.get(t[n],"globalEval"))}ge.tbody=ge.tfoot=ge.colgroup=ge.caption=ge.thead,ge.th=ge.td,y.option||(ge.optgroup=ge.option=[1,"<select multiple='multiple'>","</select>"]);var me=/<|&#?\w+;/;function xe(e,t,n,r,i){for(var o,a,s,u,l,c,f=t.createDocumentFragment(),p=[],d=0,h=e.length;d<h;d++)if((o=e[d])||0===o)if("object"===w(o))S.merge(p,o.nodeType?[o]:o);else if(me.test(o)){a=a||f.appendChild(t.createElement("div")),s=(de.exec(o)||["",""])[1].toLowerCase(),u=ge[s]||ge._default,a.innerHTML=u[1]+S.htmlPrefilter(o)+u[2],c=u[0];while(c--)a=a.lastChild;S.merge(p,a.childNodes),(a=f.firstChild).textContent=""}else p.push(t.createTextNode(o));f.textContent="",d=0;while(o=p[d++])if(r&&-1<S.inArray(o,r))i&&i.push(o);else if(l=ie(o),a=ve(f.appendChild(o),"script"),l&&ye(a),n){c=0;while(o=a[c++])he.test(o.type||"")&&n.push(o)}return f}var be=/^([^.]*)(?:\.(.+)|)/;function we(){return!0}function Te(){return!1}function Ce(e,t){return e===function(){try{return E.activeElement}catch(e){}}()==("focus"===t)}function Ee(e,t,n,r,i,o){var a,s;if("object"==typeof t){for(s in"string"!=typeof n&&(r=r||n,n=void 0),t)Ee(e,s,n,r,t[s],o);return e}if(null==r&&null==i?(i=n,r=n=void 0):null==i&&("string"==typeof n?(i=r,r=void 0):(i=r,r=n,n=void 0)),!1===i)i=Te;else if(!i)return e;return 1===o&&(a=i,(i=function(e){return S().off(e),a.apply(this,arguments)}).guid=a.guid||(a.guid=S.guid++)),e.each(function(){S.event.add(this,t,i,r,n)})}function Se(e,i,o){o?(Y.set(e,i,!1),S.event.add(e,i,{namespace:!1,handler:function(e){var t,n,r=Y.get(this,i);if(1&e.isTrigger&&this[i]){if(r.length)(S.event.special[i]||{}).delegateType&&e.stopPropagation();else if(r=s.call(arguments),Y.set(this,i,r),t=o(this,i),this[i](),r!==(n=Y.get(this,i))||t?Y.set(this,i,!1):n={},r!==n)return e.stopImmediatePropagation(),e.preventDefault(),n&&n.value}else r.length&&(Y.set(this,i,{value:S.event.trigger(S.extend(r[0],S.Event.prototype),r.slice(1),this)}),e.stopImmediatePropagation())}})):void 0===Y.get(e,i)&&S.event.add(e,i,we)}S.event={global:{},add:function(t,e,n,r,i){var o,a,s,u,l,c,f,p,d,h,g,v=Y.get(t);if(V(t)){n.handler&&(n=(o=n).handler,i=o.selector),i&&S.find.matchesSelector(re,i),n.guid||(n.guid=S.guid++),(u=v.events)||(u=v.events=Object.create(null)),(a=v.handle)||(a=v.handle=function(e){return"undefined"!=typeof S&&S.event.triggered!==e.type?S.event.dispatch.apply(t,arguments):void 0}),l=(e=(e||"").match(P)||[""]).length;while(l--)d=g=(s=be.exec(e[l])||[])[1],h=(s[2]||"").split(".").sort(),d&&(f=S.event.special[d]||{},d=(i?f.delegateType:f.bindType)||d,f=S.event.special[d]||{},c=S.extend({type:d,origType:g,data:r,handler:n,guid:n.guid,selector:i,needsContext:i&&S.expr.match.needsContext.test(i),namespace:h.join(".")},o),(p=u[d])||((p=u[d]=[]).delegateCount=0,f.setup&&!1!==f.setup.call(t,r,h,a)||t.addEventListener&&t.addEventListener(d,a)),f.add&&(f.add.call(t,c),c.handler.guid||(c.handler.guid=n.guid)),i?p.splice(p.delegateCount++,0,c):p.push(c),S.event.global[d]=!0)}},remove:function(e,t,n,r,i){var o,a,s,u,l,c,f,p,d,h,g,v=Y.hasData(e)&&Y.get(e);if(v&&(u=v.events)){l=(t=(t||"").match(P)||[""]).length;while(l--)if(d=g=(s=be.exec(t[l])||[])[1],h=(s[2]||"").split(".").sort(),d){f=S.event.special[d]||{},p=u[d=(r?f.delegateType:f.bindType)||d]||[],s=s[2]&&new RegExp("(^|\\.)"+h.join("\\.(?:.*\\.|)")+"(\\.|$)"),a=o=p.length;while(o--)c=p[o],!i&&g!==c.origType||n&&n.guid!==c.guid||s&&!s.test(c.namespace)||r&&r!==c.selector&&("**"!==r||!c.selector)||(p.splice(o,1),c.selector&&p.delegateCount--,f.remove&&f.remove.call(e,c));a&&!p.length&&(f.teardown&&!1!==f.teardown.call(e,h,v.handle)||S.removeEvent(e,d,v.handle),delete u[d])}else for(d in u)S.event.remove(e,d+t[l],n,r,!0);S.isEmptyObject(u)&&Y.remove(e,"handle events")}},dispatch:function(e){var t,n,r,i,o,a,s=new Array(arguments.length),u=S.event.fix(e),l=(Y.get(this,"events")||Object.create(null))[u.type]||[],c=S.event.special[u.type]||{};for(s[0]=u,t=1;t<arguments.length;t++)s[t]=arguments[t];if(u.delegateTarget=this,!c.preDispatch||!1!==c.preDispatch.call(this,u)){a=S.event.handlers.call(this,u,l),t=0;while((i=a[t++])&&!u.isPropagationStopped()){u.currentTarget=i.elem,n=0;while((o=i.handlers[n++])&&!u.isImmediatePropagationStopped())u.rnamespace&&!1!==o.namespace&&!u.rnamespace.test(o.namespace)||(u.handleObj=o,u.data=o.data,void 0!==(r=((S.event.special[o.origType]||{}).handle||o.handler).apply(i.elem,s))&&!1===(u.result=r)&&(u.preventDefault(),u.stopPropagation()))}return c.postDispatch&&c.postDispatch.call(this,u),u.result}},handlers:function(e,t){var n,r,i,o,a,s=[],u=t.delegateCount,l=e.target;if(u&&l.nodeType&&!("click"===e.type&&1<=e.button))for(;l!==this;l=l.parentNode||this)if(1===l.nodeType&&("click"!==e.type||!0!==l.disabled)){for(o=[],a={},n=0;n<u;n++)void 0===a[i=(r=t[n]).selector+" "]&&(a[i]=r.needsContext?-1<S(i,this).index(l):S.find(i,this,null,[l]).length),a[i]&&o.push(r);o.length&&s.push({elem:l,handlers:o})}return l=this,u<t.length&&s.push({elem:l,handlers:t.slice(u)}),s},addProp:function(t,e){Object.defineProperty(S.Event.prototype,t,{enumerable:!0,configurable:!0,get:m(e)?function(){if(this.originalEvent)return e(this.originalEvent)}:function(){if(this.originalEvent)return this.originalEvent[t]},set:function(e){Object.defineProperty(this,t,{enumerable:!0,configurable:!0,writable:!0,value:e})}})},fix:function(e){return e[S.expando]?e:new S.Event(e)},special:{load:{noBubble:!0},click:{setup:function(e){var t=this||e;return pe.test(t.type)&&t.click&&A(t,"input")&&Se(t,"click",we),!1},trigger:function(e){var t=this||e;return pe.test(t.type)&&t.click&&A(t,"input")&&Se(t,"click"),!0},_default:function(e){var t=e.target;return pe.test(t.type)&&t.click&&A(t,"input")&&Y.get(t,"click")||A(t,"a")}},beforeunload:{postDispatch:function(e){void 0!==e.result&&e.originalEvent&&(e.originalEvent.returnValue=e.result)}}}},S.removeEvent=function(e,t,n){e.removeEventListener&&e.removeEventListener(t,n)},S.Event=function(e,t){if(!(this instanceof S.Event))return new S.Event(e,t);e&&e.type?(this.originalEvent=e,this.type=e.type,this.isDefaultPrevented=e.defaultPrevented||void 0===e.defaultPrevented&&!1===e.returnValue?we:Te,this.target=e.target&&3===e.target.nodeType?e.target.parentNode:e.target,this.currentTarget=e.currentTarget,this.relatedTarget=e.relatedTarget):this.type=e,t&&S.extend(this,t),this.timeStamp=e&&e.timeStamp||Date.now(),this[S.expando]=!0},S.Event.prototype={constructor:S.Event,isDefaultPrevented:Te,isPropagationStopped:Te,isImmediatePropagationStopped:Te,isSimulated:!1,preventDefault:function(){var e=this.originalEvent;this.isDefaultPrevented=we,e&&!this.isSimulated&&e.preventDefault()},stopPropagation:function(){var e=this.originalEvent;this.isPropagationStopped=we,e&&!this.isSimulated&&e.stopPropagation()},stopImmediatePropagation:function(){var e=this.originalEvent;this.isImmediatePropagationStopped=we,e&&!this.isSimulated&&e.stopImmediatePropagation(),this.stopPropagation()}},S.each({altKey:!0,bubbles:!0,cancelable:!0,changedTouches:!0,ctrlKey:!0,detail:!0,eventPhase:!0,metaKey:!0,pageX:!0,pageY:!0,shiftKey:!0,view:!0,"char":!0,code:!0,charCode:!0,key:!0,keyCode:!0,button:!0,buttons:!0,clientX:!0,clientY:!0,offsetX:!0,offsetY:!0,pointerId:!0,pointerType:!0,screenX:!0,screenY:!0,targetTouches:!0,toElement:!0,touches:!0,which:!0},S.event.addProp),S.each({focus:"focusin",blur:"focusout"},function(e,t){S.event.special[e]={setup:function(){return Se(this,e,Ce),!1},trigger:function(){return Se(this,e),!0},_default:function(){return!0},delegateType:t}}),S.each({mouseenter:"mouseover",mouseleave:"mouseout",pointerenter:"pointerover",pointerleave:"pointerout"},function(e,i){S.event.special[e]={delegateType:i,bindType:i,handle:function(e){var t,n=e.relatedTarget,r=e.handleObj;return n&&(n===this||S.contains(this,n))||(e.type=r.origType,t=r.handler.apply(this,arguments),e.type=i),t}}}),S.fn.extend({on:function(e,t,n,r){return Ee(this,e,t,n,r)},one:function(e,t,n,r){return Ee(this,e,t,n,r,1)},off:function(e,t,n){var r,i;if(e&&e.preventDefault&&e.handleObj)return r=e.handleObj,S(e.delegateTarget).off(r.namespace?r.origType+"."+r.namespace:r.origType,r.selector,r.handler),this;if("object"==typeof e){for(i in e)this.off(i,t,e[i]);return this}return!1!==t&&"function"!=typeof t||(n=t,t=void 0),!1===n&&(n=Te),this.each(function(){S.event.remove(this,e,n,t)})}});var ke=/<script|<style|<link/i,Ae=/checked\s*(?:[^=]|=\s*.checked.)/i,Ne=/^\s*<!(?:\[CDATA\[|--)|(?:\]\]|--)>\s*$/g;function je(e,t){return A(e,"table")&&A(11!==t.nodeType?t:t.firstChild,"tr")&&S(e).children("tbody")[0]||e}function De(e){return e.type=(null!==e.getAttribute("type"))+"/"+e.type,e}function qe(e){return"true/"===(e.type||"").slice(0,5)?e.type=e.type.slice(5):e.removeAttribute("type"),e}function Le(e,t){var n,r,i,o,a,s;if(1===t.nodeType){if(Y.hasData(e)&&(s=Y.get(e).events))for(i in Y.remove(t,"handle events"),s)for(n=0,r=s[i].length;n<r;n++)S.event.add(t,i,s[i][n]);Q.hasData(e)&&(o=Q.access(e),a=S.extend({},o),Q.set(t,a))}}function He(n,r,i,o){r=g(r);var e,t,a,s,u,l,c=0,f=n.length,p=f-1,d=r[0],h=m(d);if(h||1<f&&"string"==typeof d&&!y.checkClone&&Ae.test(d))return n.each(function(e){var t=n.eq(e);h&&(r[0]=d.call(this,e,t.html())),He(t,r,i,o)});if(f&&(t=(e=xe(r,n[0].ownerDocument,!1,n,o)).firstChild,1===e.childNodes.length&&(e=t),t||o)){for(s=(a=S.map(ve(e,"script"),De)).length;c<f;c++)u=e,c!==p&&(u=S.clone(u,!0,!0),s&&S.merge(a,ve(u,"script"))),i.call(n[c],u,c);if(s)for(l=a[a.length-1].ownerDocument,S.map(a,qe),c=0;c<s;c++)u=a[c],he.test(u.type||"")&&!Y.access(u,"globalEval")&&S.contains(l,u)&&(u.src&&"module"!==(u.type||"").toLowerCase()?S._evalUrl&&!u.noModule&&S._evalUrl(u.src,{nonce:u.nonce||u.getAttribute("nonce")},l):b(u.textContent.replace(Ne,""),u,l))}return n}function Oe(e,t,n){for(var r,i=t?S.filter(t,e):e,o=0;null!=(r=i[o]);o++)n||1!==r.nodeType||S.cleanData(ve(r)),r.parentNode&&(n&&ie(r)&&ye(ve(r,"script")),r.parentNode.removeChild(r));return e}S.extend({htmlPrefilter:function(e){return e},clone:function(e,t,n){var r,i,o,a,s,u,l,c=e.cloneNode(!0),f=ie(e);if(!(y.noCloneChecked||1!==e.nodeType&&11!==e.nodeType||S.isXMLDoc(e)))for(a=ve(c),r=0,i=(o=ve(e)).length;r<i;r++)s=o[r],u=a[r],void 0,"input"===(l=u.nodeName.toLowerCase())&&pe.test(s.type)?u.checked=s.checked:"input"!==l&&"textarea"!==l||(u.defaultValue=s.defaultValue);if(t)if(n)for(o=o||ve(e),a=a||ve(c),r=0,i=o.length;r<i;r++)Le(o[r],a[r]);else Le(e,c);return 0<(a=ve(c,"script")).length&&ye(a,!f&&ve(e,"script")),c},cleanData:function(e){for(var t,n,r,i=S.event.special,o=0;void 0!==(n=e[o]);o++)if(V(n)){if(t=n[Y.expando]){if(t.events)for(r in t.events)i[r]?S.event.remove(n,r):S.removeEvent(n,r,t.handle);n[Y.expando]=void 0}n[Q.expando]&&(n[Q.expando]=void 0)}}}),S.fn.extend({detach:function(e){return Oe(this,e,!0)},remove:function(e){return Oe(this,e)},text:function(e){return $(this,function(e){return void 0===e?S.text(this):this.empty().each(function(){1!==this.nodeType&&11!==this.nodeType&&9!==this.nodeType||(this.textContent=e)})},null,e,arguments.length)},append:function(){return He(this,arguments,function(e){1!==this.nodeType&&11!==this.nodeType&&9!==this.nodeType||je(this,e).appendChild(e)})},prepend:function(){return He(this,arguments,function(e){if(1===this.nodeType||11===this.nodeType||9===this.nodeType){var t=je(this,e);t.insertBefore(e,t.firstChild)}})},before:function(){return He(this,arguments,function(e){this.parentNode&&this.parentNode.insertBefore(e,this)})},after:function(){return He(this,arguments,function(e){this.parentNode&&this.parentNode.insertBefore(e,this.nextSibling)})},empty:function(){for(var e,t=0;null!=(e=this[t]);t++)1===e.nodeType&&(S.cleanData(ve(e,!1)),e.textContent="");return this},clone:function(e,t){return e=null!=e&&e,t=null==t?e:t,this.map(function(){return S.clone(this,e,t)})},html:function(e){return $(this,function(e){var t=this[0]||{},n=0,r=this.length;if(void 0===e&&1===t.nodeType)return t.innerHTML;if("string"==typeof e&&!ke.test(e)&&!ge[(de.exec(e)||["",""])[1].toLowerCase()]){e=S.htmlPrefilter(e);try{for(;n<r;n++)1===(t=this[n]||{}).nodeType&&(S.cleanData(ve(t,!1)),t.innerHTML=e);t=0}catch(e){}}t&&this.empty().append(e)},null,e,arguments.length)},replaceWith:function(){var n=[];return He(this,arguments,function(e){var t=this.parentNode;S.inArray(this,n)<0&&(S.cleanData(ve(this)),t&&t.replaceChild(e,this))},n)}}),S.each({appendTo:"append",prependTo:"prepend",insertBefore:"before",insertAfter:"after",replaceAll:"replaceWith"},function(e,a){S.fn[e]=function(e){for(var t,n=[],r=S(e),i=r.length-1,o=0;o<=i;o++)t=o===i?this:this.clone(!0),S(r[o])[a](t),u.apply(n,t.get());return this.pushStack(n)}});var Pe=new RegExp("^("+ee+")(?!px)[a-z%]+$","i"),Re=function(e){var t=e.ownerDocument.defaultView;return t&&t.opener||(t=C),t.getComputedStyle(e)},Me=function(e,t,n){var r,i,o={};for(i in t)o[i]=e.style[i],e.style[i]=t[i];for(i in r=n.call(e),t)e.style[i]=o[i];return r},Ie=new RegExp(ne.join("|"),"i");function We(e,t,n){var r,i,o,a,s=e.style;return(n=n||Re(e))&&(""!==(a=n.getPropertyValue(t)||n[t])||ie(e)||(a=S.style(e,t)),!y.pixelBoxStyles()&&Pe.test(a)&&Ie.test(t)&&(r=s.width,i=s.minWidth,o=s.maxWidth,s.minWidth=s.maxWidth=s.width=a,a=n.width,s.width=r,s.minWidth=i,s.maxWidth=o)),void 0!==a?a+"":a}function Fe(e,t){return{get:function(){if(!e())return(this.get=t).apply(this,arguments);delete this.get}}}!function(){function e(){if(l){u.style.cssText="position:absolute;left:-11111px;width:60px;margin-top:1px;padding:0;border:0",l.style.cssText="position:relative;display:block;box-sizing:border-box;overflow:scroll;margin:auto;border:1px;padding:1px;width:60%;top:1%",re.appendChild(u).appendChild(l);var e=C.getComputedStyle(l);n="1%"!==e.top,s=12===t(e.marginLeft),l.style.right="60%",o=36===t(e.right),r=36===t(e.width),l.style.position="absolute",i=12===t(l.offsetWidth/3),re.removeChild(u),l=null}}function t(e){return Math.round(parseFloat(e))}var n,r,i,o,a,s,u=E.createElement("div"),l=E.createElement("div");l.style&&(l.style.backgroundClip="content-box",l.cloneNode(!0).style.backgroundClip="",y.clearCloneStyle="content-box"===l.style.backgroundClip,S.extend(y,{boxSizingReliable:function(){return e(),r},pixelBoxStyles:function(){return e(),o},pixelPosition:function(){return e(),n},reliableMarginLeft:function(){return e(),s},scrollboxSize:function(){return e(),i},reliableTrDimensions:function(){var e,t,n,r;return null==a&&(e=E.createElement("table"),t=E.createElement("tr"),n=E.createElement("div"),e.style.cssText="position:absolute;left:-11111px;border-collapse:separate",t.style.cssText="border:1px solid",t.style.height="1px",n.style.height="9px",n.style.display="block",re.appendChild(e).appendChild(t).appendChild(n),r=C.getComputedStyle(t),a=parseInt(r.height,10)+parseInt(r.borderTopWidth,10)+parseInt(r.borderBottomWidth,10)===t.offsetHeight,re.removeChild(e)),a}}))}();var Be=["Webkit","Moz","ms"],$e=E.createElement("div").style,_e={};function ze(e){var t=S.cssProps[e]||_e[e];return t||(e in $e?e:_e[e]=function(e){var t=e[0].toUpperCase()+e.slice(1),n=Be.length;while(n--)if((e=Be[n]+t)in $e)return e}(e)||e)}var Ue=/^(none|table(?!-c[ea]).+)/,Xe=/^--/,Ve={position:"absolute",visibility:"hidden",display:"block"},Ge={letterSpacing:"0",fontWeight:"400"};function Ye(e,t,n){var r=te.exec(t);return r?Math.max(0,r[2]-(n||0))+(r[3]||"px"):t}function Qe(e,t,n,r,i,o){var a="width"===t?1:0,s=0,u=0;if(n===(r?"border":"content"))return 0;for(;a<4;a+=2)"margin"===n&&(u+=S.css(e,n+ne[a],!0,i)),r?("content"===n&&(u-=S.css(e,"padding"+ne[a],!0,i)),"margin"!==n&&(u-=S.css(e,"border"+ne[a]+"Width",!0,i))):(u+=S.css(e,"padding"+ne[a],!0,i),"padding"!==n?u+=S.css(e,"border"+ne[a]+"Width",!0,i):s+=S.css(e,"border"+ne[a]+"Width",!0,i));return!r&&0<=o&&(u+=Math.max(0,Math.ceil(e["offset"+t[0].toUpperCase()+t.slice(1)]-o-u-s-.5))||0),u}function Je(e,t,n){var r=Re(e),i=(!y.boxSizingReliable()||n)&&"border-box"===S.css(e,"boxSizing",!1,r),o=i,a=We(e,t,r),s="offset"+t[0].toUpperCase()+t.slice(1);if(Pe.test(a)){if(!n)return a;a="auto"}return(!y.boxSizingReliable()&&i||!y.reliableTrDimensions()&&A(e,"tr")||"auto"===a||!parseFloat(a)&&"inline"===S.css(e,"display",!1,r))&&e.getClientRects().length&&(i="border-box"===S.css(e,"boxSizing",!1,r),(o=s in e)&&(a=e[s])),(a=parseFloat(a)||0)+Qe(e,t,n||(i?"border":"content"),o,r,a)+"px"}function Ke(e,t,n,r,i){return new Ke.prototype.init(e,t,n,r,i)}S.extend({cssHooks:{opacity:{get:function(e,t){if(t){var n=We(e,"opacity");return""===n?"1":n}}}},cssNumber:{animationIterationCount:!0,columnCount:!0,fillOpacity:!0,flexGrow:!0,flexShrink:!0,fontWeight:!0,gridArea:!0,gridColumn:!0,gridColumnEnd:!0,gridColumnStart:!0,gridRow:!0,gridRowEnd:!0,gridRowStart:!0,lineHeight:!0,opacity:!0,order:!0,orphans:!0,widows:!0,zIndex:!0,zoom:!0},cssProps:{},style:function(e,t,n,r){if(e&&3!==e.nodeType&&8!==e.nodeType&&e.style){var i,o,a,s=X(t),u=Xe.test(t),l=e.style;if(u||(t=ze(s)),a=S.cssHooks[t]||S.cssHooks[s],void 0===n)return a&&"get"in a&&void 0!==(i=a.get(e,!1,r))?i:l[t];"string"===(o=typeof n)&&(i=te.exec(n))&&i[1]&&(n=se(e,t,i),o="number"),null!=n&&n==n&&("number"!==o||u||(n+=i&&i[3]||(S.cssNumber[s]?"":"px")),y.clearCloneStyle||""!==n||0!==t.indexOf("background")||(l[t]="inherit"),a&&"set"in a&&void 0===(n=a.set(e,n,r))||(u?l.setProperty(t,n):l[t]=n))}},css:function(e,t,n,r){var i,o,a,s=X(t);return Xe.test(t)||(t=ze(s)),(a=S.cssHooks[t]||S.cssHooks[s])&&"get"in a&&(i=a.get(e,!0,n)),void 0===i&&(i=We(e,t,r)),"normal"===i&&t in Ge&&(i=Ge[t]),""===n||n?(o=parseFloat(i),!0===n||isFinite(o)?o||0:i):i}}),S.each(["height","width"],function(e,u){S.cssHooks[u]={get:function(e,t,n){if(t)return!Ue.test(S.css(e,"display"))||e.getClientRects().length&&e.getBoundingClientRect().width?Je(e,u,n):Me(e,Ve,function(){return Je(e,u,n)})},set:function(e,t,n){var r,i=Re(e),o=!y.scrollboxSize()&&"absolute"===i.position,a=(o||n)&&"border-box"===S.css(e,"boxSizing",!1,i),s=n?Qe(e,u,n,a,i):0;return a&&o&&(s-=Math.ceil(e["offset"+u[0].toUpperCase()+u.slice(1)]-parseFloat(i[u])-Qe(e,u,"border",!1,i)-.5)),s&&(r=te.exec(t))&&"px"!==(r[3]||"px")&&(e.style[u]=t,t=S.css(e,u)),Ye(0,t,s)}}}),S.cssHooks.marginLeft=Fe(y.reliableMarginLeft,function(e,t){if(t)return(parseFloat(We(e,"marginLeft"))||e.getBoundingClientRect().left-Me(e,{marginLeft:0},function(){return e.getBoundingClientRect().left}))+"px"}),S.each({margin:"",padding:"",border:"Width"},function(i,o){S.cssHooks[i+o]={expand:function(e){for(var t=0,n={},r="string"==typeof e?e.split(" "):[e];t<4;t++)n[i+ne[t]+o]=r[t]||r[t-2]||r[0];return n}},"margin"!==i&&(S.cssHooks[i+o].set=Ye)}),S.fn.extend({css:function(e,t){return $(this,function(e,t,n){var r,i,o={},a=0;if(Array.isArray(t)){for(r=Re(e),i=t.length;a<i;a++)o[t[a]]=S.css(e,t[a],!1,r);return o}return void 0!==n?S.style(e,t,n):S.css(e,t)},e,t,1<arguments.length)}}),((S.Tween=Ke).prototype={constructor:Ke,init:function(e,t,n,r,i,o){this.elem=e,this.prop=n,this.easing=i||S.easing._default,this.options=t,this.start=this.now=this.cur(),this.end=r,this.unit=o||(S.cssNumber[n]?"":"px")},cur:function(){var e=Ke.propHooks[this.prop];return e&&e.get?e.get(this):Ke.propHooks._default.get(this)},run:function(e){var t,n=Ke.propHooks[this.prop];return this.options.duration?this.pos=t=S.easing[this.easing](e,this.options.duration*e,0,1,this.options.duration):this.pos=t=e,this.now=(this.end-this.start)*t+this.start,this.options.step&&this.options.step.call(this.elem,this.now,this),n&&n.set?n.set(this):Ke.propHooks._default.set(this),this}}).init.prototype=Ke.prototype,(Ke.propHooks={_default:{get:function(e){var t;return 1!==e.elem.nodeType||null!=e.elem[e.prop]&&null==e.elem.style[e.prop]?e.elem[e.prop]:(t=S.css(e.elem,e.prop,""))&&"auto"!==t?t:0},set:function(e){S.fx.step[e.prop]?S.fx.step[e.prop](e):1!==e.elem.nodeType||!S.cssHooks[e.prop]&&null==e.elem.style[ze(e.prop)]?e.elem[e.prop]=e.now:S.style(e.elem,e.prop,e.now+e.unit)}}}).scrollTop=Ke.propHooks.scrollLeft={set:function(e){e.elem.nodeType&&e.elem.parentNode&&(e.elem[e.prop]=e.now)}},S.easing={linear:function(e){return e},swing:function(e){return.5-Math.cos(e*Math.PI)/2},_default:"swing"},S.fx=Ke.prototype.init,S.fx.step={};var Ze,et,tt,nt,rt=/^(?:toggle|show|hide)$/,it=/queueHooks$/;function ot(){et&&(!1===E.hidden&&C.requestAnimationFrame?C.requestAnimationFrame(ot):C.setTimeout(ot,S.fx.interval),S.fx.tick())}function at(){return C.setTimeout(function(){Ze=void 0}),Ze=Date.now()}function st(e,t){var n,r=0,i={height:e};for(t=t?1:0;r<4;r+=2-t)i["margin"+(n=ne[r])]=i["padding"+n]=e;return t&&(i.opacity=i.width=e),i}function ut(e,t,n){for(var r,i=(lt.tweeners[t]||[]).concat(lt.tweeners["*"]),o=0,a=i.length;o<a;o++)if(r=i[o].call(n,t,e))return r}function lt(o,e,t){var n,a,r=0,i=lt.prefilters.length,s=S.Deferred().always(function(){delete u.elem}),u=function(){if(a)return!1;for(var e=Ze||at(),t=Math.max(0,l.startTime+l.duration-e),n=1-(t/l.duration||0),r=0,i=l.tweens.length;r<i;r++)l.tweens[r].run(n);return s.notifyWith(o,[l,n,t]),n<1&&i?t:(i||s.notifyWith(o,[l,1,0]),s.resolveWith(o,[l]),!1)},l=s.promise({elem:o,props:S.extend({},e),opts:S.extend(!0,{specialEasing:{},easing:S.easing._default},t),originalProperties:e,originalOptions:t,startTime:Ze||at(),duration:t.duration,tweens:[],createTween:function(e,t){var n=S.Tween(o,l.opts,e,t,l.opts.specialEasing[e]||l.opts.easing);return l.tweens.push(n),n},stop:function(e){var t=0,n=e?l.tweens.length:0;if(a)return this;for(a=!0;t<n;t++)l.tweens[t].run(1);return e?(s.notifyWith(o,[l,1,0]),s.resolveWith(o,[l,e])):s.rejectWith(o,[l,e]),this}}),c=l.props;for(!function(e,t){var n,r,i,o,a;for(n in e)if(i=t[r=X(n)],o=e[n],Array.isArray(o)&&(i=o[1],o=e[n]=o[0]),n!==r&&(e[r]=o,delete e[n]),(a=S.cssHooks[r])&&"expand"in a)for(n in o=a.expand(o),delete e[r],o)n in e||(e[n]=o[n],t[n]=i);else t[r]=i}(c,l.opts.specialEasing);r<i;r++)if(n=lt.prefilters[r].call(l,o,c,l.opts))return m(n.stop)&&(S._queueHooks(l.elem,l.opts.queue).stop=n.stop.bind(n)),n;return S.map(c,ut,l),m(l.opts.start)&&l.opts.start.call(o,l),l.progress(l.opts.progress).done(l.opts.done,l.opts.complete).fail(l.opts.fail).always(l.opts.always),S.fx.timer(S.extend(u,{elem:o,anim:l,queue:l.opts.queue})),l}S.Animation=S.extend(lt,{tweeners:{"*":[function(e,t){var n=this.createTween(e,t);return se(n.elem,e,te.exec(t),n),n}]},tweener:function(e,t){m(e)?(t=e,e=["*"]):e=e.match(P);for(var n,r=0,i=e.length;r<i;r++)n=e[r],lt.tweeners[n]=lt.tweeners[n]||[],lt.tweeners[n].unshift(t)},prefilters:[function(e,t,n){var r,i,o,a,s,u,l,c,f="width"in t||"height"in t,p=this,d={},h=e.style,g=e.nodeType&&ae(e),v=Y.get(e,"fxshow");for(r in n.queue||(null==(a=S._queueHooks(e,"fx")).unqueued&&(a.unqueued=0,s=a.empty.fire,a.empty.fire=function(){a.unqueued||s()}),a.unqueued++,p.always(function(){p.always(function(){a.unqueued--,S.queue(e,"fx").length||a.empty.fire()})})),t)if(i=t[r],rt.test(i)){if(delete t[r],o=o||"toggle"===i,i===(g?"hide":"show")){if("show"!==i||!v||void 0===v[r])continue;g=!0}d[r]=v&&v[r]||S.style(e,r)}if((u=!S.isEmptyObject(t))||!S.isEmptyObject(d))for(r in f&&1===e.nodeType&&(n.overflow=[h.overflow,h.overflowX,h.overflowY],null==(l=v&&v.display)&&(l=Y.get(e,"display")),"none"===(c=S.css(e,"display"))&&(l?c=l:(le([e],!0),l=e.style.display||l,c=S.css(e,"display"),le([e]))),("inline"===c||"inline-block"===c&&null!=l)&&"none"===S.css(e,"float")&&(u||(p.done(function(){h.display=l}),null==l&&(c=h.display,l="none"===c?"":c)),h.display="inline-block")),n.overflow&&(h.overflow="hidden",p.always(function(){h.overflow=n.overflow[0],h.overflowX=n.overflow[1],h.overflowY=n.overflow[2]})),u=!1,d)u||(v?"hidden"in v&&(g=v.hidden):v=Y.access(e,"fxshow",{display:l}),o&&(v.hidden=!g),g&&le([e],!0),p.done(function(){for(r in g||le([e]),Y.remove(e,"fxshow"),d)S.style(e,r,d[r])})),u=ut(g?v[r]:0,r,p),r in v||(v[r]=u.start,g&&(u.end=u.start,u.start=0))}],prefilter:function(e,t){t?lt.prefilters.unshift(e):lt.prefilters.push(e)}}),S.speed=function(e,t,n){var r=e&&"object"==typeof e?S.extend({},e):{complete:n||!n&&t||m(e)&&e,duration:e,easing:n&&t||t&&!m(t)&&t};return S.fx.off?r.duration=0:"number"!=typeof r.duration&&(r.duration in S.fx.speeds?r.duration=S.fx.speeds[r.duration]:r.duration=S.fx.speeds._default),null!=r.queue&&!0!==r.queue||(r.queue="fx"),r.old=r.complete,r.complete=function(){m(r.old)&&r.old.call(this),r.queue&&S.dequeue(this,r.queue)},r},S.fn.extend({fadeTo:function(e,t,n,r){return this.filter(ae).css("opacity",0).show().end().animate({opacity:t},e,n,r)},animate:function(t,e,n,r){var i=S.isEmptyObject(t),o=S.speed(e,n,r),a=function(){var e=lt(this,S.extend({},t),o);(i||Y.get(this,"finish"))&&e.stop(!0)};return a.finish=a,i||!1===o.queue?this.each(a):this.queue(o.queue,a)},stop:function(i,e,o){var a=function(e){var t=e.stop;delete e.stop,t(o)};return"string"!=typeof i&&(o=e,e=i,i=void 0),e&&this.queue(i||"fx",[]),this.each(function(){var e=!0,t=null!=i&&i+"queueHooks",n=S.timers,r=Y.get(this);if(t)r[t]&&r[t].stop&&a(r[t]);else for(t in r)r[t]&&r[t].stop&&it.test(t)&&a(r[t]);for(t=n.length;t--;)n[t].elem!==this||null!=i&&n[t].queue!==i||(n[t].anim.stop(o),e=!1,n.splice(t,1));!e&&o||S.dequeue(this,i)})},finish:function(a){return!1!==a&&(a=a||"fx"),this.each(function(){var e,t=Y.get(this),n=t[a+"queue"],r=t[a+"queueHooks"],i=S.timers,o=n?n.length:0;for(t.finish=!0,S.queue(this,a,[]),r&&r.stop&&r.stop.call(this,!0),e=i.length;e--;)i[e].elem===this&&i[e].queue===a&&(i[e].anim.stop(!0),i.splice(e,1));for(e=0;e<o;e++)n[e]&&n[e].finish&&n[e].finish.call(this);delete t.finish})}}),S.each(["toggle","show","hide"],function(e,r){var i=S.fn[r];S.fn[r]=function(e,t,n){return null==e||"boolean"==typeof e?i.apply(this,arguments):this.animate(st(r,!0),e,t,n)}}),S.each({slideDown:st("show"),slideUp:st("hide"),slideToggle:st("toggle"),fadeIn:{opacity:"show"},fadeOut:{opacity:"hide"},fadeToggle:{opacity:"toggle"}},function(e,r){S.fn[e]=function(e,t,n){return this.animate(r,e,t,n)}}),S.timers=[],S.fx.tick=function(){var e,t=0,n=S.timers;for(Ze=Date.now();t<n.length;t++)(e=n[t])()||n[t]!==e||n.splice(t--,1);n.length||S.fx.stop(),Ze=void 0},S.fx.timer=function(e){S.timers.push(e),S.fx.start()},S.fx.interval=13,S.fx.start=function(){et||(et=!0,ot())},S.fx.stop=function(){et=null},S.fx.speeds={slow:600,fast:200,_default:400},S.fn.delay=function(r,e){return r=S.fx&&S.fx.speeds[r]||r,e=e||"fx",this.queue(e,function(e,t){var n=C.setTimeout(e,r);t.stop=function(){C.clearTimeout(n)}})},tt=E.createElement("input"),nt=E.createElement("select").appendChild(E.createElement("option")),tt.type="checkbox",y.checkOn=""!==tt.value,y.optSelected=nt.selected,(tt=E.createElement("input")).value="t",tt.type="radio",y.radioValue="t"===tt.value;var ct,ft=S.expr.attrHandle;S.fn.extend({attr:function(e,t){return $(this,S.attr,e,t,1<arguments.length)},removeAttr:function(e){return this.each(function(){S.removeAttr(this,e)})}}),S.extend({attr:function(e,t,n){var r,i,o=e.nodeType;if(3!==o&&8!==o&&2!==o)return"undefined"==typeof e.getAttribute?S.prop(e,t,n):(1===o&&S.isXMLDoc(e)||(i=S.attrHooks[t.toLowerCase()]||(S.expr.match.bool.test(t)?ct:void 0)),void 0!==n?null===n?void S.removeAttr(e,t):i&&"set"in i&&void 0!==(r=i.set(e,n,t))?r:(e.setAttribute(t,n+""),n):i&&"get"in i&&null!==(r=i.get(e,t))?r:null==(r=S.find.attr(e,t))?void 0:r)},attrHooks:{type:{set:function(e,t){if(!y.radioValue&&"radio"===t&&A(e,"input")){var n=e.value;return e.setAttribute("type",t),n&&(e.value=n),t}}}},removeAttr:function(e,t){var n,r=0,i=t&&t.match(P);if(i&&1===e.nodeType)while(n=i[r++])e.removeAttribute(n)}}),ct={set:function(e,t,n){return!1===t?S.removeAttr(e,n):e.setAttribute(n,n),n}},S.each(S.expr.match.bool.source.match(/\w+/g),function(e,t){var a=ft[t]||S.find.attr;ft[t]=function(e,t,n){var r,i,o=t.toLowerCase();return n||(i=ft[o],ft[o]=r,r=null!=a(e,t,n)?o:null,ft[o]=i),r}});var pt=/^(?:input|select|textarea|button)$/i,dt=/^(?:a|area)$/i;function ht(e){return(e.match(P)||[]).join(" ")}function gt(e){return e.getAttribute&&e.getAttribute("class")||""}function vt(e){return Array.isArray(e)?e:"string"==typeof e&&e.match(P)||[]}S.fn.extend({prop:function(e,t){return $(this,S.prop,e,t,1<arguments.length)},removeProp:function(e){return this.each(function(){delete this[S.propFix[e]||e]})}}),S.extend({prop:function(e,t,n){var r,i,o=e.nodeType;if(3!==o&&8!==o&&2!==o)return 1===o&&S.isXMLDoc(e)||(t=S.propFix[t]||t,i=S.propHooks[t]),void 0!==n?i&&"set"in i&&void 0!==(r=i.set(e,n,t))?r:e[t]=n:i&&"get"in i&&null!==(r=i.get(e,t))?r:e[t]},propHooks:{tabIndex:{get:function(e){var t=S.find.attr(e,"tabindex");return t?parseInt(t,10):pt.test(e.nodeName)||dt.test(e.nodeName)&&e.href?0:-1}}},propFix:{"for":"htmlFor","class":"className"}}),y.optSelected||(S.propHooks.selected={get:function(e){var t=e.parentNode;return t&&t.parentNode&&t.parentNode.selectedIndex,null},set:function(e){var t=e.parentNode;t&&(t.selectedIndex,t.parentNode&&t.parentNode.selectedIndex)}}),S.each(["tabIndex","readOnly","maxLength","cellSpacing","cellPadding","rowSpan","colSpan","useMap","frameBorder","contentEditable"],function(){S.propFix[this.toLowerCase()]=this}),S.fn.extend({addClass:function(t){var e,n,r,i,o,a,s,u=0;if(m(t))return this.each(function(e){S(this).addClass(t.call(this,e,gt(this)))});if((e=vt(t)).length)while(n=this[u++])if(i=gt(n),r=1===n.nodeType&&" "+ht(i)+" "){a=0;while(o=e[a++])r.indexOf(" "+o+" ")<0&&(r+=o+" ");i!==(s=ht(r))&&n.setAttribute("class",s)}return this},removeClass:function(t){var e,n,r,i,o,a,s,u=0;if(m(t))return this.each(function(e){S(this).removeClass(t.call(this,e,gt(this)))});if(!arguments.length)return this.attr("class","");if((e=vt(t)).length)while(n=this[u++])if(i=gt(n),r=1===n.nodeType&&" "+ht(i)+" "){a=0;while(o=e[a++])while(-1<r.indexOf(" "+o+" "))r=r.replace(" "+o+" "," ");i!==(s=ht(r))&&n.setAttribute("class",s)}return this},toggleClass:function(i,t){var o=typeof i,a="string"===o||Array.isArray(i);return"boolean"==typeof t&&a?t?this.addClass(i):this.removeClass(i):m(i)?this.each(function(e){S(this).toggleClass(i.call(this,e,gt(this),t),t)}):this.each(function(){var e,t,n,r;if(a){t=0,n=S(this),r=vt(i);while(e=r[t++])n.hasClass(e)?n.removeClass(e):n.addClass(e)}else void 0!==i&&"boolean"!==o||((e=gt(this))&&Y.set(this,"__className__",e),this.setAttribute&&this.setAttribute("class",e||!1===i?"":Y.get(this,"__className__")||""))})},hasClass:function(e){var t,n,r=0;t=" "+e+" ";while(n=this[r++])if(1===n.nodeType&&-1<(" "+ht(gt(n))+" ").indexOf(t))return!0;return!1}});var yt=/\r/g;S.fn.extend({val:function(n){var r,e,i,t=this[0];return arguments.length?(i=m(n),this.each(function(e){var t;1===this.nodeType&&(null==(t=i?n.call(this,e,S(this).val()):n)?t="":"number"==typeof t?t+="":Array.isArray(t)&&(t=S.map(t,function(e){return null==e?"":e+""})),(r=S.valHooks[this.type]||S.valHooks[this.nodeName.toLowerCase()])&&"set"in r&&void 0!==r.set(this,t,"value")||(this.value=t))})):t?(r=S.valHooks[t.type]||S.valHooks[t.nodeName.toLowerCase()])&&"get"in r&&void 0!==(e=r.get(t,"value"))?e:"string"==typeof(e=t.value)?e.replace(yt,""):null==e?"":e:void 0}}),S.extend({valHooks:{option:{get:function(e){var t=S.find.attr(e,"value");return null!=t?t:ht(S.text(e))}},select:{get:function(e){var t,n,r,i=e.options,o=e.selectedIndex,a="select-one"===e.type,s=a?null:[],u=a?o+1:i.length;for(r=o<0?u:a?o:0;r<u;r++)if(((n=i[r]).selected||r===o)&&!n.disabled&&(!n.parentNode.disabled||!A(n.parentNode,"optgroup"))){if(t=S(n).val(),a)return t;s.push(t)}return s},set:function(e,t){var n,r,i=e.options,o=S.makeArray(t),a=i.length;while(a--)((r=i[a]).selected=-1<S.inArray(S.valHooks.option.get(r),o))&&(n=!0);return n||(e.selectedIndex=-1),o}}}}),S.each(["radio","checkbox"],function(){S.valHooks[this]={set:function(e,t){if(Array.isArray(t))return e.checked=-1<S.inArray(S(e).val(),t)}},y.checkOn||(S.valHooks[this].get=function(e){return null===e.getAttribute("value")?"on":e.value})}),y.focusin="onfocusin"in C;var mt=/^(?:focusinfocus|focusoutblur)$/,xt=function(e){e.stopPropagation()};S.extend(S.event,{trigger:function(e,t,n,r){var i,o,a,s,u,l,c,f,p=[n||E],d=v.call(e,"type")?e.type:e,h=v.call(e,"namespace")?e.namespace.split("."):[];if(o=f=a=n=n||E,3!==n.nodeType&&8!==n.nodeType&&!mt.test(d+S.event.triggered)&&(-1<d.indexOf(".")&&(d=(h=d.split(".")).shift(),h.sort()),u=d.indexOf(":")<0&&"on"+d,(e=e[S.expando]?e:new S.Event(d,"object"==typeof e&&e)).isTrigger=r?2:3,e.namespace=h.join("."),e.rnamespace=e.namespace?new RegExp("(^|\\.)"+h.join("\\.(?:.*\\.|)")+"(\\.|$)"):null,e.result=void 0,e.target||(e.target=n),t=null==t?[e]:S.makeArray(t,[e]),c=S.event.special[d]||{},r||!c.trigger||!1!==c.trigger.apply(n,t))){if(!r&&!c.noBubble&&!x(n)){for(s=c.delegateType||d,mt.test(s+d)||(o=o.parentNode);o;o=o.parentNode)p.push(o),a=o;a===(n.ownerDocument||E)&&p.push(a.defaultView||a.parentWindow||C)}i=0;while((o=p[i++])&&!e.isPropagationStopped())f=o,e.type=1<i?s:c.bindType||d,(l=(Y.get(o,"events")||Object.create(null))[e.type]&&Y.get(o,"handle"))&&l.apply(o,t),(l=u&&o[u])&&l.apply&&V(o)&&(e.result=l.apply(o,t),!1===e.result&&e.preventDefault());return e.type=d,r||e.isDefaultPrevented()||c._default&&!1!==c._default.apply(p.pop(),t)||!V(n)||u&&m(n[d])&&!x(n)&&((a=n[u])&&(n[u]=null),S.event.triggered=d,e.isPropagationStopped()&&f.addEventListener(d,xt),n[d](),e.isPropagationStopped()&&f.removeEventListener(d,xt),S.event.triggered=void 0,a&&(n[u]=a)),e.result}},simulate:function(e,t,n){var r=S.extend(new S.Event,n,{type:e,isSimulated:!0});S.event.trigger(r,null,t)}}),S.fn.extend({trigger:function(e,t){return this.each(function(){S.event.trigger(e,t,this)})},triggerHandler:function(e,t){var n=this[0];if(n)return S.event.trigger(e,t,n,!0)}}),y.focusin||S.each({focus:"focusin",blur:"focusout"},function(n,r){var i=function(e){S.event.simulate(r,e.target,S.event.fix(e))};S.event.special[r]={setup:function(){var e=this.ownerDocument||this.document||this,t=Y.access(e,r);t||e.addEventListener(n,i,!0),Y.access(e,r,(t||0)+1)},teardown:function(){var e=this.ownerDocument||this.document||this,t=Y.access(e,r)-1;t?Y.access(e,r,t):(e.removeEventListener(n,i,!0),Y.remove(e,r))}}});var bt=C.location,wt={guid:Date.now()},Tt=/\?/;S.parseXML=function(e){var t,n;if(!e||"string"!=typeof e)return null;try{t=(new C.DOMParser).parseFromString(e,"text/xml")}catch(e){}return n=t&&t.getElementsByTagName("parsererror")[0],t&&!n||S.error("Invalid XML: "+(n?S.map(n.childNodes,function(e){return e.textContent}).join("\n"):e)),t};var Ct=/\[\]$/,Et=/\r?\n/g,St=/^(?:submit|button|image|reset|file)$/i,kt=/^(?:input|select|textarea|keygen)/i;function At(n,e,r,i){var t;if(Array.isArray(e))S.each(e,function(e,t){r||Ct.test(n)?i(n,t):At(n+"["+("object"==typeof t&&null!=t?e:"")+"]",t,r,i)});else if(r||"object"!==w(e))i(n,e);else for(t in e)At(n+"["+t+"]",e[t],r,i)}S.param=function(e,t){var n,r=[],i=function(e,t){var n=m(t)?t():t;r[r.length]=encodeURIComponent(e)+"="+encodeURIComponent(null==n?"":n)};if(null==e)return"";if(Array.isArray(e)||e.jquery&&!S.isPlainObject(e))S.each(e,function(){i(this.name,this.value)});else for(n in e)At(n,e[n],t,i);return r.join("&")},S.fn.extend({serialize:function(){return S.param(this.serializeArray())},serializeArray:function(){return this.map(function(){var e=S.prop(this,"elements");return e?S.makeArray(e):this}).filter(function(){var e=this.type;return this.name&&!S(this).is(":disabled")&&kt.test(this.nodeName)&&!St.test(e)&&(this.checked||!pe.test(e))}).map(function(e,t){var n=S(this).val();return null==n?null:Array.isArray(n)?S.map(n,function(e){return{name:t.name,value:e.replace(Et,"\r\n")}}):{name:t.name,value:n.replace(Et,"\r\n")}}).get()}});var Nt=/%20/g,jt=/#.*$/,Dt=/([?&])_=[^&]*/,qt=/^(.*?):[ \t]*([^\r\n]*)$/gm,Lt=/^(?:GET|HEAD)$/,Ht=/^\/\//,Ot={},Pt={},Rt="*/".concat("*"),Mt=E.createElement("a");function It(o){return function(e,t){"string"!=typeof e&&(t=e,e="*");var n,r=0,i=e.toLowerCase().match(P)||[];if(m(t))while(n=i[r++])"+"===n[0]?(n=n.slice(1)||"*",(o[n]=o[n]||[]).unshift(t)):(o[n]=o[n]||[]).push(t)}}function Wt(t,i,o,a){var s={},u=t===Pt;function l(e){var r;return s[e]=!0,S.each(t[e]||[],function(e,t){var n=t(i,o,a);return"string"!=typeof n||u||s[n]?u?!(r=n):void 0:(i.dataTypes.unshift(n),l(n),!1)}),r}return l(i.dataTypes[0])||!s["*"]&&l("*")}function Ft(e,t){var n,r,i=S.ajaxSettings.flatOptions||{};for(n in t)void 0!==t[n]&&((i[n]?e:r||(r={}))[n]=t[n]);return r&&S.extend(!0,e,r),e}Mt.href=bt.href,S.extend({active:0,lastModified:{},etag:{},ajaxSettings:{url:bt.href,type:"GET",isLocal:/^(?:about|app|app-storage|.+-extension|file|res|widget):$/.test(bt.protocol),global:!0,processData:!0,async:!0,contentType:"application/x-www-form-urlencoded; charset=UTF-8",accepts:{"*":Rt,text:"text/plain",html:"text/html",xml:"application/xml, text/xml",json:"application/json, text/javascript"},contents:{xml:/\bxml\b/,html:/\bhtml/,json:/\bjson\b/},responseFields:{xml:"responseXML",text:"responseText",json:"responseJSON"},converters:{"* text":String,"text html":!0,"text json":JSON.parse,"text xml":S.parseXML},flatOptions:{url:!0,context:!0}},ajaxSetup:function(e,t){return t?Ft(Ft(e,S.ajaxSettings),t):Ft(S.ajaxSettings,e)},ajaxPrefilter:It(Ot),ajaxTransport:It(Pt),ajax:function(e,t){"object"==typeof e&&(t=e,e=void 0),t=t||{};var c,f,p,n,d,r,h,g,i,o,v=S.ajaxSetup({},t),y=v.context||v,m=v.context&&(y.nodeType||y.jquery)?S(y):S.event,x=S.Deferred(),b=S.Callbacks("once memory"),w=v.statusCode||{},a={},s={},u="canceled",T={readyState:0,getResponseHeader:function(e){var t;if(h){if(!n){n={};while(t=qt.exec(p))n[t[1].toLowerCase()+" "]=(n[t[1].toLowerCase()+" "]||[]).concat(t[2])}t=n[e.toLowerCase()+" "]}return null==t?null:t.join(", ")},getAllResponseHeaders:function(){return h?p:null},setRequestHeader:function(e,t){return null==h&&(e=s[e.toLowerCase()]=s[e.toLowerCase()]||e,a[e]=t),this},overrideMimeType:function(e){return null==h&&(v.mimeType=e),this},statusCode:function(e){var t;if(e)if(h)T.always(e[T.status]);else for(t in e)w[t]=[w[t],e[t]];return this},abort:function(e){var t=e||u;return c&&c.abort(t),l(0,t),this}};if(x.promise(T),v.url=((e||v.url||bt.href)+"").replace(Ht,bt.protocol+"//"),v.type=t.method||t.type||v.method||v.type,v.dataTypes=(v.dataType||"*").toLowerCase().match(P)||[""],null==v.crossDomain){r=E.createElement("a");try{r.href=v.url,r.href=r.href,v.crossDomain=Mt.protocol+"//"+Mt.host!=r.protocol+"//"+r.host}catch(e){v.crossDomain=!0}}if(v.data&&v.processData&&"string"!=typeof v.data&&(v.data=S.param(v.data,v.traditional)),Wt(Ot,v,t,T),h)return T;for(i in(g=S.event&&v.global)&&0==S.active++&&S.event.trigger("ajaxStart"),v.type=v.type.toUpperCase(),v.hasContent=!Lt.test(v.type),f=v.url.replace(jt,""),v.hasContent?v.data&&v.processData&&0===(v.contentType||"").indexOf("application/x-www-form-urlencoded")&&(v.data=v.data.replace(Nt,"+")):(o=v.url.slice(f.length),v.data&&(v.processData||"string"==typeof v.data)&&(f+=(Tt.test(f)?"&":"?")+v.data,delete v.data),!1===v.cache&&(f=f.replace(Dt,"$1"),o=(Tt.test(f)?"&":"?")+"_="+wt.guid+++o),v.url=f+o),v.ifModified&&(S.lastModified[f]&&T.setRequestHeader("If-Modified-Since",S.lastModified[f]),S.etag[f]&&T.setRequestHeader("If-None-Match",S.etag[f])),(v.data&&v.hasContent&&!1!==v.contentType||t.contentType)&&T.setRequestHeader("Content-Type",v.contentType),T.setRequestHeader("Accept",v.dataTypes[0]&&v.accepts[v.dataTypes[0]]?v.accepts[v.dataTypes[0]]+("*"!==v.dataTypes[0]?", "+Rt+"; q=0.01":""):v.accepts["*"]),v.headers)T.setRequestHeader(i,v.headers[i]);if(v.beforeSend&&(!1===v.beforeSend.call(y,T,v)||h))return T.abort();if(u="abort",b.add(v.complete),T.done(v.success),T.fail(v.error),c=Wt(Pt,v,t,T)){if(T.readyState=1,g&&m.trigger("ajaxSend",[T,v]),h)return T;v.async&&0<v.timeout&&(d=C.setTimeout(function(){T.abort("timeout")},v.timeout));try{h=!1,c.send(a,l)}catch(e){if(h)throw e;l(-1,e)}}else l(-1,"No Transport");function l(e,t,n,r){var i,o,a,s,u,l=t;h||(h=!0,d&&C.clearTimeout(d),c=void 0,p=r||"",T.readyState=0<e?4:0,i=200<=e&&e<300||304===e,n&&(s=function(e,t,n){var r,i,o,a,s=e.contents,u=e.dataTypes;while("*"===u[0])u.shift(),void 0===r&&(r=e.mimeType||t.getResponseHeader("Content-Type"));if(r)for(i in s)if(s[i]&&s[i].test(r)){u.unshift(i);break}if(u[0]in n)o=u[0];else{for(i in n){if(!u[0]||e.converters[i+" "+u[0]]){o=i;break}a||(a=i)}o=o||a}if(o)return o!==u[0]&&u.unshift(o),n[o]}(v,T,n)),!i&&-1<S.inArray("script",v.dataTypes)&&S.inArray("json",v.dataTypes)<0&&(v.converters["text script"]=function(){}),s=function(e,t,n,r){var i,o,a,s,u,l={},c=e.dataTypes.slice();if(c[1])for(a in e.converters)l[a.toLowerCase()]=e.converters[a];o=c.shift();while(o)if(e.responseFields[o]&&(n[e.responseFields[o]]=t),!u&&r&&e.dataFilter&&(t=e.dataFilter(t,e.dataType)),u=o,o=c.shift())if("*"===o)o=u;else if("*"!==u&&u!==o){if(!(a=l[u+" "+o]||l["* "+o]))for(i in l)if((s=i.split(" "))[1]===o&&(a=l[u+" "+s[0]]||l["* "+s[0]])){!0===a?a=l[i]:!0!==l[i]&&(o=s[0],c.unshift(s[1]));break}if(!0!==a)if(a&&e["throws"])t=a(t);else try{t=a(t)}catch(e){return{state:"parsererror",error:a?e:"No conversion from "+u+" to "+o}}}return{state:"success",data:t}}(v,s,T,i),i?(v.ifModified&&((u=T.getResponseHeader("Last-Modified"))&&(S.lastModified[f]=u),(u=T.getResponseHeader("etag"))&&(S.etag[f]=u)),204===e||"HEAD"===v.type?l="nocontent":304===e?l="notmodified":(l=s.state,o=s.data,i=!(a=s.error))):(a=l,!e&&l||(l="error",e<0&&(e=0))),T.status=e,T.statusText=(t||l)+"",i?x.resolveWith(y,[o,l,T]):x.rejectWith(y,[T,l,a]),T.statusCode(w),w=void 0,g&&m.trigger(i?"ajaxSuccess":"ajaxError",[T,v,i?o:a]),b.fireWith(y,[T,l]),g&&(m.trigger("ajaxComplete",[T,v]),--S.active||S.event.trigger("ajaxStop")))}return T},getJSON:function(e,t,n){return S.get(e,t,n,"json")},getScript:function(e,t){return S.get(e,void 0,t,"script")}}),S.each(["get","post"],function(e,i){S[i]=function(e,t,n,r){return m(t)&&(r=r||n,n=t,t=void 0),S.ajax(S.extend({url:e,type:i,dataType:r,data:t,success:n},S.isPlainObject(e)&&e))}}),S.ajaxPrefilter(function(e){var t;for(t in e.headers)"content-type"===t.toLowerCase()&&(e.contentType=e.headers[t]||"")}),S._evalUrl=function(e,t,n){return S.ajax({url:e,type:"GET",dataType:"script",cache:!0,async:!1,global:!1,converters:{"text script":function(){}},dataFilter:function(e){S.globalEval(e,t,n)}})},S.fn.extend({wrapAll:function(e){var t;return this[0]&&(m(e)&&(e=e.call(this[0])),t=S(e,this[0].ownerDocument).eq(0).clone(!0),this[0].parentNode&&t.insertBefore(this[0]),t.map(function(){var e=this;while(e.firstElementChild)e=e.firstElementChild;return e}).append(this)),this},wrapInner:function(n){return m(n)?this.each(function(e){S(this).wrapInner(n.call(this,e))}):this.each(function(){var e=S(this),t=e.contents();t.length?t.wrapAll(n):e.append(n)})},wrap:function(t){var n=m(t);return this.each(function(e){S(this).wrapAll(n?t.call(this,e):t)})},unwrap:function(e){return this.parent(e).not("body").each(function(){S(this).replaceWith(this.childNodes)}),this}}),S.expr.pseudos.hidden=function(e){return!S.expr.pseudos.visible(e)},S.expr.pseudos.visible=function(e){return!!(e.offsetWidth||e.offsetHeight||e.getClientRects().length)},S.ajaxSettings.xhr=function(){try{return new C.XMLHttpRequest}catch(e){}};var Bt={0:200,1223:204},$t=S.ajaxSettings.xhr();y.cors=!!$t&&"withCredentials"in $t,y.ajax=$t=!!$t,S.ajaxTransport(function(i){var o,a;if(y.cors||$t&&!i.crossDomain)return{send:function(e,t){var n,r=i.xhr();if(r.open(i.type,i.url,i.async,i.username,i.password),i.xhrFields)for(n in i.xhrFields)r[n]=i.xhrFields[n];for(n in i.mimeType&&r.overrideMimeType&&r.overrideMimeType(i.mimeType),i.crossDomain||e["X-Requested-With"]||(e["X-Requested-With"]="XMLHttpRequest"),e)r.setRequestHeader(n,e[n]);o=function(e){return function(){o&&(o=a=r.onload=r.onerror=r.onabort=r.ontimeout=r.onreadystatechange=null,"abort"===e?r.abort():"error"===e?"number"!=typeof r.status?t(0,"error"):t(r.status,r.statusText):t(Bt[r.status]||r.status,r.statusText,"text"!==(r.responseType||"text")||"string"!=typeof r.responseText?{binary:r.response}:{text:r.responseText},r.getAllResponseHeaders()))}},r.onload=o(),a=r.onerror=r.ontimeout=o("error"),void 0!==r.onabort?r.onabort=a:r.onreadystatechange=function(){4===r.readyState&&C.setTimeout(function(){o&&a()})},o=o("abort");try{r.send(i.hasContent&&i.data||null)}catch(e){if(o)throw e}},abort:function(){o&&o()}}}),S.ajaxPrefilter(function(e){e.crossDomain&&(e.contents.script=!1)}),S.ajaxSetup({accepts:{script:"text/javascript, application/javascript, application/ecmascript, application/x-ecmascript"},contents:{script:/\b(?:java|ecma)script\b/},converters:{"text script":function(e){return S.globalEval(e),e}}}),S.ajaxPrefilter("script",function(e){void 0===e.cache&&(e.cache=!1),e.crossDomain&&(e.type="GET")}),S.ajaxTransport("script",function(n){var r,i;if(n.crossDomain||n.scriptAttrs)return{send:function(e,t){r=S("<script>").attr(n.scriptAttrs||{}).prop({charset:n.scriptCharset,src:n.url}).on("load error",i=function(e){r.remove(),i=null,e&&t("error"===e.type?404:200,e.type)}),E.head.appendChild(r[0])},abort:function(){i&&i()}}});var _t,zt=[],Ut=/(=)\?(?=&|$)|\?\?/;S.ajaxSetup({jsonp:"callback",jsonpCallback:function(){var e=zt.pop()||S.expando+"_"+wt.guid++;return this[e]=!0,e}}),S.ajaxPrefilter("json jsonp",function(e,t,n){var r,i,o,a=!1!==e.jsonp&&(Ut.test(e.url)?"url":"string"==typeof e.data&&0===(e.contentType||"").indexOf("application/x-www-form-urlencoded")&&Ut.test(e.data)&&"data");if(a||"jsonp"===e.dataTypes[0])return r=e.jsonpCallback=m(e.jsonpCallback)?e.jsonpCallback():e.jsonpCallback,a?e[a]=e[a].replace(Ut,"$1"+r):!1!==e.jsonp&&(e.url+=(Tt.test(e.url)?"&":"?")+e.jsonp+"="+r),e.converters["script json"]=function(){return o||S.error(r+" was not called"),o[0]},e.dataTypes[0]="json",i=C[r],C[r]=function(){o=arguments},n.always(function(){void 0===i?S(C).removeProp(r):C[r]=i,e[r]&&(e.jsonpCallback=t.jsonpCallback,zt.push(r)),o&&m(i)&&i(o[0]),o=i=void 0}),"script"}),y.createHTMLDocument=((_t=E.implementation.createHTMLDocument("").body).innerHTML="<form></form><form></form>",2===_t.childNodes.length),S.parseHTML=function(e,t,n){return"string"!=typeof e?[]:("boolean"==typeof t&&(n=t,t=!1),t||(y.createHTMLDocument?((r=(t=E.implementation.createHTMLDocument("")).createElement("base")).href=E.location.href,t.head.appendChild(r)):t=E),o=!n&&[],(i=N.exec(e))?[t.createElement(i[1])]:(i=xe([e],t,o),o&&o.length&&S(o).remove(),S.merge([],i.childNodes)));var r,i,o},S.fn.load=function(e,t,n){var r,i,o,a=this,s=e.indexOf(" ");return-1<s&&(r=ht(e.slice(s)),e=e.slice(0,s)),m(t)?(n=t,t=void 0):t&&"object"==typeof t&&(i="POST"),0<a.length&&S.ajax({url:e,type:i||"GET",dataType:"html",data:t}).done(function(e){o=arguments,a.html(r?S("<div>").append(S.parseHTML(e)).find(r):e)}).always(n&&function(e,t){a.each(function(){n.apply(this,o||[e.responseText,t,e])})}),this},S.expr.pseudos.animated=function(t){return S.grep(S.timers,function(e){return t===e.elem}).length},S.offset={setOffset:function(e,t,n){var r,i,o,a,s,u,l=S.css(e,"position"),c=S(e),f={};"static"===l&&(e.style.position="relative"),s=c.offset(),o=S.css(e,"top"),u=S.css(e,"left"),("absolute"===l||"fixed"===l)&&-1<(o+u).indexOf("auto")?(a=(r=c.position()).top,i=r.left):(a=parseFloat(o)||0,i=parseFloat(u)||0),m(t)&&(t=t.call(e,n,S.extend({},s))),null!=t.top&&(f.top=t.top-s.top+a),null!=t.left&&(f.left=t.left-s.left+i),"using"in t?t.using.call(e,f):c.css(f)}},S.fn.extend({offset:function(t){if(arguments.length)return void 0===t?this:this.each(function(e){S.offset.setOffset(this,t,e)});var e,n,r=this[0];return r?r.getClientRects().length?(e=r.getBoundingClientRect(),n=r.ownerDocument.defaultView,{top:e.top+n.pageYOffset,left:e.left+n.pageXOffset}):{top:0,left:0}:void 0},position:function(){if(this[0]){var e,t,n,r=this[0],i={top:0,left:0};if("fixed"===S.css(r,"position"))t=r.getBoundingClientRect();else{t=this.offset(),n=r.ownerDocument,e=r.offsetParent||n.documentElement;while(e&&(e===n.body||e===n.documentElement)&&"static"===S.css(e,"position"))e=e.parentNode;e&&e!==r&&1===e.nodeType&&((i=S(e).offset()).top+=S.css(e,"borderTopWidth",!0),i.left+=S.css(e,"borderLeftWidth",!0))}return{top:t.top-i.top-S.css(r,"marginTop",!0),left:t.left-i.left-S.css(r,"marginLeft",!0)}}},offsetParent:function(){return this.map(function(){var e=this.offsetParent;while(e&&"static"===S.css(e,"position"))e=e.offsetParent;return e||re})}}),S.each({scrollLeft:"pageXOffset",scrollTop:"pageYOffset"},function(t,i){var o="pageYOffset"===i;S.fn[t]=function(e){return $(this,function(e,t,n){var r;if(x(e)?r=e:9===e.nodeType&&(r=e.defaultView),void 0===n)return r?r[i]:e[t];r?r.scrollTo(o?r.pageXOffset:n,o?n:r.pageYOffset):e[t]=n},t,e,arguments.length)}}),S.each(["top","left"],function(e,n){S.cssHooks[n]=Fe(y.pixelPosition,function(e,t){if(t)return t=We(e,n),Pe.test(t)?S(e).position()[n]+"px":t})}),S.each({Height:"height",Width:"width"},function(a,s){S.each({padding:"inner"+a,content:s,"":"outer"+a},function(r,o){S.fn[o]=function(e,t){var n=arguments.length&&(r||"boolean"!=typeof e),i=r||(!0===e||!0===t?"margin":"border");return $(this,function(e,t,n){var r;return x(e)?0===o.indexOf("outer")?e["inner"+a]:e.document.documentElement["client"+a]:9===e.nodeType?(r=e.documentElement,Math.max(e.body["scroll"+a],r["scroll"+a],e.body["offset"+a],r["offset"+a],r["client"+a])):void 0===n?S.css(e,t,i):S.style(e,t,n,i)},s,n?e:void 0,n)}})}),S.each(["ajaxStart","ajaxStop","ajaxComplete","ajaxError","ajaxSuccess","ajaxSend"],function(e,t){S.fn[t]=function(e){return this.on(t,e)}}),S.fn.extend({bind:function(e,t,n){return this.on(e,null,t,n)},unbind:function(e,t){return this.off(e,null,t)},delegate:function(e,t,n,r){return this.on(t,e,n,r)},undelegate:function(e,t,n){return 1===arguments.length?this.off(e,"**"):this.off(t,e||"**",n)},hover:function(e,t){return this.mouseenter(e).mouseleave(t||e)}}),S.each("blur focus focusin focusout resize scroll click dblclick mousedown mouseup mousemove mouseover mouseout mouseenter mouseleave change select submit keydown keypress keyup contextmenu".split(" "),function(e,n){S.fn[n]=function(e,t){return 0<arguments.length?this.on(n,null,e,t):this.trigger(n)}});var Xt=/^[\s\uFEFF\xA0]+|[\s\uFEFF\xA0]+$/g;S.proxy=function(e,t){var n,r,i;if("string"==typeof t&&(n=e[t],t=e,e=n),m(e))return r=s.call(arguments,2),(i=function(){return e.apply(t||this,r.concat(s.call(arguments)))}).guid=e.guid=e.guid||S.guid++,i},S.holdReady=function(e){e?S.readyWait++:S.ready(!0)},S.isArray=Array.isArray,S.parseJSON=JSON.parse,S.nodeName=A,S.isFunction=m,S.isWindow=x,S.camelCase=X,S.type=w,S.now=Date.now,S.isNumeric=function(e){var t=S.type(e);return("number"===t||"string"===t)&&!isNaN(e-parseFloat(e))},S.trim=function(e){return null==e?"":(e+"").replace(Xt,"")},"function"==typeof define&&define.amd&&define("jquery",[],function(){return S});var Vt=C.jQuery,Gt=C.$;return S.noConflict=function(e){return C.$===S&&(C.$=Gt),e&&C.jQuery===S&&(C.jQuery=Vt),S},"undefined"==typeof e&&(C.jQuery=C.$=S),S});
+</script>
+<meta name="viewport" content="width=device-width, initial-scale=1" />
+<style type="text/css">html{font-family:sans-serif;-webkit-text-size-adjust:100%;-ms-text-size-adjust:100%}body{margin:0}article,aside,details,figcaption,figure,footer,header,hgroup,main,menu,nav,section,summary{display:block}audio,canvas,progress,video{display:inline-block;vertical-align:baseline}audio:not([controls]){display:none;height:0}[hidden],template{display:none}a{background-color:transparent}a:active,a:hover{outline:0}abbr[title]{border-bottom:1px dotted}b,strong{font-weight:700}dfn{font-style:italic}h1{margin:.67em 0;font-size:2em}mark{color:#000;background:#ff0}small{font-size:80%}sub,sup{position:relative;font-size:75%;line-height:0;vertical-align:baseline}sup{top:-.5em}sub{bottom:-.25em}img{border:0}svg:not(:root){overflow:hidden}figure{margin:1em 40px}hr{height:0;-webkit-box-sizing:content-box;-moz-box-sizing:content-box;box-sizing:content-box}pre{overflow:auto}code,kbd,pre,samp{font-family:monospace,monospace;font-size:1em}button,input,optgroup,select,textarea{margin:0;font:inherit;color:inherit}button{overflow:visible}button,select{text-transform:none}button,html input[type=button],input[type=reset],input[type=submit]{-webkit-appearance:button;cursor:pointer}button[disabled],html input[disabled]{cursor:default}button::-moz-focus-inner,input::-moz-focus-inner{padding:0;border:0}input{line-height:normal}input[type=checkbox],input[type=radio]{-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;padding:0}input[type=number]::-webkit-inner-spin-button,input[type=number]::-webkit-outer-spin-button{height:auto}input[type=search]{-webkit-box-sizing:content-box;-moz-box-sizing:content-box;box-sizing:content-box;-webkit-appearance:textfield}input[type=search]::-webkit-search-cancel-button,input[type=search]::-webkit-search-decoration{-webkit-appearance:none}fieldset{padding:.35em .625em .75em;margin:0 2px;border:1px solid silver}legend{padding:0;border:0}textarea{overflow:auto}optgroup{font-weight:700}table{border-spacing:0;border-collapse:collapse}td,th{padding:0}@media print{*,:after,:before{color:#000!important;text-shadow:none!important;background:0 0!important;-webkit-box-shadow:none!important;box-shadow:none!important}a,a:visited{text-decoration:underline}a[href]:after{content:" (" attr(href) ")"}abbr[title]:after{content:" (" attr(title) ")"}a[href^="javascript:"]:after,a[href^="#"]:after{content:""}blockquote,pre{border:1px solid #999;page-break-inside:avoid}thead{display:table-header-group}img,tr{page-break-inside:avoid}img{max-width:100%!important}h2,h3,p{orphans:3;widows:3}h2,h3{page-break-after:avoid}.navbar{display:none}.btn>.caret,.dropup>.btn>.caret{border-top-color:#000!important}.label{border:1px solid #000}.table{border-collapse:collapse!important}.table td,.table th{background-color:#fff!important}.table-bordered td,.table-bordered th{border:1px solid #ddd!important}}@font-face{font-family:'Glyphicons Halflings';src:url(data:application/vnd.ms-fontobject;base64,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);src:url(data:application/vnd.ms-fontobject;base64,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) format('embedded-opentype'),url(data:application/font-woff;base64,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) format('woff'),url(data:application/font-sfnt;base64,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) format('truetype'),url(data:image/svg+xml;base64,<?xml version="1.0" standalone="no"?>
<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN" "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd" >
<svg xmlns="http://www.w3.org/2000/svg">
<metadata></metadata>
<defs>
<font id="glyphicons_halflingsregular" horiz-adv-x="1200" >
<font-face units-per-em="1200" ascent="960" descent="-240" />
<missing-glyph horiz-adv-x="500" />
<glyph horiz-adv-x="0" />
<glyph horiz-adv-x="400" />
<glyph unicode=" " />
<glyph unicode="*" d="M600 1100q15 0 34 -1.5t30 -3.5l11 -1q10 -2 17.5 -10.5t7.5 -18.5v-224l158 158q7 7 18 8t19 -6l106 -106q7 -8 6 -19t-8 -18l-158 -158h224q10 0 18.5 -7.5t10.5 -17.5q6 -41 6 -75q0 -15 -1.5 -34t-3.5 -30l-1 -11q-2 -10 -10.5 -17.5t-18.5 -7.5h-224l158 -158 q7 -7 8 -18t-6 -19l-106 -106q-8 -7 -19 -6t-18 8l-158 158v-224q0 -10 -7.5 -18.5t-17.5 -10.5q-41 -6 -75 -6q-15 0 -34 1.5t-30 3.5l-11 1q-10 2 -17.5 10.5t-7.5 18.5v224l-158 -158q-7 -7 -18 -8t-19 6l-106 106q-7 8 -6 19t8 18l158 158h-224q-10 0 -18.5 7.5 t-10.5 17.5q-6 41 -6 75q0 15 1.5 34t3.5 30l1 11q2 10 10.5 17.5t18.5 7.5h224l-158 158q-7 7 -8 18t6 19l106 106q8 7 19 6t18 -8l158 -158v224q0 10 7.5 18.5t17.5 10.5q41 6 75 6z" />
<glyph unicode="+" d="M450 1100h200q21 0 35.5 -14.5t14.5 -35.5v-350h350q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-350v-350q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v350h-350q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5 h350v350q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xa0;" />
<glyph unicode="&#xa5;" d="M825 1100h250q10 0 12.5 -5t-5.5 -13l-364 -364q-6 -6 -11 -18h268q10 0 13 -6t-3 -14l-120 -160q-6 -8 -18 -14t-22 -6h-125v-100h275q10 0 13 -6t-3 -14l-120 -160q-6 -8 -18 -14t-22 -6h-125v-174q0 -11 -7.5 -18.5t-18.5 -7.5h-148q-11 0 -18.5 7.5t-7.5 18.5v174 h-275q-10 0 -13 6t3 14l120 160q6 8 18 14t22 6h125v100h-275q-10 0 -13 6t3 14l120 160q6 8 18 14t22 6h118q-5 12 -11 18l-364 364q-8 8 -5.5 13t12.5 5h250q25 0 43 -18l164 -164q8 -8 18 -8t18 8l164 164q18 18 43 18z" />
<glyph unicode="&#x2000;" horiz-adv-x="650" />
<glyph unicode="&#x2001;" horiz-adv-x="1300" />
<glyph unicode="&#x2002;" horiz-adv-x="650" />
<glyph unicode="&#x2003;" horiz-adv-x="1300" />
<glyph unicode="&#x2004;" horiz-adv-x="433" />
<glyph unicode="&#x2005;" horiz-adv-x="325" />
<glyph unicode="&#x2006;" horiz-adv-x="216" />
<glyph unicode="&#x2007;" horiz-adv-x="216" />
<glyph unicode="&#x2008;" horiz-adv-x="162" />
<glyph unicode="&#x2009;" horiz-adv-x="260" />
<glyph unicode="&#x200a;" horiz-adv-x="72" />
<glyph unicode="&#x202f;" horiz-adv-x="260" />
<glyph unicode="&#x205f;" horiz-adv-x="325" />
<glyph unicode="&#x20ac;" d="M744 1198q242 0 354 -189q60 -104 66 -209h-181q0 45 -17.5 82.5t-43.5 61.5t-58 40.5t-60.5 24t-51.5 7.5q-19 0 -40.5 -5.5t-49.5 -20.5t-53 -38t-49 -62.5t-39 -89.5h379l-100 -100h-300q-6 -50 -6 -100h406l-100 -100h-300q9 -74 33 -132t52.5 -91t61.5 -54.5t59 -29 t47 -7.5q22 0 50.5 7.5t60.5 24.5t58 41t43.5 61t17.5 80h174q-30 -171 -128 -278q-107 -117 -274 -117q-206 0 -324 158q-36 48 -69 133t-45 204h-217l100 100h112q1 47 6 100h-218l100 100h134q20 87 51 153.5t62 103.5q117 141 297 141z" />
<glyph unicode="&#x20bd;" d="M428 1200h350q67 0 120 -13t86 -31t57 -49.5t35 -56.5t17 -64.5t6.5 -60.5t0.5 -57v-16.5v-16.5q0 -36 -0.5 -57t-6.5 -61t-17 -65t-35 -57t-57 -50.5t-86 -31.5t-120 -13h-178l-2 -100h288q10 0 13 -6t-3 -14l-120 -160q-6 -8 -18 -14t-22 -6h-138v-175q0 -11 -5.5 -18 t-15.5 -7h-149q-10 0 -17.5 7.5t-7.5 17.5v175h-267q-10 0 -13 6t3 14l120 160q6 8 18 14t22 6h117v100h-267q-10 0 -13 6t3 14l120 160q6 8 18 14t22 6h117v475q0 10 7.5 17.5t17.5 7.5zM600 1000v-300h203q64 0 86.5 33t22.5 119q0 84 -22.5 116t-86.5 32h-203z" />
<glyph unicode="&#x2212;" d="M250 700h800q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#x231b;" d="M1000 1200v-150q0 -21 -14.5 -35.5t-35.5 -14.5h-50v-100q0 -91 -49.5 -165.5t-130.5 -109.5q81 -35 130.5 -109.5t49.5 -165.5v-150h50q21 0 35.5 -14.5t14.5 -35.5v-150h-800v150q0 21 14.5 35.5t35.5 14.5h50v150q0 91 49.5 165.5t130.5 109.5q-81 35 -130.5 109.5 t-49.5 165.5v100h-50q-21 0 -35.5 14.5t-14.5 35.5v150h800zM400 1000v-100q0 -60 32.5 -109.5t87.5 -73.5q28 -12 44 -37t16 -55t-16 -55t-44 -37q-55 -24 -87.5 -73.5t-32.5 -109.5v-150h400v150q0 60 -32.5 109.5t-87.5 73.5q-28 12 -44 37t-16 55t16 55t44 37 q55 24 87.5 73.5t32.5 109.5v100h-400z" />
<glyph unicode="&#x25fc;" horiz-adv-x="500" d="M0 0z" />
<glyph unicode="&#x2601;" d="M503 1089q110 0 200.5 -59.5t134.5 -156.5q44 14 90 14q120 0 205 -86.5t85 -206.5q0 -121 -85 -207.5t-205 -86.5h-750q-79 0 -135.5 57t-56.5 137q0 69 42.5 122.5t108.5 67.5q-2 12 -2 37q0 153 108 260.5t260 107.5z" />
<glyph unicode="&#x26fa;" d="M774 1193.5q16 -9.5 20.5 -27t-5.5 -33.5l-136 -187l467 -746h30q20 0 35 -18.5t15 -39.5v-42h-1200v42q0 21 15 39.5t35 18.5h30l468 746l-135 183q-10 16 -5.5 34t20.5 28t34 5.5t28 -20.5l111 -148l112 150q9 16 27 20.5t34 -5zM600 200h377l-182 112l-195 534v-646z " />
<glyph unicode="&#x2709;" d="M25 1100h1150q10 0 12.5 -5t-5.5 -13l-564 -567q-8 -8 -18 -8t-18 8l-564 567q-8 8 -5.5 13t12.5 5zM18 882l264 -264q8 -8 8 -18t-8 -18l-264 -264q-8 -8 -13 -5.5t-5 12.5v550q0 10 5 12.5t13 -5.5zM918 618l264 264q8 8 13 5.5t5 -12.5v-550q0 -10 -5 -12.5t-13 5.5 l-264 264q-8 8 -8 18t8 18zM818 482l364 -364q8 -8 5.5 -13t-12.5 -5h-1150q-10 0 -12.5 5t5.5 13l364 364q8 8 18 8t18 -8l164 -164q8 -8 18 -8t18 8l164 164q8 8 18 8t18 -8z" />
<glyph unicode="&#x270f;" d="M1011 1210q19 0 33 -13l153 -153q13 -14 13 -33t-13 -33l-99 -92l-214 214l95 96q13 14 32 14zM1013 800l-615 -614l-214 214l614 614zM317 96l-333 -112l110 335z" />
<glyph unicode="&#xe001;" d="M700 650v-550h250q21 0 35.5 -14.5t14.5 -35.5v-50h-800v50q0 21 14.5 35.5t35.5 14.5h250v550l-500 550h1200z" />
<glyph unicode="&#xe002;" d="M368 1017l645 163q39 15 63 0t24 -49v-831q0 -55 -41.5 -95.5t-111.5 -63.5q-79 -25 -147 -4.5t-86 75t25.5 111.5t122.5 82q72 24 138 8v521l-600 -155v-606q0 -42 -44 -90t-109 -69q-79 -26 -147 -5.5t-86 75.5t25.5 111.5t122.5 82.5q72 24 138 7v639q0 38 14.5 59 t53.5 34z" />
<glyph unicode="&#xe003;" d="M500 1191q100 0 191 -39t156.5 -104.5t104.5 -156.5t39 -191l-1 -2l1 -5q0 -141 -78 -262l275 -274q23 -26 22.5 -44.5t-22.5 -42.5l-59 -58q-26 -20 -46.5 -20t-39.5 20l-275 274q-119 -77 -261 -77l-5 1l-2 -1q-100 0 -191 39t-156.5 104.5t-104.5 156.5t-39 191 t39 191t104.5 156.5t156.5 104.5t191 39zM500 1022q-88 0 -162 -43t-117 -117t-43 -162t43 -162t117 -117t162 -43t162 43t117 117t43 162t-43 162t-117 117t-162 43z" />
<glyph unicode="&#xe005;" d="M649 949q48 68 109.5 104t121.5 38.5t118.5 -20t102.5 -64t71 -100.5t27 -123q0 -57 -33.5 -117.5t-94 -124.5t-126.5 -127.5t-150 -152.5t-146 -174q-62 85 -145.5 174t-150 152.5t-126.5 127.5t-93.5 124.5t-33.5 117.5q0 64 28 123t73 100.5t104 64t119 20 t120.5 -38.5t104.5 -104z" />
<glyph unicode="&#xe006;" d="M407 800l131 353q7 19 17.5 19t17.5 -19l129 -353h421q21 0 24 -8.5t-14 -20.5l-342 -249l130 -401q7 -20 -0.5 -25.5t-24.5 6.5l-343 246l-342 -247q-17 -12 -24.5 -6.5t-0.5 25.5l130 400l-347 251q-17 12 -14 20.5t23 8.5h429z" />
<glyph unicode="&#xe007;" d="M407 800l131 353q7 19 17.5 19t17.5 -19l129 -353h421q21 0 24 -8.5t-14 -20.5l-342 -249l130 -401q7 -20 -0.5 -25.5t-24.5 6.5l-343 246l-342 -247q-17 -12 -24.5 -6.5t-0.5 25.5l130 400l-347 251q-17 12 -14 20.5t23 8.5h429zM477 700h-240l197 -142l-74 -226 l193 139l195 -140l-74 229l192 140h-234l-78 211z" />
<glyph unicode="&#xe008;" d="M600 1200q124 0 212 -88t88 -212v-250q0 -46 -31 -98t-69 -52v-75q0 -10 6 -21.5t15 -17.5l358 -230q9 -5 15 -16.5t6 -21.5v-93q0 -10 -7.5 -17.5t-17.5 -7.5h-1150q-10 0 -17.5 7.5t-7.5 17.5v93q0 10 6 21.5t15 16.5l358 230q9 6 15 17.5t6 21.5v75q-38 0 -69 52 t-31 98v250q0 124 88 212t212 88z" />
<glyph unicode="&#xe009;" d="M25 1100h1150q10 0 17.5 -7.5t7.5 -17.5v-1050q0 -10 -7.5 -17.5t-17.5 -7.5h-1150q-10 0 -17.5 7.5t-7.5 17.5v1050q0 10 7.5 17.5t17.5 7.5zM100 1000v-100h100v100h-100zM875 1000h-550q-10 0 -17.5 -7.5t-7.5 -17.5v-350q0 -10 7.5 -17.5t17.5 -7.5h550 q10 0 17.5 7.5t7.5 17.5v350q0 10 -7.5 17.5t-17.5 7.5zM1000 1000v-100h100v100h-100zM100 800v-100h100v100h-100zM1000 800v-100h100v100h-100zM100 600v-100h100v100h-100zM1000 600v-100h100v100h-100zM875 500h-550q-10 0 -17.5 -7.5t-7.5 -17.5v-350q0 -10 7.5 -17.5 t17.5 -7.5h550q10 0 17.5 7.5t7.5 17.5v350q0 10 -7.5 17.5t-17.5 7.5zM100 400v-100h100v100h-100zM1000 400v-100h100v100h-100zM100 200v-100h100v100h-100zM1000 200v-100h100v100h-100z" />
<glyph unicode="&#xe010;" d="M50 1100h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5zM650 1100h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v400 q0 21 14.5 35.5t35.5 14.5zM50 500h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5zM650 500h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400 q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe011;" d="M50 1100h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM450 1100h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200 q0 21 14.5 35.5t35.5 14.5zM850 1100h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM50 700h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200 q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM450 700h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM850 700h200q21 0 35.5 -14.5t14.5 -35.5v-200 q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM50 300h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM450 300h200 q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM850 300h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5 t35.5 14.5z" />
<glyph unicode="&#xe012;" d="M50 1100h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM450 1100h700q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-700q-21 0 -35.5 14.5t-14.5 35.5v200 q0 21 14.5 35.5t35.5 14.5zM50 700h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM450 700h700q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-700 q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM50 300h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM450 300h700q21 0 35.5 -14.5t14.5 -35.5v-200 q0 -21 -14.5 -35.5t-35.5 -14.5h-700q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe013;" d="M465 477l571 571q8 8 18 8t17 -8l177 -177q8 -7 8 -17t-8 -18l-783 -784q-7 -8 -17.5 -8t-17.5 8l-384 384q-8 8 -8 18t8 17l177 177q7 8 17 8t18 -8l171 -171q7 -7 18 -7t18 7z" />
<glyph unicode="&#xe014;" d="M904 1083l178 -179q8 -8 8 -18.5t-8 -17.5l-267 -268l267 -268q8 -7 8 -17.5t-8 -18.5l-178 -178q-8 -8 -18.5 -8t-17.5 8l-268 267l-268 -267q-7 -8 -17.5 -8t-18.5 8l-178 178q-8 8 -8 18.5t8 17.5l267 268l-267 268q-8 7 -8 17.5t8 18.5l178 178q8 8 18.5 8t17.5 -8 l268 -267l268 268q7 7 17.5 7t18.5 -7z" />
<glyph unicode="&#xe015;" d="M507 1177q98 0 187.5 -38.5t154.5 -103.5t103.5 -154.5t38.5 -187.5q0 -141 -78 -262l300 -299q8 -8 8 -18.5t-8 -18.5l-109 -108q-7 -8 -17.5 -8t-18.5 8l-300 299q-119 -77 -261 -77q-98 0 -188 38.5t-154.5 103t-103 154.5t-38.5 188t38.5 187.5t103 154.5 t154.5 103.5t188 38.5zM506.5 1023q-89.5 0 -165.5 -44t-120 -120.5t-44 -166t44 -165.5t120 -120t165.5 -44t166 44t120.5 120t44 165.5t-44 166t-120.5 120.5t-166 44zM425 900h150q10 0 17.5 -7.5t7.5 -17.5v-75h75q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5 t-17.5 -7.5h-75v-75q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v75h-75q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5h75v75q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe016;" d="M507 1177q98 0 187.5 -38.5t154.5 -103.5t103.5 -154.5t38.5 -187.5q0 -141 -78 -262l300 -299q8 -8 8 -18.5t-8 -18.5l-109 -108q-7 -8 -17.5 -8t-18.5 8l-300 299q-119 -77 -261 -77q-98 0 -188 38.5t-154.5 103t-103 154.5t-38.5 188t38.5 187.5t103 154.5 t154.5 103.5t188 38.5zM506.5 1023q-89.5 0 -165.5 -44t-120 -120.5t-44 -166t44 -165.5t120 -120t165.5 -44t166 44t120.5 120t44 165.5t-44 166t-120.5 120.5t-166 44zM325 800h350q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-350q-10 0 -17.5 7.5 t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe017;" d="M550 1200h100q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5zM800 975v166q167 -62 272 -209.5t105 -331.5q0 -117 -45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5 t-184.5 123t-123 184.5t-45.5 224q0 184 105 331.5t272 209.5v-166q-103 -55 -165 -155t-62 -220q0 -116 57 -214.5t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5q0 120 -62 220t-165 155z" />
<glyph unicode="&#xe018;" d="M1025 1200h150q10 0 17.5 -7.5t7.5 -17.5v-1150q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v1150q0 10 7.5 17.5t17.5 7.5zM725 800h150q10 0 17.5 -7.5t7.5 -17.5v-750q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v750 q0 10 7.5 17.5t17.5 7.5zM425 500h150q10 0 17.5 -7.5t7.5 -17.5v-450q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v450q0 10 7.5 17.5t17.5 7.5zM125 300h150q10 0 17.5 -7.5t7.5 -17.5v-250q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5 v250q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe019;" d="M600 1174q33 0 74 -5l38 -152l5 -1q49 -14 94 -39l5 -2l134 80q61 -48 104 -105l-80 -134l3 -5q25 -44 39 -93l1 -6l152 -38q5 -43 5 -73q0 -34 -5 -74l-152 -38l-1 -6q-15 -49 -39 -93l-3 -5l80 -134q-48 -61 -104 -105l-134 81l-5 -3q-44 -25 -94 -39l-5 -2l-38 -151 q-43 -5 -74 -5q-33 0 -74 5l-38 151l-5 2q-49 14 -94 39l-5 3l-134 -81q-60 48 -104 105l80 134l-3 5q-25 45 -38 93l-2 6l-151 38q-6 42 -6 74q0 33 6 73l151 38l2 6q13 48 38 93l3 5l-80 134q47 61 105 105l133 -80l5 2q45 25 94 39l5 1l38 152q43 5 74 5zM600 815 q-89 0 -152 -63t-63 -151.5t63 -151.5t152 -63t152 63t63 151.5t-63 151.5t-152 63z" />
<glyph unicode="&#xe020;" d="M500 1300h300q41 0 70.5 -29.5t29.5 -70.5v-100h275q10 0 17.5 -7.5t7.5 -17.5v-75h-1100v75q0 10 7.5 17.5t17.5 7.5h275v100q0 41 29.5 70.5t70.5 29.5zM500 1200v-100h300v100h-300zM1100 900v-800q0 -41 -29.5 -70.5t-70.5 -29.5h-700q-41 0 -70.5 29.5t-29.5 70.5 v800h900zM300 800v-700h100v700h-100zM500 800v-700h100v700h-100zM700 800v-700h100v700h-100zM900 800v-700h100v700h-100z" />
<glyph unicode="&#xe021;" d="M18 618l620 608q8 7 18.5 7t17.5 -7l608 -608q8 -8 5.5 -13t-12.5 -5h-175v-575q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v375h-300v-375q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v575h-175q-10 0 -12.5 5t5.5 13z" />
<glyph unicode="&#xe022;" d="M600 1200v-400q0 -41 29.5 -70.5t70.5 -29.5h300v-650q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5v1100q0 21 14.5 35.5t35.5 14.5h450zM1000 800h-250q-21 0 -35.5 14.5t-14.5 35.5v250z" />
<glyph unicode="&#xe023;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 1027q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5 t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5t-57 214.5t-155.5 155.5t-214.5 57zM525 900h50q10 0 17.5 -7.5t7.5 -17.5v-275h175q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v350q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe024;" d="M1300 0h-538l-41 400h-242l-41 -400h-538l431 1200h209l-21 -300h162l-20 300h208zM515 800l-27 -300h224l-27 300h-170z" />
<glyph unicode="&#xe025;" d="M550 1200h200q21 0 35.5 -14.5t14.5 -35.5v-450h191q20 0 25.5 -11.5t-7.5 -27.5l-327 -400q-13 -16 -32 -16t-32 16l-327 400q-13 16 -7.5 27.5t25.5 11.5h191v450q0 21 14.5 35.5t35.5 14.5zM1125 400h50q10 0 17.5 -7.5t7.5 -17.5v-350q0 -10 -7.5 -17.5t-17.5 -7.5 h-1050q-10 0 -17.5 7.5t-7.5 17.5v350q0 10 7.5 17.5t17.5 7.5h50q10 0 17.5 -7.5t7.5 -17.5v-175h900v175q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe026;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 1027q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5 t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5t-57 214.5t-155.5 155.5t-214.5 57zM525 900h150q10 0 17.5 -7.5t7.5 -17.5v-275h137q21 0 26 -11.5t-8 -27.5l-223 -275q-13 -16 -32 -16t-32 16l-223 275q-13 16 -8 27.5t26 11.5h137v275q0 10 7.5 17.5t17.5 7.5z " />
<glyph unicode="&#xe027;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 1027q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5 t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5t-57 214.5t-155.5 155.5t-214.5 57zM632 914l223 -275q13 -16 8 -27.5t-26 -11.5h-137v-275q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v275h-137q-21 0 -26 11.5t8 27.5l223 275q13 16 32 16 t32 -16z" />
<glyph unicode="&#xe028;" d="M225 1200h750q10 0 19.5 -7t12.5 -17l186 -652q7 -24 7 -49v-425q0 -12 -4 -27t-9 -17q-12 -6 -37 -6h-1100q-12 0 -27 4t-17 8q-6 13 -6 38l1 425q0 25 7 49l185 652q3 10 12.5 17t19.5 7zM878 1000h-556q-10 0 -19 -7t-11 -18l-87 -450q-2 -11 4 -18t16 -7h150 q10 0 19.5 -7t11.5 -17l38 -152q2 -10 11.5 -17t19.5 -7h250q10 0 19.5 7t11.5 17l38 152q2 10 11.5 17t19.5 7h150q10 0 16 7t4 18l-87 450q-2 11 -11 18t-19 7z" />
<glyph unicode="&#xe029;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 1027q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5 t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5t-57 214.5t-155.5 155.5t-214.5 57zM540 820l253 -190q17 -12 17 -30t-17 -30l-253 -190q-16 -12 -28 -6.5t-12 26.5v400q0 21 12 26.5t28 -6.5z" />
<glyph unicode="&#xe030;" d="M947 1060l135 135q7 7 12.5 5t5.5 -13v-362q0 -10 -7.5 -17.5t-17.5 -7.5h-362q-11 0 -13 5.5t5 12.5l133 133q-109 76 -238 76q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5h150q0 -117 -45.5 -224 t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5q192 0 347 -117z" />
<glyph unicode="&#xe031;" d="M947 1060l135 135q7 7 12.5 5t5.5 -13v-361q0 -11 -7.5 -18.5t-18.5 -7.5h-361q-11 0 -13 5.5t5 12.5l134 134q-110 75 -239 75q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5h-150q0 117 45.5 224t123 184.5t184.5 123t224 45.5q192 0 347 -117zM1027 600h150 q0 -117 -45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5q-192 0 -348 118l-134 -134q-7 -8 -12.5 -5.5t-5.5 12.5v360q0 11 7.5 18.5t18.5 7.5h360q10 0 12.5 -5.5t-5.5 -12.5l-133 -133q110 -76 240 -76q116 0 214.5 57t155.5 155.5t57 214.5z" />
<glyph unicode="&#xe032;" d="M125 1200h1050q10 0 17.5 -7.5t7.5 -17.5v-1150q0 -10 -7.5 -17.5t-17.5 -7.5h-1050q-10 0 -17.5 7.5t-7.5 17.5v1150q0 10 7.5 17.5t17.5 7.5zM1075 1000h-850q-10 0 -17.5 -7.5t-7.5 -17.5v-850q0 -10 7.5 -17.5t17.5 -7.5h850q10 0 17.5 7.5t7.5 17.5v850 q0 10 -7.5 17.5t-17.5 7.5zM325 900h50q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-50q-10 0 -17.5 7.5t-7.5 17.5v50q0 10 7.5 17.5t17.5 7.5zM525 900h450q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-450q-10 0 -17.5 7.5t-7.5 17.5v50 q0 10 7.5 17.5t17.5 7.5zM325 700h50q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-50q-10 0 -17.5 7.5t-7.5 17.5v50q0 10 7.5 17.5t17.5 7.5zM525 700h450q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-450q-10 0 -17.5 7.5t-7.5 17.5v50 q0 10 7.5 17.5t17.5 7.5zM325 500h50q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-50q-10 0 -17.5 7.5t-7.5 17.5v50q0 10 7.5 17.5t17.5 7.5zM525 500h450q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-450q-10 0 -17.5 7.5t-7.5 17.5v50 q0 10 7.5 17.5t17.5 7.5zM325 300h50q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-50q-10 0 -17.5 7.5t-7.5 17.5v50q0 10 7.5 17.5t17.5 7.5zM525 300h450q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-450q-10 0 -17.5 7.5t-7.5 17.5v50 q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe033;" d="M900 800v200q0 83 -58.5 141.5t-141.5 58.5h-300q-82 0 -141 -59t-59 -141v-200h-100q-41 0 -70.5 -29.5t-29.5 -70.5v-600q0 -41 29.5 -70.5t70.5 -29.5h900q41 0 70.5 29.5t29.5 70.5v600q0 41 -29.5 70.5t-70.5 29.5h-100zM400 800v150q0 21 15 35.5t35 14.5h200 q20 0 35 -14.5t15 -35.5v-150h-300z" />
<glyph unicode="&#xe034;" d="M125 1100h50q10 0 17.5 -7.5t7.5 -17.5v-1075h-100v1075q0 10 7.5 17.5t17.5 7.5zM1075 1052q4 0 9 -2q16 -6 16 -23v-421q0 -6 -3 -12q-33 -59 -66.5 -99t-65.5 -58t-56.5 -24.5t-52.5 -6.5q-26 0 -57.5 6.5t-52.5 13.5t-60 21q-41 15 -63 22.5t-57.5 15t-65.5 7.5 q-85 0 -160 -57q-7 -5 -15 -5q-6 0 -11 3q-14 7 -14 22v438q22 55 82 98.5t119 46.5q23 2 43 0.5t43 -7t32.5 -8.5t38 -13t32.5 -11q41 -14 63.5 -21t57 -14t63.5 -7q103 0 183 87q7 8 18 8z" />
<glyph unicode="&#xe035;" d="M600 1175q116 0 227 -49.5t192.5 -131t131 -192.5t49.5 -227v-300q0 -10 -7.5 -17.5t-17.5 -7.5h-50q-10 0 -17.5 7.5t-7.5 17.5v300q0 127 -70.5 231.5t-184.5 161.5t-245 57t-245 -57t-184.5 -161.5t-70.5 -231.5v-300q0 -10 -7.5 -17.5t-17.5 -7.5h-50 q-10 0 -17.5 7.5t-7.5 17.5v300q0 116 49.5 227t131 192.5t192.5 131t227 49.5zM220 500h160q8 0 14 -6t6 -14v-460q0 -8 -6 -14t-14 -6h-160q-8 0 -14 6t-6 14v460q0 8 6 14t14 6zM820 500h160q8 0 14 -6t6 -14v-460q0 -8 -6 -14t-14 -6h-160q-8 0 -14 6t-6 14v460 q0 8 6 14t14 6z" />
<glyph unicode="&#xe036;" d="M321 814l258 172q9 6 15 2.5t6 -13.5v-750q0 -10 -6 -13.5t-15 2.5l-258 172q-21 14 -46 14h-250q-10 0 -17.5 7.5t-7.5 17.5v350q0 10 7.5 17.5t17.5 7.5h250q25 0 46 14zM900 668l120 120q7 7 17 7t17 -7l34 -34q7 -7 7 -17t-7 -17l-120 -120l120 -120q7 -7 7 -17 t-7 -17l-34 -34q-7 -7 -17 -7t-17 7l-120 119l-120 -119q-7 -7 -17 -7t-17 7l-34 34q-7 7 -7 17t7 17l119 120l-119 120q-7 7 -7 17t7 17l34 34q7 8 17 8t17 -8z" />
<glyph unicode="&#xe037;" d="M321 814l258 172q9 6 15 2.5t6 -13.5v-750q0 -10 -6 -13.5t-15 2.5l-258 172q-21 14 -46 14h-250q-10 0 -17.5 7.5t-7.5 17.5v350q0 10 7.5 17.5t17.5 7.5h250q25 0 46 14zM766 900h4q10 -1 16 -10q96 -129 96 -290q0 -154 -90 -281q-6 -9 -17 -10l-3 -1q-9 0 -16 6 l-29 23q-7 7 -8.5 16.5t4.5 17.5q72 103 72 229q0 132 -78 238q-6 8 -4.5 18t9.5 17l29 22q7 5 15 5z" />
<glyph unicode="&#xe038;" d="M967 1004h3q11 -1 17 -10q135 -179 135 -396q0 -105 -34 -206.5t-98 -185.5q-7 -9 -17 -10h-3q-9 0 -16 6l-42 34q-8 6 -9 16t5 18q111 150 111 328q0 90 -29.5 176t-84.5 157q-6 9 -5 19t10 16l42 33q7 5 15 5zM321 814l258 172q9 6 15 2.5t6 -13.5v-750q0 -10 -6 -13.5 t-15 2.5l-258 172q-21 14 -46 14h-250q-10 0 -17.5 7.5t-7.5 17.5v350q0 10 7.5 17.5t17.5 7.5h250q25 0 46 14zM766 900h4q10 -1 16 -10q96 -129 96 -290q0 -154 -90 -281q-6 -9 -17 -10l-3 -1q-9 0 -16 6l-29 23q-7 7 -8.5 16.5t4.5 17.5q72 103 72 229q0 132 -78 238 q-6 8 -4.5 18.5t9.5 16.5l29 22q7 5 15 5z" />
<glyph unicode="&#xe039;" d="M500 900h100v-100h-100v-100h-400v-100h-100v600h500v-300zM1200 700h-200v-100h200v-200h-300v300h-200v300h-100v200h600v-500zM100 1100v-300h300v300h-300zM800 1100v-300h300v300h-300zM300 900h-100v100h100v-100zM1000 900h-100v100h100v-100zM300 500h200v-500 h-500v500h200v100h100v-100zM800 300h200v-100h-100v-100h-200v100h-100v100h100v200h-200v100h300v-300zM100 400v-300h300v300h-300zM300 200h-100v100h100v-100zM1200 200h-100v100h100v-100zM700 0h-100v100h100v-100zM1200 0h-300v100h300v-100z" />
<glyph unicode="&#xe040;" d="M100 200h-100v1000h100v-1000zM300 200h-100v1000h100v-1000zM700 200h-200v1000h200v-1000zM900 200h-100v1000h100v-1000zM1200 200h-200v1000h200v-1000zM400 0h-300v100h300v-100zM600 0h-100v91h100v-91zM800 0h-100v91h100v-91zM1100 0h-200v91h200v-91z" />
<glyph unicode="&#xe041;" d="M500 1200l682 -682q8 -8 8 -18t-8 -18l-464 -464q-8 -8 -18 -8t-18 8l-682 682l1 475q0 10 7.5 17.5t17.5 7.5h474zM319.5 1024.5q-29.5 29.5 -71 29.5t-71 -29.5t-29.5 -71.5t29.5 -71.5t71 -29.5t71 29.5t29.5 71.5t-29.5 71.5z" />
<glyph unicode="&#xe042;" d="M500 1200l682 -682q8 -8 8 -18t-8 -18l-464 -464q-8 -8 -18 -8t-18 8l-682 682l1 475q0 10 7.5 17.5t17.5 7.5h474zM800 1200l682 -682q8 -8 8 -18t-8 -18l-464 -464q-8 -8 -18 -8t-18 8l-56 56l424 426l-700 700h150zM319.5 1024.5q-29.5 29.5 -71 29.5t-71 -29.5 t-29.5 -71.5t29.5 -71.5t71 -29.5t71 29.5t29.5 71.5t-29.5 71.5z" />
<glyph unicode="&#xe043;" d="M300 1200h825q75 0 75 -75v-900q0 -25 -18 -43l-64 -64q-8 -8 -13 -5.5t-5 12.5v950q0 10 -7.5 17.5t-17.5 7.5h-700q-25 0 -43 -18l-64 -64q-8 -8 -5.5 -13t12.5 -5h700q10 0 17.5 -7.5t7.5 -17.5v-950q0 -10 -7.5 -17.5t-17.5 -7.5h-850q-10 0 -17.5 7.5t-7.5 17.5v975 q0 25 18 43l139 139q18 18 43 18z" />
<glyph unicode="&#xe044;" d="M250 1200h800q21 0 35.5 -14.5t14.5 -35.5v-1150l-450 444l-450 -445v1151q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe045;" d="M822 1200h-444q-11 0 -19 -7.5t-9 -17.5l-78 -301q-7 -24 7 -45l57 -108q6 -9 17.5 -15t21.5 -6h450q10 0 21.5 6t17.5 15l62 108q14 21 7 45l-83 301q-1 10 -9 17.5t-19 7.5zM1175 800h-150q-10 0 -21 -6.5t-15 -15.5l-78 -156q-4 -9 -15 -15.5t-21 -6.5h-550 q-10 0 -21 6.5t-15 15.5l-78 156q-4 9 -15 15.5t-21 6.5h-150q-10 0 -17.5 -7.5t-7.5 -17.5v-650q0 -10 7.5 -17.5t17.5 -7.5h150q10 0 17.5 7.5t7.5 17.5v150q0 10 7.5 17.5t17.5 7.5h750q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 7.5 -17.5t17.5 -7.5h150q10 0 17.5 7.5 t7.5 17.5v650q0 10 -7.5 17.5t-17.5 7.5zM850 200h-500q-10 0 -19.5 -7t-11.5 -17l-38 -152q-2 -10 3.5 -17t15.5 -7h600q10 0 15.5 7t3.5 17l-38 152q-2 10 -11.5 17t-19.5 7z" />
<glyph unicode="&#xe046;" d="M500 1100h200q56 0 102.5 -20.5t72.5 -50t44 -59t25 -50.5l6 -20h150q41 0 70.5 -29.5t29.5 -70.5v-600q0 -41 -29.5 -70.5t-70.5 -29.5h-1000q-41 0 -70.5 29.5t-29.5 70.5v600q0 41 29.5 70.5t70.5 29.5h150q2 8 6.5 21.5t24 48t45 61t72 48t102.5 21.5zM900 800v-100 h100v100h-100zM600 730q-95 0 -162.5 -67.5t-67.5 -162.5t67.5 -162.5t162.5 -67.5t162.5 67.5t67.5 162.5t-67.5 162.5t-162.5 67.5zM600 603q43 0 73 -30t30 -73t-30 -73t-73 -30t-73 30t-30 73t30 73t73 30z" />
<glyph unicode="&#xe047;" d="M681 1199l385 -998q20 -50 60 -92q18 -19 36.5 -29.5t27.5 -11.5l10 -2v-66h-417v66q53 0 75 43.5t5 88.5l-82 222h-391q-58 -145 -92 -234q-11 -34 -6.5 -57t25.5 -37t46 -20t55 -6v-66h-365v66q56 24 84 52q12 12 25 30.5t20 31.5l7 13l399 1006h93zM416 521h340 l-162 457z" />
<glyph unicode="&#xe048;" d="M753 641q5 -1 14.5 -4.5t36 -15.5t50.5 -26.5t53.5 -40t50.5 -54.5t35.5 -70t14.5 -87q0 -67 -27.5 -125.5t-71.5 -97.5t-98.5 -66.5t-108.5 -40.5t-102 -13h-500v89q41 7 70.5 32.5t29.5 65.5v827q0 24 -0.5 34t-3.5 24t-8.5 19.5t-17 13.5t-28 12.5t-42.5 11.5v71 l471 -1q57 0 115.5 -20.5t108 -57t80.5 -94t31 -124.5q0 -51 -15.5 -96.5t-38 -74.5t-45 -50.5t-38.5 -30.5zM400 700h139q78 0 130.5 48.5t52.5 122.5q0 41 -8.5 70.5t-29.5 55.5t-62.5 39.5t-103.5 13.5h-118v-350zM400 200h216q80 0 121 50.5t41 130.5q0 90 -62.5 154.5 t-156.5 64.5h-159v-400z" />
<glyph unicode="&#xe049;" d="M877 1200l2 -57q-83 -19 -116 -45.5t-40 -66.5l-132 -839q-9 -49 13 -69t96 -26v-97h-500v97q186 16 200 98l173 832q3 17 3 30t-1.5 22.5t-9 17.5t-13.5 12.5t-21.5 10t-26 8.5t-33.5 10q-13 3 -19 5v57h425z" />
<glyph unicode="&#xe050;" d="M1300 900h-50q0 21 -4 37t-9.5 26.5t-18 17.5t-22 11t-28.5 5.5t-31 2t-37 0.5h-200v-850q0 -22 25 -34.5t50 -13.5l25 -2v-100h-400v100q4 0 11 0.5t24 3t30 7t24 15t11 24.5v850h-200q-25 0 -37 -0.5t-31 -2t-28.5 -5.5t-22 -11t-18 -17.5t-9.5 -26.5t-4 -37h-50v300 h1000v-300zM175 1000h-75v-800h75l-125 -167l-125 167h75v800h-75l125 167z" />
<glyph unicode="&#xe051;" d="M1100 900h-50q0 21 -4 37t-9.5 26.5t-18 17.5t-22 11t-28.5 5.5t-31 2t-37 0.5h-200v-650q0 -22 25 -34.5t50 -13.5l25 -2v-100h-400v100q4 0 11 0.5t24 3t30 7t24 15t11 24.5v650h-200q-25 0 -37 -0.5t-31 -2t-28.5 -5.5t-22 -11t-18 -17.5t-9.5 -26.5t-4 -37h-50v300 h1000v-300zM1167 50l-167 -125v75h-800v-75l-167 125l167 125v-75h800v75z" />
<glyph unicode="&#xe052;" d="M50 1100h600q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-600q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 800h1000q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1000q-21 0 -35.5 14.5t-14.5 35.5v100 q0 21 14.5 35.5t35.5 14.5zM50 500h800q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 200h1100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100 q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe053;" d="M250 1100h700q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-700q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 800h1100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5v100 q0 21 14.5 35.5t35.5 14.5zM250 500h700q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-700q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 200h1100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100 q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe054;" d="M500 950v100q0 21 14.5 35.5t35.5 14.5h600q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-600q-21 0 -35.5 14.5t-14.5 35.5zM100 650v100q0 21 14.5 35.5t35.5 14.5h1000q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1000 q-21 0 -35.5 14.5t-14.5 35.5zM300 350v100q0 21 14.5 35.5t35.5 14.5h800q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5zM0 50v100q0 21 14.5 35.5t35.5 14.5h1100q21 0 35.5 -14.5t14.5 -35.5v-100 q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5z" />
<glyph unicode="&#xe055;" d="M50 1100h1100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 800h1100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5v100 q0 21 14.5 35.5t35.5 14.5zM50 500h1100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 200h1100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100 q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe056;" d="M50 1100h100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM350 1100h800q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5v100 q0 21 14.5 35.5t35.5 14.5zM50 800h100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM350 800h800q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-800 q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 500h100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM350 500h800q21 0 35.5 -14.5t14.5 -35.5v-100 q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 200h100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM350 200h800 q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe057;" d="M400 0h-100v1100h100v-1100zM550 1100h100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM550 800h500q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-500 q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM267 550l-167 -125v75h-200v100h200v75zM550 500h300q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-300q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM550 200h600 q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-600q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe058;" d="M50 1100h100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM900 0h-100v1100h100v-1100zM50 800h500q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-500 q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM1100 600h200v-100h-200v-75l-167 125l167 125v-75zM50 500h300q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-300q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 200h600 q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-600q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe059;" d="M75 1000h750q31 0 53 -22t22 -53v-650q0 -31 -22 -53t-53 -22h-750q-31 0 -53 22t-22 53v650q0 31 22 53t53 22zM1200 300l-300 300l300 300v-600z" />
<glyph unicode="&#xe060;" d="M44 1100h1112q18 0 31 -13t13 -31v-1012q0 -18 -13 -31t-31 -13h-1112q-18 0 -31 13t-13 31v1012q0 18 13 31t31 13zM100 1000v-737l247 182l298 -131l-74 156l293 318l236 -288v500h-1000zM342 884q56 0 95 -39t39 -94.5t-39 -95t-95 -39.5t-95 39.5t-39 95t39 94.5 t95 39z" />
<glyph unicode="&#xe062;" d="M648 1169q117 0 216 -60t156.5 -161t57.5 -218q0 -115 -70 -258q-69 -109 -158 -225.5t-143 -179.5l-54 -62q-9 8 -25.5 24.5t-63.5 67.5t-91 103t-98.5 128t-95.5 148q-60 132 -60 249q0 88 34 169.5t91.5 142t137 96.5t166.5 36zM652.5 974q-91.5 0 -156.5 -65 t-65 -157t65 -156.5t156.5 -64.5t156.5 64.5t65 156.5t-65 157t-156.5 65z" />
<glyph unicode="&#xe063;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 173v854q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5 t155.5 -155.5t214.5 -57z" />
<glyph unicode="&#xe064;" d="M554 1295q21 -72 57.5 -143.5t76 -130t83 -118t82.5 -117t70 -116t49.5 -126t18.5 -136.5q0 -71 -25.5 -135t-68.5 -111t-99 -82t-118.5 -54t-125.5 -23q-84 5 -161.5 34t-139.5 78.5t-99 125t-37 164.5q0 69 18 136.5t49.5 126.5t69.5 116.5t81.5 117.5t83.5 119 t76.5 131t58.5 143zM344 710q-23 -33 -43.5 -70.5t-40.5 -102.5t-17 -123q1 -37 14.5 -69.5t30 -52t41 -37t38.5 -24.5t33 -15q21 -7 32 -1t13 22l6 34q2 10 -2.5 22t-13.5 19q-5 4 -14 12t-29.5 40.5t-32.5 73.5q-26 89 6 271q2 11 -6 11q-8 1 -15 -10z" />
<glyph unicode="&#xe065;" d="M1000 1013l108 115q2 1 5 2t13 2t20.5 -1t25 -9.5t28.5 -21.5q22 -22 27 -43t0 -32l-6 -10l-108 -115zM350 1100h400q50 0 105 -13l-187 -187h-368q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5v182l200 200v-332 q0 -165 -93.5 -257.5t-256.5 -92.5h-400q-165 0 -257.5 92.5t-92.5 257.5v400q0 165 92.5 257.5t257.5 92.5zM1009 803l-362 -362l-161 -50l55 170l355 355z" />
<glyph unicode="&#xe066;" d="M350 1100h361q-164 -146 -216 -200h-195q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5l200 153v-103q0 -165 -92.5 -257.5t-257.5 -92.5h-400q-165 0 -257.5 92.5t-92.5 257.5v400q0 165 92.5 257.5t257.5 92.5z M824 1073l339 -301q8 -7 8 -17.5t-8 -17.5l-340 -306q-7 -6 -12.5 -4t-6.5 11v203q-26 1 -54.5 0t-78.5 -7.5t-92 -17.5t-86 -35t-70 -57q10 59 33 108t51.5 81.5t65 58.5t68.5 40.5t67 24.5t56 13.5t40 4.5v210q1 10 6.5 12.5t13.5 -4.5z" />
<glyph unicode="&#xe067;" d="M350 1100h350q60 0 127 -23l-178 -177h-349q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5v69l200 200v-219q0 -165 -92.5 -257.5t-257.5 -92.5h-400q-165 0 -257.5 92.5t-92.5 257.5v400q0 165 92.5 257.5t257.5 92.5z M643 639l395 395q7 7 17.5 7t17.5 -7l101 -101q7 -7 7 -17.5t-7 -17.5l-531 -532q-7 -7 -17.5 -7t-17.5 7l-248 248q-7 7 -7 17.5t7 17.5l101 101q7 7 17.5 7t17.5 -7l111 -111q8 -7 18 -7t18 7z" />
<glyph unicode="&#xe068;" d="M318 918l264 264q8 8 18 8t18 -8l260 -264q7 -8 4.5 -13t-12.5 -5h-170v-200h200v173q0 10 5 12t13 -5l264 -260q8 -7 8 -17.5t-8 -17.5l-264 -265q-8 -7 -13 -5t-5 12v173h-200v-200h170q10 0 12.5 -5t-4.5 -13l-260 -264q-8 -8 -18 -8t-18 8l-264 264q-8 8 -5.5 13 t12.5 5h175v200h-200v-173q0 -10 -5 -12t-13 5l-264 265q-8 7 -8 17.5t8 17.5l264 260q8 7 13 5t5 -12v-173h200v200h-175q-10 0 -12.5 5t5.5 13z" />
<glyph unicode="&#xe069;" d="M250 1100h100q21 0 35.5 -14.5t14.5 -35.5v-438l464 453q15 14 25.5 10t10.5 -25v-1000q0 -21 -10.5 -25t-25.5 10l-464 453v-438q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v1000q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe070;" d="M50 1100h100q21 0 35.5 -14.5t14.5 -35.5v-438l464 453q15 14 25.5 10t10.5 -25v-438l464 453q15 14 25.5 10t10.5 -25v-1000q0 -21 -10.5 -25t-25.5 10l-464 453v-438q0 -21 -10.5 -25t-25.5 10l-464 453v-438q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5 t-14.5 35.5v1000q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe071;" d="M1200 1050v-1000q0 -21 -10.5 -25t-25.5 10l-464 453v-438q0 -21 -10.5 -25t-25.5 10l-492 480q-15 14 -15 35t15 35l492 480q15 14 25.5 10t10.5 -25v-438l464 453q15 14 25.5 10t10.5 -25z" />
<glyph unicode="&#xe072;" d="M243 1074l814 -498q18 -11 18 -26t-18 -26l-814 -498q-18 -11 -30.5 -4t-12.5 28v1000q0 21 12.5 28t30.5 -4z" />
<glyph unicode="&#xe073;" d="M250 1000h200q21 0 35.5 -14.5t14.5 -35.5v-800q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v800q0 21 14.5 35.5t35.5 14.5zM650 1000h200q21 0 35.5 -14.5t14.5 -35.5v-800q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v800 q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe074;" d="M1100 950v-800q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5v800q0 21 14.5 35.5t35.5 14.5h800q21 0 35.5 -14.5t14.5 -35.5z" />
<glyph unicode="&#xe075;" d="M500 612v438q0 21 10.5 25t25.5 -10l492 -480q15 -14 15 -35t-15 -35l-492 -480q-15 -14 -25.5 -10t-10.5 25v438l-464 -453q-15 -14 -25.5 -10t-10.5 25v1000q0 21 10.5 25t25.5 -10z" />
<glyph unicode="&#xe076;" d="M1048 1102l100 1q20 0 35 -14.5t15 -35.5l5 -1000q0 -21 -14.5 -35.5t-35.5 -14.5l-100 -1q-21 0 -35.5 14.5t-14.5 35.5l-2 437l-463 -454q-14 -15 -24.5 -10.5t-10.5 25.5l-2 437l-462 -455q-15 -14 -25.5 -9.5t-10.5 24.5l-5 1000q0 21 10.5 25.5t25.5 -10.5l466 -450 l-2 438q0 20 10.5 24.5t25.5 -9.5l466 -451l-2 438q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe077;" d="M850 1100h100q21 0 35.5 -14.5t14.5 -35.5v-1000q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v438l-464 -453q-15 -14 -25.5 -10t-10.5 25v1000q0 21 10.5 25t25.5 -10l464 -453v438q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe078;" d="M686 1081l501 -540q15 -15 10.5 -26t-26.5 -11h-1042q-22 0 -26.5 11t10.5 26l501 540q15 15 36 15t36 -15zM150 400h1000q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1000q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe079;" d="M885 900l-352 -353l352 -353l-197 -198l-552 552l552 550z" />
<glyph unicode="&#xe080;" d="M1064 547l-551 -551l-198 198l353 353l-353 353l198 198z" />
<glyph unicode="&#xe081;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM650 900h-100q-21 0 -35.5 -14.5t-14.5 -35.5v-150h-150 q-21 0 -35.5 -14.5t-14.5 -35.5v-100q0 -21 14.5 -35.5t35.5 -14.5h150v-150q0 -21 14.5 -35.5t35.5 -14.5h100q21 0 35.5 14.5t14.5 35.5v150h150q21 0 35.5 14.5t14.5 35.5v100q0 21 -14.5 35.5t-35.5 14.5h-150v150q0 21 -14.5 35.5t-35.5 14.5z" />
<glyph unicode="&#xe082;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM850 700h-500q-21 0 -35.5 -14.5t-14.5 -35.5v-100q0 -21 14.5 -35.5 t35.5 -14.5h500q21 0 35.5 14.5t14.5 35.5v100q0 21 -14.5 35.5t-35.5 14.5z" />
<glyph unicode="&#xe083;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM741.5 913q-12.5 0 -21.5 -9l-120 -120l-120 120q-9 9 -21.5 9 t-21.5 -9l-141 -141q-9 -9 -9 -21.5t9 -21.5l120 -120l-120 -120q-9 -9 -9 -21.5t9 -21.5l141 -141q9 -9 21.5 -9t21.5 9l120 120l120 -120q9 -9 21.5 -9t21.5 9l141 141q9 9 9 21.5t-9 21.5l-120 120l120 120q9 9 9 21.5t-9 21.5l-141 141q-9 9 -21.5 9z" />
<glyph unicode="&#xe084;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM546 623l-84 85q-7 7 -17.5 7t-18.5 -7l-139 -139q-7 -8 -7 -18t7 -18 l242 -241q7 -8 17.5 -8t17.5 8l375 375q7 7 7 17.5t-7 18.5l-139 139q-7 7 -17.5 7t-17.5 -7z" />
<glyph unicode="&#xe085;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM588 941q-29 0 -59 -5.5t-63 -20.5t-58 -38.5t-41.5 -63t-16.5 -89.5 q0 -25 20 -25h131q30 -5 35 11q6 20 20.5 28t45.5 8q20 0 31.5 -10.5t11.5 -28.5q0 -23 -7 -34t-26 -18q-1 0 -13.5 -4t-19.5 -7.5t-20 -10.5t-22 -17t-18.5 -24t-15.5 -35t-8 -46q-1 -8 5.5 -16.5t20.5 -8.5h173q7 0 22 8t35 28t37.5 48t29.5 74t12 100q0 47 -17 83 t-42.5 57t-59.5 34.5t-64 18t-59 4.5zM675 400h-150q-10 0 -17.5 -7.5t-7.5 -17.5v-150q0 -10 7.5 -17.5t17.5 -7.5h150q10 0 17.5 7.5t7.5 17.5v150q0 10 -7.5 17.5t-17.5 7.5z" />
<glyph unicode="&#xe086;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM675 1000h-150q-10 0 -17.5 -7.5t-7.5 -17.5v-150q0 -10 7.5 -17.5 t17.5 -7.5h150q10 0 17.5 7.5t7.5 17.5v150q0 10 -7.5 17.5t-17.5 7.5zM675 700h-250q-10 0 -17.5 -7.5t-7.5 -17.5v-50q0 -10 7.5 -17.5t17.5 -7.5h75v-200h-75q-10 0 -17.5 -7.5t-7.5 -17.5v-50q0 -10 7.5 -17.5t17.5 -7.5h350q10 0 17.5 7.5t7.5 17.5v50q0 10 -7.5 17.5 t-17.5 7.5h-75v275q0 10 -7.5 17.5t-17.5 7.5z" />
<glyph unicode="&#xe087;" d="M525 1200h150q10 0 17.5 -7.5t7.5 -17.5v-194q103 -27 178.5 -102.5t102.5 -178.5h194q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-194q-27 -103 -102.5 -178.5t-178.5 -102.5v-194q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v194 q-103 27 -178.5 102.5t-102.5 178.5h-194q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5h194q27 103 102.5 178.5t178.5 102.5v194q0 10 7.5 17.5t17.5 7.5zM700 893v-168q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v168q-68 -23 -119 -74 t-74 -119h168q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-168q23 -68 74 -119t119 -74v168q0 10 7.5 17.5t17.5 7.5h150q10 0 17.5 -7.5t7.5 -17.5v-168q68 23 119 74t74 119h-168q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5h168 q-23 68 -74 119t-119 74z" />
<glyph unicode="&#xe088;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 1027q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5 t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5t-57 214.5t-155.5 155.5t-214.5 57zM759 823l64 -64q7 -7 7 -17.5t-7 -17.5l-124 -124l124 -124q7 -7 7 -17.5t-7 -17.5l-64 -64q-7 -7 -17.5 -7t-17.5 7l-124 124l-124 -124q-7 -7 -17.5 -7t-17.5 7l-64 64 q-7 7 -7 17.5t7 17.5l124 124l-124 124q-7 7 -7 17.5t7 17.5l64 64q7 7 17.5 7t17.5 -7l124 -124l124 124q7 7 17.5 7t17.5 -7z" />
<glyph unicode="&#xe089;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 1027q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5 t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5t-57 214.5t-155.5 155.5t-214.5 57zM782 788l106 -106q7 -7 7 -17.5t-7 -17.5l-320 -321q-8 -7 -18 -7t-18 7l-202 203q-8 7 -8 17.5t8 17.5l106 106q7 8 17.5 8t17.5 -8l79 -79l197 197q7 7 17.5 7t17.5 -7z" />
<glyph unicode="&#xe090;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 1027q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5q0 -120 65 -225 l587 587q-105 65 -225 65zM965 819l-584 -584q104 -62 219 -62q116 0 214.5 57t155.5 155.5t57 214.5q0 115 -62 219z" />
<glyph unicode="&#xe091;" d="M39 582l522 427q16 13 27.5 8t11.5 -26v-291h550q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-550v-291q0 -21 -11.5 -26t-27.5 8l-522 427q-16 13 -16 32t16 32z" />
<glyph unicode="&#xe092;" d="M639 1009l522 -427q16 -13 16 -32t-16 -32l-522 -427q-16 -13 -27.5 -8t-11.5 26v291h-550q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5h550v291q0 21 11.5 26t27.5 -8z" />
<glyph unicode="&#xe093;" d="M682 1161l427 -522q13 -16 8 -27.5t-26 -11.5h-291v-550q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v550h-291q-21 0 -26 11.5t8 27.5l427 522q13 16 32 16t32 -16z" />
<glyph unicode="&#xe094;" d="M550 1200h200q21 0 35.5 -14.5t14.5 -35.5v-550h291q21 0 26 -11.5t-8 -27.5l-427 -522q-13 -16 -32 -16t-32 16l-427 522q-13 16 -8 27.5t26 11.5h291v550q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe095;" d="M639 1109l522 -427q16 -13 16 -32t-16 -32l-522 -427q-16 -13 -27.5 -8t-11.5 26v291q-94 -2 -182 -20t-170.5 -52t-147 -92.5t-100.5 -135.5q5 105 27 193.5t67.5 167t113 135t167 91.5t225.5 42v262q0 21 11.5 26t27.5 -8z" />
<glyph unicode="&#xe096;" d="M850 1200h300q21 0 35.5 -14.5t14.5 -35.5v-300q0 -21 -10.5 -25t-24.5 10l-94 94l-249 -249q-8 -7 -18 -7t-18 7l-106 106q-7 8 -7 18t7 18l249 249l-94 94q-14 14 -10 24.5t25 10.5zM350 0h-300q-21 0 -35.5 14.5t-14.5 35.5v300q0 21 10.5 25t24.5 -10l94 -94l249 249 q8 7 18 7t18 -7l106 -106q7 -8 7 -18t-7 -18l-249 -249l94 -94q14 -14 10 -24.5t-25 -10.5z" />
<glyph unicode="&#xe097;" d="M1014 1120l106 -106q7 -8 7 -18t-7 -18l-249 -249l94 -94q14 -14 10 -24.5t-25 -10.5h-300q-21 0 -35.5 14.5t-14.5 35.5v300q0 21 10.5 25t24.5 -10l94 -94l249 249q8 7 18 7t18 -7zM250 600h300q21 0 35.5 -14.5t14.5 -35.5v-300q0 -21 -10.5 -25t-24.5 10l-94 94 l-249 -249q-8 -7 -18 -7t-18 7l-106 106q-7 8 -7 18t7 18l249 249l-94 94q-14 14 -10 24.5t25 10.5z" />
<glyph unicode="&#xe101;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM704 900h-208q-20 0 -32 -14.5t-8 -34.5l58 -302q4 -20 21.5 -34.5 t37.5 -14.5h54q20 0 37.5 14.5t21.5 34.5l58 302q4 20 -8 34.5t-32 14.5zM675 400h-150q-10 0 -17.5 -7.5t-7.5 -17.5v-150q0 -10 7.5 -17.5t17.5 -7.5h150q10 0 17.5 7.5t7.5 17.5v150q0 10 -7.5 17.5t-17.5 7.5z" />
<glyph unicode="&#xe102;" d="M260 1200q9 0 19 -2t15 -4l5 -2q22 -10 44 -23l196 -118q21 -13 36 -24q29 -21 37 -12q11 13 49 35l196 118q22 13 45 23q17 7 38 7q23 0 47 -16.5t37 -33.5l13 -16q14 -21 18 -45l25 -123l8 -44q1 -9 8.5 -14.5t17.5 -5.5h61q10 0 17.5 -7.5t7.5 -17.5v-50 q0 -10 -7.5 -17.5t-17.5 -7.5h-50q-10 0 -17.5 -7.5t-7.5 -17.5v-175h-400v300h-200v-300h-400v175q0 10 -7.5 17.5t-17.5 7.5h-50q-10 0 -17.5 7.5t-7.5 17.5v50q0 10 7.5 17.5t17.5 7.5h61q11 0 18 3t7 8q0 4 9 52l25 128q5 25 19 45q2 3 5 7t13.5 15t21.5 19.5t26.5 15.5 t29.5 7zM915 1079l-166 -162q-7 -7 -5 -12t12 -5h219q10 0 15 7t2 17l-51 149q-3 10 -11 12t-15 -6zM463 917l-177 157q-8 7 -16 5t-11 -12l-51 -143q-3 -10 2 -17t15 -7h231q11 0 12.5 5t-5.5 12zM500 0h-375q-10 0 -17.5 7.5t-7.5 17.5v375h400v-400zM1100 400v-375 q0 -10 -7.5 -17.5t-17.5 -7.5h-375v400h400z" />
<glyph unicode="&#xe103;" d="M1165 1190q8 3 21 -6.5t13 -17.5q-2 -178 -24.5 -323.5t-55.5 -245.5t-87 -174.5t-102.5 -118.5t-118 -68.5t-118.5 -33t-120 -4.5t-105 9.5t-90 16.5q-61 12 -78 11q-4 1 -12.5 0t-34 -14.5t-52.5 -40.5l-153 -153q-26 -24 -37 -14.5t-11 43.5q0 64 42 102q8 8 50.5 45 t66.5 58q19 17 35 47t13 61q-9 55 -10 102.5t7 111t37 130t78 129.5q39 51 80 88t89.5 63.5t94.5 45t113.5 36t129 31t157.5 37t182 47.5zM1116 1098q-8 9 -22.5 -3t-45.5 -50q-38 -47 -119 -103.5t-142 -89.5l-62 -33q-56 -30 -102 -57t-104 -68t-102.5 -80.5t-85.5 -91 t-64 -104.5q-24 -56 -31 -86t2 -32t31.5 17.5t55.5 59.5q25 30 94 75.5t125.5 77.5t147.5 81q70 37 118.5 69t102 79.5t99 111t86.5 148.5q22 50 24 60t-6 19z" />
<glyph unicode="&#xe104;" d="M653 1231q-39 -67 -54.5 -131t-10.5 -114.5t24.5 -96.5t47.5 -80t63.5 -62.5t68.5 -46.5t65 -30q-4 7 -17.5 35t-18.5 39.5t-17 39.5t-17 43t-13 42t-9.5 44.5t-2 42t4 43t13.5 39t23 38.5q96 -42 165 -107.5t105 -138t52 -156t13 -159t-19 -149.5q-13 -55 -44 -106.5 t-68 -87t-78.5 -64.5t-72.5 -45t-53 -22q-72 -22 -127 -11q-31 6 -13 19q6 3 17 7q13 5 32.5 21t41 44t38.5 63.5t21.5 81.5t-6.5 94.5t-50 107t-104 115.5q10 -104 -0.5 -189t-37 -140.5t-65 -93t-84 -52t-93.5 -11t-95 24.5q-80 36 -131.5 114t-53.5 171q-2 23 0 49.5 t4.5 52.5t13.5 56t27.5 60t46 64.5t69.5 68.5q-8 -53 -5 -102.5t17.5 -90t34 -68.5t44.5 -39t49 -2q31 13 38.5 36t-4.5 55t-29 64.5t-36 75t-26 75.5q-15 85 2 161.5t53.5 128.5t85.5 92.5t93.5 61t81.5 25.5z" />
<glyph unicode="&#xe105;" d="M600 1094q82 0 160.5 -22.5t140 -59t116.5 -82.5t94.5 -95t68 -95t42.5 -82.5t14 -57.5t-14 -57.5t-43 -82.5t-68.5 -95t-94.5 -95t-116.5 -82.5t-140 -59t-159.5 -22.5t-159.5 22.5t-140 59t-116.5 82.5t-94.5 95t-68.5 95t-43 82.5t-14 57.5t14 57.5t42.5 82.5t68 95 t94.5 95t116.5 82.5t140 59t160.5 22.5zM888 829q-15 15 -18 12t5 -22q25 -57 25 -119q0 -124 -88 -212t-212 -88t-212 88t-88 212q0 59 23 114q8 19 4.5 22t-17.5 -12q-70 -69 -160 -184q-13 -16 -15 -40.5t9 -42.5q22 -36 47 -71t70 -82t92.5 -81t113 -58.5t133.5 -24.5 t133.5 24t113 58.5t92.5 81.5t70 81.5t47 70.5q11 18 9 42.5t-14 41.5q-90 117 -163 189zM448 727l-35 -36q-15 -15 -19.5 -38.5t4.5 -41.5q37 -68 93 -116q16 -13 38.5 -11t36.5 17l35 34q14 15 12.5 33.5t-16.5 33.5q-44 44 -89 117q-11 18 -28 20t-32 -12z" />
<glyph unicode="&#xe106;" d="M592 0h-148l31 120q-91 20 -175.5 68.5t-143.5 106.5t-103.5 119t-66.5 110t-22 76q0 21 14 57.5t42.5 82.5t68 95t94.5 95t116.5 82.5t140 59t160.5 22.5q61 0 126 -15l32 121h148zM944 770l47 181q108 -85 176.5 -192t68.5 -159q0 -26 -19.5 -71t-59.5 -102t-93 -112 t-129 -104.5t-158 -75.5l46 173q77 49 136 117t97 131q11 18 9 42.5t-14 41.5q-54 70 -107 130zM310 824q-70 -69 -160 -184q-13 -16 -15 -40.5t9 -42.5q18 -30 39 -60t57 -70.5t74 -73t90 -61t105 -41.5l41 154q-107 18 -178.5 101.5t-71.5 193.5q0 59 23 114q8 19 4.5 22 t-17.5 -12zM448 727l-35 -36q-15 -15 -19.5 -38.5t4.5 -41.5q37 -68 93 -116q16 -13 38.5 -11t36.5 17l12 11l22 86l-3 4q-44 44 -89 117q-11 18 -28 20t-32 -12z" />
<glyph unicode="&#xe107;" d="M-90 100l642 1066q20 31 48 28.5t48 -35.5l642 -1056q21 -32 7.5 -67.5t-50.5 -35.5h-1294q-37 0 -50.5 34t7.5 66zM155 200h345v75q0 10 7.5 17.5t17.5 7.5h150q10 0 17.5 -7.5t7.5 -17.5v-75h345l-445 723zM496 700h208q20 0 32 -14.5t8 -34.5l-58 -252 q-4 -20 -21.5 -34.5t-37.5 -14.5h-54q-20 0 -37.5 14.5t-21.5 34.5l-58 252q-4 20 8 34.5t32 14.5z" />
<glyph unicode="&#xe108;" d="M650 1200q62 0 106 -44t44 -106v-339l363 -325q15 -14 26 -38.5t11 -44.5v-41q0 -20 -12 -26.5t-29 5.5l-359 249v-263q100 -93 100 -113v-64q0 -21 -13 -29t-32 1l-205 128l-205 -128q-19 -9 -32 -1t-13 29v64q0 20 100 113v263l-359 -249q-17 -12 -29 -5.5t-12 26.5v41 q0 20 11 44.5t26 38.5l363 325v339q0 62 44 106t106 44z" />
<glyph unicode="&#xe109;" d="M850 1200h100q21 0 35.5 -14.5t14.5 -35.5v-50h50q21 0 35.5 -14.5t14.5 -35.5v-150h-1100v150q0 21 14.5 35.5t35.5 14.5h50v50q0 21 14.5 35.5t35.5 14.5h100q21 0 35.5 -14.5t14.5 -35.5v-50h500v50q0 21 14.5 35.5t35.5 14.5zM1100 800v-750q0 -21 -14.5 -35.5 t-35.5 -14.5h-1000q-21 0 -35.5 14.5t-14.5 35.5v750h1100zM100 600v-100h100v100h-100zM300 600v-100h100v100h-100zM500 600v-100h100v100h-100zM700 600v-100h100v100h-100zM900 600v-100h100v100h-100zM100 400v-100h100v100h-100zM300 400v-100h100v100h-100zM500 400 v-100h100v100h-100zM700 400v-100h100v100h-100zM900 400v-100h100v100h-100zM100 200v-100h100v100h-100zM300 200v-100h100v100h-100zM500 200v-100h100v100h-100zM700 200v-100h100v100h-100zM900 200v-100h100v100h-100z" />
<glyph unicode="&#xe110;" d="M1135 1165l249 -230q15 -14 15 -35t-15 -35l-249 -230q-14 -14 -24.5 -10t-10.5 25v150h-159l-600 -600h-291q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5h209l600 600h241v150q0 21 10.5 25t24.5 -10zM522 819l-141 -141l-122 122h-209q-21 0 -35.5 14.5 t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5h291zM1135 565l249 -230q15 -14 15 -35t-15 -35l-249 -230q-14 -14 -24.5 -10t-10.5 25v150h-241l-181 181l141 141l122 -122h159v150q0 21 10.5 25t24.5 -10z" />
<glyph unicode="&#xe111;" d="M100 1100h1000q41 0 70.5 -29.5t29.5 -70.5v-600q0 -41 -29.5 -70.5t-70.5 -29.5h-596l-304 -300v300h-100q-41 0 -70.5 29.5t-29.5 70.5v600q0 41 29.5 70.5t70.5 29.5z" />
<glyph unicode="&#xe112;" d="M150 1200h200q21 0 35.5 -14.5t14.5 -35.5v-250h-300v250q0 21 14.5 35.5t35.5 14.5zM850 1200h200q21 0 35.5 -14.5t14.5 -35.5v-250h-300v250q0 21 14.5 35.5t35.5 14.5zM1100 800v-300q0 -41 -3 -77.5t-15 -89.5t-32 -96t-58 -89t-89 -77t-129 -51t-174 -20t-174 20 t-129 51t-89 77t-58 89t-32 96t-15 89.5t-3 77.5v300h300v-250v-27v-42.5t1.5 -41t5 -38t10 -35t16.5 -30t25.5 -24.5t35 -19t46.5 -12t60 -4t60 4.5t46.5 12.5t35 19.5t25 25.5t17 30.5t10 35t5 38t2 40.5t-0.5 42v25v250h300z" />
<glyph unicode="&#xe113;" d="M1100 411l-198 -199l-353 353l-353 -353l-197 199l551 551z" />
<glyph unicode="&#xe114;" d="M1101 789l-550 -551l-551 551l198 199l353 -353l353 353z" />
<glyph unicode="&#xe115;" d="M404 1000h746q21 0 35.5 -14.5t14.5 -35.5v-551h150q21 0 25 -10.5t-10 -24.5l-230 -249q-14 -15 -35 -15t-35 15l-230 249q-14 14 -10 24.5t25 10.5h150v401h-381zM135 984l230 -249q14 -14 10 -24.5t-25 -10.5h-150v-400h385l215 -200h-750q-21 0 -35.5 14.5 t-14.5 35.5v550h-150q-21 0 -25 10.5t10 24.5l230 249q14 15 35 15t35 -15z" />
<glyph unicode="&#xe116;" d="M56 1200h94q17 0 31 -11t18 -27l38 -162h896q24 0 39 -18.5t10 -42.5l-100 -475q-5 -21 -27 -42.5t-55 -21.5h-633l48 -200h535q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-50v-50q0 -21 -14.5 -35.5t-35.5 -14.5t-35.5 14.5t-14.5 35.5v50h-300v-50 q0 -21 -14.5 -35.5t-35.5 -14.5t-35.5 14.5t-14.5 35.5v50h-31q-18 0 -32.5 10t-20.5 19l-5 10l-201 961h-54q-20 0 -35 14.5t-15 35.5t15 35.5t35 14.5z" />
<glyph unicode="&#xe117;" d="M1200 1000v-100h-1200v100h200q0 41 29.5 70.5t70.5 29.5h300q41 0 70.5 -29.5t29.5 -70.5h500zM0 800h1200v-800h-1200v800z" />
<glyph unicode="&#xe118;" d="M200 800l-200 -400v600h200q0 41 29.5 70.5t70.5 29.5h300q42 0 71 -29.5t29 -70.5h500v-200h-1000zM1500 700l-300 -700h-1200l300 700h1200z" />
<glyph unicode="&#xe119;" d="M635 1184l230 -249q14 -14 10 -24.5t-25 -10.5h-150v-601h150q21 0 25 -10.5t-10 -24.5l-230 -249q-14 -15 -35 -15t-35 15l-230 249q-14 14 -10 24.5t25 10.5h150v601h-150q-21 0 -25 10.5t10 24.5l230 249q14 15 35 15t35 -15z" />
<glyph unicode="&#xe120;" d="M936 864l249 -229q14 -15 14 -35.5t-14 -35.5l-249 -229q-15 -15 -25.5 -10.5t-10.5 24.5v151h-600v-151q0 -20 -10.5 -24.5t-25.5 10.5l-249 229q-14 15 -14 35.5t14 35.5l249 229q15 15 25.5 10.5t10.5 -25.5v-149h600v149q0 21 10.5 25.5t25.5 -10.5z" />
<glyph unicode="&#xe121;" d="M1169 400l-172 732q-5 23 -23 45.5t-38 22.5h-672q-20 0 -38 -20t-23 -41l-172 -739h1138zM1100 300h-1000q-41 0 -70.5 -29.5t-29.5 -70.5v-100q0 -41 29.5 -70.5t70.5 -29.5h1000q41 0 70.5 29.5t29.5 70.5v100q0 41 -29.5 70.5t-70.5 29.5zM800 100v100h100v-100h-100 zM1000 100v100h100v-100h-100z" />
<glyph unicode="&#xe122;" d="M1150 1100q21 0 35.5 -14.5t14.5 -35.5v-850q0 -21 -14.5 -35.5t-35.5 -14.5t-35.5 14.5t-14.5 35.5v850q0 21 14.5 35.5t35.5 14.5zM1000 200l-675 200h-38l47 -276q3 -16 -5.5 -20t-29.5 -4h-7h-84q-20 0 -34.5 14t-18.5 35q-55 337 -55 351v250v6q0 16 1 23.5t6.5 14 t17.5 6.5h200l675 250v-850zM0 750v-250q-4 0 -11 0.5t-24 6t-30 15t-24 30t-11 48.5v50q0 26 10.5 46t25 30t29 16t25.5 7z" />
<glyph unicode="&#xe123;" d="M553 1200h94q20 0 29 -10.5t3 -29.5l-18 -37q83 -19 144 -82.5t76 -140.5l63 -327l118 -173h17q19 0 33 -14.5t14 -35t-13 -40.5t-31 -27q-8 -4 -23 -9.5t-65 -19.5t-103 -25t-132.5 -20t-158.5 -9q-57 0 -115 5t-104 12t-88.5 15.5t-73.5 17.5t-54.5 16t-35.5 12l-11 4 q-18 8 -31 28t-13 40.5t14 35t33 14.5h17l118 173l63 327q15 77 76 140t144 83l-18 32q-6 19 3.5 32t28.5 13zM498 110q50 -6 102 -6q53 0 102 6q-12 -49 -39.5 -79.5t-62.5 -30.5t-63 30.5t-39 79.5z" />
<glyph unicode="&#xe124;" d="M800 946l224 78l-78 -224l234 -45l-180 -155l180 -155l-234 -45l78 -224l-224 78l-45 -234l-155 180l-155 -180l-45 234l-224 -78l78 224l-234 45l180 155l-180 155l234 45l-78 224l224 -78l45 234l155 -180l155 180z" />
<glyph unicode="&#xe125;" d="M650 1200h50q40 0 70 -40.5t30 -84.5v-150l-28 -125h328q40 0 70 -40.5t30 -84.5v-100q0 -45 -29 -74l-238 -344q-16 -24 -38 -40.5t-45 -16.5h-250q-7 0 -42 25t-66 50l-31 25h-61q-45 0 -72.5 18t-27.5 57v400q0 36 20 63l145 196l96 198q13 28 37.5 48t51.5 20z M650 1100l-100 -212l-150 -213v-375h100l136 -100h214l250 375v125h-450l50 225v175h-50zM50 800h100q21 0 35.5 -14.5t14.5 -35.5v-500q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v500q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe126;" d="M600 1100h250q23 0 45 -16.5t38 -40.5l238 -344q29 -29 29 -74v-100q0 -44 -30 -84.5t-70 -40.5h-328q28 -118 28 -125v-150q0 -44 -30 -84.5t-70 -40.5h-50q-27 0 -51.5 20t-37.5 48l-96 198l-145 196q-20 27 -20 63v400q0 39 27.5 57t72.5 18h61q124 100 139 100z M50 1000h100q21 0 35.5 -14.5t14.5 -35.5v-500q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v500q0 21 14.5 35.5t35.5 14.5zM636 1000l-136 -100h-100v-375l150 -213l100 -212h50v175l-50 225h450v125l-250 375h-214z" />
<glyph unicode="&#xe127;" d="M356 873l363 230q31 16 53 -6l110 -112q13 -13 13.5 -32t-11.5 -34l-84 -121h302q84 0 138 -38t54 -110t-55 -111t-139 -39h-106l-131 -339q-6 -21 -19.5 -41t-28.5 -20h-342q-7 0 -90 81t-83 94v525q0 17 14 35.5t28 28.5zM400 792v-503l100 -89h293l131 339 q6 21 19.5 41t28.5 20h203q21 0 30.5 25t0.5 50t-31 25h-456h-7h-6h-5.5t-6 0.5t-5 1.5t-5 2t-4 2.5t-4 4t-2.5 4.5q-12 25 5 47l146 183l-86 83zM50 800h100q21 0 35.5 -14.5t14.5 -35.5v-500q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v500 q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe128;" d="M475 1103l366 -230q2 -1 6 -3.5t14 -10.5t18 -16.5t14.5 -20t6.5 -22.5v-525q0 -13 -86 -94t-93 -81h-342q-15 0 -28.5 20t-19.5 41l-131 339h-106q-85 0 -139.5 39t-54.5 111t54 110t138 38h302l-85 121q-11 15 -10.5 34t13.5 32l110 112q22 22 53 6zM370 945l146 -183 q17 -22 5 -47q-2 -2 -3.5 -4.5t-4 -4t-4 -2.5t-5 -2t-5 -1.5t-6 -0.5h-6h-6.5h-6h-475v-100h221q15 0 29 -20t20 -41l130 -339h294l106 89v503l-342 236zM1050 800h100q21 0 35.5 -14.5t14.5 -35.5v-500q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5 v500q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe129;" d="M550 1294q72 0 111 -55t39 -139v-106l339 -131q21 -6 41 -19.5t20 -28.5v-342q0 -7 -81 -90t-94 -83h-525q-17 0 -35.5 14t-28.5 28l-9 14l-230 363q-16 31 6 53l112 110q13 13 32 13.5t34 -11.5l121 -84v302q0 84 38 138t110 54zM600 972v203q0 21 -25 30.5t-50 0.5 t-25 -31v-456v-7v-6v-5.5t-0.5 -6t-1.5 -5t-2 -5t-2.5 -4t-4 -4t-4.5 -2.5q-25 -12 -47 5l-183 146l-83 -86l236 -339h503l89 100v293l-339 131q-21 6 -41 19.5t-20 28.5zM450 200h500q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-500 q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe130;" d="M350 1100h500q21 0 35.5 14.5t14.5 35.5v100q0 21 -14.5 35.5t-35.5 14.5h-500q-21 0 -35.5 -14.5t-14.5 -35.5v-100q0 -21 14.5 -35.5t35.5 -14.5zM600 306v-106q0 -84 -39 -139t-111 -55t-110 54t-38 138v302l-121 -84q-15 -12 -34 -11.5t-32 13.5l-112 110 q-22 22 -6 53l230 363q1 2 3.5 6t10.5 13.5t16.5 17t20 13.5t22.5 6h525q13 0 94 -83t81 -90v-342q0 -15 -20 -28.5t-41 -19.5zM308 900l-236 -339l83 -86l183 146q22 17 47 5q2 -1 4.5 -2.5t4 -4t2.5 -4t2 -5t1.5 -5t0.5 -6v-5.5v-6v-7v-456q0 -22 25 -31t50 0.5t25 30.5 v203q0 15 20 28.5t41 19.5l339 131v293l-89 100h-503z" />
<glyph unicode="&#xe131;" d="M600 1178q118 0 225 -45.5t184.5 -123t123 -184.5t45.5 -225t-45.5 -225t-123 -184.5t-184.5 -123t-225 -45.5t-225 45.5t-184.5 123t-123 184.5t-45.5 225t45.5 225t123 184.5t184.5 123t225 45.5zM914 632l-275 223q-16 13 -27.5 8t-11.5 -26v-137h-275 q-10 0 -17.5 -7.5t-7.5 -17.5v-150q0 -10 7.5 -17.5t17.5 -7.5h275v-137q0 -21 11.5 -26t27.5 8l275 223q16 13 16 32t-16 32z" />
<glyph unicode="&#xe132;" d="M600 1178q118 0 225 -45.5t184.5 -123t123 -184.5t45.5 -225t-45.5 -225t-123 -184.5t-184.5 -123t-225 -45.5t-225 45.5t-184.5 123t-123 184.5t-45.5 225t45.5 225t123 184.5t184.5 123t225 45.5zM561 855l-275 -223q-16 -13 -16 -32t16 -32l275 -223q16 -13 27.5 -8 t11.5 26v137h275q10 0 17.5 7.5t7.5 17.5v150q0 10 -7.5 17.5t-17.5 7.5h-275v137q0 21 -11.5 26t-27.5 -8z" />
<glyph unicode="&#xe133;" d="M600 1178q118 0 225 -45.5t184.5 -123t123 -184.5t45.5 -225t-45.5 -225t-123 -184.5t-184.5 -123t-225 -45.5t-225 45.5t-184.5 123t-123 184.5t-45.5 225t45.5 225t123 184.5t184.5 123t225 45.5zM855 639l-223 275q-13 16 -32 16t-32 -16l-223 -275q-13 -16 -8 -27.5 t26 -11.5h137v-275q0 -10 7.5 -17.5t17.5 -7.5h150q10 0 17.5 7.5t7.5 17.5v275h137q21 0 26 11.5t-8 27.5z" />
<glyph unicode="&#xe134;" d="M600 1178q118 0 225 -45.5t184.5 -123t123 -184.5t45.5 -225t-45.5 -225t-123 -184.5t-184.5 -123t-225 -45.5t-225 45.5t-184.5 123t-123 184.5t-45.5 225t45.5 225t123 184.5t184.5 123t225 45.5zM675 900h-150q-10 0 -17.5 -7.5t-7.5 -17.5v-275h-137q-21 0 -26 -11.5 t8 -27.5l223 -275q13 -16 32 -16t32 16l223 275q13 16 8 27.5t-26 11.5h-137v275q0 10 -7.5 17.5t-17.5 7.5z" />
<glyph unicode="&#xe135;" d="M600 1176q116 0 222.5 -46t184 -123.5t123.5 -184t46 -222.5t-46 -222.5t-123.5 -184t-184 -123.5t-222.5 -46t-222.5 46t-184 123.5t-123.5 184t-46 222.5t46 222.5t123.5 184t184 123.5t222.5 46zM627 1101q-15 -12 -36.5 -20.5t-35.5 -12t-43 -8t-39 -6.5 q-15 -3 -45.5 0t-45.5 -2q-20 -7 -51.5 -26.5t-34.5 -34.5q-3 -11 6.5 -22.5t8.5 -18.5q-3 -34 -27.5 -91t-29.5 -79q-9 -34 5 -93t8 -87q0 -9 17 -44.5t16 -59.5q12 0 23 -5t23.5 -15t19.5 -14q16 -8 33 -15t40.5 -15t34.5 -12q21 -9 52.5 -32t60 -38t57.5 -11 q7 -15 -3 -34t-22.5 -40t-9.5 -38q13 -21 23 -34.5t27.5 -27.5t36.5 -18q0 -7 -3.5 -16t-3.5 -14t5 -17q104 -2 221 112q30 29 46.5 47t34.5 49t21 63q-13 8 -37 8.5t-36 7.5q-15 7 -49.5 15t-51.5 19q-18 0 -41 -0.5t-43 -1.5t-42 -6.5t-38 -16.5q-51 -35 -66 -12 q-4 1 -3.5 25.5t0.5 25.5q-6 13 -26.5 17.5t-24.5 6.5q1 15 -0.5 30.5t-7 28t-18.5 11.5t-31 -21q-23 -25 -42 4q-19 28 -8 58q6 16 22 22q6 -1 26 -1.5t33.5 -4t19.5 -13.5q7 -12 18 -24t21.5 -20.5t20 -15t15.5 -10.5l5 -3q2 12 7.5 30.5t8 34.5t-0.5 32q-3 18 3.5 29 t18 22.5t15.5 24.5q6 14 10.5 35t8 31t15.5 22.5t34 22.5q-6 18 10 36q8 0 24 -1.5t24.5 -1.5t20 4.5t20.5 15.5q-10 23 -31 42.5t-37.5 29.5t-49 27t-43.5 23q0 1 2 8t3 11.5t1.5 10.5t-1 9.5t-4.5 4.5q31 -13 58.5 -14.5t38.5 2.5l12 5q5 28 -9.5 46t-36.5 24t-50 15 t-41 20q-18 -4 -37 0zM613 994q0 -17 8 -42t17 -45t9 -23q-8 1 -39.5 5.5t-52.5 10t-37 16.5q3 11 16 29.5t16 25.5q10 -10 19 -10t14 6t13.5 14.5t16.5 12.5z" />
<glyph unicode="&#xe136;" d="M756 1157q164 92 306 -9l-259 -138l145 -232l251 126q6 -89 -34 -156.5t-117 -110.5q-60 -34 -127 -39.5t-126 16.5l-596 -596q-15 -16 -36.5 -16t-36.5 16l-111 110q-15 15 -15 36.5t15 37.5l600 599q-34 101 5.5 201.5t135.5 154.5z" />
<glyph unicode="&#xe137;" horiz-adv-x="1220" d="M100 1196h1000q41 0 70.5 -29.5t29.5 -70.5v-100q0 -41 -29.5 -70.5t-70.5 -29.5h-1000q-41 0 -70.5 29.5t-29.5 70.5v100q0 41 29.5 70.5t70.5 29.5zM1100 1096h-200v-100h200v100zM100 796h1000q41 0 70.5 -29.5t29.5 -70.5v-100q0 -41 -29.5 -70.5t-70.5 -29.5h-1000 q-41 0 -70.5 29.5t-29.5 70.5v100q0 41 29.5 70.5t70.5 29.5zM1100 696h-500v-100h500v100zM100 396h1000q41 0 70.5 -29.5t29.5 -70.5v-100q0 -41 -29.5 -70.5t-70.5 -29.5h-1000q-41 0 -70.5 29.5t-29.5 70.5v100q0 41 29.5 70.5t70.5 29.5zM1100 296h-300v-100h300v100z " />
<glyph unicode="&#xe138;" d="M150 1200h900q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-900q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5zM700 500v-300l-200 -200v500l-350 500h900z" />
<glyph unicode="&#xe139;" d="M500 1200h200q41 0 70.5 -29.5t29.5 -70.5v-100h300q41 0 70.5 -29.5t29.5 -70.5v-400h-500v100h-200v-100h-500v400q0 41 29.5 70.5t70.5 29.5h300v100q0 41 29.5 70.5t70.5 29.5zM500 1100v-100h200v100h-200zM1200 400v-200q0 -41 -29.5 -70.5t-70.5 -29.5h-1000 q-41 0 -70.5 29.5t-29.5 70.5v200h1200z" />
<glyph unicode="&#xe140;" d="M50 1200h300q21 0 25 -10.5t-10 -24.5l-94 -94l199 -199q7 -8 7 -18t-7 -18l-106 -106q-8 -7 -18 -7t-18 7l-199 199l-94 -94q-14 -14 -24.5 -10t-10.5 25v300q0 21 14.5 35.5t35.5 14.5zM850 1200h300q21 0 35.5 -14.5t14.5 -35.5v-300q0 -21 -10.5 -25t-24.5 10l-94 94 l-199 -199q-8 -7 -18 -7t-18 7l-106 106q-7 8 -7 18t7 18l199 199l-94 94q-14 14 -10 24.5t25 10.5zM364 470l106 -106q7 -8 7 -18t-7 -18l-199 -199l94 -94q14 -14 10 -24.5t-25 -10.5h-300q-21 0 -35.5 14.5t-14.5 35.5v300q0 21 10.5 25t24.5 -10l94 -94l199 199 q8 7 18 7t18 -7zM1071 271l94 94q14 14 24.5 10t10.5 -25v-300q0 -21 -14.5 -35.5t-35.5 -14.5h-300q-21 0 -25 10.5t10 24.5l94 94l-199 199q-7 8 -7 18t7 18l106 106q8 7 18 7t18 -7z" />
<glyph unicode="&#xe141;" d="M596 1192q121 0 231.5 -47.5t190 -127t127 -190t47.5 -231.5t-47.5 -231.5t-127 -190.5t-190 -127t-231.5 -47t-231.5 47t-190.5 127t-127 190.5t-47 231.5t47 231.5t127 190t190.5 127t231.5 47.5zM596 1010q-112 0 -207.5 -55.5t-151 -151t-55.5 -207.5t55.5 -207.5 t151 -151t207.5 -55.5t207.5 55.5t151 151t55.5 207.5t-55.5 207.5t-151 151t-207.5 55.5zM454.5 905q22.5 0 38.5 -16t16 -38.5t-16 -39t-38.5 -16.5t-38.5 16.5t-16 39t16 38.5t38.5 16zM754.5 905q22.5 0 38.5 -16t16 -38.5t-16 -39t-38 -16.5q-14 0 -29 10l-55 -145 q17 -23 17 -51q0 -36 -25.5 -61.5t-61.5 -25.5t-61.5 25.5t-25.5 61.5q0 32 20.5 56.5t51.5 29.5l122 126l1 1q-9 14 -9 28q0 23 16 39t38.5 16zM345.5 709q22.5 0 38.5 -16t16 -38.5t-16 -38.5t-38.5 -16t-38.5 16t-16 38.5t16 38.5t38.5 16zM854.5 709q22.5 0 38.5 -16 t16 -38.5t-16 -38.5t-38.5 -16t-38.5 16t-16 38.5t16 38.5t38.5 16z" />
<glyph unicode="&#xe142;" d="M546 173l469 470q91 91 99 192q7 98 -52 175.5t-154 94.5q-22 4 -47 4q-34 0 -66.5 -10t-56.5 -23t-55.5 -38t-48 -41.5t-48.5 -47.5q-376 -375 -391 -390q-30 -27 -45 -41.5t-37.5 -41t-32 -46.5t-16 -47.5t-1.5 -56.5q9 -62 53.5 -95t99.5 -33q74 0 125 51l548 548 q36 36 20 75q-7 16 -21.5 26t-32.5 10q-26 0 -50 -23q-13 -12 -39 -38l-341 -338q-15 -15 -35.5 -15.5t-34.5 13.5t-14 34.5t14 34.5q327 333 361 367q35 35 67.5 51.5t78.5 16.5q14 0 29 -1q44 -8 74.5 -35.5t43.5 -68.5q14 -47 2 -96.5t-47 -84.5q-12 -11 -32 -32 t-79.5 -81t-114.5 -115t-124.5 -123.5t-123 -119.5t-96.5 -89t-57 -45q-56 -27 -120 -27q-70 0 -129 32t-93 89q-48 78 -35 173t81 163l511 511q71 72 111 96q91 55 198 55q80 0 152 -33q78 -36 129.5 -103t66.5 -154q17 -93 -11 -183.5t-94 -156.5l-482 -476 q-15 -15 -36 -16t-37 14t-17.5 34t14.5 35z" />
<glyph unicode="&#xe143;" d="M649 949q48 68 109.5 104t121.5 38.5t118.5 -20t102.5 -64t71 -100.5t27 -123q0 -57 -33.5 -117.5t-94 -124.5t-126.5 -127.5t-150 -152.5t-146 -174q-62 85 -145.5 174t-150 152.5t-126.5 127.5t-93.5 124.5t-33.5 117.5q0 64 28 123t73 100.5t104 64t119 20 t120.5 -38.5t104.5 -104zM896 972q-33 0 -64.5 -19t-56.5 -46t-47.5 -53.5t-43.5 -45.5t-37.5 -19t-36 19t-40 45.5t-43 53.5t-54 46t-65.5 19q-67 0 -122.5 -55.5t-55.5 -132.5q0 -23 13.5 -51t46 -65t57.5 -63t76 -75l22 -22q15 -14 44 -44t50.5 -51t46 -44t41 -35t23 -12 t23.5 12t42.5 36t46 44t52.5 52t44 43q4 4 12 13q43 41 63.5 62t52 55t46 55t26 46t11.5 44q0 79 -53 133.5t-120 54.5z" />
<glyph unicode="&#xe144;" d="M776.5 1214q93.5 0 159.5 -66l141 -141q66 -66 66 -160q0 -42 -28 -95.5t-62 -87.5l-29 -29q-31 53 -77 99l-18 18l95 95l-247 248l-389 -389l212 -212l-105 -106l-19 18l-141 141q-66 66 -66 159t66 159l283 283q65 66 158.5 66zM600 706l105 105q10 -8 19 -17l141 -141 q66 -66 66 -159t-66 -159l-283 -283q-66 -66 -159 -66t-159 66l-141 141q-66 66 -66 159.5t66 159.5l55 55q29 -55 75 -102l18 -17l-95 -95l247 -248l389 389z" />
<glyph unicode="&#xe145;" d="M603 1200q85 0 162 -15t127 -38t79 -48t29 -46v-953q0 -41 -29.5 -70.5t-70.5 -29.5h-600q-41 0 -70.5 29.5t-29.5 70.5v953q0 21 30 46.5t81 48t129 37.5t163 15zM300 1000v-700h600v700h-600zM600 254q-43 0 -73.5 -30.5t-30.5 -73.5t30.5 -73.5t73.5 -30.5t73.5 30.5 t30.5 73.5t-30.5 73.5t-73.5 30.5z" />
<glyph unicode="&#xe146;" d="M902 1185l283 -282q15 -15 15 -36t-14.5 -35.5t-35.5 -14.5t-35 15l-36 35l-279 -267v-300l-212 210l-308 -307l-280 -203l203 280l307 308l-210 212h300l267 279l-35 36q-15 14 -15 35t14.5 35.5t35.5 14.5t35 -15z" />
<glyph unicode="&#xe148;" d="M700 1248v-78q38 -5 72.5 -14.5t75.5 -31.5t71 -53.5t52 -84t24 -118.5h-159q-4 36 -10.5 59t-21 45t-40 35.5t-64.5 20.5v-307l64 -13q34 -7 64 -16.5t70 -32t67.5 -52.5t47.5 -80t20 -112q0 -139 -89 -224t-244 -97v-77h-100v79q-150 16 -237 103q-40 40 -52.5 93.5 t-15.5 139.5h139q5 -77 48.5 -126t117.5 -65v335l-27 8q-46 14 -79 26.5t-72 36t-63 52t-40 72.5t-16 98q0 70 25 126t67.5 92t94.5 57t110 27v77h100zM600 754v274q-29 -4 -50 -11t-42 -21.5t-31.5 -41.5t-10.5 -65q0 -29 7 -50.5t16.5 -34t28.5 -22.5t31.5 -14t37.5 -10 q9 -3 13 -4zM700 547v-310q22 2 42.5 6.5t45 15.5t41.5 27t29 42t12 59.5t-12.5 59.5t-38 44.5t-53 31t-66.5 24.5z" />
<glyph unicode="&#xe149;" d="M561 1197q84 0 160.5 -40t123.5 -109.5t47 -147.5h-153q0 40 -19.5 71.5t-49.5 48.5t-59.5 26t-55.5 9q-37 0 -79 -14.5t-62 -35.5q-41 -44 -41 -101q0 -26 13.5 -63t26.5 -61t37 -66q6 -9 9 -14h241v-100h-197q8 -50 -2.5 -115t-31.5 -95q-45 -62 -99 -112 q34 10 83 17.5t71 7.5q32 1 102 -16t104 -17q83 0 136 30l50 -147q-31 -19 -58 -30.5t-55 -15.5t-42 -4.5t-46 -0.5q-23 0 -76 17t-111 32.5t-96 11.5q-39 -3 -82 -16t-67 -25l-23 -11l-55 145q4 3 16 11t15.5 10.5t13 9t15.5 12t14.5 14t17.5 18.5q48 55 54 126.5 t-30 142.5h-221v100h166q-23 47 -44 104q-7 20 -12 41.5t-6 55.5t6 66.5t29.5 70.5t58.5 71q97 88 263 88z" />
<glyph unicode="&#xe150;" d="M400 300h150q21 0 25 -11t-10 -25l-230 -250q-14 -15 -35 -15t-35 15l-230 250q-14 14 -10 25t25 11h150v900h200v-900zM935 1184l230 -249q14 -14 10 -24.5t-25 -10.5h-150v-900h-200v900h-150q-21 0 -25 10.5t10 24.5l230 249q14 15 35 15t35 -15z" />
<glyph unicode="&#xe151;" d="M1000 700h-100v100h-100v-100h-100v500h300v-500zM400 300h150q21 0 25 -11t-10 -25l-230 -250q-14 -15 -35 -15t-35 15l-230 250q-14 14 -10 25t25 11h150v900h200v-900zM801 1100v-200h100v200h-100zM1000 350l-200 -250h200v-100h-300v150l200 250h-200v100h300v-150z " />
<glyph unicode="&#xe152;" d="M400 300h150q21 0 25 -11t-10 -25l-230 -250q-14 -15 -35 -15t-35 15l-230 250q-14 14 -10 25t25 11h150v900h200v-900zM1000 1050l-200 -250h200v-100h-300v150l200 250h-200v100h300v-150zM1000 0h-100v100h-100v-100h-100v500h300v-500zM801 400v-200h100v200h-100z " />
<glyph unicode="&#xe153;" d="M400 300h150q21 0 25 -11t-10 -25l-230 -250q-14 -15 -35 -15t-35 15l-230 250q-14 14 -10 25t25 11h150v900h200v-900zM1000 700h-100v400h-100v100h200v-500zM1100 0h-100v100h-200v400h300v-500zM901 400v-200h100v200h-100z" />
<glyph unicode="&#xe154;" d="M400 300h150q21 0 25 -11t-10 -25l-230 -250q-14 -15 -35 -15t-35 15l-230 250q-14 14 -10 25t25 11h150v900h200v-900zM1100 700h-100v100h-200v400h300v-500zM901 1100v-200h100v200h-100zM1000 0h-100v400h-100v100h200v-500z" />
<glyph unicode="&#xe155;" d="M400 300h150q21 0 25 -11t-10 -25l-230 -250q-14 -15 -35 -15t-35 15l-230 250q-14 14 -10 25t25 11h150v900h200v-900zM900 1000h-200v200h200v-200zM1000 700h-300v200h300v-200zM1100 400h-400v200h400v-200zM1200 100h-500v200h500v-200z" />
<glyph unicode="&#xe156;" d="M400 300h150q21 0 25 -11t-10 -25l-230 -250q-14 -15 -35 -15t-35 15l-230 250q-14 14 -10 25t25 11h150v900h200v-900zM1200 1000h-500v200h500v-200zM1100 700h-400v200h400v-200zM1000 400h-300v200h300v-200zM900 100h-200v200h200v-200z" />
<glyph unicode="&#xe157;" d="M350 1100h400q162 0 256 -93.5t94 -256.5v-400q0 -165 -93.5 -257.5t-256.5 -92.5h-400q-165 0 -257.5 92.5t-92.5 257.5v400q0 165 92.5 257.5t257.5 92.5zM800 900h-500q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5 v500q0 41 -29.5 70.5t-70.5 29.5z" />
<glyph unicode="&#xe158;" d="M350 1100h400q165 0 257.5 -92.5t92.5 -257.5v-400q0 -165 -92.5 -257.5t-257.5 -92.5h-400q-163 0 -256.5 92.5t-93.5 257.5v400q0 163 94 256.5t256 93.5zM800 900h-500q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5 v500q0 41 -29.5 70.5t-70.5 29.5zM440 770l253 -190q17 -12 17 -30t-17 -30l-253 -190q-16 -12 -28 -6.5t-12 26.5v400q0 21 12 26.5t28 -6.5z" />
<glyph unicode="&#xe159;" d="M350 1100h400q163 0 256.5 -94t93.5 -256v-400q0 -165 -92.5 -257.5t-257.5 -92.5h-400q-165 0 -257.5 92.5t-92.5 257.5v400q0 163 92.5 256.5t257.5 93.5zM800 900h-500q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5 v500q0 41 -29.5 70.5t-70.5 29.5zM350 700h400q21 0 26.5 -12t-6.5 -28l-190 -253q-12 -17 -30 -17t-30 17l-190 253q-12 16 -6.5 28t26.5 12z" />
<glyph unicode="&#xe160;" d="M350 1100h400q165 0 257.5 -92.5t92.5 -257.5v-400q0 -163 -92.5 -256.5t-257.5 -93.5h-400q-163 0 -256.5 94t-93.5 256v400q0 165 92.5 257.5t257.5 92.5zM800 900h-500q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5 v500q0 41 -29.5 70.5t-70.5 29.5zM580 693l190 -253q12 -16 6.5 -28t-26.5 -12h-400q-21 0 -26.5 12t6.5 28l190 253q12 17 30 17t30 -17z" />
<glyph unicode="&#xe161;" d="M550 1100h400q165 0 257.5 -92.5t92.5 -257.5v-400q0 -165 -92.5 -257.5t-257.5 -92.5h-400q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5h450q41 0 70.5 29.5t29.5 70.5v500q0 41 -29.5 70.5t-70.5 29.5h-450q-21 0 -35.5 14.5t-14.5 35.5v100 q0 21 14.5 35.5t35.5 14.5zM338 867l324 -284q16 -14 16 -33t-16 -33l-324 -284q-16 -14 -27 -9t-11 26v150h-250q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5h250v150q0 21 11 26t27 -9z" />
<glyph unicode="&#xe162;" d="M793 1182l9 -9q8 -10 5 -27q-3 -11 -79 -225.5t-78 -221.5l300 1q24 0 32.5 -17.5t-5.5 -35.5q-1 0 -133.5 -155t-267 -312.5t-138.5 -162.5q-12 -15 -26 -15h-9l-9 8q-9 11 -4 32q2 9 42 123.5t79 224.5l39 110h-302q-23 0 -31 19q-10 21 6 41q75 86 209.5 237.5 t228 257t98.5 111.5q9 16 25 16h9z" />
<glyph unicode="&#xe163;" d="M350 1100h400q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-450q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h450q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-165 0 -257.5 92.5t-92.5 257.5v400 q0 165 92.5 257.5t257.5 92.5zM938 867l324 -284q16 -14 16 -33t-16 -33l-324 -284q-16 -14 -27 -9t-11 26v150h-250q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5h250v150q0 21 11 26t27 -9z" />
<glyph unicode="&#xe164;" d="M750 1200h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -10.5 -25t-24.5 10l-109 109l-312 -312q-15 -15 -35.5 -15t-35.5 15l-141 141q-15 15 -15 35.5t15 35.5l312 312l-109 109q-14 14 -10 24.5t25 10.5zM456 900h-156q-41 0 -70.5 -29.5t-29.5 -70.5v-500 q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5v148l200 200v-298q0 -165 -93.5 -257.5t-256.5 -92.5h-400q-165 0 -257.5 92.5t-92.5 257.5v400q0 165 92.5 257.5t257.5 92.5h300z" />
<glyph unicode="&#xe165;" d="M600 1186q119 0 227.5 -46.5t187 -125t125 -187t46.5 -227.5t-46.5 -227.5t-125 -187t-187 -125t-227.5 -46.5t-227.5 46.5t-187 125t-125 187t-46.5 227.5t46.5 227.5t125 187t187 125t227.5 46.5zM600 1022q-115 0 -212 -56.5t-153.5 -153.5t-56.5 -212t56.5 -212 t153.5 -153.5t212 -56.5t212 56.5t153.5 153.5t56.5 212t-56.5 212t-153.5 153.5t-212 56.5zM600 794q80 0 137 -57t57 -137t-57 -137t-137 -57t-137 57t-57 137t57 137t137 57z" />
<glyph unicode="&#xe166;" d="M450 1200h200q21 0 35.5 -14.5t14.5 -35.5v-350h245q20 0 25 -11t-9 -26l-383 -426q-14 -15 -33.5 -15t-32.5 15l-379 426q-13 15 -8.5 26t25.5 11h250v350q0 21 14.5 35.5t35.5 14.5zM50 300h1000q21 0 35.5 -14.5t14.5 -35.5v-250h-1100v250q0 21 14.5 35.5t35.5 14.5z M900 200v-50h100v50h-100z" />
<glyph unicode="&#xe167;" d="M583 1182l378 -435q14 -15 9 -31t-26 -16h-244v-250q0 -20 -17 -35t-39 -15h-200q-20 0 -32 14.5t-12 35.5v250h-250q-20 0 -25.5 16.5t8.5 31.5l383 431q14 16 33.5 17t33.5 -14zM50 300h1000q21 0 35.5 -14.5t14.5 -35.5v-250h-1100v250q0 21 14.5 35.5t35.5 14.5z M900 200v-50h100v50h-100z" />
<glyph unicode="&#xe168;" d="M396 723l369 369q7 7 17.5 7t17.5 -7l139 -139q7 -8 7 -18.5t-7 -17.5l-525 -525q-7 -8 -17.5 -8t-17.5 8l-292 291q-7 8 -7 18t7 18l139 139q8 7 18.5 7t17.5 -7zM50 300h1000q21 0 35.5 -14.5t14.5 -35.5v-250h-1100v250q0 21 14.5 35.5t35.5 14.5zM900 200v-50h100v50 h-100z" />
<glyph unicode="&#xe169;" d="M135 1023l142 142q14 14 35 14t35 -14l77 -77l-212 -212l-77 76q-14 15 -14 36t14 35zM655 855l210 210q14 14 24.5 10t10.5 -25l-2 -599q-1 -20 -15.5 -35t-35.5 -15l-597 -1q-21 0 -25 10.5t10 24.5l208 208l-154 155l212 212zM50 300h1000q21 0 35.5 -14.5t14.5 -35.5 v-250h-1100v250q0 21 14.5 35.5t35.5 14.5zM900 200v-50h100v50h-100z" />
<glyph unicode="&#xe170;" d="M350 1200l599 -2q20 -1 35 -15.5t15 -35.5l1 -597q0 -21 -10.5 -25t-24.5 10l-208 208l-155 -154l-212 212l155 154l-210 210q-14 14 -10 24.5t25 10.5zM524 512l-76 -77q-15 -14 -36 -14t-35 14l-142 142q-14 14 -14 35t14 35l77 77zM50 300h1000q21 0 35.5 -14.5 t14.5 -35.5v-250h-1100v250q0 21 14.5 35.5t35.5 14.5zM900 200v-50h100v50h-100z" />
<glyph unicode="&#xe171;" d="M1200 103l-483 276l-314 -399v423h-399l1196 796v-1096zM483 424v-230l683 953z" />
<glyph unicode="&#xe172;" d="M1100 1000v-850q0 -21 -14.5 -35.5t-35.5 -14.5h-150v400h-700v-400h-150q-21 0 -35.5 14.5t-14.5 35.5v1000q0 20 14.5 35t35.5 15h250v-300h500v300h100zM700 1000h-100v200h100v-200z" />
<glyph unicode="&#xe173;" d="M1100 1000l-2 -149l-299 -299l-95 95q-9 9 -21.5 9t-21.5 -9l-149 -147h-312v-400h-150q-21 0 -35.5 14.5t-14.5 35.5v1000q0 20 14.5 35t35.5 15h250v-300h500v300h100zM700 1000h-100v200h100v-200zM1132 638l106 -106q7 -7 7 -17.5t-7 -17.5l-420 -421q-8 -7 -18 -7 t-18 7l-202 203q-8 7 -8 17.5t8 17.5l106 106q7 8 17.5 8t17.5 -8l79 -79l297 297q7 7 17.5 7t17.5 -7z" />
<glyph unicode="&#xe174;" d="M1100 1000v-269l-103 -103l-134 134q-15 15 -33.5 16.5t-34.5 -12.5l-266 -266h-329v-400h-150q-21 0 -35.5 14.5t-14.5 35.5v1000q0 20 14.5 35t35.5 15h250v-300h500v300h100zM700 1000h-100v200h100v-200zM1202 572l70 -70q15 -15 15 -35.5t-15 -35.5l-131 -131 l131 -131q15 -15 15 -35.5t-15 -35.5l-70 -70q-15 -15 -35.5 -15t-35.5 15l-131 131l-131 -131q-15 -15 -35.5 -15t-35.5 15l-70 70q-15 15 -15 35.5t15 35.5l131 131l-131 131q-15 15 -15 35.5t15 35.5l70 70q15 15 35.5 15t35.5 -15l131 -131l131 131q15 15 35.5 15 t35.5 -15z" />
<glyph unicode="&#xe175;" d="M1100 1000v-300h-350q-21 0 -35.5 -14.5t-14.5 -35.5v-150h-500v-400h-150q-21 0 -35.5 14.5t-14.5 35.5v1000q0 20 14.5 35t35.5 15h250v-300h500v300h100zM700 1000h-100v200h100v-200zM850 600h100q21 0 35.5 -14.5t14.5 -35.5v-250h150q21 0 25 -10.5t-10 -24.5 l-230 -230q-14 -14 -35 -14t-35 14l-230 230q-14 14 -10 24.5t25 10.5h150v250q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe176;" d="M1100 1000v-400l-165 165q-14 15 -35 15t-35 -15l-263 -265h-402v-400h-150q-21 0 -35.5 14.5t-14.5 35.5v1000q0 20 14.5 35t35.5 15h250v-300h500v300h100zM700 1000h-100v200h100v-200zM935 565l230 -229q14 -15 10 -25.5t-25 -10.5h-150v-250q0 -20 -14.5 -35 t-35.5 -15h-100q-21 0 -35.5 15t-14.5 35v250h-150q-21 0 -25 10.5t10 25.5l230 229q14 15 35 15t35 -15z" />
<glyph unicode="&#xe177;" d="M50 1100h1100q21 0 35.5 -14.5t14.5 -35.5v-150h-1200v150q0 21 14.5 35.5t35.5 14.5zM1200 800v-550q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5v550h1200zM100 500v-200h400v200h-400z" />
<glyph unicode="&#xe178;" d="M935 1165l248 -230q14 -14 14 -35t-14 -35l-248 -230q-14 -14 -24.5 -10t-10.5 25v150h-400v200h400v150q0 21 10.5 25t24.5 -10zM200 800h-50q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5h50v-200zM400 800h-100v200h100v-200zM18 435l247 230 q14 14 24.5 10t10.5 -25v-150h400v-200h-400v-150q0 -21 -10.5 -25t-24.5 10l-247 230q-15 14 -15 35t15 35zM900 300h-100v200h100v-200zM1000 500h51q20 0 34.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-34.5 -14.5h-51v200z" />
<glyph unicode="&#xe179;" d="M862 1073l276 116q25 18 43.5 8t18.5 -41v-1106q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v397q-4 1 -11 5t-24 17.5t-30 29t-24 42t-11 56.5v359q0 31 18.5 65t43.5 52zM550 1200q22 0 34.5 -12.5t14.5 -24.5l1 -13v-450q0 -28 -10.5 -59.5 t-25 -56t-29 -45t-25.5 -31.5l-10 -11v-447q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v447q-4 4 -11 11.5t-24 30.5t-30 46t-24 55t-11 60v450q0 2 0.5 5.5t4 12t8.5 15t14.5 12t22.5 5.5q20 0 32.5 -12.5t14.5 -24.5l3 -13v-350h100v350v5.5t2.5 12 t7 15t15 12t25.5 5.5q23 0 35.5 -12.5t13.5 -24.5l1 -13v-350h100v350q0 2 0.5 5.5t3 12t7 15t15 12t24.5 5.5z" />
<glyph unicode="&#xe180;" d="M1200 1100v-56q-4 0 -11 -0.5t-24 -3t-30 -7.5t-24 -15t-11 -24v-888q0 -22 25 -34.5t50 -13.5l25 -2v-56h-400v56q75 0 87.5 6.5t12.5 43.5v394h-500v-394q0 -37 12.5 -43.5t87.5 -6.5v-56h-400v56q4 0 11 0.5t24 3t30 7.5t24 15t11 24v888q0 22 -25 34.5t-50 13.5 l-25 2v56h400v-56q-75 0 -87.5 -6.5t-12.5 -43.5v-394h500v394q0 37 -12.5 43.5t-87.5 6.5v56h400z" />
<glyph unicode="&#xe181;" d="M675 1000h375q21 0 35.5 -14.5t14.5 -35.5v-150h-105l-295 -98v98l-200 200h-400l100 100h375zM100 900h300q41 0 70.5 -29.5t29.5 -70.5v-500q0 -41 -29.5 -70.5t-70.5 -29.5h-300q-41 0 -70.5 29.5t-29.5 70.5v500q0 41 29.5 70.5t70.5 29.5zM100 800v-200h300v200 h-300zM1100 535l-400 -133v163l400 133v-163zM100 500v-200h300v200h-300zM1100 398v-248q0 -21 -14.5 -35.5t-35.5 -14.5h-375l-100 -100h-375l-100 100h400l200 200h105z" />
<glyph unicode="&#xe182;" d="M17 1007l162 162q17 17 40 14t37 -22l139 -194q14 -20 11 -44.5t-20 -41.5l-119 -118q102 -142 228 -268t267 -227l119 118q17 17 42.5 19t44.5 -12l192 -136q19 -14 22.5 -37.5t-13.5 -40.5l-163 -162q-3 -1 -9.5 -1t-29.5 2t-47.5 6t-62.5 14.5t-77.5 26.5t-90 42.5 t-101.5 60t-111 83t-119 108.5q-74 74 -133.5 150.5t-94.5 138.5t-60 119.5t-34.5 100t-15 74.5t-4.5 48z" />
<glyph unicode="&#xe183;" d="M600 1100q92 0 175 -10.5t141.5 -27t108.5 -36.5t81.5 -40t53.5 -37t31 -27l9 -10v-200q0 -21 -14.5 -33t-34.5 -9l-202 34q-20 3 -34.5 20t-14.5 38v146q-141 24 -300 24t-300 -24v-146q0 -21 -14.5 -38t-34.5 -20l-202 -34q-20 -3 -34.5 9t-14.5 33v200q3 4 9.5 10.5 t31 26t54 37.5t80.5 39.5t109 37.5t141 26.5t175 10.5zM600 795q56 0 97 -9.5t60 -23.5t30 -28t12 -24l1 -10v-50l365 -303q14 -15 24.5 -40t10.5 -45v-212q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5v212q0 20 10.5 45t24.5 40l365 303v50 q0 4 1 10.5t12 23t30 29t60 22.5t97 10z" />
<glyph unicode="&#xe184;" d="M1100 700l-200 -200h-600l-200 200v500h200v-200h200v200h200v-200h200v200h200v-500zM250 400h700q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-12l137 -100h-950l137 100h-12q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5zM50 100h1100q21 0 35.5 -14.5 t14.5 -35.5v-50h-1200v50q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe185;" d="M700 1100h-100q-41 0 -70.5 -29.5t-29.5 -70.5v-1000h300v1000q0 41 -29.5 70.5t-70.5 29.5zM1100 800h-100q-41 0 -70.5 -29.5t-29.5 -70.5v-700h300v700q0 41 -29.5 70.5t-70.5 29.5zM400 0h-300v400q0 41 29.5 70.5t70.5 29.5h100q41 0 70.5 -29.5t29.5 -70.5v-400z " />
<glyph unicode="&#xe186;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM500 700h-200v-100h200v-300h-300v100h200v100h-200v300h300v-100zM900 700v-300l-100 -100h-200v500h200z M700 700v-300h100v300h-100z" />
<glyph unicode="&#xe187;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM500 300h-100v200h-100v-200h-100v500h100v-200h100v200h100v-500zM900 700v-300l-100 -100h-200v500h200z M700 700v-300h100v300h-100z" />
<glyph unicode="&#xe188;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM500 700h-200v-300h200v-100h-300v500h300v-100zM900 700h-200v-300h200v-100h-300v500h300v-100z" />
<glyph unicode="&#xe189;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM500 400l-300 150l300 150v-300zM900 550l-300 -150v300z" />
<glyph unicode="&#xe190;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM900 300h-700v500h700v-500zM800 700h-130q-38 0 -66.5 -43t-28.5 -108t27 -107t68 -42h130v300zM300 700v-300 h130q41 0 68 42t27 107t-28.5 108t-66.5 43h-130z" />
<glyph unicode="&#xe191;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM500 700h-200v-100h200v-300h-300v100h200v100h-200v300h300v-100zM900 300h-100v400h-100v100h200v-500z M700 300h-100v100h100v-100z" />
<glyph unicode="&#xe192;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM300 700h200v-400h-300v500h100v-100zM900 300h-100v400h-100v100h200v-500zM300 600v-200h100v200h-100z M700 300h-100v100h100v-100z" />
<glyph unicode="&#xe193;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM500 500l-199 -200h-100v50l199 200v150h-200v100h300v-300zM900 300h-100v400h-100v100h200v-500zM701 300h-100 v100h100v-100z" />
<glyph unicode="&#xe194;" d="M600 1191q120 0 229.5 -47t188.5 -126t126 -188.5t47 -229.5t-47 -229.5t-126 -188.5t-188.5 -126t-229.5 -47t-229.5 47t-188.5 126t-126 188.5t-47 229.5t47 229.5t126 188.5t188.5 126t229.5 47zM600 1021q-114 0 -211 -56.5t-153.5 -153.5t-56.5 -211t56.5 -211 t153.5 -153.5t211 -56.5t211 56.5t153.5 153.5t56.5 211t-56.5 211t-153.5 153.5t-211 56.5zM800 700h-300v-200h300v-100h-300l-100 100v200l100 100h300v-100z" />
<glyph unicode="&#xe195;" d="M600 1191q120 0 229.5 -47t188.5 -126t126 -188.5t47 -229.5t-47 -229.5t-126 -188.5t-188.5 -126t-229.5 -47t-229.5 47t-188.5 126t-126 188.5t-47 229.5t47 229.5t126 188.5t188.5 126t229.5 47zM600 1021q-114 0 -211 -56.5t-153.5 -153.5t-56.5 -211t56.5 -211 t153.5 -153.5t211 -56.5t211 56.5t153.5 153.5t56.5 211t-56.5 211t-153.5 153.5t-211 56.5zM800 700v-100l-50 -50l100 -100v-50h-100l-100 100h-150v-100h-100v400h300zM500 700v-100h200v100h-200z" />
<glyph unicode="&#xe197;" d="M503 1089q110 0 200.5 -59.5t134.5 -156.5q44 14 90 14q120 0 205 -86.5t85 -207t-85 -207t-205 -86.5h-128v250q0 21 -14.5 35.5t-35.5 14.5h-300q-21 0 -35.5 -14.5t-14.5 -35.5v-250h-222q-80 0 -136 57.5t-56 136.5q0 69 43 122.5t108 67.5q-2 19 -2 37q0 100 49 185 t134 134t185 49zM525 500h150q10 0 17.5 -7.5t7.5 -17.5v-275h137q21 0 26 -11.5t-8 -27.5l-223 -244q-13 -16 -32 -16t-32 16l-223 244q-13 16 -8 27.5t26 11.5h137v275q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe198;" d="M502 1089q110 0 201 -59.5t135 -156.5q43 15 89 15q121 0 206 -86.5t86 -206.5q0 -99 -60 -181t-150 -110l-378 360q-13 16 -31.5 16t-31.5 -16l-381 -365h-9q-79 0 -135.5 57.5t-56.5 136.5q0 69 43 122.5t108 67.5q-2 19 -2 38q0 100 49 184.5t133.5 134t184.5 49.5z M632 467l223 -228q13 -16 8 -27.5t-26 -11.5h-137v-275q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v275h-137q-21 0 -26 11.5t8 27.5q199 204 223 228q19 19 31.5 19t32.5 -19z" />
<glyph unicode="&#xe199;" d="M700 100v100h400l-270 300h170l-270 300h170l-300 333l-300 -333h170l-270 -300h170l-270 -300h400v-100h-50q-21 0 -35.5 -14.5t-14.5 -35.5v-50h400v50q0 21 -14.5 35.5t-35.5 14.5h-50z" />
<glyph unicode="&#xe200;" d="M600 1179q94 0 167.5 -56.5t99.5 -145.5q89 -6 150.5 -71.5t61.5 -155.5q0 -61 -29.5 -112.5t-79.5 -82.5q9 -29 9 -55q0 -74 -52.5 -126.5t-126.5 -52.5q-55 0 -100 30v-251q21 0 35.5 -14.5t14.5 -35.5v-50h-300v50q0 21 14.5 35.5t35.5 14.5v251q-45 -30 -100 -30 q-74 0 -126.5 52.5t-52.5 126.5q0 18 4 38q-47 21 -75.5 65t-28.5 97q0 74 52.5 126.5t126.5 52.5q5 0 23 -2q0 2 -1 10t-1 13q0 116 81.5 197.5t197.5 81.5z" />
<glyph unicode="&#xe201;" d="M1010 1010q111 -111 150.5 -260.5t0 -299t-150.5 -260.5q-83 -83 -191.5 -126.5t-218.5 -43.5t-218.5 43.5t-191.5 126.5q-111 111 -150.5 260.5t0 299t150.5 260.5q83 83 191.5 126.5t218.5 43.5t218.5 -43.5t191.5 -126.5zM476 1065q-4 0 -8 -1q-121 -34 -209.5 -122.5 t-122.5 -209.5q-4 -12 2.5 -23t18.5 -14l36 -9q3 -1 7 -1q23 0 29 22q27 96 98 166q70 71 166 98q11 3 17.5 13.5t3.5 22.5l-9 35q-3 13 -14 19q-7 4 -15 4zM512 920q-4 0 -9 -2q-80 -24 -138.5 -82.5t-82.5 -138.5q-4 -13 2 -24t19 -14l34 -9q4 -1 8 -1q22 0 28 21 q18 58 58.5 98.5t97.5 58.5q12 3 18 13.5t3 21.5l-9 35q-3 12 -14 19q-7 4 -15 4zM719.5 719.5q-49.5 49.5 -119.5 49.5t-119.5 -49.5t-49.5 -119.5t49.5 -119.5t119.5 -49.5t119.5 49.5t49.5 119.5t-49.5 119.5zM855 551q-22 0 -28 -21q-18 -58 -58.5 -98.5t-98.5 -57.5 q-11 -4 -17 -14.5t-3 -21.5l9 -35q3 -12 14 -19q7 -4 15 -4q4 0 9 2q80 24 138.5 82.5t82.5 138.5q4 13 -2.5 24t-18.5 14l-34 9q-4 1 -8 1zM1000 515q-23 0 -29 -22q-27 -96 -98 -166q-70 -71 -166 -98q-11 -3 -17.5 -13.5t-3.5 -22.5l9 -35q3 -13 14 -19q7 -4 15 -4 q4 0 8 1q121 34 209.5 122.5t122.5 209.5q4 12 -2.5 23t-18.5 14l-36 9q-3 1 -7 1z" />
<glyph unicode="&#xe202;" d="M700 800h300v-380h-180v200h-340v-200h-380v755q0 10 7.5 17.5t17.5 7.5h575v-400zM1000 900h-200v200zM700 300h162l-212 -212l-212 212h162v200h100v-200zM520 0h-395q-10 0 -17.5 7.5t-7.5 17.5v395zM1000 220v-195q0 -10 -7.5 -17.5t-17.5 -7.5h-195z" />
<glyph unicode="&#xe203;" d="M700 800h300v-520l-350 350l-550 -550v1095q0 10 7.5 17.5t17.5 7.5h575v-400zM1000 900h-200v200zM862 200h-162v-200h-100v200h-162l212 212zM480 0h-355q-10 0 -17.5 7.5t-7.5 17.5v55h380v-80zM1000 80v-55q0 -10 -7.5 -17.5t-17.5 -7.5h-155v80h180z" />
<glyph unicode="&#xe204;" d="M1162 800h-162v-200h100l100 -100h-300v300h-162l212 212zM200 800h200q27 0 40 -2t29.5 -10.5t23.5 -30t7 -57.5h300v-100h-600l-200 -350v450h100q0 36 7 57.5t23.5 30t29.5 10.5t40 2zM800 400h240l-240 -400h-800l300 500h500v-100z" />
<glyph unicode="&#xe205;" d="M650 1100h100q21 0 35.5 -14.5t14.5 -35.5v-50h50q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-300q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5h50v50q0 21 14.5 35.5t35.5 14.5zM1000 850v150q41 0 70.5 -29.5t29.5 -70.5v-800 q0 -41 -29.5 -70.5t-70.5 -29.5h-600q-1 0 -20 4l246 246l-326 326v324q0 41 29.5 70.5t70.5 29.5v-150q0 -62 44 -106t106 -44h300q62 0 106 44t44 106zM412 250l-212 -212v162h-200v100h200v162z" />
<glyph unicode="&#xe206;" d="M450 1100h100q21 0 35.5 -14.5t14.5 -35.5v-50h50q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-300q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5h50v50q0 21 14.5 35.5t35.5 14.5zM800 850v150q41 0 70.5 -29.5t29.5 -70.5v-500 h-200v-300h200q0 -36 -7 -57.5t-23.5 -30t-29.5 -10.5t-40 -2h-600q-41 0 -70.5 29.5t-29.5 70.5v800q0 41 29.5 70.5t70.5 29.5v-150q0 -62 44 -106t106 -44h300q62 0 106 44t44 106zM1212 250l-212 -212v162h-200v100h200v162z" />
<glyph unicode="&#xe209;" d="M658 1197l637 -1104q23 -38 7 -65.5t-60 -27.5h-1276q-44 0 -60 27.5t7 65.5l637 1104q22 39 54 39t54 -39zM704 800h-208q-20 0 -32 -14.5t-8 -34.5l58 -302q4 -20 21.5 -34.5t37.5 -14.5h54q20 0 37.5 14.5t21.5 34.5l58 302q4 20 -8 34.5t-32 14.5zM500 300v-100h200 v100h-200z" />
<glyph unicode="&#xe210;" d="M425 1100h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5zM425 800h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5 t17.5 7.5zM825 800h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5zM25 500h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v150 q0 10 7.5 17.5t17.5 7.5zM425 500h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5zM825 500h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5 v150q0 10 7.5 17.5t17.5 7.5zM25 200h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5zM425 200h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5 t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5zM825 200h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe211;" d="M700 1200h100v-200h-100v-100h350q62 0 86.5 -39.5t-3.5 -94.5l-66 -132q-41 -83 -81 -134h-772q-40 51 -81 134l-66 132q-28 55 -3.5 94.5t86.5 39.5h350v100h-100v200h100v100h200v-100zM250 400h700q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-12l137 -100 h-950l138 100h-13q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5zM50 100h1100q21 0 35.5 -14.5t14.5 -35.5v-50h-1200v50q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe212;" d="M600 1300q40 0 68.5 -29.5t28.5 -70.5h-194q0 41 28.5 70.5t68.5 29.5zM443 1100h314q18 -37 18 -75q0 -8 -3 -25h328q41 0 44.5 -16.5t-30.5 -38.5l-175 -145h-678l-178 145q-34 22 -29 38.5t46 16.5h328q-3 17 -3 25q0 38 18 75zM250 700h700q21 0 35.5 -14.5 t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-150v-200l275 -200h-950l275 200v200h-150q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5zM50 100h1100q21 0 35.5 -14.5t14.5 -35.5v-50h-1200v50q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe213;" d="M600 1181q75 0 128 -53t53 -128t-53 -128t-128 -53t-128 53t-53 128t53 128t128 53zM602 798h46q34 0 55.5 -28.5t21.5 -86.5q0 -76 39 -183h-324q39 107 39 183q0 58 21.5 86.5t56.5 28.5h45zM250 400h700q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-13 l138 -100h-950l137 100h-12q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5zM50 100h1100q21 0 35.5 -14.5t14.5 -35.5v-50h-1200v50q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe214;" d="M600 1300q47 0 92.5 -53.5t71 -123t25.5 -123.5q0 -78 -55.5 -133.5t-133.5 -55.5t-133.5 55.5t-55.5 133.5q0 62 34 143l144 -143l111 111l-163 163q34 26 63 26zM602 798h46q34 0 55.5 -28.5t21.5 -86.5q0 -76 39 -183h-324q39 107 39 183q0 58 21.5 86.5t56.5 28.5h45 zM250 400h700q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-13l138 -100h-950l137 100h-12q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5zM50 100h1100q21 0 35.5 -14.5t14.5 -35.5v-50h-1200v50q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe215;" d="M600 1200l300 -161v-139h-300q0 -57 18.5 -108t50 -91.5t63 -72t70 -67.5t57.5 -61h-530q-60 83 -90.5 177.5t-30.5 178.5t33 164.5t87.5 139.5t126 96.5t145.5 41.5v-98zM250 400h700q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-13l138 -100h-950l137 100 h-12q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5zM50 100h1100q21 0 35.5 -14.5t14.5 -35.5v-50h-1200v50q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe216;" d="M600 1300q41 0 70.5 -29.5t29.5 -70.5v-78q46 -26 73 -72t27 -100v-50h-400v50q0 54 27 100t73 72v78q0 41 29.5 70.5t70.5 29.5zM400 800h400q54 0 100 -27t72 -73h-172v-100h200v-100h-200v-100h200v-100h-200v-100h200q0 -83 -58.5 -141.5t-141.5 -58.5h-400 q-83 0 -141.5 58.5t-58.5 141.5v400q0 83 58.5 141.5t141.5 58.5z" />
<glyph unicode="&#xe218;" d="M150 1100h900q21 0 35.5 -14.5t14.5 -35.5v-500q0 -21 -14.5 -35.5t-35.5 -14.5h-900q-21 0 -35.5 14.5t-14.5 35.5v500q0 21 14.5 35.5t35.5 14.5zM125 400h950q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-283l224 -224q13 -13 13 -31.5t-13 -32 t-31.5 -13.5t-31.5 13l-88 88h-524l-87 -88q-13 -13 -32 -13t-32 13.5t-13 32t13 31.5l224 224h-289q-10 0 -17.5 7.5t-7.5 17.5v50q0 10 7.5 17.5t17.5 7.5zM541 300l-100 -100h324l-100 100h-124z" />
<glyph unicode="&#xe219;" d="M200 1100h800q83 0 141.5 -58.5t58.5 -141.5v-200h-100q0 41 -29.5 70.5t-70.5 29.5h-250q-41 0 -70.5 -29.5t-29.5 -70.5h-100q0 41 -29.5 70.5t-70.5 29.5h-250q-41 0 -70.5 -29.5t-29.5 -70.5h-100v200q0 83 58.5 141.5t141.5 58.5zM100 600h1000q41 0 70.5 -29.5 t29.5 -70.5v-300h-1200v300q0 41 29.5 70.5t70.5 29.5zM300 100v-50q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v50h200zM1100 100v-50q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v50h200z" />
<glyph unicode="&#xe221;" d="M480 1165l682 -683q31 -31 31 -75.5t-31 -75.5l-131 -131h-481l-517 518q-32 31 -32 75.5t32 75.5l295 296q31 31 75.5 31t76.5 -31zM108 794l342 -342l303 304l-341 341zM250 100h800q21 0 35.5 -14.5t14.5 -35.5v-50h-900v50q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe223;" d="M1057 647l-189 506q-8 19 -27.5 33t-40.5 14h-400q-21 0 -40.5 -14t-27.5 -33l-189 -506q-8 -19 1.5 -33t30.5 -14h625v-150q0 -21 14.5 -35.5t35.5 -14.5t35.5 14.5t14.5 35.5v150h125q21 0 30.5 14t1.5 33zM897 0h-595v50q0 21 14.5 35.5t35.5 14.5h50v50 q0 21 14.5 35.5t35.5 14.5h48v300h200v-300h47q21 0 35.5 -14.5t14.5 -35.5v-50h50q21 0 35.5 -14.5t14.5 -35.5v-50z" />
<glyph unicode="&#xe224;" d="M900 800h300v-575q0 -10 -7.5 -17.5t-17.5 -7.5h-375v591l-300 300v84q0 10 7.5 17.5t17.5 7.5h375v-400zM1200 900h-200v200zM400 600h300v-575q0 -10 -7.5 -17.5t-17.5 -7.5h-650q-10 0 -17.5 7.5t-7.5 17.5v950q0 10 7.5 17.5t17.5 7.5h375v-400zM700 700h-200v200z " />
<glyph unicode="&#xe225;" d="M484 1095h195q75 0 146 -32.5t124 -86t89.5 -122.5t48.5 -142q18 -14 35 -20q31 -10 64.5 6.5t43.5 48.5q10 34 -15 71q-19 27 -9 43q5 8 12.5 11t19 -1t23.5 -16q41 -44 39 -105q-3 -63 -46 -106.5t-104 -43.5h-62q-7 -55 -35 -117t-56 -100l-39 -234q-3 -20 -20 -34.5 t-38 -14.5h-100q-21 0 -33 14.5t-9 34.5l12 70q-49 -14 -91 -14h-195q-24 0 -65 8l-11 -64q-3 -20 -20 -34.5t-38 -14.5h-100q-21 0 -33 14.5t-9 34.5l26 157q-84 74 -128 175l-159 53q-19 7 -33 26t-14 40v50q0 21 14.5 35.5t35.5 14.5h124q11 87 56 166l-111 95 q-16 14 -12.5 23.5t24.5 9.5h203q116 101 250 101zM675 1000h-250q-10 0 -17.5 -7.5t-7.5 -17.5v-50q0 -10 7.5 -17.5t17.5 -7.5h250q10 0 17.5 7.5t7.5 17.5v50q0 10 -7.5 17.5t-17.5 7.5z" />
<glyph unicode="&#xe226;" d="M641 900l423 247q19 8 42 2.5t37 -21.5l32 -38q14 -15 12.5 -36t-17.5 -34l-139 -120h-390zM50 1100h106q67 0 103 -17t66 -71l102 -212h823q21 0 35.5 -14.5t14.5 -35.5v-50q0 -21 -14 -40t-33 -26l-737 -132q-23 -4 -40 6t-26 25q-42 67 -100 67h-300q-62 0 -106 44 t-44 106v200q0 62 44 106t106 44zM173 928h-80q-19 0 -28 -14t-9 -35v-56q0 -51 42 -51h134q16 0 21.5 8t5.5 24q0 11 -16 45t-27 51q-18 28 -43 28zM550 727q-32 0 -54.5 -22.5t-22.5 -54.5t22.5 -54.5t54.5 -22.5t54.5 22.5t22.5 54.5t-22.5 54.5t-54.5 22.5zM130 389 l152 130q18 19 34 24t31 -3.5t24.5 -17.5t25.5 -28q28 -35 50.5 -51t48.5 -13l63 5l48 -179q13 -61 -3.5 -97.5t-67.5 -79.5l-80 -69q-47 -40 -109 -35.5t-103 51.5l-130 151q-40 47 -35.5 109.5t51.5 102.5zM380 377l-102 -88q-31 -27 2 -65l37 -43q13 -15 27.5 -19.5 t31.5 6.5l61 53q19 16 14 49q-2 20 -12 56t-17 45q-11 12 -19 14t-23 -8z" />
<glyph unicode="&#xe227;" d="M625 1200h150q10 0 17.5 -7.5t7.5 -17.5v-109q79 -33 131 -87.5t53 -128.5q1 -46 -15 -84.5t-39 -61t-46 -38t-39 -21.5l-17 -6q6 0 15 -1.5t35 -9t50 -17.5t53 -30t50 -45t35.5 -64t14.5 -84q0 -59 -11.5 -105.5t-28.5 -76.5t-44 -51t-49.5 -31.5t-54.5 -16t-49.5 -6.5 t-43.5 -1v-75q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v75h-100v-75q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v75h-175q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5h75v600h-75q-10 0 -17.5 7.5t-7.5 17.5v150 q0 10 7.5 17.5t17.5 7.5h175v75q0 10 7.5 17.5t17.5 7.5h150q10 0 17.5 -7.5t7.5 -17.5v-75h100v75q0 10 7.5 17.5t17.5 7.5zM400 900v-200h263q28 0 48.5 10.5t30 25t15 29t5.5 25.5l1 10q0 4 -0.5 11t-6 24t-15 30t-30 24t-48.5 11h-263zM400 500v-200h363q28 0 48.5 10.5 t30 25t15 29t5.5 25.5l1 10q0 4 -0.5 11t-6 24t-15 30t-30 24t-48.5 11h-363z" />
<glyph unicode="&#xe230;" d="M212 1198h780q86 0 147 -61t61 -147v-416q0 -51 -18 -142.5t-36 -157.5l-18 -66q-29 -87 -93.5 -146.5t-146.5 -59.5h-572q-82 0 -147 59t-93 147q-8 28 -20 73t-32 143.5t-20 149.5v416q0 86 61 147t147 61zM600 1045q-70 0 -132.5 -11.5t-105.5 -30.5t-78.5 -41.5 t-57 -45t-36 -41t-20.5 -30.5l-6 -12l156 -243h560l156 243q-2 5 -6 12.5t-20 29.5t-36.5 42t-57 44.5t-79 42t-105 29.5t-132.5 12zM762 703h-157l195 261z" />
<glyph unicode="&#xe231;" d="M475 1300h150q103 0 189 -86t86 -189v-500q0 -41 -42 -83t-83 -42h-450q-41 0 -83 42t-42 83v500q0 103 86 189t189 86zM700 300v-225q0 -21 -27 -48t-48 -27h-150q-21 0 -48 27t-27 48v225h300z" />
<glyph unicode="&#xe232;" d="M475 1300h96q0 -150 89.5 -239.5t239.5 -89.5v-446q0 -41 -42 -83t-83 -42h-450q-41 0 -83 42t-42 83v500q0 103 86 189t189 86zM700 300v-225q0 -21 -27 -48t-48 -27h-150q-21 0 -48 27t-27 48v225h300z" />
<glyph unicode="&#xe233;" d="M1294 767l-638 -283l-378 170l-78 -60v-224l100 -150v-199l-150 148l-150 -149v200l100 150v250q0 4 -0.5 10.5t0 9.5t1 8t3 8t6.5 6l47 40l-147 65l642 283zM1000 380l-350 -166l-350 166v147l350 -165l350 165v-147z" />
<glyph unicode="&#xe234;" d="M250 800q62 0 106 -44t44 -106t-44 -106t-106 -44t-106 44t-44 106t44 106t106 44zM650 800q62 0 106 -44t44 -106t-44 -106t-106 -44t-106 44t-44 106t44 106t106 44zM1050 800q62 0 106 -44t44 -106t-44 -106t-106 -44t-106 44t-44 106t44 106t106 44z" />
<glyph unicode="&#xe235;" d="M550 1100q62 0 106 -44t44 -106t-44 -106t-106 -44t-106 44t-44 106t44 106t106 44zM550 700q62 0 106 -44t44 -106t-44 -106t-106 -44t-106 44t-44 106t44 106t106 44zM550 300q62 0 106 -44t44 -106t-44 -106t-106 -44t-106 44t-44 106t44 106t106 44z" />
<glyph unicode="&#xe236;" d="M125 1100h950q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-950q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5zM125 700h950q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-950q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5 t17.5 7.5zM125 300h950q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-950q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe237;" d="M350 1200h500q162 0 256 -93.5t94 -256.5v-500q0 -165 -93.5 -257.5t-256.5 -92.5h-500q-165 0 -257.5 92.5t-92.5 257.5v500q0 165 92.5 257.5t257.5 92.5zM900 1000h-600q-41 0 -70.5 -29.5t-29.5 -70.5v-600q0 -41 29.5 -70.5t70.5 -29.5h600q41 0 70.5 29.5 t29.5 70.5v600q0 41 -29.5 70.5t-70.5 29.5zM350 900h500q21 0 35.5 -14.5t14.5 -35.5v-300q0 -21 -14.5 -35.5t-35.5 -14.5h-500q-21 0 -35.5 14.5t-14.5 35.5v300q0 21 14.5 35.5t35.5 14.5zM400 800v-200h400v200h-400z" />
<glyph unicode="&#xe238;" d="M150 1100h1000q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-50v-200h50q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-50v-200h50q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-50v-200h50q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5 t-35.5 -14.5h-1000q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5h50v200h-50q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5h50v200h-50q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5h50v200h-50q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe239;" d="M650 1187q87 -67 118.5 -156t0 -178t-118.5 -155q-87 66 -118.5 155t0 178t118.5 156zM300 800q124 0 212 -88t88 -212q-124 0 -212 88t-88 212zM1000 800q0 -124 -88 -212t-212 -88q0 124 88 212t212 88zM300 500q124 0 212 -88t88 -212q-124 0 -212 88t-88 212z M1000 500q0 -124 -88 -212t-212 -88q0 124 88 212t212 88zM700 199v-144q0 -21 -14.5 -35.5t-35.5 -14.5t-35.5 14.5t-14.5 35.5v142q40 -4 43 -4q17 0 57 6z" />
<glyph unicode="&#xe240;" d="M745 878l69 19q25 6 45 -12l298 -295q11 -11 15 -26.5t-2 -30.5q-5 -14 -18 -23.5t-28 -9.5h-8q1 0 1 -13q0 -29 -2 -56t-8.5 -62t-20 -63t-33 -53t-51 -39t-72.5 -14h-146q-184 0 -184 288q0 24 10 47q-20 4 -62 4t-63 -4q11 -24 11 -47q0 -288 -184 -288h-142 q-48 0 -84.5 21t-56 51t-32 71.5t-16 75t-3.5 68.5q0 13 2 13h-7q-15 0 -27.5 9.5t-18.5 23.5q-6 15 -2 30.5t15 25.5l298 296q20 18 46 11l76 -19q20 -5 30.5 -22.5t5.5 -37.5t-22.5 -31t-37.5 -5l-51 12l-182 -193h891l-182 193l-44 -12q-20 -5 -37.5 6t-22.5 31t6 37.5 t31 22.5z" />
<glyph unicode="&#xe241;" d="M1200 900h-50q0 21 -4 37t-9.5 26.5t-18 17.5t-22 11t-28.5 5.5t-31 2t-37 0.5h-200v-850q0 -22 25 -34.5t50 -13.5l25 -2v-100h-400v100q4 0 11 0.5t24 3t30 7t24 15t11 24.5v850h-200q-25 0 -37 -0.5t-31 -2t-28.5 -5.5t-22 -11t-18 -17.5t-9.5 -26.5t-4 -37h-50v300 h1000v-300zM500 450h-25q0 15 -4 24.5t-9 14.5t-17 7.5t-20 3t-25 0.5h-100v-425q0 -11 12.5 -17.5t25.5 -7.5h12v-50h-200v50q50 0 50 25v425h-100q-17 0 -25 -0.5t-20 -3t-17 -7.5t-9 -14.5t-4 -24.5h-25v150h500v-150z" />
<glyph unicode="&#xe242;" d="M1000 300v50q-25 0 -55 32q-14 14 -25 31t-16 27l-4 11l-289 747h-69l-300 -754q-18 -35 -39 -56q-9 -9 -24.5 -18.5t-26.5 -14.5l-11 -5v-50h273v50q-49 0 -78.5 21.5t-11.5 67.5l69 176h293l61 -166q13 -34 -3.5 -66.5t-55.5 -32.5v-50h312zM412 691l134 342l121 -342 h-255zM1100 150v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1000q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5h1000q21 0 35.5 -14.5t14.5 -35.5z" />
<glyph unicode="&#xe243;" d="M50 1200h1100q21 0 35.5 -14.5t14.5 -35.5v-1100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5v1100q0 21 14.5 35.5t35.5 14.5zM611 1118h-70q-13 0 -18 -12l-299 -753q-17 -32 -35 -51q-18 -18 -56 -34q-12 -5 -12 -18v-50q0 -8 5.5 -14t14.5 -6 h273q8 0 14 6t6 14v50q0 8 -6 14t-14 6q-55 0 -71 23q-10 14 0 39l63 163h266l57 -153q11 -31 -6 -55q-12 -17 -36 -17q-8 0 -14 -6t-6 -14v-50q0 -8 6 -14t14 -6h313q8 0 14 6t6 14v50q0 7 -5.5 13t-13.5 7q-17 0 -42 25q-25 27 -40 63h-1l-288 748q-5 12 -19 12zM639 611 h-197l103 264z" />
<glyph unicode="&#xe244;" d="M1200 1100h-1200v100h1200v-100zM50 1000h400q21 0 35.5 -14.5t14.5 -35.5v-900q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v900q0 21 14.5 35.5t35.5 14.5zM650 1000h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400 q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5zM700 900v-300h300v300h-300z" />
<glyph unicode="&#xe245;" d="M50 1200h400q21 0 35.5 -14.5t14.5 -35.5v-900q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v900q0 21 14.5 35.5t35.5 14.5zM650 700h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v400 q0 21 14.5 35.5t35.5 14.5zM700 600v-300h300v300h-300zM1200 0h-1200v100h1200v-100z" />
<glyph unicode="&#xe246;" d="M50 1000h400q21 0 35.5 -14.5t14.5 -35.5v-350h100v150q0 21 14.5 35.5t35.5 14.5h400q21 0 35.5 -14.5t14.5 -35.5v-150h100v-100h-100v-150q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v150h-100v-350q0 -21 -14.5 -35.5t-35.5 -14.5h-400 q-21 0 -35.5 14.5t-14.5 35.5v800q0 21 14.5 35.5t35.5 14.5zM700 700v-300h300v300h-300z" />
<glyph unicode="&#xe247;" d="M100 0h-100v1200h100v-1200zM250 1100h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5zM300 1000v-300h300v300h-300zM250 500h900q21 0 35.5 -14.5t14.5 -35.5v-400 q0 -21 -14.5 -35.5t-35.5 -14.5h-900q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe248;" d="M600 1100h150q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-150v-100h450q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-900q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5h350v100h-150q-21 0 -35.5 14.5 t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5h150v100h100v-100zM400 1000v-300h300v300h-300z" />
<glyph unicode="&#xe249;" d="M1200 0h-100v1200h100v-1200zM550 1100h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5zM600 1000v-300h300v300h-300zM50 500h900q21 0 35.5 -14.5t14.5 -35.5v-400 q0 -21 -14.5 -35.5t-35.5 -14.5h-900q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe250;" d="M865 565l-494 -494q-23 -23 -41 -23q-14 0 -22 13.5t-8 38.5v1000q0 25 8 38.5t22 13.5q18 0 41 -23l494 -494q14 -14 14 -35t-14 -35z" />
<glyph unicode="&#xe251;" d="M335 635l494 494q29 29 50 20.5t21 -49.5v-1000q0 -41 -21 -49.5t-50 20.5l-494 494q-14 14 -14 35t14 35z" />
<glyph unicode="&#xe252;" d="M100 900h1000q41 0 49.5 -21t-20.5 -50l-494 -494q-14 -14 -35 -14t-35 14l-494 494q-29 29 -20.5 50t49.5 21z" />
<glyph unicode="&#xe253;" d="M635 865l494 -494q29 -29 20.5 -50t-49.5 -21h-1000q-41 0 -49.5 21t20.5 50l494 494q14 14 35 14t35 -14z" />
<glyph unicode="&#xe254;" d="M700 741v-182l-692 -323v221l413 193l-413 193v221zM1200 0h-800v200h800v-200z" />
<glyph unicode="&#xe255;" d="M1200 900h-200v-100h200v-100h-300v300h200v100h-200v100h300v-300zM0 700h50q0 21 4 37t9.5 26.5t18 17.5t22 11t28.5 5.5t31 2t37 0.5h100v-550q0 -22 -25 -34.5t-50 -13.5l-25 -2v-100h400v100q-4 0 -11 0.5t-24 3t-30 7t-24 15t-11 24.5v550h100q25 0 37 -0.5t31 -2 t28.5 -5.5t22 -11t18 -17.5t9.5 -26.5t4 -37h50v300h-800v-300z" />
<glyph unicode="&#xe256;" d="M800 700h-50q0 21 -4 37t-9.5 26.5t-18 17.5t-22 11t-28.5 5.5t-31 2t-37 0.5h-100v-550q0 -22 25 -34.5t50 -14.5l25 -1v-100h-400v100q4 0 11 0.5t24 3t30 7t24 15t11 24.5v550h-100q-25 0 -37 -0.5t-31 -2t-28.5 -5.5t-22 -11t-18 -17.5t-9.5 -26.5t-4 -37h-50v300 h800v-300zM1100 200h-200v-100h200v-100h-300v300h200v100h-200v100h300v-300z" />
<glyph unicode="&#xe257;" d="M701 1098h160q16 0 21 -11t-7 -23l-464 -464l464 -464q12 -12 7 -23t-21 -11h-160q-13 0 -23 9l-471 471q-7 8 -7 18t7 18l471 471q10 9 23 9z" />
<glyph unicode="&#xe258;" d="M339 1098h160q13 0 23 -9l471 -471q7 -8 7 -18t-7 -18l-471 -471q-10 -9 -23 -9h-160q-16 0 -21 11t7 23l464 464l-464 464q-12 12 -7 23t21 11z" />
<glyph unicode="&#xe259;" d="M1087 882q11 -5 11 -21v-160q0 -13 -9 -23l-471 -471q-8 -7 -18 -7t-18 7l-471 471q-9 10 -9 23v160q0 16 11 21t23 -7l464 -464l464 464q12 12 23 7z" />
<glyph unicode="&#xe260;" d="M618 993l471 -471q9 -10 9 -23v-160q0 -16 -11 -21t-23 7l-464 464l-464 -464q-12 -12 -23 -7t-11 21v160q0 13 9 23l471 471q8 7 18 7t18 -7z" />
<glyph unicode="&#xf8ff;" d="M1000 1200q0 -124 -88 -212t-212 -88q0 124 88 212t212 88zM450 1000h100q21 0 40 -14t26 -33l79 -194q5 1 16 3q34 6 54 9.5t60 7t65.5 1t61 -10t56.5 -23t42.5 -42t29 -64t5 -92t-19.5 -121.5q-1 -7 -3 -19.5t-11 -50t-20.5 -73t-32.5 -81.5t-46.5 -83t-64 -70 t-82.5 -50q-13 -5 -42 -5t-65.5 2.5t-47.5 2.5q-14 0 -49.5 -3.5t-63 -3.5t-43.5 7q-57 25 -104.5 78.5t-75 111.5t-46.5 112t-26 90l-7 35q-15 63 -18 115t4.5 88.5t26 64t39.5 43.5t52 25.5t58.5 13t62.5 2t59.5 -4.5t55.5 -8l-147 192q-12 18 -5.5 30t27.5 12z" />
<glyph unicode="&#x1f511;" d="M250 1200h600q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-150v-500l-255 -178q-19 -9 -32 -1t-13 29v650h-150q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5zM400 1100v-100h300v100h-300z" />
<glyph unicode="&#x1f6aa;" d="M250 1200h750q39 0 69.5 -40.5t30.5 -84.5v-933l-700 -117v950l600 125h-700v-1000h-100v1025q0 23 15.5 49t34.5 26zM500 525v-100l100 20v100z" />
</font>
</defs></svg> ) format('svg')}.glyphicon{position:relative;top:1px;display:inline-block;font-family:'Glyphicons Halflings';font-style:normal;font-weight:400;line-height:1;-webkit-font-smoothing:antialiased;-moz-osx-font-smoothing:grayscale}.glyphicon-asterisk:before{content:"\2a"}.glyphicon-plus:before{content:"\2b"}.glyphicon-eur:before,.glyphicon-euro:before{content:"\20ac"}.glyphicon-minus:before{content:"\2212"}.glyphicon-cloud:before{content:"\2601"}.glyphicon-envelope:before{content:"\2709"}.glyphicon-pencil:before{content:"\270f"}.glyphicon-glass:before{content:"\e001"}.glyphicon-music:before{content:"\e002"}.glyphicon-search:before{content:"\e003"}.glyphicon-heart:before{content:"\e005"}.glyphicon-star:before{content:"\e006"}.glyphicon-star-empty:before{content:"\e007"}.glyphicon-user:before{content:"\e008"}.glyphicon-film:before{content:"\e009"}.glyphicon-th-large:before{content:"\e010"}.glyphicon-th:before{content:"\e011"}.glyphicon-th-list:before{content:"\e012"}.glyphicon-ok:before{content:"\e013"}.glyphicon-remove:before{content:"\e014"}.glyphicon-zoom-in:before{content:"\e015"}.glyphicon-zoom-out:before{content:"\e016"}.glyphicon-off:before{content:"\e017"}.glyphicon-signal:before{content:"\e018"}.glyphicon-cog:before{content:"\e019"}.glyphicon-trash:before{content:"\e020"}.glyphicon-home:before{content:"\e021"}.glyphicon-file:before{content:"\e022"}.glyphicon-time:before{content:"\e023"}.glyphicon-road:before{content:"\e024"}.glyphicon-download-alt:before{content:"\e025"}.glyphicon-download:before{content:"\e026"}.glyphicon-upload:before{content:"\e027"}.glyphicon-inbox:before{content:"\e028"}.glyphicon-play-circle:before{content:"\e029"}.glyphicon-repeat:before{content:"\e030"}.glyphicon-refresh:before{content:"\e031"}.glyphicon-list-alt:before{content:"\e032"}.glyphicon-lock:before{content:"\e033"}.glyphicon-flag:before{content:"\e034"}.glyphicon-headphones:before{content:"\e035"}.glyphicon-volume-off:before{content:"\e036"}.glyphicon-volume-down:before{content:"\e037"}.glyphicon-volume-up:before{content:"\e038"}.glyphicon-qrcode:before{content:"\e039"}.glyphicon-barcode:before{content:"\e040"}.glyphicon-tag:before{content:"\e041"}.glyphicon-tags:before{content:"\e042"}.glyphicon-book:before{content:"\e043"}.glyphicon-bookmark:before{content:"\e044"}.glyphicon-print:before{content:"\e045"}.glyphicon-camera:before{content:"\e046"}.glyphicon-font:before{content:"\e047"}.glyphicon-bold:before{content:"\e048"}.glyphicon-italic:before{content:"\e049"}.glyphicon-text-height:before{content:"\e050"}.glyphicon-text-width:before{content:"\e051"}.glyphicon-align-left:before{content:"\e052"}.glyphicon-align-center:before{content:"\e053"}.glyphicon-align-right:before{content:"\e054"}.glyphicon-align-justify:before{content:"\e055"}.glyphicon-list:before{content:"\e056"}.glyphicon-indent-left:before{content:"\e057"}.glyphicon-indent-right:before{content:"\e058"}.glyphicon-facetime-video:before{content:"\e059"}.glyphicon-picture:before{content:"\e060"}.glyphicon-map-marker:before{content:"\e062"}.glyphicon-adjust:before{content:"\e063"}.glyphicon-tint:before{content:"\e064"}.glyphicon-edit:before{content:"\e065"}.glyphicon-share:before{content:"\e066"}.glyphicon-check:before{content:"\e067"}.glyphicon-move:before{content:"\e068"}.glyphicon-step-backward:before{content:"\e069"}.glyphicon-fast-backward:before{content:"\e070"}.glyphicon-backward:before{content:"\e071"}.glyphicon-play:before{content:"\e072"}.glyphicon-pause:before{content:"\e073"}.glyphicon-stop:before{content:"\e074"}.glyphicon-forward:before{content:"\e075"}.glyphicon-fast-forward:before{content:"\e076"}.glyphicon-step-forward:before{content:"\e077"}.glyphicon-eject:before{content:"\e078"}.glyphicon-chevron-left:before{content:"\e079"}.glyphicon-chevron-right:before{content:"\e080"}.glyphicon-plus-sign:before{content:"\e081"}.glyphicon-minus-sign:before{content:"\e082"}.glyphicon-remove-sign:before{content:"\e083"}.glyphicon-ok-sign:before{content:"\e084"}.glyphicon-question-sign:before{content:"\e085"}.glyphicon-info-sign:before{content:"\e086"}.glyphicon-screenshot:before{content:"\e087"}.glyphicon-remove-circle:before{content:"\e088"}.glyphicon-ok-circle:before{content:"\e089"}.glyphicon-ban-circle:before{content:"\e090"}.glyphicon-arrow-left:before{content:"\e091"}.glyphicon-arrow-right:before{content:"\e092"}.glyphicon-arrow-up:before{content:"\e093"}.glyphicon-arrow-down:before{content:"\e094"}.glyphicon-share-alt:before{content:"\e095"}.glyphicon-resize-full:before{content:"\e096"}.glyphicon-resize-small:before{content:"\e097"}.glyphicon-exclamation-sign:before{content:"\e101"}.glyphicon-gift:before{content:"\e102"}.glyphicon-leaf:before{content:"\e103"}.glyphicon-fire:before{content:"\e104"}.glyphicon-eye-open:before{content:"\e105"}.glyphicon-eye-close:before{content:"\e106"}.glyphicon-warning-sign:before{content:"\e107"}.glyphicon-plane:before{content:"\e108"}.glyphicon-calendar:before{content:"\e109"}.glyphicon-random:before{content:"\e110"}.glyphicon-comment:before{content:"\e111"}.glyphicon-magnet:before{content:"\e112"}.glyphicon-chevron-up:before{content:"\e113"}.glyphicon-chevron-down:before{content:"\e114"}.glyphicon-retweet:before{content:"\e115"}.glyphicon-shopping-cart:before{content:"\e116"}.glyphicon-folder-close:before{content:"\e117"}.glyphicon-folder-open:before{content:"\e118"}.glyphicon-resize-vertical:before{content:"\e119"}.glyphicon-resize-horizontal:before{content:"\e120"}.glyphicon-hdd:before{content:"\e121"}.glyphicon-bullhorn:before{content:"\e122"}.glyphicon-bell:before{content:"\e123"}.glyphicon-certificate:before{content:"\e124"}.glyphicon-thumbs-up:before{content:"\e125"}.glyphicon-thumbs-down:before{content:"\e126"}.glyphicon-hand-right:before{content:"\e127"}.glyphicon-hand-left:before{content:"\e128"}.glyphicon-hand-up:before{content:"\e129"}.glyphicon-hand-down:before{content:"\e130"}.glyphicon-circle-arrow-right:before{content:"\e131"}.glyphicon-circle-arrow-left:before{content:"\e132"}.glyphicon-circle-arrow-up:before{content:"\e133"}.glyphicon-circle-arrow-down:before{content:"\e134"}.glyphicon-globe:before{content:"\e135"}.glyphicon-wrench:before{content:"\e136"}.glyphicon-tasks:before{content:"\e137"}.glyphicon-filter:before{content:"\e138"}.glyphicon-briefcase:before{content:"\e139"}.glyphicon-fullscreen:before{content:"\e140"}.glyphicon-dashboard:before{content:"\e141"}.glyphicon-paperclip:before{content:"\e142"}.glyphicon-heart-empty:before{content:"\e143"}.glyphicon-link:before{content:"\e144"}.glyphicon-phone:before{content:"\e145"}.glyphicon-pushpin:before{content:"\e146"}.glyphicon-usd:before{content:"\e148"}.glyphicon-gbp:before{content:"\e149"}.glyphicon-sort:before{content:"\e150"}.glyphicon-sort-by-alphabet:before{content:"\e151"}.glyphicon-sort-by-alphabet-alt:before{content:"\e152"}.glyphicon-sort-by-order:before{content:"\e153"}.glyphicon-sort-by-order-alt:before{content:"\e154"}.glyphicon-sort-by-attributes:before{content:"\e155"}.glyphicon-sort-by-attributes-alt:before{content:"\e156"}.glyphicon-unchecked:before{content:"\e157"}.glyphicon-expand:before{content:"\e158"}.glyphicon-collapse-down:before{content:"\e159"}.glyphicon-collapse-up:before{content:"\e160"}.glyphicon-log-in:before{content:"\e161"}.glyphicon-flash:before{content:"\e162"}.glyphicon-log-out:before{content:"\e163"}.glyphicon-new-window:before{content:"\e164"}.glyphicon-record:before{content:"\e165"}.glyphicon-save:before{content:"\e166"}.glyphicon-open:before{content:"\e167"}.glyphicon-saved:before{content:"\e168"}.glyphicon-import:before{content:"\e169"}.glyphicon-export:before{content:"\e170"}.glyphicon-send:before{content:"\e171"}.glyphicon-floppy-disk:before{content:"\e172"}.glyphicon-floppy-saved:before{content:"\e173"}.glyphicon-floppy-remove:before{content:"\e174"}.glyphicon-floppy-save:before{content:"\e175"}.glyphicon-floppy-open:before{content:"\e176"}.glyphicon-credit-card:before{content:"\e177"}.glyphicon-transfer:before{content:"\e178"}.glyphicon-cutlery:before{content:"\e179"}.glyphicon-header:before{content:"\e180"}.glyphicon-compressed:before{content:"\e181"}.glyphicon-earphone:before{content:"\e182"}.glyphicon-phone-alt:before{content:"\e183"}.glyphicon-tower:before{content:"\e184"}.glyphicon-stats:before{content:"\e185"}.glyphicon-sd-video:before{content:"\e186"}.glyphicon-hd-video:before{content:"\e187"}.glyphicon-subtitles:before{content:"\e188"}.glyphicon-sound-stereo:before{content:"\e189"}.glyphicon-sound-dolby:before{content:"\e190"}.glyphicon-sound-5-1:before{content:"\e191"}.glyphicon-sound-6-1:before{content:"\e192"}.glyphicon-sound-7-1:before{content:"\e193"}.glyphicon-copyright-mark:before{content:"\e194"}.glyphicon-registration-mark:before{content:"\e195"}.glyphicon-cloud-download:before{content:"\e197"}.glyphicon-cloud-upload:before{content:"\e198"}.glyphicon-tree-conifer:before{content:"\e199"}.glyphicon-tree-deciduous:before{content:"\e200"}.glyphicon-cd:before{content:"\e201"}.glyphicon-save-file:before{content:"\e202"}.glyphicon-open-file:before{content:"\e203"}.glyphicon-level-up:before{content:"\e204"}.glyphicon-copy:before{content:"\e205"}.glyphicon-paste:before{content:"\e206"}.glyphicon-alert:before{content:"\e209"}.glyphicon-equalizer:before{content:"\e210"}.glyphicon-king:before{content:"\e211"}.glyphicon-queen:before{content:"\e212"}.glyphicon-pawn:before{content:"\e213"}.glyphicon-bishop:before{content:"\e214"}.glyphicon-knight:before{content:"\e215"}.glyphicon-baby-formula:before{content:"\e216"}.glyphicon-tent:before{content:"\26fa"}.glyphicon-blackboard:before{content:"\e218"}.glyphicon-bed:before{content:"\e219"}.glyphicon-apple:before{content:"\f8ff"}.glyphicon-erase:before{content:"\e221"}.glyphicon-hourglass:before{content:"\231b"}.glyphicon-lamp:before{content:"\e223"}.glyphicon-duplicate:before{content:"\e224"}.glyphicon-piggy-bank:before{content:"\e225"}.glyphicon-scissors:before{content:"\e226"}.glyphicon-bitcoin:before{content:"\e227"}.glyphicon-btc:before{content:"\e227"}.glyphicon-xbt:before{content:"\e227"}.glyphicon-yen:before{content:"\00a5"}.glyphicon-jpy:before{content:"\00a5"}.glyphicon-ruble:before{content:"\20bd"}.glyphicon-rub:before{content:"\20bd"}.glyphicon-scale:before{content:"\e230"}.glyphicon-ice-lolly:before{content:"\e231"}.glyphicon-ice-lolly-tasted:before{content:"\e232"}.glyphicon-education:before{content:"\e233"}.glyphicon-option-horizontal:before{content:"\e234"}.glyphicon-option-vertical:before{content:"\e235"}.glyphicon-menu-hamburger:before{content:"\e236"}.glyphicon-modal-window:before{content:"\e237"}.glyphicon-oil:before{content:"\e238"}.glyphicon-grain:before{content:"\e239"}.glyphicon-sunglasses:before{content:"\e240"}.glyphicon-text-size:before{content:"\e241"}.glyphicon-text-color:before{content:"\e242"}.glyphicon-text-background:before{content:"\e243"}.glyphicon-object-align-top:before{content:"\e244"}.glyphicon-object-align-bottom:before{content:"\e245"}.glyphicon-object-align-horizontal:before{content:"\e246"}.glyphicon-object-align-left:before{content:"\e247"}.glyphicon-object-align-vertical:before{content:"\e248"}.glyphicon-object-align-right:before{content:"\e249"}.glyphicon-triangle-right:before{content:"\e250"}.glyphicon-triangle-left:before{content:"\e251"}.glyphicon-triangle-bottom:before{content:"\e252"}.glyphicon-triangle-top:before{content:"\e253"}.glyphicon-console:before{content:"\e254"}.glyphicon-superscript:before{content:"\e255"}.glyphicon-subscript:before{content:"\e256"}.glyphicon-menu-left:before{content:"\e257"}.glyphicon-menu-right:before{content:"\e258"}.glyphicon-menu-down:before{content:"\e259"}.glyphicon-menu-up:before{content:"\e260"}*{-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}:after,:before{-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}html{font-size:10px;-webkit-tap-highlight-color:rgba(0,0,0,0)}body{font-family:"Helvetica Neue",Helvetica,Arial,sans-serif;font-size:14px;line-height:1.42857143;color:#333;background-color:#fff}button,input,select,textarea{font-family:inherit;font-size:inherit;line-height:inherit}a{color:#337ab7;text-decoration:none}a:focus,a:hover{color:#23527c;text-decoration:underline}a:focus{outline:thin dotted;outline:5px auto -webkit-focus-ring-color;outline-offset:-2px}figure{margin:0}img{vertical-align:middle}.carousel-inner>.item>a>img,.carousel-inner>.item>img,.img-responsive,.thumbnail a>img,.thumbnail>img{display:block;max-width:100%;height:auto}.img-rounded{border-radius:6px}.img-thumbnail{display:inline-block;max-width:100%;height:auto;padding:4px;line-height:1.42857143;background-color:#fff;border:1px solid #ddd;border-radius:4px;-webkit-transition:all .2s ease-in-out;-o-transition:all .2s ease-in-out;transition:all .2s ease-in-out}.img-circle{border-radius:50%}hr{margin-top:20px;margin-bottom:20px;border:0;border-top:1px solid #eee}.sr-only{position:absolute;width:1px;height:1px;padding:0;margin:-1px;overflow:hidden;clip:rect(0,0,0,0);border:0}.sr-only-focusable:active,.sr-only-focusable:focus{position:static;width:auto;height:auto;margin:0;overflow:visible;clip:auto}[role=button]{cursor:pointer}.h1,.h2,.h3,.h4,.h5,.h6,h1,h2,h3,h4,h5,h6{font-family:inherit;font-weight:500;line-height:1.1;color:inherit}.h1 .small,.h1 small,.h2 .small,.h2 small,.h3 .small,.h3 small,.h4 .small,.h4 small,.h5 .small,.h5 small,.h6 .small,.h6 small,h1 .small,h1 small,h2 .small,h2 small,h3 .small,h3 small,h4 .small,h4 small,h5 .small,h5 small,h6 .small,h6 small{font-weight:400;line-height:1;color:#777}.h1,.h2,.h3,h1,h2,h3{margin-top:20px;margin-bottom:10px}.h1 .small,.h1 small,.h2 .small,.h2 small,.h3 .small,.h3 small,h1 .small,h1 small,h2 .small,h2 small,h3 .small,h3 small{font-size:65%}.h4,.h5,.h6,h4,h5,h6{margin-top:10px;margin-bottom:10px}.h4 .small,.h4 small,.h5 .small,.h5 small,.h6 .small,.h6 small,h4 .small,h4 small,h5 .small,h5 small,h6 .small,h6 small{font-size:75%}.h1,h1{font-size:36px}.h2,h2{font-size:30px}.h3,h3{font-size:24px}.h4,h4{font-size:18px}.h5,h5{font-size:14px}.h6,h6{font-size:12px}p{margin:0 0 10px}.lead{margin-bottom:20px;font-size:16px;font-weight:300;line-height:1.4}@media (min-width:768px){.lead{font-size:21px}}.small,small{font-size:85%}.mark,mark{padding:.2em;background-color:#fcf8e3}.text-left{text-align:left}.text-right{text-align:right}.text-center{text-align:center}.text-justify{text-align:justify}.text-nowrap{white-space:nowrap}.text-lowercase{text-transform:lowercase}.text-uppercase{text-transform:uppercase}.text-capitalize{text-transform:capitalize}.text-muted{color:#777}.text-primary{color:#337ab7}a.text-primary:focus,a.text-primary:hover{color:#286090}.text-success{color:#3c763d}a.text-success:focus,a.text-success:hover{color:#2b542c}.text-info{color:#31708f}a.text-info:focus,a.text-info:hover{color:#245269}.text-warning{color:#8a6d3b}a.text-warning:focus,a.text-warning:hover{color:#66512c}.text-danger{color:#a94442}a.text-danger:focus,a.text-danger:hover{color:#843534}.bg-primary{color:#fff;background-color:#337ab7}a.bg-primary:focus,a.bg-primary:hover{background-color:#286090}.bg-success{background-color:#dff0d8}a.bg-success:focus,a.bg-success:hover{background-color:#c1e2b3}.bg-info{background-color:#d9edf7}a.bg-info:focus,a.bg-info:hover{background-color:#afd9ee}.bg-warning{background-color:#fcf8e3}a.bg-warning:focus,a.bg-warning:hover{background-color:#f7ecb5}.bg-danger{background-color:#f2dede}a.bg-danger:focus,a.bg-danger:hover{background-color:#e4b9b9}.page-header{padding-bottom:9px;margin:40px 0 20px;border-bottom:1px solid #eee}ol,ul{margin-top:0;margin-bottom:10px}ol ol,ol ul,ul ol,ul ul{margin-bottom:0}.list-unstyled{padding-left:0;list-style:none}.list-inline{padding-left:0;margin-left:-5px;list-style:none}.list-inline>li{display:inline-block;padding-right:5px;padding-left:5px}dl{margin-top:0;margin-bottom:20px}dd,dt{line-height:1.42857143}dt{font-weight:700}dd{margin-left:0}@media (min-width:768px){.dl-horizontal dt{float:left;width:160px;overflow:hidden;clear:left;text-align:right;text-overflow:ellipsis;white-space:nowrap}.dl-horizontal dd{margin-left:180px}}abbr[data-original-title],abbr[title]{cursor:help;border-bottom:1px dotted #777}.initialism{font-size:90%;text-transform:uppercase}blockquote{padding:10px 20px;margin:0 0 20px;font-size:17.5px;border-left:5px solid #eee}blockquote ol:last-child,blockquote p:last-child,blockquote ul:last-child{margin-bottom:0}blockquote .small,blockquote footer,blockquote small{display:block;font-size:80%;line-height:1.42857143;color:#777}blockquote .small:before,blockquote footer:before,blockquote small:before{content:'\2014 \00A0'}.blockquote-reverse,blockquote.pull-right{padding-right:15px;padding-left:0;text-align:right;border-right:5px solid #eee;border-left:0}.blockquote-reverse .small:before,.blockquote-reverse footer:before,.blockquote-reverse small:before,blockquote.pull-right .small:before,blockquote.pull-right footer:before,blockquote.pull-right small:before{content:''}.blockquote-reverse .small:after,.blockquote-reverse footer:after,.blockquote-reverse small:after,blockquote.pull-right .small:after,blockquote.pull-right footer:after,blockquote.pull-right small:after{content:'\00A0 \2014'}address{margin-bottom:20px;font-style:normal;line-height:1.42857143}code,kbd,pre,samp{font-family:monospace}code{padding:2px 4px;font-size:90%;color:#c7254e;background-color:#f9f2f4;border-radius:4px}kbd{padding:2px 4px;font-size:90%;color:#fff;background-color:#333;border-radius:3px;-webkit-box-shadow:inset 0 -1px 0 rgba(0,0,0,.25);box-shadow:inset 0 -1px 0 rgba(0,0,0,.25)}kbd kbd{padding:0;font-size:100%;font-weight:700;-webkit-box-shadow:none;box-shadow:none}pre{display:block;padding:9.5px;margin:0 0 10px;font-size:13px;line-height:1.42857143;color:#333;word-break:break-all;word-wrap:break-word;background-color:#f5f5f5;border:1px solid #ccc;border-radius:4px}pre code{padding:0;font-size:inherit;color:inherit;white-space:pre-wrap;background-color:transparent;border-radius:0}.pre-scrollable{max-height:340px;overflow-y:scroll}.container{padding-right:15px;padding-left:15px;margin-right:auto;margin-left:auto}@media (min-width:768px){.container{width:750px}}@media (min-width:992px){.container{width:970px}}@media (min-width:1200px){.container{width:1170px}}.container-fluid{padding-right:15px;padding-left:15px;margin-right:auto;margin-left:auto}.row{margin-right:-15px;margin-left:-15px}.col-lg-1,.col-lg-10,.col-lg-11,.col-lg-12,.col-lg-2,.col-lg-3,.col-lg-4,.col-lg-5,.col-lg-6,.col-lg-7,.col-lg-8,.col-lg-9,.col-md-1,.col-md-10,.col-md-11,.col-md-12,.col-md-2,.col-md-3,.col-md-4,.col-md-5,.col-md-6,.col-md-7,.col-md-8,.col-md-9,.col-sm-1,.col-sm-10,.col-sm-11,.col-sm-12,.col-sm-2,.col-sm-3,.col-sm-4,.col-sm-5,.col-sm-6,.col-sm-7,.col-sm-8,.col-sm-9,.col-xs-1,.col-xs-10,.col-xs-11,.col-xs-12,.col-xs-2,.col-xs-3,.col-xs-4,.col-xs-5,.col-xs-6,.col-xs-7,.col-xs-8,.col-xs-9{position:relative;min-height:1px;padding-right:15px;padding-left:15px}.col-xs-1,.col-xs-10,.col-xs-11,.col-xs-12,.col-xs-2,.col-xs-3,.col-xs-4,.col-xs-5,.col-xs-6,.col-xs-7,.col-xs-8,.col-xs-9{float:left}.col-xs-12{width:100%}.col-xs-11{width:91.66666667%}.col-xs-10{width:83.33333333%}.col-xs-9{width:75%}.col-xs-8{width:66.66666667%}.col-xs-7{width:58.33333333%}.col-xs-6{width:50%}.col-xs-5{width:41.66666667%}.col-xs-4{width:33.33333333%}.col-xs-3{width:25%}.col-xs-2{width:16.66666667%}.col-xs-1{width:8.33333333%}.col-xs-pull-12{right:100%}.col-xs-pull-11{right:91.66666667%}.col-xs-pull-10{right:83.33333333%}.col-xs-pull-9{right:75%}.col-xs-pull-8{right:66.66666667%}.col-xs-pull-7{right:58.33333333%}.col-xs-pull-6{right:50%}.col-xs-pull-5{right:41.66666667%}.col-xs-pull-4{right:33.33333333%}.col-xs-pull-3{right:25%}.col-xs-pull-2{right:16.66666667%}.col-xs-pull-1{right:8.33333333%}.col-xs-pull-0{right:auto}.col-xs-push-12{left:100%}.col-xs-push-11{left:91.66666667%}.col-xs-push-10{left:83.33333333%}.col-xs-push-9{left:75%}.col-xs-push-8{left:66.66666667%}.col-xs-push-7{left:58.33333333%}.col-xs-push-6{left:50%}.col-xs-push-5{left:41.66666667%}.col-xs-push-4{left:33.33333333%}.col-xs-push-3{left:25%}.col-xs-push-2{left:16.66666667%}.col-xs-push-1{left:8.33333333%}.col-xs-push-0{left:auto}.col-xs-offset-12{margin-left:100%}.col-xs-offset-11{margin-left:91.66666667%}.col-xs-offset-10{margin-left:83.33333333%}.col-xs-offset-9{margin-left:75%}.col-xs-offset-8{margin-left:66.66666667%}.col-xs-offset-7{margin-left:58.33333333%}.col-xs-offset-6{margin-left:50%}.col-xs-offset-5{margin-left:41.66666667%}.col-xs-offset-4{margin-left:33.33333333%}.col-xs-offset-3{margin-left:25%}.col-xs-offset-2{margin-left:16.66666667%}.col-xs-offset-1{margin-left:8.33333333%}.col-xs-offset-0{margin-left:0}@media (min-width:768px){.col-sm-1,.col-sm-10,.col-sm-11,.col-sm-12,.col-sm-2,.col-sm-3,.col-sm-4,.col-sm-5,.col-sm-6,.col-sm-7,.col-sm-8,.col-sm-9{float:left}.col-sm-12{width:100%}.col-sm-11{width:91.66666667%}.col-sm-10{width:83.33333333%}.col-sm-9{width:75%}.col-sm-8{width:66.66666667%}.col-sm-7{width:58.33333333%}.col-sm-6{width:50%}.col-sm-5{width:41.66666667%}.col-sm-4{width:33.33333333%}.col-sm-3{width:25%}.col-sm-2{width:16.66666667%}.col-sm-1{width:8.33333333%}.col-sm-pull-12{right:100%}.col-sm-pull-11{right:91.66666667%}.col-sm-pull-10{right:83.33333333%}.col-sm-pull-9{right:75%}.col-sm-pull-8{right:66.66666667%}.col-sm-pull-7{right:58.33333333%}.col-sm-pull-6{right:50%}.col-sm-pull-5{right:41.66666667%}.col-sm-pull-4{right:33.33333333%}.col-sm-pull-3{right:25%}.col-sm-pull-2{right:16.66666667%}.col-sm-pull-1{right:8.33333333%}.col-sm-pull-0{right:auto}.col-sm-push-12{left:100%}.col-sm-push-11{left:91.66666667%}.col-sm-push-10{left:83.33333333%}.col-sm-push-9{left:75%}.col-sm-push-8{left:66.66666667%}.col-sm-push-7{left:58.33333333%}.col-sm-push-6{left:50%}.col-sm-push-5{left:41.66666667%}.col-sm-push-4{left:33.33333333%}.col-sm-push-3{left:25%}.col-sm-push-2{left:16.66666667%}.col-sm-push-1{left:8.33333333%}.col-sm-push-0{left:auto}.col-sm-offset-12{margin-left:100%}.col-sm-offset-11{margin-left:91.66666667%}.col-sm-offset-10{margin-left:83.33333333%}.col-sm-offset-9{margin-left:75%}.col-sm-offset-8{margin-left:66.66666667%}.col-sm-offset-7{margin-left:58.33333333%}.col-sm-offset-6{margin-left:50%}.col-sm-offset-5{margin-left:41.66666667%}.col-sm-offset-4{margin-left:33.33333333%}.col-sm-offset-3{margin-left:25%}.col-sm-offset-2{margin-left:16.66666667%}.col-sm-offset-1{margin-left:8.33333333%}.col-sm-offset-0{margin-left:0}}@media (min-width:992px){.col-md-1,.col-md-10,.col-md-11,.col-md-12,.col-md-2,.col-md-3,.col-md-4,.col-md-5,.col-md-6,.col-md-7,.col-md-8,.col-md-9{float:left}.col-md-12{width:100%}.col-md-11{width:91.66666667%}.col-md-10{width:83.33333333%}.col-md-9{width:75%}.col-md-8{width:66.66666667%}.col-md-7{width:58.33333333%}.col-md-6{width:50%}.col-md-5{width:41.66666667%}.col-md-4{width:33.33333333%}.col-md-3{width:25%}.col-md-2{width:16.66666667%}.col-md-1{width:8.33333333%}.col-md-pull-12{right:100%}.col-md-pull-11{right:91.66666667%}.col-md-pull-10{right:83.33333333%}.col-md-pull-9{right:75%}.col-md-pull-8{right:66.66666667%}.col-md-pull-7{right:58.33333333%}.col-md-pull-6{right:50%}.col-md-pull-5{right:41.66666667%}.col-md-pull-4{right:33.33333333%}.col-md-pull-3{right:25%}.col-md-pull-2{right:16.66666667%}.col-md-pull-1{right:8.33333333%}.col-md-pull-0{right:auto}.col-md-push-12{left:100%}.col-md-push-11{left:91.66666667%}.col-md-push-10{left:83.33333333%}.col-md-push-9{left:75%}.col-md-push-8{left:66.66666667%}.col-md-push-7{left:58.33333333%}.col-md-push-6{left:50%}.col-md-push-5{left:41.66666667%}.col-md-push-4{left:33.33333333%}.col-md-push-3{left:25%}.col-md-push-2{left:16.66666667%}.col-md-push-1{left:8.33333333%}.col-md-push-0{left:auto}.col-md-offset-12{margin-left:100%}.col-md-offset-11{margin-left:91.66666667%}.col-md-offset-10{margin-left:83.33333333%}.col-md-offset-9{margin-left:75%}.col-md-offset-8{margin-left:66.66666667%}.col-md-offset-7{margin-left:58.33333333%}.col-md-offset-6{margin-left:50%}.col-md-offset-5{margin-left:41.66666667%}.col-md-offset-4{margin-left:33.33333333%}.col-md-offset-3{margin-left:25%}.col-md-offset-2{margin-left:16.66666667%}.col-md-offset-1{margin-left:8.33333333%}.col-md-offset-0{margin-left:0}}@media (min-width:1200px){.col-lg-1,.col-lg-10,.col-lg-11,.col-lg-12,.col-lg-2,.col-lg-3,.col-lg-4,.col-lg-5,.col-lg-6,.col-lg-7,.col-lg-8,.col-lg-9{float:left}.col-lg-12{width:100%}.col-lg-11{width:91.66666667%}.col-lg-10{width:83.33333333%}.col-lg-9{width:75%}.col-lg-8{width:66.66666667%}.col-lg-7{width:58.33333333%}.col-lg-6{width:50%}.col-lg-5{width:41.66666667%}.col-lg-4{width:33.33333333%}.col-lg-3{width:25%}.col-lg-2{width:16.66666667%}.col-lg-1{width:8.33333333%}.col-lg-pull-12{right:100%}.col-lg-pull-11{right:91.66666667%}.col-lg-pull-10{right:83.33333333%}.col-lg-pull-9{right:75%}.col-lg-pull-8{right:66.66666667%}.col-lg-pull-7{right:58.33333333%}.col-lg-pull-6{right:50%}.col-lg-pull-5{right:41.66666667%}.col-lg-pull-4{right:33.33333333%}.col-lg-pull-3{right:25%}.col-lg-pull-2{right:16.66666667%}.col-lg-pull-1{right:8.33333333%}.col-lg-pull-0{right:auto}.col-lg-push-12{left:100%}.col-lg-push-11{left:91.66666667%}.col-lg-push-10{left:83.33333333%}.col-lg-push-9{left:75%}.col-lg-push-8{left:66.66666667%}.col-lg-push-7{left:58.33333333%}.col-lg-push-6{left:50%}.col-lg-push-5{left:41.66666667%}.col-lg-push-4{left:33.33333333%}.col-lg-push-3{left:25%}.col-lg-push-2{left:16.66666667%}.col-lg-push-1{left:8.33333333%}.col-lg-push-0{left:auto}.col-lg-offset-12{margin-left:100%}.col-lg-offset-11{margin-left:91.66666667%}.col-lg-offset-10{margin-left:83.33333333%}.col-lg-offset-9{margin-left:75%}.col-lg-offset-8{margin-left:66.66666667%}.col-lg-offset-7{margin-left:58.33333333%}.col-lg-offset-6{margin-left:50%}.col-lg-offset-5{margin-left:41.66666667%}.col-lg-offset-4{margin-left:33.33333333%}.col-lg-offset-3{margin-left:25%}.col-lg-offset-2{margin-left:16.66666667%}.col-lg-offset-1{margin-left:8.33333333%}.col-lg-offset-0{margin-left:0}}table{background-color:transparent}caption{padding-top:8px;padding-bottom:8px;color:#777;text-align:left}th{}.table{width:100%;max-width:100%;margin-bottom:20px}.table>tbody>tr>td,.table>tbody>tr>th,.table>tfoot>tr>td,.table>tfoot>tr>th,.table>thead>tr>td,.table>thead>tr>th{padding:8px;line-height:1.42857143;vertical-align:top;border-top:1px solid #ddd}.table>thead>tr>th{vertical-align:bottom;border-bottom:2px solid #ddd}.table>caption+thead>tr:first-child>td,.table>caption+thead>tr:first-child>th,.table>colgroup+thead>tr:first-child>td,.table>colgroup+thead>tr:first-child>th,.table>thead:first-child>tr:first-child>td,.table>thead:first-child>tr:first-child>th{border-top:0}.table>tbody+tbody{border-top:2px solid #ddd}.table .table{background-color:#fff}.table-condensed>tbody>tr>td,.table-condensed>tbody>tr>th,.table-condensed>tfoot>tr>td,.table-condensed>tfoot>tr>th,.table-condensed>thead>tr>td,.table-condensed>thead>tr>th{padding:5px}.table-bordered{border:1px solid #ddd}.table-bordered>tbody>tr>td,.table-bordered>tbody>tr>th,.table-bordered>tfoot>tr>td,.table-bordered>tfoot>tr>th,.table-bordered>thead>tr>td,.table-bordered>thead>tr>th{border:1px solid #ddd}.table-bordered>thead>tr>td,.table-bordered>thead>tr>th{border-bottom-width:2px}.table-striped>tbody>tr:nth-of-type(odd){background-color:#f9f9f9}.table-hover>tbody>tr:hover{background-color:#f5f5f5}table col[class*=col-]{position:static;display:table-column;float:none}table td[class*=col-],table th[class*=col-]{position:static;display:table-cell;float:none}.table>tbody>tr.active>td,.table>tbody>tr.active>th,.table>tbody>tr>td.active,.table>tbody>tr>th.active,.table>tfoot>tr.active>td,.table>tfoot>tr.active>th,.table>tfoot>tr>td.active,.table>tfoot>tr>th.active,.table>thead>tr.active>td,.table>thead>tr.active>th,.table>thead>tr>td.active,.table>thead>tr>th.active{background-color:#f5f5f5}.table-hover>tbody>tr.active:hover>td,.table-hover>tbody>tr.active:hover>th,.table-hover>tbody>tr:hover>.active,.table-hover>tbody>tr>td.active:hover,.table-hover>tbody>tr>th.active:hover{background-color:#e8e8e8}.table>tbody>tr.success>td,.table>tbody>tr.success>th,.table>tbody>tr>td.success,.table>tbody>tr>th.success,.table>tfoot>tr.success>td,.table>tfoot>tr.success>th,.table>tfoot>tr>td.success,.table>tfoot>tr>th.success,.table>thead>tr.success>td,.table>thead>tr.success>th,.table>thead>tr>td.success,.table>thead>tr>th.success{background-color:#dff0d8}.table-hover>tbody>tr.success:hover>td,.table-hover>tbody>tr.success:hover>th,.table-hover>tbody>tr:hover>.success,.table-hover>tbody>tr>td.success:hover,.table-hover>tbody>tr>th.success:hover{background-color:#d0e9c6}.table>tbody>tr.info>td,.table>tbody>tr.info>th,.table>tbody>tr>td.info,.table>tbody>tr>th.info,.table>tfoot>tr.info>td,.table>tfoot>tr.info>th,.table>tfoot>tr>td.info,.table>tfoot>tr>th.info,.table>thead>tr.info>td,.table>thead>tr.info>th,.table>thead>tr>td.info,.table>thead>tr>th.info{background-color:#d9edf7}.table-hover>tbody>tr.info:hover>td,.table-hover>tbody>tr.info:hover>th,.table-hover>tbody>tr:hover>.info,.table-hover>tbody>tr>td.info:hover,.table-hover>tbody>tr>th.info:hover{background-color:#c4e3f3}.table>tbody>tr.warning>td,.table>tbody>tr.warning>th,.table>tbody>tr>td.warning,.table>tbody>tr>th.warning,.table>tfoot>tr.warning>td,.table>tfoot>tr.warning>th,.table>tfoot>tr>td.warning,.table>tfoot>tr>th.warning,.table>thead>tr.warning>td,.table>thead>tr.warning>th,.table>thead>tr>td.warning,.table>thead>tr>th.warning{background-color:#fcf8e3}.table-hover>tbody>tr.warning:hover>td,.table-hover>tbody>tr.warning:hover>th,.table-hover>tbody>tr:hover>.warning,.table-hover>tbody>tr>td.warning:hover,.table-hover>tbody>tr>th.warning:hover{background-color:#faf2cc}.table>tbody>tr.danger>td,.table>tbody>tr.danger>th,.table>tbody>tr>td.danger,.table>tbody>tr>th.danger,.table>tfoot>tr.danger>td,.table>tfoot>tr.danger>th,.table>tfoot>tr>td.danger,.table>tfoot>tr>th.danger,.table>thead>tr.danger>td,.table>thead>tr.danger>th,.table>thead>tr>td.danger,.table>thead>tr>th.danger{background-color:#f2dede}.table-hover>tbody>tr.danger:hover>td,.table-hover>tbody>tr.danger:hover>th,.table-hover>tbody>tr:hover>.danger,.table-hover>tbody>tr>td.danger:hover,.table-hover>tbody>tr>th.danger:hover{background-color:#ebcccc}.table-responsive{min-height:.01%;overflow-x:auto}@media screen and (max-width:767px){.table-responsive{width:100%;margin-bottom:15px;overflow-y:hidden;-ms-overflow-style:-ms-autohiding-scrollbar;border:1px solid #ddd}.table-responsive>.table{margin-bottom:0}.table-responsive>.table>tbody>tr>td,.table-responsive>.table>tbody>tr>th,.table-responsive>.table>tfoot>tr>td,.table-responsive>.table>tfoot>tr>th,.table-responsive>.table>thead>tr>td,.table-responsive>.table>thead>tr>th{white-space:nowrap}.table-responsive>.table-bordered{border:0}.table-responsive>.table-bordered>tbody>tr>td:first-child,.table-responsive>.table-bordered>tbody>tr>th:first-child,.table-responsive>.table-bordered>tfoot>tr>td:first-child,.table-responsive>.table-bordered>tfoot>tr>th:first-child,.table-responsive>.table-bordered>thead>tr>td:first-child,.table-responsive>.table-bordered>thead>tr>th:first-child{border-left:0}.table-responsive>.table-bordered>tbody>tr>td:last-child,.table-responsive>.table-bordered>tbody>tr>th:last-child,.table-responsive>.table-bordered>tfoot>tr>td:last-child,.table-responsive>.table-bordered>tfoot>tr>th:last-child,.table-responsive>.table-bordered>thead>tr>td:last-child,.table-responsive>.table-bordered>thead>tr>th:last-child{border-right:0}.table-responsive>.table-bordered>tbody>tr:last-child>td,.table-responsive>.table-bordered>tbody>tr:last-child>th,.table-responsive>.table-bordered>tfoot>tr:last-child>td,.table-responsive>.table-bordered>tfoot>tr:last-child>th{border-bottom:0}}fieldset{min-width:0;padding:0;margin:0;border:0}legend{display:block;width:100%;padding:0;margin-bottom:20px;font-size:21px;line-height:inherit;color:#333;border:0;border-bottom:1px solid #e5e5e5}label{display:inline-block;max-width:100%;margin-bottom:5px;font-weight:700}input[type=search]{-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}input[type=checkbox],input[type=radio]{margin:4px 0 0;margin-top:1px\9;line-height:normal}input[type=file]{display:block}input[type=range]{display:block;width:100%}select[multiple],select[size]{height:auto}input[type=file]:focus,input[type=checkbox]:focus,input[type=radio]:focus{outline:thin dotted;outline:5px auto -webkit-focus-ring-color;outline-offset:-2px}output{display:block;padding-top:7px;font-size:14px;line-height:1.42857143;color:#555}.form-control{display:block;width:100%;height:34px;padding:6px 12px;font-size:14px;line-height:1.42857143;color:#555;background-color:#fff;background-image:none;border:1px solid #ccc;border-radius:4px;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075);box-shadow:inset 0 1px 1px rgba(0,0,0,.075);-webkit-transition:border-color ease-in-out .15s,-webkit-box-shadow ease-in-out .15s;-o-transition:border-color ease-in-out .15s,box-shadow ease-in-out .15s;transition:border-color ease-in-out .15s,box-shadow ease-in-out .15s}.form-control:focus{border-color:#66afe9;outline:0;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 8px rgba(102,175,233,.6);box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 8px rgba(102,175,233,.6)}.form-control::-moz-placeholder{color:#999;opacity:1}.form-control:-ms-input-placeholder{color:#999}.form-control::-webkit-input-placeholder{color:#999}.form-control[disabled],.form-control[readonly],fieldset[disabled] .form-control{background-color:#eee;opacity:1}.form-control[disabled],fieldset[disabled] .form-control{cursor:not-allowed}textarea.form-control{height:auto}input[type=search]{-webkit-appearance:none}@media screen and (-webkit-min-device-pixel-ratio:0){input[type=date].form-control,input[type=time].form-control,input[type=datetime-local].form-control,input[type=month].form-control{line-height:34px}.input-group-sm input[type=date],.input-group-sm input[type=time],.input-group-sm input[type=datetime-local],.input-group-sm input[type=month],input[type=date].input-sm,input[type=time].input-sm,input[type=datetime-local].input-sm,input[type=month].input-sm{line-height:30px}.input-group-lg input[type=date],.input-group-lg input[type=time],.input-group-lg input[type=datetime-local],.input-group-lg input[type=month],input[type=date].input-lg,input[type=time].input-lg,input[type=datetime-local].input-lg,input[type=month].input-lg{line-height:46px}}.form-group{margin-bottom:15px}.checkbox,.radio{position:relative;display:block;margin-top:10px;margin-bottom:10px}.checkbox label,.radio label{min-height:20px;padding-left:20px;margin-bottom:0;font-weight:400;cursor:pointer}.checkbox input[type=checkbox],.checkbox-inline input[type=checkbox],.radio input[type=radio],.radio-inline input[type=radio]{position:absolute;margin-top:4px\9;margin-left:-20px}.checkbox+.checkbox,.radio+.radio{margin-top:-5px}.checkbox-inline,.radio-inline{position:relative;display:inline-block;padding-left:20px;margin-bottom:0;font-weight:400;vertical-align:middle;cursor:pointer}.checkbox-inline+.checkbox-inline,.radio-inline+.radio-inline{margin-top:0;margin-left:10px}fieldset[disabled] input[type=checkbox],fieldset[disabled] input[type=radio],input[type=checkbox].disabled,input[type=checkbox][disabled],input[type=radio].disabled,input[type=radio][disabled]{cursor:not-allowed}.checkbox-inline.disabled,.radio-inline.disabled,fieldset[disabled] .checkbox-inline,fieldset[disabled] .radio-inline{cursor:not-allowed}.checkbox.disabled label,.radio.disabled label,fieldset[disabled] .checkbox label,fieldset[disabled] .radio label{cursor:not-allowed}.form-control-static{min-height:34px;padding-top:7px;padding-bottom:7px;margin-bottom:0}.form-control-static.input-lg,.form-control-static.input-sm{padding-right:0;padding-left:0}.input-sm{height:30px;padding:5px 10px;font-size:12px;line-height:1.5;border-radius:3px}select.input-sm{height:30px;line-height:30px}select[multiple].input-sm,textarea.input-sm{height:auto}.form-group-sm .form-control{height:30px;padding:5px 10px;font-size:12px;line-height:1.5;border-radius:3px}.form-group-sm select.form-control{height:30px;line-height:30px}.form-group-sm select[multiple].form-control,.form-group-sm textarea.form-control{height:auto}.form-group-sm .form-control-static{height:30px;min-height:32px;padding:6px 10px;font-size:12px;line-height:1.5}.input-lg{height:46px;padding:10px 16px;font-size:18px;line-height:1.3333333;border-radius:6px}select.input-lg{height:46px;line-height:46px}select[multiple].input-lg,textarea.input-lg{height:auto}.form-group-lg .form-control{height:46px;padding:10px 16px;font-size:18px;line-height:1.3333333;border-radius:6px}.form-group-lg select.form-control{height:46px;line-height:46px}.form-group-lg select[multiple].form-control,.form-group-lg textarea.form-control{height:auto}.form-group-lg .form-control-static{height:46px;min-height:38px;padding:11px 16px;font-size:18px;line-height:1.3333333}.has-feedback{position:relative}.has-feedback .form-control{padding-right:42.5px}.form-control-feedback{position:absolute;top:0;right:0;z-index:2;display:block;width:34px;height:34px;line-height:34px;text-align:center;pointer-events:none}.form-group-lg .form-control+.form-control-feedback,.input-group-lg+.form-control-feedback,.input-lg+.form-control-feedback{width:46px;height:46px;line-height:46px}.form-group-sm .form-control+.form-control-feedback,.input-group-sm+.form-control-feedback,.input-sm+.form-control-feedback{width:30px;height:30px;line-height:30px}.has-success .checkbox,.has-success .checkbox-inline,.has-success .control-label,.has-success .help-block,.has-success .radio,.has-success .radio-inline,.has-success.checkbox label,.has-success.checkbox-inline label,.has-success.radio label,.has-success.radio-inline label{color:#3c763d}.has-success .form-control{border-color:#3c763d;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075);box-shadow:inset 0 1px 1px rgba(0,0,0,.075)}.has-success .form-control:focus{border-color:#2b542c;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 6px #67b168;box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 6px #67b168}.has-success .input-group-addon{color:#3c763d;background-color:#dff0d8;border-color:#3c763d}.has-success .form-control-feedback{color:#3c763d}.has-warning .checkbox,.has-warning .checkbox-inline,.has-warning .control-label,.has-warning .help-block,.has-warning .radio,.has-warning .radio-inline,.has-warning.checkbox label,.has-warning.checkbox-inline label,.has-warning.radio label,.has-warning.radio-inline label{color:#8a6d3b}.has-warning .form-control{border-color:#8a6d3b;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075);box-shadow:inset 0 1px 1px rgba(0,0,0,.075)}.has-warning .form-control:focus{border-color:#66512c;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 6px #c0a16b;box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 6px #c0a16b}.has-warning .input-group-addon{color:#8a6d3b;background-color:#fcf8e3;border-color:#8a6d3b}.has-warning .form-control-feedback{color:#8a6d3b}.has-error .checkbox,.has-error .checkbox-inline,.has-error .control-label,.has-error .help-block,.has-error .radio,.has-error .radio-inline,.has-error.checkbox label,.has-error.checkbox-inline label,.has-error.radio label,.has-error.radio-inline label{color:#a94442}.has-error .form-control{border-color:#a94442;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075);box-shadow:inset 0 1px 1px rgba(0,0,0,.075)}.has-error .form-control:focus{border-color:#843534;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 6px #ce8483;box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 6px #ce8483}.has-error .input-group-addon{color:#a94442;background-color:#f2dede;border-color:#a94442}.has-error .form-control-feedback{color:#a94442}.has-feedback label~.form-control-feedback{top:25px}.has-feedback label.sr-only~.form-control-feedback{top:0}.help-block{display:block;margin-top:5px;margin-bottom:10px;color:#737373}@media (min-width:768px){.form-inline .form-group{display:inline-block;margin-bottom:0;vertical-align:middle}.form-inline .form-control{display:inline-block;width:auto;vertical-align:middle}.form-inline .form-control-static{display:inline-block}.form-inline .input-group{display:inline-table;vertical-align:middle}.form-inline .input-group .form-control,.form-inline .input-group .input-group-addon,.form-inline .input-group .input-group-btn{width:auto}.form-inline .input-group>.form-control{width:100%}.form-inline .control-label{margin-bottom:0;vertical-align:middle}.form-inline .checkbox,.form-inline .radio{display:inline-block;margin-top:0;margin-bottom:0;vertical-align:middle}.form-inline .checkbox label,.form-inline .radio label{padding-left:0}.form-inline .checkbox input[type=checkbox],.form-inline .radio input[type=radio]{position:relative;margin-left:0}.form-inline .has-feedback .form-control-feedback{top:0}}.form-horizontal .checkbox,.form-horizontal .checkbox-inline,.form-horizontal .radio,.form-horizontal .radio-inline{padding-top:7px;margin-top:0;margin-bottom:0}.form-horizontal .checkbox,.form-horizontal .radio{min-height:27px}.form-horizontal .form-group{margin-right:-15px;margin-left:-15px}@media (min-width:768px){.form-horizontal .control-label{padding-top:7px;margin-bottom:0;text-align:right}}.form-horizontal .has-feedback .form-control-feedback{right:15px}@media (min-width:768px){.form-horizontal .form-group-lg .control-label{padding-top:14.33px;font-size:18px}}@media (min-width:768px){.form-horizontal .form-group-sm .control-label{padding-top:6px;font-size:12px}}.btn{display:inline-block;padding:6px 12px;margin-bottom:0;font-size:14px;font-weight:400;line-height:1.42857143;text-align:center;white-space:nowrap;vertical-align:middle;-ms-touch-action:manipulation;touch-action:manipulation;cursor:pointer;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;background-image:none;border:1px solid transparent;border-radius:4px}.btn.active.focus,.btn.active:focus,.btn.focus,.btn:active.focus,.btn:active:focus,.btn:focus{outline:thin dotted;outline:5px auto -webkit-focus-ring-color;outline-offset:-2px}.btn.focus,.btn:focus,.btn:hover{color:#333;text-decoration:none}.btn.active,.btn:active{background-image:none;outline:0;-webkit-box-shadow:inset 0 3px 5px rgba(0,0,0,.125);box-shadow:inset 0 3px 5px rgba(0,0,0,.125)}.btn.disabled,.btn[disabled],fieldset[disabled] .btn{cursor:not-allowed;filter:alpha(opacity=65);-webkit-box-shadow:none;box-shadow:none;opacity:.65}a.btn.disabled,fieldset[disabled] a.btn{pointer-events:none}.btn-default{color:#333;background-color:#fff;border-color:#ccc}.btn-default.focus,.btn-default:focus{color:#333;background-color:#e6e6e6;border-color:#8c8c8c}.btn-default:hover{color:#333;background-color:#e6e6e6;border-color:#adadad}.btn-default.active,.btn-default:active,.open>.dropdown-toggle.btn-default{color:#333;background-color:#e6e6e6;border-color:#adadad}.btn-default.active.focus,.btn-default.active:focus,.btn-default.active:hover,.btn-default:active.focus,.btn-default:active:focus,.btn-default:active:hover,.open>.dropdown-toggle.btn-default.focus,.open>.dropdown-toggle.btn-default:focus,.open>.dropdown-toggle.btn-default:hover{color:#333;background-color:#d4d4d4;border-color:#8c8c8c}.btn-default.active,.btn-default:active,.open>.dropdown-toggle.btn-default{background-image:none}.btn-default.disabled,.btn-default.disabled.active,.btn-default.disabled.focus,.btn-default.disabled:active,.btn-default.disabled:focus,.btn-default.disabled:hover,.btn-default[disabled],.btn-default[disabled].active,.btn-default[disabled].focus,.btn-default[disabled]:active,.btn-default[disabled]:focus,.btn-default[disabled]:hover,fieldset[disabled] .btn-default,fieldset[disabled] .btn-default.active,fieldset[disabled] .btn-default.focus,fieldset[disabled] .btn-default:active,fieldset[disabled] .btn-default:focus,fieldset[disabled] .btn-default:hover{background-color:#fff;border-color:#ccc}.btn-default .badge{color:#fff;background-color:#333}.btn-primary{color:#fff;background-color:#337ab7;border-color:#2e6da4}.btn-primary.focus,.btn-primary:focus{color:#fff;background-color:#286090;border-color:#122b40}.btn-primary:hover{color:#fff;background-color:#286090;border-color:#204d74}.btn-primary.active,.btn-primary:active,.open>.dropdown-toggle.btn-primary{color:#fff;background-color:#286090;border-color:#204d74}.btn-primary.active.focus,.btn-primary.active:focus,.btn-primary.active:hover,.btn-primary:active.focus,.btn-primary:active:focus,.btn-primary:active:hover,.open>.dropdown-toggle.btn-primary.focus,.open>.dropdown-toggle.btn-primary:focus,.open>.dropdown-toggle.btn-primary:hover{color:#fff;background-color:#204d74;border-color:#122b40}.btn-primary.active,.btn-primary:active,.open>.dropdown-toggle.btn-primary{background-image:none}.btn-primary.disabled,.btn-primary.disabled.active,.btn-primary.disabled.focus,.btn-primary.disabled:active,.btn-primary.disabled:focus,.btn-primary.disabled:hover,.btn-primary[disabled],.btn-primary[disabled].active,.btn-primary[disabled].focus,.btn-primary[disabled]:active,.btn-primary[disabled]:focus,.btn-primary[disabled]:hover,fieldset[disabled] .btn-primary,fieldset[disabled] .btn-primary.active,fieldset[disabled] .btn-primary.focus,fieldset[disabled] .btn-primary:active,fieldset[disabled] .btn-primary:focus,fieldset[disabled] .btn-primary:hover{background-color:#337ab7;border-color:#2e6da4}.btn-primary .badge{color:#337ab7;background-color:#fff}.btn-success{color:#fff;background-color:#5cb85c;border-color:#4cae4c}.btn-success.focus,.btn-success:focus{color:#fff;background-color:#449d44;border-color:#255625}.btn-success:hover{color:#fff;background-color:#449d44;border-color:#398439}.btn-success.active,.btn-success:active,.open>.dropdown-toggle.btn-success{color:#fff;background-color:#449d44;border-color:#398439}.btn-success.active.focus,.btn-success.active:focus,.btn-success.active:hover,.btn-success:active.focus,.btn-success:active:focus,.btn-success:active:hover,.open>.dropdown-toggle.btn-success.focus,.open>.dropdown-toggle.btn-success:focus,.open>.dropdown-toggle.btn-success:hover{color:#fff;background-color:#398439;border-color:#255625}.btn-success.active,.btn-success:active,.open>.dropdown-toggle.btn-success{background-image:none}.btn-success.disabled,.btn-success.disabled.active,.btn-success.disabled.focus,.btn-success.disabled:active,.btn-success.disabled:focus,.btn-success.disabled:hover,.btn-success[disabled],.btn-success[disabled].active,.btn-success[disabled].focus,.btn-success[disabled]:active,.btn-success[disabled]:focus,.btn-success[disabled]:hover,fieldset[disabled] .btn-success,fieldset[disabled] .btn-success.active,fieldset[disabled] .btn-success.focus,fieldset[disabled] .btn-success:active,fieldset[disabled] .btn-success:focus,fieldset[disabled] .btn-success:hover{background-color:#5cb85c;border-color:#4cae4c}.btn-success .badge{color:#5cb85c;background-color:#fff}.btn-info{color:#fff;background-color:#5bc0de;border-color:#46b8da}.btn-info.focus,.btn-info:focus{color:#fff;background-color:#31b0d5;border-color:#1b6d85}.btn-info:hover{color:#fff;background-color:#31b0d5;border-color:#269abc}.btn-info.active,.btn-info:active,.open>.dropdown-toggle.btn-info{color:#fff;background-color:#31b0d5;border-color:#269abc}.btn-info.active.focus,.btn-info.active:focus,.btn-info.active:hover,.btn-info:active.focus,.btn-info:active:focus,.btn-info:active:hover,.open>.dropdown-toggle.btn-info.focus,.open>.dropdown-toggle.btn-info:focus,.open>.dropdown-toggle.btn-info:hover{color:#fff;background-color:#269abc;border-color:#1b6d85}.btn-info.active,.btn-info:active,.open>.dropdown-toggle.btn-info{background-image:none}.btn-info.disabled,.btn-info.disabled.active,.btn-info.disabled.focus,.btn-info.disabled:active,.btn-info.disabled:focus,.btn-info.disabled:hover,.btn-info[disabled],.btn-info[disabled].active,.btn-info[disabled].focus,.btn-info[disabled]:active,.btn-info[disabled]:focus,.btn-info[disabled]:hover,fieldset[disabled] .btn-info,fieldset[disabled] .btn-info.active,fieldset[disabled] .btn-info.focus,fieldset[disabled] .btn-info:active,fieldset[disabled] .btn-info:focus,fieldset[disabled] .btn-info:hover{background-color:#5bc0de;border-color:#46b8da}.btn-info .badge{color:#5bc0de;background-color:#fff}.btn-warning{color:#fff;background-color:#f0ad4e;border-color:#eea236}.btn-warning.focus,.btn-warning:focus{color:#fff;background-color:#ec971f;border-color:#985f0d}.btn-warning:hover{color:#fff;background-color:#ec971f;border-color:#d58512}.btn-warning.active,.btn-warning:active,.open>.dropdown-toggle.btn-warning{color:#fff;background-color:#ec971f;border-color:#d58512}.btn-warning.active.focus,.btn-warning.active:focus,.btn-warning.active:hover,.btn-warning:active.focus,.btn-warning:active:focus,.btn-warning:active:hover,.open>.dropdown-toggle.btn-warning.focus,.open>.dropdown-toggle.btn-warning:focus,.open>.dropdown-toggle.btn-warning:hover{color:#fff;background-color:#d58512;border-color:#985f0d}.btn-warning.active,.btn-warning:active,.open>.dropdown-toggle.btn-warning{background-image:none}.btn-warning.disabled,.btn-warning.disabled.active,.btn-warning.disabled.focus,.btn-warning.disabled:active,.btn-warning.disabled:focus,.btn-warning.disabled:hover,.btn-warning[disabled],.btn-warning[disabled].active,.btn-warning[disabled].focus,.btn-warning[disabled]:active,.btn-warning[disabled]:focus,.btn-warning[disabled]:hover,fieldset[disabled] .btn-warning,fieldset[disabled] .btn-warning.active,fieldset[disabled] .btn-warning.focus,fieldset[disabled] .btn-warning:active,fieldset[disabled] .btn-warning:focus,fieldset[disabled] .btn-warning:hover{background-color:#f0ad4e;border-color:#eea236}.btn-warning .badge{color:#f0ad4e;background-color:#fff}.btn-danger{color:#fff;background-color:#d9534f;border-color:#d43f3a}.btn-danger.focus,.btn-danger:focus{color:#fff;background-color:#c9302c;border-color:#761c19}.btn-danger:hover{color:#fff;background-color:#c9302c;border-color:#ac2925}.btn-danger.active,.btn-danger:active,.open>.dropdown-toggle.btn-danger{color:#fff;background-color:#c9302c;border-color:#ac2925}.btn-danger.active.focus,.btn-danger.active:focus,.btn-danger.active:hover,.btn-danger:active.focus,.btn-danger:active:focus,.btn-danger:active:hover,.open>.dropdown-toggle.btn-danger.focus,.open>.dropdown-toggle.btn-danger:focus,.open>.dropdown-toggle.btn-danger:hover{color:#fff;background-color:#ac2925;border-color:#761c19}.btn-danger.active,.btn-danger:active,.open>.dropdown-toggle.btn-danger{background-image:none}.btn-danger.disabled,.btn-danger.disabled.active,.btn-danger.disabled.focus,.btn-danger.disabled:active,.btn-danger.disabled:focus,.btn-danger.disabled:hover,.btn-danger[disabled],.btn-danger[disabled].active,.btn-danger[disabled].focus,.btn-danger[disabled]:active,.btn-danger[disabled]:focus,.btn-danger[disabled]:hover,fieldset[disabled] .btn-danger,fieldset[disabled] .btn-danger.active,fieldset[disabled] .btn-danger.focus,fieldset[disabled] .btn-danger:active,fieldset[disabled] .btn-danger:focus,fieldset[disabled] .btn-danger:hover{background-color:#d9534f;border-color:#d43f3a}.btn-danger .badge{color:#d9534f;background-color:#fff}.btn-link{font-weight:400;color:#337ab7;border-radius:0}.btn-link,.btn-link.active,.btn-link:active,.btn-link[disabled],fieldset[disabled] .btn-link{background-color:transparent;-webkit-box-shadow:none;box-shadow:none}.btn-link,.btn-link:active,.btn-link:focus,.btn-link:hover{border-color:transparent}.btn-link:focus,.btn-link:hover{color:#23527c;text-decoration:underline;background-color:transparent}.btn-link[disabled]:focus,.btn-link[disabled]:hover,fieldset[disabled] .btn-link:focus,fieldset[disabled] .btn-link:hover{color:#777;text-decoration:none}.btn-group-lg>.btn,.btn-lg{padding:10px 16px;font-size:18px;line-height:1.3333333;border-radius:6px}.btn-group-sm>.btn,.btn-sm{padding:5px 10px;font-size:12px;line-height:1.5;border-radius:3px}.btn-group-xs>.btn,.btn-xs{padding:1px 5px;font-size:12px;line-height:1.5;border-radius:3px}.btn-block{display:block;width:100%}.btn-block+.btn-block{margin-top:5px}input[type=button].btn-block,input[type=reset].btn-block,input[type=submit].btn-block{width:100%}.fade{opacity:0;-webkit-transition:opacity .15s linear;-o-transition:opacity .15s linear;transition:opacity .15s linear}.fade.in{opacity:1}.collapse{display:none}.collapse.in{display:block}tr.collapse.in{display:table-row}tbody.collapse.in{display:table-row-group}.collapsing{position:relative;height:0;overflow:hidden;-webkit-transition-timing-function:ease;-o-transition-timing-function:ease;transition-timing-function:ease;-webkit-transition-duration:.35s;-o-transition-duration:.35s;transition-duration:.35s;-webkit-transition-property:height,visibility;-o-transition-property:height,visibility;transition-property:height,visibility}.caret{display:inline-block;width:0;height:0;margin-left:2px;vertical-align:middle;border-top:4px dashed;border-top:4px solid\9;border-right:4px solid transparent;border-left:4px solid transparent}.dropdown,.dropup{position:relative}.dropdown-toggle:focus{outline:0}.dropdown-menu{position:absolute;top:100%;left:0;z-index:1000;display:none;float:left;min-width:160px;padding:5px 0;margin:2px 0 0;font-size:14px;text-align:left;list-style:none;background-color:#fff;-webkit-background-clip:padding-box;background-clip:padding-box;border:1px solid #ccc;border:1px solid rgba(0,0,0,.15);border-radius:4px;-webkit-box-shadow:0 6px 12px rgba(0,0,0,.175);box-shadow:0 6px 12px rgba(0,0,0,.175)}.dropdown-menu.pull-right{right:0;left:auto}.dropdown-menu .divider{height:1px;margin:9px 0;overflow:hidden;background-color:#e5e5e5}.dropdown-menu>li>a{display:block;padding:3px 20px;clear:both;font-weight:400;line-height:1.42857143;color:#333;white-space:nowrap}.dropdown-menu>li>a:focus,.dropdown-menu>li>a:hover{color:#262626;text-decoration:none;background-color:#f5f5f5}.dropdown-menu>.active>a,.dropdown-menu>.active>a:focus,.dropdown-menu>.active>a:hover{color:#fff;text-decoration:none;background-color:#337ab7;outline:0}.dropdown-menu>.disabled>a,.dropdown-menu>.disabled>a:focus,.dropdown-menu>.disabled>a:hover{color:#777}.dropdown-menu>.disabled>a:focus,.dropdown-menu>.disabled>a:hover{text-decoration:none;cursor:not-allowed;background-color:transparent;background-image:none;filter:progid:DXImageTransform.Microsoft.gradient(enabled=false)}.open>.dropdown-menu{display:block}.open>a{outline:0}.dropdown-menu-right{right:0;left:auto}.dropdown-menu-left{right:auto;left:0}.dropdown-header{display:block;padding:3px 20px;font-size:12px;line-height:1.42857143;color:#777;white-space:nowrap}.dropdown-backdrop{position:fixed;top:0;right:0;bottom:0;left:0;z-index:990}.pull-right>.dropdown-menu{right:0;left:auto}.dropup .caret,.navbar-fixed-bottom .dropdown .caret{content:"";border-top:0;border-bottom:4px dashed;border-bottom:4px solid\9}.dropup .dropdown-menu,.navbar-fixed-bottom .dropdown .dropdown-menu{top:auto;bottom:100%;margin-bottom:2px}@media (min-width:768px){.navbar-right .dropdown-menu{right:0;left:auto}.navbar-right .dropdown-menu-left{right:auto;left:0}}.btn-group,.btn-group-vertical{position:relative;display:inline-block;vertical-align:middle}.btn-group-vertical>.btn,.btn-group>.btn{position:relative;float:left}.btn-group-vertical>.btn.active,.btn-group-vertical>.btn:active,.btn-group-vertical>.btn:focus,.btn-group-vertical>.btn:hover,.btn-group>.btn.active,.btn-group>.btn:active,.btn-group>.btn:focus,.btn-group>.btn:hover{z-index:2}.btn-group .btn+.btn,.btn-group .btn+.btn-group,.btn-group .btn-group+.btn,.btn-group .btn-group+.btn-group{margin-left:-1px}.btn-toolbar{margin-left:-5px}.btn-toolbar .btn,.btn-toolbar .btn-group,.btn-toolbar .input-group{float:left}.btn-toolbar>.btn,.btn-toolbar>.btn-group,.btn-toolbar>.input-group{margin-left:5px}.btn-group>.btn:not(:first-child):not(:last-child):not(.dropdown-toggle){border-radius:0}.btn-group>.btn:first-child{margin-left:0}.btn-group>.btn:first-child:not(:last-child):not(.dropdown-toggle){border-top-right-radius:0;border-bottom-right-radius:0}.btn-group>.btn:last-child:not(:first-child),.btn-group>.dropdown-toggle:not(:first-child){border-top-left-radius:0;border-bottom-left-radius:0}.btn-group>.btn-group{float:left}.btn-group>.btn-group:not(:first-child):not(:last-child)>.btn{border-radius:0}.btn-group>.btn-group:first-child:not(:last-child)>.btn:last-child,.btn-group>.btn-group:first-child:not(:last-child)>.dropdown-toggle{border-top-right-radius:0;border-bottom-right-radius:0}.btn-group>.btn-group:last-child:not(:first-child)>.btn:first-child{border-top-left-radius:0;border-bottom-left-radius:0}.btn-group .dropdown-toggle:active,.btn-group.open .dropdown-toggle{outline:0}.btn-group>.btn+.dropdown-toggle{padding-right:8px;padding-left:8px}.btn-group>.btn-lg+.dropdown-toggle{padding-right:12px;padding-left:12px}.btn-group.open .dropdown-toggle{-webkit-box-shadow:inset 0 3px 5px rgba(0,0,0,.125);box-shadow:inset 0 3px 5px rgba(0,0,0,.125)}.btn-group.open .dropdown-toggle.btn-link{-webkit-box-shadow:none;box-shadow:none}.btn .caret{margin-left:0}.btn-lg .caret{border-width:5px 5px 0;border-bottom-width:0}.dropup .btn-lg .caret{border-width:0 5px 5px}.btn-group-vertical>.btn,.btn-group-vertical>.btn-group,.btn-group-vertical>.btn-group>.btn{display:block;float:none;width:100%;max-width:100%}.btn-group-vertical>.btn-group>.btn{float:none}.btn-group-vertical>.btn+.btn,.btn-group-vertical>.btn+.btn-group,.btn-group-vertical>.btn-group+.btn,.btn-group-vertical>.btn-group+.btn-group{margin-top:-1px;margin-left:0}.btn-group-vertical>.btn:not(:first-child):not(:last-child){border-radius:0}.btn-group-vertical>.btn:first-child:not(:last-child){border-top-right-radius:4px;border-bottom-right-radius:0;border-bottom-left-radius:0}.btn-group-vertical>.btn:last-child:not(:first-child){border-top-left-radius:0;border-top-right-radius:0;border-bottom-left-radius:4px}.btn-group-vertical>.btn-group:not(:first-child):not(:last-child)>.btn{border-radius:0}.btn-group-vertical>.btn-group:first-child:not(:last-child)>.btn:last-child,.btn-group-vertical>.btn-group:first-child:not(:last-child)>.dropdown-toggle{border-bottom-right-radius:0;border-bottom-left-radius:0}.btn-group-vertical>.btn-group:last-child:not(:first-child)>.btn:first-child{border-top-left-radius:0;border-top-right-radius:0}.btn-group-justified{display:table;width:100%;table-layout:fixed;border-collapse:separate}.btn-group-justified>.btn,.btn-group-justified>.btn-group{display:table-cell;float:none;width:1%}.btn-group-justified>.btn-group .btn{width:100%}.btn-group-justified>.btn-group .dropdown-menu{left:auto}[data-toggle=buttons]>.btn input[type=checkbox],[data-toggle=buttons]>.btn input[type=radio],[data-toggle=buttons]>.btn-group>.btn input[type=checkbox],[data-toggle=buttons]>.btn-group>.btn input[type=radio]{position:absolute;clip:rect(0,0,0,0);pointer-events:none}.input-group{position:relative;display:table;border-collapse:separate}.input-group[class*=col-]{float:none;padding-right:0;padding-left:0}.input-group .form-control{position:relative;z-index:2;float:left;width:100%;margin-bottom:0}.input-group-lg>.form-control,.input-group-lg>.input-group-addon,.input-group-lg>.input-group-btn>.btn{height:46px;padding:10px 16px;font-size:18px;line-height:1.3333333;border-radius:6px}select.input-group-lg>.form-control,select.input-group-lg>.input-group-addon,select.input-group-lg>.input-group-btn>.btn{height:46px;line-height:46px}select[multiple].input-group-lg>.form-control,select[multiple].input-group-lg>.input-group-addon,select[multiple].input-group-lg>.input-group-btn>.btn,textarea.input-group-lg>.form-control,textarea.input-group-lg>.input-group-addon,textarea.input-group-lg>.input-group-btn>.btn{height:auto}.input-group-sm>.form-control,.input-group-sm>.input-group-addon,.input-group-sm>.input-group-btn>.btn{height:30px;padding:5px 10px;font-size:12px;line-height:1.5;border-radius:3px}select.input-group-sm>.form-control,select.input-group-sm>.input-group-addon,select.input-group-sm>.input-group-btn>.btn{height:30px;line-height:30px}select[multiple].input-group-sm>.form-control,select[multiple].input-group-sm>.input-group-addon,select[multiple].input-group-sm>.input-group-btn>.btn,textarea.input-group-sm>.form-control,textarea.input-group-sm>.input-group-addon,textarea.input-group-sm>.input-group-btn>.btn{height:auto}.input-group .form-control,.input-group-addon,.input-group-btn{display:table-cell}.input-group .form-control:not(:first-child):not(:last-child),.input-group-addon:not(:first-child):not(:last-child),.input-group-btn:not(:first-child):not(:last-child){border-radius:0}.input-group-addon,.input-group-btn{width:1%;white-space:nowrap;vertical-align:middle}.input-group-addon{padding:6px 12px;font-size:14px;font-weight:400;line-height:1;color:#555;text-align:center;background-color:#eee;border:1px solid #ccc;border-radius:4px}.input-group-addon.input-sm{padding:5px 10px;font-size:12px;border-radius:3px}.input-group-addon.input-lg{padding:10px 16px;font-size:18px;border-radius:6px}.input-group-addon input[type=checkbox],.input-group-addon input[type=radio]{margin-top:0}.input-group .form-control:first-child,.input-group-addon:first-child,.input-group-btn:first-child>.btn,.input-group-btn:first-child>.btn-group>.btn,.input-group-btn:first-child>.dropdown-toggle,.input-group-btn:last-child>.btn-group:not(:last-child)>.btn,.input-group-btn:last-child>.btn:not(:last-child):not(.dropdown-toggle){border-top-right-radius:0;border-bottom-right-radius:0}.input-group-addon:first-child{border-right:0}.input-group .form-control:last-child,.input-group-addon:last-child,.input-group-btn:first-child>.btn-group:not(:first-child)>.btn,.input-group-btn:first-child>.btn:not(:first-child),.input-group-btn:last-child>.btn,.input-group-btn:last-child>.btn-group>.btn,.input-group-btn:last-child>.dropdown-toggle{border-top-left-radius:0;border-bottom-left-radius:0}.input-group-addon:last-child{border-left:0}.input-group-btn{position:relative;font-size:0;white-space:nowrap}.input-group-btn>.btn{position:relative}.input-group-btn>.btn+.btn{margin-left:-1px}.input-group-btn>.btn:active,.input-group-btn>.btn:focus,.input-group-btn>.btn:hover{z-index:2}.input-group-btn:first-child>.btn,.input-group-btn:first-child>.btn-group{margin-right:-1px}.input-group-btn:last-child>.btn,.input-group-btn:last-child>.btn-group{z-index:2;margin-left:-1px}.nav{padding-left:0;margin-bottom:0;list-style:none}.nav>li{position:relative;display:block}.nav>li>a{position:relative;display:block;padding:10px 15px}.nav>li>a:focus,.nav>li>a:hover{text-decoration:none;background-color:#eee}.nav>li.disabled>a{color:#777}.nav>li.disabled>a:focus,.nav>li.disabled>a:hover{color:#777;text-decoration:none;cursor:not-allowed;background-color:transparent}.nav .open>a,.nav .open>a:focus,.nav .open>a:hover{background-color:#eee;border-color:#337ab7}.nav .nav-divider{height:1px;margin:9px 0;overflow:hidden;background-color:#e5e5e5}.nav>li>a>img{max-width:none}.nav-tabs{border-bottom:1px solid #ddd}.nav-tabs>li{float:left;margin-bottom:-1px}.nav-tabs>li>a{margin-right:2px;line-height:1.42857143;border:1px solid transparent;border-radius:4px 4px 0 0}.nav-tabs>li>a:hover{border-color:#eee #eee #ddd}.nav-tabs>li.active>a,.nav-tabs>li.active>a:focus,.nav-tabs>li.active>a:hover{color:#555;cursor:default;background-color:#fff;border:1px solid #ddd;border-bottom-color:transparent}.nav-tabs.nav-justified{width:100%;border-bottom:0}.nav-tabs.nav-justified>li{float:none}.nav-tabs.nav-justified>li>a{margin-bottom:5px;text-align:center}.nav-tabs.nav-justified>.dropdown .dropdown-menu{top:auto;left:auto}@media (min-width:768px){.nav-tabs.nav-justified>li{display:table-cell;width:1%}.nav-tabs.nav-justified>li>a{margin-bottom:0}}.nav-tabs.nav-justified>li>a{margin-right:0;border-radius:4px}.nav-tabs.nav-justified>.active>a,.nav-tabs.nav-justified>.active>a:focus,.nav-tabs.nav-justified>.active>a:hover{border:1px solid #ddd}@media (min-width:768px){.nav-tabs.nav-justified>li>a{border-bottom:1px solid #ddd;border-radius:4px 4px 0 0}.nav-tabs.nav-justified>.active>a,.nav-tabs.nav-justified>.active>a:focus,.nav-tabs.nav-justified>.active>a:hover{border-bottom-color:#fff}}.nav-pills>li{float:left}.nav-pills>li>a{border-radius:4px}.nav-pills>li+li{margin-left:2px}.nav-pills>li.active>a,.nav-pills>li.active>a:focus,.nav-pills>li.active>a:hover{color:#fff;background-color:#337ab7}.nav-stacked>li{float:none}.nav-stacked>li+li{margin-top:2px;margin-left:0}.nav-justified{width:100%}.nav-justified>li{float:none}.nav-justified>li>a{margin-bottom:5px;text-align:center}.nav-justified>.dropdown .dropdown-menu{top:auto;left:auto}@media (min-width:768px){.nav-justified>li{display:table-cell;width:1%}.nav-justified>li>a{margin-bottom:0}}.nav-tabs-justified{border-bottom:0}.nav-tabs-justified>li>a{margin-right:0;border-radius:4px}.nav-tabs-justified>.active>a,.nav-tabs-justified>.active>a:focus,.nav-tabs-justified>.active>a:hover{border:1px solid #ddd}@media (min-width:768px){.nav-tabs-justified>li>a{border-bottom:1px solid #ddd;border-radius:4px 4px 0 0}.nav-tabs-justified>.active>a,.nav-tabs-justified>.active>a:focus,.nav-tabs-justified>.active>a:hover{border-bottom-color:#fff}}.tab-content>.tab-pane{display:none}.tab-content>.active{display:block}.nav-tabs .dropdown-menu{margin-top:-1px;border-top-left-radius:0;border-top-right-radius:0}.navbar{position:relative;min-height:50px;margin-bottom:20px;border:1px solid transparent}@media (min-width:768px){.navbar{border-radius:4px}}@media (min-width:768px){.navbar-header{float:left}}.navbar-collapse{padding-right:15px;padding-left:15px;overflow-x:visible;-webkit-overflow-scrolling:touch;border-top:1px solid transparent;-webkit-box-shadow:inset 0 1px 0 rgba(255,255,255,.1);box-shadow:inset 0 1px 0 rgba(255,255,255,.1)}.navbar-collapse.in{overflow-y:auto}@media (min-width:768px){.navbar-collapse{width:auto;border-top:0;-webkit-box-shadow:none;box-shadow:none}.navbar-collapse.collapse{display:block!important;height:auto!important;padding-bottom:0;overflow:visible!important}.navbar-collapse.in{overflow-y:visible}.navbar-fixed-bottom .navbar-collapse,.navbar-fixed-top .navbar-collapse,.navbar-static-top .navbar-collapse{padding-right:0;padding-left:0}}.navbar-fixed-bottom .navbar-collapse,.navbar-fixed-top .navbar-collapse{max-height:340px}@media (max-device-width:480px) and (orientation:landscape){.navbar-fixed-bottom .navbar-collapse,.navbar-fixed-top .navbar-collapse{max-height:200px}}.container-fluid>.navbar-collapse,.container-fluid>.navbar-header,.container>.navbar-collapse,.container>.navbar-header{margin-right:-15px;margin-left:-15px}@media (min-width:768px){.container-fluid>.navbar-collapse,.container-fluid>.navbar-header,.container>.navbar-collapse,.container>.navbar-header{margin-right:0;margin-left:0}}.navbar-static-top{z-index:1000;border-width:0 0 1px}@media (min-width:768px){.navbar-static-top{border-radius:0}}.navbar-fixed-bottom,.navbar-fixed-top{position:fixed;right:0;left:0;z-index:1030}@media (min-width:768px){.navbar-fixed-bottom,.navbar-fixed-top{border-radius:0}}.navbar-fixed-top{top:0;border-width:0 0 1px}.navbar-fixed-bottom{bottom:0;margin-bottom:0;border-width:1px 0 0}.navbar-brand{float:left;height:50px;padding:15px 15px;font-size:18px;line-height:20px}.navbar-brand:focus,.navbar-brand:hover{text-decoration:none}.navbar-brand>img{display:block}@media (min-width:768px){.navbar>.container .navbar-brand,.navbar>.container-fluid .navbar-brand{margin-left:-15px}}.navbar-toggle{position:relative;float:right;padding:9px 10px;margin-top:8px;margin-right:15px;margin-bottom:8px;background-color:transparent;background-image:none;border:1px solid transparent;border-radius:4px}.navbar-toggle:focus{outline:0}.navbar-toggle .icon-bar{display:block;width:22px;height:2px;border-radius:1px}.navbar-toggle .icon-bar+.icon-bar{margin-top:4px}@media (min-width:768px){.navbar-toggle{display:none}}.navbar-nav{margin:7.5px -15px}.navbar-nav>li>a{padding-top:10px;padding-bottom:10px;line-height:20px}@media (max-width:767px){.navbar-nav .open .dropdown-menu{position:static;float:none;width:auto;margin-top:0;background-color:transparent;border:0;-webkit-box-shadow:none;box-shadow:none}.navbar-nav .open .dropdown-menu .dropdown-header,.navbar-nav .open .dropdown-menu>li>a{padding:5px 15px 5px 25px}.navbar-nav .open .dropdown-menu>li>a{line-height:20px}.navbar-nav .open .dropdown-menu>li>a:focus,.navbar-nav .open .dropdown-menu>li>a:hover{background-image:none}}@media (min-width:768px){.navbar-nav{float:left;margin:0}.navbar-nav>li{float:left}.navbar-nav>li>a{padding-top:15px;padding-bottom:15px}}.navbar-form{padding:10px 15px;margin-top:8px;margin-right:-15px;margin-bottom:8px;margin-left:-15px;border-top:1px solid transparent;border-bottom:1px solid transparent;-webkit-box-shadow:inset 0 1px 0 rgba(255,255,255,.1),0 1px 0 rgba(255,255,255,.1);box-shadow:inset 0 1px 0 rgba(255,255,255,.1),0 1px 0 rgba(255,255,255,.1)}@media (min-width:768px){.navbar-form .form-group{display:inline-block;margin-bottom:0;vertical-align:middle}.navbar-form .form-control{display:inline-block;width:auto;vertical-align:middle}.navbar-form .form-control-static{display:inline-block}.navbar-form .input-group{display:inline-table;vertical-align:middle}.navbar-form .input-group .form-control,.navbar-form .input-group .input-group-addon,.navbar-form .input-group .input-group-btn{width:auto}.navbar-form .input-group>.form-control{width:100%}.navbar-form .control-label{margin-bottom:0;vertical-align:middle}.navbar-form .checkbox,.navbar-form .radio{display:inline-block;margin-top:0;margin-bottom:0;vertical-align:middle}.navbar-form .checkbox label,.navbar-form .radio label{padding-left:0}.navbar-form .checkbox input[type=checkbox],.navbar-form .radio input[type=radio]{position:relative;margin-left:0}.navbar-form .has-feedback .form-control-feedback{top:0}}@media (max-width:767px){.navbar-form .form-group{margin-bottom:5px}.navbar-form .form-group:last-child{margin-bottom:0}}@media (min-width:768px){.navbar-form{width:auto;padding-top:0;padding-bottom:0;margin-right:0;margin-left:0;border:0;-webkit-box-shadow:none;box-shadow:none}}.navbar-nav>li>.dropdown-menu{margin-top:0;border-top-left-radius:0;border-top-right-radius:0}.navbar-fixed-bottom .navbar-nav>li>.dropdown-menu{margin-bottom:0;border-top-left-radius:4px;border-top-right-radius:4px;border-bottom-right-radius:0;border-bottom-left-radius:0}.navbar-btn{margin-top:8px;margin-bottom:8px}.navbar-btn.btn-sm{margin-top:10px;margin-bottom:10px}.navbar-btn.btn-xs{margin-top:14px;margin-bottom:14px}.navbar-text{margin-top:15px;margin-bottom:15px}@media (min-width:768px){.navbar-text{float:left;margin-right:15px;margin-left:15px}}@media (min-width:768px){.navbar-left{float:left!important}.navbar-right{float:right!important;margin-right:-15px}.navbar-right~.navbar-right{margin-right:0}}.navbar-default{background-color:#f8f8f8;border-color:#e7e7e7}.navbar-default .navbar-brand{color:#777}.navbar-default .navbar-brand:focus,.navbar-default .navbar-brand:hover{color:#5e5e5e;background-color:transparent}.navbar-default .navbar-text{color:#777}.navbar-default .navbar-nav>li>a{color:#777}.navbar-default .navbar-nav>li>a:focus,.navbar-default .navbar-nav>li>a:hover{color:#333;background-color:transparent}.navbar-default .navbar-nav>.active>a,.navbar-default .navbar-nav>.active>a:focus,.navbar-default .navbar-nav>.active>a:hover{color:#555;background-color:#e7e7e7}.navbar-default .navbar-nav>.disabled>a,.navbar-default .navbar-nav>.disabled>a:focus,.navbar-default .navbar-nav>.disabled>a:hover{color:#ccc;background-color:transparent}.navbar-default .navbar-toggle{border-color:#ddd}.navbar-default .navbar-toggle:focus,.navbar-default .navbar-toggle:hover{background-color:#ddd}.navbar-default .navbar-toggle .icon-bar{background-color:#888}.navbar-default .navbar-collapse,.navbar-default .navbar-form{border-color:#e7e7e7}.navbar-default .navbar-nav>.open>a,.navbar-default .navbar-nav>.open>a:focus,.navbar-default .navbar-nav>.open>a:hover{color:#555;background-color:#e7e7e7}@media (max-width:767px){.navbar-default .navbar-nav .open .dropdown-menu>li>a{color:#777}.navbar-default .navbar-nav .open .dropdown-menu>li>a:focus,.navbar-default .navbar-nav .open .dropdown-menu>li>a:hover{color:#333;background-color:transparent}.navbar-default .navbar-nav .open .dropdown-menu>.active>a,.navbar-default .navbar-nav .open .dropdown-menu>.active>a:focus,.navbar-default .navbar-nav .open .dropdown-menu>.active>a:hover{color:#555;background-color:#e7e7e7}.navbar-default .navbar-nav .open .dropdown-menu>.disabled>a,.navbar-default .navbar-nav .open .dropdown-menu>.disabled>a:focus,.navbar-default .navbar-nav .open .dropdown-menu>.disabled>a:hover{color:#ccc;background-color:transparent}}.navbar-default .navbar-link{color:#777}.navbar-default .navbar-link:hover{color:#333}.navbar-default .btn-link{color:#777}.navbar-default .btn-link:focus,.navbar-default .btn-link:hover{color:#333}.navbar-default .btn-link[disabled]:focus,.navbar-default .btn-link[disabled]:hover,fieldset[disabled] .navbar-default .btn-link:focus,fieldset[disabled] .navbar-default .btn-link:hover{color:#ccc}.navbar-inverse{background-color:#222;border-color:#080808}.navbar-inverse .navbar-brand{color:#9d9d9d}.navbar-inverse .navbar-brand:focus,.navbar-inverse .navbar-brand:hover{color:#fff;background-color:transparent}.navbar-inverse .navbar-text{color:#9d9d9d}.navbar-inverse .navbar-nav>li>a{color:#9d9d9d}.navbar-inverse .navbar-nav>li>a:focus,.navbar-inverse .navbar-nav>li>a:hover{color:#fff;background-color:transparent}.navbar-inverse .navbar-nav>.active>a,.navbar-inverse .navbar-nav>.active>a:focus,.navbar-inverse .navbar-nav>.active>a:hover{color:#fff;background-color:#080808}.navbar-inverse .navbar-nav>.disabled>a,.navbar-inverse .navbar-nav>.disabled>a:focus,.navbar-inverse .navbar-nav>.disabled>a:hover{color:#444;background-color:transparent}.navbar-inverse .navbar-toggle{border-color:#333}.navbar-inverse .navbar-toggle:focus,.navbar-inverse .navbar-toggle:hover{background-color:#333}.navbar-inverse .navbar-toggle .icon-bar{background-color:#fff}.navbar-inverse .navbar-collapse,.navbar-inverse .navbar-form{border-color:#101010}.navbar-inverse .navbar-nav>.open>a,.navbar-inverse .navbar-nav>.open>a:focus,.navbar-inverse .navbar-nav>.open>a:hover{color:#fff;background-color:#080808}@media (max-width:767px){.navbar-inverse .navbar-nav .open .dropdown-menu>.dropdown-header{border-color:#080808}.navbar-inverse .navbar-nav .open .dropdown-menu .divider{background-color:#080808}.navbar-inverse .navbar-nav .open .dropdown-menu>li>a{color:#9d9d9d}.navbar-inverse .navbar-nav .open .dropdown-menu>li>a:focus,.navbar-inverse .navbar-nav .open .dropdown-menu>li>a:hover{color:#fff;background-color:transparent}.navbar-inverse .navbar-nav .open .dropdown-menu>.active>a,.navbar-inverse .navbar-nav .open .dropdown-menu>.active>a:focus,.navbar-inverse .navbar-nav .open .dropdown-menu>.active>a:hover{color:#fff;background-color:#080808}.navbar-inverse .navbar-nav .open .dropdown-menu>.disabled>a,.navbar-inverse .navbar-nav .open .dropdown-menu>.disabled>a:focus,.navbar-inverse .navbar-nav .open .dropdown-menu>.disabled>a:hover{color:#444;background-color:transparent}}.navbar-inverse .navbar-link{color:#9d9d9d}.navbar-inverse .navbar-link:hover{color:#fff}.navbar-inverse .btn-link{color:#9d9d9d}.navbar-inverse .btn-link:focus,.navbar-inverse .btn-link:hover{color:#fff}.navbar-inverse .btn-link[disabled]:focus,.navbar-inverse .btn-link[disabled]:hover,fieldset[disabled] .navbar-inverse .btn-link:focus,fieldset[disabled] .navbar-inverse .btn-link:hover{color:#444}.breadcrumb{padding:8px 15px;margin-bottom:20px;list-style:none;background-color:#f5f5f5;border-radius:4px}.breadcrumb>li{display:inline-block}.breadcrumb>li+li:before{padding:0 5px;color:#ccc;content:"/\00a0"}.breadcrumb>.active{color:#777}.pagination{display:inline-block;padding-left:0;margin:20px 0;border-radius:4px}.pagination>li{display:inline}.pagination>li>a,.pagination>li>span{position:relative;float:left;padding:6px 12px;margin-left:-1px;line-height:1.42857143;color:#337ab7;text-decoration:none;background-color:#fff;border:1px solid #ddd}.pagination>li:first-child>a,.pagination>li:first-child>span{margin-left:0;border-top-left-radius:4px;border-bottom-left-radius:4px}.pagination>li:last-child>a,.pagination>li:last-child>span{border-top-right-radius:4px;border-bottom-right-radius:4px}.pagination>li>a:focus,.pagination>li>a:hover,.pagination>li>span:focus,.pagination>li>span:hover{z-index:3;color:#23527c;background-color:#eee;border-color:#ddd}.pagination>.active>a,.pagination>.active>a:focus,.pagination>.active>a:hover,.pagination>.active>span,.pagination>.active>span:focus,.pagination>.active>span:hover{z-index:2;color:#fff;cursor:default;background-color:#337ab7;border-color:#337ab7}.pagination>.disabled>a,.pagination>.disabled>a:focus,.pagination>.disabled>a:hover,.pagination>.disabled>span,.pagination>.disabled>span:focus,.pagination>.disabled>span:hover{color:#777;cursor:not-allowed;background-color:#fff;border-color:#ddd}.pagination-lg>li>a,.pagination-lg>li>span{padding:10px 16px;font-size:18px;line-height:1.3333333}.pagination-lg>li:first-child>a,.pagination-lg>li:first-child>span{border-top-left-radius:6px;border-bottom-left-radius:6px}.pagination-lg>li:last-child>a,.pagination-lg>li:last-child>span{border-top-right-radius:6px;border-bottom-right-radius:6px}.pagination-sm>li>a,.pagination-sm>li>span{padding:5px 10px;font-size:12px;line-height:1.5}.pagination-sm>li:first-child>a,.pagination-sm>li:first-child>span{border-top-left-radius:3px;border-bottom-left-radius:3px}.pagination-sm>li:last-child>a,.pagination-sm>li:last-child>span{border-top-right-radius:3px;border-bottom-right-radius:3px}.pager{padding-left:0;margin:20px 0;text-align:center;list-style:none}.pager li{display:inline}.pager li>a,.pager li>span{display:inline-block;padding:5px 14px;background-color:#fff;border:1px solid #ddd;border-radius:15px}.pager li>a:focus,.pager li>a:hover{text-decoration:none;background-color:#eee}.pager .next>a,.pager .next>span{float:right}.pager .previous>a,.pager .previous>span{float:left}.pager .disabled>a,.pager .disabled>a:focus,.pager .disabled>a:hover,.pager .disabled>span{color:#777;cursor:not-allowed;background-color:#fff}.label{display:inline;padding:.2em .6em .3em;font-size:75%;font-weight:700;line-height:1;color:#fff;text-align:center;white-space:nowrap;vertical-align:baseline;border-radius:.25em}a.label:focus,a.label:hover{color:#fff;text-decoration:none;cursor:pointer}.label:empty{display:none}.btn .label{position:relative;top:-1px}.label-default{background-color:#777}.label-default[href]:focus,.label-default[href]:hover{background-color:#5e5e5e}.label-primary{background-color:#337ab7}.label-primary[href]:focus,.label-primary[href]:hover{background-color:#286090}.label-success{background-color:#5cb85c}.label-success[href]:focus,.label-success[href]:hover{background-color:#449d44}.label-info{background-color:#5bc0de}.label-info[href]:focus,.label-info[href]:hover{background-color:#31b0d5}.label-warning{background-color:#f0ad4e}.label-warning[href]:focus,.label-warning[href]:hover{background-color:#ec971f}.label-danger{background-color:#d9534f}.label-danger[href]:focus,.label-danger[href]:hover{background-color:#c9302c}.badge{display:inline-block;min-width:10px;padding:3px 7px;font-size:12px;font-weight:700;line-height:1;color:#fff;text-align:center;white-space:nowrap;vertical-align:middle;background-color:#777;border-radius:10px}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.btn-group-xs>.btn .badge,.btn-xs .badge{top:0;padding:1px 5px}a.badge:focus,a.badge:hover{color:#fff;text-decoration:none;cursor:pointer}.list-group-item.active>.badge,.nav-pills>.active>a>.badge{color:#337ab7;background-color:#fff}.list-group-item>.badge{float:right}.list-group-item>.badge+.badge{margin-right:5px}.nav-pills>li>a>.badge{margin-left:3px}.jumbotron{padding-top:30px;padding-bottom:30px;margin-bottom:30px;color:inherit;background-color:#eee}.jumbotron .h1,.jumbotron h1{color:inherit}.jumbotron p{margin-bottom:15px;font-size:21px;font-weight:200}.jumbotron>hr{border-top-color:#d5d5d5}.container .jumbotron,.container-fluid .jumbotron{border-radius:6px}.jumbotron .container{max-width:100%}@media screen and (min-width:768px){.jumbotron{padding-top:48px;padding-bottom:48px}.container .jumbotron,.container-fluid .jumbotron{padding-right:60px;padding-left:60px}.jumbotron .h1,.jumbotron h1{font-size:63px}}.thumbnail{display:block;padding:4px;margin-bottom:20px;line-height:1.42857143;background-color:#fff;border:1px solid #ddd;border-radius:4px;-webkit-transition:border .2s ease-in-out;-o-transition:border .2s ease-in-out;transition:border .2s ease-in-out}.thumbnail a>img,.thumbnail>img{margin-right:auto;margin-left:auto}a.thumbnail.active,a.thumbnail:focus,a.thumbnail:hover{border-color:#337ab7}.thumbnail .caption{padding:9px;color:#333}.alert{padding:15px;margin-bottom:20px;border:1px solid transparent;border-radius:4px}.alert h4{margin-top:0;color:inherit}.alert .alert-link{font-weight:700}.alert>p,.alert>ul{margin-bottom:0}.alert>p+p{margin-top:5px}.alert-dismissable,.alert-dismissible{padding-right:35px}.alert-dismissable .close,.alert-dismissible .close{position:relative;top:-2px;right:-21px;color:inherit}.alert-success{color:#3c763d;background-color:#dff0d8;border-color:#d6e9c6}.alert-success hr{border-top-color:#c9e2b3}.alert-success .alert-link{color:#2b542c}.alert-info{color:#31708f;background-color:#d9edf7;border-color:#bce8f1}.alert-info hr{border-top-color:#a6e1ec}.alert-info .alert-link{color:#245269}.alert-warning{color:#8a6d3b;background-color:#fcf8e3;border-color:#faebcc}.alert-warning hr{border-top-color:#f7e1b5}.alert-warning .alert-link{color:#66512c}.alert-danger{color:#a94442;background-color:#f2dede;border-color:#ebccd1}.alert-danger hr{border-top-color:#e4b9c0}.alert-danger .alert-link{color:#843534}@-webkit-keyframes progress-bar-stripes{from{background-position:40px 0}to{background-position:0 0}}@-o-keyframes progress-bar-stripes{from{background-position:40px 0}to{background-position:0 0}}@keyframes progress-bar-stripes{from{background-position:40px 0}to{background-position:0 0}}.progress{height:20px;margin-bottom:20px;overflow:hidden;background-color:#f5f5f5;border-radius:4px;-webkit-box-shadow:inset 0 1px 2px rgba(0,0,0,.1);box-shadow:inset 0 1px 2px rgba(0,0,0,.1)}.progress-bar{float:left;width:0;height:100%;font-size:12px;line-height:20px;color:#fff;text-align:center;background-color:#337ab7;-webkit-box-shadow:inset 0 -1px 0 rgba(0,0,0,.15);box-shadow:inset 0 -1px 0 rgba(0,0,0,.15);-webkit-transition:width .6s ease;-o-transition:width .6s ease;transition:width .6s ease}.progress-bar-striped,.progress-striped .progress-bar{background-image:-webkit-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:-o-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);-webkit-background-size:40px 40px;background-size:40px 40px}.progress-bar.active,.progress.active .progress-bar{-webkit-animation:progress-bar-stripes 2s linear infinite;-o-animation:progress-bar-stripes 2s linear infinite;animation:progress-bar-stripes 2s linear infinite}.progress-bar-success{background-color:#5cb85c}.progress-striped .progress-bar-success{background-image:-webkit-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:-o-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent)}.progress-bar-info{background-color:#5bc0de}.progress-striped .progress-bar-info{background-image:-webkit-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:-o-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent)}.progress-bar-warning{background-color:#f0ad4e}.progress-striped .progress-bar-warning{background-image:-webkit-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:-o-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent)}.progress-bar-danger{background-color:#d9534f}.progress-striped .progress-bar-danger{background-image:-webkit-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:-o-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent)}.media{margin-top:15px}.media:first-child{margin-top:0}.media,.media-body{overflow:hidden;zoom:1}.media-body{width:10000px}.media-object{display:block}.media-object.img-thumbnail{max-width:none}.media-right,.media>.pull-right{padding-left:10px}.media-left,.media>.pull-left{padding-right:10px}.media-body,.media-left,.media-right{display:table-cell;vertical-align:top}.media-middle{vertical-align:middle}.media-bottom{vertical-align:bottom}.media-heading{margin-top:0;margin-bottom:5px}.media-list{padding-left:0;list-style:none}.list-group{padding-left:0;margin-bottom:20px}.list-group-item{position:relative;display:block;padding:10px 15px;margin-bottom:-1px;background-color:#fff;border:1px solid #ddd}.list-group-item:first-child{border-top-left-radius:4px;border-top-right-radius:4px}.list-group-item:last-child{margin-bottom:0;border-bottom-right-radius:4px;border-bottom-left-radius:4px}a.list-group-item,button.list-group-item{color:#555}a.list-group-item .list-group-item-heading,button.list-group-item .list-group-item-heading{color:#333}a.list-group-item:focus,a.list-group-item:hover,button.list-group-item:focus,button.list-group-item:hover{color:#555;text-decoration:none;background-color:#f5f5f5}button.list-group-item{width:100%;text-align:left}.list-group-item.disabled,.list-group-item.disabled:focus,.list-group-item.disabled:hover{color:#777;cursor:not-allowed;background-color:#eee}.list-group-item.disabled .list-group-item-heading,.list-group-item.disabled:focus .list-group-item-heading,.list-group-item.disabled:hover .list-group-item-heading{color:inherit}.list-group-item.disabled .list-group-item-text,.list-group-item.disabled:focus .list-group-item-text,.list-group-item.disabled:hover .list-group-item-text{color:#777}.list-group-item.active,.list-group-item.active:focus,.list-group-item.active:hover{z-index:2;color:#fff;background-color:#337ab7;border-color:#337ab7}.list-group-item.active .list-group-item-heading,.list-group-item.active .list-group-item-heading>.small,.list-group-item.active .list-group-item-heading>small,.list-group-item.active:focus .list-group-item-heading,.list-group-item.active:focus .list-group-item-heading>.small,.list-group-item.active:focus .list-group-item-heading>small,.list-group-item.active:hover .list-group-item-heading,.list-group-item.active:hover .list-group-item-heading>.small,.list-group-item.active:hover .list-group-item-heading>small{color:inherit}.list-group-item.active .list-group-item-text,.list-group-item.active:focus .list-group-item-text,.list-group-item.active:hover .list-group-item-text{color:#c7ddef}.list-group-item-success{color:#3c763d;background-color:#dff0d8}a.list-group-item-success,button.list-group-item-success{color:#3c763d}a.list-group-item-success .list-group-item-heading,button.list-group-item-success .list-group-item-heading{color:inherit}a.list-group-item-success:focus,a.list-group-item-success:hover,button.list-group-item-success:focus,button.list-group-item-success:hover{color:#3c763d;background-color:#d0e9c6}a.list-group-item-success.active,a.list-group-item-success.active:focus,a.list-group-item-success.active:hover,button.list-group-item-success.active,button.list-group-item-success.active:focus,button.list-group-item-success.active:hover{color:#fff;background-color:#3c763d;border-color:#3c763d}.list-group-item-info{color:#31708f;background-color:#d9edf7}a.list-group-item-info,button.list-group-item-info{color:#31708f}a.list-group-item-info .list-group-item-heading,button.list-group-item-info .list-group-item-heading{color:inherit}a.list-group-item-info:focus,a.list-group-item-info:hover,button.list-group-item-info:focus,button.list-group-item-info:hover{color:#31708f;background-color:#c4e3f3}a.list-group-item-info.active,a.list-group-item-info.active:focus,a.list-group-item-info.active:hover,button.list-group-item-info.active,button.list-group-item-info.active:focus,button.list-group-item-info.active:hover{color:#fff;background-color:#31708f;border-color:#31708f}.list-group-item-warning{color:#8a6d3b;background-color:#fcf8e3}a.list-group-item-warning,button.list-group-item-warning{color:#8a6d3b}a.list-group-item-warning .list-group-item-heading,button.list-group-item-warning .list-group-item-heading{color:inherit}a.list-group-item-warning:focus,a.list-group-item-warning:hover,button.list-group-item-warning:focus,button.list-group-item-warning:hover{color:#8a6d3b;background-color:#faf2cc}a.list-group-item-warning.active,a.list-group-item-warning.active:focus,a.list-group-item-warning.active:hover,button.list-group-item-warning.active,button.list-group-item-warning.active:focus,button.list-group-item-warning.active:hover{color:#fff;background-color:#8a6d3b;border-color:#8a6d3b}.list-group-item-danger{color:#a94442;background-color:#f2dede}a.list-group-item-danger,button.list-group-item-danger{color:#a94442}a.list-group-item-danger .list-group-item-heading,button.list-group-item-danger .list-group-item-heading{color:inherit}a.list-group-item-danger:focus,a.list-group-item-danger:hover,button.list-group-item-danger:focus,button.list-group-item-danger:hover{color:#a94442;background-color:#ebcccc}a.list-group-item-danger.active,a.list-group-item-danger.active:focus,a.list-group-item-danger.active:hover,button.list-group-item-danger.active,button.list-group-item-danger.active:focus,button.list-group-item-danger.active:hover{color:#fff;background-color:#a94442;border-color:#a94442}.list-group-item-heading{margin-top:0;margin-bottom:5px}.list-group-item-text{margin-bottom:0;line-height:1.3}.panel{margin-bottom:20px;background-color:#fff;border:1px solid transparent;border-radius:4px;-webkit-box-shadow:0 1px 1px rgba(0,0,0,.05);box-shadow:0 1px 1px rgba(0,0,0,.05)}.panel-body{padding:15px}.panel-heading{padding:10px 15px;border-bottom:1px solid transparent;border-top-left-radius:3px;border-top-right-radius:3px}.panel-heading>.dropdown .dropdown-toggle{color:inherit}.panel-title{margin-top:0;margin-bottom:0;font-size:16px;color:inherit}.panel-title>.small,.panel-title>.small>a,.panel-title>a,.panel-title>small,.panel-title>small>a{color:inherit}.panel-footer{padding:10px 15px;background-color:#f5f5f5;border-top:1px solid #ddd;border-bottom-right-radius:3px;border-bottom-left-radius:3px}.panel>.list-group,.panel>.panel-collapse>.list-group{margin-bottom:0}.panel>.list-group .list-group-item,.panel>.panel-collapse>.list-group .list-group-item{border-width:1px 0;border-radius:0}.panel>.list-group:first-child .list-group-item:first-child,.panel>.panel-collapse>.list-group:first-child .list-group-item:first-child{border-top:0;border-top-left-radius:3px;border-top-right-radius:3px}.panel>.list-group:last-child .list-group-item:last-child,.panel>.panel-collapse>.list-group:last-child .list-group-item:last-child{border-bottom:0;border-bottom-right-radius:3px;border-bottom-left-radius:3px}.panel>.panel-heading+.panel-collapse>.list-group .list-group-item:first-child{border-top-left-radius:0;border-top-right-radius:0}.panel-heading+.list-group .list-group-item:first-child{border-top-width:0}.list-group+.panel-footer{border-top-width:0}.panel>.panel-collapse>.table,.panel>.table,.panel>.table-responsive>.table{margin-bottom:0}.panel>.panel-collapse>.table caption,.panel>.table caption,.panel>.table-responsive>.table caption{padding-right:15px;padding-left:15px}.panel>.table-responsive:first-child>.table:first-child,.panel>.table:first-child{border-top-left-radius:3px;border-top-right-radius:3px}.panel>.table-responsive:first-child>.table:first-child>tbody:first-child>tr:first-child,.panel>.table-responsive:first-child>.table:first-child>thead:first-child>tr:first-child,.panel>.table:first-child>tbody:first-child>tr:first-child,.panel>.table:first-child>thead:first-child>tr:first-child{border-top-left-radius:3px;border-top-right-radius:3px}.panel>.table-responsive:first-child>.table:first-child>tbody:first-child>tr:first-child td:first-child,.panel>.table-responsive:first-child>.table:first-child>tbody:first-child>tr:first-child th:first-child,.panel>.table-responsive:first-child>.table:first-child>thead:first-child>tr:first-child td:first-child,.panel>.table-responsive:first-child>.table:first-child>thead:first-child>tr:first-child th:first-child,.panel>.table:first-child>tbody:first-child>tr:first-child td:first-child,.panel>.table:first-child>tbody:first-child>tr:first-child th:first-child,.panel>.table:first-child>thead:first-child>tr:first-child td:first-child,.panel>.table:first-child>thead:first-child>tr:first-child th:first-child{border-top-left-radius:3px}.panel>.table-responsive:first-child>.table:first-child>tbody:first-child>tr:first-child td:last-child,.panel>.table-responsive:first-child>.table:first-child>tbody:first-child>tr:first-child th:last-child,.panel>.table-responsive:first-child>.table:first-child>thead:first-child>tr:first-child td:last-child,.panel>.table-responsive:first-child>.table:first-child>thead:first-child>tr:first-child th:last-child,.panel>.table:first-child>tbody:first-child>tr:first-child td:last-child,.panel>.table:first-child>tbody:first-child>tr:first-child th:last-child,.panel>.table:first-child>thead:first-child>tr:first-child td:last-child,.panel>.table:first-child>thead:first-child>tr:first-child th:last-child{border-top-right-radius:3px}.panel>.table-responsive:last-child>.table:last-child,.panel>.table:last-child{border-bottom-right-radius:3px;border-bottom-left-radius:3px}.panel>.table-responsive:last-child>.table:last-child>tbody:last-child>tr:last-child,.panel>.table-responsive:last-child>.table:last-child>tfoot:last-child>tr:last-child,.panel>.table:last-child>tbody:last-child>tr:last-child,.panel>.table:last-child>tfoot:last-child>tr:last-child{border-bottom-right-radius:3px;border-bottom-left-radius:3px}.panel>.table-responsive:last-child>.table:last-child>tbody:last-child>tr:last-child td:first-child,.panel>.table-responsive:last-child>.table:last-child>tbody:last-child>tr:last-child th:first-child,.panel>.table-responsive:last-child>.table:last-child>tfoot:last-child>tr:last-child td:first-child,.panel>.table-responsive:last-child>.table:last-child>tfoot:last-child>tr:last-child th:first-child,.panel>.table:last-child>tbody:last-child>tr:last-child td:first-child,.panel>.table:last-child>tbody:last-child>tr:last-child th:first-child,.panel>.table:last-child>tfoot:last-child>tr:last-child td:first-child,.panel>.table:last-child>tfoot:last-child>tr:last-child th:first-child{border-bottom-left-radius:3px}.panel>.table-responsive:last-child>.table:last-child>tbody:last-child>tr:last-child td:last-child,.panel>.table-responsive:last-child>.table:last-child>tbody:last-child>tr:last-child th:last-child,.panel>.table-responsive:last-child>.table:last-child>tfoot:last-child>tr:last-child td:last-child,.panel>.table-responsive:last-child>.table:last-child>tfoot:last-child>tr:last-child th:last-child,.panel>.table:last-child>tbody:last-child>tr:last-child td:last-child,.panel>.table:last-child>tbody:last-child>tr:last-child th:last-child,.panel>.table:last-child>tfoot:last-child>tr:last-child td:last-child,.panel>.table:last-child>tfoot:last-child>tr:last-child th:last-child{border-bottom-right-radius:3px}.panel>.panel-body+.table,.panel>.panel-body+.table-responsive,.panel>.table+.panel-body,.panel>.table-responsive+.panel-body{border-top:1px solid #ddd}.panel>.table>tbody:first-child>tr:first-child td,.panel>.table>tbody:first-child>tr:first-child th{border-top:0}.panel>.table-bordered,.panel>.table-responsive>.table-bordered{border:0}.panel>.table-bordered>tbody>tr>td:first-child,.panel>.table-bordered>tbody>tr>th:first-child,.panel>.table-bordered>tfoot>tr>td:first-child,.panel>.table-bordered>tfoot>tr>th:first-child,.panel>.table-bordered>thead>tr>td:first-child,.panel>.table-bordered>thead>tr>th:first-child,.panel>.table-responsive>.table-bordered>tbody>tr>td:first-child,.panel>.table-responsive>.table-bordered>tbody>tr>th:first-child,.panel>.table-responsive>.table-bordered>tfoot>tr>td:first-child,.panel>.table-responsive>.table-bordered>tfoot>tr>th:first-child,.panel>.table-responsive>.table-bordered>thead>tr>td:first-child,.panel>.table-responsive>.table-bordered>thead>tr>th:first-child{border-left:0}.panel>.table-bordered>tbody>tr>td:last-child,.panel>.table-bordered>tbody>tr>th:last-child,.panel>.table-bordered>tfoot>tr>td:last-child,.panel>.table-bordered>tfoot>tr>th:last-child,.panel>.table-bordered>thead>tr>td:last-child,.panel>.table-bordered>thead>tr>th:last-child,.panel>.table-responsive>.table-bordered>tbody>tr>td:last-child,.panel>.table-responsive>.table-bordered>tbody>tr>th:last-child,.panel>.table-responsive>.table-bordered>tfoot>tr>td:last-child,.panel>.table-responsive>.table-bordered>tfoot>tr>th:last-child,.panel>.table-responsive>.table-bordered>thead>tr>td:last-child,.panel>.table-responsive>.table-bordered>thead>tr>th:last-child{border-right:0}.panel>.table-bordered>tbody>tr:first-child>td,.panel>.table-bordered>tbody>tr:first-child>th,.panel>.table-bordered>thead>tr:first-child>td,.panel>.table-bordered>thead>tr:first-child>th,.panel>.table-responsive>.table-bordered>tbody>tr:first-child>td,.panel>.table-responsive>.table-bordered>tbody>tr:first-child>th,.panel>.table-responsive>.table-bordered>thead>tr:first-child>td,.panel>.table-responsive>.table-bordered>thead>tr:first-child>th{border-bottom:0}.panel>.table-bordered>tbody>tr:last-child>td,.panel>.table-bordered>tbody>tr:last-child>th,.panel>.table-bordered>tfoot>tr:last-child>td,.panel>.table-bordered>tfoot>tr:last-child>th,.panel>.table-responsive>.table-bordered>tbody>tr:last-child>td,.panel>.table-responsive>.table-bordered>tbody>tr:last-child>th,.panel>.table-responsive>.table-bordered>tfoot>tr:last-child>td,.panel>.table-responsive>.table-bordered>tfoot>tr:last-child>th{border-bottom:0}.panel>.table-responsive{margin-bottom:0;border:0}.panel-group{margin-bottom:20px}.panel-group .panel{margin-bottom:0;border-radius:4px}.panel-group .panel+.panel{margin-top:5px}.panel-group .panel-heading{border-bottom:0}.panel-group .panel-heading+.panel-collapse>.list-group,.panel-group .panel-heading+.panel-collapse>.panel-body{border-top:1px solid #ddd}.panel-group .panel-footer{border-top:0}.panel-group .panel-footer+.panel-collapse .panel-body{border-bottom:1px solid #ddd}.panel-default{border-color:#ddd}.panel-default>.panel-heading{color:#333;background-color:#f5f5f5;border-color:#ddd}.panel-default>.panel-heading+.panel-collapse>.panel-body{border-top-color:#ddd}.panel-default>.panel-heading .badge{color:#f5f5f5;background-color:#333}.panel-default>.panel-footer+.panel-collapse>.panel-body{border-bottom-color:#ddd}.panel-primary{border-color:#337ab7}.panel-primary>.panel-heading{color:#fff;background-color:#337ab7;border-color:#337ab7}.panel-primary>.panel-heading+.panel-collapse>.panel-body{border-top-color:#337ab7}.panel-primary>.panel-heading .badge{color:#337ab7;background-color:#fff}.panel-primary>.panel-footer+.panel-collapse>.panel-body{border-bottom-color:#337ab7}.panel-success{border-color:#d6e9c6}.panel-success>.panel-heading{color:#3c763d;background-color:#dff0d8;border-color:#d6e9c6}.panel-success>.panel-heading+.panel-collapse>.panel-body{border-top-color:#d6e9c6}.panel-success>.panel-heading .badge{color:#dff0d8;background-color:#3c763d}.panel-success>.panel-footer+.panel-collapse>.panel-body{border-bottom-color:#d6e9c6}.panel-info{border-color:#bce8f1}.panel-info>.panel-heading{color:#31708f;background-color:#d9edf7;border-color:#bce8f1}.panel-info>.panel-heading+.panel-collapse>.panel-body{border-top-color:#bce8f1}.panel-info>.panel-heading .badge{color:#d9edf7;background-color:#31708f}.panel-info>.panel-footer+.panel-collapse>.panel-body{border-bottom-color:#bce8f1}.panel-warning{border-color:#faebcc}.panel-warning>.panel-heading{color:#8a6d3b;background-color:#fcf8e3;border-color:#faebcc}.panel-warning>.panel-heading+.panel-collapse>.panel-body{border-top-color:#faebcc}.panel-warning>.panel-heading .badge{color:#fcf8e3;background-color:#8a6d3b}.panel-warning>.panel-footer+.panel-collapse>.panel-body{border-bottom-color:#faebcc}.panel-danger{border-color:#ebccd1}.panel-danger>.panel-heading{color:#a94442;background-color:#f2dede;border-color:#ebccd1}.panel-danger>.panel-heading+.panel-collapse>.panel-body{border-top-color:#ebccd1}.panel-danger>.panel-heading .badge{color:#f2dede;background-color:#a94442}.panel-danger>.panel-footer+.panel-collapse>.panel-body{border-bottom-color:#ebccd1}.embed-responsive{position:relative;display:block;height:0;padding:0;overflow:hidden}.embed-responsive .embed-responsive-item,.embed-responsive embed,.embed-responsive iframe,.embed-responsive object,.embed-responsive video{position:absolute;top:0;bottom:0;left:0;width:100%;height:100%;border:0}.embed-responsive-16by9{padding-bottom:56.25%}.embed-responsive-4by3{padding-bottom:75%}.well{min-height:20px;padding:19px;margin-bottom:20px;background-color:#f5f5f5;border:1px solid #e3e3e3;border-radius:4px;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.05);box-shadow:inset 0 1px 1px rgba(0,0,0,.05)}.well blockquote{border-color:#ddd;border-color:rgba(0,0,0,.15)}.well-lg{padding:24px;border-radius:6px}.well-sm{padding:9px;border-radius:3px}.close{float:right;font-size:21px;font-weight:700;line-height:1;color:#000;text-shadow:0 1px 0 #fff;filter:alpha(opacity=20);opacity:.2}.close:focus,.close:hover{color:#000;text-decoration:none;cursor:pointer;filter:alpha(opacity=50);opacity:.5}button.close{-webkit-appearance:none;padding:0;cursor:pointer;background:0 0;border:0}.modal-open{overflow:hidden}.modal{position:fixed;top:0;right:0;bottom:0;left:0;z-index:1050;display:none;overflow:hidden;-webkit-overflow-scrolling:touch;outline:0}.modal.fade .modal-dialog{-webkit-transition:-webkit-transform .3s ease-out;-o-transition:-o-transform .3s ease-out;transition:transform .3s ease-out;-webkit-transform:translate(0,-25%);-ms-transform:translate(0,-25%);-o-transform:translate(0,-25%);transform:translate(0,-25%)}.modal.in .modal-dialog{-webkit-transform:translate(0,0);-ms-transform:translate(0,0);-o-transform:translate(0,0);transform:translate(0,0)}.modal-open .modal{overflow-x:hidden;overflow-y:auto}.modal-dialog{position:relative;width:auto;margin:10px}.modal-content{position:relative;background-color:#fff;-webkit-background-clip:padding-box;background-clip:padding-box;border:1px solid #999;border:1px solid rgba(0,0,0,.2);border-radius:6px;outline:0;-webkit-box-shadow:0 3px 9px rgba(0,0,0,.5);box-shadow:0 3px 9px rgba(0,0,0,.5)}.modal-backdrop{position:fixed;top:0;right:0;bottom:0;left:0;z-index:1040;background-color:#000}.modal-backdrop.fade{filter:alpha(opacity=0);opacity:0}.modal-backdrop.in{filter:alpha(opacity=50);opacity:.5}.modal-header{min-height:16.43px;padding:15px;border-bottom:1px solid #e5e5e5}.modal-header .close{margin-top:-2px}.modal-title{margin:0;line-height:1.42857143}.modal-body{position:relative;padding:15px}.modal-footer{padding:15px;text-align:right;border-top:1px solid #e5e5e5}.modal-footer .btn+.btn{margin-bottom:0;margin-left:5px}.modal-footer .btn-group .btn+.btn{margin-left:-1px}.modal-footer .btn-block+.btn-block{margin-left:0}.modal-scrollbar-measure{position:absolute;top:-9999px;width:50px;height:50px;overflow:scroll}@media (min-width:768px){.modal-dialog{width:600px;margin:30px auto}.modal-content{-webkit-box-shadow:0 5px 15px rgba(0,0,0,.5);box-shadow:0 5px 15px rgba(0,0,0,.5)}.modal-sm{width:300px}}@media (min-width:992px){.modal-lg{width:900px}}.tooltip{position:absolute;z-index:1070;display:block;font-family:"Helvetica Neue",Helvetica,Arial,sans-serif;font-size:12px;font-style:normal;font-weight:400;line-height:1.42857143;text-align:left;text-align:start;text-decoration:none;text-shadow:none;text-transform:none;letter-spacing:normal;word-break:normal;word-spacing:normal;word-wrap:normal;white-space:normal;filter:alpha(opacity=0);opacity:0;line-break:auto}.tooltip.in{filter:alpha(opacity=90);opacity:.9}.tooltip.top{padding:5px 0;margin-top:-3px}.tooltip.right{padding:0 5px;margin-left:3px}.tooltip.bottom{padding:5px 0;margin-top:3px}.tooltip.left{padding:0 5px;margin-left:-3px}.tooltip-inner{max-width:200px;padding:3px 8px;color:#fff;text-align:center;background-color:#000;border-radius:4px}.tooltip-arrow{position:absolute;width:0;height:0;border-color:transparent;border-style:solid}.tooltip.top .tooltip-arrow{bottom:0;left:50%;margin-left:-5px;border-width:5px 5px 0;border-top-color:#000}.tooltip.top-left .tooltip-arrow{right:5px;bottom:0;margin-bottom:-5px;border-width:5px 5px 0;border-top-color:#000}.tooltip.top-right .tooltip-arrow{bottom:0;left:5px;margin-bottom:-5px;border-width:5px 5px 0;border-top-color:#000}.tooltip.right .tooltip-arrow{top:50%;left:0;margin-top:-5px;border-width:5px 5px 5px 0;border-right-color:#000}.tooltip.left .tooltip-arrow{top:50%;right:0;margin-top:-5px;border-width:5px 0 5px 5px;border-left-color:#000}.tooltip.bottom .tooltip-arrow{top:0;left:50%;margin-left:-5px;border-width:0 5px 5px;border-bottom-color:#000}.tooltip.bottom-left .tooltip-arrow{top:0;right:5px;margin-top:-5px;border-width:0 5px 5px;border-bottom-color:#000}.tooltip.bottom-right .tooltip-arrow{top:0;left:5px;margin-top:-5px;border-width:0 5px 5px;border-bottom-color:#000}.popover{position:absolute;top:0;left:0;z-index:1060;display:none;max-width:276px;padding:1px;font-family:"Helvetica Neue",Helvetica,Arial,sans-serif;font-size:14px;font-style:normal;font-weight:400;line-height:1.42857143;text-align:left;text-align:start;text-decoration:none;text-shadow:none;text-transform:none;letter-spacing:normal;word-break:normal;word-spacing:normal;word-wrap:normal;white-space:normal;background-color:#fff;-webkit-background-clip:padding-box;background-clip:padding-box;border:1px solid #ccc;border:1px solid rgba(0,0,0,.2);border-radius:6px;-webkit-box-shadow:0 5px 10px rgba(0,0,0,.2);box-shadow:0 5px 10px rgba(0,0,0,.2);line-break:auto}.popover.top{margin-top:-10px}.popover.right{margin-left:10px}.popover.bottom{margin-top:10px}.popover.left{margin-left:-10px}.popover-title{padding:8px 14px;margin:0;font-size:14px;background-color:#f7f7f7;border-bottom:1px solid #ebebeb;border-radius:5px 5px 0 0}.popover-content{padding:9px 14px}.popover>.arrow,.popover>.arrow:after{position:absolute;display:block;width:0;height:0;border-color:transparent;border-style:solid}.popover>.arrow{border-width:11px}.popover>.arrow:after{content:"";border-width:10px}.popover.top>.arrow{bottom:-11px;left:50%;margin-left:-11px;border-top-color:#999;border-top-color:rgba(0,0,0,.25);border-bottom-width:0}.popover.top>.arrow:after{bottom:1px;margin-left:-10px;content:" ";border-top-color:#fff;border-bottom-width:0}.popover.right>.arrow{top:50%;left:-11px;margin-top:-11px;border-right-color:#999;border-right-color:rgba(0,0,0,.25);border-left-width:0}.popover.right>.arrow:after{bottom:-10px;left:1px;content:" ";border-right-color:#fff;border-left-width:0}.popover.bottom>.arrow{top:-11px;left:50%;margin-left:-11px;border-top-width:0;border-bottom-color:#999;border-bottom-color:rgba(0,0,0,.25)}.popover.bottom>.arrow:after{top:1px;margin-left:-10px;content:" ";border-top-width:0;border-bottom-color:#fff}.popover.left>.arrow{top:50%;right:-11px;margin-top:-11px;border-right-width:0;border-left-color:#999;border-left-color:rgba(0,0,0,.25)}.popover.left>.arrow:after{right:1px;bottom:-10px;content:" ";border-right-width:0;border-left-color:#fff}.carousel{position:relative}.carousel-inner{position:relative;width:100%;overflow:hidden}.carousel-inner>.item{position:relative;display:none;-webkit-transition:.6s ease-in-out left;-o-transition:.6s ease-in-out left;transition:.6s ease-in-out left}.carousel-inner>.item>a>img,.carousel-inner>.item>img{line-height:1}@media all and (transform-3d),(-webkit-transform-3d){.carousel-inner>.item{-webkit-transition:-webkit-transform .6s ease-in-out;-o-transition:-o-transform .6s ease-in-out;transition:transform .6s ease-in-out;-webkit-backface-visibility:hidden;backface-visibility:hidden;-webkit-perspective:1000px;perspective:1000px}.carousel-inner>.item.active.right,.carousel-inner>.item.next{left:0;-webkit-transform:translate3d(100%,0,0);transform:translate3d(100%,0,0)}.carousel-inner>.item.active.left,.carousel-inner>.item.prev{left:0;-webkit-transform:translate3d(-100%,0,0);transform:translate3d(-100%,0,0)}.carousel-inner>.item.active,.carousel-inner>.item.next.left,.carousel-inner>.item.prev.right{left:0;-webkit-transform:translate3d(0,0,0);transform:translate3d(0,0,0)}}.carousel-inner>.active,.carousel-inner>.next,.carousel-inner>.prev{display:block}.carousel-inner>.active{left:0}.carousel-inner>.next,.carousel-inner>.prev{position:absolute;top:0;width:100%}.carousel-inner>.next{left:100%}.carousel-inner>.prev{left:-100%}.carousel-inner>.next.left,.carousel-inner>.prev.right{left:0}.carousel-inner>.active.left{left:-100%}.carousel-inner>.active.right{left:100%}.carousel-control{position:absolute;top:0;bottom:0;left:0;width:15%;font-size:20px;color:#fff;text-align:center;text-shadow:0 1px 2px rgba(0,0,0,.6);filter:alpha(opacity=50);opacity:.5}.carousel-control.left{background-image:-webkit-linear-gradient(left,rgba(0,0,0,.5) 0,rgba(0,0,0,.0001) 100%);background-image:-o-linear-gradient(left,rgba(0,0,0,.5) 0,rgba(0,0,0,.0001) 100%);background-image:-webkit-gradient(linear,left top,right top,from(rgba(0,0,0,.5)),to(rgba(0,0,0,.0001)));background-image:linear-gradient(to right,rgba(0,0,0,.5) 0,rgba(0,0,0,.0001) 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1);background-repeat:repeat-x}.carousel-control.right{right:0;left:auto;background-image:-webkit-linear-gradient(left,rgba(0,0,0,.0001) 0,rgba(0,0,0,.5) 100%);background-image:-o-linear-gradient(left,rgba(0,0,0,.0001) 0,rgba(0,0,0,.5) 100%);background-image:-webkit-gradient(linear,left top,right top,from(rgba(0,0,0,.0001)),to(rgba(0,0,0,.5)));background-image:linear-gradient(to right,rgba(0,0,0,.0001) 0,rgba(0,0,0,.5) 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1);background-repeat:repeat-x}.carousel-control:focus,.carousel-control:hover{color:#fff;text-decoration:none;filter:alpha(opacity=90);outline:0;opacity:.9}.carousel-control .glyphicon-chevron-left,.carousel-control .glyphicon-chevron-right,.carousel-control .icon-next,.carousel-control .icon-prev{position:absolute;top:50%;z-index:5;display:inline-block;margin-top:-10px}.carousel-control .glyphicon-chevron-left,.carousel-control .icon-prev{left:50%;margin-left:-10px}.carousel-control .glyphicon-chevron-right,.carousel-control .icon-next{right:50%;margin-right:-10px}.carousel-control .icon-next,.carousel-control .icon-prev{width:20px;height:20px;font-family:serif;line-height:1}.carousel-control .icon-prev:before{content:'\2039'}.carousel-control .icon-next:before{content:'\203a'}.carousel-indicators{position:absolute;bottom:10px;left:50%;z-index:15;width:60%;padding-left:0;margin-left:-30%;text-align:center;list-style:none}.carousel-indicators li{display:inline-block;width:10px;height:10px;margin:1px;text-indent:-999px;cursor:pointer;background-color:#000\9;background-color:rgba(0,0,0,0);border:1px solid #fff;border-radius:10px}.carousel-indicators .active{width:12px;height:12px;margin:0;background-color:#fff}.carousel-caption{position:absolute;right:15%;bottom:20px;left:15%;z-index:10;padding-top:20px;padding-bottom:20px;color:#fff;text-align:center;text-shadow:0 1px 2px rgba(0,0,0,.6)}.carousel-caption .btn{text-shadow:none}@media screen and (min-width:768px){.carousel-control .glyphicon-chevron-left,.carousel-control .glyphicon-chevron-right,.carousel-control .icon-next,.carousel-control .icon-prev{width:30px;height:30px;margin-top:-15px;font-size:30px}.carousel-control .glyphicon-chevron-left,.carousel-control .icon-prev{margin-left:-15px}.carousel-control .glyphicon-chevron-right,.carousel-control .icon-next{margin-right:-15px}.carousel-caption{right:20%;left:20%;padding-bottom:30px}.carousel-indicators{bottom:20px}}.btn-group-vertical>.btn-group:after,.btn-group-vertical>.btn-group:before,.btn-toolbar:after,.btn-toolbar:before,.clearfix:after,.clearfix:before,.container-fluid:after,.container-fluid:before,.container:after,.container:before,.dl-horizontal dd:after,.dl-horizontal dd:before,.form-horizontal .form-group:after,.form-horizontal .form-group:before,.modal-footer:after,.modal-footer:before,.nav:after,.nav:before,.navbar-collapse:after,.navbar-collapse:before,.navbar-header:after,.navbar-header:before,.navbar:after,.navbar:before,.pager:after,.pager:before,.panel-body:after,.panel-body:before,.row:after,.row:before{display:table;content:" "}.btn-group-vertical>.btn-group:after,.btn-toolbar:after,.clearfix:after,.container-fluid:after,.container:after,.dl-horizontal dd:after,.form-horizontal .form-group:after,.modal-footer:after,.nav:after,.navbar-collapse:after,.navbar-header:after,.navbar:after,.pager:after,.panel-body:after,.row:after{clear:both}.center-block{display:block;margin-right:auto;margin-left:auto}.pull-right{float:right!important}.pull-left{float:left!important}.hide{display:none!important}.show{display:block!important}.invisible{visibility:hidden}.text-hide{font:0/0 a;color:transparent;text-shadow:none;background-color:transparent;border:0}.hidden{display:none!important}.affix{position:fixed}@-ms-viewport{width:device-width}.visible-lg,.visible-md,.visible-sm,.visible-xs{display:none!important}.visible-lg-block,.visible-lg-inline,.visible-lg-inline-block,.visible-md-block,.visible-md-inline,.visible-md-inline-block,.visible-sm-block,.visible-sm-inline,.visible-sm-inline-block,.visible-xs-block,.visible-xs-inline,.visible-xs-inline-block{display:none!important}@media (max-width:767px){.visible-xs{display:block!important}table.visible-xs{display:table!important}tr.visible-xs{display:table-row!important}td.visible-xs,th.visible-xs{display:table-cell!important}}@media (max-width:767px){.visible-xs-block{display:block!important}}@media (max-width:767px){.visible-xs-inline{display:inline!important}}@media (max-width:767px){.visible-xs-inline-block{display:inline-block!important}}@media (min-width:768px) and (max-width:991px){.visible-sm{display:block!important}table.visible-sm{display:table!important}tr.visible-sm{display:table-row!important}td.visible-sm,th.visible-sm{display:table-cell!important}}@media (min-width:768px) and (max-width:991px){.visible-sm-block{display:block!important}}@media (min-width:768px) and (max-width:991px){.visible-sm-inline{display:inline!important}}@media (min-width:768px) and (max-width:991px){.visible-sm-inline-block{display:inline-block!important}}@media (min-width:992px) and (max-width:1199px){.visible-md{display:block!important}table.visible-md{display:table!important}tr.visible-md{display:table-row!important}td.visible-md,th.visible-md{display:table-cell!important}}@media (min-width:992px) and (max-width:1199px){.visible-md-block{display:block!important}}@media (min-width:992px) and (max-width:1199px){.visible-md-inline{display:inline!important}}@media (min-width:992px) and (max-width:1199px){.visible-md-inline-block{display:inline-block!important}}@media (min-width:1200px){.visible-lg{display:block!important}table.visible-lg{display:table!important}tr.visible-lg{display:table-row!important}td.visible-lg,th.visible-lg{display:table-cell!important}}@media (min-width:1200px){.visible-lg-block{display:block!important}}@media (min-width:1200px){.visible-lg-inline{display:inline!important}}@media (min-width:1200px){.visible-lg-inline-block{display:inline-block!important}}@media (max-width:767px){.hidden-xs{display:none!important}}@media (min-width:768px) and (max-width:991px){.hidden-sm{display:none!important}}@media (min-width:992px) and (max-width:1199px){.hidden-md{display:none!important}}@media (min-width:1200px){.hidden-lg{display:none!important}}.visible-print{display:none!important}@media print{.visible-print{display:block!important}table.visible-print{display:table!important}tr.visible-print{display:table-row!important}td.visible-print,th.visible-print{display:table-cell!important}}.visible-print-block{display:none!important}@media print{.visible-print-block{display:block!important}}.visible-print-inline{display:none!important}@media print{.visible-print-inline{display:inline!important}}.visible-print-inline-block{display:none!important}@media print{.visible-print-inline-block{display:inline-block!important}}@media print{.hidden-print{display:none!important}}
+</style>
+<script>/*!
+ * Bootstrap v3.3.5 (http://getbootstrap.com)
+ * Copyright 2011-2015 Twitter, Inc.
+ * Licensed under the MIT license
+ */
+if("undefined"==typeof jQuery)throw new Error("Bootstrap's JavaScript requires jQuery");+function(a){"use strict";var b=a.fn.jquery.split(" ")[0].split(".");if(b[0]<2&&b[1]<9||1==b[0]&&9==b[1]&&b[2]<1)throw new Error("Bootstrap's JavaScript requires jQuery version 1.9.1 or higher")}(jQuery),+function(a){"use strict";function b(){var a=document.createElement("bootstrap"),b={WebkitTransition:"webkitTransitionEnd",MozTransition:"transitionend",OTransition:"oTransitionEnd otransitionend",transition:"transitionend"};for(var c in b)if(void 0!==a.style[c])return{end:b[c]};return!1}a.fn.emulateTransitionEnd=function(b){var c=!1,d=this;a(this).one("bsTransitionEnd",function(){c=!0});var e=function(){c||a(d).trigger(a.support.transition.end)};return setTimeout(e,b),this},a(function(){a.support.transition=b(),a.support.transition&&(a.event.special.bsTransitionEnd={bindType:a.support.transition.end,delegateType:a.support.transition.end,handle:function(b){return a(b.target).is(this)?b.handleObj.handler.apply(this,arguments):void 0}})})}(jQuery),+function(a){"use strict";function b(b){return this.each(function(){var c=a(this),e=c.data("bs.alert");e||c.data("bs.alert",e=new d(this)),"string"==typeof b&&e[b].call(c)})}var c='[data-dismiss="alert"]',d=function(b){a(b).on("click",c,this.close)};d.VERSION="3.3.5",d.TRANSITION_DURATION=150,d.prototype.close=function(b){function c(){g.detach().trigger("closed.bs.alert").remove()}var e=a(this),f=e.attr("data-target");f||(f=e.attr("href"),f=f&&f.replace(/.*(?=#[^\s]*$)/,""));var g=a(f);b&&b.preventDefault(),g.length||(g=e.closest(".alert")),g.trigger(b=a.Event("close.bs.alert")),b.isDefaultPrevented()||(g.removeClass("in"),a.support.transition&&g.hasClass("fade")?g.one("bsTransitionEnd",c).emulateTransitionEnd(d.TRANSITION_DURATION):c())};var e=a.fn.alert;a.fn.alert=b,a.fn.alert.Constructor=d,a.fn.alert.noConflict=function(){return a.fn.alert=e,this},a(document).on("click.bs.alert.data-api",c,d.prototype.close)}(jQuery),+function(a){"use strict";function b(b){return this.each(function(){var d=a(this),e=d.data("bs.button"),f="object"==typeof b&&b;e||d.data("bs.button",e=new c(this,f)),"toggle"==b?e.toggle():b&&e.setState(b)})}var c=function(b,d){this.$element=a(b),this.options=a.extend({},c.DEFAULTS,d),this.isLoading=!1};c.VERSION="3.3.5",c.DEFAULTS={loadingText:"loading..."},c.prototype.setState=function(b){var c="disabled",d=this.$element,e=d.is("input")?"val":"html",f=d.data();b+="Text",null==f.resetText&&d.data("resetText",d[e]()),setTimeout(a.proxy(function(){d[e](null==f[b]?this.options[b]:f[b]),"loadingText"==b?(this.isLoading=!0,d.addClass(c).attr(c,c)):this.isLoading&&(this.isLoading=!1,d.removeClass(c).removeAttr(c))},this),0)},c.prototype.toggle=function(){var a=!0,b=this.$element.closest('[data-toggle="buttons"]');if(b.length){var c=this.$element.find("input");"radio"==c.prop("type")?(c.prop("checked")&&(a=!1),b.find(".active").removeClass("active"),this.$element.addClass("active")):"checkbox"==c.prop("type")&&(c.prop("checked")!==this.$element.hasClass("active")&&(a=!1),this.$element.toggleClass("active")),c.prop("checked",this.$element.hasClass("active")),a&&c.trigger("change")}else this.$element.attr("aria-pressed",!this.$element.hasClass("active")),this.$element.toggleClass("active")};var d=a.fn.button;a.fn.button=b,a.fn.button.Constructor=c,a.fn.button.noConflict=function(){return a.fn.button=d,this},a(document).on("click.bs.button.data-api",'[data-toggle^="button"]',function(c){var d=a(c.target);d.hasClass("btn")||(d=d.closest(".btn")),b.call(d,"toggle"),a(c.target).is('input[type="radio"]')||a(c.target).is('input[type="checkbox"]')||c.preventDefault()}).on("focus.bs.button.data-api blur.bs.button.data-api",'[data-toggle^="button"]',function(b){a(b.target).closest(".btn").toggleClass("focus",/^focus(in)?$/.test(b.type))})}(jQuery),+function(a){"use strict";function b(b){return this.each(function(){var d=a(this),e=d.data("bs.carousel"),f=a.extend({},c.DEFAULTS,d.data(),"object"==typeof b&&b),g="string"==typeof b?b:f.slide;e||d.data("bs.carousel",e=new c(this,f)),"number"==typeof b?e.to(b):g?e[g]():f.interval&&e.pause().cycle()})}var c=function(b,c){this.$element=a(b),this.$indicators=this.$element.find(".carousel-indicators"),this.options=c,this.paused=null,this.sliding=null,this.interval=null,this.$active=null,this.$items=null,this.options.keyboard&&this.$element.on("keydown.bs.carousel",a.proxy(this.keydown,this)),"hover"==this.options.pause&&!("ontouchstart"in document.documentElement)&&this.$element.on("mouseenter.bs.carousel",a.proxy(this.pause,this)).on("mouseleave.bs.carousel",a.proxy(this.cycle,this))};c.VERSION="3.3.5",c.TRANSITION_DURATION=600,c.DEFAULTS={interval:5e3,pause:"hover",wrap:!0,keyboard:!0},c.prototype.keydown=function(a){if(!/input|textarea/i.test(a.target.tagName)){switch(a.which){case 37:this.prev();break;case 39:this.next();break;default:return}a.preventDefault()}},c.prototype.cycle=function(b){return b||(this.paused=!1),this.interval&&clearInterval(this.interval),this.options.interval&&!this.paused&&(this.interval=setInterval(a.proxy(this.next,this),this.options.interval)),this},c.prototype.getItemIndex=function(a){return this.$items=a.parent().children(".item"),this.$items.index(a||this.$active)},c.prototype.getItemForDirection=function(a,b){var c=this.getItemIndex(b),d="prev"==a&&0===c||"next"==a&&c==this.$items.length-1;if(d&&!this.options.wrap)return b;var e="prev"==a?-1:1,f=(c+e)%this.$items.length;return this.$items.eq(f)},c.prototype.to=function(a){var b=this,c=this.getItemIndex(this.$active=this.$element.find(".item.active"));return a>this.$items.length-1||0>a?void 0:this.sliding?this.$element.one("slid.bs.carousel",function(){b.to(a)}):c==a?this.pause().cycle():this.slide(a>c?"next":"prev",this.$items.eq(a))},c.prototype.pause=function(b){return b||(this.paused=!0),this.$element.find(".next, .prev").length&&a.support.transition&&(this.$element.trigger(a.support.transition.end),this.cycle(!0)),this.interval=clearInterval(this.interval),this},c.prototype.next=function(){return this.sliding?void 0:this.slide("next")},c.prototype.prev=function(){return this.sliding?void 0:this.slide("prev")},c.prototype.slide=function(b,d){var e=this.$element.find(".item.active"),f=d||this.getItemForDirection(b,e),g=this.interval,h="next"==b?"left":"right",i=this;if(f.hasClass("active"))return this.sliding=!1;var j=f[0],k=a.Event("slide.bs.carousel",{relatedTarget:j,direction:h});if(this.$element.trigger(k),!k.isDefaultPrevented()){if(this.sliding=!0,g&&this.pause(),this.$indicators.length){this.$indicators.find(".active").removeClass("active");var l=a(this.$indicators.children()[this.getItemIndex(f)]);l&&l.addClass("active")}var m=a.Event("slid.bs.carousel",{relatedTarget:j,direction:h});return a.support.transition&&this.$element.hasClass("slide")?(f.addClass(b),f[0].offsetWidth,e.addClass(h),f.addClass(h),e.one("bsTransitionEnd",function(){f.removeClass([b,h].join(" ")).addClass("active"),e.removeClass(["active",h].join(" ")),i.sliding=!1,setTimeout(function(){i.$element.trigger(m)},0)}).emulateTransitionEnd(c.TRANSITION_DURATION)):(e.removeClass("active"),f.addClass("active"),this.sliding=!1,this.$element.trigger(m)),g&&this.cycle(),this}};var d=a.fn.carousel;a.fn.carousel=b,a.fn.carousel.Constructor=c,a.fn.carousel.noConflict=function(){return a.fn.carousel=d,this};var e=function(c){var d,e=a(this),f=a(e.attr("data-target")||(d=e.attr("href"))&&d.replace(/.*(?=#[^\s]+$)/,""));if(f.hasClass("carousel")){var g=a.extend({},f.data(),e.data()),h=e.attr("data-slide-to");h&&(g.interval=!1),b.call(f,g),h&&f.data("bs.carousel").to(h),c.preventDefault()}};a(document).on("click.bs.carousel.data-api","[data-slide]",e).on("click.bs.carousel.data-api","[data-slide-to]",e),a(window).on("load",function(){a('[data-ride="carousel"]').each(function(){var c=a(this);b.call(c,c.data())})})}(jQuery),+function(a){"use strict";function b(b){var c,d=b.attr("data-target")||(c=b.attr("href"))&&c.replace(/.*(?=#[^\s]+$)/,"");return a(d)}function c(b){return this.each(function(){var c=a(this),e=c.data("bs.collapse"),f=a.extend({},d.DEFAULTS,c.data(),"object"==typeof b&&b);!e&&f.toggle&&/show|hide/.test(b)&&(f.toggle=!1),e||c.data("bs.collapse",e=new d(this,f)),"string"==typeof b&&e[b]()})}var d=function(b,c){this.$element=a(b),this.options=a.extend({},d.DEFAULTS,c),this.$trigger=a('[data-toggle="collapse"][href="#'+b.id+'"],[data-toggle="collapse"][data-target="#'+b.id+'"]'),this.transitioning=null,this.options.parent?this.$parent=this.getParent():this.addAriaAndCollapsedClass(this.$element,this.$trigger),this.options.toggle&&this.toggle()};d.VERSION="3.3.5",d.TRANSITION_DURATION=350,d.DEFAULTS={toggle:!0},d.prototype.dimension=function(){var a=this.$element.hasClass("width");return a?"width":"height"},d.prototype.show=function(){if(!this.transitioning&&!this.$element.hasClass("in")){var b,e=this.$parent&&this.$parent.children(".panel").children(".in, .collapsing");if(!(e&&e.length&&(b=e.data("bs.collapse"),b&&b.transitioning))){var f=a.Event("show.bs.collapse");if(this.$element.trigger(f),!f.isDefaultPrevented()){e&&e.length&&(c.call(e,"hide"),b||e.data("bs.collapse",null));var g=this.dimension();this.$element.removeClass("collapse").addClass("collapsing")[g](0).attr("aria-expanded",!0),this.$trigger.removeClass("collapsed").attr("aria-expanded",!0),this.transitioning=1;var h=function(){this.$element.removeClass("collapsing").addClass("collapse in")[g](""),this.transitioning=0,this.$element.trigger("shown.bs.collapse")};if(!a.support.transition)return h.call(this);var i=a.camelCase(["scroll",g].join("-"));this.$element.one("bsTransitionEnd",a.proxy(h,this)).emulateTransitionEnd(d.TRANSITION_DURATION)[g](this.$element[0][i])}}}},d.prototype.hide=function(){if(!this.transitioning&&this.$element.hasClass("in")){var b=a.Event("hide.bs.collapse");if(this.$element.trigger(b),!b.isDefaultPrevented()){var c=this.dimension();this.$element[c](this.$element[c]())[0].offsetHeight,this.$element.addClass("collapsing").removeClass("collapse in").attr("aria-expanded",!1),this.$trigger.addClass("collapsed").attr("aria-expanded",!1),this.transitioning=1;var e=function(){this.transitioning=0,this.$element.removeClass("collapsing").addClass("collapse").trigger("hidden.bs.collapse")};return a.support.transition?void this.$element[c](0).one("bsTransitionEnd",a.proxy(e,this)).emulateTransitionEnd(d.TRANSITION_DURATION):e.call(this)}}},d.prototype.toggle=function(){this[this.$element.hasClass("in")?"hide":"show"]()},d.prototype.getParent=function(){return a(this.options.parent).find('[data-toggle="collapse"][data-parent="'+this.options.parent+'"]').each(a.proxy(function(c,d){var e=a(d);this.addAriaAndCollapsedClass(b(e),e)},this)).end()},d.prototype.addAriaAndCollapsedClass=function(a,b){var c=a.hasClass("in");a.attr("aria-expanded",c),b.toggleClass("collapsed",!c).attr("aria-expanded",c)};var e=a.fn.collapse;a.fn.collapse=c,a.fn.collapse.Constructor=d,a.fn.collapse.noConflict=function(){return a.fn.collapse=e,this},a(document).on("click.bs.collapse.data-api",'[data-toggle="collapse"]',function(d){var e=a(this);e.attr("data-target")||d.preventDefault();var f=b(e),g=f.data("bs.collapse"),h=g?"toggle":e.data();c.call(f,h)})}(jQuery),+function(a){"use strict";function b(b){var c=b.attr("data-target");c||(c=b.attr("href"),c=c&&/#[A-Za-z]/.test(c)&&c.replace(/.*(?=#[^\s]*$)/,""));var d=c&&a(c);return d&&d.length?d:b.parent()}function c(c){c&&3===c.which||(a(e).remove(),a(f).each(function(){var d=a(this),e=b(d),f={relatedTarget:this};e.hasClass("open")&&(c&&"click"==c.type&&/input|textarea/i.test(c.target.tagName)&&a.contains(e[0],c.target)||(e.trigger(c=a.Event("hide.bs.dropdown",f)),c.isDefaultPrevented()||(d.attr("aria-expanded","false"),e.removeClass("open").trigger("hidden.bs.dropdown",f))))}))}function d(b){return this.each(function(){var c=a(this),d=c.data("bs.dropdown");d||c.data("bs.dropdown",d=new g(this)),"string"==typeof b&&d[b].call(c)})}var e=".dropdown-backdrop",f='[data-toggle="dropdown"]',g=function(b){a(b).on("click.bs.dropdown",this.toggle)};g.VERSION="3.3.5",g.prototype.toggle=function(d){var e=a(this);if(!e.is(".disabled, :disabled")){var f=b(e),g=f.hasClass("open");if(c(),!g){"ontouchstart"in document.documentElement&&!f.closest(".navbar-nav").length&&a(document.createElement("div")).addClass("dropdown-backdrop").insertAfter(a(this)).on("click",c);var h={relatedTarget:this};if(f.trigger(d=a.Event("show.bs.dropdown",h)),d.isDefaultPrevented())return;e.trigger("focus").attr("aria-expanded","true"),f.toggleClass("open").trigger("shown.bs.dropdown",h)}return!1}},g.prototype.keydown=function(c){if(/(38|40|27|32)/.test(c.which)&&!/input|textarea/i.test(c.target.tagName)){var d=a(this);if(c.preventDefault(),c.stopPropagation(),!d.is(".disabled, :disabled")){var e=b(d),g=e.hasClass("open");if(!g&&27!=c.which||g&&27==c.which)return 27==c.which&&e.find(f).trigger("focus"),d.trigger("click");var h=" li:not(.disabled):visible a",i=e.find(".dropdown-menu"+h);if(i.length){var j=i.index(c.target);38==c.which&&j>0&&j--,40==c.which&&j<i.length-1&&j++,~j||(j=0),i.eq(j).trigger("focus")}}}};var h=a.fn.dropdown;a.fn.dropdown=d,a.fn.dropdown.Constructor=g,a.fn.dropdown.noConflict=function(){return a.fn.dropdown=h,this},a(document).on("click.bs.dropdown.data-api",c).on("click.bs.dropdown.data-api",".dropdown form",function(a){a.stopPropagation()}).on("click.bs.dropdown.data-api",f,g.prototype.toggle).on("keydown.bs.dropdown.data-api",f,g.prototype.keydown).on("keydown.bs.dropdown.data-api",".dropdown-menu",g.prototype.keydown)}(jQuery),+function(a){"use strict";function b(b,d){return this.each(function(){var e=a(this),f=e.data("bs.modal"),g=a.extend({},c.DEFAULTS,e.data(),"object"==typeof b&&b);f||e.data("bs.modal",f=new c(this,g)),"string"==typeof b?f[b](d):g.show&&f.show(d)})}var c=function(b,c){this.options=c,this.$body=a(document.body),this.$element=a(b),this.$dialog=this.$element.find(".modal-dialog"),this.$backdrop=null,this.isShown=null,this.originalBodyPad=null,this.scrollbarWidth=0,this.ignoreBackdropClick=!1,this.options.remote&&this.$element.find(".modal-content").load(this.options.remote,a.proxy(function(){this.$element.trigger("loaded.bs.modal")},this))};c.VERSION="3.3.5",c.TRANSITION_DURATION=300,c.BACKDROP_TRANSITION_DURATION=150,c.DEFAULTS={backdrop:!0,keyboard:!0,show:!0},c.prototype.toggle=function(a){return this.isShown?this.hide():this.show(a)},c.prototype.show=function(b){var d=this,e=a.Event("show.bs.modal",{relatedTarget:b});this.$element.trigger(e),this.isShown||e.isDefaultPrevented()||(this.isShown=!0,this.checkScrollbar(),this.setScrollbar(),this.$body.addClass("modal-open"),this.escape(),this.resize(),this.$element.on("click.dismiss.bs.modal",'[data-dismiss="modal"]',a.proxy(this.hide,this)),this.$dialog.on("mousedown.dismiss.bs.modal",function(){d.$element.one("mouseup.dismiss.bs.modal",function(b){a(b.target).is(d.$element)&&(d.ignoreBackdropClick=!0)})}),this.backdrop(function(){var e=a.support.transition&&d.$element.hasClass("fade");d.$element.parent().length||d.$element.appendTo(d.$body),d.$element.show().scrollTop(0),d.adjustDialog(),e&&d.$element[0].offsetWidth,d.$element.addClass("in"),d.enforceFocus();var f=a.Event("shown.bs.modal",{relatedTarget:b});e?d.$dialog.one("bsTransitionEnd",function(){d.$element.trigger("focus").trigger(f)}).emulateTransitionEnd(c.TRANSITION_DURATION):d.$element.trigger("focus").trigger(f)}))},c.prototype.hide=function(b){b&&b.preventDefault(),b=a.Event("hide.bs.modal"),this.$element.trigger(b),this.isShown&&!b.isDefaultPrevented()&&(this.isShown=!1,this.escape(),this.resize(),a(document).off("focusin.bs.modal"),this.$element.removeClass("in").off("click.dismiss.bs.modal").off("mouseup.dismiss.bs.modal"),this.$dialog.off("mousedown.dismiss.bs.modal"),a.support.transition&&this.$element.hasClass("fade")?this.$element.one("bsTransitionEnd",a.proxy(this.hideModal,this)).emulateTransitionEnd(c.TRANSITION_DURATION):this.hideModal())},c.prototype.enforceFocus=function(){a(document).off("focusin.bs.modal").on("focusin.bs.modal",a.proxy(function(a){this.$element[0]===a.target||this.$element.has(a.target).length||this.$element.trigger("focus")},this))},c.prototype.escape=function(){this.isShown&&this.options.keyboard?this.$element.on("keydown.dismiss.bs.modal",a.proxy(function(a){27==a.which&&this.hide()},this)):this.isShown||this.$element.off("keydown.dismiss.bs.modal")},c.prototype.resize=function(){this.isShown?a(window).on("resize.bs.modal",a.proxy(this.handleUpdate,this)):a(window).off("resize.bs.modal")},c.prototype.hideModal=function(){var a=this;this.$element.hide(),this.backdrop(function(){a.$body.removeClass("modal-open"),a.resetAdjustments(),a.resetScrollbar(),a.$element.trigger("hidden.bs.modal")})},c.prototype.removeBackdrop=function(){this.$backdrop&&this.$backdrop.remove(),this.$backdrop=null},c.prototype.backdrop=function(b){var d=this,e=this.$element.hasClass("fade")?"fade":"";if(this.isShown&&this.options.backdrop){var f=a.support.transition&&e;if(this.$backdrop=a(document.createElement("div")).addClass("modal-backdrop "+e).appendTo(this.$body),this.$element.on("click.dismiss.bs.modal",a.proxy(function(a){return this.ignoreBackdropClick?void(this.ignoreBackdropClick=!1):void(a.target===a.currentTarget&&("static"==this.options.backdrop?this.$element[0].focus():this.hide()))},this)),f&&this.$backdrop[0].offsetWidth,this.$backdrop.addClass("in"),!b)return;f?this.$backdrop.one("bsTransitionEnd",b).emulateTransitionEnd(c.BACKDROP_TRANSITION_DURATION):b()}else if(!this.isShown&&this.$backdrop){this.$backdrop.removeClass("in");var g=function(){d.removeBackdrop(),b&&b()};a.support.transition&&this.$element.hasClass("fade")?this.$backdrop.one("bsTransitionEnd",g).emulateTransitionEnd(c.BACKDROP_TRANSITION_DURATION):g()}else b&&b()},c.prototype.handleUpdate=function(){this.adjustDialog()},c.prototype.adjustDialog=function(){var a=this.$element[0].scrollHeight>document.documentElement.clientHeight;this.$element.css({paddingLeft:!this.bodyIsOverflowing&&a?this.scrollbarWidth:"",paddingRight:this.bodyIsOverflowing&&!a?this.scrollbarWidth:""})},c.prototype.resetAdjustments=function(){this.$element.css({paddingLeft:"",paddingRight:""})},c.prototype.checkScrollbar=function(){var a=window.innerWidth;if(!a){var b=document.documentElement.getBoundingClientRect();a=b.right-Math.abs(b.left)}this.bodyIsOverflowing=document.body.clientWidth<a,this.scrollbarWidth=this.measureScrollbar()},c.prototype.setScrollbar=function(){var a=parseInt(this.$body.css("padding-right")||0,10);this.originalBodyPad=document.body.style.paddingRight||"",this.bodyIsOverflowing&&this.$body.css("padding-right",a+this.scrollbarWidth)},c.prototype.resetScrollbar=function(){this.$body.css("padding-right",this.originalBodyPad)},c.prototype.measureScrollbar=function(){var a=document.createElement("div");a.className="modal-scrollbar-measure",this.$body.append(a);var b=a.offsetWidth-a.clientWidth;return this.$body[0].removeChild(a),b};var d=a.fn.modal;a.fn.modal=b,a.fn.modal.Constructor=c,a.fn.modal.noConflict=function(){return a.fn.modal=d,this},a(document).on("click.bs.modal.data-api",'[data-toggle="modal"]',function(c){var d=a(this),e=d.attr("href"),f=a(d.attr("data-target")||e&&e.replace(/.*(?=#[^\s]+$)/,"")),g=f.data("bs.modal")?"toggle":a.extend({remote:!/#/.test(e)&&e},f.data(),d.data());d.is("a")&&c.preventDefault(),f.one("show.bs.modal",function(a){a.isDefaultPrevented()||f.one("hidden.bs.modal",function(){d.is(":visible")&&d.trigger("focus")})}),b.call(f,g,this)})}(jQuery),+function(a){"use strict";function b(b){return this.each(function(){var d=a(this),e=d.data("bs.tooltip"),f="object"==typeof b&&b;(e||!/destroy|hide/.test(b))&&(e||d.data("bs.tooltip",e=new c(this,f)),"string"==typeof b&&e[b]())})}var c=function(a,b){this.type=null,this.options=null,this.enabled=null,this.timeout=null,this.hoverState=null,this.$element=null,this.inState=null,this.init("tooltip",a,b)};c.VERSION="3.3.5",c.TRANSITION_DURATION=150,c.DEFAULTS={animation:!0,placement:"top",selector:!1,template:'<div class="tooltip" role="tooltip"><div class="tooltip-arrow"></div><div class="tooltip-inner"></div></div>',trigger:"hover focus",title:"",delay:0,html:!1,container:!1,viewport:{selector:"body",padding:0}},c.prototype.init=function(b,c,d){if(this.enabled=!0,this.type=b,this.$element=a(c),this.options=this.getOptions(d),this.$viewport=this.options.viewport&&a(a.isFunction(this.options.viewport)?this.options.viewport.call(this,this.$element):this.options.viewport.selector||this.options.viewport),this.inState={click:!1,hover:!1,focus:!1},this.$element[0]instanceof document.constructor&&!this.options.selector)throw new Error("`selector` option must be specified when initializing "+this.type+" on the window.document object!");for(var e=this.options.trigger.split(" "),f=e.length;f--;){var g=e[f];if("click"==g)this.$element.on("click."+this.type,this.options.selector,a.proxy(this.toggle,this));else if("manual"!=g){var h="hover"==g?"mouseenter":"focusin",i="hover"==g?"mouseleave":"focusout";this.$element.on(h+"."+this.type,this.options.selector,a.proxy(this.enter,this)),this.$element.on(i+"."+this.type,this.options.selector,a.proxy(this.leave,this))}}this.options.selector?this._options=a.extend({},this.options,{trigger:"manual",selector:""}):this.fixTitle()},c.prototype.getDefaults=function(){return c.DEFAULTS},c.prototype.getOptions=function(b){return b=a.extend({},this.getDefaults(),this.$element.data(),b),b.delay&&"number"==typeof b.delay&&(b.delay={show:b.delay,hide:b.delay}),b},c.prototype.getDelegateOptions=function(){var b={},c=this.getDefaults();return this._options&&a.each(this._options,function(a,d){c[a]!=d&&(b[a]=d)}),b},c.prototype.enter=function(b){var c=b instanceof this.constructor?b:a(b.currentTarget).data("bs."+this.type);return c||(c=new this.constructor(b.currentTarget,this.getDelegateOptions()),a(b.currentTarget).data("bs."+this.type,c)),b instanceof a.Event&&(c.inState["focusin"==b.type?"focus":"hover"]=!0),c.tip().hasClass("in")||"in"==c.hoverState?void(c.hoverState="in"):(clearTimeout(c.timeout),c.hoverState="in",c.options.delay&&c.options.delay.show?void(c.timeout=setTimeout(function(){"in"==c.hoverState&&c.show()},c.options.delay.show)):c.show())},c.prototype.isInStateTrue=function(){for(var a in this.inState)if(this.inState[a])return!0;return!1},c.prototype.leave=function(b){var c=b instanceof this.constructor?b:a(b.currentTarget).data("bs."+this.type);return c||(c=new this.constructor(b.currentTarget,this.getDelegateOptions()),a(b.currentTarget).data("bs."+this.type,c)),b instanceof a.Event&&(c.inState["focusout"==b.type?"focus":"hover"]=!1),c.isInStateTrue()?void 0:(clearTimeout(c.timeout),c.hoverState="out",c.options.delay&&c.options.delay.hide?void(c.timeout=setTimeout(function(){"out"==c.hoverState&&c.hide()},c.options.delay.hide)):c.hide())},c.prototype.show=function(){var b=a.Event("show.bs."+this.type);if(this.hasContent()&&this.enabled){this.$element.trigger(b);var d=a.contains(this.$element[0].ownerDocument.documentElement,this.$element[0]);if(b.isDefaultPrevented()||!d)return;var e=this,f=this.tip(),g=this.getUID(this.type);this.setContent(),f.attr("id",g),this.$element.attr("aria-describedby",g),this.options.animation&&f.addClass("fade");var h="function"==typeof this.options.placement?this.options.placement.call(this,f[0],this.$element[0]):this.options.placement,i=/\s?auto?\s?/i,j=i.test(h);j&&(h=h.replace(i,"")||"top"),f.detach().css({top:0,left:0,display:"block"}).addClass(h).data("bs."+this.type,this),this.options.container?f.appendTo(this.options.container):f.insertAfter(this.$element),this.$element.trigger("inserted.bs."+this.type);var k=this.getPosition(),l=f[0].offsetWidth,m=f[0].offsetHeight;if(j){var n=h,o=this.getPosition(this.$viewport);h="bottom"==h&&k.bottom+m>o.bottom?"top":"top"==h&&k.top-m<o.top?"bottom":"right"==h&&k.right+l>o.width?"left":"left"==h&&k.left-l<o.left?"right":h,f.removeClass(n).addClass(h)}var p=this.getCalculatedOffset(h,k,l,m);this.applyPlacement(p,h);var q=function(){var a=e.hoverState;e.$element.trigger("shown.bs."+e.type),e.hoverState=null,"out"==a&&e.leave(e)};a.support.transition&&this.$tip.hasClass("fade")?f.one("bsTransitionEnd",q).emulateTransitionEnd(c.TRANSITION_DURATION):q()}},c.prototype.applyPlacement=function(b,c){var d=this.tip(),e=d[0].offsetWidth,f=d[0].offsetHeight,g=parseInt(d.css("margin-top"),10),h=parseInt(d.css("margin-left"),10);isNaN(g)&&(g=0),isNaN(h)&&(h=0),b.top+=g,b.left+=h,a.offset.setOffset(d[0],a.extend({using:function(a){d.css({top:Math.round(a.top),left:Math.round(a.left)})}},b),0),d.addClass("in");var i=d[0].offsetWidth,j=d[0].offsetHeight;"top"==c&&j!=f&&(b.top=b.top+f-j);var k=this.getViewportAdjustedDelta(c,b,i,j);k.left?b.left+=k.left:b.top+=k.top;var l=/top|bottom/.test(c),m=l?2*k.left-e+i:2*k.top-f+j,n=l?"offsetWidth":"offsetHeight";d.offset(b),this.replaceArrow(m,d[0][n],l)},c.prototype.replaceArrow=function(a,b,c){this.arrow().css(c?"left":"top",50*(1-a/b)+"%").css(c?"top":"left","")},c.prototype.setContent=function(){var a=this.tip(),b=this.getTitle();a.find(".tooltip-inner")[this.options.html?"html":"text"](b),a.removeClass("fade in top bottom left right")},c.prototype.hide=function(b){function d(){"in"!=e.hoverState&&f.detach(),e.$element.removeAttr("aria-describedby").trigger("hidden.bs."+e.type),b&&b()}var e=this,f=a(this.$tip),g=a.Event("hide.bs."+this.type);return this.$element.trigger(g),g.isDefaultPrevented()?void 0:(f.removeClass("in"),a.support.transition&&f.hasClass("fade")?f.one("bsTransitionEnd",d).emulateTransitionEnd(c.TRANSITION_DURATION):d(),this.hoverState=null,this)},c.prototype.fixTitle=function(){var a=this.$element;(a.attr("title")||"string"!=typeof a.attr("data-original-title"))&&a.attr("data-original-title",a.attr("title")||"").attr("title","")},c.prototype.hasContent=function(){return this.getTitle()},c.prototype.getPosition=function(b){b=b||this.$element;var c=b[0],d="BODY"==c.tagName,e=c.getBoundingClientRect();null==e.width&&(e=a.extend({},e,{width:e.right-e.left,height:e.bottom-e.top}));var f=d?{top:0,left:0}:b.offset(),g={scroll:d?document.documentElement.scrollTop||document.body.scrollTop:b.scrollTop()},h=d?{width:a(window).width(),height:a(window).height()}:null;return a.extend({},e,g,h,f)},c.prototype.getCalculatedOffset=function(a,b,c,d){return"bottom"==a?{top:b.top+b.height,left:b.left+b.width/2-c/2}:"top"==a?{top:b.top-d,left:b.left+b.width/2-c/2}:"left"==a?{top:b.top+b.height/2-d/2,left:b.left-c}:{top:b.top+b.height/2-d/2,left:b.left+b.width}},c.prototype.getViewportAdjustedDelta=function(a,b,c,d){var e={top:0,left:0};if(!this.$viewport)return e;var f=this.options.viewport&&this.options.viewport.padding||0,g=this.getPosition(this.$viewport);if(/right|left/.test(a)){var h=b.top-f-g.scroll,i=b.top+f-g.scroll+d;h<g.top?e.top=g.top-h:i>g.top+g.height&&(e.top=g.top+g.height-i)}else{var j=b.left-f,k=b.left+f+c;j<g.left?e.left=g.left-j:k>g.right&&(e.left=g.left+g.width-k)}return e},c.prototype.getTitle=function(){var a,b=this.$element,c=this.options;return a=b.attr("data-original-title")||("function"==typeof c.title?c.title.call(b[0]):c.title)},c.prototype.getUID=function(a){do a+=~~(1e6*Math.random());while(document.getElementById(a));return a},c.prototype.tip=function(){if(!this.$tip&&(this.$tip=a(this.options.template),1!=this.$tip.length))throw new Error(this.type+" `template` option must consist of exactly 1 top-level element!");return this.$tip},c.prototype.arrow=function(){return this.$arrow=this.$arrow||this.tip().find(".tooltip-arrow")},c.prototype.enable=function(){this.enabled=!0},c.prototype.disable=function(){this.enabled=!1},c.prototype.toggleEnabled=function(){this.enabled=!this.enabled},c.prototype.toggle=function(b){var c=this;b&&(c=a(b.currentTarget).data("bs."+this.type),c||(c=new this.constructor(b.currentTarget,this.getDelegateOptions()),a(b.currentTarget).data("bs."+this.type,c))),b?(c.inState.click=!c.inState.click,c.isInStateTrue()?c.enter(c):c.leave(c)):c.tip().hasClass("in")?c.leave(c):c.enter(c)},c.prototype.destroy=function(){var a=this;clearTimeout(this.timeout),this.hide(function(){a.$element.off("."+a.type).removeData("bs."+a.type),a.$tip&&a.$tip.detach(),a.$tip=null,a.$arrow=null,a.$viewport=null})};var d=a.fn.tooltip;a.fn.tooltip=b,a.fn.tooltip.Constructor=c,a.fn.tooltip.noConflict=function(){return a.fn.tooltip=d,this}}(jQuery),+function(a){"use strict";function b(b){return this.each(function(){var d=a(this),e=d.data("bs.popover"),f="object"==typeof b&&b;(e||!/destroy|hide/.test(b))&&(e||d.data("bs.popover",e=new c(this,f)),"string"==typeof b&&e[b]())})}var c=function(a,b){this.init("popover",a,b)};if(!a.fn.tooltip)throw new Error("Popover requires tooltip.js");c.VERSION="3.3.5",c.DEFAULTS=a.extend({},a.fn.tooltip.Constructor.DEFAULTS,{placement:"right",trigger:"click",content:"",template:'<div class="popover" role="tooltip"><div class="arrow"></div><h3 class="popover-title"></h3><div class="popover-content"></div></div>'}),c.prototype=a.extend({},a.fn.tooltip.Constructor.prototype),c.prototype.constructor=c,c.prototype.getDefaults=function(){return c.DEFAULTS},c.prototype.setContent=function(){var a=this.tip(),b=this.getTitle(),c=this.getContent();a.find(".popover-title")[this.options.html?"html":"text"](b),a.find(".popover-content").children().detach().end()[this.options.html?"string"==typeof c?"html":"append":"text"](c),a.removeClass("fade top bottom left right in"),a.find(".popover-title").html()||a.find(".popover-title").hide()},c.prototype.hasContent=function(){return this.getTitle()||this.getContent()},c.prototype.getContent=function(){var a=this.$element,b=this.options;return a.attr("data-content")||("function"==typeof b.content?b.content.call(a[0]):b.content)},c.prototype.arrow=function(){return this.$arrow=this.$arrow||this.tip().find(".arrow")};var d=a.fn.popover;a.fn.popover=b,a.fn.popover.Constructor=c,a.fn.popover.noConflict=function(){return a.fn.popover=d,this}}(jQuery),+function(a){"use strict";function b(c,d){this.$body=a(document.body),this.$scrollElement=a(a(c).is(document.body)?window:c),this.options=a.extend({},b.DEFAULTS,d),this.selector=(this.options.target||"")+" .nav li > a",this.offsets=[],this.targets=[],this.activeTarget=null,this.scrollHeight=0,this.$scrollElement.on("scroll.bs.scrollspy",a.proxy(this.process,this)),this.refresh(),this.process()}function c(c){return this.each(function(){var d=a(this),e=d.data("bs.scrollspy"),f="object"==typeof c&&c;e||d.data("bs.scrollspy",e=new b(this,f)),"string"==typeof c&&e[c]()})}b.VERSION="3.3.5",b.DEFAULTS={offset:10},b.prototype.getScrollHeight=function(){return this.$scrollElement[0].scrollHeight||Math.max(this.$body[0].scrollHeight,document.documentElement.scrollHeight)},b.prototype.refresh=function(){var b=this,c="offset",d=0;this.offsets=[],this.targets=[],this.scrollHeight=this.getScrollHeight(),a.isWindow(this.$scrollElement[0])||(c="position",d=this.$scrollElement.scrollTop()),this.$body.find(this.selector).map(function(){var b=a(this),e=b.data("target")||b.attr("href"),f=/^#./.test(e)&&a(e);return f&&f.length&&f.is(":visible")&&[[f[c]().top+d,e]]||null}).sort(function(a,b){return a[0]-b[0]}).each(function(){b.offsets.push(this[0]),b.targets.push(this[1])})},b.prototype.process=function(){var a,b=this.$scrollElement.scrollTop()+this.options.offset,c=this.getScrollHeight(),d=this.options.offset+c-this.$scrollElement.height(),e=this.offsets,f=this.targets,g=this.activeTarget;if(this.scrollHeight!=c&&this.refresh(),b>=d)return g!=(a=f[f.length-1])&&this.activate(a);if(g&&b<e[0])return this.activeTarget=null,this.clear();for(a=e.length;a--;)g!=f[a]&&b>=e[a]&&(void 0===e[a+1]||b<e[a+1])&&this.activate(f[a])},b.prototype.activate=function(b){this.activeTarget=b,this.clear();var c=this.selector+'[data-target="'+b+'"],'+this.selector+'[href="'+b+'"]',d=a(c).parents("li").addClass("active");d.parent(".dropdown-menu").length&&(d=d.closest("li.dropdown").addClass("active")),
+d.trigger("activate.bs.scrollspy")},b.prototype.clear=function(){a(this.selector).parentsUntil(this.options.target,".active").removeClass("active")};var d=a.fn.scrollspy;a.fn.scrollspy=c,a.fn.scrollspy.Constructor=b,a.fn.scrollspy.noConflict=function(){return a.fn.scrollspy=d,this},a(window).on("load.bs.scrollspy.data-api",function(){a('[data-spy="scroll"]').each(function(){var b=a(this);c.call(b,b.data())})})}(jQuery),+function(a){"use strict";function b(b){return this.each(function(){var d=a(this),e=d.data("bs.tab");e||d.data("bs.tab",e=new c(this)),"string"==typeof b&&e[b]()})}var c=function(b){this.element=a(b)};c.VERSION="3.3.5",c.TRANSITION_DURATION=150,c.prototype.show=function(){var b=this.element,c=b.closest("ul:not(.dropdown-menu)"),d=b.data("target");if(d||(d=b.attr("href"),d=d&&d.replace(/.*(?=#[^\s]*$)/,"")),!b.parent("li").hasClass("active")){var e=c.find(".active:last a"),f=a.Event("hide.bs.tab",{relatedTarget:b[0]}),g=a.Event("show.bs.tab",{relatedTarget:e[0]});if(e.trigger(f),b.trigger(g),!g.isDefaultPrevented()&&!f.isDefaultPrevented()){var h=a(d);this.activate(b.closest("li"),c),this.activate(h,h.parent(),function(){e.trigger({type:"hidden.bs.tab",relatedTarget:b[0]}),b.trigger({type:"shown.bs.tab",relatedTarget:e[0]})})}}},c.prototype.activate=function(b,d,e){function f(){g.removeClass("active").find("> .dropdown-menu > .active").removeClass("active").end().find('[data-toggle="tab"]').attr("aria-expanded",!1),b.addClass("active").find('[data-toggle="tab"]').attr("aria-expanded",!0),h?(b[0].offsetWidth,b.addClass("in")):b.removeClass("fade"),b.parent(".dropdown-menu").length&&b.closest("li.dropdown").addClass("active").end().find('[data-toggle="tab"]').attr("aria-expanded",!0),e&&e()}var g=d.find("> .active"),h=e&&a.support.transition&&(g.length&&g.hasClass("fade")||!!d.find("> .fade").length);g.length&&h?g.one("bsTransitionEnd",f).emulateTransitionEnd(c.TRANSITION_DURATION):f(),g.removeClass("in")};var d=a.fn.tab;a.fn.tab=b,a.fn.tab.Constructor=c,a.fn.tab.noConflict=function(){return a.fn.tab=d,this};var e=function(c){c.preventDefault(),b.call(a(this),"show")};a(document).on("click.bs.tab.data-api",'[data-toggle="tab"]',e).on("click.bs.tab.data-api",'[data-toggle="pill"]',e)}(jQuery),+function(a){"use strict";function b(b){return this.each(function(){var d=a(this),e=d.data("bs.affix"),f="object"==typeof b&&b;e||d.data("bs.affix",e=new c(this,f)),"string"==typeof b&&e[b]()})}var c=function(b,d){this.options=a.extend({},c.DEFAULTS,d),this.$target=a(this.options.target).on("scroll.bs.affix.data-api",a.proxy(this.checkPosition,this)).on("click.bs.affix.data-api",a.proxy(this.checkPositionWithEventLoop,this)),this.$element=a(b),this.affixed=null,this.unpin=null,this.pinnedOffset=null,this.checkPosition()};c.VERSION="3.3.5",c.RESET="affix affix-top affix-bottom",c.DEFAULTS={offset:0,target:window},c.prototype.getState=function(a,b,c,d){var e=this.$target.scrollTop(),f=this.$element.offset(),g=this.$target.height();if(null!=c&&"top"==this.affixed)return c>e?"top":!1;if("bottom"==this.affixed)return null!=c?e+this.unpin<=f.top?!1:"bottom":a-d>=e+g?!1:"bottom";var h=null==this.affixed,i=h?e:f.top,j=h?g:b;return null!=c&&c>=e?"top":null!=d&&i+j>=a-d?"bottom":!1},c.prototype.getPinnedOffset=function(){if(this.pinnedOffset)return this.pinnedOffset;this.$element.removeClass(c.RESET).addClass("affix");var a=this.$target.scrollTop(),b=this.$element.offset();return this.pinnedOffset=b.top-a},c.prototype.checkPositionWithEventLoop=function(){setTimeout(a.proxy(this.checkPosition,this),1)},c.prototype.checkPosition=function(){if(this.$element.is(":visible")){var b=this.$element.height(),d=this.options.offset,e=d.top,f=d.bottom,g=Math.max(a(document).height(),a(document.body).height());"object"!=typeof d&&(f=e=d),"function"==typeof e&&(e=d.top(this.$element)),"function"==typeof f&&(f=d.bottom(this.$element));var h=this.getState(g,b,e,f);if(this.affixed!=h){null!=this.unpin&&this.$element.css("top","");var i="affix"+(h?"-"+h:""),j=a.Event(i+".bs.affix");if(this.$element.trigger(j),j.isDefaultPrevented())return;this.affixed=h,this.unpin="bottom"==h?this.getPinnedOffset():null,this.$element.removeClass(c.RESET).addClass(i).trigger(i.replace("affix","affixed")+".bs.affix")}"bottom"==h&&this.$element.offset({top:g-b-f})}};var d=a.fn.affix;a.fn.affix=b,a.fn.affix.Constructor=c,a.fn.affix.noConflict=function(){return a.fn.affix=d,this},a(window).on("load",function(){a('[data-spy="affix"]').each(function(){var c=a(this),d=c.data();d.offset=d.offset||{},null!=d.offsetBottom&&(d.offset.bottom=d.offsetBottom),null!=d.offsetTop&&(d.offset.top=d.offsetTop),b.call(c,d)})})}(jQuery);</script>
+<script>/**
+* @preserve HTML5 Shiv 3.7.2 | @afarkas @jdalton @jon_neal @rem | MIT/GPL2 Licensed
+*/
+// Only run this code in IE 8
+if (!!window.navigator.userAgent.match("MSIE 8")) {
+!function(a,b){function c(a,b){var c=a.createElement("p"),d=a.getElementsByTagName("head")[0]||a.documentElement;return c.innerHTML="x<style>"+b+"</style>",d.insertBefore(c.lastChild,d.firstChild)}function d(){var a=t.elements;return"string"==typeof a?a.split(" "):a}function e(a,b){var c=t.elements;"string"!=typeof c&&(c=c.join(" ")),"string"!=typeof a&&(a=a.join(" ")),t.elements=c+" "+a,j(b)}function f(a){var b=s[a[q]];return b||(b={},r++,a[q]=r,s[r]=b),b}function g(a,c,d){if(c||(c=b),l)return c.createElement(a);d||(d=f(c));var e;return e=d.cache[a]?d.cache[a].cloneNode():p.test(a)?(d.cache[a]=d.createElem(a)).cloneNode():d.createElem(a),!e.canHaveChildren||o.test(a)||e.tagUrn?e:d.frag.appendChild(e)}function h(a,c){if(a||(a=b),l)return a.createDocumentFragment();c=c||f(a);for(var e=c.frag.cloneNode(),g=0,h=d(),i=h.length;i>g;g++)e.createElement(h[g]);return e}function i(a,b){b.cache||(b.cache={},b.createElem=a.createElement,b.createFrag=a.createDocumentFragment,b.frag=b.createFrag()),a.createElement=function(c){return t.shivMethods?g(c,a,b):b.createElem(c)},a.createDocumentFragment=Function("h,f","return function(){var n=f.cloneNode(),c=n.createElement;h.shivMethods&&("+d().join().replace(/[\w\-:]+/g,function(a){return b.createElem(a),b.frag.createElement(a),'c("'+a+'")'})+");return n}")(t,b.frag)}function j(a){a||(a=b);var d=f(a);return!t.shivCSS||k||d.hasCSS||(d.hasCSS=!!c(a,"article,aside,dialog,figcaption,figure,footer,header,hgroup,main,nav,section{display:block}mark{background:#FF0;color:#000}template{display:none}")),l||i(a,d),a}var k,l,m="3.7.2",n=a.html5||{},o=/^<|^(?:button|map|select|textarea|object|iframe|option|optgroup)$/i,p=/^(?:a|b|code|div|fieldset|h1|h2|h3|h4|h5|h6|i|label|li|ol|p|q|span|strong|style|table|tbody|td|th|tr|ul)$/i,q="_html5shiv",r=0,s={};!function(){try{var a=b.createElement("a");a.innerHTML="<xyz></xyz>",k="hidden"in a,l=1==a.childNodes.length||function(){b.createElement("a");var a=b.createDocumentFragment();return"undefined"==typeof a.cloneNode||"undefined"==typeof a.createDocumentFragment||"undefined"==typeof a.createElement}()}catch(c){k=!0,l=!0}}();var t={elements:n.elements||"abbr article aside audio bdi canvas data datalist details dialog figcaption figure footer header hgroup main mark meter nav output picture progress section summary template time video",version:m,shivCSS:n.shivCSS!==!1,supportsUnknownElements:l,shivMethods:n.shivMethods!==!1,type:"default",shivDocument:j,createElement:g,createDocumentFragment:h,addElements:e};a.html5=t,j(b)}(this,document);
+};
+</script>
+<script>/*! Respond.js v1.4.2: min/max-width media query polyfill * Copyright 2013 Scott Jehl
+ * Licensed under https://github.com/scottjehl/Respond/blob/master/LICENSE-MIT
+ *  */
+
+// Only run this code in IE 8
+if (!!window.navigator.userAgent.match("MSIE 8")) {
+!function(a){"use strict";a.matchMedia=a.matchMedia||function(a){var b,c=a.documentElement,d=c.firstElementChild||c.firstChild,e=a.createElement("body"),f=a.createElement("div");return f.id="mq-test-1",f.style.cssText="position:absolute;top:-100em",e.style.background="none",e.appendChild(f),function(a){return f.innerHTML='&shy;<style media="'+a+'"> #mq-test-1 { width: 42px; }</style>',c.insertBefore(e,d),b=42===f.offsetWidth,c.removeChild(e),{matches:b,media:a}}}(a.document)}(this),function(a){"use strict";function b(){u(!0)}var c={};a.respond=c,c.update=function(){};var d=[],e=function(){var b=!1;try{b=new a.XMLHttpRequest}catch(c){b=new a.ActiveXObject("Microsoft.XMLHTTP")}return function(){return b}}(),f=function(a,b){var c=e();c&&(c.open("GET",a,!0),c.onreadystatechange=function(){4!==c.readyState||200!==c.status&&304!==c.status||b(c.responseText)},4!==c.readyState&&c.send(null))};if(c.ajax=f,c.queue=d,c.regex={media:/@media[^\{]+\{([^\{\}]*\{[^\}\{]*\})+/gi,keyframes:/@(?:\-(?:o|moz|webkit)\-)?keyframes[^\{]+\{(?:[^\{\}]*\{[^\}\{]*\})+[^\}]*\}/gi,urls:/(url\()['"]?([^\/\)'"][^:\)'"]+)['"]?(\))/g,findStyles:/@media *([^\{]+)\{([\S\s]+?)$/,only:/(only\s+)?([a-zA-Z]+)\s?/,minw:/\([\s]*min\-width\s*:[\s]*([\s]*[0-9\.]+)(px|em)[\s]*\)/,maxw:/\([\s]*max\-width\s*:[\s]*([\s]*[0-9\.]+)(px|em)[\s]*\)/},c.mediaQueriesSupported=a.matchMedia&&null!==a.matchMedia("only all")&&a.matchMedia("only all").matches,!c.mediaQueriesSupported){var g,h,i,j=a.document,k=j.documentElement,l=[],m=[],n=[],o={},p=30,q=j.getElementsByTagName("head")[0]||k,r=j.getElementsByTagName("base")[0],s=q.getElementsByTagName("link"),t=function(){var a,b=j.createElement("div"),c=j.body,d=k.style.fontSize,e=c&&c.style.fontSize,f=!1;return b.style.cssText="position:absolute;font-size:1em;width:1em",c||(c=f=j.createElement("body"),c.style.background="none"),k.style.fontSize="100%",c.style.fontSize="100%",c.appendChild(b),f&&k.insertBefore(c,k.firstChild),a=b.offsetWidth,f?k.removeChild(c):c.removeChild(b),k.style.fontSize=d,e&&(c.style.fontSize=e),a=i=parseFloat(a)},u=function(b){var c="clientWidth",d=k[c],e="CSS1Compat"===j.compatMode&&d||j.body[c]||d,f={},o=s[s.length-1],r=(new Date).getTime();if(b&&g&&p>r-g)return a.clearTimeout(h),h=a.setTimeout(u,p),void 0;g=r;for(var v in l)if(l.hasOwnProperty(v)){var w=l[v],x=w.minw,y=w.maxw,z=null===x,A=null===y,B="em";x&&(x=parseFloat(x)*(x.indexOf(B)>-1?i||t():1)),y&&(y=parseFloat(y)*(y.indexOf(B)>-1?i||t():1)),w.hasquery&&(z&&A||!(z||e>=x)||!(A||y>=e))||(f[w.media]||(f[w.media]=[]),f[w.media].push(m[w.rules]))}for(var C in n)n.hasOwnProperty(C)&&n[C]&&n[C].parentNode===q&&q.removeChild(n[C]);n.length=0;for(var D in f)if(f.hasOwnProperty(D)){var E=j.createElement("style"),F=f[D].join("\n");E.type="text/css",E.media=D,q.insertBefore(E,o.nextSibling),E.styleSheet?E.styleSheet.cssText=F:E.appendChild(j.createTextNode(F)),n.push(E)}},v=function(a,b,d){var e=a.replace(c.regex.keyframes,"").match(c.regex.media),f=e&&e.length||0;b=b.substring(0,b.lastIndexOf("/"));var g=function(a){return a.replace(c.regex.urls,"$1"+b+"$2$3")},h=!f&&d;b.length&&(b+="/"),h&&(f=1);for(var i=0;f>i;i++){var j,k,n,o;h?(j=d,m.push(g(a))):(j=e[i].match(c.regex.findStyles)&&RegExp.$1,m.push(RegExp.$2&&g(RegExp.$2))),n=j.split(","),o=n.length;for(var p=0;o>p;p++)k=n[p],l.push({media:k.split("(")[0].match(c.regex.only)&&RegExp.$2||"all",rules:m.length-1,hasquery:k.indexOf("(")>-1,minw:k.match(c.regex.minw)&&parseFloat(RegExp.$1)+(RegExp.$2||""),maxw:k.match(c.regex.maxw)&&parseFloat(RegExp.$1)+(RegExp.$2||"")})}u()},w=function(){if(d.length){var b=d.shift();f(b.href,function(c){v(c,b.href,b.media),o[b.href]=!0,a.setTimeout(function(){w()},0)})}},x=function(){for(var b=0;b<s.length;b++){var c=s[b],e=c.href,f=c.media,g=c.rel&&"stylesheet"===c.rel.toLowerCase();e&&g&&!o[e]&&(c.styleSheet&&c.styleSheet.rawCssText?(v(c.styleSheet.rawCssText,e,f),o[e]=!0):(!/^([a-zA-Z:]*\/\/)/.test(e)&&!r||e.replace(RegExp.$1,"").split("/")[0]===a.location.host)&&("//"===e.substring(0,2)&&(e=a.location.protocol+e),d.push({href:e,media:f})))}w()};x(),c.update=x,c.getEmValue=t,a.addEventListener?a.addEventListener("resize",b,!1):a.attachEvent&&a.attachEvent("onresize",b)}}(this);
+};
+</script>
+<style>h1 {font-size: 34px;}
+       h1.title {font-size: 38px;}
+       h2 {font-size: 30px;}
+       h3 {font-size: 24px;}
+       h4 {font-size: 18px;}
+       h5 {font-size: 16px;}
+       h6 {font-size: 12px;}
+       code {color: inherit; background-color: rgba(0, 0, 0, 0.04);}
+       pre:not([class]) { background-color: white }</style>
+<script>
+
+/**
+ * jQuery Plugin: Sticky Tabs
+ *
+ * @author Aidan Lister <aidan@php.net>
+ * adapted by Ruben Arslan to activate parent tabs too
+ * http://www.aidanlister.com/2014/03/persisting-the-tab-state-in-bootstrap/
+ */
+(function($) {
+  "use strict";
+  $.fn.rmarkdownStickyTabs = function() {
+    var context = this;
+    // Show the tab corresponding with the hash in the URL, or the first tab
+    var showStuffFromHash = function() {
+      var hash = window.location.hash;
+      var selector = hash ? 'a[href="' + hash + '"]' : 'li.active > a';
+      var $selector = $(selector, context);
+      if($selector.data('toggle') === "tab") {
+        $selector.tab('show');
+        // walk up the ancestors of this element, show any hidden tabs
+        $selector.parents('.section.tabset').each(function(i, elm) {
+          var link = $('a[href="#' + $(elm).attr('id') + '"]');
+          if(link.data('toggle') === "tab") {
+            link.tab("show");
+          }
+        });
+      }
+    };
+
+
+    // Set the correct tab when the page loads
+    showStuffFromHash(context);
+
+    // Set the correct tab when a user uses their back/forward button
+    $(window).on('hashchange', function() {
+      showStuffFromHash(context);
+    });
+
+    // Change the URL when tabs are clicked
+    $('a', context).on('click', function(e) {
+      history.pushState(null, null, this.href);
+      showStuffFromHash(context);
+    });
+
+    return this;
+  };
+}(jQuery));
+
+window.buildTabsets = function(tocID) {
+
+  // build a tabset from a section div with the .tabset class
+  function buildTabset(tabset) {
+
+    // check for fade and pills options
+    var fade = tabset.hasClass("tabset-fade");
+    var pills = tabset.hasClass("tabset-pills");
+    var navClass = pills ? "nav-pills" : "nav-tabs";
+
+    // determine the heading level of the tabset and tabs
+    var match = tabset.attr('class').match(/level(\d) /);
+    if (match === null)
+      return;
+    var tabsetLevel = Number(match[1]);
+    var tabLevel = tabsetLevel + 1;
+
+    // find all subheadings immediately below
+    var tabs = tabset.find("div.section.level" + tabLevel);
+    if (!tabs.length)
+      return;
+
+    // create tablist and tab-content elements
+    var tabList = $('<ul class="nav ' + navClass + '" role="tablist"></ul>');
+    $(tabs[0]).before(tabList);
+    var tabContent = $('<div class="tab-content"></div>');
+    $(tabs[0]).before(tabContent);
+
+    // build the tabset
+    var activeTab = 0;
+    tabs.each(function(i) {
+
+      // get the tab div
+      var tab = $(tabs[i]);
+
+      // get the id then sanitize it for use with bootstrap tabs
+      var id = tab.attr('id');
+
+      // see if this is marked as the active tab
+      if (tab.hasClass('active'))
+        activeTab = i;
+
+      // remove any table of contents entries associated with
+      // this ID (since we'll be removing the heading element)
+      $("div#" + tocID + " li a[href='#" + id + "']").parent().remove();
+
+      // sanitize the id for use with bootstrap tabs
+      id = id.replace(/[.\/?&!#<>]/g, '').replace(/\s/g, '_');
+      tab.attr('id', id);
+
+      // get the heading element within it, grab it's text, then remove it
+      var heading = tab.find('h' + tabLevel + ':first');
+      var headingText = heading.html();
+      heading.remove();
+
+      // build and append the tab list item
+      var a = $('<a role="tab" data-toggle="tab">' + headingText + '</a>');
+      a.attr('href', '#' + id);
+      a.attr('aria-controls', id);
+      var li = $('<li role="presentation"></li>');
+      li.append(a);
+      tabList.append(li);
+
+      // set it's attributes
+      tab.attr('role', 'tabpanel');
+      tab.addClass('tab-pane');
+      tab.addClass('tabbed-pane');
+      if (fade)
+        tab.addClass('fade');
+
+      // move it into the tab content div
+      tab.detach().appendTo(tabContent);
+    });
+
+    // set active tab
+    $(tabList.children('li')[activeTab]).addClass('active');
+    var active = $(tabContent.children('div.section')[activeTab]);
+    active.addClass('active');
+    if (fade)
+      active.addClass('in');
+
+    if (tabset.hasClass("tabset-sticky"))
+      tabset.rmarkdownStickyTabs();
+  }
+
+  // convert section divs with the .tabset class to tabsets
+  var tabsets = $("div.section.tabset");
+  tabsets.each(function(i) {
+    buildTabset($(tabsets[i]));
+  });
+};
+
+</script>
+<style type="text/css">.hljs-literal {
+color: #990073;
+}
+.hljs-number {
+color: #099;
+}
+.hljs-comment {
+color: #998;
+font-style: italic;
+}
+.hljs-keyword {
+color: #900;
+font-weight: bold;
+}
+.hljs-string {
+color: #d14;
+}
+</style>
+<script src="data:application/javascript;base64,/*! highlight.js v9.12.0 | BSD3 License | git.io/hljslicense */
!function(e){var n="object"==typeof window&&window||"object"==typeof self&&self;"undefined"!=typeof exports?e(exports):n&&(n.hljs=e({}),"function"==typeof define&&define.amd&&define([],function(){return n.hljs}))}(function(e){function n(e){return e.replace(/&/g,"&amp;").replace(/</g,"&lt;").replace(/>/g,"&gt;")}function t(e){return e.nodeName.toLowerCase()}function r(e,n){var t=e&&e.exec(n);return t&&0===t.index}function a(e){return k.test(e)}function i(e){var n,t,r,i,o=e.className+" ";if(o+=e.parentNode?e.parentNode.className:"",t=B.exec(o))return w(t[1])?t[1]:"no-highlight";for(o=o.split(/\s+/),n=0,r=o.length;r>n;n++)if(i=o[n],a(i)||w(i))return i}function o(e){var n,t={},r=Array.prototype.slice.call(arguments,1);for(n in e)t[n]=e[n];return r.forEach(function(e){for(n in e)t[n]=e[n]}),t}function u(e){var n=[];return function r(e,a){for(var i=e.firstChild;i;i=i.nextSibling)3===i.nodeType?a+=i.nodeValue.length:1===i.nodeType&&(n.push({event:"start",offset:a,node:i}),a=r(i,a),t(i).match(/br|hr|img|input/)||n.push({event:"stop",offset:a,node:i}));return a}(e,0),n}function c(e,r,a){function i(){return e.length&&r.length?e[0].offset!==r[0].offset?e[0].offset<r[0].offset?e:r:"start"===r[0].event?e:r:e.length?e:r}function o(e){function r(e){return" "+e.nodeName+'="'+n(e.value).replace('"',"&quot;")+'"'}s+="<"+t(e)+E.map.call(e.attributes,r).join("")+">"}function u(e){s+="</"+t(e)+">"}function c(e){("start"===e.event?o:u)(e.node)}for(var l=0,s="",f=[];e.length||r.length;){var g=i();if(s+=n(a.substring(l,g[0].offset)),l=g[0].offset,g===e){f.reverse().forEach(u);do c(g.splice(0,1)[0]),g=i();while(g===e&&g.length&&g[0].offset===l);f.reverse().forEach(o)}else"start"===g[0].event?f.push(g[0].node):f.pop(),c(g.splice(0,1)[0])}return s+n(a.substr(l))}function l(e){return e.v&&!e.cached_variants&&(e.cached_variants=e.v.map(function(n){return o(e,{v:null},n)})),e.cached_variants||e.eW&&[o(e)]||[e]}function s(e){function n(e){return e&&e.source||e}function t(t,r){return new RegExp(n(t),"m"+(e.cI?"i":"")+(r?"g":""))}function r(a,i){if(!a.compiled){if(a.compiled=!0,a.k=a.k||a.bK,a.k){var o={},u=function(n,t){e.cI&&(t=t.toLowerCase()),t.split(" ").forEach(function(e){var t=e.split("|");o[t[0]]=[n,t[1]?Number(t[1]):1]})};"string"==typeof a.k?u("keyword",a.k):x(a.k).forEach(function(e){u(e,a.k[e])}),a.k=o}a.lR=t(a.l||/\w+/,!0),i&&(a.bK&&(a.b="\\b("+a.bK.split(" ").join("|")+")\\b"),a.b||(a.b=/\B|\b/),a.bR=t(a.b),a.e||a.eW||(a.e=/\B|\b/),a.e&&(a.eR=t(a.e)),a.tE=n(a.e)||"",a.eW&&i.tE&&(a.tE+=(a.e?"|":"")+i.tE)),a.i&&(a.iR=t(a.i)),null==a.r&&(a.r=1),a.c||(a.c=[]),a.c=Array.prototype.concat.apply([],a.c.map(function(e){return l("self"===e?a:e)})),a.c.forEach(function(e){r(e,a)}),a.starts&&r(a.starts,i);var c=a.c.map(function(e){return e.bK?"\\.?("+e.b+")\\.?":e.b}).concat([a.tE,a.i]).map(n).filter(Boolean);a.t=c.length?t(c.join("|"),!0):{exec:function(){return null}}}}r(e)}function f(e,t,a,i){function o(e,n){var t,a;for(t=0,a=n.c.length;a>t;t++)if(r(n.c[t].bR,e))return n.c[t]}function u(e,n){if(r(e.eR,n)){for(;e.endsParent&&e.parent;)e=e.parent;return e}return e.eW?u(e.parent,n):void 0}function c(e,n){return!a&&r(n.iR,e)}function l(e,n){var t=N.cI?n[0].toLowerCase():n[0];return e.k.hasOwnProperty(t)&&e.k[t]}function p(e,n,t,r){var a=r?"":I.classPrefix,i='<span class="'+a,o=t?"":C;return i+=e+'">',i+n+o}function h(){var e,t,r,a;if(!E.k)return n(k);for(a="",t=0,E.lR.lastIndex=0,r=E.lR.exec(k);r;)a+=n(k.substring(t,r.index)),e=l(E,r),e?(B+=e[1],a+=p(e[0],n(r[0]))):a+=n(r[0]),t=E.lR.lastIndex,r=E.lR.exec(k);return a+n(k.substr(t))}function d(){var e="string"==typeof E.sL;if(e&&!y[E.sL])return n(k);var t=e?f(E.sL,k,!0,x[E.sL]):g(k,E.sL.length?E.sL:void 0);return E.r>0&&(B+=t.r),e&&(x[E.sL]=t.top),p(t.language,t.value,!1,!0)}function b(){L+=null!=E.sL?d():h(),k=""}function v(e){L+=e.cN?p(e.cN,"",!0):"",E=Object.create(e,{parent:{value:E}})}function m(e,n){if(k+=e,null==n)return b(),0;var t=o(n,E);if(t)return t.skip?k+=n:(t.eB&&(k+=n),b(),t.rB||t.eB||(k=n)),v(t,n),t.rB?0:n.length;var r=u(E,n);if(r){var a=E;a.skip?k+=n:(a.rE||a.eE||(k+=n),b(),a.eE&&(k=n));do E.cN&&(L+=C),E.skip||(B+=E.r),E=E.parent;while(E!==r.parent);return r.starts&&v(r.starts,""),a.rE?0:n.length}if(c(n,E))throw new Error('Illegal lexeme "'+n+'" for mode "'+(E.cN||"<unnamed>")+'"');return k+=n,n.length||1}var N=w(e);if(!N)throw new Error('Unknown language: "'+e+'"');s(N);var R,E=i||N,x={},L="";for(R=E;R!==N;R=R.parent)R.cN&&(L=p(R.cN,"",!0)+L);var k="",B=0;try{for(var M,j,O=0;;){if(E.t.lastIndex=O,M=E.t.exec(t),!M)break;j=m(t.substring(O,M.index),M[0]),O=M.index+j}for(m(t.substr(O)),R=E;R.parent;R=R.parent)R.cN&&(L+=C);return{r:B,value:L,language:e,top:E}}catch(T){if(T.message&&-1!==T.message.indexOf("Illegal"))return{r:0,value:n(t)};throw T}}function g(e,t){t=t||I.languages||x(y);var r={r:0,value:n(e)},a=r;return t.filter(w).forEach(function(n){var t=f(n,e,!1);t.language=n,t.r>a.r&&(a=t),t.r>r.r&&(a=r,r=t)}),a.language&&(r.second_best=a),r}function p(e){return I.tabReplace||I.useBR?e.replace(M,function(e,n){return I.useBR&&"\n"===e?"<br>":I.tabReplace?n.replace(/\t/g,I.tabReplace):""}):e}function h(e,n,t){var r=n?L[n]:t,a=[e.trim()];return e.match(/\bhljs\b/)||a.push("hljs"),-1===e.indexOf(r)&&a.push(r),a.join(" ").trim()}function d(e){var n,t,r,o,l,s=i(e);a(s)||(I.useBR?(n=document.createElementNS("http://www.w3.org/1999/xhtml","div"),n.innerHTML=e.innerHTML.replace(/\n/g,"").replace(/<br[ \/]*>/g,"\n")):n=e,l=n.textContent,r=s?f(s,l,!0):g(l),t=u(n),t.length&&(o=document.createElementNS("http://www.w3.org/1999/xhtml","div"),o.innerHTML=r.value,r.value=c(t,u(o),l)),r.value=p(r.value),e.innerHTML=r.value,e.className=h(e.className,s,r.language),e.result={language:r.language,re:r.r},r.second_best&&(e.second_best={language:r.second_best.language,re:r.second_best.r}))}function b(e){I=o(I,e)}function v(){if(!v.called){v.called=!0;var e=document.querySelectorAll("pre code");E.forEach.call(e,d)}}function m(){addEventListener("DOMContentLoaded",v,!1),addEventListener("load",v,!1)}function N(n,t){var r=y[n]=t(e);r.aliases&&r.aliases.forEach(function(e){L[e]=n})}function R(){return x(y)}function w(e){return e=(e||"").toLowerCase(),y[e]||y[L[e]]}var E=[],x=Object.keys,y={},L={},k=/^(no-?highlight|plain|text)$/i,B=/\blang(?:uage)?-([\w-]+)\b/i,M=/((^(<[^>]+>|\t|)+|(?:\n)))/gm,C="</span>",I={classPrefix:"hljs-",tabReplace:null,useBR:!1,languages:void 0};return e.highlight=f,e.highlightAuto=g,e.fixMarkup=p,e.highlightBlock=d,e.configure=b,e.initHighlighting=v,e.initHighlightingOnLoad=m,e.registerLanguage=N,e.listLanguages=R,e.getLanguage=w,e.inherit=o,e.IR="[a-zA-Z]\\w*",e.UIR="[a-zA-Z_]\\w*",e.NR="\\b\\d+(\\.\\d+)?",e.CNR="(-?)(\\b0[xX][a-fA-F0-9]+|(\\b\\d+(\\.\\d*)?|\\.\\d+)([eE][-+]?\\d+)?)",e.BNR="\\b(0b[01]+)",e.RSR="!|!=|!==|%|%=|&|&&|&=|\\*|\\*=|\\+|\\+=|,|-|-=|/=|/|:|;|<<|<<=|<=|<|===|==|=|>>>=|>>=|>=|>>>|>>|>|\\?|\\[|\\{|\\(|\\^|\\^=|\\||\\|=|\\|\\||~",e.BE={b:"\\\\[\\s\\S]",r:0},e.ASM={cN:"string",b:"'",e:"'",i:"\\n",c:[e.BE]},e.QSM={cN:"string",b:'"',e:'"',i:"\\n",c:[e.BE]},e.PWM={b:/\b(a|an|the|are|I'm|isn't|don't|doesn't|won't|but|just|should|pretty|simply|enough|gonna|going|wtf|so|such|will|you|your|they|like|more)\b/},e.C=function(n,t,r){var a=e.inherit({cN:"comment",b:n,e:t,c:[]},r||{});return a.c.push(e.PWM),a.c.push({cN:"doctag",b:"(?:TODO|FIXME|NOTE|BUG|XXX):",r:0}),a},e.CLCM=e.C("//","$"),e.CBCM=e.C("/\\*","\\*/"),e.HCM=e.C("#","$"),e.NM={cN:"number",b:e.NR,r:0},e.CNM={cN:"number",b:e.CNR,r:0},e.BNM={cN:"number",b:e.BNR,r:0},e.CSSNM={cN:"number",b:e.NR+"(%|em|ex|ch|rem|vw|vh|vmin|vmax|cm|mm|in|pt|pc|px|deg|grad|rad|turn|s|ms|Hz|kHz|dpi|dpcm|dppx)?",r:0},e.RM={cN:"regexp",b:/\//,e:/\/[gimuy]*/,i:/\n/,c:[e.BE,{b:/\[/,e:/\]/,r:0,c:[e.BE]}]},e.TM={cN:"title",b:e.IR,r:0},e.UTM={cN:"title",b:e.UIR,r:0},e.METHOD_GUARD={b:"\\.\\s*"+e.UIR,r:0},e});hljs.registerLanguage("sql",function(e){var t=e.C("--","$");return{cI:!0,i:/[<>{}*#]/,c:[{bK:"begin end start commit rollback savepoint lock alter create drop rename call delete do handler insert load replace select truncate update set show pragma grant merge describe use explain help declare prepare execute deallocate release unlock purge reset change stop analyze cache flush optimize repair kill install uninstall checksum restore check backup revoke comment",e:/;/,eW:!0,l:/[\w\.]+/,k:{keyword:"abort abs absolute acc acce accep accept access accessed accessible account acos action activate add addtime admin administer advanced advise aes_decrypt aes_encrypt after agent aggregate ali alia alias allocate allow alter always analyze ancillary and any anydata anydataset anyschema anytype apply archive archived archivelog are as asc ascii asin assembly assertion associate asynchronous at atan atn2 attr attri attrib attribu attribut attribute attributes audit authenticated authentication authid authors auto autoallocate autodblink autoextend automatic availability avg backup badfile basicfile before begin beginning benchmark between bfile bfile_base big bigfile bin binary_double binary_float binlog bit_and bit_count bit_length bit_or bit_xor bitmap blob_base block blocksize body both bound buffer_cache buffer_pool build bulk by byte byteordermark bytes cache caching call calling cancel capacity cascade cascaded case cast catalog category ceil ceiling chain change changed char_base char_length character_length characters characterset charindex charset charsetform charsetid check checksum checksum_agg child choose chr chunk class cleanup clear client clob clob_base clone close cluster_id cluster_probability cluster_set clustering coalesce coercibility col collate collation collect colu colum column column_value columns columns_updated comment commit compact compatibility compiled complete composite_limit compound compress compute concat concat_ws concurrent confirm conn connec connect connect_by_iscycle connect_by_isleaf connect_by_root connect_time connection consider consistent constant constraint constraints constructor container content contents context contributors controlfile conv convert convert_tz corr corr_k corr_s corresponding corruption cos cost count count_big counted covar_pop covar_samp cpu_per_call cpu_per_session crc32 create creation critical cross cube cume_dist curdate current current_date current_time current_timestamp current_user cursor curtime customdatum cycle data database databases datafile datafiles datalength date_add date_cache date_format date_sub dateadd datediff datefromparts datename datepart datetime2fromparts day day_to_second dayname dayofmonth dayofweek dayofyear days db_role_change dbtimezone ddl deallocate declare decode decompose decrement decrypt deduplicate def defa defau defaul default defaults deferred defi defin define degrees delayed delegate delete delete_all delimited demand dense_rank depth dequeue des_decrypt des_encrypt des_key_file desc descr descri describ describe descriptor deterministic diagnostics difference dimension direct_load directory disable disable_all disallow disassociate discardfile disconnect diskgroup distinct distinctrow distribute distributed div do document domain dotnet double downgrade drop dumpfile duplicate duration each edition editionable editions element ellipsis else elsif elt empty enable enable_all enclosed encode encoding encrypt end end-exec endian enforced engine engines enqueue enterprise entityescaping eomonth error errors escaped evalname evaluate event eventdata events except exception exceptions exchange exclude excluding execu execut execute exempt exists exit exp expire explain export export_set extended extent external external_1 external_2 externally extract failed failed_login_attempts failover failure far fast feature_set feature_value fetch field fields file file_name_convert filesystem_like_logging final finish first first_value fixed flash_cache flashback floor flush following follows for forall force form forma format found found_rows freelist freelists freepools fresh from from_base64 from_days ftp full function general generated get get_format get_lock getdate getutcdate global global_name globally go goto grant grants greatest group group_concat group_id grouping grouping_id groups gtid_subtract guarantee guard handler hash hashkeys having hea head headi headin heading heap help hex hierarchy high high_priority hosts hour http id ident_current ident_incr ident_seed identified identity idle_time if ifnull ignore iif ilike ilm immediate import in include including increment index indexes indexing indextype indicator indices inet6_aton inet6_ntoa inet_aton inet_ntoa infile initial initialized initially initrans inmemory inner innodb input insert install instance instantiable instr interface interleaved intersect into invalidate invisible is is_free_lock is_ipv4 is_ipv4_compat is_not is_not_null is_used_lock isdate isnull isolation iterate java join json json_exists keep keep_duplicates key keys kill language large last last_day last_insert_id last_value lax lcase lead leading least leaves left len lenght length less level levels library like like2 like4 likec limit lines link list listagg little ln load load_file lob lobs local localtime localtimestamp locate locator lock locked log log10 log2 logfile logfiles logging logical logical_reads_per_call logoff logon logs long loop low low_priority lower lpad lrtrim ltrim main make_set makedate maketime managed management manual map mapping mask master master_pos_wait match matched materialized max maxextents maximize maxinstances maxlen maxlogfiles maxloghistory maxlogmembers maxsize maxtrans md5 measures median medium member memcompress memory merge microsecond mid migration min minextents minimum mining minus minute minvalue missing mod mode model modification modify module monitoring month months mount move movement multiset mutex name name_const names nan national native natural nav nchar nclob nested never new newline next nextval no no_write_to_binlog noarchivelog noaudit nobadfile nocheck nocompress nocopy nocycle nodelay nodiscardfile noentityescaping noguarantee nokeep nologfile nomapping nomaxvalue nominimize nominvalue nomonitoring none noneditionable nonschema noorder nopr nopro noprom nopromp noprompt norely noresetlogs noreverse normal norowdependencies noschemacheck noswitch not nothing notice notrim novalidate now nowait nth_value nullif nulls num numb numbe nvarchar nvarchar2 object ocicoll ocidate ocidatetime ociduration ociinterval ociloblocator ocinumber ociref ocirefcursor ocirowid ocistring ocitype oct octet_length of off offline offset oid oidindex old on online only opaque open operations operator optimal optimize option optionally or oracle oracle_date oradata ord ordaudio orddicom orddoc order ordimage ordinality ordvideo organization orlany orlvary out outer outfile outline output over overflow overriding package pad parallel parallel_enable parameters parent parse partial partition partitions pascal passing password password_grace_time password_lock_time password_reuse_max password_reuse_time password_verify_function patch path patindex pctincrease pctthreshold pctused pctversion percent percent_rank percentile_cont percentile_disc performance period period_add period_diff permanent physical pi pipe pipelined pivot pluggable plugin policy position post_transaction pow power pragma prebuilt precedes preceding precision prediction prediction_cost prediction_details prediction_probability prediction_set prepare present preserve prior priority private private_sga privileges procedural procedure procedure_analyze processlist profiles project prompt protection public publishingservername purge quarter query quick quiesce quota quotename radians raise rand range rank raw read reads readsize rebuild record records recover recovery recursive recycle redo reduced ref reference referenced references referencing refresh regexp_like register regr_avgx regr_avgy regr_count regr_intercept regr_r2 regr_slope regr_sxx regr_sxy reject rekey relational relative relaylog release release_lock relies_on relocate rely rem remainder rename repair repeat replace replicate replication required reset resetlogs resize resource respect restore restricted result result_cache resumable resume retention return returning returns reuse reverse revoke right rlike role roles rollback rolling rollup round row row_count rowdependencies rowid rownum rows rtrim rules safe salt sample save savepoint sb1 sb2 sb4 scan schema schemacheck scn scope scroll sdo_georaster sdo_topo_geometry search sec_to_time second section securefile security seed segment select self sequence sequential serializable server servererror session session_user sessions_per_user set sets settings sha sha1 sha2 share shared shared_pool short show shrink shutdown si_averagecolor si_colorhistogram si_featurelist si_positionalcolor si_stillimage si_texture siblings sid sign sin size size_t sizes skip slave sleep smalldatetimefromparts smallfile snapshot some soname sort soundex source space sparse spfile split sql sql_big_result sql_buffer_result sql_cache sql_calc_found_rows sql_small_result sql_variant_property sqlcode sqldata sqlerror sqlname sqlstate sqrt square standalone standby start starting startup statement static statistics stats_binomial_test stats_crosstab stats_ks_test stats_mode stats_mw_test stats_one_way_anova stats_t_test_ stats_t_test_indep stats_t_test_one stats_t_test_paired stats_wsr_test status std stddev stddev_pop stddev_samp stdev stop storage store stored str str_to_date straight_join strcmp strict string struct stuff style subdate subpartition subpartitions substitutable substr substring subtime subtring_index subtype success sum suspend switch switchoffset switchover sync synchronous synonym sys sys_xmlagg sysasm sysaux sysdate sysdatetimeoffset sysdba sysoper system system_user sysutcdatetime table tables tablespace tan tdo template temporary terminated tertiary_weights test than then thread through tier ties time time_format time_zone timediff timefromparts timeout timestamp timestampadd timestampdiff timezone_abbr timezone_minute timezone_region to to_base64 to_date to_days to_seconds todatetimeoffset trace tracking transaction transactional translate translation treat trigger trigger_nestlevel triggers trim truncate try_cast try_convert try_parse type ub1 ub2 ub4 ucase unarchived unbounded uncompress under undo unhex unicode uniform uninstall union unique unix_timestamp unknown unlimited unlock unpivot unrecoverable unsafe unsigned until untrusted unusable unused update updated upgrade upped upper upsert url urowid usable usage use use_stored_outlines user user_data user_resources users using utc_date utc_timestamp uuid uuid_short validate validate_password_strength validation valist value values var var_samp varcharc vari varia variab variabl variable variables variance varp varraw varrawc varray verify version versions view virtual visible void wait wallet warning warnings week weekday weekofyear wellformed when whene whenev wheneve whenever where while whitespace with within without work wrapped xdb xml xmlagg xmlattributes xmlcast xmlcolattval xmlelement xmlexists xmlforest xmlindex xmlnamespaces xmlpi xmlquery xmlroot xmlschema xmlserialize xmltable xmltype xor year year_to_month years yearweek",literal:"true false null",built_in:"array bigint binary bit blob boolean char character date dec decimal float int int8 integer interval number numeric real record serial serial8 smallint text varchar varying void"},c:[{cN:"string",b:"'",e:"'",c:[e.BE,{b:"''"}]},{cN:"string",b:'"',e:'"',c:[e.BE,{b:'""'}]},{cN:"string",b:"`",e:"`",c:[e.BE]},e.CNM,e.CBCM,t]},e.CBCM,t]}});hljs.registerLanguage("r",function(e){var r="([a-zA-Z]|\\.[a-zA-Z.])[a-zA-Z0-9._]*";return{c:[e.HCM,{b:r,l:r,k:{keyword:"function if in break next repeat else for return switch while try tryCatch stop warning require library attach detach source setMethod setGeneric setGroupGeneric setClass ...",literal:"NULL NA TRUE FALSE T F Inf NaN NA_integer_|10 NA_real_|10 NA_character_|10 NA_complex_|10"},r:0},{cN:"number",b:"0[xX][0-9a-fA-F]+[Li]?\\b",r:0},{cN:"number",b:"\\d+(?:[eE][+\\-]?\\d*)?L\\b",r:0},{cN:"number",b:"\\d+\\.(?!\\d)(?:i\\b)?",r:0},{cN:"number",b:"\\d+(?:\\.\\d*)?(?:[eE][+\\-]?\\d*)?i?\\b",r:0},{cN:"number",b:"\\.\\d+(?:[eE][+\\-]?\\d*)?i?\\b",r:0},{b:"`",e:"`",r:0},{cN:"string",c:[e.BE],v:[{b:'"',e:'"'},{b:"'",e:"'"}]}]}});hljs.registerLanguage("perl",function(e){var t="getpwent getservent quotemeta msgrcv scalar kill dbmclose undef lc ma syswrite tr send umask sysopen shmwrite vec qx utime local oct semctl localtime readpipe do return format read sprintf dbmopen pop getpgrp not getpwnam rewinddir qqfileno qw endprotoent wait sethostent bless s|0 opendir continue each sleep endgrent shutdown dump chomp connect getsockname die socketpair close flock exists index shmgetsub for endpwent redo lstat msgctl setpgrp abs exit select print ref gethostbyaddr unshift fcntl syscall goto getnetbyaddr join gmtime symlink semget splice x|0 getpeername recv log setsockopt cos last reverse gethostbyname getgrnam study formline endhostent times chop length gethostent getnetent pack getprotoent getservbyname rand mkdir pos chmod y|0 substr endnetent printf next open msgsnd readdir use unlink getsockopt getpriority rindex wantarray hex system getservbyport endservent int chr untie rmdir prototype tell listen fork shmread ucfirst setprotoent else sysseek link getgrgid shmctl waitpid unpack getnetbyname reset chdir grep split require caller lcfirst until warn while values shift telldir getpwuid my getprotobynumber delete and sort uc defined srand accept package seekdir getprotobyname semop our rename seek if q|0 chroot sysread setpwent no crypt getc chown sqrt write setnetent setpriority foreach tie sin msgget map stat getlogin unless elsif truncate exec keys glob tied closedirioctl socket readlink eval xor readline binmode setservent eof ord bind alarm pipe atan2 getgrent exp time push setgrent gt lt or ne m|0 break given say state when",r={cN:"subst",b:"[$@]\\{",e:"\\}",k:t},s={b:"->{",e:"}"},n={v:[{b:/\$\d/},{b:/[\$%@](\^\w\b|#\w+(::\w+)*|{\w+}|\w+(::\w*)*)/},{b:/[\$%@][^\s\w{]/,r:0}]},i=[e.BE,r,n],o=[n,e.HCM,e.C("^\\=\\w","\\=cut",{eW:!0}),s,{cN:"string",c:i,v:[{b:"q[qwxr]?\\s*\\(",e:"\\)",r:5},{b:"q[qwxr]?\\s*\\[",e:"\\]",r:5},{b:"q[qwxr]?\\s*\\{",e:"\\}",r:5},{b:"q[qwxr]?\\s*\\|",e:"\\|",r:5},{b:"q[qwxr]?\\s*\\<",e:"\\>",r:5},{b:"qw\\s+q",e:"q",r:5},{b:"'",e:"'",c:[e.BE]},{b:'"',e:'"'},{b:"`",e:"`",c:[e.BE]},{b:"{\\w+}",c:[],r:0},{b:"-?\\w+\\s*\\=\\>",c:[],r:0}]},{cN:"number",b:"(\\b0[0-7_]+)|(\\b0x[0-9a-fA-F_]+)|(\\b[1-9][0-9_]*(\\.[0-9_]+)?)|[0_]\\b",r:0},{b:"(\\/\\/|"+e.RSR+"|\\b(split|return|print|reverse|grep)\\b)\\s*",k:"split return print reverse grep",r:0,c:[e.HCM,{cN:"regexp",b:"(s|tr|y)/(\\\\.|[^/])*/(\\\\.|[^/])*/[a-z]*",r:10},{cN:"regexp",b:"(m|qr)?/",e:"/[a-z]*",c:[e.BE],r:0}]},{cN:"function",bK:"sub",e:"(\\s*\\(.*?\\))?[;{]",eE:!0,r:5,c:[e.TM]},{b:"-\\w\\b",r:0},{b:"^__DATA__$",e:"^__END__$",sL:"mojolicious",c:[{b:"^@@.*",e:"$",cN:"comment"}]}];return r.c=o,s.c=o,{aliases:["pl","pm"],l:/[\w\.]+/,k:t,c:o}});hljs.registerLanguage("ini",function(e){var b={cN:"string",c:[e.BE],v:[{b:"'''",e:"'''",r:10},{b:'"""',e:'"""',r:10},{b:'"',e:'"'},{b:"'",e:"'"}]};return{aliases:["toml"],cI:!0,i:/\S/,c:[e.C(";","$"),e.HCM,{cN:"section",b:/^\s*\[+/,e:/\]+/},{b:/^[a-z0-9\[\]_-]+\s*=\s*/,e:"$",rB:!0,c:[{cN:"attr",b:/[a-z0-9\[\]_-]+/},{b:/=/,eW:!0,r:0,c:[{cN:"literal",b:/\bon|off|true|false|yes|no\b/},{cN:"variable",v:[{b:/\$[\w\d"][\w\d_]*/},{b:/\$\{(.*?)}/}]},b,{cN:"number",b:/([\+\-]+)?[\d]+_[\d_]+/},e.NM]}]}]}});hljs.registerLanguage("diff",function(e){return{aliases:["patch"],c:[{cN:"meta",r:10,v:[{b:/^@@ +\-\d+,\d+ +\+\d+,\d+ +@@$/},{b:/^\*\*\* +\d+,\d+ +\*\*\*\*$/},{b:/^\-\-\- +\d+,\d+ +\-\-\-\-$/}]},{cN:"comment",v:[{b:/Index: /,e:/$/},{b:/={3,}/,e:/$/},{b:/^\-{3}/,e:/$/},{b:/^\*{3} /,e:/$/},{b:/^\+{3}/,e:/$/},{b:/\*{5}/,e:/\*{5}$/}]},{cN:"addition",b:"^\\+",e:"$"},{cN:"deletion",b:"^\\-",e:"$"},{cN:"addition",b:"^\\!",e:"$"}]}});hljs.registerLanguage("go",function(e){var t={keyword:"break default func interface select case map struct chan else goto package switch const fallthrough if range type continue for import return var go defer bool byte complex64 complex128 float32 float64 int8 int16 int32 int64 string uint8 uint16 uint32 uint64 int uint uintptr rune",literal:"true false iota nil",built_in:"append cap close complex copy imag len make new panic print println real recover delete"};return{aliases:["golang"],k:t,i:"</",c:[e.CLCM,e.CBCM,{cN:"string",v:[e.QSM,{b:"'",e:"[^\\\\]'"},{b:"`",e:"`"}]},{cN:"number",v:[{b:e.CNR+"[dflsi]",r:1},e.CNM]},{b:/:=/},{cN:"function",bK:"func",e:/\s*\{/,eE:!0,c:[e.TM,{cN:"params",b:/\(/,e:/\)/,k:t,i:/["']/}]}]}});hljs.registerLanguage("bash",function(e){var t={cN:"variable",v:[{b:/\$[\w\d#@][\w\d_]*/},{b:/\$\{(.*?)}/}]},s={cN:"string",b:/"/,e:/"/,c:[e.BE,t,{cN:"variable",b:/\$\(/,e:/\)/,c:[e.BE]}]},a={cN:"string",b:/'/,e:/'/};return{aliases:["sh","zsh"],l:/\b-?[a-z\._]+\b/,k:{keyword:"if then else elif fi for while in do done case esac function",literal:"true false",built_in:"break cd continue eval exec exit export getopts hash pwd readonly return shift test times trap umask unset alias bind builtin caller command declare echo enable help let local logout mapfile printf read readarray source type typeset ulimit unalias set shopt autoload bg bindkey bye cap chdir clone comparguments compcall compctl compdescribe compfiles compgroups compquote comptags comptry compvalues dirs disable disown echotc echoti emulate fc fg float functions getcap getln history integer jobs kill limit log noglob popd print pushd pushln rehash sched setcap setopt stat suspend ttyctl unfunction unhash unlimit unsetopt vared wait whence where which zcompile zformat zftp zle zmodload zparseopts zprof zpty zregexparse zsocket zstyle ztcp",_:"-ne -eq -lt -gt -f -d -e -s -l -a"},c:[{cN:"meta",b:/^#![^\n]+sh\s*$/,r:10},{cN:"function",b:/\w[\w\d_]*\s*\(\s*\)\s*\{/,rB:!0,c:[e.inherit(e.TM,{b:/\w[\w\d_]*/})],r:0},e.HCM,s,a,t]}});hljs.registerLanguage("python",function(e){var r={keyword:"and elif is global as in if from raise for except finally print import pass return exec else break not with class assert yield try while continue del or def lambda async await nonlocal|10 None True False",built_in:"Ellipsis NotImplemented"},b={cN:"meta",b:/^(>>>|\.\.\.) /},c={cN:"subst",b:/\{/,e:/\}/,k:r,i:/#/},a={cN:"string",c:[e.BE],v:[{b:/(u|b)?r?'''/,e:/'''/,c:[b],r:10},{b:/(u|b)?r?"""/,e:/"""/,c:[b],r:10},{b:/(fr|rf|f)'''/,e:/'''/,c:[b,c]},{b:/(fr|rf|f)"""/,e:/"""/,c:[b,c]},{b:/(u|r|ur)'/,e:/'/,r:10},{b:/(u|r|ur)"/,e:/"/,r:10},{b:/(b|br)'/,e:/'/},{b:/(b|br)"/,e:/"/},{b:/(fr|rf|f)'/,e:/'/,c:[c]},{b:/(fr|rf|f)"/,e:/"/,c:[c]},e.ASM,e.QSM]},s={cN:"number",r:0,v:[{b:e.BNR+"[lLjJ]?"},{b:"\\b(0o[0-7]+)[lLjJ]?"},{b:e.CNR+"[lLjJ]?"}]},i={cN:"params",b:/\(/,e:/\)/,c:["self",b,s,a]};return c.c=[a,s,b],{aliases:["py","gyp"],k:r,i:/(<\/|->|\?)|=>/,c:[b,s,a,e.HCM,{v:[{cN:"function",bK:"def"},{cN:"class",bK:"class"}],e:/:/,i:/[${=;\n,]/,c:[e.UTM,i,{b:/->/,eW:!0,k:"None"}]},{cN:"meta",b:/^[\t ]*@/,e:/$/},{b:/\b(print|exec)\(/}]}});hljs.registerLanguage("julia",function(e){var r={keyword:"in isa where baremodule begin break catch ccall const continue do else elseif end export false finally for function global if import importall let local macro module quote return true try using while type immutable abstract bitstype typealias ",literal:"true false ARGS C_NULL DevNull ENDIAN_BOM ENV I Inf Inf16 Inf32 Inf64 InsertionSort JULIA_HOME LOAD_PATH MergeSort NaN NaN16 NaN32 NaN64 PROGRAM_FILE QuickSort RoundDown RoundFromZero RoundNearest RoundNearestTiesAway RoundNearestTiesUp RoundToZero RoundUp STDERR STDIN STDOUT VERSION catalan e|0 eu|0 eulergamma golden im nothing pi γ π φ ",built_in:"ANY AbstractArray AbstractChannel AbstractFloat AbstractMatrix AbstractRNG AbstractSerializer AbstractSet AbstractSparseArray AbstractSparseMatrix AbstractSparseVector AbstractString AbstractUnitRange AbstractVecOrMat AbstractVector Any ArgumentError Array AssertionError Associative Base64DecodePipe Base64EncodePipe Bidiagonal BigFloat BigInt BitArray BitMatrix BitVector Bool BoundsError BufferStream CachingPool CapturedException CartesianIndex CartesianRange Cchar Cdouble Cfloat Channel Char Cint Cintmax_t Clong Clonglong ClusterManager Cmd CodeInfo Colon Complex Complex128 Complex32 Complex64 CompositeException Condition ConjArray ConjMatrix ConjVector Cptrdiff_t Cshort Csize_t Cssize_t Cstring Cuchar Cuint Cuintmax_t Culong Culonglong Cushort Cwchar_t Cwstring DataType Date DateFormat DateTime DenseArray DenseMatrix DenseVecOrMat DenseVector Diagonal Dict DimensionMismatch Dims DirectIndexString Display DivideError DomainError EOFError EachLine Enum Enumerate ErrorException Exception ExponentialBackOff Expr Factorization FileMonitor Float16 Float32 Float64 Function Future GlobalRef GotoNode HTML Hermitian IO IOBuffer IOContext IOStream IPAddr IPv4 IPv6 IndexCartesian IndexLinear IndexStyle InexactError InitError Int Int128 Int16 Int32 Int64 Int8 IntSet Integer InterruptException InvalidStateException Irrational KeyError LabelNode LinSpace LineNumberNode LoadError LowerTriangular MIME Matrix MersenneTwister Method MethodError MethodTable Module NTuple NewvarNode NullException Nullable Number ObjectIdDict OrdinalRange OutOfMemoryError OverflowError Pair ParseError PartialQuickSort PermutedDimsArray Pipe PollingFileWatcher ProcessExitedException Ptr QuoteNode RandomDevice Range RangeIndex Rational RawFD ReadOnlyMemoryError Real ReentrantLock Ref Regex RegexMatch RemoteChannel RemoteException RevString RoundingMode RowVector SSAValue SegmentationFault SerializationState Set SharedArray SharedMatrix SharedVector Signed SimpleVector Slot SlotNumber SparseMatrixCSC SparseVector StackFrame StackOverflowError StackTrace StepRange StepRangeLen StridedArray StridedMatrix StridedVecOrMat StridedVector String SubArray SubString SymTridiagonal Symbol Symmetric SystemError TCPSocket Task Text TextDisplay Timer Tridiagonal Tuple Type TypeError TypeMapEntry TypeMapLevel TypeName TypeVar TypedSlot UDPSocket UInt UInt128 UInt16 UInt32 UInt64 UInt8 UndefRefError UndefVarError UnicodeError UniformScaling Union UnionAll UnitRange Unsigned UpperTriangular Val Vararg VecElement VecOrMat Vector VersionNumber Void WeakKeyDict WeakRef WorkerConfig WorkerPool "},t="[A-Za-z_\\u00A1-\\uFFFF][A-Za-z_0-9\\u00A1-\\uFFFF]*",a={l:t,k:r,i:/<\//},n={cN:"number",b:/(\b0x[\d_]*(\.[\d_]*)?|0x\.\d[\d_]*)p[-+]?\d+|\b0[box][a-fA-F0-9][a-fA-F0-9_]*|(\b\d[\d_]*(\.[\d_]*)?|\.\d[\d_]*)([eEfF][-+]?\d+)?/,r:0},o={cN:"string",b:/'(.|\\[xXuU][a-zA-Z0-9]+)'/},i={cN:"subst",b:/\$\(/,e:/\)/,k:r},l={cN:"variable",b:"\\$"+t},c={cN:"string",c:[e.BE,i,l],v:[{b:/\w*"""/,e:/"""\w*/,r:10},{b:/\w*"/,e:/"\w*/}]},s={cN:"string",c:[e.BE,i,l],b:"`",e:"`"},d={cN:"meta",b:"@"+t},u={cN:"comment",v:[{b:"#=",e:"=#",r:10},{b:"#",e:"$"}]};return a.c=[n,o,c,s,d,u,e.HCM,{cN:"keyword",b:"\\b(((abstract|primitive)\\s+)type|(mutable\\s+)?struct)\\b"},{b:/<:/}],i.c=a.c,a});hljs.registerLanguage("coffeescript",function(e){var c={keyword:"in if for while finally new do return else break catch instanceof throw try this switch continue typeof delete debugger super yield import export from as default await then unless until loop of by when and or is isnt not",literal:"true false null undefined yes no on off",built_in:"npm require console print module global window document"},n="[A-Za-z$_][0-9A-Za-z$_]*",r={cN:"subst",b:/#\{/,e:/}/,k:c},i=[e.BNM,e.inherit(e.CNM,{starts:{e:"(\\s*/)?",r:0}}),{cN:"string",v:[{b:/'''/,e:/'''/,c:[e.BE]},{b:/'/,e:/'/,c:[e.BE]},{b:/"""/,e:/"""/,c:[e.BE,r]},{b:/"/,e:/"/,c:[e.BE,r]}]},{cN:"regexp",v:[{b:"///",e:"///",c:[r,e.HCM]},{b:"//[gim]*",r:0},{b:/\/(?![ *])(\\\/|.)*?\/[gim]*(?=\W|$)/}]},{b:"@"+n},{sL:"javascript",eB:!0,eE:!0,v:[{b:"```",e:"```"},{b:"`",e:"`"}]}];r.c=i;var s=e.inherit(e.TM,{b:n}),t="(\\(.*\\))?\\s*\\B[-=]>",o={cN:"params",b:"\\([^\\(]",rB:!0,c:[{b:/\(/,e:/\)/,k:c,c:["self"].concat(i)}]};return{aliases:["coffee","cson","iced"],k:c,i:/\/\*/,c:i.concat([e.C("###","###"),e.HCM,{cN:"function",b:"^\\s*"+n+"\\s*=\\s*"+t,e:"[-=]>",rB:!0,c:[s,o]},{b:/[:\(,=]\s*/,r:0,c:[{cN:"function",b:t,e:"[-=]>",rB:!0,c:[o]}]},{cN:"class",bK:"class",e:"$",i:/[:="\[\]]/,c:[{bK:"extends",eW:!0,i:/[:="\[\]]/,c:[s]},s]},{b:n+":",e:":",rB:!0,rE:!0,r:0}])}});hljs.registerLanguage("cpp",function(t){var e={cN:"keyword",b:"\\b[a-z\\d_]*_t\\b"},r={cN:"string",v:[{b:'(u8?|U)?L?"',e:'"',i:"\\n",c:[t.BE]},{b:'(u8?|U)?R"',e:'"',c:[t.BE]},{b:"'\\\\?.",e:"'",i:"."}]},s={cN:"number",v:[{b:"\\b(0b[01']+)"},{b:"(-?)\\b([\\d']+(\\.[\\d']*)?|\\.[\\d']+)(u|U|l|L|ul|UL|f|F|b|B)"},{b:"(-?)(\\b0[xX][a-fA-F0-9']+|(\\b[\\d']+(\\.[\\d']*)?|\\.[\\d']+)([eE][-+]?[\\d']+)?)"}],r:0},i={cN:"meta",b:/#\s*[a-z]+\b/,e:/$/,k:{"meta-keyword":"if else elif endif define undef warning error line pragma ifdef ifndef include"},c:[{b:/\\\n/,r:0},t.inherit(r,{cN:"meta-string"}),{cN:"meta-string",b:/<[^\n>]*>/,e:/$/,i:"\\n"},t.CLCM,t.CBCM]},a=t.IR+"\\s*\\(",c={keyword:"int float while private char catch import module export virtual operator sizeof dynamic_cast|10 typedef const_cast|10 const for static_cast|10 union namespace unsigned long volatile static protected bool template mutable if public friend do goto auto void enum else break extern using asm case typeid short reinterpret_cast|10 default double register explicit signed typename try this switch continue inline delete alignof constexpr decltype noexcept static_assert thread_local restrict _Bool complex _Complex _Imaginary atomic_bool atomic_char atomic_schar atomic_uchar atomic_short atomic_ushort atomic_int atomic_uint atomic_long atomic_ulong atomic_llong atomic_ullong new throw return and or not",built_in:"std string cin cout cerr clog stdin stdout stderr stringstream istringstream ostringstream auto_ptr deque list queue stack vector map set bitset multiset multimap unordered_set unordered_map unordered_multiset unordered_multimap array shared_ptr abort abs acos asin atan2 atan calloc ceil cosh cos exit exp fabs floor fmod fprintf fputs free frexp fscanf isalnum isalpha iscntrl isdigit isgraph islower isprint ispunct isspace isupper isxdigit tolower toupper labs ldexp log10 log malloc realloc memchr memcmp memcpy memset modf pow printf putchar puts scanf sinh sin snprintf sprintf sqrt sscanf strcat strchr strcmp strcpy strcspn strlen strncat strncmp strncpy strpbrk strrchr strspn strstr tanh tan vfprintf vprintf vsprintf endl initializer_list unique_ptr",literal:"true false nullptr NULL"},n=[e,t.CLCM,t.CBCM,s,r];return{aliases:["c","cc","h","c++","h++","hpp"],k:c,i:"</",c:n.concat([i,{b:"\\b(deque|list|queue|stack|vector|map|set|bitset|multiset|multimap|unordered_map|unordered_set|unordered_multiset|unordered_multimap|array)\\s*<",e:">",k:c,c:["self",e]},{b:t.IR+"::",k:c},{v:[{b:/=/,e:/;/},{b:/\(/,e:/\)/},{bK:"new throw return else",e:/;/}],k:c,c:n.concat([{b:/\(/,e:/\)/,k:c,c:n.concat(["self"]),r:0}]),r:0},{cN:"function",b:"("+t.IR+"[\\*&\\s]+)+"+a,rB:!0,e:/[{;=]/,eE:!0,k:c,i:/[^\w\s\*&]/,c:[{b:a,rB:!0,c:[t.TM],r:0},{cN:"params",b:/\(/,e:/\)/,k:c,r:0,c:[t.CLCM,t.CBCM,r,s,e]},t.CLCM,t.CBCM,i]},{cN:"class",bK:"class struct",e:/[{;:]/,c:[{b:/</,e:/>/,c:["self"]},t.TM]}]),exports:{preprocessor:i,strings:r,k:c}}});hljs.registerLanguage("ruby",function(e){var b="[a-zA-Z_]\\w*[!?=]?|[-+~]\\@|<<|>>|=~|===?|<=>|[<>]=?|\\*\\*|[-/+%^&*~`|]|\\[\\]=?",r={keyword:"and then defined module in return redo if BEGIN retry end for self when next until do begin unless END rescue else break undef not super class case require yield alias while ensure elsif or include attr_reader attr_writer attr_accessor",literal:"true false nil"},c={cN:"doctag",b:"@[A-Za-z]+"},a={b:"#<",e:">"},s=[e.C("#","$",{c:[c]}),e.C("^\\=begin","^\\=end",{c:[c],r:10}),e.C("^__END__","\\n$")],n={cN:"subst",b:"#\\{",e:"}",k:r},t={cN:"string",c:[e.BE,n],v:[{b:/'/,e:/'/},{b:/"/,e:/"/},{b:/`/,e:/`/},{b:"%[qQwWx]?\\(",e:"\\)"},{b:"%[qQwWx]?\\[",e:"\\]"},{b:"%[qQwWx]?{",e:"}"},{b:"%[qQwWx]?<",e:">"},{b:"%[qQwWx]?/",e:"/"},{b:"%[qQwWx]?%",e:"%"},{b:"%[qQwWx]?-",e:"-"},{b:"%[qQwWx]?\\|",e:"\\|"},{b:/\B\?(\\\d{1,3}|\\x[A-Fa-f0-9]{1,2}|\\u[A-Fa-f0-9]{4}|\\?\S)\b/},{b:/<<(-?)\w+$/,e:/^\s*\w+$/}]},i={cN:"params",b:"\\(",e:"\\)",endsParent:!0,k:r},d=[t,a,{cN:"class",bK:"class module",e:"$|;",i:/=/,c:[e.inherit(e.TM,{b:"[A-Za-z_]\\w*(::\\w+)*(\\?|\\!)?"}),{b:"<\\s*",c:[{b:"("+e.IR+"::)?"+e.IR}]}].concat(s)},{cN:"function",bK:"def",e:"$|;",c:[e.inherit(e.TM,{b:b}),i].concat(s)},{b:e.IR+"::"},{cN:"symbol",b:e.UIR+"(\\!|\\?)?:",r:0},{cN:"symbol",b:":(?!\\s)",c:[t,{b:b}],r:0},{cN:"number",b:"(\\b0[0-7_]+)|(\\b0x[0-9a-fA-F_]+)|(\\b[1-9][0-9_]*(\\.[0-9_]+)?)|[0_]\\b",r:0},{b:"(\\$\\W)|((\\$|\\@\\@?)(\\w+))"},{cN:"params",b:/\|/,e:/\|/,k:r},{b:"("+e.RSR+"|unless)\\s*",k:"unless",c:[a,{cN:"regexp",c:[e.BE,n],i:/\n/,v:[{b:"/",e:"/[a-z]*"},{b:"%r{",e:"}[a-z]*"},{b:"%r\\(",e:"\\)[a-z]*"},{b:"%r!",e:"![a-z]*"},{b:"%r\\[",e:"\\][a-z]*"}]}].concat(s),r:0}].concat(s);n.c=d,i.c=d;var l="[>?]>",o="[\\w#]+\\(\\w+\\):\\d+:\\d+>",u="(\\w+-)?\\d+\\.\\d+\\.\\d(p\\d+)?[^>]+>",w=[{b:/^\s*=>/,starts:{e:"$",c:d}},{cN:"meta",b:"^("+l+"|"+o+"|"+u+")",starts:{e:"$",c:d}}];return{aliases:["rb","gemspec","podspec","thor","irb"],k:r,i:/\/\*/,c:s.concat(w).concat(d)}});hljs.registerLanguage("yaml",function(e){var b="true false yes no null",a="^[ \\-]*",r="[a-zA-Z_][\\w\\-]*",t={cN:"attr",v:[{b:a+r+":"},{b:a+'"'+r+'":'},{b:a+"'"+r+"':"}]},c={cN:"template-variable",v:[{b:"{{",e:"}}"},{b:"%{",e:"}"}]},l={cN:"string",r:0,v:[{b:/'/,e:/'/},{b:/"/,e:/"/},{b:/\S+/}],c:[e.BE,c]};return{cI:!0,aliases:["yml","YAML","yaml"],c:[t,{cN:"meta",b:"^---s*$",r:10},{cN:"string",b:"[\\|>] *$",rE:!0,c:l.c,e:t.v[0].b},{b:"<%[%=-]?",e:"[%-]?%>",sL:"ruby",eB:!0,eE:!0,r:0},{cN:"type",b:"!!"+e.UIR},{cN:"meta",b:"&"+e.UIR+"$"},{cN:"meta",b:"\\*"+e.UIR+"$"},{cN:"bullet",b:"^ *-",r:0},e.HCM,{bK:b,k:{literal:b}},e.CNM,l]}});hljs.registerLanguage("css",function(e){var c="[a-zA-Z-][a-zA-Z0-9_-]*",t={b:/[A-Z\_\.\-]+\s*:/,rB:!0,e:";",eW:!0,c:[{cN:"attribute",b:/\S/,e:":",eE:!0,starts:{eW:!0,eE:!0,c:[{b:/[\w-]+\(/,rB:!0,c:[{cN:"built_in",b:/[\w-]+/},{b:/\(/,e:/\)/,c:[e.ASM,e.QSM]}]},e.CSSNM,e.QSM,e.ASM,e.CBCM,{cN:"number",b:"#[0-9A-Fa-f]+"},{cN:"meta",b:"!important"}]}}]};return{cI:!0,i:/[=\/|'\$]/,c:[e.CBCM,{cN:"selector-id",b:/#[A-Za-z0-9_-]+/},{cN:"selector-class",b:/\.[A-Za-z0-9_-]+/},{cN:"selector-attr",b:/\[/,e:/\]/,i:"$"},{cN:"selector-pseudo",b:/:(:)?[a-zA-Z0-9\_\-\+\(\)"'.]+/},{b:"@(font-face|page)",l:"[a-z-]+",k:"font-face page"},{b:"@",e:"[{;]",i:/:/,c:[{cN:"keyword",b:/\w+/},{b:/\s/,eW:!0,eE:!0,r:0,c:[e.ASM,e.QSM,e.CSSNM]}]},{cN:"selector-tag",b:c,r:0},{b:"{",e:"}",i:/\S/,c:[e.CBCM,t]}]}});hljs.registerLanguage("fortran",function(e){var t={cN:"params",b:"\\(",e:"\\)"},n={literal:".False. .True.",keyword:"kind do while private call intrinsic where elsewhere type endtype endmodule endselect endinterface end enddo endif if forall endforall only contains default return stop then public subroutine|10 function program .and. .or. .not. .le. .eq. .ge. .gt. .lt. goto save else use module select case access blank direct exist file fmt form formatted iostat name named nextrec number opened rec recl sequential status unformatted unit continue format pause cycle exit c_null_char c_alert c_backspace c_form_feed flush wait decimal round iomsg synchronous nopass non_overridable pass protected volatile abstract extends import non_intrinsic value deferred generic final enumerator class associate bind enum c_int c_short c_long c_long_long c_signed_char c_size_t c_int8_t c_int16_t c_int32_t c_int64_t c_int_least8_t c_int_least16_t c_int_least32_t c_int_least64_t c_int_fast8_t c_int_fast16_t c_int_fast32_t c_int_fast64_t c_intmax_t C_intptr_t c_float c_double c_long_double c_float_complex c_double_complex c_long_double_complex c_bool c_char c_null_ptr c_null_funptr c_new_line c_carriage_return c_horizontal_tab c_vertical_tab iso_c_binding c_loc c_funloc c_associated  c_f_pointer c_ptr c_funptr iso_fortran_env character_storage_size error_unit file_storage_size input_unit iostat_end iostat_eor numeric_storage_size output_unit c_f_procpointer ieee_arithmetic ieee_support_underflow_control ieee_get_underflow_mode ieee_set_underflow_mode newunit contiguous recursive pad position action delim readwrite eor advance nml interface procedure namelist include sequence elemental pure integer real character complex logical dimension allocatable|10 parameter external implicit|10 none double precision assign intent optional pointer target in out common equivalence data",built_in:"alog alog10 amax0 amax1 amin0 amin1 amod cabs ccos cexp clog csin csqrt dabs dacos dasin datan datan2 dcos dcosh ddim dexp dint dlog dlog10 dmax1 dmin1 dmod dnint dsign dsin dsinh dsqrt dtan dtanh float iabs idim idint idnint ifix isign max0 max1 min0 min1 sngl algama cdabs cdcos cdexp cdlog cdsin cdsqrt cqabs cqcos cqexp cqlog cqsin cqsqrt dcmplx dconjg derf derfc dfloat dgamma dimag dlgama iqint qabs qacos qasin qatan qatan2 qcmplx qconjg qcos qcosh qdim qerf qerfc qexp qgamma qimag qlgama qlog qlog10 qmax1 qmin1 qmod qnint qsign qsin qsinh qsqrt qtan qtanh abs acos aimag aint anint asin atan atan2 char cmplx conjg cos cosh exp ichar index int log log10 max min nint sign sin sinh sqrt tan tanh print write dim lge lgt lle llt mod nullify allocate deallocate adjustl adjustr all allocated any associated bit_size btest ceiling count cshift date_and_time digits dot_product eoshift epsilon exponent floor fraction huge iand ibclr ibits ibset ieor ior ishft ishftc lbound len_trim matmul maxexponent maxloc maxval merge minexponent minloc minval modulo mvbits nearest pack present product radix random_number random_seed range repeat reshape rrspacing scale scan selected_int_kind selected_real_kind set_exponent shape size spacing spread sum system_clock tiny transpose trim ubound unpack verify achar iachar transfer dble entry dprod cpu_time command_argument_count get_command get_command_argument get_environment_variable is_iostat_end ieee_arithmetic ieee_support_underflow_control ieee_get_underflow_mode ieee_set_underflow_mode is_iostat_eor move_alloc new_line selected_char_kind same_type_as extends_type_ofacosh asinh atanh bessel_j0 bessel_j1 bessel_jn bessel_y0 bessel_y1 bessel_yn erf erfc erfc_scaled gamma log_gamma hypot norm2 atomic_define atomic_ref execute_command_line leadz trailz storage_size merge_bits bge bgt ble blt dshiftl dshiftr findloc iall iany iparity image_index lcobound ucobound maskl maskr num_images parity popcnt poppar shifta shiftl shiftr this_image"};return{cI:!0,aliases:["f90","f95"],k:n,i:/\/\*/,c:[e.inherit(e.ASM,{cN:"string",r:0}),e.inherit(e.QSM,{cN:"string",r:0}),{cN:"function",bK:"subroutine function program",i:"[${=\\n]",c:[e.UTM,t]},e.C("!","$",{r:0}),{cN:"number",b:"(?=\\b|\\+|\\-|\\.)(?=\\.\\d|\\d)(?:\\d+)?(?:\\.?\\d*)(?:[de][+-]?\\d+)?\\b\\.?",r:0}]}});hljs.registerLanguage("awk",function(e){var r={cN:"variable",v:[{b:/\$[\w\d#@][\w\d_]*/},{b:/\$\{(.*?)}/}]},b="BEGIN END if else while do for in break continue delete next nextfile function func exit|10",n={cN:"string",c:[e.BE],v:[{b:/(u|b)?r?'''/,e:/'''/,r:10},{b:/(u|b)?r?"""/,e:/"""/,r:10},{b:/(u|r|ur)'/,e:/'/,r:10},{b:/(u|r|ur)"/,e:/"/,r:10},{b:/(b|br)'/,e:/'/},{b:/(b|br)"/,e:/"/},e.ASM,e.QSM]};return{k:{keyword:b},c:[r,n,e.RM,e.HCM,e.NM]}});hljs.registerLanguage("makefile",function(e){var i={cN:"variable",v:[{b:"\\$\\("+e.UIR+"\\)",c:[e.BE]},{b:/\$[@%<?\^\+\*]/}]},r={cN:"string",b:/"/,e:/"/,c:[e.BE,i]},a={cN:"variable",b:/\$\([\w-]+\s/,e:/\)/,k:{built_in:"subst patsubst strip findstring filter filter-out sort word wordlist firstword lastword dir notdir suffix basename addsuffix addprefix join wildcard realpath abspath error warning shell origin flavor foreach if or and call eval file value"},c:[i]},n={b:"^"+e.UIR+"\\s*[:+?]?=",i:"\\n",rB:!0,c:[{b:"^"+e.UIR,e:"[:+?]?=",eE:!0}]},t={cN:"meta",b:/^\.PHONY:/,e:/$/,k:{"meta-keyword":".PHONY"},l:/[\.\w]+/},l={cN:"section",b:/^[^\s]+:/,e:/$/,c:[i]};return{aliases:["mk","mak"],k:"define endef undefine ifdef ifndef ifeq ifneq else endif include -include sinclude override export unexport private vpath",l:/[\w-]+/,c:[e.HCM,i,r,a,n,t,l]}});hljs.registerLanguage("java",function(e){var a="[À-ʸa-zA-Z_$][À-ʸa-zA-Z_$0-9]*",t=a+"(<"+a+"(\\s*,\\s*"+a+")*>)?",r="false synchronized int abstract float private char boolean static null if const for true while long strictfp finally protected import native final void enum else break transient catch instanceof byte super volatile case assert short package default double public try this switch continue throws protected public private module requires exports do",s="\\b(0[bB]([01]+[01_]+[01]+|[01]+)|0[xX]([a-fA-F0-9]+[a-fA-F0-9_]+[a-fA-F0-9]+|[a-fA-F0-9]+)|(([\\d]+[\\d_]+[\\d]+|[\\d]+)(\\.([\\d]+[\\d_]+[\\d]+|[\\d]+))?|\\.([\\d]+[\\d_]+[\\d]+|[\\d]+))([eE][-+]?\\d+)?)[lLfF]?",c={cN:"number",b:s,r:0};return{aliases:["jsp"],k:r,i:/<\/|#/,c:[e.C("/\\*\\*","\\*/",{r:0,c:[{b:/\w+@/,r:0},{cN:"doctag",b:"@[A-Za-z]+"}]}),e.CLCM,e.CBCM,e.ASM,e.QSM,{cN:"class",bK:"class interface",e:/[{;=]/,eE:!0,k:"class interface",i:/[:"\[\]]/,c:[{bK:"extends implements"},e.UTM]},{bK:"new throw return else",r:0},{cN:"function",b:"("+t+"\\s+)+"+e.UIR+"\\s*\\(",rB:!0,e:/[{;=]/,eE:!0,k:r,c:[{b:e.UIR+"\\s*\\(",rB:!0,r:0,c:[e.UTM]},{cN:"params",b:/\(/,e:/\)/,k:r,r:0,c:[e.ASM,e.QSM,e.CNM,e.CBCM]},e.CLCM,e.CBCM]},c,{cN:"meta",b:"@[A-Za-z]+"}]}});hljs.registerLanguage("stan",function(e){return{c:[e.HCM,e.CLCM,e.CBCM,{b:e.UIR,l:e.UIR,k:{name:"for in while repeat until if then else",symbol:"bernoulli bernoulli_logit binomial binomial_logit beta_binomial hypergeometric categorical categorical_logit ordered_logistic neg_binomial neg_binomial_2 neg_binomial_2_log poisson poisson_log multinomial normal exp_mod_normal skew_normal student_t cauchy double_exponential logistic gumbel lognormal chi_square inv_chi_square scaled_inv_chi_square exponential inv_gamma weibull frechet rayleigh wiener pareto pareto_type_2 von_mises uniform multi_normal multi_normal_prec multi_normal_cholesky multi_gp multi_gp_cholesky multi_student_t gaussian_dlm_obs dirichlet lkj_corr lkj_corr_cholesky wishart inv_wishart","selector-tag":"int real vector simplex unit_vector ordered positive_ordered row_vector matrix cholesky_factor_corr cholesky_factor_cov corr_matrix cov_matrix",title:"functions model data parameters quantities transformed generated",literal:"true false"},r:0},{cN:"number",b:"0[xX][0-9a-fA-F]+[Li]?\\b",r:0},{cN:"number",b:"0[xX][0-9a-fA-F]+[Li]?\\b",r:0},{cN:"number",b:"\\d+(?:[eE][+\\-]?\\d*)?L\\b",r:0},{cN:"number",b:"\\d+\\.(?!\\d)(?:i\\b)?",r:0},{cN:"number",b:"\\d+(?:\\.\\d*)?(?:[eE][+\\-]?\\d*)?i?\\b",r:0},{cN:"number",b:"\\.\\d+(?:[eE][+\\-]?\\d*)?i?\\b",r:0}]}});hljs.registerLanguage("javascript",function(e){var r="[A-Za-z$_][0-9A-Za-z$_]*",t={keyword:"in of if for while finally var new function do return void else break catch instanceof with throw case default try this switch continue typeof delete let yield const export super debugger as async await static import from as",literal:"true false null undefined NaN Infinity",built_in:"eval isFinite isNaN parseFloat parseInt decodeURI decodeURIComponent encodeURI encodeURIComponent escape unescape Object Function Boolean Error EvalError InternalError RangeError ReferenceError StopIteration SyntaxError TypeError URIError Number Math Date String RegExp Array Float32Array Float64Array Int16Array Int32Array Int8Array Uint16Array Uint32Array Uint8Array Uint8ClampedArray ArrayBuffer DataView JSON Intl arguments require module console window document Symbol Set Map WeakSet WeakMap Proxy Reflect Promise"},a={cN:"number",v:[{b:"\\b(0[bB][01]+)"},{b:"\\b(0[oO][0-7]+)"},{b:e.CNR}],r:0},n={cN:"subst",b:"\\$\\{",e:"\\}",k:t,c:[]},c={cN:"string",b:"`",e:"`",c:[e.BE,n]};n.c=[e.ASM,e.QSM,c,a,e.RM];var s=n.c.concat([e.CBCM,e.CLCM]);return{aliases:["js","jsx"],k:t,c:[{cN:"meta",r:10,b:/^\s*['"]use (strict|asm)['"]/},{cN:"meta",b:/^#!/,e:/$/},e.ASM,e.QSM,c,e.CLCM,e.CBCM,a,{b:/[{,]\s*/,r:0,c:[{b:r+"\\s*:",rB:!0,r:0,c:[{cN:"attr",b:r,r:0}]}]},{b:"("+e.RSR+"|\\b(case|return|throw)\\b)\\s*",k:"return throw case",c:[e.CLCM,e.CBCM,e.RM,{cN:"function",b:"(\\(.*?\\)|"+r+")\\s*=>",rB:!0,e:"\\s*=>",c:[{cN:"params",v:[{b:r},{b:/\(\s*\)/},{b:/\(/,e:/\)/,eB:!0,eE:!0,k:t,c:s}]}]},{b:/</,e:/(\/\w+|\w+\/)>/,sL:"xml",c:[{b:/<\w+\s*\/>/,skip:!0},{b:/<\w+/,e:/(\/\w+|\w+\/)>/,skip:!0,c:[{b:/<\w+\s*\/>/,skip:!0},"self"]}]}],r:0},{cN:"function",bK:"function",e:/\{/,eE:!0,c:[e.inherit(e.TM,{b:r}),{cN:"params",b:/\(/,e:/\)/,eB:!0,eE:!0,c:s}],i:/\[|%/},{b:/\$[(.]/},e.METHOD_GUARD,{cN:"class",bK:"class",e:/[{;=]/,eE:!0,i:/[:"\[\]]/,c:[{bK:"extends"},e.UTM]},{bK:"constructor",e:/\{/,eE:!0}],i:/#(?!!)/}});hljs.registerLanguage("tex",function(c){var e={cN:"tag",b:/\\/,r:0,c:[{cN:"name",v:[{b:/[a-zA-Zа-яА-я]+[*]?/},{b:/[^a-zA-Zа-яА-я0-9]/}],starts:{eW:!0,r:0,c:[{cN:"string",v:[{b:/\[/,e:/\]/},{b:/\{/,e:/\}/}]},{b:/\s*=\s*/,eW:!0,r:0,c:[{cN:"number",b:/-?\d*\.?\d+(pt|pc|mm|cm|in|dd|cc|ex|em)?/}]}]}}]};return{c:[e,{cN:"formula",c:[e],r:0,v:[{b:/\$\$/,e:/\$\$/},{b:/\$/,e:/\$/}]},c.C("%","$",{r:0})]}});hljs.registerLanguage("xml",function(s){var e="[A-Za-z0-9\\._:-]+",t={eW:!0,i:/</,r:0,c:[{cN:"attr",b:e,r:0},{b:/=\s*/,r:0,c:[{cN:"string",endsParent:!0,v:[{b:/"/,e:/"/},{b:/'/,e:/'/},{b:/[^\s"'=<>`]+/}]}]}]};return{aliases:["html","xhtml","rss","atom","xjb","xsd","xsl","plist"],cI:!0,c:[{cN:"meta",b:"<!DOCTYPE",e:">",r:10,c:[{b:"\\[",e:"\\]"}]},s.C("<!--","-->",{r:10}),{b:"<\\!\\[CDATA\\[",e:"\\]\\]>",r:10},{b:/<\?(php)?/,e:/\?>/,sL:"php",c:[{b:"/\\*",e:"\\*/",skip:!0}]},{cN:"tag",b:"<style(?=\\s|>|$)",e:">",k:{name:"style"},c:[t],starts:{e:"</style>",rE:!0,sL:["css","xml"]}},{cN:"tag",b:"<script(?=\\s|>|$)",e:">",k:{name:"script"},c:[t],starts:{e:"</script>",rE:!0,sL:["actionscript","javascript","handlebars","xml"]}},{cN:"meta",v:[{b:/<\?xml/,e:/\?>/,r:10},{b:/<\?\w+/,e:/\?>/}]},{cN:"tag",b:"</?",e:"/?>",c:[{cN:"name",b:/[^\/><\s]+/,r:0},t]}]}});hljs.registerLanguage("markdown",function(e){return{aliases:["md","mkdown","mkd"],c:[{cN:"section",v:[{b:"^#{1,6}",e:"$"},{b:"^.+?\\n[=-]{2,}$"}]},{b:"<",e:">",sL:"xml",r:0},{cN:"bullet",b:"^([*+-]|(\\d+\\.))\\s+"},{cN:"strong",b:"[*_]{2}.+?[*_]{2}"},{cN:"emphasis",v:[{b:"\\*.+?\\*"},{b:"_.+?_",r:0}]},{cN:"quote",b:"^>\\s+",e:"$"},{cN:"code",v:[{b:"^```w*s*$",e:"^```s*$"},{b:"`.+?`"},{b:"^( {4}|	)",e:"$",r:0}]},{b:"^[-\\*]{3,}",e:"$"},{b:"\\[.+?\\][\\(\\[].*?[\\)\\]]",rB:!0,c:[{cN:"string",b:"\\[",e:"\\]",eB:!0,rE:!0,r:0},{cN:"link",b:"\\]\\(",e:"\\)",eB:!0,eE:!0},{cN:"symbol",b:"\\]\\[",e:"\\]",eB:!0,eE:!0}],r:10},{b:/^\[[^\n]+\]:/,rB:!0,c:[{cN:"symbol",b:/\[/,e:/\]/,eB:!0,eE:!0},{cN:"link",b:/:\s*/,e:/$/,eB:!0}]}]}});hljs.registerLanguage("json",function(e){var i={literal:"true false null"},n=[e.QSM,e.CNM],r={e:",",eW:!0,eE:!0,c:n,k:i},t={b:"{",e:"}",c:[{cN:"attr",b:/"/,e:/"/,c:[e.BE],i:"\\n"},e.inherit(r,{b:/:/})],i:"\\S"},c={b:"\\[",e:"\\]",c:[e.inherit(r)],i:"\\S"};return n.splice(n.length,0,t,c),{c:n,k:i,i:"\\S"}});"></script>
+
+<style type="text/css">
+  code{white-space: pre-wrap;}
+  span.smallcaps{font-variant: small-caps;}
+  span.underline{text-decoration: underline;}
+  div.column{display: inline-block; vertical-align: top; width: 50%;}
+  div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;}
+  ul.task-list{list-style: none;}
+    </style>
+
+<style type="text/css">code{white-space: pre;}</style>
+<script type="text/javascript">
+if (window.hljs) {
+  hljs.configure({languages: []});
+  hljs.initHighlightingOnLoad();
+  if (document.readyState && document.readyState === "complete") {
+    window.setTimeout(function() { hljs.initHighlighting(); }, 0);
+  }
+}
+</script>
+
+
+
+
+
+<style type="text/css">
+/* for pandoc --citeproc since 2.11 */
+div.csl-bib-body { }
+div.csl-entry {
+  clear: both;
+}
+.hanging div.csl-entry {
+  margin-left:2em;
+  text-indent:-2em;
+}
+div.csl-left-margin {
+  min-width:2em;
+  float:left;
+}
+div.csl-right-inline {
+  margin-left:2em;
+  padding-left:1em;
+}
+div.csl-indent {
+  margin-left: 2em;
+}
+</style>
+
+
+
+
+<style type="text/css">
+.main-container {
+  max-width: 940px;
+  margin-left: auto;
+  margin-right: auto;
+}
+img {
+  max-width:100%;
+}
+.tabbed-pane {
+  padding-top: 12px;
+}
+.html-widget {
+  margin-bottom: 20px;
+}
+button.code-folding-btn:focus {
+  outline: none;
+}
+summary {
+  display: list-item;
+}
+details > summary > p:only-child {
+  display: inline;
+}
+pre code {
+  padding: 0;
+}
+</style>
+
+
+
+<!-- tabsets -->
+
+<style type="text/css">
+.tabset-dropdown > .nav-tabs {
+  display: inline-table;
+  max-height: 500px;
+  min-height: 44px;
+  overflow-y: auto;
+  border: 1px solid #ddd;
+  border-radius: 4px;
+}
+
+.tabset-dropdown > .nav-tabs > li.active:before, .tabset-dropdown > .nav-tabs.nav-tabs-open:before {
+  content: "\e259";
+  font-family: 'Glyphicons Halflings';
+  display: inline-block;
+  padding: 10px;
+  border-right: 1px solid #ddd;
+}
+
+.tabset-dropdown > .nav-tabs.nav-tabs-open > li.active:before {
+  content: "\e258";
+  font-family: 'Glyphicons Halflings';
+  border: none;
+}
+
+.tabset-dropdown > .nav-tabs > li.active {
+  display: block;
+}
+
+.tabset-dropdown > .nav-tabs > li > a,
+.tabset-dropdown > .nav-tabs > li > a:focus,
+.tabset-dropdown > .nav-tabs > li > a:hover {
+  border: none;
+  display: inline-block;
+  border-radius: 4px;
+  background-color: transparent;
+}
+
+.tabset-dropdown > .nav-tabs.nav-tabs-open > li {
+  display: block;
+  float: none;
+}
+
+.tabset-dropdown > .nav-tabs > li {
+  display: none;
+}
+</style>
+
+<!-- code folding -->
+
+
+
+
 </head>
-<body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-article">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav">
-<li>
-  <a href="../../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu">
-<li>
-      <a href="../../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul>
-</li>
-<li>
-  <a href="../../news/index.html">News</a>
-</li>
-      </ul>
-<ul class="nav navbar-nav navbar-right">
-<li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul>
+
+<body>
+
+
+<div class="container-fluid main-container">
+
+
+
+
+<div id="header">
+
+
+
+<h1 class="title toc-ignore">Testing hierarchical parent degradation
+kinetics with residue data on dimethenamid and dimethenamid-P</h1>
+<h4 class="author">Johannes Ranke</h4>
+<h4 class="date">Last change on 5 January 2023, last compiled on 13
+Februar 2025</h4>
+
 </div>
-<!--/.nav-collapse -->
-  </div>
-<!--/.container -->
+
+<div id="TOC">
+true
 </div>
-<!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header toc-ignore">
-      <h1 data-toc-skip>Testing hierarchical parent degradation kinetics
-with residue data on dimethenamid and dimethenamid-P</h1>
-                        <h4 data-toc-skip class="author">Johannes
-Ranke</h4>
-            
-            <h4 data-toc-skip class="date">Last change on 5 January
-2023, last compiled on 30 October 2023</h4>
-      
-      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/prebuilt/2022_dmta_parent.rmd" class="external-link"><code>vignettes/prebuilt/2022_dmta_parent.rmd</code></a></small>
-      <div class="hidden name"><code>2022_dmta_parent.rmd</code></div>
-
-    </div>
-
-    
-    
-<div class="section level2">
-<h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a>
-</h2>
+
+<div id="introduction" class="section level1">
+<h1>Introduction</h1>
 <p>The purpose of this document is to demonstrate how nonlinear
 hierarchical models (NLHM) based on the parent degradation models SFO,
 FOMC, DFOP and HS can be fitted with the mkin package.</p>
@@ -157,7 +398,7 @@ FOMC, DFOP and HS can be fitted with the mkin package.</p>
 173340 (Application of nonlinear hierarchical models to the kinetic
 evaluation of chemical degradation data) of the German Environment
 Agency carried out in 2022 and 2023.</p>
-<p>The mkin package is used in version 1.2.6. It contains the test data
+<p>The mkin package is used in version 1.2.9. It contains the test data
 and the functions used in the evaluations. The <code>saemix</code>
 package is used as a backend for fitting the NLHM, but is also loaded to
 make the convergence plot function available.</p>
@@ -165,21 +406,19 @@ make the convergence plot function available.</p>
 also provides the <code>kable</code> function that is used to improve
 the display of tabular data in R markdown documents. For parallel
 processing, the <code>parallel</code> package is used.</p>
-<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://pkgdown.jrwb.de/mkin/">mkin</a></span><span class="op">)</span></span>
-<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://yihui.org/knitr/" class="external-link">knitr</a></span><span class="op">)</span></span>
-<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va">saemix</span><span class="op">)</span></span>
-<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va">parallel</span><span class="op">)</span></span>
-<span><span class="va">n_cores</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/parallel/detectCores.html" class="external-link">detectCores</a></span><span class="op">(</span><span class="op">)</span></span>
-<span><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/Sys.info.html" class="external-link">Sys.info</a></span><span class="op">(</span><span class="op">)</span><span class="op">[</span><span class="st">"sysname"</span><span class="op">]</span> <span class="op">==</span> <span class="st">"Windows"</span><span class="op">)</span> <span class="op">{</span></span>
-<span>  <span class="va">cl</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/parallel/makeCluster.html" class="external-link">makePSOCKcluster</a></span><span class="op">(</span><span class="va">n_cores</span><span class="op">)</span></span>
-<span><span class="op">}</span> <span class="kw">else</span> <span class="op">{</span></span>
-<span>  <span class="va">cl</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/parallel/makeCluster.html" class="external-link">makeForkCluster</a></span><span class="op">(</span><span class="va">n_cores</span><span class="op">)</span></span>
-<span><span class="op">}</span></span></code></pre></div>
+<pre class="r"><code>library(mkin)
+library(knitr)
+library(saemix)
+library(parallel)
+n_cores &lt;- detectCores()
+if (Sys.info()[&quot;sysname&quot;] == &quot;Windows&quot;) {
+  cl &lt;- makePSOCKcluster(n_cores)
+} else {
+  cl &lt;- makeForkCluster(n_cores)
+}</code></pre>
 </div>
-<div class="section level2">
-<h2 id="data">Data<a class="anchor" aria-label="anchor" href="#data"></a>
-</h2>
+<div id="data" class="section level1">
+<h1>Data</h1>
 <p>The test data are available in the mkin package as an object of class
 <code>mkindsg</code> (mkin dataset group) under the identifier
 <code>dimethenamid_2018</code>. The following preprocessing steps are
@@ -201,37 +440,37 @@ and <code>Elliot 2</code>) are combined, resulting in dimethenamid
 (DMTA) data from six soils.</li>
 </ul>
 <p>The following commented R code performs this preprocessing.</p>
-<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="co"># Apply a function to each of the seven datasets in the mkindsg object to create a list</span></span>
-<span><span class="va">dmta_ds</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fl">7</span>, <span class="kw">function</span><span class="op">(</span><span class="va">i</span><span class="op">)</span> <span class="op">{</span></span>
-<span>  <span class="va">ds_i</span> <span class="op">&lt;-</span> <span class="va">dimethenamid_2018</span><span class="op">$</span><span class="va">ds</span><span class="op">[[</span><span class="va">i</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">data</span>                     <span class="co"># Get a dataset</span></span>
-<span>  <span class="va">ds_i</span><span class="op">[</span><span class="va">ds_i</span><span class="op">$</span><span class="va">name</span> <span class="op">==</span> <span class="st">"DMTAP"</span>, <span class="st">"name"</span><span class="op">]</span> <span class="op">&lt;-</span>  <span class="st">"DMTA"</span>              <span class="co"># Rename DMTAP to DMTA</span></span>
-<span>  <span class="va">ds_i</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/subset.html" class="external-link">subset</a></span><span class="op">(</span><span class="va">ds_i</span>, <span class="va">name</span> <span class="op">==</span> <span class="st">"DMTA"</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"name"</span>, <span class="st">"time"</span>, <span class="st">"value"</span><span class="op">)</span><span class="op">)</span> <span class="co"># Select data</span></span>
-<span>  <span class="va">ds_i</span><span class="op">$</span><span class="va">time</span> <span class="op">&lt;-</span> <span class="va">ds_i</span><span class="op">$</span><span class="va">time</span> <span class="op">*</span> <span class="va">dimethenamid_2018</span><span class="op">$</span><span class="va">f_time_norm</span><span class="op">[</span><span class="va">i</span><span class="op">]</span>  <span class="co"># Normalise time</span></span>
-<span>  <span class="va">ds_i</span>                                                       <span class="co"># Return the dataset</span></span>
-<span><span class="op">}</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># Use dataset titles as names for the list elements</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">dmta_ds</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">sapply</a></span><span class="op">(</span><span class="va">dimethenamid_2018</span><span class="op">$</span><span class="va">ds</span>, <span class="kw">function</span><span class="op">(</span><span class="va">ds</span><span class="op">)</span> <span class="va">ds</span><span class="op">$</span><span class="va">title</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># Combine data for Elliot soil to obtain a named list with six elements</span></span>
-<span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot"</span><span class="op">]</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/cbind.html" class="external-link">rbind</a></span><span class="op">(</span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 1"</span><span class="op">]</span><span class="op">]</span>, <span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 2"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span> <span class="co">#</span></span>
-<span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 1"</span><span class="op">]</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="cn">NULL</span></span>
-<span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 2"</span><span class="op">]</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="cn">NULL</span></span></code></pre></div>
+<pre class="r"><code># Apply a function to each of the seven datasets in the mkindsg object to create a list
+dmta_ds &lt;- lapply(1:7, function(i) {
+  ds_i &lt;- dimethenamid_2018$ds[[i]]$data                     # Get a dataset
+  ds_i[ds_i$name == &quot;DMTAP&quot;, &quot;name&quot;] &lt;-  &quot;DMTA&quot;              # Rename DMTAP to DMTA
+  ds_i &lt;- subset(ds_i, name == &quot;DMTA&quot;, c(&quot;name&quot;, &quot;time&quot;, &quot;value&quot;)) # Select data
+  ds_i$time &lt;- ds_i$time * dimethenamid_2018$f_time_norm[i]  # Normalise time
+  ds_i                                                       # Return the dataset
+})
+
+# Use dataset titles as names for the list elements
+names(dmta_ds) &lt;- sapply(dimethenamid_2018$ds, function(ds) ds$title)
+
+# Combine data for Elliot soil to obtain a named list with six elements
+dmta_ds[[&quot;Elliot&quot;]] &lt;- rbind(dmta_ds[[&quot;Elliot 1&quot;]], dmta_ds[[&quot;Elliot 2&quot;]]) #
+dmta_ds[[&quot;Elliot 1&quot;]] &lt;- NULL
+dmta_ds[[&quot;Elliot 2&quot;]] &lt;- NULL</code></pre>
 <p>The following tables show the 6 datasets.</p>
-<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="kw">for</span> <span class="op">(</span><span class="va">ds_name</span> <span class="kw">in</span> <span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">dmta_ds</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
-<span>    <span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="fu"><a href="../../reference/mkin_long_to_wide.html">mkin_long_to_wide</a></span><span class="op">(</span><span class="va">dmta_ds</span><span class="op">[[</span><span class="va">ds_name</span><span class="op">]</span><span class="op">]</span><span class="op">)</span>,</span>
-<span>      caption <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste</a></span><span class="op">(</span><span class="st">"Dataset"</span>, <span class="va">ds_name</span><span class="op">)</span>,</span>
-<span>      label <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span><span class="st">"tab:"</span>, <span class="va">ds_name</span><span class="op">)</span>, booktabs <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span></span>
-<span>    <span class="fu"><a href="https://rdrr.io/r/base/cat.html" class="external-link">cat</a></span><span class="op">(</span><span class="st">"\n\\clearpage\n"</span><span class="op">)</span></span>
-<span><span class="op">}</span></span></code></pre></div>
-<table class="table">
+<pre class="r"><code>for (ds_name in names(dmta_ds)) {
+    print(kable(mkin_long_to_wide(dmta_ds[[ds_name]]),
+      caption = paste(&quot;Dataset&quot;, ds_name),
+      label = paste0(&quot;tab:&quot;, ds_name), booktabs = TRUE))
+    cat(&quot;\n\\clearpage\n&quot;)
+}</code></pre>
+<table>
 <caption>Dataset Calke</caption>
-<thead><tr class="header">
+<thead>
+<tr class="header">
 <th align="right">time</th>
 <th align="right">DMTA</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="right">0</td>
@@ -263,12 +502,14 @@ and <code>Elliot 2</code>) are combined, resulting in dimethenamid
 </tr>
 </tbody>
 </table>
-<table class="table">
+<table>
 <caption>Dataset Borstel</caption>
-<thead><tr class="header">
+<thead>
+<tr class="header">
 <th align="right">time</th>
 <th align="right">DMTA</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="right">0.000000</td>
@@ -336,12 +577,14 @@ and <code>Elliot 2</code>) are combined, resulting in dimethenamid
 </tr>
 </tbody>
 </table>
-<table class="table">
+<table>
 <caption>Dataset Flaach</caption>
-<thead><tr class="header">
+<thead>
+<tr class="header">
 <th align="right">time</th>
 <th align="right">DMTA</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="right">0.0000000</td>
@@ -489,12 +732,14 @@ and <code>Elliot 2</code>) are combined, resulting in dimethenamid
 </tr>
 </tbody>
 </table>
-<table class="table">
+<table>
 <caption>Dataset BBA 2.2</caption>
-<thead><tr class="header">
+<thead>
+<tr class="header">
 <th align="right">time</th>
 <th align="right">DMTA</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="right">0.0000000</td>
@@ -594,12 +839,14 @@ and <code>Elliot 2</code>) are combined, resulting in dimethenamid
 </tr>
 </tbody>
 </table>
-<table class="table">
+<table>
 <caption>Dataset BBA 2.3</caption>
-<thead><tr class="header">
+<thead>
+<tr class="header">
 <th align="right">time</th>
 <th align="right">DMTA</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="right">0.0000000</td>
@@ -699,12 +946,14 @@ and <code>Elliot 2</code>) are combined, resulting in dimethenamid
 </tr>
 </tbody>
 </table>
-<table class="table">
+<table>
 <caption>Dataset Elliot</caption>
-<thead><tr class="header">
+<thead>
+<tr class="header">
 <th align="right">time</th>
 <th align="right">DMTA</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="right">0.000000</td>
@@ -901,25 +1150,24 @@ and <code>Elliot 2</code>) are combined, resulting in dimethenamid
 </tbody>
 </table>
 </div>
-<div class="section level2">
-<h2 id="separate-evaluations">Separate evaluations<a class="anchor" aria-label="anchor" href="#separate-evaluations"></a>
-</h2>
+<div id="separate-evaluations" class="section level1">
+<h1>Separate evaluations</h1>
 <p>In order to obtain suitable starting parameters for the NLHM fits,
 separate fits of the four models to the data for each soil are generated
 using the <code>mmkin</code> function from the <code>mkin</code>
 package. In a first step, constant variance is assumed. Convergence is
 checked with the <code>status</code> function.</p>
-<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">deg_mods</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"FOMC"</span>, <span class="st">"DFOP"</span>, <span class="st">"HS"</span><span class="op">)</span></span>
-<span><span class="va">f_sep_const</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/mmkin.html">mmkin</a></span><span class="op">(</span></span>
-<span>  <span class="va">deg_mods</span>,</span>
-<span>  <span class="va">dmta_ds</span>,</span>
-<span>  error_model <span class="op">=</span> <span class="st">"const"</span>,</span>
-<span>  quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu"><a href="../../reference/status.html">status</a></span><span class="op">(</span><span class="va">f_sep_const</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
-<thead><tr class="header">
+<pre class="r"><code>deg_mods &lt;- c(&quot;SFO&quot;, &quot;FOMC&quot;, &quot;DFOP&quot;, &quot;HS&quot;)
+f_sep_const &lt;- mmkin(
+  deg_mods,
+  dmta_ds,
+  error_model = &quot;const&quot;,
+  quiet = TRUE)
+
+status(f_sep_const) |&gt; kable()</code></pre>
+<table>
+<thead>
+<tr class="header">
 <th align="left"></th>
 <th align="left">Calke</th>
 <th align="left">Borstel</th>
@@ -927,7 +1175,8 @@ checked with the <code>status</code> function.</p>
 <th align="left">BBA 2.2</th>
 <th align="left">BBA 2.3</th>
 <th align="left">Elliot</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="left">SFO</td>
@@ -972,11 +1221,11 @@ to converge. All separate fits with constant variance converged, with
 the sole exception of the HS fit to the BBA 2.2 data. To prepare for
 fitting NLHM using the two-component error model, the separate fits are
 updated assuming two-component error.</p>
-<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">f_sep_tc</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_sep_const</span>, error_model <span class="op">=</span> <span class="st">"tc"</span><span class="op">)</span></span>
-<span><span class="fu"><a href="../../reference/status.html">status</a></span><span class="op">(</span><span class="va">f_sep_tc</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
-<thead><tr class="header">
+<pre class="r"><code>f_sep_tc &lt;- update(f_sep_const, error_model = &quot;tc&quot;)
+status(f_sep_tc) |&gt; kable()</code></pre>
+<table>
+<thead>
+<tr class="header">
 <th align="left"></th>
 <th align="left">Calke</th>
 <th align="left">Borstel</th>
@@ -984,7 +1233,8 @@ updated assuming two-component error.</p>
 <th align="left">BBA 2.2</th>
 <th align="left">BBA 2.3</th>
 <th align="left">Elliot</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="left">SFO</td>
@@ -1028,9 +1278,8 @@ updated assuming two-component error.</p>
 converge with constant variance did converge, but other non-SFO fits
 failed to converge.</p>
 </div>
-<div class="section level2">
-<h2 id="hierarchichal-model-fits">Hierarchichal model fits<a class="anchor" aria-label="anchor" href="#hierarchichal-model-fits"></a>
-</h2>
+<div id="hierarchichal-model-fits" class="section level1">
+<h1>Hierarchichal model fits</h1>
 <p>The following code fits eight versions of hierarchical models to the
 data, using SFO, FOMC, DFOP and HS for the parent compound, and using
 either constant variance or two-component error for the error model. The
@@ -1042,18 +1291,18 @@ all eight versions in parallel (given a sufficient number of computing
 cores being available) to save execution time.</p>
 <p>Convergence plots and summaries for these fits are shown in the
 appendix.</p>
-<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">f_saem</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/mhmkin.html">mhmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">f_sep_const</span>, <span class="va">f_sep_tc</span><span class="op">)</span>, transformations <span class="op">=</span> <span class="st">"saemix"</span><span class="op">)</span></span></code></pre></div>
+<pre class="r"><code>f_saem &lt;- mhmkin(list(f_sep_const, f_sep_tc), transformations = &quot;saemix&quot;)</code></pre>
 <p>The output of the <code>status</code> function shows that all fits
 terminated successfully.</p>
-<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="../../reference/status.html">status</a></span><span class="op">(</span><span class="va">f_saem</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
-<thead><tr class="header">
+<pre class="r"><code>status(f_saem) |&gt; kable()</code></pre>
+<table>
+<thead>
+<tr class="header">
 <th align="left"></th>
 <th align="left">const</th>
 <th align="left">tc</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="left">SFO</td>
@@ -1079,16 +1328,17 @@ terminated successfully.</p>
 </table>
 <p>The AIC and BIC values show that the biphasic models DFOP and HS give
 the best fits.</p>
-<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_saem</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span>digits <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
-<thead><tr class="header">
+<pre class="r"><code>anova(f_saem) |&gt; kable(digits = 1)</code></pre>
+<table>
+<thead>
+<tr class="header">
 <th align="left"></th>
 <th align="right">npar</th>
 <th align="right">AIC</th>
 <th align="right">BIC</th>
 <th align="right">Lik</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="left">SFO const</td>
@@ -1154,22 +1404,22 @@ it shows the lowest AIC and BIC values in the first set of fits when
 combined with the two-component error model. Therefore, the DFOP model
 was selected for further refinements of the fits with the aim to make
 the model fully identifiable.</p>
-<div class="section level3">
-<h3 id="parameter-identifiability-based-on-the-fisher-information-matrix">Parameter identifiability based on the Fisher Information
-Matrix<a class="anchor" aria-label="anchor" href="#parameter-identifiability-based-on-the-fisher-information-matrix"></a>
-</h3>
+<div id="parameter-identifiability-based-on-the-fisher-information-matrix" class="section level2">
+<h2>Parameter identifiability based on the Fisher Information
+Matrix</h2>
 <p>Using the <code>illparms</code> function, ill-defined statistical
 model parameters such as standard deviations of the degradation
 parameters in the population and error model parameters can be
 found.</p>
-<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="../../reference/illparms.html">illparms</a></span><span class="op">(</span><span class="va">f_saem</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
-<thead><tr class="header">
+<pre class="r"><code>illparms(f_saem) |&gt; kable()</code></pre>
+<table>
+<thead>
+<tr class="header">
 <th align="left"></th>
 <th align="left">const</th>
 <th align="left">tc</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="left">SFO</td>
@@ -1200,34 +1450,32 @@ be obtained for this standard deviation when using different starting
 values.</p>
 <p>The thus identified overparameterisation is addressed by removing the
 random effect for <code>k2</code> from the parameter model.</p>
-<div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">f_saem_dfop_tc_no_ranef_k2</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_saem</span><span class="op">[[</span><span class="st">"DFOP"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span>,</span>
-<span>  no_random_effect <span class="op">=</span> <span class="st">"k2"</span><span class="op">)</span></span></code></pre></div>
+<pre class="r"><code>f_saem_dfop_tc_no_ranef_k2 &lt;- update(f_saem[[&quot;DFOP&quot;, &quot;tc&quot;]],
+  no_random_effect = &quot;k2&quot;)</code></pre>
 <p>For the resulting fit, it is checked whether there are still
 ill-defined parameters,</p>
-<div class="sourceCode" id="cb11"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="../../reference/illparms.html">illparms</a></span><span class="op">(</span><span class="va">f_saem_dfop_tc_no_ranef_k2</span><span class="op">)</span></span></code></pre></div>
+<pre class="r"><code>illparms(f_saem_dfop_tc_no_ranef_k2)</code></pre>
 <p>which is not the case. Below, the refined model is compared with the
 previous best model. The model without random effect for <code>k2</code>
 is a reduced version of the previous model. Therefore, the models are
 nested and can be compared using the likelihood ratio test. This is
 achieved with the argument <code>test = TRUE</code> to the
 <code>anova</code> function.</p>
-<div class="sourceCode" id="cb12"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_saem</span><span class="op">[[</span><span class="st">"DFOP"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span>, <span class="va">f_saem_dfop_tc_no_ranef_k2</span>, test <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span> <span class="op">|&gt;</span></span>
-<span>  <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span>format.args <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>digits <span class="op">=</span> <span class="fl">4</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
+<pre class="r"><code>anova(f_saem[[&quot;DFOP&quot;, &quot;tc&quot;]], f_saem_dfop_tc_no_ranef_k2, test = TRUE) |&gt;
+  kable(format.args = list(digits = 4))</code></pre>
+<table>
 <colgroup>
-<col width="38%">
-<col width="7%">
-<col width="8%">
-<col width="8%">
-<col width="9%">
-<col width="8%">
-<col width="4%">
-<col width="15%">
+<col width="38%" />
+<col width="7%" />
+<col width="8%" />
+<col width="8%" />
+<col width="9%" />
+<col width="8%" />
+<col width="4%" />
+<col width="15%" />
 </colgroup>
-<thead><tr class="header">
+<thead>
+<tr class="header">
 <th align="left"></th>
 <th align="right">npar</th>
 <th align="right">AIC</th>
@@ -1236,7 +1484,8 @@ achieved with the argument <code>test = TRUE</code> to the
 <th align="right">Chisq</th>
 <th align="right">Df</th>
 <th align="right">Pr(&gt;Chisq)</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="left">f_saem_dfop_tc_no_ranef_k2</td>
@@ -1264,35 +1513,35 @@ achieved with the argument <code>test = TRUE</code> to the
 random effect for <code>k2</code>. The p value of the likelihood ratio
 test is much greater than 0.05, indicating that the model with the
 higher likelihood (here the model with random effects for all
-degradation parameters <code>f_saem[["DFOP", "tc"]]</code>) does not fit
+degradation parameters <code>f_saem[[&quot;DFOP&quot;, &quot;tc&quot;]]</code>) does not fit
 significantly better than the model with the lower likelihood (the
 reduced model <code>f_saem_dfop_tc_no_ranef_k2</code>).</p>
 <p>Therefore, AIC, BIC and likelihood ratio test suggest the use of the
 reduced model.</p>
 <p>The convergence of the fit is checked visually.</p>
 <div class="figure" style="text-align: center">
-<img src="2022_dmta_parent_files/figure-html/convergence-saem-dfop-tc-no-ranef-k2-1.png" alt="Convergence plot for the NLHM DFOP fit with two-component error and without a random effect on 'k2'" width="864"><p class="caption">
+<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABsAAAAYACAIAAABHMxqdAAAACXBIWXMAAB2HAAAdhwGP5fFlAAAgAElEQVR4nOzdeVzU5f7//2uGfREEVFDEnTAFXEkxy9wyLcWlXNJOuebyUSs9ampK2tFcOqWkHY+mLUdPi5i5gVKnNFPTcgEERRDJBQVBZBm2gfn9cf18fydkEIZlGHjcb+d2bu95zzUzL7B6+37O67oulU6nEwAAAAAAAABQGrWpCwAAAAAAAABQexEgAgAAAAAAADCIABEAAAAAAACAQQSIAAAAAAAAAAwiQAQAAAAAAABgEAEiAAAAAAAAAIMIEAEAAAAAAAAYRIAIAAAAAAAAwCACRAAAAAAAAAAGESACAAAAAAAAMIgAEQAAAAAAAIBBBIgAAAAAAAAADCJABAAAAAAAAGAQASIAAAAAAAAAgwgQAQAAAAAAABhEgAgAAAAAAADAIAJEAAAAAAAAAAYRIAIAAAAAAAAwiAARAAAAAAAAgEEEiAAAAAAAAAAMIkAEAAAAAAAAYBABIgAAAAAAAACDCBABAAAAAAAAGESACAAAAAAAAMAgAkQAAAAAAAAABhEgAgAAAAAAADCIABEAAAAAAACAQQSIAAAAAAAAAAwiQAQAAAAAAABgEAEiAAAAAAAAAIMIEAEAAAAAAAAYRIAIAAAAAAAAwCACRAAAAAAAAAAGESACAAAAAAAAMIgAEQAAAAAAAIBBBIgAAAAAAAAADCJABAAAAAAAAGAQASIAAAAAAAAAgwgQAQAAAAAAABhEgAgAAAAAAADAIAJEAAAAAAAAAAYRIAIAAAAAAAAwiAARAAAAAAAAgEEEiEDFfP7556q/UqvVDRs29PPze+ONN/7880/9wVOnTtUf+dtvv+k/++677+o/+/nnnwshBg8erHqUiRMnKm9y6dIl5fyoUaNq5pcAAID01FNPyWvQhQsXTF0LAADlxfULqCgCRKCydDrd/fv3o6OjN2zY4Ovre+zYMUMjT506pf/w9OnTlf/0r7/+Wjk+dOhQdna20W+1bdu2IUOGuLq6enh4jB49OjIysvLlAQCg78yZM3Z2dg0bNjR1IQAAPFp2dvbChQsDAgIaNGjQqlWrIUOGhIWFmboowDQsTV0AYK4aNWr03HPPCSG0Wm10dHRMTExxcXFWVtakSZMuXbpkaVnKv1ynTp2aO3eu8rDUALFfv36NGjWSx4WFhUo+OHz4cEdHR3kcGBiojP/qq6+U47y8vAMHDowdO7aiP4tWq50xY8a2bduUM99+++2+ffv27t0rf0YAACovJSVl+vTpeXl5pi4EAIBHS01N7d69uzLJLDs7OykpKSwsbObMmZs2bTJtbUDNI0AEjNSuXbsvv/xSeRgRETFy5Mjs7OyEhIQvvvhi0qRJ+oOtra0LCgr0OxCvXr169+5d5Snl/N///nflOCcnRwkQP/roo5YtW5aoITIy8tKlS0IIS0tLrVYrhPj222+NCBD/+c9/yvSwbdu2r732Wnx8/Oeff56fnz9+/PirV686OztX9A0BAFBERUWdOXPm7Nmz//3vf9PT001dDgAA5TJ//nyZHvr4+EyYMOHOnTtbt27Nz8/fvHnzoEGDhg0bZuoCgRrFFGagagwcOHDRokXyeO3atSWe7dy5sxDi2rVrt2/flmdk+6G1tXWHDh2M/lCl/fDNN9+0sLAQQoSFhRkxi/njjz8WQtja2h4+fHjp0qWfffbZ9OnThRDp6em7du0yujwAQD20Zs0auapUp06dsrKyhBD//ve/J0+evGnTJtJDAECtVeL6pdPpQkNDhRANGjQ4fvz40qVLQ0JCPvroIzlYfyEpoJ4gQASqjLK3SVxcXG5urv5T3bt3t7KyEkIo+6jIALFLly62trZGf+I333wjD6ZNm9anTx8hRG5u7oEDByr0JvHx8devXxdCPPHEE23btpUnX3rpJXlw9OhRo8sDANQ3R44cWbx4sRCiWbNmBw8ebNCggRDC399/+AMqlcrUNQIAUNLD16/r16/n5OQIIXr06KGsMTVgwAB5EBUVZapSAVMhQASqTLNmzeRsX51Ol5iYqP+Uvb19p06dhBAnT56UZ2SA2LNnT6M/7syZMwkJCUIIf3//du3ajRw5Up7/9ttvK/Q+V65ckQfNmzdXTirH8fHxRlcIAKhXEhISxo4dW1xc7ODgsH//fuVSMnXq1O8ekP3yAADUHqVev+zs7NavX79+/fo5c+YoI2VnvRBCWZ4eqD8IEIGq5OHhIQ9ktKdPZoVyGUStVnv27FlRuQBRaZsfNWqUEEJp66joLOaMjAx54ODgoJyUPSNCiHv37hldIQCg/sjOzh4+fPi9e/fUavV///vfrl27mroiAAAezdD1q3HjxvPmzZs3b97QoUOVwcoU5kGDBpmgVsCkCBCBqlTGzCy5dfKZM2eKioqioqLkHGejA0SdTqfMX5YBoqenZ48ePYQQubm5Bw8eLP9bKbth6k+mtre3L/EsAABlmDlzZnR0tBDi9ddf17/XAgCgNivn9au4uHjOnDlffPGFEKJDhw5vvPFGzZUI1A4EiEBVSk5OlgfKYoIKGSBqNJqoqCg5f9nd3b1Vq1bGfdCJEyfkwoWPPfZYx44d5UllFrOSLZaHtbW1PNBoNMpJJTe0s7MzrkIAQL0SGRkpD/bu3SsXjQIAoPYrz/Xr+vXrffv2DQkJEUJ07tz5yJEjcukqoF4hQASqzO3bt+/fvy+EUKlUrVu3LvFs69at3d3dhRCnTp2SW6nISNE4yv7LcXFxqgcWLFggT1ZoFrOLi4s8UFb00D9u2LCh0UUCAOoVJycnIURycvL69etNXQsAAOVV9vVr3759nTp1OnbsmBDi1VdfPXHihKenZ02XCNQCBIhAldmxY4c88Pb2LrVxTyaGp06dquQOKsXFxbt37y5jQIVmMXt7e8uDpKQk5aRyrDwLAEAZRowYcejQIXm8bt06pSUfAIDarOzr18qVK+UKiY6Ojrt27frss8+YoYV6iwARqBo//fTT+++/L48XLlxY6hiZGP7444+xsbGiEgHizz//fPv2bSFEkyZNJvxVr1695Jjy78Xs7e0tJ0H//vvvSgP/1q1b5UG/fv2MKxIAUK8sW7bsySeffP7554UQOTk577zzjqkrAgDg0cq4fu3cuXPZsmU6nc7e3v7YsWPjxo0zXZmA6VmaugDAXCUkJEycOFEIodVqL168GBkZWVRUJIRo27bt3/72t1JfIjsQb9y4IYSwsLDo3r27cR+t7L/86quvrl27Vv+p2NjYDh06CCEOHTqUk5Ojv7FyGSZNmjRv3ryioqL+/fsPGzYsLi7u+PHjQohGjRqNHj3auCIBAPWK3EbsvffeO3TokE6n27Fjx5w5c/z9/U1dFwAAZTF0/dLpdMuWLZNjmjdvvnHjRv1XtWjR4t133zVBuYDpECACRkpNTf3ss89KnGzduvXOnTstLUv/N6t79+6WlpZarVYI4efnV850rwStVhsaGiqPhw8fXuLZxx9/vG3btgkJCbm5uQcOHBgzZkx53nP27NmHDh368ccf7969u337dnnS1dV19+7dLA8MACi/zp07jxo1avfu3cXFxfPnzz9y5IipKwIA4NEevn5FRUVdvXpVPhsXFxcXF1diPAEi6humMANVoEGDBh07dpw/f/6FCxd69OhhaJi9vX2nTp3ksdHzl3/44Ye0tDQhRJMmTUp9k2HDhsmD8s9itrKyCg8PX7duna+vr52dXfv27V977bXffvutT58+xhUJAKi3VqxYoVarhRARERHh4eGmLgcAgHIpcf1KTEw0dUVA7aLS6XSmrgEAAAAAAABALUUHIgAAAAAAAACDWAMRqLNOnz79yy+/PHLYhAkT3N3da6AeAAAAAABgjggQgTrrp59+WrRo0SOHPfPMMwSIAAAAAADAENZABAAAAAAAAGAQayACAAAAAAAAMIgAEQAAAAAAAIBBBIgAAAAAAAAADCJABAAAAAAAAGAQASIAAAAAAAAAgwgQAQAAAAAAABhEgAgAAAAAAADAIAJEAAAAAAAAAAYRIAIAAAAAAAAwiAARAAAAAAAAgEEEiAAAAAAAAAAMIkAEAAAAAAAAYBABIgAAAAAAAACDCBABAAAAAAAAGESACAAAAAAAAMAgAkQAAAAAAAAABhEgAgAAAAAAADCIABEAAAAAAACAQQSIAAAAAAAAAAwiQAQAAAAAAABgEAEiAAAAAAAAAIMIEAEAAAAAAAAYRIAIAAAAAAAAwCACRAAAAAAAAAAGWZq6AAAAAABA7ZWUlBQbG/vcc8/Jh9evX9+4ceOFCxeKioo6d+48adKkjh07mrZCAEB1U+l0OlPXAAAAAACojf7xj38sW7ZsyJAh+/fvF0Ls379//PjxWVlZygBra+u1a9fOnTvXdDUCAKodU5gBAAAAAKX4+uuvly5d6uDgMGLECCHEnTt3Jk6cWFhYuGLFigsXLly5cmX79u1ubm5vvfXWiRMnTF0sAKAa0YEIAAAAAChFjx49YmJiLl682KJFCyHEli1bpk+f/vHHH8+aNUsZEx0d3a1bt0GDBu3bt890lQIAqhcdiAAAAACAUsTExDz11FMyPRRCREVFCSFeeukl/TG+vr49e/Y8d+6cCeoDANQUAkQAAAAAQCkcHBxu376tPPTw8Ch1mJWVlVarramiAAAmQIAIAAAAACjFwIEDL1y4cPToUfmwd+/eQojvv/9ef0xSUtKpU6d69OhhgvoAADXFIjg42NQ1AAAAAABqnYCAgG+++ebTTz8tKipq3bp1p06d0tPT161b16JFCx8fH5VK9euvv44bN+727dvr1q177LHHTF0vAKC6sIkKAAAAAKB0Fy9efO65527cuCGEcHR0dHd3T0hIEEJYWlpaWlrm5eVZWlp+/PHHr7/+uqkrBQBUIwJEAAAAAIBBhYWFX3311ZYtWyIjI7OysuRJtVrdunXrF1988f/+7/+aN29u2goBANWNABEAAAAAUC4ajUaj0VhaWjo6OlpaWpq6HABADSFABAAAAAAAAGAQuzADAAAAAAAAMIiec2MUFBTk5OSYugoAqF4NGzZUqVSmrgJViesXgPqA61fdw/ULQH1Qy69fBIgVlp+f37Zt25s3b5q6EABCCGEthMWD4wIhikxZS10zZcqUrVu3mroKVBmuX0CtwvWr+nD9qmO4fgG1Ctev6lPLr18EiBWWkZFx8+ZNtVrt7Oxs6loAiBE5OY8XFMjj7xwcYq2tTVtP3aDVarOysi5evGjqQlCVuH4BtQrXr+rA9atqpaamnjhxovzjg4KCqqMMrl9ArcL1qzqYxfWLANFIjRs3vn37tqmrACD2jxlz+Ztv5PH27dt9Ro82bT11w8mTJ3v16mXqKlAtuH4BtQTXr+rA9atqXbx4cfjw4eUfX637c3L9AmoJrl/VwSyuXwSIAAAAAICSnnnmmaioqODg4NDQUCHEjBkz6AEEgHqLABEAAAAAUApfX9/du3d369bt7NmzCxcubNmypakrAgCYBgEiAADVLicnR6VS2dvb6588ceLEzp07IyMjGzZs6O/vP336dC8vL1NVCACAIWPHjj179qypqwAAmBIBIgAA1c7R0bF9+/axsbHKmSVLlqxevVpZLurAgQMhISGbN2+eMGGCiWoEAKB0AQEB7dq1s7Tk5hEA6i+uAX+xZcuWR+6ZXVhYKIRIT0+vkYoAAHVQWFjYqlWr3NzcVqxY0adPH0tLy19++WXp0qVTpkzp1q3b448/buoCAQD4f5555pkrV66YugoAgCkRIP7Fr7/++scff5RnpIwRAQAwwocffqhWqw8ePNijRw95xsfHp2vXrk888cTKlSt37dpl2vIAAAAAQB8B4l98+umn8+bN02q1ZYxJSEgYM2aMWq2usaoAAHVMZGRk165dlfRQ6tq1a/fu3VlkCgAAAEBtQ4D4F1ZWVp06dSp7jI2NTc0UYxb++c9/Xrp06V//+heJKgCUX2FhYZMmTR4+36xZM/11Ek3r5MmTwcHBxcXFypmGDRtu27bN2dnZhFUBAEwiISEhIiIiJiYmLS1No9G4ubl5enp6enoOHTq0adOmRr9tRkbG77//XvaYe/fuCSHKbvKoVlevXn3jjTfefvvtwMBAU9UAACZHgIhKWbt27Z07d2bOnNm5c2dT1wIAZiMgICAyMlKn06lUKuWkTqe7ePGiv7+/CQvTd/To0SNHjpQ4OW3atIEDB5qkHgCASSQmJs6cOTM8PLzUZ2fNmhUUFLR+/fpWrVoZ8eZTp07dvXt3eUaacA36gwcP7t+/383NjQARQH1GgAjjFRUV3b17Vwjx+++/EyACQNkSExOHDBni7e3t7e3do0ePw4cPr1y5ctmyZcqA999/Py4ubuTIkSYsUt/8+fN79+6dl5cnHy5evPjMmTMmbAABANS8tLS0gQMHJiQkdOzYMSgoyNfX183NzcnJKTMzMz09/dKlSwcOHAgNDb1w4cLx48fd3d0r+v7jxo3LzMzU73Z/WHZ29qlTpyrxQ1SWvBQmJCSYsAYAMDkCRBgvPT29qKhICPH7779PmTLFuDf58ccfV6xYsX379rZt21ZpdQBQizz99NPx8fFhYWFhYWHKSSVA1Gq1AQEB58+f79Chw9KlS01X5l9YWlr27t1befjhhx8KIXQ6nekqAgDUtMWLFyckJKxevXrRokWlDggODt6xY8e0adOWLVu2ZcuWir7/yJEjH/nNWXR0tJ+fn37Pfg0rKCgQQsTHx5uqAACoDVi3DiWdPn26RYsWc+bMyc7OLmOYVquNi4uTx49cuETfzZs3dTrdzp077e3t9+7d++WXXx47dmzHjh3p6ekRERHcmgKok44ePXrz5s2cnJyoqKjvvvtu/fr106dPHzBggHy2qKjo/PnzvXr1+v777x0cHExbqiEWFhZCCPm9EQCgnjh27JiPj4+h9FCaOHFinz59jh8/XmNV1bDCwkIhRHJyclZWlqlrAQCToQMR/78///wzKCho/vz5V69evX79ekhIiJub2/Llyw2Nf+mll/bt2yePo6Ki8vPzy7O9zL59+4KCggYNGvTbb7/l5ub++OOPSUlJQohffvnl2rVrO3fuDA4OLuNDAcCs2dvb+/r6+vr6ljhvZWWVlJTUokULk1RVTnKzrLJnmQEA6pjU1FQ/P79HDvPy8rpw4UIN1GMSMkAUQly9evWRW24CQF1FByKERqORLYHnz59/9913ZaInhEhLSyvjVWfOnFFuIwsKCs6dO3fo0CH9qXml+vzzz4UQhw8fzsjIEEIkJib++eefQojTp0/L165YseLcuXNFRUVytzV9xcXFr7zyyvTp07l9BVDHqNXqWp4eCgJEAKiXAgMDT548Wfbs3ZSUlPDw8Dq8wYicwiyYxQygfiNArO+uXbvWuHHj119//YcffhBCXLly5cCBA/IpZeH8hxUVFd25c0f/zLFjx0aMGDFixIgyZrfl5OSEh4er1erx48fPmjVLCHH16tUbN27Iz5IbqxUXF//yyy9jxoxp0aLF9evX9V/+5Zdf/uc//9myZcucOXPK3oWtoKDgwIEDGo3mkT8+ANSk/Pz8U6dO/fHHH8qZw4cPDx8+PDAwcNq0aT/++KMJa3skAkQAqIfmzJmj1Wp79uy5ceNGpc9AkZycvG3btoCAgJSUlEmTJpmkwhpQaoB4//79sLAw1l8CUH8QIFa7zMzMHTt2/FvPd999Z+qi/p9Dhw5pNJqjR4+eOHFCnlGSwdzcXP2RGzZsaN68+aVLl4QQKSkpykacsmtmz549BQUF+fn527ZtK3UBFJ1Ot3z5co1GExgY+J///Ocf//iHEOLy5cvK9VgIYWdnJ4Q4cODAd999l52dHRoaqjyl1WrfeecdIYRKpdq0adOzzz5b4v1jY2P9/Pw2bNgghNi0adPQoUNffvnlyl/RdTrd/v37Y2Njyzk+Pj5+x44dlf/coqKiEydOZGZmbt26ddKkSXv37l26dOny5cuVf3i2bds2ZcoUtkMFzMjXX3/duHHjwMDA7t27P/vss3l5eZs3b37uuee+//77U6dObd26deDAgUuWLDF1mQaxBiIA1EMDBw4MCQnJzMycO3duq1atGjZs2KZNm86dO7dr187V1bVZs2ZTp069devW5s2bhw8fbupiq4tyw5KSkqKcXLhw4ZAhQ/bv32+iogCgprEGYrX74IMPVqxYUeLkiRMnakmT/6+//iqEkNuhODg45OTkKE+VCBB37dp18+bNkJCQTZs2JScnK+eHDRv28ccfnz59Wj6cPn16kyZNSvQnZmdnT5gw4fvvv7eyslq8eLEQwtnZ2cXFRc5T9vX1tbS0PH/+/MSJEzdv3hwRESFftW/fvhkzZmzbtk2n03l7e1+/fr19+/abN28OCgr6448/jh8/7ufn5+zsLAfPnTs3Ojr6jTfeaNy4sbyQf//99//+979ff/31R/4SioqKiouL8/Pz1Wq1vb29cl6n002fPv3f//63Wq1evHjxypUrH/lWU6ZMOXr0qIWFxblz5y5cuLB79+6EhITMzMz+/fs/8rX6Vq1atWzZMktLSxkR7tixQ3nqm2++GTp06Lx58zIzM4cMGfLIfevkDxIWFtahQwe5BmXLli1LDCgoKLh69Wr79u0rVKQQorCw8KOPPmrTps2KFSteeOGFd99919Ky5H9VfvnllwULFiQlJYWHh/v7+1f0I4C64cyZM+PGjbOzswsKCkpLS4uIiHj11Ve///77jh07rlu37vHHHz9//vz8+fNXrVo1cODAZ555xtT1loIORACon2bMmDFgwIBt27ZFRETExsYmJiYKISwsLBo3bty9e/eRI0dOnDjRw8PD1GWW1w8//JCYmDh16tTyv0RZAzE/P185eeXKFSGE/m0RANRxOlRQVFSUEEKtVpdz/OXLl2fMmDHtgebNmwshwsPDq7XI8tNfdWvcuHFWVlbKw+eff14ZpuyR4uLiotFo9L9q++abb5QUT5Gdna28trCwcPDgwUIIV1fXQ4cOKee7dOkiB7/44ot3797dt29fenq6SqWSJ1UqlaWlZefOneXDp59+WgixYsUKnU73/PPPy5NeXl7R0dH379/v1auXEEJGV+3atbO2tpYDfHx8iouLdTrdRx991Lp167CwsK1bt+bl5en/BgoLCwMCAry9vVu0aNG6detbt24pT7333ntCCDs7OysrK5VKtWTJkvT09FJ/jQUFBfHx8ZmZmfIXKPt0xINsVKVSrVmzpvx/KDk5OW5ubvIdunXrNmLECGtr6xkzZrz66qvyB3zppZeUP6Pp06cPGzYsPz+/jDf87LPPxIOb/759+z48YMyYMUKIbdu27dmzR/7Gyunvf/+7/p/7vHnzlKeKioqioqKioqKaNGmi/EHrvzYrK0v/t12GgoKCTZs2JSQklPrsvtGj1wkh/3fp66/LXzzKIFuSAwMDTV1I3TF06FALC4s//vhDPpTbWTZo0CA5OVkZc+XKFWtra/3/9lat27dvCyHc3d2Ne/nLL78shNi5c2fVVgXUW1y/qgPXr+pWXFycnZ199+7doqKiGvvQit5/la1t27ZCiJs3b5b/Ja+88or82+yUKVOUk+3atRNCrF+/vkqqAswI16/qYBbXLwLECqvkBey5554TQsj1MkwrNzdXfn+oCA4OfuKJJ5SH/fr1kyMLCwu/+eYb5fyhQ4e2bNmiPDx27Jj+q6TLly8rH3To0CEhRJMmTa5cuaJfwIgRI+Tgt956SznZqlUrIUTv3r2VjEyhUqni4+N1Ot3atWuVky1btpTTll1cXL766itPT095PiAgQGajbdq0Wbx4scwl5f936NBB5lZr166dNGnSpk2b9D/Fy8vr/ffff+edd4KDg9VqtVqtPnjw4ObNm+Wzw4cP/9e//pWSkqL/g0RGRnbu3FmlUg0bNkx5n6ZNm8rgTKVSqdVqlUoVGhp648aN4uLiFStWtG7detiwYUocmZqaqtVqDx06tG/fPn9//z59+gghevbsqfwa5UY3+fn5Xl5eojTBwcGG/qALCwvl328UP/30k3zqxIkTQ4cO/eSTT/Sf3bhx46hRo7y9vU+fPq3T6Xbt2rVu3brCwkL5kmvXrt24cUN5uRL4ynTS1dVVyWf1s8XevXvb2Nio1eq4uDilsB49ejg4OFy7dq1EwRcvXizxVzr5Z+Tv779gwYLdu3eX+Aur/gXso4kT161bd+jQoQrFoHiYWVzAzEvz5s2feuop5aFc/jUoKKjEsN69e7ds2bKaaqhkgDhhwgQhxJdfflm1VQH1Fjdg1YHrV51UtQGi/Cu6oW+mSyW/axdCvPLKK/JMcXGxra1t2X8JB+oqrl/VwSyuX0xhrmkycNGZerXde/fueXt7y6ZChZeXV69evU6fPu3h4XH79m1lCnNwcLBcslBKSkqSd6FS06ZNfXx8lCnM0o0bNx577DF5fOHCBSHE+PHjS8RYbdq0kQf6M2cHDBjw+eef//Of/2zfvv2tW7fi4uLGjh0bEhIihAgMDJRfGMrJfTY2Nj4+PpGRkQsXLhRCLF26dMyYMVFRUbLUsWPH6nS6+fPnX716ddWqVfLN5a89Jibm5ZdfHjBgwNKlS4UQ//nPf5RPb968+fXr12VnkDR79uwhQ4bIal944YW9e/fu3bv39OnTn376qRzwwQcfLF68WC6Msm/fPvnR/fv3Hzdu3J07d3799dc2bdqEh4e/9957o0aNsrKy6tev3+HDh4UQiYmJTZo0CQoKGjZs2OTJk93d3W/evKl8rq2t7dq1a5XfoVwd0traeufOnaGhof/73/+uXr06ZsyYzz77TKVS6XS6lStXtm7deuzYsaNGjdJoNM8+++zLL78s08bNmzfHx8f7+PgsWbLkzJkzISEhb7755pkzZywtLWfPnv3HH3+UWLplzpw58qBv377z58+XXZ9HjhzZvn3722+/vWvXLp1ON3bs2J49e3722Wc6nW7gwIE//PDD6BSej7MAACAASURBVNGj4+Lizp49e+DAAQ8Pj6lTp8bFxVlYWLi4uDz77LMbNmxYtGjRp59++q9//cvHxycsLOytt9767bffhBDdunWzs7Nr1KhRp06dPv3006SkpC5durRo0eLSpUuykVOn03388cdCiMjIyMjISCHExIkTt2/fLoT45ZdfTp06lX7ihNuD4nfs2HFBCPkPybhx4zp16uTk5CSEKCoqsrW1vXr1amxsbEpKiq2trbW1tfyt5ubmqtVq2UVrbW3t4OAg30r/WGFlZWVra5uVlVXivK2trXw35a2kgoKCzMxMZeW4zMxMjUYj20XlRuRCiOLiYhm/Svb29iX+xXyYnZ2d/DurIisrqzwLYrq5ufXr1++Rw1Ad7t+/r//Phjx+uH3bxcVF/nNeCzGFGQBQKp1OJ/8y/Mi/w9QGchpyhVYSV9ZAVA5SU1PlhpP6C0ABQB1nquTSfFXyGzCZRh08eLBqq6oo/T1AFUeOHPnxxx8tLCz+9re/CSG6dOkiByvNgI0bNxZCLF26VC4a8uKLL65Zs6a4uPjhxQG/+OIL+dq9e/fK5f/kUob6YmNjX3nlle3btyvdbTqdTqvVKv19RUVFeXl58hcuhNiyZYtyfv78+du3b4+KilKSl5MnT+p0urS0tHnz5ikNnufPn5fTmZs3b966dWshxPjx45s1a1ai2i5durz33nvBwcFarXbfvn3u7u7yrt7GxkbpttPpdEFBQXK8q6vrhg0bvvrqKxllqlSqWbNmhYSEODs7q1QqWYm+7OzsVq1a2dnZKVOb33///Weeeebh5QKFEE8++eSECRN++eWXMv74CgsLZfdiXFzc77///v777wshLCwshg4dqrxPq1atxo8fP2fOHEdHRyHEvn37dDpdTk6O7PHs2rXr7NmzlcFeXl7jxo1TqVTe3t5CiB49eowaNUp5tkGDBuLBDHEbGxv9ZSLd3NwyMjKuXLmSn58vW0E7dOjQs2dP+ezixYuVms+cOSPHy1ROxm0lDB48+KmnnpLHoaGh8oVyf3AnJye1Wv3UU0/Jl//973+fOXOmHDnhwddf64SY1K3bnDlzlAngeNi5c+ce8V8HnU5nJt+AmZeAgAAPD4/c3Fz58ODBg0KIDh066HfLFhQUeHp69uzZs5pqqGQH4sSJE4UQ27dvr9qqgHqLDo7qwPXLJM6fPy//mlFN71+1HYjyW9iLFy+W/yXKAkrDhw+XZ+TfbIUQM2bMqJKqADPC9as6mMX1iw7EmlZLOhCV7ichhLOz8/3794UQXl5e7du3z8zMTEpK+uKLL5QOxPT0dCHEU089NWDAgOXLl9+8eTM1NVUIMWHCBJmpKY1yCtlM98cffyjbsfn5+ZUY0759+y+++KLESbkeszxWq9U2NjYdO3Zs0aJFRkbG6NGjlfPr1q2Tx88++2x4eLidnV3Xrl2FEK6uruvXr1ferVOnTiNHjvzqq69mzJjh4eEREhLy3nvv3b17t3///pmZmfPnz9+wYUNhYeGiRYuUNx86dOiff/5ZUFDw+uuv9+jRQ5kTLYSYM2fOgQMHLCws0tPT586dK082atRo27Zt8vfw6quvJicnP/zbcHBwiI6OLioqSkpKeuedd9q0abNw4cKFCxfeunVr1qxZe/fu7dChQ7t27caNG+fl5dWlSxf9hK5UlpaWLi4uQgiZ93Xr1k2r1S5dulT2Eq5cufK///1vTEzMtWvX5Pjx48fLbNHe3n7r1q0jR448e/bs2bNn5TtcuXLlvffeGzdu3Hvvvefu7i43q8nPz09NTY2KigoODg4MDHz66afz8vIGDx68efPmO3fu9O/f393dfciQIS+88IKzs7PMW6dOnbply5aYmBghhJub2549e3r37q3U3L17965du8oPFQ+26Jk+fXrTpk1feumlyMjICRMmhIWFKePfeecde3v7gIAAOV9+/vz5r7/+euPGjb/77rsxY8bIfwBsbW1ffPHFRuHh4u5d+aoFCxb4jB69fPny3bt3//TTT/Hx8bJb0NLSMjc318vLy9fX18PDIy8vT6vVajQa+TuRvYFCiPz8fHlSCJGXl1diHyEhRGFhYWFhofwDkst0ykW15RbkWq1WeSvJysrKycmpuLjY2tpaq9U6OzvLXkWVStWwYUM5RvaQKi/Jzs5WFupWlNj9VqPRFBUVKd+cW1pa2tnZlZpHl9CkSRMfH59HDkN1GD9+/BtvvDFq1Ki5c+empqYuXLjQ1dU1JiZmxYoVy5YtU6lU8quRmzdvTp8+3dTFlo4ORABAHSC7CI3rQFQ2Ubl+/bo8eHhiCgDUVQSINa0WBogDBgwIDQ0VQsgZr/b29srUTjlABogfffSR3Fv53Llzf/75pxBC2cz34chMLu+lNA+q1eoOHToYV6rs6cvPz1cCF30zZswIDw/v0aOHsnFKCR999NHTTz/92muv2dnZTZo0SQjRqlWro0ePRkZGvvLKK40bN75y5Yp+t50QwtraWk4WLvFW/fr1u3///vr164ODg1u0aOHl5fX888/PnDlTmYTYoEED2az3MNk35+fnt3fvXuVks2bN9uzZc+TIET8/v4f7IitkyZIlDRs2DAsL69Kly9KlS/v27fv00097eHj06tUrICBg3rx5ysgBAwbcuXNnz549Bw8eHDFixIgRI+Lj4+UscjmpXB7b2Nj89NNPhYWFcirK0aNH8/Ly5FY2rVq1un79+sMzbe3s7L7++uvRo0fHxsYuWLBADta3Y8eO0aNHJyQkyGng2dnZa9euldHY448/7uHhcfHixf3797dp0+aHH36IiYkZPHjw448/Hh8fb2VlNXnyZLlgzciRIw8fPhwcHJyenr5+/frnnntu/5gxl/XW6BRCuLq6yj2LKvMrBarWzJkz9+/ff+jQIbksrKOj46+//vrmm28GBwd/8cUXjz322MWLF69fv+7t7T1//nxTF1s6AkQAQKl8fHyio6NNXUW5FBYWygtZhQLEh3dhVgLE7OzsKi0QAGovAsSaVtsCRGdn58DAwNDQUGVuqXgwvVSu6yEeBIhubm6yxUlOUhg8eHCnTp3kAG9vb7VarX9XKTsQL1++LB+2bt1aTqQ1Thnh2rBhw7755ptu3boZGuDu7j5jxowSJzt37iz3d16wYEGFKnFwcHjjjTecnJxeeukluaF2JalUqkGDBlX+fYQQs2bNmjVrljx+8sknf/311+bNm5dapJ2d3fjx48ePHy8f6q9BqU92gMrjEvvkyP7Hh/n6+sbExNy6davUPzJ/f//IyMh79+65u7srM0EUffr06dOnj5yYnJ6evmTJkj179sTGxgohXn75Zf037NevHwv5wexYWVkdPnz4s88+O3bsmJWV1ezZs/39/b/77rtJkybt2bPn6tWrVlZWo0eP/uSTT0oscFl7ECACAEpla2vbsWNHU1dRLkoCWMkORNktIehABFCfECDWtFpyAybnLAshBg8e3LRpU/Gg/VCSt68lOhBdXV31Q8DNmzcrO/A6ODjMmjVLo9Hcv3//1q1bJ06ckNfUS5cuCSFUKtXgwYOr72d5eL/mauXs7Pzmm2/W5CcaR1mIsOaVEfhaW1u7u7s/8h1cXV0/+eSTIUOGDBs2zNvbW+6iA5g7CwuLyZMnT548WTnj5OS0e/fulJSUlJSUxx57zFAndS1RYio9AABmR+mQqNDl7OEAUem4pAMRQP1BgFjTalUH4sKFC1etWhUZGalSqfz9/ZVn9TsQCwoKsrKyrKysZHpoZ2eXm5vr4+Mj9+JQbNy4UR4kJyc3a9YsNjb25s2bsgPxxIkTJgyzYL6GDh169OjR9u3bu7q6mroWoBo1adJEztCv5WrJF2AAAJNISEiIiIiIiYlJS0vTaDRubm6enp6enp5Dhw6V7QhmoUo6EHU63W+//SbPsAszgPqDALGm1aoA0cPDQ61Wd+7c+dKlS/pzXa2trS0sLAoKCoqKiu7duyeEcHV1lZV7enrGx8eXMYG0adOmw4cP37t376RJkxISEtRqtTLTGaiohxdSBMxdNd2ApaWlHT16tOx+Ctl7/vAuPeVEgAgA9VNiYuLMmTPDw8NLfXbWrFlBQUHr168v0V5QOxkXIJZYAzEuLi49Pd3a2lp2WlR5kQBQOxEg1rRaEiDK20hl94+Hd0GxtbXNycnJzc1V5i/L8y1atIiPj+/fv38Zb75p06Zff/31yJEjQojWrVvLfkYAqOeq9QZs2rRpe/bsKc9I+bWQEQgQAaAeSktLGzhwYEJCQseOHYOCgnx9fd3c3JycnDIzM9PT0y9dunTgwIHQ0NALFy4cP368PCvVmJYSIFZmCvOJEyeEEL169fr555+Zwgyg/iBArGm1JECUHYil7mss2dnZyQAxLS1N6AWI7777bo8ePV544YUy3rxZs2aHDh0aNGhQUVGRsq0HANRn1X0D9tprr8k1Cstw//59+dWOcQgQAaAeWrx4cUJCwurVqxctWlTqgODg4B07dkybNm3ZsmVbtmyp4fIqSgkQK9SPXyJAvHDhghCib9++BIgA6hUCxJpmRgGiECIvL69EB2Lv3r179+79yPfv3r37rVu3LC0tH3lDCwD1QXXfgA0dOnTo0KFlj4mOjvbz81P2v6ooNlEBgHro2LFjPj4+hi5e0sSJE3fu3Hn8+PEaq8poVbKJiuzlb9mypaWlZX5+fmFhoZWVVVVXCgC1jtrUBdQ7tSRAlJc9FxcXQwNsbGyEEHl5ebID0c3NraIfYWNjQ3oIAFI5b8D69OlTa2/A6EAEgHooNTW1PEv0enl5paSk1EA9laREgZVZA1F2HTo6OjZo0EAIkZCQ8MMPP1RxoQBQ+xAg1rRaEiCWWAPxYbIDUVkD0YgAEQCgqAM3YASIAFAPBQYGnjx5Mj4+vowxKSkp4eHhgYGBNVaV0apkF2a5cYqjo6Ojo6MQYvz48QMHDlT2ZQaAuooAsabVkgCx/FOYH9mrCAB4pDpwA0aACAD10Jw5c7Rabc+ePTdu3JiUlFTi2eTk5G3btgUEBKSkpEyaNMkkFVaIMoXZuACxqKhIq9XKDsQGDRrIAPHixYtCiOTk5CquFQBqGQLEmlYbbsCKi4uzsrJUKpWTk5OhMTJA1Gg08lrYqFGjmqsPAOqcOnADxhqIAFAPDRw4MCQkJDMzc+7cua1atWrYsGGbNm06d+7crl07V1fXZs2aTZ069datW5s3bx4+fLipi30043ZhllOYbW1thRAFBQWZmZlCCCcnJxkg6s9rBoA6jE1U6qP79+8XFxc7OzuXsUahvEDm5eVFRUUJITp06FBz9QFAnSNvwOY+4Ozs7Orq6uTklJ2dnZ6eLnu9LS0ta/MNmOygpwMRAOqbGTNmDBgwYNu2bREREbGxsYmJiUIICwuLxo0bd+/efeTIkRMnTvTw8DB1meVixBTmgoICnU5nZWVla2ubl5eXn59fYgqzRIAIoM4jQKxptaED8ZELIIoHHYg5OTmxsbFCCF9f35qpDQDqKnO/AZPXL5MvwQEAqHne3t5r1qxZs2aNTqfTaDR5eXkuLi7yumBejAgQZfuhtbW1bLDIz89XNlEhQARQrxAg1rTasAbi3bt3hRCNGzcuY4wMEPft25ednd28eXPWQASAyjPrG7Da8AUYAMC0VCqVg4ODg4ODqQsxknEdiEIIKysrGxsboRcgNmjQwNPTUxkm2xIBoA4jQKxptSFATE1NFeULEL/44gshhJ+fX80UBgD1hDnegLEGIgDA3CmbqJT/ciYDRBsbG2trayFEZmZmQUGBtbW1jY2Nv7+/MiwnJ6eqiwWA2oUAsabVhgAxJSVFPCpAlC36EgEiANQAnU6n3KWYupZS0IEIADB3le9AlHO5GjRoIITQDxCZwgygzjOzADEnJ0elUtnb2+ufPHHixM6dOyMjIxs2bOjv7z99+nQvLy9TVfhItSFALH8HorW1dZs2bUaOHFlDlQFAPRYZGdm5c2dh6muEIQSIAABzZ3SAKFsOxYMAUa5+6O/vr1Kp5FWbKcwA6jwzCxAdHR3bt28vt/WQlixZsnr1auVe68CBAyEhIZs3b54wYYKJanyE2hMgNmnSpIwxMkDs27dveHh4DZUFAKjFCBABAOauMpuoyAAxLS1NPOhAbNCgQevWra9evSroQARQD5jHwu2GhIWFrVq1ytXVddOmTdHR0ZcuXdq6dau9vf2UKVP0Q8ZapTbcgJWnA7FDhw5CiNGjR9dQTQBQ7/n4+ERHR0dHR5u6kNKxBiIAwNxV7RRmIUSPHj3kAR2IAOo8M+tALOHDDz9Uq9UHDx5U/sPt4+PTtWvXJ554YuXKlbt27TJteaWqDR2I5VkDcdy4cQMGDCh7DACgCtna2nbs2NHUVRhUG74AAwCgMpQAsfzfh5XagSinMAshNm3aFBAQ8NZbb9GBCKDOM+8OxMjIyK5duyrpodS1a9fu3bufPXvWVFXVfjJAdHd3L3sY6SEAQEGACAAwd0bswiwzxxJrIDo5OclnXVxcnn32WcEUZgD1gHl3IBYWFpa6kF+zZs1q7RTm2tCBWJ4pzACA6pCQkBARERETE5OWlqbRaNzc3Dw9PT09PYcOHdq0aVNTV1cWAkQAgLmrzCYq1tbW4q+bqEhyOjMBIoA6z7wDxICAgMjISJ1OJ1M5SafTXbx40d/f34SFlaE23ICVZwozAKBqJSYmzpw509DOVLNmzQoKClq/fn2rVq1qtq7yqg3XLwAAKqNqN1GRZJhIgAigzjO/ADExMXHIkCHe3t7e3t49evQ4fPjwypUrly1bpgx4//334+LiRo4cacIiy2DyDsSEhASNRmNra6v/vRkAoFqlpaUNHDgwISGhY8eOQUFBvr6+bm5uTk5OmZmZ6enply5dOnDgQGho6IULF44fP/7IJSZMgk1UAADmrjIdiKVuoiIeBIhsogKgzjOzAPHpp5+Oj48PCwsLCwtTTioBolarDQgIOH/+fIcOHZYuXWq6Msti2gAxOzv7qaeeEkL4+PiYpAAAqJ8WL16ckJCwevXqRYsWlTogODh4x44d06ZNW7Zs2ZYtW2q4vPKgAxEAYO6M7kC0srKytbUVpU1hltlifn5+Xl6eHAMAdZKZBYhHjx4VQmg0mqtXr8bHxyckJMTHx1+7dk0+W1RUdP78+V69en3++ecODg5GvH9iYmJERETZY27evGnEOytMGyAeOnQoOTnZz8/v4MGDJikAAOqnY8eO+fj4GEoPpYkTJ+7cufP48eM1VlWFECACAMxXenr6jRs3Kt+BKDsNPTw89Mc4Ojrm5+dnZ2cTIAKow8wsQJTs7e19fX19fX1LnLeyskpKSmrRooXR7zx37tz9+/eXZ6TRd1CmDRB3794thJg8ebKnp6dJCgCA+ik1NdXPz++Rw7y8vC5cuFAD9RiBABEAYL5eeeWV8PBwV1dX+bCiAaKVlZUMEKV27drpj3F0dExLS8vOzm7UqFEV1QsAtY5ZBoiGqNXqyqSHQoiFCxd6enqWfXd07969b7/9Vn/blgox4Q1YUVFRWFiYSqUaMWJEzX86ANRngYGBERER8fHxJW459KWkpISHhwcGBtZkYeXHGogAAPN18+bN4uJiOQFZVGITFenhAFGwDCKAus5cA8SYmJjQ0NC4uLiMjIzCwkJHR0d3d/fOnTuPGTPGycnJ6Ld98sknn3zyybLHREdHVyZANGEHovxarEmTJpWMWQEAFTVnzpywsLCePXsuW7YsKCioZcuW+s8mJycfPHhw5cqVKSkpkyZNMlWRZaMDEQBgvpTJy1L5vw/Ly8sTQtjY2LRt21aecXR0bNq0qf4YuacKGzEDqNvML0C8fPny7NmzDa1UOG/evClTpqxatYrlJx4mv3Bzc3MzdSEAUO8MHDgwJCRk7gPOzs6urq5OTk7Z2dnp6en37t0TQlhaWm7evHn48OGmLrZ0BIgAAPMlc0BF+TsQZSzo6Oj49NNPyzNt27Yt0U1iZ2cnhMjNza2CQgGgtjKzADE5OXnQoEHXr1+fOHFiv379CgoKvv766yNHjixfvrxDhw4xMTFffvnlhx9+mJSU9O2338pbndrGhDdg6enpggARAExkxowZAwYM2LZtW0RERGxsbGJiohDCwsKicePG3bt3Hzly5MSJE0ssyl6rECACAMxXiXSv/B2IOTk5QggHB4cWLVq0bt06MTHR29u7xBjZvFKiyREA6hgzCxCXL1+elJT01VdfjRkzRp6ZNGnS3/72t5CQkHPnzo0ePXr58uVz584NCQlZu3Zt2ZtdmopppzALIZSVgwEANczb23vNmjVr1qzR6XQajSYvL8/FxaV2ft31MAJEAID5UtK9pk2bJicny5UNy0MJEIUQffr0SUxMfHg5YxkglmhyBIA6xjxuWhSnTp3q1KmTkh5Kb7/9dnp6utw9WaVSbdy4sXnz5l9++aWJanwEkweIdCACgMmpVCoHBwc3NzdzSQ8Fm6gAAMyZku71799fVORypkxhFkLMnDmzb9++48aNKzFG7q9CgAigbjOb+xYpKSmpxMLzQggvLy8hxIULF5QzXbt2lVPDaiETBohyCjMdiAAAI9CBCAAwUzqdTnYg+vn5DRo0SFRkDUT9DsSAgID//e9//v7+JcbQgQigPjCzALFjx46///57idUlzp49K4RQgkWdTnf58uXmzZuboL5yMHmASAciAMAIBIgAADOVn5+v0+lsbGwiIyNlO4VxHYiGsAYigPrAzALEESNG3Lp1a/LkyVlZWfLM1atXZ82aJYTo27evEOLGjRtjx469fPnyCy+8YMpCDTP5FGY6EAEARiBABACYKdkbKGM+S0tLYdQuzGWMYQozgPrAzDZReeutt7777rudO3ceOHDAz89Po9FER0cXFBRMnTq1V69eQog333xz9+7dvr6+y5YtM3WxpTN5gEgHIgDACKyBCAAwU5UJEPWnMBvCFGYA9YGZdSBaWFj873//e/vtt9Vq9fHjx8+ePevk5PTBBx988skncoC3t/fy5ctPnTrVsGFD05ZqiKk6OJKSkuS6kC4uLjX80QCAOoAORACAmdIPEOX3YVXbgcgUZgD1gZl1IAohbG1tV61atWrVqj///NPCwsLT01P/2VWrVpmqsHIyVQdiYGBgcnKyoAMRAGAUAkQAgJmS0Z5+B2L5G+rL04EopzATIAKo28wvQFS0aNHC1CUYwyQB4r1792R6KAgQAQBGIUAEAJip6l4DkSnMAOoDM5vCXAeYJEC8efOmcswmKgAAIxAgAgDMVHWvgcgmKgDqAwLEmmbaAPGxxx4r++IHAECp2EQFAGCmjA4QdTqdRqNRqVT29vZlDKMDEUB9QIBY02q+g0Or1SYkJAghXnrppXPnztXY5wIA6hI6EAEAZkquTij7BCsUIObm5hYXF9va2spv0Qyxs7MTlQgQ79y5Y9wLAaAmESDWtBroQJw9e7aLi8uNGzfkw759+86aNUsI4e3tXfZXZwAAGEKACAAwU0Z3IJZnAURRuSnMR44cadq06erVq414LQDUJALEmlYDAeJPP/2UkZFx6dIlIYRWqz116pQ837x58+r7UABA3UaACAAwU7m5ueJBgFihFTkqFCAatwvzqVOndDpdTEyMEa8FgJpEgFgHXb9+XTy42t24cUP5eo0AEQBgNNZABACYKaM7EMuzg4qo3BqI165dE0Lcv3/fiNcCQE0iQKxp1d2BeO/evczMTPEgQExMTFSeIkAEABiNDkQAgJmSvYHVN4VZvrNxHYjyfo0AEUDtR4BY06r7BkxZ+jArK0sIkZSUpDzl6elZTR8KAKjzCBABAGZKvwOxQg31dCACgMLS1AXUO9Xdgfjnn3/KA/0OxI4dO/bv379JkybV9KEAgDqPABEAYKaqewqz0ZuoaLVa2f9BgAig9qMDsaZVbYC4e/fuhg0b/vzzz8oZuQCieBAgym+03nrrrQ0bNlTJJwIA6icCRACAmZKTi2XMVx0Bop2dnTAqQLx+/bqshAARQO1HB2JNq9obsCNHjty/f//48ePPPPPMhg0b1q5d26pVK/mUnMIsA0TlJAAAxmETFQBAlduxY8emTZvKHiP3UK5MB4bRHYjyo+3t7cseZvQuzMqC9ZmZmTqdTvaaAEDtRIBY06q2A1F2vGdkZAghvv/++1u3bt26dUs+JTsQ5Yzmli1bVsnHAQDqLToQAQBV7ocffvjjjz/KM7LyAaIRHYgFBQVCCGtr67KHyWhSpo0Voiw/VVRUlJOT88jdWgDAhAgQa1r1BYgJCQlCCHt7e41GIx4EiHfv3hVCsPohAKCSCBABAFVu+/btb731VtljEhISxowZIy9Dxim1A3HDhg0ajebtt98uMTgtLW3w4MEvv/zyG2+8oT/3uQyl7sI8dOjQrKysn376qYy+wjt37ijH9+/fJ0AEUJsRINa0agoQCwoKbt68aWlpGRcXt3nz5lWrVmVnZ+fn52dnZ1tbWzdo0KBKPg4AUG8RIAIAqpyNjU23bt0eOaaSn/LwLsxarXbBggUFBQWTJ08u0Wzx+++/nzlzRqVSlT9AVDZRSUxMbN26tTwZFhZWVFR07949V1dXQy8sESB6enoa9wMCQA1gExUzlpOTc+/ePSFERkbGtWvXioqKvLy8PD09+/fvL4TIzs5OS0sTQri5uZm4UACA+WMNRACAmZI5oAwQ1Wq1Wq3W6XRyevJvv/1WYrCcwpWamir+uvtKGaysrCwsLLRarY+Pz82bN4UQWq1WXjGDgoKmTZtm6IUpKSnKMfuoAKjlCBBrWhV2cCgbLmdkZMj5y23bthVCyNb37Ozs9PR0QYAIAKgKdCACAMyUfgeieDCLWTp9+nSJwbIJo0IBovLmhYWFcoqYMp35+PHjO3bsMPQq2YFoZWUlhAgNDc3MzCz3zwQANY0AsaZV4RRmeXESQty7d+/q1atCiDZt2ogHAWJWVpa8+JXRD8QpOQAAIABJREFUMw8AQDkRIAIAzFQZAaKhDsTs7GyNRlPRAFEIIUNA/fUQtVqtXKT+YTJAbNWqlRDigw8+eOedd8r3AwGACRAg1rTqCBAzMjL0A0S54iFTmAEAVYgAEQBgpuT+yEoOqB8gnjlzpsRgGSAKIVJTU8sfICpjsrKyxEMbqsiTQoj09HQ5dVqSU5i9vb3lw127duk/CwC1Cpuo1LQqvAG7deuWPLh//75ca8PLy0vodSDKFRJdXFwq/1kAgHqOABEAYBa0Wu3JkyeVCC8zM/PEiRMqlap58+byjFzVV8rIyCgqKtI/I5swRAUDRKUD0VCA6O7unpyc3K5du0GDBu3Zs0cIUVRUdPfuXbVa3a5dOzns7t27YWFhQUFBxvzYAFDNCBDNmFziUAhRVFQkw0QnJyfxoAMxJydHfnvWqFEj09UIAKgJISEhH330UdljZFOD0S3wbKICADAL69atW7x4cYmTkyZNat++vTzW70AUQhQWFuoHiMZ1IJYdIMp5zVeuXNFoNJGRkcoHFRUVNW7cWH/Jqd9++40AEUDtZK4BYkJCQkRERExMTFpamkajcXNz8/T09PT0HDp0aNOmTU1dXVmqcAqz/lIackMVGR1aWlra2Njk5+fLVJE1EAGgzjt//rxcy+KRjL4A0YEIADALffv2ffbZZ/UvWC4uLuvXr1ceNmzYUO6RIhUWFirxn6h0gPjwGojiQaoo31l5f7kAYpMmTeSSU1ZWVoWFheyjAqDWMr8AMTExcebMmeHh4aU+O2vWrKCgoPXr18uVaGuhKrwB0w8QZVbo4OAgHzZo0CA/Pz8pKUkQIAJAPbB169YlS5aUPSYuLm7w4MHyMmQEAkQAgFno2bPn4cOHyxjg5eV15coV5WFhYaH+s8ZNYR41alRSUlJaWprMCuW2LQr9ADEzM7OwsNDKykougOju7j5u3LiUlBSVSrVixQoCRAC1lpkFiGlpaQMHDkxISOjYsWNQUJCvr6+bm5uTk1NmZmZ6evqlS5cOHDgQGhp64cKF48ePu7u7m7peg6qkAzEnJ0c5lhPT5BRmIYSjo+Pdu3dlgMgUZgCo89RqtdxHqwyGtoAs/0cIAkQAgPmTC8crtFqt/kPjOhAXL17s5OQ0e/bsMqYwy7ZHnU6Xlpbm4eEhV7Fv2rSpm5vbihUrvvvuO2UkANRCZhYgLl68OCEhYfXq1YsWLSp1QHBw8I4dO6ZNm7Zs2bItW7bUcHnlIW/AqnAKs7W1tbJXl9w+RTyYyywDRDZRAQBUHmsgAgDqhjICxMzMTCX7S01NlY2E5QkQxYNbsDJ2YVbmTd+9e9fDwyMuLk4Ioeygov9yAKiFjJzHZCrHjh3z8fExlB5KEydO7NOnz/Hjx2usqgqpwjUQZQei/pqPSoDYsmVLIYTchVmuqQEAQGXQgQgAqBtKBIhKN4bQ26ZSCJGamiqfKmeAKGeDlbEGojI5Wh7IAPGxxx6TJ52dnQUdiABqMTMLEFNTU8uzR4qXl5dcUaIWqvJNVDw9PZV3VgLE559/Xh64uLh4e3tX/rMAAPUcASIAoG5o3ry5/kP9NRCV+ctCiHv37skc0Nraujxv+3AHYuvWrXv27Cn+OoVZ+ZQSAaJ8OQEigFrLzALEwMDAkydPxsfHlzEmJSUlPDw8MDCwxqqqkCq8AZMdiC1atJAP7e3tlaXxg4KC5PFrr72mv6cYAADGIUAEANQNJToQ9QNEGf81bNhQHpd/DURRWoDYs2fP0aNHi79uoiKESEtLKy4ulne1SoAoGxiZwgyg1jKzAHHOnDlarbZnz54bN26UC/zpS05O3rZtW0BAQEpKyqRJk0xS4SNVeQeiEiDKK5bUtGnTPn362NjYzJw5s/IfBAAAASIAoG5QAkQ5a1h/DcTs7GzxYJEoZT3ECgWIGRkZU6ZMWbBggXyhfqqoBIh37969ceOGRqNp2rSpsg2m/gxoAKiFzGwTlYEDB4aEhMx9wNnZ2dXV1cnJKTs7Oz09XS75Z2lpuXnz5uHDh5u62NJV+RqISoCozF+W9u3bd+/evRJfrwEAYBw2UQEA1A0NGzZ0dXXNyMjw8PC4f/++fgeiDBCbNWsWGxublZXl4OAgKrgGYlxcnJybLPQCRBkL6ncgXrp0Sei1HwohHBwc1Gq1RqPRarWWlmZ2nw6gPjC//zDNmDFjwIAB27Zti4iIiI2NTUxMFEJYWFg0bty4e/fuI0eOnDhxooeHh6nLrAklAkT9DkQhhKOjY4lIEQAAo6lUKpVKpdPpdDqd/DIMAAAz9fXXX2dmZq5bt078dQpziQ5EGeRVqANRn42NjTIxWaPRyNs3IcTdu3dPnjwphOjatasyWKVSNWjQ4P79+1lZWS4uLpX44QCgWphfgCiE8Pb2XrNmzZo1a3Q6nUajycvLc3FxUZb/q4y33377/fffL89Io1sIq2oKmE6ny83NValUSo/hw1csAACqkFqtLioqKi4ult2IAACYqQEDBgghPvzwQ1FagNioUSNra+uCggLZOVjOANHR0VF+06ac0Z/CrL89y+3bt69fvy6EeOqpp/TfwcnJyVCAmJycfPv27S5dulTs5wSAqmNmayCWoFKpHBwc3NzcqiQ9rBlVNYVZo9HodDo7O7smTZrIM/QbAgCqFcsgAgDqEisrK1HaGoiOjo4y+JOLzpczQLSwsLC3t9c/ox8gpqenK5945MiRn3/+WaVSPRwgitKWQfzwww9btmzZvXt3ZXI0ANQ8s+xAFELExMSEhobGxcVlZGQUFhY6Ojq6u7t37tx5zJgxyjK0Rli9evXq1avLHhMdHe3n52f07K0qDBCFEPb29m5ubvIMHYgAgGpFgAgAqEtknPdwB6IMENPS0uTJcgaIQggnJ6ecnBylD9HW1lZZA1HGgj169Bg2bNiqVasyMjJ8fX0bNWqk/3L9BRP17dy5UxZ5+fJl/WUTAaAmmV+AePny5dmzZ0dERJT67Lx586ZMmbJq1SpbW9v/j737DoviXPsHfrP0KoiggAULohSVJmhQbOixEDB6LNEkxxqRGE2iicYSX02IJkZPjCUxGKOisWMHxQ5WkCYgSu8KAoJUKfv747mc3x5YYIGF2YXv54/3mp0ZhnvzXsdhvnM/z9PGhUlIWgEim0FDU1NTVVVVS0uruLgYHYgAANCqsI4KAAC0JyxAdHd3HzZs2I0bN0gkQOS6UhQVFSVf0sTQ0DA7O9vW1vbx48ckMgdiYWFhYWEhEXXq1GnVqlUeHh6ff/75xx9/XOvH6+tAzMnJYRtpaWnN+Z4AANIgZwFidnb2hAkT0tPT582bN2bMmLdv3x4/fvzq1avfffedhYVFbGzs4cOHd+zYkZqaevLkSdkc1yz1DkQi6tKlS3FxMToQAQCgVaEDEQAA2hMWIJaXlz98+JDtqTWEmZrSfkhEvr6+qamp586d4wJEfX19TU3NgoKCjIwMIurUqRMRmZmZ+fv71/1xbsWVWvtzc3PZBps5EQCAF7IYsTXgu+++S01NPXr06F9//TV37tz58+dfuXLlo48++u2335ycnDZu3JiQkLBs2bIzZ8789NNPfBcrntQ7EImIjWJGgAgAAK0KASIAALQnXGthWVkZe0Cr24HYpADRyspq8uTJ3MgwVVVVBQWFPn36EFFYWBi9CxDrI3YIc1FRUXl5OdtGByIA8EjOAsQHDx4MHjx45syZojvXrFmTn59/4cIFIlJQUNi5c2f37t0PHz7MU42NaKUORMIiKgAA0MoQIAIAQHvCOhCJSCgUBgUFpaWlsQBRU1OzeR2ITK2fNTMzIyLWk9jwfP1ihzBz7YeEDkQA4JWcBYipqam9evWqtbNHjx5EFBkZye2xtbVNTk5u08ok1vKnr/v37xsbG+/fv5/eBYisAxEBIgAAtCoEiAAA0J5wASIRjR49+v3332fDvJrdgcjUChD79etHRDExMdRYgMge60QTQ3o3AaKhoSGhAxEAeCVnAaKlpWVoaGhFRYXoTtYNzgWLQqHw2bNn3bt356E+CbS8A/H+/fvZ2dmnT5+md0OYJ06caGJiMnz4cGkVCQAAUBcWUQEAgPZEdHWUmpqapKQkNv+gtrZ2SzoQucYOtrBn3759iejt27fU2BBm9kibmpoqupMFiLa2toqKillZWVVVVU2tBwBAKuQsQJw6dWpWVtaCBQu4mWWTkpK8vLyIaPTo0USUkZExa9asZ8+eTZkyhc9C69fyAJF9dzaEmQWIc+fOzcjIGDJkiJRqBAAAEAMdiAAA0J6oqKiIfnzz5g3r/tPU1JRKByK7PhvCXOuQWKampkSUkpIiupOVZGxs3LVr16qqquzs7KbWAwAgFXIWIH755ZfDhg07cuRIjx49RowYYWdnN3DgwOjo6EWLFrH+uy+++OLEiRNWVlYbNmzgu1jxWh4gsr56hgWIAAAAbQABIgAAtCeiQ5iZFy9eUAtWYWbEdiAyDXcgNhAgGhgYdOvWjYhevnzZ1HoAAKRCzgJERUXFGzdurFmzRiAQBAcHh4WF6ejo/PLLL3v37mUnmJmZfffddw8ePNDV1eW31PpIN0BkcyACAAC0AQSIAADQnogOYWbYY5q0hjCzn+3evTt3kYYDRBMTE2Vl5ezsbG7ZZXo3hLlr167sCff169dNrQcAQCpq/4sp+9TU1Ly9vb29vdPS0hQVFU1MTESPent781WYhKQbIOrp6UmhJgAAAAlgDkQAAGhP6nYgEpFAIFBXV5fiIioCgaBHjx4JCQnU2CIqioqKPXr0SEpKSktL69+/P9vJOhC7dOnCwsfCwsKm1gMAIBVy1oEoqmfPnrXSQ7nQ8gCxuLiY23Z2dpZCTQAAABJAByIAALQnYgNEDQ0NgUDARhOTNAJEIurduzfbaDhApHfrqMTFxT169IiIysvLHz58SERGRkboQAQAfslfB6K8k2KAqK2tjZWXAQCgzSBABACA9kRsgMgGIDs5OQ0ZMiQiIqLlQ5hJJEBseAgzvZsGccaMGRUVFX5+fuHh4QkJCQMHDnR2dr506RKhAxEA+CPHHYhyquVPX9wQ5jFjxoi95wEAALQGBIgAANCe1J0DkUTiv6+//pqIDA0Nm3rZlnQgTp48WUVFpaKigogePXp08+ZNItq2bZuamhqGMAMAv9CB2NakNQdi7969ly5dKrWyAAAAGoMAEQAA2hOx3RhcYjh79mwDAwMHB4emXra+DkQ1NbVG+xmnTZv24sWL3bt3r1+/PiYmJi0tjYgGDBhARBjCDAD8QgdiW5PWEOaAgIDx48dLrSwAAIDGYBEVAABoT8QGiB4eHtz2uHHjGh10XJeSkpK6ujoRqampsT0sQGy0/ZDR09P74IMPiOjJkydZWVkCgaB79+70bvgzOhABgC/oQGxr0goQufdaAAAAbQMdiAAA0J7UDRCVlJTmzp3b8isvXrw4JyeH9QwS0cCBA7t06WJjYyPhj5uZmamoqCQnJxORkZGRiooKoQMRAPiGALGtSWsIs6amptRqAgAAkAACRAAAaE9qBYhdunSZPXu2kZFRy6/83//+V/SjtrZ2enq62CkX6yusf//+0dHRRNSjRw+2Ex2IAMAvBIhtDQEiAEAHVFJSoqCgoKGhIbrz3r17R44ciYqK0tXVHTRo0JIlS7iHBNmEABEAANqTWoneP//8M27cuFb6XdxwZglZW1vXChDRgQgA/MIciG2thQFiWVlZdXW1mpqa5O+vAACAd1paWnZ2dqJ71q5d6+zsvGfPnuDg4IsXL3p7e1taWvr6+vJVoSQwByIAQMd0+fJlT0/PcePGffrpp+Hh4XVPWLVq1b///e+2L6yFanUgytQkUdx89z179mQb6EAEAH4hQGxrLQwQ0X4IANAO+Pv7e3t7d+7ceffu3dHR0XFxcX/++aeGhsbChQufPn3Kd3X1QgciAEAHtGTJksmTJ//+++/Xr1/ft2+fnZ1drfG5RHTt2rVTp07xUl5LyHKA6OHhwaY+5AJEdCACAL8QILa1Zj995eTkfPDBB6dPnyYEiAAAcm7Hjh0CgeDSpUtLly61tLQ0NzdfuHDh5cuXq6qqNm/ezHd19UKACADQ0Rw/fvyPP/7o06fPyZMn4+Lijhw50q1bty+++OLcuXN8lyYFshwg6urqsvHUbAVneteBWFRUVFFRwWdlANBRYRhsW2t2B+L169f9/PyePXtGMnZvAwCApoqKirK1tXV0dBTdaWtra29vHxYWxldVjUKACADQ0ezatUtNTS0wMLBPnz5EZG5ubmFhMWLEiCVLlowZM0ZbW5vvAltElgNEItq7d29AQMDkyZPZRyUlJS0treLi4t69e0dERBgaGvJbHgB0NOhAbGvNDhBZs3peXh7J3r0NAACapLKyUuzf/cbGxtnZ2W1fj4QQIAIAdDRxcXHDhw9n6SEzZMiQ33777cWLFz///DOPhUmFjAeIPXv2XLx4sejc96wJMTs7+5tvvuGvLgDooBAgtrVmB4hv3rwhooKCAiKqtY4nAADIFwcHh6ioqFr3AqFQGBMTM2jQIL6qahQWUQEA6GjKysrqvjf65JNPHBwcfvnll7S0NF6qkhbRbE5ZWbmpCyW3PScnJwMDAzU1tYMHD8r7f3wAkDsIENtaswPEoqIiInr79i3J3ssxAABoVHJy8qRJk5YvX75r1y5HR8eMjIxa0x1u2bLl+fPnzs7OfFXYKHQgAgB0NGZmZg8ePHj58qXoTgUFhb1791ZUVCxYsECubwqiHYhy8YR14sSJ9PT0KVOmCIXCCxcu8F0OAHQsCBDbWgsDREYubm8AAMAZOXKkvr6+v7//zp07ly1btmnTJiLiAsSqqiobG5tvv/3WwsJi3bp1vFbaEHQgAgB0NAsXLiwvL3dxcXn48KFoVmhnZ7dq1apr1659/PHHbKSUPOICRDU1NScnJ36LkYRAIFBVVXVzcyMiBIgA0MawiEpba3b7hmiAiFWYAQDky+3bt4motLQ0KSkpISEhMTExISEhJSWFHa2uro6IiBg+fPjBgwdl+V94dCACAHQ0Xl5eUVFR+/btc3JyUlZWDgkJGTx4MDu0adOm5OTkI0eO+Pn5yemtgQsQr169OmLECH6LkdykSZMUFRVv3bpVXl4u+8OuAaDdQIAoN0QDRHV1dR4rAQCA5tHQ0LCysrKysqq1X1lZOTU1tWfPnrxUJTl0IAIAdEB79uwZNmyYr69vUlKS6DgqZWXlo0ePvvfeezt37kxISOCxwmbjAkRVVVV+K2mSLl269OnTJz4+PikpycLCgu9yAKCjwBDmttbsIcyFhYXcNl40AQC0JwKBQPbTQ0IHIgBAh6SoqPif//zn2rVrSUlJQ4YMET0kEAiWLVsWHx+fnZ0dHBzMV4XNxi2iInePV2xd7KSkJL4LAYAOBAFiW5PKEGb5ekUGAADq6urdunU7ceIE34W0CDoQAQBArG7dur333nt8V9FkctqBSER9+/YlosTERL4LAYAOBAFiW5PKIipyd4cDAOjgysvLX758OXPmzFmzZuXl5fFdTjOhAxEAANoT+Q0Q0YEIAG1PzgLEkpKS0tLSWjvv3bvn5eU1YsQINze3tWvXpqen81KbhKQSIMpdjz0AAJibm+/fvz8gIMDS0nL//v3y2MeHDkQAAGhPuCHMCBABABolZwGilpaWnZ2d6J61a9c6Ozvv2bMnODj44sWL3t7elpaWvr6+fFXYKASIAAAdk4KCwvz582NjY4cOHbpw4UJLS8vTp0/zXVTTsA5EBIgAANA+yHsHIoYwA0Bbku9VmP39/b29vfX19Tdt2uTi4qKkpBQUFLRu3bqFCxfa2dkNHDiQ7wLFaGqAWFhYOGPGjKlTp4q2XsrdHQ4AABhjY+Pz58+fOnXq22+/nT59ev/+/efNm/fRRx+ZmJjwXVrjWAcihjADAHQQubm59+7dk/x8d3f31iumNXABooqKCr+VNBWbAzE5OVkoFLIHTACA1ibfAeKOHTsEAsGlS5ccHR3ZHnNzc1tb26FDh27evPno0aP8lidWUwPE8PDwq1evZmZmiu5EByIAgFybPn26h4fHn3/+uWnTpjVr1qxdu3b06NFjxowZMWKEg4ODzP4jjyHMAAAdSkxMjIeHh+TnN2OUFb/ktwNRS0urc+fO+fn5eXl5Xbp04bscAOgQ5DtAjIqKsrW15dJDxtbW1t7ePiwsjK+qGtbUALG8vJyIcnJyRHfK3R0OAABqUVJS8vT0XLBggZ+f359//nnjxo3r168TkYqKSkVFBd/ViYdFVAAAOpRRo0Y9efJk48aNbM4NT0/PTp068V2UNLEAUSAQcEmiHDE0NMzPz8/JyUGACABtQ74DxMrKSkNDw7r7jY2Nnz592vb1SKKpT19v374lolpLdspscwoAADSJiorKzJkzZ86cmZKScvny5cDAwBs3bvBdVL3QgQgA0NFYWVmdOnXKzs4uLCzsm2++6dWrF98VSZOampqmpqa2tjbfhTSHoaFhXFxcTk6OhYUF37UAQIcgZ4uo1OLg4BAVFVWrm08oFMbExAwaNIivqhrW1A5E1odSK3BEgAgA0M6YmpouXbrUz8+v1hsjmYIORACAjmnWrFl8l9AqlJSU7t+/f/PmTb4LaY6uXbsS0cuXL/kuBAA6CvkLEJOTkydNmrR8+fJdu3Y5OjpmZGRs3rxZ9IQtW7Y8f/7c2dmZrwob1tQAkXUg1oIhzAAA7ZWSkuwODkAHIgBAx+Tg4NCvXz9ZvkM1m7W19YABA/iuojnYULxaU10BALQeObsHjBw5MiEhwd/f39/fn9u5efPmDRs2EFFVVZWDg0NERISFhcW6dev4K7MhzZsDsRZ0IAIAyJfy8vJ2sEgiOhABADqmUaNGxcfH810F/A/WgYgAEQDajJwFiLdv3yai0tLSpKSkhISExMTEhISElJQUdrS6ujoiImL48OEHDx7U1NTks9D6oQMRAKADah//bqMDEQAAQEawDkQMYQaANiNnASKjoaFhZWVlZWVVa7+ysnJqamrPnj2bfeW7d+/6+vo23FtRUFBATUkAa2neHIi1oAMRAEBOJSYmBgYGxsbG5uXllZaW6uvrm5iYmJiYuLm5GRkZNfuyycnJgYGBDZ+TmZnZ7OszCBABAABkBOtAPHTo0OvXr0+cOMF3OQDQ/sllgFgfgUDQkvSQiLZu3XrhwgVJzmyzAFFsByICRAAAuZOcnLx06dKAgACxR728vNzd3bdt22ZqatqMiy9fvlzC+1dLBiBjCDMAQIfVSi/AXr16devWrYYfjtLT05t9/XaMdSBWVFScOnWqvLycPSGGh4cbGxuzbBEAQLrkMkCsqKgIDw9XVla2s7Nje65cubJ3796XL19aW1vPnDlz7Nixzbvyr7/+OmXKlIbPyczM3LRpE3uIagapdCC2j6FwAAAdR15enqura2JioqWlpbu7u5WVlb6+vo6OTlFRUX5+flxc3MWLF0+fPh0ZGRkcHNyMv/tXr15tYmLSaAf9yZMnWzIVIzoQAQA6oFZ9AbZ48WI/Pz9JzsTrq1pYgEhEQqEwJSVlwIAB8fHx9vb2zs7ObOIvAADpkr8A8fjx44sWLXrz5g0Rubq6nj9//q+//vLy8mJHHzx44OPjs2bNmh9++KEZF+/du/fixYsbPic6OnrTpk3NuDiDDkQAgA7o22+/TUxM/PHHH1evXi32hI0bNx44cGDx4sUbNmz4448/mnr94cOHDx8+vOFzoqOjWxggogMRAKCjae0XYAsWLGh0cefCwsKrV6+2g7XIpEv0v3ZycvKAAQNCQ0NramoeP35cU1PT7H4XAID6yFmAGBISMnv2bHV1dXd397y8vMDAwE8++eTcuXOWlpY///zzwIEDIyIiVq5c6e3t7erqOmrUKL7rFaOpT1/oQAQAaAfu3Lljbm5eX3rIzJs378iRI8HBwW1WVVOhAxEAoKNp7RdgkydPnjx5csPnREdHW1tbI0CsRVtbe8KECcHBwSUlJUlJSUQUHR1NROxjv379+C4QANobOXsvsXnzZoFAEBQUdPbs2aCgoNWrV584cUJFReXatWsTJ040NTX18PAICAhQUVHZtm0b38WKhw5EAIAOKDc3V5Iponr06JGTk9MG9TQPOhABADoaCV+Aubi4yPILsPYqICBg3bp1RJSSkkLvAkTRDQAAKZKzADE8PHz48OG2trbs42effUZEY8aM6datG3dOv379hg4dKrP/aLZ8DkQlJSXWAwIAAPJi2LBh9+/fT0hIaOCcnJycgICAYcOGtVlVTYUORACAjqZ9vABrx3r37k1ErAPxyZMnbKfMPgsDgFyTswCxsLCwU6dO3Ee2LbqH0dPTKygoaNPKJNaSDkTW+oHxywAAcufzzz+vqqpycnLauXNnampqraPZ2dk+Pj4ODg45OTnz58/npUJJsNsQAkQAgI6jfbwAa8f69OlDRElJScXFxawPkYgCAwOLi4v5LAsA2iM5CxDZ1LDl5eXs4507d4goNDRUNI+rrKwMCwuzsLDgp8TGtKQDUUdHhzB+GQBADrm6uv72229FRUXLly83NTXV1dXt06fPkCFD+vXr17lzZ2Nj40WLFmVlZe3Zs8fDw4PvYuvFOhAxhBkAoONoHy/A2jE212FCQkJ0dLRQKNTX11dUVLxz586CBQv4Lg0A2hs5CxDnzJnz4sWLadOmXb169ciRI4sXL+7cuXNsbOymTZtYJFddXb1y5crMzMxG5+LlS0sCRNZriQARAEAeeXp6xsTEfP311zY2NhUVFcnJyZGRkSkpKaqqqvb29t7e3unp6Z9++infZTYEcyACAHQ07eMFWDump6dnZmZWXFx86tQpInJxcbl06RIR3b9/n+/SAKC9kbNVmJcuXXrhwoXLly9fvnyZiLS0tO7evfvFF19s3Ljx0KFD/fv3j4mJSU9PNzMzW7lyJd/FiteSAFFXVzc1NRUBIgA1atAHAAAgAElEQVSAnDIzM9u6devWrVuFQmFpaWl5ebmenh5L5eQC5kAEAOiAPD09x40b5+PjExgY+PTp0+TkZCJSVFQ0MDCwt7f/4IMP5s2bJzolPbSxkSNHxsfHHz58mIj69u3r6uqqoaGRkZFRVFTERrABAEiF3Dy0MMrKyleuXPHx8fn4448XLFgQHBw8aNAgPz+/adOmJScnBwQEvHjxYsaMGQ8ePJDZlK0lcyCyDkTMgQgAIO8UFBQ0NTX19fXlKD0kdCACAHRU7AVYWFhYaWlpcXHxq1ev3r59m52dHRISsmbNGqSH/Bo5ciQRsUVs+vXrJxAIBgwYIBQKY2Nj+S4NANoVOetAJCJFRcUFCxaIzumgo6Nz6tSpnJycnJyc/v37q6io8Fheo5r69FWrA5EwhBkAoJ0SCoXspZHMvihCByIAQAfHXoBpamryXQj8fy4uLtw2mxLR0tIyLCwsJibGycmJv7oAoL2Rp8aHhhkaGlpZWcl4ekjoQAQAgHpERUWpqanJ8lsidCACAEBdQqGwoqJCtO8B2lKvXr3s7e3ZNgsQ2YKi6EAEAOlqPwGivGjhHIiEDkQAAOAJOhABAKAu2X8B1u7NmTOHiFRVVbt3707vAsRnz57xXBYAtC8IENsaVmEGAACxzM3No6Ojo6Oj+S6kXggQAQAAZNCsWbM0NTXt7e3ZWAFDQ0MiysvL47suAGhX5G8ORHmHIcwAACCWmpqapaUl31U0BEOYAQCgLvYCjO8qOrRu3bo9f/5cW1ubfdTT0yOigoICXosCgPYGAWJba3YHooKCAjoQAQCAR+hABACAumT/BVhHYGxszG2zma9ev37NXzkA0A4hQGxrTW3f4DoQVVVVu3XrRu860gEAQB4lJiYGBgbGxsbm5eWVlpbq6+ubmJiYmJi4ubkZGRnxXV0j0IEIAAAg+9CBCACtAQFiW2v2EGZ1dfUpU6ZcuXJl2LBhrVUcAAC0muTk5KVLlwYEBIg96uXl5e7uvm3bNlNT07atqwnQgQgA0GHJ9QuwjkZFRUVDQ6O0tLS0tFRDQ4PvcgCgnUCA2NaaGiCWl5ezDXV1dQUFhfHjx7dWZQAA0Gry8vJcXV0TExMtLS3d3d2trKz09fV1dHSKiory8/Pj4uIuXrx4+vTpyMjI4ODgrl278l2veC0PEDMzM42NjdmtEAAA5EI7eAHWAenq6paWlr5+/RoBIgBICwLEttaSDsTWqgkAAFrZt99+m5iY+OOPP65evVrsCRs3bjxw4MDixYs3bNjwxx9/tHF5EmphgHj27NmpU6du3br166+/lmpdAADQWtrHC7AOSE9PLysrq6CgQHRuRACAlkCA2NaaFCAKhcLKykq2jQARAEB+3blzx9zcvL70kJk3b96RI0eCg4PbrKqmauEciI8fPyaiiIgIadYEAACtqX28AOuAsI4KAEidgO8COpwmBYjcEsxEhOZzAAD5lZubK8kUUT169MjJyWmDepqnhQFiamoqEWVmZkqzJgAAaE0SvgBzcXGR5RdgHRDWUQEAqUOA2NaaHSCiAxEAQH4NGzbs/v37CQkJDZyTk5MTEBAgyytltXAIMwJEAAC50z5egHVA6EAEAKlDgNjWmhQgsgkQ2QMbZq8AAJBfn3/+eVVVlZOT086dO1mOJio7O9vHx8fBwSEnJ2f+/Pm8VCgJqXQgZmVlSbMmAABoTe3jBVgHhA5EAJA6BIhtTfIAUSgUsmlEunXrFhoa+vvvv7d6cQAA0DpcXV1/++23oqKi5cuXm5qa6urq9unTZ8iQIf369evcubOxsfGiRYuysrL27Nnj4eHBd7H1akkHYnV1NYsOy8rK8DwDACAv2scLsA4IHYgAIHUIENua5O0bsbGx69evJyJVVVU7OzsdHZ1WLw4AAFqNp6dnTEzM119/bWNjU1FRkZycHBkZmZKSoqqqam9v7+3tnZ6e/umnn/JdZkNaEiBmZWVxy4JJcRRzXl7etGnTLly4IK0LAgCAqPbxAqwDQoAIAFKHVZjbmuQdiCUlJWxDVVW1dWsCAIA2YWZmtnXr1q1btwqFwtLS0vLycj09PfZiSS60ZAizaN9KZmamlZWVVEq6ffv2mTNnSktL3dzcpHJBAACoxdPTc9y4cT4+PoGBgU+fPk1OTiYiRUVFAwMDe3v7Dz74YN68ed26deO7TPgfGMIMAFKHALGtSR4gciuoqKiotG5NAADQthQUFDQ1NTU1NfkupGla0oGYlpbGbUtxGkT2aPTy5UtpXRAAAOqS9xdgHRALEPPy8uoeqqysVFZWbvOKAEDu4R/9tiZ5gMgN9cL6ywAAIAta0oFYVFTEbUtxCDMbnIWlPwEA2gZ7Aaavr4/0UMb16NGD/rf9n4iSk5NHjx6toaGxc+dOnuoCADmGf/fbWlM7EI2MjFavXt3qZQEAADSmJR2I7K0Y66nPyMiQVklcgCjJjRUAAKCD6N27NxGx8eacLVu23Lp1q6qqasWKFVevXuWpNACQVwgQ21pTA0RHR0d3d/dWLwsAAKAxLelAZAGiqakp1WmIaAkWIFZWVmKaJwAAAE7nzp07depUVFT06tUrInr58uWGDRsuXrxIRFOnThUKhb6+vuzMmpqa/Px8PmsFADmBALGtSR4gvn37ljABIgAAyIyWdyD269ePpBogcrkhpkEEAAAQJdqEuGLFis2bN2dlZRkZGXl7exORv78/u6EvWLDA2Nj4yZMn/FYLALIPAWJba2oHIgJEAACQES3pQKyqqqJWCBBZByIhQAQAAPhfXIAYGhp6/PhxtvNf//rXgAED+vfv/+rVq7t37yYnJx8+fLiiouKnn37itVgAkAPyugpzYmJiYGBgbGxsXl5eaWmpvr6+iYmJiYmJm5ubkZER39U1pKkdiKqqqq1eEwAAgARa3oGop6enp6dXUFCQm5trYGDQ8pK4ABHrqAAAAIjiAsT4+HihUDhhwgRlZeXPP/+ciKZPn+7t7T1z5sySkhJ2Wz9+/PiGDRvMzMx4LhoAZJj8BYjJyclLly4NCAgQe9TLy8vd3X3btm1sliUZJHn7BoYwAwCATGlJgMh+SklJydTUtKCgICUlpRkBYk1Njaenp5WV1bJly9iewsJCtoEORAAAAFFcgMiaV9zd3T09Pdmhb7755uDBg5mZmUTUqVMne3v769evz5kz5969e0pK8hcRAEDbkLN/HfLy8lxdXRMTEy0tLd3d3a2srPT19XV0dIqKivLz8+Pi4i5evHj69OnIyMjg4OCuXbvyXa8YTR3CjA5EAACQES1fREVZWblXr17h4eGpqakODg5NvUhKSsq+fft0dHS8vLxYMehABAAAEIsFiElJSeyO2atXL+6Qjo7OsWPH9u/f/+WXX1pbWxcWFlpbW4eEhNy7d2/kyJG8VQwAsk3O5kD89ttvExMTf/zxx+jo6B9++GH27Nnjx493cnIaP378rFmzNm7cGBoa+tdff6WkpGzYsIHvYsXDIioAACCnWj6EWUlJiT3ANG8axKKiIvZ/nz59yvZgDkQAAACx+vbtS0RJSUnsntuzZ0/Ro87OzgcOHLC2tiaiTp06TZ48mYgeP37ckt9YWVl59+7d6urqq1ev2traamtr3717tyUXBACZImcB4p07d8zNzVevXt3AOfPmzXNxcQkODm6zqpoEi6gAAICcYi0MzQsQ2SIqysrKFhYWRBQSElLfmZWVldzayrUUFxezjQcPHrBrlpSUsD1ZWVnNqAoAAKC9MjU1FQgEaWlpaWlp9L8diHXZ2NgQUXh4eLN/XWFh4YQJE5ydnd3d3adMmRIeHl5cXDx//vyysrJmXxMAZIqcBYi5ubmSrJHSo0cPmR3KJHmAyJo1ECACAICMYB2ILRnCrKSk5OLiQkQ3b96s71Y4Y8aMnj17ir2PcwHiw4cPiej169fcRdjTUVBQkJWVVWBgYDMqBAAAaE/U1NRMTEwqKytLS0s7d+6sra3dwMm2trZEFBYW1uxfN3/+/Js3bxLRpUuXKisrV65caWlp+fz58xMnTjT7mgAgU+QsQBw2bNj9+/cTEhIaOCcnJycgIGDYsGFtVlWTYA5EAACQUy0Zwsx1IJqbm3fv3j0nJycmJkbsmTExMcXFxRkZGXUP1Q0QiUhfX5+IUlNTS0tL582bFxMTc+nSpWZUCAAA0M706dOHbdQav1yXtbW1srJyXFxcaWlpM36Rn5/fmTNn2HosROTq6vrTTz95eXkR0fnz55txQQCQQXIWIH7++edVVVVOTk47d+6sO31Sdna2j4+Pg4NDTk7O/PnzeamwUZgDEQAA5FTLF1FhazuOHj2aiG7duiX2TDYqmb1Fq+XNmzdsIzY2tqysjAWIvXv31tbWLioqWrp0aWJiIr2bKhEAAKCD4wLEhscvE5GqquqgQYOqq6vZJCGPHz9m7YQSYinh2rVrfX19P//884MHDyooKLi5uSkoKFy9elXsPR0A5I6cBYiurq6//fZbUVHR8uXLTU1NdXV1+/TpM2TIkH79+nXu3NnY2HjRokVZWVl79uzx8PDgu1jxMAciAADIKal0INK7iZbi4+PFntlAgMh1IFZVVUVGRubn5xORrq4uey46ePAgO4oAEQAAgN6to0JEgwcPbvTkcePGEdHly5crKirGjx8/ZsyYlStXNvzcWl1dnZmZSURssZSxY8eam5v/+uuvbNqx7t2729jYFBcXX7x4seXfBQB4p8R3AU3m6ek5btw4Hx+fwMDAp0+fJicnE5GioqKBgYG9vf0HH3wwb968bt26Ne/ilZWVsbGx7CGnPqy7odkkb99gHYgYwgwAADJCWh2IbCAVm7WwLhYglpeX1z3EBYhE9PjxY3a1Xr16qaioREdHE5GtrW1YWBgCRAAAAHr3xm706NFr1qxp9ORJkyZt3br17NmzPXv2ZK/ofvnlF01Nzf/7v/+re3J6evrKlSsvXrxYWlr66aefJiQkaGtrDxo0qNZp8+fPDwsL27Jly7Rp06TxhQCAT/IXIBKRmZnZ1q1bt27dKhQKS0tLy8vL9fT02FNNCy1YsODw4cOSnNm8x6cmwRBmAACQKdLqQGQNg3WnIiGi8vJydubq1at/+umnK1eusB9hWIBoZGSUnZ39+PFjNvthv379uHvlvHnzECACAAAwkyZNevTo0aBBgyTpShk+fLiurm5iYuLy5cuJaMKECdevX//++++nTp06ZMgQ0TMzMzPt7Oxyc3PZx3379gmFQkdHR/ZiT9T8+fM3b94cGhp679694cOH1zp64cKF9PR0W1tbNTU1IyOjrl27Nv+rAkDrk8sAkaOgoKCpqampqSmtC7733nuxsbENn1NWVhYbG8tGIjePgoKCUCgUCoUNXwSLqAAAgExpdgfiy5cv2VuxRjsQWfshEUVGRhJRcnJy//79uaMsQBw5cuTx48ejoqK6d+9ORGZmZuyosbHxqFGjCEOYAQAA3nFwcJDwTCUlpV27dh05csTf35+Ifvnll7/++mv79u1ff/311atXiejQoUP9+/d3cnI6cuRIbm7ue++9d+zYsSlTprBb9uTJk+teU11dfc6cOdu3b9+3b9/r168nTZrEHfroo498fX25j4MHD46IiGjJNwWA1iaXAWJFRUV4eLiysrKdnR3bc+XKlb179758+dLa2nrmzJljx45t3pU//fTTTz/9tOFzoqOjra2t2yBARAciAADIlOZ1ID58+HD48OFsEiXWTmhgYKCurp6Xl1dcXKylpSV6MhcgMnl5eaIf2SIqVlZWx48fT09PZ8Oc+/Xrp6amRkRTp07t1KkTIUAEAABoljlz5syZM+fgwYN5eXmWlpbr1q3bv39/YGBgdHR0bm7uJ598MnDgwNjY2AsXLhDRF1980b1790WLFn322Wdjxoz57LPPxF5zwoQJ27dvP3jw4MGDB+/evcv6EIOCgnx9fbW1tadOnRoUFJScnPz8+fM2/aoA0HTyFyAeP3580aJF7BHC1dX1/Pnzf/31F1shnogePHjg4+OzZs2aH374gdcyGyLhOipYRAUAAGQK60BsaoAYHx/PNS2yDkQFBYWePXs+e/YsLS3NwsJC9ORaAWJoaKient6AAQPYR9aB2LdvXyUlpVevXhUWFiooKPTr12/w4MEPHjwYPHgwu3UiQAQAAGi2Tz75hG3o6enNnTt39+7df/zxR3p6OhElJCRkZ2ffv39fVVV1/PjxRLRkyZKePXu6uLjUHb/MjBw5UkNDo7S0lIj+/PPPY8eOPXv2jD0Rf/nllxs3biQibW3t4uLioqIiHR2dNvmKANAccrYKc0hIyOzZs6urq93d3Z2dnQMDAz/55JMvv/zS0tLy8uXLycnJfn5+ffr08fb2vnXrFt/F1kvCABGLqAAAgExhHYhNHcJcVlbGbXMTGrJpEEeNGhUSEiJ6sugyKUT0+eef29nZcSsys6OdOnUyNDSsqampqKgwMjJiM5k4Ojqqqalpa2srKCgUFxe3wVTFAAAA7R4bn+fj48NWUq6srPz777+rq6tHjBihra1NRIqKim5ubg0Ef2pqalOmTFFRUVFQUDh69Ohvv/129erVK1euaGpqcm1AbPbDly9ftsVXAoDmkrMAcfPmzQKBICgo6OzZs0FBQatXrz5x4oSKisq1a9cmTpxoamrq4eEREBCgoqKybds2voutV5MCRHQgAgCAjGjeEGbR9ZS5AJFNg5ibm3v37l3Rk2t1IBJRaWlpYWEh22YBopaWlrGxMdvDTYDICAQCLS0toVDIRioAAABAS1hbW3t6epaXl1dXV7M3dkePHiUiJycnyS/i6+ubkZExefJk9oTL/PTTTwYGBmwbASKAXJCzIczh4eHDhw+3tbVlHz/77LMtW7aMGTOmW7du3Dn9+vUbOnRodHQ0TzU2TsJJ6LGICgAAyJTmLaIi2oHIjW+aMmXKP//8U1JSIhovkrgAkYjYuCd6NweiaIBoaWlZ62QdHZ03b94UFRWx+RABAACgJf773/9WV1f36NEjNzd3586d7EHb3t5e8isoKysbGBicOHHiq6++ysjIcHV1zc/P9/T05E4wNDQkopycHKkXDwBSJGcBYmFhoejzANuu+4Sgp6cXFRXVppU1BToQAQBAHrU8QOQ6EN3d3VetWrVx48ZaAWKtIcy1rsCOamtrm5iYsD1WVla1TtbR0cnMzEQHIgAAgFSoqKj88ccfRLR7925uZ5MCREZdXX3Pnj1iD6EDEUAuyNkQ5gEDBoSGhnIPG3fu3CGi0NBQ0TCusrIyLCys1qTsMgWLqAAAgDxq3hBmsQEiEbGlk0WPksQdiEZGRmyP2ACRsI4KAACAtJmbm7MNIyMj7k2eVCBABJALchYgzpkz58WLF9OmTbt69eqRI0cWL17cuXPn2NjYTZs2sTyuurp65cqVmZmZkydP5rvYemERFQAAkEctDxBFl2hUV1en/50hkerpQOQCRK4DseEhzIQAEQAAQNocHR1tbGyUlJRmzZol3StjCDOAXJCzIcxLly69cOHC5cuXL1++TERaWlp379794osvNm7ceOjQof79+8fExKSnp5uZma1cuZLvYuuFIcwAACCPmjeEWTQirBsg1upA5LLCujtrampKS0sVFBQ0NDRYgGhsbNy5c+daJ7MAkVt3BQAAAKRCW1s7LCysNa6MDkQAuSBnHYjKyspXrlzx8fH5+OOPFyxYEBwcPGjQID8/v2nTpiUnJwcEBLx48WLGjBkPHjxgA6NkU5OGMKMDEQAAGrV+/XpFRUWFBllbW5MEd58GtMEQ5gY6EEtKSoRCoaampkAgGDRokJaW1oQJE+qejA5EAAAA+YIAEUAuyFkHIhEpKiouWLBgwYIF3B4dHZ1Tp07l5OTk5OT0799f9lv20IEIAADSVV5e3tTGwGZgHYjSHcIs+RyILBPU1tYmIhMTk5ycHLHv2BAgAgAAyBc2tXFaWhrfhQBAQ+SsA7EBhoaGVlZWchG3YREVAACQrp9//lnYmCdPntC7e1DzsA7ElgxhFu1AFDsHYgMBYnZ2Nr1rUmA/zgLNWliA+Pr16yYVCQAAAHzp06ePgYFBenr6s2fP+K4FAOrVfgJEOSLhHFJYRAUAAGRNM0Yx19eBKPkqzOycrKwsImp02UczMzMiio6OlrxCAAAA4JFAIBg/fjwR+fv7810LANQLASIPJOlArK6urq6uVlRUZI9qAAAAsqAZ66jUNwdio6swz5kzZ8WKFfSuAzEzM5OIuPWX62Nvb09EoaGh7OOTJ09WrVolNpcEAAAAGTFp0iQiOnv2LN+FAEC9ECDyQJIAERMgAgCADGqNDsSbN2/OmzevoKCgtLRUdAJ1bW1tPT09ehcgStiBaG5urq2tnZaWlpubS0QrV67ctm3bwYMHJS8YAAAA2tjEiRN1dHRu3759+fJlvmsBAPEQIPJAkgARSzADAIAMakYHYqNzIO7evfvvv/++dOnSJ5988vDhQ9ETNDQ06H8DxEY7EAUCgY2NDRGFhoYWFRXdunWLiK5fvy55wQAAANDG9PT0Nm7cSERbtmzhuxYAEA8BIg/QgQgAAHKqNToQ2fjiuLg4f39/BQUFBwcH7gQWILIrsCHMjXYgEpGtrS0RPXnyJCAggN1Pb9261QarVAMAAECzzZo1i4ji4uL4LgQAxFNq/BSQtobbN4RC4YoVK1h0iAARAABkSkvmQFRUVBRdN5nrQGQNhidOnCgpKbGysjp69ChbCKV5HYhEZGhoSEQFBQUJCQlsT35+fkREBAsWAQAAQAZ169ZNTU0tNze3pKREU1OT73IAoDZ0IPKg4Q7E9PT0nTt3btu2jRAgAgCAjJG8A/HcuXOJiYkkEiCKth+SSAciOyE+Pp6IRo8ezU3foa6uzkLGJi2iQkTa2tpE9ObNm+TkZCIyMjIiopCQENFzDh8+vH79+oZHAwAAAECbUVBQ6NGjBxGlpaWJ7o+JiamqquKpKAD4/xAg8qDhADE7O5vbZg9XAAAAMoK1EDYaID558sTDw2PixImVlZVcgCg6ASKJdCCKjnEeM2YMd+8T7UAsLy/Pz89XUVExMDBotEgWIBYVFaWmphLR5MmTiSg6Olr0nFWrVn3//fctHCclumY0AAAAtJCpqSkRsds3c+zYMSsrq2XLlvFWEwC8gwCRBw0HiGyIFoPObQAAkCmsA3Hr1q3Tpk1j0wuKVVBQQETx8fH79u2rrKxkO2t1IKqqqgoEgoqKCi6GU1FRGTNmDNeByM2BWFpaypZUNjQ0ZPfQhuno6BBRYWFhWlqagoLCpEmTSCRAXLBggZGREVvuOTw8vCnf/n9s3769U6dOgYGBzb4CAAD8/vvv5ubmfRs0ceJEamwGeWgfevXqRUSRkZH//ve/z549S0QHDx4kon379oWGhvJcHECHhzkQedBwgPjixQtuGwEiAADIFBYg7t69u7y8PDExceDAgWJP41ZePnToELezVgciEampqZWWlubn57OPLi4uOjo6XC7JdSCWlZWxc/T19SUpknUgxsfHV1RUGBoaDh06lEQCxFu3bnG32rCwsA8//JCIqqqqBg4c2KtXr2vXrknyK+jdwixnzpxxdXWV8EcAAKCW+/fvP3/+XJIzESB2BCxAPHDgwLNnzzIyMkaMGHH9+nUiqqmpOXDggL29Pd8FAnRoCBB5UN8M9HPmzKmpqTE3N+f2IEAEAACZwm5hLB+sqKio7zQuQGSdg0ytDkQiUldXLy0tffPmDfvo5uZGRCoqKgKBoKamRnQIc15eHhF17txZkiJZByKbVLFXr14mJiZ6enqvXr06cuSIu7s7l1eSSAdidnZ2QkJCcnJyRUUF1wLZMDb8OSgoSJKTAQBALB8fn++++67hc54/fz5x4kTRZbigvWIBIsuUIyMjz549W1lZqaur+/r169jYWL6rA+joECDyQGwHYmJi4tGjR4noo48+4nayBycAAAAZwToQGUkCxNevX3M7xXYg0rsbooeHB2sGJCJVVdWysjI1NTVuERUWIDapA5FNuM4eRaytre/cuTN37lw9Pb3CwkLuzIiICKFQqKCg8OrVKyKqrq5OSUkRfZNXn4qKiqSkJCKKjY3Ny8uTsDAAAKhFWVm5T58+DZ/DltKCjoDdtdkfBmVlZbt37yaiBQsW/PLLL0+fPuW5OIAOD69xZAXrzSaiqKgobqeWlhZP5QAAAIgh2gAiSYAomtbVDRBZPkhEioqKfn5+XAzHegDrdiBKmNOxDkSmZ8+eRPTVV1/961//MjY2LigoEH2Bl5+fz5Z6ZNcnIrZyNKeqqur3339nqzkTUXFx8Z49e169ehUfH89WkhEKhWhCBAAAkIpaaTIbKPDxxx9raWm9fPmSzbDcPM+ePcvJyWlpfQAdGwJEHogdwnzjxg22IboiJDoQAQBAptTXgRgUFPTw4cO6h0RvdnWHMHMLLte637UwQGQdiAzrZXj//ff9/f3Hjx9f97uwuRFZByIRJSQkiJ5z6dIlT0/PFStWsI9r16718vJycnL6888/6d2QAn9/f0mqAgAAgIYZGxtzfxswBgYG1tbWbHCA5E2IJSUlRUVF3MeLFy9aWVlNmTLl+++/v337thQLBuhQECDyQOwQ5lu3brEN0ecxzIEIAAAyRbQDkWszrKysdHV1Zatk1jokqoEORG6DYQ8PoouoNClA1NLS4hZrNjEx4fZ369ZN9DRHR0eqEyDW6kBkzyp37tyZN2/e6dOn9+3bx87ZuXMnEbHlUy5evIip/QEAAFpOIBDUakIcM2aMgoICW7RNtNWmYQ4ODgMHDmR/jRQWFs6ePbuqqiokJGT9+vVLliwhopqampMnT4quXwoAjUKAyIO6AWJNTY3YhmoEiAAAIFPEdiCWlJRUVFQUFBRwe8QGiA10INYKEHV1dYlIT09PVVVVIBCUl5ezxVgkDBAFAgE3B4ixsTG3nwsQNTQ0Bg8ePHv2bPNIHMEAACAASURBVHoXINY3hJnN4/769eu///57xowZ5eXl77///o4dO4YNG6alpbVmzZqePXtmZWXZ2dllZGRIUhsAAAA0gAsQHz9+/Pfff+/YsYOIrKysiOjAgQNsguOGFRYWPn36NCsri/XoREdHFxcXc0fZWOb/+7//mzFjhpubmyQXBAAGASIP6gaIZWVlYpsXECACAIBMETsHYllZGdvgZjxsagdirSHMhw4dunDhQo8ePRQUFFhoyNZelHAVZhIZxSw2QJwxY0ZERISDgwMRhYeHu7i4/PTTT+wQCxCrqqo2b948e/ZsboZiejcc283NbcWKFffu3Xvz5s2oUaNYChkeHi56JgAAADRP3759iUhNTc3GxuaTTz4xMjIiogULFnTv3j04OHj58uWNXiE+Pp5tfPnllxs3bmSDCbihCUKhcN68ed9//z0RhYaG7tq1q5W+CED7gwCRB3UDxJKSErFnIkAEAACZIrYDse6SKS3sQBw0aNCUKVPYNutEePLkCUncgUjvAkQFBQXRYcvcNruOpaWlQCCIiYm5c+cOt8RnfHx8cXFxcHDwhg0bjh07xpZYETV48GDRjz/++KOnpycRZWZmSlgbAAAA1IcFiEZGRlzkR0RdunQ5ceKEmpranj17Dh061PAVuOmMnz59umnTpjNnzhDRmDFjuBMuX75MRNOnTyeiv//+W8pfAKD9QoDIg7oBIvfcUgsCRAAAkCliA0SpdyCKYg8SlZWV1JQAkS3E3KVLF7YeC1MrQNTS0hKdt5GIFBUVq6urQ0JCYmJiuJ2ampoeHh6GhobsBEtLS9Ef4SZmwhBmAACAljMzMyOiHj161No/bNiw//73v0S0f/9+ImpgSWWuA5GIhEIhW+ts8eLFc+fOnTVrFhGpq6sHBAT4+vpqa2tHRUXhDg4gIQSIPKi7CnOtAJEbIIYAEQAAZIrYIcx1OxBFFwTjSN6BKEp0MvWmdiCKjl8mIjYMikSGQp8+fXrVqlX9+vVjH9myKg8ePGAjprt06UJE5ubmfn5+bAVnMzOzulln9+7dCQEiAACANIwaNcrLy2vt2rV1D3344Yfq6urBwcHu7u5GRkb1rafMOhB//fXXmzdvcjsHDx58+PBhX1/fn3/+OTAw0NXVVVVVddy4cUKh8NKlS630XZrh22+/nTZtGmZmBNkkZwFiSUlJ3Wa9e/fueXl5jRgxws3Nbe3atenp6bzUJrn6hjBzTdrsrQshQAQAABnDRX4kjQ5EPT09ttFoByIRCQQC7vxGsQ7EWgGijo4O+0VcEKmqqvrTTz99+eWX7CMbN+3n53f37l0i+u677/T19d3d3YnI2tqa6oxfZthCzxjCDAAA0HJqamq7du1i7+1q0dbWdnNzq6mpOX/+fE1NDRubXMv69evPnj1LREOGDBk1atSMGTOISFVVlf05oaiouHLlyvfee4+dPHnyZCI6f/58632dJikuLt62bduZM2fYzC0AskbOAkQtLS07OzvRPWvXrnV2dt6zZ09wcPDFixe9vb0tLS19fX35qrB5WIDIDa2ysbFhGwgQAQBApogmaJLPgcgGPtftQGTRG0nWgWhpaSk6gLphYjsQ6d2ttlYnIwsHicjd3V1BQSEkJCQyMpKI3n///VevXm3YsIGIZs6c+f7773/22Wd1fxc6EAEAANrG999/P336dFNTUyK6c+dOraNFRUVbt24tKioiInNzcyI6cODAtGnTli1bVvePECJyc3NTVFS8fv069wcMv4KCgticLSEhIXzXAiCGnAWItfj7+3t7e3fu3Hn37t3R0dFxcXF//vmnhobGwoUL2VpLsqm+Iczs30GBQMA9njXQkQEAAND2Ro4cyW1L3oHYqVMnEteByAV8knQgLlu2TPI6xXYgEtGgQYNUVFS4McuMlZWVQCDQ1ta2sLC4cOECyxO1tbVFJ2Dq1avXuXPnnJ2d6/6url27Kikp5ebmih24DQAAANJiZmZ28uTJ2NhYVVXVqKiogoIC0aOBgYGVlZV9+vTZvn17165diUhDQ+PUqVM///yz2KsZGho6OztXVFTIyCjmGzdusA0EiCCb5DtA3LFjh0AguHTp0tKlSy0tLc3NzRcuXHj58uWqqqrNmzfzXV296ltExdjYePr06f/5z38MDAzYfnQgAgCATKkbIL558+bly5dsT30Boq6uLonrQOQCvgY6EI2NjXv27NmjR4+PPvpI8jqdnJxUVVVHjBhRa/8///yTkJBQa2p2XV3dw4cPHz58mIgmT578+++/KyoqOjg4iK7/2ABFRUUjIyOhUJiVlSV5hQAAANA86urqQ4cOrampqdWEyJZXXrx48RdffCHhpaZOnUpEAQEBUi+ySQ4cODBv3rzAwED2MTQ0lN96AMQS08crR6KiomxtbdmU5xxbW1t7e/uwsDC+qmpUfXMgampqHjx4kIiOHj3K9mtpafFRIAAAgHhGRkYDBgyIi4ujdwGira0tm62ciNigIaoTILK5C5vXgaigoPDkyRMFBQXR6RcbNXfu3Llz59bdr6amVndhRyL68MMPue3hw4eHhYXV7V5sQPfu3dPT0zMyMnr37i35TwEAAEDzjB8/Pigo6Ny5c2yqYoZ18E2cOFHy67A3o/fv35d6hY0qKSlhDUOZmZleXl5sPIeKikpNTU10dDR3FEB2yHcHYmVlpaGhYd39xsbG2dnZbV+PhBoIENnHuhsAAAAy4siRI//+97/pXYCYlJTEHaq1CjO3ZDO7WddNCSXpQCQiHR0dNqdhmxk0aBBbgllCbEw0W7sZAAAAWtu0adOI6Pz589yCxSUlJampqaqqqhYWFpJfx9raWlNTMzEx8dWrV61SaD02bNigq6vLFm37/vvvudlghg4damdnV1VVVd8a0wA8ku8A0cHBISoqSjSJIyKhUBgTEzNo0CC+qmpUfUOYuScrbgMBIgAAyBpbW1u2WnFFRUVlZaXolL6vX79mG6wDkUv9Zs+e/cMPP6xevbrWpbS0tNhkhfI+5+/QoUOJ6OHDh3wXAgAA0CEMHDhw4MCBeXl5wcHBbM+zZ8+EQqGZmZnY9VLqo6Sk5ODgIBQKHzx40DqVinfr1q2qqqoff/wxLy+PDUNkXFxcXF1diejKlSttWQ+AJOQvQExOTp40adLy5ct37drl6OiYkZFRa7rDLVu2PH/+XOw05zJCwg5EZWXluqO9AABAHpWUlLB3RaLu3bvn5eU1YsQINze3tWvXpqen81JbM6iqqhJRRUXF27dvRffXmgORTX1IRF26dPn2229rLV3CsCbEhjsQZR8LEB89elTfCUKhcNGiRWxBZwAAAGi5sWPHksh6I2wZ1QEDBjT1Ok5OTtTmbwGfPXtGRP7+/suXLy8rKxs/fjx7peri4jJhwgRCgAgySc7mQBw5cmRCQoK/v7+/vz+3c/Pmzewv8qqqKgcHh4iICAsLi3Xr1vFXZiMk7EBE+yEAQLuhpaU1YMAA9qcts3bt2h9//JG7F1y8ePG3337bs2eP2Jn7ZA0XINZad7hWgMgWX6YGGwyNjY3j4uLkvQNx8ODBqqqqT58+vXjxYnh4eO/evUNCQj744AMXFxd2QkREhI+Pj5qa2saNG7mR3QAAANBsbNBhVFTUqlWrfH19//WvfxHRwIEDm3odW1tbInry5InUK6zP69evc3JyiKimpubIkSNEtGrVqrCwsPv3748YMUJJSUlHR+fZs2c5OTliZ2wD4IucBYhsIoDS0tKkpKSEhITExMSEhISUlBR2tLq6OiIiYvjw4QcPHpTl9I09OYiO+aoVILLiZfkrAABAS/j7+3t7e+vr62/atMnFxUVJSSkoKGjdunULFy60s7Nrxt++bUzCAJHrQGwgH+zbt++NGzeaNOGgDFJVVbWxsXnw4MG0adPevn0rEAhqamr27NkTFxfXt29fetdHUF5enpaWZmpqWvcKb9++PXbs2KRJk+T9PwUAAEDbGDx4MBFFRkaGhIS8ePHi0KFD1KwOREtLSyKKjo6WeoX1ef78ORFZWFhoa2s/fPhw06ZN48aNGzduHHfCkCFD7ty5Ex4ezroRAWSEnAWIjIaGhpWVlZWVVa39ysrKqampPXv2bPaVjx07tn///obPKS4upv/tH2yqRocw9+rVy9HR0cHBodm/AgAAZNmOHTsEAsGlS5ccHR3ZHnNzc1tb26FDh27evPno0aP8lteoRgNEtl+SDkRvb++xY8e6ubm1Vq1tZdiwYQ8ePGBjutk7wqqqqk2bNs2aNevFixe+vr7stISEBLEB4p9//vnZZ58tWbJk7969bVg1AACAvLKyslJUVIyLixNt0GFpYJOYmZmpqKgkJyeXlpa2zZAIFiBaW1v7+PjEx8fb2NjUOsHW1hYBIsgguQwQ6yMQCFqSHhKRn5/ftWvXJDmzJQFiXawDkQsQVVRU2ngOVwAAaEtRUVG2trZcesjY2tra29uHhYXxVZXkmjoHYgNTHHbp0mXmzJmtVWgbGjFixI4dO7iPhoaGOTk5hw4dYg0RnPj4eNEWA87Zs2cJy7AAAABITENDo1+/fmwyQSISCATLli1rxmKqysrK/fv3j46OfvLkia6urrm5ubQrrY0FiP3799fS0qqbHhIR2xkeHt7alQA0iZwFiOrq6p06ddq5c+eMGTNa4/o+Pj6LFy9uOBxMSUlZtGhRSyYwqjuEmXUgyvsMUAAAIKHKykqxk9oYGxuLzpMos+rrQCwvL2dv7yWfA7HdcHZ2VlBQ4P6EmD17dmhoaGxs7ODBg9XU1Nj/Z+/fv79u3br4+Pjt27dzP1hQULBu3To2SUt0dHR5ebmamho/3wEAAECu2NjYsABxyJAhZ86c6d27d/OuY2VlFR0dPXbs2JKSEh8fn//85z/JycliF3+TCi5ArO8EFiDeu3fv+fPnDZzWqJqamsDAwNjYWEdHx2HDhoWFhb1+/ZotPgPQDHIWIJaXl5eXl8+cOfPMmTO7d+/W19eX7vW1tbUb/Z9TyydHqG8RFUx6CADQQTg4OERFRQmFQnZHYIRCYUxMTDPenLc9FiCWl5fXChCJqKCgQFVVtbKyUlFRUVtbm+3sCAGigYGBubl5XFwc+2hjY/PLL79UV1erqKiwPX5+fh988EF+fv6vv/7q5eXF5kYkopMnT+7Zs4dtV1ZWRkZG1mpNBQAAALGcnJyOHTtGRAMGDGh2ekhEgwYNOnbsGGvrOXDgQEJCwpYtW9avX79p0yap1SqChZ4NJIMDBw7s1KlTRkbGiBEjXr58Kck1q6qqHj165OjoqKioWFNT8+LFC2Nj42XLlnF/Y3Tp0uXVq1eKiorPnj3j/ggBaBL5WwfQ3Nx8//79AQEBlpaW+/fvr66u5ruiJqtvDsSO8HwFANBhJScnT5o0afny5bt27XJ0dMzIyNi8ebPoCVu2bHn+/LmzszNfFUquvg5EIiooKGDth6qqqlwnXQe5wW3dunXp0qVs+nZ7e3tFRUUuPSQirpGhpqZm9+7d3P7Q0FAisra2Zu0G7CMAAAA0ysnJiW20JD0kIk9Pz+3bt1+9elVTU/PevXunT58mos2bN7M10KRLKBTGx8dTgwGikpLSjRs3dHR0cnJycnNzG75gZGSks7Ozh4fHe++9x/7AWL9+vYmJybFjx65evUpEs2fPNjY2fvXqFRFVV1ezbwfQDPIXICooKMyfPz82Nnbo0KELFy60tLSUu/8BNLqICgAAtDMjR47U19f39/ffuXPnsmXL2AttLkCsqqqysbH59ttvLSws1q1bx2ulEqlvDkQiys/PZwGimpoaO01FRUVJSc5GPDTP+++/v3v37mPHjv3zzz91J3EfMGCAg4MDC4jZjIdMSEgIEe3du9fLy4uIDh06JN15lgEAANorGxsb9rayhQGirq7uF1984erqOnnyZC7go9aZmzgzM7OkpMTQ0JCbKlosW1tb1ieYmpra8AU3bdp09+7dS5cuEdHJkycfPXoUFBRERF5eXomJiaqqqgcPHkxNTc3OzmbL9Pn5+Unty0AHI38BImNsbHz+/PmTJ0/W1NRMnz7d3Nx8y5YtmZmZfNclkfqGMHeQBg0AgA7o9u3b7O/FJ0+e+Pn5bdu2bcmSJdxiGtXV1REREcOHDz937pxcvEwS24Gop6dHRAUFBWynmpoa+5u+o93dBg8ePGvWrLr7lZWVHz16dPPmTRUVldTUVPbusKysLCYmRklJycbGZvbs2d26dXv06NGHH36YkZHRwjJevXpVWVnZwosAAADIMhUVFQcHBxJp828hV1dX0Y8tn76srkYnQOT06tWLiFJSUho4Jy0t7dy5c9zH4OBgR0fH4OBgIsrPzxcKhVZWVsrKykpKSt26dXN3d9fQ0Hj48GFaWloLvwV0TPIaIDLTp0+PjY3ds2dPUVHRmjVrevbsOW7cOG9v76CgINb+IJvQgQgA0DFpaGhYWVl5eHh89dVXe/fu9ff3Z/uVlZVTU1Pv3r3betN1S5fYANHY2JhEhjB32ACxYUpKSmZmZjU1NWy2xIiIiMrKSgsLCw0NDQ0NjfXr1xPRsWPHZs6c6ezsHBIScuHChbFjx2ZnZ4u9WmpqamJiYt39CQkJJiYmH330Uat+l4ZFR0cvX748JyeHxxoAAKDd27179969e11cXKRyteHDh7MNLS0tko0AseEOxEOHDlVXVzs7O0+aNIn7M1I0ahgyZAi3raGh4ebmJhQKWSsiQFPJd4BIREpKSp6enqmpqceOHRs9evSNGzfWrl07cuRIbuVHGVR3FeY3b94QAkQAgI5KIBD07NmT7yqaQMIAkZ2GALEWCwsLImLLbbOxUax7goiWLl165swZIrp3797du3dnzJjx/fff37hx4++//657HaFQOGLECCsrK9ZoIOr27dtv3769ceNGa36PRqxYsWLnzp2+vr481gAAAO2etbX1kiVL2CN2yw0YMICNqJgwYYKSklJ8fDz3p051dXVWVpYk3f0hISH5+fliD5WUlJw4cYKIzM3NG72Oqakp1RMgPnz4cPXq1ZGRkWw88urVqy9durRixQruBBsbGwMDA/rfAJGI2MvFw4cPN/rbAeqS+wCRUVFRmTlz5rVr15KSknbv3u3h4cFN3C6zuNcCRUVFpaWlmpqa3GqVAADQLqWmpgYEBHAf09PTV61aNX78+LFjx3711VcxMTE81tYkDQSI+fn5SUlJRNS1a1d0IIo1cOBAehcg3r9/n4iGDRvGHXV3dzc0NGTbKSkpjx49IqIrV67k5eXVus7Tp0/T09PLy8snT57MYkcO+6nc3NysrKxW/Cb1e/r0KYsvIyMjeSkAAACgGQQCAVuYZciQIWZmZlVVVZcvX2Z3tOnTp5uYmJiamhYWFor92bKyMiL65ptv2GoNYs9Zv379zZs3O3fu7O7u3mgx9Q1hvn379rBhw7Zu3Tp79uzw8HBtbW02K46Xl1dWVhZrpbK2tl6+fLmuru6ECRNEf3bChAk6OjqxsbFsTRWAJmknASLH1NR06dKlfn5+df/Olh3s9QgXILJxSUZGRnzWBAAAreyHH37o06cPt/zuhQsXLC0tt23bFhgYeOPGje3bt9va2v7666/8FikhRUVFZWXlmpoaNgUHw3Ugsqm7hw//f+zdd1yTV/v48ZOw9xIUEXGjonUP3FUR9x6PFas4C1qtFrVWq1brpq3b2to6+lhb9ywqWkepgtSBA6GoqIgoS2XJzu+P+9v88gBhKBDG5/3qH8nJyZ0rp3e8wpVzn9OBAmKepALi2bNnnz596u/vL1Q2kRRCyOXy3r17CyHs7OyUjRcvXrS1tb127ZrqcS5fviyEMDExSUhIGD16dHBwsNT+5s0b5aLvN2/eVCgUZ8+eVffXTj5CQ0NzvGLhffHFF9L3nFu3br3dEQAA0IgJEyY0atRowIABbdu2FUIMHTq0R48ePj4+v//+uxDi2bNn0o2oqCjlAiOJiYn169c3NDRs3br1mjVrhBBnzpzJsdGcr69vzZo1t2/fLoQ4efJkYWYgqruEed26dVKSvXfvnkKh6Nu3r/TLrhDC1tZWugq7UaNGCxYsePnyZf369VWfq62tLX1hK3BzZyC3ilZAVCrLGz7q6uoKIZSzNqTZAdLHGABQIf32228LFy40MjIaMmSIEOLFixfu7u4ZGRlLly4NCgoKCwv76aefrKysZs+eLVWFyj7pq2pCQoKyRfolTFlA7Ny5MwXEPLVs2VImk129etXV1fXJkyfm5uZSSVFpzpw5Y8aMOXz4sOqXmYyMjFWrVkm3N27cOH78eGm/xWXLlk2aNCk9Pf3jjz8WQhw/frxq1arKeX83b96cN2+ei4uLl5dXkYLMzs7u0aNHu3btdu/eLYTYtm3bypUrN27cKFU81QkPDx83btyHH3548OBBU1NTuVx+79499nIBAJQj0kYLzZo1+/rrr9u1ayc1zpw5U1kQPHnyZGRkZJMmTVq0aCE1BgYG3r9/Xwih/OEtOTl54cKF0uTBZ8+eKRSKX3/9NSIiIjEx8b333lP94TAfderUkclkISEhFy9eVDZGREQcP35cT0/PyclJanF3d1d91kcffdS8efPBgwerO2yVKlUEBUS8lbJbZctTamqqtANJuSatdaictcEMRACo8L755htjY+M7d+5Iax0eOXIkLi5u06ZN06ZNkzrUq1evTZs2rVq1WrVq1bFjxzQabKHo6eklJSWpFhClX8IiIyOvXbumpaXl7OyclpbWrl27PLckrswaNGhw9uzZXr16SXMGO3TokGPlpiZNmkhLB3bo0OHSpUt9+vQxNDQ8ceLE4cOHw8PDa9WqtWTJEuXiSh07dvzwww8PHTr0xx9/HDhwYMyYMdIfMzKZTKFQbNq0SfqaUcgL5K9du3b16lUnJ6fAwMDIyEghxOTJkx0cHDw8PKTJDnZ2dhEREeq+jC1cuFBal11bW3v79u3z589/8OBBaGhokyZN3na0AADQDCsrqz///DMgIKBz585hYWFCiH79+p08efLUqVPR0dFSIr5y5UrXrl1v3Lih+kQ7O7vIyMi1a9deunRp3rx5Q4cOXblypbK2OHHixEIGYG5uPmPGjPXr148aNSosLExa8Wzfvn1ZWVnDhw9/7733FixY4Ojo2KtXL9VnDRw4cODAgfkcVlobkUuY8RbK2QxEPT09afpeuUYBEQAqm+Dg4M6dOyt3Srl9+7YQYsSIEap9mjRp0r59+xzfQcusHDMQtbS0pALi33//nZ6e3qBBA1NTU2tra39//8mTJ2sy0DKpe/fuypqatG5RnubMmdOlS5fNmzcfOHBgxIgRWVlZe/fuffTokfRHi1wu79ixY/PmzS0sLMaNGyeEGD9+fHp6+qhRo/bt2+fj42NsbKy8uirPzZpz+/DDDz09Pbt27SrNWJTJZOnp6WPGjFEoFE5OTmZmZpGRkdIfUblFRETs379fW1t7+vTpgYGBI0aMkBZu//zzz99//31mOgAAyh0dHZ1OnToplyqeNGlS48aN4+LifH19pZYzZ84IIaQvb8oljOfNmyfdCAgIkH4qvnTpUnBwsLa29oULF6ZPn174AL755psOHTq8ePFCeRWCtGvK8OHDP/zww/79+3/77bdFnWIlFRDJy3gL5ayAWDFQQASAysbIyOj58+fKu9WqVcuzm46OTmZmZmkF9U6kJbqlFNaiRYs1a9ZYWVmJf7ObdHUM8tGxY0fphrTiYZ769+9/8eLF2rVrCyFGjRolhNi+ffuCBQuEEH379s3KyvLz85Muc546dapMJpMGf86cOSNGjHB1dT1w4ICBgcH48eMNDAyio6MTExPziSc9PT0wMDA4OFhLS0v6XykdVgghTUX09vaW5jhIl6gLIVJTU2fMmNGrV6+jR49GRUX16NEjIyNj+PDhGzdulEqH/fr1E0IcP378woULv/76q/SsmTNnNmvWLCkp6a2HDgCA0rRv3z43N7cOHTp079590aJFUqP0+5+Pj49CoZAKiB4eHkKImjVrTps2bdu2bbNnzxb/flM6f/58RkZGo0aNunbtWqQNo+Vy+dq1a2Uy2cqVK5cuXRoVFXXlyhUDA4PevXvXqFHj+PHjffr0Kerbkb6kMQMRb4ECogbkWUBkDUQAqMBcXFyCgoKUS9h06tRJCHH06FHVPo8fP/b391eutlPG1atXTwhx584dIcTw4cNnz55taWmpfFT1NvIkFRAdHBxyLICoTq9evczNzcPDw/fu3SuEaNmypeqjjo6Ou3btsrOzGzBgQKtWraRGV1fXuLi4HTt2SCVIaXdsdcaMGSOtFj9w4MCoqKg2bdpYW1uvXLlSOiEdHBx69uwpnbd79uyRJiHu2rVr48aNvr6+kyZNWrFiRVhYWLNmzb755hvlMUeNGmVhYSHdlk7+qKioLVu23Lp1i81VAADlRY0aNX7++ee//vrL1NR05MiR3bt3b9iw4d69e01MTG7cuDFkyJDQ0FBdXd1PPvnkgw8+WLx4sVwunzJlypo1a6Qf0oQQqampQghlgi6SDh06bN68WUtLa8mSJRMmTMjOzu7du7exsfFbvx1mIOKtUUDUgBwFRGkTFWYgAkAFtmLFiurVq/fr12/ZsmVPnjzp1q3bxx9/7OXltWfPnrS0tKysrD///LNv375v3ryZNGmSpoMtFGlTP2nJcOlyZgsLC+WP6sqyEdTp06dPz549pemEhaGrqzt37lypbityFRCFEGPHjn369GmOBTQNDAyEEHXr1hX5XsUcGhp66NAh6baLi4uOjs7ly5elDV78/f0jIiJu376tra3duXNnIcT58+elxZWkhRotLCxiY2Ol7cXXrl2r+n3G0NDw448/1tHREUKcOXNm5MiRGzdulObYSuvKAwBQvshksnPnzt27d69KlSrSTP+jR49mZWX169fP3Nx8z549EyZMkHpqaWnt27dv48aN0p//Qog2bdq8V03yKgAAIABJREFU3Yt6eHisXr1aoVCcOnVKCDF+/Ph3eQtsooK3RgFRA7iEGQAqG3t7+1OnTllYWCxatMjBwcHExOT3339PSEhwc3MzNjY2Njbu0qXLP//8s2XLlv79+2s62EJp0KCBEEIqBknLE2tpaSknHlJALJCFhYWvr2+RFoicP3/+P//807FjR0NDww4dOhT+iXXq1BFCfP/99+Hh4Xl28Pb2zs7OtrS0tLCwGDBggBBCW1tb2kRbCFGjRg1p4fbmzZuvWLGiSpUqISEh+/fv/+uvv4yMjI4ePaqlpaVQKExMTLp27ZrjyF9++eXr16/t7OwSExP379+vXMJJXSQAAJQXvXr1un379sKFC7ds2bJ///7cHQwNDadPny5lYSHEu1xlMmvWLGlXuqpVq/bt2/etjyP+nYF47949aUluoPAoIGqAagExPT398ePHMpmsRo0amo4LAFCCnJycHj58uHv37o4dO8pkMuV0sOzsbDs7u3nz5oWHh0tLzpUL0gxEiTQDUagsfUgBsYTIZLKzZ88+efKkatWqhX+WNG/x9OnTuRduDwoKmjlz5o4dO3R0dPz9/ePj4/P5QiKTyebPnz9mzBghhKenp0KhGDhwYOfOnaVVn1xdXfPc6c7AwGDEiBHSSaJQKKRFG8v+DMQTJ0507NhR3Y4xAAAIIerWrbts2TIPDw8tLS11faSFRAwMDN577723fiGZTPbTTz/NmDFj27ZtUiZ9a9K3tRs3bnTs2DEjI+NdDoXKhgKiBqgWEG/evJmamtqoUSNTU1NNxwUAKFk6Ojpjx4718/NLSEhITk6OiYl5+fJlWlra/fv3V61aVb5+Scq/gMgaiCVHX19fuclJIQ0aNKh///4GBgY+Pj4hISFS45MnT7799tvRo0dv2LAhKyvLw8ND9f9pPqQCorT4urST+MqVK7/66ivl7MLcvv322/j4+H79+unq6n755Zei6DMQnz59Kk13DQoK6tevX1BQUD6dDx8+vGbNmsIcNisra8+ePdIuMTn8+OOPly9f/umnn4oUJ1BRpaWl+fv7X7t2Tdly+vTpwYMHOzs7T5ky5dy5cxqMDSjjatWqJYRo3bq1tKbHWzMwMFi/fv2gQYPeMR5pBqIQIjExMTg4+B2PhkqFAqIGqBYQ/f39hRDt27fXcEwAgNJlaGhYpUoVc3Pzd/wZWVNq1KhhaGgo3WYGYhlnb29//PjxcePGKRSK5cuXCyEUCsWYMWNmz5597949e3v7yZMnL126tJBHa9OmzZAhQ4QQJiYm0hbSxsbGCxYskFZaVMfQ0PDQoUNPnz4dNmyYKOIMxFOnTtnb2zs4OPj4+Kxbt+73339fsGCBQqFYuHChlZWVo6PjzZs3lZ0TEhLGjRs3b96869evF3jk7du3u7m5zZkzJ/dDUo3S19e38HECFdVvv/1mbW3t7OzcunXrXr16paambtmypXfv3kePHvX39//hhx9cXFwKv6IrUNk0btxY/LuBXlmg/LYmhFBNoECByuUfLeWdVEBMSUkRQgQEBIh3Ww0BAFCOPHjwwNfXNzg4OC4uLiUlxcrKys7OTto8t3wthistvvHPP/+If9dAFCq/aVNALIPmzp27c+fOPXv2TJ8+/cmTJ35+fnK5XKFQbNiwYfDgwUU61Lp164KCgkaMGCFt0lJIurq61tbWJiYmcrn8yZMnWVlZ+VzwpWrfvn1CiGfPnn3wwQdSwd3Hx8fNze2XX34RQsTHxw8dOvTSpUvSHN7//ve/iYmJQojTp0/n3mpGlZ+fnzRrUjqNVb1+/Voqcd64cWPXrl1jx45VbhAEVDaBgYGjR482MDAYNGhQXFycr6/vuHHjjh496uTktHbt2kaNGt28edPLy2vFihUuLi7dunXTdLxAmTNhwoTq1at3795d04H8H0NDQ2tra2kTlZs3b44bN07TEaHcoICoAVIBMSkpSQhx5coVIYSzs7OGYwIAlLDw8HBPT09p+7zcpk2bNmjQIG9vb+k6l6K6fv26VGTJR7Fvt1e1alWp8sIMxHKhdu3aH3/88dq1a3fv3v3q1SshxNdff/2f//ynWrVqRT1UzZo189nTOX/6+vrVq1d/+vTp5MmTjY2N169fL5PJ1HWOj4+fPXv2iRMnhBCtWrVSXj6ZnZ39yy+/yGSyAwcOrFmzJiAgoE2bNtevX//xxx+XLVsm9fn2229DQkJ27NiRZ+3v4sWLykpHjuupY2Jili9frlAopBcaP368np6etHR9Dnv27JHL5aNHj36LcQDKi2XLlsnl8j///FOqyM+fP3/VqlUmJiZnz56V/vWoVatWkyZNnJycvL29KSACueno6EgblJUdf/zxx4kTJ+bPn88MRBQJBUQNUF7CHB4eHh4ebmVl5eTkpOmgAAAlKC4uzsXF5cGDB05OToMGDWrSpImVlZWpqWlCQkJ8fHxISMiJEycOHjwYFBTk5+dXpP0xJIsXL5aKLAXKzs4uevh5UxaeWAOxvHB1dV27du2NGzekOXqdOnV6i+rhu3Nzc1u1atWOHTuEECNGjOjcubO6nkeOHNm1a5cQwtbW9ty5c+3btw8JCRk8eLC/v//z588HDhw4dOjQbt26DRgw4PLly15eXvv27VMoFKNHj96/f39MTMzu3bv79OmTZ+1PWtywe/fuV65ciY+Pf/36tZmZmfTQV199tWHDBiFE69atMzMzb968efjw4RwHGT58+MmTJ1NTU7W0tN5//32NDCNQOm7cuNGhQwflfN7p06evWrWqe/fuqqd9vXr12rZte+fOHQ3FCKBopC+i8+fPv3btWnR0tI2NjaYjQvlAAVEDpEWjUlJSpIV1unfvznUxAFCxff755w8ePFi5cuVnn32WZ4clS5bs2LFjypQpixYt2rZtW1GP7+3tXeDaOklJSV999VUx7tmlLHQyA7G8aN68uRDi1q1b6enpWlpamvr9csGCBb/88suTJ0+EED/99FM+BUTlOoYDBw40MzM7ceLEt99+O3PmzJiYmNWrV3t7ewshLC0tFy1a1Lt3b+mKZk9Pz82bN1taWp49ezY0NHTKlCnnz5/P8ZlKTk4+dOiQTCbbtm3b4MGD7969Gx4eLg2OEEI5TXjKlCkuLi61a9c+ffp0enq68lL9Fy9eHDp0SJqimJWVdebMmQ8//LB4hwgoO1TL60II6bZqi8TCwuLWrVulGhmAd2Bra9uzZ8+zZ89OmTLlyJEjmg4H5QMFRA1QzkA8e/asEKJnz56ajggAULIuXbrk6OiornoocXd337Nnj5+f31sc39HRcd68efn3efHixVdffVWkRevyp5x+oiysSAVEmUxGAbFssrKysre3j4iIEEI0atSoGE+GIjE2Nr58+fLNmzcHDBiwf//+rVu36uvr59lTKiBu2LBh0qRJQoi6detu2rRJCFG/fv2jR48qu/Xo0aNatWrPnz+vWbPmkiVLhBCbNm3KyMho1arV7du3v//++xkzZnh7ez979szNzW3s2LF79+5NSkrq0KFDvXr1ateufffu3aCgoMuXL0dGRvbp0+eff/4xNzffvXt37969dXR0mjZtevv27XPnzvXp00d6udOnTysUChcXl65duy5cuPDUqVMUEFGBNWzY8O+//05NTZU+p5cuXRJC/P333wqFQrn+QEZGxvXr16WdIgCUFzt37mzQoMGxY8eeP39emKn0gYGBq1atWrFiRSnEhrKJiW8aoCwg3rhxQ5Sl/ZgAACUkJiamMHuk2NvbR0dHl0I8xSL3DERpExUTE5NyurV0ZaCcZ/fee+9pMAw7O7t+/fq1bNkyOTk5z6J5SkpKRkZGUFCQTCZzc3PLv9apra29YsUKV1fXS5cuKXfy0dHRuXbtmrRAoYeHx86dO8+cOfPRRx8lJCRIVyh7enoKIWrXri2EmDBhwrRp01asWNGvXz8hRPfu3QcMGKCjoyOE+OCDD4QQmzdvVr6cNEVx4MCBw4cPF0L4+voW48oAQFkzZsyY58+fDxs27MyZM3v27JkyZYqlpWVwcPDSpUuV83C9vLwiIyOljw+A8sLOzu79999XKBSnTp1KSkoaOHDg2rVr1XWOiYkZOnTooUOHRo0aRdartCggaoCygBgXFyeEKF/bbgIA3oKzs/OVK1fu37+fT5/o6OhTp06Vo221cq+BKLUoKzgog9q0aSPdaNWqlWYjEf9ehHH27NnExMQ3b94o27du3WplZeXs7JySklK7du3CTGh1d3c/deqUg4ODaqOOjs6oUaOEEH/++acQQk9PLyUlZdq0abdv37a1tR0xYoT4t4CYnZ3dqVMnMzOzhIQEIYT0LMnkyZMNDAx8fHykfWOys7OlJWhcXV0dHR3r1KkTGxsbEBBQDMMBlEmenp49evT4/fffXV1d3dzcXr9+ff78+e7duy9ZsqRevXp9+vSpXbv2hg0b6tev7+XlpelgARRN3759hRA//vjjpEmTjh8//tlnn40ePfrQoUPi301flby9vZ8+fSqECAoKevjwoUaihcYxQUADjIyMZDJZSkqKEEIulxfjclQAgLJpxowZPj4+7du3X7Ro0aBBg3KUOaKiok6ePLls2bLo6OgJEyZoKsiiyj0D0d7e/vvvv69Tp47mgkIBPvnkEwsLC21t7bFjx2o6FuHi4rJ69epDhw7t3LnTxsZGWkDt9u3b0txAac/ld9zU1cXFxdzc/NWrV6amposXL/7000//+9//CiGmT58uXXrfoUMHbW3tDz744KefflqzZs3nn38+ceLEkSNHKo9gZWU1cuTIXbt2/fLLL1988cWNGzdiY2Nr165dv359IUS/fv02bty4d+9eR0fHwu8dlJ2dHRISotqiWj8FyhQdHZ3Tp0/v3Lnz0qVLOjo6H3/88XvvvXf48OEJEyYcOnTo4cOHOjo6I0eOzGctAgBlVt++fWUymfJSgOzs7F9//TU4ONje3r5Dhw79+/ffu3ev9NGWVg6ZNGnS9u3bnz17VuT9/gonKysrISFB9YdDqSU9Pd3IyCgjI0NqTEhIiI2NjY2Nffz4cVRU1Js3bwwNDatXr25mZmZiYiKEiIyMfPTo0YMHD7KysqQFdkxMTMzNza2srKytrQ0MDOLj4+Pi4mJjY+Pj49PS0oQQKSkpaWlp5ubmysUZ8mRkZGRkZGRsbCwFqaurK80PS0xMzMzMFEKkpqY+e/YsIiJCitbExGTDhg1SnwqAAqIGyOVyPT291NRUIYSlpaWWlpamIwIAlCwXF5eNGzfO/JeZmZmlpaWpqWlSUlJ8fPzLly+FENra2lu2bBk8eLCmgy2s3DMQhRCTJ0/WUDgoFBMTk+nTp2s6iv/TsWNHY2PjsLAwIcSLFy8iIiLs7e1v3rwphGjatOmTJ0969Oixfv36d3kJQ0PD69ev+/n5NWzY0NHRcf369U+ePDEyMvroo4+kDu3atXv9+rW0wd28efN69OiRe27mqFGjdu3atW/fvufPn0v1RxcXF+khqYC4cePGI0eO+Pn5SX+W5A7jt99+c3d3HzJkyPnz52fMmHHv3r3du3fPsbVVbnv5119/NR837l3eKVBytLS0Jk6cOHHiRGWLqanpgQMHoqOjo6OjGzRooFwJF0D5UqtWrYMHD164cCE6Orpnz57Xrl374YcfgoODT58+nZmZeeTIkc8++2zdunWhoaGhoaFWVlbLli3bsWNHdHT02xUQExMTw8LC7t+//+zZsxcvXmRkZDx//tzQ0FAmkz19+jQ0NDQiIiI9PV1HR0dZK6wAZs6cqdl1Y4oRBUTNMDIyUhYQNR0LAKA0eHh49OzZc/v27b6+vvfu3QsPDxdCaGlpWVtbt27deujQoe7u7oVZwbrssLGxkclkCoWCPx3xdvT19Y8ePSqtsCaE+PHHH8ePHx8cHCyEGDp0qLQdyrurXbu2dJ2yECIwMHDOnDldunRR/QImVQ+FEHK5vG3btrmP0LNnT0tLyzt37ty5c0dq6dWrl3SjW7duNjY20dHRERERDg4O7dq18/f3V31ucnLy1q1bv/jii9TUVGmf6Pnz50sPRUVFKQuIAQEBdU+d6tatG3O4UI7Y2NjY2NgU3A9AGTZkyJAhQ4ZItydOnOjn53f79u29e/dKLXv27BkyZIi0DWDfvn2rVavWtm3b7CtXCnPkrKys169fR0ZG+vv7X7169dq1a7du3crKysrnKTKZzMTEJDExUbXF3NxcLpdnZWXJZDK5XK6lpaWnp2dhYWFra1u9enV7e3tDQ8OkpKSnT58mJycnJCQoFApbW9tatWrVrVtXR0cnPj4+Ozs7OTn55cuXMTExsbGxqampVlZWlpaWVapUsbS0lDKviYmJlpbWq1ev8n9TiYmJycnJycnJUs+0tDTp0lJjY2Np6WRdXd2qVavWqlVL+n29Vq1aFaZ6KCggaoqRkZG0ACL7VAJA5VG/fv3Vq1evXr1aoVCkpKSkpqZaWFjI5eV1PWI9PT1p1xfW4sBb6969++PHj5cuXbp8+fIvv/xyx44djRo1EkKU0HauNjY2u3btKuqzdHR0vLy8Pv/8cyFEvXr1srKypNUbhRB6enp37ty5e/dunz59UlNTAwICLl++3KFDB+nR7777btGiRTExMdJBMjIyzM3N69atGxoa+vXXX4d+8YX4d9OkW7du/dynj42Nzd9//21vb1887xYoVg8ePPD19Q0ODo6Li0tJSbGysrKzs7OzsxswYABLugMVRosWLW7fvi39mGdgYBAbGyutJWJnZ/fpp58KITp16hT1vwXE9PT0wMDA3377zc/Pz8zMbOzYsWFhYefPn79z505ycrJqT11d3caNG9evX79GjRpVq1Y1MjKytLR88+aNQqGwsbFp3Lixg4ODvr5+dnZ2+f16XLFRQNQM5TXwzEAEgEpIJpNJS6hoOpB3dfbs2ZSUFNVLmIGi0tXV7dy5s3T7yZMnERERosQKiG9t/vz5TZs2vXbt2sKFC3MsPmNtbd2tW7ebN29+/fXXP/zww7Zt26QC4unTpz08PIQQHTp0+OKLL0JCQmbNmjV16tRVq1ZJTzx+7lzovn3K42hpaUVHR3/55Zfbt28vxXcGFCw8PNzT01Pafzy3adOmDRo0yNvbu1atWqUbF4Di17Jly927d0u3p02b5u3tra+vP2fOnE8//dTMzEwIUatWrSiV/ufPn+86Y8aLFy+ULRcuXFDetrCwsLa2btasWadOnVq0aNGqVSvlrP98UD0ssyggaoa0tKcQwtzcXLORAADKCIVCkZ6eLv53ScEyTtpHAnhH7du3NzAwkLaYUygU2traDRo00HRQOfXv379///7qHnV0dJw/f/5PP/20b9++tWvX2tjYnDhxQgjx6aefent7CyF69erVpk0b5S7YOTg5OX27caOrq+uuXbs++eSTJk2alNC7KF4pKSnR0dFxcXEvX760sLBIT09PTk7OzMxMTEyUlr1X9pRWepUYGhqamZmpLhYpPeXNmzcJCQnJyckKhUK6yiw5Ofn169evXr2qUaPGwYMHraysSvXtQQghRFxcnIuLy4MHD5ycnAYNGtSkSRMrKytTU9OEhIT4+PiQkJATJ04cPHgwKCjIz89PubkWgHKqY8eO0o0aNWp8/vnnCoXigw8+aNmypbJD7dq1Vecffvfddy+EaNy4cffu3UeNGvXw4cOtW7dmZ2d/9dVXLVq0kDYwQYVBAVEzHBwcrl69KoTgmxAAQHLr1q3mzZsLIRQKhaZjAUqVmZnZzZs3DQwM5syZ89tvvzVs2LA8LqxZu3btAQMGHDlyZOvWrYsXL/b19RVCDBs2THpULpcr/yrLbdGiRY7vvz916tRNmzZ5enqeP3++hDbZUygUL168ePToUWxsrLa2trb2//0tYGhoqPzpQqFQPHv27OXLl9LGlEKI7OzsmJiY58+fP3369PXr1zExMXFxcVK9rySCzO3JkydxcXF8bdaIzz///MGDBytXrpQWQcttyZIlO3bsmDJlyqJFi7Zt21bK4QEoXq1bt/b09NyyZUuzZs0sLCyk38BU5Z5rvG7dupkzZ0q3O3Xq9OGHH5ZCnNCI8lpALO9rcNSrV0+6wSXMAAAA0pTDX375pXfv3k5OTpoO5y3NmDHjyJEje/fudXd3Dw0NNTMzUzflME/Lli07cODAn3/+OXTo0HXr1lWrVk05TS8rKysgICAyMjIlJUVbW1tHR0dHR0fayT07O7tevXrKJRFevXr14sWL+/fvR0VFZWdnp6amxsXFPXnyJD4+PiUlJSoq6s2bN8X1fvX19W1sbCwtLa2srF69emVkZKSrq6ulpWVqaiqXy6WL3SRmZmbKS9KSkpKk+YbSXV1d3aysLFNTUz09PVNTUxMTE5lMZmVlVaVKFWNjY3NzczMzMzMzM2tr6+IKG0Vy6dIlR0dHddVDibu7+549e/z8/EotKgAlZ926dQ4ODv369cvz0dwFxDFjxpR4TCgbyl8BsWKswaEsILKJCgBA4ujoqNzjFaic5HL5+PHjNR3F2+vUqZOenl5YWNivv/4qhOjRo4dyil9hmJubHzlypG/fvseOHTt27Ji2tnanTp3atm0bEBBw7dq1pKSkYgnS2tra3t7e1tY2KysrMzNTakxJSVHONxRCVK1a1crKSlm+lMlkVapUqVq1qr29vZmZmY2NjfSoaokQFVVMTEzTpk0L7GZvbx8UFFQK8QAoaTo6OnPnzlX3qIGBgb6+vkhNle6amJhwnXLlUc4KiBVmDQ4KiACAHPT19cvvxCsAQggdHZ2mTZv+/fff0jVfgwYNKuoR2rVrFxgYOHv27MuXL798+fLChQvK1ejr16/fvHlzAwODzMzMjIyMjIyMxMREaTX6sLAwaQVVIYSZmVmVKlXq1atnZ2enra2tq6traWlZs2ZNKysrY2NjGxsbY2PjYnvDqAScnZ19fX3v37+v/Pslt+jo6FOnTjk7O5dmYAA0xcjISFlArF69umaDQWkqZwXECrMGB5cwAwAAVDwtW7b8+++/Y2JidHR08tl0JR916tQ5cuSIECIhIeHo0aPPnj2zt7fv3bs3XxqhETNmzPDx8Wnfvv2iRYsGDRrk4OCg+mhUVNTJkyeXLVsWHR09YcIETQUJoDQZGxuLuDjptp2dnWaDQWkqZwXECrMGh62trZGRUXJyMt8FAaBSKe9r+ALIX4sWLaQbLi4u7/g1z9TUdOzYscURFPD2XFxcNm7cOPNfZmZmlpaW0vqb8fHx0v7a2traW7ZsGTx4sKaDBVAanJycIh8/lm7z9bVSKWcFxAqzBodMJmvcuHFgYCCfNwCoJCrGGr4A8teuXTshRNWqVbds2aLpWIDi4eHh0bNnz+3bt/v6+t67dy88PFwIoaWlZW1t3bp166FDh7q7u1erVk3TYQIoJapLYeSYlYyKrZwVECvSGhw///xzWFhYPm8EAFBhVJg1fAHkr0WLFkeOHGncuDF/U6EiqV+//urVq1evXq1QKFJSUlJTUy0sLJQ7awOotN577z1Nh4DSU84KiBVpDQ5HR0dHR0dNRwEAKA0VZg1fAAV6i71TgPJCJpMZGRkZGRlpOhAAZYJMJtN0CCg95ayAyBocAIDyqMKs4QsAqJxKaA3f58+f//nnn/n3iYiIeOvjAwCKSzkrIIoSXoMjOjr64sWL+fchgQEAiqrCrOELAKhsSnQNXw8PD2nb8QJlZ2e/xfEBAMWl/BUQRUmuwTF16lQSGACg2FWkNXwBAJVHSa/h6+Hhoaenl//fVqmpqcePHzcwMHiH9wEAeFflsoCoVOxrcEyZMkVHRyf/PiQwAEBRVaQ1fAEAlUdJr+Hbq1evXr165d/nxYsX1apVMzU1LerBAQDFqJwVEA0MDMzMzDZs2DBy5MiSOH6fPn369OmTfx8SGACgqFjDFwBQHrGGLwBAUgyX/Zam1NTUFy9ejBo16j//+U9cXJymwwEAoLA8PDzu3r07d+7cFi1apKWlhYeHBwUFPXr0SE9Pr3Xr1itWrIiIiJg6daqmwwQA4P+LiYkpzB4p9vb20dHRpRAPAEBTytkMRCGEo6Pj3LlzZ8+e7eTktHz58vHjx2tpaZV+GOnp6WfPni1MtwsXLlhZWbG7uTpZWVmxsbE2NjYMkTrSEL3FmjKVhCwkRHnqHDt2TPHokQaDKbMyMjKEEO3atStk/+Dg4JIMp/IquTV8C4/8VVzIXwUif+WP/FUY5C+NKztr+JK/igv5q0Dkr/yRvwqjYuYvRbkihGjYsKFCoYiMjBwwYIAQwtHR8cCBA6UZA7+tAWVKTyGm/PtfHU0HU8F07NixNP91RUkjfwFlCvmr5JC/itGZM2e0tLSsrKzWr1//6NGjHI8+e/bshx9+qFmzplwuP3z4cAnFQP4CyhTyV8kp4/lLplAoND1ERSCTyRo2bHjv3j3p7oEDBz7//POwsLAGDRq4u7uPHTvWzs6upGNQKBQff/xxaGhoYTrfvXs3KirK0NBQX1+/pAMrp5KTk9PS0oyMjPT09DQdSxnFEBUoKSkpPT3d2NhYV1dX07GUUYmJiRkZGU2bNi3ST6nu7u4ffPBByUWFUkb+Kl7841wghqhA5K8Ckb/Kgq1bt86cOVOaTaNuDd9NmzaV3Coc5K/ixT/OBWKICkT+KlDFzF+arV8Wlfh3BqJSRkbGli1bqlWrJoSQy+U9evRYvnz5pUuX3rx5o6kgVXl5eQkh1q5dq+lAyi5PT08hxObNmzUdSNk1fvx4IcSOHTs0HUjZJe2q9Ntvv2k6kLKrX79+QogTJ05oOhCUG+SvApG/CkT+KhD5q0DkrzLin3/+kdbwVVbltLS0qlWrJq3hGxUVpekA/z/yV4HIXwUifxWI/FWgCpm/yt8aiDloa2t7eHhMnDjx8OHDP/zwwx9//HHu3DkhhK6ublpamqajAwAAAIDyrSys4QsA0KwK8o++rq7uqFGjzp49+/BM3ojsAAAgAElEQVThw82bNw8ePJgp6wAAAABQjGQymZGRkZWVFdVDAKhsKtq/+7Vq1fL09Dx8+HBcXJymYwEAAACACk6hUKSlpXH5FwBUbBWtgKikrV3ur84GAAAAgDLu1q1b+vr6XAEGABVbOauypaamymQyTUcBAAAAAAAAVBblrIDINuoAAAAAUHY4OjreuXNH01EAAEpWOSsgAgAAAADKDn19fScnJ01HAQAoWRV2DUQAAAAAAAAA744ZiAAAAACA/Dx48MDX1zc4ODguLi4lJcXKysrOzs7Ozm7AgAG2traajg4AUOIoIAIAAAAA8hYeHu7p6Xnq1Kk8H502bdqgQYO8vb1r1apVunEBAEoVBcSSZWRkJIQwNjbWdCBlF0NUIIaoQAxRgRgiFBXnTIEYogIxRAViiArEEGlcXFyci4vLgwcPnJycBg0a1KRJEysrK1NT04SEhPj4+JCQkBMnThw8eDAoKMjPz69q1aqajpdzpmAMUYEYogIxRAWqkEMkUygUmo6hInv9+vXx48eHDx+ur6+v6VjKqJcvX/7+++8jRozQ1dXVdCxlVExMjK+v78iRI7W1qfjn7fnz5xcuXBg5cqRczrqueXv69Only5dHjBghk8k0HQvKB/JXgchfBSJ/FYj8VSDyl8ZNnTr1+++/X7ly5Weffaauz44dO6ZMmTJhwoRt27aVZmx5In8ViPxVIPJXgchfBaqQ+YsCIgAAAAAgD40aNVIoFCEhIfl369mzZ1RU1N27d0snKgBA6aNaDAAAAADIQ0xMTGH2SLG3t4+Oji6FeAAAmkIBEQAAAACQB2dn5ytXrty/fz+fPtHR0adOnXJ2di61qAAApY8CIgAAAAAgDzNmzMjMzGzfvv2GDRseP36c49GoqKjt27e3adMmOjp6woQJGokQAFA6WAMRAAAAAJC3rVu3zpw5MyMjQwhhZmZmaWlpamqalJQUHx//8uVLIYS2tvamTZumTp2q6UgBACWIAiIAAAAAQK2wsLDt27f7+vreu3cvNTVVCKGlpWVtbV2jRo2hQ4e6u7tXq1ZN0zECAEoWBUQAAAAAQMEUCkVKSkpqaqqFhYVcznJYAFCJUEAEAAAAAAAAoBa/GgEAAAAAAABQiwIiAAAAAAAAALUoIAIAAAAAAABQiwIiAAAAAAAAALUoIAIAAAAAAABQiwIiAAAAAAAAALUoIAIAAAAAAABQiwIiStaFCxdiYmI0HQVQ8Xl7e2/evFnTUQAVB/kLKB3kL6B4kb+A0lEJ8xcFxJLi6+s7cOBAa2vrxo0be3h4xMfHazoiDbhz5877779/+fLlPB8tzBBV4GHcunVrq1atTExMbGxsunTpsm/fvtx9KvMQJSYmenl5tWjRwtjYuHbt2oMHD75+/XrubpV5iFT9/PPPc+bMOXDgQO6HGCIUFeeDIH/li/yVP/JXkZC/UIw4HwT5K1/kr/yRv4qkkuYvBUrA1q1btbS0dHV1u3bt2qBBAyFE3bp1Hz58qOm4Stvw4cOFEEeOHMn9UGGGqKIOY2Zm5tSpU4UQenp6Xbt2ff/99/X19YUQU6dOVe1WmYcoMTGxdu3aQohq1ar179/f2dlZCCGTyY4fP67arTIPkarw8HBTU1MhRLdu3XI8xBChqDgfJOSvPJG/CkT+KhLyF4oR54OE/JUn8leByF9FUmnzFwXE4vfPP//o6OhYWVn9888/UsuKFSuEEL1799ZsYKXm/Pnza9asadWqlVSkzp3ACjNEFXgYd+zYIYRwdHSMioqSWu7fv1+nTh0hxMmTJ6WWSj5E8+bNE0JMmDAhKytLajl58qRMJrO1tVX2qeRDpJSZmdmxY0cTE5PcCYwhQlFxPpC/8kf+KhD5q/DIXyhGnA/kr/yRvwpE/iq8ypy/KCAWv7lz5woh1q9fr9ro5OQkhLh//76moipN9erVU53lmjuBFWaIKvAw9ujRQwjh7++v2rh//34hxEcffSTdreRD1KxZM319/ZSUFNXG9u3bCyHCw8Olu5V8iJSWLl0qk8l+/PHH3AmMIUJRcT6Qv/JH/ioQ+avwyF8oRpwP5K/8kb8KRP4qvMqcv1gDsfj5+voKIQYNGqTaKN2VHqrwLly48OjRo0ePHnl4eOTZoTBDVIGH8eHDhzo6Om3atFFtbNq0qRAiLCxMulvJh8je3n7YsGEGBgaqjVpaWkKIpKQk6W4lHyJJQEDA0qVLp0+f7urqmvtRhghFxflA/sof+atA5K9CIn+heHE+kL/yR/4qEPmrkCp5/tLWdAAVjUKhuHfvnqmpqYODg2p7kyZNhBDBwcEaiqtU2dnZSTfMzMxyP1qYIarYw3jo0CGZTCaX/0/5/tq1a0KIunXrCoZIiOPHj+do+fPPPwMDA2vXrt2wYUPBEAkhhEhKSnJzc6tXr97q1atzL7vLEKGoOB8E+asg5K8Ckb8Kg/yF4sX5IMhfBSF/FYj8VRjkLwqIxSwlJSU1NdXW1jZHu5WVlRAiLi5OE0GVLYUZooo9jM2bN8/RcvPmTS8vL5lMJi3uyxApBQQEfP311xEREYGBgQ0bNty7d6+2trZgiIQQQsyYMePx48dXrlzJ8VOhhCFCUXE+FIiPFfmr8Mhf+SB/oXhxPhSIjxX5q/DIX/kgf3EJczFLTU0VQkg78qiSWlJSUjQQUxlTmCGqPMOoUCh27drVpUuXqKior7/+umXLloIhUhEXFxcUFHT37t2srCw9PT3lP6kM0cGDB3fs2LF48WLlatk5MEQoKs6HAvGxUkX+yh/5Sx3yF4od50OB+FipIn/lj/ylDvlLUEAsdhYWFlpaWsplApQSEhLEv3XlSq4wQ1RJhvHatWvt27cfP368kZHR4cOHZ82aJbUzREp9+/YNDQ1NSEg4d+7ckydPXF1db926JSr9EEVGRk6ZMsXZ2fmzzz5T16eSDxHeAudDgfhYKZG/CkT+yhP5CyWB86FAfKyUyF8FIn/lifwl4RLmYiaXy62trXNfDy+1KBenqMwKM0QVfhgzMjIWL168Zs0aPT29hQsXzp07V9oGXsIQ5da9e/clS5ZMnz599+7d3t7elXyIjhw5Eh8fL5fL3dzcpBbp16rg4ODRo0fr6Ojs3r27kg8R3gLnQ4H4WAnyV9GRv1SRv1ASOB8KxMdKkL+KjvylivwlYQZi8atRo8arV6+eP3+u2hgSEiLKwwlROgozRBV4GLOzsz/88MOVK1d269bt3r17y5YtU81ekso8RNevX+/Tp8/GjRtztEvL98bExEh3K/MQSf76669f/3Xs2DEhRHR09K+//rp//36pA0OEouJ8KFAl/1iRv/JH/iok8heKHedDgSr5x4r8lT/yVyGRvyggFr8hQ4YoFIoTJ06oNp44cUJbW3vAgAGaiqpMKcwQVeBh3LJly6+//jpmzJjTp0/XrFkzzz6VeYjMzMxOnTr13//+N0e7tCmVk5OTdLcyD9G0adMU/+vp06dCiG7duikUijdv3kjdKvMQ4e1wPhSokn+syF/5I38ViPyFEsL5UKBK/rEif+WP/FUg8tf/UaC4PXv2TFtb28HB4cWLF1LLjh07hBDDhg3TbGClT1og4MiRIznaCzNEFXgY69evb2homJCQkE+fSj5E0sK033//vbIlODi4atWqurq6d+7ckVoq+RDloJrAlBgiFBXngxL5K0/krwKRv4qK/IViwfmgRP7KE/mrQOSvoqqc+YsCYon47rvv5HK5ra2tu7u7i4uLtrZ23bp1Hz58qOm4Spu6BKYo3BBVyGGMiooSQujr6zfPi5eXl7JnpR0ihULx999/GxkZCSGcnJyGDBnSqVMnHR0dmUy2bt061W6VeYhyyDOBKRgiFB3ng4T8lRv5qzDIX0VF/kJx4XyQkL9yI38VBvmrqCpn/qKAWFIOHz7cv39/Kyurhg0bTpo0KSoqStMRaUA+CUxRuCGqeMP4119/5TMjePjw4aqdK+cQSUJDQ8eNG2dnZ6enp1e3bt3BgwdfvXo1d7fKPESq1CUwBUOEouN8UJC/8kL+KiTyV5GQv1CMOB8U5K+8kL8KifxVJJUzf8kUCkU+HycAAAAAAAAAlRmbqAAAAAAAAABQiwIiAAAAAAAAALUoIAIAAAAAAABQiwIiAAAAAAAAALUoIAIAAAAAAABQiwIiAAAAAAAAALUoIAIAAAAAAABQiwIiAAAAAAAAALUoIAIAAAAAAABQiwIiAAAAAAAAALUoIAIAAAAAAABQiwIiAAAAAAAAALUoIAIAAAAAAABQiwIiAAAAAAAAALUoIAIAAAAAAABQiwIiAAAAAAAAALUoIAIAAAAAAABQiwIiAAAAAAAAALUoIAIAAAAAAABQiwIiAAAAAAAAALUoIAIAAAAAAABQiwIiAAAAAAAAALUoIAIAAAAAAABQiwIiAAAAAAAAALUoIAIAAAAAAABQiwIiAAAAAAAAALUoIAIAAAAAAABQiwIiAAAAAAAAALUoIAIAAAAAAABQiwIiAAAAAAAAALUoIAIAAAAAAABQiwIiAAAAAAAAALUoIAIAAAAAAABQiwIiAAAAAAAAALUoIAJvyd7eXiaTvXjxQtOB/J/JkyfLZLLQ0FBNBwIAKNPIXwCA8oj8BWgWBUSgeCgUirS0tMzMzAr5cgCAior8BQAoj8hfQCmjgAgUj6tXr+rr68+fP19TLzd//nx/f38HB4fSCQAAUDGQvwAA5RH5Cyhl2poOAEDxqFOnTp06dTQdBQAARUP+AgCUR+QvVDbMQATKkMTERE29dEZGRlZWlqZeHQBQrpG/AADlEfkLKDwKiEAx6NOnT/v27YUQ3t7eMplsz549yof27Nnj6upqbW1tY2Pj6up6+vRp1SfOmTNHJpPFxcXt3r27Zs2affv2ldoTExPnzp3btm1bU1NTa2vrdu3afffddwqFIp+X8/DwyLGIb2Zm5ooVKzp16mRmZla/fv1hw4bduHFD9dXnz58vk8kePXo0depUU1NTHR0de3t7d3f3qKioEhkmAEAZQ/4CAJRH5C+g9FFABIrB1KlTP/nkEyHE+++/v27dulatWknt48aNc3Nzu3btWps2bRo3bnzp0qXevXuvWLEix9P37ds3adKkpk2bDho0SAgRGxvbpEmTtWvXpqSkuLq6tm7d+u7dux4eHkuWLMn/5VSlpKR07NhxwYIF9+/f79y5s4WFxdGjR9u1a7dr164cPT08PHbu3NmzZ8/Jkyfr6+vv3LmzX79+/BoGAJUB+QsAUB6RvwANUAB4KzVq1BBCPH/+XLrr7+8vhPDy8lJ22LdvnxBi8ODBCQkJUsv9+/fr1q0rl8sDAgKkFi8vLyGEjY1NUFCQ8onLly8XQsyfP1/Z8uzZM1NT0zp16ihbcr/cRx99JIQICQmR7i5evFgIMXLkyJSUFKnl4sWLpqam5ubmsbGxUstnn30mhDA0NAwMDJRa0tLSmjVrJoRQjQcAUJGQvwAA5RH5C9AsZiACJWX58uUGBga7du0yMTGRWurWrbtmzZrs7Oyff/5ZteeECRPee+895d2OHTt+9913s2fPVrbY2tpWrVo1Ojq68K++bt06U1PT7777zsDAQGrp0qXLrFmzXr16tXPnTtWe06ZNa926tXRbV1d34MCBQghm0QNApUX+AgCUR+QvoESxCzNQIrKysu7du2dra5sjV8XGxgohbt68qdrYpk0b1btdu3bt2rWrQqF49OjRo0ePwsPDL1y4EBYWZmxsXMhXf/bs2evXr3v16mVhYaHa3qdPny+//DIkJES10dnZWfWukZFRIV8FAFDxkL8AAOUR+QsoaRQQgRIRGRmZnp7++PHj6dOn5340x25ftra2qnczMjKWLl26devWuLg4mUxma2vbsmXLatWqJSUlFfLVnz59mvuwQgg7OzshxOPHj1UbLS0tC3lYAECFR/4CAJRH5C+gpHEJM1AiqlWrpqWl5eLikufaATl+AZPL/+eT6Obm9tVXX/Xv3/+vv/5KSkqKjIw8fvy4tORHISnXB8nRLk2ML9KhAACVCvkLAFAekb+AkkYBESgRurq6derUuXbtWnJysmq7n5/fxx9/fPr0aXVPfPPmzfHjx5s1a7Zz584OHToYGhpK7a9fvy78q1evXt3U1NTf3//Vq1eq7dLrNmrUqAjvBABQmZC/AADlEfkLKGkUEIHilJqaqrw9a9as+Pj4Dz74QJnDnj59OmLEiE2bNklT2fMkl8szMzNfvnz55s0bqSUzM3P58uVhYWFZWVkKhULdy+Uwc+bM169fe3p6pqWlSS1//fWXt7e3ubn5+PHj3/b9AQAqJvIXAKA8In8BpYY1EIHiIa2wu3//fplM5ubm1rZt2ylTphw9evTYsWM1a9Zs165dfHx8YGBgdnb2ihUrmjRpou44enp6bm5uO3bsqFu3bs+ePeVyuZ+fnxCiXbt2AQEB7u7uCxYsqF+/fu6Xy3GcefPm+fj47N279+LFi23bto2Ojr569apcLt++fbu1tXVJjgQAoDwhfwEAyiPyF1DKmIEIFI/GjRt7eXnp6ent3Lnz2bNnQggtLS0fH5+NGzc2adLE39//0aNH3bt39/HxmT9/fv6H2rJly+LFi42NjQ8ePHjr1q1hw4YFBQWtX7++SZMm+/fvj4yMzPPlcjAyMrp8+fKyZcscHBz++OOP58+fDxo0KCAgYOzYsSXx9gEA5RT5CwBQHpG/gFImyzEjFwAAAAAAAACUmIEIAAAAAAAAQC0KiAAAAAAAAADUooAIAAAAAAAAQC0KiAAAAAAAAADUooAIAAAAAAAAQC0KiAAAAAAAAADUooAIAAAAAAAAQC0KiAAAAAAAAADUooAIAAAAAAAAQC0KiAAAAAAAAADUooAIAAAAAAAAQC0KiAAAAAAAAADUooAIAAAAAAAAQC0KiAAAAAAAAADUooAIAAAAAAAAQC0KiAAAAAAAAADUooAIAAAAAAAAQC0KiAAAAAAAAADUooAIAAAAAAAAQC0KiAAAAAAAAADUooAIAAAAAAAAQC0KiAAAAAAAAADUooAIAAAAAAAAQC0KiAAAAAAAAADUooAIAAAAAAAAQC0KiAAAAAAAAADUooAIAAAAAAAAQC0KiAAAAAAAAADUooAIAAAAAAAAQC0KiAAAAAAAAADUooAIAAAAAAAAQC0KiAAAAAAAAADUooAIAAAAAAAAQC0KiAAAAAAAAADUooAIAAAAAAAAQC0KiAAAAAAAAADUooAIAAAAAAAAQC0KiAAAAAAAAADUooAIAAAAAAAAQC0KiAAAAAAAAADUooAIAAAAAAAAQC0KiAAAAAAAAADUooAIAAAAAAAAQC0KiAAAAAAAAADUooAIAAAAAAAAQC0KiAAAAAAAAADUooAIAAAAAAAAQC0KiAAAAAAAAADUooAIAAAAAAAAQC0KiAAAAAAAAADUooAIAAAAAAAAQC0KiAAAAAAAAADUooAIAAAAAAAAQC0KiAAAAAAAAADUooAIAAAAAAAAQC0KiAAAAAAAAADUooAIAAAAAAAAQC0KiAAAAAAAAADUooAIAAAAAAAAQC0KiAAAAAAAAADUooAIAAAAAAAAQC0KiAAAAAAAAADUooAIAAAAAAAAQC0KiAAAAAAAAADUooAIAAAAAAAAQC0KiAAAAAAAAADUooAIAAAAAAAAQC0KiAAAAAAAAADUooAIAAAAAAAAQC0KiAAAAAAAAADUooAIAAAAAAAAQC0KiAAAAAAAAADUooAIAAAAAAAAQC0KiAAAAAAAAADUooAIAAAAAAAAQC0KiAAAAAAAAADUooAIAACAErdr1y7Z/5LL5ebm5k2bNv3kk0+ePHmi2nny5MmqPQMCAlQf/fLLL1Uf3bVrlxCiT58+soK4u7srDxISEqJsHzZsWOkMAgAASp07d5bSUFBQkKZjAQpGAREAAABFk5SUNG/evDZt2piYmNSqVatv374+Pj5FPYhCoXj9+vWdO3fWr1/fpEmTS5cuqevp7++vevfq1atvE/T/+u2335S3f//996SkpLc+1Pbt2/v27WtpaVmtWrWRI0feunXr3cMDAJQvxZIZ8xcYGGhgYGBubl68hwUKSVvTAQAAAKA8iYmJad26tXLOYFJS0uPHj318fDw9PTdv3lzg06tUqdK7d28hRGZm5p07d4KDg7OzsxMTEydMmBASEqKtnce3U39//5kzZyrv5llA7N69e5UqVaTbGRkZyvrg4MGDjY2NpdvOzs7K/r/++qvydmpq6okTJ/7zn/8UGHwOmZmZHh4e27dvV7bs37//2LFjR44ckd4jAKAyeMfMWBjR0dEfffRRampqsRwNeAsUEAEAAFAEXl5e0t9Ijo6Obm5uL168+OGHH9LS0rZs2eLq6jpw4MD8n16vXr2ff/5ZedfX13fo0KFJSUkPHjzYvXv3hAkTVDvr6uqmp6erzkB8+PBhbGys8iFl+5w5c5S3k5OTlQXEdevWOTg45Ijh1q1bISEhQghtbe3MzEwhxP79+9+igPjNN99I1cO6deuOHz/+/v37u3btSktLGzNmzMOHD83MzIp6QABAefSOmTEft2/fDgwMvH79+t69e+Pj44svZKDIuIQZKNOOHj3apUsXS0vLvn37BgYGzpo1S1om4+LFi5oODQBQNBcvXhw5cmTDhg1NTEzatGkzefLkx48fq3ZwcXGR/pH39fUdNmxY9erVLSws+vbt+8cffwghzpw54+rqamlp6eDgMGnSpJiYGNXnZmZmLl++vFu3bubm5vb29kOHDr1y5UruGAqTVu7cuTNmzBhHR0dDQ8OqVau2bdt2w4YNb968kR5VKBQHDx4UQpiYmPj5+S1cuHDjxo3r1q2THlW9LriQXFxcPvvsM+n2mjVrcjzavHlzIcSjR4+eP38utUjTD3V1dRs3blzU11JSTj+cNWuWlpaWEMLHx+ctrmLetGmTEEJfX//06dMLFy7cuXPnRx99JISIj4//5Zdf3jo8AKgkyIw5rF69Wnr1Zs2aJSYmCiG+//77iRMnbt68meohNE8BoKzK8XeUqalp586dpdsXLlzQdHQAgCKYO3euTCbL8TXMxMRk3759yj49e/aU2q2trVW7GRgYzJ07N8e1vc7OzllZWdITY2NjO3TokOPgMpnsu+++U42hMGnl8uXLOjo6ub8xDh48ODs7W6FQKP+069mzp/LIYWFhUmPTpk3VjcDOnTulPu3bt8/xUGRkpDLmlJQUhUIxadIkqcXT01OK58iRI1LnWbNmCSHatWvXvn17qc/OnTtzHFC1FPjo0aPcwdStW1d6NCwsrHv37tLtvXv35ve/MBflu+7SpYuy8dy5c1LjqFGjinQ0AKhsyIydOnWS+ty8eVOhUJw+fVoulwshqlevHhERIfX5/vvvB/9LGi59ff3i+R8AFBEzEIEyKjQ09IsvvhBCyGSy0aNHT5w4MSPj/7F353FR1fv/wN+zsC+DjOwgiyyyaO6CaWZKeS0uXiuzrlZa6lVLb6am2cWF0kzLm3Y1y7RNWy1LFBLNVBJNQ9lBGEDZB9kGGIdt5vfH5+f5TqzDMMww+Ho+vo/v48yZM2feQNcz8zrvz+fTfOHCBUPXBQAAPXby5Ml33nlHpVLxeLznn39+x44dLLSqq6t78cUXpVJpm+MrKioiIiKef/55W1tbIrpz584777wjEomWLl36wAMPsGMSExOvXr3KtqOjoy9evEhEs2bNOnr06LZt2ywtLVUq1bJly1JTU9kxGl5Wli9f3tzcTETPPPPMnj17Xn31VSsrKyI6duxYeno6EVlYWOzcuXPnzp0rVqzgXsW6JIiIm22wR1xdXdloX5VKlZ+fr/6UpaXlfffdx35etod1IHLpoRauXLkikUiIaMSIEb6+vrNnz2b7v/vuux6dh/ty6O7uzu3ktnNzc7WuEABgwMOVsQ2JRDJ37lylUmllZXX8+HHuarJo0aIf72It8wAGY9D4EgA69fjjj7P/kW7ZsoXtUV/GCx2IAABGJCQkhP3r/cEHH7A9SqWS+3d+5cqVbCfXZ/HKK6+wPVzXHp/PT01NZTu57OyLL75QqVQlJSVmZmZEFBAQ0NLSwo7Zs2cPO+aZZ55hezS5rMhkMnt7ezs7u0mTJnHFP/300+yYI0eOdPYDPvvss+yYTZs2dXZMFx2IKpUqICCAPfvzzz+r1DoQV69e/dJLLxHRlClTVCpVc3OzhYUFEX311VdadyC++uqr7KnNmzerVKqioiLW02FhYVFXV9dZ/e1xg5QXLVrE7SwpKWE7fXx8ND8VAMC9BldGlVoHYkJCAvuF8Pl8dh3sEOu4RAciGAo6EAH6KXb3TCgUvvzyy2zPjBkzxo8fb9CiAACgx+RyOetQcHR0XLJkCdvJ4/HWr1/Ptq9cudLmJY8++ijbmDBhAtvw9vbmvmtxOxsbG4nozz//ZBuPPvoo15vAfSniOik0uazY2NhUVlZWV1dfuHChrq7u8uXLe/bs+emnn9iz6iuWcJRK5YoVKz7//HMiCgoK+ve//635b0Zd+1FsHLZ08pUrV1pbW1NTU9mcU1p3IKpUqm+//ZZts9+Sm5sb+5XeuXPnxIkTmp+KWwrT3Nyc22lpadnmWQAAaANXxjaWLVuWlpZGREuWLImIiOj2eACDQIAI0B81NDSwZbzc3d3t7Oy4/aNHjzZcUQAAoI0bN26oVCoiCgwMVJ+tKSQkhKVm2dnZbV7CDXfiYjUbGxvu2TZZGxuNS0Tvvfce7y5XV1e2s6ioqLW1VfPLSmNjY3R09PDhw0UiUWho6IoVK+RyeWc/WmFh4dSpU1lPx8iRI0+dOqX1usOlpaVsg5udkMMCRLlcnpqaysYvOzk5eXl5afdGFy9eLCwsJCJ/f//g4GC2kxvFzGWLmjA1NWUb6r8iLjdknZIAANAeroxtpKSksI1jx441NDR0ezyAQQi7PwQA9K6hoYFdU9tMDMx9V1hndboAACAASURBVAEAAKPT5usN+z6jUqk67F/QHLcso1AobHPVYOrq6pqamjS8rPztb387e/YsETk7O0+bNm3MmDHXrl374osv2p/2559/fv7556urq4noueee27dvn9aRWVlZWW1tLRHxeDxvb+82z3p7ezs5OZWXl1+6dIkFiCxS1A63/vKNGzfatz2ytZg1nK9q0KBBbIOb6Ep9W/3rKAAAdAhXRo6tra1MJistLd25c+fGjRs1fBWAPqEDEaA/cnR0ZN9eCgsL1W9wsc52AAAwIv7+/uwLUmZmZmtrK7c/LS1NqVQSUWBgYG/Oz7XsvfXWW3c6Ymdnp+Fl5cKFC+w7UnBwcEFBwZdffvnKK69wY3LVRUdHz5o1q7q62tra+siRI59++mlvGu4OHTrENvz8/Do8D0sMuQBR6/HLSqXy+++/7+KAHo1i9vPzYxvc+pvq29yzAADQBq6MbfzjH/84efIk296xYwfXlQ/QryBABOin2KKTjY2NH330EduTmJjIrl4AAGBELC0tg4KCiKi8vPzjjz9mO1Uq1VtvvcW2x44d25vzDx8+nG2cP3+e21lQUPDyyy+//PLL27dvZ3s0uaxwCwcHBASw6ecbGxt/+eWXNu94+PDhqKgolUplaWl5/vx5bi55dXV1dZWVlZWVlV2M82LOnj379ttvs+3XXnutw2NYYnjmzJnMzEzqRYD422+/lZWVEZGjo+O8v5o4cSI7RvO1mP38/Ngg6KtXr3Kjz7g/MVtOFAAA2sOVsc1TUVFR999/P5vnsaGhga0NDdDfYAgzQD+1du3ayMhIIlq9enVqaqqZmRlbU8zQdQEAQI+9/fbbbE705cuXJyUlBQcHx8TEnD59mohsbGw2bNjQm5OPGjVq5syZJ0+ePHHixDPPPPPkk08WFxfv3r07JyeHiD788EN2mCaXlSFDhrCN48ePv/zyyz4+PkeOHCkoKGA7WVeISqWKiopie9zd3Xfv3t3mDJs3byaixYsXs8HCb7zxRnR0tPoxEolkwYIFRNTS0pKenp6SksLaT4YOHcqtWdkG60AsKioiIoFAoPUXy2+++YZtPPfcc++88476U5mZmezb7MmTJxsaGqysrDQ54cKFC1999dXW1tZp06b9/e9/v3HjRkJCAhENHjx4zpw52hUJAHAvwJVR/RjWj/nmm2+ePHlSpVIdOnRoxYoVI0aM6M0vAUD39L3sMwBo7KmnnlL/X6tAIOBupv3222+Grg4AAHpg1apV7Wfcs7a2/vrrr7ljpk+fzvZfunSJ7cnKymJ7Ro4cyR3Gred44MABtic3N9fHx6f9x7wVK1YolUruhd1eVpqamrg9jKWlJTfh4KJFi1QqVXJychcfLLk6586dy/a88cYbbM+nn37axQu9vb25n1qlUr344ots/+rVq1UqVUNDAzdHFfcWXB/ip59+2ua3XV9fz525oKCA7WxubhaLxWzn77//3v5vxA15U/+jdK2pqWnatGltfhZ7e3tcpgEAuoUr46RJk9ie69evsz1PPPEE2xMeHt7+N8Yuhebm5jr6CwD0DIYwA/RfX3311Y4dO8aPH29raztp0qSff/4Z46EAAIzUu+++e+bMmdmzZ/v7+1tZWY0aNeqFF15ITU1t89VFO0OHDk1OTl6zZs348eOtra09PDwee+yx+Pj4999/X/27WbeXFRMTk7i4uKefftrJycnFxWX27NlXrlxZunQpe/bjjz+WyWT5+fm9L5hjY2MTHBy8evXq5OTkCRMmdHaYpaUlG2hGvRi/fPr06crKSiJydHTs8CR///vf2Ybmo5jZb2zHjh0hISEWFhbDhg17/vnnL1++PGXKFO2KBAC4d+DK2N6WLVv4fD4RxcfHx8XF6eq0ADrBU2FEJEC/JJFIWlpaiMjLy4tNt0FEs2bN+umnn4jo5s2bXDs9AABAt3BZAQAAUIcrI0CPoAMRoJ964YUXhg0bNmzYsJdeeqm+vl6lUh0+fPj48eNE5OPjg4sZAAD0CC4rAAAA6nBlBOgRdCAC9FNXr16dOnUqm8VJKBSamZk1NDQQEVvYa8yYMYYuEAAAjAkuK5r7448/Lly40O1h8+bNc3Jy0kM9AADQF3BlBOgRBIgA/VdZWdn27dt//fXXgoICgUDg5eX14IMPrlmzxsXFxdClAQCA8cFlRUPbt29ft25dt4ddvXoVXy8BAIwarowAmkOACAAAAAAAAAAAAJ3CHIgAAAAAAAAAAADQKQSIAAAAAAAAAAAA0CkEiAAAAAAAAAAAANApBIgAAAAAAAAAAADQKQSIAAAAAAAAAAAA0CkEiAAAAAAAAAAAANApBIgAAAAAAAAAAADQKQSIAAAAAAAAAAAA0CkEiAAAAAAAAAAAANApBIgAAAAAAAAAAADQKQSIAAAAAAAAAAAA0CkEiAAAAAAAAAAAANApBIgAAAAAAAAAAADQKaGhCwAAALiHSCSS+Pj4jIyMyspKuVwuFovd3Nzc3NwiIiJcXFwMXR0AAAAAAEAHeCqVytA1AAAADHz5+fnLli2Li4vr8FmhUBgZGblz504vLy/91gUAAAAAANANBIgAAAB9rrKycsKECRKJJDg4ODIyMiQkRCwW29raymSyqqqqrKysmJiYP//809fXNyEhwcnJydD1AgAAAAAA/B8EiAAAAH1uyZIlH3300bZt29atW9fZMYcOHVq8ePHChQv379+vz9oAAAAAAAC6hgARAACgzwUGBqpUqqysrK4Pmz59emlpaXp6un6qAgAAAAAA0ARWYQYAAOhzFRUVmqyR4uHhIZVK9VAPAAAAAACA5hAgAgAA9LmwsLDExMTc3NwujpFKpXFxcWFhYXqrCgAAAAAAQBMIEAEAAPrcihUrWlpaQkNDd+/effPmzTbPlpaWHjhwYNy4cVKpdOHChQapEAAAAAAAoDOYAxEAAEAf9u3bt3LlyubmZiISiUT29va2trb19fVVVVXV1dVEJBQKP/jggyVLlhi6UgAAAAAAgL9AgAgAAKAnOTk5Bw4ciI+Pz8zMVCgURCQQCBwcHNzd3WfPnr1gwQJnZ2dD1wgAAAAAANAWAkQAAAB9U6lUcrlcoVAMGjSIz8d0IgAAAAAA0K8hQAQAAAAAAAAAAIBOCQ1dAAAAwD1EIpHEx8dnZGRUVlbK5XKxWOzm5ubm5hYREeHi4mLo6gAAAAAAADqADkQAAAB9yM/PX7ZsWVxcXIfPCoXCyMjInTt3enl56bcuAAAAAACAbiBABAAA6HOVlZUTJkyQSCTBwcGRkZEhISFisdjW1lYmk1VVVWVlZcXExPz555++vr4JCQlOTk6GrhcAAAAAAOD/IEAEAADoc0uWLPnoo4+2bdu2bt26zo45dOjQ4sWLFy5cuH//fn3WBgAAAAAA0DUEiAAAAH0uMDBQpVJlZWV1fdj06dNLS0vT09P1UxUAAAAAAIAm+IYuAAAAYOCrqKjQZI0UDw8PqVSqh3oAAAAAAAA0hwARAACgz4WFhSUmJubm5nZxjFQqjYuLCwsL01tVAAAAAAAAmkCACAAA0OdWrFjR0tISGhq6e/fumzdvtnm2tLT0wIED48aNk0qlCxcuNEiFAAAAAAAAncEciAAAAPqwb9++lStXNjc3E5FIJLK3t7e1ta2vr6+qqqquriYioVD4wQcfLFmyxNCVAgAAAAAA/AUCRAAAAD3Jyck5cOBAfHx8ZmamQqEgIoFA4ODg4O7uPnv27AULFjg7Oxu6RgAAAAAAgLYQIAIAAOibSqWSy+UKhWLQoEF8PqYTAQAAAACAfg0BIgAAAAAAAAAAAHRKaOgCjFJTU1NDQ4OhqwAA6Ft2dnY8Hs/QVQw0EokkPj4+IyOjsrJSLpeLxWI3Nzc3N7eIiAgXF5e+fndcvwDgXoDr18CD6xcA3Av6+/VLBT2kUCjc3NwM/XcDgP/PlMji7v8JDF3MAPPiiy8a+l/cASUvL2/GjBmd/baFQuHjjz+en5/fdwXg+gXQr+D61Xdw/RpgcP0C6Fdw/eo7/fz6hQ7EHqupqSkuLubz+SKRyNC1AAD9o6EhsKmJbf9oZZVpamrYegaGlpaWurq69PR0QxcycFRWVoaHh0skkuDg4MjIyJCQELFYbGtrK5PJqqqqsrKyYmJijh49mpycnJCQ4OTk1Bc14PoF0K/g+tUXcP0akHD9AuhXcP3qC0Zx/UKAqCUHB4eysjJDVwEAdPypp7K//ZZtHzx4MGDOHMPWMzAkJiZOnDjR0FUMKK+//rpEItm2bdu6des6PGDTpk2HDh1avHhxVFTU/v37+64SXL8A+glcv/oCrl8DGK5fAP0Erl99wSiuXwgQAQAA+tz58+cDAgI6Sw+ZBQsWHD58OCEhQYvzX7ly5ejRo10fw2aPwhxSAAAAAADQUwgQAQAA+lxFRcXw4cO7PczDwyM5OVmL82/ZsiUmJkaTI+vr67U4PwAAAAAA3MsQIAIAAPS5sLCw+Pj43NxcX1/fzo6RSqVxcXFhYWFanP/dd9+dPHmySqXq4pjy8vJdu3bx+Xwtzg8AAAAAAPcyBIgAAAB9bsWKFbGxsaGhoVFRUZGRkZ6enurPlpaWnjhxIjo6WiqVLly4UIvz+/v7r127tutj0tLSdu3apcXJAQAAAADgHocAEQAAoM+Fh4fv2bNn5V0ikcje3t7W1ra+vr6qqqq6upqIhELh3r17Z82aZehiAQAAAAAA/gIBIgAAgD4sXbp0+vTpBw4ciI+Pz8zMzM/PJyKBQODg4DB27NjZs2cvWLDA2dnZ0GUCAAAAAAC0hQARAABAT/z8/LZv3759+3aVSiWXyxUKxaBBgzApIQAAAAAA9HMIEAGgn1IoFGZmZjwer6qq6oUXXliyZMmMGTMMXRSAbvB4PCsrKysrK0MX0pXExMRNmzYplUpuj52d3YEDB0QikQGrAgCAgeTIkSOHDh3q+pimpiYiqqmp0fCcuH4BAPQFBIgA0B8VFxcHBQVNmTLlxx9/PHLkyLFjx/Ly8hAgwgAgkUji4+MzMjIqKyvlcrlYLHZzc3Nzc4uIiHBxcTF0dX9x7ty5U6dOtdn5z3/+E7M0AgCArvz888+nT5/W5MjGxkYNz9nh9eu555577LHHelYcAACoQYAIAP3RhQsXZDLZ8ePHN23alJOTQ0QpKSkZGRlBQUGGLg1AS/n5+cuWLYuLi+vw2eXLl0dGRu7cudPLy0u/dXVq9erVkyZNUigU7OG+fft++OGHgoICgxYFAAADyieffLJkyZLW1tYujikoKFi0aJHmM360uX699957sbGx5eXlva0VAODehgARAPqjpKQktnHw4EFu5zfffLN58+aenury5cuLFi1qaWnZtGnTnDlzdFYiQE9UVlaGh4dLJJLg4ODIyMiQkBCxWGxrayuTyaqqqrKysmJiYo4ePZqcnJyQkODk5GToeomIhELhpEmTuIfXr1//4Ycfbt68acCSAABggLGyspo6dWrXx6SlpfXonG2uX6dPn46NjZVKpdrUBwAAdyFABID+iAsQS0pKuJ3Jyck9PU9qauq0adMaGhqIaO7cubm5ua+//npPT6JQKGQymaOjY09fCMB5/fXXJRLJtm3b1q1b1+EBmzZtOnTo0OLFi6Oiovbv36/n8jTBWiPRgQgAAMaFfYRDgAgA0EsIEAGg31GpVCxAnDJlyrlz54jIy8uroKBAPUzURGpq6qOPPtrQ0PDMM8+MHz9+1apVGzZsSE9P//jjjy0tLdsf39zcfP78+e+++y4/P5/H45mZmVlaWl64cKG4uJiIzMzM3Nzc/Pz8fH19w8PDw8PDOzwJQIfOnz8fEBDQWXrILFiw4PDhwwkJCXqrqkcQIAIAgDFCgAgAoBMIEAGg3ykoKKiurnZ2dp4/f/65c+fMzMz279//yCOP9ChAlMlkM2bMKCkpmTJlyieffGJubu7l5TV//vwjR478/vvvr7/+OhvXnJKSUlFRceXKlaSkpFOnTsnl8vanMjU1tbS0rKmpycvLy8vL++WXX/73v//Z2to+8cQTjzzyyMMPP2xnZ6e7nx4GpoqKiuHDh3d7mIeHhxadtvrBAsT8/HxDFwIAANADCBABAHQCASIA9DvZ2dlEFBQUNGvWrK+//nru3LlTp07l8/nl5eWtra0CgUCTk0RHR5eUlISFhcXFxZmbmxNRZGTk5cuXn3766eTk5CVLlkRFRdXU1Kiv6Mfn84OCgp566qnx48fzeLz6+vqSkpLw8HB/f38+n3/nzp1bt27l5OQkJycfP3788uXLBw8ePHjwII/HCwgIGDVqlK+v74gRI7y9vQcNGuTl5aX5VN9wLwgLC4uPj8/NzfX19e3sGKlUGhcXFxYWps/CNDd48GAbG5va2tqamhqE5gAAYCwQIAIA6AQCRADodyQSCRH5+vqKxeL4+Hi2c/DgwVKptLy83NXVtdszXL9+/f333+fz+R988AFLD5nAwMBr16599913r7zySklJCUsMXV1dhw0bNn78+IceesjNza2zc1pYWAQEBAQEBDz22GMbNmzIzMyMiYk5efLkpUuXsrKysrKy1A+2sbEZO3ZsQEBAWFjY1KlTPTw8tPxdwECxYsWK2NjY0NDQqKioyMhIT09P9WdLS0tPnDgRHR0tlUoXLlxoqCK75enpmZaWVlBQMHLkSEPXAgAAoBG2NBkCRACAXkKACADaqK2tnTlzZkRERNdzumknNzeXiIYOHaq+09XVVSqVlpSUdBsgqlSqJUuWNDc3r1y5cvTo0W2e5fF4c+bMefLJJ4uKikQika2trXZFBgYGBgYGrlmzpqWlJTk5OS0tLScnJyUlpbS0tKysrKio6OzZs2fPnv3www+JyMLCIiQkZOjQoV5eXjNmzBgzZoy1tbV27wtGKjw8fM+ePSvvEolE9vb2tra29fX1VVVV1dXVRCQUCvfu3Ttr1ixDF9spb2/vtLS0/Px8BIgAAGAsHBwc+Hx+RUWFUqnEABEAAK0hQAQAbRw5cuTixYv19fV9ESCyDsT2AeL169c1mQbx66+//uOPP9zc3N56663OjuHxeLrqChQKhWPGjBkzZoz6TqlUevXq1aysrNOnT1+9epVNs3jlyhUievvtt3k83tChQ4OCghobGx0cHMLCwh588MGgoCCd1AP91tKlS6dPn37gwIH4+PjMzEw2maBAIHBwcBg7duzs2bMXLFjg7Oxs6DK7gnVUAADA6AiFwkGDBlVWVlZWVjo4OBi6HAAAY4UAEQC08cUXXxBRbW2tbk9bVlZmaWnZWYBIRN0GiK2trRs3biSizZs3W1lZ6bY8zTk6Os6cOXPmzJmrVq0iotra2pSUlJs3b6ampp48eTInJyc3N5c1WhLRl19+SUSenp6LFy+eNWtWYGAgj8czVOXQp/z8/LZv3759+3aVSiWXyxUKxaBBg4yoG8Lb25uwjgoAABgbR0fHyspKqVSKABEAQGsIEAGgx27dunXp0iUikslkOjxtXV3dsGHDvLy88vLyqF2AyGYnPHHixL59+3bt2vXQQw+x/cXFxdwxxcXF17//Picnx9fX9/nnn9dhbb0kEokmT548efJkItq+fXtLS0tmZmZubq6ZmVlhYeG5c+d+/fXXmzdvbtiwYcOGDUQkFAptbGxUKpWnp6eDgwOPx2PrV4hEIm9vb4FAUFtba2JiYmJiUl5eXltbq1AobG1tFQqFlZWViYmJn5+fi4sLj8cTiUQikcjNzc2AWSp0iMfjWVlZGd3fBQEiAAAYI0dHx8zMTKlUGhwcbOhaAACMFQJEAOix3NxclUpFRDKZTKVS6apd7sqVK7W1tcnJyUTk5ORkY2Oj/iwbcRwTE0NE77//PhcgFhYWDrp7TGpq6pdHjhDR2rVrNVys2SCEQuHw4cOHDx/OHi5ZskSlUv3yyy9Hjhw5e/ZsUVFRS0sLmxSvpqZGV+9oYWFhZmZ27do1d3d3nZwT7kEIEAEAwBixdVTKy8sNXQgAgBEzsgCxoaGBx+NZWlqq77x48eLhw4dTUlLs7OxGjBjxr3/9CwueAvQpbhxxa2trfX19m6RPa2yKQKb9/eFHHnlEIBC0trYS0a+//trY2GhmZkZEzc3N3DHnzp27mpcnFovnz5+vk5L0hsfjzZgxY8aMGexhc3NzY2OjQqHIz8+vra1tbW2trKysr6+/ffs2ixft7OyUSqVcLndychKJRJaWlnK5XCAQyOVyuVyek5Nz+/ZtlUpVXV1dU1NTVlbW0NBQV1fHfmMAWvPx8SGi/Px8Hd45AAAA6GsIEAEAes/IAkRra+thw4ZlZmZyezZs2LBt2zbWDEVEMTExe/bs2bt377x58wxUI8DApz4RYW1tbV8EiGPHjm3zrLu7+/Tp03/55Rciqq+vP3/+/P33329paakeILKxz/Pnzzc3N9dJSYbChidbW1sPHjxYJydsampqbGzU1V8K7lk2Njb29vZVVVUVFRWOjo6GLgcAAEAj7JqFABEAoDeMLEBsIzY2duvWrWKxeMuWLVOmTBEKhRcuXHjjjTdefPHFMWPGBAYGGrpAgIGpTYCoqyGx6gFim0WNmeXLl8fHx7u7u9+6dWvp0qX5+fk//fRTS0uL+jF8Pn/RokU6qWcgMTU1NTU1NXQV966KioqLFy9qfnxkZGTfFdNLPj4+VVVVeXl5CBABAMBYsA5EqVRq6EIAAIyYcQeIu3bt4vP5J06cmDBhAtsTEBAwevTo8ePHR0dHHzlyxLDlARhQUVHRoEGD+miJhtLSUm5bVwsx19bW3rp1i3vYYYAYERFRW1sbGxs7Z84ctlLztWvX1DsQieiJJ54ICgrSSUkAupKenj5r1izNj+fa6vshb2/vq1ev5uXlhYaGGroWAAAAjWAIMwBA7xl3gJiSkjJ69GguPWRGjx49duzYpKQkQ1UFYHDFxcV+fn6TJ08+depUX5yfdSCyefd0FSCyj3Tm5uYKhUIsFrOp1tqztrZWzwdlMpl6B6KVldWWLVt0Ug+ADj344IOpqambNm06evQoES1dulQkEhm6KC2x/22y6QIAAACMAgJEAIDeM+4Asbm5ucMhVK6ururzJALcay5evKhQKBITE/tooQMWIAYEBFy7dk1XAWJFRQURjRo1atq0aQEBAV2U7efnZ2JiwhoPZTKZegfiwYMHAwICdFIPgG6FhIR8//33Y8aMSUpKeu211zw9PQ1dkZawEDMAABgdBIgAAL3HN3QBvTJu3LiUlJQ2Q71UKlV6evqIESMMVRWAwV29epWI6uvri4qKdH5ylUrFAsRhw4YRkUwm08lp2aw0Tk5O0dHRXS+CZGpqOnToULZdUVGhVCp1UgCAHsydO9fQJfQWOhABAMDoYA5EAIDeM74AMT8/f+bMmStXrvzggw8mTJhQVFQUHR2tfsDbb79948aNSZMmGapCAMPasWMHGyZJRBkZGTo/f3V1tUKhEIlEbm5upLs5EG/fvk1EGi46HBwczDaKi4t18u4A+jFu3DhfX1+h0Ijb/xEgAgCA0bGwsLCxsVEoFLr64AoAcA8ysu8wDzzwQG5ubmxsbGxsLLczOjo6KiqKiFpaWsaNG3f9+vWgoKA33njDcGUCGExaWtratWu5h5mZmY888oiuTt7U1MTn81lXo5ubm62tLemuA5ENYXZwcNDk4FGjRrGQtLi42F8nbw+gFw8++GBOTo6hq+gVT09PoVBYVFTU2NhoZmZm6HIAAAA04uTkVFdXV15ebrzTEAMAGJaRBYjnzp0jIrlcnpeXl5ubK5FIcnNzCwoK2LOtra3Xr1+fOHHiZ5991keLzwL0c+x/IxwdTgba0tLi7+/v5OS0YcMGIvLy8mIBoq5u5LJBJRoGiK+88gqPx9uwYUNZWZlO3h0ANCQUCj08PPLz8wsKCjDlKAAAGAsnJ6fc3Nzy8nJ/f9x9BgDQhpEFiIylpWVISEhISEib/SYmJjdv3hwyZIhBqgLoD86fP09EM2fO9Pf3/+9//5uVlaWrM5eVld28ebOwsJD1T3l5ebH7t7pdREXDANHS0nLOnDkbNmxobW3VybsDgOZ8fHzy8/Pz8vIQIAIA3DsyMjKOHj1648aNmpqa5uZma2trJyenkSNHPvXUU+ymcj/n7OxMRLj3DACgNaMMEDvD5/ORHsK9LDY29tdffyWid999VyAQ/Pe//2VTBMbFxY0aNYrNHq01tm6dUqlMTEwkQweIRGQUH1UBBiRfX98zZ85IJBJDFwIAAPqQnZ398ssvx8fHd/jsq6+++uKLL27dutXc3FzPhfUI+ySMABEAQGvGGiAa+x0wAJ2rqamJiIhobW11d3cPCAhgUxNKpdKTJ08++uijc+bM+eabb3pzfm7dut9//53UAsSamhoiksvl77333pNPPql1RxILEB0dHTU8Hv9LBzAUtgw6AkQAgHtBaWnpI488UlhYuGDBgoceeqipqembb745derUxo0bg4KCMjIyvvjii127dt28efO7777j8/vvEp0sQGR3xAEAQAvGFyAOjDtgADp3/fr11tbWgICA48eP83g8kUhkbm5eV1f3ySefEFFpaWkvz88FiOzOrZeXl729Pd0NEX788cf//Oc/GRkZR44c0e78Pe1ANDc3NzExaW5u1u7tAEBrCBABAO4dGzduvHnz5tdff/3UU0+xPQsXLnz22Wf37Nlz7dq1OXPmbNy4ceXKlXv27HnnnXfWrVtn2Gq7wIYwI0AEANBa/71H1CF2B+zMmTMLFiz44osvPvnkk4cffpiINm7c+M0332zcuNHBwWHXrl3//Oc/lUqloYsF0Kvr168T0ZQpU/z8/Nge1s33888/ky7WSuYCRMbLy8vLy8vCwqK4uFgmk5WUlBBRdna2didXqVQ9DRAJTYgABsICxNzcXEMXAgAAfe7SpUv33Xcflx4y69evr6qqOn78OBHxeLzdu3e7u7t/8cUXBqpRIxjCDADQS0YWBFC5EAAAIABJREFUILI7YEeOHDl48OC8efMWLlz4yy+/zJ8/f8+ePaGhoZs2bcrNzX355Zd/+OGHd955x9DFAugVCxBHjhzJ7WGfk1paWkgXAaL6DVsrKysHBweBQODv769SqbKystinMa0Dhfr6+qamJmtrazMzM81fhQARwCBYgJifn497dQAAA97Nmzc9PT3b7PTw8CCi5ORkbs/o0aPz8/P1WlkPYREVAIBeMrIAccDcAQPQufYBovp8grrtQOTeJTAwkIiysrJYvCiTybQbGMJWYulpIGhjY6PFewFAL7F5hxUKBVumCQAABrDg4OCrV682Njaq70xKSiIiLlhUqVTZ2dnu7u4GqE9jGMIMANBLRhYgDpg7YAC61drampmZyefzR4wYwe3UbYCo/nnr0UcfZRvDhg0joszMTO7ZnJycbk91+PDh//znP+q9SyxAZKuyaA4diACGwqZKwDSIAAAD3j/+8Y+SkpIXXnihrq6O7cnLy1u+fDkRTZ06lYiKiormzp2bnZ392GOPGbLQ7nCLqKhUKkPXAgBglIwsQBwwd8AAdKu2trapqcnW1tbKyorbyT4nMc3NzXK5vDdvod6B+Pe//51tsA7E06dPFxYWsj3dBoibN2+eN2/em2++ef78efX6qecBIjoQAQzF19eXMA0iAMA9YNWqVWFhYYcPH/bw8Jg8efKYMWMCAwPT0tIWLVo0ceJEInrllVe+/fbbkJCQqKgoQxfbFXNzczs7u6ampurqakPXAgBglIxsFeZ//OMfa9eufeGFF/bt28eyg/Z3wF599dXs7OxXXnlFi/NfunTp2LFjXR/T0NBARPX19VqcH6CPdBjAcR2IfD5fqVTKZDJLS0vtzj958mQ2RNrS0tLPzy84OJjtf+ihh1xcXK5evcod2W2g8OOPP7KNc+fOPfjgg13U3y3WgWhlZUUNDT16IQD0EhZiBgC4RwgEgl9//XXLli0ffvhhQkICEQ0ePHj9+vUrV65kB/j5+W3cuHHNmjXqt7H7Jycnp5qamrKyMnt7e0PXAgBgfIwsQFy1atWPP/54+PDhmJiY4cOHy+XytLS0pqYm9Ttg33//vdZ3wLZu3crmUuxWAwIL6E86DOBYB6K5ubmrq2teXp5MJmOTv/RUc3Mz+7zI4/Hy8vKsra25pwYPHvzNN9888MAD3J6ioqKuz1ZVVcU2Lly4wO1kI6y1CxCdnJwoL69HLwSAXkIHIgDAvcPc3Hzr1q1bt269deuWQCBwc3NTf3br1q2GKqynnJ2ds7Ozy8rKgoKCDF0LAIDxMbIAsa/vgO3atWvKlCls1drOlJeX79q1i883stHfMLB1ESD6+fmZmppSL6ZBZHE5j8ebOXOm+rBo5v777xeJRKwArpIucAFiYmJic3OziYlJZ/V364EHHvjqq688PT2rECAC6BcCRACAe9CQIUMMXUKvuLi4EBZiBgDQlpEFiNTHd8CGDh366quvdn1MWlrarl27evMuADrXYQA3fvz4GTNmzJ49+6uvviKivLy8w4cPz58/f/To0T06ORuw7+rqGhMT0/5ZPp8fFhYWFxfHHtbU1HRxqsbGxoaGBjMzM29v76ysrJSUlDFjxpC2qzDPmzdv3rx5x596qqpHLwOAXmOLqOTm5qpUKh6PZ+hyAADAkFQqVVNTExGZmZkZupausLE4paWlhi4EAMAoGV+AyDH2O2AAOtRhgGhjYxMbG0tEJ06cIKJVq1YVFxd/8sknp06dCg0N1fzkLEBUH7ncxv333x8XFzd48ODbt2933YHI2g/t7e3Hjh2blZV17do1FiCy7kisqgxgLEQikYODQ0VFRVlZGWvoAACAe1ZKSsrIkSOJSIsFjv/4448ffvih62MqKiq0O3kbLEBEByIAgHaMOEDsTP+/A5afn3/t2rXZs2cbuhAYOLoeAsz2FxcXE1FdXd0HH3yg2wAxIiJi69atERERhw4d6jpArKysJCJ7e/uRI0d++eWX165d06R+AOiHfH19KyoqcnJyECACAIDWoqOjOxzj0l7vA0QMYQYA6I0BGCD25g6Yfixfvjw2Nvby5cvjx483dC1gfFQqlUKhsLCw4Pa0tLTcvn2bOg/guM6+iRMnXrx4MSkpqUfv2G2AeN9991VVVTU0NBw6dKizIcyff/75gQMHli1bRkT29vajRo0iIrayMyFABDBCfn5+iYmJOTk56ssoAQDAPSggICAtLU2717733nsPPPCAUqns4hhdzUGPIcwAAL0xAAPE/o9lJYWFhQgQQQuLFi369ttvMzIy3N3d2Z7Q0NA///yTOh8CzO1fsGDB1atXb9y40dDQoPlCQ90GiERkbm4uFAqJSCaTdTgn2t69ey9fvmxvb093OxCJKCUlRalU8vl8BIgARodNg5iTk2PoQgAAwMDMzc2Dg4O1e62fn9+aNWu6PkZXc9BjCDMAQG8MwKWE2R0wrW+C6UFraysRSaVSQxcCRuny5ct1dXXZ2dnsYUtLC9dR2G0HYmhoaHBwcGtra3JysubvyALEbgNHoVBoZWXV2trKjlfX3NzM3vHixYtEJBaL7e3thwwZUl9fn5eXR3fnQESACGBEECACAIBxwRBmAIDeGIAdiL25A6YfLS0thAARtMWmEayurmYPq6qquNH6XQeIFhYWw4YNGzVq1LVr165duzZx4kQN37GhoYG660Bk7OzsGhoaamtrbWxs1Penp6crFAq6Owc260P09fW9deuWRCJ59tlnExMTCYuoABgVf39/QoAIAHDPkEgk8fHxGRkZlZWVcrlcLBa7ubm5ublFREQYy2S4gwcPNjExqaqqamxs7LfT5QMA9FsDMEDs/9CBCFpTqVRtAkQ2+yHTdYA4YsQIoVA4fvz4gwcPJiQkLF++XMM31WQIM1dAcXFxTU0NN7yaYSOsOSxA9PHx+fXXX69fv87Swy7qB4B+yM/Pj8fj5ebmsokIDF0OAAD0lfz8/GXLlsXFxXX47PLlyyMjI3fu3Onl5aXfunqMx+M5OjoWFxeXl5cPGTLE0OUAABgZYw0QjfoOGOtAZK1YAD0ik8nYIuM9ChDHjRvn6uo6d+5cIpo2bRoRxcfHt7a2CgQCTd60RwEi3Z3lU925c+fUH3IBIhGdOnWq2/oBoB+ytrZ2dnYuLS0tLCz09PQ0dDkAANAnKisrw8PDJRJJcHBwZGRkSEiIWCy2tbWVyWRVVVVZWVkxMTFHjx5NTk5OSEhwcnIydL3dcHFxKS4uLi0tRYAIANBTxhcgDoA7YBjCPDDk5+e///77a9ascXNz09ubcnEhCxBnzpx55swZ7tnOAjh/f//i4mK27evr6+fnl5OTc+XKldDQ0K7fbvny5bm5ucOHD6deBIjnz58/fPiwiYmJtbU1K1s9QPz999+7rR8A+id/f//S0tKcnBwEiAAAA9Xrr78ukUi2bdu2bt26Dg/YtGnToUOHFi9eHBUVtX//fj2X11Os1wQLMQMAaMHIAsSBcQcMQ5gHhjfeeOPIkSNOTk7r16/X25tyAWJNTY1CoYiPj2d5NKNhADdjxoycnJzTp093HSCqVKpDhw7duXOnpKSENJ4DkdoFiB9//LFSqVy3bl1eXt7XX39NRA4ODnQ3QGxsbCQiFxeX8PBwTEYDoLWrV69+//33XR+j8853f3//c+fOZWdnT58+XbdnBgCAfuL8+fMBAQGdpYfMggULDh8+nJCQoLeqtIYAEQBAa0YWIA6MO2DoQBwAampqfvzxRyIqLy/vo7eQSCS7d+9eu3ateocjmwCRiKqrqzMyMtTTQ9I4QBw1ahQRces4d6a0tPTOnTtExNY017wDsaamRn1nUVERET300EOvvfbaqFGj8vPzWXDJAkTmzTffXLhwoSbFA0CHNm/eHBMTo8mRSqVSV2/K1lG5ceOGrk4IAAD9TUVFBRuM0jUPD4/k5GQ91NNLCBABALRmZAHiwLgDxjoQq6qqWlpahEIj+xPcy2pqaqKjo59//vnhw4d/9tlnLFxTn4JQt9avX//dd9+5urq+9tpr3E71IcwpKSnqx9vZ2bVZ+7gzQ4cOJSKJRNL1YW0O0HoIc1lZGRE5Ozvb2tquXbuW2y8Wi+3s7Fja6Ovrq0nlANCZd999d9KkSV0fU15evmvXLh0ueBIQEEAIEAEABrSwsLD4+Pjc3NwuPq1JpdK4uLiwsDB9FqYdBIgAAFozsvRqYNwBY11jKpWqoqKi/y/5ApyffvrpvffeKy4u/t///rdlyxa2s48Ww8nOzj527BiptRwynQWI69evnzdvnoa5AGv9y87Ojo6OXr58OZuRsL28vDz1h1ZWVt2emQWIMplMqVS++eab//vf/55++mk2ArrD/9RXrVoVFRVFd2MIANCav7+/+s2GDqWlpe3atUu3b0oatDMDAIDxWrFiRWxsbGhoaFRUVGRkZJtJb0tLS0+cOBEdHS2VSo1iNAkCRAAAremsDUE/wsLCEhMTc3Nzuzim/98BYx2IhFHMxqC4uNjb23vz5s10t7Hu2rVrX375ZVVVFfv81BcB4nfffRcYGNjc3Exqqy0znQWIEydODAoK0vD8rq6u5ubmVVVVUVFRBw4c6OwwLToQ2cSjFy9e3Lt378aNG6VS6fvvvy+TyczNzQcNGtT++P/85z///e9/N2/e3G9nLAWALvj4+JiYmNy6dUuhUBi6FgAA6BPh4eF79uyRyWQrV6708vKys7Pz8fEZOXKkr6+vvb29q6vrokWLSkpK9u7dO2vWLEMX2z0EiAAAWjOyAHHFihUtLS2hoaG7d+++efNmm2dLS0sPHDgwbty4fn4HjJu3rqqqyrCVQLdSU1MLCgrYzGINDQ1ElJuby5K1mTNnUi8CxDt37nBRchuXL19WqVQscesiQMzJyeH2Dx48WPO35vP53t7ebLtNm6E69mPa2tqyh5oEiI8//rhYLD537tz27dvp7mSLROTs7Mzj8Tp8ycqVK1kTIgAYHRMTE29v79bW1q5v7AEAgFFbunRpenr62rVrR40a1djYmJ+fn5ycXFBQYGZmNnbs2K1btxYWFi5ZssTQZWoEASIAgNaMLEAcGHfAuACxzVRx0A/V1dXR3Vn8WICoVCrPnz9PRH5+fqRtgHj79m0XF5cnnniiw2cLCwuJ6PHHH6fOA8Samhr10c09ChBJbQGT9kE88/777584cYLu5qSk8SrMbJZDtnDKokWL2H5nZ+celQcAxmLYsGFElJWVZehCAACgD/n5+W3fvj0pKUkul9fX19++fbupqam0tPTKlSvr1683ok96zs7OfD5fKpV2diMfAAA6Y2QBIg2IO2Dc5arNYrXQD9XX1xNReXm5UqlkASIRpaamEpGLi4u1tXVjY6NMJuvpaTMzM2tra8+ePau+s7KycuXKldevX2cBIpvus02AyM011tra2tDQYGpq6ubmZmJi0tPPbWwdFeokQLx06dKqVatqa2vNzc3nzp3LdmoSIBLR008/zZoNRSIRl+Njrk+AgYpNYIppEAEA7hE8Hs/KykosFutwSS59EgqFgwcPbm1txVxSAAA9ZZT/7hv7HTB0IBoR1oHY3Nx8+/ZtFiYSkVKpJKJBgwY5ODgQ0a5duzw9PdPS0jQ/bXFxMRHV1taqD6A4cuTI7t27lyxZwgLE++67j/6aMkskkoyMDDs7Ozc3N7ZHLBafOHHizJkzGqZ7nNmzZ7M5E2/dutXmKYVC8cILLyiVyuXLl2dlZT344IMsENTwLTw8PFjlY8eOdXFxEYvFhAARYOBCgAgAAMaFfS5lq/wBAIDmjDJA5BjpHTB0IBoRLjQsLS3lOhAZOzs7FiAeOHDg1q1bFy5caPPajz/+eOnSpR2Oj+A+sqiP+0tKSiKiP/74o6ioSCAQBAcH0187EI8fP05Ef/vb3xwdHdkee3v7++67b/LkyT39uaZMmZKenj5o0KCGhgaRSHT58mW2Pzc396mnnsrIyAgMDNy5c6enp6dIJHruuedmzZrFTYbYrcjISCJiCxmxmBIBIsBAhSHMAABgXFxdXQkBIgBAzxlT7jZgoAPRiLAOROoyQGTz/anPSEhEra2ta9as+fDDD2/cuNH+tKwDkToKEBlnZ2d7e3uBQCCTybgIMj4+nogiIiK4Nlt7e/ve/HRsIWmZTHbmzBm2Z/HixT///LOZmdlnn31mbm7Odh46dOjHH3/U/LSrV6/+9NNP16xZQ0RjxowhtRHTADDAsAAxOztbpVIZuhYAAIDusQAR66gAAPQUAkR9a21t5b5lIUDs/7roQBw0aJD60iVt1tROSkpif18WL7bRvgNRoVBkZmYKBAI2XnjIkCF8Pl8kEimVSu6/k4yMDCIaM2aM+hDm3vx0LECkuwsuK5XKP//8kxU/btw4rU9rbW393HPPsY7FLVu2xMTEzJkzpzd1AkC/JRaLHRwcZDIZWjkAAMAooAMRAEA7CBD1TX1AKwLE/q/rDkT2+YNp04HILZDCNRuqa9+BmJqa2tzcHBgYOH78eCLy8PAgokGDBtHdUcx37ty5deuWqampj48PFyD2sgORLSRNRHl5eUSUm5srk8k8PDzYuGOdsLGxefTRRwUCga5OCAD9DWtCzMzMNHQhAAAA3WNT66ADEQCgpxAg6hs3fpkwB6IxUO9A5LaJyNLS0tTUNDAwkNvTpgPxt99+YxsddiByAeK1a9fYBksSQ0JCWLMei/bUA8QbN24olcqhQ4cKhUIuuOxlB+K6det27NhBdzsQ2Rjq0aNH9+acAHCvYf8SIkAEAACjgA5EAADtIEDUN3QgGheuA7GkpIR1ILIhxnZ2dkQUEhLCHck6ENPS0j755BOVSsUtyty+A1GlUrGPLCKRqKKioqCggO6myWKx+KWXXjp27Nhrr71Gfw0QWcLIOn101YEoFov//e9/C4XC4uLixsZGNn4ZASIA9AgCRAAAMCIIEAEAtIMAUd/QgWhcuK7DlJQUFiA6OTnR3Whv2LBh3ODcqqqqY8eOjRs37sUXXzx79iz3oaR9gFhVVaVQKEQi0f33309EV65cobv/MdjZ2ZmamkZGRtrY2NBfA8Ts7GwiCggIILUAsZcdiEQkFAo9PT2VSuWOHTt2795NRL2Z/RAA7kEIEAEAwIggQAQA0A4CRH1jASKfz6fOOxA///xztt4u6JNKpZo6deqTTz6pvpPrQJRIJGyQsre3N93tQLSwsPDx8WEHVFZW7tixQ6FQEFFiYiLXato+QExOTiYiPz8/FtX98ccfdPc/BpFIpH6keoDIVlBhHYjcEOZediAy7EfYvHlzU1PT0qVLH3744d6fEwDuHQgQAQDAiDg5OQkEAqlUqt7YAQAA3UKAqG8sV2I5UYcdiBUVFc8999wLL7yg78rueTKZ7Lfffjt27JhSqeR2sg5ET09PlUrV0tLC4/G8vLzoboBIRMHBwWyjurqaG7bMMkF2ZPs5EFk6PHXqVBYgsoHDHQaIjo6ORFRaWqpSqc6fP09EbIkVBwcHU1NT0lGAOHz4cCJqaWkxMTHZuXMnFjwBgB7x8PCwtrYuLy9vMxUsAABAPyQUCh0cHJRKZVlZmaFrAQAwJggQ9Y3d6bKysjI3N29ubpbL5W0OYFPpcY1voDd37twhopaWFvXOUPaHCA8PZw8tLS2dnZ1JLUB8/PHHnZ2dTU1NW1tbZTIZ23n58mUiGjdunFAorKioaGpqIqKPP/74iy++IKJTp06xc7LkjrXtcEOY1UsaMmQIEd26dSs9Pb20tNTNzY11+vB4PDaKefDgwb3/wSMiItjG2LFjLS0te39CALin8Hg81hzNGqUBAAD6OfZBGqOYAQB6BAGivrEORIFAwKKi9k2ILL1qbGzUf233ODb6mO5muAzrQJw2bRp7aGVlxeZA5Fr/5s2bV1pa6uHhwR6yJVbKy8uJyNvb283NTaVS5efnV1dX/+tf/1q4cGFubu7169ctLCwmT57s4eFhaWlZVlZWW1vbdYB4+vRp9TKIaNWqVfPnz2df2ntp8uTJDg4ORPTAAw/0/mwAcA8KCgoiBIgAAGAk2HRA7ScaAgCALiBA1DfWgSgUCtlg1fbTILIuNgSI+sc6EEktQGxublYoFEKhcMyYMWyPlZVVZGTkQw899NRTT6m/lssTR44cye308PBgD5OSkq5evapUKltaWvbu3atUKv38/MzNzXk8nr+/PxFlZ2d3OITZ09OTiG7evPnbb78R0fTp07mnXnrppc8//9zExKT3P7hAIHj66ad5PN7MmTN7fzYAuAexABHTIAIAgFFgHYgIEAEAegQBor5xHYhsrCgXWnFYkKRUKtm4V9Cb9gEiG79sbW3t4+NjZmZGRFZWVsOGDTtz5gxbQJnDrYY8Y8YMbqeHhwdLHv/880+21DIRffrpp6S2CgpbVTk7O7vDDkQPDw8ej1dYWJiYmEhEbd5Uh957772ioiJ0IAKAdtCBCAAARgQLMQMAaAEBor5xHYhsEYz2KSHXk4gmRD1rP4T5+PHjRGRjYyMQCPz8/IjIysqqw9e6u7sT0YQJExYtWsTt9PX1HT16NBElJSVxASJbUpnd9iS1ALHDDkRLS8vBgwc3NjZKpVJHR0duxWedEwgEXKYJANBTLEBMT083dCEAAADdQwciAIAWECDqGxcgso62LgJELs8C/WjTgZienv78888TkbW1NRGx1UvYdnvbtm379ttvExISvL29LSwsiMjd3T0oKIh1ICYlJbF1mYVCITu+TQdienp6hx2IdHcaRCIKDQ3V1U8KAKBb3t7elpaWxcXF7Sf2BQAA6G+wiAoAgBYQIOobN4S5TQfixo0b16xZQ3fnQCR0IOpFUlJSfHw82+YS29u3bxNRTk4Oe2hra0t3A8TOOhAHDx785JNPsnyQLdM8a9YsHo/n7Ozs5uZWW1tbUlIiEonGjRvHjuc6ECdOnMjn80+ePNnU1GRubm5ubt7mzGwaRCIKCwvTyY8MAKBzfD6f/SOJUcwAAND/YREVAAAtIEDUt86GMG/btm3nzp1VVVUIEPXpySeffPjhhw8fPkztOhBv3bpFRF5eXsuWLSOi++67j4jYasVd8/b2JqInnniCPWSjmNkZWL8hqXUgenl5zZo1i/1n0Gb8MsN6GIOCgtos2wIA0K+EhIQQRjEDAIAxwBBmAAAtIEDUN64Dka2f29TUFBcX9+GHHzY3NxORRCLBEGZ9Ki8vJ6LFixc3NDRwAWJhYaFcLmcB4tKlS5999lkiioyM/Oqrr7Zu3drtOT/66KMffvhhypQp7KF6gMjWXCa1DkQi+ve//8022o9fJqL169ffunUrPT2d5ZIAAP1TcHAwIUAEAABjYG9vb2FhIZPJ6uvrDV0LAIDREBq6gHtO+w7E5cuXcxNw5OXlYREV7cjlcrawteaam5sbGhrYa2/evMkltidOnJg0aRJbsYQbQSwQCObOnavJaYcOHTp06FDu4ahRo9jGyJEjuR5D9RVL7r//fldXVzbGuf3ZeDyeh4dHj34uAAD9YwFiWlqaoQsBAADonqurq0QiKS4u5kYIAQBA1xAg6hsLENXnQOQSQ0KAqK3r169PmDBh9erVb731loYviY2NPXv2LPewuLiY60AkomvXrt24cYPU1jDRGteBOHLkSNZ2amJi4ujoyB3A5/MjIyP37dvXYQciAIBRwBBmAADQQllZ2YULF7o+prCwUOfv6+7uLpFIioqKECACAGgIAaK+sSHMXAdiY2OjXC7nnpVIJJgDUQtnz55tampKTk7W/CUrV67klkkhouLi4jZjxllzIteBqDUPDw9fX9+amhr27drd3d3Hx4fP/8vsAU8//fSHH36Ijy8AYLw8PDxEIlFZWVlFRYUm08UCAAAQ0dKlS48dO6bJkUqlUofvi2kQAQB6CgGivrUZwlxTU6NSqbhn1TsQMQeiJioqKv72t7+VlZWR2ioo3WpsbMzLy1PfU1xczP1pJk2adPXq1fr6elNTU7akci9dvny5ubmZ/cUlEkmb9JCIJk+enJub2/tuRwAAQ+HxeEFBQYmJiWlpaVOnTjV0OQAAYByWLl1qZmbWdThYW1t76tQpHo+nw/d1d3cnoqKiIh2eEwBgYEOAqG/cIiosTqqurlZ/Ni8vj2s8RAeiJi5duvTnn3+ybfVezi6oVKrU1FT2h+AUFRWxEcTR0dHr1q175513XnvttSFDhrQP+7Rgb2/PbbO/e3tsykUAGPAkEkl8fHxGRkZlZaVcLheLxW5ubm5ubhERES4uLoaurleGDx+emJiYmpqKABEAADT08MMPP/zww10fk5aWNnz4cN0GiOhABADoKQSI+sa1uZmZmVG7ALGwsFAgELBtBIiaUL/qa9iB+K9//evAgQPcQ1NT06ampuLiYvYXMTc3J6JXXnmlsLBw3Lhxuq4XAO5d+fn5y5Yti4uL6/DZ5cuXR0ZG7ty508vLS7916czw4cMJ66gAAIAxQIAIANBTCBD1rc0iKm0CRKVSyTXwYwizJrQIEE+cOKE+SsLf3z8tLa24uJj1/lhYWBCRiYnJnj17dF0sANy7Kisrw8PDJRJJcHBwZGRkSEiIWCy2tbWVyWRVVVVZWVkxMTFHjx5NTk5OSEhwcnIydL3aYDO9pqamGroQAACAbmAIMwBATyFA1DduERW2IK96gOjr65ubm8s9RAeiJnoaIFZWVra50xgUFMQCRPbVlwWIAAC69frrr0skkm3btq1bt67DAzZt2nTo0KHFixdHRUXt379fz+XpBNeBqFQqdTL/AwAAQB9BByIAQE/h872+tVlERT1AnD59+ssvv7xo0aJZs2YRAkTNsKv+W2+9RZrNgZiSktJmz7Bhw4RCYUVFhUQiobtDmAEAdOv8+fMBAQGdpYfMggULpkyZkpCQoLeqdIvN51hfX5+fn2/oWgAAALri7OwsEAikUmlzc7OhawEAMA7oQPyLpKSkb7/9tutjKioqevNfPsBrAAAgAElEQVQWXSyiYm1tvWPHDiJavXo1YQizZliA+OCDD5JmHYjJyclEFBYW5uHhwf7WYrF45syZP//88++//07oQASAvlFRUcEa9Lrm4eHB/pkyUiNGjCguLk5OTh46dKihawEAAOiUUCh0cXEpKioqKSnx9PQ0dDkAAEYAAeJfbNy4MSYmRpMj1SfR65EuOhAtLS3ZBmuCG5AdiHfu3Dl48ODjjz/u7OyskxOyANHX15fH4ykUCpVK1fUCbawDcf78+ZMnT2YBokgkOnDggJeXF2tgRAciAPSFsLCw+Pj43NxcX1/fzo6RSqVxcXFhYWH6LEy3RowYERsbm5KSMnv2bEPXAgAA0BUPD4+ioqKioiIEiAAAmkCA+Bc7d+6cNGlS18eUl5fv2rVL69mduuhAtLKyYhtsOeABGSB+9tlnL730UnZ29u7du3t/toaGhpqaGnNzcwcHBzMzM4VC0djY2HUCmJOTQ0RBQUEODg5sj52dnYODg6enZ2ZmJqEDEQD6xooVK2JjY0NDQ6OioiIjI9t8VyktLT1x4kR0dLRUKl24cKGhiuy9ESNGENZRAQAAY8DWUSksLDR0IQAAxgEB4l8EBAS89tprXR+Tlpa2a9curd+iTQcie8i0CRAH5BDmpKQkIrp9+7ZOzsbWTXN1deXxeBYWFgqFQi6Xdx0gSqVSInJ2dhaLxXw+X6lU2tnZEZG7uzsCRADoO+Hh4Xv27Fl5l0gksre3t7W1ra+vr6qqYjeThELh3r172TS4RooFiEY9ChsAAO4RHh4ehIWYAQA0hkVU9K1NgKiOCxAH8BDm69evE5FMJuv9qVpbW1966SUiCgwMpLvBH5sGsbm5+eTJkx0msCxAdHBwEAqFgwYNIiIuQGQHYAgzAPSRpUuXpqenr127dtSoUY2Njfn5+cnJyQUFBWZmZmPHjt26dWthYeGSJUsMXWavDBs2zNzcPC8vTyf/zgMAAPQd9vkfASIAgIYQIOobN4SZtRmqG/BDmFtaWtLS0khHAWJKSsrp06fFYvGePXvorwHixx9//Oijj7777rttXtLY2FhbW2tiYsKiw6CgIAsLC3bvkf1/QgciAPQlPz+/7du3JyUlyeXy+vr627dvNzU1lZaWXrlyZf369bqaHNaAhEJhUFCQSqVqv+Q9AABAv4IAEQCgRxAg6psmHYgDdQjzjRs3WMBXV1fXxWFbtmwJDAzsdpgzO0lQUJC3tzfdXYKGnb+goIC9XZuXVFRUqFQqBwcHttBKTEzMjRs37O3tCR2IAKBfPB7PysqKzaVg6Fp0bOTIkXS33xwAAKDfYg0EmAMRAEBDmANR37gORBMTkzZPDfghzNy0+l13IH711VdZWVnJycnTpk3r4rCGhgZS+6WxzkG2kjILH9kCzerY+GVHR0f20NbW1tbWlm1zASI6EAGgT0kkkvj4+IyMjMrKSrlcLhaL3dzc3NzcIiIiXFxcDF2dDtx3332EaRABAKDfQwciAECPIEDUt+bmZiIyMTFp34FobW3NNthTAy9ArKioYBtddCC2trbm5+cTUU1NTddn6zBAZG2bLChs/2mgvLyciJycnNqfDQEiAPS1/Pz8ZcuWxcXFdfjs8uXLIyMjd+7c6eXlpd+6dIx1ICJABACAfs7FxUUoFJaVlTU3N7fv7QAAgDYQIOob14HYPkBkg3DpbgfiwBvCzDUedtaBePz48X379rHktLa2tuuzsWbDHnUgsgTTwcGh/dnYEAYej9d+bkoAgN6rrKwMDw+XSCTBwcGRkZEhISFisdjW1lYmk1VVVWVlZcXExBw9ejQ5OTkhIaHD+xzGYuTIkTweLy0traWlRSjExwwAAOinBAKBi4tLYWFhcXGxsd+9AwDQA3yy17cuAsQBP4SZyw0bGxubmpra/wb27t3L9eZo2IHIpa5so6qqiu4GhfX19TU1NWyRZabNEGZ1dnZ2TzzxhJmZGZseEQBAt15//XWJRLJt27Z169Z1eMCmTZsOHTq0ePHiqKio/fv367k8HbK1tfXx8ZFIJFlZWSEhIYYuBwAAoFMeHh6FhYWFhYUIEAEAujXQ5m7v/zRfRGXgBYjqTYUdNiGq79RuCPO8efM2bdrELcDSpgmxiwCRiL777rsvv/yyux8CAEAb58+fDwgI6Cw9ZBYsWDBlypSEhAS9VfX/2LvzuCjL9X/g17AOAsMmguwqiAoqCgS45BJoaYZabqe0cFeOmlqa6AF/ppDpiQ4YmppmnkpLywwVxSyJcEMQRBYBAVEQEJTFYWd+f9xfnzOxyTLDwwyf9x/nNfPMzcNFnRc0n7mu+5aTESNGEFF8fDzfhQAAALTGysqKcI4KAEDbIEDsas12IE6ePHnu3LlGRkbsqbKewiyTANHPzy80NJRaCBCJKDAwkLuPs7Oz9HZjrDOxpQARAEB+ioqK2nJGiqWlJfuoQ6EhQAQAAIWAg5gBANoOAWJXa7YD8Ysvvvj++++5p8ragSidDzZ7jor0xWYDxKysrKCgoPXr17PTS0lqhJkLENkxNUx1dfXp06e5pykpKURkZmbWmZ8CAKADPDw8rly5kpGR0cqawsLCiIgIDw+PLqtKThAgAgCAQkCACADQdggQuxoXIEof1iG9Tx8p7x6IrANRRUWFWuhAZAHisGHDiOjPP/9cu3Yt+8fFYW9Ha2trf/rpp0YdiFyS2Ah3Emhqauq1a9dEItGYMWNk9AMBALTV6tWr6+rq3N3dQ0JCcnJyGr2an59/8OBBV1fXwsLChQsX8lKhDHEBokQi4bsWAACAFrEA8f79+3wXAgCgABAgdjVuhFldXZ1dUVVVNTAwkF6jrB2ILEA0NTWlVjsQd+3aRUTZ2dmff/75rVu3pBfExcWxBz/88ENLI8zM4MGD33nnHSJKTExsaGggosOHDxPRnDlzuC8BAOgyXl5eoaGhZWVla9assbGx0dfX79+/v5OTk62traGhoZmZ2ZIlS/Ly8sLCwqZPn853sZ1lampqZmZWWlqamZnJdy0AAAAtQgciAEDbIUDsak1HmI2MjFhTHodli9KjuMqBBYgWFhbUXAdiVVVVbW2tpqamiYkJd7HRm09uIO7q1astBYgvvfQSEQ0fPvzo0aMWFhYVFRXffPPNggULzp49S0RvvvmmHH4yAIAXW7FixZ07dzZs2DBixIjq6uqsrKyEhITs7GxNTU0XF5fAwMDc3Nxly5bxXaZsODs7E9HNmzf5LgQAAKBFCBABANpOje8Cepymh6j07t270RplDRBZaGhpaXn9+vWmHYjsiq6urp6eHnfx3r170mtYgKiqqlpRUcFmABvtgainpzdt2rTr16+zPsfhw4c/ePBg6dKl3D/MgQMHyueHAwB4MTs7u507d+7cuVMikYjF4qqqKgMDg0afIXXMzZs3f/zxx9bXsIOkusbIkSN//fXXuLi4OXPmdNk3BQAAmdPS0tLT0wsJCZk9ezbftciesbGxlpZWcXHxs2fPMKUEANA6BIhdrdkOxEZrWIBYU1PTxbXJlUQiKSsrEwgE5ubm1FwHIruiq6srvSPkvXv31qxZk52d/fPPP9+/fz8/P793794WFha3bt1KTk6mJnsg2tjYLF++vKam5r333iOiCRMmnDlzhksP1dXV2ceMAAD8EggE2traMnyvsnXr1vDw8LasZLs6yBvrQOT2nQAAAAVVVVVVVVU1Z86cn3766Ysvvmj6zkWhCQQCCwuL9PT03NzcQYMG8V0OAEC3hgCxqzUNEJt2ILKXFK4DcdKkSWpqamxSuKny8vKGhgYdHR224WPTAJHrQBSJRCoqKuwtbmZm5pUrV6qqqh49evTbb78R0dixY4no1q1bFRUVJBUgsgf9+vXr3bv3tm3b2MU1a9ZcvHgxIiKCPbWxsVFTw//nAYBPmZmZkZGRycnJ7DR5IyMjc3Nzc3PzadOm9e3bt8O33b179wtPiCooKAgODpZJw+MLcSPMEolEIBB0wXcEAAA5sbe337Bhw7p16xwcHHbs2PHee++pqqryXZTMWFlZpaen379/HwEiAEDrEKZ0NW6EWUVFRU1Nra6uTjlGmMVicWRkJBFVVFTo6Og0XcASQz09PZFIRM0dosKusPRQJBI9ffqUiG7cuFFVVUVEpaWlLEB85ZVXpOeauRFmT0/PWbNmLV++XPqeampqp06dunDhwhtvvEFEAwYMkNXPCwDQXllZWStXruQ+0mjE19fX29t79+7dNjY2Hbi5vb39xo0bW1+TlJQUHBzcgZt3gJmZmZmZWV5eXmZmpq2tbdd8UwAAkAeBQLBw4cJXX311+fLlixcv3rVr144dO5RmY3ErKyvCQcwAAG2AALGrcR2IRKShoVFXV2dsbNxojZqamkAgqKurU6DGDXZAChHl5eU1u88gWyASidjgQ3FxcaMFXAciEenr67MAkbUZsi///fffiWjixInS83dcB2Lv3r1/+OGHpt9XU1Nz2rRpRkZGxcXFCBABgC/FxcVeXl6ZmZkODg7e3t6Ojo5GRkYikaisrKykpCQ1NTU8PPzkyZMJCQnR0dHSZ0kpLhcXl9OnT8fGxiJABABQAmZmZqdPnz5x4oSfn99bb701cOBAHx+f+fPns+2JFBcCRACANsIpzF2N60AkIk1NTWpuD0RSwG0QuZHkvLy8ZhewAFFPT491XD5+/LjRAukA0dDQkIik5+yys7MfPXqkq6s7ePDgfv36cdfbuIPYkCFDCB2IAMAfPz+/zMzMoKCgpKSkHTt2zJs3b9KkSe7u7pMmTZo7d+7WrVtjY2MPHTqUnZ3t7+/Pd7GywaaYY2Nj+S4EAABk5q233kpOTg4LCysrK9u0aZOVlZWnp2dgYOCff/7JxoYUDgJEAIA2QoDY1Rp1IFJzeyCSAm6DyHUgPnz4sJUFrQSI3CEqROTn57dq1arXX3+dezUlJYWI2Cec0vN9bQwQJ02apK6u/sINwgAA5CQqKsre3v6jjz5qZY2Pj8+4ceOio6O7rCq5cnFxIQSIAABKR01NbcWKFTk5OceOHZswYcKlS5c2b9788ssv6+np8V1aRyBABABoIwSIXY0FiKwDkbUZNhsgKtw2iNIjzM0uYDPL+vr6bGS7qKio0QLpDsQ333wzJCRkwoQJ3KssQDQzMyOigQMHsuZNgUCgpaXVlvK2bNny9OlTV1fX9v1UAAAyUlRU1JYzUiwtLQsLC7ugni7AfuXGxsayP3wAAKBMNDQ05syZc/HixXv37n3xxRfTp08XCoV8F9URCBABANoIeyDKXUZGRnBwMPf2KTExkdrQgai4AWJLHYgJCQlENGTIkNZHmNkRK8zo0aO5x8nJyfS8A1FDQ2PYsGE3btzQ0tJq+3Gi3HErAABdz8PDIzIyMiMjo5UNAQsLCyMiIjw8PLqyMPkxNjbu169fVlZWSkrK0KFD+S4HAADkwsbGZuXKlStXrlTQj4usrKwEAkFubm5DQ0Pb31kAAPRA+BUpd998801YWNj+59inW6zD38bGRigUSu/ox1G4PRBf2IF48+ZNInJ2dhaJRJqamuXl5Y32SZHuQGScnJy4CeX09HR63oFIzxtb2ji/DADAu9WrV9fV1bm7u4eEhOTk5DR6NT8//+DBg66uroWFhQsXLuSlQnl46aWXiOj69et8FwIAAHLHOiQUjlAo7NOnT01NzaNHj/iuBQCgW1PI3/KKZf369dbW1uzsFMbIyGjUqFFEdOrUqdLS0qanMJPS7YEokUji4uLo+Z76RkZGeXl5jx8/trCw4NY0DRDV1dW3bdt26NChO3fusCy1UYCIpkIAUBReXl6hoaFrntPT0zM0NBSJRBUVFSUlJU+ePCEiNTW1sLCw6dOn812szLi6uh4/fvzGjRuLFi3iuxYAAOiIqqoqgUDAdxXyZW1tXVBQkJOTw73XAACAphAgyp2enl5Lb5x0dXWl8zJpCjfC3PopzBkZGaWlpebm5qampkRkbGzclgCRiNatW2dgYMD143B/1NmIX7PZKwBA97RixQpPT8+DBw9GRkampKRkZWURkaqqqrGxsYuLy8yZM318fNgvSaWBDkQAAEXHdh5XbtbW1tevX8/JyVGaXUQAAOQBAWI3pXABonQHYlVVVaNNlO/cuUNETk5O7Gmz2yBWVFQQkY6OTqM7Sx/oxvZAJCJ7e/vw8PD+/fvL8EcAAJA3Ozu7nTt37ty5UyKRiMXiqqoqAwMDJd5xycXFRV1dPTEx8dmzZ9h0AgBAWUkkEjYt1IG0sb6+vunOHo08ePCgg5W1jbW1NRG9sAwAgB5OUQPE7OzsioqKQYMGsb02EhMTv/nmm6KiIgcHh6lTpzo4OPBdYGdxeyCKxeL3339/ypQp3XyojetArK2tvXnzpvT5J/T8CGYTExP2tNkAUSwWU3NTydLHqkiPFUydOlVWxQMAdDGBQKCtra30mZqWltbQoUPj4uLi4uLGjh3LdzkAACAXiYmJrFFAIpG092sXL1789ddft2VlQ0NDe2/eRggQAQDaQvECxF9//XXZsmX5+flE1K9fv9OnTxcVFU2ePJlr1vvXv/61a9eu1atX81pmZ3F7IMbExBw4cODu3bvdPEBkHYi9e/d+/PjxlStXGgWIbHsvQ0ND9pSNHhcVFUmvaSlA5DoQ1dTU+vbtK5fqAQC6RE5OTkpKyquvvsqe5ubmhoSEJCQk1NfXOzk5LVy4UAk+AGvEzc0tLi7u6tWrCBABAKApFxeXqKio1tfU1NQ8ePBAflsxsgAxOztbTvcHAFAOChYg3rx509vbm4jc3d1VVVVv3Lgxb948dXV1fX39gICAQYMGJScnb9++/f333x81apSLiwvf9XYcN8LMjgNj4Vp3xgLESZMmfffdd1euXGn0aklJCREZGBiwp6wDsb0B4tChQ1muCgCgiHbs2OHv7z9lyhQWIP76669vv/022/6ViC5durRnz55PP/10zZo1vJYpY25ubnv37r127RrfhQAAgLzY29snJSV17Gt9fX19fX1bX5OUlDR06FB5B4joQAQAaJ2C7bu0bds2Ijpz5syVK1eio6MvXryYkpISHx9//PhxX1/fV155ZdWqVb///ruqqmpQUBDfxXYKFyAWFhYSUVVVFd8VvQALENm74qZvFBt1ILK9Cxv9d0ZlZSW1GiC6u7vLvGwAgK5x/PjxLVu2aGtrz5gxg4gKCgp8fHxqa2u3bduWkJCQnp5+6NAhIyOjdevWxcTE8F2sLLFf3U0/WAIAAKUhFAodHBwUt4keASIAQFsoWAdiQkKCh4fHa6+9xp6OHTvW1dU1OTl5woQJ3JohQ4a4ubklJibyVKNsNOpArK6u5ruiF3j69CkRubq6ikSihw8fFhYW9unTh3uV7YHIBYiurq5EFBsbK32HZ8+eEQJEAFBSn332mY6OTlJSkpWVFRGdOnWquLh4z549XNuFra2tq6urs7PzJ598cvr0aV6LlaWBAwf27t07Ly8vJyeHvUMDAADoVvT09AwMDJ48eVJUVMS2WgIAgKYULEAsKioaNmyY9BULCws2HiutT58+N2/e7MK6ZI/N6tbU1LAORNad152xQ1T09PSGDRsWHR2dmJjo6enJvcriRW6EeeDAgSKRKDc399GjR6ampuxiSx2IQqFQKBRWVVUhQAQAxZWcnDx27FiWHhLR7du3iWjWrFnSaxwdHd3d3ePj43moT24EAoGbmxsbHUCACACguDIzMyMjI5OTk4uLi8VisZGRkbm5ubm5+bRp05Rgm3IbG5snT55kZ2cjQAQAaImCBYi2traxsbENDQ0qKv83fP3BBx+w7jZpaWlpiv6rn+tALCgoIMUZYeYCxISEBOkAsdEeiCoqKs7Ozr///ntsbOzrr7/OLra0ByIRLVu2rKioaODAgfL+KQAA5ERbW5t1lDPcZyeNqKur19XVdVVRXcTDw4MFiHPnzuW7FgAAaLesrKyVK1dGREQ0+6qvr6+3t/fu3bttbGy6ti5ZsrGxiY+Pz87OZpNSAADQlILtgejp6Zmfn79kyRKuI8/NzW3KlCnSa/bv35+cnPzKK6/wUaDMKFaA+PTp06qqKl1d3V69eg0fPpyIEhISpBewAJEbYSYiZ2dnIrp16xZ7Wl9fX1VVpaKioqmp2fT+n3/++bfffiu/+gEA5M3LyyshIeHy5cvs6ZgxY4jol19+kV6Tk5Nz9epVNzc3HuqTp9GjRxO2QQQAUEzFxcVeXl4REREODg5+fn7ffffd+fPnr1y5cv78+e+//z4gIGD48OEnT5708vJib1sUFEs/cRAzAEArFCxADAgI6Nev36FDhwwNDd95551Gr4aGhrq5uS1btkxbW3vTpk28VCgrLEC8cuVKbm4udfsAMS8vj4jMzMyIiAWI165dk26iaXSIChFZWloSERvQJqn5ZfkdrwYAwKPAwEAzM7OpU6d+/PHH9+/fHz9+/KpVqz744INvv/22urq6vr7+zz//nDJlSmVl5eLFi/kuVsZcXV3V1dXj4+MrKir4rgUAANrHz88vMzMzKCgoKSlpx44d8+bNmzRpkru7+6RJk+bOnbt169bY2NhDhw5lZ2f7+/vzXWzH4RwVAIAXUrAAUSQSJSQkbNiwwd7ens3MSjt79uz169ednZ2joqIUfdyV7YEYHBz8+PFjIqqtra2vr+e7qBZJB4hOTk5mZmZ3797dsGEDe7W+vr60tFRVVVUkEnFfYmRkRM8PV6FW55cBAJSApaVlRESEgYGBv7+/tbW1rq7u2bNny8rK3nnnHR0dHR0dnZdffvnu3bthYWHcxg5KQ1tbe/jw4XV1dY3OzgIAgO4vKirK3t7+o48+amWNj4/PuHHjoqOju6wqmUMHIgDACylYgEhEurq6O3fuvHXr1q+//tropR07dmRmZsbGxo4cOZKX2mSIdSBK685NiNIBoqam5g8//EBER48eZa8+ffpUIpHo6+tzO1cSUe/evQkBIgD0JA4ODvfu3fvmm29Gjx4tEAgyMzPZ9YaGBnNz840bN2ZlZS1btozfIuWETTH/9ddffBcCAADtU1RU1JYzUiwtLbnRIkXUr18/IsrKyuK7EACA7kvBDlFpnRLkhpxmA0RtbW1einkh6QCRiEaPHi0SiR4/flxSUmJoaNjoBBWGdSCy/kpCgAgAPYO6uvr8+fPnz59PRGKxWCwWq6mp6ejoqKkp1Z/jpkaNGvWf//xHoZtTAAB6Jg8Pj8jIyIyMDFtb25bWFBYWRkREeHh4dGVhssUCxOzsbIlEgi2VAACapajvWJKTk0+ePHn37t2nT5/W1tbq6OiYmJg4OTnNmTNHek5WcSluByJjZ2d38+bNu3fvuru7N90AkVoYYdbS0uqymgEA+NWrV6+e86nJ2LFjiSgmJqa+vl5VVZXvcgAAoK1Wr1597tw5d3d3f39/b29vtlcgJz8//8yZMx9//HFhYeHChQv5KrLzdHV1Wd9DYWGhiYkJ3+UAAHRHihcgpqWlrVq1KjIystlX169fv3jx4sDAQKFQ2MWFyRbbA5GI3nzzzbi4uKysLEUMENPT093d3VvpQMQIMwBAT9C3b9/+/fvfu3fv9u3bTk5OfJcDAABt5eXlFRoauuY5PT09Q0NDkUhUUVFRUlLCGgXU1NTCwsKmT5/Od7Gd0q9fv5KSkqysLASIAADNUrA9EPPz8ydPnvzbb7/5+PgcPXr0q6++mjRpEhEFBAQcP348ICDA2Ng4ODj47bffbmho4LvYTuE6EPX19VlfXucDxGPHjh0/fryzlTWnaYDIDrFJT08norKyMiLS1dWV/hKRSKShoVFeXh4WFmZmZnbp0iVCgAgAoLxYE+Kff/7JdyEAANA+K1asuHPnzoYNG0aMGFFdXZ2VlZWQkJCdna2pqeni4hIYGJibm6sEe/hiG0QAgNYpWIAYEBCQk5Pz3XffHTp06J133lm4cOH58+fnz58fGhrq7u6+devWjIyMVatW/fTTT59++infxXYKFyBqa2uzbspOBogNDQ0LFiyYP3++RCKRQX1/12wHIhHdvXuXngeITUfLWRPiunXr8vPzv//+e0KACACgvFiAGBUVxXchAADQbnZ2djt37oyLixOLxRUVFY8fP66pqcnPz79x48amTZtMTU35LlAG+vfvTwgQAQBapmAB4tWrV4cPHz5nzhzpi5s2bSopKWGHMgsEgpCQEAsLC+4IYAUl8wCxsrKytra2tra2srJSBvX9HTsLpU+fPtwVFiCyDsTy8nJqOUCsrq7mViJABABQVlyAKI/PsQAAoGsIBAJtbW0jIyMVFQV7I/lC6EAEAGidgv3ez8nJabRxLxFZWloSUUJCAndl5MiRiv6rn9sDsVevXrIKENmDZ8+edbK2RiQSSWVlpUAgkI7/2L8U1pnYegcidxNCgAgAoLwGDhxoZmZWWFiYlpbGdy0AAACNIUAEAGidggWIDg4OsbGxrGeNExcXR0RcsCiRSNLS0iwsLHioT3aadiA2+qnbi51SIv1AViorKxsaGoRCofTnkMbGxgKB4PHjxw0NDc3ugUjPA0QNDQ0uSUSACACgxFgT4uXLl/kuBAAAoDEWIN67d4/vQgAAuikFO4V5xowZGzZsWLRo0d69e1kgde/ePV9fXyKaMGECET148GD9+vVpaWlr167twP0TExOPHz9eX1/fypqioqIO1d4+8hhhZg9k3oHY7AHK6urqhoaGxcXFxcXFrXcgvvTSS7q6uufOnWt6EwAAUCbjx48/fvz45cuXlWCvfQAAUDLW1tYqKiq5ubm1tbXcezEAAOAoWIC4bt26n3/++dtvvw0PDx86dKhYLE5KSqqpqVmyZMmoUaOIaO3atSdOnHB0dPT39+/A/Tdv3hweHt6WlfI+5Vl+AaLMOxCbDRCJqE+fPsXFxYWFhS3tgci2W54wYYK6ujoCRAAApTd+/Hgi+uOPP3iuAwAAoAlNTU1zc/Pc3Nzc3PkfdQ4AACAASURBVFx2oAoAAEhTsABRVVX10qVL27Zt27dvX3R0NBH17t1706ZNa9asYQvs7OwCAgI+/PBDbW3tDtw/KChozJgxra8pKCgIDg6W97bB3B6ICh0gpqSkFBYWttSBuHLlSk1NzeXLl9+5c4ddQYAIAKDEBg0aZGZmlpeXl5aWZm9vz3c5AAAAf9O/f//c3Nx79+4hQAQAaErBAkQiEgqFgYGBgYGB9+/fV1VVNTc3l341MDCwMzd3dHR0dHRsfU1SUlJwcHBnvktbqKn9378aWQWI8tsDkc1EN01sTUxMiKigoKClANHU1HTz5s1E5ObmJhQKq6qqECACACi3l19++dixY7///jsCRAAA6G769+9/+fJlbIMIANAsBTtERZqVlVWj9FCZKNApzOzOWlpaja736dOHiLgR5qaHqHA0NTXd3d2puRQSAACUCduw+NKlS3wXAgAA0BgOYgYAaIXidSC+kEQiqampISJNTU2+a+k4RT9EhaQCxJY6EKV98MEHqqqqEydOlG1tAADQrXABYkNDg7w3AwEAAGgXNrmMDkQAgGYp4X+7JyYmCoVCFropLi5A1NHRYUlodXV1Z27Y9SPM7QoQp06devHiRSVuKQUAACKys7OztrYuLi6+ffs237UAAAD8zYABA4goMzOT70IAALojJQwQlYMSnMLM7YH4whFmAADoOTDFDAAA3RMCRACAVihhgGhvb5+UlJSUlMR3IZ0ivQci216QSwA7Rt57ILY0wpydnV1bWysUCrmfCAAAejJPT08iunjxIt+FAAAA/I2xsbFIJHr69GlJSQnftQAAdDtKGCAKhUIHBwcHBwe+C+kUrgORO0QlKyurM82DXd+BaGVlRUSpqamE9kMAAHjO09NTIBBcvny5k1tzAAAAyBzbBhFNiAAATSlhgKgcWICooaGhrq7O9kAMDw9fvXp1h28ojw7Ev/76a/z48Zs3b6bmAkRzc/NevXqxA21a3wARAAB6DhMTE0dHx2fPnl27do3vWgAAAP6GTTFnZGTwXQgAQLejqKcwZ2ZmRkZGJicnFxcXi8ViIyMjc3Nzc3PzadOm9e3bl+/qZIAN/LKTSbgDYbKzszt8Q3kcorJo0aK0tDT2uGmAKBAIBgwYwLbJR4AIAAAcLy+v27dvX7hw4eWXX+a7FgAAgP/BNogAAC1RvAAxKytr5cqVERERzb7q6+vr7e29e/duGxubrq1LxlgHYqMAkZ1G0jEyH2GWSCQ5OTnc06YBIhHZ2dmxANHQ0FAm3xQAAJSAl5fXZ599dv78+e3bt/NdCwAAwP8gQAQAaImCBYjFxcVeXl6ZmZkODg7e3t6Ojo5GRkYikaisrKykpCQ1NTU8PPzkyZMJCQnR0dHsFGAFxTYNZLmbgYEBuyiTAFFWI8xFRUXSB0M3GyDa2tqyB+gxAQAAzrhx47S0tOLi4goLC9mJWwAAAN0BAkQAgJYoWIDo5+eXmZkZFBT00UcfNbtg69athw8fXrp0qb+//5dfftnF5cmQtbX1999/zwK4MWPGhIWFrVy5slt1IObm5ko/bakDkT2YPn26TL4pAAAoAS0trbFjx164cOG3336bN28e3+UAAAD8H/b+6969e3wXAgDQ7SjYISpRUVH29vYtpYeMj4/PuHHjoqOju6wqOZk7d66LiwsRCQSCuXPnElFFRUWH7ybzPRDbEiAOHDiQiAYMGDBs2DCZfFMAAFAOkydPJqKWNiQBAADghaWlpVAozMvLk+HJkwAAykHBAsSioqK2nJFiaWlZWFjYBfV0GTbRXF5eLpFIOnYHmY8wswBRS0uLPW02QPTw8Ni8efPevXtl8h0BAEBpvPrqq0QUERHR0NDAdy0AAAD/R0VFpV+/fhKJBFPMAACNKFiA6OHhceXKlYyMjFbWFBYWRkREeHh4dFlVXUBNTU0oFNbX10tvO9guMu9AvH//PhE5OTmxp80GiOrq6tu3b/fy8pLJdwQAAKUxZMgQa2vrwsLCW7du8V0LAADA/7BdmFp/ywkA0AMpWIC4evXquro6d3f3kJAQ6SOAmfz8/IMHD7q6uhYWFi5cuJCXCuWHa0Ls2JfLaQ9ENmRNzw+MBgAAaKPXXnuNiM6cOcN3IQAAwJsdO3YYvsjo0aOJqMOTWO3FtkFEgAgA0IiCBYheXl6hoaFlZWVr1qyxsbHR19fv37+/k5OTra2toaGhmZnZkiVL8vLywsLClO/UDpFIRERlZWUd+3KZjzA/fPiQiJydndlTbpYZAACgLaZMmUIIEAEAeraCgoInL9Lhd0Adg4OYAQCapWCnMBPRihUrPD09Dx48GBkZmZKSkpWVRUSqqqrGxsYuLi4zZ8708fExNTXlu0zZ624diGyXyaFDh7KnzY4wAwAAtMTT01NLS+vGjRsFBQUmJiZ8lwMAADwICQn5f//v/7W+JiUlZfTo0QKBoGtKYiPM6enpXfPtAAAUheIFiERkZ2e3c+fOnTt3SiQSsVhcVVVlYGCgoqJg3ZTtJasAsaampqGhofP/uNgngaamprq6uuXl5RhhBgDgUVxc3A8//ND6mqKioq4ppo20tLTGjRsXERERERHx7rvv8l0OAADww8DAoPUFbBKry7ARZgSIAACNKGSAyBEIBNra2j0kupJJgCgQCCQSSVVVVecbBlmAKBKJVq9enZ2djeYRAAAeBQQEhIeHt2Vltzr1+PXXX4+IiAgPD0eACAAA3YS1tbVQKHz48OGzZ896yDtNAIC2UOwAsUfR0dGhTgSI7PhmHR2d8vLyyspKLkBMSkqysrJq78d6dXV1YrFYVVVVW1t7+/btHSsJAABkZdeuXWPGjGl9TUFBQXBwcLdq2H/jjTdWrVoVERFRXV2tqanJdzkAAACkoqLSv3//5OTkjIyM4cOH810OAEB3gQBRYXSmA1EikdTW1goEAjZuzMJEImJ/FKdOnXr69Ol23ZCVoaOj02V7kQAAQCsGDRo0aNCg1tckJSUFBwd3TT1tZGlpOXz48Fu3bv3xxx+TJ0/muxwAAAAiIjs7u+Tk5PT0dASIAACcbtSGAK1jAWJFRUUHvra2tpaI1NXVhUIhPe9GJKL8/PyGhoYOHDHG5pf19PQ6UAwAAADnjTfeIKL2fo4FAAAgPwMHDiSiu3fv8l0IAEA3ggBRYbAAkSV37cUFiFpaWiR1oAq7XlJS0sb7xMTEfPjhh2KxmHUgspIAAAA6zNvbm4hOnTolkUj4rgUAAIAIBzEDADQHAaLC6EwHYk1NDRFpaGg06kBkAeKTJ0/aeJ+goKDdu3dHRERwJ6h0oBgAAADOiBEjrK2t8/Lybty4wXctAAAAROhABABoDgJEhdGZPRBZgNh0hJldr66ufvbsWVvuw3oVMzIySktLCQEiAAB0mkAgYE2IP/30E9+1AAAAED3vQESACAAgDQGiwuhMgMg6DTU0NJodYaY2TzGz756RkYERZgAAkJWZM2cS0cmTJ/kuBAAAgIjIzMxMJBI9fvy47Xs9AQAoPQSICkNHR4c6N8LctAOxurqaPWjjn0bWeJiZmYlDVAAAQFbGjBnTp0+fjIyM27dv810LAAAAEZoQAQCaQICoMLS1tYmojbPGjXAdiM3ugUhtDhBZbsgFiOhABACAzlNVVZ0+fToRnThxgu9aAAAAiJ5vg5iWlsZ3IQAA3QUCRIXRmQCR60BkI8yN9kCktgWIEomETS7n5uYWFRUR9kAEAAAZmTVrFhEdP36c70IAAACIcI4KAEATCBAVRmdGmBt1IHZsD8SKior6+noiamhoSExMJHQgAgCAjIwfP753795paWl37tzhuxYAAACyt7cndCACAEhBgKgwOh8gNu1AbFeAyMaWGfYGDx2IAAAgE2pqajNmzCA0IQIAQPeAABEAoBEEiApDJiPMjToQuRHmJ0+evPAm0gFiXl4eIUAEAADZmTNnDiFABACA7sHe3l4gEKSnp7MZLAAAQICoMDofIDY9RKVdeyCyI5gZ1rqIABEAAGRl/PjxJiYmd+/ejY+P57sWAADo6bS1tS0sLKqrq7Ozs/muBQCgW0CAqDB69eqloqJSWVnZgQ/BWjqFuV0BonQHIoM9EAEAQFZUVVVnz55NRN9//z3ftQAAAGCKGQDgbxAgKgyBQNCrVy+JRCIWi9v7tY32QORGmOvq6tiD9o4wM/r6+u2tBAAAoCXz5s0jom+//bahoYHvWgAAoKcbNGgQEaWmpvJdCABAt4AAUZF0eIq50QhzdXW19HVq29ks0iPMjKGhYXsrAQAAaIm7u3v//v3z8vKioqL4rgUAAHo6BIgAANIQICqSDh/E3FIHIncKc1u6GsvLy4mIRZAMOhABAECGBALBP/7xDyI6evQo37UAAEBPx0aYESACADAIEBVJ5zsQNTU1qbk9ENsSILIORHNzc/ZUU1OT1QMAACAr77zzDhGdOHGC+6wLAACAF4MHDyai5ORkvgsBAOgWECAqEpl3ILYxQHz27FlcXBzbA5ELEA0MDNpbBgAAQOvs7e3d3NzKysp+/vlnvmsBAAB69uxZ03cKMTExvr6+Y8eOnTZt2ubNm3Nzc3mpTd7Mzc319PSKi4sfP37Mdy0AAPxDgKhIWIDYgQ7ERgEi14HYlhHmCxcuWFlZOTs7HzlyhIgsLCzYdQSIAAAgDwsWLCAi9kcHAAD4paOj4+zsLH1l8+bNY8aMCQsLi46ODg8PDwwMdHBw+O9//8tXhXKFKWYAAA4CREXCRoY70IHY6BCVph2IrYSSX331VUlJCREVFxcTkZWVFbuOABEAAORh3rx5QqHw4sWLytrSAgCguM6dOxcYGGhoaPjFF18kJSWlpqYeOHCgV69eixcvTklJ4bs62WNTzEr5owEAtBcCREXS4T0QuQ5EFiA27UCsr6/njmZuJC4ujohsbW2JyMXFZcaMGew6jmAGAAB5MDAwmD59ekNDw9dff813LQAA8DfBwcEqKipnzpxZuXKlg4ODvb394sWLz549W1dX9/HHH/NdnexhG0QAAA4CREXS4T0QWadh0xFmrgORWphiLisry8zMFAqFUVFRx44di4mJsbGxYS+hAxEAAORk4cKFRHT48GGJRMJ3LQAA8D+JiYkjR450c3OTvjhy5EgXFxfWdqBk0IEIAMBBgKhIOnkKs6amZqMOROkAsdnzLuPj4yUSybBhw/r27Ttnzhx1dXVdXV32EjoQAQBATl555RUbG5usrKzffvuN71oAAOB/amtr+/Tp0/S6mZlZfn5+19cjb+hABADgIEBUJPIbYaYWOhDZB4kjR47krmhpaWloaBA6EAEAQG5UVFR8fHyI6MCBA3zXAgAA/+Pq6pqYmNioPVwikdy5c2fYsGF8VSU//fv319LSevDgQWlpKd+1AADwTI3vArqXlJSU06dPt76moKCga4ppqsMjzI1OYWZ3ePr0qXQW2WwumZ2dTUQDBw6Uvqirq1tcXKyvr9/eMgAAANpo0aJFH3/88alTpwoKCkxMTPguBwCg58rKypoyZYqdnZ2dnZ2bm9v58+c//vhjf39/bsEnn3xy9+7dmTNn8liknKiqqg4cODAhISE1NbXR4DYAQE+DAPFvNmzYEB4e3paVDQ0N8i6mqU6OMKurqxsZGZmYmBQUFPzxxx/e3t5lZWVEpKKi0tDQ0GwHIosaRSKR9EWRSFRcXIwRZgAAkB9zc/PXX3/91KlThw4d2rRpE9/lAAD0UC+//HJGRsa5c+fOnTvHXeQCxLq6OldX11u3bg0ZMmTLli38lSlHQ4YMSUhISE5ORoAIAD0cAsS/2bZtm4ODQ+trioqKDh06pKLCw/Q360Ds8AizhoaGQCB4/fXXv/rqq4CAAJYeEpFIJHr69GkrASL7vhy2DSJGmAEAQK6WL19+6tSp/fv3b9iwQVVVle9yAAB6osuXLxORWCy+d+9eRkZGZmZmRkYGm1Iiovr6+lu3bo0aNerIkSOs10H5DBkyhLANIgAAAsRGRowYMWLEiNbXJCUlHTp0qGvqaYTtYNjsaSet40aYieiNN9746quvoqKiuFf19PTaFSCyUbK+ffu2twwAAMjMzIyMjExOTi4uLhaLxUZGRubm5ubm5tOmTcPv1Ua8vLxsbW0zMjLOnDnzxhtv8F0OAEDP1atXL0dHR0dHx0bX1dXVc3JyrKyseKmqa7CfGgEiAAACREXCAsTq6ur2fiEbYWaHn3h6egqFQu4cFSLS19fPyclpNkAsLy+nJgFiWFhYQkKCs7Nze8sAAOjJsrKyVq5cGRER0eyrvr6+3t7eu3fvtrGx6dq6ui8VFZWVK1euW7cuNDQUASIAQDekoqKi3OkhEbEBtTt37vBdCAAAzxAgKhJNTU2SOkO57aQDxF69eo0ZM+bixYvcq3p6etTCKcysA5HNLHNsbW1tbW3bWwMAQE9WXFzs5eWVmZnp4ODg7e3t6OhoZGQkEonKyspKSkpSU1PDw8NPnjyZkJAQHR2NM0M4Pj4+//rXv3777bfk5GQ2RAYAAN2NRCJhbzfYuxUlww5ivn//fmlpKXvfBADQMyFAVCTsDOUOBIjSI8xENGnSJOkAkZ2n3OzWis2OMAMAQHv5+fllZmYGBQV99NFHzS7YunXr4cOHly5d6u/v/+WXX3Zxed2Wvr7+u+++GxYWFhISsm/fPr7LAQCAZiQmJjo5ORGRRCJp79eeOHHihX/12FuSDtxcJlRVVQcNGhQfH5+SkuLu7s5LDQAA3QECREXCRphlEiBu2LCBe5UFiG3fAxEAANorKirK3t6+pfSQ8fHx+fbbb6Ojo7usKoWwatWqvXv3Hj16dPv27b179+a7HAAAkKUffvhBurOhFXwFiETk6OgYHx+flJSEABEAejIEiIqkYyPMiYmJmZmZ9HyEmYiGDx8eGhq6Y8eOR48eURtGmBEgAgB0UlFR0dChQ1+4zNLSMiEhoQvqUSCDBg167bXXzp49u2/fvi1btvBdDgAANGZvb5+UlNSxrz148ODSpUtbX5Odnb1kyRIVFZWOfYvOY+eodPhnBABQDggQFUnHOhC9vb2zs7NJqgORiP75z3/++OOPLEBkHYjbtm2rr6/X0tKaPXs22+JQIpE8e/ZMIBBoa2vL7ocAAOiJPDw8IiMjMzIyWtlDtrCwMCIiwsPDoysLUwjr168/e/bsnj17PvjgA/anEAAAug+hUMhOGukAkUjk6enZ+hrekzv20/FeBgAAv3j7GAc6oGMBYn5+PnsgHSASkUgkYg9YgCiRSLZv37558+agoCB2XSwWNzQ0aGlpqaqqdrJyAIAebvXq1XV1de7u7iEhITk5OY1ezc/PP3jwoKura2Fh4cKFC3mpsDubOHHiyJEjCwoKvvnmG75rAQCAHofNECQmJvJdCAAAn9CBqEhYgFhdXd32L3n27Bm3nhthZrizlbkkkXn69Cl7UF5eTk2OYAYAgA7w8vIKDQ1d85yenp6hoaFIJKqoqCgpKXny5AkRqamphYWFTZ8+ne9iu6MNGzbMnTt39+7dixYtwsdaAABdLzMzMzIyMjk5ubi4WCwWGxkZmZubm5ubT5s2rW/fvnxXJ1+Wlpb6+vpFRUWFhYV9+vThuxwAAH4gQFQkHdgDkb0pZZoNENXV1RtNKLPckLABIgCATK1YscLT0/PgwYORkZEpKSlZWVlEpKqqamxs7OLiMnPmTB8fH1NTU77L7KbeeustW1vb9PT0EydOzJkzh+9yAAB6kKysrJUrV0ZERDT7qq+vr7e39+7du21sbLq2rq4jEAgcHBz++uuvpKSkiRMn8l0OAAA/ECAqEi0tLfp7gFhXV5eSkuLo6CgQCJr9EukAsdkRZgSIAABdxs7ObufOnTt37pRIJGKxuKqqysDAgMdd4RWIqqrqhg0bli5dGhgYOHv27Jb+6gEAgGwVFxd7eXllZmY6ODh4e3s7OjoaGRmJRKKysrKSkpLU1NTw8PCTJ08mJCRER0ebmJjwXa+8DB069K+//rp9+zYCRADosfCmRZFoaGgIBILq6uqGhgZ2JTg4eNiwYUeOHGnpS1oJEFkHooaGhq2trZra/6JklhsSAkQAALlh51MZGRkhPWy7BQsWWFhYJCYmnjp1iu9aAAB6Cj8/v8zMzKCgoKSkpB07dsybN2/SpEnu7u6TJk2aO3fu1q1bY2NjDx06lJ2d7e/vz3excoRtEAEA8L5FkQgEgkbbIObm5hJRXFwce3rkyJFx48Zxp6ZQqyPMXAfikCFDHj586OPjw66jAxEAQH4yMzP37du3evXqt99+e8aMGYsXLw4ICNi/f7/0r25olqam5saNG4no448/lkgkfJcDANAjREVF2dvbf/TRR62s8fHxGTduXHR0dJdV1fWGDRtGCBABoGfDCLOCEQqFlZWVVVVV0uPMGRkZ7NXvvvsuKioqOjp61qxZ7EpbOhCJqE+fPra2tuw6OhABAOQBe0h13uLFiz/55JP4+PhTp07NmDGD73IAAJRfUVERa75rnaWlZUJCQhfUw5dhw4YJBII7d+7U19fjLC8A6JkQICqYRueoVFZWElF6ejp7WlJSQkQFBQXc+hd2IHIXBw4cyB6gAxEAQOawh5RMCIVCPz8/X1/fgIAAb29vDIADAMibh4dHZGRkRkYG123QVGFhYUREhIeHR1cW1sVEIpG1tXV2dnZ6evqgQYP4LgcAgAf4L28Fw0aYuQCRPcjOzq6traXncWFhYSG3/oUdiNxFb2/vPXv2qKmp1dTU1NTU0PMkEQEiAEDnYQ8pWVm0aJG1tfXt27ePHTvGdy0AAMpv9erVdXV17u7uISEhOTk5jV7Nz88/ePCgq6trYWHhwoULeamwy2CKGQB6OASICqZRgMg6EOvq6m7duhUREVFcXEwtBIgCgaBXr17St2rUgaiuru7r68tSRRYdsg252BUAAOgM7CElK5qamixjDQgIYB+eAQCA/Hh5eYWGhpaVla1Zs8bGxkZfX79///5OTk62traGhoZmZmZLlizJy8sLCwubPn0638XKFwJEAOjhECAqmGY7EIlo1qxZr7322tOnT6m5APGdd975+uuvGwWIVlZW6urqlpaW0hdZXFhRUfHNN98EBQURkb6+vvx+HACAHqKoqKhv374vXGZpaSn9Oxya9e677w4ZMiQjI2P//v181wIAoPxWrFhx586dDRs2jBgxorq6OisrKyEhITs7W1NT08XFJTAwMDc3d9myZXyXKXcsQLx16xbfhQAA8AN7ICqYlgJE6YGCpgHivHnzpkyZ0uhWFhYWd+/eNTIykr7IBpYrKir+/PNPIpo0adKCBQtk/lMAAPQ02ENKhlRVVQMDA6dPn75t27b58+ezhnoAAJAfOzu7nTt37ty5UyKRiMXiqqoqAwODnrYRrZOTExEp91kxAACt6Fm/9JUACxCrq6vZUzbC3MiDBw+4c5lZgGhgYNDs3WxsbBpNKHMjzKmpqUS0cePGtrTMAABA67CHlGx5e3uPGTOmsLCQNcsDAEDXEAgE2traRkZGPS09JKIBAwbo6Og8ePDg8ePHfNcCAMCDHvd7X9G11IEoLScnx97ePiUlhV4UIDbFdSCyL8cRYwAAMoE9pGTus88+EwgEn3/+eVZWFt+1AACA8lNRUWFTzGhCBICeCQGigmn2EJWmGhoa0tLSiKioqIiIGs0pt4J1IN67d6+4uFhPT8/MzKzzNQMAAGEPKVlzdXWdP39+VVXVhx9+yHctAADQIwwfPpwQIAJAT4U9EBVMWzoQmYKCgpqampKSEnV19bYHiKwDMTY2ltB+CAAga/LbQyo+Pv748eOtr2EfKSmToKCgn3/++eTJkxcvXvT09OS7HAAAUHIjRowgovj4eL4LAQDgAQJEBaOpqUltCxAfPXr06NEjiUTSp0+ftr87ZR2IN27cIASIAAByw/aQ0tbWltUN/f39w8PD27KyoaFBVt+Ud2ZmZlu2bNm4ceOqVasSEhI0NDT4rggAAJQZAkQA6MkQICqYF44wq6iosDeHBQUF+fn5RNSuU1BYByLbALF///6yKBkAAP4nMzMzMjIyOTm5uLhYLBYbGRmZm5ubm5tPmzatM4dWffrpp2PGjGl9TUFBQXBwsJJte//+++8fPnw4NTV19+7dfn5+fJcDAADKbOjQoerq6qmpqWKxuFevXnyXAwDQpRAgKhjpAFEikXDHMXMCAgJUVFT+9a9/sQ5EIjI1NW37/VmAyG6LDRABAGQoKytr5cqVERERzb7q6+vr7e29e/duGxubDtx88ODBgwcPbn1NUlJScHBwB27enWloaHzxxReenp7bt2+fPXu2ra0t3xUBAIDS0tTUHDx4cGJi4u3bt93c3PguBwCgSyFAVDDSAWJVVZVEImm0wNraesCAAUSUn5+fnZ1N7exAZCPMDAJEAABZKS4u9vLyyszMdHBw8Pb2dnR0NDIyEolEZWVlJSUlqamp4eHhJ0+eTEhIiI6ONjEx4bteRTJx4sT58+d/8803y5Ytu3jxokAg4LsiAABQWiNGjEhMTIyPj0eACAA9DQJEBcMCRNYhyGJEgUDAxYiqqqqOjo56enpEdPXq1atXr1I7OxD19fW5xwgQAQBkxc/PLzMzMygo6KOPPmp2wdatWw8fPrx06VJ/f/8vv/yyi8tTdP/+97/PnTt36dKlgwcPLlmyhO9yAABAaY0cOfLIkSNxcXHsKWaZAaDnUKqNkHqCRh2IJBX5ubi4PHr0yNnZuVFi2K4ORDs7O+4xAkQAAFmJioqyt7dvKT1kfHx8xo0bFx0d3WVVKY3evXuHhoYS0QcffJCbm8t3OQAAoLRGjhxJRJcuXQoICFi9erWBgQHOVAGAHgIBooJhpzCzDkR2goqBgQE7d1JPT693795EpKOjI32yZ7sCRG4LLQ0NDWNjY9kVDgDQoxUVFbXlt7GlpWVhYWEX1KN85syZM3PmzLKysoULFzbd3wMAhDYizwAAIABJREFUAEAmnJycVFRUMjMzt23bFhoaWlNTc+HCBb6LAgDoCggQFYyWlhY9jw5ZB6JQKGQzy9LbF0o3IbZrhNnIyIjlhqampthGCgBAVjw8PK5cuZKRkdHKmsLCwoiICA8Pjy6rSsns3bvX2Nj44sWLe/bs4bsWAABQTjo6Ovb29tJXbt68yVcxAABdSZH2QCwqKoqJiWn7em9vb/kVwxcWIIrFYnoeIwqFQpFIVFRUJB0gOjo63r9/38bG5sGDB+09knLQoEFFRUWYXwYAkKHVq1efO3fO3d3d39/f29vb2tpa+tX8/PwzZ858/PHHhYWFCxcu5KtIRdenT5/9+/fPmDFjw4YNEyZMcHR05LsiAABQQs7OzikpKdxTBIgA0EMoUoB4586d6dOnt329Uk4wsdnkZ8+ekVQHIvtJRSIRt+y7774rKioyMjIqLS1lc81tN2jQoD///BMBIgCADHl5eYWGhq55Tk9Pz9DQUCQSVVRUlJSUPHnyhIjU1NTCwsLa9ZcOGpk+ffqSJUsOHDgwb96869evs0/dAAAAZGjVqlVisTg8PLympoaIsrKySkpKDA0N+a4LAEC+FClAHD9+/O3bt7du3Xry5EkiWrFiBRvd7VGaBohaWlrq6ur09xHmXr16sfYWHR2d9n4L1rJhZWUlo5IBAICIaMWKFZ6engcPHoyMjExJScnKyiIiVVVVY2NjFxeXmTNn+vj4tGvTCWhWcHBwdHR0UlLSmjVr9u/fz3c5AACgbF566aWTJ086Ozuzs5glEsnNmze9vLz4rgsAQL4UKUAkIkdHxxMnTrBf1hs3bmw0AtYTSAeI3AiziooK/T1A7Iz33nuvvr5+9uzZMrkbAABw7Ozsdu7cuXPnTolEIhaLq6qqDAwM2O9wkBVtbe1jx465u7sfOHBgzJgxCxYs4LsiAABQQq+88kpcXNzw4cMTEhJu3LiBABEAlJ5CvmmZO3cu3yXwpmmAqKWlxaJD6RHmzhCJRGvXrjU3N5fJ3QAAoCmBQKCtrW1kZIT0UB6GDRvGzlFZsWJFQkIC3+UAAIASCgoKSk9P/+CDD4goNja22TW5ubn79u07f/5815YGACAXCvm+xdXV1dbWVk1NwdonZaLZPRBdXV3V1NScnJx4Lg4AAFp19uxZNsi8bNmy+Pj4pgs+/PDDWbNmdX1hymfhwoVLliwRi8UzZswoLi7muxwAAFA2qqqqtra2Li4uRHTjxo2mC7Zs2TJgwIAVK1ZMnz69pKSkywsEAJAxhQwQx48fn56e3jNb5JoNEN9///3S0tIxY8bwXBwAALRs+fLlU6dO3bdv32+//bZ//35nZ+fPP/+80ZqLFy+eOHGCl/KUT0hIyEsvvZSVlTVr1qza2lq+ywEAACU0cOBAPT29Bw8e5OfnS1+vr6//5JNP6urqTExMqqqqjh07xleFAACyopABYk/WdIS5V69e3P8CAED3dPz48S+//LJ///4//vhjamrqt99+a2pqunbt2l9++YXv0pSWUCj8+eefzczMfv/995UrV/JdDgAAKCEVFRVXV1ciiomJkb5eVFRUX19vbGwcHBxMRAcPHmxoaOCnRAAAGUGAqGCaPUSF55oAAOBF9uzZIxQKIyMj33rrLXt7+3/84x9nz57V0dFZvnx5eXk539UpLTMzs1OnTvXq1evgwYOffPIJ3+UAAIASmjhxIhE12ujw0aNHRGRqajp9+vS+ffvGx8d/+umn/NQHACAjirqNYHJy8smTJ+/evfv06dPa2lodHR0TExMnJ6c5c+bI6iyR7klTU1NNTa2mpqa2tpYbYea7KAAAeIHU1NRRo0b179+fu+Lk5BQaGurj47Nr165t27bxWJtyc3V1PXr06KxZs/z8/MzNzefPn893RQAA8D/x8fHHjx9vfU1RUVHXFNMxkydP9vPzazZANDEx0dLS+vrrr1999dUdO3b4+vqy0y8BABSR4gWIaWlpq1atioyMbPbV9evXL168ODAwUIljNW1t7dLS0mfPnlVXVxMCRAAARVBZWdl0dundd98NCwv797//vXjxYisrK14K6wlmzpwZHBy8Zs2aRYsWGRoaTp06le+KAADg//j7+4eHh7dlZbcdAR4xYoSJicn9+/dTUlIGDx7MLnIdiEQ0adKkl19++fLly8eOHVuyZAmftQIAdIKCBYj5+fmTJ0/Ozc318fGZOHFiTU3N8ePHL1y4EBAQMGTIkOTk5KNHjwYHB+fk5Pz4448qKso5oN2rV6/S0lKxWMw6EDU1NfmuCAAAXsDOzu7q1asFBQUmJibcRYFAsHfvXjc3t0WLFp0/f15Z/2x1B6tXr3706FFQUNCsWbPOnj07fvx4visCAAAiok8//fSFR0EWFBQEBwd327+SAoFgwoQJx44di4mJYQHirl27vv76a3oeIBLRokWLLl++fOTIkTYGiLdu3dqzZ09QUJCxsbHcCgcAaB8FCxADAgJycnKOHTs2Z84cdmXhwoULFiwIDQ2Nj4+fPXt2QEDAmjVrQkNDP/30048++ojfauWE2waRdSAiQAQA6P4WL178z3/+c9y4cUeOHHF1deXeBTk7O3/44YeffPLJggUL9u7dy2+Rym3Hjh1Pnz7du3fvtGnTzp0798L3qwAA0AUGDx7Mde21JCkpiR1F0m25ubkdO3bs6tWrixYtqq2t9ff3Z60eXIA4c+bMhQsXXrt2TSwWt+X0Sz8/v3Pnzg0ZMmTdunXyLR0AoM266cc4Lbl69erw4cO59JDZtGlTSUnJr7/+SkQCgSAkJMTCwuLo0aM81Sh3CBABABSOr6/v0qVL09LS3N3dhUJhQkIC99K2bdvmzJnDzmVOTU3lsUjlJhAI9uzZ8+6771ZUVEyZMuXPP//kuyIAAFAS7u7uRHTt2jUiSkxMZOkhSQWI2traw4YNq6uri42NfeHdHj16dOnSJSK6evWqvCoGAGg/BQsQc3JyrK2tG120tLQkIuk3YyNHjszKyurSyrpQowAReyACACiEsLCww4cPv/LKKxYWFhKJhLuurq7+3XffhYSEmJmZcW85QB5UVFS++uqrBQsWlJeXv/baaxcvXuS7IgAAUAYjRozQ1NRMTk4uLy+/fv06d1163xIWMr4wEzxw4EDfvn3ZGz0EiADQrSjYCLODg0NsbGx1dbV0211cXBwRccGiRCJJS0uzsLDowP0zMjJOnTpVX1/fypqCgoIO3FmG0IEIAKCIVFVV33vvvffee6/pSyoqKqtWrVq1atWjR48yMzO7vLQeRFVV9fDhwxoaGgcPHnz99de///77GTNm8F0UAAAoNk1NTScnp2vXrl2/fp31ITJcByIRubm5hYWFNc0EU1JS/vGPf+zYsWPKlClEdOXKFe6l3Nzchw8fmpuby7l8AIA2UbAAccaMGRs2bFi0aNHevXt1dXWJ6N69e76+vkQ0YcIEInrw4MH69evT0tLWrl3bgfuvXbu2+58ChgARAEBZmZqaSr/ZAHlQUVHZv3+/jo7O559/PmvWrJCQkJUrV/JdFAAAKDZ3d/dr167FxMTcuHGDu2hmZsY9HjVqFP09H2R+/fXXW7duffHFFyxAfPDgARGtWbPm1q1bly9f/uOPP95+++2u+AEAAF5EwQLEdevW/fzzz99++214ePjQoUPFYnFSUlJNTc2SJUvYb+S1a9eeOHHC0dHR39+/A/ffsmWLg4ND62uePXu2Z88eluLxAgEiAABAZwgEguDgYGNj4y1btvj6+mZlZe3cubPbnu8JAADd36hRo/7zn/9ER0dnZGQIBILPPvtMLBYbGBhwC2xtbU1NTR89enTx4sUxY8Zw+1Clp6cT0dWrVyUSiUAgYAHikiVLoqKiLl++fODAAQSIANBNKFiAqKqqeunSpW3btu3bty86OpqIevfuvWnTpjVr1rAFdnZ2AQEBH374YccCPjc3Nzc3t9bXFBQU7NmzR0dHpwP3lwkEiAAAAJ3n5+dnYWGxZMmS3bt3371797///S8bbgAAAGgvDw8PIvr9999ra2vNzc3ff//9pmvGjBlz4sQJLy+vt95668cff2QXWYBYUlKSmpo6ePBgFiBaWFi8/fbbGzduvHz5ckpKyuDBgyMjI6OiojZt2tSWQ5wBAORB8T5sFwqFgYGBJSUlOTk5Dx48KCoqWrdunaqqKns1MDBw69atPLYHdgEEiAAAADKxYMGCiIgIQ0PD06dPe3h43L17l++KAABAIVlaWlpaWtbW1hLRgAEDml0zduxY9iAiIqKuro49ZgEiEcXExJSWlpaXl+vo6Ojp6YlEorlz5xLRDz/8QERLly7dvn372LFjpUekAQC6kuIFiBwrK6ueuaEsAkQAAABZmTBhwvXr1x0cHO7cuePq6nrixAm+KwIAAIXE9tSilgNEtms/EVVUVCQmJrIH+fn57GJMTMzDhw+JiDsO9M033ySiU6dOJSYmZmdnE1FcXNzEiRO5LwEA6EoKHCC2RCKRVFdXs3BNKSFABAAAkKEBAwZcvXp19uzZZWVls2bNWrlyZWVlJd9FAQCAghk9ejR70L9//2YXDB069OzZs6+99hoR/fXXX0SUkZEhkUjYfogxMTHc/DJbP2HCBJFIdOvWrdDQUCKaO3fuqFGjKioq/vjjD7n/MAAATSjYHohtkZiY6OTkREQSiYTvWuRCJBIRUWlpKQsQNTQ0+K4IAABAseno6Bw/fnzMmDEffvjh3r17o6Kijh49OmLECL7rAgAAhcEFiLa2ti2tee211x4+fHju3Lnw8PB//vOfbH7Z09Pz999/T0tLCwsLI6kAUUNDY/LkyT/++OOhQ4eIaN68ecnJyTExMX/99de8efPk/vMAdMLZs2fPnz/PumW1tbWbphba2tp9+/atqakRCoUmJiY2NjbW1tZmZmbc9nTQDSlhgKj0TExMiOjRo0csQOQO8AIAAIDOWLVq1dixY//xj3/cuXPHzc1ty5YtmzZtUldX57suAABQAMOHD9fV1S0vL29phJmZPHmyjo7OhQsXPv30U7Zn4pAhQ8Ri8aVLl3755ReSChCJaOzYsT/++GNDQ4O6uvqECRMMDQ2JiJ0mCiBb//3vfwcPHuzs7NzhOzQ0NNy4cSM1NfXEiRPh4eEduIOGhoa1tXWfPn2MjY0tLS319fW1tLRqamqMjY3V1NTY3qB9+vSR/pKKigqxWFxRUcGe6urqqqiolJaWcgvU1dUNDAzYKbiampr6+vra2tq6urpqakjD2k0J/5HZ29snJSXxXYUc9e3bl4jy8/OrqqoII8wAAACy4+TkdPPmzY0bN+7ZsycgIODEiRMHDhxwc3Pjuy4AAOjuVFVV33///ZiYmGHDhrWyzNLS8siRI2+++ebhw4ddXV2JyN7eXigUXrp0ydLS0tra+q233uIWc+euuLq66urquri4aGpq3r59OyYmhttyEaDzoqOj58+f37t37127do0ePdrOzq69d/jll182btyYlpbGnurq6q5fv37IkCFEVF5ezp0axCkvLy8sLFRTU6usrMzPz8/JycnJycnLy0tPT+dOFpIrLS0tIyMjExMTAwMDkUikq6urqalpYGDAXhWJRKqqqjo6OqxBsk+fPo1aI8vLy4VCIfcxM/tCXV1dbW1tJT4qXQkDRKFQ6ODgwHcVcmRmZkZEeXl52AMRAABA5rS0tEJCQt58883Fixffvn171KhRS5cu3bFjB+v7AAAAaMm2bdvasuyNN97Q0dG5e/cue0Nnb28/depUsVi8fPnyRt2LQ4cO1dPTKy0tfeWVV4hIKBTOnz//4MGDU6dOzcnJYXtbAXTekSNHiOjx48c+Pj7Ozs6xsbFt/9orV65s3br1woULRGRlZfXyyy8PHDhw2bJljVoF26KysjInJ+fx48cFBQV5eXlPnjyprKzU0NAoKiqqr69/8pz0l2hra2tra7MGQyIqLS2VSCT6+vrcgpqamidPnjx79oyIqqqq2D1LS0srKysfPHjANh6VLRUVFT09PSIyMDAwMjI6c+aMsbGxzL8LL5QwQFR6pqamAoGgoKBAIBAQ9kAEAACQg3HjxiUmJm7fvn3Xrl379u374Ycftm7d+v/Zu8+4KK728f9nl6UICAgKIiIqKhaMsYO9d8WKjSSWWNBoYqImxtxqrNGYO3YNJtbYY4yCsWAssYuaIKJgQ2OhCdJEUGD/D+Z373+/wLCLAZbyeb/yYPfMmZmLw2wuufbMnIkTJ3JHMwDgX1KpVB4eHidOnJDWVnZzc5NmfuXsaWRk1L59+0OHDvXo0UNq2bBhQ3BwcFBQ0B9//DFgwIDx48cLIb799lupYAHklJCQMGzYsODg4ODg4GxFvdevXyuVytevX+/bt08IYWtrm5CQcO3atbCwsLp16+pz8MWLF28LDhZC2NjYLFiwYMKECf/mX0rlypXT87z/XmpqakxMTGxsbGJiYkJCQkpKSlpamube54SEBLVanZyc/OrVq5iYmJiYmKysLM2+SqXSwsIiLS1NegqBEEIqayYlJUnHkd6+ePHi6dOniYmJFBAN7P79+4GBgbdu3YqLi0tNTbWzs3NycnJycurbt690h28pZmZmVqFChfj4eOktMxABACgM5cqVW7RokY+Pz9SpU0+cODF16tS1a9cuWrRo4MCB0nd4AAC8nbZt2544cUIIYWdnV7FixTx6bt68+d69ey1atJDeGhkZeXl5BQUFHT9+vGLFihs3bhRC+Pv7r1y50tvbuwgiRzZRUVFmZmbaU950SkpKOnz4sFqtHjp06L9ZM+Tly5cWFhZ594mNje3atWtwcLAQYsuWLTNnztRsCggIGDt2rIuLy/DhwxMTE1u1anX+/PmxY8du2rTpp59+yrWiLYmLi9O8Dg4OtrW1nTx58ieffFKy7tUwNzevXr169erVC/zIGRkZycnJarU6ISHB3t5eMzuyFFAaOoB8i4iI6NmzZ61atXx9fVevXr1z587ffvvtp59+mj9//oQJE6pVqzZ48GDpm5xSTFMkNTY2VipL3i8RAICSol69eoGBgQcPHnRzcwsPDx88eHCLFi0CAgLUarWhQwMAlFS9evWS/o7TOd/K1tZWUz2UdOvWTQhx7NgxPz8/IYSdnV1UVNQHH3zw7NmzQosX/4e/v//+/fuFENHR0XXr1vXw8NDMRMvb6dOnBwwY0KVLlxEjRowcOfKrr7566xguXLhgY2MzYcIEaVrVunXrLl68uGjRIu07fJ8+fdqhQ4fg4GCpSL1582bNswiTkpKGDh0aExMTFBQ0e/ZsIcSXX34phHjvvfeEEMuXL+/Ro8f169eFEI8fP96yZcvUqVOtra137NghhLhx44bmFFWrVr1169b8+fNLVvWwUKlUqgoVKtja2tasWbM0VQ9FiZuBGBcX17Vr1/v37zdo0MDLy8vd3d3Ozs7KyiopKSk+Pj4sLCwgIGD//v3BwcHnzp2TVisulRwdHUNDQwXTDwEAKBL9+vXr1avXTz/9tGDBgqtXr/bt27dJkyZffPHFoEGD+CYPAJBfzZo1++GHHyZMmNCuXbv87tu0adNKlSpFREQ8fvxYqVRevXp1+vTp+/fv/+6777777rvCiBbaZs+evXjxYoVCMWnSpJs3byYmJiYmJv7888+jR48WQjx58qRJkyajRo1atmxZth3PnDnTs2dPaSlUe3v7+Pj4ZcuWdevWrWPHjm8RxrFjxzIyMvz8/Pz8/ExNTaXnaQohIiMj16xZI72eOHHirVu3GjZseOTIkRYtWoSFhXXq1OmPP/4wNja+ePFiamqqQqFQq9WvXr1q3bp17969hRAdOnRYv379tGnTjh07Fh0dbW9vf+LECc3du2PGjDl16lR6bGyV/4Xx6aefluLCC7IpYf/k/fLLL+/fv79kyZKbN28uWrRo+PDh3bp18/Dw6Nat27Bhw+bNm3f16tVNmzY9fPhwzpw5hg62EEnrqAghzMzMDBsJAABlhEqlmjBhwt27d1esWOHk5HT9+nVvb283N7c1a9YkJycbOjoAQAnz4YcfRkVFLVq0KL87KpXKfv36CSEyMjI6duxYvXp1aQbZtm3btB/TVoolJycfO3bMID/svHnzFi9eLIRQq9Vr1649c+aM1D5r1qy1a9eGhobu27cvNjb2u+++CwoK0ux18uTJatWqvffee2lpae3bt69fv/7BgwfnzJmTlZU1cOBAGxsbc3Nzb29vTRFQCPHPP//Ur1+/SZMmKSkpuUZy5coVIYRSqbSyskpPT5fmGCoUik2bNkVHR0dFRZ06der48eMqler48eNOTk67du1ycHA4e/asn5/f9evXz549K4Tw8fExNzd3dHTcvXu35sgTJ0588OBBuXLl/v777+PHj5uamg4cOHDixImjRo16/fr1Tz/9pB2Gk5NTQY0tir8SVkD8888/3dzcvvjiizz6jB49un379ufOnSuyqIqe5hZmZiACAFCUypUr9/HHH9+7d2/dunWurq737t2bMmVK1apVp06deuvWLUNHBwAoSSpVqvR2D9UdMGCA9GLEiBFCiMaNG7u4uDx//vzmzZsFGV8x8+TJkx07dmzbtu2DDz7o0aPHtm3bijiAxMTEb775xsjIaN++fZ07d27evPl77703d+7cHj16REdHf/TRR7169fL39xdCZGVleXt7P3jwQNpx9uzZjx8/fvz4cZUqVY4ePRoaGurh4TFr1qxmzZolJCRIKwLv27dPKuoJIU6cONGqVavbt2//9ddfY8eO1Tw1JS0tTSqbqtVqqUD56NGjmJiYw4cPSy/69+//6tWrTp06ValSpVOnTq9fv27Xrl3lypWFEO3atRszZowQ4qOPPmrVqpV0C/aQIUOCg4OvX79etWpV7Z/U0dGxZ8+eQggjI6Pr16/v379//fr1mzdvvnbt2oQJE8zNzYtkvFHslLACYmxsrD5rpDg7O8fExBRBPIYi/S9AUEAEAMAQzMzMfH19w8PD9+/f3759+6SkpNWrVzdo0KB169Y//vijZgk/AAAKQ5cuXSpUqGBhYTFo0CCppUOHDkKI06dPGzCqbNLT02/fvi2EeP78+bRp0/r16/dvJuwfOHDAxcXFx8fngw8+OHDggBBi7dq1BRar3jGkp6d36NBh8ODBJ06cuHLlyrZt2+bNmydNJ6xevfo///xz6tQppVLZvHnzhw8ftmvX7u7du2fPnr106ZJKpRJC/Oc//9HcRKhSqY4cOeLv7x8VFfXJJ58IIS5duiSdpVevXk+fPvX09LSystq7d690Z/qJEyccHBwGDx4shLh//35cXJyjo2PVqlVNTU179eplbm5eqVKlTz/9VAghfaMpnVFTaxZCSDVBIUR6enpYWJhSqWzdunWtWrU05QVtI0eOFEL4+PhoP6azSZMmGzZs6NOnTyGMLkqAElZA9PT0vHjx4r179/LoExMTc/ToUU9PzyKLqujZ2dlJLyggAgBgKEZGRgMHDjx9+vSNGzcmTZpkbW194cKFcePGOTo6ent7Hzhw4NWrV4aOEQBQCpmamp4/f/7ixYvW1tZSS6EWEH/55Zfz58/ndy9fX9/69evXr1+/Xr16K1as8Pf3f7s1Q169erV169YVK1ZkZWV17NhRc8/s1atXpYqb/l6+fPngwQPNQiL5tWfPHiHE0KFDs7WbmJh8/fXX33zzjfS2efPmJ0+e7Nix49OnTydPniyVO2fMmBEZGTlx4kTtHStWrNinTx8HBwcPDw8hxOXLl4ODg0eOHPnmzZsZM2acPXt227ZtCoXiP//5z+HDh/v27ZuUlHTgwIFt27YtX75cCJGz6NGmTZumTZsKIQYNGvTrr7+OGjXKx8dHs9XT07NChQpCCGne66effprHyicDBw68fPnyhg0b3m6sUDqpS5Tjx48bGRnZ2dmtXLny4cOH2bY+e/Zs48aN1apVUyqVBw4cKKQYoqKihBAODg6FdHx9HD58WPr1vfvuuwYMAygODnl7fyuE9F/Ynj2GDqeUuHDhghDC09PT0IGgIIWEhAghlEqloQMptV6+fLl169ZOnTppllWxtLT09vbetWtXQkKCoaNDsUP+Kgzkr8ITEREREhLy5s0b6W1wcPBnn332/vvvL1269ObNm4V6avKXPiIiIoQQtra2mZmZBXvkR48eCSEqVaqUmJjYo0eP+fPnp6en//DDD/fu3ctjr/j4eO3n9bdr106lUhkZGUVERGTr+fz582rVqnXr1k3uUCtWrJAOolQqo6OjIyIivv/++6lTpwohmjdvfubMGT1/kPPnz0vlsxEjRui5i7aEhAQTExOVSvX8+fNcO2RlZX3//ffz588PDw9Xq9UvXrywsLBQKBTSAgZ//PFHHgeXfn2WlpZShB9++KFmkzTlUPqnhZubm2ZITU1Nr1+/nvNQZ8+eHTBgwKNHj3I90cmTJ3ft2vX999/PnTv3rS8V8ldhKBH5q4QVENVq9bp164yNjaXPjLW1dY0aNRo1auTq6ip90oQQKpVqw4YNhRdAcSggar5padmypQHDAIoDElhhKBEJDPnFH2BF5vHjx8uXL2/evLnmyVbGxsadOnVatmxZcHBwVlaWoQNEsUD+Kgzkr8Jw6NAhzVOkatSoERIScvLkSc1fZEIIExOTlStXFl4A5C891ahRQwhx7dq1gj3svn37pF+0r6+v9EK6p7Vr165yu8TGxo4aNUoI0blz5/Pnz0vL+A4ZMkQIsXbt2mydJ0+eLB327NmzuR7Ny8tL6uDh4aFpTEpK0kxF3L17d94/QmJioouLi3bpTfuLvZcvX27atEl73LZu3VqlShU3NzcrK6ujR49Kjbt27RJCdOjQIe9zaZswYYLmM/Ly5cu8O2s+ZX369Hn16pWmPTg4WKoeNm7cODk5+auvvqpfv767u/uOHTv0j6Rgkb8KQ4nIXyXsFmYhhK+vb2ho6MyZMxs3bpyenh4REREcHPy4+8anAAAgAElEQVTw4UNTU9NmzZotXrz48ePHmg9qaaWZacwtzAAAFDdVq1b97LPPrly5Is2S6Nixo1qtPnny5MyZMxs1auTo6DhixAg/P7+wsDBDRwoAOly7ds3LyysqKsrDw6N169ZPnz4dPnz4Z599ZmNjs2bNmhMnTqxatcrGxuaTTz65evWqoYMt6zp16iSEOHnyZLb26OjovHe8e/eutKRGrjSrCfv5+UkvpPx19uxZuSd1zJw5c8uWLUIIX1/fVq1ade7cWaFQdO/eXQhx/Phx7Z5RUVE//PCD9Nrb29vJyek///mPdge1Wq25e9rb21vTXr58+d27d3fr1k0IMX369Jx3WEdHR7u7u7dq1UoIERAQ8OjRIxMTk6FDh7Zr1y49PT0gIEDTc+7cuWPGjGnatOmwYcOkJY+3bNny7Nmz8PDwpKSkAQMGtGrVqlatWtJdw5pqpj6mT59uYWEhhGjatKnOhUeWLl06bty4c+fO+fv7a0/efOedd06cOHH06NELFy5YWlouWLAgNDQ0JCREWj8HKFKGrmD+K1lZWSkpKc+fPy/wedp5KA4zEOPi4qRfXx5f+wBlBN+AFYYS8Q0Y8osZHAb04sWLPXv2jB49WjNdQmJvb+/l5fXNN9+cOnUqKSnJ0GGiSJG/CgP5q8D169dPoVD8/vvv0ts///zTyMhICHHy5ElNn9DQUJVKNXDgwEKKgfylpx07dgghqlevfurUKU3j0aNHFQrF4sWL5fZKSkqqXr26EOLgwYM5t37zzTfOzs7amWv16tX79u1zd3cXQgQGBmofR3qRmZlZqVIlIcS+ffu0D/XPP/8IIaysrHr37l2/fv05c+ZMnTpVmn7YqlUrGxsb6fjlypVLTEzU7CWtB1K1atU7d+7knMKfmZnZrFkz6fKQ7uf96quvunTp8uOPP2qeDxgaGipVHletWqVWq6WlV1xdXX18fE6dOhUZGWlubq5QKMqXLy+EGDVqVEZGhvT66NGjmtmREmNj45y3YOdt48aNQoiFCxfma69ijvxVGEpE/irZBUSDKA4FxMzMTClz9+nTx4BhAMUBCawwlIgEhvziD7Bi4vbt22vXrvX29tbcrCRRKpX16tUbOXLksmXLjh8/HhUVZehIUbjIX4WB/FXgXFxcWrVqpd3i4eFhZWWVrZu0lmshxUD+0lNsbGzVqlWFEC4uLpoZNl988YUQws3NLddd1qxZo6ncNW3aNDk5Wa1Wh4eHX716Va1Wa8+U79WrlxBC81ueMWOGEOLzzz+/c+fO1atXq1WrJoTw8/PbunWrtOBnzZo1c56uQYMGuc5q2r59e0JCwo0bN9q0aSOEaN++/bx5844dO/bq1Svp5sJhw4bJ/dTR0dHSrMCpU6cGBQVpHh6i8fXXX1tZWSkUCumxgHFxcZowbG1tpUWKBwwYEBoaWq5cOYVCIVUYNfEHBwfv3Llz3bp1EydO/Pvvv9/i9/L06dNS9vQS8ldhKBH5S5XrBxjFnFKptLGxiYuL4xZmAABKlrp169atW3fSpElCiPv371+4cOHy5ctXrlwJDg6+ffv27du3pSkkQoiKFSu6u7vX/Z/atWtXq1ZNpeIfbwCKTmxs7DvvvKPdUrVq1fj4+Gzd7O3tr127VoRxIRcVK1a8e/dugwYNHjx4cPz48QYNGjg5Od28eVMIER4efvv27Xr16mXb5YcffkhISLC1tTUyMrp27Vrt2rUDAwPbt2+fkpISGhqqefphw4YNAwICfvvtt1q1akktPXv2/Pbbb7///vulS5daWlpKd/4uW7YsISFBultOKjhm069fv9DQUCGEkZGRi4tL27Ztt27dampq2rdvX2tr64YNG44aNercuXNnzpw5c+aMEKJGjRoRERFmZmbTpk2T+6nt7e3nzJlz8ODBPXv2XL58Wa1W165d++7du0KI1q1bnz9//uuvv87KymrXrp1U5bS1tb169eq2bdv27t37xx9/HDhwwMTE5JtvvqlTp86MGTPmz58vlVxbtmwpHf+dd97J9hHIL2kRFaAU4N+gJZWdnR0FRAAoce7fvx8YGHjr1q24uLjU1FQ7OzsnJycnJ6e+fftmm4+GssDV1dXV1fW9994TQrx+/TokJOT69evBwcE3btwICQl5/vz56dOnT58+relvYmJSvXr1mjVr1qhRo3r16i4uLtWqVXNxcXFwcJBuTQCAglWrVq2rV69mZWVp1pefPn265nlKGuHh4dJdqzAsMzOzUaNGzZkzp2fPnkKIgQMH3rhxQ9q0ZMmSDh069OvXr2LFilJLcnLyrVu3TExMnj59evXq1VGjRt2/f79Tp05Sgbh58+apqalCiJ9//tnLy0uhUEiT9SQdO3bs37//b7/9JoSQqofm5ub37t0TQlSvXj0rK0taRCWb/v37L1myRAixfv36cePGCSHatm1rY2NjbW0tdRg2bFhgYGD16tXj4+N37twprU28evXqFi1a5PFTN2nSpH79+rdu3YqOjnZ2dj537ly/fv0SExO3b9/u6uqalZVVoUKFzZs3a4/S+PHjBw8e/OGHHx48ePDzzz+vU6eOEGLs2LELFixITk4WQkgPTwSgjQJiSSXNDKeACAAlRURExKRJk44ePZrr1smTJ3t5eS1fvlx6DhHKIBMTk6ZNmzZt2lTT8vjxY2lOYlhY2J07d+7evfvkyZM7d+7cuXMn275GRkaVK1euWrWqo6Pj+++/r/03HgD8G126dPnvf/87bty4NWvWlCtXTmjNzNLw8/O7detWrgUjFL2xY8du27bt2bNnb968+fXXX4UQJiYmCoVi+/bt27dvt7OzO378eK1ataZNm3bgwIHMzMymTZuamZm1adNm0qRJn332WWxsrLm5eUZGRkJCghCicuXKQ4YMMTExyXmiDRs2qFSqoKCgR48eValSZcyYMQsXLqxVq9bvv/9eu3btXGNr3ry5s7Pzs2fPpPqmFK12BwsLi927d0uvK1WqtHjx4nfeeWfMmDE6f+pffvnlyy+/jIqK2rFjh729/aVLl6R2aSi8vLxq1qyZbRdbW9tff/315cuX0jonQohq1aq1adPm7NmzLi4u0nd7ALRRQCyppIWYKSACQIkQFxfXtWvX+/fvN2jQwMvLy93d3c7OzsrKKikpKT4+PiwsLCAgYP/+/cHBwefOnXNwcDB0vCgWnJ2dnZ2dpSUmJWlpaQ8ePLh//35ERMSjR48ePXr0+PHjx48fR0VFPX369OnTp0KI06dP9+rVi38hACgQc+fOPXDgwKZNm3bu3Dlo0KCff/5Ze+vq1at//vnnK1euWFhYzJo1y1BBQluVKlWkG3iPHDnSp0+frKyshg0bLl26dM6cOenp6deuXZs9e3bVqlU3bdok9ffw8JBedO3aVXoxdOjQvn37RkVFdejQwdbWNtfqoRDCwcFh3759q1at+vjjj/v06TN79uwWLVp07tw5j+WGFQrFiRMn4uPjpWc15u3LL780MTEZNGiQZvZrHurVq3fgwIGc7T4+PnnvqKkeSj7//PMXL15s3LhRMykSgAYFxJKKGYgAUIJ8+eWX9+/fX7JkifRgnZzmzZu3efPm8ePHz5kz54cffiji8FBSmJmZ1a9fv379+tnaX79+HRkZ+eTJk4kTJ968eTMwMLBPnz4GiRBAKWNlZRUcHLxw4cJjx44lJiZm2/r7779fuXKladOmfn5+0k2gKD569uw5Y8aMpUuXNmnSpHPnzp07d46Pj3d2dj527Jg0mdTZ2fnx48eae3Xd3d0dHR0jIyPHjh3bunVrPc/i6+trb2/fvXt3MzOzvn376uyv/3ViYWExd+5cPTsXlN69e/fu3buITwqUFBQQSyoKiABQgvz5559ubm5y1UPJ6NGjd+zYce7cubc4/l9//bVnz568+8TGxr7FkVEimJiYuLi4uLi4DB8+fPbs2TNmzNi2bZtmq6mpac75IGq1Wro9TaFQqNVqIYS1tbU0yyMzM/PNmzdmZmYFHmdycnJGRoaVlZX0xMaUlJQ3b95IMWiWAdWWmpqanp6uHX9GRkZycrJKpSpfvnzO/m/evMnIyJD+MM7WnpKSUq5cuTdv3kgrk0rtRkZGVlZWmg5CCBsbm5wreOYtLS3NyMjI2Ng4726JiYnSMqBCiBcvXggh6l27pvkZVq9eHfPLL7nuaGRkpFars7Ky8hVVKePs7Lxo0aLCuCahj/Llyy9dunTp0qU5Ny1atGjt2rU5bw5FMbFo0aJ33323S5cu0ltbW9v3339/w4YNqampzZs337Fjx9GjRwcOHChtVSgUO3fuDA8P1796KIQwNjYeNmxYwYcOoPihgFhSOTk5CSEqVKhg6EAAALrFxsY2bNhQZzdnZ+fg4OC3OP6cOXMCAgL06VnGyxClnre399y5c8PCwsLCwgwdC3TwEaLR/16fO3fubT75ZYmPj0/jxo0NHQWya9KkiaFDQF6MjIyyVfeWLFly4cKFGzdujBgxonbt2tkeVtihQ4cOHToUaYgASg4KiCXVxIkTHRwc+vfvb+hAAAC6eXp6BgYG3rt3r1atWnJ9YmJijh496unp+RbHX7ZsWZs2bfLuk5KSsnDhQmm+FUqrWrVqBQcHP3jw4NWrV5rG9PR0aSVNbZpJf5rFVaVpcUIIaZ5XRkZGRkaGUqlUKBSZmZmaHVUqVUZGhqmpqTQ3MD09PduRpUZjY2NpdqE2S0tLY2PjxMREqZBtYWEhPVorKysr562RRkZGpqam0tGk+E1NTaUJjJoJg1IwUn9jY2O1Wq1SqaQZlJpNSqVSpVJZWFi8fv1aqVQaGRlp5hhmZmYmJSVJx5GmNGoGQU8KhcLU1FStVqenp0tnNDExef36dc6e1tbWKSkpmZmZmmmPz5Yte3P1qrR1ypQpVm3b5nqKzMxMhUKhzyPASrEKFSpQPSzO1Gq1dNm/xd1R0nLzmnnBuXr8+PHbB4f/y8bG5tSpU0eOHBkyZIihYwFQwlBALKksLCx0PhEWAFBMTJ069ciRIx4eHnPmzPHy8nJxcdHeGhkZefjw4QULFsTExOiz1GBO9erVq1evXt59oqOjFy5cmPPuTpQyuT4kEcWQ/y+/hP+vgNimTRs3/phHiXXjxo13331XCJF3HTBXEyZMkFYK1okZ9AXF1tZ25MiRho4CQMlDAREAgELXtWvX1atXf/w/1tbWtra2VlZWKSkp8fHx0qQnlUq1bt06ppYDAMqO0aNHSw9FzUNaWpq/vz8PwQQAw6KACABAUfD19e3SpcuPP/4YGBh4+/btiIgIIYSRkVGlSpWaNWs2cODA0aNHV65c2dBhAgCQP25ubjdv3ny7ffv06aNz1fjo6OjKlStbW1u/3SkAAAWCAiIAAEWkdu3a0kKWarU6NTU1LS2tQoUKZfzRZgCAks7MzKxBgwaGjgIAULgoIAIAUNQUCoWFhYWFhYWhAwEAAAAA3SggAgAAAADycv/+/cDAwFu3bsXFxaWmptrZ2Tk5OTk5OfXt29fR0dHQ0QEACh0FRAAAAABA7iIiIiZNmnT06NFct06ePNnLy2v58uXVq1cv2rgAAEWKAuJbev369YkTJ/Tpdvr0aTs7O4VCUQRRlUSZmZnPnz+3t7dniORIQ+Tg4GDoQIopRViY5tI5dOiQ+uFDAwZTbL1580YI0bJlSz3737p1qzDDgSGRvwoK+Usn8lfeyF/6IH8ZXFxcXNeuXe/fv9+gQQMvLy93d3c7OzsrK6ukpKT4+PiwsLCAgID9+/cHBwefO3euUD/v5K+CQv7SifyVN/KXPkpn/lIjn2JiYgz9SwPw/+sixPj//VfT0MGUMq1btzb0/3FRkMhfQLFC/io85K8CNH78eCHEkiVL8uizadMmlUo1fvz4QoqB/AUUK+SvwlPM85dCrVYbeohKGLVaPWXKlPDwcH06h4aGRkZGmpubm5mZFXZgJdTLly/T09MtLCxMTU0NHUsxxRDplJKS8vr1a0tLSxMTE0PHUkwlJye/efOmYcOG+foqdfTo0SNGjCi8qFDEyF8Fi/8568QQ6UT+0on8ZXD16tVTq9VhYWF5d+vSpUtkZGRoaGhhxED+Klj8z1knhkgn8pdOpTN/GbR8WfpNnz5dCPHtt98aOpDia9KkSUKItWvXGjqQ4mvUqFFCiM2bNxs6kOLL29tbCLFnzx5DB1J89e7dWwgREBBg6EBQYpC/dCJ/6UT+0on8pRP5y+Ds7Ow6dOigs9uoUaMqVqxYBPHoRP7SifylE/lLJ/KXTqUyfymLumAJAAAAACgJPD09L168eO/evTz6xMTEHD161NPTs8iiAgAUPQqIAAAAAIBcTJ06NSMjw8PDY9WqVY8ePcq2NTIy8scff2zevHlMTMyYMWMMEiEAoGiwCjMAAAAAIBddu3ZdvXr1x/9jbW1ta2trZWWVkpISHx//4sULIYRKpVq3bl3//v0NHSwAoBBRQAQAAAAA5M7X17dLly4//vhjYGDg7du3IyIihBBGRkaVKlVq1qzZwIEDR48eXblyZUOHCQAoXBQQAQAAAACyateuvXTp0qVLl6rV6tTU1LS0tAoVKiiVPA4LAMoQCogAAAAAAN0UCoWFhYWFhYWhAwEAFDW+NQIAAAAAAAAgiwIiAAAAAAAAAFkUEAEAAAAAAADIooAIAAAAAAAAQBYFxMIlPWDY0tLS0IEUXwyRTgyRTgyRTgwR8otrRieGSCeGSCeGSCeGCPnFNaMTQ6QTQ6QTQ6RTqRwihVqtNnQMpVliYqK/v//gwYPNzMwMHUsx9eLFi99//33IkCEmJiaGjqWYio2NDQwM9Pb2VqlYNj13UVFRp0+f9vb2Vir5UiR3T548uXDhwpAhQxQKhaFjQclA/tKJ/KUT+Usn8pdO5C/kF/lLJ/KXTuQvnchfOpXK/EUBEQAAAAAAAIAsqsUAAAAAAAAAZFFABAAAAAAAACCLAiIAAAAAAAAAWRQQAQAAAAAAAMiigAgAAAAAAABAFgVEAAAAAAAAALIoIAIAAAAAAACQRQERAAAAAAAAgCwKiAAAAAAAAABkUUAEAAAAAAAAIIsCIgAAAAAAAABZFBABAAAAAAAAyKKACAAAAAAAAEAWBUQAAAAAAAAAsiggonCdPn06NjbW0FEApd/y5cvXrl1r6CiA0oP8BRQN8hdQsMhfQNEog/mLAmJhCQwM7NevX6VKlerXr+/r6xsfH2/oiAzg5s2bHTt2vHDhQq5b9RmiUjyM69evb9q0afny5e3t7du1a7d3796cfcryECUnJ0+fPr1x48aWlpY1atTo37//9evXc3Yry0Okbfv27TNmzPjll19ybmKIkF9cD4L8lSfyV97IX/lC/kIB4noQ5K88kb/yRv7KlzKav9QoBOvXrzcyMjIxMWnfvn2dOnWEEK6urg8ePDB0XEVt8ODBQojffvst5yZ9hqi0DmNGRsaECROEEKampu3bt+/YsaOZmZkQYsKECdrdyvIQJScn16hRQwhRuXLlPn36eHp6CiEUCoW/v792t7I8RNoiIiKsrKyEEB06dMi2iSFCfnE9SMhfuSJ/6UT+yhfyFwoQ14OE/JUr8pdO5K98KbP5iwJiwbtz546xsbGdnd2dO3eklsWLFwshevToYdjAisypU6eWLVvWtGlTqUidM4HpM0SleBg3b94shHBzc4uMjJRa7t27V7NmTSHE4cOHpZYyPkSff/65EGLMmDGZmZlSy+HDhxUKhaOjo6ZPGR8ijYyMjNatW5cvXz5nAmOIkF9cD+SvvJG/dCJ/6Y/8hQLE9UD+yhv5Syfyl/7Kcv6igFjwZs6cKYRYuXKldmODBg2EEPfu3TNUVEWpVq1a2rNccyYwfYaoFA9j586dhRCXLl3Sbty3b58QYuLEidLbMj5EjRo1MjMzS01N1W708PAQQkREREhvy/gQacyfP1+hUPz00085ExhDhPzieiB/5Y38pRP5S3/kLxQgrgfyV97IXzqRv/RXlvMXz0AseIGBgUIILy8v7UbprbSp1Dt9+vTDhw8fPnzo6+ubawd9hqgUD+ODBw+MjY2bN2+u3diwYUMhxN27d6W3ZXyInJ2dBw0aVK5cOe1GIyMjIURKSor0towPkeTy5cvz58//6KOPunfvnnMrQ4T84nogf+WN/KUT+UtP5C8ULK4H8lfeyF86kb/0VMbzl8rQAZQ2arX69u3bVlZWLi4u2u3u7u5CiFu3bhkoriLl5OQkvbC2ts65VZ8hKt3D+OuvvyoUCqXy/5Tvr127JoRwdXUVDJEQ/v7+2VrOnj0bFBRUo0aNunXrCoZICCFESkqKj49PrVq1li5dmvOxuwwR8ovrQZC/dCF/6UT+0gf5CwWL60GQv3Qhf+lE/tIH+YsCYgFLTU1NS0tzdHTM1m5nZyeEiIuLM0RQxYs+Q1S6h/Hdd9/N1vL3339Pnz5doVBID/dliDQuX7783XffPX78OCgoqG7durt27VKpVIIhEkIIMXXq1EePHl28eDHbV4UShgj5xfWgEx8r8pf+yF95IH+hYHE96MTHivylP/JXHshf3MJcwNLS0oQQ0oo82qSW1NRUA8RUzOgzRGVnGNVq9datW9u1axcZGfndd981adJEMERa4uLigoODQ0NDMzMzTU1NNf9LZYj279+/efPmuXPnap6WnQ1DhPzietCJj5U28lfeyF9yyF8ocFwPOvGx0kb+yhv5Sw75S1BALHAVKlQwMjLSPCZAIykpSfyvrlzG6TNEZWQYr1275uHhMWrUKAsLiwMHDkybNk1qZ4g0evXqFR4enpSU9Mcff/zzzz/du3e/ceOGKPND9PTp0/Hjx3t6en7xxRdyfcr4EOEtcD3oxMdKg/ylE/krV+QvFAauB534WGmQv3Qif+WK/CXhFuYCplQqK1WqlPN+eKlF83CKskyfISr1w/jmzZu5c+cuW7bM1NT0q6++mjlzprQMvIQhyqlTp07z5s376KOPtm3btnz58jI+RL/99lt8fLxSqfTx8ZFapG+rbt26NXz4cGNj423btpXxIcJb4HrQiY+VIH/lH/lLG/kLhYHrQSc+VoL8lX/kL23kLwkzEAte1apVExISoqKitBvDwsJESbggioY+Q1SKhzErK+v9999fsmRJhw4dbt++vWDBAu3sJSnLQ3T9+vWePXuuXr06W7v0+N7Y2FjpbVkeIsn58+d3/8+hQ4eEEDExMbt37963b5/UgSFCfnE96FTGP1bkr7yRv/RE/kKB43rQqYx/rMhfeSN/6Yn8RQGx4A0YMECtVgcEBGg3BgQEqFSqvn37GiqqYkWfISrFw7hu3brdu3ePHDny2LFj1apVy7VPWR4ia2vro0eP/vzzz9napUWpGjRoIL0ty0M0efJk9f/15MkTIUSHDh3UavWrV6+kbmV5iPB2uB50KuMfK/JX3shfOpG/UEi4HnQq4x8r8lfeyF86kb/+HzUK2rNnz1QqlYuLS3R0tNSyefNmIcSgQYMMG1jRkx4Q8Ntvv2Vr12eISvEw1q5d29zcPCkpKY8+ZXyIpAfT+vn5aVpu3brl4OBgYmJy8+ZNqaWMD1E22glMgyFCfnE9aJC/ckX+0on8lV/kLxQIrgcN8leuyF86kb/yq2zmLwqIhWLDhg1KpdLR0XH06NFdu3ZVqVSurq4PHjwwdFxFTS6BqfUbolI5jJGRkUIIMzOzd3Mzffp0Tc8yO0Rqtfrq1asWFhZCiAYNGgwYMKBNmzbGxsYKhWLFihXa3cryEGWTawJTM0TIP64HCfkrJ/KXPshf+UX+QkHhepCQv3Iif+mD/JVfZTN/UUAsLAcOHOjTp4+dnV3dunU//PDDyMhIQ0dkAHkkMLV+Q1T6hvH8+fN5zAgePHiwdueyOUSS8PDwDz74wMnJydTU1NXVtX///leuXMnZrSwPkTa5BKZmiJB/XA9q8lduyF96In/lC/kLBYjrQU3+yg35S0/kr3wpm/lLoVar8/g4AQAAAAAAACjLWEQFAAAAAAAAgCwKiAAAAAAAAABkUUAEAAAAAAAAIIsCIgAAAAAAAABZFBABAAAAAAAAyKKACAAAAAAAAEAWBUQAAAAAAAAAsiggAgAAAAAAAJBFAREAAAAAAACALAqIAAAAAAAAAGRRQAQAAAAAAAAgiwIiAAAAAAAAAFkUEAEAAAAAAADIooAIAAAAAAAAQBYFRAAAAAAAAACyKCACAAAAAAAAkEUBEQAAAAAAAIAsCogAAAAAAAAAZFFABAAAAAAAACCLAiIAAAAAAAAAWRQQAQAAAAAAAMiigAgAAAAAAABAFgVEAAAAAAAAALIoIAIAAAAAAACQRQERAAAAAAAAgCwKiAAAAAAAAABkUUAEAAAAAAAAIIsCIgAAAAAAAABZFBABAAAAAAAAyKKACAAAAAAAAEAWBUQAAAAAAAAAsiggAgAAAAAAAJBFAREAAAAAAACALAqIAAAAAAAAAGRRQATekrOzs0KhiI6ONnQg/8+4ceMUCkV4eLihAwEAFGvkLwBASUT+AgyLAiJQMNRqdXp6ekZGRqk8HQCgtCJ/AQBKIvIXUMQoIAIF48qVK2ZmZrNmzTLU6WbNmnXp0iUXF5eiCQAAUDqQvwAAJRH5CyhiKkMHAKBg1KxZs2bNmoaOAgCA/CF/AQBKIvIXyhpmIALFSHJysqFO/ebNm8zMTEOdHQBQopG/AAAlEfkL0B8FRKAA9OzZ08PDQwixfPlyhUKxY8cOzaYdO3Z07969UqVK9vb23bt3P3bsmPaOM2bMUCgUcXFx27Ztq1atWq9evaT25OTkmTNntmjRwsrKqlKlSi1bttywYYNarc7jdL6+vtke4puRkbF48eI2bdpYW1vXrl170KBBf/31l/bZZ82apVAoHj58OGHCBCsrK2NjY2dn59GjR0dGRhbKMAEAihnyFwCgJCJ/AUWPAiJQACZMmPDJJ58IITp27LhixYqmTZtK7R988P54sKIAACAASURBVIGPj8+1a9eaN29ev379P//8s0ePHosXL862+969ez/88MOGDRt6eXkJIZ4/f+7u7v7tt9+mpqZ27969WbNmoaGhvr6+8+bNy/t02lJTU1u3bj179ux79+61bdu2QoUKBw8ebNmy5datW7P19PX13bJlS5cuXcaNG2dmZrZly5bevXvzbRgAlAXkLwBASUT+AgxADeCtVK1aVQgRFRUlvb106ZIQYvr06ZoOe/fuFUL0798/KSlJarl3756rq6tSqbx8+bLUMn36dCGEvb19cHCwZsdFixYJIWbNmqVpefbsmZWVVc2aNTUtOU83ceJEIURYWJj0du7cuUIIb2/v1NRUqeXMmTNWVlY2NjbPnz+XWr744gshhLm5eVBQkNSSnp7eqFEjIYR2PACA0oT8BQAoichfgGExAxEoLIsWLSpXrtzWrVvLly8vtbi6ui5btiwrK2v79u3aPceMGfPOO+9o3rZu3XrDhg2ffvqppsXR0dHBwSEmJkb/s69YscLKymrDhg3lypWTWtq1azdt2rSEhIQtW7Zo95w8eXKzZs2k1yYmJv369RNCMIseAMos8hcAoCQifwGFilWYgUKRmZl5+/ZtR0fHbLnq+fPnQoi///5bu7F58+bab9u3b9++fXu1Wv3w4cOHDx9GREScPn367t27lpaWep792bNniYmJ3bp1q1ChgnZ7z549v/7667CwMO1GT09P7bcWFhZ6ngUAUPqQvwAAJRH5CyhsFBCBQvH06dPXr18/evToo48+yrk122pfjo6O2m/fvHkzf/789evXx8XFKRQKR0fHJk2aVK5cOSUlRc+zP3nyJOdhhRBOTk5CiEePHmk32tra6nlYAECpR/4CAJRE5C+gsHELM1AoKleubGRk1LVr11yfHZDtGzCl8v98En18fBYuXNinT5/z58+npKQ8ffrU399feuSHnjTPB8nWLk2Mz9ehAABlCvkLAFASkb+AwkYBESgUJiYmNWvWvHbt2suXL7Xbz507N2XKlGPHjsnt+OrVK39//0aNGm3ZsqVVq1bm5uZSe2Jiov5nr1KlipWV1aVLlxISErTbpfPWq1cvHz8JAKAsIX8BAEoi8hdQ2CggAgUpLS1N83ratGnx8fEjRozQ5LAnT54MGTJkzZo10lT2XCmVyoyMjBcvXrx69UpqycjIWLRo0d27dzMzM9Vqtdzpsvn4448TExMnTZqUnp4utZw/f3758uU2NjajRo16258PAFA6kb8AACUR+QsoMjwDESgY0hN29+3bp1AofHx8WrRoMX78+IMHDx46dKhatWotW7aMj48PCgrKyspavHixu7u73HFMTU19fHw2b97s6urapUsXpVJ57tw5IUTLli0vX748evTo2bNn165dO+fpsh3n888/P3LkyK5du86cOdOiRYuYmJgrV64olcoff/yxUqVKhTkSAICShPwFACiJyF9AEWMGIlAw6tevP336dFNT0y1btjx79kwIYWRkdOTIkdWrV7u7u1+6dOnhw4edOnU6cuTIrFmz8j7UunXr5s6da2lpuX///hs3bgwaNCg4OHjlypXu7u779u17+vRprqfLxsLC4sKFCwsWLHBxcTl58mRUVJSXl9fly5ffe++9wvjxAQAlFPkLAFASkb+AIqbINiMXAAAAAAAAADSYgQgAAAAAAABAFgVEAAAAAAAAALIoIAIAAAAAAACQRQERAAAAAAAAgCwKiAAAAAAAAABkUUAEAAAAAAAAIIsCIgAAAAAAAABZFBABAAAAAAAAyKKACAAAAAAAAEAWBUQAAAAAAAAAsiggAgAAAAAAAJBFAREAAAAAAACALAqIAAAAAAAAAGRRQAQAAAAAAAAgiwIiAAAAAAAAAFkUEAEAAAAAAADIooAIAAAAAAAAQBYFRAAAAAAAAACyKCACAAAAAAAAkEUBEQAAAAAAAIAsCogAAAAAAAAAZFFABAAAAAAAACCLAiIAAAAAAAAAWRQQAQAAAAAAAMiigAgAAAAAAABAFgVEAAAAAAAAALIoIAIAAAAAAACQRQERAAAAAAAAgCwKiAAAAAAAAABkUUAEAAAAAAAAIIsCIgAAAAAAAABZFBABAAAAAAAAyKKACAAAAAAAAEAWBUQAAAAAAAAAsiggAgAAAAAAAJBFAREAAAAAAACALAqIAAAAAAAAAGRRQAQAAAAAAAAgiwIiAAAAAAAAAFkUEAEAAAAAAADIooAIAAAAAAAAQBYFRAAAAAAAAACyKCACAAAAAAAAkEUBEQAAAAAAAIAsCogAAAAAAAAAZFFABAAAAAAAACCLAiIAAAAAAAAAWRQQAQAAAAAAAMiigAgAAAAAAABAFgVEAAAAAAAAALIoIAIAAAAAAACQRQERAAAAAAAAgCwKiAAAAAAAAABkUUAEAAAAAAAAIIsCIgAAAAAAAABZFBABAAAAAAAAyKKACAAAAAAAAEAWBUQAAAAAAAAAsiggAgAAAAAAAJBFAREAAAAAAACALAqIAAAAAAAAAGRRQAQAAAAAAAAgiwIiAAAAAAAAAFkUEAEAAAAAAADIooAIAAAAAAAAQBYFRAAAAAAAAACyKCACAAAAAAAAkEUBEQAAAAAAAIAsCogAAAAAAAAAZFFABAAAAAAAACCLAiIAAAAAAAAAWRQQAQAAAAAAAMiigAgAAAAAAABAFgVEAAAAAAAAALIoIAIAAAAAAACQRQERAAAAAAAAgCwKiAAAAAAAoPRr27atQqFQKBTBwcGGjgUoYSggAmVCSkrK559/3rx58/Lly1evXr1Xr15HjhwxdFAAgLIrKCioXLlyNjY2hg4EAID8IYWhbFIZOgAAhS42NrZZs2b//POP9DYlJeXRo0dHjhyZNGnS2rVrDRsbAKAMiomJmThxYlpamqEDAQAgf0hhKLMoIAKl3/Tp06XqoZubm4+PT3R09MaNG9PT09etW9e9e/d+/foZOkAAQJkQEhISFBR0/fr1Xbt2xcfHGzocAAD0RQoDuIUZkHXmzBlvb++6deuWL1++efPm48aNe/TokXaHrl27Sk/QCAwMHDRoUJUqVSpUqNCrV6+TJ08KIY4fP969e3dbW1sXF5cPP/wwNjZWe9+MjIxFixZ16NDBxsbG2dl54MCBFy9ezBnDwYMH27VrZ2tr26tXr6CgoGnTpklnPHPmjKbPzZs3R44c6ebmZm5u7uDg0KJFi1WrVr169Uraqlar9+/fL4QoX778uXPnvvrqq9WrV69YsULaumfPnrwHQZ8AAABlUN7ZJ1d+fn5jx45du3Ytf3oBAAwrKyvr66+/bty4sa2tbe/evfft25d3f1IYINQAcjNz5kyFQpHt81K+fPm9e/dq+nTp0kVqr1Spkna3cuXKzZw5U6X6PzN8PT09MzMzpR2fP3/eqlWrbAdXKBQbNmzQjmHZsmXaHaysrNq2bSu9Pn36tNTnwoULxsbGOT/a/fv3z8rKUqvVmqJnly5dNEe+e/eu1NiwYcM8BkGfAAAAZZDO7JMrPz+//v8jJVkzM7OiDBsAUMa1adNGSlitW7fOlsLmzp2bx46kMEChVqtz/uMPKON+//333r17CyEUCsUHH3zQoEGDI0eOSPMKrays7t69a29vL4To2rXriRMnpF369u1rZ2f366+/JiUlSS12dnbe3t6hoaF//vmn1HL58uUWLVoIIT755JOVK1cKIfr37//ee+/duXNnwYIFqampSqXy77//btiwoRAiPDy8UaNG6enpCoVi2LBh5ubmO3fu1MzsOH36dPv27YUQTZo0+euvv4QQI0aM8PT0fPjw4YYNG16+fCmECAkJcXd3j42N3bZtmxCiTp06ffv2lXb/66+/mjRpIoTw9PS8cOFCroOgZwAAgDJIZ/bReQRjY+OMjAwzM7O8Jy0CAFCA2rZte+7cOem1q6trv379bt68GRgYKLVo/l7LGykMZZShK5hAcaT5y2fNmjVSS1ZW1qBBg6TGjz/+WGrUzECcNm2a1LJlyxapRalUhoSESI0eHh5S4/bt29Vq9bNnz0xNTYUQbm5uGRkZUp/Vq1dLfUaMGCG1aE43f/58qUV73WRpAmBSUpKtra2NjU2bNm00wQ8fPlzqs3PnTrkf8P3335f6zJs3T66PPgEAAMqgt84+2qR5+kzfAAAUJc0MxFq1ar148UJqnDRpktQ4aNAgfQ5CCkPZxDMQgexSU1NDQ0OFEPb29hMmTJAaFQrFrFmzpNdBQUHZdpGmKwohWrZsKb2oUaOGpgqpaUxPTxdCXLt2TXrRu3dvIyMjaZOmWnf27FnpxdWrV4UQKpVqypQpUkuPHj2yfSFWvnz5uLi4Fy9enD17Njk5+fLly6tXrz548KC09fXr1zl/uqysrKlTp0pzEuvXr//JJ5/IjYM+AQAAyqC3yD4AABQrH3/8sY2NjfR6zpw50l3Jmr/FAOTEKsxAdnfu3FGr1UKIevXqaT/H0N3dXaFQqNXq8PDwbLtYWlpKLzSPTSxfvrxma7ZnKd6/f1968d///ve///1vtkM9efIkMzMzLS1NWje5atWqmsQmhGjSpMmVK1e0+6enpy9btmzv3r2hoaFqXU8kePz4sY+Pj3RL9bvvvhsQEGBtbZ1rz5cvX+oZAACgDMpX9gEAoLiRHhslcXBwqFy5cmRkZExMzIsXLypUqGDAwIBiiwIiICtb4U9afVitVv/LuRWadbtUKlW2hVYkycnJr1+/lv4ey9bBxMQkW+eePXueOnVKCFG5cuXOnTs3bdr0r7/+2r59e87DHjp0aNSoUS9evBBCfPDBB+vXry9XrpxckC9fvtQzAABAGaR/9gEAoPjT3BmmVHKbJpA7CohAdnXq1JEKhbdv387MzNTkkps3b2ZlZQkh6tWr92+O7+rqKr1YtGjRzJkz5bpZWlqmpKQ8fvw4NTXV3NxcE4N2n7Nnz0p/vzVo0ODatWvSoxUnTpyY82gLFiyQlhWztLT08/PTPKlKjr29vT4BAADKIP2zDwAAxVNISIhmTci4uLhnz54JISpXrix3hxYAiutAdubm5vXr1xdCREdHb9y4UWpUq9WLFi2SXjdr1uzfHF8zW16zOrMQ4uHDh1OmTJkyZcrSpUullkaNGgkh0tPT/fz8pJaLFy9Kf7Bp3Lt3T3rh5uYm/f2Wnp5+7NixbGfcsWPHnDlz1Gq1ubn5n3/+mWv1MDk5OS4uLi4uLjU1Vf8AAABlkJ7ZJ2dmAQCgmFi9enVSUpL0evHixdJMkVatWkktpDAgJ2YgArn45ptv+vbtK4SYPHny9evXGzRoEBAQcOLECSFE+fLlZ8+e/W8O3rhx4169ev3++++HDx8eMWLEkCFDnj59umrVqrt37wohNmzYIHWbOXOml5eXEGL69OkhISGmpqbSIs7ah6pWrZr0wt/ff8qUKTVr1ty5c+fDhw+lRikLqtXqOXPmSC1Vq1ZdtWpVtiN8/fXXQojx48fv3r1bCPHVV18tWLBAzwAAAGWQPtlH5JZZAAAoJu7cudOiRQsvL69bt24FBAQIIZRK5dy5c6WtpDAgJwqIQC769Onz6aeffv/991lZWZpJiEIIS0vLjRs3VqlS5V8ef9WqVWFhYQ8ePNi1a9euXbs07VOnTh0/frz0ul+/fkOHDt2zZ09mZuamTZuEEEZGRg0bNgwJCdH0b9eundTy5s2bNWvWCCHMzc09PT0vXrwohLh48eLo0aNDQkIePHgg9b9z586dO3e0I3n33XelAmJO+gQAACiD9Mk+ho4RAIC8mJiYhIeHL1u2THqrUCgWLlz4zjvvGDYqoDjjFmYgd999990ff/wxcODAOnXqWFhYNG7ceOzYsSEhIUOHDv33B3d1dQ0ODp4xY0aLFi0sLS2dnZ379OkTGBi4cuVK7ZVbdu3a9e2337Zo0cLKyqpNmzaHDh3q1KmT9nGMjY2PHj06fPhwBwcHR0fHgQMHBgUF+fr6Sls3btyYlJQUERHx1nHqDAAAUAbpk30MGyEAAHnz9/cfN26cm5ubtbV19+7df/3111mzZhk6KKBYU3BDIlA83b9/PyMjQwhRvXp16QlTQoj+/fsfPHhQCPHo0SPNHWSlNQAAAAAAAFAcMAMRKKbGjh1bt27dunXrfvTRRykpKWq1eseOHf7+/kKImjVrFkHxzuABAAAAAACA4oAZiEAxdfXq1Y4dO6akpAghVCqVqanpy5cvhRDSSspNmzYt9QEAAAAAAIDiwGjevHmGjgFALqpUqTJmzJjMzMzU1NSXL18aGxvXq1dv+PDhe/bsqVOnTlkIAAAAAAAAFAfMQAQAAAAAAAAgi2cgAgAAAAAAAJBFAREAAAAAAACALAqIAAAAAAAAAGRRQAQAAAAAAAAgiwIiAAAAAAAAAFkUEAEAAAAAAADIooAIAAAAAAAAQBYFRAAAAAAAAACyKCACAAAAAAAAkEUBEQAAAAAAAIAsCogAAAAAAAAAZFFABAAAAAAAACCLAiIAAAAAAAAAWRQQAQAAAAAAAMiigAgAAAAAAABAFgVEAAAAAAAAALIoIAIAAAAAAACQRQERAAAAAAAAgCwKiAAAAAAAAABkUUAEAAAAAAAAIIsCIgAAAAAAAABZFBABAAAAAAAAyKKACAAAAAAAAEAWBUQAAAAAAAAAsiggAgAAAAAAAJBFAREAAAAAAACALAqIAAAAAAAAAGRRQAQAAAAAAAAgiwIiAAAAAAAAAFkUEAEAAAAAAADIooAIAAAAAAAAQBYFRAAAAAAAAACyKCACAAAAAAAAkEUBEQAAAAAAAIAsCogAAAAAAAAAZKkMHQAAAMD/x96dx0VZtn0DP2eGVfYd2WQVcAABNzAXSrG0DNQyzdwVt9Qyy7Iysly4U7tzocwtvdNcI5cIBTMVBVMgVpF9kV1W2YZt3j/Ox+udWMZhgLlm4Pf9PH/Mcs01x9hzMzO/Oc7jBAAAuZaZmRkeHp6SklJeXl5fX29gYGBubm5ubj59+vTBgwezXR0AAPQ5jlAoZLsGAAAAAAAAkEfZ2dmrV68OCwvr9F4lJSU/P79du3ZZW1vLti4AAJApBIjSaGpqqqurY7sKAIC+paury+Fw2K4CehPevwBgIMD7Vy8qLy8fM2ZMZmYmn8/38/NzcXExMDDQ1tauqampqKhITU29cuVKTEyMvb19ZGSkiYlJH5WB9y8AGAjk/f1LCN3U2Nhobm7O9n83APg/KoSoP/s/HtvF9DPLli1j+y8u9Ca8fwHIFbx/9R28f/WigIAAQsiOHTvEHHP06FElJaWAgIA+qgHvXwByBe9ffUfO37/QgdhtJSUlpqamXC5XR0eH7VoAgMyoq3NuaqKXQzQ0HqqosFtP/9DS0vL06VNvb++7d++yXQv0Grx/AcgVvH/1Bbx/9TpnZ2ehUJiamir+sMmTJxcVFSUnJ/dFDXj/ApAreP/qCwrx/oVNVKRkZGRUXFzMdhUAQC6/9dajs2fp5aNHjzrOns1uPf1DVFTU2LFj2a4C+gTevwDkBN6/+gLev3pdWVmZq6vrcw+ztLSMj4/v00rw/gUgJ/D+1RcU4v2Ly3YBAAAAAAAAII+8vb2joqIyMjLEHFNaWhoWFubt7S2zqgAAQPYQIAIAAAAAAEAn1q1b19LS4uXltXfv3tzc3Hb3FhUVHT58eNSoUaWlpUuWLGGlQgAAkA2FXMIcGhp6+fLl9PR0Ozu7lStXenh4tDvgww8/zMnJOXfuHCvlAQAAAAAA9AO+vr779u1b/4yOjo6+vr62tnZtbW1FRUVlZSUhRElJKTg42N/fn+1iAQCgDylegLhy5cqDBw/Sy9evXz906NCePXvee+890WMiIiL++ecfNqoDAAAAAADoP1atWjV58uTDhw+Hh4c/fPgwOzubEMLj8YyMjEaOHDlz5szFixebmpqyXSYAAPQtBQsQz5w5c/DgQVtb26CgIFdX15iYmI0bN77//vs2NjZ+fn5sVwcAAAAAANDfODg4BAUFBQUFCYXC+vr6xsZGPT09LhfjsAAABhAFCxD379+vpqYWHh5ua2tLCHF0dBw2bNj48eNXrlz50ksvaWlpsV0gAAAAAABA/8ThcDQ0NDQ0NNguBAAAZE3BfjVKTU0dO3YsTQ8pd3f3ffv2FRcXf/PNNywWBgAAAAAAMAAJhUKBQCAQCNguBAAA+pCCdSA2NDS0tbW1u3HhwoXBwcG7d+9etmyZlZUVK4WBJAoKCtasWbN27dpJkyaxXQsAAACbqqurAwICZsyYMWfOHLZrAQDokYSEBHd3d0KIUCjs7mNPnTp17Ngx8cc0NTURQqqqqiQ8Z1RUVGBgoOjXRl1d3cOHD+vo6HS3PAAAYChYgOjg4BAdHV1SUmJiYsLcyOFwvv/++zFjxixduvTq1asYxiG3IiIiLl68qKGhgQARAAAGuC+++OLs2bOPHj2SnwDx0KFDISEh586dU+jFiTt27IiOjj579uzt27erqqreeOMNtisCAHEuXboUEREhyZGSdzjevHnz2rVr7W585513MDQfAKAnFCxAXLZs2bvvvjtx4sTjx4+PGjWKyQpHjBjx4Ycf7ty5c8GCBd9//z27RUJXGhoaCCFPnjxhuxAAAAA25efnBwcHE0JSUlK2bdvm7+/P5/P79BmTkpKGDh3a3NwsJhzctm1bbm7u5cuX5SfTbKehoSEkJMTPz6+rVyEUCnft2lVRUXHlypUlS5bU1tZmZGTY2NiIP61AIDh27Ji/vz+2kQWQjqOjY1JSknSPPXLkyIoVK1pbW8Uck5OTs3z5csnbRDZu3Dhu3LjGxkZ69fvvv//1119zcnKkqxAAACgFa9Zbs2ZNQEDAo0ePvLy81NTU4uPjmbu2bt361ltvnTx50tTUNDU1lcUioSv0Z0MEiAAAMMDFx8c3NzcTQpqbmz/77LPPPvtM9N6WlpaoqKiOM1sYbW1t3Zo1dvbsWVdX13Hjxmlra3f6O+uBAwdcXFxyc3MJIT/++OPx48clPzkhpLCwcObMmbdu3erWo8Roa2v79ttvHzx40O72H374Yd68eTt27OjqgVlZWRUVFYSQLVu21NTUtLW1nT17lrn32rVrCQkJoaGhjx49En3UyZMnV61atXjx4k7PKRAI2h3/XKmpqV9++WVkZKSYY2pqagoLC7t1WgC5paamxufzpfshREND48UXX5wslpeXV7fOqaSkNG7cuHYPz8vLk6I8AABgKFiASAgJDg4+duzYpEmTLCwsRKdsKCsrnzp1au/evWZmZszPTSBX6Led8vJytgsBAABgU7vvsVlZWaJXd+/ePXbs2EOHDnX18FmzZg0ZMqSmpqbjXcnJyYWFhbt37/7rr7+YG7/77jtCyP3799va2o4cOdLxUd98801ycjK9fOPGjUWLFt2+fVvyl7Nnz56QkJCDBw9K/hDxjh07tmHDhvXr17e7PSMjgxASEhLS8SE3b96cNm0aMyMlJSWFXjhz5gy9UFBQ8Morr4wcOfLVV19tt66Z/iAdFhYmmjYy1q5d6+zsfOPGDQmLz8rKGj58eGBg4Ny5c8XkvCtXrhRNGOlLA4C+MGTIEEII/Y0EAACkpngBIo/HW7RoUURERFZWFh3Wy+ByuWvXrk1PTy8qKhL/qy+wAh2IAAAAhJD8/HxCCLNgtt3XWvoZJjExsdPHlpeXX758uaSkhIn8CCFffPHFmjVrLly44Onp6ezsvHHjRpq+NTc3x8XF3b17lzkyNja2XXyZmJjY8Xt1u5xRKBSuXLkyKCiIueXmzZvFxcWEkKamphMnThBCHj9+LNmrFycxMXHw4MEBAQGEkLS0tHb30n+3lJSUrKyssrIy5vaCgoIZM2b88ccf7V4Il8uNi4t7+PAhPbNQKKSNn0lJSZmZmcxhzL/kW2+9dfjw4XZP+s8//wiFQrrkXBKhoaF0w4fHjx/TfxlRtbW1J06cuHz5MpNsUp9++mmn2S4A9BwCRACAXqF4AaIkTE1NX3jhBbargPZogFhXV0eHIQIAAAxMNMJbsGCBkpISIaS6urqqqio7O/udd96JjY2NjY0lhHQ1ris8PJwOC6N9iw0NDXFxcVu3bg0ODn7jjTeamppoZ2JmZub8+fM1NTUnT55MCOHxePThQqHwt99+Ez3h77//TgjhcrkcDmfRokWTJk3icDjnzp2bNm3aq6++ShO31NTUgwcPbtmyhb6Dx8TEvPjii/PmzSOE/PjjjzTLKygoEP+q4+LiDh06JGaT1oqKiqlTpxYXF9Pl20+ePKmurhY9gMko/f39TUxMLly4QK9u2rSpsrLSy8uLvszPP/987Nixnp6e8+fPJ4TQlYz3798nhAwfPtzNzY151RSd3TZ79mxCyM2bN+mN8fHx69ate/LkCf0PcenSJfErjqurq+k/Du1V9PX1JYR89913EydOPHPmzO3bt+vq6ggh27ZtW7hw4euvv95xifq6devS09PF/QsCgFQQIAIA9AoF20RFEkKhkP7wq6qq2t3HPn369O+//xbz0ZYQUllZSQgRP+gXOsUs5KmoqDA3N2e3GAAAALbQTrqpU6du3LjRx8cnJSUlJyfnyy+//O233yorK2lQlZ2d3e5RT58+LS4uDgsLo1ezsrKEQqGHhwdd/crlckUzqbq6up9//pkQUlFRweFwNm/e/NVXXzk5OaWmpn7//ffXr1/PyMj43//+N3z48J9++okQ8tNPP40ZM2bo0KGEkLlz554+ffqPP/4ghFhbW9vY2CxdupQQ0tTU9Pfff0+cOPHy5ctCofDmzZsPHjzYtGkTfcbnTvRbsWLF/fv3dXR0aFRHXb9+PTQ01M3NbeHChV9//XVBQcELL7zwn//8Z9GiRenp6RkZGSNGjKBH1tfX03838qw9c/v27bNmzWpubr5y5Qoh5OTJk6dPnw4PD9+0adPWrVsJIZGRkcePH6+oqLh+/TpNBpcvX66lpbVw4cLjx48vX75ceDKd/QAAIABJREFUXV29rKyspKRER0dn5cqVZ8+ezcnJKS4u/uWXX/78888rV64oKyvTeLSpqenNN9+8ceOGiopKu9eVlJRUW1s7ffp0Ozu7u3fv0sXjO3fujIiIoL2NUVFRzc3NixcvPnLkyC+//EIIsbW19fX1FRw5Qlpa6EnGjx8ff/v2vHnzJkyYoKura2trO3To0PT09EGDBg0bNkxPT8/Q0FD8Py8hpKysLDU1NS8vLzc3t6amRllZmcfjNTY2ampqqqur19bW1tTU1NfX6+joEEKUlZVVVFTq6urU1dXNzMx0dXXpR9zKysrGxsa6urrq6modHZ3Bgwdramrq6uqSZ5+B9fT0dHV1dXR0dHV1BQIB3dCmoaHh8ePHNTU1ampqysrKTU1NXC6XJqrNzc21tbVCobCoqKi0tLS8vLyiooLeWF1dzfw/rZKSkp6enpaWVrsXVVNT09raqqWldf78eQMDg+f+IwB0ZGJioqamVlZWVldXp9C7zAMAsKsfBogJCQl0abP4HLBTy5Yt63T8TUcY5CcFJkB88uQJAkQAABhQmpqaxo4dy+fzjx8/TjsQraysjIyMbGxsUlJSzp8/f/HiRULItWvX6PE5OTmnTp16/fXXNTU16S3vvPPO5cuXmV7CrKys3NxcZn+PHTt27Ny5s6qqSvTzj6mpaXFx8aRJk7788stp06Y5OztbWVmlpqbS7ebOnz+flpb26NEjOzu7uXPn0nZIQsipU6dmzpwZHh5+6NChwsLCwsJCJjWbPHnynDlzaPNja2trQEBAfX39jBkz/vzzz+rq6oqKCn19/U5ffktLC039tm3b9uabb3I4HELIkydPXn31VYFAwOPxtLS0Dhw4wOVy9+/f7+7uzufz09PTr1y5wuVyhw8ffu3atddee621tVVNTU1TU5OOQ4mNjT18+PD9+/erq6v5fL6tre3mzZs3b97MPOkLL7wwfvz4urq6jIwM2pjp7Ozs6elpaWkZGxv73nvvHTx4kGZ8w4YNs7W1JYRkZ2e/9957zPriH3/8kRBiYWHB5XLv3r17/vz5t99+mzl/SEhIUVHRpk2bamtr6csZP358RUWFjY2Np6eno6Mj/XemXZw///zz5MmTc3NzhwwZkpGRweFwNv72GykpoaeaP3/+z4mJ9+/fp52SnbKwsBg/fvzw4cPNzc09PDycnZ0FAkFlZWVcXFxcXFxYWNjdu3el+PSrEJSVlcvLyxEgyl5ZWZnoDITn8vPz67tipMbhcCwtLdPT0/Py8pydndkuBwBAUfXDALEnZsyYQTfvE6O2tjY6Olo29fQzTICI+BUAAPqZ5ubmiooKExOTrg7IysqKiYmJi4vbuXNnQUEBl8u1sLAghFhbWxNCfvjhBxr9tDxrSauvr583b56pqemdO3dotkUXSbS0tPj4+Pz111/0hPRgJSWl5cuXr1ix4tSpU6tXr2aedOfOnUpKShMnTuRwOHQf0lmzZh07dkxDQ6Ouri4mJoauAv7444+Z9JAQwuFw3nzzzVmzZmVlZf3111+tra3MFiItLS2nT59mjoyLiyOE+Pn5PXr0qLq6uqCgoKsAMTU1le5xl5CQcP36dSMjo0WLFmVmZtLPBq2trW+++WZbW9u6devoz8D29vaEkMDAwMDAwICAgPT0dLr4w9zc/PXXX//222/19PQqKyuXL19Ozz916tSOT8rhcOje0GvXrt2/fz8hZNiwYbq6uleuXHF3d//ll1+2bNly9epVQsjIkSMtLCyUlZULCwvPnz/PnIEmgy4uLn5+fqtWrfrpp59mzpyppqZGCKmqquq4Tcrdu3cNDQ3pVtdjxoyhASKtpLm5eeHChYSQOXPm0PzUwcHh6bMAUUdH59y5c7///ruZmVl1dfWdO3eePHni7OxcUVGRk5NTXl5eVVX1+PHjX375hfYwdkpDQ8PV1dXKysra2lpfX7+5uVkoFKqoqNTU1DQ1NWloaGhpaQ0aNIhmqQKBoKmpSUtLq66urri4uKamRktLi8vl6ujoqKura2ho6OrqlpeXl5SU0NZFQgjtQ6yqqqqsrKyqqqqvr+dyua2trQKBQE1NzczMTE9Pr6Ghobm5WVVVlcPh0NxZSUmJ9hWamJiYmpoaGhrq6uoqKytra2vr6OhwuVzm/7UqKiroP7goLS0tJSUlCwsLMf/jgr6TnJzs7+8v+fFyG2FbW1unp6fn5OQgQAQAkFo/DBAdHR3pLBspzJkzZ86cOeKPSUpKcnV1pZ/8oFtEOxDZrQQAAKB3LV++/OTJk7Gxsa6ursyN2dnZAoHAycmJPFu2TPdBbmlpGTx4MI1X6HCudj+t8Xg8mpcVFxe/9tprAoHg5MmTxcXFgwYN2rdvn4+Pj52dXUxMzGeffUYIeemll5YtW6anp0cvi57Hw8ODjvxjfP755xoaGq+++urUqVNpt6Ozs/OiRYs6viIulxsREfHtt99u2LCBEKKsrNzW1tba2kojTg8Pj+TkZDo0Zvz48T///HNKSkpBQQF9+ZcvX167du2ZM2fGjBlDCLl06RJdU0xf15EjR+7du8es0X733Xf379/f1tY2YcKEb775ht5oZ2fHVHL06FFmdIylpeVXX301ceLE0tJSutcKLbXdxsrtrFy58sCBA0ZGRnTjGjc3N09Pz5iYGCsrK3rAG2+8wePxLC0ts7KyOo6psba2fuutt957773w8HCaYObl5c2ePZv5YMPj8ZSUlOjVH3744eWXXyaEjB49+vjx4w4ODmvWrHFzc1u0aFFeXp6Tk9Mnn3xCH2VmZvZI5FkmT55MB1Z2SigUPnz4MDIy8tGjR/n5+dHR0fn5+erq6pqamnw+393d/YUXXpg6dSqWZ0Lv8vHxSUxMDAwMpD82rFq1ii6BVzj0p5quZssCAIAk+mGAqKamxufz2a4COoEORACAzMzM8PDwlJSU8vLy+vp6AwMDc3Nzc3Pz6dOnDx48mO3q4Pna2toePHgwcuRIpnOKENLc3BwSEtLS0rJv374FCxaMGzeOEFJZWTl69Oi6urrExEQ7OztmA5DvvvuOEOLo6Eiv0rGDlIODQ3p6urKy8ksvvXT16lVdXd2qqiq6iTDdscTZ2XnJkiUtLS3Kysp1dXW0we3dd9+dMWMGPYOjo2NoaOiNGze++eYbFRUVml2KsrGx2bdvX1tbG21CJIRs375dtP2wnbFjx9ILc+bMWbVq1YkTJ3744QdCyGeffXb+/PlffvnFwsLC1taWTiaZO3fu7t27lyxZcvDgwdzc3AsXLtAAcceOHbRZcvHixUePHj137lxra6uRkZGFhYVAINi1a1d1dbWamtp3333HrJWmXUJ8Pt/T0/N///sfIURZWbm5udnS0lJDQ8PPz49uNUMIUVNTy8zMNDMzE/Nfjc/nnz17VnSMoL+/f0xMDB3AZ2VlRf+T2djY0NXZ9B9nxIgRsbGxQqHQ2tpaT09v2bJlBw4cqKiooFMj7927Rwjh8XgmJiZ37txpa2sbPXr00KFDZ86cSZ9i+vTp//3vf9etW/fuu+8SQqKjow8ePLho0SLp8hcOhzNs2LBhw4ZJ8ViAnnBxcTl//jz9n8OmTZvobx4KB/uoAAD0XD8MEEFuoQMRAAay7Ozs1atXMztgtLNmzRo/P79du3bRLgmQWz/88MOaNWv27dtHUyEqOjqarvE8dOjQ4cOH9+zZk5mZeevWLfp+t2HDhosXLzIBIr3x9ddfp1dffvllY2Pj0tLSIUOGvPjii+np6d7e3mPGjLl69ernn3++bds2OlyFBls0VlNSUho5cmRcXBxdFMxsM0JNnTp10KBB33zzzbBhwzru+EFxuVwXF5d79+5ZW1szlXTKw8PD0NCQx+Pt2bPH0NBQQ0Pjhx9+sLa29vPzs7KyOn/+PB15RgPEqqqqgICAtra2yMhIQsiDBw9u37599OhROv2QEOLn55eXl0c7HydOnHju3Dl6+4kTJ9o974QJE06fPj169GglJaWCgoKpU6caGxsvXLiQebFOTk5035hhw4aJTw+pdi2Kc+bM+c9//jNu3LjU1NQPP/yQxsE2NjaEEGtr60mTJh05cmTu3Ll6enoRERE05N2/f/+rr746bdo0eoa6ujplZeXk5GRjY2OaCT5+/FhJSYlZpGJpaZmWlsY84+DBgwMDA59bJ4B8mjNnDpPaKyJ0IAIA9JxCBoihoaGXL19OT0+3s7NbuXKlh4dHuwM+/PDDnJwc5lMpyAl0IALAgFVeXu7r65uZmcnn8/38/FxcXAwMDLS1tWtqaioqKlJTU69cuXLhwoX4+PjIyEiM+pJPDx8+dHBwoJEQ3XmDio2NFd24QygUvv/++/Qyl8tVU1O7dOnSrVu3EhISCCH6+voVFRVcLpfZiVhNTW3VqlVffvmlt7f3Cy+88OOPP9Jxe15eXr6+vvHx8SdOnHB3d//nn3/IswCREBIRESEQCPbs2VNdXc2sw2V4e3tv2rTJx8dHzMvx9PS8d+/eihUrRFspO1JRUUlMTORyubR9z83N7dKlS3Z2djweb+TIkYWFhXRfCzql0czMrLCwkJlLGBsb+8EHH9BdQfT09ObOnTt58uSnT5/SAHHChAlinpfD4bz11lv08vXr1+mFyZMnM1nhoEGDbG1tMzIyhg8fLuY8XbG3ty8tLaXTDBm0LfTNN99cs2aNpaXlkiVLZs+e/ccff0yfPp0eMGXKFPoaaW/p7t27HRwcmIe3OxtAfzJq1Ch7e3sx3cpyDgEiAEDPKd57wMqVKw8ePEgvX79+/dChQ3v27HnvvfdEj4mIiKCfs0GuoAMRAAaszZs3Z2Zm7tix4+OPP+70gMDAwGPHjgUEBGzZsoV5mwN2CYXC06dPe3l52djY/PLLL2+//fbo0aPpjDk60JD68ssvacPd22+/Td/g7t69u2TJkjFjxpiYmBw/fvx///vfxIkT6cHbtm3bvHnz+PHjacse9cEHH3A4nDlz5jg6Oo4ZM2bo0KEcDofuCrJ37965c+eOHj3azs6uqqqKWcE6aNCgQYMGffXVV51WrqKisnPnTvGv7osvvqCD+Z7770DnBjKYNI0QwiwKnjdvnrKy8rRp0y5cuLB69Wq6NLi6uprZU3js2LEHDhwghPj7+9Ol2eIDxE616zR0cXGROkAkneV9q1atMjEx8ff319LS+uKLLwghenp6zKRFQgiPxwsJCUlPT6crygEGDh8fn/T0dLarkB4CRACAnlOwAPHMmTMHDx60tbUNCgpydXWNiYnZuHHj+++/b2NjQ1fQgDxDgAgAA9atW7ccHR27Sg+pxYsXnzx5kkZRIA9u3Ljx9ttvT5s27ffff9+9ezch5O+//6Z3iQaIdHHxTz/9RPfYFQqFTU1Nqqqq9F6BQEBH+FHe3t6FhYXtVhYzcRURmY1I6ejovPLKK4SQ/fv3h4SEtNsjpSdMTExWrlzZW2dTU1NbsGABIWTFihUtLS2ffPKJvr6+6LgxZi8XdXX1kydP9iT4Y6xevbqlpWXWrFk9PA9DQ0Nj/vz54o8ZPXr06NGje+sZAUA2zMzM1NTUSkpK6urqsNcQAIB0xC1akUP79+9XU1MLDw9/4403HB0d33777dDQUE1NzZUrVz59+pTt6uA5ECACwIBVVlYmyR4plpaWpaWlMqgHJEGnFiYkJERHR9M9QETvunfv3qxZs3Jzc+luwswPmRwOh0kPCSG+vr5WVlaic/HU1NTErxru1Lx5886fP6+trS31y5GZNWvW1NTU7N+/39jYWElJycLCghDi6enJHDBt2rR169b1/Il8fX0vX75Mzw8AIAaHw6HTHrCPCgCA1BQsQExNTR07diyds0O5u7vv27evuLj4m2++YbEwkAQCRAAYsLy9vaOiojIyMsQcU1paGhYW5u3tLbOqQDwa5hYUFISEhBBCvLy8mLsqKip2797966+/HjhwoK6uTk9PT1dXt9OT0H02/vrrL0KIpqamvr6+LEqXA6+99lpJSUlVVVVoaOjXX38tuvAZAED26Cpm+pMPAABIQcECxIaGBjpVR9TChQtHjRq1e/fuvLw8VqoCCSFABIABa926dS0tLV5eXnv37u3Y/lBUVHT48OFRo0aVlpYuWbKElQoHsubm5ubm5o630wBRKBSeOnWKENJuqSxdbE73A6G793ZFU1NzwoQJ69ev37JlSy+WrRA0NDRcXV0//fRT0a5MAADZwxhEAIAeUrAA0cHBITo6uqSkRPRGDofz/fffCwSCpUuXdowXQX4wAWJdXV1DQwO7xQAAyJKvr+++fftqamrWr19vbW2tq6tra2vr7u5ub2+vr69vZma2fPnywsLC4OBgf39/tosdWIRCoZubm4eHR8e7mOXkdC3ztGnTRGcXFhUVEULo3sriA0Tqv//974cfftgrNQMAQHfRP9ToQAQAkJqCBYjLli1rbGycOHHivXv3RLPCESNGfPjhhxEREQsWLMAwRLlFA0QlJSWCJkQAGHhWrVqVnJz80UcfeXh4CASC7Ozs+Pj4nJwcVVXVkSNHbt++PT8/f8WKFWyXOeBUVlampqYmJydXVla2u0t0HqWWlpaTk5Po1smUUCgkkgWIAADAIgSIAAA9pGC7MK9ZsyYhIeHHH3/08vJSVla+f/8+s4Xf1q1bs7OzT548GRISgj5E+UQDxMGDB+fn5z958sTS0pLtigAAZMrBwSEoKCgoKEgoFNbX1zc2Nurp6Umxnwb0ItpISAjJz8/X09MTvUs0QPTw8OByuZaWlp1++USACAAg5xAgAgD0kOJ9aQkODj527NikSZMsLCzoz/6UsrLyqVOn9u7da2Zm1tjYyGKF0BUaINL2DXQgAsBAxuFwNDQ0DAwMkB6yTjRAFL09Nze3sLCQEPLBBx84OzvT2ZR0w19jY2PRIzkczsSJE2VULgAASIXuw5mVlcV2IQAAikrxvrfweLxFixZFRERkZWW5u7uL3sXlcteuXZuenl5UVERHm4NcQYAIACCGUCgUCATMuFiQDSZAZLZiKy8vT0xMtLe3p3d9/fXXKSkpCxcuJITQ3vnXXntt0KBBzBmmT5/O5/NlXTcAAHSHoaGhlpZWdXV1VVUV27UAACgkBVvCLCFTU1NTU1O2q4B/aW1tbWlp4XK59D8NAkQAgHYSEhLoD2Oi/fUSOnHixL59+8Q/kG403HHSX1du3769adOmpqYm5hZjY+NTp07p6up2tzx5RtsMybMOxJKSEjs7Ow6H09LSQgjR1tZWU1NjDp4wYcK33347adKkoqKiP/74w93dvamp6euvv2alcgAA6BZra+vExMSsrCxPT0+2awEAUDz9M0AEOUR7alRVVY2MjAgCRACAXhUWFvbgwQNJjhQNBMWLioqKiopqd+PNmzf9/Py6V5x8Ky4uphdogBgeHl5XV8fc22618rRp0+jbmZqaWnx8/IEDB8aOHSvDYgEAQHq2trYIEAEApIYAEWSEfmVVVVU1NDQkCBABADpwdHRMSkqS7rFHjhz54IMPxB+TmZn51ltvST51cePGjVOmTGltbaVXd+3adfr06dzcXOkqlFvtOhBv3Lghem9XCxpmzpw5c+bMvq4NAAB6EcYgAgD0BAJEkBG6sw3TgVhWVsZ2RQAA8kVNTU3qUXrq6uojRowQf4yqqmq3zsnlckVnDbu7u58+fbrdTiP9ADMDMTIy0szM7OnTp8xdb7zxxvz581mqCwAAehkCRACAnkCACDLCLGGmHYgIEAEAFIuVlRUR2WlEQT148CA+Pn7JkiUcDofeQgNELpfb2trKhImEEFVV1V9++UVJCZ+UAAD6CQSIAAA9gY/FICPtZiAiQAQAUCz9I0Bcu3ZtdHS0u7s707BJQ8O//vpLRUVFV1f3xx9/VFZWDgoKcnV1RXoIANCfIEAEAOgJfDIGGcEmKgAACq1/BIgVFRWEkOzsbBogVlVV1dXVaWpqjh8/nh6we/duQsjw4cNdXV1ZrBMAAHqdjY0Nl8vNzc1taWnBT0QAAN0l6SR1gB4SXcLM4XCePHnS1tbGdlEAACApMzMzZWXl4uJiyfdxlkM1NTVEJAZ9/PgxIcTS0rLdYXPnznVxcZFxbQAA0KdUVVXNzc1bWloU/ccwAABW4IcXkBEmQFRSUtLV1a2srKyqqtLX12e7LgCAPldWVnb37l3Jj/fz8+u7YqTG4/HMzMxyc3MfP35MV4EpotraWtIhQDQ3N2ezJgAAkBVbW9v8/PysrCzFfSMDAGALAkSQBaFQeOPGDfJsD1AjI6PKysqysjIEiAAwECQnJ/v7+0t+vFAo7LtiesLKyio3NzcvL09Bv3e1tbXV1dURQnJzc+kthYWFBAEiAMCAYWdnd/PmzYyMjMmTJ7NdCwCAgkGACLJw/fr1zz//nDwLEA0NDdPS0srKyhwdHdkuDQCgz/n4+CQmJgYGBl64cIEQsmrVKh0dHbaLkgZd6qu4K7/q6upoOMsEiPn5+YQQCwsLNssCAABZsbOzI4RkZmayXQgAgOJBgAiyQIfWE5EORIJ9VABgIHFxcTl//vyIESNiY2M3bdo0ZMgQtiuSBi2bSd8UztOnT+kFJgMtKCgg6EAEABgwECACAEgNm6iALDQ3N9ML6urqhBBDQ0NCSFlZGZs1AQDI3Jw5c9guoUf6TYBYXl5OL9MZiOhABAAYIBAgAgBIDQEiyAINEG1sbFauXEmedSAiQASAgWbUqFH29vZKSora/t9vAkTy7FVgBiIAwIBib29PCMnKypLbccMAAHILASLIQlNTEyHE19d34sSJBEuYAWCg8vHxSU9PV9y4igaIijsDUTRATExMJFjCDAAwwOjq6hoYGNTW1paUlLBdCwCAgkGACLIgEAgIISoqKvQqDRBLS0vZrAkAALqJCRAVtHFDNEBMSEhoa2urrKzkcrl0sAYAAAwEtAkRq5gBALoLASLIAl3C3C5AxBJmAADFMmjQIENDw8bGRgVt3KitrSWEaGhoEELi4+OfPn3a2tqqpaXF4/HYLg0AQK4JBILo6OiYmBjmlqtXr/r7+3t7ewcEBFy/fp3F2rqLBogZGRlsFwIAoGAQIIIs0CXMTIBobGxMECACACgghR6DSDsQx4wZQwhJSEioqqoihOjq6rJcFgCAfDtz5oyRkZG3t/fIkSOnTJnS2NgYHBz8yiuvXLx4MTo6+tChQ76+vp9++inbZUoKHYgAANJBgAiy0C5AxBJmAAAFZW1tTQjJyclhuQ6p0ABx+PDhmpqaBQUFaWlphBA9PT226wIAkF/379+fO3dua2urn5/fuHHjwsPDFy5cuGHDBj6fHxoamp2dHRISYmtru3379r/++ovtYiVCN2JOT09nuxAAAAWjqBtBgmKhS5iVlZXpVaYDUSgUcjgcNisDAIDu6AcdiNra2l5eXhEREWfPniXoQAQAEOurr77icrm3b9/29PQkhHzyySc7d+7U0tKKiIgwNTUlhFhbW7u4uPD5/F27dvn4+LBcrgQcHBwIljADAHQfOhBBFmgHIhMgqqqqamlpNTU11dTUsFoXAAB0Tz/oQNTS0po+fTohhAaI6EAEABAjLi5u7NixND0khLz77ruEkJdeeommh5S9vf3o0aOTkpLYKbGb6BJmdCACAHQXOhD/JS8vLywsTPwxBQUFsimmP2m3iQohxNjY+OnTp2VlZTo6OuzVBQAA3dM/AsQpU6asX7+e/oiFDkQAADGqq6tFP67Tyx0/wOvp6SUkJMi0MmkZGhrq6elVVlaWlZXRwUoAACAJBIj/snbt2kuXLklyZFtbW18X05+0m4FICDEyMsrMzCwtLaW/AQIAgEKgAaKCLmGmuzBraWlZW1s7ODjQ9hN0IAIAiOHk5PTgwYPGxkY1NTVCyK1btwghDx48EJ1E1NzcHBsbO2zYMDYL7Q57e/v79+9nZGQgQAQAkBwCxH/54IMPRLvxO1VZWXnu3DlM7uuWdkuYybN9VLARMwCAYqEzEHNychRxiC1tOdTW1iaE2NraIkAEAHiuefPmvffee7NmzVq/fn1ZWdmmTZv09fVTUlK2bt26ZcsWDofT2tq6cePGgoKClStXsl2spBwcHO7fv5+Wlubt7c12LQAACgMB4r9MmDBhwoQJ4o9JSkpCgNhdnS5hJggQAQAUjba2tr6+fkVFRVlZGf1LrkBoB6KmpiYhxMbGht6IJcwAAGKsXr368uXLoaGhoaGhhBBNTc07d+68//77gYGBJ06cGDp0aHJycn5+voODw8aNG9kuVlJ0HxWMQQQA6BZsogKy0OkSZoIAEQBAASnuGMT6+npCyKBBg8izV0HQgQgAIJaysvLVq1cPHz68YMGCpUuXRkZGurm5hYSEzJo1Kzs7OywsrLi4ePbs2dHR0XSNs0JAgAgAIAV0IIIsdBUglpaWslYTAABIxdraOjY2NicnZ/To0WzX0j0IEAEApMDj8ZYuXbp06VLmFm1t7fPnz5eWlpaWlg4dOlT0Q75CGDp0KCEkLS2N7UIAABQJAkSQhY4zEOnCNwSIAAAKhy7+zc7OZruQbmtoaCAIEAEAeomxsbHCzbKgaICYnp6uiPN8AQDYgiXMIAtdzUBEgAgAoHAUN0CkHYjq6uoEMxABAHqPUCgUCAQCgYDtQiSlo6NjbGxcV1dXWFjIdi0AAAoDHYggCx2XMJuYmBAEiAAACkhxA0TagUgDRGNjY01NzdraWnQgAgD0UEJCgru7OyFEKBR297E3btw4ffq0+GMqKyulO7kYDg4OpaWlaWlp5ubmvXhaAIB+DAEiyELHABEdiAAACqp/BIiEkM8//zw3N9fU1JTVogAABrT//ve/ly5dkuTI3g0QHR0d79y5k5aW9uKLL/biaQEA+jEEiCALHWcgGhkZcbncsrKytrY2LhdL6QEAFIa1tTWHw8nNzVWsP+DNzc3Nzc1KSkrMr1kfffQRuyUBAPQPjo6OSUlJ0j32wIFAiYpgAAAgAElEQVQD06dPb2trE3NMQUHB1q1be/cdB/uoAAB0FwJEkIWOMxCVlJT09PTKy8vLy8vpjswAAKAQ1NXVTUxMiouLCwoKLC0t2S5HUu3aDwEAoLeoqanx+XzpHmthYbFs2TLxxyQlJW3dulW683eFBoiPHj3q3dMCAPRjCtM4AAqt4xJmglXMAAAKSxFXMYtuwQwAAAOco6MjQQciAEB3oAMRZKGrAPHhw4elpaVS/2IJAACssLW1jYqKysrKmjBhAtu1SEp0C2YAAOiuzMzM8PDwlJSU8vLy+vp6AwMDc3Nzc3Pz6dOnDx48mO3qus3Ozo7H42VnZzc1NbX7kgIAAJ1CgAiy0HEGIkEHIgCAwrK1tSXoQAQAGBiys7NXr14dFhbW6b1r1qzx8/PbtWuXtbW1bOvqEVVVVWtr68zMzMzMTGdnZ7bLAQBQAAgQQRY6zkAkCBABABQWXcKclZXFdiHdgA5EAAAplJeX+/r6ZmZm8vl8Pz8/FxcXAwMDbW3tmpqaioqK1NTUK1euXLhwIT4+PjIy0sTEhO16u8HJySkzMzM1NRUBIgCAJBAggix02oFIFzsUFBSwUxMAAEiLdiAqVoCIDkQAACls3rw5MzNzx44dH3/8cacHBAYGHjt2LCAgYMuWLQcPHpRxeT3h5OT0+++/p6amsl0IAIBiwCYqIAs0QFRVVRW9URFn8AMAAFHMP+DoQAQAkMKtW7ccHR27Sg+pxYsXT5w4MTIyUmZV9Qq6jwo2YgYAkBACRJCFTjsQFXEFHAAAEEIsLCxUVVWLi4tpKqcQaAciAkQAgG4pKyuTZI8US0tLhRtM5OTkRAhBByIAgIQQIIIsdDoDURFXwAEAACGEy+UOGTJEKBQq0N9wBIgAAFLw9vaOiorKyMgQc0xpaWlYWJi3t7fMquoVCBABALoFASL0gvz8/PT09K7ubWtra2lp4XA4Skr/mrlpbGysoaFRUVFRXV3d9zUCAEBvUrgfgWizJGYgAgB0y7p161paWry8vPbu3Zubm9vu3qKiosOHD48aNaq0tHTJkiWsVCg1IyMjAwOD6urqwsJCtmsBAFAACBChF/j4+Hh6ejY2Nna8q6ysbNSoUaRD+yEhhMPhYBUzAAwoAoEgOjo6JiaGueXq1av+/v7e3t4BAQHXr19nsbbusrOzI4RkZmayXYiksIkKAIAUfH199+3bV1NTs379emtra11dXVtbW3d3d3t7e319fTMzs+XLlxcWFgYHB/v7+7NdbLehCREAQHIIEKEXFBcX19bWFhUVdbwrISEhNjaWdBYgkmcNLIo1hh8AQDpnzpwxMjLy9vYeOXLklClTGhsbg4ODX3nllYsXL0ZHRx86dMjX1/fTTz9lu0xJKVyAiE1UAACks2rVquTk5I8++sjDw0MgEGRnZ8fHx+fk5Kiqqo4cOXL79u35+fkrVqxgu0xpODs7E0IePnzIdiEAAApA6fmHADwP3SOlpKSEdhSKEggE9EK7HVQoa2trQkhOTg692tjYmJeXN3To0D6rFACAHffv3587d666urqfn195eXl4ePjChQsvXrzI5/O/+eYbZ2fnf/75Z+PGjdu3b/f19fXx8WG73uejAaICtZCjAxEAQGoODg5BQUFBQUFCobC+vr6xsVFPT4/LVfhmFASIAACSU/g/+sA6oVDY0tJCCCkpKel4L7OuudMORDMzM0JIcXExvfruu+86OTndu3evr2oFAGDJV199xeVyb9++/dtvv92+ffvjjz8+e/asiopKRETE1KlTra2t/f39w8LCVFRUdu3axXaxElG4DkRsogIA0HMcDkdDQ8PAwKAfpIcEASIAQHco5N/9/jRDqh+gOywTqQJEExMT0Qfm5+cLhcI///yzTwoFAGBPXFzc2LFjPT096dV3332XEPLSSy+Zmpoyx9jb248ePTopKYmdErvJ1taWw+Hk5OS0trayXYtEsIQZAADaQYAIACA5xQsQ+9kMqX6gFwNEut4ZHYgA0P9UV1fr6OgwV+ll0VsoPT29yspKmVYmLXV1dTMzs6ampvz8fLZrkQg6EAEAoB0rKysNDY2ioiJFefMFAGCRggWIdIZUa2urn5/fuHHj6AypDRs28Pn80NDQ7OzskJAQW1vb7du3//XXX2wXO1DQAYikiwCRmYEoSYBITxUVFdUXdQIAsMjJyenBgwfMbyq3bt0ihDx48EAoFDLHNDc3x8bGDhs2jJ0Su4+uYs7IyGC7EInQDkTMQAQAAAaXy3V0dCRoQgQAkICCBYj9b4ZUPyBhB2Knm6h0GiCWlpbm5ub2RakAAGyZN29ecXHxrFmzrl27dvLkyYCAAH19/ZSUlK1bt9IMsbW1dePGjQUFBa+++irbxUrK3t6eKE6AiA5EAADoiM/nE0JSUlLYLgQAQN4pWIDY/2ZI9QPiA0TxHYjGxsYcDqesrKytrY2INDMmJyf3Sa0AACxZvXr1pEmTQkNDX3755Xfeeae6uvrGjRsvvfRSYGCgvb391KlTbWxs9u7d6+DgsHHjRraLlRQCRAAAUHR0DCICRACA51KwALH/zZDqB8QvYRY/A1FZWVlfX7+lpaW8vJyIpI2dngoAQHEpKytfvXr18OHDCxYsWLp0aWRkpJubW0hIyKxZs7Kzs8PCwoqLi2fPnh0dHa2mpsZ2sZJSrI2Y6VuMAv3zAgCADNDJIQgQAQCeS4ntArqHmSFFvwCIzpDicDj0GIWbIaXoetKBSAgxMTEpLy8vKSkxMjJissji4uI+qBQAgE08Hm/p0qVLly5lbtHW1j5//nxpaWlpaenQoUO7+jsptxSrA5G+W3U6TwMAAAYsuoQZ658AAJ5LwToQ++UMKUXHBIjV1dV0RL0oJkDs6jub6BhE8c2MAAD9krGxsYuLi8Klh+RZgJiZmUnHUMg5BIgAANCRra3toEGDCgoKqqur2a4FAECuKViA2C9nSCk6JkAkhGRnZ7e7ly5htrKymjdvXqcPpwEibTlEgAgAA5lQKBQIBMzvLvJPW1vbxMSkoaGhsLCQ7Vqer6WlhRCipKRgay8AAKBPcblcJycnoVCIVcwAAOIp2MdoOkPqp59+unXrlrKy8tq1a+kMqSVLlvz6669ZWVnKysqzZ8/+/vvvMeRIZkQDxKysLLoKgEG/CX/xxRcLFizo9OF0A5x2HYhYwgwAA1BCQoK7uzshhPbUd8udO3d+/vln8Z2AdDqwFCcXw97evqSkJCMjw8LCohdP2xfQgQgAAJ1ycXGJjY1NTk729vZmuxYAAPmlYAEi6Y8zpBQdk/oRQrKystrdSzsQxeS5okuYmb4bBIgAAN0SFBR0+fJlSY7s3QDRwcHhzp076enpPj4+vXjavoAAEQAAOoUxiAAAklC8ALErxsbGxsbGbFcxEEmyhFlVVbWrh9MORCxhBgBwdHRMSkqS7rHffffda6+9Jv6YgoKCrVu3crm9Ob3EwcGBEJKent6L5+wj9N0KS5gBAKAdGiBK/RYMADBA9MOP0UKhkOZQYkIr6EXtljC3u5c2FYrpQGQCxObmZqFQqKSkxOFwqqqqBAIB/gsCwICipqbWbgqE5GxsbAICAsQfk5SUtHXrVunO3xUFChDpDER0IAIAQDsuLi4EASIAwPP0wwCxJzOkYmJizp07J/6YsrIyKSvrp2hcO3jw4KKiop50IDKxr66ubkFBQUlJiZWVVR/WDQAAPaZAASKWMAMAQKesrKy0tbWLi4vLysqMjIzYLgcAQE71wwCxJwIDA69cuSLJkeIH1Q8otKfDycmpqKhIig5EZhdmGiCqqKiYmpoiQASA/iozMzM8PDwlJaW8vLy+vt7AwMDc3Nzc3Hz69OmDBw9mu7puc3Bw4HA4mZmZbW1tvbs4utdhF2YAAOgUh8Ph8/lRUVHJycnyP9IXAIAt/fBjdE9mSO3atWvcuHHijykpKfn222/l/GuSLNHgz9DQUFtbu6ampqqqSldXl7n3uZuoGBsb83i8J0+e1NXVEUJUVVVpT2JRUVGflw4AIEPZ2dmrV68OCwvr9N41a9b4+fnt2rXL2tpatnX1iIaGhpmZWUFBQV5enpxXjhmIAADQFVdX16ioqMTERASIAABd6Ycfo3syQ8rR0XHTpk3ij0lKSvr222+lO3+/xCwKMzU1rampKS4u7hggilnCzOPxDA0NS0pKHj9+TJ51IBJsxAwA/Ut5ebmvr29mZiafz/fz83NxcTEwMKC/u1RUVKSmpl65cuXChQvx8fGRkZG0NVtRDB06tKCgIC0tTSECRBUVFbYLAQAAuePq6koISUxMZLsQAAD51Q8DRJAx2oFIA8S0tLSioiInJyfm3ucuYSaEmJqalpSU5OXlEUJUVFToIj4EiADQn2zevDkzM3PHjh0ff/xxpwcEBgYeO3YsICBgy5YtBw8elHF5PTF06NAbN248evRoypQpbNciDmYgAgBAVxAgAgA8F9bhQk8xPR2ddg4+twORPNtHhQkQ0YEIAP3PrVu3HB0du0oPqcWLF0+cODEyMlJmVfWKoUOHEkXYRwUzEAEAoCs0QExKSsKkewCAriBAhJ5ipkq1C/4qKiroujYiWYCYn59PRAJEzEAEgP6krKxMkj1SLC0tS0tLZVBPL6IB4qNHj9guRByhUNjS0sLhcHg8Htu1AACA3NHX17ewsKitre24JyQAAFAIEKGnmA7EdkuP//nnn/T0dPoj3nOXMBNCcnNzCToQAaCf8vb2joqKysjIEHNMaWlpWFiYt7e3zKrqFY6OjoSQtLQ0tgsRh2k/5HA4bNcCAADyyM3NjRASHx/PdiEAAHJKkRbylJWV3b17V/Lj/fz8+q4YYNAZiB2Dv+zsbOYYSQLEnJwcQoiqqipmIAJA/7Nu3bo//vjDy8try5Ytfn5+Q4YMEb23qKjo999//+qrr0pLS5csWcJWkdKxsbFRUVHJy8traGhQV1dnu5zOYQtmAAAQz83NLTQ0NDExcdasWWzXAgAgjxTpk3RycrK/v7/kxwuFwr4rBhiiuzATkeCPdhQSQjgcjvhdL2liyHQg0qtFRUVCoRCtIgDQP/j6+u7bt2/9Mzo6Ovr6+tra2rW1tRUVFZWVlYQQJSWl4ODgbr3TyQMlJSVbW9vU1NT09HTaviGHsIMKAACIhw5EAADxFClA9PHxSUxMDAwMvHDhAiFk1apVOjo6bBcF7WcgMrMLaUchIURFRUV8DkgTw6qqKnqwurq6trZ2TU1NVVWVnp5eX9YOACA7q1atmjx58uHDh8PDwx8+fEjbtHk8npGR0ciRI2fOnLl48WL6h1ThODo6pqamPnr0SG4DRLqEGQEiAAB0Zfjw4QQBIgBA1xQpQCSEuLi4nD9/fsSIEbGxsZs2bWq3BAxY0dUuzEyAKH79Mnm2hJmivYqDBw+uqakpLi5GgAgA/YmDg0NQUFBQUJBQKKyvr29sbNTT0+NyFX4esZOT08WLF1NTU9kupEvoQAQA6Inc3NyHDx++8sor9Gp+fv7evXvj4+NbW1vd3d2XLFnC5/PZrbDnHB0d1dXVc3JyqqqqdHV12S4HAEDuKOSXljlz5rBdAvx/zLcyIyMjHo9XXl5Ob2GWMIvfgpk860CkaICIjZgBoH/jcDgaGhoGBgb9ID0kz/ZRkeeNmDEDEQBAatu2bbO1tT1w4AC9evnyZT6fv2vXrvDw8D///HPPnj2enp7fffcdu0X2HI/H4/P5QqEwISGB7VoAAOSRQn5vGTVqlL29Pb4GyAlmExUej2dsbNzW1lZSUtLc3FxQUEAPeG4HopaWlqamJr1M00ZmDGIf1g0AAL1E/gNELGEGAJDOmTNnPvvsMw0NjRkzZhBCSkpKFi9e3NzcvHXr1vj4+PT09KNHjxoYGGzYsKFb213KJ3d3d4JVzAAAXVDIDM7Hxyc9PZ3tKuD/iLZ1mJmZFRUVFRUVNTU1tba20gOe24FICDE1Nc3IyCAiS5gJAkQAAAXh5ORECHn06JHcbn6FJcwAANLZs2ePpqZmUlKSlZUVIeS3334rLy/fv3//mjVr6AH29vajRo0aMWLEzp07L126xGqxPYUxiAAAYihkByLIFWYGIhEJ/h4+fMgc8NwORCKyihkBIgCAwtHX1zcyMnr69GlhYSHbtXQOASIAgHRSUlLGjx9P00NCSGJiIiHkzTffFD3GxcXFy8srLi6Ohfp6Fe1A/Oeff9guBABAHiFAhJ4S/VbGBH8pKSnk2aI2SToQ2QoQt27d+s477zQ0NPT1EwEA9G+0CVH01yO5QpcwY/gJAEB3aWhoMHskkn9vfihKWVmZ/qVVaO7u7lwuNykpiX7BAQAAUQgQoac6DRCTk5MJIXPnztXV1R05cuRzT8IEiDKegXjw4MGTJ0+uXLmyr58IAKB/c3Z2JnIcIKIDEQBAOr6+vvHx8Tdv3qRXx40bRwi5ePGi6DG5ubnR0dFjxoxhob5epampaWdnJxAI5PbtDACARQgQoafoJir0WxmzezINEH19fYuKir7//vvnnqRdByI9j+ivnX1EIBAQQk6cOFFaWtrXzwUA0I/RDsTU1FS2C+kcAkQAAOls377dzMzs1Vdf/eqrr/Ly8nx8fNauXbtx48aTJ08KBILW1tbbt29PmzatoaFh2bJlbBfbCzw9PQkh/WA5NgBAr0OACD0lOgPRzMyMEFJQUEB/tRs2bJgkAxBJhwCRnkcGs7Ro+kkIYfaMBgAAKdAORLkNELGEGQBAOpaWlmFhYXp6elu2bBkyZIiWllZoaGhNTc0777yjqampqak5YcKEtLS04ODg1157je1ie4GHhwdBgAgA0BkEiNBTom0dtHPw3r17dXV15ubmurq6Ep6EJobkWYCop6enpqZWU1NTX1/fJ0U/wwSIcjv4HwBAIWAJMwBAf8Xn87Oysk6cOPHCCy9wOJzMzEx6e1tbm7m5+aZNm7Kzs1esWMFukb0FASIAQFfwUzz0VMcZiE+ePCGEDB06VPKTtJuBSG/Jzs4uLCy0t7fv3YJFMQEidnwGAOgJS0tLDQ2NoqKiiooKfX19tstpDwEiAEBPKCsrz58/f/78+YSQ+vr6+vp6JSUlTU3N/tfZTZcwx8bGtrW1cbnotgEA+P/wNxF6SnQGopmZmbq6Or1dugCRdiASQhwcHAghP//8c1hYWC9WK6q5uVkoFNLL6EAEAOgJLpcrz2MQaYDY/77oAgDI3qBBgwwNDXV1dfvlH1VDQ0NLS8va2tr09HS2awEAkC/98I8+yJjoDEQlJSU+n//gwQPyLAGUEF2z3NjYyASII0aMuHbt2pdffsnhcAoLC+niaEnk5+d//vnnGzZscHNzE38k035I0IEIANBjw4YNi4mJSUlJGTt2LNu1tEdnIKIDEQCgLwiFQvq5mllLJLmHDx9eunRJ/DElJSVSViaVESNG5Ofnx8TEODo6yvJ5AQDkHAJE6Kl268KGDx8uRYDI4XDommUmQBw5ciS9IBQK8/LyJA8Qz549e/z4cQ0NjQMHDog/EgEiAEAvkucxiFjCDADQdxISEtzd3QkhzOIeyX300UdXrlyR5Mi2trZuVyYVT0/P3377LTY29u2335bNMwIAKAQEiNBTNIZjgr/hw4fTC91awkyeDT1kfrdkAkTSzfXFxcXFRLJdlUUDRCxhBgDooWHDhhFCUlJS2C6kE1jCDAAgn7Zu3crn88UfU1ZWdvToUZlNJBwxYgQhJCYmRjZPBwCgKPBJGnqq3bcyGiDyeDwbG5tuncfGxubu3bvMxs1WVlYmJiZ0wUK30r3S0lIJH0IDRB6P19raig5EAIAeogFicnIy24V0AkuYAQD6jqOjY1JSknSP9fDwoBsfi5GUlHT06FHpzi+FUaNGEUJiYmKwjwoAgCj8QYSeEp2BSAjx8PDQ1tb28PDo7gyUXbt2nT59esqUKcwtu3fvpqshupXudTdAHDx4MIfDKS4ultmyCACAfsnW1lZdXf3x48fV1dVs19IeljADAPQdNTU1Pp//3EZCRWFkZGRpafn06dO0tDS2awEAkCMIEKGn2n0r09LSevTo0fXr17t7HlNT07feekv0V7558+atXr2aSNWBWFJS0traKv5IGiBqamoaGBi0tLTQBwIAgHR4PJ6Tk5NQKJTDjZgRIAIAgORoE+L9+/fZLgQAQI5gCTNIr7y8/MmTJx2/lUm+4clzmZmZEakCxJaWlrKyMvGVMNMbzczMnjx50q29ngEAoCM+nx8XF5ecnDxmzBi2a/kXuoQZMxABAKSWmZkZHh6ekpJSXl5eX19vYGBgbm5ubm4+ffr0wYMHs11dLxsxYsSvv/4aExMzf/58tmsBAJAX+CQN0nv99dcfPHhAewb7qK1DigCxrKyMXnhuIMgEiEZGRgkJCYWFhZ6enj0oFgBgoKPr1+RwDCI6EAEApJadnb169eqwsLBO712zZo2fn9+uXbusra1lW1cfoh2If//9N9uFAADIEQSIIL2srCxmI2MdHZ2+eIruBohVVVUCgYBefm4gKNqB2K1nAQCATtEAUepR+n0HASIAgHTKy8t9fX0zMzP5fL6fn5+Li4uBgYG2tnZNTU1FRUVqauqVK1cuXLgQHx8fGRlpYmLCdr29Y9SoURwOJy4urrm5Ge8dAAAUAkSQXlVVFb2gr6+vrq7eF09hZGSkrKxcXl4uEAieuyvLw4cP/fz8mKvPDQQRIAIA9C4XFxcilx2IWMIMACCdzZs3Z2Zm7tix4+OPP+70gMDAwGPHjgUEBGzZsuXgwYMyLq+P6OrqOjg4pKWlJSYmYokSAACFTVRASgKBoLGxkV42Nzfvo2fhcrkmJiZCoVCSjZjv3r2bnp7OXO1ugFhQUNCzYgEABjpra2stLa2CgoLy8nK2a/kXdCACAEjn1q1bjo6OXaWH1OLFiydOnBgZGSmzqmRg9OjRBKuYAQBEIEAEKTHth6QvA0Tm5JKke0ygST33IUyASJ8CHYgAAD3E4XCcnZ2J/DUh0gARHYgAAN1VVlYmyR4plpaWdCfDfgMbMQMAtIMAEaRUXV3NXLawsOi7J5IiQLS0tJTkIfT7pOgS5j///DMqKqqHBQMADGSurq5E/sYg0iXM6EAEAOgub2/vqKiojIwMMceUlpaGhYV5e3vLrCoZGDNmDEEHIgCACASIII07d+58++23zFUZdCBK0h5IA8Tx48d/9913kjyk3RLmrKysl19+edq0aV0d39raevny5crKyu6UDwAwsNAxiImJiWwX8i9YwgwAIJ1169a1tLR4eXnt3bs3Nze33b1FRUWHDx8eNWpUaWnpkiVLWKmwj7i7u6uqqqakpNTU1LBdCwCAXECACNL44IMPfvjhB+ZqnwaIkg8opAHilClTxo4dK8lDmADR2NhYSUmppqampaWlqqqqq4jw119/ff311z/99NPuvgQAgIGDdiDKW4CITVQAAKTj6+u7b9++mpqa9evXW1tb6+rq2trauru729vb6+vrm5mZLV++vLCwMDg42N/fn+1ie5Oqqqq7u3tbWxtWMQMAUPgk/S+///778ePHxR9Dl+4KhUKZVCSnRNcvE7lZwtzQ0EAIUVNTMzIyUlFRKS8vb2xsVFNT6+p4gUBACFFVVeXxeKampo8fP6a3FxYW6unpdTy+uLiYEJKamirV6wAAGBCYJcxCoZDD4bBdzv9BByIAgNRWrVo1efLkw4cPh4eHP3z4MDs7mxDC4/GMjIxGjhw5c+bMxYsXm5qasl1m7/Py8rp37969e/cmTZrEdi0AAOxDgPgvR44cCQkJkeTIAR4g1tfXi16Vq01U1NTUuFyuqalpXl5eYWGhra1tV8czHYj0WZgAsaCggM/ndzy+rq6OEJKVlSXNywAAICQ3N/fhw4evvPIKvZqfn7937974+PjW1lZ3d/clS5Z0+sdHsRgbG5uamhYXF+fm5lpbW7Ndzv9BgAgA0BMODg5BQUFBQUFCobC+vr6xsVFPT4/L7eer2egYxOjoaLYLAQCQCwgQ/+XHH398++23xYeD+fn5H3zwQb9/vxSPCRDpb4/29vZ991zdnYFIWw7Nzc3z8vIKCgrEBIii3ydFM9B2YWV9fb26ujqHw6EB4uPHj5uammjsCAAguW3btm3ZsmXatGk0QLx8+fK8efOePn1K7/3zzz/379//n//8Z/369ayW2Qvc3NyKi4sTEhIQIAIA9DMcDkdDQ0NDQ4PtQmSBbgtz9+5dueqpBwBgCwLEfzE0NHzjjTfEHyNvO0uygkZphJDPPvvsk08+UVVV7bvn6u4MRCZAJIT89NNPrq6uurq6nR7frgORuV30uTIzM11dXRctWhQcHFxbW0sIaW1tzc3NdXBw6MFrAoAB58yZM5999pmWltaMGTMIISUlJYsXL25ubt66daufn9+gQYNu37796aefbtiwYdSoUXSQq+Jyc3O7du1aQkLC66+/znYt/4cGiJiBCAAAkrO2tjYzMyssLMzIyMCHfwCAAd1GB9Jpa2ujUR0hRF9fv0/TQ0KIlpaWtrZ2Q0NDRUUFISQiIuLvv//u9MiOAeLRo0eDg4O7OrMkAWJ6enpDQwOdnUwDRIJVzADQfXv27NHU1ExKSqKbVP7222/l5eW7du36/PPP3dzc7O3tFy9efO3aNSUlpZ07d7JdbE/J4T4qdBMVdCACAEC30FXMUVFRbBcCAMA+BIjQbQ0NDcwq766a+3oX3aSloKCgpqbmlVde6aqlRTRAHDt2LF1oIKZjVJIAkW7M8uTJE4IAEQB6ICUlZfz48VZWVvQqDdfefPNN0WNcXFy8vLzi4uJYqK9Xubm5EUISEhLYLuT/wy7MAAAgBbomAAEiAABBgAhSYNYvE1kFiDTde/z4cXFxcWtra0lJSbtdXCjRAHH27Nl//vknIeThw4ddnbarADE2NvaP/8fefcc1de//A/8kIRKmCIgIIsgQFRRQseKoo8Vq1aq97uu4jiW1Kt4AACAASURBVFpX9dZfta1dtP1Wr1V7W7GuarW2rqqtdeJqXXVURUGGqCxBluBgh5XfH+/LeZxmESDJIeH1/KOPGE5yPqGIJ6+835/3iRN0m070+PFjxgsQafYcAIDubGxsaJI70TStUiqVUtRl0jp37iyVSu/fv6/2d7Ug6Bc+KhABAKBe+vbtyxi7fPmy0AsBABAeAkSoN/4bwpYtWxrhjFwFIlUCMsa4icl8/ACRMda9e3eRSJSUlFRdXa32abVUIM6ePZtu04stKSkpLS3lAsTnz5/r4VUBQHMSHh4eExNz/vx5+mO/fv0YY7/99hv/mPT09KtXr1K3lEmztLTs1KlTdXV10+lizsvLY4w5OzsLvRAAADAl3bt3l8lkcXFxuP4HAECACPXGDxCN82aMq0DkAkSlmSq5ublVVVVKAaK9vb27u3tZWZmmgkF+gNiuXTuxWGxhYbF06VILC4ucnBz6KrUwM8by8/O50kvajB8AQHcrVqxwc3MbPnz4559//vDhw4EDB7711lvvvPPOrl275HJ5dXX1xYsXX3311bKyMu4DDJMWFBTEGIuJiRF6If9D/2rwPysCAACok6WlZY8ePWpqaq5evSr0WgAABIYAEeqNcjQ3N7e1a9d26dLFCGekt3yaKhBjY2Pd3d0XLFhAAaKVlRX3JVqepi5mfoBobW29cuXKL7/88ssvv2zbtm1NTU12djbjpaWPHz/mKhARIAJAfXl4eERFRbVq1erjjz/29PS0s7M7fvx4YWHhlClTbG1tbW1tX3zxxXv37m3YsGHEiBFCL1YPmlSASHtfiMViTZ3jAAAAmlAX859//in0QgAABIYAEdS4evVqly5d1qxZU1NTo/pVytR8fX2XLFlCg0oMjVqYNVUgxsXFVVdX//XXX0oViIyxTp06Mcbu3bun9mn5ASJjbNmyZW+//Tb/dIxXgYgAEQAaKSAgICUlZefOnX379hWJRMnJyXR/TU2Nu7v7u+++m5qa+uabbwq7SH0JDg5mTSZApCp1Z2dn7hc+AACAjihAvHjxotALAQAQGMYRghpRUVGJiYlLly61traeP3++0lcpQLS2tjbaerRXINJcgpSUFDs7O/b3ANHT05Np2DCRqQSIHH6AiApEANAjqVQ6derUqVOnMsZKS0tLS0stLCxsbW3NbzpwSEgIYywmJqampkYsFvjTSvQvAwBAg/Xr108sFl+7dq2iogIfRAFAc4YKRFAjNzeXbqjd/55amG1sbIy2Hi7Ro2nI7O+ZIPUaFxYW0h75/ACRHwWqqleAmJ+fzwWIZjAjtZEqKiqmTp0aGRkp9EIATJi1tbWzs7ODg4P5pYeMMScnJw8Pj+LiYq7QUkAIEAEAoMEcHR07d+5cVlZ28+ZNodcCACAkM3zTAo3HBYhqozfjVyA6OzvLZLKnT58+fPiQ7uG3MFMFIqstDFQNEDMyMtQ+raYAkZvZsm7dOu5CITMzkys8RAViYmLiTz/9tGvXLk9Pz9dee03o5QCYCYVCQb+XLC0t6/vYzMzMqKgotftOcJTGTxlaSEhIRkbGrVu3/Pz8jHleVQgQAQCgMfr37x8fH3/hwoWwsDCh1wIAIBgEiKAGFyCqjd6MHyCKRKJ27do9ePCA205LbYBI9FWBePz4cf7miWlpadxtBIj0M6BQKFavXo0AEUBfYmNjaetAhUJR38cuWLDg8OHDuhypPWTUo+Dg4MOHD9++fXv8+PHGOaMmWVlZDAEiAAA0VP/+/Tdt2nThwoV3331X6LUAAAgGASKoQb3AjBe9/fTTTytXrvzll1/8/f2N38LMGKMAsaCggP6Ym5tbWVkplUrZ3wNEqVQqkUi4P7q5uUkkkpycnKqqKtUmQe0B4oMHD/h3pqamcrcRIHKd3c+ePRN2JQBA/v3vf9c5Yvjp06f79+83zvArVrsN4q1bt4xzOi0oQHRzcxN6IQAAYJIGDBjAGPvzzz+rq6v57zUAAJoVBIigBlUgSiSSgoKCsrIyKyurdevWJSQk/PHHH/7+/savQGS1oR6xt7cvLCx89OiRl5cX+3uAyC8/ZIxZWFi0adMmKysrOzvbw8ND6Tk1BYj0JlOpSIfrnmbGChCPHTs2d+7cPXv29OvXzwinqxduODVNvgYAvfD394+Li2vYYwcNGjRo0CDtx8TFxRk/QIyOjjbO6bQoKipijDk4OAi9EAAAMEnu7u7e3t4pKSkxMTHdu3cXejkAAMLAEBX4n4SEBEdHx2+++aa0tLSoqEgmk1E8l5mZ+fTpU3oHSB3NggSIXPwnlUoDAgJYbXVkRUUFV5bIVAJEprWLWVOA6OzsrHownYVmiRpniMqpU6cyMzMvXLhghHPVF1eByCWJANB4MpksICCAfsWZAU9Pz9atW+fl5WnaiNZoqqurGWOoGQEAgAajIsTz588LvRAAAMEgQIT/iY6Ofvr06YkTJ6j8sE2bNlz0du7cOXr3RW8ChWphphsdO3akMJEywdzcXIVCwRXU6CVAtLOz0zTBwN7enhmrAvH58+eMMblcboRz1RcqEAFAF02kCJEqyukTIAAAgAYYOHAgQ4AIAM0bWpibr4KCgsDAwJEjR27ZsoXVRkIZGRmqAeL169fpIRTDCdvCHBYW1rJlS1abZlL/so+PD21ZqBogUh1lcnKy6nNqChAZY05OTrRnlhIHB4dnz54ZJ0Ck7QWbZkLHVSA2zeUBNGXJycmnT59OSEgoKCgoLS11cnJyd3d3d3cfOXJk27ZthV6dnnXv3v3UqVPR0dGjRo0ScBkIEAEAoJFon5Dz589jG0QAaLYQIDZfDx48yMnJOXXqFP2RAsTMzEylADEjI4ObfdwUWpj79OlDpXmUZubn5zPGfH198/LyCgsLVQPEwMBAxlhsbKzqc9ILUX0I0xwgOjk5paWlGTNARAUigNlITU2dP39+VFSU2q8uWLBg1KhRa9asoY89zANtFHXz5k1hl0FTrY22+SMAAJgfDw8PbIMIAM0cAsTmizaVz8rKoo/RKBIqLCy8d+8eY6xNmzaU2WVkZMTHx9NDMjMzFQqFsC3Mffr0oSEDlGY+ffqUMebo6Ojh4REfH6+aBnbr1o2pCxArKioyMzMtLCzc3d1VT6e0DaKVlRV9fzw9PW/evGmgAPHhw4cLFixYvHjxyy+/zJp2gMhVIFZXV3PjsAFAi4KCgvDw8OTk5ICAgFGjRgUGBjo5OdFIqCdPnty9e/fo0aMHDx6MiYm5dOlSmzZthF6vfvTo0YMxduPGDWGXgT0QAQCg8QYNGpSSkvLHH38gQASA5gkBYvNVXFzMGKusrMzJyXF3d+dqym7dusUYa926NQWIf/31V0FBgaOjo0gkKigoyM/PF6QC0dnZuWPHjhKJpGPHjpSsUQUiBYitWrXSFCB26dJFIpEkJSXJ5XL+zobJycnV1dW+vr6aWpj5f2zXrt39+/cZYx06dGAGG6Lyyy+/HD16VCKRNP0AkT87paysDAEiQJ2WL1+enJy8cuXK9957T+0BERER27dvnzNnzscff7x582YjL89AOnTo4OTklJubm5mZyX0OZHxoYQYAgMYbNGjQtm3bfv/99//3//6f0GsBABAALqabncrKyh49ekyZMoUqEFltKR8XCVHDspOTEwWIlCcGBgZyHc2CVCCKRKI7d+7ExMSIRCJuJezvASJT149sZWXVsWPHqqqqhIQE/v1UaNmxY0e1p1OqQOSqFClANFAF4t27dxlv2oBJ7IHImuoKAZqaCxcu+Pv7a0oPyYwZMwYMGHDp0iWjrcrQRCIRlWkIW4SIABEAABpv8ODBIpHo4sWLxtnOCACgqcHFdLOTm5sbHR198ODBwsJCuoe/syFjLCkpiTHm5OTUvn17Vrt1VEBAANfRTA+kecTG1KJFC6p0a9u2rVQqzc3NlcvldQaIjLGuXbsyxrhGbEIvU1OAyK9AlMlkrVu3ptu0N5mBLhoSExMZYxkZGY8fP66pqaGEt2lWICJABKivx48f6zIjxcPDIy8vzwjrMZqePXsyobdBRIAIAACN17Zt206dOhUVFXETJgEAmhXzuZieNm2alZWV0KswATR6uLy8nErwmEoFIu0V5eTk5OTkxPUpBwcHU54oYIDIEYvF7u7uCoUiMzOTCxBpeWobq318fBhjaWlp/Du1VyDyA0QrKytHR0e6bYQKRMZYdHR0YWEhveNtmgEiv4UZASKALsLCwq5cuULz4jXJy8uLiooKCwsz2qqMgLZBFPa9FgJEAADQi5deeokxdvbsWaEXAgAgAPO5mK6srESQoQsukFKarcyPhFhtgsbNPu7fv7/gFYh8XJrJBYhDhw4dPXr0jBkzVA/mVs6/kwJEf39/tc/Pb2G2tram74aFhQU9lSECxCdPnnBlR9HR0dS/zJpqPIcKRID6WrRoUVVVVe/evdetW5eenq701ezs7K1bt4aGhubl5c2cOVOQFRpIaGgoY+z69etUzy4IBIgAAIbQDAs4KEA8ffq00AsBABCAKQ1R+fbbbw8cOKDpq7TD3aBBg7h7/vjjD2Msy9RQBSJj7M6dO3RDe4CYlJTk7u7euXNn2psvPT29qKhIJBLZ2dkZdd1/R0Hew4cPuQCxTZs2v/76q9qDKW18+PAh/86cnBzG29xQidoKRDc3NxrDYoghKlz5IWMsJSWFCxCbfgWi0k8OAKgVHh4eGRm5uFbLli0dHR3t7e2Li4ufPHlCv8osLCw2bNgwevRooRerT+3bt2/btm12dnZKSgrVgxsfAkQAAENohgUcgwcPlkqlV65cKSwsFLacAgDA+EwpQCwrKzt37pz2Y+o8ALhA6smTJ3SjzgpEGgpMMVxiYmJNTY2NjY1EIjHiqpVxRYVcgKjlYK5ckX8n1dBpGgWjVIFIAWK7du1oE8bGVyAeOHCgQ4cO1NlHUlJSGGNSqbSysrKwsLCJB4j03ROJRAqForldOAI02Lx5815++eWtW7eePn06MTExNTWVMSaRSFq3bt2zZ8/XX399xowZrq6uQi9T/3r06HH06NHr168jQAQAMC0o4FBib28fGhp6+fLlCxcujBgxQujlAAAYlSkFiO+8806bNm0WLlyoUCi+/vrrV155hf/V+fPnHz58ODMzU6jlmQquApFDpXn8plSxWEyR3ODBg3fv3j1x4kRWm9nR7BHBP3CrV4DIlSvy79Q+S9rPz8/Z2dnBweHBgwe2tra09WGnTp1EIpGFhUVVVVVVVZWFRR1/ff773//GxsZu3bpVKWy9ffv2+PHj/f39aWoKef78OWPM3d09LS2t6QeIFDfb29s/f/4cASKA7vz8/FatWrVq1SqFQlFaWlpeXt6qVSuzD7Z69epFASL9a2J8CBABABoGBRyqwsPDL1++fOrUKQSIANDcmNjF9NSpU2/fvt2tW7fZs2evWrXK0dHRvRZNz3DnEXqxTZRqIEXjjPkViC1btqTMa8qUKeXl5UOHDmWMubu7SyQSerhpBYgODg729vZFRUV0MNEeILZq1SozMzM2Nnb+/PnLli0bOHDg5cuX//vf/zLGdC9CXLdu3Y4dO6jIiO/HH39UKBRKm6BRgEivq7CwkBtx0zTjOYqbqTAzNjbW3t4+IiJC4DUBmBSRSGRjY+Pk5NQcUq1evXoxxv766y+hFoAAEQCgYd55552dO3fa29vb2dlt27Yt8+9ee+01xhj/HqHXawxDhgxhjJ06dUrohQAAGJvpXUx36NDh/Pnzn3766caNG7t3737z5k2hV2RiVCsQFQpFRkYGBYgymYz9fQdAjlQq5XrrBA8QqSv5wYMHxcXFLVq0sLW11eV4rou5oqKisrJSKpW2aNFC00MsLS2trKy+/fbbUaNGMcbCwsLoVVPhoS4BIqVslFRyqqur9+zZwxgrKyvjusgZYzSahgLEq1evvvvuu3R/U65ApADxxo0bRUVF165dE3pRANBEvfDCCyKR6ObNmwYaYV8nBIgAAA2GAg4lL7zwgqOjY1JSkmqVAACAeTPJi2mJRPLRRx9dunSpsrIyLCzsiy++qK6uFnpRJkMpQKRQ7OHDhxQJ+fr6Mg0BIquN4VhTChAZYw4ODnUer9TFTNEeXfTUF1UgJiQk1DlRlL6l/N7wHTt2rF27Njs7m/746NEj7kv8CkR6r0uaZoBIL4oKPwsKClhTXScANAUODg5+fn5lZWVxcXGCLAABIgBAY6CAg08ikdC2jyhCBIDmxoQvpl944YXbt29PmTLlww8/7N+/v9KIDNBEKejp3Lkz4wWI/v7+THOASPEWawIBYqtWrezs7Og9ofb+ZcINYv7mm2969uxJHxhq6l/WjgLEsLCwjz/+WPuRSgHi06dPZ86cyZUWMsaysrK421SB2K5dO+6efv36saYazPErEKmOsmmuEwCaiBdeeIExJlSpMgJEAIBGQgEHH+3vFBUVJfRCAACMyrQvpm1tbb///vv9+/cnJSX9+eefQi/HNChVIAYEBDCdA0RPT0+6IXiAyHjlkJpWq3rww4cP9+/ff/PmzdOnT7PGBYiMsdWrV3M7FaqqrKysqqpivADx7t27SkWLqhWI7dq1E4lEdM+cOXOYKeyBiApEAKgTBYhXr14V5OwIEAEA9AIFHGTo0KEikejMmTO4AAaAZsUcLqbHjh0bGxu7fv369evXC70WE6D071yXLl0YY9weiKNHj+7cuTPtiKyqSQWI3GI6depU58FcgEhzVGj8cSMDRLlcvmvXLk2HcUNpuD0QaYA1H78CkQJER0dHblW0j0wTvC6prq6Wy+VisZiax1GBCAB16t27N0OACABg+lDAwRhr165d165di4uLm+13AACaJwuhF6Af7u7uCxYsEHoVpoFfgSgWi6mFOTU1tby8XCwW9+zZMyEhQdNjvby86EZTCBC5CsSgoKA6D6a0MT09/dmzZ0xPASI9oabDuACRq0Dklyu2atXq6dOn/ApEamFu2bKlvb19cXExY6x169ZSqbSyspLmvTRgqQZCL83KyopG7tBqESACgBZBQUE2Njb37t3Lz893dnY28tkRIAIA6NfYsWPDwsIOHTok9EIEM2zYsNjY2OPHjw8ePFjotQAAGIkZXkwrFAq5XI44QxMKEK2srBhjNjY2lAlScZxMJuP6Z9VqUhWI9QoQ1VYgNmyICk1hJloaN1QDRH4FYkhICFNXgWhvb899b52cnCwtLVnT62Kml2ZtbU0BIsHfOADQwsLCokePHgqF4q+//jL+2REgAgDoHRVwNNsajldffZUxdvToUaEXAgBgPGZSgcgXGxsbHBzMGKtzSK6q7777bvPmzdqPofSkAU/eRFDQ4+PjExcXZ2dnR5kglcJRqqhFEwwQRSJRt27d6jzY3d3dwsIiKyuL3kZSuV/jKxAbEyD+/vvvmioQ6R4nJyeZTFZcXCyXy+3s7BqwVAOhV2RlZcX/gUGACADa9e7d+8KFC1euXKE3XcaEABEAwKAUCgXVKNCH3/WSl5d3/vx57cc0wc0W+/bt6+TklJSU9ODBA19fX6GXAwBgDGYYIDbGxYsXb968qcuRphsg0r/u/v7+cXFxDg4ONjY2zs7O+fn5TIeKPDs7O0dHxydPnjSFAJHSzPbt2+syhdnCwsLNze3hw4f8OxsfIGZmZioUCrVlm0oBYk1NzYMHD7ivUsadm5tLf1QoFBQgchWIdnZ2lpaWdBHW1LI56lm2s7NDBSIA6K5v376MscuXLxv/1AgQAQAMqjEFHHPnzv311191OZJ+mTcREokkPDx87969x44dW7x4sdDLAQAwBjMMECkaa9hjt27dumTJkurqai3HJCcnT5gwwXTfh1DQExQU1L9/f5qg0r59ewoQ66xAZIx5eno2kQCxR48eI0eOfOWVV3Q8vn379noPEMvKyvLz81u3bq16GFd4SDeePn3Kj9i6du3KGMvLy6P8sbi4uLq62sbGxsLComXLlqx2tHTTbGGmscuOjo4IEAFAd3369BGJRNeuXauqquLvBWEECBABAJqsN954o85/FJ4/f37q1CntWy0Z38iRI/fu3XvkyBEEiADQTJhhgCiTyQICAhr22BYtWtCnZ1o0oDK/SeH6C7h/6jw9PaOjo5luAeKQIUNSUlIo/xKWlZXV4cOHdT/e09Pz0qVL/HsauQeiSCRSKBQZGRlqA0SlCkQK3Tjt27d3cHB49uzZ06dPHR0duf5lVtsezg8Qm1o2R2OXlQJE/nAeQ3jw4MHYsWOXL18+fvx4g54IAAzE2dnZ19f3/v37MTExPXr0MOapESACABhUYwo4hg0bNmzYMO3HxMXFde3atakFiMOHD5dKpRcuXHj27JmDg4PQywEAMDhcTDc7lEa1aNGCu4fb2VCXAPE///nPs2fPuIeYENU1N7IC0d3dnWnek0VtgEizR1u0aOHg4NCmTRtW28XMTVBhfw8QKaFrmgGik5MT/wemurq6srLScCc9f/58TEzMwYMHDXcKADA06mL+888/jXxeBIgAAAZFBRwNruEwUS1btuzXr19lZWVUVJTQawEAMAZcTDc7qjsc1ytANF16DxD9/f0ZY5mZmWoPUxsgdu/e3cPDg/r4XFxcGGN5eXmsNkA06QpEZuAixGfPnnH/BQAThQARAADMyahRoxhjhw4dEnohAADGYIYtzKAdpTxqKxAb1tJrKry8vJTuaWSA2K5dO6Y50lIbILq6uh45ckQikTDG+BWI/BZmmglD8WLTDBC5PRCVfmDkcnnDvqW6oIyV/gsAJqpfv36MMaXdJIwAASIAQOMlJyefPn06ISGhoKCgtLTUycnJ3d3d3d195MiRbdu2FXp1whg9evTbb7997Nix8vJypU/WAQDMDwJEs3Xo0KGbN29GRERQXMVRbWEeOHDg0KFDY2Njhw8fbuxVGhGXk3p4eFDfccMCUy5ApLxPU98uFyCWlJQwXgsz953XFCCOHTs2NjZ21qxZrKkOUeFamJU2fzRo0EnRISoQAUyav7+/i4tLVlZWampqhw4djHZemo2GABEAoGFSU1Pnz5+vqVF3wYIFo0aNWrNmjeqn9WbP09MzODj41q1bv//++6uvvir0cgAADMuUAsTHjx9fvnxZ9+OppLzZ+uijj+Li4kaPHq20V71qC3OrVq1OnDhh7PUZXfv27WnsSXBwMAWIjaxApM2SNfXtqq1ApMZkwg8QKRejJ/Tw8Ni+fTsd05T3QHR0dHR1deXfb4QAERWIACZNJBL16dPn0KFDFy9eNGaASBWISh+nAQCALgoKCsLDw5OTkwMCAkaNGhUYGOjk5GRvb19YWPjkyZO7d+8ePXr04MGDMTExly5douvbZmX06NG3bt365ZdfECACgNkzpQAxPj5+9OjRuh+vUCgMt5imjxKrtLQ0pQCRUh5TnyXdAFZWVi4uLrm5uSEhIUeOHGGM2draNuB5aAqzRCKhh1dVVak9jAsQ6cbjx49Z7RAVwg8QuUhO6UmaZgtzfn4+Y4yuHa2trSkhZQbeA/Hp06cMFYgApq9fv34UIE6bNs1oJ6XrgaY2vhMAwCQsX748OTl55cqV7733ntoDIiIitm/fPmfOnI8//njz5s1GXp7g/vGPf3zyySeHDh3atGkTvU0AADBXpvQ7buDAgXfu3ImIiKBJrPPmzaOWT+B79OjR9evXR48eXVRUxBhLTU1VOkB1D8Tmw8vLKzc3NyAgQCqVVlZWNqaF2dramr6HmtI9pRZm1YhQdQqz6s9zg1uYd+7c6e3tTduN6R1lebRXY5s2bbifMYO2WlOXd3l5uVwub4bxN4DZ6N+/P2Ps4sWLxjwptTCjAhEAoAEuXLjg7++vKT0kM2bM2LVrl/G3uG0KAgIC/P39k5KSLly4MHjwYKGXAwBgQKYUIDLGAgMDDxw40KNHj+jo6HfffVd1ri689dZbv/766x9//EG5VVpamtIBzTlAHDJkSEJCAo1CTklJaUwLMxcg1rkHoqYWZv4UZn4LM1/DWpjT09OnT5/u5+d37969ej1QR9weiIwxV1dXLkA0aKUkV3v47NmzZtgdA2A2unfvbmdnl5SUlJWV5ebmZpyTYogKAECDPX78uGvXrnUe5uHhERMTY4T1NEH/+Mc/VqxYceDAAQSIAGDeTPJieuLEiUIvoeminOWvv/6iji3VALHZtjAzxj777LPCwkJfX98xY8b4+fn5+/s34Em4AJFuNHgPRDs7O8ZYcXEx+3tNH1/DWpipWO/Ro0f1epTuuCnMrLaOkhhhD0SGbRABTJyFhUWfPn2YcWcxI0AEAGiwsLCwK1euPHjwQMsxeXl5UVFRYWFhRltVkzJu3DjG2C+//EIF7wAA5sokL6ZDQ0N9fX2xx4RaVBB3+/Zt+mN6errSAapTmJuhNWvW3Lt3TzWw0wX94NnY2FCAqKkCkdsZkG7QvoH8scVWVlasNmfUFCBq75LWhM5YWlpqiKytvLy8tLTU0tKS6jf5c1SMsAciQ4AIYPpefPFFxtiFCxeMdkbsgQgA0GCLFi2qqqrq3bv3unXrVN9ZZGdnb926NTQ0NC8vb+bMmYKsUHDBwcF+fn65ubnG/KcNAMD4TDJAHDhw4P37993d3YVeSFNEH3xxHQTYA1HvKDe0srKi76Gm1IzbELCioqK8vLygoEAkEvEjQn6AqGkPxIa1MHPFjzk5OfV6oC6UNnPkB4iG2wNRoVDQnp4Mc1QATN+AAQMYY+fPnzfaGbEHIgBAg4WHh0dGRhYWFi5evNjLy8vBwcHb2zs4ONjX19fR0dHNze2NN97IysrasGFDvcZdmpnx48czxvbt2yf0QgAADMgkA0TQgoYCJyUl0R9LSkpo/i+HAq/m2cKsF/XdA5ExdvXq1YqKCl9fXwoECY1wocM07YGotoU5Kyvr3LlzWlbInTo7O1u311QPVAnILZUfIBquhbm4uJhrCUGACGDqevXqZWNjEx8fT1OkjAAtzAAAc/emswAAIABJREFUjTFv3rz4+Phly5aFhITI5fLU1NSYmJi0tDRLS8uePXuuWLEiIyPjzTffFHqZQpowYQJj7ODBg5reGgAAmAFcTJsb+keLvwGHUhEiWpgbqb57IDLGTp06xRjr27cv/wDVFmYdA8SZM2cOGjQoPj5e0woNWoGoVCxpnD0Q+W3LaGEGMHVSqbRv374KhcJorV5oYQYAaCQ/P79Vq1ZFR0eXlpYWFxfn5+dXVFRkZ2dfv379/fff53+i3Dx17do1ICAgPz//zJkzQq8FAMBQECCaG6pA5FMKEFGB2EiUG9rY2OhegfjLL78wlQDR0tJSLBbL5fLq6mqqqtNxiArFgloqdwxagahULNmrVy9vb29aueECRH7VISoQAczAwIEDGWN//PGHcU6HCkQAAH0RiUQ2NjZOTk74papk0qRJjLE9e/YIvRAAAEPB731zo2OAiArEBqMhKjpWIFKBHnWUKwWIrLYI8cmTJ3K53MrKSjXVVRsglpSUcP/VcmpmlApENze35OTksWPHqq5T7ydVvQ1gHqZNm0a/DZqPQYMGMcZ+//1345wOeyACAIChTZo0SSQSHTp0iBulCABgZhAgmht+gEgpT1paGv8AtDA3ko57IFLA17FjR/qjtbV1p06dlI6hyIDqBFX7l5mGAJEuSnQJEBtfgbh27dr58+dT9x9jLCcnJyUlRXW1atepRwgQwbxVVlYabgZR09SzZ097e/ukpKTMzExDn0uhUCgUCpFIhBZmAAAwHG9v77CwsKKiot9++03otQAAGISF0AsAPeMHiEFBQRcuXEALs365uLgwxtq2bau9ArGwsJAx1qVLl4sXLzLGfH19Vd+70hyVrKwsVp8AkaJDLZ9t6rEC8csvv8zLy/vggw/c3d3lcnmXLl1ou0algdG0Tk3fisajtmWRSKRQKOgbC2Bavv322wMHDmj6akJCAqstyiNG6+0VioWFxYABA44cOfL7779PmzbNoOfCBogAAGAcU6ZMuXz58o8//kjtzAAAZgYBornhB4ghISGqASIqEBtpypQpbdu2ffHFF+k9v6YKRMq8AgMD6Y9+fn6qx/ArEFU3QGSNbmGmaLIxqNyvqKiIMZacnEzpIdMQIBq6AtHJySk/P7+4uNhAZwEwnLKyMu3D0xljdR5gZl566aUjR46cPXvW0AEiNkAEAADjmDBhwttvv3369Ons7Oy2bdsKvRwAAD3D9bS5UapAFIlE6enp9PaJvlpTU2NhYYGtoBpMIpEMGTJEJpNpqUCsrKwsLS21sLDg2pa5XmY+ChAp5lOK5IhqMFdRUUH/i3UJEFNTU+fNm3fo0CFdX9vfVVRU0Kkps0tOTua+ZOQWZkpj3d3dWW2aCWBa3nnnnZ07d9rb29vZ2W3bti3z71577TXGGP8eoddrDC+99BJjzAjTKrEBIgAAGIejo+Pw4cOrqqp++uknodcCAKB/CBDNDb8gztXV1dXVtaKi4tGjR3QPJqjokZY9ELlJIx4eHnSPlgpEajTWMUDkckNdWphLS0s3bdq0cuVKXV6OKq5ZWDVAFKQCsV27dtxiAEzO1KlTb9++3a1bt9mzZ69atcrR0dG9Fu1m4M4j9GKNISAgoG3btllZWYmJiQY9ESoQAQDAaKisfseOHUIvBABA/3A9bW6oPI326XNwcOjQoQNjjAZfMMZon35sgKgXWioQqWKuZcuWlHkxrQFibm4uY8ze3l71AC0Boi4ViISLj+uLq/WjzO7BgwfclwQZokKpCgJEMF0dOnQ4f/78p59+unHjxu7du9+8eVPoFQlJJBJREeLp06cNeiIKELEHIgAAGMGrr77apk2bhISEv/76S+i1AADoGQJEc0MB4vLlyydMmBAcHOzt7c14ASKlTjY2NgKu0GzoUoFoY2PTunVrkUjUuXNn1cP4FYh6DxC5fr3c3Fzq4KsvrgKRziVgBSIFsqhABDMgkUg++uijS5cuVVZWhoWFffHFFw3762keXn75ZcbYqVOnDHoWChDRwgwAAEYglUqnTJnCGPv++++FXgsAgJ4hQDQ3FCDOnTt37969VlZWSgEihS+2trYCrtBsaKlApACRyvR27969b98+Jycn1cOob7FeFYhc53KdLcw+Pj70x6qqKjoFY+zRo0fTp0+Pjo6u++WpVCAKuAciv4UZeyCCGXjhhRdu3749ZcqUDz/8sH///hkZGUKvSBivvPKKSCQ6d+6c4X57MLQwAwCAcc2aNYsxtmfPHi2f9wMAmCJcT5sbChAtLP43XxsBouFoqUDkWpgZYy+//PK4cePUPoNBW5gDAgK4e7hxzD/88MPOnTs3b96s7YXV4u+BWF1dnZaWxn0JFYgAjWRra/v999/v378/KSnpzz//FHo5wnB1de3atWtJScnly5cNdxYEiAAAYEydO3fu27dvYWHhvn37hF4LAIA+4XrarCgUiurqapFIxPVqIUA0nDorENXOReGjAJEONk6ASLuxcAWJ2vErEJ8/f86PStVWINIOm4ZA36K2bdtKJJKysrLm3PIJZmbs2LGxsbHr169fv3690GsRxiuvvMIYO3nypOFOgQARAACMbPbs2Yyx7777TuiFAADoE66nzYpS+SFDgGhIde6BqJSyqaIAkeg4hVn3FuaBAwcOHTq0ffv2jBcgXr9+nTGWl5enfWGEX4H49OlT7n6JRKL0I0SvlF61IVAFooODA23fiSJEMCfu7u4LFixYsGCB0AsRxtChQxljUVFRhjsFAkQAADCyCRMmODg4XL169datW0KvBQBAbyzqPgRMB4VZ/ADRzc1NJpPl5eUVFxfb2toiQNQjqkCss4VZC36AqMcKRMoWnZ2dT5w4ERER8emnn1KAmJmZSTd0DBD5FYgUILq6uubk5Dg4OCjNM23VqhVjjB8y6hdX0Wlra1tYWFhcXFzn9xbAFCkUCipqpr/79VJZWRkfH6+9Ppe/k2kT0a9fPzs7u9jY2EePHtGkdb1DgAgAAEZmZWU1ffr0b775ZtOmTTruHQQA0PQhQDQrVIFIwRYRiUQdOnRITExMTk4OCgqiSAgBol6IRCKpVFpZWVlZWcn/nrPa2r06Qy4aokL03sJM6aSbmxurrUC8ceMGHfD48WPtC+O/CjoXhYOBgYHjx4/38vJSOpIqEA0UIFZWVpaUlFhYWNja2tKPLv0YX7t27fPPP1+9erUhTgogiNjY2ODgYMaYQqGo72NnzZr1448/6nIkBWpNRIsWLQYNGnT48OGoqCjadV7vECACAIDxzZ07d926dbt27Vq1alWdbUkAACYBAaJZUW1hZoz5+PhwASIqEPWLAsSKigqlANEIFYh1tjCrBojp6el0QGFhYXl5uUwm07481RZmR0fHb775RvVIg1YgcntEikQiOzs7VtvCvHv37mPHjg0YMKCTIc4KYGr69euXkJCg/ZiysrKEhASlCmLBDRs27PDhw8ePH0eACAAAZqNTp06DBw8+e/bsjh07/v3vfwu9HAAAPUCAaFY0BYisdhtEKt2iFAYaj3JD+rbzcXv2aX84P8JTmzbSNosNa2Gm8kZHR0duPfn5+dwxeXl5tD2iFlwLc0lJyZMnT7hnU+Xg4CAWi58/f15TU6P3d+m0eMoo+XsgZmdnMw0t5AAmyt/fPy4urmGPnTNnzpw5c7QfExcX17Vr16YWIA4fPlwkEp06dUoulzegd7tOCBABAEAQb7311tmzZzds2LBo0SL8MwQAZgC/yMyKlgDxwYMHrDZ1QgWivqgGfERfLcxisbhFixbctmiMV3ioJUCkaciUTtIaqIivoKCAO0aXbRC5CsSioiLtkahYLLa3t6+pqeEeootvvvnmjTfeqHOkMn9KNb8CkV4CAkQwJzKZLCAggD9CvTnw8PAIDAwsLi6+dOmSIZ4fASIAAAhixIgRXl5e9+/fP378uNBrAQDQA1xPmxUtASLtnY8WZv3SNIi5vi3MFhYW/DCRT6mLmcsN5XK5auUjY0yhUFCASE9OuRtlcFSBSMVHumyDqDpEhcoA1WpAF/OKFSu2bt2alpam9qsFBQX+/v7vvvsuP7ukH10EiABmZsSIEYyxo0ePGuLJESACAIAgJBLJwoULGWNqtwACADA5uJ42KwgQjYxamLnyQI6OLcxcgGhnZ6epqVBTgMg0bIMol8tramosLS3p3TKFmFQYSAEi7YpYrwpEboiKHgPEkpISWgOFm6ri4+Pv3bu3d+9e2rqxTZs2rLYCkZLN3NxchgARTFBycvKmTZsWLVr0z3/+c8yYMbNnz/7kk0+2bNlCXfnN08iRIxljhw8fNsSTI0AEAAChzJo1y87O7uzZs7GxsUKvBQCgsbAHolmhMEUpQOzQoYNEIsnIyCgpKUGAqF+aKhApFNO9hVlt/zJRChD5oeE333zz9OnTlStX8ncN409QYYzZ2tqKxeLi4uLq6mqqOuzSpcujR490CRANWoHIFR5q6nqmEDYjI+P3339njPXo0YPxKhArKiroXGrLMAGaptTU1Pnz50dFRan96oIFC0aNGrVmzRrVQedm74UXXnBxcUlJSYmPj9d7BzcCRAAAEIqDg8OsWbO+/vrrr776aseOHUIvBwCgUXA9/TeffPKJVCoVadW1a1fGmEKhEHqxalCYojQRuEWLFn369KmqqtqyZQsCRP1SW4GoUCh0rEDk/kdoiRq1VCBGRET897//nTFjBv94pQBRLBbb2dkpFIqioiLaA7FLly6MsZycnDpfXVMIEBUKxa+//soYCw0NZbwA8fHjx/R3EBWIYCoKCgrCw8OjoqICAgKWL1++e/fukydPXrly5eTJk3v27Pnkk0+CgoIOHjwYHh5O1bXNilgsHj58OGPst99+0/uTI0AEAAABLV682MLCYs+ePZmZmUKvBQCgUXA9/TfFxcUmXdCktoWZMfbee+8xxtasWUNhDQJEfVFbgVhSUlJVVWVtbU1f1aJnz549e/Zk9alA5AeI9K54z549/DRQNbukJ3/27Bm1MIeFhTHG7t69W+er4w9soSnMjQkQ4+Pj//Of/3Dfq9TUVLrBDxCrqqp69+49ffp07oXQMiQSCb8C8fnz51zCggARTMXy5cuTk5NXrlwZFxf3xRdfTJo0aciQIb179x4yZMjEiRMjIiJu3Ljx/fffp6Wlffzxx0IvVgCjRo1ihgkQaVKTRCLR+zMDAADUycvLa+zYsRUVFV9//bXQawEAaBQEiH+zdu1aRV3u3LnDaidRNDWaAsRhw4a5urpmZWUlJSUxBIj6o7YCUcfyQ3r43r17/fz8XnvtNU3HKAWI1Bzt4uLCP4afKlKZoZOTE3cPlTdmZmZWVlba29tTEkc/xtpRgCgSiaqrq7OysljjAsQPP/zw/fff37JlC/2Rq0Dk6hwZY+np6deuXTty5AjjBYiMsc6dO9MPraurK2Ns/fr1O3fupC+ZdOIPzcqFCxf8/f3p4xxNZsyYMWDAAAMNI27iwsPDra2tr1+/Tr9t9IiqlZvmv9oAANAcLFu2jDG2efNmulAHADBRCBDNiqYAUSQSdezYkdWGMggQ9UVtBaLuASJjzMfH5969e0uXLtV0AAWI586do3pDenLq5+Vw8SKrDRCdnZ25e6gCkaboODs7e3t7W1tbZ2Rk8BM6tShApBdCeyY2JkCkARHffPMNvRBaD/t7BSIdQ3kof7jKG2+8QTcmT548ceJEuVz+/fff0z2oQART8fjx47Zt29Z5mIeHhy5blJofa2vr8PBwhUKh9yJEtDADADTe8ePH582b9/LLL7/55pu3bt1SPWDp0qXjxo0z/sJMQkhIyNChQ4uLiyMjI4VeCwBAw+F62qxoChAZY35+ftxtBIj60sgKRF1QgPj222//8ssvTIcAkfqUVSsQU1JSGGPOzs5isZhmFMTFxWk5r1wur66ubtGiBSWDCoVCLBZrabWm16slQKSm4/v37x84cGDQoEGHDh2i+1UDxIqKisrKSnqlAQEBkZGRixYtogOkUukrr7zCeHWLCBDBVISFhV25cuXBgwdajsnLy4uKiqJ9BpqhMWPGMMZo21M9QgszAEAjzZ07d/jw4Zs2bTp79uyWLVt69Oih2o175syZAwcOCLI8k/DBBx8wxtatW8f/jBwAwLQgQDQrWgJEX19f7jYCRH1pfAVinbgJy9nZ2TSeRSQSKQWI5eXl3G3VFmalCkTGWGBgIKsrQKTyQ2tra+6nhQY6azqenpkGPavFFVVNnTr13LlzMpnM0dGR/T1A5DZzLCkpoW9jRETEwoUL+c+jFGIiQARTsWjRItrlc926denp6Upfzc7O3rp1a2hoaF5e3syZMwVZoeBGjhwplUrPnTtHm67qC1qYAQAaY9++fZs3b/b29t6/f//du3d37drl6ur69ttvG2LXWjPWr1+/gQMHPn36dP369UKvBQCggRAgmhVdAkSJRMKN6IVG4ioQafYxoc8VtQxWrhcuQCwqKioqKqqurrazs+vUqRP/GNUKRH4LM62E9hykzM7f35/V1iRqQq/I2trazs6O7uFuqOXh4cEYe/jwodqvFhUVcSNZqGAzJiZm7dq1TF0FImOstLRUUw6r9I3FHohgKsLDwyMjIwsLCxcvXuzl5eXg4ODt7R0cHOzr6+vo6Ojm5vbGG29kZWVt2LBh9OjRQi9WGI6OjgMHDqysrKSNUPUFLcwAAI2xfv16mUx2+vTpsWPH+vv7T548+fjx47a2tnPnzuXvZA11oiFpX331Ff/qFwDAhOB62qxQNRalWkq4FuagoCAUYugLVSCuWrWqZcuWV65coTsNVIFYWFjIPbOnpyedmsJiChDj4uL+/PNPKvRTrUDMzMxktSEgfVV7jQ/lfVZWVlxuqKV/mdUGiBkZGWq/qrSnm6Wlpa+vLz2h2gCxuLhYxwARFYhgQubNmxcfH79s2bKQkBC5XJ6amhoTE5OWlmZpadmzZ88VK1ZkZGS8+eabQi9TSGPHjmWM6bcJDi3MAACNcffu3T59+nh7e3P3BAcHR0ZG5uTkrF69WsCFmZxBgwYNHDjwyZMnGMcMACYKAaJZ0VKB6OPjQ/UX//nPf4y9LPNFWe2VK1cqKytjYmLoTsMFiLTDYKtWrSQSCVUR0n/Ly8srKyvDwsL69etH44lVA0TK5qgfmeoQtexXyHgtzDoGiK6uri1atMjLy+OXQ3JoA0SuAqhDhw7cjor8z65VW5hVv41oYQaT5ufnt2rVqujo6NLS0uLi4vz8/IqKiuzs7OvXr7///vs0Z7w5GzNmjEQiOX36tB63iEILMwBAY5SVlVEpN9/06dNDQ0PXrl2rqfsE1Pr0008ZY1999ZV+N+sAADAOBIhmRUuAaGtr+9lnn/3f//1feHi40ddltqgMkC6qiouL6U4jVCAyxn788cd9+/bROBS5XJ6dnc0tgKlrYabGYUoDaS6KLhWIugeIYrHY3d1doVBQqaMSqkCkUeCMMR8fH+4J1VYglpSUaOoERwszmAeRSGRjY+Pk5ITWWr7WrVsPHDhQLpfrcWsttDADADSGn5/f1atX6cNgjkgk2rhxo1wunzVrlmq8CJq8+OKLQ4YMef78+apVq4ReCwBAveF62qxoCRAZYx988AHN/wJ94XeLl5SU0A39BohcbMevQGSMBQUFjR8/nuLF8vLyR48e8R+lWoFIDFeByLRug0gBYs+ePakISFOAyK9ApAARLcwAzc348eMZY/v27dPXE6KFGQCgMWbPnl1eXj5gwIBr167xs8IePXosXbr0zJkz06ZNw2aIuvviiy9EIlFkZKTaD90BAJoyBIhmRXuACHpHFYiEKwDU7xCV9957j8YQK1UgEplMxhiTy+VKlyD8AJG/EgoQdalAVB2iomOAqHYbRPrU2tPT08XFhWkIEKuqqrghzjk5OVVVVTY2NqobespkMv63HQEigJl5/fXXpVLpmTNn9NXehRZmAIDGWLBgwZw5c5KSknr37i2TybhNexhjn3322YQJE2gu8927dwVcpAnp2bPn+PHjy8rKaKYKAIAJQYBoVhAgGhk/3jJQC7Orq+u//vUvpiFAVKpApA3UxGIx1RgSigsJpYENq0DUPoWZaQ0QqQLRxcXF09OT1c4EpyfkAsTnz59zH2tnZWUxzd9DfiSKFmYAM+Ps7PzSSy9VVFToa5QKWpgBABppw4YN27dvf+mll9q1a0efyhCpVLp79+5169a5ubmVl5cLuELT8sUXX7Ro0eKHH37gp7EAAE0frqfNQWFh4bFjx6qrqxEgGhm/0I9rYaZiwDZt2ujrLFylHr+FmXAViBQgjhkzhlbFb9bjH08ViLa2ti1atCguLqaNEdWq7xRmxli7du1Y7ctXQne6urr++9//Hj9+/IsvvkjLEIvFpaWl1GBIZyT0cnQJEFGBCGB+Jk2axBjbu3evXp4NLcwAAI0kkUj+9a9/nTlzJiUlJTg4mP8lsVj81ltv3b9/Pzs7+9KlS0Kt0LT4+PjMnz+/pqbmnXfeEXotAAD1gADRHERERIwYMWLfvn0UpiBANJr27dtzt4uLixUKRUFBwf379y0sLDp16qSvs3ABYp0ViH369Pnqq6/WrVvHf7hqBSLToYu5AXsg0gH8WS4c+nw1MDBw0qRJ+/btoxxTJBLZ2dkpFAraN4cfINI0FU1n5N+PABHA/IwZM8bKyur8+fNqK5rrCy3MAABG4Orq2rdvX6FXYTI++ugjJyenM2fOHD58WOi1AADoCkmTOaCd42JjY6k/VHXbODAQfoAYHR3t7++fnJxcU1MTEBBAtYF6oUsFIpX4ubu7T5kyRenhqhWIjDFHR8fc3NynT59S17OqBgSIVlZWjDHVBpYnT56kpaXZ2Nj4+/srfcnR0fH58+f5+fkODg78AJGmqWhqmqYKRGtr69LSUrQwA5gfOzu7kSNH/vzzz7t373733Xcb+WxoYQYAMDSFQkF9LfTZdr3I5fK4uDjtxyQnJzdwZU2Vo6NjRETEW2+9tWTJkiFDhujxjQMAgOEgQDQHNO/i3r177u7uDBWIRsQPENPT07nbQUFBejyLlZWVVCqVy+U0ikRLBSL9ACixs7OTSqVUqccFiIaoQKRLH/pp5Lt165ZCoQgKClJtIXRzc0tNTc3KyvL19eU/UJcKxNatW6enp6MCEcAs/fOf//z555937tzZ+AARLcwAAIYWGxtLrc38HRJ1NGvWrF27dulyJH8GtBmYO3fuli1b7ty5s3bt2g8++EDo5QAA1A1Jk8CWLVt2+/bto0eP8gfL1hclL0lJSf3792cIEI3Iw8NDJBKpXirpN0BkjNnb21NzNGOsdevW3P2U2e3atSs3N1csFtMuhKpatWpFY0y4NLDOOSoNmMKsqQIxOjqaMda9e3fVh7i5ubHakSn1rUBEgAhgxoYNG+bs7JyQkHDr1q2QkJDGPBVamAEAmrLBgwfXOcG5rKwsISHBzH6TW1hYREZGDho0aMWKFZMnT+7QoYPQKwIAqAOSJoHt2bMnMzMzPj6+MW+QKOtJTk6Wy+UMAaIRyWQyFxcXKgwkjo6OT548aeTbXVUUIFKRY9u2bbn7qQKR6vWWLVtmbW2t9uFcgFjfCsR6DVHRVIF4584dxpjSltuEXotqgEhPoumMFCC6uLgwTGEGMFNSqXTSpEmRkZE7d+5s5G9UVCACABiav79/nW3ImsycOXPmzJnaj4mLi+vatauZBYiMsQEDBkyZMuXHH39cuHDhsWPHhF4OAEAdsCWQwKhcKykpSe1Xc3NzXVxcFi5cqP1JKG2Ry+UPHjxgCBCNi/ad5Ozfv/+rr74aPHiwfs9CUVpNTY1IJOLvWshtmGJlZbVq1SpND6e4UCQS8fdAZForEFVbmDXVA3I0VSDSGASlbxRRrUDkv8nXdEauhZlhiAqA+Zo+fTpjbPfu3Y38a44KRAAAQ5PJZAEBAQEBAUIvxPSsXr3a0dHx+PHje/fuFXotAAB1QIAoMO0B4v379x8/fnz06FHtT8LVfMXHxzMEiMbF3wZRLBYPHDjw7bff1nupC1eL5+joyN+dmrtNRXmaUIBobW3NjRGgjRTrFSDqWIHIDxB//PFHGxubGzduMA37M/IDRPoxpmRT+xn79u1rY2MzaNAghgARwHz16NEjMDAwLy+vkUUZGKICAABNVps2bb788kvG2OLFi/Pz84VeDgCANrieFhg1HWsKECmLycjI4Hd3ajqMMZaQkMAQIBpXWFgY9760VatWBnqPykVp/P5lxqtA1B4gUirHlR+y2uK+wsJCTQ/hAkRbW1uq3GlAC/OVK1dKS0uLi4uZ1gCRWrDpjPwdHjVVIA4bNqy4uHjs2LEMASKAWZsxYwZj7Pvvv2/Mk6CFGQBAL5KTkzdt2rRo0aJ//vOfY8aMmT179ieffLJlyxa6kIMGmzlz5uDBg/Py8hYtWiT0WgAAtEHSJKTq6mqKPzTtHExZTE1Nzb1799RuIcc/jNXmQVKpVP9rBQ2WLFkybdo0Ly+vkpISZ2dnA52Fe2alALFeFYj8PI7SwKKiIk0P4QJEsVjs5eX1/Plz/vRntVRbmLng287OTm3+qNrCzP8eam+app9z7IEIYMamTp36/vvvnzhxIisri35dNABamAEAGik1NXX+/PlRUVFqv7pgwYJRo0atWbPGy8vLuOsyEyKR6LvvvuvWrduePXvGjh37+uuvC70iAAD1UIEoJC5quXfvnuokX/4B2meTKY2tQAWikTk7O1Nxn5OTk4FOwY0wbkyAqFqBqCVA5KYwM8auXr0aFxdX58+VagUid1tt+SFTFyDyKxC11zxSgIgKRAAz1rp169dee62qqmr79u0NfhJUIAIANEZBQUF4eHhUVFRAQMDy5ct379598uTJK1eunDx5cs+ePZ988klQUNDBgwfDw8P5cwWhXry9vWk387lz5+LbCABNFgJEIXH5YElJSWZmpuoBXP6iqceiI2K0AAAgAElEQVRZ6TCCANH4bGxsmCEDxLCwMLqhrwpEHVuYqajQxcVF6bxqqVYg1hkgtmzZ0s7Orri4OCsriw6m2cr8RWoiEokkEolCoVAbvgOAeZgzZw5jbNu2bbSVYQNgD0QAgMZYvnx5cnLyypUr4+Livvjii0mTJg0ZMqR3795DhgyZOHFiRETEjRs3vv/++7S0tI8//ljoxZqw+fPnh4eHP378eNasWbi4BYCmyVSTpuTk5NOnTyckJBQUFJSWljo5Obm7u7u7u48cOVKXpKOJoA0QSWJiooeHh9IB9apAtLa2ptAHAaLxGboCMTg4mP7/NmwPRNUKxDpbmEtKSlhtMKojS0tLkUgkl8sVCgV1C9YZIDLGXn755V9//XX37t300+vj4yORSKhiqM5dF6VSaXV19aNHj3RfJACYlpdeesnb2zslJeXkyZPDhg1rwDMgQAQAaIwLFy74+/u/9957Wo6ZMWPGrl27Ll26ZLRVmR+RSLR9+/Zu3bodO3bs22+/XbhwodArAgBQZnrX06mpqcOGDfP19Z03b15kZOTu3bsPHTq0bdu2zz777M0332zfvv3YsWPT0tKEXqZO+LVaaiNCLn9JTEzU9CQVFRXV1dVSqbRz5850DwJE46NsznB7IEql0tDQUKaSxOlYgejn58cY8/b25u6ps4X52bNnrHZYs45EIpFMJlMoFNwPti4B4vTp0xljP/zwAwWILVu2bNeuHX+RWlAXc2pqqu6LbKTCwsJ9+/Zt2LDhxo0bFRUVRjsvQLMlFovfeOMNxtjmzZsb9gwIEAEAGuPx48e61Gd4eHjk5eUZYT1mzN3dfcuWLYyxpUuX3r59W+jlAAAoM7GkifbgSE5ODggIGDVqVGBgoJOTk729fWFh4ZMnT+7evXv06NGDBw/GxMRcunSpTZs2Qq+3DvwAUW1EyN8ksaamRu37H8porKys/P39b968yRAgCsHQLcyMsYiIiN27dw8ZMoR/p44ViH379k1MTOzQoQN3T50tzE+ePGH1f0UymaysrKysrIzambkAsX379poe8uqrr1pZWcXFxdHyrK2tvb2909PTmW4ViOzvlbwGdeTIkTlz5uTk5NAfXVxcPv7444CAgIEDBxpnAQDN08yZMyMiIo4ePfrw4UMtv0w0QYAIANAYYWFhp0+ffvDgga+vr6Zj8vLyoqKiuC13oMH+8Y9/zJs3b+PGjePHj79x40adF8MAAMZkYtfTZrYHh2oF4sWLF69du8bdyeUvZWVlmsoq6UmsrKxQgSggQ7cwM8YGDhy4ZcsWpcsIHSsQGWOdOnXiDmZ1VSAWFxfL5XIbGxv+Q3ShtA0i/QBPnz593Lhxmh4ilUpp8bRjtJWVFRd01lmBSD/q/L9HBvLs2bO33npr1KhROTk5vXr1mjZtWseOHfPy8hYuXDho0KDQ0NB9+/ZhnAuAgbi4uIwbN666unrTpk0NeDgCRACAxli0aFFVVVXv3r3XrVtHH/HyZWdnb926NTQ0NC8vb+bMmYKs0Mx89dVXISEh9+/fnzFjBjZDBIAmxcSup3Xcg2PAgAEmsQcHVU7RINq7d+9WVFSEh4cPGTKE2yqen4xMnDjx4MGDqk/CVSByASKVZYExhYSESKXSkJAQI59XxwpEVdr3QGxY+SFTGcRMNyIiIrQ/FQWF1PZibW1NAaKFhQXNgNbCOBWINTU1o0ePXr9+vVgs/vLLL69evfrDDz/cvXt38+bNkydPdnV1vXHjxsSJE9u1azdixIgNGzYYdDEAzdP8+fMZY1u3bm3ABwYIEAEAGiM8PDwyMrKwsHDx4sVeXl4ODg7e3t7BwcG+vr6Ojo5ubm5vvPFGVlbWhg0bRo8eLfRizYFMJtu/f7+Dg8Mvv/xCo5kBAJoIE7ueNrM9OOiNkJ+fn42NTU5Ozr179+RyeWFhIVdsSPkLpSTXr19ft26d6pPQMTKZrEuXLnQPKhCN76OPPnr27FnPnj2NfF7dKxCVyGQyqVQql8vVbuRXUFDAGHN0dKzvetRWINKdWlCASBWI1MLMdCg/ZLV/NWjiiuFs2rTp/Pnzbdu2vXXr1tKlS2k+jEgkmjNnzq5du1JTUzdv3hwYGJiXl3fs2LEFCxbMnTs3OTnZoEsCaG7CwsJ69uz5+PHj3bt31/exCBABABpp3rx58fHxy5YtCwkJkcvlqampMTExaWlplpaWPXv2XLFiRUZGxptvvin0Ms2Hj4/PTz/9JBaLP/zww+PHjwu9HACA/zGx6+mwsLArV648ePBAyzEmtAcH133cqVMnxtjFixfp/oSEBP4BAQEBSvfzcRmNr68v5SkIEAVRZ7mcITS4ApFp3QZRXxWINBdFxwCRGyZOFYi6BIhG+FGvqKhYsWIFY2z9+vVdu3ZVPUAmk82ZM+fOnTt37txZv369RCLZvHlzcHDwTz/9pHpwZmbmxYsX79+/T7WfJSUlsbGxqhWUcrl88+bN3bt3F4vFbm5uo0ePXrJkycaNG3fs2PHjjz/+9NNPY8eO7dWr18KFC9X+TuArKyuLioqKjIzcsmXL5s2bt27dyhU4A5iWRYsWMcbUfpCmHQJEAIDG8/PzW7VqVXR0dGlpaXFxcX5+fkVFRXZ29vXr199//31XV1ehF2huhg8f/umnn1ZXV0+aNCk+Pl7o5QAAMGZyQ1QWLVp04sSJ3r17f/zxx6NGjfL09OR/NTs7+9ixY59//nkT34NjzZo1+/btO3HiBAUHVDx48+ZNLkBMTEwcMWIEq41UXnzxxZiYGIVCkZ+f//jx49atW/OfjQsQpVKpr69vYmIiAsTmQyqVisXimpqahgWIT548KSoqUh0e3eAKRAoQ61uBSDtIEhoH1KFDh0GDBtV5OiN06+/atevRo0fdunUbM2aM9iMDAwMDAwNDQ0NXr1594MCBqVOnbty4sUuXLn379m3btu3evXt///33hw8fcsfLZDL6RkmlUldXV6lUKpVKW7VqVVlZmZaWRv8LGGPZ2dm//fab2jNev35948aNwcHBbm5uLi4u5eXl+fn5VlZW1tbWrq6uFRUV0dHRsbGxJSUl/EcFBQXRRG8A0zJhwoR33303Jibm7NmzL730ku4PRIAIAKBHIpHIxsaG5geCQX3wwQfx8fF79+4dMWLE1atXm/6AUAAweyaWNNEeHItrtWzZ0tHR0d7evri4+MmTJ0+fPmWMWVhYNPE9OLZv356QkHD+/HmaumBpaUndx5oqEPv16/evf/1r5syZt2/fTkhIGDBgAP/Z+BlNaGhoYmIiPgNsVmQyWWlpqYODQ30fqGUbRKpAbGQLc2VlZVVVlYWFRZ0xH7/Y0Nra2tbWNiUlRZfTGSFApKEN77zzDnUu16lXr1779+/fvn37smXLLl++fPny5a1bt3JfbdWqlb+/f15eXm5ubklJiUQi8fT0TEtLy8jIUHqenj17Ll26dPTo0ampqXfu3ElJSbl3715lZWV1dXVFRUVoaGjv3r337NmzY8eO6Ojo6OhoTesRiUS9evXq3r17TU2NQqHo3Llz9+7dG/SdABBYixYtFixY8OGHH3711VcIEAEAwOyJRCKaDnr16tURI0b88ccf/A/dAQCMz8QCRMbYvHnzXn755a1bt54+fToxMTE1NZUxJpFIWrdu3bNnz9dff33GjBlNOUGrqKi4f/8+YywhIcHDw4MxJpPJaP5JZmYmHZOYmEg3uP0NQ0JCQkJCbt++nZiYqCVA/Pbbb9966y3j78QHArKxsSktLW1Au7GWQcxU/tbIFmYdyw+ZSoCo++lUi23pUwTGWGpqanZ2dp8+fXR/NlUJCQl//fVXy5Ytx44dW68HzpgxY9y4cYcPH87Jydm9e7dUKn3llVdef/31wMBALsWQy+W0hWVFRUVOTo5EIiksLCwpKRGJRHZ2dh07dqTD/P39/f391Z6lf//+X375ZUxMTEFBQV5enkwma926dVlZWUlJSU5OjkgkCgoK6tatm1LNMoDpmjt37sqVK0+cOBEXFxcYGKjjoxAgAgCAibKysjp8+HCfPn1u3LgxduzYw4cPt2jRQuhFAUDzZXoBIqvdg2PVqlUKhaK0tLS8vLxVq1am8t6AyogYYwkJCS4uLuzv809IYmKiQqEQiUTcJomMMTpGacszhUJBO0LSMba2tkgPm5vIyMjHjx83oAKxzj0QG1mBaIQAUbUC8c6dO+n79l24cOHYsWOZmZkJCQlcEtcAX3/9NWNswoQJurwKJba2tpMnT2aMLVmyRO0B3ACcFi1atG/fnjHm7u7egLP07du3vo8CAR0/fvzIkSP379/38fGZO3eu6uj2pUuXpqWl7d+/X5DlNXFOTk4zZ86MjIxcvXr1Dz/8oOOjECACAIDpat26dVRUVN++fU+ePDl16tTdu3dLJBKhFwUAzZRpX0/THhxOTk4m9MaASwATEhIoZ5HJZN7e3tw0DMZYYWHho0ePGK8CkdUGiFxxIvn6668pnmhAwAHmYcKECQsXLmzAA+tsYTZaBaLSHoi6n041QMzKyvrwww83bNiQnp5eXV39ww8/5Ofn6/6EfCdOnNi6daulpeXixYsb9gwASubOnTt8+PBNmzadPXt2y5YtPXr0oJCa78yZMwcOHBBkeSZhyZIlFhYWe/bsSU9P1/EhCBABAMCk+fj4nDhxomXLlj///PPs2bMxEA8AhGKG19MKhUIul6sONtVFTU1Nenp6ilZco7HuHj16xD38ypUrdGdSUhINN7C0tJRIJDSImUPDtvgViDSLWWkI140bN+gGP38E0IVSC/OKFSscHBzu3bvHGjFERbUCUZeKQq4C0crKql6fqaq2MN+9ezc5OZn744oVK1q3bt2nTx9uJomOrl27NnHiRIVC8dlnnykVCAM0zL59+zZv3uzt7b1///67d+/u2rXL1dX17bff1jQkB9Ty8vKaNGlSZWXlmjVrdHwIAkQAADB1ISEhx44ds7W13bFjx5w5c5AhAoAgzPB6OjY2ViaTNSxQmzt3rpeXl49Ww4YNY4wpFAodn3PDhg3t2rXjHs7Vm8jlckoDaancdk4+Pj6sNijkVyC2b9/ezs4uOzubqsMIN2sCFYhQX0otzOfOnXv+/Pnt27eZ0SsQuQCxvruXqlYg3r17V6FQBAcHHz58uFWrVnTMlStXXn/9dd3/zu7cuXPQoEGFhYWTJ09eunRpvZYEoMn69etlMtnp06fHjh3r7+8/efLk48eP29razp07V20hMGjy3nvvicXibdu25eTk6HI8AkQAADADffv2PXr0qI2NzbZt22bOnFldXS30igCg2cH19N906tTJuy6enp6MMd3rpDw9PX19ffnP0KNHj9DQUMYYzU6lzIUKDBljL774Ivt7gEgRjEgkokoofhEiAkRoMIrtiouL6Y9Uo0d5oh4rEOvVwlzfTQBVA0RKCYcMGTJy5Miffvrp66+/fvDggYuLy4ULF65fv17nE5aVlc2ZM2f69Ol0Y8eOHToOXwao0927d/v06ePt7c3dExwcHBkZmZOTs3r1agEXZnK6dOkyZsyYsrIyHb9vCBABAMA8DBgwgOoQf/jhh0mTJlVUVAi9IgBoXszwetrf3z8uLi4uLq4Bj12yZElyXa5du8bqU5w1fPjw+/fv85/hxo0bgwYNYoxRu6jaAJG2SuQ2SaQvKXUxFxcX5+Xl0W0EiFBfNjY2jDHqo2e1VYf8ALGRFYilpaWsnhWIjQ8QSe/evRljr7766uLFi9u3bz9p0iTG2M8//6zpeSoqKtatWzd06FAfH5/vvvvOyspq27Ztmzdv1vT8AA1QVlam2nA0ffr00NDQtWvXPnz4UJBVmagPP/xQJBJt2rQpNze3zoMRIAIAgNkYMGDAyZMnHRwc9u/fP2LECDQxAIAxmeH1tEwmCwgI4PK4pokaluldDQ1j5RY8YMAAxlh8fLxCoVCq4VIKEFNTU7knRIAI9UV1f6oViAqF4unTpyKRqAEViBQgNngKc30DRNU9EBljYWFhQ4YM4d8zbtw4xtj+/furqqpUj8/IyHjttdcWL1588uTJ7OzsoKCgK1euzJw5s14rAaiTn5/f1atXlQIvkUi0ceNGuVw+a9Ys7Geku+Dg4FGjRpWWlq5atarOgxEgAgCAOenTp88ff/zh6up6+vTpAQMGZGVlCb0iAGgucD0tDH6+SZmLl5eXm5ubu7u7l5dX27Zti4qK0tPT1VYgcsWVXP8yq52oC6A7foBYUVFBH2AWFRU9f/68qqqqZcuWauM57SgupKpGIwSIqhWCixYtunDhAhVXcvr06dOxY8eHDx9u27aNf39JScnkyZN9fHxOnjzp4uKyd+/e6OjomzdvBgUF1WsZALqYPXt2eXn5gAEDrl27xs8Ke/TosXTp0jNnzkybNg11BLqLiIgQi8UbN26sc7IZAkQAADAzwcHBf/75Z8eOHW/duvXCCy/cunVL6BUBQLOA62lhdO7cmdtFkfJBsVh8+/btW7duiUQiqk+8c+eOXC4XiURcgEj3KwWIo0aN+uyzzyZOnGj8VwEmjd/CzE3mKSwsbPAGiIwx2qbzyJEj1dXVRtsDkR8j9u3bVzX3FIlEK1asYIwtW7bswIED3P3Lli3bs2ePQqH45z//efXq1QkTJoSEhNRrDDSA7hYsWDBnzpykpKTevXvLZLKYmBjuS5999tmECRNoLvPdu3cFXKQJCQoKGjduXHl5+eeff679SASIAABgfry9vS9fvty/f//MzMz/3969x0Vd53sc/84ww20QBhACRUgBzYC85A1rU1c5LquF5aUbZxMzXbzttl5K21Wz1KNpR3NT24cu2j1Ns7QVoz2ZKV4yU1PRlWuiwHC/3wbm/PHb5jELDDMgMMzM6/kX850fM18+/fAzvfn+vr9f/epXrezVAwAdhc/TluHi4qLfSl+fD/r4+Pj4+IhfgsLz58/rdDpnZ2f9bRx69+7t6elZUFAg3XoyJydHCBEZGfmXv/zFw8Oj638KWDXDFYhSaCgMAsR2bIAohBg/fnz//v2zsrI+//zzLruEWf/trXjiiSeeeeaZsrKy6dOnBwYG9uzZc+TIkTt27HBycjp37tz777/ft2/fNr010A7bt29PSEgYP358QECA4W3BlUrlhx9++NZbb/Xq1UtaeA5zvPrqqwqF4u9//7u0obAxBIgAAJvk7e2dlJQUFxdXWVn51FNPLVmypMXtegCgo/B52mIiIiKkL6Q9EA1JAaJ001h9vGj41E8//SSEkC524+JltI9hgNh8BWL7AkS5XD579mwhxLFjx6QA0dXV1eR33eUlzOYEiDKZ7IMPPti+fbtKpbp161ZhYeG5c+ccHBx27NgxZMiQNr0p0G4ODg4zZ878+uuv09PTBw8ebPiUXC5fuHDhzZs3c3JyTp48aakZWpcBAwbMmjVLq9WuWLGilcMIEAEAtsrJyenvf//7tm3bFArF5s2bx44de+vWLUtPCoDNavMeZxaUn5+fnJxs/vExMTGdN5m7FxERcfDgQdEsIhS/pITffPONaLaAKyIi4rvvvrty5UpUVJR0w1wCRLSP/hLmY8eOvfjii9JgeXn53QSI4pezNy0tLTAwUJh9CbO0lrBXr15tei/zA0RJfHz8008/XVJS4ujoePjw4UGDBkn3awa6Dz8/Pz8/P0vPwmqsWrXqgw8+OHjwYHJy8ujRo1s8hgARAGDbFixYMHTo0CeffPLUqVODBw/+29/+NnXqVEtPCoANsqYA8erVq1OmTDH/eMMLxLohKWcRLa1AHDx48PDhw1tZgShtgygFiOanJ4Ah/QrEd955JyUlRRq8yxWIQoiQkBAhRGpq6siRI4V5AaJcLn/77bd1Ol3z34XWRUdHf/vtt7169cr7ZWNQk9RqtVqtFkLMnTu3Te8FdAGdTldXVyda6gtoUa9evRYvXrxmzZrFixcnJyfrd/wwRIAIALB5o0eP/vHHH+Pi4o4cOTJt2rTnnntuy5Yt0odeAOgo1hQgjh079qefflq9evWBAweEEPHx8Va98Z/+EubmKxAdHR3PnTs3e/bs3bt3N1+BKIS4fPmy4BJm3B39CkTD7cPKysqky5nbHSDee++9CoUiOzv7woULQoiAgABzvmvOnDnteK9JkyZNmjTp8JNP5rXjm4Hu5/Lly9Klze34A9iqVavWrVtnzuZH3fyva221dOnSXbt2nTlz5qOPPnrmmWeaH0CACACwBz179vziiy927NixdOnSvXv3JiUl7dix47HHHrP0vADYDmsKEIUQ4eHhn3766YMPPnjhwoWXXnopKCjI0jNqv5CQEBcXl+rq6uYBomT9+vVnzpyJjo42HIyIiJDJZFevXm1oaGAFIu6GtAJRnxhK9Jcwt+8uzEIIpVIZGBiYnp7+1VdfCSHGjx/fEZMFYEJFRYWZW6e3uEzPerm5ua1duzYuLm7ZsmWPPfaY/q7uegSIAAA7IZPJ5s2bFxUVFRcXd+rUqZiYmKlTp27ZssXMv+gDQOus8vP0U089ZekpdAAHB4dx48Z5eHgYu3GEj4/PlStX3njjDcNBDw+PoKCg6urqmzdvsgIRd0NagVhRUWF419e7v4RZCBEcHCyEaGxsDAsL4/MKYL4BAwZcuXLlitmX5BvavHmzzpTc3FwhhI+PT0dP3MJ+97vfjRgx4vbt22vXrm3+LAEiAMCuhIaGnjhxYuvWrT169Dhw4MDAgQP/53/+p7a21tLzAmD1rPLz9PDhw0NCQqS7Lli1Q4cOZWZmenp6tum79FcxswIRd0OhUDRf/arVaqV7t91NgChtgyiEmDhxYrtfBLBDzs7OYWFhYWFhlp6IlZHL5W+99ZZcLn/zzTf1O7rqESACAOyNXC5ftGjRtWvXpk2bVlFRsXz58oEDB37yySc2to0JgC5mlZ+nx44de/PmTWML96yIUqlsx9a2DzzwgBDi8uXLrEDEXdJf6xcbG5ucnOzr6yuESE1NFXe3Rkm6fcojjzyyePHijpgmAJgwcuTI2bNn19XVzZs3r8n/HREgAgDsU0BAwP79+7/++uuIiIiMjIynnnpqxIgRiYmJlp4XAGtl9Yv47NDQoUOFEMnJydXV1QqFwtXV1dIzgrVSqVQFBQVCiCFDhkRGRrq7u2s0mry8PGH2zU9a9Nxzz40ePTokJMTGtloDOkRaWlpSUtK1a9cKCwurqqq8vb179+7du3fvRx991N/f39Kzs2Lr168/dOjQ8ePH9+7dO3PmTP04ASIAwJ6NHz/+xx9/TEhIWL169fnz56OjoyMjI//85z9HR0fzWR1AmxAgWp/IyEghxKlTpwTXL+Pu6Fcg9u/fXxicTi4uLj179rybVw4NDb3LuQG2JyMjY968ecb+8j9//vyYmJhNmzbde++9XTsvG+Hl5fXmm2/GxsYuXrw4Ojr6nnvukcYJEAEAds7BwWH27NnPPvvs9u3bN27cePr06UmTJg0ePHjp0qXTp09XKpWWniAA68Dnaevj7+8fFBRUV1cnCBBxd5oEiPrL4Xv37s0fJIGOVVhYGBUVlZiYGBYWtmLFig8//PDYsWOnT58+duzYRx99tGrVqkGDBh04cCAqKkpaBYx2ePbZZ6Ojo4uKihYsWKAfJEAEAEAI4eLisnjx4oyMjM2bN/v7+1+8ePHZZ5/t16/f+vXr8/PzLT07AFaAFYhWKTIyMisrS7ABIu6OdCNmpVLZt29fIYR+X9E+ffpYclqALVqxYkVaWtr69etffvnlFg9YvXp1QkLCnDlzVq5c+c4773Tx9GzGzp07IyIiPv3003379s2YMUMQIAJAB2ELDtvg6ur6pz/9af78+e+9996WLVuuXr26YsWKV1999fHHH589e/a4cePomACM4V8HqyTdpEKwAhF3R1qB2LdvX+nKBWl7TUGACHSCEydODBgwwFh6KImLixszZszJkye7bFa2JzAw8I033hBCzJs3Lzc3VxAgAsBdy8jIiI6ODgkJiY+P37Zt24cffnjo0KHdu3evWbNm7ty5gYGB06ZNy8zMtPQ00QZOTk6zZ8/+6aefvvrqq5iYGK1W+/HHH0+YMKFfv35/+ctfUlJSLD1BAN0RKxCt0pAhQ6QvWIGIuyGtQJSuXxZCDBs2TPqCABHocPn5+RERESYP69Onz6VLl7pgPjbshRde+OyzzxITE+Pi4t58800CRAC4G9IWHGlpaWFhYTExMeHh4d7e3u7u7mVlZUVFRdevXz9y5MiBAwcuXbp08uRJ/f6zsAoymSwqKioqKio7O3v37t179+7NyMh4/fXXX3/99cGDB0+fPn3q1KkDBgyw9DQBdBcEiFZp0KBBMplMp9MRIOJuSCsQ9QHi0KFD5XJ5Y2MjASLQ4SIjI5OSklJTU0NCQowdo9FoEhMTpTtlod1kMtnu3bsHDRqUmJiYmJjo6ekpCBABoL3YgsMeBAQErFq1auXKlSdOnHjvvfcOHjx48eLFixcvvvLKKwMHDnz00UcnTZo0evRohYL0ALBrfJ62Smq1WrpNJ5cw425IZ5F+4WGPHj2kMDEgIMCCswJs0qJFi7Ra7ahRo9566y1pE1tDOTk5u3btGj58uEajmTVrlkVmaEt69er1/vvvDxkyRCaTFRcXCwJEAGgvtuCwHzKZbMyYMbt27crNzT18+PDMmTO9vb1TUlI2btw4ZsyYnj17Tp06devWrefPn6+pqRFC5Obmnjhx4ujRo2lpaSkpKSdOnEhPT9fpdFVVVUIIrVZ78+bNffv2/e///u+3336blZUlXRNQUVGRnp5++vTp06dPZ2Zm1tbW6ieQk5OTl5d369Ytw0FD0itotdrLly9fuXKloqKi9Z+ovr6+uLi4uLg4PT29vLy8pKRE+lTQIq1WW1xcXFBQoNFofv75Z+kby8vLa2pq7ty506ZKVlRUNDQ0SF9LtSovL6+srBRCFBcXNzY25uXlffPNN6mpqW16WdZ/OlwAAB4SSURBVHPU1tZKb6TT6crLyw2fKi8vb3K3nMbGxoqKivr6+uavU1lZ2eI47Bx/Q7BWgwcPzsjIIEDE3Vi2bNmjjz4aHh6uH5k/f/7777/PAiigw0VFRW3btu0Pv/Dw8PDy8nJ3d6+oqCgqKpI+zioUiu3bt0+ZMsXSk7UFEydOnDhx4sKFC//6178KAkQAaC+24LBDjo6OkydPnjx5ckNDw3fffXfkyJF//OMfKSkpBw8ePHjwoHSMq6urFBQ24eDg0NDQ4OHhUV1dXVdX1/xZIYQ+XBNCKBQKf39/pVJZVFRUUlIiDXp5eQUEBCiVyoKCgoiIiNra2vT0dJVK9dNPP3l4eOh0utLSUiGETCYLCAiora1Vq9Vubm4ajcbBwUGtVv/888/SDAsKCvRZpLOzc319fUNDg5eXl1wuLyoqamxsdHJycnFx0b9vi6SfyM3NTS6XNzQ0+Pr6enl5aTQaKb6sqKhQKBTOzs5qtTowMLCkpKSysjItLU2tVisUCplMlp+fr9Pp9HOQ8kQ9Ly8vNzc3Hx+fwsLCnJwc/T0t+/btm56eLoS4//77XV1dKyoqysrK6urqcnJyfHx8VCqVUqm8dOlSSUnJoEGDFApFZWXljRs3VCpVWVmZVJbS0tKysjJ3d3eFQqFWq0tLSwsLC4UQSqXSx8dHLpffvn1bmphare7du7dKpcrOzpbJZA0NDbW1tdJHUz8/P1dXV0dHx7S0NK1W6+/vL/2kkWlp9/7yIzz55JOXnnzSxcXF2dm5efWqqqqcnJyk/+5SuZrnkh4eHtb1OU2lUjk6OjYZbGxsLC0tVavVMpnMcFytVh89etTX17cLJ9iJCBCt1YgRIz777DP2GcHdUCgUTT4RLliwYMGCBZaaD2Db4uPjJ0yYsGvXrqSkpJSUlIyMDCGEg4ODj4/PsGHDnnjiibi4OD8/P0tP06Zs2rQpOTn5woUL7PgBAO3DFhz2zMHBYezYsWPHjt20aVNWVtbXX3+dnJx86tSpjIyMqqoqtVo9cOBAFxeXa9euqdVqLy+vzMzMO3fuKJXK0tJSmUwWFBQUFhbWq1evq1evZmVlFRYW1tbWKpXKoKAgLy+vxsbGwsLC27dv37p1S3o7Ly8vBwcHmUym0WiKioqkwSbXbUhhX0hIiJRqSd+r0Wj0B+iPLy4ulsvl0k4mKpVKmpiLi4v+lYUQtbW1hgmji4uLTCZzdnaWy+XSOj7pAE9PT/3SxYyMDOkjnF59fX11dXVxcbF+XKlU6o+X8keVStXY2FhdXS3tH6pSqR544IH09PS8vLyioiIp8RRCSKGh4RdN3ksIkZ2dbfjwxx9/1H9dVlamVCodHByksri4uJSVlQkhpB/Z1dVV+k9juKDS1dW1pKSkeYQq5a3S/ej09N94v2iqurq6urq62bAQQhhbT6onxcFWpJV1rM2fcnFxKS8vJ0CEhS1cuDAgIICFKgBgRUJDQzds2LBhwwbpAp+amhpPT0/r+qOrdXFycjp69Og///nPyZMnW3ouAGCVFi1adPTo0VGjRq1cuTImJiYoKMjw2ZycnC+//PK1115jCw6bFxQU9Pzzzz///PPSw/Ly8hYvhquurnZ2di4pKZHyuCbParXaxsZGw9VbtbW1ubm5DQ0N7u7uPXv2lAZTU1Olq4CdnZ3Pnz/v7e0dHBys0WiGDh1aV1en0+mkI+vr67Ozs11dXYuLi6uqqnx9fUtLS8vLy0NDQ+VyeU1NjVqt1s8hPz/fxcXFzc0tNzdXoVB4eno6ODhUV1dLqwtb+cGrq6tdXFxKS0t1Op1CocjJySkrK/P09JSiSTc3t/r6+tra2vz8/Nu3b7u5ubm7u/fp06eoqEipVCoUCg8PD/3ekU2KptPpNBpNZWVlcXGxi4tLQECARqORy+W1tbUajUaKnG7evFlbW+vm5tajRw8nJye1Wp2fn6/Vauvq6vr37+/r63v+/HlHR0dHR8f+/fvX1dWp1WqtVnvr1q0ePXr07NmzuLi4oaFBiiyltUfSMsaGhoZ7771X+giamZlZUVFRUlLi6+vr7OysVCqdnJw8PDyEEHl5eTU1NRUVFcHBwU5OTjk5OdJV4ddWrMj76ivpp/jkk08GzJhRWVnZfMGpEMLV1bW2trahoUHaat/NzU2pVDY5prS0VLo43Vq0+MPK5XIPD4+SkhL9glNJz549benP2ASI1kqlUsXGxlp6FgCA9pDJZCqVSroTOjqVr6/v008/belZAIC1YgsOtMjYVlpSYCeFa801vw2Lk5NTk1RaCGG43DUsLEz6YuDAgU0OUyqVffv2FULoL8trZSd3Hx8f6QvDqz2aR5zNScdIgZoQIjQ0tPk0XF1dPT099bemFEK4uro2f6kmRZPJZE0uKNQnTfoftvlP3WQCDz/8cJM3VSgUUlnEL/8h9MmsEMLR0bFJwaVt8VvUq1cvw4f6O23eUavz/vPIVj7WOjk5GXt9ib621sLY6d36U7aBABEAAAAA0DK24AAACAJEAAAAAEAr2IIDAECACAAAAAAwjS04AMBuESACAAAAANpJp9NJtxQwudlZi0zeQkG6kywAwLIIEAEAAAAA7XT58uXBgwcLIZrcftQcf/zjH7du3WrOke14cQBAByJABAAAAABYgJeXl8n7ljY2NpaWlrLlIgBYFgFiO9XV1X399dfmHHb8+HFvb2+ZTNYFs7JGDQ0NBQUFvr6+lMgYqUT33HOPpSfSTcmuX9efOl988YUuM9OCk+m26uvrhRAjR4408/hr16515nRgSfSvjkL/Mon+1Tr6lznoX1ZhwIABV65cad/3rly5cuXKla0fk5eX5+fn5+7uTv/qEPQvk+hfraN/mcMm+5eMpeBtlZ+f7+vra+lZAPi3CUL0++Xrr4VIt+RcbM1DDz108uRJS88CHYb+BXQr9K/OQ/+yMfQvoFuhf3Webt6/WIHYZj179pw/f/6NGzfMOfjq1as5OTmurq7Ozs6dPTErVVlZWVtbq1Kp2rfpsj2gRK27IMSJioq6ujo3NzdHR0cvS8+neyovL6+vr4+IiGjTn1Lj4uI6b0roevSvjsU/ziZRotbRv8xB/4Kgf3U0/nE2iRK1jv5lDtvsXzp0piVLlggh3njjDUtPpPuaN2+eEOLtt9+29ES6r5kzZwohEhISLD2R7mvGjBlCiE8++cTSE+m+Jk2aJIQ4cuSIpScCq0H/Mon+ZRL9yyT6l0n0r+4jNTV1x44dCxcufOaZZ6ZMmfL888+vXLnynXfeuXPnjqWn9h/oXybRv0yif5lE/zLJJvsXKxABAAAAAC3LyMiYN29eYmJii8/Onz8/JiZm06ZN9957b9fOCwDQpQgQAQAAAAAtKCwsjIqKSktLCwsLi4mJCQ8P9/b2dnd3LysrKyoqun79+pEjRw4cOHDp0qWTJ09y0wkAsGEEiAAAAACAFqxYsSItLW39+vUvv/xyiwesXr06ISFhzpw50hXNXTw9AECXkVt6AgAAAACA7ujEiRMDBgwwlh5K4uLixowZ053vHAoAuHsEiAAAAACAFuTn5/v7+5s8rE+fPhqNpgvmAwCwFAJEAAAAAEALIiMjT58+nZqa2soxGo0mMTExMjKyy2YFAOh6BIgAAAAAgBYsWrRIq9WOGjXqrbfeysrKavJsTk7Orl27hg8frtFoZs2aZZEZAgC6BjdRAQAAAAC0ICoqatu2bX/4hYeHh5eXl7u7e0VFRVFRUXFxsRBCoVBs3759ypQplp4sAKATESACAAAAAFoWHx8/YcKEXbt2JSUlpaSkZGRkCCEcHBx8fHyGDRv2xBNPxMXF+fn5WXqaAIDORYAIAAAAADAqNDR0w4YNGzZs0Ol0VVVVNTU1np6ecjnbYQGAHSFA7FwqlUoI4ebmZumJdF+UyCRKZBIlMokSoa04Z0yiRCZRIpMokUmUqLuRyWQqlUr679I9cc6YRIlMokQmUSKTbLJEMp1OZ+k52LLS0tLDhw9PmzbN2dnZ0nPppoqLi//xj39Mnz7d0dHR0nPppvLz85OSkmbMmKFQkPi3LDc39/jx4zNmzOAv4cZkZ2cnJydPnz5dJpNZei6wDvQvk+hfJtG/TKJ/mUT/QlvRv0yif5lE/zKJ/mWSTfYvAkQAAAAAAAAARpEWAwAAAAAAADCKABEAAAAAAACAUQSIAAAAAAAAAIwiQAQAAAAAAABgFAEiAAAAAAAAAKMIEAEAAAAAAAAYRYAIAAAAAAAAwCgCRAAAAAAAAABGESACAAAAAAAAMIoAEQAAAAAAAIBRBIgAAAAAAAAAjCJABAAAAAAAAGAUASIAAAAAAAAAowgQAQAAAAAAABhFgIjOdfz48fz8fEvPArB9mzZtevvtty09C8B20L+ArkH/AjoW/QvoGnbYvwgQO0tSUtJjjz3m4+Nz//33x8fHFxUVWXpGFnDlypVx48YlJye3+Kw5JbLhMu7YsePBBx/s0aOHr6/vI488sm/fvubH2HOJysvLlyxZMmTIEDc3t759+06ZMuXChQvND7PnEhl67733li5d+umnnzZ/ihKhrTgfBP2rVfSv1tG/2oT+hQ7E+SDoX62if7WO/tUmdtq/dOgEO3bscHBwcHR0HDNmTP/+/YUQwcHB6enplp5XV5s2bZoQ4tChQ82fMqdEtlpGrVY7d+5cIYSTk9OYMWPGjRvn7OwshJg7d67hYfZcovLy8r59+woh/Pz8Jk+eHBkZKYSQyWSHDx82PMyeS2QoIyPD3d1dCDF27NgmT1EitBXng4T+1SL6l0n0rzahf6EDcT5I6F8ton+ZRP9qE7vtXwSIHe9f//qXUqn09vb+17/+JY2sW7dOCPGb3/zGshPrMt98883GjRsffPBBKaRu3sDMKZENlzEhIUEIMWDAgJycHGkkNTW1X79+Qogvv/xSGrHzEr300ktCiFmzZjU0NEgjX375pUwm8/f31x9j5yXS02q1Dz30UI8ePZo3MEqEtuJ8oH+1jv5lEv3LfPQvdCDOB/pX6+hfJtG/zGfP/YsAseMtW7ZMCLF161bDwbCwMCFEamqqpWbVlUJCQgxXuTZvYOaUyIbLOH78eCHEmTNnDAf3798vhPj9738vPbTzEg0aNMjZ2bmqqspwcNSoUUKIjIwM6aGdl0hvzZo1Mpls9+7dzRsYJUJbcT7Qv1pH/zKJ/mU++hc6EOcD/at19C+T6F/ms+f+xR6IHS8pKUkIERMTYzgoPZSesnnHjx/PzMzMzMyMj49v8QBzSmTDZUxPT1cqlcOHDzccjIiIEELcvHlTemjnJerTp8/UqVNdXFwMBx0cHIQQFRUV0kM7L5Hk7Nmza9asWbBgwcSJE5s/S4nQVpwP9K/W0b9Mon+Zif6FjsX5QP9qHf3LJPqXmey8fyksPQFbo9PpUlJS3N3dg4KCDMfDw8OFENeuXbPQvLpU7969pS88PDyaP2tOiWy7jAcPHpTJZHL5f8T3P/zwgxAiODhYUCIhDh8+3GTku++++/777/v27XvfffcJSiSEEKKioiI2NjYkJGTDhg3Nt92lRGgrzgdB/zKF/mUS/csc9C90LM4HQf8yhf5lEv3LHPQvAsQOVlVVVVNT4+/v32Tc29tbCFFYWGiJSXUv5pTItss4ePDgJiMXL15csmSJTCaTNvelRHpnz57dvHnzrVu3vv/++/vuu++jjz5SKBSCEgkhhFi0aFFWVtbp06eb/KlQQonQVpwPJvFrRf8yH/2rFfQvdCzOB5P4taJ/mY/+1Qr6F5cwd7CamhohhHRHHkPSSFVVlQXm1M2YUyL7KaNOp9u7d+8jjzySk5OzefPmoUOHCkpkoLCw8NKlS1evXm1oaHByctL/k0qJDhw4kJCQsGrVKv1u2U1QIrQV54NJ/FoZon+1jv5lDP0LHY7zwSR+rQzRv1pH/zKG/iUIEDucp6eng4ODfpsAvbKyMvFLrmznzCmRnZTxhx9+GDVq1MyZM1Uq1Wefffbiiy9K45RI77e//e2NGzfKysr++c9//vzzzxMnTrx8+bKw+xLdvn17zpw5kZGRL7/8srFj7LxEaAfOB5P4tdKjf5lE/2oR/QudgfPBJH6t9OhfJtG/WkT/knAJcweTy+U+Pj7Nr4eXRvSbU9gzc0pk82Wsr69ftWrVxo0bnZyc/vznPy9btky6DbyEEjX361//evXq1QsWLHj33Xc3bdpk5yU6dOhQUVGRXC6PjY2VRqS/Vl27du3pp59WKpXvvvuunZcI7cD5YBK/VoL+1Xb0L0P0L3QGzgeT+LUS9K+2o38Zon9JWIHY8QICAkpKSnJzcw0Hr1+/LqzhhOga5pTIhsvY2Nj4u9/9bv369WPHjk1JSXnttdcMu5fEnkt04cKF6Ojobdu2NRmXtu/Nz8+XHtpziSSnTp36+BdffPGFEEKj0Xz88cf79++XDqBEaCvOB5Ps/NeK/tU6+peZ6F/ocJwPJtn5rxX9q3X0LzPRvwgQO97jjz+u0+mOHDliOHjkyBGFQvHoo49aalbdijklsuEybt++/eOPP3722WePHTsWGBjY4jH2XCIPD4/ExMT333+/ybh0U6qwsDDpoT2XaP78+br/lJ2dLYQYO3asTqerrq6WDrPnEqF9OB9MsvNfK/pX6+hfJtG/0Ek4H0yy818r+lfr6F8m0b/+TYeOdufOHYVCERQUlJeXJ40kJCQIIaZOnWrZiXU9aYOAQ4cONRk3p0Q2XMbQ0FBXV9eysrJWjrHzEkkb0/7tb3/Tj1y7du2ee+5xdHS8cuWKNGLnJWrCsIHpUSK0FeeDHv2rRfQvk+hfbUX/QofgfNCjf7WI/mUS/aut7LN/ESB2ip07d8rlcn9//7i4uKioKIVCERwcnJ6ebul5dTVjDUxnXolssow5OTlCCGdn58EtWbJkif5Iuy2RTqc7f/68SqUSQoSFhT3++OMPP/ywUqmUyWRbtmwxPMyeS9REiw1MR4nQdpwPEvpXc/Qvc9C/2or+hY7C+SChfzVH/zIH/aut7LN/ESB2ls8++2zy5Mne3t733Xff7Nmzc3JyLD0jC2ilgenMK5HtlfHUqVOtrAieNm2a4cH2WSLJjRs3nnvuud69ezs5OQUHB0+ZMuXcuXPND7PnEhky1sB0lAhtx/mgo3+1hP5lJvpXm9C/0IE4H3T0r5bQv8xE/2oT++xfMp1O18qvEwAAAAAAAAB7xk1UAAAAAAAAABhFgAgAAAAAAADAKAJEAAAAAAAAAEYRIAIAAAAAAAAwigARAAAAAAAAgFEEiAAAAAAAAACMIkAEAAAAAAAAYBQBIgAAAAAAAACjCBABAAAAAAAAGEWACAAAAAAAAMAoAkQAAAAAAAAARhEgAgAAAAAAADCKABEAAAAAAACAUQSIAAAAAAAAAIwiQAQAAAAAAABgFAEiAAAAAAAAAKMIEAEAAAAAAAAYRYAIAAAAAAAAwCgCRAAAAAAAAABGESACAAAAAAAAMIoAEQAAAAAAAIBRBIgAAAAAAAAAjCJABAAAAAAAAGAUASIAAAAAAAAAowgQAQAAAAAAABhFgAgAAAAAAADAKAJEAAAAAAAAAEYRIAIAAAAAAAAwigARAAAAAAAAgFEEiAAAAAAAAACMIkAEAAAAAAAAYBQBIgAAAAAAAACjCBABAAAAAAAAGEWACAAAAAAAAMAoAkSgnfr06SOTyfLy8iw9kX974YUXZDLZjRs3LD0RAEC3Rv8CAFgj+hdgWQSIQMfQ6XS1tbVardYm3w4AYKvoXwAAa0T/AroYASLQMc6dO+fs7Lx8+XJLvd3y5cvPnDkTFBTUNRMAANgG+hcAwBrRv4AuprD0BAB0jH79+vXr18/SswAAoG3oXwAAa0T/gr1hBSLQjZSXl1vqrevr6xsaGiz17gAAq0b/AgBYI/oXYD4CRKADREdHjxo1SgixadMmmUz2wQcf6J/64IMPJk6c6OPj4+vrO3HixGPHjhl+49KlS2UyWWFh4bvvvhsYGPjb3/5WGi8vL1+2bNmIESPc3d19fHxGjhy5c+dOnU7XytvFx8c32cRXq9WuW7fu4Ycf9vDwCA0NnTp16o8//mj47suXL5fJZJmZmXPnznV3d1cqlX369ImLi8vJyemUMgEAuhn6FwDAGtG/gK5HgAh0gLlz5/7xj38UQowbN27Lli0PPvigNP7cc8/Fxsb+8MMPw4cPv//++0+cOPGb3/xm3bp1Tb593759s2fPjoiIiImJEUIUFBSEh4e/8cYbVVVVEydOHDZs2NWrV+Pj41evXt362xmqqqp66KGHXnnlldTU1F/96leenp6ff/75yJEj9+7d2+TI+Pj4PXv2TJgw4YUXXnB2dt6zZ8+kSZP4axgA2AP6FwDAGtG/AAvQAWiXgIAAIURubq708MyZM0KIJUuW6A/Yt2+fEGLKlCllZWXSSGpqanBwsFwuP3v2rDSyZMkSIYSvr++lS5f037h27VohxPLly/Ujd+7ccXd379evn36k+dv9/ve/F0Jcv35derhq1SohxIwZM6qqqqSRb7/91t3dXa1WFxQUSCMvv/yyEMLV1fX777+XRmprawcNGiSEMJwPAMCW0L8AANaI/gVYFisQgc6ydu1aFxeXvXv39ujRQxoJDg7euHFjY2Pje++9Z3jkrFmzHnjgAf3Dhx56aOfOnX/605/0I/7+/vfcc49GozH/3bds2eLu7r5z504XFxdp5JFHHnnxxRdLSkr27NljeOT8+fOHDRsmfe3o6PjYY48JIVhFDwB2i/4FALBG9C+gU3EXZqBTNDQ0pKSk+Pv7N+lVBQUFQoiLFy8aDg4fPtzw4ZgxY8aMGaPT6TIzMzMzMzMyMo4fP37z5k03Nzcz3/3OnTulpaX/9V//5enpaTgeHR396quvXr9+3XAwMjLS8KFKpTLzXQAAtof+BQCwRvQvoLMRIAKd4vbt23V1dVlZWQsWLGj+bJO7ffn7+xs+rK+vX7NmzY4dOwoLC2Uymb+//9ChQ/38/CoqKsx89+zs7OYvK4To3bu3ECIrK8tw0MvLy8yXBQDYPPoXAMAa0b+AzsYlzECn8PPzc3BwiIqKanHvgCZ/AZPL/+M3MTY29vXXX588efKpU6cqKipu3759+PBhacsPM+n3B2kyLi2Mb9NLAQDsCv0LAGCN6F9AZyNABDqFo6Njv379fvjhh8rKSsPxkydPLly48NixY8a+sbq6+vDhw4MGDdqzZ8/o0aNdXV2l8dLSUvPfvVevXu7u7mfOnCkpKTEcl9534MCBbfhJAAD2hP4FALBG9C+gsxEgAh2ppqZG//WLL75YVFT0zDPP6HtYdnb29OnT//rXv0pL2Vskl8u1Wm1xcXF1dbU0otVq165de/PmzYaGBp1OZ+ztmvjDH/5QWlo6b9682tpaaeTUqVObNm1Sq9UzZ85s788HALBN9C8AgDWifwFdhj0QgY4h7bC7f/9+mUwWGxs7YsSIOXPmfP7551988UVgYODIkSOLioq+//77xsbGdevWhYeHG3sdJyen2NjYhISE4ODgCRMmyOXykydPCiFGjhx59uzZuLi4V155JTQ0tPnbNXmdl1566ejRox999NG33347YsQIjUZz7tw5uVy+a9cuHx+fzqwEAMCa0L8AANaI/gV0MVYgAh3j/vvvX7JkiZOT0549e+7cuSOEcHBwOHr06LZt28LDw8+cOZOZmfnrX//66NGjy5cvb/2ltm/fvmrVKjc3twMHDly+fHnq1KmXLl3aunVreHj4/v37b9++3eLbNaFSqZKTk1977bWgoKD/+7//y83NjYmJOXv27H//9393xo8PALBS9C8AgDWifwFdTNZkRS4AAAAAAAAA6LECEQAAAAAAAIBRBIgAAAAAAAAAjCJABAAAAAAAAGAUASIAAAAAAAAAowgQAQAAAAAAABhFgAgAAAAAAADAKAJEAAAAAAAAAEYRIAIAAAAAAAAwigARAAAAAAAAgFEEiAAAAAAAAACMIkAEAAAAAAAAYBQBIgAAAAAAAACjCBABAAAAAAAAGEWACAAAAAAAAMAoAkQAAAAAAAAARhEgAgAAAAAAADCKABEAAAAAAACAUQSIAAAAAAAAAIwiQAQAAAAAAABgFAEiAAAAAAAAAKMIEAEAAAAAAAAYRYAIAAAAAAAAwCgCRAAAAAAAAABGESACAAAAAAAAMIoAEQAAAAAAAIBRBIgAAAAAAAAAjCJABAAAAAAAAGAUASIAAAAAAAAAowgQAQAAAAAAABhFgAgAAAAAAADAKAJEAAAAAAAAAEYRIAIAAAAAAAAw6v8BiJ6Ln6tzlAMAAAAASUVORK5CYII=" alt="Convergence plot for the NLHM DFOP fit with two-component error and without a random effect on &#39;k2&#39;" width="864" />
+<p class="caption">
 Convergence plot for the NLHM DFOP fit with two-component error and
 without a random effect on ‘k2’
 </p>
 </div>
 <p>All parameters appear to have converged to a satisfactory degree. The
 final fit is plotted using the plot method from the mkin package.</p>
-<div class="sourceCode" id="cb13"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_saem_dfop_tc_no_ranef_k2</span><span class="op">)</span></span></code></pre></div>
+<pre class="r"><code>plot(f_saem_dfop_tc_no_ranef_k2)</code></pre>
 <div class="figure" style="text-align: center">
-<img src="2022_dmta_parent_files/figure-html/plot-saem-dfop-tc-no-ranef-k2-1.png" alt="Plot of the final NLHM DFOP fit" width="864"><p class="caption">
+<img src="data:image/png;base64,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" alt="Plot of the final NLHM DFOP fit" width="864" />
+<p class="caption">
 Plot of the final NLHM DFOP fit
 </p>
 </div>
 <p>Finally, a summary report of the fit is produced.</p>
-<div class="sourceCode" id="cb14"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">f_saem_dfop_tc_no_ranef_k2</span><span class="op">)</span></span></code></pre></div>
-<pre><code>saemix version used for fitting:      3.2 
-mkin version used for pre-fitting:  1.2.6 
-R version used for fitting:         4.3.1 
-Date of fit:     Mon Oct 30 11:19:13 2023 
-Date of summary: Mon Oct 30 11:19:14 2023 
+<pre class="r"><code>summary(f_saem_dfop_tc_no_ranef_k2)</code></pre>
+<pre><code>saemix version used for fitting:      3.3 
+mkin version used for pre-fitting:  1.2.9 
+R version used for fitting:         4.4.2 
+Date of fit:     Thu Feb 13 16:33:33 2025 
+Date of summary: Thu Feb 13 16:33:34 2025 
 
 Equations:
 d_DMTA/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
@@ -1304,7 +1553,7 @@ Data:
 
 Model predictions using solution type analytical 
 
-Fitted in 8.975 s
+Fitted in 3.778 s
 Using 300, 100 iterations and 9 chains
 
 Variance model: Two-component variance function 
@@ -1339,11 +1588,11 @@ DMTA_0    98.256267 96.286112 100.22642
 k1         0.064037  0.033281   0.09479
 k2         0.008469  0.006002   0.01094
 g          0.954167  0.914460   0.99387
-a.1        1.061795  0.863943   1.25965
-b.1        0.029550  0.022529   0.03657
-SD.DMTA_0  2.068581  0.427706   3.70946
+a.1        1.061795  0.878608   1.24498
+b.1        0.029550  0.022593   0.03651
+SD.DMTA_0  2.068581  0.427178   3.70998
 SD.k1      0.598285  0.258235   0.93833
-SD.g       1.016689  0.360057   1.67332
+SD.g       1.016689  0.360061   1.67332
 
 Correlation: 
    DMTA_0  k1      k2     
@@ -1353,22 +1602,21 @@ g  -0.0521 -0.0286 -0.2744
 
 Random effects:
             est.  lower  upper
-SD.DMTA_0 2.0686 0.4277 3.7095
+SD.DMTA_0 2.0686 0.4272 3.7100
 SD.k1     0.5983 0.2582 0.9383
 SD.g      1.0167 0.3601 1.6733
 
 Variance model:
        est.   lower   upper
-a.1 1.06180 0.86394 1.25965
-b.1 0.02955 0.02253 0.03657
+a.1 1.06180 0.87861 1.24498
+b.1 0.02955 0.02259 0.03651
 
 Estimated disappearance times:
       DT50  DT90 DT50back DT50_k1 DT50_k2
 DMTA 11.45 41.32    12.44   10.82   81.85</code></pre>
 </div>
-<div class="section level3">
-<h3 id="alternative-check-of-parameter-identifiability">Alternative check of parameter identifiability<a class="anchor" aria-label="anchor" href="#alternative-check-of-parameter-identifiability"></a>
-</h3>
+<div id="alternative-check-of-parameter-identifiability" class="section level2">
+<h2>Alternative check of parameter identifiability</h2>
 <p>The parameter check used in the <code>illparms</code> function is
 based on a quadratic approximation of the likelihood surface near its
 optimum, which is calculated using the Fisher Information Matrix (FIM).
@@ -1378,13 +1626,12 @@ approach has recently been implemented in mkin.</p>
 of the saem algorithm with different parameter combinations, sampled
 from the range of the parameters obtained for the individual datasets
 fitted separately using nonlinear regression.</p>
-<div class="sourceCode" id="cb16"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">f_saem_dfop_tc_multi</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/multistart.html">multistart</a></span><span class="op">(</span><span class="va">f_saem</span><span class="op">[[</span><span class="st">"DFOP"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span>, n <span class="op">=</span> <span class="fl">50</span>, cores <span class="op">=</span> <span class="fl">15</span><span class="op">)</span></span></code></pre></div>
-<div class="sourceCode" id="cb17"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/par.html" class="external-link">par</a></span><span class="op">(</span>mar <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">6.1</span>, <span class="fl">4.1</span>, <span class="fl">2.1</span>, <span class="fl">2.1</span><span class="op">)</span><span class="op">)</span></span>
-<span><span class="fu"><a href="../../reference/parplot.html">parplot</a></span><span class="op">(</span><span class="va">f_saem_dfop_tc_multi</span>, lpos <span class="op">=</span> <span class="st">"bottomright"</span>, ylim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.3</span>, <span class="fl">10</span><span class="op">)</span>, las <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span></code></pre></div>
+<pre class="r"><code>f_saem_dfop_tc_multi &lt;- multistart(f_saem[[&quot;DFOP&quot;, &quot;tc&quot;]], n = 50, cores = 15)</code></pre>
+<pre class="r"><code>par(mar = c(6.1, 4.1, 2.1, 2.1))
+parplot(f_saem_dfop_tc_multi, lpos = &quot;bottomright&quot;, ylim = c(0.3, 10), las = 2)</code></pre>
 <div class="figure" style="text-align: center">
-<img src="2022_dmta_parent_files/figure-html/multistart-full-par-1.png" alt="Scaled parameters from the multistart runs, full model" width="960"><p class="caption">
+<img src="data:image/png;base64,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" alt="Scaled parameters from the multistart runs, full model" width="960" />
+<p class="caption">
 Scaled parameters from the multistart runs, full model
 </p>
 </div>
@@ -1395,36 +1642,34 @@ parameter <code>g</code>.</p>
 <p>The parameter boxplots of the multistart runs with the reduced model
 shown below indicate that all runs give similar results, regardless of
 the starting parameters.</p>
-<div class="sourceCode" id="cb18"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">f_saem_dfop_tc_no_ranef_k2_multi</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/multistart.html">multistart</a></span><span class="op">(</span><span class="va">f_saem_dfop_tc_no_ranef_k2</span>,</span>
-<span>  n <span class="op">=</span> <span class="fl">50</span>, cores <span class="op">=</span> <span class="fl">15</span><span class="op">)</span></span></code></pre></div>
-<div class="sourceCode" id="cb19"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/par.html" class="external-link">par</a></span><span class="op">(</span>mar <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">6.1</span>, <span class="fl">4.1</span>, <span class="fl">2.1</span>, <span class="fl">2.1</span><span class="op">)</span><span class="op">)</span></span>
-<span><span class="fu"><a href="../../reference/parplot.html">parplot</a></span><span class="op">(</span><span class="va">f_saem_dfop_tc_no_ranef_k2_multi</span>, ylim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.5</span>, <span class="fl">2</span><span class="op">)</span>, las <span class="op">=</span> <span class="fl">2</span>,</span>
-<span>  lpos <span class="op">=</span> <span class="st">"bottomright"</span><span class="op">)</span></span></code></pre></div>
+<pre class="r"><code>f_saem_dfop_tc_no_ranef_k2_multi &lt;- multistart(f_saem_dfop_tc_no_ranef_k2,
+  n = 50, cores = 15)</code></pre>
+<pre class="r"><code>par(mar = c(6.1, 4.1, 2.1, 2.1))
+parplot(f_saem_dfop_tc_no_ranef_k2_multi, ylim = c(0.5, 2), las = 2,
+  lpos = &quot;bottomright&quot;)</code></pre>
 <div class="figure" style="text-align: center">
-<img src="2022_dmta_parent_files/figure-html/multistart-reduced-par-1.png" alt="Scaled parameters from the multistart runs, reduced model" width="960"><p class="caption">
+<img src="data:image/png;base64,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" alt="Scaled parameters from the multistart runs, reduced model" width="960" />
+<p class="caption">
 Scaled parameters from the multistart runs, reduced model
 </p>
 </div>
 <p>When only the parameters of the top 25% of the fits are shown (based
 on a feature introduced in mkin 1.2.2 currently under development), the
 scatter is even less as shown below.</p>
-<div class="sourceCode" id="cb20"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/par.html" class="external-link">par</a></span><span class="op">(</span>mar <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">6.1</span>, <span class="fl">4.1</span>, <span class="fl">2.1</span>, <span class="fl">2.1</span><span class="op">)</span><span class="op">)</span></span>
-<span><span class="fu"><a href="../../reference/parplot.html">parplot</a></span><span class="op">(</span><span class="va">f_saem_dfop_tc_no_ranef_k2_multi</span>, ylim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.5</span>, <span class="fl">2</span><span class="op">)</span>, las <span class="op">=</span> <span class="fl">2</span>, llquant <span class="op">=</span> <span class="fl">0.25</span>,</span>
-<span>  lpos <span class="op">=</span> <span class="st">"bottomright"</span><span class="op">)</span></span></code></pre></div>
+<pre class="r"><code>par(mar = c(6.1, 4.1, 2.1, 2.1))
+parplot(f_saem_dfop_tc_no_ranef_k2_multi, ylim = c(0.5, 2), las = 2, llquant = 0.25,
+  lpos = &quot;bottomright&quot;)</code></pre>
 <div class="figure" style="text-align: center">
-<img src="2022_dmta_parent_files/figure-html/multistart-reduced-par-llquant-1.png" alt="Scaled parameters from the multistart runs, reduced model, fits with the top 25\% likelihood values" width="960"><p class="caption">
+<img src="data:image/png;base64,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" alt="Scaled parameters from the multistart runs, reduced model, fits with the top 25\% likelihood values" width="960" />
+<p class="caption">
 Scaled parameters from the multistart runs, reduced model, fits with the
 top 25% likelihood values
 </p>
 </div>
 </div>
 </div>
-<div class="section level2">
-<h2 id="conclusions">Conclusions<a class="anchor" aria-label="anchor" href="#conclusions"></a>
-</h2>
+<div id="conclusions" class="section level1">
+<h1>Conclusions</h1>
 <p>Fitting the four parent degradation models SFO, FOMC, DFOP and HS as
 part of hierarchical model fits with two different error models and
 normal distributions of the transformed degradation parameters works
@@ -1436,39 +1681,35 @@ was fully identifiable and showed the lowest values for the model
 selection criteria AIC and BIC. The reliability of the identification of
 all model parameters was confirmed using multiple starting values.</p>
 </div>
-<div class="section level2">
-<h2 id="acknowledgements">Acknowledgements<a class="anchor" aria-label="anchor" href="#acknowledgements"></a>
-</h2>
+<div id="acknowledgements" class="section level1">
+<h1>Acknowledgements</h1>
 <p>The helpful comments by Janina Wöltjen of the German Environment
 Agency are gratefully acknowledged.</p>
 </div>
-<div class="section level2">
-<h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a>
-</h2>
+<div id="references" class="section level1">
+<h1>References</h1>
 <div id="refs" class="references csl-bib-body hanging-indent">
 <div id="ref-duchesne_2021" class="csl-entry">
 Duchesne, Ronan, Anissa Guillemin, Olivier Gandrillon, and Fabien
 Crauste. 2021. <span>“Practical Identifiability in the Frame of
 Nonlinear Mixed Effects Models: The Example of the in Vitro
-Erythropoiesis.”</span> <em>BMC Bioinformatics</em> 22 (478). <a href="https://doi.org/10.1186/s12859-021-04373-4" class="external-link">https://doi.org/10.1186/s12859-021-04373-4</a>.
+Erythropoiesis.”</span> <em>BMC Bioinformatics</em> 22 (478). <a href="https://doi.org/10.1186/s12859-021-04373-4">https://doi.org/10.1186/s12859-021-04373-4</a>.
 </div>
 </div>
 </div>
-<div class="section level2">
-<h2 id="appendix">Appendix<a class="anchor" aria-label="anchor" href="#appendix"></a>
-</h2>
-<div class="section level3">
-<h3 id="hierarchical-model-fit-listings">Hierarchical model fit listings<a class="anchor" aria-label="anchor" href="#hierarchical-model-fit-listings"></a>
-</h3>
+<div id="appendix" class="section level1">
+<h1>Appendix</h1>
+<div id="hierarchical-model-fit-listings" class="section level2">
+<h2>Hierarchical model fit listings</h2>
 <caption>
 Hierarchical mkin fit of the SFO model with error model const
 </caption>
 <pre><code>
-saemix version used for fitting:      3.2 
-mkin version used for pre-fitting:  1.2.6 
-R version used for fitting:         4.3.1 
-Date of fit:     Mon Oct 30 11:18:56 2023 
-Date of summary: Mon Oct 30 11:21:30 2023 
+saemix version used for fitting:      3.3 
+mkin version used for pre-fitting:  1.2.9 
+R version used for fitting:         4.4.2 
+Date of fit:     Thu Feb 13 16:33:26 2025 
+Date of summary: Thu Feb 13 16:34:39 2025 
 
 Equations:
 d_DMTA/dt = - k_DMTA * DMTA
@@ -1478,7 +1719,7 @@ Data:
 
 Model predictions using solution type analytical 
 
-Fitted in 1.899 s
+Fitted in 0.792 s
 Using 300, 100 iterations and 9 chains
 
 Variance model: Constant variance 
@@ -1530,17 +1771,17 @@ Estimated disappearance times:
       DT50  DT90
 DMTA 12.24 40.65
 
-</code></pre>
-<p></p>
+</pre>
+<p></code></p>
 <caption>
 Hierarchical mkin fit of the SFO model with error model tc
 </caption>
 <pre><code>
-saemix version used for fitting:      3.2 
-mkin version used for pre-fitting:  1.2.6 
-R version used for fitting:         4.3.1 
-Date of fit:     Mon Oct 30 11:19:00 2023 
-Date of summary: Mon Oct 30 11:21:30 2023 
+saemix version used for fitting:      3.3 
+mkin version used for pre-fitting:  1.2.9 
+R version used for fitting:         4.4.2 
+Date of fit:     Thu Feb 13 16:33:28 2025 
+Date of summary: Thu Feb 13 16:34:39 2025 
 
 Equations:
 d_DMTA/dt = - k_DMTA * DMTA
@@ -1550,7 +1791,7 @@ Data:
 
 Model predictions using solution type analytical 
 
-Fitted in 5.364 s
+Fitted in 2.245 s
 Using 300, 100 iterations and 9 chains
 
 Variance model: Two-component variance function 
@@ -1578,13 +1819,13 @@ Likelihood computed by importance sampling
   798.3 797.1 -393.2
 
 Optimised parameters:
-               est.     lower    upper
-DMTA_0    97.271822 95.703157 98.84049
-k_DMTA     0.056638  0.029110  0.08417
-a.1        2.660081  2.230398  3.08976
-b.1        0.001665 -0.006911  0.01024
-SD.DMTA_0  1.545520  0.145035  2.94601
-SD.k_DMTA  0.606422  0.262274  0.95057
+               est.    lower    upper
+DMTA_0    97.271822 95.70316 98.84049
+k_DMTA     0.056638  0.02911  0.08417
+a.1        2.660081  2.27492  3.04525
+b.1        0.001665 -0.14451  0.14784
+SD.DMTA_0  1.545520  0.14301  2.94803
+SD.k_DMTA  0.606422  0.26227  0.95057
 
 Correlation: 
        DMTA_0
@@ -1592,29 +1833,29 @@ k_DMTA 0.0169
 
 Random effects:
             est.  lower  upper
-SD.DMTA_0 1.5455 0.1450 2.9460
+SD.DMTA_0 1.5455 0.1430 2.9480
 SD.k_DMTA 0.6064 0.2623 0.9506
 
 Variance model:
-        est.     lower   upper
-a.1 2.660081  2.230398 3.08976
-b.1 0.001665 -0.006911 0.01024
+        est.   lower  upper
+a.1 2.660081  2.2749 3.0452
+b.1 0.001665 -0.1445 0.1478
 
 Estimated disappearance times:
       DT50  DT90
 DMTA 12.24 40.65
 
-</code></pre>
-<p></p>
+</pre>
+<p></code></p>
 <caption>
 Hierarchical mkin fit of the FOMC model with error model const
 </caption>
 <pre><code>
-saemix version used for fitting:      3.2 
-mkin version used for pre-fitting:  1.2.6 
-R version used for fitting:         4.3.1 
-Date of fit:     Mon Oct 30 11:18:57 2023 
-Date of summary: Mon Oct 30 11:21:30 2023 
+saemix version used for fitting:      3.3 
+mkin version used for pre-fitting:  1.2.9 
+R version used for fitting:         4.4.2 
+Date of fit:     Thu Feb 13 16:33:27 2025 
+Date of summary: Thu Feb 13 16:34:39 2025 
 
 Equations:
 d_DMTA/dt = - (alpha/beta) * 1/((time/beta) + 1) * DMTA
@@ -1624,7 +1865,7 @@ Data:
 
 Model predictions using solution type analytical 
 
-Fitted in 2.944 s
+Fitted in 1.409 s
 Using 300, 100 iterations and 9 chains
 
 Variance model: Constant variance 
@@ -1681,17 +1922,17 @@ Estimated disappearance times:
       DT50  DT90 DT50back
 DMTA 11.41 42.53     12.8
 
-</code></pre>
-<p></p>
+</pre>
+<p></code></p>
 <caption>
 Hierarchical mkin fit of the FOMC model with error model tc
 </caption>
 <pre><code>
-saemix version used for fitting:      3.2 
-mkin version used for pre-fitting:  1.2.6 
-R version used for fitting:         4.3.1 
-Date of fit:     Mon Oct 30 11:19:01 2023 
-Date of summary: Mon Oct 30 11:21:30 2023 
+saemix version used for fitting:      3.3 
+mkin version used for pre-fitting:  1.2.9 
+R version used for fitting:         4.4.2 
+Date of fit:     Thu Feb 13 16:33:28 2025 
+Date of summary: Thu Feb 13 16:34:39 2025 
 
 Equations:
 d_DMTA/dt = - (alpha/beta) * 1/((time/beta) + 1) * DMTA
@@ -1701,7 +1942,7 @@ Data:
 
 Model predictions using solution type analytical 
 
-Fitted in 6.228 s
+Fitted in 2.811 s
 Using 300, 100 iterations and 9 chains
 
 Variance model: Two-component variance function 
@@ -1734,11 +1975,11 @@ Optimised parameters:
 DMTA_0    99.10577 97.33296 100.87859
 alpha      5.46260  2.52199   8.40321
 beta      81.66080 30.46664 132.85497
-a.1        1.50219  1.23601   1.76836
-b.1        0.02893  0.02099   0.03687
-SD.DMTA_0  1.61887 -0.03636   3.27411
+a.1        1.50219  1.25801   1.74636
+b.1        0.02893  0.02048   0.03739
+SD.DMTA_0  1.61887 -0.03843   3.27618
 SD.alpha   0.58145  0.17364   0.98925
-SD.beta    0.68205  0.21108   1.15303
+SD.beta    0.68205  0.21108   1.15302
 
 Correlation: 
       DMTA_0  alpha  
@@ -1747,30 +1988,30 @@ beta  -0.1430  0.2467
 
 Random effects:
             est.    lower  upper
-SD.DMTA_0 1.6189 -0.03636 3.2741
+SD.DMTA_0 1.6189 -0.03843 3.2762
 SD.alpha  0.5814  0.17364 0.9892
 SD.beta   0.6821  0.21108 1.1530
 
 Variance model:
        est.   lower   upper
-a.1 1.50219 1.23601 1.76836
-b.1 0.02893 0.02099 0.03687
+a.1 1.50219 1.25801 1.74636
+b.1 0.02893 0.02048 0.03739
 
 Estimated disappearance times:
       DT50  DT90 DT50back
 DMTA 11.05 42.81    12.89
 
-</code></pre>
-<p></p>
+</pre>
+<p></code></p>
 <caption>
 Hierarchical mkin fit of the DFOP model with error model const
 </caption>
 <pre><code>
-saemix version used for fitting:      3.2 
-mkin version used for pre-fitting:  1.2.6 
-R version used for fitting:         4.3.1 
-Date of fit:     Mon Oct 30 11:18:57 2023 
-Date of summary: Mon Oct 30 11:21:30 2023 
+saemix version used for fitting:      3.3 
+mkin version used for pre-fitting:  1.2.9 
+R version used for fitting:         4.4.2 
+Date of fit:     Thu Feb 13 16:33:27 2025 
+Date of summary: Thu Feb 13 16:34:39 2025 
 
 Equations:
 d_DMTA/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
@@ -1782,7 +2023,7 @@ Data:
 
 Model predictions using solution type analytical 
 
-Fitted in 3.231 s
+Fitted in 1.638 s
 Using 300, 100 iterations and 9 chains
 
 Variance model: Constant variance 
@@ -1844,17 +2085,17 @@ Estimated disappearance times:
       DT50 DT90 DT50back DT50_k1 DT50_k2
 DMTA 11.79 42.8    12.88   11.09   76.46
 
-</code></pre>
-<p></p>
+</pre>
+<p></code></p>
 <caption>
 Hierarchical mkin fit of the DFOP model with error model tc
 </caption>
 <pre><code>
-saemix version used for fitting:      3.2 
-mkin version used for pre-fitting:  1.2.6 
-R version used for fitting:         4.3.1 
-Date of fit:     Mon Oct 30 11:19:01 2023 
-Date of summary: Mon Oct 30 11:21:30 2023 
+saemix version used for fitting:      3.3 
+mkin version used for pre-fitting:  1.2.9 
+R version used for fitting:         4.4.2 
+Date of fit:     Thu Feb 13 16:33:28 2025 
+Date of summary: Thu Feb 13 16:34:39 2025 
 
 Equations:
 d_DMTA/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
@@ -1866,7 +2107,7 @@ Data:
 
 Model predictions using solution type analytical 
 
-Fitted in 6.71 s
+Fitted in 3.024 s
 Using 300, 100 iterations and 9 chains
 
 Variance model: Two-component variance function 
@@ -1901,12 +2142,12 @@ DMTA_0    98.347470 96.380815 100.31413
 k1         0.064524  0.034279   0.09477
 k2         0.008304  0.005843   0.01076
 g          0.952128  0.909578   0.99468
-a.1        1.068907  0.868694   1.26912
-b.1        0.029265  0.022262   0.03627
-SD.DMTA_0  2.065796  0.428485   3.70311
+a.1        1.068907  0.883665   1.25415
+b.1        0.029265  0.022318   0.03621
+SD.DMTA_0  2.065796  0.427951   3.70364
 SD.k1      0.583703  0.251796   0.91561
-SD.k2      0.004167 -7.832168   7.84050
-SD.g       1.064450  0.397476   1.73142
+SD.k2      0.004167 -7.831228   7.83956
+SD.g       1.064450  0.397479   1.73142
 
 Correlation: 
    DMTA_0  k1      k2     
@@ -1916,114 +2157,259 @@ g  -0.0464 -0.0269 -0.2713
 
 Random effects:
               est.   lower  upper
-SD.DMTA_0 2.065796  0.4285 3.7031
+SD.DMTA_0 2.065796  0.4280 3.7036
 SD.k1     0.583703  0.2518 0.9156
-SD.k2     0.004167 -7.8322 7.8405
+SD.k2     0.004167 -7.8312 7.8396
 SD.g      1.064450  0.3975 1.7314
 
 Variance model:
        est.   lower   upper
-a.1 1.06891 0.86869 1.26912
-b.1 0.02927 0.02226 0.03627
+a.1 1.06891 0.88367 1.25415
+b.1 0.02927 0.02232 0.03621
 
 Estimated disappearance times:
       DT50  DT90 DT50back DT50_k1 DT50_k2
 DMTA 11.39 41.36    12.45   10.74   83.48
 
-</code></pre>
-<p></p>
+</pre>
+<p></code></p>
 <caption>
 Hierarchical mkin fit of the HS model with error model const
 </caption>
 <pre><code>
-saemix version used for fitting:      3.2 
-mkin version used for pre-fitting:  1.2.6 
-R version used for fitting:         4.3.1 
-Date of fit:     Mon Oct 30 11:18:59 2023 
-Date of summary: Mon Oct 30 11:21:30 2023 
+saemix version used for fitting:      3.3 
+mkin version used for pre-fitting:  1.2.9 
+R version used for fitting:         4.4.2 
+Date of fit:     Thu Feb 13 16:33:28 2025 
+Date of summary: Thu Feb 13 16:34:39 2025 
 
 Equations:
-d_DMTA/dt = - ifelse(time 
-<p></p></code>
+d_DMTA/dt = - ifelse(time &lt;= tb, k1, k2) * DMTA
+
+Data:
+155 observations of 1 variable(s) grouped in 6 datasets
+
+Model predictions using solution type analytical 
+
+Fitted in 2.301 s
+Using 300, 100 iterations and 9 chains
+
+Variance model: Constant variance 
+
+Starting values for degradation parameters:
+  DMTA_0       k1       k2       tb 
+97.82176  0.06931  0.02997 11.13945 
+
+Fixed degradation parameter values:
+None
+
+Starting values for random effects (square root of initial entries in omega):
+       DMTA_0 k1 k2 tb
+DMTA_0  97.82  0  0  0
+k1       0.00  1  0  0
+k2       0.00  0  1  0
+tb       0.00  0  0  1
+
+Starting values for error model parameters:
+a.1 
+  1 
+
+Results:
+
+Likelihood computed by importance sampling
+  AIC   BIC logLik
+  714 712.1   -348
+
+Optimised parameters:
+              est.    lower    upper
+DMTA_0    98.16102 96.47747 99.84456
+k1         0.07876  0.05261  0.10491
+k2         0.02227  0.01706  0.02747
+tb        13.99089 -7.40049 35.38228
+a.1        1.82305  1.60700  2.03910
+SD.DMTA_0  1.88413  0.56204  3.20622
+SD.k1      0.34292  0.10482  0.58102
+SD.k2      0.19851  0.01718  0.37985
+SD.tb      1.68168  0.58064  2.78272
+
+Correlation: 
+   DMTA_0  k1      k2     
+k1  0.0142                
+k2  0.0001 -0.0025        
+tb  0.0165 -0.1256 -0.0301
+
+Random effects:
+            est.   lower  upper
+SD.DMTA_0 1.8841 0.56204 3.2062
+SD.k1     0.3429 0.10482 0.5810
+SD.k2     0.1985 0.01718 0.3798
+SD.tb     1.6817 0.58064 2.7827
+
+Variance model:
+     est. lower upper
+a.1 1.823 1.607 2.039
+
+Estimated disappearance times:
+      DT50  DT90 DT50back DT50_k1 DT50_k2
+DMTA 8.801 67.91    20.44   8.801   31.13
+
+</pre>
+<p></code></p>
 <caption>
 Hierarchical mkin fit of the HS model with error model tc
 </caption>
 <pre><code>
-saemix version used for fitting:      3.2 
-mkin version used for pre-fitting:  1.2.6 
-R version used for fitting:         4.3.1 
-Date of fit:     Mon Oct 30 11:19:02 2023 
-Date of summary: Mon Oct 30 11:21:30 2023 
+saemix version used for fitting:      3.3 
+mkin version used for pre-fitting:  1.2.9 
+R version used for fitting:         4.4.2 
+Date of fit:     Thu Feb 13 16:33:29 2025 
+Date of summary: Thu Feb 13 16:34:39 2025 
 
 Equations:
-d_DMTA/dt = - ifelse(time 
-<p></p></code>
-</pre></pre>
+d_DMTA/dt = - ifelse(time &lt;= tb, k1, k2) * DMTA
+
+Data:
+155 observations of 1 variable(s) grouped in 6 datasets
+
+Model predictions using solution type analytical 
+
+Fitted in 3.264 s
+Using 300, 100 iterations and 9 chains
+
+Variance model: Two-component variance function 
+
+Starting values for degradation parameters:
+  DMTA_0       k1       k2       tb 
+98.45190  0.07525  0.02576 19.19375 
+
+Fixed degradation parameter values:
+None
+
+Starting values for random effects (square root of initial entries in omega):
+       DMTA_0 k1 k2 tb
+DMTA_0  98.45  0  0  0
+k1       0.00  1  0  0
+k2       0.00  0  1  0
+tb       0.00  0  0  1
+
+Starting values for error model parameters:
+a.1 b.1 
+  1   1 
+
+Results:
+
+Likelihood computed by importance sampling
+    AIC BIC logLik
+  667.1 665 -323.6
+
+Optimised parameters:
+              est.    lower    upper
+DMTA_0    97.76571 95.81350 99.71791
+k1         0.05855  0.03080  0.08630
+k2         0.02337  0.01664  0.03010
+tb        31.09638 29.38289 32.80987
+a.1        1.08835  0.90059  1.27611
+b.1        0.02964  0.02261  0.03667
+SD.DMTA_0  2.04877  0.42553  3.67200
+SD.k1      0.59166  0.25621  0.92711
+SD.k2      0.30698  0.09561  0.51835
+SD.tb      0.01274 -0.10915  0.13464
+
+Correlation: 
+   DMTA_0  k1      k2     
+k1  0.0160                
+k2 -0.0070 -0.0024        
+tb -0.0668 -0.0103 -0.2013
+
+Random effects:
+             est.    lower  upper
+SD.DMTA_0 2.04877  0.42553 3.6720
+SD.k1     0.59166  0.25621 0.9271
+SD.k2     0.30698  0.09561 0.5183
+SD.tb     0.01274 -0.10915 0.1346
+
+Variance model:
+       est.   lower   upper
+a.1 1.08835 0.90059 1.27611
+b.1 0.02964 0.02261 0.03667
+
+Estimated disappearance times:
+      DT50  DT90 DT50back DT50_k1 DT50_k2
+DMTA 11.84 51.71    15.57   11.84   29.66
+
+</pre>
+<p></code></p>
 </div>
-<div class="section level3">
-<h3 id="hierarchical-model-convergence-plots">Hierarchical model convergence plots<a class="anchor" aria-label="anchor" href="#hierarchical-model-convergence-plots"></a>
-</h3>
+<div id="hierarchical-model-convergence-plots" class="section level2">
+<h2>Hierarchical model convergence plots</h2>
 <div class="figure" style="text-align: center">
-<img src="2022_dmta_parent_files/figure-html/convergence-saem-sfo-const-1.png" alt="Convergence plot for the NLHM SFO fit with constant variance" width="864"><p class="caption">
+<img src="data:image/png;base64,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" alt="Convergence plot for the NLHM SFO fit with constant variance" width="864" />
+<p class="caption">
 Convergence plot for the NLHM SFO fit with constant variance
 </p>
 </div>
 <div class="figure" style="text-align: center">
-<img src="2022_dmta_parent_files/figure-html/convergence-saem-sfo-tc-1.png" alt="Convergence plot for the NLHM SFO fit with two-component error" width="864"><p class="caption">
+<img src="data:image/png;base64,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" alt="Convergence plot for the NLHM SFO fit with two-component error" width="864" />
+<p class="caption">
 Convergence plot for the NLHM SFO fit with two-component error
 </p>
 </div>
 <div class="figure" style="text-align: center">
-<img src="2022_dmta_parent_files/figure-html/convergence-saem-fomc-const-1.png" alt="Convergence plot for the NLHM FOMC fit with constant variance" width="864"><p class="caption">
+<img src="data:image/png;base64,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" alt="Convergence plot for the NLHM FOMC fit with constant variance" width="864" />
+<p class="caption">
 Convergence plot for the NLHM FOMC fit with constant variance
 </p>
 </div>
 <div class="figure" style="text-align: center">
-<img src="2022_dmta_parent_files/figure-html/convergence-saem-fomc-tc-1.png" alt="Convergence plot for the NLHM FOMC fit with two-component error" width="864"><p class="caption">
+<img src="data:image/png;base64,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" alt="Convergence plot for the NLHM FOMC fit with two-component error" width="864" />
+<p class="caption">
 Convergence plot for the NLHM FOMC fit with two-component error
 </p>
 </div>
 <div class="figure" style="text-align: center">
-<img src="2022_dmta_parent_files/figure-html/convergence-saem-dfop-const-1.png" alt="Convergence plot for the NLHM DFOP fit with constant variance" width="864"><p class="caption">
+<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABsAAAAYACAIAAABHMxqdAAAACXBIWXMAAB2HAAAdhwGP5fFlAAAgAElEQVR4nOzdeVxU1/3/8TOAiiwDMiLqiKKREAV3jGCaulRqTGIwZjFps4GRRKyYRJuqjWhjgyGhTSqp1oZqNrMpMXFFJ4sijcQtggiojIigGHDGDRAQmN8f9+d8p8ggoM6dy7yef/Rx59zDnU/aR7m5bz7nXJXJZBIAAAAAAAAA0BQnuQsAAAAAAAAAYL8IEAEAAAAAAABYRYAIAAAAAAAAwCoCRAAAAAAAAABWESACAAAAAAAAsIoAEQAAAAAAAIBVBIgAAAAAAAAArCJABAAAAAAAAGAVASIAAAAAAAAAqwgQAQAAAAAAAFhFgAgAAAAAAADAKgJEAAAAAAAAAFYRIAIAAAAAAACwigARAAAAAAAAgFUEiAAAAAAAAACsIkAEAAAAAAAAYBUBIgAAAAAAAACrCBABAAAAAAAAWEWACAAAAAAAAMAqAkQAAAAAAAAAVhEgAgAAAAAAALCKABEAAAAAAACAVQSIAAAAAAAAAKwiQAQAAAAAAABgFQEiAAAAAAAAAKsIEAEAAAAAAABYRYAIAAAAAAAAwCoCRAAAAAAAAABWESACAAAAAAAAsIoAEQAAAAAAAIBVBIgAAAAAAAAArCJABAAAAAAAAGAVASIAAAAAAAAAqwgQAQAAAAAAAFhFgAgAAAAAAADAKgJEAAAAAAAAAFYRIAIAAAAAAACwigARAAAAAAAAgFUEiAAAAAAAAACsIkAEAAAAAAAAYBUBIgAAAAAAAACrCBABAAAAAAAAWEWACAAAAAAAAMAqAkSgdT788EPV/3JycvL29h40aNBLL7106tQpy8kzZsywnPnTTz9Znv3LX/5iefbDDz8UQkyaNEl1I1FRUeaL5Ofnm8cfeeQR2/yXAACA5N5775XuQVlZWXLXAgBAS3H/AlqLABG4WSaT6eLFizk5Of/4xz9CQkLS09OtzczMzLT8uHfv3pv/9i+++MJ8vHXr1oqKijZfKiUl5f777/fx8enevfvjjz+enZ198+UBAGBp3759nTt39vb2lrsQAABurKKi4k9/+tPIkSM9PT0DAgLuv//+bdu2yV0UIA8XuQsAlKpr16733XefEKKuri4nJyc3N7ehoeHy5cvR0dH5+fkuLk38nyszM3POnDnmj00GiOPHj+/atat0fPXqVXM+OGXKFA8PD+k4PDzcPP/zzz83H1dXV2/evPmJJ55o7T9LXV3dzJkzU1JSzCPr1q3buHHj119/Lf0zAgBw88rKyl588cXq6mq5CwEA4MbKy8tDQ0PNi8wqKiqKioq2bdsWGxv7z3/+U97aANsjQATaqH///h9//LH5o06nmzp1akVFhV6v/+ijj6Kjoy0nd+zYsba21rID8cSJE+fOnTOfMo//8Y9/NB9XVlaaA8R33323T58+jWrIzs7Oz88XQri4uNTV1Qkh1q1b14YA8e9//7uUHt5xxx3PPfdcQUHBhx9+WFNT8/vf//7EiRNeXl6tvSAAAGaHDx/et2/fwYMHP/vsM6PRKHc5AAC0yLx586T0MCgo6Kmnnvrll1/ef//9mpqaFStWTJw48aGHHpK7QMCmWMIM3BoRERHz58+Xjt96661GZ4cOHSqEOHny5NmzZ6URqf2wY8eOAwcObPOXmtsPX375ZWdnZyHEtm3b2rCK+b333hNCuLq6bt++/bXXXvvggw9efPFFIYTRaPz000/bXB4AwAElJiZKu0oNGTLk8uXLQoh///vf06dP/+c//0l6CACwW43uXyaTKTU1VQjh6emZkZHx2muvJScnv/vuu9Jky42kAAdBgAjcMuZ3mxw7duzKlSuWp0JDQzt06CCEML9HRQoQhw0b5urq2uZv/PLLL6WDmJiYMWPGCCGuXLmyefPmVl2koKCguLhYCHH33Xffcccd0uBjjz0mHezatavN5QEAHM2OHTsWLlwohOjZs+eWLVs8PT2FEIMHD55yjUqlkrtGAAAau/7+VVxcXFlZKYQYNWqUeY+pCRMmSAeHDx+Wq1RALgSIwC3Ts2dPabWvyWQqLCy0POXm5jZkyBAhxJ49e6QRKUAMCwtr89ft27dPr9cLIQYPHty/f/+pU6dK4+vWrWvVdY4fPy4d9OrVyzxoPi4oKGhzhQAAh6LX65944omGhgZ3d/dNmzaZbyUzZszYcI3ULw8AgP1o8v7VuXPnpKSkpKSkuLg480yps14IYd6eHnAcBIjArdS9e3fpQIr2LElZobQNYl1d3cGDB8XNBYjmtvlHHnlECGFu62jtKuYLFy5IB+7u7uZBqWdECHH+/Pk2VwgAcBwVFRVTpkw5f/68k5PTZ599Nnz4cLkrAgDgxqzdv3x9fefOnTt37tzJkyebJ5uXME+cOFGGWgFZESACt1IzK7OkVyfv27evvr7+8OHD0hrnNgeIJpPJvH5ZChC1Wu2oUaOEEFeuXNmyZUvLL2V+G6blYmo3N7dGZwEAaEZsbGxOTo4Q4oUXXrB81gIAwJ618P7V0NAQFxf30UcfCSEGDhz40ksv2a5EwD4QIAK3UmlpqXRg3kzQTAoQq6qqDh8+LK1f9vPzCwgIaNsX/fjjj9LGhXfeeWdwcLA0aF7FbM4WW6Jjx47SQVVVlXnQnBt27ty5bRUCABxKdna2dPD1119Lm0YBAGD/WnL/Ki4uHjduXHJyshBi6NChO3bskLauAhwKASJwy5w9e/bixYtCCJVK1bdv30Zn+/bt6+fnJ4TIzMyUXqUiRYptY37/8rFjx1TXvPrqq9Jgq1Yxd+nSRTow7+hheezt7d3mIgEADkWtVgshSktLk5KS5K4FAICWav7+tXHjxiFDhqSnpwshnn322R9//FGr1dq6RMAOECACt8yaNWukg8DAwCYb96TEMDMz8ybfoNLQ0LB+/fpmJrRqFXNgYKB0UFRUZB40H5vPAgDQjIcffnjr1q3S8dtvv21uyQcAwJ41f/9aunSptEOih4fHp59++sEHH7BCCw6LABG4NX744Yc333xTOv7Tn/7U5BwpMfzuu+/y8vLETQSIO3fuPHv2rBCiW7duT/2v0aNHS3Na/i7mwMBAaRH0/v37zQ3877//vnQwfvz4thUJAHAo8fHx99xzzwMPPCCEqKysXLRokdwVAQBwY83cv9auXRsfH28ymdzc3NLT05988kn5ygTk5yJ3AYBS6fX6qKgoIURdXd2RI0eys7Pr6+uFEHfcccczzzzT5I9IHYglJSVCCGdn59DQ0LZ9tfn9y88+++xbb71leSovL2/gwIFCiK1bt1ZWVlq+WLkZ0dHRc+fOra+v/81vfvPQQw8dO3YsIyNDCNG1a9fHH3+8bUUCAByK9Bqxv/71r1u3bjWZTGvWrImLixs8eLDcdQEA0Bxr9y+TyRQfHy/N6dWr1/Llyy1/qnfv3n/5y19kKBeQDwEi0Ebl5eUffPBBo8G+ffuuXbvWxaXp/2eFhoa6uLjU1dUJIQYNGtTCdK+Rurq61NRU6XjKlCmNzg4YMOCOO+7Q6/VXrlzZvHnztGnTWnLN2bNnb9269bvvvjt37tzq1aulQR8fn/Xr17M9MACg5YYOHfrII4+sX7++oaFh3rx5O3bskLsiAABu7Pr71+HDh0+cOCGdPXbs2LFjxxrNJ0CEo2EJM3ALeHp6BgcHz5s3Lysra9SoUdamubm5DRkyRDpu8/rlb7/91mAwCCG6devW5EUeeugh6aDlq5g7dOiQlpb29ttvh4SEdO7c+a677nruued++umnMWPGtK1IAIDDev31152cnIQQOp0uLS1N7nIAAGiRRvevwsJCuSsC7IvKZDLJXQMAAAAAAAAAO0UHIgAAAAAAAACr2AMRaLf27t27e/fuG0576qmn/Pz8bFAPAAAAAABQIgJEoN364Ycf5s+ff8NpY8eOJUAEAAAAAADWsAciAAAAAAAAAKvYAxEAAAAAAACAVQSIAAAAAAAAAKwiQAQAAAAAAABgFQEiAAAAAAAAAKsIEAEAAAAAAABYRYAIAAAAAAAAwCoCRAAAAAAAAABWESACAAAAAAAAsIoAEQAAAAAAAIBVBIgAAAAAAAAArCJABAAAAAAAAGAVASIAAAAAAAAAqwgQAQAAAAAAAFhFgAgAAAAAAADAKgJEAAAAAAAAAFYRIAIAAAAAAACwigARAAAAAAAAgFUEiAAAAAAAAACsIkAEAAAAAAAAYBUBIgAAAAAAAACrCBABAAAAAAAAWEWACAAAAAAAAMAqAkQAAAAAAAAAVhEgAgAAAAAAALCKABEAAAAAAACAVQSIAAAAAAAAAKwiQAQAAAAAAABgFQEiAAAAAAAAAKsIEAEAAAAAAABYRYAIAAAAAAAAwCoCRAAAAAAAAABWESACAAAAAAAAsIoAEQAAAAAAAIBVBIgAAAAAAAAArCJABAAAAAAAAGAVASIAAAAAAAAAqwgQAQAAAAAAAFjlIncBilRbW1tZWSl3FQBwe3l7e6tUKrmrwK3E/QuAI+D+1f5w/wLgCOz8/kWA2Go1NTV33HHH6dOn5S4EgBBCdBTC+dpxrRD1ctbS3jz//PPvv/++3FXgluH+BdgV7l+3D/evdob7F2BXuH/dPnZ+/yJAbLULFy6cPn3aycnJy8tL7loAiIcrKwfU1krHG9zd8zp2lLee9qGuru7y5ctHjhyRuxDcSty/ALvC/et24P7VLnH/AuwK96/bQRH3LwLENvL19T179qzcVQAQm6ZNO/rll9Lx6tWrgx5/XN562oc9e/aMHj1a7iraJ71er9PpcnNzDQZDVVWVRqPRarVarXby5Mk9evSwQQHcvwA7wf3rduD+1Y5x/wLsBPev20ER9y8CRAAAbKGwsDA2NjYtLa3Js7NmzYqMjExKSgoICLBtXQAAAABwAwSIAADcdgaDISIiQq/XBwcHR0ZGhoSEaDQatVp96dIlo9GYn5+/efPm1NTUrKysjIwMPz8/uesFAAAAgP9DgAgAwG23cOFCvV6/bNmy+fPnNzlhyZIla9asiYmJiY+PX7VqlY3LAwAAAIBmOMldAAAA7V96enpQUJC19FASFRU1ZsyYjIwMm1UFAAAAAC1BgAgAwG1XXl7eknek+Pv7l5WV2aAeAAAAAGg5AkQAAG678PDwPXv2FBQUNDOnrKwsLS0tPDzcZlUBAAAAQEsQIAIAcNvFxcXV1dWFhYUtX768qKio0dnS0tKUlJSRI0eWlZVFR0fLUiEAAAAAWMNLVGTw2WefffPNNykpKR4eHnLXAgCwhYiIiOTk5DnXeHl5+fj4qNXqiooKo9F4/vx5IYSLi8uKFSumTJkid7H/3549e5YsWdLQ0GAe8fb2TklJ8fLykrEqAABuLZPJFBsb269fvz/+8Y9y1wIA9osAUQarVq3atWtXbGzsr3/9a7lrAQDYyMyZMydMmJCSkqLT6fLy8goLC4UQzs7Ovr6+oaGhU6dOjYqK6t69u9xl/p9du3bt2LGj0WBMTExERIQs9QAAcDvo9fp//etfGo2m5QHihQsX0tPTH3jgAWdn59taGwDYDwJEGVy9etX8nwAAxxEYGJiYmJiYmGgymaqqqqqrq7t06eLkZKfbicybN+9Xv/pVdXW19HHhwoX79u2rr6+XtyoAAG4taYfi8+fP19fXtyQQrKysXLhw4cqVK//zn/+w8QgAx2GnDy3tm7QcrK6uTu5CAADyUKlU7u7uGo3GbtNDIYSLi8uvfvWrCdf4+vqKa7cwAADaDSlAbGhoMBgMN5z8/vvve3p6fvLJJ0KIdevW3fbiAMBu0IEoA6l9gwARABxQbm5uamrqsWPHLly4cPXqVQ8PDz8/v6FDh06bNk2tVstdXXOkrJMAEQDQzkgBohDCYDB069at+ck6nc5kMl2+fFkI8f333589e9auth8BgNuHAFEGdCACgAM6evTo7NmzdTpdk2fnzp37/PPPJyQkuLq62riwFiJABAC0S3q9Xjo4d+6ctTlHjx794x//GB8ff/jwYfNgbW1t3759d+7cOWrUqNteJQDIjQBRBlIHInsgAoDjKC0tnThxYnFxcVRU1Pjx42tra7/44osdO3YsXrx44MCBubm5H3/88TvvvFNUVLRu3Tr7XNdMgAgAaJfMHYhZWVnDhg3z8PC4fs5XX321adOmnj17Hj9+XBq5++676+rqDh48GB8fv337dtuVCwAyscdHlHaPDkQAcDSLFy8uKir69NNPV69e/dRTT0VHR2/fvv3pp59OTk4OCwtbsmRJQUHB7Nmzv/rqq7feekvuYptGgAgAaH/q6+sLCwul49mzZz/00ENNTpO2R9y1a5f5ZWLPP//8t99+q1ard+zYcfDgQdtUCwAyIkCUAQEiADiazMzMIUOGTJs2zXJwwYIFRqNx06ZNQgiVSrV8+fJevXp9/PHHMtV4AyqVShAgAgDal02bNtXU1Jg/Wq5QtmQ0GoUQ+fn5Qoj777//m2++ee6557p06fL0008LIb755hubFAsAciJAlAEvUQEAR1NUVNSnT59Gg/7+/kKIrKws88jw4cPNfRD2RupANJlMchcCAECr1dbWlpeXNxr8/vvvFy1aJITo16+fNGIwGCzzRDPLFzSHh4c/9NBDHTp0EEJMmjRJCMESZgCOgABRBnQgAoCjCQ4O3r9/f6NnEmnFkzlYNJlMR48e7dWrlwz1tQBLmAEAyvX73/++V69ep06dMo/U1dU98MADOTk5Pj4+L730kjRoMpnOnj17/Y9LHYgSy78Ijh07tlOnTvv377dMGBctWvTUU0/xJzcA7QwBogx4iQoAOJqHH374zJkz06dPv3z5sjRy4sSJWbNmCSHGjRsnhCgpKXniiSeOHj364IMPylmodQSIAADlOn78eG1trWWb/y+//FJdXa3RaH788ce+ffuax0tLS6//cct8UFpAIHF3dw8LC6uvr9+7d680Ul9fn5SUtHbt2iavAwDKRYAoAzoQAcDRvPLKK+Hh4WvXrvX397/33ntHjBgxYMCAnJycGTNmjB49Wgjx8ssvf/nllyEhIfHx8XIX2zQCRACAclVVVQkhKioqzCOnT58WQgQEBAQFBXXt2tU8fsMAsXfv3pan7rzzTiFEUVGR9PHkyZPV1dWNvgsA2gECRBmwByIAOBpnZ+fvv/9+wYIFTk5OGRkZBw8eVKvVf/vb31auXClNCAwMXLx4cWZmpre3t7ylWkOACABQritXrgghLl26ZB6RgsIePXoIIXx9fc3jZ86cafSzJpPp/Pnz0rFKperZs6flWSlPlALEsrIy8wtVzGsOAKB9cJG7AEdEByIAOCBXV9eEhISEhIRTp045OztrtVrLswkJCXIV1kIEiAAA5bq+A1EKCqU0sG/fvnPnzs3IyPjpp5+u70C8fPmyefspX19fV1dXy7PSlojS7oqTJ082r2WmAxFAO0MHogzoQAQAR9a7d+9G6aEiECACAJRL6kC07AqUljBLAaKTk1NSUtL06dNFU0uYLd+gcv27zqQAsaioqK6u7tChQ+ZxAkQA7QwdiDKgAxEAHJZer9fpdLm5uQaDoaqqSqPRaLVarVY7efJkaRWV3SJABAAoVENDg7QvoWWAKHUgWv5JT7oRNwoQS0pKZsyYYf5o+QYVibSE+dSpUwUFBbW1teZxljADaGcIEGVAgAgADqiwsDA2NjYtLa3Js7NmzYqMjExKSgoICGjDxRMSEpKSkpqfI919zp0714brCwJEAIBiVVdXm0wm0VSAaLmhoXRcUlJi+bMbN2789ttvzR+v70DUarXOzs5nzpzJysqyHKcDEUA7Q4AoA5YwA4CjMRgMERERer0+ODg4MjIyJCREo9Go1epLly4Zjcb8/PzNmzenpqZmZWVlZGT4+fm19vqlpaXm/d2b1+YEkAARAKBQ0gaI4kYBYmBgoEqlKigoaGhokO56Qojy8nLpoHv37gaDYezYsY0u3qFDh549exYXF+t0OiHEM888U1lZmZqaSoAIoJ0hQJQBHYgA4GgWLlyo1+uXLVs2f/78JicsWbJkzZo1MTEx8fHxq1atau31k5OTX3/99ebn5OXl3XPPPSqVqrUXlxAgAgAUyhwgWoZ6lnsgSjw9PbVabUlJycmTJ/v16ycNmgPERx555L333mvy+nfccUdxcbH0/uWxY8fq9XrBEmYA7Y5SA0Tl7iEl6EAEAMeTnp4eFBRkLT2UREVFrV27NiMjo21f0aVLl+YnqNXqtl1ZQoAIAFAo6Q0qwiLUMxgMRqPR09PT19fXcuZdd91VUlKSl5dnDhClrT/uvffemJgYa9efNm3azp07pZnBwcFlZWWCJcwA2h3lBYi3dQ8p25Cevq5evSp3IQAAGykvLx80aNANp/n7+zfaQcl+ECACABTq+iXMOTk5Qojg4OBGjfkDBgz49ttv8/PzH3jgAWlEigXj4+MHDx5s7frPPPPMn//8Z6PROGHChBEjRuzbt08QIAJodxQWIN7uPaRsgw5EAHA04eHhOp2uoKCgf//+1uaUlZWlpaWFh4fbsrCWI0AEAFh65pln1q1bZ27us2fXdyAePnxYCHH93/buuusuIUReXp55RFrC3LVr12au7+bm9q9//euHH3546623nJ2dPTw8BAEigHZHYQHi7d5DyjbYAxEAHE1cXNy2bdvCwsLi4+MjIyP79Oljeba0tHTLli1Lly4tKyuLjo6Wq8jmST0aBIgAAMnVq1erq6vlrqJFLDsQjxw5snfv3u3btwshQkJCGs0cMGCAECI/P9/8g1IHYqOVztd77LHHHnvsMelYChDZAxFAO6OwANEGe0jZAAEiADiaiIiI5OTkOdd4eXn5+Pio1eqKigqj0Si9QNnFxWXFihVTpkyRu9imSR2IJpNJ7kIAADbyz3/+c/369dbO5ubmCiHGjRtnHvnhhx9sUVbrmTsQKyoqpk6deuzYMenj9QGiuQOxpKRk0qRJ0kpncaMOxEY8PT0FHYgA2h2FBYjtYA8pwRJmAHBIM2fOnDBhQkpKik6ny8vLKywsFEI4Ozv7+vqGhoZOnTo1Kiqqe/fucpdpFUuYAcDRXLlyZefOnc3PueEEe2DuQDQajb/88otKpTKZTCqVasiQIY1m9ujRw9vb22g0fvjhh+b00NPTs1OnTi3/OqkDcffu3Y8//vibb77ZoUMHf3//W/HPAQByUliA2A72kBK8RAUAHFVgYGBiYmJiYqLJZKqqqqquru7SpYsUzNk/AkQAcDTz5s3z8/P7wx/+YDKZ3n333YkTJ1qejY2N3bhxY0lJiVzltZy5A1Facx0YGJiQkFBZWanRaK6fPGDAgD179nz++efmkRuuX25EChCrq6vXrVu3ZcsWjUZz6tSptlcPAPZBGQ8tZnFxcXV1dWFhYcuXLy8qKmp0trS0NCUlZeTIkfa8h5T50YsORABwWCqVyt3dXaPRKCU9FASIAOCQnn766UOHDg0ePPj5559PTEz08fHRXuPm5iaE0FqQu1irzB2Ikv79+z/66KPPPvtsk5OlbRCl9sMOHTqIVq5fFteWMJu/uri4uLa2trU1A4C9Ucxzi0TaQ+rSpUtz5swJCAjw9vbu16/f0KFD+/fv7+Pj07NnzxkzZpw5c8ae95CS1i8LAkQAwP8ymUw1NTU1NTVyF9I0AkQAcEx9+/bdtWvXX/7yl5UrVw4fPvzAgQNyV9RqjQLEwMDAZiZLAaLkoYceEm3tQLQkbXYMAIqmsABRCDFz5swjR468+uqrw4YNq6mpKSwszMrKOnnyZKdOnUJDQxMSEoqLi1944QW5y7SKDkQAQJOys7NdXV1dXV3lLqRpBIgA4LCcnZ0XLVqUkZFx9erV8PDwN954w9wVoQjmJcySZrbDEkIMHDhQOujevfsjjzwihGjtDobXB4hGo7FVVwAAO6SwPRAlit5Dig5EAIASESACgIMbNWrUoUOH4uLiXnvttS1btijl+Utc60B0dnaWnsWa70AcO3bs+PHjv//++5EjRz7++OMqlWrChAmt+rrOnTu7uLhYPu4RIAJoBxQZIJpJe0i5u7vLXUgrmB+9eIkKAMBSUFCQ+YWPdkilUgkCRABwbB4eHqtXr77//vtfeOEFBYViUgfiK6+88sknn/zyyy8hISHNTHZzc9PpdNu3bx8yZIizs/MTTzzRhm/UarXl5eVeXl6lpaWCABFAu6DsALFJJpNJ2qS2U6dOctfSBDoQAQBNcnV1DQ4OlrsKq6ROE5PJJHchAACZPfroo+Hh4V9//bXchbSU1IHYv3//PXv2nDlzplevXs3Pd3JymjRp0s18486dO6urq7t27Tp9+vSNGzeyByKAdqAdBojZ2dlDhw4V9vqQwx6IAAAlYgkzAMBMq9XOmjVL7ipaSgoQ3dzc+vTp06dPHxt8Y0BAgHTQr18/IYTBYLDBlwLAbdUOA0Q7R4AIAI5Mr9frdLrc3FyDwVBVVaXRaLRarVarnTx5co8ePeSurjkEiADgyJR7/xLXljC7ubnZ/qu7dOkieAszgHahHQaIdr6HFEuYAcAxFRYWxsbGpqWlNXl21qxZkZGRSUlJ5p4Fe0OACACOSen3L3GtA7Fz5862/2ofHx/BHogA2oV2GCDa+R5SvEQFAByQwWCIiIjQ6/XBwcGRkZEhISEajUatVl+6dMloNObn52/evDk1NTUrKysjI8PPz0/ueptAgAgADqgd3L/EtQ5EAkQAuBntMEC0c3QgAoADWrhwoV6vX7Zs2fz585ucsGTJkjVr1sTExMTHx69atcrG5bUEASIAOKB2cP8S11o3OnbsaPuvlgJEljADaAeUGiDm5uampqYeO3bswoULV69e9fDw8PPzGzp06LRp09Rq9c1c+cKFC82/feXixYs3c332QAQAB5Senh4UFGTt6UsSFRW1du3ajIwMm1XVKgSIAOCA2sH9S1zr4XB2drb9V9OBCKDdUF6AePTo0dmzZ+t0uibPzp079/nnn09ISHB1dW3DxefMmSgauUUAACAASURBVLN8+fKWzCwvL2/D9QUdiADgkMrLywcNGnTDaf7+/llZWTaopw1UKpUgQAQAB9MO7l+CABEAbgWFBYilpaUTJ04sLi6OiooaP358bW3tF198sWPHjsWLFw8cODA3N/fjjz9+5513ioqK1q1bJ/VKtErXrl2l92Q1o76+/tKlS813KTaDDkQAcEDh4eE6na6goKB///7W5pSVlaWlpYWHh9uysJaT7qptvv0BAJSoHdy/hKwBokajEUL88ssvJpNJ+lMcAChUqyM2eS1evLioqOjTTz9dvXr1U089FR0dvX379qeffjo5OTksLGzJkiUFBQWzZ8/+6quv3nrrrTZcf9GiRcYb+e9//yuuNWK0gbkDkZeoAIDjiIuLq6urCwsLW758eVFRUaOzpaWlKSkpI0eOLCsri46OlqXCG2IJMwA4oNt9/3r11VdVN9K9e3dxEyvAxLXWDRcXGbpnunTp0rNnz8uXLxcUFNj+2wHgFlJYgJiZmTlkyJBp06ZZDi5YsMBoNG7atEkIoVKpli9f3qtXr48//limGm+ADkQAcEARERHJycmXLl2aM2dOQECAt7d3v379hg4d2r9/fx8fn549e86YMePMmTMrVqyYMmWK3MU2jQARABzQ7b5/dezYsYWdGTfTAi9jB6IQIjQ0VAhx4MABWb4dAG4VhQWIRUVFffr0aTTo7+8vhLDcdGP48OGFhYU2razFCBABwDHNnDnzyJEjr7766rBhw2pqagoLC7Oysk6ePNmpU6fQ0NCEhITi4uIXXnhB7jKtIkAEAMd0W+9ff/3rXxsaGkzNOnz4sLiJFWBC1g5EIcSIESMEASIA5VPYHojBwcH79++vqanp1KmTefDgwYNCCHOwaDKZjh492qtXL3lKvBFeogIADiswMDAxMTExMdFkMlVVVVVXV3fp0qUNO/bKggARAByWou9fQu4ORClA3L9/vyzfDgC3imJ+6UsefvjhM2fOTJ8+/fLly9LIiRMnZs2aJYQYN26cEKKkpOSJJ544evTogw8+KGeh1tGBCABQqVTu7u4ajUZBT18EiAAAJd6/xLUAUa6ahw0bJoSQ+igBQLkU1oH4yiuvbNiwYe3atZs3bx40aFBVVVVOTk5tbe2MGTNGjx4thHj55ZfXr18fEhISHx8vd7FN4yUqAAAlIkAEADTJZDLV1tYKISxXidkVqXWjQ4cOsnx7jx493NzcDAbD5cuXPT09ZakBAG6ekv5wJIRwdnb+/vvvFyxY4OTklJGRcfDgQbVa/be//W3lypXShMDAwMWLF2dmZnp7e8tbqjWWHYg3sxMwAAC2RIAIAGhSdna2q6urq6ur3IVYJe8SZpVK1bt3byHE9a+xBgAFUVgHohDC1dU1ISEhISHh1KlTzs7OWq3W8mxCQoJchbWQuQNROpZrK18AAFqFABEAoFDyBohCiICAgPz8/JMnT4aEhMhVAwDcJAWnV9KfcRTH8tGrrq6OABEAoAgEiACAJgUFBeXk5MhdRXOkJczyBohCiJMnT8pVAADcPKWmV7m5uampqceOHbtw4cLVq1c9PDz8/PyGDh06bdo0tVotd3XNaRQgylgJAAAtR4AIAGiSq6trcHCw3FU0R+pAlLF1o0+fPoIlzAAUTnkB4tGjR2fPnq3T6Zo8O3fu3Oeffz4hIcFu9+CwXMJMgAgAUAoCRACAQtnDEmZBByIAhVNYgFhaWjpx4sTi4uKoqKjx48fX1tZ+8cUXO3bsWLx48cCBA3Nzcz/++ON33nmnqKho3bp10qOOvaEDEQCgRASIAODI9Hq9TqfLzc01GAxVVVUajUar1Wq12smTJ/fo0UPu6m5A9gCxb9++QojCwkK5CgCAm6ewAHHx4sVFRUWff/75tGnTpJHo6OhnnnkmOTn5559/fvzxxxcvXjxnzpzk5OS33npr/vz58lbbJMsOxKtXr8pYCQAALUeACACOqbCwMDY2Ni0trcmzs2bNioyMTEpKkprs7JPsAWJQUJBKpcrLy6upqenUqZNcZQDAzbDHHr1mZGZmDhkyxJweShYsWGA0Gjdt2iSEUKlUy5cv79Wr18cffyxTjTdAByIAQIlUKpUQwmQyyV0IAMB2DAZDREREWlpacHDwwoULP/300+3bt+/Zs2f79u2fffbZ4sWLhwwZkpqaGhER8csvv8hdrFXSY5eMeyB6e3uHhIRUVVXt3btXrhoA4CYprAOxqKho7NixjQb9/f2FEFlZWeaR4cOHW9skUXbsgQgAUCI6EAHAAS1cuFCv1y9btsza6q4lS5asWbMmJiYmPj5+1apVNi6vJUwmU0NDg0qlkneHq3Hjxh0+fHjnzp333nuvjGUAQJsprAMxODh4//79NTU1loMHDx4U195sJYQwmUxHjx7t1auXDPW1AB2IAIBbrqGhobCw8ESzSkpKbuYrCBABwAGlp6cHBQU1vzdUVFTUmDFjMjIybFZVq8i+flki9cF8991315/au3dvnz59vvrqK1vXBACtobAA8eGHHz5z5sz06dMvX74sjZw4cWLWrFlCiHHjxgkhSkpKnnjiiaNHjz744INyFmodHYgAgFsuJiamX79+dzRr0qRJ4iYSQAJEAHBA5eXlLXlHir+/f1lZmQ3qaQM7CRDHjRvXuXPn3bt36/X6Rqe++uqrU6dOffnll7IUBgAtpLAlzK+88sqGDRvWrl27efPmQYMGVVVV5eTk1NbWzpgxY/To0UKIl19+ef369SEhIfHx8XIX2zTLRy9eogIAuCUGDRrUr1+/5ufU1taWlJRIWxm2AQEiADig8PBwnU5XUFDQv39/a3PKysrS0tLCw8NtWVjL2UmA6O3t/eSTT65evXrlypVJSUmWp37++WchxJEjR2QqDQBaRGEdiM7Ozt9///2CBQucnJwyMjIOHjyoVqv/9re/rVy5UpoQGBi4ePHizMxMb29veUu1hiXMAIBbbs6cOfob2bZtm7j2LpQ2IEAEAAcUFxdXV1cXFha2fPnyoqKiRmdLS0tTUlJGjhxZVlYWHR0tS4U3JPsbVMxeeOEFIcT69esbjUsB4rFjx+gvAWDP5P812lqurq4JCQkJCQmnTp1ydnbWarWWZxMSEuQqrIUIEAEASkSACAAOKCIiIjk5ec41Xl5ePj4+arW6oqLCaDSeP39eCOHi4rJixYopU6bIXWzT7KQDUQgRGhrapUuXoqKikpIS85b9p06dKi8vF0LU1tYeP3584MCBstYIAFYprAPRUu/evRulh4pguQei5TEAAPaMABEAHNPMmTOPHDny6quvDhs2rKamprCwMCsr6+TJk506dQoNDU1ISCguLpZ66+yT/QSITk5Od999txBiz5495sFDhw6Zj3NycmQoCwBaRsEBokIRIAIAlIgAEQAcVmBgYGJi4sGDB6uqqioqKs6dO1dbW1taWrpv374FCxZ0795d7gKbYz9LmIUQ0sb9P/74o3kkPz9fXMs34+Pjjx07JldtANA8AkRbs3z0IkAEACgFASIAQKVSubu7azQa6aagCPbTgSiuBYgZGRnmkZMnTwohXnjhhX79+h09ejQlJUWu2gCgeYr5vd9u0IEIAFAiAkQAgBLZVYB4zz33dO7c+eDBg2fPnpVGCgsLhRCTJk164403hBAnTpyQsz4AsI4A0dboQAQAKBEBIgBAiexqCXPnzp3HjRvX0NCQlpYmjUgBYt++ffv27SuuNSQCgB0iQLQ1OhABAEpEgAgAUCK76kAUQtx///1CiG3btgkhTCZTUVGREKJPnz5SgEgHIgC7RYBoa3QgAgCUiAARAKBE9hYgTpgwQVx7j8rZs2erq6t9fX09PDy6devm4eFx/vz5CxcuyF0jADSBANHWCBABAEpEgAgAUCK7WsIshLjzzju9vb1LSkpKS0vN65elUwEBAYJVzADsFQGirbGEGQCgRCqVSghhMpnkLgQAgFawtw5ElUo1bNgwIcSBAwfy8/OFEP369ZNOsYoZgD0jQLQ1OhABAEpEByIAQInsLUAUQoSGhgoh9u/f/8033wghfv3rX0vjBIgA7Jm9NHI7DjoQAQBKRIAIAFAiOwwQ7777biHE+vXrCwoKnJ2dp06dKo0PHDhQCJGdnS1ncQBgBQGirdGBCACOTK/X63S63Nxcg8FQVVWl0Wi0Wq1Wq508eXKPHj3krq45BIgAACWytz0QhRD33Xdfv379jhw5IoQYN26cn5+fND506FAhxKFDh+QsDgCssKNfow6CDkQAcEyFhYWxsbFpaWlNnp01a1ZkZGRSUpK0gbodIkAEACiRHXYgenh4fP755xMmTBg6dOg777xjHh80aJCzs/ORI0fc3d23b9/+q1/9SsYiAaARAkRbowMRAByQwWCIiIjQ6/XBwcGRkZEhISEajUatVl+6dMloNObn52/evDk1NTUrKysjI8PciWBXCBABAEpkhwGiEGLkyJEXL15sNOjm5hYUFJSbm1tVVbV3714CRAB2hQDR1iwfvaR2egBAu7dw4UK9Xr9s2bL58+c3OWHJkiVr1qyJiYmJj49ftWqVjctrCQJEAIAS2eES5mYMHTo0NzdXCHHp0iW5awGA/8FbmG2NJcwA4IDS09ODgoKspYeSqKioMWPGZGRk2KyqViFABAAokX12IFrz9NNPd+vWTRAgArA/BIi2xhJmAHBA5eXlLXlHir+/f1lZmQ3qaQMCRACAEimrA/G+++5bunSpEOLy5cty1wIA/4MA0dboQAQABxQeHr5nz56CgoJm5pSVlaWlpYWHh9usqlYhQAQAKJGyOhCFEGq1WhAgArA/BIi2RgciADiguLi4urq6sLCw5cuXFxUVNTpbWlqakpIycuTIsrKy6OhoWSq8IQJEAIASKS5A9PT0FCxhBmB/lNHI3Z4QIAKAA4qIiEhOTp5zjZeXl4+Pj1qtrqioMBqN58+fF0K4uLisWLFiypQpchfbNAJEAIASKWsJs7jWgUiACMDeKObXaLvBEmYAcEwzZ86cMGFCSkqKTqfLy8srLCwUQjg7O/v6+oaGhk6dOjUqKqp79+5yl2kVASIAQIkU14HIEmYA9okA0dakRy8nJ6eGhgbpr2EAAAcRGBiYmJiYmJhoMpmqqqqqq6u7dOkiBXP2jwARAKBECg0Q6UAEYG8IEG1NevTq0KFDTU0Nj2EA4JhUKpW7u7u7u7vchbQCASIAQIkUt4SZPRAB2CdldD20J9JfwDp27ChYwgwADqampiYzM/PAgQPmke3bt0+ZMiU8PDwmJua7776TsbYbUqlUQgiTySR3IQAAtIJCOxBZwgzA3hAg2pq5A1EQIAKAI/niiy98fX3Dw8NDQ0N/+9vfVldXr1ix4r777vvmm28yMzPff//9iIiIP//5z3KXaRUdiAAAJVJcgNixY8dOnTpdvXr1ypUrctcCAP+HANHW6EAEAAe0b9++J598sr6+PjIy8le/+pVOp3v22WdfeeWV4ODgrVu3FhYWbtiwoV+/fgkJCTt37pS72KYRIAIAlEhxS5gF2yACsEtK+jXaPtCBCAAOaOnSpU5OTrt37x4+fLgQYsGCBW+++aanp+e3334rvXk5ICAgJCQkODg4KSlp7NixMpfbFAJEAIASKa4DUQihVqvLy8svX77s5+cndy0A8P/RgWhrdCACgAP6+eefR48eLaWHQog//OEPQojx48dL6aGkf//+d999d05Ojjwl3ggBIgBAiZQYIPIeFQB2iADR1uhABAAHdPHiRS8vL/NH6dhyRNKlS5fz58/btLIWI0AEACiRtIRZWQEiS5gB2CECRFuTHr2kDkTpZgYAaPfuuuuu/fv3V1dXSx/T09OFEPv377d8qfHVq1cPHjw4cOBAeUq8EQJEAIASSU0bStwDkRcxA7ArBIi2xhJmAHBAv//978+ePfvII4/s2LFj7dq1MTExPj4+ubm5r7/+upQh1tfXz5s37/Tp0w888IDcxTaNABEAHNnJkydzcnLMDRDZ2dnz5s179tln33rrrSNHjshbW/OUuIT5+g5Eg8GQlZUlX0UAwEtUbI4lzADggGJjYzdt2rR169atW7cKITw8PP773/++/PLLS5Ys+eijj+68884jR44UFxcHBgbOmzdP7mKbRoAIAI5p06ZNL7zwQmlpqRCib9++GzduLC8vnzhx4tWrV6UJixYtevvtt+Pi4mQt0yolBog+Pj5CiPLycvPIo48+unv37iNHjgQFBclXFwCHRoBoa3QgAoAD6tChw/bt2z/44IP09PQOHTrMnj178ODBGzZsiI6O/uqrr06cONGhQ4fHH3985cqVrq6uchfbNAJEAHBABw4ciIyMFEKEhYU5Ozvv27fvySef7NChg7e39+LFi++6667c3Ny//vWvL7300ujRo0NDQ+WutwlS16SyljBrtVohxOnTp6WPv/zyS3p6ekNDw5YtWwgQAchFSb9G2wc6EAHAMTk7O0+fPn369OnmEbVavX79+rKysrKysjvvvFP625LdIkAEAAf0+uuvCyG2bNkyadIkIcTu3bvHjRtXX1///fffjxs3Tgjxm9/85je/+c2QIUOWLVuWmpoqc7lNUWIHYqMA8euvv5buv999990rr7wiZ2UAHBh7INoaHYgAAEvdunULCQmx8/RQECACgEPKysoKDw+X0kMhxL333jty5Ei1Wi2lh5KBAweOGjUqOztbphpvoB0EiJs3b5YO0tPTa2trZSsLgGOjA9HW6EAEAEem1+t1Ol1ubq7BYKiqqtJoNFqtVqvVTp48uUePHm2+rMFg2LVrV/O3leLi4jZfXxAgAoBDKi8vHzx4sOVIr169jEZjo2ndunU7cOCADetqBaUHiHV1dbt27RJC+Pr6lpeXHz16dNCgQTLXB8AhESDaGh2IAOCYCgsLY2Nj09LSmjw7a9asyMjIpKSkgICANlw8Jibmq6++asnMNieABIgA4ID69++/f//+hoYG6S4ghJg3b57BYGg07ejRo76+vjavrkWUuAdir169hBCnT582mUwHDhy4fPlyUFCQFCBeuHBB7uoAOCgl/RptH+hABAAHZDAYIiIi9Hp9cHBwZGRkSEiIRqNRq9WXLl0yGo35+fmbN29OTU3NysrKyMjw8/Nr7fWfe+65G/ZWXLx4cceOHSqVqq3/EEKlUplMJpPJdDMXAQAoyIQJE/7+97/PmDHjvffe69y5sxBi1KhRjeb8+9//zs3Nfe6552SorwWU2IHo7u7u5eV18eJFo9H4ww8/CCHGjRt36tQpIcTFixflrg6AgyJAtDUCRABwQAsXLtTr9cuWLZs/f36TE5YsWbJmzZqYmJj4+PhVq1a19vqTJ0+ePHly83NycnIGDRp0M9mfk5NTfX19Q0ODsh7DAABttnjx4g0bNqxevfrTTz995JFHPvnkE8uzycnJn3zyyd69e93d3RcsWCBXkc1TYoAohNBqtRcvXjx9+vR///tfIcSYMWM2btwoCBAByIeXqNgaS5gBwAGlp6cHBQVZSw8lUVFRY8aMycjIsFlVrcUqZgBwNGq1Oisr69VXXw0KCro+utq6devevXtHjBiRnp5+5513ylLhDSk0QDSvYi4oKBBChISEeHl5CQJEAPIhQLSF0tLSE9dUVlaKax2I0n4cAIB2r7y8vCXvSPH39y8rK7NBPW1DgAgADsjT0zMxMfHQoUObNm1qdOqNN97Q6/X79+8fPny4LLW1hBL3QBRCSIHsTz/9JK1c7tOnjxQgXrp0SebKADgqAsTbbuXKlT179rzjmp07dwo6EAHAwYSHh+/Zs0dqIrCmrKwsLS0tPDzcZlW1FgEiAMDS8OHD+/XrJ3cVN6DQDsQHHnhACLF69eqqqiqNRuPp6alWqwUBIgD5KOzvMErk7+/fv39/y8ctb2/vESNGCAJEAHAYcXFx27ZtCwsLi4+Pj4yM7NOnj+XZ0tLSLVu2LF26tKysLDo6Wq4ib4gAEQAcll6v1+l0ubm5BoNBirS0Wq1Wq508eXJLWuxlpNAAcfz48d7e3sXFxUII6V8bpACRJcwA5EKAeNs9+OCDDz74YKPB7777TvAMBgAOIyIiIjk5ec41Xl5ePj4+arW6oqLCaDSeP39eCOHi4rJixYopU6bIXaxVBIgA4IAKCwtjY2PT0tKaPDtr1qzIyMikpKSAgADb1tVS0hJmxQWIHTt2fOCBB9auXSuuBYjSEuYtW7aYTKb33ntPuikDgM0QIMpDuoGxByIAOI6ZM2dOmDAhJSVFp9Pl5eUVFhYKIZydnX19fUNDQ6dOnRoVFdW9e3e5y2wOASIAOBqDwRAREaHX64ODgyMjI0NCQjQajVqtvnTpktFozM/P37x5c2pqalZWVkZGhp+fn9z1NkHqQFTcHohCiCYDxKKiopUrV77yyiv9+/eXuT4ADkZ5v0bbB+kGxhJmAHAogYGBiYmJiYmJJpOpqqqqurq6S5cuCuogIEAEAEezcOFCvV6/bNmy+fPnNzlhyZIla9asiYmJiY+PX7VqlY3LawmFLmEWQkycONHZ2bm+vl7q7pSWMEtOnz7d2gDRaDR+9NFHTz/9tEajubV1AnAQinloaWekGxgBIgA4JpVK5e7urtFoFJQeCgJEAHA86enpQUFB1tJDSVRU1JgxYzIyMmxWVasoN0D08fEJCwsTQkhvqpE6ECWnT5+2nHnkyBHpZc3NWLly5csvv7x8+fLbUCkAh6CwDsTKykqVSuXm5mY5+OOPP65duzY7O9vb23vw4MEvvviiv7+/XBW2EAEiAKARk8lUW1srhOjUqZPctTSNABEAHE15efmgQYNuOM3f3z8rK8sG9bSBtG2UEpcwCyH+8Y9/bNu27be//a343w7EM2fOmI8vXrwovQ47Ly+vmUudO3dOCJGfn3/bigXQzins16iHh8ddd91l+Zvxz3/+87Jly0wmk/Rx8+bNycnJK1aseOqpp2SqsUUIEAEAjWRnZw8dOlQIYb6p2RsCRABwNOHh4TqdrqCgoJkFs2VlZWlpaeHh4bYsrOWU24EohBgxYsSIESOkY8sOxKKiotra2o4dOwohjEZjbW2tXq9vaGhoZmWD9Prm48eP3+aSAbRbCgsQG9m2bVtCQoJGo3n99dfHjBnj4uKye/fu11577fnnnx8xYsSAAQPkLtAqAkQAgOIQIAKAo4mLi9u2bVtYWFh8fHxkZKT0Ng+z0tLSLVu2LF26tKysLDo6ug3X//rrr//1r381/1hUUVEhWvPXtWPHjr377rvma0qtkQoNEC1ZBojvvffexo0bjx8/3rFjx6qqKiHE1atXz507161bN2s/bg4QTSaTSqWyQcEA2hllB4jvvPOOk5PTli1bRo0aJY0EBQUNHz787rvvXrp06aeffipvec0gQAQANBIUFJSTkyN3Fc0hQAQARxMREZGcnDznGi8vLx8fH7VaXVFRYTQaz58/L4RwcXFZsWLFlClT2nD9Tz75ZPv27S2Z2fIAce3atStXrmw06O3t3brK7E+HDh06d+585coV6eOpU6eysrJGjhwpBYhCiLNnzzYTIF64cEEIUVFRcebMGa1Wa4OCAbQzyg4Qs7Ozhw8fbk4PJcOHDw8NDT148KBcVbUEASIAoBFXV9fg4GC5q2gOASIAOKCZM2dOmDAhJSVFp9Pl5eUVFhYKIZydnX19fUNDQ6dOnRoVFdW9e/e2Xfz9999/8cUXm59z8uTJGTNmtPy1Y3Pnzg0ICLh69ap5pFevXna7wrpVvLy8zAGiECIzM3PkyJHmkTNnzqSnp4eGhkqvXmlE6kAUQhw/fpwAEUAbKDtAvHr1apN/Y+nZs2fzO8jKjgARAKA40oonu92iEQBwmwQGBiYmJiYmJppMpqqqqurq6i5durQ80WtGly5dJkyY0Pyc1rbnq9XqqKiomyjKfvXq1au8vNz8FPnTTz/Nnj3b3IGYlpb2j3/8Y9iwYU0200gdiEKIY8eOjR071ib1AmhXlB0gjhw5Mjs7u9EmDiaT6ciRI4MHD5axsBsiQAQAx6TX63U6XW5ursFgqKqq0mg0Wq1Wq9VOnjy5R48ecld3A3QgAoCDU6lU7u7u7u7uchfioFJTU0tLS++//36j0SiEyMzMFEKYA0Tpo9Qiej1zB6LlG5wBoOWUFyAWFhbef//9gYGBgYGBo0aN2r59+9KlS+Pj480T3nzzzWPHjk2dOlXGIm+IABEAHE1hYWFsbGxaWlqTZ2fNmhUZGZmUlBQQEGDbulqBABEAcD2TyVRbWyuE6NSpk9y1tHO9e/fu3bt3cnJySUnJokWL9Hp9TU2NOUCUXhdz4cKFixcvWr5xRWIOEKWX0gBAayksQPz1r39dUFCwbdu2bdu2mQfNAWJdXd3IkSMPHTo0cODA1157Tb4yb4wAEQAcisFgiIiI0Ov1wcHBkZGRISEhGo1GrVZfunTJaDTm5+dv3rw5NTU1KysrIyPDz89P7nqbRoAIALhednb20KFDBXtc2Mrvfvc7IcSbb75ZW1tbWVlpDhCrq6ulg1OnTg0aNMjyRyoqKsybQl6+fNmGxQJoPxQWIO7atUsIUVVVdeLEiYKCAr1eX1BQcPLkSelsfX39oUOHRo8e/eGHH9p5Xz0BIgA4lIULF+r1+mXLls2fP7/JCUuWLFmzZk1MTEx8fPyqVatsXF4LESACAGAn3N3dz58/bxkgmhUVFTUKEM3th4IAEUBbKSxAlLi5uYWEhISEhDQa79ChQ1FRUe/evWWpqlUIEAHAoaSnpwcFBVlLDyVRUVFr167NyMiwWVWtRYAIALheUFBQa99zgpvn4eEhhKioqLB8L7OkqKio0Yjla1UIEAG0zS14c5b9cHJyUkR6KK4FiHV1dXIXAgCwhfLy8pa8I8Xf37+srMwG9bQNASIA4Hqurq7BwcHBwcFyF+JYpCV3TXYgnjp1yvLjl19++dBDD5k/EiACaJt2FSBKTCZTTU1NgjQyggAAIABJREFUTU2N3IU0x8XFRdCBCAAOIzw8fM+ePQUFBc3MKSsrS0tLCw8Pt1lVrUWACACAnTAHiFIHokqlMp9q1IFo/qjRaAQvUQHQVopcwtw8RWziyxJmAHAocXFx27ZtCwsLi4+Pj4yM7NOnj+XZ0tLSLVu2LF26tKysLDo6Wq4ib4gAEQAcll6v1+l0ubm5BoOhqqpKo9FotVqtVjt58uSWtNjjlpMCxKqqKqkDcdiwYeZ1yo06EM0vV+nVq5fBYKADEUDbtMMAUREIEAHAoURERCQnJ8+5xsvLy8fHR61WV1RUGI3G8+fPCyFcXFxWrFgxZcoUuYu1igARABxQYWFhbGxsWlpak2dnzZoVGRmZlJQUEBBg27ocnZubm7BYwjx16tS8vDytVltQUHDmzBnLmZYBYlZWFgEigLZphwGiIjbxJUAEAEczc+bMCRMmpKSk6HS6vLy8wsJCIYSzs7Ovr29oaOjUqVOjoqK6d+8ud5nNIUAEAEdjMBgiIiL0en1wcHBkZGRISIhGo1Gr1ZcuXTIajfn5+Zs3b05NTc3KysrIyPDz85O7XgfSaA/EwMDA48ePu7q6du3atbS01GQymRc1mwNEKeQlQATQNu0wQJQ28ZW7ihuw5wDxzJkzixYteumllwYNGiR3LQDQrgQGBiYmJiYmJppMpqqqqurq6i5dukipnCIQIAKAo1m4cKFer1+2bNn8+fObnLBkyZI1a9bExMTEx8evWrXKxuU5skZ7ILq5uWm1WiGEj4+P0Wg8d+6cr6+vNFOaMGnSpFdffXXFihWVlZUNDQ0K+tcPAHaC3xrysOcAccOGDatXr/773/8udyEA0G6pVCp3d3eNRqOsf30nQAQAR5Oenh4UFGQtPZRERUWNGTMmIyPDZlVBXLcHorSiWQjRs2dPIYTlKmapA/Gxxx7r3bu3m5ubyWSqrKyUoWIACqek5xazmpqazMzMAwcOmEe2b98+ZcqU8PDwmJiY7777TsbaWsjJyUmlUjU0NNjhm16kG8z+/fvlLgQAYF8IEAHA0ZSXl7fkHSn+/v5lZWU2qAdmjfZA7Ny5szQu/e91fYDo6uoqhPD09BSsYgbQJspbwvzFF1/MmDFD+pUXERGxcePG1atXz5o1SzqbmZmZkpKyYMGCN954ow0X37Jly4cfftj8nIsXL4pb8YpnJyen+vr6hoYGqRvRftTV1Qkh8vLyKisrpb9rAQAgCBABwPGEh4frdLqCgoL+/ftbm1NWVpaWlhYeHm7LwtBoD8RGHYilpaXmmdISZnOAePbs2YqKCtsXDEDpFBYg7tu378knn+zcuXNkZKTBYNDpdM8+++w333wTHBz89ttvDxgw4NChQ/PmzUtISIiIiBg7dmxrr/+f//xnw4YNLZl58wGis7NzfX19fX29fQaI9fX1hw4duueee+QuBwBgL6Tt2O2wdx4AcJvExcVt27YtLCwsPj4+MjKyT58+lmdLS0u3bNmydOnSsrKy6OhouYp0TM0HiHQgArjlFBYgLl261MnJaffu3cOHDxdCLFiw4M033/T09Pz222+lN1cGBASEhIQEBwcnJSW1IUD897///bvf/a75R6Pi4uK5c+fe/K5VLi4utbW1dXV1HTt2vMlL3VrmnRn3799PgAgAMJP+4iX9nQkA4AgiIiKSk5PnXOPl5eXj46NWqysqKoxG4/nz54UQLi4uK1asmDJlitzFOhYPDw8hREVFhdRg2GgJ86JFiyorK5ctWyb+N0BUq9VCiEuXLslUNQAFU1iA+PPPP48ePVpKD4UQf/jDH958883x48dL6aGkf//+d999d05OThuu37Vr10cffbT5OW278vXs9j0q5ifDkpISeSsBANgVqbtB6nQAADiImTNnTpgwISUlRafT5eXlFRYWCiH+H3vnHdfU2f7hJyEhgbCXiCwRRMW9Clq1DlqtVVpta4e+bq3Yaq3W1i6ttlprl9pXq6+jtdpltXXUhdpWUXChCDIURAHZM4QQEkh+f9w/ns/Tk0ESgST0vv7wE5KTc55zEpOc63zv+7Gzs/P29h44cOCkSZNmzpzJno4hbQP9UtaZQCSEJCQkwA1WIIJ2xAQigiBmYGMCsbq62tXVlf4Jt9l7AHd395s3b7bpyEzHajtJUYFohXITQRAEsSA07GDpgSAIgiBtSlhY2Pr169evX6/RaORyuUKhcHd3f/iSLORhoCXMkECkArFXr14CgaChoQF695MmgQgRRSxhRhDEbGzsQ79bt25Xr16FT0BCyLlz5wghV69eZYuOVSpVUlJSjx49LDNEo7HaBCIdEhapIQiCICz0XMXSA0EQBEEsA4/Hk0gknp6eaA8tDnwpS6VShULB5/NFIhHc37Vr18TEREJIVVUV3KPdAxGvBSIIYgY29rn/8ssvFxUVTZ48+dSpU/v27Zs3b56Hh0daWtrq1avBITY2Ni5btuzBgwfjx4+39GCbwWo7SdEhWeHYEARBEAuCCUQEQRAEsRJAIBYXFxNCHBwcYKIzIDg4mBBCE4jsLMz4VY4giNnYWAlzbGzskSNHjh07duzYMUKIk5PThQsXlixZsmrVqj179nTt2vXWrVt5eXlhYWHLli2z9GCbQSAQEKtMIKJARBAEQXSCZx0IgiAIYiWAQMzKyiKEhIaGsg9Bjy+pVKrRaHg8HptAxHbGCIKYjY0JRKFQePLkyW+//fbcuXNCofC1117r3bv3b7/9NmvWrIMHD969e1coFD7//PNbt26Fz0drxmpLmFEgIgiCIDrBEmYEQRAEsRLgS5kQIhQKt2/fzj4kEAgkEkltba1MJnN2dmZ7IKJARBDEbGxMIBJC7OzsZs+ePXv2bHqPi4vLr7/+WlJSUlJS0rVrV3t7ewsOz3hQICIIgiAtRWpq6h9//GF4GahyehhQICIIgiCIlUAF4nPPPTd48GDOo66urrW1tZs2bZo2bRomEBEEaRFsTyDqw8fHx8fHx9KjMAEUiAiCIEhLsWLFiqNHjxqzpFqtNnsrWMKMIAiCIFYCFYgzZ87UftTNza2goOC99967efNmfX09n8+HnA0IROiKiCAIYhK2KhDT0tIOHDhw+/btqqoqlUrl5OTUoUOHvn37TpkyxcXFxdKjMwoUiAiCIEhL8fHHH/fu3dvwd0ppaemuXbseZt5MFIgIgiAIYiWIxeJ+/frxeLxRo0ZpPwptEAkhCQkJhBCRSASzrEAhMyYQEQQxA9sTiJmZma+99lpcXJzOR5cuXTpnzpy1a9diD0SzQYGIIAhic/Tu3bt3796Gl0lNTd21a9fDbAVLmBEEQRDEekhKStL3EBWIeXl5pKl+mWAJM4IgD4GNCcTCwsInnngiLy9v5syZo0aNUiqVP//886lTp1auXNmjR4+0tLTvv//+yy+/vH///v79+x8mZNEGWK1ApENCgYggCIKwYAIRQRAEQWwCKhABCB7SG1jCjCCIGVi1YtNm5cqV9+/f/+GHH3bt2jV16tRZs2adPHly2rRpmzdvjoyMXLVqVVZW1muvvXbw4MFPP/3U0oNtBmhCoVQqLT0QLphARBAEQXSCAhFBEARBbAKOQKQJRCgmwAQigiBmYGMCMTExsU+fPlOmTGHvXLFiRUVFxZEjRwghPB5v06ZN/v7+33//vYXGaCwikYgQAlNiWRUoEBEEQRCdYAkzgiAIgtgE+gQi9kBEEMRsbEwg3r9/PygoiHNnQEAAISQ5OZne079//5ycnDYdmenAZ7c1C0QrLK9GEARBLAgmEBEEQRDEJtAnELEHIoIgZmNjAjEiIuLq1av19fXsndA7lopFjUaTmZnp7+9vgfGZAnyIW2H7CUwgIgiCIDpBgYggCIIgNgEIRDorAEcgWuFJKIIg1o+NCcRnnnmmoKBg9uzZNTU1cM/du3cXLlxICBk5ciQhJD8//4UXXsjMzHzqqacsOVAjsP4EIgpEBEEQhAVLmBEEQRDEJgCB2KdPH3CInElUMIGIIIgZ2JhAfOONN6Kiovbt2xcQEDBs2LABAwZ07949NTV17ty5Q4YMIYQsWbLkl19+6dmz5wcffGDpwTaD1SYQaeWySqWCGxqNpqCgwHIjQhAEQawCBwcHgUCgUCjwChOCIAiCWDNQn9e3b1+ozMMSZgRBHh4bE4h2dnZnz55dsWIFn8+Pj49PSkpycXH5/PPPt27dCguEhYWtXLkyMTHRzc3NskNtFhtKIK5Zs6ZTp04nTpyw3KAQBEEQqwBOPDCEiCAIgiDWzPDhwxMSEjZu3BgaGkpQICII0hIILD0AkxGLxWvXrl27dm1ubq6dnV2nTp3YR9euXWupgZkKfIhbs0CkUcTbt28TQk6fPj127FiLDQtBEASxApycnKRSqUwm43RnRxAEQRDEqoiMjCSEdOnS5ezZs7SEWSAQCIVClUqlVCrt7e0tOkAEQWwM2xOIlMDAQEsP4aGw2hJm7QQiXKG6fPmyxcaEIAiCWAfQBhHnUUEQBEEQmyAsLIwwCURCiKOjY3V1tVwuR4GIIIhJ2FgJc3vChkqYoVTt2rVr2PQKQRDkX46zszNBgYggCIIgNkJMTMzQoUMnTZpE78EqZgRBzMOGE4i2jkgkIjYiEOHbRS6Xp6Wl9e7d22IjQxAEsX2ys7Pj4uLS0tLKy8vlcrmnp2enTp06deo0YcKEjh07Wnp0zePl5UUIKS4utvRAEARBEARpnq5du8bHx7P3QJDFCivhEASxclAgWgy48mOFH9zUG9JZmOnlqZs3b6JARBAEMY+cnJzY2Fh9E1ItXLgwJibms88+Cw4ObttxmQa0Hi4oKLD0QBAEQRAEMQdMICIIYh4oEC2G1U6iQudO4SQQCSEPHjywzJiQVqaioiIxMXHcuHE8Hs+Cw5g+fbqvr+/69estOAYEaSXKy8ujo6Ozs7MjIiJiYmJ69uzp6enp4uIilUorKioyMjKOHj164MCB5OTk+Pj4Dh06WHq8evHz8yMoEBEEQRDEZkGBiCCIeaBAtBg2NIkK9EAkhBQWFlpmTCaiUqni4uJGjRrFdgtGDPDWW2/t2LHj0KFDEydOtNQYKisr9+zZ4+LiggIRaZe888472dnZ69ate/vtt3UusGrVqt27d8+bN++DDz7Ytm1bGw/PeFAgIgiCIIhNY7WVcAiCWDk4iYrFMGYSlYaGhvfee+/kyZNtNaj/3yjcoFFEennKVgTizp07x48f/9VXX1l6IDZDWVkZISQlJcWCY6ivryeEKJVKC44BQVqPc+fOhYeH67OHwMyZM0eMGMFpVGRtoEBEEARBEJsGzkMxgYggiKmgQLQYxiQQr1+//vHHH69cubKtBkWI/klUiO2cMZaXl9N/EWMAkZ2ZmWnBMaBARNo3paWlxsyREhAQUFJS0gbjMRsUiAiCIAhi02AJM4Ig5oEC0WKAQARpoo+amhpCiFQqbaMxEUK0BKJaraYxSVtJIML0L3RHkGaxBoEIY1Cr1Wq12oLDQJBWIioqKiEhISsry8AyJSUlJ06ciIqKarNRmQEIRGyJiyAIgiA2CgpEBEHMAwWixYDouOEEIjwKGrHN4AhEuVyu0WgEAgGxHYEII6ezSCPNAvLu9u3bFhwDzR5iCBFplyxatKihoSEyMnLTpk3379/nPFpYWLhjx45BgwaVlJTMmjXLIiM0El9fXzs7u5KSErxIgyAIgiC2CApEBEHMAwWixTBmFmYQiDKZrI3GRAjREogwg4qHh4ezs7NcLq+qqmrLwZgHjJz2cESaBd6HVVVVFqydpP8X0Pwi7ZLo6OjNmzdLpdLFixcHBwe7ubmFhIT07ds3NDTUw8PDz89v7ty5BQUFW7Zsefrppy09WEMIBAIfHx+1Wl1UVGTpsSAIgiAIYjISiYQw82QiCIIYCQpEi2G1CUTq3aCYFK5NOTo6QvcumwghYgLRVGgpfUZGhqXGgAlEpN2zYMGCW7duLV++vF+/fvX19Tk5OcnJyffu3ROJRAMHDly7dm1eXt78+fMtPczmCQsLI4QkJiZaeiAIgiAIgpiMk5MTafOQCoIg7QAUiBbDmElU4NHGxsa2TJiDfePxeHCbCkQbapwP6hAFovHQ9J8FZ56hEhNfOKQdExYWtn79+qSkJLlcLpPJysrKlEplYWHhlStXVqxY4evra+kBGsUzzzxDCPnxxx8tPRAEQRCkjTh27NiCBQvGjBkzf/7869evay/w5ptvPvfcc20/MMQMIIGIAhFBEFNBgWgxjClhpt6wLT/fQSCKRCLCCESJROLl5UUIqaioaLORmA3sAvbnMh5rKB+mAhETiMi/AR6PJ5FIPD09+Xzb+yJ+/vnn+Xz+sWPH2jggjyAIgliEV155Zfz48d98882ZM2e2b98+YMCAr776irPM6dOnf/31V4sMDzEVZ2dnggIRQRDTsb3zlnaDdQpEKFvm8/lCoZAQ0tDQAN0xHB0dQSnahNzBHoimYg0CkY4BzS/SLnFwcPD19f3ll18sPZAWwM/PLyIiQqFQZGdnW3osCIIgSOvy888/b9u2LSQkZP/+/RkZGfv27fP19V2yZMmhQ4csPTTETLCEGUEQ80CBaDHYHoj79u3bvHmz9jJUqbRZygPcjUAgoAKRljDb29sTJiZmzWAPRFOh7zQLCmLsgYi0bxQKRXFx8ZQpU1544QUL9gpoKXx8fAghZWVllh4IgiAI0rp8/fXXYrE4Li7u2WefDQ8Pf+mll44dO+bk5PTKK69gDt1GQYGIIIh5oEC0GCAQQdwsWrRo0aJF1dXVnGVoh8Q2+3ynAlEgEBBdAtEm5A6oQwyyGY819B/EEmak3RMeHr5z584TJ05ERETs3LnTplPS0NQCBSKCIEi7JyMjY8iQISEhIfSevn37bt68uaioaMOGDRYcGGI2KBARBDEPFIgWQygU2tnZqVSqxsZGUCfa7QVpCXMbJxDt7OyoQIQSZolEYj0CceXKld99952BBTCBaBJKpVKtVsPthz9oCQkJQUFBR44cMfWJ1iAxEaRV4fF4s2bNSktLGzx48Jw5cyIiIg4cOGDpQZmJp6cnsei0SwiCIEjbUFdXR38oUqZPnz5o0KDPP/88NzfXIqNCHgYUiAiCmAcKREtCJ2IG4VVVVcVZoO0TiJCI0ZlAtHgPxLVr13766acFBQWrV69evnz55MmTv/zyS51L4iQqJsE24nx4eXf+/Pnc3NyTJ0+a+kQUiMi/BD8/v8OHD+/fv1+tVkM52CeffPLgwQNLj8s0MIGIIAjyLyEsLCwxMbG4uJi9k8fjbd26tb6+fvbs2dp6EbFyQCBiBTqCIKaCAtGS0CrmZgVi2/dAtLYS5srKyvfee++dd96BQu+SkpKDBw9u375d58LtTyDKZLILFy600spbViDC2syYrRtLmJF/Fc8++2xaWtqWLVukUumKFSsCAwPHjBmzdu3a8+fPG55cy0rABCKCIMi/hDlz5igUihEjRly6dIl1hQMGDHjzzTdPnz79n//8B1WUbYGzMCMIYh4oEC0JTSBC7q+yspKzgDX0QLSSEuaUlBSNRtPY2FhaWkrvpMeHA1iw9hRkW758+aOPPnrq1KnWWLm1CcT29MIhiAEEAsGCBQvu37//008/jRw58uzZs+++++7w4cNdXV0tPbTmwQQigiDIv4SFCxfOmzcvMzMzMjJSLBYnJyfTh1avXj1lyhSYlzkjI8OCg0RMAkuYEQQxDxSIlgQEIv3sPnPmTGJiIrsAJhApN2/ehBslJSX0Tn0C0SIJxE2bNgUGBmZnZ7fGyouKiui/LU5rCERtG94smEBE/p3Y29tPmTLl9OnTd+/e/e9///v000/DV4OVgwlEBEGQfw9btmzZvXv36NGj/f39NRoNvV8oFP7www+bNm3y8/Ozifg8AqBARBDEPFAgWhKhUEgYfbNly5YxY8aw38p0EpW2TyDa2dkRaxKIKSkpcIPtwKLvl4pFBOKZM2fy8vKuX7/eGisHVUoVW8vCrhYTiAhiKYKDg2NjY3/77TebsHKYQEQQBPn3YGdnN2PGDLjW1bdvX/YhPp//2muv3blzp7CwMD4+3lIjRExCLBYLhcL6+nr81Y0giEmgQLQkIOlYJVdbW8tKMZ0lzGq1uvWaZHESiI2NjVZSwkwTiAYEYnp6OgwPvgvbWCDCplvJ8cGettKLbm0JRPwpg/zLgY9fKwcSiCgQEQRBEMDX13fo0KGWHgViLC0yj0p5efmMGTP+/vvvFhoUgiDWjg2cpbRjQCBylFNdXR1MrkL0lDD/+OOPU6dOff/991evXt0iw9BoNL/88ktkZGRQUBBINzs7O+0eiPBQswJxz549np6e48ePf5gh/f3331euXHnjjTf4/P933GlpaXCDLWFWKpWNjY1wGP/666+RI0cuXrz4q6++gqG2sYdqVYHYqgnE1hCIVVVVarWavnzGgCXMSPtGoVDweDxLj6LFgASiTYQlEQRBkBYhOzs7Li4uLS2tvLxcLpd7enp26tSpU6dOEyZM6Nixo6VHh5iGk5NTZWWlTCbz8PAweyUnTpz47rvvampqRowY0YJjQxDEakGBaEm0E4jknzZHZwIRGuHdv3+/pYZx/vz5F154YejQofHx8TCdi85JVGAwhuWOVCqdMWOGj4/PQ3bre/PNN69cuTJ69Oh+/foRQjQaDVWobAKREKJQKCQSCSHk3r17hJC8vDxioRJmTCCya2tsbKyurnZ3dzf+iZhARNo3IpHI0kNoSZycnMRicW1t7Zw5c7Zv327S1QIEQRDEtsjJyYmNjT1x4oTORxcuXBgTE/PZZ58FBwe37bgQ84ETqIdskwXBjlbq0o4giBWCAtGS6Ewg0r6H7G02gUhLROVyuaOj48MPAyIkFy5cSElJMSAQpVIpaU4g1tbWajQadhcIIVVVVbNnz37xxRefffZZI4cEHfToXiuVStoakk0gEkLq6urg+w/GCQfHggKxVR2fDSUQCSGVlZUoEBFEm3aT4PD29s7Ly9u5c+fy5cu7du1q6eEgCIIgrUJ5eXl0dHR2dnZERERMTEzPnj09PT1dXFykUmlFRUVGRsbRo0cPHDiQnJwcHx/foUMHS48XMQpnZ2fCCMT169fv37//5MmT0KLESOCUDQUigvx7QIFoSfSVMGvfhmAdAMufOXPG2dn5+++/f+mllx5yGNT37dixY9q0aUSPQDSmByKMjSOAEhISDh48WFlZabxArK6uJszus4dIO4HI7gX82f56IMKhaCU72RqTqBBCKioqQkJCzBgGljAj7ZVWTXAcPXp0z549hpeBj1Z2qq6H4eOPP3733Xfz8vLS0tJQICIIgrRX3nnnnezs7HXr1r399ts6F1i1atXu3bvnzZv3wQcfbNu2rY2Hh5gHZyLmjRs3FhYWXrp06cknnzR+JdAKmXN2hiBIOwYFoiXRWcKsLRDt7e0zMjKqq6tdXV1Jk6ABX3bz5s0WFIhxcXEvvvgi+adAhEepQDQsyOBRjrmDAZt0bcqAQOQkEKmuAtHJDsBqeyCuXbs2JCTkhRdeMH7lbZBA5PF4Go2mZROIJj0RE4hI+6a1Exy7d+8+ePCgMUu2lECcNm3ajRs3vvjii/T09KeffrpF1okgCIJYG+fOnQsPD9dnD4GZM2fu27cPZ2G2IViBCJNok6aWUMYDdWy1tbU1NTUQaVQqlZcuXYqKirKJGeEQBDEV/I9tSaBplL4EYkNDg0qlEggEAwYMSEhIuHz5cnR0NNHTJFEfFRUVMpksMDDQwDKg3gghGRkZoPm0E4iOjo4gEA3LHervNBoNnS4ABmz8tam6ujrYCt071rFyJgujy1hJCXOzjq+goODdd9/18fExSSC2wSQqEolEJpM9fPqPTSCa9EQUiEj7prUTHNu2bXvxxRcNy8G8vLylS5e2YL/C7t27E0LS09NbaoUIgiCItVFaWtqrV69mFwsICEhOTm6D8SAtAisQz507B3eaKhAhgUgIKSoqAoH45Zdfvv3221u3bn3llVdacLQIglgJKBAtieEEItxwcHCIjIxMSEi4dOmStkBstqZ17NixqampDx48MNCNjiYQNRoNfH8YKGE2JoFICFGpVLA8vbOysnLmzJkzZ84cPny44TFD/JDoSSByUCgUlZWVs2bNyszMJLYgEGGEJSUlMpkMvrmNoQ0mUXF2dpbJZEbKu2+++SY8PHzkyJH61kYeQiBiCTPSLmntBIeXl1ezbSJSU1PNWLMBUCAiCIK0e6KiouLi4rKyskJDQ/UtU1JScuLEiaioKDPWn5ubq6+5B+XBgwdmrBkxgJubG2kqGDJbIEICkRBSXFwcFhZGmn4SZGVltdxIEQSxIlAgWhLDPRDB64FAJIRcvHgR7meXbzaBmJeXV1dXl52dPXDgQH3L0A3V1dX9/fff5KF7IMITqUAEo6TRaL799luFQtGyArGuri4xMfH3339nt2V2D0S1Wh0TExMcHLx582ZTnwsbbVYgUkOXk5NjzOVcQkhjY6ORKzcPKhALCwuNEYj37t1bsGBB165dQdrqXBvBEmYE+SftMsERERFBCElPT2dT5wiCIEh7YtGiRcePH4+MjPzggw9iYmKCgoLYRwsLC//44481a9aUlJTMmjXLjPW/9tprhw8fNmZJtVptxvoRnUCzFCgRu3HjBtz5MAlEuJGbm0uwKyKCtF9QIFoSViB6eHh4eXndvn1bO4H42GOP8fn8v/76C6ZdNqmEGVael5dnQCCCIuzTp09iYuKdO3dgYCAQVSqVXC7n8Xi0hNlIgcg6IJOMJ9ElEA1sVKFQsOt/yB6IJSUlR48edXNzM0MgGplANEMg0uPQ2glEYtxBg/HouxRMB8kpNm8WFIhI+6a1ExwWwc3NzdPTs7y8vLy83MvLy9LDQRAEQVqe6OjozZs3L27C1dXVw8PDxcVFJpNVVFTABWOBQLBlyxbz+uEuXbrU19fX8DKVlZX79++9UlN4AAAgAElEQVTHK1UtCAjEoqIitVoNJ4CkJQTi/fv3iVbPegRB2g0oEC0JW8IcGBjYv39/bYHo6Ojo4+MzaNCgS5cunT179qmnntJXwlxXV5eent6/f392E7BAfn6+gWFAAhFqnOE2TSBOnTpVo9E4Ojry+XyRSEQeWiAao8BMLWFmh/SQJcxSqdTw5gxghkA0cs30oLVGAlGj0cB3PNRTGyPvYJna2lqdVdh0tKZWImMJM9K+ae0Eh6Vwd3cvLy+vqqpCgYggCNJeWbBgwZgxY3bs2BEXF5eeng4/Yu3s7Ly9vQcOHDhp0qSZM2c2KwH1MXz48Gbrk1JTU1EgtizwehUXF+fm5tbV1XXs2LGysrK0tLS2tlYikcAyarV68ODBAoEgMTFRew01NTX0RztEDtVqNSQMMIGIIO2VFuukjpgBm0AUCAQODg6EsWYgsyAXNn78eELIsWPHiP5A37JlywYMGAA1yBSaQORseuvWrSdPnoTbIA2hEUZjYyNhBCKoIvgWMbWEmd7JSkNjFJipJcwtKBAhNGdtAtH4BOLVq1dv3bpl5GqBdevWQdzS+AQiPbA6fxwYEIh0uh5toqOjaUE0JhCRdgkkOKRS6eLFi4ODg93c3EJCQvr27RsaGurh4eHn5zd37tyCggKzExyWAi4+mdqyAEEQBLEtwsLC1q9fn5SUJJfLZTJZWVmZUqksLCy8cuXKihUrzLaHiKWgJcwZGRmEkO7du8OlTYgQAtevX7927dqlS5d0/jin8UPSdFJQXFwMp0IoEBGkvYIC0ZKwCUQ7OzuOQCwtLSWEQKYDCpBBOekrYYaYIY2gw5qhVwhHIObn58fGxr766qvwJ2gddpYVKhABMwSivgSiSQKRzu5iagIRtq7RaEx1iCBt1Wq1GSE4iycQa2trH330UZhpx3ju3r0LN1pKIOoLEr799tseHh4pKSk6V3j69GmQ19pjKC0tffzxx3/55ZdmB4YgVs6CBQtu3bq1fPnyfv361dfX5+TkJCcn37t3TyQSDRw4cO3atXl5efPnz7f0ME0DLj5VVVVZeiAIgiBIW8Dj8SQSiaenJ5+PJ5I2DDjfoqIiEIjdunXz9PQk/7wiePz4cbgBkzVzYAUizKZC5WNpaSk2rESQdgl+7lsSwwlEEIje3t6kSeGBUNNXwkyn96X3UJXDKWEGTUbjYGwCEWhZgWjStNF0eESrByIYLg6cBGJDQ0NjYyM1XIYF4s6dO19//XXqrQjTts/UEKJKpdJoNMSIHTRVINbU1EDytNlRZWZm1tfXw9vGeOjRM14g0mVouxNKfX09/bnAWVVycrJSqdQ57wqnMybnPXb+/Pm4uLjdu3c3OzAEsX7aX4IDE4gIgiCIRqOpr69vpen+kNaAJhBh3uTw8HBoTMS6QlqvRs/OWEAaisViQkhFRQVpmkGFENLY2EgnaGZZu3btk08+aUxPfARBrBMUiJaETSAaFoiOjo6kSfnpK2GG+1l/RGUWJ4EIz6LraQ2ByJo7sxOInBJmDw8P7YUVCgVHVCkUCmMEokwmW7x48caNG9mgPv12NPUHED0sxicQ2at2CoXio48+0pnOW7Nmzeuvv04XM7BmcHMNDQ0mlQDThbt06QJ/ZmZmHjhwwMBTDCQQ2d2HY/LXX39NnDgxNTUV3mY6jw+NmnKGBICY0PnDBUFsl3aT4MAEIoIgCHLz5k2xWAwuCbEJHB0dnZ2dFQrFt99+Swjp168fnPHRiElDQ8OlS5fgts6pEeGrPzg4mGgJRKKnUGnLli3Hjx8HZYkgiC1i2+cttg6bQNQuYQbBxApE7QSiYYFIZU1BQQEbI4f1UOelLRDt7OxgbABs/SETiLASY8ScdgLRsEDkDKmurs4YgXjo0CH4gmSfblICMT4+/tFHH01KSiJmCUTqSX/99ddvvvnm/fffX716tfbyOiOlOqHhPo6PM2Y8mzZteuqpp+DPBQsWPPvsszdu3ND3FAMCkX1zwjHZu3fvkSNHRo4cmZ2dTfQ4UMMJRBSIyL8HW0xwwHcHJhARBEEQxLaAugelUhkbGzts2DCOQHzw4AE9bdEpECGrGBgYSJoEIu2MRHRNxFxbW1tQUED+maJAEMS2QIFoSUwtYYYPdFAw0BvRyASiSqViY+ScBCKstvVKmOHO999/n5g7CzNsVKdA5JQwk3/O1GEgi/fTTz/BDVYy0m9HY8Z5+PDhCxcu7Nu3jxgUiBcvXvzjjz+0hySXyxsaGi5evPjcc88tXbqU6InwsDbQmAQi0fJxhoHxBAUFCYVC+BOGUVhYqO8p9IhplzBrC0T4bVFWVgaTsuncBY7xzMzMZMOYMB4UiMi/AVtMcEAJM/w/vXLliqnzOCEIgiDtgPDw8NTU1NTUVEsPBDEBqGL29PT8/PPPyT/PNwkh9+7do0saEIgw9QoIxKysLEKIi4sL0ZUzuHPnDnR8QoGIILYLCkRLYmoJM1sEev78eaKrvaDOBCL5p/iDTahUKoglaicQhUJhC5Yww8BcXV3Jw5UwQ2dfDtoJRLZzx6pVqyQSyZ9//qn9RGgYzBmqSSXMcNyuXbtGDArEyZMnT5w4ke4U6zSrq6vBwcELofO7WdsR60NnAnH//v23b9828CwYub29PRWI8HrB7wCd0F0wJoHImXnZmATi1atXJ06cSP80NYFYW1vLXv8khECzOSOfjiCISdASZqlUasY8TgiCIEg7QCwWR0REREREWHogiAl07NiREDJz5ky4cskRiGyXJ50nKXBnhw4dRCKRXC5XKBRQbzR06FCi1UGLMFN9okBEENsFBaIlgdZX+kqY9QlEUDBwzmZkApH8U5Nxkn3aAlEkEmkLRKFQyOPx6GwhOmETiI2Nje+///7Ro0fhTrgY9TAC0dvb+z//+c/YsWPha4nuowGBuGXLFrlcDrKVAxVSOhOIxowThnf9+nV21mbOE2tra4uKitRqNQ30cQQiW/enU3KxNtDAqDQaDf1Wpsft6tWrzz//fGxsrIG9gPEIhUIqEGErBgSiSSXMxghE2MfOnTsvWrQI7rl37x6NN8Ihqqmp0Wg0arU6Pj7eQBKzvLx8wIAB4eHh9GhUV1f36tWre/fuJlV2I4hFsMUEB3x3bN++vVevXjAhjM6+6QiCIAiCWBVz586NiYl544034E/zEohOTk5QJVZcXJybm2tnZxcTE0MIof0TKSgQEaQdgALRkpiUQOTxeHV1dWq1GuyJi4sLn89XKpV0EmHDCURWk1GTwiqeZgUij8cTCoUajUalUu3du3f69OnaRoYViKmpqR999NEbb7wBd0IC0XAR7v/+978BAwbAxSuiJTpFItF33313/Pjxxx57jD6lrq6OU6esreFYzyWXyyFdr1MgmpRAhOFJpdKsrCx9ApFefKPflA8jEA0cPYVCQZ9OnwIrN1CMTHQJxGYTiPSIaa+5WYHIHp/Gxkb4E45kaGjo+PHj6aNXr15l96KxsVEul//000/Dhg1btWqVvrFt3749MzOzoaHh6NGjcM8bb7xx9+7d/Pz8vXv36nsWglgJtpjggBJmtVpNW6cbTj0jCIIgNkp2dvY333yzaNGil19++ZlnnpkzZ87KlSu3b99u+KcmYrVER0f//vvvkEMkehKIcH5qIIFIBWJSUlJDQ0NAQMDIkSMJIRcvXuQsT38eoEBEENsFBaIlMb4HIo/Hc3Bw0Gg0dXV1sLxYLIblqbKB++vr63VaMJ0JxPr6+j179igUCh6PB4IPEIlEoJMA+DohTVXM9fX1Gzdu3LNnz9tvv83ZI7aEGbaSlZUF/s7Z2ZnP56tUqq1bt2p31QUOHDiQlJSUk5OjPU4YFTsMwHACEWBb9c2ZMyc8PDwpKYket4dMIBJCrl271qxApGKXHS1HIDZbwkyrzrXRObUOmMTy8vKTJ0/qO+acEmalUml8ArGwsJATR4VhsNXuBhKIEydODAoKkslkME5HR0f2XXflyhW4QQ+RVCqFl/Lnn39m18lKc1qZfubMGdjc999/D/ds3rzZQHgWQRDzYC8+ATRigCAIgrQPcnJyxo0bFxoaumDBgs2bN//www+///77zp07V69ePX/+/MDAwGeffZYNrCG2iE6B2KNHD2Iwgejs7AwC8fLly4SQLl26hIWFeXt7FxUV0VAIABkOggIRQWwZFIiWxMAszHV1dTKZTCwWOzs7w8JQxVxVVaVWq+3t7fl8PrSr4Fg2wviUZkuYExISpk+frtFoHB0dqZ4jhIjF4qeeemrYsGF9+/YlWgJRqVSCG/r666/Z7hiEsWMqlQpuazQaKMcTi8WwidjY2HfffVfnAeEk7Di7Rr0hO1TtSVQMJxDv3LmjVqtv3ryp88iwArGqqqp3797vvfceffTu3bsjRoygM6LQ4bGTlDUrEDkJRHbilGYTiER/CJHdLn0KjLC4uHjs2LGLFy/W+UQzEojsznJqFWk8ljQnEDUazZ9//llcXFxQUADjdHBwaFYgwtPv3bvHThLN2gqFQuHt7c3j8f7++2+lUpmUlKRSqXr06NGpU6fU1NTjx4/r2ykEaTPaWYIDBSKCIEj7pry8PDo6+sSJExEREe+8884PP/xw8uTJhISEkydP/vjjjytXruzTp8+BAweio6O1m9sgNoTOEuaePXsS4xKI8NM9NDSUx+MNGTKEEJKQkMAuT98e2OoEQWwXQfOLIK2GgRJm8E0w1TIgkUjKyspA64BB4yQWqZopLS3t0qULMaKEmeo/R0dHNtYnEon69+9/7ty5995778aNG3QYVCDC2jQaTVpaGsy9BbAlzLB3hBAoshaJRCKRCEarz+MYFogtkkAEG5Wfn6/zyNDwpkKhuHbtWkpKSkNDw0cffQR3/vnnn+fOnQsICIBKWzo8VmJyBCKt6dMnENkEIqT/WD1KtCYYqa+vB5XMwUACEaAJxMbGxoSEhEGDBsGGqECEo0pDjsYkEAkhBQUF7LuUCsSysjJYs7ZAPHv27Llz52bMmAHjlMvlNIHIvrK0zIE6VioQCSF//PEH2G24n51e54knnrh+/fqtW7eSkpLgWujQoUPDw8OXLVu2bt26cePG8Xg8fbuGIK1KTk5ObGzsiRMndD66cOHCmJiYzz77LDg4uG3H9VBACTMLCkQEQZD2xDvvvJOdnb1u3Trt2iNg1apVu3fvnjdv3gcffLBt27Y2Hh7SUrACUaVS5ebm8vl8AwKRk0BMSUkhhAQGBhJC+vfvf+jQoVu3brHL05MLNoG4fPlye3t7eraFIIiVgwlES2KghJmtXwZAG8EVG8gewvLgwhobG6nWoR/KzSYQCwoK6Mo5AhFuLFu2bM+ePTNmzIA/OQKREELLjQG2hJmj0kQiEQybEPLgwYO0tDTtA6JPINIeiOww6FOMSSBmZ2dv3LhRoVDAV5c+gcgmEGHX2G848Fzx8fHBwcH79+9nPR0rENk6WbohYwSizsE/fAKRc3vv3r3Dhg379NNP4U8YOZtAhANivEDUHp7hBOKKFSs+/PDDXbt20dHqTCAWFxf7+vouWrSIHqKamhq6+/SN19jYyNnEkCFDHnnkEULIlStXoH/zI488Mm/ePC8vr/j4+I0bN8JiqampZhdQKJVK1krTvC1ART+CsLTXBAdNIPJ4PLDz2AMRQRCkPXHu3Lnw8HB99hCYOXPmiBEj4uPj22xUSIsDAjE+Pn7+/PkpKSkqlapz585wNmrMJCoQVoCOiuHh4YSQzMzMU6dOTZkypaKiorGxkWYC6C/wqqqqDRs2rFu3Dqc6RBBbAROIlsRACTN8sLICET7TQaawAnHo0KEHDx6Mjo6mS+ps5Md6K22BKJFI7Ozs7OzsaFoQ7ndzc5s2bRp9ItxvjEDUnqyZljADcXFx0FMjMTHxk08++eqrr4KDg81IIBoziYpMJnvrrbcOHDjg6+sLUzw/ePCAPkr35bXXXqO9Oerr6+/evUsIqaioUKvVMF82jAdim6+++iocf/LPBKJGo1EqlXSoJiUQCSE1NTWenmycjisQ9TVnbDaBSO8EN0F7BcJ47O3teTyeQCCgR+NhBCLU3SuVSrVarZ2ghDpNOqWJvgSiTCaTyWTHjh2jR0wqldLdp4HK3NxcOo8QbPrJJ58khOzatWvVqlXwS+WRRx5xdnbetm3b5MmTlyxZ8tZbb3Xt2jUtLc3Pz+/cuXOdO3fWt6ccUlJSNm7cmJSUdPv27draWhcXl7CwsJCQkIsXL1ZVVQ0fPjwnJ6e4uBhe0z59+jzyyCPBwcFpaWmBgYEhISEjR44MCQnhrLOxsfHgwYNnz5719/dvaGh48OCBh4dHQEDAjBkzaOsApH3QXhMc9vb2fn5+crl8//79jY2N48aNy8jIaGhoYGfiQhAEQWyX0tLSXr16NbtYQEBAcnJyG4wHaSXgl2dhYeH27dvj4uIIId27d4df9QZKmGkCEfD19SWEdOvWjRCSkZGxdu3av//+OygoyNvbW61WOzs719TUUIEIVxzVavWdO3f69OnT2juIIMjDY6u/77Ozs+Pi4tLS0srLy+VyuaenZ6dOnTp16jRhwgQ6k5T102wJs+EEIvyr0WgyMjKGDx9Ol6SBLJMSiIQQoVAILoZGBTnQBCJ1OgYEIme6Dyhhpn+ePn0auvKtWrXq5MmTY8aMefXVV+nAQGZBsCsjIwMi8dQusZqptrYWDqCrq6tYLC4uLtbZSRC+n/Lz82FU2glElUr19ddfszsCuwaXy+B7kXVh7JwknD6MbBmyvlmY7e3tlUqlVCo1nEBUq9UcY8i+pqdOncrKyoqNjeXcbziBCPtLB0ZLmOFfYwQi60A5AhG2QhOIcrmc45Hr6urg0NF3jr4EIsC+UmwJMz3+nKzTjh07goKCBgwYALsgFApfeuklmNN20qRJX3/99YoVK2pqaqAvZ35+/uDBg0eOHDlmzJjLly8rFIpZs2Y99thjiYmJvXv3dnJygnX+/PPPX3311YgRI7755hsQ0Dwez83Nraqq6tq1a9euXYPFaGE+/NJKTk7m/Izm8/l9+vQZMGBAfn5+aWmpQqEoKCjg8Xg6D/WaNWvefPPNqVOnFhUVaTQaWq+N2C5GJjj27dtncwmOixcvNjQ0QOuMkJCQ7Ozs9PR0Y842EQRBEOsnKioqLi4uKysrNDRU3zIlJSUnTpyIiopqy4EhLQt76Rp+qBsWiDSByKYfQCCGhYXx+fzs7GzIUmzYsAEehfP0mpoaqVTq4uJCe57cvn0bBSKC2AS2JxDbUw+pZkuY2e5y4Pi0eyASQhQKBauZqIRqtgcitT9Qg2Zvbw+ChtOGj6JdwsyZcI0tYWZzYYQQsVjMesm///67oaGhpqbm7NmzdMxUD4nFYnt7++rqarlcPnHiRAj96Uwg3rt3z9/fnxCyd+/eo0ePbtu2jePg3N3dKysrYZy0MJCTQDx8+DBbkQojgQQiIaSsrExbILKwJczkn4eduiFOAtHLy6ugoIAziQrR+nquq6vjCDh25QsXLszKygoJCRk7dix7v+EEIgyAijlawgz/0sUqKys1Go3OdoHw6otEovr6evZIEkJ+++03QkiPHj3++OMPpVLJKS4mhBQXF3OUKJtApAKRx+PBjrMLGyMQgT59+oCiXbBgAa1ZhiM2bdq0wsLCBQsW9O7d+/bt28ePH9+/f//+/fthgZ9++ikiIuLmzZtOTk4LFy6sqqq6fPlyTk5OVVVVYmIiIWTy5MnLli0LDQ318vIqKyvLyspKT093dnaOjIxMSEhwc3MbOHCgs7NzY2PjkSNHSkpK0tPTg4KCiouLc3NzDx8+fP369evXr3NG261bt6lTp8pkMh6P5+/vL5VKf//990uXLi1btmzZsmWwTK9evcaPHz9kyJDo6Gh9fl8bjUZz4cKFEydO3L1719/ff/z48SNGjDDyuUiL044THGwn3AEDBmRnZ1+7dg0FIoIgSPtg0aJFx48fj4yM/OCDD2JiYtjPfEJIYWHhH3/8sWbNmpKSklmzZllqkMjDo1370qNHj2YTiE5OTmyFDShCR0fHgICA+/fvczpNeXp6qlSqmpqakpISFxcX+jMem58giK1gYwIRekhlZ2dHRETExMT07NnT09PTxcVFKpVWVFRkZGQcPXr0wIEDycnJ8fHxHTp0sPR4m4FNINrZ2Tk5OUGuu7S0VDuBCJ/pIKTYEmZCSH19PRtAowat2QQiFJNOmTIFQjE6pzlmMb4HIqesmGglEGtqav7+++/8/HxYEhQSHbCDgwMIxLq6Oprdo09nTWJVVRU4LHt7ezgsHIEYEhJy7do1+IajlpBtftfQ0DBr1izO9yItYSbMTGH6BKJ2ApHepgoPXtAPP/zw0KFDhBGIhhOIdIt8Pl8ikbBNAElTPfvq1as5AtFwAhEO+IMHD6A0myYiSZNGBBobG6urq7XnVyVN76WAgICsrCx23tikpKSTJ0+6uLjExsZu2LCBCkSxWEyHTWu62dHSBCIMQyAQeHp6areB0ykQdU7XIBKJHn300cTExCVLlnAecnFxcXFxAW2t0WhOnTqVkJCwZs2aRx99dMiQIRs2bLh586ajo2Ntbe369evpsx555JHHHnssJCRk1qxZtDDTy8vLy8srMjIS/nzuuefo8gKB4Nlnn+Vsuq6u7sqVK0lJScHBwQEBAUKhsGPHjgqFIiAggLPk22+/ffz48Y0bNyYkJEgkkoaGhpSUFMjh2tnZhYaGRkZGymQytVodHh5eW1tbUlJy+/btiIiIkJCQsLAwJycne3v7c+fO/f7775mZmXS1GzZsiImJ+fHHH+lHB9KW/EsSHP379//ll1+SkpJo81wEQRDEpomOjt68efPiJlxdXT08PFxcXGQyWUVFBfwcFQgEW7Zsefrppy09WMR8qECElAAhpHv37lCRY3gSla5du8I9dnZ29Oy1W7dudLpOiqenp6OjY05Ozp07d8RiMZ2mGQUigtgKNiYQ21kPKU4Ckcfj9e7d+8KFCzdu3NDugaidQKRBpPr6etYf0diXvgQimzIjhMyfPx8KJE0ViHw+v6Kiorq62tXVlbNF7RJmtgdiWFjYnTt3Hn/88U6dOrFjpnqIhtHq6urondolzF27dk1NTYV2fkKhUKdA7Ny5My0ypU6KjfVVVVXRL0UnJyeYfrqsrIxKRnrDDIFIn1JaWlpdXb1q1Sr4E7KlBQUFKpVKIpF88cUXO3fuvHz5MmfwoAIDAgKSkpImTZp0/vx5duUw7ISEhKKiomZ7INLbYAyVSmVJSYmvry+nhJndekVFhU6BCE8JDAzMyspik5swY8nkyZPB3ctkMvAgHTp0oD8g4CXw8PDo3Llzfn5+cXExm0B0dXW1t7fv3LmzWq3WFoisP62tra2trZVIJBUVFTrnVD569Cg0N9D14P/D4/GeeOKJJ554YsGCBd7e3nw+f+bMmfv373/ppZdyc3N37twZEhLi4uJy6NCh//3vf/S3kdk4ODgMHz6c7TZggHHjxo0bNw5uK5XKM2fOJCQkHD16NCUlJTMzk9WCFO1sIyEkICBgypQpPXv2zMjI2Lp166FDhzIzM7Eg2iL8SxIc0ECAfuoiCIIg7YAFCxaMGTNmx44dcXFx6enpECAAWzRw4MBJkybNnDkTalcR24UKxKFDh96/f7+4uLhnz54QpJBKpZyF6+vrlUqlUCgUiUQBAQEODg51dXXe3t5weksIiY6OPnnyJOdZnp6eEonkzJkzGRkZCxYsoCcIt2/flslkERERQ4YM+fHHH1txJxEEeThsTCC2sx5SHIFICOnbty8IRH09EFskgciZl4NKFirmjOyB2KlTp7y8vLy8PG2B2NDQwG5RIBDY2dlRgfjGG2988cUXd+7coZ34amtr1Wo11XC0HV51dTUthdYuYe7Zs2dqair4Mn0JRPbXDKdOGWAzdO7u7qNGjTp37hydTYUYIRD1lTArFIrGxkaRSGRnZyeXy9lqXxCIUFjt7u4+b968+Pj4y5cvf/vttxEREdRVUbPm5eUFe8eunG6UnV2E6Ekg0jcDfV3y8/N9fX05JczsfkG/P21gDe7u7oQQmUxWWVk5d+7cGTNmgND08vKCF0itVkNO0MfHJzc3F6Qt/PvYY48dOHDgrbfe+vTTT9kEopOT06VLl1xdXSdPnqy9Xc5ulpSUdO7cWSaTOesapIODg/E5O5pW7tq167vvvksI6dy5M632feONN4xcTythb28PPnH16tX19fVpaWnx8fFOTk4SiQQKqH18fIKCgpKSkqCqGpxsv379Ro8ePXr0aBqZfP3112/duoX20FL8SxIc8Aa7deuWpQeCIAiCtCRhYWHr169fv369RqORy+UKhcLd3R2mGUTaB1Qg+vn5/fTTTwqFQiKRwJmCdgKRxg8JIXw+v0uXLqmpqexp19KlS8PCws6cOXP58mVoBEQI8fDwgAuoBw8eZPOJcHU8NzdXZy97BEGsBxsTiO2shxR86dISZtJ06pWcnKzdA9FACTOnB6LxCUSAboX6IyMTiP7+/nl5eXB5CrZLP/RVKhVbxczO+kIIGTJkiLOz89SpU9kxs7qTVrOytcYcgWhnZwczfNE7YQH2i0cgEIDnArRDbeSf04DQlbCVxWaXMMPyjo6OfD5fLpez+hK+X2HTMEIoEDh8+HCHDh22b9/OWQPdfXqU2C9y+BnHjofeT+9sbGyE2V3o65KXlzdw4EB9JczknwKaBV59mClFLpefOnXqwIEDWVlZEyZMIIQ4OzuzM3rDPVAUTNfg4+NDmt7AVCDCbsJ/ATqBCV0DtFtmh2RYILZXRCJRv379+vXrp/3QsGHDDD+3Q4cO1t/YoX3zb0hweHl5eXh4VFRUFBcX4/sNQRCk/cHj8SQSiXa/PMTWEYvF8AO+Y8eONMXi6uoqEAikUqlKpWLPFOgMKvAnlIVx5jKdOHHixIkTU1JSduzYsWnTJkKIp6dneHg4IQSyPtjD15wAACAASURBVH5+fk8++eSpU6dyc3MhrlhRUSGTyTgnAgiCWA82dtUoKioqISGBTYdpY0M9pEAaQqkvpIRg/qnz589DNs1wAlFfCbPxPRAB7QSiPoFIjQ+sDQqQqZXr3bs3bUjX0NDAOjVYIV2ts7Mz58SytraWHZWjoyM76zRnVDBOkUgUFhZGH9WZQOT8vmF1JF2PToHIzm1idgIR/J2DgwOsk9WXERERvr6+oNigBR79puSYQdJ02Dk9jHUKRDhoOhOI9E8qEGFO6sbGRj6fD29FjkDkzHZC4QhEkCA3b95MT0+n42QnupFIJJzfAfDq09Gyuwlwlod5cmQyGewmKG+IN+KFSsS2gARHUlKSXC6XyWRlZWVKpbKwsPDKlSsrVqywdXsIQIYa+xkhCIIgiA3B4/HgxznrAfl8vpeXl0aj4ZxGwZkI/OwnTV/9On/G9OrV66OPPoLbnp6ebFOgDz/88H//+9/IkSMJIXv37oU7aYFasxQVFf3www+ceTvbmPXr13/11VcWHACCtDE2JhAXLVrU0NAQGRm5adMm7bashYWFO3bsGDRokK30kKJNIkiTQOzdu7e3t3dubi6UuxrugciWMOtMILICkc0Dsl7JyclJuzRYn0AEGVdbW9vQ0MDj8fz8/EiTF1OpVHTWEfjTgECUSCQcgcjJ0Dk4OMC2WIHI6YEIzfLYR7UFoqOjIysQOV8wMLdyswKxvLxcoVCsWbNG53wdRH8CkRYgw8BYgSgSiSCvRwh56aWXCPMFrD3zCbz0MFoajeQIRNgohBk5CcTRo0ezE3zTd0JBQQFbv0yMFoiwBioQ4XXXaDTHjh0jZglEdjcBzvLwO4bGVAMDA0mTQNTZ1BlBrB9IcHh6era/+i84N7h48WLXrl1feeUVSw8HQRAEQRCjgPMmjgeEyiE6gSHASSBGR0eLxeLHHntM52qdnZ3hJMXT0zMoKIieEkK/oFGjRhFCIIhAdM24yIGe6r7zzjsvv/zyb7/9ZuzumUtWVtbPP//M9tAH6urqVqxY8eabb3Ja/yNIO8bGzlugh5RUKl28eHFwcLCbm1tISEjfvn1DQ0M9PDz8/Pzmzp1bUFBgKz2kWIEIt0Ui0Y0bN6Ay187Oji2/1S5hph/utAciuBtq0EAAwdmpvh6IbJW0kQIR5JpAIIABgBdj108IUalU7NY5kUknJyftBCJHIBpIIMINe3t72nuRMJOosEZJIpGwWooDHF6OQISVwD7C4MvKyk6dOvXBBx9QgUi/LKmYM1zCrJ1AFAqF8Bb19vaG6XrpOqFz4oULF1QqFRvNg9FSgcg2M6ZHjwrEhoaGxYsXp6WlEULWrVsHAg7GQ1+pqqoqtn6ZmJVArKuro+IY1m+MQIQfIvB2MiaBqFMgwvHEBCKCWBtQnbRx48Y7d+5s27Zty5Ytlh4RgiAIgiDNAz/OOZXIOgUiJ4E4atSourq6//znP/rWDBVXnp6ednZ2Tz31lLe396pVq6CYDAQixXACcffu3c7OzgcPHiSEQOAGzndaleeff/6FF17QnmKhtLRUo9E0NDSwZ6wI0r6xMYFICFmwYMGtW7eWL1/er1+/+vr6nJyc5OTke/fuiUSigQMHrl27Ni8vb/78+ZYeplFoJxAJIX5+fkuWLCGEcJIpIMLgkgt8WC9atGjNmjWE6YEIxcicBCI8UV8JM+TagGYnUYEvFZhbQyAQgASkCUR2SVrCDIKSTSDa2dk5ODh4eXmxu88RiLSE2UAPRHt7e3BY9E4wUByBaKBFCxwudhM0gQj7CN+X5eXlHEvl7+8Pg6drYKf8pgMwUMIsFAoff/zxd999d9euXfAom0Dctm3bo48++uabb3733Xek6RUEOQgGmRhMIMrl8vT09E2bNsGu0elEYDz0laqurmanYCYmCkToU9nQ0ACzYFNaI4EIWVe6m/ATBPqEokBEEGsDEoh0fqoPP/xQ34cJgiAIgiDWQ1BQkFAoZJtEkaYTIk4reWgpBr/JjWHGjBmRkZH9+/cnhPz6668lJSUrV66Eh/z9/eGnPmBYIF66dEmj0cCsLKDt9JWItRTnz5+/fv06ISQ7O5vzEJyMEK02WVZFdnb2zp07MSOJtBS2JxBJO+ohpVMgEkKmTp06cOBAzkS0rF4BT2Rvbw/TJtAEIvgsTgIRDJo+gcgJ8cENwwlEnQJRO4EIAhEuYYGRhNVKJBIej8fn86FAG0yTMQlE6qQ8PDxg61S6waPwFDYMaEAg8ni87t27k6YelHQlMEg6zTQhpKKigjOdiKenJ2wa9Gt9fX1+fj59lGYDDZQwC4VCgUDw0UcfPfXUU3APtPkjhNTV1cEFro0bN8LlNWNKmDkJRDaf6OjoqLOEubq62rwSZnitBQIBrJadXZoYJxDhP6nxPRDhVwV9k6BARBBrhp3eyt/fv6SkZP/+/RYcD4IgCIIgxnDgwIGMjAw4A6LoTCCmpqYSQmAiTWNYsmRJQkKCm5ubzkfZ6QENlzDD5UkoIANt19oCcceOHXADpiiQyWS0fI0KRHrDCpkzZ86cOXNOnz5t6YEg7QSbFIgUW+8hpU8gOjo6XrlyhVP2xZYz09tgu2gPRJ0JRHAxVPBpNBpWh7Gf4yaVMAuFQgMJRI5AhBWCR6NuCJ4O5agmlTB7e3tfvHjx4MGDTk5OPB6PDl7bFRoQiBEREdozhFKBCIC30haIHh4eoMnc3d3pAAghcFtbIOpMIHI2PXbsWGgerFAorl27xj5kTAkzJ4HIajWaQNQWiJwSZtb6EV0C8dChQ2fPnqW5RSq12d2BI8PeI5FIWE9NtASiqT0QQSDClQOlUmmj//0RpL0SERHx/PPPE0I6d+4M+YKffvrJ0oNCEARBEKQZoD8Y504DArFXr14tsl1TBSLEF+A8sbUnbbt16xbcgAkYxo0bFxIS8v7771+6dMn6BWJpaen58+cJISkpKZYeC9JOaIcn3hqNhjOpiNWi3QPRAGyzQioQQclxBCIngcgRiHV1dWwLWNbsGCkQjUkg0hJm8INQawyr5QjE4OBgojULs85JVNhRDRo0KDAw0M7OjvpBmkBkoaXQ2owYMYKVtnQlbPk2CMTKykqOQHR3d4c9ouE+AA4mVXuwR/pKmLWHNHjwYFiMM8+4MSXMnAQiZyYZwwLRyASiXC6fNGnSc889RxOI9Niylx+1E4guLi5Lly59/fXX4cVycnKC94CBBCKbLSVaAhHSmqWlpbCb2q8jgiAWhMfj7du377PPPtu5c+eAAQMIIXl5eVevXnV0dPzyyy8tPToEQRAEQUxAp0AEIdUaAjE1NdVAvS0ViAqFAk4EKisrW6qC+MaNGzExMXQ6F4DWmd29e1cul1+7dq2+vv6jjz4aNmwYZBKJFQvEI0eOwCSibdApEvmX0A4F4s2bN8Visb4uflaFvgSiTkAOArRxIU0gsiXMNIHIKWHWaDTDhg17/PHH2dVqJxB5PJ6RAtHHx4fH45WUlGg0GqqlwKzRBOKIESO++eabzz77jBgUiJxZmLV7IPJ4PJ0qkJomNhDHDlg7gQiLDR8+XKdAZPfdzc3N0dFRoVDQ3B/seFRUFOwmDfcBcPyPHDmycOHC+vp6Tgkz+72rUyDCqkpKSjjfmnC/zhJmeAuVl5eDaaUJRFYv0jgnZxIV7RJmtqck0SUQ1Wp1RUUFvMFYgRgUFESNIUcgvvLKK88+++zgwYO//PJLeOlpkwF4ukwmgxWyr5S+Eub6+noej4cCEUGsHIFAsHTp0pEjR9K5tr7//vu6urqvvvoKu/AgCIIgiA0BAvHBgweTJ0/++OOPCSH5+fmVlZXe3t4t1ToMLjf6+/t37ty5pKTkypUrOhdTq9UQyHjw4AGbMmmREGJ9ff3UqVMPHz78xRdfsHfSM7hz5875+PjU1dW5urp6enqqVKqbN2/CQ1YrEE+ePAk3aI4SQR6SdigQbQiTBKLOBCLYLjqJirOzs729PZ0BmS1hfvvtt7t16xYfH3/hwgV2tdoJRHt7e7Ysl4UzC7NIJHJzc1OpVIcOHQItFRoa+vnnnxNmFmaxWDx//ny4PMUpYYavnMDAQIFAoFQqdTov+G7o27fv7t27dQpE6rx0JhB1CsTXXntt/Pjx48aNa1YgOjg4aM/UXFVV9corr+gUiPAaQfn5X3/9xSlhZpszGhCI2rAJRE4JM/SR/Oyzz/bt20eYCCpNIAoEAjq9jL5JVKjs49R0c3KX1CcWFRWRfwpEd3d3KrVZgcjj8f773//Sqdw4s4fD00tLSxsbGyUSCftycASil5eXUCgECW5vbw/jLC0thfcMCkQEsVq8vb35fH5paenhw4cJIbm5uVBKgyAIgiCITQACMT4+/uDBg6tXr66oqLh69SohpG/fvi21iaCgoB07duzcuXP8+PGEkGPHjulcrKysDE5e5HI5W7DFKd4yjz179oBlO3HiBC3Xy8/P12g0HTt2hI5JEHro0aNHaGgoIYRmFa1WIObk5MCN9PR0tgYRQcymHQrE8PDw1NRU6Mtg5ZhUwuzi4kJFj74eiCKRCMwL+CMQRnCPUqnUeXFGO4GoL35I/plABAX25JNPEkKee+45EFsCgQBsDi1hZtfGSSBOnz795ZdffuGFF7SrlalABEM0ZMiQ6dOn6xwS7bgHDTG1B6xtFWNjY48ePers7NysQBSLxeDF6HSiIpEItkIFIrt++H4FkpOTaWs/7UPK6TZIN6dzH0EL6ixh5ig/Dw8PPp/PTqIC6lBnCXNNTQ0oQmoz6drg3chJIFIBCkeDjXy6urrCgRIKhbAXsIOOjo5sg0J9ApFohR9Zgcjj8VxcXOiLKxaLocJdoVDASFAgIojVIhAIvLy81Go1LfOBuaEQBEEQBLEJ4AQHziuVSuX+/fvPnj1LCHn00UdbcCuzZ89+/PHHx40bRwg5c+aMzmUgxABs2LCB3r579+7DDyAzMxNu5OfnU5MA9cuhoaFs1jIkJAQyHHT+FqsViDB+e3t7qVTKzvlpJNnZ2X369Pn5559bYWiIrdIOBaJYLI6IiIiIiLD0QJrHpAQiYUKI2j0QwQRRvVVbWyuXy3NycgQCgXYrXEII1TraszAbKRBhwHv27PH19W1oaAD9JxQKYSW0hJk1ZRyBGBERsXfv3rCwMG2ByOldqC+aR/6ZdzMygUg9nRkJRDoSTgLR2dn5mWeeefrpp+lzb968SVv7aZtB4xOI27dvnzZtGmkqha6rq4PXGgQip3AAhKZGo6H9FuGY6BSIGo0GdCQdDBWgcFT1CUTtBCIViLSiHF4RzsGHY8sRiNCYgzPLCisQnZ2d+Xw+KxBJU/QSlAQKRASxZuh/ebhB630QBEEQBLF+OnbsyJ60/vTTTyAQR40a1eLbglN4ffOo0EgHIeSPP/4gTae0LSIQ4QQHTvFgBwkheXl5hJCAgIDw8HC6ZJcuXeCsnJ4cWadAVCqVxcXFAoFg4MCBhJDs7GxT1/Ddd9/dvHnz999/b4XRIbZKOxSINoRJCUTCtEHUl0AUi8U0gZiUlNTQ0NCzZ09OtguAeWyJiQlEWDktYSaE8Pl82smOMAlEnQKxa9euPB5P2+3qTCCy7smAQIS90xaIY8eOHTp06IQJExwdHTkV2XQHzUgg6hOIYWFhBw8eDAsLo89NTk7mlDCz6BSIfD6fs6RAIJgzZw71j/C6jxgxoqKiAjKGnAQiNcj0Ap12ApGd7ga+7bRLmEHncQQi/RP2SyAQ0KPh5uZmjEDUmUBkt0hh37R0vhq6j6RJIEIsHwUiglgz9L/85MmTCVPvgyAIgiCI9SMWi9lznAsXLqSlpTk5OT3yyCMtvi04GYEO+9qPsgIR6Nq1K2lRgTho0CDSNMszaRKI/v7+O3bs2Lx5M9wZEhLC9hYj1ioQCwoK1Gq1n58fHFW2p7+RnDhxgjATtCIIIcQmT7zr6+uvX78uFAqh3yoh5OTJk1u3bi0uLu7Vq9eUKVNGjx5t3prLy8v/+usvwy3e4XOkRTAvgcgKKc4kKmwC8fLly4SQwYMH61xzly5dYCp67R6IBuafgZWDSKIKDMYALSFoApGWMLMCcfjw4YWFhWydL8C2OwQ4pcHNJhBho/b29gKBAATZmDFjli5dSp8OzgswnEBkt+Xg4ABejM7lQh8Fmevq6grjhJeG1V6ZmZkQ7KeTqLDoe7nFYjGr7dzd3Vn76e7uXlhYePny5StXruhMIIJBLi4upgIRhsdOogIJRB6Pp9FoYL+0E4iwI/oSiHQX2AQiHBCTBKJIJLKzs4MEIkdz9+jR49VXX62qqtq7dy+8RXUmEFEgIoj1Q//Ljx079rvvvisuLi4vL2enBUMQBEEQxJrp3bt3RkYGIYTH48GpxIgRI3TmIR4SsVjs6upaXV1dWVlJG6xTwOt17NixqKgIDOPgwYMzMjJaUCD26tXrzJkzVAjSBGJISMjChQvfffddqVTapUsXtpiaWKtAhJplf39/CAyZKhDLysquXbtGUCAi/8T2Trx//vnnuXPngj2Jjo4+fPjwrl27Fi5cCI8mJibu2LFjxYoVMEWUqcybN8/I9kwtMo8k2x7OeIFI44eEEKFQyOfzVSoVCDI2gQjTVw0aNEjnh0VYWBhks83ogcgZMCxPE4i0hBn0E6fZHycxx67WvBJmNoEIT4RoHitGHR0dqUDk8Xh0YZ0CkT0gYrEYjjZ9uelIXnnlFYlEMm3atIsXLxJdAlGlUt24cQOeYmQPRFgYKsQB9rUmzOzbUqlUp0CkTTDptxocB+0SZnd394qKCo5ANJxA5AhEtgeidgIR1smZCwVe6E6dOtF7HB0dYUc4CUShULh58+b4+Pi9e/fCmlEgIohhNm7cuGnTJsPLwP/iNu6iTT+mevbs2a1bt6tXr2ZkZAwdOrQtx4AgCIIgiNn06tXrl19+IYRERkYmJCQQQtjGTS2Lr69vdXV1cXGxtkBMTk4mhKxbt27y5MkDBw7MzMzs06ePSCQqKiqSy+U659s0HioQCSMEQZt27tyZEMLj8V566aW//vqrT58+tGGiWCxWKpUwJ6QxBYVtCY1PwhklFBEaT3x8PJwCQ04IQQAbO/G+cuXKiy++6ODgEBMTU15eHhcXN3369EOHDkVERGzYsKF79+43btxYtmzZ2rVro6OjH3vsMVPXP2PGjGb/5ysUiiNHjhiI6RmPqSXMYKk4H6ZisVgul8ME887OznSuXugzNWDAgL/++kt7VV26dIEb2glE8wQiTSBySpgNrI2zWlYgciY/MbIHIjxRWyBKJBIaIRSJRDTTpy+BKBKJwJ3RBKL2SDw9PV9//XV6D7w0HAsGjXWNL2HW3lOOQOzevXt8fDwhRCqVwgEHj0ahBhl6IM6ZM2f+/Pl0tewszJ6enhUVFfpKmHUmEDl/mtED8f333+/bt+/IkSPpPRKJRKdABAYPHrx8+XIIFOsUiHC9EQUighBCUlJSjLwC38YCET5YnJycgoKCunfvfvXq1fT0dBSICIIgCGIr9O7dG268/PLLCQkJdnZ2MTExrbStDh06ZGZmFhcXd+/enfPQpUuXCCGDBw92cnIaN25cZmZmeHh4UFDQ7du3c3JyHmYKBKVSWVFRIRQKYaNwilRXV5eQkMDn8yMjI2GxrVu3wg16CtajR4+ioqKCgoLCwkJ/f3+zB9AaQAIxICAAzrNMTSDSOm5MICIsNnbivWbNGj6ff/78+f79+xNCVqxY8cknnzg7O58+fRoyDsHBwT179oyIiPjss8/MEIgTJkyYMGGC4WWKi4t9fX11+g5TMbWEmZ2NlyISieRyOVwGCQwMBJNVVlYGV1ECAgJ0rhnmnif/TCAaOYkKZ8A6E4g6S5gNrxYEYnR0dFBQUGRkJFsqbrxAZJWWzmGze6dTIBJC3N3d4ejRBKKBkegrYaZ7pLOEWZ9ApEt6enqWl5dztv7f//63sbFx165dUqkUdB7Hb9IadogxTp06FZrmavdA9PLyunPnDnw70sE4OTlBWlM7gRgVFQWX4CisQHRzczOmhDkqKioqKoq9x8fHh+1YzMHe3n79+vVwm9MDEaYGAm+OAhFBCCHbt29/5513DC9z+/btcePGseH3NgC+nXv06MHj8Xr06EGarucjCIIgCGIT9O3blxDSsWPHCRMmLFu2bPTo0ZwQQwsC1x05NcJwT25urqurK8xn8sknn7z00kuDBg3atGnT7du3c3NzH0YgFhcXazQaHx8f2DqcIl24cEGhUPTv31+77wrtgdizZ0+hUFhQUJCXl9c2AlGj0ezYsSMqKqpnz56Gl6QlzHCuZGoCkb4EKBARFhs78b5+/fqQIUPAHhJCXn311U8++WTUqFFsIWdoaOjgwYPp5OvWjHk9ELUFIiEEGhoGBQXBMg8ePKisrBQKhR4eHtquys3NDT4ceTyeSQlEkUhEmwwa7oFYU1MDeTfjBSKEBJ9++unY2FjyT/dkUgkz3Ue6DBtmZF2e9jGHwbu5ucEnpoEEIgUOYMeOHUmTU2P7LRJzE4gBAQHl5eWcrQuFQvhmqq6uhq6X2nFUtmqY3tYuYaaimTOYDh065OTkwFGFTRBC6uvrExMTOUPlJBB79erF5/Mh9k/0CERt/Pz8ICqrUyCycBKItP8p0X8wEeRfBZ/PB7FuAM6nU9sQFRUVEBAwZcoU0lQEBF9YCIIgCILYBIGBgdu3b+/UqVNgYGBeXh5NDLQGcF4P1VQs0N9/0KBBcB1UJBLBhCdw0gclaGYDp36+vr4gRkEgnjlzhhCic3IFViDW1tZeunQpLy+Pk5NoJc6fPz9v3rwBAwZcvXrV8JIXLlwghISGhoI6NDWBSF8CLGFGWGxMIFZXV7PCC25rhwHd3d3BSlg5pgrEoKAgwkygDICcUqvVjo6OPj4+8HF269YtuIrC5/M5aw4NDf3kk0/gWRKJhH202UlU4CmQbtOXQIT7IWFOjBOIkBWHUla6dTMmUSFGJBANC0QYLZWPxiQQ33zzzZCQkBdeeAH+dHFxkcvlHh4eFRUV9CnGJxBZgXjjxg3O1kmTaJNKpToFIu2BCBgQiHAlDQQi+xqBQIQjQBOIOi9YcXogQjthejUS1snpgaiNn58f3Gg20ssRiL169aIuWyKR4NcaglgtnTt3zs3NhduBgYGkReciQxAEQRCkDZg7dy7c4ExA3OJAzEVbIIIOo9XEFDgZhBNJs4H5nX19fZ2dncVisUwmk8vlSUlJhJBhw4ZpL88KxIKCAtKGv21SUlIIIUlJSfn5+QYyj4mJideuXfP29h4zZgyYUFMTiPQlwAQiwtKmdUwPD/Rfp8Goc+fOEUKuXr3KdnRSqVRJSUlQJ2XlmNoDceLEiceOHfvoo4/YO6mcCgoK4vF4YHAggAkXcDiarHfv3pMnT+aYMqDZBCJhPI7OHoi0hJmzTsMsWbKE9mQ0VSByEoh0eA9Zwky3yxFb2kG5gICA119/nZoyWIAG8cg/E4j0VW62hBlksXbzYMMCUSwWsztLR0Xn5iZNAhGeCFei2MHMmzfviSeegAtohgWiQCCA14XmWNlaBiMTiHRClWYFIn0/wCFycHCA/+POzs4Q/0QQxPqBC2DUJyIIgiAIgrDACax2CTO09dfWeS0iEGkCkTSd0ZSWlt6+fZsQ0q1bN+3l3d3d4QSqV69e8NsG6oXbgFu3bhFCNBrN0aNHDSy2a9cuQsjs2bPFYrF5szDTl0CpVHLm0kT+zdiYQHz55ZeLioomT5586tSpffv2zZs3z8PDIy0tbfXq1eAQGxsbly1b9uDBg/Hjx1t6sM1jagKRz+ePGzeOo1qonALlBNdDsrKySNMFHM6awex06NBBJBLRTojsQ0YKRGqdQOjApQlawqw9PAM4Ojr+8ssv4JJ8fHzoammjrmYFIt1KiycQ2fXExsauWLHC8L7A8voEIlVszSYQZ8+ePXHixOeff56zAOxvVVWVUqnk8/kcBawvgQiHhZ1EBcYJZpAdzMyZM0+cOAGvQn19fX5+fnp6us7vG1rCzMmxAsaXMMMNUxOIpKmK+ZlnnrG2Kc8QBNFHx44dhUJhcXEx/hJFEARBEEQbnQlEqVSalJRkb2+vPQlbiwhEqFmGTcN5EPRVtLe3h+4rHHg8Xmxs7PTp0/39/UEgtlkCEQQiIeS3334zsBgErSZNmkSawjFmJxAJVjEjDDYmEGNjY0ePHn3s2LEnnnhi6tSp1dXVf/7556hRo1atWhUaGjpu3LjOnTtv2rQpLCxs2bJllh5s85gqEHVC5VRwcDBpEoiNjY1ETwLx/9i777imrv9/4CcJAWSKyJItKCooOAt1t1JwFZy1brRina171tW6Wu1wUDcV0dZVtWIBUWsVt1JxIKAsZUemGnby++N8vb/7CVlAQhiv56OPPuByczkcA+/cd97nfej55ubmCQkJZ8+eZX+JZq/kJ30UViBKzVcq1K1bt9jY2PDwcKbNBIfDYbKBchKIvXr1Gj16NFNUTx+io6PDfgi7bFCZCkQmKydRgbh48eLq24FJoNVwH3zwAfu7MzkvGpA4HI6sf246bD09PQ8Pj3PnzlVvjkvHQzcPoS0p2SlRdg9EDocjtQKRLvul16FljNX/jegslZWV+fr6du3aVWq5ELOEWWruj26YwGzZJguTQFS+ByLTxnj69On9+vVrFL/pAEDxeDwrKyuRSMRs7QcAAADAkJpAvH79emVlZc+ePavfqNIEovweiKdOnaLLeGWhbZ3ofTQt+PDx8RGJRG3btpV11/bzzz//9ttvhBC6jrjeEohxcXGEEC0trUuXLtF7NGZ1JuP169eJiYn0jpK8v7etXQ9EumoNq5iB0cgSiHw+PzIy8sCBA5MnT54+fXp0dHSXLl3OnDkzatSolJSUiIiI7OzssWPH3r59W34jvwaipkuYpZJagUhJrUBkny+R+hk6dOjXX389f/58Od9OVgJRVgWikglEQoipYnfNQgAAIABJREFUqamvry+Hw2GOKJNANDIyOnny5Pjx49kPkfi5Fi1atHr1avZoJX4EidHKqkBU5kkVFBQUGRn56aef0k/t7OxsbW2Zb0oTiHI2/aDfQk5nYppoo++S0ZNNTEy4XO6IESM+//xzPp/P7nvIPKmqVyBW3327+jDKysqys7PLysqSkpKqj4SpQJSogqQmTpxYXFxcvYJSQi0qEIcNG0Y/6N2797///ssu9gSAhg9tEAEAAEAW9jYmDNqeq3oDRKJEBWJqaupnn33G3C1KRROItEyBDoCubqQ7PstH78GfP39eWlp64MCBP//88/r16zXN1ikpJyfn9evXJiYmY8aMEYlEwcHBN27cMDY23rVrF/u0mzdvisXiXr160bu8WlQgFhYWlpaWGhkZ0dlABSIwGtkmKoQQHo83ffr06dOnM0eMjIxOnTqVm5ubm5vbvn175TNWGqfaCkTaRpDdh05OBaJUJiYmP/30k/xvV6MeiHw+n50QrCnme7H7ISrzEIlsVMeOHb/99ttNmzaJRCKpS5g5HA4NEtV7IOrq6mpra9PVdsokEK2trelabH19/Xfv3g0fPpzD4TAPpCldOQlEmiqVs/cITSAyFYiEkOPHjxcWFg4fPpw9AxIXYSoQxWJxZWUlh8NhV/xVHw9TgUjf0aK9gSVoaWnRizAlgRIU7qBCapJApM+BNm3aDBgwQOFlAaDBQhtEAAAAkIXWW0gkEGmCj95JSZCTQPTy8qqsrJw4caJIJMrNzRUKhbJuKtkViExrfkJI+/btFQ64TZs23bp1i4mJmTFjRmhoKD04bNiw8+fPK3xsTSUkJBBCOnbsOGXKlN9///38+fOpqanl5eUhISFz585lTouOjiaEfPjhh/TTFi1a6OjolJSUlJaWKryfLSoqGjt2LO01b2FhQW/oUIEIjEZWgSiHubm5m5tbI8oeEhUlEJm/AnR1rYGBAXOkpglEZTB/dpmsk0QFYvVtnWtt2rRpTk5OPj4+zFYbSg5PajaKDlhqBSKTdGNXIHI4HHoyc7UaTR1NjdG8nvIViDSBqLACkb6pRf+h+/bty2QPCSttx04RMhWItPyQz+ez6//lLGGm+6jISiD26tVrzZo1Erv61IiFhQX9LVC4hNnNzU1LSyswMBAdDwEatXpuFQQAAACNiJ6enr6+fklJCTsnyE7wSZCVQCwsLLx9+/b9+/d/+OEHekRO+5S8vDzm+suXL79+/TrdUEHJfVlXrVpFCKHZQ7rmNzY2VpkH1lRaWhohxN7evm/fvnw+/+HDh7QjWUxMDFPzKBaLT506RQjx9vZmHqh8EeLNmzcvXry4Y8cOggQiSNP4KhCpuLi406dPJyYmFhYWVlRUGBgYWFhYeHh4fPbZZwozEQ2HCpcwa2lpMe+QtG7dmu4DJbXeTVUJRFkViOzr1zGBuHr1ambpcY2GJzWBqK2tXVZWJrUC0dDQkP5ZZCcQdXV1afmksbExe8mwklatWnX//n3a0pHdp9LY2JgWukul5BJmWi8pdTxMApH9jhlTgUgTiMzqY0pWBeK7d+9ow0SpCUSaLF6/fr2soSqDx+MNHDjw1atXzOY5snh6ehYWFirclQUAGrh27doRQh4/flzrK5SWllbvtwsAAABNg5mZ2bt37wQCAXNPVIsEYkpKCv2AyRump6fTFyHVsa+vo6PTp0+fXbt2DRo06LPPPlNmwP7+/u7u7jRpeObMGW9v74yMjJKSEjltuGqHLuCwt7fX09Pr3r377du3aU6wqqrq33//9ff3J4RER0enpKTY2dn169ePeWDLli2zs7N/++235cuXy/8WNEcpEokIIZaWlrQFFpYwA6PxVSAmJCR88sknrq6ua9asCQ0NDQsLi4yMPH36dFBQUGBgoI2NzcKFC6t3Em2YmF2GSZ2XMDs5OTHZOvq3z8bGplu3btWvrKYEIk3AaWlpWVhYLFu2rH///nX/XrUentSufHR+ZCUQ2efQt2iYM2k6Ultbm/3vpdCUKVN27txJvwV7F+b4+PirV6/KepQyFYjMqnCpCUQmxUab5jKX5XA4paWldC22RAWirAQi041Y6vt1qrp7j4qKio+PVyaBjuwhQBNA95i6efNm7R5eWFhob2/v4+Oj0kEBAABAQ1F9FbOcBCKtrqieQExNTWU+pndP6enppaWldK9RtpKSkurXd3Bw+Prrr5XMAHK53C1bthBCrKys+vTp4+DgIBKJkpOTlXlsjdDsHm0n3bdvX3qQVo0wK6b37dtHCBk/fjz71pXu9LJixYpbt24p8y0oKysrVCCChEaWQMzKyvLx8bl8+XJAQMCRI0cOHjz4ySefEELWrl17/PjxtWvXmpmZ/fTTTxMmTKBZ8wZOhT0Q2fXVXl5eBgYGp06donmo+lnCXFJSQt43PdyyZQvtwlD/K8ql9kCk5Cxhlkgg0vwjEzDo1eqyLQ/zWCMjI0tLS6n5TUphApHL5TI1hlL/KZmvshOIdEtrsVhcVFREWBsoU7KWMDPxNTs7u/o3QvkPANSCq6ursbFxWlqaMhsxb9++fd68ebTmmgoPD8/Nzb1y5cqjR4/UOUwAAADQDNrTn/Z8p2pRgcgkEM3MzAICAgghDx8+bNOmjbW19apVq5gFv3QTkjdv3mhra9dlIaOvr29oaOipU6e4XK6zszMhROoulHXELGEmhNA0iI+Pz19//cXlcv/444+8vLznz5///vvvfD7/yy+/ZD/w0KFDdCPKyMhIOdePi4tjrxGxsrKiN9c0gSgSiR49etQo0iygPo0sgbh27dq0tLRjx44dOnRo4sSJ06ZNi4yMnDRp0s6dOz09PdetW/fixYt58+b9+eef33//vaYHq5hKljDT5BRtgEjt3r07NzeXVnmQ+qpApDd4zEHafrH+E4iDBg3q3bv3mDFjqn9J+QpEGpyYg3VPIDJzLiczSClMIBJWu0D5S5jZCUTy/h+OSSCyq/mqX0dbW5u9+w1d+EwxT1Q5nRwBAGThcrl0F8XLly/LP7O8vHz16tW7du1ivxl+4cIF+kFwcPDZs2fV8eocAAAANKj6Rsy1XsK8YMGCBw8edO3alRBy7ty5goKCnJycTZs2de/e/dmzZ4SQO3fu0DsdqRevkQkTJtB9S2gC8cWLF3W8YHXMEmZCyKBBgx48eHDmzBkXFxdfX1+hUBgSErJmzZqqqqopU6ZItMyytbUNDAwkhFy6dEnWxVNSUtzd3ZkXWoRVgUiXMO/YscPd3V3hImho2hpZAvH27dvu7u4SzQhWrFiRn59Pq3Y5HM6OHTtsbGyOHDmioTHWgEoqEL28vPT09AYPHswc4XA47HLr+kkgUkxSydnZWVtbmxZL1ycXF5fo6GhfX9/qX6LJQWUqEB0cHIKCgnbt2kUP0gRiXeZN+QRi3759O3bsOGTIEDnnyE8g0m9hZGTE3kGMvK/NpAlEiR6ItNcvG4fDkZX8ZfZcxmYmALWTlJS0Z8+e+fPnT5gwYcSIEV988cXatWv37duXlZWl6aHVE7roZvr06ewXqdXFxsbShiRMG6Oqqqrw8HD68Z49e0aMGDF+/Hg1DxYAAADqlcQS5vj4+KKiIi0tLamruORXIPbt29fW1pZuyEnfdBwyZMgHH3yQkpKyaNEiwlpoxdzj1J2aEohisZidQCSEdOvWjd71jx49mhBy4MCB48ePt2jRYs2aNdUfPmDAAD6ff+fOHXo/WF10dDRtf8+QWMJ87do1Qsj27dtr3YgGmoBGtggxLS1twIABEgfplo7srY66desWFRVVnwOrHZUkECdPnjx58mQ5J9RnApFdgRgfHy9nrW79U74CkRAya9Ys5jQVLmFWmED08PCIi4uTf478XaFtbW1nzJjRsWNHiY6N9B9u69atpFoFIu35KEFHR4duwSzBzMwsNzdXS0uLXaIIAMpISUmZPXt2RESE1K/OmTPHz89v27ZtDg4O9Tuu+rZgwYLU1NQDBw4sW7Zs8ODBstrL3rlzh36QkpIycOBAQsj169fz8/Pbt2/P5XLj4+MJIffu3UtLS5OzMxUAAAA0LuwKxMjISFoaYmpqKvXuQ19fn8vlCoXC8PDwW7durV27lsfjLV68+MqVK4QQ+pqKXdQyfPjwDz/80N3dnSbjmLdv616ByKBlHCpfJCEQCIRCYatWrZgFZwyasqR3kXPnzqXpEQmGhoa9e/e+evXq+fPnJ06cWP2Ee/fuSRyRWML84MEDQohIJLp06VJXFfxA0Cg1sgSiq6vr/fv3y8rK2KmTmJgYwsrEi8XihISE+q99qwV2AlHlmzQx6qcHosRBQoijo2NdvpHKKd8DUYIKlzCrZItwJoEodUhcLpe2zpVA//r//fffhBA+n6+jo8Pj8WiXw+oViPTizCYqbDSiowEiQE3l5eV5e3snJSW5urr6+fm5ubmZmpoaGRkVFxfn5+fHx8eHhYWdPn06NjY2OjrawsJC0+NVIz09vaCgoEuXLj19+nTo0KEHDx5s06ZN9dNu375NP2AqEE+fPk0IGTlypLa29oYNGwghYrH42LFjK1asqK+xAwA0LwKBoEbVRn5+fuobDDQT9HYjPT19165dT548oQdlJfg4HI6BgUFxcfGUKVMEAsHQoUPbtWv3888/V1VVcTictm3bEkLatm3L5XJp8z5XV1faaysnJ4eoJ4FIExHp6emquiBF11zTXKEE9sHq5VaMMWPGXL169cSJE1ITiHfv3pU4wl7CLBAIaMqV/G97SmhuGlkWYMSIEUuXLp0+ffqvv/5Kkz7Jyclz5swhhNDyhPT09EWLFiUkJCxYsEDDY1UCO4Govh1mJXrV1U8FYgNUowpENpr1q2MCkcPhiMVihRWIypCfQJSl+rbL+vr6NEUoNYEo63lCIzoaIALU1MqVK5OSkjZv3iyrd8y6deuCg4MDAwPXrFmzd+/eeh5ePePz+Zs3b54yZUpERMTRo0eXLFlS/RzmnfCgoKDExMRjx46dOXOGEDJy5EhDQ8ONGze2aNHi7du3K1euzMnJ+fnnn+v1BwAAaB6ePn3q7++v/Pnsba8AaocuYT558uTx48eZg3ISfIaGhsXFxbRiMSUl5dWrV1VVVc7OzosWLaL3Taampp988gldAtKpUycTExMej5efn19RUcEkEKW+l1k79FIqbE1TXFysra1NU/k9e/asfoKlpaW+vj7tVOjm5ibrOqNGjZo/f/7FixffvHkjcVtaXl4eGxvL5XK1tLREIlFlZaW2tnbr1q2ZFeK0/JDe0ubm5hKsRWuuGm66R6qFCxeeOXPm6NGjYWFhnTt3FgqFT548KS8vnzFjBm1ZumDBglOnTrm5uUld+d/QMAlEXV1d9XWU00gPxAZI+R6IEureA5HD4YwePbqiokIldaa1SyCymx7SH1xPT48mEGUtYZZ6HRrRG3KmGKBhunbtmouLi/zO0wEBAUePHo2Ojq63UWnQuHHj8vPz58yZk5CQQAgRCoXsP1MikYjZPzE/P//EiROtW7fOyMhwcnLq0aMHh8N5+PChiYnJypUrQ0JCzp49iwQiAIA6DBgw4PHjx+vWraM14LNmzWJeiAKoiZeXl4ODA/MygJKfQGQ+Tk1NpQsXpk+fzt6JeNasWREREW3atKG9Ds3MzLKzswUCAU3z0Tc1VTV+U1NTHR2dgoKCkpKSut/9CYVCZ2fnNm3aWFlZEUJo0kMCh8NxcnJ69OiRsbGx1PXLlIWFRfv27Z89e5aens7egjU9Pf3QoUOlpaVdunSJioo6duzYggULLCwsOBwOLTTJz88PCQkhhHTt2jUmJiY3N5c06bUyIEcjywLweLwrV65s2LBhz5499BardevWK1as+Oqrr+gJ7dq1W7t27ZIlS9RX0KdCTNJQraPVSA/EBqjWFYh1X8JMCDlx4kRdHs6mwgpEQgiXy5XaqlLWxbGEGaB2BAJB586dFZ5ma2vLbunbtHXo0IEQ8vfff9vZ2eXm5t65c8fd3Z1+KSsrq7y8nM/nM7vABwUFEUKWLVtGWyDRN9iDg4P/+uuvtLS0jIwM2iKdEFJaWlrHv9gAUM9KS0vT09PT09MzMzMzMzNTU1PT0tKysrIKCgqEQmFZWZmJicn169dVWCIEynNzczt16lT37t1jYmKWLVuGtrOgbi1btoyKipo+fTrdtYOi646lYicQk5OT6X5r3t7e7HOGDh26cOHCLl26MFfLzs5OTU0tKCjQ1tZmXl2oBIfDsbKySk1NzczMlNjWshaePn0qEAgEAsGjR48IIb1795Z6Gk0gurm5yf9BaOFIQUEBc6SoqMjNzY3urPLtt9+am5vb2dkRQmi+kuZbL1269O7dOwMDg9WrV48cOVIgECCB2Gw1viyArq7upk2bNm3a9PLlSx6Px9wwUJs2bdLUwGqhUScQpfZAbMh5JTrgWiQQXVxcuFyu1H4TGsE0UqzRPyW7tIdOBT1ibGwsdQeD6t15KSQQAWrHy8srKirqxYsXcv6Y5ObmRkREeHl51efANMjFxYWw1vicOHGCSSDSPjtdunR5/PhxeXk5c75EgQCXy/3ggw8iIyNv3bpFtyD8+++//f399+7dGxAQUG8/CMgXFBS0d+/e8+fP03sSaObKysoSExPj4+OfPn368OHDZ8+evXr1qqSkRP6jRCIR83YCaMS4ceNo33mAeuDs7PzPP//o6enRTR1nz569ePFiWSfTBVJ0de3x48eLi4vbtWvXrVs39jk8Hm/79u3Mp7TZ9MOHDwkhlpaWKt8csk2bNipJIO7ZsycyMpJ+LBaLra2tZWXw6ctLOeuXKXYCcf/+/ZWVldbW1kVFRQYGBl988cWnn35KCOnevbuJicknn3xC3icQ6eLouXPn0vQleiA2Z404C9AEXoY26gRiU1rCrKenx+fzaa+H6g/s0qVLZmYm/evZEKi2AlFqA0QiO4Ho4OCgra3dtHd4AFCH+fPnh4eHe3p6rlmzxs/PT+L1X1ZW1oULF7799tvc3Nxp06ZpapD1rE2bNgYGBnRrP0JIVFTUxo0bCSHl5eX//fcfIaRt27bDhg0TCARdu3YtLCycOXNm9b/SXl5e7ARiSEhIRUXFpk2bpkyZImt/5yYmJCQkPz//s88+a9mypfo2ZKu1ly9fLlmyRCgUnj9/njathuagtLQ0JycnIyOD/l8gEKSlpaWnp6elpaWkpNA93Nh0dHQsLS3t7OzatGljaWnp6Ojo6OhoZWVlamqqq6vbokULfX19qS/SoN707NnT2dkZbyFDveFyudbW1snJyYSQdevW0SIGqXbt2jVu3DhbW9sBAwbQBk2zZ8+WnxOk9zL0xQYttVMtes3MzMy6XKSwsHD27Nm0r2ifPn3c3NzoSx2pRo8effHixQkTJsi/JpNA3Llz5/z588n7LV8WLVq0bt06eo69vX1+fj79mH0LbG9vb2pqSttHottps4UYoElNI4EotaavAaJ5N3a/P3YOdOPGjXS5nNTHNqh8GbPiuNY9ENkViFIbIJJqCURm5zILC4uHDx+i/Q1ATXl7e+/cufOr94yNjVu1amVkZPT27dv8/Hz6VrCWllZQUFCN2tU3ahwOx8XF5cGDBz179nz8+PGDBw/y8vJMTU1Hjhx54cIFQoidnR3zclaWfv36EUKioqIIIVVVVfSDFy9ehIeHDx06VO0/g6YJhcIvvviioqJiwYIF7u7utJii4UhJSfH39xcKhYS1K45CJSUl2traNe0N/e7du8zMzHbt2tV4lFBzYrE4IyMjNTU1KysrKysrPT09JycnLy8vLy9PIBDk5eUVFhbKeqyWllb79u07dOjQsWNHDw8PNzc3R0fHRtF6qJkbMGDA8+fPNT0KaF7s7OySk5P5fL78Sg4HBwcHBwfm/UhDQ8OpU6fKvzJdEE2LatXRG4FeMzMzMysra+nSpbNmzZLau1C+p0+fMnm6JUuW0PJAWXr16qXMawB6I5mbm7t69WoOh6OlpUV3i5a1d3OrVq1oaSchxMLCgsfjmZqa5ubm0spQaIYabrqnOWCKI9jJHZWTSOrV8f1b+UuYG3IF4pYtWwYPHkyLsSl2AlHqHqANU90rEOkPXqMKxNatW9NidW1tbXbPXQBQ3qxZswYNGnTgwIGoqKhnz57RJt88Hs/MzKxHjx4jR44MCAiQ0+KnSerQocODBw/8/f2NjY0vXbo0e/bsI0eO/PPPP/SrynTa6tOnT8uWLR8/fpycnPzkyZP8/Hz6Snfr1q19+vQJDg7ev3//0aNHPTw81PyjKGv79u0uLi7Dhg1TydViY2MrKipoEX1sbOzbt29l1Y9L+O2332bPnr1v376JEyeqZCTVVVRUjBo16tGjRxYWFjk5OUomEHNycjp06ODl5fX3338r/73evXs3duzYiIiIhw8fKtNsFJQnEolycnISExPT0tLob1lCQsLz58/l3z1qa2ubm5tbW1tbWlpaWVlZWlpaW1vb2tra2dk5OTmhlhAAlEH3A7GwsFBmSYGBgUHr1q1fv349Y8YMqR3e2dhLmDt16qSKwf4PZiPmM2fOhIaGVlVV1SKB+PjxY+ZjV1dXlQyM1o5cu3atpKTExcVl4cKFM2fO1NXV9fT0lHo+n883MjKiHRLpa1Rzc3MkEJszJBA1qX4qECWSes12ExUbGxuJom4ej0fvMxvXC1kmgaiSHohKJhCtrKyYBGINxwsA/1+7du22bt26detWsVgsFApLS0tNTEyayUpbqb7++mttbe3p06cPHTq0f//+J06c8PT0pAVrRLkEIp/PHzJkyLFjx6ZOnUp3V/viiy9Onz59/fp15v5h7ty5169fV3mHo1qIiYlZvHixtbU1fcNfquzs7H379s2cOZNd/P769evr16+7uLhI3Oc8ePCAEDJp0qQbN24kJCSkpaXJuseorKwMDw/v378/baR74cKFkpKSadOmubi4GBsbX7x4cebMmSp8F1AsFn/11Vf//fefk5PTrVu3bGxs4uPjAwMDL1y48N9//9F+VVKFhYUVFhaGh4ffvn1b1v2MhOXLl//yyy9lZWVisXjKlCktWrQICwuTVV+vclu2bKFjqJ9vVz+KiorCw8MjIyNv376dlJQktfmgubl527ZtraysbGxsrKysrK2tTU1NTUxMzMzMTExM5OyXCgCgJJpAVP691d69e1+9enXBggUKz6QRlq6vUkfvaZpAfPz4MX2NV7vq3SdPntAPjIyMHB0dVTIwGhzpO7U9e/YMDAwsKyujzSJkPcTU1JQmEOmk0bXkpaWlKhkPNDoNN93THDAJRCXrBer4Xag67k3J9FdqdD0QpdLS0qqoqGhc+3XWrgKx7glEujNsHRPQAEBxOBx9fX2s2uvRo8ehQ4cIIRYWFpMmTdq1a1dwcDDzVSX3+hw9evSxY8euX79O3jfxcXBwWLVqlZ6eHofD0dbWvnHjRkRExODBg9X0UyiPbg2ZkZFRWFgotT7izZs3vr6+sbGxubm5u3btYo5/+eWXp0+f5nK59+/f79q1K3P8/v37hJDu3btnZmYmJCSkpKTISiAePXp06tSps2fP9vX1HTt2LH1tUFFRsWPHjsLCwrCwMD6fP3PmTIU/wr179ywsLOT0oS4vL79y5cqdO3d+/fVXXV3dkJAQMzMzd3f3e/fu7d+/nxASExPj6+srf4oIIf7+/lu2bFG4DI0QcuLECeZOhja0mjBhwpAhQ+bOnavwsXVUVla2atUqkUjk7+9PdxVv1J4+fRoeHh4VFXX16lVm8yJCiJmZmZOTk5OTk52dXefOnTt06NC+fXv8+WqGkpKSoqKi4uLi8vLyhEKhqamptbW1tbX18OHD1dFFDqCmCcTTp0+Xl5cr0w6YiZUcDkcdCcQuXbpwudyIiAiaqnvx4kUtLkIrEGfPnj148GBVvdlME4i0U2TPnj0JIfPmzZP/EFNTU9qJkqlAJISgArHZQgJRk5jUnlqXMEsk9epYQca8Xmx0FYhS0QRi46qqU+0mKkr2QGReFzauuQJoaNLS0p49e8ZkT169erVjx47Y2NiqqioPD49p06apaolKY0R/dvpy2dPTs0OHDkrOxogRI3bs2LF58+a5c+euXLmSELJixQo/Pz8XFxctLa3169evW7euQSUQCSHPnj2Tesfyyy+/0HdrTp48+fPPP9OoWllZefHiRUKISCQ6ffo0O4FIKxB79OhB5y01NVXigunp6d9888348eMTExPpZQUCAU238Xg8kUh05swZGk12794tP4GYmpr677//Tps2zdLSMiYmRlZ34O3bt9N/Bfrt6KKtadOmMUuY5TSVr6iooF0s7ezsXr58uW3bNl9f39atW0t9dXHnzp1du3bNmjUrJSWFz+cbGhoyTd/Dw8PDw8PbtWvn4+NDq+fU9AZnRkYGLWAJDg7eunWrOr6FulVUVERERJw7d+7SpUtpaWn0II/HGzhw4NChQwcMGODq6tq43mcFdUhJSZk9e3ZERITUr86ZM8fPz2/btm0ODg71Oy5o4gYMGGBrazt8+HAlz+fxeEpuJubh4TFo0KBLly516NBBVjlFXXh4eAQHB0+ZMoUm2goLCwUCgZx9YKp78+bNo0ePCCGrV69WYYKe/eYlTSAqRBtQGhoa0pQFjf6oQGy2mu+yqYagMW6iwufz6avwRtcDUSo6OY0rKVa7BGLXrl319PRoupD+M9HSelkFPhIJROatv8Y1VwANysaNG9u2bbt792766fnz511dXbdt2xYVFXXlypUff/yxW7duv/zyi2YHqUFubm7Mx5s2bQoODlb+Tal58+ZlZmYyeSsOh+Pq6kof/tFHH5H3q3XU4fLly6NHj46PjyeEVFZWjho1ytzcXCAQiESi8vLy5OTk33777dq1a4SQ77777vbt2/RRs2bN2rNnT/Wr/fnnn4QQXV3d3NzcAwcO0IP37t178+YN/fPL7gwoFArj4+P5fH6XLl3ZiqBBAAAgAElEQVTofTs7gVhQUDB9+vRevXr99ttv27Zto7khgUBw5swZeoKLi0u/fv3evXuXl5dHCHn8+PHhw4dl/ZiFhYXu7u5Tp04ViUSZmZmBgYGyzmSuP2TIEKbV48yZM0eNGkU/zsjIkPXYmzdvFhcXu7q6xsfH83i8+Ph4a2trWYWES5cuDQ0N/fLLLwkhvr6+zFIve3t7utB7/fr1RUVFXbp06dKli9RFuHXHLEU/cuRI49qSUiwWR0dHBwYGWllZffrppwcPHkxLS7OwsAgICDh27Fh2dvaVK1cWLVrUvXt3ZA8hLy/P29s7IiLC1dV15cqVx44di4yMvHXrVmRk5O+//7527Vp3d/fTp097e3vn5ORoerDQpHTo0OHly5dyIk5dbNy4UUdHZ+TIkeq4OCHEx8eH/WlsbGxISMjjx48XLVpEG0PJcf36dX9//8LCwp49e6q2vJepHdHS0lKyPTRNIDI3g7QslOk2A82OGGooOzubEGJhYaGSq9Fq5CVLlqjkarKwVzGXlpbW8Wo0gXX27Fn6aVVVFXPxqKioOg+2XtG/oU+ePNH0QGqAuQs6f/58TR+7aNEiQsjUqVPFYnFpaWlkZGRFRYXUMyXubHfs2EE/KC4urusPoGp/jR37AyH0v/jjxzU9nCbi5s2bhBAvLy9ND6Tp+OOPPwghhoaGBw8eFIvF2dnZtOPMhg0bYmNjnz9/fujQISsrKy6Xe+PGDTWNgRapcblcNV2/jpjysVatWtU9VDHKysroWubc3FxVXZO6fv26n58fXcxrZGS0fPnytm3b0h/hyJEjvr6+tra29AW3jo4OXWvM5XKZbYLt7OwkLkj31TEyMlq7di09Z/Xq1WVlZTRHNm3aNH19fQ6Hs2bNms6dO/fq1YsmE93d3cXvn2DDhg0LDw9PTEwsLy/v378/8zfc3t6+T58+Eq8Ax44de/DgQWbOCSF8Pp/u+chWUVHh7+9PdyYxNDT8/PPPdXV1uVxuRkYGc87t27d1dXV37NiRnZ3N5XJbtGgxYcKExMRE9nVEItEPP/xACPnyyy+rT+aRI0f27dtHc4ULFy4Ui8Xt27enYzM3NxeJRBLnx8fHs5ta/vDDD2KxmPbdCwoKevPmDf2Y2bjz999/r+U/szTHjh1bsWKFSCQKDQ1lxvDo0aOaXufs6NFM/Hpw6JAKR8hWUVHx4MGDo0eP7t27d+/evYcPH16xYgW7nVbnzp03b9788OHDqqoqNY2hPiF+qRxN32zevFnOOYcOHdLS0goMDFTTGBp4/IJGSigUqvX67HI/etdJK29omJOlvLycnmxgYPDs2TPVDomuciCEdOzYUcmHzJ8/nxDSp08f+unx48cJIcttbHD/pXKNIn4hgVhjqk0g0lqwdevWqeRqsjBFgnw+v/qr8Jqib4OEhYUxR5jCw3/++aeOF69ntJL8+fPnmh5IzdCS1Vqkazdu3EgImTFjhsIz2TdFhJCTJ0/SD1R4V68qSCCqQ6MIYI1Lr169DAwM0tLS6Kc0R79r1y72OY8fP9bW1h4+fLiaxtDwb8BoZfScOXNUe1lvb29CyCFVJ2iYjbmqV0oOGDBA4kjfvn0JIUOGDKE1hpRETvPrr78mhHz22WeVlZW7du2il2UWLJ85c+azzz5jX5OGsICAALFYzNQ2EkLs7e3pWmkrK6urV69qa2tzuVx6MruabOPGjcXFxTSgrFixIiAgQOrkf/vtt8xDaJ5u9OjRhJBt27Yx58yYMYMQ4uvrSztaDhkyROqM0Z+d/Qw/depUaGhoWloau7tTRESEWCxmV4XcvXs3NDRUIBAwD1y1ahV7Kv777z+xWDxq1Cg+n09j+p07d5ycnJgTevfuLXVIZWVlK1eu9PX1lRpSRSLRgQMHRo0alZOTwxysqqqit3bx8fF0BxVq586d1S/+7bffxsTESP3WYrF4nbMzE79+nDJF1mm1U1pa+scff9Atzok0tra2K1asqJ4ybuwQv1SuQ4cOLi4uCk/7+OOPO3XqpKYxNPz4BVDdBx98IPXPr42NjZw3bOiqBWdn54SEBJUP6eXLl3QMY8aMUfIh69evJ4SMHj2afkpfb0zV0sL9l8o1iviFJcwaRmsD1d2Imt6EfP/993v27Kn7NpS0/QF7tTI7QVnHi9czOuBGtzEIvRmoxaoimvyVdS/Bhh6IAKoVFxfXt29fZusJei80ZswY9jlubm6enp50F4jmqXfv3jwej2ajVGjixImEEJUvD3/69Cn9IDQ0tEePHoSQjRs30hZ+V69eZU6j9w90jxdPT8+uXbsyyTLawZAQcvfu3StXruzYsYPP5y9evJjH482ZM+e7774jhPz3338cDmfv3r1+fn4bNmygYWvUqFH6+voCgYAQ0r17d0KIs7MzvRSfz09LS1uzZg0hZMyYMf3793d2dhaJRAKBgMfj7dq1i8vlbt68+bvvvvvyyy8NDQ1pUrJ///5028rQ0FC62SJVWVm5efNm5lPaR3L8+PGEkNOnT2dmZrq7u//0009//fUXISQuLu7IkSOEEH9/f6kzZm1tTQh5+PDh3r17CSGPHz8eN27c5MmTd+/eLRKJ6CuiFi1a9OvXj/zvkvaRI0dOnDjxm2++YY7QHbepNm3auLu7E0KOHj364sULOhW9evV68uTJnj17goKC9PX1b968Sd8AlnD06NFNmzZFRETQ2SaE5OTk/Pnnnxs3bszPz1+/fj3d1HvDhg3MQ548eVJQUEAISUxMpEuY6VD//fdfiYuHhoZ+8803ErlO9ty+evWK+fTy5csrVqxg6nBrLTs7+8KFC7NmzbKwsBg3btzZs2eLiopcXFxGjx4dGBgYGBg4adKkhQsX/vvvv6mpqZs2bZLY1xugOoFAoMwiSltbW4ULMwGaFbq5lp6eHo/H++ijj5hInZ6efuPGDakPKSws/P333wkhw4YNYyrxVYgpimQHWfnorz9duUwIoS9lKysrVT42aBw0ncFsfFRbgUhTh7t371bJ1WTx9PS0t7evrKxUydXoOqbLly8zR5g/KLdv31bJt6g3ixcv9vX1VdXM1Bv6cv/evXs1fWBpaWlISMjr168Vnnnp0iX2H4qUlBQtLS19ff1ajVe9UIGoDo3iHbDGxcLComvXrsyntKqLXdZEffzxx5aWlmoaQ8Ov4BAKhcnJySq/bFlZGX35271797osZE5ISBg0aND69evfvHlTWVnZokULDodDF+rm5+dfuXJF/H7FNP3LyefztbS0EhMTmQbtkZGRYrH45s2bn3/+OSHku++++/vvv3fu3EkIoX3f586dy3y7oqIiQ0NDQshHH33EHNy+ffvgwYOLioq+//57es2bN2/SLx0+fPj48eNMk01CyOHDh8WsUj4bGxuxWPzu3Tv2DyUUCu/evUs/psucvby8mGJz2tuxTZs29vb23bp1owcLCgo4HE6LFi22bdtGWAWYHA6Hw+Ho6ekVFRVJnUCmYyAhhHbfYyaKELJ///7p06f/+OOP9GRa+c6uTGTmoaKigq7mpjWG06ZNk/8PR7ctOnbsWPUvMUl8Pp9fXFx85MgR5jv6+PjQGy0ul6utrT1kyJCYmJhFixYxu9/8+OOPNFVKc6ytW7cuKytjX/zjjz8mhDg5OYnF4ufPnw8cOHDdunVMJ5CoqKiJ74PXD4S4E0IIMTQ09PHx+eeff/Ly8q5du9avX79OnTrRJKbUHy0xMTE4OHj+/PkfffSRra2txPu43bt33717N1P43EwgfqncsGHDdHR05K/XycnJsbS0rF0FvUgkyleEZlsacvwCqG7Tpk2EEG9vbxp5f/rpJ/I+E7d06VKpD/H09KR/wC9cuKCOIYlEIhop/vzzTyUfIhQKg4KCmJesIpFIR0eHHb+2jh+v7sXgzUSjiF9IINaYahOIRkZGhJDffvtNJVeTpaKiQuJFbV3QYop///2XOdKtWzf6l+7Bgweq+i4gx6pVq7p06SLrDk0lmNVwfD7f3Ny8vLz8xIkT586dU993rDUkENWhUQSwxmXixIlcLvfq1av0U7qnx759+9jnpKam6uvr+/n5qWkMDT+BqD5//PEHbYdHX7Jv2rRp1KhR5eXlSj48JibGysqKqR2YP3/+8+fPmZSchAkTJhgYGMydOzcmJobGSpoi5HK5BQUF9BzaJoL26WMLCQlhX+qrr76S9Sq/rKzM1dW1devWJSUl7ONv3rxhyszp6tQVK1bQT2Ut42WkpqbSnbWYZybdEWXw4MFlZWXsnrm0gx57jTCTd5s8ebKs61dUVLCbMhPWCgBdXV2JoFZQUODj43Po0KEtW7bQAs+2bdvSLz18+JAQ4uzsvHTpUvK/PVWkosnWL774ovp46GJkugVNSEiIubk5IaR///5Mzrdv375Tp06lH0vU4M+ePZsO7M6dO7QEMjQ0lLn4vXv36A+rpaVVXl6+fPly+ih3d/fs7GyxWDxjxgyJBCLzBJNq8ODBkZGRWVlZf//9908//TRv3jy6w7WEVq1a9e/ff+XKlXFxcfKnpalC/FK5ixcv8ng8U1PTX375JTU1VeKrmZmZ+/fvt7Oz43K5Z86cqcX1aZtXZXA4HFX8QAD15MqVK4S124FIJHr27FlkZCQhpEePHlIfQiO4nZ2dxLt9KkS3UU5KSqr1FZydnSXiV58+faKjo0NCQlJSUsRisfKvr4CtUcQvJBBrTLUJRPrK9eTJkyq5Wv345ptvnJyc2IUz9L19QkhsbKwGBwYqxGxneezYMbVmKusOCUR1aBQBrHF5+fKljY2Nvr7+hg0baEHQvHnzjIyMQkNDS0tLKysrr1271qlTJy6XW4v9kZTUnBOIYrH47t27hBBDQ0OBQEAry2jNoByXL1/OysoSi8Xjxo1j38QaGxvTDKCPj48y35ruzszeXuDVq1dSu2fcv3+f/cCKior4+HhZlxUKhYWFhdWP07SjgYEBbbFElxUTQiZOnKhwqHTlVLt27ej2YrS2rnq79xEjRjD381wud/jw4cwmy9euXZNzfdrmkmrfvn1ycjLtrLJlyxY5jyotLeVyuXw+n64Y+PXXXwkhEyZMkD8/DLqDjYODg0QbaPqHzsXFZd26deR9h5Z+/fqJRKILFy60adOGw+GcP3++srKSWcKsr6/PbMzi7e1Nb8MyMjLoltn29vaHDx8uKSmhu8owP2lCQkLv3r3J+yb6n3766aRJk1q2bMm+ATu+cqVYLM7MzFy2bFnnzp0NDAxat269fv366Ojo77//XlbvESMjozFjxmzZsiU8PDw5ObkB9imuf4hf6hAUFMTUtxobGzs6Orq7uzs5ObF3dN2zZ0/tLv7999+bmpqayEV/BbS0tFT7cwGoW2JiosSWle/evdPR0eHxePn5+RIn062NdXR0VFj6U92BAwdWr15dlyt8/PHH1SvoKW1tbTc3Ny6X27lz571790q0enz9+vXt27eVWQzXPDWK+IUEYo2pNoFIX4aGh4er5GqaMmnSJPono9m+3d30pKam0n/Thv/kRAJRHRpFAGt0njx5YmNjQ3+zDAwMmOotLS0tmmuoyw2YMpp5AlH8/u0u5k2vwMBAOWGLrpjr0aNHaGgoU3pmZWVFM0F0M+UFCxbUejB0Vys7Ozt3d3e6VJnL5b59+7bWF2Q8f/5cV1eXKWV99+7dihUrxo8fr0zji4qKClpdyOFwpk6dSttHSpTKit+3VCeEuLq6ZmVlVVVV0Z2jO3ToIH+vto8++oi5zaBLj9euXbty5UqFO7zR/ompqamlpaW0LdSBAwcU/jhUVVUVXTIWGRlZUlLC9C2hbTFnzJiRlpZGq0ENDAyYjGRVVRXzXumLFy/omP39/ePj42kGkzYLbt26tUgkEgqFHTt2pOd4eXnRQs7JkyfTvbBPnTpFbxePHj3KTv/Nb9VKyfglEAjWrVvXvXt3U1PTDz74YN68edu3bz9//nwDf4dPIxC/1CQxMXHp0qVdu3ZlkuM8Hs/S0rJHjx6bNm2i77Woj2rvvwA0i+60RntTbN++3dnZ+dWrV2KxOCUlhRBia2ur6QEq8MUXX7ATiEt9fDp06MDlcpn114wPP/zwu++++/XXXzMyMjZs2EDfOtXW1p40aVJUVBQKFSU0iviFBGKNqTaA0cUy8t+ub/gWLlxI/0Y0uu2MQZbXr1/Tf9OGv7M2Eojq0CgCWGNUXl4eEhLSu3dvmjCiuFyuk5PTsmXL6MtH9UEC8fHjxxI7Juvp6dGuiOXl5fv27aM9Cim6JzKDrmn98ssvT58+zRw8e/ZsrQdTWVm5bds2un0wrXB0dHSs+89ICQSCWhejJSQkzJo1i/ZkpJnT69evS5xDd03hcDjMot27d+9aWVkdOXJE/sVzc3PpPjOEkP379ys/Kpq3bdWqFc3Nubq61ujGgzai6tGjh6mp6dChQ+lBuvE03Qz91q1bHh4ecnpC0bzqwYMHxWJxeXk580RilmyXlZUdOnSIqU/s1atXeXn5nDlzCCG0YNPDwyMnJ4f9pPqxWzfEL5VD/FI3kUj09u3b169fy9lGVuWQQISmZOvWrYSQzz//XPy+tT0tCaQtpHr27KnpASrw/PnzLW5uTPyKCQ5++/ZtSkqKSCRauXLl6NGjs7Kyfv/99+qbL3E4nM6dOzPNTLp06cL0cRaLxS9fvrx8+fJXX301Z86cHTt2SF1j0bQ1ivilRUCj6O8P03C9kTIzM6MfSNyYQePF7MLc6LaoBmjI+Hz+pEmTaNW2UCgUCoVaWloGBgb441k/3Nzcpk+fTrcApoRCYVhY2MSJE/v27Xvnzh0jIyOBQECzZufOnaPntGzZcu3atRMnTrx9+3a/fv0MDQ29vLxu3brVt2/fTz/9tNaD4fF4ixYtoh97eHj88ccfKtwPt3p3ReW1b98+KCiod+/eEydOLC8v53A41Qc2cODAMWPGDBgwYMKECfRIz549MzMzFV7czMxs0KBB9vb2aWlpNCeoJEdHxxs3btDtFGxtbX/77TeJDUPkmzFjxtatW+la5vDwcIFAYGZmFhsbSwjx8PAghCjcAH316tUnT56kK7X5fL6XlxfdWdvPz4+eoK2tHRAQkJGR8c0333A4nN27d/P5fNrWkOZbP/74Y3Nzc/qzm5qahoaGlh86lBgTo/xPAdAQcDgcfX19ug8kANTCuHHjVqxY8ddff2VkZNDNykJCQtavX0/fZKLNMRoyZ2fnTp06JbxveKWnp8f8TaCrKwgh48aNGzp06M6dO/Py8p4/fx4REWFjY3Pw4MGBAwempaX9+uuvJ06cePTo0Ycffqirq2tubi4UCpn6FWr16tXt27dv3769kZGRubl5+/btu3Tp0rFjx+qvmcVicVZWVnR09OPHj3Nzc42NjY2Njd+8eUMIadGihYGBQZcuXTw8PJiUhXxisbiwsLCiouLt27eEEG1tbfqj6evrS7RCbp5wx6JhNIHY2GMw89tYo1fz0JDp6Ojw+fyKigp2FycAUCE9Pb3G/u5RY7RkyZIDBw5UVVU5OjpmZWWVlpaeO3dOJBLduXOHEFJcXBwdHe3l5eXj45OSkmJlZbVhw4Z+/frRNbPDhg2jF9m/f//WrVtpnkglo/L39z969OjkyZNVcjWV+Pzzz/fv3x8XF7ds2TJmRxGGgYHBiRMnan3xw4cPv3jxglnzqwy6z4mRkdG5c+c+/PDDmr6Ib9269dWrVydPnpycnPzu3buzZ8/ev3//yZMnXC63S5cuylxh2rRp06ZNYz49duzYhx9+KBQKfXx82KfNnTs3LCysX79+dH+Vnj17EkKqqqoIIbTgsVevXmlpaf379/f19T0fHFyjnwKgwRKLxeXl5QRvPAMowc7Ork+fPteuXVu/fr1IJCKEvHz58saNG40lgagkQ0PDlStX0o8LCwv19PRo7La3t9+yZcuqVau2bNly+PDhjIyMly9fEkLMzMwsLCz8/f3NzMz+/PPPf//99/79+/SdP0aLFi3c3d29vLy0tbWzsrJSU1OTk5NzcnIqKiqUHBWfz6d70DFHtLS0DA0Ny8vL8/PzMzIy0tPT6V+z6uzs7Dp37kx7qvD5/NatW7ds2dLIyMjR0dHKykpbW7tly5bVXzI1MUggapipqWl2draS6fAGCxWITZKBgUFBQQHeaQFQraSkpKioqLi4uLy8PKFQaGpqam1tbW1tPXz48OprPWqkoKBA/gnFxcV1uX7T4OTktHjx4mvXrl28eLG4uNjGxiYqKurRo0eEkE6dOsXFxfn7+zs7O//333/GxsabNm1iduBlc3V1DQkJUeGoXFxc6BgaDrppuJou3r9/f9ocUHk0Hzd79mzaOqoWPDw8Hj169Msvv3z99dcLFy6klQXOzs7sfgLKs7GxiYuLq6yslHgPuGXLlnQNGtW7d+/x48cfO3asb9++rq6uhJAhQ4acPHnS39+/dj8FQMP06NEjWswrFos1PRaARsDb2/vatWt0QzYulysSia5fv06TiU0mgchGN69jMzQ03Lhx48aNG9+9e/f69Ws+n8/eZm3+/PmZmZmpqalJSUlv3rzJzs5+9uzZf//9l5SUdPv2bXacpczNzWli0dra+vXr10KhkAZ3oVBYUFDw4MGDZ8+e0dfJFRUVDx8+lD9aExMTHo9nZGRECCkrK6Ob27x9+/bly5c01ymHjo6Og4ODtbU1bWnC5XLbtm27du3aJvPmCtI9Gnb27FmBQMB0zGmkkEBskoyMjAoKClAhBaAqKSkps2fPjoiIkPrVOXPm+Pn5bdu2jVZa1dT8+fN37typzJm4u9uyZQv9wMDAwNPT89atWykpKY6Ojjt37vz444/fvHlDl7LOmjVLavYQNMLPzy8hIYGuCK6LYcOGLV68mGYPW7RoMXz48Fpfiun1IV9QUJCVlRWz3dzUqVOHDBlCW2ADAEDzROvTS0pKCCHDhw8/d+7cpUuXaJatSSYQ5ZDVEqFNmzZt2rT58MMP2QeLiopu3br18OHDyspKW1tba2trZ2dnKysr5dNz7969e/r0KfOeulgsrqysfPv2rYGBgZ6enqWlpYODg9SrVVVVpaSkxMbG5uXlEULKy8sFAkFRUVFhYWFSUpJAIKioqMjPzy8sLExISEhISGA/dvTo0d26dVNyhA0c0j0a5uDgULt7xQaFeR2MBGJTsnjx4kePHtnZ2Wl6IABNQV5enre3d1JSkqurq5+fn5ubm6mpqZGRUXFxcX5+fnx8fFhY2OnTp2NjY6Ojo2vx2tHCwsLExET+OSKRqKioiMvl1vaHaIJGjRp169YtQsi4ceP69u3r7e199+7doqIiQojEa1bQOLqQvI6cnJxOnTo1adIkX1/fw4cP071i1MrY2Hjbtm3sI8geQtPj4uLy5H1DNABQqEePHhwORywWa2trr1279ty5c//88w/9kqWlpWbH1pAZGxv7+vr6+vrW+gr6+vq9evWqxQN5PJ6zs7PCNzKFQmFycnJWVlZhYSEhpLKysmXLlk0me0iQQASVYCoQsdy1KZk7d66mhwDQdKxcuTIpKWnz5s3Lly+XesK6deuCg4MDAwPXrFnD3uVDSatWrVq1apX8c3JyciwtLeuyvUbTM2rUqCVLlojF4s8++4zP51+8eHHr1q3Lly/ncDheXl6aHh2ohZ+fX25uro6OjqpaWAKArq4uXaQPAMowNTV1dHRMTk7+5JNPunbtam1tnZGRQb/U3CoQmxg9PT03Nzc3NzdND0RdUIYAKmBkZGRsbKyrq4sEIgCAVNeuXXNxcZGVPaQCAgL69+8fHR1db6MCBweHr776aubMme7u7vTIsGHDOBxOx44dkWltwnR1dZE9BAAADerduzchZPz48YSQr7/+2svLq02bNhwOx97eXtNDA5AJFYigGpGRkRUVFXRTaQAAkCAQCDp37qzwNFtb29jY2HoYDzB++ukn9qeurq5hYWGOjo6aGg8AQMOkvk3AAJqhH3/8cezYsUOHDiWELF68ePHixc+ePUtJSWkC/c2gCUMCEVTjgw8+0PQQAAAaLi8vr6ioqBcvXshpnpKbmxsREYGVsxo3ZMgQTQ8BAKABUesmYADNU+vWrYcNG8Y+0rFjx44dO2pqPADKQAIRAABA7ebPnx8eHu7p6blmzRo/Pz+J9SlZWVkXLlz49ttvc3Nzp02bpqlBAgAASFD3JmAAANBYIIEIAACgdt7e3jt37vzqPWNj41atWhkZGb19+zY/P7+goIAQoqWlFRQU5O/vr+nBAgAA/B91bwIGAACNBRKIAAAA9WHWrFmDBg06cOBAVFQUbXNDCOHxeGZmZj169Bg5cmRAQIClpaWmhwkAAPD/KbkJ2NGjR7EJGABA04YEIgAAQD1p167d1q1bt27dKhaLhUJhaWmpiYkJl8vV9LgAAACkwyZgAABA4aYFAACgvnE4HH19fVNTU2QPAQCgIfPy8rp169aLFy/knINNwAAAmgPctwAAADQIYrG4rKysrKxM0wMBAAD4P/Pnz6+srPT09NyxY0daWprEV7Oysg4cONCzZ09sAgYA0ORhCXMtlZeXX7p0SZnTrl69ampqyuFw6mFUjVFVVdXr16/Nzc0xRbLQKcKudrJw4uOZp85ff/0lTk3V4GAarIqKCkLIBx98oOT5cXFx6hwOSPfo0SMPDw9CiFgsVt93QfxSFcQvhRC/5EP8Ugbil8Y1nE3AEL9UBfFLIcQv+RC/lNEk4xdHrXcpTZJAIDA3N9f0KADg/wwipO37jy8RkqzJsTQ1vXv3RkP0+hQbG6vWBCLiF0CDgvilPohfKvf8+XNmE7DS0lLyfhMwGxubetgEDPELoEFB/FKfBh6/kECsMbFYPG/evISEBGVOfvr0aVZWlp6enq6urroH1ki9e/eurKxMX19fR0dH02NpoDBFCr19+7a8vNzAwEBbW1vTY2mg3rx5U1FR0blz5xq9lRoQEDB+/Hj1jQoklJaWJnHpg1MAACAASURBVCUlEUJcXV3VcX3EL9XCH2eFMEUKIX4phPjV0GhkEzDEL9XCH2eFMEUKIX4p1DTjlxjUafHixYSQH374QdMDabhmz55NCNm9e7emB9JwTZ06lRASHBys6YE0XGPHjiWEHD9+XNMDabiGDh1KCAkLC9P0QKDRQPxSCPFLIcQvhRC/FEL8gppC/FII8UshxC+FEL8UapLxC5uoAAAAAAAAAAAAgEzYRAUAAKD+JCUlRUVFxcXF5eXlCYVCU1NTa2tra2vr4cOHW1lZaXp0AAAAAAAAUiCBCAAAUB9SUlJmz54dEREh9atz5szx8/Pbtm2bg4ND/Y4LAAAAAABAASQQAQAA1C4vL8/b2zspKcnV1dXPz8/Nzc3U1NTIyKi4uDg/Pz8+Pj4sLOz06dOxsbHR0dE16rUMAAAAAACgbkggAgAAqN3KlSuTkpI2b968fPlyqSesW7cuODg4MDBwzZo1e/furefhAQAAAAAAyIFNVAAAANTu2rVrLi4usrKHVEBAQP/+/aOjo+ttVAAAAAAAAMpAAhEAAEDtBAKBMnuk2Nra5ubm1sN4AAAAAAAAlIcEIgAAgNp5eXndunXrxYsXcs7Jzc2NiIjw8vKqt1EBAAAAAAAoAwlEAAAAtZs/f35lZaWnp+eOHTvS0tIkvpqVlXXgwIGePXvm5uZOmzZNIyMEAAAAAACQBZuoAAAAqJ23t/fOnTu/es/Y2LhVq1ZGRkZv377Nz88vKCgghGhpaQUFBfn7+2t6sAAAAAAAAP8DCUT10tfXJ4QYGBhoeiANF6ZIIUyRQpgihTBFDcGsWbMGDRp04MCBqKioZ8+epaSkEEJ4PJ6ZmVmPHj1GjhwZEBBgaWmp6WH+HzxnFMIUKYQpUghTpBCmCGoKzxmFMEUKYYoUwhQp1CSniCMWizU9hqasqKjo/Pnzo0eP1tXV1fRYGqiCgoK///57zJgx2tramh5LAyUQCKKiosaOHaulhYy/dNnZ2VevXh07diyXi7YM0qWnp9+8eXPMmDEcDkfTYwFCCBGLxUKhsLS01MTEpGE+bxG/FEL8UgjxSyHEL4UQv6CmEL8UQvxSCPFLIcQvhZpk/EICEQAAAAAAAAAAAGRCthgAAAAAAAAAAABkQgIRAAAAAAAAAAAAZEICEQAAAAAAAAAAAGRCAhEAAAAAAAAAAABkQgIRAAAAAAAAAAAAZEICEQAAAAAAAAAAAGRCAhEAAAAAAAAAAABkQgIRAAAAAAAAAAAAZEICEQAAAAAAAAAAAGRCAhEAAAAAAAAAAABkQgIRAAAAAAAAAAAAZEICEQAAAAAAAAAAAGRCAhEAAAAAAAAAAABkQgIRAAAAAAAAAAAAZEICEdTr6tWrAoFA06MAaPq2bdu2e/duTY8CoOlA/AKoH4hfAKqF+AVQP5ph/EICUV2ioqI+/fRTMzOzTp06zZo1Kz8/X9Mj0oAnT54MHDjw5s2bUr+qzBQ14Wn89ddfu3fvbmhoaG5u3q9fvxMnTlQ/pzlP0Zs3bxYvXty1a1cDAwNHR0d/f/+YmJjqpzXnKWI7cuTIkiVLTp06Vf1LmCKoKTwfCOKXXIhf8iF+1QjiF6gQng8E8UsuxC/5EL9qpJnGLzGowa+//srj8bS1tfv379++fXtCiJOTU3JysqbHVd9Gjx5NCDl79mz1LykzRU11GisrK2fOnEkI0dHR6d+//8CBA3V1dQkhM2fOZJ/WnKfozZs3jo6OhBBLS8thw4Z5eXkRQjgczvnz59mnNecpYktJSTEyMiKEDBgwQOJLmCKoKTwfKMQvqRC/FEL8qhHEL1AhPB8oxC+pEL8UQvyqkWYbv5BAVL3ExEQ+n29qapqYmEiPbNq0iRDi6+ur2YHVm3/++ef777/v3r07TVJXD2DKTFETnsbg4GBCiIuLS1ZWFj3y4sWLtm3bEkIuXLhAjzTzKVq2bBkhZNq0aVVVVfTIhQsXOByOlZUVc04znyJGZWVl7969DQ0NqwcwTBHUFJ4PiF/yIX4phPilPMQvUCE8HxC/5EP8UgjxS3nNOX4hgah6S5cuJYT88ssv7IOurq6EkBcvXmhqVPXJ2dmZXeVaPYApM0VNeBo//vhjQsjt27fZB0+ePEkI+fLLL+mnzXyK3N3ddXV1hUIh+6CnpychJCUlhX7azKeIsWHDBg6Hc/DgweoBDFMENYXnA+KXfIhfCiF+KQ/xC1QIzwfEL/kQvxRC/FJec45f6IGoelFRUYQQPz8/9kH6Kf1Sk3f16tXU1NTU1NRZs2ZJPUGZKWrC05icnMzn83v27Mk+2LlzZ0LI8+fP6afNfIpsbW1HjRrVokUL9kEej0cIefv2Lf20mU8RdefOnQ0bNsydO9fHx6f6VzFFUFN4PiB+yYf4pRDil5IQv0C18HxA/JIP8UshxC8lNfP4paXpATQ1YrH42bNnRkZG9vb27ONubm6EkLi4OA2Nq15ZW1vTD4yNjat/VZkpatrT+Oeff3I4HC73f9L3Dx48IIQ4OTkRTBEh58+flzhy/fr1e/fuOTo6dujQgWCKCCGEvH37duLEic7Ozlu3bq3edhdTBDWF5wNB/FIE8UshxC9lIH6BauH5QBC/FEH8UgjxSxmIX0ggqphQKCwtLbWyspI4bmpqSgjJy8vTxKAaFmWmqGlPo4eHh8SRhw8fLl68mMPh0Oa+mCLGnTt3tm/f/urVq3v37nXo0OH333/X0tIimCJCCCHz589PS0u7deuWxFuFFKYIagrPB4Xwa4X4pTzELzkQv0C18HxQCL9WiF/KQ/ySA/ELS5hVrLS0lBBCd+Rho0eEQqEGxtTAKDNFzWcaxWLx4cOH+/Xrl5WVtX379m7duhFMEUteXl5sbOzTp0+rqqp0dHSYP6mYotOnTwcHB69du5bpli0BUwQ1heeDQvi1YkP8kg/xSxbEL1A5PB8Uwq8VG+KXfIhfsiB+ESQQVc7ExITH4zFtAhjFxcXkfV65mVNmiprJND548MDT03Pq1Kn6+vpnzpxZsGABPY4pYgwZMiQhIaG4uPjy5csvX7708fF59OgRafZTlJGRERgY6OXltXz5clnnNPMpglrA80Eh/FoxEL8UQvySCvEL1AHPB4Xwa8VA/FII8UsqxC8KS5hVjMvlmpmZVV8PT48wzSmaM2WmqMlPY0VFxdq1a7///nsdHZ3Vq1cvXbqUbgNPYYqq++ijj9atWzd37tyQkJBt27Y18yk6e/Zsfn4+l8udOHEiPULfrYqLi/v888/5fH5ISEgznyKoBTwfFMKvFUH8qjnELzbEL1AHPB8Uwq8VQfyqOcQvNsQvChWIqmdjY1NYWJidnc0+GB8fTxrDE6J+KDNFTXgaRSLR5MmTN2/ePGDAgGfPnn377bfs6EU15ymKiYkZPHjwzp07JY7T9r0CgYB+2pyniLpx48Yf7/3111+EkNzc3D/++OPkyZP0BEwR1BSeDwo1818rxC/5EL+UhPgFKofng0LN/NcK8Us+xC8lIX4hgah6I0aMEIvFYWFh7INhYWFaWlrDhw/X1KgaFGWmqAlPY1BQ0B9//DFhwoTIyEg7Ozup5zTnKTI2No6IiAgNDZU4TjelcnV1pZ825ymaM2eO+H+lp6cTQgYMGCAWi0tKSuhpzXmKoHbwfFComf9aIX7Jh/ilEOIXqAmeDwo1818rxC/5EL8UQvz6P2JQtczMTC0tLXt7+5ycHHokODiYEDJq1CjNDqz+0QYBZ8+elTiuzBQ14Wls166dnp5ecXGxnHOa+RTRxrT79u1jjsTFxVlYWGhraz958oQeaeZTJIEdwBiYIqgpPB8YiF9SIX4phPhVU4hfoBJ4PjAQv6RC/FII8aummmf8QgJRLfbs2cPlcq2srAICAry9vbW0tJycnJKTkzU9rvomK4CJlZuiJjmNWVlZhBBdXV0PaRYvXsyc2WynSCwW379/X19fnxDi6uo6YsSIPn368Pl8Dofz888/s09rzlMkQWoAE2OKoObwfKAQv6pD/FIG4ldNIX6BquD5QCF+VYf4pQzEr5pqnvELCUR1OXPmzLBhw0xNTTt06PDFF19kZWVpekQaICeAiZWboqY3jTdu3JBTETx69Gj2yc1ziqiEhIQpU6ZYW1vr6Og4OTn5+/vfvXu3+mnNeYrYZAUwMaYIag7PBzHilzSIX0pC/KoRxC9QITwfxIhf0iB+KQnxq0aaZ/ziiMViOb9OAAAAAAAAAAAA0JxhExUAAAAAAAAAAACQCQlEAAAAAAAAAAAAkAkJRAAAAAAAAAAAAJAJCUQAAAAAAAAAAACQCQlEAAAAAAAAAAAAkAkJRAAAAAAAAAAAAJAJCUQAAAAAAAAAAACQCQlEAAAAAAAAAAAAkAkJRAAAAAAAAAAAAJAJCUQAAAAAAAAAAACQCQlEAAAAAAAAAAAAkAkJRAAAAAAAAAAAAJAJCUQAAAAAAAAAAACQCQlEAAAAAAAAAAAAkAkJRAAAAAAAAAAAAJAJCUQAAAAAAAAAAACQCQlEAAAAAAAAAAAAkAkJRAAAAAAAAAAAAJAJCUQAAAAAAAAAAACQCQlEAAAAAAAAAAAAkAkJRAAAAAAAAAAAAJAJCUQAAAAAAAAAAACQCQlEAAAAAAAAAAAAkAkJRAAAAAAAAAAAAJAJCUQAAAAAAAAAAACQCQlEAAAAAAAAAAAAkAkJRAAAAAAAAAAAAJAJCUQAAAAAAAAAAACQCQlEAAAAAAAAAAAAkAkJRAAAAAAAAAAAAJAJCUQAAAAAAAAAAACQCQlEAAAAAAAAAAAAkAkJRAAAAAAAAAD4f+zdZ1hU19738TUwUqUIShWxQFDBXsCSaBRji2JMLFGTKHaNLVY0EqNRY0mxxRKP5dhiPYkNlGiiQUQRC4iigqCIKE3pnXle7PvMw0EHkTaMfj/Xfd3XZu01e/8HT1zOb9ZeCwBUIkAEAAAAAAAAoBIBIlBGdnZ2Mpns6dOn6i7k/4wdO1Ymk925c0fdhQAAqjXGLwCAJmL8AtSLABGoGAqFIicnJz8//428HQDgTcX4BQDQRIxfQBUjQAQqxuXLl/X09Ly8vNR1Oy8vr8DAQHt7+6opAADwZmD8AgBoIsYvoIrJ1V0AgIrRsGHDhg0bqrsKAABeD+MXAEATMX7hbcMMRKAaSUtLU9et8/LyCgoK1HV3AIBGY/wCAGgixi+g9AgQgQrQu3dvNzc3IcTq1atlMtmePXuUp/bs2dOzZ886depYWFj07Nnz1KlTRV84e/ZsmUyWlJT073//u169en369JHa09LS5syZ0759e2Nj4zp16ri6um7atEmhUJRwu4kTJxZbxDc/P3/ZsmWdO3c2MTFxdHT8+OOPr127VvTuXl5eMpksOjp6/PjxxsbGNWrUsLOzGzVqVFxcXKX8mgAA1QzjFwBAEzF+AVWPABGoAOPHj58+fboQ4v333//555/btGkjtX/xxRcjRowIDg5u165d06ZNz58/36tXr2XLlhV7+YEDB8aMGdOsWTMPDw8hRGJioouLy6pVqzIzM3v27Nm2bduwsLCJEycuWrSo5NsVlZmZ2alTpwULFkRERLz77ru1atX6448/XF1dd+7cWaznxIkTd+zY4e7uPnbsWD09vR07dvTt25dvwwDgbcD4BQDQRIxfgBooAJRJ3bp1hRBPnjyRfgwMDBRCzJo1S9nhwIEDQogBAwakpqZKLREREY0aNdLS0rp06ZLUMmvWLCGEhYXFjRs3lC9cunSpEMLLy0vZ8vjxY2Nj44YNGypbXrzdhAkThBDh4eHSj998840QYvDgwZmZmVLLuXPnjI2NTU1NExMTpZZ58+YJIQwMDIKCgqSWnJycFi1aCCGK1gMAeJMwfgEANBHjF6BezEAEKsvSpUv19fV37txpZGQktTRq1GjlypWFhYW7du0q2tPT07N58+bKHzt16rRp06avvvpK2WJtbW1paRkfH1/6u//888/GxsabNm3S19eXWt57770ZM2Y8f/58x44dRXtOnjy5bdu20rGOjk7//v2FEMyiB4C3FuMXAEATMX4BlYpdmIFKUVBQcPv2bWtr62JjVWJiohDi+vXrRRvbtWtX9McuXbp06dJFoVBER0dHR0dHRUX9/fff9+7dq1mzZinv/vjx45SUlA8++KBWrVpF23v37v3tt9+Gh4cXbezQoUPRHw0NDUt5FwDAm4fxCwCgiRi/gMpGgAhUitjY2Nzc3AcPHnz55Zcvni2225e1tXXRH/Py8hYvXrxx48akpCSZTGZtbd26dWsrK6v09PRS3v3Ro0cvXlYIYWtrK4R48OBB0UYzM7NSXhYA8MZj/AIAaCLGL6Cy8QgzUCmsrKy0tbV79Ojx0rUDin0DpqX1P/8ljhgx4rvvvvvwww8vXLiQnp4eGxt77NgxacmPUlKuD1KsXZoY/1qXAgC8VRi/AACaiPELqGwEiECl0NHRadiwYXBwcEZGRtF2f3//KVOmnDp1StULs7Kyjh071qJFix07dnTs2NHAwEBqT0lJKf3dbWxsjI2NAwMDnz9/XrRdum+TJk1e450AAN4mjF8AAE3E+AVUNgJEoCJlZ2crj2fMmJGcnDxs2DDlGPbo0aNBgwatX79emsr+UlpaWvn5+c+ePcvKypJa8vPzly5deu/evYKCAoVCoep2xUybNi0lJWXSpEk5OTlSy4ULF1avXm1qajpy5Miyvj8AwJuJ8QsAoIkYv4AqwxqIQMWQVtg9ePCgTCYbMWJE+/btx40b98cffxw9erRevXqurq7JyclBQUGFhYXLli1zcXFRdR1dXd0RI0Zs3769UaNG7u7uWlpa/v7+QghXV9dLly6NGjVqwYIFjo6OL96u2HXmzp3r4+Ozb9++c+fOtW/fPj4+/vLly1paWlu3bq1Tp05l/iYAAJqE8QsAoIkYv4AqxgxEoGI0bdp01qxZurq6O3bsePz4sRBCW1vbx8dn3bp1Li4ugYGB0dHR3bp18/Hx8fLyKvlSv/zyyzfffFOzZs3Dhw+HhIR8/PHHN27cWLNmjYuLy8GDB2NjY196u2IMDQ0DAgKWLFlib29/9uzZJ0+eeHh4XLp06bPPPquMtw8A0FCMXwAATcT4BVQxWbEZuQAAAAAAAACgxAxEAAAAAAAAACoRIAIAAAAAAABQiQARAAAAAAAAgEoEiAAAAAAAAABUIkAEAAAAAAAAoBIBIgAAAAAAAACVCBABAAAAAAAAqESACAAAAAAAAEAlAkQAAAAAAAAAKhEgAgAAAAAAAFCJABEAAAAAAACASgSIAAAAAAAAAFQiQAQAAAAAAACgEgEiAAAAAAAAAJUIEAEAAAAAAACoRIAIAAAAAAAAQCUCRAAAAAAAAAAqESACAAAAAAAAUIkAEQAAAAAAAIBKBIgAAAAAAAAAVCJABAAAAAAAAKASASIAAAAAAAAAlQgQAQAAAAAAAKhEgAgAAAAAAABAJQJEAAAAAAAAACoRIAIAAAAAAABQiQARAAAAAAAAgEoEiAAAAAAAAABUIkAEAAAAAAAAoBIBIgAAAAAAAACVCBABAAAAAAAAqESACAAAAAAAAEAlAkQAAAAAAAAAKhEgAgAAAAAAAFCJABEAAAAAAACASgSIAAAAAAAAAFQiQAQAAAAAAACgEgEiAAAAAAAAAJUIEAEAAAAAAACoRIAIAAAAAAAAQCUCRAAAAAAAAAAqESACAAAAAAAAUIkAEQAAAAAAAIBKBIgAAAAAAAAAVCJABAAAAAAAAKASASIAAAAAAAAAlQgQAQAAAAAAAKhEgAgAAAAAAABAJQJEAAAAAAAAACoRIAIAAAAAAABQiQARAAAAAAAAgEoEiAAAAAAAAABUIkAEAAAAAAAAoBIBIgAAAAAAAACVCBABAAAAAAAAqESACAAAAAAAAEAlAkQAAAAAAAAAKhEgAgAAAAAAAFCJABEAAAAAAACASgSIAAAAAAAAAFQiQAQAAAAAAACgEgEiAAAAAAAAAJUIEAEAAAAAAACoRIAIAAAAAAAAQCUCRAAAAAAAAAAqESACAAAAAAAAUIkAEQAAAAAAAIBKBIgAAAAAAAAAVCJABAAAAAAAAKASASIAAAAAAAAAlQgQAQAAAAAAAKhEgAgAAAAAAABAJQJEAAAAAAAAACoRIAIAAAAAAABQiQARAAAAAAAAgEoEiAAAAAAAAABUIkAEAABApdu5c6fsf2lpaZmamjZr1mz69OkPHz4s2nns2LFFe166dKno2W+//bbo2Z07dwohevfuLXuVUaNGKS8SHh6ubP/444+r5pcAAIDSu+++Kw1DN27cUHctwKsRIAIAAOD1pKenz507t127dkZGRvXr1+/Tp4+Pj8/rXkShUKSkpNy8eXPNmjUuLi7nz59X1TMwMLDoj5cvXy5L0f9r//79yuOTJ0+mp6eX+VJbt27t06ePmZmZlZXV4MGDQ0JCyl8eAECzVMjIWLKgoCB9fX1TU9OKvSxQSnJ1FwAAAABNkpCQ0LZtW+WcwfT09AcPHvj4+EyaNGnDhg2vfHnt2rV79eolhMjPz7958+atW7cKCwvT0tI8PT3Dw8Pl8pf86zQwMHDatGnKH18aIHbr1q127drScV5enjIfHDBgQM2aNaXjDh06KPv/9ttvyuPs7Ozjx48PHTr0lcUXk5+fP3HixK1btypbDh48ePTo0d9//116jwCAt0E5R8bSiI+PnzBhQnZ2doVcDSgDAkQAAAC8hlmzZkmfkZycnEaMGPH06dNff/01Jyfnl19+6dmzZ//+/Ut+uYODw65du5Q/+vn5DRw4MD09PTIy8t///renp2fRzjo6Orm5uUVnIN6/fz8xMVF5Stk+e/Zs5XFGRoYyQPz555/t7e2L1RASEhIeHi6EkMvl+fn5QoiDBw+WIUD88ccfpfSwUaNGI0eOjIiI2LlzZ05OzvDhw+/fv29iYvK6FwQAaKJyjowlCA0NDQoKunr16r59+5KTkyuuZOC18QgzUK398ccf7733npmZWZ8+fYKCgmbMmCEtk3Hu3Dl1lwYAeD3nzp0bPHhw48aNjYyM2rVrN3bs2AcPHhTt0KNHD+kveT8/v48//tjGxqZWrVp9+vQ5e/asEOL06dM9e/Y0MzOzt7cfM2ZMQkJC0dfm5+cvXbq0a9eupqamdnZ2AwcOvHjx4os1lGZYuXnz5vDhw52cnAwMDCwtLdu3b7927dqsrCzprEKhOHz4sBDCyMjI39//66+/Xrdu3c8//yydLfpccCn16NFj3rx50vHKlSuLnW3ZsqUQIjo6+smTJ1KLNP1QR0enadOmr3svJeX0wxkzZmhrawshfHx8yvAU8/r164UQenp6p06d+vrrr3fs2DFhwgQhRHJy8t69e8tcHgC8JRgZi1mxYoV09xYtWqSlpQkhtmzZMnr06A0bNpAeQv0UAKqrYp+jjI2N3333Xen477//Vnd1AIDXMGfOHJlMVuyfYUZGRgcOHFD2cXd3l9rr1KlTtJu+vv6cOXOKPdvboUOHgoIC6YWJiYkdO3YsdnGZTLZp06aiNZRmWAkICKhRo8aL/2IcMGBAYWGhQqFQfrRzd3dXXvnevXtSY7NmzVT9Bnbs2CH1cXNzK3YqNjZWWXNmZqZCoRgzZozUMmnSJKme33//Xeo8Y8YMIYSrq6ubm5vUZ8eOHcUuWDQKjI6OfrGYRo0aSWfv3bvXrVs36Xjfvn0l/RG+QPmu33vvPWXjmTNnpMYhQ4a81tUA4G3DyNi5c2epz/Xr1xUKxalTp7S0tIQQNjY2MTExUp8tW7YM+C/p16Wnp1cxfwDAa2IGIlBN3blzZ+HChUIImUz26aefjh49Oi8v759//lF3XQCA13by5MmVK1cqFAqZTDZy5MhVq1ZJoVVaWtqYMWPi4+OL9U9ISOjXr9/IkSONjY2FEFlZWStXrjQxMZk4ceJ7770n9bl48eKVK1ek4yVLlgQEBAghBgwYcPjw4eXLlxsYGCgUikmTJoWGhkp9SjmsTJ48OS8vTwgxbNiwdevWzZw509DQUAjx+++/h4WFCSH09fVXr169evXqqVOnKl8lzZIQQihXG3wtNjY20tO+CoUiKiqq6CkDA4MWLVpI71dqkWYgKtPDMggKCoqMjBRCNG/e3MHBYeDAgVL7wYMHX+s6yg+HdevWVTYqjyMiIspcIQC88RgZi4mMjBw6dGhhYaGhoeGxY8eUo8nYsWP/81/SlHlAbdQaXwJQ6eOPP5b+I128eLHUUnQbL2YgAoAGcXFxkf72Xr9+vdRSWFio/Ht+2rRpUqNynsWMGTOkFuWsPS0trdDQUKlRmZ3t2rVLoVA8fvxYV1dXCOHk5JSfny/1WbdundRn2LBhUktphpXU1FQzMzNTU9POnTsri//000+lPnv37lX1Bj///HOpz6JFi1T1KWEGokKhcHJyks4ePXpUUWQG4qxZs7788kshRJcuXRQKRV5enr6+vhBi3759ZZ6BOHPmTOnUt99+q1AoHj16JM3p0NfXT0tLU1X/i5QPKY8dO1bZ+PjxY6mxYcOGpb8UALxtGBkVRWYg+vv7S78QLS0taRx8KWnGJTMQoS7MQASqKenbM7lcPmXKFKmlV69e7du3V2tRAIDXlpmZKc1QsLCwGD9+vNQok8m8vLyk46CgoGIv6du3r3Tg6uoqHTRo0ED5WUvZmJOTI4QIDg6WDvr27aucm6D8UKScSVGaYcXIyCgpKenZs2f//PNPWlrapUuX1q1b98cff0hni+5YolRYWDh16tR///vfQoimTZtOnz699L+Zol58ik1J2jo5KCiooKAgNDRUWnOqzDMQFQrFgQMHpGPpt2Rrayv9SrOysk6cOFH6Sym3wtTT01M2GhgYFDsLACiGkbGYSZMm3bx5Uwgxfvz4fv36vbI/oBYEiEB1lJGRIW3jVbduXVNTU2V769at1VcUAKAs7t69q1AohBBNmjQpulqTi4uLpwb/UQAAIABJREFUlJrduXOn2EuUjzspYzUjIyPl2WJZm/Q0rhDixx9/lP2XjY2N1Pjo0aOCgoLSDys5OTlLlixp1qyZiYmJm5vb1KlTMzMzVb21mJiY999/X5rT0bJly9OnT5d53+G4uDjpQLk6oZIUIGZmZoaGhkrPL1taWtavX79sNwoICIiJiRFCvPPOO87OzlKj8ilmZbZYGjo6OtJB0V+RMjeUZkoCAF7EyFhMSEiIdPD7779nZGS8sj+gFvJXdwFQ5TIyMqQxtdjCwMrPKgAAjVPs4430eUahULx0/kLpKbdllMvlxUYNSVpaWm5ubimHld69e//1119CCCsrq+7du7dp0+batWu7du168bJHjx4dOXLks2fPhBBffPHFxo0byxyZPXnyJCUlRQghk8kaNGhQ7GyDBg0sLS2fPn0aGBgoBYhSpFg2yv2X7969++K0R2kv5lKuV1WrVi3pQLnQVdHjoh9HAQAvxcioZGxsnJqaGhcXt3r16m+++aaUrwKqEjMQgerIwsJC+vQSExNT9AsuaWY7AECDvPPOO9IHpNu3bxcUFCjbb968WVhYKIRo0qRJea6vnLK3dOnSrJcxNTUt5bDyzz//SJ+RnJ2do6Ojd+/ePWPGDOUzuUUtWbJkwIABz549q1mz5t69e3fs2FGeCXfbt2+XDhwdHV96HSkxVAaIZX5+ubCw8NChQyV0eK2nmB0dHaUD5f6bRY+VZwEAxTAyFvPRRx+dPHlSOl61apVyVj5QrRAgAtWUtOlkTk7Oli1bpJaLFy9KoxcAQIMYGBg0bdpUCPH06dNff/1ValQoFEuXLpWO27ZtW57rN2vWTDo4f/68sjE6OnrKlClTpkxZsWKF1FKaYUW5cbCTk5O0/HxOTs6pU6eK3XHPnj3e3t4KhcLAwOD8+fPKteSLSktLS0pKSkpKKuE5L8lff/31/fffS8dz5859aR8pMTxz5szt27dFOQLEv//++8mTJ0IICwuLEf+rY8eOUp/S78Xs6OgoPQR95coV5dNnyj9iaTtRAMCLGBmLnfL29u7UqZO0zmNGRoa0NzRQ3fAIM1BNzZkzx8PDQwgxa9as0NBQXV1daU8xddcFAHht33//vbQm+uTJk69evers7Hz8+PE///xTCGFkZLRgwYLyXLxVq1Z9+vQ5efLkiRMnhg0bNmjQoNjY2LVr1967d08IsWnTJqlbaYaVevXqSQfHjh2bMmVKw4YN9+7dGx0dLTVKs0IUCoW3t7fUUrdu3bVr1xa7wrfffiuEGDdunPSw8Ndff71kyZKifSIjI0eNGiWEyM/PDwsLCwkJkaafNGrUSLlnZTHSDMRHjx4JIbS1tcv8wXL//v3SwRdffLFy5cqip27fvi19mj158mRGRoahoWFpLujp6Tlz5syCgoLu3bv379//7t27/v7+QojatWsPHjy4bEUCwNuAkbFoH2k+5nfffXfy5EmFQrF9+/apU6c2b968PL8EoOJV9bbPAEptyJAhRf9r1dbWVn6Z9vfff6u7OgDAa/jqq69eXHGvZs2av/32m7KPu7u71B4YGCi1hIeHSy0tW7ZUdlPu57h161apJSIiomHDhi/+M2/q1KmFhYXKF75yWMnNzVW2SAwMDJQLDo4dO1ahUNy4caOEf1gq6xw6dKjU8vXXX0stO3bsKOGFDRo0UL5rhUIxZswYqX3WrFkKhSIjI0O5RpXyFsp5iDt27Cj2205PT1deOTo6WmrMy8szNzeXGi9cuPDin5Hykbeifygly83N7d69e7H3YmZmxjANAK/EyNi5c2ep5fr161LLJ598IrX06NHjxd+YNBTq6elV0J8A8Hp4hBmovvbt27dq1ar27dsbGxt37tz56NGjPA8FABrqhx9+OHPmzMCBA9955x1DQ8NWrVqNHj06NDS02EeXsmnUqNGNGzdmz57dvn37mjVr2tnZffjhh35+fmvWrCn62eyVw0qNGjV8fX0//fRTS0tLa2vrgQMHBgUFTZw4UTr766+/pqamRkVFlb9gJSMjI2dn51mzZt24ccPV1VVVNwMDA+lBM1GO55f//PPPpKQkIYSFhcVLL9K/f3/poPRPMUu/sVWrVrm4uOjr6zdu3HjkyJGXLl3q0qVL2YoEgLcHI+OLFi9erKWlJYTw8/Pz9fWtqMsCFUKm4IlIoFqKjIzMz88XQtSvX19abkMIMWDAgD/++EMI8eDBA+V0egAAXolhBQCAohgZgdfCDESgmho9enTjxo0bN2785ZdfpqenKxSKPXv2HDt2TAjRsGFDBjMAwGthWAEAoChGRuC1MAMRqKauXLny/vvvS6s4yeVyXV3djIwMIYS0sVebNm3UXSAAQJMwrJTe5cuX//nnn1d2GzFihKWlZRXUAwCoDIyMwGshQASqrydPnqxYseLs2bPR0dHa2tr169fv2rXr7Nmzra2t1V0aAEDzMKyU0ooVK+bNm/fKbleuXOHjJQBoNEZGoPQIEAEAAAAAAACoxBqIAAAAAAAAAFQiQAQAAAAAAACgEgEiAAAAAAAAAJUIEAEAAAAAAACoRIAIAAAAAAAAQCUCRAAAAAAAAAAqESACAAAAAAAAUIkAEQAAAAAAAIBKBIgAAAAAAAAAVCJABAAAAAAAAKASASIAAAAAAAAAlQgQAQAAAAAAAKhEgAgAAAAAAABAJbm6CwAA4C0SGRnp5+d369atpKSkzMxMc3NzW1tbW1vbfv36WVtbq7s6AAAAAHgJmUKhUHcNAAC8+aKioiZNmuTr6/vSs3K53MPDY/Xq1fXr16/augAAAADgFQgQAQCodElJSa6urpGRkc7Ozh4eHi4uLubm5sbGxqmpqcnJyeHh4cePHw8ODnZwcPD397e0tFR3vQAAAADw/xEgAgBQ6caPH79ly5bly5fPmzdPVZ/t27ePGzfO09Nz8+bNVVkbAAAAAJSMABEAgErXpEkThUIRHh5ecjd3d/e4uLiwsLCqqQoAAAAASoNdmAEAqHQJCQml2SPFzs4uPj6+CuoBAAAAgNIjQAQAoNJ16NDh4sWLERERJfSJj4/39fXt0KFDlVUFAAAAAKVBgAgAQKWbOnVqfn6+m5vb2rVrHzx4UOxsXFzc1q1b27VrFx8f7+npqZYKAQAAAEAV1kAEAKAqbNy4cdq0aXl5eUIIExMTMzMzY2Pj9PT05OTkZ8+eCSHkcvn69evHjx+v7koBAAAA4H8QIAIAUEXu3bu3detWPz+/27dvZ2dnCyG0tbXr1KlTt27dgQMHjho1ysrKSt01AgAAAEBxBIgAAFQ1hUKRmZmZnZ1dq1YtLS2WEwEAAABQrREgAgAAAAAAAFBJru4CAAB4i0RGRvr5+d26dSspKSkzM9Pc3NzW1tbW1rZfv37W1tbqrg4AAAAAXoIZiAAAVIWoqKhJkyb5+vq+9KxcLvfw8Fi9enX9+vWrti4AAAAAeAUCRAAAKl1SUpKrq2tkZKSzs7OHh4eLi4u5ubmxsXFqampycnJ4ePjx48eDg4MdHBz8/f0tLS3VXS8AAAAA/H8EiAAAVLrx48dv2bJl+fLl8+bNU9Vn+/bt48aN8/T03Lx5c1XWBgAAAAAl07AAMSMjQyaTGRgYFG0MCAjYs2dPSEiIqalp8+bNJ0yYYGdnp64KAQB4UZMmTRQKRXh4eMnd3N3d4+LiwsLCqqYqAAAAACgNLXUX8Hpq1qzZpk2boi0LFizo3LnzL7/84u/vf/z48WXLljk7O+/evVtdFQIA8KKEhITS7JFiZ2cXHx9fBfUAAAAAQOlpWIBYjI+Pz7Jly8zMzDZs2HDz5s3w8PBff/3VwMBgzJgxt2/fVnd1AAD8nw4dOly8eDEiIqKEPvHx8b6+vh06dKiyqgAAAACgNDQ7QPzpp5+0tLROnDgxadIkZ2dnJyenMWPGnDx5Mj8/f8mSJequDgCA/zN16tT8/Hw3N7e1a9c+ePCg2Nm4uLitW7e2a9cuPj7e09NTLRUCAAAAgCoatgaiTCZr3LixcnahlZWVnZ1dUFBQsW5ubm7Pnz9/5VJTAABUmY0bN06bNi0vL08IYWJiYmZmZmxsnJ6enpyc/OzZMyGEXC5fv379+PHj1V0pAAAAAPwPuboLKJe8vDwLC4sX221sbHiEGQBQrUycONHd3X3r1q1+fn63b9+OiooSQmhra9epU6dt27YDBw4cNWqUlZWVussEAAAAgOI0ewZir169wsLCHj58KJPJlH0UCkXjxo0tLCz++ecfNZUJAEBJFApFZmZmdnZ2rVq1tLQ0ezkRAMDbIDIy0s/P79atW0lJSZmZmebm5ra2tra2tv369SvNLmEAAE2neTMQo6Ki+vTp4+jo6Ojo6OrqeurUqSVLlnh7eys7fP/993fv3h04cKAaiwQAoAQymczQ0NDQ0FDdhQAA8ApRUVGTJk3y9fV96dnJkyd7eHisXr26fv36VVsXAKBKadgMxC5dukRERDx+/Lhoo1wul5aUys/Pb9eu3fXr15s2bXr58uXK+2CWm5ubkZFRSRcHgGrC1NS06PxuVAj1zuBg/ALwNmD8qkBJSUmurq6RkZHOzs4eHh4uLi7m5ubGxsapqanJycnh4eHHjx8PDg52cHDw9/e3tLSspDIYvwC8Dar7+KXQQBkZGaGhof/5z39Wr149YcKEXr16Se3Z2dlCiI4dO967d6/y7p6dnW1ra6vuPzcA/0dHCP3//p+2uot5w4wZM6by/i59C92/f79Xr16qfttyufzjjz+OioqqvAIYv4BqhfGr8jB+VaBx48YJIZYvX15Cn23btsnl8nHjxlVSDYxfQLXC+FV5qvn4pWEzEEtWWFj46NGjevXqVepdnj59amVlpaWlZWJiUqk3AlAaH2VkNMnNlY7/Y2h4W0dHvfW8GfLz89PS0jp06BAQEKDuWt4Q1WEGB+MXUK0wflUGxq8K16RJE4VCER4eXnI3d3f3uLi4sLCwyqiB8QuoVhi/KoNGjF+atwZiCbS0tCo7PVSqU6fOkydPquZeAEpwbMiQOwcOSMfbtm1zGjxYvfW8GS5evNixY0d1V/FGmT9/fmRk5PLly+fNm/fSDosWLdq+ffu4ceO8vb03b95ceZUwfgHVBONXZWD8qnAJCQnNmjV7ZTc7O7sbN25UaiWMX0A1wfhVGTRi/NLUAPHWrVuHDx++e/fu8+fP8/LyatasaWlp2bJlyyFDhhgbG6u7OgAA/sf58+ednJxUpYeSUaNG7dmzx9/fvwzXX7Vq1YoVKwoLC0voI51NSkoqw/UBAG+nDh06+Pn5RUREODg4qOoTHx/v6+vboUOHqiwMAFDFNC9AvHPnzpQpU/z8/F56dubMmWPGjFm2bJmenl4VFwYAgCqVPYPj/v37pUwGCwoKynB9AMDbaerUqT4+Pm5ubt7e3h4eHvb29kXPxsXFnThxYsmSJfHx8Z6enuoqEgBQBTQsQIyLi+vZs2dMTMyoUaO6deuWm5u7f//+06dPf/PNN02bNr1169auXbt++umnBw8eHDx4UEtLS931AgAgROXP4Pjll1+WLVtWcp/bt2936tSpWu/sBgCoZnr06LFu3bpp/2ViYmJmZmZsbJyenp6cnPzs2TMhhFwu/+WXXwYMGKDuYgEAlUjDIrZvvvnmwYMHe/fu3bZt24gRIzw9PU+dOvXZZ5+tW7fOzc1t0aJFERERU6ZMOXLkyMqVK9VdLAAA/2fq1Kn5+flubm5r16598OBBsbNxcXFbt25t165dmWdwyGSyWq/CEh8AgDKYOHFiWFjYnDlzWrVqlZOTExUVdePGjejoaF1d3bZt2y5btiwmJmb8+PHqLhMAULk0LEAMDAxs0aLFkCFDijZ6eXklJycfO3ZMCCGTydauXVu3bt1du3apqUYAAIqTZnCkpqZOmzatfv36pqamDRs2bNmypYODg5mZmY2NzdixYx8/fswMDgBANeTo6LhixYqrV69mZmamp6cnJibm5ubGxcUFBQV5eXlZWVmpu0AAQKXTsADxwYMHxdbdEELY2dkJIYouGtW6deuoqKgqrQwAgBIxgwMAoOlkMpmhoaG5uTmrRQHA20bD1kB0dna+cuVKTk6Orq6usvHq1atCCGWwqFAo7ty5U7duXfWUCACACtIMjhUrVigUiszMzOzs7Fq1avEZDACg0RQKRW5urhCi6Gc0AMAbRsMCxI8++mjOnDmjR4/euHGjkZGREOL+/fuTJ08WQrz//vtCiEePHs2cOfPOnTszZswow/UDAgJ27dpVWFhYQp+srCwhRHp6elneADRcTEzMl19+OXv27M6dO6u7FgAaTJrBYWhoqO5CSnLx4sVFixYVHRNNTU23bt1qYmKixqoAANVNSEhIy5YthRAKheJ1X7t3797t27eX3EdKJ58/f17KazJ+AUBl0LAA8auvvvrPf/6zZ8+e48ePN2vWLDMz8+bNm7m5uWPHju3YsaMQYsaMGYcOHXJxcfH29i7D9b///ntpLcVXysjIKMP1oek2bdp09OjRJ0+eXLp0Sd21vGliYmLEf1ckqLYSExNjYmKSkpKEEPn5+UlJSdJfBbq6uubm5vXq1bO2tq5Tp04VVJKVlfX48eMnT55IX2kIIXJzc1P/q6CgICcnR1qlKC8vT9ohMSUlxdzcfMuWLTVr1qyCCqFKZGSkn5/frVu3kpKSMjMzzc3NbW1tbW1t+/XrZ21tre7q/se5c+dOnz5drHH48OGs0ggAqChHjx79888/S9MzJyenlNd86fg1cuTIvn37vl5xAIAiNCxA1NbWPnv27OLFizdt2uTv7y+EqF27tpeX17Rp06QOjo6O33zzzezZs8s2rWPNmjUffvhhyX1iY2MXL17ME2dvJ19fXyHE5cuXAwMD3dzcKvDK2dnZenp6FXhBzXL9+vX33nuvoKDAy8tr9OjRL8YoOTk5hw8f7tGjR6XGc1lZWZcvXz59+vSVK1du3boVGxtrZGSUmpr6WhfR09OrV6+ehYWFnp6evr6+np6erq5uRkZGWlpabm7u06dPExMT8/LytLW1a9Wqpa2tbWxsbGRkVLNmTUtLSwMDAwMDA7lcLrXUqFFDCJGRkREbG5uYmPjkyZPHjx8/f/48JSXldauSyGQyLy+vZs2aleG1KL+oqKhJkyZJf428aPLkyR4eHqtXr65fv37V1qXSrFmzOnfunJ2dLf24cePGI0eOREdHq7UoAEC14+TkdPPmzbK99l//+tf48eMLCgpK6BMdHT127NjSf/4qNn6tXr361KlTiYmJZasQACDRsABRCKGnp7ds2bJly5Y9fPhQW1vb1ta26Nlly5aV5+INGjQYN25cyX1u3ry5ePHi8twFGurp06fXrl2TjocOHXru3LkXt/Qpm5CQEDc3t9mzZ3/77bcVckFVQkNDhw8fvnjx4uozgSg/Pz83N/ejjz5KS0sTQixcuHD9+vWBgYFFM5TY2Ng+ffqEhISMHj1669atFV7DkydPdu/efeLEiYCAAOkZGaViOZ2ZmZm1tbWUb8rlcjMzM2k2X1ZWVmJi4sOHDx8/fvzs2bO7d+/evXv3lfeVJgaWjY6OjrW1tY2NjfLLErlcbmxsbGpqamRkJJfLdXR0DA0NDQwMdHV1TUxMtLS0jI2NLSwsSA/VJSkpqUePHpGRkc7Ozh4eHi4uLubm5sbGxqmpqcnJyeHh4cePHz98+PCNGzf8/f0tLS3VXa8QQsjl8qLLNVy/fv3IkSMPHjxQY0kAgGpIT0/P2dm5bK81NDSUlqIqweumk8XGr5MnT546dUp6ggQAUGaaFyAq1atXT90l4K1w8ODBtm3bKhQKHx8fhULRpUuXvLy8gICA1atXr1u3rkJu4evrm5WVtXHjxoULF8rllfhfpY+PT2ho6JQpU3r27Kmvry81ZmRkLFy40M/Pb9++fS4uLpV39xfl5ua6urpGRESkp6c7OTn9+OOPy5Ytu3DhwvDhwy9cuCD1ycrK6t+/f0hIiBDi3r17ytdmZ2efO3eue/fu5bn7rl27du/e7e/vn5+fL4TQ1tZu3rz5Bx980KlTp+bNm9vb26emptaqVeu1LpuVlXX//v2kpKTc3NzMzMycnJy8vDwpxZPL5ZaWlnXq1NHW1i4sLExJScnLy0tPT09JSUlPT3/69GlWVlZWVlZubm5GRob0JLIQQl9f39ra2tLS0sLCwtbW1tTU1MTExNTUtMxvHGoxf/78yMjI5cuXz5s376UdFi1atH379nHjxnl7e2/evLmKyysNKdZnBiIAQLOYm5sLIQgQAaCcNDhAVIVdwFCB/v7778GDB9va2j5+/FgmkwkhJkyYYG1t3bVrV2XCVX6XL18WQiQkJPj5+fXu3buiLvuiR48eSf9/8+bN06dPv3Hjxs8//xwTE3PmzBkhxJAhQwICAqpyeekffvjh+vXr0rGnp2efPn06depkZ2cXEBDw6NEjaS/1X3/99erVq1ZWVk+ePCmaXKxatcrb23vDhg1lWzQxLCysX79+UVFRQggdHZ2PPvro008/7dGjR7Fg7nXTQyGEvr5+Kb+El/45i7fE+fPnnZycVKWHklGjRu3Zs0daoKMaIkAEAGii2rVrCyF4hBkAyukNXMgvJCRET0/vbV5ODhXo1KlTQojY2FiFQlFYWGhvb//JJ5+0a9euRo0aISEhFbUZd1BQkHTw+++/V8gFVZECRCHEkSNHCgsLR44cuWPHjjNnzpiamjZp0uTWrVtubm7Dhg2zsLCIi4uTehYUFOTl5RW9yA8//LB///7yF5OTk7Ny5UohhI6OjlwuHzFihBDCxMTE3d1dCHHy5EkhhEKh2LBhgxDi559/1tbWjo2NVRbj5+cnhCjlrkdFpaWlfffdd506dYqKinJxcdm5c2dCQsKRI0cGDRrEtD5UnoSEhNLskWJnZxcfH18F9ZSBFCBKsTsAAJpCChCZgQgA5fQGBohABfrrr7+kAy0trS5duvz0009yudzAwKBly5YFBQXl34s5MzPT09Pz4cOH0o937twp5wVLJu10LIS4ePGijY3N9evXpSem16xZ4+vr6+LiEh4evm/fvoSEhHPnzkk933///SZNmmRnZ+fn5w8fPvzDDz+cNWvW0KFDlyxZUs5i/Pz8nj9/3rJly1OnTh09etTGxkZq79OnjxDixIkTQgh/f/+7d+9Kua2NjU1BQYGUgebm5l65ckUIcf78+cLCwtLf9NixY46OjgsXLkxJSRk0aFBQUNDnn39ubGxczvcCvFKHDh0uXrwYERFRQp/4+HhfX98OHTpUWVWvpXbt2kZGRikpKc+fP1d3LQAAlJb0zAczEAGgnN7AR5jLswsYUFRqampwcLBMJlMoFB988IGPj4/yVMeOHYOCggICAsqzBp8Q4tChQ9u3bxdCNGzY8P79+5X9bKCUvjk4OERERDx9+lRHR+fAgQPt27eXJkb9+9//btOmjUKhEEIEBwcPHTo0KSnJ399foVCMGzcuLS2t6ARJb2/vTZs2eXp6liFJfPjw4cGDBw8ePCiEGDRoUNeuXYue7du3r1wu9/X1jY2NvXjxohCiX79+2tra9vb2MTExDx48aNCgwdWrV7OysoQQmZmZpfznYFZW1vTp07ds2SKE6NSp03fffVfsvkClmjp1qo+Pj5ubm7e3t4eHR7EtmOLi4k6cOLFkyZL4+HhPT091FflK9vb2N2/ejI6ObtmypbprAQBUuoSEhICAgNL39/DwqLxiyoxHmAGgQryBAWJ5dgEDigoMDMzPz+/YseM333zTqlWroqc6d+68Zs2a1/oX1UsdP35cCOHh4bFgwQI3N7fY2Nj8/PxK2kclNzc3Pj6+Ro0ao0aNWrBgwaBBg/bv3y8t7Chp1arV0KFDf/vtN4VCERwcLIQICgqS8sRdu3Ypu8lkMm9v78WLFz9+/HjDhg2LFi3S1tZ+rUqGDBkSGBgohNDS0vrkk0+KnbW2th44cOCBAwfWr18v7WXcvn17IUT9+vX9/f2ljFUKFuVyeX5+/qNHjwxfdcfIyMhBgwZdu3ZNT09v+fLl06ZNK/rGgSrQo0ePdevWTfsvExMTMzMzY2Pj9PT05ORkaUtuuVz+yy+/VJ9N0l/UoEGDmzdvRkVFESACwNsgLCzstUYl6d+N1Q2bqABAhXgDA0SgokhPKLu5uX3wwQfFTnXs2FEIERgYWFhYqKVVfCmAtWvX+vn5tWnTJjw83NTUNCUlZc+ePVpaWpMmTTIyMlqxYoXULTc3V1pjcc2aNfb29jY2No8ePXr06JG00FiZrVy50sHBYeDAgXv27GnZsqWzs3NycvLYsWN79OhRWFhYt27dmTNnuri4uLu7vxii7d69++uvv3Z2dr569apCoZAeE5b07t27c+fO586da9iw4aJFi3r16jVs2LCoqKjg4GAp4CullJSUoKAgXV3d77//3sXF5Z133nmxz1dffXXgwIF169ZJW0UrA0QhxIMHD4QQkZGRQoiRI0du3br14cOHTUq8o5+f35AhQ549e+bo6Hj48OFmzZqVvlqgAk2cONHd3X3r1q1+fn63b9+WFhPU1tauU6dO27ZtBw4cOGrUKCsrK3WXWRL2UQGAt0rXrl1DQ0MXLVp0+PBhIcTEiROrcre9ilK7dm2ZTJaUlKRQKPgKGQDKTFMDxMjISD8/v1u3biUlJWVmZpqbm9va2tra2vbr1680q9QDr5SdnS3NknN1dX3xrI2NTYMGDaKiosLCwooFUqmpqfPnz8/IyJBmF0qmT59eq1atjRs3CiEiIiK6dOkyderUs2fPpqamNm/eXHqYsX79+o8ePYqOji5PgHjlypW5c+eampoaGxuPGDGibdu2c+fOvXDhwpEjR6RtSezs7HR1dfv37//Sl2tpaTVt2rRu3bqPHj1yd3dPS0sTQrz//vutW7detWqVTCabP3++1NPNza1Xr14bN278888/lQFiSEjIsGHDli5dKj2p4CAOAAAgAElEQVTAEhcXd+jQoS+++KLoIoMBAQEFBQVubm7Tp09X9S5cXV2HDBmyf//+jIyMWrVqSSGjg4ODEOLGjRtCiNjYWCFEz549g4ODc69dU3WdpKSklStX/vDDDwUFBQMGDNi5cyfLHUK9HB0dV6xYsWLFCoVCkZmZmZ2dXatWrRe/hKi2GjRoINhHBQDeJi4uLocOHWrTps3Vq1fnzp1bbAkOjVCjRg0jI6PU1NTU1FRNDEABoJrQvAAxKipq0qRJvr6+Lz07efJkDw+P1atXl3MOF95y8fHxjRs3lh4qVDW9rmPHjlFRUStXrty2bVuNGjWkxjVr1qxevTojI0P6UUtLS9ri4+TJk3Xr1pUajxw5cuLEiREjRuzevVsIMWjQIKnd3t7e399fmmFXZtL3w8+fP582bZoQIjg4WHn97OxsIYSyjBLMmzfPy8vr7Nmz0o+//vpro0aNXuzm7u6+cePGDRs29OvXT0pRf/7557CwMC8vr5CQkDFjxowYMeLs2bO7d+8+ffq0XC43NDQUQkjbs3Tu3LnkGn766acrV65ERkZ27dpV+q64e/fuMpns1KlTXbp0kR5ttrW1HTZs2I0iAaJyj+bk5OQxY8b88ccfhYWF2traixYtWrhwoQbFNHjjyWQyQ0ND6T8KDUKACABvp6FDh169elXdVZSdubl5ampqUlISASIAlJmGBYhJSUk9evSIjIx0dnb28PBwcXExNzc3NjZOTU1NTk4ODw8/fvz44cOHb9y44e/vb2lpqe56oakOHTokpYcWFhaqwuhRo0YdPnx49+7drq6uX375pRBCoVCsWrVKmhyno6OTl5d39uzZuLi4Tz/99OTJk40bNxZCaGtrFxYW5uTkjB492s/PTyaTDR8+XLpg0WcDFQpFaGioQqGwtrbW0dG5cOFCr169SrPU4JEjR6SDW7duiZetRNOvX79XXmTy5MmfffbZihUrQkJCunbt+tL0UAjRp0+fLl26nDt37quvvvLz88vKypLiy9u3b3t7ex84cEDazujy5cuNGjVKTU29dOnSb7/9tnLlSlGKANHa2vrevXu3bt1SftFdt27d9u3bX7p06fz581KLjY2Nh4fHjdmzla8aPXr00Ph4hULxww8/PH78WE9Pz93dfeHCha/1kDUAVQgQAeDt1K5dOwcHh0pap7sK1K5dOyoqKjExsWHDhuquBQA0lYaNAfPnz4+MjFy+fPm8efNe2mHRokXbt28fN26ct7f35s2bq7g8vDEOHTokhLC3tx85cqSqPt27d9+0adPIkSN///13KUC8efNmbGxs7dq1J0+ePGjQoIcPH3bp0iUrK8vAwODq1avSR+7AwMDQ0FBPT09pR+Pu3btLH8iFEFJOJ6VjGzdunDx5shBCS0urXr160dHRM2fOXL169Usr6dSpU0pKyvXr1+/du3f37l1p22ghhPJAei+xsbHjxo1T5pUlMzY2Xrp0acl99PT0duzY0aBBg7CwMCGEj49PamqqtrZ2QUGB9NsQQkyePPnAgQMJCQmiyJbTn376qbu7+ytrkMlkxTZE+vjjj6WFKaWzUrpqbGwsUlOlxpycnK+++kr5a9m3b5+dnV1p3i+A0pA+d0VFRbGMFAC8Vbp27Xrv3j11V1F2bMQMAOWnYQ/0nT9/3snJSVV6KBk1alSXLl38/f2rrCq8YZKSks6fP6+rq3vjxo1FixaV0LNfv35yufz8+fOhoaFCCGlHlL59+y5atMjZ2bl3795CCH19/a5duxYWFiYlJVlZWbVp02bQoEGtWrXq0aPH4cOHpRhRMmDAAFNT07Nnz/7555/79+8XQjg4OBQWFkpzEn/44QdpTUalkJAQHx+fxMTEgICAsLCwS5cu+fj4CCEGDhzYpk2bwYMHf/3110IILS2tdevW/fXXX8+fP9+wYUPF/q7q1atnYGAQFxeXkpIilTd79mxvb29lh759+548eVLav2/btm0JCQl2dnZ79+7V09Mrw+2+/PLLVatWScd16tTR0dERQjg6Oio71K5du3fv3mPHjj1w4MD58+dJD4GKZWRkZGZmlpmZKX0rAACARiBABIDy07AZiAkJCaXZQdXOzk7aaQEog8DAwIKCgnffffeVi6SYmZl17Njx/PnzzZs39/X1lRYN7NmzZ7FuvXv3ljYwGTx4sEwmq1mz5ksXkalVq9acOXPmz58/aNCg9PR0HR2d4ODgOXPmbN682d7e/sGDB1euXHFzc5M6KxSK/v37P3z48Mcff5RaTp8+feHCBSHEJ598MnToUCHEn3/+KYRwdnaWJkhWBi0tLScnp2vXrt2+fVt6U506dWrWrNnixYulDg0aNGjcuPG2bduOHj365MkTIUS3bt3KfDt9ff1Zs2YtXbr0+fPntra2UmOjRo3uBAdLx3379p2xY0d53hFQGRISEgICAkrfX9qGqHpq2LBhcnLy/fv3LSws1F0LAAClQoAIAOWnYQFihw4d/Pz8IiIipP1YXyo+Pt7X17dDhw5VWRgqXEhIyPXr13v27Fn1a1levnxZqNh8+UVTpky5detWYmLixYsXg4ODhRAdO3Ys1qdXr17SwbBhw0q+2qxZs65fv37gwAEhhLu7u7Gx8caNGxcuXLhr1y4vL6+YmBhlz6tXr0rbrSijuv/85z937tzR1tb+4IMPpJYuXbrMnz+/e/fupXkjZda4ceNr166Fh4dfu3ZNCNG6dWsLCwvpQWaZTCYt7FirVq3mzZtfv35dlC9AlDRp0uTixYs2NjYvnurSpUs5Lw5UhrCwMGkebim9uHpp9dGgQYMrV67cv39f+X0GAADVnLm5uRAiKSlJ3YUAgAbTsABx6tSpPj4+bm5u3t7eHh4eyt0VJHFxcSdOnFiyZEl8fLynp6e6ikT5KRSKgQMHRkZGmpiY3LlzpyozxD/++EN6rLiU22588sknWVlZn3/+uZ+fX3x8vLm5ebH/WQohHBwchg4dmpeX98pQskaNGnv37nVwcFi1apW0/KJMJrO1tZUexX348GHROqUDabMXIYT0GHXHjh3NzMyUV3vlOoblJ20Os379+uTkZCsrKynXs7GxiYmJsba2Vj6qPGPGjLVr1zZu3Pijjz4q/x0vXryonIFYlMbtaYu3RNeuXUNDQxctWiRtNDRx4kTN3QVSWgbx/v376i4EAIDSkmYgsv4GAJSHhgWIPXr0WLdu3bT/MjExMTMzMzY2Tk9PT05OlpIUuVz+yy+/vNZcD1Q3AQEBkZGRQoiUlJQNGzaMHTu2ahazu3fv3sCBAwsLC0WpA0QhRJMmTYQQFy9eFEK0bt36pX327dtXyqtpa2svXbp04cKFRVcJrFevnhCi6AxEX19fIYSlpeXTp0+FENIzzkKIHj16lPJGFUXa50SafdmmTRup0d7ePiYmRrk/jBDi888///zzzyvkju3bt9++fXvTpk0r5GpA1XBxcTl06FCbNm2uXr06d+7cF79p0BRsxAwA0Dh16tQRPMIMAOWjYZuoCCEmTpwYFhY2Z86cVq1a5eTkREVF3bhxIzo6WldXt23btsuWLYuJiRk/fry6y0S57Nq1S/w3mVqyZImLi0tycnIV3Hfbtm1Semhvb1+3bt1SvsrJyUm537GqAPF1Fdtj5MUZiHfu3BFCnD592tbW1sTERLnZy4srMFa2Xr16zZgxY968eR9++OG0adOkRinxLBogVqBx48ZdunSp8hZ2BCqPtD6pRmMGIgBA47AGIgCUn4bNQJQ4OjquWLFixYoVCoUiMzMzOzu7Vq1aWlqaF4bipfz9/f/1r39paWnt3Lmze/fuKSkpqampR44cGTNmTCXdMS8vr7CwsEaNGjt37hRCrFq16rViOCMjo7p160rTA9u1a1cZFdra2mpra8fFxeXn58vl8uTk5NTUVGNj4+bNm4eEhOTk5JiYmMycOVNHR6eSCiiBoaGhciMXJSliKLo/cgXS0tIq/fxQoFpp166dg4ODXK6Rg6+EABEAoHEIEAGg/DQ7dJPJZIaGhubm5qSHbxIvL6/8/Pw5c+a0adNm//790sYj+/fvF0Kkp6e/++67Q4YMyc/Pr6jb5eXltW7d2tHRMTo6Oi4uztLSctasWaXZ7LsoaR3AZs2affjhhxVVWFE1atSwsrLKz8/v1q3b9evXpYcHpfl9ZmZm1tbWBgYGoaGh165dqybBxIQJE7y9vceNG6fuQoDqpWvXrvfu3XvpCp6awt7eXi6XP3r0KCcnR921AABQKgSIAFB+5G6oXjIzMy9fvqytrT1//nwhRM+ePTds2KCjo/PXX389e/ZswoQJ/v7+Bw4cmDt3boXcbteuXZ988snNmzdjYmKkVQVL2OC7BJ9//nmzZs327dunq6tbIYW9SHoo+J9//uncubOfn58QQtrgWMnGxsbKyqqS7v66bG1tv/322+pTD4CKIpfL7ezsCgsLo6Oj1V0LAAClIs04SU5OllYrAgCUAQEiqpfLly/n5ua2aNHCyMhIajE1NXV1dS0oKJg1a9aePXtq1qypra29YcOGlJSU8t9u5syZR48elY6PHz8u/vt03usaMWJESEiItGhjJZEmRTZu3DgjI2P58uXihQARAKoGTzEDADSLtra2qalpQUGBtOsmAKAMCBBRXcTFxW3ZsmXjxo1CiM6dOxc99d577wkhtm3bJoRYsWJF165dc3Jyfv/997Ld6Ny5cwsWLFi6dOkXX3yRkJCgo6PTokULIcRff/0lhGjUqFE530glWbNmze3btw8ePCiESE1NFZW2RQkAlEyaqR0ZGanuQgAAKC2eYgaAcqoWy6UBQoipU6ceOnRIOu7UqVPRU++9997SpUuFEO3bt58wYUKNGjXOnDlz4MCBL7744nXvkpaWNmTIkKdPnypbGjRoMGvWrM8++yw7O1uUdQZiFdDT05NWWuzRo8dLH2EGgKohfdFCgAgA0CC1a9e+e/duQkKCk5OTumsBAI3EDERUhYSEhMGDB/v6+u7fv79oeKdUUFDw559/CiGGDRs2duzYvn37Fj3boUMHuVwuk8l++uknLS2t/v37CyH8/f0jIyNPnjzp7e195syZUlby448/Pn36tHbt2srFCu3t7Ys+elxtA0Slbdu22djYyGQyKU8EgCpGgAgA0Dh16tQRzEAEgHJgBiIqXURExPTp00+cOHH06NGcnBwzM7MTJ064ubkV7RMcHPz8+XNHR8c9e/a8eAUjI6OtW7fm5uZ27NhRCGFpaWllZfXkyRMnJ6eCggIhxNKlS9evXz9x4sSQkJDPPvts+vTpo0aNevE6BQUFmzdvFkIcPnz4/PnzCxcuFELY29s3btxYLpdLOztX20eYlerWrXvhwoXo6Gi+PgWgFtLfkxEREeouBACA0pICxISEBHUXAgCaihmIqFyXL192cnI6ceKEECInJ0cIkZyc/P333xfrdvbsWSFEt27dVF3niy++GDt2rPJHFxcXIURBQUG9evWGDh2qUChmzpwZGRm5cOHCkJCQ0aNHK5+GLuqvv/6Ki4t755133nvvvaZNm0qN9vb2+vr6S5Ys0dHRsbGxsbS0LO97rnz169fv2rWruqsA8JaSAsSoqCj2sgQAaAppDUQCRAAoMwJEVK7ly5cXFha2b9++6HS5CxcuKBSKot1Onz4thOjevXspLysFiEIILy+vffv2ffbZZ1lZWcOHDz927JgQQqFQ7N+/v2j/xMTE/fv3r127VggxbNgwIUSTJk2kU9JKgvPmzYuKigoODpbJZGV5nwDw1qhZs6alpWV2dnZsbKy6awEAoFR4hBkAyokAEZUoIiLi6NGj+vr6R48elZ4pbteuXd26dRMTE2/duqXs9vz5c39//xo1anzwwQelvLIyQOzSpYsQYtGiRUKIS5cuKRSK999/Xwhx5coVZWeFQjFo0KChQ4ceO3bM2NjY09NTCOHg4FCjRg0hhL29vdTNxsbGysqqvO8ZAN4Cjo6OgmUQAQCag0eYAaCcCBBRiX777bfCwsLBgwdbWlqOGDGiW7duCxYsePfdd4UQ/v7+ym6nT5/Oy8vr3LmziYlJKa/cvHlzIYSFhYW0kUiDBg1atGghnVq2bFnNmjWjo6OlLxgLCgqWL1/+999/a2lpyeXy3bt329nZCSFq1KjRunVrXV1dtiIBgNfl4OAgWAYRAKA5CBABoJwIEFHxtm7d6uvrK4Q4ePCgEGLw4MFCCFtb2zNnznh4eHTq1EkIcfnyZanz9evXp0+fLoT48MMPS3+L1q1bT5s27YcfflA+cSxtzfzOO++4ubm1atVKCHHlypXs7Ozhw4cv+H/s3Xlc1NXi//EzLCKLyCKKIIoIooKGigulqSl2r2aQWdq9qeGOa9e03CJ/mgv3S3lzodzb7GZlaalgqDeVXHJhERCRARHZHRdEZJ/fH58bl0wRYYbPzPB6/jXL4TNvy4fDvOcsS5cKIbZv356TkzNq1Kjqixw8eDA5OVnaDwUAUHccxAwA0C/sgQgADcQpzNCwqKioqVOntmjR4s0334yPj7e1tR02bFjNAdJUwYsXL0p3169fn5OT8+yzz06ZMqXur2JsbPyvf/2r5iNvvPHGTz/99NZbbwkhfH19T5w4MX/+fKVSWVZW1rJlyx07dowePfqBi9jZ2dnZ2dXjzwgATRwzEAEA+oU9EAGggZiBCE1Sq9Vvv/22EOLu3bsrV640MjJasWJFs2bNao7x9vZWKBSJiYnS8Z2XLl0SQqxcudLa2rohL+3m5hYTE/P6668LIcaPH29tbX3p0qXKysouXbr8+OOPf24PAQD1RoEIANAvLGEGgAaiQIQmXblyJTY2tvruJ598Mnv27AfG2NjYuLi4FBcXS2vfkpOThRA1z2huuJ49e548eXLWrFlnzpy5dOnSs88+q8GLAwCkQ1RSU1PVarXcWQAAeDwLCwsLC4v79+/fu3dP7iwAoJdYwgxNOn/+vBBi2LBhN27c8Pb2njp16kOHde/e/dq1axcvXmzRosXt27dtbW3btGmj2SReXl4bN27U7DUBAJKWLVs6ODgUFBTk5ua2bdtW7jgAADyeg4NDRkZGQUGBpaWl3FkAQP8wAxGadO7cOSHEs88+GxMT88UXXzxqWPfu3YUQe/bskaYfcg4yAOgdaRXzlStX5A4CAECdsIoZABqCAhGaJM1A7N27d+3DXnvtNQsLi6+++mrRokWCAhEA9JC0ipkCEQCgL6QCMT8/X+4gAKCXKBChMRUVFTExMUIIX1/f2kf26NFj8+bNQogzZ84IIby8vBohHgBAgygQAQD6pXXr1oIZiABQX+yB+Afx8fG7d++urKysZQxvOY9y4MCBwsJCLy8v6b25dq+99lpISEh6erqtre348eMbIR4AQIMoEAEA+oUZiADQEBSIf7B06dL9+/fXZWRVVZW2w+idLVu2CCEmTZpUl8HGxsaLFy+ePn16WFhYXQpHAIBO6dy5s6BABAA0zJUrV/bu3Vv7Z6u8vDyNvBZ7IAJAQ1Ag/sGaNWsGDBhQ+5i8vLx169YZGbH6+w9UKtWhQ4fMzMwmTpxYxx+ZOnXq3/72Nw5BAwB95OHhoVAoUlNTq6qqeE8EANTP/PnzG20CB0uYAaAhKBD/wNvb29vbu/YxCQkJ69ata5w8eiQqKqqysnLIkCH29vZ1/ynaQwDQU1ZWVo6Ojjk5OZmZmR06dJA7DgBAL7377ruP3Q+9oKBgx44dCoWiga/FEmYAaAgKRGjGoUOHhBDPP/+83EEAAI2kc+fOOTk5V65coUAEANRP3759+/btW/uYhIQEjRSIzEAEgIagQIQGqNXqn3/+WQgxfPhwubMAQFP0ySefrFu3rqKiopYxZWVlQgi1Wq2pF+3cufOxY8cuX748bNgwTV0TAAAtYQYiADQEBSI0IDY2Njs729nZuXv37nJnAYCm6NSpUykpKXUZqdkCUQhRx9cFAEBezEAEgIagQIQGHDhwQAgxcuTIhq8sAADUw7Zt2957773ax6SkpPz1r3/V4IEnnp6eggIRAKAnLCwsLC0t7927V1RUZGVlJXccANAzFIjQgIMHDwohRowYIXcQAGiiTE1N3dzcah9TXFys2ReVZiBevnxZs5cFAEBLHBwc7t27l5+fT4EIAE9KY9MQ0JTFxsYKIYYMGSJ3EABA43FzczM1Nb127VpJSYncWQAAeDxpFTPbIAJAPVAgoqGKi4vv379vYWFhbW0tdxYAQOMxNTXt2LFjZWVlamqq3FkAAHg8CkQAqDcKRDTUzZs3hRB2dnZyBwEANLYuXboIIZKTk+UOAgDA41EgAkC9USCioSgQAaDJks5RYRtEAIBeoEAEgHqjQERDUSACQJNFgQgA0CMODg5CiIKCArmDAID+oUBEQ1EgAkCTxRJmAIAeYQYiANQbBSIaigIRAJosqUC8fPmyWq2WOwsAAI9BgQgA9UaBiIZSqVSCAhEAmiR7e3sHB4fCwsKsrCy5swAA8BgUiABQbxSIaJCSkpKMjAwhhI2NjdxZAAAyYBUzAEBftGnTRlAgAkC9UCCi/m7cuNGrV6+PP/5YMAMRAJqqrl27CiEuXbokdxAAAB7DwcHByMiooKCgqqpK7iwAoGcoEFF/b7zxRvUnRgpEAGiaKBABAPrCxMTE1ta2srJS2sYdAFB3FIiop7KyssOHD1ffpUAEgKaJAhEAoEekbRDz8vLkDgIAeoYCEfVx+vTphQsXlpaWGhn9968QBSIANE0UiAAAPcI5KgBQPxSIqI85c+asX79eCDFixAiFQiEoEAGgqXJxcbGyssrLy1OpVHJnAQDgMaRzVJiBCABPykTuAE/m3r17CoXCwsKi5oMnT57ctWtXfHy8jY1Njx49ZsyY4eLiIlfCJuLatWvSjeeff76iokKpVDo5OckbCQAgC4VC0aVLl3Pnzl26dGnAgAFyxwEAoDYUiABQP3o2A9HKyqp37941H1m6dOmAAQPCw8Ojo6P379+/evVqLy+vL7/8Uq6EBmnv3r09e/Y8cOCAdLesrKygoEC63b9//4iIiJSUFFNTU/kCAgDk1K1bNyFEUlKS3EEAAHgMljADQP3oWYH4gIiIiNWrV9vZ2W3atCkhISE5OXnr1q0WFhZTpkxhMyYN+vLLL2NjY0eNGvXee++p1eqcnBy1Wm1jY7Nu3boH+lwAQBMkFYi88wIAdB8zEAGgfvRsCfMD1q1bZ2RkdODAgX79+kmPeHp69urVq2/fvitXrvzqq6/kjWcwUlNThRBqtXrFihVCiOeff14I0blz5zfffFPmZAAAHcAMRACAvqBABID60e8ZiPHx8b169apuDyW9evXy9fW9cOGCXKkMj1KpFEJ8+umnxsbGK1as8Pf3F0I4OzvLnQsAoBOkAjExMVHuIAAAPIa0hJkCEQCelH4XiOXl5dIbwAOcnJxycnIaP49Bys3NLSoqsrOzmzhx4qZNm+zs7IqLiwUFIgDgdx07drSwsMjKyrp9+7bcWQAAqI00A5E9EAHgSel3gdinT5/4+Hi1Wl3zQbVanZiY2KNHD7lSGRhp/bK7u7sQYvr06f/85z+lxykQAQASIyOjrl27ClYxAwB0HkuYAaB+9K9ATE9PHzFixLx58zZu3NivX7/r16+vXLmy5oC1a9empKQMGDBAroQGRlq/3KlTJ+mutH5ZUCACAGrw9vYWrGIGAOg8CwsLKyurkpKSwsJCubMAgD7Rs0NUnn322dTU1IiIiIiIiOoHV65cGRISIoSoqKjo06dPbGxst27dli1bJl9Mg/JAgdi+fXsPD48rV660a9dO1lwAAB3i5eUlKBABwECVlpbGxMSYmpr27t1beuTQoUMff/xxXl5e9+7dx44dO3ToUHkTPpE2bdoUFRXl5eVZW1vLnQUA9IaeFYjHjh0TQhQXF6elpaWmpiqVytTU1KtXr0rPVlZWxsbGPv3005999pmlpaWcQQ1IWlqaqFEgCiHWrl176NCh/v37yxcKAKBbpAIxISFB7iAAAA3bvXv31KlT7969K4Tw9/f/8ccfd+zYMWvWLOnZ06dPb9u2bfHixatWrZI15hNwdHRUKpW5ubkeHh5yZwEAvaFnBaLEwsLC29tbWi1Vk6mpaUZGRvv27WVJZagemIEohBg9evTo0aPlSwQA0DksYQYAg3T27NnXXnvN3Nw8ICBApVJFRUVNnDhx3759Xl5e//d//9e1a9fY2NgFCxasXr3a399/8ODBcuetE7ZBBIB60MsC8VGMjIxoDzVOmoHo5uYmdxAAgO5ycXGxsbHJzc0tKChwcHCQOw4AQDNWrlxpZGR04sSJXr16CSEWL168du3aFi1aHD582NHRUQjh6urq7e3t5eUVFhamLwWilDw3N1fuIACgT/TvEBUhRGlp6enTp8+fP1/9yKFDhwIDA/38/KZNm3bkyBEZsxmYoqKi/Pz85s2bt23bVu4sAADdpVAounXrJljFDACGJSYm5umnn5baQyHE7NmzhRDPPfec1MFJ3N3d+/btq0f//jMDEQDqQf9mIBreHhy6TJp+2LFjRyMjveyaAUDXKJXKqKiopKQklUpVXFxsb2/v7Ozs7Ow8atQoff+qxtvb++TJkxcvXhwyZIjcWQAAmnHnzp2WLVtW35Vu13xEYmtrGx8f36jJGoACEQDqQc8KRIPcg0OXSRsgsn4ZABouPT195syZkZGRD3121qxZAQEBYWFhrq6ujZtLY7p37y6YgQgAhqVLly7nzp0rKSlp3ry5EOL48eNCiHPnzqnVaoVCIY0pLy+/cOGCNA9dL0gFIkuYAeCJ6FmBaJB7cOiyPx/BDACoB5VK5e/vr1Qqvby8AgICvL297e3tra2tCwsLb968mZycvH///j179sTFxUVHR0sfbPSOVCBevHhR7iAAAI35+9///uabb7788svz5s0rKCh455137OzskpKSVqxYERISolAoKisrFyxYkJWVNesG9B4AACAASURBVGPGDLnD1pX0yZEZiADwRPSsQPzzHhxr167V9z04dFlGRoYQQn+nwwCAjliyZIlSqVyzZs2iRYseOmD58uU7d+6cNm1aSEjI5s2bGzmeRkgHMSckJFRVVbHxBQAYhpkzZ/70008HDx48ePCgEMLKyurXX3/9xz/+sXz58s8//7xz586JiYmZmZkeHh4LFiyQO2xdMQMRAOpBz36/r/seHLdu3WrUZAYqMzNTCOHi4iJ3EADQb8ePH/f09HxUeygJCgoaNGhQdHR0o6XSLGk/x6KiImn2OgDAAJiamh46dGjbtm0TJkyYPHlydHR0jx49fvjhh5dffjk9PT0yMjI3N/fVV189ffq0tMZZL1TPQFSr1XJnAQC9oWczELW9B0daWtrhw4drH5OVlVWPK+ud2NjYf/zjHykpKUIIJycnueMAgH4rKCiQVvjWzsXFJS4urhHyaEmPHj2ysrLi4+Pd3d3lzgIA0AxjY+PJkydPnjy5+hFra+vvvvsuPz8/Pz+/c+fOzZo1kzFePZibm0u7iNy5c8fGxkbuOACgH/SsQNT2HhxvvvnmTz/9VJeRVVVV9bi+Hvnmm29++eUX6Xa7du1kzQIAes/Pzy8qKio1NbWWZi0/Pz8yMtLPz68xg2nWU089FRERER8fP3r0aLmzAAC0q3Xr1q1bt5Y7RT05OjoWFhbm5uZSIAJAHelZgajtPTgWL17crl27ysrKWsbcunXr22+/rZ7waKiqJ1oaGRkxAxEAGmju3LkRERH9+/cPCQkJCAjo0KFDzWdzcnIOHDiwcuXK/Pz8SZMmyRWy4ThHBQCaILVaXVZWJoQwMzN70p+tqqq6evVq7WOuX79ev2C1cHR0TElJycnJ6dKli8YvDgAGSc8KRGkPjk8//fT48eOmpqZz5syR9uCYNGnS999/n5aWZmpq+uqrr3788cf124PDz8/vsVM/EhISmkKBmJOTI91wdHQ0MdGzvycAoGv8/f03bNgw73ctW7a0s7OztrYuKiq6efOmtG+viYlJeHh4YGCg3GHrr0ePHkIIvV6FDQB4UvHx8T4+PkKIemwpOG3atO3bt9dlpGZXgEnbIHKOCgDUnf4VQ4a3B4duys7Olm44OzvLmwQADENwcPCwYcO2bdsWFRV16dKl9PR0IYSxsbGDg4Ovr+/o0aODgoKkzzP6q0uXLs2bN09LSyssLLS2tpY7DgBA13Xv3t3Nza32MWVlZdevX9fsBA4KRAB4UvpXID6KXu/BoYOqC0Q2QAQATfHw8AgNDQ0NDVWr1cXFxSUlJba2tkZGRnLn0hgTE5Nu3bpduHAhPj5+wIABcscBADQGT0/PhISE+v2sNDG/9jEJCQndu3fXRoGYl5enwWsCgGEznA8t1dRqdWlpaWlpqdxB9FhJScnt27el28xABACNUygUlpaW9vb2htQeSqRVbLGxsXIHAQA0kubNm3t5eXl5eckd5AlIBWL1rk0AgMcynBmI1RqyBwckOTk51f/1OnXqJG8YADAkSqUyKioqKSlJpVIVFxfb29s7Ozs7OzuPGjWqbdu2cqfTgKeeekqwDSIAQLexhBkAnpQBFohoOOm7uB49eixZsiQgIEDuOABgCNLT02fOnBkZGfnQZ2fNmhUQEBAWFubq6tq4uTRM+g6PAhEADIyBfQFGgQgAT8oAC8SG7MEBSVZWlhDC3d197NixcmcBAEOgUqn8/f2VSqWXl1dAQIC3t7e9vb21tXVhYeHNmzeTk5P379+/Z8+euLi46OjoNm3ayJ23/nx8fBQKRUJCQkVFhYmJAf6aAQBNjUF+ASaVnhSIAFB3BvibvbQHh9wp9FJubu706dPnzJmTkZEhfv9eDgDQcEuWLFEqlWvWrFm0aNFDByxfvnznzp3Tpk0LCQnZvHlzI8fTIGtrazc3N6VSmZyc7O3tLXccAECDGOoXYA4ODsbGxjdu3ODrLgCoI/6txP989tlnP/74Y1VVVUpKihBi4MCBcicCAANx/PhxT0/PR7WHkqCgoF27dkVHRzdaKi3p2bOnUqmMiYmhQAQAfWeoX4AZGxs7ODjk5ubm5eVxaCQA1IW+FogGtgeHjjhx4oQQIiIiorKyslOnTmPGjJE7EQAYiIKCgu7duz92mIuLiwHsHtizZ8/vvvsuJiZm/PjxcmcBADSIAX8B1rZt29zc3JycHApEAKgL/SsQDXIPDl1QWVn566+/SjeEENOnT2cyPwBoip+fX1RUVGpqqru7+6PG5OfnR0ZG+vn5NWYwbejZs6cQIiYmRu4gAICGMuAvwNq2bRsTEyOdHgkAeCw9a4gMdQ8OXRAfH3/79u3qu88995yMYQDAwMydOzciIqJ///4hISEBAQEdOnSo+WxOTs6BAwdWrlyZn58/adIkuUJqSnWBqFarFQqF3HEAAPVnwF+ASQvXKBABoI70rEA01D04dMG5c+eqb9vY2Pj4+MgYBgAMjL+//4YNG+b9rmXLlnZ2dtbW1kVFRTdv3rx165YQwsTEJDw8PDAwUO6wDeXo6Ojk5JSdna1UKmv5wAkA0H0G/AUYBSIAPBEjuQM8mTruwTFo0CC924NDdklJSUIIadrmwIEDjY2N5U4EAAYlODg4MTHx7bff7tmzZ2lpaXp6elxc3NWrV83MzHx9fVevXp2ZmTl9+nS5Y2pG7969hRDnz5+XOwgAoEGkL8AKCwvnzZvn6upqY2Pj5ubm4+Pj7u5uZ2fn5OQ0derU7OxsffwCjAIRAJ6Ins1ANOA9OGQnFYhLly79+eef58+fL3ccADBAHh4eoaGhoaGharW6uLi4pKTE1tbWyEgDX+b9+9//3rFjR+1jioqKhBBqtbrhL/dYvXr1+umnny5cuDB27NhGeDkAgPYEBwcPGzZs27ZtUVFRly5dSk9PF7+fYuzr6zt69OigoCBHR0e5Yz4xCkQAeCJ6ViAa8B4cspMKxL/+9a9z5syROwsAGDiFQmFpaWlpaampC+7bt+/w4cN1Gdk4BSIzEAHAkGjvCzAZUSACwBPRswLRgPfgkFdhYWFWVlbz5s07duwodxYAMGRKpTIqKiopKUmlUhUXF9vb2zs7Ozs7O48aNUr6JFM/27Ztmz59emVlZS1jrl69OnXq1Mb5vCcViBcuXOAcFQAwJBr/AkxGFIgA8ET0rEBsUpvQN6ZLly6p1WpPT0+2PgQALUlPT585c2ZkZORDn501a1ZAQEBYWJirq2s9Lm5lZTVkyJDaxyQkJNTjyvXj5OTEOSoAAF3m6OioUCjy8vKqqqr0fTYlADQCPSsQheHuwSGvlJQUIUSXLl3kDgIAhkmlUvn7+yuVSi8vr4CAAG9vb3t7e2tr68LCwps3byYnJ+/fv3/Pnj1xcXHR0dHSeVb6ztfX98cffzx37hwFIgBAB5mZmdnZ2alUqhs3brRu3VruOACg6/SvQBQGugeHvJRKpRCiU6dOcgcBAMO0ZMkSpVK5Zs2aRYsWPXTA8uXLd+7cOW3atJCQkM2bNzdyPG3o3bu3VCCOGzdO7iwAADxE27ZtVSpVdnY2BSIAPJZ+l27SHhz29va0hw2UlpYmKBABQGuOHz/u6en5qPZQEhQUNGjQoOjo6EZLpVW+vr5CiHPnzskdBACAh3NychJsgwgAdUPvBiGYgQgAWlZQUFCXM1JcXFzy8/MbIU8j6Nevn0KhOH/+fO1HuwAAIBepQMzOzpY7CADoAQpECEGBCABa5ufnd+rUqdTU1FrG5OfnR0ZG+vn5NVoqrbK3t+/YsWNRUdGlS5fkzgIAwENI3+1RIAJAXVAgQhQVFeXn55uZmUlfwQEANG7u3LkVFRX9+/dfv359RkbGA8/m5ORs27atT58++fn5kyZNkiWhNvTt21cI8dtvv8kdBACAh5AKRJYwA0BdUCBCpKSkqNXqjh07spUkAGiJv7//hg0bCgsL582b5+rqamNj4+bm5uPj4+7ubmdn5+TkNHXq1Ozs7PDw8MDAQLnDakyfPn2EEGfPnpU7CAAAD8EeiABQd3p5CjM06M6dO3/729+EEE899ZTcWQDAkAUHBw8bNmzbtm1RUVGXLl1KT08XQhgbGzs4OPj6+o4ePTooKMjR0VHumJokzUA8c+aM3EEAAHgI9kAEgLqjQGzqjh8/fvnyZQ8Pj48++kjuLABg4Dw8PEJDQ0NDQ9VqdXFxcUlJia2trQHP/u7du7epqWl8fPy9e/csLS3ljgMAwB9QIAJA3RnshxbU0eXLl4UQI0aMaNOmjdxZAKCpUCgUlpaW9vb2BtweCiHMzc27d+9eWVl5/vx5ubMAAPAgR0dHhUKRl5dXVVUldxYA0HWG/LkFdZGSkiKE6Ny5s9xBAMDwZWRkREZGVt/NzMxcuHDh8OHDhw4d+tZbbyUmJsqYTUv69+8vWMUMANBJZmZm9vb25eXl+fn5cmcBAF1HgdjUSTMQKRABQNtWrVrl5ua2adMm6e5PP/3k5eUVFhYWFRV19OjRDz/8sFevXoa3m0S/fv0EBSIAQFc5OzsLVjEDQB1QIDZ10gxET09PuYMAgCHbvXv3smXLLC0tX3rpJSFEXl5eUFBQeXn5ihUr4uLirly5smPHDnt7+/nz5588eVLusJokzUA0sD8UAMBgSNsgZmVlyR0EAHQdh6g0aYWFhbm5uebm5tI3bwAALfnwww+trKwSEhLat28vhNi7d69Kpdq4ceOsWbOkAe7u7n369Ondu/fatWt//PFHWcNqkoeHR6tWrXJycjIyMjp06CB3HAAA/oAZiABQR8xAbNKqN0A07F38AUB2SUlJAwcOlNpDIcTFixeFEK+88krNMd7e3v3794+JiZEhn9YoFAppFfOpU6fkzgIAwIM4iBkA6ojaqOkKDw/fsWOHEMLDw0PuLABg4CwtLXNzc6vvOjo6PnSYqalpRUVFY4VqJH5+foICEQCgk1jCDAB1RIHYRF2+fHnWrFkff/yxoEAEAO3z9/ePi4s7duyYdHfAgAFCiH379tUck5GRcfr0aWm+niF55plnBAUiAEAnsYQZAOqIArGJyszMrL7NEcwAoG2rV692cnIaOXLkypUrr127Nnjw4Dlz5ixYsGDXrl2lpaWVlZUnTpwYMWLE/fv3p0yZIndYDevbt6+pqWlMTExRUZHcWQAA+ANmIAJAHVEgNlE1V9IxAxEAtM3FxSUyMtLW1jYkJKRDhw4tWrQ4ePBgYWHh66+/bmVlZWVl9eyzz6akpISHh7/wwgtyh9UwCwuLp556qqKi4ty5c3JnAQDgD5iBCAB1RIHYROXk5FTfpkAEgEbg5eWVlpb2+eefP/PMMwqFQqlUSo9XVVU5Ozu/88476enp06dPlzeklkirmH/99Ve5gwAA8AcODg7NmjVTqVQlJSVyZwEAnWYidwDdkpube+LEidrH1Fz8q7+qZyBaW1u3bt1a3jAA0ESYmpqOHz9+/PjxQoji4uLi4mITExMrKysTEwN/O37mmWc++uij6OhouYMAAPAHRkZGbdu2zcjIyM7OdnNzkzsOAOguA//E8qSCg4P37t1bl5FVVVXaDqNVeXl50o3u3bvLmwQAmiYLCwsLCwu5UzSSgQMHCiFOnjxZWVlpbGwsdxwAAP7H2dk5IyPj+vXrFIgAUAsKxD8IDg42MzOrvRy8c+fOzz//rFAoGi2VZpWXlz/zzDPx8fFCiPfff1+aCAMAgPY4Ojq6ubmlpaVdvHjRx8dH7jgAAPyPtA0i56gAQO0oEP9g+PDhw4cPr31MQkJC9+7d9bdAVCqVZ8+elW4HBAS0b99e3jwAgKZg4MCBaWlpJ06coEAEAOgUCkQAqAsKxCYnIyOj+nbbtm1lTAIAaDoGDhz42WefHT9+fM6cOXJnAQDoih07doSHh9c+5v79+0IItVqtpQwUiABQFxSITc61a9ekG82aNbOzs5M3DACgiXj22WeFEMeOHVOr1fo7ix8AoFlHjx49f/58XUZqr0Bs166dEOL69etauj4AGAYKxCan+q3R2dmZj3AAgMbh4eHh5OSUnZ19+fLlLl26yB0HAKATtm/f/o9//KP2MUqlcuzYsUZGRlrKwAxEAKgLCsSmZdWqVV9//bUQIjAwcMaMGXLHAQA0IQMHDty9e/exY8coEAEAEjMzs969ez92jFYzUCACQF1o62sc6KDY2Nhly5alpKQIIWbOnPn888/LnQgA0IQMHjxYCPHLL7/InAMAgBratWunUChycnIqKyvlzgIAuosCsQnZsmVL9e0OHTrImAQA0ARJBaK0DaLcWQAA+K9mzZq1bt26vLw8Pz9f7iwAoLsoEJuKpKSkL774QrqtUCikrYIBAGg0Xbp0cXJyysnJuXz5stxZAAD4H85RAYDHokBsKsaMGVNUVPTiiy+am5u7urpaWFjInQgA0OQMGjRIsIoZAKBjKBAB4LEoEJuErKysS5cu2drafv311xcuXPjPf/4jdyIAQFMkrWI+evSo3EEAAPgfCkQAeCxOYW4SYmJihBA9e/Y0Nzfn7EsAgFyee+45IcTRo0erqqqMjPgWEwCgEygQAeCx+N29SYiNjRVC+Pj4yB0EANCkubu7d+jQQaVSXbx4Ue4sAAD8FwUiADwWBWKTEBcXJygQAQA6QJqEeOTIEbmDAADwXxSIAPBYFIgGbs6cOX5+ftHR0UKIp556Su44AICmbujQoYICEQCgSygQAeCx2APRwO3atevWrVtCiI4dO3br1k3uOACApm7YsGEKheLYsWOlpaVmZmZyxwEAQLi4uCgUiuvXr7NFLwA8Cv84GrL79+/funXL1NT0zTffPHv2rIkJfTEAQGZt2rTx9va+d+/emTNn5M4CAIAQQpiZmbVq1aqsrCw/P1/uLACgoygQDVlWVpYQol27duvWrbO3t5c7DgAAQgjh7+8vhPj555/lDgIAwH+1b99eCJGZmSl3EADQURSIhiw7O1sI4eTkJHcQAAD+RyoQDx06JHcQAAD+y8XFRVAgAsCjUSAaMgpEAIAOGjRokLm5+YULF1gpBgDQEVKBeO3aNbmDAICOokA0WGfOnPn666+FEG3btpU7CwAA/2Nubj5w4MCqqirOYgYA6AhmIAJA7fSsQLx3715xcfEDD548eXLWrFkDBw4cNWrU0qVL+UdfEhwcvG/fPsEMRACA7nn++eeFEJGRkXIHAQBACApEAHgcPSsQraysevfuXfORpUuXDhgwIDw8PDo6ev/+/atXr/by8vryyy/lSqg7cnNzpRvOzs7yJgEA4AF//etfhRARERFVVVVyZwEAgAIRAB5DzwrEB0RERKxevdrOzm7Tpk0JCQnJyclbt261sLCYMmXKpUuX5E4ns5s3b0o3mIEIANA1Xbt2dXNzKygoiImJkTsLAAAUiADwGCZyB2iQdevWGRkZHThwoF+/ftIjnp6evXr16tu378qVK7/66it540nUanVCQkJZWVn1I61aterQoYNWX/TevXulpaXSbem9EAAAnTJ8+PBPPvnk4MGDD6wtAACg8Tk7O5uYmOTk5JSVlTVr1kzuOACgc/R7BmJ8fHyvXr2q20NJr169fH19L1y4IFeqB3zwwQc9evTwraFTp06JiYlafVFp+qGNjU14eLiHh4dWXwsAgHoYMWKEEOLAgQNyBwEAQBgbGzs5OVVVVWVlZcmdBQB0kX4XiOXl5a1bt/7z405OTjk5OY2f56F8fX379u3b+3ctW7asrKzMyMjQ6otKBWKHDh2Cg4O1+kIAANTPsGHDzM3Nz549m5eXJ3cWAABE+/bthRDXrl2TOwgA6CL9XsLcp0+f+Ph4tVqtUCiqH1Sr1YmJiT169JAxWE2DBw8+c+ZM9d3AwMB9+/aVl5dr9UVv3LghhLC3t9fqqwAAdMT+/fs///zz2sfcuXNHCKFWqxsl0eOZm5sPHjw4IiIiMjJy4sSJcscBADR1FIgAUAv9KxDT09NHjBjh4eHh4eHRr1+/Q4cOrVy5MiQkpHrA2rVrU1JSRo8eLWPIWkgbatTcElEbpBmIdnZ2Wn0VAICO2Llz5/fff1+XkbpTIAohXnjhhYiIiP3791MgAgBkR4EIALXQswLx2WefTU1NjYiIiIiIqH6wukCsqKjo06dPbGxst27dli1bJl/M2jRmgcgMRABoIjZv3vzaa6/VXg5mZma+9dZbRkY6tHvJqFGjZs+eHRkZWVpaamZmJnccAECTxkHMAFALPSsQjx07JoQoLi5OS0tLTU1VKpWpqalXr16Vnq2srIyNjX366ac/++wzS0tLOYM+mqmpqdB+gahSqQQzEAGgyWjVqtWYMWNqH5OQkNA4YerOxcXlqaeeio2N/eWXX55//nm54wAAmjQKRACohZ4ViBILCwtvb29vb+8HHjc1Nc3IyJBmnussaQaitvdAZAkzAEAvvPjii7Gxsfv27aNABADIS/ogqe3jLgFAT+nQOqaGMzIy0vH2ULAHIgAANQQGBgoh9u3bp1ObMwIAasrIyIiMjKy+m5mZuXDhwuHDhw8dOvStt95KTEyUMZsGdejQQVAgAsAj6FmBaG5u7ujo+M0338gdpP7YAxEAgGo+Pj4dOnTIzs4+e/as3FkAAA+xatUqNze3TZs2SXd/+uknLy+vsLCwqKioo0ePfvjhh7169froo4/kDakRNjY2LVu2LCoqunXrltxZAEDn6FmBWFJSkpeXN3bs2HHjxknb/OmdxiwQbW1ttfoqAAA0kEKhCAgIEELU8RRpAEBj2r1797JlyywtLV966SUhRF5eXlBQUHl5+YoVK+Li4q5cubJjxw57e/v58+efPHlS7rAawCpmAHgUPSsQhRCenp7bt2+PjIz08vLavn17ZWWl3ImeTOMcoiJt/evk5KTVVwEAoOFGjx4thNizZ4/cQQAAD/rwww+trKwSEhImTZokhNi7d69KpQoLC3v33Xd79Ojh7u4eFBT0888/m5iYrF27Vu6wGsAqZgB4FP0rEBUKxaRJk5KSkvr27TtlyhQvLy/9+sghFYhaPUSltLQ0MzPTxMREev8DAECXDRgwoHXr1qmpqfHx8XJnAQD8QVJS0sCBA6s3mr948aIQ4pVXXqk5xtvbu3///jExMTLk0zQKRAB4FP0rECVOTk4//vjjt99+W1VVNWbMGE9Pz7Vr12ZlZcmd6/Ea4RTmtLS0yspKV1dXqawEAECXGRsbS0ep6Nc3ggDQFFhaWubm5lbfdXR0fOgwU1PTioqKxgqlRSxhBoBH0dcCUTJmzJikpKTw8PDCwsLFixe3b99+2LBhq1evPnHiRElJidzpHq4R9kBMTU0VQnh4eGjvJQAA0CBpMsvu3bvlDgIA+AN/f/+4uLhjx45JdwcMGCCE2LdvX80xGRkZp0+f7tevnwz5NI0ZiADwKCZyB2goExOT4ODgyZMn//DDD1u3bj169OiRI0eEEM2aNSstLX3Sq925c+exp0BevXq1flEljVYguru7a+8lAADQoMGDB7dq1ery5cuJiYleXl5yxwEA/Nfq1at/+eWXkSNHvvPOOxMnThw8ePCcOXMWLFhgYWExZswYExOTkydPzpgx4/79+1OmTJE7rAZQIALAo+h9gShp1qzZ2LFjx44de/Xq1YMHD0ZFRR09erQe15kyZcp3331Xl5FVVVX1uL5olAJRqVQKITp16qS9lwAAQINMTExGjx69ZcuW3bt3r1ixQu44AID/cnFxiYyM/Mtf/hISEhISEmJlZdWmTZvCwsLXX3/9jTfeMDExKSkpMTExCQ8Pf+GFF+QOqwEUiADwKAZSIFZzdXWdOXPmzJkz67cHx7hx427fvl37mKKiotOnTysUinoFbLwCkRmIAAA9Mnbs2C1btnz11VcUiACgU7y8vNLS0r7++uvNmzfHx8dLnzWEEFVVVc7OzmPGjJk9e3a7du3kDakpjo6OzZs3LygoKCoqsrKykjsOAOgQQysQq5mY1OeP9vLLL7/88su1j0lISOjevXu9C8RGOIU5OztbCGEw7+IAgKZg0KBBjo6OSqXywoULvXr1kjsOAOB/TE1Nx48fP378eCFEcXFxcXGxiYmJlZVV/T5z6TKFQtGhQ4fLly9nZGSwpQYA1KRnh6iUlJTExcXJnaJBGmEGYkFBgRDCwcFBey8BAIBmGRsbv/rqq0KIf//733JnAQA8koWFRatWrWxsbAyvPZSwihkAHkrP/tE3MzOTO0JDNUKBqFKphBD29vbaewkAADTutddeW79+/VdffRUaGmpkpGffcQJAk6VWq6VPN/X4sJaQkHDgwIHax+Tl5dUzWb24urqKBp+cCQCGR88KxLpoyBtYI5CWMGuvQLxz505ZWZm1tbVu/vEBAHiUfv36derUSalUHjt2bMiQIXLHAQDUSXx8vI+PjxBCrVY/6c8uXrx4//79dRlZ70MsnxQzEAHgoQywQGzIG1gj0PYMRNYvAwD0lEKheO21195///0vvviCAhEAmoJVq1b16NGjsrKyljEFBQU7duxotJnpzEAEgIcywAJRx0kFovYOUblx44YQolWrVlq6PgAA2jN+/Pj3339/z549mzZtMjc3lzsOAODxPD09ExIS6vezPXr06NGjR+1jEhISduzYUb/r10PHjh2FEOnp6Y32igCgFwxwgyHpDaze72HaxgxEAAAepXPnzv369SssLPzhhx/kzgIAqJPmzZt7eXkZzJnF0gxECkQAeIABFog6/gam7QKRGYgAAL02ceJEIcRnn30mdxAAQFPk6OhoYWFx48aNu3fvyp0FAHQIS5gbm7YPUZFmIFIgAgD01Lhx4+bPn3/48OHMzEwXFxe54wAAhBBCqVRGRUUlJSWpVKri4mJ7e3tnZ2dnZ+dRo0a1bdtW7nSapFAo2rdvn5ycnJGR4e3tLXccANAV+log6u8bGHsgAgBQC1tb28DAwK+//vrTTz9999135Y4DAE1denr6zJkzIyMjH/rsrFmzAgICBk6REQAAIABJREFUwsLCpJW/hqFjx47Jycnp6ekUiABQTf8KRH1/A2ucJczsgQgA0F+TJ0/++uuvd+7cuXTp0kY7dhMA8Gcqlcrf31+pVHp5eQUEBHh7e9vb21tbWxcWFt68eTM5OXn//v179uyJi4uLjo5u06aN3Hk1g3NUAODP9KxANIA3MKlALC0t1dL1c3NzBTMQAQD67LnnnnN1dU1PTz9y5Ii/v7/ccQCg6VqyZIlSqVyzZs2iRYseOmD58uU7d+6cNm1aSEjI5s2bGzmellAgAsCf6VmBaABvYFpdwpyXl3f06FFjY2MfHx9tXB8AgEZgZGQ0adKkkJCQrVu3UiACgIyOHz/u6en5qA9fkqCgoF27dkVHRzdaKm2jQASAP9OzZUF1fAMbNGiQzr6BaXUJ8+bNm0tLS1988cX27dtr4/oAADSOyZMnm5iY7Nu3Ly8vT+4sANB0FRQU1GWLeRcXl/z8/EbI0zik7bAoEAGgJj0rEA3gDUyrBaJUm77xxhvauDgAAI3GycnphRdeKCsr2759u9xZAKDp8vPzO3XqVGpqai1j8vPzIyMj/fz8Gi2VtnXq1EkIkZaWJncQANAhelYgGsAbmKmpqdDaEmZpA8QOHTpo4+IAADSm4OBgIcSWLVsqKyvlzgIATdTcuXMrKir69++/fv36jIyMB57NycnZtm1bnz598vPzJ02aJEtCbbCxsbG1tS0qKtLZWSkA0Pj0bA/EuXPnRkRE9O/fPyQkJCAg4IGmLCcn58CBAytXrtTlNzBjY2NjY+PKysrKykpjY2PNXlx6h2vdurVmLwsA0BSlUhkVFZWUlKRSqYqLi+3t7Z2dnZ2dnUeNGlWXKfZNir+/v7u7e2pq6v79+wMCAuSOAwBNkb+//4YNG+b9rmXLlnZ2dtbW1kVFRTdv3rx165YQwsTEJDw8PDAwUO6wmuTm5nb+/Pm0tDQ+WwGARM8KRMN4AzM1Na2srCwrKzM3N9fgZauqqlQqlUKh4AhmANBB6enpM2fOjIyMfOizs2bNCggICAsLk/ZdghBCoVDMnDlz/vz5GzdupEAEALkEBwcPGzZs27ZtUVFRly5dknYGNDY2dnBw8PX1HT16dFBQkKOjo9wxNaxjx47nz59PT0/v37+/3FkAQCfoWYEoDOINrFmzZiUlJeXl5ZotEFUqVUVFhb29vbRKGgCgO1Qqlb+/v1Kp9PLyCggI8Pb2tre3t7a2LiwsvHnzZnJy8v79+/fs2RMXFxcdHd2mTRu58+qKoKCgkJCQI0eOJCYmenl5yR0HAJooDw+P0NDQ0NBQtVpdXFxcUlJia2trZKRn22E9ETc3N8E2iABQg/4ViEL/38C0dI4K65cBQGctWbJEqVSuWbNm0aJFDx2wfPnynTt3Tps2LSQkZPPmzY0cT2fZ2NhMmDAhPDx8/fr1/GcBANkpFApLS0tLS0u5g2hdx44dBQcxA0ANelO6PZT0BmZvb69H7aGgQASApuf48eOenp6Pag8lQUFBgwYNio6ObrRUemHu3LkKheLLL79UqVRyZwEANBXSQcxKpVLuIACgK/SpdzMYWi0QWfgGADqooKCgLmekuLi4cODjAzw9PUeOHFlcXPzxxx/LnQUA0FRIS5gpEAGgGgWiDDRYIN67d++nn36SLlVQUCCEcHBwaPhlAQCa5efnd+rUqdTU1FrG5OfnR0ZG+vn5NVoqffHWW28JITZu3FhSUiJ3FgBAk9ChQwdTU9OsrCzeegBAQoEoA+mQE40UiKGhoS+++OL27dsFS5gBQIfNnTu3oqKif//+69evz8jIeODZnJycbdu29enTJz8/f9KkSbIk1GWDBw/29fXNy8v7/PPP5c4CAGgSTExMXFxcqqqqrl69KncWANAJFIgykGYglpeXN/xSiYmJQghpSkteXp6gQAQAneTv779hw4bCwsJ58+a5urra2Ni4ubn5+Pi4u7vb2dk5OTlNnTo1Ozs7PDw8MDBQ7rC6aOHChUKIsLCwyspKubMAAJoEtkEEgJooEGUgFYjDhw9PS0tr4KWkK2RnZ4vfzwhzdnZucEAAgOYFBwcnJia+/fbbPXv2LC0tTU9Pj4uLu3r1qpmZma+v7+rVqzMzM6dPny53TB318ssvu7u7X7ly5dtvv5U7CwCgSZAKxIZ/ZAMAw0CBKANzc3MhxI0bN2bOnNnAS0lfiGVlZd27d+/MmTNCiN69ezc8IQBAGzw8PEJDQy9cuFBcXFxUVHTjxo2ysrKcnJyzZ88uXrzY0dFR7oC6y9jY+J133hFCrFmzRq1Wyx0HAGD4mIEIADVRIMpg2bJlCxYssLW1PXTo0OXLl+t9nfz8/Lt37wohTp061bJly8LCwo4dOzo5OWkuKQBAKxQKhaWlpb29vZERb8R1NWHCBBcXl/j4+L1798qdBQBg+KQCsfYD0ACg6TCRO0BTNHTo0KFDh546derXX3/Nz8/39PSs33Wqp9NXVFRIN55++mnNRAQAaIdSqYyKikpKSlKpVMXFxfb29s7Ozs7OzqNGjWrbtq3c6XRas2bN3nnnndmzZ69YsSIwMFChUMidCABgyJiBCAA1USDKxtraWghRWFhY7yv8+c3smWeeaVAmAIDWpKenz5w5MzIy8qHPzpo1KyAgICwszNXVtXFz6ZMpU6asXbs2Njb2hx9+GD16tNxxAACGzN3dXaFQpKenV1ZWGhsbyx0HAGRGgSibhheINTf0tbOzCwoK+vvf/66BZAAATVOpVP7+/kql0svLKyAgwNvb297e3traurCw8ObNm8nJyfv379+zZ09cXFx0dHSbNm3kzqujzMzMlixZMnPmzOXLlwcGBrIAHACgPRYWFm3bts3Ozs7MzOTrPQCgQJRNwwvEvLy86tt+fn5hYWEaiAUA0IIlS5Yolco1a9YsWrTooQOWL1++c+fOadOmhYSEbN68uZHj6ZHJkyeHhoZevHjx3//+N1+bAQC0yt3dPTs7OzU1lQIRAPjqXjYtW7YUDSsQVSqVEMLCwkII0bNnT00FAwBo3PHjxz09PR/VHkqCgoIGDRoUHR3daKn0UbNmzd577z0hxHvvvVdWViZ3HACAIXN3dxecowIAQggKRBk1fAaiVCBOmzatR48er776qsaSAQA0raCgoC5npLi4uOTn5zdCHr02YcKEbt26KZXKLVu2yJ0FAGDIOEcFAKpRIMqmRYsWomEFYkFBgRBiwoQJcXFx3bt311gyAICm+fn5nTp1qvYpDPn5+ZGRkX5+fo2WSk8ZGxuvXr1aCLFixYo7d+7IHQcAYLA8PDwEMxABQAhBgSijhs9AvHHjhhDC3t5eY5kAANoxd+7cioqK/v37r1+/PiMj44Fnc3Jytm3b1qdPn/z8/EmTJsmSUL8EBAQMHDiwoKBgzZo1cmcBABgsqUC8cuWK3EEAQH4UiLLR1B6IrVq10lgmAIB2+Pv7b9iwobCwcN68ea6urjY2Nm5ubj4+Pu7u7nZ2dk5OTlOnTs3Ozg4PDw8MDJQ7rH744IMPjIyMPvroo7S0NLmzAAAMk4eHh0KhUCqVVVVVcmcBAJlRIMpGmoFY77VXxcXF9+/fNzc3lw5RAQDouODg4MTExLfffrtnz56lpaXp6elxcXFXr141MzPz9fVdvXp1Zmbm9OnT5Y6pN/r06TN+/PiSkpKFCxfKnQUAYJgsLS0dHR1LSkquX78udxYAkJmJ3AF0S2lpaUJCQu1jNLWHbgOXMEvrl5l+CAB6xMPDIzQ0NDQ0VK1WFxcXl5SU2NraGhlp4Mu848eP79q1q/Yxt27dEkKo1eqGv5yOWL169ffff//9999HRUX5+/vLHQcAYIA8PDxycnKuXLnSvn17ubMAgJwoEP9g0qRJX331VV1GNnwS+xMtYc7JyQkICJg1a9bEiROlR6T1y2yACAD6SKFQWFpaWlpaauqCH3zwwY8//liXkYZUIDo5Ob377rtvv/32nDlz4uLizMzM5E4EADA0Hh4ex48fv3LlytChQ+XOAgByokD8g2HDhl2+fLn2Mffv309KSlIoFA18rSdawnzy5MmzZ89++umnFIgAoNeUSmVUVFRSUpJKpSouLra3t3d2dnZ2dh41alTbtm3rfdkNGzaMHDmy9jFZWVkrVqzQyIRH3TFv3rydO3deunQpLCxs6dKlcscBABiazp07C85RAQAKxAcEBQUFBQXVPiYhIaF79+6aKhDv3r1bl8E3b94UQqSnp1c/UlBQIIRwcHBoYAwAQONIT0+fOXNmZGTkQ5+dNWtWQEBAWFiYq6trPS7evn37adOm1T4mISFhxYoV9bi4LmvWrNmmTZuGDh36/vvvjx071t3dXe5EAACDIh3EnJKSIncQAJAZBaJsLC0tjY2N7927V1FRYWLymP8R0sZV169fLy8vNzU1Fb/vgcgMRADQCyqVyt/fX6lUenl5BQQEeHt729vbW1tbFxYW3rx5Mzk5ef/+/Xv27ImLi4uOjm7Tpo3cefXJkCFDJkyY8Nlnn02bNu3IkSMN/4YPAIBq0gxECkQAoECUjUKhsLa2vnXrVmFhoZ2dXe2DpZXOlZWVmZmZbm5u4vc5iY/9QQCALliyZIlSqVyzZs2iRYseOmD58uU7d+6cNm1aSEjI5s2bGzmevgsLC4uIiPjPf/6zdevWx87EBACg7tzd3Y2NjdPT06tncgDaU1lZWVlZ2axZM7kCVFVVbd26tU+fPr169ZIrA3QWBaKcpALxzp07j+0Bb9++Ld1IT0+XCkSpUrSxsdF2SABAwx0/ftzT0/NR7aEkKCho165d0dHRjZbKYLRq1Wr9+vXjxo1buHDhX/7yFw7KBAB9UV5enpiYWFlZWcsYpVLZaHn+zMzMrH379unp6WlpaZ6enjImQVMwYsSIxMTElJQUCwsLWQIcOXJkxowZJiYmo0ePfvfdd729vWWJAd1EgSgnOzu7jIyMmzdvduzYsfaR1QXi1atXaz4iHeUMANBxBQUF3bt3f+wwFxeXuLi4RshjeMaOHfvtt9/u2bNn0qRJUVFRLGQGAL0wefLkL774oi4jq6qqtB3mUTw9PdPT01NSUigQoVU3btyIiopSq9WvvPKKi4vLJ5988ucxt2/fnjx58ksvvfT666/fuXNn4cKFsbGxzs7Oq1at6tatW8MzJCUlCSEqKiq++eabqqqqb7/9tuHXhMGgQJSTtMtVfn7+Y0dKC5ZFjQJR2hXR1tZWW+EAAJrj5+cXFRWVmppayykf+fn5kZGRfn5+jRnMkISHh584ceLIkSPr16+fN2+e3HEAAI83YMAAqbCoxf3795OSkmT8Zqhz586RkZFsgwgtSU5OTkhIGDNmTHR0tFqtFkIcPHhQCLFixYrWrVs/MPjEiRPff/99VFSUv7//559/vnXrViHE2bNnXVxc1q9f3/Awly9fFkK88MIL+/fvv379esMvCENiJHeAJk365yAvL++xI6UFy0KItLQ06UZhYaFgBiIA6Im5c+dWVFT0799//fr1GRkZDzybk5Ozbdu2Pn365OfnT5o0SZaEBqB169ZbtmwRQixatCghIUHuOACAx5s2bdq5x9m9e7cQQt4CUfxerAAaN3369FdeeeXs2bMnTpyo+fjPP/+cmZn5wGDp18i7d++uWbPm0KFDQogXX3xRCHHt2jWNhElOThZCjBgxQgiRnZ2tkWvCYFAgykmagViXAlGabyhqFIjSI+yBCAB6wd/ff8OGDYWFhfPmzXN1dbWxsXFzc/Px8XF3d7ezs3Nycpo6dWp2dnZ4eHhgYKDcYfVYQEDA1KlTS0pKxo0bV1xcLHccAIAhkFYuUyBCS6Se7tdff923b1/Nx8ePH9+3b19pTmK16qJw79690dHRxsbGs2bNEporEKW/50OGDBFC5ObmPvDqaOIoEOUkzUCsyxLm6gLxypUr0g1pD0QKRADQF8HBwYmJiW+//XbPnj1LS0vT09Pj4uKuXr1qZmbm6+u7evXqzMzM6dOnyx1T761bt65r166JiYmsYgYAaAQFIrTqxo0bQogFCxYolUoPD4+aRzDn5ubm5ORU3924ceMvv/wi3c7IyCgtLe3du7ePj4+oc4EYHBz83HPP1bxmTYWFhdnZ2ebm5p07d7azsysrK5OyARL2QJRT3fdAlJYwm5ubq1SqW7du2drasoQZAPSOh4dHaGhoaGioWq0uLi4uKSmxtbU1MuLLPE2ytLTcvXt3//79t23bNnDgwAkTJsidCACg39q1a2dpaZmXlyd9EJM7DgxKeXm59GG/srLS2Nj46NGjn3zyyYEDB2JjY6UBly9fdnJyEkKcOHFizpw50oNWVlZFRUVCiJdeesnBwUEqCu7du2dpafmoFzp48GBMTMzOnTtLS0sHDRp0/vz5Fi1aPDBG2gHG09PTyMiobdu2N2/ezMnJcXBw0MKfG3qJDy1yquMeiPfv3y8pKTE3N+/SpYsQIjU1Va1WcwozAOgvhUJhaWlpb29Pe6gN3bt337hxoxBixowZ1b9/AwBQPwqFQpqEyDkq0LgbN25ULxP29vZu167d+++/Lx2NIqn+W3f48OHqB1944QUhhImJycSJExUKRbt27YQQf94wUQgRGxu7bt26u3fvvv7668uWLSstLRVCXLlyZdGiRX8evHfvXiHEs88+K4SQWku2QURNfG6RUx1nIFYfuCyd3Zmamnr37t3KykorKytTU9NGyAkA0IiDBw8GBwcPGzZs+vTpMTExfx6wcOHCV155pfGDGZ6goKBp06bdv3//pZdeUqlUcscBAOg3qUCUzpcAntTFixdHjhx57tw56W55eXn136Waa4Sffvpp6YaHh0f1g9Vr5//zn/9IN0xMTF577TUhREBAQNu2bYUQ7du3F49YxTxlypT58+ePGzeuele0Ll26mJiYbNmy5YHdotVq9TfffCOEGDt2rBBCuvKjFjujaaJAlFMdZyBWb3coFYhXrlyRJjkz/RAA9MiMGTNGjhz5ySefHDlyZMuWLb179/7Xv/71wJjDhw9/9913ssQzPOvXr+/Xr9/Vq1fHjBlTXl4udxwAgB5jG0Qolcrjx4/X72e//PLLgwcPbtmyRbq7ZMmSrl27Sr/y1SwQ/fz8pBstW7b8+9//3rVrVyHEb7/9du3atVu3bp05c0Z61snJadSoUYcOHaqeqPioAjErK+vChQtCiIMHD1Y/OHLkyPbt21dUVGRlZdUcHBERkZGR0b59eykGMxDxZxSIcnJwcFAoFDdu3KiqqqplWHVdKBWI6enpFIgAoF927969efNmNze3b7/9Njk5edeuXY6Ojv/4xz8eOG4PGmRmZvb99987OTn98ssvwcHBcscBAOgxaS8pZiA2WVVVVX/5y1+GDBkSHx9fjx9PTEwUQpw9e1a6u2fPHiHE+++/r1arCwoKpAfNzc0HDx5c/SNffvml1DD++uuvXbp0GTBgQFlZmbQdYadOnRQKxfDhw6t35OzQoYMQ4s/95oEDB6rXR1fveNitWzepHKxZIFZVVS1evFgIMW/ePIVCIZiBiIehQJSTqampnZ1dRUVF7WcbSZONbWxspBmLBQUFHMEMAPpl48aNzZs3j4qKGjNmjKen59/+9reDBw9aWVnNmDHj7t27cqczWE5OTvv27bOwsNi+ffvq1avljgMA0FdSgXjp0iW5g0Aehw4dSk1Nraqq2r59ez1+PCkpSQiRkJBQXFyckpKSnp4uhIiLixsyZIi0XnjSpEmZmZkuLi41f8rDw6NNmzaWlpYlJSVJSUnOzs5nzpzZsWPHpk2bHrj+uHHjmjdv/uWXX27durV655aEhIRVq1YJIaRC8J///Kd0vnPXrl2dnZ3FH2cXHj9+PD4+vn379rNmzZIekWY1pqWl1ePPC0NFgSizbt26CSFOnTpVyxjps2WLFi3s7e2FEDdv3qRABAD9kpyc/PTTT7u5uVU/4uPjs2HDhtzc3P/7v/+TMZjB8/X13bVrl7Gx8bJlyz777DO54wAA9FLnzp2NjIyUSiV7YjRN1YuFd+3aVVZWVsvIrKysTp06/b//9/+qH7l3715GRoYQoqKiIjY29tChQ0II6Ri9Y8eOSWOcnZ2lD/s1mZqapqSk5Obm/vbbb3v37j1//nzHjh2DgoKkpc01eXp6rlq1Sq1WT5s2zcnJaceOHUKIhQsXXrt2rV+/ft9///177703derUl19+uVOnTj4+Pg/MQNy0aZNUNY4bN87MzEx6UGoqpOoTkFAgymz48OFCiIiIiFrGFBYWCiGsra2lf1NUKhUFIgDol/v37/95t4qJEyf26dPngw8+eOim19CUwMDAf/3rX2q1eurUqfv375c7DgBA/5ibm7u6upaXl6empsqdBTKQVi5bWVmpVCppPfKjnDp1Ki0tbdWqVdV/VS5dulT9S+Bvv/0WHR0thHj11Vdr/lSrVq0eejVra2srKytfX9+AgADpCNZHmT9//s6dO7t27VpWVjZjxoyzZ8+ePHlSCPHDDz8EBgYuX77c2Nj4q6++Sk1NNTc3rzkD8fLly7Nnz5aOeJYOd5a4ubmZmZldu3atqKio9v84aDooEGU2cuRI8ce9Cf6segainZ2dEEKlUrEHIgDoFw8Pj9OnTz9wapZCofj4449LS0snT55c+2a4aKDZs2cvXbq0vLz81VdfrT7EEACAupOmfbENYtOUm5srfj/k5MqVK7WMvHr1qhCivLx85cqV0t3Zs2cLISwtLYUQhw8flk41efPNN6UFxZJHFYhP5I033khKSpo+fXp5efmCBQsKCws7duwobWX4gJoHpPz222/Sg3Z2dtWnuAghTExMOnfurFarWbmPahSIMvPx8WnduvX169elWc0PJc1AbNGiha2trZGR0e3bt8+fPy9+3yoVAKD7pkyZUlJSMmjQoDNnztTsCnv37r1w4cLDhw9PmDCBzRC16v333w8ODr5///6oUaNOnDghdxwAgJ5hG8Qm6+7du/fu3bO0tOzVq5cQIiUl5f+zd+cBNaf9/8evc9oXJUkpa0QUWYps2besgzFmMGSyJMuY6baNGTMMxvI1hqxjrGPMMMY+xoRJE8mSokVKZCmSLFG0nd8fn9v5nTtOhep06vn4q3Odq3OuLuf0dl5dn+sqoLMUIAohdu/e7ezsPHTo0NDQULlc/vXXX8tkshMnTly7ds3IyKhly5atWrVSfpd02kGxGDVqlHh5oEq7du1e20cKECMiIs6dOycd7eLm5ubv76+rq6vajauYkQ8BoobJZDKpFBWwGF76SGlmZqajo2Nubp6bm7t7924hRL9+/UptnACAd+Hr6ztu3LjY2Fh3d3dDQ8OIiAjlXfPmzfvggw+kc5lZ11Ci/P39vby8nj175unpqdx1CACAopBWIBIgVkDS8kMbG5sGDRqIoq1ANDIyyszMjIqKkuK5devW+fn5NWvWLDMzU6FQNG3aVFdXd/78+ePGjdu4ceOwYcPc3d2La7StWrWSDmsWQrRt2/a1faRLmGNjYzt16iRtp7Zw4cIPP/wwXzcCROSjZQHis2fPMjIy8jWePn3a19e3Q4cO/fr1++KLL27duqWRsb21evXqiQIDROUKRCGEtA3i06dPGzduLL2fAQBaYc2aNZs3b+7atWuNGjVUt63Q09P75ZdfVq5caWtr+/z5cw2OsNyTy+UbN24cNWrU06dP+/TpExAQoOkRAQC0BmFKhaUMEB0cHERhAaJ0ZeHYsWNVG6Wli8oFQNLNTp06rV+//pNPPtm5c6epqWlxjVZHR0d6Ijc3t3w7LSrZ2tpKRzNnZGTEx8fLZDJXV9dXu7m5uQkhuG4DSloWIJqamrZs2VK15Ysvvmjfvv2aNWuCg4MPHTq0cOFCJyenn3/+WVMjfAv169cXQly7dk1dB+UKRPEyQBT/u78pAKDs09HRGT169LFjxxISEpo1a6Z6l1wunzx5clxcXHJysrS1NkqIXC7ftGmTt7f3s2fP+vXrt2fPHk2PCACgHZR7ILJtcUnLyspatGhReHh4CT1+QEBAcnJy0fvnCxCvXr2ak5OjrrO0AvHLL79cs2aNdKWwjo6OlD7PnDlzwIABQojOnTu/0w9QmOXLl//5558hISGvnuwsMTY29vb2Hj58uLRDYrt27V57QKuHh4eBgcHZs2fT0tJKdMDQFloWIOZz5MiRhQsXVqlSZfXq1ZGRkVeuXPnxxx+lN4MWrS2XViAWECAqT2EWQkjnqAghXFxcSmV0AIDSY2Njo263GhQXuVy+YcOGadOmvXjx4oMPPvD399f0iAAAWqBy5cq2trYZGRkFbF6PYnH48OHZs2d//vnnJfHg//zzT48ePby9vYv+LcoA0cbGxtzc/MGDBw0bNpTONc0nNTX16dOnlStXrlq1qo+PT/v27YUQDg4ORkZGQggjI6O9e/devXr1/fffL6af5vXMzc179+6to6NTQJ8NGzb8/PPPsbGx165dO378+Gv7mJiYtG/fPjc399ixY0+ePNm+ffuzZ89KZsjQDtodIH7//fdyufzw4cMTJ050cnJq2LCht7f3n3/+mZOTI515pBWkFYhvdAmzEKJJkyalMjoAAMobmUy2fPnyRYsW5eXlTZ48+fPPP2c5CQCgUFzFXDqkfQPPnz//2uq8b9++pk2bSoeEqNq/f/9XX3118+bNSZMmTZw4Ufre5cuXb9y4UbWbdLWi8ujholAGiEKIOXPmWFtbJyQkzJ0799We0vLDOnXqSDdbt24t/nfpj0wmk5YxlhGVKlWyt7dXPQ86nx49egghAgMDhw8f/vHHH//www+lODqUOdodIF66dKlFixbS21KpRYsWrq6u0uHoWkH6DXLt2jXVLbFUvXoJs3SkeimOEQCA8mbmzJlbt27V19dfvnz5gAEDpD/XAQCgjpOTkxBCiz5pah1pg5GtW7cKIZ48efLa8463bNly+fLlvn37qh49l52d7e3tPX+GDPHSAAAgAElEQVT+fAcHh9WrV69du3br1q3379///PPPJ0+erPygnZWVtXfvXiFEampq0c9OUA0Q/fz8/v77b11d3VWrVknjVBUbGyterhASQgwfPnzUqFGfffbZm8xB2dK0aVMhxNatWw8dOiSEOH/+vKZHBE3S7gAxOzv7teed29ravtGmBpplZmZmbW2dkZGh7leYFCBKKxClS5gbNGhgYGBQmoMEAKD8GTly5N9//21paXno0CF3d3fp//0AALxW9+7dhRA7duxQt/ID72jPnj2HDh1KSkqSbkpLEfO5cOGCECI9Pf3PP/9UNh4/fjw1NVUIkZWVZW9vL4SYM2fO5cuXhRDPnz+XEkAhREhIyMOHD6Wvi77HomqAKIRo2rTpsmXL8vLyxo0bl+9/DtJGatJ2mUIIa2vrLVu2tGrVqohPVAZJq52UJ9mW3MaU0AraHSC6ubldunQp369vhUIRFRUlJeXaQvoVo27fRtVLmKtWrSpe/h0AAAC8o44dO549e9bZ2TkmJsbNzW3Xrl2aHhEAoIzq1auXnZ1dbGzsqVOnND2W8mn//v2qN0NDQ/N1SElJuX37tvS1anj322+/CSFatWo1cuTI8PDwBg0aJCUlbdmyRbr35s2b0hdSpCjx9vYuyuGr6enpISEh4mWUJpk6daq3t3dWVta0adOUjcHBwdJReI6OjoU+rLaoU6eO6gXON27cePTokQbHA83SvgDx+vXrnp6eU6dO9ff3b9269e3bt/Ntd/jdd99dvXpV2q9UW0gBYnR09C+//PLHH3/ku1d1BWLfvn379+/v4+NT+oMEAKBcsre3DwkJGTZsWHp6+gcffDBhwoTMzExNDwoAUObo6OgMGzZMCPH3339reizl0PPnz48ePSqTyeRyuXS1+LZt25SLByXS9eOGhoZCJUDMzs4+cOCAEGL79u3btm2rVKmSp6eneJkqCiESExMXLFiwd+/eyMhIIYS0JDAlJWX16tUFD+nChQsffPDBw4cPO3Xq1LBhQ9W7Fi5caGxs/Ndffz148EAIERcX5+HhcfLkSaGyArEc0NHRkQ59lclkTk5OCoVi3759qh0KOJAa5Y+WBYgeHh6WlpZHjhxZuXLl5MmT582bJ4RQBog5OTnNmzefPXt248aN58yZo9GRvhlpO959+/aNHDlyxIgRqmsqX7x4kZWVpa+vL/2WrFWr1v79+z08PDQ2VgAAyh1TU9OdO3f6+/sbGhquX7++ZcuWbHEFAHiVq6urECIqKkrTAymHgoKCnj171rx586tXr547d27AgAHp6emLFi1S7SOt/ezTp48QIjY2VjoU+OTJk2lpaU5OTspzAnr27CmEyMrKkm7u3bt3zpw548aNu3TpkhBixowZQ4YMkcvl4eHhL168OHPmjLKn0vXr10NDQz/99NMjR45I35Kvg5WVVatWrRQKxenTp4UQoaGh0qd4uVyeL2rUdtLSy3r16nXs2FEI4eXlpVzRKYQ4fPiwxkaGUqer6QG8GSnRz8jISEhIiI+Pv3btWnx8vHTUkRAiNzc3PDy8bdu2W7duNTEx0eRA35AUIEonSWVmZq5evdrJyalz587if69fBgAAJcfX17d9+/YfffRRdHS0u7v77NmzZ8+eXcDRhACAikZaGSctZEPxOn78uBCie/fu0pK3L7/8cv/+/X/88ce0adPmzJnz+eefV61aVToFePz48UeOHLl7966FhUVISIh0Lsp7772nfKiOHTuamJhI8aIQQlqfmJqampaWJoRo3779oEGDnJycoqOj/fz8/P39p0+fvnjxYuW3L126dPr06UIIaTnkwYMHe/Xq9eqA27VrFxgYOGjQoB49eihDw1q1ahkbG5fIBGmIFCC6uLhMnjw5MjIyKCgoKSnJ4uW9+/bt++TBA+ms11f9888/O3bsCAgISE9Pt7S0TElJyc7Orly5spWVlY2NjaWlpa2tra6ubuXKlR8/fmxtbV2jRg0PD4/XHnSBskDLAkSJsbGxs7Ozs7NzvnY9Pb3ExMRatWppZFTvIt8i58mTJ1etWjUlJUUmk6kewQwAAEqUi4vL+fPnZ82atWrVqm+++WbPnj0bNmxo06aNpscFACgTGjZsqKend+3atefPn0uXiBVFbm7uxIkTnZ2dJ0+eXKLD02pSgNi1a1fpZosWLWrUqHH79u2PP/7433//TU1Nbdq0aXp6+uDBg7t3796gQYPw8PDs7Owff/zxn3/+EUL0799f+VBGRkb+/v5eXl7STeUZIHl5edWqVZPyqVatWkVHR69du1YIsWPHjkWLFsnl/71Ac/v27dIXCoXCyclJuiD6Ve3atRNC5OTkHDlyRNqZ0d7eXvmk5UaXLl1WrFjRt29fR0fHDRs2ODo6pqSkKAPEjIyMmTNnOjo6jh07Vk9PLzAwMC0trXv37tWqVQsNDe3evXtubq7UU3l8TWZmZnJysrQa9FU6Ojpdu3YdOnSom5tb48aNdXV18/Ly7t69m5iYqKen17JlS5lMVuI/M9TQygBRHblcro3poRCievXqDg4Od+7cMTU1TUlJEUKkpqbGx8c7ODiwAhEAgNJkZGS0YsWKQYMGeXt7R0ZGtm/f3tvbe+HCher+ug4AqDj09fUdHByio6NjYmKaN29exO+Ki4vbsGGDhYUFAaI6aWlpFy9eNDQ0VB5mIJPJPD09N2zY8O+//wohAgICrl69KoQYP368EMLJyUk6EXjnzp3p6enGxsb5/jlGjx4tl8tjYmK+++471Xbp6mYhRJs2bbZs2SLFW3fu3Bk1atSYMWN27Nhx9erVyMhIQ0PDFy9eKBSKtm3bqhtzmzZt9PT0srOzFQqFFIeFhISUv9Vznp6e6enpRkZGQoiGDRva2dk9v3NHtcPGjRuFEH5+fsoWY2PjAQMGnDhxIjc3d+TIkX5+fjVr1kxOTq5WrZqxsfGjR49SUlLu3bt3//795OTk3NzcR48emZqa3r9/PzY29uTJk3///be0zaiBgYGdnV1SUtLz58+lR65bt+7YsWPHjh0rnS6LUqaVAWJiYmJMTIxyFfGtW7dWrlwZERGRm5vbrFmzMWPGSAvLtUtYWFheXt60adM2bdoktQQHB69cuVLalNTc3FyjowMAoGLx8PCIiIhYsGDB0qVLN2zYsHv37rlz506cOFFPT0/TQwMAaJKzs3N0dHRkZGTRA0RpedrDhw+nTJnSvXv3fv36Fdw/ISFhx44dY8aMsbOze9fhlnk7d+6UjjnOy8tzdXWVgirJhx9++OOPP0p7C+bl5V2/fl1HR8fd3V0IsWjRovbt269du1ZK7lxdXXV184cbH3/88cOHDxcvXqxQKKytrbOzs52cnNasWSPd+/7772/YsOHChQu6uro5OTk///zzjh07lKcRdO3aNTU1NTQ0VFpm+FqVK1c+duzYihUrpGuoa9WqVf7SQ4nqP0qXLl0UL1doCiH09PREdraZmdmTJ08MDQ3t7e1r1Kjx999/79y5Uwjh6uq6YcMGaa2uhcV/ly0aGxvb2tqqe66HDx/u2bPn6NGjFy9evHbtWkJCghDCxsamVq1ad+/evX79+uzZs7/55ps+ffoMGjSoV69e/H23NGnZISpCiAULFtjb2yvPSzp48KCTk9OyZcsCAgJOnDixfPnyFi1aSDsjaBdTU1MzMzPVN9KmTZv8/f3XrVsnhCjgDQYAAEqCkZHRt99+e+nSpR49ejx8+PDTTz91cnLavXu36llnAFBhffzxx6qxQsXxFtsg3rp1S/pi1apVn3/+eaH9582b99VXXzk6Ohb8LNnZ2dp+Bu79+/c/+uijVatWrVq1Sgjh4uKiem+nTp3Wr1+vr68/evRoqcXFxUW6OK9mzZoTJkxQruhUt9mIhYXFnj17vv/++2PHjt25cycwMNDU1FR5V1BQ0Jo1a0JCQnx9fStXrqxQKCwsLFq0aCGE6Nmz54oVKz7//PPBgwcXMH4PD4+RI0dKX48bN+5tp0Gb5EtU3d3dDQ0NT548qVAoMjMzo6Kijh49evLkyU2bNoWEhJw5c6boV/pLLCwsvL29d+/eHR8fn56efu3atcePHycnJ4eGhl6/fv3o0aP9+vXLycn5448/RowYYWVl1bJly0WLFiUlJRXrT4nX07IA8bfffpszZ46JiYm0Q+q9e/e8vLyys7PnzZsXERERFxe3adMmS0vLzz77TDoLSeuo/onpzJkzyq9r166tieEAAFDRNWzY8OjRowcPHmzUqFFcXNzQoUNdXV0PHjxIjAiggsvOzlZeV1ihNGnSRAhx+fLlon+LtAJRcu3aNWmXqgJIq+qePn1awBG3WVlZzZo1a9myZdGHURIyMzPV3ZWTk6PcfFCduLg41ZtNmzbN12Hs2LGPHz/evHmzdAFivvRq5MiR0ifoAnYrfu+99z799FNnZ2dDQ0PlLocSY2NjHx8fV1dXf3//L7/8Ugjx1Vdf/fnnn2vXrh07dqy7u/uyZcsKPZ21ffv2+vr6tWvX/uyzzwruWT7km2pfX9/bt283a9ZMtdHDw8PLy8vd3V1HR+ddnsvU1NTe3l55IIRcLu/Ro8eBAwdu3rzp7+/fvXt3Q0PDsLCw2bNn165du1evXps2bSrgBYl3p2WXMC9fvtzU1DQyMlLa63Dfvn0PHjzw9/f39fWVOtSvX9/Nza1ly5bfffeddNaSdlFdaaj616Q6depoYDQAAEAIIUTfvn179eq1efPmefPmhYWF9e/fv1mzZjNnzhwyZMg7/ucYAMqs1atX//777+rujY6OFkJ07txZ2SKdZVHuFRAg7tmzJzIy8ssvv8wXVClXIAoh8vLyIiIiOnTokJKS4uPjM27cOAcHhzp16ii/JTc3NyYmRvpa2uZP+Y1ZWVnK9Vy//PKL9E9w+/btGjVq5BtJXl7er7/+2qFDh5o1a77jz1uA0NDQDh06fPPNN7NmzVJtP3Xq1OTJkyMiIgwMDNavX69co/eqQgNEIYT0I3///fdWVlbKD/4SAwODrVu3Hj16VLmz4Vv77LPPhg4dKs3khAkTiv6NVlZWFy9eNDc3ryALcp2cnKTLlqWbMpms9C8itrW19fX19fX1ffHixbFjx7Zs2bJ///6jR48ePXr0008/9fDwcHJyko7DNjc3r1OnjpOTk4GBQSkPslzSsgAxOjq6Q4cOypNSpN/a77//vmofZ2dnd3f3ixcvamB870zdJhesQAQAQLN0dXXHjh07cuTIH3/8ccmSJeHh4cOGDbO3t586daqXlxfHnQEofzIzMwMDAwvuU2iH8sfe3t7ExOT27dsPHz5UbusmmTVrVlxc3IABA/ItyFJdgSiECA8P79Chw759+/7444+//vorMzPTz89vyZIl0r3SEc/S6RzSp9qjR48eOnTo1q1bQUFBAQEBjo6O169fX7BggdR/+/bt1tbW33zzTbt27X755Rep8eDBg8OHDx80aNCePXtKaB6EELt3787Ozl66dOnkyZOVlwYLIRYsWHDx4kW5XJ6ZmTlq1Ki4uDg/Pz/lOjJV8fHxQog6dercuHFDLpdL4exrOTo6btu27dX2rl27Kg9ufkev5rBF1Lhx42IZgFbQ0dGpUqWKuHdP0wMRQggDA4M+ffr06dMnNTX14MGDGzZsOHPmzOHDh/Mt3dXV1a1fv37z5s2bNm0q5UWcwfJ2tOwSZhMTk7t37ypv2tjYvLabnp6elm4GIQWIhoaG+fZoJ0AEAKAsMDQ0nDx5cnx8/Lp16xwcHBISEqZOnVqjRo3Jkye/0X5YAFD2+fn5bdu2zczMrFKlSj/99NPt/9W/f38hhGqLpsdbSuRyubQN4quLEB88eCBeXoCcl5c3derU//znP+LlCsR27dpJB4BIsWBERIQQIiMjQ6FQrFmzJiUl5dKlS3l5eVI16dy5s4GBQVxc3NOnT/38/Pz9/ffv3//w4cNevXrVr1+/SZMm8fHxMplMCDF79uxPPvnk5s2b+/btk84UFkJcuHBB+UTFS/W69ZMnTwohHj58+NNPPykbc3JygoODhRA3b95cuXKlXC6fP39+7dq1k5OTX300KUD85JNPpFkt9HphlAUNGjTQ9BDyq1q1qpeXV0hIyM2bN3fv3r1gwQIfH59Ro0YNGDCgcePGCoXiypUrO3funDVrVr9+/aysrOrUqdOvX78ZM2b8+uuv0osQRaFlKxC7d+/+yy+/nDx5smPHjkII6Yj3/fv3jx07VtknMTHxzJkz3bp1e4vHz8vLu3HjRsF9SrQ02tjY+Pr62tjY/PDDD6mpqcp2AkQAAMoOAwOD8ePHjx079sCBAz/88ENgYKC/v7+/v7+7u7uXl9fQoUMrV66s6TECQDEYOXJk+/btR44c6e3tPWnSpMWLFyuv0zQ2Nhbqr6Aq35o2bXr27NmIiAgPDw9lY15e3qNHj8TLAHHJkiUrV64UQrz33nvSR8gDBw7ExcW5u7ufP39evAwQLS0tTUxMbt682a5du/j4+B9++EF6kBYtWqSmpoaFhZ08eVK6VFkIYWBgIH1ItLW1dXJy6tKli3TtcP369VNTUx89ehQXF+fo6CheRoc3btx48uTJa5f+FYW0KEf1dOMdO3b4+Ph069Zt165dz549UwaUS5cunTBhgnSV6Pnz59PT0x0dHe3s7CZPnuzk5OTj43P16tU///zzk08+yfcUUnbTrVu3rl27Wltbv904UcqqV69eyC6emlOzZs1XL9t//vx5TEzMxYsXo6KiwsLCzp8/n5iYmJiYeOjQIamDnZ1dhw4dpIhf2i6z1AeuHbQsQFy4cGFgYGCfPn1mzJgxatSoTp06TZ482c/Pz9jYeMiQIbq6uqdPn54wYUJmZqa3t/dbPP64ceNU/3hSgLy8vLd4/KLw9/cXQmzZskUZIFpaWqouCAcAAGWBXC4fOHDgwIEDIyMj169f//PPP585c+bMmTNTp07t06fPhx9+6OnpWUF2RAJQjtWtW/fkyZMLFy6cN29eQEDAzz//rPGDOzTO1dV148aNZ8+elW7GxMSEhIQMHDhQ+pAYGhp6586db7/9Vrq3ffv2CoXC2Ni4SpUqLi4uBgYGUVFRjx49khYwxsTEBAYGDh06VIrS9u/fL11c2bhx44cPH4aFhS1btkz52fP777+vU6fOo0ePhg0bJpPJlANYtGjR9u3bDxw4cOnSJekCZynaUygUp06dOn/+/PDhw+3t7d/oZ8zKymrRooW+vn5YWJgQIjMzs1u3btJRpXv37v36669r1KiRm5vbrl27p0+fRkREbNmyZfz48eLlVpjKzTG7dOni6+s7derUU6dOjRgxIjMzMy4u7tGjR927dxcvA8T69etzSSlKjqGhYfPmzZs3by7dzMvLu3r1amRkZHR0dFhYWEhIyJ07d3799ddff/1VCKGnp+fs7NyyZcu2bdu2adOmQYMG+bY0rci0LECsWbPmX3/91atXr6+++uqrr74yNTW1trZ+8uTJiBEjRo8eraur+/z5c11d3TVr1vTt2/ctHr9JkyaF/mLNzc1NTExU/TtMSTA3N1d+/aa/6wEAQGlydnZetWrVkiVL9uzZs23btuPHj+/Zs2fPnj2mpqaenp4DBgzw9PRkTSIA7aWjo/Pll1/26NFj+PDhbdq0mTt37syZMzU9KE1q3bq1ECI0NFS6OWbMmDNnzkgXFAshgoOD69ev//z5c2NjY+kK5UqVKvXr108IYWho2KJFi5CQkJ07dz558sTW1tbKysrFxUX5yKdPn5YWdTZu3FhHR2f9+vXSLpPVqlXT09MbPHhwtWrVlJ2bNGliZGRkaGjYt2/fiIiIAwcOfPjhh4sXLw4PD1dmjjNmzLh8+fL169c3bdr04MGDHTt2jBo1SvXD5mslJSVt2LAhKipKCHH27FlbW9vAwMDTp09bWVmNGjVq2bJla9eulU5YnjBhgp6e3rBhw1avXi0FiAEBAUKILl26KB9NunAwODjY09PzwoULcrn88ePH4eHh1tbWjx49srCwID1EaZLL5Y6OjtJaXSGEdIHzqVOngoODz507Fxsbe/HixYsXL27cuFEIYWRk5Ojo2Lx5827duvXq1SvftqcVjZYFiEIIJyenhISEX3/9df369ZcuXbp27ZrUnpeXZ2dnN2TIkEmTJr317qdTp06dOnVqwX3u3btnY2NT0icNKT9m9O3bV9o4AwAAlGVGRkYjRowYMWLEnTt3du3a9dtvv509e3bXrl27du3S1dVt3759r169evbs6eLiovyQCQBapHXr1uHh4VOmTJkzZ87hw4cr8qocZ2dnY2Pj+Pj4tLQ0hUIhrQQ8fvy4soO0UWCXLl0aN26sq6s7b948HR0d6a62bduGhIQsW7ZMCCEtiapfv76JicmzZ8+kb7x27ZpMJnN0dKxevbryATdv3uzp6ZlvGEZGRsHBwfr6+oaGhtL5xXl5edKCQSVpnaN0mvOMGTN++umnuLi4VatWFfwDtmnT5ubNm9LX7u7u0gpKIcS33347duzYHTt2SBsaSlUvOzvbxsbm8uXLp06datq06alTp3R1dVW3FHNxcalUqVJcXFxCQoJyl8avv/5a+ujdsGHDIkw5UFJkMlmjRo0aNWokXcb67NmziIiIs2fPBgcHnz179tatW1KeuGnTJl1dXQ8Pj4EDBw4ZMkT17VlxaOUvfT09vZEjRwYHBz958uTZs2f3799/+PDhixcv4uPjv/vuu7dOD8sU5UYVo0ePVt1ZAwAAlHF2dnbTpk07c+bMjRs3fvjhh65du8pkssDAwJkzZzZv3tzGxmbYsGHr1q2Ljo5WKBSaHiwAvAFTU9NNmzbt3r07Njb21KlTmh6Oxujq6rZs2VKKDo8dOyYt91MuFaxfv77UrXHjxosXL16wYIEyPRRCtG3bVgiRkJAghPDy8hJCyOVyZ2dn1cevVauWiYmJra2tFK65urr26NHjtSNp0aKF9L2urq4ymczOzs7X1/enn37y8fH54osvlN2io6OTkpJ27NghhNi0aVNKSorUnpWVJY1EVUJCws2bN/X19aWFgVKpysjIMDQ0HDp0qEwmU64u9PX1FULo6elJmxsuXrz4+PHjWVlZ7u7uquvudXR0pEWIyvRQCLF3714pclUuBAPKAhMTk7Zt23766ae///77zZs3Hz16dPr06eXLl0v/nTtx4sSUKVNq1qzZp0+fTZs2VZzDoyRaGSCqMjY2rlq1auXKlUv6muJSpvyF+9Zb3gIAAM2qVavWlClTjh07dv/+/d27d3/yySc1a9ZMSUn57bfffHx8nJycqlWr1r9//0WLFv3zzz9PnpTZHckB4H8MGTLk0qVL0uFRmh6Lxri5uQkhzp8///fff0std+7cEUJ07tz5m2++kVqkw5rz6dmzZ4cOHYQQzZo1GzRokNQorR9URmmNGzeWvhg/fnzdunW3b99e6KddaavKoKAgf3//MWPGrFmzZtq0acp7X7x4MWvWLGldZEZGxs8//yy1T5o0ycHBQdrZUElaUNmjR4+lS5dKLZ9++uno0aMXL14sfUqVVhc6ODhIl3ILIaZMmVKpUqWDBw8uWLBACNG7d+98w5Ou4FYlnT0tWIGIss3c3LxNmzbTpk2T/ju3Y8eOgQMH6ujoSIcC1axZ08nJafLkyTt37kxKStL0YEtcuQrdJAqFIisrSwghHQKlpZTbUlSqVEmzIwEAAO/I3Nx8yJAhQ4YMEUJcvXr1xIkTQUFBQUFBd+7cOXjw4MGDB4UQcrncwcGhRYsWzZo1a9asWZMmTSrm1TEAtIK00k3To9Ak6SSZCxcuxMbGqrZXqVJFGau9NkA0MTE5ceLErl273N3dlTtadOrU6ccff/Tx8dm8eXN4eHijRo2k9mnTpqnmgAWTckklS0vLGjVqKFdI7dy5UwjRt2/fQ4cOBQcHT5gwwcjIaN++fXl5eatWrZLWRUqkANHNzU2KNYUQI0aMUD05Z9CgQUFBQYMHD1aOv1q1alOnTv3222+lA6ZfPZCgf//+vr6+ynX3rVq1Onv2rHRqKCsQoS3Mzc0/+uijjz766MGDB3v27Dl8+HBgYGB0dHR0dLT015Rq1ao1bdq0YcOG1atXt7KysrGxcXZ2Lk9nWpTDAPHSpUvNmjUTL9daayllgMgKRAAAypMGDRo0aNBgwoQJQogbN26cOnUqNDT07Nmz4eHhsbGxsbGx0mc8IUSVKlWcnJwaNWokbfXdoEGD2rVrl7NLLgCUA+VjAcebatGihRAiJCQkNTVVX1/f2tr61q1bQggLCwt7e/tatWqlpaUpFxLmo6ur+9FHH6m2fPTRRx4eHnZ2dmZmZhMmTOjVq1exDLJZs2bKADE7O1sul3/xxReHDh06cOCAubn5qlWr7t+/L4TYu3fvvXv3fv/992vXri1ZsuTcuXNCiFatWjk7O1tYWJiZmSmPr5WYmZlt2rQp33NNnDhx8eLF2dnZdevWVSaPSnZ2du3atTt79mxubm5ubu6MGTPef/996dJvAkRoHUtLy3Hjxo0bNy4nJ+fMmTNBQUGnTp06depUSkrKsWPHjh07puwpl8sjIyOVfxLQdvwftIwiQASAcunatWsBAQHR0dEPHjzIyMiwtLS0s7Ozs7Pr168fy80qoDp16tSpU2f48OFCiOzs7MjIyLCwsPDw8MuXL1++fDktLe3ff//9999/lf319PRq165tb29ft27dOnXq1K5du1atWrVr17axsSFYBKAp77KAY/Xq1cuXLy+4j5ROlrXVIQ0aNKhUqdK9e/eEEE2bNq1Xr54UIFapUkUmkwUHBz9//tzIyKjoDyht5T969OjRo0cX1yBdXFwOHTrk4OCQmJiYlZXVsmXL1q1bV61aVVr6N3v2bCGETCZ78eJFkyZNpDDxvffeCwsLk8lkbm5u+vr6UVFRurq6RTkwp3r16gMGDPj999/79+//2g6HDx9OS0vz8vKKiorq2UoJLXcAACAASURBVLPn9OnTv/vuO0NDw3r16hXXzwuUMumUPGmLTyFEYmJiVFRUXFzcvXv3UlJSkpOTc3Nzy9P/8Mvh/zUbNmwYGRmp6VG8Ky5hBoBy5vr16xMnTvzrr79ee6+vr++AAQOWLVtWp06d0h0Xygo9Pb3mzZurrvJISkqKjo6+cuVKTEzM1atX4+Libt26FR8fHx8fn+975XK5jY1NjRo1bGxsRo0apdxUCwDKuPPnz796iIdWkMvlzZs3DwoKEkK0bNmyfv36v//+uxDCwsJCCFGzZk0Nj08IIUTnzp0XLFgwdOhQIcSCBQs8PT1lMlmbNm2krTMePnwohPDz81u+fLmUHgohfvnll4yMDHt7e0tLSyHEG2UfCxcutLGx+eyzz157r5mZmZmZWUBAQE5OjqGh4aJFi1xcXIyMjPT09N7xxwTKiNq1a9euXVvToyhB5TBANDQ0fO1mE9pFChBlMpmpqammxwIAeFcPHjzo3r37tWvXnJycBgwY4OzsbGlpaWZm9uTJk7S0tCtXrhw6dGjPnj0RERHBwcHW1taaHi/KBFtbW1tbW2mvesmLFy8SEhISEhKuX7+emJiYmJh469atmzdv3r17NykpSdq9OygoqG/fvvr6+pobOICK5V0WcGzcuPHLL78suE9qamrr1q2trKze7ilKjpeXlxQgtmjRQnkSSJUqVTQ6qP/RtWvX27dvV69ePS8vr0ePHtLmjN7e3gkJCVevXs3OzjY2Nv7iiy8MDAx2797dsWPHDRs2/PLLL0IIV1fXt3g6BweHVatWFdxHV1dXuWR+2LBhb/EsADSlHAaI5YMUIJqYmOjo6Gh6LACAdzV79uxr164tWrRo5syZr+3w9ddfb968edy4cV999dX69etLeXjQFgYGBo0aNXp1J53s7Ox79+7dunXL29s7Ojr6xIkTxbV/FgAU6l0WcOjo6BR6woCJiYkQQnleR9kxevToq1ev7tixo3fv3hYWFjKZTKFQSCsQyw47OzshhFwu9/DwkFr69+/fv39/d3f30NDQDz74wNzcfP78+fPnzw8KCtqwYcOTJ0/EyzOmAUCVtgaI5X4PqcqVKws2QASA8iIoKKhhw4bq0kOJl5fXjh07goOD3+Lxw8LCdu3aVXAf5dVJKH/09PRq1KhRo0aNDz74YO7cuX5+fnv37tX0oFAIm/PnlfvU/Pjjj+nHj2tyNGVb1apV586dy7palEELFy5cuHCh9LWjo2NMTEy1atU0O6Qi8vLyunPnjuoRzy4uLlIGKt52BSKA8k37AsQKsoeUtbW1TCbjKjYAKB/u37/fpEmTQrvVrFkzIiLiLR5/7ty5hw4dKkpP6cRDlFeDBw/++uuvo6KioqKiND0WFGKEEC4vvz527NjbvPMrkkGDBrVs2VLTo6jQyv0Cjne3adOmmJgYbTludfz48ePHj1dtMTc3b9iw4ZUrV2xsbFq1aqWpgQEos7QsQKw4e0jVqlXr8OHD5XsDTgCoONq0aRMQEBAfH1+/fn11fVJSUv766682bdq8xeMvXbpUeQCcOk+fPv32229Z216+OTk5BQUFJSUlPXr0SNNjQSEyf/wx+/x56euxY8fqsd5HPSsrK9JDDaogCzjenbu7u7u7u6ZH8U5+//33mJiYXr16GRsba3osAMocLQsQK9QeUr1799b0EAAAxWPKlClHjhxxd3f/6quvBgwYkO/vQ8nJyYcPH54/f35KSsqYMWPe4vEdHR0dHR0L7nPv3r1vv/3WyMjoLR4fWqTQKBllxMHjx2NfBojdunVrOHSoZscDvFbFWcABIYSTk1M5OI8UQAnRsgCxpPeQAgCgJHTv3n3VqlVTXzI3N69SpYqZmdnTp0/T0tIePnwohNDV1V2zZs3AgQM1PVgAAP6rQi3gAAAUQK7pAbyZ+/fvF2WLjZo1a6akpJTCeAAAKCIfH5+oqKjp06c3b978xYsX169fj4iIuHHjhoGBgaur68KFC2/dupVvNyIAADSriAs4OnbsyAIOACjftGwFYknvIQUAQMlxcHBYvHjx4sWLFQpFRkbG8+fPLSws5HIt+2MeAKDiKOlDwAAA2kLLPrRMmTIlJyfH3d195cqViYmJ+e5NTk7euHGjm5vbW+8hBQBAKZDJZCYmJpaWlqSHAICyrE2bNiEhIfHx8QX0YQEHAFQEWva5RdpD6smTJ1OnTq1Tp07lypXt7e2bNWtWv379KlWq2Nrajh07NikpiT2kAAAAAOAdsYADACDRskuYhRA+Pj7dunXbuHFjQEBATEzM9evXhRA6OjpWVlaurq6DBg3y8vKysbHR9DABAAAAQLtxCBgAQKJ9AaIoG3tIZWVlHTt2rCjdAgMDLS0tZTJZKYxKG+Xm5qamplarVo0pUkeaImtra00PpIySXbmifOkcOHBAceOGBgdTZmVnZwshWrduXcT+0dHRJTkcaBL1q7hQvwpF/SoY9asoqF9lQRlZwEH9Ki7Ur0JRvwpG/SqKclm/ZAqFQtNj0DL379+vVq2apkcB4L+6CWH/8utjQiRocizlTbt27ThRsTyhfgFlCvWr5FC/So5GFnBQv4AyhfpVcsp4/dLKFYiaVbVqVV9f39jY2KJ0joqKSk5ONjY2NjQ0LOmBaalnz569ePHCxMTEwMBA02Mpo5iigoUJEfT0aVZWlqmpqb6+fhVNj6dsSk9Pz87ObtKkyRv9KdXLy6vkhoTSR/0qXvxyLhRTVDDqV1FQv8oa6RAwExOT0nxS6lfx4pdzoZiiglG/iqJ81i8FSpKfn58QYunSpZoeSNk1ceJEIcTq1as1PZCya/To0UKIzZs3a3ogZdfQoUOFEL/99pumB1J29enTRwhx6NAhTQ8EWoP6VSjqV6GoX4WifhWK+oU3Rf0qFPWrUNSvQlG/ClUu65eWncIMAAAAAAAAoDQRIAIAAAAAAABQiwARAAAAAAAAgFoEiAAAAAAAAADUIkAEAAAAAAAAoBYBIgAAAAAAAAC1CBABAAAAAAAAqEWACAAAAAAAAEAtAkQAAAAAAAAAahEgAgAAAAAAAFCLALFkmZiYCCFMTU01PZCyiykqFFNUKKaoUEwR3hSvmUIxRYViigrFFBWKKcKb4jVTKKaoUExRoZiiQpXLKZIpFApNj6E8e/z48cGDB4cMGWJoaKjpsZRRDx8+/PPPP99//319fX1Nj6WMun//fkBAwNChQ3V1dTU9ljLq7t27gYGBQ4cOlcv5o8jr3b59+/Tp0++//75MJtP0WKAdqF+Fon4VivpVKOpXoahfeFPUr0JRvwpF/SoU9atQ5bJ+ESACAAAAAAAAUIu0GAAAAAAAAIBaBIgAAAAAAAAA1CJABAAAAAAAAKAWASIAAAAAAAAAtQgQAQAAAAAAAKhFgAgAAAAAAABALQJEAAAAAAAAAGoRIAIAAAAAAABQiwARAAAAAAAAgFoEiAAAAAAAAADUIkAEAAAAAAAAoBYBIgAAAAAAAAC1CBABAAAAAAAAqEWACAAAAAAAAEAtAkSUrMDAwPv372t6FED5t2zZstWrV2t6FED5Qf0CSgf1Cyhe1C+gdFTA+kWAWFICAgL69+9vZWXVuHFjHx+ftLQ0TY9IAyIjIzt37nz69OnX3luUKSrH07h27dqWLVtWqlSpWrVqHh4eu3bterVPRZ6i9PR0Pz+/5s2bm5qa1q1bd+DAgWFhYa92q8hTpGr79u3/+c9/fv/991fvYorwpng9COpXgahfBaN+vRHqF4oRrwdB/SoQ9atg1K83UkHrlwIlYO3atTo6Ovr6+h07dmzQoIEQol69egkJCZoeV2kbMmSIEGLfvn2v3lWUKSqv05iTkzN+/HghhIGBQceOHTt37mxoaCiEGD9+vGq3ijxF6enpdevWFULY2Nj07du3TZs2QgiZTHbw4EHVbhV5ilRdv37dzMxMCNGpU6d8dzFFeFO8HiTUr9eifhWK+vVGqF8oRrweJNSv16J+FYr69UYqbP0iQCx+V69e1dPTs7S0vHr1qtSycOFCIUSvXr00O7BS888//yxZsqRly5ZSSP1qASvKFJXjady8ebMQomHDhsnJyVJLfHy8vb29EOLw4cNSSwWfohkzZgghxowZk5ubK7UcPnxYJpNVr15d2aeCT5FSTk5Ou3btKlWq9GoBY4rwpng9UL8KRv0qFPWr6KhfKEa8HqhfBaN+FYr6VXQVuX4RIBa/6dOnCyF++OEH1UYnJychRHx8vKZGVZrq16+vusr11QJWlCkqx9PYtWtXIcSZM2dUG3fv3i2EmDBhgnSzgk+Ri4uLoaFhRkaGaqO7u7sQ4vr169LNCj5FSvPmzZPJZD/99NOrBYwpwpvi9UD9Khj1q1DUr6KjfqEY8XqgfhWM+lUo6lfRVeT6xR6IxS8gIEAIMWDAANVG6aZ0V7kXGBh448aNGzdu+Pj4vLZDUaaoHE9jQkKCnp6em5ubamOTJk2EEHFxcdLNCj5FNWvWHDx4sJGRkWqjjo6OEOLp06fSzQo+RZLQ0NB58+ZNmjSpZ8+er97LFOFN8XqgfhWM+lUo6lcRUb9QvHg9UL8KRv0qFPWriCp4/dLV9ADKG4VCERMTY2ZmVrt2bdV2Z2dnIUR0dLSGxlWq7OzspC/Mzc1fvbcoU1S+p/GPP/6QyWRy+f/E9xcuXBBC1KtXTzBFQhw8eDBfy7///nvu3Lm6des6OjoKpkgIIcTTp09HjBhRv379xYsXv7rtLlOEN8XrQVC/CkP9KhT1qyioXyhevB4E9asw1K9CUb+KgvpFgFjMMjIynj9/Xr169XztlpaWQogHDx5oYlBlS1GmqHxPY7NmzfK1hIeH+/n5yWQyaXNfpkgpNDT0//7v/27dunXu3DlHR8edO3fq6uoKpkgIIcSUKVMSExNDQkLy/alQwhThTfF6KBRvK+pX0VG/CkD9QvHi9VAo3lbUr6KjfhWA+sUlzMXs+fPnQgjpRB5VUktGRoYGxlTGFGWKKs40KhSKrVu3enh4JCcn/9///V+LFi0EU6TiwYMHERERUVFRubm5BgYGyl+pTNGePXs2b948d+5c5W7Z+TBFeFO8HgrF20oV9atg1C91qF8odrweCsXbShX1q2DUL3WoX4IAsdhZWFjo6OgotwlQevLkiXiZK1dwRZmiCjKNFy5ccHd3Hz16tImJyd69e6dNmya1M0VKnp6esbGxT548OX78+M2bN3v27Hnp0iVR4afozp0748aNa9OmzcyZM9X1qeBThLfA66FQvK2UqF+Fon69FvULJYHXQ6F4WylRvwpF/Xot6peES5iLmVwut7KyevV6eKlFuTlFRVaUKSr305idnT137twlS5YYGBjMmTNn+vTp0jHwEqboVV26dPn6668nTZq0bdu2ZcuWVfAp2rdvX1pamlwuHzFihNQi/bUqOjr6ww8/1NPT27ZtWwWfIrwFXg+F4m0lqF9vjvqlivqFksDroVC8rQT1681Rv1RRvySsQCx+NWrUePTo0d27d1Ubr1y5IrThBVE6ijJF5Xga8/LyPv7440WLFnXq1CkmJmb+/Pmq1UtSkacoLCysd+/eq1atytcubd97//596WZFniLJqVOnfn3pwIEDQoiUlJRff/119+7dUgemCG+K10OhKvjbivpVMOpXEVG/UOx4PRSqgr+tqF8Fo34VEfWLALH4vffeewqF4tChQ6qNhw4d0tXV7devn6ZGVaYUZYrK8TSuWbPm119/HT58+NGjR2vVqvXaPhV5iszNzf/666+ff/45X7t0KJWTk5N0syJPka+vr+J/3b59WwjRqVMnhUKRmZkpdavIU4S3w+uhUBX8bUX9Khj1q1DUL5QQXg+FquBvK+pXwahfhaJ+/ZcCxS0pKUlXV7d27dr37t2TWjZv3iyEGDx4sGYHVvqkDQL27duXr70oU1SOp9HBwcHY2PjJkycF9KngUyRtTLthwwZlS3R0tLW1tb6+fmRkpNRSwacoH9UCpsQU4U3xelCifr0W9atQ1K83Rf1CseD1oET9ei3qV6GoX2+qYtYvAsQSsW7dOrlcXr16dS8vr+7du+vq6tarVy8hIUHT4ypt6gqYomhTVC6nMTk5WQhhaGjY7HX8/PyUPSvsFCkUivPnz5uYmAghnJyc3nvvvfbt2+vp6clkshUrVqh2q8hTlM9rC5iCKcKb4/UgoX69ivpVFNSvN0X9QnHh9SChfr2K+lUU1K83VTHrFwFiSdm7d2/fvn0tLS0dHR29vb2Tk5M1PSINKKCAKYo2ReVvGk+dOlXAiuAhQ4aodq6YUySJjY0dNWqUnZ2dgYFBvXr1Bg4cePbs2Ve7VeQpUqWugCmYIrw5Xg8K6tfrUL+KiPr1RqhfKEa8HhTUr9ehfhUR9euNVMz6JVMoFAW8nQAAAAAAAABUZByiAgAAAAAAAEAtAkQAAAAAAAAAahEgAgAAAAAAAFCLABEAAAAAAACAWgSIAAAAAAAAANQiQAQAAAAAAACgFgEiAAAAAAAAALUIEAEAAAAAAACoRYAIAAAAAAAAQC0CRAAAAAAAAABqESACAAAAAAAAUIsAEQAAAAAAAIBaBIgAAAAAAAAA1CJABAAAAAAAAKAWASIAAAAAAAAAtQgQAQAAAAAAAKhFgAgAAAAAAABALQJEAAAAAAAAAGoRIAIAAAAAAABQiwARAAAAAAAAgFoEiAAAAAAAAADUIkAEAAAAAAAAoBYBIgAAAAAAAAC1CBABAAAAAAAAqEWACAAAAAAAAEAtAkQAAAAAAAAAahEgAgAAAAAAAFCLABEAAAAAAACAWgSIAAAAAAAAANQiQAQAAAAAAACgFgEiAAAAAAAAALUIEAEAAAAAAACoRYAIAAAAAAAAQC0CRAAAAAAAAABqESACb6lmzZoymezevXuaHsh/jR07ViaTxcbGanogAIAyjfoFANBG1C9AswgQgeKhUChevHiRk5NTLp8OAFBeUb8AANqI+gWUMgJEoHicPXvW0NBw1qxZmnq6WbNmnTlzpnbt2qUzAABA+UD9AgBoI+oXUMp0NT0AAMXD3t7e3t5e06MAAODNUL8AANqI+oWKhhWIQBmSnp6uqafOzs7Ozc3V1LMDALQa9QsAoI2oX0DRESACxaB3797u7u5CiGXLlslksh07dijv2rFjR8+ePa2srKpVq9azZ8+jR4+qfuN//vMfmUz24MGDbdu21apVy9PTU2pPT0+fPn16q1atzMzMrKysWrduvW7dOoVCUcDT+fj45NvENycnZ+HChe3btzc3N3dwcBg8ePDFixdVn33WrFkymezGjRvjx483MzPT09OrWbOml5dXcnJyiUwTAKCMoX4BALQR9QsofQSIQDEYP378p59+KoTo3LnzihUrWrZsKbWPGjVqxIgRFy5ccHNza9y4cVBQUK9evRYuXJjv23ft2uXt7d2kSZMBAwYIIVJTU52dnZcuXZqRkdGzZ09XV9eoqCgfH5+vv/664KdTlZGR0a5duy+++CI+Pr5Dhw4WFhb79+9v3br11q1b8/X08fHZsmVLt27dxo4da2houGXLlj59+vDXMACoCKhfAABtRP0CNEAB4K3UqFFDCHH37l3p5pkzZ4QQfn5+yg67du0SQgwcOPDJkydSS3x8fL169eRyeWhoqNTi5+cnhKhWrVpERITyGxcsWCCEmDVrlrIlKSnJzMzM3t5e2fLq002YMEEIceXKFenm3LlzhRBDhw7NyMiQWk6ePGlmZla5cuXU1FSpZebMmUIIY2Pjc+fOSS0vXrxwcXERQqiOBwBQnlC/AADaiPoFaBYrEIGSsmDBAiMjo61bt1aqVElqqVev3pIlS/Ly8rZv367ac8yYMU2bNlXebNeu3bp16z777DNlS/Xq1a2trVNSUor+7CtWrDAzM1u3bp2RkZHU4uHhMW3atEePHm3ZskW1p6+vr6urq/S1vr5+//79hRCsogeACov6BQDQRtQvoERxCjNQInJzc2NiYqpXr56vVqWmpgohwsPDVRvd3NxUb3bs2LFjx44KheLGjRs3bty4fv16YGBgXFycqalpEZ89KSnp8ePHPXr0sLCwUG3v3bv3N998c+XKFdXGNm3aqN40MTEp4rMAAMof6hcAQBtRv4CSRoAIlIg7d+5kZWUlJiZOmjTp1XvznfZVvXp11ZvZ2dnz5s1bu3btgwcPZDJZ9erVW7RoYWNj8/Tp0yI+++3bt199WCGEnZ2dECIxMVG1sUqVKkV8WABAuUf9AgBoI+oXUNK4hBkoETY2Njo6Ot27d3/t3gH5/gIml//PO3HEiBHffvtt3759T5069fTp0zt37hw8eFDa8qOIlPuD5GuXFsa/0UMBACoU6hcAQBtRv4CSRoAIlAh9fX17e/sLFy48e/ZMtT04OHjy5MlHjx5V942ZmZkHDx50cXHZsmVL27ZtjY2NpfbHjx8X/dltbW3NzMzOnDnz6NEj1XbpeRs1avQGPwkAoCKhfgEAtBH1CyhpBIhAcXr+/Lny62nTpqWlpX300UfKGnb79u3333/f399fWsr+WnK5PCcn5+HDh5mZmVJLTk7OggUL4uLicnNzFQqFuqfLZ+rUqY8fP544ceKLFy+kllOnTi1btqxy5cqjR49+258PAFA+Ub8AANqI+gWUGvZABIqHtMPu7t27ZTLZiBEjWrVqNW7cuP379x84cKBWrVqtW7dOS0s7d+5cXl7ewoULnZ2d1T2OgYHBiBEjNm/eXK9evW7dusnl8uDgYCFE69atQ0NDvby8vvjiCwcHh1efLt/jzJgx48iRIzt37jx58mSrVq1SUlLOnj0rl8s3btxoZWVVkjMBANAm1C8AgDaifgGljBWIQPFo3Lixn5+fgYHBli1bkpKShBA6OjpHjhxZtWqVs7PzmTNnbty40aVLlyNHjsyaNavgh1qzZs3cuXNNTU337Nlz6dKlwYMHR0RE/PDDD87Ozrt3775z585rny4fExOT06dPz58/v3bt2idOnLh79+6AAQNCQ0NHjhxZEj8+AEBLUb8AANqI+gWUMlm+FbkAAAAAAAAAoMQKRAAAAAAAAABqESACAAAAAAAAUIsAEQAAAAAAAIBaBIgAAAAAAAAA1CJABAAAAAAAAKAWASIAAAAAAAAAtQgQAQAAAAAAAKhFgAgAAAAAAABALQJEAAAAAAAAAGoRIAIAAAAAAABQiwARAAAAAAAAgFoEiAAAAAAAAADUIkAEAAAAAAAAoBYBIgAAAAAAAAC1CBABAAAAAAAAqEWACAAAAAAAAEAtAkQAAAAAAAAAahEgAgAAAAAAAFCLABEAAAAAAACAWgSIAAAAAAAAANQiQAQAAAAAAACgFgEiAAAAAAAAALUIEAEAAAAAAACoRYAIAAAAAAAAQC0CRAAAAAAAAABqESACAAAAAAAAUIsAEQAAAAAAAIBaBIgAAAAAAAAA1CJABAAAAAAAAKAWASIAAAAAAAAAtQgQAQAAAAAAAKhFgAgAAAAAAABALQJEAAAAAAAAAGoRIAIAAAAAAABQiwARAAAAAAAAgFoEiAAAAAAAAADUIkAEAAAAAAAAoBYBIgAAAAAAAAC1CBABAAAAAAAAqEWACAAAAAAAAEAtAkQAAAAAAAAAahEgAgAAAAAAAFCLABEAAAAAAACAWgSIAAAAAAAAANQiQAQAAAAAAACgFgEiAAAAAAAAALUIEAEAAAAAAACoRYAIAAAAAAAAQC0CRAAAAAAAAABqESACAAAAAAAAUIsAEQAAAAAAAIBaBIgAAAAAAAAA1CJABAAAAAAAAKAWASIAAAAAAAAAtQgQAQAAAAAAAKhFgAgAAAAAAABALQJEAAAAAAAAAGoRIAIAAAAAAABQiwARAAAAAAAAgFoEiAAAAAAAAADUIkAEAAAAAAAAoBYBIgAAAAAAAAC1CBABAAAAAAAAqEWACAAAAAAAAEAtAkQAAAAAAAAAahEgAgAAAAAAAFCLABEAAAAAAACAWgSIAAAAAAAAANQiQAQAAAAAAACgFgEiAAAAAAAAALUIEAEAAAAAAACoRYAIAAAAAAAAQC0CRAAAAAAAAABqESACAAAAAAAAUIsAEQAAAAAAAIBaBIgAAAAAAAAA1CJABPBfHTp0kMlkMpksIiJC02MBAAAAAABlBQEiUD49ffp0xowZbm5ulSpVqlOnjqen55EjR8r4IwMAkA9FBwBQppw7d87IyKhy5cqaHghQ2mQKhULTYwBQzO7fv+/q6nrz5s187RMnTly9erW67+rQoUNwcLAQIjw83MXFpRgfGQCAt0DRAQCUKSkpKb179w4LCzM0NMzMzNT0cIBSxQpEoBzy8/OTPm41bNhw/vz5kyZNMjAwEEKsWbPmwIEDZfORAQDIh6IDACgLLl++vGnTpkmTJjVq1CgsLEzTwwE0gwAR+P9Onjw5dOhQR0fHSpUqubm5jR07NjExUbVD9+7dpV0CAwICBg8ebGtra2Fh4enpeeLECSHE33//3bNnzypVqtSuXdvb2/v+/fuq35uTk7NgwYJOnTpVrly5Zs2agwYNCgkJeXUM+/fv9/DwqFKliqen57lz56ZNmyY948mTJ5V9IiMjhw8f3rBhQ2NjY2tr61atWq1cuVL5FzCFQrFnzx4hRKVKlYKDg+fMmbNq1aoVK1ZI9/72229Fn5DFixdLz+7i4pKenl6MjwwAKDmUsyIOAABQARVcfV5rw4YNn3zyyerVq9PS0kptnECZowCgUCgUiunTp8tksnxvkEqVKu3atUvZp1u3blK7lZWVajcjI6Pp06fr6ur+P/buPC6qco8f+DMzDLuI4IIsXsAFFTdEAlyyXCo102t1037d1DL3pSy7ZWpdKs3UFjXNJe2W1jXzVu6l5YaZiiaISgoCArIjDA7LAHN+f3x/Pr/jbMzKzMDn/bqv+xrPHA6PSHPO+Zzv93nEG+Pj4xsaGugLS0pKBg4cqHFwiUTy+eefxzUwMQAAIABJREFUi8fw4Ycfinfw8fEZMmQIvT5+/Djt8/vvv8vlcu3/lsePH69WqwVB4HeJI0aM4Ee+ceMGbezdu7e+n8DgwYNpn0uXLgmC8PPPP0ulUsZYYGBgTk6OJUcGAIAmg9OZkQMAAIAWqNGzj06bN28efw+dZN3d3Zty2ACOAAEigCAIwoEDB/hd0JQpU1atWjVs2DB+11FYWEi78TsuxtjYsWOnTJni4+PDt/j7+8+aNevBBx/kW86ePUtfuGDBAn5m2rNnz4oVKzw9PRljUqk0JSWF9klLS6POLIlEMmnSpBdffNHDw4Mfit/wREVF0ZZnn3123bp1r776qpeXF225fPmyIAhFRUWrV69evXr13r17+V+QV9rHx8fr+yGIA8T09PQ2bdowxry8vC5cuEA7mH1kAABoGjidGT8AAABogRo9+zSKHrMhQIQWCAEigCAIQq9evei0sX79etqiVquffPJJ2rhgwQLayO+4XnnlFdry5Zdf0hapVMpPOXFxcbTx66+/FgTh9u3bdCcTERFRX19P+6xbt46fumgL/3YJCQm0RbzQJN3wKBQKPz8/X1/fwYMH88FPmjSJ9vnmm2/0/QWff/552uedd97Rtw8PEBMTE+kHIpVKxbdtZh8ZAACaBk5nRg4AAABaILPPPmIIEKHFwhyIAKyqqurKlSuMsfbt28+YMYM2SiSSN998k16fP39e40vGjBlDL2JjY+lFWFgYv23jG2traxljFy5coBdjxoyRyWT0Fr+9OXXqFL1ISkpijLm4uMybN4+2PPbYYw888ID4+7Zq1aq0tPTOnTunTp2qrKw8e/bsunXrfvrpJ3pXpVJp/+3UavX8+fO/+uorxljPnj1ffvnlRn8gs2fPTk1NZYzNmDFj7Nix+nYz48gAAGA7OJ0ZPwAAAGiBzDj7AACHABGAXb9+XRAExliPHj3EEz/16tWLZrj466+/NL7E29ubXvB5plq1asXf1Zh8KiMjg1589NFHknsCAwNpY25ubkNDg1KppIUmg4ODfX19+df2799f41vX1ta+++67vXv3bt26dVxc3Pz586uqqvT91XJych5++GEqD+nXr98vv/zSunXrRn4cjKWkpNCLH3/8UalUWvHIAABgOzidMcaMHwAAALRAJp19AEDMpfFdAFoMjTslujUSBMHCh1F8rS4XFxeNmelJZWWlSqWiuz6NHVxdXTV2HjVq1LFjxxhjAQEBw4cPj46O/vPPP7/++mvtw+7du3fKlCl37txhjE2ePHnjxo3iSaAM8/HxUSgU+fn5q1evfvvtt614ZAAAsLWWfDpTKpVGDgAAAFog488+AKABFYgArFu3bnSvde3atYaGBr49NTVVrVYzxnr06GHJ8Tt37kwv3n///WpdfH1927dvT2UgOTk54odg1ErMnTp1ik54kZGRWVlZO3bseOWVV2gCew3vvvvu+PHj79y54+3t/c0333z55ZfGZ3x///vfDx48SK9XrVqVn59vrSMDAIDt4HTGGDNyAAAA0AIZf/YBAG0IEAGYp6dnz549GWOFhYVbtmyhjYIgvP/++/R6wIABlhy/d+/e9OLkyZN8Y1ZW1rx58+bNm7dy5Ura0rdvX8ZYbW3t5s2bacuZM2foDMelp6fTi4iICJrJvra29ueff9b4jjt37ly2bJkgCJ6enidPnuQTA4tVVlaWlpaWlpZqF+0vW7Zs0KBBNDGWUqlcunSpSUcGAAC7wOnM+AEAAEALZOTZx8CNEkBLhhZmAMYY++CDD2i1kDlz5ly8eDEyMnL//v1Hjx5ljLVq1eqtt96y5OBRUVGjR48+ePDggQMHnn322aeffjovL2/t2rU3btxgjH3++ee02+uvvz5u3DjG2GuvvXb58mU3Nzda9VJ8qE6dOtGLffv2zZs3Lzw8/JtvvsnKyqKNVGAiCMKyZctoS3Bw8Nq1azWO8O9//5sxNn369P/+97+MsSVLlrz77rvifaiA5b333jt48KAgCNu3b58/f36fPn2MPDIAANgLTmdGDgAAAFogY84+zOCNEkCL1sSrPgM4rIULF2pMGsUY8/b2/u9//8v3GTFiBG3/448/aEtaWhpt6devH9+NLw25detW2pKenh4eHq79H+D8+fPVajX/wmeeeUb8rkwm4+Uex48fFwRBpVLxLcTT0zM+Pp5ev/TSS4IgJCcnG/hPno9z4sSJtGXJkiW0ZfDgwbTl0qVLtOWpp56iLSNHjjT+yAAAYEc4nRkzAAAAaIGMOfsIes4sHM2x6+7u3rRjB7A/tDAD/D9r1qz59ddfJ0yY0K1bNy8vr6ioqBdffPHy5csaNyHm6dy5c3Jy8qJFix544AFvb++QkJDHH3/8yJEjn376qfg279tvv121atUDDzzg4+MzePDgvXv3Dhs2THwcuVx++PDhSZMmdejQoWPHjhMmTDh//vysWbPo3S1btigUiszMTMsHTBISEqRSKWPsyJEjhw8ftuKRAQDARnA6M2YAAADQAhlz9rHvCAEcmURAQweAY8jIyKivr2eMhYaG0pQcjLHx48f/9NNPjLHs7Gxecg8AAOCw7H46s/sAAAAAAJofVCACOIoXX3yxe/fu3bt3nzt37t27dwVB2Llz5759+xhj4eHhuNsBAACnYPfTmd0HAAAAAND8oAIRwFEkJSU9/PDDd+/eZYy5uLi4ubkplUrGGC09GR0dbe8BAgAANM7upzO7DwAAAACg+ZG988479h4DADDGWGBg4AsvvNDQ0FBVVaVUKuVyeY8ePSZNmrRr165u3brZe3QAAABGsfvpzO4DAAAAAGh+UIEIAAAAAAAAAAAAemEORAAAAAAAAAAAANALASIAAAAAAAAAAADohQARAAAAAAAAAAAA9EKACAAAAAAAAAAAAHohQAQAAAAAAAAAAAC9ECACAAAAAAAAAACAXggQAQAAAAAAAAAAQC8EiAAAAAAAAAAAAKAXAkQAAAAAAAAAAADQCwEiAAAAAAAAAAAA6IUAEQAAAAAAAAAAAPRCgAgAAAAAAAAAAAB6IUAEAAAAAAAAAAAAvRAgAgAAAAAAAAAAgF4IEAEAAAAAAAAAAEAvBIgAAAAAAAAAAACgFwJEAAAAAAAAAAAA0AsBIgAAAAAAAAAAAOiFABEAAAAAAAAAAAD0QoAIAAAAAAAAAAAAeiFABAAAAAAAAAAAAL0QIAIAAAAAAAAAAIBeCBABAAAAAAAAAABALwSIAAAAAAAAAAAAoBcCRAAAAAAAAAAAANALASIAAAAAAAAAAADohQARAAAAAAAAAAAA9EKACAAAAAAAAAAAAHohQAQAAAAAAAAAAAC9ECACAAAAAAAAAACAXggQAQAAAAAAAAAAQC8EiAAAAAAAAAAAAKAXAkQAAAAAAAAAAADQCwEiAAAAAAAAAAAA6IUAEQAAAAAAAAAAAPRCgAgAAAAAAAAAAAB6IUAEAAAAAAAAAAAAvVzsPQDTKJVKiUTi6ekp3vj777/v3LkzJSXF19e3T58+M2fODAkJsekwVCqVUqm06bcAALA7X19fiURi71GANeH8BQAtAc5fzQ/OXwDQEjj6+UtwKoyx7t27i7csXrxY4+fbqlWrr7/+2nZjqKmpCQoKstM/FwBocmXM497/ZPYeTDMzbdo0232WQtPD+QvAoeD8ZTs4fzUzOH8BOBScv2zHwc9fTlaBqOHQoUPLly/39/dPSEgYOnSoi4vLqVOnlixZMm3atOjo6B49etjim5aXl+fl5Uml0tatW9vi+ABgkr8rlT1UKnr9g5fXNVdX+46neaivr6+srLxy5Yq9BwLWhPMXgEPB+csWcP5qlnD+AnAoOH/ZglOcv5w7QPz444+lUumBAwdiY2NpS0RERP/+/R944IF33333m2++sd23bteuXUFBge2ODwBG2vfMM3999x293rZtW8Q//mHf8TQPZ86cGThwoL1HATaB8xeAg8D5yxZw/mrGcP4CcBA4f9mCU5y/nHsRlZSUlP79+/P0kPTv33/AgAEXL16016gAAAAAAAAAAACaDecOEOvq6tq3b6+9PTAwMD8/v+nHAwAAAAAAAAAA0Mw4dwtzTExMSkqKIAjidVQEQbhy5UqfPn3sODAAAAAAAIBmIyMj48iRI1evXi0tLa2qqvL39w8KCgoKCho7dmzHjh3tPToAALA55wsQMzMzR48e3bVr165du8bGxv7888/vvvvusmXL+A4ffPDB9evXJ0yYYMdBAgAAAAAANAOZmZmzZ88+fPiwznfnzJkzbty41atXh4aGNu24AACgSTlZgPjggw+mp6cfOnTo0KFDfCMPEOvr62NiYi5dutSzZ88lS5bYb5gAAAC6oYIDAACcSGlp6ciRIzMyMiIjI8eNG9erVy9/f38fHx+FQlFWVpaWlrZ///49e/YkJycnJiZ26NDB3uMFAABbcbIA8cSJE4yxqqqqmzdvpqenZ2RkpKenZ2Vl0bsNDQ2XLl0aOHDgf/7zHy8vL3sOFAAA4H6o4AAAAKezePHijIyMFStWvPHGGzp3eOedd7Zv3z59+vRly5Zt2rSpiYcHAABNxskCROLp6dmrV69evXppbJfL5dnZ2Z06dbLLqAAAAPRBBQcAADijkydPRkRE6EsPydSpU3fu3JmYmNhkowIAgKbn3Kswa5BKpUgPAZyaWq2ePn36xx9/bO+BAFgZr+BITU19//33J02a9Mgjj8TFxT3yyCMTJ0585513kpKStm3blpWVJZ7VF8BasrKyxo0bh9t7ADBVcXGxMTNshISEFBUVNcF4zPPtt99OnDjx7t279h4IAIATa1YBIhEEoba2tra21rwvLy8vv2NQRUWFdQcMxtu5c+ePP/5o71GADeXm5m7ZsuWTTz6x90AArMzICo6hQ4ci4gFbOHTo0N69e3fu3GnvgQCAk4mPjz9z5kx6erqBfYqKig4fPhwfH99kozLVpk2bdu3a9ccff9h7IAAATqwZBogpKSnu7u7u7u5mfO2CBQvatGnjZ1BERARjrLi42NoDh0bU1NQ8//zzzz33nL0HAjZUV1fHGFOpVPYeCICVNY8KDnBe9GDV7MerANBizZ8/v76+Pi4ubu3atdnZ2Rrv5ufnb926NSYmpqio6IUXXrDLCI3R0NDAGMMZFgDAEk45B6LttGvXrk2bNob3aWhoUCgUgiA0zZCAU6lUarVaqVTeuXOn0X+mRv35559paWmTJk2yytjAWurr69m9GBGgOYmPjz9y5Eh6enqXLl307eP4FRzgvOjBDB7PAICpRo4cuW7dugX3tG7d2s/Pz8fH5+7du2VlZXfu3GGMubi4bNiwYfz48fYerF5074YSEAAASzTDCsSIiIjU1NTU1FQzvnbJkiVljTl9+jRjTCKRWHvg0Ah6csgYu337tuVHmzZt2rPPPvvXX39ZfiiwIgoQ6f8BmpPmUcEBzgv13QBgtlmzZl25cuX111+Pioqqra3NzMxMTk7Oyspyc3MbMGDA8uXLc3JyZsyYYe9hGoIAEQDAcs2wAtHd3T0yMtLeowDr4wFifn6+5f/E9Lz01q1b1JMODoJucVGBCM1P86jgAOdFn6toYQYA83Tt2nXlypUrV64UBKGqqqqmpqZNmzZSqdMUo1CAiBZmAABLNMMAEZorXpWWn59v+dGqqqqYlYoZwYpQgQjN2KxZs0aMGLF169YjR45cu3YtMzOTMSaTydq1azdgwIAJEyZMnTo1ICDA3sOE5gkViABgFRKJxMvLy8vLy94DMQ0qEAEALOeUAeLBgwf37dt348aNzp07z5w5MyoqSmOHRYsWZWVl7d692y7DAxuxbgszBYhWySLBijAHIjRvzl7BAc4LcyACgO0IgkAfL25ubvYei26oQAQAsJzzBYgzZ87ctGkTvf7111+3bNny0Ucfvfzyy+J9jh49eunSJXuMDmxIrVbTC6ukftXV1dY6FFgRxcSCINTX17u4aH5AVVVVbd++/amnnurQoYM9RgdgNU5awQHOCy3MAGA7KSkp/fr1Y/dyOpMcP37822+/NbwPXbdXVlaaNzyGABEAwBqcLEDctWvXpk2bwsPDV65c2bt37wsXLrz22muvvPJKWFjYuHHj7D06sC0rtjDX1dXR0dDC7Gj4v7LOAPHbb7+dO3duZmbm6tWrm3xoADbn+BUc4LzQwgwAjunjjz/eu3evMXtS/5B50MIMAGA5JwsQ169f7+7ufuTIkfDwcMZYREREz549hwwZMnPmzGHDhrVq1creAwQbsmILM7/+QAWio+HNyzqnQbx79y6z7PIRwJFZUsGxdu3aTz/91PA+9ClaWlpq3vDAqaGFGQBsJyIiIjU11byv/eyzzx5//HHDJ768vLyEhARLZvyg41dUVNTW1uIpHQCAeZwsQExLSxs4cCClh6Rfv37r1q2bOnXqqlWrEhIS7Dg2sDXxKswWHoonUKhAdDQ8N9Q5DSK1sWOJFQBtycnJN2/eNGZP/BfUMqGFGQBsx93dPTIy0ryvDQ4Ofumllwzvk5qaauGNHg8oi4uLg4ODLTkUAECL5WQBYnV1NZ8Ij5s8efKGDRvWrFkzbdq0Tp062WVg0ASsGCDSRCqMsYKCAgsPBdaFABFaMksqOLZs2fLWW28Z3uf69eujRo3Cmi0tEyoQAQAYY0VFRQgQAQDM42QBYteuXf/444/CwkLxEgoSiWTjxo2xsbEvvvjizz//jFuj5ooHiFVVVXfu3GnTpo3Zh+IViNXV1RYeCqxLPAei9rv0O8B/EwCaGUsqOKRSqbg8Xye0/7dkmAMRACyUlZV19+7d7t270yzVKSkpX331VXFxcWRk5JgxY8w+fzUNXoOCdVQAAMzmZFnbtGnTampqhg4devbsWXEpYnR09KJFi44ePfr8889bsj4XODJxbGRh6zGvQLT8UGBdqEAEALAFtDADgNn27dsXGBgYFhbWu3fvbt26paamHjt2bMCAAWvWrPnqq6/+9a9/9e/ff+3atfYepiG8hRkBIgCA2ZwsQJwzZ8706dP/+uuvuLg4d3f35ORk/lZCQsIzzzyzc+fOgICAtLQ0Ow4SbMSKAaK4DAcBokMxvIgKKhChWXr++ec9PDzsPQpo5tDCDADmuXDhwrhx4woKCuLi4gYNGpSXlzdp0qRXX33V19d3/fr1R48eXbt2ra+v78svv5yUlGTvweqFABEAwHJO1sLMGNuwYUN8fPyOHTtu3rwpXq5LLpd/8803gwYNWrt2bXp6uh1HCDZiowAxLy/PkkOBdRmuQKTfAVQgQjNTV1dXU1Nj71FAM4cWZgAwD61ecuDAgVGjRjHGTp069fDDDzc0NPz2228PP/wwY2z48OHDhw/v27fvihUr9uzZY+fh6oEAEQDAcs4XIMpksilTpkyZMkX7LalUOm/evHnz5hUUFGRkZDT50OA+v/zyS1hYWNeuXa11QLQwtwT8X1lngEgXfwgQwel89tln33//vb53r169yhij2zBy7NixphgWtCQUHaKFGQBMlZycHB8fT+khY2zIkCExMTFXr14Vn7Z69uwZGxubkpJipzE2DgEiAIDlnC9ANEZAQEBAQIC9R9Gi3bp167HHHouKirpw4YK1jokAsSUwpgIRLczgdKqrq48fP254n0Z3ALAEfaiq1er6+npaAAEAwBjFxcV9+vQRbwkODi4rK9PYrX379la87Lc6BIgAAJbDFSTYRFlZmSAI1q0DtXoLs6+vb3l5OQJEh2J4FWYsogJO6rXXXuvQocPcuXMFQfjkk08effRR8buzZ8/eu3dvbm6uvYYHLQF/KqNSqRAgAoDxunTpkpSUpFarpdL/N3v+a6+9VlpaqrHbX3/91a5duyYfnbF4gFhYWGjfkQAAOC8nW0QFnAXN51VRUaFUKq11TAoQ3d3dmZUqELt06WL5ocC6MAciNFf//Oc/L1261KdPn2nTpq1cudLPzy/oHk9PT8ZYkIi9BwvNEJ/9EF3MAGCSESNG5Ofnv/TSS7yDJzY2dvTo0eJ9Nm/efPXq1eHDh9tjgKZBBSIAgNkQIIJN8AUBrLhECYVHwcHBzEoViJ07d7b8UGBdhgNEqkBECzM4qbCwsBMnTvz73//euHFj//79HbnVC5ofcQWifUcCAM7l7bffDgsL27Ztm5+f33PPPafx7rp162JjY2fMmOHl5fXmm2/aZYTGELcwi9fhBAAA4yFABJvgAaIV4zmKlkJCQiQSSX5+PmVJ5qEAsUuXLnQoBFKOg9/i6iwzxByI4OxkMtnSpUsTExPr6uri4+Pff/99/D5D0+C5IQJEADCJj49PcnLy66+/HhERUVFRofHuwYMHz507Fx0dffLkyW7dutllhMbgoaFKpdL+WwAAgDEQIIJN8A4pK1YgUmLo6enp7+9fV1dXXFxs9qGoBaN169b+/v719fWWHAqsy/AqzJgDEZqH2NjYS5cuPffcc0uWLBkyZEhOTo69RwTNH/9QRQszAJiqVatWK1euvHTp0r59+zTeev/99zMyMpKSkvr372+XsRmJAkSJRMLQxQwAYC4EiGATfJIUq1cguri4BAYGMsuiSapA9PT0pLnGrJhygoVQgQgthLe397Zt23bv3v3XX3+dPn3a3sOB5g8tzABgC/379w8PD7f3KBpHAaK/vz/DOioAAOZCgAg2Ybs5EGUymeWpHwJEh2W4AhGLqEAz89RTT6WkpKxfv379+vX2Hgs0c2hhBoCWjALEgIAAhgARAMBcCBDBJniHVGZm5ptvvnns2DHLj0ntq1Kp1PLUjwokPTw8ECA6GmMWUUGACM1JUFDQnDlz5syZY++BQDOHFmYAaMkQIAIAWA4BItgEr0A8ePDgBx988PTTT5eUlFh4TIqNrFKBSAEiKhAdkOEWZgSIAADmQQUiALRkCBABACyHABFsggeIlPWUlpa+++67Fh6T2lddXFys1cKMCkQHZLgCEXMgAgCYB3MgAkBLhgARAMByCBDBJrQ7pM6ePWvhMTXmQLRkeRZegRgcHMwQIDoStDADAJhKpVIZjgXVajV/9IIWZgBogShA7NChA0OACABgLhd7DwCaJ16ByOXm5lp4TKsvouLh4eHj42PhocC6eDiIVZgBAIwhCELPnj1dXV2vXr2qbx/xIxlUIAJAC4QKRAAAy6ECEWxCO0AsKCiwsHBMI0C0JJHUWIXZ8nATrAUViAAAJqmurs7IyLh27VpRUZG+fcSfqMePHx84cOAff/zRJKMDAHAICBABACyHABFsggJEmUxGf5TJZA0NDQUFBZYckweI/v7+7u7u5eXlSqXSvEPxFmY/Pz8PDw+FQnH37l1LxgbWggpEAACT0BmNMXbjxg19+4irDr/88sszZ84cPHjQ5iMDAHAYCBABACyHABFsggLEsLAwxphUKu3ZsyezuFOYB4gSicTCykFKHj09PRljNA0iihAdBCoQAQBMQjX1jLHr16/n5OSUlZVp7yP+RK2oqGC6GgUAAJoxChB9fHy8vLyqqqpQOgAAYAYEiGATdGfSvXt3xljHjh3Dw8OZxSEdDxCZxakfr0C0/FBgXahABAAwCa9ATEpK6t69+4gRI7T30X4kgwARAFqm9u3bMxQhAgCYBQEi2ATdmURFRTHGIiIirLLYsThAtGQdFUEQampqJBKJu7s7uxcgYh0VB4EKRAAAk/AKxMOHD1dVVV25coU+KsW0F07BWswA0KLQB6NUKqWFmC2cWAkAoGVCgAg2QXcmcXFxe/fu/eKLL6yyVom1KhBramrUarWbm5tUKrXwUGB1hgNE+h3g+6CCBgCAVyDevHmTMaZSqbRXU9H+REWACAAtCrUwSyQSmgYRASIAgBkQIIJNULLj7u4+duzY0NBQSwoGOe0AMScn59NPP01KSjLpOHwJZvojAkSHYriFmZ4e02/Cjh07fHx8Dh8+3JTDAwBwNDxA5G7duqWxRTtA1P4qAIBmTCNARAszAIAZXOw9AGieKEB0c3OjP4aEhDAbtDD/73//KyoqGjhw4OnTp40/jngCRIYA0cFoVCCmpKSEhob6+PjQRnEL84ULF+rq6i5duhRpp6ECADgC3sLM/fbbb126dPHz8+Nb0MIMAC0cDxCphRkBIgCAGVCBCDbBKxDpjxQg5uTkWHJM7QpE6tLKysoy6Th0r+Xh4UF/tEp1JFiLOEC8evVqVFTUCy+8wN8VL6JCd786O50BAFoO7VrCN998c8KECeItWEQFAFo4jQARLcwAAGZAgAg2QXcmPKQLDg6WSCR5eXl08jYPRUsuLi7sXiJJCgoKTEqRKED08vLiY2OoQHQY/J+yvr4+PT1drVbfvn2bv0vRoSAIarWafscQIAJAC6ezGfnq1aviP4orEPv168cQIAJAC4MWZgAAyyFABJug6jDewuzu7u7v719bW1tcXGz2MfnqaYyxDh06yOVyvj0/P9/449C9Fg8327dv7+rqWlJSgrspR0ARIWOsrq6upKSE3X/fy5cWrauro+3owgOAFo6eivETLikpKRF/PNKzlsGDB//4448ffvghs/eH59GjRz///HPD+6hUqsWLF5s0RQkAgD5YRAUAwHIIEMEmKKTjLcxMtOyJ2ccUVyBKpVJqPSYmHVapVDLRHIgSiSQoKEgQBBQhOgJxBSK1qItrDHm8yCsQda61AgDQctAJt0+fPuKNgiCIH63RB6mXl9e4cePatm3L7B0gzp07d9asWdqLvYidPn16xYoVy5Yta7JRAUAzhhZmAADLIUAEm9CYA5Hdazq2JKQTz4HI7iWSxKTDaiyiYpWxgbWI50CkelWdFYj19fV096u9MgAAQItCJ7UHHnhALpf7+fnx8nzx3L70UUlv0anZWkX3giAcOnSotLTUpK+iJ3kVFRUG9qHKSvEsFgAAZuMBYseOHRlamAEAzIIAEWyCwh3tCkRjQrpFixZ16NBBuyuZAkRqYWb3T4NoRoDIW5iZldZ4AauZJk/xAAAgAElEQVQQB4h0R4oAEQDAAAraAgICTp06derUqYsXLz788MPs/gCRKhBdXV3ZvWZnawWIv/766+jRoxcuXGjSV9FHt87ZGzX2KSwsvHjxouE9AQAaxQNEDw+PVq1a1dTUlJeX23tQAABOBgEi2ATdmYinZDK+hTkxMbGoqOjUqVMa2ylApBZmdn+AaFL2p7EKs0ljA1vjAeJPP/20Z88edn9EyFuYESACABBeVh8bG9uzZ89evXpFRkay+2v3KEAUVyBaq4W5rKyM3R9WGoPGQ6djfejj/c6dO9HR0aYGlAAABmAaRAAA8yBABOurq6traGhwcXHhYR8zpU2Y7itSU1M1tlurhZnuWLRbmBEgOgIeIKpUqrt377L750DkFYgNDQ0IEAEAmK6nYjRHsHYLsy0qEHnMZ9JX0Qe44QBRHHGikRkALMQrEBkCRAAAcyFABOvTngCRmRLSUWCUkpKisV0jQDS7AhFzIDoy7UVRDFcgiuNFAIAWSHteDgoQxaGbuC2Azs7l5eUdO3ZMTEy08LvTh7CpAaIxFYjij3frLvmiVCoff/zxzz77zIrHBAAHJw4QaRpEBIgAAKZCgAjWpz0BIrsX0hlecpFQhHT58mWN7RoBYmhoKLt3BWBhCzMqEB2H4QBRXIFI98OoQASAFk77qVhgYCC7vwKRFi3x8vJi987OarW6oKDgzJkzFn53MwJEQRDoo974CkTrBojHjh07cODA1q1brXhMAHBw2hWI2vOtAwCAYQgQwfp0ViAGBwdLJJK8vDyeAelDdyOZmZnUwcppBIh9+/Zdu3bt7t275XJ5YWGh8UGSdgsz5kB0HNoBorgIhVcgzps3j54bowIRAFo47adibdu2ZfeHenQ+9fb2ZozJZDI+wQgFi5agk69CoeCfz42qq6ujO3lj5kAk1g0Qz507xxhTKBRWPCYAODjtABELMQMAmAoBIlgfXeiLV1ChP3bo0KGurq7RfgGKhARBuHbtmni7RoAokUjmzZs3aNCgwMBAtVptfAOydrFG27ZtPTw8ysrKLL+VAgvpDBDpmo+JKhD37dtHa+ehAhEAWjjtp2Lay6SIA0QmOkFbK0BUq9UVFRUmfQkzpQLRuh/1f/zxB0OACNDCoAIRAMByLo3vAmAi8WTtYiEhIQUFBbdu3aLuKn14TdmtW7diYmL4do0AUXzY7OzsnJyc8PBwY4anXawhkUhCQkKuX7+ek5PTvXt3Yw4CNqIdIFKzGy0eql3hggARgFy5cmX//v2G90G1RbOkPQei9jIpGgGiu7s7RYeWB4j8lH3nzh0/Pz9jvsTIANFGFYiCICQlJTEEiAAtDOZABACwHAJEsD4DAeL58+dzcnLi4uIMfLk4QBRv1xcgdurUiZnSgKxdrEFju379+q1btxAg2pc4QJw8efLOnTvr6+tVKhUFiNr972hhBiBvvPFGowEiaXQeCXAu2gEivRYHiOI5EJlojhHrBoimfknTL6Jy9OjRyspKGqpKpaqurhb/3ACgGUMFIgCA5RAggvXpbGFmRid9/J5BY0+KlvjMTZzxy7MQnQEijc34g4CN8H99mUz2xRdf/PTTT+Xl5XyjdgUiAkQA8t577/Xu3dtwOFhcXLxt2zapFLOXNCva83JQPmi4ApFe2DdA3Lx584ULFw4dOqTzd5JfS9TW1lql2LykpOTRRx8Vb1EoFAgQAVoI7QARFYgAAKZy1gAxIyPjyJEjV69eLS0traqq8vf3DwoKCgoKGjt2LBWlgx3RvQGVjIkZudgxr0HTiPPoxlj7NsPUNZS1izWY6WWMYCM8IgwMDJTJZFTHym8dtcMRtDADkL59+/bt29fwPqmpqdu2bWua8UCT0Z6Xw5gWZnphlwCRf26Xlpb+8ssvhYWFOq/caLcFCxZ8+OGHVqlALC8v1ziJKBSKDh06WH5kAHB84gCxffv2Li4uJSUldXV12jcsAACgj/MFiJmZmbNnzz58+LDOd+fMmTNu3LjVq1eHhoY27bjg/zPQwsyMqPLTV4FoYA5E7Z0N0NfCbNJBwBZOnjxJvzwTJkwYNGgQu/dbxO82tSsQa2pqNFbrBgBoUbRPam5ubhKJRKVSCYJAd8u2XkSFmRUgkvLycgMBYqtWrbS/xDziRJVgGkSAlkkqlbZr1y4/P7+wsDA4ONjewwEAcBpOFiCWlpaOHDkyIyMjMjJy3LhxvXr18vf39/HxUSgUZWVlaWlp+/fv37NnT3JycmJiIp4q24u+ANHINmHz5kA0vvuYChl4/QUxtQ8arK66unrEiBH0r/z999/TTS89Fua/EtoViNevXz94/XojZVcAAM0X5WIaK4O5ubnV1NTU1NTQdsecA5GUl5fr3E0cIFqlAhEBIkBLptHJFBAQkJ+fX1BQgAARAMB4ThYgLl68OCMjY8WKFW+88YbOHd55553t27dPnz592bJlmzZtauLhAbEkQFSr1Q0NDVKpVCaTFRYW1tbW8kIJ6waIGlM0Yg5Eu1MqlXRXKZPJKD1kRlQgAgC0ZJcvX1Yqlb6+vhrzcri7u4sDRNtVIFrSwkyaLEAUH8TV1VWlUiFABGg5xC3MjLHAwMA///zz9u3bdh0UAICTcbKZ1E+ePBkREaEvPSRTp04dOnRoYmJik40KNOgLEDt06ODm5lZcXGxg4UW6FXFxcQkKChIEITc3l7+lL0D08/Nr1apVRUWFvpsQDXQLoTE8qkDMzc2lywtoevyWUrxODlUgGpgDEQCgJVu3bh1j7Pnnn9eYIJhqDHlkZrs5EPnnc1lZmalfQsTn7hUrVnz33Xf0mgbv6ekplUrr6+stf4AkrkDs2rUrY0yhUAiC8P333xcVFVl4cIBmTKlUal+6//7773PmzBkyZMjYsWPfeustx58FSCNApJkTsBAzAIBJnCxALC4uNmaNlJCQEFwL2pG+AFEqlYaEhAiCYOAig1ZQkcvlVBKYnZ3N39IXIDIT6wdpeBoViF5eXm3btq2ursZvjr3wMhZxgEi/RQZamAEAWrLdu3czxmbPnq2xXWMhZo0WZltUIBo/Ha1GC3NFRQW9yMvLW7x48cKFC+mP/FqCRmv5NIjiCsSePXsyxhQKxeHDh59++mn+TQFAm7e3d3R0tHjLW2+9NXjw4A0bNiQmJu7fv3/58uWRkZE7duyw1wiNgQARAMByThYgxsfHnzlzJj093cA+RUVFhw8fjo+Pb7JRgQadPcKk0bVK+ArO2pkgZYvidIkzKUCkGyrt4aGL2b74zaF4OTyNORD5Ct0AAFBTU1NeXu7u7h4REaHxljhAFASBgkKzKxBpqQGdb/HPZ+MDPn0ViJWVlYyxoqIius/nT/vofG15FzOPU93d3cPDwxljCoWCnhpeu3bNwoMDtByHDh1avny5n5/fZ599lpqampaWtmXLFk9Pz2nTpjnyf0oIEAEALOdkAeL8+fPr6+vj4uLWrl0rrk0j+fn5W7dujYmJKSoqeuGFF+wyQmD6KxAZY3/729/Y/XWFGniASHuK4zxrVSDqyze1ax6hKRmoQEQLMwCAtpKSEsaYv7+/9lt0jouOjl62bFl1dXVDQ4OHhwc/gfIAUaVSadQDalOr1X369BkwYIDOd/nns+UBYnV1NWOsrq6OtvBrCToRmBcg3rp1KyMjg17zADE0NLR169aMMYVCQREq3wcAGvXxxx9LpdIDBw7Mnj07MjIyIiJi2rRpBw8erK+vf/fdd+09Or0QIAIAWM7JAsSRI0euW7dOoVAsWLAgNDTU19c3PDy8X79+Xbp08fPzCwwMfOmll27fvr1hw4bx48fbe7AtFw8Btd9qNOnTCBBt0cJM30I73wwNDWUIEO2H38SKb4YRIAIA6EMBYtu2bbXfooiwqqrq119/pZMjLz9k9z9Ca7QIsbq6uqSkJDc3V2dEyD+6Gw0itb+E8ACRT7JGVYFWaWEeNGhQ//796TtSBBkTE7N9+3YfHx/GmEKhoM7riooK+mECQKNSUlL69+8fGxsr3ti/f/8BAwZcvHjRXqNqFAJEAADLOVmAyBibNWvWlStXXn/99aioqNra2szMzOTk5KysLDc3twEDBixfvjwnJ2fGjBn2HmaLZqACsdEqPwMtzE1TgYgWZnuhX5suXbrs37+fb8QiKgAA+jQaIDLGUlJSevXqxUQTIIrfZYwplcoTJ07MmDGjuLhY53fhhXvUYqzBjABRIwrkcyBSBSJjjEaiESCaUYFYV1eXm5urUCio/5r+IgMGDIiLi6MAsaKiguenM2bMGDhw4K+//mrqdwFoaerq6tq3b6+9PTAw0JHzOASIAACW0zGdnOPr2rXrypUrV65cKQhCVVVVTU1NmzZtNNYfBDuyvIXZxcXFpApE2jkrK8uS4TU6NrAp+qdv3759ly5d+EaNRVQsX4UTAKDZMCZA5GubiCsQxa+VSuWqVasOHDiQlJR08uRJcc7Y0NDQ0NDAA0SFQqHdLm1GC3OjFYgUIFJiaEkLM48mCwoKgoODxTMga1QgMsb+97//McZ27949fPhwU78RQIsSExOTkpIiCAIP4xhjgiBcuXKlT58+dhyYYRoBYkBAgEQiKSwsVKvVuIsEADCSc39cSiQSLy8vf39/fO47FAOLqGjPbKhBuwKRzvfs3gIaBgJEY7I/lUqlVqvlcrn27wwqEO1LZ7CrUYGIABEAgKOgzXCAyIlDwxkzZixdupQm7lAqlXTiu3jx4okTJ8RfMmLEiIiICF54aKMKRI05ENn9FYh8ERUzWpj5kakCkS5O6CejHSAS49eSBmhRMjMzR48evWDBgvXr18fGxubm5mpMd/jBBx9cv3598ODB9hqhqVxdXf39/evr6/UVXwMAgLZmmLsJglBbW2v5an1gNsNzIEokkpycHH1JEKWEcrnc09OzXbt2tbW1fOVHal/VGSAGBQXJ5fKCgoJG/935DYn2W6hAtC+dvzaYAxEAQJ/S0lJmdIAoLi0MCQlJSEgICgpijCmVypycHNrOEzdy7ty5rKwsflpUKBTa38iKqzDzAFHnHIhmXNdpBIhUgUg/GWrALCoq4i3MdHVhuwBREITff//dvJkcAezrwQcf9Pf3P3To0Nq1a+fNm5eQkMAY4wFifX19VFTU4sWLe/bsuWTJEruOVC+N8kMSGBjIGLt9+7Z9xgQA4ISaYYCYkpLi7u6ufelsjFWrVrVr187PoEGDBrF75yHQyUALs5ubW0BAQF1dnb6ztThF0kj0DLQwy2Sy4OBgtVrN74L04S1R2m+1bdvWy8urrKxM5z0S2JrOXxuNFmYEiAAAZNOmTd9++y3TEyBqPycTVyASihQLCwt50MZ7fhljdXV11FPMy3N0ViDyRMyKi6hozIFodgszP3JBQQG7FyDSTyYgIIAxlp+fT4nh4MGD33jjDWbEkjJm27Vr16BBgyh5AXAuJ06cyMvLUyqVly9f/uGHH1avXj1z5swRI0bQuw0NDZcuXRo4cOBPP/0kflDhUBAgAgBYRTMMEC2RkZFRUlJyxyCkS40yECCyxrqYDQSIVJzo4qJ74s5Gl2chBtqrJRKJkQcBWzDQwow5EAEANCQkJFy/fp0ZXYFIDctidKuflpbGt4ivcHiYyANE61Ygjho1iqYd1F5E5bfffjt58qTlqzAbaGH28/NzdXUtLy+neSTffvvtMWPGMFtWINJaDWfOnGGMJSYmPvTQQ6mpqTb6XgC24Onp2atXr/Hjx7/66qsbN248dOgQbZfL5dnZ2adPnxbPYe1oECACAFhFMwwQIyIiUlNTzbss27hxY1ljTp8+zbTOQCBmTICoL6TTDhD50igGKhDZvVujRrM/Ay3Mxh8EbKHRFma1Wo3KXwAAwusBjQkQ16xZs3LlSo19jAwQqaGYWXsOxN69e//97393dXWtrq6maI9XIKamps6dO5d3DJjdwsz/CtotzBKJhIoQ09PTGWNeXl7007BdBSKNn5aemD9//okTJ/bt22ej7wXQlKRSKT2Ad2QIEAEArMIpV2E2zN3dPTIy0ryvlUgkbdq0MbwPTbwNBlg3QDSmhbnRw3IGKhCZias5g3U1uogK+pcBADget7Vr1077XY0AsX///tonvlatWjHGkpOTGWMSiUQQBHELs5EViGavwkyf9v7+/vn5+aWlpYGBgbwCkTGWn59v0xZmxlhAQMCtW7coW/T29qYA0XYViDT+kpKSzZs3//nnn0xPIAvgpARBMPyQ3gCag8hwl0lubq75g9MTIHbs2JHdqw4GAABjNMMAEezOmABRX0gn7lMOCwsT70n5kb4Vt6l4sNHsz/DYjDwI2ALdUmq0qGtUINplYAAAjqa6uprfbBtTgejr66u9T69evRhjly9fZox16tQpOztbHBHy14bnQDS7ApE+3tu2bZufn19SUhIYGEiRqEwma2hoKCsro+eFNlqFmd2bBpE0WQUiY2zjxo30AgEiNCcpKSn9+vVjZk0TP3PmzC1bthizp9mdKAYCRFQgAgAYzykDxIMHD+7bt+/GjRudO3eeOXNmVFSUxg6LFi3KysravXu3XYYHloR0ZrcwG1k8aEwFIlqY7UJck8KJF1ERP5qWSqVqtZr+v2mHCQBgf5RzyeXy8ePHUxeeBo0AUWd3xYABA/jrXr16ZWdn66xA5C3M1poDUXyip/STJiKkCsQNGzYsXbq0qKiIPt6ttQpzWloaLbPGfzKUHZAmq0BkjPE5dhAgApDu3buHh4cb3kelUllYhKgNLcwAAKZyvgBx5syZmzZtote//vrrli1bPvroo5dfflm8z9GjRy9dumSP0QFj1gsQNWYkNGYORCMDRMNjQ4BoF/RrY3gORL59+PDh69atmzZtWmJiYtMOEwDA/ihA7Nix43fffadzB43nZDorEPv27evq6kofsI899tiBAwcML6JieBVmUxdR4RWI7F6ASBWIHh4e7du3p9RSLpdLJBKzW5j5X6G8vDw6OpqOL25h5nt6e3t7eHhIJJLq6mp6OmXq92oUHz9/GFZZWcn0rAsH4HRoDnrzvnbhwoULFy40vE9qamrv3r3NnoPewByIaGEGADCeky2ismvXrk2bNoWHh+/evTstLW3nzp0BAQGvvPLKTz/9ZO+hwf+nMwniQkNDJRJJdna2ztoxcYDo4+PTpk0bpVJJNxKGA8SQkBCZTJaXl2e4i8rw/CyYA9GOdFYgildhFlcgenh4RERE6AuCAQCaNwoQqWhOJ3EFokwm0zl9s5ubW+/evRljgwYNio+PZ6LEjZlegWhqC7O+CkQKEGlP+pC3vAJREAQ+ZaR2C7NUKqX00MvLSxAEG3Uxa48fFYjQnNAc9GZPQ29rOgPEgIAAqVRaWFhoePpFAADgnCxAXL9+vbu7+5EjR5566qmIiIhnn3324MGD3t7eM2fOxHWY4zAc0nl6erZr1662tpYmNdegsRRvjx492L1+H/H0iNrkcnlQUFBDQwP1KOljuIU5ICDA3d29uLjYdk1MoI+BRVR0BohMf0gNANC8mRQgtm7dWl/ZzkMPPcQYmzt3buvWrZmeVZj59ZXhCsS6ujojpycTPy7SDhA9PT11BoiWzIEoxi8AeAuzl5cX/XxsOg0iAkQAO9IZIMrl8nbt2jU0NOi8JQEAAG1OFiCmpaUNHDhQPE1Gv3791q1bV1BQsGrVKjsODBhjgiAMGDDgkUceMVyByAy2G2ukhDTBJa1XaHgRFcOH5Qy3MEskEqyjYi8a2TERd66JS1bp3hgViADQMpkUIOrsXybvv//+uXPnJk6cSCWK4gpE7XpDAxWI1BxgZBGiMS3MtCeFfRauwqxRfcl/Ml26dKEX3t7e4hc2eoKIABGc3cGDB2fNmjVixIgZM2bQlbmGRYsWPf30000/MGPoDBAZY0FBQQzTIAIAGM3JAkSam0Zj4+TJk2NiYtasWXPr1i27jApIRUXFhQsXjh49SpfsBsIdAyGdRopEC7rRjJaGW5gNH5YzXB1p5EHAFgwEiPSW+L99qkBEgAgAtpOVlcX7Xh2NSQGizhVUiJubW0xMDGNMuwJROy7UDrzUanVDQ4NUKqVvZ1KAqK+FmdoUaE8jKxAFQaAjaKCrkc6dO4s38p9M9+7dPT09mejHiApEAH1mzpw5ZsyYzz///Ndff928eXN0dPQnn3yisc/Ro0e///57uwyvUYYDxLy8PDuMCQDACTlZgNi1a9c//vijsLBQvFEikWzcuLG2tvbFF1/Ekqx2RNfcgiBcv36dWSlAFFcgUnGihQGi4RZmxlhYWBhjLDMz08BBwBZ0tjCLF1ERtzDTXR9amAHARjIyMrp27frcc8/ZeyC6WStA5Nzc3Nzc3FQqFaV47P5qRKIdKfIoUPxZ3Sh9LczaFYhGzoG4aNGijh07Jicna2ynv4LG6q78AkAmk/Xt25ehAhGgMc1gDnp9ASKto4IAEQDASE4WIE6bNq2mpmbo0KFnz54VZ4XR0dGLFi06evTo888/jwsye+EP7ekeoNEAUWdIpxEg9urVSy6X//XXX9XV1ZQf6ZsDkRmX/RluYWaoQLQfnRWI4jkQ6T95Ly+voUOHPvnkkwwViABgM5mZmfX19Trb9BwBnWfpUYpOFCB27NgxKirKyKZCKkKsqKiYOnWqu7v7vn37NHbQvr7iUaD4s7pR+lqY9c2BSP9voDAwNTW1vr5eI0BUq9WVlZUSiYSuDThxtNq/f3+GCkSAxjSDOehRgQgAYBV6sxjHNGfOnJSUlM2bN8fFxcnl8vPnz9PTY8ZYQkJCZmbmzp07f/jhB9Qh2oXGNbeBcMdA0qeRIrm5uYWFhV2/fv3mzZtGtjAbDhDRwuywjKxA9PPzO378uPhdAOeSkZFx5MiRq1evlpaWVlVV+fv7BwUFBQUFjR07lq/qAHZ3584dxlheXl5DQ4OB84690AmXl85po7c6d+586tQpI4/p4+NTVFRUUlKya9eu2tpa7cCrvLxcrVaLZyLmp2wzKhC1W5h5BWKHDh1oT3EF4vr167t06bJgwQLtA9JPQ2MZhMrKSkEQWrVqxRuiiThAjI6OZoy1atWK/tjEFYj19fVY+xWcgr456KdOnbpq1aqEhAQ7js1ICBABAKzCyQJExtiGDRvi4+N37Nhx8+ZN8Xp/crn8m2++GTRo0Nq1a9PT0+04whbLigGiuMyQAsTMzMxGA0TjKxDRwuyADFQg0k2p9io6aGEG55KZmTl79uzDhw/rfHfOnDnjxo1bvXo1PcYAe0lMTGzfvj1Nn1dXV5eXl9epUyd7D0oTnXANVCBGR0evWLFi0KBBxh+TKhCPHTtWXV0tkUi0l1Sur68vKioKCAjgW3gLsxUrED08PKKiosLCwjIzM+lkzU/Zhw4d0hkgUuSnMb8N9S+3bt3a399fvF18ATBmzJiHHnro//yf/0N/bOIKRHZvbhYAB6dvDvoNGzasWbNm2rRpDvghqQGLqAAAWIXzBYgymWzKlClTpkzRfksqlc6bN2/evHkFBQUZGRlNPrSWzvgAMTQ0VCqV5uTk1NfXa7Qka6dI9MDz5s2barVaIpEYWIU5ODhYLpfn5+fX1NSISwzEGm1h7tatm4uLy5UrV4qKingXFTQBAxWI9FtBCbL4F6BPnz5SqZSh4hicQWlp6ciRIzMyMiIjI8eNG9erVy9/f38fHx+FQlFWVpaWlrZ///49e/YkJycnJibyCixoYgUFBUOHDo2IiJg6dSptycrKcrR741deeWXz5s3M4ByIMpnsjTfeMOmwtFrxwYMHGWP/+Mc/9u7dy+dD5AoKCsQBIm9hNqkCUbyIiqenp6ura3V1tUql4n3Z7u7u69ate/zxx6l4kJ/Qr169qvOAdPmhESDSjI0+Pj4aAaL48qB9+/bHjh3jf2zKCkRPT8+qqiojI1cA++Jz0IvPTTQHfWxs7Isvvvjzzz8buD53BKhABACwCof+rDdbQECASU/dwSqMDxDd3Nw6duxYV1eXm5vLN3711VfR0dE3b95k9weIVBJ45coVdv91vzaZTNapUydBEAw0IDfawty6devRo0erVKodO3YY+F5gFQcPHty+fTu9NrAKs7gCUVyCOnPmzOLi4iYbLYAlFi9enJGRsWLFitTU1Pfff3/SpEmPPPJIXFzcI488MnHixHfeeScpKWnbtm1ZWVnLli2z92BbrtzcXLVaff36dZ5GZWdn23dI2g4fPkxZm4EA0QwUn504cYIx9vjjj2/cuPGtt97i99uUDuTn54u/hH9um1SBKF5Ehd07syuVytraWqlUSifoMWPGnDx5ctu2bYyxYcOGjRs3ztXVNTc3V3shF6anApH21KhAlMlkBkrXm7ICkZ5QNnEF4oYNG/75z3+eOXOmKb8pNAPNYA56LKICAGAVzTNABLswPkBkujqFv/jii4sXL/7www9MV4B47tw5xpjGTEbGHFZDoy3MjLEXXniBMfbtt98a/l5guRkzZrz44os3btxgeioQtRdR0XjE7evr22SjBbDEyZMnIyIiDBeFTZ06dejQoYmJiU02KtBAzyQaGhouXLhAWxwwQOSFftYNEOloVHXYu3fvyZMnv/fee3x+QLrNFgeI+/btoxljTJ0DUePT3sPDgzFWVlZGr/kd/pAhQ+ik37Fjxx9//LFHjx6CIFy7dk37gDrnQKQWZo0KRMNn/6asQKQAsckqEOvq6qZPnz5nzpwdO3YMHjz4iy++aJrvC83DnDlzpk+f/tdff8XFxbm7u4sXLEpISHjmmWdoXea0tDQ7DtIwfQGin5+fh4eHQqFw8AAUAMBBIEAEq7EwQLx8+TK7N2+9dgtzamoqu3fBbdJhNTTawszuzaquUWcBtqBUKgVBOHToEDOiAlHnJJhSqVT7chDAARUXFxuzRkpISEhRUVETjAd04kXNSUlJ9MIB19SyaYBIqJ2ZMRYSEkIv6PTKz4yXLl164oknJk+ezMxdhZl/2lOAWFpayl/rFOYuCPIAACAASURBVBkZyfR0MRtoYdaoQDTcx0ABYnl5eVVV1b59+4zMQ40kDhBlMhmNyqYViNeuXZs3b97gwYNDQkJCQkK2bNni4eHxz3/+U61WT58+/b333jt8+LD2TJcAOm3YsGH79u3Dhw8PDg7WnoN+7dq1gYGBNTU1dhyhYfoCRIYuZgAAUyBABKuxJEDMycmh6JBoLKLC7l1kN1qBSGmjgQCx0RZmZuMmJhCjGypaU0LjlpI0WoGocwuAA4qPjz9z5ozhNb6KiooOHz4cHx/fZKMCDbSaBxOVoZ04cWL79u0OVYfYBAEiX9+Zz/9Ia/vwAJHK/ej/LaxApFCPolsD60r37NmT6QoQq6ur6fFSaWmpOI8TVyB6eHi0adMmJibmscceMzAw+hZ//vnnRx999MQTT2zcuNGYv46RxAGiu7s7lXbaLkD87rvvYmJi1q9ff/r06dzc3MLCwoiIiBMnTnz11VcffvihWq1eunTpqFGjhg4diuUjwBg0B/3Ro0dv3rzZr18/8Vs0B/2NGzfy8/MdtoLeQIAYHBzMGBPPqgQAAPrgxhusRpy4SSQSwyvk8qVR6I9UfsiJv7ZNmza8TdXICkR+WG3GtDDTupY0wxTYFP1zHD9+XKVSacyKRcSrMOtbhhsBIjiF+fPn19fXx8XFrV27VjuNys/P37p1a0xMTFFREc2iAHahMa2qr69venr6Cy+88Nprr9lrSNrsEiBqVCCK5yI0dQ5E+uTnxYBUdUg/ed4xrY0STO2bfH7toVarxf98vALRxcXlt99+O3LkyLlz53bu3GlgYLGxsRKJ5MKFCxSMnj592pi/jpHob00/WA8PD/qb2qiFefv27RMnTlQqlc8999wvv/ySnp5+9erVtLS0mJgYxtiiRYvWrFkzbty4gICAU6dOxcTE8HpbAEs48hz0qEAEALAK3HiD1dBFPN2BGC4/ZKYEiHxnZkSAqHFYbca0MLu5ubm4uPBIC6xLrVZTONvQ0ECZYHV1dVlZmc4KRPEqzPoqENHCDE5h5MiR69atUygUCxYsCA0N9fX1DQ8P79evX5cuXfz8/AIDA1966aXbt29v2LBh/Pjx9h5sy6URIH799de9e/dmjNFUrQ7C1gGiTCbjrcTGBIimrsJMfY4aASJ17hsIEGn1Z40+ZXb/lIXid/kqzIyxuLg4mpzEMH9//86dOyuVSgrUaPJla6HLD3ogygNEW1QgHj16dMGCBYIgrF69+uuvvx45cmTnzp179Ogh3mfhwoU//vjj5cuXH3roodu3bw8bNuzixYtWHwmA40AFIgCAVSBABKuhAJFmKTI1QKRp0Xl4ZLsA0ZgWZnbvPgpFiLYwZcqU9u3b5+bmivu5lEqlgQpEegsViODsZs2adeXKlddffz0qKqq2tjYzMzM5OTkrK8vNzW3AgAHLly/PycmZMWOGvYfZomkEiAMGDDh+/DgzODNG07NRgMirDsV9xPoCRPGCA6ZWIGoEiOIW5kYDRI2VUtj93Q/iAJG3MBszJC42NpYxRkvoZGdna+eV5qG1a11cXHgFIr2wboBYUVExduzYkSNHVlZWTpw48dVXXzW8f9u2bX/55ZeJEydWVlaOHTsWs69CM4YKRAAAq8CNN1gNXcRTsUajAWJgYKCHh0dRURHdhFClQLdu3ehdfQFio3Mg+vv7t27dWqFQ8HmsNBjTwszudTGbNw2iUqk0kGBCSkqKUqm8du2aRoBooAKR3sIciNAMdO3adeXKlRcvXqyqqrp7925JSYlKpcrPzz9//vybb75JEQnYkXYLs5+fn4+Pj0KhoGWC7U6tVvPUycCMgWbgcaTOAJE6iHmgptHCbFIFIi30bGoLc4cOHVhjAWJ5eTl/zVuYjRkSRw9B+U/4/PnzJn25TgcOHIiLi2OMubm50dWFjVqYly5dun//fj8/v2XLlm3evNmYL5HL5f/5z39oJsTnnnvOpou6ANgRAkQAAKvAjTdYDV3ER0VFSSSSRi/ZJRKJeL5CqvXr1asXvWt2BSJrrAjRmBZmZtk6KpMnT+7WrZvhpRJaMr5cpvhWExWI0NJIJBIvLy9/f3/8AjsUevjUtWtXxpi7uzuFXJSdOchyzHQWk0qlEyZMoNOotegMEP/2t7/RC39/f29v7+rqavFjP9K6dWvjKxAFQaitrZVIJPxJnriF2UAk6u/vL5fLy8vLxQ+f2P0tzOJRmVeBSAEfl5GRYdKX63Ts2DF64ebmRn9ZW1Qg0jyqUqn02LFj//73vw1EsRpcXV2/+eabgICAI0eOLFiwwFrjAXAoaGEGALAK3LeA1VAwFBoaevDgwV27djW6vzjpowCRqhfZ/aswM6sGiEa2MFuyjkpOTk5DQ0NOTo4ZX9sS0O9JUVERKhABNFC2opGPGEmtVmdnZ980CDdIjaI6uEcffZTdm66O3evedZAuZvo89Pb23rNnj+HFykylM0AMCgpydXV1cXHx8PCgUzAVIYqjurCwMOMrEGtrawVBcHV15R/dRrYwSySSDh06CIJAA7h9+/by5csrKirEj/ooNCTmVSBqBIhULGmhs2fP0gtxBaJ1A8SysrLRo0dXV1ePHz++T58+pn55YGDg//73P3d39w0bNmzYsMEqQwJwKKhABACwCpfGdwEwDl9E5aGHHjJmf+0AUV8FYufOnemFtQJEm1YgUgmGVe46miWqFiksLMQciAAaUlJS+vXrx+7d6phk5syZW7ZsMWZPMw7eQqhUqoqKCrlcPnz48PXr17dp04a2O1QFopFnMTPoDBBdXFy+/PLL+vp6mUzWvn37mzdvFhUVdenSRRwgdu7cmSo3jalA1JgAkRm9iApjLCAgIDc3t6CgoFOnTm+//fbWrVs9PT2ptZnwUWVnZ9Na56ZWIPLVY8SjtUR9fT1fn8R2FYgJCQmXLl2KiIhYt26deUeIj4//4osvnnvuuQULFoSFhY0aNcoqAwNwfAEBAS4uLsXFxSqVyhYfrQAAzQkCRLAa8SrMxqCkj/qDKEDs0qWLh4dHdXW1RoDYqVMnNze3+vp64wNEfW1HdHvTaNWGJRWIGgFiTU1NXV2d8c1EzZsgCLwCUVyrcvfuXZ3/NPRHVCACNKp79+68WFsflUqFIkQD8vLyBEFo167dgw8+GBQUNGzYMNpOAeLJkyfnzZtn99vLJg4QGWOTJk2iF3QKpqRPHCCGh4f/+eefzLgKRH0BYqMViEw0DaIgCAcOHGCMZWRkiEfLKxDHjx9PE4nYPUC8fPkyv5bgFYju7u5WXIX59u3bmzZtkkql3333XWBgoNnHefbZZ69du/bee+898cQTK1aseO211ywfG4CD0HcNyRiTyWSBgYG3bt26ffs2fdoDAIA+CBDBOsaPH0/3D8YHiF26dGH3B4heXl5hYWFXr17VSJGoAkKlUjXaeszulStaGCBasQLxoYceys7OpoVWzThaM1NdXU3XcNoViDrviumPqECEFiIiIiI1NdW8r124cOHChQsN75Oamtq7d2+dPVzAGEtKSmKM9e3b18/PT5y0xsTESCSSvXv3Llmy5MMPP7TfABmzZYCocxVmMXGAKF6FOTw8XPywxzDtAJFe00nT8LIwtMpQYWHhpUuXaD3omzdv0rVEq1atKisreax569YtxphEIml07TUNVg8Q09LS+GsbVSB+8sknNTU1Tz31lBnNyxoSEhJUKtWqVasWLVqUm5v7ySefWD48AEdgoIWZMRYcHHzr1q2cnBwEiAAAhuHGG8y0evXqsWPHUjuqIAj79u2jfMeSCkRPT8/4+Hi5XM5nbecmTpz4/PPPG3NYcS6pjaIojTkWtVGNwJNPPjlz5kxjvqn2t+ABYnJyckFBwe3bt009TrPEM1ntORB1ZrsuLi4SiaSurk4QBH1Pj5GGQLPh7u4eGRlJ68BC0zt37hxjLCYmRmP7oEGDtm7dyhjjvah2ZORMvmbQV4HIUQFgYWHh77//zk9qcrk8JCRE/LDHMH0ViKTRFmbGWEFBwS+//EJbMjMz6TqEKu+oAlGtVldUVEgkkh9//NHf37/RIYlZPUAsLS3lr20xB6JCoaAFl//1r39ZeCjGmEQiWbly5ffff+/h4fHpp58iQIRmw3CAGBISwrCOCgCAERAggpm2bt26f//+5cuXM1FZGTMxQJRKpdnZ2XV1dTxA/Pzzz/Py8iIiIsweWEhIiFwuz8vL0zkLIV2sG1mB2NDQkJiYaOoAxAFibW0t3X5QcxaIA0SNVZh1ltVIJBI+DSIqEAHAps6fP88Ye+CBB7TfGjRoEHOMdVSavoWZowrEpKSkIUOG8B9Fp06dXFxcLKlAND5ApHLC4uJimt+QMZaZmUm1kLQMQklJSUlJiUKhaGhoaN269RNPPNHoeDRYPUAsKyvjr7UrEI2JXA1btmxZRUXFQw89NGDAAAsPxU2YMGH79u0SieTVV1/duXOntQ4L4LCwEDMAgJHQwgxmomvijz766NVXX+XpITMlQHRzc6OWgezs7OrqaolE4unpKZVKTW040uDi4hIaGnrjxo3MzMyePXtqvGvSHIjs/i4tI/EA8dq1a9TWze71fAGVijCjKxBpi0qlqqurwxyI0DxkZWXdvXu3e/fuVAqdkpLy1VdfFRcXR0ZGjhkzBuWH9tLQ0HDhwgWmqwKRMRYaGiqVSnNycurr6xutYbcpuweIFy9eFJ/0afUzSyoQxa8NB4g0oaFCoRDPMkxzHVIF4qlTp4KDgw8fPswY42vgmKTJKhCtMgdiYmLiunXr5HL5mjVrLBynhmeeeSY3N/e1116bPHlyeXn57NmzrV7sX1RUlJycfO7cucrKyrKysvLycm9vbzc3t4CAgNra2sDAwE6dOrVt29bNzU0mk/3tb38z7x8UgDTawswQIAIAGAEBIphDEIQ7d+4wxmpray9fvsxnDPH29ta4+Dasc+fOt27dSk1NVavVHh4e1kqCOnfufOPGjYyMDLMDRH4fZUmA+I9//INPZ4YKRMIrEGtrawsLC8Xb9d0V88IWVCCCs9u3b9+MGTNo7rawsLC9e/cWFxc/+uijPHZZunTpqlWr5s+fb9dhtlAlJSV3795t27atztW63NzcAgMDc3Nzc3JywsLCmn54nO0CRE9PT4lEIgiC4RbmvLw8vuXQoUNRUVHs/gWvdPryyy+/+uqrXbt2USQnvlQQvzY8ByIFiJWVldSqLJVK1Wr15cuXGWMdO3akfWpra6mS1EECRHraSj9YNze3Pn36SKXSvn37Wt7CXFdXN2vWLLVavWTJkv79+1s4Tm2vvvpqVVXVsmXL5s6d+/XXX48aNWr//v03b96MjIxcvnz54MGDzT6yWq1etmzZypUrjf/rSySS2NjYAwcO+Pn5mf19oSVDgAgAYBUIEMEcCoWCX/alp6dTzWBgYODu3btNOk7nzp2PHTtGKRsv+rMcraNCVQkaTJoDkYkq5ozHA0TxXRYCRCL+eebk5Ii319fXSyQS7X8aXtiCCkRwahcuXBg3bhxjLC4uTiaTnT9/ftKkSXK53NfX9+233+7evfvVq1ffe++9l19+eeDAgVbsRgQj0eMNAxVw4eHhubm5mZmZzTVApD4ApVJpuAKR7sMZYx4eHo899hi9brQC8euvvz527NjPP//ctm1bZm4LM72rUCiorK9bt25paWk05TG1MBNqcDYvQNS4FLFWBWJQUFBubq6bm9uTTz5ZWVlJ30Uul6staGH+4YcfUlNTu3bt+uabb1o4SH2WLl3ao0eP+fPnnz179uzZs7Tx1KlTw4cPX7169dy5c80oS2xoaJg+ffq2bdtkMtmQIUOio6MDAgJ8fX39/PwqKytramry8vI8PT1zcnJu375dUlKiUqlUKlV6enpSUlJxcTECRDCPMXMgiq9LAQBAJwSIYA7xnD7p6el9+/ZljAUGBg4cONCk49CCJykpKcyqASId9ubNm9pvmTQHImOsrq6upqZGfKvTKLqDunv3LpVIEASIRLywdUlJCWPMxcWlvr7+zp07giC4urpqX9thDkRoHhISEhhjBw4cGDVqFGPs1KlTDz/8cENDw2+//fbwww8zxoYPHz58+PC+ffuuWLFiz549dh5uy0OPNwxUwIWFhZ08efLmzZvDhg1rwnFpsl2AyBjz9vZuNEDkqB6QGK5ArKqqosuGP//8c8iQIcyyFubKykpK5Xr27JmWlkanBiogIllZWczBKhAjIiIoQGSiqx1vb292547ZR961axdjbM6cOSZdopjqqaeeGj169LfffpuUlDRs2LBBgwatWrXqk08+mT9//o4dOxYvXvz4449rn5d1qqiouHLlypIlS44dO+bl5bV3717j/1Oqqfm/7J13XFPX+8dPFiSAbARBhgwZgooLxbpF24KotFZr61as2qqts1rrqKNWq62rVq2jKlJHpQ5Q8St1iygCDjYqQ2RD2CPJ74/nx33dJiGEJJAEn/cffV1O7r0598bee87nfJ7nqeHz+VLdwQgiD+hARBAEUQk48UYUgZ7TJz09vdl5V1O0qoAo1YEo59SL3hm65iUP8BVv3ryhZ4nCHIgA3YEI4eHGxsak8f5I/TeAAiLSPoiPjx8wYACoh4SQQYMG9e3b19DQENRDwMPDw8fHBx6JSBsDj3oZaXzBeKj2OiqtKiDC5Tf1NjczM6Mvv9EFRKkORJFIlJOTs379emNj45SUFELIkydPlCmiQuVAhEEI3QpqaWlJ9U0ZB2Ir5UDs2rUrkaidrcCoiYLP54eHhzOZzAkTJijZw2bR09ObNWvWb7/9NmHCBGtr6507d/7999/W1tYPHz4cN26cg4PDxo0bMzMzpVauI4TExcWtWbOme/fuJiYmAwcOjIqKsrS0vHLlSouEeC6Xi+ohogyyBUQrKysOh5OXlydPJSgEQZB3GXQgIooACRCNjY1LS0vT0tKoGsotPY+LiwtpVPpUKCDCaVNTUyU/AgdisyHM9Dkkn883MzOT86uFQiHoXPT4ZaIlDkSRSFRdXa3CH0ISuhrL5/MJIaampoWFhW/fviVNzKZgXlpXV9dUCLOc3gcEUS8FBQXdu3ent3Tu3Jnu5gY6duwIpTyQNqbZlTBHR0fShLe9LVGjgMhkMi0tLSmTDr3imVQH4pYtW1avXm1oaFhfXw/aYlxcHMhMyoQw5+XlVVVVcblcKysr6iNDQ0MjIyMwtoMDUbFYVy6XC/kK4U9VORBHjBjx+++/i+VlVkZAPHfuXE1NzdChQ6F6TBszfvz4UaNGHTp0aN++fSkpKWvWrFmzZg0hxNbWduDAgZ06dYJ3vY6OTnFx8V9//QXjIh6P5+jo6Ofnt2rVKiXL5SFIS5EtILJYrE6dOmVmZubk5Kg3SQWCIIiGgwIiogggIPbp0+f69etpaWnNGjeawsnJicFgwMhShbpVly5dWCzW69ev6+vrxaKVW1qFmbQwDSLlv9BGAfGbb77Zv39/fHw8eCVaA6kCIiEECqpInTpS81IUEBGtxtnZ+dGjR0KhkPo3vHTpUrqbG0hOTsaptVpo9kX2LgiIRkZGpNEYLhUrKysQEKdPn7506VKqHbx1YgJiUlISaXzUAyUlJdAoNYRZV1dX9tsZHIggyZmbm9MtkHQBEd7aijkQmUymrq4upRsqKSCWl5eXlpYymczx48fn5eVB/kcK2WqpbI4ePUoImTp1qjLdUwZ9ff1FixYtXLjwxo0be/fuvXXrVkVFRVZWVmhoqNiebDb7iy++GD9+/PDhw9VbwRx5l5EtIBJC7OzsMjMz1V4mC0EQRMPBFzmiCDB8d3Z2jo2NLS4uhhTmCgiI+vr6nTp1evPmDVGpgKijo2Nvb5+RkfHy5UsxLUzOIir0a2lRIWZKQKSXGCZaIiAmJCTU1NSkpqa2noBIV2PpAiJM3WU4EGWEMKOAiGgFI0eO3LFjx5w5c/bs2QOWKx8fH7F9Dhw48OLFi+nTp6uhf+88zToQoTwXvO/USKsKiOvXr4+KipJRw4cy/X366afdunWj2kFAFJPb6NIhRVxcHGnCgQh1DGTQoUMHyh5oZmYGcidgaGhI1xOJogIi9KempobD4UASZMVOQghJSUnp3r27UCg0NTVlMpli6iFRwoH48uXL27dv6+vrf/zxxwp3TyUwGAxI3koIEYlESUlJ9+/fLykpAW20pqaGzWYPHz7czc1Nvf1EkGYFRHj+ZGZmtl2fEARBtBAUEBFFAAHR1NTUxcUlOjo6Pj6eKCQgEkKcnZ1VLiDCaTMyMtLS0sS0MDmLqEh1IFZVVTXbSUpABLWLQityIEJkGVVfuzWgOxBBmaVHmaEDEWnHrF279vz584cPHw4JCfnoo49OnDhB/3T37t0nTpx4+PChvr5+69VURWTQrAPR0tJSX1+/qKiorKyMLl21Ma0qIA4fPlx2ZrpOnTrBhliAMAiCtbW19EZ6JTEKSFoiVUCkK5JSYTKZ+vr68FI2MzMTcyBaWlrSd1ZGQCwpKQE/ozICYlpaGtyQprKgKCwgXrhwQSQSBQYGKuNhVDkMBsPd3d3d3V3dHUEQKcgpIGIhZgRBENlg8QFEEUBANDExgWyDICAqpgBCwROFD5d9WrE0iAKBQCgUslisZstuSDoQHz16ZGJismnTJqn7V1ZW3r9/n0ikkKeorq5uKr+45gA9bOoSVIJkCDN9jocORKQdY2hoGB8fv3z5cldXV0lhJTw8/OHDh717975161brWYARGTQrIDIYDIhiVq8JsVUFxGahHIhSBUR5HIhQ4URqCHOzAiKhVW6hC4h6enpsNnvPnj2LFy+m9lRGQCSNcdzKvLipY5vKxqiwgHj58mVCSEBAgGKHI8g7CAqICIIgKgEFREQR6A5E0qjTKeZAhDMQVQuIUuuoyJkAkRBiY2NDTc/A7PDkyZO6uronT55I3X/FihW+vr7Xrl2Tob4pn4u9tWkDARFuJoSQ00OYARkOxPr6enQgItpOhw4dtm7dGhcXd/HiRbGPNm3alJ6e/ujRo169eqmlb0izIcxEM9IggqlN0wREqSHMIJSLZVSEpSDFHIikCQERNpycnD766CNqT5UIiMq8uCkBsamsppSds0WUl5ffvHmTzWa///77CvcNQd41UEBEEARRCSggIoogJiDCfEBbBER5cnhbWVmlpKRMmTKFNDoQIYmhpBnhxIkT1tbWERERhJC3b9/KUN9aVZhTCW3mQIR5HdxYIyMjSgGUXYW5qWkzCohIO6BXr14gTiHqQp5qYJqQBlETHIhsNlssiBsERCqE+dy5c/r6+pBN7MaNG9BI1xwVFhCpdSa6gEg10ntFr9HcImA0YmRkxGQyqewZCgCvVHd39+XLl0vdwd/fX4HTRkRE1NXV+fr6KlZmGkHeTeQpokJQQEQQBGkOFBARRRATEAHNFxDlTIAI2NvbW1tbk0ZbSlMC4s2bN3Nzc8GQUldXJ0N9EytPqYFQORB//PHHoUOH0sONVQWVu4o0/hxcLldyEkiHciA25Q9CARFBEOWR34H4LguIYJozNjYWm4eLhTCfOHGiqqoKXohOTk6ws5eXl9j+AI/HYzKZHA7H1dW12Q5Q7wsHBwdKLhSzIhJC9PX14Q2uACBo8ng8qbZK+YEDR44cOWjQIKk7DB48mH4f5CQsLIwQMm7cOMV6hSDvJlhEBUEQRCWggIgoQklJCSHE2NhYeQHR2dkZXueqFRC7dOnC4XAyMzPpOd3lD2EGQM8Co1xhYSEhpKqqSmwf+tSiKQERom61RUCsr68/derUzZs3nz9/rvKvAFGS7pvQ0dHp2LEjbEudulNFVOCHkBQZUUBEEER50IEoD126dGGxWPb29mLt9CIqIpHozp070K6jo2NoaAhKX1MCoo6Ozs6dO3ft2iWPmka9Ary8vKAoM6HphpSkSKmWCgACIpfLlZrYUX7glSrjolgslljhl2apq6sLDw8nhIwdO1axXiHIu0mzAqK5ubmenl5JSQmMNhEEQRCpoICIKAKV2MjIyIhSfxRTAPX09Dp37qzw4U3B4XAcHBwEAgF9ptdSARH0LNkORNkCor29va2tLaxqalEIM0xQpea/VxJJAVFXV5fKDyXVgUgVUUEBEUGQVoLP5+fn55PmHIhQngvqCKsLtYcw37t37++//xZrp5v1kpKSYMmNNCp6ME5wdHSkbq+YrLZw4cIvvvhCng6AVshkMj09PVksFgi+dBs7CAT01c2WoloBkQrQlgrcN/m5fft2WVlZ9+7dMeMBgrSIZgVEgmkQEQRB5AAFREQRSktLSePEgCoYqpgDkTQO9FUrIBJpUczKOBCbEhDpDkdJAfHff/9NS0uDuY2GOxDr6uogl2VDQ0MbC4jyOBApARFDmBEEUS0ikcjT0xNS2cp+kdnb27PZ7OzsbDUWxYLnc0uFJxXSr18/SBZGh+5ApOyHpFHag1UiExMTWC8kMn15soETOjo60qVDSkBksVjwjgCpVzFUJSDCgbIFRPkHJMCVK1cIIR988IFiXUKQdxZ5BER4smEUM4IgiAxQQERajEAgqKioYDKZoK8pLyBC1VFY91Mh0LGUlBSqRf4iKgDMQ2QLiPSpRW1tLV1ANDAwsLe319HRocqAKHQdbQR1aW3sQNTR0ZHHgVhXVwdWUHQgIgiiWvh8PmU5ke1A5HA49vb2QqHw5cuXbdI1KajXgdgUdK2NbvyHhUaYlltbW1NlUhQWEOEVQEVDiwmI1DdqiwOxpb8jCIhYfxlBWoo8AiIkZ3j9+nUb9QlBEEQLQQERaTFlZWUikcjQ0BBS+ykvIG7cuPHBgwdjxoxRWRcJIdIExBYVUSGNExXQrSAgq0UhzB4eHjBSoTx0ilxGWyEpIEKgumpR3oGIAiKCIKqFirclcrzIwNqmxjSImikg0kOYIUsyANLe9u3bQ0ND/fz8AgICoF1hARHWGvv16wd/glyoyQKi7CulD0hmz5796tUrGTtnZmY+hFclegAAIABJREFUe/bM0NBw4MCBinUJQd5ZMIQZQRBEJaCAiLQY0JWoVOVU2USFBURdXV0fHx+V9I0OCIjJyclUi8IhzBUVFTATaFEIs4eHB2ygA5ECBESowgzQcyBiERUEQdqeoqIialu2A5E01lFRYxpEzRQQ6SHMdAERhgrW1tYTJ05kMBgBAQHwxFZYQJw9e/aVK1e+/vpr+FPSgThz5swPP/ywT58+Cl5J2zoQ6QOS8vLy69evy9j54sWLhBA/P7+WBj4jCIIhzAiCICoBBUSkxUACRGNjY/iTciCqPImhkoCyKRnCLP/I29zcnBBSWFhI+VOqq6thCEIh24EIG9olILZeDsSGhoaamhoWi0Wpz+S/VZjlKaIiOweiZJlsBEEQ2dAFRHQgKgaLxWKz2QKBoL6+XtKBSGFubu7n56erq6tw0hIOhzN69GgqBaSkgPjNN99cvnxZmQEJiAi2trYgIF69ehUSBLcUBXIgPn78WMbOFy5cIIQEBgYq0BkEecdBARFBEEQloICItBgxB6KTkxMoOM0aN9oYGxsbfX39vLw8UDxJy3MggrCVn58PCRAJISKRSMyMII8DUUtDmFUuIIK6p6enR0//Tw9hliogUg5EeXIgJiYmqrbPCIK0e1rkQITYWHQgSkKZEOGdCz2kLxcB58+fz8rKsra2VsmXOjg4UP9VFfPnz4+Pj589ezZofxs2bAgLC1PgPPKEMIv9jk0JiCKR6I8//oiKimKxWP7+/gp0BkGQZoEciGpMcYsgCKL5yKukaAiVlZUMBkNsYfnevXsnT55MSEgwNjbu3r37F198ofJyHAgdMQeirq7ujBkz3r59a2JiotZ+icNgMLp27frkyZOUlBTIl9TSHIgmJiYcDqe0tDQnJ4dqrK6uphsKpDoQdXV1zc3NBwwYAO3a5UCsq6uDG6VyARHil/X19cUERGp+JXXqLuZAlC0garhKiyCIpjF58uSrV69SfzbrQAQBMTU1tXW71TSaLCBWVFTU1NSAA9HJySkxMVHMgUhoocEqYePGjRMmTFBtFhQmk9m9e3dC0/7oWTLlp6UhzISQhISEuro6sR9XJBIFBQWBiPnVV1/Rc4AgCCInQqGQEALZ25vC3t5eV1c3KyuroqJC01wRCIIgGoKWCYgGBgZubm50k9Hq1au3bNlCRZVeunRp9+7d+/bt+/zzz9XUx/aPmAOREHLw4EH1dUcWICAmJyeDgNjSEGYGg2FhYfHmzZuMjAyqUSwNotQqzOPGjQsNDaXatUtABJmPtKaASJ896ujoUNKzYkVU6MNBkD4RBEHk5MKFC9RDT09Pr1mLepcuXVgs1uvXryWFnrZBYwVEqo4KCIiurq5SBUTVwuVyWyOHMnVy2JBMfywP8oQwi/2OtbW1mzdvHjt2rLe3N9UYFRUVFhZmbGy8d+/eyZMnK9ATBFEV6enpkZGRL168KCoqqqqqMjMzs7GxsbGxGTNmTKdOndTdO1nIE8LMZrOdnZ2fP3+ekpLSq1evtuoagiCINqHdIcwRERGbN282NTXdu3fvs2fPkpKSDh48qKenN3v2bIxkbD3EHIiaDKRBpOqotFRAJI1RzK9fv6ZaxCYSUkOYxb5Cu0KYqTSCbeNA5HK5ZmZmurq6Ojo6MhyI1dXV1dXVTCZTdmYrDb/JCIJoGtQzfODAgVu3bm12fx0dHXt7+4aGBrGCuTk5OWfOnPnyyy9NTU3ppnUxysrKFC7KAWisgAhyW3V1dVlZGYPBCAoKMjAw8PX1VXe/FEdJAVGeEGZJwXr9+vW9evUKDAy8c+cOtOzfv58QsnjxYlQPETXy8uXLDz74wNnZed68ebt37w4JCQkLC/vjjz82bNgwd+5cOzu7jz/+WHYZcfUij4BICHF3dyeYDwdBEKRptMyBKMbOnTuZTObly5ep9WdXV9devXr169fvhx9+CAkJUW/32iuSDkSNRVUCIj2hsgwHYlMCopY6EOGHViGQxNDAwIAuIBobG7NYrJCQEJFIJLWeMtzM4uJikUjUoUMH2YM/dCAiCCI/DQ0N1EPjq6++mjhxojxHubi4ZGRkpKamUjXE4PDz58+zWCyBQBAdHR0UFCR5YHl5uYuLi4uLy927dxXus8YKiPBgz8/PFwgERkZGU6ZMmTJliro7pRTDhw8PDQ2tqqpSrDyXPCHM9Dcak8k88eef58+fv3bt2sWLFyMiIl68eFFaWnr+/HkOhzNnzhwF+oAgKqGoqMjPzy89Pb1bt25jx4719PQ0MzMzNDTk8/nFxcVJSUmXLl06d+5cfHz8nTt3LC0t1d1fKYiVQGwKFBARBEFko90CYkJCQq9evcSiV3r16tWnT5/Y2Fh19ardgw5EalskEtEdiFT5EbGpHVUGpOVX0HZICogQMqxCKAcidX/YbDYEuEmdbANw9yAmTmqVFTooICIIIj/0B7j8qeWcnZ2vXr0qVkflxYsXhBAo19tUhsS7d+8WFBSUlJTU19e36E0k2WcNFBDBavf27VtCiKblRFaMzz77LDc3d9myZfT3/o4dO27cuHH69Gm6HX7//v08Hm/atGn0w+UJYaajq6v72WefffbZZwUFBbNmzbp48eK2bdvu3r3b0NCwdOlSVZWdQRAFWLVqVXp6+pYtW1auXCl1h3Xr1h05ciQ4OPj777///fff27h78oMORARBECXR7hDm+vp6qn4rHWtr69zc3LbvzzuCFjkQ3dzcGAxGSkoKTOpAXZK/CjNp2oEIWlhtbS19SZPKgSj2FdrlQKSsFip3IErmQDQyMmp2MAd3D8qkooCIIIgKUUxAhDoqKSkpVItAIKAX7qR/RAeMhw0NDfS8ui2loKCgRb1tM9qfgEgIAZWQLiDu27fv8uXLz549o1oqKysXLFjwxRdfiB0rjwORDvVmtLCwWL9+PSHk4MGDL1688PDw2LBhgxIXgSDKcuvWLVdX16bUQ2DGjBlDhgyhQu81DTlDmLt160YIiY6O1vBBO4IgiLrQbgGxb9++CQkJYqZ0kUj0/PlzqKCHtAZaJCDq6+t37ty5trYW0rIo7EAE9Qqoqqo6f/58hw4djh49Sp98kuZCmDU8PZ9UB6KcER9yIpkDUZ5JJhXCTJqoskIHBUQEQeRHMQERIpfpKiHUVKH+lC0gEiWKOAuFwtzcXAaDoYF+NHiww/JtuxEQQf6j1tUEAgEsKEIoBpCdnS0UCmtqasQUB3lyINKhJ/fw9vaeNGkSIcTY2PjcuXPyq5AI0hoUFBTIUyPF1tY2Pz+/DfqjAHIKiJ6enp6enjk5OZMnT759+3abdA1BEESb0D4B8eXLlx9++OGiRYv27Nnj4+OTnZ39ww8/0Hf48ccfU1JS3nvvPXX1sN2jRSHMhBA3NzdCSFJSElFCQKRTXV397NkzkUj09OlTsVz47SwHYkNDg2KJn5pCMQER7h4IiOhARBBEhVACIoPBMDc3l/MoSQFRLJxZqj5YX18fHR0N200pjM1SUFBQV1dnZmYmvyzVZrRLByIod9T7MScnB97yYgIibFBvT6ClDkS6gEgIOXXq1Js3b5KSkmAYgyBqZMCAAffv3xd70ImRn59/5cqVAQMGtFmvWoScAiKTyVy3bh0h5Ny5c5MnT8ZRJYIgiBhaJiAOHjzYzMwsIiJi165dX331FcR0UAJiQ0ODt7f3qlWrPDw8vvvuO7X2tD3z5s0bQohm5kiWpDUERJgV1NbWigmIVAizVudApIuGqo1iVt6BiAIigiAqBAREfX39b7/9VnaFdzoODg46OjrZ2dnUY1NsXp2Xlyf58CwuLqaergo7EKG+swbaD0k7dSCKhTBTRWbpvy9VdJv+9hQIBHV1dUwmU0wWlIHknp06ddKWsRbSvlm4cGFDQ0P//v137dpFTwsO5ObmHjp0qG/fvvn5+TNnzlRLD5tFTgGREBIUFLRr164uXbpkZ2efP38+IiJCY22VCIIgbY+WFVG5efMmIaSqqiojIyMtLS09PT0tLY0azwkEgri4OF9f32PHjunr66uzo+0XoVD48uVLBoPh6Oio7r7IBQiIkA5ZaoJC2dCNlgwGQyQSUQJiTU1NUyHMUnMgal0IMyFk1apVhw8fZjJVs9IgKSCampo2exTcPdBqUUBEEESFwDPcyclp06ZN8h/FYrGcnJwSExNTU1MhXwpdEIQ3RXJycr9+/ehH0fPoKexABK3KxsZGscNbFXAggoCoLTEKzSIWwkxluqQ7ECkBkf72hHdWi4yiGugqRRDAz89v9+7dixoxMjIyNTU1NDSsqKgoLi6GMndsNnvfvn3jxo1Td2elI7+AyGAwvvrqKzabPX/+/Hnz5hUVFY0aNerq1aut30cEQRAtQMsEREBPTw9SVIi1czic169f29nZqaVX7wg5OTk1NTVWVlbNZqPTEOj11EBdapEDka5EGxoalpWV0QXEdhnCDDMlCwuLysrKY8eOLV26VPL/NcVQJoQZwByICIKoEBAQ5feIUbi4uCQmJqakpICACJacKVOmQOaHf/75JyEhQaqAqKenV1VV1S4FRLiNILG1G9+cWAgztWLdrIDY0vhlotC/QwRpM+bNmzdy5MhDhw5FRkYmJibC/+ksFsvCwqJPnz5BQUEzZsywsrJSdzebRH4BEfjss8+WLFkCOdAjIyP79++/aNGiTz/9VMYh1dXVFy5cKC8vnzVrlvxfhCAIol1oWQizbJhMJqqHrQ0Eajk5Oam7I/JCFxAVCGGmK1Zgl1NAQNSuEGaYUXt5eX3wwQeEkPj4eFV9BSUg6ujowNBKHgGR/hOggIggiApRWEAUS4MIBpyZM2eGhIQMHDiQSHtywtqMi4sLl8vNyckRy5cnJ5osIIKBDpQ1zeyhAoiFMEt1IErNgaiAgIgORETDcXFx2bp1a2xsbFVVVUVFRWFhYV1dXW5ubkxMzLfffqvJ6iFpuYBoaGgYEBBAHRsdHT1r1iwXF5fff/+9qUMmTZo0adKkOXPmRERECIXClStXzpgxQygUwhnmzZu3ceNGpa8DQRBEzbQrAREQiUS1tbVisaWIqgAB0dnZWd0dkRcrKysTE5OSkpK8vDwFBES6AxHUrurqapgHSuZApAREqTkQNVzbEquXoqOj4+XlRQh5+vSpqr6ioqKCNIqAcIvkCWGmhy1jCDOCICoEnuEKCIiurq6EkOTkZPgTBESI2+3RowchJCEhQewQUJQMDAycnZ1FIpFiJkTNFxABzczSqABiIczyOxDhnxY6EJF2CYPB0NfXNzMzU1WKmzagpQIiIeSzzz4jhHTv3n3YsGG+vr7V1dVpaWnz5s0LCQl59uzZ1KlTnzx5sm/fPmdn59u3b6empl68eBEO3L59+4QJE7Zu3Xr06FHIvhUeHr5///7169eXl5e3wsUhCIK0HVoZwiybhISEnj17ksZXRYsICQk5cuSI7H1ABFHg5O2DjIwMolUOREKIu7v7vXv3Xrx4oaSACGpXVVUVTAyqq6ubyoEo9hUwK9BwUVvMDqNaAZHP58fExMAcDAwdXC63trZWnjxZdNEQHYgIgrSUtLS0s2fPLl68WNLhpbADUUxABDkJFpkgqDkhIUEkEtEnq5QlzdLS8tmzZ3fu3Jk4ceKECRNalH5RkwVE+m3UzB4qgFgIM9SYJk0UUZF0IGIOROTdQSQSQaiNZkrhCgiIgYGBISEhPj4+jo6O1dXVhw8ffvv27caNGxcvXuzp6RkVFXXmzBmBQFBfX+/v7z9o0CCRSDRu3LiIiIioqCjqJMePH+fxeN9//z0hpKGh4fbt2x9++KHKrw5BEKTNaIcCojJcuHDh+vXr8uz5zgqIWhfCTCQExBYVUdHX14eM+ITmQGyqiApVhVkbQ5ipeRHA4XCoabDyJ1+2bNmBAwdA/gNNFsaX8oQwo4CIIIgyfP7559HR0Z06dZo2bZrYR6oSEOkORCsrK0tLy7y8vMzMTHt7e+oQKgciHHvq1KnU1NQjR460SECEevQWFhYt7XAb0C4diGIhzAUFBbBBORCFQmFeXh5sUwKiSCQKCQkh6EBE3iWUMXDcunXr5MmTsveBx6zC8y8FBEQGg0ElPeTxeAsWLCCE3Lt378aNG1FRUSwWq66uTigU2tnZZWZmhoeHs9nsDRs29OjR4+zZs76+vh9//PHo0aNPnjx57NgxCGQmhJw7d+69994zNDRU7CoQBEHUTjsUEF1dXZ89e6bYsX/88cfcuXMFAoGMfV69ejVnzhwtMu2rFlhpp0+KNB8PDw9CyIsXL0CuapEDkcVicblcmDyAA7GiokIsB6KBgUFdIzKKqGh4FeY3b97Q/9TR0XF0dNTX18/Ozs7Nzc3Pz3dwcDAyMlLs5FCXE9y7MB9TTEDEEGYEaYqzZ8/KyM0EvIMO+kuXLkVHRxNCYmNjmxIQFXB+dezYkUqOYW5uXl5ezmKxqDlht27d8vLyEhMT6e9KsGDzeDwQEGNiYgghubm5r1+/lv+VCr8g3RqvOdCrY8Fzvh1AD2Gur68HCYPQBMTy8nJKGqAExNjY2K1bt5IWDpZQQETeWX7++ecLFy7Is2dbCohSOXDgQJ8+fUpLS8eMGbN9+/aYmBh/f//33nsvISFh06ZNXl5eXl5e69atg51nzJhx9OhRFou1cOFCOzu7b7755vDhw8+ePYuOjhaJRH///be3t7ejoyN18sTERFdX13d2jokgiFbQDgVELpfbrVs3xY7V19cfNmyY7H0UVifbB/n5+YSQjh07qrsjLYASEKEmZosEREKIgYEBKIZ9+/b9/fffr127Zm5uTmgC4tixYw8ePKinp6e9VZgbGhrgl6XQ0dFhMpmurq6xsbFubm58Pt/W1vb169eKjb3ogV0tFRD19PRYLBbI+s06EDVcpUWQ1uOvv/5CB70kYWFhsPHkyRPJTxV2IBJCXF1dHzx4kJKSwuFwRCKRkZER9Xh0c3O7ceNGUlLS+++/T+1PhTBDARZqtSM6Olp+mQmepc0+CdUCpcO2m/hl8t8Q5sLCQpFIxGQyhUIhJSDy+XxqZ+pNBzpj7969m82KQwcFRESrUcbAsXv3bn9/f9n75OTkbNiwQWFxTVUCopOTU2ho6Lp161auXOnk5AQhWdeuXXv8+DHUHqRz+PDh5cuXM5nMrl27VldX79q169WrVw8fPty3b196evqOHTvs7OwePXoEpvIjR47MnDlz0aJFv/zyi5KdRBAEaT3aoYCItCraKCCCoPzixQtvb2/ScgFRX18fopaGDBni5OSUnp4O0b5UCDOXy+XxeBwOp76+HnwKWhfCnJ+fL2a8BdHT2dk5NjYWJkhZWVlVVVWKOV8kBcRRo0bp6uq6uLg0eyzk6oY+oAMRQZri0KFDc+fOlb3PO+igp4Se+Ph4saSERDkBsWvXrg8ePEhOToZwXfpyiFiAM0AJiPApxYMHDz755BM5v5ReikrToHrVbuKXCSFsNltHRwciDGAkAGtpUgXES5cueXh4fPDBB/Azde7cuUVvTMyBiGg1yhg47OzsgoODZe/z7NmzDRs2KHZ+ojoBkRAyevTo0aNH01ssLS2bymzo5uYGGzwe7+XLl1OnTj1+/DhEQxNCMjMz7ezsNmzYsGTJErAt79mzZ+rUqb169VK+nwiCIK3BOzSLQJSnurq6vLxcV1dX4VBWtdC5c2dDQ8P8/HwIpG1RDkRCCxbj8XiTJ08mhIDWRlVhhkE/KG6glGldCDPEL5uZmVEtlIBI3w2Kx5WWloqVnxYjLS1t6tSpiYmJVAu9xDMYOvbu3fvs2TM5J1eUbog5EBGkKYyMjEY2R//+/dXdzbYGpBxCCJ/PT09PF/tUGQERpoVJSUmgJdFLQlEf0fenciCamprSF+EePXok/5dqsoAYFBQE19WeHIiEFsUMC6iOjo4sFquyshLe6XQB8d9//509ezZR9GdCARFBWg8VCojKMG7cONjo1avXypUrfX196+rqVqxYMWHCBFhzEggEe/fuJYQUFxefPHlS9ngbQRCk7UEBEWkBsPyumenbZcBgMNzd3QkhcXFxRKEQZtigQs8AKoQZJp/NCoia7EAEaZUeRidVQOTz+Xw+39HRcciQIf7+/j/99JPUs506der48ePHjx+nWiQdiC0CBUQEQRQDlj1gdQReAXRUIiBCvGqzDkQqByKheVIIIfS1FtlAnS4dHR14Pmsa5ubmhw4d0tfX9/PzU3dfVAkVxQxDoI4dO8IaKhRihn9gFEVFRVRjs5Z5MTp16qSiLiNIqxAeHj5v3ryRI0fOnTtXalKIZcuWTZgwoe07Jg8aIiB+8MEHw4YNW7Zs2ePHj7ds2XL37t1ff/0V8iEyGIxly5YRQq5du0YImT179ueffx4YGEgfQiMIgqgdFBCRFqCN8csARFWkpqYShUKYYYPL5dI9JvQQZtKouIHvQOtCmMGBaGdnR7U05UBMTEwsKSl5+PBheHj44cOHpZ4NZlAwuQIkHYgtgpqGtSiEWSAQiJXJRhDkXQOknL59+xJpUh19EailgAiYmJgo6UC0tbXV09PLzc2lPwapEGZCExA5HE5hYWFhYaE836jJ9kNgzJgxFRUVVN3S9gFViJkSEOG3ht+d7kAkhNTW1tbW1ir2S2lXeTrkXeOLL77w9/ffv3////73vwMHDvTu3VsyVd/169fPnj2rlu41i4YIiDwe78aNG/QF+C+//PLixYv+/v4hISFbt27t1KlTdnb27t27z58/TwiJjIzs16/fpk2b6urqzp8/P3fuXMpWjyAIoha0KQdiQUHBvXv35N9/7NixrdeZdxPtFRA9PT1JY+ixwg5ESQGRHsIMU1CpAqK2hDDLEBAhw2N5eTm9WDNVj1KM4uJi8l9fhlociB9++GFcXNyLFy/oodkIgmgIcXFx27dvX79+PSShVwmZmZnffffd4sWLqQRS8CDq16/flStXJAVEZRyITk5OHA7n9evX4OCmOxChAtWTJ0+SkpJ8fHygUVJA1NXV7d69e0xMTFJS0nvvvdfsN8KDVDNLMLdjxEKYLSwsJAVEfX196jXH5/MVcyAiiMby119//f77746Ojlu3bvXy8nr8+PHSpUu//vrrLl26aMtsS0MERKkEBAQEBATAtp+f359//rlo0SJCyNy5c2/evPnixYvvvvuOzWbv3LkzLy+vtrb26NGj6uwugiDvNtokID5//pzKHCEP71ShybZB2wVEQLEciLq6ukwmk578saGhAeRCeggzGEkMDQ3pZ9DGEGbQQK2srAwNDfl8fs+ePWNiYsrLy9PS0qh9qCzyYkgKiJQDkclkKjBXV0BAzMjIgBiQffv2rVmzRvZRJSUlycnJvXv3bqm4jCCIwhw9evTkyZPOzs7r1q1T1TnPnDlz/PhxDofzxx9/QAvdgSiWlJA0CoiK5Z7T0dFxdHRMTk5++PAh+a8DkRDi5ub25MmTxMREMQERVlBAQLSxsXF3d4+JiUlMTJRHQNR8B2K7RHYIMwiIVlZWVIbNsrIy/KWQdsaePXu4XG5kZKSjoyMhxNXV1cPDY9CgQV988cXw4cNRK1chc+fOvXz5clFR0ZgxY/bs2VNbW7tv377ly5evWbMGjAjHjh0bOnTo9OnT1d1TBEHeUbRJQBw6dOjTp0/XrVt37tw5Qsi8efO0q5RHO4AaPau7Iy2GXhhOMQcizDDFpoiglMGEEFRC0NRMTU3pu2l+CDMIiJIORAaDceDAgYKCgrt374KASK9CUFdXV1lZKWmHERMQa2pqqBLPPB5PgeVfGJvyeLxmxV8QEE+ePLl7925o2bNnz/Lly5lM5rBhw4yMjC5fvgztjx49+vnnn0eNGpWUlLRz5876+vphw4ZduXJFM5OLIUj7A1ak5M8AKA9gkaafE6QcEBCTk5OFQiG9CLUyDkRCiJubW3Jy8v3798l/HYiEEEi8S+8J3YHo4+PTuXNnf39/SHsnqWxKBWUptSAWwizVgdipUyfq5YgORKT9kZSU5OvrC+oh0LNnz927d8+YMWPbtm3KFEduMzTZgUjH19c3IyPj1q1bfn5+bDabzWYvXbr02LFjz58/J4SMGjXq2rVr8+fPNzMz27t37+vXr6dOndqhQ4cFCxa8fPmSx+NhKlUEQVobbRIQCSGenp5nz57t3bt3bGzsihUrMF9MG6O9DkRra2tTU1MQthTLgQgCophmTS9eTBeexAREzQ9hzsvLI4R07tyZaqEuZ+LEiYSQhIQEQoiYA5EQUlJS0qyAqGT8MmmcMMszbW5oaKirq1u4cCH0gcfj5efnh4eH5+bm3r17lxASGxvLZDK//PJL+DM0NJQQwmKxDA0No6KiJk6ceOrUKSyFiSBtAPi1VSsgwloIdc66urra2loOh2NpadmpU6fc3NzMzEwHBwdqfyUFRHd393/++QeUI7HlJUkBkV5ExdTUNCsrixACWa5QQNRkqBDmjIwMQoiNjY1UAZHan8/n4y+FtDOqq6uFQqFY47Rp0/bt2/fzzz/Pnj2bvgKtmWiLgEgIMTQ0pCKaCSEMBuP48eOHDh0yMDBYv379ihUrdu3aNXbsWLiiVatWEULu3r178eJFS0vL1NRU+iIZgiCIytHKR8ykSZPU3YV3FCoBkLo7ogiUCbGlFjOYA8AUokOHDiwWi/ooJyeHNMqF1GnFIp2JNoQwv337lhDSqVMnyuIndpfASSHmQCRNRDFDbkRKQFSyggr17fK4ORoaGq5cuVJcXGxubh4UFLR69WpCyI8//khFMW/YsMHX1/fu3bsmJiY9e/YkhDCZzIiIiJs3b5qYmISFhc2ZM0eBHiII0lLAz5WSkkI5lMV48+bNixcvWnROEBBLS0thdYduBJNU9IgqBERqW+zNCB/RlUG6A1FsNzkvE2UptQA/2Zs3b54+faqrq9ujRw8qhPnw4cMRERHkvwJiWVkZOhCRdoaLi8uDBw9gsZmCwWD89ttvtbW1s2bNkpQm7UeQAAAgAElEQVQXNQ0tEhAl8fb23rt379atW7lc7i+//LJ27Vq4HE9Pz+7du+vp6YWGhlZWVmZkZNy8ebOpk0Cy3Rs3brRhxxEEaYdopYDYt29fZ2fnlmayQ5RHex2IhJDp06c7OTmNHj26f//+LTqQ7kBkMBj0/IZSHYjGxsZiq38aHsIsEolgUGhpaSlbQMzNzRUbPkrWURGJRNBI1aZU3oEI3y6nAxFMhcuXLz937tzUqVOZTObDhw+Li4uhqMI///xTXV39ySefZGVlXbt2zdfXd9u2bX5+fj179rx586aBgcGJEycgSQKCIK0KCIi1tbVg7JLE39/f29sbdpMTWAshjcod6Djw6GhtAREWJChglJKRkQG6IWlCQHR2dtbR0cnMzKQ/J5sCBEQsotLGWFtbE0KOHz8uFAr79evH5XJBQCwpKfnqq69A/EUHItK+mT17dk1NzZAhQ6Kjo+laYe/evZctW3b9+vWpU6fSM19rIFotINJhMBiQzmvPnj1Pnz6Nj4+/fPky9V7YtGmTnZ3d1KlTCSG5ubne3t6//vorfHTo0KG7d+9u3LhRbV1HEKRdoJUC4tChQ1NTU21sbNTdkXcOSmZSd0cUYebMmWlpaVeuXBGLL24WuoBI/hunRs94SE1BxTJhEY0PYS4uLq6rqzM2NuZyuVR8t5iACLJpcnKy2LGSAmJ5eTlcqcodiHIKiJAmxs/PjxBia2u7fPnyYcOGbd68OTo6evLkyQwGw8HB4eDBg/r6+hYWFnfv3v3mm2/gWC8vr59++okQsmLFCo39sRCk3UApg01FMWdnZ9fV1UH+BDkBByJ1TroRzMPDg0h4/WpqaohyAiJMR01NTcVqSevq6np7ewsEgoMHD0ILvYgKBZvNdnFxEQqF8kQxg8iIslQbA4tP//77LyFk0KBBpHEY8Pz5c+rtJiYgogMRaWcsWLAgODg4OTm5f//+XC43Pj6e+mjDhg0TJ048efKklZWVnNkY1EK7ERCBoKCgBQsWwPbQoUNTUlLAWvi///0vKyvrxIkTd+7cOXr0aFxc3Jo1a5YuXerr63vy5ElCyM2bNyF8SjanT5/evHlzq14CgiBailYKiIi60GoBUWHoIcxEIg0ikXAgSgqUGh7CDD+rlZUVoSWIlOpAFEuASKSFMEPyQUJIRUUFDNdU5UCUZzImEAggsRr8KISQLVu23Lhx49tvv2Wz2SdPnkxPT4+JiRErk00RHBzs7u6enp7+559/KtBPBEHkpKysjHokNhXACzYu+aOYq6qqoDAudRScgR7CLHY2JR2IBgYGtra2hJA+ffpIzku/++47QsjGjRvhW+g5EOmAsilPLkj0tamF3r17U9tDhw4ljQJiTEwM1d6lS5d+/frBvzR0ICLtkn379h05cmTEiBGdO3eG0R3A4XBCQkJ27dplbW0NSzKaSTsTEMWwtrYeNmzY4cOHhw8f7unpKRKJBg0a9P333xNCysvLf/755/v374NDXygUrl69WvYyuUAgCA4OXr16tYyAaARB3llQQETkRSgUFhQUMBgMLQ1hVhgZDkRCCJvNBjWKUtwkHYgaHsIMQwp5BMSXL1+KHSvpQKQERKFQCNKh8gIi3FKxO98UYGuS/BWALl26mJubN3Usi8WCOT9VxBlBkNaAHpgsVSJsaGiAuaj8AiJlP6SOknQgqjaEmTTqklDlWYzAwEB3d/eCgoK4uDjSRAgzaUkaRJSl1EK3bt1gANCpU6dhw4aRxnXE7Oxsah8jI6Po6Ohvv/2WoAMRaaewWKzp06dfv349IyNDLGMDk8n86quvUlNTc3Nz79y5o64eyqZ9C4jAjBkz/ve//0VGRtrY2LBYrIaGBi6Xy2KxfH19IRG8tbU1l8s9duzYjh07pJ5BKBRWV1fHxcXBatyBAwfa9AIQBNEGUEBE5KW4uLi+vt7ExKSlRUi0HXAUUsZDMQeiqakpDEeoEsZNORA1NioWBEQwlsrOgSipgcoQEEnj7F35EOYPPvhg/fr1UBGlWerr63V0dBSeY3/88ceWlpbx8fH37t1T7AwIgjQLOIXhOUPXznbs2OHj41NUVARiGWm5gAiPYkkB0dLS0szMjKqvAigvIAYEBHC53DFjxkj91NfXlxDy8OFD0rSAiA5EDYfD4fTo0YMQMm3aNHhFSq5mwToi/LesrAx/KeTdxMrKauDAgeruhXTeBQERsLKyys7OfvDggamp6fz589+8eXP79u2tW7eyWKxvvvkGApn//PPPxMREupP06dOnv/3228SJE62srCCZOCHk3LlzPXr06NmzZ0NDA/0rnj59Ghoa2rdv3z179hBCcnNzZVfRuXPnzpIlS+RJ9YtoGuXl5cuWLWtRMhmk3YMCIiIv72b8MiFk2LBhe/fupVKBiM0cqFBZNzc32GjKgVhfX09/VbclycnJpqamP//8s1h7dHR0YGDg3bt3iYQDkdoA6E4KyCgPSIYw0yVFmL0r70Dk8Xjff/+9l5eXnPs3ZT+UBx0dnZkzZxJCfvvtN4VPgiCIbMCBCMa9xMREau5x+vTphw8fRkVFKSwg9u3b19DQMD8/v6CggF5EhUirowICImUwV4Avv/yyurrax8dH6qf9+vUj/xUQJZ+BYAxBB6ImM2/evN69e1MZx2QLiKWlpZWVlQwGA38pBNEc3h0BEejTp09RUdHPP//csWNHJpPp7+9fVla2ZMmSwMBAc3PzFy9eeHh49O3bF1bUBALBhAkT5s+ff/bsWT6fD1E4Dg4OtbW1CQkJ8fHxc+bMOXLkSG1tbUNDw7Nnz7y9vT/99NNHjx6tW7du+/btnTt3Xrx4sYzOLF++fMeOHRcuXKA3Xr58Wf58QVQ5MqSN2bdv3/bt23/44Qd1dwTRIFBAROTlnRUQWSzW/PnzqWqbkg5E2KB2kHQgMhgMfX19kUikrsW3mJiYkpKS8PBwsfbjx49fvHgRcvzLE8IMuLq6Utt5eXmUKioSiW7fvn379m3qU1U5EFuKMgIiIWTu3LksFuvMmTNQdhxBENUSGRk5ZcoUQkjXrl2tra2rqqpevXoFH8EKRHx8PCUgFhYWyvl/YlZWFiHE1taWCgqm50AkhHh6ehJCoM4SoLwDUTZ0AbGpHIhdu3blcDjp6enNpg/DKszqYtq0aY8ePaLiDOjDADMzs86dO8NLB9ofP34sFAr19PSYTBxjI4im8K4JiJLAu4PNZn/00UfQ8vjx4x9++OHEiRM9e/akl0msra1lMBiXL1/u1asXvB+PHj06e/ZsOzs7Pz+/AwcOCAQCV1dXa2vroqKiZcuWCYXCgwcP6unpzZo1S1Lpq6qqevz4MSHkwYMHVGN0dPT48eOnT5/+7NkzqjEzM7OoqEiy53/99ZeJicnSpUtVdi8IIYSkpKSofF6Wk5OjfDlygUDQlulEjx079tdffzX1KYi89LpJCIKDG0Re3lkBUYxmHYhSqzxD3j2I2mt74H2cmZkp1g7hxhBbDUUk5REQu3btSm2HhobOmzcPtsPCwgYPHgzrlhDnpSoHYktRUkC0t7cPCAiora09duyYqrqEIAjFnTt3IL+ShYWFWHFkMDXTBUQitwkRqjw5OztT5xRLRQdev7YUED09PXk8XmpqamlpKcwHJAVEHR0dFxcXgUDQbAFTrMKsIVDDAC6Xm5aWlpiYCFohOBBTUlIIJkBEEA0DBUSKdevW/fDDD9evX2cwGCdPnly4cCGoeKamptSyR0BAgIeHx8OHD9PS0mBGIBQK8/Pz//333z/++IMQEhoaumTJEkIIg8GwtbWtqamprq4+fPjwsGHDPDw83n//faru4oMHDyAD0v3796FFKBROmzYNArOCg4N37dp1/vz5OXPmODk5+fj4WFlZeXt7g+ZICImKipo2bVptbe3PP/8s5lgUiUT03McyePLkiViekLi4OHd39wkTJih2D8+fP//LL7+INebn57u6ug4YMEAZv6RIJBoxYoS9vb1kjFdLqaqqunfvnuzot4iIiOnTp0+ePDkqKopqLCkp8fPzW7ZsWWRkJAzA0tPTYVQWHx8fGBhIH0ch7yAoICLyggIiIFaCg5ILbWxsYPIgVb2ysLAg/60b0JaAUJiVlSX2FqHnK/T29ia0HIjQYYqmHIiEEOodD/9CbG1tjxw58v777xNpDkStEBAJITNmzCCEnDlzRhXdQRDkP1BL656eniDqnTlz5ocffqiurgZhUUxAlHOoSgmIVFCw2gVENpvt4eEhEoliY2OFQqGurq5UV5pkx6SCIcwagqGhIcgQAwcONDY2pn4RGAMA+DMhiEaBAiKFlZXVd999N2LEiCFDhpSXl5eUlPj6+u7fvz89PT0pKcnLy4vBYEBJKBaL1blz50uXLoWFhXXr1g3G8FVVVf379+/Zs+f06dODgoKOHDly8OBBBoMxfvx4Y2Pj6OjoxMTEq1evbt++Hb6OikyKi4sDZe2ff/5JTk6G+dT9+/cXLVoUFBR06NChhoaG9PT0vLy8uLi4oKCg+vr69PT0sWPH1tbWQp4QsVxMixYtsra2vnLlCtWya9euwMBAMf0uPz9/4MCBQ4YMgTc+cPHiRaFQGBERQfdFJiQkDB06lOqwSCTKzMxMTEx8/vz527dvIVRiy5YtY8eOnTJlytdff+3u7t6nT5/Hjx8/ffqUEHLlypXKysrnz59DPUYxBALB7du36X0ghLx9+/bChQt0v+H58+dv3ryZn59PjxurqKioqanJzc3l8/mEkJqaGnmSYi1btmzgwIHHjx9vagc+nx8cHEwIEQqFw4cP/+STT3777bd///137969169f3759++jRo2FPoVC4du3aN2/erF279uLFi5s2bWr225F2DFvdHUC0BhQQgSlTppSXl8fExISFhRGaA5EQ4u7uHh0drbEOxNra2ry8PAhVBqh8hR06dIAZLDgQzc3N6TZD8t95Ebh7LC0tDQwM0tPT4WVGGkusQEjCtWvXiDQHolaEMBNCRo8e3aFDh0ePHr169crBwUEVnUIQ5P+BsfKGDRumTJkCg2mwFVhbW8OfmZmZEI8M0EOcZJCamkoIcXZ2BpHu2bNn8LCSISBCT1pPQCSEeHp6Pn78+N9//yVN+NOhY2fOnGlWQAR1VSyNBtL2sNlsAwOD8vJyPz8/ejv8NCwWi8ViOTk5qal3CIJIAQVESXbt2rVixYrnz5/v378f8owbGxuHhYVlZmYOGDCA2g0edGPHjo2MjBw1ahSTyQTznamp6blz52Cf3Nzcjh07Xr58ee7cuUFBQXv27AkLC9u3b19VVRU4FnV1dWtra6Ojo/v37w8J9datWxcbG5uYmAgS3sqVK0tKSvbv308IsbS0zMzMnDFjRl1dXXl5eWBg4F9//WVra5uQkODs7Mzj8fbs2VNVVbVv3z5CyL59+8LDww8dOjRp0qRTp07V1NSEhYV9+umn0LHKysq9e/dWV1dXV1eHh4ePHz+eEPLq1StKntu8eTOVnHHr1q03b9787LPPnj59+vTp0/nz54MySDFw4MD79+9TWZshbsDHx0dXVzc6OjoyMhLaDxw48MMPP4h5JpYuXfrLL79Mnjz55MmTWVlZUVFR5ubmU6ZMKS4uNjExGThw4G+//WZtbb1mzRrYf+/evTwez8bGhs/nT58+ncvlvn371sPDY9WqVdOnTw8MDAwMDOzbt6+9vT31FZWVlTdu3Pj+++9XrVrF5XL//vtvuEA3NzdIqHL79u0nT558+eWXMExavXp1dna2j4+Pg4NDWFjYmTNnzpw5Y2xsDIMiOzu7zMxMFxeXHj16nD17dseOHf/888/r168JIWFhYQ8ePIiPj1c8gTSizaCAiMgLCoiAsbHxqlWrwLdPCOnYsSP10ezZs4VCYf/+/SWPUq+ASDkNMzMz6QIi1d63b18Wi0UaBcRBgwaJjbF4PN6QIUNu3rxJCBk4cOCKFSt69uw5ZMgQa2trmNOSRgERzgAzdtAWtS6EmRDC5XIDAgJOnTp17tw56rdGEEQlgDsA3iaQlxCgZ1CFWCczM7OioiJ5HIi1tbVZWVlsNtvBwQGeM8+fP4fHUZcuXWCfjh07WlhYFBQU5OTk2NjYCIXC4uJiJpOp/BNDBqBaXr9+nTRmimhqn2Z1UljyaUqFRNoSMzOz8vLykSNH0hsdHBw+/vjjbt26zZgxA38mBNEoUECUxMvLSzI9uqOjo6Ojo9T9hw8fPmfOHE9PT8m6YfBCDwgIyMnJIYRERESkp6cHBwenpqZmZWX17dt36NCh27ZtO3/+/MmTJ588eeLo6Dh9+nSoTPXPP/+wWKyAgICSkpJLly716dMnMDBw5syZUDCaEBIcHMzlcj/99NPdu3enp6cTQt5//33KtRcRESEQCEQi0ZEjR6Dl3LlzkyZNWrBgQUFBQWxsbEZGBrSfPHmyS5cuX3/9NSzpsdlsHR2dy5cvd+7c2dTUNCsrC0KmsrKyRo0alZCQUFNTY2pqamFhIRAI3r59SwiBspNiCASCqqqqiRMngl3DxsYmJyfHz88vODh48uTJBw8eHD16dHR0NKiuISEhVVVV4eHhdXV1DAZDJBJZWVm9ffv20qVL6enpU6ZMefHiRceOHfPz8+/duxcUFMRisQQCAfVdMTExH3/8sUAgOH78+PHjx93c3E6fPv3555/7+vrm5ORcunQJ/p1/8skn1CHJyck+Pj7+/v7r1q2bOnXqq1evjh492rFjx4CAgL1793I4nAMHDnTv3v23336bP38+acwk06tXrzt37hw5csTPz+/SpUtnz54lhMDNJ4RUV1cPHjx4Yn19D6n/UBQiLy+vsLAwOzs7Nze3oqKisLCwqqqqrKysQ4cOJiYmbDZbKBSWlZUJBAI+n9+hQwcdHR0WiwVlBgghBgYGHA6HyWTCSl5tbW1VVRU0whiPy+XKcLGAwE0IYbFYYJqBM8CnVVVVYtZRsR1YLFaHDh1kRJ3b2Nj4+voqc380ChQQEXmBNBMoIAJQtVNfX3/ixIlU4+zZs2fPni11fxAQ1RXCTOUkzszMhDUogHIgUqInhDAPHjxY8iTbt2/38fGBcK0ff/yRNCqDlAMRcilCqhQQEGNiYoKDg7WuiArw8ccfnzp16vTp0yggIohqoWcD9PDwgDE0IeTOnTvUPmBJ8PHxCQ8Ply0gVlVVrV27tlu3bgKBwNnZmcPh2NjYmJiYFBQUwEmoCleEkJ49e0ZGRt6+fXvSpElv376tr6+3srJqbQciISQmJoYoLSDCkk+ryp2InPz4448pKSm9evWiN0L1LXV1CUEQGaCAqDwsFuvAgQPy7Dlx4sTNmzeD99DExOTgwYN1dXXbtm07evRoeXk5j8c7f/48VRBs7NixsGFiYgLBBxC8HBIS8vLlSx6PN3z4cELI8uXLKysrx40bt3Tp0pSUFH19/YULF8bGxl69epUQMn/+/Ojo6OfPn9fW1oaHh/v7+0dERFD9cXR0zMrK+vvvvy9cuFBfX89msxsaGnx8fEA4y8nJAd2TENK9e/fc3FwofTZ//vydO3dSSeHLysp+/fXXjIwMNpudkJDg5+eXkJBw6dIlQoixsTHkCrS3t1+4cOGSJUvu3bsXHx+fnJy8ZcuWrl27wnVNnDjxzJkzYWFhTCYTFKt+/frdv3//5cuXQUFBCQkJq1atIoSsXbv21KlTCQkJFRUVlHoIfSaECAQCHo8HC7FJSUnDhw8vLCxMSEigLtbd3T0xMRFGVgMGDDAwMIiJibl8+XJ4eDj8X/DkyRNCCNy3vXv3du/enRAyZ86c+Ph4Doezf/9+Gxubv/76i8fjgaT4ySef3L1799NPP3348GFmZuZ77723fPly+uSOEJKTk/Of/FYS1NXVZWRk1NfXZ2dnP3r0qLKysqioKC0tLSsrq7i4uKKiAmaR7Zj4+Hi41e0AFBARecnOziaEUFUI33FgRrp582a6dVwGkFJQExyIVKNIJCotLWUwGPb29tRSlZOTU3R0NJXzgk6fPn3OnTtHT4aor6/PZrOrq6vr6+s5HA44EOFFC/Phw4cPDxgwQBtzIBJCPvjgA3jpvn79Ws5fGUEQeYCBLyzDGBoa2tnZQVAMtbhNCIG4IWdnZzAhvnnzxtraWurZrl27tn37dlj8cHFxgcZu3brduXOnvLycy+XSsxAEBARERkaeOXNm0qRJVNXm1rlKQvWEEALj/qYERBcXF11d3VevXlVUVDSVO496YqOAqAnQ1w4RBNF8UEBsS9auXdu3b9/U1FQul/vZZ5+ZmpqKRCIHBwdII7hy5UrZSgqHw9m4cWPv3r2DgoJGjx4Ny42dO3cGRbJDhw6rV6/euHHjsGHD3r59GxoaqqOjExwczGAwSktLJ0yYEBUVRVcPDx48OG3atF9//XXZsmX19fULFizYuHHjjRs3vL29mUzmgwcPPvroo0GDBuXl5W3cuHHr1q3m5ua//PLLxIkTx4wZQ++VkZHR999/T28RCoVeXl4CgSA0NHTQoEE8Hu/06dOdO3fevHkzBF9v2bKFNFbWmj179sGDB6dMmbJt27YVK1ZUVVVt2rTp0KFDTCbTycnp5s2b3333XVRUVJ8+fWbNmhUcHCwQCK5cuVJYWLhx40YejxcZGXn16tVZs2YxmcyTJ09++umn3bt3j42NLSwsZDAY4AydOHGiSCTq2LFjamrq7du3N2zYsHfvXm9v77y8vG+++SYkJIQQ4uvr6+7uDndy/vz5c+bMgWths9kQPz5//nwrKyv6SMPGxgYciFT97iFDhpw+fZpx8iRpdHfu2bNn182bVVVVsbGxpqamdXV1FRUVNjY2HTt2LC8vr6urS0pKkl1bxsLCwsLCwtraulOnToaGhubm5jwez8TEhM/nl5SUCAQCNpvdoUMH8Bjy+XyYdTY0NMA0s7y8vKGhAfyJ8E/IwMCAz+c3NDSAMbC6ulpGbeva2lpYS6bOQHcs6unpSa406+joUCJ4Q0NDZWWljAwzJiYm7SmxCQqIiLyAgGhjY6PujmgEn3/++ahRo+jxy7LRhByIhBB6WjHwgRsZGb18+ZJqPHjw4E8//dTUdY0bN06sxdDQsLi4mM/nm5mZwdoRxAzOmDHj6dOnO3fuTEtL01IHIo/HCwgICA0NPXHixOrVq5U/IYIgAIzhQEAkhHh6eoKACOjr61dWVoLiZmBg4OnpefPmzadPnzYlIII7HvYfNmwYdU7wM7q6ukJ+BuCjjz76+uuvIyIiKioq4HnY2qtitra2RkZGkOqhqUtgs9nu7u5xcXHPnz+XDA0DysvL6+vrO3ToAM9YBEEQRH5QQGxLdHR0xKYMDAZj9+7dR48etbCwWLFihTwnGT9+/P/+97+ePXuKtQ8dOpQKJbayslq8eDH1kZmZWXh4+N27d1++fOnu7r5mzZrS0tJp06ZxOJylS5fq6elZWVkFBQURQuC/hJDY2FjqcKoo84kTJ+TpIZPJjIuLI4RwOJzExEQejweZ8QsLC5OSknx8fPh8PrgF+/Xrt2fPHkKIv7+/v78/HE7pcYQQY2Nj2IGCzWaDN3PKlCkMBoPD4XzyySe7du3q37//+PHjs7OzIafWtm3bBg8e/Pvvv9OP7dq1a9euXWfNmgV/Wlpa7t+///r16/n5+atXr/7www8bGhrS0tK2bt0qeVH0oI2m8PT09PT0vJicnNwoIGZkZMQ3blOzTmoD7pWTk5OBgYGJiUmfPn3Mzc2NjIycnJzs7Ow6duyor69POT0RzQcFREQuKisrS0tLuVyuWA3idxn51UOibgGRciBCIktAajotDofTouuiC4h0ByJpDNzLy8uj3EY1NTVtU5hSVcmnZs2aFRoaun///uXLl+OkHUFUBT2EmRASHBxcUVHx6tUrkBF9fHxu3LgBHxkYGHh5eYGAKNUZTQjJz8+HjZEjR3799dewHRAQAGvpYkNhGxubvn37RkdHP3jwAFbFWtuByGAwunXrdu/ePUIIPQWtGJ6ennFxcbdu3XJzc5O6iI0JEBEEQRQGBUS1ExAQEBAQ0KJDIHi5RXC53BEjRsA2NZYAICBXtVCzA7HFSDc3t/T09NDQ0OHDh6enpw8cOFDhZCnUxMrAwAD0StI4r9y4caOtre3777/f7Ek6dOhw+vTpBw8ewFDq6NGjinWmKRgMxteLF1tbW0+YMKG2tpbL5err6798+bK0tFRfX5/H49nb22P8RLuBqe4OINoBZT/EV69iQAizWnIgNjQ0iBVKBmA6quTTHBLNgrlGTECEdJn5+fkgIK5YsWLWrFlDhw5V5uuaBRyyqsrUOWLEiG7dumVnZ3t7e0MBGQRBlIcewkwICQwM/Pfff6nUq25ubvDAJI0ORCIzPyAIiIsXL7548SIEMhNC/P39IbUrRBDTgbx1T58+bZsQZkIIVLckTYcwk8YVl+XLl5uamh46dEhyB0yAiCAIojAoICJtjLm5+Zdffunh4TFmzJhWWvzT0dH56quvqOQtshkyZMiKFSvoMRkq5JdfftmxY8fSpUvt7e27du1qZ2dnZmbWp0+fkSNHDhgwoGfPnjh6aU+ggIjIBSSXxfhlhVGjA7G4uBiGTYQQeg0pmI4q+UoDAREESnoIM2l0aObn50MIc0BAwKFDh2Skh1AJS5cuPXLkCL1QjDIwGIyffvrJysrq+fPnI0aMoOq7IQiiDGIhzAClshkbG1OlmcGBSBpTIkoFBMQBAwaInfDAgQPTpk2bOnWq2P7UCdsmhJnQKk3LEBD9/f1tbGzMzc2FQiE9eRPA5/OjoqIIOhARBEEUAgVEBGk9ZARYIO0PFBARucAKKkqiRgGRnoGilRyIICCKORBBQKRCmNumfIq1tfX06dNVOED88MMPs7KyVq9eLRAIZs+eDVmEofw0giCKIRbCDNAFRCq9OjgQGQxGYmIiVYuQori42NfX9/r160Sa79jLy+vo0aN2dnaS7YSQR48eJVZ8gQwAACAASURBVCUlEU1yIGZnZ4NKSC+nCCxevBjKweMaPoIgiAKggIggCKISMAciIhcoICoJ2EZKSkoaGhomT57s4eGxbt26tvlqcBoyGAyRSEQXEDMyMojS01FwFNIFREkHIrjl26Z8SmvAZrM3btxoYGDw7bffTpkyZcmSJZmZmfb29o6Ojra2toMHD54xYwaTiYsxiLykp6dHRka+ePGiqKioqqrKzMzMxsbGxsZmzJgxMgQmbeTmzZvOzs6S1nWxEGaALiBSPjsDAwNDQ0N7e/tXr16lpqa6ubnRD3nw4MH9+/dhW/7krV5eXgwGg7I0dunSpQWXpBDwjUwms9kleldXVx0dnYyMjMrKSqq6HyEEtE6CDkQEQRCFQAERQRBEJaCAiDRPVFTUgQMHCIYwKwGLxYI6XKmpqWfOnOnUqVObCYhgezQzMyssLKRCmA8dOrRy5UrSCiHMlAORx+MZGhry+fy3b9+StnIgth4rV6588+bNnj17MjMzWSzW69evoeDDn3/++ffff587d05MDUEQSV6+fDl//vwrV65I/XTBggVjx47dvn27g4ND2/arVYiPjx86dOjQoUPBVUdHqgPRxsbGzMysqKjI2NiYugNQdql79+6vXr1KSEgQExDfvHlDbcsvIBoZGYEi2b1792XLljVVGVmFmJqarly5ksPhNFtkkMPhuLu7x8fHP3v2jCrHXFNTA9HWBB2ICIIgCoECIoIgiEpA1wzSPIcOHXr9+jWLxVJVarl3E5gJQx0VMOC0DaDfQRwf5UCMjY2FjRbVXJZERggzdXKpYoE2smvXrrS0tOvXr1dXV6ekpERGRu7du9fCwiI8PHzQoEFQ8hVBmqKoqMjPz+/KlSvdunVbtWpVSEjI1atX79+/f/Xq1VOnTq1du7ZHjx7nzp3z8/OjV0vXXmJiYgghjx49EgqFYh9JzYFICOnZsychxMLColu3buBcpgREIi2wNzc3FzY4HE6L1kI2bNgwb968O3fufP755/IfpQybN29ev369PHuK5Xz85ZdfjIyMKKkUHYgIgiAKgAIigiCISkAHItI8EL8cFhYGRS0RxTAwMCgoKGh7ARHm2HZ2drGxsZSAmJmZSQiZO3fujBkzlDm5pIBIhTATQiwtLdPS0mC7HQiIhBBHR0dHR0dCiIuLi4uLy8iRI4cMGTJ48OBHjx49evTI2to6MDBQ3X1ENJRVq1alp6dv2bIFzL+SrFu37siRI8HBwd9///3vv//ext1TOfHx8YSQioqKjIwMZ2dn+kdSQ5gJITt37oyMjBw0aBCLxXJ3d3/+/DmUY25KQKRkNXNz8xZNC6dMmTJlypSWXU9bIXaxUVFR9NQT6EBEEARRABQQEQRBVAI6EJHmAQGxa9eu6u6IdgNWGggorq2tlXTltBLgQLS3tye0KswgIH7xxRfGxsbKnBwExLKyMiIRwkxo9kYOh0MXFtsT3bp1S0hIWLVqFSFkxowZMmrFIu84t27dcnV1bUo9BGbMmDFkyJA7d+60Wa9aDxAQCU0LA+OhSCSCB5GkgOjl5fXNN9+A9/DMmTNXrlyBxLvNCoiSFVS0F7hY6u5BqgQKdCAiCIIoAAqICIIgKgEFRKQZRCIRTNIwAaKS0EOYSRuaEKWGMENGLeXLj4KAmJWVFRwcDGn+pQqI7cN+2BQ2NjYbN24MCgoqLi4ePHjwjh07rl+/DqIqglAUFBTIUyPF1tY2Pz+/DfqjQvbs2fPTTz/RW0QikZiA+PDhQyMjo23bttXV1QmFQl1dXdmlh9zc3EaNGgXbzs7Oenp6mZmZpaWl9H0oAbEN8hi2GT169CCExMfHw3QXFnsIIVwuV09Pj6pPjSAIgsgPCogIgiAqAQVEpBkKCwtrampMTEzoFSERBVCvgAgORBAQ+Xx+aWmpvr6+mZmZkicHAfHSpUsHDx589eoV+W8Is7m5OWxoewWVZmEwGCEhIR999FFpaemSJUv8/Pz69OkD9a8RBBgwYMD9+/epoH6p5OfnX7lyZcCAAW3WK5WwZMmSlStXgr0aePXqFWQ2II1munv37tXV1V26dKmp+GUZsFgsT09PuigJgIC4YcOGbdu2KX8VGoKVlZWVlVVZWdnr16/5fH5JSQm0z58/v7CwEEMBEARBFAAFRARBEJWAAiLSDDk5OQTth6oAFFhqjt1mAiKVA5E0hjCD/RBalAQioAUCAdVCdyBS6bratwMR0NXVPXv27D///DNx4kR7e/u0tLQhQ4bcunVL3f1CNIWFCxc2NDT0799/165dYnGphJDc3NxDhw717ds3Pz9/5syZaumhYlRVVdXV1Ympe1AHxsjIiBASFxdHGh878fHx8Ohr6TMBfHlwKkAgEOTl5TGZzJUrV3p4eKjgSjQG6mIp+yEhxNbW9l14kCIIgrQGKCAiCIKoBBQQkWaABIiQiApRBrU4EEUiEczk6Q5EmJQqH79MaB5DCroDkRIQ270DkSIwMDA0NPT27duOjo7Pnj0bOXJkaGioujuFaAR+fn67d+/m8/mLFi1ycHAwNjZ2dHTs2bOns7OzqamptbX1nDlz3rx5s2/fvnHjxqm7sy2gvLwcNujqXkVFBSHE29ubx+O9fv16xIgRf//9NyGkrKwsMTGRtNCBSGiBvVRLfn6+QCAwNzdvf/lVoRr15cuXQWiGGS/cAQRBEEQBUEBE/q+9ew+Iqs7/P/6Z4SooFxEFES8oooI3BAU1r6BdVCytrb5uqWsqumntamn2LVdXXcvK0ry0llpZW+rmHVM3wVsqeQFBEUHAVBQQFRHkOr8/ztf5zYIDA57hzOX5+GvmzGHm7ceBN/Picz4fALIgQEQtmIEoF0UCxNu3b5eUlLi6ukpTgUpLS8vLy3fu3ClkmoEo7ZGqy2pnIOry9fU9f/787Nmzy8rKJkyY8NtvvyldEUxCdHR0cnLyW2+91bNnz5KSkoyMjISEhMzMTAcHh5CQkMWLF//+++9TpkxRusy60V6qrBsgSqmim5tb165dNRrNL7/8Ii1xIIQ4ceKEqHuAKGVqugGidP2yJa1+qPXqq6/a29t/9dVXW7duFUKMHz8+JSVl8ODBStcFAOaKABEAZEGAiFowA1EuigSI0gKI3t7eUq5XWlq6cePG1atXCyF69uz5+M/frFmzKr+NPTJAtJ4ZiFoODg4ffPBBdHT0gwcPRowYUWXtNlgtf3//pUuXnj59uqioqLCwMC8vr7S0NDs7Oz4+fu7cuV5eXkoXWGePnIEoHWzSpIkU/On69ddfRd3/qNCtWze1Wp2cnCzt9i4eLs5gyL40Zqdz586vv/56ZWXlxo0bhRDt27cPCAhQuigAAABYOwJE1ELaWrddu3ZKF2L2pACxgddAlK5fbtGihUqlsre312g00qfuiRMnRkdHP/7z29nZScsg6h7R3rbaGYhan3766fDhw2/evBkaGvr6668nJiaeOXNGuxc2rJlKpZI2Mqp5M2LTp52BmJKS8uDBA+l2DQHigQMHRN1nIDZp0qRDhw4lJSXSFdDCogNEIcTYsWOFEJWVlUKmP/YAgDVjBiIAyMJW6QJg6qTZIn369FG6ELMnBYjSNiaioQJE6WO8tFeyvb19aWmptDZZy5Yt5fotytPTU7tPqOAS5v9mZ2f3008/zZo1a+3atStXrly5cqUQomXLlu+9996kSZNsbGyULhCmRaPRSPmyg4NDXb/25s2btW7aI+1kIi/tDMTy8vKkpKSQkBChEyAOGjTI3t6+oqKioqLCx8enrKwsJydH1D1AFEL06NEjNTX1zJkz3bp1E5YeIIaEhLi6ut69e9fNzW3o0KFKlwMA5o0AEQBkQYCImvz+++/Xrl1r2rQp1089PilA1GqYALGoqEg8vIJYivakAFHGbQc8PT1TU1O1d7mEuYpGjRp9/vnn06ZNW7Zs2bFjx8rKyjIyMqZOnfrJJ5/079+/rKysQ4cOTz75ZGhoqNKVQnmJiYnSlD3po06dTJ06ddu2bYacKc1rk4t2BqIQ4syZM1UCxM6dO1+7du2zzz5buHChn59fnz59li1bJur1R4WePXv++OOPZ86cefXVV1NSUqSpiJYaINra2g4aNGj79u2jR4+uR5oMANBFgAgAsiBARE200w/puI/P2dlZ927DBIj379/XvrT0KVQKEHVjvsdUZR8V3WiycePGdnZ2ZWVlVjsDUSswMHD9+vXS7S1btsyZM+fixYsXL16Ujrz33nt/+MMfli9fbo5L4MFETJ06tdY/DNy9e3ffvn3y/jyXAkRbW9vy8vLTp09LB7UBohCiWbNm/fv3F0IEBgYOGzZMChDrMQNRupL37Nmz165d6969uzRV01IDRCHEtGnTMjMzZ8yYoXQhAGD2CBABQBYEiKiJtHtsWFiY0oVYApOagWi8AFH3mVUqlZubW25urjXPQKxu7Nixo0ePjo2NvXz5slqtTkhI2LBhww8//LBnz54//vGPo0ePHjx4sK0tP5ytUUBAQFJSUv2+dvjw4cOHD6/5nKSkpK5du8r7CUrKCnv27BkfH3/mzBndg9ofesOGDTt+/HhQUJD25149/qgQHBysUqni4+N3796tXUjUggPEYcOGDRs2TOkqAMASECACgCz4jIqaZGVlCSH8/f2VLsQSKBIg6s5ANN4lzLp3qzyzu7t7bm4uMxCrsLW1jYiI0N7961//+uabb27btm3VqlWrVq1q2bLlzJkz3dzcvLy8BgwYUGWbGlgwR0fHwMBApauoG2kG4oABA3777bfExMTy8nJbW1vdGYgSaSFdZ2fn1q1bX7lypR4zED09PUeOHLljx445c+ZoDzJpFwBQKwJEAJCFeW/+CGO7evWqEMLX11fpQiyB4jMQG+ASZpVKVSVAbNq0qbDuNRAN0bZt259++uncuXPz5s3r1KnT9evX33777SlTpkRFRbm7u3fq1OnTTz8tKytTukzgEaSs0NfX18/Pr7i4WFqasHqAqBUcHCzqdQmzEGL+/PkqlUp31yYLnoEIAJALASIAyIIAETWRtuwkQJSFNcxArJ5LSvuoMAPREEFBQX//+9/Pnz+/efPmyZMnT548+YknnnBycrp48eIbb7zRo0ePX375Reka8bj27NkTHR0dERExZcoU7QW/umbPnv388883fGH1Js1AdHFx6dWrlxDi1KlT4uHPmUcGiNIuK498qFY9e/YMDw/X3nV3d69fEAkAsCoEiAAgCwJE6FVRUZGdna1Wq1u2bKl0LZZA8RmIRloDsVOnTtpfyKrnklKAyAxEw6lUqrFjx65du3bt2rWHDh26d+/e9u3bO3bseP78+cjIyMWLF1dUVChdI+pp6tSpzzzzzJo1a/7zn/988cUXvXr1Wr58eZVzDhw4sGXLFkXKqx8pQGzSpIk0tVDaR6WGGYivvfba//7v/06dOrV+Lzd+/HjtbR8fn/o9CQDAqhAgAoAsCBChV3Z2dnl5uZeXl4yz1ayZSc1AlDFA7NWrV2pqaqdOnR75tJ07dxZCdOjQQa6XszZqtXrUqFHnzp17//33hRDz5s0LCQnZsGHD3bt3lS4NdfPDDz+sXbvWz89v8+bNKSkpmzZt8vLyevPNN7dv3650aY9FygqrzECsIUBs3rz5ggUL2rRpU7+Xe+GFF3x9fUeNGvXGG2/Mnz+/3mUDAAx0//596Q/Suo4dOzZ9+vQnnnhi5MiR8+bNky5aMlkEiAAgC3PdRCU9PX3//v3nz5+/detWUVGRh4eHj4+Pj4/PyJEjWRFJLly/LC9XV1e1Wl1ZWSndVXYNRHlD4Q4dOri4uIhHBYjz5s2bMGECE4Uek729/fz588PCwqZMmXL27NkJEyZMnDixT58+zz333MiRIzt16lRYWGhvby9jLgzZrVy50tHRcf/+/X5+fkKIgICALl26PPHEE1OnTh0yZEj9Luk1BdoZiAEBASqV6uzZs+Xl5TUEiI/J1dX1ypUrsj8tAECfxo0bd+rUSVriVjJv3rwlS5ZIqZwQYteuXStWrFi1atW4ceMUqrEWBIgAIAvzm4GYkZHx1FNPdejQITo6esWKFd999922bdu+/PLLBQsWTJkypXXr1mPHjs3MzFS6TEsgBYitWrVSuhALYWdnp5tuN2SAqDsDUToie9IkrXJYPZdUqVSkh3J58sknU1NT169fP2DAAEdHx+PHj7/11ludO3d2cXFp0qSJo6Nj//79V6xYkZeXp3SleISUlJS+fftK6aGkR48eK1asuHHjxocffqhgYY9JuwZi06ZN27VrV1RUdOHCBe20RKWrAwDILCYmZvHixU2bNv3888+TkpJSUlL++c9/Ojk5TZo0STdkNCkEiAAgCzObgXjr1q3IyMj09PTAwMCoqKigoCAPDw8XF5eCgoL8/PyUlJRdu3Zt3bo1ISHhyJEjLVq0ULpe88YMRNm1adPm2rVr0u2GvIRZdw1E6Vco2S9LlwJEZsAZm4ODw/jx48ePH19cXLx9+/Z9+/Zt27bt9u3bjo6OlZWVR48ePXr06FtvvfXyyy/PmTPH399f6Xrx/xUXF2snIGu9+uqrq1at+uijjyZNmtS6dWtFCntM2gBRCBEaGnr58uUjR46UlpYyJRYALNInn3yiVqt3797dp08f6UhAQEBwcHDv3r0XLlz43XffKVveIxEgAoAszGwG4jvvvJOenr5kyZKkpKRFixa99NJLw4YNCwsLGzZs2Isvvjh//vzffvvtq6++yszMfO+995Qu1uxJS1kFBAQoXYjl0F32q+FnIEqXMEuMNAORvKDBNGrU6MUXX/zqq6/y8/Nv3759//79/Pz877777plnniktLf3qq686d+789NNPv/vuu2vXrr106ZLS9UL4+/sfP3785s2bugdVKtXq1atLSkr+9Kc/VY8XzcKdO3eEEK6uruLhDsuxsbHCONcvAwAUl5iYGBwcrE0PJcHBwSEhIdI+WiaIABEAZGFmAeKhQ4cCAgLmzJlTwzkTJkwYOHDgkSNHGqwqi1RRUbFv3z4hREREhNK1WI6GDxCrz0CUNNglzGgAbm5uarXa2dn5pZde2rVr18WLFydPnmxraxsTE7No0aKpU6d27Nixe/fukydPXrVqlZmmVBZg0qRJDx48GDhw4IkTJ3T/F3r16jV79uwDBw688sor0pW/ZuTGjRv379+XLgUQDwPEgwcPimrbRgEALENZWVnz5s2rH2/ZsmV2dnbD12MIAkQAkIWZXcKcm5vbtWvXWk/z9fVNSEhogHos2MmTJ2/duuXv78/+uTLSvURRqTUQJVzCbME6dOiwdu3aRYsW7du379KlS6mpqXv27ElMTExMTBRChIaGhoaGKl2jNZo+fXpiYuIXX3wRFhZmZ2cXHx/fvXt36aEFCxZkZGRs2rTpp59+Mq+ENy0tTQihvVi+V69earU6NzdXsH4uAFio0NDQxMREjUajm8dpNJrk5ORu3bopWFgNCBABQBZmFiCGh4fv378/LS2thlQrJydn79694eHhDVmY5YmLixNCDB8+XOlCLIqyMxAb4BJmZiCajmbNmr388svS7ZKSkhMnTpw7d06tVvfq1UvZwqzZqlWrwsPDv/3228uXL2s3rxRC2NnZfffdd/369fvss8+kSM5cpKamCp0AsUmTJp07d05OThZCkFMDgMXIyMh4+umn/f39/f39+/Tp8/PPPy9cuFB3wah//OMfqampzz33nIJF1oAAEQBkYWYB4owZM2JiYsLCwt57772oqCjdOEYIkZ2dvXv37oULF+bk5EycOFGpIi2DtFCX7oaheHxKBYjMQLRyDg4OAwYMGDBggNKFWDsbGxtpD5zqD6nV6tdff/3111+/ceNGenp6g5dWT9Lymrrb9fTu3VsKEKXLmQEA5m7AgAFpaWkxMTExMTHag9oAsby8PDQ09OzZs126dHn33XeVK7MmBIgAIAszCxAjIyNXrFgx8yFXV9emTZu6uLgUFhZKOwkIIWxtbVetWjV69Gili9UrJiZmz549H3/8sSlP17p165YQwsPDQ+lCLIqfn5+rq6ubm1tWVlZOTo6xX66ysvLBgwcqlap6uscmKoBp8vLy8vLyUroKQ1UPEENDQ9evXy+YgQgAlkK6LKmoqOjy5ctpaWnp6elpaWmZmZnSoxUVFWfPnu3bt+/GjRulv1ibIAJEAJCFmQWIQojo6OiIiIh169bt37//woULGRkZQggbGxtPT8+QkJDnnntuwoQJJv7p69133z19+nRERERUVFTNZ+bn5zdu3Fgbyly/fr1FixY2NjbGr1Hk5+cLAkS5NWrUKCUlxdHR0cvL6+7du8XFxVLuZiTFxcUajcbJyUn6hYlLmAHIq8oaiEIIaV9OV1dX1s8FAEvi5OQUFBQUFBRU5bidnV1WVpbuMt8miAARAGRhZrswS/z9/ZcuXXr69OmioqLCwsK8vLzS0tLs7Oz4+Pi5c+eaeHpYWlqalJQkhDh8+HDNZ2ZnZ/v6+o4ZM0a6e+zYsVatWr399ttGL1EI8XAGYtOmTRvm5ayHl5eXm5ub9C69ceOGUV9L9/plwSXMAOR25coV8d+LM/To0eO1115bsGCBWm2Wv2AAAOpErVabeHooCBABQCbmNwNRl0qlcnZ2NtnZ8o90/vz50tJSYUCAePHixaKiopiYmNzcXE9Pz3Pnzmk0mu3bty9btqxOr/j999/fvHnzjTfeKC0tValUBiZHXMJsVF5eXllZWdnZ2e3atTPeq+huwSyMfAmztE8LASJgPSorK+/evatSqdzc3LQH1Wr1F198oWBVAICGp9FopA84ute7GP61d+7cqfmcgoKCelb28CUEASIAPDbzDhAf6XEaWAM4c+aM9sb9+/drSD+l6WkVFRW7d+8eP368tKtJWlra9evXW7ZsaeDLVVRUTJ48ubCw0M3N7d1333Vzczt37pwh7ZNLmI2qIWcgStGe+O/vCNlnILq4uAidsBKAxSsoKKisrHRxcbG1tcDfJQAAhktMTOzRo4d4GNXVybRp09asWWPImfV4ct0vJEAEgMdkgb/0P04Di42N/f7772s+R9qqxfAnT01NXb58eUVFhXQ3Pj5eulFWVvbdd9+99tprVc6fMWNGixYt5s2bp02Xtm/fPn78eO3dw4cPd+rUafr06UuWLOnUqZOnp2cNr56UlFRYWCiEmDBhghDi2rVrSUlJXbt2rbnmysrK27dvq9Vq3XklkJG3t7cwfoDYkDMQR40atWjRoueff17epwVgsqQJI7QJAMDj8PPz8/DwqKysrOGcioqKx5mESIAIALKwwADxcXzyySc7duww5EzDA8RNmzatXr26ysFp06atWrVqwYIFr7zyiu68sBs3bqxYsUKtVr/88svadCk2NraiokKagSiEOHz4cHx8/NGjR6Oiom7fvv3DDz+88MIL+l79+PHjVY4cOnSo1gDx9u3blZWVTZs2bZgNW6yQNAMxOzvbqK9y7949IUSTJk2ku46OjtqHjLGJyjvvvCPvcwIwNYsXL/7mm2+OHDni4eFx9+5dIYSrq6vSRQEAFBYQECAt8l4Ps2fPnj17ds3nSBMg6p0A1nvqIgBAlwUGiI/TwFauXDlixIiae8y1a9fqtDz8X//617Zt25aVlWmPtGrV6qmnnoqLi0tOTo6Lixs2bJj2od9//10IUVlZuW7dOm2AeOfOncTERO3duLg4aW8TaS7kli1bDAwQg4ODT58+HRcXN3369Jpr5vplY5MCxLNnz966dcvd3X39+vVDhgyRfT1E6f/R3d1dutu4cWPpho2NDfsbAKiHmJiYlJSU3bt3v/LKK8xABABIHB0dAwMDla6iFsxABIDHZIEB4uM0MF9f3+rXFFeRlJS0YMECw5/TxcVFuny4ihEjRiQnJ8fGxlYPEIUQH330kZT7eHt7Z2dn/+Mf/7h8+bIQQqVSJScn604li4uL02g0+jriiRMnhBAODg4qleqzzz7r37//4cOHpfPz8/NnzJgxZMiQiRMnVvkqtmA2NukS5l27dr300ktvv/32pEmTnn322X//+9/yvooUMWv/H7UBIludAKif8vJyIcTq1aszMjKk1UKYgQgAMHFcwgwAsmAWkmIGDRokhIiLi9M9ePXqVSFEy5YtS0pKpCmHY8eOFUL8+OOP0t3evXtrNJri4uKmTZv269fP3d09JyfnD3/4w/Xr16u/xIMHDy5dumRraxsfH//rr7/269fP29v7xo0bKSkpJSUlkZGRmzZteuedd6rPuGQLZmOTZiAKIbKysqRZPDk5ObK/ijQDURsgaq9lln0HFQBWQgoQjx8/Pn/+/D179ghmIAIATB4BIgDIggBRMf369ZOiPWmTE4k0A3HGjBkdOnSQjvzpT38KCQmRLjh1cnIaOXKkdDwiIuLIkSNPP/20EGLz5s1r166t/hIXL14sLy/39/fv2rWrNFVESi1jY2PPnDlz+vRpIcTNmzcvXLhQ5Qu5hNnYgoKCwsLChBB37twpLi4WD7cjkFeVS5i1ASIzEAHUjxQgSnbv3i0IEAEAJo8AEQBkQYComCZNmvTq1ausrOzIkSPSkQ8//FC6iNXX11dK+tRqdWBgYHx8fM+ePYUQXl5er776alRUVGho6NSpU4UQM2fODA8PF0IcPHiw+ktIa0HqXtA9ePBg6WTd0LDK10obuQgCRGNycnKShv3OnTsPHjwQxgkQq1zCTIAI4DHpBojXrl0TBIgAAJNHgAgAsrDANRDNyJAhQ06cOHHw4MEnn3yysLBwzpw5lZWVQghfX9/BgwevW7euWbNmtra2QojBgwefOnWqRYsWrVq12rZtm/YZQkNDY2JiPDw8Tpw4ERMTs2XLluXLl2tzouTkZPHfAaL2uunWrVsLIfz8/C5fvhwbGyttq3LmzJm9e/emp6fHx8d7enpKV0/DSBwdHR0dHR88eCDNE2yAGYjaNRC5hBlA/Ugbgn34a9tYTgAAIABJREFU4YfaHTMJEAHAguXm5h47dszw86OiooxXTL0RIAKALMwpQLSMBqZr8ODBS5Ys+eWXX4QQ58+fl9JDIUSrVq06duzYuHFjaeKhEGLYsGHLli3TXtesy9XVNTg4OD4+/sUXXywoKAgJCYmOjpYekgLEoKAg7cn+/v6+vr6///771q1bhRBTpkx5++23Dx48mJeXN3bsWN0FGT/55JN+/foZ5Z+Nh1xdXR88eJCdnS2EuH//fllZmbzRHjMQAchLmoE4evTo5cuXMwMRACxecnLy6NGjDT+/+tLqpoAAEQBkYU4BomU0MF39+vVzcHA4c+bM7du3pbBPCKFWq318fOzt7bOyspydnaWDkZGRBw4cCA4OfuTzDBkyJD4+vqCgQAhx4MCB6Ojo69evp6enJyYmiv+egSidvHHjxszMTCHEiBEjVq9enZmZuXjx4ri4OBcXF+lJhBAhISFG+TdDh5ub282bN6XtcYQQd+7c8fT0lPH5q2yi4ujoaGdnJ3tMCcB6SAGinZ1dv379fvzxR6EzxxkAYHkGDRp07ty5+fPnS5MPoqOjXV1dlS6qzggQAUAW5hQgWkYD0+Xk5BQWFhYXFxcXFycFiC1btoyMjJQmiGlzH8nQoUP1Pc/QoUOXLl0q3Y6Nja2oqBg3blxsbKxGo2ncuHHHjh11T5YCRCGEnZ2dv7//0KFDv/zyy/Xr1wsh3njjjW+++SYjI8PFxcXf31/ufy6qkmbuaAPEu3fvGiNA1P1436RJk/z8fGYgAqgfKUC0tbXt37+/FCAyAxEALFtQUNCWLVt69ep1+vTpt99+u02bNkpXVGcEiAAgC3MKEIVFNLAqIiIi4uLiDhw4kJ6eLoRYvXr1qFGj6vok0kzGkpISIUR+fv7Zs2dTU1OlTtmjRw8bGxvdk4cOHWpjY1NRURESEmJnZycFiNICfJ06dRo6dOi6det69uwp7fsMo5IScN0ZiPI+f5VLmAUBIoDHow0Qn3jiCekIASIAWIMXX3zx9OnTSldRTwSIACALswyJXnzxRaVLkE1ERIQQYt++fQkJCUKILl261ONJnJyc+vbtKx7ukrFnzx5pWT3xqCuRfXx89u/fv3379r179wohhgwZou2mnTt3fvbZZ1Uq1bBhw+r570FdSB+8tf9Z8gaIZWVl9+7ds7Oz0+6dIh6+Q7iEGUD9aAPErl27Sn8C4RJmALAGoaGhHTp0kHZ3NDsEiAAgC7PsAWbdwKoICQlxdXW9dOmSEKJdu3Z+fn71e565c+e6uLgMGTJk5syZX3/9tXY/lkcuZTh48GDt7RYtWnTr1i0hIUGtVnfs2LFHjx7Xr1/38vKqXxmoEylAvHv3rnRXxgCxoKCgT58+Qgh3d3fd35akfVSYgQigfrQBoo2Nzccff3zx4sW2bdsqXRQAwOgGDRokfWAxRwSIACALs5yBKDUwHx8fpQuRga2t7VdffeXq6urs7Lx169Z6XzgcGRm5bdu2cePG2djYpKWlSQdtbGwM2Uk5MjJSCNGmTRsnJychBOlhg6myiKeMAeLevXtTUlJEtZU0pQCRGYgA6qesrEw8/BkyceLEpUuX8nkMAGDiCBABQBZmGSBamOeee+7SpUsXL17s2bPnYz5V06ZNtTs1P/nkk2fPnjVkbshTTz0lhOjRo8djvjrqqsraYdqpiLoKCwsNf8Ly8vKgoKBnn312//790pHOnTvrnsAMRACPQzsDUelCAAAwFAEiAMiCANEkeHp6yjWhUrt8YdeuXYOCggz5kiFDhuzbt2/NmjWyFADDVQkQq89AXLlypYuLy7Zt2wx8wqtXryYnJ8fExEgB4s8///z999/rniCtgUiACKB+CBABAGaHABEAZEGAaGm0AaKvr6/hXxUZGdm8eXPjVAS9ar2Eee/evRqN5uOPPzbwCfPy8oQQJSUlWVlZzZo1i4iIcHBw0D2BS5gB1FtlZWVlZaVara73ahsAADQ8AkQAkAWfASxNeHi4i4uLEKJ169ZK14Ja1DoD8cyZM0KIw4cPnz9/3pAnlAJESdeuXat/yOcSZgD1xvRDAIA5IkAEAFkQIFoaOzu7P/zhD02aNNEuhgiTVSVAvHz58pkzZ9q2bWtjY/Ptt9/euHHj+vXr0kOHDh0y5Al1A8T27dtXP4EZiADqjQARAGCOCBABQBZ8DLBAa9eu/fTTTxs1aqR0IaiFh4eH7t1Tp07t3r07KytLCHHo0CHdR4uLiw15Qt0AsUOHDtVPkGanOjo61q9gACZr/vz5n332Wc3nVFRUiIefo+qBABEAYI4IEAFAFnwMsEAqlYr00Cw0a9ZMe7tFixY3b97ctWuXdDcvL0+6flmlUmk0mgcPHhjyhLUGiCNGjNi3b9/LL7/8WHUDMD0FBQW3b9826ksQIAIAzBEBIgDIgkuYAcW4u7trlymMjIwUQpw8eVK6m5eXd+HCBfFwLUu5AsS2bdvu2LGjX79+j1c4AJPz8ccfFxQU5Nfo6NGj4jE+QREgAgDMEQEiAMiCjwGAYmxsbNzc3PLz84UQgwcP/vbbb7WXFubl5ZWUlAghunXrlpWVJd2ulTZAVKlUj1wDEYAFkxY5rYG0iEG9lZWVCQJEAIC5IUAEAFkwAxFQkrTQoUql6ty5s+7xvLy81NRUIUS3bt2EwTMQc3NzpRstW7Zs3LixzLUCsG7SDER2YQIAmBcCRACQBfMIACV5eHhcunTJ3t6+TZs2usfz8vI0Go27u3urVq1EHS9hnj17dkREhDGqBWDNuIQZAGCOCBABQBbMQASUJM1AbNSokZeXl3Zej6Ojo/SLTseOHaUdk7Ozs/fs2VPr3qlSgPjXv/512LBhxq0bgPUhQAQAAACsFgEioCRtgKhWq6XJho6OjtLGKUInQNyxY8czzzyzZ8+eGp6qsrIyPz9fpVJJzwkA8iJABACYI2YgAoAsCBABJUlhn5QSSrmhp6dns2bNpEe7dOkiPSS5ceNGDU9179698vLyJk2a8PEegDEQIAIAzBEBIgDIggARUJJ2BqIQwtfXVwjRvHlzbYDYt29fBwcH7cn379+v4amkR52dnY1XLQBrRoAIADBHBIgAIAsCREBJ1WcgNm/e3NPTUzrYu3dv3QCxsLCwhqcqKioSQjg5ORm1YABWiwARAGCOCBABQBYEiICSdGcgagNE6WBYWJijo6P0kKTmALG4uFj7VAAgOwJEAIA5IkAEAFnwMQBQkrRxipQYPvvssydPnpw6dWpCQoIQYvjw4UIIZiACMBFlZWWCABEAYG4IEAFAFnwMAJQUHh6+ZcuW3r17CyGaN2/+5ZdfCiF69eoVGBgYGhoqHl7dLCFABKAgaQainZ2d0oUAAFAHBIgAIAsCREBhY8aMqXJErVaHh4dLtw3fREUKENlEBYCRcAkzAMAcESACgCxYAxEwacxABGAiCBABAOaIABEAZEGACJi0ugaIbKICwEgIEAEA5ogAEQBkQYAImDRmIAIwEQSIAABzRIAIALIgQARMGgEiABNBgAgAMEcEiAAgCwJEwKTZ2NhoP64TIAJQEAEiAMAcESACgCwIEAFTp52EWHOAWFxcLFgDEYDRECACAMwRASIAyIIAETB1Dg4O0o3CwkLpF6BHYgYiAKMqKysTBIgAAHNDgAgAsiBABEyddgZiZWWlNM3wkQgQARiVNAPRzs5O6UIAAKgDAkQAkAUBImDqDNxHhQARgFFxCTMAwBwRIAKALAgQAVNnYIAoTU4kQARgJASIAABzRIAIALIgQARMnW6AeO/ePelGTk5OUlKS7mnSDEQ2UQFgJASIAABzRIAIALIgQARMXfUZiMeOHQsKCurZs+e1a9e0D3EJMwCjIkAEAJgjAkQAkAUBImDqtLswi4cB4syZM3Nzc8vLyy9evKh9iAARgFERIAIAzBEBIgDIgo8BgKnTnYFYUFAghNBOPLxy5Yr2IQJEwCykp6fv37///Pnzt27dKioq8vDw8PHx8fHxGTlypLe3t9LV1aSiokIIYWNjo3QhAAAFmG//IkAEAFkQIAKmTjdAvHXrVmVlZW5urnRXN0BkExXAxGVkZEybNm3v3r2PfHT69OlRUVHLli1r27Ztw9ZlqLKyMsEMRACwPubevwgQAUAWfAwATJ10CXOzZs3y8vJyc3Pz8vKkCwmFEL///rsQoqysrLKykk1UAFN269atyMjI9PT0wMDAqKiooKAgDw8PFxeXgoKC/Pz8lJSUXbt2bd26NSEh4ciRIy1atFC63keQfvLY2dkpXQgAoOFYQP8iQAQAWRAgAqauVatWQojg4OB9+/bl5eXduHFD+5AUIIaEhBQWFkobNDMDETBN77zzTnp6+pIlS+bMmfPIE+bPn79+/frJkye/9957a9eubeDyDMEaiABghSygfwEAZMEmKoCpW7hw4cmTJ1955RUhRF5e3s2bN4UQ0h94r1y5UlxcnJiYePnyZdZABEzZoUOHAgIC9H36kkyYMGHgwIFHjhxpsKrqhAARAKyQBfQvZiACgCzMLEC8f/++lJLoOnbs2PTp05944omRI0fOmzdPmpMFWAwHB4fQ0NBmzZoJnQAxNDRUCHHlypXr169rz3R3d+ezPWCacnNzDVlj3tfXNycnpwHqqQcCRACwQhbQvwgQAUAWZhYgNm7cuFevXrpH5s2b179//1WrVh05cmTXrl2LFy8ODAz89ttvlaoQMBJPT08hRG5urnQJs7+/v5ub2/379xMTE7XnjBo1SrH6ANQoPDz8119/TUtLq+GcnJycvXv3hoeHN1hVdUKACABWyAL6FwEiAMjCzALEKmJiYhYvXty0adPPP/88KSkpJSXln//8p5OT06RJky5cuKB0dYCcqsxA9PLykra6O3z4sPaccePGKVQdgFrMmDGjvLw8LCzss88+y8rKqvJodnb2unXrQkNDc3JyJk6cqEiFtSJABAArZAH9iwARAGRh3h8DPvnkE7VavXv37j59+khHAgICgoODe/fuvXDhwu+++07Z8gAZVQkQW7Ro0bFjx7Nnzx48eFAIYWNjExQUNHjwYIWrBKBHZGTkihUrZj7k6uratGlTFxeXwsLC/Pz827dvCyFsbW1XrVo1evRopYt9NAJEALBCFtC/CBABQBbm/TEgMTExODhYmx5KgoODQ0JCTp8+rVRVgDE4OTk5OTkVFRWlp6cLIVq0aBEQECCEkC5hXrJkyezZsxUuEUCNoqOjIyIi1q1bt3///gsXLmRkZAghbGxsPD09Q0JCnnvuuQkTJnh5eSldpl4EiABgncy9fxEgAoAszPtjQFlZWfPmzasfb9myJZcww/I0a9bsypUrp06dEkL4+fnl5eUJISorK4UQPj4+ChcHwAD+/v5Lly5dunSpRqMpKip68OCBu7u7Wm0ey4mUlZUJAkQAsEpm3b8IEAFAFub9MSA0NDQxMVGj0ej2A41Gk5yc3K1bNwULA4zB09PzypUrJSUlHh4e/v7+d+7c0T7UqlUrBQsDUFcqlcrZ2dnZ2VnpQupAmoFoZ2endCEAAMWYY/8iQAQAWZjHX410ZWRkPP300zNnzly5cmWfPn2uXr26cOFC3RP+8Y9/pKam9u/fX6kKASPx9vaWboSFhalUqo4dO1Z/CID50mg0JSUlJSUlShfyaFzCDAB4JBPvXwSIACALM/sYMGDAgLS0tJiYmJiYGO3BhQsXvvfee0KI8vLy0NDQs2fPdunS5d1331WuTMAoXnnllV27dgkhwsLChBBubm4tW7a8fv16ixYt2rRpo3R1AB5XYmJijx49xMOPOnVy9OjRb7/9VlrTQB9pqXvDn/zy5cuffPJJaWmpdDchIUEIYWNjU9faAACWzcT7V3Jycl2rAgBUZ2YBYlxcnBCiqKjo8uXLaWlp6enpaWlpmZmZ0qMVFRVnz57t27fvxo0bzWtePWCIMWPGdO/ePSEhYcCAAdKRTZs2paamPv/88/b29srWBkBZS5cu3blzpyFnGv4BbMOGDStXrqxy0NXVtW6VAQCgX4P1ryZNmtStMgDAfzOzAFHi5OQUFBQUFBRU5bidnV1WVlbr1q0VqQowNrVavX379t9++00bIA4aNGjQoEGKFgVANgEBAUlJSfX72k8//XTEiBE1n1NQUDB79mzDE8C//OUvbdq0qaio0B5xd3fv27dv/SoEAFgqs+hf4eHh9asQACAxywBRH7VaTXoIy9amTRuuVgYslaOjY2BgYP2+tl27dpMnT675nJs3b86ePdvR0dHA53Rzc/vTn/5Uv3oAANaD/gUA1sCiAkSJRqORFrxwcHCo69devHhx27ZtNZ9z8+bNelYGAAAAAAAAmBsLDBAfZxHfWbNmSZtU1KrmhX4BAAAAAAAAy2CBAeLj+Nvf/lbr9Pv79++vXLmycePGDVMSAAAAAAAAoCALDBAfZxHf4ODg4ODgms+5efPmypUr2eUZAAAAAAAA1sACA8THWcQXAADZ5ebmHjt2zPDzo6KijFcMAAAGon8BALQsMEAEAMCkJCcnjx492vDz67GGLwAAsqN/AQC0zDVATE9P379///nz52/dulVUVOTh4eHj4+Pj4zNy5Ehvb+8GKKC0tPTAgQOGnBYbG+vh4aFSqRqgKnNUUVGRl5fXvHlzhkgfaYhatGihdCEmSpWSon3r7NixQ5OZqWAxJqusrEwI0adPHwPPP3/+vDHLsTqDBg06d+7c/Pnzt27dKoSIjo52dXVVqhj6l1zoX7Wif9WM/mUI+pey6F8Wif5VK/pXzehfhrDI/qUyuz8TZWRkTJs2be/evY981NbWNioqatmyZW3btjVSAbm5uc2bNzfSkwOoqwgh/B7ePiDEZSVrsTT9+vU7cuSI0lVYlF69ep0+fTozM7NNmzYN/+r0L8Ck0L+Mh/4lO/oXAC36l/GYeP8ysxmIt27dioyMTE9PDwwMjIqKCgoK8vDwcHFxKSgoyM/PT0lJ2bVr19atWxMSEo4cOWKkvxg0a9Zs+vTpFy9eNOTk5OTk7OxsJycnR0dHYxRjAe7fv19SUuLs7Ozg4KB0LSaKIarZaSEOFRaWlpY2btzY3t6+qdL1mKZ79+6VlZV17dq1Tj8YJ0yYYLySrNOLL754+vRppV6d/iUvfjjXiiGqGf3LEPQvE0H/siT8cK4VQ1Qz+pchLLN/aczK5MmThRBLliyp4ZyvvvrK1tZ28uTJDVZVDWbNmiWE+PDDD5UuxHRNmzZNCPH5558rXYjpGj9+vBBi/fr1Shdiul544QUhxA8//KB0IabrmWeeEULs2rVL6UKs3cGDBzt06HD16lWlC6kd/atW9K9a0b9qRf+qFf3LRNC/LAn9q1b0r1rRv2plkf3LzGYgHjp0KCAgYM6cOTWcM2HChE2bNpnytE8AgHUaNGjQpUuXlK4CAIC6oX8BANRKF1A3ubm5huyR4uvrm5OT0wD1AAAAAAAAAJbNzALE8PDwX3/9NS0trYZzcnJy9u7dGx4e3mBVAQAAAAAAAJbKzALEGTNmlJeXh4WFffbZZ1lZWVUezc7OXrduXWhoaE5OzsSJExWpEAAAAAAAALAkZrYGYmRk5IoVK2Y+5Orq2rRpUxcXl8LCwvz8/Nu3bwshbG1tV61aNXr0aKWLBQAAAAAAAMyemQWIQojo6OiIiIh169bt37//woULGRkZQggbGxtPT8+QkJDnnntuwoQJXl5eSpcJAAAAAAAAWALzCxCFEP7+/kuXLl26dKlGoykqKnrw4IG7u7tabWaXYwMAAAAAAACmzywDRC2VSuXs7Ozs7Kx0IQAAAAAAAIBlYtYeAAAAAAAAAL0IEAEAAAAAAADoRYBoXNLl1Y0bN1a6ENPFENWKIaoVQ1Qrhgh1xXumVgxRrRiiWjFEtWKIUFe8Z2rFENWKIaoVQ1QrixwilUajUboGS3b37t2dO3eOHTvW0dFR6VpM1O3bt/fs2fP888/b29srXYuJys3N3b9//wsvvGBra96LlhrPjRs3YmNjX3jhBTZT0ufq1avHjh17/vnnVSqV0rXAPNC/akX/qhX9q1b0r1rRv1BX9K9a0b9qRf+qFf2rVhbZvwgQAQAAAAAAAOhFWgwAAAAAAABALwJEAAAAAAAAAHoRIAIAAAAAAADQiwARAAAAAAAAgF4EiAAAAAAAAAD0IkAEAAAAAAAAoBcBIgAAAAAAAAC9CBABAAAAAAAA6EWACAAAAAAAAEAvAkQAAAAAAAAAehEgAgAAAAAAANCLABEAAAAAAACAXgSIAAAAAAAAAPQiQAQAAAAAAACgFwEijCs2NjY3N1fpKgDLt2zZss8//1zpKgDLQf8CGgb9C5AX/QtoGFbYvwgQjWX//v2jRo3y9PTs0qVLdHR0fn6+0hUpICkpafDgwceOHXvko4YMkQUP4+rVq3v16tWkSZPmzZsPGDDgxx9/rH6ONQ/RvXv3Zs2a1bNnz8aNG7dr12706NGnT5+ufpo1D5Gub775Zvbs2Vu2bKn+EEOEuuL9IOhfNaJ/1Yz+VSf0L8iI94Ogf9WI/lUz+ledWGn/0sAIVq9ebWNjY29vP3DgwI4dOwoh2rdvf/nyZaXramhjx44VQmzbtq36Q4YMkaUOY3l5+ZQpU4QQDg4OAwcOHDx4sKOjoxBiypQpuqdZ8xDdu3evXbt2QggvL68RI0aEh4cLIVQq1c6dO3VPs+Yh0pWRkeHi4iKEGDRoUJWHGCLUFe8HCf3rkehftaJ/1Qn9CzLi/SChfz0S/atW9K86sdr+RYAov9TUVDs7Ow8Pj9TUVOnI4sWLhRBPPvmksoU1mIMHD37wwQe9evWSQurqDcyQIbLgYVy/fr0QIiAgIDs7WzqSlpbm5+cnhNi9e7d0xMqH6O233xZCTJw4saKiQjqye/dulUrl7e2tPcfKh0irvLy8X79+TZo0qd7AGCLUFe8H+lfN6F+1on8Zjv4FGfF+oH/VjP5VK/qX4ay5fxEgyu+tt94SQnz66ae6BwMDA4UQaWlpSlXVkDp06KA7y7V6AzNkiCx4GIcOHSqEOH78uO7BzZs3CyGmTp0q3bXyIerevbujo2NRUZHuwbCwMCFERkaGdNfKh0hrwYIFKpXqyy+/rN7AGCLUFe8H+lfN6F+1on8Zjv4FGfF+oH/VjP5VK/qX4ay5f7EGovz2798vhIiKitI9KN2VHrJ4sbGxmZmZmZmZ0dHRjzzBkCGy4GG8fPmynZ1daGio7sGuXbsKIS5duiTdtfIh8vX1HTNmTKNGjXQP2tjYCCEKCwulu1Y+RJITJ04sWLDgz3/+8/Dhw6s/yhChrng/0L9qRv+qFf3LQPQvyIv3A/2rZvSvWtG/DGTl/ctW6QIsjUajuXDhgouLS5s2bXSPBwUFCSHOnz+vUF0NysfHR7rh6upa/VFDhsiyh/Hf//63SqVSq/8rvj916pQQon379oIhEmLnzp1Vjhw+fDg+Pr5du3adOnUSDJEQQojCwsJx48Z16NBh6dKl1ZfdZYhQV7wfBP2rNvSvWtG/DEH/grx4Pwj6V23oX7WifxmC/kWAKLOioqIHDx54e3tXOe7h4SGEuHXrlhJFmRZDhsiyh7FHjx5Vjpw9e3bWrFkqlUpa3Jch0jpx4sRHH330+++/x8fHd+rU6fvvv7e1tRUMkRBCiBkzZmRlZf36669V/lQoYYhQV7wfasW3Ff3LcPSvGtC/IC/eD7Xi24r+ZTj6Vw3oX1zCLLMHDx4IIaQdeXRJR4qKihSoycQYMkTWM4wajWbjxo0DBgzIzs7+6KOPgoODBUOk49atWwkJCcnJyRUVFQ4ODtofqQzR1q1b169f//7772tXy66CIUJd8X6oFd9WuuhfNaN/6UP/gux4P9SKbytd9K+a0b/0oX8JAkTZubu729jYaJcJ0CooKBAPc2UrZ8gQWckwnjp1KiwsbPz48c7Ozj/99NObb74pHWeItJ5++umLFy8WFBT85z//uXLlyvDhwxMTE4XVD9G1a9cmT54cHh4+Z84cfedY+RChHng/1IpvKy36V63oX49E/4Ix8H6oFd9WWvSvWtG/Hon+JeESZpmp1WpPT8/q18NLR7SLU1gzQ4bI4oexrKzs/fff/+CDDxwcHN5999233npL2gZewhBVN2TIkPnz5//5z3/++uuvly1bZuVDtG3btvz8fLVaPW7cOOmI9Neq8+fPv/TSS3Z2dl9//bWVDxHqgfdDrfi2EvSvuqN/6aJ/wRh4P9SKbytB/6o7+pcu+peEGYjya9Wq1Z07d27cuKF7MCUlRZjDG6JhGDJEFjyMlZWVr7zyypIlSwYNGnThwoWFCxfqdi+JNQ/R6dOnn3rqqRUrVlQ5Li3fm5ubK9215iGSHD169F8P7dixQwiRk5Pzr3/9a/PmzdIJDBHqivdDraz824r+VTP6l4HoX5Ad74daWfm3Ff2rZvQvA9G/CBDl9+yzz2o0ml27duke3LVrl62t7ciRI5WqyqQYMkQWPIyrVq3617/+9T//8z8///xz69atH3mONQ+Rq6vr3r17v/322yrHpU2pAgMDpbvWPETTp0/X/LerV68KIQYNGqTRaIqLi6XTrHmIUD+8H2pl5d9W9K+a0b9qRf+CkfB+qJWVf1vRv2pG/6oV/ev/aCC369ev29ratmnT5ubNm9KR9evXCyHGjBmjbGENT1ogYNu2bVWOGzJEFjyM/v7+Tk5OBQUFNZxj5UMkLUz7xRdfaI+cP3++RYsW9vb2SUlJ0hErH6IqdBuYFkOEuuL9oEX/eiT6V63oX3VF/4IseD9o0b8eif5VK/pXXVln/yJANIo1a9ao1Wpvb+8JEyZERkba2tq2b9/+8uXLStfV0PQ1MI1hQ2SRw5idnS2EcHR07PEos2bN0p5ptUOk0Wh+++03Z2dnIURgYOCzzz7bv39/Ozs7lUq1fPly3dOseYiqeGQD0zBEqDveDxL6V3X0L0PQv+qK/gW58H5lSlIMAAAJpUlEQVSQ0L+qo38Zgv5VV9bZvwgQjeWnn34aMWKEh4dHp06dJk2alJ2drXRFCqihgWkMGyLLG8ajR4/WMCN47Nixuidb5xBJLl68+Oqrr/r4+Dg4OLRv33706NEnT56sfpo1D5EufQ1MwxCh7ng/aOhfj0L/MhD9q07oX5AR7wcN/etR6F8Gon/ViXX2L5VGo6nh2wkAAAAAAACANWMTFQAAAAAAAAB6ESACAAAAAAAA0IsAEQAAAAAAAIBeBIgAAAAAAAAA9CJABAAAAAAAAKAXASIAAAAAAAAAvQgQAQAAAAAAAOhFgAgAAAAAAABALwJEAAAAAAAAAHoRIAIAAAAAAADQiwARAAAAAAAAgF4EiAAAAAAAAAD0IkAEAAAAAAAAoBcBIgAAAAAAAAC9CBABAAAAAAAA6EWACAAAAAAAAEAvAkQAAAAAAAAAehEgAgAAAAAAANCLABEAAAAAAACAXgSIAAAAAAAAAPQiQAQAAAAAAACgFwEiAAAAAAAAAL0IEAEAAAAAAADoRYAIAAAAAAAAQC8CRAAAAAAAAAB6ESACAAAAAAAA0IsAEQAAAAAAAIBeBIgAAAAAAAAA9CJABAAAAAAAAKAXASIAAAAAAAAAvQgQAQAAAAAAAOhFgAgAAAAAAABALwJEAAAAAAAAAHoRIAIAAAAAAADQiwARqCdfX1+VSnXz5k2lC/k/r732mkqlunjxotKFAABMGv0LAGCO6F+AsggQAXloNJqSkpLy8nKLfDkAgKWifwEAzBH9C2hgBIiAPE6ePOno6Dh37lylXm7u3LnHjx9v06ZNwxQAALAM9C8AgDmifwENzFbpAgDIw8/Pz8/PT+kqAACoG/oXAMAc0b9gbZiBCJiQe/fuKfXSZWVlFRUVSr06AMCs0b8AAOaI/gUYjgARkMFTTz0VFhYmhFi2bJlKpdq0aZP2oU2bNg0fPtzT07N58+bDhw//+eefdb9w9uzZKpXq1q1bX3/9devWrZ9++mnp+L179956663evXu7uLh4enr26dNnzZo1Go2mhpeLjo6usohveXn54sWL+/fv7+rq6u/vP2bMmDNnzui++ty5c1UqVWZm5pQpU1xcXOzs7Hx9fSdMmJCdnW2UYQIAmBj6FwDAHNG/gIZHgAjIYMqUKW+88YYQYvDgwcuXL+/Vq5d0/NVXXx03btypU6dCQ0O7dOly6NChJ598cvHixVW+/Mcff5w0aVLXrl2joqKEEHl5eUFBQR9++GFRUdHw4cNDQkKSk5Ojo6Pnz59f88vpKioq6tev37x589LS0p544gl3d/ft27f36dNn48aNVc6Mjo7esGFDRETEa6+95ujouGHDhmeeeYa/hgGANaB/AQDMEf0LUIAGQL20atVKCHHjxg3p7vHjx4UQs2bN0p7w448/CiFGjx5dUFAgHUlLS2vfvr1arT5x4oR0ZNasWUKI5s2bJyQkaL9w0aJFQoi5c+dqj1y/ft3FxcXPz097pPrLTZ06VQiRkpIi3X3//feFEC+88EJRUZF0JC4uzsXFxc3NLS8vTzoyZ84cIYSTk1N8fLx0pKSkpHv37kII3XoAAJaE/gUAMEf0L0BZzEAEjGXRokWNGjXauHFjkyZNpCPt27f/4IMPKisrv/nmG90zJ06c2K1bN+3dfv36rVmz5i9/+Yv2iLe3d4sWLXJycgx/9eXLl7u4uKxZs6ZRo0bSkQEDBrz55pt37tzZsGGD7pnTp08PCQmRbtvb248aNUoIwSx6ALBa9C8AgDmifwFGxS7MgFFUVFRcuHDB29u7Sq/Ky8sTQpw9e1b3YGhoqO7dgQMHDhw4UKPRZGZmZmZmZmRkxMbGXrp0qXHjxga++vXr1+/evTts2DB3d3fd40899dTf/va3lJQU3YPh4eG6d52dnQ18FQCA5aF/AQDMEf0LMDYCRMAorl27VlpampWV9ec//7n6o1V2+/L29ta9W1ZWtmDBgtWrV9+6dUulUnl7ewcHB3t5eRUWFhr46levXq3+tEIIHx8fIURWVpbuwaZNmxr4tAAAi0f/AgCYI/oXYGxcwgwYhZeXl42NTWRk5CPXDqjyFzC1+r++E8eNG/f3v/99xIgRR48eLSwsvHbt2s6dO6UlPwykXR+kynFpYnydngoAYFXoXwAAc0T/AoyNABEwCnt7ez8/v1OnTt2/f1/3+JEjR15//fWff/5Z3xcWFxfv3Lmze/fuGzZs6Nu3r5OTk3T87t27hr96y5YtXVxcjh8/fufOHd3j0ut27ty5Dv8SAIA1oX8BAMwR/QswNgJEQE4PHjzQ3n7zzTfz8/NffvllbQ+7evXq888/v3LlSmkq+yOp1ery8vLbt28XFxdLR8rLyxctWnTp0qWKigqNRqPv5aqYOXPm3bt3p02bVlJSIh05evTosmXL3Nzcxo8fX99/HwDAMtG/AADmiP4FNBjWQATkIa2wu3nzZpVKNW7cuN69e0+ePHn79u07duxo3bp1nz598vPz4+PjKysrFy9eHBQUpO95HBwcxo0bt379+vbt20dERKjV6iNHjggh+vTpc+LEiQkTJsybN8/f37/6y1V5nrfffjsmJub777+Pi4vr3bt3Tk7OyZMn1Wr1unXrPD09jTkSAABzQv8CAJgj+hfQwJiBCMijS5cus2bNcnBw2LBhw/Xr14UQNjY2MTExK1asCAoKOn78eGZm5pAhQ2JiYubOnVvzU61ater9999v3Ljx1q1bExMTx4wZk5CQ8OmnnwYFBW3evPnatWuPfLkqnJ2djx07tnDhwjZt2vzyyy83btyIioo6ceLEH//4R2P88wEAZor+BQAwR/QvoIGpqszIBQAAAAAAAAAtZiACAAAAAAAA0IsAEQAAAAAAAIBeBIgAAAAAAAAA9CJABAAAAAAAAKAXASIAAAAAAAAAvQgQAQAAAAAAAOhFgAgAAAAAAABALwJEAAAAAAAAAHoRIAIAAAAAAADQiwARAAAAAAAAgF4EiAAAAAAAAAD0IkAEAAAAAAAAoBcBIgAAAAAAAAC9CBABAAAAAAAA6EWACAAAAAAAAEAvAkQAAAAAAAAAehEgAgAAAAAAANCLABEAAAAAAACAXgSIAAAAAAAAAPQiQAQAAAAAAACgFwEiAAAAAAAAAL0IEAEAAAAAAADoRYAIAAAAAAAAQC8CRAAAAAAAAAB6ESACAAAAAAAA0IsAEQAAAAAAAIBeBIgAAAAAAAAA9CJABAAAAAAAAKAXASIAAAAAAAAAvQgQAQAAAAAAAOhFgAgAAAAAAABALwJEAAAAAAAAAHr9P5MgoXyPNDIOAAAAAElFTkSuQmCC" alt="Convergence plot for the NLHM DFOP fit with constant variance" width="864" />
+<p class="caption">
 Convergence plot for the NLHM DFOP fit with constant variance
 </p>
 </div>
 <div class="figure" style="text-align: center">
-<img src="2022_dmta_parent_files/figure-html/convergence-saem-dfop-tc-1.png" alt="Convergence plot for the NLHM DFOP fit with two-component error" width="864"><p class="caption">
+<img src="data:image/png;base64,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" alt="Convergence plot for the NLHM DFOP fit with two-component error" width="864" />
+<p class="caption">
 Convergence plot for the NLHM DFOP fit with two-component error
 </p>
 </div>
 <div class="figure" style="text-align: center">
-<img src="2022_dmta_parent_files/figure-html/convergence-saem-hs-const-1.png" alt="Convergence plot for the NLHM HS fit with constant variance" width="864"><p class="caption">
+<img src="data:image/png;base64,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" alt="Convergence plot for the NLHM HS fit with constant variance" width="864" />
+<p class="caption">
 Convergence plot for the NLHM HS fit with constant variance
 </p>
 </div>
 <div class="figure" style="text-align: center">
-<img src="2022_dmta_parent_files/figure-html/convergence-saem-hs-tc-1.png" alt="Convergence plot for the NLHM HS fit with two-component error" width="864"><p class="caption">
+<img src="data:image/png;base64,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" alt="Convergence plot for the NLHM HS fit with two-component error" width="864" />
+<p class="caption">
 Convergence plot for the NLHM HS fit with two-component error
 </p>
 </div>
 </div>
-<div class="section level3">
-<h3 id="session-info">Session info<a class="anchor" aria-label="anchor" href="#session-info"></a>
-</h3>
-<pre><code>R version 4.3.1 (2023-06-16)
-Platform: x86_64-pc-linux-gnu (64-bit)
-Running under: Ubuntu 22.04.3 LTS
+<div id="session-info" class="section level2">
+<h2>Session info</h2>
+<pre><code>R version 4.4.2 (2024-10-31)
+Platform: x86_64-pc-linux-gnu
+Running under: Debian GNU/Linux 12 (bookworm)
 
 Matrix products: default
-BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0 
-LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
+BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.11.0 
+LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.11.0
 
 locale:
- [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
- [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
- [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
- [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
+ [1] LC_CTYPE=de_DE.UTF-8       LC_NUMERIC=C              
+ [3] LC_TIME=de_DE.UTF-8        LC_COLLATE=de_DE.UTF-8    
+ [5] LC_MONETARY=de_DE.UTF-8    LC_MESSAGES=de_DE.UTF-8   
+ [7] LC_PAPER=de_DE.UTF-8       LC_NAME=C                 
  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
-[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
+[11] LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=C       
 
-time zone: Europe/Zurich
+time zone: Europe/Berlin
 tzcode source: system (glibc)
 
 attached base packages:
@@ -2031,60 +2417,73 @@ attached base packages:
 [8] base     
 
 other attached packages:
-[1] saemix_3.2 npde_3.3   knitr_1.44 mkin_1.2.6
+[1] saemix_3.3      npde_3.5        knitr_1.49      mkin_1.2.9     
+[5] rmarkdown_2.29  nvimcom_0.9-167
 
 loaded via a namespace (and not attached):
- [1] sass_0.4.7        utf8_1.2.3        generics_0.1.3    stringi_1.7.12   
- [5] lattice_0.21-9    digest_0.6.33     magrittr_2.0.3    evaluate_0.22    
- [9] grid_4.3.1        fastmap_1.1.1     rprojroot_2.0.3   jsonlite_1.8.7   
-[13] mclust_6.0.0      gridExtra_2.3     purrr_1.0.1       fansi_1.0.4      
-[17] scales_1.2.1      codetools_0.2-19  textshaping_0.3.6 jquerylib_0.1.4  
-[21] cli_3.6.1         rlang_1.1.1       munsell_0.5.0     cachem_1.0.8     
-[25] yaml_2.3.7        tools_4.3.1       memoise_2.0.1     dplyr_1.1.2      
-[29] colorspace_2.1-0  ggplot2_3.4.2     vctrs_0.6.3       R6_2.5.1         
-[33] zoo_1.8-12        lifecycle_1.0.3   stringr_1.5.0     fs_1.6.3         
-[37] MASS_7.3-60       ragg_1.2.5        pkgconfig_2.0.3   desc_1.4.2       
-[41] pkgdown_2.0.7     bslib_0.5.1       pillar_1.9.0      gtable_0.3.3     
-[45] glue_1.6.2        systemfonts_1.0.4 xfun_0.40         tibble_3.2.1     
-[49] lmtest_0.9-40     tidyselect_1.2.0  rstudioapi_0.15.0 htmltools_0.5.6.1
-[53] nlme_3.1-163      rmarkdown_2.23    compiler_4.3.1   </code></pre>
+ [1] jsonlite_1.8.9    gtable_0.3.6      dplyr_1.1.4       compiler_4.4.2   
+ [5] tidyselect_1.2.1  colorout_1.3-2    tinytex_0.54      gridExtra_2.3    
+ [9] jquerylib_0.1.4   scales_1.3.0      yaml_2.3.10       fastmap_1.2.0    
+[13] lattice_0.22-6    ggplot2_3.5.1     R6_2.5.1          generics_0.1.3   
+[17] lmtest_0.9-40     MASS_7.3-61       tibble_3.2.1      munsell_0.5.1    
+[21] bslib_0.8.0       pillar_1.9.0      rlang_1.1.4       utf8_1.2.4       
+[25] cachem_1.1.0      xfun_0.49         sass_0.4.9        cli_3.6.3        
+[29] magrittr_2.0.3    digest_0.6.37     grid_4.4.2        mclust_6.1.1     
+[33] lifecycle_1.0.4   nlme_3.1-166      vctrs_0.6.5       evaluate_1.0.1   
+[37] glue_1.8.0        codetools_0.2-20  zoo_1.8-12        fansi_1.0.6      
+[41] colorspace_2.1-1  tools_4.4.2       pkgconfig_2.0.3   htmltools_0.5.8.1</code></pre>
 </div>
-<div class="section level3">
-<h3 id="hardware-info">Hardware info<a class="anchor" aria-label="anchor" href="#hardware-info"></a>
-</h3>
-<pre><code>CPU model: Intel(R) Xeon(R) Gold 6134 CPU @ 3.20GHz</code></pre>
-<pre><code>MemTotal:       247605564 kB</code></pre>
+<div id="hardware-info" class="section level2">
+<h2>Hardware info</h2>
+<pre><code>CPU model: AMD Ryzen 9 7950X 16-Core Processor</code></pre>
+<pre><code>MemTotal:       64927788 kB</code></pre>
 </div>
 </div>
-  </div>
 
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
 
-        <nav id="toc" data-toggle="toc"><h2 data-toc-skip>Contents</h2>
-    </nav>
-</div>
+
 
 </div>
 
+<script>
 
+// add bootstrap table styles to pandoc tables
+function bootstrapStylePandocTables() {
+  $('tr.odd').parent('tbody').parent('table').addClass('table table-condensed');
+}
+$(document).ready(function () {
+  bootstrapStylePandocTables();
+});
 
-      <footer><div class="copyright">
-  <p></p>
-<p>Developed by Johannes Ranke.</p>
-</div>
 
-<div class="pkgdown">
-  <p></p>
-<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
+</script>
 
-      </footer>
-</div>
+<!-- tabsets -->
+
+<script>
+$(document).ready(function () {
+  window.buildTabsets("TOC");
+});
+
+$(document).ready(function () {
+  $('.tabset-dropdown > .nav-tabs > li').click(function () {
+    $(this).parent().toggleClass('nav-tabs-open');
+  });
+});
+</script>
 
-  
+<!-- code folding -->
 
 
-  
+<!-- dynamically load mathjax for compatibility with self-contained -->
+<script>
+  (function () {
+    var script = document.createElement("script");
+    script.type = "text/javascript";
+    script.src  = "https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML";
+    document.getElementsByTagName("head")[0].appendChild(script);
+  })();
+</script>
 
-  </body>
+</body>
 </html>
diff --git a/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-dfop-const-1.png b/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-dfop-const-1.png
index 4c74de78..91c027a5 100644
--- a/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-dfop-const-1.png
+++ b/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-dfop-const-1.png
diff --git a/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-dfop-tc-1.png b/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-dfop-tc-1.png
index 4bd1ceb1..678b76e2 100644
--- a/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-dfop-tc-1.png
+++ b/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-dfop-tc-1.png
diff --git a/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-dfop-tc-no-ranef-k2-1.png b/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-dfop-tc-no-ranef-k2-1.png
index 612478e5..a3bc320e 100644
--- a/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-dfop-tc-no-ranef-k2-1.png
+++ b/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-dfop-tc-no-ranef-k2-1.png
diff --git a/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-fomc-const-1.png b/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-fomc-const-1.png
index 953ffb3c..da63e191 100644
--- a/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-fomc-const-1.png
+++ b/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-fomc-const-1.png
diff --git a/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-fomc-tc-1.png b/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-fomc-tc-1.png
index 21ca0bce..00958654 100644
--- a/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-fomc-tc-1.png
+++ b/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-fomc-tc-1.png
diff --git a/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-hs-const-1.png b/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-hs-const-1.png
index f15137d0..72a996ac 100644
--- a/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-hs-const-1.png
+++ b/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-hs-const-1.png
diff --git a/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-hs-tc-1.png b/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-hs-tc-1.png
index 322668f0..46cfa3f8 100644
--- a/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-hs-tc-1.png
+++ b/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-hs-tc-1.png
diff --git a/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-sfo-const-1.png b/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-sfo-const-1.png
index 4ceb281f..6dc7a6e2 100644
--- a/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-sfo-const-1.png
+++ b/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-sfo-const-1.png
diff --git a/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-sfo-tc-1.png b/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-sfo-tc-1.png
index 07383871..8df26d78 100644
--- a/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-sfo-tc-1.png
+++ b/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-sfo-tc-1.png
diff --git a/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/multistart-full-par-1.png b/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/multistart-full-par-1.png
index bd89ef9f..f90b19e7 100644
--- a/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/multistart-full-par-1.png
+++ b/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/multistart-full-par-1.png
diff --git a/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/multistart-reduced-par-1.png b/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/multistart-reduced-par-1.png
index 97502695..f6f1e43d 100644
--- a/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/multistart-reduced-par-1.png
+++ b/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/multistart-reduced-par-1.png
diff --git a/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/multistart-reduced-par-llquant-1.png b/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/multistart-reduced-par-llquant-1.png
index b74347bc..ef6bc8bd 100644
--- a/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/multistart-reduced-par-llquant-1.png
+++ b/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/multistart-reduced-par-llquant-1.png
diff --git a/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/plot-saem-dfop-tc-no-ranef-k2-1.png b/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/plot-saem-dfop-tc-no-ranef-k2-1.png
index 277b7c18..cd506f57 100644
--- a/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/plot-saem-dfop-tc-no-ranef-k2-1.png
+++ b/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/plot-saem-dfop-tc-no-ranef-k2-1.png
diff --git a/docs/articles/prebuilt/2022_dmta_parent_files/figure-latex/convergence-saem-dfop-const-1.pdf b/docs/articles/prebuilt/2022_dmta_parent_files/figure-latex/convergence-saem-dfop-const-1.pdf
new file mode 100644
index 00000000..20f31bba
--- /dev/null
+++ b/docs/articles/prebuilt/2022_dmta_parent_files/figure-latex/convergence-saem-dfop-const-1.pdf
diff --git a/docs/articles/prebuilt/2022_dmta_parent_files/figure-latex/convergence-saem-dfop-tc-1.pdf b/docs/articles/prebuilt/2022_dmta_parent_files/figure-latex/convergence-saem-dfop-tc-1.pdf
new file mode 100644
index 00000000..079a4547
--- /dev/null
+++ b/docs/articles/prebuilt/2022_dmta_parent_files/figure-latex/convergence-saem-dfop-tc-1.pdf
diff --git a/docs/articles/prebuilt/2022_dmta_parent_files/figure-latex/convergence-saem-dfop-tc-no-ranef-k2-1.pdf b/docs/articles/prebuilt/2022_dmta_parent_files/figure-latex/convergence-saem-dfop-tc-no-ranef-k2-1.pdf
new file mode 100644
index 00000000..d732c9d6
--- /dev/null
+++ b/docs/articles/prebuilt/2022_dmta_parent_files/figure-latex/convergence-saem-dfop-tc-no-ranef-k2-1.pdf
diff --git a/docs/articles/prebuilt/2022_dmta_parent_files/figure-latex/convergence-saem-fomc-const-1.pdf b/docs/articles/prebuilt/2022_dmta_parent_files/figure-latex/convergence-saem-fomc-const-1.pdf
new file mode 100644
index 00000000..8a7cf103
--- /dev/null
+++ b/docs/articles/prebuilt/2022_dmta_parent_files/figure-latex/convergence-saem-fomc-const-1.pdf
diff --git a/docs/articles/prebuilt/2022_dmta_parent_files/figure-latex/convergence-saem-fomc-tc-1.pdf b/docs/articles/prebuilt/2022_dmta_parent_files/figure-latex/convergence-saem-fomc-tc-1.pdf
new file mode 100644
index 00000000..7556db36
--- /dev/null
+++ b/docs/articles/prebuilt/2022_dmta_parent_files/figure-latex/convergence-saem-fomc-tc-1.pdf
diff --git a/docs/articles/prebuilt/2022_dmta_parent_files/figure-latex/convergence-saem-hs-const-1.pdf b/docs/articles/prebuilt/2022_dmta_parent_files/figure-latex/convergence-saem-hs-const-1.pdf
new file mode 100644
index 00000000..6e8b9df1
--- /dev/null
+++ b/docs/articles/prebuilt/2022_dmta_parent_files/figure-latex/convergence-saem-hs-const-1.pdf
diff --git a/docs/articles/prebuilt/2022_dmta_parent_files/figure-latex/convergence-saem-hs-tc-1.pdf b/docs/articles/prebuilt/2022_dmta_parent_files/figure-latex/convergence-saem-hs-tc-1.pdf
new file mode 100644
index 00000000..08da9919
--- /dev/null
+++ b/docs/articles/prebuilt/2022_dmta_parent_files/figure-latex/convergence-saem-hs-tc-1.pdf
diff --git a/docs/articles/prebuilt/2022_dmta_parent_files/figure-latex/convergence-saem-sfo-const-1.pdf b/docs/articles/prebuilt/2022_dmta_parent_files/figure-latex/convergence-saem-sfo-const-1.pdf
new file mode 100644
index 00000000..ba3698f3
--- /dev/null
+++ b/docs/articles/prebuilt/2022_dmta_parent_files/figure-latex/convergence-saem-sfo-const-1.pdf
diff --git a/docs/articles/prebuilt/2022_dmta_parent_files/figure-latex/convergence-saem-sfo-tc-1.pdf b/docs/articles/prebuilt/2022_dmta_parent_files/figure-latex/convergence-saem-sfo-tc-1.pdf
new file mode 100644
index 00000000..7baff5bb
--- /dev/null
+++ b/docs/articles/prebuilt/2022_dmta_parent_files/figure-latex/convergence-saem-sfo-tc-1.pdf
diff --git a/docs/articles/prebuilt/2022_dmta_parent_files/figure-latex/multistart-full-par-1.pdf b/docs/articles/prebuilt/2022_dmta_parent_files/figure-latex/multistart-full-par-1.pdf
new file mode 100644
index 00000000..3621b5a5
--- /dev/null
+++ b/docs/articles/prebuilt/2022_dmta_parent_files/figure-latex/multistart-full-par-1.pdf
diff --git a/docs/articles/prebuilt/2022_dmta_parent_files/figure-latex/multistart-reduced-par-1.pdf b/docs/articles/prebuilt/2022_dmta_parent_files/figure-latex/multistart-reduced-par-1.pdf
new file mode 100644
index 00000000..93d185be
--- /dev/null
+++ b/docs/articles/prebuilt/2022_dmta_parent_files/figure-latex/multistart-reduced-par-1.pdf
diff --git a/docs/articles/prebuilt/2022_dmta_parent_files/figure-latex/multistart-reduced-par-llquant-1.pdf b/docs/articles/prebuilt/2022_dmta_parent_files/figure-latex/multistart-reduced-par-llquant-1.pdf
new file mode 100644
index 00000000..31818088
--- /dev/null
+++ b/docs/articles/prebuilt/2022_dmta_parent_files/figure-latex/multistart-reduced-par-llquant-1.pdf
diff --git a/docs/articles/prebuilt/2022_dmta_parent_files/figure-latex/plot-saem-dfop-tc-no-ranef-k2-1.pdf b/docs/articles/prebuilt/2022_dmta_parent_files/figure-latex/plot-saem-dfop-tc-no-ranef-k2-1.pdf
new file mode 100644
index 00000000..607a03ed
--- /dev/null
+++ b/docs/articles/prebuilt/2022_dmta_parent_files/figure-latex/plot-saem-dfop-tc-no-ranef-k2-1.pdf
diff --git a/docs/articles/prebuilt/2022_dmta_pathway.html b/docs/articles/prebuilt/2022_dmta_pathway.html
index 2c3f326a..c9f87cd8 100644
--- a/docs/articles/prebuilt/2022_dmta_pathway.html
+++ b/docs/articles/prebuilt/2022_dmta_pathway.html
@@ -1,155 +1,396 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en">
+
+<html>
+
 <head>
-<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
-<meta charset="utf-8">
-<meta http-equiv="X-UA-Compatible" content="IE=edge">
-<meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P • mkin</title>
-<!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous">
-<script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../../bootstrap-toc.css">
-<script src="../../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous">
-<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous">
-<!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../../pkgdown.css" rel="stylesheet">
-<script src="../../pkgdown.js"></script><meta property="og:title" content="Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P">
-<meta property="og:description" content="mkin">
-<!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]-->
+
+<meta charset="utf-8" />
+<meta name="generator" content="pandoc" />
+<meta http-equiv="X-UA-Compatible" content="IE=EDGE" />
+
+
+<meta name="author" content="Johannes Ranke" />
+
+
+<title>Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</title>
+
+<script>// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
+// be compatible with the behavior of Pandoc < 2.8).
+document.addEventListener('DOMContentLoaded', function(e) {
+  var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
+  var i, h, a;
+  for (i = 0; i < hs.length; i++) {
+    h = hs[i];
+    if (!/^h[1-6]$/i.test(h.tagName)) continue;  // it should be a header h1-h6
+    a = h.attributes;
+    while (a.length > 0) h.removeAttribute(a[0].name);
+  }
+});
+</script>
+<script>/*! jQuery v3.6.0 | (c) OpenJS Foundation and other contributors | jquery.org/license */
+!function(e,t){"use strict";"object"==typeof module&&"object"==typeof module.exports?module.exports=e.document?t(e,!0):function(e){if(!e.document)throw new Error("jQuery requires a window with a document");return t(e)}:t(e)}("undefined"!=typeof window?window:this,function(C,e){"use strict";var t=[],r=Object.getPrototypeOf,s=t.slice,g=t.flat?function(e){return t.flat.call(e)}:function(e){return t.concat.apply([],e)},u=t.push,i=t.indexOf,n={},o=n.toString,v=n.hasOwnProperty,a=v.toString,l=a.call(Object),y={},m=function(e){return"function"==typeof e&&"number"!=typeof e.nodeType&&"function"!=typeof e.item},x=function(e){return null!=e&&e===e.window},E=C.document,c={type:!0,src:!0,nonce:!0,noModule:!0};function b(e,t,n){var r,i,o=(n=n||E).createElement("script");if(o.text=e,t)for(r in c)(i=t[r]||t.getAttribute&&t.getAttribute(r))&&o.setAttribute(r,i);n.head.appendChild(o).parentNode.removeChild(o)}function w(e){return null==e?e+"":"object"==typeof e||"function"==typeof e?n[o.call(e)]||"object":typeof e}var f="3.6.0",S=function(e,t){return new S.fn.init(e,t)};function p(e){var t=!!e&&"length"in e&&e.length,n=w(e);return!m(e)&&!x(e)&&("array"===n||0===t||"number"==typeof t&&0<t&&t-1 in e)}S.fn=S.prototype={jquery:f,constructor:S,length:0,toArray:function(){return s.call(this)},get:function(e){return null==e?s.call(this):e<0?this[e+this.length]:this[e]},pushStack:function(e){var t=S.merge(this.constructor(),e);return t.prevObject=this,t},each:function(e){return S.each(this,e)},map:function(n){return this.pushStack(S.map(this,function(e,t){return n.call(e,t,e)}))},slice:function(){return this.pushStack(s.apply(this,arguments))},first:function(){return this.eq(0)},last:function(){return this.eq(-1)},even:function(){return this.pushStack(S.grep(this,function(e,t){return(t+1)%2}))},odd:function(){return this.pushStack(S.grep(this,function(e,t){return t%2}))},eq:function(e){var t=this.length,n=+e+(e<0?t:0);return this.pushStack(0<=n&&n<t?[this[n]]:[])},end:function(){return this.prevObject||this.constructor()},push:u,sort:t.sort,splice:t.splice},S.extend=S.fn.extend=function(){var e,t,n,r,i,o,a=arguments[0]||{},s=1,u=arguments.length,l=!1;for("boolean"==typeof a&&(l=a,a=arguments[s]||{},s++),"object"==typeof a||m(a)||(a={}),s===u&&(a=this,s--);s<u;s++)if(null!=(e=arguments[s]))for(t in e)r=e[t],"__proto__"!==t&&a!==r&&(l&&r&&(S.isPlainObject(r)||(i=Array.isArray(r)))?(n=a[t],o=i&&!Array.isArray(n)?[]:i||S.isPlainObject(n)?n:{},i=!1,a[t]=S.extend(l,o,r)):void 0!==r&&(a[t]=r));return a},S.extend({expando:"jQuery"+(f+Math.random()).replace(/\D/g,""),isReady:!0,error:function(e){throw new Error(e)},noop:function(){},isPlainObject:function(e){var t,n;return!(!e||"[object Object]"!==o.call(e))&&(!(t=r(e))||"function"==typeof(n=v.call(t,"constructor")&&t.constructor)&&a.call(n)===l)},isEmptyObject:function(e){var t;for(t in e)return!1;return!0},globalEval:function(e,t,n){b(e,{nonce:t&&t.nonce},n)},each:function(e,t){var n,r=0;if(p(e)){for(n=e.length;r<n;r++)if(!1===t.call(e[r],r,e[r]))break}else for(r in e)if(!1===t.call(e[r],r,e[r]))break;return e},makeArray:function(e,t){var n=t||[];return null!=e&&(p(Object(e))?S.merge(n,"string"==typeof e?[e]:e):u.call(n,e)),n},inArray:function(e,t,n){return null==t?-1:i.call(t,e,n)},merge:function(e,t){for(var n=+t.length,r=0,i=e.length;r<n;r++)e[i++]=t[r];return e.length=i,e},grep:function(e,t,n){for(var r=[],i=0,o=e.length,a=!n;i<o;i++)!t(e[i],i)!==a&&r.push(e[i]);return r},map:function(e,t,n){var r,i,o=0,a=[];if(p(e))for(r=e.length;o<r;o++)null!=(i=t(e[o],o,n))&&a.push(i);else for(o in e)null!=(i=t(e[o],o,n))&&a.push(i);return g(a)},guid:1,support:y}),"function"==typeof Symbol&&(S.fn[Symbol.iterator]=t[Symbol.iterator]),S.each("Boolean Number String Function Array Date RegExp Object Error Symbol".split(" "),function(e,t){n["[object "+t+"]"]=t.toLowerCase()});var d=function(n){var e,d,b,o,i,h,f,g,w,u,l,T,C,a,E,v,s,c,y,S="sizzle"+1*new Date,p=n.document,k=0,r=0,m=ue(),x=ue(),A=ue(),N=ue(),j=function(e,t){return e===t&&(l=!0),0},D={}.hasOwnProperty,t=[],q=t.pop,L=t.push,H=t.push,O=t.slice,P=function(e,t){for(var n=0,r=e.length;n<r;n++)if(e[n]===t)return n;return-1},R="checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|ismap|loop|multiple|open|readonly|required|scoped",M="[\\x20\\t\\r\\n\\f]",I="(?:\\\\[\\da-fA-F]{1,6}"+M+"?|\\\\[^\\r\\n\\f]|[\\w-]|[^\0-\\x7f])+",W="\\["+M+"*("+I+")(?:"+M+"*([*^$|!~]?=)"+M+"*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|("+I+"))|)"+M+"*\\]",F=":("+I+")(?:\\((('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|((?:\\\\.|[^\\\\()[\\]]|"+W+")*)|.*)\\)|)",B=new RegExp(M+"+","g"),$=new RegExp("^"+M+"+|((?:^|[^\\\\])(?:\\\\.)*)"+M+"+$","g"),_=new RegExp("^"+M+"*,"+M+"*"),z=new RegExp("^"+M+"*([>+~]|"+M+")"+M+"*"),U=new RegExp(M+"|>"),X=new RegExp(F),V=new RegExp("^"+I+"$"),G={ID:new RegExp("^#("+I+")"),CLASS:new RegExp("^\\.("+I+")"),TAG:new RegExp("^("+I+"|[*])"),ATTR:new RegExp("^"+W),PSEUDO:new RegExp("^"+F),CHILD:new RegExp("^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\("+M+"*(even|odd|(([+-]|)(\\d*)n|)"+M+"*(?:([+-]|)"+M+"*(\\d+)|))"+M+"*\\)|)","i"),bool:new RegExp("^(?:"+R+")$","i"),needsContext:new RegExp("^"+M+"*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\("+M+"*((?:-\\d)?\\d*)"+M+"*\\)|)(?=[^-]|$)","i")},Y=/HTML$/i,Q=/^(?:input|select|textarea|button)$/i,J=/^h\d$/i,K=/^[^{]+\{\s*\[native \w/,Z=/^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/,ee=/[+~]/,te=new RegExp("\\\\[\\da-fA-F]{1,6}"+M+"?|\\\\([^\\r\\n\\f])","g"),ne=function(e,t){var n="0x"+e.slice(1)-65536;return t||(n<0?String.fromCharCode(n+65536):String.fromCharCode(n>>10|55296,1023&n|56320))},re=/([\0-\x1f\x7f]|^-?\d)|^-$|[^\0-\x1f\x7f-\uFFFF\w-]/g,ie=function(e,t){return t?"\0"===e?"\ufffd":e.slice(0,-1)+"\\"+e.charCodeAt(e.length-1).toString(16)+" ":"\\"+e},oe=function(){T()},ae=be(function(e){return!0===e.disabled&&"fieldset"===e.nodeName.toLowerCase()},{dir:"parentNode",next:"legend"});try{H.apply(t=O.call(p.childNodes),p.childNodes),t[p.childNodes.length].nodeType}catch(e){H={apply:t.length?function(e,t){L.apply(e,O.call(t))}:function(e,t){var n=e.length,r=0;while(e[n++]=t[r++]);e.length=n-1}}}function se(t,e,n,r){var i,o,a,s,u,l,c,f=e&&e.ownerDocument,p=e?e.nodeType:9;if(n=n||[],"string"!=typeof t||!t||1!==p&&9!==p&&11!==p)return n;if(!r&&(T(e),e=e||C,E)){if(11!==p&&(u=Z.exec(t)))if(i=u[1]){if(9===p){if(!(a=e.getElementById(i)))return n;if(a.id===i)return n.push(a),n}else if(f&&(a=f.getElementById(i))&&y(e,a)&&a.id===i)return n.push(a),n}else{if(u[2])return H.apply(n,e.getElementsByTagName(t)),n;if((i=u[3])&&d.getElementsByClassName&&e.getElementsByClassName)return H.apply(n,e.getElementsByClassName(i)),n}if(d.qsa&&!N[t+" "]&&(!v||!v.test(t))&&(1!==p||"object"!==e.nodeName.toLowerCase())){if(c=t,f=e,1===p&&(U.test(t)||z.test(t))){(f=ee.test(t)&&ye(e.parentNode)||e)===e&&d.scope||((s=e.getAttribute("id"))?s=s.replace(re,ie):e.setAttribute("id",s=S)),o=(l=h(t)).length;while(o--)l[o]=(s?"#"+s:":scope")+" "+xe(l[o]);c=l.join(",")}try{return H.apply(n,f.querySelectorAll(c)),n}catch(e){N(t,!0)}finally{s===S&&e.removeAttribute("id")}}}return g(t.replace($,"$1"),e,n,r)}function ue(){var r=[];return function e(t,n){return r.push(t+" ")>b.cacheLength&&delete e[r.shift()],e[t+" "]=n}}function le(e){return e[S]=!0,e}function ce(e){var t=C.createElement("fieldset");try{return!!e(t)}catch(e){return!1}finally{t.parentNode&&t.parentNode.removeChild(t),t=null}}function fe(e,t){var n=e.split("|"),r=n.length;while(r--)b.attrHandle[n[r]]=t}function pe(e,t){var n=t&&e,r=n&&1===e.nodeType&&1===t.nodeType&&e.sourceIndex-t.sourceIndex;if(r)return r;if(n)while(n=n.nextSibling)if(n===t)return-1;return e?1:-1}function de(t){return function(e){return"input"===e.nodeName.toLowerCase()&&e.type===t}}function he(n){return function(e){var t=e.nodeName.toLowerCase();return("input"===t||"button"===t)&&e.type===n}}function ge(t){return function(e){return"form"in e?e.parentNode&&!1===e.disabled?"label"in e?"label"in e.parentNode?e.parentNode.disabled===t:e.disabled===t:e.isDisabled===t||e.isDisabled!==!t&&ae(e)===t:e.disabled===t:"label"in e&&e.disabled===t}}function ve(a){return le(function(o){return o=+o,le(function(e,t){var n,r=a([],e.length,o),i=r.length;while(i--)e[n=r[i]]&&(e[n]=!(t[n]=e[n]))})})}function ye(e){return e&&"undefined"!=typeof e.getElementsByTagName&&e}for(e in d=se.support={},i=se.isXML=function(e){var t=e&&e.namespaceURI,n=e&&(e.ownerDocument||e).documentElement;return!Y.test(t||n&&n.nodeName||"HTML")},T=se.setDocument=function(e){var t,n,r=e?e.ownerDocument||e:p;return r!=C&&9===r.nodeType&&r.documentElement&&(a=(C=r).documentElement,E=!i(C),p!=C&&(n=C.defaultView)&&n.top!==n&&(n.addEventListener?n.addEventListener("unload",oe,!1):n.attachEvent&&n.attachEvent("onunload",oe)),d.scope=ce(function(e){return a.appendChild(e).appendChild(C.createElement("div")),"undefined"!=typeof e.querySelectorAll&&!e.querySelectorAll(":scope fieldset div").length}),d.attributes=ce(function(e){return e.className="i",!e.getAttribute("className")}),d.getElementsByTagName=ce(function(e){return e.appendChild(C.createComment("")),!e.getElementsByTagName("*").length}),d.getElementsByClassName=K.test(C.getElementsByClassName),d.getById=ce(function(e){return a.appendChild(e).id=S,!C.getElementsByName||!C.getElementsByName(S).length}),d.getById?(b.filter.ID=function(e){var t=e.replace(te,ne);return function(e){return e.getAttribute("id")===t}},b.find.ID=function(e,t){if("undefined"!=typeof t.getElementById&&E){var n=t.getElementById(e);return n?[n]:[]}}):(b.filter.ID=function(e){var n=e.replace(te,ne);return function(e){var t="undefined"!=typeof e.getAttributeNode&&e.getAttributeNode("id");return t&&t.value===n}},b.find.ID=function(e,t){if("undefined"!=typeof t.getElementById&&E){var n,r,i,o=t.getElementById(e);if(o){if((n=o.getAttributeNode("id"))&&n.value===e)return[o];i=t.getElementsByName(e),r=0;while(o=i[r++])if((n=o.getAttributeNode("id"))&&n.value===e)return[o]}return[]}}),b.find.TAG=d.getElementsByTagName?function(e,t){return"undefined"!=typeof t.getElementsByTagName?t.getElementsByTagName(e):d.qsa?t.querySelectorAll(e):void 0}:function(e,t){var n,r=[],i=0,o=t.getElementsByTagName(e);if("*"===e){while(n=o[i++])1===n.nodeType&&r.push(n);return r}return o},b.find.CLASS=d.getElementsByClassName&&function(e,t){if("undefined"!=typeof t.getElementsByClassName&&E)return t.getElementsByClassName(e)},s=[],v=[],(d.qsa=K.test(C.querySelectorAll))&&(ce(function(e){var t;a.appendChild(e).innerHTML="<a id='"+S+"'></a><select id='"+S+"-\r\\' msallowcapture=''><option selected=''></option></select>",e.querySelectorAll("[msallowcapture^='']").length&&v.push("[*^$]="+M+"*(?:''|\"\")"),e.querySelectorAll("[selected]").length||v.push("\\["+M+"*(?:value|"+R+")"),e.querySelectorAll("[id~="+S+"-]").length||v.push("~="),(t=C.createElement("input")).setAttribute("name",""),e.appendChild(t),e.querySelectorAll("[name='']").length||v.push("\\["+M+"*name"+M+"*="+M+"*(?:''|\"\")"),e.querySelectorAll(":checked").length||v.push(":checked"),e.querySelectorAll("a#"+S+"+*").length||v.push(".#.+[+~]"),e.querySelectorAll("\\\f"),v.push("[\\r\\n\\f]")}),ce(function(e){e.innerHTML="<a href='' disabled='disabled'></a><select disabled='disabled'><option/></select>";var t=C.createElement("input");t.setAttribute("type","hidden"),e.appendChild(t).setAttribute("name","D"),e.querySelectorAll("[name=d]").length&&v.push("name"+M+"*[*^$|!~]?="),2!==e.querySelectorAll(":enabled").length&&v.push(":enabled",":disabled"),a.appendChild(e).disabled=!0,2!==e.querySelectorAll(":disabled").length&&v.push(":enabled",":disabled"),e.querySelectorAll("*,:x"),v.push(",.*:")})),(d.matchesSelector=K.test(c=a.matches||a.webkitMatchesSelector||a.mozMatchesSelector||a.oMatchesSelector||a.msMatchesSelector))&&ce(function(e){d.disconnectedMatch=c.call(e,"*"),c.call(e,"[s!='']:x"),s.push("!=",F)}),v=v.length&&new RegExp(v.join("|")),s=s.length&&new RegExp(s.join("|")),t=K.test(a.compareDocumentPosition),y=t||K.test(a.contains)?function(e,t){var n=9===e.nodeType?e.documentElement:e,r=t&&t.parentNode;return e===r||!(!r||1!==r.nodeType||!(n.contains?n.contains(r):e.compareDocumentPosition&&16&e.compareDocumentPosition(r)))}:function(e,t){if(t)while(t=t.parentNode)if(t===e)return!0;return!1},j=t?function(e,t){if(e===t)return l=!0,0;var n=!e.compareDocumentPosition-!t.compareDocumentPosition;return n||(1&(n=(e.ownerDocument||e)==(t.ownerDocument||t)?e.compareDocumentPosition(t):1)||!d.sortDetached&&t.compareDocumentPosition(e)===n?e==C||e.ownerDocument==p&&y(p,e)?-1:t==C||t.ownerDocument==p&&y(p,t)?1:u?P(u,e)-P(u,t):0:4&n?-1:1)}:function(e,t){if(e===t)return l=!0,0;var n,r=0,i=e.parentNode,o=t.parentNode,a=[e],s=[t];if(!i||!o)return e==C?-1:t==C?1:i?-1:o?1:u?P(u,e)-P(u,t):0;if(i===o)return pe(e,t);n=e;while(n=n.parentNode)a.unshift(n);n=t;while(n=n.parentNode)s.unshift(n);while(a[r]===s[r])r++;return r?pe(a[r],s[r]):a[r]==p?-1:s[r]==p?1:0}),C},se.matches=function(e,t){return se(e,null,null,t)},se.matchesSelector=function(e,t){if(T(e),d.matchesSelector&&E&&!N[t+" "]&&(!s||!s.test(t))&&(!v||!v.test(t)))try{var n=c.call(e,t);if(n||d.disconnectedMatch||e.document&&11!==e.document.nodeType)return n}catch(e){N(t,!0)}return 0<se(t,C,null,[e]).length},se.contains=function(e,t){return(e.ownerDocument||e)!=C&&T(e),y(e,t)},se.attr=function(e,t){(e.ownerDocument||e)!=C&&T(e);var n=b.attrHandle[t.toLowerCase()],r=n&&D.call(b.attrHandle,t.toLowerCase())?n(e,t,!E):void 0;return void 0!==r?r:d.attributes||!E?e.getAttribute(t):(r=e.getAttributeNode(t))&&r.specified?r.value:null},se.escape=function(e){return(e+"").replace(re,ie)},se.error=function(e){throw new Error("Syntax error, unrecognized expression: "+e)},se.uniqueSort=function(e){var t,n=[],r=0,i=0;if(l=!d.detectDuplicates,u=!d.sortStable&&e.slice(0),e.sort(j),l){while(t=e[i++])t===e[i]&&(r=n.push(i));while(r--)e.splice(n[r],1)}return u=null,e},o=se.getText=function(e){var t,n="",r=0,i=e.nodeType;if(i){if(1===i||9===i||11===i){if("string"==typeof e.textContent)return e.textContent;for(e=e.firstChild;e;e=e.nextSibling)n+=o(e)}else if(3===i||4===i)return e.nodeValue}else while(t=e[r++])n+=o(t);return n},(b=se.selectors={cacheLength:50,createPseudo:le,match:G,attrHandle:{},find:{},relative:{">":{dir:"parentNode",first:!0}," ":{dir:"parentNode"},"+":{dir:"previousSibling",first:!0},"~":{dir:"previousSibling"}},preFilter:{ATTR:function(e){return e[1]=e[1].replace(te,ne),e[3]=(e[3]||e[4]||e[5]||"").replace(te,ne),"~="===e[2]&&(e[3]=" "+e[3]+" "),e.slice(0,4)},CHILD:function(e){return e[1]=e[1].toLowerCase(),"nth"===e[1].slice(0,3)?(e[3]||se.error(e[0]),e[4]=+(e[4]?e[5]+(e[6]||1):2*("even"===e[3]||"odd"===e[3])),e[5]=+(e[7]+e[8]||"odd"===e[3])):e[3]&&se.error(e[0]),e},PSEUDO:function(e){var t,n=!e[6]&&e[2];return G.CHILD.test(e[0])?null:(e[3]?e[2]=e[4]||e[5]||"":n&&X.test(n)&&(t=h(n,!0))&&(t=n.indexOf(")",n.length-t)-n.length)&&(e[0]=e[0].slice(0,t),e[2]=n.slice(0,t)),e.slice(0,3))}},filter:{TAG:function(e){var t=e.replace(te,ne).toLowerCase();return"*"===e?function(){return!0}:function(e){return e.nodeName&&e.nodeName.toLowerCase()===t}},CLASS:function(e){var t=m[e+" "];return t||(t=new RegExp("(^|"+M+")"+e+"("+M+"|$)"))&&m(e,function(e){return t.test("string"==typeof e.className&&e.className||"undefined"!=typeof e.getAttribute&&e.getAttribute("class")||"")})},ATTR:function(n,r,i){return function(e){var t=se.attr(e,n);return null==t?"!="===r:!r||(t+="","="===r?t===i:"!="===r?t!==i:"^="===r?i&&0===t.indexOf(i):"*="===r?i&&-1<t.indexOf(i):"$="===r?i&&t.slice(-i.length)===i:"~="===r?-1<(" "+t.replace(B," ")+" ").indexOf(i):"|="===r&&(t===i||t.slice(0,i.length+1)===i+"-"))}},CHILD:function(h,e,t,g,v){var y="nth"!==h.slice(0,3),m="last"!==h.slice(-4),x="of-type"===e;return 1===g&&0===v?function(e){return!!e.parentNode}:function(e,t,n){var r,i,o,a,s,u,l=y!==m?"nextSibling":"previousSibling",c=e.parentNode,f=x&&e.nodeName.toLowerCase(),p=!n&&!x,d=!1;if(c){if(y){while(l){a=e;while(a=a[l])if(x?a.nodeName.toLowerCase()===f:1===a.nodeType)return!1;u=l="only"===h&&!u&&"nextSibling"}return!0}if(u=[m?c.firstChild:c.lastChild],m&&p){d=(s=(r=(i=(o=(a=c)[S]||(a[S]={}))[a.uniqueID]||(o[a.uniqueID]={}))[h]||[])[0]===k&&r[1])&&r[2],a=s&&c.childNodes[s];while(a=++s&&a&&a[l]||(d=s=0)||u.pop())if(1===a.nodeType&&++d&&a===e){i[h]=[k,s,d];break}}else if(p&&(d=s=(r=(i=(o=(a=e)[S]||(a[S]={}))[a.uniqueID]||(o[a.uniqueID]={}))[h]||[])[0]===k&&r[1]),!1===d)while(a=++s&&a&&a[l]||(d=s=0)||u.pop())if((x?a.nodeName.toLowerCase()===f:1===a.nodeType)&&++d&&(p&&((i=(o=a[S]||(a[S]={}))[a.uniqueID]||(o[a.uniqueID]={}))[h]=[k,d]),a===e))break;return(d-=v)===g||d%g==0&&0<=d/g}}},PSEUDO:function(e,o){var t,a=b.pseudos[e]||b.setFilters[e.toLowerCase()]||se.error("unsupported pseudo: "+e);return a[S]?a(o):1<a.length?(t=[e,e,"",o],b.setFilters.hasOwnProperty(e.toLowerCase())?le(function(e,t){var n,r=a(e,o),i=r.length;while(i--)e[n=P(e,r[i])]=!(t[n]=r[i])}):function(e){return a(e,0,t)}):a}},pseudos:{not:le(function(e){var r=[],i=[],s=f(e.replace($,"$1"));return s[S]?le(function(e,t,n,r){var i,o=s(e,null,r,[]),a=e.length;while(a--)(i=o[a])&&(e[a]=!(t[a]=i))}):function(e,t,n){return r[0]=e,s(r,null,n,i),r[0]=null,!i.pop()}}),has:le(function(t){return function(e){return 0<se(t,e).length}}),contains:le(function(t){return t=t.replace(te,ne),function(e){return-1<(e.textContent||o(e)).indexOf(t)}}),lang:le(function(n){return V.test(n||"")||se.error("unsupported lang: "+n),n=n.replace(te,ne).toLowerCase(),function(e){var t;do{if(t=E?e.lang:e.getAttribute("xml:lang")||e.getAttribute("lang"))return(t=t.toLowerCase())===n||0===t.indexOf(n+"-")}while((e=e.parentNode)&&1===e.nodeType);return!1}}),target:function(e){var t=n.location&&n.location.hash;return t&&t.slice(1)===e.id},root:function(e){return e===a},focus:function(e){return e===C.activeElement&&(!C.hasFocus||C.hasFocus())&&!!(e.type||e.href||~e.tabIndex)},enabled:ge(!1),disabled:ge(!0),checked:function(e){var t=e.nodeName.toLowerCase();return"input"===t&&!!e.checked||"option"===t&&!!e.selected},selected:function(e){return e.parentNode&&e.parentNode.selectedIndex,!0===e.selected},empty:function(e){for(e=e.firstChild;e;e=e.nextSibling)if(e.nodeType<6)return!1;return!0},parent:function(e){return!b.pseudos.empty(e)},header:function(e){return J.test(e.nodeName)},input:function(e){return Q.test(e.nodeName)},button:function(e){var t=e.nodeName.toLowerCase();return"input"===t&&"button"===e.type||"button"===t},text:function(e){var t;return"input"===e.nodeName.toLowerCase()&&"text"===e.type&&(null==(t=e.getAttribute("type"))||"text"===t.toLowerCase())},first:ve(function(){return[0]}),last:ve(function(e,t){return[t-1]}),eq:ve(function(e,t,n){return[n<0?n+t:n]}),even:ve(function(e,t){for(var n=0;n<t;n+=2)e.push(n);return e}),odd:ve(function(e,t){for(var n=1;n<t;n+=2)e.push(n);return e}),lt:ve(function(e,t,n){for(var r=n<0?n+t:t<n?t:n;0<=--r;)e.push(r);return e}),gt:ve(function(e,t,n){for(var r=n<0?n+t:n;++r<t;)e.push(r);return e})}}).pseudos.nth=b.pseudos.eq,{radio:!0,checkbox:!0,file:!0,password:!0,image:!0})b.pseudos[e]=de(e);for(e in{submit:!0,reset:!0})b.pseudos[e]=he(e);function me(){}function xe(e){for(var t=0,n=e.length,r="";t<n;t++)r+=e[t].value;return r}function be(s,e,t){var u=e.dir,l=e.next,c=l||u,f=t&&"parentNode"===c,p=r++;return e.first?function(e,t,n){while(e=e[u])if(1===e.nodeType||f)return s(e,t,n);return!1}:function(e,t,n){var r,i,o,a=[k,p];if(n){while(e=e[u])if((1===e.nodeType||f)&&s(e,t,n))return!0}else while(e=e[u])if(1===e.nodeType||f)if(i=(o=e[S]||(e[S]={}))[e.uniqueID]||(o[e.uniqueID]={}),l&&l===e.nodeName.toLowerCase())e=e[u]||e;else{if((r=i[c])&&r[0]===k&&r[1]===p)return a[2]=r[2];if((i[c]=a)[2]=s(e,t,n))return!0}return!1}}function we(i){return 1<i.length?function(e,t,n){var r=i.length;while(r--)if(!i[r](e,t,n))return!1;return!0}:i[0]}function Te(e,t,n,r,i){for(var o,a=[],s=0,u=e.length,l=null!=t;s<u;s++)(o=e[s])&&(n&&!n(o,r,i)||(a.push(o),l&&t.push(s)));return a}function Ce(d,h,g,v,y,e){return v&&!v[S]&&(v=Ce(v)),y&&!y[S]&&(y=Ce(y,e)),le(function(e,t,n,r){var i,o,a,s=[],u=[],l=t.length,c=e||function(e,t,n){for(var r=0,i=t.length;r<i;r++)se(e,t[r],n);return n}(h||"*",n.nodeType?[n]:n,[]),f=!d||!e&&h?c:Te(c,s,d,n,r),p=g?y||(e?d:l||v)?[]:t:f;if(g&&g(f,p,n,r),v){i=Te(p,u),v(i,[],n,r),o=i.length;while(o--)(a=i[o])&&(p[u[o]]=!(f[u[o]]=a))}if(e){if(y||d){if(y){i=[],o=p.length;while(o--)(a=p[o])&&i.push(f[o]=a);y(null,p=[],i,r)}o=p.length;while(o--)(a=p[o])&&-1<(i=y?P(e,a):s[o])&&(e[i]=!(t[i]=a))}}else p=Te(p===t?p.splice(l,p.length):p),y?y(null,t,p,r):H.apply(t,p)})}function Ee(e){for(var i,t,n,r=e.length,o=b.relative[e[0].type],a=o||b.relative[" "],s=o?1:0,u=be(function(e){return e===i},a,!0),l=be(function(e){return-1<P(i,e)},a,!0),c=[function(e,t,n){var r=!o&&(n||t!==w)||((i=t).nodeType?u(e,t,n):l(e,t,n));return i=null,r}];s<r;s++)if(t=b.relative[e[s].type])c=[be(we(c),t)];else{if((t=b.filter[e[s].type].apply(null,e[s].matches))[S]){for(n=++s;n<r;n++)if(b.relative[e[n].type])break;return Ce(1<s&&we(c),1<s&&xe(e.slice(0,s-1).concat({value:" "===e[s-2].type?"*":""})).replace($,"$1"),t,s<n&&Ee(e.slice(s,n)),n<r&&Ee(e=e.slice(n)),n<r&&xe(e))}c.push(t)}return we(c)}return me.prototype=b.filters=b.pseudos,b.setFilters=new me,h=se.tokenize=function(e,t){var n,r,i,o,a,s,u,l=x[e+" "];if(l)return t?0:l.slice(0);a=e,s=[],u=b.preFilter;while(a){for(o in n&&!(r=_.exec(a))||(r&&(a=a.slice(r[0].length)||a),s.push(i=[])),n=!1,(r=z.exec(a))&&(n=r.shift(),i.push({value:n,type:r[0].replace($," ")}),a=a.slice(n.length)),b.filter)!(r=G[o].exec(a))||u[o]&&!(r=u[o](r))||(n=r.shift(),i.push({value:n,type:o,matches:r}),a=a.slice(n.length));if(!n)break}return t?a.length:a?se.error(e):x(e,s).slice(0)},f=se.compile=function(e,t){var n,v,y,m,x,r,i=[],o=[],a=A[e+" "];if(!a){t||(t=h(e)),n=t.length;while(n--)(a=Ee(t[n]))[S]?i.push(a):o.push(a);(a=A(e,(v=o,m=0<(y=i).length,x=0<v.length,r=function(e,t,n,r,i){var o,a,s,u=0,l="0",c=e&&[],f=[],p=w,d=e||x&&b.find.TAG("*",i),h=k+=null==p?1:Math.random()||.1,g=d.length;for(i&&(w=t==C||t||i);l!==g&&null!=(o=d[l]);l++){if(x&&o){a=0,t||o.ownerDocument==C||(T(o),n=!E);while(s=v[a++])if(s(o,t||C,n)){r.push(o);break}i&&(k=h)}m&&((o=!s&&o)&&u--,e&&c.push(o))}if(u+=l,m&&l!==u){a=0;while(s=y[a++])s(c,f,t,n);if(e){if(0<u)while(l--)c[l]||f[l]||(f[l]=q.call(r));f=Te(f)}H.apply(r,f),i&&!e&&0<f.length&&1<u+y.length&&se.uniqueSort(r)}return i&&(k=h,w=p),c},m?le(r):r))).selector=e}return a},g=se.select=function(e,t,n,r){var i,o,a,s,u,l="function"==typeof e&&e,c=!r&&h(e=l.selector||e);if(n=n||[],1===c.length){if(2<(o=c[0]=c[0].slice(0)).length&&"ID"===(a=o[0]).type&&9===t.nodeType&&E&&b.relative[o[1].type]){if(!(t=(b.find.ID(a.matches[0].replace(te,ne),t)||[])[0]))return n;l&&(t=t.parentNode),e=e.slice(o.shift().value.length)}i=G.needsContext.test(e)?0:o.length;while(i--){if(a=o[i],b.relative[s=a.type])break;if((u=b.find[s])&&(r=u(a.matches[0].replace(te,ne),ee.test(o[0].type)&&ye(t.parentNode)||t))){if(o.splice(i,1),!(e=r.length&&xe(o)))return H.apply(n,r),n;break}}}return(l||f(e,c))(r,t,!E,n,!t||ee.test(e)&&ye(t.parentNode)||t),n},d.sortStable=S.split("").sort(j).join("")===S,d.detectDuplicates=!!l,T(),d.sortDetached=ce(function(e){return 1&e.compareDocumentPosition(C.createElement("fieldset"))}),ce(function(e){return e.innerHTML="<a href='#'></a>","#"===e.firstChild.getAttribute("href")})||fe("type|href|height|width",function(e,t,n){if(!n)return e.getAttribute(t,"type"===t.toLowerCase()?1:2)}),d.attributes&&ce(function(e){return e.innerHTML="<input/>",e.firstChild.setAttribute("value",""),""===e.firstChild.getAttribute("value")})||fe("value",function(e,t,n){if(!n&&"input"===e.nodeName.toLowerCase())return e.defaultValue}),ce(function(e){return null==e.getAttribute("disabled")})||fe(R,function(e,t,n){var r;if(!n)return!0===e[t]?t.toLowerCase():(r=e.getAttributeNode(t))&&r.specified?r.value:null}),se}(C);S.find=d,S.expr=d.selectors,S.expr[":"]=S.expr.pseudos,S.uniqueSort=S.unique=d.uniqueSort,S.text=d.getText,S.isXMLDoc=d.isXML,S.contains=d.contains,S.escapeSelector=d.escape;var h=function(e,t,n){var r=[],i=void 0!==n;while((e=e[t])&&9!==e.nodeType)if(1===e.nodeType){if(i&&S(e).is(n))break;r.push(e)}return r},T=function(e,t){for(var n=[];e;e=e.nextSibling)1===e.nodeType&&e!==t&&n.push(e);return n},k=S.expr.match.needsContext;function A(e,t){return e.nodeName&&e.nodeName.toLowerCase()===t.toLowerCase()}var N=/^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i;function j(e,n,r){return m(n)?S.grep(e,function(e,t){return!!n.call(e,t,e)!==r}):n.nodeType?S.grep(e,function(e){return e===n!==r}):"string"!=typeof n?S.grep(e,function(e){return-1<i.call(n,e)!==r}):S.filter(n,e,r)}S.filter=function(e,t,n){var r=t[0];return n&&(e=":not("+e+")"),1===t.length&&1===r.nodeType?S.find.matchesSelector(r,e)?[r]:[]:S.find.matches(e,S.grep(t,function(e){return 1===e.nodeType}))},S.fn.extend({find:function(e){var t,n,r=this.length,i=this;if("string"!=typeof e)return this.pushStack(S(e).filter(function(){for(t=0;t<r;t++)if(S.contains(i[t],this))return!0}));for(n=this.pushStack([]),t=0;t<r;t++)S.find(e,i[t],n);return 1<r?S.uniqueSort(n):n},filter:function(e){return this.pushStack(j(this,e||[],!1))},not:function(e){return this.pushStack(j(this,e||[],!0))},is:function(e){return!!j(this,"string"==typeof e&&k.test(e)?S(e):e||[],!1).length}});var D,q=/^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]+))$/;(S.fn.init=function(e,t,n){var r,i;if(!e)return this;if(n=n||D,"string"==typeof e){if(!(r="<"===e[0]&&">"===e[e.length-1]&&3<=e.length?[null,e,null]:q.exec(e))||!r[1]&&t)return!t||t.jquery?(t||n).find(e):this.constructor(t).find(e);if(r[1]){if(t=t instanceof S?t[0]:t,S.merge(this,S.parseHTML(r[1],t&&t.nodeType?t.ownerDocument||t:E,!0)),N.test(r[1])&&S.isPlainObject(t))for(r in t)m(this[r])?this[r](t[r]):this.attr(r,t[r]);return this}return(i=E.getElementById(r[2]))&&(this[0]=i,this.length=1),this}return e.nodeType?(this[0]=e,this.length=1,this):m(e)?void 0!==n.ready?n.ready(e):e(S):S.makeArray(e,this)}).prototype=S.fn,D=S(E);var L=/^(?:parents|prev(?:Until|All))/,H={children:!0,contents:!0,next:!0,prev:!0};function O(e,t){while((e=e[t])&&1!==e.nodeType);return e}S.fn.extend({has:function(e){var t=S(e,this),n=t.length;return this.filter(function(){for(var e=0;e<n;e++)if(S.contains(this,t[e]))return!0})},closest:function(e,t){var n,r=0,i=this.length,o=[],a="string"!=typeof e&&S(e);if(!k.test(e))for(;r<i;r++)for(n=this[r];n&&n!==t;n=n.parentNode)if(n.nodeType<11&&(a?-1<a.index(n):1===n.nodeType&&S.find.matchesSelector(n,e))){o.push(n);break}return this.pushStack(1<o.length?S.uniqueSort(o):o)},index:function(e){return e?"string"==typeof e?i.call(S(e),this[0]):i.call(this,e.jquery?e[0]:e):this[0]&&this[0].parentNode?this.first().prevAll().length:-1},add:function(e,t){return this.pushStack(S.uniqueSort(S.merge(this.get(),S(e,t))))},addBack:function(e){return this.add(null==e?this.prevObject:this.prevObject.filter(e))}}),S.each({parent:function(e){var t=e.parentNode;return t&&11!==t.nodeType?t:null},parents:function(e){return h(e,"parentNode")},parentsUntil:function(e,t,n){return h(e,"parentNode",n)},next:function(e){return O(e,"nextSibling")},prev:function(e){return O(e,"previousSibling")},nextAll:function(e){return h(e,"nextSibling")},prevAll:function(e){return h(e,"previousSibling")},nextUntil:function(e,t,n){return h(e,"nextSibling",n)},prevUntil:function(e,t,n){return h(e,"previousSibling",n)},siblings:function(e){return T((e.parentNode||{}).firstChild,e)},children:function(e){return T(e.firstChild)},contents:function(e){return null!=e.contentDocument&&r(e.contentDocument)?e.contentDocument:(A(e,"template")&&(e=e.content||e),S.merge([],e.childNodes))}},function(r,i){S.fn[r]=function(e,t){var n=S.map(this,i,e);return"Until"!==r.slice(-5)&&(t=e),t&&"string"==typeof t&&(n=S.filter(t,n)),1<this.length&&(H[r]||S.uniqueSort(n),L.test(r)&&n.reverse()),this.pushStack(n)}});var P=/[^\x20\t\r\n\f]+/g;function R(e){return e}function M(e){throw e}function I(e,t,n,r){var i;try{e&&m(i=e.promise)?i.call(e).done(t).fail(n):e&&m(i=e.then)?i.call(e,t,n):t.apply(void 0,[e].slice(r))}catch(e){n.apply(void 0,[e])}}S.Callbacks=function(r){var e,n;r="string"==typeof r?(e=r,n={},S.each(e.match(P)||[],function(e,t){n[t]=!0}),n):S.extend({},r);var i,t,o,a,s=[],u=[],l=-1,c=function(){for(a=a||r.once,o=i=!0;u.length;l=-1){t=u.shift();while(++l<s.length)!1===s[l].apply(t[0],t[1])&&r.stopOnFalse&&(l=s.length,t=!1)}r.memory||(t=!1),i=!1,a&&(s=t?[]:"")},f={add:function(){return s&&(t&&!i&&(l=s.length-1,u.push(t)),function n(e){S.each(e,function(e,t){m(t)?r.unique&&f.has(t)||s.push(t):t&&t.length&&"string"!==w(t)&&n(t)})}(arguments),t&&!i&&c()),this},remove:function(){return S.each(arguments,function(e,t){var n;while(-1<(n=S.inArray(t,s,n)))s.splice(n,1),n<=l&&l--}),this},has:function(e){return e?-1<S.inArray(e,s):0<s.length},empty:function(){return s&&(s=[]),this},disable:function(){return a=u=[],s=t="",this},disabled:function(){return!s},lock:function(){return a=u=[],t||i||(s=t=""),this},locked:function(){return!!a},fireWith:function(e,t){return a||(t=[e,(t=t||[]).slice?t.slice():t],u.push(t),i||c()),this},fire:function(){return f.fireWith(this,arguments),this},fired:function(){return!!o}};return f},S.extend({Deferred:function(e){var o=[["notify","progress",S.Callbacks("memory"),S.Callbacks("memory"),2],["resolve","done",S.Callbacks("once memory"),S.Callbacks("once memory"),0,"resolved"],["reject","fail",S.Callbacks("once memory"),S.Callbacks("once memory"),1,"rejected"]],i="pending",a={state:function(){return i},always:function(){return s.done(arguments).fail(arguments),this},"catch":function(e){return a.then(null,e)},pipe:function(){var i=arguments;return S.Deferred(function(r){S.each(o,function(e,t){var n=m(i[t[4]])&&i[t[4]];s[t[1]](function(){var e=n&&n.apply(this,arguments);e&&m(e.promise)?e.promise().progress(r.notify).done(r.resolve).fail(r.reject):r[t[0]+"With"](this,n?[e]:arguments)})}),i=null}).promise()},then:function(t,n,r){var u=0;function l(i,o,a,s){return function(){var n=this,r=arguments,e=function(){var e,t;if(!(i<u)){if((e=a.apply(n,r))===o.promise())throw new TypeError("Thenable self-resolution");t=e&&("object"==typeof e||"function"==typeof e)&&e.then,m(t)?s?t.call(e,l(u,o,R,s),l(u,o,M,s)):(u++,t.call(e,l(u,o,R,s),l(u,o,M,s),l(u,o,R,o.notifyWith))):(a!==R&&(n=void 0,r=[e]),(s||o.resolveWith)(n,r))}},t=s?e:function(){try{e()}catch(e){S.Deferred.exceptionHook&&S.Deferred.exceptionHook(e,t.stackTrace),u<=i+1&&(a!==M&&(n=void 0,r=[e]),o.rejectWith(n,r))}};i?t():(S.Deferred.getStackHook&&(t.stackTrace=S.Deferred.getStackHook()),C.setTimeout(t))}}return S.Deferred(function(e){o[0][3].add(l(0,e,m(r)?r:R,e.notifyWith)),o[1][3].add(l(0,e,m(t)?t:R)),o[2][3].add(l(0,e,m(n)?n:M))}).promise()},promise:function(e){return null!=e?S.extend(e,a):a}},s={};return S.each(o,function(e,t){var n=t[2],r=t[5];a[t[1]]=n.add,r&&n.add(function(){i=r},o[3-e][2].disable,o[3-e][3].disable,o[0][2].lock,o[0][3].lock),n.add(t[3].fire),s[t[0]]=function(){return s[t[0]+"With"](this===s?void 0:this,arguments),this},s[t[0]+"With"]=n.fireWith}),a.promise(s),e&&e.call(s,s),s},when:function(e){var n=arguments.length,t=n,r=Array(t),i=s.call(arguments),o=S.Deferred(),a=function(t){return function(e){r[t]=this,i[t]=1<arguments.length?s.call(arguments):e,--n||o.resolveWith(r,i)}};if(n<=1&&(I(e,o.done(a(t)).resolve,o.reject,!n),"pending"===o.state()||m(i[t]&&i[t].then)))return o.then();while(t--)I(i[t],a(t),o.reject);return o.promise()}});var W=/^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/;S.Deferred.exceptionHook=function(e,t){C.console&&C.console.warn&&e&&W.test(e.name)&&C.console.warn("jQuery.Deferred exception: "+e.message,e.stack,t)},S.readyException=function(e){C.setTimeout(function(){throw e})};var F=S.Deferred();function B(){E.removeEventListener("DOMContentLoaded",B),C.removeEventListener("load",B),S.ready()}S.fn.ready=function(e){return F.then(e)["catch"](function(e){S.readyException(e)}),this},S.extend({isReady:!1,readyWait:1,ready:function(e){(!0===e?--S.readyWait:S.isReady)||(S.isReady=!0)!==e&&0<--S.readyWait||F.resolveWith(E,[S])}}),S.ready.then=F.then,"complete"===E.readyState||"loading"!==E.readyState&&!E.documentElement.doScroll?C.setTimeout(S.ready):(E.addEventListener("DOMContentLoaded",B),C.addEventListener("load",B));var $=function(e,t,n,r,i,o,a){var s=0,u=e.length,l=null==n;if("object"===w(n))for(s in i=!0,n)$(e,t,s,n[s],!0,o,a);else if(void 0!==r&&(i=!0,m(r)||(a=!0),l&&(a?(t.call(e,r),t=null):(l=t,t=function(e,t,n){return l.call(S(e),n)})),t))for(;s<u;s++)t(e[s],n,a?r:r.call(e[s],s,t(e[s],n)));return i?e:l?t.call(e):u?t(e[0],n):o},_=/^-ms-/,z=/-([a-z])/g;function U(e,t){return t.toUpperCase()}function X(e){return e.replace(_,"ms-").replace(z,U)}var V=function(e){return 1===e.nodeType||9===e.nodeType||!+e.nodeType};function G(){this.expando=S.expando+G.uid++}G.uid=1,G.prototype={cache:function(e){var t=e[this.expando];return t||(t={},V(e)&&(e.nodeType?e[this.expando]=t:Object.defineProperty(e,this.expando,{value:t,configurable:!0}))),t},set:function(e,t,n){var r,i=this.cache(e);if("string"==typeof t)i[X(t)]=n;else for(r in t)i[X(r)]=t[r];return i},get:function(e,t){return void 0===t?this.cache(e):e[this.expando]&&e[this.expando][X(t)]},access:function(e,t,n){return void 0===t||t&&"string"==typeof t&&void 0===n?this.get(e,t):(this.set(e,t,n),void 0!==n?n:t)},remove:function(e,t){var n,r=e[this.expando];if(void 0!==r){if(void 0!==t){n=(t=Array.isArray(t)?t.map(X):(t=X(t))in r?[t]:t.match(P)||[]).length;while(n--)delete r[t[n]]}(void 0===t||S.isEmptyObject(r))&&(e.nodeType?e[this.expando]=void 0:delete e[this.expando])}},hasData:function(e){var t=e[this.expando];return void 0!==t&&!S.isEmptyObject(t)}};var Y=new G,Q=new G,J=/^(?:\{[\w\W]*\}|\[[\w\W]*\])$/,K=/[A-Z]/g;function Z(e,t,n){var r,i;if(void 0===n&&1===e.nodeType)if(r="data-"+t.replace(K,"-$&").toLowerCase(),"string"==typeof(n=e.getAttribute(r))){try{n="true"===(i=n)||"false"!==i&&("null"===i?null:i===+i+""?+i:J.test(i)?JSON.parse(i):i)}catch(e){}Q.set(e,t,n)}else n=void 0;return n}S.extend({hasData:function(e){return Q.hasData(e)||Y.hasData(e)},data:function(e,t,n){return Q.access(e,t,n)},removeData:function(e,t){Q.remove(e,t)},_data:function(e,t,n){return Y.access(e,t,n)},_removeData:function(e,t){Y.remove(e,t)}}),S.fn.extend({data:function(n,e){var t,r,i,o=this[0],a=o&&o.attributes;if(void 0===n){if(this.length&&(i=Q.get(o),1===o.nodeType&&!Y.get(o,"hasDataAttrs"))){t=a.length;while(t--)a[t]&&0===(r=a[t].name).indexOf("data-")&&(r=X(r.slice(5)),Z(o,r,i[r]));Y.set(o,"hasDataAttrs",!0)}return i}return"object"==typeof n?this.each(function(){Q.set(this,n)}):$(this,function(e){var t;if(o&&void 0===e)return void 0!==(t=Q.get(o,n))?t:void 0!==(t=Z(o,n))?t:void 0;this.each(function(){Q.set(this,n,e)})},null,e,1<arguments.length,null,!0)},removeData:function(e){return this.each(function(){Q.remove(this,e)})}}),S.extend({queue:function(e,t,n){var r;if(e)return t=(t||"fx")+"queue",r=Y.get(e,t),n&&(!r||Array.isArray(n)?r=Y.access(e,t,S.makeArray(n)):r.push(n)),r||[]},dequeue:function(e,t){t=t||"fx";var n=S.queue(e,t),r=n.length,i=n.shift(),o=S._queueHooks(e,t);"inprogress"===i&&(i=n.shift(),r--),i&&("fx"===t&&n.unshift("inprogress"),delete o.stop,i.call(e,function(){S.dequeue(e,t)},o)),!r&&o&&o.empty.fire()},_queueHooks:function(e,t){var n=t+"queueHooks";return Y.get(e,n)||Y.access(e,n,{empty:S.Callbacks("once memory").add(function(){Y.remove(e,[t+"queue",n])})})}}),S.fn.extend({queue:function(t,n){var e=2;return"string"!=typeof t&&(n=t,t="fx",e--),arguments.length<e?S.queue(this[0],t):void 0===n?this:this.each(function(){var e=S.queue(this,t,n);S._queueHooks(this,t),"fx"===t&&"inprogress"!==e[0]&&S.dequeue(this,t)})},dequeue:function(e){return this.each(function(){S.dequeue(this,e)})},clearQueue:function(e){return this.queue(e||"fx",[])},promise:function(e,t){var n,r=1,i=S.Deferred(),o=this,a=this.length,s=function(){--r||i.resolveWith(o,[o])};"string"!=typeof e&&(t=e,e=void 0),e=e||"fx";while(a--)(n=Y.get(o[a],e+"queueHooks"))&&n.empty&&(r++,n.empty.add(s));return s(),i.promise(t)}});var ee=/[+-]?(?:\d*\.|)\d+(?:[eE][+-]?\d+|)/.source,te=new RegExp("^(?:([+-])=|)("+ee+")([a-z%]*)$","i"),ne=["Top","Right","Bottom","Left"],re=E.documentElement,ie=function(e){return S.contains(e.ownerDocument,e)},oe={composed:!0};re.getRootNode&&(ie=function(e){return S.contains(e.ownerDocument,e)||e.getRootNode(oe)===e.ownerDocument});var ae=function(e,t){return"none"===(e=t||e).style.display||""===e.style.display&&ie(e)&&"none"===S.css(e,"display")};function se(e,t,n,r){var i,o,a=20,s=r?function(){return r.cur()}:function(){return S.css(e,t,"")},u=s(),l=n&&n[3]||(S.cssNumber[t]?"":"px"),c=e.nodeType&&(S.cssNumber[t]||"px"!==l&&+u)&&te.exec(S.css(e,t));if(c&&c[3]!==l){u/=2,l=l||c[3],c=+u||1;while(a--)S.style(e,t,c+l),(1-o)*(1-(o=s()/u||.5))<=0&&(a=0),c/=o;c*=2,S.style(e,t,c+l),n=n||[]}return n&&(c=+c||+u||0,i=n[1]?c+(n[1]+1)*n[2]:+n[2],r&&(r.unit=l,r.start=c,r.end=i)),i}var ue={};function le(e,t){for(var n,r,i,o,a,s,u,l=[],c=0,f=e.length;c<f;c++)(r=e[c]).style&&(n=r.style.display,t?("none"===n&&(l[c]=Y.get(r,"display")||null,l[c]||(r.style.display="")),""===r.style.display&&ae(r)&&(l[c]=(u=a=o=void 0,a=(i=r).ownerDocument,s=i.nodeName,(u=ue[s])||(o=a.body.appendChild(a.createElement(s)),u=S.css(o,"display"),o.parentNode.removeChild(o),"none"===u&&(u="block"),ue[s]=u)))):"none"!==n&&(l[c]="none",Y.set(r,"display",n)));for(c=0;c<f;c++)null!=l[c]&&(e[c].style.display=l[c]);return e}S.fn.extend({show:function(){return le(this,!0)},hide:function(){return le(this)},toggle:function(e){return"boolean"==typeof e?e?this.show():this.hide():this.each(function(){ae(this)?S(this).show():S(this).hide()})}});var ce,fe,pe=/^(?:checkbox|radio)$/i,de=/<([a-z][^\/\0>\x20\t\r\n\f]*)/i,he=/^$|^module$|\/(?:java|ecma)script/i;ce=E.createDocumentFragment().appendChild(E.createElement("div")),(fe=E.createElement("input")).setAttribute("type","radio"),fe.setAttribute("checked","checked"),fe.setAttribute("name","t"),ce.appendChild(fe),y.checkClone=ce.cloneNode(!0).cloneNode(!0).lastChild.checked,ce.innerHTML="<textarea>x</textarea>",y.noCloneChecked=!!ce.cloneNode(!0).lastChild.defaultValue,ce.innerHTML="<option></option>",y.option=!!ce.lastChild;var ge={thead:[1,"<table>","</table>"],col:[2,"<table><colgroup>","</colgroup></table>"],tr:[2,"<table><tbody>","</tbody></table>"],td:[3,"<table><tbody><tr>","</tr></tbody></table>"],_default:[0,"",""]};function ve(e,t){var n;return n="undefined"!=typeof e.getElementsByTagName?e.getElementsByTagName(t||"*"):"undefined"!=typeof e.querySelectorAll?e.querySelectorAll(t||"*"):[],void 0===t||t&&A(e,t)?S.merge([e],n):n}function ye(e,t){for(var n=0,r=e.length;n<r;n++)Y.set(e[n],"globalEval",!t||Y.get(t[n],"globalEval"))}ge.tbody=ge.tfoot=ge.colgroup=ge.caption=ge.thead,ge.th=ge.td,y.option||(ge.optgroup=ge.option=[1,"<select multiple='multiple'>","</select>"]);var me=/<|&#?\w+;/;function xe(e,t,n,r,i){for(var o,a,s,u,l,c,f=t.createDocumentFragment(),p=[],d=0,h=e.length;d<h;d++)if((o=e[d])||0===o)if("object"===w(o))S.merge(p,o.nodeType?[o]:o);else if(me.test(o)){a=a||f.appendChild(t.createElement("div")),s=(de.exec(o)||["",""])[1].toLowerCase(),u=ge[s]||ge._default,a.innerHTML=u[1]+S.htmlPrefilter(o)+u[2],c=u[0];while(c--)a=a.lastChild;S.merge(p,a.childNodes),(a=f.firstChild).textContent=""}else p.push(t.createTextNode(o));f.textContent="",d=0;while(o=p[d++])if(r&&-1<S.inArray(o,r))i&&i.push(o);else if(l=ie(o),a=ve(f.appendChild(o),"script"),l&&ye(a),n){c=0;while(o=a[c++])he.test(o.type||"")&&n.push(o)}return f}var be=/^([^.]*)(?:\.(.+)|)/;function we(){return!0}function Te(){return!1}function Ce(e,t){return e===function(){try{return E.activeElement}catch(e){}}()==("focus"===t)}function Ee(e,t,n,r,i,o){var a,s;if("object"==typeof t){for(s in"string"!=typeof n&&(r=r||n,n=void 0),t)Ee(e,s,n,r,t[s],o);return e}if(null==r&&null==i?(i=n,r=n=void 0):null==i&&("string"==typeof n?(i=r,r=void 0):(i=r,r=n,n=void 0)),!1===i)i=Te;else if(!i)return e;return 1===o&&(a=i,(i=function(e){return S().off(e),a.apply(this,arguments)}).guid=a.guid||(a.guid=S.guid++)),e.each(function(){S.event.add(this,t,i,r,n)})}function Se(e,i,o){o?(Y.set(e,i,!1),S.event.add(e,i,{namespace:!1,handler:function(e){var t,n,r=Y.get(this,i);if(1&e.isTrigger&&this[i]){if(r.length)(S.event.special[i]||{}).delegateType&&e.stopPropagation();else if(r=s.call(arguments),Y.set(this,i,r),t=o(this,i),this[i](),r!==(n=Y.get(this,i))||t?Y.set(this,i,!1):n={},r!==n)return e.stopImmediatePropagation(),e.preventDefault(),n&&n.value}else r.length&&(Y.set(this,i,{value:S.event.trigger(S.extend(r[0],S.Event.prototype),r.slice(1),this)}),e.stopImmediatePropagation())}})):void 0===Y.get(e,i)&&S.event.add(e,i,we)}S.event={global:{},add:function(t,e,n,r,i){var o,a,s,u,l,c,f,p,d,h,g,v=Y.get(t);if(V(t)){n.handler&&(n=(o=n).handler,i=o.selector),i&&S.find.matchesSelector(re,i),n.guid||(n.guid=S.guid++),(u=v.events)||(u=v.events=Object.create(null)),(a=v.handle)||(a=v.handle=function(e){return"undefined"!=typeof S&&S.event.triggered!==e.type?S.event.dispatch.apply(t,arguments):void 0}),l=(e=(e||"").match(P)||[""]).length;while(l--)d=g=(s=be.exec(e[l])||[])[1],h=(s[2]||"").split(".").sort(),d&&(f=S.event.special[d]||{},d=(i?f.delegateType:f.bindType)||d,f=S.event.special[d]||{},c=S.extend({type:d,origType:g,data:r,handler:n,guid:n.guid,selector:i,needsContext:i&&S.expr.match.needsContext.test(i),namespace:h.join(".")},o),(p=u[d])||((p=u[d]=[]).delegateCount=0,f.setup&&!1!==f.setup.call(t,r,h,a)||t.addEventListener&&t.addEventListener(d,a)),f.add&&(f.add.call(t,c),c.handler.guid||(c.handler.guid=n.guid)),i?p.splice(p.delegateCount++,0,c):p.push(c),S.event.global[d]=!0)}},remove:function(e,t,n,r,i){var o,a,s,u,l,c,f,p,d,h,g,v=Y.hasData(e)&&Y.get(e);if(v&&(u=v.events)){l=(t=(t||"").match(P)||[""]).length;while(l--)if(d=g=(s=be.exec(t[l])||[])[1],h=(s[2]||"").split(".").sort(),d){f=S.event.special[d]||{},p=u[d=(r?f.delegateType:f.bindType)||d]||[],s=s[2]&&new RegExp("(^|\\.)"+h.join("\\.(?:.*\\.|)")+"(\\.|$)"),a=o=p.length;while(o--)c=p[o],!i&&g!==c.origType||n&&n.guid!==c.guid||s&&!s.test(c.namespace)||r&&r!==c.selector&&("**"!==r||!c.selector)||(p.splice(o,1),c.selector&&p.delegateCount--,f.remove&&f.remove.call(e,c));a&&!p.length&&(f.teardown&&!1!==f.teardown.call(e,h,v.handle)||S.removeEvent(e,d,v.handle),delete u[d])}else for(d in u)S.event.remove(e,d+t[l],n,r,!0);S.isEmptyObject(u)&&Y.remove(e,"handle events")}},dispatch:function(e){var t,n,r,i,o,a,s=new Array(arguments.length),u=S.event.fix(e),l=(Y.get(this,"events")||Object.create(null))[u.type]||[],c=S.event.special[u.type]||{};for(s[0]=u,t=1;t<arguments.length;t++)s[t]=arguments[t];if(u.delegateTarget=this,!c.preDispatch||!1!==c.preDispatch.call(this,u)){a=S.event.handlers.call(this,u,l),t=0;while((i=a[t++])&&!u.isPropagationStopped()){u.currentTarget=i.elem,n=0;while((o=i.handlers[n++])&&!u.isImmediatePropagationStopped())u.rnamespace&&!1!==o.namespace&&!u.rnamespace.test(o.namespace)||(u.handleObj=o,u.data=o.data,void 0!==(r=((S.event.special[o.origType]||{}).handle||o.handler).apply(i.elem,s))&&!1===(u.result=r)&&(u.preventDefault(),u.stopPropagation()))}return c.postDispatch&&c.postDispatch.call(this,u),u.result}},handlers:function(e,t){var n,r,i,o,a,s=[],u=t.delegateCount,l=e.target;if(u&&l.nodeType&&!("click"===e.type&&1<=e.button))for(;l!==this;l=l.parentNode||this)if(1===l.nodeType&&("click"!==e.type||!0!==l.disabled)){for(o=[],a={},n=0;n<u;n++)void 0===a[i=(r=t[n]).selector+" "]&&(a[i]=r.needsContext?-1<S(i,this).index(l):S.find(i,this,null,[l]).length),a[i]&&o.push(r);o.length&&s.push({elem:l,handlers:o})}return l=this,u<t.length&&s.push({elem:l,handlers:t.slice(u)}),s},addProp:function(t,e){Object.defineProperty(S.Event.prototype,t,{enumerable:!0,configurable:!0,get:m(e)?function(){if(this.originalEvent)return e(this.originalEvent)}:function(){if(this.originalEvent)return this.originalEvent[t]},set:function(e){Object.defineProperty(this,t,{enumerable:!0,configurable:!0,writable:!0,value:e})}})},fix:function(e){return e[S.expando]?e:new S.Event(e)},special:{load:{noBubble:!0},click:{setup:function(e){var t=this||e;return pe.test(t.type)&&t.click&&A(t,"input")&&Se(t,"click",we),!1},trigger:function(e){var t=this||e;return pe.test(t.type)&&t.click&&A(t,"input")&&Se(t,"click"),!0},_default:function(e){var t=e.target;return pe.test(t.type)&&t.click&&A(t,"input")&&Y.get(t,"click")||A(t,"a")}},beforeunload:{postDispatch:function(e){void 0!==e.result&&e.originalEvent&&(e.originalEvent.returnValue=e.result)}}}},S.removeEvent=function(e,t,n){e.removeEventListener&&e.removeEventListener(t,n)},S.Event=function(e,t){if(!(this instanceof S.Event))return new S.Event(e,t);e&&e.type?(this.originalEvent=e,this.type=e.type,this.isDefaultPrevented=e.defaultPrevented||void 0===e.defaultPrevented&&!1===e.returnValue?we:Te,this.target=e.target&&3===e.target.nodeType?e.target.parentNode:e.target,this.currentTarget=e.currentTarget,this.relatedTarget=e.relatedTarget):this.type=e,t&&S.extend(this,t),this.timeStamp=e&&e.timeStamp||Date.now(),this[S.expando]=!0},S.Event.prototype={constructor:S.Event,isDefaultPrevented:Te,isPropagationStopped:Te,isImmediatePropagationStopped:Te,isSimulated:!1,preventDefault:function(){var e=this.originalEvent;this.isDefaultPrevented=we,e&&!this.isSimulated&&e.preventDefault()},stopPropagation:function(){var e=this.originalEvent;this.isPropagationStopped=we,e&&!this.isSimulated&&e.stopPropagation()},stopImmediatePropagation:function(){var e=this.originalEvent;this.isImmediatePropagationStopped=we,e&&!this.isSimulated&&e.stopImmediatePropagation(),this.stopPropagation()}},S.each({altKey:!0,bubbles:!0,cancelable:!0,changedTouches:!0,ctrlKey:!0,detail:!0,eventPhase:!0,metaKey:!0,pageX:!0,pageY:!0,shiftKey:!0,view:!0,"char":!0,code:!0,charCode:!0,key:!0,keyCode:!0,button:!0,buttons:!0,clientX:!0,clientY:!0,offsetX:!0,offsetY:!0,pointerId:!0,pointerType:!0,screenX:!0,screenY:!0,targetTouches:!0,toElement:!0,touches:!0,which:!0},S.event.addProp),S.each({focus:"focusin",blur:"focusout"},function(e,t){S.event.special[e]={setup:function(){return Se(this,e,Ce),!1},trigger:function(){return Se(this,e),!0},_default:function(){return!0},delegateType:t}}),S.each({mouseenter:"mouseover",mouseleave:"mouseout",pointerenter:"pointerover",pointerleave:"pointerout"},function(e,i){S.event.special[e]={delegateType:i,bindType:i,handle:function(e){var t,n=e.relatedTarget,r=e.handleObj;return n&&(n===this||S.contains(this,n))||(e.type=r.origType,t=r.handler.apply(this,arguments),e.type=i),t}}}),S.fn.extend({on:function(e,t,n,r){return Ee(this,e,t,n,r)},one:function(e,t,n,r){return Ee(this,e,t,n,r,1)},off:function(e,t,n){var r,i;if(e&&e.preventDefault&&e.handleObj)return r=e.handleObj,S(e.delegateTarget).off(r.namespace?r.origType+"."+r.namespace:r.origType,r.selector,r.handler),this;if("object"==typeof e){for(i in e)this.off(i,t,e[i]);return this}return!1!==t&&"function"!=typeof t||(n=t,t=void 0),!1===n&&(n=Te),this.each(function(){S.event.remove(this,e,n,t)})}});var ke=/<script|<style|<link/i,Ae=/checked\s*(?:[^=]|=\s*.checked.)/i,Ne=/^\s*<!(?:\[CDATA\[|--)|(?:\]\]|--)>\s*$/g;function je(e,t){return A(e,"table")&&A(11!==t.nodeType?t:t.firstChild,"tr")&&S(e).children("tbody")[0]||e}function De(e){return e.type=(null!==e.getAttribute("type"))+"/"+e.type,e}function qe(e){return"true/"===(e.type||"").slice(0,5)?e.type=e.type.slice(5):e.removeAttribute("type"),e}function Le(e,t){var n,r,i,o,a,s;if(1===t.nodeType){if(Y.hasData(e)&&(s=Y.get(e).events))for(i in Y.remove(t,"handle events"),s)for(n=0,r=s[i].length;n<r;n++)S.event.add(t,i,s[i][n]);Q.hasData(e)&&(o=Q.access(e),a=S.extend({},o),Q.set(t,a))}}function He(n,r,i,o){r=g(r);var e,t,a,s,u,l,c=0,f=n.length,p=f-1,d=r[0],h=m(d);if(h||1<f&&"string"==typeof d&&!y.checkClone&&Ae.test(d))return n.each(function(e){var t=n.eq(e);h&&(r[0]=d.call(this,e,t.html())),He(t,r,i,o)});if(f&&(t=(e=xe(r,n[0].ownerDocument,!1,n,o)).firstChild,1===e.childNodes.length&&(e=t),t||o)){for(s=(a=S.map(ve(e,"script"),De)).length;c<f;c++)u=e,c!==p&&(u=S.clone(u,!0,!0),s&&S.merge(a,ve(u,"script"))),i.call(n[c],u,c);if(s)for(l=a[a.length-1].ownerDocument,S.map(a,qe),c=0;c<s;c++)u=a[c],he.test(u.type||"")&&!Y.access(u,"globalEval")&&S.contains(l,u)&&(u.src&&"module"!==(u.type||"").toLowerCase()?S._evalUrl&&!u.noModule&&S._evalUrl(u.src,{nonce:u.nonce||u.getAttribute("nonce")},l):b(u.textContent.replace(Ne,""),u,l))}return n}function Oe(e,t,n){for(var r,i=t?S.filter(t,e):e,o=0;null!=(r=i[o]);o++)n||1!==r.nodeType||S.cleanData(ve(r)),r.parentNode&&(n&&ie(r)&&ye(ve(r,"script")),r.parentNode.removeChild(r));return e}S.extend({htmlPrefilter:function(e){return e},clone:function(e,t,n){var r,i,o,a,s,u,l,c=e.cloneNode(!0),f=ie(e);if(!(y.noCloneChecked||1!==e.nodeType&&11!==e.nodeType||S.isXMLDoc(e)))for(a=ve(c),r=0,i=(o=ve(e)).length;r<i;r++)s=o[r],u=a[r],void 0,"input"===(l=u.nodeName.toLowerCase())&&pe.test(s.type)?u.checked=s.checked:"input"!==l&&"textarea"!==l||(u.defaultValue=s.defaultValue);if(t)if(n)for(o=o||ve(e),a=a||ve(c),r=0,i=o.length;r<i;r++)Le(o[r],a[r]);else Le(e,c);return 0<(a=ve(c,"script")).length&&ye(a,!f&&ve(e,"script")),c},cleanData:function(e){for(var t,n,r,i=S.event.special,o=0;void 0!==(n=e[o]);o++)if(V(n)){if(t=n[Y.expando]){if(t.events)for(r in t.events)i[r]?S.event.remove(n,r):S.removeEvent(n,r,t.handle);n[Y.expando]=void 0}n[Q.expando]&&(n[Q.expando]=void 0)}}}),S.fn.extend({detach:function(e){return Oe(this,e,!0)},remove:function(e){return Oe(this,e)},text:function(e){return $(this,function(e){return void 0===e?S.text(this):this.empty().each(function(){1!==this.nodeType&&11!==this.nodeType&&9!==this.nodeType||(this.textContent=e)})},null,e,arguments.length)},append:function(){return He(this,arguments,function(e){1!==this.nodeType&&11!==this.nodeType&&9!==this.nodeType||je(this,e).appendChild(e)})},prepend:function(){return He(this,arguments,function(e){if(1===this.nodeType||11===this.nodeType||9===this.nodeType){var t=je(this,e);t.insertBefore(e,t.firstChild)}})},before:function(){return He(this,arguments,function(e){this.parentNode&&this.parentNode.insertBefore(e,this)})},after:function(){return He(this,arguments,function(e){this.parentNode&&this.parentNode.insertBefore(e,this.nextSibling)})},empty:function(){for(var e,t=0;null!=(e=this[t]);t++)1===e.nodeType&&(S.cleanData(ve(e,!1)),e.textContent="");return this},clone:function(e,t){return e=null!=e&&e,t=null==t?e:t,this.map(function(){return S.clone(this,e,t)})},html:function(e){return $(this,function(e){var t=this[0]||{},n=0,r=this.length;if(void 0===e&&1===t.nodeType)return t.innerHTML;if("string"==typeof e&&!ke.test(e)&&!ge[(de.exec(e)||["",""])[1].toLowerCase()]){e=S.htmlPrefilter(e);try{for(;n<r;n++)1===(t=this[n]||{}).nodeType&&(S.cleanData(ve(t,!1)),t.innerHTML=e);t=0}catch(e){}}t&&this.empty().append(e)},null,e,arguments.length)},replaceWith:function(){var n=[];return He(this,arguments,function(e){var t=this.parentNode;S.inArray(this,n)<0&&(S.cleanData(ve(this)),t&&t.replaceChild(e,this))},n)}}),S.each({appendTo:"append",prependTo:"prepend",insertBefore:"before",insertAfter:"after",replaceAll:"replaceWith"},function(e,a){S.fn[e]=function(e){for(var t,n=[],r=S(e),i=r.length-1,o=0;o<=i;o++)t=o===i?this:this.clone(!0),S(r[o])[a](t),u.apply(n,t.get());return this.pushStack(n)}});var Pe=new RegExp("^("+ee+")(?!px)[a-z%]+$","i"),Re=function(e){var t=e.ownerDocument.defaultView;return t&&t.opener||(t=C),t.getComputedStyle(e)},Me=function(e,t,n){var r,i,o={};for(i in t)o[i]=e.style[i],e.style[i]=t[i];for(i in r=n.call(e),t)e.style[i]=o[i];return r},Ie=new RegExp(ne.join("|"),"i");function We(e,t,n){var r,i,o,a,s=e.style;return(n=n||Re(e))&&(""!==(a=n.getPropertyValue(t)||n[t])||ie(e)||(a=S.style(e,t)),!y.pixelBoxStyles()&&Pe.test(a)&&Ie.test(t)&&(r=s.width,i=s.minWidth,o=s.maxWidth,s.minWidth=s.maxWidth=s.width=a,a=n.width,s.width=r,s.minWidth=i,s.maxWidth=o)),void 0!==a?a+"":a}function Fe(e,t){return{get:function(){if(!e())return(this.get=t).apply(this,arguments);delete this.get}}}!function(){function e(){if(l){u.style.cssText="position:absolute;left:-11111px;width:60px;margin-top:1px;padding:0;border:0",l.style.cssText="position:relative;display:block;box-sizing:border-box;overflow:scroll;margin:auto;border:1px;padding:1px;width:60%;top:1%",re.appendChild(u).appendChild(l);var e=C.getComputedStyle(l);n="1%"!==e.top,s=12===t(e.marginLeft),l.style.right="60%",o=36===t(e.right),r=36===t(e.width),l.style.position="absolute",i=12===t(l.offsetWidth/3),re.removeChild(u),l=null}}function t(e){return Math.round(parseFloat(e))}var n,r,i,o,a,s,u=E.createElement("div"),l=E.createElement("div");l.style&&(l.style.backgroundClip="content-box",l.cloneNode(!0).style.backgroundClip="",y.clearCloneStyle="content-box"===l.style.backgroundClip,S.extend(y,{boxSizingReliable:function(){return e(),r},pixelBoxStyles:function(){return e(),o},pixelPosition:function(){return e(),n},reliableMarginLeft:function(){return e(),s},scrollboxSize:function(){return e(),i},reliableTrDimensions:function(){var e,t,n,r;return null==a&&(e=E.createElement("table"),t=E.createElement("tr"),n=E.createElement("div"),e.style.cssText="position:absolute;left:-11111px;border-collapse:separate",t.style.cssText="border:1px solid",t.style.height="1px",n.style.height="9px",n.style.display="block",re.appendChild(e).appendChild(t).appendChild(n),r=C.getComputedStyle(t),a=parseInt(r.height,10)+parseInt(r.borderTopWidth,10)+parseInt(r.borderBottomWidth,10)===t.offsetHeight,re.removeChild(e)),a}}))}();var Be=["Webkit","Moz","ms"],$e=E.createElement("div").style,_e={};function ze(e){var t=S.cssProps[e]||_e[e];return t||(e in $e?e:_e[e]=function(e){var t=e[0].toUpperCase()+e.slice(1),n=Be.length;while(n--)if((e=Be[n]+t)in $e)return e}(e)||e)}var Ue=/^(none|table(?!-c[ea]).+)/,Xe=/^--/,Ve={position:"absolute",visibility:"hidden",display:"block"},Ge={letterSpacing:"0",fontWeight:"400"};function Ye(e,t,n){var r=te.exec(t);return r?Math.max(0,r[2]-(n||0))+(r[3]||"px"):t}function Qe(e,t,n,r,i,o){var a="width"===t?1:0,s=0,u=0;if(n===(r?"border":"content"))return 0;for(;a<4;a+=2)"margin"===n&&(u+=S.css(e,n+ne[a],!0,i)),r?("content"===n&&(u-=S.css(e,"padding"+ne[a],!0,i)),"margin"!==n&&(u-=S.css(e,"border"+ne[a]+"Width",!0,i))):(u+=S.css(e,"padding"+ne[a],!0,i),"padding"!==n?u+=S.css(e,"border"+ne[a]+"Width",!0,i):s+=S.css(e,"border"+ne[a]+"Width",!0,i));return!r&&0<=o&&(u+=Math.max(0,Math.ceil(e["offset"+t[0].toUpperCase()+t.slice(1)]-o-u-s-.5))||0),u}function Je(e,t,n){var r=Re(e),i=(!y.boxSizingReliable()||n)&&"border-box"===S.css(e,"boxSizing",!1,r),o=i,a=We(e,t,r),s="offset"+t[0].toUpperCase()+t.slice(1);if(Pe.test(a)){if(!n)return a;a="auto"}return(!y.boxSizingReliable()&&i||!y.reliableTrDimensions()&&A(e,"tr")||"auto"===a||!parseFloat(a)&&"inline"===S.css(e,"display",!1,r))&&e.getClientRects().length&&(i="border-box"===S.css(e,"boxSizing",!1,r),(o=s in e)&&(a=e[s])),(a=parseFloat(a)||0)+Qe(e,t,n||(i?"border":"content"),o,r,a)+"px"}function Ke(e,t,n,r,i){return new Ke.prototype.init(e,t,n,r,i)}S.extend({cssHooks:{opacity:{get:function(e,t){if(t){var n=We(e,"opacity");return""===n?"1":n}}}},cssNumber:{animationIterationCount:!0,columnCount:!0,fillOpacity:!0,flexGrow:!0,flexShrink:!0,fontWeight:!0,gridArea:!0,gridColumn:!0,gridColumnEnd:!0,gridColumnStart:!0,gridRow:!0,gridRowEnd:!0,gridRowStart:!0,lineHeight:!0,opacity:!0,order:!0,orphans:!0,widows:!0,zIndex:!0,zoom:!0},cssProps:{},style:function(e,t,n,r){if(e&&3!==e.nodeType&&8!==e.nodeType&&e.style){var i,o,a,s=X(t),u=Xe.test(t),l=e.style;if(u||(t=ze(s)),a=S.cssHooks[t]||S.cssHooks[s],void 0===n)return a&&"get"in a&&void 0!==(i=a.get(e,!1,r))?i:l[t];"string"===(o=typeof n)&&(i=te.exec(n))&&i[1]&&(n=se(e,t,i),o="number"),null!=n&&n==n&&("number"!==o||u||(n+=i&&i[3]||(S.cssNumber[s]?"":"px")),y.clearCloneStyle||""!==n||0!==t.indexOf("background")||(l[t]="inherit"),a&&"set"in a&&void 0===(n=a.set(e,n,r))||(u?l.setProperty(t,n):l[t]=n))}},css:function(e,t,n,r){var i,o,a,s=X(t);return Xe.test(t)||(t=ze(s)),(a=S.cssHooks[t]||S.cssHooks[s])&&"get"in a&&(i=a.get(e,!0,n)),void 0===i&&(i=We(e,t,r)),"normal"===i&&t in Ge&&(i=Ge[t]),""===n||n?(o=parseFloat(i),!0===n||isFinite(o)?o||0:i):i}}),S.each(["height","width"],function(e,u){S.cssHooks[u]={get:function(e,t,n){if(t)return!Ue.test(S.css(e,"display"))||e.getClientRects().length&&e.getBoundingClientRect().width?Je(e,u,n):Me(e,Ve,function(){return Je(e,u,n)})},set:function(e,t,n){var r,i=Re(e),o=!y.scrollboxSize()&&"absolute"===i.position,a=(o||n)&&"border-box"===S.css(e,"boxSizing",!1,i),s=n?Qe(e,u,n,a,i):0;return a&&o&&(s-=Math.ceil(e["offset"+u[0].toUpperCase()+u.slice(1)]-parseFloat(i[u])-Qe(e,u,"border",!1,i)-.5)),s&&(r=te.exec(t))&&"px"!==(r[3]||"px")&&(e.style[u]=t,t=S.css(e,u)),Ye(0,t,s)}}}),S.cssHooks.marginLeft=Fe(y.reliableMarginLeft,function(e,t){if(t)return(parseFloat(We(e,"marginLeft"))||e.getBoundingClientRect().left-Me(e,{marginLeft:0},function(){return e.getBoundingClientRect().left}))+"px"}),S.each({margin:"",padding:"",border:"Width"},function(i,o){S.cssHooks[i+o]={expand:function(e){for(var t=0,n={},r="string"==typeof e?e.split(" "):[e];t<4;t++)n[i+ne[t]+o]=r[t]||r[t-2]||r[0];return n}},"margin"!==i&&(S.cssHooks[i+o].set=Ye)}),S.fn.extend({css:function(e,t){return $(this,function(e,t,n){var r,i,o={},a=0;if(Array.isArray(t)){for(r=Re(e),i=t.length;a<i;a++)o[t[a]]=S.css(e,t[a],!1,r);return o}return void 0!==n?S.style(e,t,n):S.css(e,t)},e,t,1<arguments.length)}}),((S.Tween=Ke).prototype={constructor:Ke,init:function(e,t,n,r,i,o){this.elem=e,this.prop=n,this.easing=i||S.easing._default,this.options=t,this.start=this.now=this.cur(),this.end=r,this.unit=o||(S.cssNumber[n]?"":"px")},cur:function(){var e=Ke.propHooks[this.prop];return e&&e.get?e.get(this):Ke.propHooks._default.get(this)},run:function(e){var t,n=Ke.propHooks[this.prop];return this.options.duration?this.pos=t=S.easing[this.easing](e,this.options.duration*e,0,1,this.options.duration):this.pos=t=e,this.now=(this.end-this.start)*t+this.start,this.options.step&&this.options.step.call(this.elem,this.now,this),n&&n.set?n.set(this):Ke.propHooks._default.set(this),this}}).init.prototype=Ke.prototype,(Ke.propHooks={_default:{get:function(e){var t;return 1!==e.elem.nodeType||null!=e.elem[e.prop]&&null==e.elem.style[e.prop]?e.elem[e.prop]:(t=S.css(e.elem,e.prop,""))&&"auto"!==t?t:0},set:function(e){S.fx.step[e.prop]?S.fx.step[e.prop](e):1!==e.elem.nodeType||!S.cssHooks[e.prop]&&null==e.elem.style[ze(e.prop)]?e.elem[e.prop]=e.now:S.style(e.elem,e.prop,e.now+e.unit)}}}).scrollTop=Ke.propHooks.scrollLeft={set:function(e){e.elem.nodeType&&e.elem.parentNode&&(e.elem[e.prop]=e.now)}},S.easing={linear:function(e){return e},swing:function(e){return.5-Math.cos(e*Math.PI)/2},_default:"swing"},S.fx=Ke.prototype.init,S.fx.step={};var Ze,et,tt,nt,rt=/^(?:toggle|show|hide)$/,it=/queueHooks$/;function ot(){et&&(!1===E.hidden&&C.requestAnimationFrame?C.requestAnimationFrame(ot):C.setTimeout(ot,S.fx.interval),S.fx.tick())}function at(){return C.setTimeout(function(){Ze=void 0}),Ze=Date.now()}function st(e,t){var n,r=0,i={height:e};for(t=t?1:0;r<4;r+=2-t)i["margin"+(n=ne[r])]=i["padding"+n]=e;return t&&(i.opacity=i.width=e),i}function ut(e,t,n){for(var r,i=(lt.tweeners[t]||[]).concat(lt.tweeners["*"]),o=0,a=i.length;o<a;o++)if(r=i[o].call(n,t,e))return r}function lt(o,e,t){var n,a,r=0,i=lt.prefilters.length,s=S.Deferred().always(function(){delete u.elem}),u=function(){if(a)return!1;for(var e=Ze||at(),t=Math.max(0,l.startTime+l.duration-e),n=1-(t/l.duration||0),r=0,i=l.tweens.length;r<i;r++)l.tweens[r].run(n);return s.notifyWith(o,[l,n,t]),n<1&&i?t:(i||s.notifyWith(o,[l,1,0]),s.resolveWith(o,[l]),!1)},l=s.promise({elem:o,props:S.extend({},e),opts:S.extend(!0,{specialEasing:{},easing:S.easing._default},t),originalProperties:e,originalOptions:t,startTime:Ze||at(),duration:t.duration,tweens:[],createTween:function(e,t){var n=S.Tween(o,l.opts,e,t,l.opts.specialEasing[e]||l.opts.easing);return l.tweens.push(n),n},stop:function(e){var t=0,n=e?l.tweens.length:0;if(a)return this;for(a=!0;t<n;t++)l.tweens[t].run(1);return e?(s.notifyWith(o,[l,1,0]),s.resolveWith(o,[l,e])):s.rejectWith(o,[l,e]),this}}),c=l.props;for(!function(e,t){var n,r,i,o,a;for(n in e)if(i=t[r=X(n)],o=e[n],Array.isArray(o)&&(i=o[1],o=e[n]=o[0]),n!==r&&(e[r]=o,delete e[n]),(a=S.cssHooks[r])&&"expand"in a)for(n in o=a.expand(o),delete e[r],o)n in e||(e[n]=o[n],t[n]=i);else t[r]=i}(c,l.opts.specialEasing);r<i;r++)if(n=lt.prefilters[r].call(l,o,c,l.opts))return m(n.stop)&&(S._queueHooks(l.elem,l.opts.queue).stop=n.stop.bind(n)),n;return S.map(c,ut,l),m(l.opts.start)&&l.opts.start.call(o,l),l.progress(l.opts.progress).done(l.opts.done,l.opts.complete).fail(l.opts.fail).always(l.opts.always),S.fx.timer(S.extend(u,{elem:o,anim:l,queue:l.opts.queue})),l}S.Animation=S.extend(lt,{tweeners:{"*":[function(e,t){var n=this.createTween(e,t);return se(n.elem,e,te.exec(t),n),n}]},tweener:function(e,t){m(e)?(t=e,e=["*"]):e=e.match(P);for(var n,r=0,i=e.length;r<i;r++)n=e[r],lt.tweeners[n]=lt.tweeners[n]||[],lt.tweeners[n].unshift(t)},prefilters:[function(e,t,n){var r,i,o,a,s,u,l,c,f="width"in t||"height"in t,p=this,d={},h=e.style,g=e.nodeType&&ae(e),v=Y.get(e,"fxshow");for(r in n.queue||(null==(a=S._queueHooks(e,"fx")).unqueued&&(a.unqueued=0,s=a.empty.fire,a.empty.fire=function(){a.unqueued||s()}),a.unqueued++,p.always(function(){p.always(function(){a.unqueued--,S.queue(e,"fx").length||a.empty.fire()})})),t)if(i=t[r],rt.test(i)){if(delete t[r],o=o||"toggle"===i,i===(g?"hide":"show")){if("show"!==i||!v||void 0===v[r])continue;g=!0}d[r]=v&&v[r]||S.style(e,r)}if((u=!S.isEmptyObject(t))||!S.isEmptyObject(d))for(r in f&&1===e.nodeType&&(n.overflow=[h.overflow,h.overflowX,h.overflowY],null==(l=v&&v.display)&&(l=Y.get(e,"display")),"none"===(c=S.css(e,"display"))&&(l?c=l:(le([e],!0),l=e.style.display||l,c=S.css(e,"display"),le([e]))),("inline"===c||"inline-block"===c&&null!=l)&&"none"===S.css(e,"float")&&(u||(p.done(function(){h.display=l}),null==l&&(c=h.display,l="none"===c?"":c)),h.display="inline-block")),n.overflow&&(h.overflow="hidden",p.always(function(){h.overflow=n.overflow[0],h.overflowX=n.overflow[1],h.overflowY=n.overflow[2]})),u=!1,d)u||(v?"hidden"in v&&(g=v.hidden):v=Y.access(e,"fxshow",{display:l}),o&&(v.hidden=!g),g&&le([e],!0),p.done(function(){for(r in g||le([e]),Y.remove(e,"fxshow"),d)S.style(e,r,d[r])})),u=ut(g?v[r]:0,r,p),r in v||(v[r]=u.start,g&&(u.end=u.start,u.start=0))}],prefilter:function(e,t){t?lt.prefilters.unshift(e):lt.prefilters.push(e)}}),S.speed=function(e,t,n){var r=e&&"object"==typeof e?S.extend({},e):{complete:n||!n&&t||m(e)&&e,duration:e,easing:n&&t||t&&!m(t)&&t};return S.fx.off?r.duration=0:"number"!=typeof r.duration&&(r.duration in S.fx.speeds?r.duration=S.fx.speeds[r.duration]:r.duration=S.fx.speeds._default),null!=r.queue&&!0!==r.queue||(r.queue="fx"),r.old=r.complete,r.complete=function(){m(r.old)&&r.old.call(this),r.queue&&S.dequeue(this,r.queue)},r},S.fn.extend({fadeTo:function(e,t,n,r){return this.filter(ae).css("opacity",0).show().end().animate({opacity:t},e,n,r)},animate:function(t,e,n,r){var i=S.isEmptyObject(t),o=S.speed(e,n,r),a=function(){var e=lt(this,S.extend({},t),o);(i||Y.get(this,"finish"))&&e.stop(!0)};return a.finish=a,i||!1===o.queue?this.each(a):this.queue(o.queue,a)},stop:function(i,e,o){var a=function(e){var t=e.stop;delete e.stop,t(o)};return"string"!=typeof i&&(o=e,e=i,i=void 0),e&&this.queue(i||"fx",[]),this.each(function(){var e=!0,t=null!=i&&i+"queueHooks",n=S.timers,r=Y.get(this);if(t)r[t]&&r[t].stop&&a(r[t]);else for(t in r)r[t]&&r[t].stop&&it.test(t)&&a(r[t]);for(t=n.length;t--;)n[t].elem!==this||null!=i&&n[t].queue!==i||(n[t].anim.stop(o),e=!1,n.splice(t,1));!e&&o||S.dequeue(this,i)})},finish:function(a){return!1!==a&&(a=a||"fx"),this.each(function(){var e,t=Y.get(this),n=t[a+"queue"],r=t[a+"queueHooks"],i=S.timers,o=n?n.length:0;for(t.finish=!0,S.queue(this,a,[]),r&&r.stop&&r.stop.call(this,!0),e=i.length;e--;)i[e].elem===this&&i[e].queue===a&&(i[e].anim.stop(!0),i.splice(e,1));for(e=0;e<o;e++)n[e]&&n[e].finish&&n[e].finish.call(this);delete t.finish})}}),S.each(["toggle","show","hide"],function(e,r){var i=S.fn[r];S.fn[r]=function(e,t,n){return null==e||"boolean"==typeof e?i.apply(this,arguments):this.animate(st(r,!0),e,t,n)}}),S.each({slideDown:st("show"),slideUp:st("hide"),slideToggle:st("toggle"),fadeIn:{opacity:"show"},fadeOut:{opacity:"hide"},fadeToggle:{opacity:"toggle"}},function(e,r){S.fn[e]=function(e,t,n){return this.animate(r,e,t,n)}}),S.timers=[],S.fx.tick=function(){var e,t=0,n=S.timers;for(Ze=Date.now();t<n.length;t++)(e=n[t])()||n[t]!==e||n.splice(t--,1);n.length||S.fx.stop(),Ze=void 0},S.fx.timer=function(e){S.timers.push(e),S.fx.start()},S.fx.interval=13,S.fx.start=function(){et||(et=!0,ot())},S.fx.stop=function(){et=null},S.fx.speeds={slow:600,fast:200,_default:400},S.fn.delay=function(r,e){return r=S.fx&&S.fx.speeds[r]||r,e=e||"fx",this.queue(e,function(e,t){var n=C.setTimeout(e,r);t.stop=function(){C.clearTimeout(n)}})},tt=E.createElement("input"),nt=E.createElement("select").appendChild(E.createElement("option")),tt.type="checkbox",y.checkOn=""!==tt.value,y.optSelected=nt.selected,(tt=E.createElement("input")).value="t",tt.type="radio",y.radioValue="t"===tt.value;var ct,ft=S.expr.attrHandle;S.fn.extend({attr:function(e,t){return $(this,S.attr,e,t,1<arguments.length)},removeAttr:function(e){return this.each(function(){S.removeAttr(this,e)})}}),S.extend({attr:function(e,t,n){var r,i,o=e.nodeType;if(3!==o&&8!==o&&2!==o)return"undefined"==typeof e.getAttribute?S.prop(e,t,n):(1===o&&S.isXMLDoc(e)||(i=S.attrHooks[t.toLowerCase()]||(S.expr.match.bool.test(t)?ct:void 0)),void 0!==n?null===n?void S.removeAttr(e,t):i&&"set"in i&&void 0!==(r=i.set(e,n,t))?r:(e.setAttribute(t,n+""),n):i&&"get"in i&&null!==(r=i.get(e,t))?r:null==(r=S.find.attr(e,t))?void 0:r)},attrHooks:{type:{set:function(e,t){if(!y.radioValue&&"radio"===t&&A(e,"input")){var n=e.value;return e.setAttribute("type",t),n&&(e.value=n),t}}}},removeAttr:function(e,t){var n,r=0,i=t&&t.match(P);if(i&&1===e.nodeType)while(n=i[r++])e.removeAttribute(n)}}),ct={set:function(e,t,n){return!1===t?S.removeAttr(e,n):e.setAttribute(n,n),n}},S.each(S.expr.match.bool.source.match(/\w+/g),function(e,t){var a=ft[t]||S.find.attr;ft[t]=function(e,t,n){var r,i,o=t.toLowerCase();return n||(i=ft[o],ft[o]=r,r=null!=a(e,t,n)?o:null,ft[o]=i),r}});var pt=/^(?:input|select|textarea|button)$/i,dt=/^(?:a|area)$/i;function ht(e){return(e.match(P)||[]).join(" ")}function gt(e){return e.getAttribute&&e.getAttribute("class")||""}function vt(e){return Array.isArray(e)?e:"string"==typeof e&&e.match(P)||[]}S.fn.extend({prop:function(e,t){return $(this,S.prop,e,t,1<arguments.length)},removeProp:function(e){return this.each(function(){delete this[S.propFix[e]||e]})}}),S.extend({prop:function(e,t,n){var r,i,o=e.nodeType;if(3!==o&&8!==o&&2!==o)return 1===o&&S.isXMLDoc(e)||(t=S.propFix[t]||t,i=S.propHooks[t]),void 0!==n?i&&"set"in i&&void 0!==(r=i.set(e,n,t))?r:e[t]=n:i&&"get"in i&&null!==(r=i.get(e,t))?r:e[t]},propHooks:{tabIndex:{get:function(e){var t=S.find.attr(e,"tabindex");return t?parseInt(t,10):pt.test(e.nodeName)||dt.test(e.nodeName)&&e.href?0:-1}}},propFix:{"for":"htmlFor","class":"className"}}),y.optSelected||(S.propHooks.selected={get:function(e){var t=e.parentNode;return t&&t.parentNode&&t.parentNode.selectedIndex,null},set:function(e){var t=e.parentNode;t&&(t.selectedIndex,t.parentNode&&t.parentNode.selectedIndex)}}),S.each(["tabIndex","readOnly","maxLength","cellSpacing","cellPadding","rowSpan","colSpan","useMap","frameBorder","contentEditable"],function(){S.propFix[this.toLowerCase()]=this}),S.fn.extend({addClass:function(t){var e,n,r,i,o,a,s,u=0;if(m(t))return this.each(function(e){S(this).addClass(t.call(this,e,gt(this)))});if((e=vt(t)).length)while(n=this[u++])if(i=gt(n),r=1===n.nodeType&&" "+ht(i)+" "){a=0;while(o=e[a++])r.indexOf(" "+o+" ")<0&&(r+=o+" ");i!==(s=ht(r))&&n.setAttribute("class",s)}return this},removeClass:function(t){var e,n,r,i,o,a,s,u=0;if(m(t))return this.each(function(e){S(this).removeClass(t.call(this,e,gt(this)))});if(!arguments.length)return this.attr("class","");if((e=vt(t)).length)while(n=this[u++])if(i=gt(n),r=1===n.nodeType&&" "+ht(i)+" "){a=0;while(o=e[a++])while(-1<r.indexOf(" "+o+" "))r=r.replace(" "+o+" "," ");i!==(s=ht(r))&&n.setAttribute("class",s)}return this},toggleClass:function(i,t){var o=typeof i,a="string"===o||Array.isArray(i);return"boolean"==typeof t&&a?t?this.addClass(i):this.removeClass(i):m(i)?this.each(function(e){S(this).toggleClass(i.call(this,e,gt(this),t),t)}):this.each(function(){var e,t,n,r;if(a){t=0,n=S(this),r=vt(i);while(e=r[t++])n.hasClass(e)?n.removeClass(e):n.addClass(e)}else void 0!==i&&"boolean"!==o||((e=gt(this))&&Y.set(this,"__className__",e),this.setAttribute&&this.setAttribute("class",e||!1===i?"":Y.get(this,"__className__")||""))})},hasClass:function(e){var t,n,r=0;t=" "+e+" ";while(n=this[r++])if(1===n.nodeType&&-1<(" "+ht(gt(n))+" ").indexOf(t))return!0;return!1}});var yt=/\r/g;S.fn.extend({val:function(n){var r,e,i,t=this[0];return arguments.length?(i=m(n),this.each(function(e){var t;1===this.nodeType&&(null==(t=i?n.call(this,e,S(this).val()):n)?t="":"number"==typeof t?t+="":Array.isArray(t)&&(t=S.map(t,function(e){return null==e?"":e+""})),(r=S.valHooks[this.type]||S.valHooks[this.nodeName.toLowerCase()])&&"set"in r&&void 0!==r.set(this,t,"value")||(this.value=t))})):t?(r=S.valHooks[t.type]||S.valHooks[t.nodeName.toLowerCase()])&&"get"in r&&void 0!==(e=r.get(t,"value"))?e:"string"==typeof(e=t.value)?e.replace(yt,""):null==e?"":e:void 0}}),S.extend({valHooks:{option:{get:function(e){var t=S.find.attr(e,"value");return null!=t?t:ht(S.text(e))}},select:{get:function(e){var t,n,r,i=e.options,o=e.selectedIndex,a="select-one"===e.type,s=a?null:[],u=a?o+1:i.length;for(r=o<0?u:a?o:0;r<u;r++)if(((n=i[r]).selected||r===o)&&!n.disabled&&(!n.parentNode.disabled||!A(n.parentNode,"optgroup"))){if(t=S(n).val(),a)return t;s.push(t)}return s},set:function(e,t){var n,r,i=e.options,o=S.makeArray(t),a=i.length;while(a--)((r=i[a]).selected=-1<S.inArray(S.valHooks.option.get(r),o))&&(n=!0);return n||(e.selectedIndex=-1),o}}}}),S.each(["radio","checkbox"],function(){S.valHooks[this]={set:function(e,t){if(Array.isArray(t))return e.checked=-1<S.inArray(S(e).val(),t)}},y.checkOn||(S.valHooks[this].get=function(e){return null===e.getAttribute("value")?"on":e.value})}),y.focusin="onfocusin"in C;var mt=/^(?:focusinfocus|focusoutblur)$/,xt=function(e){e.stopPropagation()};S.extend(S.event,{trigger:function(e,t,n,r){var i,o,a,s,u,l,c,f,p=[n||E],d=v.call(e,"type")?e.type:e,h=v.call(e,"namespace")?e.namespace.split("."):[];if(o=f=a=n=n||E,3!==n.nodeType&&8!==n.nodeType&&!mt.test(d+S.event.triggered)&&(-1<d.indexOf(".")&&(d=(h=d.split(".")).shift(),h.sort()),u=d.indexOf(":")<0&&"on"+d,(e=e[S.expando]?e:new S.Event(d,"object"==typeof e&&e)).isTrigger=r?2:3,e.namespace=h.join("."),e.rnamespace=e.namespace?new RegExp("(^|\\.)"+h.join("\\.(?:.*\\.|)")+"(\\.|$)"):null,e.result=void 0,e.target||(e.target=n),t=null==t?[e]:S.makeArray(t,[e]),c=S.event.special[d]||{},r||!c.trigger||!1!==c.trigger.apply(n,t))){if(!r&&!c.noBubble&&!x(n)){for(s=c.delegateType||d,mt.test(s+d)||(o=o.parentNode);o;o=o.parentNode)p.push(o),a=o;a===(n.ownerDocument||E)&&p.push(a.defaultView||a.parentWindow||C)}i=0;while((o=p[i++])&&!e.isPropagationStopped())f=o,e.type=1<i?s:c.bindType||d,(l=(Y.get(o,"events")||Object.create(null))[e.type]&&Y.get(o,"handle"))&&l.apply(o,t),(l=u&&o[u])&&l.apply&&V(o)&&(e.result=l.apply(o,t),!1===e.result&&e.preventDefault());return e.type=d,r||e.isDefaultPrevented()||c._default&&!1!==c._default.apply(p.pop(),t)||!V(n)||u&&m(n[d])&&!x(n)&&((a=n[u])&&(n[u]=null),S.event.triggered=d,e.isPropagationStopped()&&f.addEventListener(d,xt),n[d](),e.isPropagationStopped()&&f.removeEventListener(d,xt),S.event.triggered=void 0,a&&(n[u]=a)),e.result}},simulate:function(e,t,n){var r=S.extend(new S.Event,n,{type:e,isSimulated:!0});S.event.trigger(r,null,t)}}),S.fn.extend({trigger:function(e,t){return this.each(function(){S.event.trigger(e,t,this)})},triggerHandler:function(e,t){var n=this[0];if(n)return S.event.trigger(e,t,n,!0)}}),y.focusin||S.each({focus:"focusin",blur:"focusout"},function(n,r){var i=function(e){S.event.simulate(r,e.target,S.event.fix(e))};S.event.special[r]={setup:function(){var e=this.ownerDocument||this.document||this,t=Y.access(e,r);t||e.addEventListener(n,i,!0),Y.access(e,r,(t||0)+1)},teardown:function(){var e=this.ownerDocument||this.document||this,t=Y.access(e,r)-1;t?Y.access(e,r,t):(e.removeEventListener(n,i,!0),Y.remove(e,r))}}});var bt=C.location,wt={guid:Date.now()},Tt=/\?/;S.parseXML=function(e){var t,n;if(!e||"string"!=typeof e)return null;try{t=(new C.DOMParser).parseFromString(e,"text/xml")}catch(e){}return n=t&&t.getElementsByTagName("parsererror")[0],t&&!n||S.error("Invalid XML: "+(n?S.map(n.childNodes,function(e){return e.textContent}).join("\n"):e)),t};var Ct=/\[\]$/,Et=/\r?\n/g,St=/^(?:submit|button|image|reset|file)$/i,kt=/^(?:input|select|textarea|keygen)/i;function At(n,e,r,i){var t;if(Array.isArray(e))S.each(e,function(e,t){r||Ct.test(n)?i(n,t):At(n+"["+("object"==typeof t&&null!=t?e:"")+"]",t,r,i)});else if(r||"object"!==w(e))i(n,e);else for(t in e)At(n+"["+t+"]",e[t],r,i)}S.param=function(e,t){var n,r=[],i=function(e,t){var n=m(t)?t():t;r[r.length]=encodeURIComponent(e)+"="+encodeURIComponent(null==n?"":n)};if(null==e)return"";if(Array.isArray(e)||e.jquery&&!S.isPlainObject(e))S.each(e,function(){i(this.name,this.value)});else for(n in e)At(n,e[n],t,i);return r.join("&")},S.fn.extend({serialize:function(){return S.param(this.serializeArray())},serializeArray:function(){return this.map(function(){var e=S.prop(this,"elements");return e?S.makeArray(e):this}).filter(function(){var e=this.type;return this.name&&!S(this).is(":disabled")&&kt.test(this.nodeName)&&!St.test(e)&&(this.checked||!pe.test(e))}).map(function(e,t){var n=S(this).val();return null==n?null:Array.isArray(n)?S.map(n,function(e){return{name:t.name,value:e.replace(Et,"\r\n")}}):{name:t.name,value:n.replace(Et,"\r\n")}}).get()}});var Nt=/%20/g,jt=/#.*$/,Dt=/([?&])_=[^&]*/,qt=/^(.*?):[ \t]*([^\r\n]*)$/gm,Lt=/^(?:GET|HEAD)$/,Ht=/^\/\//,Ot={},Pt={},Rt="*/".concat("*"),Mt=E.createElement("a");function It(o){return function(e,t){"string"!=typeof e&&(t=e,e="*");var n,r=0,i=e.toLowerCase().match(P)||[];if(m(t))while(n=i[r++])"+"===n[0]?(n=n.slice(1)||"*",(o[n]=o[n]||[]).unshift(t)):(o[n]=o[n]||[]).push(t)}}function Wt(t,i,o,a){var s={},u=t===Pt;function l(e){var r;return s[e]=!0,S.each(t[e]||[],function(e,t){var n=t(i,o,a);return"string"!=typeof n||u||s[n]?u?!(r=n):void 0:(i.dataTypes.unshift(n),l(n),!1)}),r}return l(i.dataTypes[0])||!s["*"]&&l("*")}function Ft(e,t){var n,r,i=S.ajaxSettings.flatOptions||{};for(n in t)void 0!==t[n]&&((i[n]?e:r||(r={}))[n]=t[n]);return r&&S.extend(!0,e,r),e}Mt.href=bt.href,S.extend({active:0,lastModified:{},etag:{},ajaxSettings:{url:bt.href,type:"GET",isLocal:/^(?:about|app|app-storage|.+-extension|file|res|widget):$/.test(bt.protocol),global:!0,processData:!0,async:!0,contentType:"application/x-www-form-urlencoded; charset=UTF-8",accepts:{"*":Rt,text:"text/plain",html:"text/html",xml:"application/xml, text/xml",json:"application/json, text/javascript"},contents:{xml:/\bxml\b/,html:/\bhtml/,json:/\bjson\b/},responseFields:{xml:"responseXML",text:"responseText",json:"responseJSON"},converters:{"* text":String,"text html":!0,"text json":JSON.parse,"text xml":S.parseXML},flatOptions:{url:!0,context:!0}},ajaxSetup:function(e,t){return t?Ft(Ft(e,S.ajaxSettings),t):Ft(S.ajaxSettings,e)},ajaxPrefilter:It(Ot),ajaxTransport:It(Pt),ajax:function(e,t){"object"==typeof e&&(t=e,e=void 0),t=t||{};var c,f,p,n,d,r,h,g,i,o,v=S.ajaxSetup({},t),y=v.context||v,m=v.context&&(y.nodeType||y.jquery)?S(y):S.event,x=S.Deferred(),b=S.Callbacks("once memory"),w=v.statusCode||{},a={},s={},u="canceled",T={readyState:0,getResponseHeader:function(e){var t;if(h){if(!n){n={};while(t=qt.exec(p))n[t[1].toLowerCase()+" "]=(n[t[1].toLowerCase()+" "]||[]).concat(t[2])}t=n[e.toLowerCase()+" "]}return null==t?null:t.join(", ")},getAllResponseHeaders:function(){return h?p:null},setRequestHeader:function(e,t){return null==h&&(e=s[e.toLowerCase()]=s[e.toLowerCase()]||e,a[e]=t),this},overrideMimeType:function(e){return null==h&&(v.mimeType=e),this},statusCode:function(e){var t;if(e)if(h)T.always(e[T.status]);else for(t in e)w[t]=[w[t],e[t]];return this},abort:function(e){var t=e||u;return c&&c.abort(t),l(0,t),this}};if(x.promise(T),v.url=((e||v.url||bt.href)+"").replace(Ht,bt.protocol+"//"),v.type=t.method||t.type||v.method||v.type,v.dataTypes=(v.dataType||"*").toLowerCase().match(P)||[""],null==v.crossDomain){r=E.createElement("a");try{r.href=v.url,r.href=r.href,v.crossDomain=Mt.protocol+"//"+Mt.host!=r.protocol+"//"+r.host}catch(e){v.crossDomain=!0}}if(v.data&&v.processData&&"string"!=typeof v.data&&(v.data=S.param(v.data,v.traditional)),Wt(Ot,v,t,T),h)return T;for(i in(g=S.event&&v.global)&&0==S.active++&&S.event.trigger("ajaxStart"),v.type=v.type.toUpperCase(),v.hasContent=!Lt.test(v.type),f=v.url.replace(jt,""),v.hasContent?v.data&&v.processData&&0===(v.contentType||"").indexOf("application/x-www-form-urlencoded")&&(v.data=v.data.replace(Nt,"+")):(o=v.url.slice(f.length),v.data&&(v.processData||"string"==typeof v.data)&&(f+=(Tt.test(f)?"&":"?")+v.data,delete v.data),!1===v.cache&&(f=f.replace(Dt,"$1"),o=(Tt.test(f)?"&":"?")+"_="+wt.guid+++o),v.url=f+o),v.ifModified&&(S.lastModified[f]&&T.setRequestHeader("If-Modified-Since",S.lastModified[f]),S.etag[f]&&T.setRequestHeader("If-None-Match",S.etag[f])),(v.data&&v.hasContent&&!1!==v.contentType||t.contentType)&&T.setRequestHeader("Content-Type",v.contentType),T.setRequestHeader("Accept",v.dataTypes[0]&&v.accepts[v.dataTypes[0]]?v.accepts[v.dataTypes[0]]+("*"!==v.dataTypes[0]?", "+Rt+"; q=0.01":""):v.accepts["*"]),v.headers)T.setRequestHeader(i,v.headers[i]);if(v.beforeSend&&(!1===v.beforeSend.call(y,T,v)||h))return T.abort();if(u="abort",b.add(v.complete),T.done(v.success),T.fail(v.error),c=Wt(Pt,v,t,T)){if(T.readyState=1,g&&m.trigger("ajaxSend",[T,v]),h)return T;v.async&&0<v.timeout&&(d=C.setTimeout(function(){T.abort("timeout")},v.timeout));try{h=!1,c.send(a,l)}catch(e){if(h)throw e;l(-1,e)}}else l(-1,"No Transport");function l(e,t,n,r){var i,o,a,s,u,l=t;h||(h=!0,d&&C.clearTimeout(d),c=void 0,p=r||"",T.readyState=0<e?4:0,i=200<=e&&e<300||304===e,n&&(s=function(e,t,n){var r,i,o,a,s=e.contents,u=e.dataTypes;while("*"===u[0])u.shift(),void 0===r&&(r=e.mimeType||t.getResponseHeader("Content-Type"));if(r)for(i in s)if(s[i]&&s[i].test(r)){u.unshift(i);break}if(u[0]in n)o=u[0];else{for(i in n){if(!u[0]||e.converters[i+" "+u[0]]){o=i;break}a||(a=i)}o=o||a}if(o)return o!==u[0]&&u.unshift(o),n[o]}(v,T,n)),!i&&-1<S.inArray("script",v.dataTypes)&&S.inArray("json",v.dataTypes)<0&&(v.converters["text script"]=function(){}),s=function(e,t,n,r){var i,o,a,s,u,l={},c=e.dataTypes.slice();if(c[1])for(a in e.converters)l[a.toLowerCase()]=e.converters[a];o=c.shift();while(o)if(e.responseFields[o]&&(n[e.responseFields[o]]=t),!u&&r&&e.dataFilter&&(t=e.dataFilter(t,e.dataType)),u=o,o=c.shift())if("*"===o)o=u;else if("*"!==u&&u!==o){if(!(a=l[u+" "+o]||l["* "+o]))for(i in l)if((s=i.split(" "))[1]===o&&(a=l[u+" "+s[0]]||l["* "+s[0]])){!0===a?a=l[i]:!0!==l[i]&&(o=s[0],c.unshift(s[1]));break}if(!0!==a)if(a&&e["throws"])t=a(t);else try{t=a(t)}catch(e){return{state:"parsererror",error:a?e:"No conversion from "+u+" to "+o}}}return{state:"success",data:t}}(v,s,T,i),i?(v.ifModified&&((u=T.getResponseHeader("Last-Modified"))&&(S.lastModified[f]=u),(u=T.getResponseHeader("etag"))&&(S.etag[f]=u)),204===e||"HEAD"===v.type?l="nocontent":304===e?l="notmodified":(l=s.state,o=s.data,i=!(a=s.error))):(a=l,!e&&l||(l="error",e<0&&(e=0))),T.status=e,T.statusText=(t||l)+"",i?x.resolveWith(y,[o,l,T]):x.rejectWith(y,[T,l,a]),T.statusCode(w),w=void 0,g&&m.trigger(i?"ajaxSuccess":"ajaxError",[T,v,i?o:a]),b.fireWith(y,[T,l]),g&&(m.trigger("ajaxComplete",[T,v]),--S.active||S.event.trigger("ajaxStop")))}return T},getJSON:function(e,t,n){return S.get(e,t,n,"json")},getScript:function(e,t){return S.get(e,void 0,t,"script")}}),S.each(["get","post"],function(e,i){S[i]=function(e,t,n,r){return m(t)&&(r=r||n,n=t,t=void 0),S.ajax(S.extend({url:e,type:i,dataType:r,data:t,success:n},S.isPlainObject(e)&&e))}}),S.ajaxPrefilter(function(e){var t;for(t in e.headers)"content-type"===t.toLowerCase()&&(e.contentType=e.headers[t]||"")}),S._evalUrl=function(e,t,n){return S.ajax({url:e,type:"GET",dataType:"script",cache:!0,async:!1,global:!1,converters:{"text script":function(){}},dataFilter:function(e){S.globalEval(e,t,n)}})},S.fn.extend({wrapAll:function(e){var t;return this[0]&&(m(e)&&(e=e.call(this[0])),t=S(e,this[0].ownerDocument).eq(0).clone(!0),this[0].parentNode&&t.insertBefore(this[0]),t.map(function(){var e=this;while(e.firstElementChild)e=e.firstElementChild;return e}).append(this)),this},wrapInner:function(n){return m(n)?this.each(function(e){S(this).wrapInner(n.call(this,e))}):this.each(function(){var e=S(this),t=e.contents();t.length?t.wrapAll(n):e.append(n)})},wrap:function(t){var n=m(t);return this.each(function(e){S(this).wrapAll(n?t.call(this,e):t)})},unwrap:function(e){return this.parent(e).not("body").each(function(){S(this).replaceWith(this.childNodes)}),this}}),S.expr.pseudos.hidden=function(e){return!S.expr.pseudos.visible(e)},S.expr.pseudos.visible=function(e){return!!(e.offsetWidth||e.offsetHeight||e.getClientRects().length)},S.ajaxSettings.xhr=function(){try{return new C.XMLHttpRequest}catch(e){}};var Bt={0:200,1223:204},$t=S.ajaxSettings.xhr();y.cors=!!$t&&"withCredentials"in $t,y.ajax=$t=!!$t,S.ajaxTransport(function(i){var o,a;if(y.cors||$t&&!i.crossDomain)return{send:function(e,t){var n,r=i.xhr();if(r.open(i.type,i.url,i.async,i.username,i.password),i.xhrFields)for(n in i.xhrFields)r[n]=i.xhrFields[n];for(n in i.mimeType&&r.overrideMimeType&&r.overrideMimeType(i.mimeType),i.crossDomain||e["X-Requested-With"]||(e["X-Requested-With"]="XMLHttpRequest"),e)r.setRequestHeader(n,e[n]);o=function(e){return function(){o&&(o=a=r.onload=r.onerror=r.onabort=r.ontimeout=r.onreadystatechange=null,"abort"===e?r.abort():"error"===e?"number"!=typeof r.status?t(0,"error"):t(r.status,r.statusText):t(Bt[r.status]||r.status,r.statusText,"text"!==(r.responseType||"text")||"string"!=typeof r.responseText?{binary:r.response}:{text:r.responseText},r.getAllResponseHeaders()))}},r.onload=o(),a=r.onerror=r.ontimeout=o("error"),void 0!==r.onabort?r.onabort=a:r.onreadystatechange=function(){4===r.readyState&&C.setTimeout(function(){o&&a()})},o=o("abort");try{r.send(i.hasContent&&i.data||null)}catch(e){if(o)throw e}},abort:function(){o&&o()}}}),S.ajaxPrefilter(function(e){e.crossDomain&&(e.contents.script=!1)}),S.ajaxSetup({accepts:{script:"text/javascript, application/javascript, application/ecmascript, application/x-ecmascript"},contents:{script:/\b(?:java|ecma)script\b/},converters:{"text script":function(e){return S.globalEval(e),e}}}),S.ajaxPrefilter("script",function(e){void 0===e.cache&&(e.cache=!1),e.crossDomain&&(e.type="GET")}),S.ajaxTransport("script",function(n){var r,i;if(n.crossDomain||n.scriptAttrs)return{send:function(e,t){r=S("<script>").attr(n.scriptAttrs||{}).prop({charset:n.scriptCharset,src:n.url}).on("load error",i=function(e){r.remove(),i=null,e&&t("error"===e.type?404:200,e.type)}),E.head.appendChild(r[0])},abort:function(){i&&i()}}});var _t,zt=[],Ut=/(=)\?(?=&|$)|\?\?/;S.ajaxSetup({jsonp:"callback",jsonpCallback:function(){var e=zt.pop()||S.expando+"_"+wt.guid++;return this[e]=!0,e}}),S.ajaxPrefilter("json jsonp",function(e,t,n){var r,i,o,a=!1!==e.jsonp&&(Ut.test(e.url)?"url":"string"==typeof e.data&&0===(e.contentType||"").indexOf("application/x-www-form-urlencoded")&&Ut.test(e.data)&&"data");if(a||"jsonp"===e.dataTypes[0])return r=e.jsonpCallback=m(e.jsonpCallback)?e.jsonpCallback():e.jsonpCallback,a?e[a]=e[a].replace(Ut,"$1"+r):!1!==e.jsonp&&(e.url+=(Tt.test(e.url)?"&":"?")+e.jsonp+"="+r),e.converters["script json"]=function(){return o||S.error(r+" was not called"),o[0]},e.dataTypes[0]="json",i=C[r],C[r]=function(){o=arguments},n.always(function(){void 0===i?S(C).removeProp(r):C[r]=i,e[r]&&(e.jsonpCallback=t.jsonpCallback,zt.push(r)),o&&m(i)&&i(o[0]),o=i=void 0}),"script"}),y.createHTMLDocument=((_t=E.implementation.createHTMLDocument("").body).innerHTML="<form></form><form></form>",2===_t.childNodes.length),S.parseHTML=function(e,t,n){return"string"!=typeof e?[]:("boolean"==typeof t&&(n=t,t=!1),t||(y.createHTMLDocument?((r=(t=E.implementation.createHTMLDocument("")).createElement("base")).href=E.location.href,t.head.appendChild(r)):t=E),o=!n&&[],(i=N.exec(e))?[t.createElement(i[1])]:(i=xe([e],t,o),o&&o.length&&S(o).remove(),S.merge([],i.childNodes)));var r,i,o},S.fn.load=function(e,t,n){var r,i,o,a=this,s=e.indexOf(" ");return-1<s&&(r=ht(e.slice(s)),e=e.slice(0,s)),m(t)?(n=t,t=void 0):t&&"object"==typeof t&&(i="POST"),0<a.length&&S.ajax({url:e,type:i||"GET",dataType:"html",data:t}).done(function(e){o=arguments,a.html(r?S("<div>").append(S.parseHTML(e)).find(r):e)}).always(n&&function(e,t){a.each(function(){n.apply(this,o||[e.responseText,t,e])})}),this},S.expr.pseudos.animated=function(t){return S.grep(S.timers,function(e){return t===e.elem}).length},S.offset={setOffset:function(e,t,n){var r,i,o,a,s,u,l=S.css(e,"position"),c=S(e),f={};"static"===l&&(e.style.position="relative"),s=c.offset(),o=S.css(e,"top"),u=S.css(e,"left"),("absolute"===l||"fixed"===l)&&-1<(o+u).indexOf("auto")?(a=(r=c.position()).top,i=r.left):(a=parseFloat(o)||0,i=parseFloat(u)||0),m(t)&&(t=t.call(e,n,S.extend({},s))),null!=t.top&&(f.top=t.top-s.top+a),null!=t.left&&(f.left=t.left-s.left+i),"using"in t?t.using.call(e,f):c.css(f)}},S.fn.extend({offset:function(t){if(arguments.length)return void 0===t?this:this.each(function(e){S.offset.setOffset(this,t,e)});var e,n,r=this[0];return r?r.getClientRects().length?(e=r.getBoundingClientRect(),n=r.ownerDocument.defaultView,{top:e.top+n.pageYOffset,left:e.left+n.pageXOffset}):{top:0,left:0}:void 0},position:function(){if(this[0]){var e,t,n,r=this[0],i={top:0,left:0};if("fixed"===S.css(r,"position"))t=r.getBoundingClientRect();else{t=this.offset(),n=r.ownerDocument,e=r.offsetParent||n.documentElement;while(e&&(e===n.body||e===n.documentElement)&&"static"===S.css(e,"position"))e=e.parentNode;e&&e!==r&&1===e.nodeType&&((i=S(e).offset()).top+=S.css(e,"borderTopWidth",!0),i.left+=S.css(e,"borderLeftWidth",!0))}return{top:t.top-i.top-S.css(r,"marginTop",!0),left:t.left-i.left-S.css(r,"marginLeft",!0)}}},offsetParent:function(){return this.map(function(){var e=this.offsetParent;while(e&&"static"===S.css(e,"position"))e=e.offsetParent;return e||re})}}),S.each({scrollLeft:"pageXOffset",scrollTop:"pageYOffset"},function(t,i){var o="pageYOffset"===i;S.fn[t]=function(e){return $(this,function(e,t,n){var r;if(x(e)?r=e:9===e.nodeType&&(r=e.defaultView),void 0===n)return r?r[i]:e[t];r?r.scrollTo(o?r.pageXOffset:n,o?n:r.pageYOffset):e[t]=n},t,e,arguments.length)}}),S.each(["top","left"],function(e,n){S.cssHooks[n]=Fe(y.pixelPosition,function(e,t){if(t)return t=We(e,n),Pe.test(t)?S(e).position()[n]+"px":t})}),S.each({Height:"height",Width:"width"},function(a,s){S.each({padding:"inner"+a,content:s,"":"outer"+a},function(r,o){S.fn[o]=function(e,t){var n=arguments.length&&(r||"boolean"!=typeof e),i=r||(!0===e||!0===t?"margin":"border");return $(this,function(e,t,n){var r;return x(e)?0===o.indexOf("outer")?e["inner"+a]:e.document.documentElement["client"+a]:9===e.nodeType?(r=e.documentElement,Math.max(e.body["scroll"+a],r["scroll"+a],e.body["offset"+a],r["offset"+a],r["client"+a])):void 0===n?S.css(e,t,i):S.style(e,t,n,i)},s,n?e:void 0,n)}})}),S.each(["ajaxStart","ajaxStop","ajaxComplete","ajaxError","ajaxSuccess","ajaxSend"],function(e,t){S.fn[t]=function(e){return this.on(t,e)}}),S.fn.extend({bind:function(e,t,n){return this.on(e,null,t,n)},unbind:function(e,t){return this.off(e,null,t)},delegate:function(e,t,n,r){return this.on(t,e,n,r)},undelegate:function(e,t,n){return 1===arguments.length?this.off(e,"**"):this.off(t,e||"**",n)},hover:function(e,t){return this.mouseenter(e).mouseleave(t||e)}}),S.each("blur focus focusin focusout resize scroll click dblclick mousedown mouseup mousemove mouseover mouseout mouseenter mouseleave change select submit keydown keypress keyup contextmenu".split(" "),function(e,n){S.fn[n]=function(e,t){return 0<arguments.length?this.on(n,null,e,t):this.trigger(n)}});var Xt=/^[\s\uFEFF\xA0]+|[\s\uFEFF\xA0]+$/g;S.proxy=function(e,t){var n,r,i;if("string"==typeof t&&(n=e[t],t=e,e=n),m(e))return r=s.call(arguments,2),(i=function(){return e.apply(t||this,r.concat(s.call(arguments)))}).guid=e.guid=e.guid||S.guid++,i},S.holdReady=function(e){e?S.readyWait++:S.ready(!0)},S.isArray=Array.isArray,S.parseJSON=JSON.parse,S.nodeName=A,S.isFunction=m,S.isWindow=x,S.camelCase=X,S.type=w,S.now=Date.now,S.isNumeric=function(e){var t=S.type(e);return("number"===t||"string"===t)&&!isNaN(e-parseFloat(e))},S.trim=function(e){return null==e?"":(e+"").replace(Xt,"")},"function"==typeof define&&define.amd&&define("jquery",[],function(){return S});var Vt=C.jQuery,Gt=C.$;return S.noConflict=function(e){return C.$===S&&(C.$=Gt),e&&C.jQuery===S&&(C.jQuery=Vt),S},"undefined"==typeof e&&(C.jQuery=C.$=S),S});
+</script>
+<meta name="viewport" content="width=device-width, initial-scale=1" />
+<style type="text/css">html{font-family:sans-serif;-webkit-text-size-adjust:100%;-ms-text-size-adjust:100%}body{margin:0}article,aside,details,figcaption,figure,footer,header,hgroup,main,menu,nav,section,summary{display:block}audio,canvas,progress,video{display:inline-block;vertical-align:baseline}audio:not([controls]){display:none;height:0}[hidden],template{display:none}a{background-color:transparent}a:active,a:hover{outline:0}abbr[title]{border-bottom:1px dotted}b,strong{font-weight:700}dfn{font-style:italic}h1{margin:.67em 0;font-size:2em}mark{color:#000;background:#ff0}small{font-size:80%}sub,sup{position:relative;font-size:75%;line-height:0;vertical-align:baseline}sup{top:-.5em}sub{bottom:-.25em}img{border:0}svg:not(:root){overflow:hidden}figure{margin:1em 40px}hr{height:0;-webkit-box-sizing:content-box;-moz-box-sizing:content-box;box-sizing:content-box}pre{overflow:auto}code,kbd,pre,samp{font-family:monospace,monospace;font-size:1em}button,input,optgroup,select,textarea{margin:0;font:inherit;color:inherit}button{overflow:visible}button,select{text-transform:none}button,html input[type=button],input[type=reset],input[type=submit]{-webkit-appearance:button;cursor:pointer}button[disabled],html input[disabled]{cursor:default}button::-moz-focus-inner,input::-moz-focus-inner{padding:0;border:0}input{line-height:normal}input[type=checkbox],input[type=radio]{-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;padding:0}input[type=number]::-webkit-inner-spin-button,input[type=number]::-webkit-outer-spin-button{height:auto}input[type=search]{-webkit-box-sizing:content-box;-moz-box-sizing:content-box;box-sizing:content-box;-webkit-appearance:textfield}input[type=search]::-webkit-search-cancel-button,input[type=search]::-webkit-search-decoration{-webkit-appearance:none}fieldset{padding:.35em .625em .75em;margin:0 2px;border:1px solid silver}legend{padding:0;border:0}textarea{overflow:auto}optgroup{font-weight:700}table{border-spacing:0;border-collapse:collapse}td,th{padding:0}@media print{*,:after,:before{color:#000!important;text-shadow:none!important;background:0 0!important;-webkit-box-shadow:none!important;box-shadow:none!important}a,a:visited{text-decoration:underline}a[href]:after{content:" (" attr(href) ")"}abbr[title]:after{content:" (" attr(title) ")"}a[href^="javascript:"]:after,a[href^="#"]:after{content:""}blockquote,pre{border:1px solid #999;page-break-inside:avoid}thead{display:table-header-group}img,tr{page-break-inside:avoid}img{max-width:100%!important}h2,h3,p{orphans:3;widows:3}h2,h3{page-break-after:avoid}.navbar{display:none}.btn>.caret,.dropup>.btn>.caret{border-top-color:#000!important}.label{border:1px solid #000}.table{border-collapse:collapse!important}.table td,.table th{background-color:#fff!important}.table-bordered td,.table-bordered th{border:1px solid #ddd!important}}@font-face{font-family:'Glyphicons Halflings';src:url(data:application/vnd.ms-fontobject;base64,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);src:url(data:application/vnd.ms-fontobject;base64,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) format('embedded-opentype'),url(data:application/font-woff;base64,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) format('woff'),url(data:application/font-sfnt;base64,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) format('truetype'),url(data:image/svg+xml;base64,<?xml version="1.0" standalone="no"?>
<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN" "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd" >
<svg xmlns="http://www.w3.org/2000/svg">
<metadata></metadata>
<defs>
<font id="glyphicons_halflingsregular" horiz-adv-x="1200" >
<font-face units-per-em="1200" ascent="960" descent="-240" />
<missing-glyph horiz-adv-x="500" />
<glyph horiz-adv-x="0" />
<glyph horiz-adv-x="400" />
<glyph unicode=" " />
<glyph unicode="*" d="M600 1100q15 0 34 -1.5t30 -3.5l11 -1q10 -2 17.5 -10.5t7.5 -18.5v-224l158 158q7 7 18 8t19 -6l106 -106q7 -8 6 -19t-8 -18l-158 -158h224q10 0 18.5 -7.5t10.5 -17.5q6 -41 6 -75q0 -15 -1.5 -34t-3.5 -30l-1 -11q-2 -10 -10.5 -17.5t-18.5 -7.5h-224l158 -158 q7 -7 8 -18t-6 -19l-106 -106q-8 -7 -19 -6t-18 8l-158 158v-224q0 -10 -7.5 -18.5t-17.5 -10.5q-41 -6 -75 -6q-15 0 -34 1.5t-30 3.5l-11 1q-10 2 -17.5 10.5t-7.5 18.5v224l-158 -158q-7 -7 -18 -8t-19 6l-106 106q-7 8 -6 19t8 18l158 158h-224q-10 0 -18.5 7.5 t-10.5 17.5q-6 41 -6 75q0 15 1.5 34t3.5 30l1 11q2 10 10.5 17.5t18.5 7.5h224l-158 158q-7 7 -8 18t6 19l106 106q8 7 19 6t18 -8l158 -158v224q0 10 7.5 18.5t17.5 10.5q41 6 75 6z" />
<glyph unicode="+" d="M450 1100h200q21 0 35.5 -14.5t14.5 -35.5v-350h350q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-350v-350q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v350h-350q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5 h350v350q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xa0;" />
<glyph unicode="&#xa5;" d="M825 1100h250q10 0 12.5 -5t-5.5 -13l-364 -364q-6 -6 -11 -18h268q10 0 13 -6t-3 -14l-120 -160q-6 -8 -18 -14t-22 -6h-125v-100h275q10 0 13 -6t-3 -14l-120 -160q-6 -8 -18 -14t-22 -6h-125v-174q0 -11 -7.5 -18.5t-18.5 -7.5h-148q-11 0 -18.5 7.5t-7.5 18.5v174 h-275q-10 0 -13 6t3 14l120 160q6 8 18 14t22 6h125v100h-275q-10 0 -13 6t3 14l120 160q6 8 18 14t22 6h118q-5 12 -11 18l-364 364q-8 8 -5.5 13t12.5 5h250q25 0 43 -18l164 -164q8 -8 18 -8t18 8l164 164q18 18 43 18z" />
<glyph unicode="&#x2000;" horiz-adv-x="650" />
<glyph unicode="&#x2001;" horiz-adv-x="1300" />
<glyph unicode="&#x2002;" horiz-adv-x="650" />
<glyph unicode="&#x2003;" horiz-adv-x="1300" />
<glyph unicode="&#x2004;" horiz-adv-x="433" />
<glyph unicode="&#x2005;" horiz-adv-x="325" />
<glyph unicode="&#x2006;" horiz-adv-x="216" />
<glyph unicode="&#x2007;" horiz-adv-x="216" />
<glyph unicode="&#x2008;" horiz-adv-x="162" />
<glyph unicode="&#x2009;" horiz-adv-x="260" />
<glyph unicode="&#x200a;" horiz-adv-x="72" />
<glyph unicode="&#x202f;" horiz-adv-x="260" />
<glyph unicode="&#x205f;" horiz-adv-x="325" />
<glyph unicode="&#x20ac;" d="M744 1198q242 0 354 -189q60 -104 66 -209h-181q0 45 -17.5 82.5t-43.5 61.5t-58 40.5t-60.5 24t-51.5 7.5q-19 0 -40.5 -5.5t-49.5 -20.5t-53 -38t-49 -62.5t-39 -89.5h379l-100 -100h-300q-6 -50 -6 -100h406l-100 -100h-300q9 -74 33 -132t52.5 -91t61.5 -54.5t59 -29 t47 -7.5q22 0 50.5 7.5t60.5 24.5t58 41t43.5 61t17.5 80h174q-30 -171 -128 -278q-107 -117 -274 -117q-206 0 -324 158q-36 48 -69 133t-45 204h-217l100 100h112q1 47 6 100h-218l100 100h134q20 87 51 153.5t62 103.5q117 141 297 141z" />
<glyph unicode="&#x20bd;" d="M428 1200h350q67 0 120 -13t86 -31t57 -49.5t35 -56.5t17 -64.5t6.5 -60.5t0.5 -57v-16.5v-16.5q0 -36 -0.5 -57t-6.5 -61t-17 -65t-35 -57t-57 -50.5t-86 -31.5t-120 -13h-178l-2 -100h288q10 0 13 -6t-3 -14l-120 -160q-6 -8 -18 -14t-22 -6h-138v-175q0 -11 -5.5 -18 t-15.5 -7h-149q-10 0 -17.5 7.5t-7.5 17.5v175h-267q-10 0 -13 6t3 14l120 160q6 8 18 14t22 6h117v100h-267q-10 0 -13 6t3 14l120 160q6 8 18 14t22 6h117v475q0 10 7.5 17.5t17.5 7.5zM600 1000v-300h203q64 0 86.5 33t22.5 119q0 84 -22.5 116t-86.5 32h-203z" />
<glyph unicode="&#x2212;" d="M250 700h800q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#x231b;" d="M1000 1200v-150q0 -21 -14.5 -35.5t-35.5 -14.5h-50v-100q0 -91 -49.5 -165.5t-130.5 -109.5q81 -35 130.5 -109.5t49.5 -165.5v-150h50q21 0 35.5 -14.5t14.5 -35.5v-150h-800v150q0 21 14.5 35.5t35.5 14.5h50v150q0 91 49.5 165.5t130.5 109.5q-81 35 -130.5 109.5 t-49.5 165.5v100h-50q-21 0 -35.5 14.5t-14.5 35.5v150h800zM400 1000v-100q0 -60 32.5 -109.5t87.5 -73.5q28 -12 44 -37t16 -55t-16 -55t-44 -37q-55 -24 -87.5 -73.5t-32.5 -109.5v-150h400v150q0 60 -32.5 109.5t-87.5 73.5q-28 12 -44 37t-16 55t16 55t44 37 q55 24 87.5 73.5t32.5 109.5v100h-400z" />
<glyph unicode="&#x25fc;" horiz-adv-x="500" d="M0 0z" />
<glyph unicode="&#x2601;" d="M503 1089q110 0 200.5 -59.5t134.5 -156.5q44 14 90 14q120 0 205 -86.5t85 -206.5q0 -121 -85 -207.5t-205 -86.5h-750q-79 0 -135.5 57t-56.5 137q0 69 42.5 122.5t108.5 67.5q-2 12 -2 37q0 153 108 260.5t260 107.5z" />
<glyph unicode="&#x26fa;" d="M774 1193.5q16 -9.5 20.5 -27t-5.5 -33.5l-136 -187l467 -746h30q20 0 35 -18.5t15 -39.5v-42h-1200v42q0 21 15 39.5t35 18.5h30l468 746l-135 183q-10 16 -5.5 34t20.5 28t34 5.5t28 -20.5l111 -148l112 150q9 16 27 20.5t34 -5zM600 200h377l-182 112l-195 534v-646z " />
<glyph unicode="&#x2709;" d="M25 1100h1150q10 0 12.5 -5t-5.5 -13l-564 -567q-8 -8 -18 -8t-18 8l-564 567q-8 8 -5.5 13t12.5 5zM18 882l264 -264q8 -8 8 -18t-8 -18l-264 -264q-8 -8 -13 -5.5t-5 12.5v550q0 10 5 12.5t13 -5.5zM918 618l264 264q8 8 13 5.5t5 -12.5v-550q0 -10 -5 -12.5t-13 5.5 l-264 264q-8 8 -8 18t8 18zM818 482l364 -364q8 -8 5.5 -13t-12.5 -5h-1150q-10 0 -12.5 5t5.5 13l364 364q8 8 18 8t18 -8l164 -164q8 -8 18 -8t18 8l164 164q8 8 18 8t18 -8z" />
<glyph unicode="&#x270f;" d="M1011 1210q19 0 33 -13l153 -153q13 -14 13 -33t-13 -33l-99 -92l-214 214l95 96q13 14 32 14zM1013 800l-615 -614l-214 214l614 614zM317 96l-333 -112l110 335z" />
<glyph unicode="&#xe001;" d="M700 650v-550h250q21 0 35.5 -14.5t14.5 -35.5v-50h-800v50q0 21 14.5 35.5t35.5 14.5h250v550l-500 550h1200z" />
<glyph unicode="&#xe002;" d="M368 1017l645 163q39 15 63 0t24 -49v-831q0 -55 -41.5 -95.5t-111.5 -63.5q-79 -25 -147 -4.5t-86 75t25.5 111.5t122.5 82q72 24 138 8v521l-600 -155v-606q0 -42 -44 -90t-109 -69q-79 -26 -147 -5.5t-86 75.5t25.5 111.5t122.5 82.5q72 24 138 7v639q0 38 14.5 59 t53.5 34z" />
<glyph unicode="&#xe003;" d="M500 1191q100 0 191 -39t156.5 -104.5t104.5 -156.5t39 -191l-1 -2l1 -5q0 -141 -78 -262l275 -274q23 -26 22.5 -44.5t-22.5 -42.5l-59 -58q-26 -20 -46.5 -20t-39.5 20l-275 274q-119 -77 -261 -77l-5 1l-2 -1q-100 0 -191 39t-156.5 104.5t-104.5 156.5t-39 191 t39 191t104.5 156.5t156.5 104.5t191 39zM500 1022q-88 0 -162 -43t-117 -117t-43 -162t43 -162t117 -117t162 -43t162 43t117 117t43 162t-43 162t-117 117t-162 43z" />
<glyph unicode="&#xe005;" d="M649 949q48 68 109.5 104t121.5 38.5t118.5 -20t102.5 -64t71 -100.5t27 -123q0 -57 -33.5 -117.5t-94 -124.5t-126.5 -127.5t-150 -152.5t-146 -174q-62 85 -145.5 174t-150 152.5t-126.5 127.5t-93.5 124.5t-33.5 117.5q0 64 28 123t73 100.5t104 64t119 20 t120.5 -38.5t104.5 -104z" />
<glyph unicode="&#xe006;" d="M407 800l131 353q7 19 17.5 19t17.5 -19l129 -353h421q21 0 24 -8.5t-14 -20.5l-342 -249l130 -401q7 -20 -0.5 -25.5t-24.5 6.5l-343 246l-342 -247q-17 -12 -24.5 -6.5t-0.5 25.5l130 400l-347 251q-17 12 -14 20.5t23 8.5h429z" />
<glyph unicode="&#xe007;" d="M407 800l131 353q7 19 17.5 19t17.5 -19l129 -353h421q21 0 24 -8.5t-14 -20.5l-342 -249l130 -401q7 -20 -0.5 -25.5t-24.5 6.5l-343 246l-342 -247q-17 -12 -24.5 -6.5t-0.5 25.5l130 400l-347 251q-17 12 -14 20.5t23 8.5h429zM477 700h-240l197 -142l-74 -226 l193 139l195 -140l-74 229l192 140h-234l-78 211z" />
<glyph unicode="&#xe008;" d="M600 1200q124 0 212 -88t88 -212v-250q0 -46 -31 -98t-69 -52v-75q0 -10 6 -21.5t15 -17.5l358 -230q9 -5 15 -16.5t6 -21.5v-93q0 -10 -7.5 -17.5t-17.5 -7.5h-1150q-10 0 -17.5 7.5t-7.5 17.5v93q0 10 6 21.5t15 16.5l358 230q9 6 15 17.5t6 21.5v75q-38 0 -69 52 t-31 98v250q0 124 88 212t212 88z" />
<glyph unicode="&#xe009;" d="M25 1100h1150q10 0 17.5 -7.5t7.5 -17.5v-1050q0 -10 -7.5 -17.5t-17.5 -7.5h-1150q-10 0 -17.5 7.5t-7.5 17.5v1050q0 10 7.5 17.5t17.5 7.5zM100 1000v-100h100v100h-100zM875 1000h-550q-10 0 -17.5 -7.5t-7.5 -17.5v-350q0 -10 7.5 -17.5t17.5 -7.5h550 q10 0 17.5 7.5t7.5 17.5v350q0 10 -7.5 17.5t-17.5 7.5zM1000 1000v-100h100v100h-100zM100 800v-100h100v100h-100zM1000 800v-100h100v100h-100zM100 600v-100h100v100h-100zM1000 600v-100h100v100h-100zM875 500h-550q-10 0 -17.5 -7.5t-7.5 -17.5v-350q0 -10 7.5 -17.5 t17.5 -7.5h550q10 0 17.5 7.5t7.5 17.5v350q0 10 -7.5 17.5t-17.5 7.5zM100 400v-100h100v100h-100zM1000 400v-100h100v100h-100zM100 200v-100h100v100h-100zM1000 200v-100h100v100h-100z" />
<glyph unicode="&#xe010;" d="M50 1100h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5zM650 1100h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v400 q0 21 14.5 35.5t35.5 14.5zM50 500h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5zM650 500h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400 q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe011;" d="M50 1100h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM450 1100h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200 q0 21 14.5 35.5t35.5 14.5zM850 1100h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM50 700h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200 q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM450 700h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM850 700h200q21 0 35.5 -14.5t14.5 -35.5v-200 q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM50 300h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM450 300h200 q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM850 300h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5 t35.5 14.5z" />
<glyph unicode="&#xe012;" d="M50 1100h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM450 1100h700q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-700q-21 0 -35.5 14.5t-14.5 35.5v200 q0 21 14.5 35.5t35.5 14.5zM50 700h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM450 700h700q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-700 q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM50 300h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM450 300h700q21 0 35.5 -14.5t14.5 -35.5v-200 q0 -21 -14.5 -35.5t-35.5 -14.5h-700q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe013;" d="M465 477l571 571q8 8 18 8t17 -8l177 -177q8 -7 8 -17t-8 -18l-783 -784q-7 -8 -17.5 -8t-17.5 8l-384 384q-8 8 -8 18t8 17l177 177q7 8 17 8t18 -8l171 -171q7 -7 18 -7t18 7z" />
<glyph unicode="&#xe014;" d="M904 1083l178 -179q8 -8 8 -18.5t-8 -17.5l-267 -268l267 -268q8 -7 8 -17.5t-8 -18.5l-178 -178q-8 -8 -18.5 -8t-17.5 8l-268 267l-268 -267q-7 -8 -17.5 -8t-18.5 8l-178 178q-8 8 -8 18.5t8 17.5l267 268l-267 268q-8 7 -8 17.5t8 18.5l178 178q8 8 18.5 8t17.5 -8 l268 -267l268 268q7 7 17.5 7t18.5 -7z" />
<glyph unicode="&#xe015;" d="M507 1177q98 0 187.5 -38.5t154.5 -103.5t103.5 -154.5t38.5 -187.5q0 -141 -78 -262l300 -299q8 -8 8 -18.5t-8 -18.5l-109 -108q-7 -8 -17.5 -8t-18.5 8l-300 299q-119 -77 -261 -77q-98 0 -188 38.5t-154.5 103t-103 154.5t-38.5 188t38.5 187.5t103 154.5 t154.5 103.5t188 38.5zM506.5 1023q-89.5 0 -165.5 -44t-120 -120.5t-44 -166t44 -165.5t120 -120t165.5 -44t166 44t120.5 120t44 165.5t-44 166t-120.5 120.5t-166 44zM425 900h150q10 0 17.5 -7.5t7.5 -17.5v-75h75q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5 t-17.5 -7.5h-75v-75q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v75h-75q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5h75v75q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe016;" d="M507 1177q98 0 187.5 -38.5t154.5 -103.5t103.5 -154.5t38.5 -187.5q0 -141 -78 -262l300 -299q8 -8 8 -18.5t-8 -18.5l-109 -108q-7 -8 -17.5 -8t-18.5 8l-300 299q-119 -77 -261 -77q-98 0 -188 38.5t-154.5 103t-103 154.5t-38.5 188t38.5 187.5t103 154.5 t154.5 103.5t188 38.5zM506.5 1023q-89.5 0 -165.5 -44t-120 -120.5t-44 -166t44 -165.5t120 -120t165.5 -44t166 44t120.5 120t44 165.5t-44 166t-120.5 120.5t-166 44zM325 800h350q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-350q-10 0 -17.5 7.5 t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe017;" d="M550 1200h100q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5zM800 975v166q167 -62 272 -209.5t105 -331.5q0 -117 -45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5 t-184.5 123t-123 184.5t-45.5 224q0 184 105 331.5t272 209.5v-166q-103 -55 -165 -155t-62 -220q0 -116 57 -214.5t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5q0 120 -62 220t-165 155z" />
<glyph unicode="&#xe018;" d="M1025 1200h150q10 0 17.5 -7.5t7.5 -17.5v-1150q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v1150q0 10 7.5 17.5t17.5 7.5zM725 800h150q10 0 17.5 -7.5t7.5 -17.5v-750q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v750 q0 10 7.5 17.5t17.5 7.5zM425 500h150q10 0 17.5 -7.5t7.5 -17.5v-450q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v450q0 10 7.5 17.5t17.5 7.5zM125 300h150q10 0 17.5 -7.5t7.5 -17.5v-250q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5 v250q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe019;" d="M600 1174q33 0 74 -5l38 -152l5 -1q49 -14 94 -39l5 -2l134 80q61 -48 104 -105l-80 -134l3 -5q25 -44 39 -93l1 -6l152 -38q5 -43 5 -73q0 -34 -5 -74l-152 -38l-1 -6q-15 -49 -39 -93l-3 -5l80 -134q-48 -61 -104 -105l-134 81l-5 -3q-44 -25 -94 -39l-5 -2l-38 -151 q-43 -5 -74 -5q-33 0 -74 5l-38 151l-5 2q-49 14 -94 39l-5 3l-134 -81q-60 48 -104 105l80 134l-3 5q-25 45 -38 93l-2 6l-151 38q-6 42 -6 74q0 33 6 73l151 38l2 6q13 48 38 93l3 5l-80 134q47 61 105 105l133 -80l5 2q45 25 94 39l5 1l38 152q43 5 74 5zM600 815 q-89 0 -152 -63t-63 -151.5t63 -151.5t152 -63t152 63t63 151.5t-63 151.5t-152 63z" />
<glyph unicode="&#xe020;" d="M500 1300h300q41 0 70.5 -29.5t29.5 -70.5v-100h275q10 0 17.5 -7.5t7.5 -17.5v-75h-1100v75q0 10 7.5 17.5t17.5 7.5h275v100q0 41 29.5 70.5t70.5 29.5zM500 1200v-100h300v100h-300zM1100 900v-800q0 -41 -29.5 -70.5t-70.5 -29.5h-700q-41 0 -70.5 29.5t-29.5 70.5 v800h900zM300 800v-700h100v700h-100zM500 800v-700h100v700h-100zM700 800v-700h100v700h-100zM900 800v-700h100v700h-100z" />
<glyph unicode="&#xe021;" d="M18 618l620 608q8 7 18.5 7t17.5 -7l608 -608q8 -8 5.5 -13t-12.5 -5h-175v-575q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v375h-300v-375q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v575h-175q-10 0 -12.5 5t5.5 13z" />
<glyph unicode="&#xe022;" d="M600 1200v-400q0 -41 29.5 -70.5t70.5 -29.5h300v-650q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5v1100q0 21 14.5 35.5t35.5 14.5h450zM1000 800h-250q-21 0 -35.5 14.5t-14.5 35.5v250z" />
<glyph unicode="&#xe023;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 1027q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5 t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5t-57 214.5t-155.5 155.5t-214.5 57zM525 900h50q10 0 17.5 -7.5t7.5 -17.5v-275h175q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v350q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe024;" d="M1300 0h-538l-41 400h-242l-41 -400h-538l431 1200h209l-21 -300h162l-20 300h208zM515 800l-27 -300h224l-27 300h-170z" />
<glyph unicode="&#xe025;" d="M550 1200h200q21 0 35.5 -14.5t14.5 -35.5v-450h191q20 0 25.5 -11.5t-7.5 -27.5l-327 -400q-13 -16 -32 -16t-32 16l-327 400q-13 16 -7.5 27.5t25.5 11.5h191v450q0 21 14.5 35.5t35.5 14.5zM1125 400h50q10 0 17.5 -7.5t7.5 -17.5v-350q0 -10 -7.5 -17.5t-17.5 -7.5 h-1050q-10 0 -17.5 7.5t-7.5 17.5v350q0 10 7.5 17.5t17.5 7.5h50q10 0 17.5 -7.5t7.5 -17.5v-175h900v175q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe026;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 1027q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5 t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5t-57 214.5t-155.5 155.5t-214.5 57zM525 900h150q10 0 17.5 -7.5t7.5 -17.5v-275h137q21 0 26 -11.5t-8 -27.5l-223 -275q-13 -16 -32 -16t-32 16l-223 275q-13 16 -8 27.5t26 11.5h137v275q0 10 7.5 17.5t17.5 7.5z " />
<glyph unicode="&#xe027;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 1027q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5 t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5t-57 214.5t-155.5 155.5t-214.5 57zM632 914l223 -275q13 -16 8 -27.5t-26 -11.5h-137v-275q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v275h-137q-21 0 -26 11.5t8 27.5l223 275q13 16 32 16 t32 -16z" />
<glyph unicode="&#xe028;" d="M225 1200h750q10 0 19.5 -7t12.5 -17l186 -652q7 -24 7 -49v-425q0 -12 -4 -27t-9 -17q-12 -6 -37 -6h-1100q-12 0 -27 4t-17 8q-6 13 -6 38l1 425q0 25 7 49l185 652q3 10 12.5 17t19.5 7zM878 1000h-556q-10 0 -19 -7t-11 -18l-87 -450q-2 -11 4 -18t16 -7h150 q10 0 19.5 -7t11.5 -17l38 -152q2 -10 11.5 -17t19.5 -7h250q10 0 19.5 7t11.5 17l38 152q2 10 11.5 17t19.5 7h150q10 0 16 7t4 18l-87 450q-2 11 -11 18t-19 7z" />
<glyph unicode="&#xe029;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 1027q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5 t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5t-57 214.5t-155.5 155.5t-214.5 57zM540 820l253 -190q17 -12 17 -30t-17 -30l-253 -190q-16 -12 -28 -6.5t-12 26.5v400q0 21 12 26.5t28 -6.5z" />
<glyph unicode="&#xe030;" d="M947 1060l135 135q7 7 12.5 5t5.5 -13v-362q0 -10 -7.5 -17.5t-17.5 -7.5h-362q-11 0 -13 5.5t5 12.5l133 133q-109 76 -238 76q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5h150q0 -117 -45.5 -224 t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5q192 0 347 -117z" />
<glyph unicode="&#xe031;" d="M947 1060l135 135q7 7 12.5 5t5.5 -13v-361q0 -11 -7.5 -18.5t-18.5 -7.5h-361q-11 0 -13 5.5t5 12.5l134 134q-110 75 -239 75q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5h-150q0 117 45.5 224t123 184.5t184.5 123t224 45.5q192 0 347 -117zM1027 600h150 q0 -117 -45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5q-192 0 -348 118l-134 -134q-7 -8 -12.5 -5.5t-5.5 12.5v360q0 11 7.5 18.5t18.5 7.5h360q10 0 12.5 -5.5t-5.5 -12.5l-133 -133q110 -76 240 -76q116 0 214.5 57t155.5 155.5t57 214.5z" />
<glyph unicode="&#xe032;" d="M125 1200h1050q10 0 17.5 -7.5t7.5 -17.5v-1150q0 -10 -7.5 -17.5t-17.5 -7.5h-1050q-10 0 -17.5 7.5t-7.5 17.5v1150q0 10 7.5 17.5t17.5 7.5zM1075 1000h-850q-10 0 -17.5 -7.5t-7.5 -17.5v-850q0 -10 7.5 -17.5t17.5 -7.5h850q10 0 17.5 7.5t7.5 17.5v850 q0 10 -7.5 17.5t-17.5 7.5zM325 900h50q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-50q-10 0 -17.5 7.5t-7.5 17.5v50q0 10 7.5 17.5t17.5 7.5zM525 900h450q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-450q-10 0 -17.5 7.5t-7.5 17.5v50 q0 10 7.5 17.5t17.5 7.5zM325 700h50q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-50q-10 0 -17.5 7.5t-7.5 17.5v50q0 10 7.5 17.5t17.5 7.5zM525 700h450q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-450q-10 0 -17.5 7.5t-7.5 17.5v50 q0 10 7.5 17.5t17.5 7.5zM325 500h50q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-50q-10 0 -17.5 7.5t-7.5 17.5v50q0 10 7.5 17.5t17.5 7.5zM525 500h450q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-450q-10 0 -17.5 7.5t-7.5 17.5v50 q0 10 7.5 17.5t17.5 7.5zM325 300h50q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-50q-10 0 -17.5 7.5t-7.5 17.5v50q0 10 7.5 17.5t17.5 7.5zM525 300h450q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-450q-10 0 -17.5 7.5t-7.5 17.5v50 q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe033;" d="M900 800v200q0 83 -58.5 141.5t-141.5 58.5h-300q-82 0 -141 -59t-59 -141v-200h-100q-41 0 -70.5 -29.5t-29.5 -70.5v-600q0 -41 29.5 -70.5t70.5 -29.5h900q41 0 70.5 29.5t29.5 70.5v600q0 41 -29.5 70.5t-70.5 29.5h-100zM400 800v150q0 21 15 35.5t35 14.5h200 q20 0 35 -14.5t15 -35.5v-150h-300z" />
<glyph unicode="&#xe034;" d="M125 1100h50q10 0 17.5 -7.5t7.5 -17.5v-1075h-100v1075q0 10 7.5 17.5t17.5 7.5zM1075 1052q4 0 9 -2q16 -6 16 -23v-421q0 -6 -3 -12q-33 -59 -66.5 -99t-65.5 -58t-56.5 -24.5t-52.5 -6.5q-26 0 -57.5 6.5t-52.5 13.5t-60 21q-41 15 -63 22.5t-57.5 15t-65.5 7.5 q-85 0 -160 -57q-7 -5 -15 -5q-6 0 -11 3q-14 7 -14 22v438q22 55 82 98.5t119 46.5q23 2 43 0.5t43 -7t32.5 -8.5t38 -13t32.5 -11q41 -14 63.5 -21t57 -14t63.5 -7q103 0 183 87q7 8 18 8z" />
<glyph unicode="&#xe035;" d="M600 1175q116 0 227 -49.5t192.5 -131t131 -192.5t49.5 -227v-300q0 -10 -7.5 -17.5t-17.5 -7.5h-50q-10 0 -17.5 7.5t-7.5 17.5v300q0 127 -70.5 231.5t-184.5 161.5t-245 57t-245 -57t-184.5 -161.5t-70.5 -231.5v-300q0 -10 -7.5 -17.5t-17.5 -7.5h-50 q-10 0 -17.5 7.5t-7.5 17.5v300q0 116 49.5 227t131 192.5t192.5 131t227 49.5zM220 500h160q8 0 14 -6t6 -14v-460q0 -8 -6 -14t-14 -6h-160q-8 0 -14 6t-6 14v460q0 8 6 14t14 6zM820 500h160q8 0 14 -6t6 -14v-460q0 -8 -6 -14t-14 -6h-160q-8 0 -14 6t-6 14v460 q0 8 6 14t14 6z" />
<glyph unicode="&#xe036;" d="M321 814l258 172q9 6 15 2.5t6 -13.5v-750q0 -10 -6 -13.5t-15 2.5l-258 172q-21 14 -46 14h-250q-10 0 -17.5 7.5t-7.5 17.5v350q0 10 7.5 17.5t17.5 7.5h250q25 0 46 14zM900 668l120 120q7 7 17 7t17 -7l34 -34q7 -7 7 -17t-7 -17l-120 -120l120 -120q7 -7 7 -17 t-7 -17l-34 -34q-7 -7 -17 -7t-17 7l-120 119l-120 -119q-7 -7 -17 -7t-17 7l-34 34q-7 7 -7 17t7 17l119 120l-119 120q-7 7 -7 17t7 17l34 34q7 8 17 8t17 -8z" />
<glyph unicode="&#xe037;" d="M321 814l258 172q9 6 15 2.5t6 -13.5v-750q0 -10 -6 -13.5t-15 2.5l-258 172q-21 14 -46 14h-250q-10 0 -17.5 7.5t-7.5 17.5v350q0 10 7.5 17.5t17.5 7.5h250q25 0 46 14zM766 900h4q10 -1 16 -10q96 -129 96 -290q0 -154 -90 -281q-6 -9 -17 -10l-3 -1q-9 0 -16 6 l-29 23q-7 7 -8.5 16.5t4.5 17.5q72 103 72 229q0 132 -78 238q-6 8 -4.5 18t9.5 17l29 22q7 5 15 5z" />
<glyph unicode="&#xe038;" d="M967 1004h3q11 -1 17 -10q135 -179 135 -396q0 -105 -34 -206.5t-98 -185.5q-7 -9 -17 -10h-3q-9 0 -16 6l-42 34q-8 6 -9 16t5 18q111 150 111 328q0 90 -29.5 176t-84.5 157q-6 9 -5 19t10 16l42 33q7 5 15 5zM321 814l258 172q9 6 15 2.5t6 -13.5v-750q0 -10 -6 -13.5 t-15 2.5l-258 172q-21 14 -46 14h-250q-10 0 -17.5 7.5t-7.5 17.5v350q0 10 7.5 17.5t17.5 7.5h250q25 0 46 14zM766 900h4q10 -1 16 -10q96 -129 96 -290q0 -154 -90 -281q-6 -9 -17 -10l-3 -1q-9 0 -16 6l-29 23q-7 7 -8.5 16.5t4.5 17.5q72 103 72 229q0 132 -78 238 q-6 8 -4.5 18.5t9.5 16.5l29 22q7 5 15 5z" />
<glyph unicode="&#xe039;" d="M500 900h100v-100h-100v-100h-400v-100h-100v600h500v-300zM1200 700h-200v-100h200v-200h-300v300h-200v300h-100v200h600v-500zM100 1100v-300h300v300h-300zM800 1100v-300h300v300h-300zM300 900h-100v100h100v-100zM1000 900h-100v100h100v-100zM300 500h200v-500 h-500v500h200v100h100v-100zM800 300h200v-100h-100v-100h-200v100h-100v100h100v200h-200v100h300v-300zM100 400v-300h300v300h-300zM300 200h-100v100h100v-100zM1200 200h-100v100h100v-100zM700 0h-100v100h100v-100zM1200 0h-300v100h300v-100z" />
<glyph unicode="&#xe040;" d="M100 200h-100v1000h100v-1000zM300 200h-100v1000h100v-1000zM700 200h-200v1000h200v-1000zM900 200h-100v1000h100v-1000zM1200 200h-200v1000h200v-1000zM400 0h-300v100h300v-100zM600 0h-100v91h100v-91zM800 0h-100v91h100v-91zM1100 0h-200v91h200v-91z" />
<glyph unicode="&#xe041;" d="M500 1200l682 -682q8 -8 8 -18t-8 -18l-464 -464q-8 -8 -18 -8t-18 8l-682 682l1 475q0 10 7.5 17.5t17.5 7.5h474zM319.5 1024.5q-29.5 29.5 -71 29.5t-71 -29.5t-29.5 -71.5t29.5 -71.5t71 -29.5t71 29.5t29.5 71.5t-29.5 71.5z" />
<glyph unicode="&#xe042;" d="M500 1200l682 -682q8 -8 8 -18t-8 -18l-464 -464q-8 -8 -18 -8t-18 8l-682 682l1 475q0 10 7.5 17.5t17.5 7.5h474zM800 1200l682 -682q8 -8 8 -18t-8 -18l-464 -464q-8 -8 -18 -8t-18 8l-56 56l424 426l-700 700h150zM319.5 1024.5q-29.5 29.5 -71 29.5t-71 -29.5 t-29.5 -71.5t29.5 -71.5t71 -29.5t71 29.5t29.5 71.5t-29.5 71.5z" />
<glyph unicode="&#xe043;" d="M300 1200h825q75 0 75 -75v-900q0 -25 -18 -43l-64 -64q-8 -8 -13 -5.5t-5 12.5v950q0 10 -7.5 17.5t-17.5 7.5h-700q-25 0 -43 -18l-64 -64q-8 -8 -5.5 -13t12.5 -5h700q10 0 17.5 -7.5t7.5 -17.5v-950q0 -10 -7.5 -17.5t-17.5 -7.5h-850q-10 0 -17.5 7.5t-7.5 17.5v975 q0 25 18 43l139 139q18 18 43 18z" />
<glyph unicode="&#xe044;" d="M250 1200h800q21 0 35.5 -14.5t14.5 -35.5v-1150l-450 444l-450 -445v1151q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe045;" d="M822 1200h-444q-11 0 -19 -7.5t-9 -17.5l-78 -301q-7 -24 7 -45l57 -108q6 -9 17.5 -15t21.5 -6h450q10 0 21.5 6t17.5 15l62 108q14 21 7 45l-83 301q-1 10 -9 17.5t-19 7.5zM1175 800h-150q-10 0 -21 -6.5t-15 -15.5l-78 -156q-4 -9 -15 -15.5t-21 -6.5h-550 q-10 0 -21 6.5t-15 15.5l-78 156q-4 9 -15 15.5t-21 6.5h-150q-10 0 -17.5 -7.5t-7.5 -17.5v-650q0 -10 7.5 -17.5t17.5 -7.5h150q10 0 17.5 7.5t7.5 17.5v150q0 10 7.5 17.5t17.5 7.5h750q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 7.5 -17.5t17.5 -7.5h150q10 0 17.5 7.5 t7.5 17.5v650q0 10 -7.5 17.5t-17.5 7.5zM850 200h-500q-10 0 -19.5 -7t-11.5 -17l-38 -152q-2 -10 3.5 -17t15.5 -7h600q10 0 15.5 7t3.5 17l-38 152q-2 10 -11.5 17t-19.5 7z" />
<glyph unicode="&#xe046;" d="M500 1100h200q56 0 102.5 -20.5t72.5 -50t44 -59t25 -50.5l6 -20h150q41 0 70.5 -29.5t29.5 -70.5v-600q0 -41 -29.5 -70.5t-70.5 -29.5h-1000q-41 0 -70.5 29.5t-29.5 70.5v600q0 41 29.5 70.5t70.5 29.5h150q2 8 6.5 21.5t24 48t45 61t72 48t102.5 21.5zM900 800v-100 h100v100h-100zM600 730q-95 0 -162.5 -67.5t-67.5 -162.5t67.5 -162.5t162.5 -67.5t162.5 67.5t67.5 162.5t-67.5 162.5t-162.5 67.5zM600 603q43 0 73 -30t30 -73t-30 -73t-73 -30t-73 30t-30 73t30 73t73 30z" />
<glyph unicode="&#xe047;" d="M681 1199l385 -998q20 -50 60 -92q18 -19 36.5 -29.5t27.5 -11.5l10 -2v-66h-417v66q53 0 75 43.5t5 88.5l-82 222h-391q-58 -145 -92 -234q-11 -34 -6.5 -57t25.5 -37t46 -20t55 -6v-66h-365v66q56 24 84 52q12 12 25 30.5t20 31.5l7 13l399 1006h93zM416 521h340 l-162 457z" />
<glyph unicode="&#xe048;" d="M753 641q5 -1 14.5 -4.5t36 -15.5t50.5 -26.5t53.5 -40t50.5 -54.5t35.5 -70t14.5 -87q0 -67 -27.5 -125.5t-71.5 -97.5t-98.5 -66.5t-108.5 -40.5t-102 -13h-500v89q41 7 70.5 32.5t29.5 65.5v827q0 24 -0.5 34t-3.5 24t-8.5 19.5t-17 13.5t-28 12.5t-42.5 11.5v71 l471 -1q57 0 115.5 -20.5t108 -57t80.5 -94t31 -124.5q0 -51 -15.5 -96.5t-38 -74.5t-45 -50.5t-38.5 -30.5zM400 700h139q78 0 130.5 48.5t52.5 122.5q0 41 -8.5 70.5t-29.5 55.5t-62.5 39.5t-103.5 13.5h-118v-350zM400 200h216q80 0 121 50.5t41 130.5q0 90 -62.5 154.5 t-156.5 64.5h-159v-400z" />
<glyph unicode="&#xe049;" d="M877 1200l2 -57q-83 -19 -116 -45.5t-40 -66.5l-132 -839q-9 -49 13 -69t96 -26v-97h-500v97q186 16 200 98l173 832q3 17 3 30t-1.5 22.5t-9 17.5t-13.5 12.5t-21.5 10t-26 8.5t-33.5 10q-13 3 -19 5v57h425z" />
<glyph unicode="&#xe050;" d="M1300 900h-50q0 21 -4 37t-9.5 26.5t-18 17.5t-22 11t-28.5 5.5t-31 2t-37 0.5h-200v-850q0 -22 25 -34.5t50 -13.5l25 -2v-100h-400v100q4 0 11 0.5t24 3t30 7t24 15t11 24.5v850h-200q-25 0 -37 -0.5t-31 -2t-28.5 -5.5t-22 -11t-18 -17.5t-9.5 -26.5t-4 -37h-50v300 h1000v-300zM175 1000h-75v-800h75l-125 -167l-125 167h75v800h-75l125 167z" />
<glyph unicode="&#xe051;" d="M1100 900h-50q0 21 -4 37t-9.5 26.5t-18 17.5t-22 11t-28.5 5.5t-31 2t-37 0.5h-200v-650q0 -22 25 -34.5t50 -13.5l25 -2v-100h-400v100q4 0 11 0.5t24 3t30 7t24 15t11 24.5v650h-200q-25 0 -37 -0.5t-31 -2t-28.5 -5.5t-22 -11t-18 -17.5t-9.5 -26.5t-4 -37h-50v300 h1000v-300zM1167 50l-167 -125v75h-800v-75l-167 125l167 125v-75h800v75z" />
<glyph unicode="&#xe052;" d="M50 1100h600q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-600q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 800h1000q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1000q-21 0 -35.5 14.5t-14.5 35.5v100 q0 21 14.5 35.5t35.5 14.5zM50 500h800q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 200h1100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100 q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe053;" d="M250 1100h700q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-700q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 800h1100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5v100 q0 21 14.5 35.5t35.5 14.5zM250 500h700q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-700q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 200h1100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100 q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe054;" d="M500 950v100q0 21 14.5 35.5t35.5 14.5h600q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-600q-21 0 -35.5 14.5t-14.5 35.5zM100 650v100q0 21 14.5 35.5t35.5 14.5h1000q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1000 q-21 0 -35.5 14.5t-14.5 35.5zM300 350v100q0 21 14.5 35.5t35.5 14.5h800q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5zM0 50v100q0 21 14.5 35.5t35.5 14.5h1100q21 0 35.5 -14.5t14.5 -35.5v-100 q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5z" />
<glyph unicode="&#xe055;" d="M50 1100h1100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 800h1100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5v100 q0 21 14.5 35.5t35.5 14.5zM50 500h1100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 200h1100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100 q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe056;" d="M50 1100h100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM350 1100h800q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5v100 q0 21 14.5 35.5t35.5 14.5zM50 800h100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM350 800h800q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-800 q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 500h100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM350 500h800q21 0 35.5 -14.5t14.5 -35.5v-100 q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 200h100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM350 200h800 q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe057;" d="M400 0h-100v1100h100v-1100zM550 1100h100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM550 800h500q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-500 q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM267 550l-167 -125v75h-200v100h200v75zM550 500h300q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-300q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM550 200h600 q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-600q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe058;" d="M50 1100h100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM900 0h-100v1100h100v-1100zM50 800h500q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-500 q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM1100 600h200v-100h-200v-75l-167 125l167 125v-75zM50 500h300q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-300q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 200h600 q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-600q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe059;" d="M75 1000h750q31 0 53 -22t22 -53v-650q0 -31 -22 -53t-53 -22h-750q-31 0 -53 22t-22 53v650q0 31 22 53t53 22zM1200 300l-300 300l300 300v-600z" />
<glyph unicode="&#xe060;" d="M44 1100h1112q18 0 31 -13t13 -31v-1012q0 -18 -13 -31t-31 -13h-1112q-18 0 -31 13t-13 31v1012q0 18 13 31t31 13zM100 1000v-737l247 182l298 -131l-74 156l293 318l236 -288v500h-1000zM342 884q56 0 95 -39t39 -94.5t-39 -95t-95 -39.5t-95 39.5t-39 95t39 94.5 t95 39z" />
<glyph unicode="&#xe062;" d="M648 1169q117 0 216 -60t156.5 -161t57.5 -218q0 -115 -70 -258q-69 -109 -158 -225.5t-143 -179.5l-54 -62q-9 8 -25.5 24.5t-63.5 67.5t-91 103t-98.5 128t-95.5 148q-60 132 -60 249q0 88 34 169.5t91.5 142t137 96.5t166.5 36zM652.5 974q-91.5 0 -156.5 -65 t-65 -157t65 -156.5t156.5 -64.5t156.5 64.5t65 156.5t-65 157t-156.5 65z" />
<glyph unicode="&#xe063;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 173v854q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5 t155.5 -155.5t214.5 -57z" />
<glyph unicode="&#xe064;" d="M554 1295q21 -72 57.5 -143.5t76 -130t83 -118t82.5 -117t70 -116t49.5 -126t18.5 -136.5q0 -71 -25.5 -135t-68.5 -111t-99 -82t-118.5 -54t-125.5 -23q-84 5 -161.5 34t-139.5 78.5t-99 125t-37 164.5q0 69 18 136.5t49.5 126.5t69.5 116.5t81.5 117.5t83.5 119 t76.5 131t58.5 143zM344 710q-23 -33 -43.5 -70.5t-40.5 -102.5t-17 -123q1 -37 14.5 -69.5t30 -52t41 -37t38.5 -24.5t33 -15q21 -7 32 -1t13 22l6 34q2 10 -2.5 22t-13.5 19q-5 4 -14 12t-29.5 40.5t-32.5 73.5q-26 89 6 271q2 11 -6 11q-8 1 -15 -10z" />
<glyph unicode="&#xe065;" d="M1000 1013l108 115q2 1 5 2t13 2t20.5 -1t25 -9.5t28.5 -21.5q22 -22 27 -43t0 -32l-6 -10l-108 -115zM350 1100h400q50 0 105 -13l-187 -187h-368q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5v182l200 200v-332 q0 -165 -93.5 -257.5t-256.5 -92.5h-400q-165 0 -257.5 92.5t-92.5 257.5v400q0 165 92.5 257.5t257.5 92.5zM1009 803l-362 -362l-161 -50l55 170l355 355z" />
<glyph unicode="&#xe066;" d="M350 1100h361q-164 -146 -216 -200h-195q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5l200 153v-103q0 -165 -92.5 -257.5t-257.5 -92.5h-400q-165 0 -257.5 92.5t-92.5 257.5v400q0 165 92.5 257.5t257.5 92.5z M824 1073l339 -301q8 -7 8 -17.5t-8 -17.5l-340 -306q-7 -6 -12.5 -4t-6.5 11v203q-26 1 -54.5 0t-78.5 -7.5t-92 -17.5t-86 -35t-70 -57q10 59 33 108t51.5 81.5t65 58.5t68.5 40.5t67 24.5t56 13.5t40 4.5v210q1 10 6.5 12.5t13.5 -4.5z" />
<glyph unicode="&#xe067;" d="M350 1100h350q60 0 127 -23l-178 -177h-349q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5v69l200 200v-219q0 -165 -92.5 -257.5t-257.5 -92.5h-400q-165 0 -257.5 92.5t-92.5 257.5v400q0 165 92.5 257.5t257.5 92.5z M643 639l395 395q7 7 17.5 7t17.5 -7l101 -101q7 -7 7 -17.5t-7 -17.5l-531 -532q-7 -7 -17.5 -7t-17.5 7l-248 248q-7 7 -7 17.5t7 17.5l101 101q7 7 17.5 7t17.5 -7l111 -111q8 -7 18 -7t18 7z" />
<glyph unicode="&#xe068;" d="M318 918l264 264q8 8 18 8t18 -8l260 -264q7 -8 4.5 -13t-12.5 -5h-170v-200h200v173q0 10 5 12t13 -5l264 -260q8 -7 8 -17.5t-8 -17.5l-264 -265q-8 -7 -13 -5t-5 12v173h-200v-200h170q10 0 12.5 -5t-4.5 -13l-260 -264q-8 -8 -18 -8t-18 8l-264 264q-8 8 -5.5 13 t12.5 5h175v200h-200v-173q0 -10 -5 -12t-13 5l-264 265q-8 7 -8 17.5t8 17.5l264 260q8 7 13 5t5 -12v-173h200v200h-175q-10 0 -12.5 5t5.5 13z" />
<glyph unicode="&#xe069;" d="M250 1100h100q21 0 35.5 -14.5t14.5 -35.5v-438l464 453q15 14 25.5 10t10.5 -25v-1000q0 -21 -10.5 -25t-25.5 10l-464 453v-438q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v1000q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe070;" d="M50 1100h100q21 0 35.5 -14.5t14.5 -35.5v-438l464 453q15 14 25.5 10t10.5 -25v-438l464 453q15 14 25.5 10t10.5 -25v-1000q0 -21 -10.5 -25t-25.5 10l-464 453v-438q0 -21 -10.5 -25t-25.5 10l-464 453v-438q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5 t-14.5 35.5v1000q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe071;" d="M1200 1050v-1000q0 -21 -10.5 -25t-25.5 10l-464 453v-438q0 -21 -10.5 -25t-25.5 10l-492 480q-15 14 -15 35t15 35l492 480q15 14 25.5 10t10.5 -25v-438l464 453q15 14 25.5 10t10.5 -25z" />
<glyph unicode="&#xe072;" d="M243 1074l814 -498q18 -11 18 -26t-18 -26l-814 -498q-18 -11 -30.5 -4t-12.5 28v1000q0 21 12.5 28t30.5 -4z" />
<glyph unicode="&#xe073;" d="M250 1000h200q21 0 35.5 -14.5t14.5 -35.5v-800q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v800q0 21 14.5 35.5t35.5 14.5zM650 1000h200q21 0 35.5 -14.5t14.5 -35.5v-800q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v800 q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe074;" d="M1100 950v-800q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5v800q0 21 14.5 35.5t35.5 14.5h800q21 0 35.5 -14.5t14.5 -35.5z" />
<glyph unicode="&#xe075;" d="M500 612v438q0 21 10.5 25t25.5 -10l492 -480q15 -14 15 -35t-15 -35l-492 -480q-15 -14 -25.5 -10t-10.5 25v438l-464 -453q-15 -14 -25.5 -10t-10.5 25v1000q0 21 10.5 25t25.5 -10z" />
<glyph unicode="&#xe076;" d="M1048 1102l100 1q20 0 35 -14.5t15 -35.5l5 -1000q0 -21 -14.5 -35.5t-35.5 -14.5l-100 -1q-21 0 -35.5 14.5t-14.5 35.5l-2 437l-463 -454q-14 -15 -24.5 -10.5t-10.5 25.5l-2 437l-462 -455q-15 -14 -25.5 -9.5t-10.5 24.5l-5 1000q0 21 10.5 25.5t25.5 -10.5l466 -450 l-2 438q0 20 10.5 24.5t25.5 -9.5l466 -451l-2 438q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe077;" d="M850 1100h100q21 0 35.5 -14.5t14.5 -35.5v-1000q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v438l-464 -453q-15 -14 -25.5 -10t-10.5 25v1000q0 21 10.5 25t25.5 -10l464 -453v438q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe078;" d="M686 1081l501 -540q15 -15 10.5 -26t-26.5 -11h-1042q-22 0 -26.5 11t10.5 26l501 540q15 15 36 15t36 -15zM150 400h1000q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1000q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe079;" d="M885 900l-352 -353l352 -353l-197 -198l-552 552l552 550z" />
<glyph unicode="&#xe080;" d="M1064 547l-551 -551l-198 198l353 353l-353 353l198 198z" />
<glyph unicode="&#xe081;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM650 900h-100q-21 0 -35.5 -14.5t-14.5 -35.5v-150h-150 q-21 0 -35.5 -14.5t-14.5 -35.5v-100q0 -21 14.5 -35.5t35.5 -14.5h150v-150q0 -21 14.5 -35.5t35.5 -14.5h100q21 0 35.5 14.5t14.5 35.5v150h150q21 0 35.5 14.5t14.5 35.5v100q0 21 -14.5 35.5t-35.5 14.5h-150v150q0 21 -14.5 35.5t-35.5 14.5z" />
<glyph unicode="&#xe082;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM850 700h-500q-21 0 -35.5 -14.5t-14.5 -35.5v-100q0 -21 14.5 -35.5 t35.5 -14.5h500q21 0 35.5 14.5t14.5 35.5v100q0 21 -14.5 35.5t-35.5 14.5z" />
<glyph unicode="&#xe083;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM741.5 913q-12.5 0 -21.5 -9l-120 -120l-120 120q-9 9 -21.5 9 t-21.5 -9l-141 -141q-9 -9 -9 -21.5t9 -21.5l120 -120l-120 -120q-9 -9 -9 -21.5t9 -21.5l141 -141q9 -9 21.5 -9t21.5 9l120 120l120 -120q9 -9 21.5 -9t21.5 9l141 141q9 9 9 21.5t-9 21.5l-120 120l120 120q9 9 9 21.5t-9 21.5l-141 141q-9 9 -21.5 9z" />
<glyph unicode="&#xe084;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM546 623l-84 85q-7 7 -17.5 7t-18.5 -7l-139 -139q-7 -8 -7 -18t7 -18 l242 -241q7 -8 17.5 -8t17.5 8l375 375q7 7 7 17.5t-7 18.5l-139 139q-7 7 -17.5 7t-17.5 -7z" />
<glyph unicode="&#xe085;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM588 941q-29 0 -59 -5.5t-63 -20.5t-58 -38.5t-41.5 -63t-16.5 -89.5 q0 -25 20 -25h131q30 -5 35 11q6 20 20.5 28t45.5 8q20 0 31.5 -10.5t11.5 -28.5q0 -23 -7 -34t-26 -18q-1 0 -13.5 -4t-19.5 -7.5t-20 -10.5t-22 -17t-18.5 -24t-15.5 -35t-8 -46q-1 -8 5.5 -16.5t20.5 -8.5h173q7 0 22 8t35 28t37.5 48t29.5 74t12 100q0 47 -17 83 t-42.5 57t-59.5 34.5t-64 18t-59 4.5zM675 400h-150q-10 0 -17.5 -7.5t-7.5 -17.5v-150q0 -10 7.5 -17.5t17.5 -7.5h150q10 0 17.5 7.5t7.5 17.5v150q0 10 -7.5 17.5t-17.5 7.5z" />
<glyph unicode="&#xe086;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM675 1000h-150q-10 0 -17.5 -7.5t-7.5 -17.5v-150q0 -10 7.5 -17.5 t17.5 -7.5h150q10 0 17.5 7.5t7.5 17.5v150q0 10 -7.5 17.5t-17.5 7.5zM675 700h-250q-10 0 -17.5 -7.5t-7.5 -17.5v-50q0 -10 7.5 -17.5t17.5 -7.5h75v-200h-75q-10 0 -17.5 -7.5t-7.5 -17.5v-50q0 -10 7.5 -17.5t17.5 -7.5h350q10 0 17.5 7.5t7.5 17.5v50q0 10 -7.5 17.5 t-17.5 7.5h-75v275q0 10 -7.5 17.5t-17.5 7.5z" />
<glyph unicode="&#xe087;" d="M525 1200h150q10 0 17.5 -7.5t7.5 -17.5v-194q103 -27 178.5 -102.5t102.5 -178.5h194q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-194q-27 -103 -102.5 -178.5t-178.5 -102.5v-194q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v194 q-103 27 -178.5 102.5t-102.5 178.5h-194q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5h194q27 103 102.5 178.5t178.5 102.5v194q0 10 7.5 17.5t17.5 7.5zM700 893v-168q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v168q-68 -23 -119 -74 t-74 -119h168q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-168q23 -68 74 -119t119 -74v168q0 10 7.5 17.5t17.5 7.5h150q10 0 17.5 -7.5t7.5 -17.5v-168q68 23 119 74t74 119h-168q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5h168 q-23 68 -74 119t-119 74z" />
<glyph unicode="&#xe088;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 1027q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5 t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5t-57 214.5t-155.5 155.5t-214.5 57zM759 823l64 -64q7 -7 7 -17.5t-7 -17.5l-124 -124l124 -124q7 -7 7 -17.5t-7 -17.5l-64 -64q-7 -7 -17.5 -7t-17.5 7l-124 124l-124 -124q-7 -7 -17.5 -7t-17.5 7l-64 64 q-7 7 -7 17.5t7 17.5l124 124l-124 124q-7 7 -7 17.5t7 17.5l64 64q7 7 17.5 7t17.5 -7l124 -124l124 124q7 7 17.5 7t17.5 -7z" />
<glyph unicode="&#xe089;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 1027q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5 t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5t-57 214.5t-155.5 155.5t-214.5 57zM782 788l106 -106q7 -7 7 -17.5t-7 -17.5l-320 -321q-8 -7 -18 -7t-18 7l-202 203q-8 7 -8 17.5t8 17.5l106 106q7 8 17.5 8t17.5 -8l79 -79l197 197q7 7 17.5 7t17.5 -7z" />
<glyph unicode="&#xe090;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 1027q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5q0 -120 65 -225 l587 587q-105 65 -225 65zM965 819l-584 -584q104 -62 219 -62q116 0 214.5 57t155.5 155.5t57 214.5q0 115 -62 219z" />
<glyph unicode="&#xe091;" d="M39 582l522 427q16 13 27.5 8t11.5 -26v-291h550q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-550v-291q0 -21 -11.5 -26t-27.5 8l-522 427q-16 13 -16 32t16 32z" />
<glyph unicode="&#xe092;" d="M639 1009l522 -427q16 -13 16 -32t-16 -32l-522 -427q-16 -13 -27.5 -8t-11.5 26v291h-550q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5h550v291q0 21 11.5 26t27.5 -8z" />
<glyph unicode="&#xe093;" d="M682 1161l427 -522q13 -16 8 -27.5t-26 -11.5h-291v-550q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v550h-291q-21 0 -26 11.5t8 27.5l427 522q13 16 32 16t32 -16z" />
<glyph unicode="&#xe094;" d="M550 1200h200q21 0 35.5 -14.5t14.5 -35.5v-550h291q21 0 26 -11.5t-8 -27.5l-427 -522q-13 -16 -32 -16t-32 16l-427 522q-13 16 -8 27.5t26 11.5h291v550q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe095;" d="M639 1109l522 -427q16 -13 16 -32t-16 -32l-522 -427q-16 -13 -27.5 -8t-11.5 26v291q-94 -2 -182 -20t-170.5 -52t-147 -92.5t-100.5 -135.5q5 105 27 193.5t67.5 167t113 135t167 91.5t225.5 42v262q0 21 11.5 26t27.5 -8z" />
<glyph unicode="&#xe096;" d="M850 1200h300q21 0 35.5 -14.5t14.5 -35.5v-300q0 -21 -10.5 -25t-24.5 10l-94 94l-249 -249q-8 -7 -18 -7t-18 7l-106 106q-7 8 -7 18t7 18l249 249l-94 94q-14 14 -10 24.5t25 10.5zM350 0h-300q-21 0 -35.5 14.5t-14.5 35.5v300q0 21 10.5 25t24.5 -10l94 -94l249 249 q8 7 18 7t18 -7l106 -106q7 -8 7 -18t-7 -18l-249 -249l94 -94q14 -14 10 -24.5t-25 -10.5z" />
<glyph unicode="&#xe097;" d="M1014 1120l106 -106q7 -8 7 -18t-7 -18l-249 -249l94 -94q14 -14 10 -24.5t-25 -10.5h-300q-21 0 -35.5 14.5t-14.5 35.5v300q0 21 10.5 25t24.5 -10l94 -94l249 249q8 7 18 7t18 -7zM250 600h300q21 0 35.5 -14.5t14.5 -35.5v-300q0 -21 -10.5 -25t-24.5 10l-94 94 l-249 -249q-8 -7 -18 -7t-18 7l-106 106q-7 8 -7 18t7 18l249 249l-94 94q-14 14 -10 24.5t25 10.5z" />
<glyph unicode="&#xe101;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM704 900h-208q-20 0 -32 -14.5t-8 -34.5l58 -302q4 -20 21.5 -34.5 t37.5 -14.5h54q20 0 37.5 14.5t21.5 34.5l58 302q4 20 -8 34.5t-32 14.5zM675 400h-150q-10 0 -17.5 -7.5t-7.5 -17.5v-150q0 -10 7.5 -17.5t17.5 -7.5h150q10 0 17.5 7.5t7.5 17.5v150q0 10 -7.5 17.5t-17.5 7.5z" />
<glyph unicode="&#xe102;" d="M260 1200q9 0 19 -2t15 -4l5 -2q22 -10 44 -23l196 -118q21 -13 36 -24q29 -21 37 -12q11 13 49 35l196 118q22 13 45 23q17 7 38 7q23 0 47 -16.5t37 -33.5l13 -16q14 -21 18 -45l25 -123l8 -44q1 -9 8.5 -14.5t17.5 -5.5h61q10 0 17.5 -7.5t7.5 -17.5v-50 q0 -10 -7.5 -17.5t-17.5 -7.5h-50q-10 0 -17.5 -7.5t-7.5 -17.5v-175h-400v300h-200v-300h-400v175q0 10 -7.5 17.5t-17.5 7.5h-50q-10 0 -17.5 7.5t-7.5 17.5v50q0 10 7.5 17.5t17.5 7.5h61q11 0 18 3t7 8q0 4 9 52l25 128q5 25 19 45q2 3 5 7t13.5 15t21.5 19.5t26.5 15.5 t29.5 7zM915 1079l-166 -162q-7 -7 -5 -12t12 -5h219q10 0 15 7t2 17l-51 149q-3 10 -11 12t-15 -6zM463 917l-177 157q-8 7 -16 5t-11 -12l-51 -143q-3 -10 2 -17t15 -7h231q11 0 12.5 5t-5.5 12zM500 0h-375q-10 0 -17.5 7.5t-7.5 17.5v375h400v-400zM1100 400v-375 q0 -10 -7.5 -17.5t-17.5 -7.5h-375v400h400z" />
<glyph unicode="&#xe103;" d="M1165 1190q8 3 21 -6.5t13 -17.5q-2 -178 -24.5 -323.5t-55.5 -245.5t-87 -174.5t-102.5 -118.5t-118 -68.5t-118.5 -33t-120 -4.5t-105 9.5t-90 16.5q-61 12 -78 11q-4 1 -12.5 0t-34 -14.5t-52.5 -40.5l-153 -153q-26 -24 -37 -14.5t-11 43.5q0 64 42 102q8 8 50.5 45 t66.5 58q19 17 35 47t13 61q-9 55 -10 102.5t7 111t37 130t78 129.5q39 51 80 88t89.5 63.5t94.5 45t113.5 36t129 31t157.5 37t182 47.5zM1116 1098q-8 9 -22.5 -3t-45.5 -50q-38 -47 -119 -103.5t-142 -89.5l-62 -33q-56 -30 -102 -57t-104 -68t-102.5 -80.5t-85.5 -91 t-64 -104.5q-24 -56 -31 -86t2 -32t31.5 17.5t55.5 59.5q25 30 94 75.5t125.5 77.5t147.5 81q70 37 118.5 69t102 79.5t99 111t86.5 148.5q22 50 24 60t-6 19z" />
<glyph unicode="&#xe104;" d="M653 1231q-39 -67 -54.5 -131t-10.5 -114.5t24.5 -96.5t47.5 -80t63.5 -62.5t68.5 -46.5t65 -30q-4 7 -17.5 35t-18.5 39.5t-17 39.5t-17 43t-13 42t-9.5 44.5t-2 42t4 43t13.5 39t23 38.5q96 -42 165 -107.5t105 -138t52 -156t13 -159t-19 -149.5q-13 -55 -44 -106.5 t-68 -87t-78.5 -64.5t-72.5 -45t-53 -22q-72 -22 -127 -11q-31 6 -13 19q6 3 17 7q13 5 32.5 21t41 44t38.5 63.5t21.5 81.5t-6.5 94.5t-50 107t-104 115.5q10 -104 -0.5 -189t-37 -140.5t-65 -93t-84 -52t-93.5 -11t-95 24.5q-80 36 -131.5 114t-53.5 171q-2 23 0 49.5 t4.5 52.5t13.5 56t27.5 60t46 64.5t69.5 68.5q-8 -53 -5 -102.5t17.5 -90t34 -68.5t44.5 -39t49 -2q31 13 38.5 36t-4.5 55t-29 64.5t-36 75t-26 75.5q-15 85 2 161.5t53.5 128.5t85.5 92.5t93.5 61t81.5 25.5z" />
<glyph unicode="&#xe105;" d="M600 1094q82 0 160.5 -22.5t140 -59t116.5 -82.5t94.5 -95t68 -95t42.5 -82.5t14 -57.5t-14 -57.5t-43 -82.5t-68.5 -95t-94.5 -95t-116.5 -82.5t-140 -59t-159.5 -22.5t-159.5 22.5t-140 59t-116.5 82.5t-94.5 95t-68.5 95t-43 82.5t-14 57.5t14 57.5t42.5 82.5t68 95 t94.5 95t116.5 82.5t140 59t160.5 22.5zM888 829q-15 15 -18 12t5 -22q25 -57 25 -119q0 -124 -88 -212t-212 -88t-212 88t-88 212q0 59 23 114q8 19 4.5 22t-17.5 -12q-70 -69 -160 -184q-13 -16 -15 -40.5t9 -42.5q22 -36 47 -71t70 -82t92.5 -81t113 -58.5t133.5 -24.5 t133.5 24t113 58.5t92.5 81.5t70 81.5t47 70.5q11 18 9 42.5t-14 41.5q-90 117 -163 189zM448 727l-35 -36q-15 -15 -19.5 -38.5t4.5 -41.5q37 -68 93 -116q16 -13 38.5 -11t36.5 17l35 34q14 15 12.5 33.5t-16.5 33.5q-44 44 -89 117q-11 18 -28 20t-32 -12z" />
<glyph unicode="&#xe106;" d="M592 0h-148l31 120q-91 20 -175.5 68.5t-143.5 106.5t-103.5 119t-66.5 110t-22 76q0 21 14 57.5t42.5 82.5t68 95t94.5 95t116.5 82.5t140 59t160.5 22.5q61 0 126 -15l32 121h148zM944 770l47 181q108 -85 176.5 -192t68.5 -159q0 -26 -19.5 -71t-59.5 -102t-93 -112 t-129 -104.5t-158 -75.5l46 173q77 49 136 117t97 131q11 18 9 42.5t-14 41.5q-54 70 -107 130zM310 824q-70 -69 -160 -184q-13 -16 -15 -40.5t9 -42.5q18 -30 39 -60t57 -70.5t74 -73t90 -61t105 -41.5l41 154q-107 18 -178.5 101.5t-71.5 193.5q0 59 23 114q8 19 4.5 22 t-17.5 -12zM448 727l-35 -36q-15 -15 -19.5 -38.5t4.5 -41.5q37 -68 93 -116q16 -13 38.5 -11t36.5 17l12 11l22 86l-3 4q-44 44 -89 117q-11 18 -28 20t-32 -12z" />
<glyph unicode="&#xe107;" d="M-90 100l642 1066q20 31 48 28.5t48 -35.5l642 -1056q21 -32 7.5 -67.5t-50.5 -35.5h-1294q-37 0 -50.5 34t7.5 66zM155 200h345v75q0 10 7.5 17.5t17.5 7.5h150q10 0 17.5 -7.5t7.5 -17.5v-75h345l-445 723zM496 700h208q20 0 32 -14.5t8 -34.5l-58 -252 q-4 -20 -21.5 -34.5t-37.5 -14.5h-54q-20 0 -37.5 14.5t-21.5 34.5l-58 252q-4 20 8 34.5t32 14.5z" />
<glyph unicode="&#xe108;" d="M650 1200q62 0 106 -44t44 -106v-339l363 -325q15 -14 26 -38.5t11 -44.5v-41q0 -20 -12 -26.5t-29 5.5l-359 249v-263q100 -93 100 -113v-64q0 -21 -13 -29t-32 1l-205 128l-205 -128q-19 -9 -32 -1t-13 29v64q0 20 100 113v263l-359 -249q-17 -12 -29 -5.5t-12 26.5v41 q0 20 11 44.5t26 38.5l363 325v339q0 62 44 106t106 44z" />
<glyph unicode="&#xe109;" d="M850 1200h100q21 0 35.5 -14.5t14.5 -35.5v-50h50q21 0 35.5 -14.5t14.5 -35.5v-150h-1100v150q0 21 14.5 35.5t35.5 14.5h50v50q0 21 14.5 35.5t35.5 14.5h100q21 0 35.5 -14.5t14.5 -35.5v-50h500v50q0 21 14.5 35.5t35.5 14.5zM1100 800v-750q0 -21 -14.5 -35.5 t-35.5 -14.5h-1000q-21 0 -35.5 14.5t-14.5 35.5v750h1100zM100 600v-100h100v100h-100zM300 600v-100h100v100h-100zM500 600v-100h100v100h-100zM700 600v-100h100v100h-100zM900 600v-100h100v100h-100zM100 400v-100h100v100h-100zM300 400v-100h100v100h-100zM500 400 v-100h100v100h-100zM700 400v-100h100v100h-100zM900 400v-100h100v100h-100zM100 200v-100h100v100h-100zM300 200v-100h100v100h-100zM500 200v-100h100v100h-100zM700 200v-100h100v100h-100zM900 200v-100h100v100h-100z" />
<glyph unicode="&#xe110;" d="M1135 1165l249 -230q15 -14 15 -35t-15 -35l-249 -230q-14 -14 -24.5 -10t-10.5 25v150h-159l-600 -600h-291q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5h209l600 600h241v150q0 21 10.5 25t24.5 -10zM522 819l-141 -141l-122 122h-209q-21 0 -35.5 14.5 t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5h291zM1135 565l249 -230q15 -14 15 -35t-15 -35l-249 -230q-14 -14 -24.5 -10t-10.5 25v150h-241l-181 181l141 141l122 -122h159v150q0 21 10.5 25t24.5 -10z" />
<glyph unicode="&#xe111;" d="M100 1100h1000q41 0 70.5 -29.5t29.5 -70.5v-600q0 -41 -29.5 -70.5t-70.5 -29.5h-596l-304 -300v300h-100q-41 0 -70.5 29.5t-29.5 70.5v600q0 41 29.5 70.5t70.5 29.5z" />
<glyph unicode="&#xe112;" d="M150 1200h200q21 0 35.5 -14.5t14.5 -35.5v-250h-300v250q0 21 14.5 35.5t35.5 14.5zM850 1200h200q21 0 35.5 -14.5t14.5 -35.5v-250h-300v250q0 21 14.5 35.5t35.5 14.5zM1100 800v-300q0 -41 -3 -77.5t-15 -89.5t-32 -96t-58 -89t-89 -77t-129 -51t-174 -20t-174 20 t-129 51t-89 77t-58 89t-32 96t-15 89.5t-3 77.5v300h300v-250v-27v-42.5t1.5 -41t5 -38t10 -35t16.5 -30t25.5 -24.5t35 -19t46.5 -12t60 -4t60 4.5t46.5 12.5t35 19.5t25 25.5t17 30.5t10 35t5 38t2 40.5t-0.5 42v25v250h300z" />
<glyph unicode="&#xe113;" d="M1100 411l-198 -199l-353 353l-353 -353l-197 199l551 551z" />
<glyph unicode="&#xe114;" d="M1101 789l-550 -551l-551 551l198 199l353 -353l353 353z" />
<glyph unicode="&#xe115;" d="M404 1000h746q21 0 35.5 -14.5t14.5 -35.5v-551h150q21 0 25 -10.5t-10 -24.5l-230 -249q-14 -15 -35 -15t-35 15l-230 249q-14 14 -10 24.5t25 10.5h150v401h-381zM135 984l230 -249q14 -14 10 -24.5t-25 -10.5h-150v-400h385l215 -200h-750q-21 0 -35.5 14.5 t-14.5 35.5v550h-150q-21 0 -25 10.5t10 24.5l230 249q14 15 35 15t35 -15z" />
<glyph unicode="&#xe116;" d="M56 1200h94q17 0 31 -11t18 -27l38 -162h896q24 0 39 -18.5t10 -42.5l-100 -475q-5 -21 -27 -42.5t-55 -21.5h-633l48 -200h535q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-50v-50q0 -21 -14.5 -35.5t-35.5 -14.5t-35.5 14.5t-14.5 35.5v50h-300v-50 q0 -21 -14.5 -35.5t-35.5 -14.5t-35.5 14.5t-14.5 35.5v50h-31q-18 0 -32.5 10t-20.5 19l-5 10l-201 961h-54q-20 0 -35 14.5t-15 35.5t15 35.5t35 14.5z" />
<glyph unicode="&#xe117;" d="M1200 1000v-100h-1200v100h200q0 41 29.5 70.5t70.5 29.5h300q41 0 70.5 -29.5t29.5 -70.5h500zM0 800h1200v-800h-1200v800z" />
<glyph unicode="&#xe118;" d="M200 800l-200 -400v600h200q0 41 29.5 70.5t70.5 29.5h300q42 0 71 -29.5t29 -70.5h500v-200h-1000zM1500 700l-300 -700h-1200l300 700h1200z" />
<glyph unicode="&#xe119;" d="M635 1184l230 -249q14 -14 10 -24.5t-25 -10.5h-150v-601h150q21 0 25 -10.5t-10 -24.5l-230 -249q-14 -15 -35 -15t-35 15l-230 249q-14 14 -10 24.5t25 10.5h150v601h-150q-21 0 -25 10.5t10 24.5l230 249q14 15 35 15t35 -15z" />
<glyph unicode="&#xe120;" d="M936 864l249 -229q14 -15 14 -35.5t-14 -35.5l-249 -229q-15 -15 -25.5 -10.5t-10.5 24.5v151h-600v-151q0 -20 -10.5 -24.5t-25.5 10.5l-249 229q-14 15 -14 35.5t14 35.5l249 229q15 15 25.5 10.5t10.5 -25.5v-149h600v149q0 21 10.5 25.5t25.5 -10.5z" />
<glyph unicode="&#xe121;" d="M1169 400l-172 732q-5 23 -23 45.5t-38 22.5h-672q-20 0 -38 -20t-23 -41l-172 -739h1138zM1100 300h-1000q-41 0 -70.5 -29.5t-29.5 -70.5v-100q0 -41 29.5 -70.5t70.5 -29.5h1000q41 0 70.5 29.5t29.5 70.5v100q0 41 -29.5 70.5t-70.5 29.5zM800 100v100h100v-100h-100 zM1000 100v100h100v-100h-100z" />
<glyph unicode="&#xe122;" d="M1150 1100q21 0 35.5 -14.5t14.5 -35.5v-850q0 -21 -14.5 -35.5t-35.5 -14.5t-35.5 14.5t-14.5 35.5v850q0 21 14.5 35.5t35.5 14.5zM1000 200l-675 200h-38l47 -276q3 -16 -5.5 -20t-29.5 -4h-7h-84q-20 0 -34.5 14t-18.5 35q-55 337 -55 351v250v6q0 16 1 23.5t6.5 14 t17.5 6.5h200l675 250v-850zM0 750v-250q-4 0 -11 0.5t-24 6t-30 15t-24 30t-11 48.5v50q0 26 10.5 46t25 30t29 16t25.5 7z" />
<glyph unicode="&#xe123;" d="M553 1200h94q20 0 29 -10.5t3 -29.5l-18 -37q83 -19 144 -82.5t76 -140.5l63 -327l118 -173h17q19 0 33 -14.5t14 -35t-13 -40.5t-31 -27q-8 -4 -23 -9.5t-65 -19.5t-103 -25t-132.5 -20t-158.5 -9q-57 0 -115 5t-104 12t-88.5 15.5t-73.5 17.5t-54.5 16t-35.5 12l-11 4 q-18 8 -31 28t-13 40.5t14 35t33 14.5h17l118 173l63 327q15 77 76 140t144 83l-18 32q-6 19 3.5 32t28.5 13zM498 110q50 -6 102 -6q53 0 102 6q-12 -49 -39.5 -79.5t-62.5 -30.5t-63 30.5t-39 79.5z" />
<glyph unicode="&#xe124;" d="M800 946l224 78l-78 -224l234 -45l-180 -155l180 -155l-234 -45l78 -224l-224 78l-45 -234l-155 180l-155 -180l-45 234l-224 -78l78 224l-234 45l180 155l-180 155l234 45l-78 224l224 -78l45 234l155 -180l155 180z" />
<glyph unicode="&#xe125;" d="M650 1200h50q40 0 70 -40.5t30 -84.5v-150l-28 -125h328q40 0 70 -40.5t30 -84.5v-100q0 -45 -29 -74l-238 -344q-16 -24 -38 -40.5t-45 -16.5h-250q-7 0 -42 25t-66 50l-31 25h-61q-45 0 -72.5 18t-27.5 57v400q0 36 20 63l145 196l96 198q13 28 37.5 48t51.5 20z M650 1100l-100 -212l-150 -213v-375h100l136 -100h214l250 375v125h-450l50 225v175h-50zM50 800h100q21 0 35.5 -14.5t14.5 -35.5v-500q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v500q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe126;" d="M600 1100h250q23 0 45 -16.5t38 -40.5l238 -344q29 -29 29 -74v-100q0 -44 -30 -84.5t-70 -40.5h-328q28 -118 28 -125v-150q0 -44 -30 -84.5t-70 -40.5h-50q-27 0 -51.5 20t-37.5 48l-96 198l-145 196q-20 27 -20 63v400q0 39 27.5 57t72.5 18h61q124 100 139 100z M50 1000h100q21 0 35.5 -14.5t14.5 -35.5v-500q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v500q0 21 14.5 35.5t35.5 14.5zM636 1000l-136 -100h-100v-375l150 -213l100 -212h50v175l-50 225h450v125l-250 375h-214z" />
<glyph unicode="&#xe127;" d="M356 873l363 230q31 16 53 -6l110 -112q13 -13 13.5 -32t-11.5 -34l-84 -121h302q84 0 138 -38t54 -110t-55 -111t-139 -39h-106l-131 -339q-6 -21 -19.5 -41t-28.5 -20h-342q-7 0 -90 81t-83 94v525q0 17 14 35.5t28 28.5zM400 792v-503l100 -89h293l131 339 q6 21 19.5 41t28.5 20h203q21 0 30.5 25t0.5 50t-31 25h-456h-7h-6h-5.5t-6 0.5t-5 1.5t-5 2t-4 2.5t-4 4t-2.5 4.5q-12 25 5 47l146 183l-86 83zM50 800h100q21 0 35.5 -14.5t14.5 -35.5v-500q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v500 q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe128;" d="M475 1103l366 -230q2 -1 6 -3.5t14 -10.5t18 -16.5t14.5 -20t6.5 -22.5v-525q0 -13 -86 -94t-93 -81h-342q-15 0 -28.5 20t-19.5 41l-131 339h-106q-85 0 -139.5 39t-54.5 111t54 110t138 38h302l-85 121q-11 15 -10.5 34t13.5 32l110 112q22 22 53 6zM370 945l146 -183 q17 -22 5 -47q-2 -2 -3.5 -4.5t-4 -4t-4 -2.5t-5 -2t-5 -1.5t-6 -0.5h-6h-6.5h-6h-475v-100h221q15 0 29 -20t20 -41l130 -339h294l106 89v503l-342 236zM1050 800h100q21 0 35.5 -14.5t14.5 -35.5v-500q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5 v500q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe129;" d="M550 1294q72 0 111 -55t39 -139v-106l339 -131q21 -6 41 -19.5t20 -28.5v-342q0 -7 -81 -90t-94 -83h-525q-17 0 -35.5 14t-28.5 28l-9 14l-230 363q-16 31 6 53l112 110q13 13 32 13.5t34 -11.5l121 -84v302q0 84 38 138t110 54zM600 972v203q0 21 -25 30.5t-50 0.5 t-25 -31v-456v-7v-6v-5.5t-0.5 -6t-1.5 -5t-2 -5t-2.5 -4t-4 -4t-4.5 -2.5q-25 -12 -47 5l-183 146l-83 -86l236 -339h503l89 100v293l-339 131q-21 6 -41 19.5t-20 28.5zM450 200h500q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-500 q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe130;" d="M350 1100h500q21 0 35.5 14.5t14.5 35.5v100q0 21 -14.5 35.5t-35.5 14.5h-500q-21 0 -35.5 -14.5t-14.5 -35.5v-100q0 -21 14.5 -35.5t35.5 -14.5zM600 306v-106q0 -84 -39 -139t-111 -55t-110 54t-38 138v302l-121 -84q-15 -12 -34 -11.5t-32 13.5l-112 110 q-22 22 -6 53l230 363q1 2 3.5 6t10.5 13.5t16.5 17t20 13.5t22.5 6h525q13 0 94 -83t81 -90v-342q0 -15 -20 -28.5t-41 -19.5zM308 900l-236 -339l83 -86l183 146q22 17 47 5q2 -1 4.5 -2.5t4 -4t2.5 -4t2 -5t1.5 -5t0.5 -6v-5.5v-6v-7v-456q0 -22 25 -31t50 0.5t25 30.5 v203q0 15 20 28.5t41 19.5l339 131v293l-89 100h-503z" />
<glyph unicode="&#xe131;" d="M600 1178q118 0 225 -45.5t184.5 -123t123 -184.5t45.5 -225t-45.5 -225t-123 -184.5t-184.5 -123t-225 -45.5t-225 45.5t-184.5 123t-123 184.5t-45.5 225t45.5 225t123 184.5t184.5 123t225 45.5zM914 632l-275 223q-16 13 -27.5 8t-11.5 -26v-137h-275 q-10 0 -17.5 -7.5t-7.5 -17.5v-150q0 -10 7.5 -17.5t17.5 -7.5h275v-137q0 -21 11.5 -26t27.5 8l275 223q16 13 16 32t-16 32z" />
<glyph unicode="&#xe132;" d="M600 1178q118 0 225 -45.5t184.5 -123t123 -184.5t45.5 -225t-45.5 -225t-123 -184.5t-184.5 -123t-225 -45.5t-225 45.5t-184.5 123t-123 184.5t-45.5 225t45.5 225t123 184.5t184.5 123t225 45.5zM561 855l-275 -223q-16 -13 -16 -32t16 -32l275 -223q16 -13 27.5 -8 t11.5 26v137h275q10 0 17.5 7.5t7.5 17.5v150q0 10 -7.5 17.5t-17.5 7.5h-275v137q0 21 -11.5 26t-27.5 -8z" />
<glyph unicode="&#xe133;" d="M600 1178q118 0 225 -45.5t184.5 -123t123 -184.5t45.5 -225t-45.5 -225t-123 -184.5t-184.5 -123t-225 -45.5t-225 45.5t-184.5 123t-123 184.5t-45.5 225t45.5 225t123 184.5t184.5 123t225 45.5zM855 639l-223 275q-13 16 -32 16t-32 -16l-223 -275q-13 -16 -8 -27.5 t26 -11.5h137v-275q0 -10 7.5 -17.5t17.5 -7.5h150q10 0 17.5 7.5t7.5 17.5v275h137q21 0 26 11.5t-8 27.5z" />
<glyph unicode="&#xe134;" d="M600 1178q118 0 225 -45.5t184.5 -123t123 -184.5t45.5 -225t-45.5 -225t-123 -184.5t-184.5 -123t-225 -45.5t-225 45.5t-184.5 123t-123 184.5t-45.5 225t45.5 225t123 184.5t184.5 123t225 45.5zM675 900h-150q-10 0 -17.5 -7.5t-7.5 -17.5v-275h-137q-21 0 -26 -11.5 t8 -27.5l223 -275q13 -16 32 -16t32 16l223 275q13 16 8 27.5t-26 11.5h-137v275q0 10 -7.5 17.5t-17.5 7.5z" />
<glyph unicode="&#xe135;" d="M600 1176q116 0 222.5 -46t184 -123.5t123.5 -184t46 -222.5t-46 -222.5t-123.5 -184t-184 -123.5t-222.5 -46t-222.5 46t-184 123.5t-123.5 184t-46 222.5t46 222.5t123.5 184t184 123.5t222.5 46zM627 1101q-15 -12 -36.5 -20.5t-35.5 -12t-43 -8t-39 -6.5 q-15 -3 -45.5 0t-45.5 -2q-20 -7 -51.5 -26.5t-34.5 -34.5q-3 -11 6.5 -22.5t8.5 -18.5q-3 -34 -27.5 -91t-29.5 -79q-9 -34 5 -93t8 -87q0 -9 17 -44.5t16 -59.5q12 0 23 -5t23.5 -15t19.5 -14q16 -8 33 -15t40.5 -15t34.5 -12q21 -9 52.5 -32t60 -38t57.5 -11 q7 -15 -3 -34t-22.5 -40t-9.5 -38q13 -21 23 -34.5t27.5 -27.5t36.5 -18q0 -7 -3.5 -16t-3.5 -14t5 -17q104 -2 221 112q30 29 46.5 47t34.5 49t21 63q-13 8 -37 8.5t-36 7.5q-15 7 -49.5 15t-51.5 19q-18 0 -41 -0.5t-43 -1.5t-42 -6.5t-38 -16.5q-51 -35 -66 -12 q-4 1 -3.5 25.5t0.5 25.5q-6 13 -26.5 17.5t-24.5 6.5q1 15 -0.5 30.5t-7 28t-18.5 11.5t-31 -21q-23 -25 -42 4q-19 28 -8 58q6 16 22 22q6 -1 26 -1.5t33.5 -4t19.5 -13.5q7 -12 18 -24t21.5 -20.5t20 -15t15.5 -10.5l5 -3q2 12 7.5 30.5t8 34.5t-0.5 32q-3 18 3.5 29 t18 22.5t15.5 24.5q6 14 10.5 35t8 31t15.5 22.5t34 22.5q-6 18 10 36q8 0 24 -1.5t24.5 -1.5t20 4.5t20.5 15.5q-10 23 -31 42.5t-37.5 29.5t-49 27t-43.5 23q0 1 2 8t3 11.5t1.5 10.5t-1 9.5t-4.5 4.5q31 -13 58.5 -14.5t38.5 2.5l12 5q5 28 -9.5 46t-36.5 24t-50 15 t-41 20q-18 -4 -37 0zM613 994q0 -17 8 -42t17 -45t9 -23q-8 1 -39.5 5.5t-52.5 10t-37 16.5q3 11 16 29.5t16 25.5q10 -10 19 -10t14 6t13.5 14.5t16.5 12.5z" />
<glyph unicode="&#xe136;" d="M756 1157q164 92 306 -9l-259 -138l145 -232l251 126q6 -89 -34 -156.5t-117 -110.5q-60 -34 -127 -39.5t-126 16.5l-596 -596q-15 -16 -36.5 -16t-36.5 16l-111 110q-15 15 -15 36.5t15 37.5l600 599q-34 101 5.5 201.5t135.5 154.5z" />
<glyph unicode="&#xe137;" horiz-adv-x="1220" d="M100 1196h1000q41 0 70.5 -29.5t29.5 -70.5v-100q0 -41 -29.5 -70.5t-70.5 -29.5h-1000q-41 0 -70.5 29.5t-29.5 70.5v100q0 41 29.5 70.5t70.5 29.5zM1100 1096h-200v-100h200v100zM100 796h1000q41 0 70.5 -29.5t29.5 -70.5v-100q0 -41 -29.5 -70.5t-70.5 -29.5h-1000 q-41 0 -70.5 29.5t-29.5 70.5v100q0 41 29.5 70.5t70.5 29.5zM1100 696h-500v-100h500v100zM100 396h1000q41 0 70.5 -29.5t29.5 -70.5v-100q0 -41 -29.5 -70.5t-70.5 -29.5h-1000q-41 0 -70.5 29.5t-29.5 70.5v100q0 41 29.5 70.5t70.5 29.5zM1100 296h-300v-100h300v100z " />
<glyph unicode="&#xe138;" d="M150 1200h900q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-900q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5zM700 500v-300l-200 -200v500l-350 500h900z" />
<glyph unicode="&#xe139;" d="M500 1200h200q41 0 70.5 -29.5t29.5 -70.5v-100h300q41 0 70.5 -29.5t29.5 -70.5v-400h-500v100h-200v-100h-500v400q0 41 29.5 70.5t70.5 29.5h300v100q0 41 29.5 70.5t70.5 29.5zM500 1100v-100h200v100h-200zM1200 400v-200q0 -41 -29.5 -70.5t-70.5 -29.5h-1000 q-41 0 -70.5 29.5t-29.5 70.5v200h1200z" />
<glyph unicode="&#xe140;" d="M50 1200h300q21 0 25 -10.5t-10 -24.5l-94 -94l199 -199q7 -8 7 -18t-7 -18l-106 -106q-8 -7 -18 -7t-18 7l-199 199l-94 -94q-14 -14 -24.5 -10t-10.5 25v300q0 21 14.5 35.5t35.5 14.5zM850 1200h300q21 0 35.5 -14.5t14.5 -35.5v-300q0 -21 -10.5 -25t-24.5 10l-94 94 l-199 -199q-8 -7 -18 -7t-18 7l-106 106q-7 8 -7 18t7 18l199 199l-94 94q-14 14 -10 24.5t25 10.5zM364 470l106 -106q7 -8 7 -18t-7 -18l-199 -199l94 -94q14 -14 10 -24.5t-25 -10.5h-300q-21 0 -35.5 14.5t-14.5 35.5v300q0 21 10.5 25t24.5 -10l94 -94l199 199 q8 7 18 7t18 -7zM1071 271l94 94q14 14 24.5 10t10.5 -25v-300q0 -21 -14.5 -35.5t-35.5 -14.5h-300q-21 0 -25 10.5t10 24.5l94 94l-199 199q-7 8 -7 18t7 18l106 106q8 7 18 7t18 -7z" />
<glyph unicode="&#xe141;" d="M596 1192q121 0 231.5 -47.5t190 -127t127 -190t47.5 -231.5t-47.5 -231.5t-127 -190.5t-190 -127t-231.5 -47t-231.5 47t-190.5 127t-127 190.5t-47 231.5t47 231.5t127 190t190.5 127t231.5 47.5zM596 1010q-112 0 -207.5 -55.5t-151 -151t-55.5 -207.5t55.5 -207.5 t151 -151t207.5 -55.5t207.5 55.5t151 151t55.5 207.5t-55.5 207.5t-151 151t-207.5 55.5zM454.5 905q22.5 0 38.5 -16t16 -38.5t-16 -39t-38.5 -16.5t-38.5 16.5t-16 39t16 38.5t38.5 16zM754.5 905q22.5 0 38.5 -16t16 -38.5t-16 -39t-38 -16.5q-14 0 -29 10l-55 -145 q17 -23 17 -51q0 -36 -25.5 -61.5t-61.5 -25.5t-61.5 25.5t-25.5 61.5q0 32 20.5 56.5t51.5 29.5l122 126l1 1q-9 14 -9 28q0 23 16 39t38.5 16zM345.5 709q22.5 0 38.5 -16t16 -38.5t-16 -38.5t-38.5 -16t-38.5 16t-16 38.5t16 38.5t38.5 16zM854.5 709q22.5 0 38.5 -16 t16 -38.5t-16 -38.5t-38.5 -16t-38.5 16t-16 38.5t16 38.5t38.5 16z" />
<glyph unicode="&#xe142;" d="M546 173l469 470q91 91 99 192q7 98 -52 175.5t-154 94.5q-22 4 -47 4q-34 0 -66.5 -10t-56.5 -23t-55.5 -38t-48 -41.5t-48.5 -47.5q-376 -375 -391 -390q-30 -27 -45 -41.5t-37.5 -41t-32 -46.5t-16 -47.5t-1.5 -56.5q9 -62 53.5 -95t99.5 -33q74 0 125 51l548 548 q36 36 20 75q-7 16 -21.5 26t-32.5 10q-26 0 -50 -23q-13 -12 -39 -38l-341 -338q-15 -15 -35.5 -15.5t-34.5 13.5t-14 34.5t14 34.5q327 333 361 367q35 35 67.5 51.5t78.5 16.5q14 0 29 -1q44 -8 74.5 -35.5t43.5 -68.5q14 -47 2 -96.5t-47 -84.5q-12 -11 -32 -32 t-79.5 -81t-114.5 -115t-124.5 -123.5t-123 -119.5t-96.5 -89t-57 -45q-56 -27 -120 -27q-70 0 -129 32t-93 89q-48 78 -35 173t81 163l511 511q71 72 111 96q91 55 198 55q80 0 152 -33q78 -36 129.5 -103t66.5 -154q17 -93 -11 -183.5t-94 -156.5l-482 -476 q-15 -15 -36 -16t-37 14t-17.5 34t14.5 35z" />
<glyph unicode="&#xe143;" d="M649 949q48 68 109.5 104t121.5 38.5t118.5 -20t102.5 -64t71 -100.5t27 -123q0 -57 -33.5 -117.5t-94 -124.5t-126.5 -127.5t-150 -152.5t-146 -174q-62 85 -145.5 174t-150 152.5t-126.5 127.5t-93.5 124.5t-33.5 117.5q0 64 28 123t73 100.5t104 64t119 20 t120.5 -38.5t104.5 -104zM896 972q-33 0 -64.5 -19t-56.5 -46t-47.5 -53.5t-43.5 -45.5t-37.5 -19t-36 19t-40 45.5t-43 53.5t-54 46t-65.5 19q-67 0 -122.5 -55.5t-55.5 -132.5q0 -23 13.5 -51t46 -65t57.5 -63t76 -75l22 -22q15 -14 44 -44t50.5 -51t46 -44t41 -35t23 -12 t23.5 12t42.5 36t46 44t52.5 52t44 43q4 4 12 13q43 41 63.5 62t52 55t46 55t26 46t11.5 44q0 79 -53 133.5t-120 54.5z" />
<glyph unicode="&#xe144;" d="M776.5 1214q93.5 0 159.5 -66l141 -141q66 -66 66 -160q0 -42 -28 -95.5t-62 -87.5l-29 -29q-31 53 -77 99l-18 18l95 95l-247 248l-389 -389l212 -212l-105 -106l-19 18l-141 141q-66 66 -66 159t66 159l283 283q65 66 158.5 66zM600 706l105 105q10 -8 19 -17l141 -141 q66 -66 66 -159t-66 -159l-283 -283q-66 -66 -159 -66t-159 66l-141 141q-66 66 -66 159.5t66 159.5l55 55q29 -55 75 -102l18 -17l-95 -95l247 -248l389 389z" />
<glyph unicode="&#xe145;" d="M603 1200q85 0 162 -15t127 -38t79 -48t29 -46v-953q0 -41 -29.5 -70.5t-70.5 -29.5h-600q-41 0 -70.5 29.5t-29.5 70.5v953q0 21 30 46.5t81 48t129 37.5t163 15zM300 1000v-700h600v700h-600zM600 254q-43 0 -73.5 -30.5t-30.5 -73.5t30.5 -73.5t73.5 -30.5t73.5 30.5 t30.5 73.5t-30.5 73.5t-73.5 30.5z" />
<glyph unicode="&#xe146;" d="M902 1185l283 -282q15 -15 15 -36t-14.5 -35.5t-35.5 -14.5t-35 15l-36 35l-279 -267v-300l-212 210l-308 -307l-280 -203l203 280l307 308l-210 212h300l267 279l-35 36q-15 14 -15 35t14.5 35.5t35.5 14.5t35 -15z" />
<glyph unicode="&#xe148;" d="M700 1248v-78q38 -5 72.5 -14.5t75.5 -31.5t71 -53.5t52 -84t24 -118.5h-159q-4 36 -10.5 59t-21 45t-40 35.5t-64.5 20.5v-307l64 -13q34 -7 64 -16.5t70 -32t67.5 -52.5t47.5 -80t20 -112q0 -139 -89 -224t-244 -97v-77h-100v79q-150 16 -237 103q-40 40 -52.5 93.5 t-15.5 139.5h139q5 -77 48.5 -126t117.5 -65v335l-27 8q-46 14 -79 26.5t-72 36t-63 52t-40 72.5t-16 98q0 70 25 126t67.5 92t94.5 57t110 27v77h100zM600 754v274q-29 -4 -50 -11t-42 -21.5t-31.5 -41.5t-10.5 -65q0 -29 7 -50.5t16.5 -34t28.5 -22.5t31.5 -14t37.5 -10 q9 -3 13 -4zM700 547v-310q22 2 42.5 6.5t45 15.5t41.5 27t29 42t12 59.5t-12.5 59.5t-38 44.5t-53 31t-66.5 24.5z" />
<glyph unicode="&#xe149;" d="M561 1197q84 0 160.5 -40t123.5 -109.5t47 -147.5h-153q0 40 -19.5 71.5t-49.5 48.5t-59.5 26t-55.5 9q-37 0 -79 -14.5t-62 -35.5q-41 -44 -41 -101q0 -26 13.5 -63t26.5 -61t37 -66q6 -9 9 -14h241v-100h-197q8 -50 -2.5 -115t-31.5 -95q-45 -62 -99 -112 q34 10 83 17.5t71 7.5q32 1 102 -16t104 -17q83 0 136 30l50 -147q-31 -19 -58 -30.5t-55 -15.5t-42 -4.5t-46 -0.5q-23 0 -76 17t-111 32.5t-96 11.5q-39 -3 -82 -16t-67 -25l-23 -11l-55 145q4 3 16 11t15.5 10.5t13 9t15.5 12t14.5 14t17.5 18.5q48 55 54 126.5 t-30 142.5h-221v100h166q-23 47 -44 104q-7 20 -12 41.5t-6 55.5t6 66.5t29.5 70.5t58.5 71q97 88 263 88z" />
<glyph unicode="&#xe150;" d="M400 300h150q21 0 25 -11t-10 -25l-230 -250q-14 -15 -35 -15t-35 15l-230 250q-14 14 -10 25t25 11h150v900h200v-900zM935 1184l230 -249q14 -14 10 -24.5t-25 -10.5h-150v-900h-200v900h-150q-21 0 -25 10.5t10 24.5l230 249q14 15 35 15t35 -15z" />
<glyph unicode="&#xe151;" d="M1000 700h-100v100h-100v-100h-100v500h300v-500zM400 300h150q21 0 25 -11t-10 -25l-230 -250q-14 -15 -35 -15t-35 15l-230 250q-14 14 -10 25t25 11h150v900h200v-900zM801 1100v-200h100v200h-100zM1000 350l-200 -250h200v-100h-300v150l200 250h-200v100h300v-150z " />
<glyph unicode="&#xe152;" d="M400 300h150q21 0 25 -11t-10 -25l-230 -250q-14 -15 -35 -15t-35 15l-230 250q-14 14 -10 25t25 11h150v900h200v-900zM1000 1050l-200 -250h200v-100h-300v150l200 250h-200v100h300v-150zM1000 0h-100v100h-100v-100h-100v500h300v-500zM801 400v-200h100v200h-100z " />
<glyph unicode="&#xe153;" d="M400 300h150q21 0 25 -11t-10 -25l-230 -250q-14 -15 -35 -15t-35 15l-230 250q-14 14 -10 25t25 11h150v900h200v-900zM1000 700h-100v400h-100v100h200v-500zM1100 0h-100v100h-200v400h300v-500zM901 400v-200h100v200h-100z" />
<glyph unicode="&#xe154;" d="M400 300h150q21 0 25 -11t-10 -25l-230 -250q-14 -15 -35 -15t-35 15l-230 250q-14 14 -10 25t25 11h150v900h200v-900zM1100 700h-100v100h-200v400h300v-500zM901 1100v-200h100v200h-100zM1000 0h-100v400h-100v100h200v-500z" />
<glyph unicode="&#xe155;" d="M400 300h150q21 0 25 -11t-10 -25l-230 -250q-14 -15 -35 -15t-35 15l-230 250q-14 14 -10 25t25 11h150v900h200v-900zM900 1000h-200v200h200v-200zM1000 700h-300v200h300v-200zM1100 400h-400v200h400v-200zM1200 100h-500v200h500v-200z" />
<glyph unicode="&#xe156;" d="M400 300h150q21 0 25 -11t-10 -25l-230 -250q-14 -15 -35 -15t-35 15l-230 250q-14 14 -10 25t25 11h150v900h200v-900zM1200 1000h-500v200h500v-200zM1100 700h-400v200h400v-200zM1000 400h-300v200h300v-200zM900 100h-200v200h200v-200z" />
<glyph unicode="&#xe157;" d="M350 1100h400q162 0 256 -93.5t94 -256.5v-400q0 -165 -93.5 -257.5t-256.5 -92.5h-400q-165 0 -257.5 92.5t-92.5 257.5v400q0 165 92.5 257.5t257.5 92.5zM800 900h-500q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5 v500q0 41 -29.5 70.5t-70.5 29.5z" />
<glyph unicode="&#xe158;" d="M350 1100h400q165 0 257.5 -92.5t92.5 -257.5v-400q0 -165 -92.5 -257.5t-257.5 -92.5h-400q-163 0 -256.5 92.5t-93.5 257.5v400q0 163 94 256.5t256 93.5zM800 900h-500q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5 v500q0 41 -29.5 70.5t-70.5 29.5zM440 770l253 -190q17 -12 17 -30t-17 -30l-253 -190q-16 -12 -28 -6.5t-12 26.5v400q0 21 12 26.5t28 -6.5z" />
<glyph unicode="&#xe159;" d="M350 1100h400q163 0 256.5 -94t93.5 -256v-400q0 -165 -92.5 -257.5t-257.5 -92.5h-400q-165 0 -257.5 92.5t-92.5 257.5v400q0 163 92.5 256.5t257.5 93.5zM800 900h-500q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5 v500q0 41 -29.5 70.5t-70.5 29.5zM350 700h400q21 0 26.5 -12t-6.5 -28l-190 -253q-12 -17 -30 -17t-30 17l-190 253q-12 16 -6.5 28t26.5 12z" />
<glyph unicode="&#xe160;" d="M350 1100h400q165 0 257.5 -92.5t92.5 -257.5v-400q0 -163 -92.5 -256.5t-257.5 -93.5h-400q-163 0 -256.5 94t-93.5 256v400q0 165 92.5 257.5t257.5 92.5zM800 900h-500q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5 v500q0 41 -29.5 70.5t-70.5 29.5zM580 693l190 -253q12 -16 6.5 -28t-26.5 -12h-400q-21 0 -26.5 12t6.5 28l190 253q12 17 30 17t30 -17z" />
<glyph unicode="&#xe161;" d="M550 1100h400q165 0 257.5 -92.5t92.5 -257.5v-400q0 -165 -92.5 -257.5t-257.5 -92.5h-400q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5h450q41 0 70.5 29.5t29.5 70.5v500q0 41 -29.5 70.5t-70.5 29.5h-450q-21 0 -35.5 14.5t-14.5 35.5v100 q0 21 14.5 35.5t35.5 14.5zM338 867l324 -284q16 -14 16 -33t-16 -33l-324 -284q-16 -14 -27 -9t-11 26v150h-250q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5h250v150q0 21 11 26t27 -9z" />
<glyph unicode="&#xe162;" d="M793 1182l9 -9q8 -10 5 -27q-3 -11 -79 -225.5t-78 -221.5l300 1q24 0 32.5 -17.5t-5.5 -35.5q-1 0 -133.5 -155t-267 -312.5t-138.5 -162.5q-12 -15 -26 -15h-9l-9 8q-9 11 -4 32q2 9 42 123.5t79 224.5l39 110h-302q-23 0 -31 19q-10 21 6 41q75 86 209.5 237.5 t228 257t98.5 111.5q9 16 25 16h9z" />
<glyph unicode="&#xe163;" d="M350 1100h400q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-450q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h450q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-165 0 -257.5 92.5t-92.5 257.5v400 q0 165 92.5 257.5t257.5 92.5zM938 867l324 -284q16 -14 16 -33t-16 -33l-324 -284q-16 -14 -27 -9t-11 26v150h-250q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5h250v150q0 21 11 26t27 -9z" />
<glyph unicode="&#xe164;" d="M750 1200h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -10.5 -25t-24.5 10l-109 109l-312 -312q-15 -15 -35.5 -15t-35.5 15l-141 141q-15 15 -15 35.5t15 35.5l312 312l-109 109q-14 14 -10 24.5t25 10.5zM456 900h-156q-41 0 -70.5 -29.5t-29.5 -70.5v-500 q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5v148l200 200v-298q0 -165 -93.5 -257.5t-256.5 -92.5h-400q-165 0 -257.5 92.5t-92.5 257.5v400q0 165 92.5 257.5t257.5 92.5h300z" />
<glyph unicode="&#xe165;" d="M600 1186q119 0 227.5 -46.5t187 -125t125 -187t46.5 -227.5t-46.5 -227.5t-125 -187t-187 -125t-227.5 -46.5t-227.5 46.5t-187 125t-125 187t-46.5 227.5t46.5 227.5t125 187t187 125t227.5 46.5zM600 1022q-115 0 -212 -56.5t-153.5 -153.5t-56.5 -212t56.5 -212 t153.5 -153.5t212 -56.5t212 56.5t153.5 153.5t56.5 212t-56.5 212t-153.5 153.5t-212 56.5zM600 794q80 0 137 -57t57 -137t-57 -137t-137 -57t-137 57t-57 137t57 137t137 57z" />
<glyph unicode="&#xe166;" d="M450 1200h200q21 0 35.5 -14.5t14.5 -35.5v-350h245q20 0 25 -11t-9 -26l-383 -426q-14 -15 -33.5 -15t-32.5 15l-379 426q-13 15 -8.5 26t25.5 11h250v350q0 21 14.5 35.5t35.5 14.5zM50 300h1000q21 0 35.5 -14.5t14.5 -35.5v-250h-1100v250q0 21 14.5 35.5t35.5 14.5z M900 200v-50h100v50h-100z" />
<glyph unicode="&#xe167;" d="M583 1182l378 -435q14 -15 9 -31t-26 -16h-244v-250q0 -20 -17 -35t-39 -15h-200q-20 0 -32 14.5t-12 35.5v250h-250q-20 0 -25.5 16.5t8.5 31.5l383 431q14 16 33.5 17t33.5 -14zM50 300h1000q21 0 35.5 -14.5t14.5 -35.5v-250h-1100v250q0 21 14.5 35.5t35.5 14.5z M900 200v-50h100v50h-100z" />
<glyph unicode="&#xe168;" d="M396 723l369 369q7 7 17.5 7t17.5 -7l139 -139q7 -8 7 -18.5t-7 -17.5l-525 -525q-7 -8 -17.5 -8t-17.5 8l-292 291q-7 8 -7 18t7 18l139 139q8 7 18.5 7t17.5 -7zM50 300h1000q21 0 35.5 -14.5t14.5 -35.5v-250h-1100v250q0 21 14.5 35.5t35.5 14.5zM900 200v-50h100v50 h-100z" />
<glyph unicode="&#xe169;" d="M135 1023l142 142q14 14 35 14t35 -14l77 -77l-212 -212l-77 76q-14 15 -14 36t14 35zM655 855l210 210q14 14 24.5 10t10.5 -25l-2 -599q-1 -20 -15.5 -35t-35.5 -15l-597 -1q-21 0 -25 10.5t10 24.5l208 208l-154 155l212 212zM50 300h1000q21 0 35.5 -14.5t14.5 -35.5 v-250h-1100v250q0 21 14.5 35.5t35.5 14.5zM900 200v-50h100v50h-100z" />
<glyph unicode="&#xe170;" d="M350 1200l599 -2q20 -1 35 -15.5t15 -35.5l1 -597q0 -21 -10.5 -25t-24.5 10l-208 208l-155 -154l-212 212l155 154l-210 210q-14 14 -10 24.5t25 10.5zM524 512l-76 -77q-15 -14 -36 -14t-35 14l-142 142q-14 14 -14 35t14 35l77 77zM50 300h1000q21 0 35.5 -14.5 t14.5 -35.5v-250h-1100v250q0 21 14.5 35.5t35.5 14.5zM900 200v-50h100v50h-100z" />
<glyph unicode="&#xe171;" d="M1200 103l-483 276l-314 -399v423h-399l1196 796v-1096zM483 424v-230l683 953z" />
<glyph unicode="&#xe172;" d="M1100 1000v-850q0 -21 -14.5 -35.5t-35.5 -14.5h-150v400h-700v-400h-150q-21 0 -35.5 14.5t-14.5 35.5v1000q0 20 14.5 35t35.5 15h250v-300h500v300h100zM700 1000h-100v200h100v-200z" />
<glyph unicode="&#xe173;" d="M1100 1000l-2 -149l-299 -299l-95 95q-9 9 -21.5 9t-21.5 -9l-149 -147h-312v-400h-150q-21 0 -35.5 14.5t-14.5 35.5v1000q0 20 14.5 35t35.5 15h250v-300h500v300h100zM700 1000h-100v200h100v-200zM1132 638l106 -106q7 -7 7 -17.5t-7 -17.5l-420 -421q-8 -7 -18 -7 t-18 7l-202 203q-8 7 -8 17.5t8 17.5l106 106q7 8 17.5 8t17.5 -8l79 -79l297 297q7 7 17.5 7t17.5 -7z" />
<glyph unicode="&#xe174;" d="M1100 1000v-269l-103 -103l-134 134q-15 15 -33.5 16.5t-34.5 -12.5l-266 -266h-329v-400h-150q-21 0 -35.5 14.5t-14.5 35.5v1000q0 20 14.5 35t35.5 15h250v-300h500v300h100zM700 1000h-100v200h100v-200zM1202 572l70 -70q15 -15 15 -35.5t-15 -35.5l-131 -131 l131 -131q15 -15 15 -35.5t-15 -35.5l-70 -70q-15 -15 -35.5 -15t-35.5 15l-131 131l-131 -131q-15 -15 -35.5 -15t-35.5 15l-70 70q-15 15 -15 35.5t15 35.5l131 131l-131 131q-15 15 -15 35.5t15 35.5l70 70q15 15 35.5 15t35.5 -15l131 -131l131 131q15 15 35.5 15 t35.5 -15z" />
<glyph unicode="&#xe175;" d="M1100 1000v-300h-350q-21 0 -35.5 -14.5t-14.5 -35.5v-150h-500v-400h-150q-21 0 -35.5 14.5t-14.5 35.5v1000q0 20 14.5 35t35.5 15h250v-300h500v300h100zM700 1000h-100v200h100v-200zM850 600h100q21 0 35.5 -14.5t14.5 -35.5v-250h150q21 0 25 -10.5t-10 -24.5 l-230 -230q-14 -14 -35 -14t-35 14l-230 230q-14 14 -10 24.5t25 10.5h150v250q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe176;" d="M1100 1000v-400l-165 165q-14 15 -35 15t-35 -15l-263 -265h-402v-400h-150q-21 0 -35.5 14.5t-14.5 35.5v1000q0 20 14.5 35t35.5 15h250v-300h500v300h100zM700 1000h-100v200h100v-200zM935 565l230 -229q14 -15 10 -25.5t-25 -10.5h-150v-250q0 -20 -14.5 -35 t-35.5 -15h-100q-21 0 -35.5 15t-14.5 35v250h-150q-21 0 -25 10.5t10 25.5l230 229q14 15 35 15t35 -15z" />
<glyph unicode="&#xe177;" d="M50 1100h1100q21 0 35.5 -14.5t14.5 -35.5v-150h-1200v150q0 21 14.5 35.5t35.5 14.5zM1200 800v-550q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5v550h1200zM100 500v-200h400v200h-400z" />
<glyph unicode="&#xe178;" d="M935 1165l248 -230q14 -14 14 -35t-14 -35l-248 -230q-14 -14 -24.5 -10t-10.5 25v150h-400v200h400v150q0 21 10.5 25t24.5 -10zM200 800h-50q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5h50v-200zM400 800h-100v200h100v-200zM18 435l247 230 q14 14 24.5 10t10.5 -25v-150h400v-200h-400v-150q0 -21 -10.5 -25t-24.5 10l-247 230q-15 14 -15 35t15 35zM900 300h-100v200h100v-200zM1000 500h51q20 0 34.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-34.5 -14.5h-51v200z" />
<glyph unicode="&#xe179;" d="M862 1073l276 116q25 18 43.5 8t18.5 -41v-1106q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v397q-4 1 -11 5t-24 17.5t-30 29t-24 42t-11 56.5v359q0 31 18.5 65t43.5 52zM550 1200q22 0 34.5 -12.5t14.5 -24.5l1 -13v-450q0 -28 -10.5 -59.5 t-25 -56t-29 -45t-25.5 -31.5l-10 -11v-447q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v447q-4 4 -11 11.5t-24 30.5t-30 46t-24 55t-11 60v450q0 2 0.5 5.5t4 12t8.5 15t14.5 12t22.5 5.5q20 0 32.5 -12.5t14.5 -24.5l3 -13v-350h100v350v5.5t2.5 12 t7 15t15 12t25.5 5.5q23 0 35.5 -12.5t13.5 -24.5l1 -13v-350h100v350q0 2 0.5 5.5t3 12t7 15t15 12t24.5 5.5z" />
<glyph unicode="&#xe180;" d="M1200 1100v-56q-4 0 -11 -0.5t-24 -3t-30 -7.5t-24 -15t-11 -24v-888q0 -22 25 -34.5t50 -13.5l25 -2v-56h-400v56q75 0 87.5 6.5t12.5 43.5v394h-500v-394q0 -37 12.5 -43.5t87.5 -6.5v-56h-400v56q4 0 11 0.5t24 3t30 7.5t24 15t11 24v888q0 22 -25 34.5t-50 13.5 l-25 2v56h400v-56q-75 0 -87.5 -6.5t-12.5 -43.5v-394h500v394q0 37 -12.5 43.5t-87.5 6.5v56h400z" />
<glyph unicode="&#xe181;" d="M675 1000h375q21 0 35.5 -14.5t14.5 -35.5v-150h-105l-295 -98v98l-200 200h-400l100 100h375zM100 900h300q41 0 70.5 -29.5t29.5 -70.5v-500q0 -41 -29.5 -70.5t-70.5 -29.5h-300q-41 0 -70.5 29.5t-29.5 70.5v500q0 41 29.5 70.5t70.5 29.5zM100 800v-200h300v200 h-300zM1100 535l-400 -133v163l400 133v-163zM100 500v-200h300v200h-300zM1100 398v-248q0 -21 -14.5 -35.5t-35.5 -14.5h-375l-100 -100h-375l-100 100h400l200 200h105z" />
<glyph unicode="&#xe182;" d="M17 1007l162 162q17 17 40 14t37 -22l139 -194q14 -20 11 -44.5t-20 -41.5l-119 -118q102 -142 228 -268t267 -227l119 118q17 17 42.5 19t44.5 -12l192 -136q19 -14 22.5 -37.5t-13.5 -40.5l-163 -162q-3 -1 -9.5 -1t-29.5 2t-47.5 6t-62.5 14.5t-77.5 26.5t-90 42.5 t-101.5 60t-111 83t-119 108.5q-74 74 -133.5 150.5t-94.5 138.5t-60 119.5t-34.5 100t-15 74.5t-4.5 48z" />
<glyph unicode="&#xe183;" d="M600 1100q92 0 175 -10.5t141.5 -27t108.5 -36.5t81.5 -40t53.5 -37t31 -27l9 -10v-200q0 -21 -14.5 -33t-34.5 -9l-202 34q-20 3 -34.5 20t-14.5 38v146q-141 24 -300 24t-300 -24v-146q0 -21 -14.5 -38t-34.5 -20l-202 -34q-20 -3 -34.5 9t-14.5 33v200q3 4 9.5 10.5 t31 26t54 37.5t80.5 39.5t109 37.5t141 26.5t175 10.5zM600 795q56 0 97 -9.5t60 -23.5t30 -28t12 -24l1 -10v-50l365 -303q14 -15 24.5 -40t10.5 -45v-212q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5v212q0 20 10.5 45t24.5 40l365 303v50 q0 4 1 10.5t12 23t30 29t60 22.5t97 10z" />
<glyph unicode="&#xe184;" d="M1100 700l-200 -200h-600l-200 200v500h200v-200h200v200h200v-200h200v200h200v-500zM250 400h700q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-12l137 -100h-950l137 100h-12q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5zM50 100h1100q21 0 35.5 -14.5 t14.5 -35.5v-50h-1200v50q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe185;" d="M700 1100h-100q-41 0 -70.5 -29.5t-29.5 -70.5v-1000h300v1000q0 41 -29.5 70.5t-70.5 29.5zM1100 800h-100q-41 0 -70.5 -29.5t-29.5 -70.5v-700h300v700q0 41 -29.5 70.5t-70.5 29.5zM400 0h-300v400q0 41 29.5 70.5t70.5 29.5h100q41 0 70.5 -29.5t29.5 -70.5v-400z " />
<glyph unicode="&#xe186;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM500 700h-200v-100h200v-300h-300v100h200v100h-200v300h300v-100zM900 700v-300l-100 -100h-200v500h200z M700 700v-300h100v300h-100z" />
<glyph unicode="&#xe187;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM500 300h-100v200h-100v-200h-100v500h100v-200h100v200h100v-500zM900 700v-300l-100 -100h-200v500h200z M700 700v-300h100v300h-100z" />
<glyph unicode="&#xe188;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM500 700h-200v-300h200v-100h-300v500h300v-100zM900 700h-200v-300h200v-100h-300v500h300v-100z" />
<glyph unicode="&#xe189;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM500 400l-300 150l300 150v-300zM900 550l-300 -150v300z" />
<glyph unicode="&#xe190;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM900 300h-700v500h700v-500zM800 700h-130q-38 0 -66.5 -43t-28.5 -108t27 -107t68 -42h130v300zM300 700v-300 h130q41 0 68 42t27 107t-28.5 108t-66.5 43h-130z" />
<glyph unicode="&#xe191;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM500 700h-200v-100h200v-300h-300v100h200v100h-200v300h300v-100zM900 300h-100v400h-100v100h200v-500z M700 300h-100v100h100v-100z" />
<glyph unicode="&#xe192;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM300 700h200v-400h-300v500h100v-100zM900 300h-100v400h-100v100h200v-500zM300 600v-200h100v200h-100z M700 300h-100v100h100v-100z" />
<glyph unicode="&#xe193;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM500 500l-199 -200h-100v50l199 200v150h-200v100h300v-300zM900 300h-100v400h-100v100h200v-500zM701 300h-100 v100h100v-100z" />
<glyph unicode="&#xe194;" d="M600 1191q120 0 229.5 -47t188.5 -126t126 -188.5t47 -229.5t-47 -229.5t-126 -188.5t-188.5 -126t-229.5 -47t-229.5 47t-188.5 126t-126 188.5t-47 229.5t47 229.5t126 188.5t188.5 126t229.5 47zM600 1021q-114 0 -211 -56.5t-153.5 -153.5t-56.5 -211t56.5 -211 t153.5 -153.5t211 -56.5t211 56.5t153.5 153.5t56.5 211t-56.5 211t-153.5 153.5t-211 56.5zM800 700h-300v-200h300v-100h-300l-100 100v200l100 100h300v-100z" />
<glyph unicode="&#xe195;" d="M600 1191q120 0 229.5 -47t188.5 -126t126 -188.5t47 -229.5t-47 -229.5t-126 -188.5t-188.5 -126t-229.5 -47t-229.5 47t-188.5 126t-126 188.5t-47 229.5t47 229.5t126 188.5t188.5 126t229.5 47zM600 1021q-114 0 -211 -56.5t-153.5 -153.5t-56.5 -211t56.5 -211 t153.5 -153.5t211 -56.5t211 56.5t153.5 153.5t56.5 211t-56.5 211t-153.5 153.5t-211 56.5zM800 700v-100l-50 -50l100 -100v-50h-100l-100 100h-150v-100h-100v400h300zM500 700v-100h200v100h-200z" />
<glyph unicode="&#xe197;" d="M503 1089q110 0 200.5 -59.5t134.5 -156.5q44 14 90 14q120 0 205 -86.5t85 -207t-85 -207t-205 -86.5h-128v250q0 21 -14.5 35.5t-35.5 14.5h-300q-21 0 -35.5 -14.5t-14.5 -35.5v-250h-222q-80 0 -136 57.5t-56 136.5q0 69 43 122.5t108 67.5q-2 19 -2 37q0 100 49 185 t134 134t185 49zM525 500h150q10 0 17.5 -7.5t7.5 -17.5v-275h137q21 0 26 -11.5t-8 -27.5l-223 -244q-13 -16 -32 -16t-32 16l-223 244q-13 16 -8 27.5t26 11.5h137v275q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe198;" d="M502 1089q110 0 201 -59.5t135 -156.5q43 15 89 15q121 0 206 -86.5t86 -206.5q0 -99 -60 -181t-150 -110l-378 360q-13 16 -31.5 16t-31.5 -16l-381 -365h-9q-79 0 -135.5 57.5t-56.5 136.5q0 69 43 122.5t108 67.5q-2 19 -2 38q0 100 49 184.5t133.5 134t184.5 49.5z M632 467l223 -228q13 -16 8 -27.5t-26 -11.5h-137v-275q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v275h-137q-21 0 -26 11.5t8 27.5q199 204 223 228q19 19 31.5 19t32.5 -19z" />
<glyph unicode="&#xe199;" d="M700 100v100h400l-270 300h170l-270 300h170l-300 333l-300 -333h170l-270 -300h170l-270 -300h400v-100h-50q-21 0 -35.5 -14.5t-14.5 -35.5v-50h400v50q0 21 -14.5 35.5t-35.5 14.5h-50z" />
<glyph unicode="&#xe200;" d="M600 1179q94 0 167.5 -56.5t99.5 -145.5q89 -6 150.5 -71.5t61.5 -155.5q0 -61 -29.5 -112.5t-79.5 -82.5q9 -29 9 -55q0 -74 -52.5 -126.5t-126.5 -52.5q-55 0 -100 30v-251q21 0 35.5 -14.5t14.5 -35.5v-50h-300v50q0 21 14.5 35.5t35.5 14.5v251q-45 -30 -100 -30 q-74 0 -126.5 52.5t-52.5 126.5q0 18 4 38q-47 21 -75.5 65t-28.5 97q0 74 52.5 126.5t126.5 52.5q5 0 23 -2q0 2 -1 10t-1 13q0 116 81.5 197.5t197.5 81.5z" />
<glyph unicode="&#xe201;" d="M1010 1010q111 -111 150.5 -260.5t0 -299t-150.5 -260.5q-83 -83 -191.5 -126.5t-218.5 -43.5t-218.5 43.5t-191.5 126.5q-111 111 -150.5 260.5t0 299t150.5 260.5q83 83 191.5 126.5t218.5 43.5t218.5 -43.5t191.5 -126.5zM476 1065q-4 0 -8 -1q-121 -34 -209.5 -122.5 t-122.5 -209.5q-4 -12 2.5 -23t18.5 -14l36 -9q3 -1 7 -1q23 0 29 22q27 96 98 166q70 71 166 98q11 3 17.5 13.5t3.5 22.5l-9 35q-3 13 -14 19q-7 4 -15 4zM512 920q-4 0 -9 -2q-80 -24 -138.5 -82.5t-82.5 -138.5q-4 -13 2 -24t19 -14l34 -9q4 -1 8 -1q22 0 28 21 q18 58 58.5 98.5t97.5 58.5q12 3 18 13.5t3 21.5l-9 35q-3 12 -14 19q-7 4 -15 4zM719.5 719.5q-49.5 49.5 -119.5 49.5t-119.5 -49.5t-49.5 -119.5t49.5 -119.5t119.5 -49.5t119.5 49.5t49.5 119.5t-49.5 119.5zM855 551q-22 0 -28 -21q-18 -58 -58.5 -98.5t-98.5 -57.5 q-11 -4 -17 -14.5t-3 -21.5l9 -35q3 -12 14 -19q7 -4 15 -4q4 0 9 2q80 24 138.5 82.5t82.5 138.5q4 13 -2.5 24t-18.5 14l-34 9q-4 1 -8 1zM1000 515q-23 0 -29 -22q-27 -96 -98 -166q-70 -71 -166 -98q-11 -3 -17.5 -13.5t-3.5 -22.5l9 -35q3 -13 14 -19q7 -4 15 -4 q4 0 8 1q121 34 209.5 122.5t122.5 209.5q4 12 -2.5 23t-18.5 14l-36 9q-3 1 -7 1z" />
<glyph unicode="&#xe202;" d="M700 800h300v-380h-180v200h-340v-200h-380v755q0 10 7.5 17.5t17.5 7.5h575v-400zM1000 900h-200v200zM700 300h162l-212 -212l-212 212h162v200h100v-200zM520 0h-395q-10 0 -17.5 7.5t-7.5 17.5v395zM1000 220v-195q0 -10 -7.5 -17.5t-17.5 -7.5h-195z" />
<glyph unicode="&#xe203;" d="M700 800h300v-520l-350 350l-550 -550v1095q0 10 7.5 17.5t17.5 7.5h575v-400zM1000 900h-200v200zM862 200h-162v-200h-100v200h-162l212 212zM480 0h-355q-10 0 -17.5 7.5t-7.5 17.5v55h380v-80zM1000 80v-55q0 -10 -7.5 -17.5t-17.5 -7.5h-155v80h180z" />
<glyph unicode="&#xe204;" d="M1162 800h-162v-200h100l100 -100h-300v300h-162l212 212zM200 800h200q27 0 40 -2t29.5 -10.5t23.5 -30t7 -57.5h300v-100h-600l-200 -350v450h100q0 36 7 57.5t23.5 30t29.5 10.5t40 2zM800 400h240l-240 -400h-800l300 500h500v-100z" />
<glyph unicode="&#xe205;" d="M650 1100h100q21 0 35.5 -14.5t14.5 -35.5v-50h50q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-300q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5h50v50q0 21 14.5 35.5t35.5 14.5zM1000 850v150q41 0 70.5 -29.5t29.5 -70.5v-800 q0 -41 -29.5 -70.5t-70.5 -29.5h-600q-1 0 -20 4l246 246l-326 326v324q0 41 29.5 70.5t70.5 29.5v-150q0 -62 44 -106t106 -44h300q62 0 106 44t44 106zM412 250l-212 -212v162h-200v100h200v162z" />
<glyph unicode="&#xe206;" d="M450 1100h100q21 0 35.5 -14.5t14.5 -35.5v-50h50q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-300q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5h50v50q0 21 14.5 35.5t35.5 14.5zM800 850v150q41 0 70.5 -29.5t29.5 -70.5v-500 h-200v-300h200q0 -36 -7 -57.5t-23.5 -30t-29.5 -10.5t-40 -2h-600q-41 0 -70.5 29.5t-29.5 70.5v800q0 41 29.5 70.5t70.5 29.5v-150q0 -62 44 -106t106 -44h300q62 0 106 44t44 106zM1212 250l-212 -212v162h-200v100h200v162z" />
<glyph unicode="&#xe209;" d="M658 1197l637 -1104q23 -38 7 -65.5t-60 -27.5h-1276q-44 0 -60 27.5t7 65.5l637 1104q22 39 54 39t54 -39zM704 800h-208q-20 0 -32 -14.5t-8 -34.5l58 -302q4 -20 21.5 -34.5t37.5 -14.5h54q20 0 37.5 14.5t21.5 34.5l58 302q4 20 -8 34.5t-32 14.5zM500 300v-100h200 v100h-200z" />
<glyph unicode="&#xe210;" d="M425 1100h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5zM425 800h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5 t17.5 7.5zM825 800h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5zM25 500h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v150 q0 10 7.5 17.5t17.5 7.5zM425 500h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5zM825 500h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5 v150q0 10 7.5 17.5t17.5 7.5zM25 200h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5zM425 200h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5 t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5zM825 200h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe211;" d="M700 1200h100v-200h-100v-100h350q62 0 86.5 -39.5t-3.5 -94.5l-66 -132q-41 -83 -81 -134h-772q-40 51 -81 134l-66 132q-28 55 -3.5 94.5t86.5 39.5h350v100h-100v200h100v100h200v-100zM250 400h700q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-12l137 -100 h-950l138 100h-13q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5zM50 100h1100q21 0 35.5 -14.5t14.5 -35.5v-50h-1200v50q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe212;" d="M600 1300q40 0 68.5 -29.5t28.5 -70.5h-194q0 41 28.5 70.5t68.5 29.5zM443 1100h314q18 -37 18 -75q0 -8 -3 -25h328q41 0 44.5 -16.5t-30.5 -38.5l-175 -145h-678l-178 145q-34 22 -29 38.5t46 16.5h328q-3 17 -3 25q0 38 18 75zM250 700h700q21 0 35.5 -14.5 t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-150v-200l275 -200h-950l275 200v200h-150q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5zM50 100h1100q21 0 35.5 -14.5t14.5 -35.5v-50h-1200v50q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe213;" d="M600 1181q75 0 128 -53t53 -128t-53 -128t-128 -53t-128 53t-53 128t53 128t128 53zM602 798h46q34 0 55.5 -28.5t21.5 -86.5q0 -76 39 -183h-324q39 107 39 183q0 58 21.5 86.5t56.5 28.5h45zM250 400h700q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-13 l138 -100h-950l137 100h-12q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5zM50 100h1100q21 0 35.5 -14.5t14.5 -35.5v-50h-1200v50q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe214;" d="M600 1300q47 0 92.5 -53.5t71 -123t25.5 -123.5q0 -78 -55.5 -133.5t-133.5 -55.5t-133.5 55.5t-55.5 133.5q0 62 34 143l144 -143l111 111l-163 163q34 26 63 26zM602 798h46q34 0 55.5 -28.5t21.5 -86.5q0 -76 39 -183h-324q39 107 39 183q0 58 21.5 86.5t56.5 28.5h45 zM250 400h700q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-13l138 -100h-950l137 100h-12q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5zM50 100h1100q21 0 35.5 -14.5t14.5 -35.5v-50h-1200v50q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe215;" d="M600 1200l300 -161v-139h-300q0 -57 18.5 -108t50 -91.5t63 -72t70 -67.5t57.5 -61h-530q-60 83 -90.5 177.5t-30.5 178.5t33 164.5t87.5 139.5t126 96.5t145.5 41.5v-98zM250 400h700q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-13l138 -100h-950l137 100 h-12q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5zM50 100h1100q21 0 35.5 -14.5t14.5 -35.5v-50h-1200v50q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe216;" d="M600 1300q41 0 70.5 -29.5t29.5 -70.5v-78q46 -26 73 -72t27 -100v-50h-400v50q0 54 27 100t73 72v78q0 41 29.5 70.5t70.5 29.5zM400 800h400q54 0 100 -27t72 -73h-172v-100h200v-100h-200v-100h200v-100h-200v-100h200q0 -83 -58.5 -141.5t-141.5 -58.5h-400 q-83 0 -141.5 58.5t-58.5 141.5v400q0 83 58.5 141.5t141.5 58.5z" />
<glyph unicode="&#xe218;" d="M150 1100h900q21 0 35.5 -14.5t14.5 -35.5v-500q0 -21 -14.5 -35.5t-35.5 -14.5h-900q-21 0 -35.5 14.5t-14.5 35.5v500q0 21 14.5 35.5t35.5 14.5zM125 400h950q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-283l224 -224q13 -13 13 -31.5t-13 -32 t-31.5 -13.5t-31.5 13l-88 88h-524l-87 -88q-13 -13 -32 -13t-32 13.5t-13 32t13 31.5l224 224h-289q-10 0 -17.5 7.5t-7.5 17.5v50q0 10 7.5 17.5t17.5 7.5zM541 300l-100 -100h324l-100 100h-124z" />
<glyph unicode="&#xe219;" d="M200 1100h800q83 0 141.5 -58.5t58.5 -141.5v-200h-100q0 41 -29.5 70.5t-70.5 29.5h-250q-41 0 -70.5 -29.5t-29.5 -70.5h-100q0 41 -29.5 70.5t-70.5 29.5h-250q-41 0 -70.5 -29.5t-29.5 -70.5h-100v200q0 83 58.5 141.5t141.5 58.5zM100 600h1000q41 0 70.5 -29.5 t29.5 -70.5v-300h-1200v300q0 41 29.5 70.5t70.5 29.5zM300 100v-50q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v50h200zM1100 100v-50q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v50h200z" />
<glyph unicode="&#xe221;" d="M480 1165l682 -683q31 -31 31 -75.5t-31 -75.5l-131 -131h-481l-517 518q-32 31 -32 75.5t32 75.5l295 296q31 31 75.5 31t76.5 -31zM108 794l342 -342l303 304l-341 341zM250 100h800q21 0 35.5 -14.5t14.5 -35.5v-50h-900v50q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe223;" d="M1057 647l-189 506q-8 19 -27.5 33t-40.5 14h-400q-21 0 -40.5 -14t-27.5 -33l-189 -506q-8 -19 1.5 -33t30.5 -14h625v-150q0 -21 14.5 -35.5t35.5 -14.5t35.5 14.5t14.5 35.5v150h125q21 0 30.5 14t1.5 33zM897 0h-595v50q0 21 14.5 35.5t35.5 14.5h50v50 q0 21 14.5 35.5t35.5 14.5h48v300h200v-300h47q21 0 35.5 -14.5t14.5 -35.5v-50h50q21 0 35.5 -14.5t14.5 -35.5v-50z" />
<glyph unicode="&#xe224;" d="M900 800h300v-575q0 -10 -7.5 -17.5t-17.5 -7.5h-375v591l-300 300v84q0 10 7.5 17.5t17.5 7.5h375v-400zM1200 900h-200v200zM400 600h300v-575q0 -10 -7.5 -17.5t-17.5 -7.5h-650q-10 0 -17.5 7.5t-7.5 17.5v950q0 10 7.5 17.5t17.5 7.5h375v-400zM700 700h-200v200z " />
<glyph unicode="&#xe225;" d="M484 1095h195q75 0 146 -32.5t124 -86t89.5 -122.5t48.5 -142q18 -14 35 -20q31 -10 64.5 6.5t43.5 48.5q10 34 -15 71q-19 27 -9 43q5 8 12.5 11t19 -1t23.5 -16q41 -44 39 -105q-3 -63 -46 -106.5t-104 -43.5h-62q-7 -55 -35 -117t-56 -100l-39 -234q-3 -20 -20 -34.5 t-38 -14.5h-100q-21 0 -33 14.5t-9 34.5l12 70q-49 -14 -91 -14h-195q-24 0 -65 8l-11 -64q-3 -20 -20 -34.5t-38 -14.5h-100q-21 0 -33 14.5t-9 34.5l26 157q-84 74 -128 175l-159 53q-19 7 -33 26t-14 40v50q0 21 14.5 35.5t35.5 14.5h124q11 87 56 166l-111 95 q-16 14 -12.5 23.5t24.5 9.5h203q116 101 250 101zM675 1000h-250q-10 0 -17.5 -7.5t-7.5 -17.5v-50q0 -10 7.5 -17.5t17.5 -7.5h250q10 0 17.5 7.5t7.5 17.5v50q0 10 -7.5 17.5t-17.5 7.5z" />
<glyph unicode="&#xe226;" d="M641 900l423 247q19 8 42 2.5t37 -21.5l32 -38q14 -15 12.5 -36t-17.5 -34l-139 -120h-390zM50 1100h106q67 0 103 -17t66 -71l102 -212h823q21 0 35.5 -14.5t14.5 -35.5v-50q0 -21 -14 -40t-33 -26l-737 -132q-23 -4 -40 6t-26 25q-42 67 -100 67h-300q-62 0 -106 44 t-44 106v200q0 62 44 106t106 44zM173 928h-80q-19 0 -28 -14t-9 -35v-56q0 -51 42 -51h134q16 0 21.5 8t5.5 24q0 11 -16 45t-27 51q-18 28 -43 28zM550 727q-32 0 -54.5 -22.5t-22.5 -54.5t22.5 -54.5t54.5 -22.5t54.5 22.5t22.5 54.5t-22.5 54.5t-54.5 22.5zM130 389 l152 130q18 19 34 24t31 -3.5t24.5 -17.5t25.5 -28q28 -35 50.5 -51t48.5 -13l63 5l48 -179q13 -61 -3.5 -97.5t-67.5 -79.5l-80 -69q-47 -40 -109 -35.5t-103 51.5l-130 151q-40 47 -35.5 109.5t51.5 102.5zM380 377l-102 -88q-31 -27 2 -65l37 -43q13 -15 27.5 -19.5 t31.5 6.5l61 53q19 16 14 49q-2 20 -12 56t-17 45q-11 12 -19 14t-23 -8z" />
<glyph unicode="&#xe227;" d="M625 1200h150q10 0 17.5 -7.5t7.5 -17.5v-109q79 -33 131 -87.5t53 -128.5q1 -46 -15 -84.5t-39 -61t-46 -38t-39 -21.5l-17 -6q6 0 15 -1.5t35 -9t50 -17.5t53 -30t50 -45t35.5 -64t14.5 -84q0 -59 -11.5 -105.5t-28.5 -76.5t-44 -51t-49.5 -31.5t-54.5 -16t-49.5 -6.5 t-43.5 -1v-75q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v75h-100v-75q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v75h-175q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5h75v600h-75q-10 0 -17.5 7.5t-7.5 17.5v150 q0 10 7.5 17.5t17.5 7.5h175v75q0 10 7.5 17.5t17.5 7.5h150q10 0 17.5 -7.5t7.5 -17.5v-75h100v75q0 10 7.5 17.5t17.5 7.5zM400 900v-200h263q28 0 48.5 10.5t30 25t15 29t5.5 25.5l1 10q0 4 -0.5 11t-6 24t-15 30t-30 24t-48.5 11h-263zM400 500v-200h363q28 0 48.5 10.5 t30 25t15 29t5.5 25.5l1 10q0 4 -0.5 11t-6 24t-15 30t-30 24t-48.5 11h-363z" />
<glyph unicode="&#xe230;" d="M212 1198h780q86 0 147 -61t61 -147v-416q0 -51 -18 -142.5t-36 -157.5l-18 -66q-29 -87 -93.5 -146.5t-146.5 -59.5h-572q-82 0 -147 59t-93 147q-8 28 -20 73t-32 143.5t-20 149.5v416q0 86 61 147t147 61zM600 1045q-70 0 -132.5 -11.5t-105.5 -30.5t-78.5 -41.5 t-57 -45t-36 -41t-20.5 -30.5l-6 -12l156 -243h560l156 243q-2 5 -6 12.5t-20 29.5t-36.5 42t-57 44.5t-79 42t-105 29.5t-132.5 12zM762 703h-157l195 261z" />
<glyph unicode="&#xe231;" d="M475 1300h150q103 0 189 -86t86 -189v-500q0 -41 -42 -83t-83 -42h-450q-41 0 -83 42t-42 83v500q0 103 86 189t189 86zM700 300v-225q0 -21 -27 -48t-48 -27h-150q-21 0 -48 27t-27 48v225h300z" />
<glyph unicode="&#xe232;" d="M475 1300h96q0 -150 89.5 -239.5t239.5 -89.5v-446q0 -41 -42 -83t-83 -42h-450q-41 0 -83 42t-42 83v500q0 103 86 189t189 86zM700 300v-225q0 -21 -27 -48t-48 -27h-150q-21 0 -48 27t-27 48v225h300z" />
<glyph unicode="&#xe233;" d="M1294 767l-638 -283l-378 170l-78 -60v-224l100 -150v-199l-150 148l-150 -149v200l100 150v250q0 4 -0.5 10.5t0 9.5t1 8t3 8t6.5 6l47 40l-147 65l642 283zM1000 380l-350 -166l-350 166v147l350 -165l350 165v-147z" />
<glyph unicode="&#xe234;" d="M250 800q62 0 106 -44t44 -106t-44 -106t-106 -44t-106 44t-44 106t44 106t106 44zM650 800q62 0 106 -44t44 -106t-44 -106t-106 -44t-106 44t-44 106t44 106t106 44zM1050 800q62 0 106 -44t44 -106t-44 -106t-106 -44t-106 44t-44 106t44 106t106 44z" />
<glyph unicode="&#xe235;" d="M550 1100q62 0 106 -44t44 -106t-44 -106t-106 -44t-106 44t-44 106t44 106t106 44zM550 700q62 0 106 -44t44 -106t-44 -106t-106 -44t-106 44t-44 106t44 106t106 44zM550 300q62 0 106 -44t44 -106t-44 -106t-106 -44t-106 44t-44 106t44 106t106 44z" />
<glyph unicode="&#xe236;" d="M125 1100h950q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-950q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5zM125 700h950q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-950q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5 t17.5 7.5zM125 300h950q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-950q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe237;" d="M350 1200h500q162 0 256 -93.5t94 -256.5v-500q0 -165 -93.5 -257.5t-256.5 -92.5h-500q-165 0 -257.5 92.5t-92.5 257.5v500q0 165 92.5 257.5t257.5 92.5zM900 1000h-600q-41 0 -70.5 -29.5t-29.5 -70.5v-600q0 -41 29.5 -70.5t70.5 -29.5h600q41 0 70.5 29.5 t29.5 70.5v600q0 41 -29.5 70.5t-70.5 29.5zM350 900h500q21 0 35.5 -14.5t14.5 -35.5v-300q0 -21 -14.5 -35.5t-35.5 -14.5h-500q-21 0 -35.5 14.5t-14.5 35.5v300q0 21 14.5 35.5t35.5 14.5zM400 800v-200h400v200h-400z" />
<glyph unicode="&#xe238;" d="M150 1100h1000q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-50v-200h50q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-50v-200h50q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-50v-200h50q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5 t-35.5 -14.5h-1000q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5h50v200h-50q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5h50v200h-50q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5h50v200h-50q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe239;" d="M650 1187q87 -67 118.5 -156t0 -178t-118.5 -155q-87 66 -118.5 155t0 178t118.5 156zM300 800q124 0 212 -88t88 -212q-124 0 -212 88t-88 212zM1000 800q0 -124 -88 -212t-212 -88q0 124 88 212t212 88zM300 500q124 0 212 -88t88 -212q-124 0 -212 88t-88 212z M1000 500q0 -124 -88 -212t-212 -88q0 124 88 212t212 88zM700 199v-144q0 -21 -14.5 -35.5t-35.5 -14.5t-35.5 14.5t-14.5 35.5v142q40 -4 43 -4q17 0 57 6z" />
<glyph unicode="&#xe240;" d="M745 878l69 19q25 6 45 -12l298 -295q11 -11 15 -26.5t-2 -30.5q-5 -14 -18 -23.5t-28 -9.5h-8q1 0 1 -13q0 -29 -2 -56t-8.5 -62t-20 -63t-33 -53t-51 -39t-72.5 -14h-146q-184 0 -184 288q0 24 10 47q-20 4 -62 4t-63 -4q11 -24 11 -47q0 -288 -184 -288h-142 q-48 0 -84.5 21t-56 51t-32 71.5t-16 75t-3.5 68.5q0 13 2 13h-7q-15 0 -27.5 9.5t-18.5 23.5q-6 15 -2 30.5t15 25.5l298 296q20 18 46 11l76 -19q20 -5 30.5 -22.5t5.5 -37.5t-22.5 -31t-37.5 -5l-51 12l-182 -193h891l-182 193l-44 -12q-20 -5 -37.5 6t-22.5 31t6 37.5 t31 22.5z" />
<glyph unicode="&#xe241;" d="M1200 900h-50q0 21 -4 37t-9.5 26.5t-18 17.5t-22 11t-28.5 5.5t-31 2t-37 0.5h-200v-850q0 -22 25 -34.5t50 -13.5l25 -2v-100h-400v100q4 0 11 0.5t24 3t30 7t24 15t11 24.5v850h-200q-25 0 -37 -0.5t-31 -2t-28.5 -5.5t-22 -11t-18 -17.5t-9.5 -26.5t-4 -37h-50v300 h1000v-300zM500 450h-25q0 15 -4 24.5t-9 14.5t-17 7.5t-20 3t-25 0.5h-100v-425q0 -11 12.5 -17.5t25.5 -7.5h12v-50h-200v50q50 0 50 25v425h-100q-17 0 -25 -0.5t-20 -3t-17 -7.5t-9 -14.5t-4 -24.5h-25v150h500v-150z" />
<glyph unicode="&#xe242;" d="M1000 300v50q-25 0 -55 32q-14 14 -25 31t-16 27l-4 11l-289 747h-69l-300 -754q-18 -35 -39 -56q-9 -9 -24.5 -18.5t-26.5 -14.5l-11 -5v-50h273v50q-49 0 -78.5 21.5t-11.5 67.5l69 176h293l61 -166q13 -34 -3.5 -66.5t-55.5 -32.5v-50h312zM412 691l134 342l121 -342 h-255zM1100 150v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1000q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5h1000q21 0 35.5 -14.5t14.5 -35.5z" />
<glyph unicode="&#xe243;" d="M50 1200h1100q21 0 35.5 -14.5t14.5 -35.5v-1100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5v1100q0 21 14.5 35.5t35.5 14.5zM611 1118h-70q-13 0 -18 -12l-299 -753q-17 -32 -35 -51q-18 -18 -56 -34q-12 -5 -12 -18v-50q0 -8 5.5 -14t14.5 -6 h273q8 0 14 6t6 14v50q0 8 -6 14t-14 6q-55 0 -71 23q-10 14 0 39l63 163h266l57 -153q11 -31 -6 -55q-12 -17 -36 -17q-8 0 -14 -6t-6 -14v-50q0 -8 6 -14t14 -6h313q8 0 14 6t6 14v50q0 7 -5.5 13t-13.5 7q-17 0 -42 25q-25 27 -40 63h-1l-288 748q-5 12 -19 12zM639 611 h-197l103 264z" />
<glyph unicode="&#xe244;" d="M1200 1100h-1200v100h1200v-100zM50 1000h400q21 0 35.5 -14.5t14.5 -35.5v-900q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v900q0 21 14.5 35.5t35.5 14.5zM650 1000h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400 q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5zM700 900v-300h300v300h-300z" />
<glyph unicode="&#xe245;" d="M50 1200h400q21 0 35.5 -14.5t14.5 -35.5v-900q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v900q0 21 14.5 35.5t35.5 14.5zM650 700h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v400 q0 21 14.5 35.5t35.5 14.5zM700 600v-300h300v300h-300zM1200 0h-1200v100h1200v-100z" />
<glyph unicode="&#xe246;" d="M50 1000h400q21 0 35.5 -14.5t14.5 -35.5v-350h100v150q0 21 14.5 35.5t35.5 14.5h400q21 0 35.5 -14.5t14.5 -35.5v-150h100v-100h-100v-150q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v150h-100v-350q0 -21 -14.5 -35.5t-35.5 -14.5h-400 q-21 0 -35.5 14.5t-14.5 35.5v800q0 21 14.5 35.5t35.5 14.5zM700 700v-300h300v300h-300z" />
<glyph unicode="&#xe247;" d="M100 0h-100v1200h100v-1200zM250 1100h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5zM300 1000v-300h300v300h-300zM250 500h900q21 0 35.5 -14.5t14.5 -35.5v-400 q0 -21 -14.5 -35.5t-35.5 -14.5h-900q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe248;" d="M600 1100h150q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-150v-100h450q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-900q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5h350v100h-150q-21 0 -35.5 14.5 t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5h150v100h100v-100zM400 1000v-300h300v300h-300z" />
<glyph unicode="&#xe249;" d="M1200 0h-100v1200h100v-1200zM550 1100h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5zM600 1000v-300h300v300h-300zM50 500h900q21 0 35.5 -14.5t14.5 -35.5v-400 q0 -21 -14.5 -35.5t-35.5 -14.5h-900q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe250;" d="M865 565l-494 -494q-23 -23 -41 -23q-14 0 -22 13.5t-8 38.5v1000q0 25 8 38.5t22 13.5q18 0 41 -23l494 -494q14 -14 14 -35t-14 -35z" />
<glyph unicode="&#xe251;" d="M335 635l494 494q29 29 50 20.5t21 -49.5v-1000q0 -41 -21 -49.5t-50 20.5l-494 494q-14 14 -14 35t14 35z" />
<glyph unicode="&#xe252;" d="M100 900h1000q41 0 49.5 -21t-20.5 -50l-494 -494q-14 -14 -35 -14t-35 14l-494 494q-29 29 -20.5 50t49.5 21z" />
<glyph unicode="&#xe253;" d="M635 865l494 -494q29 -29 20.5 -50t-49.5 -21h-1000q-41 0 -49.5 21t20.5 50l494 494q14 14 35 14t35 -14z" />
<glyph unicode="&#xe254;" d="M700 741v-182l-692 -323v221l413 193l-413 193v221zM1200 0h-800v200h800v-200z" />
<glyph unicode="&#xe255;" d="M1200 900h-200v-100h200v-100h-300v300h200v100h-200v100h300v-300zM0 700h50q0 21 4 37t9.5 26.5t18 17.5t22 11t28.5 5.5t31 2t37 0.5h100v-550q0 -22 -25 -34.5t-50 -13.5l-25 -2v-100h400v100q-4 0 -11 0.5t-24 3t-30 7t-24 15t-11 24.5v550h100q25 0 37 -0.5t31 -2 t28.5 -5.5t22 -11t18 -17.5t9.5 -26.5t4 -37h50v300h-800v-300z" />
<glyph unicode="&#xe256;" d="M800 700h-50q0 21 -4 37t-9.5 26.5t-18 17.5t-22 11t-28.5 5.5t-31 2t-37 0.5h-100v-550q0 -22 25 -34.5t50 -14.5l25 -1v-100h-400v100q4 0 11 0.5t24 3t30 7t24 15t11 24.5v550h-100q-25 0 -37 -0.5t-31 -2t-28.5 -5.5t-22 -11t-18 -17.5t-9.5 -26.5t-4 -37h-50v300 h800v-300zM1100 200h-200v-100h200v-100h-300v300h200v100h-200v100h300v-300z" />
<glyph unicode="&#xe257;" d="M701 1098h160q16 0 21 -11t-7 -23l-464 -464l464 -464q12 -12 7 -23t-21 -11h-160q-13 0 -23 9l-471 471q-7 8 -7 18t7 18l471 471q10 9 23 9z" />
<glyph unicode="&#xe258;" d="M339 1098h160q13 0 23 -9l471 -471q7 -8 7 -18t-7 -18l-471 -471q-10 -9 -23 -9h-160q-16 0 -21 11t7 23l464 464l-464 464q-12 12 -7 23t21 11z" />
<glyph unicode="&#xe259;" d="M1087 882q11 -5 11 -21v-160q0 -13 -9 -23l-471 -471q-8 -7 -18 -7t-18 7l-471 471q-9 10 -9 23v160q0 16 11 21t23 -7l464 -464l464 464q12 12 23 7z" />
<glyph unicode="&#xe260;" d="M618 993l471 -471q9 -10 9 -23v-160q0 -16 -11 -21t-23 7l-464 464l-464 -464q-12 -12 -23 -7t-11 21v160q0 13 9 23l471 471q8 7 18 7t18 -7z" />
<glyph unicode="&#xf8ff;" d="M1000 1200q0 -124 -88 -212t-212 -88q0 124 88 212t212 88zM450 1000h100q21 0 40 -14t26 -33l79 -194q5 1 16 3q34 6 54 9.5t60 7t65.5 1t61 -10t56.5 -23t42.5 -42t29 -64t5 -92t-19.5 -121.5q-1 -7 -3 -19.5t-11 -50t-20.5 -73t-32.5 -81.5t-46.5 -83t-64 -70 t-82.5 -50q-13 -5 -42 -5t-65.5 2.5t-47.5 2.5q-14 0 -49.5 -3.5t-63 -3.5t-43.5 7q-57 25 -104.5 78.5t-75 111.5t-46.5 112t-26 90l-7 35q-15 63 -18 115t4.5 88.5t26 64t39.5 43.5t52 25.5t58.5 13t62.5 2t59.5 -4.5t55.5 -8l-147 192q-12 18 -5.5 30t27.5 12z" />
<glyph unicode="&#x1f511;" d="M250 1200h600q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-150v-500l-255 -178q-19 -9 -32 -1t-13 29v650h-150q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5zM400 1100v-100h300v100h-300z" />
<glyph unicode="&#x1f6aa;" d="M250 1200h750q39 0 69.5 -40.5t30.5 -84.5v-933l-700 -117v950l600 125h-700v-1000h-100v1025q0 23 15.5 49t34.5 26zM500 525v-100l100 20v100z" />
</font>
</defs></svg> ) format('svg')}.glyphicon{position:relative;top:1px;display:inline-block;font-family:'Glyphicons Halflings';font-style:normal;font-weight:400;line-height:1;-webkit-font-smoothing:antialiased;-moz-osx-font-smoothing:grayscale}.glyphicon-asterisk:before{content:"\2a"}.glyphicon-plus:before{content:"\2b"}.glyphicon-eur:before,.glyphicon-euro:before{content:"\20ac"}.glyphicon-minus:before{content:"\2212"}.glyphicon-cloud:before{content:"\2601"}.glyphicon-envelope:before{content:"\2709"}.glyphicon-pencil:before{content:"\270f"}.glyphicon-glass:before{content:"\e001"}.glyphicon-music:before{content:"\e002"}.glyphicon-search:before{content:"\e003"}.glyphicon-heart:before{content:"\e005"}.glyphicon-star:before{content:"\e006"}.glyphicon-star-empty:before{content:"\e007"}.glyphicon-user:before{content:"\e008"}.glyphicon-film:before{content:"\e009"}.glyphicon-th-large:before{content:"\e010"}.glyphicon-th:before{content:"\e011"}.glyphicon-th-list:before{content:"\e012"}.glyphicon-ok:before{content:"\e013"}.glyphicon-remove:before{content:"\e014"}.glyphicon-zoom-in:before{content:"\e015"}.glyphicon-zoom-out:before{content:"\e016"}.glyphicon-off:before{content:"\e017"}.glyphicon-signal:before{content:"\e018"}.glyphicon-cog:before{content:"\e019"}.glyphicon-trash:before{content:"\e020"}.glyphicon-home:before{content:"\e021"}.glyphicon-file:before{content:"\e022"}.glyphicon-time:before{content:"\e023"}.glyphicon-road:before{content:"\e024"}.glyphicon-download-alt:before{content:"\e025"}.glyphicon-download:before{content:"\e026"}.glyphicon-upload:before{content:"\e027"}.glyphicon-inbox:before{content:"\e028"}.glyphicon-play-circle:before{content:"\e029"}.glyphicon-repeat:before{content:"\e030"}.glyphicon-refresh:before{content:"\e031"}.glyphicon-list-alt:before{content:"\e032"}.glyphicon-lock:before{content:"\e033"}.glyphicon-flag:before{content:"\e034"}.glyphicon-headphones:before{content:"\e035"}.glyphicon-volume-off:before{content:"\e036"}.glyphicon-volume-down:before{content:"\e037"}.glyphicon-volume-up:before{content:"\e038"}.glyphicon-qrcode:before{content:"\e039"}.glyphicon-barcode:before{content:"\e040"}.glyphicon-tag:before{content:"\e041"}.glyphicon-tags:before{content:"\e042"}.glyphicon-book:before{content:"\e043"}.glyphicon-bookmark:before{content:"\e044"}.glyphicon-print:before{content:"\e045"}.glyphicon-camera:before{content:"\e046"}.glyphicon-font:before{content:"\e047"}.glyphicon-bold:before{content:"\e048"}.glyphicon-italic:before{content:"\e049"}.glyphicon-text-height:before{content:"\e050"}.glyphicon-text-width:before{content:"\e051"}.glyphicon-align-left:before{content:"\e052"}.glyphicon-align-center:before{content:"\e053"}.glyphicon-align-right:before{content:"\e054"}.glyphicon-align-justify:before{content:"\e055"}.glyphicon-list:before{content:"\e056"}.glyphicon-indent-left:before{content:"\e057"}.glyphicon-indent-right:before{content:"\e058"}.glyphicon-facetime-video:before{content:"\e059"}.glyphicon-picture:before{content:"\e060"}.glyphicon-map-marker:before{content:"\e062"}.glyphicon-adjust:before{content:"\e063"}.glyphicon-tint:before{content:"\e064"}.glyphicon-edit:before{content:"\e065"}.glyphicon-share:before{content:"\e066"}.glyphicon-check:before{content:"\e067"}.glyphicon-move:before{content:"\e068"}.glyphicon-step-backward:before{content:"\e069"}.glyphicon-fast-backward:before{content:"\e070"}.glyphicon-backward:before{content:"\e071"}.glyphicon-play:before{content:"\e072"}.glyphicon-pause:before{content:"\e073"}.glyphicon-stop:before{content:"\e074"}.glyphicon-forward:before{content:"\e075"}.glyphicon-fast-forward:before{content:"\e076"}.glyphicon-step-forward:before{content:"\e077"}.glyphicon-eject:before{content:"\e078"}.glyphicon-chevron-left:before{content:"\e079"}.glyphicon-chevron-right:before{content:"\e080"}.glyphicon-plus-sign:before{content:"\e081"}.glyphicon-minus-sign:before{content:"\e082"}.glyphicon-remove-sign:before{content:"\e083"}.glyphicon-ok-sign:before{content:"\e084"}.glyphicon-question-sign:before{content:"\e085"}.glyphicon-info-sign:before{content:"\e086"}.glyphicon-screenshot:before{content:"\e087"}.glyphicon-remove-circle:before{content:"\e088"}.glyphicon-ok-circle:before{content:"\e089"}.glyphicon-ban-circle:before{content:"\e090"}.glyphicon-arrow-left:before{content:"\e091"}.glyphicon-arrow-right:before{content:"\e092"}.glyphicon-arrow-up:before{content:"\e093"}.glyphicon-arrow-down:before{content:"\e094"}.glyphicon-share-alt:before{content:"\e095"}.glyphicon-resize-full:before{content:"\e096"}.glyphicon-resize-small:before{content:"\e097"}.glyphicon-exclamation-sign:before{content:"\e101"}.glyphicon-gift:before{content:"\e102"}.glyphicon-leaf:before{content:"\e103"}.glyphicon-fire:before{content:"\e104"}.glyphicon-eye-open:before{content:"\e105"}.glyphicon-eye-close:before{content:"\e106"}.glyphicon-warning-sign:before{content:"\e107"}.glyphicon-plane:before{content:"\e108"}.glyphicon-calendar:before{content:"\e109"}.glyphicon-random:before{content:"\e110"}.glyphicon-comment:before{content:"\e111"}.glyphicon-magnet:before{content:"\e112"}.glyphicon-chevron-up:before{content:"\e113"}.glyphicon-chevron-down:before{content:"\e114"}.glyphicon-retweet:before{content:"\e115"}.glyphicon-shopping-cart:before{content:"\e116"}.glyphicon-folder-close:before{content:"\e117"}.glyphicon-folder-open:before{content:"\e118"}.glyphicon-resize-vertical:before{content:"\e119"}.glyphicon-resize-horizontal:before{content:"\e120"}.glyphicon-hdd:before{content:"\e121"}.glyphicon-bullhorn:before{content:"\e122"}.glyphicon-bell:before{content:"\e123"}.glyphicon-certificate:before{content:"\e124"}.glyphicon-thumbs-up:before{content:"\e125"}.glyphicon-thumbs-down:before{content:"\e126"}.glyphicon-hand-right:before{content:"\e127"}.glyphicon-hand-left:before{content:"\e128"}.glyphicon-hand-up:before{content:"\e129"}.glyphicon-hand-down:before{content:"\e130"}.glyphicon-circle-arrow-right:before{content:"\e131"}.glyphicon-circle-arrow-left:before{content:"\e132"}.glyphicon-circle-arrow-up:before{content:"\e133"}.glyphicon-circle-arrow-down:before{content:"\e134"}.glyphicon-globe:before{content:"\e135"}.glyphicon-wrench:before{content:"\e136"}.glyphicon-tasks:before{content:"\e137"}.glyphicon-filter:before{content:"\e138"}.glyphicon-briefcase:before{content:"\e139"}.glyphicon-fullscreen:before{content:"\e140"}.glyphicon-dashboard:before{content:"\e141"}.glyphicon-paperclip:before{content:"\e142"}.glyphicon-heart-empty:before{content:"\e143"}.glyphicon-link:before{content:"\e144"}.glyphicon-phone:before{content:"\e145"}.glyphicon-pushpin:before{content:"\e146"}.glyphicon-usd:before{content:"\e148"}.glyphicon-gbp:before{content:"\e149"}.glyphicon-sort:before{content:"\e150"}.glyphicon-sort-by-alphabet:before{content:"\e151"}.glyphicon-sort-by-alphabet-alt:before{content:"\e152"}.glyphicon-sort-by-order:before{content:"\e153"}.glyphicon-sort-by-order-alt:before{content:"\e154"}.glyphicon-sort-by-attributes:before{content:"\e155"}.glyphicon-sort-by-attributes-alt:before{content:"\e156"}.glyphicon-unchecked:before{content:"\e157"}.glyphicon-expand:before{content:"\e158"}.glyphicon-collapse-down:before{content:"\e159"}.glyphicon-collapse-up:before{content:"\e160"}.glyphicon-log-in:before{content:"\e161"}.glyphicon-flash:before{content:"\e162"}.glyphicon-log-out:before{content:"\e163"}.glyphicon-new-window:before{content:"\e164"}.glyphicon-record:before{content:"\e165"}.glyphicon-save:before{content:"\e166"}.glyphicon-open:before{content:"\e167"}.glyphicon-saved:before{content:"\e168"}.glyphicon-import:before{content:"\e169"}.glyphicon-export:before{content:"\e170"}.glyphicon-send:before{content:"\e171"}.glyphicon-floppy-disk:before{content:"\e172"}.glyphicon-floppy-saved:before{content:"\e173"}.glyphicon-floppy-remove:before{content:"\e174"}.glyphicon-floppy-save:before{content:"\e175"}.glyphicon-floppy-open:before{content:"\e176"}.glyphicon-credit-card:before{content:"\e177"}.glyphicon-transfer:before{content:"\e178"}.glyphicon-cutlery:before{content:"\e179"}.glyphicon-header:before{content:"\e180"}.glyphicon-compressed:before{content:"\e181"}.glyphicon-earphone:before{content:"\e182"}.glyphicon-phone-alt:before{content:"\e183"}.glyphicon-tower:before{content:"\e184"}.glyphicon-stats:before{content:"\e185"}.glyphicon-sd-video:before{content:"\e186"}.glyphicon-hd-video:before{content:"\e187"}.glyphicon-subtitles:before{content:"\e188"}.glyphicon-sound-stereo:before{content:"\e189"}.glyphicon-sound-dolby:before{content:"\e190"}.glyphicon-sound-5-1:before{content:"\e191"}.glyphicon-sound-6-1:before{content:"\e192"}.glyphicon-sound-7-1:before{content:"\e193"}.glyphicon-copyright-mark:before{content:"\e194"}.glyphicon-registration-mark:before{content:"\e195"}.glyphicon-cloud-download:before{content:"\e197"}.glyphicon-cloud-upload:before{content:"\e198"}.glyphicon-tree-conifer:before{content:"\e199"}.glyphicon-tree-deciduous:before{content:"\e200"}.glyphicon-cd:before{content:"\e201"}.glyphicon-save-file:before{content:"\e202"}.glyphicon-open-file:before{content:"\e203"}.glyphicon-level-up:before{content:"\e204"}.glyphicon-copy:before{content:"\e205"}.glyphicon-paste:before{content:"\e206"}.glyphicon-alert:before{content:"\e209"}.glyphicon-equalizer:before{content:"\e210"}.glyphicon-king:before{content:"\e211"}.glyphicon-queen:before{content:"\e212"}.glyphicon-pawn:before{content:"\e213"}.glyphicon-bishop:before{content:"\e214"}.glyphicon-knight:before{content:"\e215"}.glyphicon-baby-formula:before{content:"\e216"}.glyphicon-tent:before{content:"\26fa"}.glyphicon-blackboard:before{content:"\e218"}.glyphicon-bed:before{content:"\e219"}.glyphicon-apple:before{content:"\f8ff"}.glyphicon-erase:before{content:"\e221"}.glyphicon-hourglass:before{content:"\231b"}.glyphicon-lamp:before{content:"\e223"}.glyphicon-duplicate:before{content:"\e224"}.glyphicon-piggy-bank:before{content:"\e225"}.glyphicon-scissors:before{content:"\e226"}.glyphicon-bitcoin:before{content:"\e227"}.glyphicon-btc:before{content:"\e227"}.glyphicon-xbt:before{content:"\e227"}.glyphicon-yen:before{content:"\00a5"}.glyphicon-jpy:before{content:"\00a5"}.glyphicon-ruble:before{content:"\20bd"}.glyphicon-rub:before{content:"\20bd"}.glyphicon-scale:before{content:"\e230"}.glyphicon-ice-lolly:before{content:"\e231"}.glyphicon-ice-lolly-tasted:before{content:"\e232"}.glyphicon-education:before{content:"\e233"}.glyphicon-option-horizontal:before{content:"\e234"}.glyphicon-option-vertical:before{content:"\e235"}.glyphicon-menu-hamburger:before{content:"\e236"}.glyphicon-modal-window:before{content:"\e237"}.glyphicon-oil:before{content:"\e238"}.glyphicon-grain:before{content:"\e239"}.glyphicon-sunglasses:before{content:"\e240"}.glyphicon-text-size:before{content:"\e241"}.glyphicon-text-color:before{content:"\e242"}.glyphicon-text-background:before{content:"\e243"}.glyphicon-object-align-top:before{content:"\e244"}.glyphicon-object-align-bottom:before{content:"\e245"}.glyphicon-object-align-horizontal:before{content:"\e246"}.glyphicon-object-align-left:before{content:"\e247"}.glyphicon-object-align-vertical:before{content:"\e248"}.glyphicon-object-align-right:before{content:"\e249"}.glyphicon-triangle-right:before{content:"\e250"}.glyphicon-triangle-left:before{content:"\e251"}.glyphicon-triangle-bottom:before{content:"\e252"}.glyphicon-triangle-top:before{content:"\e253"}.glyphicon-console:before{content:"\e254"}.glyphicon-superscript:before{content:"\e255"}.glyphicon-subscript:before{content:"\e256"}.glyphicon-menu-left:before{content:"\e257"}.glyphicon-menu-right:before{content:"\e258"}.glyphicon-menu-down:before{content:"\e259"}.glyphicon-menu-up:before{content:"\e260"}*{-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}:after,:before{-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}html{font-size:10px;-webkit-tap-highlight-color:rgba(0,0,0,0)}body{font-family:"Helvetica Neue",Helvetica,Arial,sans-serif;font-size:14px;line-height:1.42857143;color:#333;background-color:#fff}button,input,select,textarea{font-family:inherit;font-size:inherit;line-height:inherit}a{color:#337ab7;text-decoration:none}a:focus,a:hover{color:#23527c;text-decoration:underline}a:focus{outline:thin dotted;outline:5px auto -webkit-focus-ring-color;outline-offset:-2px}figure{margin:0}img{vertical-align:middle}.carousel-inner>.item>a>img,.carousel-inner>.item>img,.img-responsive,.thumbnail a>img,.thumbnail>img{display:block;max-width:100%;height:auto}.img-rounded{border-radius:6px}.img-thumbnail{display:inline-block;max-width:100%;height:auto;padding:4px;line-height:1.42857143;background-color:#fff;border:1px solid #ddd;border-radius:4px;-webkit-transition:all .2s ease-in-out;-o-transition:all .2s ease-in-out;transition:all .2s ease-in-out}.img-circle{border-radius:50%}hr{margin-top:20px;margin-bottom:20px;border:0;border-top:1px solid #eee}.sr-only{position:absolute;width:1px;height:1px;padding:0;margin:-1px;overflow:hidden;clip:rect(0,0,0,0);border:0}.sr-only-focusable:active,.sr-only-focusable:focus{position:static;width:auto;height:auto;margin:0;overflow:visible;clip:auto}[role=button]{cursor:pointer}.h1,.h2,.h3,.h4,.h5,.h6,h1,h2,h3,h4,h5,h6{font-family:inherit;font-weight:500;line-height:1.1;color:inherit}.h1 .small,.h1 small,.h2 .small,.h2 small,.h3 .small,.h3 small,.h4 .small,.h4 small,.h5 .small,.h5 small,.h6 .small,.h6 small,h1 .small,h1 small,h2 .small,h2 small,h3 .small,h3 small,h4 .small,h4 small,h5 .small,h5 small,h6 .small,h6 small{font-weight:400;line-height:1;color:#777}.h1,.h2,.h3,h1,h2,h3{margin-top:20px;margin-bottom:10px}.h1 .small,.h1 small,.h2 .small,.h2 small,.h3 .small,.h3 small,h1 .small,h1 small,h2 .small,h2 small,h3 .small,h3 small{font-size:65%}.h4,.h5,.h6,h4,h5,h6{margin-top:10px;margin-bottom:10px}.h4 .small,.h4 small,.h5 .small,.h5 small,.h6 .small,.h6 small,h4 .small,h4 small,h5 .small,h5 small,h6 .small,h6 small{font-size:75%}.h1,h1{font-size:36px}.h2,h2{font-size:30px}.h3,h3{font-size:24px}.h4,h4{font-size:18px}.h5,h5{font-size:14px}.h6,h6{font-size:12px}p{margin:0 0 10px}.lead{margin-bottom:20px;font-size:16px;font-weight:300;line-height:1.4}@media (min-width:768px){.lead{font-size:21px}}.small,small{font-size:85%}.mark,mark{padding:.2em;background-color:#fcf8e3}.text-left{text-align:left}.text-right{text-align:right}.text-center{text-align:center}.text-justify{text-align:justify}.text-nowrap{white-space:nowrap}.text-lowercase{text-transform:lowercase}.text-uppercase{text-transform:uppercase}.text-capitalize{text-transform:capitalize}.text-muted{color:#777}.text-primary{color:#337ab7}a.text-primary:focus,a.text-primary:hover{color:#286090}.text-success{color:#3c763d}a.text-success:focus,a.text-success:hover{color:#2b542c}.text-info{color:#31708f}a.text-info:focus,a.text-info:hover{color:#245269}.text-warning{color:#8a6d3b}a.text-warning:focus,a.text-warning:hover{color:#66512c}.text-danger{color:#a94442}a.text-danger:focus,a.text-danger:hover{color:#843534}.bg-primary{color:#fff;background-color:#337ab7}a.bg-primary:focus,a.bg-primary:hover{background-color:#286090}.bg-success{background-color:#dff0d8}a.bg-success:focus,a.bg-success:hover{background-color:#c1e2b3}.bg-info{background-color:#d9edf7}a.bg-info:focus,a.bg-info:hover{background-color:#afd9ee}.bg-warning{background-color:#fcf8e3}a.bg-warning:focus,a.bg-warning:hover{background-color:#f7ecb5}.bg-danger{background-color:#f2dede}a.bg-danger:focus,a.bg-danger:hover{background-color:#e4b9b9}.page-header{padding-bottom:9px;margin:40px 0 20px;border-bottom:1px solid #eee}ol,ul{margin-top:0;margin-bottom:10px}ol ol,ol ul,ul ol,ul ul{margin-bottom:0}.list-unstyled{padding-left:0;list-style:none}.list-inline{padding-left:0;margin-left:-5px;list-style:none}.list-inline>li{display:inline-block;padding-right:5px;padding-left:5px}dl{margin-top:0;margin-bottom:20px}dd,dt{line-height:1.42857143}dt{font-weight:700}dd{margin-left:0}@media (min-width:768px){.dl-horizontal dt{float:left;width:160px;overflow:hidden;clear:left;text-align:right;text-overflow:ellipsis;white-space:nowrap}.dl-horizontal dd{margin-left:180px}}abbr[data-original-title],abbr[title]{cursor:help;border-bottom:1px dotted #777}.initialism{font-size:90%;text-transform:uppercase}blockquote{padding:10px 20px;margin:0 0 20px;font-size:17.5px;border-left:5px solid #eee}blockquote ol:last-child,blockquote p:last-child,blockquote ul:last-child{margin-bottom:0}blockquote .small,blockquote footer,blockquote small{display:block;font-size:80%;line-height:1.42857143;color:#777}blockquote .small:before,blockquote footer:before,blockquote small:before{content:'\2014 \00A0'}.blockquote-reverse,blockquote.pull-right{padding-right:15px;padding-left:0;text-align:right;border-right:5px solid #eee;border-left:0}.blockquote-reverse .small:before,.blockquote-reverse footer:before,.blockquote-reverse small:before,blockquote.pull-right .small:before,blockquote.pull-right footer:before,blockquote.pull-right small:before{content:''}.blockquote-reverse .small:after,.blockquote-reverse footer:after,.blockquote-reverse small:after,blockquote.pull-right .small:after,blockquote.pull-right footer:after,blockquote.pull-right small:after{content:'\00A0 \2014'}address{margin-bottom:20px;font-style:normal;line-height:1.42857143}code,kbd,pre,samp{font-family:monospace}code{padding:2px 4px;font-size:90%;color:#c7254e;background-color:#f9f2f4;border-radius:4px}kbd{padding:2px 4px;font-size:90%;color:#fff;background-color:#333;border-radius:3px;-webkit-box-shadow:inset 0 -1px 0 rgba(0,0,0,.25);box-shadow:inset 0 -1px 0 rgba(0,0,0,.25)}kbd kbd{padding:0;font-size:100%;font-weight:700;-webkit-box-shadow:none;box-shadow:none}pre{display:block;padding:9.5px;margin:0 0 10px;font-size:13px;line-height:1.42857143;color:#333;word-break:break-all;word-wrap:break-word;background-color:#f5f5f5;border:1px solid #ccc;border-radius:4px}pre code{padding:0;font-size:inherit;color:inherit;white-space:pre-wrap;background-color:transparent;border-radius:0}.pre-scrollable{max-height:340px;overflow-y:scroll}.container{padding-right:15px;padding-left:15px;margin-right:auto;margin-left:auto}@media (min-width:768px){.container{width:750px}}@media (min-width:992px){.container{width:970px}}@media (min-width:1200px){.container{width:1170px}}.container-fluid{padding-right:15px;padding-left:15px;margin-right:auto;margin-left:auto}.row{margin-right:-15px;margin-left:-15px}.col-lg-1,.col-lg-10,.col-lg-11,.col-lg-12,.col-lg-2,.col-lg-3,.col-lg-4,.col-lg-5,.col-lg-6,.col-lg-7,.col-lg-8,.col-lg-9,.col-md-1,.col-md-10,.col-md-11,.col-md-12,.col-md-2,.col-md-3,.col-md-4,.col-md-5,.col-md-6,.col-md-7,.col-md-8,.col-md-9,.col-sm-1,.col-sm-10,.col-sm-11,.col-sm-12,.col-sm-2,.col-sm-3,.col-sm-4,.col-sm-5,.col-sm-6,.col-sm-7,.col-sm-8,.col-sm-9,.col-xs-1,.col-xs-10,.col-xs-11,.col-xs-12,.col-xs-2,.col-xs-3,.col-xs-4,.col-xs-5,.col-xs-6,.col-xs-7,.col-xs-8,.col-xs-9{position:relative;min-height:1px;padding-right:15px;padding-left:15px}.col-xs-1,.col-xs-10,.col-xs-11,.col-xs-12,.col-xs-2,.col-xs-3,.col-xs-4,.col-xs-5,.col-xs-6,.col-xs-7,.col-xs-8,.col-xs-9{float:left}.col-xs-12{width:100%}.col-xs-11{width:91.66666667%}.col-xs-10{width:83.33333333%}.col-xs-9{width:75%}.col-xs-8{width:66.66666667%}.col-xs-7{width:58.33333333%}.col-xs-6{width:50%}.col-xs-5{width:41.66666667%}.col-xs-4{width:33.33333333%}.col-xs-3{width:25%}.col-xs-2{width:16.66666667%}.col-xs-1{width:8.33333333%}.col-xs-pull-12{right:100%}.col-xs-pull-11{right:91.66666667%}.col-xs-pull-10{right:83.33333333%}.col-xs-pull-9{right:75%}.col-xs-pull-8{right:66.66666667%}.col-xs-pull-7{right:58.33333333%}.col-xs-pull-6{right:50%}.col-xs-pull-5{right:41.66666667%}.col-xs-pull-4{right:33.33333333%}.col-xs-pull-3{right:25%}.col-xs-pull-2{right:16.66666667%}.col-xs-pull-1{right:8.33333333%}.col-xs-pull-0{right:auto}.col-xs-push-12{left:100%}.col-xs-push-11{left:91.66666667%}.col-xs-push-10{left:83.33333333%}.col-xs-push-9{left:75%}.col-xs-push-8{left:66.66666667%}.col-xs-push-7{left:58.33333333%}.col-xs-push-6{left:50%}.col-xs-push-5{left:41.66666667%}.col-xs-push-4{left:33.33333333%}.col-xs-push-3{left:25%}.col-xs-push-2{left:16.66666667%}.col-xs-push-1{left:8.33333333%}.col-xs-push-0{left:auto}.col-xs-offset-12{margin-left:100%}.col-xs-offset-11{margin-left:91.66666667%}.col-xs-offset-10{margin-left:83.33333333%}.col-xs-offset-9{margin-left:75%}.col-xs-offset-8{margin-left:66.66666667%}.col-xs-offset-7{margin-left:58.33333333%}.col-xs-offset-6{margin-left:50%}.col-xs-offset-5{margin-left:41.66666667%}.col-xs-offset-4{margin-left:33.33333333%}.col-xs-offset-3{margin-left:25%}.col-xs-offset-2{margin-left:16.66666667%}.col-xs-offset-1{margin-left:8.33333333%}.col-xs-offset-0{margin-left:0}@media (min-width:768px){.col-sm-1,.col-sm-10,.col-sm-11,.col-sm-12,.col-sm-2,.col-sm-3,.col-sm-4,.col-sm-5,.col-sm-6,.col-sm-7,.col-sm-8,.col-sm-9{float:left}.col-sm-12{width:100%}.col-sm-11{width:91.66666667%}.col-sm-10{width:83.33333333%}.col-sm-9{width:75%}.col-sm-8{width:66.66666667%}.col-sm-7{width:58.33333333%}.col-sm-6{width:50%}.col-sm-5{width:41.66666667%}.col-sm-4{width:33.33333333%}.col-sm-3{width:25%}.col-sm-2{width:16.66666667%}.col-sm-1{width:8.33333333%}.col-sm-pull-12{right:100%}.col-sm-pull-11{right:91.66666667%}.col-sm-pull-10{right:83.33333333%}.col-sm-pull-9{right:75%}.col-sm-pull-8{right:66.66666667%}.col-sm-pull-7{right:58.33333333%}.col-sm-pull-6{right:50%}.col-sm-pull-5{right:41.66666667%}.col-sm-pull-4{right:33.33333333%}.col-sm-pull-3{right:25%}.col-sm-pull-2{right:16.66666667%}.col-sm-pull-1{right:8.33333333%}.col-sm-pull-0{right:auto}.col-sm-push-12{left:100%}.col-sm-push-11{left:91.66666667%}.col-sm-push-10{left:83.33333333%}.col-sm-push-9{left:75%}.col-sm-push-8{left:66.66666667%}.col-sm-push-7{left:58.33333333%}.col-sm-push-6{left:50%}.col-sm-push-5{left:41.66666667%}.col-sm-push-4{left:33.33333333%}.col-sm-push-3{left:25%}.col-sm-push-2{left:16.66666667%}.col-sm-push-1{left:8.33333333%}.col-sm-push-0{left:auto}.col-sm-offset-12{margin-left:100%}.col-sm-offset-11{margin-left:91.66666667%}.col-sm-offset-10{margin-left:83.33333333%}.col-sm-offset-9{margin-left:75%}.col-sm-offset-8{margin-left:66.66666667%}.col-sm-offset-7{margin-left:58.33333333%}.col-sm-offset-6{margin-left:50%}.col-sm-offset-5{margin-left:41.66666667%}.col-sm-offset-4{margin-left:33.33333333%}.col-sm-offset-3{margin-left:25%}.col-sm-offset-2{margin-left:16.66666667%}.col-sm-offset-1{margin-left:8.33333333%}.col-sm-offset-0{margin-left:0}}@media (min-width:992px){.col-md-1,.col-md-10,.col-md-11,.col-md-12,.col-md-2,.col-md-3,.col-md-4,.col-md-5,.col-md-6,.col-md-7,.col-md-8,.col-md-9{float:left}.col-md-12{width:100%}.col-md-11{width:91.66666667%}.col-md-10{width:83.33333333%}.col-md-9{width:75%}.col-md-8{width:66.66666667%}.col-md-7{width:58.33333333%}.col-md-6{width:50%}.col-md-5{width:41.66666667%}.col-md-4{width:33.33333333%}.col-md-3{width:25%}.col-md-2{width:16.66666667%}.col-md-1{width:8.33333333%}.col-md-pull-12{right:100%}.col-md-pull-11{right:91.66666667%}.col-md-pull-10{right:83.33333333%}.col-md-pull-9{right:75%}.col-md-pull-8{right:66.66666667%}.col-md-pull-7{right:58.33333333%}.col-md-pull-6{right:50%}.col-md-pull-5{right:41.66666667%}.col-md-pull-4{right:33.33333333%}.col-md-pull-3{right:25%}.col-md-pull-2{right:16.66666667%}.col-md-pull-1{right:8.33333333%}.col-md-pull-0{right:auto}.col-md-push-12{left:100%}.col-md-push-11{left:91.66666667%}.col-md-push-10{left:83.33333333%}.col-md-push-9{left:75%}.col-md-push-8{left:66.66666667%}.col-md-push-7{left:58.33333333%}.col-md-push-6{left:50%}.col-md-push-5{left:41.66666667%}.col-md-push-4{left:33.33333333%}.col-md-push-3{left:25%}.col-md-push-2{left:16.66666667%}.col-md-push-1{left:8.33333333%}.col-md-push-0{left:auto}.col-md-offset-12{margin-left:100%}.col-md-offset-11{margin-left:91.66666667%}.col-md-offset-10{margin-left:83.33333333%}.col-md-offset-9{margin-left:75%}.col-md-offset-8{margin-left:66.66666667%}.col-md-offset-7{margin-left:58.33333333%}.col-md-offset-6{margin-left:50%}.col-md-offset-5{margin-left:41.66666667%}.col-md-offset-4{margin-left:33.33333333%}.col-md-offset-3{margin-left:25%}.col-md-offset-2{margin-left:16.66666667%}.col-md-offset-1{margin-left:8.33333333%}.col-md-offset-0{margin-left:0}}@media (min-width:1200px){.col-lg-1,.col-lg-10,.col-lg-11,.col-lg-12,.col-lg-2,.col-lg-3,.col-lg-4,.col-lg-5,.col-lg-6,.col-lg-7,.col-lg-8,.col-lg-9{float:left}.col-lg-12{width:100%}.col-lg-11{width:91.66666667%}.col-lg-10{width:83.33333333%}.col-lg-9{width:75%}.col-lg-8{width:66.66666667%}.col-lg-7{width:58.33333333%}.col-lg-6{width:50%}.col-lg-5{width:41.66666667%}.col-lg-4{width:33.33333333%}.col-lg-3{width:25%}.col-lg-2{width:16.66666667%}.col-lg-1{width:8.33333333%}.col-lg-pull-12{right:100%}.col-lg-pull-11{right:91.66666667%}.col-lg-pull-10{right:83.33333333%}.col-lg-pull-9{right:75%}.col-lg-pull-8{right:66.66666667%}.col-lg-pull-7{right:58.33333333%}.col-lg-pull-6{right:50%}.col-lg-pull-5{right:41.66666667%}.col-lg-pull-4{right:33.33333333%}.col-lg-pull-3{right:25%}.col-lg-pull-2{right:16.66666667%}.col-lg-pull-1{right:8.33333333%}.col-lg-pull-0{right:auto}.col-lg-push-12{left:100%}.col-lg-push-11{left:91.66666667%}.col-lg-push-10{left:83.33333333%}.col-lg-push-9{left:75%}.col-lg-push-8{left:66.66666667%}.col-lg-push-7{left:58.33333333%}.col-lg-push-6{left:50%}.col-lg-push-5{left:41.66666667%}.col-lg-push-4{left:33.33333333%}.col-lg-push-3{left:25%}.col-lg-push-2{left:16.66666667%}.col-lg-push-1{left:8.33333333%}.col-lg-push-0{left:auto}.col-lg-offset-12{margin-left:100%}.col-lg-offset-11{margin-left:91.66666667%}.col-lg-offset-10{margin-left:83.33333333%}.col-lg-offset-9{margin-left:75%}.col-lg-offset-8{margin-left:66.66666667%}.col-lg-offset-7{margin-left:58.33333333%}.col-lg-offset-6{margin-left:50%}.col-lg-offset-5{margin-left:41.66666667%}.col-lg-offset-4{margin-left:33.33333333%}.col-lg-offset-3{margin-left:25%}.col-lg-offset-2{margin-left:16.66666667%}.col-lg-offset-1{margin-left:8.33333333%}.col-lg-offset-0{margin-left:0}}table{background-color:transparent}caption{padding-top:8px;padding-bottom:8px;color:#777;text-align:left}th{}.table{width:100%;max-width:100%;margin-bottom:20px}.table>tbody>tr>td,.table>tbody>tr>th,.table>tfoot>tr>td,.table>tfoot>tr>th,.table>thead>tr>td,.table>thead>tr>th{padding:8px;line-height:1.42857143;vertical-align:top;border-top:1px solid #ddd}.table>thead>tr>th{vertical-align:bottom;border-bottom:2px solid #ddd}.table>caption+thead>tr:first-child>td,.table>caption+thead>tr:first-child>th,.table>colgroup+thead>tr:first-child>td,.table>colgroup+thead>tr:first-child>th,.table>thead:first-child>tr:first-child>td,.table>thead:first-child>tr:first-child>th{border-top:0}.table>tbody+tbody{border-top:2px solid #ddd}.table .table{background-color:#fff}.table-condensed>tbody>tr>td,.table-condensed>tbody>tr>th,.table-condensed>tfoot>tr>td,.table-condensed>tfoot>tr>th,.table-condensed>thead>tr>td,.table-condensed>thead>tr>th{padding:5px}.table-bordered{border:1px solid #ddd}.table-bordered>tbody>tr>td,.table-bordered>tbody>tr>th,.table-bordered>tfoot>tr>td,.table-bordered>tfoot>tr>th,.table-bordered>thead>tr>td,.table-bordered>thead>tr>th{border:1px solid #ddd}.table-bordered>thead>tr>td,.table-bordered>thead>tr>th{border-bottom-width:2px}.table-striped>tbody>tr:nth-of-type(odd){background-color:#f9f9f9}.table-hover>tbody>tr:hover{background-color:#f5f5f5}table col[class*=col-]{position:static;display:table-column;float:none}table td[class*=col-],table th[class*=col-]{position:static;display:table-cell;float:none}.table>tbody>tr.active>td,.table>tbody>tr.active>th,.table>tbody>tr>td.active,.table>tbody>tr>th.active,.table>tfoot>tr.active>td,.table>tfoot>tr.active>th,.table>tfoot>tr>td.active,.table>tfoot>tr>th.active,.table>thead>tr.active>td,.table>thead>tr.active>th,.table>thead>tr>td.active,.table>thead>tr>th.active{background-color:#f5f5f5}.table-hover>tbody>tr.active:hover>td,.table-hover>tbody>tr.active:hover>th,.table-hover>tbody>tr:hover>.active,.table-hover>tbody>tr>td.active:hover,.table-hover>tbody>tr>th.active:hover{background-color:#e8e8e8}.table>tbody>tr.success>td,.table>tbody>tr.success>th,.table>tbody>tr>td.success,.table>tbody>tr>th.success,.table>tfoot>tr.success>td,.table>tfoot>tr.success>th,.table>tfoot>tr>td.success,.table>tfoot>tr>th.success,.table>thead>tr.success>td,.table>thead>tr.success>th,.table>thead>tr>td.success,.table>thead>tr>th.success{background-color:#dff0d8}.table-hover>tbody>tr.success:hover>td,.table-hover>tbody>tr.success:hover>th,.table-hover>tbody>tr:hover>.success,.table-hover>tbody>tr>td.success:hover,.table-hover>tbody>tr>th.success:hover{background-color:#d0e9c6}.table>tbody>tr.info>td,.table>tbody>tr.info>th,.table>tbody>tr>td.info,.table>tbody>tr>th.info,.table>tfoot>tr.info>td,.table>tfoot>tr.info>th,.table>tfoot>tr>td.info,.table>tfoot>tr>th.info,.table>thead>tr.info>td,.table>thead>tr.info>th,.table>thead>tr>td.info,.table>thead>tr>th.info{background-color:#d9edf7}.table-hover>tbody>tr.info:hover>td,.table-hover>tbody>tr.info:hover>th,.table-hover>tbody>tr:hover>.info,.table-hover>tbody>tr>td.info:hover,.table-hover>tbody>tr>th.info:hover{background-color:#c4e3f3}.table>tbody>tr.warning>td,.table>tbody>tr.warning>th,.table>tbody>tr>td.warning,.table>tbody>tr>th.warning,.table>tfoot>tr.warning>td,.table>tfoot>tr.warning>th,.table>tfoot>tr>td.warning,.table>tfoot>tr>th.warning,.table>thead>tr.warning>td,.table>thead>tr.warning>th,.table>thead>tr>td.warning,.table>thead>tr>th.warning{background-color:#fcf8e3}.table-hover>tbody>tr.warning:hover>td,.table-hover>tbody>tr.warning:hover>th,.table-hover>tbody>tr:hover>.warning,.table-hover>tbody>tr>td.warning:hover,.table-hover>tbody>tr>th.warning:hover{background-color:#faf2cc}.table>tbody>tr.danger>td,.table>tbody>tr.danger>th,.table>tbody>tr>td.danger,.table>tbody>tr>th.danger,.table>tfoot>tr.danger>td,.table>tfoot>tr.danger>th,.table>tfoot>tr>td.danger,.table>tfoot>tr>th.danger,.table>thead>tr.danger>td,.table>thead>tr.danger>th,.table>thead>tr>td.danger,.table>thead>tr>th.danger{background-color:#f2dede}.table-hover>tbody>tr.danger:hover>td,.table-hover>tbody>tr.danger:hover>th,.table-hover>tbody>tr:hover>.danger,.table-hover>tbody>tr>td.danger:hover,.table-hover>tbody>tr>th.danger:hover{background-color:#ebcccc}.table-responsive{min-height:.01%;overflow-x:auto}@media screen and (max-width:767px){.table-responsive{width:100%;margin-bottom:15px;overflow-y:hidden;-ms-overflow-style:-ms-autohiding-scrollbar;border:1px solid #ddd}.table-responsive>.table{margin-bottom:0}.table-responsive>.table>tbody>tr>td,.table-responsive>.table>tbody>tr>th,.table-responsive>.table>tfoot>tr>td,.table-responsive>.table>tfoot>tr>th,.table-responsive>.table>thead>tr>td,.table-responsive>.table>thead>tr>th{white-space:nowrap}.table-responsive>.table-bordered{border:0}.table-responsive>.table-bordered>tbody>tr>td:first-child,.table-responsive>.table-bordered>tbody>tr>th:first-child,.table-responsive>.table-bordered>tfoot>tr>td:first-child,.table-responsive>.table-bordered>tfoot>tr>th:first-child,.table-responsive>.table-bordered>thead>tr>td:first-child,.table-responsive>.table-bordered>thead>tr>th:first-child{border-left:0}.table-responsive>.table-bordered>tbody>tr>td:last-child,.table-responsive>.table-bordered>tbody>tr>th:last-child,.table-responsive>.table-bordered>tfoot>tr>td:last-child,.table-responsive>.table-bordered>tfoot>tr>th:last-child,.table-responsive>.table-bordered>thead>tr>td:last-child,.table-responsive>.table-bordered>thead>tr>th:last-child{border-right:0}.table-responsive>.table-bordered>tbody>tr:last-child>td,.table-responsive>.table-bordered>tbody>tr:last-child>th,.table-responsive>.table-bordered>tfoot>tr:last-child>td,.table-responsive>.table-bordered>tfoot>tr:last-child>th{border-bottom:0}}fieldset{min-width:0;padding:0;margin:0;border:0}legend{display:block;width:100%;padding:0;margin-bottom:20px;font-size:21px;line-height:inherit;color:#333;border:0;border-bottom:1px solid #e5e5e5}label{display:inline-block;max-width:100%;margin-bottom:5px;font-weight:700}input[type=search]{-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}input[type=checkbox],input[type=radio]{margin:4px 0 0;margin-top:1px\9;line-height:normal}input[type=file]{display:block}input[type=range]{display:block;width:100%}select[multiple],select[size]{height:auto}input[type=file]:focus,input[type=checkbox]:focus,input[type=radio]:focus{outline:thin dotted;outline:5px auto -webkit-focus-ring-color;outline-offset:-2px}output{display:block;padding-top:7px;font-size:14px;line-height:1.42857143;color:#555}.form-control{display:block;width:100%;height:34px;padding:6px 12px;font-size:14px;line-height:1.42857143;color:#555;background-color:#fff;background-image:none;border:1px solid #ccc;border-radius:4px;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075);box-shadow:inset 0 1px 1px rgba(0,0,0,.075);-webkit-transition:border-color ease-in-out .15s,-webkit-box-shadow ease-in-out .15s;-o-transition:border-color ease-in-out .15s,box-shadow ease-in-out .15s;transition:border-color ease-in-out .15s,box-shadow ease-in-out .15s}.form-control:focus{border-color:#66afe9;outline:0;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 8px rgba(102,175,233,.6);box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 8px rgba(102,175,233,.6)}.form-control::-moz-placeholder{color:#999;opacity:1}.form-control:-ms-input-placeholder{color:#999}.form-control::-webkit-input-placeholder{color:#999}.form-control[disabled],.form-control[readonly],fieldset[disabled] .form-control{background-color:#eee;opacity:1}.form-control[disabled],fieldset[disabled] .form-control{cursor:not-allowed}textarea.form-control{height:auto}input[type=search]{-webkit-appearance:none}@media screen and (-webkit-min-device-pixel-ratio:0){input[type=date].form-control,input[type=time].form-control,input[type=datetime-local].form-control,input[type=month].form-control{line-height:34px}.input-group-sm input[type=date],.input-group-sm input[type=time],.input-group-sm input[type=datetime-local],.input-group-sm input[type=month],input[type=date].input-sm,input[type=time].input-sm,input[type=datetime-local].input-sm,input[type=month].input-sm{line-height:30px}.input-group-lg input[type=date],.input-group-lg input[type=time],.input-group-lg input[type=datetime-local],.input-group-lg input[type=month],input[type=date].input-lg,input[type=time].input-lg,input[type=datetime-local].input-lg,input[type=month].input-lg{line-height:46px}}.form-group{margin-bottom:15px}.checkbox,.radio{position:relative;display:block;margin-top:10px;margin-bottom:10px}.checkbox label,.radio label{min-height:20px;padding-left:20px;margin-bottom:0;font-weight:400;cursor:pointer}.checkbox input[type=checkbox],.checkbox-inline input[type=checkbox],.radio input[type=radio],.radio-inline input[type=radio]{position:absolute;margin-top:4px\9;margin-left:-20px}.checkbox+.checkbox,.radio+.radio{margin-top:-5px}.checkbox-inline,.radio-inline{position:relative;display:inline-block;padding-left:20px;margin-bottom:0;font-weight:400;vertical-align:middle;cursor:pointer}.checkbox-inline+.checkbox-inline,.radio-inline+.radio-inline{margin-top:0;margin-left:10px}fieldset[disabled] input[type=checkbox],fieldset[disabled] input[type=radio],input[type=checkbox].disabled,input[type=checkbox][disabled],input[type=radio].disabled,input[type=radio][disabled]{cursor:not-allowed}.checkbox-inline.disabled,.radio-inline.disabled,fieldset[disabled] .checkbox-inline,fieldset[disabled] .radio-inline{cursor:not-allowed}.checkbox.disabled label,.radio.disabled label,fieldset[disabled] .checkbox label,fieldset[disabled] .radio label{cursor:not-allowed}.form-control-static{min-height:34px;padding-top:7px;padding-bottom:7px;margin-bottom:0}.form-control-static.input-lg,.form-control-static.input-sm{padding-right:0;padding-left:0}.input-sm{height:30px;padding:5px 10px;font-size:12px;line-height:1.5;border-radius:3px}select.input-sm{height:30px;line-height:30px}select[multiple].input-sm,textarea.input-sm{height:auto}.form-group-sm .form-control{height:30px;padding:5px 10px;font-size:12px;line-height:1.5;border-radius:3px}.form-group-sm select.form-control{height:30px;line-height:30px}.form-group-sm select[multiple].form-control,.form-group-sm textarea.form-control{height:auto}.form-group-sm .form-control-static{height:30px;min-height:32px;padding:6px 10px;font-size:12px;line-height:1.5}.input-lg{height:46px;padding:10px 16px;font-size:18px;line-height:1.3333333;border-radius:6px}select.input-lg{height:46px;line-height:46px}select[multiple].input-lg,textarea.input-lg{height:auto}.form-group-lg .form-control{height:46px;padding:10px 16px;font-size:18px;line-height:1.3333333;border-radius:6px}.form-group-lg select.form-control{height:46px;line-height:46px}.form-group-lg select[multiple].form-control,.form-group-lg textarea.form-control{height:auto}.form-group-lg .form-control-static{height:46px;min-height:38px;padding:11px 16px;font-size:18px;line-height:1.3333333}.has-feedback{position:relative}.has-feedback .form-control{padding-right:42.5px}.form-control-feedback{position:absolute;top:0;right:0;z-index:2;display:block;width:34px;height:34px;line-height:34px;text-align:center;pointer-events:none}.form-group-lg .form-control+.form-control-feedback,.input-group-lg+.form-control-feedback,.input-lg+.form-control-feedback{width:46px;height:46px;line-height:46px}.form-group-sm .form-control+.form-control-feedback,.input-group-sm+.form-control-feedback,.input-sm+.form-control-feedback{width:30px;height:30px;line-height:30px}.has-success .checkbox,.has-success .checkbox-inline,.has-success .control-label,.has-success .help-block,.has-success .radio,.has-success .radio-inline,.has-success.checkbox label,.has-success.checkbox-inline label,.has-success.radio label,.has-success.radio-inline label{color:#3c763d}.has-success .form-control{border-color:#3c763d;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075);box-shadow:inset 0 1px 1px rgba(0,0,0,.075)}.has-success .form-control:focus{border-color:#2b542c;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 6px #67b168;box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 6px #67b168}.has-success .input-group-addon{color:#3c763d;background-color:#dff0d8;border-color:#3c763d}.has-success .form-control-feedback{color:#3c763d}.has-warning .checkbox,.has-warning .checkbox-inline,.has-warning .control-label,.has-warning .help-block,.has-warning .radio,.has-warning .radio-inline,.has-warning.checkbox label,.has-warning.checkbox-inline label,.has-warning.radio label,.has-warning.radio-inline label{color:#8a6d3b}.has-warning .form-control{border-color:#8a6d3b;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075);box-shadow:inset 0 1px 1px rgba(0,0,0,.075)}.has-warning .form-control:focus{border-color:#66512c;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 6px #c0a16b;box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 6px #c0a16b}.has-warning .input-group-addon{color:#8a6d3b;background-color:#fcf8e3;border-color:#8a6d3b}.has-warning .form-control-feedback{color:#8a6d3b}.has-error .checkbox,.has-error .checkbox-inline,.has-error .control-label,.has-error .help-block,.has-error .radio,.has-error .radio-inline,.has-error.checkbox label,.has-error.checkbox-inline label,.has-error.radio label,.has-error.radio-inline label{color:#a94442}.has-error .form-control{border-color:#a94442;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075);box-shadow:inset 0 1px 1px rgba(0,0,0,.075)}.has-error .form-control:focus{border-color:#843534;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 6px #ce8483;box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 6px #ce8483}.has-error .input-group-addon{color:#a94442;background-color:#f2dede;border-color:#a94442}.has-error .form-control-feedback{color:#a94442}.has-feedback label~.form-control-feedback{top:25px}.has-feedback label.sr-only~.form-control-feedback{top:0}.help-block{display:block;margin-top:5px;margin-bottom:10px;color:#737373}@media (min-width:768px){.form-inline .form-group{display:inline-block;margin-bottom:0;vertical-align:middle}.form-inline .form-control{display:inline-block;width:auto;vertical-align:middle}.form-inline .form-control-static{display:inline-block}.form-inline .input-group{display:inline-table;vertical-align:middle}.form-inline .input-group .form-control,.form-inline .input-group .input-group-addon,.form-inline .input-group .input-group-btn{width:auto}.form-inline .input-group>.form-control{width:100%}.form-inline .control-label{margin-bottom:0;vertical-align:middle}.form-inline .checkbox,.form-inline .radio{display:inline-block;margin-top:0;margin-bottom:0;vertical-align:middle}.form-inline .checkbox label,.form-inline .radio label{padding-left:0}.form-inline .checkbox input[type=checkbox],.form-inline .radio input[type=radio]{position:relative;margin-left:0}.form-inline .has-feedback .form-control-feedback{top:0}}.form-horizontal .checkbox,.form-horizontal .checkbox-inline,.form-horizontal .radio,.form-horizontal .radio-inline{padding-top:7px;margin-top:0;margin-bottom:0}.form-horizontal .checkbox,.form-horizontal .radio{min-height:27px}.form-horizontal .form-group{margin-right:-15px;margin-left:-15px}@media (min-width:768px){.form-horizontal .control-label{padding-top:7px;margin-bottom:0;text-align:right}}.form-horizontal .has-feedback .form-control-feedback{right:15px}@media (min-width:768px){.form-horizontal .form-group-lg .control-label{padding-top:14.33px;font-size:18px}}@media (min-width:768px){.form-horizontal .form-group-sm .control-label{padding-top:6px;font-size:12px}}.btn{display:inline-block;padding:6px 12px;margin-bottom:0;font-size:14px;font-weight:400;line-height:1.42857143;text-align:center;white-space:nowrap;vertical-align:middle;-ms-touch-action:manipulation;touch-action:manipulation;cursor:pointer;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;background-image:none;border:1px solid transparent;border-radius:4px}.btn.active.focus,.btn.active:focus,.btn.focus,.btn:active.focus,.btn:active:focus,.btn:focus{outline:thin dotted;outline:5px auto -webkit-focus-ring-color;outline-offset:-2px}.btn.focus,.btn:focus,.btn:hover{color:#333;text-decoration:none}.btn.active,.btn:active{background-image:none;outline:0;-webkit-box-shadow:inset 0 3px 5px rgba(0,0,0,.125);box-shadow:inset 0 3px 5px rgba(0,0,0,.125)}.btn.disabled,.btn[disabled],fieldset[disabled] .btn{cursor:not-allowed;filter:alpha(opacity=65);-webkit-box-shadow:none;box-shadow:none;opacity:.65}a.btn.disabled,fieldset[disabled] a.btn{pointer-events:none}.btn-default{color:#333;background-color:#fff;border-color:#ccc}.btn-default.focus,.btn-default:focus{color:#333;background-color:#e6e6e6;border-color:#8c8c8c}.btn-default:hover{color:#333;background-color:#e6e6e6;border-color:#adadad}.btn-default.active,.btn-default:active,.open>.dropdown-toggle.btn-default{color:#333;background-color:#e6e6e6;border-color:#adadad}.btn-default.active.focus,.btn-default.active:focus,.btn-default.active:hover,.btn-default:active.focus,.btn-default:active:focus,.btn-default:active:hover,.open>.dropdown-toggle.btn-default.focus,.open>.dropdown-toggle.btn-default:focus,.open>.dropdown-toggle.btn-default:hover{color:#333;background-color:#d4d4d4;border-color:#8c8c8c}.btn-default.active,.btn-default:active,.open>.dropdown-toggle.btn-default{background-image:none}.btn-default.disabled,.btn-default.disabled.active,.btn-default.disabled.focus,.btn-default.disabled:active,.btn-default.disabled:focus,.btn-default.disabled:hover,.btn-default[disabled],.btn-default[disabled].active,.btn-default[disabled].focus,.btn-default[disabled]:active,.btn-default[disabled]:focus,.btn-default[disabled]:hover,fieldset[disabled] .btn-default,fieldset[disabled] .btn-default.active,fieldset[disabled] .btn-default.focus,fieldset[disabled] .btn-default:active,fieldset[disabled] .btn-default:focus,fieldset[disabled] .btn-default:hover{background-color:#fff;border-color:#ccc}.btn-default .badge{color:#fff;background-color:#333}.btn-primary{color:#fff;background-color:#337ab7;border-color:#2e6da4}.btn-primary.focus,.btn-primary:focus{color:#fff;background-color:#286090;border-color:#122b40}.btn-primary:hover{color:#fff;background-color:#286090;border-color:#204d74}.btn-primary.active,.btn-primary:active,.open>.dropdown-toggle.btn-primary{color:#fff;background-color:#286090;border-color:#204d74}.btn-primary.active.focus,.btn-primary.active:focus,.btn-primary.active:hover,.btn-primary:active.focus,.btn-primary:active:focus,.btn-primary:active:hover,.open>.dropdown-toggle.btn-primary.focus,.open>.dropdown-toggle.btn-primary:focus,.open>.dropdown-toggle.btn-primary:hover{color:#fff;background-color:#204d74;border-color:#122b40}.btn-primary.active,.btn-primary:active,.open>.dropdown-toggle.btn-primary{background-image:none}.btn-primary.disabled,.btn-primary.disabled.active,.btn-primary.disabled.focus,.btn-primary.disabled:active,.btn-primary.disabled:focus,.btn-primary.disabled:hover,.btn-primary[disabled],.btn-primary[disabled].active,.btn-primary[disabled].focus,.btn-primary[disabled]:active,.btn-primary[disabled]:focus,.btn-primary[disabled]:hover,fieldset[disabled] .btn-primary,fieldset[disabled] .btn-primary.active,fieldset[disabled] .btn-primary.focus,fieldset[disabled] .btn-primary:active,fieldset[disabled] .btn-primary:focus,fieldset[disabled] .btn-primary:hover{background-color:#337ab7;border-color:#2e6da4}.btn-primary .badge{color:#337ab7;background-color:#fff}.btn-success{color:#fff;background-color:#5cb85c;border-color:#4cae4c}.btn-success.focus,.btn-success:focus{color:#fff;background-color:#449d44;border-color:#255625}.btn-success:hover{color:#fff;background-color:#449d44;border-color:#398439}.btn-success.active,.btn-success:active,.open>.dropdown-toggle.btn-success{color:#fff;background-color:#449d44;border-color:#398439}.btn-success.active.focus,.btn-success.active:focus,.btn-success.active:hover,.btn-success:active.focus,.btn-success:active:focus,.btn-success:active:hover,.open>.dropdown-toggle.btn-success.focus,.open>.dropdown-toggle.btn-success:focus,.open>.dropdown-toggle.btn-success:hover{color:#fff;background-color:#398439;border-color:#255625}.btn-success.active,.btn-success:active,.open>.dropdown-toggle.btn-success{background-image:none}.btn-success.disabled,.btn-success.disabled.active,.btn-success.disabled.focus,.btn-success.disabled:active,.btn-success.disabled:focus,.btn-success.disabled:hover,.btn-success[disabled],.btn-success[disabled].active,.btn-success[disabled].focus,.btn-success[disabled]:active,.btn-success[disabled]:focus,.btn-success[disabled]:hover,fieldset[disabled] .btn-success,fieldset[disabled] .btn-success.active,fieldset[disabled] .btn-success.focus,fieldset[disabled] .btn-success:active,fieldset[disabled] .btn-success:focus,fieldset[disabled] .btn-success:hover{background-color:#5cb85c;border-color:#4cae4c}.btn-success .badge{color:#5cb85c;background-color:#fff}.btn-info{color:#fff;background-color:#5bc0de;border-color:#46b8da}.btn-info.focus,.btn-info:focus{color:#fff;background-color:#31b0d5;border-color:#1b6d85}.btn-info:hover{color:#fff;background-color:#31b0d5;border-color:#269abc}.btn-info.active,.btn-info:active,.open>.dropdown-toggle.btn-info{color:#fff;background-color:#31b0d5;border-color:#269abc}.btn-info.active.focus,.btn-info.active:focus,.btn-info.active:hover,.btn-info:active.focus,.btn-info:active:focus,.btn-info:active:hover,.open>.dropdown-toggle.btn-info.focus,.open>.dropdown-toggle.btn-info:focus,.open>.dropdown-toggle.btn-info:hover{color:#fff;background-color:#269abc;border-color:#1b6d85}.btn-info.active,.btn-info:active,.open>.dropdown-toggle.btn-info{background-image:none}.btn-info.disabled,.btn-info.disabled.active,.btn-info.disabled.focus,.btn-info.disabled:active,.btn-info.disabled:focus,.btn-info.disabled:hover,.btn-info[disabled],.btn-info[disabled].active,.btn-info[disabled].focus,.btn-info[disabled]:active,.btn-info[disabled]:focus,.btn-info[disabled]:hover,fieldset[disabled] .btn-info,fieldset[disabled] .btn-info.active,fieldset[disabled] .btn-info.focus,fieldset[disabled] .btn-info:active,fieldset[disabled] .btn-info:focus,fieldset[disabled] .btn-info:hover{background-color:#5bc0de;border-color:#46b8da}.btn-info .badge{color:#5bc0de;background-color:#fff}.btn-warning{color:#fff;background-color:#f0ad4e;border-color:#eea236}.btn-warning.focus,.btn-warning:focus{color:#fff;background-color:#ec971f;border-color:#985f0d}.btn-warning:hover{color:#fff;background-color:#ec971f;border-color:#d58512}.btn-warning.active,.btn-warning:active,.open>.dropdown-toggle.btn-warning{color:#fff;background-color:#ec971f;border-color:#d58512}.btn-warning.active.focus,.btn-warning.active:focus,.btn-warning.active:hover,.btn-warning:active.focus,.btn-warning:active:focus,.btn-warning:active:hover,.open>.dropdown-toggle.btn-warning.focus,.open>.dropdown-toggle.btn-warning:focus,.open>.dropdown-toggle.btn-warning:hover{color:#fff;background-color:#d58512;border-color:#985f0d}.btn-warning.active,.btn-warning:active,.open>.dropdown-toggle.btn-warning{background-image:none}.btn-warning.disabled,.btn-warning.disabled.active,.btn-warning.disabled.focus,.btn-warning.disabled:active,.btn-warning.disabled:focus,.btn-warning.disabled:hover,.btn-warning[disabled],.btn-warning[disabled].active,.btn-warning[disabled].focus,.btn-warning[disabled]:active,.btn-warning[disabled]:focus,.btn-warning[disabled]:hover,fieldset[disabled] .btn-warning,fieldset[disabled] .btn-warning.active,fieldset[disabled] .btn-warning.focus,fieldset[disabled] .btn-warning:active,fieldset[disabled] .btn-warning:focus,fieldset[disabled] .btn-warning:hover{background-color:#f0ad4e;border-color:#eea236}.btn-warning .badge{color:#f0ad4e;background-color:#fff}.btn-danger{color:#fff;background-color:#d9534f;border-color:#d43f3a}.btn-danger.focus,.btn-danger:focus{color:#fff;background-color:#c9302c;border-color:#761c19}.btn-danger:hover{color:#fff;background-color:#c9302c;border-color:#ac2925}.btn-danger.active,.btn-danger:active,.open>.dropdown-toggle.btn-danger{color:#fff;background-color:#c9302c;border-color:#ac2925}.btn-danger.active.focus,.btn-danger.active:focus,.btn-danger.active:hover,.btn-danger:active.focus,.btn-danger:active:focus,.btn-danger:active:hover,.open>.dropdown-toggle.btn-danger.focus,.open>.dropdown-toggle.btn-danger:focus,.open>.dropdown-toggle.btn-danger:hover{color:#fff;background-color:#ac2925;border-color:#761c19}.btn-danger.active,.btn-danger:active,.open>.dropdown-toggle.btn-danger{background-image:none}.btn-danger.disabled,.btn-danger.disabled.active,.btn-danger.disabled.focus,.btn-danger.disabled:active,.btn-danger.disabled:focus,.btn-danger.disabled:hover,.btn-danger[disabled],.btn-danger[disabled].active,.btn-danger[disabled].focus,.btn-danger[disabled]:active,.btn-danger[disabled]:focus,.btn-danger[disabled]:hover,fieldset[disabled] .btn-danger,fieldset[disabled] .btn-danger.active,fieldset[disabled] .btn-danger.focus,fieldset[disabled] .btn-danger:active,fieldset[disabled] .btn-danger:focus,fieldset[disabled] .btn-danger:hover{background-color:#d9534f;border-color:#d43f3a}.btn-danger .badge{color:#d9534f;background-color:#fff}.btn-link{font-weight:400;color:#337ab7;border-radius:0}.btn-link,.btn-link.active,.btn-link:active,.btn-link[disabled],fieldset[disabled] .btn-link{background-color:transparent;-webkit-box-shadow:none;box-shadow:none}.btn-link,.btn-link:active,.btn-link:focus,.btn-link:hover{border-color:transparent}.btn-link:focus,.btn-link:hover{color:#23527c;text-decoration:underline;background-color:transparent}.btn-link[disabled]:focus,.btn-link[disabled]:hover,fieldset[disabled] .btn-link:focus,fieldset[disabled] .btn-link:hover{color:#777;text-decoration:none}.btn-group-lg>.btn,.btn-lg{padding:10px 16px;font-size:18px;line-height:1.3333333;border-radius:6px}.btn-group-sm>.btn,.btn-sm{padding:5px 10px;font-size:12px;line-height:1.5;border-radius:3px}.btn-group-xs>.btn,.btn-xs{padding:1px 5px;font-size:12px;line-height:1.5;border-radius:3px}.btn-block{display:block;width:100%}.btn-block+.btn-block{margin-top:5px}input[type=button].btn-block,input[type=reset].btn-block,input[type=submit].btn-block{width:100%}.fade{opacity:0;-webkit-transition:opacity .15s linear;-o-transition:opacity .15s linear;transition:opacity .15s linear}.fade.in{opacity:1}.collapse{display:none}.collapse.in{display:block}tr.collapse.in{display:table-row}tbody.collapse.in{display:table-row-group}.collapsing{position:relative;height:0;overflow:hidden;-webkit-transition-timing-function:ease;-o-transition-timing-function:ease;transition-timing-function:ease;-webkit-transition-duration:.35s;-o-transition-duration:.35s;transition-duration:.35s;-webkit-transition-property:height,visibility;-o-transition-property:height,visibility;transition-property:height,visibility}.caret{display:inline-block;width:0;height:0;margin-left:2px;vertical-align:middle;border-top:4px dashed;border-top:4px solid\9;border-right:4px solid transparent;border-left:4px solid transparent}.dropdown,.dropup{position:relative}.dropdown-toggle:focus{outline:0}.dropdown-menu{position:absolute;top:100%;left:0;z-index:1000;display:none;float:left;min-width:160px;padding:5px 0;margin:2px 0 0;font-size:14px;text-align:left;list-style:none;background-color:#fff;-webkit-background-clip:padding-box;background-clip:padding-box;border:1px solid #ccc;border:1px solid rgba(0,0,0,.15);border-radius:4px;-webkit-box-shadow:0 6px 12px rgba(0,0,0,.175);box-shadow:0 6px 12px rgba(0,0,0,.175)}.dropdown-menu.pull-right{right:0;left:auto}.dropdown-menu .divider{height:1px;margin:9px 0;overflow:hidden;background-color:#e5e5e5}.dropdown-menu>li>a{display:block;padding:3px 20px;clear:both;font-weight:400;line-height:1.42857143;color:#333;white-space:nowrap}.dropdown-menu>li>a:focus,.dropdown-menu>li>a:hover{color:#262626;text-decoration:none;background-color:#f5f5f5}.dropdown-menu>.active>a,.dropdown-menu>.active>a:focus,.dropdown-menu>.active>a:hover{color:#fff;text-decoration:none;background-color:#337ab7;outline:0}.dropdown-menu>.disabled>a,.dropdown-menu>.disabled>a:focus,.dropdown-menu>.disabled>a:hover{color:#777}.dropdown-menu>.disabled>a:focus,.dropdown-menu>.disabled>a:hover{text-decoration:none;cursor:not-allowed;background-color:transparent;background-image:none;filter:progid:DXImageTransform.Microsoft.gradient(enabled=false)}.open>.dropdown-menu{display:block}.open>a{outline:0}.dropdown-menu-right{right:0;left:auto}.dropdown-menu-left{right:auto;left:0}.dropdown-header{display:block;padding:3px 20px;font-size:12px;line-height:1.42857143;color:#777;white-space:nowrap}.dropdown-backdrop{position:fixed;top:0;right:0;bottom:0;left:0;z-index:990}.pull-right>.dropdown-menu{right:0;left:auto}.dropup .caret,.navbar-fixed-bottom .dropdown .caret{content:"";border-top:0;border-bottom:4px dashed;border-bottom:4px solid\9}.dropup .dropdown-menu,.navbar-fixed-bottom .dropdown .dropdown-menu{top:auto;bottom:100%;margin-bottom:2px}@media (min-width:768px){.navbar-right .dropdown-menu{right:0;left:auto}.navbar-right .dropdown-menu-left{right:auto;left:0}}.btn-group,.btn-group-vertical{position:relative;display:inline-block;vertical-align:middle}.btn-group-vertical>.btn,.btn-group>.btn{position:relative;float:left}.btn-group-vertical>.btn.active,.btn-group-vertical>.btn:active,.btn-group-vertical>.btn:focus,.btn-group-vertical>.btn:hover,.btn-group>.btn.active,.btn-group>.btn:active,.btn-group>.btn:focus,.btn-group>.btn:hover{z-index:2}.btn-group .btn+.btn,.btn-group .btn+.btn-group,.btn-group .btn-group+.btn,.btn-group .btn-group+.btn-group{margin-left:-1px}.btn-toolbar{margin-left:-5px}.btn-toolbar .btn,.btn-toolbar .btn-group,.btn-toolbar .input-group{float:left}.btn-toolbar>.btn,.btn-toolbar>.btn-group,.btn-toolbar>.input-group{margin-left:5px}.btn-group>.btn:not(:first-child):not(:last-child):not(.dropdown-toggle){border-radius:0}.btn-group>.btn:first-child{margin-left:0}.btn-group>.btn:first-child:not(:last-child):not(.dropdown-toggle){border-top-right-radius:0;border-bottom-right-radius:0}.btn-group>.btn:last-child:not(:first-child),.btn-group>.dropdown-toggle:not(:first-child){border-top-left-radius:0;border-bottom-left-radius:0}.btn-group>.btn-group{float:left}.btn-group>.btn-group:not(:first-child):not(:last-child)>.btn{border-radius:0}.btn-group>.btn-group:first-child:not(:last-child)>.btn:last-child,.btn-group>.btn-group:first-child:not(:last-child)>.dropdown-toggle{border-top-right-radius:0;border-bottom-right-radius:0}.btn-group>.btn-group:last-child:not(:first-child)>.btn:first-child{border-top-left-radius:0;border-bottom-left-radius:0}.btn-group .dropdown-toggle:active,.btn-group.open .dropdown-toggle{outline:0}.btn-group>.btn+.dropdown-toggle{padding-right:8px;padding-left:8px}.btn-group>.btn-lg+.dropdown-toggle{padding-right:12px;padding-left:12px}.btn-group.open .dropdown-toggle{-webkit-box-shadow:inset 0 3px 5px rgba(0,0,0,.125);box-shadow:inset 0 3px 5px rgba(0,0,0,.125)}.btn-group.open .dropdown-toggle.btn-link{-webkit-box-shadow:none;box-shadow:none}.btn .caret{margin-left:0}.btn-lg .caret{border-width:5px 5px 0;border-bottom-width:0}.dropup .btn-lg .caret{border-width:0 5px 5px}.btn-group-vertical>.btn,.btn-group-vertical>.btn-group,.btn-group-vertical>.btn-group>.btn{display:block;float:none;width:100%;max-width:100%}.btn-group-vertical>.btn-group>.btn{float:none}.btn-group-vertical>.btn+.btn,.btn-group-vertical>.btn+.btn-group,.btn-group-vertical>.btn-group+.btn,.btn-group-vertical>.btn-group+.btn-group{margin-top:-1px;margin-left:0}.btn-group-vertical>.btn:not(:first-child):not(:last-child){border-radius:0}.btn-group-vertical>.btn:first-child:not(:last-child){border-top-right-radius:4px;border-bottom-right-radius:0;border-bottom-left-radius:0}.btn-group-vertical>.btn:last-child:not(:first-child){border-top-left-radius:0;border-top-right-radius:0;border-bottom-left-radius:4px}.btn-group-vertical>.btn-group:not(:first-child):not(:last-child)>.btn{border-radius:0}.btn-group-vertical>.btn-group:first-child:not(:last-child)>.btn:last-child,.btn-group-vertical>.btn-group:first-child:not(:last-child)>.dropdown-toggle{border-bottom-right-radius:0;border-bottom-left-radius:0}.btn-group-vertical>.btn-group:last-child:not(:first-child)>.btn:first-child{border-top-left-radius:0;border-top-right-radius:0}.btn-group-justified{display:table;width:100%;table-layout:fixed;border-collapse:separate}.btn-group-justified>.btn,.btn-group-justified>.btn-group{display:table-cell;float:none;width:1%}.btn-group-justified>.btn-group .btn{width:100%}.btn-group-justified>.btn-group .dropdown-menu{left:auto}[data-toggle=buttons]>.btn input[type=checkbox],[data-toggle=buttons]>.btn input[type=radio],[data-toggle=buttons]>.btn-group>.btn input[type=checkbox],[data-toggle=buttons]>.btn-group>.btn input[type=radio]{position:absolute;clip:rect(0,0,0,0);pointer-events:none}.input-group{position:relative;display:table;border-collapse:separate}.input-group[class*=col-]{float:none;padding-right:0;padding-left:0}.input-group .form-control{position:relative;z-index:2;float:left;width:100%;margin-bottom:0}.input-group-lg>.form-control,.input-group-lg>.input-group-addon,.input-group-lg>.input-group-btn>.btn{height:46px;padding:10px 16px;font-size:18px;line-height:1.3333333;border-radius:6px}select.input-group-lg>.form-control,select.input-group-lg>.input-group-addon,select.input-group-lg>.input-group-btn>.btn{height:46px;line-height:46px}select[multiple].input-group-lg>.form-control,select[multiple].input-group-lg>.input-group-addon,select[multiple].input-group-lg>.input-group-btn>.btn,textarea.input-group-lg>.form-control,textarea.input-group-lg>.input-group-addon,textarea.input-group-lg>.input-group-btn>.btn{height:auto}.input-group-sm>.form-control,.input-group-sm>.input-group-addon,.input-group-sm>.input-group-btn>.btn{height:30px;padding:5px 10px;font-size:12px;line-height:1.5;border-radius:3px}select.input-group-sm>.form-control,select.input-group-sm>.input-group-addon,select.input-group-sm>.input-group-btn>.btn{height:30px;line-height:30px}select[multiple].input-group-sm>.form-control,select[multiple].input-group-sm>.input-group-addon,select[multiple].input-group-sm>.input-group-btn>.btn,textarea.input-group-sm>.form-control,textarea.input-group-sm>.input-group-addon,textarea.input-group-sm>.input-group-btn>.btn{height:auto}.input-group .form-control,.input-group-addon,.input-group-btn{display:table-cell}.input-group .form-control:not(:first-child):not(:last-child),.input-group-addon:not(:first-child):not(:last-child),.input-group-btn:not(:first-child):not(:last-child){border-radius:0}.input-group-addon,.input-group-btn{width:1%;white-space:nowrap;vertical-align:middle}.input-group-addon{padding:6px 12px;font-size:14px;font-weight:400;line-height:1;color:#555;text-align:center;background-color:#eee;border:1px solid #ccc;border-radius:4px}.input-group-addon.input-sm{padding:5px 10px;font-size:12px;border-radius:3px}.input-group-addon.input-lg{padding:10px 16px;font-size:18px;border-radius:6px}.input-group-addon input[type=checkbox],.input-group-addon input[type=radio]{margin-top:0}.input-group .form-control:first-child,.input-group-addon:first-child,.input-group-btn:first-child>.btn,.input-group-btn:first-child>.btn-group>.btn,.input-group-btn:first-child>.dropdown-toggle,.input-group-btn:last-child>.btn-group:not(:last-child)>.btn,.input-group-btn:last-child>.btn:not(:last-child):not(.dropdown-toggle){border-top-right-radius:0;border-bottom-right-radius:0}.input-group-addon:first-child{border-right:0}.input-group .form-control:last-child,.input-group-addon:last-child,.input-group-btn:first-child>.btn-group:not(:first-child)>.btn,.input-group-btn:first-child>.btn:not(:first-child),.input-group-btn:last-child>.btn,.input-group-btn:last-child>.btn-group>.btn,.input-group-btn:last-child>.dropdown-toggle{border-top-left-radius:0;border-bottom-left-radius:0}.input-group-addon:last-child{border-left:0}.input-group-btn{position:relative;font-size:0;white-space:nowrap}.input-group-btn>.btn{position:relative}.input-group-btn>.btn+.btn{margin-left:-1px}.input-group-btn>.btn:active,.input-group-btn>.btn:focus,.input-group-btn>.btn:hover{z-index:2}.input-group-btn:first-child>.btn,.input-group-btn:first-child>.btn-group{margin-right:-1px}.input-group-btn:last-child>.btn,.input-group-btn:last-child>.btn-group{z-index:2;margin-left:-1px}.nav{padding-left:0;margin-bottom:0;list-style:none}.nav>li{position:relative;display:block}.nav>li>a{position:relative;display:block;padding:10px 15px}.nav>li>a:focus,.nav>li>a:hover{text-decoration:none;background-color:#eee}.nav>li.disabled>a{color:#777}.nav>li.disabled>a:focus,.nav>li.disabled>a:hover{color:#777;text-decoration:none;cursor:not-allowed;background-color:transparent}.nav .open>a,.nav .open>a:focus,.nav .open>a:hover{background-color:#eee;border-color:#337ab7}.nav .nav-divider{height:1px;margin:9px 0;overflow:hidden;background-color:#e5e5e5}.nav>li>a>img{max-width:none}.nav-tabs{border-bottom:1px solid #ddd}.nav-tabs>li{float:left;margin-bottom:-1px}.nav-tabs>li>a{margin-right:2px;line-height:1.42857143;border:1px solid transparent;border-radius:4px 4px 0 0}.nav-tabs>li>a:hover{border-color:#eee #eee #ddd}.nav-tabs>li.active>a,.nav-tabs>li.active>a:focus,.nav-tabs>li.active>a:hover{color:#555;cursor:default;background-color:#fff;border:1px solid #ddd;border-bottom-color:transparent}.nav-tabs.nav-justified{width:100%;border-bottom:0}.nav-tabs.nav-justified>li{float:none}.nav-tabs.nav-justified>li>a{margin-bottom:5px;text-align:center}.nav-tabs.nav-justified>.dropdown .dropdown-menu{top:auto;left:auto}@media (min-width:768px){.nav-tabs.nav-justified>li{display:table-cell;width:1%}.nav-tabs.nav-justified>li>a{margin-bottom:0}}.nav-tabs.nav-justified>li>a{margin-right:0;border-radius:4px}.nav-tabs.nav-justified>.active>a,.nav-tabs.nav-justified>.active>a:focus,.nav-tabs.nav-justified>.active>a:hover{border:1px solid #ddd}@media (min-width:768px){.nav-tabs.nav-justified>li>a{border-bottom:1px solid #ddd;border-radius:4px 4px 0 0}.nav-tabs.nav-justified>.active>a,.nav-tabs.nav-justified>.active>a:focus,.nav-tabs.nav-justified>.active>a:hover{border-bottom-color:#fff}}.nav-pills>li{float:left}.nav-pills>li>a{border-radius:4px}.nav-pills>li+li{margin-left:2px}.nav-pills>li.active>a,.nav-pills>li.active>a:focus,.nav-pills>li.active>a:hover{color:#fff;background-color:#337ab7}.nav-stacked>li{float:none}.nav-stacked>li+li{margin-top:2px;margin-left:0}.nav-justified{width:100%}.nav-justified>li{float:none}.nav-justified>li>a{margin-bottom:5px;text-align:center}.nav-justified>.dropdown .dropdown-menu{top:auto;left:auto}@media (min-width:768px){.nav-justified>li{display:table-cell;width:1%}.nav-justified>li>a{margin-bottom:0}}.nav-tabs-justified{border-bottom:0}.nav-tabs-justified>li>a{margin-right:0;border-radius:4px}.nav-tabs-justified>.active>a,.nav-tabs-justified>.active>a:focus,.nav-tabs-justified>.active>a:hover{border:1px solid #ddd}@media (min-width:768px){.nav-tabs-justified>li>a{border-bottom:1px solid #ddd;border-radius:4px 4px 0 0}.nav-tabs-justified>.active>a,.nav-tabs-justified>.active>a:focus,.nav-tabs-justified>.active>a:hover{border-bottom-color:#fff}}.tab-content>.tab-pane{display:none}.tab-content>.active{display:block}.nav-tabs .dropdown-menu{margin-top:-1px;border-top-left-radius:0;border-top-right-radius:0}.navbar{position:relative;min-height:50px;margin-bottom:20px;border:1px solid transparent}@media (min-width:768px){.navbar{border-radius:4px}}@media (min-width:768px){.navbar-header{float:left}}.navbar-collapse{padding-right:15px;padding-left:15px;overflow-x:visible;-webkit-overflow-scrolling:touch;border-top:1px solid transparent;-webkit-box-shadow:inset 0 1px 0 rgba(255,255,255,.1);box-shadow:inset 0 1px 0 rgba(255,255,255,.1)}.navbar-collapse.in{overflow-y:auto}@media (min-width:768px){.navbar-collapse{width:auto;border-top:0;-webkit-box-shadow:none;box-shadow:none}.navbar-collapse.collapse{display:block!important;height:auto!important;padding-bottom:0;overflow:visible!important}.navbar-collapse.in{overflow-y:visible}.navbar-fixed-bottom .navbar-collapse,.navbar-fixed-top .navbar-collapse,.navbar-static-top .navbar-collapse{padding-right:0;padding-left:0}}.navbar-fixed-bottom .navbar-collapse,.navbar-fixed-top .navbar-collapse{max-height:340px}@media (max-device-width:480px) and (orientation:landscape){.navbar-fixed-bottom .navbar-collapse,.navbar-fixed-top .navbar-collapse{max-height:200px}}.container-fluid>.navbar-collapse,.container-fluid>.navbar-header,.container>.navbar-collapse,.container>.navbar-header{margin-right:-15px;margin-left:-15px}@media (min-width:768px){.container-fluid>.navbar-collapse,.container-fluid>.navbar-header,.container>.navbar-collapse,.container>.navbar-header{margin-right:0;margin-left:0}}.navbar-static-top{z-index:1000;border-width:0 0 1px}@media (min-width:768px){.navbar-static-top{border-radius:0}}.navbar-fixed-bottom,.navbar-fixed-top{position:fixed;right:0;left:0;z-index:1030}@media (min-width:768px){.navbar-fixed-bottom,.navbar-fixed-top{border-radius:0}}.navbar-fixed-top{top:0;border-width:0 0 1px}.navbar-fixed-bottom{bottom:0;margin-bottom:0;border-width:1px 0 0}.navbar-brand{float:left;height:50px;padding:15px 15px;font-size:18px;line-height:20px}.navbar-brand:focus,.navbar-brand:hover{text-decoration:none}.navbar-brand>img{display:block}@media (min-width:768px){.navbar>.container .navbar-brand,.navbar>.container-fluid .navbar-brand{margin-left:-15px}}.navbar-toggle{position:relative;float:right;padding:9px 10px;margin-top:8px;margin-right:15px;margin-bottom:8px;background-color:transparent;background-image:none;border:1px solid transparent;border-radius:4px}.navbar-toggle:focus{outline:0}.navbar-toggle .icon-bar{display:block;width:22px;height:2px;border-radius:1px}.navbar-toggle .icon-bar+.icon-bar{margin-top:4px}@media (min-width:768px){.navbar-toggle{display:none}}.navbar-nav{margin:7.5px -15px}.navbar-nav>li>a{padding-top:10px;padding-bottom:10px;line-height:20px}@media (max-width:767px){.navbar-nav .open .dropdown-menu{position:static;float:none;width:auto;margin-top:0;background-color:transparent;border:0;-webkit-box-shadow:none;box-shadow:none}.navbar-nav .open .dropdown-menu .dropdown-header,.navbar-nav .open .dropdown-menu>li>a{padding:5px 15px 5px 25px}.navbar-nav .open .dropdown-menu>li>a{line-height:20px}.navbar-nav .open .dropdown-menu>li>a:focus,.navbar-nav .open .dropdown-menu>li>a:hover{background-image:none}}@media (min-width:768px){.navbar-nav{float:left;margin:0}.navbar-nav>li{float:left}.navbar-nav>li>a{padding-top:15px;padding-bottom:15px}}.navbar-form{padding:10px 15px;margin-top:8px;margin-right:-15px;margin-bottom:8px;margin-left:-15px;border-top:1px solid transparent;border-bottom:1px solid transparent;-webkit-box-shadow:inset 0 1px 0 rgba(255,255,255,.1),0 1px 0 rgba(255,255,255,.1);box-shadow:inset 0 1px 0 rgba(255,255,255,.1),0 1px 0 rgba(255,255,255,.1)}@media (min-width:768px){.navbar-form .form-group{display:inline-block;margin-bottom:0;vertical-align:middle}.navbar-form .form-control{display:inline-block;width:auto;vertical-align:middle}.navbar-form .form-control-static{display:inline-block}.navbar-form .input-group{display:inline-table;vertical-align:middle}.navbar-form .input-group .form-control,.navbar-form .input-group .input-group-addon,.navbar-form .input-group .input-group-btn{width:auto}.navbar-form .input-group>.form-control{width:100%}.navbar-form .control-label{margin-bottom:0;vertical-align:middle}.navbar-form .checkbox,.navbar-form .radio{display:inline-block;margin-top:0;margin-bottom:0;vertical-align:middle}.navbar-form .checkbox label,.navbar-form .radio label{padding-left:0}.navbar-form .checkbox input[type=checkbox],.navbar-form .radio input[type=radio]{position:relative;margin-left:0}.navbar-form .has-feedback .form-control-feedback{top:0}}@media (max-width:767px){.navbar-form .form-group{margin-bottom:5px}.navbar-form .form-group:last-child{margin-bottom:0}}@media (min-width:768px){.navbar-form{width:auto;padding-top:0;padding-bottom:0;margin-right:0;margin-left:0;border:0;-webkit-box-shadow:none;box-shadow:none}}.navbar-nav>li>.dropdown-menu{margin-top:0;border-top-left-radius:0;border-top-right-radius:0}.navbar-fixed-bottom .navbar-nav>li>.dropdown-menu{margin-bottom:0;border-top-left-radius:4px;border-top-right-radius:4px;border-bottom-right-radius:0;border-bottom-left-radius:0}.navbar-btn{margin-top:8px;margin-bottom:8px}.navbar-btn.btn-sm{margin-top:10px;margin-bottom:10px}.navbar-btn.btn-xs{margin-top:14px;margin-bottom:14px}.navbar-text{margin-top:15px;margin-bottom:15px}@media (min-width:768px){.navbar-text{float:left;margin-right:15px;margin-left:15px}}@media (min-width:768px){.navbar-left{float:left!important}.navbar-right{float:right!important;margin-right:-15px}.navbar-right~.navbar-right{margin-right:0}}.navbar-default{background-color:#f8f8f8;border-color:#e7e7e7}.navbar-default .navbar-brand{color:#777}.navbar-default .navbar-brand:focus,.navbar-default .navbar-brand:hover{color:#5e5e5e;background-color:transparent}.navbar-default .navbar-text{color:#777}.navbar-default .navbar-nav>li>a{color:#777}.navbar-default .navbar-nav>li>a:focus,.navbar-default .navbar-nav>li>a:hover{color:#333;background-color:transparent}.navbar-default .navbar-nav>.active>a,.navbar-default .navbar-nav>.active>a:focus,.navbar-default .navbar-nav>.active>a:hover{color:#555;background-color:#e7e7e7}.navbar-default .navbar-nav>.disabled>a,.navbar-default .navbar-nav>.disabled>a:focus,.navbar-default .navbar-nav>.disabled>a:hover{color:#ccc;background-color:transparent}.navbar-default .navbar-toggle{border-color:#ddd}.navbar-default .navbar-toggle:focus,.navbar-default .navbar-toggle:hover{background-color:#ddd}.navbar-default .navbar-toggle .icon-bar{background-color:#888}.navbar-default .navbar-collapse,.navbar-default .navbar-form{border-color:#e7e7e7}.navbar-default .navbar-nav>.open>a,.navbar-default .navbar-nav>.open>a:focus,.navbar-default .navbar-nav>.open>a:hover{color:#555;background-color:#e7e7e7}@media (max-width:767px){.navbar-default .navbar-nav .open .dropdown-menu>li>a{color:#777}.navbar-default .navbar-nav .open .dropdown-menu>li>a:focus,.navbar-default .navbar-nav .open .dropdown-menu>li>a:hover{color:#333;background-color:transparent}.navbar-default .navbar-nav .open .dropdown-menu>.active>a,.navbar-default .navbar-nav .open .dropdown-menu>.active>a:focus,.navbar-default .navbar-nav .open .dropdown-menu>.active>a:hover{color:#555;background-color:#e7e7e7}.navbar-default .navbar-nav .open .dropdown-menu>.disabled>a,.navbar-default .navbar-nav .open .dropdown-menu>.disabled>a:focus,.navbar-default .navbar-nav .open .dropdown-menu>.disabled>a:hover{color:#ccc;background-color:transparent}}.navbar-default .navbar-link{color:#777}.navbar-default .navbar-link:hover{color:#333}.navbar-default .btn-link{color:#777}.navbar-default .btn-link:focus,.navbar-default .btn-link:hover{color:#333}.navbar-default .btn-link[disabled]:focus,.navbar-default .btn-link[disabled]:hover,fieldset[disabled] .navbar-default .btn-link:focus,fieldset[disabled] .navbar-default .btn-link:hover{color:#ccc}.navbar-inverse{background-color:#222;border-color:#080808}.navbar-inverse .navbar-brand{color:#9d9d9d}.navbar-inverse .navbar-brand:focus,.navbar-inverse .navbar-brand:hover{color:#fff;background-color:transparent}.navbar-inverse .navbar-text{color:#9d9d9d}.navbar-inverse .navbar-nav>li>a{color:#9d9d9d}.navbar-inverse .navbar-nav>li>a:focus,.navbar-inverse .navbar-nav>li>a:hover{color:#fff;background-color:transparent}.navbar-inverse .navbar-nav>.active>a,.navbar-inverse .navbar-nav>.active>a:focus,.navbar-inverse .navbar-nav>.active>a:hover{color:#fff;background-color:#080808}.navbar-inverse .navbar-nav>.disabled>a,.navbar-inverse .navbar-nav>.disabled>a:focus,.navbar-inverse .navbar-nav>.disabled>a:hover{color:#444;background-color:transparent}.navbar-inverse .navbar-toggle{border-color:#333}.navbar-inverse .navbar-toggle:focus,.navbar-inverse .navbar-toggle:hover{background-color:#333}.navbar-inverse .navbar-toggle .icon-bar{background-color:#fff}.navbar-inverse .navbar-collapse,.navbar-inverse .navbar-form{border-color:#101010}.navbar-inverse .navbar-nav>.open>a,.navbar-inverse .navbar-nav>.open>a:focus,.navbar-inverse .navbar-nav>.open>a:hover{color:#fff;background-color:#080808}@media (max-width:767px){.navbar-inverse .navbar-nav .open .dropdown-menu>.dropdown-header{border-color:#080808}.navbar-inverse .navbar-nav .open .dropdown-menu .divider{background-color:#080808}.navbar-inverse .navbar-nav .open .dropdown-menu>li>a{color:#9d9d9d}.navbar-inverse .navbar-nav .open .dropdown-menu>li>a:focus,.navbar-inverse .navbar-nav .open .dropdown-menu>li>a:hover{color:#fff;background-color:transparent}.navbar-inverse .navbar-nav .open .dropdown-menu>.active>a,.navbar-inverse .navbar-nav .open .dropdown-menu>.active>a:focus,.navbar-inverse .navbar-nav .open .dropdown-menu>.active>a:hover{color:#fff;background-color:#080808}.navbar-inverse .navbar-nav .open .dropdown-menu>.disabled>a,.navbar-inverse .navbar-nav .open .dropdown-menu>.disabled>a:focus,.navbar-inverse .navbar-nav .open .dropdown-menu>.disabled>a:hover{color:#444;background-color:transparent}}.navbar-inverse .navbar-link{color:#9d9d9d}.navbar-inverse .navbar-link:hover{color:#fff}.navbar-inverse .btn-link{color:#9d9d9d}.navbar-inverse .btn-link:focus,.navbar-inverse .btn-link:hover{color:#fff}.navbar-inverse .btn-link[disabled]:focus,.navbar-inverse .btn-link[disabled]:hover,fieldset[disabled] .navbar-inverse .btn-link:focus,fieldset[disabled] .navbar-inverse .btn-link:hover{color:#444}.breadcrumb{padding:8px 15px;margin-bottom:20px;list-style:none;background-color:#f5f5f5;border-radius:4px}.breadcrumb>li{display:inline-block}.breadcrumb>li+li:before{padding:0 5px;color:#ccc;content:"/\00a0"}.breadcrumb>.active{color:#777}.pagination{display:inline-block;padding-left:0;margin:20px 0;border-radius:4px}.pagination>li{display:inline}.pagination>li>a,.pagination>li>span{position:relative;float:left;padding:6px 12px;margin-left:-1px;line-height:1.42857143;color:#337ab7;text-decoration:none;background-color:#fff;border:1px solid #ddd}.pagination>li:first-child>a,.pagination>li:first-child>span{margin-left:0;border-top-left-radius:4px;border-bottom-left-radius:4px}.pagination>li:last-child>a,.pagination>li:last-child>span{border-top-right-radius:4px;border-bottom-right-radius:4px}.pagination>li>a:focus,.pagination>li>a:hover,.pagination>li>span:focus,.pagination>li>span:hover{z-index:3;color:#23527c;background-color:#eee;border-color:#ddd}.pagination>.active>a,.pagination>.active>a:focus,.pagination>.active>a:hover,.pagination>.active>span,.pagination>.active>span:focus,.pagination>.active>span:hover{z-index:2;color:#fff;cursor:default;background-color:#337ab7;border-color:#337ab7}.pagination>.disabled>a,.pagination>.disabled>a:focus,.pagination>.disabled>a:hover,.pagination>.disabled>span,.pagination>.disabled>span:focus,.pagination>.disabled>span:hover{color:#777;cursor:not-allowed;background-color:#fff;border-color:#ddd}.pagination-lg>li>a,.pagination-lg>li>span{padding:10px 16px;font-size:18px;line-height:1.3333333}.pagination-lg>li:first-child>a,.pagination-lg>li:first-child>span{border-top-left-radius:6px;border-bottom-left-radius:6px}.pagination-lg>li:last-child>a,.pagination-lg>li:last-child>span{border-top-right-radius:6px;border-bottom-right-radius:6px}.pagination-sm>li>a,.pagination-sm>li>span{padding:5px 10px;font-size:12px;line-height:1.5}.pagination-sm>li:first-child>a,.pagination-sm>li:first-child>span{border-top-left-radius:3px;border-bottom-left-radius:3px}.pagination-sm>li:last-child>a,.pagination-sm>li:last-child>span{border-top-right-radius:3px;border-bottom-right-radius:3px}.pager{padding-left:0;margin:20px 0;text-align:center;list-style:none}.pager li{display:inline}.pager li>a,.pager li>span{display:inline-block;padding:5px 14px;background-color:#fff;border:1px solid #ddd;border-radius:15px}.pager li>a:focus,.pager li>a:hover{text-decoration:none;background-color:#eee}.pager .next>a,.pager .next>span{float:right}.pager .previous>a,.pager .previous>span{float:left}.pager .disabled>a,.pager .disabled>a:focus,.pager .disabled>a:hover,.pager .disabled>span{color:#777;cursor:not-allowed;background-color:#fff}.label{display:inline;padding:.2em .6em .3em;font-size:75%;font-weight:700;line-height:1;color:#fff;text-align:center;white-space:nowrap;vertical-align:baseline;border-radius:.25em}a.label:focus,a.label:hover{color:#fff;text-decoration:none;cursor:pointer}.label:empty{display:none}.btn .label{position:relative;top:-1px}.label-default{background-color:#777}.label-default[href]:focus,.label-default[href]:hover{background-color:#5e5e5e}.label-primary{background-color:#337ab7}.label-primary[href]:focus,.label-primary[href]:hover{background-color:#286090}.label-success{background-color:#5cb85c}.label-success[href]:focus,.label-success[href]:hover{background-color:#449d44}.label-info{background-color:#5bc0de}.label-info[href]:focus,.label-info[href]:hover{background-color:#31b0d5}.label-warning{background-color:#f0ad4e}.label-warning[href]:focus,.label-warning[href]:hover{background-color:#ec971f}.label-danger{background-color:#d9534f}.label-danger[href]:focus,.label-danger[href]:hover{background-color:#c9302c}.badge{display:inline-block;min-width:10px;padding:3px 7px;font-size:12px;font-weight:700;line-height:1;color:#fff;text-align:center;white-space:nowrap;vertical-align:middle;background-color:#777;border-radius:10px}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.btn-group-xs>.btn .badge,.btn-xs .badge{top:0;padding:1px 5px}a.badge:focus,a.badge:hover{color:#fff;text-decoration:none;cursor:pointer}.list-group-item.active>.badge,.nav-pills>.active>a>.badge{color:#337ab7;background-color:#fff}.list-group-item>.badge{float:right}.list-group-item>.badge+.badge{margin-right:5px}.nav-pills>li>a>.badge{margin-left:3px}.jumbotron{padding-top:30px;padding-bottom:30px;margin-bottom:30px;color:inherit;background-color:#eee}.jumbotron .h1,.jumbotron h1{color:inherit}.jumbotron p{margin-bottom:15px;font-size:21px;font-weight:200}.jumbotron>hr{border-top-color:#d5d5d5}.container .jumbotron,.container-fluid .jumbotron{border-radius:6px}.jumbotron .container{max-width:100%}@media screen and (min-width:768px){.jumbotron{padding-top:48px;padding-bottom:48px}.container .jumbotron,.container-fluid .jumbotron{padding-right:60px;padding-left:60px}.jumbotron .h1,.jumbotron h1{font-size:63px}}.thumbnail{display:block;padding:4px;margin-bottom:20px;line-height:1.42857143;background-color:#fff;border:1px solid #ddd;border-radius:4px;-webkit-transition:border .2s ease-in-out;-o-transition:border .2s ease-in-out;transition:border .2s ease-in-out}.thumbnail a>img,.thumbnail>img{margin-right:auto;margin-left:auto}a.thumbnail.active,a.thumbnail:focus,a.thumbnail:hover{border-color:#337ab7}.thumbnail .caption{padding:9px;color:#333}.alert{padding:15px;margin-bottom:20px;border:1px solid transparent;border-radius:4px}.alert h4{margin-top:0;color:inherit}.alert .alert-link{font-weight:700}.alert>p,.alert>ul{margin-bottom:0}.alert>p+p{margin-top:5px}.alert-dismissable,.alert-dismissible{padding-right:35px}.alert-dismissable .close,.alert-dismissible .close{position:relative;top:-2px;right:-21px;color:inherit}.alert-success{color:#3c763d;background-color:#dff0d8;border-color:#d6e9c6}.alert-success hr{border-top-color:#c9e2b3}.alert-success .alert-link{color:#2b542c}.alert-info{color:#31708f;background-color:#d9edf7;border-color:#bce8f1}.alert-info hr{border-top-color:#a6e1ec}.alert-info .alert-link{color:#245269}.alert-warning{color:#8a6d3b;background-color:#fcf8e3;border-color:#faebcc}.alert-warning hr{border-top-color:#f7e1b5}.alert-warning .alert-link{color:#66512c}.alert-danger{color:#a94442;background-color:#f2dede;border-color:#ebccd1}.alert-danger hr{border-top-color:#e4b9c0}.alert-danger .alert-link{color:#843534}@-webkit-keyframes progress-bar-stripes{from{background-position:40px 0}to{background-position:0 0}}@-o-keyframes progress-bar-stripes{from{background-position:40px 0}to{background-position:0 0}}@keyframes progress-bar-stripes{from{background-position:40px 0}to{background-position:0 0}}.progress{height:20px;margin-bottom:20px;overflow:hidden;background-color:#f5f5f5;border-radius:4px;-webkit-box-shadow:inset 0 1px 2px rgba(0,0,0,.1);box-shadow:inset 0 1px 2px rgba(0,0,0,.1)}.progress-bar{float:left;width:0;height:100%;font-size:12px;line-height:20px;color:#fff;text-align:center;background-color:#337ab7;-webkit-box-shadow:inset 0 -1px 0 rgba(0,0,0,.15);box-shadow:inset 0 -1px 0 rgba(0,0,0,.15);-webkit-transition:width .6s ease;-o-transition:width .6s ease;transition:width .6s ease}.progress-bar-striped,.progress-striped .progress-bar{background-image:-webkit-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:-o-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);-webkit-background-size:40px 40px;background-size:40px 40px}.progress-bar.active,.progress.active .progress-bar{-webkit-animation:progress-bar-stripes 2s linear infinite;-o-animation:progress-bar-stripes 2s linear infinite;animation:progress-bar-stripes 2s linear infinite}.progress-bar-success{background-color:#5cb85c}.progress-striped .progress-bar-success{background-image:-webkit-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:-o-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent)}.progress-bar-info{background-color:#5bc0de}.progress-striped .progress-bar-info{background-image:-webkit-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:-o-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent)}.progress-bar-warning{background-color:#f0ad4e}.progress-striped .progress-bar-warning{background-image:-webkit-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:-o-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent)}.progress-bar-danger{background-color:#d9534f}.progress-striped .progress-bar-danger{background-image:-webkit-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:-o-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent)}.media{margin-top:15px}.media:first-child{margin-top:0}.media,.media-body{overflow:hidden;zoom:1}.media-body{width:10000px}.media-object{display:block}.media-object.img-thumbnail{max-width:none}.media-right,.media>.pull-right{padding-left:10px}.media-left,.media>.pull-left{padding-right:10px}.media-body,.media-left,.media-right{display:table-cell;vertical-align:top}.media-middle{vertical-align:middle}.media-bottom{vertical-align:bottom}.media-heading{margin-top:0;margin-bottom:5px}.media-list{padding-left:0;list-style:none}.list-group{padding-left:0;margin-bottom:20px}.list-group-item{position:relative;display:block;padding:10px 15px;margin-bottom:-1px;background-color:#fff;border:1px solid #ddd}.list-group-item:first-child{border-top-left-radius:4px;border-top-right-radius:4px}.list-group-item:last-child{margin-bottom:0;border-bottom-right-radius:4px;border-bottom-left-radius:4px}a.list-group-item,button.list-group-item{color:#555}a.list-group-item .list-group-item-heading,button.list-group-item .list-group-item-heading{color:#333}a.list-group-item:focus,a.list-group-item:hover,button.list-group-item:focus,button.list-group-item:hover{color:#555;text-decoration:none;background-color:#f5f5f5}button.list-group-item{width:100%;text-align:left}.list-group-item.disabled,.list-group-item.disabled:focus,.list-group-item.disabled:hover{color:#777;cursor:not-allowed;background-color:#eee}.list-group-item.disabled .list-group-item-heading,.list-group-item.disabled:focus .list-group-item-heading,.list-group-item.disabled:hover .list-group-item-heading{color:inherit}.list-group-item.disabled .list-group-item-text,.list-group-item.disabled:focus .list-group-item-text,.list-group-item.disabled:hover .list-group-item-text{color:#777}.list-group-item.active,.list-group-item.active:focus,.list-group-item.active:hover{z-index:2;color:#fff;background-color:#337ab7;border-color:#337ab7}.list-group-item.active .list-group-item-heading,.list-group-item.active .list-group-item-heading>.small,.list-group-item.active .list-group-item-heading>small,.list-group-item.active:focus .list-group-item-heading,.list-group-item.active:focus .list-group-item-heading>.small,.list-group-item.active:focus .list-group-item-heading>small,.list-group-item.active:hover .list-group-item-heading,.list-group-item.active:hover .list-group-item-heading>.small,.list-group-item.active:hover .list-group-item-heading>small{color:inherit}.list-group-item.active .list-group-item-text,.list-group-item.active:focus .list-group-item-text,.list-group-item.active:hover .list-group-item-text{color:#c7ddef}.list-group-item-success{color:#3c763d;background-color:#dff0d8}a.list-group-item-success,button.list-group-item-success{color:#3c763d}a.list-group-item-success .list-group-item-heading,button.list-group-item-success .list-group-item-heading{color:inherit}a.list-group-item-success:focus,a.list-group-item-success:hover,button.list-group-item-success:focus,button.list-group-item-success:hover{color:#3c763d;background-color:#d0e9c6}a.list-group-item-success.active,a.list-group-item-success.active:focus,a.list-group-item-success.active:hover,button.list-group-item-success.active,button.list-group-item-success.active:focus,button.list-group-item-success.active:hover{color:#fff;background-color:#3c763d;border-color:#3c763d}.list-group-item-info{color:#31708f;background-color:#d9edf7}a.list-group-item-info,button.list-group-item-info{color:#31708f}a.list-group-item-info .list-group-item-heading,button.list-group-item-info .list-group-item-heading{color:inherit}a.list-group-item-info:focus,a.list-group-item-info:hover,button.list-group-item-info:focus,button.list-group-item-info:hover{color:#31708f;background-color:#c4e3f3}a.list-group-item-info.active,a.list-group-item-info.active:focus,a.list-group-item-info.active:hover,button.list-group-item-info.active,button.list-group-item-info.active:focus,button.list-group-item-info.active:hover{color:#fff;background-color:#31708f;border-color:#31708f}.list-group-item-warning{color:#8a6d3b;background-color:#fcf8e3}a.list-group-item-warning,button.list-group-item-warning{color:#8a6d3b}a.list-group-item-warning .list-group-item-heading,button.list-group-item-warning .list-group-item-heading{color:inherit}a.list-group-item-warning:focus,a.list-group-item-warning:hover,button.list-group-item-warning:focus,button.list-group-item-warning:hover{color:#8a6d3b;background-color:#faf2cc}a.list-group-item-warning.active,a.list-group-item-warning.active:focus,a.list-group-item-warning.active:hover,button.list-group-item-warning.active,button.list-group-item-warning.active:focus,button.list-group-item-warning.active:hover{color:#fff;background-color:#8a6d3b;border-color:#8a6d3b}.list-group-item-danger{color:#a94442;background-color:#f2dede}a.list-group-item-danger,button.list-group-item-danger{color:#a94442}a.list-group-item-danger .list-group-item-heading,button.list-group-item-danger .list-group-item-heading{color:inherit}a.list-group-item-danger:focus,a.list-group-item-danger:hover,button.list-group-item-danger:focus,button.list-group-item-danger:hover{color:#a94442;background-color:#ebcccc}a.list-group-item-danger.active,a.list-group-item-danger.active:focus,a.list-group-item-danger.active:hover,button.list-group-item-danger.active,button.list-group-item-danger.active:focus,button.list-group-item-danger.active:hover{color:#fff;background-color:#a94442;border-color:#a94442}.list-group-item-heading{margin-top:0;margin-bottom:5px}.list-group-item-text{margin-bottom:0;line-height:1.3}.panel{margin-bottom:20px;background-color:#fff;border:1px solid transparent;border-radius:4px;-webkit-box-shadow:0 1px 1px rgba(0,0,0,.05);box-shadow:0 1px 1px rgba(0,0,0,.05)}.panel-body{padding:15px}.panel-heading{padding:10px 15px;border-bottom:1px solid transparent;border-top-left-radius:3px;border-top-right-radius:3px}.panel-heading>.dropdown .dropdown-toggle{color:inherit}.panel-title{margin-top:0;margin-bottom:0;font-size:16px;color:inherit}.panel-title>.small,.panel-title>.small>a,.panel-title>a,.panel-title>small,.panel-title>small>a{color:inherit}.panel-footer{padding:10px 15px;background-color:#f5f5f5;border-top:1px solid #ddd;border-bottom-right-radius:3px;border-bottom-left-radius:3px}.panel>.list-group,.panel>.panel-collapse>.list-group{margin-bottom:0}.panel>.list-group .list-group-item,.panel>.panel-collapse>.list-group .list-group-item{border-width:1px 0;border-radius:0}.panel>.list-group:first-child .list-group-item:first-child,.panel>.panel-collapse>.list-group:first-child .list-group-item:first-child{border-top:0;border-top-left-radius:3px;border-top-right-radius:3px}.panel>.list-group:last-child .list-group-item:last-child,.panel>.panel-collapse>.list-group:last-child .list-group-item:last-child{border-bottom:0;border-bottom-right-radius:3px;border-bottom-left-radius:3px}.panel>.panel-heading+.panel-collapse>.list-group .list-group-item:first-child{border-top-left-radius:0;border-top-right-radius:0}.panel-heading+.list-group .list-group-item:first-child{border-top-width:0}.list-group+.panel-footer{border-top-width:0}.panel>.panel-collapse>.table,.panel>.table,.panel>.table-responsive>.table{margin-bottom:0}.panel>.panel-collapse>.table caption,.panel>.table caption,.panel>.table-responsive>.table caption{padding-right:15px;padding-left:15px}.panel>.table-responsive:first-child>.table:first-child,.panel>.table:first-child{border-top-left-radius:3px;border-top-right-radius:3px}.panel>.table-responsive:first-child>.table:first-child>tbody:first-child>tr:first-child,.panel>.table-responsive:first-child>.table:first-child>thead:first-child>tr:first-child,.panel>.table:first-child>tbody:first-child>tr:first-child,.panel>.table:first-child>thead:first-child>tr:first-child{border-top-left-radius:3px;border-top-right-radius:3px}.panel>.table-responsive:first-child>.table:first-child>tbody:first-child>tr:first-child td:first-child,.panel>.table-responsive:first-child>.table:first-child>tbody:first-child>tr:first-child th:first-child,.panel>.table-responsive:first-child>.table:first-child>thead:first-child>tr:first-child td:first-child,.panel>.table-responsive:first-child>.table:first-child>thead:first-child>tr:first-child th:first-child,.panel>.table:first-child>tbody:first-child>tr:first-child td:first-child,.panel>.table:first-child>tbody:first-child>tr:first-child th:first-child,.panel>.table:first-child>thead:first-child>tr:first-child td:first-child,.panel>.table:first-child>thead:first-child>tr:first-child th:first-child{border-top-left-radius:3px}.panel>.table-responsive:first-child>.table:first-child>tbody:first-child>tr:first-child td:last-child,.panel>.table-responsive:first-child>.table:first-child>tbody:first-child>tr:first-child th:last-child,.panel>.table-responsive:first-child>.table:first-child>thead:first-child>tr:first-child td:last-child,.panel>.table-responsive:first-child>.table:first-child>thead:first-child>tr:first-child th:last-child,.panel>.table:first-child>tbody:first-child>tr:first-child td:last-child,.panel>.table:first-child>tbody:first-child>tr:first-child th:last-child,.panel>.table:first-child>thead:first-child>tr:first-child td:last-child,.panel>.table:first-child>thead:first-child>tr:first-child th:last-child{border-top-right-radius:3px}.panel>.table-responsive:last-child>.table:last-child,.panel>.table:last-child{border-bottom-right-radius:3px;border-bottom-left-radius:3px}.panel>.table-responsive:last-child>.table:last-child>tbody:last-child>tr:last-child,.panel>.table-responsive:last-child>.table:last-child>tfoot:last-child>tr:last-child,.panel>.table:last-child>tbody:last-child>tr:last-child,.panel>.table:last-child>tfoot:last-child>tr:last-child{border-bottom-right-radius:3px;border-bottom-left-radius:3px}.panel>.table-responsive:last-child>.table:last-child>tbody:last-child>tr:last-child td:first-child,.panel>.table-responsive:last-child>.table:last-child>tbody:last-child>tr:last-child th:first-child,.panel>.table-responsive:last-child>.table:last-child>tfoot:last-child>tr:last-child td:first-child,.panel>.table-responsive:last-child>.table:last-child>tfoot:last-child>tr:last-child th:first-child,.panel>.table:last-child>tbody:last-child>tr:last-child td:first-child,.panel>.table:last-child>tbody:last-child>tr:last-child th:first-child,.panel>.table:last-child>tfoot:last-child>tr:last-child td:first-child,.panel>.table:last-child>tfoot:last-child>tr:last-child th:first-child{border-bottom-left-radius:3px}.panel>.table-responsive:last-child>.table:last-child>tbody:last-child>tr:last-child td:last-child,.panel>.table-responsive:last-child>.table:last-child>tbody:last-child>tr:last-child th:last-child,.panel>.table-responsive:last-child>.table:last-child>tfoot:last-child>tr:last-child td:last-child,.panel>.table-responsive:last-child>.table:last-child>tfoot:last-child>tr:last-child th:last-child,.panel>.table:last-child>tbody:last-child>tr:last-child td:last-child,.panel>.table:last-child>tbody:last-child>tr:last-child th:last-child,.panel>.table:last-child>tfoot:last-child>tr:last-child td:last-child,.panel>.table:last-child>tfoot:last-child>tr:last-child th:last-child{border-bottom-right-radius:3px}.panel>.panel-body+.table,.panel>.panel-body+.table-responsive,.panel>.table+.panel-body,.panel>.table-responsive+.panel-body{border-top:1px solid #ddd}.panel>.table>tbody:first-child>tr:first-child td,.panel>.table>tbody:first-child>tr:first-child th{border-top:0}.panel>.table-bordered,.panel>.table-responsive>.table-bordered{border:0}.panel>.table-bordered>tbody>tr>td:first-child,.panel>.table-bordered>tbody>tr>th:first-child,.panel>.table-bordered>tfoot>tr>td:first-child,.panel>.table-bordered>tfoot>tr>th:first-child,.panel>.table-bordered>thead>tr>td:first-child,.panel>.table-bordered>thead>tr>th:first-child,.panel>.table-responsive>.table-bordered>tbody>tr>td:first-child,.panel>.table-responsive>.table-bordered>tbody>tr>th:first-child,.panel>.table-responsive>.table-bordered>tfoot>tr>td:first-child,.panel>.table-responsive>.table-bordered>tfoot>tr>th:first-child,.panel>.table-responsive>.table-bordered>thead>tr>td:first-child,.panel>.table-responsive>.table-bordered>thead>tr>th:first-child{border-left:0}.panel>.table-bordered>tbody>tr>td:last-child,.panel>.table-bordered>tbody>tr>th:last-child,.panel>.table-bordered>tfoot>tr>td:last-child,.panel>.table-bordered>tfoot>tr>th:last-child,.panel>.table-bordered>thead>tr>td:last-child,.panel>.table-bordered>thead>tr>th:last-child,.panel>.table-responsive>.table-bordered>tbody>tr>td:last-child,.panel>.table-responsive>.table-bordered>tbody>tr>th:last-child,.panel>.table-responsive>.table-bordered>tfoot>tr>td:last-child,.panel>.table-responsive>.table-bordered>tfoot>tr>th:last-child,.panel>.table-responsive>.table-bordered>thead>tr>td:last-child,.panel>.table-responsive>.table-bordered>thead>tr>th:last-child{border-right:0}.panel>.table-bordered>tbody>tr:first-child>td,.panel>.table-bordered>tbody>tr:first-child>th,.panel>.table-bordered>thead>tr:first-child>td,.panel>.table-bordered>thead>tr:first-child>th,.panel>.table-responsive>.table-bordered>tbody>tr:first-child>td,.panel>.table-responsive>.table-bordered>tbody>tr:first-child>th,.panel>.table-responsive>.table-bordered>thead>tr:first-child>td,.panel>.table-responsive>.table-bordered>thead>tr:first-child>th{border-bottom:0}.panel>.table-bordered>tbody>tr:last-child>td,.panel>.table-bordered>tbody>tr:last-child>th,.panel>.table-bordered>tfoot>tr:last-child>td,.panel>.table-bordered>tfoot>tr:last-child>th,.panel>.table-responsive>.table-bordered>tbody>tr:last-child>td,.panel>.table-responsive>.table-bordered>tbody>tr:last-child>th,.panel>.table-responsive>.table-bordered>tfoot>tr:last-child>td,.panel>.table-responsive>.table-bordered>tfoot>tr:last-child>th{border-bottom:0}.panel>.table-responsive{margin-bottom:0;border:0}.panel-group{margin-bottom:20px}.panel-group .panel{margin-bottom:0;border-radius:4px}.panel-group .panel+.panel{margin-top:5px}.panel-group .panel-heading{border-bottom:0}.panel-group .panel-heading+.panel-collapse>.list-group,.panel-group .panel-heading+.panel-collapse>.panel-body{border-top:1px solid #ddd}.panel-group .panel-footer{border-top:0}.panel-group .panel-footer+.panel-collapse .panel-body{border-bottom:1px solid #ddd}.panel-default{border-color:#ddd}.panel-default>.panel-heading{color:#333;background-color:#f5f5f5;border-color:#ddd}.panel-default>.panel-heading+.panel-collapse>.panel-body{border-top-color:#ddd}.panel-default>.panel-heading .badge{color:#f5f5f5;background-color:#333}.panel-default>.panel-footer+.panel-collapse>.panel-body{border-bottom-color:#ddd}.panel-primary{border-color:#337ab7}.panel-primary>.panel-heading{color:#fff;background-color:#337ab7;border-color:#337ab7}.panel-primary>.panel-heading+.panel-collapse>.panel-body{border-top-color:#337ab7}.panel-primary>.panel-heading .badge{color:#337ab7;background-color:#fff}.panel-primary>.panel-footer+.panel-collapse>.panel-body{border-bottom-color:#337ab7}.panel-success{border-color:#d6e9c6}.panel-success>.panel-heading{color:#3c763d;background-color:#dff0d8;border-color:#d6e9c6}.panel-success>.panel-heading+.panel-collapse>.panel-body{border-top-color:#d6e9c6}.panel-success>.panel-heading .badge{color:#dff0d8;background-color:#3c763d}.panel-success>.panel-footer+.panel-collapse>.panel-body{border-bottom-color:#d6e9c6}.panel-info{border-color:#bce8f1}.panel-info>.panel-heading{color:#31708f;background-color:#d9edf7;border-color:#bce8f1}.panel-info>.panel-heading+.panel-collapse>.panel-body{border-top-color:#bce8f1}.panel-info>.panel-heading .badge{color:#d9edf7;background-color:#31708f}.panel-info>.panel-footer+.panel-collapse>.panel-body{border-bottom-color:#bce8f1}.panel-warning{border-color:#faebcc}.panel-warning>.panel-heading{color:#8a6d3b;background-color:#fcf8e3;border-color:#faebcc}.panel-warning>.panel-heading+.panel-collapse>.panel-body{border-top-color:#faebcc}.panel-warning>.panel-heading .badge{color:#fcf8e3;background-color:#8a6d3b}.panel-warning>.panel-footer+.panel-collapse>.panel-body{border-bottom-color:#faebcc}.panel-danger{border-color:#ebccd1}.panel-danger>.panel-heading{color:#a94442;background-color:#f2dede;border-color:#ebccd1}.panel-danger>.panel-heading+.panel-collapse>.panel-body{border-top-color:#ebccd1}.panel-danger>.panel-heading .badge{color:#f2dede;background-color:#a94442}.panel-danger>.panel-footer+.panel-collapse>.panel-body{border-bottom-color:#ebccd1}.embed-responsive{position:relative;display:block;height:0;padding:0;overflow:hidden}.embed-responsive .embed-responsive-item,.embed-responsive embed,.embed-responsive iframe,.embed-responsive object,.embed-responsive video{position:absolute;top:0;bottom:0;left:0;width:100%;height:100%;border:0}.embed-responsive-16by9{padding-bottom:56.25%}.embed-responsive-4by3{padding-bottom:75%}.well{min-height:20px;padding:19px;margin-bottom:20px;background-color:#f5f5f5;border:1px solid #e3e3e3;border-radius:4px;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.05);box-shadow:inset 0 1px 1px rgba(0,0,0,.05)}.well blockquote{border-color:#ddd;border-color:rgba(0,0,0,.15)}.well-lg{padding:24px;border-radius:6px}.well-sm{padding:9px;border-radius:3px}.close{float:right;font-size:21px;font-weight:700;line-height:1;color:#000;text-shadow:0 1px 0 #fff;filter:alpha(opacity=20);opacity:.2}.close:focus,.close:hover{color:#000;text-decoration:none;cursor:pointer;filter:alpha(opacity=50);opacity:.5}button.close{-webkit-appearance:none;padding:0;cursor:pointer;background:0 0;border:0}.modal-open{overflow:hidden}.modal{position:fixed;top:0;right:0;bottom:0;left:0;z-index:1050;display:none;overflow:hidden;-webkit-overflow-scrolling:touch;outline:0}.modal.fade .modal-dialog{-webkit-transition:-webkit-transform .3s ease-out;-o-transition:-o-transform .3s ease-out;transition:transform .3s ease-out;-webkit-transform:translate(0,-25%);-ms-transform:translate(0,-25%);-o-transform:translate(0,-25%);transform:translate(0,-25%)}.modal.in .modal-dialog{-webkit-transform:translate(0,0);-ms-transform:translate(0,0);-o-transform:translate(0,0);transform:translate(0,0)}.modal-open .modal{overflow-x:hidden;overflow-y:auto}.modal-dialog{position:relative;width:auto;margin:10px}.modal-content{position:relative;background-color:#fff;-webkit-background-clip:padding-box;background-clip:padding-box;border:1px solid #999;border:1px solid rgba(0,0,0,.2);border-radius:6px;outline:0;-webkit-box-shadow:0 3px 9px rgba(0,0,0,.5);box-shadow:0 3px 9px rgba(0,0,0,.5)}.modal-backdrop{position:fixed;top:0;right:0;bottom:0;left:0;z-index:1040;background-color:#000}.modal-backdrop.fade{filter:alpha(opacity=0);opacity:0}.modal-backdrop.in{filter:alpha(opacity=50);opacity:.5}.modal-header{min-height:16.43px;padding:15px;border-bottom:1px solid #e5e5e5}.modal-header .close{margin-top:-2px}.modal-title{margin:0;line-height:1.42857143}.modal-body{position:relative;padding:15px}.modal-footer{padding:15px;text-align:right;border-top:1px solid #e5e5e5}.modal-footer .btn+.btn{margin-bottom:0;margin-left:5px}.modal-footer .btn-group .btn+.btn{margin-left:-1px}.modal-footer .btn-block+.btn-block{margin-left:0}.modal-scrollbar-measure{position:absolute;top:-9999px;width:50px;height:50px;overflow:scroll}@media (min-width:768px){.modal-dialog{width:600px;margin:30px auto}.modal-content{-webkit-box-shadow:0 5px 15px rgba(0,0,0,.5);box-shadow:0 5px 15px rgba(0,0,0,.5)}.modal-sm{width:300px}}@media (min-width:992px){.modal-lg{width:900px}}.tooltip{position:absolute;z-index:1070;display:block;font-family:"Helvetica Neue",Helvetica,Arial,sans-serif;font-size:12px;font-style:normal;font-weight:400;line-height:1.42857143;text-align:left;text-align:start;text-decoration:none;text-shadow:none;text-transform:none;letter-spacing:normal;word-break:normal;word-spacing:normal;word-wrap:normal;white-space:normal;filter:alpha(opacity=0);opacity:0;line-break:auto}.tooltip.in{filter:alpha(opacity=90);opacity:.9}.tooltip.top{padding:5px 0;margin-top:-3px}.tooltip.right{padding:0 5px;margin-left:3px}.tooltip.bottom{padding:5px 0;margin-top:3px}.tooltip.left{padding:0 5px;margin-left:-3px}.tooltip-inner{max-width:200px;padding:3px 8px;color:#fff;text-align:center;background-color:#000;border-radius:4px}.tooltip-arrow{position:absolute;width:0;height:0;border-color:transparent;border-style:solid}.tooltip.top .tooltip-arrow{bottom:0;left:50%;margin-left:-5px;border-width:5px 5px 0;border-top-color:#000}.tooltip.top-left .tooltip-arrow{right:5px;bottom:0;margin-bottom:-5px;border-width:5px 5px 0;border-top-color:#000}.tooltip.top-right .tooltip-arrow{bottom:0;left:5px;margin-bottom:-5px;border-width:5px 5px 0;border-top-color:#000}.tooltip.right .tooltip-arrow{top:50%;left:0;margin-top:-5px;border-width:5px 5px 5px 0;border-right-color:#000}.tooltip.left .tooltip-arrow{top:50%;right:0;margin-top:-5px;border-width:5px 0 5px 5px;border-left-color:#000}.tooltip.bottom .tooltip-arrow{top:0;left:50%;margin-left:-5px;border-width:0 5px 5px;border-bottom-color:#000}.tooltip.bottom-left .tooltip-arrow{top:0;right:5px;margin-top:-5px;border-width:0 5px 5px;border-bottom-color:#000}.tooltip.bottom-right .tooltip-arrow{top:0;left:5px;margin-top:-5px;border-width:0 5px 5px;border-bottom-color:#000}.popover{position:absolute;top:0;left:0;z-index:1060;display:none;max-width:276px;padding:1px;font-family:"Helvetica Neue",Helvetica,Arial,sans-serif;font-size:14px;font-style:normal;font-weight:400;line-height:1.42857143;text-align:left;text-align:start;text-decoration:none;text-shadow:none;text-transform:none;letter-spacing:normal;word-break:normal;word-spacing:normal;word-wrap:normal;white-space:normal;background-color:#fff;-webkit-background-clip:padding-box;background-clip:padding-box;border:1px solid #ccc;border:1px solid rgba(0,0,0,.2);border-radius:6px;-webkit-box-shadow:0 5px 10px rgba(0,0,0,.2);box-shadow:0 5px 10px rgba(0,0,0,.2);line-break:auto}.popover.top{margin-top:-10px}.popover.right{margin-left:10px}.popover.bottom{margin-top:10px}.popover.left{margin-left:-10px}.popover-title{padding:8px 14px;margin:0;font-size:14px;background-color:#f7f7f7;border-bottom:1px solid #ebebeb;border-radius:5px 5px 0 0}.popover-content{padding:9px 14px}.popover>.arrow,.popover>.arrow:after{position:absolute;display:block;width:0;height:0;border-color:transparent;border-style:solid}.popover>.arrow{border-width:11px}.popover>.arrow:after{content:"";border-width:10px}.popover.top>.arrow{bottom:-11px;left:50%;margin-left:-11px;border-top-color:#999;border-top-color:rgba(0,0,0,.25);border-bottom-width:0}.popover.top>.arrow:after{bottom:1px;margin-left:-10px;content:" ";border-top-color:#fff;border-bottom-width:0}.popover.right>.arrow{top:50%;left:-11px;margin-top:-11px;border-right-color:#999;border-right-color:rgba(0,0,0,.25);border-left-width:0}.popover.right>.arrow:after{bottom:-10px;left:1px;content:" ";border-right-color:#fff;border-left-width:0}.popover.bottom>.arrow{top:-11px;left:50%;margin-left:-11px;border-top-width:0;border-bottom-color:#999;border-bottom-color:rgba(0,0,0,.25)}.popover.bottom>.arrow:after{top:1px;margin-left:-10px;content:" ";border-top-width:0;border-bottom-color:#fff}.popover.left>.arrow{top:50%;right:-11px;margin-top:-11px;border-right-width:0;border-left-color:#999;border-left-color:rgba(0,0,0,.25)}.popover.left>.arrow:after{right:1px;bottom:-10px;content:" ";border-right-width:0;border-left-color:#fff}.carousel{position:relative}.carousel-inner{position:relative;width:100%;overflow:hidden}.carousel-inner>.item{position:relative;display:none;-webkit-transition:.6s ease-in-out left;-o-transition:.6s ease-in-out left;transition:.6s ease-in-out left}.carousel-inner>.item>a>img,.carousel-inner>.item>img{line-height:1}@media all and (transform-3d),(-webkit-transform-3d){.carousel-inner>.item{-webkit-transition:-webkit-transform .6s ease-in-out;-o-transition:-o-transform .6s ease-in-out;transition:transform .6s ease-in-out;-webkit-backface-visibility:hidden;backface-visibility:hidden;-webkit-perspective:1000px;perspective:1000px}.carousel-inner>.item.active.right,.carousel-inner>.item.next{left:0;-webkit-transform:translate3d(100%,0,0);transform:translate3d(100%,0,0)}.carousel-inner>.item.active.left,.carousel-inner>.item.prev{left:0;-webkit-transform:translate3d(-100%,0,0);transform:translate3d(-100%,0,0)}.carousel-inner>.item.active,.carousel-inner>.item.next.left,.carousel-inner>.item.prev.right{left:0;-webkit-transform:translate3d(0,0,0);transform:translate3d(0,0,0)}}.carousel-inner>.active,.carousel-inner>.next,.carousel-inner>.prev{display:block}.carousel-inner>.active{left:0}.carousel-inner>.next,.carousel-inner>.prev{position:absolute;top:0;width:100%}.carousel-inner>.next{left:100%}.carousel-inner>.prev{left:-100%}.carousel-inner>.next.left,.carousel-inner>.prev.right{left:0}.carousel-inner>.active.left{left:-100%}.carousel-inner>.active.right{left:100%}.carousel-control{position:absolute;top:0;bottom:0;left:0;width:15%;font-size:20px;color:#fff;text-align:center;text-shadow:0 1px 2px rgba(0,0,0,.6);filter:alpha(opacity=50);opacity:.5}.carousel-control.left{background-image:-webkit-linear-gradient(left,rgba(0,0,0,.5) 0,rgba(0,0,0,.0001) 100%);background-image:-o-linear-gradient(left,rgba(0,0,0,.5) 0,rgba(0,0,0,.0001) 100%);background-image:-webkit-gradient(linear,left top,right top,from(rgba(0,0,0,.5)),to(rgba(0,0,0,.0001)));background-image:linear-gradient(to right,rgba(0,0,0,.5) 0,rgba(0,0,0,.0001) 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1);background-repeat:repeat-x}.carousel-control.right{right:0;left:auto;background-image:-webkit-linear-gradient(left,rgba(0,0,0,.0001) 0,rgba(0,0,0,.5) 100%);background-image:-o-linear-gradient(left,rgba(0,0,0,.0001) 0,rgba(0,0,0,.5) 100%);background-image:-webkit-gradient(linear,left top,right top,from(rgba(0,0,0,.0001)),to(rgba(0,0,0,.5)));background-image:linear-gradient(to right,rgba(0,0,0,.0001) 0,rgba(0,0,0,.5) 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1);background-repeat:repeat-x}.carousel-control:focus,.carousel-control:hover{color:#fff;text-decoration:none;filter:alpha(opacity=90);outline:0;opacity:.9}.carousel-control .glyphicon-chevron-left,.carousel-control .glyphicon-chevron-right,.carousel-control .icon-next,.carousel-control .icon-prev{position:absolute;top:50%;z-index:5;display:inline-block;margin-top:-10px}.carousel-control .glyphicon-chevron-left,.carousel-control .icon-prev{left:50%;margin-left:-10px}.carousel-control .glyphicon-chevron-right,.carousel-control .icon-next{right:50%;margin-right:-10px}.carousel-control .icon-next,.carousel-control .icon-prev{width:20px;height:20px;font-family:serif;line-height:1}.carousel-control .icon-prev:before{content:'\2039'}.carousel-control .icon-next:before{content:'\203a'}.carousel-indicators{position:absolute;bottom:10px;left:50%;z-index:15;width:60%;padding-left:0;margin-left:-30%;text-align:center;list-style:none}.carousel-indicators li{display:inline-block;width:10px;height:10px;margin:1px;text-indent:-999px;cursor:pointer;background-color:#000\9;background-color:rgba(0,0,0,0);border:1px solid #fff;border-radius:10px}.carousel-indicators .active{width:12px;height:12px;margin:0;background-color:#fff}.carousel-caption{position:absolute;right:15%;bottom:20px;left:15%;z-index:10;padding-top:20px;padding-bottom:20px;color:#fff;text-align:center;text-shadow:0 1px 2px rgba(0,0,0,.6)}.carousel-caption .btn{text-shadow:none}@media screen and (min-width:768px){.carousel-control .glyphicon-chevron-left,.carousel-control .glyphicon-chevron-right,.carousel-control .icon-next,.carousel-control .icon-prev{width:30px;height:30px;margin-top:-15px;font-size:30px}.carousel-control .glyphicon-chevron-left,.carousel-control .icon-prev{margin-left:-15px}.carousel-control .glyphicon-chevron-right,.carousel-control .icon-next{margin-right:-15px}.carousel-caption{right:20%;left:20%;padding-bottom:30px}.carousel-indicators{bottom:20px}}.btn-group-vertical>.btn-group:after,.btn-group-vertical>.btn-group:before,.btn-toolbar:after,.btn-toolbar:before,.clearfix:after,.clearfix:before,.container-fluid:after,.container-fluid:before,.container:after,.container:before,.dl-horizontal dd:after,.dl-horizontal dd:before,.form-horizontal .form-group:after,.form-horizontal .form-group:before,.modal-footer:after,.modal-footer:before,.nav:after,.nav:before,.navbar-collapse:after,.navbar-collapse:before,.navbar-header:after,.navbar-header:before,.navbar:after,.navbar:before,.pager:after,.pager:before,.panel-body:after,.panel-body:before,.row:after,.row:before{display:table;content:" "}.btn-group-vertical>.btn-group:after,.btn-toolbar:after,.clearfix:after,.container-fluid:after,.container:after,.dl-horizontal dd:after,.form-horizontal .form-group:after,.modal-footer:after,.nav:after,.navbar-collapse:after,.navbar-header:after,.navbar:after,.pager:after,.panel-body:after,.row:after{clear:both}.center-block{display:block;margin-right:auto;margin-left:auto}.pull-right{float:right!important}.pull-left{float:left!important}.hide{display:none!important}.show{display:block!important}.invisible{visibility:hidden}.text-hide{font:0/0 a;color:transparent;text-shadow:none;background-color:transparent;border:0}.hidden{display:none!important}.affix{position:fixed}@-ms-viewport{width:device-width}.visible-lg,.visible-md,.visible-sm,.visible-xs{display:none!important}.visible-lg-block,.visible-lg-inline,.visible-lg-inline-block,.visible-md-block,.visible-md-inline,.visible-md-inline-block,.visible-sm-block,.visible-sm-inline,.visible-sm-inline-block,.visible-xs-block,.visible-xs-inline,.visible-xs-inline-block{display:none!important}@media (max-width:767px){.visible-xs{display:block!important}table.visible-xs{display:table!important}tr.visible-xs{display:table-row!important}td.visible-xs,th.visible-xs{display:table-cell!important}}@media (max-width:767px){.visible-xs-block{display:block!important}}@media (max-width:767px){.visible-xs-inline{display:inline!important}}@media (max-width:767px){.visible-xs-inline-block{display:inline-block!important}}@media (min-width:768px) and (max-width:991px){.visible-sm{display:block!important}table.visible-sm{display:table!important}tr.visible-sm{display:table-row!important}td.visible-sm,th.visible-sm{display:table-cell!important}}@media (min-width:768px) and (max-width:991px){.visible-sm-block{display:block!important}}@media (min-width:768px) and (max-width:991px){.visible-sm-inline{display:inline!important}}@media (min-width:768px) and (max-width:991px){.visible-sm-inline-block{display:inline-block!important}}@media (min-width:992px) and (max-width:1199px){.visible-md{display:block!important}table.visible-md{display:table!important}tr.visible-md{display:table-row!important}td.visible-md,th.visible-md{display:table-cell!important}}@media (min-width:992px) and (max-width:1199px){.visible-md-block{display:block!important}}@media (min-width:992px) and (max-width:1199px){.visible-md-inline{display:inline!important}}@media (min-width:992px) and (max-width:1199px){.visible-md-inline-block{display:inline-block!important}}@media (min-width:1200px){.visible-lg{display:block!important}table.visible-lg{display:table!important}tr.visible-lg{display:table-row!important}td.visible-lg,th.visible-lg{display:table-cell!important}}@media (min-width:1200px){.visible-lg-block{display:block!important}}@media (min-width:1200px){.visible-lg-inline{display:inline!important}}@media (min-width:1200px){.visible-lg-inline-block{display:inline-block!important}}@media (max-width:767px){.hidden-xs{display:none!important}}@media (min-width:768px) and (max-width:991px){.hidden-sm{display:none!important}}@media (min-width:992px) and (max-width:1199px){.hidden-md{display:none!important}}@media (min-width:1200px){.hidden-lg{display:none!important}}.visible-print{display:none!important}@media print{.visible-print{display:block!important}table.visible-print{display:table!important}tr.visible-print{display:table-row!important}td.visible-print,th.visible-print{display:table-cell!important}}.visible-print-block{display:none!important}@media print{.visible-print-block{display:block!important}}.visible-print-inline{display:none!important}@media print{.visible-print-inline{display:inline!important}}.visible-print-inline-block{display:none!important}@media print{.visible-print-inline-block{display:inline-block!important}}@media print{.hidden-print{display:none!important}}
+</style>
+<script>/*!
+ * Bootstrap v3.3.5 (http://getbootstrap.com)
+ * Copyright 2011-2015 Twitter, Inc.
+ * Licensed under the MIT license
+ */
+if("undefined"==typeof jQuery)throw new Error("Bootstrap's JavaScript requires jQuery");+function(a){"use strict";var b=a.fn.jquery.split(" ")[0].split(".");if(b[0]<2&&b[1]<9||1==b[0]&&9==b[1]&&b[2]<1)throw new Error("Bootstrap's JavaScript requires jQuery version 1.9.1 or higher")}(jQuery),+function(a){"use strict";function b(){var a=document.createElement("bootstrap"),b={WebkitTransition:"webkitTransitionEnd",MozTransition:"transitionend",OTransition:"oTransitionEnd otransitionend",transition:"transitionend"};for(var c in b)if(void 0!==a.style[c])return{end:b[c]};return!1}a.fn.emulateTransitionEnd=function(b){var c=!1,d=this;a(this).one("bsTransitionEnd",function(){c=!0});var e=function(){c||a(d).trigger(a.support.transition.end)};return setTimeout(e,b),this},a(function(){a.support.transition=b(),a.support.transition&&(a.event.special.bsTransitionEnd={bindType:a.support.transition.end,delegateType:a.support.transition.end,handle:function(b){return a(b.target).is(this)?b.handleObj.handler.apply(this,arguments):void 0}})})}(jQuery),+function(a){"use strict";function b(b){return this.each(function(){var c=a(this),e=c.data("bs.alert");e||c.data("bs.alert",e=new d(this)),"string"==typeof b&&e[b].call(c)})}var c='[data-dismiss="alert"]',d=function(b){a(b).on("click",c,this.close)};d.VERSION="3.3.5",d.TRANSITION_DURATION=150,d.prototype.close=function(b){function c(){g.detach().trigger("closed.bs.alert").remove()}var e=a(this),f=e.attr("data-target");f||(f=e.attr("href"),f=f&&f.replace(/.*(?=#[^\s]*$)/,""));var g=a(f);b&&b.preventDefault(),g.length||(g=e.closest(".alert")),g.trigger(b=a.Event("close.bs.alert")),b.isDefaultPrevented()||(g.removeClass("in"),a.support.transition&&g.hasClass("fade")?g.one("bsTransitionEnd",c).emulateTransitionEnd(d.TRANSITION_DURATION):c())};var e=a.fn.alert;a.fn.alert=b,a.fn.alert.Constructor=d,a.fn.alert.noConflict=function(){return a.fn.alert=e,this},a(document).on("click.bs.alert.data-api",c,d.prototype.close)}(jQuery),+function(a){"use strict";function b(b){return this.each(function(){var d=a(this),e=d.data("bs.button"),f="object"==typeof b&&b;e||d.data("bs.button",e=new c(this,f)),"toggle"==b?e.toggle():b&&e.setState(b)})}var c=function(b,d){this.$element=a(b),this.options=a.extend({},c.DEFAULTS,d),this.isLoading=!1};c.VERSION="3.3.5",c.DEFAULTS={loadingText:"loading..."},c.prototype.setState=function(b){var c="disabled",d=this.$element,e=d.is("input")?"val":"html",f=d.data();b+="Text",null==f.resetText&&d.data("resetText",d[e]()),setTimeout(a.proxy(function(){d[e](null==f[b]?this.options[b]:f[b]),"loadingText"==b?(this.isLoading=!0,d.addClass(c).attr(c,c)):this.isLoading&&(this.isLoading=!1,d.removeClass(c).removeAttr(c))},this),0)},c.prototype.toggle=function(){var a=!0,b=this.$element.closest('[data-toggle="buttons"]');if(b.length){var c=this.$element.find("input");"radio"==c.prop("type")?(c.prop("checked")&&(a=!1),b.find(".active").removeClass("active"),this.$element.addClass("active")):"checkbox"==c.prop("type")&&(c.prop("checked")!==this.$element.hasClass("active")&&(a=!1),this.$element.toggleClass("active")),c.prop("checked",this.$element.hasClass("active")),a&&c.trigger("change")}else this.$element.attr("aria-pressed",!this.$element.hasClass("active")),this.$element.toggleClass("active")};var d=a.fn.button;a.fn.button=b,a.fn.button.Constructor=c,a.fn.button.noConflict=function(){return a.fn.button=d,this},a(document).on("click.bs.button.data-api",'[data-toggle^="button"]',function(c){var d=a(c.target);d.hasClass("btn")||(d=d.closest(".btn")),b.call(d,"toggle"),a(c.target).is('input[type="radio"]')||a(c.target).is('input[type="checkbox"]')||c.preventDefault()}).on("focus.bs.button.data-api blur.bs.button.data-api",'[data-toggle^="button"]',function(b){a(b.target).closest(".btn").toggleClass("focus",/^focus(in)?$/.test(b.type))})}(jQuery),+function(a){"use strict";function b(b){return this.each(function(){var d=a(this),e=d.data("bs.carousel"),f=a.extend({},c.DEFAULTS,d.data(),"object"==typeof b&&b),g="string"==typeof b?b:f.slide;e||d.data("bs.carousel",e=new c(this,f)),"number"==typeof b?e.to(b):g?e[g]():f.interval&&e.pause().cycle()})}var c=function(b,c){this.$element=a(b),this.$indicators=this.$element.find(".carousel-indicators"),this.options=c,this.paused=null,this.sliding=null,this.interval=null,this.$active=null,this.$items=null,this.options.keyboard&&this.$element.on("keydown.bs.carousel",a.proxy(this.keydown,this)),"hover"==this.options.pause&&!("ontouchstart"in document.documentElement)&&this.$element.on("mouseenter.bs.carousel",a.proxy(this.pause,this)).on("mouseleave.bs.carousel",a.proxy(this.cycle,this))};c.VERSION="3.3.5",c.TRANSITION_DURATION=600,c.DEFAULTS={interval:5e3,pause:"hover",wrap:!0,keyboard:!0},c.prototype.keydown=function(a){if(!/input|textarea/i.test(a.target.tagName)){switch(a.which){case 37:this.prev();break;case 39:this.next();break;default:return}a.preventDefault()}},c.prototype.cycle=function(b){return b||(this.paused=!1),this.interval&&clearInterval(this.interval),this.options.interval&&!this.paused&&(this.interval=setInterval(a.proxy(this.next,this),this.options.interval)),this},c.prototype.getItemIndex=function(a){return this.$items=a.parent().children(".item"),this.$items.index(a||this.$active)},c.prototype.getItemForDirection=function(a,b){var c=this.getItemIndex(b),d="prev"==a&&0===c||"next"==a&&c==this.$items.length-1;if(d&&!this.options.wrap)return b;var e="prev"==a?-1:1,f=(c+e)%this.$items.length;return this.$items.eq(f)},c.prototype.to=function(a){var b=this,c=this.getItemIndex(this.$active=this.$element.find(".item.active"));return a>this.$items.length-1||0>a?void 0:this.sliding?this.$element.one("slid.bs.carousel",function(){b.to(a)}):c==a?this.pause().cycle():this.slide(a>c?"next":"prev",this.$items.eq(a))},c.prototype.pause=function(b){return b||(this.paused=!0),this.$element.find(".next, .prev").length&&a.support.transition&&(this.$element.trigger(a.support.transition.end),this.cycle(!0)),this.interval=clearInterval(this.interval),this},c.prototype.next=function(){return this.sliding?void 0:this.slide("next")},c.prototype.prev=function(){return this.sliding?void 0:this.slide("prev")},c.prototype.slide=function(b,d){var e=this.$element.find(".item.active"),f=d||this.getItemForDirection(b,e),g=this.interval,h="next"==b?"left":"right",i=this;if(f.hasClass("active"))return this.sliding=!1;var j=f[0],k=a.Event("slide.bs.carousel",{relatedTarget:j,direction:h});if(this.$element.trigger(k),!k.isDefaultPrevented()){if(this.sliding=!0,g&&this.pause(),this.$indicators.length){this.$indicators.find(".active").removeClass("active");var l=a(this.$indicators.children()[this.getItemIndex(f)]);l&&l.addClass("active")}var m=a.Event("slid.bs.carousel",{relatedTarget:j,direction:h});return a.support.transition&&this.$element.hasClass("slide")?(f.addClass(b),f[0].offsetWidth,e.addClass(h),f.addClass(h),e.one("bsTransitionEnd",function(){f.removeClass([b,h].join(" ")).addClass("active"),e.removeClass(["active",h].join(" ")),i.sliding=!1,setTimeout(function(){i.$element.trigger(m)},0)}).emulateTransitionEnd(c.TRANSITION_DURATION)):(e.removeClass("active"),f.addClass("active"),this.sliding=!1,this.$element.trigger(m)),g&&this.cycle(),this}};var d=a.fn.carousel;a.fn.carousel=b,a.fn.carousel.Constructor=c,a.fn.carousel.noConflict=function(){return a.fn.carousel=d,this};var e=function(c){var d,e=a(this),f=a(e.attr("data-target")||(d=e.attr("href"))&&d.replace(/.*(?=#[^\s]+$)/,""));if(f.hasClass("carousel")){var g=a.extend({},f.data(),e.data()),h=e.attr("data-slide-to");h&&(g.interval=!1),b.call(f,g),h&&f.data("bs.carousel").to(h),c.preventDefault()}};a(document).on("click.bs.carousel.data-api","[data-slide]",e).on("click.bs.carousel.data-api","[data-slide-to]",e),a(window).on("load",function(){a('[data-ride="carousel"]').each(function(){var c=a(this);b.call(c,c.data())})})}(jQuery),+function(a){"use strict";function b(b){var c,d=b.attr("data-target")||(c=b.attr("href"))&&c.replace(/.*(?=#[^\s]+$)/,"");return a(d)}function c(b){return this.each(function(){var c=a(this),e=c.data("bs.collapse"),f=a.extend({},d.DEFAULTS,c.data(),"object"==typeof b&&b);!e&&f.toggle&&/show|hide/.test(b)&&(f.toggle=!1),e||c.data("bs.collapse",e=new d(this,f)),"string"==typeof b&&e[b]()})}var d=function(b,c){this.$element=a(b),this.options=a.extend({},d.DEFAULTS,c),this.$trigger=a('[data-toggle="collapse"][href="#'+b.id+'"],[data-toggle="collapse"][data-target="#'+b.id+'"]'),this.transitioning=null,this.options.parent?this.$parent=this.getParent():this.addAriaAndCollapsedClass(this.$element,this.$trigger),this.options.toggle&&this.toggle()};d.VERSION="3.3.5",d.TRANSITION_DURATION=350,d.DEFAULTS={toggle:!0},d.prototype.dimension=function(){var a=this.$element.hasClass("width");return a?"width":"height"},d.prototype.show=function(){if(!this.transitioning&&!this.$element.hasClass("in")){var b,e=this.$parent&&this.$parent.children(".panel").children(".in, .collapsing");if(!(e&&e.length&&(b=e.data("bs.collapse"),b&&b.transitioning))){var f=a.Event("show.bs.collapse");if(this.$element.trigger(f),!f.isDefaultPrevented()){e&&e.length&&(c.call(e,"hide"),b||e.data("bs.collapse",null));var g=this.dimension();this.$element.removeClass("collapse").addClass("collapsing")[g](0).attr("aria-expanded",!0),this.$trigger.removeClass("collapsed").attr("aria-expanded",!0),this.transitioning=1;var h=function(){this.$element.removeClass("collapsing").addClass("collapse in")[g](""),this.transitioning=0,this.$element.trigger("shown.bs.collapse")};if(!a.support.transition)return h.call(this);var i=a.camelCase(["scroll",g].join("-"));this.$element.one("bsTransitionEnd",a.proxy(h,this)).emulateTransitionEnd(d.TRANSITION_DURATION)[g](this.$element[0][i])}}}},d.prototype.hide=function(){if(!this.transitioning&&this.$element.hasClass("in")){var b=a.Event("hide.bs.collapse");if(this.$element.trigger(b),!b.isDefaultPrevented()){var c=this.dimension();this.$element[c](this.$element[c]())[0].offsetHeight,this.$element.addClass("collapsing").removeClass("collapse in").attr("aria-expanded",!1),this.$trigger.addClass("collapsed").attr("aria-expanded",!1),this.transitioning=1;var e=function(){this.transitioning=0,this.$element.removeClass("collapsing").addClass("collapse").trigger("hidden.bs.collapse")};return a.support.transition?void this.$element[c](0).one("bsTransitionEnd",a.proxy(e,this)).emulateTransitionEnd(d.TRANSITION_DURATION):e.call(this)}}},d.prototype.toggle=function(){this[this.$element.hasClass("in")?"hide":"show"]()},d.prototype.getParent=function(){return a(this.options.parent).find('[data-toggle="collapse"][data-parent="'+this.options.parent+'"]').each(a.proxy(function(c,d){var e=a(d);this.addAriaAndCollapsedClass(b(e),e)},this)).end()},d.prototype.addAriaAndCollapsedClass=function(a,b){var c=a.hasClass("in");a.attr("aria-expanded",c),b.toggleClass("collapsed",!c).attr("aria-expanded",c)};var e=a.fn.collapse;a.fn.collapse=c,a.fn.collapse.Constructor=d,a.fn.collapse.noConflict=function(){return a.fn.collapse=e,this},a(document).on("click.bs.collapse.data-api",'[data-toggle="collapse"]',function(d){var e=a(this);e.attr("data-target")||d.preventDefault();var f=b(e),g=f.data("bs.collapse"),h=g?"toggle":e.data();c.call(f,h)})}(jQuery),+function(a){"use strict";function b(b){var c=b.attr("data-target");c||(c=b.attr("href"),c=c&&/#[A-Za-z]/.test(c)&&c.replace(/.*(?=#[^\s]*$)/,""));var d=c&&a(c);return d&&d.length?d:b.parent()}function c(c){c&&3===c.which||(a(e).remove(),a(f).each(function(){var d=a(this),e=b(d),f={relatedTarget:this};e.hasClass("open")&&(c&&"click"==c.type&&/input|textarea/i.test(c.target.tagName)&&a.contains(e[0],c.target)||(e.trigger(c=a.Event("hide.bs.dropdown",f)),c.isDefaultPrevented()||(d.attr("aria-expanded","false"),e.removeClass("open").trigger("hidden.bs.dropdown",f))))}))}function d(b){return this.each(function(){var c=a(this),d=c.data("bs.dropdown");d||c.data("bs.dropdown",d=new g(this)),"string"==typeof b&&d[b].call(c)})}var e=".dropdown-backdrop",f='[data-toggle="dropdown"]',g=function(b){a(b).on("click.bs.dropdown",this.toggle)};g.VERSION="3.3.5",g.prototype.toggle=function(d){var e=a(this);if(!e.is(".disabled, :disabled")){var f=b(e),g=f.hasClass("open");if(c(),!g){"ontouchstart"in document.documentElement&&!f.closest(".navbar-nav").length&&a(document.createElement("div")).addClass("dropdown-backdrop").insertAfter(a(this)).on("click",c);var h={relatedTarget:this};if(f.trigger(d=a.Event("show.bs.dropdown",h)),d.isDefaultPrevented())return;e.trigger("focus").attr("aria-expanded","true"),f.toggleClass("open").trigger("shown.bs.dropdown",h)}return!1}},g.prototype.keydown=function(c){if(/(38|40|27|32)/.test(c.which)&&!/input|textarea/i.test(c.target.tagName)){var d=a(this);if(c.preventDefault(),c.stopPropagation(),!d.is(".disabled, :disabled")){var e=b(d),g=e.hasClass("open");if(!g&&27!=c.which||g&&27==c.which)return 27==c.which&&e.find(f).trigger("focus"),d.trigger("click");var h=" li:not(.disabled):visible a",i=e.find(".dropdown-menu"+h);if(i.length){var j=i.index(c.target);38==c.which&&j>0&&j--,40==c.which&&j<i.length-1&&j++,~j||(j=0),i.eq(j).trigger("focus")}}}};var h=a.fn.dropdown;a.fn.dropdown=d,a.fn.dropdown.Constructor=g,a.fn.dropdown.noConflict=function(){return a.fn.dropdown=h,this},a(document).on("click.bs.dropdown.data-api",c).on("click.bs.dropdown.data-api",".dropdown form",function(a){a.stopPropagation()}).on("click.bs.dropdown.data-api",f,g.prototype.toggle).on("keydown.bs.dropdown.data-api",f,g.prototype.keydown).on("keydown.bs.dropdown.data-api",".dropdown-menu",g.prototype.keydown)}(jQuery),+function(a){"use strict";function b(b,d){return this.each(function(){var e=a(this),f=e.data("bs.modal"),g=a.extend({},c.DEFAULTS,e.data(),"object"==typeof b&&b);f||e.data("bs.modal",f=new c(this,g)),"string"==typeof b?f[b](d):g.show&&f.show(d)})}var c=function(b,c){this.options=c,this.$body=a(document.body),this.$element=a(b),this.$dialog=this.$element.find(".modal-dialog"),this.$backdrop=null,this.isShown=null,this.originalBodyPad=null,this.scrollbarWidth=0,this.ignoreBackdropClick=!1,this.options.remote&&this.$element.find(".modal-content").load(this.options.remote,a.proxy(function(){this.$element.trigger("loaded.bs.modal")},this))};c.VERSION="3.3.5",c.TRANSITION_DURATION=300,c.BACKDROP_TRANSITION_DURATION=150,c.DEFAULTS={backdrop:!0,keyboard:!0,show:!0},c.prototype.toggle=function(a){return this.isShown?this.hide():this.show(a)},c.prototype.show=function(b){var d=this,e=a.Event("show.bs.modal",{relatedTarget:b});this.$element.trigger(e),this.isShown||e.isDefaultPrevented()||(this.isShown=!0,this.checkScrollbar(),this.setScrollbar(),this.$body.addClass("modal-open"),this.escape(),this.resize(),this.$element.on("click.dismiss.bs.modal",'[data-dismiss="modal"]',a.proxy(this.hide,this)),this.$dialog.on("mousedown.dismiss.bs.modal",function(){d.$element.one("mouseup.dismiss.bs.modal",function(b){a(b.target).is(d.$element)&&(d.ignoreBackdropClick=!0)})}),this.backdrop(function(){var e=a.support.transition&&d.$element.hasClass("fade");d.$element.parent().length||d.$element.appendTo(d.$body),d.$element.show().scrollTop(0),d.adjustDialog(),e&&d.$element[0].offsetWidth,d.$element.addClass("in"),d.enforceFocus();var f=a.Event("shown.bs.modal",{relatedTarget:b});e?d.$dialog.one("bsTransitionEnd",function(){d.$element.trigger("focus").trigger(f)}).emulateTransitionEnd(c.TRANSITION_DURATION):d.$element.trigger("focus").trigger(f)}))},c.prototype.hide=function(b){b&&b.preventDefault(),b=a.Event("hide.bs.modal"),this.$element.trigger(b),this.isShown&&!b.isDefaultPrevented()&&(this.isShown=!1,this.escape(),this.resize(),a(document).off("focusin.bs.modal"),this.$element.removeClass("in").off("click.dismiss.bs.modal").off("mouseup.dismiss.bs.modal"),this.$dialog.off("mousedown.dismiss.bs.modal"),a.support.transition&&this.$element.hasClass("fade")?this.$element.one("bsTransitionEnd",a.proxy(this.hideModal,this)).emulateTransitionEnd(c.TRANSITION_DURATION):this.hideModal())},c.prototype.enforceFocus=function(){a(document).off("focusin.bs.modal").on("focusin.bs.modal",a.proxy(function(a){this.$element[0]===a.target||this.$element.has(a.target).length||this.$element.trigger("focus")},this))},c.prototype.escape=function(){this.isShown&&this.options.keyboard?this.$element.on("keydown.dismiss.bs.modal",a.proxy(function(a){27==a.which&&this.hide()},this)):this.isShown||this.$element.off("keydown.dismiss.bs.modal")},c.prototype.resize=function(){this.isShown?a(window).on("resize.bs.modal",a.proxy(this.handleUpdate,this)):a(window).off("resize.bs.modal")},c.prototype.hideModal=function(){var a=this;this.$element.hide(),this.backdrop(function(){a.$body.removeClass("modal-open"),a.resetAdjustments(),a.resetScrollbar(),a.$element.trigger("hidden.bs.modal")})},c.prototype.removeBackdrop=function(){this.$backdrop&&this.$backdrop.remove(),this.$backdrop=null},c.prototype.backdrop=function(b){var d=this,e=this.$element.hasClass("fade")?"fade":"";if(this.isShown&&this.options.backdrop){var f=a.support.transition&&e;if(this.$backdrop=a(document.createElement("div")).addClass("modal-backdrop "+e).appendTo(this.$body),this.$element.on("click.dismiss.bs.modal",a.proxy(function(a){return this.ignoreBackdropClick?void(this.ignoreBackdropClick=!1):void(a.target===a.currentTarget&&("static"==this.options.backdrop?this.$element[0].focus():this.hide()))},this)),f&&this.$backdrop[0].offsetWidth,this.$backdrop.addClass("in"),!b)return;f?this.$backdrop.one("bsTransitionEnd",b).emulateTransitionEnd(c.BACKDROP_TRANSITION_DURATION):b()}else if(!this.isShown&&this.$backdrop){this.$backdrop.removeClass("in");var g=function(){d.removeBackdrop(),b&&b()};a.support.transition&&this.$element.hasClass("fade")?this.$backdrop.one("bsTransitionEnd",g).emulateTransitionEnd(c.BACKDROP_TRANSITION_DURATION):g()}else b&&b()},c.prototype.handleUpdate=function(){this.adjustDialog()},c.prototype.adjustDialog=function(){var a=this.$element[0].scrollHeight>document.documentElement.clientHeight;this.$element.css({paddingLeft:!this.bodyIsOverflowing&&a?this.scrollbarWidth:"",paddingRight:this.bodyIsOverflowing&&!a?this.scrollbarWidth:""})},c.prototype.resetAdjustments=function(){this.$element.css({paddingLeft:"",paddingRight:""})},c.prototype.checkScrollbar=function(){var a=window.innerWidth;if(!a){var b=document.documentElement.getBoundingClientRect();a=b.right-Math.abs(b.left)}this.bodyIsOverflowing=document.body.clientWidth<a,this.scrollbarWidth=this.measureScrollbar()},c.prototype.setScrollbar=function(){var a=parseInt(this.$body.css("padding-right")||0,10);this.originalBodyPad=document.body.style.paddingRight||"",this.bodyIsOverflowing&&this.$body.css("padding-right",a+this.scrollbarWidth)},c.prototype.resetScrollbar=function(){this.$body.css("padding-right",this.originalBodyPad)},c.prototype.measureScrollbar=function(){var a=document.createElement("div");a.className="modal-scrollbar-measure",this.$body.append(a);var b=a.offsetWidth-a.clientWidth;return this.$body[0].removeChild(a),b};var d=a.fn.modal;a.fn.modal=b,a.fn.modal.Constructor=c,a.fn.modal.noConflict=function(){return a.fn.modal=d,this},a(document).on("click.bs.modal.data-api",'[data-toggle="modal"]',function(c){var d=a(this),e=d.attr("href"),f=a(d.attr("data-target")||e&&e.replace(/.*(?=#[^\s]+$)/,"")),g=f.data("bs.modal")?"toggle":a.extend({remote:!/#/.test(e)&&e},f.data(),d.data());d.is("a")&&c.preventDefault(),f.one("show.bs.modal",function(a){a.isDefaultPrevented()||f.one("hidden.bs.modal",function(){d.is(":visible")&&d.trigger("focus")})}),b.call(f,g,this)})}(jQuery),+function(a){"use strict";function b(b){return this.each(function(){var d=a(this),e=d.data("bs.tooltip"),f="object"==typeof b&&b;(e||!/destroy|hide/.test(b))&&(e||d.data("bs.tooltip",e=new c(this,f)),"string"==typeof b&&e[b]())})}var c=function(a,b){this.type=null,this.options=null,this.enabled=null,this.timeout=null,this.hoverState=null,this.$element=null,this.inState=null,this.init("tooltip",a,b)};c.VERSION="3.3.5",c.TRANSITION_DURATION=150,c.DEFAULTS={animation:!0,placement:"top",selector:!1,template:'<div class="tooltip" role="tooltip"><div class="tooltip-arrow"></div><div class="tooltip-inner"></div></div>',trigger:"hover focus",title:"",delay:0,html:!1,container:!1,viewport:{selector:"body",padding:0}},c.prototype.init=function(b,c,d){if(this.enabled=!0,this.type=b,this.$element=a(c),this.options=this.getOptions(d),this.$viewport=this.options.viewport&&a(a.isFunction(this.options.viewport)?this.options.viewport.call(this,this.$element):this.options.viewport.selector||this.options.viewport),this.inState={click:!1,hover:!1,focus:!1},this.$element[0]instanceof document.constructor&&!this.options.selector)throw new Error("`selector` option must be specified when initializing "+this.type+" on the window.document object!");for(var e=this.options.trigger.split(" "),f=e.length;f--;){var g=e[f];if("click"==g)this.$element.on("click."+this.type,this.options.selector,a.proxy(this.toggle,this));else if("manual"!=g){var h="hover"==g?"mouseenter":"focusin",i="hover"==g?"mouseleave":"focusout";this.$element.on(h+"."+this.type,this.options.selector,a.proxy(this.enter,this)),this.$element.on(i+"."+this.type,this.options.selector,a.proxy(this.leave,this))}}this.options.selector?this._options=a.extend({},this.options,{trigger:"manual",selector:""}):this.fixTitle()},c.prototype.getDefaults=function(){return c.DEFAULTS},c.prototype.getOptions=function(b){return b=a.extend({},this.getDefaults(),this.$element.data(),b),b.delay&&"number"==typeof b.delay&&(b.delay={show:b.delay,hide:b.delay}),b},c.prototype.getDelegateOptions=function(){var b={},c=this.getDefaults();return this._options&&a.each(this._options,function(a,d){c[a]!=d&&(b[a]=d)}),b},c.prototype.enter=function(b){var c=b instanceof this.constructor?b:a(b.currentTarget).data("bs."+this.type);return c||(c=new this.constructor(b.currentTarget,this.getDelegateOptions()),a(b.currentTarget).data("bs."+this.type,c)),b instanceof a.Event&&(c.inState["focusin"==b.type?"focus":"hover"]=!0),c.tip().hasClass("in")||"in"==c.hoverState?void(c.hoverState="in"):(clearTimeout(c.timeout),c.hoverState="in",c.options.delay&&c.options.delay.show?void(c.timeout=setTimeout(function(){"in"==c.hoverState&&c.show()},c.options.delay.show)):c.show())},c.prototype.isInStateTrue=function(){for(var a in this.inState)if(this.inState[a])return!0;return!1},c.prototype.leave=function(b){var c=b instanceof this.constructor?b:a(b.currentTarget).data("bs."+this.type);return c||(c=new this.constructor(b.currentTarget,this.getDelegateOptions()),a(b.currentTarget).data("bs."+this.type,c)),b instanceof a.Event&&(c.inState["focusout"==b.type?"focus":"hover"]=!1),c.isInStateTrue()?void 0:(clearTimeout(c.timeout),c.hoverState="out",c.options.delay&&c.options.delay.hide?void(c.timeout=setTimeout(function(){"out"==c.hoverState&&c.hide()},c.options.delay.hide)):c.hide())},c.prototype.show=function(){var b=a.Event("show.bs."+this.type);if(this.hasContent()&&this.enabled){this.$element.trigger(b);var d=a.contains(this.$element[0].ownerDocument.documentElement,this.$element[0]);if(b.isDefaultPrevented()||!d)return;var e=this,f=this.tip(),g=this.getUID(this.type);this.setContent(),f.attr("id",g),this.$element.attr("aria-describedby",g),this.options.animation&&f.addClass("fade");var h="function"==typeof this.options.placement?this.options.placement.call(this,f[0],this.$element[0]):this.options.placement,i=/\s?auto?\s?/i,j=i.test(h);j&&(h=h.replace(i,"")||"top"),f.detach().css({top:0,left:0,display:"block"}).addClass(h).data("bs."+this.type,this),this.options.container?f.appendTo(this.options.container):f.insertAfter(this.$element),this.$element.trigger("inserted.bs."+this.type);var k=this.getPosition(),l=f[0].offsetWidth,m=f[0].offsetHeight;if(j){var n=h,o=this.getPosition(this.$viewport);h="bottom"==h&&k.bottom+m>o.bottom?"top":"top"==h&&k.top-m<o.top?"bottom":"right"==h&&k.right+l>o.width?"left":"left"==h&&k.left-l<o.left?"right":h,f.removeClass(n).addClass(h)}var p=this.getCalculatedOffset(h,k,l,m);this.applyPlacement(p,h);var q=function(){var a=e.hoverState;e.$element.trigger("shown.bs."+e.type),e.hoverState=null,"out"==a&&e.leave(e)};a.support.transition&&this.$tip.hasClass("fade")?f.one("bsTransitionEnd",q).emulateTransitionEnd(c.TRANSITION_DURATION):q()}},c.prototype.applyPlacement=function(b,c){var d=this.tip(),e=d[0].offsetWidth,f=d[0].offsetHeight,g=parseInt(d.css("margin-top"),10),h=parseInt(d.css("margin-left"),10);isNaN(g)&&(g=0),isNaN(h)&&(h=0),b.top+=g,b.left+=h,a.offset.setOffset(d[0],a.extend({using:function(a){d.css({top:Math.round(a.top),left:Math.round(a.left)})}},b),0),d.addClass("in");var i=d[0].offsetWidth,j=d[0].offsetHeight;"top"==c&&j!=f&&(b.top=b.top+f-j);var k=this.getViewportAdjustedDelta(c,b,i,j);k.left?b.left+=k.left:b.top+=k.top;var l=/top|bottom/.test(c),m=l?2*k.left-e+i:2*k.top-f+j,n=l?"offsetWidth":"offsetHeight";d.offset(b),this.replaceArrow(m,d[0][n],l)},c.prototype.replaceArrow=function(a,b,c){this.arrow().css(c?"left":"top",50*(1-a/b)+"%").css(c?"top":"left","")},c.prototype.setContent=function(){var a=this.tip(),b=this.getTitle();a.find(".tooltip-inner")[this.options.html?"html":"text"](b),a.removeClass("fade in top bottom left right")},c.prototype.hide=function(b){function d(){"in"!=e.hoverState&&f.detach(),e.$element.removeAttr("aria-describedby").trigger("hidden.bs."+e.type),b&&b()}var e=this,f=a(this.$tip),g=a.Event("hide.bs."+this.type);return this.$element.trigger(g),g.isDefaultPrevented()?void 0:(f.removeClass("in"),a.support.transition&&f.hasClass("fade")?f.one("bsTransitionEnd",d).emulateTransitionEnd(c.TRANSITION_DURATION):d(),this.hoverState=null,this)},c.prototype.fixTitle=function(){var a=this.$element;(a.attr("title")||"string"!=typeof a.attr("data-original-title"))&&a.attr("data-original-title",a.attr("title")||"").attr("title","")},c.prototype.hasContent=function(){return this.getTitle()},c.prototype.getPosition=function(b){b=b||this.$element;var c=b[0],d="BODY"==c.tagName,e=c.getBoundingClientRect();null==e.width&&(e=a.extend({},e,{width:e.right-e.left,height:e.bottom-e.top}));var f=d?{top:0,left:0}:b.offset(),g={scroll:d?document.documentElement.scrollTop||document.body.scrollTop:b.scrollTop()},h=d?{width:a(window).width(),height:a(window).height()}:null;return a.extend({},e,g,h,f)},c.prototype.getCalculatedOffset=function(a,b,c,d){return"bottom"==a?{top:b.top+b.height,left:b.left+b.width/2-c/2}:"top"==a?{top:b.top-d,left:b.left+b.width/2-c/2}:"left"==a?{top:b.top+b.height/2-d/2,left:b.left-c}:{top:b.top+b.height/2-d/2,left:b.left+b.width}},c.prototype.getViewportAdjustedDelta=function(a,b,c,d){var e={top:0,left:0};if(!this.$viewport)return e;var f=this.options.viewport&&this.options.viewport.padding||0,g=this.getPosition(this.$viewport);if(/right|left/.test(a)){var h=b.top-f-g.scroll,i=b.top+f-g.scroll+d;h<g.top?e.top=g.top-h:i>g.top+g.height&&(e.top=g.top+g.height-i)}else{var j=b.left-f,k=b.left+f+c;j<g.left?e.left=g.left-j:k>g.right&&(e.left=g.left+g.width-k)}return e},c.prototype.getTitle=function(){var a,b=this.$element,c=this.options;return a=b.attr("data-original-title")||("function"==typeof c.title?c.title.call(b[0]):c.title)},c.prototype.getUID=function(a){do a+=~~(1e6*Math.random());while(document.getElementById(a));return a},c.prototype.tip=function(){if(!this.$tip&&(this.$tip=a(this.options.template),1!=this.$tip.length))throw new Error(this.type+" `template` option must consist of exactly 1 top-level element!");return this.$tip},c.prototype.arrow=function(){return this.$arrow=this.$arrow||this.tip().find(".tooltip-arrow")},c.prototype.enable=function(){this.enabled=!0},c.prototype.disable=function(){this.enabled=!1},c.prototype.toggleEnabled=function(){this.enabled=!this.enabled},c.prototype.toggle=function(b){var c=this;b&&(c=a(b.currentTarget).data("bs."+this.type),c||(c=new this.constructor(b.currentTarget,this.getDelegateOptions()),a(b.currentTarget).data("bs."+this.type,c))),b?(c.inState.click=!c.inState.click,c.isInStateTrue()?c.enter(c):c.leave(c)):c.tip().hasClass("in")?c.leave(c):c.enter(c)},c.prototype.destroy=function(){var a=this;clearTimeout(this.timeout),this.hide(function(){a.$element.off("."+a.type).removeData("bs."+a.type),a.$tip&&a.$tip.detach(),a.$tip=null,a.$arrow=null,a.$viewport=null})};var d=a.fn.tooltip;a.fn.tooltip=b,a.fn.tooltip.Constructor=c,a.fn.tooltip.noConflict=function(){return a.fn.tooltip=d,this}}(jQuery),+function(a){"use strict";function b(b){return this.each(function(){var d=a(this),e=d.data("bs.popover"),f="object"==typeof b&&b;(e||!/destroy|hide/.test(b))&&(e||d.data("bs.popover",e=new c(this,f)),"string"==typeof b&&e[b]())})}var c=function(a,b){this.init("popover",a,b)};if(!a.fn.tooltip)throw new Error("Popover requires tooltip.js");c.VERSION="3.3.5",c.DEFAULTS=a.extend({},a.fn.tooltip.Constructor.DEFAULTS,{placement:"right",trigger:"click",content:"",template:'<div class="popover" role="tooltip"><div class="arrow"></div><h3 class="popover-title"></h3><div class="popover-content"></div></div>'}),c.prototype=a.extend({},a.fn.tooltip.Constructor.prototype),c.prototype.constructor=c,c.prototype.getDefaults=function(){return c.DEFAULTS},c.prototype.setContent=function(){var a=this.tip(),b=this.getTitle(),c=this.getContent();a.find(".popover-title")[this.options.html?"html":"text"](b),a.find(".popover-content").children().detach().end()[this.options.html?"string"==typeof c?"html":"append":"text"](c),a.removeClass("fade top bottom left right in"),a.find(".popover-title").html()||a.find(".popover-title").hide()},c.prototype.hasContent=function(){return this.getTitle()||this.getContent()},c.prototype.getContent=function(){var a=this.$element,b=this.options;return a.attr("data-content")||("function"==typeof b.content?b.content.call(a[0]):b.content)},c.prototype.arrow=function(){return this.$arrow=this.$arrow||this.tip().find(".arrow")};var d=a.fn.popover;a.fn.popover=b,a.fn.popover.Constructor=c,a.fn.popover.noConflict=function(){return a.fn.popover=d,this}}(jQuery),+function(a){"use strict";function b(c,d){this.$body=a(document.body),this.$scrollElement=a(a(c).is(document.body)?window:c),this.options=a.extend({},b.DEFAULTS,d),this.selector=(this.options.target||"")+" .nav li > a",this.offsets=[],this.targets=[],this.activeTarget=null,this.scrollHeight=0,this.$scrollElement.on("scroll.bs.scrollspy",a.proxy(this.process,this)),this.refresh(),this.process()}function c(c){return this.each(function(){var d=a(this),e=d.data("bs.scrollspy"),f="object"==typeof c&&c;e||d.data("bs.scrollspy",e=new b(this,f)),"string"==typeof c&&e[c]()})}b.VERSION="3.3.5",b.DEFAULTS={offset:10},b.prototype.getScrollHeight=function(){return this.$scrollElement[0].scrollHeight||Math.max(this.$body[0].scrollHeight,document.documentElement.scrollHeight)},b.prototype.refresh=function(){var b=this,c="offset",d=0;this.offsets=[],this.targets=[],this.scrollHeight=this.getScrollHeight(),a.isWindow(this.$scrollElement[0])||(c="position",d=this.$scrollElement.scrollTop()),this.$body.find(this.selector).map(function(){var b=a(this),e=b.data("target")||b.attr("href"),f=/^#./.test(e)&&a(e);return f&&f.length&&f.is(":visible")&&[[f[c]().top+d,e]]||null}).sort(function(a,b){return a[0]-b[0]}).each(function(){b.offsets.push(this[0]),b.targets.push(this[1])})},b.prototype.process=function(){var a,b=this.$scrollElement.scrollTop()+this.options.offset,c=this.getScrollHeight(),d=this.options.offset+c-this.$scrollElement.height(),e=this.offsets,f=this.targets,g=this.activeTarget;if(this.scrollHeight!=c&&this.refresh(),b>=d)return g!=(a=f[f.length-1])&&this.activate(a);if(g&&b<e[0])return this.activeTarget=null,this.clear();for(a=e.length;a--;)g!=f[a]&&b>=e[a]&&(void 0===e[a+1]||b<e[a+1])&&this.activate(f[a])},b.prototype.activate=function(b){this.activeTarget=b,this.clear();var c=this.selector+'[data-target="'+b+'"],'+this.selector+'[href="'+b+'"]',d=a(c).parents("li").addClass("active");d.parent(".dropdown-menu").length&&(d=d.closest("li.dropdown").addClass("active")),
+d.trigger("activate.bs.scrollspy")},b.prototype.clear=function(){a(this.selector).parentsUntil(this.options.target,".active").removeClass("active")};var d=a.fn.scrollspy;a.fn.scrollspy=c,a.fn.scrollspy.Constructor=b,a.fn.scrollspy.noConflict=function(){return a.fn.scrollspy=d,this},a(window).on("load.bs.scrollspy.data-api",function(){a('[data-spy="scroll"]').each(function(){var b=a(this);c.call(b,b.data())})})}(jQuery),+function(a){"use strict";function b(b){return this.each(function(){var d=a(this),e=d.data("bs.tab");e||d.data("bs.tab",e=new c(this)),"string"==typeof b&&e[b]()})}var c=function(b){this.element=a(b)};c.VERSION="3.3.5",c.TRANSITION_DURATION=150,c.prototype.show=function(){var b=this.element,c=b.closest("ul:not(.dropdown-menu)"),d=b.data("target");if(d||(d=b.attr("href"),d=d&&d.replace(/.*(?=#[^\s]*$)/,"")),!b.parent("li").hasClass("active")){var e=c.find(".active:last a"),f=a.Event("hide.bs.tab",{relatedTarget:b[0]}),g=a.Event("show.bs.tab",{relatedTarget:e[0]});if(e.trigger(f),b.trigger(g),!g.isDefaultPrevented()&&!f.isDefaultPrevented()){var h=a(d);this.activate(b.closest("li"),c),this.activate(h,h.parent(),function(){e.trigger({type:"hidden.bs.tab",relatedTarget:b[0]}),b.trigger({type:"shown.bs.tab",relatedTarget:e[0]})})}}},c.prototype.activate=function(b,d,e){function f(){g.removeClass("active").find("> .dropdown-menu > .active").removeClass("active").end().find('[data-toggle="tab"]').attr("aria-expanded",!1),b.addClass("active").find('[data-toggle="tab"]').attr("aria-expanded",!0),h?(b[0].offsetWidth,b.addClass("in")):b.removeClass("fade"),b.parent(".dropdown-menu").length&&b.closest("li.dropdown").addClass("active").end().find('[data-toggle="tab"]').attr("aria-expanded",!0),e&&e()}var g=d.find("> .active"),h=e&&a.support.transition&&(g.length&&g.hasClass("fade")||!!d.find("> .fade").length);g.length&&h?g.one("bsTransitionEnd",f).emulateTransitionEnd(c.TRANSITION_DURATION):f(),g.removeClass("in")};var d=a.fn.tab;a.fn.tab=b,a.fn.tab.Constructor=c,a.fn.tab.noConflict=function(){return a.fn.tab=d,this};var e=function(c){c.preventDefault(),b.call(a(this),"show")};a(document).on("click.bs.tab.data-api",'[data-toggle="tab"]',e).on("click.bs.tab.data-api",'[data-toggle="pill"]',e)}(jQuery),+function(a){"use strict";function b(b){return this.each(function(){var d=a(this),e=d.data("bs.affix"),f="object"==typeof b&&b;e||d.data("bs.affix",e=new c(this,f)),"string"==typeof b&&e[b]()})}var c=function(b,d){this.options=a.extend({},c.DEFAULTS,d),this.$target=a(this.options.target).on("scroll.bs.affix.data-api",a.proxy(this.checkPosition,this)).on("click.bs.affix.data-api",a.proxy(this.checkPositionWithEventLoop,this)),this.$element=a(b),this.affixed=null,this.unpin=null,this.pinnedOffset=null,this.checkPosition()};c.VERSION="3.3.5",c.RESET="affix affix-top affix-bottom",c.DEFAULTS={offset:0,target:window},c.prototype.getState=function(a,b,c,d){var e=this.$target.scrollTop(),f=this.$element.offset(),g=this.$target.height();if(null!=c&&"top"==this.affixed)return c>e?"top":!1;if("bottom"==this.affixed)return null!=c?e+this.unpin<=f.top?!1:"bottom":a-d>=e+g?!1:"bottom";var h=null==this.affixed,i=h?e:f.top,j=h?g:b;return null!=c&&c>=e?"top":null!=d&&i+j>=a-d?"bottom":!1},c.prototype.getPinnedOffset=function(){if(this.pinnedOffset)return this.pinnedOffset;this.$element.removeClass(c.RESET).addClass("affix");var a=this.$target.scrollTop(),b=this.$element.offset();return this.pinnedOffset=b.top-a},c.prototype.checkPositionWithEventLoop=function(){setTimeout(a.proxy(this.checkPosition,this),1)},c.prototype.checkPosition=function(){if(this.$element.is(":visible")){var b=this.$element.height(),d=this.options.offset,e=d.top,f=d.bottom,g=Math.max(a(document).height(),a(document.body).height());"object"!=typeof d&&(f=e=d),"function"==typeof e&&(e=d.top(this.$element)),"function"==typeof f&&(f=d.bottom(this.$element));var h=this.getState(g,b,e,f);if(this.affixed!=h){null!=this.unpin&&this.$element.css("top","");var i="affix"+(h?"-"+h:""),j=a.Event(i+".bs.affix");if(this.$element.trigger(j),j.isDefaultPrevented())return;this.affixed=h,this.unpin="bottom"==h?this.getPinnedOffset():null,this.$element.removeClass(c.RESET).addClass(i).trigger(i.replace("affix","affixed")+".bs.affix")}"bottom"==h&&this.$element.offset({top:g-b-f})}};var d=a.fn.affix;a.fn.affix=b,a.fn.affix.Constructor=c,a.fn.affix.noConflict=function(){return a.fn.affix=d,this},a(window).on("load",function(){a('[data-spy="affix"]').each(function(){var c=a(this),d=c.data();d.offset=d.offset||{},null!=d.offsetBottom&&(d.offset.bottom=d.offsetBottom),null!=d.offsetTop&&(d.offset.top=d.offsetTop),b.call(c,d)})})}(jQuery);</script>
+<script>/**
+* @preserve HTML5 Shiv 3.7.2 | @afarkas @jdalton @jon_neal @rem | MIT/GPL2 Licensed
+*/
+// Only run this code in IE 8
+if (!!window.navigator.userAgent.match("MSIE 8")) {
+!function(a,b){function c(a,b){var c=a.createElement("p"),d=a.getElementsByTagName("head")[0]||a.documentElement;return c.innerHTML="x<style>"+b+"</style>",d.insertBefore(c.lastChild,d.firstChild)}function d(){var a=t.elements;return"string"==typeof a?a.split(" "):a}function e(a,b){var c=t.elements;"string"!=typeof c&&(c=c.join(" ")),"string"!=typeof a&&(a=a.join(" ")),t.elements=c+" "+a,j(b)}function f(a){var b=s[a[q]];return b||(b={},r++,a[q]=r,s[r]=b),b}function g(a,c,d){if(c||(c=b),l)return c.createElement(a);d||(d=f(c));var e;return e=d.cache[a]?d.cache[a].cloneNode():p.test(a)?(d.cache[a]=d.createElem(a)).cloneNode():d.createElem(a),!e.canHaveChildren||o.test(a)||e.tagUrn?e:d.frag.appendChild(e)}function h(a,c){if(a||(a=b),l)return a.createDocumentFragment();c=c||f(a);for(var e=c.frag.cloneNode(),g=0,h=d(),i=h.length;i>g;g++)e.createElement(h[g]);return e}function i(a,b){b.cache||(b.cache={},b.createElem=a.createElement,b.createFrag=a.createDocumentFragment,b.frag=b.createFrag()),a.createElement=function(c){return t.shivMethods?g(c,a,b):b.createElem(c)},a.createDocumentFragment=Function("h,f","return function(){var n=f.cloneNode(),c=n.createElement;h.shivMethods&&("+d().join().replace(/[\w\-:]+/g,function(a){return b.createElem(a),b.frag.createElement(a),'c("'+a+'")'})+");return n}")(t,b.frag)}function j(a){a||(a=b);var d=f(a);return!t.shivCSS||k||d.hasCSS||(d.hasCSS=!!c(a,"article,aside,dialog,figcaption,figure,footer,header,hgroup,main,nav,section{display:block}mark{background:#FF0;color:#000}template{display:none}")),l||i(a,d),a}var k,l,m="3.7.2",n=a.html5||{},o=/^<|^(?:button|map|select|textarea|object|iframe|option|optgroup)$/i,p=/^(?:a|b|code|div|fieldset|h1|h2|h3|h4|h5|h6|i|label|li|ol|p|q|span|strong|style|table|tbody|td|th|tr|ul)$/i,q="_html5shiv",r=0,s={};!function(){try{var a=b.createElement("a");a.innerHTML="<xyz></xyz>",k="hidden"in a,l=1==a.childNodes.length||function(){b.createElement("a");var a=b.createDocumentFragment();return"undefined"==typeof a.cloneNode||"undefined"==typeof a.createDocumentFragment||"undefined"==typeof a.createElement}()}catch(c){k=!0,l=!0}}();var t={elements:n.elements||"abbr article aside audio bdi canvas data datalist details dialog figcaption figure footer header hgroup main mark meter nav output picture progress section summary template time video",version:m,shivCSS:n.shivCSS!==!1,supportsUnknownElements:l,shivMethods:n.shivMethods!==!1,type:"default",shivDocument:j,createElement:g,createDocumentFragment:h,addElements:e};a.html5=t,j(b)}(this,document);
+};
+</script>
+<script>/*! Respond.js v1.4.2: min/max-width media query polyfill * Copyright 2013 Scott Jehl
+ * Licensed under https://github.com/scottjehl/Respond/blob/master/LICENSE-MIT
+ *  */
+
+// Only run this code in IE 8
+if (!!window.navigator.userAgent.match("MSIE 8")) {
+!function(a){"use strict";a.matchMedia=a.matchMedia||function(a){var b,c=a.documentElement,d=c.firstElementChild||c.firstChild,e=a.createElement("body"),f=a.createElement("div");return f.id="mq-test-1",f.style.cssText="position:absolute;top:-100em",e.style.background="none",e.appendChild(f),function(a){return f.innerHTML='&shy;<style media="'+a+'"> #mq-test-1 { width: 42px; }</style>',c.insertBefore(e,d),b=42===f.offsetWidth,c.removeChild(e),{matches:b,media:a}}}(a.document)}(this),function(a){"use strict";function b(){u(!0)}var c={};a.respond=c,c.update=function(){};var d=[],e=function(){var b=!1;try{b=new a.XMLHttpRequest}catch(c){b=new a.ActiveXObject("Microsoft.XMLHTTP")}return function(){return b}}(),f=function(a,b){var c=e();c&&(c.open("GET",a,!0),c.onreadystatechange=function(){4!==c.readyState||200!==c.status&&304!==c.status||b(c.responseText)},4!==c.readyState&&c.send(null))};if(c.ajax=f,c.queue=d,c.regex={media:/@media[^\{]+\{([^\{\}]*\{[^\}\{]*\})+/gi,keyframes:/@(?:\-(?:o|moz|webkit)\-)?keyframes[^\{]+\{(?:[^\{\}]*\{[^\}\{]*\})+[^\}]*\}/gi,urls:/(url\()['"]?([^\/\)'"][^:\)'"]+)['"]?(\))/g,findStyles:/@media *([^\{]+)\{([\S\s]+?)$/,only:/(only\s+)?([a-zA-Z]+)\s?/,minw:/\([\s]*min\-width\s*:[\s]*([\s]*[0-9\.]+)(px|em)[\s]*\)/,maxw:/\([\s]*max\-width\s*:[\s]*([\s]*[0-9\.]+)(px|em)[\s]*\)/},c.mediaQueriesSupported=a.matchMedia&&null!==a.matchMedia("only all")&&a.matchMedia("only all").matches,!c.mediaQueriesSupported){var g,h,i,j=a.document,k=j.documentElement,l=[],m=[],n=[],o={},p=30,q=j.getElementsByTagName("head")[0]||k,r=j.getElementsByTagName("base")[0],s=q.getElementsByTagName("link"),t=function(){var a,b=j.createElement("div"),c=j.body,d=k.style.fontSize,e=c&&c.style.fontSize,f=!1;return b.style.cssText="position:absolute;font-size:1em;width:1em",c||(c=f=j.createElement("body"),c.style.background="none"),k.style.fontSize="100%",c.style.fontSize="100%",c.appendChild(b),f&&k.insertBefore(c,k.firstChild),a=b.offsetWidth,f?k.removeChild(c):c.removeChild(b),k.style.fontSize=d,e&&(c.style.fontSize=e),a=i=parseFloat(a)},u=function(b){var c="clientWidth",d=k[c],e="CSS1Compat"===j.compatMode&&d||j.body[c]||d,f={},o=s[s.length-1],r=(new Date).getTime();if(b&&g&&p>r-g)return a.clearTimeout(h),h=a.setTimeout(u,p),void 0;g=r;for(var v in l)if(l.hasOwnProperty(v)){var w=l[v],x=w.minw,y=w.maxw,z=null===x,A=null===y,B="em";x&&(x=parseFloat(x)*(x.indexOf(B)>-1?i||t():1)),y&&(y=parseFloat(y)*(y.indexOf(B)>-1?i||t():1)),w.hasquery&&(z&&A||!(z||e>=x)||!(A||y>=e))||(f[w.media]||(f[w.media]=[]),f[w.media].push(m[w.rules]))}for(var C in n)n.hasOwnProperty(C)&&n[C]&&n[C].parentNode===q&&q.removeChild(n[C]);n.length=0;for(var D in f)if(f.hasOwnProperty(D)){var E=j.createElement("style"),F=f[D].join("\n");E.type="text/css",E.media=D,q.insertBefore(E,o.nextSibling),E.styleSheet?E.styleSheet.cssText=F:E.appendChild(j.createTextNode(F)),n.push(E)}},v=function(a,b,d){var e=a.replace(c.regex.keyframes,"").match(c.regex.media),f=e&&e.length||0;b=b.substring(0,b.lastIndexOf("/"));var g=function(a){return a.replace(c.regex.urls,"$1"+b+"$2$3")},h=!f&&d;b.length&&(b+="/"),h&&(f=1);for(var i=0;f>i;i++){var j,k,n,o;h?(j=d,m.push(g(a))):(j=e[i].match(c.regex.findStyles)&&RegExp.$1,m.push(RegExp.$2&&g(RegExp.$2))),n=j.split(","),o=n.length;for(var p=0;o>p;p++)k=n[p],l.push({media:k.split("(")[0].match(c.regex.only)&&RegExp.$2||"all",rules:m.length-1,hasquery:k.indexOf("(")>-1,minw:k.match(c.regex.minw)&&parseFloat(RegExp.$1)+(RegExp.$2||""),maxw:k.match(c.regex.maxw)&&parseFloat(RegExp.$1)+(RegExp.$2||"")})}u()},w=function(){if(d.length){var b=d.shift();f(b.href,function(c){v(c,b.href,b.media),o[b.href]=!0,a.setTimeout(function(){w()},0)})}},x=function(){for(var b=0;b<s.length;b++){var c=s[b],e=c.href,f=c.media,g=c.rel&&"stylesheet"===c.rel.toLowerCase();e&&g&&!o[e]&&(c.styleSheet&&c.styleSheet.rawCssText?(v(c.styleSheet.rawCssText,e,f),o[e]=!0):(!/^([a-zA-Z:]*\/\/)/.test(e)&&!r||e.replace(RegExp.$1,"").split("/")[0]===a.location.host)&&("//"===e.substring(0,2)&&(e=a.location.protocol+e),d.push({href:e,media:f})))}w()};x(),c.update=x,c.getEmValue=t,a.addEventListener?a.addEventListener("resize",b,!1):a.attachEvent&&a.attachEvent("onresize",b)}}(this);
+};
+</script>
+<style>h1 {font-size: 34px;}
+       h1.title {font-size: 38px;}
+       h2 {font-size: 30px;}
+       h3 {font-size: 24px;}
+       h4 {font-size: 18px;}
+       h5 {font-size: 16px;}
+       h6 {font-size: 12px;}
+       code {color: inherit; background-color: rgba(0, 0, 0, 0.04);}
+       pre:not([class]) { background-color: white }</style>
+<script>
+
+/**
+ * jQuery Plugin: Sticky Tabs
+ *
+ * @author Aidan Lister <aidan@php.net>
+ * adapted by Ruben Arslan to activate parent tabs too
+ * http://www.aidanlister.com/2014/03/persisting-the-tab-state-in-bootstrap/
+ */
+(function($) {
+  "use strict";
+  $.fn.rmarkdownStickyTabs = function() {
+    var context = this;
+    // Show the tab corresponding with the hash in the URL, or the first tab
+    var showStuffFromHash = function() {
+      var hash = window.location.hash;
+      var selector = hash ? 'a[href="' + hash + '"]' : 'li.active > a';
+      var $selector = $(selector, context);
+      if($selector.data('toggle') === "tab") {
+        $selector.tab('show');
+        // walk up the ancestors of this element, show any hidden tabs
+        $selector.parents('.section.tabset').each(function(i, elm) {
+          var link = $('a[href="#' + $(elm).attr('id') + '"]');
+          if(link.data('toggle') === "tab") {
+            link.tab("show");
+          }
+        });
+      }
+    };
+
+
+    // Set the correct tab when the page loads
+    showStuffFromHash(context);
+
+    // Set the correct tab when a user uses their back/forward button
+    $(window).on('hashchange', function() {
+      showStuffFromHash(context);
+    });
+
+    // Change the URL when tabs are clicked
+    $('a', context).on('click', function(e) {
+      history.pushState(null, null, this.href);
+      showStuffFromHash(context);
+    });
+
+    return this;
+  };
+}(jQuery));
+
+window.buildTabsets = function(tocID) {
+
+  // build a tabset from a section div with the .tabset class
+  function buildTabset(tabset) {
+
+    // check for fade and pills options
+    var fade = tabset.hasClass("tabset-fade");
+    var pills = tabset.hasClass("tabset-pills");
+    var navClass = pills ? "nav-pills" : "nav-tabs";
+
+    // determine the heading level of the tabset and tabs
+    var match = tabset.attr('class').match(/level(\d) /);
+    if (match === null)
+      return;
+    var tabsetLevel = Number(match[1]);
+    var tabLevel = tabsetLevel + 1;
+
+    // find all subheadings immediately below
+    var tabs = tabset.find("div.section.level" + tabLevel);
+    if (!tabs.length)
+      return;
+
+    // create tablist and tab-content elements
+    var tabList = $('<ul class="nav ' + navClass + '" role="tablist"></ul>');
+    $(tabs[0]).before(tabList);
+    var tabContent = $('<div class="tab-content"></div>');
+    $(tabs[0]).before(tabContent);
+
+    // build the tabset
+    var activeTab = 0;
+    tabs.each(function(i) {
+
+      // get the tab div
+      var tab = $(tabs[i]);
+
+      // get the id then sanitize it for use with bootstrap tabs
+      var id = tab.attr('id');
+
+      // see if this is marked as the active tab
+      if (tab.hasClass('active'))
+        activeTab = i;
+
+      // remove any table of contents entries associated with
+      // this ID (since we'll be removing the heading element)
+      $("div#" + tocID + " li a[href='#" + id + "']").parent().remove();
+
+      // sanitize the id for use with bootstrap tabs
+      id = id.replace(/[.\/?&!#<>]/g, '').replace(/\s/g, '_');
+      tab.attr('id', id);
+
+      // get the heading element within it, grab it's text, then remove it
+      var heading = tab.find('h' + tabLevel + ':first');
+      var headingText = heading.html();
+      heading.remove();
+
+      // build and append the tab list item
+      var a = $('<a role="tab" data-toggle="tab">' + headingText + '</a>');
+      a.attr('href', '#' + id);
+      a.attr('aria-controls', id);
+      var li = $('<li role="presentation"></li>');
+      li.append(a);
+      tabList.append(li);
+
+      // set it's attributes
+      tab.attr('role', 'tabpanel');
+      tab.addClass('tab-pane');
+      tab.addClass('tabbed-pane');
+      if (fade)
+        tab.addClass('fade');
+
+      // move it into the tab content div
+      tab.detach().appendTo(tabContent);
+    });
+
+    // set active tab
+    $(tabList.children('li')[activeTab]).addClass('active');
+    var active = $(tabContent.children('div.section')[activeTab]);
+    active.addClass('active');
+    if (fade)
+      active.addClass('in');
+
+    if (tabset.hasClass("tabset-sticky"))
+      tabset.rmarkdownStickyTabs();
+  }
+
+  // convert section divs with the .tabset class to tabsets
+  var tabsets = $("div.section.tabset");
+  tabsets.each(function(i) {
+    buildTabset($(tabsets[i]));
+  });
+};
+
+</script>
+<style type="text/css">.hljs-literal {
+color: #990073;
+}
+.hljs-number {
+color: #099;
+}
+.hljs-comment {
+color: #998;
+font-style: italic;
+}
+.hljs-keyword {
+color: #900;
+font-weight: bold;
+}
+.hljs-string {
+color: #d14;
+}
+</style>
+<script src="data:application/javascript;base64,/*! highlight.js v9.12.0 | BSD3 License | git.io/hljslicense */
!function(e){var n="object"==typeof window&&window||"object"==typeof self&&self;"undefined"!=typeof exports?e(exports):n&&(n.hljs=e({}),"function"==typeof define&&define.amd&&define([],function(){return n.hljs}))}(function(e){function n(e){return e.replace(/&/g,"&amp;").replace(/</g,"&lt;").replace(/>/g,"&gt;")}function t(e){return e.nodeName.toLowerCase()}function r(e,n){var t=e&&e.exec(n);return t&&0===t.index}function a(e){return k.test(e)}function i(e){var n,t,r,i,o=e.className+" ";if(o+=e.parentNode?e.parentNode.className:"",t=B.exec(o))return w(t[1])?t[1]:"no-highlight";for(o=o.split(/\s+/),n=0,r=o.length;r>n;n++)if(i=o[n],a(i)||w(i))return i}function o(e){var n,t={},r=Array.prototype.slice.call(arguments,1);for(n in e)t[n]=e[n];return r.forEach(function(e){for(n in e)t[n]=e[n]}),t}function u(e){var n=[];return function r(e,a){for(var i=e.firstChild;i;i=i.nextSibling)3===i.nodeType?a+=i.nodeValue.length:1===i.nodeType&&(n.push({event:"start",offset:a,node:i}),a=r(i,a),t(i).match(/br|hr|img|input/)||n.push({event:"stop",offset:a,node:i}));return a}(e,0),n}function c(e,r,a){function i(){return e.length&&r.length?e[0].offset!==r[0].offset?e[0].offset<r[0].offset?e:r:"start"===r[0].event?e:r:e.length?e:r}function o(e){function r(e){return" "+e.nodeName+'="'+n(e.value).replace('"',"&quot;")+'"'}s+="<"+t(e)+E.map.call(e.attributes,r).join("")+">"}function u(e){s+="</"+t(e)+">"}function c(e){("start"===e.event?o:u)(e.node)}for(var l=0,s="",f=[];e.length||r.length;){var g=i();if(s+=n(a.substring(l,g[0].offset)),l=g[0].offset,g===e){f.reverse().forEach(u);do c(g.splice(0,1)[0]),g=i();while(g===e&&g.length&&g[0].offset===l);f.reverse().forEach(o)}else"start"===g[0].event?f.push(g[0].node):f.pop(),c(g.splice(0,1)[0])}return s+n(a.substr(l))}function l(e){return e.v&&!e.cached_variants&&(e.cached_variants=e.v.map(function(n){return o(e,{v:null},n)})),e.cached_variants||e.eW&&[o(e)]||[e]}function s(e){function n(e){return e&&e.source||e}function t(t,r){return new RegExp(n(t),"m"+(e.cI?"i":"")+(r?"g":""))}function r(a,i){if(!a.compiled){if(a.compiled=!0,a.k=a.k||a.bK,a.k){var o={},u=function(n,t){e.cI&&(t=t.toLowerCase()),t.split(" ").forEach(function(e){var t=e.split("|");o[t[0]]=[n,t[1]?Number(t[1]):1]})};"string"==typeof a.k?u("keyword",a.k):x(a.k).forEach(function(e){u(e,a.k[e])}),a.k=o}a.lR=t(a.l||/\w+/,!0),i&&(a.bK&&(a.b="\\b("+a.bK.split(" ").join("|")+")\\b"),a.b||(a.b=/\B|\b/),a.bR=t(a.b),a.e||a.eW||(a.e=/\B|\b/),a.e&&(a.eR=t(a.e)),a.tE=n(a.e)||"",a.eW&&i.tE&&(a.tE+=(a.e?"|":"")+i.tE)),a.i&&(a.iR=t(a.i)),null==a.r&&(a.r=1),a.c||(a.c=[]),a.c=Array.prototype.concat.apply([],a.c.map(function(e){return l("self"===e?a:e)})),a.c.forEach(function(e){r(e,a)}),a.starts&&r(a.starts,i);var c=a.c.map(function(e){return e.bK?"\\.?("+e.b+")\\.?":e.b}).concat([a.tE,a.i]).map(n).filter(Boolean);a.t=c.length?t(c.join("|"),!0):{exec:function(){return null}}}}r(e)}function f(e,t,a,i){function o(e,n){var t,a;for(t=0,a=n.c.length;a>t;t++)if(r(n.c[t].bR,e))return n.c[t]}function u(e,n){if(r(e.eR,n)){for(;e.endsParent&&e.parent;)e=e.parent;return e}return e.eW?u(e.parent,n):void 0}function c(e,n){return!a&&r(n.iR,e)}function l(e,n){var t=N.cI?n[0].toLowerCase():n[0];return e.k.hasOwnProperty(t)&&e.k[t]}function p(e,n,t,r){var a=r?"":I.classPrefix,i='<span class="'+a,o=t?"":C;return i+=e+'">',i+n+o}function h(){var e,t,r,a;if(!E.k)return n(k);for(a="",t=0,E.lR.lastIndex=0,r=E.lR.exec(k);r;)a+=n(k.substring(t,r.index)),e=l(E,r),e?(B+=e[1],a+=p(e[0],n(r[0]))):a+=n(r[0]),t=E.lR.lastIndex,r=E.lR.exec(k);return a+n(k.substr(t))}function d(){var e="string"==typeof E.sL;if(e&&!y[E.sL])return n(k);var t=e?f(E.sL,k,!0,x[E.sL]):g(k,E.sL.length?E.sL:void 0);return E.r>0&&(B+=t.r),e&&(x[E.sL]=t.top),p(t.language,t.value,!1,!0)}function b(){L+=null!=E.sL?d():h(),k=""}function v(e){L+=e.cN?p(e.cN,"",!0):"",E=Object.create(e,{parent:{value:E}})}function m(e,n){if(k+=e,null==n)return b(),0;var t=o(n,E);if(t)return t.skip?k+=n:(t.eB&&(k+=n),b(),t.rB||t.eB||(k=n)),v(t,n),t.rB?0:n.length;var r=u(E,n);if(r){var a=E;a.skip?k+=n:(a.rE||a.eE||(k+=n),b(),a.eE&&(k=n));do E.cN&&(L+=C),E.skip||(B+=E.r),E=E.parent;while(E!==r.parent);return r.starts&&v(r.starts,""),a.rE?0:n.length}if(c(n,E))throw new Error('Illegal lexeme "'+n+'" for mode "'+(E.cN||"<unnamed>")+'"');return k+=n,n.length||1}var N=w(e);if(!N)throw new Error('Unknown language: "'+e+'"');s(N);var R,E=i||N,x={},L="";for(R=E;R!==N;R=R.parent)R.cN&&(L=p(R.cN,"",!0)+L);var k="",B=0;try{for(var M,j,O=0;;){if(E.t.lastIndex=O,M=E.t.exec(t),!M)break;j=m(t.substring(O,M.index),M[0]),O=M.index+j}for(m(t.substr(O)),R=E;R.parent;R=R.parent)R.cN&&(L+=C);return{r:B,value:L,language:e,top:E}}catch(T){if(T.message&&-1!==T.message.indexOf("Illegal"))return{r:0,value:n(t)};throw T}}function g(e,t){t=t||I.languages||x(y);var r={r:0,value:n(e)},a=r;return t.filter(w).forEach(function(n){var t=f(n,e,!1);t.language=n,t.r>a.r&&(a=t),t.r>r.r&&(a=r,r=t)}),a.language&&(r.second_best=a),r}function p(e){return I.tabReplace||I.useBR?e.replace(M,function(e,n){return I.useBR&&"\n"===e?"<br>":I.tabReplace?n.replace(/\t/g,I.tabReplace):""}):e}function h(e,n,t){var r=n?L[n]:t,a=[e.trim()];return e.match(/\bhljs\b/)||a.push("hljs"),-1===e.indexOf(r)&&a.push(r),a.join(" ").trim()}function d(e){var n,t,r,o,l,s=i(e);a(s)||(I.useBR?(n=document.createElementNS("http://www.w3.org/1999/xhtml","div"),n.innerHTML=e.innerHTML.replace(/\n/g,"").replace(/<br[ \/]*>/g,"\n")):n=e,l=n.textContent,r=s?f(s,l,!0):g(l),t=u(n),t.length&&(o=document.createElementNS("http://www.w3.org/1999/xhtml","div"),o.innerHTML=r.value,r.value=c(t,u(o),l)),r.value=p(r.value),e.innerHTML=r.value,e.className=h(e.className,s,r.language),e.result={language:r.language,re:r.r},r.second_best&&(e.second_best={language:r.second_best.language,re:r.second_best.r}))}function b(e){I=o(I,e)}function v(){if(!v.called){v.called=!0;var e=document.querySelectorAll("pre code");E.forEach.call(e,d)}}function m(){addEventListener("DOMContentLoaded",v,!1),addEventListener("load",v,!1)}function N(n,t){var r=y[n]=t(e);r.aliases&&r.aliases.forEach(function(e){L[e]=n})}function R(){return x(y)}function w(e){return e=(e||"").toLowerCase(),y[e]||y[L[e]]}var E=[],x=Object.keys,y={},L={},k=/^(no-?highlight|plain|text)$/i,B=/\blang(?:uage)?-([\w-]+)\b/i,M=/((^(<[^>]+>|\t|)+|(?:\n)))/gm,C="</span>",I={classPrefix:"hljs-",tabReplace:null,useBR:!1,languages:void 0};return e.highlight=f,e.highlightAuto=g,e.fixMarkup=p,e.highlightBlock=d,e.configure=b,e.initHighlighting=v,e.initHighlightingOnLoad=m,e.registerLanguage=N,e.listLanguages=R,e.getLanguage=w,e.inherit=o,e.IR="[a-zA-Z]\\w*",e.UIR="[a-zA-Z_]\\w*",e.NR="\\b\\d+(\\.\\d+)?",e.CNR="(-?)(\\b0[xX][a-fA-F0-9]+|(\\b\\d+(\\.\\d*)?|\\.\\d+)([eE][-+]?\\d+)?)",e.BNR="\\b(0b[01]+)",e.RSR="!|!=|!==|%|%=|&|&&|&=|\\*|\\*=|\\+|\\+=|,|-|-=|/=|/|:|;|<<|<<=|<=|<|===|==|=|>>>=|>>=|>=|>>>|>>|>|\\?|\\[|\\{|\\(|\\^|\\^=|\\||\\|=|\\|\\||~",e.BE={b:"\\\\[\\s\\S]",r:0},e.ASM={cN:"string",b:"'",e:"'",i:"\\n",c:[e.BE]},e.QSM={cN:"string",b:'"',e:'"',i:"\\n",c:[e.BE]},e.PWM={b:/\b(a|an|the|are|I'm|isn't|don't|doesn't|won't|but|just|should|pretty|simply|enough|gonna|going|wtf|so|such|will|you|your|they|like|more)\b/},e.C=function(n,t,r){var a=e.inherit({cN:"comment",b:n,e:t,c:[]},r||{});return a.c.push(e.PWM),a.c.push({cN:"doctag",b:"(?:TODO|FIXME|NOTE|BUG|XXX):",r:0}),a},e.CLCM=e.C("//","$"),e.CBCM=e.C("/\\*","\\*/"),e.HCM=e.C("#","$"),e.NM={cN:"number",b:e.NR,r:0},e.CNM={cN:"number",b:e.CNR,r:0},e.BNM={cN:"number",b:e.BNR,r:0},e.CSSNM={cN:"number",b:e.NR+"(%|em|ex|ch|rem|vw|vh|vmin|vmax|cm|mm|in|pt|pc|px|deg|grad|rad|turn|s|ms|Hz|kHz|dpi|dpcm|dppx)?",r:0},e.RM={cN:"regexp",b:/\//,e:/\/[gimuy]*/,i:/\n/,c:[e.BE,{b:/\[/,e:/\]/,r:0,c:[e.BE]}]},e.TM={cN:"title",b:e.IR,r:0},e.UTM={cN:"title",b:e.UIR,r:0},e.METHOD_GUARD={b:"\\.\\s*"+e.UIR,r:0},e});hljs.registerLanguage("sql",function(e){var t=e.C("--","$");return{cI:!0,i:/[<>{}*#]/,c:[{bK:"begin end start commit rollback savepoint lock alter create drop rename call delete do handler insert load replace select truncate update set show pragma grant merge describe use explain help declare prepare execute deallocate release unlock purge reset change stop analyze cache flush optimize repair kill install uninstall checksum restore check backup revoke comment",e:/;/,eW:!0,l:/[\w\.]+/,k:{keyword:"abort abs absolute acc acce accep accept access accessed accessible account acos action activate add addtime admin administer advanced advise aes_decrypt aes_encrypt after agent aggregate ali alia alias allocate allow alter always analyze ancillary and any anydata anydataset anyschema anytype apply archive archived archivelog are as asc ascii asin assembly assertion associate asynchronous at atan atn2 attr attri attrib attribu attribut attribute attributes audit authenticated authentication authid authors auto autoallocate autodblink autoextend automatic availability avg backup badfile basicfile before begin beginning benchmark between bfile bfile_base big bigfile bin binary_double binary_float binlog bit_and bit_count bit_length bit_or bit_xor bitmap blob_base block blocksize body both bound buffer_cache buffer_pool build bulk by byte byteordermark bytes cache caching call calling cancel capacity cascade cascaded case cast catalog category ceil ceiling chain change changed char_base char_length character_length characters characterset charindex charset charsetform charsetid check checksum checksum_agg child choose chr chunk class cleanup clear client clob clob_base clone close cluster_id cluster_probability cluster_set clustering coalesce coercibility col collate collation collect colu colum column column_value columns columns_updated comment commit compact compatibility compiled complete composite_limit compound compress compute concat concat_ws concurrent confirm conn connec connect connect_by_iscycle connect_by_isleaf connect_by_root connect_time connection consider consistent constant constraint constraints constructor container content contents context contributors controlfile conv convert convert_tz corr corr_k corr_s corresponding corruption cos cost count count_big counted covar_pop covar_samp cpu_per_call cpu_per_session crc32 create creation critical cross cube cume_dist curdate current current_date current_time current_timestamp current_user cursor curtime customdatum cycle data database databases datafile datafiles datalength date_add date_cache date_format date_sub dateadd datediff datefromparts datename datepart datetime2fromparts day day_to_second dayname dayofmonth dayofweek dayofyear days db_role_change dbtimezone ddl deallocate declare decode decompose decrement decrypt deduplicate def defa defau defaul default defaults deferred defi defin define degrees delayed delegate delete delete_all delimited demand dense_rank depth dequeue des_decrypt des_encrypt des_key_file desc descr descri describ describe descriptor deterministic diagnostics difference dimension direct_load directory disable disable_all disallow disassociate discardfile disconnect diskgroup distinct distinctrow distribute distributed div do document domain dotnet double downgrade drop dumpfile duplicate duration each edition editionable editions element ellipsis else elsif elt empty enable enable_all enclosed encode encoding encrypt end end-exec endian enforced engine engines enqueue enterprise entityescaping eomonth error errors escaped evalname evaluate event eventdata events except exception exceptions exchange exclude excluding execu execut execute exempt exists exit exp expire explain export export_set extended extent external external_1 external_2 externally extract failed failed_login_attempts failover failure far fast feature_set feature_value fetch field fields file file_name_convert filesystem_like_logging final finish first first_value fixed flash_cache flashback floor flush following follows for forall force form forma format found found_rows freelist freelists freepools fresh from from_base64 from_days ftp full function general generated get get_format get_lock getdate getutcdate global global_name globally go goto grant grants greatest group group_concat group_id grouping grouping_id groups gtid_subtract guarantee guard handler hash hashkeys having hea head headi headin heading heap help hex hierarchy high high_priority hosts hour http id ident_current ident_incr ident_seed identified identity idle_time if ifnull ignore iif ilike ilm immediate import in include including increment index indexes indexing indextype indicator indices inet6_aton inet6_ntoa inet_aton inet_ntoa infile initial initialized initially initrans inmemory inner innodb input insert install instance instantiable instr interface interleaved intersect into invalidate invisible is is_free_lock is_ipv4 is_ipv4_compat is_not is_not_null is_used_lock isdate isnull isolation iterate java join json json_exists keep keep_duplicates key keys kill language large last last_day last_insert_id last_value lax lcase lead leading least leaves left len lenght length less level levels library like like2 like4 likec limit lines link list listagg little ln load load_file lob lobs local localtime localtimestamp locate locator lock locked log log10 log2 logfile logfiles logging logical logical_reads_per_call logoff logon logs long loop low low_priority lower lpad lrtrim ltrim main make_set makedate maketime managed management manual map mapping mask master master_pos_wait match matched materialized max maxextents maximize maxinstances maxlen maxlogfiles maxloghistory maxlogmembers maxsize maxtrans md5 measures median medium member memcompress memory merge microsecond mid migration min minextents minimum mining minus minute minvalue missing mod mode model modification modify module monitoring month months mount move movement multiset mutex name name_const names nan national native natural nav nchar nclob nested never new newline next nextval no no_write_to_binlog noarchivelog noaudit nobadfile nocheck nocompress nocopy nocycle nodelay nodiscardfile noentityescaping noguarantee nokeep nologfile nomapping nomaxvalue nominimize nominvalue nomonitoring none noneditionable nonschema noorder nopr nopro noprom nopromp noprompt norely noresetlogs noreverse normal norowdependencies noschemacheck noswitch not nothing notice notrim novalidate now nowait nth_value nullif nulls num numb numbe nvarchar nvarchar2 object ocicoll ocidate ocidatetime ociduration ociinterval ociloblocator ocinumber ociref ocirefcursor ocirowid ocistring ocitype oct octet_length of off offline offset oid oidindex old on online only opaque open operations operator optimal optimize option optionally or oracle oracle_date oradata ord ordaudio orddicom orddoc order ordimage ordinality ordvideo organization orlany orlvary out outer outfile outline output over overflow overriding package pad parallel parallel_enable parameters parent parse partial partition partitions pascal passing password password_grace_time password_lock_time password_reuse_max password_reuse_time password_verify_function patch path patindex pctincrease pctthreshold pctused pctversion percent percent_rank percentile_cont percentile_disc performance period period_add period_diff permanent physical pi pipe pipelined pivot pluggable plugin policy position post_transaction pow power pragma prebuilt precedes preceding precision prediction prediction_cost prediction_details prediction_probability prediction_set prepare present preserve prior priority private private_sga privileges procedural procedure procedure_analyze processlist profiles project prompt protection public publishingservername purge quarter query quick quiesce quota quotename radians raise rand range rank raw read reads readsize rebuild record records recover recovery recursive recycle redo reduced ref reference referenced references referencing refresh regexp_like register regr_avgx regr_avgy regr_count regr_intercept regr_r2 regr_slope regr_sxx regr_sxy reject rekey relational relative relaylog release release_lock relies_on relocate rely rem remainder rename repair repeat replace replicate replication required reset resetlogs resize resource respect restore restricted result result_cache resumable resume retention return returning returns reuse reverse revoke right rlike role roles rollback rolling rollup round row row_count rowdependencies rowid rownum rows rtrim rules safe salt sample save savepoint sb1 sb2 sb4 scan schema schemacheck scn scope scroll sdo_georaster sdo_topo_geometry search sec_to_time second section securefile security seed segment select self sequence sequential serializable server servererror session session_user sessions_per_user set sets settings sha sha1 sha2 share shared shared_pool short show shrink shutdown si_averagecolor si_colorhistogram si_featurelist si_positionalcolor si_stillimage si_texture siblings sid sign sin size size_t sizes skip slave sleep smalldatetimefromparts smallfile snapshot some soname sort soundex source space sparse spfile split sql sql_big_result sql_buffer_result sql_cache sql_calc_found_rows sql_small_result sql_variant_property sqlcode sqldata sqlerror sqlname sqlstate sqrt square standalone standby start starting startup statement static statistics stats_binomial_test stats_crosstab stats_ks_test stats_mode stats_mw_test stats_one_way_anova stats_t_test_ stats_t_test_indep stats_t_test_one stats_t_test_paired stats_wsr_test status std stddev stddev_pop stddev_samp stdev stop storage store stored str str_to_date straight_join strcmp strict string struct stuff style subdate subpartition subpartitions substitutable substr substring subtime subtring_index subtype success sum suspend switch switchoffset switchover sync synchronous synonym sys sys_xmlagg sysasm sysaux sysdate sysdatetimeoffset sysdba sysoper system system_user sysutcdatetime table tables tablespace tan tdo template temporary terminated tertiary_weights test than then thread through tier ties time time_format time_zone timediff timefromparts timeout timestamp timestampadd timestampdiff timezone_abbr timezone_minute timezone_region to to_base64 to_date to_days to_seconds todatetimeoffset trace tracking transaction transactional translate translation treat trigger trigger_nestlevel triggers trim truncate try_cast try_convert try_parse type ub1 ub2 ub4 ucase unarchived unbounded uncompress under undo unhex unicode uniform uninstall union unique unix_timestamp unknown unlimited unlock unpivot unrecoverable unsafe unsigned until untrusted unusable unused update updated upgrade upped upper upsert url urowid usable usage use use_stored_outlines user user_data user_resources users using utc_date utc_timestamp uuid uuid_short validate validate_password_strength validation valist value values var var_samp varcharc vari varia variab variabl variable variables variance varp varraw varrawc varray verify version versions view virtual visible void wait wallet warning warnings week weekday weekofyear wellformed when whene whenev wheneve whenever where while whitespace with within without work wrapped xdb xml xmlagg xmlattributes xmlcast xmlcolattval xmlelement xmlexists xmlforest xmlindex xmlnamespaces xmlpi xmlquery xmlroot xmlschema xmlserialize xmltable xmltype xor year year_to_month years yearweek",literal:"true false null",built_in:"array bigint binary bit blob boolean char character date dec decimal float int int8 integer interval number numeric real record serial serial8 smallint text varchar varying void"},c:[{cN:"string",b:"'",e:"'",c:[e.BE,{b:"''"}]},{cN:"string",b:'"',e:'"',c:[e.BE,{b:'""'}]},{cN:"string",b:"`",e:"`",c:[e.BE]},e.CNM,e.CBCM,t]},e.CBCM,t]}});hljs.registerLanguage("r",function(e){var r="([a-zA-Z]|\\.[a-zA-Z.])[a-zA-Z0-9._]*";return{c:[e.HCM,{b:r,l:r,k:{keyword:"function if in break next repeat else for return switch while try tryCatch stop warning require library attach detach source setMethod setGeneric setGroupGeneric setClass ...",literal:"NULL NA TRUE FALSE T F Inf NaN NA_integer_|10 NA_real_|10 NA_character_|10 NA_complex_|10"},r:0},{cN:"number",b:"0[xX][0-9a-fA-F]+[Li]?\\b",r:0},{cN:"number",b:"\\d+(?:[eE][+\\-]?\\d*)?L\\b",r:0},{cN:"number",b:"\\d+\\.(?!\\d)(?:i\\b)?",r:0},{cN:"number",b:"\\d+(?:\\.\\d*)?(?:[eE][+\\-]?\\d*)?i?\\b",r:0},{cN:"number",b:"\\.\\d+(?:[eE][+\\-]?\\d*)?i?\\b",r:0},{b:"`",e:"`",r:0},{cN:"string",c:[e.BE],v:[{b:'"',e:'"'},{b:"'",e:"'"}]}]}});hljs.registerLanguage("perl",function(e){var t="getpwent getservent quotemeta msgrcv scalar kill dbmclose undef lc ma syswrite tr send umask sysopen shmwrite vec qx utime local oct semctl localtime readpipe do return format read sprintf dbmopen pop getpgrp not getpwnam rewinddir qqfileno qw endprotoent wait sethostent bless s|0 opendir continue each sleep endgrent shutdown dump chomp connect getsockname die socketpair close flock exists index shmgetsub for endpwent redo lstat msgctl setpgrp abs exit select print ref gethostbyaddr unshift fcntl syscall goto getnetbyaddr join gmtime symlink semget splice x|0 getpeername recv log setsockopt cos last reverse gethostbyname getgrnam study formline endhostent times chop length gethostent getnetent pack getprotoent getservbyname rand mkdir pos chmod y|0 substr endnetent printf next open msgsnd readdir use unlink getsockopt getpriority rindex wantarray hex system getservbyport endservent int chr untie rmdir prototype tell listen fork shmread ucfirst setprotoent else sysseek link getgrgid shmctl waitpid unpack getnetbyname reset chdir grep split require caller lcfirst until warn while values shift telldir getpwuid my getprotobynumber delete and sort uc defined srand accept package seekdir getprotobyname semop our rename seek if q|0 chroot sysread setpwent no crypt getc chown sqrt write setnetent setpriority foreach tie sin msgget map stat getlogin unless elsif truncate exec keys glob tied closedirioctl socket readlink eval xor readline binmode setservent eof ord bind alarm pipe atan2 getgrent exp time push setgrent gt lt or ne m|0 break given say state when",r={cN:"subst",b:"[$@]\\{",e:"\\}",k:t},s={b:"->{",e:"}"},n={v:[{b:/\$\d/},{b:/[\$%@](\^\w\b|#\w+(::\w+)*|{\w+}|\w+(::\w*)*)/},{b:/[\$%@][^\s\w{]/,r:0}]},i=[e.BE,r,n],o=[n,e.HCM,e.C("^\\=\\w","\\=cut",{eW:!0}),s,{cN:"string",c:i,v:[{b:"q[qwxr]?\\s*\\(",e:"\\)",r:5},{b:"q[qwxr]?\\s*\\[",e:"\\]",r:5},{b:"q[qwxr]?\\s*\\{",e:"\\}",r:5},{b:"q[qwxr]?\\s*\\|",e:"\\|",r:5},{b:"q[qwxr]?\\s*\\<",e:"\\>",r:5},{b:"qw\\s+q",e:"q",r:5},{b:"'",e:"'",c:[e.BE]},{b:'"',e:'"'},{b:"`",e:"`",c:[e.BE]},{b:"{\\w+}",c:[],r:0},{b:"-?\\w+\\s*\\=\\>",c:[],r:0}]},{cN:"number",b:"(\\b0[0-7_]+)|(\\b0x[0-9a-fA-F_]+)|(\\b[1-9][0-9_]*(\\.[0-9_]+)?)|[0_]\\b",r:0},{b:"(\\/\\/|"+e.RSR+"|\\b(split|return|print|reverse|grep)\\b)\\s*",k:"split return print reverse grep",r:0,c:[e.HCM,{cN:"regexp",b:"(s|tr|y)/(\\\\.|[^/])*/(\\\\.|[^/])*/[a-z]*",r:10},{cN:"regexp",b:"(m|qr)?/",e:"/[a-z]*",c:[e.BE],r:0}]},{cN:"function",bK:"sub",e:"(\\s*\\(.*?\\))?[;{]",eE:!0,r:5,c:[e.TM]},{b:"-\\w\\b",r:0},{b:"^__DATA__$",e:"^__END__$",sL:"mojolicious",c:[{b:"^@@.*",e:"$",cN:"comment"}]}];return r.c=o,s.c=o,{aliases:["pl","pm"],l:/[\w\.]+/,k:t,c:o}});hljs.registerLanguage("ini",function(e){var b={cN:"string",c:[e.BE],v:[{b:"'''",e:"'''",r:10},{b:'"""',e:'"""',r:10},{b:'"',e:'"'},{b:"'",e:"'"}]};return{aliases:["toml"],cI:!0,i:/\S/,c:[e.C(";","$"),e.HCM,{cN:"section",b:/^\s*\[+/,e:/\]+/},{b:/^[a-z0-9\[\]_-]+\s*=\s*/,e:"$",rB:!0,c:[{cN:"attr",b:/[a-z0-9\[\]_-]+/},{b:/=/,eW:!0,r:0,c:[{cN:"literal",b:/\bon|off|true|false|yes|no\b/},{cN:"variable",v:[{b:/\$[\w\d"][\w\d_]*/},{b:/\$\{(.*?)}/}]},b,{cN:"number",b:/([\+\-]+)?[\d]+_[\d_]+/},e.NM]}]}]}});hljs.registerLanguage("diff",function(e){return{aliases:["patch"],c:[{cN:"meta",r:10,v:[{b:/^@@ +\-\d+,\d+ +\+\d+,\d+ +@@$/},{b:/^\*\*\* +\d+,\d+ +\*\*\*\*$/},{b:/^\-\-\- +\d+,\d+ +\-\-\-\-$/}]},{cN:"comment",v:[{b:/Index: /,e:/$/},{b:/={3,}/,e:/$/},{b:/^\-{3}/,e:/$/},{b:/^\*{3} /,e:/$/},{b:/^\+{3}/,e:/$/},{b:/\*{5}/,e:/\*{5}$/}]},{cN:"addition",b:"^\\+",e:"$"},{cN:"deletion",b:"^\\-",e:"$"},{cN:"addition",b:"^\\!",e:"$"}]}});hljs.registerLanguage("go",function(e){var t={keyword:"break default func interface select case map struct chan else goto package switch const fallthrough if range type continue for import return var go defer bool byte complex64 complex128 float32 float64 int8 int16 int32 int64 string uint8 uint16 uint32 uint64 int uint uintptr rune",literal:"true false iota nil",built_in:"append cap close complex copy imag len make new panic print println real recover delete"};return{aliases:["golang"],k:t,i:"</",c:[e.CLCM,e.CBCM,{cN:"string",v:[e.QSM,{b:"'",e:"[^\\\\]'"},{b:"`",e:"`"}]},{cN:"number",v:[{b:e.CNR+"[dflsi]",r:1},e.CNM]},{b:/:=/},{cN:"function",bK:"func",e:/\s*\{/,eE:!0,c:[e.TM,{cN:"params",b:/\(/,e:/\)/,k:t,i:/["']/}]}]}});hljs.registerLanguage("bash",function(e){var t={cN:"variable",v:[{b:/\$[\w\d#@][\w\d_]*/},{b:/\$\{(.*?)}/}]},s={cN:"string",b:/"/,e:/"/,c:[e.BE,t,{cN:"variable",b:/\$\(/,e:/\)/,c:[e.BE]}]},a={cN:"string",b:/'/,e:/'/};return{aliases:["sh","zsh"],l:/\b-?[a-z\._]+\b/,k:{keyword:"if then else elif fi for while in do done case esac function",literal:"true false",built_in:"break cd continue eval exec exit export getopts hash pwd readonly return shift test times trap umask unset alias bind builtin caller command declare echo enable help let local logout mapfile printf read readarray source type typeset ulimit unalias set shopt autoload bg bindkey bye cap chdir clone comparguments compcall compctl compdescribe compfiles compgroups compquote comptags comptry compvalues dirs disable disown echotc echoti emulate fc fg float functions getcap getln history integer jobs kill limit log noglob popd print pushd pushln rehash sched setcap setopt stat suspend ttyctl unfunction unhash unlimit unsetopt vared wait whence where which zcompile zformat zftp zle zmodload zparseopts zprof zpty zregexparse zsocket zstyle ztcp",_:"-ne -eq -lt -gt -f -d -e -s -l -a"},c:[{cN:"meta",b:/^#![^\n]+sh\s*$/,r:10},{cN:"function",b:/\w[\w\d_]*\s*\(\s*\)\s*\{/,rB:!0,c:[e.inherit(e.TM,{b:/\w[\w\d_]*/})],r:0},e.HCM,s,a,t]}});hljs.registerLanguage("python",function(e){var r={keyword:"and elif is global as in if from raise for except finally print import pass return exec else break not with class assert yield try while continue del or def lambda async await nonlocal|10 None True False",built_in:"Ellipsis NotImplemented"},b={cN:"meta",b:/^(>>>|\.\.\.) /},c={cN:"subst",b:/\{/,e:/\}/,k:r,i:/#/},a={cN:"string",c:[e.BE],v:[{b:/(u|b)?r?'''/,e:/'''/,c:[b],r:10},{b:/(u|b)?r?"""/,e:/"""/,c:[b],r:10},{b:/(fr|rf|f)'''/,e:/'''/,c:[b,c]},{b:/(fr|rf|f)"""/,e:/"""/,c:[b,c]},{b:/(u|r|ur)'/,e:/'/,r:10},{b:/(u|r|ur)"/,e:/"/,r:10},{b:/(b|br)'/,e:/'/},{b:/(b|br)"/,e:/"/},{b:/(fr|rf|f)'/,e:/'/,c:[c]},{b:/(fr|rf|f)"/,e:/"/,c:[c]},e.ASM,e.QSM]},s={cN:"number",r:0,v:[{b:e.BNR+"[lLjJ]?"},{b:"\\b(0o[0-7]+)[lLjJ]?"},{b:e.CNR+"[lLjJ]?"}]},i={cN:"params",b:/\(/,e:/\)/,c:["self",b,s,a]};return c.c=[a,s,b],{aliases:["py","gyp"],k:r,i:/(<\/|->|\?)|=>/,c:[b,s,a,e.HCM,{v:[{cN:"function",bK:"def"},{cN:"class",bK:"class"}],e:/:/,i:/[${=;\n,]/,c:[e.UTM,i,{b:/->/,eW:!0,k:"None"}]},{cN:"meta",b:/^[\t ]*@/,e:/$/},{b:/\b(print|exec)\(/}]}});hljs.registerLanguage("julia",function(e){var r={keyword:"in isa where baremodule begin break catch ccall const continue do else elseif end export false finally for function global if import importall let local macro module quote return true try using while type immutable abstract bitstype typealias ",literal:"true false ARGS C_NULL DevNull ENDIAN_BOM ENV I Inf Inf16 Inf32 Inf64 InsertionSort JULIA_HOME LOAD_PATH MergeSort NaN NaN16 NaN32 NaN64 PROGRAM_FILE QuickSort RoundDown RoundFromZero RoundNearest RoundNearestTiesAway RoundNearestTiesUp RoundToZero RoundUp STDERR STDIN STDOUT VERSION catalan e|0 eu|0 eulergamma golden im nothing pi γ π φ ",built_in:"ANY AbstractArray AbstractChannel AbstractFloat AbstractMatrix AbstractRNG AbstractSerializer AbstractSet AbstractSparseArray AbstractSparseMatrix AbstractSparseVector AbstractString AbstractUnitRange AbstractVecOrMat AbstractVector Any ArgumentError Array AssertionError Associative Base64DecodePipe Base64EncodePipe Bidiagonal BigFloat BigInt BitArray BitMatrix BitVector Bool BoundsError BufferStream CachingPool CapturedException CartesianIndex CartesianRange Cchar Cdouble Cfloat Channel Char Cint Cintmax_t Clong Clonglong ClusterManager Cmd CodeInfo Colon Complex Complex128 Complex32 Complex64 CompositeException Condition ConjArray ConjMatrix ConjVector Cptrdiff_t Cshort Csize_t Cssize_t Cstring Cuchar Cuint Cuintmax_t Culong Culonglong Cushort Cwchar_t Cwstring DataType Date DateFormat DateTime DenseArray DenseMatrix DenseVecOrMat DenseVector Diagonal Dict DimensionMismatch Dims DirectIndexString Display DivideError DomainError EOFError EachLine Enum Enumerate ErrorException Exception ExponentialBackOff Expr Factorization FileMonitor Float16 Float32 Float64 Function Future GlobalRef GotoNode HTML Hermitian IO IOBuffer IOContext IOStream IPAddr IPv4 IPv6 IndexCartesian IndexLinear IndexStyle InexactError InitError Int Int128 Int16 Int32 Int64 Int8 IntSet Integer InterruptException InvalidStateException Irrational KeyError LabelNode LinSpace LineNumberNode LoadError LowerTriangular MIME Matrix MersenneTwister Method MethodError MethodTable Module NTuple NewvarNode NullException Nullable Number ObjectIdDict OrdinalRange OutOfMemoryError OverflowError Pair ParseError PartialQuickSort PermutedDimsArray Pipe PollingFileWatcher ProcessExitedException Ptr QuoteNode RandomDevice Range RangeIndex Rational RawFD ReadOnlyMemoryError Real ReentrantLock Ref Regex RegexMatch RemoteChannel RemoteException RevString RoundingMode RowVector SSAValue SegmentationFault SerializationState Set SharedArray SharedMatrix SharedVector Signed SimpleVector Slot SlotNumber SparseMatrixCSC SparseVector StackFrame StackOverflowError StackTrace StepRange StepRangeLen StridedArray StridedMatrix StridedVecOrMat StridedVector String SubArray SubString SymTridiagonal Symbol Symmetric SystemError TCPSocket Task Text TextDisplay Timer Tridiagonal Tuple Type TypeError TypeMapEntry TypeMapLevel TypeName TypeVar TypedSlot UDPSocket UInt UInt128 UInt16 UInt32 UInt64 UInt8 UndefRefError UndefVarError UnicodeError UniformScaling Union UnionAll UnitRange Unsigned UpperTriangular Val Vararg VecElement VecOrMat Vector VersionNumber Void WeakKeyDict WeakRef WorkerConfig WorkerPool "},t="[A-Za-z_\\u00A1-\\uFFFF][A-Za-z_0-9\\u00A1-\\uFFFF]*",a={l:t,k:r,i:/<\//},n={cN:"number",b:/(\b0x[\d_]*(\.[\d_]*)?|0x\.\d[\d_]*)p[-+]?\d+|\b0[box][a-fA-F0-9][a-fA-F0-9_]*|(\b\d[\d_]*(\.[\d_]*)?|\.\d[\d_]*)([eEfF][-+]?\d+)?/,r:0},o={cN:"string",b:/'(.|\\[xXuU][a-zA-Z0-9]+)'/},i={cN:"subst",b:/\$\(/,e:/\)/,k:r},l={cN:"variable",b:"\\$"+t},c={cN:"string",c:[e.BE,i,l],v:[{b:/\w*"""/,e:/"""\w*/,r:10},{b:/\w*"/,e:/"\w*/}]},s={cN:"string",c:[e.BE,i,l],b:"`",e:"`"},d={cN:"meta",b:"@"+t},u={cN:"comment",v:[{b:"#=",e:"=#",r:10},{b:"#",e:"$"}]};return a.c=[n,o,c,s,d,u,e.HCM,{cN:"keyword",b:"\\b(((abstract|primitive)\\s+)type|(mutable\\s+)?struct)\\b"},{b:/<:/}],i.c=a.c,a});hljs.registerLanguage("coffeescript",function(e){var c={keyword:"in if for while finally new do return else break catch instanceof throw try this switch continue typeof delete debugger super yield import export from as default await then unless until loop of by when and or is isnt not",literal:"true false null undefined yes no on off",built_in:"npm require console print module global window document"},n="[A-Za-z$_][0-9A-Za-z$_]*",r={cN:"subst",b:/#\{/,e:/}/,k:c},i=[e.BNM,e.inherit(e.CNM,{starts:{e:"(\\s*/)?",r:0}}),{cN:"string",v:[{b:/'''/,e:/'''/,c:[e.BE]},{b:/'/,e:/'/,c:[e.BE]},{b:/"""/,e:/"""/,c:[e.BE,r]},{b:/"/,e:/"/,c:[e.BE,r]}]},{cN:"regexp",v:[{b:"///",e:"///",c:[r,e.HCM]},{b:"//[gim]*",r:0},{b:/\/(?![ *])(\\\/|.)*?\/[gim]*(?=\W|$)/}]},{b:"@"+n},{sL:"javascript",eB:!0,eE:!0,v:[{b:"```",e:"```"},{b:"`",e:"`"}]}];r.c=i;var s=e.inherit(e.TM,{b:n}),t="(\\(.*\\))?\\s*\\B[-=]>",o={cN:"params",b:"\\([^\\(]",rB:!0,c:[{b:/\(/,e:/\)/,k:c,c:["self"].concat(i)}]};return{aliases:["coffee","cson","iced"],k:c,i:/\/\*/,c:i.concat([e.C("###","###"),e.HCM,{cN:"function",b:"^\\s*"+n+"\\s*=\\s*"+t,e:"[-=]>",rB:!0,c:[s,o]},{b:/[:\(,=]\s*/,r:0,c:[{cN:"function",b:t,e:"[-=]>",rB:!0,c:[o]}]},{cN:"class",bK:"class",e:"$",i:/[:="\[\]]/,c:[{bK:"extends",eW:!0,i:/[:="\[\]]/,c:[s]},s]},{b:n+":",e:":",rB:!0,rE:!0,r:0}])}});hljs.registerLanguage("cpp",function(t){var e={cN:"keyword",b:"\\b[a-z\\d_]*_t\\b"},r={cN:"string",v:[{b:'(u8?|U)?L?"',e:'"',i:"\\n",c:[t.BE]},{b:'(u8?|U)?R"',e:'"',c:[t.BE]},{b:"'\\\\?.",e:"'",i:"."}]},s={cN:"number",v:[{b:"\\b(0b[01']+)"},{b:"(-?)\\b([\\d']+(\\.[\\d']*)?|\\.[\\d']+)(u|U|l|L|ul|UL|f|F|b|B)"},{b:"(-?)(\\b0[xX][a-fA-F0-9']+|(\\b[\\d']+(\\.[\\d']*)?|\\.[\\d']+)([eE][-+]?[\\d']+)?)"}],r:0},i={cN:"meta",b:/#\s*[a-z]+\b/,e:/$/,k:{"meta-keyword":"if else elif endif define undef warning error line pragma ifdef ifndef include"},c:[{b:/\\\n/,r:0},t.inherit(r,{cN:"meta-string"}),{cN:"meta-string",b:/<[^\n>]*>/,e:/$/,i:"\\n"},t.CLCM,t.CBCM]},a=t.IR+"\\s*\\(",c={keyword:"int float while private char catch import module export virtual operator sizeof dynamic_cast|10 typedef const_cast|10 const for static_cast|10 union namespace unsigned long volatile static protected bool template mutable if public friend do goto auto void enum else break extern using asm case typeid short reinterpret_cast|10 default double register explicit signed typename try this switch continue inline delete alignof constexpr decltype noexcept static_assert thread_local restrict _Bool complex _Complex _Imaginary atomic_bool atomic_char atomic_schar atomic_uchar atomic_short atomic_ushort atomic_int atomic_uint atomic_long atomic_ulong atomic_llong atomic_ullong new throw return and or not",built_in:"std string cin cout cerr clog stdin stdout stderr stringstream istringstream ostringstream auto_ptr deque list queue stack vector map set bitset multiset multimap unordered_set unordered_map unordered_multiset unordered_multimap array shared_ptr abort abs acos asin atan2 atan calloc ceil cosh cos exit exp fabs floor fmod fprintf fputs free frexp fscanf isalnum isalpha iscntrl isdigit isgraph islower isprint ispunct isspace isupper isxdigit tolower toupper labs ldexp log10 log malloc realloc memchr memcmp memcpy memset modf pow printf putchar puts scanf sinh sin snprintf sprintf sqrt sscanf strcat strchr strcmp strcpy strcspn strlen strncat strncmp strncpy strpbrk strrchr strspn strstr tanh tan vfprintf vprintf vsprintf endl initializer_list unique_ptr",literal:"true false nullptr NULL"},n=[e,t.CLCM,t.CBCM,s,r];return{aliases:["c","cc","h","c++","h++","hpp"],k:c,i:"</",c:n.concat([i,{b:"\\b(deque|list|queue|stack|vector|map|set|bitset|multiset|multimap|unordered_map|unordered_set|unordered_multiset|unordered_multimap|array)\\s*<",e:">",k:c,c:["self",e]},{b:t.IR+"::",k:c},{v:[{b:/=/,e:/;/},{b:/\(/,e:/\)/},{bK:"new throw return else",e:/;/}],k:c,c:n.concat([{b:/\(/,e:/\)/,k:c,c:n.concat(["self"]),r:0}]),r:0},{cN:"function",b:"("+t.IR+"[\\*&\\s]+)+"+a,rB:!0,e:/[{;=]/,eE:!0,k:c,i:/[^\w\s\*&]/,c:[{b:a,rB:!0,c:[t.TM],r:0},{cN:"params",b:/\(/,e:/\)/,k:c,r:0,c:[t.CLCM,t.CBCM,r,s,e]},t.CLCM,t.CBCM,i]},{cN:"class",bK:"class struct",e:/[{;:]/,c:[{b:/</,e:/>/,c:["self"]},t.TM]}]),exports:{preprocessor:i,strings:r,k:c}}});hljs.registerLanguage("ruby",function(e){var b="[a-zA-Z_]\\w*[!?=]?|[-+~]\\@|<<|>>|=~|===?|<=>|[<>]=?|\\*\\*|[-/+%^&*~`|]|\\[\\]=?",r={keyword:"and then defined module in return redo if BEGIN retry end for self when next until do begin unless END rescue else break undef not super class case require yield alias while ensure elsif or include attr_reader attr_writer attr_accessor",literal:"true false nil"},c={cN:"doctag",b:"@[A-Za-z]+"},a={b:"#<",e:">"},s=[e.C("#","$",{c:[c]}),e.C("^\\=begin","^\\=end",{c:[c],r:10}),e.C("^__END__","\\n$")],n={cN:"subst",b:"#\\{",e:"}",k:r},t={cN:"string",c:[e.BE,n],v:[{b:/'/,e:/'/},{b:/"/,e:/"/},{b:/`/,e:/`/},{b:"%[qQwWx]?\\(",e:"\\)"},{b:"%[qQwWx]?\\[",e:"\\]"},{b:"%[qQwWx]?{",e:"}"},{b:"%[qQwWx]?<",e:">"},{b:"%[qQwWx]?/",e:"/"},{b:"%[qQwWx]?%",e:"%"},{b:"%[qQwWx]?-",e:"-"},{b:"%[qQwWx]?\\|",e:"\\|"},{b:/\B\?(\\\d{1,3}|\\x[A-Fa-f0-9]{1,2}|\\u[A-Fa-f0-9]{4}|\\?\S)\b/},{b:/<<(-?)\w+$/,e:/^\s*\w+$/}]},i={cN:"params",b:"\\(",e:"\\)",endsParent:!0,k:r},d=[t,a,{cN:"class",bK:"class module",e:"$|;",i:/=/,c:[e.inherit(e.TM,{b:"[A-Za-z_]\\w*(::\\w+)*(\\?|\\!)?"}),{b:"<\\s*",c:[{b:"("+e.IR+"::)?"+e.IR}]}].concat(s)},{cN:"function",bK:"def",e:"$|;",c:[e.inherit(e.TM,{b:b}),i].concat(s)},{b:e.IR+"::"},{cN:"symbol",b:e.UIR+"(\\!|\\?)?:",r:0},{cN:"symbol",b:":(?!\\s)",c:[t,{b:b}],r:0},{cN:"number",b:"(\\b0[0-7_]+)|(\\b0x[0-9a-fA-F_]+)|(\\b[1-9][0-9_]*(\\.[0-9_]+)?)|[0_]\\b",r:0},{b:"(\\$\\W)|((\\$|\\@\\@?)(\\w+))"},{cN:"params",b:/\|/,e:/\|/,k:r},{b:"("+e.RSR+"|unless)\\s*",k:"unless",c:[a,{cN:"regexp",c:[e.BE,n],i:/\n/,v:[{b:"/",e:"/[a-z]*"},{b:"%r{",e:"}[a-z]*"},{b:"%r\\(",e:"\\)[a-z]*"},{b:"%r!",e:"![a-z]*"},{b:"%r\\[",e:"\\][a-z]*"}]}].concat(s),r:0}].concat(s);n.c=d,i.c=d;var l="[>?]>",o="[\\w#]+\\(\\w+\\):\\d+:\\d+>",u="(\\w+-)?\\d+\\.\\d+\\.\\d(p\\d+)?[^>]+>",w=[{b:/^\s*=>/,starts:{e:"$",c:d}},{cN:"meta",b:"^("+l+"|"+o+"|"+u+")",starts:{e:"$",c:d}}];return{aliases:["rb","gemspec","podspec","thor","irb"],k:r,i:/\/\*/,c:s.concat(w).concat(d)}});hljs.registerLanguage("yaml",function(e){var b="true false yes no null",a="^[ \\-]*",r="[a-zA-Z_][\\w\\-]*",t={cN:"attr",v:[{b:a+r+":"},{b:a+'"'+r+'":'},{b:a+"'"+r+"':"}]},c={cN:"template-variable",v:[{b:"{{",e:"}}"},{b:"%{",e:"}"}]},l={cN:"string",r:0,v:[{b:/'/,e:/'/},{b:/"/,e:/"/},{b:/\S+/}],c:[e.BE,c]};return{cI:!0,aliases:["yml","YAML","yaml"],c:[t,{cN:"meta",b:"^---s*$",r:10},{cN:"string",b:"[\\|>] *$",rE:!0,c:l.c,e:t.v[0].b},{b:"<%[%=-]?",e:"[%-]?%>",sL:"ruby",eB:!0,eE:!0,r:0},{cN:"type",b:"!!"+e.UIR},{cN:"meta",b:"&"+e.UIR+"$"},{cN:"meta",b:"\\*"+e.UIR+"$"},{cN:"bullet",b:"^ *-",r:0},e.HCM,{bK:b,k:{literal:b}},e.CNM,l]}});hljs.registerLanguage("css",function(e){var c="[a-zA-Z-][a-zA-Z0-9_-]*",t={b:/[A-Z\_\.\-]+\s*:/,rB:!0,e:";",eW:!0,c:[{cN:"attribute",b:/\S/,e:":",eE:!0,starts:{eW:!0,eE:!0,c:[{b:/[\w-]+\(/,rB:!0,c:[{cN:"built_in",b:/[\w-]+/},{b:/\(/,e:/\)/,c:[e.ASM,e.QSM]}]},e.CSSNM,e.QSM,e.ASM,e.CBCM,{cN:"number",b:"#[0-9A-Fa-f]+"},{cN:"meta",b:"!important"}]}}]};return{cI:!0,i:/[=\/|'\$]/,c:[e.CBCM,{cN:"selector-id",b:/#[A-Za-z0-9_-]+/},{cN:"selector-class",b:/\.[A-Za-z0-9_-]+/},{cN:"selector-attr",b:/\[/,e:/\]/,i:"$"},{cN:"selector-pseudo",b:/:(:)?[a-zA-Z0-9\_\-\+\(\)"'.]+/},{b:"@(font-face|page)",l:"[a-z-]+",k:"font-face page"},{b:"@",e:"[{;]",i:/:/,c:[{cN:"keyword",b:/\w+/},{b:/\s/,eW:!0,eE:!0,r:0,c:[e.ASM,e.QSM,e.CSSNM]}]},{cN:"selector-tag",b:c,r:0},{b:"{",e:"}",i:/\S/,c:[e.CBCM,t]}]}});hljs.registerLanguage("fortran",function(e){var t={cN:"params",b:"\\(",e:"\\)"},n={literal:".False. .True.",keyword:"kind do while private call intrinsic where elsewhere type endtype endmodule endselect endinterface end enddo endif if forall endforall only contains default return stop then public subroutine|10 function program .and. .or. .not. .le. .eq. .ge. .gt. .lt. goto save else use module select case access blank direct exist file fmt form formatted iostat name named nextrec number opened rec recl sequential status unformatted unit continue format pause cycle exit c_null_char c_alert c_backspace c_form_feed flush wait decimal round iomsg synchronous nopass non_overridable pass protected volatile abstract extends import non_intrinsic value deferred generic final enumerator class associate bind enum c_int c_short c_long c_long_long c_signed_char c_size_t c_int8_t c_int16_t c_int32_t c_int64_t c_int_least8_t c_int_least16_t c_int_least32_t c_int_least64_t c_int_fast8_t c_int_fast16_t c_int_fast32_t c_int_fast64_t c_intmax_t C_intptr_t c_float c_double c_long_double c_float_complex c_double_complex c_long_double_complex c_bool c_char c_null_ptr c_null_funptr c_new_line c_carriage_return c_horizontal_tab c_vertical_tab iso_c_binding c_loc c_funloc c_associated  c_f_pointer c_ptr c_funptr iso_fortran_env character_storage_size error_unit file_storage_size input_unit iostat_end iostat_eor numeric_storage_size output_unit c_f_procpointer ieee_arithmetic ieee_support_underflow_control ieee_get_underflow_mode ieee_set_underflow_mode newunit contiguous recursive pad position action delim readwrite eor advance nml interface procedure namelist include sequence elemental pure integer real character complex logical dimension allocatable|10 parameter external implicit|10 none double precision assign intent optional pointer target in out common equivalence data",built_in:"alog alog10 amax0 amax1 amin0 amin1 amod cabs ccos cexp clog csin csqrt dabs dacos dasin datan datan2 dcos dcosh ddim dexp dint dlog dlog10 dmax1 dmin1 dmod dnint dsign dsin dsinh dsqrt dtan dtanh float iabs idim idint idnint ifix isign max0 max1 min0 min1 sngl algama cdabs cdcos cdexp cdlog cdsin cdsqrt cqabs cqcos cqexp cqlog cqsin cqsqrt dcmplx dconjg derf derfc dfloat dgamma dimag dlgama iqint qabs qacos qasin qatan qatan2 qcmplx qconjg qcos qcosh qdim qerf qerfc qexp qgamma qimag qlgama qlog qlog10 qmax1 qmin1 qmod qnint qsign qsin qsinh qsqrt qtan qtanh abs acos aimag aint anint asin atan atan2 char cmplx conjg cos cosh exp ichar index int log log10 max min nint sign sin sinh sqrt tan tanh print write dim lge lgt lle llt mod nullify allocate deallocate adjustl adjustr all allocated any associated bit_size btest ceiling count cshift date_and_time digits dot_product eoshift epsilon exponent floor fraction huge iand ibclr ibits ibset ieor ior ishft ishftc lbound len_trim matmul maxexponent maxloc maxval merge minexponent minloc minval modulo mvbits nearest pack present product radix random_number random_seed range repeat reshape rrspacing scale scan selected_int_kind selected_real_kind set_exponent shape size spacing spread sum system_clock tiny transpose trim ubound unpack verify achar iachar transfer dble entry dprod cpu_time command_argument_count get_command get_command_argument get_environment_variable is_iostat_end ieee_arithmetic ieee_support_underflow_control ieee_get_underflow_mode ieee_set_underflow_mode is_iostat_eor move_alloc new_line selected_char_kind same_type_as extends_type_ofacosh asinh atanh bessel_j0 bessel_j1 bessel_jn bessel_y0 bessel_y1 bessel_yn erf erfc erfc_scaled gamma log_gamma hypot norm2 atomic_define atomic_ref execute_command_line leadz trailz storage_size merge_bits bge bgt ble blt dshiftl dshiftr findloc iall iany iparity image_index lcobound ucobound maskl maskr num_images parity popcnt poppar shifta shiftl shiftr this_image"};return{cI:!0,aliases:["f90","f95"],k:n,i:/\/\*/,c:[e.inherit(e.ASM,{cN:"string",r:0}),e.inherit(e.QSM,{cN:"string",r:0}),{cN:"function",bK:"subroutine function program",i:"[${=\\n]",c:[e.UTM,t]},e.C("!","$",{r:0}),{cN:"number",b:"(?=\\b|\\+|\\-|\\.)(?=\\.\\d|\\d)(?:\\d+)?(?:\\.?\\d*)(?:[de][+-]?\\d+)?\\b\\.?",r:0}]}});hljs.registerLanguage("awk",function(e){var r={cN:"variable",v:[{b:/\$[\w\d#@][\w\d_]*/},{b:/\$\{(.*?)}/}]},b="BEGIN END if else while do for in break continue delete next nextfile function func exit|10",n={cN:"string",c:[e.BE],v:[{b:/(u|b)?r?'''/,e:/'''/,r:10},{b:/(u|b)?r?"""/,e:/"""/,r:10},{b:/(u|r|ur)'/,e:/'/,r:10},{b:/(u|r|ur)"/,e:/"/,r:10},{b:/(b|br)'/,e:/'/},{b:/(b|br)"/,e:/"/},e.ASM,e.QSM]};return{k:{keyword:b},c:[r,n,e.RM,e.HCM,e.NM]}});hljs.registerLanguage("makefile",function(e){var i={cN:"variable",v:[{b:"\\$\\("+e.UIR+"\\)",c:[e.BE]},{b:/\$[@%<?\^\+\*]/}]},r={cN:"string",b:/"/,e:/"/,c:[e.BE,i]},a={cN:"variable",b:/\$\([\w-]+\s/,e:/\)/,k:{built_in:"subst patsubst strip findstring filter filter-out sort word wordlist firstword lastword dir notdir suffix basename addsuffix addprefix join wildcard realpath abspath error warning shell origin flavor foreach if or and call eval file value"},c:[i]},n={b:"^"+e.UIR+"\\s*[:+?]?=",i:"\\n",rB:!0,c:[{b:"^"+e.UIR,e:"[:+?]?=",eE:!0}]},t={cN:"meta",b:/^\.PHONY:/,e:/$/,k:{"meta-keyword":".PHONY"},l:/[\.\w]+/},l={cN:"section",b:/^[^\s]+:/,e:/$/,c:[i]};return{aliases:["mk","mak"],k:"define endef undefine ifdef ifndef ifeq ifneq else endif include -include sinclude override export unexport private vpath",l:/[\w-]+/,c:[e.HCM,i,r,a,n,t,l]}});hljs.registerLanguage("java",function(e){var a="[À-ʸa-zA-Z_$][À-ʸa-zA-Z_$0-9]*",t=a+"(<"+a+"(\\s*,\\s*"+a+")*>)?",r="false synchronized int abstract float private char boolean static null if const for true while long strictfp finally protected import native final void enum else break transient catch instanceof byte super volatile case assert short package default double public try this switch continue throws protected public private module requires exports do",s="\\b(0[bB]([01]+[01_]+[01]+|[01]+)|0[xX]([a-fA-F0-9]+[a-fA-F0-9_]+[a-fA-F0-9]+|[a-fA-F0-9]+)|(([\\d]+[\\d_]+[\\d]+|[\\d]+)(\\.([\\d]+[\\d_]+[\\d]+|[\\d]+))?|\\.([\\d]+[\\d_]+[\\d]+|[\\d]+))([eE][-+]?\\d+)?)[lLfF]?",c={cN:"number",b:s,r:0};return{aliases:["jsp"],k:r,i:/<\/|#/,c:[e.C("/\\*\\*","\\*/",{r:0,c:[{b:/\w+@/,r:0},{cN:"doctag",b:"@[A-Za-z]+"}]}),e.CLCM,e.CBCM,e.ASM,e.QSM,{cN:"class",bK:"class interface",e:/[{;=]/,eE:!0,k:"class interface",i:/[:"\[\]]/,c:[{bK:"extends implements"},e.UTM]},{bK:"new throw return else",r:0},{cN:"function",b:"("+t+"\\s+)+"+e.UIR+"\\s*\\(",rB:!0,e:/[{;=]/,eE:!0,k:r,c:[{b:e.UIR+"\\s*\\(",rB:!0,r:0,c:[e.UTM]},{cN:"params",b:/\(/,e:/\)/,k:r,r:0,c:[e.ASM,e.QSM,e.CNM,e.CBCM]},e.CLCM,e.CBCM]},c,{cN:"meta",b:"@[A-Za-z]+"}]}});hljs.registerLanguage("stan",function(e){return{c:[e.HCM,e.CLCM,e.CBCM,{b:e.UIR,l:e.UIR,k:{name:"for in while repeat until if then else",symbol:"bernoulli bernoulli_logit binomial binomial_logit beta_binomial hypergeometric categorical categorical_logit ordered_logistic neg_binomial neg_binomial_2 neg_binomial_2_log poisson poisson_log multinomial normal exp_mod_normal skew_normal student_t cauchy double_exponential logistic gumbel lognormal chi_square inv_chi_square scaled_inv_chi_square exponential inv_gamma weibull frechet rayleigh wiener pareto pareto_type_2 von_mises uniform multi_normal multi_normal_prec multi_normal_cholesky multi_gp multi_gp_cholesky multi_student_t gaussian_dlm_obs dirichlet lkj_corr lkj_corr_cholesky wishart inv_wishart","selector-tag":"int real vector simplex unit_vector ordered positive_ordered row_vector matrix cholesky_factor_corr cholesky_factor_cov corr_matrix cov_matrix",title:"functions model data parameters quantities transformed generated",literal:"true false"},r:0},{cN:"number",b:"0[xX][0-9a-fA-F]+[Li]?\\b",r:0},{cN:"number",b:"0[xX][0-9a-fA-F]+[Li]?\\b",r:0},{cN:"number",b:"\\d+(?:[eE][+\\-]?\\d*)?L\\b",r:0},{cN:"number",b:"\\d+\\.(?!\\d)(?:i\\b)?",r:0},{cN:"number",b:"\\d+(?:\\.\\d*)?(?:[eE][+\\-]?\\d*)?i?\\b",r:0},{cN:"number",b:"\\.\\d+(?:[eE][+\\-]?\\d*)?i?\\b",r:0}]}});hljs.registerLanguage("javascript",function(e){var r="[A-Za-z$_][0-9A-Za-z$_]*",t={keyword:"in of if for while finally var new function do return void else break catch instanceof with throw case default try this switch continue typeof delete let yield const export super debugger as async await static import from as",literal:"true false null undefined NaN Infinity",built_in:"eval isFinite isNaN parseFloat parseInt decodeURI decodeURIComponent encodeURI encodeURIComponent escape unescape Object Function Boolean Error EvalError InternalError RangeError ReferenceError StopIteration SyntaxError TypeError URIError Number Math Date String RegExp Array Float32Array Float64Array Int16Array Int32Array Int8Array Uint16Array Uint32Array Uint8Array Uint8ClampedArray ArrayBuffer DataView JSON Intl arguments require module console window document Symbol Set Map WeakSet WeakMap Proxy Reflect Promise"},a={cN:"number",v:[{b:"\\b(0[bB][01]+)"},{b:"\\b(0[oO][0-7]+)"},{b:e.CNR}],r:0},n={cN:"subst",b:"\\$\\{",e:"\\}",k:t,c:[]},c={cN:"string",b:"`",e:"`",c:[e.BE,n]};n.c=[e.ASM,e.QSM,c,a,e.RM];var s=n.c.concat([e.CBCM,e.CLCM]);return{aliases:["js","jsx"],k:t,c:[{cN:"meta",r:10,b:/^\s*['"]use (strict|asm)['"]/},{cN:"meta",b:/^#!/,e:/$/},e.ASM,e.QSM,c,e.CLCM,e.CBCM,a,{b:/[{,]\s*/,r:0,c:[{b:r+"\\s*:",rB:!0,r:0,c:[{cN:"attr",b:r,r:0}]}]},{b:"("+e.RSR+"|\\b(case|return|throw)\\b)\\s*",k:"return throw case",c:[e.CLCM,e.CBCM,e.RM,{cN:"function",b:"(\\(.*?\\)|"+r+")\\s*=>",rB:!0,e:"\\s*=>",c:[{cN:"params",v:[{b:r},{b:/\(\s*\)/},{b:/\(/,e:/\)/,eB:!0,eE:!0,k:t,c:s}]}]},{b:/</,e:/(\/\w+|\w+\/)>/,sL:"xml",c:[{b:/<\w+\s*\/>/,skip:!0},{b:/<\w+/,e:/(\/\w+|\w+\/)>/,skip:!0,c:[{b:/<\w+\s*\/>/,skip:!0},"self"]}]}],r:0},{cN:"function",bK:"function",e:/\{/,eE:!0,c:[e.inherit(e.TM,{b:r}),{cN:"params",b:/\(/,e:/\)/,eB:!0,eE:!0,c:s}],i:/\[|%/},{b:/\$[(.]/},e.METHOD_GUARD,{cN:"class",bK:"class",e:/[{;=]/,eE:!0,i:/[:"\[\]]/,c:[{bK:"extends"},e.UTM]},{bK:"constructor",e:/\{/,eE:!0}],i:/#(?!!)/}});hljs.registerLanguage("tex",function(c){var e={cN:"tag",b:/\\/,r:0,c:[{cN:"name",v:[{b:/[a-zA-Zа-яА-я]+[*]?/},{b:/[^a-zA-Zа-яА-я0-9]/}],starts:{eW:!0,r:0,c:[{cN:"string",v:[{b:/\[/,e:/\]/},{b:/\{/,e:/\}/}]},{b:/\s*=\s*/,eW:!0,r:0,c:[{cN:"number",b:/-?\d*\.?\d+(pt|pc|mm|cm|in|dd|cc|ex|em)?/}]}]}}]};return{c:[e,{cN:"formula",c:[e],r:0,v:[{b:/\$\$/,e:/\$\$/},{b:/\$/,e:/\$/}]},c.C("%","$",{r:0})]}});hljs.registerLanguage("xml",function(s){var e="[A-Za-z0-9\\._:-]+",t={eW:!0,i:/</,r:0,c:[{cN:"attr",b:e,r:0},{b:/=\s*/,r:0,c:[{cN:"string",endsParent:!0,v:[{b:/"/,e:/"/},{b:/'/,e:/'/},{b:/[^\s"'=<>`]+/}]}]}]};return{aliases:["html","xhtml","rss","atom","xjb","xsd","xsl","plist"],cI:!0,c:[{cN:"meta",b:"<!DOCTYPE",e:">",r:10,c:[{b:"\\[",e:"\\]"}]},s.C("<!--","-->",{r:10}),{b:"<\\!\\[CDATA\\[",e:"\\]\\]>",r:10},{b:/<\?(php)?/,e:/\?>/,sL:"php",c:[{b:"/\\*",e:"\\*/",skip:!0}]},{cN:"tag",b:"<style(?=\\s|>|$)",e:">",k:{name:"style"},c:[t],starts:{e:"</style>",rE:!0,sL:["css","xml"]}},{cN:"tag",b:"<script(?=\\s|>|$)",e:">",k:{name:"script"},c:[t],starts:{e:"</script>",rE:!0,sL:["actionscript","javascript","handlebars","xml"]}},{cN:"meta",v:[{b:/<\?xml/,e:/\?>/,r:10},{b:/<\?\w+/,e:/\?>/}]},{cN:"tag",b:"</?",e:"/?>",c:[{cN:"name",b:/[^\/><\s]+/,r:0},t]}]}});hljs.registerLanguage("markdown",function(e){return{aliases:["md","mkdown","mkd"],c:[{cN:"section",v:[{b:"^#{1,6}",e:"$"},{b:"^.+?\\n[=-]{2,}$"}]},{b:"<",e:">",sL:"xml",r:0},{cN:"bullet",b:"^([*+-]|(\\d+\\.))\\s+"},{cN:"strong",b:"[*_]{2}.+?[*_]{2}"},{cN:"emphasis",v:[{b:"\\*.+?\\*"},{b:"_.+?_",r:0}]},{cN:"quote",b:"^>\\s+",e:"$"},{cN:"code",v:[{b:"^```w*s*$",e:"^```s*$"},{b:"`.+?`"},{b:"^( {4}|	)",e:"$",r:0}]},{b:"^[-\\*]{3,}",e:"$"},{b:"\\[.+?\\][\\(\\[].*?[\\)\\]]",rB:!0,c:[{cN:"string",b:"\\[",e:"\\]",eB:!0,rE:!0,r:0},{cN:"link",b:"\\]\\(",e:"\\)",eB:!0,eE:!0},{cN:"symbol",b:"\\]\\[",e:"\\]",eB:!0,eE:!0}],r:10},{b:/^\[[^\n]+\]:/,rB:!0,c:[{cN:"symbol",b:/\[/,e:/\]/,eB:!0,eE:!0},{cN:"link",b:/:\s*/,e:/$/,eB:!0}]}]}});hljs.registerLanguage("json",function(e){var i={literal:"true false null"},n=[e.QSM,e.CNM],r={e:",",eW:!0,eE:!0,c:n,k:i},t={b:"{",e:"}",c:[{cN:"attr",b:/"/,e:/"/,c:[e.BE],i:"\\n"},e.inherit(r,{b:/:/})],i:"\\S"},c={b:"\\[",e:"\\]",c:[e.inherit(r)],i:"\\S"};return n.splice(n.length,0,t,c),{c:n,k:i,i:"\\S"}});"></script>
+
+<style type="text/css">
+  code{white-space: pre-wrap;}
+  span.smallcaps{font-variant: small-caps;}
+  span.underline{text-decoration: underline;}
+  div.column{display: inline-block; vertical-align: top; width: 50%;}
+  div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;}
+  ul.task-list{list-style: none;}
+    </style>
+
+<style type="text/css">code{white-space: pre;}</style>
+<script type="text/javascript">
+if (window.hljs) {
+  hljs.configure({languages: []});
+  hljs.initHighlightingOnLoad();
+  if (document.readyState && document.readyState === "complete") {
+    window.setTimeout(function() { hljs.initHighlighting(); }, 0);
+  }
+}
+</script>
+
+
+
+
+
+<style type="text/css">
+/* for pandoc --citeproc since 2.11 */
+div.csl-bib-body { }
+div.csl-entry {
+  clear: both;
+}
+.hanging div.csl-entry {
+  margin-left:2em;
+  text-indent:-2em;
+}
+div.csl-left-margin {
+  min-width:2em;
+  float:left;
+}
+div.csl-right-inline {
+  margin-left:2em;
+  padding-left:1em;
+}
+div.csl-indent {
+  margin-left: 2em;
+}
+</style>
+
+
+
+
+<style type="text/css">
+.main-container {
+  max-width: 940px;
+  margin-left: auto;
+  margin-right: auto;
+}
+img {
+  max-width:100%;
+}
+.tabbed-pane {
+  padding-top: 12px;
+}
+.html-widget {
+  margin-bottom: 20px;
+}
+button.code-folding-btn:focus {
+  outline: none;
+}
+summary {
+  display: list-item;
+}
+details > summary > p:only-child {
+  display: inline;
+}
+pre code {
+  padding: 0;
+}
+</style>
+
+
+
+<!-- tabsets -->
+
+<style type="text/css">
+.tabset-dropdown > .nav-tabs {
+  display: inline-table;
+  max-height: 500px;
+  min-height: 44px;
+  overflow-y: auto;
+  border: 1px solid #ddd;
+  border-radius: 4px;
+}
+
+.tabset-dropdown > .nav-tabs > li.active:before, .tabset-dropdown > .nav-tabs.nav-tabs-open:before {
+  content: "\e259";
+  font-family: 'Glyphicons Halflings';
+  display: inline-block;
+  padding: 10px;
+  border-right: 1px solid #ddd;
+}
+
+.tabset-dropdown > .nav-tabs.nav-tabs-open > li.active:before {
+  content: "\e258";
+  font-family: 'Glyphicons Halflings';
+  border: none;
+}
+
+.tabset-dropdown > .nav-tabs > li.active {
+  display: block;
+}
+
+.tabset-dropdown > .nav-tabs > li > a,
+.tabset-dropdown > .nav-tabs > li > a:focus,
+.tabset-dropdown > .nav-tabs > li > a:hover {
+  border: none;
+  display: inline-block;
+  border-radius: 4px;
+  background-color: transparent;
+}
+
+.tabset-dropdown > .nav-tabs.nav-tabs-open > li {
+  display: block;
+  float: none;
+}
+
+.tabset-dropdown > .nav-tabs > li {
+  display: none;
+}
+</style>
+
+<!-- code folding -->
+
+
+
+
 </head>
-<body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-article">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav">
-<li>
-  <a href="../../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu">
-<li>
-      <a href="../../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul>
-</li>
-<li>
-  <a href="../../news/index.html">News</a>
-</li>
-      </ul>
-<ul class="nav navbar-nav navbar-right">
-<li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul>
-</div>
-<!--/.nav-collapse -->
-  </div>
-<!--/.container -->
-</div>
-<!--/.navbar -->
 
-      
+<body>
+
 
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header toc-ignore">
-      <h1 data-toc-skip>Testing hierarchical pathway kinetics with
+<div class="container-fluid main-container">
+
+
+
+
+<div id="header">
+
+
+
+<h1 class="title toc-ignore">Testing hierarchical pathway kinetics with
 residue data on dimethenamid and dimethenamid-P</h1>
-                        <h4 data-toc-skip class="author">Johannes
-Ranke</h4>
-            
-            <h4 data-toc-skip class="date">Last change on 20 April 2023,
-last compiled on 30 October 2023</h4>
-      
-      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/prebuilt/2022_dmta_pathway.rmd" class="external-link"><code>vignettes/prebuilt/2022_dmta_pathway.rmd</code></a></small>
-      <div class="hidden name"><code>2022_dmta_pathway.rmd</code></div>
-
-    </div>
-
-    
-    
-<div class="section level2">
-<h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a>
-</h2>
+<h4 class="author">Johannes Ranke</h4>
+<h4 class="date">Last change on 20 April 2023, last compiled on 13
+Februar 2025</h4>
+
+</div>
+
+<div id="TOC">
+true
+</div>
+
+<div id="introduction" class="section level1">
+<h1>Introduction</h1>
 <p>The purpose of this document is to test demonstrate how nonlinear
 hierarchical models (NLHM) based on the parent degradation models SFO,
 FOMC, DFOP and HS, with parallel formation of two or more metabolites
@@ -158,7 +399,7 @@ can be fitted with the mkin package.</p>
 173340 (Application of nonlinear hierarchical models to the kinetic
 evaluation of chemical degradation data) of the German Environment
 Agency carried out in 2022 and 2023.</p>
-<p>The mkin package is used in version 1.2.6, which is currently under
+<p>The mkin package is used in version 1.2.9, which is currently under
 development. It contains the test data, and the functions used in the
 evaluations. The <code>saemix</code> package is used as a backend for
 fitting the NLHM, but is also loaded to make the convergence plot
@@ -167,28 +408,26 @@ function available.</p>
 also provides the <code>kable</code> function that is used to improve
 the display of tabular data in R markdown documents. For parallel
 processing, the <code>parallel</code> package is used.</p>
-<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://pkgdown.jrwb.de/mkin/">mkin</a></span><span class="op">)</span></span>
-<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://yihui.org/knitr/" class="external-link">knitr</a></span><span class="op">)</span></span>
-<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va">saemix</span><span class="op">)</span></span>
-<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va">parallel</span><span class="op">)</span></span>
-<span><span class="va">n_cores</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/parallel/detectCores.html" class="external-link">detectCores</a></span><span class="op">(</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># We need to start a new cluster after defining a compiled model that is</span></span>
-<span><span class="co"># saved as a DLL to the user directory, therefore we define a function</span></span>
-<span><span class="co"># This is used again after defining the pathway model</span></span>
-<span><span class="va">start_cluster</span> <span class="op">&lt;-</span> <span class="kw">function</span><span class="op">(</span><span class="va">n_cores</span><span class="op">)</span> <span class="op">{</span></span>
-<span>  <span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/Sys.info.html" class="external-link">Sys.info</a></span><span class="op">(</span><span class="op">)</span><span class="op">[</span><span class="st">"sysname"</span><span class="op">]</span> <span class="op">==</span> <span class="st">"Windows"</span><span class="op">)</span> <span class="op">{</span></span>
-<span>    <span class="va">ret</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/parallel/makeCluster.html" class="external-link">makePSOCKcluster</a></span><span class="op">(</span><span class="va">n_cores</span><span class="op">)</span></span>
-<span>  <span class="op">}</span> <span class="kw">else</span> <span class="op">{</span></span>
-<span>    <span class="va">ret</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/parallel/makeCluster.html" class="external-link">makeForkCluster</a></span><span class="op">(</span><span class="va">n_cores</span><span class="op">)</span></span>
-<span>  <span class="op">}</span></span>
-<span>  <span class="kw"><a href="https://rdrr.io/r/base/function.html" class="external-link">return</a></span><span class="op">(</span><span class="va">ret</span><span class="op">)</span></span>
-<span><span class="op">}</span></span></code></pre></div>
+<pre class="r"><code>library(mkin)
+library(knitr)
+library(saemix)
+library(parallel)
+n_cores &lt;- detectCores()
+
+# We need to start a new cluster after defining a compiled model that is
+# saved as a DLL to the user directory, therefore we define a function
+# This is used again after defining the pathway model
+start_cluster &lt;- function(n_cores) {
+  if (Sys.info()[&quot;sysname&quot;] == &quot;Windows&quot;) {
+    ret &lt;- makePSOCKcluster(n_cores)
+  } else {
+    ret &lt;- makeForkCluster(n_cores)
+  }
+  return(ret)
+}</code></pre>
 </div>
-<div class="section level2">
-<h2 id="data">Data<a class="anchor" aria-label="anchor" href="#data"></a>
-</h2>
+<div id="data" class="section level1">
+<h1>Data</h1>
 <p>The test data are available in the mkin package as an object of class
 <code>mkindsg</code> (mkin dataset group) under the identifier
 <code>dimethenamid_2018</code>. The following preprocessing steps are
@@ -209,41 +448,41 @@ and <code>Elliot 2</code>) are combined, resulting in dimethenamid
 (DMTA) data from six soils.</li>
 </ul>
 <p>The following commented R code performs this preprocessing.</p>
-<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="co"># Apply a function to each of the seven datasets in the mkindsg object to create a list</span></span>
-<span><span class="va">dmta_ds</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fl">7</span>, <span class="kw">function</span><span class="op">(</span><span class="va">i</span><span class="op">)</span> <span class="op">{</span></span>
-<span>  <span class="va">ds_i</span> <span class="op">&lt;-</span> <span class="va">dimethenamid_2018</span><span class="op">$</span><span class="va">ds</span><span class="op">[[</span><span class="va">i</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">data</span>                     <span class="co"># Get a dataset</span></span>
-<span>  <span class="va">ds_i</span><span class="op">[</span><span class="va">ds_i</span><span class="op">$</span><span class="va">name</span> <span class="op">==</span> <span class="st">"DMTAP"</span>, <span class="st">"name"</span><span class="op">]</span> <span class="op">&lt;-</span>  <span class="st">"DMTA"</span>              <span class="co"># Rename DMTAP to DMTA</span></span>
-<span>  <span class="va">ds_i</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/subset.html" class="external-link">subset</a></span><span class="op">(</span><span class="va">ds_i</span>, select <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"name"</span>, <span class="st">"time"</span>, <span class="st">"value"</span><span class="op">)</span><span class="op">)</span> <span class="co"># Select data</span></span>
-<span>  <span class="va">ds_i</span><span class="op">$</span><span class="va">time</span> <span class="op">&lt;-</span> <span class="va">ds_i</span><span class="op">$</span><span class="va">time</span> <span class="op">*</span> <span class="va">dimethenamid_2018</span><span class="op">$</span><span class="va">f_time_norm</span><span class="op">[</span><span class="va">i</span><span class="op">]</span>  <span class="co"># Normalise time</span></span>
-<span>  <span class="va">ds_i</span>                                                       <span class="co"># Return the dataset</span></span>
-<span><span class="op">}</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># Use dataset titles as names for the list elements</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">dmta_ds</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">sapply</a></span><span class="op">(</span><span class="va">dimethenamid_2018</span><span class="op">$</span><span class="va">ds</span>, <span class="kw">function</span><span class="op">(</span><span class="va">ds</span><span class="op">)</span> <span class="va">ds</span><span class="op">$</span><span class="va">title</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># Combine data for Elliot soil to obtain a named list with six elements</span></span>
-<span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot"</span><span class="op">]</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/cbind.html" class="external-link">rbind</a></span><span class="op">(</span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 1"</span><span class="op">]</span><span class="op">]</span>, <span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 2"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span> <span class="co">#</span></span>
-<span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 1"</span><span class="op">]</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="cn">NULL</span></span>
-<span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 2"</span><span class="op">]</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="cn">NULL</span></span></code></pre></div>
+<pre class="r"><code># Apply a function to each of the seven datasets in the mkindsg object to create a list
+dmta_ds &lt;- lapply(1:7, function(i) {
+  ds_i &lt;- dimethenamid_2018$ds[[i]]$data                     # Get a dataset
+  ds_i[ds_i$name == &quot;DMTAP&quot;, &quot;name&quot;] &lt;-  &quot;DMTA&quot;              # Rename DMTAP to DMTA
+  ds_i &lt;- subset(ds_i, select = c(&quot;name&quot;, &quot;time&quot;, &quot;value&quot;)) # Select data
+  ds_i$time &lt;- ds_i$time * dimethenamid_2018$f_time_norm[i]  # Normalise time
+  ds_i                                                       # Return the dataset
+})
+
+# Use dataset titles as names for the list elements
+names(dmta_ds) &lt;- sapply(dimethenamid_2018$ds, function(ds) ds$title)
+
+# Combine data for Elliot soil to obtain a named list with six elements
+dmta_ds[[&quot;Elliot&quot;]] &lt;- rbind(dmta_ds[[&quot;Elliot 1&quot;]], dmta_ds[[&quot;Elliot 2&quot;]]) #
+dmta_ds[[&quot;Elliot 1&quot;]] &lt;- NULL
+dmta_ds[[&quot;Elliot 2&quot;]] &lt;- NULL</code></pre>
 <p>The following tables show the 6 datasets.</p>
-<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="kw">for</span> <span class="op">(</span><span class="va">ds_name</span> <span class="kw">in</span> <span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">dmta_ds</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
-<span>  <span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span></span>
-<span>    <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="fu"><a href="../../reference/mkin_long_to_wide.html">mkin_long_to_wide</a></span><span class="op">(</span><span class="va">dmta_ds</span><span class="op">[[</span><span class="va">ds_name</span><span class="op">]</span><span class="op">]</span><span class="op">)</span>,</span>
-<span>      caption <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste</a></span><span class="op">(</span><span class="st">"Dataset"</span>, <span class="va">ds_name</span><span class="op">)</span>,</span>
-<span>      booktabs <span class="op">=</span> <span class="cn">TRUE</span>, row.names <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span><span class="op">)</span></span>
-<span>    <span class="fu"><a href="https://rdrr.io/r/base/cat.html" class="external-link">cat</a></span><span class="op">(</span><span class="st">"\n\\clearpage\n"</span><span class="op">)</span></span>
-<span><span class="op">}</span></span></code></pre></div>
-<table class="table">
+<pre class="r"><code>for (ds_name in names(dmta_ds)) {
+  print(
+    kable(mkin_long_to_wide(dmta_ds[[ds_name]]),
+      caption = paste(&quot;Dataset&quot;, ds_name),
+      booktabs = TRUE, row.names = FALSE))
+    cat(&quot;\n\\clearpage\n&quot;)
+}</code></pre>
+<table>
 <caption>Dataset Calke</caption>
-<thead><tr class="header">
+<thead>
+<tr class="header">
 <th align="right">time</th>
 <th align="right">DMTA</th>
 <th align="right">M23</th>
 <th align="right">M27</th>
 <th align="right">M31</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="right">0</td>
@@ -296,15 +535,17 @@ and <code>Elliot 2</code>) are combined, resulting in dimethenamid
 </tr>
 </tbody>
 </table>
-<table class="table">
+<table>
 <caption>Dataset Borstel</caption>
-<thead><tr class="header">
+<thead>
+<tr class="header">
 <th align="right">time</th>
 <th align="right">DMTA</th>
 <th align="right">M23</th>
 <th align="right">M27</th>
 <th align="right">M31</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="right">0.000000</td>
@@ -420,15 +661,17 @@ and <code>Elliot 2</code>) are combined, resulting in dimethenamid
 </tr>
 </tbody>
 </table>
-<table class="table">
+<table>
 <caption>Dataset Flaach</caption>
-<thead><tr class="header">
+<thead>
+<tr class="header">
 <th align="right">time</th>
 <th align="right">DMTA</th>
 <th align="right">M23</th>
 <th align="right">M27</th>
 <th align="right">M31</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="right">0.0000000</td>
@@ -684,15 +927,17 @@ and <code>Elliot 2</code>) are combined, resulting in dimethenamid
 </tr>
 </tbody>
 </table>
-<table class="table">
+<table>
 <caption>Dataset BBA 2.2</caption>
-<thead><tr class="header">
+<thead>
+<tr class="header">
 <th align="right">time</th>
 <th align="right">DMTA</th>
 <th align="right">M23</th>
 <th align="right">M27</th>
 <th align="right">M31</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="right">0.0000000</td>
@@ -864,15 +1109,17 @@ and <code>Elliot 2</code>) are combined, resulting in dimethenamid
 </tr>
 </tbody>
 </table>
-<table class="table">
+<table>
 <caption>Dataset BBA 2.3</caption>
-<thead><tr class="header">
+<thead>
+<tr class="header">
 <th align="right">time</th>
 <th align="right">DMTA</th>
 <th align="right">M23</th>
 <th align="right">M27</th>
 <th align="right">M31</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="right">0.0000000</td>
@@ -1044,15 +1291,17 @@ and <code>Elliot 2</code>) are combined, resulting in dimethenamid
 </tr>
 </tbody>
 </table>
-<table class="table">
+<table>
 <caption>Dataset Elliot</caption>
-<thead><tr class="header">
+<thead>
+<tr class="header">
 <th align="right">time</th>
 <th align="right">DMTA</th>
 <th align="right">M23</th>
 <th align="right">M27</th>
 <th align="right">M31</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="right">0.000000</td>
@@ -1393,9 +1642,8 @@ and <code>Elliot 2</code>) are combined, resulting in dimethenamid
 </tbody>
 </table>
 </div>
-<div class="section level2">
-<h2 id="separate-evaluations">Separate evaluations<a class="anchor" aria-label="anchor" href="#separate-evaluations"></a>
-</h2>
+<div id="separate-evaluations" class="section level1">
+<h1>Separate evaluations</h1>
 <p>As a first step to obtain suitable starting parameters for the NLHM
 fits, we do separate fits of several variants of the pathway model used
 previously <span class="citation">(Ranke et al. 2021)</span>, varying
@@ -1403,71 +1651,71 @@ the kinetic model for the parent compound. Because the SFORB model often
 provides faster convergence than the DFOP model, and can sometimes be
 fitted where the DFOP model results in errors, it is included in the set
 of parent models tested here.</p>
-<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="kw">if</span> <span class="op">(</span><span class="op">!</span><span class="fu"><a href="https://rdrr.io/r/base/files2.html" class="external-link">dir.exists</a></span><span class="op">(</span><span class="st">"dmta_dlls"</span><span class="op">)</span><span class="op">)</span> <span class="fu"><a href="https://rdrr.io/r/base/files2.html" class="external-link">dir.create</a></span><span class="op">(</span><span class="st">"dmta_dlls"</span><span class="op">)</span></span>
-<span><span class="va">m_sfo_path_1</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span></span>
-<span>  DMTA <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"M23"</span>, <span class="st">"M27"</span>, <span class="st">"M31"</span><span class="op">)</span><span class="op">)</span>,</span>
-<span>  M23 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span>
-<span>  M27 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span>
-<span>  M31 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"M27"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span>
-<span>  name <span class="op">=</span> <span class="st">"m_sfo_path"</span>, dll_dir <span class="op">=</span> <span class="st">"dmta_dlls"</span>,</span>
-<span>  unload <span class="op">=</span> <span class="cn">TRUE</span>, overwrite <span class="op">=</span> <span class="cn">TRUE</span>,</span>
-<span>  quiet <span class="op">=</span> <span class="cn">TRUE</span></span>
-<span><span class="op">)</span></span>
-<span><span class="va">m_fomc_path_1</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span></span>
-<span>  DMTA <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"FOMC"</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"M23"</span>, <span class="st">"M27"</span>, <span class="st">"M31"</span><span class="op">)</span><span class="op">)</span>,</span>
-<span>  M23 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span>
-<span>  M27 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span>
-<span>  M31 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"M27"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span>
-<span>  name <span class="op">=</span> <span class="st">"m_fomc_path"</span>, dll_dir <span class="op">=</span> <span class="st">"dmta_dlls"</span>,</span>
-<span>  unload <span class="op">=</span> <span class="cn">TRUE</span>, overwrite <span class="op">=</span> <span class="cn">TRUE</span>,</span>
-<span>  quiet <span class="op">=</span> <span class="cn">TRUE</span></span>
-<span><span class="op">)</span></span>
-<span><span class="va">m_dfop_path_1</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span></span>
-<span>  DMTA <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"M23"</span>, <span class="st">"M27"</span>, <span class="st">"M31"</span><span class="op">)</span><span class="op">)</span>,</span>
-<span>  M23 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span>
-<span>  M27 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span>
-<span>  M31 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"M27"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span>
-<span>  name <span class="op">=</span> <span class="st">"m_dfop_path"</span>, dll_dir <span class="op">=</span> <span class="st">"dmta_dlls"</span>,</span>
-<span>  unload <span class="op">=</span> <span class="cn">TRUE</span>, overwrite <span class="op">=</span> <span class="cn">TRUE</span>,</span>
-<span>  quiet <span class="op">=</span> <span class="cn">TRUE</span></span>
-<span><span class="op">)</span></span>
-<span><span class="va">m_sforb_path_1</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span></span>
-<span>  DMTA <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFORB"</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"M23"</span>, <span class="st">"M27"</span>, <span class="st">"M31"</span><span class="op">)</span><span class="op">)</span>,</span>
-<span>  M23 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span>
-<span>  M27 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span>
-<span>  M31 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"M27"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span>
-<span>  name <span class="op">=</span> <span class="st">"m_sforb_path"</span>, dll_dir <span class="op">=</span> <span class="st">"dmta_dlls"</span>,</span>
-<span>  unload <span class="op">=</span> <span class="cn">TRUE</span>, overwrite <span class="op">=</span> <span class="cn">TRUE</span>,</span>
-<span>  quiet <span class="op">=</span> <span class="cn">TRUE</span></span>
-<span><span class="op">)</span></span>
-<span><span class="va">m_hs_path_1</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span></span>
-<span>  DMTA <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"HS"</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"M23"</span>, <span class="st">"M27"</span>, <span class="st">"M31"</span><span class="op">)</span><span class="op">)</span>,</span>
-<span>  M23 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span>
-<span>  M27 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span>
-<span>  M31 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"M27"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span>
-<span>  name <span class="op">=</span> <span class="st">"m_hs_path"</span>, dll_dir <span class="op">=</span> <span class="st">"dmta_dlls"</span>,</span>
-<span>  unload <span class="op">=</span> <span class="cn">TRUE</span>, overwrite <span class="op">=</span> <span class="cn">TRUE</span>,</span>
-<span>  quiet <span class="op">=</span> <span class="cn">TRUE</span></span>
-<span><span class="op">)</span></span>
-<span><span class="va">cl</span> <span class="op">&lt;-</span> <span class="fu">start_cluster</span><span class="op">(</span><span class="va">n_cores</span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">deg_mods_1</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span></span>
-<span>  sfo_path_1 <span class="op">=</span> <span class="va">m_sfo_path_1</span>,</span>
-<span>  fomc_path_1 <span class="op">=</span> <span class="va">m_fomc_path_1</span>,</span>
-<span>  dfop_path_1 <span class="op">=</span> <span class="va">m_dfop_path_1</span>,</span>
-<span>  sforb_path_1 <span class="op">=</span> <span class="va">m_sforb_path_1</span>,</span>
-<span>  hs_path_1 <span class="op">=</span> <span class="va">m_hs_path_1</span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">sep_1_const</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/mmkin.html">mmkin</a></span><span class="op">(</span></span>
-<span>  <span class="va">deg_mods_1</span>,</span>
-<span>  <span class="va">dmta_ds</span>,</span>
-<span>  error_model <span class="op">=</span> <span class="st">"const"</span>,</span>
-<span>  quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu"><a href="../../reference/status.html">status</a></span><span class="op">(</span><span class="va">sep_1_const</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
-<thead><tr class="header">
+<pre class="r"><code>if (!dir.exists(&quot;dmta_dlls&quot;)) dir.create(&quot;dmta_dlls&quot;)
+m_sfo_path_1 &lt;- mkinmod(
+  DMTA = mkinsub(&quot;SFO&quot;, c(&quot;M23&quot;, &quot;M27&quot;, &quot;M31&quot;)),
+  M23 = mkinsub(&quot;SFO&quot;),
+  M27 = mkinsub(&quot;SFO&quot;),
+  M31 = mkinsub(&quot;SFO&quot;, &quot;M27&quot;, sink = FALSE),
+  name = &quot;m_sfo_path&quot;, dll_dir = &quot;dmta_dlls&quot;,
+  unload = TRUE, overwrite = TRUE,
+  quiet = TRUE
+)
+m_fomc_path_1 &lt;- mkinmod(
+  DMTA = mkinsub(&quot;FOMC&quot;, c(&quot;M23&quot;, &quot;M27&quot;, &quot;M31&quot;)),
+  M23 = mkinsub(&quot;SFO&quot;),
+  M27 = mkinsub(&quot;SFO&quot;),
+  M31 = mkinsub(&quot;SFO&quot;, &quot;M27&quot;, sink = FALSE),
+  name = &quot;m_fomc_path&quot;, dll_dir = &quot;dmta_dlls&quot;,
+  unload = TRUE, overwrite = TRUE,
+  quiet = TRUE
+)
+m_dfop_path_1 &lt;- mkinmod(
+  DMTA = mkinsub(&quot;DFOP&quot;, c(&quot;M23&quot;, &quot;M27&quot;, &quot;M31&quot;)),
+  M23 = mkinsub(&quot;SFO&quot;),
+  M27 = mkinsub(&quot;SFO&quot;),
+  M31 = mkinsub(&quot;SFO&quot;, &quot;M27&quot;, sink = FALSE),
+  name = &quot;m_dfop_path&quot;, dll_dir = &quot;dmta_dlls&quot;,
+  unload = TRUE, overwrite = TRUE,
+  quiet = TRUE
+)
+m_sforb_path_1 &lt;- mkinmod(
+  DMTA = mkinsub(&quot;SFORB&quot;, c(&quot;M23&quot;, &quot;M27&quot;, &quot;M31&quot;)),
+  M23 = mkinsub(&quot;SFO&quot;),
+  M27 = mkinsub(&quot;SFO&quot;),
+  M31 = mkinsub(&quot;SFO&quot;, &quot;M27&quot;, sink = FALSE),
+  name = &quot;m_sforb_path&quot;, dll_dir = &quot;dmta_dlls&quot;,
+  unload = TRUE, overwrite = TRUE,
+  quiet = TRUE
+)
+m_hs_path_1 &lt;- mkinmod(
+  DMTA = mkinsub(&quot;HS&quot;, c(&quot;M23&quot;, &quot;M27&quot;, &quot;M31&quot;)),
+  M23 = mkinsub(&quot;SFO&quot;),
+  M27 = mkinsub(&quot;SFO&quot;),
+  M31 = mkinsub(&quot;SFO&quot;, &quot;M27&quot;, sink = FALSE),
+  name = &quot;m_hs_path&quot;, dll_dir = &quot;dmta_dlls&quot;,
+  unload = TRUE, overwrite = TRUE,
+  quiet = TRUE
+)
+cl &lt;- start_cluster(n_cores)
+
+deg_mods_1 &lt;- list(
+  sfo_path_1 = m_sfo_path_1,
+  fomc_path_1 = m_fomc_path_1,
+  dfop_path_1 = m_dfop_path_1,
+  sforb_path_1 = m_sforb_path_1,
+  hs_path_1 = m_hs_path_1)
+
+sep_1_const &lt;- mmkin(
+  deg_mods_1,
+  dmta_ds,
+  error_model = &quot;const&quot;,
+  quiet = TRUE)
+
+status(sep_1_const) |&gt; kable()</code></pre>
+<table>
+<thead>
+<tr class="header">
 <th align="left"></th>
 <th align="left">Calke</th>
 <th align="left">Borstel</th>
@@ -1475,7 +1723,8 @@ of parent models tested here.</p>
 <th align="left">BBA 2.2</th>
 <th align="left">BBA 2.3</th>
 <th align="left">Elliot</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="left">sfo_path_1</td>
@@ -1528,11 +1777,11 @@ of parent models tested here.</p>
 constant variance converged (status OK). Most fits with DFOP or SFORB
 for the parent converged as well. The fits with HS for the parent did
 not converge with default settings.</p>
-<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">sep_1_tc</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">sep_1_const</span>, error_model <span class="op">=</span> <span class="st">"tc"</span><span class="op">)</span></span>
-<span><span class="fu"><a href="../../reference/status.html">status</a></span><span class="op">(</span><span class="va">sep_1_tc</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
-<thead><tr class="header">
+<pre class="r"><code>sep_1_tc &lt;- update(sep_1_const, error_model = &quot;tc&quot;)
+status(sep_1_tc) |&gt; kable()</code></pre>
+<table>
+<thead>
+<tr class="header">
 <th align="left"></th>
 <th align="left">Calke</th>
 <th align="left">Borstel</th>
@@ -1540,7 +1789,8 @@ not converge with default settings.</p>
 <th align="left">BBA 2.2</th>
 <th align="left">BBA 2.3</th>
 <th align="left">Elliot</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="left">sfo_path_1</td>
@@ -1595,25 +1845,24 @@ different data sets when applying the DFOP and SFORB model and some
 additional convergence problems when using the FOMC model for the
 parent.</p>
 </div>
-<div class="section level2">
-<h2 id="hierarchichal-model-fits">Hierarchichal model fits<a class="anchor" aria-label="anchor" href="#hierarchichal-model-fits"></a>
-</h2>
+<div id="hierarchichal-model-fits" class="section level1">
+<h1>Hierarchichal model fits</h1>
 <p>The following code fits two sets of the corresponding hierarchical
 models to the data, one assuming constant variance, and one assuming
 two-component error.</p>
-<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">saem_1</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/mhmkin.html">mhmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">sep_1_const</span>, <span class="va">sep_1_tc</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
+<pre class="r"><code>saem_1 &lt;- mhmkin(list(sep_1_const, sep_1_tc))</code></pre>
 <p>The run time for these fits was around two hours on five year old
 hardware. After a recent hardware upgrade these fits complete in less
 than twenty minutes.</p>
-<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="../../reference/status.html">status</a></span><span class="op">(</span><span class="va">saem_1</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
-<thead><tr class="header">
+<pre class="r"><code>status(saem_1) |&gt; kable()</code></pre>
+<table>
+<thead>
+<tr class="header">
 <th align="left"></th>
 <th align="left">const</th>
 <th align="left">tc</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="left">sfo_path_1</td>
@@ -1644,16 +1893,17 @@ than twenty minutes.</p>
 </table>
 <p>According to the <code>status</code> function, all fits terminated
 successfully.</p>
-<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">saem_1</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span>digits <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
-<thead><tr class="header">
+<pre class="r"><code>anova(saem_1) |&gt; kable(digits = 1)</code></pre>
+<table>
+<thead>
+<tr class="header">
 <th align="left"></th>
 <th align="right">npar</th>
 <th align="right">AIC</th>
 <th align="right">BIC</th>
 <th align="right">Lik</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="left">sfo_path_1 const</td>
@@ -1740,22 +1990,22 @@ assume a discontinuity, so the SFORB model is preferable from a
 mechanistic viewpoint. In addition, the information criteria AIC and BIC
 are very similar for HS and SFORB. Therefore, the SFORB model is
 selected here for further refinements.</p>
-<div class="section level3">
-<h3 id="parameter-identifiability-based-on-the-fisher-information-matrix">Parameter identifiability based on the Fisher Information
-Matrix<a class="anchor" aria-label="anchor" href="#parameter-identifiability-based-on-the-fisher-information-matrix"></a>
-</h3>
+<div id="parameter-identifiability-based-on-the-fisher-information-matrix" class="section level2">
+<h2>Parameter identifiability based on the Fisher Information
+Matrix</h2>
 <p>Using the <code>illparms</code> function, ill-defined statistical
 model parameters such as standard deviations of the degradation
 parameters in the population and error model parameters can be
 found.</p>
-<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="../../reference/illparms.html">illparms</a></span><span class="op">(</span><span class="va">saem_1</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
-<thead><tr class="header">
+<pre class="r"><code>illparms(saem_1) |&gt; kable()</code></pre>
+<table>
+<thead>
+<tr class="header">
 <th align="left"></th>
 <th align="left">const</th>
 <th align="left">tc</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="left">sfo_path_1</td>
@@ -1794,22 +2044,22 @@ two-component error, the random effect for the rate constant from
 reversibly bound DMTA to the free DMTA (<code>k_DMTA_bound_free</code>)
 is not well-defined. Therefore, the fit is updated without assuming a
 random effect for this parameter.</p>
-<div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">saem_sforb_path_1_tc_reduced</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">saem_1</span><span class="op">[[</span><span class="st">"sforb_path_1"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span>,</span>
-<span>  no_random_effect <span class="op">=</span> <span class="st">"log_k_DMTA_bound_free"</span><span class="op">)</span></span>
-<span><span class="fu"><a href="../../reference/illparms.html">illparms</a></span><span class="op">(</span><span class="va">saem_sforb_path_1_tc_reduced</span><span class="op">)</span></span></code></pre></div>
+<pre class="r"><code>saem_sforb_path_1_tc_reduced &lt;- update(saem_1[[&quot;sforb_path_1&quot;, &quot;tc&quot;]],
+  no_random_effect = &quot;log_k_DMTA_bound_free&quot;)
+illparms(saem_sforb_path_1_tc_reduced)</code></pre>
 <p>As expected, no ill-defined parameters remain. The model comparison
 below shows that the reduced model is preferable.</p>
-<div class="sourceCode" id="cb11"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">saem_1</span><span class="op">[[</span><span class="st">"sforb_path_1"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span>, <span class="va">saem_sforb_path_1_tc_reduced</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span>digits <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
-<thead><tr class="header">
+<pre class="r"><code>anova(saem_1[[&quot;sforb_path_1&quot;, &quot;tc&quot;]], saem_sforb_path_1_tc_reduced) |&gt; kable(digits = 1)</code></pre>
+<table>
+<thead>
+<tr class="header">
 <th align="left"></th>
 <th align="right">npar</th>
 <th align="right">AIC</th>
 <th align="right">BIC</th>
 <th align="right">Lik</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="left">saem_sforb_path_1_tc_reduced</td>
@@ -1828,9 +2078,8 @@ below shows that the reduced model is preferable.</p>
 </tbody>
 </table>
 <p>The convergence plot of the refined fit is shown below.</p>
-<div class="sourceCode" id="cb12"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">saem_sforb_path_1_tc_reduced</span><span class="op">$</span><span class="va">so</span>, plot.type <span class="op">=</span> <span class="st">"convergence"</span><span class="op">)</span></span></code></pre></div>
-<p><img src="2022_dmta_pathway_files/figure-html/saem-sforb-path-1-tc-reduced-convergence-1.png" width="700" style="display: block; margin: auto;"></p>
+<pre class="r"><code>plot(saem_sforb_path_1_tc_reduced$so, plot.type = &quot;convergence&quot;)</code></pre>
+<p><img src="data:image/png;base64,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" width="672" style="display: block; margin: auto;" /></p>
 <p>For some parameters, for example for <code>f_DMTA_ilr_1</code> and
 <code>f_DMTA_ilr_2</code>, i.e. for two of the parameters determining
 the formation fractions of the parallel formation of the three
@@ -1842,16 +2091,15 @@ the parameter estimates very much, and it is proposed that the fit is
 acceptable. No numeric convergence criterion is implemented in
 saemix.</p>
 </div>
-<div class="section level3">
-<h3 id="alternative-check-of-parameter-identifiability">Alternative check of parameter identifiability<a class="anchor" aria-label="anchor" href="#alternative-check-of-parameter-identifiability"></a>
-</h3>
+<div id="alternative-check-of-parameter-identifiability" class="section level2">
+<h2>Alternative check of parameter identifiability</h2>
 <p>As an alternative check of parameter identifiability <span class="citation">(Duchesne et al. 2021)</span>, multistart runs were
 performed on the basis of the refined fit shown above.</p>
-<div class="sourceCode" id="cb13"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">saem_sforb_path_1_tc_reduced_multi</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/multistart.html">multistart</a></span><span class="op">(</span><span class="va">saem_sforb_path_1_tc_reduced</span>,</span>
-<span>  n <span class="op">=</span> <span class="fl">32</span>, cores <span class="op">=</span> <span class="fl">10</span><span class="op">)</span></span></code></pre></div>
-<div class="sourceCode" id="cb14"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">saem_sforb_path_1_tc_reduced_multi</span><span class="op">)</span></span></code></pre></div>
+<pre class="r"><code>saem_sforb_path_1_tc_reduced_multi &lt;- multistart(saem_sforb_path_1_tc_reduced,
+  n = 32, cores = 10)</code></pre>
+<pre><code> 
+  (subscript) logical subscript too long</code></pre>
+<pre class="r"><code>print(saem_sforb_path_1_tc_reduced_multi)</code></pre>
 <pre><code>&lt;multistart&gt; object with 32 fits:
  E OK 
  7 25 
@@ -1864,11 +2112,11 @@ the SAEM algorithm leads to parameter combinations for the degradation
 model that the numerical integration routine cannot cope with. Because
 of this variation of initial parameters, some of the model fits take up
 to two times more time than the original fit.</p>
-<div class="sourceCode" id="cb16"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/par.html" class="external-link">par</a></span><span class="op">(</span>mar <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">12.1</span>, <span class="fl">4.1</span>, <span class="fl">2.1</span>, <span class="fl">2.1</span><span class="op">)</span><span class="op">)</span></span>
-<span><span class="fu"><a href="../../reference/parplot.html">parplot</a></span><span class="op">(</span><span class="va">saem_sforb_path_1_tc_reduced_multi</span>, ylim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.5</span>, <span class="fl">2</span><span class="op">)</span>, las <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span></code></pre></div>
+<pre class="r"><code>par(mar = c(12.1, 4.1, 2.1, 2.1))
+parplot(saem_sforb_path_1_tc_reduced_multi, ylim = c(0.5, 2), las = 2)</code></pre>
 <div class="figure" style="text-align: center">
-<img src="2022_dmta_pathway_files/figure-html/unnamed-chunk-2-1.png" alt="Parameter boxplots for the multistart runs that succeeded" width="960"><p class="caption">
+<img src="data:image/png;base64,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" alt="Parameter boxplots for the multistart runs that succeeded" width="960" />
+<p class="caption">
 Parameter boxplots for the multistart runs that succeeded
 </p>
 </div>
@@ -1878,124 +2126,113 @@ independent of the starting parameters, and there are no remaining
 ill-defined parameters.</p>
 </div>
 </div>
-<div class="section level2">
-<h2 id="plots-of-selected-fits">Plots of selected fits<a class="anchor" aria-label="anchor" href="#plots-of-selected-fits"></a>
-</h2>
+<div id="plots-of-selected-fits" class="section level1">
+<h1>Plots of selected fits</h1>
 <p>The SFORB pathway fits with full and reduced parameter distribution
 model are shown below.</p>
-<div class="sourceCode" id="cb17"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">saem_1</span><span class="op">[[</span><span class="st">"sforb_path_1"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div>
+<pre class="r"><code>plot(saem_1[[&quot;sforb_path_1&quot;, &quot;tc&quot;]])</code></pre>
 <div class="figure" style="text-align: center">
-<img src="2022_dmta_pathway_files/figure-html/unnamed-chunk-3-1.png" alt="SFORB pathway fit with two-component error" width="700"><p class="caption">
+<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABUAAAAYACAIAAAA41HWdAAAACXBIWXMAAB2HAAAdhwGP5fFlAAAgAElEQVR4nOzdeVwU9f8H8Pfswc1yCiy3IIciHqGieaWpeNRXFNMyzaPUvErTKI8sMzCPSvM2w/sAtF9m3mWo5AGSYArKIbfcyL3LXvP7Y20jzhWFZeX1fPh4tPOZ93zmPTtu63tn5vNhWJYlAAAAAAAAAGjbOJpOAAAAAAAAAACahgIeAAAAAAAAQAuggAcAAAAAAADQAijgAQAAAAAAALQACngAAAAAAAAALYACHgAAAAAAAEALoIAHANACeXl5TB2GhoY9e/ZcsmRJaWmpRrJauHAhwzAxMTHP0smsWbMYhnnw4MHzyup5YYni8qVShQYmW83Ozq57umu6efMmPaf3vxmWLVumygGeUZPnevHixcrIWqe71mKb/RwBAMDzxdN0AgAAoC49Pb0BAwYoX7MsW1hYGB8fHxsbGxYWdufOHTMzM82mpw6WZSUSCZfL5fHa+hfQhVTxvPOPF/gYLe5trJEE9PX1+/fvX+8qgUDQyslAi2rkXLu7u7fQTrXowwgAACr4XzYAgNawsbG5ePFizZaMjAx/f//bt29/9tlnW7du1VRi6ouKiurbt+/SpUs3bNigbFm2bNl7773n5OSk2cTqKhIpiKhYpNBUAnZ2drVON7yonv1cN+NzVPfDCAAAbR8KeAAALebo6Lh58+ZBgwZdunRJ07k0k4uLi4uLi6azgPaFrc5QiB82GcZwdDmGPYij3wopPSN8jgAA2gkU8AAA2q1Lly5E9PBh09VIvcRisZ6e3nPNqFVJpVIOh8PlcjWdCGgPRbXozsskr1Qnltdhso7rtpbOCAAAQE0YxA4AQLvdv3+fiOzt7VUtMpksODh4wIABJiYmbm5uAQEBt2/frrlJx44dBw4ceP36dW9vb319fYFAMGTIkD179tSMmTp1KsMw6enpNRuV42bdu3evoWTKy8sDAwP79OkjEAg6dOjg6+u7c+dOln0yDtyoUaP69u1LRBs3bmQY5vDhw0Q0d+7cWoNvNZm/chC1tLS0OXPmCAQCPp/v4OAwY8aMnJycp3zzXjSNv//qx0gkks8//7xfv34CgcDHx2fRokVlZWW19sWy7KFDh0aMGGFubq5l7z9Hl6PnTkQMz5xr8kq9fziCJ0+kcwy6ajRXdT3t56jeDyMAALR9uAIPAO1Obm5uSEhI3YKkpTk7O8+cOVNHR+c59pmdnb1o0SIiUg1uV1VVNWTIkKioKGtr64EDB+bn5588efLUqVM//PDDtGnTVBtmZmaOHDlSIpEoN7xx40ZERMSNGzdqlfFPpbCw0MfHJyMjw8vLy8/Pr6Ki4urVq3Pnzs3JyVm9ejURzZkzx9PTc9OmTUOGDBk7dqyPj0/dTtTMn4jmzp176dKlESNG2NraXrp0ad++fXFxcdHR0e32anyT77+aMSUlJcOGDYuJienYsePQoUNTU1M3b9587ty5CxcuODo6qna3bt26s2fPjhgxYtSoUefPn9+3b9/ff/8dFRXF4WjBtQEd53Xie36sokrHZTOj61g3QJazTVL2J6PnwrOe2frpPbsmP0fqfBgBAKANQgEPAO2LXC4fOnRoQkKCRvb+999/b9vW/Ntx8/LyRo0apXzNsmxRUdHdu3fFYrGtre0333yjbF+/fn1UVNTEiRP37dunr69PRFeuXHn99dcXLVr02muvWVhYKMPS09OdnJzOnTvn6elJRMnJyWPGjPnxxx8nT548dOjQ5qW3e/fujIyMZcuWBQcHK1tycnI8PT0PHTqkLA79/f2FQuGmTZt8fHw+/PDDejtRM39l+59//tmrVy8ikkgkffr0uX379r1797p161azw3IJu/dOpVjOcoje7GJgb8yt1a5cTCiUlkmevGaIOlvyM8tkRBSbL/3qWvnfBRKZ4km70JDzXndDHS5Tq4eaau2reZKTkxmGqdt+69atesutJt9/NWO++uqrmJiYjz76aMOGDcpq/Jtvvlm6dOmqVav27dun2t2FCxciIyOV779YLPb19Y2JiUlKSvLw8HiWo35GiqoEeWGYOpEcfXeF6IH4/hs8s9G1VrHyKnnBfiLiGHaTZn3ddF9cI75wPnGa/yhKQ+eaiCIjIxsaoL4RTX6O1PkwAgBAG4QCHgDal7y8PE1V70QUERHxLJuLRKJz586pFhmGcXJyGjp06Nq1a1WV7aZNmwQCwc6dO5X/aieiQYMGLV68ePXq1fv27VuyZIlq8w0bNiirdyLq1KnTtm3bhg8f/u233za7gO/fv//OnTsDAgJULUKh0Nra+qnurFY///nz5yurRyLS0dH53//+FxcXl5OTU6uA/yNdvPlWuWpxqa9xve213M6TKF/EF0rjC6W12l+y1ulrp9N4DzX31TwNTS1mbFx/t+q8/03GVFZWbtmyxdHR8euvv1ZdS1+8ePGpU6dKS0tr7q7m+6+npzdy5Mg7d+7k5uZqtoCX5WyRFRxVP54VJUpFiQ2tlRf9LFevH46RD9fkFfX3W0sj08iZmJg0o0P1P0cAAKBdUMADQPsiFAr79u1748YNjezd39//WTZ3dnZOTU1tJODRo0elpaUjRoyoNSf8qFGjVq9erXxaXmX06P9ceBw2bJixsXF8fHyz0xs8ePDgwYNZlk1LS0tLS0tNTY2IiEhKSjIyMlKzh6fKv1+/fjUXDQ0N6+3Tz0VvtcSkUqLgcxl/N/267crF5Meyon9mjOMy1MWSn/hYdiFV3MOKP9RJ9+8CmUTOKtudTXl9bHXq9lBTrX01z9NOLabO+99kTGJiokQiGTZsGJ/PV23F4XDq/vakempDycDA4GkPsCXw7ZczBp2JVavulpdeVpRGMDo2POtZxDz5tYKV5MryfiAins37DL+DOv0wfGuuYGCzc6bnPWXgU32OAABAu6CAB4D2hWGYixcvhoWFFRQUtPKuu3Tp8tprr7XoLrKysohIKBTWarezsyOimiPSmZiY1K14HRwcHjx4wLJsQ3fzNk4qlX755Zc7duwoKipiGEYoFL700ks2NjYVFRXPPX8iMjc3V6dPXS4zxaue2rKhdpUj8VUXUsVdLPnzfeq/4t1kD61Mnfe/yZi0tDQisrW1bXJ3VlZWLXMcz4TRtecLF6oZzLd5XxTny1ZnMHwLntWTERbE8a8Rq+AJ5+s4fdViabasp/ocAQCAdkEBDwDtjpGR0cyZWjkwVZOUY9Hn5ubWalfeIF1zpPrS0tKqqqpaV02zs7NtbGwaqd7Lyxu7Y3zKlClhYWHTpk2bPXt2jx49lJ337t1b/St+6ucPdanz/jcZoyzd8/Pza3WuUChYlq05QGDzfuVpWzh6Oo6fVye9K80M4pr7MzwTedHPirI/Gb4l3+5jTSfXfPgcAQC8wLRgqFgAAFCTra2tQCC4ceNGSUlJzfbz588TUefOnWs2njlzpubi5cuXS0tLlbPKq4jFYtVrlmWjo6Mb2rVIJDp16lT37t337dv38ssvq34aqPXg9HPMH2pS5/1XJ8bT01N5w7xc/p+70Dt16mRtbV1rtrkXANdiPEfQn5UWyLI3kkIsyficlPfh85rz5Hkbgc8RAMALDAU8AMAL5cMPPywtLZ03b151dbWy5c8//9y4caOpqen06dNrRgYGBiYlJSlfP3z4cO7cuUSknJSO/rlB+ueff1bFf/PNN408Ic/hcGQy2ePHj0UikbJFJpMFBQUlJSXJ5fJahV/N3wWanT/UpM77r06MiYnJjBkzEhMTV61apTpr27ZtS01NHTly5Itw1b0OHacgIo40d7ckdSlbncEx6MqzekfTST0r9T9HjXwYAQCgDcIt9AAAL5RPPvnk7NmzR48evXz5cp8+ffLz85VTc+/Zs6dDh39H5BIIBCYmJt27d+/bty8R3bx5s6qqaurUqaqR7SZOnPj9999/+umnV65ccXFxiYmJuX79upOTU0MP0Orq6k6ZMmXv3r2urq7Dhg3jcDiRkZFE5Ovre/PmzRkzZqxYscLNzU05WFp4eDjDMFOmTOnTp0/z8oda1Hz/1YkJCgqKjIwMDg4ODw/v3r17enp6dHS0lZXVhg0bNH2ULYJj2J1nNUWWf0BWcJiI+M5riXmm+f+a4dGjR6oZImsxNDQ8fvz403aozueoyQ8jAAC0QbgCDwDwQjE0NLx27dqaNWucnJwuXbqUm5s7duzYmzdvTp06tWaYgYFBZGTku+++m5eXFxMT06tXr127dh04cEAV4Ovre/r06f79+0dGRm7duvXGjRvvv/9+YGBgI7vevn37559/bmRkdOLEiTt37gQEBMTFxW3evLlr167h4eHZ2dlE1KVLl6VLl+rq6u7bt+/Ro0fNzr8VKGdxt3u2udxbkzrvvzox1tbWf/3118cffywQCM6ePVtaWjp79uw7d+7UHRTthcF3WMFwBUTENR/LFQxoMv65q6qqOteA33//vRkdqvM5avLDCAAAbRDz4j3PBgAAjevYsaNYLFZ/evacnBxdXV01R31/keRVyq0MuS/gXeNQh6wwXJa/T7fTbkbHTtO5AAAANAgFPABAu/O0BTwAAAAAtAW4hR4AAAAAAABAC6CABwAAAAAAANACuIUeAKDdyc7OZlnW3t5e04kAAAAAwFNAAQ8AAAAAAACgBXALPQAAAAAAAIAWQAEPAAAAAAAAoAVQwAMAAAAAAABoARTwAAAAAAAAAFoABTwAgDYpKCgIDAwcMWKEUCg0Nzfv3bv34sWL8/LynrafhQsXMgwTExOjXOzXr5++vv7zThaaKTQ0lKnDysqqf//+x44da15kTcePH1dGBgYGtvzRAAAAwHODAh4AQGv8+uuv3t7eGzZs+OOPP2xsbGxsbO7cubNp06ZOnTpFRERoOjt4zoRC4bB/DB48WCAQXLt27a233vr000+bHamkqu3DwsLUz2fHjh0+Pj7GxsZWVlaDBg1Sc9vmbQUAAAD14mk6AQAALaAQicvPXqmKipPmFTJcro6TndErvoYDfIhhWi2H2NhYf39/hmG2bNny7rvvKi+Yl5eXf/3118HBwZMmTYqLi7OxsWm1fF48IpZ9M6+wE5/3tbkpv86Z3VpafqKyKsTKoiOvlb46/fz89u7dW7PlzJkzY8eO3bBhw7vvvuvm5taMSCIqLy8/ffq0iYmJk5PTnTt3bt686evr23gmcrl8/vz5u3bt0tXV7du3L4fDuX79+qRJky5durRz587nuxUAAAA0AlfgAQCaIM3KfbRk7eMjv1QnpyvKK+UlZaK4hILN+/KCtrPVklZLY+HChXK5/Oeff16wYIHqdndjY+OgoKC5c+fm5+eHhIS0WjIvJDlLhXL5mSrRoqLHUpatuWpTadm2svJChaL6v+2tbPTo0RMmTFAoFFFRUc2OPHnypFgsDggImDx5Mql3Ef7gwYO7du3y8PBIS0uLiIi4dOnS3bt3XVxcdu3adebMmee7FQAAADQCBTwAQGPYakle8A5ZfpFuJyfrFfMc965z2B1kOe9trqmxKDahcOfR1kkjLCwsMjJy6NChY8aMqbt29uzZRHTixInWSeZFZcRhfrSyMONwLonECwqLJf/U6t+Xlu8qq+ASrTM39eTzNZukk5MTEZWWljY7Unn//OTJkwMCAogoLCyMbepXiUOHDhHR/v37Vbd4uLq6rlu3johOnTr1fLcCAACARqCABwBoTPnFP2X5RbqujjZrFun37MIxNuSamxgN7Wfz1UeMrk5l5C1JxqNWSENZnC9YsKDetT169GBZVjUiHRGVl5cHBgb26dNHIBB06NDB19d3586dTdZpKqWlpb6+vgzDbNu2TdV4+PBhPz+/Dh06WFlZ+fn5nT9//hkOqI3y5PNDrCzMOJwr4uqFhcUSlv2+tHxHWTmXaL2F2WgDzY/zFx0dTUQ9e/ZsXmRxcfGFCxeEQuGQIUM6derUo0ePrKys69evN97Vw4cP+Xx+7969azZ6e3sTUVJS0vPdCgAAABqBZ+ABoD2SF5fIS8rqXcUTWnH09VSLVTF3ich4RH9pZk6tSIOeXSpvxFbdjNNxtFWn/1o9PxVlMebp6alOcGFhoY+PT0ZGhpeXl5+fX0VFxdWrV+fOnZuTk7N69eomNy8tLR0xYkR0dPS2bdvmzZunbJw2bdqBAwcsLCz69OlTVVV15cqVCxcuBAUFLV++vHlH1JpSpDKxej9ecBly5/NDrCxm5hddEVf/Lzc/XSbnEH1gInDi8e5JpE1u20K/i8tkspycnC1btly6dGn06NH9+vVrXuRPP/0klUrffPNNDodDRBMmTIiNjQ0NDX355Zcb2ftPP/3EMIxyExXlD0aurq7PdysAAABoBKP+BRkAgBeDorIqc9YKtoFiTK+ru80XH6gWsz/4Uvoon+HzWKms3niuuanD7q/U6V+vs6vNmsXNy9nIyKiysrKystLAwKDJ4ODg4BUrVixbtiw4OFjZkpOT4+npaWlpmZKSomxZuHDh1q1bb9265ePjQ0T9+vWLjY0ViUSlpaXDhw+/devWjh075syZowwODw+fOHGiv7//gQMHjI2NiSglJcXPzy81NfX69et9+vRp3kG1jj9E4nmFxerHLzYRzBYY3ZdK38orVLPsr7XtUyZYj9DQ0DfffLPeVYMHDz579qxqEAT1I5WGDRv2+++/q877/fv3O3fuLBQKs7KyalXajYuNjR09enRubu6tW7deeumlFt0KAAAAVHAFHgDaHUZPz6CXtzS3sN61Bi95/SdYh09Eel3c5OWVtSLlJeXy4sd8YQc1+9f36drsnOVyed2LmQ3p37//zp07lU84KwmFQmtr65yc2jcR1FJSUqK89r5161ZV9U5EQUFB+vr6+/fvV1bvROTq6rp+/fqAgICDBw+28QLelc/rpasjUvMKPDE9dPlEdKFKrKrejRnGgc9rcr4B1bbPi1Ao9PL692+jWCxOSEi4fPmyv79/eHi4QCB42si8vLyIiAh3d3dl9U5Enp6eXl5e9+7di4yMHDRokDpZsSx74MCBhQsXlpeXf/vtt2rW4c3bCgAAAGpBAQ8A7Q7D5XT4aKaawTpO9pK0bN3OrqYTRtZalbtqk7z4sdGrte89fqr+1WRpaZmVlZWamtq5c+d6A8RisVwu19PT43K5gwcPHjx4MMuyaWlpaWlpqampERERSUlJRkaNXRyWy+XKa+9ElJ+fX7M9ISFBKBQePHiwZnxhYSERxcbGPofDa0mOPN5BK8un2kT13PsiU0FIWcVjhcKSw9liaa7TirMGUn2Tw1VXV8+bNy8kJGTOnDlHjx592sjw8HC5XC6Xy6dOnVozkojCwsLUKeBjYmLmzZsXFRVlY2Nz4MABf39/dQ6keVsBAABAPVgAAGiY6G5iasD8tMmLRX8/+LdVoSg5cT41YH7G9E/klVWtkMbrr79ORCdOnGgooFOnTkSUkJDAsqxEIlm5cqWFhQURMQxja2v72muv2djYGBkZqeKV4+HdunVLudi3b18iMjAwOHTokKmpqY6OTnx8vHJVenp6I18i3bt3b8nj1oDNJWWeGdleGdmnK6tYlk2QSPpl5XhmZM/OL6xWKFonB+VA8dOnT6+7qrKyUk9Pj8PhiESip4pkWbZ///4NnUdra2uZTNZIShKJZNmyZVwu18DAYOXKlWVlZeocSPO2AgAAgIZgFHoAgMboebkZD+/PVktyV2/JC95Rcvzs4yO/PPp43eMjvxDDmM+exGmVkclHjhxJRJs2bap3bVpaWnJysrW1tXKUuylTpnz11Vevvfban3/+WVFRkZ2dferUKXt7+8Z3weFwzp49+/bbb69bt04ikcyZM4dlWSKysbHhcrnDhw+v91uk7V+BfyqbS8t3lJXzGOY7S3PlmPPKcelNOZwr4urFRY/lms7QwMDAwcFBoVAUFBQ8VWRmZua1a9fs7OwUdX6G8PT0zMvLu3LlSkNdKRSKd955Z+3ata+88kpCQsKaNWtUD1M0onlbAQAAQCNQwAMANMFi1iTTiaMZPk/0172SY6dLf7ogScvimptYBc4y7Nf0bF7PxbRp0ywsLK5evRoeHl5rFcuyK1eupH+KfJFIdOrUqe7du+/bt+/ll19WDXrX5MzhOjo6ypuoZ82a1b9//6tXr/7444/KdhcXl5iYmMrK/4wCEBkZuXDhwhdpMrkCuXxnWTmPYb61MBteY74ATz5/r5WFKYdzSST+U1ytwQyJSCwWZ2Zm6unp2drWnvug8cjQ0FCWZcePH8/UeRBg/PjxyoCGutq+ffuxY8fefvvt8+fPOzo6qplq87YCAACARqCABwBoCodjOnG0/a41lgunmr4xyvTNMVbL3rffttqgd7dWS8HQ0FA5Jftbb7319ddfV1VVKdurqqrmzp17+PBhc3PztWvXEhGHw5HJZI8fPxaJRMoYmUwWFBSUlJQkl8tZNcZyYxhm165dfD4/MDAwLy+PiBYvXlxcXDx58mRVDZ+VlfXGG29s3brVzs6uJY5XIzpwuavMTH6wNB9eZ7Y/Tz7/kJXlIhNBH10djeSmJJVKFy1aJBaL/fz8uFzuU0Uqb7avObShyrhx44joxIkTMln9Uy18//33BgYGO3bsaGSnYrE4Li4uLi5O9XdMna0AAADgqWAaOQAArfHjjz9+9NFHZWVlXC7Xy8vL0NDw7t275eXl5ubmBw4cGDNmjDJs5syZe/fuFQqFw4YN43A4kZGRRGRpaXnz5s1p06atWLHCzc2toWnkVPtasWJFcHDwW2+9deTIEblcPmbMmPPnz5ubm/v6+hYXF0dHRysUiuDg4GXLlmnkrXixKSeHs7Oz8/b2VjVWV1ffu3cvPz/fzMzs1q1bLi4u6kcmJye7ublZWVnl5OTUO5eBk5NTRkbGhQsXhg8fXmtVbm6uUCjU09NTPqBRy7BhwzZs2EBE9+7d69q1KxGJxWJdXV01twIAAICnglHoAQC0xrvvvuvn57d27do7d+7Ex8dzOJxu3br16dNnxYoVyiHrlLZv3+7o6HjkyJETJ054eHgEBASsWrXq7t277733Xnh4+PTp093c3Jrc12effRYWFnb06NFp06b5+fmdPXt227Zt4eHhN27c0NHRGTp06JIlS5Q37UMLyc7Ozs7OVi1yOJyOHTsOHz58/fr1te6fbzJSefnd39+/oZkIx40bt3nz5tDQ0LoF/MOHD4lILBbXO96BcvTEupq3FQAAADQOV+ABAAAAAAAAtACegQcAAAAAAADQAijgAQAAAAAAALQACngAAAAAAAAALYACHgAAAAAAAEALoIAHAAAAAAAA0AIo4AEAAAAAAAC0AAp4AAAAAAAAAC2AAh4A4CkUSQoq5RWazgIAAAAA2iMU8AAA6soRZy27Py8o6RMZK2vlXYeGhjJ1WFlZ9e/f/9ixY62cjPoWLlzIMExMTIymE2kOWZE0JzhVnFTV+rtW/3Q37y/G8ePHlZGBgYEtfzQAAADw3PA0nQAAgNY4+uhHGSvNrc7+rfDXkR38Wz8BoVDo5eWlfC2VSrOysq5du3bt2rXY2Nivv/66RXfNsqxEIuFyuTxeu/jikBVKksbcFt2rKNid5Xamp34Xo9bPQf3T/bR/MVS1fVhY2Pr169XMZ8eOHXv27ElMTNTX1/f09FywYMHEiRMb36S8vHz16tW///57UlJShw4dunfvvmrVqpdeeknNPQIAAEAtuAIPAKCWuLJbd8tv63B0iOhUXliZrKT1c/Dz87v4j4iIiOTk5NOnT/N4vA0bNiQlJbXorqOiovT09JYtW9aie2kjVNU7o8uRFUqSRt8WxWvguQn1T/dT/cUoLy8/ffq0iYlJt27d0tPTb9682WQmcrn8/fffnzdv3r1793x8fLp27RodHT1p0qT333+/ka0qKiq6d+/+zTff5ObmDhkyRCgUnjx5slevXr/++msz3g0AAAAgFPAAAOqQsbLQRyFENEH4Tg9Bb5G86kTOIU0nRUQ0evToCRMmKBSKqKgoTefyglBV73puBl63+5mMsXxSw9/T/NgH6p/uRiJPnjwpFosDAgImT55MRGFhYU3u9+DBg7t27fLw8EhLS4uIiLh06dLdu3ddXFx27dp15syZhrb66quvUlNTZ86cmZ2dferUqWvXrp0+fZqIZs+e3fShAgAAQH1QwAMANO23glO51Y+EevZDLEa9afcuj+FHFv+eWtWyF73V5OTkRESlpaXN7qG8vPz5paPdalbvbmdf0nHUczno/aSGH9Mmanj1T3dDkcr75ydPnhwQEEBEYWFhLMs23tWhQ4eIaP/+/TY2NsoWV1fXdevWEdGpU6ca2urcuXN6enpbt27lcJ78Y2P06NG+vr45OTlpaWlN5g8AAAB1oYAHAGhCmaz0VH44Eb1pO5PLcK10bIZ1eI0l9sijPSw1Ufm0gujoaCLq2bOnqkUmkwUHBw8YMMDExMTNzS0gIOD27ds1N/n4448ZhikqKjpw4ICjo+Po0aOV7b/99tvIkSPt7OyMjIy6du0aHBxcVVVFRKNGjerbty8Rbdy4kWGYw4cPq7o6fPiwn59fhw4drKys/Pz8zp8/3wqH3EJqVe98G10iYnQ4baqGr3u6nyqyuLj4woULQqFwyJAhnTp16tGjR1ZW1vXr1xvv6uHDh3w+v3fv3jUbvb29iaiRZzccHBwCAgL09fVrNnK5XCKqqND8TyEAAADaCAU8AEATTuQcFMmregh6exs/GXzrf9YTTfhmKZUPokoiNZWVTCbLzMwMDAy8dOnS6NGj+/Xrp2yvqqrq37//ihUrkpOTBw4caGZmdvLkSV9f3/3799fqISws7L333vP29h47diwRHTp0aPjw4b///rtQKBw+fHhxcfGKFStmzJhBRHPmzFm0aBERDRkyZNOmTT4+Psoepk2bNmXKlJiYmN69e3fp0uXKlSsjR44MDg5uvXfh+am3eldqCzV8Q6f7aSN/+uknqVT65ptvKq+KT5gwgYhCQ0Mb3/tPP9KFq7MAACAASURBVP0UHR2tupCupJxcwNXVtaGtTp06pbx0r3L16tXo6OiOHTt6eno2vkcAAACoF9PkjXMAAC8YGSvbnPpVfnUOEfU1GzzOZnIj7Rmih6sTlxCRKd+Cx3BV7VeKL+7L3MZj+KZ8M4aYJvt5RqGhoW+++Wa9qwYPHnz27FnVdc4vvvhi9erVEydO3Ldvn7LxypUrr7/+OofDSU5OtrCwIKKPP/5448aNVlZWFy9e7Natm3JDDw+PzMzM+Ph4Z2dnIhKJRD169EhMTMzPz+/QocPNmzf79u27dOnSDRs2KOPDw8MnTpzo7+9/4MABY2NjIkpJSfHz80tNTb1+/XqfPn2IaOHChVu3br1165aq5m994oTK1Ol3FVXyJ8tcxmG9u2CExX/aWZI8ErMSluEzfKEuw2X+3f6feFFceeLIv+TlMuIyura6pMuppx8VDmP/tZvJKMvm5az+6VY/UmnYsGG///676ozcv3+/c+fOQqEwKyurVn3euNjY2NGjR+fm5t66davJUeVv3rz5zTffZGZmRkdHe3p6Hj16VHn1HgAAAJ5Wu5gNCACgJqlC8rAqUSSvIqK0quTG249k/8gSy+PwH0sLa7YPNB/2R+G5dFFKoSRfnX6ei5qzhRGRWCxOSEi4fPmyv79/eHi4QCAgok2bNgkEgp07d6oqt0GDBi1evHj16tX79u1bsmSJavOZM2eqqnciys7ONjAwUFb4RKSvr3/q1KmCgoJaFaBKUFCQvr7+/v37ldU7Ebm6uq5fvz4gIODgwYPKAr4tkDyqFsVX1HzWQZxYKRhhUbediFgpK8kQ1+pBGS8tlMrLZUREcrY6U9x4P0QkflDZ7AJeSZ3T/VSReXl5ERER7u7uqt9TPD09vby87t27FxkZOWjQIHWyYln2wIEDCxcuLC8v//bbb9WZE66oqCguLi4nJ0cul+vq6hYVFamzIwAAAKgLBTwAtDv6XIMNnX+olFcQkRnfopH2myVXEyvvCXgmq9y+kZO8ZjxDzNt2s9YmL+NxeEtdvuxo4NZk/8/Oz89v7969NVuqq6vnzZsXEhIyZ86co0ePPnr0qLS0dMSIEWZmZjXDRo0atXr16vv379dsrPVI88SJE/fu3dulS5e33npr4MCBvr6+7u7u7u7u9WYil8sTEhKEQuHBgwdrthcWFhJRbGzssxzm8yV41dw7aYBCpFAuMlxGx1Gvbrsotjz9/QR5pczEz9JunZvqIrwq/smd8xzG7ktX07FWDfVTa6tn0eTpftrI8PBwuVwul8unTp1aM5KIwsLC1CngY2Ji5s2bFxUVZWNjc+DAAX9/f3UOZPTo0cpxFi5dujRp0iQ/P7/o6OiaPx4BAACAmlDAA0B7ZMA1NOAaNt4uUUiO5xwgIl+zQWXyJ0N5K8tyJR6H72rokVx5/1LhmTlOSxrqp0Xp6upu2bLlyJEjYWFhe/fuzcrKIiKhUFgrzM7OjojS09NrNtYK27Vrl4+Pz969ezdu3LhhwwaGYXx8fD7++OOJEyfW3W92drZEIklPT1+wYEHdtW1tWPuaD7Q31K7bUZ9vq5vsH1t6vpBjyHUO8WJ4/95In/d9RvbyJOIwTjs6W7xd++1tqP/nrtbp1tNr8DeCeiOV48+npKSkpKTUij9+/PjmzZuVI8zVSyqVfv755+vXr9fV1V25cmVgYKDqzgv1DR069IsvvliwYMGBAwc2btz4tJsDAAAACngAgPpFFv9WJCkgoosFpy4WNDhXFhFFlUT+z3qSUM++tVL7DwMDAwcHh6SkpIKCAnt7eyLKzc2tFZOTk0NEyrUqtZ555vP58+fPnz9/fmFh4Y0bNy5fvhwSEjJp0iQiqlvD29jYcLncoUOHXrhw4bkfkaYY+pp0+rlHsn/s45/yiCHnH5/U8I1X762s5ul2cHBQPzIzM/PatWt2dnaZmZkMw9SM7Ny58/37969cuTJkyJB6u1IoFO+8886xY8deffXVkJAQR0fHJvP866+/VqxYMXr06IULF9ZsVw5fV1BQ0PShAgAAQB0YhR4AoH4O+h07Grg567s2+aezkbeAb6qpPMVicWZmpp6enq2tra2trUAguHHjRklJSc0Y5exunTt3bqiThw8fLl68+MiRI0RkaWn52muvbdiw4ccffySi06dP143X0dFxcXGJiYmprKys2R4ZGblw4ULtnUxOWcNzjXmPT+SlvXuPlbFtqnqn/57up4oMDQ1lWXb8+PG1qnciGj9+PDU6Fv327duPHTv29ttvnz9/Xp3qnYhMTEzOnTtXaxR6IoqPjyeimk/sAwAAgPpwBR4AoH5uhp0/c9ug6SyaIJVKFy1aJBaLx44dq7z/+cMPP1yzZs28efP27t2rq6tLRH/++efGjRtNTU2nT5/eUD8CgWDTpk329vZDhgxR3VqfkJBARG5u/z7eLxb/O8Db4sWL582bN3ny5CNHjhgaGhJRVlbWG2+8kZubO2fOnBY52lZh6Gvi+n/dU/xjH5/Iq06pqootJw7jtL1NVO91T7f6kcr75wMCAuoGjxs3Ljg4+MSJE1u3buXx6vmHwffff29gYLBjx45GdioWix88eEBE3bp1YxjG1dXVx8cnKirqhx9+mDVrljImISEhKChIR0dnzJgxT3PcAAAA8AQKeAAArXHx4sVRo0apFqurq+/du5efn29mZvbtt98qGz/55JOzZ88ePXr08uXLffr0yc/Pj4qK4nA4e/bs6dChQ0M9W1pazp07d8eOHW5ubgMGDDA1NU1ISLhz546Li8vs2bOJyMjIiIjCw8MZhpkyZUqfPn1mz5598uTJX375xdHR0dfXt7i4ODo6WqFQBAcHd+3atYXfiZZl1NfU9eceKf6x/1bvUzRQvatzutWMTE5OjomJsbKyGjhwYN0d9erVy9HRMSMj448//hg+fHittbm5uUlJSXp6evWOcjds2DDlzIIpKSk9evQgIrFYrPzlaNeuXYMHD549e/bmzZvd3d0LCgpu3rwpk8m+++47XIEHAABoHhTwAABaIzs7Ozs7W7XI4XA6duw4fPjw9evXq26oNjQ0vHbt2rp1686cOXPp0iVLS8uxY8euXLlSWVw1YvPmzV27dg0JCYmOjpZIJM7OzsuWLVu0aJGVlRURdenSZenSpWFhYfv27Rs6dCgRcbncs2fPbtu2LTw8/MaNGzo6OkOHDl2yZMnIkSNb7A1oPUZ9TTv93DPz4wdWCxzNJ9loJAd1TreakcrL7/7+/g1N9j5u3LjNmzeHhobWLeAfPnxIRGKxuN7JBTp16tRQ/j4+Pn/99VdwcPBvv/125swZe3v7MWPGLF++vNb0BwAAAKA+hmXrzF0LAAAAAAAAAG0MBrEDAAAAAAAA0AIo4AEAAAAAAAC0AAp4AAAAAAAAAC2AAh4AAAAAAABAC6CABwAAAAAAANACKOABAAAAAAAAtAAKeAAAAAAAAAAtgAIeAAAAAAAAQAuggAcAAAAAAADQAijgAQAAAAAAALQACngAAAAAAAAALYACHgAAAAAAAEALoIAHAAAAAAAA0AIo4AEAAAAAAAC0AAp4AAAAAAAAAC2AAh4AAAAAAABAC6CABwAAAAAAANACKOABAAAAAAAAtAAKeAAAAAAAAAAtgAIeAAAAAAAAQAuggAcAAAAAAADQAijgAQAAAAAAALQACngAAAAAAAAALYACHgAAAAAAAEALoIAHAAAAAAAA0AIo4AEAAAAAAAC0AAp4AAAAAAAAAC2AAh4AAAAAAABAC6CABwAAAAAAANACKOABAAAAAAAAtAAKeAAAAAAAAAAtgAIeAAAAAAAAQAuggAcAAAAAAADQAjxNJ6CVDhw4EB8fr+ksAABAkyZMmNCrVy9NZwHPE77fAQCgjX+/MyzLajoHLfP48WNzc3NNZwEAABo2ePDgiIgITWcBzw2+3wEAgNr89zuuwD81iURCREZGRitXrtR0LgAAoAEZGRnbt29Xfh3ACwPf7wAA7ZxWfL+jgG8mQ0PDTz75RNNZAACABly/fn379u2azgJaBL7fAQDaLa34fscgdgAAAAAAAABaAAU8AAAAAAAAgBbALfQtRVEZW/1gMilETYdyDHU7H+foe7Z8UgAAAAAAAKCtUMC3FFZaxEpy1IstIVlJy2YDAAAAAAAAWg4FfEvhmr7KFQyUl13lWU3lO36pbLxVco3LcHua+CoXJWlL5YUnuKYjOMZ9NZcpAAAAAAAAaAE8A9+C+B3XE8OTFRxlJTkMzzRVkr8z+4ft2buyZCUMz5StzpAX/UyMDt/pK01nCgAAAAAAAG0dCvgWxNH35FlNJVYmTV/OEnskew9LrIJVHM3+kYgk6cuIlfNt5nD03TSdKQAAAAAAALR1uIW+5SjkJX9wDH0Ybri8NCI98T3Tivt9y3WJYSXyS9mJmWZl1xiugDHoLC+7yhUMIGI0nTAAAAAAAAC0XSjgW4q86OfqpHdVi1bFP80kIl0iIpIQFccTESsvk6TMIyJdj2NcMz+N5AkAAAAAAABaAQV8S+EYvUQcXVJUcwy7S0QPeAqxKM1RmmFPRDodM/QcsmRcAx09F0XlXeIYcAy7aTpfAAAAAAAAaNPwDHxLYXSd+TZziUhGdDxrABHpWhc8Sp2ZnfiOjmUBEYU/elnGEhHx7RYzOkKNJgsAAAAAAABtHQr4FsS3W1rJs+JUxr18zLPixkscfZGD1w9OfXdy9KvLIvoP/MmZU3W3gmfLF87XdKYAAAAAAADQ1uEW+trKysrkcnkjAaWlper2xTW8UjprlGGQ7TtHrpx6b0jvOMOXooghhZx7M3XQkEk7iOhy+YI3OPrPnjYAAAAAAAC82HAF/j8WL15sYmJi3igPDw8iKigoaLK3suvFtpN7iO678SyLc2cUPr74GnFYYtii0+NL303nmZdU3fOwm+xWEa32LwIAAAAAAADQXqGA/w8zNQgEAiJiWbbxripulDx8J8LYJP9a2pssMW/KDlRd9ZI9NpXmW0puuo2XhrHEuZE0SWCcnzQ5oup2eascHwAAAAAAAGgr3EL/H6tWrVq1alXjMXfv3vX29maYJqZtL/6/cPf9nxCHdSEiIoYnsw8KVq6y++fFUP8vyZ9YBacofItBz8nPmDwAAAAAAAC8wHAFvqWUvE7EEBHJSkykeR1YljkTKXUfW9HZv+L3mzJiGWleB1mZgIgYYsvGNfFzAAAAAAAAALRzKOBbikOf8ffLOhJRVZzXg9nfX+T8b/7aqux8WUaebP7aqjOcgAeztogTOhHRnXJ3p55jNJ0vAAAAAAAAtGko4FvKg/S75V/NUYh0BYOvCVyTyoKmV4ifXGbPzFPkfvG6wDPeyPcvRZW+OPjdxMx4zWYLAAAAAAAAbRwK+JZi+qWp8W3X/F/HEpHlB3u9I6S2RkLVWv2rSR0W7iWivLA3BDEuRuuMNJYoAAAAAAAAaAMMYtdSDtkY+jNFZfv8dV+7Yuacqhfwh9shz2TKVq7NfumsvkNmcaV9WegoBYc52MFwjWbTBQCAdiAmJuap4n18fFooEwAAAGgGFPAt5ZK3ce5E2/fDH4k2Tjb77Fuz2cccTg6gx0/W5lreJOJVrX1HLtfZ9qZtamdDjSYLAADtQq9evZ4qvsk5U6EFKUTSrPWsrKTJQIZnwhPOY/hWrZAUAABoFgr4lrKwl/EuLmevg3zGt/1N75wTdIt3GpdLIU/WPsgWV/31clmUz49LrVO8zT/yMtZosgAA0C4sWLBA0ymAuuTlN6WPNqkZzOg68qxntmg+AADQFqCAbylvdTF4q4vBF6fvHV5p/u6OmR23BY4aemuFqoBPU+Run8GwLGMnL+glE1phMAIAAGhxW7Zs0XQKoC6uoD9H310hSuQKBnEtx9UbIy+9Ki/6ieGZcS38Wzk9AADQCBTwLUVRdU+S9O5Sw0JFN4m4mxEr1nVxEunwGYmUJaLsfIXgu2CBHieYoaJUI4H5YdL10HTKAAAATyQlJWVnZ7/yyiuaTqQdY/h85/XVCf6Kyr903HYzfOvaAaxE+mgrEfHtlzE8cw1kCAAArQ4XflsKW52lED3g6BXxTMuNTHM4BiI+j1wd/n3DU0W5OrZ5OsI8oU6KXlSyBlMFAACoiWXZoKCgMWPGaDqR9o5rMphr5sfKK6SZwXXXSnO2s+IUjr4Hz3p6q6cGAACagQK+pXDNRnCMexNR6YVXHr6z7e7iHVFZ73g6//uGzyv+8Ap3KBFV3fZO8tcv+61IY7kCAEC7xLLs559/7urqavxfhoaG+/fvd3V11XSCQDpOa4mjK8s/pKj4q2Y7K82XZX9LRHynIGL4GsoOAABaGwr4lsPIHn9GCkYwJLLYljPz8MAwnTEWdnaq1dUPEl6WX1YQN33/TJKzKQFxsiKpBtMFAID2ZufOnV9++WVhYaFQKKyoqDAyMvL09BQKhSKRyNfXd8+ePZpOEIjR68i3nk2kkKQvI/p3UgBpxpesvJxrNppr+qoG0wMAgFaGAr4FKaq7lP72CsOXeUzer8/K/ujZ9bTnJ6q1lgm/80j+M/8NUZYtERFb83sZAACgxYWEhBgZGd2/fz8xMXHq1Km9evWKjo5OTExcs2ZNYWGhl5eXphMEIiKefSDDt1aUR8mL/k/ZoqiMkxUcJUZHx2m1ZnMDAIBWhgK+BQleteBYrlRU6Rt3u3Hoi1PGLOn1cVetzU4pk4qM3N4eZlDCZTms8EAnniVugQMAgNbz8OHDAQMGCIVCInrllVdu376tbP/0009Zll2zZo1Gs4MnGK4R32E5EUnSPyNFFRErSVtGpOAL5zF6nTSdHQAAtCoU8C3Let5LkqLZRGT92q6g8Ul6HV05PK5yVXa+In2HPz/LlOWwZ4PO/tH7okYzBQCAdkcikRgZGSlfOzs7P3r0qKqqioh4PN6AAQMuXsQXU1vBs5rCMezJSh5JH22RFZ5QlF9n+FY8u8WazgsAAFobCvgWZz5huVzsqOOU1cnj1LqADH1nR2U7y1Ls2U5EJDGSFTsX/1Z0OlucodFMAQCgffHw8Lh//77ydceOHVmWjYuLUy4qFIoHDx5oLjWohaPjHETESB9tlmZ8RkR8h88YrkDTWQEAQGtDAd/iFIzOPudlRNRhRqhjflGfQqFqVZIsI8NDT7eMP35+gFmq6aGsXZpLEwAA2p0hQ4bcvXt3+fLlhYWFzs7OFhYWu3fvJiKRSPT777/b29trOkH4F8e4H9diHClErCSXY9iNZzVZ0xkBAIAGoIBvceWVDyrFKVk8J45Rhf2atX2GpKlWJfX9o+zH36SBp+1HnHv/N75+4r3YsmjNZQoAAO3LqlWrHB0d165de+LECYZh5s6du2/fvp49e3p6eubk5Lz11luaThD+Q8dxNXH0iRgd5/X4JxwAQPvE03QCLz69jE8+KL2sfK3vldgtX0pPBpGl/Mq/J4hTacSTxRlVhkHXIrr69eQxOC8AANDijI2NY2Jidu/e7enpSUSrVq1KTEz85Zdf5HL5zJkzP/nkkyZ7aAXvvPNOeHi4SCTSdCKax+ja63qGkqyEY+yr6VwAAEAzUCi2POUjaiwp5DwOT+blylWtuZesUFQYyvjE161iiJUXm4xe0uNG2B8DfIdrLFsAAGhPLC0tly9frnzN5/NDQ0NFIhGHw9HV1dVsYipSqVQsFms6i7aCKxio6RQAAECTUMC3OKlCxiEihjg8GRG52HMM9JgqMUtE+Y8VRdKKDkaMMlIhEBsVceTTWUrQYL4AANCu6evrt/Iet23bdvz48YbWxsfHE9GQIUNULX/88UdrpAUAAND2oIBvcfqOy8Ul5xlSKCT8q78sMrlj46L72V3xI+Xa1ddHf9vzto4wl4iyjrxNRGxHTWYLAADtx7179xoP8PLyaoU0RCJRRERE4zFNBjTu77//PnPmTOMxFRUVRKScSA8AAKBtQgHf4rgGXfnW02R5ezk60h7Gt3Kuzbcy7UKlTwr4kqpiZfUuTnKR/TSopLf+4PA+Gs0XAADai65duzYewLJsK6SxdOlSa2vrBQsWsCy7adMmPz+/mmvnzZv3yy+/ZGVlPcsuli9f/uuvv6oTWV5e/iw7AgAAaFEo4FucvPQyKysi4hNJTUb8wbMofula8aWjT9YaJ14m0iOWkZcbmYyIMLeczNXnNtofAADA87FgwYJaLeXl5dHR0fHx8U5OToGBga2WydSpUwcMGDB16tT33ntvwYIF69atU93Jb2BgQER2dnbP0n9wcHD37t1lMlkjMQUFBSEhIRwORncHAIC2CwV8i5MkzWJlBU8WGDLsFTdILtv4TwEfnyIl0iOGNXzpjmHPu/eHD+Ea8Rw2umsqWwAAaD+2bNlSt5Fl2Y0bNwYGBjZ5g/3z1bFjx8uXLwcHB3/55ZcXL148dOiQj4/P8+rc29vb29u78Zi7d++GhIQ8rz0CAAC0BPzM3ILyqxRLL5U8EHd+six/8m572hmqYhJSFXL5P+srzYnocWhuayYJAABQE8MwH3/8cd++fXfs2FFcXNyau+ZyuZ999llkZKRUKu3Xr19QUJBc9R0JAAAAKOBbVEyu5P8SRTMT1slZHhFV3XdTthvaGJnp6ylfV4rYmGtdiIiIefTVXNJlnHZ30Uy6AAAA//Dx8WFZViqVtv6ufX19Y2Njp0yZsnLlyoEDB2ZmZrZ+DgAAAG0TCvgWJNDhMAyVyk0OFrxDRAZeiaJ7HkRkyivy8Pz34YUMRTwRie55lN7pfnltpNFIM00lDAAAoPTw4UOBQGBtba2RvRsZGYWEhISHhz948ODPP//USA4AAABtEJ6Bb0EdDDiuprxqOXuiYskEixNG3FK5fSkr4zE8mY+79MbtJ2H3E8U00CB93ZJNmx0KfO37PL7c33yoRhMHAIB2IT09vW6jWCz++eefz549O2DAgNZPqaYJEyb069fv559/1mwaAAAAbQcK+Bbkbs47P6mD8rWsaLMkabqRSW5mah+HjlHenf699+Fusrz4wrDdq7qXDOEZlmWkiqT9aSgRlclKBDxTzaQOAADtgLOzc0OreDzeqlWrWjGX+tnZ2c2fP1/TWQAAALQVz1TAy2SyP/74Iyws7IcffnheCb2oeBZjZVkeCtEDc5d71WLjrp3+HZXn7yTFrg9WX+5jaMfhfmo3obehCxGdzD12Mu/YZLtZwyzHaC5rAAB4kU2ZMqXedisrq4kTJ/r6+rZyPqAp8qKfFZVxTcdxdLjmr3MMurZ8RgAAUL/mFPByufzKlSuhoaEnTpwoLCwkIhTw6tB1PyC609eQrbzJ79fd5Rqfx0hlLBGl5yreH2KdLJXHSiQRcodBXIPH0qJT+eFEFPZobz+zwYZcI03nDgAAL6CDBw9qOgVoCxTVybOJVWvAQkXl37oeR1o6IQAAaMhTFPAKhSIyMjIsLOz48eN5eXnKRkdHR39//5bJrTEpKSkXL16Mj48vKiqqqqqysLCws7Ozs7N7/fXXhUJh6+ejDkbfvVQwyqT0TG8malvVEXfHsfceyoiIZdmkO3G7+7685/bdMQ52ZGYSkvm9gpUTkYyVHcjaMdfpY03nDgAAAC8qDq/DJFn+IYZvxRPOrT9EVip9tImIeB3eatXUAADgv5ou4FmWvX79emhoaHh4eE5OjrLR09Nz/Pjx48aN69WrVwtnWFtqauq8efPOnTtX79r58+ePHTt248aNjTzXp0E27jvKojrxGel7JgvueHDuPXzSfvv2bV8Tq3HfhOh5eyQtHXavPI6IBDzTMlnJrdI/M8VvOOg5azBtAAB4MXzzzTdPFb9kyZIWygTaFL7DKnnRL6w0n2PgzTV9tW6AJO1TIuIKBnLNX2/17AAA4F+NFfA3b94MCwsLCwvLyspStnh5eVVXVycnJyckJLRKerUVFRUNHz48JSXFy8tr7NixXbt2tbCwEAgEZWVlxcXF9+/f//XXX0+cOBEXFxcZGampyW8awXAFh4vfn26xRZ953N2De+Tsk/bbt2/rzn6f4fNE8Um70548hPam7cx9Wdskiurd6d+u8fheY0kDAMCLYunSpU8VjwK+nWD4HXh2H0kzvpCmL+eaRBLDr7lWIUqU5YUQw+U7r9VUhgAAoFR/AR8YGBgWFqaaXaZHjx4TJkyYMGGCh4fH1KlTk5OTWzHD/1i+fHlKSsratWs//fTTegO++OKLvXv3zp49e9WqVbt27Wrl9Gopl7Ahdyqq5f9pPFU8938mh815xd3c+UQiZePFazGnHlG/l7yuyP8skpU8aSw8Nd5myrHsH7PFGVElV/uYDmzl/AEA4AVTd0q2bdu2Xbx4sWfPngEBAc7Ozo8fP75w4cKpU6cmT568bt06jSQJGsEXzpXlH1KIEmV5e3k2s2uukqYvJ1bKs36XY+ClqfQAAECp/gJ+w4YNRNStW7fJkydPmDDB1dW1dbNq0JUrVzw8PBqq3pVmzJhx+PDhyMjIVsuqIREZ4u9vVdRt/zj9u03OC64ZzGSY1SxLRJSb+mDZxdzIgd0u8s4QkbWuUCQXpVYlzXX6+ELhL8WSggNZO18y6cv77y/iAAAAT2Xs2LE1F8PCwn777bcvv/zys88+UzUuWLBg165d77///uDBg2fPnl2nD3hBMTo6TqurH7wtzVrLtQxgeBbKZvnjc/KS3xmeKd9hmWYTBAAAIuI0si49PT0xMTEtLU2hULRaQo0rKChQZ4w6BweH/Pz8VsincX4d9dYMMgn0Na75p5MZL6q898t/39Rx+8jJUv9JqEL+sXPOL7bRUj7LsDTLcNbCjstmOiy01LGa7bCYiKrklSdzQzV5MAAA8MLZvXt3x44da1bvSnPmzOncuXNYWJhGsgJN4ZqN5pq+yspKpJlfP2liJZL0z4iIb/+pqqQHAAANqr+ADw8PDwgIqK6uDgkJGTZsmL29/UcffRQTE9PKydXVr1+/69evN34Pf35+/rlz5/r169dqa2NRuwAAIABJREFUWTVEh8tM7mIwp6dRzT/OJk/uehjqpNvF1kYVXJx27erjS0Tk+cDA/Gaeq4HHAPNXicjdqIuHUVciOpf/f6XSx5o4DgAAeDHdunXL09Oz3lWdOnW6detWK+cDGsd3CiaGL8vfq6iKJyJpzk5WnMzRd+dZz9R0agAAQNRQAT9hwoTjx4/n5+cfPHhwzJgxhYWF3333Xa9evTw8PK5fv05ErPLO71b3wQcfyGSyvn37fv/996pH9FVycnL27NnTu3fv/Pz8mTPb+jdNsUjR1c5dtRgWeZglBZe4Y06bVkZEUY13eLbjYg5x5CTfm7VNE5kCAMCLydbWNi4uTi6X12qXy+WxsbEODg4ayQo0iKPvzrOeTqxcmraMlRbIsr+hf6p6TacGAABEjd9Cb2xsPGXKlF9//TU3N/eHH3549dVXU1JSUlJSiMjBweGDDz64fPlyK99dP3z48C1btpSVlX344YfOzs6mpqYuLi49evTo1KmTubm5ra3trFmzHj16tH37do3MTv9UisWK3v0GqRZT76YTkV+H//FMBGUlecXJ9yvlFZXyiiJJAYfhKK/G3ym7lSrS2AiCAADwghkwYEB2dnZgYGDNb3OFQvHJJ59kZmb2799fg7mBpvDtlzE8M3nZleqE8ay8jGvmV+/EcgAAoBHMU11Lz8vLCw8PP3bs2LVr15QbWllZjRs3bufOnS2WYT2SkpL27Nlz8eLFhIQEsVhMRFwut0OHDvb29uPHj58xY4aNjU2TnTTb3bt3vb29ORxO3UsW6phz7vFvaWIi+qCX0Zu2hTZ2Lsp2ri53yrUxHC7T+OZ2eo6YUg4AQLOuX7/+8ssv9+vX79q1a5rO5ZkUFhb26NEjOzu7a9euY8eOtbe3z8rK+uWXX/7++297e/vY2FgLi3b02PMzfr+/SGS5P0jSAomIGB397n8yep00nREAQGvQiu/3+gv4zz77zNXVdfr06Q1tlpmZGRoaeuzYMeWD8Zq6o55l2aqqKrFYbGZmxuE0djeBmpYsWfLtt9+qE8kwTPPuPvj64sXhvM+5jNxUj2NtwOk49EZe0ZN+Th+1cnP59xa1ctLZqdNVxvznuIx5Jpu99jdjvwAA8LxoxRe8mhITExcvXnzmzJmajWPHjt2wYYObm5umstIIFPD/YmXiOwMVovt84QK+0xpNZwMA0Eq04vu9/mnkvvrqq2HDhjVSwDs4OCxdunTp0qXJycnHjh1rqeyawjCMoaGhoaHh8+rQyMiIx+PJZLLn1WFd44y/c6AE5Wu2irq7cy9cf1LA596veMVZp0Zs5RtGTn8xggcV98z45t0EvYiom7FPy+UGAADtjbu7++nTpxMTE+Pj4x89euTo6NilSxcXFxdN5wUaxfB03PfJi07ybRdoOhUAAPiP+gt49XXq1GnlypXPJRX1VVdX3759m8/n+/g8qWbPnz+/Y8eOvLw8b2/vSZMmvfpqMx/WWr169erVqxuPUf5CzzBN3OveENfOn0oSxhJRsVzYwczV2/HahevFylV30mzeFnQhIkVFNKuoZrgGw12/e5WYn/OOuRi49xD0bt4eAQAAGufu7u7u7t50HLQbHH0Pjn2gprMAAIDanrWAb32hoaGzZs0qLy8nouHDh//yyy8hISHz589Xrr1x48aePXuWLVsWFBSk0TQbVvW38r/m3Bx5WU7PblL6Z37323cz5GXFqkBWUc1WZ3L0Oo63ebv10wQAgBfPgwcPiMjJyUlPT0+12AgPD4/WSAsAAADUo2UFfHR09FtvvaWvrz927NiioqKLFy9Omzbt5MmTXl5eGzZs6Ny5c2xs7NKlS4ODg4cPH/7KK69oOt96MHqu/77mWXrb8umfO+rjHhBj0IMV3SVWRkQM15T4lv/P3n3HN1W9fwB/7spOV9p0D0pbZimbAiJ8QRAQZMmQPQqIgIJMUZAfG0FQBJQhKggIsmWUvZXZyezeK03S7OQm997fH6mlQEsLdlA871f/aO89OXlSscnnnnPPqZ0qEQRBkDeRfdf3GzdutGvXruTHF6itNW4QBEEQBClTuQE+LS1t9erVlexl3rx5VVRPBZYuXYrj+NWrV1u2bAkAn3/++apVq6RS6blz5+wrzwcEBDRt2rRJkyZr1659PQM84dyTcOrGFJ0HAI5RedHHXBw7qTQcAGj0tjRtR38yprgpOdF4K0ncvkUtVosgCIK8SSIiIgDAzc3N/uNHH31Uq+UgCIIgCPJyyg3wSUlJ8+fPr2QvNRbgo6OjO3ToYE/vADBt2rRVq1Z17dq19L5xQUFBbdu2vXfvXs2U9Aoo/+WM5jKADThW1HJxs3riSzF6+6kbFzb698AAABc2VG43WvN28NZ/Qfl4AECy4fHXyQsHeY7s4fZ+bVaPIAiC1Fnbtm0r/eMPP/xQW5UgCIIgCPIKyg3wgYGBo0ePrslSKkOj0Tg6Opb8aP++9BE7Z2fnuLi4Gq3sZeDCBqR8rC1/OwCA7U7DVm4lAT7qgXloDxEARwWsEDTNs+Ze11+84TyqPwBgGGbl6CN5e8OdOzuQz75kBEEQBEEQBEEQ5M32ogD/1Vdf1WQpldGwYcM7d+6YzWb76jtXrlwBgDt37nAcV7IsvNVqjYqKaty4cW0WWpEEwVwvOCgANQC8Faz98Z/jUY8YAI5weZ9w/J+ka5ru7HX95ZtOw/tiBBEoCglzaB2rvXNNda63fFAtFo8gCIK8SRQKhVAolEgkALB///4TJ04EBwdPnjy5ZKY9giAIgiCvCby2C3g5I0aMyMvLGzRo0JkzZ3bv3j1p0iQXF5cHDx4sWbLEvtAOwzCzZ8/Ozs5+7733arvYF/nlIbU+62MAAMBaN7SWHI95zNg4Ps/vKwDgBwfw/L2ZIp0p6r797IfeEeHOnZtK0V3xCIIgSBUwGo1Dhw6Vy+WxsbEA8Pvvvw8dOnTnzp0LFy4MDw9XKBS1XSCCIAiCIE+pYwH+448/7tat28mTJ999992RI0dqNJqLFy927dp18eLFQUFBvXr1qlev3oYNG4KDg2fPnl3bxb6ImML3FQ5LMocAx/m643KX4ukDRjOXYhiMCQLtP0o6twUA/YW/7T/KeR6T/Gb6CQNrpWYEQRDkDbNu3br9+/c3aNDAPti+du1amUx2+PDh5cuXp6SkfPvtt7VdIIIgCIIgT6ljAZ6iqNOnT2/fvn306NETJky4du1as2bNDh8+PGjQoNTU1MjIyLy8vCFDhty4ccM+x/615SnBGSC+zp4PGABAy0ZEyamo9CeT/8X/a4eRhDHqPqPW1HyRCIIgyJtt7969Xl5e0dHRISEhBQUFUVFR48aN69+//4IFC0JDQ48fP17bBSIIgiAI8pSyA/yMGTMGDhxYw6VUEkEQEyZM+PXXX7dv3x4WFgYADg4OBw4cyMvLi4+P1+v1+/btc3Fxqe0yKzC0kQgA/taFE869AKBVoyeLEdy+8WT5PUIqEbZqCgyrv3y75otEEARB3mxpaWnt2rUTCoUAcOPGDY7jSnZgbdSoUWpqam0WhyAIgiDIc8pexG7x4sUAoNFUdtT3+XXga55cLpfL5bVdRWUR/1w54fkvN6kvtG785Db4K7svMusZQlI8Ji/t2t54M1Z/+aZj/3dK2twquvZTxobJ/rNaOrarwaoRBEGQN4qjo2NmZqb9+6tXr2IY1q5d8duKWq3m8Xi1VxqCIAiCIGUoO8A7OTm9VC/2BeSQV4AJ6mHC8a0abcYwsP8WE0zJse2uhN18257hBc0bCxrVxx2kpR/FcoyVo/fmbG8qbcHD0QcsBEEQ5FWEhoZeuHAhPT3d2dn5999/b926taurKwDk5uZev369SZMmtV0ggiCvI0b1pzVnA3C2Clvi4jBe4HoArAaqQpD/iHK3kQMAPp/fpEkTHK9j98nXLXozEzWza+iEU4HecclZLADYwBabEsdrz2/0d7vTOB1HW+csnUk8/ah2zm9HKo5kmFLvaP7q4NylNgpHEARB6rzZs2efOXOmUaNGIpFIqVQuWLAAAPbs2TNv3jyj0Th+/PjaLhBBkNcRU3SG1d+pTEvW9IgXsArw13ppKgSpW8oO8M2aNYuLi7NYLHl5eQMGDBg4cGDnzp0JgiizMVKhfQ+Nex4YSx9hAQCAsnEX297yTsHS7/9fmxaDk7No+9l49mFYatM77f7+8mCAWYyPkYo9n/7lY4CN951+WnE0RPxab3ePIAiCvM66d+++c+fOZcuWZWdnjx8/fuLEiQBw+/btrKysKVOmTJgwobYLRBDkdUR5fWpT7AeO5gV+h4ubldGCYy2JYzlLJuU5DaV3BKlaZQf42NjY5OTkQ4cOHTx4cPPmzZs2bXJ1de3Xr9/AgQPfeecddFPcy7qTR99TWJ85SNm4ZRvTvLNMAJDt6tzS1+l3KLCfSgrJhFSg0i0r+qcUXmrhWdalEz9h4ES/mQDAcsz3aSvFhCTCb0Y1vw4EQRDkTTNq1KhRo0aVPjJ9+vQvvvjCPpceQRDkeZggiPKYZM3daFPsFshHPT9D3qbYzVkyMZ4n5T2zVipEkDdYudPj69evP2fOnBs3bmRmZn7//fehoaG//PLLe++95+bmNmLEiEOHDhmNxvIeizyjrSc/yJmsX+orSEws25gWmGUCgL//53q7i1OYuH5J+7+L4g5/7AYA8ixr8+7xjJ55QecXlKditXf+Ul+6XXS9ul8IgiAI8sYLDAysrfR+8uTJKVOmvPPOO5MnT46Ojn6+wZw5cwYPHlzzhb0OrNkWxfYs1sTWdiEIAgBA+szDKDmru8UojzxzimP01oylAED5/R/gotqoDkHeZC+6B97O29t72rRp06ZNKywsPHr06KFDhw4cOLBnzx6RSNSzZ8+BAwf26dPndViF/nV2I8eSpC5e5yNMFDvL+2t/lV60gAUArZQI96ZERYzclC/8ETNZOAAozMrq8L8IWXtv9cQIOhUSB8U0PN2qzJ4NjP5Y3j779/tyfg5zaM3D+TXymhAEQZA3xK1bt3bt2hUTE6PX66Ojo0+fPu3t7d20adMaLuOjjz7asmWL/fvz589v27Zt3bp1M2Y8Nbns3LlzMTExNVzY68CSakrsFUVnmYuOKurvD8OFaH0ipJZhhITyXUCnzKDTFwqdewIuLDlly/6Gs+bj0jak6we1WCGCvKle4g3A1dV1woQJJ06cUCgUu3fv7tWrV2Rk5MiRI+vQ5m21ZWVnx6ODXO1fG9rfbyGOdvFNFIQkC0KS5Z4Jjdj7/g6PhO7q5g2eTJVXxzxw87xMuqkAwKYtHoHXnrysv3yrdM+H83brGR0AYICprIWRimcvgiIIgiDICyxbtiw8PHzjxo3Xrl2zZ+Pjx4+HhoauXLmyJsvYt2/fli1bAgMD//jjj0ePHu3evdvDw2PmzJlHjx6tyTJqkuGGRv1HfmValqR3wEB3UZU8JBaNwyOvA1I+Che34Ohsa873JQc5S5o17wcAnOe/Ei0+jyDVoeIR+Oc5ODj07NnTYrEolcpLly7RNF3lZb1heATW1I2yf885R5i1P3LWwsw/hjIXWwKAcYLn2u78xR/Ht2n6xd9xxQP1N+NtXbEudKYX50Q2OBQGAMCy6p2HgANhaAPCxREAcsyZl5RnAAPggAMOAE4WHOzo3FXGc6uVl4kgCILULZGRkQsXLqxfv/7XX38dHx+/ePFiABg6dOiBAwcWLFjQsmXLd999t2Yq2bhxo0AgOHv2bGBgIAA0aNCgcePGnTp1+uijj7p27SqVSivs4cWUSuXly5cZ5kW3pGVmZv7LZ6k8zYnClFHxHM0a43TeS4PKa8ZoLtnyFLkrUgSBtFNPkctIr/z16YzamvPFRY/ZARj/n2EY0pFw6ATYq3yoQ5B/AecFrDTf72XNWU+6fYjxfQGATv8SWAvpNhyXlD17FEGQf+nl/tYXFBQcOXLkwIEDFy9etNlsIpFo4MCBgwYNqqbi3hhFFvZmDs0UXy7ne/HnhFjn8d6/nHJkkHM+js+DATm+v7bzbMFfA1B8Mf6vGFZxcyTnRDa7HU558gEAcFzUppnh72jduetOQ3oDwN6cn1iOAQAcw1mOBQCapQ/m7Zrk91mtvEwEQRCkblm3bp1AIDhz5kxgYGBubq794FtvvXXr1q2AgIBvvvmmxgL8o0ePOnToYE/vds2bN//+++/HjRu3Zs2aJUuW/Mv+J02adOjQocq0ZNlqH9wuSe+AY/nr0wGgzAzPaq9bHg4AAPmUfw7ZwH168bd0xlONeYHfkfLR1VczgpQJl7YjZP0Z5WFr5hJe0DZGe4VRnQBCTPkurO3SEOSNVakAn5OTc/jw4YMHD165coVhGIlE8sEHHwwaNKh3794iEVqaomJfXtacSjGX/EhA730NdjQQPj686lrTmd1ci6zB32VGz5CLOoYBnLG3iXnM5Lo78663KE7vAAAg7fm24e9o3dnrjoPejdLfuq+LsUf33m4D7+tjUo1JOGA31Ve7yHqi7eUQBEGQCkVHR7dv3750bLbz9fVt3br1vXv3aqwSk8n0fHIeM2bM5s2bv/nmm4iICD8/v3/T/9ixYyvcDVej0Zw5cwbDqnfSr/aMMmV0PEez8ul+0i4uKcPj8tenczbOZ2XwMy2tqkDWKMFFelu+Py+kOUYWF8bqGd1VNWtmSRklaaNnjXcBI3Fp22otG0HKw/NfalJH2goPEvIx1rTPAYDyno3xPGq7LgR5Y70owGdkZNh3krt+/TrHcU5OTsOHD//ggw969OghEKAdHV/CgAbCZz4PnGOXhsDQMd5bpy17f9ISg2OBdci3BQbPBvV9ziVnsQBgY9nPFmtwyvCtiddVWPzbFjQJ5vl50Rk52lvRB5x3AQDLsV4C334eH/bhBn/xeJqSVgDA3uyfFoWsxdB9RwiCIEhFhEJhmcddXFwSExNrrIzg4OAbN27k5+e7u7uXHMQw7IcffmjXrt2ECRNOnz6N46++clvfvn379u374jb37t0LDQ2t1gCvPaNM/jCOs7Dy6X4uH7jTmebAPc1ShscVfJ8BAKUzPJ1hTuyTKmk5WP7xz5QvLmiwBUotUotLjQm9oqy5lno/ruIHAek+Hhc2rL6yEeQFMJ435TnNmr2GfjycY3QYP4DymFLxwxAEeVVlvxeuWbMmPDzc399/5syZjx49Gj9+/MmTJwsKCnbu3Pn++++j9P6yuvkLvu/uXPprcqdu54q6C3Hz5OCN847VU7tTwIHnKdf2YU8uqXhlxFk57jOlupB5Migh7fEWAJxK3ZNvycExHACGeY0nMIKH8wd4jAAADPB0U/J11YUaf5UIgiBIHdO8efNbt27pdLpnjuv1+ps3b4aFhdVYJREREWazuXPnzjdv3iw9FN+qVas5c+acO3du9OjRz9dZtzxJ79P8HLq6JLx7N2VkvPG2JnBPM4yPF3yfkb2g+IqJNZ9OePcunWay5g/DBQ05OtWat6V0V/wgUfCJFk69o/hBdzizlPKeVxsvCEGKUd4zMJ4Xx+gAgOe/FNCOSHWBOdH4uMvtnKUptV0I8tLKHoGfO3cuAPD5/O7du3fp0oUkyYSEhISEhPJ6+fTTT6urwDcUn8DWZM97y+FKW+JoH4dhisvh4o4xorAH7TnytxPFiwJm37w5Zc68+zQtwZ+MBog7t806cvhycBoAsBzbwrFdU2kL+6n2zp0vK08nGh4CwIHcXa0c2wsJdIMDgiAIUq7Ro0efO3duzJgxv/zyS8lBg8EwYsQItVr9wQc1twXU1KlT4+Litm7dGh4eTlHU7du3Sy4fLFmyJDU1dffu3YcPH66BG9SryVPpvZtLyodxrJkFDHJXpXrOr2cfh8/fkAEA3iuCLYkGOtOMEYz84+uYQxswP7JlLedYLYZLSjokxIz8480AYIhpxKu/j/QahRGScp8eQaoVLuL5LbEkRRCO/yNc+tR2NS9ywWR+bLVOcpA+f0eNhmW3aPV9RcJGPKoWKqtZ5kRjYq8oa57FcEfL6m0+q0NquyLkJbxoCr3FYjl+/Pjx48cr7AUF+FeQa/XcWzhmvHzLXM2qNT6H9h8kd5kutM968vfk0Y2bOMtscZOVfhQuFFwchlt4LHBA4uRgzzElpzDAmkpbJBoeYgBaW9GJggMfeKL1bBAEQZByjRo16uzZs7t27YqMjLRvCtu9e/eoqCiVStWzZ8+PP/64JovZvHlz+/btf/vtt5SUFI7jSo5TFLVnz56OHTtu2LAhKSmpJkuqKrrL6uRhcRzNun/iJ+3qkjIsjjWzrhO8Hf7nkjruXu6qVPeZ/vV+aZo65l7+hgxcSnp+Xs97SVDR0YOU+1KmAACAY2lb1jfPdIsLAAAk4bes2bcwHp90H1/jrwxBihGugwR8P0zUoLYLqcBWrT6WphOstrUy59IZXsOy4xXKB7TVzHGLeI61Vl+NKEnvopYOpvv6gk2ZAIAyfB1SdoDfuHFjDdfx37RD8dEEn+OsIUZTsO8j6jRmswaK3V2d9IVFHACwet2623eJ8LZTHJ5sn5NhSrnrnAIAgIGYkGxNf+rtnGYtAGD/1HNG8WcXWU9XnrzmXg+CIAhS1+zcubNPnz4rVqx4+PAhAFy8eLF+/frLly+fOHFidS/n9gyCIMaOHTt27NjnT+E4Pn369OnTp+fl5SUnJ9dkVVVC/3cRR7O4AOcHCkvSu9+3DQGDeljT1LH38teny6f68usLzQ8N+qtq+Lye+2f+GP6etWATJVdxdAjGTwaOJWQDMEoOAPq/8gS+x4GycbZGGPkQMAHhXEP7BVQHRmPL/ipJ2tnFeQD60FKH4dI2tV1Cxb50dhyvUEYaTQBQkuHt6d3jt4Lxe4vCvmsEHWu3xuplSTIm9o6y5lkkHZ2CDjXX/1WU8mFccYZfFYJW0KoTyg7wU6dOreE6/pvMrCDL63PvtGnzi5aLOD1Hi3WXO7QLTT9x1Vrc4PatDY0avS8SeZPFVwkVdD5XnNBBYyvS2IrK69zGWVXWQhTgEQRBkBcbMmTIkCFDGIbJzs728PDg8Xi1XVG5PDw8PDzq3urWHp/5G6O1mhOFGTMTgONK0jsAOPWT1/ulaerYe/YP0Dw/gf/mRvZHuU7mTNF6AMB4CfZ3fkZZvBOe4J99AzDyIQAAZ2WNDwmed42+qirCaGyJfaONUdrCHTmsrqFstFdtV4S8yZryqF/dZOMUykijieW4b2TOJo6bqFD6/FwwenkexkLhwDinI80l7Z1qu9JqYUkqXv/Snt5xMeHQXRa4txnK8HXLS+wDb7VaOY57nd/X6xASw3gERhHYt/d6febawtMcDQCY+BPK26O95Y+SAN84Pra/g9SDfDLHJ1Ta0pF01tjUTaUtGkpCn+/Zxtmuqc4X0vkefK8gEVqTFkEQBCmbTqfr3bt3//79Z82aBQAEQfzLrdqQ8mA83HWclyayEBgOFxBuET6lPyJL/+fCCxBaEo0A4PCOjBdQsi8AgRGMPbpzVgojbYBxwAhZC4YLjYBhGMHnGPsmtcw/0+/qmJL0TrrybIV0+rRHAIAyPFKtGvGon91k4xTKMybzp4WqAobx+kUxenkeBgAYsAYmqX9M0JuY4Z9P7/bjKMPXORUE+MzMzPXr19+9ezc1NTU7OxsAfHx86tWr16pVq5kzZ/r4+NRIkW8gAoddfV22xxgik8z9PZd4Wvpj/HrCsBnCMFP7n6cDmOzNkm7cmOYoLf3AO5q/NDY1ANzTRd/TRb/gKfIsOQ/0sSVL3CEIgiBIaVKpNC0t7eLFi/YAj1Qf/TV16sh7wAAvQECnmRP7RgcfbyFsKgEARmtL6h9jSTSSrhSjthXuyOb5CTxmBwAALgwm5aNt+T8DYABc4a8fuo7dY1WKbSoXYcMEyvMjW+FBYMwAQDi8TTh1r93X+AoYrS2pX7QxSsvzF4ScbFX0Z0HW/MT0qQ9ZmnWLQJ8wkWpkz/BjFcoLZkvX/eri9P7PRTB7hq9/IEzaybk2q6wiCVbrQYNxXD6h6BPzTHpnALZr9b4k0ft1zvAcY0mK4MxplWiKUV6fELL+1V1RrSs3wFsslhkzZuzYsYOmixdFl0qlGIZlZGRkZGRcvnx548aNERER69evR2Pyr6a1B++yi+VsGjwyN+8Wegmj3ADjGWPMLRrUF/CizTQAQHp6emZmpq+vb8mjGkmadXD+n5WjK+xfgAvriYIrbIYgCIL8Zy1btmzixIlxcXHNmjWr7VreQDaOO2E0tRfwmatFrJnFBXj9Pc1ylqdoThQm9okmDocqvUn3YY8MtzQ8P0H9A2FpY++bHui151T2AA8AlO9CRnmEs6kxyiZokGB+HCRokETJCzFKznI2zloAgAGGUwEravWFvgpGa0t6P9pwpzi98/wF8ml+AJA1PzFz5mMAeKkMrzSxBAZOgrJ3R0aQ5/mQhDuOt/5dOX5xcXrn1xN6LgjM+PQRa2RYA5P8QeybkeGPGUw7dYZWg1Jccy2S9k5BR1rgQhwAGIAFSvUxoymEonqLhA7dZfV2hSYPiS3YlClq5eAy5LW5WYk1M+pIYM2Vacvo/vpPB/hp06Zt377d0dHxk08+GTVqVEBAgIODAwDodLq0tLTffvtt69atmzdvttlsW7ZsKa8T5MWCnEkAoHDARU3tR1JHxruOCGjZKP6vWJv9yPXr14cNG1byEGdKFuFX2TX/72r+9hXWk/Nem/8DEQRBkNdJnz59vv322+7du0+YMKF169aenp44/lQEateuXW3V9ga4bLbMVxX5kMTOGT6OUVrNqcLEfjFBh8OAAU1kobpfbKEnKblv5gUIgw6GZc54bHqg5/kI/H9oVNIDRjpT3rPp9C8AwyXt7yh/jxA0SAbgSI+PrNmr7SPzpHwcLmpSiy/zFTyf3u3HXy3Dsxz03q8QktilEWjdH6RSdCw7QaH03aMoSe96H16jY82F9UQ8f0HSgBjWYM/wMfUPNK/rGT46R78NAAAgAElEQVTCQXLdbElowndNs5geGcwJBlGYtCS9izHs/1wcAQBYrui4AjjARYQgRFzbVRf7IX3NI338UvcJZO4mjO/LD/4FsDKu07G6G3Ta54ALKa//xM5oZQf4Xbt2bd++3dfX99atW8+sFiOVSkNDQ1evXj1r1qy2bdtu3bq1c+fOw4cPr5Fq3zTvBwsDHMkmrk92myRdKeOD4PZhZEmAv3TpUukAX3nZ5oxNaavdeO5LG2zg4fyqqRhBEAR5g7i6utq/WblyZZkNSm/nhrysDgJ+Mx4vjqZHF6l27mwMox9oThUmDYg1/t7okdnS7JJOqrKVpHfdVTXPRxB8qiW/nrB0J6THJFvBLtb0CADcxl0hZPM5awGr+xtYCwBgpBPlO792Xt6rKi+9271Chmc4rtDEUvjrM+W3zjPZuNu5dEcfPvEm/lLt6d3tt4KS9K7yppb+7Ntcav6GE0o6OAUdbv5PhmffgAzvhOO/ymUTv+F4dFrrs7qE3lFBx1osrcccM5qEGPaDm6w5jwccZH6WoPw1BxcR9f8IEzWXVtxv9YvT3r1ddB0A/uCkw/k+nCWTNT0k3UY815ClUz4DAMprBlY31/J8WWUH+G3btgHA/v37X7DWq1wu379/f7t27bZu3VrzAT45Ofns2bMPHjxQKpVGo1Emk3l7e3t7e/ft29fT07OGi3llGECYnCp9RNhEor8R0mkc8c3O4iMXL158QQ+6s9c0B0+7fzmV8nn2v5Qn39tXGJBpSjuS//uQUtvFIwiCIIjd7Nmza7uEN5kQw35yk0UolLE0PUKt2vlrY8cxDzSnCs1D7u/b6uPiQIZk2AJ+avKC9A4AgJFUwErLwwEYhrOmBJJ0wqVtLQ8HAOAALOXzOUbKqrzyGO3tzWlfD/Ea+47re1XeecrQOMMdLT9QGHKqFeVdxgCDfJofx0L2gsTMzxL49UQO3VyqvAbkxX6I0m+K0q/v5vR+8HP/IOs4e3o33taNX5yHcQAAPD+B19FmFpHhjMk8S6n+Rub8TIZPGRIbmtwJFxEV9f36csDxbV6uE78HmJ7W+qzuXt+o+9t9hc1EW9xkbfg84CBz5mPF9ix7epd2fi2uVjAc83vODvv3f2luvCuPcMlcbM1YQri8jxFPXV+wFexiDdEYz5vyml4bldaCsgN8fHy8k5NTeHj4ix/ctm1bmUwWHx9fDYWVKzU19eOPP46MjCzz7NSpU/v167d27dqAgICarKqqCEMlqr1+bZtKeZSBtgIAJCQkZGVllbdeoK1AZStUaw6fcZ0++plTOEaM852+PHFumjGpustGEARB6qI1a9bUdglvOAmObf8nw48uUs3cGlQ42hJ2UbdoUnazUy2F/sLkD2JflN4BAIBw7EI49WCKzgCANWsVRtnnTbC4MIR0H1flNVs5ek/2dhtnPZT7WxunDo5kFX+a56wsAGAkDmS5w7u4AAcMgOM4G5oDUgu0NAsAGssb+MufpyqKp61tXCicxDgrhwmJoGMtBEGibbRggkJ5xmT+Qauf7iiVdHDy39Qodfx9YDlMSGD8Or+8gj3DR/yT4edPyBAcatba5zVN7wBwrvB4niVbzvds49jxRMGB7Yb0+dJwVnfDlr2O8vuqpBnH6K2ZKwCA578U8DftelN5yv7nWFRUFBpaxhZlzwsNDVWpVFVa0osolcru3btHRkY2adJkwYIFe/bsOX369N9//3369Om9e/d+9dVXYWFhBw8e7N69e35+fo1VVYWEYVKOxW35wa0aPbm28oJBeGmPt4DADdfu2grVz58NENZf2fCHqQHzqqVWBEEQBEEqYs/wYTxeLsPM02vWr/cu6OzIV1pT+kQn9onSXVXzfAUhkeWmdztewErA+QAYZ1OzpkT75zfKfwVg1Ase9WpOFxwtpPMBwMyaDuTuqvL+6x9oLmrpYE4wJLx715preb5B4Y7szFmPAcB3bQPHd6t+fgHyX9aAolrzecuMQs7KAQBnYrJmPWbNbCiP+slN1pCiAigSAOgsc87/JQPLAYCt0MqZ2VquuyqIcdxHSG5Y53Onu1SkY8kP7hnuaF/P9K6zaf/M3w8AI7wn9nUfIuO5ZZhS7rsMAMCtuZs5c3JJS1vWas5agEvb/hfWritR7vUkPr9Sd00LBIKKG1WdBQsWJCcnr1y58t69e8uXL//www979OgRHh7eo0ePYcOGLV68+M6dOzt27EhLS1u0aFFNFvbK9j00fna+yMIUX+MUNZMCBmxUUOfWlQrwpJuLuENLjmG0x8tu48qTiwlJ1daMIAiCIEjlSXBsolSCA8YBUAK80b4wh3dk1gLacFvL8xWEnGpZau/3smGCQNI9AoCD4s2dWMK5F+HUrcpLVVuVJwoOAsBonykkRv2luphqTKzapyCcyOBjLUQtHUp2pS59tnBHdsanjwDAd20Dt8nVvJkcx7CGONYQU+EXR+dVbyVITfnUUbpL7ur7P5l9lXUMw7TnVSlDY+0Z/rCHW1+RkM4yJ/aKsqSaAAAw8FkdXLJlet3FACxQqU8ZzTwK+3OD/913pKzG9rjbneL0fuA1Su8AcCjvNyNjaObQiuGYkwUHB3mMAoBdquuY6wfA0XRG8Qg8Z06x5m8DwHkBq+A12viu2lWwD/zr5sqVKw0aNJg//0XrtYwbN2737t3Xrl2rsar+jQOPTVF59KimohbuPAAgHEmev5C+F9z5fXJV8X0fcP78+fIezlloUuYMgOlOXDTdjecF+km6hgvDGpXXHkEQBEGQmqFj2c1afR+RUMOys1VqFjgXHFex7Dit6tffmpgj7nPJpiZ/NK8wvdtRPnOZwv2cVQEAgPF4/kuro+YDuTstrLm1Y4cusncL6fyTBYf25GxfELQKq9IPx/YMn/h+tDFKm9g7KvhkS8qTD5VL70lq2/m0pzaUYjgOAFgOtkTrn2ncM1Dg71juZ11r5jJrzreVqhgXCls+wEinSjUGsBWqDdfvWtOzOZajPNxE4c15Af+JtbXqCozA/Lc1BgJUe/MAB3uGD9wXhgvwUukdA4zz+TpEPsW34h5fY3qWu22xnDKZ/jSYhBi2xU3WgCInbYCAdx7LcqyAgXBZoPTt1yi9Z5hSr6rOERjxnvyD9SlLzKxppPfkEHGTBMP9M45tehCnGNUJRnOBcOxKp30OrIWUj8HFLWq76hpVxwK8QqGozNx+X1/f2NjYGqjn33MV4gCQq2dbuBcfETWT6q+GtPuSFPAxs4UDgIyMjOTk5Pr16z/zWGtOQcHKH6y5CgDgOLDmKqy5CsP1u5K328imjsKIp6ZXXFaeSTQ8GO3zMQ/n1cDrQhAEQZD/uNsW+hedfr/eYAOgOW6wWDTbyXGSQhlL04M1Su0a+TvmM2H0FkisOBvj4haU1yeU7xd0ygwAoDwmY4JnPxX8G5aEkazhvpWzvmtVvQvgwqSY1Ht7AxdOF7LmWxrNMT5ePOOS8pxMenz075+xOMP3jTJG6+wZXnOqsDJj72tv6c6mlrEjNMNxX9/UPXMwUW1b27Xc1I0JAuzf4KJGgJU985SzpHM2NUbJMaKyG2tpDp4u2n+SY5iSI0UHTkk6t5NNHobxqv6WB+SlxNF0mo15XyTECMz/x8YATzJ80tDYuGV+wR8+ZtLenPQOAD9odTt0egAQYFjxqnUst2xZoTbHChgAB8qvUoytnEStHGq70mJ7c7azHPuuW78rqrNm1gQAh/N2Tw2YvyZ54QnVpU7uEYKc9da0BeC/nCk6gxESyndBbZdc08oN8H///XdlonJaWlpVllOR9u3bnz17NikpKSgoqLw2BQUFkZGR7du3r8nCXpmHmACAAuOTv/LCZpKiY04W2j08VH/pTvFmchcuXHgmwHMWOn/ZJluBkhfgIwpvXvT7cVwkcOjbTXvsvP7KbVwqdhn3Qen2dzV/39NFiwjJcO+I6n9ZCIIgCPJf11koaMvn37JYAKCbULDYxQkH2O4m65+nyGZsAPCJ+RBuuMpU1A8AsJqrlNcnpNtIm2IfZ80jfap2+wCW1d3irAoSoHhfQTrTfmtf8frvVlPJamasocoGSAgnMuhIi8Q+UaZ4/aO3b1vzLADgu66B28QXzZyf2UYa5ESWviOZ5bhtMQYcwyY2fypj83B48SLqpOuHtpyNrDkJl3Yg5SOfb8AxOvrxKACgvGdVcrmBogORRb8fBwwTd2ojbN4QI0nL41Td+b/0l2+yZrN8zsTKdFKTzDbu2zs6Hf3UenV382gA+DPJ9EhlLX3cW0JMaSmp0zOVl6u1cTT9gLbOd3J4KsNjoD+v8r+sYmzwJqV3ALD+sxtoCz6vJZ8HLJc+5aF2Tx4rIlZv8u58UNPhuCaxb3TQ0RbiNrWf4W8XXX+svy8lHZo5tFqb/BWJUT4CvzRTcpTmxlsu3a6qzu21wXh+AGt6bEkYBQCkz1yMktd21TWt3ABvMBju3btXk6VUxieffHLq1Knw8PBFixb169fP39+/9Nnc3NwTJ04sXbq0oKBg/PjxtVXkS3EX4wCQZ3jy9i0KkwKAJqVh51bpJQH+4sWLEyc+9Udfe/qqrUDJr+/nsXQmxqPMcY/MD5IwkvD4v09yP/9GG3nFoU9X0u3JzisfeI56qI87X3iis6yHt8CvJl4bgiAIgvyHpVptcTRt//6mxRJP02E83imTKZcpfnPfJhn7leEqAMYLWI1RbslWW5aN6SQsteA1x9CpMzlGB55Tt2j13YSCoCYnq6FSnPL5nE79zADUDknnGfWX8fDi4WgOuE1pqzNNqQOlTZspdgDOp7znVuETkzIq+HhLe4YHrOL0DgANXMgG7Z7aRMrKcttiDAQGc9u93ObV1qyVrDkJAGz5P9nyf3pBSzptHuHSt8Ip9LZCteZgJOC4fO5EUeviYTBxx1bSHm/lLlxvvBlrin4gbNH4pYosj4Xj+uUpfElig6uLEHs2U2/Q6H7TG35xkzWuaMz/fqF1W4yhzFN382h7ki+BY/BhY5GzoA4vyT7VUTq9UPWrTg8AJRmeZaBofx6HYYR914OqTu+2nA10xmKAilf1x/j+wrAbgFflEmPtBPyrZouCZf82W+YoVJ98mafancuKiZWbfR60FenbO9QnSfcjyqR+0UHHWohb12aGp1n6j9xfAWCAx4gjeXs54N5169feufOihBkXlKdm119yR/PXXV1UL/k4j8yvgDVhgvqUx+RaLLi2lB3ga3hcvfK6d+/+/ffff/oPR0dHFxcXBwcHvV6vUqnUajUAkCS5efPm/v3rxlKE7mICAPINTy4lC0Ouuww7yzebOrcmYUvxwQvnjtPZ67FSf6AZ1TXCSew4uJd9OpbT0PfyFm+g07IdB/QQtW9huHbHdPeetOfbJe39hIEfekVcUZ0VVXoOGIIgCIIgr8zIsSzAcIlYz7LHjKYIhXK4RLxdq+cAZjk5XDSZbxIdwbknqCNZYzwvcMOSgsK7HP0+Jlzh4mxfMsuWt5VjdMAPmEYO+0ujzbDZlrtU9jbsl8K4Dc3JWOLFFH0gkPOFDWyc9XbR9WYOrcWEpLfPzJVJ8yXKQwBAeUzBBPWq9qntGT5rboLDOzKXDz2qtvMXK5lCDwAY6YLxnxre4FgLZ3pkD12VnEJvvBHDWW3iji1L0rsd5ePh2L+7etcRw7U7VRXgAYDmuGtmy0cK1Y9uT2X4dRrtNq2efC7Vl6mlB++Hd51VpqdWWf8zyXQjh+4bJAz3euq+yyAXsk6ndwB4W8Df6Opiz/AccJ87OdpwWLvSo7HR1OG4BgAAgyofe7epjlYmvYP9lg1Gi1VpgO8mFHQTCu7T1oj8Qv+ZKaojmpL0/pmjw0QHCferPH3iA9X+vKT3aznDRyoOF9IFfsJ6FMZLNDx0IJ16ywcKCVEXWY8LhaeO5+3rIx/8R+6vP+kTFjp2YTWXeQErAPsv3hpcdoB/ZmT7tTJlypR33nln+/btZ8+effjwYWpqKgAQBOHm5ta6deuBAweOGzfOw6NG3wD+DfsIfH7JCDxrtinGySM4AGjFkFIxpjNwAJCv0N278lXDek/WwBQ0BAB/XkDxhWpBk2Cvr+eR7jIA4AX6Gq7dsRUon3murq69urr2qu5XhCAIgry2vvnmm5dqP2vWrGqq5L+gGY8X6+MJAPb3+GNG01atHgNY4Ow4UiKOkEoAgDOvMGku2gp2k/KxsxybTlQojxlMALDCxRm3FVmzVgPAj87z/6I5d4KY7FBd28oczz/0iAyYycR4qg5zls8Pqc5HFhxu5dh+asC8+uIGQwVOgaYiMy4Wen9WHc9OyqiAn5pUR88VPK/bSFvedtZ4DzCCs6n5DXbj0vCSs5ZHHzCmh4DzgbXw/JdWZgq9NTsfAARNQp4/JWgaAgDWrCpbzZ6PYbvkrmMUhbcslokK5VY3mQjDAOA7jc6e3tfLnCscfgcADKBHvWfj4iOV9UYO3cqDN6yxqKoKfn10EvC/d3WZXqjaqTOwAFk25hJtif/au7NIYDtY4LM62O2jKp45T7gOZfVRAIAJg3FemcsZsoz2OnAMRrljlGvVPrtdE5L8cYmSOaKxiPC1pdI7lCzpB1C7GV5v050qOAwAuebsn7O+BwALY5r7cBIUb7+BPdDHJRkfA0CuOeuO1/gOfv+Hi5vVfJ2vgzq2iJ1dcHDw6tWrV69ezXGc0Wg0m83Ozs44XgVXBOfMmbN27drKtOS4Sl1Iq5B9BD6vZAQeFxAu7zGq4zalM+ti7BBGnv6r+O6jq0mdQju2BQDOprYV/AoAlkcB3Lu2kq549YrDPGefsEfVyf+4CIIgSPWZPfvl7p1GAb5KEAAtBfw/jSYOgIdBaKlYhQnqUR6TrTkb6PT5LZqc3uYms2d4KwcrtSs4mypB2H4b0cmVwH9yk/mR1fLOXkjnn1Ecs+FOZseuAs0FXdqCcxYjANzV/P1QH9dIFNzBcAsA/iB9upgyGkiqPmmzRiZvTZqkk7NDV5eKW1chjKACVlgevA+AAXB02ueC0PP2LZYZdSRTdB7D+RxrIRw6ES59K9UhywIAEGV8IrUvLcwxVbmduDdJ/OrmOkZReNdCT1Iot7rJtmn1P2p19vT+jrBGd3quW0oy/G86AwDIcHyH3DV4h6dtjZWUVf1Cg5RHhC13I2fJ5Oh8fuglwJ+9LEJnLAXNFQDgBW54wSbf/0bGjMfM3nxGhK/e6vu4lSiEosaXuiCIEZj/1sYszRYdKUjqF93gfGtBw5qescsCQ+E8C2u2csU3blg4i4V5aptJmrUAAAYYEOL/bHqHOhrgS2AYJhaLxeIq+xcmFApxHGfZqvzz+mIexVPon9wDz/NfalKfJWVFd/mtOre8XhLgr8QIPl34FQDQqXMAgFEHWZJ8TDEPKM/Oz/Rpin4IADy/F21YorUVSQgHHKvb86AQBEGQl3LkyJFnjmzatOns2bMtWrQYNGhQQECAWq0+c+bMn3/+OXz48NWrV9dKkXWaheH4xLNTl/8wGJeoigAglEfF09YIhXK7myyMVzzzk/SeY1PsY3W3mcJDLVwH2TP8I+09uuBXDCMWSue5Evgvbq71q+26/KG8PfZPzMstxoWAU+o//fnNk3FHAFiTvKiPNbWnLScTl9wi3BW5v34Z/HXVPjtrZJIHx+ouq7Fv0+v9Gur0vlvV9v+ip9bdsOX/jPG8ODoHMII1xJjv9cD4vgAsU3QBADiOBgzjcIpO/5Lnv6TCZEV6uAIAnZAG3To8c8ryOBUAKM8qfnWlM3yf3IJchkHpvZLC+bwGPCrOQgNARyE/mCIBoDrSOwAA4PygLeb7vYHRWjOWUQErSp/jrAVM3vcAgEvaEM49qqcA0J5XAsCjZsKkMBEfwxKs1tlK9VqZc8nkXozE3Cb6FB0tYDQ2wx1tzQd4B9Lp28Y/51iyliXOtbL0jMCFgaIQAADWjN3rBpaMp1onXDKW0QdG+X1FeX1aE+XWqjqZ39LT0yMjI0t+zMzMnDNnTo8ePbp16zZr1qz79++/cs9LlixhGIZ7ofj4eADAKndnUYXEFObrQDjwsJIBfYwfQHl9DMD52HI6t37ybn3p0iWWZVnTY1vBL4ARhMsCACjaf5JOyy7doebYecvjFEIqEbYs9yarbHPGZw/Gb0pfzVXuhhwEQRDkzdDvaRaL5dy5c0uWLImKivriiy9GjBgxbdq0Y8eO/fjjj3v27Dl5sjrWS3uT/RCtb7Ej/6HyqYW7/zAYF6uKOIAFzo573d3eFwn1LBehUMb+s74dRkgo3y8AgM5YBKyxBZ+3zU02V/M1wVn/EA8tEjSo1vQOACRW3Lka418kfTCAQdZkDDgAcOYsXW1ZHMABKogDrKRlVSlJ77iY4Kxc6pj4omOKl+2EwDAXIS4Xv/RnWpvyEKM8zNE5AAAcAwCs/i6jPMIojwGjBwDgOOA4tuiCLfcHzqausENRuzDAMP2VW5ak9NLHmSKt5vAZABCFN3/ZIitkz/BSHMtlGAxglYsTSu8VsnLcJ0p1nIWW4jgPw44ZTCuLNNX6jLi0Pe7QCQCs+Vs5S2bpU3TKpxxrBcD5QZurr4C0HSF6J6LJDcP3M3N3O7o44Hik0TRbqS4ZQtTfKEoZGgscyEZ6yobXzs3IOEYcy9tHs5Zw57dDpS3FhERMSMSEFGNNleuAg8q2rNvq3gj88uXLFy1a1Lt37549ewLAn3/+OWLECJ2ueOfPCxcubNy48euvv/700zpz9eXEYDeWg9LXAyivWXTqb+68HLdgwtmBUGsZAFCpVDExMU1EK4CzkR4TeQEDRO0KjTdjcz9fI+7QklffnzNbjHfvWR6nAIa5TByCl//n25F0EuKiaM3Ni4Wnurr2rv6XiCAIgryOtm7dWq9evYULFz5zfPLkyd99993+/fsnTZpUK4XVUQ8LbRaGO5NqXnhFu6STQ2NXqnR6HykRA8AKmTMA2Ne0KxmHJ+UjbAW/sPooa84Gymd+I/0Z1nxNhztslX4cSvECqvmeuPG+08f7TgcAlmOWJ3zapuiAH6vb4jUslhdgThjDAxZkA74I3lHlz8sameQhcbrLakrOCz7ZUvVHft7q1NQx8S87Do9jcHKw23PzHipGeXxky/8VOJqQtmV0twDjA2chHLqw+pscawIADBcBKePoTFI+AiNlFXfo5e7Q823tqct5X33n8F4XYfNGGElaHqVo/rzAqDWChvXFHVq+dJWVcMBg1LEcBsAB7NUb/icUiKponKk8nDnVpjpWmYXZcEEw4fJetRbzsuzp/ZLJLMPxn+WueQxjvx8eAD53cqy+5+UH/WCKDgOOoVOm8xsVT4ZiDTGMOhIAKPkoTFDuJtn/0kGDcZGX1XeH35IJmY7nigTjHu34ueF4rTrSaAKAtTJn042i5P4xjJ6RjfT039wI8NrZKzDR8PCu5m8ezh/oUWpbR5xP+X5Bp8zAeF47nAbf1cW87dJ9rO/U0g+0Ff5BJ03CKDnpOfXZTt9EdSzA79u378svv5RKpQMGDACA/Pz8cePGWa3WJUuW9OvXTyQSXb169Ysvvvjss8/atGnTocOz85deT2Lquf9JCHHBtuHuU7/DcfztlvjRS8VXx04f/b5hr/MY6UT5zAMAtxljVb8c1p29pr98Cy7fKn6oVOIwsIctr5CzMRhJPNszAABISIexvlM3pa2+ojqHAjyCIMh/1p07dzp27FjmqaCgoCtXrtRwPW+G+4XW6Hz6ejZtlnJfqYoA4AtnxxGS4vmoBMAKmbMN4KTRNEmhuujlLjBbbAVKTDAb9CMM2d/qnQfa0hbKAX5x+MRCuFw1m+co1eOlkoWqotFS8QBxNS4qdkF5KtWcdUXYvL/xpjVjcfP6Gy2Mggb8Eq9JlW/tU5zeL6ns6V3QUOy1MBAAXi3DxxbQPAJ725f/UjVggkDK8yNrzgaOYzCeB0fnAWCM9hIAACECxki4fWjL/wkjJJTvl5Xs03nsIM5q0527rjl0RnPoTMlxQdMQ+awJUA25+juNzn7f+0Inx606Xcn98P8mwwc6kTgGgU5lf4wEAGvWClvhgcp1honaZgMufOViqtYz6T2YIoMpsmRNO6jODI/xvCn3CGveFkZzmdVewx3eAuDoxEkAgOEi0n9JNT3vOZN5kaqIBRjWXh56wi2xT7QmstBx3KNtPzeI0BZFGk31ogydRyXVenpnOXZ39lYOOCkWuPXRzWA3VckpDKADz8+RzmhhjL4L2FXVuf+59vQX1v/nkWZr5hIAoPy+woiX20uyjqpjAX7dunUSieTevXt+fn4AcOTIEaVSuXHjxqlTiy+3BAUFtWnTplWrVqtWrTp27FitFvuv0LnvmR6dFDZM7NqWPHqpeDLemciDn/YiKJ8F9svAGEXJJg5xfL+r8c49W74CoyheoK+odahy6+/6y7cAxx37v1Ne//alZWVUzd1phiAIgrxuvLy8YmNjGYYhiKc+qTMMExMT4+tbxUsxv/GeWqGMAxcCD6GoYRLRMMlTd5MSAF/LnCU4lqY3Fn29lY66b39k1vSOLfnX4P57crYwnQoaHDi1B4dPVChPGU3nTGYrx6XabFBtDIz+WN4+APByX5abMtfTGm15PBIAzvICTmv+amMe7C3wq6iPyno+vduPv1qGZzj4+IxaQGBxE1563m/xAgT6u6R8gq3gJwzjc5wZgADGiAuDWd11ACB95mKUvJIdYgQu++hDSbf2+su3rGnZHMtSnnJR++aiVk2rNb3b73vvKOCXXtPulTP80Eai+s5ke69yL4gQskH2AE/KR5U3N8FW+DtH5xEuvV+f9A4AX6qLLpnMMgL/tdSdKZ0E/O9kzp8o1Tt1Bi+CGCOt4u0eGO1VW/4OAACOAZwC1mp+PIRw6sFZMllzIgCAMIhO+RQAcJ4v5f9/AFX5TyXNauMA5jk5jJVKoBkEH2/xT4aH7T83WHkpp11EOmNgaze9A0ABnZthSgWAKw/eLlQ1b9lslVRYz9UAACAASURBVFT85FaUR5hsBmQ01l9zEbRRYYK7mhslAd6avY6zZOHiMNJtWO2UXuPqWIB/8OBBp06d7OkdAOy3ow8ePLh0m6ZNm4aHh0dHR9dCfVWAo5MmsaYkz1lmVm8EgO7hFEDx7Rx/Rev0FncHxW6bYg8AkJ4fk66DSXdXh/e6lO5C3KmN/vItzeEz0u4d8PIv1bdybF+NrwNBEAR57b311lvbtm2bO3fumjVrSjZzYVl23rx5mZmZvXujKVovIa7Aejr1qdsvA0jyiEfZEZQAmJ+WU/D1NtpqxUiC8vfmGMbxMGb+8I4LWwgANr8VjXgCAPjS2XGBqsjKcW4EMcWheHBp+V/aewrrzr4uVNV92j6ct1vP6BpLmqUXNPouce7ukOHA0Rjf1+o6jlWd25u9fXb9qhkhLC+9271Chmc5jmGBfqVi7AsQ0CmfMEWncElLVh+Fka4cqwOWwaUdbQW/YIJ6lPtL30jCDw7gBwe8UkUv4VuNdss/O8bZ73v3JokdbrLRBcq7FnqKQrXVzYX/Shn+hyj993f1G7o7vVe/7OxNOPcknLoxRecB41F+Xz3fgDXEWnM2AMbj+f3fKxRQfQjAPAhiu5vsmXUlOgsFG2TOs1VqrErDsx1TeIhRPr2AKGNilEdLfuIMcYwhDgAYjCC9Z2FkVc4CiHCQDBCLZP9sjiBsJg061iKpb7QmslA6jJl7R8u9BukdADz43pP8ZqqsypjoxgDQmBrUzrOwdANFwY9y/e1pJDx2H9fB+X/2gxydbc3dCIDxAlbW0cXdXsFLBHiFQiEUCiUSCQDs37//xIkTwcHBkydPdnOruVFcsVicl/dkC83y9nunKMpWnVepq1aunplxrmhMqLh3fQGwtE19ChgD6QzgDADg74kH+eJJmSwA0Fa4dkffs2Os/YGs9iq4Dn6+Q2HzRoKmIeZ7CdrjF52Gvl73HSEIgiCvjxUrVpw8eXLdunVnzpzp16+fj49PVlbWsWPH4uPjfXx8li9fXtsF1iXpWpv9hmClqeK9bBidXvHtz5zVKu3WwXlUf1wiAoD1OfneasfJuk2XBF2/sTX71cboOXZ1kZYDIDBMwTA/afXTHKUAcDbVnKlj8gysr7TcSc4vJceceUl5BsfwYd4TTidAnDHsPgxpAvt4/iv6OXa5obn5QB8Xq70T5tD63z9X6th7uksqypMffLKlILiMYQavhYFg4/K+SUsdEx98sqWkvdO/f9IXKFmAgHDuzupjOFshAOBOXRnVMQDg+a8E/OVm5tcMHcs+k97t/Ehyp1w2ukB5y2K5bLb0eKUF7YosLACoTC+6xZ3yX8Fo3rIV/EK6j8NFz2wuyNFp8wFYyvNjTFC/7MfXkhUu5f5z6iwU3Pb2rI4nJT0/til2A2elvGfjosZ0+uccnV9ylufzOSYMoTMWcZZMUj6matO7nezprQ1FzaVBf7ZI6hutu6wGgNchvduFO3cGgC/YfADWh9eyt/ypqRCcUztTTBu5/paf/1c4WbxTPZ3+JbAm0nUwLv0PDUxW6kKF0WgcOnSoXC6PjY0FgN9//33o0KE7d+5cuHBheHi4QvHS64W+su7du8fGxl6+fNn+41tvvQUAR48eLd0mPT39xo0b7dq1q7Gq/qX7hdY7efSRBBMAAM6nvGYCABDu6TNW5R9bBRi80+7JnhYXEvvy6q17qmVZnIf1AQDtnxcYja7CAjJMKVsz1udZsitsiSAIgrxJXF1dL1y40Lt373v37i1fvnzKlCnLly+Pj4/v16/fhQsXZLKKV+36z2I5uJRhOZlsLvk6+yjhWtMO8c0b7/IMjm/eeITZx3jDueyvm+6Ga5tYg0nYsonsow/t6R0A5CLhFeePku8P262flmNjhhcoxuQri1i2s1Dwk6uLN0nIytpgvErszfmJ5Ziusl4+An/7kXPsSmHzKMKlj5iQ9HUfAgB7s7fbOOsLuylXgtV6y1K8n7M12wIAlAePkvMAwMpxp4wm3dM7+AqbSgADzsrZ8iseWbenTNurbwGM8/xXAWA2xT7SpTcAAMbDSSfOpiIcuxDO775yv9VKiuPfyJx/dpM9v+a8H0nulrsucHJ8W1CNlx5wYQjpPh44xpr2+TOnbIV/sLobGCUnvT+rvgLqEFwYTHpEAACjvUbI+vMb7C2ZJI87diZ95gLO5yyZGOlE+S6omZLsGZ4fKHSb6POapPcSJhsHT++xbYfxvAnpeADO/HAmo9MCAKu7ySiPAi6gfBfVQqG1p1LvBOvWrdu/f3+DBg3sg+1r166VyWSHDx9evnx5SkrKt99+W81FPrFixQovL6/33ntv6dKlGRkZXbp0mT59+uzZs3fv3m2xWBiGuXr1au/evU0mU0RERI1V9S+5iQgASFBbp55Ray0ssCYgJMDkB2zagw+/ZsTE3cOfTJQ4ceI4nbkcAHC+P0tnltcnv2GgsFVT1mzRHD5bYQEP9fE31Jc3pa22sOaqeEEIgiBInRESEnLixInHjx8fPnx406ZNf/75Z3Jy8pEjR4KDg2u7tNfahXTzhJOq6WfVJV/xCrOE0FfqwRxty04FAGm3DoBhrMlMp2fTGTlzxMLDXj5+roMW/Hg8UG9UMKyWY9sJ+Btkzu0E/HOe7h9KqmVn5ijNzfu6GAwwL4Hf7aLrmaZUAMi15N8x59wuun676LqUdOTjggI673zhK+4sOKNQPbZAuV9vBID6+8P4gUJjtC6xd5RJRc9Qqj9Tqn/SPfnVqQ/lp028Dxx4fhHo1L/im89tLAcAHHCnFUcjFUcqbP88XNqGcB0IrJnjWFzcgvScbFMeA4yk/FdU/ODa01skbM3nlXnKmyRGScWCal6LnvKZj5EyRnuVUR1/cpQ1WTOXwn9pRbHKoLznYZQrq7vBqI7h4hbFF4Ywgh+4ETiaTl8E//w+a6wkUXNpk7gOvusbvFbpHf65JGe2PTUBxHgnPnvGsrz/UzI6MbCPFeuGFaz+0ZI0B4CjvD/D+D61UmptqdQU+r1793p5eUVHRwuFwoKCgqioqFmzZvXv379///6///778ePHa2yWna+vb2RkZM+ePRctWrRo0SKJROLu7q7VakeOHDl27FiSJM1mM0mSmzdv7tOnT82U9O/VcyQc+LiB5iJTzB82xFvmbgDOCgCcNc7NGgcAb7ciBTww0wAAaVn6hGQsxJ9gTQlMwW+Ew9vldev8YV9T1H3d6asO73Uh3VxeUEAX2btXVeeyzRknCg4O9BhRxS8PQRAEee2FhISEhITUdhV1SSsP3uCGogIjczXTwnIAABkWvz+Ug4e57r2pD49IKmPTNTGFXeu6ncz/BheHWZLqAWSyNlvByh+NMQ/si9hhFCVu35wXEpgvc1LwiiffZdhsBQzrU862MlXibOExAOCA25n1AwBkqd8BGBSrva1PP/hMyzOKY++69XuFpxgpFS9TaxariwBgiLco5FSrhF53jbG6a73u3vzJx8mZ6iMqnoagPpSfNv4+Z+M8vwj0/LzeSz3LvpyfAcBPGNhY0uxlK+T5LTGpTjHqk4LGJ6zZa4GzkR4f4aJGL9vPfwpGOlG+n9Ops+n0hUKndwAXwH9yRbHKwEhHymcBnfoZnfaF0KkHL/A7OmH8/7N33vFRFG0cf2bL9buUS++k0EtC70VAqYKIiFJFRECKFRAsKF0BARFERaqCIErvTYqE3gKEQHq/y93l+t22ef+4EJIQQkKNvPf98Mdldnb22WXvdn8zTyF9+yNxCJuzBDtuF3k0uCkP0+6j+lVbAGPS04vJ7Cmtu0nR7pLtrAM7rwIZQAeOe9YGPm0qJeDT0tJeeuklqVQKAPHx8Rjjjh07ujbVqVNnz549T86+e6lXr15KSsrGjRtXrFhx5cqV5ORkV7sgCMHBwf379x83blxIyH9pGkYlJv5503fCQcPxTIYHCeU/gstbgZAYY6f9RvOTHk27BC1r21hyML5oeXz3Ca5mOAlAUIFjKhhWFBEsb9vEevxc4cadPuOHVtBTTEjeC5+8ImNhmQSzjMAQQFDEfyzToRs3bty4qRJnzpxZt27dpUuXLBbLxYsX9+3bFxwcXL9+/WdtV7XGS0LM7ehhcAhv79brHQIAZJr4pbkTunvuaaGI7+Rx5KT5BX9FKT/H+h4aSrsMAIki5hLy8wCgW7IO81xxEjs2R2M5djY1M/fTMW+aRXQbidgk4KsMM0xbsMbXx1/Hm4/ovV7xQ+LH7Ejf2aenirobGIwKI5IBAsTBzTzLlhisKa/7cId4UyEnAb4yGKcbCnnAbwTLI3c3Pv3SWfU12+cjMqJ2xEXTFFROvW9Nsq++ai25NifcqUZ+/uqnADAsgQ+SaAGQhERvNZR3i6xUEDgSBdFBE9isuc5bwzGrRZS3q2Svm4qh/IZz+asE2zU2dzkd/AFmstncH1w3+f9PRrFKQvkP4zRrBeslNvcHOvhjcb2dAIBZLZe9AADo8NmA6AeN8byRb+UXnbVY2FIBMLyAAeBkFjP+gAEABJvdfkVA9fu2q+U17LVYRCLHtTSAM4oOlwDAdqKZtLG4erkQPHkqpc08PDwyM4u8tY8fP44QKo4wNxgMIlH53jtPDpqmhwwZMmTIEACw2Ww2m42iKIVCQVH/VampEhOu2C2EecEcDwhh7AQAaZ0znZwJgFD3NuhgfFHnPSfY9weJEUHxxuOEPK6CYb3e6G2Lv2Q9ed5n3JCKi5cESkKm11xYssXA6D5JHCUi6CV117s1vBs3btw8r8ycOfOLL77A+K4g2rlz59KlS2fPnv3pp2VDW90UczaXSTZwADCgdtHS8bRjJiPvsTx/7JTg2Z8Ez0tKaz861hsA5CLUI0pKInAmTeL1dspnAKFsSUfl2y/fwDxXnMROty5X+1si08405e36Jrm0jd2xLCTQifFIrf4Kw7x3Iefz4RlEhiPj9zxqeR2GxwCQpOeMjlIvvpFelIyqwqusncODt+tSjVEAdzONOXkMgK9m1r2VWyozWYCcWNv74f17X1fIAeArg3GGwchjHC9hLq0O/3xYeuh1B9UvgdsZZz6ir8za+4ks51Vt+aH4FkvROoTeXJTJuLaaqqSABwA6aAKnXY+dWQBAh05F1JNNnldNYHi89LxFV/pGOp/HAMCO2/ZEfalLXdOLGtagdBwHIumI2c7rfdicBZTvG0zaNBCKbvInb/t/DkIUMcdxrQebvZDyGejy+mYzZ2De5Mrq/6zNK4fD6U45jVoEPSmtdyjdsSnRVu6mDBOfYXKFwSPwrQMAl+zEcJIAAFH4HEdCVwCB1QRZ/lXJX0iSNqr9hCysnlRKmDVo0ODw4cPp6eleXl4bN25s2rSpj48PAOTm5p48ebJevXoPHOHJIZPJZLL7Vkr7D3HbwAHA7UKmGZcDJV6kSLENMPRsR3+0oKg4zakrnN6IvT0YzOaVP9YdKD+13+R3sZN5iNKj36fNFjDv4PlfsxaPCvuoqru7cePGjZvqz969ez///POoqKhvvvnm6tWr06dPB4DXX3/9zz//nDp1auPGjV96qZpm8Hq2cAIM3qErkzXNj9asrDlcLdYLAhEqytga3ZQxFKUQM+uQmBAwb8YArGEvf7EhIS0KWENSMaIp3Zqc9PGJIGBqv+KlSHN2p4wFnipRzUgRQq1yxVla25gJ6UQmAwDCYV1838v6YSFAoVF79GUMaxMiXturoqC5Mjh5nGrkjc5y8r85eezkcZkWKyP4SB9+WbVYw88qNAGAKkgUsrsx9L5su2y+2ekck2rHPA78LDJwSkWe87M6eAwvrSGNrG3odhsAzHkpBwFszlkjIWXjIj6VU7Ka3lVZgSCkorCvnLfeJmT1KL/hVT+//yRXtOwPF8rP3XA+j3Ep+WIQwMsxUi9JqXuAVLUnvXvx+p3OpCGC5RwQsnILy/2fcCaXOZDq+KC5stypNELZkvR+mddvYzNniKJXCNYrnOY3QCJR+Iynb+oDcfJ49F69p4Q4M8z/CR2iojoH9+9MKBpTAW9zmt8wOwZwovNmilvAl8PHH3+8f//+OnXqyGQynU43depUAPj9998nT55ss9lGjHAHbDwGXE9JG0fRodOYlPcBKAAOMyIkYo47e7bz31U3SnI92QEAvAAHT7Ovd/epIAt9MdLYKodvWTjTP/oDafai2ITThSdeCRjsK3pSX103bty4cfOsWLhwoUQi2b9/f2RkZG5urquxbdu2Z86ciYiIWLBggVvAlwtFQJsQ8T8ZzpKNCtIcTqcjoeglU0I4JXCnAwbMA7gST/MmzJsEwy0ACRDItPOIbmWm+VIDwJDwAlXnGNf3R534YBI/wQfaNAGAFhw0eDfdS8Ok1pNsGRs4empm4xvmyasz5w0LrREgEpVW0+1Cq5Z13FNMnBrqVyZZ1Dfx5o03bB1Cxd91KbUELSKRtCrL+wAgmM8ClLpK/TC+LBgzOQ4AespkddQyfjub8nah9QwAwAPVOwDkWfhT2YxQYqnjovESQE0A0BfWRIBEjhdynTmrnBdaeDf3FBPBVam0R6r7iUkPQlYX0BNMOlCtaBIgWtLV0+wsdQ/suG2Pz2F6R0tbll56DVKSZdS7C1H4DHvhAcFyDgDo4A+QKOiJ2lydWXnZejDN0S5U3P4+X0ZRxCx74X6uYDPlP5zJnAkg0IGjkST6KdtZGVgeeAxlJvIegnPGf7MdGS/7v46g7A9I8wBxPR/aUfoQyQYeAKskyFdKAgCbpwWep/19W4bd9aYRRXwjCvuaKzgNkChYrI9o4X+OSgn4rl27rl27dubMmdnZ2SNGjHjnnXcA4OzZs1lZWWPGjHn77befsJHPIXYOMyVu1hQD55r/dnDY6jGIkq1BtosAACQGgIb4LAD0aAPXizQ17DnBDX5nMqJ9n4Rtf+SuPqk/DACBkhAzZ7Rw5h/S502PWfjAHd24cePGzX+LixcvtmrVKjIyskx7aGho06ZNExISnr5JycnJBw4cuH79uk6ns9lsarU6ODg4ODi4d+/egYFPpETzQ/DnTfux0uodAFIcUeeszZvJT6fQ0Rggir19xdZwr6EHEBAXfrErs89MKC3SuEDrMYIOYpIjAPLUYwZplySaL4UCBmnNa9I4fK1xg0aLsPN2Td0mk/cIYLIcqreuijWMOE6ZtT5maqAiupX/re4XGiVaJq3J7LKncZjvowbNikkkJku9VRfYBQDQ2QWPRwu2Z5KGc/pt97Z/VuKzK7tP6GwoWPeDpG47v/dCHzjsnHjzgdQyRXOK8i9+e9q1ktwCAFIA9oMpQcvOf6FqnvDV05O5XNI5TkkQ3kQ5/00sxjdZrr7owbcHAugZJS3TmKhn43OYJgGigXUr5eWKxBF0wBg2ZxESh/4fZhQriWtqqYKihkgUTAeOZbMXOJOGYrYA0X5U8ON3dGUxM/PWJBEh/jRqNvFMZ6N0jPaXjEWMwHjTPu28u5TZelHDXCsoPyLG5MAmBwcAIPYCADDjwhTH1+087vYgJJxGBwCkh+qJmF6NqaxnUXHMeTHjx4+fNm2ay5feTZU4lc0M31XW9c7F8ouW5RctsfJP1sYMEjAiSVawyjzkGgyoe1tq/tqibntP8di7orx0jwLCRQ/y7n795IT8+7Q5GbaUi8bTcR4tntAR3bhx48bNs8KVofZevL29b9269TQtSU1NHTt27N69e8vd+t577/Xp02f+/PkRERFP06pykZAIUFnvz0ayy83kpwEgkr3tamkou9JQdgUAgAEAUApmpfUYAAhsjrjmDcdNL8sxovBQGGDsN9bTZ0TPWqGBhFyWIxzKWySYT6gyJiaaD+udqXZZrDJmR+N6nhQAQE06Zk/jUx3OxSZa9G8lhPzZiChvRfRRwI8pHxQSB9z5RAAmAIC/c81IAAwgAFCIB4wBoZAFbQjJg9U7AIxrrIj0vPv6erbwpJbRZmZ3AYB34xSuxmvmi+n2VB+R7zuxLz6ek6l+WATcJ0/rQRCr/dQ1SieBcmD8rlZ/xulc4at+oqXgS0KHTALApHdvVy56NxVAB33AaX/HTC4A0KHTEPn49ec+zbZMexoAHNbt7eLT87GPX3n+yFnFCAwAbMld19SjtZQsNSXUN0ZKE8hZ2gno8+MmAeNmgXTfGBkA2OIv2i8niqLCW77aoWQ3wWq3njgHAJL6/3dFTx8+Odm9E/ZuKomEQl4SguHvtnACtrIYACQkIaYglWt8xNTtBdUeAQhCZgcABLh5PcrXC2kNGABMFv7kqXPFtQAeI5zAnC485vrs4O1tvV4IkoTlODJ+zVr6vVvAu3Hjxs3zRWxs7JkzZ8xms1JZqlyzxWI5ffp0o0aNnpolOp2ua9euycnJ9erV69OnT/369dVqtUqlMplMer0+MTFx586dW7ZsuXz58okTJ/z9n3FUV0011cRfVOgUAIPJKbg86m45ampYPz9aAwAmrMqnA2O4m3pCrSfU0VwSy8mQjaJUJgAARJDBWFQrx5m51nsAqLqole29AIA3AnAtwXZW0UiwXmlRsDIbAGSxyugdcWQJySqpKZ8xNvyzZemeR/W3B16K3hj72DX8Y4EOn81qfgfeTCqaEHX3faAzHLI7VATxq6+6nogGgC0mbdcb9Shg8xQvRUrCKzlsfV+6/h2/gyumc4mpP/ogT5eAn9Si6Da28o0/vfGzhTfbKV+AZk/g5J49MgI1EYv+dTiHa3QlNXyxevcnyVr0k0pCjDm9YDxWppGQx2JnJu/MLNmIJOEVZ1z+f4SU02FfMbdHEfJYym/wYx/ewOp2aYpqQG7L29DSs72CUla8SwU8igt9kvXaOeO/YkLiLw7KsKfs0GweEDisZAcRiV6pWXYS+csTJgFDtBflcgDh/OpmH9qCsy8qZXphYC9CIQMAJj1bt/x3vtAkqRstrlm1epPPAVX4YrvLzDwu4vzp+KGlXj5yLXzb9RoAGNNYPq6JAgAw86390lFCcADCvEVOKqwEAV1b0r/vKcomsnPnzqoKeNvpy5jn5a0bV9Dnx/SFLGZJRH0eMy9UGgkAE2tMm3JjtJUzb8/b8HLAG1U6ohs3bty4qc4MHTr04MGDw4YNW716dXGj1WodNGiQwWDo37//U7Nk6tSpycnJc+bMmTJlSrkdpk+fvmrVqlGjRn3xxRcrVqyo6viCIKSnp5dMtn8vWVlZrg8pKSn3bg0JCSkuu/PZMWOZ5F4AACBdlPvh7LApPCZVyPSTbfQIya/eQoGXoAcMNGWD4mU2LFCKPZ697+7JZhR9cNp9mbQXJTEBzmya1bAAwNdDGYZMMJQ6UrafdMaosHm/plkOGk69vq/N1u6u2FKLxaLRaMo9u5L2F1Nuf5tVBEBiXI6jYBXHJ4TAmUTWRN58dsWtzYekbZUYz2Ls0qzMFICQkJCeuRMEzLKI7q+a9Y3d0UUqqdL4POZ/z1oJAC2pTvsBoPR/XA/fVzflrd6YvbK+Mpa6U53rgeNzOhYETPmKHup8n3b/jwG+pkSXeHgzN3+V2ru2TOrAeEyB/ozT6UMSK33Vcrs95aHGN5loAKqgoCAlpSif/0pmQZ4z99PoOdHyWgDApk/jtBvLHbksiJY1SwdC+ujn+3z17yOu7U3I65cstve4xt/m+M0pFIWZWHnL1vwNg4NHPcT4WqMVAFihnJ/EytiDAa+xLgcAfxSEMEKADmp3tPfuGiAOqth+jEtJesrHy3fCUO2i1ea9x0z7jyMfL3AyYLQAAPb2sL3yQkpq6mP8/8rJySl3l2oFqvhhVkyZMjMY4/Hjx/9/lplJSEho0KABQRA8zz+4d3nkWfn5p80lJ7QwwJ5kBwCEe5D1fIoeM13Fi7qIlmCBQgRn5VVy0vTXYXbItKI8DTVr1rx582aVjpv51hTeYg2c+4k4KqzcDlomf8qN0RhwX//XS2r1palzL5jiSUR9X3+thHgecv67cePGzaNw6tSp1q1bt2rV6t9//33WtjwqQ4cOXbdunVQq9fPzS09P79Kly4ULF/R6fbdu3Xbv3o2qXsTk4ahTpw7GODExseJuXbp0yc3NvXbtWlXHHzly5MqVKx/WOgCAdu3aHTtWtOSYoGXG7Cu0criw0IAJESIIAECUGAFeHTOksfwCAJiQx0+qMR8b5wKAIylaUrPIrx6z9IEDYU1MDRAg0kNPqwtYQciQELXibokwk3O1uehYA0WPkWnvZBcfeqh93DWh1JWJXJoBJPXVgSN19/nzYj4ipaGvR4DT6QwLC7vfC2tJ+13cr7//mFXyhi+J9Ck3prapTP+Kxz+0zBEZTGh4zxfl2zMGDXRev+7aOqh/s58+vg0YX/P7ZDg9fIqnxwARVaXxd+Vu2aJZZ8q0bh1wPHxhKmAhZWxI8db2Hdq1XxGb7cgYEDS8m29flz3htWrZGzYyH9iPHY57x1+8cCv0SyA4HL0tTt5M9biu5xPtjySSkF9Xy9u2Q8bCP2vGfGs0xTucPiSx2tcnROAfenyfgbNUHd4q2DjN9M8qAKg9sEarKQ0BwFvkM7/OLwDAaTcwyWMBYN+/rLXstQQA8PHx6dBEhFktoWotqbvrWV2fp9//nT36w+nOn7t7twmAZ2KPb0OvXmvaA0BxwjgCEV/WXOiHAise/6OV+7bctBe3CIJw4tQZMjwOANsTT5TqjXFo/snTG5dUbE+t/hGtP2tk1zml3mJAECaJyHCkxaqaT6gxteLzrbEkDdGi0Y0VnzS/4zggOC23buz68MsmHj40IgHAyDp2ZN5ekXTRzDoBoFWrVrt27QJAiPKo+PoUn2/F17OaP98rtQLvLjPzeDmdw/ydZC93U7qRTzcWzQscJt6qW/vPIFEOAMhJEwbo2oISiwgnIwBAUlLS5cuXq+TfqOjcyrj1gP6njYFzPym3sNzi1FkYsJxSllTvGPCg4JFXzedZzP6Uvsj1rXPjxo0bN88Ha9eu7dWr1+zZs2/cuAEAR44ciYqKmjVr1jvvvPPUjxu8FAAAIABJREFU1DsAaLXaBg0aPLBbaGjo5cuXH2L8Bg0aPDD0j2EY1yJ8uT0bN77rv1bfV3R8sJ8gCL16vXV3Mv2TExjQvOxPN8S8DghU2BjCZeYbG6n5dMyWSCIlEF3a5wuQSwCBAWOSFZNEjBiJMIMBKYOx9zeT6UBf20WkWZYFABpSh8OJSFTaJARv7NHUPeTP08K+6Xu9DBff85hM03STJk3uN7lf0n4XRf3zLeBROjugmAIApZg6mVUqUZ+vFD3E+B99f+3vOSY/svCt9d1/cYjhzrWd9Y4GMEaUT7PIqecwliIkCELlxzdzpj26vwAA7KjLjNa3ABAI3b5tV9whLCxMQkgBYEf+ptZenVSUB03TUR98qO/XH1+8wH06uYyGj23/lqXPFbmR5wFu97kYvS1O2kTxMNfz6fef8ZUwey7RpOlAjZbF4FLvUTQlCMTDjy9YAGMfyuETGUlQRPMP67r66JmCg9pdXXx7Ur4DufyVguV8ukb1/eZy8mgMf6ND+9jdAIQofOZjPt/q09+/FvSbB5QYADQ03XhVPgBYGQEAJhwsFBHg8dm/iuLVvtzr8OfdZHVPyn4EcZ9HuaR7PWWsQ7AnW28KWNiY/etHNaZXPP7+VEeZrzwZ7op9QNLad79ZLZWnQsSZPlaK06wu2ZnAeMqokLy8ImnJAmkeXJsHAKZoKiHHmS0hJJdMZ66YLyQRtaPIcu5PpaCwRPoS4SyfS4co7vgmCHb7xTiCze/9+d2evgAfAHwAEgBXzoXr9nM1AIAOmUyHTAEAIYNdSs89Enhin/Rola6nw+Go/ovwlVqBf/HFF48fP37t2rXIyMgffvhh3Lhxrr0yMzMjIiI6d+68f//+J29qdeHRV+B5DMcynPbSCRvGHzQAhlretMuF3oWf4++65rGYpxBZ5MI0YJrHrsPprs/Tpk2bOXNm5Y8rOJzZE2bw+kKf9wYrOrUss/Wi8fT3aXMAYHzEpyXz1W3JXb9H+3czVZvTxmMA8FWtRaGSiMof1I0bN26eP56nFfhieJ7Pzs4OCAi417HwKdC7d+8DBw4kJCRER9+3nJJGo2nUqFGzZs22b9/+JGx4xOd71I95ABgImNvs057ObRgQYCJ99Dw23zdq80iCLj/Tchl4rrGy7SFWw9zqccGRaAUARKMaaxp4vlyq7szs/pe77S3gaQGWi3+tt8wpOD6KnF5PGVtVmwvsQtt1GlaolDMmAtg1wLdWlSqrAwCA40Z/wXgIIZG0WborwxlX8Bdz+20AENXaTHmVTUxdGc4bT/2QNs/1GWPyWPxShPj2LcvPfz4m/JNmnm0AIJPjB2sKNDzfUiJe7uMtuTNFdeB0gaRfgtzIG7p7RcpFhj/zSRXlWod/CNuePoWC0C1XYxQEhGCZWt1R+hgS1+nsglpKAMDPGYtOGY4SQETIYlJsN2mC/qHeBoqgBMs5R8KLQIikjU4jcdkUBs7EAXzhAcpvuCjyu0c3pnoSn80M3aXj759wviTRXtS+159I9aiSnNQfXpm5BAARiPiq5nccZr9K+hgAAPC4iE8bV5jNysriKxq2ZIFGg0OYeLAQEKzt6e1qEXFZ9fKaVbJ8+yEq9JSibZ4zx4tWR8lqnTP+GywJz3akq2QN9knGSBD6wce7dYkki5kf3tT+lH27qeyrNaHMGWJNa+/WwWIAAMzYL8ZiJheQCJH3cwTGmDMCAB36OR38oTPZltT9ApvjBIDgWTH+E8t3PS6X/8TzvVK/wtWwzMx/GhJBp/Cyv60TD4GAQUpDj6iS2TsH2i+tAsdZwAgQBoBXOjp3HS7atnHjxioJeEIi9h7SV7t4tWH9NlnzRoS81IzpqqylABAmiyyTbd5PHCBg/oIpXkYqbbz5x/T5s2otrfxB3bhx48ZNteWPP/7w8fHp3LkzAJAkGRYWVnLTzp07161b93QsmTBhwp49e1q2bPnFF1/06dMnPLyUHsjNzd21a9eMGTM0Gs2IESOejkkPh6gjXiT+sGPeITm2AOL9Rq9xZtQtqd4NO15M2jXMK4/FJBJqycjrVtWoLQE9dmBACEPO1Fd93s3N/y7dkWiVNlAoO3prvs9IHnr1ymfR2nZerhGif83qtreAo9Df74v54GBO+0GKNXFKTmpb78iOoZIWQVWYf/EUE72iJRpbKQmSbeHTCrlgBRnhWeotUSVCQYqHqUclqbXadjYSY4ZJeU8UvRIAmLQPAQDJYx9OvQNArKr5O2Hvu7JbCxhdEPE0KXT37QeA/cSlHApoQlT8YhNKkWv91MM0uniHc6RW95OvWobQoTMF4lcT5Ea+sIvnC+tjgUKIQvqNebdfvhi9LVbe3KOcw1cnHBh/oDMYBYFGiMX4M4NhDeUTRVH532dgRgj4KOKe2tuVwqXe9YzutOEYAHTx6fVy4ICJCcNYgV2VtfSdsPcJRVPK5zWuYBOT/qW45uqS+/LGI3zhAUQq6dDnOcy2ZbDo0ogAtnSOt64bNTo7fidWMSZOXrJdQT/xTJNOwbElbz0AAAid1b2CJWEA0Ma7k6s49B85vzZQxdHovr8Pchq1Ci61Nd8iAAACaBNyR7bgCIepjmC7jiSRpKp9ueNgVsMbdgPAddLbwpsB4LXAYXWUDa5ZLmU70r1otcF2tZ1Ce5zzfa9AX6zhMz64WfBzFgBEn7POfCPj87cigot/bZCIDvuSuT0aUZ6S2HOILJuQb6XZYstf83bBZ0gURAeOLlbvktpyR5Ite9qt806mx6T7Tg3/F6nsNGr1KTPzvOL6dbWzZea0kDhmnuNyFyAEDdSS0bbebTIkEpHDwQBAcnLypUuXYmOrMOMub9fUfPCk49qtws17vIf3K27fodlk4cwAkGFLGXG57707sphheQYAch1ZV8znGiqbVvkM3bhx48ZNNWPgwIEAMHHixAULFpBkKW126dKl9evXPzUB37Vr1++//37iHTw8PLy9vVUqlcVi0ev1BoMBACiKWrZsWd++5TykqgOelGl6rc+UVgtYwW72lCssgEEWd1UWW3KdA3n1PtCwRsbNDaMlp0PgPMprzcV03QsYIYSt59tbL9Syjr4OGKQNFDE7G1Nq+oyei/gtp/6M2wsHh5xpoHxjjybiUAFHoYWDQ876K+EaAIQBhAHA9XRrfDbzd78q1PelCLi3TPrKK9bZ/5q6RUqmtn5M68+Egg4cy+Ys5gr+pkKm8nm/AGcERIhj1j543/tAIrKVV8fiP3e9xhs47feZOwDwjFpL/O9kybqXcIpa46ceptGddzKjtLpBqVj+6jVFIV/Y2aPTH3FITABA+Iq6AKDfmHe776XordVaw7uy1rni3lf4qOcbTacczuGagh8XFTqXZwMAk+kIW1T74TQ8ACxNmyOAICVlA4KHE0B0VvfcX7A93vBPv4BBapEvHTad0+/k9dt40wlS1bZoH8yxaVMBgA6ZjGi/x3Oe1RUZhYAqdXFdFSYlJPIQP+3aEDvyNxeyegCQk8re/gNcja8FDr1gjLfzNi2Tf0C7o4ffq/fbnceQYy7lfFRgEwAAA2Sa7rbTfrNUaa9gNp8OmYxEAWVHAWCSxwDAJdLHLo+z2FOj5LVaeLVDgLr7vvJX3m8EIgBArJs/IHDhJhs7pkC/RO1VY2q6S70X+lGeGi78iu3PDdnhb98dnPIZwOWvFszxXM53dOgXZY6Y4zQO0y8CgMKQ6UQquNS7orUnsan+r2tTB36WE/B1eh6iAj6JqMrlrNZU6t4qLjNTpv3pl5l5jhFTCABClGXntgl5HGvojwUqpMZ8T3U3hQx1bR9VvHXz5s2OyqUhLEY9cgCQhGnPUSbjboAHCVWZU6+cs5AbN27cuKn+SKXSxYsX9+zZ02g0PltLxowZc+3atUmTJsXFxTmdztTU1MuXL6elpYnF4qZNm86ePTszM/Pdd999tkZWQKg4ozN9oLnzVHPnKR9FFsCduXlU8jGNAbC0fmKgf4LBj0I8ju36E6JZQFhgRJK6MyhvEWAAhPwmhFNqGgA6LKqVNziI5PHHv2X9tD2336ECnoLFw3zP1lfWCD4cGfZ3ZNjWl+ulRYb9XTti97S29DM480pAh01HpCcAZm6+yeb/AgCUd79KFn6vDCFK8h/jKg6zHOY25vxacWeXhvcnyYJLprvqfVNjdEdxIRKFr6jrPTCAN3G3+16ynnnGX437UVK9r/b1qSuil/l4txaLu8/KdS7PBgoRNFGwMjvj/cTKuTyX5Yb5Spr9NgAMDnqHAAIABgaPkJEyDNgVv4BEgXTw+wDApn0KuEjjcXk/CfZEJImk/Ec+rjOtPvCY36vdmuVIf9aGlMWlz12f+wUOLq4bp6I8e/gVrdgVK/xymXK0sOPvmpL/+m8tAADAULKxzdZah4xdgLeymV/fO4hgvcRpN3FA7BPXyXFkIUBvBo1EgACgm19fP3GgjtEGSUKtvDna8fcghZzB+PSHN1zqPSlOti2+vu/EUACwnTEmdjiL74YbI1HEHACCzfkBO5LLHPRj6wofoeCyKPa95GbXuxWpd2pT/RG2wl2vqP6ZGwoEyvkqOe/btIe8uNWPSq3AV58yM88HnAD7Uu1mZ6lfU9fzHQHaeN1WohUa+dHLWsy+6piy2KiOsRoBfu7XSdhxJ+fArxs2bB8zbqxKMUZV2QKPdGig6sV2pj3/6Ff9GfDlBFdjD79XK5iTc+PGjRs3zyujR49GCC1cuLBly5Y7duyoIAT9KRATEzNv3rx58+ZhjG02m8Ph8PLyIojqWOT8XpKd9U+a27ZRntBCi5yZPYPDrvoM3QwCAQQC4LXiPsdzqH7qouLMJkHtpeFkcVdVrc9gjBDC+o19C9ZqAYDyE3EaJmPsdVJBevb2VUuJHj/Wyfaj8xemex0zgAj2fr3ronQMsODrf9RbZrXzNrUstWuQKNGScJtDTaGahhjQ0cuZm28I9iQAQIREFPXDYxz8uuXKBeNpCSFFCF02nUswX6yvrKj2eDhFvZeB/EdkKAr5hC6qV++svReDSBT+Y13MYsOW/Nt9L8XsiJM1qV7x8AKAS737k+QaP3U4RQGABKEvlhp0a/UCgQgOC4ARjQpWZmMOh39fG4iqLcT/mLEQAAIlIa28OxU3Dg0Z+2P6/DT77QTzhfrKxnTgeE6zXrAlcJq1lP9bmDOw2fMBQBQxF4jHEIpf3Tig3b4pd81R0d6Ztb8vLk9YHfgjZxWLGQAIkoR28O5actNLvn1P6A/nO3NcPvZvh04od4Q6ajpUVao6piBAtoUHgFBV0TqfhLB/7T8ygM7GAJx2A194tMz/MmZzAQQWUW87LvxF19DLGp83njpvPOXaGiIJ1zhz8525gOEf3b7p6m51vzBGrNMDQFKc7NSWmgt8valZagKh/EUZtovmm53O1jrSDFEIAAh5LOU7gNNuZNK+FNdcC3eeC9iRivN/AiBOcVPGDk7H+RzVyoPaVH+4rVDL820l4nFjAi1KRfp7N3K+SgaA52MdvlICfsiQIQcOHFi3bt3evXv9/PwAoGvXrsVlZsaOHfuEjXze2HHb/vHhwnI3HUx3HEwvlRbVQ0z07SsJPsiyE8/dJKhau+Q9mudJpRK73QEAeampkoSrfu3aljva/fAc2NN66oLzejJ2Mkhc5WRFjOAUPY8/ym7cuHHzfwhJkt9++22dOnXGjh3bvHnzzZs3u0Liny0IIblcLpfLH9y12rDndb8jN2cA/6IvnLOhoQVrX5fWvSVvegkAEO2XqPxmYY6+r/dWAvEAEPLG2syDjfzHrgIAhHAB4W8qGA5gkzZQxOxqrFmakfdNWurQqzXWNvDs7QsAwV9HEyIi/8eMw9MOp7ZO4eLlACBwnlOipy1OmZliS3o1cHCS9cbBgl3tvLu4Yl+rG5RXN05aR7DfAAAq7AsgHluuRAELG7NXAkAv//4EEJty12zIWfl1zcUkKse7UMcLapLYa7PnL8mIKuTN3tR38wL3Gw0/+aoBAAFI72S2QxQKX1HXcqqQzXHmLUiP/P3BVRKeJjYBX3IyJdU7YMianKRblgkkIngsUIhAgFmMaKRbkwMAVdLwe/L/NnOFADA6/OOS7c09227L35jryPo5Y8niequBkIjCvnLeeovNmk2q+7GZMzBnID06kp5d7zPwfxgTZ9yh2QwAGiZvv3ZH8cr2M+eW9cYFY7zrs54pmHhtWJkOPC5Khv2v/shLvn1CJGWTDgLAiIbyEQ1L/eTmW4TW6/MRgqNvFoVCYFZjv3AFMHPnz9xy7ZFiToq5IMFyxZF+r7cCjzlAIGA+5cMrEWulAJAUJ5u1PnyxQkYhBADBM2MQoLxF6SU1PFdgsBxrIo76iy/clTP5NRCaKjq1UHbrwKRPBcF5Avp1HCSDfOZmE9mK5UGstVAv8G0l4vdUypfztK/1Ub6C6qSPfX40fGVj4KtJmZnng3ah4qH15UzppK87b9stDG4RKK7hVep5U8+H7nmVSR6fCRgwT5lPNlC2i3+xY51tey66OtQ6sP/Vbi9WyQBCLguc+RFvtT2Eek8wX1yUOqOnX/9XAt6s6r5u3Lhx46Z6MnLkyOjo6FdffbVbt25LliwZM2bMs7bov0eIkhjSNJZJe5vL+9G35/KMI9N1W0bIGr+PCIH0mnpNJx3ht9Kl3kEgpP65od9+Ka6RgXkCkfxCz08uzov48VVcs7sfqaKCvogCgDIaPvCzyGMj/knQXomR1zkOGAA19egYKonoFzjol4zFB7Q7W3t1PKE/tCFn5ceRXz3bS3E/RLXWOa+0QeJIOuBx3mBHdHuyHOm+Iv+uvr0REMf0B3IdWUd0e7r49CrTc4vV9pm+sLNUctTuCB6hbn7cqtRzH0zK+fa74CG4QOvkBs3M70aLwxbWQmICczh91HU2x0l6UAEflyN4ni0KAu0O9JMh5OFaisSQNTlJsywTkQjzGJFI/kOtIB9JyptXsFOoqobnBGZr/u8AEKtqfm/tobHhk7+4OcHMFe7QbOrtN4BU9yXyfxFMJ5nk0XzhAUA0HTHvsZ9vSTDLClYHIZMg0VNdA9+Su87O2wIlIbmOrJ35m31wh08O8mam1Pu8mREA4OfL1rUJ1uJGBNA8SLT8Ja8nZBiLWUAIMAYAh1B+peo7liBWYCroUBKZCAEAWeJ+QbQfHTCKzV1KSGtjTodZLR0xi/LqAQAgOBw3XsFMnkHR2svyrxmJtJ49+iublBnTyBUeLNgJAEOWD1OskwLAzSay1b/X4AThA33hEoQ6SCUAEDQzGmOcvzjDdtF8s8v5iGWemnk/CVabrEVdRbtLyo7ndWv89auznTe3yNvsZQk5NbEP5DKolceeX8LyCQ4EvolI9J5KObpAbxQErcCrBweCgF0aXtZIqXpRXdmLWy2pQi2QAQMGDBgw4NmWmXk+8JESX7Yt64iVa+H/yXC+GyfvEFZqcdt8rPBW7wuu4CWHnLDEN1G2i3+xEWzbU9Th4l9/Zc2dG0JXrawLFeBT5TowAAAgJxUI0I78TR6U1ws+3R9qDDdu3LhxU+3o2LHj6dOne/XqNXbs2ISEhPslr3VTMXTIZL5gsyw2Qdku3vxvq4LVo0lFtvl0TcX0lAF+awUgrMnRyqgkAJDWuQUAiBQSxXH7pd0w4HfaETsVhCunXNAXUdgp5C/OSB2WUOd0C0mMLM+Zc6hgFwIUI6/relNvJo+ymv9tSIlbibyzHem+XGgUMBbjiZuaDTHyOndtIiSEJAoq7e4rIRHcSc3zeCEkUdLmeY93TCtv2Zb/BwC8HjTClWF7QNBbS1Jnbcvb2NKzvYIq9boVRlEkQofsDgB4ublPg92+t3pdqH/IPHlc1oKFIWM+zW56wKwDYHOdkesbZIxLNPyVT6qo6K1xssbVy3/eRWBx4sl71Lsrhh8AIn9v+BAafnn6AhazJKJGhb9/79ZgSWgjVbNLpjM78ja/5NNXRIhE4XMcCZ14w14AoALfJaQ1H++ZFmO/nGjcesB5/TbmeUBIHBWm6tlJ3u5pZFbOsKec0B+iEDU+Yurm3DUXjad35BzMsbQvtwijgxMcXKmWW3oOw0MnE3wAclKOMGAAJaW6n5OsQ3BYOTMgJCMr69nkslZS+qeACpnMFWwS7ImU/wgu/1cuZyntOwxIOZs5EzN5hLyhzJkEAEclDUdFfllu0ntWYM7dOKlapwCA7ChR/OaaO3295xWafrNYJ+gMS9ReLg0fPCuGyXEaNufbzhkzRp8S+dlkzRp4vv4Jr3+VVCf7fRKkX8uIa60FAHHwJzZVgCeYdek2Q64TgkkAuM2x72r1Jix0kEo+8lABBusFEwAQUoLy/89r2CqLuDJlZtw8XsbsM6zv7d04oOjGKlLvPBYoIDhI66RiE5sHCMv7d8qcuEDMsU4A0KSlzjhxckWnDk/HwhqymLdCx6/MWHxUt9ct4N24cePmeSI6Ojo+Pv61115btmyZWOwOlXoYEOVJh05lUj8KnLLB+kpj3R+daT8Rm2vrmTxDFMvkn+hsXdVb8fNHCAmAMAgICBQU+a2/lcrj+UIBWwTBkyAAADOCI9kOAKSCJOUkAGzN+53DHADs1mwB6CRCTP28DiiPQQCDXMfOv1AU/JAy1lHaqiqV436lppTHuFfUf2MGZ2veBgtnqqNoWFzjOlbVrIGy8VXzha35GwYHl8p6yGMMd1L/WgUsrS+J2U6nvX+7cR63Yuw1RSHHNKGkFGLSkm/3vcQVMLI4KnRhLWndFOz0QeKQp31ulSZvYZpmWSaiEOZKqXcA8HhJfVfDU0i3JkcUJA6cVrYydEkKOf0l0xkA4DH33tVB5fahgY8RTATgPckf9vLrDwCkR2e+cD+QKkLZijceLe5JSGOQKPixnGbh5j2Fm3YDxkASpKdSsNqdt9O1i1fbL9/weW8wPGGP4N+zf8GAu/r2DhAHDQwacdV0IYfYuH1gkyBJqYv5/kHDsUxmURev9qGlVKKcrmIGgqpQyBowYAAwc6aKe2IsGDlDBWUaHggiFXToVCblfd6wl1DECZaLbM4iyn84m7sMAAnSumLrlSxCcZoKunmr/CKCDsGeFtzQ7EUqDXxQKvPFKZ7ug6Z5eQDAbxbr+ALDaA/FWJXSkWg17tACACIxqciUxtbxmzQKEOIVXzqThiK02nfCUF5n5AqVEv+Xu24J3d/nfPBpy8QhqX9uiNaH0lcZFgA3E4uXqL1EgDI/ulnwSzYhJaI2NZI1qmzisGpLpQT8tWvXKu5Qr169x2GMG3Dy+N9sxiXgi9U7iIll3wSNm5gVft76/tbG20818Kh/+QXfiP05N117EXt2wdMS8ADQ2qujN61WUtW3qoobN27cuKkMAQEBSmWpVxlPT889e/ZMmDBh+fLlz8qq/zqU3zBH7ioCEtRLT2pHdWI1TnmnRK/YU4JDYv5+oFbi77Wrs1fv/RgQIrDpdFefWo3W+RFDNQV5PO+qF49ZnDoswbhTS3pQ0Vtj6SAxAHjRajmpYAWGwQxCAovpQiSXYcYBlI6QC4AxYASIACQmJHdqofOC9SoAoPJCXu+HjEZD6/83sg/kOrKO6vYSiHgj+O2S7QODRlxPunxUt6+D+qWSHuB5PC8A9JZL99kcq8yWNprZcfYV4XMqPAYPjqsAhFja+AainpQL9CMimHgAwBxGBAr/qa7366WKexVp+DeuYEYAAN7IlT/KHZycHe6krcf3yV//KnO7DZ8LAOC84tT9dncDb2KShpTqSshkTZOBkFTtlO7BdvZq4R+7EEl6vtFL+VI7QirBLGs9cV6/aovl6GlRWJDq5SeYvON04fEk63UV5dHL7zUA8BX5d/XttUfz91/an6dGz0UlVtYpAgGAnH6qZeQaqZouqLuSww/4nwUAERJ50I96G1N+Q7j8NYL1IunRRbBcYnOXCubTINgp716sfhsC+JOKLuSMhVz55RusZOgtj0Ef7RG+63lLrhPShyQQa+p7veI3zcvDgvE2q+17ozk6jfXtfAU7BEQj71eTcKFN1buza46G9O5NenTijUd43XcAYD3aGDuSdT1DF68Ie2tUWp0zttfeuL1gXTgE0wCQzLGZLC+akqz9Kcul3pWdvB/x9KsDlRLw9evXr7gDrmIlMzcVkG7ioIR6J8RErX+avRVFm77OU+WyBCv82LTfZMflV19C+1cV7bLvzz/xvHlPMxlBbUX1SuXixo0bN24egtzcclIQucqt9+7dW6+/b8EhNxXAA/l+0idLwobJIlZuf+OFPn/wfm/+BAC63/rnIj/fbEa7ZqDkxVNSsZl3SDULBmT9diF2X+PN/r5pHFeDojCLU4deLdyhJT2omB13nbdfD3qrq2/vqYnvIYx8ZGS+FSnCf4W0VySkOLpRvIkQT018z87bPoqcHqOMde3C5a9kUj9G4nA6YPQzuxwAADBZb0hhuR98vP3IsonljtgdXxuMkzxV3WVVXvPfkLOSx/wLPj3KJOUKlIR0Unc/WLBzQ/bKSVEzitv7ymUvyqQyhHrJHOMLDNtxqCtVPZMWKjAiACA9KCCANxQJIVJFioI1mC9EohBEVt9VO8q/KD4CiQlxjXIuoyhQhETIlXqM8n2A/7C/JPiHBr+bubLVo0tCa9dD5kwAIFStUXlu0gAgWK9iTkdIox5LOnrjlr0A4DW8n6p70ZIVomlFp5akpyp/1jLj3weUPTsh8oloZkZg/sxdCwCvBg6RkjJXY2//Af8ajiZbb542HG/p1f5JHLdKeNGPP6ibJADdmZIoDSGKmOO41p3TbaK8enCGXbzpOBBSjHkk2DmvHm+GlVNhrhgThqE6sHoQM/bWmtvjNmjZ1GEJAPWdL6svOJ0AEJbC+vRPwnYe0ajmwWaGX45yAHSQX/EIdPgM/moHwDxmY523Q3LjmAmaAp0Yn1wVrRyREnLa8uGQ9H9mhrb5LvefF5S7dLltf9M/T+odKingx40bV6bFbDafPXv2+vXr4eHhkyb+sRWhAAAgAElEQVRNegKG/Z+ilhANfGnzyTvqXULU+qeZtJ6iHcCahTWuXS40e1O78Usf5s3qOyj//dViO3YCQGpq6vnz55s2fcgQIPv5hIIVG3xGvylt/DCeFHbeRiBC/Mhzq27cuHHj5klz8+ZNAAgPD5dIJMV/lktkZGRkZEV+tm7uB4kgJKjDGftLzaX7Wr2xXlMzslaNDM7gkcMGRNX9ByN07gV1Ftf9BfGms7YXfdre8iy8njPteMCUGrFyUmDrpb1jL6neD6c7Z/5rEjAGABNX6BQ+kxASi50AgP6H6n4V2KWV7OCOf6Yt1M6x8bPkXoc3Sn79quZ3BCIxV8hmzgEAUfiMR1//fES0vJDAsMM0ujV+6pIa/ojdMVFnYDE2CkJVx7xoPJ1gvignFX39B967tU/AwPjCY4mWqxeM8Y09Wha3yxACgPYSyTRP1Sy2z7i8deqAm5YzbeQtpmd+cpPTswBAelDhS2tnfpwk2PRRv08gpCAKnwno4XIHPQ2YdAcAIBES7PztPpeit8XKm991k7RfMd/qdVGw8EhEYEZgshz3H6kICSGTiGQV9Qia6NBtEWw3SK9udOD4e7djJtd+qSkAiMLnPHrot2CzO5MzkFik7NKmzCZpXF062J/NzmfTs0SRTyTId4/mLx2jDZNGtvW+u8gvIaT9Agatyly6OXdNnEfz5/I1WEqhWR08lKJypkUIZQtS3ZfX/Q0IgJCBYKN8BnCatUBIlBFzVOIH/EecleKJOsMRcEzeHT3vjob/fVFoZld5uyw8pn9qsXqXN1Eaf5MAgGC1gW+R/CZk9ejA8ZxmLZs7CODS8to1dILQTiIe7aGc8GPoO++m1zlj6/tOChLgtQQ7APASFPMcqXeopID//vvv723EGM+fP3/SpEkPdLB3UxlcX48V3b3i/EU32p0BHiOA6B1x0noKANhts3/bnOKb+xAAFqT8V9ymAz7SPjRsX8Yt1+6bN29+aAHPW+283liwbH3QwmmkSlGlfTHgTxPHUIieEj3LR+T/cAa4cePGjZunQ+3atQEgPj6+RYsWxX9WgNvD7uHwlpBHnFPj4Ehb2VamrRwAKC9jo9HzXVuLA4Jbem+DCdtcn7kc4AB4fXjhjgUl197TjVz6XZ9nJYCyWH7lmvmvmU+21j7WWfn3r7kDMm0NCXF4tmPHMf2BjupubNZczOlIVTvSu/fTOet7wWw+CHYAWKIQpjCFN53spJzMb9WePiQJAGeczLeGQn+Mu6nCBiqq5rTPYe6P3NUAUEtR/4blarl9asnrnTee+iNnVUNVkzIlu1NYbpne9N6HOZbsoerFn6kG/K2Mmyqp2/hWrwuYwzHb42RNVJJ6CsOmdwipiUlvLImtWrmfp0zwjGhnmt24qwCJCd7MldTwLvXO6VkkJrBTULTxDJkd8xgOiSg6fLbzxitc1jeUzwBEl30DZDO+AMFGql8hVGUl90PAF5oBY0rticrL2UwH+rHZ+ZzeKHoCU44GVrdXuxUA3gx+GwGy87aNOb829WzdQNm4rXfno7p9qbZbezR/9w144/Efuxrwep37zuOIwmfYDXs5/W5RyCSBzRcslwAwHTQRPUi9AwCN0GK1V5GG3xU1t/ttpOPeeD/Df3JQl+/ysEMgaBRzsJm8iRIARFFhTEaOLf6yKOJuHgo67Es69AvDhrkAMAyggUrZUSp+V6svlMDNySG1Xr9FcBgTgAQAAKqXz/Ok3uEhktgVgxD65JNP/vrrr+XLl8+YMcPb+7m6Lk+f6W1Vr9WWxvmLAMB/dFj66GsYQ8rgq3XPtNwn5SbrDDzA1CSUdcW4tr/qoKhHB8eR/t3xvhVFu//xxx9z5859OC96Rbum1n/O2C/f0C3/3W/yqCrtiwCFSCOumy/PT/7ys5hvFVT1dTBz48aNGzcjR44EAF9fX9efo0c/Y8/q5xIew8+XLTbW1ztw+Dv+P4mRJY8JChDlYJ68lNFe41XWnbipJyk5b5I1PIVoznSsLuVFR++MK06z9FZDebdICSfAyswlSZaiJZPL1z50MF6N6i2UiAyHcY3ucHNmzbFzyXY0bQaAnfl/tpdFcvm/AiLpiIojvJ8gXP4qJvVD12cC4JviDdngqnPVAOBPV4tGhmPPVCnV2WXTWY0zFwAuGOOLK2CXi5bJv2g808zzroxMYblh2oKuC/KbHjDbVHX/ZV9sLdp3+fa02AZr615sBQKmfEQAIArN8uhyAPNk9owh5tO3QhfWqrx5TxkkIiLXNUgZcrWMhickRBn1Hv1XLCEvG8XwcJAeHUmvl3jDPjZztihycclNgvksV7AFCIkobPpjORYhkwAAb7YCxvcmq+NNZgAg5BW6DDwsm3JWOwVHC892NeX1AGBb/sbj+oMXTWfm1F4mJxVvBo2cfXvKXu3fbb07+4j8AMAVD///UGEbiYLpwHFs9recYS/lP4LLX41EgXTQhEruXlLDj9tRY16PFEUh33lODgYgaBRzpJk8tug3UNmlteXoaePW/XR4kLyVK+oFMMvpV/3JpGdTfuo2daN9AN7S6AoFoV8a7j8qleOw04MUG3leBKSA8J/a7JDbwTOjn8iFeBY8qjtQkyZN4uPjWZZ9LNZUnuTk5AMHDly/fl2n09lsNrVaHRwcHBwc3Lt378DAwKdszGPBS0K0DSl6qHsPChBYPmN8IpfPXG8Wv3l3TV4OE1RKn6+v1TtvPVeDbrswUvha8vKAgnG/ip2sEwDS09OPHTvWocNDpbJDSD12UM6Hs2xnr1iPn5W3a1alvcdFTJmf/GWKLSnXmRVD1XnwDm7cuHHj5hnx888/l/zTnanuSUAi2Pqqz00dR+JJTv0OkVCQ4/ezTD9cRWq3i3p3iLm7Uhdr7OfJxAMCaAKutGFeffd69d0LDrCdvjugB0aIDnihxiJFYaGr5QZJOgAae6v9xKwJN7XrMyKxfqhMnyaOBIBwSSSb+gFglvIZgEg5ZnKRKKDymkLAcCmfaeBHP2rabLLIpw+JggCJAEAA0PI8g4FEIGDAAL5gFPMmhEggqqa+asrrtfPuUnHJaxdiQlJLcTdC0KXeC3jBO1AMAIRd8NBPsgceq2ne8VvOgUFBXYt7smlTAbjCXT2daaF0QHUvylCuhkcU4gwsISEER9XUu5XF8TnOjqGSioPKReFz7MajnGY95T+ckMfdaRaY9E8BMB00oTKLsZWB9FRRvt6cVm+/miRtWGomhcsrcCZnIJoSRTyeXPcluWW9cabwBADIKeVuzV9mzniwYAcAWDjT4tRZsapmAOBFq/VswZ+5a0eHfwwAL8dIHDxu5PdUC9Q/K+jg9zntb4L1MpP6CQDQYV9X6YtMIzTVUxXvcBZ6UVN2Ry7tlQp6rox6BwBxrUiPPp2NWw9qF6w0RoaKo8Oxk7FfvcnrjYimfcYNvgkwXKMzCsKANPzq4GROx6peVAf9VHfjt0nb2kiD9fzID7LyF6UDxsGzHof7STUAPaJ3XI8ePU6ePGk0lp9m8EmQmpo6duzYvXv3lruVoqg+ffrMnz8/IiLiCRmQkJDQoEEDgiB4nn8S49s4LKMQABSszs4YnwgYkJ/IeDzuDzETPi3txd8MbICIzmOCpn2n6nSy15yII9svuXYcOXJkmTezKmE5dKpg+W+EXBq0YCrlU7UElSxmsu0ZEbLnZ2bLjRs3birg1KlTrVu3btWq1b///vusbXHz2HgSz3fszMScnpA32n7qpy5ospbzD299ofgd136+HmZzSvS+v8pGtKyFpvivjr9p8iyOc01eJLj8yphBBYwWRcxxYpzP82HUAxZv/k6yf3y48OMWyjFxVYuquwfBkfCiYDlPB39Ch051NZkFYUB+QRrHAcDrctGUrJcF2w067KvKL9w9CmkcNzi/QCcI7SXi7328C2am5s1LRSRSbTwY6LX8Jl3nRPSe8R4eAMDrdziThgpmRfKw79VD6obMrfmfWFTFjFCs4bFTAICHUO8AMP+0eflFy/wXPF+p+YDMgmz652zuUkLZXFJvr+v25TTrmZTxSBQojT1X1XmZCjBuPWhYv5X09vSbPEocVTQvwOUXaL79mUnLVnZurR7z5uM6VjG/Zn5/Qn+oMj0RoB8a/C4hpD9csPxy2brtVZ8w1ePxdKjmcAWbmdujAKDkPVBJ8nh+mEaXwXFKgjALQqgZFiw1RE+IkNa+J6AGY/O+44V/7ObNluI2UY0Q9btviKPDRxfo/7E7Sqr3qA0NkZhgMJ6gM/xjd4z8x/HC+DTMYv+JYQ/U8P+J53ulVuDT09PvbXQ4HFu3bt2zZ0/btm0ft1X3RafTde3aNTk5uV69en369Klfv75arVapVCaTSa/XJyYm7ty5c8uWLZcvXz5x4oS//38vJPtohvOdPfqZ7T1eryPzGR4MABnjE7GGodtcOL+9BtlSCb8Z6DwGAE5w3XrAyf4vOo5sL9p306ZNixcvlske8rdS0bmV7UKC7fRl3fLf/D97r0rlNGkkcqt3N27cuKn+LFiwoEr9P/rooydkyf8JSByKxKHYmaVxyjJRWKg4g7k9llT3dW0lfQdwOYtcnwnajwqehGT1kKiEIyGb7bjeCzCm/d8pMzIPJCbkAACEDNF+mM0FwYkJj1yk9OPzKWAR5Q2kEjvTAQBRHiZBeFuru86wWwP8YsoLJC5G7xAAwGCvcla5eyBE4XMc115ic5dQfoNdi7EXGTbnzvyIumC1YLuBJDXogHcrHOexcdjuKFbvIoSCPo8EgLx5qZZh7Zm/ttWCG7vz14PHe4AZR+IXiADtqjf+Q+odXOvwaxukDLpq3FtASElAINh4ZUfvqM2NCGkVMrRbWAEALMyDF/mokMn/Y+++w5uq3geAv+eurCYdabp3aZllryLKEBwIFhFRtgyZsn5s+ILsIbIEZW9QhgLKlDIEWbLLLJTuvdKmabPu+v2RWksXBToons+T5zE99+Tc9yIkfXPPeQ+XsV/QX+MzD5HqHiKfy8YvgBe/Gftcqq4djGGPTPceJ09bJqntSztrOG22+eFTkedpDxf7Ad0r8FwF3teEqCg76+yYdHPKdd1liqDbOXSOyAuPM0Y5Mk4t7fKTIA3jIiVkAHAn1ZJjFp5mcW9mAi9ybNxcwZLwTCOtBi4LCLk5YnChVoJ2G0coGpY2UkH2Xo+hNzg6zM7SnQPTiJnqbU5MYPHeCCk/eMemY7D5cRSblIYYmvHxYHzzl8SPUtm0e8rV7xdeOHsHAAah79X2O/V5wb007iqb6IH3U1fHAcAbcB++XAl8GXezKYqaPXt2hYXzPDNmzIiMjFy8ePG0adNK7DBnzpxt27YNGzZs9uzZGzZsKLHP60xrFAQRLiVYrEUjHL90BxHixobLMthvP46yd5VaP/SefGK/6MtuHVOW92+S8n9OanNaJgDk5OQcPHiwX79+L3129bAvzOFRxrBw/enLys4vWXTELJhu6a42sW1lfSPDMAzDXh+TJk16of44ga8Q5if9+0jzp8tx2t847W/F+whsmiVmEiGrI210paDRGDEQRBFIG9pnftH+ImFwmaeM64cIRhp0WjRGmh58gMAcpvj4w5xdSbS/Q+NLKu1BS+QoxLianEcPTs98YGG9KcqlcrbaKhGhbEE5fspl/GKJmyMJ2HrBZB6TobWIYh8bRZQprWf2DwBg8Jwvq4htxsqjj43Cn6LaSCX0P3cpCnL4zGWfu05fOU6/WuT7511cQ0hiLLEetNcgj8U1Jnu3QhLCb09+Dg8AL5G9v9jpSBvac4YlarwldpbM/gMucbnIplr/v1fwiSjSecbI7H3Hck6cN4dHmcOjAACRpE3HYIeBn1TSAnh3qVdP1/4AwIv8rMdjAaCnS//3NB/n8bnTH43MsKQFKuo1VL1kDemaSLSksMlrSzwk6P4s0kIwzqUl8IWz960atS1B5K+HN5oGpWVuc1IH0iWsQUAMLQ2qLQ0qWo2iIcOgAU/YTFbV0cF/b0NUqGw+g9BQa33uj518ttSPHnA/dXWcorWdXTdNea/5tVSuBL60nNDJyalXr17WSrZV48KFC7Vr1y4te7caNGjQnj17Ll68WGVRVSB/ewoAIrML6s2C4yD3wzmGpv+LU2VwfEb+vBFVd2cWUJjivVY5+9/pFhC6JdPavn379ldJ4ElbpcPgz9JXbs0+cOKlE/i/tGd+StzkKw+Y6DdHTr5YUVkMwzCsUh0+fLhIyw8//BAaGtqkSZNPP/3Ux8cnKyvr1KlTR44c6dOnz9KlS6slyDcPoWwt5N1hRTqLd3SikpHEg7DJ/6Vf0F8TLUkAAIgEkSdUwQWvEnTnxby7AMD4Lgd4NgFDAACs8gPS7l0++wwbv4TxXWZNlT/M2QUAi1VTdKnJO1LmIQDe85uhWrM1e9/hpFYSL5/LcZnZfGYWkjC0hwsqtqN7ESKv5zN/Q/IGgH7nMw9Fky5nWOeuIAZLpB+wUrP2FyRkx9B+J7VRA+G4Wt3lpaMqPylC7WRFt/vKz+G/Fe0+PiGrH264PAEJJwDAFD/NY3HdmpW9W1lz+LgJj0VW8Pq+buVl71aUU38udauQd9cSPYXL/AWAYLyXVEYdN0RT9v1CbHt+YA6P4nU5hI1CEuhDKl9xoUe5nM44mmJOdJK4dnTsAgAK0qabc6+fk7b8lLSlnrJRkT0O3mjWdT7lWX8tQiklKgyiaM3eGzLMZo2D9R2JRmiV2n5MhvaCyTw4LfOQi0bzvHeYwuRNVbqkdMO9XHOkUVq3hOxD5MXsI+kAQCopaUClfN1TlcqVwO/atauy4yin9PT0oKCg53bz9PQMCwurgngqXIA9hQCisjlegIKvyIeOC0iXyxL+77EogryZynAzJ+CSnmyp2sO83wr2L/1IF7olv+e5c+ciIyP9/f1fOgDFW015bTaSMi89QnPb4NPpR6INEetil030m/PS42AYhmEVLiQkpPCP+/fvP3369Lx582bNmlXQ+PXXX2/YsGHEiBHt2rUbNuzFtibBSkR7TNWn7Jch7U/akeNdlojmBNp/A6FqI+SFme51BCAABBB5RKlojxkFrzI/HQIgEvI6lGOvIgP2CJQ/zGRdFSTyXsTr2nJp2yjnL2mvuZz2OAgG3u69dGX79zO/Q5YUVtFsuNDxrsVizd6dX+R34sIMN+9n7z1qic6fOktIJTYdWtv17krIS51txyX/wCb8+x2Qc9q6qf88t/yT3vmwUSMyZ0MmZEouq22qrQ6u2yw/4MXUNUN8fpyC6INAgyWpvcvEvjUxe7dCEsL7xyr78yQYn2WmBx9w6XsAgNL0JWyaVuLJpBJZ4yr9q6Lnco6k7geAPm5DKZSfOr3r2OWCNjTRFHcm4/j7mpAyB3hzIMaNkNYSTBEAQNp3QbRT8T689rDIZQMgQv1JiYPkCUI6zxfO3q0YhNY4OozJyLpkMmUJwgsl8H47G0T1u6c7nvHkw1sBx/I34S4g8mLs8IdZB1JJJVXrt8bS4mvsa5qqm0ZVIYKDg69cufL06dMy+qSlpZ08eTI4OLiMPq8tOY3clKSFF2NzuMLtmq88/H9r4rO1gee3gQBgOZvVWMJckbS00M61XZODW+VPUBEE4dUXDqi6dVR2fvm6Bna0w5RaC7xkfrIKXfuEYRiGVbiNGzf6+voWzt6thg8fXrdu3f3791dLVG8eRNk9lEwGgN4OG2nXkQBgiZ0OIm+JmQYg0K4jESIAAFFOiHa0voRN/FZkMxEgptaW4gOObW6z/n17kgBCFkg5DwKRZ2NmIMaN8V5AyBvY+CzZocrtm7tDBDRKMSnsn3vvL5296w6Hpi3ZYIlOIBRySaAv7e4smC05J84nT1vGZ+tLexVp18map5O27xqRAgCS5C0ppy8ppy+tO8YhWV2QBQBALO27iyshE6hKbnP87bp30J3qAACiwNh+uKLmZu9Vj1C2tFZ2QKQN7VX0/aSmO5iy28Dn1Vc2bqhqVtBIILK32xAA+C1lr47Lqr7oqhYiab/vrE9Fcyzjt5zxW1n4QShbiFw2AJD2XUjVOyWOoSHJP92cf3J2LD4biEFovcbhL3eXEqfQlxUXQ/jtDrLt4shlWCI+um18+G+tO2v2rt2bYs3eFS1tX2jk11PJd+Bf2yI3Y8eOPXHiROvWrWfPnh0SEuLt7V34aHJy8rFjx+bPn5+WljZ48ODSBnnN1bKnEvX80yzOz+6Z/zuqjg4AIPIipabNTw39k9EDNZlj19UxfcuQz3yu/H3X2m3btm3z5s2TSovOEHtF5ifReZdvsUlpCCHay03xdnPGy620zg6045zAFRUbAIZhGFbhbty48dZbJS+YqlWr1oULF6o4njdYnqr/k7htgbIngGjEuAt5d81P+gn6q4h2Eil7URQAQDA9FfTXCWULEAxc4nIAIO0/JOT1yh6Z9pjOZ/zK51zgtcco50GU8yAAkD/pz4uWE4rut+iGNEIr1fYlZu8iwNa7eVHZz9wzCNdyAHApwTzzgg4AeJ3ecIuFul2867iP/aw+TZMAwCakpK/ebolOyFz/k9O0kkvQETbNKc3nXPpeIOXJ3iv8Yoa7s1G0134h5y8ubTui7CSBO8wPOosAEa5zPrOxe4k/1YrlNsc/c/s3vCFTVrcPkvlWdzhVxMCJS67kZJmeqVn4IIMFgAPhhmvJ5oJGBFDLnh7T3KbEbzYY7wVmNo3S9EN0zashXYY4Y/Rf2tMEIr9wK5pZ1FU2cuS7Ruierrh3+j3Nx9ZGaw3IRxms5Nl/cJ4qqiaWtVt4OcdBRhTekIJUvUPaf8hnnRAMD7j0/ZTmi39783ls3GwAAEQx3vPKGFZV+kIeBGD/Ust8rDl8VN97uhMZER/dDjjWRFbP5o3M3qG0beTQi1QgB4BX3Ivuhaxbt27cuHHWnedtbW0dHBxUKlVubq5Wq83KygIAiqLWrl07fHhlVTSt7G3kFl/J2RyW938tlaOblryqJ3bkw8xdyW7f+LtM9hH010wP3jeJrgFdU7VarbXD7t27+/btW1HxiGZLxo+78y7deqYVIdUH7zgM+hReYSkdhmFYDVUjtpkpj3r16uXk5MTGxpLPZnc8z/v6+tra2t67d6+6Yqt6Ffj5fvCx8emzWXFUNpedcX6r/yALyK/AzHYw09p+mZzTBq0ETocotchlEhIfaZPblsd9uKwTgGh5s6dAqZ57Oi5lsyVmMpL4yBpdAULK51wwPwwxEfLumuPZtIYVwY+mdmgcHYuVr0vN49vsSitxzJKvq4djwQbXvE6fOGaeYDC6r/mGdi25IpTIppnuNBd5vaTur2ziciHnMuU8lNedEU3RjM+3guEhl7adtO0gqXuw/DFgFetWiuWzw5nl7EwiuDbQ2U76H/rdb2nkzMe5D97XhHzuNqjIoeORpjGh5b33bichbgxyrlmzOgysGLQlRSUhbg965ksZ0RxjDGsJAosoe2mTMETm79nOxs9jE1cCAO0+gfasuhrnz8RmEaw5PKVhAn5vkro69kWz9xrx+V7yHfjXucjNyJEjO3XqtHnz5tDQ0EePHkVHRwMASZIajaZ58+Y9evQYNGiQi4tLVYZUsWrZUwDwNIsrrYNdN6fMXcnZR9JdJvsQyhZI6ic1RQ344t1VP/5q7bB+/foKTODTV283XLtLyKTKD96R1vUXecF077E+9FLOifOAkMPgns8d4c/Mk39pzwzy/NpD6v3czhiGYViVadu27aZNm6ZMmbJs2TLiny9kBUGYOnVqfHx8ly5VUVfszcOLMOO8jhWK39todVrXuZNtaE7W37eZJk0Utx8YGiQb74NaR9q9R7uPMz34SDDHsHFzuOyTAEB7TCpP9g4AlPMgLm2bYHjIpqynXceYY2YAwGabYXKp60a1/RRtVgTLDUzPKJ7DOyvITR86pOU984XFxUTziUhTW3fJh/5SAMjaeUgwmuz7fGzrqGzo9O+8VtJWKWvWIO+v6+aHT0tL4BHtRLmNZ+PnszEzGP/1pgeduNTNAEDIahPKlpbY6YAo2mdRea4RqyRNXZgdXR1yzM/8dT0QbrgQb/6sjvwdz2c2CHC1If9T2fsN3eXHuQ8AQMtm7Ej4scjRHEFW1zUow2TiBFZO2vjI/QHgUQarNQl1HWmHQn9QCKClK1OzsncAsL6HCcVu0yKJD+0yik1aLXJZXNIq2nMWAIjmGDZpDQAgyp5ym1CRcYi8+ekw0RRVjq6Idh/vu6trVO+7OaGZ4W9fEznxDbv3blVyAv+aF7kJCAhYunTp0qVLRVE0GAwmk8ne3p6oiFvB1l9iytOz8iYdBDjQABCjKzWBV73rQNqQhts5lngT4ymlNP3Y+HlfdRdXr8ufT3Hx4sV79+6Vp9rfc+VduW24dhcxtOvSybRb/tdv8hZBirbNUr5ZnXPivPK9trTHc74uSTDFRhsilj6dOcFvtp+8hM0dMQzDsGqxaNGi48ePr1ix4tSpUyEhIR4eHgkJCb///vu9e/c8PDwWLlxY3QHWSCSCbR853E2zFG58rOV+izAeypvewfZCN/sjf9Fr4/ijGaqQHorRADTtvYCQBZDKlrz+Gpu0GgAQ7UC7TynvKRFJey80P/qES1xu4fKQ4UEC6XnRbrB13fs2jeOg9IzScviO3kW3cMvjxBORptpq6ot6cgCIif4beMG7+SBU7AY+5WgPAHxOqcvgAYB2+5pL/0kwPhbyblGaflzaDgCgfZawsf8DkadcRxGyOuW9TKxytPUo+nfgWrIZ4qG+I93Fv4KXZNYs93Ly559ez75UYgcnn9+sxRsIRKxtsEdKyL46oT0ba/6/Fsri/7LeJLT7ZC59t8hmsknfU5o+SOpviZkBIgcAtPfCgnvyFUMw8VnHQDA/vycAr/uLcfjY7+eG1hz+jczeoZxV6MsocrN69er9+/dXV5VahJBCoVAoKqyWIMMwCJW8rKDK1HekuvhLmziXWgceSQhlJ3X24TTdiQzFVwBflnYAACAASURBVO4Z9p85JizyUV3s0L7t2XN/WfusX7/+hx9+ePVgjH+HAYBoYc1P4woSeACQBPradGitP3Ux78ptu88+LHuQL9wG69jsm7orx9MOfu1T1haAGIZhWFVydHQ8e/bshAkTjh8/fv/+/YL2kJCQZcuWqdXqaoytRgt2Z4Ldn/kcPxFl+i3CqLDxkbqPYhNXtmM2SYPO+Dz8WMjhKdcRhCwAACQBuw2364AoAADt92IlaUnb9qTDR7z2GEr6DgD2OEzd5OxmXfeuJoltGseB6RmRLDckPfNnZ0f5i6yUJJUKPlvPa7MpjUORQ1xGFgAQqjL38UIM4/WN+ckANn6BpP4fgv4yoWwFXDafcxFR9rT7pBe6TAyrSp+69q+lqCOIwnN7ukjcpUSpOzK8gUgF7TXPEjkaRM4SO4tyHclnnQAAQl6f0nxe8edy/ZpNXI4kXpLA7SXuUCjo/7bETANCRruNAwBCSvj93DB9fbyqk1rWoCo2Gqxi5Urga0SRmwEDBhw4cMBoLHnLwXJasGDBggULyu5jXSP3omUCyo8m0JrO9mX30XzloT+rpV0k32izjxuI86pOct3JYb39CxL4Xbt2LV68WKUq19S7MnDabOuTzPU/MR4ujJ9nwSFp3Vr6UxfZpNTnDkIheqT35EtZZwMU1bZDDIZhGFaiwMDAY8eOPXny5OHDh0lJSV5eXvXq1fPz86vuuN5YtNtELv1nIe+OOWKokHMJkQogbdikVdajhKK5kHsNSdxF433W+O9XKoQ8iLR7t+yRGa/5huzTSDDfk7Ye5fNF4ap1apLYoXEcmJ7xhGXjOK7Oi1R4ltatlXfldu6ZK3ZffFS4ndfpjTfvWzuUPQLp0I207cDrznGpW6WNroFgMoa1AgDacxainvMLD4ZVIxVl+7ZDp+qO4jVFaXpzyRsEw10+64SQewMAABDj+11l7HFGu0/kMvaJ5jjBEE5pehc7LliiJwIA7TYWSTysTYSUcB7/xi7dLVcC7+bmFhYWxvN88SI3d+7c8fT0LO2FVYllWZPJVN1RVBFlO/tGSe0AwDE7RwA4o+rdTXeyS5Pb7u7uiYmJAKDX63/++eeKquQnbx5kuHEv7duNrkunkLYvMyuGQAR+B8QwDHut6PX6Ll26dO/efeLEiYGBgYGBeIlTlSAVtOdsS+QoPvMQAIh8HpdQtJaQaE5k4+YWbkGUrax5TNkDI6kv4zHdmLyxXsAKZbGa82qS2OesiWFLyd4Fs8gmW5/aiAYPSa4tkotmGwBQdQk0hV/Un/2FtNUr2gUTci8AxCakpH+/QzAY5c0a0G7P3wGO9lnE332bS91MOQ3gs46J5jhCVody6v/cF2IY9rpCjO93pgfvA4gimw4AlPoTQtm6Uk5FyGjP/1mejmDj5pAOXYtM0efSdgu5txDjRruNrZSzv37KlcC/JkVufvjhh19++aW0ow8fPgSADh06FLScO3euKsKqPpZYUycHZjvANqLlxxJv0hw7qE+XBct+sh5dt27dqyfwlKsGHj5lArwFg9H08Gnask0uc8YhigQAU3gkANCuL7Nxa5wxKtEU19q+HcK7rGIYhlUTpVIZExNz7ty5KtsLFrOiNF9wqVuE3JuIcRctiQCIcvkKEfISO3OpW0ReT6o/K8/ItNs46wzSEikQqs+UeO9dMIa1FM1x1h+6AXSrCwBgvJ1/WD3E+t/DprtgiW2Wd6U9m5gKoki7atQjy1U0l5DVoZwGcKlbLdHjBcMDAKB9vwVUrt9CMQyrAgsv52y9m1e8Pdci+q9PLtyCAKYGq75qpCCULSj1J1zmQQAAxNBec4u/vKJQjr241O2C/iqXtLJwiXuRz2XjFwEA4z0fSnkXffOU663zNSlyYzQa//zzz7L7PLdDTfFYy2lkhIOs1FkopgjDo+ZXHT51dlrgGMvx6Zohjgmzv/wgdfEK0rr/TVhY2OXLl9u0afMqYSjaNM09cyXn6DmnqcPSV2w1h0dl7TjoMOQzc0RM7tmrgJAiuMlLDLsncVNE3qN7+tuDPb+m0AtM5MMwDMMq0IIFC7766qu7d+82bNiwumP5T0GM32o2fj7tMdUSPUnIvYUoe9qjhBoxvPaYmLQKUXa05/RyDn3fwrpRpENJlX11ghDLcQ2Z4kV2CETIRAAgZIh2NnJihkGwkxLKQmWzRbMFIAkAuDQjm5BCSCU2HVrb9e5KyMu77pf2/B+feVjQXwMA0iGEVL1dzhdiVa+2A00REGCPv2F5YTKKAAA5VfNuUEkpRCAoYfeMYmgSFexyT3vP57KOg2CiPSYVTF+vHIjxWWy69y6b9AOl6Yuk/tZWLnGZyKYSyhak+pPKPPvrpVz/Ml+TIjeTJk1ydnb++uuvRVFctWrV+++/X/joqFGjfv/994SEhKoJplKlG4RuB9IbOzP7u5f6Z0uqKFEE3ZG0bgvct4Bhv/zT0eR3rvKbH33w9u/H/rT2WbFixSsm8LJGdeUtGhqu301btF7esmHexRs5J85bElLM4VEiy6q6tKM9XV9i2BDnL9bGLLmadV5F2X7hNvhVIsQwDMNeWteuXVetWtW5c+chQ4Y0b97c1dW1yJYurVq1qq7Y3jAeSpJA4KnK/72LkNeX1N4LAIz3EtOD99mk1ZSmL5I8uyZRtFjivgEA2mMGoooWkCtRNMf1Sk13o8gdGkd36plZ9Gk8PyAtM5bjDjpr6ha7D0/7LDU/6o4QKW1wUkY78wZBI3/mbwKfedgcMQiRdjad1qi6OdMeLogqOku/bIiypz0mW2KmAyFhvCvxTh326nrXk38SKJPWwCy02s1oo3zfT9LKvdRa1K+tiS2VE1s+MzU91yI22ppiw6CwwaVuOIUYN4n/D3zORdp1TGVHSCgaU5peXPpeS9xcSeBOABBN0WzKBgCC8V5SYnG7N1V5v1p7TYrc9O/fv23btv379x86dOjXX3+9dOlSmSz/q1+5XA4A7u7uVRxSZZBRSBDhfjrLi0CW8reRdmZsWqpyr+reu2Lc0hJ+MxFjnQZwyWtH9VL8fiy/z+HDhyMjI/39/V8lGM34LzPW7sq7cjv3z7+tLaZ7jwEh5fvv2A/89OXGrKdsNL3W4p+SNvnKA14lNgzDMOxVODo6Wp8sXry4xA7Vuy3LmyRIQ18d4GxfbA9t640jPvOgJW6OJGBL4UNs8o+iKZKQ1aacvyznWdxJsrGEuW22WHeMK8jhC7L3+gztQ5fwux9p2460f5/P+oONX8T4rS6SvYNgyv8qwWs25dy0nMEURzkPFcxxhKIpkryxxaXeGDh7fzkuCvIj//9SOXoAUt2DVPeomnPRXnN57TFee4TXnSVtO1piZ4Bgppz6EzYv/75UE73Y3JjXociNr6/v+fPnFy1aNG/evNDQ0N27dzdr1qx6Q6pwNgxyVpApeXyGgXdWlPoNt+3HTrlXdarjWt82mmiOu+MwpEHKhrdrX2vSuMHtO/cBgOf5VatWrVmz5lWCQRJGM3GI6nFU3qVbbHKaYLZIfDxs3g1mvF/puxJPmc9Uf7zDMIZhWHWaNAlv4lV11KUsi2O85xuzTvKZBwXnIYQqf96cyKZxiSsAgPZeCOVea8YgtEWjHp6eed1s6Z+WscPJ0ZMiM3hhcHpmLMfVoenNGrWslG10GO/FRt2fXNpuymlgkd+G2eS1FVN2DlGM96JXGgHDsP8wRDtRbuPZ+PlszAzwXshnnUSkDe05s7rjqmolJ/CPHz8GAG9vb6lUWvBjGWrXrl3hkZWBJMlZs2a99957ffv2DQ4O/uabb6ZNe9N2F5/SWvkwg9XIy5qfZtdNkzgzQv9XVojCe5Uu54DFtpFDNz7z4LgvA78cn7/SYevWrXPmzHn1ZQ6S2n6S2pU44cLIG1ItyT6yV5osgGEYhr2QZcuWVXcIzzh+/PiRI0ciIiL8/f1HjBjRpEnRMiuTJ0+OiYk5cOBAtYRXSRDjRruNYROWWmKnSxucBUQCABs3T+T1pH2X5+4eV4QMoQ3/5PAD0zJWqR1mZGVFslwdmt7mpLYraW18fhhSX9p5GJu8xhI7XVr/ZMF8VNGSbN3ljvZZhMvOYRhWvWjXUVzaTsH42PxkAABQ7pMQ7VzdQVW1kt+I69SpAwBXr161Ln6z/liGapli16pVqzt37owdO/Z///vfsWPHiNI/k2qikABZSMBzZuBIfGWey2uTSqqbXHYy/Vp93W+cBCGAT1pcmumiTEzRA4DBYPhxyafTRv+7Ep5QNCI1n1du9C9uV+L6q1kX3nX8qLfbYAK92LI6DMMwrMJFREQkJia2b9++ys44YsSIDRs2WJ+fOXNm06ZNK1asGD9+fOE+p0+fvnPnTpWFVGVot/Fc+k9C3l0ufQ/lNEDIC+PSfwbEvNxa8cI5fN+0dA7gudm7FeUxhcvYL+iv8ZmHCubEsnFzgM8jHUJI2w5lvxzDMKzSEVLGe775yQAQDEjqR7uMqO6AqkHJCfzQoUMBQKPRWH8cMeI1/aOxsbHZunVrly5dhg8frtVqqzucaqAZll/vca32a3s+GXIBACjCPKonN3Ntfp+1Wy+M6npHJimYMofkjj3KPx+vajS3bXMz++qZjGNmwTTYs9LLYGAYhmFlEEVx4cKFBw4cyMsrYVehyrBv374NGzb4+fktXbo0KCjo5s2bkyZNmjBhgq+vb0hISNXEUJ0IKeM1xxwxhI1fQDqEWGKmAwi06ygkrfVy48kQWuRg/3FKmlEUKUALHOyem70DACJtaM8ZlqhxlthZMvsPgJALuTe4jAO47ByG/QdZ3zPIUhbdVCPSoRtp257X/cl4LwRCUt3hVIOSE/hNmzYV/nHdunVVEsxL6tmzZ3Bw8OHDh6s7kOqhv5BluKGzaWQBxb+Ng0OYJdtM+jwRANK04vbfLSM/++fvN4IKrNOYc/xPQZ9n16sLvNo/76a2rafUmr8lbrWcVDy/N4ZhGFYRRFGcM2fO7t2709LSCrfzPG80GoOCgqoskrVr10ql0tDQUGt93Nq1a9erV+/tt98eMWJEx44dlUrlc0eo6Uj1J0TKJkF/1fzoEyHvNqKdKPf/K/MVgjm8l2CMLPEYD8Dy/F4RCAAB4IhxsMpvjGc56sZTTv241O1C3m02aQ3tMcUSMw1ApF3H4LJzGPZfI6fQ2OY2xUtvvg6YwF2iKZJQNKruQKrH6/i/5CW4u7uPHj169OjR1R1IRUrJ448+NT53cULy3MjE2ZGZW7tbf+SQM5BKlQ0a3vvfAu+rfqY42TvW54SicQWuYcs5ei77wInMTftefSh/ee1FdX7Eu8phGIZVmfXr18+bNy8jI8PV1TU3N9fGxqZOnTqurq5Go7FVq1abN2+uskjCw8PbtGlTeHebxo0br1mzJiUl5XVbqF9pEOOzFIAQ8m4DAO01G5Flfm0hckLuLdEcU+KDMMc4c/EefLwbH+/Bx2vMjwamZcRzfDnCIBifhQCITVrNJq4Qcm8ixoV2G//812EY9sYZ11w5oMHreGsNkTb/2ewdXiiBT09Pz83NtT7fv3//wIEDFyxYkJ6eXjmBYfDd3/pxp7PPxZrL6CPk8ZZkCwBkH/mAy3QAAEpMRfQAABjb1866tR4AJCTrd++/CAAASFJrU2mjvQTN+C+RhNGfupj105EKHBYABJF/kveQE7mKHRbDMAwrsHXrVhsbm/Dw8CdPnvTv37958+bXr19/8uTJ/PnzMzIy6tevX2WRGI1GQRCKNA4cOLBFixbLly+Pi4urskiqEaFoSDn1hfztjns/pzdiaI+pAIBoJ2mja7Imt60PU8ObYzxOh7icnOBxhvNbAwBAyG+qxyTz/MC0jMhzmYkzInhdWZ+thDKYVH8CgpGNXwAAtNdcwJPjMAzDXhvlSuANBsPnn3/u5OQUFhYGAHv37v3888937tw5a9as1q1b4xy+krgoSAC4k2oprYNg4CN7hVnijNYf03cNtT7hUn4CwkUtfTKkb3BB52+3ZbMcUOoQRLtUYJCSQF/N/w1GJKk7+Ifu9zMVOPJf2jNLns5Y9HRamiWlAofFMAzDCkRFRbVt29bV1RUA2rdvf/v2bWv7tGnTRFGcP39+lUUSEBBw9erV1NTUwo0IoXXr1pnN5iFDhhRP799ItNc82m0sU2tjeX5Do5yHEPK6IpvGZ51AEh8k8TEw3n11NpdFV5XMb5lbQyZ1CwDQ7hMWutRvzDDOZ3WZn4alfh8X0e122Tk84zUHCCkAEDbNKcfPKurqMOy/SdD/bY4YYo4Y9NwHG78AoBpKg2M1S7kS+BUrVuzfv7927drWsnbfffedWq0+dOjQwoULo6KiVq1aVclB/kc1dKIB4G46W+JRwcBH9gzTn88CAITAZdwG1/HfWg+RdlkgpADA2G7XJf/UqotNFnYdtXCZh41hLUAszyS68pI3a6Ae3Q8Qytp1WH/mckUNW1fZUMM4xxieLngy2SyYKmpYDMMwrIDFYrGxsbE+9/HxSUpKMhgMAEBRVNu2bUNDQ6sskqFDh5pMpnbt2v3999+Fc/VmzZpNnjz59OnTAwYM0Ov1Lz3+okWLHJ7nrbfegmraWKcAouxor7mELOD5XQEAUbT3YgDgEpeJbCoAZPFCAs/XY+htGrU8fZeQdwcx7rTraBsCrbolThibQFlEpKQMt3Iiut3ms//N4UU2Q8i7U/AQuUzaeTCiHCiXr4S8sMKHQChrYiCGYcVx6T/zmQf5zMPPfbCJK0Uup7rjxV535VoL/fPPP7u5ud2+fVsmk6Wlpd26dWvixIndu3fv3r373r17jx49unDhwsoO9D/ImsCHpbFisaJz+dn7hSwAQDTK6+vE5/yzUs7aW0AiEG4aGNDddtMBnfXItztMfbrQcrk9VPQ+bTbvtBDyDNotBzLX/4xI0qZ9q1cf04lxmRO4ck/ixmxWS71mNfMxDMPeDLVr1w4PD7c+9/X1FUUxLCwsODgYAARBePz4cZVFMnr06Lt3727cuLF169Y0TV+/fr1Ro/z1jfPmzYuOjt6zZ8+hQ4de+j58cnJyVlZWxcX7uiBt25H2H/BZJ9n4hYzf9x4U+aers5JAtJBrSlgCAIz3AiBkOaGZyf3vUxZROcrDZ6JvRJdbhls5ER/dqnW0CWVPg8ibwlqLXGbx8S1PhxdpoRx7MbU2VMW1YdibgnIZxqXvAZGjPaYRstol9rHEzxNN0ZRTX0TZVnF4WI1TrjvwMTExrVq1kslkAHD16lVRFAs2hq1bt250dHTlxfdf5qIgnRVkjlmIfXaeW5Hs3XdnkG6WT/KhnlymPQBYJO0BAAjReD8QAAb3c5Yy+S+MTxHW7bfQ3pUyJVL1YTv7/t1BFDN+2G24eb9CxpSR8qFe4yf5zyPxzvAYhmGVoEOHDvfv358xY0ZGRoaPj49ard64cSMAGI3GM2fOeHh4VGUwP/7447Zt2959910PD4/Ct8Fpmv7pp5++//57Nzc3k+klJ2StWbNG+zyXLl0CAPT67ZlUNsZ7ERASLm2PkHsLANQkwSDEJiwR2TRC2YpUh+SEZkb1viuYBKfRngHf1qadmYDjTaW1FYYw/dOutzktC4hEUm/IrwvVuJRHQ+vpkNSvrGgwDCuGkNejnAYCgKC/Qqq7F38AZSeaohFpQ3vOrO5gsRqgXAm8ra1tfHy89flff/2FEGrVKv8Wa1ZWFsMwpb8UeyVBGhoAQqNNnx3OPB+XP2ktdWVs4ezdrpvmHY38fgeX9O1fAICefyQiOQDIgh6xqZqGTmnDev67QeKynZzWUlnbAtmGdLLv3x0AeK2ukk5xNev89exLlTQ4hmHYf83s2bO9vLwWL17866+/IoRGjhy5ffv2Jk2a1KlTJzk5uXfv59VRq1AkSX755ZenT5+Oiopq3Lhx4UMEQYwZMyYiIiI5OfnixYsvN77986hUqoq4jqqGpL608zAAwRI73bp6VjRFcSmbAQjGZ0lOqLYge/dYGmh9yTM5fLfbnJZlvJcAIFHkJIE7pEHnij8o568AADFutNuY6rxaDKuZaM+ZiHLgdef5rBNFj4kcGzsDACiPKYh2robgsJqmXAl8UFDQnTt3YmNjc3Jy9u7d27x5c0dHRwBITk6+dOlS4U1fsIplnUV/PsFyK8VyJjb/toNgzJ9AaNPW3u4jRwBQEkS38f66PzrmPa2l5lOvSIMBACEglbkAMPVLqYNd/hR0nZ6dO3du5QVsG9LJe/dyZee3KmNwQRS2xH+/LnbZuthlRt5QRs97+lthOTcqIwYMw7A3iVKpvHnz5sKFC+vUqQMAs2fP7tWrV3h4eHJy8uDBg6dOnVrdARbl4uJiXamOFWb9vV/QX+MzDwGAJWYqiBbKeWDuZc/i2btVkRxeYBtTjp+CYLLEzSk+vsjnWivSM17zgJBXyTVh2BsFUfa0xxQAsMTOLFJIgkvdLBge/fNNHIY9X7kS+EmTJnEcV7duXT8/v4SEhEGDBgHATz/91LJlS4PBMHgw3ri7sjTU0AAQp+MAIMecn7fL6uXv5qI/p439OhwEEQA0TWwlTWwzvh8sAmqed5FNdwQAQm4EADslmjrEoWDM9evXP3jwoPJiRpLKmpFBIGKo13gZKb+efelk+uHSuiWbE76PXrgmZlG8MaaSIsEwDHtjODo6zpgxo127dgBA0/S+ffu0Wq1er9+yZYtUKq3u6OCbb76haVwG5TkQaUN7zgAAS+wsPvMwn30akUoua3TkFyVn71a0MxPwexOJn8wQpo/8LIz2mgeEnM88JOQUnenGJX4nsqmEsgXp2KMqrgfD3kSU8xBCXk80RbMp6wsaRS6LTVgGAIz3YiAkpb8aw/5VrgS+c+fOO3fu9PT0NJlMgwcP/uqrrwDg+vXrCQkJI0eOHDJkSCUH+d/V0ImmCMgw8gBwNTG/HL1DH1f3hfn1aTN3JhXk8C7DPIwPAzP/fpshzJZY98LjDAsx1/LM/wWI5/lp06ZV3TVUqFZ2b88NXPme5uNg+3al9dmbuJUXeUEUdiduEPFWHBiGYS9IJpNJJK/L75GCIHBcWRueYVaUUz9C0Ui0JJmfDgMA2mMqn2MvWgQAkPjKSnsVYUtRGgYAuAwWMa602zgAsMROL7xbzT/5BsF4Ly5WVBfDsHJDFO29CP75RszaxsYvEDktaduOtH+/WoPDapJyVaEHgP79+/fv379wy5gxY2bOnGmdS49VElsJsbubetaFnIgs1iL+W3rXeZwXACTOjACAzJ1JAOC9to5DT+eYSU+yV/V12Pq3onlYRnx9R88HIqlEIsfQxvn/V6/3hDDry48ePRoaGtq5c+cquAQh12COiJE1rgsVVBbIkXH+wq3USR93c27e09+yPo/Ie3RTd6W5bZsKOS+GYdgbYPny5S/Uf+LEiZUUCVbRCMZnsenBRyCyhCyAcvlK6cp4rakbNzY8fvITUQSnkZ4AkLknWXc03WNJIOMtFfL4yJ5heX/rGE9prcONAYB2G8ul7xHy7nHpu601t+CfGb+UUz/Cpll1Xh+G1XykbTvS/kM+6wQbv4DxWyMYw7m0nQX7QWJYOZU3gS8OL32vGi1cGSlVQur7bw6PIHNnEiBIamnD5HKcXq09EOI4YH+2L3uJ+VSBhE55hwDQx63j2rZpePHyXevLJ0+efOvWLYIo1xSMV5H18xH9H38pgps4jh2AKmEa5MPcu2ujF7dTv/eJS18SkXsTtxY+ujdxS0NlMwZPScIwDAMAgEmTJr1Qf5zA1yCEMphy7Mll/EJ7LwbEAIDjl24AEDc2PGHKE4QAKBQ/4TGIYLirr/VL47gJj3MvZjGe0oDjTfPv0hNSxmuOOWIwG7+QdOiOKFtrzS1E2tCe/6veq8OwNwPjvdCoO8ul/UQ5DWLjF4DIUS4jCHnd6o4Lq0leIIG/du3arl277ty5k5ube/v27T/++MPd3b1BgwaVF9x/k94iZpue2efWwosAIAoQn8MXblcM83AvyOF3JEl25LdHRPVVM5dqWZ78Kevwfu52EZBF01+SvnPZ1+a3rhLWTXTDwsJ27NhhLWdQqWzatcy7eCPvym0uS+c8dTihVFT4KSyi5Y/03+7pbwXbt0+xJAKAI+NEITrFnKhlM/9I/62bc68KPymGYVhNdPhw0QIiP/zwQ2hoaJMmTT799FMfH5+srKxTp04dOXKkT58+S5curZYgsZfG+P9Ie88vXMi6IIePn/zE2iLxlZmjjY/aXhPNwjPZOwAAkOpPiNQtQs4lLvE72uub/OLY7pNwcWwMqxBI6ku7DGeTvjc/6StaUhDlYC1uh2HlhwpvtVqGBQsWzJ49u6CzKIpjxoxZu3btokWLpk+fXpkRvnbu378fFBREEATP88/v/YJyLeLbe9IK6tWVjSTg908d7bYnJn0TCci6dwykvGv7/v6mkHvYHJFfm+CEvOsGx8W/ZfYX8+4MWeK297eH1nY3N7cnT54oFBWfURfBJqSkLvyRS9dSLo7OM0fTrpqKHT/G8HRL/PcZljSEkIk3AsBon6lSQrY8ag4A0ASzqPYPaqaCT4ph2H/ZlStX2rRpExwcfPny5eqO5ZXs37//iy++mDt37qxZswq3b9iwYcSIERs2bBg2rJqrInMcx/N81azJr9TP9+oV89VD7c/JAGDfXeO5os6DJld5HYsoVOv3Jsp37It0FvLumu51AERRzoO4lA1I6itreAWX18KwiiLyetOd5iKbBgCM73eUM64m9hqpEZ/v5ZpBffLkyVmzZvn5+f36669z5syxNn7++ecuLi4zZsz4448/KjHA/xgphRo50Z4qsvCDIhAAEAiKtNdV0/ZSwmWiT87/vKzZe+q7th/sb6rdlZS0sJ5o0xoAjEi2RvV/yQIh+K4GRH/zZZJUml8lPikp6dtvv62Ci6I9XFwXTWT8PLmUjOQZ35kfR1Xs+D7yWnMDV7Wwa2vN3uvYBDWzDa6vbBykbAoArGA5BeW+eQAAIABJREFUmLK7Ys+IYRj2Zti4caOvr2+R7B0Ahg8fXrdu3f3791dLVIVRFPX6VNSroTK2JWr3pQACQJD1W/rjd2/wOhZJCZETY4c/NEcbi/QnFA0pp/4gWriUDQDAeC/E2TuGVSBEKmnPWQBAyOsVFJvAsPIrVwK/YsUKqVR66tSpHj16FFSta9u27bVr1wiCeNGKOFgZRBDTDXyOWSj84AQRAESAIu1ZJkEQAQCUE7y3zHONGu78/v6mhITI3JGUvjGRT5iZS7utUf1fLu1iT5CkIoh2G+flAmP6qAtOt3Tp0sePH1fBdZH2ti7zxsua1hf0eSnz1hpu3KvY8RNMsZe1ZwGAQERvtyEP9Hd2Jqzv4tSDQhQAXMm6EJH3qGLPiGEY9ga4ceOGdQf44mrVqnXjxo0qjgercBnbEuPGPQZR9FwW6LmiNgCYo42Ml7TuXy1t2tpb4k0RXW4Vz+Fpz5mIVAEAaduetP+wGuLGsDca5dRXErBVUnsfoJevR4b9Z5Urgb99+3ZwcHDxqnWenp7Nmze/f/9+JQT2H5WeJ4RncjqzWPhhPSSKUKQ9Uc9fSrAAQDMJs3xC3Z7LGxASAgAc+rgCQPK3UlmTe8dV/SxGYcW4pIjAS6x2BCGvO7GPwdkxf9q82WweNmxYOZdRvCJCKnGaOsymQ2vRbElbtskSFVeBg/+ctFkAAQDaObznKfO5oA39M/Pkyuh5tRTWoiDiT4mb8ZZyGIZhRbi5uYWFhRWfMc7z/J07dzw9PaslKqyiFM7eNSM8NV95eK+rp+qsDjzRTFpXUevXRqXl8IjW0D5LCHl92qcqZuph2H8PItWfIIlHdYeB1UjlLUIuk5W8iaiDg4PJZKq4eP7r3JTkzDaqPvUUhR9KhgQAEkGR9q+bKT8OkFpfKC20SZvjQDdKw+Rd08FfugGU7P++TiBOaNlk89OuDwR+udKGXjL2384XLlzYsmVL1VwdIknHUX3te3ej3ZyRvNRtaV/U9exLj3MfAICMlH/i0gcA+roPa23/DiuwSeZ4FWUHALHGyMvacxV1RgzDsDdD27ZtExMTp0yZYq1vaiUIwtSpU+Pj4996661qjA17Rfo/tXFjw0HIz96tjep+rrUONWa8pQBAKEj/XxopWtla4k1Pu98p8nJK01va8CIhC6jquDEMw7AylWvaRuPGja9du6bX65VKZeH23Nzcv//+u1GjRpUT23/U4IZFq8rdS2fvpfM2EjT/HVV5RiDkpNMoz6S5kanfx3biRMPFXFaKaJrgstinH0OtQ0N7dV6/75TtyYs6a/8pU6Z07drVxcWlgq+kRAjZfvq+7afvV9R4FsGyL3m79XmQTdObuqvW57UVDdS0E4WoVEvy1azzAPBryq7mdm0khLSiTo1hGFbTLVq06Pjx4ytWrDh16lRISIiHh0dCQsLvv/9+7949Dw+PhQsXVneA2MsjVRRiCNEsFJ8hX0DQcVy6BQAox4rf5xXDMAyrDOVK4AcMGHD69OmBAwdu3769oDEvL69v375ZWVk9e/asrOiwl6UZ4ZFzIsMSZTRFGAQJQZsEMPGEjOCy2MjPOvrvDV01KaL5bSY3zwIAWVlZgwYNOn78OEIlbDj/mruvv6W1pFufX9NdvKa7WFrPbDZrR/wP/T1Gykh5VUWHYRj2WnN0dDx79uyECROOHz9eeEFcSEjIsmXL1Gp1Ga/FXnPypir/vQ2jet9N+yEeADyWBMKzH/JsovnJhzfNUUZ5I6X/L/hmDIZhWM1QrgS+f//+oaGhu3btOnnypJOTEwB07tz51q1bWq32gw8+GDVqVCUH+V9nTatfKLcmGIJU07nXdEhCEGbBZEMm+tH+d02EjGDTIWHGV54LZ8wcSk9fbbH2P3ny5Jo1a8aOHVvx0ZePYDIT0pcpcusp9aUQxYmcirJVUMoS+xj4XB2bDQhdzf7rQW5YP/fhLezwvFAMwzAAgMDAwGPHjj158uThw4dJSUleXl716tUrXvUGq4lUndV+P5ecwxfO3msdbULZ4zvwGIZhNUN518Dv3Llz3759gYGBycnJAHDu3DlHR8d169YdPXq0Jt62rVlcFSQA2EnK+z9LNAtR/e7pTmQgKSGaBUJB5uyv990OH76trWAUCBmR+3etzH29Rvei2zT+dz75lClTwsLCKuUCnsf0KDKu/6S0pRsEfd6LvjbBFMOJHADkcLpkU0KJDx2bDQAgiq5SDz2XcyT1QIVfAoZhWI0WGBjYvXv3UaNGde3aFWfvbxJrDk9IibQf4hOmPbGWc8XZO4ZhWM31AlsX9OrVq1evXjzPJyYmuri4MAxTeWE9V2RkZGho6MOHDzMzMw0Gg1qtdnd3d3d379atm6urazUGVhkYCgAg0yT0OJixN0TNkGV9Y/JM9m4SAEBaR/HBO04fAAgHXSJ7hukvZBEyIn3Lp7LAx9vn3mw1gM/SsQBgNps/++yzW7du2djYVMll/Yu0tSEUMsP1e0mTFjuOHySt61/+1zZStRjuPdG6A3zZJIS0hd1bT/PCbWm7VwgWwzDsjcLz/P3791NSUko8+v77FVayBKsuRe7DO43xiuhyC2fvGIZhNdQL7z1IkqSXl1fhlosXL7Zt27biQnqO6OjoUaNGnTx5ssSjo0ePDgkJ+e6773x8fKospMrW2Ik+EmEkAYWlsVeTLO94ljXVPG1dvO5EBhAgmgTShgIEhps5+j+1GW1VeZRY75dG1hweMWTigjG+m6eunZrUdwZrfW1ERMTEiRM3bNhQJZf1L9rN2e276emrtpnDo1LmrLb7rItdj/eAKNeMAwIRrezeLv+5atvUL/zj+cxTEXmPujp/5iJxe7GgMQzDar7k5OQuXbrcuVO0AnmBqtlnFKtsqs5q391BUX3vpf0Qn7kzmddz8qaqgN+bkHZ4D2oMw7AaptQ3bo7jtmzZcv369bS0tICAgGHDhtWuXRsAjEbj5cuXU1JSdDpdbm7utWvXfv311yr7gM/MzOzcuXNkZGT9+vVDQkIaNGigVqtVKlVOTo5Wqw0PDz969Oivv/4aFhZ28eJFZ2fnqomqsg0MUjRyos/Gmn+8lXsu1lx2Aq/s4IBoJLIiogj/Q41yL2cnfROZ+E3khJ+943lukdq+6y+NHre/bnyYx1tUeWFLu787cmA3dseR/MXwGzdu7Nix4+eff14lV/YvytHeZe747H3HdIdOZe89arrzyHFMf8rZsbLP+3f2hfDc+39nX/jEpU8Xp08r+3QYhmGvlenTp9+5c6dRo0YhISFyOS7w+Saz/cDRb09QVN97+dn7kSakLc7eMQzDah5UYu5tMBjatWt348aNghaFQnH8+HGaprt165aZmVmkf5Ul8MOHD9+4cePixYunTZtWWp9t27YNGzZs8ODBlXQn+f79+0FBQQRB8DxfGeOXJiyN7XEww11JXujrVEa3tHXxCZOfIAARwK67k/ePdR82v8ommWN+9J/ZgSEAupzN6z0+HlgRAGRNVX/uiukRPfKdL7OfxOZfjp2d3Z07d7y9vaviqoox3n2csXYXr80mZFKHoZ/ZtGtVqafL4XSHUvb8pT3jJfOdHfBdpZ4Lw7A3xpUrV9q0aRMcHHz58uXqjuWVuLu7y+XyBw8eVO+yuNdEdX2+vySRN93vLOTdfn5PRNOeM2m3cQCgv5CVczLDZaovzt4xDMOKqxGf7yW/fS9fvvzGjRtqtXr8+PH+/v4xMTHff//9wIEDJRJJZmbme++916RJE4VCgRBydHRs0aJFlYV74cKF2rVrl5G9A8CgQYP27Nlz8WKp24nVUEEaWiMnEvV8hJYLcCj1c1feRElICcEkEBIi+3AaALjO9Isb/chjUULb9oGWUG2viYnAgfUufWynyI9SZ9FyZvdC+TuD9SYLAEB2dnbfT+qcXO9KFp7DTkgktfcRioaVfZmyhrXdV8zI3LQ379KtjLW75S0bEbJK3LldRdkO9BjVzblXkf3hLYKZIV6mKj6GYVhNwXFcUlLS8OHDcfZeMwmiJbFcHUVWtOTXOFC+Y698x74Sg8IwDMMqWcl54KFDh0iSPH/+fP36+QuGP/nkkwYNGvA8v3LlyvHjx1dhhM9IT08PCgp6bjdPT8/qKqheeQgE7b2kB8INZ2NNAQ6l1pmzaW3nt69R1BdhgvGfHF4U5Y2UhjB9yzExTS7kEhxwNKJY8ekX6pzuv79jyACA+v7knJGyaavzS8Fdum1asil5xpBnclpe+3sVJPAAQNjINRMGK9o05XPyKjV7L+BAPzNX/+fELecyT7R1ePcjp55qRlMFAWAYhlU9QRAkEklSUlJ1B4K9FETTnjMtUeMQ4yZrfB2IElZA8Lpz5kc9EKmk3SdUfYAYhmFYZSi5TlhUVFS9evUKsncAqFOnTr169QCgf//+VRRaSYKDg69cufL06dMy+qSlpZ08eTI4OLjKoqoyHbwlAHAuzlx2N9W7Dn57GxEyQjALiEHZv6VTahpI1OyMnmBFa/b+Z0+7ubM0PzuMFBAJAAZC+fUA//eC/y1Fu3ir+eSd+oRdJyCVAIAQTbtW6S7x8laNlZ2rZ7d2jcSZF/k/M/+YGzFREGvCREoMw7AXxzDMgAED/vjjj8Ir5rAahHLqR9g0FS1JbNL3JRwWOTZmBgDQHlMRXdbiOwzDMKwGKTmB1+l0Gk3RG4/W7dnUanWlB1W6sWPHchzXunXr77//PjY2tsjR5OTkzZs3t2jRIi0tbfDgwdUSYaV620PCkOhWiiXbJJTdsyCHFy0iYlDOWS3wIgCICChWPN/TbstcF5FAXXIPESIPAHJBjywJG/4nd3LI36NOEMSBEy/fv/UH8HoAEEWWy6z+7dNFloXKL7jQybHrwjpr33Lo2ETVkkBkZZ8OwzCsusyYMaNjx47t2rWbOnXqoUOHzhdT3QFiZSMY78UAiE1aLZrjixzjUjYIxnAk9aOch1ZLcBiGYVhlKHUpNVFsE6/iLVWvc+fOa9asGfcPW1tbBwcHlUqVm5ur1WqzsrIAgKKoH3/8sXv37tUdbMWT06ilK3MxwXw92dLZ9zlzy1XvOvjtaxT1eZhgFBCDRIsIAEiE6AbSzXNdBAIBQBJ6ZiGckwPaMU/RbVwuxwMA5BrEnpPyzm9RauwRAABV6TXhyybo8xK+nkup7RyG9pLWq1Wp53KRuA/xfGbGQbol9VT670HKpkGqpghQpZ4dwzCsavj6+lqffPvttyV2wNvIveYIZUtS/QmfedASP1dSa3NBu8hmsInLAIDxWQK4pAuGYdgbpObVIB05cmSnTp02b94cGhr66NGj6OhoACBJUqPRNG/evEePHoMGDXJxcanuMCvLhJZKjZxo4lKugkOqjg5+extFfR4mmASwFqYH8Hhido2xJPpJAOAP2UdjclY58anW/rTn9M71Wi+N6zPx2/y6OLHJwhdT846uUchtfSj1x5VySeWGGJq0V1niklK+Wa1o29xhQHfS3rbKzn5Hd+1MxrEzGcfcpJ6T/ebZ0rgIEIZhNV7ZRWGxGoHxnm/MOsln/Co4DSZUbayNbPxCkdORth1Iu87VGx6GYRhWsUreRg4h1KlTp9DQ0MKNH3744cmTJ1+rL+NFUTQYDCaTyd7evkImCEybNm3p0qXl6YkQEoTnzGN/TeSc0Vpz+Ke9HRPNXLuD2Te62q1c5mo92sxyY2P6wPyupI28RazIZozo12jj/pSCEbq+Qx88fJa2b1vQwiZ+JxgePPfUCElIp76k6u2KuhaRZXWHT+sOnRItLCGT2n76geqj9oiuiu+hWNFyJuP4mYxjWkvG7MDvvGX+VXBSDMNeTzVimxnsRdWwbeQKYROWsAlLCUVDadA5AEIw3Dfdaw+ApA3/ImR1qjs6DMOwGqNGfL7XvDvwhSGEFAqFQqGoqAFJ8g1c8Kx61yHgZLPdN9JXd5E55YJtnvB3O3kdmk7keb0g3GSa32BaNLdcBwDgc9mYmbTP4rU77kXEeJ+7ZrCOcPQCO3rqzg0b3kIIAQAIZjbhWxDZ8pxdFNkKTOARTdt99qFNu5babb8art/N2n1Yf/qifd8QRevGgCp3WjuNmA803d9z/DiPz1VSqoL2e/pbKsoW5/MYhr1hIiIiEhMT27dvX92BYM9Hu43n0n8S8u5yaT9RTv3YmOkg8pTrKJy9YxiGvXlKvQNP07St7TPzk3U6Hcuyjo4lLIROT0+vrABLcvz48SNHjkRERPj7+48YMaJJkyZFOkyePDkmJubAgUopulZDv6H/PDUjmuVkBKTxQlMJs1GjjmW5AWmZeaLgIqT+ntyJBOuEAlLe/AkApPwZ0Hmk7v7Tf69x/PjxK1eutD63xEzjUjYgxpXxXlTi6QRzLBs3BwBJG5wibJpXxhUZw8Kzdhy0xCUBgG33Tvb9qqHqQYYldcqj4QDgKw/o5z7MVx5QIcNe0p6NNkZ84TaEQjX7KzYMe1PViG/oX4UoioMGDTpw4EBeXl51x1J1aujnuxWf8av56VBEa2jP/1mixiHKXtr4JqLwai8Mw7AXUCM+30tND1iWzcjIKN5eYmNVGjFixIYNG6zPz5w5s2nTphUrVhTZmv706dN37typjuiqSHgmu++RcWxzG3tpeRcOLHGwG5KRmczxTSXMRkd1Sq+7Ns7MzuW1eqdnpBDOB216f5a7BwAAeNOTL5FoVtkIv61UdByWG5ucv1Jg1apVCKEVK1YAAO0xjc/4RbQkAyJJh27FT8c9GQAAlObzSsreAUDWqI7su2n6s1d0B08RKmUlnaVsasbpI6eef2aejDZEXMo6VyEJfLoldXv8Wh4EO8q+q3OvVx8QwzCsNKIozpkzZ/fu3WlpaYXbeZ43Go1BQUHVFRj2okjHHkTqJkH/tyVqAgDQnrNw9o5hGPZGKjn9y31BVRbuvn37NmzY4Ofnd+DAgfDw8D179ri4uEyYMOG3336rshheB4cjjDvv520Je4EbI5O0Wckc30zCbNKo5QLkXc/J2J6k/iFph5OaQmiVaqKBsLH2FHL+4vXXAMDFkfjtexcnR5uCQVauXDl79mz4f/buOzyqomsA+Jnbtrf0kEISktARBKSoSNFPRFGxoSJNfW3YFQULFnwVCy8KioKCqCiKCtgoAhZApHdCCCQhPdlssn3v3jbz/bEhhBAgIBDR+T08end2dvbcTYFz78wZAMTZ+ZSJACAXPQ843Oi9NN9arfYHYE18ygt/8UxPgmEsl1+cPPMl27WDzu4bHQcCdGPiHVM7zH0sY9INCSPq251y5RbveumYT6Y55pW+qwEGgO+rFnoV9xmLlaIo6hgffPDByy+/7HK5EhMTA4GA2Wxu165dYmKiKIq9evX66KOPTj4E9XeBhLTXABgAzBg7cXGjWjoeiqIo6qxoOoE3naJzFu67776r1+tXrlx50003tW3b9vbbb1+6dKnZbL7vvvv8fv85C6PFXZGmB4BFeaKokss+d96/ovakL7lErxtiNMyOjTYihDjUelYHYFD5y/mZm0OzYxyE0b9pffrYV3Ua8PaqX9bHRB9ZTDF58uRJkyYRqQjp0pGuNZGKpMLxmve3I388v8j5DwEAGzWMSIWAQ2fuvE+BdOCQ6qw5N+8lMEJny4VG9sgPwuels2ceeuPxnDu/rvjklIbKCeza598dOVaJuqCc/uuZoqizaO7cuWazOTc3Ny8vb+TIkT169Ni8eXNeXt7kyZNdLlfHjh1bOkDqFDCmblz8GEC8kP4GoH9gTR+KoigKjpfA/23l5ub27ds3IyOjvqVr164zZsyorKx88803WzCwc6x7gtA+mvdLuMirlvq1X4ulk77kMZt1arTDeLjSm+3K6ITHWxOVFI7Z01Nkv0+I1cWN0HQZDV/CGNqy0Td37tz555W/OhxH5fD3j+wo5txIpCIA0KrnS/uGHfmTW9/+WXjvELlw/Jk88+ZRXe6KZ6aWPviS862PamZ/Wf7U68Vjniq551nn67NCW3afgwCujr8xy9Re1EIrq39UiNzMV2GCPy6eAQAC0nWwdAWATZ4/CkMHzmKgFEX9uxUUFFxyySWJiYkA0L9//+3bt0faJ0yYQAiZPHlyi0ZXZ9SoUQaDoaWjOD8I6W8Zex5iLH1aOhCKoijqbDnPEnhRFI/dvG306NE9e/acOnVqcXFxi0TVIhZcF73qtlgVEwDQ8OkUYE98LsNyqUOpkMRdgVSOeznKYcqeDXBkKCHrw8hBt27dVqxY2bCo4ayvg3e+GFKIA/GxAICEeNbWn7X1Z6yXAOIBgDG2iwzFGDv9hbM8TVyUzXLFxQhBaMMO/8/r5IISHAhptd7Q5t3OKbOq355HtLO7BWC2qePEzNdebffei9n/45EQaVSIvKRywbra1aLW9KyEX1xLa5RqALip1ci7Uh5iEQcAH5W8Q+BvtHcjRVH/JLIsm81166TS0tLKy8tDoRAAcBx3ySWXNNpNtqUoihIOn86KpH8lBIyxpWOgKIqizqLzrMZ1VlbWhg0bqqqq4uPj6xsRQu+//36vXr3uuuuuFStWnJEN4f/+LAKyCGxVUAOA00vwEIfafHOBuDtg6l2XmTOm7qz9Cs3zMwCwUdcwxiPli3r27Llq1arBgwfX1NTNS/96pez2ly1avJQtHkFkJ589nzH3kA9NxL51rPVS1nGlXPQc0mdwCXf91VM9DQxjv/Wa4B/bSDAEDAMYA4CuXQafnBhavy24bgvrsEaNvuFsR5GgS2r4sDhU+H3VVwDwWemsu1Mf6Wm/uOGzQS3wTdV8AIgSYgZGX8UgdkjcsB+qvq4Il250r+ntuOxsR0tR1L9Q27Ztc3NzI8fp6emEkJ07d/bp0wcAMMb79+8/N2G8995733zzzfGezcnJAYABAwbUt/z666/nIiyKoiiK+vs5z3Ldu+++OxwOX3bZZRs3bmx4K7579+7jx49ftWrVqFGj/lWL4T1hDABNbQXYLIyJjWTvVdOKKl4tAEyENjMwY5CRMdR6aqPOPXr0WLNmTXJycn3Lqg3ygEFDnMogACIfmojFPLVqDiCWS35KKXsTAIS01+Hw/edzzPfDLzgYMlzQPmXWZNsN/8cY9FJuQWDVH6zdDCzjX/q75vae45AyTNl3pz7SztxZA9Wv+ho9+3X5PFkLA8DY5AcZxALANfE3mzkrAMwvm3169fAoiqJObMCAAXv27HnmmWdcLldaWlp0dPTs2bMBQBTF1atXN/yFf1aJovjb8UUq5DdsOTdRURRFUdTf0HmWwI8bN+6ee+7Zv39/79699Xr9zp076596+eWXhw8fHqlLX38/4R9PO0Nzq50zSypeLSx9+gDi495vveKG+O/u9SD/MasVOnTo8Mcff7Rt27a+ZWuO3Pu6L9fvZnBgi7TveiAKFzdKc31LVC9rG8DaLz8z8Z06ceseALDdPJh12By3X5s8a3LU6Bv4VvGAGGP3TkTTxJ3n+psEAerrGPBUm8mzO38zMOaq+vbvqr68b/fwtbWrAaCD+YKOlq6Rdh4JI5PvBYCQFvyhauE5jpaiqH+DSZMmpaamvvbaa99++y1C6P777583b163bt3atWtXUVFx2223nZswnnzyyU8//dRqtVosljlz5pQe7dprrwWAhi3nJiqKoiiK+hs6z6bQA8DMmTP79Okzf/78goIC0uDWM8/zX3zxxcUXXzx9+vSDBw+2YIRnj0vEWypk3CBpf3973XSDpflH3aHVcdA3SWfgmrs2Pm1ux/xhO5zvl3Ax/JgnO6xy1uyWlXtdtR/FRtfXvYtITU1du3btVVf02LqzruJAVQ0e8kDt648Y7r2pAhiBhMs1388ADHA2ueAxLnY4Y+l92qd82lSXGwCE1FaRh4zRYB060Dp0IAB4Fi4Nbdp1zmrUH4tBR104YxGnYCnyVb024aiN33vaLl5q+LZILFhR/d2A6KuihdhzGCZFUf98Fotl69ats2fPbteuHQBMmjQpLy/v+++/1zTtzjvvfPrpJnYnOUtGjhx5ySWXjBw58u67737wwQdff/31+sJ1RqMRAJKSkk44AEVRFEX9K5x/CTzLsmPGjBkzZsyxTzEM89BDDz300EOVlZX5+fnnPLSz7t5ltTucSpNPPbSy8YbhY7qYnu9rbebIlksd6fM6Fdyxu3xyQYqNm/efVnc4Xdsl+d7qmlnH5PCx0dal0wKjn+eX/1EXjKLC41PFbbnaO08RPV4BAABEq1kCAESt1bVEAo8EHkIiCctgbFy7GIclAEC6urn94q79ni++N1zY0TywDxfjONeBAiTpUyLZe6axXZapQ337bzUrVKLekHjHtMLJGtE+LX3/sYxJ5z48iqL+2WJiYp555pnIMc/zX331lSiKDMPodLpzHEl6evrvv//+6quvvvzyyytXrpw/f3737t3PcQwURVEU9Td3/iXwzZGQkJCQkNDSUZx5fvkUZsy7xVMrtG67JjZlatviR3JLnz6Qnqibd3XMKKdriyTf6ayZHRtlPbo0oNnIfP2G6ZWPwm/MC9dPg5j/k7znoPbJZFNmSoPOLbQPvJCWJO7whbbtsVx+VK04IETctjfSIdKglFVKB4ukg0Wer5fpO2WbL7vI2OsCxqA/N3GqRP2k9AMAYICx8rYPit6KtGugbfNsiBzrGJ1EpN3+bQeC+7JM7c9NYBRF/Rt8/vnn3bt3j9x+rxe59b1p06by8vLrr7/+XMbDsuzzzz//f//3fyNGjOjTp88LL7wwYcKEMzLywoULP/zwwxP3CQQCAEBOu64MRVEURZ19/8wE/p9qYl/LkryjpspXh7SN5TIAZDm4ttF8fTvLwP3dzKc6fsxdSapLLp9cUDh2b9qcjp8Ojbmz2rVTlkc7a2bFRsWxbF0/RscYO0Fwx6R79N27ZNz9XK4vWHexYMd+7eLR/tceNoy9TojctmfMPU7zbP8a84A+4o59ns9/0LVpLaQfrsOEce1nS5TSSi4uWt8efqzCAAAgAElEQVQxO9JmveoyISXRv2p9aOPO8O794d370YdfGS/qYr60p/6C9og9u3UiKsJlPsUNABjwNu/GJvtIWIoc/FqzPNPU7kAwJ0GXbOVsTXamKIpqvjvuuOPtt99ulMBHfPbZZ5999pnH4zn3UfXq1WvHjh0PP/zwc88999NPP52RzWW++eabVatW/fVxKIqiKKpl0QT+fPJljrjqUNPVyA+41QNutWFLpp3LcpxyDp/wdLrm16reLiocvSfljaz5/2l1V3VNrqIMr3J9EBvVlq+7RiBkTAvvHghArrmM+fP3r4bdeGtekRZ5KiCSh14P/bhWmTnRkBDDKGVv4sBWLu4O1jH4XFakN/XtFvyjS2jTrooJbxp6dtalp2BRDG3Zo5RWIo6NuX8E4tj6zvpO2fpO2TgoBjdsD/35KxE3alUHvN98G9qQaB8+5HhvgfTpjOmCvxinntUjQM3cCDBOSNjkWTeraCoClGbMHJvyYLK+9V8MgKKofxtVVSdOnFj/8Lvvvju2LFw4HF64cKHN1mIXCs1m89y5c4cMGXLvvffW1tb+9QHnzJlzzz33nLiP2+2+5ZZboqKi/vrbURRFUdRZQhP488mF8XxerdIw1ZM07AzWNVgFxqavW6xu4FC7BjfkT0nSK5msnSt/Kb/kybz4Mun9FN3WRRXTJsWNwK7/xUT10+sAgDF15WJvUau/IlJpVmrZH9/fc89jHy3+5cj6/BXrlZ53qG9N7DZ8QJnmWal5ViI+ho25hYsbyRiauNVz5iEU+9hY9/zv/MvXhjbsCG3YEWnm4mNi7r9d3yn72FcwJoNlUF8hdY5Ws7a+UTrw9XHfgtEZe5YAOs3POSJWiH+p7ds+5eT3uBjEZpna+1RPF2uPff5dhaEDBaG8+gReJUqVVNFKn4KguZULKYr6d9I07a233qp/+Ouvvx5vW/Xx48efq6CadtNNN/Xp02fJkiV/fSiLxXL55SfZGKWqqgoAOI7+04iiKIr6+0J0rdep2rNnT+fOnRmG0TStpWOBNcXhsUvdAIAADBxaeVtsgok96auao/aLiqJx+8jhywXhWH7i56k1KcIEh+0OswkAiOIMb+9GcAhYvb7jamnvVR8vdk2YrviDR5XZu3zQZe9OHtTatAKH9kZaGHMPLm4EG3Ud4s5FxTit1iNu36c4XYwgCG1S9Z3bnnhWvOqcJxc8BgCs40pgGhfAI6qmOmsY/V4gXtY2QNd+0VkM/fhkLDvliiR9an26/knpzN9rfjax5gusPUanPMCfw8kOFPUv9Oeff/bt27dPnz7r169v6VhOGSFk06ZNkePevXs/+uijt95667HdzGZzhw4dEPoXXROsqqpKSEiIj4+vrKxs6VgoiqKoFnBe/P1OLzP/QwzO0C8rCL+10f/WQPsZGTDq9sTgRm/1nLLIQ3218sYdxU/PT/0veLdJ8uQou4mP45KeUEomgxZWK98Ltn7zyls/7n3Hk4+NGrpmq1g/zqrVv3des37s2LEvTXwpiizVar7FgS1yYAsUPsVaL2Wjh7KOqxF/FndHY6Ps5kF9mt+fixulVn2CgzsYc3c+qfHdJ9+y371LPooa7UcIBdf30gK7DBe0R8Jfugl/GgRGaDR5vpOl2z7/Lqdc+af796Hxt8Tr6vbPq1VcPtWbqk9j0Jm5skNR1PkOIdSrV6/I8eDBgy+77LL6h39DL7zwwquvvqooTe/AQlEURVH/Nme3QBd1tukO7/T+3MXW1jYuSn/GvqDOGcV12TsDxgssAMA7lRdvL4ovUZaFxOleHwDwrR5EulQAUJ1fTgpFXeOYw/Arf5que/VBg0F/5NqQoiizZ8/ucOHN079NZDruFDJnsfZBAKB5f5ELHhO3tQ/vvUopfxsHd0HzloKfZYyQNgUAKWX/I1LjdaHmyy5y3FqEGBzanu1bVuJ8fXbx2KedU2b5V6xVa1qg1FO97rY+U9p/8Eb7WS9k/68+eweAN/MnvZz3xLg9Iz4t/eAchJEb2PN52UcKkc/Be1EU9dctW7asYZ15jLHf72/BeI6FMVZV9eT9KIqiKOrfgSbw57cYIwsALIIEE/vLbbHPHL3x+5sb/Vd+VX1Km89FOGcUl048AADAoTZfXtB2TU/70DgAsLrUl249FF+ihDABAECC0PoVAADAY90vpqjFWbVzGQYeHZ2wa9uWfol9G47pdrvHjx+f1bbz3EUePnuhoXue0GYm6xgMiMP+DUrxS+Hdl4lb28kH71VdXxG54jQ/kTOBsfRio68DHFZKXm70FAmvZgy7EWe3XvuJY8S1uqw0IiuhLbtrPvyq7KGXNH+gRQKuFyPEpxrSG7b0j74yQZck4fBu/9aG7Zs86zZ61rpk5xl89zAWpxW8vNr14xelH53BYSmKOuOcTue0adNmzpxZ3xIMBseNG2c2m202W6dOnaZNm9aC4VEURVEUdTx0Cv35zcwjAIgyNH0h5quckFvCe1xyn1a65o95VPb+RRfbkBgASJmW7V1eTRRiqVVfuaHwuUXpMzuxD1gtTtuQYn3vzuENHaW9s2ruQkQBAC7pReZJZprv5ZVRa97Dc0s8R+5jl5SU3HvvvR999NGUKVMGDryNi70NtKDmXa15VmveX4hUqroWgmshACB9G9Z6MWPpBaxFKXwUcDNu6jI6XfvFjLFz80/2WNKhp7FzXuRYdX2t1SyBBktACVYAgGgBrbyfkN7KNmyr5vaLO/aJ2/YSTWP0jXePJ4oKDEJsi01fvzL2uitjrwuoPhYd+WH3qu76DecHRA8emXzfGXmvj0vejdx7X+teeXX8TTFC3BkZlqKoM2vp0qUjR46sra3t37//Aw88EGkcOXLk4sWLASA2Nnbv3r2PP/74pk2bFixY0KKRUhRFURTVGE3gz28cgwBAxzZdZMgrYwDId6vNT+Drs3fEMhlfdI5k7wDAJ+jab+69f+AWrVYxBrQ3rsl/+nvi74SXh0SL9ekvpBsZguPVcgBgDB1Kn+3q+c7J2ri7X7/vlu23LK7+8ZX5U9zkyAzzzZs3Dxo0qFu3bo8++uiIESPYqGvZqGsBAIt52LNa8/2OfX+ScL4azgfnp6fwcWgB7F71FxN4zfl5w4sFhChNzOsnKiEA4UOgBVmHzTygt3lA72OHIrJSev8kHJZ02Wn6Dln6jpm6rDTEn+sF8wBg5o6ammHjHKOS79vp23JIzDewxvp2r+L+sHiahbO1Nra52DHQcvSrTqxSKtvsqav2gQn5oHjqc5mvn5HgKYo6g/Lz84cPHx4KhR544IFhw4ZFGjdt2rR48WKr1bp69eoePXoUFBTcfPPNX3755T333DNgwICWDZiiKIqiqIboFPrzW7SBeaWf7aVLj9qq9+5ltQ+v9BCAyA4DvmZPofetqDmcvaOMBUey9wh9prFL/iWGrhYA4GTyzJjief5gpYYP8NmLjDfVd6v56gH3wmrWxiX9N7P0yf3uWZVX7uq3cdraEcJN/NEXjLZv3z569OguXbp88sknkiQBAGPI5hLv17X90tCjQN/5F6H1K2zUdYg/5kYuEoCzIT4e6VszhraMsRNiI+XiERtzQzNP9ng4x+C6N+FsgFgAQGwUIyQzfCICBgAQFw2R2u+sGVjLicZiWT45gchKeHee56ufKie9UzxqfOVz09zzvwtt2U2UllzV2T968CPpz03r8PFNiaPqG12Kc19g90bP2oXl836oWljfToAUifl+1XeCAd879DoAMTDGW1vdCQAFwf27/dvOXvwURZ2ed999NxAIfPTRR++99179tmrz588HgAkTJvTo0QMAMjIyFixYgBBqOMe+pbzwwgvhcLilo6AoiqKovwuawJ/3butg7J965AY7JpDjUn/KF+ftCp7qUKG9dbWLrFdE266KObYD4pmMeZ2QngEAW60aU66MG1/ef4l3kfXRAGMBgMoD/aumJ0Sy97IJB7SAxlo46WBI/tj/9nvTFpnnXcFd1mhTopycnDFjxqSlpT377LOHDh06/E4sY+rGJY7TZc8TMt5uHAeRQfUSpYqEi7C4H4f2EC1S956orm+IXHaqJ96QkPkBRPZgw7KQMQMAgOF0F6xn7AMIYDbqatZ2caTYnq7NrBMPhVgm4aVHUua+Fjf+buuQ/kJaElG1cG6+d8lK55RZ7vlnYGfjM6uNse0r7WbcmfLQ1XE39Y++sr59Xe3ql/KeeGTvqPH7/pMf3H/sC7f7NpaFiwFgRNJ/rogdGsXHAMCc4unkb1GVkKKoI5YvX56cnDx69OiGjStXrgSAO+64o74lOzs7Kytr//4mft7PMY7jdLpTWAVGURRFUf9sdAr9Pw2D4KVLrPevcL+x0X+qyZN9aFzlG4dwQPMudxU/lJs6ox0cPTdfLgnnDdlGwhgIbB1gydgj9v3R2/dHrzjHsP/555LVBYHnbw9amcJJyWTCARzQHDfGp/yv7cEbdoS2+qpmlFz0Wr8pzyTmqPs/SP9m/b5fCTkSYGVl5auvvjplypR2Awc+98ADNw8dynF135yMIRsQD0QBAMSYGHMPgkOgBQj2gxokJNhwxrtS8opS8gpizUifhfTpjD4D6TMYfRukT2/uZnWIE9KnyAWPEyzi0G7G3AMHtsgH79XcK4DR8Yn3h/deAwCMMZONGtKc8ViL2dirq7FXVwDAwZCUWxDOK5QLSgxdOzTqqdV6fSvW8HExQmaqkJIITAtcX0vUJSfqkhs1ZhrbtTN3LhLza2VXreJqA20j7bt8W7+r+jJGiNvp2wIAcUJi36gBAHB/6/GvHnzap3qWORcPifurcyIoijqDysrKevfuzTT49VJWVpabm9uhQ4eUlJSGPRMTE7dto/NoKIqiKOrvhSbw/xCYwP5aRcMAAIlm9j9dzc6Q9l2eSACcQW1P9VE76CZbWbuuieRQn2Vs+3P3vCu3aX7VNa8MABrm8HJJeP+gLUq5BAS2DrK+91YScPD+6+SGd6vjD4owuo0bngOAFQ/HDJlUjIPYeW1U6uz2nI7N+r7bgWu3h7b6qj8stY9M6PAJeaf4mV9+fXXXR+9++eWXDfcHwhjnrFp1+6pVj8bF3XrrrSNHjuzRowcgHgkJRCoBAIKDRDoEnAMYHWJ0wMUgtRZLxZGXI8aMjG2JVEQUFwluh+B2reHpsSZGSEG6VKRLQboURpeChBSkT0V8HBx9oYKLG6sUv0pUl1oxS9dhsZQzTHMvAwA+4X4p/xEAQAC6tt+cxpeJMRkN3TsZundq8tng+m3eb1fUnYtOEFJbCWlJQuskvnWSkJbEGBpXyDtnEvXJT7WZTICENbHhmvnicGFh6EBh6EDk4QNp4yMHRtYUKyQ45collQsGRA9u+BKKoloWwzCCIDRsWbVqFQAMGjSoUc+amppzFxZFURRFUc1DE/h/iE93Byevb3qJ8md7Qp/tCTVsyXJwy4c3fTva0MWSveLCY3P4Rtn7O+8kCTyaGRv17a2G8UNsly723PC+K7pCAYAbpjsBYOtAy9v/jROqncNMxhFmU30OLxeEAODnEVGfZcQ/9/4HkydPnjFjxpw5c7xeb8MwnE7n9OnTp0+fnpnRZtg1XYd0LuzRgWMQAAIsFYF06HDKTRrm3gQHhKQnWcfg0me2+FdtsQz0xY/TiFSAw4UkXEBUNxZzQcxtfM6MDgnJjC4ZCa2Q0Arx8Uhoxbd+Wc4fB4CVkte52OFq9QLExyFzd1L+NgAwjquRrnXzvzrNZL68LyAk5RVKB4vUKpd04JB04FDkKcRzif99QshIOeEAJ4fFA4CbsbaCMTGGrEZtCFCjVPzquBuzTO3ePDgJA+5suTDVkBFp/67qS6dcCQAqUT4ofuux9EmR9oDqL5dK4oVEG+/4iydCUdTpadOmTU5ODiGkfjXT999/D8ck8Bjj/Pz81NTUFgiRoiiKoqjjown8P0SRXzt5p8Oc4ok6N87hCUl4Kn3/FUdl7zyPPoiNukin66nTAcCPN8O6Yfbh/6sa8nFtZJDua4MvT6z6+jrzV33wgkCwu064/6FWxjt9BAOycr/9J5YATHZ7J8TETp069a5Jk67+cE7l/M/CO3c0CuZgQf6b0/PfBEiMYYZexl97GX/phZwu/jY+8V4AUCpmaq6v6zszpi6s4/9KnthfPcsLkCXmQGiHI+unbohFAEBUL5FLiFRKpCIslRCplMglRCohSjUJ52vh/CY/DexfT5RKJMQzho7KgbsAABDLx9+Fxf2Ij0VcVPM/9pNi9Drr1f3h6v4AgIMhubBULiqXi8uUQ2WaP4j0jVeBhjbvCq7fxsfH8ikJfKt4PikeCScqca95f5H23djMYHTtFrL2K07cBwFa6lyMAbOI+0/qY/Xt1yfcHi3Ebvb84ZKde3zby8MlrfQpADC7+H97/NsBIFaIf6nt23omUnoQFCIHVL+dj0LQ9H4KFEWdKX369HnvvfcWLFhw++23A8ChQ4d++uknQRD69+/fsNuMGTNEURw4cGDLRElRFEVR1HHQBP4fQs+cQubDoZMsrj4qh/+kvHZhJRYxANl7uf2dt1vpePRhbPSFOgEAWIAp0Q4FYEeub+BCDwBwdl71KKDg9B/dT/3oFhP56f9LloNBflwJwcBF82qN8sbdJfcuSfMyZIrbW4vxAr+oH35r2vBbbysqrPh8/ldffuX2uBuFVOHCs7+VZn8r2S1o4EUfD7kl+/+uuCzatajuacQAwULr10qeOFA9qxQAWAenudXAOveBq7dHcnjE2RBnA+Mx09dxGEvFRC4lcgWRyohSSeQKIlcSuYwo1QBAwgUAoMlVdf2JFs49vK4bCYiPRnwM4uMQFw18DOKiEB+DuGjERwMXjTgH4qIi1exPCWMy6jtl6ztln6BP4NcNoU27jjxGiIuN4pPi+aQEffs2xl4XNOqPhJRINQGkS0FcdJNjErWGSCWAOKQ7+Z23EvHQbt9WALgqbpiZO1KQP0HX6ubE0cMSRjyyd5SohWYVvzUp9mVx694uQXMgPq7aEMAEN6yAMDX/xbxgDof4duZOj2VMqk/jNaJpRBUYWr+Kos6YCRMmzJkzZ/To0atXr27duvXHH38sSdItt9xis9XtZoIx/uyzzyZOnMhx3IMPPtiy0VIURVEU1QhN4P8hRncxKoSI6lF1677MCQEAx4CKIc3OXZTIMwixCPqlnDwjMnSxZC27MO/KrTioRbJ325C4j96K1TMw+3D2HsECTI12/BeHBIkAQPSdrUJbfP41btbKsXYeisUp69TaOWVIIob2JqRn1BqF8arPW6wviv4AxrO9daXvJ9ito1MuVtv1vHf9bavEX39SV/6JN0tEbhSYx08WrZYWrX4cALJbs5d15y7pxl3ajW2VfVP5f2OqZ5UAgLGzue1vPYsfzq35vCKwzn3gym1Zyy9E3HGucTB6xpANhiZSZangUc35CQBwcaNV56cABJCOjbqKyFWguojiJKqXyBVErjjxh4k4++Fk3oE4B3BRhw8ciLMjzg6sDXF2xDkAncKPZMwDd4R671VKK5XyKqW0Uq2oVp01qrNG3J7j+/GX1I9fZyymhv01txXQdUC+YfjWuk4/NDmmlHuTJpVwcWMYQ9uTBjCz6A0AYBEb1kILyj86tkOyvvWB4L4SsejnqU+0z9VnAmSCABCF9Do8bj/06Rbplm3qUCmV+1RPiXhIIyqH6uYR/PfAU4fEfDNr6WLtfnfqo0eCxGEZy6e0TT1FURHJycmLFy8eMWLE3LlzIy2tW7eeNm1a5PiHH364+eabJUlCCE2fPr1Dh8a1NimKoiiKalk0gf+HSDCxz/VtnM98lRMiADe2M/54QDzkUR065t3/syeYmns32NjVkr2ie95VW7Ffsw2Jy/i88wKEAaAV13gEFuDZS5N2f4TIf3Kr/lcU92AqUUlgvYe1ckkvZ1a8WoDCWN/eJO4LAgAgCHYwfvNtSWjw4YAJDDLqR1vMqkvOu3wrzlcGcpdc1/+amA/TP7lm9rKcFeu0TSESgmPkFWl5RdqHiyQASI/+uZO37AK2Y/e23a5ZPRzpmNazOgBAzecVgQ2eA4NPmMMfhy59quj6kmBJdX5S19JhEWvpe6QHlojqIko1UVxErQHFRdQaoriIWkuUGlBriOomSi1RPaB6mrMpAGLNwNkRa0ecHTgbYq3A2hBnQ6wNOCtibcDZEGtDnBVYO2O2mvv1rH8t0bBa5VJKKpQKJ2MyNsreAaB62uumS1fzMYwWWBdYlQJEDyyLOBaxLNIJiOMIDhC5DIDV/BtwuIjRn2idf61SUyWVA4BGtFWun058Xmv7edvn6pHAEw2DppGwVD11Dhl3h3lAbwC4IfGOGxLvkLHEIKY+eweAVvrUsnBJQPPvD+49cppAntv/UI1cLTBCT/sld6U8XP+UW6kJaH4HF91wOgBFUY0MHjw4Nzd3zZo1+/btS0lJufnmm43GuvIWoijGxMR07dr1qaee6tevX8vGSVEURVHUsVDDiaxUc+zZs6dz584Mw2jaKSw7bxGZH1QQgCd6Wfol6x742V3m16INzDuXO/okCSd/8WHhvGBgrSd6VCvEnzz79SxxFo7dQxQS92BqaJsvsN4TaY+9O0k6FPatqgEOgVa3O/i4NVmeWK795mBsqXqgq6FTnGHM8HzIFwHAcom9zeJujIHBIe3A9Tvc66s3ads2tP9sZeF+VzMSYc5o7Nm9e+8ePbp27Rr/g9WxzMABZ+5tb5jDV2va1RXOXnrd2zFRjS5IaADja9xrxfAPiXGx7sXSwf9E2hljZ32XNc3+5I4gqhvUWqK6iVpLVDeobqK6Dx94In9A8xDVA0Q9+XANINYMrAWxVuCsiLUCa0WsDXE2YM2ItRz+b+QpS3jr88CtaObIfMLdqudOtbqWi7KxMQ42ysZazA07qESZtP+RWuVEdaqJhkFTgaDL89oMu3wCFxcNhIT3Hqie9rHm9SOOTfn49ZNW1/epHh2j1zFHus0unrbbtzWoBVob2ryQPfVwPOrDe0aGsQgAXa0XPZz+TH3/HP9On+qx89HpxsyG41DUafvzzz/79u3bp0+f9evXt3Qs1BlTVVWVkJAQHx9fWVnZ0rFQFEVRLeC8+Pud3oH/V+gUy39/Y8xjqz1rSqTRP9aM625+qLvlBKvmy/yaw8AYOQQA+myTPrvxvdzjsV8flw6dCsfucb5bHPdgKgAE1nti706SyyXfqhouRsj6qRtr4wqWOud4A55YDgAefbzc7FIBgDAAGADAna0/8N/WCqu1BYY1sllLuh64fscl63v1K+428/NHdldVrtqgrNqobt6rKsfJdtVQ6M+1a/9cuzbyUGCFDNK67e9tsjtmX/rmoLbt2mVkZBSq2E/IKjF8X3XtB7FHcngN4CFX7a9iGADyFDUh5iYofhHkMgCka/910+93MpHZ8iSg5d+6w9Q1LWnKcVe2Ey0AqodEknnVTTQfqF6ieYnqBc1LVB/RvKB6ieYDzUdUD9ECoAUInGQOf50T/7iTo3fT02T3F5NImCUKRxQOh3kEBsbqYB021m4x9e1uuvjCV9vNPPEblj36ilJayScnJk17BiIlrxHSd8puNfWZknufJarmX77GNuz/TjyIlbM3arkn9TEAkHC44e16DnGXRA3aF9jlVdy6BsvmRS00teDFyEWjLFP7iZmvHT5d8kPVQkmuyArvTzGk2U5SjxAxpi6M5aITh0pRFEVRFEVRZxVN4P/JEEKEEJ4BALDrmTlDot7Z4p+5LTB9S2B/jTrjCgfbVDG7/bXKkIWuZCv7++1xp/GmDXP4+IdTW73UpmpakXepK5K9GzqaD6nqqCGCn9gBYILdesG7HQ59Wib/XMseXsDvyAs7LttZa2G+bmeQsvVx7Sx9H21dqZLgJq/rw1u7Pz2jewf+6bE4IJJ1O/Af2+Vff4zb5S7X4LgTImRNzoUDufgA5C1/87rpAMDzfJs2bQLpGWJa+k8ZGcM6dPjwoh7xMTEawMM1ddl7R0HooxMAwNDhOynnei7xXsTHn8YHEoED2t6ufyqVUmCdR65W0ud0bLIbYs3AmhEkN3NYovlA8xPVB5qPaH7QfET1Es0Lmp9oftACRAuA5j+c6nuI4jruWEdf0FGqP7XfcEwfgojMY5knIV14dyribMAYgDEg1gbIEPpzrxYAhreCzsIIVqR3IG4PF8+YL+6CxWIk2BBrjizyZ+0WfWbr8P5Ccee+kybwx3PsvfTbk+4+tpuBNd6adGdBMM+t1FxgPbLiwK/6vqv88rnwpngSAoDGtRaagLyd18mMzsLZoviY04uZoiiKoiiKov4KmsD/k+lYEFVIs9V9lRkEj/W0dI0TnvjF83Nh2BnSEs1NrIffWikDQMWp7EsXkVujztkZmNDHGn19XBrpdGjsnqrpxZ4fq6UCkYsRspZ2M3Qwq4TcUunyE4wAnnfYbjOb4BpzVitd7tJaAPBEc39cb+tzUDHtFY0uJXtzEDYHAWryAaxXRAupet+qfvZrVho75gKA2YAG92GT+vW+ZdULmkncS/a/N6Q435sj79guV1efOFRFUXJzcyG3bk/4HwASAGw2G5ecLCW24pOTWyWnDO3QbnPr1mlpaQkJ6foLd5/qp9FQffYeeej+qhIAjpfDn5K6afNCUjP7q1Vz5cInAAAxFoL9rOMKLuEBUD1K1Wzs+xMYA2ARCLCW7iAkg+YlWgBwiGhB0LxECwIoSCezOhkgiIO1jQYXMhq/nf2myP+XhXc9fThiDrFmYEzmyyXjRZjgX4JrFgFrQ6wecWaksyGdAemjGMECjA6xVkACsGbEGA4/5IG1IEYPpzIT/oqYoXBMxm3lbE9n/teWOwyUJsorNOnF/GcUAgBwadTlY1PqqnOrRF1SuSCMRTNr6WW/NFF/5OKLjGUOscyp70FAURRFURRFUU2iCfw/WadYfkul3D76qL3BB7TWLbslpszfdPYOAB6JAAA+9bcNxWYAACAASURBVNoI41a6D3lUUSPvXuFwDIsD6HRo7J6G2TsAyACBhtk7AAC8VaAM4YBRwV6jBmxs3oLMB6xmpVKq3O0vyvHpD4ZthRIyMnJxGABVfzCq9fRnCYlMyCZ7F9/38ZI2bwwt6B7sMnd5ly3DRg57O7Gade6q2ru7aM+uvN3bt2wvqyxrTvxerxe8Xti7FwDcAGMOtwuCkJiY2KpVq7i4uKSkpPj4+ISEhMTExPj4+EijIJyopgAOaDkX/qlUSoBQ6ox2vhU1nh+c7q8qQYP0ec3N4ZeJYq2GR5ibWMsQwHiG1z/GYkrkTv7jzMWNVsvexnIJwX4ATnOv4pOeAi4K+zYC4gCHAYAREnQdlwFqakt5ohAtCJqP4DDgEFE9gEXAItF8RAsQOYD9LhyuATmE1QBofrW2iuFUZMSMiWUEjag+ICpRPQAexgCHt4Gvm/9PVCAqQLCZHwkgZATEE8bGcAKwZmD0iNEDawbEIdYOiEWsBRAPrDGS/wNjRogD1gKIRZwdgMnirGHG0OzvdNLXenGBXCZqYpwuob7VKVUsdX4bOS4WCx5OfzZyXCNXP7//4TAWjaxpaPwtV8ZeF2nHRPvZ9YOCZRNr6Wa7yMEf2c8PE8ycbItH6i8hKg7tBTj51xwJiX9lxg1FURRFUdRZQhP4f7L3r3RUBrVkS+NEPd7ExjeoRb+1Up65LXB5mv66bIPxFOu0N+QRNQAo8eHIQ8ewOIbv7Pq0vNWLbSLZOwAYEXolym5nmIENSpd96lI239N68kfFWMbD/+eMtphgvJlP0KUk6FKuiAGAqneKy549AABIYEquH4TL1qQnLweAyrWDdhs6uRK5995o9fi4EiDQY5G7ZJEbALIhPhvib2IvJxg8Rk8eyS/PcBVyxaWGirziA+Xl5c0/L1mWi4qKioqKjtchKioqNjbWaI+uwNaLs+LTW8VGRUXFxMRER0fbDfba+4oMLp0FWTrM6B4zplXMmFYFt+32/OB0f1MJ0Nwc/qkat0pgr6y8GnXUgvAAxldWVtdqWo6ifhbX9NbuR0Esn/melHMtACCGI1iVD00ExAFgQLpICT2+zbtNZ+8AgHjE2YGzN/O7pHzCm/LBItZhTZ75EuJ5AAAiEy2kFh+smjIdeNXQJY2LNRLJjZUQKAGCA6CEAYlCRqwuK5loXsAS4BDBQRIOqdXFwGJGUIBTEYsJCQEBwF58aoX/jg+xCAkALGF4hDhAPCAW5ApCVABAXPRtuBwEHWIcIOUp5W8jxANrigP2hegBbs0dJEwbUyYO7gAAxFoEHEzmjRVSSNH87gal/g6J+QvL50WO84J772v9ZOTYKVe+uP+xMBbNnPX6+NsGxlwVaVeJ8pPz20jCf5H9kmghtn4on+rViGpgjfrD10KoEwvvG4Z965rXlzNeVAiM+eQdKYqiKIqiziGawP+TOfSMQ3/yG3pbKuTfiqXfiqWpm/w/3XwmF/farom1XRPbqPEGk7FRCwGSk2FMXHRBxQ07sYxrXirUIZTwZHrk2YbZe+aSrt36OYg8Lbzzj5CKRqRN8qdGA5CtA82zXku688VyXiKAoLSNDmGIqlYNfg0A7Mh2EboQDgEA7L895sDStITVFcZRvxbhkkO4+BApLSalBUypqkind5q1tbW1tXXzyb/dfNxuzF2M/Ql7BG9iDTW86XOTfUNU8vVpNpvNfJjD4TCbzSaTyWQyWSwWnU5ntVr76w2rRHFxMAQA9Tl8ffbOANxtaW6mwVovZa39Nd9vBIcRa8GBLQAAjAlwsO5Z+6DT+xyO5Rg9rOr5t1W3r+zR/0bfeZOQ2ZrIirhxZ+3874hqZQz6qLsmo2N2JWwaIYHfNiplVTgYwkERh0Qse0g4CDhAFMk8oItl0IUES6D5gahE82rVLu9PPyEOI06LJPxIJwMDjCAjhrDRPBdnJ6ofiEKUSiAaAADRCBEBAHATt2iJWqM65zUZWixA/Xd5+PABC3Bk5/ritaGSCYg1AaBE1jYVizLiNEBGn0fa9wcgFliLAUu3KXlhLSwrKM4ZVsTNwFoBoFr1u1w/aMBUAsu6lw2KHw6IRYyuUqn9sPjtEGEAocvjbr487kYAQJw1oAbmlc6UsWRizZfHXtPG2LYufiDbvBswwTpG387cSWhQ5+/fgwR2NLuvShQv0tEEnqIoiqKovxeawFNwd1dzspX9dHeo2Ke24KaC+r62zB+6HRy6Hcu4/MUCAEh4Mr1R9m7p5wAAJCToO//27C/eGnMs24oAgB6hNddb911kePO6Aj6Akwvk4hdThddKAACxKOWd9kKSUFUdfqfCs62/xR0IWrJNo4b2aFN+QRcvtng1Q0CbPyF+xeVa8oqDV724q4o4y0lVJXZWEmclqXIT7xk5QYxxw1S/zn6A10/+WqPRqAkCttqmCsJck7FDVBTDcTt1epllWZO5n9W8zGb7Va83GAwmk0kQBKPRqNPp9Hq9wWDged5sNrMsa7VaAcDhcJCol6Cmv54nkaQdAIBELl4gIeN/Z+RkIwztMy2D+/mXr1GrXFWvfXDUcwwTN/G+5mbvAIBQZNP4ZuLiwBwerFZUE0nGYhgHReyTsCQTMYxDYctVl+k715WUJ+F8cWdPIASABH7uFS5IRpwGnMbwiuPWVcBqAIA9WfoeTwAWAUsEi4Bl7Kn2r/kDsSpiMQgqQhgJKmIR0snAMGwUy+h1gINAFIJFwBIQhageAADVrQOoy57l0vpSEyxA9/rofWWKb2Xk0A5wW327nCO5v65vH1/fXrxRLK67k88AjAZGikzFd84QOQcAIM4exqJeKpeB0wBKdInJxmwAQKwlqInbfBsDBHGIb2fpmmruBACIMUhANnv+CAPLMroulgtjDG0iBQiBNe0N7MFE4zhHVuRCAFNXmzBAMEIczwh/2736GEs3zbu2mX0Rf+KNCSiKoiiKoloATeD/7ZYXSr8XiQCQYWcz7Ow7m/37alUAIAC9PqnCALFGJs7IRuuRUWAf6W6OMZ7FNbrmi+2ZP3Q7MHQbkUn5iwXBjT7vMhccnb1HIH3GXoeTTVYBIIlllyXGPVLj/rUVPLkkfer1hVwAp04qAgDEoswfL7RcagcAK8BjSnyBqlZrWq0V13xg3adpW2XZq9XN+WcBKkbErOjTzVGlmnz4Rpm9GXTYrwY9wZyS4tVVZUGfK+ytDgRrPJIrr1XQo7hIZbVU68KAz95nEhEKhSAUAo8HAKoAqo5+9juA7053ZLuFBQCDjuh1AMjqiBuOEGIYxmq2AgHBIJjNZgCIXAsAAJ1OZzQaAYDjOIvFAgAIIbu9blKA2WzmeR4A6q8XQFoUXNIpvHI9CUsAYOF5BjF8fEzUnTeFEqJEtzvyQofDcUxof5Wxe6fmdEP6NlzUDWrNtwDIPHiv1fA2UTCWZNBejWTvQHhdx4VcTFrDV2m2gFo9V3XWEkUlkkwkiahH6j5ar+4fNfamhv2l/fsrX54KDGF0CgAgXgGGMLwKrAYMMXZNswzuRzQvAAFNJETSPN7gL6sJxoiXgQHEakiPATGMICOeYeMsrIUHgLqLApErC7IMrAhAWMBGggEANC/RvABAJBAA0uujEd2amBM51AH0qW+vzVdq69bzI4Ajm+bVLmw4OyWz/kMAEBu0s4cb/YyOi8zqZ/Qa8D7VgxFSkM7OOQycBbEWAMBEK1e9GhAO8TH6FD3rAABADGKtTqlSAxVYc5wuiUVcZKkCAIg4pCIDhwQja4pMW6gLlTECU1eHArFWqKsjwESmMAAAYgRgjAAgtHlf3N4ZCEGAdJ1/RlzjCUfS/hE4lAMAXMJYoAsTKIqiKIr6+zlfE/j8/PyVK1fm5OTU1NSEQqHo6OikpKSkpKShQ4cmJia2dHTnk0dX1irHST9dIgaAWhHvr6lbZLz6UHhsZ1NWFNcnSdCxp79a/gTMF9szF3U9eMMOIpO67F3HZH3fzXzxUWu/vwwES5JVAIAQLMuO4xGaGRP1gKv21yR44nAO3zB7j2jDc234uu95AvCgqzaSvbcTuLmxMTIhtRqewHv3ZMgAQHS6++OiI6lAHO6wotZTq2l+jIOYBAmxElKXHBDCVNQgZw1b7lI9bjd4VXdteiDQ6uMCj+TxQcAXHa6SfJLol+Vm12c7hzx+DQA8/rpHhaXbztEbfzLteM9YjBaWYQEAEJitZkEnAACRMQJks9gAABjE6JnINQUiYyAALDKb6q4dIB7BMVcEbDYbwxy59lR/oSGCQekGtwZEA5AZ2+Nc9DA9H2YqlkeqnXFxt7Glm027cxpVKzRe0UOnO2oiuoCQQdCRsORx2DwFBZFGq9XKsizExvJ3jlJrPVhSEMZmhgOMsRgGAjgk6rv1Y6MzGw7F6L1SoaC63ACAQyKQoybHWK8dFDVqWMMWaX9BxbN1sycQqwGnAQCjU4HBAMTUt619+BDAConMucAhpazM88X3SKcAQwAA6WSNJRwA4lUA0LVrpctM8qoeVXUDaFbOzoZ9cv4homlgVANGWWMIz6oGmeUwQryMdCywCKsBBjAAsFgiuC7lZwCO/ASq1Q1/2Rz5TR3c1rCOQf1XDsNR18bYw9cITnOty+FPB0AjQMQ9VwIbzbAmQIhPeoKLHYH9WyLZOyBBSJ38V96EoiiKoijqLDn/EvjCwsIHHnhg+fLlTT47bty466677q233kpLSzu3cZ2vjBzjlZt7A7kqqE3Z4AOAoZmGNSVhUalLKiIl6zUCALDXJbefXdHwVTFGbtVtMc1P+C39ozIXdT14w04iY0bHZB6TvZeq6ktuLwCQEPB/IL5t3cgzY6Lura5ZkwRPfN9myju1XR9LN/WwHe9dtknSL2IYADrp+M9iY/QIAUA8yy6Mjx7jrNkhy5skaX1YukSvAwALw7wbc9R8Wg0giLEXkxDB4YTYECaP/O4JyTjVyDzYyzzIoHc8IuX23YRFDclo7ZJ2ezvqJVXxeLySz+v1eGSfL3tBSbullSIRy6PlpddxWjiIg0EcDCaEglGiGAwGQ6FQjcfjF8NYbO4+Z/8M/pC//tjj95zz918O0OjXy/sA75/d93wCGMRYzVZ0uIok0ep+uGxmK4MYQAAocj2BAAHdzlXGNycDi+r7AyZYlEyCnmMQYoAQEvmxJJoGALZSp275Ucu/iarJhVUCAT3LQWQSCsakbjYKEdJlezYShHiAukrs2G8P/FZBNA1Ap2Osevaovzt0bdONPbtEji0WC8toamWlb9laxGLEYEUgkl5DrGYKsnqWMeiBT0s2dO9UFT6oYEkjWqIQx3oD4pY9CIHJTioSFZUjHNJiazlDiAEW8wl2khxVHCpAJGwxswn6RIsqKEWFhBBFwJ4YiUEEAExB1qRqiAF7nBnriKLWze/ggWGJcjjYul93ZiPhOWfk2K6u4WJHhPcPrzudNtPp7XeKoiiKov6ezrMEvqam5oorrsjPz+/YseN1113XqVOn6Ohoq9Xq8/lqa2tzc3N//PHHb7/9dufOnevWrYuPp5sAndxL/awbyuWGLTk1yq4qBQBu7XCk2hwBCEi4fTTvDOEin0oAvFLT6+UJAfnoZ8oD6jO/e5PMrIlHS/PDe1xKky+8cK6z4cMOd6X8Z23twGnZ5j52ANhUIddPBAgjrLMgLJLgb4AY9NmeI/ntJWCosZADrUA3t71Jd6IyXYkcJyDUXuDmHc7eI3QIzYuLvqu6dpckJx9/hTYLYGUYKwOHbwqC4ELYD0YLc0dks7csY7v1F0Vy+EuH5d61qoepexQk1n1PVrxWWPGzA7jO+vbm9usvwjwKYgwAQUIS2KMudeyTFaem1fp9iiRXej2zq1xBKYy9XqIoOBSK09Q2qqqExZAYlkLBLAK6UFBVVb/fLyrKLq8vLEmaLCkhUVMVNRgkGCs+HwBooRBWmv5CUC0CE9zk1QqP70xcwtiz89T652yDn05p/D/h2y9O5QUbAb49tZCOcvBkHapO1uEoqTFLli1OTONqAQCERDbmltOMi6IoiqIo6iw7zxL4Z555Jj8//7XXXpswYUKTHV588cWPP/74nnvumTRp0qxZs85xeOejoZmGoZlH3WuauT2wq0pBAP/td9zb1y4R769VggoBAAwQlLCCIawSAOBZJtaIACCsQq1Ytyp4SZ54vKGOJ6eN6dm2JutBWcytYhHUho+dJoAAQNbIi+saF5l7sLu1R7IOAJwh/MYGn6Q1ca1Bx6JPL4juEi1EsmV3GM/YGpA0wjNwR0fTZ3HREiF6hLwSfndrIHR0db+ggpfmhwkBpkGqHelS5FezZlUgBJFnEh9JnzK1QFBw7qAtm6d38LU1AUDGp2UZn5QDkGC6odXK7ohHkcsBokpW7BMllXAsGpppiDMyANBe4DM07iunyavoP4liNVuUgcDVAcM+nZwvaCJAvE7/blzd7AAFk29yRZ+EDTy6NtNgP7wHgYLJjwfD0jE1Cu3Kli7OawGQlvqZwncAAFeNe12pFJIUORzoGMczWK4JBAEgGAztqQyENQiHRVUOA4CqyKIYJAAskBQS0jMIAPx+v6KqBSFVVFUp4DfwiEEgBUOKImMCkkbCPh8hGAAIgOT1AAAHgAgJ+M5MpUCKOg3FLudrU16f9ZweAPRtPwc4K+uDKIqiKIqi/rrzLIFfs2ZN27Ztj5e9R4wdO/bzzz9ft66Zm/0eZeLEiVOmTGlOT0JasF57y4sxMMtvabw/XPePKz0SaRvFfXdjNAAQgB1VckUA+yQcUklIIUGFHPKpOdUKIZDp4Oq3uFtyQAQAnkGAoGssn3R44/quCfyr6/1yU+n3iWXY60bYUSUvPv61g0Qze0F03armP8vkT3bXrVE38cz4XpbIbfl1pfLcXcddu46bCg0TqN+FrCRGePKxjLemFQgK7v5QznMPpnXdF7j852oAKInXjb8v7a5c8enedbW4Vh8KT/7DFzl2hXB9+8rC8EvrffwgghgAAso2WFQZBgC+O6AEWC2Fn6hxT412AMDPheHn1tRlwhWBIyP8XBh+8pcmb+Rm3RD1Sv/W3NVtr4k83kfC722tm3h8X7R5fK+6EZbmh2eudB/vcxjSzfzk4Z7LCsIP/lzXc0yD9uUF4XE/uxtvIXjYSxeaH7+orueiXVVPrKobYWQWGdOxbibFmmLxuZ9L619C5DBRj6yGHpykXpVWt6x9Z5U8a0N55HhAqu7yNL3H4yGEHHSri/JEAMDhAGhHll0TrJFwoGMs155dCWo1AITCIMnwu7+/BLo0O5tm40KBgKIoXgkfdKuAAQgQOURUBQCAqcv4rIws4LqpKbJGIrNUcMjHI8LVL8AnEJIxFn3H+zCpFmE2YABgbQMYU7eWjoWiKIqiKOq4zrMEvrq6unPnziftlpKSsnPnKU4ZPUUI0Vs0J4EAusUL3ZqxjiGSwG8cE2sTGk9Zv7mtUdKIisEdxrVi3U34gEIYBO9uDWyvkg0cWnZLLAAcdKteCTMI9U3mYwx141yRrv/i2ugasYlF/gyCfqlH5thfmaGfeaXDLWKeRf+XfmQTrCvT9dOvsPuPWS+wtkSqFnGyhRUOT3j/MV8UFfL/7N13eBRV2wbwZ7Ynm03bhHTSCZ1AQEIPAgpKE/kUVECKSBVUmqiAVFHwVTrYEFARRGkiGLAB0qWFSE8ghIT0sr3MfH8MrGsaAZKdbHL/Lq/L3TMnu3cOk919Zs+ckYuYhr7SUHex7Zz/qHjVrTi3sCHnxCZ24YpUxsoRkS7C9c4XTadKRH2j/32ubmGKGe3c8w2sVESD7c5feDxUoX6SKRZxDEdd811DA8R3l/9i6YBBf1Nh2aPTh0jEkz3cu4Qo3mij0lo4EdFzjf59hM4hinGt3MqaxUASZkjjGKXtbodg2ehYtyITK2ZoQMy/UzPaB8lGtVBqzGUcsRAR9Y3+t2fbANkLjV0LjKxUxPSxa28TIBvU2LXIWOa/BdMz4t9x6BTlOyhPoTGzDDHPt1RG+Nwty9382X8oWGe36KKVJT6SXEwT41TN693t2VlrLYopLjCyEhG91loVe689U2tV/FVU5tkfYoZea+0W65Wt/7sZfwDmT+Mot+K3RAy9FufWyv/ugZ7bGuu8w0XaMseBofGt3NoE3O2ZXmyde7goS2eVipgpbVWP3Wu/VWx958/C/FL/FhzHkr5oYpyqtd0jzP+rKDe/QEzcK7FuLf2ktgwfHMop1JaxMgJr0AyMljW5N2J3tNYvzmv1Zo41aHqHS2PUd9uzddZvknVGK7Fmo9X4nyNcDEPdwxTRXhKNRmM2m/MN7N7rBjPLMUQdQ+ThHhIiMhgMmQXaX28Y9WVed9KgifUVBbjd/RssMrFH001GKydmmJZ+Ui+JWa/XE5HGzJ29Yyq5gibHWnRFDFG4578H+HRm7kq+xcJyDFGYp0RkKOKPnxqtXFqR1e4gGscxd/9v0RV5yUUu9xYIMLNcrp5liRgiD7lIIWGISFOQbjGbiIgTSeObcDNGuDCMSBb134sdAgAAANQwjHN9k9ynT5/ExMSkpKSoqKjy+mRlZbVo0aJNmzY7d+6sjgx37tzx9/f38/PLzMysjscX3KrTmqXHihmiq2MebD1//hv4pr4y/hv4yotck0FEf4+oV7qAr8BLu/KOpBtdpcz5kf4P9HTVpOPXWRnF1lCV5NcXS85NICLDRe0/HY9zBpaIXJq4NTz82L/Lj1VC61uZOmKXqdXdXUqe2D8+J/dXvfEpVxf+S3h4RKbrky1ZXzEST5e4y8RI7/8D4Jw4c7bhVEPObp17adCb0pB3KvnjR44cad++fbt27f7666/qCQgCqPXv7wAAUDGneH+vxmt6V4fXXnvNYrHEx8cvW7bsxo0bJbZmZGR89tlnbdq0ycrKGjFihCAJawFvhYjoP2d3w6NTNFQ2OtxW6itVxns8aPVORIcC6x0P9C9dvRPRSh/14SB/VO9VRRb+gTRkprzJXlTvtRsj9ZUEvfbvXZFSGjxNwDwAAAAAleFkU+h79OixfPnySfd4eHh4e3u7u7trNJq8vLz8/Hwikkgkq1at6t+/v9BhnVX7IDlD1MgX1UsVU8S4Nkvp/JA/K6roWJt3hVvhwTAyadBUoUOAI0iD3zJnrCNWR0SyqDXEyIROBAAAAHAfTlbAE9HYsWO7d+/+2WefJSYm/vPPPykpKUQkFot9fX1bt249YMCA4cOH+/vXiDnVTqq+u3hzf3WI6gFms/PqKcUFRkuU1wP/II/Bys8A4EiMTB79mfHSiyJVa7F3b6HTAAAAANyf8xXwRBQdHb148eLFixdzHKfT6QwGg5eXlwhfQlad1v4P803U+qfVX1/QjWulvH/X/xIzxHHkJnuwf0H+fHkJ5voDwMMSe/VStDgmkocIHQQAAACgUpyygLdhGEapVCqVD1wxQnXwU4reeMztIX7wr2F+xD7wegzT27qfy84Z0+JhnrE6tPGX7yzWtQ3BqQcAzkTkEi10BAAAAIDKcu4CHmoHH8XDzJ6IUUtODqtB50r8r5vH/7p5CJ0CAAAAAABqLRTwDyk/P79Hjx4P+lN///23wWBQKBT37wqlsCyr1+vlcrlEgv32YZhMJrPZjBkrD02n00kkEpkMS509DKvVajQamzVr5uFRG45zFRYWCh0Bqgve36sWf7Yj3rvLZDabTSaTq6srw+B8wJL0ej3DMPibKg1/UxWwWCxms7lVq1YuLi4P9whO8f7uZNeBrwm0Wq2Pj4/BYBA6CAAACKl37967du0SOgVUGby/AwAA1fj3dxTwD+PSpUtpaWkP+lMWi6VXr15isXjhwoXVkarWO3r06I8//hgfH//MM88IncUpLV++/NatWxMnTgwODhY6i/O5ffv2J598EhgYOGnSJKGzOKWdO3cePnx43Lhxtenvt1WrVt7e3kKngKqE9/cqd+bMmW+//TY2Nnbw4MFCZ6lxNm7cmJSUNGTIkKZNmwqdpWbhOG7GjBkMw7z//vtCZ6lx8DdVgU2bNp0/f/7dd9/t3Pkhr9zMq+nv7xw4islkIiKpVCp0EGe1atUqIho7dqzQQZxV69atiejEiRNCB3FKp0+fJqLY2FihgziriRMnEtGyZcuEDgJQ9fD+XoGvv/6aiF544QWhg9REAwYMIKJt27YJHaTGsVgsRCQWi4UOUhN9++23RDRo0CChg9REAwcOJKKtW7cKHaR64dJrAAAAAAAAAE4ABTwAAAAAAACAE0ABDwAAAAAAAOAEUMADAAAAAAAAOAEU8AAAAAAAAABOAAU8AAAAAAAAgBNAAQ8AAAAAAADgBFDAAwAAAAAAADgBFPAAAAAAAAAATgAFPAAAAAAAAIATkAgdoA6RSqWPP/64i4uL0EGcVZs2bQIDAzt16iR0EGfVrVu34uLiyMhIoYM4pfDw8EaNGnXr1k3oIM6qU6dO27Zte+yxx4QOAlD18P5egZYtWwYHB3fp0kXoIDVRQkLCiRMnWrRoIXSQGkcsFnfv3l0qlQodpCbi/6YSEhKEDlITJSQkHDt2LDY2Vugg1YvhOE7oDAAAAAAAAABwH5hCDwAAAAAAAOAEUMADAAAAAAAAOAEU8AAAAAAAAABOAAU8AAAAAAAAgBNAAQ8AAAAAAADgBFDAAwAAAAAAADgBFPAAAAAAAAAATgAFPAAAAAAAAIATQAEPAAAAAAAA4ARQwAMAAAAAAAA4ARTwAAAAAAAAAE4ABTwAAAAAAACAE0ABDwAAAAAAAOAEUMADAAAAAAAAOAEU8AAAAAAAAABOAAU8QC3x+++/Z2dnC53CiWEAAQCgui1ZsmTlypVCpwBwSvioxkMB7yCJiYl9+/b19fVt3Ljx2LFj8/LyhE5Uc3Xu3Jkpi1arte+GIbWXlJTUtWvXv/76q8ytlRmrOj6eFQwgdsgKrF69Oi4uTqVS1atXr3Pnzlu2bCndB7sf1G7Ye22q6gWhFtu4cePUqVO///770pvq7MikpaUNGzYsmWGS0wAAIABJREFUICDA3d09Pj4eu42NxWJZtmxZfHy8h4dHaGho//79T5w4Ubpb3RkcfNb9FwfVb/Xq1WKxWCaTdenSpUGDBkQUGRl5/fp1oXPVUIGBgXK5vGEpWq3W1gdDWsLAgQOJaPv27aU3VWasMJ4VDCB2yDJZLJZXX32ViORyeZcuXbp27apQKIjo1Vdfte+G3Q9qN+y9vCp8QajFUlJS3N3diSghIaHEpjo7MklJScHBwSKRKDY2tmfPnkqlkojmz59v36duDo7Vau3evTsR+fn5Pf300x07dhSLxQzDbN682b5bnRocfNa1QQFf7S5fviyVStVq9eXLl/mWhQsXElHPnj2FDVYzabVahmF69epVQR8Mqc1vv/32wQcfxMXF8cfjSr+oVWas6vJ43ncAsUOW58svvySimJiYjIwMvuXq1asRERFE9NNPP/Et2P2gdsPea1NVLwi1mMVi6dChg0qlKl3A19mRMZlMwcHBSqXyt99+41tSU1NVKpVIJEpNTeVb6uzgfPbZZ0T01FNP6XQ6vuXkyZOurq4qlcpgMPAtdWRw8Fm3NBTw1W7atGlE9Mknn9g3NmnShIiuXr0qVKoa6/z580T02muvVdAHQ2oTFRVlP6Gm9ItaZcaqLo/nfQcQO2R5unXrRkRHjx61b9y6dSsRjRkzhr+L3Q9qN+y9NlX1glCLzZ07l2GYzz//vHQBX2dHZsOGDUQ0d+5c+8bPP/+8d+/ee/fu5e/W2cEZOnQoEe3bt8++sV+/fkR05MgR/m4dGRx81i0N58BXu8TERCLi/+Rs+Lv8JrB39epVIoqOjq6gD4bU5vfff09NTU1NTR07dmyZHSozVnV5PO87gNghy3P9+nWpVNqmTRv7xmbNmhHRlStX+LvY/aB2w95rU1UvCLXVsWPH5s6dO2HChCeffLL01jo7MuvWrSOiYcOG2TeOGDFi165dtoGqs4MjlUqJqMSCbfw52/wmqjODg8+6pUmEDlDLcRz3zz//uLu7h4aG2rc3bdqUiJKTkwXKVXNdu3aNiHJycnr16nXixAmpVNqiRYvJkyf37NmT74AhtRcUFMTf8PDwKL21MmNVx8ez4gEk7JDl++GHHxiGEYn+cxT41KlTRBQZGUnY/aC2w95rr0peEGorjUbz0ksvRUVFLV68uPSiWXV5ZM6ePevm5la/fv3ff//98OHDGRkZzZs379+/f7169fgOdXlwJk6c+N13302fPl2tVnfq1KmwsHDFihUHDx7s0aMHP5m87gwOPuuWhgK+eul0OoPBEBAQUKJdrVYTUW5urhChajT+C8/33nsvKCiobdu2mZmZiYmJ+/bte/vtt+fPn08Y0gdRmbHCeFYMO2R5YmNjS7ScOXNmypQpDMPwa1lh94PaDXuvvSp5QaitXnvttRs3bhw5csTFxaX01jo7Mjqdrri4ODIyctKkScuWLbO1z5w5c/369b1796Y6PDhE1KJFi8OHD3ft2rVXr162xueee+6rr77ib9flwbFXNz9sYAp99TIYDETELzpqj2/R6XQCZKrZUlNTJRLJnDlz0tLSfvrpp1OnTh06dEitVi9atOjIkSOEIX0QlRkrjGfFsENWBsdxX331VefOnTMyMpYuXdqqVSvC7ge1Hfbe8jz0C0KttG3bti+//HL27Nm2JbhKqLMjw/9q165dW79+/Zo1azIzMzMzM5ctW6bVal944YW0tDSqw4NDRMXFxW+88UZeXl6jRo2GDBnSp08flUq1Y8cOfsFIqtuDY69ufthAAV+9vLy8xGKxRqMp0V5UVET3DvyAvZ9//tlsNs+ePZthGL6lXbt27733HsuymzZtIgzpg6jMWGE8K4Yd8r5OnToVHx//8ssvK5XKH3/88fXXX+fbsftB7Ya9t0yP8oJQ+6Snp48ePbpdu3YzZswor0/dHBkikkjuzgJeuXLlq6++6ufn5+fnN3HixJkzZxYXFy9fvpzq8OAQ0fDhww8cODBnzpwLFy5s2LBh586dly5dioqKGjdu3L59+6huD469uvlhAwV89RKJRL6+vqVPeeJbbCd1QMUSEhKI6Ny5c4QhfRCVGSuM50PADskzm80zZ85s27ZtUlLSO++8c/ny5f79+9u2YveD2g17bwmP/oJQ+2zfvj0vL08kEr300kuDBw8ePHjwuHHjiCg5OXnw4MH8MuN1c2SIyM3NTSQSSSSS//u//7Nv59cVO3PmDNXhwcnKyvrhhx/Cw8NnzZpl+/4gICDgww8/JKI1a9ZQHR6cEurmhw0U8NUuODi4oKAgMzPTvvHixYvknHtMtWJZ1mg0WiyWEu38YVpPT0/+Loa08iozVhjP8mCHrADLskOHDl20aFFCQsI///wzb948/vrG9rD7Qe2Gvdemql4QaqXDhw9vvmfnzp1ElJWVtXnzZv4ye1RXR0YikYSHh0skEttX8TxXV1cislqt/N26OThZWVkcx8XExNiqd16jRo2IyDYadXNwSquDHzZQwFe7Z555huO43bt32zfu3r1bIpH06dNHqFQ1061btxQKRYnr0BDRwYMHiah58+b8XQxp5VVmrDCe5cEOWYFVq1Zt3rz5xRdf3LdvX/369cvsg90PajfsvTZV9YJQy4wfP77E1Ztv3bpF964Dr9fr+W51cGR4AwcONBgM/IIyNvw7rG1ZxLo5ONHR0VKp9MKFC7YDGbyzZ8/SvbXTqa4OTml18cNGtVxdHuzcvn1bIpGEhobeuXOHb+HXn3j22WeFDVYztW/fnojef/99lmX5ltOnT/v5+bm5ud24cYNvwZCWxp9ft3379hLtlRkrjCdX/gBihyxPdHS0q6trUVFRBX2w+0Hthr3XpqpeEGo9+wLeps6OzM2bNyUSSZMmTWzvp0lJSQEBATKZ7Pz583xLnR0c/syCSZMmmUwmvuXq1atRUVEMwyQmJvItdW1w8FnXBgW8I6xZs0YkEgUEBAwfPrxHjx4SiSQyMvL69etC56qJrly5wk8QiomJGTBgQHx8vEQikcvlGzZssO+GIS2hvBc1rnJjhfEsbwCxQ5YpIyODiBQKRWxZpkyZYuuJ3Q9qN+y9XFW/INRuZRbwXB0eGf4XVyqV3bt3T0hIkMvlIpHof//7X+k+dW1wcnJyoqKiiCg4OLhv376dO3eWy+VE9NZbb9l3q1ODg8+6NijgHeTHH3/s3bu3Wq1u2LDhqFGjMjIyhE5UcxUXF0+fPr1Dhw4qlSo6OnrQoEEXLlwo3Q1Daq+CFzWucmNVx8ezggHEDlna4cOHK5jYNXDgQPvO2P2gdsPeW+UvCLVYeQU8V4dHZufOnX379vXz8wsKCurdu/ehQ4dK96mbg6PT6d57773HHntMpVKFhIQ89dRTBw4cKN2t7gwOPuvaMBzHPdCUewAAAAAAAABwPCxiBwAAAAAAAOAEUMADAAAAAAAAOAEU8AAAAAAAAABOAAU8AAAAAAAAgBNAAQ8AAAAAAADgBFDAAwAAAAAAADgBFPAAAAAAAAAATgAFPAAAAAAAAIATQAEPAAAAAAAA4ARQwAMAAAAAAAA4ARTwAAAAAAAAAE4ABTwAAAAAAACAE0ABDwAAAAAAAOAEUMADAAAAAAAAOAEU8AAAAAAAAABOAAU8AAAAAAAAgBNAAQ8AAAAAAADgBFDAAwAAAAAAADgBFPAAAAAAAAAATgAFPAAAAAAAAIATQAEPAAAAAAAA4ARQwAMAAAAAAAA4ARTwAAAAAAAAAE4ABTwAAAAAAACAE0ABDwAAAAAAAOAEUMADAAAAAAAAOAEU8AAAAAAAAABOAAU8AAAAAAAAgBNAAQ8AAAAAAADgBFDAAwAAAAAAADgBFPAAAAAAAAAATgAFPAAAAAAAAIATQAEPAAAAAAAA4ARQwAMAAAAAAAA4ARTwAAAAAAAAAE4ABTwAAAAAAACAE0ABDwAAAAAAAOAEUMADAAAAAAAAOAEU8AAAAAAAAABOAAU8AAAAAAAAgBNAAQ8AAAAAAADgBFDAAwAAAAAAADgBFPAAAAAAAAAATgAFPAAAAAAAAIATQAEPAAAAAAAA4ARQwAMAAAAAAAA4ARTwAAAAAAAAAE4ABTwAAAAAAACAE0ABDwAAAAAAAOAEUMADAAAAAAAAOAEU8AAAAAAAAABOAAU8AAAAAAAAgBNAAQ8AAAAAAADgBFDAAwAAAAAAADgBFPAAAAAAAAAATgAFPAAAAAAAAIATkAgdwPlwHDds2LDk5GShgwAAgJDGjh07cuRIoVNAlcH7OwAAUI1/f2c4jhM6g5PJysry8/MTOgUAAAisffv2hw8fFjoFVBm8vwMAANX493d8A//A+EMe3t7ev/zyi9BZoAwssZskK1iyDrVMEjnnSSJHRL9mMDeftg6Sk0LoLABQhvPnzw8fPhxHwGsZvL8DANRxTvH+jgL+IUml0ri4OKFTQBm+urWqOLeQiE77Hhpd/3Wh4zyw67rLp6/8xRF3I+Di4MCaO3sHoC4zmUxCR4Dqgvd3AIA6yyne353y+0mA8hRZCg7m7edvH8v/I9t0R9g8D4oj7pv0zzjiiOhAzp50w02hEwEAAAAAQE2BAh5qlZUp77McKxcpXMWuHNHKG4uFTvRgjuT/cV132VPq3darE8tZt9xeL3QiAAAAAACoKVDAQ+1xRXvhiu4SEb0QNGpkyCQiuqm7frrwmNC5KsvIGr7P2EBEKon7+aK/XcTK88V/nys6KXQuAAAAAACoEVDAQ+2x5sZHRJyvzL+Td/eWHm0DFSFE9MWtFULnqqyf7nxfYM6rJ/NL06fqrFofqS8RfXP7cwtnFjoaAAAAAAAIDwU81BK/ZO/IN+cS0fiwaXzLpPB3GCKtpXhH5neCRquUHNOdfdk7GSITZyYihpg0Q6qPzC/LmHEgZ4/Q6QAAAAAAQHgo4KE2sLCWbRlfE1Fj99j6LhF8o6/ML9a9LRHtztpqYHVC5quE726vN3OmEEV4gTkvSFG/r9/zRETEEdGOzM2Flnxh4wEAAAAAgOBQwENt8OnNj82cScSIx4VOsW8fG/qmlJFaOcu6G/8TKltlXNScP1V4RCaSZZkziWhQ4Iin/Ab4yPxyTFnBilADq9+e+a3QGQEAAAAAQGAo4MHpZZvunCw6TERP+PZxFbnZb5KIZP38BxHR2eKTaYZUQeLdF8ux397+nIj85cEGqz7Oo10TVayUkT0XOIyIcs3ZYpL+mZuYqrsqdFIAAAAAABASCnhweitS3uc4zlXs9lzAy6W3PlXvWZXEg+O4NTeWODxapfyZ90uaPtVD6pWmT5EwkoEBQ/j21h7tm6hi9VZdsGsIR9w3t+9eHx4AAAAAAOomidABapbt27evXbvWYrFU0MdkMhFRYWGho0JBRS4Un0kzpBCRkdWPPfd8mX0sZCGiDMOt04XHWnq0dWi++zGyhh8zvyEiraWYI85NrFp7Y6ltq4k1ETE3dClEdFV78e/Co3Ee7QTLWgmZxvTL2uRO3t0ZYoTOAgAAAABQ26CA/49Nmzbt3bu3Mj0NBkN1h4HKuHlvYryVs1rJWnHnG/rrNa2A11iKdVYtEVk4CxEVWPILyl+vLtuY6bhkD87CWZalLMg03mY5a4K6p9BxAAAAAABqGxTw/7Fu3boxY8ZU3Cc1NfWVV14RiXD2QY3Qy7d/fUVYofX+EyLcxaqmqlYOiPRA1DLf+THLF12dWWQp6O8/uLl76xId9KxuxfVFelb3UtArXX2eEiRkJe3P3pVpvE1E2zI2tfbo4CZRVcnDmjmTlJHZt+SbcwstBSbWEOoSKRcpbO3XtJfyzDlEFOPW1F3iYWs/V3Qq35wrZsTN3ePcJZ629oua88WWIhEjaujWTCn+d/WEW4YbeqtOLpIHKULFjNjWbmJNfBiZ6D95AAAAAAAcAwX8f3h7e3fv3r3iPklJSY4JA5XURBUrdIRHcjj/tyJLQZhLZB+/58qced7Pf9Dm21/8krO7s/oJCSN1fMLKKLIU7MraSkQ+Mr8c051dd7YMDhpZok+WKTPHeEdr1cS4NbEV0hbOvC1jU44pS8/qEtRPtvZoz7cXmvPnXZnKF+RP+vZ7PnA4355pvP3OpQksxxJRvFfn0fXf4NvTDTcXXJ3O327t0X5c2DT+dpo+9eOUefztOI9248Pu9rlluPHBtXf52208O4wNncrfzjDcmn1pMr/cQHuvhFH1J/PtOaasdy+9ZmQNDDFP13t2QMBLfLvWqlmestDAGsSM+Anfvm09O/HtVs66L3u7zqqVimRtPTv5y4Ns45BuuGnmzAqRwk8eiHMNAAAAAKDyUMADCKnIUrgvawcRpeqvjTz7TAU9s4wZB/MOdK1hU9NNrLHIUugp9d6WsUlv1cW6t3nG/8U5l9/Yn7M7WXOum08v21z6LFPmzIvj+MK7pUfbiWFv8e13jBn7snfwt93EKlsBzxJrYo1EJBcp7L/M95B4NnGLLbYUKsSurTzibe2+Mv+u6p4aa7GIRJ3VPWztAYqg3n7/V2wpJCJbdU1EfvKAJ3z75ptziaijdzdbu7fMp61X51xTlpk1NbY7NiQTyX1k9QrMeRbOIrX7Bl5jKbqmu2TlrESUVHza9hRp+pTvMzbyt2/pb9gOHKQbbs66NIk/QNBV3XNI8N0pP0WWwk9S5ptYo0Lk0qveM7ZfjSPuUN4BM2tWStyaqlrazxQAAAAAgLoGBTyAkBhilBK3AnPefXuKGLGLyNUBkUrTWbXphpu5puwI1+h68gC+UW/VzbsyhZ8z38it+UXNeQkjeS5wuIgREXEccemGG/9oztsKeA+JZ2uP9hprsVLs1sn733kuQYr60yLna6xFSrFbhGsDW7uXVL2s6cbSYVzErq9HzCrdLhPJbMWwPQkjHeD/Yul2KSMbFDiidLtcpBhd//XS7e4Sj3kxy0q3+8kDP27yVY7pDkcUpAixtYe6Rr4a+mauKdvKWewPNHhLfVp7ts82Zho5Y5hrlK1day1O06fwSyGcLTpp+5Fr2ktfpq3gb7f36jqq/iT+dpYpc97lKRbO4ipWPuP/gu0YhJWzJubsYjlWJXGPdX9MJXEvnRkAAAAAnBQKeAAhqSTuHzX+QugUd2ksxWmGlDxTTohLWH2XCL5Rb9VN/ecVvVVHRJGuMW9HL+bbWWL1rF7KyDyknrmmLI647j69/eWBRDQ54t3VqR8aWH28Zxfbg8tFijGhU8p83oZuTav3F6tmSrGb0qXkF+MMMfZf+Nu4iF1t0/XtBciD/9dkfb4518KaAxX1be3hrtHPBb6cZcw0sHr7Ax9ExDAio9VgZA0Zxlu2xsvaC1tur+dvd/LuPjxkAn87w3Br/tVpRKSSeAzwf/Exz458O8tZD+YdYBhGJXZvrGphv6YAAAAAANQ0KOAB6hyOuDxTzh3jbS+ZOkAezDdaOMtbF8dqrRoiCpAHL2h491tfmUgerWystRarpb7tvBJsD6IUu/2v8ZdEdKzg4NobS90lHn38nuM3NVO1esb/hW9vf/7d7S+aubesseft1zRKsVvpGfJiRtzTt3/pzvVk/suabDCweiNrsF+ZL0bZZGjwmGzTHb1V19m7h/2PiEiktWr0Vt0tw43H6G4Bn1R85qtbq/jbT/j2tc1KyDTeXpn6PkOMu9Szr9/zDZSN+XaOuKvaiyISeUg9fWR+VfF7AwAAAEBloYAHqHMWXplxTXeJiDykXnwRTkRiRtzMPS7fnKOW1mvj2cHWWcyIJ4e/U95DmVjT9xkbiGhAwEsu4n9n+HfzeerPvMR0w80DOXue9O1XXb9JnacQuShELvYtIkZc5jX8AhTBy5tu0lt1OqvWW+Zja2/o1mxAwEs5xjt6VtfG499/92JLYYbxFsuxZLgR7hJtK+DPFp1YlrKQv/1c4Mu2gwuF5vzvMzdKGImnxLuL+glPqXfV/qYAAAAAQCjgAWqxv/J/+zN3f7rhhpvEfX7MctsV0RRiFw+pl788MM6jna0zQ0yZ535X7OfsH3JN2fVdIjp6dbNvFzHiwYEjl1yfvSNzc7xXZw+J1yP+LlAlXMSu9sdZiEgmkvWuN7B0z2hlo48bf5VvydVbtfZrE4S7RLfx7JBnztFZtbbpG0R0WZt8OO9X/jZL7DP+L/C3kzXnPr4+V8SIPKXqocFjG7s159vNnOl04XGZSO4l9a7vEoHV+AEAAAAqAwU8gNNjOTZFd+W6/rKVsz7p289WC+3P3p2qv0ZE3jIf+/LozYg5VfK8+ebcvVnbieiFoJEiRlRia2NVi+burc8Vndye+e2w4HFV8ozgSG4Slf36/zwPqVeZ5/DHebR7I2J2jinLwOrb251qIWWkcpFCa9VkGTOyjZl0r4A/nPfbhlur+dtDg8fYZg1kGG9tz/xWysi8ZT49fPpgET4AAAAAeyjgAZzepvS1v+fu4283V8UF3lsLfXzYjExjerBLaDV9Ab414ysja/CV+WUY0jMM6aU7BMqDz9HJP3MTu6p72lbFg1pJxIiaqlqWbo9WNlredJORNRiseg/pv/thrHubNHVKnjnHwOrDXKNt7Rc1SScKDvO33SUe3X1687dPFBxee3OpXKTwkdUbETIx1CWSbzexpmTNGVexm1rqq5b5VtevV4ucOnXqgfrHxcVVUxIAAAB4CCjgAZyG3qr7LXfvJU1SjjlrXOi0oHtrlYe7Rt/UpwQp6jdyax5odyUztawaSxqtVXMs/yARZZvu2FZBKxNH3G+5e/ElfF0mFylKrG/vKfUu87J/Xbx7+MkC8s25elbfwaurrV0qkkkZmd6qS9OnZpvu2Ar4X7J3/JD5NX/7tfC3Y93b8LczjLcSs3e7SVRqqW8H78clDN7s7mrduvUD9ec4rpqSAAAAwEPAZxoAp3Eg5ye+VhExIq212Nbeybt7iQuMOYCrWPlswJBsU+Z9ezLEdFE/4YBIUAuIGHFjVYvS7bHubVY326y1akys0UuqtrW39GibbriZZ86xcBZfu1Xxj+T9/nvuXv62q1hpW5fxRMHhbRkbXSVuvjK/F4NGu0s8qvO3qYkmTJggdAQAAAB4eCjgAWoclrPuzd6RVHz6hv7acwEv26rf9t5dzZw5UBHS0K2p4MvCMcQ8VW+AsBmgril9pb0gRf1XQ98s3bNXvQF+8sA8cw7LsfZz+3NMWVmmTDJRqu5qJ+/uZU77r92WL18udAQAAAB4eCjgAWqcy9p/+GuzMcSwxNravaU+tsW9AaACLmLXDt6Pl27vVe+ZeK/O+eZcjuMilTGOD+ZErly5kp6enpCQIHQQAAAA+BcKeAAhZRkzjhceTi4+E+oS+XzgcL6xgbLRkOAxHhLPBsrGbliFG6BKeUnV9pPwoUwcxy1YsGDr1q1arVboLAAAAPAvFPAAQlp49a0iSwERGVmjrVHEiLveu6oWAED14Thuzpw5mzZtysrKsm+3Wq16vb5Zs2ZCBQMAAIAyoYAHcBAzZzpf9HeBOT9B/aTtqulP+PbNMWU1VbVsqooVNh4A1EFr1qyZO3euu7t7QEDAlStX/P39g4ODCwsLr1y50rZt22XLlgkdEAAAAP4DBTyAI5wqPPJF2nK9VUdEYa6REa4N+HasAwcAAvriiy/c3NwuXrwYEBAwdOjQ/Pz8Xbt2EdH8+fPXr1/fpEkToQMCAADAf4iEDgBQJ9wx3tZbdeGu0YODRoa7RgsdBwCAiOj69esdO3YMCAggooSEhNOnT/PtM2bM4Dhu3rx5gqYDAACAkvANPEAVyzVlH8o/cLrwWIK6Z4L6Sb7xqXrPPu7zlELkImw2AAB7JpPJze3ulfnCwsJu376t0+lcXV0lEknHjh0TExPff/99YRMCAACAPRTwAFVJa9W8fWmCiTUS0W1Dmv0mVO8AUNPExMRcvHiRvx0eHs5x3NmzZ9u1a0dELMteunRJ0HQAAABQEqbQA1QluUjRVNWyvVfXKRHvDQ4aKXQcR7uoSdqa8ZWZMwkdBAAqpWvXrklJSTNnzszJyQkLC1Or1evWrSMivV5/4MCB4OBgoQMCAADAf6CAB3hILGc9VnBwwZXprycPzzfn8o0SRjIhbMao+pMaq1owxAib0MH0Vt2aG0t+zvpxT9YPQmcBgEqZNWtW/fr1Fy1atG3bNoZhxo4du379+pYtWzZs2DAjI2Pw4MFCByQiGjp0qIsLZjABAAAQYQo9wEPbk/3jDxmbiEglca9rtXqZdt3Zwl/T/uesHzp6dVPLfIVOBAD3oVKpTp06tW7duoYNGxLRrFmzLl++vHPnTqvVOmLEiOnTpwsdkIjIbDYbDAahUwAAANQIKOABHlIDZePm7q1bebSN9+wiE8mEjiOwO8bb+3N2M8REuDa4prv0fcaGV0PfFDoUANyfj4/PzJkz+dtSqfS7777T6/UikUgulzssw8qVK7///vvytiYnJxNR165dbS2//fabI2IBAADUPCjgAe6PI+5C8Zk/cn8JUAQP8H+Rb2ygbNwgvLGwwWqOzbe/sHCWzt49+vkPmnlx/LGCg119ejZQ4iLSAM7H8fPV9Xr977//XnGf+3YAAACoC5y1gL927VpiYmJycnJubq5Op1Or1UFBQUFBQX369OGvZwtQhVakvn+68BgRhZoibQU82CRrzp0tOqkQuTzj/4KH1Kunb/8ddzZ/k/757AZLcXIBQE124cKFijs0aeKIw3BTpkzx8/ObMGECx3Eff/zxk08+ab913LhxO3fuvHXr1qM8xddff71+/fqK+5hMJiIqKCh4lCcCAACoVs5XwKekpIwbN27v3r1lbh0/fny/fv2WLFkSFhbm2FxQm0kYST2Zfxf1k13UTwidpcZhOeu36Z8RUV//5z2kXkTUq96AQ/kHbuqvH8o70Mm7u9ABAaBcTZs2rbgDx3GOSTJkyJCOHTsOGTJk1KhREyZMWLx4sW0igKurKxEFBQU9yuPv2rVr//79lelpNBof5YkAAACqlZMV8Lm5uT169LiBbSUvAAAgAElEQVR27VqTJk369evXtGlTtVrt7u5eVFSUl5d38eLF3bt3b9u27ezZs4cOHfLz8xM6LzgllrMmFZ8Jd41WSdz5lrGhU4WNVJMdyN2TbrhZT+bf3edpvkUmkg0MGLr2xtJtGRtbe7R3EbsKmxAAyjNhwoQSLcXFxSdOnEhOTg4NDZ02bZojw4SHh//xxx8LFy6cO3duYmLipk2b4uLiqurBP//88zFjxlgslgr6pKamvvLKKyIRLtADAAA1l5MV8DNnzrx27dqiRYtmzJhRZoc5c+Z8+eWXo0ePnjVr1tq1ax0cD2qBk4V/fX97Q5Yps71Xwqj6k4WOU9NprZpdmVuIaFDQSAkjtbW39ez0W87ey9oLu7K2PhcwTLiAAFCR5cuXl27kOG7JkiXTpk277wT7KicWi999990nnnjixRdfbNeu3ezZs8t7u39QSqUyISGh4j5JSUlV8lwAAADVx8kOM//5558xMTEVv50PHz68S5cuhw4dclgqqDXMnGl16odZpkx/eVCCuqfQcZzADxmbNNbixm7NY93blNj0QtBIhpj92bsyjbcFyQYAD4dhmKlTp8bHx69evTovL8/xAdq2bXvmzJmXXnrpnXfe6dSpU1pamuMzAAAA1ExOVsBnZ2dXZo26kJCQrKwsB+SBWkbKyEbWn/Rq6JvzY5ZHKRsKHaemu21I+yMvUcSIBweNKr21vktER+9uFs6y5fZ6h0cDgEcVFxfHcZzZbBbk2d3c3L744outW7deunTp8OHDgmQAAACogZxsCn27du0SExOvXr0aFRVVXp+srKy9e/e2a9fOkcHASd0y3Pgj95eO3o+HukTyLe29EgRN5Ey+Sf+U5aw+snpH8n8vswNLLBGdKTp+ofhME1WsQ8MBwKO5fv26u7u7sKvJDBw4sF27dtu3bxcwAwAAQI3iZAX8a6+99vPPP8fHx8+aNatfv36hoaH2WzMyMn766ad58+ZlZWWNGDFCqJDgFDjittxen5izi+VYKSO1FfBQSVqrJllzjohyTFl7sn6ouPPJwr9QwAPUQDdu3CjdaDAYtm/f/vPPP3fs2NHxkUoICgoaP3680CkAAABqCicr4Hv06LF8+fJJ93h4eHh7e7u7u2s0mry8vPz8fCKSSCSrVq3q37+/0GGhRjNY9Yk5u4mY7j5P9/V/Xug4zkcpdpsQNqMy57eLiGnr1dkBkQDgQVVwyVWJRDJr1iwHZgEAAID7c7ICnojGjh3bvXv3zz77LDEx8Z9//klJSSEisVjs6+vbunXrAQMGDB8+3N/fX+iYUNO5iF3fjnrfRaz0lwcKncVZtfKIFzoCADySl156qcz2evXqPffcc23btnVwHgAAAKiY8xXwRBQdHb148eLFixdzHKfT6QwGg5eXV5VcuPXAgQNbtmypuA//PT/HcY/+dOBImcb0fdk72nt1jVY24lvCXaOFjQQAIKyNGzcKHQEAAAAegFMW8DYMwyiVSqVSWVUPuHz58h07dlSmJwp457Lrzpadd76zclaO42wFPAAAAAAAgBNx7gK+yq1cubJPnz5Wq7WCPunp6XPnzq2SL/zBMTji9mT9wHJsF/UTAwOGCh0HAEAwS5cufaD+b775ZjUlAQAAgIeAAv4/goKCRo4cWXGfpKSkuXPnOiYPVAmGmJlR7zMME6wIvX9vAIDaa8qUKQ/UHwU8AABAjYICHmqnC8Vn3CWeIS5h/F3bDQCAuqz0NdVXrlyZmJjYsmXLZ599NiwsLD8//5dfftm1a9cLL7ywePFiQUICAABAeZypgM/Ozv7rr78q379fv37VFwZqLI2l6Ktbq08VHvGXBy1suFLoOAAANUiJd8YtW7bs379/7ty57777rq1xwoQJa9euHTNmTJcuXUaPHu3wjAAAAFAuZyrgL1y48EBXd8c6c3XTxvS1pwqPuIhd+/sPFjoLAECNtm7duvDwcPvqnffqq69+8sknW7ZsQQEPAABQozhTAZ+QkHD+/Pk5c+Zs27aNiMaOHevh4SF0KKhx2nl1cRG59vF7Ti3zFToLAECNdvLkyQ4dOpS5KSoq6s8//3RwHgAAAKjY/Qv41NRUjUbTsGFDiURCROfOnduwYUN2dnaTJk2efvrpJk2aVH/IfzVt2vT777+Pi4v7+++/p0+fHhqKNcmgpFj3x2LdHxM6BQCAEwgMDDx79qzVahWLxfbtVqv1zJkzISEhQgUDAACAMlV0LbRdu3YFBgaGh4c3a9asQYMGSUlJv/32W+vWrZcuXbphw4bp06e3atVq2bJlDstqM2jQIMc/KdRMLMfuuLP5vctv5ptzhc4CAOBkOnbsmJ6ePm3aNJZlbY0sy06fPj0tLa28L+cBAKCGSyr+e2vGV0KngGpR7jfwp06d4pe6iY+PF4vFJ06cGDx4sFQq9fT0nD17dsOGDZOTk+fPnz958uT27du3bt3agZmpTZs2UVFR/IwAqMuMrOF/1+dd1l4QMaJiS5GXVC10IgAAZ7Jw4cI9e/Z89NFHv/zyS79+/YKDg2/durVz587z588HBwcvWLBA6IAAAPDAdKzm45QFLGc1s+YXgkYJHQeqWLk1MH+p859++qlXr15EdPDgwa5du1qt1l9//bVr165E1K1bt27durVo0WLRokX8SekOk5CQcOXKFUc+I9RMtw1pl7UXvKTqV+q/Xt8lXOg4AABOxsfH59dff3399df37NmTlJRka+/Xr9+HH36oVuOoKACA81md8iHLWYnot9yf+/oNcpO4CZ0IqlK5BfzZs2fbtWvHV+9E1KlTpzZt2iQnJ/PVO69x48Zt27Y9d+5ctceER5bHsiaO8//vWY48juiq2RIhlZSxrWYLd41+J/oDP3mgUowXJgCAh9GgQYOffvrp8uXLycnJt2/frl+/fuPGjSMiIoTOBQAADyNVf/WC5iwRiUhk5ayrbyyeGjlP6FBQlcot4LOzs5s3b27fEhwcnJeXV6JbvXr1Tp06VS3RoEq9nJWTZrGu9PFur5Dbt3NE8/MLv9FoJ3u4v+ruBGWwlbMyxIiYu8s3RLg2EDYPAEAt0KBBgwYN8HIKAOD0Vqd+QEReEnUfv+c2pK++qDl/VXspShkjdC6oMuUW8FFRUSdPnmRZViS6WylNmTIlN7fkOmGXLl3y9cXFupxAB4VifbFmfE6efQ1vq97lDNNaLhM2YWVc0f6z5sYSf3nQ1Mi5QmcBAHA+ly5dIqLQ0FCFQmG7W4GYGHzmAwBwGn/mJWabsohoVP1JjVTNd2dvzTPlrLu59ING64SOBlWm3AK+e/fuH3300SuvvLJixQoXFxciatu2bYk+69atS05Ofvnll6s1IlSJaZ7uZo77WqMdm5O3TO3VxUXBES3ML/xGo5UxzMdqr7gaX8CfKTq+KvUDC2eJdMUHSgCAh9GwYUMiOnr0KP+ezt+tAMdxjogFAACPjCX22/TPiaiBsnEjVXMiGhc2bf7laTmmrAO5e7qpnxI6IFSNcgv42bNn//jjj1988cU333zz7LPPbtq0yX7r8uXLN23adPz4caVS+dZbb1V/TnhUDNHbXh5E9LVG+1pu/idqr8MG4yaNVsYwn6i9ElwUQge8vxTdVQtn6enbf2DAEKGzAAA4pVGjRhGRbercmDFjBI0DAABVZkPaaiNrEDGicWHT+JYIlwYNlE0uay9suf1VV3VPUYVXEAdnUW4B7+7ufvbs2fnz5+/bt6+wsLDE1j179hw/fjwuLm7dunU4a85Z2NfwE3LzrRznRNU7EfX3H9xV3dNT6i10EAAAZ/Xpp5/a3129erVQSQAAoAoVWQoO5R8gok7ePdwlnrb2CWFvTU4eamaNX6WtHB4yUbiAUGUqOgyjUqkWL1585syZXbt2ldi0YMGCa9eunTx5slWrVtUZD6oYQzTTyyNGKrVyHBG9olI6S/VORAwxqN4BAAAAAEpYkfI+y7EKkcuQ4Fft290kbgnePYnoUN5v+aaSy5mBM3rIeRStWrXCNWacEUe0KL/wktnM/8N/Wqz9Q28QOFP5zhf/PfnCsB2Zm4UOAgBQm2VnZ2s0Gv72li1bhg0bNn/+/OzsbGFTAQBAJV3SXriqu0REg4NGlp4n/0LwKBeRK0fsqhuLhUgHVazcKfS8PXv27Nq168qVK5GRkWPGjGnZsmWJDlOnTk1NTd26dWu1JYQqw69at+neqnWHDUb+fHh+TTuh05V0KO/A+lsrWY61cBahswAA1E46nW748OFbtmw5dOhQhw4dNm/ePHjwYH7Tl19+efToUVxoBgCg5lt34yMiTsrI0vU3N6d/UbpDgCLkuu7SNd3lf7TnGymbOT4hVKGKCvgxY8asXbuWv33gwIFPP/30o48+mjx5sn2f/fv3nzlzphoDQiXkWtmPCoueVbq2Kmsl+c0abYbV+ryb8uOCol06ve28d37yPF/DT3JXvaRSyhjG4dnLdbLwCMdx/f0H9/F7TugsAAC100cffbRly5aYmBi+UF+yZIlarf7ss8+Sk5Pffvvtjz/+eMGCBUJnBACAilzXX8435xKRmTP9krOz4s7fp3/1boMlDskF1aXcAv67775bu3ZtRETE4sWLmzVrdurUqSlTprz++uvh4eH9+vVzZES4rzMm0w9a3c86/VpfdZv/1vBrioo/KSyWMMwWja6AZSUMs9LHu6NCTvfWtLMSbdZoPywsSrFY5nl7lvMMAhgVMinfkhuiCBM6CABArfXtt98GBgaePn3axcUlKyvr77//fvPNN/v379+/f//Nmzfv3r0bBTwAQA1XXx4R6dog35J3354iEj9Zr78DIkG1KreAX7FihUKhSExM5M91j4mJady4cadOncaMGfP444+rVCoHhoT76OqieFbpuk2rezU7176G56t3MdFCb8/38+9eSsBod1Ffjshw766kJn39TkRuEpWbBLsZAEA1Sk1NffLJJ11cXIjo6NGjHMclJCTwmxo1avTzzz8LGQ4AACpBIpK8Hf2B0CnAccpdxO7ixYvt27e3X6kuNjZ2+fLlmZmZH374oUOywf1ZWRq2O2/hX0XzvD0HuSn1HDc6O/eowUhEnxdr7lXvXn1cXXb413vBzc3CcZNz8w/oDUTEEr2dV7Bdq3NhmNneHm97ugv7uxRZCr5MW3Gm6ISwMQAA6g4PD4+0tDT+9sGDBxmGadu2LX83Pz9fJivjtCwAAAAQULkFvF6vZ1m2ROOwYcPatGmzdOnSmzdvVnMwqJQcvfXQLeOeawaGaJaXxyA3pYHjxubkzc4rWFJQxFfvfZUuROQjFr3r5T7GXcXX8Pv1Blv1vsbXe5BSKew38DmmrIVX3zqYt/9kwWEBYwAA1CnNmjU7c+bMjRs3ioqKNm/e3Lp1ax8fHyLKyMg4fPgwLjcDAABQ05RbwEdHRx89evTOnTv2jQzDrF692mg0jhw5snR5D0LhxLSmqDjNYrHV8Fu0OtG96v2gwfi9Vsf3nOSh4mv413LzbdX7Y3K5sPmJaMvt9VnGjDDXqEGBI4TOAgBQV0yZMsVisTRq1CgiIuLWrVvDhw8nom+++eaxxx7T6XQjRuAFGQAAoGYpt4AfNWqUwWDo0qXLsWPH7Gv1uLi4qVOn7t+/f+jQocXFxQ4JCfdh9mY/KSx+MSvnutkSJBHzjWKGqScW7dLpx2bnzsor0LB3z3Wf6KGKkUo5jiNiRru71YTqnYge93nqqXoDpkXOc5MIPJMfAKDu6NGjx4YNG0JCQgwGw4gRI1555RUiOnHixK1bt8aOHTty5EihAwIAAMB/lLuI3fjx48+dO7du3br4+HipVHrixIkWLVrwm+bOnZuSkvL111//+OOP+B6+JpDmitorpH8ZjM9nZWtZTkzURiE/ajCOzsmzchxLNNnD3U3E0L3z3i+ZzVJizMStLNJES6XdasBF4Bu6NW3o1lToFAAAdc6QIUOGDBli3zJx4sS3336bn0vvYHv27Nm1a9eVK1ciIyPHjBnTsmXLEh2mTp2ampq6detWx2cDAACoCSq6DvyqVavatWu3adOm69evc3ZLl0ul0m+++aZDhw7Lli27evVq9YeE+2BYWu3j/cyd7OtmC8PRJE+PUe7KUdm5fxmMRPSM0uVVdzf676p1a3y9jxhMa4qKJ+fmf6z2EqSGL7TkSxipUuzm+KcGAIDyCHXq+5gxY9auXcvfPnDgwKeffvrRRx9NnjzZvs/+/fvPnDkjRDoAKBtnymCLj1WmJ+PaSOQSU915AGq9igp4sVj88ssvv/zyy6U3iUSiiRMnTpw4MTMz89q1a9WVDv7LaOWu5VvYf4+lUJ6eJSIzy314pyjbmMdfde27fI1e65Js0vJ3D2mKT8hNreTy+fmFB3R6ldh1ha/fY3I5P3leqBr+oibpk5T59eT+7zX42JHPCwAAJRw/fnzjxo1nzpzRaDSnT5/et29fUFBQ06YOnRX13XffrV27NiIiYvHixc2aNTt16tSUKVNef/318PDwfv36OTIJADwQ07Wx1sI/KtOTkfq6xF2u7jwAtV5FBXxl+Pv7+/v7V0kUuK8Jv+T/esNYur1AyfrlzPpds8G+cZT9ndtkJJpKNJXIKI/0Cj7ON0/yUGlZbqNG83pu/jof73iFg86HT9Fd+Thlnok1NlA2ccwzAgBAmebPnz9r1iz7eXa7d+9esWLFwoUL33rrLYfFWLFihUKhSExM5L//j4mJady4cadOncaMGfP444+rVCqHJQFwFqbrr1vztt+/HyMVq/vLwqrrOuEi987Wwj+IGLF3b2LEZfZhC/ZzVo3IvVM1ZQCoU8ou4AsLCx/oUTw8PKoiDNzHY4HyLN1/Fh0wWbnLeRZOz4lNdy8CpyEPTnT3c5ibSCQiMnAcS8RyJGd1EjIrGMqysps12jMm02WTOZdlicjMcceNJlsBn2tlb1oszWTSarq23G1Dmok1dlE/8ULQqPv3BgCA6rF379533303MjLygw8+OH/+/Jw5c4jo+eef//7772fOnNmqVasnn3zSMUkuXrzYvn17+9n7sbGxy5cvHz58+Icffjh37txHfPwjR45s3LjRarVW0Cc/P5+I7I9lANRkrP4CZymoVE/dheqLIQ0Yb8n+mjNcF3t0lfgNL+PZi48bcneQSCGrP6f6YgDUHWUX8J6eng/0KHi3c4xXWihfaaG0b0nKNvXblsvoRC83n2M4s4uz5M1RL/hN0TVMKkk1W9Qi0fp6PlFSCREVm3LF5x4jS95W77eWZmSb7v2TuTAMSyRnmP9zc7U97PS8/MMGo0ok6qqQD3BzfUwu5+v4Pw1GjuO6lDXZPp9ld2v1fZUuHqJyL21g08H78caqFl5S9cOPBQAAPLKPPvpIoVD88ssvERERGRkZfGPHjh2PHz8eFha2dOlShxXwer2+9LK4w4YNW7Vq1dKlS0eNGlW/fv1HefxFixbt2rWrMj3xkQachSxktiG5N4nkiqa/MrLA0h04Y5ohqTtxZlnIrGrMIZLL6s82Xh5mTpsnVvdjJN7/3cyabrxFxEkDJzHykGqMAVBnVDSFXi6XN2nSRFSJeszBjEbj6dOnpVJpXFwc37Jv377Vq1ffuXOnWbNmzz//fLdu3YRN6HiMxPNKvdejbr87qXBxrO+TL3uoX8vN/0NveDkrh6/hdWkLVJa8E/L4RUw7Ecf1dHXp6+oSI5UGSsqY7DRQ6ZptZS+bzTt1+p06fahEMsrdrZ+ry5u5eVqWe9fLY7Dbf44j5LHsy1k5V8wWlrhhqkotSofqHQBAcKdPn27Xrl3pVetCQkJat26dlJTksCTR0dFHjx69c+eOn5+frZFhmNWrV7dt23bkyJH79u17lA8kH3/8ce/evSvuk56ePnfu3Or62MMajZcGsYbU+3ZkxC7S4Bli777VEgNqEZF7B7F3P2veDsvtT2RRa0t3MKW8QZxJ4jNQpGpbrUnE3n3FHl2thb+Z05fIQhfab7JkbWI1fzOyQGnga9WaAaDuKLuAb968+blz54xGY2Zm5jPPPDNgwIAuXbqIxWWf1uJg33333SuvvMJfgr5Hjx47d+784osvxo8fz289evToZ5999tZbby1YsEDQmI72s04/g/pulG6IMl8Zrvta6jlpmdqLr+GHZeU8Tjen5Wy0MpJPPGcMVLqOcncLlVR07Kanq0tPV5c0i3WnTve9RnfDYnk3r2BVYXEnhWKvTj8vv5CIbDW8rXqPkkr6Kl3Le8xcU/a54lOdvXuIyzk/CgAAHM/FxaXMdm9v7ytXrjgsxqhRoyZMmNClS5evvvqqTZs2tio6Li5u6tSp77///tChQ1evXv3Qjx8RETF69OiK+yQlJT36XP3ycKzOWnyE2DIWsinZk4jVnEYBD5UhC1ugL/jFkrNV4jdcpIq338QWH7PmbieRQlqtX7/fIw1baD3XyZL5qcR3iMi1Ed/IWTXmtIVEJAudR6JyPyKWdkdrXXNa+1JT10jPR12uC6D2Kfsw89mzZ69evfrBBx+EhISsWrWqW7du/v7+o0aN2rNnj8lkcnBEeydOnBg8eLDVau3Xr1/Hjh0TExOHDRv2xhtvNGnSZM+ePSkpKT/++GNERMTChQt///13AXM6GOPFTc3NNzGS64HvEZElfQlnviNjmI/VXi1lsjyW7Zo1V8xZkjyHrK7fcZ63Z8XVu02IRDzeXbU/0G+p2itaKsmwWn/W6d1FIo5oXn7h1xotEeWx7PCs3CtmS4RU8oWv2qucLy5yTdmLrr618daaC8Wnq/AXBwCARxEbG3v8+HH+mLg9jUZz7NixFi1aOCzJ+PHjR48efenSpfj4eIVCcfbsWdumuXPnPv/8819//bW/v//FixcdFqlqMRIvqf9YIhK5NHCJ/dul5enS//HLjDESD0nAeKHzgnNgZEHSgPFEnCl1BpH9SSisKZWfuP66Yyaui1waSuoNIc5ivjHT1mhJ/5Az3xGp2ojVzzzQo+1LMWxI0m79R1fVMQFqg3LniUVGRk6dOvXo0aNpaWnLly9v1qzZ+vXrn376aV9f3xdffPGHH37Q6QT4o5o3b55IJDp48OD27dsPHjw4Y8aMLVu2yGSy/fv39+rVKywsrH///nv37pXJZEuWLHF8PMFYGR+x+E0P9/4BvcRevTirxpw2n4iyrNZrFktX/f5442FO4hkf+Y7fg0+jEBM95eqyw7/ech/vhlJpIcsSEUc0P7/w02LN8Kzcy2ZzhFSy3lftW86Dc8T9L2VunjmngbJxQ7fmj/i7AgBAVRk6dGhOTs6wYcOKiopsjVqt9sUXX8zPzx84cKAjw6xaterLL7/s1q1bcHCw/YnoUqn0m2++WbZsWWBgoMFgcGSkqiUNmsLI/Fn9ZavmBCMPK/mfLNBy51MikgZNZ6Q+QocFpyENeoORB7Pas5bszbZGS9ZGVnuakQVJAyc6LknIu4zE21r4uzV/HxFxhhRz5loikSz0faIHWxTZwhIRWbEeBUBZmMov1pKTk7Njx44ffvhh//79JpPJ1dW1Z8+eAwYM6N27t8NWoQ8JCQkPD//zzz/5u+np6cHBwf369du+/T9X0ejUqVNaWlpqauqDPv7/s3fe4VEVXx8/c9v2tN1sOkmAhN5ROqKCAqKoIAqiQEBFEJQmTYqooFJ/KvAqVVREepOuSC8CoYTQISF9ey+3zfvHhhBCgAAhgNzPsw/P7tzZmXM3y977nTnl8OHDq1evvn0fo9G4cOFChNDN6XYeNIdy2V1ZN3jfWbzCirNehND79Qsd2oMhswduSwBnT9ySF1KvryFvveFVHZfBJEylIu835bsIsMLlnmV31vDsTuYLt0HCCKKbSqkqbe8dIYYM64Rklb65NJYA8qOEUQryLhyoJCQkJB5NDhw40Lx582bNmu3fv/9h23K/vPvuu7/88otCodDr9ZmZmW3btj127JjFYmnfvv2mTZvQg6lFcs/k5+dfunSpRYsWD2LwtLS0OnXqEARx+2T19wNvXMpeGoiYKEW9f4G8IZsMl/c9lzmeUCTJ6+4DRD8gAyT+k/Cm5ezFDxCtl9c/gkgNFly+440wZ5AlLbzbre/7tSRvLps5BskTFXUP+C/0FqxbKP07TOXv7nachSfdX+13pNRVjW0e9CDslJC4FY/F9f0uAkt0Ol3fvn379u3rcDg2bty4evXqzZs3r169mmEYv//OMV3lgt1uL75YEHh+8/JBaGjoyZMn72H8L774YuPGjWXp+VCy1E4/7DyaX0oIA8b4x1TXtVdaPrpnH/0C75VRjZru2I1WUVwGoahGRfS+fwMIgDfVqvZygjw6gMDF7m+swN3iLaTriCxp4cgqT1ZKAgkJCYnHhSVLlnTq1Gny5MlnzpwBgJ07d1apUuWrr7567733HjX1DgCRkZGRkZEP24p7hwrvzhcsFF1HudxZdNzYonbMGfnsaQBAx0+W1LvE3ULp3uALFonOg3zODLrSBD77G8wZCM3TpPbVirYksh9vWCR6L/gv9hOsWxCpoeM+q2AbJCT+89xLZoigoKD27dv7/X6z2fzPP/9UZFR89erVjxw54vP55HI5AAS24o8cOYIxLrrP4Dju2LFjNWvWvIfxZ8yY0bp169tvrRcUFMycOfOhJOf/snXwP1d9xZcOXKw4J9WtZogBDa4v5NMw3Meuj8LH+Ly5dN5MDEAnfA2olL81Fpx83lwQvXeem1TSUQPzsAwAZITshKJNY89fRlK/SfEKADSXy2owtEkQdCSJCke28wWLAICS0vBISEhIPNp069atW7dugiDk5ORERkYyDPOwLfoPg5iEr31pL3B531P6d5CssDYel/UlFhxk6ItkSNuHa5/E4wliEr72nXqOy5tDBLXkCuYBEEzCXTuu3w+8aQVmcwAAaZqC94Jg2QgASNOENy4tYSqlfRXJ4ivMMAmJ/x53J+ANBsPatWtXrly5c+dOnueVSuXrr7/epUuXB2Tczbz99tuffG5Q0+MAACAASURBVPJJly5dPv74Y6PROHLkyLCwsPT09EmTJo0fPx4hJAjC8OHDc3Jy+vfvfw/jJyUljRgx4vZ90tLSZs6ceU/m3yNH8tmPttkGN1b1qKlKDruhSFuBW5iT6lbR6IMGhe0YYK+PpuyfwZWP2cyxAECGvUQGtyl1ZMG6hcueUkYzCEXyG74mXhFrSRIHjVjp3RMumKpEvPqJL/F7gBoMnc5yE0OD31SrACAwtUdRQ6ntfK/nLSEhISHxAHE6nR07dnz11VeHDRsGACRJ3metdYmyQKgbU7quvGkFe3WCLGkRAIjuk7zxN0AMXemLh22dxOMKoapHhb/JG3/3n3sLME/p3yFUDSpsdswZ2IulVHkQbTtE246Snf2ZTOKMCrFLQuK/SZkEfG5u7po1a1atWrV7925BENRqddeuXbt06dKxY0elskJDmgcMGLBhw4ZNmzZt2rQJANRq9b59+4YMGTJx4sQlS5YkJyefPn06KysrKSlp+PDhFWnYA2XpaY/RI/yS5ulRU3XHzr+53F9Z7V1VncaqFonu40DImFvfEJChLyI6HHNGMuR5IqhlqX1E+y7B/g9iIsngts0wu9Hj9fB8tCwBIj+EvP+1Nnw+Pn7N51Z7OssRAB6MAQD7LrN5PyFAy+iEjypw9VdCQkJCouxoNJqMjIydO3cGBLxEhUFX+py3/CmY1woRfcmglmzmaMACHTWAUCQ9bNMkHmPoShMEywYsuCrecR3RejL4OcH+N5LFUdouIDgE+y5C3Qgx0UV9MJvDm1YAIErXrfh7vTx+c60523lD4gm/gAHgt9OeVedu8BINkxNLO2v1yofgBish8ehwOwF/9erV1atXr1q1at++fRjjkJCQHj16dO3a9YUXXgh4sFc8NE1v3bp18eLFu3fvpml60KBBdevWXbNmTUpKyurVqy9fvkzTdLdu3ebOnfuwLHwQBJJwliXm/irPz7A5AOBZhZJJnOo78yodPQTJE2/VH5FBdNxn7OWPRc8ZWfKSUkp0Cm5v/o8AQFf6HEhVBl9YesDCixfDB9c0LROd/3KmFUC+AAAiwLc2Rw4v9M/5UAn8Pirm2bhP7+WEJSQkJCQqhC+//PK99947efJk3bpSiZCKAzFRdMwnXNZkLmM0jh4sOvYjOpyK/e9sPEiUHQyQwfOJt6jvaxZECkFw2cI2ER1BxY7kMsfRcWMQrS9XM+8MnTBFONkSs7mkriuhrHVzIgf/hRQAoHRdStSr5wSc6xLs/lLCV/0C9t+YiZ4TsJcTb1NFS0LiSaD034upU6euWrXq0KFDAKDT6VJSUrp06dK2bVuafviJVUiSDOTSK2oJCgpauXKlwWAwGAzJyclPbPCeCDDGYvNi/LJS8ZxCDtBY+VT2Hd9F6XvyBYtFdyqX+z0dO7LEUS53OmbzCXVjSvcGAFCAajB0PEVt8Xjfs/iXRY+OzfyklXGKPuqZThrtrw4XRuiiebvSddiPGG3i17U09R/IqUpISEhIlAedOnWaNWtWu3bt+vbt27hx46ioqBIZXpo0afKwbPtvQ0cN4g2/ip409vIgAKDjxiJSyrb9JBJwnHxbrRobGlzCZfEky/YzWrQEsTmqrGqcjvqI0nZFzENI9EgokqmIFD7/Ry5jtKzm+hJHRedhwbwWCDkdN77EoSAZsf8dvZe/Qaj/dtoz/bDz7VrKYU9rirfLSCSnJNdOiSed0gX8p59+CgAymaxdu3Zt2rShKOr8+fPnz5+/1Sgff/zxgzKwzOj1er2+opcbHyl+dbqP+tlwkhwbeldV/Qgm4Wvf6fZc7iwqvAeSxRUdwP4MLm8OAGISpgTyoPweoQMAAUCG0Dq3pyv//HymTm321G/8r7HB40YEB6X5PbK0yQCwJGRIS/UL5XuCEhISEhLli05XWG98ypTS86E8lJIrTwSEnKn0uf9CHxD9hKouFd7zYRskUTFg/4V+outY0etOGDcRRAyQR6DQYstnLKJWK3o7la+1U9ydS+lDUe8B6NhRgmml4NgjWDaSYZ2KHRHZjFEAmI7+pPh9ZhEMiRjyBllu84sAYPKIwTJps/0JIsubscey/aWIrsFU6MO25ZHmdi70fr9/48aNZSmr9igI+CcTjYwIkhGxGjKD52faHQDweWhwGV2tiiA0T5Pa1wTzajbrc1nV+UXtbOY4EP1UeHdC3bh4fxJgUljIUT+bzfPTg0YvMr0dZpiNo3ohWaXqlkUsd8VIxy9UvLXAaB4XGvyGSir8LiEhIfGI8l/KF/Pog3k7CNail4S6Pql5WnAdoWM+wWxWsY5EUXb6e4MvWCQ4dpelJxnaMeBhJ1FBYEG07cCCo6iBAYgJPBNuCJakAWoQR9rrekwKC6lYE+8dRIXQsaPYjBFs5jhFSFsgCpceeMMvojsVMdF09KAyDnXVLgBAicB4if82IhbnXZ2V7ctw8o4P4qXMLLejdAH/ww8/VLAdEkWwIhS4+OItHg4DAC/gLMcN7RFKSkmhXT3CaRK9ZzH7MO6sUj5bbKW23TLjFTu//a3wxOA7ZCtk4r/wWrcIplWiPoUIag4AgQVUIFU3OzsJAGPN1myepxE6Kau3TfnSC56Nvqvj9qufb5r9DQKIrjK1B4QtdrrGW2znWG50aDB5P5+IhISEhMSDYerUqQ/bhCcFzNt8qXWx4Lz5kP98SokWOnbUzUFtZYfL/R/2Z5bJKn+WJOArFERRsSO4zHGIiZLVWIeIwtDUo352otXmx7iDQvE6XNBf6ccBfVE/aJo29PG6g6Ii+vCGxaLnNJc3l44ZAgBYcHFZkwGAif+ilFxLt0Dy/HkC+ce8NduXAQCHbHue1bVPVtV62BY9upSu6wYOHFjBdkgEMPnE5ksKhBsTeaAgIKLgSp7QZqmxRP+fOoaaw0TEQy3T3Bmu+WqC8F6+fvTXOBHHgfIc8hLXHZMIVT1ZjTUlSoMGlkW57G/YzNHyOjsBYy5jNADQ0UNL+GIJAKPN5s5X3xrBnVcSBIsxj0UAhM3rm5g3IMAI0fzF9wcDfIixB8M8Tf8B/HszdGEqJMUsSUhISEg8oSBCDqQKBCci1UDpbtGLx/5sAECU9n7moqM+YjNGACFnKv8PoVISA2HBwV4ZCligo6T7vYqGjvxAMCwRvRdE2w4q6sNAY2MZjGb8H5ks81mxqXGoHuBgaMqoyIa3V++CbYfo2FeWSYmglmTI8/dtexlAJJ0w2Z/emcudToV3R0wkn/0t5gwBZ8+KMEDi8cQtuNYW/A4Ayapa592nl+UsHJc8DUmlrG7B3dWBl3jQMATQBBJvjDmk6mAUAsJFEM/f8D0mECwgXalWVoOIIUKmWnSAeMOapSbwwy8CLrYiIHrOAmC46b8EHf0Jb1wquk/yhqWA/aLnNJLF01EDivcRAEaZrVs87iHcpcB0xQOzCMAAgDEHvA0AKIAggGTh6lKfv2eB6f/CwyLIx2sdWUJCQkJCopwgZISmmWBeg7FABbUAVEpWYNGThv3ZiAolgprefLTsUBF9eMMi0ZOO2Vwq+pObO7CZnwEWCE0TUvvq/UwkcS8gmq70hf/cW1zON6TuDUQXrua0kMtGhAQdyVlanz1qJ7XPVR1zx3smNmM09l0sy5yEdUsFCXgAMqg1GdZJsGzksibRMSO4gp8ACCZ+ys13nrchsPEkqbcnh3X5y1y8o7q6zsCEkRPPD8nwXtpr+atVWNuHbdcjiiTgHy2CGOJ0v5LZR9r+bcwJ5smqeEBD1SfBhVlqOYw/Nlt3en0qhJxY3BwyqLP3TxB9smrLCE1h0uBmvxj8PP7qmeAOleUgen2n2mDOQMd9Vnr5DULOxE3wX+zHZX0BWAAAJn5SUfxSgDEW60aPV0MwXMxoyPoUMVHyOjsFJPs0//RHef0ihXxneG99/AQAEB17/OffBVLVsvLYRDt1luPeKjD9X3hYtUegkIGEhISEhEQFgzmjYF4DACB6eeNvt+vJW/mCxUzitHufDJF0whR/emcuZxqle6uEJx32XeYL5gMQRRlqJSoQ7D/fR3SfAEKBebv3eMMibwsW4yaC0EnIBwAR4/wTz0XGDqb1795mLEr/Dnd1AiCGjv0UUGl6H3Nc9lTAHHXbccodJv4Lr207b/xD9JwG0U/p3yHUDTFvZi8PgdKiSEqC6EYho7dBZLwUgvlkkOfL3mneTCAiXll5yOne7cJf2WxYvSrvl8bBzRWklEurFCQB/xigthL8MaAaoh8dLg7DiJAgDuMhZutOry+IIBQI3AJ+Q5tMo4+57G+47MnyOjsDEt0p+HwC5olgRCm47LmYMxCqupS+x60mInWvEwULROcBACCDWpFhr5TocMDHaghiQbi2Ep3iMy8RPWm84Tc6ZujU2BZz0By5da1WP+Z1KgSwwGV/AwB09LAYZczvcvEjk+WIn+1pMP2i11WXNLyEhISExBMGovVkWEfBsgkAgKDoqMFA3+AnL5jWiK4jAACIpCJ63+d0ZFBrMuwlwfInl/UFU2V28UNsxmgQ/ZS+F6FqcJ+zSNw1WBAd/2DeXvhScBalRaABoq71ChUtwFqOmfY8rX/3NkssdFR/3vAL9l1EpJqK/ODmDlze94A5QpFERfYrx5MoAeZtovt4iUYq9CXevFp0n0SEggh+VrD/gz1nBcuGMo4ZjtsCvEwSKM3I3dCuJCJU96jq/RjPcTifkcsbykqJK9nn8x9n2f5BmhKjj7bY8gVhpjY05KYU0Tu9vml2x4jgoDZ3WSlAogTLchcKWGge2uYf01Ye80dt+5NUNS+40zcYVnSL6vWwrXsUkQT844GYD0waEuvCQqdLBMji+b+8viCCeD9IM81mT6SojkoFKb/uA0/pe0Kx8j+YzeFyvwdATMLXpW+/F4KYhK98p9oCQnTC5JsPr44IRwBakgAAOmGyP/0VLmcaFf4mycQMjHn2kr5VFZoCgED+EiSLp6M+BIBgglgQrv3MYtvg8ebxgiTgJSQkJCSeNLDgFJ2HC0PYRJ7LmXGbrqJ9N6GsfZ8zMvFfem07eOPvVEQKoW4UaBTs/wi2bYhU03Fj7nN8iXsBUVR4Ty5vNqJCkKKW6NxHyBKM+vd/c3lowf6+4/9I4ChtZ968jkfUNKZLDYvt87CQW2p4xDDxk/znenDZX5O6riVSJ2DOyGdPAwA6fnKpIRvlBXuxn2D761ZHsehlL1xL04gowDwZ3JqKHlLUYVuGf1m6mxcBAF4K2fiqdk0OGzPm7AsAsP6Cd/0Fb/HRSASbuoVXDb0X/ZLOcj85XEuc7rm6sKZyWfFD272+oWYrj3EnpSKeumHwKxx/gmX7GMyL9NriGn6n1/ex2cphbBJvzFwlcZccd/x7ynlMRapZ7PeLPgAwsPl1gxpfdJ/ZYdzQOqxdpCz6Ydv4yCEJ+McAh06g9Ji4gqZpQ4earYudLgAIIoiF4drJNjsAtJTLvaz443E2nv+0IzHIdvnzRRefY0HDCgAAf6R7KxlHJyPvOei86VRtAGe7BNnT0aWsPgIAoWogS5oHiCr11gHZrFlZWYdycw0GQ25ubv5lHc1myYM7B8W+AQBKpVKn08VGh0a5v4gIAWX8F0Ue+AxC32pDR4YEB8T/3SJ6z4HovXM/Qkkoku9hfAkJCQkJiQcKQiRg4QaXdVKFrskqLLgAXy80gzEP9wXGvB3IEFqfwuXPZTNGyKqvAkCAee7KpwBARg8GxIDgBlJ1fxNJ3CWY4wrmAwDmbdi5DwBEf4Y2a8zgYl148zoAoDD/jnvJSFldNUF8GhJ0q/HI0A5kyPOC7S8ua0qJsAsu60ssOMjQF8mQBxtITKjq3UbA3wDmAUCw76UTviEU1QHA5BE/OlBAIFRTRymR47nQEQCwwjUqRKnMdwk6BRGpvmFHPIghdIp7rAxfX8Z0UyuXuzwfmizFNXyReu8fpCmh3gFgti6st9F0luP6GMwL9dpQggCA3T7/J2Yrh3EvjbqrVC/5PuAxvzx3EQA0D312h2kjQzBvx7y/KOuHvZa/moS0OmjbvTx38eBEabWxJJKAfwywh4uEBtgg3FTG1KTpkywLAM/K5RjwMT8LAAYfP+yQfUeGD8FzEVUbNVIfVZpm/F9uYQVFn/1Qkn6jT5QNPjsol3UBwK+n3WfeKxlpXwSpfb3oOcuy+/btO3DgwNGjR48ePZqZWWpZmqMAR0s00RQRnzC0bt3FtWrVatq0aYsWLYKD71G9C9at/nNvlbGzrPqKB32hkpCQkJCQuGsIJR07ms34FAqVPBBMnLzuHkCUYF7tv9AXISqg2xETQ0e+dz9T+dM7C449RS9FV6r3SOXiHfisyXzWZAAgta/Jkhbez1wSdweiSU0Lwf43ACBEIUV10ZPmQ/I8eb1E72FABFLWxO5Tgb7JuudlGG31em8j4AGAjp8s2FvyhsVURB9CWVh5S3Sf4o2/BRLmPehzoqOH8sbfMFsAAIjWE8qaAODl8bF8FgBaxMqw4BJdgRvFgGeoyGWMltVYAwA2f2D7Gq/romMzZ/B5FkLTdEzTtyNPer7a73glSTG2+e3O/a5AABNDQ0hAv7vcH5osc3RhzeSyIvXeV6P+OFhz87u0JLE4XBfQ8CkG80K99hTLDTJZWIx7adSjbvunkbgjO0wb8/25UfLYC54zGHD78NdahbU97vg31X4IA1aQyuOOw6ecx+poGj5sSx8tJAH/yLE7y5/rFIq3KM4S7rqiGIafzS5wI8wA4gHWeTzb3F5AgL1w8gQ/oUnwWTMnYLzAOraBqmvP8CV7fF3/tcQTSBwbOxkBXm59HzOxUQwAQNNoWelzX8Pv9//5559Lly7dunWry+W6h7PgePHixYsXL15cvXo1AJAkWbdu3datW7dq1apVq1Z6vX6AySJD6KuwEOWdyssheeWAzxWSxd2qsg7mTdifDYgm5An3YK2EhITEE8L06dPvqv+wYcMekCVPIFRECm9YLHrSAQAQKXrP8oZfqPDu7NWJAIAxjxCJscAkfAWE4n4mwiAGpkCkBmMWBA8AQpQG804AjEg1IArzjpJVaiQqBDphinCyJWAeY55UN8IAck9aov8EAKajP+FNfwS6Eao61aN7rxcwdadEg4QimYpI4fN/5DJGy2quDzSymaMBC3TUh4Qi6cGeDwCQKjpuAntpAAAA9jNJ8xGlzbXz7+0zIoCL7aJ86S8BYFL9tOA6TChrYzYnEMpBhrxQNIbovcDnP/DcighgXGgwAPzucg8wWfpq1D86XQH1PvzWUry4hn+zwJQvCJyk3ssDB2/fULAcAGprGmw3bgiltR30rwHAW9EppxzHDtv2Pqfr+Jfpz2W5C2sm1yNLTdP4pHIXAt5oNCoUCrVaDQDLly//888/k5KSPvjgg/Dw8Adm3hOHzSem/GnBN7WjQ4huid0EBgzuA0CogawLXoQRC+xeZMS4cRSz6209AABEsJd78IZff673Xa2d018PW1VdkYaY6P4dRvUn7uzkk5ubO3Xq1CVLllgslnI8L0EQUlNTU1NT//e//wFAjRo1nM1boLbtslq1+jEy4vY784QiiYpI4fN/ImSJsprrSu3jS++E/dlU5PtIXrUczZaQkJD4jzF8+PC76i8J+PLkWnL4ok14Lnuy6LuA/VkISAwCxgKhaXpzBtm7hYn/ynfqOQBSXnsHkif60tqJrmOAFAAOMuR5WfWVovuk79SzgBim0mflcWISdwGhSKYi+vD5PwEieMMvTOXp7OUhIPqQLBawiP3ZAASAyMRPAURWKtt9Oh07SjCtFBx7BMtGMqyTYF4tOvYhOpyKubv/7/cMFf4WX7BQdB3BvJ3LmswkXl8oFMyrRcd+RGsFz0kAxCR+K7pS2cyxbMZoRd1nAApVGZc5BjBHRfQhVPUfqKnFNfwchxMD3F69Bwho+G4GYxbPA8CbapWk3u+fNfm/eQWPklTttmwHgHhF5YCeB4B4ReIlz/kj9v0IUJ4ve6d5S1vdSw/V2EeLMv0weDyePn36LF++fO/evS1atFi2bFn37t0DhxYtWnTw4EFJw5cXIXJibPOgi7Ybgt8u2LiT0Wxh7jkEUQ0JOxYDSTkxA1Rl8JwV2/9h/K5dSIMIBgDouPGCeb1g3fxCSLuBkd8DABP/BdxJvbvd7qlTp06bNs3tdt+qD8MwSUlJUVFREZERijBZlegkBMhrOeLM24AIlYd6Ku/y31kFRLY5qKDAeJu5zpw5A2fOwIL5ucHB+59vO6Fbt3de7qRU3tJCOna0YFolOHYHrkwljgrmtaJjH6J1dMyI25+jhISExBPO2rVrS7TMnj17+/btDRo06NKlS0JCgtVq3bZt24YNG3r06PHNN988FCP/w5BBrcnQjoJ1EwAAQpgz8XlzAQCDAAgBRuWy/Uio6lHhb/HGpezVCbLkX5n4Kb7T7TFXAIim4ydDYHsWRDqq/39g1du+xeQ96YoYGo+ox6YkXuCuBvNmAOCNy0jta4J5DR09hM0MrKeIpPZ1IqhF2QdEVAgdO4rNGMFmjpMHtWKvfg4AdNxYRAU/kBMozQQmYYov7QUAzBt+piL6AFQHAFUzX+6V8VoAJKuEXamU7g1C04xQP8UbfhG9Z2ddmBkXNhgAWgXtEmw7EKmhY0c9UCt9GMsRQgBN5bJlLg8GTAK0lMsAAAOwGMtu7RZ6muOMfKHHygk/axXF0Jvy0kvcFUftBwHAIxTqjuOOf487/i3ewc5ZA0+O2Q9KAr44ZRLwM2bMWL58ebVq1QJCfdq0aVqtdv78+enp6WPHjp01a9ZXX331gO18guhTt2RGme6XTIgBRkAzIkMnWuwmuQAA1Rj6XbV6vNUGVTDjg9wMocd6y7wOoS1jZYgOp2KGclcnfhP/KYn4AvRUova128woCMLixYvHjx+fm5t789GoqKgXX3yxWbNmjRo1qlOnDsPcnP0O+9JeEF1HAB0FrGLiJ1NRH7rd7jNnzpw6derIkSO7d+8+ffp0UUr8G6a22/NWr+q/etVQtbrLa6/17Nnz+eefJ8mSTjLFr0yKkLY3VKcXfezVCQBAx46pwAuVhISExGNJ586di79cvnz5jh07Jk2aNG7cuKLGjz766Mcff+zfv/8zzzzz/vvvV7iN/3GYhK+89r9A9APGgNC1fxFgkYroXV7bj3SlCYJlg2D5U7D/TQY/R+m68qYVVERfQpEsmFaJjv2IDqdiHnv3ioKZmbkTLmERe046ExfXflw0fNFdDQApOg/Jqv5Ex47isqcU5usl5EylCXc7JhXRmzcsEj3p/tMvYv9VQlWHCu9Z/qbfgit2fvvl6i2I1xLF1YCFzLSRy4XfAaA3nq/lc3KoxBjXcR7kaxzDPCdcXaopj0aMb5HR423b7H0h3SmERkR/CwB07ChE6x+gkTz/er7xGYW8vUI+wmLDgKvT9FmO+9Bk+Z82dLHLncZymyL1utLcQnf7/INMFg5wN5XqGOsvioeXNPz9MDhhTKrj8FbDOhGE53UdQ2ld8aMZnotH7PsVpLKD/rVGwc0elpGPJqhUWVWCWrVq2Wy2ixcvKhQKg8EQGRk5bNiwqVOnAkDdunURQidOnHjwpj4qpKWl1alThyAIQRDu3Ls86HrZlCaywmH064va8Zz1KicAQDhJ/BSuPenmJthsigyijSBfd8FLEdA5SUmTQIH/PfrFMJQhYmKcebUy6IYbgmbRTKeqhfF127dvHz58+MmTJ0tMqtPpunfv3rNnz6eeegrdKUxddB0JLLsSiiR53X03Vysxm8379u3btWvXnj17UlNTef6W+XWjo6O7d+/eu3fv2rVvTIOPBd+pZ0TPabrSBDr6k6JmLmcqlzWZUNaW1/kHpPAYCQmJCuHAgQPNmzdv1qzZ/v37H7Yt90Xbtm2vXLly6dKlmw/VrFkzOjp6x44dFW/Vw6LCru/c1c+53FkBT2lANGAOABCpkdc/Uo4ChsuZyWVNIhTV5HX3YtErWLdQYS8DgPfE09ifxVT+jtK/U15zlTu/u9zf2hyTw0I6KEumA7jM8X2N5roMPepHW96UKwCAKIR5HPKq/jHS8EV3NQCAmBimyvf+M10CB+jYT+nY0fcwpODY7U8vXKGT19x4V3v498lTPxdYvKKeNmyo0UFJeAFgaMask+5662t0VBLeDCoxgb8yx/DR3NwBAEDHA6qNvzP3b+Hbs9H25llPpeHRU7O4xA+urudx4Q2km0UOVni/vnpk01ISy90bBYLQOd9oF0UEgAFSNOrhIUFfWO2/u9wEIBFwJEmujwzX3KTJA+q9KGudWRB7G00XOb46TUsa/j75PmNKqv1Qy7DnU+IGlTiEAX954dMrngsvR3R7LbJHhZn0WFzfy/Sdy8jIaNKkiUKhAICDBw9ijNu0aRM4VKNGjStXrjw4+yQAoEomze1AFI8msrarnFCFoeQIGQVxiNnalGLYLUieRVTX0QDAi7DqnGdZuufXdOGzK6MwoJWWN9ZnJy9L9xR/zPrXBQBms+nVV9q+8MILJdS7Xiv7fmLjy3vfnj6qaqMkE3CF2/IO3jb+3Me/5y642UJC3ZjSvw1A0AnflFprVKvVvvLKK9OnTz98+LDVal23bl3fvn31+lJuU3Jzc6dPn16nTp2mTZvOnz/f6XQWHkBkoDQ9lzMNs/mBNszmcTmzAIBOmCKpdwkJCYm75ciRI9WrVy/1UNWqVY8cOVLB9jwhUDHDEB0BgVRzmANEwQPYfqSjByJ5FdF7ji9YjEgNpXsDCDmXOwv7swhVXUr/djnOdUe+PugYtN165y2ja1AI+TAeYbZu9txQRPYyx/cymvIFof73BQH1DgCYx4SMtK01XOmVhrlbTtJjvfn9zdbSj2EsWOy8yYr5CtqbKbqrAURgNsd/rjsABsCIiaKjP763Icmg1oEwQ1L7akWqdwB4JUkZJidYMnKppdBnZ3j0tBExXysJ71m2dgJ/pYCM/D0phWAQUQlQbQwA3xpGPcmrwwAAIABJREFU8ZjuELJyQOQcAJiSNTLLSea5xMDDwQp6JdmrTnlWaIsgyf7BGgDAAHEU9XGwBgGMCAmKoEgRMAIYFKS5Wb3/4/V9ZLKwGPe5lrVOSxKLwnWVaSqwD2+X6sDfK2dcJ1Pth+SEoktkKd4iCFCP6H4I0BbDWhNrqHjzHmXK5EIfHByclZUVeL5nzx6EUJMmTQIvrVZraT7VEuXJSSOLZCBvhjN4XuklVGcJfzIPBGRwfNerJiDB6hN3XfUnh9GiWHjdoglUq1KH59J2WYSwzkmKp6Ou/41IAp4OPr13Zd8egzZn5d+wE66QoUHdZcPekamVF8F68VrgCSA6ggx+ZjfrcLAOqyymVCOZyv+j48aV5eZDrVa/8sorr7zyiiiKBw4cWL169YoVK4q+YEUcOnTo0KFDw4YN69Wr18CBA6tVq0YGtSbDXhIsf3JZXzBVZgMAd3UCiB5S+yoZ1LKsn6aEhISExDWio6NPnDghCEKJ2CVBEI4fPx4XF/ewDPtvg0g1XWkce+mjwmx2mCcUSdT9lY4rbRqGqTTRf/4dLvsrUvc6osIwm8vlBjLjTCnjFk55sfqc1+wVJ7USQ+VlmvcNlbKAF2Y7nCPMVh7gZaUCAK7wfB+j2SSIg+dZ683IBwBFTbV+aHzWwDOiXyBkhG2d4UrvtMTFtRFdch+eFfChXJYhS7aLXp991RbXP4cEmxMAkIxRNqwV8uZLdOwtq+2WF0V3NQAAoj/wZaArTbpj0qLbwCTO4FX1qYje5WVkGRnXXDOuuQYAQBzlPbEa+7OimewoJscnymoo8zAPv4SO9MhkES+ABWMAaGyTV1PXOQG9GsF8JeHe62iVlNCxeLp8AkG/eqpwZXl+Sy9z/HSbAwDkCGXx/GCz9duwkI/N1gJekCPwYfjSZm+lkIXf+GM4yWrnMO6jURcv5qcjiZ/Ddb2MprMc95vLPSCo3NwEnij+yF0MAD7ROyS9z226cZhdm/97v0r3uLD1n6RMAr5OnTp///13ZmZmaGjosmXLGjdurNPpACAvL2/fvn21atV6wEY+6TRPlF1Rcz45KLzIthenxrIkAdgCoASnRqQaA3sEDub4S7yrTbxMrYo02fm+9VS1dIW74oJjN58z8/9mbv10lpflrncmCPR2l6c/H/1mpUqJQKoBc9ifJfozsOes6D6OuQLetPw5gGcBsNPF5weR2tdv0urE3W4dEATRokWLFi1aTJ06dffu3eMWLty/dq1YtOUOAAAOh+P777//4Ycf2rZtO3DgwJfafS7YdvDG36mIFADMm1YCIWMqfX5X80pISEhIBGjZsuW8efM+/fTTqVOnEte2nkRRHDlyZFZWVseOHR+uef9hqPDufP4C0Z0aeEnHf1Wq/9p9QoZ1IoOfE+x/c9nfMAnfsJnjQPSQ2teJoOblPtftCWwvlCFq8zofBWsAYLbDOdpsBYDaDN3bYDYIwuB51ibX1HvCktoXO6WKrIhoJPrF22v4mxEs9vwJs7g8IwCQwRrE0LzJ6j6Q6jmaph/eT9Hwgd/fMpW+8Np2gOgHAMACoXmK0nW5nwERHU4/3NQGhJyJ/8J/vjcAIMBZ/vgk4jyhafZ+Ys+t+WazKADAsJCgfnFqAMD8Z+7U9Txvm5o7anvvB66B1QSqSlHtlPLnFPI+BvMur+/FfINNEHUksShct8LtPu7n5DcFjbZRyMwivjlNvY4kvgkNnWSz1Zc2Mu8VhijrR1f2nk8IZRLww4cP37ZtW40aNZRKpdlsHjNmDAAsXbp05MiRHo8nJSXlARv5pPNvhB8h0HHEr7G6rBeEsdhmAGGQVqPH5FSwO3VYVQOWRhwN9a4oeovJIwgiNIgUHaFYn8/4rQRm80XveeAtM3/1fzb7Boe0pk2bzp49u2HDhrcyQPSe81u25BmX6tlMwn2SdZ+EzHFkcBtK9xYZ1gmIO1SVvyMEQbRp02ZPmzbzDaaJK1bY/ljm2bcXF3NJwhhv3759+/btCQkJ73ev2+vZw2GZowGLAJiO+gjJKt2nARISEhJPJpMnT960adOMGTO2bdvWuXPn2NjY7Ozs9evXnzp1KjY2VspQ+yAhmIQpvtMdADAZ0o4MafeApqETJgsnW/IFCwhlDcG8Bgg5U2niA5rrFmDR+W8jZY6TwoQzVGBvqasRqSbUjYu3FNfwKoJwiOJ19V5DnbCk9sWXUrkCPwBgHt+DhjfOWszlGZnEWN2HPZjKlQBAsDmsSze4/j5gnLEw5rvxZNiDTY6L5InZ2vdijT8AAADBxN9Qg6BAEPoZzTqSXBSufaBmlC9kWGcyqKXg2AsASYrzGAgU/+VuL2sRC8MTNnm8b6iUR/zsPAfXK3LND38XXPYlVoBhepJcE1lYNuvH8LC3C8w2QWQQmqfTVqWp0SGl/633+PzZvDABoc/DQor7A+TwwicWSw4vnGDZ5vL7vRN+MhlT9euHbcLjSpkEfLt27ZYsWfLll1/m5OSkpKS89957APDvv/9mZ2d/+OGHffv2fcBGPul0DlX8Y0cz4kLDadJGiYYCIZIk+0drSIB4B/nuZYvMQSTpVwie9UVvCQUABKAAUAC4QHAVtn+90PfFPF9RN4TQ0KFDp0yZQtO3W/gnFNUUMdUqx3wMok+wbeWNywXbjsAD0Xoq8j0qog+iynR1wQBDzdYQgvgsNPjmmPUYtap2ly5Zr3T2Z2fFrF51/tdf8vPzi3fIyMgYMyVj8v+Id17aPeBNWZXEGDp6SFnmlZCQkJC4GZ1O9/fffw8ZMmTTpk1paWlF7Z07d546dapW+zjJhscOQtOE0vcSLGuZQCx0+cFeGSrYdhabSQGCk708BAAAyX3p1+vMU7rX6Ljx5Tt7CQTzWv+FlBmBaIzLUNJd8EZkNVaRwc8VvhCx+bf8lDpqW7zqN5fbIYrv/mq/Ye/9pVSuwI9ICOsRbf41t0jDIwbZ1hmufnw2fk6N28zlP3fZl36BDNFEThxMqAq91smQIN2At0W313PouGPLrtAer9xmhHIhK+JjuXmZTjRlh3ZNVjcqai8QhF4GcybPP4450ujE6Z5z/QnvKRrx61SvL3PGXeRtCGBQsGat23uG5boWGPMFkcf4MhF31htaweb5MJ5pd/KAEQCL8SyH8zttKHOLhM1fhYV8YLSsdHsAoEjD5/BCL6MphxfqM8y7GnUF2i4hAVD2CKh33nnn3LlzLpdrwYIFFEUBwKBBg4xG45w5c26v/STun74a9S+xunCaBIBtHh8AtFcqAuo3nqS4g0CbER09KLBqS1eaKEtalK+fP+rqzOEZM341voMRAwCICpn0k1BcvatUqjVr1kybNu0u/oKEnAzrLKv2m6LRWSZxOqGqgzkDl/WV91ht9spQ0XvhjgOwGP/l8S5zuYeZLSUSxWz0eIeZrZkc31Wt1MRVMgz+ZOOFC0uXLm3RomQiFpdHnLvCX6+bo+eEoIP/niqr8RISEhISN5GcnPznn3+eO3duzZo1s2fP3rBhw6VLl9auXZuUlHTnN0vcH0zlmYrGV8q9GLvoPIz9GUUPEIrFpgm24odE17HymRKD+6Bd9JWSzQspawWy9HGYBkSj0h4AhUv67IU+3tQ6gmM3iDhz0NnMD9PPvXD08i4zAMSf8T03LR8AEAHhA2Ivdrq29y6AZXl++PtxAIA5DBTCLAYA/2XvzcYUx3vqPACoWj5VpN6LCOrQGgB8p87f12dSNp5X69Irfbdd8WIf2aDfXYUFsU2C2NdozuT5Ggz9nS6sAswoXwhF8tXoLd/mjD7kbbooaMgFnsMAI0OCPgzS/KzXhpNkNi/wGL+tVjW5by/Ou8WH8Ycmy0GfX0cS3+nCQghil9c32GxlbxHg8bRMNj9cq0Ropdsz3mITAfIEoc819T4vXKu6U6kmCYlyp0w78KVSuXLlcrRD4mYEDL02msOV5MznQ4oat3u9APCCQl6iM6FuTOm68aY/RNcxOvrjylpow3kM5ye+Hf4rwpjSvTF65tUZi7YW9ddoNBs3bmzduvXtbcj1ZRX4cxsENynRjqhQKiKFikgRHLv5vDmCdTtfsIgv+JkMe5mOHUkob7nmTQCoCMImils9PhEsM8JCKYQAYLPHO9JcmJ/2RYWiq0p10Oevp1I16t69e/fuqamps2fPXrp0qdd7/XosirB646HVG5s988wzI0eObN++/R3L3UlISEhIlEpycnJycvLDtkKifKDjxvnPvQWEXF5zI6K1ACCY17LZU5j4KWTIcwAg+q/6z7wOWKRjx5bDfCLOHHTW/HOuumlwlbUNSDWZ7RSKqaEqytAUheUnGnGA4fZR8Jh3AO/AvqzM0WfNP+cCgbBHeP+9zJiFleueE2i/CAiwCFcHnQvE08sSlZo2oaZFOabFOeHvxxl/ygIeAwBiCMvXyfuyr+/38yIGABFDUaPTRnrCEsKCY5vyWH5j8TkqQgcAgs1RqpGCxe7ctsd76rxgcxAqpbxmFc2Lremo8Hv56AAAoH1khyWq1hab/QurHQDaKRS9jaZLHF+DoReGa0Mehx34GYedv572FG8RMTjZ7stVb1HXzJ96yTnrhBuHYb5+4bfgBMs+g0ve0D5Qiqv3xeG6KjQVq9cG4uEHm6232odvJGN+Cte+bzSvcns8GJ9i2exr6l1NSHeeEg+BuxDwhw8f/uWXX44fP+5yuVJTU7du3RoTE1OyWLdE+bEz03cgh00Ku/43Sme5LF6IIMl6slJyOdCVJvDWjYJlvWDfSQY/04kew4f/zGNaiPli9LQT3825rt6Dg4M3b97crFmz2xvg4h1TL42389apNeZpmdKvTGRQazKotei9wOfP5Y1LBcs6wbKB1L1Gx4wkFKXs3tAI/arX9TSYbKK43eMbCrYZYSHbvb7hZmtg3X5YcFAglKgOU+gXIHrP1iAGfD/QN6ln1UVrzXOXGXMKuOJj7tq1a9euXbWTFCNSYrp/tJ5W385lTkJCQkKiBNL1/T8GGfoiGfK8YPuLNy5lEqcDABX9CRX9SVEHPnMCYIHSvUlonrrfya6pdwBwHbRfejX1r4nJ09LcxbsEkSl/1lgRQlkBINXYPOurIdubhR6rqQaAFyvLv6i1mcsYjZhISvcml/s/QlEtb0oD88+5SEnOmRuXtNnedpm1S98rlX+ve7i9M2yLFQMgjAGAjpAlb21IR8vIUKpgRqZpYQ6iCMyJAJD+om78QefNxvIifnej5dqr2tCwNhRAj/2OL1rfEP8s2J0AQKhKlqAHAM+RU6bvlojFituxl686t+wO6/uGpt29F8R5V6MCgCk2+xdW+1yH0yiIj5F6BwCTV7T7S/G/oGoCAAgXgKgEghbzjQSkAQB4U6064POnsVyaggOAilHBLMb9jZZDfr+eJJfotfEUBQDVaXpBuDbFaN7l9Q03W2fpwkr9xAMa/j2jOVDXsJ6k3iUeKmX9Xfjyyy+bNm36ww8/7N279/jx4wCwcePGOnXqTJky5UGa90QTWMt8s/p1z67tXh8APK8ovQYLYqLo6E8AgMsYw14exBt+BkJBV/t98vcXv/t+blG3sLCwHTt23FG9A8BvOfPsvLW6uk4Yo7t9T0KRxCTOUNQ/TkW+DwQtmFb5TjZnLw/CbO7NnavQ1K96XeCatN3j7WE0F1fv/YJKhhKJ/hzRexb7M0LkWUPe8pxeqVo4UVkvuWQEfdoFb6/RF2vWa7dgwQKWZe94dhISEhISIF3f/6PQ8ZMB0bzhZ9GTVuKQ4NgjWNYDoaDjPivebvCImy757iZP/HX1TijJhJ9qMpXkroP2hqPOJsuJuCCy6FEn1LLG2gUAMEYNwvc1qbJlyJ5jPfLPNQrLeTr0Ip89FQCo8B5c/nwAsG3+0DgvDynJ7/8vLj2OujShUli/WNEjXO5+ssHr0aL8umbiLZznhBMAYiZVDX8/BnMi5kRACBDU2Gj44LyrRays6NEshgEAAqHrLcF8Q0tGY3dum7iSmyLuPf8CgKxaSVdTNiPbOHMeFuzKZkmR43vHfD8k8st+6hfqAeWz/PyL59/9mLcVPeAO3gYleVejGhwUhAGMghhJkY+RegeAyc8EH+sTUfzxd49wAGBOE98Gh/7bIvKPKJ2GQCgYgIBeatXE0OBf9NpPgoPaK+UAUMbigvfJaZYrod4D1GToheHaYILY7vVd5flbvT2aIjXXvn3xFKWU1LvEwwPhMtT02LJlS4cOHapUqfLtt9+eOnVq4sSJGOO9e/e+8cYb+fn5W7ZsefHFFyvA1keEtLS0OnXqEAQhCMKde98rVx3C878bGBLtf0cfLCv8XXsp33CZ4xfrtU1khSFDrIA7LjfV0FHftwsFABD93pPNse8yAAChlFX/Y/7vp/v37180bHh4+LZt2+rXr18WGz47N8jGWT5PnnWr7fdSwWwOlz2NN/4GmANCTkd9SEUPRWRJWX6J4wP78EUtpap3APjX72dPv9SAPerXdg2pdN3Z76+d+6ZO/3Hbjj03vyUuLm7EiBH9+vVTKEpZPpeQkJC4Tw4cONC8efNmzZrt37//YdtyX0jX9+JUzPW9wmAzRvP5/0cGtZLVvJ7jFrDgO9VG9KTRcZ+VqDc25C/b+gveX1/WBrTuncGQNeSccX42oSSrrKineSaUzfKd73CMzfCqmgRXXVuf1FAAIFg2+M+/W0abOWPdS93HE0ry0KKqB3NdAz/NVSapkjc2yJt8xTg/G9GFG+wAINCI5DBiiMq/1VHWU59ufFB0CACAKEJWWeE775bXUNX8t+n1T0PANeblMyQ6817ktU9CyB0ymcstUDVroO3fo3C/HWPHlt2WhSsBoehpo5hK0cXNK5jyf4o6X1M6e1nOhdL3YirPKuOJA4BJEAOe8wCAAIaqg3bu9b9RTflq8mN5J2P1iY0XF4TKiSO9IwBgl9c3yGTlAAOAzIqC00kkAgCIGHKcAoEgRnPDxgwCGN4k6KUq5elgjwG2eLwNZEwkSV7i+AlWWy+Nut21oNQMnr/E8c8r5ALAV1a7QxSnakOLNHqeIPQymLJ4IYmiswXei3EXlXLSjXnpJf4bPBbX9zJ98WbMmCGXy7dt2/b6668HKsADQMuWLQ8fPkwQxPTp0x+khU8oq895RAydqiqK1PtFjr/M8WEE0Vh2PeEHQ6Id3cML1TsAEDIy5PnAU1nSggMnYdCgQUWdIyIi/v7775LqXXAXXzAu/hidMGpy1SlhBI15G4g3hDbdBsTEMJVnKuodJLWvgejncmb6jjfmjb+XWIquQlMfFJPrSTTdW6MqdcBaDLNON0FAJGled9HvRbKEwKNt+7e3bt997Nixbl07kjd+kbOysgYPHly5cuUZM2Z4PGW1XEJCQuJJQ7q+VwwiK15of/Rip2NQipfxDWDAf+Qu+se89Q79ygAdOxJR2sL99mvwhiWiJw0xMXTUgBL9PRwGADd3JxOvGVpCvQMAEydP3txQlqhwH7JffPW44OQBAMkrAyIBQBSu73ligcQcDRx1rWpa4VUcEVmJ80ZUXcW/6CUHjcglOOxLd13olBo1OjHombAi9Y6VZEC9Y1a88s6p0w0Oig4BkSjklXDMi77zbkJGxE2vdvszQCQZ/kkvQiF3H0jN7j+u4Ks5hqnzsj8cb1mwAjAOe+fVEuod+1nfiTOACQAARCEqpPgDszLsZxARhKiQwpNCdyHuitR7DYb+JDgIA8xwOf6l/H9eukMqvseCQIQ5BzjGRmE/+ENxfjKf5RKyHEKOUwAAEUOWQyj+uOoQMu233Ay/NxBAB6UikiQBIJPnj/rZISbLlmvREAkUFVDvI8zW313ufT4/f22Ps0i912eYpRG6eeFaJUKrruW0k5CoeMoUA5+amtqsWbObs9bFxcU1bty4eOEZifJifw4LAB0qX1963Ob1AsDzCvnN1deKEN0necMvgefZFza98cYKjisMFydkihFz15SIaRQce/xnXgdc+k8kAqAACn/YEC2vuYHQlMxmdyuQvLIsaaEYNZDNHCM6D7OXBvAFC5mEbwh1YbX5zR7v1GLpYS5w3FCLbZpSgcw23mIXnS7R6Ra9PtHjUzVv+HlCy/2urs0cf2RdHm2vsbwRRdlXbRFdHsTQldXKhR/7xnYLmvlb5NJNV1juenh8fn7+sGHDvvnmmyEDPnrvtS6akBA6JgKRt/n8JCQkJJ4sHsHr+6VLl7Zv356enm42mz0ej1arjYmJiYmJefnll6OioirenvtHZMX0hgfYDB8ApD99sObhprfZPdlt3r7VuI5AZJKqRoy80v3Mi6gQOm4Me2UYmzleEfICEHLM27nsyQDAGT5VEPexr1uaeg/AxMmTNjW80PFYQMNXXVuf1NQC4k0QlhLE9ZsNRApAFndzKNRBlNZKaa287YylHwE8hoExip12b7rrdMMDQpGcIxDyCIKKIN0iISMCqe8RiapuaGBbYwh0IbW0pvWdi5MxlStFfTPCsmCl98QZb2p6oJGOCg/t+aqySb0SnXmLHfOC9+RzmudXYZFy7kjh80QiSCWvXkXzYivz/OXe1PSIsQOYykb/mVcRqaZjR5bx4yyu3gOe8wqEptjsZG3Iyy1nEfsgEKxbedNyuHF1ihLwtAQ/Q0L2WdrBcpMwTqTpGuHP/SvvMdxttYaLzV+mJypD3H782mpTiJxY+/oN0ZoyCumVD3B7+zmFfECQZo7DOdxs5QE6KRUAIACMNls3e7wagvgxPIxGCG5U74G49+I57QBA2od/dMCANxlW6Zmop0JKVrD6j1HWJHa3ckUOCwu7cOHOxcMk7govj08aOBJBo8jrbmyBAnLtlLe84mLe6j/fC0QfqX2NNW3o2f//8vML1SxCKLzP7KX2yik+sXigESKUgAUAAEKJiNJd5rDIgugBzANxPRpf9F0SXUfvfCaEQl5jnWD/h80YIbqO+NLaUrpudPykLZymKO69t1q91uO2iXi7x/vhweNjfl5DijdcA9jMnIgxH7aqOsl9fFNT365hWWut8lZJyzcFjsqq5AS/dqyKXv65pvUH7Zr9dP74sqtnvez1xLMGg2H0xPHfTpnct2r9lFqN9W1bhfbodHPNGAkJCYknk0fn+n7lypUBAwZs2bKl1KMDBw7s3LnztGnTEhISKtKq+6S4egcEvrPu22h4r+BZmbtKFBkg2KU580dUmXSfs1P6XnzBItGTxuXNoWOGcjnfYM7kSat+dUh07OSr+kH3uEBwdfBZ06IcQklWWVVP0yoUAESvaPk9L/ilcDqCYeLkSRsbnu9wNKDhY75Myuj3QsJ36wiVGwAwRghhAHAfqadqfAL7ZM4jDYNaHgBM2Da2C3l5K+bjL7+TDDze0E+7blDwT4Nj6aePCsU3Y0VsiqZ1uRypIgW3AACIIaqurW9bYzDOywYAQBA1pqyVkujoiIhxAwWLnb2ShQWRitQxcVFQWipyRFMAwF7SsZXjmcRMOmKtN7UZ5Bv95zMcm3eT+jAAQBTiMscAABUzHNERZTHAJIg9DaZMnq/F0AvDtUEEAQDvalQXrNxK5Lkcxa9ye7o82jctfN5swVEynpEEeDFQQ8kGha6hXuA9B5o37vOzStfbYDrCs9+yjs/VwQBAIogLquj9lUHBGgCY43COMlsBoINSMdps3eDxaghifnhYXabwrnigyZJ1U875RjJmTnhYf6NlldtTg6HfVpfuQypRwRy27V2V9yuF6HhFZb3ssVzwLSNlEvD169c/fPiw0+nUaDTF210u16FDh+rVK7lI+aDJzMw8c+ZM+/btAy+zsrK+++67EydOCIJQv379lJSUWrVqVbBJ5cuxfJYTcV09rWEKfym8GJ/jOA1BNC0t/zwAABbYi+9hfwahbiyrMvfr/zu3N/V65MaY0Z9mNH3lQA47/bDzy2KpVgl1I1L7mmBeTYa2lyUtAAA7b513dVarsLZNQloF+vjP9xQsf1Lh3QlVncK3iS7f8afKmJ2Fl1eT1z+oCGrF5Uzj8ufypj985j9TgwYQ6rdFoAJx76+JQk+TxalU7K9b7dsP3hq/7yijURFqNaGQEWqVsnFtAGAYPcSN4DPHD7J93SOi6ejPP+5wJRtzXlI7DAC4vI6yavUr2ZyTYmPHtBy3IP3I7Nmznc7rGWitft+00wcXXDjeL/1In5374/p2o2slrVUpastkta6luy/OWY5L9bPd1Cppv15CQuI/zKNzfTebze3atbt06VKtWrU6d+5cu3ZtrVYbFBTkcDgsFsvZs2c3bty4atWqEydO7N27NyKiTNLooSOyYnqjQ2yGDxDEfJ1E0ETW0HO+s+7TjQ7U+rcJUCVF/OqclZv3jScorvVTQ8+4Th6zH2p4UxnXuwORdMJkf/orXO4MQtOYz50HGBnmpgAiskdfAIB70PCCgzctzgEAXd+YIvV+6Y0Tzn8sspmZSZsbMrFyJl4eM6nqld5p7kP2i6+lii517h9vxKYshmvqHQCpGp0AAOuaV0PfXAEAnLl90LN7ASBn0luil4oYGg9Dwn1O9+GRZ5sG1Du+5m4PgBUE3SCIS3WQaorU0lX+qGuan2OcX6je42ZU0/WOvsnw20GGBSvCgu/QJzSYUMgFh8v5V/2wlBx57Svqdt+IrnjXzoOuXYf5nAJAiFBv4wvSkTyRjux/+9GKWOl2l1DvAZ7Dij/OeMiaMMvueMQFPBXVX3DsASCYxGmIKnR8cHN41D82qj4AAW0VTLuCsZi3U1H9ASCJphbrdb3/n73zjo+ieP/4M9uu9/QOBEILELpYQIr0IiAqSC+CUqVXUYQACtKbNEVRLAiCgAKigvROQksjvd3lcv1u2/z+uBBCCBAQUL+/e//BKzc7Mzu7t9zsZ+YpBcY/3e791LPOA1+Wshr+a7vjvIctp94BII5hgklykV5XLuZ8M4lknb9+UbG1KvX4Obl9PEFYkf0+9wsA4DG3I3frmKjp//SIniKVeuYGDBhw6NChgQOhB9iAAAAgAElEQVQHbt26tbTQ4XD069fPbDb37t37aY2uIubPnz9nzpxOnTp5BfyePXv69etXKtV+++23VatWLV68eNy4cc9yVE+WK4UcADQLufPzIUNogV4bQJL0fVKdc1kLheLDiPaX1Pj87Pkr81eeKT30SuuGH85bkGYVO39r3HHN2StGHhd4R7IykfNc5gOCaacYOJRQt/gq67Ortkv+TKBXwAvWP4Win4FU0OGz75yMkCOCwaIHAMH9bPCQCIIbAIAOdScmua/ccF1WCqaO8pbnpFUzx1sWd3PszAhf3En9MgDUMOi+IMmBdocV4z9rRJ1v3qBrRXMVE/S2WLAtypXUy779A/UAe8smA+xbuPQ8QlZD+/oGQHcuKh56je3Td0G/oZ8nX7JxdyLSm1n3xwknNyZdHH7ldMsXWs+d8Y6M49eIuHl0ZNkTXWTZ4YUmu4jrMkxsRfLehw8fPv43+PfM7zNmzEhJSYmPj582bVqFFebOnbtly5YRI0bMmTNn/fr1j9p/ZmbmgQMHHhy4Nzs7GwAwxhs2bLj36PPPP3/v9sDRo0evXbtWYW/PN3sev2Vl05xe9R74bgQApKSkMKt5T5Lzrxq/XpuSIZaR8HVa1PzFeQxDO0GQiCACwI6czdYrzvSjtyK/9bfWdua1LH688bwUWTdCk+BO6I0Ivnhfu4BRnRCD0kdfz5qelHoj9XK9m95q6bgpQOAvv/ySivIf3H9gN034br/8VRlnCs6ZGtqjNwapk2QA4ElzJXU8X31/Qy7LkzH6GgAABaJdEGSC49sO7vYHpaHZAIAFEpECIMC8RFb3IiJE7GEcp3htZ6vzQqz9eBNWy+/3PxL0LZ62L6buYR4AHAGCopAs3TjwT/GQNSmqntJ12e6Quo6NPmQ4W7ICld6z8CxOgQ3lx08iRBFAYr7CL7eS9/MlLEoBXE7t9YLnawX9IdoWSev+KqlRhU3NYjOyCbXozltCIThy/aWs059Xsn93Skqr4NDa2Znf3B3/XFLrZSFNWUdJT2mkKlv/vs/boz6fj14/JijUcy1ZdLoJpVxauzqpVZXWb1M1Jlh548qZ787k9Citrw8JKxCFlmGSTo5vWd6CmNDTqQ0Sfy65/12VqksRkdlpmQAvuVyuxMTEsuMpcIprz9trea4Vp1x6qtcruXatWfWYU9HVz3tYShS7nDh2srj4ZJn6s3WasvXL9d8B4ArAlWdy/331H1w/2XDFxBaGSSONbMEFy6mrtkumi9bH6D81NbXCJv8qKhWFHgAGDBiwbds2mUwWEBCQnp7etm3b8+fPFxUVdejQYd++feg+qvKJs2PHjjfeeEOlUi1btmzIkCH5+fl16tRxOBwzZszo3r27XC4/evTozJkz8/Pzjx492qJFi6cxhmcQpTbdwm+94hwZpwis3AawaD/rTuwAgCS1fvRQDRs1anT9+nXvIX+9JOFaRkBAAAAsOW1bc95e24/e1dOvbNQ3Lmshl7WIUNTLrLomPnmmlJB9VHOlnvYDLLivtBSdiXTE+3SZ/LEAwOWt4m7NBgTSun8Qinr3DslzY6hg3gmYMG18UygN10oSqGa1D/sFT7YuiODTARAV0I+OmIsoAwDc5LgBBSaLKM7WafsqK15sFsy/eG68wZHqzv4/Y8A/F3RmRJuk5veloftKMa7aZv/9lNim2baC1OXLl5vN5nIV9FJ5rd5vFM6cppDKPgsOaHTbtKFUvXeRyxYZdD6/Jh8+fNzLfyJKbSX5l8zvtWrVwhiXTl73o23btrm5uYmJiY/af/fu3X/66aeH17s/NWvWLPcu6HK51Go1X1HeKQqonzRfBvJ+ZdW7t35P1HkKPRohlI6z+jiH8Lc9h3tsaSurFXbqwgJA0LL5KAIIEcSshaYJP70biPwBYC27dSP35WOMp0oocWG7lmZE0aHwZOw3DI7Ndwg7P0pruioDYby1vunbOsUAIK3WlNQEulNOC5Z8AFCpVN6dktp+9Kg4Zbn++9K9JjKjMOBbYlYVIrwIm1cEbv448gPHGSsdyIh2UXDw6m6GxN1XwlEIADgDXLU/fF8aXfGbsVCkI3XFAODJCOMdksJVw49fcxRgUwfqZQDIi2Q0Rl7mEJ1KUm4XECAMGACYGrLEG4nVUYm1PAa8iF35HffT/e7PwWRbz+5d7Vf/eLzvV0ZSl7oNoxEJCEyCNWLULwo1yxtHWQ8wfJ4REFK1Pi2Lu/nHWb7TGPuj9o9oCaUJuutba9mbbzuxeSizsKXXEh3cbne9evU4u1lwlH+lqcz4/059nUT66YtdWmqDoFQ1kITshcY1p44qdjkBICaKPLVNRRDw3ABbYopQtn/MW9yXGmPOiKI2qMP7luufVPlFLr4s2IyyzX3KjueLBMcHx6y2w+sLv//g74//QfUxDlm6TP1qTwAAUcwZP9a668cn2b+v/jOpX//52OZra3EiOzV6frLj+ve5XwRLwj6J+4x1V5xY+qH9/8vn98pafXzxxRddunRZsGCB92qPHDlSrVq1+fPnDx8+/JnN7gCwdOlSpVKZkJAQEREBALt27TKZTKtWrXr33Xe9FaKjo5s0adKoUaOFCxc+xlSdmJi4d+/eB9fJz89/jJFXHtF+NtSTMLMGgAN4x/3rIYbUtkV0AGCWTRkNWKBDJ5DqF+ZOmVL2BWjjps1e9Q4A7zZU7kl2XTVyXyQ4BtdTAABgXijag2gDonWi43Jg9uKuBFtHVVtt3ssDiNZjojMR0QZEKoSin0hdZ28gWQCgg0bzOasxm8cmDZY2KO8ML7K5QtGPgMB1PkawIiYyRBpbQxYbI6kdTcikLW2OlJB21Yo3cDmf8gVfCkX76MgPKf++NWj6qwC/HxzOjvL7Zg0hde1JbVsoPrSV23DW7WJE201l67qa1vfWdF9NBoDwru3eDwuaMGHC8uXLP/3007Iyvsjt/OvLzfL9u5VvjxoycNCGyLBmEkmpen/p4rV3fjxYGB0pqVFFGlOVqR5JSP9JKy8fPnz4eEr8S+b3wsLC2NjYh1YLDw+/dKniHbkHM2HChKCgoAfXMZvN3333HQCMGDHi3qOtW5efa2Qy2aJFi27cuFGunBCJ1/d2VNsUZdV72frHky62OB0XicJ+Cf7hu877RQJwGMvHmV1OKQAAhrqquATbBU22ZtCpwUqkdErdMo90FDOocePG52OvlhvP8SxP/AlbfCt1Xf+S/iW0EKC5EyY2Oi3CcShd2+mQ6Oqj6yOIjos3sth9taz5I53dtjCDLhmYkFo/tikJISat1tT7hwiwL8UNAMey2JFxSnTP9Z68ern5hXpViHCO5g61P9Xv1YHRHeNutjnruuYAAE0HvyqbYnNPZ0MeAIBenXc/9Q4ApL5kdpZEZkoATPWuNbzZxVtiUdl0BSqJC052VJ8cEzChawoWMBAAIrA3XaEBIQ6HS+GUYcDHmp7XVQ8aASPu9321i1Z9MLTbjRsVR6d/6PerExFtJq0EWAkcBmrheCPocIKQbhVN3ejwcCrIIa2fjDFxKqvziBEVWOM/uP/jtcc6JfqyR70y4mQ222p7QWlhyNxjCHCD5C8NtpRHHf9j15dh1N9CGgSEKEoWV4vUqgVTsevSNdcfp397beQmiZ1DAAAnk86/WCv5q8XV1vzSqmz/3sgLhKq5NKj3okWZ5frnKMUfADKpdO7cuXddvggA8MJLLf31FfxnfFLXG7948Z6YWgWx9SiPx+/a1bwGcaHLVrRu3Tog4crTu5+++k+jvuEtSZ6Y0Uz7Yg1F7aryGkeLDuV6siZ9Ptp4uILVtAf3n5+fv3v37gpb/Xuo7A58KYIgZGdnBwUFMUzl0oQ+UVQq1YsvvrhvX0kAs9GjR69evTo/P79Uo3pp2bJlampqZmbmo/bftWvXhwr4Uh711lUK0e08EwGYe3hNAMrvNSZ6A5ezjMv4gJBVl8b+efV6aoMGDUojz48YMaKcneHvGZ6h+4qCleSxtwIAQDDt9CQNreTQJDW2kfoud0ZqO+ZO7AoATLVVlH+/sjVdV1phxyUsSATLdkXzRuR9/MqwO4VNmyxYjgAAqX6BqfopkkY/dBii66b78gsAImDMAfl64K5IZcwyP73s7hfNjP6TRJc7YtsnxO0MnxaLZdmyZcuWLSsuLi7XJ2nwCxw1at6Y0Ws5wS7iV1juvSWbcYGpTA1CEh0li60ha1RXUj3qoYP04cPH/zb/Szvwpfyz83vXrl0PHjyYkJAQHX3fiaCgoKB+/fpNmjT5m3vp9+NJWdjdbH3OfroYAYQtj/EfGlZhnfwVGdkzkwCDsqUuem/9929OyHZn2O3B567MAQRHB/Kf/Dm727vd1blqQSOQFhJJCOAxFnDwzKrB06uU7ar3j6YL+WzbKOn6DiXux+7LL4rOSmUQsBY3z35tMsLY2Sdo9At+NlZsX1Xapdod5zgCQU0DFaUpv98jusSUPpdsR4oAABCEfxrjPyzMcdKS/GpJ9rhCHb1ocPhHq29JPSUmBgELF+kbn8GAEMaAwHouTt3oAgAU7+mk7bpPdMqsR17Udv5V9AQVbvnC/L0VAAgFCQCiQzjWTfPl1IAZwzMjrrql1eUh86LT+l3Bwu3XMAQRy2r6DQ198MX+kuahCWgd+ZjL8XxhUdaoOZSfLmzdPPf1FPeVG5RhJiG/hchBssZLrT83pfySEf2arFHFJvoPZuxB8+XCu979XBw2ukQZjfxkd9kCSki0rK22luHZ+feZ1n9jO3iMiQoLmPY25VfyjHF5hQUL1nE5+ZpXX9H16wYAmC92X2yMeZOkxpekvrO3muhKcl9+HrAgjT1MKBrc2zkGGHewOEJNTmp2VxiOzZcd849bh9RTzGyhfkrX5Y05XzZq3UqLbY3VRgIsNOi63D9otI9/G8mO6/HJ02mCnh+z2sD4A8B5y6lVt+IVpDK+5hol9WiP0H9ifn/kuAskSXp3v/8RFApFXl5e6cf7rabTNH0/E4sHM2/evNjYWFF8UFrHwsLCzZs3E8TTsa0mpKS6hWD5AzFBpLYkSl+BIGTwQgMJU/JtiXbe+D0AEJrWmCvgsj4GADpqMRDSMWPGlKr3yMjIpUuXluu+VYRkzvNqBV2idQlVcyDkIDoJVTPsTsdcHiGvSygbA4BoOyW6riE6GEnDRNsZRKoIVdO7Rqp6gVQ2Fuxn2bSppP51RJaMjjcfwo5LACCJWUH5VbA3XgqSVpPU2skbv+fSZwjWY67LL9Chk+mQsWW92Su4Q7IaVOBQPm8dADgDRtqYqn+6PSMLiz4PMNw9PIXocgtFFiK0RMBrNJr3339/3Lhx8W8NW/frXit3J1K9YDLmfDRvxNq1hnfeff3tt5dWiyTXfMAbzZ4baZ6bqZ4baZ60TM+NVM+N1OIffglb+2HpBObDhw8f/2l27Njh5+fXpk0buGd+37Fjx969e7dt2/ZsRjJ27Nj9+/c3b958zpw53bt3j4y8KzRJbm7uzz//PG/evIKCgiFDhjybIT02WBABAAN6kAnDbQ85JOLDxn3Z7gyiTGB6vwL/18e+QefSHrVbYpECDdgjIgmBAHLnpwJAWQ0vYAwAQpk3FyQJA2cCEBLREc1mugEBEy6lSiWfiAFAdCcC5pXSMIIALID827zXLOLmdn7VtGSnave1gyvp4LZ6pwMYfd/g/GXpme/dZNPdxs+yBLtgbWsouOmIznBP3ZJ5uq7qpXMWAPDQRPHygdotFxHFAwIMoG54EQBM33ZTPX8GAHjLGE2HzQCQ9/GbtqM2AACERIcAAMe6aU7OCY0fkq676s6LYvx21dVGqsKXxGSML7E3rIx6Z0V455ciAiBp5GPGpib1WkIh401FbFaSJNpfEt1CtC/1XO+F8ddsRjDll4zdDF1lDOaLAQAhGshHiEy+ol3594oj6Z5h+4uahzAbO+orbPJswB7W/udpQMh/0tCyLz90kL/fuIG5UxfbDx3XvdkFCAJRWjpsKntrCps+S6ZtA4QUALj0GYA5KnBwheodABDAinbaZ3QxZcgRhIEFpiyeLxu1boxGhQHWWm1TTebDLtenhn/yzvuoJBjw9pyNGHDHgJ5e9Q4ADTXNvHZMu/N39Asd/s+O8GlQKQH/UGezZxb1vV27dtu3b//jjz9atmwJAC+88AIA7N69e/jwO99Nenr6yZMn27Zt+xj9N2jQoEGDin9iSklISNi8efNjdF5JLknn1bG0Bq6QChpOyGsDwHv5hVdY7qsAv4YSBgC4zHkAQCgaUP592PQ5IDpJfWdS02rHjh2//fZbaT9Lly5VKCqYPAbG3ilETAgdOp7LXIAFuyTma3dCG9F1U1JjC8aiu/ArQKSk5jdsyigoSYgSUK4r0m+NYGsGosPyYxdt75LEP1zqSAAg5DUpvz6VuV7KrzepbcNlzOELvuIyPxJMO5mqy72LCPeDDpsqmHYCIoIip27H8hGFRSax/G6JtFY1e4HJfuSE7q0eZcvVNDPaP7pfx/7fRilXfb7FYil10AfBZCyY98H2z9ZXmzbt7bfflvrpKD+d4vmGACC63J6rya7EJOB5Uld+JY/LKwRBpEP/G1GRffjw4aOUN954AwDGjRu3ZMkSkrwr6srFixe//PLLZybg27Vrt3LlynG30Wg0er1erVbb7faioiKvAxRFUWvWrOnRo8dDe/tnqX6wkTd1XMa465jF/iPLb8IXfpaVPTUJAKyh0m0Tws/9mcfjQQAgEf0BwN/M/dn2tD6Hdiqw3Cq1q/DWmXji1VDPF7lIQgBA7vxUoyDEzrqvqQITOd9lOQIiZznyXv5SDR3AxBxpwkRKAcB11Z7c/YKs7uWQGZcwlqcN7YAFDAgBxu0OF/4Zo4CGD5OdIk59/ZLtSBEdJKm+v6G0upwKYLJnJOV/mg4A+jeC4tbXFu1CcvcLcNbqf84CBEI0knhEc3GodmcnfZ/dgGiEOUAYswx2yZnQXL4gQhFn5/KMIhtn++v5kq11jDkpOtZVc25qyMwRWZ7LDnsV6Udbw12kff1plplyEwCARFGf1db3eYhzBACwvAjlMpU/IogkFM83ooNn8Fnb+ayyN8TD58YDAJKwXNpLXFpJMRU4hKmy5LFPZ/FgAMi1P8lwS+4rNx0nL/J5BUAQTESI4qUmTOTDFj6y8rCHZaLC6CD/cock1SKoAANfYOILiqggPwCgAofwBVtF51Uubx0dMl4o/lUoPoRIFR1WcWTKf5DNVnsWzyNA07SqsjHnx2pUpz3sOY/ngNM9XyfKn9J2nY8nx5+mg7ecyTra0MH/rqnhjZAhc26OP2La39LwSpg08n7N/6NUSsDXrVv3wRWeijF5RSxYsOD333/v3Lnz1KlTBw4c2KpVqzFjxkyaNEkul/fu3ZuiqOPHj48cOdLlcg0bNuzZDOnJ4ubxrvN/0PoaMbKr7I03SUNPDqC11d4GQS2s5ABAsHIFXwAgQlZTsPzJ528GQHTYNLvdPmnSpNJ+2rdv37Nnz8qckQ4eY8vdIHUm5pr3+Qf05Qu+ZNNnA+YAc1TgENF2QnR6E6K8XbaVaHcWf7vP9utRZduqsropTNhp4I1A+XHZizBnQoCY6E2Vv2pE6ZiqK0m/PmzqBNF51Z3QngoazoTPvt/qNaK00vonAACR6kiAfcEB9z6B6s6t7H+esew+TKhV6k6tEEUCAJdXaFzxhWCzBzSM/ej9sRPnzvn000+XLl/usN7xFTTl5Y0fP/7jjz+eNm3a8OHDJRIJABAyqaxRXVmjCv4jYF7ImRiPPSzlp5M1qC2Lqy2tF1Nqt+/Dhw8f/3JkMtny5cuvX7++Y8cOjeYhmbSeKqNGjWrbtu3GjRsPHjx47dq1tLQ0ACBJ0t/fv3Hjxj179hw8ePBD/dj/DRAMUfv8c14Nnzn5BgCU1fCFn2VlTrgBAPkBkvHjovhUAaBh6VF/Mzd37S19EWfXEEqLaFVSH4yIyKCkqS94vmBCjRuzeQYRgLiF6bkkWc6WvhQkrUoFDuNzVxt6r3ecWWA/Wnyzw7nq+xqKLiGp8wXB5Da8uQEAjFt7cvlaQN7k7EDxePaGjGsxMmj6IItTwSna/ioGAE1HP2l1OQAEjo1ACLJmJuvfCIpcWwuRiNRQul6BjrNWANC/Hug3OPRylwsKl2D85jV1mz8oQ4kjG2JY/Zs/AEDhlm7BUz8DIOSNlkRtDLk1NBHzGABoN655zdNhWKbnok0SLa+zv+HLEteF0yZ485rIYSBR9K4G6pef3Tap9vXOzhMzAQCLBAIZIARYBOQEBCAiRKmAIAAA8xYADPhvae8MKw8Aha4HrTlgNkd03axUd6iKafV+57k7jhWuC1ctPx1Wd2ypH9QT7i9TscsNAMR9QgsTChkAiC737bOQdFS852p3LvsTytCLS58FAHTYtHt3gP5xhqiVf7jdWbywsNgWTdOlGn6FxXbO4yEAXpFLfer9349bdO3K+xoAXg8ZLCHuevcOkYa3MrT/zbjv6+xNk6t9+A8N8GlRKQE/evTociU2m+3MmTNXr16NjIycMmXKUxhYxYSHhx84cKBDhw5z5syZM2eOUqkMDAy0Wq1vvfXWoEGDKIpyu93eFfouXbo8vLt/H5fybDNC3ieQCACiJ0PMWQYAAwAAANugrHcUb/xGsJ8D0UXquxDyuvOmTs3KKlkQlkgkK1aseOi5zG6RFTCmTLvI4AG8UZO/kapzUDD9JJgPAACitFTwO56EVwCAiZwPRInPGBZE+8Fj5h0/izYHICS63gE0G7DTc2OApNb3fPZSACB1Hb22A48EqX5RVu8Yl/0xl7OSz1svmPcxVZaS2ootKRB1Z8ImAeAeM0WmaoR+4KtFn/9o/uJHyw+/MBHBosPFZuWBKFL+er/RAwBAp9P1nDVrz+tvZq5fZ9uymbXfCXSRnZ09ZsyYxYsXz5gxY8iQIQ/wCEUUqWr3guPoGd5oth36y3boL0SRklrV5HF1ZA3r0GH/gXdNHz58/H9m5MiRCKGlS5c2b958z549D3BBfwZUr1590aJFixYtwhg7nU63263T6Z6Wz9rT5H4avlS9S6LlmRvrhCTbjWyJYyCFKAJLZy3KCyhV73oqfllwobNYLuCA4GMJ8wdfdThbf23mGUQIkDs/VVZXqe1afl/UCx02VTB+JzpPV910K3VIFfsx8812Z0UWC0Vc8LRjkioZbE6g+btOgAAwEBJC9IgCieRuof6UG47aCkWT+2p4UklW3V4vte9l45ZsQkmGxVcHgIAxEfo3gyi/krnS/GNB9uxk79+WPYV+Q0MXDg+fvTYDrFLj528EvbdO9DBcVoik2i2C4Wx/NdX1OAPgoQIGEoo4XS9gXWL2u9dAwAAQnODyAEij5dX3N6SDJdMveK73zSA4LJKoxrNV7wBAalRM1JtC8WqEwXU5DHMUANBBJjqskJC1Jw01AAB7MnnTD4AYUtfx75yrMltjnuuvic6rlelNsIY4z71MqBTqTq0k1aNAEFyXr9t+PWbd9ztQpH7Aq/dr6A1jxOUUAMbl3rWwIPD5JgAgyyRaI9UvkfrOQtHP7sQOmM0hZNWpoIcYMLt4/OZuU4b1rvUOj4AB4KtE5w83XGXLgxTE1i6GAPnf/U0IIckDwYEzTOafnK5hhUWf+evrM8xKi22tzwf+P8XuvG8svDlaUbOJ9vl7j74a1Pe0+eg1++ULllNxmmbPfnhPj0oJ+JUrV95biDH+5JNPpkyZ8hjZXP4OderUSU1N/eabb9avX3/58uWUlJI4nKIohoaG9u7de/To0WFhFceM+feTZqUYd40Y2XVAFGAeCLqIrmISsY4k/QkCRCd23wJv9FUA7LkFgOiwqdeuXVu2bFlpJxMnTqxRo8ZDz/XmT6Ycm/B2y9/OEfoOkqgAzy2+YAsVOpHLeB8A6LCpfO5azBeRmpalk5D7yk3T5u+4zFwAkMbG6Af3YiJC+HyCTZsg2E64EjtikUUEw1R/5Ay9JRBSOnw2aejJpo4V7ec911+j/PrQUQu8eeYqw0m3Z6a5eJxa3U0hU3dpTYcGmb/ew6Zmuq+lAABiaGXL53R9uxEqBdzOGOdWawa8P3fa+3M+/eSTVatW2cvI+MzMzFGjRi1cuHDmzJmDBg2i6Yqd8/WDeuoHvsqmZbkuXnOdT3TfTHNfuem+chO++FE34FVNt/Ip7nz48OHj3wNJkh9//HGtWrXeeeedpk2bfvfdd16X+H8WhJBCoajQEey/wr0aHki4rd5ldc42O3/dDchJECVZjkRgeZEtMNAhhazSIto15IItEZk1JEQRRSUUFiq0k802PDsIY9zmm2IAQAzBhN43HhsiVXT4dDZ1Apf3fvR3J292ueY8ZwUAdRtS8/I2AChcP1BkGQAg/ejwk80Kx1937DXyJKIcQlL3C8Hf15c2KtHwCpqg7pZLmvYGr4YvWJkBAF4NX6ref1mTZZh2kxDxno4BhiK2xaniK50vdK8qI0UMCIoPtNF0PiSLSbafbISlLGMwcrmtVM8vRqSSDp8BAH9meoY5YMHcqlXnpsLtMHWEgqD0tPOCNbnNOYIVMYkcO2o/Y/XuRbBtBQAgRVncXdGwRc8+MWffnc+Y5fPWk7r2FfRgPYbdyQ89UU3sCaabsPAgE3dC2UR0XgXEkOr7Jk4WrMcA82yKgtSqgxdMpG4HDJI1qqt4rmHe3OXWvUdU7V6gg++zEhQc4LWTtx89q3ypSdlDtl+OiU4XExVGau+KP8dEzHMVH8JsDgDQkfMfHNgIAFgBZ9oEi6cCWwOPgD0CLlfZxYkAT2BRjwRYYNABwE9O1/DColdk0h8cTp96/w+R78k5bPwZAJSk+ofcir29DEyA3WXbkbs1Vt2Qetij+B/ikYPYlYIQmjx58s6dO9euXTtv3jy9/tn9jNI03b9///79+wOA0+l0Op0URSmVSop6/Mv5l/BqdcRbkwAAMA8AIHJ6z009AHBQ5gfs9m8c5rzb7/AZjiQAACAASURBVBMmdGDZkjeAiIiIGTNmVOZctQ10UhF/LrlV+3p8oKouvtqRz9sgjf1dKNoNiCLUL7LpswGRdOQCb33Bas/7aBUIIhXkpx/QU960JP07FTiIz/lU9GRgx2UAoEInAqH8OzeBkNeR1vmVz1vHZs7njd8Klt+YyIWkX6/KtC0SxRxemFZkzhWEt9VKWVxtWVxt3lTMF5gICU2HB6PbIrxQEEYUFnnzvS806EiA+Pj49957b9HixStWr+Zcd1Z809PTR4wYER8fP3PmzAEDBlQs4xFiqoYzVcM1PV8RHU7Xxeuu84mepDTqPhH4ffjw4eNfxbBhw6Kjo3v16tWhQ4cVK1aMGjXqnx7R/wJeDZ/Y4ASX4c6cdBMQBgBJtLzO2WZAEWfz2AyzAuCuRQqWLEmggwVvsDkQ9VJ3g/A8dThgQACCvWQXlMW42+6iW2cc3q3a3zPc0etzy3alpF851iiOcF3wpC7jMlt6C+VxG4E0Oy/EWo839XZkduFx63JyXvR/L93T9IqNJxFl5ZO6XfxoeERSpAwAonXUgdf9y9m5ldXwCEHogurecvOPBX7TbyIRf/uK/3dt/AiMOQFani2Ou+bgaOLrjn6vHzAWrB4cuXyWrvee9NHxRcGGxosXYjdQYVO8htY3i3gBQ1Jzfet10vSRVwkZIdgF5yX7jdZnXVftmMNAouq7G6hb/TMBxij/t/i89QAIAKigoYi5S2CL9jNCUYmMpwLeqqA95jzXXi15x3sgLwJMCm0fX7D8AXXo8JlC0S7MW6jgdyu0WBTM+wXL7yDIHcfra994hbo73G9x9ciZU0e02/dHj1OXND3KN19ttSVx/Cd6rfa1jsbVX5rWbhfMFlWbFoRSLljttgN/Wn74BQC0r3cq1xBJq9BBo7icZaS2Halt99Ar1UiIE/0DXPxdQv2rROeS07Z+deQTm961OiAhkZR6Yukty2p4n3r/z3HBeprHPABctJ6+aD39gJoFntwMV1pV+cN3N/8r/F3F26hRo5MnT5ZGPn/2yOVyubxiz5z/IhJahoKG8nkbAABRBsybWCRZopk+1S+Csf7OF3yBSC0WLFAi5xEdNvXIkSO//PJLaQ/3i113L1Oaqw7ech9Np9a2GyQhEevfny/YyqXPktY9DACea70A81TQiFJjeFIpV3dqReo06o4tEX3Xk8PU2Oq+0hoAEG2gQ5+ESwUiqeB3SX0XNnWCYDniSR5GGncwVT/1zpQejEcai2rS9GStutwabCe57JDLfcDpWmax5gr8bJ2WBKAMWspQPsYpAUhDoHYy+Yd6bWngJn9//08+/njipEk9P/jw9JbNottdWj8tLW3YsGELFiyYOXNm//7977cbDwCEQq54vqE3+t29OE5csP9+Sla3hrxJPW/QFx8+fPj4N9CqVatTp0516dLlnXfeSUhIkMl8b7FPAIIh6lws0fCA76h3APiktaZ51cQfc3eUVna6/KLyXgYAt5JX2eH9wTc++qJKRrQKqQEAEIZZC0w195oFEggBaA4HFXrSgm87uAHA3UbXdg5ZAz7SpncRLCsBRatejgEiRdP2FywS+esGIQwWNZUZJqt71TZ3fcaSMVHL+oeP35bZ9IrNa0s/c1P61A9i3FIyRElWKJhKNXz+igwACF1Q3fxjwa3BCUjA+qlVpk+Jmg4AGAoTLNazxd7RDX4zKHBIqHGy1Hb8BdXzRw19f9Q5YrH7OpJWoQPLp/vWvxmkbKGl9NSt4VeL9xY6L9kAAEhUfW+c6sWH5ILZetmx/ZqzbIl4++a8sqOwXOVhDZR9Yir7tNNh0wTj95g3AYDouCSNWuwV8+DNo5azyvs3oWpKGiqyS0c0qX1FMO9DtB+pq9jZE4s2wfgDAByxti5NG1QhiPanQidx6bO59BmkpmX5vW7MsulzAMCd+ILokkhiykdMyOKFc366C/27kxevl1tsWFhs/dxmlyJkx1j7cnMuK8+y+5B52y7ztl2ETFri9I6Qrm9XeZOS7Rw+d4PoPFt6DYSqKVByNqX814qRjA55jygTVOxHh9ON8ZvKu95dvSqdZ2Cdy95fqQihSHg6kAAht/f/GITCn9qJfDxxXtK3I4DgK7EcpqLUVeTVn8GQnhl/V8Cnpqaq1erAQF/87ScGHTZdMH6P+SLMm9yy2lLX1RZCgkI70H1rMgAA4w+ukugv3u336dObl7Z9+eWXe/Wq1E41AAQpyM2d9Ga3KCERlKzj/ihYjgjFB0H0CJbfvBlBAADzguOP044T57nsfCAJz7VkxYtNFM/FlXpDEYo4ytBbMO+RRH/25O4EIEmkpNZOwbSLTZsoFB90X3qODp9BBY1wY7jsYU+6PXZR/ECvLavht9kcB5wuAGAQ2mF35vLCUj+9oqJEPgaSOBhc8XMbHBh4Ys3q3NmzFi5cuGHDBncZGZ+amjp06ND58+fPmjWrf//+j2H04bp41XUuwXUuoejznUxEiLxpfXmz+kyV/6rThw8fPv6XiI6OPnny5GuvvbZmzRpvCE8ffx+vhk965RwwRMy+OLhtj04TqH6A6rTbxosluyA2kp4/LOL9tbf0VvCoPQqzZNag9A8+r55dTYIwjFqQV/NLM9CIBAABy0eGxc+pBgAjfzVfN/LPhUjiW93luK6REkyK2nLyeVXzYyGzvtf3/dad8CHmePPuDp6USDpY8tKF5xCFUt+4DIdMCzZmzB4ZtfStsNW7cg0nigFAb2D+GhLkjXt/PzTtDVW21k0bmJC/IsOd4rQeMGEBB8+oGjyjCgAAhozx162f5xBy8nKEtO51Bx6eyCyvCS6xYHU/ZdNT6lZ/iZ6zAEAHflQaaqcs3sj5VbfHpva9Ury3EChUfc/D1TsAbLjoyHdWHEMuxVz+dX/deVvlBTyitHT4dDZtEgAp2k5bCncq/Xt5NR+XtRDzJkAEYGAi4zkMNizq74ngwETNd1kOY66ICniLUDbyFjo5bLodr06eP08GkIvjfi7qopWKmXc7hzMkBCruiEw66G2h4AvRlcTnbaSC7zKc4XLXYncyIavhuRUHUDZufgmNJMy4QvNyf118g5pKh7OHomQ/bKnF+rnNTiO0xKDTEgQA6Pr3kNaLse75zX01WXS5kYSRxcaoe7SV1qzmbSI6b7LpU8ufwFbxPRRtZ2X1/yr9uLDYYhVxjiBM1Nz1ACMZ/BrodNiwnkTDVap7unkyrLTY1lltBEAcw5xj2eG3/eGf0ul8PEHkpOIV/27/9Cj+GSolP9LT0+8tdLvdu3bt2r9/vzeXm48nBaK0dNh09tZkACDZTA6ol2w72eRizJsIaY0y4UYRHTp53759p06dKvmM0MKFCyt5FrfosnLFTYLvZENFtB8dOplNn8Wlz8SYBwA6bAai9EKxNX/BOjY1o7Qmn2d0nrlir1/Tf9Kw0nDrTPUnKd3LQhp6SNXPsWmThKK97K3pfNFeddXlG/zDRxSavnc4McCHtzX8DrsjvtgCALN1mjoM/Y6x6E+3p2++cZ2/Pph85CXV4ODg5cuXT5kyZcGCBZs2bfJ47uSNT01NHTJkiFfGv/XWW48k4w1D+8jq1XKeuey6kMhm5LAZOcXf76cCDPJm9VVtWviC3vnw4eNZEhQUpLr7zVir1e7fv3/s2LFr1679p0b1vwfBEDG/N7m3vLay3qe1t5R+vGFkO100ffBO1JrPMyEXsI5SmGDWwIz5WyPafGN+/iuzSCECADgcODai1GqdIRAASCkUri4/GaUtSLUf76doeFpW6ze+4CPM/S7alcbPXwcAWR0FQSMkIap+FZsYe5wrYDvty0/tEUwXlawmCDZe5DD5wGWcjRnLnXXt/bcOuzXoquVnIwDcUe8A6e9eM32RQxn4al9fOmJ0WM5ZqmS7rQdB+QJIImWkrrlo/5OQutmsYMuhI4YBNwhGSQUMqOj2oarbY03b8+T1VbLYSvnobeio3XrFWTYOHC+Ke1M8ANCj+l1aXUqhwfUeLdQCFTCIz98iOhMBkPnWzPfEpisDwqTuJD5/szcuPRUwwCFvMLTAeJ3jDwQFlNs9RpIoOmgkl7OcTZ8urfOLdwO/47eFWTYBAMIkmbtqrsUITUyaigGZ3bjV9oJyA9jUSd8q4vYXg2g68iPP9de57EWk32uILrHsw1yhN64wHbmADsry3MzyXEuRRJdPptX7r/MOAjZ2azOzqBgAeijkSy3Wz6x2GqFlBl3rMil1ZPVryerXAgDR5b431Q4hrwqEHEQnIIqQVKvwvmHBhrkcAKD0d4X3835NG612ACjV8BZCpJ7DDhKgjPXEE2elxbbmdtS6jnKZN6adT8P7+PdTKeERFRV13/YUNWfOnCc2nP/fXCnkvkhwTG6mDggczBdsFZ2JtGC7RteuxV0Vig8BIrFQXFqZ1HcmFLFz5w4tLenevXvTpk0rcyIMeEnq3DRnUnzNtf7MnV1oKmgEX/C56EoCABAjzN8gwzBHwcL1bGoGHeSv6dNJWrMqFkT3lRvF3+5zXbpuXPlFwJTyxlFPA0QHSmpsE4p2s2lTROtfrssv1Aub9pnf8OFGyw8OJwB8qNd+Z3d8YC5R715DrG8C/EcaTTc57o1841o/fW3mcWJXhIaGrl69etq0afHx8Zs2bSqNNQAAKSkpgwcP9sr4fv36VVLGI4b2GthjQXAnJDlPXXSeucIXmKx7fnOdTwxdPvsxBunDhw8fj0dubu69hd5kLl27di0qKnr2Q/r/jFJCAECOP1N9X8OEjueIPNahIdUmPr53GuHBPA0AQHDYr4x6fzD+74ZbfjUV7ejm1/97LvtTADDteFOwqhBDWA8Vpfa9XGVbbMb461wBSyrJE8/pZ61PV2e6CCkhukVSR6MHOhufKf7ruPkIANRu0aDJF00zJtzwfzssaFKU9yhvZE3bcgAgZNY1RMx7OwDg7qDs4u2IsUxYLhO2kc8BAECSUICWFZyMQIa3gisovw91/ZlPWt8lwOysuDclHwCWtCnvUvfIlCRL64YRESDkx5rWjUATNhTNAMwBACKVntBpQwpNCSynIgg5UcE9pEIn8YXfiLYzvPEHyq83ADQJZhBiAWB20McSxB6y9UwXGgCIBAGhyrv0v1ZClCshta+Q2rZC8SEuawFTZam3kMv8EAtWUteR1LaRt7hi//OM5cdfZU1iy6Zzd1+5aT92pjcg7auvfCIIM4uKD7hcf7g896r3stwnUS7FRMazaeMA80y1ZYSq+b013JdaYC4HUTo6fFbZ8oFq5SqLDQA2Wu0Chilada4gfKtyIAIAwJ8kOz0dV9my6t3r9142pp1Pw/v4N4Mqk8LdGy7uXgICAvr06dOs2f9UXP6HkpCQEBsbSxCEIPytDJ/3Mv5Q8Z5k1/K22i7RMsH6p+dqdwAQgCQoDeKLCEU90XEZALyLtdLYP/44bWzdurW3LULo5FfBsdUf/luD5LXPBb23KXOljjbE11zLEAwAZNqEQofQMIgRig96rvcBgOLvWrPpwbp+3c1f7aYCDCGLpngjt3vhC4tyJi0UHc7g+e9JYqo+2fvwADBv5tJn8YXbAYBQNEgJWzLAEezEuCHDXGBZKKPevVhEcbSx6KyHVSC0zE//gvTOVoJNFN82Fj0vlbyrrsAua4PVftjlXuev15UxfsvIyIiPj9+8eXNZGe+lWrVqM2fOfOuttx7gG3//q8Kem2nOswlMRIjixcaP3NyHDx/PnBMnTrRo0eK55547fvz4Pz2WR+PGjRsAEBkZKZVKSz8+gJiYmGcxrH8HT29+ryRWjxi3JV9OE0v6ahafzZ8+8JaugOe1JFUsAI0EAJLDPw/R58+JXGbQUbe9w17dabxcwL0cId3YqQLbcvvJ4rQ3TkWufJfyNxGyGBHvS+6awJs5jkS0gIs0tN7CuSXEsrfCev9aGJ3p4miC5sRCHf3ByMh8A0MiiAtkdvQonwuGFdlZN0Yb2QIAUJDK+JprlFT5zHN5H9/K+SCF0lujd4wDZDtta96oajOikGUCGSARALDZRZaDuWKRUtIySRGcwBIBp/R//ZpB77rpbBMp6VHjLtmmlqAXwh7frcPOivU35wNAyshHWAh4AJ6bA4SiPQDgRpIV6klTLPMBkYAFMeKDoWTfyywLAAhgX3BAVEXr+3zBl2zqGMQEyxqcBaLkSkve/UiFrP7ZFZeUK87ZDHLi9ICHu6mKriT35ecBC9LYw4Sigei47L7yMiBKVv8vJI0GgPz4da5zCYRcpmr/oiSmCvCC69I1+28nsSBoerbX9e26xWZfXGwFABJghZ/+fur9wbgv1BM9mUgSJou7Uv56zQfYG28CAFPjC0rftdxRr5ZGABigj1Jx3O3O4gUAkIro+xD/avSTj1H9m8v9rrGIBPjEoOtQJmqdADDZZN7vdGkJ4s+QQLoiH0wf/9v8J+b3Sv2X2Lat4tD8Pp4sV40cAFTRUgBAql+yaTupiveRIBCSMELzPG8ujVSHSW1bQhH76ad3HD+6tatat4oJ884K+i2HO/V08VEAeC14IEMwILqx6J552JRo5Dd10sUq63qSX+ALLHxRkN/oV10JN5CE1XRvRijvWhqg/PWqV16w/Pir88yVZyngEaVjqq0m/XrZU8bTjotVbnbYFTiqFzHoPAsIYJZO86ZSYRPFyyzXQipBABqC2ORvmFlUvNfpGllomllG3ltFnMByFzysS8STtHe9dqyw2NZabRRCVlEsK+AjIiLWrl3r3Y3fsmVLud34IUOGfPTRR9OnTx8wYMAD8sZXdFVIElO1wtvouZFqXP0VUy1c0TxOFlcbPZYRgQ8fPnyUUrNmTQA4efKkd/Hd+/EBVGaV38eThSRhssnMRtHxWyLmD8qkCzk6WMIXsiSPDwz32/6eP7jcPzldPW97LNtYDAAWT8XflLK5tso3zfI+Guk3YKvjwtiQj3TRe+Kud75AWzgRIb2FEwm08o2QCtU7AAgYyuXx8vJL4S4jW0AgAgN2CPZd+V+/Ffp2uTpBk6MQjbJnJRdu6OX/9tYgJocPnqqrViKW7CeLk3tcFB3CqaaSV3qNUAB8cGv8T+c5AA4ADqd7Dqd7ynW4u5dfXf9/yzzIRM5zFf8KokeKPVMs8wEAsCBKq4wkel++/XowQaOuUL0DABXQly/YKtrPcTnL6bDp3ubcrRkAQIdOQkwQgL3ChhVCyKpTQcP43LXsrenSOvvY9OkAIh080qveAcB/wmDT6i8dJy5Yfvz1TjOEND3a6d7sAgBmscQDXwSwihVkdKsMTI1t7iutsCeLz11PBd/1PHApowEAKJ1g2imYdpZrOAKgNccncxwCwEVQg1BvUo0oxCEfSLVPQ70DQCRFNZIwA1XKdncvVZAAHxt0WoKwiiLlU+8+/q385/Ou/c/g5vEtC08RUF1X8qX85j+zg+WgBHOi4zKILhBZAECEHItOKmBQUlLSzz//XNp80vQlQLwDolMS8xWhqigdqOhwX2mFOSMd/v5rqueaOlOa6V4UbSfcV3sAZtcFAQQBZIAHQBINkmhQtLgCsF3RGBSNAeA75xmJtPZeQnlnf9gbd40vMD3Nu1IxZmWrDn4/jreu6Gn/0pC34nPqp4+0H5yXNL7KckZRHFJgTOL4zf6G56QSAGAQWmzQhVPUOqvtQ7MlieNnaNUUQqEUucpPN8Zo3mSzCwBTb2v4lRbbWquNBIjXayMrmncjIyPXrVs3Y8aMJUuWrF+/vpxv/PDhw+fNm/fee++NGDHi78dwFqx2LreAy8l3HD1LSCWyRnXkzePkDesgic+sy4cPH4/DsGHDAMDfv8SMduTIkf/ocHzchUpCtI6U+CvJPdgJAB0bGOofCEjqeJ7L9QBA4HuR3WZF/lBodIrYXkZi2VkRACzsfUWXsrk2aNawlB6NBbvA5iZGbaoTs6fBzbbnCFYEBISIp36fKzoEr3p3BzEvH2rcPupB85eZM+0r2AkAIhYBAAH63fRLS0P7cGlUuZqB4yMBIOf9jpr2hyOiMijLJpCPBgD7yeKUHhdFh5D/siFi0nfBdG62ECvo+3TSEanFwnUTV1VL1jTcpdWDFGR1/b/orRVJIungd7nspRgIBKIIJAHCTOXkc7fzMo3VqIarH+CxTzCR8e7E9lzOCsq/n5MId2ZvUjgTBTqqSD4cWwWrBwOAIOLSIHa/pbuXnraNaaIaVpHTPh06VTB+J9pOsskjROtxRAdQoZPunEwq8Z84VH0zzXHiAp9TAATBRIWKLzZeL5d15LjDLrfX772rXLbT4fT6w0sQSuP5kWpV5VOuE4r6hPpF0XqUzZpLBQ4GouRdhcuYi3kTAALeLJh2Vdi2CkDZKPnJdO23qo1+SuodAKrR1JcBFWcCIgHm6Hw5gH38q6nYhH7JkiWP1MvEiROf0Hj+AzwlE7tEI9fte2N1PXWgT8l71duFpvr5CwfZN5apRQIIiA6UNUx4d/S4NWvWeEubNGly+vRpLmsxlxVPKGKldY8AKh+zjcucx2UvJRQNpLGHAUp+jUXbaXdiewAAQm7nKRGDVOAphO0ETRIYES4AkHCYpAQAJI09TCjiSju0HzlpXP2lsmUzvzEVe1g8QTCXD+KdxOwiwPvm4lNuTz3+xvDiTyP4WwDokLzjGtW7ZqaKVcTRNLU9wE91d+jXn52umUXFHoxbSCWf+Ru8x/50u8cYzSzGg1TKqVr1vQ5RD+bWrVsLFy4stxvvJTQ0dOrUqcOGDfubMp4vMDlOXHCevOhJTvdGekESRhZXW/Vyc1mjun+nZx8+fDw2/wkTOx+Pyj9uQl/KWqtNjtBAlRIA3Dcct0Zc1XT0C55WBQCustxXdscYjSrodnDWgT+bjmWyvWrIF7d+kOqwHy9OfvWi6BAM/YKxiIu+zkMAGDBiSMyKXr93o47GX8W2f+khYd43ZCw9af4TAEhE0ohxiy4AqKWsN7nahxXWz1+Wbt65M2LxhyAqZE3OO84xKT0uCnZB1zswcq3WfaUpiG5pnX1ex+mNlxzxJ6zD6iumP1feJv/vUBkT+iTHtSLO2Ez7YmU7FRyuS40xmwcIAcanJC3e8SsJ6DtWoxpVkYNeOdjk4bzxe9Lwao8z0zdFtNdS5vFpKw5bKsjoXpaNHfUvR1bgTcDnb2HT3vP+zVRdQQU85PXsmNszvNBEA3AApX7vXlt679YzAvgzJMhAVl7CA/DFznPVAfOU/+tMtXUAAKLdeaYqYI7S9xA9t0THRVLTigoYWK6dWRRXWWy1nUe6OHcXEX49gn7uow6aon2Sz4APH5XhPzG/V7yyNWnSpArL78f/KwH/lLhZxANAjdvb7xjgPMueVY0Y6PkJcSUBSAlpFdGdTAX0KzJbP//889K2EyZMAAA6ZCxf+JXouMIXbqMCBpXtHHvSudzVAIiJii9V7+DNU6rvKhTtIZg2B799aU50Z7XIrn5F1/+QQ63Iiqs3HwBGCNfrQT7l16usegcA18WrAEBHhjyFm3EXguWI51qvcvltZ5Svhds697V17luvfvdrzbsb/Q2qexK3dJbLwilyjNF8ycOyGEsRAoCXpNKVfroxRvNWm/2Ch73EspVX7wAQFRXl3Y1fuHDh5s2by+7GZ2dnjx07dsGCBZMnT3777bcVikcLclsKFWDQdG+r6d6WN5qdJy84Tlz03ExznrzoPHUpYusiQvFUIrv48OHDh49/kLLaTxqjqPnHnQj2tRl6vv6uMGy1DPSxTPahu9PKFtroHxskv3rR9FUuAJBKMnRR9ewZyYKFRwwhukVroGTOsIiPIh4y/aU4bpwyH0VAYBDb+nVRU9rvcj8nEHHNfvm85WRDTQXRywLHR2L2VceZnxRNLlp+mpgx5XUMgmFQUMTSUDZtNIguytADyWpivhgACmwuAEi3PDy38yMhZwiKAKqikHJerLxlWdo8l+CUk4pYVcNKdUoq6PA5bMo7gLGAqCWaKd7iEIocoKpUtHw6Yi5f9LNg2rU0Kk9LmC+5mt/E7cPVAAAWN7ayIklAyO2QdQVO0cNjOY3C1RUn1qECBvD5m0VnAqGoTwX0e+jZn5dKomk6meMAYJxa5fV7H6xSnvdwh1wuAOgilz2aegcASksHDuXy1vPG76jQmYQ03H1zMGAOIYaJXis6rrgT2wu2k0zVFUgSXtooVxAGFBiNjLO/eTEArFKPcyLlFpsdAHwa3oePe6n4537XrvL2LatXrz548GBcXFyvXr2ioqLMZvOvv/66Z8+evn37Llq06OmP83+fZDMPANG6EoMxBNBEIlFJpRL5XDblHQBApEZkMwEIKmDAhk83OBwOb83w8PDevXsDABBSJmKuJ2kIlzmf1L+KqDsr8Wz6HBA9YOiZQfpF3X1erxOX6N77MudpzjU+SQd+lSoAgAhinKaZtPhCLJEviARyvFW2lePoWcfxC4imFS0qN8n9DRDtD4AAMKL9gCiZEe0iLvXXIgECkI3kiwHgbevqGO765/T8yQEx987S9Rhmb5C/+7Z69+LV8O8YzZdYFj2Kei8lIiJizZo1M2bMWLRo0caNG8vmjc/Ly5s4cWJ8fPz48eNHjx6t0Ty+URblp1N3aa3u0looKnacvAQIfOrdhw8fj4rPwu7/M6UaHiGotquBsrlWXleV1O2CYOGZKNneqdUKHQ9xfsaAt+dsxIABsJrSdA3swxCSo0UH8zw5ALAjZ0usuiGNKnDyChzLu85fBQAmdG/0V3u9ha7zJUd50y7+tmX1OAplaz/Jdjzh9M4EwIUhgQTcV47uzP3SJTgB4JvszbVj6pP3WDJWCOX/hid/E7Kf+07xegpdkhoghxdGFJo2+BsUD3OiRkwoHTKOy1oYSZwARDZr9snv8gDvoVXn7J+esemkxO99S0ra7ShMNfMGKRGtu89iDSKZaiu4zAV0+Gy4/5WWctLtSeY4EoGAYZXVVldCN5NIfnA4f3O5vPHk9jld03Sae1PZPxg6aiFn9eeD6wAAIABJREFU/AZ4C5c0kK62ChcfBgAqfCoQUkLVhDS8Kph2shlzJdU3eet71XsWL7xt3xgs5HDy2LDg/tjqQAA+De/DR4VU/BPQvXv3sh+//fbbQ4cOffjhh7Nn38lxNXr06PXr148cObJly5YjRjyLXGL/23gFfLUyP8pr/PQAAPA6n79JtJ8jNa34ot2kpjVPhKxataq02ujRo0sjn5OGV4n8TaL1Ly77YybyI2+hYD0qFP0EhOwzgbmcNPmjmiuDJWGlzUuduDR9Uj+oGdPlB8svaW4AQIBeDx7AWPYggINURLUV3wTH3JDWrIZF7L5y3XXpOgDo+nWl/B5iaPf3IeR1qYC+fMGXhLKJJGY7ABgFsVVOHgaYpVX/5WF/c7lXmka24I9elzauzt5o5TrcJO1UEjurRtjweycwFUHca9N2ycMJGAMABkhkuUcV8F7CwsJWrlw5Y8aMxYsXb9iwwem8E1DQaDTOmjXr448/Hj169Pjx4/38Kna7qiSkXqvuVEGWHaHYVhC/lgowyJ+Lkzeq6/OT9+HDx734LOz+n6Nsoa19tjkgYMKkACBvpK6+r6FpW27guAhbogsc7gc3/6votzRnEoFIEQuvBvWTkwoA6BMyaEXaAgREIZv/a+FPnQN6V9CSkAGFQAQAwJgkaBUAYMEGWABCiggpAADmsWBHgD348aPNPwA5dV8hmuFKPWY+TCFKQ+tyPVm/Gfe18y8fKb1CbCKeofs4iti1XdEfAJpKJKc9HgRw3sNWUsPTIeP4wi+xJ4sKGEjI65SWx+gpAKiiufNayIt3/r0fhCJOUvO7yowcAGoydA+FvLNcdsjl2mF3jiws6qtSbLXaMcA0rUaCIJXnNY+o3r0w/8feWYdHcXVx+IyuZC22cYMAwYMVdyjSYoWiJWiKlPIVt+IupbS4Fac4FGuhUKwUJwIEYsRt41kf/f7YEGKQABFk3ifPQ3LvmZmzS7Iz595zfsfzJypiNKsP5EJ68sAjhAPh/CK332OxMfMvNv0E5zAKVbQAAP/U9HiGdWBThmp3AYDcc8UEhZIH1KJLv0ur8yGInlbvKiokIPAxUao/y23btnl5eeWP3i2MGTOmZs2aR44cKQfHPjmisxkAqKoquuKLiqrvF1XbyZnCAQB38Dt8+HBCQoJlTiaT+fv757cmPZYDgjHJ23Lbub8QNU206RNkTrImbG0JdaELEM6TEdIRwSI8uT+G15NaVBEUuMom+xJpfKrH5BdFbv901BruBmfsPZm5/5Qx6BkqEduO7q/4skOZvw/FQrjNQzAFm/knm3UZAGwwdJxC/pOt9WC5bJ61sh11q4Xphh6R8dV2yXzvaFXdrXida/xM0+MunOHJa06bzLKdklK+Tkmz1L2PUshIBNmt1a3MynlrV52cnH7++eeoqKhp06bJZAUy6LKzs5cuXerp6TllypS8/8EyhNMbqJgE/a2A1LW/xY6cmfrTTv2tAN5cuDhfQEDgU+ZUETp37gwADRo0WLJkyf79+9evX9+jRw8AGDx4cFxcXGX7K1CYeC37OJXO/5Vm4AAgWV94PEVffCU/6Sa2RO8WpPXlbmuqk265qWnoqwNOE2c8nrwfADiedZd4tbbJLdX2VXxWV96QBw4AzqYcy6Izih6LkC6E4zgAAMARhCW91hJu84BnEdJF2vi5pHGUpHEUpvYDgOdMo6vZ7d/qvXl7Dibs5Hiuk92XQ13GAMAfKYe0TMlPAiaeH5Wafolz2CEfY0ClE5XyPWrbcQq5peTvoZkam5pOl9jHARWLvHfg6mGE27z8w5ZsfxlZjkLo1ii63EbVSiyab60aIJOaeP63F9G7n9xqgMxqlkpZqjyEIuB2fVFJdQDeol0n8t6cN4WQzoTz9wBAxcwCngUAHccDwIycNRLeiNn1tUT13yvlee9kBl/JmhQCAu8bpVJ3vH//fsuWLYud8vb2vn79epm69IlCYohShFp6yBUCIR0RcTXOEIIQdph1959/fllgNmLECGvrAnvgqFVd3P4bRrOHjvlR5HOY0ezlDI8RkeslzBEgoZ+TH4kW2ZjFrAi3+VTkODp2fnMv5Z4n3hQtU6EEHbcMAEiPxZBy/nFtfXuXHu5RBKAI6eEsbVwPrcDVUISwx10m07EL6JjZmPJfFCEmKOUAkMFx4zQpS7OWAsA2xfjzWfgutW11nwNsxlkqejqnu2961J5wGk+4zgC0GG9pHlIYNgFYBOBHG9VAK+lnItJSDw/5dOnfArVavWrVqhkzZvzyyy/r16/PysrKm9Lr9WvXrt24caOfn9/06dO9vb3f+iqFIFwcXDct1P8XoP/voTk8Wn8rQH8rABGR0sZ1rFo3kTauW1YXEhAQ+HARMuw+aCKzmG6HU4tr6wa7H+l3P9LnHxFhyPVv1HaS0u6gDqopJVGkseMr+7SdTTmaTWciCMLz/CDn0cksb43xEgQBgIHOI0PCgjhgzZzpePJ+P9fvk1jW3dLJhWfYrL+BMyOSGgim5NlsAKCipgFPAwBu05PNvAAAPJXEJG8DQM+a5/OAkFjFdfC6m/VvmP6JpSJAgknryhs+0j48mXzQz7WEHg2RNPOIyhWdz1Otm6iUA8DmHC0APDBTCSz7qk5yeaDypqS8aaFBPcUDgJHh43Jy5QBojgcAln85YkFOoirx2+yT54EA1CHJI6DnAcEAqUGWgfY7WW2PKbgFAI/JGqHK3BUZNvsqMFmouCqC23L6YCpqGqZs09mYbUdFtTWcB4TAZM3Z9FOAijFVx7x30hp5p1cnIPDxUbwKfSFq1aqVk5MTExODYQVW4liW9fLyUiqVjx49KjcP3zvKSaU2x8zRHNi+uNHGMowThhEvFsLpmLl00gbc0f9WzBft2rWzDKIoGhoaWjQC5OlUU1ATnskWee+gYmbydJqo+p5MWdMY4/NGyuaFjK/Fmu8kUQB8X76vE9w/buy2IXV4Wkbdue5r+tv8lgSNjiMnwvRPI/TPVKR8a+teDtK3W419Z3jKGNSSN0WQnitxx28BIIPjRmjSP8vcMSl7FSvy+tH13EUTb4Oiu9S21QmCZ7V03BImZSfwLCLyIL3WYKrCsq6Wfu+W751xbIOtTU2SuGw0TUrPpHl+lFw2tSzKrrKzszdu3Lhu3brU1NRCUxiG9evXb/r06Q0blrGUAJOWabgVoP/vYZ52vdOyKaLqXiUeKCAgUBo+CJXa0tCpU6eoqKjIyMiiU7Vq1XJ2dr506VLFe1VZvD8q9K8i28z971JWpqlAFnWynk0zcI5WmJ20QJzjJMN+6aQSlVEknE6lzno2nuFpALDCZFYi79Nifzmf0dF0VAx6AMig03KYbADgEGmOetUzGj3mYF+LJJi0I1RE4RbxrwKz7rwidcfex/rOnuItXcu9Rg8AKI76MXRCGqUZ5jq+re3nAJBkjp8X+j8e+PnV1rpJPF9zbCTN9EzWcACTlIpvC3aMW5edszVHhyPIBUe1M/7GD05hmUz3w6klP6ADAAACcLSPbQOHt6+bO643zMvI4gF8RWSAmRIjyBZ7m6aidy1koGMXsJnnxTXPAWkPAJz2lulJ91IeS1ZZZ1GqN/C8VOjHLlCBfBD391KtabVq1SohIWH69Olcvr6jHMfNmDEjLi7uVZvzAm+EQoTmRe/BFNU1SbP8ZSI3x6SfBADc9uuff/4575CePXsWu3+LEPaEyzQAMEeO4ek0VNESs+lpRzrkRe/msKjE6SuNgU8BYNHNnK0Buq0B+h/CZnA82lN8SapXuotie1vv53h0UtiMrQH6K2HucQmfP4pq/kvA4/J7B0oAIUmPRQBAxy/nmXRL9J5qShmj3QIAUq+Va+wcO0jElvEwmkYwOem5Ulz7ImpVjzfHmJ99bQ4fyVPJeeeLpBlLv/dF1ipfkkxk2MGatNN6Y0eJ+GdbawJBdmp1T1+srL8LSqVy9uzZUVFRa9eudXFxyT/Fsuzhw4cbNWr0+eefX758+d2vlQduZ63o0cFp+VTXTQut/foourUl3Mu9X4CAgMAHx/379318fIqd8vb2vn//fgX7I/B6lCJ09xc2f/S1y//Vp7oEAIbXtSo0vqWLdVlF7wCQQadZoncA0LO6GHMCAJKD2P5J9gk3pUYbIy3RO4tInsgnPKVRHsDSrB6TNwNUDACoVX3MthfgL8JyBEGV7U2KXpnSnhRukeZBHpM/PkqjASAik74Zb87/ZZEKKnP+TD2RRmnyVwQ4iVw72HXneO73xB2vP9YGQz8Ti2aplN8W6ff+g1Lxg1LRVEQq3qqGXEmghCXqRwB58QW5Ay9HLF8iHLF5hx34vOh9pkp5QG1nyaUfm5pxJ19jnbeDcF8grn/XEr0DACLxQXBrAEAlNTDb3kDYAQAiqYbKGgAAoBLMpgcqawwAgODoi5QEIXoXeCPiTTEZdFple1HulCpJZtmyZefPn1+7du3Fixd79erl6uoaHx9/+vTpR48eubq6Ll26tLy9/NS4Z6b4fIsrnPYOTyUgIveELIczZ87kmVm6xxUL7uhPp+zmTRGAYKTH8vxTTEqaZsVWNkdHxydJfGuu6aC6m2gGAANb94bet63o4VLvNTkGmkSocGzwF3WbfwEAALHG5/dyrmXLnhjZtRKscsTPMetumKojm3XZFLdipGR6GE2v1G2QcjmYsj2m6owB/GxrPTE985rRNEKTftzR3hHDUFlDcZ3LTPJWKm4Zm37SlHWJcJ2JO34LCO6OY6MVsiYiso1Y3MtKsjgz+5jeMCMjM5CiZqoUm+1sbpnMVYgyyCKzYGVlNWnSpPHjx+/du3fVqlURERH5Z//++++///67UaNGM2bM+OqrrwqlurwLuL2NsmfHYiY4Lm3zQQCwat5AXM8HefP9AQEBgY8AZ2fnoKAglmWLZtgFBga6ubm96kCBTw1vKx9XsUe8KaaarFZTVWsK0JUmczInMmEOMTYLJ4kjlAgToA0+zLfQ4Z4AYI9i1UgCABCRO+E0gU5YAwCiar9x2gemJ10AxQj3hdnKsS33peBgvFW3GSBwJbv9xEB7AAoAorJZv7MFaukRgHP97WuU1C3vjcik0//SnAKAQc6j0Xx52r0cBt7KvPZM9/hB9q2iqYt5WKPoLnvbV82OUcjGQKmayRXFzgod4yu/FmdylmF56fFnI0w6ipPiSI/qBaoCbSWoi/wt35b80buf3AoA5lurAMCiaVcm+/B5ILg14TqLip7Oc2Zx1S2c8anpUUfeFAO4AgBEVdZjdn3NT/sCAO4wEpUUv7AoIPAa4kzRC8OmqHDrpT4bRKi45AM+WEr1B29nZ/fPP/9MmjTp/Pnzjx+/3IPt1avX6tWrbW1f+eEl8HZYSqrqkLl1aC+237/au3dfXhJEw4YN27Rp88pTICTpucL8rD+ohz3juFovhjm9MWX5FjZHJ2lQS9G9HQA0cCAaOBAAsDF640Vc1IIifYlLoAQ9J/OqOzc7g2jtJkIAeKi7IuL3cH3yOc3xfk5Dy+uVlwThsYzNbsVrdhHqLzthSCf9MUBwwnOZZZZEkF9trSemZ143mlJY1tHyPIrguNN3mE0vKmYWm3GWipnDpB4gvdYQ8uZTlIq8AxfbqBqLyAWZ2b/r9EEU9YutTUtV2avgikQif3//kSNHHjlyZMWKFcHBwflnHzx40L9/f29v76lTpw4bNkwsLsePHs5M6f97yJsp3ZXbqEwqbVLPqkVDcb0aSNmtHQgICLz/tGrVavv27dOnT1+9ejX6YqswL8Oue/fS5rsKfPQ81T2KN8UAQLguJFwXAgCuqCxbMdmIOqbyokU6dS3dhnArPy1eFQCUCLvfQW394jeKcJnMpB3i9EFM6iHcfjBu15dJO8Zp76vUaK9qkq+w73GEZoD4g/m5pasoJI3ONHFyEqmnLpATrhQhLrIyvkMdSdxt5kyfqVrVkNXOPy7FrPo4Dt4Xv+VQ4m915Q1JtFxU8V9DopZd/0ALAMGawpmAeoY/FJLb5qauNLiJ/K4RQGNtZS999SY8gmOKVqiVb6HhYIqam5EF+aJ3AEAA5lmrKB5O6g0T0jL+cXKQv1USQbHgDiMZzW7OEEInbyacf7D0GOLpNFTeFLP7is04x2b/g+AqwnVGWV1R4JPi94SdHM9m0GnnNSf6OA6ubHfKkVLVwOcRFhYWEhKSmJjo7u5eq1atKlWqlJ9n7y3lUSO36aHuRpz5ty9sJDgCAJ2SUhIY9oyj2pvAAcAYUJc3x4tqX/JpODBv53bjxo3jx49//Wl5Om19/JbAnPvTqy72kdXlWVazdJMxOJR0d3ZcOhmVvIwPw/VPl0fMAoCeXOrn5hAAWJs0+TpWLzLxM1/vP71d7gCAkTXoWC2OEMt9Ntm+SIiqeKjo6Uzydl7RBgWOz/kXdxpHeizLb8ABZLCcHVbM/YbNukRFz+BNzwEQ3H4A4b4QKajJ/5Si/5eeEcewKhRdbWvdSlyO92ye5//666+VK1deu3at6KyDg8PEiRPHjh1rY2NTTg4wKWm6G/cN/z2kYhMtI6hMKm1a36pFQ0k9HxCS1gQEXs0HUSNXGtLS0nx9fRMSEurUqVM0wy4wMPCTWqN//2vgi2XF7ZztgfqZzRT+vlbld5UcJntf/BYjZ8g/aEIk57CeWYgKADCeZREMACS8cae9tIHEKb+lpRIeIdRi3/vA6oyBjYEziGudBdzO9Kg58DzhNttS/ef/Z+Y/Maa69sSpvu/UcrVEIvWhyyJmEiixpMYGO7Jwgx6O5xaGT44zRn/lOORLh6/L1ZNiuRprTtYV+D386Z42w8ipRMi0prnbDx10bWRcMQIWRUElPuL6twoNxjPsxPSMr62kg2SFf3M4gOWZ2REMs9XOhizT5wE257o5pBeCycS+9wEQU2BjnjOI61xCpbWMQS14UyTpuQp39C/5RAICBXmQfWtj9EoJJjWxxlf9XZeGD+L+/mYpN9WrV69evXo5ufIpc+ipIUHLphk4NwWWyXEJDCtGEC8CBwDO8Jg3xyOEw3+B+rzoXSwWDxo0qMTThlMpgTn3JZjUVewBABnbjxiDQzGVQj17XP7oHQDymr78idjW4NR6k/2B1G+qV/8NEj97FNVOrLwmIrMtBizPaNkcW6i0AJ5wnc2mnYCc67wlHctlWiEDFKDY6B0AMFUnSf3bTMpOKnYxk3qIzfyTcJ2JO/gDkruoX5MkTjioZ2VkXjKavk1NH6WQTVIqykn8FEGQbt26devWLSAg4Oeffz548GD+R8aUlJQ5c+YsWrSof//+s2bNqlmzZpk7gDvYqfp1VfXrSiek6P97aLgVQMUm6i7f0l2+ZTdxmKxNkzK/ooCAwPuGkGEnUEoUuPI7z2L2Rcdz3FBN+nOatkTvNij6u4OHexHdddzuayZlF6e9zSSsJdznE87/o+OXUzGzgTUBzyO4bdG7ebnCA38gcTsPvJvE64k2sFgbd0mVOGP0Oc3xVjYdVUR5Laa/inbuhbcQtgbpM4ycBEcH1sqtZKTjetMJPwEqIhzHAhQbZnN00hbgKcy2d9E5Vxw74VD84xwKMMda+S7+vwpM0Qaz7s5mnqdjF5NVN4hqneaZbNTKl05cx5siUUkN3GF4eVxX4OOG5qnDibsB4GsnvzD909uZ144m7RnnUaGfKhVJ8QF8aGgoAHh4eFiSeC0/voYaNWqUuWefDiaGT9KxOArOMgwAQigaAGqTufIlbOZfAIBZd9u9aU/eIb169SrUPa5YArLvAkA3+z4yXJF9+rL20k1ERKpnjsHtCh/bRNXS28qH4RkA2BvMbwrmEYAdLSbMMvFXoiW9VSvbeeTeGESoSIGryuBlvy0IriJcZ1LR0wCAcJuL4G+oUosQuONYzLorFT2TzbxARc9iUg+SnmtQ+WeWeRmK/GJnszVHuyFbuyNHF04zq2xUbydCU0oaNGiwd+/eOXPmrFmzZt++feZ8sjFms3nfvn0HDhzo0aPH5MmTX1c08Q4QLg6qr7upvu5GxyfrbwVQETGiqu7lcSEBAYH3kOrVq587d07IsPtwIVEEAMhKqn+SIIgsX+QoRhBJ8Ru2COm5wvSoA520CVd/QzhPZFIPcPrcOrIk+18Nqbm54lqKAwAjwz9OLZA9rrbC1K/JEn9Dog0R0YYIAIjUh0bqX/eUa+ZMtzKvdVP3KatLlyGEyxQm7TBvjkckNXD7YvZ1GM0e4Km81uvlBKPZz6YfL40lKmtMuM0hPZcasy8zqb/jDiNQWSMA4GkNk7AWAAiPpYC8spGhgMCr+EtzKo1KcRG7t7HpXF/RJCD7zr2smx1suxeqjvloKD6At2jS3r59u2nTpnk/voY3ysMXKASKgLUY5QFq7Ug+3sfmsSg3gLfMWvqjmkRtjh4dlnfI8OHDS3Pm7uq+7hKvz1StjQ+fZO47BQhi/72fyNujWGNrInenRYLpALQAYE86/NqRv5dEtXIrQyHbMgB3GM5mngcExdV+b3cGROQpqnGIzfyTip7J6R+ZnnTD1UMIt7kIYQ8AKMA4hbweSU5Lz7xmNK3KylliU+5rFjVq1Ni+ffvChQt/+eWXLVu25OTk9SAAjuP++OOPP/74o3HjxpMnT+7Xrx9BlMvtjXB1VH3drdipnHNX6YRk6Wf1xXWrv2mdPE/Tumt3DXeDmeQ0BMdILzdZ+6biOkIuj4BAJaPVart37967d+8pU6YIGXYfLt/UsRLjSO+CwmYVg5HnR6emB1M0AKCAcMAnsuw3mvT9alv7IncK1Ko+bj+QST1Ixc4XVd9Pui8wh48EgBBjrQF/NgAoIBwdkcn0Ol5gBEPgfH97b+uyEbFzk3j1cOifw2SVaEkgRFPr1nk/mp505bR3Sr4AKiIcviU8Fr2Lk4XAeB4ACvRERyWE249UxFg6dgFm8yWCyfPb86yOjlsGAKTHYkDLUX6YTT/JZl8tjSVneEq4zUFEnoTjWDrxFypmlrj2BQCEjl3Es1rMujumKk52V0DgtWTS6ec1JwBgoPNIFMGsCdvu6q9OJh/8PXHHvGo/oUg5bsJVFsV/Do4ePRoA7O1z82rGjh1bcR59epAY8u836pb7NAzHX401NxRP2G+KdMnATHEo8DRneAKAHNk9S6t90bFcLWrttML0aCXuNB63e11dlhxXNLduBwDm8GjgeetvekubFZYwmXk1+2KUKf8IxQIA8AANd6XkHydQWN1B1catoqVcigHBRTVPvPtpMOtuEmV7OuEnOmk9o9nHZpwmXGYApmBSdgBAI4ALPK9hOWUGakooZgEDldQgvbe8uxv5cXZ2Xrly5ezZs7dv375+/frY2Nj8s/fv3x88ePCMGTMmTpzo7++vVJZLeluxaP+8Rienai/+m6t4V2rtekaTrlmxNa/MHgCo2ETdtTuy9s1sxw5GXlHsICAgUAHI5fLo6OgrV65MmTKlsn0ReHvUUnR8w7dUO38XLNH7QzMFAHYottHeenZGdiRNxzLMq2J4wn0+m3HGolVmGeEB3ZK9oY79y1XpmGxWS3ESAqmqKvCMqhKhdmW3A48j+FtqXDEZJdsAAGfmmTLuZdXCTRT1xNDUucBjGG7Xn0nZzWlvM4k/E27z8k8x8St5WoPKP8Nsyzd9gHCZzGb/A4AcFtfRIbKR7t+L0YLLSTxFhY/iWR3hMjXXbZepTOohTnuPSTuOSqoxqb8DQpIeC8vVT4GPlWNJe82cqbGyRW15bpjTxb73jYxLscaoGxmX2tp+XrnulQfFB/Dbt2/P/+PmzZsrxJlPFxJDjCwPADqTuQZ7ScIZgAbu5Ty/98RLkZLBXRHEGMQBcDk34LUBfB7Kft2sWjchnIvRckjUsdlmrug4ABQaRwCyTMVbfsCgYsJtDm4/kIqeyWZdomJmI7iCZ3J3vzEAiwhPsS+bN0UCT5dHrpdSqZw6deoPP/xw9OjRtWvXFmrFHBcXN23atIULF/r5+X3//fclJsiUCQ7zJuiu3jHcDqRiE3VXbuuu3EatJNLG9aTNfSUNar8qDudpOmXJJjoxhXB1VH7VRVTVnadp44Mn2ScvWs5gM7xvBTgvICDwKpYsWeLv7x8cHFyvXr3K9kXgQ4Lm+ZGp6YFmCgDsUWy/g607ju9V2/pp0i0xvF9q+hG1XSEBc4RQ4y6T6diFdMyPPGsAAJHXmh3NGuS3GXch62KU0ccGP9anfEXs3g7CfYE5dAiCq8S+9xG8GJEITnvP9KQLoCLCdWbZXtrBCgMAZ6tCN1yE9FxuetSRTtyI2w9BxFUto7wpik7ZDoCSniteUR5fZqCKlphtbzb9VFU6ZTdpc9qQOMhlVH4DOuFnntXlr29HMBnh9iP1/Hs6dgFCOgFwhNNYROxdrn4KfJREGkJvZ17HESJ/kywSJb92GrY5ZvWJ5P1NVC2lWDkKfFYKZdlOU+BdYDgeABIYbJe1//icXxDSUVR9PxW3gMv+NwUZdTPw5zzLEWPmAawCVEQ4v+wDz5spwPG8OCrBFOsocsFeaLMhGFps9A4Au76w0VEF4tO/nhtnX8sRY8h/fi8P0VP8gRBDLbtKKEziDCHm0EHA5pRsituIahx4i96hiLiqyOcom32VjpnNGZ5aBgnXmZh1l/xmSSx7VKfvJCGqPh/O0xrcZUq5VmrhOD5o0KBBgwZdv3597dq1Z86cyWsiCAA6nW7Tpk2bN2/u3Lnz999/3717d7Q8C/Vxta2qf3dV/+50Qor+VoDhdgAVnaC7dkd37Y6ydyfrb4pRxwEA7d//0YkppIeL49LJqFhk+S0lPV3F9X2S56zN+fOaons7XC2oZAkIVBpffvnlunXrOnfuPGrUqMaNGzs5ORX6JLFU0gkIFCKJYYPMFACoUeyAg50rnqtgt8fe1i817TnNRNNMCEU3LdLMhXAaz2j2W261qMQHVxduTFvPHr8YBbUr43mjNFjSvNmsy3TcCtJrdZF5joqZCcATzv9DRGWsJlNFhSMAXqqjXzl/AAAgAElEQVTCj+6olS9uP4BJ/Z2KXSCqvs8ySMXMAs6Mq/1QqwZFzlT2JKtHKtNPN2I1N3nXf9LPt7Pt4iR2tUzxtIZJ/BkACM9lgLx0HlcPZjS7Od0DnkpACDXuIuQBCbwxPPAHE3bwwHdT91GLCnS+aKJq+U/6+VDdkzMpRwY4j6gsD8uJNwjgU1NTJRKJTCYDgCNHjpw7d65atWpjxozJy7QXeHdyxNw+2fB+xlNqKobTPeR1gQDI0SuSvMitadOmVZTXOS0QjuMQsReTmpF94qLhXjCblQMIQro7y9o3C2xG707Y1MthYC/HgSVekeX4HHMBCQMjAwDAA+Qfvxlv3hKguxRtOtPPjqzYgnieTubNsSXbAQCTxVMp8OYBvAVM2Q6re41JPUBHz+Y5Ix2/kqeSCbc5CJG7CXBbb9jKZmlT9k+hNYjYi3CsoNKSNm3atGnTJjw8fN26dXv27NHr9XlTPM9fvHjx4sWLVatW/e6770aOHFneefWEi0Oudn1SquFWgPFRqMin6quMDfeCAED2eevMfacMd4Jyf0vdnGTtm0maNzD8e9/w4LGiW9tydVhAQOA12Nnlfr4tX768WANB40agWDAEAQAHDNuvzo3eLdhi6F57u2GpaZE0I0GLe1pASNJ9gTlsKAAQXqvyR3QWCAwBADH+PknvFITwWMZmt2I0u3CH4ai0gEQWoznI6R4ipBPhPLHMr9utijh4lKOUKOadIdwXsBln2YyzbPY/mLIDm32VzbyAYDLCbXaZu1EUHvh9mjO1cLeuTMwwLmUuIjuQuH1qldx8eDp2Ac9qMZsvMWWHgsehpMdS05NuADzhNhfBFBXgqsBHxn8ZV6IM4Zai96Kzg5xHLwqfcintbFvbzx1FLhXvXvlRqgDeYDCMGDHiyJEj//77b8uWLQ8dOpTXw2zXrl23b98WYvh3YfUd7ZWY3Cr0LDFHIeRD9cyuCePouMU8q0OtfI8c+zPPeGCvmpz2lCUJzRQSrlmxjTMYAQARkTzDUDEJqfuOnbJNAym4SEq19Dv0TMb9ZKrouJnl2x3UFBqMyGQ2PtRNaiIval9+YMoOmKI1m3MDVw8l3IvXg6Fj5jCpBzFlW0z5btEgQuDq4aiyoymoKXBGRrObzTiFO/+PcBoPCNnbSppJpX+RtBkAflHO+IbDXSuwgrtatWobN25cvHjx1q1bN2zYkJiYmH82MjJy8uTJ8+bNq7C8esLJXvnV58qviqksMtwLNtwKENf3YZLTACDrwB95v6XAsFRsYsaeE5ZWCExKenn7KSAg8BqmTp1a2S4U4Pz582fOnAkPD69aterYsWMbNCi8eTht2rTo6OijR49WinsCebjg2AlHezWG2RRJ/rLF0ENq++cMXY8kiz0Ws/mScJkGmBRTtC7W4D0HlVTHHUYwydvo6NmiWn/kjfOsjo5bAgCE+6JyEo0rNnoHS22C8w903GI6ejZW9yodMxsAcNfpCOFQHm4U4lbm1eeGsBxJ/a4UozIntBXbXdUGBefcr6dozOmDmNTDllWbogei8qaE2488lYir30qPQODTxsyZjifvB4D6iiav6iVRVVojXP/0UOJvP3jNrVjvypdSBfBr1649cuRIjRo1LIH6mjVrbG1td+zYERISMmfOnHXr1i1durSc/fzY+DvKbKBzN9WPPjWkmzjLrnYKwgLAI6rbZ0RbG/oaAARqGjx4kJs/j2FYr8a3AYBwn8/pQLNyG2cwSpv6Wg/6knB15FnWGPj04fndWVKzo1ZeK1LB1TQV6vdelNr2RIqBzT9iZPg0A4cAuCoKyM+wPCTr2C0Bui5e4grOpSe8VrHBrZnU33HHcai0cEd0zhjKpB0FBCM8lpXJ5VCRG+m5jHo+CUFFPJNFxy5kUw8R7gsx6y5+Wb8wXFaQuPkevPWJlNQF1sru0goV/rWxsZk1a9bUqVOPHz++fv36//77L/9sXl59x44dx44d26tXL7xIM94KQH/zof7f+7rr9yx1d5zBKPGtZTOsD+HmxLOsKfBp+q5jlti+NDJ4AgIC5cfq1UVzgCuNsWPHbt261fL95cuXt2/fvnbt2h9++CG/zaVLlwIDi+/aLVDB+Ly6H4oMRV4VvVuomJ3h8oNwncWmHWdzrrMZ5zCbLyyDTMIank5B5U1wu0qQdyGcxjOafZwx1PS0N2d4ioirEo5jKuC6Zs50LGkfAPR2HkGyX1IR3/aio2+TdQ8m7qwlr8dEzwTgCKfxecX5hd12mVwBTgp8lNzIuJxFZwDA1fS/rqb/9RrL4JwHscYod4lXRblW7pTq4f733393dnYOCAiQSCQajebhw4dTpkzp3bt37969Dx06dPbs2fchgPfz8zt69KjRaKxsR0pm2S3tziBdoUGWBwDIFvEAsO+24SYz5WSNawjApB0xeTatm3o4yJMtjVgyfz/H6Y2SRnXUU0cBggAAgmHSRnWauM+M/31G1RBck7ZF2buz9Te9Xu/MvJaKeS0LpC2djjBOupQlwpGrgwuXzS++mbP7kX7S5axiE+mNDP8gmWrhIio2ae5dsJTJMSm76JjZoponC83SMXOAp3FHf1Raq6yuiKv9mJS9nD4At/ua1T3kjKHm0IGorAGnDwYEr+29qotZcsFgnJKe+a/J/KO1Ulp829vygiCIgQMHDhw48MGDB+vXrz906FD+7vE8z1+6dOnSpUvOzs7+/v7+/v4uLhWaOGTrP0Bcs6rhTqDxUSjwAADG4Gfp2w9LP6tv1aqxpFEdJy/XuHHzgOUQyXvQ1EBAQKA4wsPDExIS2rVrVzGXO3z48NatW6tUqbJy5cq6des+ePBg6tSpkyZN8vLy6tWrhBuZwMeEpaE9UeZPEqUgyRSfyWTUkpUs6IjgKsJ1BhU9nYr5UaLqCKiYN0fTyVsAUNJjeXmLxhUPKiY9FpnD/Cxd7kiPJYC8bg2lrDiXciyLzvCUVG1u3RYBYFJ+I7S3+/KZB8zYo+i5NbS3LUmjFeCJwKdGdatadeUNWWBLtJSgUjuyeC2wD5RSBfDR0dFdunSRSCQAcPv2bZ7n8+7oNWvW/PPPP193cEVB07TJZCrZ7j2ghQv5ewjCvNAjo1geAcitMhTzwAJhBLGIs3z8ZwdczjvwqzbpLwRFUVPgUwBQ9ugABUNHkb1dB1NTfVowAEga13kL915z25naVH4t1hyRyWx4qJtcJJF+80Pdxoe6XzqpvvQu+01pwm0um/6Hpawrv7Ycm3GOzbpsuZWWzZV4ijOEAADuOJKKnMhk/CHy3sXpbjEp+zhdAAAgIlcrOnqtmDmLW8/Xkif1hgdmarmNqqGoIu6UhWjUqNHu3btXrVq1bdu2zZs3F8qrT0xMXLhw4dKlS3v27Dlu3LiOHTsiFbLQgFpJ5F1ay7u0TpiwgE5OQ1AUUMQUEmEKidD/99Bp2RTDvUfAcIC8WLgSEBB4z+B5funSpUePHs0vulGubNiwQSwW//3331WqVAGAGjVq1KpVq3Xr1mPHju3QoYNcXqGlWwKVSO9qEh3F961R0W3tjaxhZeSPWiZ7lvdyb6uSy9Bwh5GMZjdnCKGTtxDOP1DRc4Az4/ZDUFmjCvC2WDCbHpiiDZtzHVN2wKy7VsAV06iUC6mnEUAGuYxGAAEA0nOF6VGHZuawqyKxOnUvABDu8ws1qBcQKBPcJV6Tqswr2e5jpFQBvFKpjIuLs3x/48YNBEHyZGkzMzPJ12ZJlSEbN248duzYq2ZDQkIAoH379nkjV65cqQi33pxsM2egC8QteT8wDxGeA56BFrZXASAmhXgWlmqZQlGkR2vA7fqh8mYAwGTmAEBRbXmeZsxhUQiAyNtd/Gp1sdfgKscAwEZcTHm3BEdWd1AOOJW+JUDXyUNcT10gfc7Sdi7LXC4hGYJbEy5TqZjZVMwsibIdoCIAAJ6iYucDAOE6u9huLm8B9Xwyk3rg5c8cZQ4bkt+AN8ebQ4cCD51xRZ36z6Zm6p5StJ8mbYRC9r1CTlbsVrwFtVr9448/zpgx48SJE+vXr79582b+WYZhTpw4ceLEierVq48ZM2b48OE2NjYV4xjPcgDAcxyC4eLa1XmKxm2UyQvXmx6FAgrAA6eroNhAQECgWHieX7Bgwf79+zWaAqInLMsajca6detWmCfPnj1r0aKFJXq34Ovru379+hEjRqxevXrRouIFUAQ+PhSiymlrfyblSA6TBQC/J+78sdoqpMRddAQjPJebQ3rRCWsQ0pnNPI9gMsK9kutsyaq/0kmbCafvKuZyhxN30zzVwrp9Navc8kZLliiTenAK9ZjkzJmEi4v9S0Fl3hTBs4VTUIsBIVFpzcpJZBAQ+BAoVQBft27df/75JyYmxtra+tChQ40bN7bo1iYlJd28ebN27dolnqFMMBqNV69efb1NiQavJzo6+uLFi6+3SUhIeJdLAMBnTmTv6hKK5QEgSccFpFCOMjRFz/E8OBmw+moCFNBdeg8AdlyvxvO5AfxntTG1nYRwy11qQiUiFoDVGTAblWWE4RkA0J24wGZpAUBc/y3zyS258dbFBfAA0MCBHFHPakeQfva17LNfV2iPVtzRn9Hs5YzP6JTthNMEAKCTNvGmyPydRcsAMjfbHBF7Ac/x5lgAHkgnoNOApwG3AZ4BNgcQAE7npll/xOm7jTpue452R47uhtG8wlb1mrLAcoUgiAEDBgwYMCAoKGjz5s0HDhzQ6QrcJsPCwqZMmfLjjz9+/fXX/v7+rVq1Km+XLBIMVs189XeCTE/CAMAcDgCASiXiOtUNd4NQqdgcFpW+46ioipu0SV1xvRpIJb17AgKfJlu2bFm0aJFCoXBycgoPD3d0dHR1dc3Ozg4PD2/atOmvv/5aYZ4Yjcb8nTItDBs2bNOmTT/99NPo0aPd3cu4KZeAQB4p5sRLaWcRQGS4PMoQ/l/GlZY2HUo8ClO0wWy+YDPOURFjAQB3mVIxonGvARF5kJ4rKuZaz3SPHmTfIlHRV04F9jkI93lsxmmS1fEAu1GHIYbnnlJvAOB0D0yPO5Xy5GTVzbh9ya2UBAQ+TUoVwE+dOvXixYs1a9aUSqXp6emzZ88GgIMHD86YMcNgMIwcObKcnXzphoODw4QJE3ieX7duXZcuBXp0jx8//vTp0/Hx8e9yiYkTJ545c6Y0lkWfM0qPkwz7qUNu1L09SB+QQnWvItn3RE+z8JkTuaaDCnjKcO8hcMiTxy+zG75sQxDOkxBRbl9NUVV3OiHFcDuQdHcGAIan54b+D2h27Mlc1TpxjfKSapj8mfy/BMpA8xwPFVqkhuCE53Lz0z5M/Ercrj8AMAlrAYDwWFqW/dhN4ZZ/eVPUy0EqKfcbJiNvjOc5On45krLzO9fp7dUDpmcYQmn665S0KUr5cHkl7B7kUb9+/S1btqxatWr//v2bN29+/Phx/lmj0bh37969e/fWrFnT399/6NCheX2kyhyyqjsVm0i4OroM7mm4/4hJTkVwnPRylTatn/zjzwBAVvVgUtKp57HU81jtpZuoWCT2rSltUk/aqA4qKxcJXwEBgfz89ttvMpns2bNnTk5Ofn5+mZmZlpvgkiVLdu/eXWEL9ABQrVq127dvp6SkODi8DIEQBNm8eXPTpk1HjRp14cIFtIjguYBAmXAo8TeGZ9rYdPaR1dkW+/PRpL0Nlc0kWMm3IdJ9sTHrEnBmROxFOI6rAFffEzie+z1xJwA0VrbIYbJzmOz8s1L1CFnS+ueSWpEgP5S4a6b3UgBARK6ASoEzIKQjQjgWe1qeyeLN0QAIKqlW/i9CQOBDpVQBfOfOnffu3btkyZKEhISRI0f6+/sDwL179+Lj48eNGzdq1KhydvIlQ4cObdWq1dChQ0ePHj1hwoSVK1daKvMBQCqVAsA7inVNnz7dycnp9TaZmZlHjx4tq1riWD2L1eVN1gXSzjndQ+CMOr7G5asvNca/6OiWv62orHMr3fV72Scvkh7O0qa+19IvppgTnTNkPEMAAO5oJ673soIrdWu8/kGO2+rqmLLk/3EcRQAAf/VjkghDTn1lx0PFRu8AYGnVrvqczbpIxy0DnuZZLWbzBabqWIaXQOXNIP0kAACCY/KmACinu8tzZgBAreojuDVnCOFpDQCgVnUAEXO6+1TU1Gqi9Sddpq8Td92vM+3W6is3gLegUCjGjx8/fvz4GzdubN68+fjx4xRVoF/g06dPJ0+ePGvWrD59+owePbp9+/Zl/nAs79xSd/VO9qm/CQ8XZc/c/yaeZjJ+O0bFJuJqW4lvTYTACXcnw91gw71g6nmc4Xag4XYgYKi4pres7Wey9s3K1iUBAYH8PH/+vFWrVpYbX7t27ebNy03ymjlz5q5duxYvXrxiRQXt5llu623btt2zZ0+TJk3yPo4aNWo0bdq0FStW+Pn5bd68+a3PbzabC61mFiUyMvKtzy/w4RKiDQrKuS9GJX0cBysI1ZX0v8L1T89pjvdzGlrisYjYi3CeRCesJj1X5Bb3fRoE5NyJM0YDwH+ZV/7LLFyyigDvQ9aNBBUAhOmfPNM98pHVRQgHwmUSHbcUwVTiOn8DUswTqTlsGGuOxu36V6KUgIDA+w/C829Zsfz8+XOFQlF+e3evgWXZZcuWLVq0yNvbe//+/Y0aNQKAQYMGHTp06K1fTul5/Phx3bp1URRl2ZJlD0tk4I20IE8KA4R5CCzBN7UV7W9qSyesoeOWbr7TcuoP5yxm1dzRpwEnMJsCSrwZe0/mnL4MAGQV9429Q5KssvoftfV5JkEI3HH+RJFPbiVh8sqoxMXPAUDaSFHtdIMSY3iOh40PdQ0diJauIgCgE8y6u9nWvexLjNfn38je/8SwsLXym9rluHfKmyKMQS2BZwAAUEJS7z9EXKWkg97oAozpURvO8BQAcKfxpMdSNu24OWI0ZtNTVH0Pp71netIFEAAeEde9hFr5shnn6LilnPEZAKCS6hrHabRNz6pEJQjavR6NRrNz587t27dHRUUVa+Dl5TVixIjhw4e7ubmV4XUz9/+RfepvACC9XEXeHryZMj4KYzOzEYJwmPuduJZ3fmMmLdNwL9h4L9j0JIJnWQBw27kcUwriNwLvHbdu3WrRokXz5s0LtXL84JDL5V27drW0Vf/nn386deqk0+ksC+LDhg17/PjxgwcPKsyZMWPGbNu2DQAIgrh37179+vUt4zRNDx069PDhw1KplOM4k8n0Fvf6IUOGHDx4sJTGFfAsIfCewPHs/LBJCabY/s7Du9r3BoAYY+SisKkYgi2u8auDyLl0ZzECWtGqe5VLkil+d/xGmqNKtCRRsb/7D7akPQAAZzIGNePNMaTXWtxhRCFLNudfc0gPQCWS+nfzEk4FBCqYD+L+/vY9ovMrzVQwGIbNnTv3888/HzJkSPPmzefPnz9z5szKcuYdcVNgQQAs8EhDwAFCKRoAuJx/AWDb5fQ8s959/QpF7wBgM7Q3bm+TfeRP6nls43+QLGt5jVAJ4WhvP3kEWSW3VtASvSMYgqtJw4Oc8J4BJcbwKALfN8rdQDaFG8K7P6STzDl+zh4bfPJieBPD//pAZ1Gty+NBEgUApyOMT9Pp/OMeCszfV1ZWu/WI2Bt39GeSNgIA4Ti2jKN3AEBwwmO5+WlvAGCSt+HqoZhdX7GkCiqpCcBRMTMBeOABVw9DrRoAAGbzJWbTnUk7Rsev4IxhdlH+aMrPrOtMzOZLACSUphGA6u9BXbdarZ41a9aMGTMuXbq0Y8eOP/74o9CGfFRU1Lx58xYsWNChQ4fhw4f36dPH8hD/jlgP6Ynb22QdPkdFxVNRuRUuZBU3228Hirw9ChnjdtaKbm0V3dpyeqMx4AnwfNHonc3RoSISqQzNfwGBj48aNWo8e/bM8r2XlxfP80FBQc2bNwcAjuNCQ0Mr0plNmzY1b958//79z58/zx9CEwRx8ODBli1b/vrrrxEREW938k6dOpX4coxGY0hISMV06xB4T7icfj7BFKsmHTvZ5bZz95BUbWHT/mbGP0eT9kzwnFWqs3xi0TsAOIldZ3kvf+PDUDHpsdAcNpyOW4zZ9kZw65dTPEtHzwIAwmWKEL0LCLyeN9iBv3v37r59+wIDA3U6XUBAwIULF1xcXOrUeZtGZWWFTqebOHHirl27mjdvjqLozZs3P7gdeA5ganrmn4bc9vUugF9yUWrvezIUZf8FzeZoLeM3b95s0aJFsWfgKdoc+pxOTuUpBrESy9u9TDlO+TkmYW4EgiGO0z0xGZ66M9783Cj1lXufaYBblxxS5kXvlh9t88Xw95KogX+kv/bolyAA94c7qF6hivcW8GyOKagZACqufxvByiVZ3Rw6iM38CwAwZXtRzROWQUazj3o+EQAQzErs+xAhCrYA4Gkm9SCd8BNvjgMA1Kou7jqzhcHXwMFgudX/lAqr9+mhUKPR7NmzZ8eOHWFhYcUaKBSKr7/+evjw4S1btnz3x1meps3PouhkjaUGnvR8y3szm5Ed/918AERcp5q0ST1Jozq4reodfRMQeAs+iBX60jBt2rQ1a9bMmjVr8uTJtra29vb2PXr02LVrl9ForFq1qkKhyAvv3xOSk5MjIyNbtmxZHicv2/u7wPuPntXNejpOx2ones3xVTTJG89hsmY9G29kDVOqLKgt961EDz9KzCE92Jx/LUmOeYNMyi4qajJCukh8732CCyIC7w8fxP29tAH8kiVL5s2bl2fM8/z333+/YcOGZcuWzZpVuuXJcuPYsWNjxozJyMiACkl7K/MbPAswKz3zzIsYfjoWMiD26/0BjmPG5z42OTg4JCYmvml9cl70rp7gnro1jjNx1v0cDA9zShnDmyMMYd0e0klmWUuV41TP59884vSs7VAnj401AUV4gD8jTTlm7lkGve+xgcSQ7xpY/ZdI3UmkenpLmjoX2B11U2CWbPwyhGe1AEg5Re8AwJuijMHNgKMAQORzBFN15lmdKbCRpfqd8FhskcEv7kiK0eynE37iqUQASBfXWyH79oqonQOGz7NWtpeIy8nht4Pn+evXr+/cufPYsWNGo7FYG29vbz8/v2HDhr0PEtC8mdKs3Gp8FAYv/tJJTxdJw9rSRnVE1TxBELgSqCg+iBt8adBqtXXq1ImNjd2yZcuYMWPmzp27ZMkSX1/fjIyM2NjYBQsWzJ8/v7J9rDiEAP5TY1/8livpf9WS1ZtatXCfwvOa48eS9jmL3RZVX4ciWKW497HCGR6bHrUDQMT1bqASHwDgmWxTUGOeThNV31004VRAoCL5IO7vpXre/euvv+bOnVulSpXjx48vWLDAMjhgwABHR8fZs2dfuHChHB0sBf369QsODt6wYcOGDRsq15O3AwNYbmvthOXeHjKyrgPAkX/z6c9/+eW7R+8IhmQeS5E2VIiqSAyB2ogeAUwm/arD80fv7ut9WB3rfaI+aoWl70uK+e4pcDwC0L2qeGAt6YJWyl7VJBTLpxi4atY4ADRyIgfWkub/KvPoHQAQTF5+0TtYZGkcx1q+p6JnAmdmElZbondEXIVw8H/1kSTuMFLi+5D0XIWQjram4NVpE06l9q2rOz8hLW1Sembq+/RciCBI27Zt9+7dm5SUtHnz5iZNmhS1iYiImDdvnpeXV6dOnfbu3avVaivezzwQEekw73u3ncvtvvtG2tQXFYuo6ITsExeT5qyNGz3bHFZ8eb+AgMCrkMvlDx48WLp0qY+PDwDMmzevf//+z549S0pKGjly5IwZMyrbQZg/fz7xHlQhCXx8JJhir2X8jSLYIJfRRWc/t+/lIHJONMVdTa/kp9yPD1RaB1f7Ac9YcuYBgE5YydNpqLwZZtOzcn0TEPggKFUN/Nq1a8Vi8cWLF6tUqZKUlNtPq1WrVnfv3vX09Pzpp58KdXSreFxcXL777rvK9eFdwACsMTSJZQGgsekuAAReT8qb7d2796sOfKZ7vC9hy1CXMT6yunmDL6P3ie6pm+M4E2c30sVmgGPEV4GZx1Ks+zoAgCWGL3YfPn/07rq6enj3ADrZbOvn7H2ifsRXQen7kgDAsg9vsV/cRulghfXwFh9+aiizd+Q9AHeZxqQe5ukU3vScip3LaPZYxknP5SUrzaIi3NEfVw9lNHvpxF9cqdAVGZOj8ao75N/2MH4xQaUaJLN6o/X8jTnaJIb90VopLpLNHkLRv2Rrv1XIGr1DWbhSqRw7duzYsWOfPXt26NCh3bt3x8TE5DfgOO7y5cuXL18eM2ZMp06d/Pz8evXqRZKVU4iOKWSy9s1k7ZvxNGMKiTA+fGx88IROSWOzK3NxQUDgA8XOzs7SHRYACII4fPiw0WhEUVQkei8ktTmOYximsr0Q+Ag5mLCD49mqVjUSTXGJpriiBjWsaqeYE08l/97Uuo1Vee4ZfIIQbnPZ9FNs9lU2629E5Mkk7wBASc/lAO9RsaGAwHtLqQL4gICA5s2bF1Wtc3Nza9y4cYl9WQRexe0EatSfGcvbKr+sJgmnGQAgeaouHRgYzqfHp1pspFJpx46vbJN2PGlfkileQyX7QG4An3E4OTd6/8E9dWNu9O7+iw8g4H3KN6J3YObxFOu+DsCDIVAbPfKJ98kCxV2FovfIr4LoZDMApO9NBIBiY3grApnR7CPUCUcwGeE2m3r+PwBgkrdbBi197Ep7ClSMO36LOwxnNAfoxF88zZFLMmfEaTfs040YrOo/28a+fqkD4IsGUxhNJ7LsJjub/DF8CEWPSE3P4bjPxOS7BPB5+Pj4LFiwYO7cuRcuXNizZ8/p06dNJlN+A5PJdPbs2bNnz9rZ2fXv398iJFlZmk8IgUvq+0jq+8CIfryZKqpsp//3vulJuLiej6SeD2ol1NQJCJSKvP6sAgIfK7HG5091wQAQqQ/drF/9Gksdq/0343IXeyGvuyxBcGvCZSoVM4eOmYOI3IGncfVw1EqQGxAQKBWlVaF/1e3cxsYmPDy87Pz5tLidaGzPsG4AACAASURBVDYx/PMsZmeOjuZ5AKhDBYt584H/5ADZFpvPP//8VW9+lCE80hAqpcmm8lZ5g6gEAwR4jtf8GsvTfF70DgCy5qq8GB6VYrnG+aBiTGFdH9LJZnk7G9fl3hFfBdHJZllLleM0z6hvHqXvTQSer3qkXmT/4PR9SYgIdV/nAx81uPobJuU3Th+U+zNCEJ4r3/gsCIk7jMDV3zBpR5nEdW7G8NlZi9JyNh3MGHZGPWK8jZNNKeojfrGzHqZJv2Uy+6emb7O3lSAIADyj6VGp6Tkc11kiHiazemPHXg2GYd27d+/evXtmZuahQ4f27Nlz586dQjZpaWmbNm3atGmTl5fXkCFDBg8eXLNmzTL04U0pVpc+56/r5mfPtX/fBAwVV/eSNKglaVCb9HSB90lQUECgIvnpp5/eyH7KlCnl5ImAQCXiIHJua/u5gdWXaIkCWk8u9CQve3DHbxnNPs74DIzhCCYn3CpZUUtA4AOiVAG8r6/v3bt3tVqtXF5go1Wn0925cyevU6vAm5Kk5wAgyYY9k52bfD4KAgHg+r2XUnzeXbq+6nBJikmtIZo9UBC1EXiRCK/qaa8e76bZGMfTvKy5Ki96tyBrrvLY4BM18glnYAk16bm1QMSlu5Nl2W+3HeQY8VWQZR/e+4QvaoVVPekb2ScwfV8S8CBvq8o+n551UlMogL+dSAHA7yGG/j4SEvs4AiSU9FxletIVgAcA3HE0Kqn+lmdCCNx+MG43gM04bU5cZ6cPnpjzk0637WTKQJnTt19ZVyVeG1J64vgete0wTfp9M/Vtavo2e9sYhhmhSc/iuM4S8Vpba7x8IlJra+tx48aNGzfu6dOnBw4cOHDgQHR0dCGbqKioJUuWWISvBg0aNGDAAA+Pwv3hKgv7SSP1N+4bA0PMTyNNTyNNTyMzD57BVApJg1ryLq2L9rETEPjomTp16hvZCwG8wEeJCBUPcx1f2V582iA44bHI/Kw/ABCuMwp39hEQEHg1pZJG8/PzS0tLGzZsWE5OTt6gXq8fMmRIZmZmv379ys29j5z6akLtiJ4hDZZ43YcgWtJBOgMfGpycZ3O2UZNXyZ4hB2+M3erQxrEbmk/bXHc7K21nQt73qdvi8x9ijjTEz4kAHhAUoTVU4uLn+Wet+zrYDHYCgOixIfmjdwCQNVdVPemLWmHp+5Oyz6ejUtRrb10oSI6ZA4Bn6fSimznwsYDKP8Ns+wAAgtsSLtPf9XQIhtn2kda9JvI5RslbyDjtIO32rmEtboaM4gxPXn+oJYZXY9h9M/WNJq0Covf81KxZc8mSJc+fP79x48a4ceNsbW2L2gQGBs6YMcPLy6tFixbr1q2Lj48valPB4LYqZe9Ojgsmuu1eqZ7mL+/cErezZrNydFduZ+w4UtneCQhUAqeK0LlzZwBo0KDBkiVL9u/fv379+h49egDA4MGD4+KKKQyuYObPn1+okEdAQODjAFN1xh1GYdZdccdvK9sXAYEPiVLtwA8dOvTvv//et2/fX3/9pVarAaBz584PHz7MyMjo2rXr+PHCEuZbMriW1KcKPjQt3dL9boBMzMXdv/aAoenciJ30roY7OXHFHWt6FGYMCEGlElXfAlv0hgAtZ+IQHFF/756yLiZuaigA2I9xBQBzpCGs20M60SxrquSMnCFYq7uTnf9YBEMcp3hkndRwRhZQcJzqieaTWpM1Uyk62GSdSQUAWUtreevC/bdVYlRj4DAEfg8x1FMT/X2kVLQx63yarZ8zJvuAW7CQHkspzoCrhyJ4mbUcx1QdVaqOnD44IX69KuvUZ9qTpuCTqLwp4fwDZt3lVSIulhh+SEpaCEUDQIeKit7zQBCkVatWrVq1Wrdu3YULFw4cOHD69OlC/ed4nr9169atW7emTJnSokWL/v379+3b19nZucKcLBZUIpY2rS9tWh8A6Lgk4+MwUdXCXfF4ljU+DCFc1ISzQ2X4KCBQEfTqVaCU98iRI5cuXVq0aNHcuXPzBidMmLB169axY8e2bdv2228r+cEax3EcL225n4CAwIcF6bWmsl0QEPjwKG1zsr179x4+fLh69eoWFforV67Y2dlt3rz57NmzlaVf9XGgxjFHDHXAMACoxUTwbM4/D15up9fr0MEDx6RF32Gez9h3CgCUvTuj8gLFz/bfutoMcuQZPnVbvMNEdwCImxqaujU+f/SO4IghWEu6ir1218l/LBVjCv8igDOyhKMIOAgd8vD4iT0vrghxk0OzzqQiYhQRozl/p8dODi32FdVVkwCw4EZO8J2c0M4P4qeHRfQKYHXvUe+0NwUhHUU1fsesu5f5mVGrem41tssaPiZcZyC4ktPeMYcOMgZ+Zk7aDFzxO05Gjs+TY87mOKrMfSodJEn26NHj0KFDycnJu3fv7ty5M4YVXqPhOO7ff/+dOHGim5tb27Ztf/311/dhTx4ACDcnRbe2oupehcb1/97XrNyaMHFx/Pj56ZsP6m8+ZHN0leKhgECFsW3bNi8vr/zRu4UxY8bUrFnzyBEhUUVAQEBAQOD94g26i/fv3z8wMNBgMMTExBgMhtDQ0LFjxxZ9ahd4I1xx7LKTg4HnAcDDFAgA/9x92S9nfo8vzzqq5UVEzm7e3X/R7TFqq1J82b7QFIIhHltq2Qxy5PRs6o4E9Xg3AIibGhra7j6daJa1VAGGaG9mka7ian82FHkVkMfLq4H32FrT3JfFDKjrGIegC7ct0Xvq9nhUinkfra/oaAMAWSc1xb4iFzk2pLbUNtmc3jeQTjIjGKK/k/2hx/DlCkI4EK4zxQ2CCfcFCOnMmyLYmNkZD+ub41fxdEp+y6cUPTI1PYfjWopFagx7YKbGpKZbfnkqC4VCMWzYsIsXLyYkJPyfvfOMj6Ja4/A7dXtJ772ShDRKElroIIjYuRZELwpiRS8qWFDsvWDHgqiodAQFpBtCTQhphPTey2Y32Tb9ftgQQgghlDSY5wO/3TNnzryTZffM/5y3fP7556NHj75wRY/n+aSkpKefftrb23vUqFEffvhhcXFxl6P1L7KoMOW4EZhaydY3te470vDJjxXzl1Uvebf55y3W7Pz+tk5EpFdITU21VYC/kMDAwNTU1D62R0RERERERKR7LkPA28AwzNvbu2P95+Tk5Gtq0g2ElRUAoJ7jWnlei6IyU2pFHZ9f0ubWTpLk+PHjLzzLSBl+wbYcGttC3DMBIQlWYN7NX7b7jvVn4o+xjTScr+Ebf6q2aXi2mVGO1oIAxiN6VIaiSqzwllOnhx7JCjh0Ovpo3oSUwtvSDTsapUEKACi+OyPDMy1vWi5hIai5+qJ7023qPWB9lP7PBsPfjagcuzAGvp0XXPC3VpVp9ExFiMLzuzCJn6xNw7eKtXwvCoKpCfenZTHpf7l9nE+GStl6rvIdU1okVTCfbz0GZ9W7Le79G0f79nj4ftfwNlxcXJ544onk5OTy8vKPP/44Pj7+QiVv865/7rnnAgICYmJi3njjjQFVhBLTqhyfmuf1wzvu779gN/dWWdQQhCTo0krDtn21r62kCsv620ARkWuPu7t7RkYGd0GmFY7j0tPTvby8+sUqERERERERkYtx0bgylmV/+OGHlJSU+vr6oKCgBQsWhISEAIDFYjly5Ehtba3BYDAajSdOnNi0aZMwAPTDoCO3ibltc9Njscq4CAIA/Amcb0zpuP0eHx+vVCovPHF3yipaxQdWadymTwCAPXXbhywIdUx1sIApZ8TxsJQ43JG0aXiuhTP83dDwTQWCowLDs42MNc+EqXGuhbXmdiidUkdT51+Cp4SRn40ofry0HKvw3uFl2N6EkKjHm0H6TXUNq6tQORawIWqvm7Th1HkOxjoLDwAteabsh4uVzUyhv5ynhIr/nmZmOSsBTMcNhbemB26NxlQDO5pRYAW6Z57euD2Cqa/x1RHibp+H9jnf82rtnnEtv4637ueaNnNNm5uk4b/L5lhlM6fItba4d18c/+lsXvqZNfVfONqHk8Slx+99PD09n3nmmWeeeaa8vHzDhg3r168/ceLEhd3S09PT09OXL18eHBx82223zZ49Oy4uDu1BRb1eB0FIfy/S30sze7LAsFResSUzjzO0EB6dA+PZxmbeaCJ9xKJ0IoOYMWPGfPfdd88///wHH3zQ/gXkef6FF16oqKiYMePahw6JiIiIiIiIXA1Il9rbbDYnJiZ29J1TKBQ7duwgCGLWrFlNTU2d+t9QAj47O3vo0KEoil64ZXFZrM81LztouCNENmeU/P76xgVyZmFBzLzl1o172lKCvfHGGy+//PKFJ/70z9JDLrmLYcHQ6BktdHPStL/cU9wBQABAAHAn0qbhWw/oCm45BWc/GUSKClae9JQGbImmSyyEC4nZEwCAylDeyLHNLKdnOD3L6pnmusaK34rsyrWIHMXXqOqfK7ErtW+/OoKC/x9RewIULxzQX2ibewP96tel9i1srq8MBSS4tK08nvROFz61hS61KOI0F2r43CaGEyDccUDoT7p4MVu/5tL9ABDCURZ7BpBeWY/gALaYzOt0hWNbfrvdtMmebwQAI6pWucyVuvwXkfrbuh23UvMbmzgB4qTkT06OvWHJ1VNaWrpx48ZNmzYdP368m98KFxeXWbNm3XLLLZMnT5bJZBfrNnCoXLScbdChSrk0PEgaHiwbGkx4uopi/gbh6NGjo0aNSkhIOHLkSH/bclU0NjZGR0dXVVVFRETMnj3b09OzsrJy27ZtWVlZnp6e6enpXZacuF65VvO7iIiIiMggZVDM711rj48++ig1NdXBwWHx4sUBAQGlpaUrV66cN2+eRCJpamqaOnVqTEyMQqFAEMTR0XHEiBF9bPTgRmD41hOCwBBGS7zKEiuXRlvle+ScozWD5YUDKed24CeNDehygAcmvjGrrszBMxB44dTNSTb1XpfYcHp0xsR3JrMN9Onwo2GZCYSnVDFSIw1RUEVm42G9Tb3b4t5lQxSdxpR0eP1z8fc5MzPmfvygeouSe9BY9V5dZmlm/KZRkhJb3AQiCZInuBD/PdAUdMZYGCjPGqIs8pdzGKKstL7ydal9C1voLycFxL/E1GRHbJ3qNG9zjXVjnf09bsaL7MPft11HsULGfNeBUDweIdvypSMS74vFmAhMLfBWhHAGpLdyQGAAdyrks+RDfze+fF/L47HGXXPMayOpDKHmS0vN15h2Iu4yv0458eVmPSeAHYo+ruqRLwBvTBW4SydmQ1AFqhp+sWT4l4uvr++SJUuWLFlSWVm5ZcuWzZs3Hzp06MLn47q6uu+///7777+Xy+VTp06dNWvWrFmznJycrokNvYFqypjWvYfZ+ibz8Qzz8QwAwLQqaViQNCxQPioWU3fhPiMiMtBwdHTcv3//M888s2PHjo4hLbNnz/7ggw9uKPUuIiIiIiIyKOh6Bz42NjYzMzMjIyM8PNzWkpubGxERwXHcJ598snjx4r41cmBxlSv0TPWnTPmKLg+dyuXGPNRqe61VIeW77JSxxxFpYNcD8UL2rKP0vxYAkE5Xyr51+OuD30b+OJIwEwCA2xNDUuIJFxIABE6o/F++OaPF98eITlnrLuSk4eiXpe8pMOXbQV81PVGh+70WVaJFscV+Sb6IHPX/JVIeqSTcJABQeGt6y942XwxMiSkStPXJeqmFqw1XBmpw45FzefJa9uuK52TwFt7+ThfjCQNdblUmaIP/iQW0TR+GrqpleCF3gSuBDgAFz1stGSMFqoL0/xx3vv/C4wJTb00fLnCtkiGbMM3EPrDIIghrW01ftxq9qey7Tb9PN/8tESgA0GHOW+S3ntHOedctWt0D53NOv5vKndPDi0pC1vZG4n0bDQ0Nf/7555YtW/bt20dR1MW6oSiakJAwc+bMGTNmREVF9ZIxVwlb32TNzrdm51uy8zldWwIL+bAI52WP9q9hIr3KoFihvyzy8/NzcnKqq6u9vb3DwsL8/f3726J+QNyBFxEREbnBGRTze9cCXqvVent7Z2ZmdmyMjIzMyspqbGy8wZfkr3KC5wz7qTN3AEAlN7TSrAx1IBxkKADwpqwPf6xa/nVb8bDbJhJr3/eXRZ8ErPNuOQAALxTMTm89oAMA02Rr1OPx5U/n0uVWALB6W6XlMgCho4bvIazAvpz3ZD1VM9fz0QkO0wVOKHs0R/d7LQCwUvbU5xkL7nkOObsrK1B8a7K+ZW9Ty94m65lzEfUcgWIMTzhLgvcOk/i3rRe0a3hJgJwqMqMKLLJkLCpv274eWAIegG3cSBc+ghBO0ujUC6Pc6aJFbMMfmP0sSfDPfWlVM8+/0WzYZbaoeMNsy9bbjBt9WFsudxTTjMOd52J2MwGVdDOCQJVZMuKAp1BZEEJ6dN2HruYt+YCQsqij7Y76vYfBYNi1a9eff/65c+dOvb6LoIx2PD09Z8yYMWPGjMmTJysUXX0pBgBMdZ31dCGVVywfEWmrOd+OwLDm4+mEuwvp6wEDIdRf5OoYFBO8yOUiCngRERGRG5xBMb937UJvMBgu9Fx1c3PLysq6wdX71YNpJmJ207jmf4qosCeKXl0/1MHdlQQAy8mQpLRz/vMTRhCE96ud1HuxOV+Fa5xw58Kz6r18fHmUf1zh7ekggCRSvv2hrfkj855JWco+Y2Z1zJkRxy5Lw+9q2FpP1bhLvRLtp8DZbPaoFGveWrf7rb35YWdi9UdGaEfbOiMSVD3JXj3JHt4JoiutLXt1Ke+UuFRZMYYHAKaeKpiRprnZyeVpb9JLqp5oX/peqNfiM1SRGcGRgD8i29X7xTAdM9R+Uuq6xE8x4lonirsUuOMdbN2PfOtRtuoTwvvVjod4UzrbsB4Qkjy/vQ+wQ9GPHexmyGWLG+EXxbxfFPPGcbkfc9sR3QbOcJAzHERwDWZ/G+46H5VHdDkCIvEhXBcx1Z8CppEM2dylh7w1ZyZY8gm3R/tAvQOARqOZM2fOnDlzGIZJSkratm3btm3bSktLL+xZWVm5atWqVatWSSSSxMTEGTNmzJw5MzDwIv4p/QTh7kK4u6imjL7wkCk5tfHLXwEAlUklof6SEH9pWKAk0AcZGKkHRW5kOI7Lzs6ura3t8ui0adP62B4RERERERGRbrho/q0L00EPiATRgx+B1aHyCE6/d5x8/YuemJslYF2BNQSz+Fnqj2WeW/IfF+eGoOcJ7xqq8u3CZd5S3we/WtByQAcARYlFvkJA86oaRIK6v+Lv/IR3qa4qvyZv3di1i798ofyxPFbH5E87GZ6e0BPDWlj9zvrNAHCP+3z0bGg3giHen4d6fx7a3GTNrzyzrnp1lHo4ecE2L+kptb/bhfr0XJ0tTIvTldaGbyoElvf+NBQ4Ad9Yi/ACICCwgv6vBtUEe7g4xiP6wtvSeRNnTNIHbo1WxGl6cgvXDoT0fceaNZGp+Qp3vq9DFINAly4F4An3JxBp1xkKeptwknDA0HqOBwBcES11moj6ruAaN7H1P/OmdLb+J7b+J1Q1Ane8B3O4DcG1553MUwjhApiKN6ZSBfNRRWSnwQVTNt9yBMEUQHoAbwVU2mf3RRDEpEmTJk2a9Nlnn2VkZGzbtu3PP/9MS0u70EWIoqjdu3fv3r178eLFwcHB06dPnzZtWmJi4oDdlrchiwlXTRplPV3A1DZYTuVYTuUAAIJjZKCPbGiI5vapCCEqeZF+oKamZsaMGenp6RfrcEMlqRURERERERn4DOyCXtcjbO0qpuojAEAA7nH8DepgFgAAnMhljZa25yRXRzTIrYkqeFgqG4LKwwBAYLm/jnzB23N+8iBez9i6eWZ6Yc1AOJMBG6Lkw9QAMM3p1mTd/mprRY4yR4niwAu8uad+gOur11g48zBNQrgq+sKjiQ5Tk5p2l1qK/mn4c5bL3Rd2MB41eBeaAaBookPA/ibOwLo864M7ktqbnUCA/Olp7kf1ANCgxZ2a2YZVle4rAjFl15vw7eodEOBaWVvSuz7W8KgiCne6h21YS5e9KglZa2tkG9fzrccRwhl37580EDUcN6++sZ7jg3CiimP3W63PNzW/72CHuDz0OHpLIJ0zz7rFrnkz35pCt6ZA2TJMOw13moNpJwNCAgCn30WXLbMNxTVt4Zq2dHkVgTMxpS+ghCPmcHvf3VsHoqKioqKiXnnllcrKyr///nvHjh379u0zmUwX9szPz8/Pz1+5cqVEIhkzZszUqVOnTZsWGRl5YQn6fgfTqhwW3QsAXLPBmltMnSm05hTR5dVUbjGVWywJ9ZdFDelvG0VuRJYtW5aenh4VFTV79my5XN7f5oiIiIiIiIhcAlHA9zWY/Sym6kMQOIqXSFCqVjX1H8F7MnfqUNrh9j5jY2UAgMqHoLJgW4tx7+EzsgJEgClOs4gvieKTedoSraSZxLV4yP7hpG9bqDmO4He6zd224TfFswjwAqrAgnfG9sSqUkvR0eaDAFBuKX49/39d9jGwegDYUb95nP0UDWHX6agq0S5nhCYsxeB9XO/8hFf9FxV1n5R5fRIi8ZNVPJtnPKpHUETgBadmFgAYCfbi301m+7YtR44HAFi8rxkFxDnXOG1FIW7lAcBWA49rZQtuORW4OVo5Wgt9COG9nNNt55p3cPp9mHYS8Bam4k0AILxfQzBVX1piw6beK1gumiS/c3LIZ5gFDU07zBYAeN1em0szR/iAn8kl47yXPMsd9DVs5gwHOd02TrcNwR0wx9txxzmoehyC2wusDjAVcK2YciSqHtU+Pt96jGs9hmBKgTMiuBbVJPb9PXbC09Nz4cKFCxcutFqt//77r03MFxUVXdiToqh9+/bt27fvhRdecHV1tSn5yZMnOzs7973Z3YPZaRQJMYqEGADgLVYqt5hraZVGhHTq1roryZKdLw3xl4T4kf7eCN5b9Q5EbnD27NkTGBh44sQJkryMhCkiIiIiIiIi/YUo4PsaVB6OO89j637UC54uUKS0ZH7n/PbUluc6BsCPiyUAgPB501ZjXOB4w7Z9t8rsJfdMcZV4/LLzq8AGXwAAEFg9W/dFhdeHwe3nBqWFzH52NvAIL+fDjyRIAnq0o1JjrRBAAIAGuq4B6rrpSfHWRrr+QgGP4MioTVFZD5z2OtjU9HON82Ne9V9VVDyb37yx3pjcjMoxpwWedSvLgIdmNYFxwoESqqmZm/Vvk1sjPXSoOitQvquICik13/NdOU7xAMCjSNEE+6ADOuAF3sQV3p7exxoeIZxxj2eY8hVM2UuYZhxT9ZFAVaKKaNypp4ncryGd1LsSRWIl5Conh3YNv83NeW2r6VejKYnik2B8mN2UBe7mROM2aFzHm0+ztd+xtd+hsiBUNZJr3mVT6Zw5gwz6DpF4A4BAV1tqVwEAYBrgjITnUgQfQNkupFLptGnTpk2btnLlyuLi4r17927fvn3Pnj1dZrCvra39+eeff/75ZwDw9/efPHny5MmTp0yZotX26QJQT0BlUllMWJeHjEkpVH6J+Vg6ACAEIQnwlgT7SkL8JEF+mH0fR5SIXLewLFtdXb1w4UJRvYuIiIiIiAwWus5CjyAIQRAazXmPiQaDgWEYR0fHC/s3NDT0loEDj6vMUstb8tnKd9jmHcDTgCmBMxYRgd50hefkeqO57bM49YcqJMgLd7yT8HoJENJ0KKX+o99Mp8ehKjfiN3X6c4cCDgSWjS9X16jt8rQAUHB3YdpzaQDgcsIl8clxCI8wMmb9mnXPT3rLReLeE6sEEKqs5azAXrKnFJW6SrpOYA4AAieUPZKjW1+LqXGH+93qv6oAgLPqvRx4wf5l/zN3u7X/p3OaeRKrtAJAqwLD4jSyI3rEtveOIYYVgVELPYWNdWWPnwFeAABUgfVcw1tzTZIgOXKVleV5ypKZIFhLCLcnmbpVwNPS8H9Q1YirGvOKuLOu4TTNtKv39vaTFL2gocksCMu0mgdUilae/81o/sVobOJ4AHDGsPuUiv+gJbKm9WzjBoE5tzSDkO4CXY053CoJWg0AVMF8rmkzIo8QzNmoLFgamQzIQA/JNhqNBw4c+Oeff3bv3l1QUNB9ZxzH4+LibGH28fHxA1+ucC1GS9ppKrfYmlvEVNVBhx9q+chI5+cX9KNtIjBIstReEpqm1Wr11KlTt23b1t+2DAjELPQiIiIiNziDYn6/qIC/rFFuqCQ3V1sHvvJ9pvKdTo0nz3Dj/ttWAd7FAS3arrZ9AtKoo6gstOrpD5o2+bJ6ewAAT3TzSxvljbKi8YUoh9658G6nPCcAyLwzs2RM8S3P3oLwKCNj/vjpt1Yv47LAdwLknV1zexuBE8oW5OjW1WJq3PFBD8OeRs10x7rPyoEX3F8LcF3ia+tWbeRuWt8wg0QeLzel/VDtWddhKxVBXJ/zcX/Z31YovumXmsvT8AJUPp9f/3WFeoqD/++RqPSqki9yur+p/LZq8LjjHDLwm6sZ7Yp5V99Sw7Jv2dspL6i0d5KiPzK0PK5WjZa2JRdkBGGH2fJjqymfYQCARJDpcukCpdzXnMw1/sE2bQfh3F8bd3sS0yRSuXcBSiJACLxREroR007qs1u7JhQXF9sy2+3bt6+lpaX7zgqFYuzYsRMmTBg/fnxsbCyOD3RHJN5kpvJLqYJSKr+Eyi+VhPq7vLioUx+6pBKRSgi3ztVDRHqJQTHB94QFCxasWbPm8OHDw4cP729b+h9RwIuIiIjc4AyK+b1rAd9lvqhuGOD5n68tVznBC3StJWM4cCY9q9Xi+hrc3Y2tXvk7tWylxdbhjknEL+8HCnQNphkvGbLFdCKv6PZMVm9PekkEFpgaCvcinbf7Yd4EAAhWoW5yEZNjBQBAAXhAFIhbUiDmJ5GgUjXeP662AiuUPpTdvKUe0+BOj3jWflwGvODxVpDL097tfRrM/PT1DXorP9lXmpZhXPlugYQ5778i6SnV3upsd7uzYoSm6ddzGh5T4eGZCbjTRXZQz6p327trouGpM7dxhoOAymXRNtWwrAAAIABJREFUqQjpdjVD9TEnKfr7VuO/FqsAgALESSVzlYrxEoHKuZk3pgGCgsADAAACICCkp0BXYnbTJSG/97PdVwHLskePHrWJ+ZMnT17yS6pSykbHqseOcBw3wjEmXItf3GUDkQWRvu9fa3uvDUxlbdUzb4EgoEq5JMhXEugjCfQhg3wxtbK/TbtuGRQTfE8oLS1dtGhRUlLSE088ER8fb2/fuT5IYmL/p8PoM0QBLyIiInKDMyjm964FvEg3XP0Ez1R9xFS8WU27u5PVFkRGAnP/C4Zt/7bllv9wieOiOwFAkA5NEtjgrPidfLmcdaLUbg7mTCPpJaUrrKSXNGhHrMRPBgC8lc9NTLGeNgIAqsDCjo4k/fs/k7DACqX/Pd28uc1nu5N6t5HTyMz9S6e38moT+9VbBRJaAATcXvTnLVzzpjq6zGrrRvrKAjdFGZP15YvzQBAwJRaROwbTdrVrela9IxLU863A2vdLmXr66jU8bymg8x/AXRfgLg9d8SD9SBHD/mw0bjNZrIIAAIs16gWSRktGPPAUYBrgDB07Y6p4zPFuzH4GQrj0k73XDL1ef/DgQVtyuzNnzlyyv1KOjIrCx8XiY2LwmFCsc9o4VCIfXtqXpfV6jkDRjd/8bj2dz+nO+zRxZwdJkK/27hmEx6D/NAcag2KC7wmXdLi7oR4SRAEvIiIicoMzKOb3ge47el1CuD3RVPmrO1laxAQFEAVmTHMsq6n96Jjh7iCU464LBDa4YGYqXy5vdW0VYgV8h5H0lQX/HVO68IwxublgRppNw6NS9PQfuZ4PuqhqlMG7YgeCegcABEd8fwwHFJo313u8GejyVGf1DgBhjsQvN9s/8JdOUc9JaAEAQADTCYP/75EeKwJNqS3Nm+qat9TRZRaqxGrY1WgLA+ZMHNvMdCHg29U7ifr/MlQzw1E1yaHgprSWPU3FczL810VdsYZHZUHSqKNXdu5AIIDAV9hpF2vUfxhNu8xWTxxDJL6E6yKm+lOUcOQ5AwAghIvA1AGCcq3HuNZjULIEVQ3H7Gbi9rMQqV9/38EVotVqb7311ltvvRUAqqur952lsrKyy/5Gs7D7KLP7KAMACrk0bkTYmITIMQkRMQ7fybE6wu2xganeAQCRkE5PzwMAtklPF5ZSBWVUYRldVM7WN7H1TYSbs/Y/M/vbRpEBytKlS/vbBBEREREREZHLQNyBv2yuyQr9twd+nyt7zCioSNRaUUFH3tW2b6aSo1V71DipJQKOF91RbjphMLq0pt2XNu7jRARDlHEa//VRCIEU3pFhTG627cNn2qV9XfYBAsgLgW8GK8Kv0V1eM5h6mnA+z93dSAtch/91xc3sPRsbl39bFlpiBgyAA/kke5efh0oVmAxHgBeYWqr86TzDzsb2UyS+Mrs7XOzvd5MGnV2t6Kjefx2qmeHImzlUjlkLzAU3pTG1lHqS/dVo+OsPgTNa04cLTF2hPMERxbTGZIRwkkTs5VsOc7q/OMN+4Ns8IFB5OGY/E7O7GVUM7V+brxV5eXn79+8/ePDgv//+W1fXXc0FGzgG0aHSsVPmj0ucPHr0aCenQRJnLghMZS1TXS+NDEFl5y09GA8ca/rmd9zdWRLgTfp7kX5epK9Hpz4i3TMoVuhFLhdxB15ERETkBmdQzO+igL9srskEf++2pvmyBxJUR/LIIalbMxa8Yba1T4xTbP+UwF3fK/lvrOmEgXAnjr26I+yLsbKstmdreYwqaHtsu4YnvCSbV24ucy4GAG+Z3/Kgj1BkQGvUlanGz1JbL2yXUvyyHyvCikwCCggP6aHK9+d5PThc+cIwZfF9WYadjYAiwAuEqwQEYOooAMCdyMiSsQAAnFC5tKCjem/4pqLyhQLHhzy8PgmxFpgKZpwSNfyFsA1r6aIn6jAXAUFd2ZqDru+Gec73tWV0482cfi+n+5vT/yOwbatLiMQHs5uG2U3DVKMBlfSn6deOnJycgwd27d/66qE0U72uRz+GISEhCQkJth/3sLAwFB18/6NMh9Mav/hZYDpUnUAQws2Z9PeSDglQTR0Dl5nH9AZkUEzwV0lBQUFVVdX48eP725C+QxTwIiIiIjc4g2J+F13o+wJOgOrW854GhruS751Zuink9iA67/PMc4/R8REcKouoXzXOdKIKU2Ihe0dGuI0tWHPKCM2YFsftCPOp1oJZaUHbYwM2ROaOSaGKzJHvR/Bf8mbOVG4pOaTbm+gwtc/v7zLQSBCNpLPgMVC8VYK+P9/r+R/aNHx0rvHFXyqtsSEd1btylDZwSzQqQ41H9M1bG0hPCQCwDXR26GGe4gFD3F/z10x3qP+6ovL5fBCg4ftKgRO8V4YGbY/Jn5HWsk9XfE9mwLpIhBx8iqs3wJ3uoetWuxhPAkAeMeR5dAZfUx8rIW9TyKfJZEr7WzD7W0BguJZDnO4vTrdDoMrY2lVs7SrAFJhmAqadgtlNG+yh8mFhYWFhYQvuUNGlz+dVux6rXfRv0pGkpKSampqLnZKXl5eXl/fTTz8BgEajiY+Pj4+PT0hIiI+P71R6c8CiGB0rHxlJl1XRxRV0cQVVXMFUVDPVdUx1nSk5lQzwlgT69LeNIv2MIAhvvfXWhg0bLjeprYiIiIiIiEivMih34Hfs2LF9+/aCgoKAgIBHH300JiamU4fnnnuutLR0w4YNvXH1K1ihX3rQsCHXfGH7q3Gv3UmtH3Zva25J21DbP1MaRv5+i2VU4Z0ZAsU7PerNVFn02xswDR60LYbwlBbMSLPmmeRRKrs5rlUvFQggHHzuwM3P3mtgm78p+1CNa94O/UqODbKiAAHf1ADAiQedD+VZQp7PsxzW2/KjY1qc07MIigjt6l3RKbEYUKWW0xHnVsgwNc61sgDgvMCr8Zdq3sw5PuThvTJUt762dP5pAAjaGasaa9eHNzeg4VtPWE9PBxCqgrb+jETtMFvMggAAUgSZIpPOVsgTpO3LLTxvTOOad3H63bwp6+wACKqIwuymYtppqDIaYPCtjDCV7zCVXeSWL67ij2awh9PZo5lcflmPvukoioaFhcXHx8fFxcXFxYWFhWFY5/+uAxaB45iKGrq4QmBY1ZTRcL5bQeOXv1pSswkfd9LXk/TzIH08CS9XZPDcXW8wKFboe4IgCK+99tqvv/5aX1/fsZ3jOIvFMnTo0MzMzP6yre8Rd+BFREREbnAGxfw++HbgH3300W+//db2et++fd99993HH3+8ePHijn327t2bnp7eH9Z1TYQTcbwa67hSwgNUGbkv7RaPKdiSV9r2oIChgARMUdhPUgXLAv6ILP5PZsM35QBgU+/yYWoACNoRWzAjzZzRas5oBQQOPX1IPk8bogwHgANNO/OMp7fXrZ/jPnCTpVOckF7HsHwXy0bp9bSTA6FbOUTyyGk0rQVQaFfvqrF2AZuiUHkXgkHiK3N9zrf2g1IEBUSBcy1t7gxN62qUozTGZH3j6iqmgTYm6wBAe4uzMqHbGvLXOwJTD/y5tSSEdCa8XgQQApU+r4P+Jblw3ErtsVoPUeh2s+N2s8UZw6bJpbcr5KEEgSqHo8rhhNfLAl3NNf/D6Xdzhn95UzpvSmcq30cIZ0w7EdNMRrUTELxzJaqBC0932ezvgfp7kPfNIAGgoVk4mskeyZIeL/BPSzvFsmyXp/A8n52dnZ2d/f333wOAUqkcNmzYyJEj4+LiRo4c6eXl1Xs3cfUgGEb6epK+nl0eFRiWazVy2fnW7Py2/jhGeLmR3h7y+Cj5iMg+tPQqEQSqHKAHy9a4A4Kpet+e/uebb755/fXX1Wq1m5tbQUGBq6urp6enwWAoKCiIi4tbuXJlfxsoIiIiIiIich6DbAd+3bp1//nPf/z9/d97772hQ4eePHlyyZIlNTU1W7dunT17dnu3mJiY9PT0Xrq1a7JC30LzsevriEThha1jHnqqxNYYGYynZ+Tbkn5bKHPWkGS8HgMA+ztdfFdHwNmg1LqV5VUvFgCAydm4Yc2m5XEfOpLOAFBuKXm94H8IIG+ErHSVeFzdXfYWryQZfsvpwhmhI1KKf+n78tCStm7dqPd2at4srnm3BAAAAc0UB7qSsuQY244hbY/r2llOfj8PRYgbN7iXa0mmcmYD8D3oi+z2+fVrGF5+VqwOk5BfO9qrOsV781auJdm2LS9QFWdbUVQRhWknoJoJmGokIOelMBxoCKzBmj5MYJskwWvYxvWc7m/c6V4y4Euu5RCVcwugMmnEAerMbIGpIwNX4Y53mUymEydOHDly5NixY0ePHm1qarr0NQAAwM3NLS4ubsSIEcOGDRs+fLiDg0Ov3tc1h9Pp6dIqurSSLqmkSyuZ2raqEKhC7r2mswuDQDOAAEIQ/WFpdzBly5maz3vSE8HtZbHZgMou1mFQrND3hBEjRuTm5ubn57u5uT3wwAPNzc3bt28HgDfffPOnn37KyMhQKPrfpeuBBx7YsGGDxWLp7QuJO/AiIiIiNziDYn4fZDvwX3zxhVQq3bNnj7+/PwCEhISEhYWNHTv20UcfnThxokp1tRsm9fX1//77b/d9Kioquu/QE5qsvEADUJCb1dzeGD8y2qbeBUbImJlE1hMAgBCobmMd7kJ6vhsMCDT+VF31ciEgQNnTinrlvf+7T7vLDuwBALxlfmPtJ//btPuP6tWL/V6+eiN7g/E+krKWzg9GhyspAEjwINGzqbP2vxaErSgMKjaVhSpHronoXr0DAGZHtAl1BOzvcbO7y4Uqtui3NzT/UWPOMp3dcEMQDJhqim1mZOHKa35rA5/2jXGEcAT0In8B3iQwDQDILWqv2QrnNIreajL/Y7GmU3QTz3cW8KgU007GtJMBgLfk8fq9nH4f13qEN53iTaeg6mNA5Zh6NKadiGrGo7LQ3r29KwLBNYTXi3TJ/+iyl6ShGyz6vWzD77jzg0zpiwBAePyPa/xDYOpQ1Qjc8U4AUCgUEyZMmDBhgu306urqw4cPJycnHz58+NSpUzx/0cUR2zrj1q1bbW/d3NyGnSU+Pn7gZ7bH7LUye60stq3OBW+lmPJquqwad3Hs1JO3WCsXLefNVsLVkfB2J73dCS830tsdd3VCsP4OsiDOfgUkXgBd/6oIbANwJsBUgAy4BYjeoLi4eMyYMW5ubgAwfvz45cuX29qXLl26evXqN95449133+1XAwEAGIaxWq39bYWIiIiIiMiAYJAJ+Nzc3FGjRtnUu43o6OjPP//8oYce+uCDD15//fWrHH/hwoXtT9jd082T+iXJbWJu2dgIAjj8W5uRcy4/0MjE/9pelM3fTx4jAAC7i/SdM6T4vqz6LysAQBqsKF+cCwD0a/B71Nq7Ft2tyFGUP5Xr/2tbfa/bXO87oU/ObEnNak0bqoq9Ygt7j0k+0kk+natV2WLgV91kJ8fPPd+fiVN+ubJqn5ds1mnz+85d7OKyPGwvtIzxlAg/VVUuzQcAzXRHw67G0odPA4DdXS7qCXZ1H5UCCJIABV1u0W+vL386z5jcbC0wk55S9XQHzU1O6vF2yAVJ9a5XUHkY7jyXrV+DKmIloeu67EPl3cs178Sd77UVjRsmIYdJyJcEwcgLjmfVlwDwv6ZmWhCmyWUTZVIFggAAKgtBZSG42+PAW7mWw7zhIGc4wJtzOP0eTr8HABDCGdOMQ9WJmGYMIvHto3vuAbjzPLZuNW/OZnV/E66LmOpPqby7BVaPSLxx+5stWYkAKOnzDkAXvhvu7u533XXXXXfdBQAGg+H48eMnTpyw/dsporgTNTU1f/31119//WV7GxAQMHz48JizDHw9j0olkmA/SbDfhYcQgiB9PakzRUx1PVNdbz6WfrYdJzxcNbdPVYzqt58mwvVRtu4ngSoj3J/BXboINRJYvTV9uAAm0ucNQAbZ/Hhl0DStVLYt5/n6+lZXV5vNZrlcjuP4mDFj9uzZ0zcC/ssvv9y4cePFjubk5ABA+8IZABw4cKAPrBIREbksLJwZRVAJKhYlFRHpXQbZA4rFYrlQOc+bN++rr7766KOPHn74YW9v76sZf8GCBcSl3D4NBsPu3buRKy6zxFv1dbsma5oBIEyW83rOuXjakUMYrmmrwHIgPaAe58LUO9nF3KSZ7ui/dmi7hgcEXN/z+2D8Gw5JjnKdAgBkYef8G9W4ZpbL3eurf/qj6sewkCgMGUxZptDzBdIQT+nbb/j9nG2e6NN1xbI9pdYl+/XzvMiblxaAAC5Pe3u8FVS9oqj2g9LSBaepUkvdZ2WcntXe4uy3JqJ5Q23pgpzG1VVOD3twJo6utDZ+X9X4fZVqrF3QzoG40tFLEF4vcU1bOP1uTr/XtnPeEa4liWveiWBKwus8Dw4pgkixc58OKwhHrZSe5/dZrFIEGSuV3CSXTZBJpbYvBSrFtJMw7SQCQGDqOcMBXn+AMxwUmDq2cSM0bgQAROKFqceg6rGYeiwi6Truuu9AMML3bSrnFqbqI2nEAbbhN4GpBwDS53W6/HXgKdzpPlQ57JLDaDSaqVOnTp3aVgaiurr65MmTJ0+ePHz48JEjR8zm7iJHioqKioqK1q1rW1Vxc3MLDw8PCwuzbdGHhYVd+Q9On4PgmOtrTwksx1TVMhU1dHk1U1FDl9ew9U10aaU5JbOTgBc43pScgkolhJcb7tLLu/SolPRZQeU/yFS8gTnciuCd81kyle8KbBOmHovZz+pFMwYSISEhubm5ttd+fn6CIGRkZCQkJAAAz/N5eXl9Y4bFYjl48GD3fS7ZQUREpB8xccaXc58kUfKNkJXk9VJrVkRkYDLIBHxQUNCxY8fq6upcXM4Vr0IQ5Ouvv46Li5s/f/4///xzNWWZb7rppptuuqn7PrYYuSt+nmZqvoxsefNDXwCA0mr+yea2QH2lHPFFXqUKAACc23bioXi+HCUmOC/ysml4geY93w9OuvWgdpt65kszEQqcH/dyW+bfcfzJjjcnNe2poSr3N+6Y4jQ4nkExBOEE4cLndrUEfWLYRX3drawAAK1SzP4uF9262sYfq7W3OLu/GgAAtR+UVq8oAgCbemdqqOq3SwBAMVLj8VaQ1yeh5oxWw87G1n1N8lgVANCV1rwJqbgjoZ7koJ5sr0zQXq/b8gjhhHsuYcqWM2UvYprE85yEBY4pXQYAuMf/ui8ORyDIDjfnXWbLTrPlJEXvsVj3WKxSBBknlUyRyxKlknZPe4Rwxh3ngOMcsPnYG5K4lkN8y2GBqmAbfoeG3wEAkfhi6lGoKgFTxyPSwN67927A1GMx+1s43Ta2+hPC+zW66DFMPRZwO655B2AKwvuVKxjT3d3d3d191qxZAMCybFZWVkpKSmpqampqanZ2NsMw3ZxbU1NTU1Ozd+9e21s7O7uYmJjIs4SHh0ulA31/A8Ex0seD9PFoX1/krRRb20i4dXYusKTnNH7+S/tZhLsL4eFCeLgSni6Elxvpc43TeWD2szH1GK4lman6kPR5q+Mh3pLP1v0ICEb4vnNtLzqQmTBhwocffvjiiy8+++yzvr6+Dg4Oq1atSkhIsFgs+/bt8/Tso/W1JUuWuLi4PPHEE4IgfPrpp9OmTet49LHHHtu2bVtlZWXfGCMiInIF/Fn7u4FtBoCd9Vtmu/6nv80REbmeGWQC/uGHH37iiScSExPXrFkzYsSIdq0+bNiw55577t13333ggQe+/vrr/jXyQnIamb+K2uL3PISo2wAAIMU4Ij0jB6DF1j5kiHMJNgUAnPKrZcpq0q1WYNzYeufK5/MBwHmRV8je4ZyeoUYzZ9acmvHiDJRCa+fWH1+UBuWdL2fbeN9Wty7BLlGJq/vmHq8GFAGuBwkH0+uZD463PBqtHOt1bmVXQMBnVRigoPu9tuCWU4Gbo91fDQAEat8v1c529vspgqmlCmam0aUWeYwqcHNbITp5tEoerXJbds4BWKB4S5bRkmWs+7QMVWCqsXbqyfbqyQ6SQHkv3HF/Qrgu5Op/4S0FbO33uNui9na27gfenINIfAnXRd2cbsMORe9RKu5RKuo5bpfZutNsyaDp3RbrbouVQJCREnKSTDpBJnXtUGmszcfe9REAnjef4VsOcYZDfOsRgSplG0qh4TcAQAhnVJWAqRNQVQIqD4fedyER6GpOvxsAUGUkp9/BNq4jSGfc7VGE9KALHwUATDOBa94JAIjUH1OPu7Kr4Dhu841fsGABADAMk5+ff/IsqampFEV1c3pzc/P+/fv3799ve4thmI+PT/v+fHh4uJ+f38DfokelEtK3CzUuiwjW3nkTVVTGVNWxDTq6vJour24/6vDIHNW0sdfWEsL3HS5rPFu7Cnee2zE1A1P2IggM7vJfVB5+ba84kFm+fPn69evfeecdHx+fhQsXLlq06M0330xPT9fpdDU1NQsXLuwzS+bOnTtmzJi5c+faJvr33ntPJmtLIiiXywHAw2OAJmcVERGptlbsb9qFIqggCDsbtoyxn+RADvRYMBGRwcsgy0IPAAsXLly1ahUAEASRkpISFRVla2cYZu7cuevWrZPL5TzPW63WgZOFfuGu5r2l5xLwfOj77DTtrp3Ezds/3r3mt7ZAWe3Ux+xvexnlhe8+S4v95EncXi8J+qF5e3z5U7kgCJ7vBzsv8gKAY1v3Y/MZjMIz7k5PfipZQLq7x2f9X41QxVzpjfYdoatqGV7IXeBKoN2JkJ+zTSuSWxCAB4Yqno9T7Sy2Ltmvvy1Y9uFErcAJZY/m6H6vRRVY4OZo5WgtVWKR+MroKmvBTWlUiUUeowraHotpL7piJdC88Yi+Za+uZV+TJdvYXmeK9JWpJ9urJzqoxtl1c/rggmv+h8r7D4JrpNEnEdwB2kJ/hwmsThKyFrObcQVj1nLcXot1r9maSlG2LwYCMIQkbpLJ/qtWXtyfgedN2XzrUa7lKN961Oa7bgPBVKgqDlXFo6qRmCIWsF5JhU0VPsw1bupRVwSTj6iEXgjts1gs6enpp86SnZ3dvZ6/EHt7+4iIiPDwcNv+fEREhJ1dZ+fwQYFA0UxVLVNVR1fUMtV1XJPebt5t0tCAjn3MKZlNq9bhDlrczZnwcCHcnQl3Z8LNGZFcRr0DuuRZtm41phkvGbLF1sI176Ly7kFwrTQ61fal6J5BkaW2hzQ2Nq5atWr06NGJiYkMw9x///3btm3jOG7u3LlffvllH7t7cBz39ttvv/7664GBgb/++uuwYcMA4J577vnjjz/64HFFzEIvInJlfFy8Irv11ETHGSa29bj+UJx27EKf//W3USIiV8KgmN8HnyD56quvEhISfv311+Li4o7TOUEQv/322+jRo1euXFlYWNiPFl7IC/GqWJdzvspbS5aNUx+czvz90enW9sY5U0bFxKnUX6YF3rwBt9eDYngq5hg7z9EbQsufyrXtw0uD5NKHEZ7ChflYxGvxEUh8NxeVoNJwVXTv3dQ1RIIDsAh6qS3E+8MVRlr4LLV1TZYpuYK6OfBchScEQ3y+CQMA3e+1hben2zQ8XdlT9Q4ACImqxturxtt7vBnI1FI2Jd+6X0eXWmzR8giGKEao/f+IxB0HdF207uAtTM03wLUAACrx5alS6sztmGYiAHCGgwKrQ0kvvjWFb00BXEu4PgLoZXgfuGLY/UrF/UqFnuf/tVj3W6zJViqHZnJoZoxMEnrR1BIoqohEFZG460IAEKyFXMsxvvUo13pUsJZw+r2cfi8AAIKhsiGoaiSqHIGphl9DT3vcboZNwGMOtyOonG3aArwJVY7kTZkgWDHNRETizTXvEJh6TDOxN9Q7AMhksoSEBFvUMQAwDJOTk9Mu6dPT01taWrofQafTJSUlJSUltbd4eHi06/nw8PAhQ4a0JyobyCASkvT3Jv29u1mt4U0WTt/CNRuowrIOZyK4g5b08XBYdC+mvbTbEeH1Cte0lTMc5PR7MO0UEGi67BUAIDxf6Il6v85wdHR88cUXba8Jgli3bp3FYkFRVCLphyhWDMNeeeWVqVOn3nfffQkJCa+++urSpUv73gwREZGec8pwPLv1lAJT3uryH0Zg0ltSjusPTXCcHqy4gbyZRET6ksG3A98Tamtri4qKRo8e3RuDX/0K/f3bm0YQHzyk+tZtssFsbfv7l5eXezq5ZI9d7f/xK4Cz/zg/8pcxb7Ljzfd6PNzwfWXFM3kAgBCoQPPOi7w83w/uKiX2YOVwJcXykOjdo4fFnEbm2f36Ah2LIsALMNpT8vPNbaWhBE4oeyRHt74WU+HeX4ZWLS+iSy2KkZrArdGY+orWqnjBfKq1ZZ+uZb/OlGIQGGFI8ghZpKr+ywrD3w3KMXaqCXaK4Zqe15Y3HTcQ7hLSq38CmLmmrVRBF5m3u0QS+D3meMfVXI4ShOMU3cRxtyrk7X+g15r11Sw3SioZK5UGEN19KAJTx7cc4VqP88YU3pQFwrmgcQR3QFXDUeUIVDkcVcYg2FXFiVhzbuZbDhNuTxA+b7CN62ye8wCAqcdJwv7kTVnW7AkAqDTyMCoLupoLXRmCIBQXF6enp2dmZmZmZmZkZJSWll7B77aPj8+QIUPCw8NDQ0Ntkl6r1faGwX0A32piquuYqjqmup6pqWeq6tjaBoHlAMD1taekEcEdO1P5Jabkk7iLI+HqiLs64c4OCIEDAFvzJV32MiL1l0UeYWq/ZcpfRWXB0sjkHlaPGxQr9D1h7dq1w4YNCw3tosrjiRMnqqurb7311r63CgCMRuNTTz21evXqhIQEFEUPHz58lY8rS5cufe+993rSE0GQqyk0IyJyQ8EK7PK8p2qp6ns9HpnsOBMA/qz948+6P7xl/q8Gf4RcT0+rIjcGg2J+vz4FfK9yBQL+nxLrb6fN7X/o9DoGnM0ftYyZen9bqKfM3uXONTl+B2oe93pKGZ9WIh/5sSAXQMAQbEXwp+5SrzYNL8D1p957yBuHWwqa2zL28wKUt7DVrZwAgACEOxGasznnEF6Y/U25b5LO9vaq1PuMaIkaAAAgAElEQVT58GaOa2EJVwkAFN2RbvinydaOKjC7O1x8vhpyyREavq2sWJKH2xFBf8fKhvbDdqjAtVrTh9s2k1HNWK55F996HJH6AqCCtRhVjsDsZ/AtyZx+H0I4SqNSEVxzzW2YXFNXxbZ9cdwwbIxUMlYmjZOQ6u5zT/JW3pTOtZ7gjSl8a4rA1HU4hqCyQFQRjSpjUUUMqogEVHbRcbocu02iY9LIZFQWaM2eyhtTAcGkQ5NQeZhN3uNuj5M+b17uzfYSLS0tmWfJyMjIzs42Go1XMI67u3tYWFhISEhoaGhoaGhwcPBVVvHoT3iebdDxVurCpHcNH/9oOpJme024Nkkji1EpicikqFyC22cAYkTlQ3lLHgg0pp2CkG2nIzJ/wu3Jbi44KCb4noAgyKeffvr0009feOjJJ5/85Zdf9Hp931vVzsaNGxcuXKjT6QDgKh9XXnrppbfffrsnPTEMY1n20v1EREQAdtZv2VCzxk3q+XrwZ7YcTDRPv5T3eBPd8JDXE2PtO9e7EREZ4AyK+V0U8JfNFQj4p/Y0/11k7diCBcBNSTNWvtlWIVkRNc3l0dXf7t8y6tmXaEb6g/Ntp61t2j5CFfOs/6sAoN/ewDbQjg953IDq3coKw36qs6WdvyQoL/ye1IT+Va8YoQ78M+aaqPdO8Gaudb+u9d/mloM66xkT7khGFo8BBCmZl83qGOUojTJBqxipsSXMs2FT77bQetyBCPqrfzQ8W/8rXfwkQrrLolMEnramDxfYJgBoC/1FFZaMOIEqJ/0/xZ3n9YYBrTx/yEodslLJVmsj17bHhQEMIYl4qSReIhkmIaWXCqYQqAq+9QRnTOGNJ3lzFvAdIsYRHJWFosoYVBGDKmNQ2RDoQTEbuvhptv5nzG6aJOQP3nTKenom4foI4b2Ca9pCFfy395YzrgmCIJSUlGRnZ58+fTorKys7OzsvL4+m6SsYSqlUBgcHh4SEDBkyJCQkJDg4ODg42JY/bPDC6fSmI2lMTQNb2yjxX0sGnOnZeahsRBmCXfRLOigm+IvBsuyyZctsrz/88MMJEybYQs07YrVa169fL5VKy8rKLhigT6mqqtq6dSsAPP744719rbq6OldXVxcXl9ra2t6+lojIdUALa1iWu8jCmZ/xXz5Uda5E6HH9oW/LPlLjmndCv5Zhg3sSEbnRGBTzuyjgL5srEPCttJBZf+55elOeJcXeqng9/p+tbQ9GC55f8TB/V+CwORLfiqJjd306vqEtO50AgMBTfi9Fq0dc6/sYZJTo2WrjeX/w1VnmA2VWJYFO9JXcGXJu01VBotGOeMuBZmWCBpX3ehpztokBBHB7QmCErOBktqHtg0ZwRB6jUsRrlQkaa76lekUhAHi+H9x6UGf4u7H/NDxvzZ7CG9MIzxcIz6Vs3Y90yf8AgPR9D3ddwFS+z1S+gyqGSiMO9HYGeAEgj2EOWahkK5VO0/TZHyICQaJJIl4qSZBIYnqSlkxgeHMOb0zjTem8MY235ILQYesMIVBZiC3MHlVEIvKhXUoygWmwpg8XuBZJ6AZMe3a7gLeeXc74BHd+8GpvuA+xZbnPzs7Ozs7Oyck5ffp0UVHRFe8oenp6BgcHBwUFBQUF2SS9v78/cdGkBgMatvY7uvR5AEAQN4HDAQEE1wmcCQBBpb4253mBKhd4KyobIo06DBdfLh0UE/zFoCiqh6npnnvuuffff7+37Rk4iAJeROSyWF3xxSHd3mj1yKf8Xux06N3Cl/JNp6c733a3W6/sB4iI9BKDYn4ffEnsBiMqEhnteW4bcLSn5KG6xiPZ59yAJzfLnayrJL4VdI0ru+L2cWlnkp5NcpK61lM1APBH1Q8Rqmi8Z5GZ1yt+Wtzv/Cx063LNACDB4ZNJ50J5zYxQamABRdST7PvGMNyh7XNBCCQ8PcF4qLn1sN50VG9ObzWltJhSWuo/tx0GeawaIRC3pX4Iiui3N+TfdDJoe6w8RtU3dp4FJX3etZ6exlR/hjvdizvP43R/A3C4y38Fuoap/gwASJ93+qB+GwIQShChBPGIWmkVhDSKPkpRx61UDs2kUHQKRX8OrSvstHcrL7VyjxCoIgpVtFWjAN7KmzJ5UxpvPMWbMnlrAW/O5s3ZtjJ1AAgi9W/T8/IIVB6GkO4AgBBOuOcSpmw5U/Yipkm0CTmm5nOBKkflEbjT3F78Q/QCBEHYctfNmTPH1kLTdH5+/pkzZ3LOkp+f38Nd+srKysrKyvYidgCA47ivr29QUFBAQEDgWfz8/EhyoOd35M1ZtheCUAMoAIDQtioo8NaS83paCwXO1M0O/KCGJMljx47ZXsfHxy9evPg//+miaLNSqQwLC+tb07rg1VdfffvttxmGuXRXERGRPqTcUpys24cj+N3uD1549F6P+Svy/7e3Yfs4+ymuEvc+t05E5HpGFPD9Q72hsai4zakeRSE2eKtjdBkA1H/1oMASQzdHKgTl9B/nvFLwlIHR19O1exv/nu7UP5mEBiw6iwAAlvP96l/817C90DLBR/JSgtqvz6u+YRpcc7OT5mYnAOBNnOmEof6rCsPORgAAAcwnW8wnWwDA7RV/QQDDXw35M076rxmqntqnWa9R1QjM8XaucRNdvkIS9INkSFsRNaZ8OfBmzOE2VN0r2R+7QYogo6SSUVIJaKCV509Q9DErdYZhhpDnFq0OWalfW41DSCJWIoklSeXFKg6iUlQ1ElWNbHvLW3lzNm/K4k2ZvCmDt+QI1iLOWsQ1tRUPQ3AtKo9A5GGoLBiVePGWArb2e9xtkUDXMFWfAgDh2xfLGb0NSZIRERERERHtLSzLFhcXnz59Oi8vLy8v78yZM3l5eT2MdmZZtrCwsFOxDwzDvLy8bJI+ICDA/ywazQAKPUDloQAI2EJZSDekc50FQaAqQaABAJH4INev2yeCIHFxcbbX06dPT0xMbH87AOF5XoxIFxEZaAggrK36TgBhjP1kFa42cZ1TsTiQziO0o0/ok9dXr37K76V+MVJE5HpFFPB9ShnLNnD8MAlpyth7Nv4XAj1xtzGnAcCUFXWG8fQSABDw3+Kv01bf8drcHys/B4BttesStIkaYlDWdu5LpvtLD5RbD5RROY26I3Od+9ESVIFRJRbDrkZAwPODYNUobcteXcO3FQIlyMOUrs/45E5ItWS0Ft6ejjuSiniNMk7jtMCzY9h870F6v27R7eSaNvMu81H1KADgW1PYxk2ASknv1/rAgG5QoegkmXSSrLN/7xErlWSlkqwUgBEDCCaJ4RIyhiSjJaQbdvE/GipFlcNR5fC2twLLW/J4UyZvzhJMp3lztsDquJZkaEluP4Muf5lrSRKs5cCbUe1ErH0t4PoCx3GbP3zHxrq6utzc3HZJn5+fX1ZW1kPtxHFcaWlpaWnpvn37Orbb29v7d8DPz8/f39/Ly6tfnPB5S2GbegcAuqab+DGBrgDeelmVFAcpO3fu7PiW53mTyaRS9bFnkIiIyCAjqyWtwHQGAA427TrYtKubnuktKUXmvAB5SF+ZJiJy/SMK+D5lYUNTGcvdR8qFjHNhFWGkP0C9wOLvqZee/IJd/ESFV6oXgkDj6ipv8PB7MrjEkm/lLVtqf3vQq9dT+AxMWB5uWt9QrO9CSJgZIeCbmo4tMgK5PVjm2+fb751o/L6q/JlcAPD6JMTpYU+2kdatq6WrKABo2a/T3Ozk90NY/k2n2AaabaQNfzUY/mrAHQiHB9zNJ1savq+URajkMSrZUCWmuvY3gpDuhPtTTOW7dNkyacR+QBC6bBmAQLg9iUgGaB7yZzWqeAmZStNpFJ1FM2do5gzN/AImAHDGsEiSiJaQUSQZThKybnLgITgqD0fl4QD32BqYqk/Z2m8EgUZ4WhAYEBgQeK657VmE1+83H3dFEFxASJRwIrxfRmRDUFkgIAPdV/zKcHFxcXFxSUxMbG+habqkpCQvL6+goCA/P7+goKCgoKCysrLnY+p0Op1Ol5qa2rERwzAPDw9fX19fX18/Pz/fs3h6euJ4L35zCfen2Ya1wLfFDhAeizH72bbXAtdK594v8C1th9yevL7Ve319/dq1ayUSyWOPPWZrMZlMzz///OrVq61Wa1hY2Pz585955pn+NVJERGTAosY1doQD3TGJ7EWQYjLFdRqOJCLSX4gCvk+xbbqvpc2KrNPtjQFV41v21NXVDdnxfKQ9k7bj/b+fXPEs/a/ZpuFvY+/4dOF7PMIf0u0d7zDNVx7YX8b3IwgA3m2VsY4ocGRBtDLI/tz/7WI9u6vYekeIzKVP9rdtNPxYBQIohqsdH/JgG+mCmacsp40SfxlTQzV8VwkAXh+HhOwfnjPsmEDz2tnO8kildrYzAOjW1Tb9UgNQAwCAgMRPJo9SyaJUtn8J52sjHW1Khjdlsg1rAcF440mEdCM8Fl+TwXsDAkESZdJEmRQArIKQTTMpFJVOMZk0Xc9xey3cXosVADCAIIK4RSF7SNWjxwWBrrAVpbv4ZqxgE/Y8ZaIKHgYAQHBE4o3KglBpMCILRGXBiCwIwfs0DqLPIEkyJCQkJOS8nROTyfR/9u47Oo7qbAP4e6fsbFfvvbkbbGzjgjEG7MT0EkzHYEKxKSYQIJQvFKcQAiGJAQMJ2EBCTUKvMS30ZtyrerN6XW2dcr8/VpbWkizLxtJqped3OJzV3dHo3evZ2X1m7twJDqEvLi7u+n9VVdXAb52t63pFRUVFRcWnn34a2i5JUlpaWmZmZnZ2dmaI7OzswzIZPlOy5JTr1ao/BSen0+qellKuZ1IsEQXKbu1K78yULKeN5Oz6zjvvXHLJJc3NzfPnz+8K8Jdccsmrr75KRAkJCdu2bbvpppu+/fbbF154IayVAsAwlW3N/9OEp8JdBcAohVnoD9ohzELfZUtAvbCuUSMemDu+pKLzy+KjCX+Z5Zn04k0J634uTGr746K4Yy+I/XnxuZtcHzczRpzTnmvrXrnwZSLKt427Pf8+NgrvI9eX6/7b8m6JL8YsfH9ZUv9L3vG/tpd2eEwiO73Acukk64T4oRi76/62reiMjbpLiz49wV/k9W7vMBdYC949yrvdXXLeJsNrxJ6b7P6uzV/qtU135r/Rfbs73aW1/Kfe80O7Z5PLt63D8O0TiuQUxXqE3XKkw3FcrOO4H3VJhd70ir/w50xOJGJcrTPlPyHFn/tjVhguZZq22a9uCgQ2BQK7VE3jPE4QPk9L7lrgPY/Xx/lEkylXlnocwuGBGu/G6WR4THmPiM5jiEitekBreJ6ZEs3jXiXRyrUO/44zudYkRs8nwWZ4dnB/edfUZ12YFMssBYJlDDPnCeY8Zs4TzDkkDGii75HB7/eXlJR0BfuSkpLS0tLS0lK//8DnZwYiPj4+IyMjIyMjMzMzPT09PT09KysrPT09LS3t4EbjG17vphncXx38SUpeZsq+z/Du8m0+lnjnNGmm/L9J8Yv7X01EzFLbp+Li4ilTpng8nmXLlp111lkLFiwgom+//XbmzJlOp/PDDz+cPn16SUnJ4sWLf/jhh48++uj4448Pb8Gapum6rigHvh/kj4dZ6AEARrmI+HzHGfghNdkkn9VkfdHcWF7lCrYwErZffXTCB/yrs9nE9gfsLJBpzlnv/4r+TsJ5Aq03iCihMlESZM1Qi9w7v2v94ujouWF9EeHR4DHq3PukpmCuNYm0tWGf2YltMusxfd2K6faOgPFuie/fOz3/3uk5OsW0ZLLtJ9lmccBn9Q+B7eiogneOKjx1fesbDURkzrcWvHuUnKzIyUruS0eWnLup+eVaIrJOceS9OiX0ZvWiQ4q/LJUuSyUirnHfLrd3k8uzucO7yeXZ5FJr/G01/rb3m2ofLD+i9FgpTm58str9fZt5nM0y2WGZYJNTBvo1V4w7W6h7ymj/kogEx4wDJpZhK1uSsiXpdJuFiHyc71LVGKH7n7bdMG5sagk+NjM2TpYnmuQJJnmcLOfLksmUIqfdqFb+TtvziBR/HjFJzv4DCYoUdw6zTiAirWkV15oEyxhl7MvB2emJBwxfCffuNrxF3Lvb8BVybyHXmrnrG8P1TUhdAlPSBHMeM+cI5jxmzmfmXEHJHMh96SORoijjx48fP358aCPnvLq6uqSkJJjngw/KyspqamoO9thxY2NjY2Pjhg0berQLgpCcnJyRkZG+V2pqakZGRmpqalpaWh83SxMscsavA0XLgj9pdU9KiUvUil93pXfBMUOKP+egaossjzzySEdHx5o1a5YuXdrV+M9//pOIbrvttunTpxNRbm7uCy+8MG7cuNWrV4c9wEuSNKgXVgAAAEQWfCgOtRNjzP/+dKdudH55TRNTymYlfXm5b0LH/SajzU+0tuoRIoopjzmr9GwrWYmoqrlMMzq/XL7f8PooDPB+nS94sb4j0Mc3/jq3ccZ/Gns0vnhG3IyU7qHmyTZx1cKYynb92a3uf+30fFsT+LYmkGoXL55ovXiSzSYP1ogGU4YixSl6m4eIlAKLFN9ZknmMVYyWjVo/EZkn2KXo/Z4/ZBKzTLRbJtpjL+xs8Zd6vZtdnk0uwSpKsTIR1T9e6dvp7voV28yosR9OJyLvtg6tUTWPs8lJ+x11b8r6vW/LCRS8ddyIGNlhZuzIfW9m5hSE38VGf+bzbwsEKjV9YyCwce/t00SiHFmaqFx8k/yM3buzufaZ2JSfM9FhynkouABXG7TqPxGRnPV76rqPIzMJlnFkGRd6Mp8Hag3fbu4tNnxF3FdseIu5v5z7K3V/JbV9ErKgwEwpgjmHmbOZkiOYs5mSxcw5wVHcIw9jLBiq582bF9ru9/uDM96Vl5eXhaipqdnfqvbHMIw9e/bs2bPnm2++6f1sQkJCSkpKRkZGWlpaampqampqSkpKWlpBrH9KvGkjY0Rc8++6gPvLg+USHznvhf1577330tPTL710nzszr1u3joguvvjirpYxY8YUFBTs2rVrqOsDAACAfiHAD7WVX7XrO7qHZIxj+XdeXvbdPV9unNMmMCHDnMMYs5fZZq+YqTQrRGSYjIbzmzItOZW+Ms55mnmYzjE2qEwiW5htLmzZZxK7PR16s9eQBTY2bp/N2G5imc4+rnXPcIp3znHeOMPxym7vs1vcxa3aH79x1XmMu45xDkbNweve/SUeU6ZZb1Xb3m0qW7o1e+0ktdZfeNIPaq1fybOq1b7m52sERchcNW6AkUHJsSg5luDV8kH5r05p/6DJu7XDt93t3dpBOici4rTrhO8Nt05EYrRkGWczj7ebx1rN42zmMTZThjn45wTbkUrB3zk3BPu0QeiD4eJsm/Vsm5WIXIaxLaBuU9VtAXW3qpapWpGqFankcdz0h+abApW/WaTN/UVc5iKrJfiLauVvuN4uxiwSo0/s/08wU7JoSiZnSEzlGvdXGr4S7gum+hLDV8L9lTxQrQeqQ+e9JyImOpk5mymZTMkUlEymZDElS1AySbQd5r4YHhRF6X1pPRH5fL6KEGVlZcEHlZWVA7xrfQ8NDQ0NDQ2bN2/u/ZQsUWKskJYopMRvn32kdM25iihwKeGikf1eIKLq6upZs2YJIaNUqqurd+7cOWHChIyMjNAlU1JSfvjhhyEvEAAAAPqDAD/U3AFOW77r+nGskKe4+Yx7Z+75c2X9hLpyb3FMecwJ1x+nNClEpMvGeyvfLZ1aQt7O5au8ZeGoOswY0YMnRPdo/L9P217Y7km2C6//LH7gq7LK7OKJ1osmWj+r9L9T7Dslb7CuUi5dus27rcNcYC14f1qgzFt05saWV+u5vtW7xdV13bv7u/bixZsa11ZbjrAnXJl+aH/IlGGOX5rWs5VRyq9y2t5p8O50661ax9dtHV+3dT0pWEUl32rOt5rHWGPO+al53MhMib05BGGWWZll7hzE7uO8SNV2qepu9exCz0sFvm9+1ra6POp3wWcN92at/jmN5N84fulsbcuT5HxZypclpzCwSy+YxMw5ojmHKCT8B1O9v4z7yri/zPCVcX8Z95VyvZ27N5O7Z85kUtzeVJ/BlHSmZDBTOlPSRuqceWazuffN7YiIc15TU1NeXl5ZWVlVVRWM9FVVVZWVlbW1tYc2k4uqUXW9UV1vENHrn6iBAL/5sng589eH4WUMb4IgmPYdpfLBBx8Q0Ykn9jxK1dTUNHRlAQAAwMAgwA81zshd2j0ocayQT0RKh3DmjedWrCk37EbuDZlSk0hEXKaqv9ZknpCfSd0zz091zhz6moen4M3CDm2oKyOal6HMy9jnauRff9r2WqF3Qbb5vHHWWWk/drJ3JcvsIlLrA2qlz3Z0VP5rU4rO3Nj6Rj0Rdc1a593s4gGDBGZKP/zHEZJuykq6KYuI1LqAb0eHb7fHt93t2+327XKrdQHvZpd3s4uIXJ+1jHl/mt6uFZ+9kYiUfKuSY1Fyrc6fxIlRI3z/YGZskkmeZJKJyBjzoG/L/Ivcz5vl5UTjiChQfjuR8aL90tf0VHJ1X6QQLwq5kpwtizmSlCNJObKUJvWcG2+/ulJ91D7NXGvivjLurzD8FXzvf4a/nGtNXGsi94aek+YJFkHJYEoGM6UxUxpTMpgphZlSBSV9RN78jDEWHAA/e/bsHk8FAoE9e/ZUVlZWVFTU1NRUVVVVVVXV1NRUVFTU1tYO8Cb2RLRpty6l/5LJB5gRcwTIy8vbvn0755ztveHiG2+8Qb0CvGEYxcXFmZmjccwXAADAcDbCv6APNzqRe1ygoWhPV8tYIT+gMJOfmzoo54ocRZG0hgARMZOQ94/J0045wMDd0WxMjExE2YcvZNpNglflbxR63y72bro82SL9qOtgM/46zvAYzS/XFp62If/1qcEMX3zOJqXAmv/qFNEp1a2qqP51EQks67HxUScdxCCCgyUnmeSkWMf87qus9XbNX+jxFXr8xR7H8bFEpLdpnk0uw2t0naiPWZyUs3aS1qJWXLeTyUzJswZH75syzXKqwsSRdpGwYJ0kJS7R6taqZbcr41/Vm14x2r9kcsJpY35doFuKVa1QU4tVrUTVGnWjUfd/GzK3usxYpiSOk+Xbo6PiDmlqRCbFMXsc2af1nCRfrd8b5iu5v4oHqoKD8LnWanh3k3d3X6uKYnIKU9KZKYWZ0pgphZmSmSmVyYlMTuy9fKQzmUzBe8j3fsowjNra2mCer6ysrKurC56xr66urqmpCT29LAp00RmZcvLyoas7fGbPnv3oo4++8MILF154IRGVlZW9/fbbJpNp/vz5oYs9/PDDXq/3hBNOCE+VAAAAsB8I8EPqnuZWj6so4O88KRTHYmPFmK9uKZz+0BjFY4jthkYBImKKkPvc5KhFgxjqRgC7iRFRnOWwTSX/q1mOiyZa3yn2CoyFpvf3S31xFmFqkumgQisTWdbfJxBR88u1RWd0ZvjJhXOZzEhgdasqqu8oDKb3uItSDtdLGCDRKVmnOa3Tui/+N2WYJ+2a693a4S/2+Eu9gUpf7PkpRBQo8ba+Xt/j15nMTOlmU7ZFyTKbsiymLLOSa7Ud5SAhslO9nPFrvek1ve0Tvfn1QMU9RCRn/F+KKSaF6PjOi+KJE9VoepmmlWlaiaqValqZptVoerGqFava6TbLPLFzMMWbHu9T7R2pkpghiVmSlCFJmZKYKooyO4he6kzd9uk9g73e0Rnm/VU8UM0D1dxfzdVaw1/JtTautZF3Z1+rMzE5gSlpTE5iphRmSmFyMpMTmCmFyQlMTjj4PhvWBEEInrfv81mfz1dTXVby6Wn1dTUTcsVJC/46Um8Q0MNtt9321FNPXXrppR9++GFWVtbatWv9fv+5554bFdU5LMQwjH/84x+33367JEnXXXddeKsFAACAHhDgh9RHPp9/69auH8cJ+YLBZq7OUR4o8NxSqHgMQnoPq3SHeNUUe2jL7mbtmvdbiCjGLMxNV47LVOZlKH0eNdhUF7j78/bfznNOSugcft+Z4Rk1v7Q3w89wElF40/v+SLGyY16MY94+95a3TnOO/3qmd7PLX+L1l3oD5V5/uU+t9ftLvf5SrytkydS785JvyQ6U+6puLxSdoinTHAz59mOiI+V0PZNi5LSbA+V3+guvJK4KtiOkxIt7LkOUKompkjiHusOej/MyTXMb/Cil+8qLnQF1l6ruUve5x6FAlCiKaZKYJonpotT1IFkSBzoIP1iGaGeWcWQZ1/sprrXwQA0PVPHAHh6o4f4qrtbxwB4eqONaczDt72elMpPjmZzMTIlMTmKmZCYlMFMikxNIimemJCYOynSP4WI2m3PyxmXG/MW/+2Ix6gQx9pRwVzRE0tPTX3311YsuumjNmjXBlqysrD//+c/Bx2+++ebixYv9fj9jbNWqVRMmTAhfpQAAANAHBPhBVN6urfhvq1fvnmBJzaLsf3zbNYC+QMjlxKRmsf224u+vyZi9ulLWKQ/pfWBMIhGRMsjhMC9auvYo+1vFvvI27c0i75tFXoHRxHh5XqZyfKZ5alL3HeD++E3Hlgb1oW871pzSPVidiSzrbxOIujN8x1etwzC998MyyW6ZtM9BDcNnBCq8gXKfv9wbKPcFyrxqjd82M4qI3N+3Ba/z75L+h4LE6zK9m12VN+0SY2U5VZGTFVO6WU5RTGmKnKaIjmG0F5KSr9Tqnza8hdR5a70Bje8I3l6+R+PN0c7TbZZyTa/UtApNq9D0Sk2r1fRaXa/V9fX+fRaOF4U3kxOj906PV6vrOwNqqiSmiKJjgHPm7cWkGCbFkLWv3GX4uFrLA7U8UNP5f7WOq3U8UMfVeq4180AND9SQu49fJSISFCbFM1Myk+OZFN950l6OY1Jc8DGTYkkYrFkhB4kYe4p58keCuee0eSPbokWLdu7c+emnn+7YsSMjI2Px4sVWa+fUCV6vNz4+fsqUKbfeemuPm/8BAADAcDCMvjqPPM9sdm9tVIlIzOfczYwaOu+lhv/t3NC1QI5SwDjnjNlc2tTHqrs9gDIAACAASURBVMc9O9mcbRk9U4L/SAuyzb8+xnli9uAGBlGgm4523HS0o7xN+1+l/5MK/zd7Alsa1C0N6qPrO1YtjOmax75DNYio983qmciynpjAVd7ySl3hKT8YHp0ElrU6MtJ7nwSzYB5jM4/pY0ONOTtJTjL5irxqtS9Q4dOaVPuxMUTk2dIROg3+PmuziaY0s5RoMqUppixz0oosMVoiokCFT4qTBdtBnZn+0ZgsZ//Bv+McKf5ngnPOj1oT0VhZHrtvsNeJajS9WteqNb1a06v2PrAwFvo6b25qWe/vvGuajbEUSUwWxUQx+H8hSRSTJTFBEA/6envBzJRspmT3/azh51ojD9RwtYEHarlax9UGrtZxtZGrjVytJd3d3wn8INHG5EQmxTE5jkmxJMUyKY7J8UyKZXIcSbFMimVy7ACPjAwNwTY13CWEQUJCws9+9rPe7eeee+6555479PUAAADAACHAD6KbZzlUg1q4/mGuj4gvv7957n8b/q6WdS3Aj5nw/JzEC/9YT0ROt1axbEfB26Pxq+ShUUR22eShO9iRFSUtiZKWTLL5NP5tTeB/Ff7CFm1sbPc7aE+HTkSl7VpRi5Yfs887i0kse81EImp5pa4zvV8cqen9ABjZ58bY58b0aI67KMU61REo9QUqvWptIFDtU6v9am0gUOUz3Lpvt5t2d572VbItcUtS61dXVt26m4gEmyinKEqmOfPh8aYss9aitr3dKMXKUrwspypSvEkwH+YoKEadYJm2Y5Du0yYSpUtiuiRSv1dbX2S32Rjbo+s1mu7mvEjVitQ+ZlM3MZYgCkcryu9io7sGorQaRr2uxx1KvFeCc9rvdwHDFxLpG7haz7UmUpu41sTVeq42cq2ZdDfXSzmV9v+nmBRDUgyTYpkUzaRYkmKYFBPyIJpJ0STFMCl6WEV9AAAAgLBDgB9EVkn4zbyoDoMfu6f21Ifr5z7d2EJtjYGO4LMKKTuXTNl4rJMzuuj+eiLSGgOFp2woeHuqZaK93xVDOJkl1vsWdETkVTkRNXuNk//V8MmFian2fU4dBzO8ZbLdMt4WdepImy1sICwT7JYJfWzYepum1vjVWr9aE9DdWsw5SURkHmczF1iD8d5f5PEXebzbOkxZ5roHy+r+WhH666JdlBJNUoJJipeleJOcZJLiTUqWJerk+M57DPKDvtlg2O8ldpLVcpK1c948l2HU6HqNptcbRp2m1+p6g67X6Hq9brQZRrWmf2D47uW8a268C+saSzWNiCTG4gUhURRjRSFWEBJFMUYQYkUhQRTihM7HB9cxgpkpGUzJ6GcRrneQWsfVZq41ca2Za82kNnGtMdhCWjNXm7nWzLUW0lo4lRzwbzLRSZ2RPoakaCZGMymKxCgmRTExiqTQH50RN4AfAAAA4GAhwA86u8DWfEf80UYi2q0Xd7Vny1lbZjuJaMd5H3/haTnm4blEpDUGis/ZNGnHMeGqFg5ZlFnwdug2mS3INofOcnf9upb1teqEeGlivDx5cfL4ODmqn7WMPmKUJEZJPa4ccZ4QO2HDbCLS2zW11k86mcfbiCju0lS9Q1f3+NW6gFrn1xpVvUPXO7z+Em+P1ea/PtV5YmzdQ+XVdxUJNlGKl+UEkynbkvHnsVKMrO7xt73XKEZJUpwsxshSrCzFDvlw/YFxCIJDEMb0usaeiHyc1+m6nQmhM9svslrWeb2NutFqGMHr7fe3ZpFovsX8SHz3lA2f+/wVmhYjCHGiECOI0QKLFoSDmzZftJNoZ+a8/hfjWjNpLXzvf6Q1733cTFor11q51kpaC9daud5Oejv3lw/sz5uY5CTRyUQnk6JJdDDRSZKTiU4mOkm0M9FJUhQTHSQ6uv8PAAAAEDkQ4IfCmCzrLpGRzguN7jNOBSx3j8/4RUZMk9a+4fwN1ibr1OePIiIxpo9v6jD8BWNOvFl86MTo0PaOAK9z63Vu/ePyzonLnIowNlYaFyednGc5OsXUe1XQRXRKorN7N2UeY8v86z5Tr+vtmlYfUBsDeqOq1ge0hoDWqJJAwQn/RackmAXDrQfceqDc5/6+PXFZhjQras+9xU3P1fT4W8wkSDGSGCtLMbIYI0nRsinbknxzNpOZ3qq5v2sTHKIULYtOSYqXmSn8Q7vNjGVJPffhK6IcK6IcRBTgvMkw6nS9WTeaDaNB15t1o8kwGnW92TCadCM42L7rFznRdY3Nft5zEgcbY9GiECMI0YIQLQhRwQeiMEtR8uVD/ATpvDx+AEtyrY30YKRvIa2V661cayO9jWttXG8jbd8fDT9XG0lt7PkaDlBMjJx+u5R85aG9FgAAAIChhAA/FHw7PKRzIioKCfDjKPeun5ZM3TynOfXn1dvKx77fGUsCJZ7wVAkDVtmunfJyo8/YJyYE7zZQ4dLG/G2fZGgRhVfOit/j1rc2qFsb1B1NapPX+K4m8F1N4JMK/ycXJnYt+Y+tng7VyI2WZqeanEr482FECCZ8Jd/a57PxV6TFX5Gmd+jBYM8Esk5zElHidZmCTdQaVa0poLVoeouqNauGW1frAmpdIHQNzgVxthnOypt2Nb9cG/pHx38905RpbnqupuHxSjFaDo4jkKIkwSGJTkl0iKJTEqMk0SkJDlFOCcNk+ybGUkQxZf/3p9M4ZyFn1xnRfbHR3/kDzYbRrOsthtFqGK0Gd3Pu1vRq6nkmP1OS3k/p3Hp1orNq6/doulMQogQhShAcAnMIgkNgTkFwMMEZ8qOdCanSQC/QZ1IUSVFMyRrQ0oav84y91s71dtLbuNZOejvX27nWTkYH11ykt3G9nXQX111cd5Hu5loLD+w58MoBAAAAhgEE+KFgHmshRsSpKOSazzwpR2xWtx35Zeq/xyy+frHUJAaXSbx1YF9VIXzqPYZb6/skHyfSjX1a3IYhS+ykXPNJuZ0X6DZ6jZ1NamGzNi6ue7RFk9e49/O24EqPSVeePbV7YPOru72cU5pDTHeIyXYxQu6qPoyIdlG0W5QcS1eLZbI946GxPRbjfkNrUfUWTWtR9WZVa9UEq2Cb7iSi2ItS9A4t2Ki3aoJVYIpARB2ftng2uA5YgGAVJ6yfZcow1/2lvPGpamYWRLso2CXzWFv67/KZIni3d7S/1yQ4RcEiClZRipYEqyhYRcEuik5JsIqC5fAf0JF6jY0Pvfa+S4fBWw0jmOfbgqleN1oNY1rITe855waRm3O3rtfsf9B+l2PNyt8SOqcJNIjubm6t1vUoQbAxZhcEK2M2gTmYYBeYlTGrwGxMcAqClTGLwKz9DOkXzEwwk5x4MG8RzrV2JuG6FgAAAIgMCPCDzre7Y/dPfyBOOtNLjO75t9b9cc6M2716k1px4jbJEIlx4uybK7+efBmlUE4YC4YDyoqSEixig/fAQYWI0pxSqn2f9BVvEeamK3PT95kGL84iPL4o5ruaQEmrtiDk3nhFLdrNH7V2/SgJlGIT05xiik1Mtolnj7XkRuNdfHgwRZCTFTm5jwninSfGOk+M7d2e+ej4hGUZWouqt2l6q6q3abpLN9o13aXp7bru0vQ2TW/XpBgpeAbeV+jxl3Zfru/6pDlxebqSZ62+o6j9g6b+yxOjJCXXUvD2UaJTan6xtum5GilaYmZBsIjmsbbEazKIkb/U6/q4WYyRBZkJNlGwS8zERKfEZCbaJWYWDmHSfrvA7IKYTv1NECAx9lZyopvzNt1o50arbrRz7jIMl2G0G9xlGO3c6DB4u2G4DO7hRm7IJf0eg7/l8fp6Dd3fnzNt1vtiO69S0YnubW6t0w2rwByMmQWmMOZkgsKYmTGHwEyMBQ8HKIzZ+j7zz5DeAQAAIILgq//gat3eUXrsd9zPdZnf+TvDf23n6NxoFnXC/+Sn74paek8tC56w5eybK7/+fun6Y+STwlgwDITBSRtw3vCpxgCXXZBtXtDrtva50dKdc5zbGtXKdr3Kpde79UqXXunqPHZQ0a6vWtgZZmrd+pXvthicUuzClUfaZ6Z2nyAtb9NEgcVaBKuE0/eHE5OYdepBzIKW9ej4lF/l6C7N6NB1ty7aRSXPSkSp9+ZZpzi0VtVw64bH0Ns1w60bbt3w6HqrZnh1w2fobZq/2Gv4DNFJLf+uc33cHLrmqJPjlRxLxXU7XP9r6b8G0S4qBdaCd6eJdrHhyarm52sFmyiYmGAVlQJryp25TGSe9e3tHzUzkQk2kclMsIjMLAiKIFgEpgiCRWQyE2yinGgKnfnPxphNElNJpIOZx8MusLeTE0s1zWUYLs47DMPDucfgHdxwGdxtcA83PJy7DKPD4D7OlZBNuN0wXvV4B/5mXGAxP7x30j4f51c2NNXperQgLHXYew89AAAAABiGIjXAFxcXr1u3bvv27U1NTR6PJy4uLi0tLS0t7bTTTktJGS532P7mBxc74XtJM1SJ/d9/cncUfdj1VIGQO/ud9qP/297VEjDR+ou/55w3BerDUSwchESr8P1lPe80Nve5+hqXnuWQPrrocN4lTmB0+RHdM7QHdF7t0qtcer3HaPToC3K6A3+bnxc2a6rBdzZRsk3sCvAb6gLnvNp5dtcisRizEG0WohQWrQjxVuHiibbQu9aXtGoGpyhFSLDiIvxBYcrs41Zn1iMd1iP7PRDASW/Tuk6hZ6+d6Pm+XWvVuFc3fIYUJwcvEEi+NUfJs+otquE3DK9huDQjwA2XZvgNw6MbXoP7Db1DD5T7eMAgEtvfbXR/29b9Vxgl/DxdTlOqbivs+Kp1f7V0EZ3SxK1zpFi5YsXOxjXVwXjPJMEywZb37yMFq9jwt6qWl2uZRSQiMUpijJhFDL4E0SmRQExiol2y5lmOOSORiALlPvcPHUTEBBKdkinTHDzAYfiMQJk3eNmC4BV0phERk1mMTXwrOaFU1bycuwzu4zzAeRs3/Jz7OW83uJ9zH+cuwwhw8nJjbMiZfx/nu1TNZRiVpH/rDyDAAwAAQESIvABfWlp6zTXXvPfee30+e+21155xxhkPPvhgdnb20NbVB+vnLapmENHXJzmqChT/27u6nsoTsjljosaD170TkSlA49on+dMDWZbccBUMw59JZDnRUk5fw+bHxkrfXJpY2a63+Y0pSd2n31Pt4pw0paxNa/YZXo17O/Q9Hd2D/xmxu+c6g4+/qPIveavzvO7ts51XHNl54KCmQ7/7s3bV4HYTs8mCWSKrLNhkZpZYjCIsyFZCp9yr9xiawaMVwSrjbP/hw0gM+UcXHZLj+D6G9DuOi3EcF9P/mnSXJpg6r+HP+ecR3i0uw60HA7+cbJLTFCJKf3BM6+v1XONGh841bnh07jcMn2H4DO4zDJ/O/dzw6nKqEkzjTGLEyPAa5DWIyLPRMPyGYBXb3mro+Lqt/3qCJhfOlVOUsp9vDV2eyWzy7rlSgqnkgs3t6/Z7iUGUxGLsIhGZx9oK3jpKsAi1D5a1vFLPxOBKBMsEW8ZfxjGJNb9UW/pWGRGRwMQo6UUin2FoZiH3UgcdoNsAAAAAhoUIC/BNTU0LFy4sLi6eOHHiGWecMWnSpLi4OKfT2d7e3tzcvHPnzrfeeus///nPpk2bPv/886SknudIh9jkFZm73mpwf9l67Jvt3mjptbrixr1P5aaMZU2cqDO9E1HcZak3H/+bsNQJI0aUIkQl9DxznmQT/3FaZ9jzaLzVZ7T6jDY/b/Ub7X5jYcig/ZxoaU6aUuvWAzpPCbluf1ez9mG5b39/9Oqp9ltndp49/qLKf+lbzZxIYHTP3KiLJnZODl/Uoq1Y12IQKSKTBbLKAhFFKYyInCZh6RHdowB8Gn9ph8encacinJxnjtp7aIATfV8TCOiciETG7KbOowNmidlNLHnfW7h7Nc454QhCn0Lnwxcsgu3oPq4AP/CIgH1lPDQ246GxhkfnAc5Vg1lE0S4SUe6LR3g2u7jXICKtVSOi4JABItLbNOLEVUN360qmRU5RiCjppmz5pVoyONdJd2lKljl4W03H8bGBKh/3GURk+AzDqxMRV7nh1omIa1xv1YjIX+ThAYMsQscXrd7N3ZMLeja5Uu/Nl+Lkxier+xxZ0Nis29dMHPjrBQAAAAiXCAvwd9xxR3Fx8X333Xfbbbf1ucA999yzdu3aq6666q677nriiScOdv3t7e3ffvtt/8uUlZUNfIVj/ztt10/Wu79s/ck/mp8yb+5qT/7NQstqm3drR/BHx6WpWY+MP9hqYTga3pnRKjGrXUy19z0hWaq9O+qHmp+pvHFOfIPH8Ki8QzV8Gveq3BXgPp37NX72mO6xx8l2cUK83Owz/DqPDZk4vaZD3928n4n7iWItwi+P7oyL75f6Vn7ReWnJng69q/29Et91/93v1d13znF2XWjwbU1gyZvNqsFlgd03P+qsveVtbVCveq/Fp3EiMkvMFNIHFondOcfZNa1gRbt+2yetbpXbTeyXMxxHJZu6XsVvvmjvUHmU0vOfWRHZ1VPsBbGde9Rmr/HoDx0+nSsiWzLJmh3V2d7uN9ZsdgcM6k0R6bzx1q4jER6Nv7TdE9C5JLLT8y1dVzR4Nf7yDo+vr740S+yMAkv03mnqAjp/s8in6lwSaGFO96EQ1eBvFfn8fa1BkdjCbHPXwRHNoHdLvO4AN0tsYY7ZtveAiG7Qe6U+l7/7ZTgUxogRkSzQMRlK11QL3CJ+k2L2a2QS6Zh0xbK3Xef0vwr/Pq+i2EdENNZuuje/x5Kflvu9pyQqZyQek6aYQ9o/q/R7VE5EpHHBYxARN7OqRs3cqs954Qhe5OEqJyI9YGzU6L+turnDmPnsJPWbNs6JdK61aUWtml/josFzzkvu458EAAAAYPiJsAD/6aefjh07dn/pPWjp0qXPPffc559/fgjrv+KKK/71r38NZEnD6Os7eF+CGb7jy5bypvLOJsayjpokpdTQ1g4iirsM6X0kGBcr1bj08XER9p4aoInxA5qXLC9aeuOc+N7tx2Yo31ya1OQ1AjpXDR7MXW1+TkSawU/I6h4FsCDbfMtMR3uAazpfPK777u5HJcmn5Fla/UbwV9xqZ/bzadzgFHoZv0Vi8VahzW+oOkkhwxHcKm/xGcFz+G3+nhXuata6AvzuZvWbPZ3zTX6dFugK8Bvr1fdL9zsSIcMp3hDbebjh8yr/01vcwccOE7txRmf7R+X+h9d37G8NukE3dR3IKPH99svOAxltPuOmkAMZXQc4emvydi/5TrHv1o87zzZXuPSbZnS3h97XoIfrptlv7F7Se+OHrb3b3w5p7+36afZfhKzhFx90Lrliuv2G6Z3t7xZ7b/hgv2vY35Kh7e+VeFes2+8abpjuWDHd3lXtinWttMXT2X5m4j7tRER0drn/gVxcAw8AAAARIMLCRkNDw+TJkw+4WEZGxqZNmw5h/YsXL25pOcAczoFA4NNPP1WUPu41tT/BDJ/1YUaRUUpEStaUxF9Vu/7XTEjvI8jEePnjcv/EhIOZgHs0ibMIcQO4mblNZsum2nu3J9nErvn2+zc5Qf784sTe7TNTTRuWJvl1TkQ+jQdCbgLIGKU7us/IL8g2rzs/waNyTjQhrvsfdFGu+d9nxXtUI3joIZQo0LEZ3fuEk/MsksDa/YYo0KKQZHhynjlgRLV4+zj8JwgsdCzDT3PNjV5ni89QRDp/fPeBjIV7D3D0XoPI6LyQJU/IUq49yt7kMxSRLR7b3X5cpnL1VHubv48aREZnFnTXMDdd+fkRNrfGBaIzQtqPSVcuP8LmCTl/7vJzTpyIFJGdmt+95Ow05YIJ1ja/IQvs5JB+mJmqnD/e2t7XUASRsdAeOzpVOW+81RUwRMZ+GjJl4/Rk07njrB1qXz3J2E9ylNAlF4+zulVDYGzhvu3njLN6VIOIzhqD9A4AAACRgfEB34BnODjttNPWrVu3devW/Pz8/S1TX19/5JFHzpgx44033hiMGurq6pKTk5OSkmpraw/qF9fNfu0v3z8qkLA44YpJ7bFElLA0NeNhpPcRYmeT+ufvOm6Z6Qg9GwwAI9JXX301Z86c2bNnf/nll+GuBQ6bQ/58BwCAkSEiPt8j7E5RK1as0DRt1qxZq1atKi8v7/FsTU3Nk08+OWPGjPr6+ssvvzwsFfZj4Vdn/vnYP9yr3Ir0PiKNi5OfWBSD9A4AAAAAAIMkwgL8woULH3744fb29htuuCE7Ozs6Ojo3N3fKlCn5+fmxsbGpqalXXnnlnj17Vq9efeaZZ4a72D6M+WgazYgiRjFL05DeAQAAAAAAYOAi72zh8uXLFyxY8OSTT65bt27Hjh2lpaVEJIpiQkLC9OnTzz777KVLlyYnD98phY/6eHq4SwAAAAAAAIDIE3kBnogKCgruv//++++/n3Pu8Xh8Pl9MTIwgRNhoAgAAAAAAAICBi8gA34UxZrPZbDZbuAsBAAAAAAAAGFw4aw0AAAAAAAAQASL7DHwY1dXVMcbCXQUAAAAcTvh8BwCA4Qxn4A9abGxsP3ehBwCAUeLoo48OdwlwOOHzHQAAaNh/viPAHzRZlgsLC/nBCwQCwV8/hN8Fzvnq1auJaPny5eEuJFJNnz6diL777rtwFxKRNmzYQERTpkwJdyGR6vrrryeiVatWhbuQw+kvf/lLWD+O4DDD5/vAHX/88UT00UcfhbuQIfL73/+eiG6//fZwFzJEvv/+eyKaNm1auAsZOnFxcUTU2NgY7kKGyAUXXEBEzz//fLgLGSJr1qwhoqVLlw5w+WH++Y4ADwAAAAAAABABEOABAAAAAAAAIgACPAAAAAAAAEAEQIAHAAAAAAAAiAAI8AAAAAAAAAARAAEeAAAAAAAAIAIgwAMAAAAAAABEAAR4AAAAAAAAgAiAAA8AAAAAAAAQARDgAQAAAAAAACIAAvzQYYwxxkRRDHchkSrYdYKAjfYQBbsOW+Chweb3I2HzgxFsFH6+j7Z39Gj7CBhtr5f2vuTRs0njLRzRxHvuuSfcNYwWgiBIkrRw4cI5c+aEu5aIlJKS0tjYuGzZstTU1HDXEpGio6MTExMvueSS0bO/PoxiY2NbW1vPP//8SZMmhbuWiJSUlOT1eq+55hqn0xnuWgAOs1H4+R4bG+twOC6//HJZlsNdy1BITk5uaWlZvnx5QkJCuGsZCrGxsS0tLRdccMHEiRPDXcsQsVqtU6dOPfnkk8NdyBBJSEjgnC9btsxqtYa7lqGQnJzc0NBw1VVXpaenh7uWw4BxzsNdAwAAAAAAAAAcwAgZSAAAAAAAAAAwsiHAAwAAAAAAAEQABHgAAAAAAACACIAADwAAAAAAABABEOABAAAAAAAAIgACPAAAAAAAAEAEQIAHAAAAAAAAiAAI8AAAAAAAAAARAAEeAAAAAAAAIAIgwAMAAAAAAABEAAR4AAAAAAAAgAiAAA8AAAAAAAAQARDgAQAAAAAAACIAAjwAAAAAAABABECABwAAAAAAAIgACPAAI8Qnn3zS0NAQ7ioiGDoQAAD69+CDDz766KPhrgLgoI3CLzkj+CUjwA+RdevWnX766QkJCRMmTFi+fHlzc3O4Kxq+5s2bx/ridrtDF0OXhtq6devxxx//5Zdf9vnsQPpqlPdnPx2IDbIfjz322LRp0xwOR2Ji4rx5815++eXey2Dzg5FttG29A3nXj1T/+Mc/brnlln//+9/hLmRwVVZWXnrppSkpKU6nc9asWSP7n1jTtFWrVs2aNSsqKiorK+vMM8/87rvvwl3U4ffjvyVGnP5fcsTvxzgMvscee0wURZPJdNxxx40ZM4aI8vLySkpKwl3XMJWamqooyrhe3G531zLo0h7OOeccInrttdd6PzWQvkJ/9tOB2CD7pGna1VdfTUSKohx33HHHH3+82Wwmoquvvjp0MWx+MLKNqq13gO/6kaq0tNTpdBLR/Pnzw13LINq6dWt6erogCFOmTFm0aJHNZiOi3/72t+Gua1Dour5gwQIiSkpKOuWUU+bOnSuKImPsxRdfDHdph9mP/JYYifb3kkfGfgwBftDt3r1bluW4uLjdu3cHW37/+98T0aJFi8Jb2PDkdrsZYyeddFI/y6BLu3z88cd//OMfp02bFjwe13s/NZC+Gs39ecAOxAa5P2vXriWisWPH1tTUBFuKiopyc3OJ6O233w62YPODkW20bb0DedePVJqmHXPMMQ6HY2QH+EAgkJ6ebrPZPv7442BLWVmZw+EQBKGsrCyspQ2KJ598kohOPvlkj8cTbPn++++tVqvD4fD5fOGt7bA4LN8SI8sBX/LI2I8hwA+6W2+9lYj++te/hjZOnDiRiIqKisJV1bC1ZcsWIlqxYkU/y6BLu+Tn54cOqOm9nxpIX43m/jxgB2KD3J8TTzyRiL7++uvQxn/9619EtGzZsuCP2PxgZBttW+9A3vUj1cqVKxljTz311MgO8M8++ywRrVy5MrTxqaeeOvXUU997771wVTV4lixZQkTvv/9+aOMZZ5xBRF999VW4qjqMDsu3xMhywJc8MvZjuAZ+0K1bt46IgruDLsEfg09BqKKiIiIqKCjoZxl0aZdPPvmkrKysrKxs+fLlfS4wkL4azf15wA7EBrk/JSUlsizPmDEjtHHy5MlEVFhYGPwRmx+MbKNt6x3Iu35E+uabb1auXHndddf99Kc/DXctg+tvf/sbEV166aWhjZdffvmbb745Il+7LMtE1GOes+AV4MGnIt1h+ZYYWQ74kkfGfkwKdwEjHOd8x44dTqczKysrtH3SpElEtH379jDVNXwVFxcTUWNj40knnfTdd9/JsnzkkUf+4he/WLRoUXABdGmotLS04IOoqKjezw6kr0Z5f/bfgYQNcv9eeeUVxpgg7HMUeP369USUl5dH2PxgpBuFW+8B3/UjUkdHx8UXX5yfn3///fePjMm9+rFp0ya73Z6ZmfnJJ5988cUXNTU1RxxxxJlnnpmYmBju0gbF9ddf/9JLL/3qV7+Ki4s79thj29raHnnkkc8++2zhPdxjUwAAIABJREFUwoVdY7Aj2o//lhhxDvi9bmTsxxDgB5fH4/H5fCkpKT3a4+LiiKipqSkcRQ1rwROe9957b1pa2syZM2tra9etW/f+++/feeedv/3tbwldejAG0lfoz/5hg9yfKVOm9GjZuHHjzTffzBgLTg+DzQ9GtlG49R7wXT8irVixory8/KuvvrJYLOGuZXB5PB6Xy5WXl3fDDTesWrWqq/2OO+54+umnTz311DDWNkiOPPLIL7744vjjjz/ppJO6Gs8999xnnnkmjFUNmVG4E6ORsh/DEPrB5fP5iCg4bWmoYIvH4wlDTcNbWVmZJEn33HNPZWXl22+/vX79+s8//zwuLu6+++776quvCF16MAbSV+jP/mGDHAjO+TPPPDNv3ryampo//elPRx11FGHzg5FulG+9fb7rR57//Oc/a9euvfvuu0fG+dj+BTfa4uLip59++vHHH6+tra2trV21apXb7b7wwgsrKyvDXeDh53K5brrppubm5vHjx19yySWnnXaaw+F4/fXXg/OcjXijfCdGkbwfQ4AfXDExMaIodnR09Ghvb2+nvYe4INS7776rqurdd9/NGAu2zJ49+9577zUM45///CehSw/GQPoK/dk/bJAHtH79+lmzZl122WU2m+3VV1+98cYbg+3Y/GBkG81b7/7e9SNMdXX1VVddNXv27Ntuuy3ctQwFSeoclvvoo49effXVSUlJSUlJ119//R133OFyuR5++OHwljcYli5d+uGHH95zzz3btm179tln33jjjV27duXn519zzTXvv/9+uKsbdKN5J0YRvh9DgB9cgiAkJCT0vmgq2NJ1nQb0b/78+US0efNmQpcejIH0FfrzEGCDDFJV9Y477pg5c+bWrVv/7//+b/fu3WeeeWbXs9j8YGQbnVtv/+/6Eea1115rbm4WBOHiiy++4IILLrjggmuuuYaItm/ffsEFFwQnMB9J7Ha7IAiSJC1evDi0PTil2caNG8NU12Cpr69/5ZVXcnJy7rrrrq5j9CkpKQ888AARPf7442GtbiiMzp0YjYj9GK6BH3Tp6enr16+vra1NTk7uaty5cyeN6PfGoTEMQ1VVURS7DgMHBX+Mjo4O/oguHbiB9BX6c3+wQfbDMIwlS5a8+OKLJ5544po1azIzM3svg80PRrbRtvUO5F0/8nzxxRc9Wurr61988UWz2Ry86dqIIUlSTk5OdXV1j488q9VKRLquh6muwVJfX885Hzt2bFd6Dxo/fjwR1dbWhqmuITXadmI0UvZjOAM/6M466yzO+VtvvRXa+NZbb0mSdNppp4WrquGpqqrKbDb3uLUDEX322WdEdMQRRwR/RJcO3ED6Cv25P9gg+7F69eoXX3zxoosuev/99/f3+YfND0a20bb1DuRdP5Jce+21Pe69XFVVRXvvA+/1esNd4OF3zjnn+Hy+4AwvXYIfeb2n/op0BQUFsixv27atx7GJTZs20d6Z2Ee80bYToxGzHxvc28wD53v27JEkKSsrq66uLtgSnBvjZz/7WXgLG57mzJlDRH/4wx8Mwwi2bNiwISkpyW63l5eXB1vQpb0Fr9B77bXXerQPpK/Qn3z/HYgNcn8KCgqsVmt7e3s/y2Dzg5FttG29A3nXj2yhAX5EqqiokCRp4sSJXR9wW7duTUlJMZlMW7ZsCW9tgyF4scANN9wQCASCLUVFRfn5+YyxdevWhbe2w+vHfEuMUPt7ySNjP4YAPxQef/xxQRBSUlKWLl26cOFCSZLy8vJKSkrCXddwVFhYGBy8NHbs2LPPPnvWrFmSJCmK8uyzz4Yuhi7tYX/7KT6wvkJ/7q8DsUH2qaamhojMZvOUvtx8881dS2Lzg5Ft9Gy9A3/Xj2AjPsDzvZu0zWZbsGDB/PnzFUURBOHPf/5zuOsaFI2Njfn5+USUnp5++umnz5s3T1EUIrr99tvDXdph9iO/JUaiPl/yiNmPIcAPkVdfffXUU0+Ni4sbN27cFVdcUVNTE+6Khi+Xy/WrX/3qmGOOcTgcBQUF559//rZt23ovhi4N1c+umQ+sr0Z5f/bTgdgge+t9UWioc845J3RhbH4wso2Srfeg3vUj1WgI8JzzN9544/TTT09KSkpLSzv11FM///zzcFc0iDwez7333nv00Uc7HI6MjIyTTz75ww8/DHdRh9+P/5YYcfp8ySNmP8Y45/28EgAAAAAAAAAYDjCJHQAAAAAAAEAEQIAHAAAAAAAAiAAI8AAAAAAAAAARAAEeAAAAAAAAIAIgwAMAAAAAAABEAAR4AAAAAAAAgAiAAA8AAAAAAAAQARDgAQAAAAAAACIAAjwAAAAAAABABECABwAAAAAAAIgACPAAAAAAAAAAEQABHgAAAAAAACACIMADAAAAAAAARAAEeAAAAAAAAIAIgAAPAAAAAAAAEAEQ4AEAAAAAAAAiAAI8AAAAAAAAQARAgAcAAAAAAACIAAjwAAAAAAAAABEAAR4AAAAAAAAgAiDAAwAAAAAAAEQABHgAAAAAAACACIAADwAAAAAAABABEOABAAAAAAAAIgACPAAAAAAAAEAEQIAHAAAAAAAAiAAI8AAAAAAAAAARAAEeAAAAAAAAIAIgwAMAAAAAAABEAAR4AAAAAAAAgAiAAA8AAAAAAAAQARDgAQAAAAAAACIAAjwAAAAAAABABECABwAAAAAAAIgACPAAAAAAAAAAEQABHgAAAAAAACACIMADAAAAAAAARAAEeAAAAAAAAIAIgAAPAAAAAAAAEAEQ4AEAAAAAAAAiAAI8AAAAAAAAQARAgAcAAAAAAACIAAjwAAAAAAAAABEAAR4AAAAAAAAgAiDAAwAAAAAAAEQABHgAAAAAAACACIAADwAAAAAAABABEOABAAAAAAAAIgACPAAAAAAAAEAEQIAHAAAAAAAAiAAI8AAAAAAAAAARAAEeAAAAAAAAIAIgwAMAAAAAAABEAAR4AAAAAAAAgAiAAA8AAAAAAAAQAaRwFxCRAoGA2+0OdxUAABBOMTEx4S4BDjN8vgMAwDD/fEeAP2iBQGDMmDHl5eXhLgQAAMJp2bJljz32WLirgMMGn+8AAEDD/vMdAf6gtbS0lJeXC4IQFRUV7loAACAMNE1zuVybNm0KdyFwOOHzHQBglIuIz3cE+EOUkJBQW1sb7iqGgq/Q0/FpS9ySVCazcNcCADAsfPXVV3PmzAl3FTAoRs/nOwAA9BARn+8I8NAfz0ZX0WkbtBa1/aPmnKcnIcMDAAAAAACEC2ahh/3qSu8ksNbX60sv28pVHu6iAAAAAAAARikEeOibd7Or6PQNWosatSh+7IfTpVi59fX60kuR4QEAAAAAAMIDAR764N3sKjx1g9asRi2Kz31usm2Gs+CtqVKs3PoGMjwAAAAAAEB4IMBDTz3SO1MEIrIc4UCGBwAAAAAACCMEeNhHn+k9yHKEI/9NZHgAAAAAAIDwQICHbr5CT+EpG7RmNfr0hNwXjghN70HWIx35r08Vo6XWN+rLr94eliIBAAAAAABGJwR46Obd4tJaVCKKu3i/d323TLbbZkQRkevTliEtDgAAAAAAYHRDgIduMWclJS7PIKKSi7e0vdPYewGu8/KrtrevaxKdUu6LRwx5gQAAAAAAAKMXAjyEYJT+xzGJyzN4wOid4bnOy6/c3vxyreiU8t+YapvuDFeZAAAAAAAAoxACPOxrPxke6R0AAAAAACC8EOChl14ZHukdAAAAAAAg7KRwFwDDEqP0P47hnBoeryxdssU2I8r1WYsYJRW8MdU6DekdAAAAAAAgDBDgYT8YZTwwhogaHq9EegcAAAAAAAg7BHjYP0YZD4wRrELra/U5aychvQMAAAAAAIQRAjz0i1Hayvy0lfnhrgMAAAAAAGC0wyR2AAAAAAAAABEAAR4AAAAAAAAgAiDAAwAAAAAAAESAiAzw77zzzvLlyxcsWHD11Vdv2LCh9wK33HLL4sWLh74wAAAAAAAAgEESeZPYLVu27Iknngg+/vDDD//+978/9NBDv/jFL0KX+eCDDzZu3BiO6gAAAAAAAAAGRYQF+JdeeumJJ57Izc29//77J0+evH79+ptvvvnGG2/Myck544wzwl3dMGV49JKLt3g2uPL/feQh3ApOa1a9m12O+bGDURsAAAyl9evXH9Ty06ZNG6RKAAAA4BBEWIB/5JFHzGbzunXrcnNziWjs2LETJkw49thjly1bdsIJJzgcjnAXOOwYHr148SbX/1qIqPD0DQVvTD2oDB+o8hWe9IO/1Ju0IjPt9wWDViYAAAyF6dOnH9TynPNBqgQAAAAOQYQF+J07d86ZMyeY3oOmTJny8MMPL1269IEHHli5cmUYaxuGutK7nKJYpzja3m08qAzfld6JqG5VBREhwwMARLTrrrsu3CUAAADAoYuwAO/1eg3D6NF46aWXrl69+k9/+tMVV1yRmZkZlsKGIcOjF5+72fW/FjnRlHpPntaiCnap5V+1hadtyH99qm3GATJ8V3q3TXcmLM8oX74DGR4AINI9/PDD4S4BAAAADl2EzUJfUFDw9ddf19XVhTYyxh577DG/3//zn/+8d7wfnTrT+yfNcqIp+dbsiut2VN9WyIhiFifr7VrRGRvc37X38+td6d06xZH36pTY85LzXjxCMAt1qyqqbiscslcBAADhUlhY+Mknn4S7CgAAANhHhAX4K664wufzHXfccd98801oVp82bdott9zywQcfLFmyxOVyhbHC4aBHeq+6vZCrnMms+V+1A8nwoek9/82pUoxMRM6fxOU+fwRThPpHkOEBAEY4zvnvfve7U045JdyFAAAAwD4iLMBfe+21V1111a5du2bNmmU2mzdt2tT11MqVK88777znnnsuOTl5586dYSwyvDqve/+kWU5RUn6dW3VnEVd50orMMe9NEx1S879qSeMxZybq7VrRmRs863tm+D7Te5DzJ3F5LyDDAwCMHJzzu+++Oy8vz7Evm832zDPP5OXlhbtAAAAA2EeEBXgiWr169dq1a0888cT09PTQ2XFlWX7++edXrVqVmprq8/nCWGF4NTxR5fpfC5NY8q3ZlTfv5n4jOIG8bWZU/mtTRIfU8modMWY7Okpv08qX7wj9XbXW33Xde8E7R4Wm9yDnT+Jyn53MTEL9IxV77i4ewpcFAACH3+OPP75y5crGxsaUlJSOjg673T5u3LiUlBSv1ztz5swnn3wy3AUCAADAPiJsEjsiEkXxsssuu+yyy3o/JQjC9ddff/3119fW1hYXH0q8fOGFF9asWdP/MoFAgIhaW1sPYf1DwPnTuLqHyrUWtfKXu8ngobd/C2b4ojM3BjM8EcWenxz6u66PmoNzzqf9rkB09r1tRJ0SH3VSfOvr9Q1PVaXei5MzAAARbM2aNXa7fefOnSkpKUuWLGlpaXnzzTeJ6Le//e3TTz89ceLEcBcIAAAA+4i8AD8QycnJycnJB16ul9dff/2DDz4YyJJ+v/8Q1j8ELBPsKXfmVN6ymwyuZFtS794nY9tmRiX9InPPb0qIc8t4e+KKfSbtjzknqeW1+rZ3Gksu3Fzw1lGWyfbe66/+dVHr6/XMJGQ/PmFwXwkAAAyykpKSuXPnpqSkENH8+fPvuuuuYPttt922du3a3/zmN3/4wx/CWiAAAADsY2QG+EP25JNPXn311bqu97NMWVnZlVdeKQjD9OoD16ctVbcXEifBIvjLvCUXbcl9bjJTOqtteaWu5r5SIhJMgndHR+Uvd2X+dVzX7zKTkPvPySVLtra91bD7pPUFbx5lneoIXfmee4vr/lzOZJbzzKSoUxOG8nUBAMBh9//s3XlYVNX/B/DPucvsw7AM+w6CGqKilpmWtljqz9LSMjV3TVP7ZrmlpZWpWaaVpeZWWmq5m2tm2Z6aKai4gewgy8wwwOx3O78/BhEQEBXB5bwenx6599w7nxkn4D1n4zhOoyn/rDYiIuLSpUt2u12lUjEM06VLl4MHD5IATxAEQRC3FRLgq9BoNI8++mjdbZKTkxunmBvjvGDDPEYMClkYe2l2WumPxvRBp9wLyJu3F2aOPIMFHDAlovSA0XHa6ki2Vrscyaiob1q5M3zq0ydidiWo2pXvGH/pvbSChZmIRZHfxHs+TdI7QRDEHa958+YVy75GRkZijE+ePNmpUycAkCTpwoULTVodQRAEQRDV3abdyMQN8x0d4jsmBAs4d1pq0DtRjBdbesCUMTzZtCE/c8QZLGD/qRGW34sdp62yMEXkV62uvoM7w3s+7SuWCKnPJNpPlAFJ7wRBEHejRx99NDk5eebMmUajMSIiwsfHZ+XKlQDgcDh++eWXkJCQpi6QIAiCIIgq7qQeeIPB8M8//9S/fZ8+fW5dMbcvBKGLmwOAYVVu7oyLIR80u/RuesluQ8luAwD4T42w/lZsO1YmC1PE7m8vC1fUfA8ZFbmuVfqQ06V7jRefTdL10pu+zXcHezJyniAI4q4xe/bszZs3f/DBB+Hh4WPHjn3llVfmzp2blJRUXFycn58/duzYpi4QAGDo0KFbtmxxOBxNXQhBEARBNL07KcCfOXOmb9++9W9feZO5e0tNGV4w8/VM73y+q+DDTGeanc91IQUlmPjy9L4uThallJwSpSADNwiCIO4GWq32+PHjK1eubNGiBQDMnj07JSVl165doiiOHDly+vTpTV0gAADP8/fy7rAEQRAEUdmdFOC7det2+vTpd999d9u2bQDwyiuv6HS6pi7qdlU1w4ctaU6p6MJPsq6Z3gGg5Iciw+rcKjeTU1HrWjlT7WkDT9OejPeLgfoRQcq4GtaoJwiCIO4ser1+5syZ7r+zLLtp0yaHw0FRlFwub7Qali5dunXr1trOnj17FgAqr1Dz66+/NkZZBEEQBHH7uZMCPAC0atVq69at7du3P3HixPTp08PDw5u6otsYgtBFsVjAxq/zsv93QR6pdJyxyiOVMfvbyUKqpHfJLhrXXjKsyNV29Qpb0sJnaBDtwTB+MlmIgtYygID2ZCgVTR8uUcZpHGeshi9zDF/mqB/Q6UcFe78QgFjUVE+RIAiCaHBKpbKRH9HhcPz22291t7lmA4IgCIK4F9xhAd7txRdfPHHiRFNXcSegUNhnzQHA+HVejekdu6TCJdlFS3MEIwcAqnZaAKBUtPegwKtvpunk2fJoR0ey1fh1XvH3BbZ/S23/lhZ+khX2aXNNF6/GekoEQRBEgzlz5kzdDeLi4hqhjClTpvj7+0+cOBFj/Omnnz711FOVz44fP37Xrl25ubm1XV4fkydPXrx4cX1aGgyGm3kggiAIgril7sgAf//99zdr1oxh7sjiGxuFwpa0oLW07b+yiK/iKqd3wcSnv3jKergEANQP6AKmROh66q95P2UrTeii5sFzY8zbCws+ynSet6X0POE9MDBkXjPGV3YLnwhBEATR0Fq1qmEvksoabTWZIUOGdOnSZciQIaNHj544ceKHH35YMRBApVIBQHBw8M3cX6PRMAwjCMI1W967C+gQBEEQd4I7MgN369YtNTW1qau4cyAInh9T7Zgz1Z7WL8mV7pCFKMK/bKnt5n1dt6SUlM/gQO/+/gWfZBUuyizemF+6zxD8XjP9yGAgA+oJgiDuEBMnTqx2xGKxHDt27OzZs+Hh4dOmTWvMYiIjI3///ff58+fPmTPn4MGD69evb9++fUPd/L333nvvvffqbpOcnBwfH48Q+TFGEARB3L7uyABP3CRnqj3lsf8EM69K0EZvbsMG3uBKRUhOBb4Z6T0gIGfyhbKfTNmvnS89aIr+vnXDVksQBEHcIp9//vnVBzHGH3/88bRp0645wL7B0TQ9a9asJ598cvDgwZ06dXrnnXfefPPNRq6BIAiCIG5nZD+we45oFdNfPCWYeV0PfeyP7W84vVeQRyqbbW8btT6e8WFtf5dggQw+JAiCuIMhhKZOnfrggw8uX768uLi48Qvo2LFjUlLSSy+99Pbbbz/88MM5OTmNXwNBEARB3J5IgL/HYMgac8Z5waaM00Sua0Wp6eu62o6xvZbJgZ59/QISH7zvxIOIQXyByz21HiTsSnfcfNUEQRBEI2vfvj3GmOf5Jnl0jUbz1Vdfbdmy5cKFC3///XeT1EAQBEEQtyEyhP7ekv9hRsluA+3JRH3f+nrTOwD0yC/kMN7g5xvNVnnnYID3zSXf2ezveukGgCxr/Lmyn0y+Y0Jc2c6yA8bAGZGBb0U13JMgCIIgbrn09HQPDw9/f/8mrKF///6dOnXauXNnE9ZAEARBELcVEuDvFSIAX8zlz89ANIr8upU8sso2vy6M5fVYtscsSQKGF4sM31fK8BjgPXPJJqsdADIEEQD0Q4Osf5gNq8q3/Mn/IAMASIYnCIK43WRlZV190Ol07ty5c//+/V26dGn8kqoJDg6eMGFCU1dBEARBELcLEuDvFaMNpixO+GJaeGBLjUd3n8qnNlltc0vKZnvpnler6r7JJA/tx6UWq4RfLDR85+/bjGUqp3c/mh6j1QCArqde2UZjO1oGALJQBX/JRTI8QRDEbSgiIqK2UwzDzJ49uxFrIQiCIAji2kiAvzdIODiDOxKIx43UrPPz8ap05nurbY65FAC09eiBH+WhBYCPSy1WXJ7hN1qtFel9s7/eh6YwJ6UPPW07WkZ7sZQScbnO0I9jc99Mzf8gQ3JKwe83u0VPkSAIgrheL730Uo3H/fz8XnjhhY4dOzZyPQRBEARB1I0E+HtC5pizz20qoNdHb24vG1JkXOerdw+A32qzv28uBYAZnroeKuW1bgNQKcPbMH6uoEgAgMvp3Z+mMSelv3S6dJ+R0cti9ibIQhRclkPZWsv6yzNGJBd+kgUAJMMTBEHcJr799tumLoEgCIIgiOtAVqG/+5UeMBVvKqA19JQE/04KuUmUhhmMabyw1WZ/p7gEA8zw1A3Rqut/w1Ee2sk6LQC407s3RdWY3pVxGlrHKFtrAaDsULGipQaxqPCTrLxZF2/J8yQIgiAIgiAIgrirkR74u5zkkHImXwCAwFnR2nDlMqwYbyw+7HQNKDI4JHwD6R0AMECeKFZ86cTYImE/UUofeKr0gInRy2L2JSjv01S+xHHK4jhlkUcquBxX4SdZlJIKnEnmwxMEQTS2RYsWXVf7yZMn36JKCIIgCIK4ASTA3+UMy3O4TIeqjdZ3XAgAKBBapvfuX2hI4wUAeFmrvYH0Psdc8r3VDgAaClklbMf4xULD+mTafsAEAGGfNa+W3gEgamPrlJ4nXGl2xk8mFHH5H2T4vxZ+A/vYEQRBEDdjypQp19WeBHiCIAiCuK2QAH+Xc2U6gEIhH8UiunyNuj12RwbvHvwO2+y2Z9TKapu616FyenfPe99js7vnw4+IFVY86iX8as5544KihVrRvMrnAmyQPHZ/u/MP/8sXcgCQOyagbU3p/XeHc6PVPtPLI5wh70yCIIiGd/We6kuXLj148GBCQkK/fv0iIiLMZvNPP/20e/fuQYMGffjhh01SJEEQBEEQtSEx6S4Xuqh54FtRrL/M/WXFvPepOo+/XK7DTtcww5U17a5pvrm0cnr3p+lRHloMsKjUUsLCuCUBK6dQ3AFTSo8TsfvaKVpWyfD2UxbBXP7BgetIyftZplnhPpXXYDjkcL5mKhYwPO5UhGvIO5MgCKLh9enTp/KXmzdv/vnnn+fMmTNr1qyKgxMnTlyxYsW4ceO6du368ssvN3qNBEEQBEHUiixid5cQLQJ/yVX5iOSUbEdLEYOuTu8zPHUjPTTL9N6V17Srz6N8b7MDgD9Nb/HX+9PlXeijPbSTdFoAMDPww9JwXQ+9YOBSep1wnrNVXFj6ozF98GnMSV4jg4pCZdGnnDHDUt7JL5YuN/jD6ZpkMgsYAMCTJm9LgiCIxrBy5crIyMjK6d1t7NixLVu23Lx5c5NURRAEQRBEbWrt51QqlTqdbsmSJS+88EJjFkTcAC7LmdLrOF/ARW2I1/XQuw/mTrlgXHspenMbXS89ADxXaDjH8QhgppfuJY0aLs+Hf8VYfMTperrAMNVTO0Jbfe56NeEMzWP8rZ/ej64yAH6sh5YG+KzM2sFDEbUhPn3w6dIfjSm9yvvhy9O7S/L/X1jw/Ji8i2XFPRJb/Gcvm5A2eyWao/f6y+maaCzmMQaAgRp1d6XilrxM9SMYvhcKVtSnJaVqIYtefqvrIQiCuHX++++/zp0713iqWbNmf/zxRyPXQxAEQRBE3WoN8E6n0+l0DhgwYPv27UuXLvXx8WnMsoj647KcKT2Pc9lOAEgffNqd4bkcp2lDPmJQxTj2TF4AAB1NPV4pHisQ6qNSHnG6MOBjLm6E9hqPtSfAr7ZToz20oz3Kr4/8Nj59wMmyQ8Wp/3fCq5+/YXUu5rH/G+HBc5oBQJdmHv9sibf3OvnAT2WlMzPGzJOOuTh3en9erXrbS4du5uW4aVLZ75ItqT4tsTMNonhA7K0uiSAI4hYJCgo6efKkKIp01Y9lRVFMSkoKDQ1tqsIIgiAIgqhRXWOVmzdvvmbNmh9//DEuLm7NmjVipZ3DiNsEl+lwp3d1R53v2BDskty934WfZGEee/X3l0cq3S2/1PtEMUyJKA0uNOYK5f+U++2Ot4tLAKC7UjHf27OhqqKUVNSmNtpHvPgirmh5TuX07vZQgje9sRUvR903mvVL8irS+7venk0+ep4JesOdyWVRSxTxv9bwp9VBxPoBABM8maR3giDuaF26dMnLy5s2bZokVUxpAkmSpk+fnpOTU1vnPEEQBEEQTaWuuIQQGjly5NmzZx944IHRo0fHxcVt27at0SojronLcab0TnSn92Y724Yuah4wOQK7pPSBp41r84BCAZMjKho/oJBt8vdtI5Pli+LQImOuIO63O6aazCLAqzrtEr23J9WQ2RkLkui48uugWCIArtKg06O+WV80kyj0wqeGiLPOCIaZfRukdwCglDFMwMvcPN3JAAAgAElEQVQAIBg2UOo2lLpttT+S9Tjmi5Aikg0Y19TFEgRB3JT58+cHBwcvXry4TZs2b7/99pdffvn222+3bdt20aJFISEh8+bNa+oCCYIgCIKo4tqJKSgoaNeuXVu2bJEkqX///s2bN1+wYEFeXl4jFEfUgctxpvQ8wWU63Omd1jIAEPRedMDkCMxLmMPqDh7V1oHXUGi1r487wz9faJhyOb2P97jW0PnrJFqEi32T7MdKZeGKoPeiKRVt/Cov+7XzlTP8H07X3IfZlfMCz3ZUm/2ZTEF4p7hEqv2ejYkNmY5YP8lyVDT9UO0UFsx87kcAIAufB5S8KaojCIJoMHq9/tChQ7169UpOTp43b94rr7wyb96806dP9+nT59ChQ2T2HEEQBEHcburb5dm/f/+zZ88uW7asrKxsxowZYWFhTzzxxPz58//880+n03lLSySuViW9/5DgTu9ugW9FUioaAOwnykr3Gqtd6M7w4QxTIkkSwDCt5pak9z5JtqOlslBF7N52AZMjmu1sS6lp41d5WRPPgYQB4I/Lq9b92Vf313cxdj2rtIm7i22z6p3h8wTRItXclsO4novq1wbRWjbkTQDgst4GyVH5FJ8zDwvFtK4r7dXzZh6CIAjiNhEbG7t3794LFy7s2LFj6dKlu3fvTktL27lzZ0xMTFOXRhAEQRBEddex2zbDMK+88sqoUaN27NixatWqQ4cO/fLLLwAgk8lcLtc1LycaCpftTOlxnMt2ah7URe9MoDVVVh4q2WOQ7CLjwwomPn3o6aiNrXVPVelC+dPpzBXK8+1PdsdLGnUIU+UON6M8vf9bKgtVxO5vJ4tQAoDmIc9mO9pefDbJtO4SAKR/FPE/c4lQad77kVKH0OloiQ8zZ0MEAphzrbH0xZLUs6DIj6bW+eqDqxZvw/hlg+mEi/vWT99BLrvhJ8L4DxOKvpFsSXz+F2zwVPdByXFBKFoHiGbD59/wnQmCIG5DsbGxsbGxTV0FQRAEQRDXcB0B3k0mkw0YMGDAgAGZmZn79u07ePDgoUOHbkVlRG0Mq3O5bCeloCK/ia+W3gHA+PUlAAicGWX65pL9pCV/fnrlAF8x732sh/aI03WS44YWGb/x0zdIhq9I70yoAu1qfTyAsjmc7qCuaqeUb4pjB5wxrbuUZLNL7wYAjSpWrXtIq/gvVKk5Zf3fpNwPV4Z2Vsh7qpR1PJAHQvEy9oSLG2YwVs7wFek9kKYjmet+b1dFySIWOM/05PMWM/qBSB4CAHzmTMACE/Aypbrv5m5OEATRNC5cuAAA4eHhCoWi4ss6NG/evDHKIgiCIAiifm485ERERIwfP378+PGCcFPDlYnr5TsmxLytkMtypg85XTH73c2V4bD8bqaUlPOszX7SguRU0NtRFWcrr1o33kNr1WpGG0wNleHPlzgznk7yT7QZQmRz14YYFRYwVG0RDXFLQ6a8ktN1awnPQMaCyPe8Pd07xpkwzv22eUCP060O20a9V2BZ6lX3YzEIrfb1GWswHXNxQ4qM6/z0oQztwHi8ofiEiwug6bV+Pj70za6IR2k70t7PiMU/8Dnvy5qtEIv3iKWHEOPJhky/yTsTBEE0lRYtWgDAkSNHOnbsWPFlHTDGdTcgCIIgCKIx3WQvJQAAc7NdnbeRTz755Isvvqi7Dcdx0KS/08hCFbE/tk/tdcJ2tPRi36TKGd74dR5IWB6lMqzJRTIq6tt4j+7l3e/V0jsAaCi0ytd7tKH4FMeNMBi/9dMH0PXN8KWS9J+Le0Au015eu/7U36ZmiTYA+G6KnxQib07T3jSlQohFCACskiQCOB+RnRxgu3+t8bHNpbqF6or93ldYLN/K7NGfBL89PLPb1pIfHr+0+Bm/+78v9liYG/JOtM+QwKsLUCK04nKGH1ZkXOHrM9dc+q/LFUDT6/x8whroPSmLmOco+UkwbqF9B3HZ7wIAG/IWYrwb5OYEQRCNb/To0QDg6+vr/nLcOLKbBkEQBEHcSWrNOU6nEyFU29m71dmzZ9PT05u6imuThSpi9rWrnOGNq/KcKfayA0YAcJyxIRkVtT5e10tfccknpWVXrzmvpajVlzP8Jqv9Nd01FrSzYfyLw7nf7jBZTgTwORaF4ml1+Vj3vvfjrGnZ6EjZ7P10QHyEvJnKfZzy6ILY8jKMX+VlrzMCgrAFMX5eV5ZwH6xRCxguPCBbuzDk2QUFJz0gdGVO1zkFIobMCed2WGy+w4IeUMgDq36+UDnD9y8wcIAbNr0DAJIFs4ET+LyPXRcGgWSnlC0Y/+ENdXOCIIjGt2rVqspfLl++vKkqIQiCIAjiBiAyOq4ySZKys7OlWpY3d0tJSenZsydFUaIoNlphNeJynKm9TrgyHGygnM+/so7g1ekdAI66XGZR6lHT3HKrhLfb7D1UCr/ae+ALRHGdxbbFarNhLMPcH5ceYIGvT5GMvr+s2SoAMK27lPXqecA45MNYv/GhtbXPF8W0ldmqaekY4NQT2jYHLRKF1rwX8Ft/zxCGfkAu76lSdlFcCf/FktQjv9AiYRpgjZ++400sXFczye5I6oC5fACQt9xB67o18P0JgrgDHT58+KGHHurUqdM///zT1LUQDSY5OTk+Pv52+PlOEARBNIk74uf7Nfoq9+3bt3v37tTU1Ojo6HHjxiUkJFRrMHXq1MzMzC1bttyyChsVRVERERF1t7Hb7Y1Sy7XJQhUxe9qd63SEz3cBhSg5khwSIIj8ulW19A4AHeW1blquodBQrbq2s+m8sMJi2WcvX46uvVz2fyodbXkQLH8ixofSPVylNQbb8TIux0mrXOoOxwGA8ugK9U7vAMB+U6CangEYlNGqIevbn1+Tw81MGz07XyPCngGeuYJ9j92RGBLoHrjvwPh1o9kiYRkCDsMMk9k9H/4aL9x1oVRs2Bzu4hja+2mS3gmCuCsZDAalUqnRaABg8+bNe/fujYmJGTt2bMVIe4IgCIIgbhN1Bfhx48atWLHC/fdffvll1apVixcvnjRpUuU2P//8c1JS0i0skKiTcW2eWCYCQiBhyYEBIcC46PMs7ePeVy9Qf71MovRFmWWL1SYC0AD/p1KO0mpaylgAkKIWOk91wWIpGzy12pLssmicNfYsJf9M3eE4oDjGb1B5epdw8Jxmdad349d52f87DwBskNyZZs9++Wyr9fFFNJ07PWXge/lDterEQXpvmqpI7y8biv9zuVQIvenpsdvucM+Hb/AMz+j7U4oosvI8QRB3H7vdPmLEiM2bN//111+dO3f+/vvvBw4c6D719ddfHzlyhGR4giDuaZgTS34Gibt2S0ZH6x699QURRO0BftOmTStWrIiKivrwww/j4+OPHz8+ZcqU119/PTIysk+fPo1ZIlGbS++mFXycCSwln9NM+ixTMPJhS1rkf5RpPVKa1jfx6i3i6w8DbLPZF5aUlUkSDfCiRj1aq6m84zqlbM74DRMK1/BZM+Utd1a+FtEo7HNPx3+7ASB35mB1p/TCz7IAIOTDWL8J9UrvoQtjPZ7wOdfl35IfinLeuBD6SXMAyJ2ewk+52JOmfUeHAMB6i3W7zXmO5wDAjvFsc6mOonxpKl8UBxca+qhVr+q0soZbxIHStGuoWxEEQdw+Fi9evHnz5ubNm7uD+scff+zj47N69eqzZ8++9dZbn3766bx585q6RoIgiCYjFK3nMibXs7Eibj+lffCW1kMQUEeA/+KLLxQKxcGDB6OiogCgefPm991338MPPzxu3LjHHntMq73GamfEreZO70hGLRwUbPSRL0IIC1jbzVvbzTul14mbyfCXBHFmcclRlwsAHlHIp3vqotga3ids6FuiaYdY+rto/pH26lH5FJ87G7FOR9oT1qOx1qNZgCBkwXWkd99xobYjpSBgADCsypXHqt3X5k5PyXn9AgAIIwLnlZQBgBdFfejjlejiDjocF/ny7QwNkrTaYqUR+p9Oe0NbyUnYlQNwzbUhKMT6A1XrxASCIIjb33fffRcUFJSYmKhUKouKik6cODF58uS+ffv27dv3+++/37NnDwnwBEHcyyhtJ0AMYIHSdEDykBrbYPtZyZGCWD1Stmzk8oh7U60B/vz58w899JA7vbu1bdv2888/HzFixMKFC+fMmdMo5RE1q0jv3ivvO2qUHj5r4/Nd8iilLEwBALH72t1Mhp9gLD7P8z4UNdNL16umRe/cEOPFhkzlMmdwWTOVukcroqxk+Vc07QBK4fXsIsshS/GWwpAFMXWPnHddtGf/7zxgCJoV5U7vF/smSk6J9mDEMiF32gVZqMJvQqhQzBd8mJHzRkrwY96IBQxgwZjH+H867f902nRe2GC1brLa3UsPrSiz7LTZe6uUz6iVsSxb/6fPZUwTCtfUpyWSBSgTTgO6e7ZRJAjiXpOZmfnUU08plUoAOHLkCMa4W7du7lMtW7bcv39/UxZHEATR1ChVS8ZvuFC4GlEqeczXNbSQnI6THQGADZmJGF1j10fck2rtoXQ4HFcvxj5s2LD7779/0aJF2dnZt7gwolaG1bnu9B71bbyipx4AOvxnBgDvFwLcDWRhipjdCbIQhfVIadbYs9d7/6fVyufVqt2BfnWkdzfGfzSlaomdGXzBisvHJC7zTQDMBk2iVGERa+Jap3epO70DABskV8ZrAaB4U2HpXsPFZ5NEq+jVz791+sOyEAVgSB98smSHwbylAABUCVrfIMWeAL/hWrWA8SST+ReHEwAwwE8OpwiQIJc9q1aFMUyhKK6xWPsUGJ4tMHxtsZrEKu9nEWCNxXrCVX1SE6LK17F30d7A+NT8B7EAgBALqEEXzCMIgmhcOp0uJyfH/fc///wTIdSxY0f3l2azWSZr6H09CIIg7jRs6FuI8RbL/hDN+64+y+d/gV3ZlLIF4zek8Wsj7k21BviYmJgjR44UFhZWPogQWr58ucvlGjVqVN17rRG3Dq1hAAALWDDzogS0iFsnlgGAVz//ijaSVZQcIgDQ2utOmCO1mjnenl5UPYafI4YN/wAAhLyFmC8EAKFovWRLRLIgNuhVdxNGf+3f/ygVHbu/naqdhzPFlvbiKdEiePXzj1gThxRUi38eoDQ0iJA+9JQr3aFqo222sy2loKJYZrqnbqyHhsf4dZN5g9U23GA0itLDCvnXvj7zvT1/DPTb6KcfqFHrKOo8z39UUta3sKjysPg0nv+4pGyUwfSP01W5GMlV/rusXCwGwVTzH8wDgMQVABbq9ZoSBEHcluLj45OSkrKyssrKyr7//vsOHTro9XoAyM/P//vvvyuPwiMIgrg3IcaTDZkOAFzmWyBV+aURc/n8pU8BgI1cSIZkEo2m1rfa6NGjJ06c2LVr13Xr1t1///3U5TjXvn37qVOnLliwYOjQocuXL2+sOokrvF8MEAxc7ozUrFfOMkZOAYyMk1TtPRQty7eCc5y2pvY+IZh4jyd9wj5rcc0bOjH+x+l6SCFXXP+qb7SuK+3VQzT/yOfMY8Pn8znzAUAW/j5Qquu8DxP8TnTqc0kgYkpDB89thhgEAIw3G7Uh2HLwHcQIgJDHUz6S+TvOXH7VeICHOe4cJ+BiGAugZ5WPBsySIwQACCBBLkuQy2Z4evzhdO21O7RUlacXRDOBNJ0vihOMxUv13g9d3ls+W/tEcPEeAHAhhUzfj0XVh99Lln8kR4r7ucNVZwmCIO4gU6ZM+emnn1q2bKlSqUwm08yZMwFg48aN06dPt9vtI0eObOoCCYIgmh7jP0ooWifZz/IFX7JBr1Uc57PfBdFGe/ehPbo0YXnEvabWAD9hwoRTp06tXLnywQcfZFn22LFjbdq0cZ+aM2dORkbGhg0bduzYQfrhm4Tfq2EAkDsjVZh18aHnAie9GfP3K4HuU47kK+k9+rvWSH6NjnSjKI03mk5z/AxPXR27wddBFj7XUXJIKNqAeQPmCyltR9rn2eu9ie1IafrgUyBi2osVzXzq04mx+9uxgXI+z2XeuM13zJV5mEJRlQtbAlReMISxPAreVXZJYBF6XKl4XKmofPAMxw83mKySBABOjF8xFi/x8eqqVBx3cePEJ5bK4ltxp+XYaWUC/MLfrnyhZDvtNHwDAAA0G/7+9T5NgiCI20r37t2/+eabuXPn5uXljRw5csyYMQBw7Nix3NzcV155ZdSoUU1dIEEQxG0A0WzEB66zfYS8jxnfFxHrDwCS9T/BuAUouSz8vaauj7i31DXYY9myZZ06dVq/fn16ejrGV0Yfsyy7cePGzp07L1my5OLFi7e+SKLc3jTHj+nO8i9aaFuNCLn/69wx2/NXAUw6qgEA70zHU++kKsqE3HYeEUtaZjik9HzuiQhFbTfMFISRRaZ8UQxl6MeUtTarG1JEs4Hj+EtLRPOPAJQsfD7A9fXk8/mui30TRavo/WJAyEexF/sm2U+UpfQ8EbEqLnNUMp/XxqufP+NdaD3aznr4fkpBBc2OojQMABhEaa3FasVST+ehdo4/8+jQXHnX+mzBSQFIld7SHMavmsyveWiXlVnsGC3ymPGVcTACrCn8HAcMRfKwKy2zZgCWAIAJHEcprz26gSAI4jY3ZMiQIUOqTN189dVX33rrLfdY+ka2b9++3bt3p6amRkdHjxs3LiEhoVqDqVOnZmZmbtmypfFrIwjiXkZ7PEJ79RTN+/mcubKozwFw+apPga8ieXhTV0fcW1DlZH4DCgoK0tLSOnfu3FAF3f6Sk5Pj4+MpihJFsZEfevBu05G8KouuDd5X1PeQEQBW9gtMiVC982Wm1iYmttAsHB468n7t8XzuvwLu4Iu+UZ41fFJTIIqDCo35othOLvtc7+1dn0nvtcCixZnUAfNFjN9LsqjPr/dywcCdbX9EKOZ1vX2jvmklWsXU3omOUxb3WVU7j+gN2XzeaMmqTRvyOYAu7mxnxotN4wX3vPcnWecHuU+CYJri/dlfqu6f+Hg9Xo8PI6wS3mu3r7FYc4Tq/44UwC7bW4ElOwGA9ulbseioaNruSh0FAIjxUbQ9TtYaJYh71uHDhx966KFOnTr9888/TV3L3WPcuHErVlQsiQoIocWLF0+aNKlym4SEhKSkpJv81aU2TfjznSCI2x92ZjhOdQKJV7T6SXKkcGnjkSxQ2eYY0DcygpW41ZypdkUz1XX2Kt4ZP99vPLO5BQQE3FPpvWl9+rjX592r/HkgtHzm9pgdBR+tzNTaRFdX76D18cuf9pnYXuMQMAC4/1tNsSSNMpjc6X2Nr8/NpHcAQLRWFr2M9unLhr17A5czvrKYve0YH7Z0jyF94ClaTcfsSVC21gKAqp1HzO4Exv9ZLqctpbHoR25vtrdd5fT+sEK+wLkCBBPt8Uisf1/3mnbudenrpqHQAI36p0D/KZ4elf/XRhgeUco14XOAUgKAaNoplf0NACA5uezyZ8eGvkXSO0EQd41///331Vdfffjhh9093gcOHEhOTm7kGjZt2rRixYqoqKgtW7acP39+w4YNAQEBr7/++g8//NDIlRAEQdQIKSLZgHHuHZf4nDkAwIa9cyvSuwhwgedrm6VcJIpGkUxhrhOGnEkXziYczhiRjGvKQXe6mmNb6XVq5KLvWRoZCvOgK//RHysFgEMdPZGEKYtIPe6jXXlfqA/rr6YUdK2fODkwftlgSueFFiz7pd77Btauuxrt+bg85mvE+NzY5cp4TcyedowPW3rAlD7oFK2mY/e3C/usRczeBEpJpb90Om/uS1iivJ7ep2x5qei34jf/zjWKUlel4nO1ERd+DYhmI+ZP0nmM1JavS5/K13d9+LYymazSK4AR/OZwPW6kD3q+7D7CZb0JWOQvfYZdOQBAqeIYv6E39jQJgiBuN3Pnzn3wwQe/+OKLv/76KykpCQD27NkTHx//wQcfNGYZX3zxhUKhOHjwYP/+/Zs3bz5o0KB9+/ZpNJpx48ZZLJbGrIQgCKI2TPAUxPpL1v8wV0BpOjD6F27Fo2yy2voWGGaYzFePBTrh4nrlFz1faLgVj3uXwJD9+nnD6lwAMG8tzBx15u7L8DXPgff09Lyuu9yiwWxENRN/Mv+WfWX7Cp1VWHnO5pJRXw0OTHtC5Z/s+q6Tn7C72H32tQ5aAACEl3EWhwGv8vWp2FDuXXPJGY4PZ5jVvj7am+t7b0DuDJ/a+0TpAVPawFPR37XWjwrGnJT+0unSfUZGH4vkA4HfwKXNyOvz6usqav++FrPaeuELLwPmGf/RlCoOAKZ6erAIdtoc9dw977iLe9lgclV9A1MACKF3FcPi6S0B4iXJlsznfcRfKp8awEYsINu/EwRxd/jxxx9nzZoVHR390UcfnT59+t133wWAAQMGbN26debMme3atXvqqacap5Lz588/9NBDlTeua9u27eeffz5ixIiFCxfOmTPnJu//5ZdfLl68uO6x8RzHAfmVhiCI2iFaw4a+zaW/CoBkER9c76pP9dROLlMjtMvuAID5Pl4Vv3SecHEvG0w2jHte3juJqA5D9uvnjavzKCUVNDs6/4MM87ZCAIhYE+fe3+ruUNcidnK5PC4ujrptAh7xcKjc6LgyZqZNipXC+GyUmteg37rrACAwk9YV0QBAIUjwZ3/KdDAJcEh0emKKx5hGCAC22Oy7bA4lQkv13t6Skct8D0v2az40ojVs6DuILV/TiM/9SHKcq0fJiPEfTns8Us8nWJHhy34ypQ08FflNfObI5NJ9RkYvi9mboIht6Ty5R7L+5vdqr8JPowZNyKZ2HOdKfkGMJxv6ZsVNJuk8Juk86vNw7vRuxxgAKIAXNapNVrsIIAHQGJ5Se33rNWWq8Q3383VfQvv0JTuFEARx11i8eLFCofjpp5+ioqLy8/PdB7t06fLvv/9GREQsWrSo0QK8w+G4el+bYcOGLVu2bNGiRaNHjw4LC6vxwno6fPhwampqfVqSAE8QRB0Yv0GS/RSShVCaDrfoIVqw7Go/nzEG0y67gwdY6ONFAyRWpHeV8l3v6+tqvVdgyJl8wZ3eoze30T7qrXnY6+LTieZthVjEkWtb3TUZvuYA37p161OnTrlcroKCgmefffa5557r2rUrTZNexyY2PF49PP7KTJvMvYXFAIktNFIxzPH2fKe4pChCHJ2geUmjBgARIKNAoAJACdQK3/Jx8ud5fp65FADe9faMZhnR9Jdg2FjPR6c9n6K9ewMASC4+72PAfL0uQ1T9Azy4M/zuhNTeiWU/mc7c97dQzDN6Wcy+BOV9GgANGzyVy3rb+7nlpfs/dZ4z20/OZDyADZlxA0P3C0RxjMHkuJze53l79lWr7pfLp5jMIgAP+IDD8UPEEKdzk8J6tPwaSlYUNKvAxbWXy8jHWgRB3AUSExM7depUud/bLTQ0tEOHDo05Ez4mJubIkSOFhYX+/v4VBxFCy5cv79ix46hRow4cOHAzPQpr1qx555136m6TkpLSs2dP0m9BEESdKFnER7f6MdrKZKt8fcYYTPvtDgAYrFGPvZzeF1bqkyeuwJAz+YJhZW5FegcAVVtts90JF59OLNlZlDE8+a7J8DUH+JMnT6alpW3fvn3btm3Lli1bunSpXq/v06fPc88998QTT8hkskau8h5XxkkfHraMbK2O9rry74VFXPaLCQCSWmgAoK1LFpMvSwnk5plLv022+5no9FBeCsCYB68L1McnrQAgUvhMLOeS4wRO9oxKCQBIHg6IASwgxAJVyz+rxGHMA5IhRUT5EUrO+I8SCr5EskBZ+PyaL3Jl8tnvASA2YNz1Pl9la23MnoTU3olV0zsAABPwslC0TnKkRq46XrQyk/HIBmjG+I+43ocAABlCSoQcGFMA8709+6hVANBDpZQAppnMIoACISVCnpEfOZMfAywCABv42gSLJpU3RjDMEK26r1qlaojlAwiCIJqQUqms8bi3t3c9u6wbxOjRoydOnNi1a9d169bdf//9FSm6ffv2U6dOXbBgwdChQ5cvX37D92cY5urPKaqx2689Ho0giEYmOVJBsl2zGWJ8kDy0EeppNJUz/EGHUyDpvQ41pXe3uzLD1zqEPjo6eurUqVOnTs3Ly9uxY8f27dvXrl27Zs0aDw+P3r179+vXr0ePHiqVqjFrvWd9eNjy/Tl7soH/of+VXXntJyyCiYdwRb5eBgB/57mST/B0GKLjcXYwn6XlkQdgHoR/IbNEzAQRAJh2mJIDLgPTOQzRAABYKAEsAADGPIh19qhjDgslFV8xAeNEw3eYy8dcNlVTB7tY9BUA0F49KHXbG3jKytbamP3tij7P9p8UrmhRaXlPxLLh81znXwDpM99hIgAwQfMAsdUuF0y8YUWu1/P+ipha36JmUTJLUuX07tZLpQSAaSZziSSZJMlX3ZrxHSQUfYtkgWzQa2Nc1KelZZmC8L659NNSSz+1apBGHcrcMd9LsSuz8r9jbRDjRTY1JYh7Qdu2bf/991+LxaLVaisft1qtR48ebdOmTaNVMmHChFOnTq1cufLBBx9kWfbYsWMVjz5nzpyMjIwNGzbs2LHj6mH2BEHcxUTzAdeFF+vZWN5yJ63rekvraWRtZbKpnh7vFpcKGAcy9AKS3mtRMe+9Wnp3U7XVRm9ve7FPYsnOosxRZyK/jgPqzs7wdc2BdwsODp44ceLEiRONRuMPP/ywffv2rVu3bty4UaVS9ejR47nnnuvdu7dOR7bUuoWsPAYAl1RlVl7ZQSMAUN28AYBC6KU4dUsfNt8mfpVvTQ3kkQcwgJz/YlyCnm+pfDpa+Se41mGrCqi3dR6depYvfUF7Pk55dHZvk0b7jZCFza720FzWW6JhIwDQuseuzP3GnCv5USyWAgCXVdeIRNG8n8ucJotcfAPPWhmnCf/yvquP057dac8nxJKfAQHt1UMW9mS1BoKRS/2/RMcZq2FVbszeK7331YQw9Isa9YMK+ZNXbRrfS6WUI/Sn0xnBMADAhr0LWKB9BwKtfloFvVTKXxzOby3W/wYGjMMAACAASURBVFzcWov1G4v1MaVimFbTQX67j0wRS39znXu2no3lLbbSno/f0noIgmhyQ4cO/fnnn4cNG7Z27dqKgzabbfDgwWazuX///o1ZzLJlyzp16rR+/fr09PTKE9FZlt24cWPnzp2XLFly8eLFxiyJIIimheShgGjAIpKHIaZ6MHPDggm7cgCxlDy4kcu71RJd3EfmMgzAIMgXxDdNZtIDfzUuy2lcnQcAAdMir07vbur7PYLfb5b92nnztkL/SeGqBG2Nze4U1w7wFfR6/ahRo0aNGlVWVrZnz57t27fv379/+/btMpnM5XJd+3riptl4nFFSvjuasM8IAFmtPQADAJw38WoWnTTwF2jePe5QAAyeCEogzIPpHCL/t9RFl8GHes8nquZVWcQC5+lugEXR8C0OGEOpWlackhznReNmAABEs+Fzr1yDWMT6Y8GMgMYgIlkgYv0r3RJLjhSQHOVtZSEN/TIAGz5fLP0dECULn8tfcp29/4juKX3EmjhAV9I7klOCgUv9v8TaMrwcodleNXzwhF3ZYumhrgBdAcAA7peb0j6AnWmCM83d5jGAxwBy1GGroN1eu+Nnh/NnhzNOxg7TanoqFcztOq4eyYLKZ0zIQ2tbNQALxdiVDYhGsqBGLo8giMY3ZMiQgwcPfvvttz/++KOfnx8AdO/e/cSJE8XFxT169Bg/fnxjFkPT9PDhw4cPH371KYqiXn311VdffbWgoCAtLa0xqyIIoglRqvsY3yFC0VpKES1vub3GNq5zz4muHCZgNFI0a+TybqlEFzfm8rz3l7TqsZfnw5MMX40sXKEfHWxcnVfwUYa6g0eNGd52rCxv1kUA8Ornr2pTc9/eHeQ6AnwFDw+PHj16uFwuk8n022+/ubddIW6dUpcEABZOmviT+Y8cFwBobeLqk1aRRTNcADKQMO6zzQgImLaYCgLMg5QNdDQwcVgAdDDDOeg+1SSdxzCtxvuqtXkoVSvGb6hQ+DVggc+aIW+5s+IUnznTPcCe8a8S7AEQG/mh62wfoGQgObBQooj7CcnLg7pQuIbLmIIYbywUI1kwG/hKg78glDJG0XIHIAopopGSB4DizQWq9h7eA/zd6V0Rq262s23OlAul+4ypvRJj9iYo4+r7/yqXNVss/qE+Lf0RM/+BS5N1Ht9Zbd9bbWc4fprJvIimB2vUAzQqj9tvGSRKGcv4DRcKV1PySPl9NT9H1/nnRVc24ze06r84QRB3rW+++aZ3797z588/d+4cAPz666/R0dHz5s0bM2YMuv0+jgwICAgICGjqKgiCaDxs6Fti8Q6x9Fex5CDt2b3aWbH4B7H0V8R4scFTmqS8W6Ryencn9spr2pEMX03YJy0QhQwrc9NeOHn1KHp7ouXis4limeD1rF/Emjt+/Dxcb4AvKirauXPn1q1bf/31V0EQVCrVc889169fv1tUHOFW4sQA4BDwExEKs1PCAK0uWimMk6PULhkFADRCzfR0XoTA+QESkMdppLBQfloq2Y9j4nDyGf6R9UV9YpUhmvL/2dsHyjoEXBnvzYbOEk07sFAilv4umn+kvXrA5W+IAIAYTzZkarWSaI9HaK9eonkfkodhVzafM1fW7EsAwEIJn/MBALhXfZOFzwVKiUUr5vKBL5K4fOCLMJeP+SKMORBKMOZAvGrRIEQjWguIAVqDaB1iPIHxRIwnoj2B8UKsHrF6SvugezN2xosN//K+9EGn8t5ONXyZ48pwKGLVMfsS2AB51Pp49x7y5f3w9cvwtHdvd4Cn9f0QVfMAG7F4NxZMtNdTgFgfGibqtC97aHbbHess1lReWFxatrzMMt5DO9rjtvuEjw19SzRtF8v+EM37aK9e1c6K5gNiyc+I0bEhM5ukPIIgmsQLL7zwwgsviKKYl5cXEBBAlqolCOL2gVi9exMiLvNNZetHgKq0Bbrk4rLnAAAbOqu2AfZ3ovM8707vz6iUFfvAt5XJVurLM7wKobl3xU5yklOyJ5VpHtDdbKhGELqoOQBcneHtSZbUp0+IJYLXs34RX98NK9hBPQP8pUuXduzYsW3btj/++EMURY1G079//379+vXq1YusY9eYBsepBsepACDncLEB4HgL7bOxyh0pDr2aYtpgTo4xD/xRMJZiADH3mEiHA90KmDjsxHjjmSs5OVzHHBroW/ElYrzY4Klc1lsAwGXOUOoeBQAu+z33WTb07Rq/J8oi5jlKf8GuXECsYNzM+A+ntA/yuQuwYEKsD+ZNwPjwlxa70iZAPfaZv34UYn2RLAjJg1TxoWELZeYfZHyBnzI+otmOBDZADgBIRt1Ahmf0/YTCryTLYUoWyobVMMNfsiUJRd8AksnC3qs4KEOon1r1nFr1j9O11mL92+nabXfchgEeMZ5syHQuczqX+ZZS93iVn4KY57NnAQAbPB2x+lpvQRDE3cJisfTq1atv376TJ08GAJqmb3KvdYIgiFuBCRgrFK2XHOeFwtVM4ISK43z+F9iZTilbMH5DmrA8yZZUvxWCdZS6DcC1R2gmurhq6d0tQV6+Lv3fzrth8rJoFdP6JlqPlPq8FBi+rOWtyPD2JMvFpxPFEsGz792T3qHuAJ+dne3eSe7vv//GGHt6eg4aNKh///5PPvmkQlF93S+iQXASPL3FYHRUWWXXwmEAKHPh9msL3UcCfBUP9PI73c3rk/uUO1IcvB6nykUkIO/TNCMhuNxtnJstUKEY6YCJA98SmpHK37Wdgqv3rjABLwtF30iOC9iVyReuPFtki3FmQPmg62E1lorkEWzAK/ylT5HMH7vyXRdfRooYqfQQAGDeBAAgmCTBBABAKZEsCLF+SBaIWD8k80dsAKIUwOgAsYi+KuViAYtWwDyINiyWSvZzomEDlngAjEDCgAFjAAnzhZgvBFsiAKjaguryavdCrpdojEKKCEoRSyljIlZFZ73iUbKrrN4ZHskiPnCefozPX8b4Db5qPhXmMmcASGzQBKSIrn4lQGeFvLNCniuIqkrfhlwYn+P5eJnsdhjvxPiPEorWSfazfMGXbNBrFcf5ghWSI5VSxjABo5uwPIIgGo1Wq83MzPz111/dAZ4gCOI2hRg2Yr7r3HN87oe0/nnE+gEA5ouES58BABsxH9CNTAquD+y8yGW9U0dfFBZLJWtiPe8mi17K+A66ZrMBGnV7uawZy16d9RPksv2Bfgju+CBakd4BwLQ+HwAaKsNjCRtX56W9cDLonej8+RlimeDVzz9iTdxdk96htgC/cOHCbdu2HT16FAD0ev3IkSP79ev3xBNPsGz1/bqIhmWwChfNQo2nMIYSZ3mwL9Gx5x/TgwjHiwQAYMzwgEv+979cQZlY+RI6AiMdYBcIGVBQeuVUYsGVNhW7azIBY7iMKQDA5SwIE0X3dwYmYLRkTwYAoNSUMqZqQRzStAdKhV35AIBdOdiVAwAAFIBEqVoxgRMoZQxSxiDa42ZeE7H0d6FgRfljXqux5GIpMGPrcbAer3jCAf+j9EP9XSkB5o1R1IjH2KA2lDL26s3nKlDqNozvQMGwgct6R958Q+VTgnGzZDmCWD8m6PU6ygipurHcl2XWL8ssQQw9VKPur1Grm3ZaKaLZ8Pmuc32FvI8Z3xfdCxBi3iDkfQwAbPj8Ol4ZgiDuMnPnzh0zZsypU6dat27d1LUQBEHUitY9Snt2F0sO8jkfyKI+AQA+azYWLbT3M7Tu0Vv3uGLpn6J5X32L1CQAXdMCyXyRZD8LQFGquPrc51+n6/2S0td0HlfvlGSRpLeLS9UUWuzjVc+qbkMV6V0WpgiaHZ392vkGzPBhn7QAAOPqvNw3UwHg7kvvUFuAnzZtGgDI5fLu3bt369aNYZiUlJSUlJTa7vLaa6/Vdoq4LsEezFe9vC+YqmzJvi7ZXmATFQx6rb2m2CH55zj0R0oLBgb4ebIPBss++KcMCeiraJ/jao4XryRcK+BJXDEAdLcpXmyphstLkp028GklQolL8pRTNe6uiSS7/PKbnMuYVnFc3nK7+1ukZDstGDaIxq3Y3cEOAAiBe8sfWgmiA9Faectt7s9Hbx6t60rrHhVLf2V8B7Lh86udFa3CuQ5HfYev9HjiDyw+kvrMa7RXSehChfNCssejZsY3FzsuSM5MxjOfeSAfIFE0bhONAIillLGU6j6kiqNUrSjVfUgWWPm2bNhssXiXaN4nlvxyZTc1ycHnzAUANuxdRF/H/hPdlPIDdkeGICwoKVtaZn1BrRqiVfvTTdYfT+u60l49RfN+PmeuLOpzAOBz5mGhlPZ8kvZ8oqmqIgii8fXu3fvTTz/t3r37qFGjOnToEBgYSFVdgLNjx45NVRtBEERlsogFjlN/CEXfMP7DAAuCcTNQcllNsx0bEOPzHJ/zPhbMtK4bpelQYxvBsB5zBUDJ5XEHauwFcV0YCPazjN9gSt2mPg9qlKR0XnjDWLzIx+splbLiuEWSRhuKT3FcDHurRhw0AskmpvU/aT1SKgtVxOxtJ49UyiIUaX2TTOvzJacUsSYO0Q2W4e/K9A51D6F3uVx79uzZs2fPNe9CAnwDivRkmKojZpQMAgCFgJFNXJ1s/2xVVuAFm/5RL32zKwsQ0AgeCKwyMH5+SSlYQDJAdw9l55ArU503nXPsTXOcLOS//j/vQEV45d01seTEjgvufm4MiFa2AEqOBaN7ojvF+gqFa4SidZLttPtWlOo+2qunWLxHclxAsiDMXUKUFosONmR6Q6V3NzZigXiqi2DczAROpFRVNodnPCFqvQ9If2GRLt462n96RMH8jIxRCKT2hZ9RUZvaeDzmXbgk3bDsb3lUjt9Eh6JZtmQ/g50Zkv2MZD8DsMV9H8T4UOrWlKYtpW5LqROQPJQJfoPPfo/PeovWPeL+dsznLcKuXErdlvEdcF31t5HJ9gT6/e5wrrXY/nW51lis66y2XirFCK2mRRONapGFz3OUHhKKNjJ+IwCxgmE9ILbKZoEEQdwD9PryBS8++OCDGhtU3o+dIAiiCSFFFOM/SshfxmW9DZILALOB45Ei6pY+qGDajgUzAIilv4mlv9XVVHLxWXPYiPerHXYvEY1oDRv6Vj0ftLdKmcELy8osk01mHqC3SgkAFkkaYyg+xXFBDL1MX/NmwLe/8vT+l1kWoojZ104o5Ao+zAh8MzJ6Z9u0vknmrYUA0DAZ/tMWfuNDFTHqO3+2QQ1qDvBffPFFI9dBuJW5pB6bDC6x+i9MfsX87BVZGpsQPzTEP9XOM2h0Jue8ZFjX2xsArp5anS0I31vtgEE8j+CBKqfeekibViKcN/Fz/ylb/lQLxm+YUPgVpYh0byDHZb0t5C8FgF1lLw98cAEAuM4+LbpyKXWC82xfd5c7Yrxp/fOM72BKHQ8AtOeTzjM9sGByD21CimZMwJiGfVkoZSzjP1woWMVnzrh6/zPGY55YJpl3PWNYpfLoXorkFHZJiEWSQ0ofcNJrYKDpqzygggNnPaHrdbmbXbJL9vOSPVmyn8X2s5I9GQsmsfRX98L7AIBYPaVujWgvyXGBv7SEDZ6MuTw+f5l7hnx9FiCp/hQAHlUqHlUqznD8VxbrAbtjl82xy+bopJCP1Go6K+SN/O0FKSLZgLH8pSVc1kxADGCRCRxbfZYEQRB3uylT7qqNlwjibiI5JMHEyULIslNXsCFvisatUtnfAHDN+YwNgtLe7+7rAgBKGYuqdiMBgGQ5grnyuamC8Vsm5PUqaz9jgc+aAQBM8FT3pMV6elWnBYBlZZY3TWYA6KqQjzEUn+S4IIZe56uvNlXzTuFO75Y/zbIQRcz+dnye82K/k5JNtPxujtnXroEzPIAiVt0QVd+O0J344brL5UpMTGRZtn379u4jBw4cWL58eWFhYXx8/IABAx5//PG673AzkpOT4+PjKYoSRfHara+TiGHWH6W5lip3zjtnn/p5ul8xDwAulpLzUl4LzZZ3YzQsmt9N93cu56FCyWqul0oZzpR/IvOGybzf7kB5yJUEY5/URPnT/dVXuustHF56wtI5WP5wqBwLZmdSBywUy5t/R3v1wGKZJbGjwYFmFO3f3C9cKNrApU+sGCFPaTqwgRNo7158zjzJdqrihthxVuKK3H+n1PGIufy5IJIxAaOv3rTzBmChxJnUAQsmeex62vv/rrxixT+4UoYjxksSfr34TLpolwCAklOSS1K21jpOWQAAEAr/sqXP4MDabg4AmMuTbCcla6JkS5RsJzFvrHwWsb6AGMzl07pH5c03AKWs7T71dEkQv7HatlptNowBoDnLDteq/0+lZBtxejwWrc6kDpgvBADE6hVt/kNMDRO3CIK42uHDhx966KFOnTr9888/TV0L0WBu6c93grgufCGX2uuEK90eubaVZ5+GHNV4pxOK1nLprwOALHo541t9HuitwKW/LhStBUCAWGWbfyqvYSyW/u461xchCmOJUjSXnBeYgLGyiAVXqi34ksucgRSRytaHq2z9Uz+fl1qWlVlogGCGyRaEOzu928WLzyS6R87H7m/H5Zand8aHFUy8LFQRs68dn++6+GySZBN9hgSGL6/+WUnjuCN+vt95AX7Tpk1jxoyxWCwA0L179127dn311VcTJlzZUgIhNGPGjHnz5t2iAhr5BzyX5fyr2zFPA5cSoTJ6Mg8llQGAz+DA8BVX3tYH7I5JJrMPTa3z1UezzGmOH1BokCMk/Y7sOixrgzHAv8GBmlqWhaj2zeVskXnATlMzH/WWTpu5nDmAJQBEez7OBk2iPDoDAEgu+3/hINVrBwtG/7ys2coGeCEAhIJVXOY0JI9QtjkMlAIAQHI6Tj6IXVmyyMVC2YCUJ44LJg4ANI94246UAMaYxwAADIr+vrWux3VsjYZdOZItUbImCkXr3EOnrkAMpYqjNPdT2g60psPVa9HXn0WSNlnt6622QlEEAH+aHqnVDNU23ueFQtG3XPr/AEAWuZjxH9Foj0sQd7o74gc8cb1IgCduE+707rxgAwDEIpLhq8CiK3U4IJk8ZjU0yvBozBudJztgoRQAaO+n5bHfXD4hOE8/ItnPAQClbC6LWeM83Q0AFPG/u+d7Vusku7FHX1RSttpiBQAvmtrs59uw6d2G8U6bvadK6U3VMLb0sNPFYdz1qoX0boz1L3NKjxMAELEmThamuNi3PKiHLGye/vyVbnnjmrzCT7MAQevsRxivJphnekf8fL/ukcBN69ixYwMHDhRFsU+fPl26dDl48OCwYcPeeOONuLi4ffv2ZWRk7NixIyoqav78+b/99ltTF9sAuBzn/7N3nvFRVF8fP3fa9mx6b6RBSEIHAan+6aiIgNK7IAI2rCgKAiJiQcFHUBQEBUVQpBcp0mtICCWkh5RN296n3efFhpBAwCBJAN3vZ18kd+7MnNkyc3/3npL5eLJnOZsRKVvwXNiemZGSKBkA6H8vs542VnXrKZM+KpVoBXFceUU2xy8zmjDAaJXCM4SgWmAR4GW1x+3U+zcplufOPl0sxGFH7rqDn4/drptzTEyQX57v8yR7bS5gkcXyxYZNz2WtnHQk/nIFBwBASOjAaQCAmGBJ/G+S+N8l8b9TgVORJJyJ/sr1LxO5BAAAkVTQzPp6N6iAiYS8OXbmcSUrXC2cZjl25hOyZrxpSOaAZF7LytuqSCVpOaxjwqWYw0AgVU9v4HHOqDTj7oo7H786SBJGej9Jh78vSdhVVZuEULZ1ZR8Rral86So263l7Sjv72Rjn1RFc8VLRdBxEx11dkYogJnso9wX5L/L2jKPpUkFYZDBWCOLf71lPUP6jSO8nSK8BlP/YRjupGzdu3Lhx4+Z2VKl3WZLSf2Y45nDu+IuGP8rut10PDIiUxK2TxH7XOOodABDtS4e8DgAICEG3rSrcki9dJdquACIAgI5YSMgTKP9xgHkuf7arA1ewAPM6Ut39H6t3syiecbKuv02CmMKy93oxNfnVYl2gN44sK9feMvjcarVPLtfO0Orra1Sq7OzpcobNn3El68nzlcvsX8WTSjJ6U0tVVy+20HG125nSL68BQMjC2Pui3h8WHrIchvPnzycI4siRI23atAGAt99++6OPPlKpVH/++WdgYCAAREZGJiYmJiQkfPLJJz169LjP5t4bbIEjc0CyM9de0Uy5YEyIXUKOa6loerh93oRLpn3arEEpMX+0UnRQAwCD0P/5er+k1R+yO0aVVhixqEAokqLKo3hAMEWhnOJx2+Lnf2Q60rWC0/DWqpiJ/WXLV+X3muT/7ZCYXxFgEQgCxBk5X54wxwM4AaBnhKS5Lw0AVMgsvnwDZosxV0H5DgMAUt0DqrkMOTVfAQDlP9YVJ18/IJKOXOS8PIgr+oTyGwFY5IqXAgCm388acIHTOD0e8476paUt1ZT9VIozywYEiviyGRXASKNl5auKckalRf2UdFfr8ABAyJpS/uP50lVIEi5tvh0IKYh20XJesJwRLWdE8xnMlQn63YJ+NwcAiCaUrUhlR8KjE6HqcCOU4I7QCD2lkA9SyE86nEZR9CUbc1qNuDGR7MaNGzdu3Li5r/DlbObjleo9dnsbyocmFaTmo9zcsRcjVyV4DbuLIGo39QgVOJUv+1G0pwMAlzebbHEEC2aucAkAABZJ74GuikV02DuC9vfKrHXSJnzZWkAUHVF7itC/xZW1zhX3/phU9qPF4oqHf1x+r4GcVbimQPI5YVRZxU/+vj7Xh6Bbrfa3dXqxfudICBTxdTxXzpr2ajGAR2+fiK8qi8YRCjJ6U8v0rmccGVYA8H8pPODF8Ho887+Ph2wF/vz58507d3apdwCYMWMGADz22GMu9e4iJiamQ4cOFy9e/AfHf+ONN9DfkZSUBA2fmLdKvSseUV/+pKldQjIkGhgjozzp6E0tvYcHCmY+a1BK1To8g9AXPl49ZFIjFgEggWHe1xkwghfVqle871SGfVU/L5UEnbJ0vMQ9Jies25OeGeq7kcN0hqMpAaJB9sS07o+vfdx77ePemwb7jk6o9O5GpJIOnwMA3LX3QbTddEzBeFAw/IlIFR36Vv2+LaRHN9JrAAhW7tp87tpcEKyE+smsQcoq9U7ICGVHz+gtreStVZGrmosOIXtYqmAR/J4LxU4xd0yaM+tma/8WOnwOFfi8JGZVpd8+ISM8OtPBL0nifpS1vSprncrEfEMFTCYUSQCiaD7DaZY5r460n411pD7C5rzMV/yCnQV/exYE0Ekq6VftpnyBZVsXaiaUaw87nA9ZoIsbN27cuHHj5i7hy9mMAcmOK1ZZYqV6B4Cgd6OC3mqCBZw3+ZL+19L7beN/FUTRkR8CACBCtKfzZWtdq+sACBDDhM+73suLDn0DANj8d7nc1wDzVMBEQh5/hwPfjurq/Qc/33e8PF7wUAkAb2n12232+rqsoUpFIsMAQD7PjyitKBMEANhpu6He3/X0qEetaDlltB43VP59TG8+aqjapN9c6sisHKIbNpc5c+vtGv+VPGQr8EajUa2+kWfL9Xf1FhdeXl4XLlyAu4dhGITuf14A9pojo/85Nt+heEQds6XVuUMmAGiiprjD+sJP8iJWNo9Y2RzzWL+pNOuplJg/WivaewAAg9AratVfdgcGOONwutT7NI+/KVcepCK/7+89Zrvut5IuCWEHSEFvJ0I/yH3xw4g3gZAENZ0bLK095QblN4IvXS1aznFFS+mw2Tc2YI7LexMA6r2YnAsmYr7duJ8v3wCAgZDQwXMRWQwAIotBrPzglB09mx3pAADOLBuhIHU/lyg6qgEACAR3n9MSkR5M5G1nT5EknJKEg+8wAMCCRbScFk0nRfNJwXJWtGeI9gwo+wEAkCSUVHUmPDoTqs51TPYuQYhG6KTDedLhjKWpCSrl442b5c6NGzdu3Lj51yCajouOjLr0JNU9kCSygc25mRrqfUelencR9G4UAGg+ys2bfAkA3OvwDYRgPCBot92hA2JCMFsEANy1d7HIAaDrpexupEOiAibxZT+ItiuCIxtRXnTom//AEivGrnrvoRS51t83iCQBYKZahQG+Npnf0uoZhPrUR2i6AqHVfj4TyrUXWbZA4EeXaid6KObrjS71/o6Xeriy3hIzOXPtlXHvo4NAxNr1JdlDU6M3tVR189KuLc6fkQ4YB82OMh/UWU4YMgcmN0/uREgfspXmRuMhE/DNmjU7e/asw+GQSqUAcPjwYQA4e/YsxhhdFzYcxyUnJzdv/k9SFy5YsGDBgr8phe1KcoMaUkeVLs1n8x2kkoze2JJUUX4KAgAS/aiyz3PNh/X2VDMTJo1clWBLMTuzbMVzs2J3VLokrDVbMQACjBGSI9RLVicfmzYBxO8dvgyxVQaWO8mAEX4/IcB00PQ7VtckmIhFjkt9Oc0yyn80klT6uvAl34j2TCSNogIm38N7cFuQNIoOfJ4r/gIA6KDppLpJ3J6gjAHnLEf1WU+lRP/eilTeSO8hiZFHLI/PnXDRetJISInojS0lTerN76gW20glqX6MVD8GAIA50ZoqmE+KpmOi+RR2FvLOjVCxEQAQ7U94PEqqOhPqLoSs6e0clJrS9MHggM0W2xqzJZPjZ+sMnxpNwxWKMSqFurZcI27cuHHjxo2b2yA60p+uY/5d0qufpOmGhjaoOnw5m9Ev2XG1FvXuIujdKCziko/z8qZcQhLk+aQ7p139w2v+TzDsr0tPLFxfH6b9qZCapewQRYfPd6YPBQA6dHaNknJ1ZqPFepN6d/GiWgUAX5vMc3WGPiGBtz/AXaAkamj4D/RGfF29j6o/9Q4AmBNBcNW0osIWxwJC2p802cNS/SaHlC4rABGHfBjr91yo9ZQBAHC1ZTk3t/KQCfhRo0a9/PLLQ4YMeemll8rLy998801vb+/Lly9/8MEH7733HkJIEITXXnutqKjo+eefv9/G/nN8J4XoN5XyOi7/hctR65I+7KZuE8D0DZcUnTICAnkHNQAUvZvlzLIhCRHwd7m5lwAAIABJREFUUgQACLqttpLve7FsLwwAICWQQ8RlFciXoeU15xoIeSIdMd/1N3YWsDkvC5ZzoYIRA1HgDAtkSjy5c55y4DBDms84rwyu2o1p8glialRiI1TtKd8hfMUm9tpcSez3AIC5Cq5oCQAwkR/9g2oZdYQKeY3XbgYg6eBXAYCJkMbtbJsx4JzlhCF7cE0Nj8F8rDKHPB0iVXX3aiCTagHRhLIdoWwHQTMARNGWLpqOCeYToukY5soE7e+C9ncAQLQf4fEo6dGF8HCJ+RooEBqrUoxUynfaHKvNlnSO+8pkXm22DFXKxyqVIQ9nHRE3bty4ceOm0SFIr4GC9jdEeZHeg2rvItr4io0AQHoNrL1Dg1HxQ7HjqhWRKOqnpFvVu4vg96Itx42Wo/qi97LdAr4hoAImuAQ8FTAJUWoW8GqzBQE8JpPFVNZpxpxmOYgcAMKAEIhXA95sTd4cqUp6/o8Kmo65EipgfK0nsnF4Y7ptQLTMX177ekx/ucws4meU8kDy5pHei2pVCEUy9bqO6NLwT5eWF/A8vn6W+lXvACCNU0T90jLn2dTyFQWYFSP+Lx4AtD9pSr+4BgAhH8b6Tw3NGZVm2q+j/JjYba0JuXuUe1vuwl28vLxcJpMplUoA2Lhx444dO2JjY6dOnern59eQFtaA47j+/fvv3185PaZUKo8dO/bKK68cOHAgKioqLi7u0qVLBQUFsbGxFy5ccK3S1zuNU2bGnmbJfDyZ13IefXyiN7RAEsJ2zpTe/Yw0Vt78fKfi97NLPs1DDBG1Lkk90BcAnJkTBO2WOh2akMvbXwNEAgBf/Dl77YM6mlRrDQzMauwp7UC0SZtvJzweZXNf5UtXk+oekvjf7+Jq/wGCFRAC4kZxezbfkTHgHJvvUHZUR29pTSpJwHDtlfSKVUWEnFQ8ola08Qie989LvtUjoj1TNB8TTccF0zHMFle1V67MV4r5uFt3PO5wfm+2HHc4MQAJ0Ecum6BSJjHuLJ1u3DQ2D0WZmVr59NNP76r/rFmzGsiSBxB3Gbl/N84rTwnGwwAYkcqq+jI1EB1YdACi6LB36eCXGtM2rsSZ0TfZmW2Tt1bFbmtDetZinuajXM2CHESiyO8SvIb+27zoBf1urmgJ4L9NeY4IeVMmalntn+C94cwYJ+i21r1/gSQhstVfEnRDZwqGvdXHdbfjjIafeabT0ISwtzrdKU1VY7LTZn+9WsL5MJL6McDH/5bpg3vHtF+X82yq6BB9J4aEf940f2a69kdN6MIYvymhropRlB8Tt7ONNL7xairfxEPxfK+TgLfZbBMmTNi4cePRo0cfffTRn3/+ecSIEa5NUVFRJ0+ebEwNLwjCmjVrDh8+TNP0zJkzW7ZsaTKZJk6c+Ntvv2GMaZoePHjw119/7e39T1xW6kKjPeCra/io9S0qvi0sfDvTd3ww5cPcpN4B4Kj2VKvMAQSIhwMX9vZqDgA8xl+ZLKks64HQ657qEBLY7GmYK6fDZruKYQimo+zVEViwAMBZR689ltEYoMImjPb+WsRoVdnUCDUFAL0VGzrI9uRzzRbq/3ijo1cL/5vlIlf4MVe4iFAkMVHLHBf/B4CkLY4QsmYN+ubUSg0N/3urojlZLvUe/WvLRl17vxuwI0cwHRVNRwXTEcyWVLUj2p/w6EKqu5Ee3ZC0SfVdrnLcGrN1h83OYQwA7STMRJWyZz1V6XTjxk1deCge8LVyt/Ff9z0pTGPiFvD/akT7ueaYq1MSOMpvOBP9dUMbdBM3qse1UMVub0151xhulX6eXzQnC5EoYmVz7+H14zv9QMHmvcWXrKxTV8TI2mYg6uYEWPcOV7CAK3JNcaJ8Z5RVkAYoSCSBUkFAACEkUjmvAuZcnTEg1Hyn3KNj1e6YLbYnJ9TxXOvKx5Z4zX+/S+MJeK7YiaTETd8rF1XqHQEEkqRGEKDRNPwXzXgdRyrJB0S9w0PyfK+TgF+wYMGcOXOaNm26devWuLi4du3a5eXlrVq16vLly++8887s2bMXLlzYCLbembKysrKysri4OIZhGvREjfmAt6dZrgxIBj3n0ccHUci4s8Kzv69hV8VN6j2VZUeVVrxjeG+QdXP1wC0nxtMrdMccTn+S3EPuFHNfRUyIrNUZIGSCfo8zcxyIzouOzonS4zZR8fiVneXczRMx/nTZtvj+csI+IWvNWUuHeV3VoxPkN/UB0WFP7YCdBYjywbyWCprGRHxYv++DaDnrmmi4M4jy5kpjM/qf44qclC/DV7CuuhSqrpXqvWJ1UeHrGZGrEz2faLwpp7qDHdnXxfzRGmJeEkp6dCNcYv56FEOZIPxosf5isZlEEQBW+fk8ept0g27cuKl3HooHfK388ccfN7V89dVX+/bta9269ZAhQyIjI/V6/d69e7dt2zZy5MjFixeHhobeFzvvC24B/++GuzafK/4MgAAQSe+nCVXb6luFil9E6wUAAERJE7YTykfug4W30fCln+UXvVe7ep91wPBHhn1pL8/HYxowxU8jgNkie0p7EO1M5CJC1bHWLs7M57Ajmw6aURUK+g+YsVefqed3DvOrpWivYLWntnMNwNLsbUZdXfdKB4/pbZTfmS2fGEwzTUvHm7/FgDCgVEXv1l7t5KGv1dxfdFzsLVqSkSS8Mh3SrdfAlQr6XQAwIWtN88iejSbgTfu0OSMuECoqdkdrWfMataWrq/d3vNSD5HJXPDw0loYP/Tgud/SDot7hIXm+10nAJyQkGAyGrKwsmUxWVlYWGBg4a9asJUuWAECLFi0QQqmpqQ1v6oNCYz7g/8xzLPq+5MPv8mkjj2gCcyIAIAlxUz3zdI6bVq6bLLM/ndUdCyZJs02ucpQA4MT4Na1ezxm/Le6HuTJJ7GrS5ynBsN95dQRgjgqYZA/+iMsYJbfuNXuMKvf7HAD+uub87IyZIdGvT/n4l76gtGyyKp4oDfyOJKCZD13r2o2g/d2ZOREAEOUjbXVWa+AvX76cl5eXn5+v0WgsFovdbjcYDDabzW63G41Gq9WqUCgAQK1WEwTh5eXl4+MTEBDg7+8fHBwcEBAQGRkZFRXlCoIQ9LucV0fW8R2TNPuF13V1afib1DsA6NZr8qZcJpVk07/aS5ve5xvEnRHtmaLpiGA8LJqOYl5b1Y6kMaS6K+nRjfDogmhfG8abLLY0ln3TU924BeTduPlP81A84OvCxo0bhw8fPm/evDlz5lRvX7ly5fPPP79y5copU6bcL9saH7eA/1cj2k6HgOioS1fSq7+k6fqGNqhWbtXwd1DvAPC/n8vzDPywprKPenreF4PrEa7wI65wMaFoIU08AOhm0Shof3NmTkK0n7TVWXRL2HndeeSH0gq7eGpcgK+sllETX76BzX7BNcvzWt5nCXHDprdRAsAvust9MntKMAsAhz1GdIv/Sl6bN5NoOeu42AcIRtbydFV25+qwWVP5io2F1MD+Z5eMTVQ0joB3qXfRIQIA5cdU1/CnnM6JZVqxZtY6i4jHl2svsSwARFP0tiC/hkjcXWUVHSLhipx0ABO7s019Ds4x2M6b5K1UrlLzdeeheL7XKYAkLy+vb9++MpkMAE6ePIkx7tGjh2tTfHz8rl27Gs6+/zg7sh15wZKcL5s3n3lJMAkAgGh0k3oHgGY0fTA4AAC4kFnctfe5/Nmk+iggGgAkCC3z9WbzP+O5MkL1COkzSLReYDPHA+aooOlMxAIGAMd+ZL/wl8q0wS98IqFsk2PkAYBA0FyW6rBsBkLiGzffT3KnQGsz2XPfmeijJy9eLJBcymhWWlo/dUqDg4Pj4+MT48PiPISEKGgeH+fhFVZrT8wWifZMQAwhjZVEyeJ2ty35KNdnQrCyY43nmffIINMhvW69JvvZC80Otyc9HtwkjoQslpDFUgETAUTRdlk0HhZMh0XTcezI4h1ZfOlqAEQoEmmP7qPU3UmvznA9Lz2H8Zs6g5ogRisV0fSDe4Fu3Lh5EPjmm2+aNGlyk3oHgKlTp37xxRcbN278Twl4N/82MLaeTLEcPMnmFmKeVz8ZQQddrdpIyJMIpWsRHgu6HZivqNpEevZudFsroQOY2G2tM/on2y+Yswad9/ifT8kneYhEkasS6qt6nIDB6BC9a5Ov9xc6+GW+fL1ovcCX/0T5j62xTXSw1+YCAB327r2o97+F8hvOl34vWs4CwGvBH++DxwGUANCn/EOXerciJRfyZq3qHQAIZTvKdyhf8St77X1J7OqbtoqWs3zFr0BIjqHZAFBh/9uA/3qgSif7TQl15tlNe7WZA89XafgUJ+dS73O81COuZ61TEmiNn49Lw7vS2jWEgPfo7RO1oUXOiAsNot5FnP/CFe2PGvXjflFrExHzwH3b75E6je/VanVBQYHr7yNHjiCEHnmk0rNIr9c3tMv6f5mzGhYA2ocwFr6yBQPALXeNSeXaCkH8PdCPDnqeL1sn2jP40u+pwKmVuzhy+NJVAAQTuQg7C5zpz2DBQvkOY647ICFpEzpgCqdZxua/LU3YXdkImM17CwCzvjPkkohbbRME4dixY/v27fvzzz/PnDlzfb3icj1efnFxcXFx8fWUhYDQuchIXVJSUvv27du3b9+hQwcvL6/KS7w0ECCTDnrBFS4uaSKLWFl7HcHwL5o5Lllsqea8KZejN7RokHtSPUMQ8kRCnkgFvQCYF60pgvGIaDosmE+K1jTRmgaa5a6M96S6O6nubpG3PmB3ODH+xWLtJpOOVyo6ul3r3bhxcxvOnj376KOP1ropJibGVavVjZuHEcxx5Z9+bzubVtVi2t7Me2ImIl2qCYn2q5K475E0hi/7gS/7ARAJWAAAQp54s3psXOhgSdyuNhn9k23nzbbz5vpV7wDw3hHjr+m2Xc/4RdeWKu9+QkiZ8LnOzElcwQLS56nqQp0rXoqdBYSiBeU36h8fXrQJ5r/0pHBn12PERC5yXOyDAQUyJTHsNwDvFOkPeul3YEQgLK70mP6zica0/Ul57TELdPg8XrdD0G4RAiaRHl2qbakcV9NBM89dCQaw5xr4Wo9Qj5j261zq3XdiSNinTTEn5oxOM+6syBxwPnZHa1mCsq2E8SfJFzyUz9bMOe/S8C9qdRzGDTdS9ujtE72pZfk3RcFzo6Rx9a/eAcC4vTxn7MV/n4av0083KSnpwIED+fn5Xl5eP//8c7t27Xx9fQFAo9EcO3YsIaGuCRvc3C0vtVM6r1htwy+INgEA5K1VtvPmnJEXota3UPf1cfU572SPO5yV7tOIYSI+cF4dyRV+RPoORZQPALB5b4HopPzHI0mk81JfzJWS6u5M9HLXhNrBfOeZEpaGF0bDLwrz6S2n1u80DACAfuo/RMu5Ms5/2MGRfYtMHgyiEIxKkAcoyNTU1NWrV69fv768vLwx3w2MITc3Nzc3d+vWrQCAEMQ1UXVq7d2lNdO5eWlksFS0pWFnIZLcKWKTkBFN1iWldz1t3F5e8mle4GuRjWT9vfGqVqfhxdX+PlJXabqQV0B0CJbTovEvo/6Q1J4K5hOi+QRX+JGEVBxSdj7GdPqeaPuXPeYvu6MZTU9QKfvLpXS9Fh1x48bNv4Dg4ODU1FRBEMiaUY6CIKSkpISF1e705MbNg4921Ubb2TRSpVT26UIq5YCxKAhsTrkk9jjGCCEMmGXz32NivuEKPgQAwILLcZqOXHSr/3YjU6Xh2Xx75Lf1qd4BoNAkCCJoLMIDJ+ABSJ+nidLvRNNxvuhTOnyeqxGzxVzxMgBgIv75RyNYhKxB562njNMSlAvH3HxnK7eJDr5qPbyVSj2UMf6KAbXFy8+VPyO99rYXAMIizzQJCJoimB2ztXoAqFXDIyaIDnmZK/iQy3ubTDrEY/LV/YZ8E99d/tvzXud0QsCsM2MyjQ4AyDHwgzZXVN83UEEu7qn2lNSP1LwpVxwgQAwR9WNSpYYfeD52R+t2Ccq/ggOsGD9bWhHPUHO8PKveXyWBvvfzAYCvTeZdNvvXvj4NUb1Y1cNb1aNe845fV++EggxdGFv8Qfa/UsPX6af72muv7d27Nz4+Xi6Xa7Xa2bNnA8D69evffPNNm802ceLEBjbyv8vjCGe+ls5VsKSKEsx86Mdxxl0VpZ/l54y4EPVjknqALwD8YrUBwBBFZSlJ0qs/6fk/wbCfK1jENPlEMB4SDPsQqaRDXnVeHSnaMwl5AhO3FlCl38TC46ZcIw8AGp8Zc8Pea8nOn5PfSU7gGYGfAcDnmlk6TrbhkhUARIf51KY/Mv/ccP78+TubLZVK4+PjmzZtGh4eHhYW5uHhIZfLPT095XK5TCZTq9VSqdThcACAXq8XRdFgMLhyEBYXF5eVlRUUFOTm5hYWFvL8neYmMYarOearOeY1mwEAwgMt3dpu6/1Ek75PvRYQcKdHnSRK1mRtUvbTKcUf5MhbqDz6+NTpw7iv/Gl3cBgGaMp2BvlLXTqckJIe3c4wj0xGk2SiZaR4fjoki8a/RPtVxrivJ+zrCeCg/E5IOh1iOn7q6PQxEzhcoRitUngS/577lxs3bu6RLl26fPvtt2+88caSJUuI6zcHURTffPPNgoKCAQMG3F/z3Lj5Z3CacsuBk4imCA+FcXOlayHlr2NCGSZCghgnAAAiBP0u58XemCsDQAAYQCSU7YGrwFwpou9znTY6WBJ/vAOv5Zjwv68yc1x/cM7Vv+bFfUrc76mHe4eJWORI68lpVlD+Y5A0BgDY/Dkg2kifpwmPzv/smIKZz3oqxXrKCAAJlyyv/1AAo/3hehDB/yVbPj1trt7fn56+LX6HnLAxyOGVNyhQ0LiW32emv3X6nPO14R7/V2D4a2029WzYgJhakuHTQTP5sh9F20W+bK3Fc9yfeQ4SW7/w/wQAlhS9elZHuxxqnQK+WM5V3/FSOVdsVtaLgL9Vvbu4VcPLEpQsxhkcd4Fl7SL+0Mer+nfoS6P5a5OZQMgsigAP/LdLxPnTrmh/0lSWoOrmpXhEnfl4snF7ec6IC1HrW6B6mhy579S1Dvy6desWLFhQVFT07LPPrly5kqKoV155ZenSpdOmTfviiy9o+j9UibrRktw4rlozByRzpazqMR/LER1gaFnUnVCQRe9llX6W7/oFEv28uxWXshjvCQoIvT4xJtozHBe6AIjSxP1s1vOiPZ2O+EA0nxV0WxETIk3cV5XJHAAulHEnipwAgEDszw3yEVP2sy/lGmyT/b8tR623kn/8dMlWWl4efemHE5u/tZmNt7M2MTGxV69enTt3btWqVVRUFHnPKSs5jsvPz09LS7t0MS3l6LJLmfqsawJfh7ccIdSiRYvevXv379+/a9eut/tyaj7M0XyYS3nRTQ+3lzR50HO3brba5ugMGCCIJHcE+csQAoCTDufkCq2AQYmInUF+fiQJAJgtEUx/ica/BOMhzGqqjpBLRZ2WdkyVdg7x6vGsOiCceuDm3d24eYh4KJLc1IWKiopWrVoVFRUlJiYOGjQoNDS0sLBw69ataWlpoaGhKSkpPj4PwRRnfeFOYvevwbTjkG71JiAIEEXSWy1LaooktDThRUTWyWmZ9BogafpTQxv5D9h41W6w1fhyfpNq1TtElSLPzzc5RhHfWtXB1d7Uh+4eXnsA3bjtuqOFzh8e9+4S+oBG2LE5L/Jl61xllUTzGcelvkBIZC1PI8nd+QQ5BZxcwokWTjL5EnHejEMk3LvRztczVBae7eolfhWPGQIAjhU6v0213iSHpgSsmBH4JQaEoHLLcfOjb+Z96q8gtvX1Oj3svPyyTRdE+//Sonm4wF6bA7weIQKgUh9irky0XQaCIZQdOJHEjhyKLxAIz93MtrdO+kR5UjkGPlJNfdGrRqomFYNcJZzvEdMBXc4zqaJD9JsaGvZJ01vDRbFTzB5xwbRXS/kxcbvaaGOkvTSlrkms/nLZkusa3qXeXbus9vN50EMyq9S7olK9u5qrynKr+/rURcM/FM/3ugr4W8nJyfHw8HD50v+naJwHfJV69+jtEzArIrNfsqyFKv545X25SsNrVsXNao86SyXf+dUYY7F5b/Il3yDKC/N6JI0hvf7Ha1YiSi1J2H2HCu2i+aTj0gARSTlRYBAnS9yrtUfMm7/w+1Xf2u22W/t7e3sPHDiwd+/evXr1CgoKurVDfSGYjjgvP8kKkvQc+/mrKKXk8VNnL168ePFvPwK1Wt2nT5+BAwcOGDDAz69m6TgRZz97wbirQt5SFfdnO+LBy+ZyE1UaPpAidgYGXHCyk8q1AoASEduD/AJqmzER7Rmi8ZBgPCSajmKh8hYsIOoSnVim7Brj27u5VydAbiXvxs1d81A84OtIRkbGK6+8snPnzuqNgwYNWrJkSWxs7P2y6r7gFvANh53HC46ZuoVL+jb5+/Xke0e7ZrN5+0EAUPXu4j1pGKJIAHBeHSHod2MngzEipE4AuL7wXgmSRmNHNgAwkUuowMl1P53BIa67ZHummSxAcRerFzzm8m05UYo4VLd8PGvSbPOP3XYd5VZOjQ/wldYytnnwBTzmyh0p7bBgkjTbyBV+JFqS6dA36dC37vY4i0+a154yvfVdQUKOtcKLfv/5iDIfJqTMOffrfE8zn9JU+fH4MO56eaVuYZLVA2/4crf8Nn9T04EhTBHUTw63yqOcZhZPOv1EEzWZaxSaedM7nmkQGZU5MNn8l14SI29+riMia7ee13GXmh8TLILflNCwz5ou1Bt/tFira/ivqqn3x2TSL329H+j19+rqvWYJKrhLDf9QPN//+dg9KiqqHu1wUx3HFeuVfslYyyr6+ERvaMHrOVkLld+kkKoOIfNisF0s+7rAb/LVFl+FPvHkzbVD6NC3hIpNmNcBAOU9kCv+AgiJJO6nO6h3ACBUHUmfwaD9TYJgv/6JtC/3Ll682GQy3dSNJMm+ffuOHz/+ySeflEga4+5PenQlvZ9gdNtaJka0iC2Y5CtnYlIsFsuRzUOPHP7r2AXyTJrN7qxlKspoNP7666+//vorQRAdOnR4+umnhwwZUvnVJVDkqoT0rqdtqeZrL6VHflN70rsHhyEKOQDM0RlKeLGXptQgiOId1TsAELI4QhZHBU4BzIuWZMF4yGI8RFnOtmBTQJcCumUmQiFRdyXVPQh1T0IW17gX5MaNmweCuLi4HTt2ZGRkXL58ubi4ODw8vHnz5u5H/AMC5nDh6xkY47AlcQ9jACfmjS6FfKWM3ZmhLzXRfcJuiXdFRL3nFRf1RgAg1SqfKc9Wpf5lIj60Gw8ihrXseUTV9yQgl1ohAEQAQEwQ5fsMV7iIkDWlAsbf1el+y7AvPWPmRPxqe1Xd9/qh8OtjugOjQp77n+/AuvTvGc6sSiXsNX0IjE4BY0QgniRZACAQISPlgCHcg/SuTb0/FCDajwqZxV17n82ciAULYkLo4Jf+wXEe86Oa/1gYkmM1+zLb5sTEBkhiAU4TMHdaxMJv8ltdtSz6uWjrrCiBQQDwZGwNZ0wnlnxW/Nqnka/cUO+kAgEtWHgQAAgglaRgE4HHQAAht7q+S0zM/1UFX2BHLpv3GgCBZDHYlg4AiAnOpwYDNHjuuuA50VnnzjuzbPnPX/5iYZAHTczxVFPVEiGJViFn+AXBIpiDGMW0EACY7aUGgB8tVgDYZbNncnwWV+ne300q+bymX/0DB4Yq9R7zeytl55tlkSxJGbutdebj5417tDnjLkb9mISohzst1F0I+NOnT69bty4lJcVisZw/f37Pnj0hISGJiYkNZ9x/lvJvC7GWdUiI0ndjmkoIOlBStfZeCYKQRbHafVrIsj35na738FjRmiZof6vehVC0EowHCCaE13wNAKTqUcHwp2D4k5Ankr5DbndqJnyeVbd7z0l24uLDJs26m7b6+vpOnzFzynOTg4OD6+1q6wYTMd9u2IudhUBI+IpNVMAEORDdm57u3kwNmHXwylT7lwcOp+3bt+/s2bO3Lp6Ionjy5MmTJ0++8cYbbdq0GTp06NChQ2NjY6M2tLj62Fndeo2yg9p3ckitp35wqNLwOkEEAMUd1XsNEEWoOhCKRCUiQNHGwRbqbbkStkAl6gX9bkG/GwCAVBKScCSJQJIwRPtSgdMQqWzY63Hjxs0DQ1xcXFycexavAcm2XT1nPDko4FkJUdclaMzh3HFphq3lAMCVOKPWJT1cGp7Nf4fX/J/r72YAx5IAAOxna+lJyGKlLU9WuR/XAyIGAESS1Qv3IGkTOnAqV/yltGWWMyNG0jQLAAGIrvzzVMir/LUPAICO/PBufdM4AQMAfzdFwXJsGcd1BwFgS8mGRzy7Kam/V/4Rauro6BqR+WsKvvrkQFe73S8hOL9D/PYsazoAzIld0kRe6Tvj4PFvGXazs4ZlhWYBAHZk2S/VDMBuH8S0CXxQCkvRQc/zZWsrHSLC5wFx16GOolVQv3CZvGJhwqQdd7bpfj1Ysv2a0iJ/SczONhWDUyJSTe+sLWjyc4vzBt7J4xMFJgmXgZAIAM1kpkI29BqfGE5dxIhCmDcQPXSL+xNZVjpYHrGiHxPqK1qFrCGplqP6Jt+9JYnIAkCksh2SRldaoO4hWi/wZWuwLd3liv9G5is79TwA5BoFAEjXcdErNNVt9pAQW4f4hnncq1hWdFTHbGmd9dR53YaS5kb70sVBBkH81MfLpeFFm5A9LNVy3KALohf8EP5BGNUUANXU8NXV+zJfb+bBzoLMXnNof9IAQOCsyFvVuwtZC1Xwe9HXXk43bi+3p1nkre9iru0BpK53qAULFrz33nvV/e23b9++fPnyDz/88O23324Y2/67BLwUkbm5zFPLyl+5IuxoAzxGNCKq+WVhAedPvSxk2ewKsuCNUAVCzsJFgn7XrYcS2SLXH4LxgGA8AACAaLnPU7dL42m0Kyd80nbLpm0ANRJjhoWFzZo163f1U8el8pe870NIJJJE0EEvcEWfI8obsxo2/13AAgBGtA9mi1WRL/UMfaat3Ep3AAAgAElEQVRn72fmz5+v1+v379+/a9euHTt21FqRPjk5OTk5efbs2S1bthw6dGjv2T3odwTdxpIHX8ADQChJVnn8yQjwuJuMdIJ+L1ewEABIgFp8tgSLaLsMtspCgNiSTAZOIVWdoM5jTTdu3DykuCfoGxpWZFfkf6JlywlAQ4PqVKKsSr1TXjQgMO6oyBmTVqXhRSwe1e9vrmzhy9xQdPOPm3dm2Tc+5R3m8YDERlUOGhHlyYnYxmECgarmHAQWrIA5jIX6LTVNenoAAK8zGDbu9BzWv0rGC7ZhovVbOqjCmh0vaa4BweoyglC2w5bzWDCT3gNJ9WP1aEmtYMDri1ZhwBJCahUsW0rXjw6ZercHMfGGI/o/eb4PAEhwUJwyIduWgbH4Re7CpQlrXH22ZtnnHK7d635juv2mFk8pcW78fU7ddwPEMBELnVeHE6pOpO/Td7u3aBcznzxvPWVkIqRxO9syETdGMiZWBAA+Uh6zrXXmgPOmP7WnBqeMeyJQJNCSyFn9PCvH0uurxQ8hzAOAJ7fD89UdrhahLBhCLxEKMmZzS837SyURWZhjEM2y1+ZJ4tZW7UiHzea1v4FgQoBTbS136f8mLaiMQvWllKs0fNudxpcBli4OehXgMx8vwiZmD0s1H9brgugFP0Q8m+jdTVr55rg0fCrLprGV6t2PJJY+8OodAJgIqf/M8LJl1zSLc2VJSnX/Wga5luOGwncyAcBndJC81cOt3qGOAn737t1z5syJjo7++OOP09LS5s6dCwDPPvvspk2bZs+e3aZNm759+zasmf8xHIHM7Ocj5q7I9z9nynoi2XHVJo1XNN3fzrUVCzj/+cu6X0pYOfHJitCXuvoCAOU/2iXg6eAXgaoM/BAN+wXTUaA8qaDnEZLwmv/DXDnlN/x26j01NXXIkCHZ2dnVG318fObMmTNt2jSGYY5u0SaXsDP26dcO9CEbfRmADp7Fl2/ArAZob9GSDABAemK2GDFB1X2rvLy8XAvsoiieO3du+/btO3bsSE5OvjXdQ2pqampq6hyA+CbNhjQfMuKyrHnzB9qR/pTDObFCizFIEGIxrhDE/prSXUEBsrrdWwnPXogOwFwpqe5JqLvV3Ii3GfKVbG4ie8FTNAAAr9/N63cDISVVnQh1D1Ldk1Ak1u8Ay40bNw8C7gn6RmBX+W9athwA9pZv7eLdK1DyN15sVeqdVFMxW1ohhsh8PNm4oyJ7+IXoDS2QhNiv3bGh6LtQaeTcuM8IVPk83ppp09nF7VmOaW0eCP8pOuR1ofwXzOuY6K83FnX94KhJxaCUiYFVHTBvcKS0xbyOiZhfzwLez+Wojwwbd9pOpcraJiCGYbPzbecuSRNaePQ9JWubQgdN5wo/dnWjAiax2dMBMcz10mUNynHdwRxbhprymtnk7UVZbx/S7unu0zdMGnlXB/kydxHGWMQUAOSaS3eWbo5TxGdYr5h4w/7ynf/zGwAAfSKlRW0FrqZrwI4se6FZGBgjC1XVGA0m+D4g8z6VkF59pUkHkCTqH3w3HBlWV875gJciqqt3ABBFAAC7gGXNlX6TQzQf5UqP6p8YE1rhQRGScADAgAr45hYWAIAikJQCJEKAs5BRGUS7TJAhGmxV6fQIGec94nvMuWarkKDbZj8TVt2DAwHvure2UuWkPTJgH/rilZNxrhj4pt70zoaJgXdxq4Z/wyY+P/Wa5cgN9T7No4aUXWY0V6l3ACgXxHd0hiUPuP88AACELoolGKLk07ycUWlR65LUA2u8sZaThuynU0Sr4DM6KOL/4v8Fg9k6ibDPPvtMKpXu3bv36aefrspa16VLl9OnTxME8emnnzakhf9FLpRz5V70xjkxTKTMesYkmHjRUXn3rVTvG0pAQS5eGVbaQdlFKgEA0msAoe4OACJvoPzHU/7jCXkLwXQcECWJ+Y4OmIqkMZgrR4SCDnun1pP++OOPnTt3rq7eCYKYPn16VlbWSy+9xDAMAHzVxytAQZ4sYhccvzkwvjEgFXTYewCAxMrwIVdqUDr8AyDkt3YnCKJ9+/bz5s07e/ZsQUHB8uXLe/ToUWt6/Cu56Qu+WJiQkJAUnvjJR58UFxc35GX8Q1zqXcSgRMSeIP/53p4IoFQQ+2tK7XVLRYlIJR3+HgCI9nQ64Dk6+OVqr1faxX1yJvwbByEHgLXK8VvUUwyyFiCygvEgd+19R1o3+7mmbNZzfPkGzJY07KW6ceOmsXBN0EdFRW3evNk1Ow8Azz77bGBg4OzZs/fs2XNfrfuXoOMqdpX9DgDR8qY85n/V/HDn/tXVe+zW1vK2HrIkZez2NpQPbdqrzR5xwWw1bC35BQAKHXmHdXtv7IgBqudku98gyosOfR0A2PzZJHbe2oErWIh5HenRjfSq54KFspbNAIBgaNLTg80vMv621/DzdtuZNEQSfMUjXIkPITMDFpAkHAAov2f5sh8ARDrohRv+zw2GU3RsLvkRAIYFj4uSx/X06Sdi8eei7+/qIFetl3JsGQDgSoDHAw8A1+x5DCEBgF+KV3OYBQBPKfFKe9Ubj9R4RaopAHimmeym9oHRD1xFHkLRGlG11Gn7W+QtVYGzIgGg8M0MVxDKrVSsKdZ8nAcAoYtiPxvpv/Zx70Hd3kW0PwIcG//qmOxNz2b8+gva1rTDX/idT/PGLBUtakJmpxEHQDARC10H4Yq/wFwxACCGdf34sGDBvOHGS6hMAo0FM3bml+oLAMDkxACgs4s/X7a5XjuzHXf45W632bfabvaYcFEiCMuN5gqh9vgNRUd19JZWpJJsu9P44mxN+zGZd1Dv1XPOh1wfLe+y2V/X6h+KrJ7B86IDZ0ViVswZk2bcfuNDt5w0ZD+VIliuq3fi4ZfvdRTw58+f79Sp060pbcLCwtq1a3fx4sUGMOw/TVo5BwCR8Yq4XW1IbxoAOI1TMPNV6p1QkMlrY9PbyfvLZa6AFi5/jmj8CwCEsrX2s03sZyOd6UMARMC8M32I/WwTNmMMAGDR6rz8hCtfSxUcx82cOXPMmDE2241U8y1atjxx4sTy5cs9PW8Ek/jLia/6eDEkWnvR+vPlWvLSNzSU33BC2Q4LJkKRRMiTsGAkVO2p24f0VxESEjJ9+vSDBw8WFxevWLGiV69eVG111C4WXHr97dfDw8P79Omzdu1as9l8a5/7QpV6VyFiR5BfAEkOUcg/8Lqh4R110/CU3whC2RazGq546U2bwinqDdvqQL7YIE3c5PPGfOVL//Pe8GTIkV3By1jf0UgSirlyvmITm/2CPTnekdqJzX9HMPwJYu1PFDdu3DwUPIAT9NnZ2StWrHjxxRdHjRo1ePDgyZMnv//++998841Go/n7nR9Ifi3+gRWdHTy7zGzytoyUnzeeumg+f7vOt6p3V3t1DZ887LDDbicRCQC/lay3CpZGupK7hwqYTMjjsSM3hrtZoIr2q3zZGkAkHbmo3s/LRITIH2klsiyiac+h/dTPDPAc2s9z+OPShDheo7WdeBQAcZqvmIgFlN9IpGwvmk8i2p8KebXeLbmV7aWbDJwuWt60k1d3ABgUOEJJeVyxXEg2nqz7QVbkL3HJRQrRAIAAhcuiHKI9RtEMAHjg1hR81TDmPzQEz4sOfLNJ9UQS1eF/Kbn2YjqIOHhetP+McFcjIpV02GwAYPPnxHmyANA9XCpYBccVq2hR2q88BwCAOcr3GULZFgAwq+GKvwQA0qMLAFhOtGM1AQBARyyQtcu1NM96MWcZANhE+a/aZwDgsi3h44yeAKB1iABQbhfeOWx0vWbu018o4+A2zNMb3tTqV5pu/qVrBGFMWcVXJvPO28h7AFB29HRp+PbbjE3P2ngGbZniOyzB6w7qvZtUsiPIf7RS4fp3l80+W6u/mwwP943gedGBr0ViVswZe9Gl4f+V6h3qni9EJqt9Ws7b29vhcNSfPW4AAFy/4RZ+NBMm9ejqBQB8GZs9OCVv8iXdhhJSRUVvbf1TEgUATygqPxfsiuNygUhEMACAACFCjkiF61+XDxIW7dWdkYqLi3v27Ll8+fLqBrzwwgtnTp/u0KFm5jwAAGgdQH/YXQ0A7x81ntaw9XnZdQIxkYsAkGjPEB0ZAAQT8dFd+Vb5+/tPnTp13759Go3m22+/7du3762F4gVB2Ldv37hx4wIDA0eNGrVz506eb/CUoXfm+Qqda+19R5Cf//Vp0aFK+bzrGv5Vra5uR0JM5EcAiNMsw85r1TdgtpgrXgYAgVGL9wQFfebj1YphisDzXfRYF8nb74Qfzok/xkQuJr36AakQ7em85v+c6cNsZ5s4Lw/iipeK1pSbJobcuHHz4PNATdDn5ub2798/JiZm2rRpy5YtW79+/ZYtW7777rsPPvhg6tSp4eHhQ4cOzcvLa0yT7p1s69XThqMMwQwNGutBeQ70HwIAPxd/L+Ba1rRup95duDQ84U2qDisGzB4ITiAQYeFNW0t/aaSLqTMTduiiV2iiV2iiV5ZPvPA6AMQ4lvrR5WYWV7av0Ow/+hpg/peK4cd1DbLo7Tt9lCQuki/XGjbtNm7ebdy63/DzdnvKZUIh9xr/HukzGEQHr93CNPmUL/4cAOjw9xH595GxDh5P3qUbtLmi+mvtRSsAbL5qv6l97HadsWYCuXK2dG/5VgRoRMgk1+K5glQ+FTAcqi2b/y17yrcYOQMANJHHYuAAwFuqeiHidQrRV8wX1JQ3AJwwHDZwdRwY/GsJnhN1q4ZHCB47bXC8keFS766F+ioo/zGEojVmi0f7rAYAOYUoLzrq5xaElND+el0KMZUhMNy190C0kT6DLWfnig5G2fEcIocBAF/0KYDop/L8IOYrAEiBCU/5bMWAzjFzA1U0ALgSQXhKieHN5a7XK+1VzW8fwjDLU00CLDWaqmt4jSCMLaso5IWmND1EWYsjahXKjp4hS+IAYUBAsXj8whLn1gq+2sJPMS9UV+/LfL0lCM32Uldp+K02+xlnLU40DyDBc6P9Z4ZjVswZd7FkcW7WoH+heoc6xsC3atXq9OnTZrNZpapxa7NYLKdOnWrZsmXD2PbfxbUCn+hHAwBb5AAAKoCxnDTCSSOpomK2tEprKS0pt4ZTVEumMl+oyFfL1oYFjAUAwIBBrL5OjgEAcxWARVcY/JEjR5555pmSkhse0TKZbMWKFWPH3inFzuA4WVo590Oa9cV9+i1DfAPvpurpvUMo21G+w/iKjQBA+Y0klG3+2XF8fX0nT548efJknU73xx9//Pzzz/v3778pfb3NZlu/fv369esDAgJGjhw5YcKEpKSkeriGuyeOpspEcaO/r1/NEIBhSjkC+Mho7CCpa6o5QtmO8h3KV/zKXntfEru6qp3NnwOijfR5mvDoDAD95bL+ctkFll1ntu62O3bZ7LvAM5EZPCZ0TD8ZSVnOCsaDgvGQaD0vmA4LpsMczEOUD6HuTqp7kJ6PIeYhyAjoxo0buOMEfWZmZqOZodVqe/funZ2dnZCQMGjQoMTERB8fHw8PD5PJpNPp0tPTt2/fvnnz5tTU1KNHjwYEPDCptu4IBry+eBUG3N/vaV/GHwD6+A06ottf7Cg4pN19a/Gw4vezKtX7jja1plmSJSlPrTzbYlLziBMRj/7fo4dfPgyA9lfs7ObdO0Qa3hiXVDfEaj5hpywdD5l69PA4NCPoy/evzXc1Pqbe/6jqqEnw+KZs+scN4/RPyGWB818x7zliOXCSzS/CAkd6qeWPtFQP7kP5eGLnPLt+l6D93SnasLOQULSm/IbX5bAWDp8oYh18LUZX2IQKW41RBEmAzi6qqxWd/qV4dYCg66qIjrBfztPtPG861d27TzfKs4QAnf1iSu57bdQdr1+AlFB1utWBnBf5zZqfAAAQdPPuvd0j3altOSIuxF/i9z/fAXvK//CW+Jh4HcZ4ae78uXGf38Vbdl8RTce44i8A33YVugokj2ciFtRxDTJ4ThQAlCzOzR2X1uSHJM8n/fqfM475VQO4FvUOAAAEE/mR41K/3rKvv2QeB/AGAI9ePlE/J4gVr7h68KUr6cDnsLOAr9gMhNR64vn86QbfcU/6jt4kaXIEUT0E4yGucAkhbeIppiNJZFdFoaBzUL5Dp8b0OrlTV2wSJCRiRRwoJxd2q1OAwAqjWYaQDeOlRpMIeJqHqkQQxpdpC3nBiyCuctwem/1pxW01vC3FnPf6VQIjABBpguDEbjPzvgR4cXQTlxuvnEByhGwYPyaTLvXxohGC6zntRID1FiuFkPpusibfX0IXxQJA2bJrxfNzAODfp96hjgJ+7Nixf/7557hx49asWVPVaLVaR40apdfrhw4d2lDW/Scpt4kai6BkUJQnBQA+Y4JlicqAlyMyB50X9HzM760Uj6i36wwAMFAuq/oyUqourG4nAADBgMgCAJI0IZQtAUC0pmJHLhAyl7czqezgUu/btm175plnqjtQhIeHb9q0qX379n9r5DudPDJ1/PEi54y9+k2DGzADR63Q4XMF/U4AwhXRfY94e3tPmDBhwoQJWq128+bN3y3+9nTOzVVuSktLP//8888//7x58+Zjx46dNGlSla9p4/BLgN/tNg1Vyofecea1CixYeM1XIDoQqQZEC9otTkQRTCgAYFYjaH8DRBGUJ3dtHhASKmg6IlUtGGaJD/O6IKy3WH+12C6y3Js6/XqG+TmgM+HRmQ57B/N60XRYMBwSjAexM1/Q/uYqZ0jIYgn1Y6S6J+nRBUhFfb0Pbty4qV8enAn62bNnZ2dnL1q06K233qq1w9y5c1evXj1lypT33ntv5cqVjWbYvXBUtz/XlulF+/Tzf8rVQiFqWNC45XmLtpRseMSzq5KqscZO+TEAINpFrtgJ1wW87ZwJi6Bo7wEA54wnSvIL2jhaAIBvYJTAZnGiEwBWZm98LuxlEWMAMDhxgamG11iAnGIaNz3ZC22UnUMYD5wbJu4GwBLcTBCODvb6zSlKmgZGIBDi+VUAYKQ7ft5yWxJIBGMrUt293s1AJOkxoIfHgB5YEEEQEHPD4Q5JQungF7nCxYJ+NwBiIhfWURD6yoi/RvmXWGoI9d+u2n64aHu6qXxcYo3HsZ+cCKi2yJFuSbtoPP6x8zzlPOfUbfQH6AsApt0sQOX3o+yys+zGd5vyG8lE3/CExwBHCpx/FG/XWFu4Wj6pOMXz3QBgTdbBP8ozAUCEtuVaAGgrk5QXQG6m5UqsMr4u13Xf4bW/CYZ9depqOkKHvI6uJ2y+A1l6/oc0q9jdJzHX0XSjJmtsWl5f3zE7KwiMkyeFbm7vBdVS9A+Ikj4aKgEAQtWB9HmK0f7+UtDnAN+5tsoTt7B5+VxJgCMnXNX5jOPiu2zxZdoXs0Xj8qabAUDe+i0kOSlaU+mwtwXTEb70O0TKAYAOGM9emweE1JXFqdQqAgArAgDUPbl7Kwmzx2ZXImTD+Euj2SKKf9qd13jeiyD0oihFqOktzqRV2FLM6f3OERYRAHg1RRl55EURer7bjLzlrDhjYjSFEAngT5LxDL3Y25OuXnkR4F0vNY3Qbpu9jvmS/zGYFfOnXbFftERtaCGJutd0DKGLYhGC0i+v+YwJivjq36beAQDdmpq7VsaOHbtu3TqZTObv75+fn9+rV6/k5GSdTtevX7+dO3fWW9GDh4GLFy8mJSURBHFrsfF6ocgs9Fxf1j1c8m1/7+rtok3AHCbVFAC8rTPsttm3BvqHUdcfDJh3XOgi2q9W9adC32ZC3xDt6Y4LXav6ACKlSX8R8oSffvpp/Pjx1T3D+/Xr99NPP3l71zjpHTA4xWe3aJ0CPjjSv/E/ftGeAYAIWezfd717ji869FvqH5tO/p6fn19rB6lU+tRTT40fP753797EwzMlKej+cGaMr2NnSex3pE+Nwi1OjHfY7BsttiY0tci7ljKb2JEtGA8JxoOi8QgWrqc5RAyhak+qe5LqHoSi1e0qILhx83Bx4sSJzp07d+rU6fjx4/fblnti3bp1Y8eOHTx48Jo1a9atWzdjxgyMsdVqHTly5NatW5cvXz59+vTGsSQ+Ph5jnJ6efuduvXr10mg0ly5dutvj79mz5/vvv7/zmMdoNO7duxcAhg0bduvWgQMHjhs37qbG+fPnp6Wl1Xq0fk/2TWtz1Mjpp4S/2tGrW/X+shE8GSVyZ0nnnhu3xIEDBz45Zozlg9zyT/IQQ0StTVQ/7vftmK9a/xEHGLa2OXipWVliU8XjCweQTupkT8NnAzrjuj19CZFvfWjW3dp/L/07rS0ts4lfNpnRU32gTiYiWt5BA4is4/EL7Hkn9IeeCHjmkw8//cf2S2hx0fhj3irHyauBK3Yk3cv1fnHG/OU5y5hEBXnwi9v1HzBwQE7HlEJH3kJcoHbkOEmPSyBxbRIKCWwGMhQjFcZGRJWJLaMqAICJWkb5j646wvEi55htdfWKR0h4tMMsdYGy5Mebs/nkJ44dkHDxMWlax6S4mzYdP368vPxGuLggwoHUML1FAvf2fahL/+GD2/eL+gQwS4fPIxSVMxQ//vhjbm5uVZ+x/7sa6GU9eCH0hz/j63L8gtiny8IqJ4ZG7Sx76kBlgeS1TwRs635zRWSVITMuuTKe1FvlWDTpNEOwxwM2x4V1DQeLq1zCwb0jQ9d3jP5mFsFwAMBVeOdM+FK0S/YlHguaFjlqoJzNmoJof8Kr9/+zd97xURRtHH9my/WWdumNhITeQkeqqHQEBAREigQQBRSxgICA0kUFBKSDCtK7dGmi1NBCCwkppF/a9bZl3j8upJGEC4QAvvl+8sfd7uzc3ObuZn7zNE6zGQAe5gVytrxgL/3e8yF7z9cAgNstp1glXiRr4yhhj1Dxks7FVlNl3R9MkvYvvrrv7iFByIqxIyrDlSRyOR7Z7UHr1sjiSnpLOe6P5abhzltRyMABQG5P1yvBR+r/GuStVVuFjMhG8yTaNI7JTNwIHFeVvw+Ptyd5ove1N8IygwBALzL+0fJg6/6vPXv/i75ccDkhqqLjycnJOXny5Es+vzsr4AFg+/btc+fOvXv3rt1uJ0kyJCTk008/jYyMLDWt93+Y5y3gAeChnnMXExIa8SbOeEGn6OhSYuuIBzDwfAlvFk53yna3UHEhUiZqdMUe9yGnO4WE/tiWDBgorw8Ewd+vXbt2zJgxPJ8fl4UQmjZt2syZMyuqRTkeeMD0f25bywFn5o6tOLzn3v4du3ZotdpS2/j7+w8bNmz48OEhIc89b20lwJst15thexqpbE8oXmPSlwOrJV17OAqfAKmgfT7m9ec53SlEe4oaXUHkkwsR3WOYtXpjO5Goi0RUWCkUs7wxitOd4nQneeNVeBTqiSiXfB97ZUdH7t9qqnlF+c8IeHhpNujd3d3r169/6tSp8puNGDHi4MGDRTWGk/Tp02fv3r1POzoAgDp16pTYOLBYLEqlkmFKd/p969u2Pj1dQ6ThU0PnO6KdC9qrash7b++ACLRv4Om82Pztzjqt29DbdvhT5E8r9brFSUhAuI31zliaRAEFABwJf37g1n2ThrSRt96+9Y0ghmj2NSKIoknnCx8WX9pxxpykLxpUdPw9f+7k085tYo3ptaX1nWlftP+/kqxRGUwot6sL8SkAbLpWDysCBwSdlJCWc0n+zX3TBBR3PL6uW432tWGHGLJIt77Cmuuc7J/D3Iz7E9OtKe1Vb0XWnOjMeMoaf9sm1Jh+ws9+sGTm8BW9P0XbRx7OPZlka6SmDrzrW1b7jhNbB43wkJKSWZArMl7EgDIIidnI0zKKZ3hDipmkkcxXCgAirUGtBKsdKeusJt0LvVw1OnODCauQROYWriRFpMhViBDo8gLsvIcQx4mpXA8PDwAwcHqOZz3k5l7hqV92mpWXWLIOfMN6in/XOLvem7vOOmetFSr++X+K9tcP9WPSlxHy5qK6RwBQifZd29A7v5fqjLhBf322FjvTPylzlTTsghABAN7e3j97jXT5Ne3X7p4H27pOaCp3pbmJEycWLOYtMeeYrMSCa3+aVTvyzfQYuvZYrx17rD+ostYj2WvKJkd74rd+GufuNmgvAKTNm6j/q+2vzPYl9tV16tS5ffuW9XZ33nCe9hrDZm/HnHH+etNXIwRpWmH75W+b7RQAKDqPoeTumLEjWtDEU9A5SFjwigzDzPji07yzvwEuJaNQnQYNOv516i+LlQDEP/qu8xZLyohh5n//KfV+Xvnjwv23rnIGFgBye7q2Xh2uclMJWcEK0YK6RC0jaZFxYp5Ec+sc3XNhYRX8f8tqTwM1XzSjA9lah/UPcWp9onYmzlrk98vJmNMvZDyOpy/5/F4BAe+A47jU1FQvLy/Bo+jr/zeqQMAXkPL5fc3K5JDtDZXdnHLYtlxvhq1xAOBQ7ISkDm++g0gZ5owAgCiFqNHVPfvP9O/fv0C9kyS5bt26x3ehqsn8KSl1WpzrQC+vn0P27tu7YcOGEydOFNy3oiCEOnToMGrUqL59+4pEzsaivxDY7O32uDGIVosaXeG1f9liRyDaHYDAjEYYupZ07Wa50RzbUgQhKyiPQc50uNFgXKDVA4AbSQyQSgfKJJ7Fd/Qwq+P1f3O6k5zuNLYW7qMjUQip7EiqOhKKts7kDaqmmpeK/5KAh5djg75nz57Hjx+/detWaGhoWW00Gk3Dhg2bNWu2f//+ivafkZHx999/l98mOTn5s88+Qwht21ZKWrjGjRs/PrYrV64UtRAWYKaN54L/5IGbVnNhsKTm4+3vqK8kqWLczF7NU153nApp0mSUUGLBOISmlqzU6xbnu4DldrPd9HHvsDbfjnqrT/TtD2OapnZyjOey9p+VSYsIRPCYvxK1zGSn3pIkdRQnP+P4daKcfwOOAmAFpfyp7qYnti+jf5x9sb0ERz/Qd1mUPtlTfH5WwHTHiUxLQ85/bQ3PXOvtLkAIxQ0vOQprl9+/3Du4+47spgGpdrfZAEAgoj8zSh9vKqu986n6yh4AACAASURBVO/3GduP/DP3TLKtkadgin9CWe3Phu83YYMA+HmWf4TO5XylPEcIgn8odTy31JeSVbFuZq+HMR9esarfkd6PbOXrGE+s6e78uKk0Qc8JX55wM+nx8SCEW6vnqQSJhLR+CVe7lJSUvLw8AFDQqT6SizymzmbMNLHqCt2fS1ZPF9I2oHlwRe9nSLCX9XoEZjQFPoAF7QnEtfOaJaUy7uQNSDB2rtB4CvoPDQ3lLXz4pkwe4PBAjzAXqpz2EY1re+sHYnvab7IRg02/EQDi+mevHTNwH+sENlvgsqlMhkfK9CmAERZC+ieGWoPqhYaG8qYb1uhOgChh+GYgRPrb7wtR3ueJi49ou5b6Ko8zVnkziCqlVHPjxo1FQUG9M7LNmEePduvaWcydb5Re0qKhpK5lVJZDvRt7ubX9tSGikOP9Ehbk871cGE+xUkyZEBCQGWms+WHdKvu+FG2fGJfouUIqvSrgJThtst7uw3kvkYvv0siTrHO8RQlf+ioYT0JCwv3796dNm/aSz+8VFvDVVKWAv9f+sjlKH3Y0QtamFI/lEmAm03K9MXCOShIIEJWfC4TyADYLAARB8y8+qPfGG29YLPnVJgQCwe+//16qr2BFictj3SWESvjK+JM/Ees9U0yHy5yR85kZ4jU5CACSk5M3bdq0cePGBw8elHqJi4vLe++9FxkZ+aJy3TkBtt7uxhsu0D6f0AHfWO/04PX/AAAhbyaqe5RJnsOkLiakjUT1/3IyGpDFeL/Z8pvBdI9hAIAEeEMiHiyTNhOWssGHbYmc9hSnO83rz2L2kVMDoghZBKnsSCo7ErImgKo2UrOaap6K/5iAd/BiN+iPHz/etWtXlUo1Y8aM3r17BwYGFj2bnp7+559/fvvttykpKbt27Xr77befxxgqcX5fljjvmu6iC+3mKBX2OEbWcDb3OACMD5rSWNkCALQ83zk908RjAOhxyjR4fDLmMJDo0MqgW8BOHpdCsBgQPjH95OAJY/xEhfdn4YNp94y3AODfy98zrPSz5vJxTZ7sP1U+X94dnWXXOB738R7cUz3g6fop0DPruGNLo923hg2oLb4DSCBueB6Jgqy33uCNV2m/r2i/L53p7VK6fdC+HFdFfP26ixwapo684eQas55ubJVIgYDf1aekY3YBW1LXxJnuAUAzy5X2lpuOgyekLZMIZY49CwGhFnoRiGhrjgpiUgHg0b5GKd5qqdaH39z/FABmhf249IJy5z3zgg7Kd2oVht+vTFp0WftPS5d2owNKL4zHGy5bb78FhKCMl8D5lmSfT+iAbypwIwCyLXyLTZkBCvLUYHWFLuRyDzCpP2I2G9uSAdGEpHb+OoS38fZk4G2AGQACCBFCAiQKfLwCESJlgpAVjp2gcqj5S3qBgC+/JZu9wx432vF4l2xwHWKuoHc0k25LfE22512XIT9pwj8JIq8Zs9elEhIyZGdD+Wsi692+vOkW8EYgJBgoxOttvDCLKXkrMEJL0yYWVfUIoF8tcaiKHtlASpa2/srguGGanIcsK0BgxwAABAAPMEEpL1EQDgDMNwxx3a+xWgYArnVX7vvBf72XhztJWG4a0r6LV3/oL2msiO151XzNYFKQUj2HKFT7YgtReFVnLMIMTng/Wnsgi1RSNQ80ljRRAABv5h70v2E4k0f7CsMORzx7PHxFeSXmd6fWyk8MNqtbt25lDKaaYmAGW28bgUDiBoUzMYtxBsf7UaVYRZiH3wJnQaIa2BoPgBHtju3piPbATBYAEOKacXltevfuUKDeaZreuXNnz549n32oWivffUeWp5Tc3dfdXfwf0fCiWtKgDfUeDLyZNjteFC5V9fTw9/efNm3a119//ffff69fv37nzp0mU7GN/7y8vGXLli1btqxly5ajRo0aOHCgTPasq6jKBgmC5lujOzHpKyj1e4LAedZbHQFjQdACbE9j0lc8KtTn7D+RQqivVNJXKomy2TcbTcct1iNmyxGzJYymB8skPaUSSdFsKMIgynME5TkCMMebrnG6U5z2FG+8zBsu8oaLTMp8RCoIZVtHwDwSvQpRCdVU84qzbds2d3f3119/HQBIkgwICCh66uDBg7/99lvVjOSNN95YtmzZxEcolUpXV1eFQmE0GnNzcx1WQYqiVqxY8ZzUeyXCYPt13SUAyGNyDml2l9/4su4fh4BXEcQad7dRWTl1j+kGTErFHNBN5MxVw5sfJb6BgGBxSj2j3y1Z5zmdktzuP3i7MOVNmLRujPEOBoyhckwyZ3OPZ9k1AMhPHJhiSTyQub2TvRUXHc/l6UiFTFgrRBgaWH4Pt7OZaA0jxulq9mIA2diFi3oTf5jiNvCCoXW4OE5DdcyMOeHGXvJjrjKEkiBVpOESIS+lcm2p8MA51DuBiDuGG9d0Fx038AXiTJzJYN9IAMCA593/tJ71vhu2AsCbfJ6o3oElifNv6K8Eylu+r2pqvd0VAAFg2ntCWbFmm1PX8JhzF3iezzudaK4L4HNF9y+kpxU0IBEJABfz/u7k1s1RGb4EhLwZ5d6Pzd5pfzhTWHN9ibNs9k7ecB7Rasr3UyfvQAEMh+FRkrYKwRku8KZHxmTM8KabpbXigTdjMGNT6YGNvCWGLFvAc1pWdyhLaOMtzlmbKPd32My1vOGShVTt1EZOfP+6SsMmviabtczPR0Y3GRqiJkkYBgCQvS71wTs3QnYFkMJrwNsAAHiz41MhJGx+wlI8YoJF8d4y0pGvGgBUQmJ6a6VMUPpHqUC9O+LeSQAOQISQFeOlOgMAlNDwqV/HsVoG0QRmeO9Ee5qWGU5mr04XZve6weYxhpO5wVsa7N1Qo33raKmeAwBSRTtybFUlpap3ACAkZMiOhg4Nf79r1AvR8C8/TlngnxgC939lxn+uFvg72czkk9oprRRt/YWWW8a7LS8KQyR1b7QqaDAtV7vHZN7l5VGreMLJgk1uUb0T1nv9gMkCANLlLV53DvMmAMh1Wd22yxcFFXQRQuvXrx8+fHilDJvDMGhfTlSGXUyjnW+713L77xhRHY70hJQMOxYhaVjs99FgMOzYsWP9+vX//FNK9BEAKBSKQYMGRUZGRkREVMlgncX+YByb9Qfp2kMY9huXsxeAJ9362u4P53L3Ue4DBKFPn95Zw3HbjebtJnMWxwGAnCDelooHyaTBVJkfCcwZef3fnO4UrzvNWwqzsCBhIKnsSCrbE8r2ziSbraaaquSV2KF3Bsf8PnHixMWLF5dwmJ8yZcr8+fOreH6PjY1du3bt8ePH79696yiSQpKkh4eHn59f3759R4wY4eXl9fxevRLn9+v6S2nWlCc2Q4Caqlp7CAqr4l3blcZ+cI9k8cEP3PZ+4dl3fka3DbkAcGFQRtRHO5ttaNZ8XQtM4OPTj99/M6ZEb/9cXsSysk+aicdHPNlrryx44D+KHmzjrWHSOp/UmDHh1hAWc7ViRAO2F4byCWvVcP94KO1VZnmU1r9pMk3cT8ETXleecOpVCaGkWeoTs5weSkoef5hSKWIb1v0BAXJsWHgIPOfU+plCZWbhrgKmntZuu2d5I1j0y1tPmK3O5BzblLKiHVgGWC4BIgFzgqBFua49pt0bz2NmMWhoy10AQAJvcaMrQJRSYkbH5H16Z0TB05gH72doWoWH/OqlPv9449fduw/xjSx1JNiebrneFHizqM5BQtGm8ARvtdxohm0pgpCfKY8hTrz7YtzNYXrsyCYAYsd6V+hC3nzXeqsj8DZC2ZbXnQNEUp4jECkHzLMZv2DeiggZRiRwOlL1Bu0/tei1TNpSLmcPIYsQ1Tte1nYKm22P7X7NctsYEyT+LjJwz/ueT7TAAwBvvmWPjeSpj6/18hdnMrdaSxcv9/eR0ZvUbuqCH0wMDz+557DD19xzHJErEO2OmWwAuGLvtjKzmBNEQ/rIBK/vc1m37ncPAalwEeVvJaiExLpurm5l2MB6ZGgeMKwbQebwnAihZW4uW03mvyxWMUHYeJ4HWOLu+qa4MIRTdzArfmg0ZjDlRrM5TGYd8Zov1Z9NSBHrOEGQ2J5o4YUorq4w7KoVAEgVHXa0ibhulRqcylLvBbxAO/wrMb87JbQ+/vjjEkcMBsPly5fv3LkTGBj4xRdfPIeB/d+BASws/vOBNSaXvaFh2voLzdcNAFC0EiyD8VGLhQeQo5LfcCZpOgBPe40mpPUFflPsCZMAgMs76jhrFnTuNWhegXoHgNmzZ1eWegcAEsHKt1w6bdEYGXwuxfZfEvCenwRa75pyNqc/eOdG+MmmAv/C30e5XD5y5MiRI0fGxMRs2LBhw4YNGo2m6LV6vX7VqlWrVq2KiIgYPXr0oEGDShRqelHQATO53INc7kFOd5J0exsAeMMFLnc/EGLaf/qz9KwmyY+V8rEK2XGLdbPRFGWz/2Yw/W4wtRYJB8ukncSlJAhApIx06Uq6dAUAbEvJLy+vO4NtSaxmI6vZCIgkpA1JZQdC2YGUNQdC+Hgn1VRTzVMjFouXLFly7969bdu2KZVOFSV+ftSsWXPBggULFizAGJvNZqvV6uLi8goV+yigkaJ5I4WzJuUCdIezYdQ9ksV/jnX/Y6IH8HjzF545voIOtP3Om+sxj/PGGzSiHPVytzfnvBHuUVff2ei4kOHtl7TnHI/JZwtE+jV5pY23EogYF/SFwIrbXFefaZgeE2a1dqvnrarB5uSZL1633YvPmPaj97zJlEfplWu+aCG/nGG3Ep0BTgBAJq7riW7rWcVf+jeDlAIA8EXX1MRdE/YgEC+GHFLZyZkaJX9lHwLoBQBiUjLAe/imlBUEEFn2zGNZB7qp+z7x8udHlpkHgBzLE+zOVt6yN+MPAKgTOJ1Im+8IYWNS5ni49+3s3t2QsZK23wdEAObpgJmlqncAUNIuowM+zWVyHE83p4dmADRVtm7pXbIuDwLUXPVaWYNBAm/aZyKTMs+eNEVU71TB/WfSfsS2FELawMlUOCVIM3AAzoX4F4fNXOOwXfO6vwEAMMtmrCnaAPP5n3ZO+5cg9BdE5X/2sC2JyzsEgASB35VQ72YGn0228RiIPMY18jYVawKA8ETLtDVJ/7RQxnkU+6ZEeNFFa/45ICT1BKHnbjc6L8603WstXbzcnxGhSSqFuuh2J4KAH8Oxjc/5Pf3B4LahO3dhJhPRnphnv0mc8tBcGFUhImyza/0BAMvSJxg5OXDYaM/fK8wgeJ2NL0vA+1GUiccZHCdCaKW7a0uRsIVI+GlOnkPDSwGUxfNJK3t4BG+qlzDsFpvDUK605x3L1JEPCQ7HdFR03Nro/Jf3AjdqXqB6BwDN8ofaA1mIRjUPNSlhHnNASMiQ7Q3vvxllvmFIHHU7/GTTKh7hS45Tv/LLli17/CDG+Pvvv//iiy+eoppLNY/z3T/6zbfNTbxoAKjpSgGA5YYBAIp+rC/Z7EYeh9O0b3EXek53mtP/jShXyncyAFDq91nNBt6UX2iB4ej3vsq8dq0wy0VkZOS0adMqd/xuYkIlQkYGX86wj2r4n6r7HbCslj3FajiTF9f3eviJpo97GYWHh8+fP3/27Nn79u1bu3bt47nuoqKixowZM3ny5EGDBo0ePfqFG+QRraZ8PmWSZzOJU8kG5wAR9sQpAJj2nYSEfs/eP4VQV4m4q0R8j2H+MJoOmCz/WG3/WG1rPdzaiMqT30joR6mHUuqhADxvusnpTvPaU5zxIm+8yhuvQuoPQEhIRWtS2YFQdiQktZ3zW6ymmmrKY+zYsQihH374oWXLlgcOHCgnh1xVghCSSqVS6X9qNnkieXs0mMGUWhAb6Vkggq6+796aijKnmwDAyluuDbjaYVs7YbaQOoiSWhcmZBESIgLxAECip3ea0LPac3l/AUB71zcUlCpvy572+8kr4bRJxGxuffvbWmMBwOW93lmL11mu383duEv9eenW3bfDxG+HiQEirbe288arfn7dtOkWBRX/kKk9pM0kbE2w3GwFPOES+q39wThAAkHgt08cW5TufKw+HQCsNtfenu+2d3vzqu5CtOEqABzM3NHGpaOSrgpfLRuHJxzXZpiK+WgkalkAuJvD9N6VXfS4q4hY+oaL/JFf9L6MrTo2L1Raq5mqDabzQ9gwq2VSFvb0/cyQ/BUAAOYJWVPKvbzkRC2LJFa4ItdfAFNr92Zd1BXOoUv7TGCzNvOmaDbrd0o9DACwPZVJW+aItnM+mK5SIFWd2cwNjseErAlvugGYI1VdefM1bM8AUgGPytMS8oiifnn2pOnA2yiPdwl5yxJ9LrliWHvDpDCx3/zykEq3pqqFywf6TNySGp5oifng5hejAyzCwuV0Gz/hhlanWU3JoCHMYZ8vddjOu6jI5QYRawQiBx6qWgYEzSj+BhAA8FYRIZ3K6ycC8KKGZ/c18NLZCr+P4qyFkqzUe5Za/5j7A0DfMMmEpvnKWS5AKlGZN9yHJM9wVhFCqzxcmwuFAEAj9KOby8ScvFMWq4gg1I8lHFX1Uudr+FwGEBActsnIZbM8l2Rnv6+xPoqBQT7TalS9egcAWUsVISZ4C5+zIU3yY3ipqznD2TzLXRMAyDs6W+L6/4en36ZFCH3++ee7d+9euXLlt99+63z98GcnKSnp7t27Xbp0cTxNTk5eunTpjRs3OI5r1KjRyJEjX8WY/AQdy/BYSCKZADVS0wBgvqYHAEnjQgF/0mIFgMfNmEzKPACgfMYjSgkAgEhB0CLr7a6OLJVfrAw4erww9W6PHj1WrFjxPN6C0Y4BIMP4FBuvLzVIQNTY2uD+m1GWaOODATdq7m+MSoueEggE/fv379+/f0JCwrp16zZs2JCWlla0gcFgWL169erVqyMiIsaMGTNo0KAqjpDH1nhb7AePssdhQDRvibFE1QIAzOYAkKzmd1azGQAQ5SIIXUWIS27nV5RaND3LRTVZqdhjstxm7CXiPsqFIKSNCGkj8PkEeAun/5fXneZ0p3nzbU57gtOeAABEqx1KnlR2QILn6FVbTTX/bUiSXLRoUe3atceNG9e8efMdO3Y4QuKrqXp8vws1XdXb7pn6DIm7tz7Q4EICgIbjlvKhgYRCwOs1Wem9P+0tzBZq/bTHxx41WoxFL/f3Oa7Jav12eJ2nHsDPCfN5zIsI8RC/McDzxtMXAWCEywc/W35JtSVf1v7bTNWaEIvcx7+fMnaG+XI0ZzCS8nImMkIQOM96uwuTvkynmiPN/SxSvRSzw+xJU4G3UeqhrGYTAE97jys16UmKgbupyS/pxGFua9qVnLwGAMAyCpvu9UN6q5r5ICeH5IEnSMuutD9GBo576jfuPHobPptss3Ol7JJYGHwrq1jNKhJBlpmTCygA0NjS/8o+hAAN9hmFACFpfcrjPVazCRBiM9eR9lQZtvGObemA2c5vT09sKmvjJ+gQUAH1/scd88575jVdXV3FIkHATFvsSCZ5Dun6NqKU9qTpwFso93cIeasnd1SpkC7dSNXrnPYvAMDWBMrrQzb9Z950GTPZiHanXHswmRsBAAAJAucV3B9O/zeXewAIMe1fil2qe4g4L8321rIEl3Srzlf097c1a7rQc12oqcuTwpMs3/+acuybUEacL317hIrZ7O2c7vTj/YjrAQBIADzt+UcMmttXvL9q6sjXy1mz180i6UzPD5HqbTUpE2KrN7anM7HDxfI2hZ7fnJHJ3gQAC1OnIIIE4GQC5K9wqtLHEbOlqHp3QCO05JGGv2C1BctKajpVL7Xv7JCUKXGAAdFIaOS+GpOc4Us1PWEEACQksI1PmRorCBA5WeuqEpG2VIbubRzX93rW2hTM8gFLa5UomK0/lpMwNBrbefXHAT7Ta1Tx8F5+ntXVOSIi4sKFC2UV2XsezJkzZ8aMGd26dXMI+AMHDgwZMsRgyC+vcvLkyZ9//nnhwoUTJ06ssiFVIsPqS1e+5SKiEPDYcssIAOIG+QIePxLwHYsLeE53mjdcQrQH7TW64CAhb0G69eZy9m45Jly9OargePPmzbdu3UqVHZBcWZgZjAGk9H/EQErKqdBdjWI6XTH+o00cfSd4Q90SPzRFCQ4O/u6772bOnHno0KHVq1cfOXKkRDhlVFTU6NGjJ0+ePGTIkDFjxjRs2PC5vwEAAOCtCbzpeomDmM159JDDtvyqRdiWiK3x8MwC3oGcIN6XSwEKLWkcQJ8MDQC8I5X2kYrl5fvHEmJS9Tqpep0GwIyG053hdac53SlsT2ezt0P2dgAgxLUIZQdS2YFUvAbk/5fJrppqKoVRo0aFhob269evS5cuS5cu/fDDD1/0iP4f4TzoFb8Fd3k3NvCeberIpIwdddfQVjPG2Vgmdp83H8zU+Ez2jo2oQQftrT/Ju1HJ68PAQ+ApJZ+ymmmM6XacOQYABvl+QADB5mk5vZF0VTYJ6xIQc+yhNX5TyopmqtYAQCrlwpqB1jtxzMN0sm55MwUhb0669eFydkts5y4YWrWUn7fFDOYNlxApR9KGvOa3cjKlDd6fk2ooOnvmV0RnefqTEw5jLAEw0nGQsW3u6BFbtGLfc8JDQpwa7JFtLmarmH1OF5XJhLvRCzsUC0JRiQg/eb5C25q2gcWMkBDtzMi38Yp5egghFPA2AJbLPeh4PxdJL6MluafCWf2sFBKvB1bsP34k3npdw9zNYdr4CUm3PkTmOl7/D5v6Penag8vZC4SI9p/x5F4AAGDNDdPBOEvRI4ZHPuElnBEIBJ82k7fzL88Ljw6cw+nOAmYxmwe8FQl8sD0NACjvcUzKIkcbRLkQssb5F2COSZwKALTvZCTwfbzDOgQesTDekmQRhUnrH2rc0UsIAC3S7DPHBq7+PUUdYxqzJDF0byNSnr8q5g2fcHnHAEBQY6nDIdGM8WKtPoFlwzT8gIWZvM7m+81SQqRfLxu1IytntYdbU4EgY9EORYdfJA3yX5R5ZLvhDJc5w+USQ0rm6l82NguQAwC4S5z1cdjq6Y4A+T+WwZpGaKmbyw0706i00j+Wm4aMRUkAgAiEGcwoKb+7Vr+7AAAWBRl0pAn+PUOzIjn+vegav9eveg0va6MK3d0oru/17I1pAFBUw+uP5cQPvslbefXHAX7zn/uX+lXkWd1j4uPjFQqFp6fnk5tWBtu2bZs2bZpUKu3Tpw8AZGZmjhgxgmGY2bNn37hxIzY2dv369W5ubpMmTXqZEw+UAwIQUQgArHFmzsgJAkSUW77R8p6dyeA4T5KsJyhmxmRSvwcAyntciXApQeCc+zmtJs4vTNQZEhJy4MCBqvFI7L0ru9MWTZKOrYLXqhpoH2HI7oakksrblZk2K/6J7SmK6tWr18GDB+Pj42fMmOHrW3Jq0ev1K1eubNSoUatWrTZt2lRQHeD5Qao6EfIWAEC5DxQ3viZufI18tMVOSBuJG18VN77myFhDyJqSLm8+18HwALEMO0+ra5eWOS1Xe9Nuf/I1Dud/9/6CkOXiJndEDS8IguaTLl0QKeMt99iMX2wx75qvBFtvd2NSFvKGS4D/Ox+/aqqpAjp06HDx4sWQkJBx48Z99NFHVbk1Xw0AcAAfZeeelHHfbQpIDREG3LO1H3x/Lalw1PLIycOp/bLYqzZhiKTukRY1aoQFiUMe/5OST+/YtSppMQBWC73bunYGAGyzAwAhEgLAR8FfIoTMnHF7+kZHYyQSFrQpH0Hgt0BIVKY9+3N7c5jmDZcAgPL5hE37CQDogBmILJm/ysHw+tJuIaJuIaI3ahBe7jc83KIkkkwAQACO491CRG/WoLw9bqrdL6uUMVvS1lZWHv7y8ZKS9Tzoon8yAQEAYgqVOF6g3gEg3hwDADbeesdww/EXZYo9SPoCAH5kT7Yhcj8d/MBcMj3h86DgTgkC5wIQTMZqe/x4AEz7fPLESmwFXEi13cpiiv4l6fK3Nkocv6lhorOe8JNCiMMpz+EAGACxmo205xgAIKQNeOM14C0AAAhhNpfN2upoz2o28eZbSBhIe5fifFGQtU4UJq15qDHtVbh3kO1Ce+5tJAgSmy7q4t6+7iiWDgCEvCXp1gcAc7pTpLIDKDuMsNXbRjbLkrYd3aKv/6KR4joaQqRntT4mxSgLxuOycm5PvJO+QG1P9QEAQt6cDvjG8UdIGwAAktRxPC1Ybu2yzQEAVzFxeIDH2EbOflsDKOpx9e6AQihCKHj8HGdgY3tcY3MZZQ+PwE11MY3oR2tyq4Kc+XtApIdVMDfEY6w/tvPxQ6NtD8xODqYScWh4Qkpmb0x7OOEe8Biq1btzOGWJTUpKevyg1Wrdu3fv4cOHX3utzAwZlc4PP/wgk8lu3brlKHWzd+/enJycn3/++aOPPnI0CA0NbdasWURExPz58/fv31/R/jmOK/XNFiUl5cl5ZZ8dy81i5ncA+OuR+b2o5Zc3XuH1/yDKhfIcVaIHK+cy9Ktks8XmeCqRSHbv3q1WV6wsZznkWHgzU2ym5DEAgJ3DyXrOV07Ga9lhB/OWv6nyV5CK/0R9eHEdWfBv9R/0u56xOFHV2+PxnJmlEhAQMGvWrKnjp6xru3xL7I7z3CW++ArjwoULFy5c+PTTT99///0xY8bUrl37+QwfAJAgaKE1uiObs4f2m4xEoXTw91x0O8jfbA7GtiQ2Z+ejSnLP0XuCBNjnpT5lsW41mv612naZzLtM5lo0PVAm6SGRyMr2bigKIQ4nxOGU1xjALG+8wjl87I1RvOE8bzjPpMxDpJxQvEYqOxDK9oQ4/Pm9nWqq+c8QGhp64cKF/v37r1ixQiisThhZpWRz3HmrDQCMbpRgfwNRvzuWaKO47601u+tN1GonfPDQN9qCa4jDDjehfSr/X3Msa5+WyQWAcYGfO46QrkogCTYrl7dYPcSezVVtL+adPZZ1oIfnOxIkZR6mAUBZSeyKggQ+tM94JmXB++pNZ60DOoo3I1EN4M1PzJQ2skG+69bah0vseacAIC5poNnsiRBuX+9AQbNQ84PbxhsAMxm5gQAAIABJREFU8MCUdUl7roWq7TPdiOfG1zUXZtkyShxEmGHiRtL2ZIwIhHmzx8hIjyEB4ip1GCakDRzhDLwlFgl8aZ8Jzl/7QUMpQQAu4ieRbeGis1kA6Fjc2C4WoH5hpaflKwrtN5XL3oXZXMAspz8lrLUT80b7/eGO0nqk69tczh7m4SzStSdgjkmeCwCCwNlAlPRB4IxcbLdrljtGUW1p2KEmlEehgZqT81RTfNkDvXWw8f1uV00XdQ/6XK95OALRiAeYr5g8MfewIGc37/kBI2+VwnI1aGqjh5sHSeJgm/v7OwBDxg9Dh4kzuOUBrjMTbVu1hERIqWcDjMKWGCp8C6LcAIDyGGy93hSb7xDShqSilTllAQCQqk6uosYAhnrudJjr83WGRSQiZCTkMly2fVUzwd3FPuMnpRFi5NZdLZscoHSx3rYzI9Ozv8+0AQAhIkoNDq0CStjhlT3VCUOq1fuTcerTExQUVOb1FDVjhrOeNs/OnTt32rZtW1CoNjo6GgD69y+W6qNevXotW7YsmrPNeUaNGrVx40ZnWpbIUlbpmG8aAEBSRMCftJbiP89mrAIASj0MPbbpPnny5Fu3bhU8XbFiRYMGDaCSuJfD9NqVzZV2D+7nMh225CdjTzawvXZlywXo3HueZRW3BIAOWzR2Dv87tIr8OJ4FRSfX4E3183ZmCkOePA8VwOYxCb1vtkxu1C685cO05N2mgwcExzSmrKJt8vLylixZsmTJkvbt248ZM6Zv377PY/XsWC2xWZvtSd8IwzcTkjrCmpsAOEJaH4pmgpE992yfJEBnsaizWPSQZbcbzXtN5nsMMytPt0ir7y4RD5RJ6wqcDphHFCFvSchb0n5fYc7A689xutO87jRvuc/lHebyDgMAEniRyg6Eon11wHw11RTFy8urRHUMlUp1+PDhCRMmrFy58kWN6v8TDIABSIBFbi5vScTMwcaxXa9aoo3ifreXEBiiLRmBArd99Z+HegeAXRm/Ox7Mi51SOKQvGMAY7r2HSBIjDAA85lYn/vhBfCc2O49Su9F+Tv2c0j6fWDK31IK7ty0DKe9xpLK97f5wJzOlYcCXtfm1Wh1FDTHGhzS7i7fJ33K+mPf38xbwNg5/ckKbZiwWGZekZQEgJpct4TfuJiaWdM5PYuch8CxaL7AArsb3tnsDEeaRMMg3aLbvY1q0CqD9v+Zy9mBOLwicDUQF6nV9eERrZEpfD59KtpU4kmbkd/VxK7VxAYhS0X5f2hO/BCA53WnKK5JN+R4AADASBAgC59psybzxCps8F/M2zOaQ8laEop0juQ8i5QW59O1JFssdIwC4DfUpqt4BwO4HhAK+NORJPF1r9fXM/CnJdFnP5thJL+H7muwou1IpHzFav8KeNEVU/9Rxb7UQIQFCAMAkzwGch6jXbHFtGE3u4A4mJsNGSMmQnQ1lbV1s9/7gtH8xyXMFwYshP1vwRCb5OyZxKisJA94KiBLWXC+4jQBASD33CFNCQoYdjbjf9arxgi743bv71gUy55o0cxHTvkIAWMNLP0jP7vTZQ/NBHaGgQvc2Fvi9gA+eA1kbVY0/GsQPvJG9MS17UxpgUI8P8JtXrd7LwykB/95775V6XK1WDxgwoEWLFpU6pPKQSqUZGYX7l2XVg6VpmmWfxnu2adOmZ8+eLb+N3W5PSUlx1M59Ouwc/vOB1cYWM8OmGzkA+Pi4dlJTmZhGvpf0UoAoJe2nYRqpaQ3H3bMzYoRaFIlywUwmm7MfEEV5lTS/Hz16tOjaa+jQocOGDXvqAT+OSky6iAiLvdhbMLMYAxAIORKCOArjOVYkdLlJOpL1HACYWV5CvQKGelUvD1UvDwBgUm1ITFCuT9CZnI6N633dfMMgDJWEHW4ScLuWz7ueo61Dr/e+v9N84MSJEyXKLJ85c+bMmTMeHh4jRowYPXp0SEgpqX2eBTpgBpd7gMs7xGn/IlWvk67d88fpyARDSmn/byr3FcsngKImqxQTlfITFus2o/mSzbbDZN5hMtcT0ANl0m4SsaQi3zVEyguL0tnTHUqe053B9gw2aytkbQWH6V7ZgVS2IxSvleW6WU01/yekp6c/fpCiqBUrVvTs2TM3N7fqh/R/i5okJyrldQSCdiIhANBewpqHm8R2vWq5aQAAqCFO2BHWKag8n9tUA3ct0949VFzRBYqVt7J8/qrJhovorvxFIguYLXAdy8yJy1mTBQAuQ3qBk7/PhEga+I0tblQ/+XLa74o9/hNnMqVlmfn7uQwABMGwS7q/aUS7ovppAAhQfXI8b2PsCcmsJhvbWbvClNokhsLoDeHzyr/I5ep4s4VQynSk5FSSjeFLTWLH38oqpmZJBJpHSewK4E3XsTWx6BFC0oA336TcenF5RwqPIkQo2hVNt15ZOHYfdEWWcIj2EIRvxuZ7pFvFqvG9X198NKGYUDcxvCOZcUjxKusIYLRzHuOU5wesZhNvvgMAtthR+c7zANj+0HI130WRyfjF8YAznLdcCc6/khCK6hx0WCDEdWV+82umfBWb+nUsISE8RhVW2BHHIbsbBgEcWRjn8lMmEChgRe189W6zA4DVayKyHeBNN1nNZrl6qOMq3hLDajYBooR1FtQ8FBjb7WqBepe3dQEAOnAup3uN1WyiPEcQknoAQHt/xGp+4y33wBIDjlhXUtnal2niJehUwbQFT4fAXxR8qPG5N6+EXbf8PDa18YEIUpa/HFdhNH+axnhQZ5YRxm11GjV7wWshRSfXGtsaxg+8wVv4avXuDKiEeHjJGTp06JYtW06ePNm+fXsAOH36dMeOHVevXh0ZWVjIJCkpqW7dup07d967d+/zGMOtW7fq169PEESJzGTOszvG8vkpbZmnMQCC+UsSQpItH08JtfiIro3w3GUyT8vVdhSLVrgXuqsxyXOZ1EWk29vCmhuKdqDT6erXr5+cnOx4WrNmzatXr1ZuwvPfbplmntM7375PmPj7Tqqyzob8kg4A0aM8XwkB7wAz+Kb/GUJChv3VVBhc5nY1p2Nje14zX9U71DvtLQQA/Ymc+Hdv8lZe/ZG/dRSxes3qjRs3ZmVlPX45QRCdO3ceO3Zsz549KzH1IJP2E/NwFiEOEzU4B4gGAMCcNbo9b75N+0+nfSc9S+dG1iijnv7DlsCyO4zmPSazlucBQEagsQr5B+VlOXYK3nLPoeR5/TnMPcrbjEhC2phUtiOU7UlZ88d98KqppizOnz/funXrVq1avXL5VmJiYgAgMDBQJBIVPC2H8PD/o/CTZ5/fKx0mwxb39nXM4Zr7Gj/R9j7uaN7RBOvW3m7NvEvJaFU+qdakpOKq0oH1Xrzxr/PA5t8QBHx4jETAEC6Deir7VihVCrbe7sobLpLKTpzuFBAicaPLpSYeK6DDFo1jf98ZwkN/9fI43+ym6oO23wmCKqEeaj4Y64+c1f95is3IN60Lgv0sPbpZ6hQLdpv9jy4qgwlzoxcVT2KnFBH+8uIWDN5ivhwI2KkEE5THIEHIs5YNyjRx9uJ3sdfObL2dH91IOrhOsaRIbhJC8syW4b8SraOP5AHAg7HeT90Jpz9ru9MboeIihZAgQgAAwJsxbwcAIISIEAMA5u3AmwGQqP4pQlqYGFjz88OUr2IBgf+P4QUavtMfWalWtn9Kbt8FGTyBTEtC2o0IHJ6Vc8lqA4B3pJJvXVVc9i5b3ChEe4gaXXFs9Nvu9uN0JymvMYKg+QBgjTGlfRuv/shf1qpwcWtPnMJm/EIq2grr5Mfwcrn7bfeHAQBQSknThBdS+/byAwPV6yZOskpbKEP3NSZlJOZwUuSd3O0ZhILSbKvT8TV3+hmskpWI+are9sDi0v8FO+S+EvP7c89GXrnMnTv39OnT3bt3//LLL4cNG9ahQ4fx48dPnjxZIpG88847FEX9+++/Y8eOtVgso0aVNEq/PHQIEI5oILUUt8CffmjLMHIEQv1rixGCxC+Cs9NtHV9TOUrKnbPaAKBt0RravI3VbAQA2mtMif4nTZpUoN4pivr9998rvVxZz1Dxvlir3l5spzlRy3IYhBThJy/U4RYWZxi5PfctIS7Uh41fQKnJ5wSikayNSnc0J67P9fCTTUu1w5eq3gFA0dmtxtYG8e/e1CxPVoP/wgULv/322927d//yyy8lHEB4nj927NixY8d8fX0/+OCDUaNG+fs7m1qmHGjvcaxmM2+5z2ZupLwiIT8TzO2yMsE4z9R7H2XY0j4M+ryZsvXT9RBMUV+oFJ8o5Uct1m1GU5TNftRseXYBT4hrEeJalNdYwCxvjOIcYt54xfEHqT8AIESIMCFCSASE4PFZlpDUEoZvruK6uNVUU+nUqlULAC5cuODwnnM8LYdXa5f/vwftJax9wVk/R8e6osTqwkl8RYG+osBSTrRszwR01/95yhodw+kMhFQiahWi6N5BUCOgRMOstSlsFuP1RRAiSxUD+UlYON1JAKB9PilfvQNA3zDJlQw7AMSb71s5CwCYrZ42mwsCrFIW23hCiJXLEgBAqOc1C1b7LpmOnA/CKhvMcVnfrzVfjgYAQiYhVQouR2tPSCGXrfbp0dF1eL+ClrXd6KgMJlRF1fN40usSYkIWwRsuIFpNKMqYKDkTpz0OAITiWcMBTiRaxxzJK/XU6uum1ddNRY8EK6kTgzye8RUrBVLRjnTp6oiAezQdY8q1pyD0FwDATKb1enNAlKjhRUS7A4Dt/jAudz/lPqCoegcA9ccBAJDyVWzypzEA4NDwq7u46Fckw4IMnkCr53if60yHZGjiGBYeqXcAIN37EZr1vP5fNnUxHTCLyz3I6U4iSkX7feHoWRQurfF7/RLDpv2+5LJ3OPwZSdeeAICEwY7ofUGNpS9EvQNAsxC5/UjE/a5XTRd1cb2vhe5qlDwpJnd7BqmgQvc1fuG296JImiicTC9VTekCfvHixRXq5bPPPquMwTwZf3//I0eOdOnSZcaMGTNmzJDJZJ6ennq9/r333hs+fDhFUVar1eH716NHj6oZ0lPgKiamtS75AR2wNzvDyHlJibntlQAA7Qo3cTM47m+rDQBeExVaCNmc3ZjJIqQNbwiahGNc4GZ85MiR9evXFzT7/PPPmzdvXulvQSUidj4WyBSxIUNrwzVdqH39ip06mmAdfyxv8UVDG19hA3UlzKkvCUGb6se+FWW+YXjwzo2aBxoT0mK77GWpdweKzm41/mgQP+imZnkyAPjNDxs0aNCgQYPu3r373dJZO7fstuuLbc+npqbOnj17zpw53bt3Hzt27FtvvUWUX3qtfJBAEDjLFjOESZlLuvcFIB9lgvn2WazQZ7OPZ9hSAWBT8s9PLeAdCBDqKRH3lIhTWE5eJKedhuO2Gc2tRcKI0iqmOAWiCHkLQt6C9vsSeLPt/ghOewwAADDmLcBbylr58mwu8AwQ1Wm9qnm1cexue3jkL9PHjh37QodTzSsA7aN2ixxYfpv0ufHpcxMAwBZvDvylTqkanpA2oNSDWc3vSOhH+4x/8us+mlRrSMIcDxJ5NtXGAaDgBBEhEghrBjl8+BUC9FmtjkFylL55sT3roenvK7LXK6GMuW7XUfPlaFIhc/twiKRpPUAIc5zx1IXc9Tv1B08JawZJ20Q4WvrISABwsqa3IGiBNbojZrUC/6+RKPTxBkzyd5z2OCFtRHk84bY/ER8ZGeZKldjTSTXwPMZKISqaYJhCqIXv006szwFB4ByL7iTwNgBMuQ9kc/ey2dspz+GEvCWiPUUNzgAQDvXOGy6UUwr+cQ0v/y1NPy8eCOS1olZ0RwLzfAn1/mgA86zRHZn0X0j3d+0PZwIA7fc1osrL2ogoFe0/1Z7wmT1pulj1BhAie9IUAEx5jqRce1Xm3akgAn9R2MHGDg1/q+4/nI4llVTN/Y0lES+RWsYcTvnsvuW2MWhD3RcYkP+qULqAnzx5coV6qTIBDwB169aNj4/funXrqlWrbt68+eDBA8dxnud9fX3feeedjz/+2M+v8rynqgrHz6u3lAQAe5I1a1WyemIg7SkAgLHZOSaedyPIojUkHOnrjijen6LJHiqXTlUpAcBms40fXzgp1qtX75tvqjSeuVTeChYt6KjaGWP2kjk1t70qkDIyZFfDmM5Rpku6zB+TvKcVSxubMOyW+apeGFKKenegeMMt+Pf6Dg0vCpO6f+ALADVrhfpPUL074q2Eo2n3diZk3Sy2cc5x3P79+/fv3x8UFBQZGTlixAhv76d0USNdupGq1zntX0zKAgASszmkoq1jw/jp4IH/I32d47GZM29L3zDQe8RT91aAX/G6KQfMlhV6wwq9oQZNvSOV9JZKXJ9lI4OQCIIXW240A95KBy9CmOcNlzjDRWwvrDSBKDfgzZi3UF6jqtV7Nf8B1qxZU/Rpdaa6ap4dh3pHFEJCIvePDAAoS8PTAbMAY0o9xJlMaVvvmFMMpbjQY4CrrkEAAKmFRezaBwiDVRLFW22zV262RMc8u4DHLKc/eAoAPL6IFNXKT0aDSFLeuQ0iiOwVm3V7jhcI+ApBSBtQ6iGs5jd70nRh+B8lX9eWwqSveFQO5ll9vuq404cHlDSqR2zI1Nrw6MYy58uYOY9KRAA4mxuhHJAomPYay6QtQZSKDpqLhH5M6mJ74hRR/b8ACCQMetSQtydOAcC07yRH5fbHUX8cgHlInRqbPOm+4VSedp8GCBS4srbrEO8amuyrNjsAIIC24mJTfMF+k+1OD8zmEpLalOfwJw6bUg9jM9fz5ttM+gpCFMTr/0G0B+1fddm+y0IQJA473OR+16v2ZOvLqd6TRt/J3ZYBALFdr9Y83KRaw5dP6QL+8ejx5cuXHz9+vHHjxv369QsKCsrLyzt27NiBAwcGDx68YMGC5z/OYtA0PXTo0KFDhwKA2Ww2m80URclkskoMEq56zCwAgEPialYma35+KAgWe0T6AYCaIGOAzeW5PSZzH6kEAHjDBd50w0K5f4M6CRDq9Mgyv2jRori4OMdjkiQ3bNjwktQB6hMm7hMmBgA7DycSLExpQW1/xlkERcSYgISOgWLRy/0vpb2ENQ80Tp0ep3ijpD8C7SUAAC6XYTT2UgU8ANgeWDCDAQGlzt/2Ppa1X2PPUMu9GgyJCO11XZgkw4dlWzZvMRgMRS9MTEz8+uuvZ86c2atXrzFjxrz++utPYZCnA7/jdGfYzA0ACBBJB82taA9F2ZS83MZbCUQ0kEdc118+rjnQ3aOfjKrk6WGgVGri8W6TOZ5hF2r1P+oMHUTCflLJa2LR020OIaEf7TORSVnAaX4T1T8FXqMBANsecrqzvP4MpzuLmfySCkzqD2zOHlLRnlS2JRRtEf1S+BlWU0011bxY8tU7iQJX1REGiuPevpb7Rwa246B1ddFjAdWIchWE/Oxkz1t7u8VriyUkPpZo+/2WSYy52de2qgb1FNYMchynSdTUSwAAlKcbAHDaCuToKQt7QjJvtggCfArUewHSds1zN+yyJ6bwJgshrUDO9gJo/+lczj4u74gjm2yx1036GngL5T6QkLd8+tG/OBz/c+eqwT4B2vdzzGpJl66IcqV9P2OztvKm62zWdsrj3YI2bOYm3nQdCXxp74/K6cpzQgAApE6NLareh2flONS7ACE7xp9k5/3kDm+KC/+hjn8TZnMBgA6cC8iJJSki6aC5tju9mbQfEKkCANp/KqKUT7yuChAEicOORGQsSnCP9JM0lD/5gqqiQL2TckoQJLJEG6s1/BMp/bPYu3fvok+3b99+4sSJ2bNnT58+veDgxx9/vGrVqrFjx7Zv33706NHPd5hlI5FIJJIKFPR6abEwGAA8JQQAWG4bAUDgn//BDaapv602DPB1rtaO8UCZlMlYBQCbxe9gQrjEzaWlSAgASUlJ8+bNK+jwww8/bNr0uRcDK4rj57r8bde+u7PvZpeeu+Wr0yVn3GCl8SUJxyoHYbDYEQfF6VjzTYMjGSkABCyrzelZ7f6s2G5XS93p1CxPTvnyPiDw/yFc1dMDAPSs9k/NLgAwsPoce5aMUhgD9cO+e3/RwkVbtmxZtWpVieKIDMPs2rVr165dISEhDoO8Wq12fuSEuBalHs5mrgWAgqSpT4eWzT2XdxIAOri+Odhv9Ec3B9mwbWXS95+HzH7qPktFRqAJSvlHSvlZi3WnyXzGYj1usR63WNUk+bZU3FcqCaz4Lh7t8wmbtYU33WQ1Wyj1ewCAhAGU+j1Qv4fZPMv1xsDqCHkzbLmPrQmsNYHVbARAhKQWoWhHKtsR8jYvydxcTTXO8NKGyFXzKlKo3lfXAQSmq/rQPY3j+lzL25UJCILWlqLhncdbRnoXd9yL07IAQAJukpvoDnkyv5IZFjmdEQCeTlQ/1pUBACh1KWXPEEWSLkreYuV0esdrVdTgjGgPyncS83AmkzSVVD7KJvskb/CnIy6PtRZ3oecwAECmibuVVWw95qcgVc9cDNxVTABA6WkQKgopFdT4Kf8xIab9p9kffMg8/IZ07Y5IOQBgVsekzAUAQdCcJ/p0eE4IQBRKnxPvvyjMZbB30ax1k1WKLukaLc+X0PCIVlN+nzNJM0jXHqSyg7OjVrQjXXtyuQcwZyKk9SmPoU/z3p8PgkBRwM+1n9yuCsEcThp7J3dbhiOlv7i+LK73NdNl/f03o8IORwiqJF3/q4hTi93Vq1cHBwcXVe8OxowZs2TJku3bt79AAf+fwcLwAKCWkgBgvWsCAHHtfNemyUrFQKn0rNW6QKuflacTMWmv5x7kgN4rH7TEzaWgMvykSZPMZrPjsZub28yZM6v4LahERJ6N8ynXT76ZNx2fy7IANFGYzcMRPiCiSs6AEV6vUsB8ytTYnE1pXp8H+XwTAgCIRsGb6icMi9buz4rtda2Ehi+q3h1+FgCwI/1XC2d2p9XZjAYAOMwCwJ6Mzc1qtRkzZsyYMWOioqJWr169efNmk6lY4pkHDx589dVXM2bM6N279+jRo19//XUnyxzS/lO53L2Aedpv6rO89+UJ8zHGIlI02G80AcQA3xG/pfxy13gzznQvVPqEFFlPAQnQUSzqKBZlcdw+k2W3yZzAsqv1xjV6YxOhoJ9U0kUiFju/niJEtP8Me1wkkzybdOtVtLAck/wtsDpS2UFYew9gjjfd4PRned3fnOE8b77Lm++yGasAkYSkAalsSyjakYpWQPwX9hOr+Q/zMofIVeM8KQbuu3/1ZqaYMLudzQDA9xcNa28UzhEEgmbego+aVL6zdFH1zmrsKVNiAcAj0i9fw+/MBHhWDV8qiCAAwHQuStaxpI3adO4yAAjDgku5rIIQMgmUZczHmNPpAYCQ5Wdxb+ol8JWTLX0qEENOe3/Ian7nLffZzPWUIxsx5uwJnwNg2vezsrzBK8qhB9bxx0tPYvdrtPnXaHPRI/5y8vSQCpgBSsVfQS3oqPKTV37IJOUxkNVs5A0X2bSfaP/pAMCkLsRMNiFvQToXYa4e568e548BSuScB4Aj3upSNTzt/REhrlVRbwhB4GyL9hjwNkHgvILS9NU8Tr56/yODkJKhuxvJ2qgAIHRf43wN37Vaw5eJUwL+ypUrbdq0KfVUaGjoEwunV+MMNg4AIMSFYnMZJsNGykhBQP5HdpvJHG23T1epAGCBVp+WuprA7HFJj+keYSE09VF27rsyqeXsmd27dxf0tnDhQje3UraNnytuYjJBx3lJypuqv2mjxBh+u2UOUVF/9HJzxEo5ysj9+76HUvAK/8y59FbnbknPWJRISEmvyUFQtoYvVb0nWR78m3uKQmQOk0UhylPok2p96Cpwy7XnHMjcPtBnBABERESsWrVqwYIFv/7666pVq+7cuVN0AHa7fceOHTt27AgPD4+MjBw2bJi7u3v5Y0aUi6jBPwDYkQnm6Ygx3X5gvg8AQ3xGE0AAQEe3LgczduSxOWse/rCg9uqn7vmJeJDkKIVslEJ21WbfbTIfNluibPYom31Onq6rRDxRqXAnnTImUO792Mz1vOE8m/oDHTDTcZC33GM1vwGi6MC5AACIJGRNCFkT8PkEsJ03RnG6s5z+LG+M4k3XeNM1SFsKiCZkTUhFW0LZtrouXTUvJy95iFw1TnIt0348wVrqqduPubndyWYqXcCXot4JhGiUtSYFAJ6ThhcQCAAImiSkEsuNu7m/7nEZ1BPRFABgltPuOGS+HE2IRbL2zubtLwdhsD8S0LYHD5mUDNrPq+gp85Vo3mShfdSkIv+uNvESnK2o9EUCQeBsW8xgJmU+6f4OotxYza+8+RYS+D5jOZii1HShIrwENq7YRs//2DvP+CiqLoyfO2V3Z7Ml2fSeECChl9CLNAugICJIkyoKKr6KAooFBKmCiIggItKVooCo9KJSQw0thCRAet9ke5l23w8blhACpJPg/n982Mzcmbkbsjvz3HPOc67n8wLGvm6kt/yeW2S0X9WY2A2KZAAgjRcCKLLUpzorxkZR9Cl950NAkrAFtiu9uMzllPdwjEU+ezUAIQlbUC6D9yU6g0O9D1XIZ3oUudapCeIvf58+WbkGUXw/X3c5mLnzqyFI92fKOU9A0jBpw02YLyBUpasnF/AA9Q4ADnt8l4Z/OGXqA9+4cWODwZCSkkLe+2ETBCE8PFytVl+5cqXaZljrqKY+seP3FqTo+f1DffgU29WmJxSdPRrua+3YNSZXG2O3t5BIVnt7vpeXOSeli1rUbQ7d+Zx3l1G52hxBGC2VrO/WNT4+3jG+TZs2MTExlXIprxA6m/hHknVYY7eHd3PX2cRhu7UJBfwrUfL53dVwR8BfGOdTpwU8ABT+mpP82jUs4KBFDX3eLOr3hjns0PAO1xDTaf396h0DXpj0SYI5TkN7FXD5vb0HtPfoOjthCoEIETAB6IvIZX7Skh13HAH5TZs2OTMviiORSMobkK8Y78eN1XGFPlL/BVF33bBuWRPmJEwDgJFBE3t49q6+qxfHgvFxkIQDAAAgAElEQVQ+i3WH2XLezgLAe2rVBFVZn1lF82XblR6AKKbFCYctsP36QEF/lPJ/UxL6UHcA0SoYT4uGY4L+mGiOBXynYpOQEoo2pKorqepKKKJdBnhPGHWiT2xZ2LZt29ChQ2fNmlUiyc5RIrdq1ar/VIZdLewD/yBEDOezWfZeYbYoxnglj5vaXtns3mZmIWqqZDfyypH/Y0bqe/GIQmFrmnBZdmerbWk9+a0hl0Sr6DU2UDPML+mlWNEs+P4vJHBegyq5bkwWO/x3bTNv+ueQrNzFq0EQSZVCGlkPENgTbgs6IxCE9+Sxbh1bVcnlCtZsN+z9h/b39v5gvCSs6BZsvRSfv3SdYDR5jn9F2fupSl7CHj9I0B2mfMfTwZ/aLrXBXL604TpS8+Kjj6wEbdflFNjE6R1V41u4PXp0hThvZ1/Nze8qk37rpZHe+wSSKwijc7XZgvB3gK+6/E+qbNJEPn8rqekHok3QHaR8xkjqfV2uM/xmssws1A1VuH3qUbL8rUAUB2TniYCPBfjVisboTy73qPffWii6eJQYIBh4h4aXhMhqWMPXift7mT45Xbp0ycjImDZtmije7fstiuKHH36Ylpb2oOC8i3IhJdEtnXAui5WEyhrujw5f28S5a4GnewhFXWLZPtk5Sv0+tai7Jmn2Nd9wcE5+jiBESyXC2jVO9U4QxPfff1/z6h0A3GXEyKaPUO+OYetf8Hw+gnk6/EmTNB6DfEO+jQIE6dMS8tdlOjY64vDu/b0FPZ/Q94JDvYcsi3KqdwCIKfw3wRzHkG4FXL6KUvfzfSWUieis6SlgwZP2ErCwJXPt/ZdzBORTU1O/+uqryMiSpYCOgPwzzzwTFRW1aNGivLy86njL+/N26bhCAHgrdGrx7fWYhg3dGgPA1sy1IoilH1zVyBEa6Cbf5OO1x99nhod6iOJuNvtNjt9vsdoevF7p8JsFzLIpMwBAKNgt6I8iyoMOfFS+McGQ6h508AxZ04NMm1vSqK20/yTCrQVgXjSc4NIX2OKet5wLs18fwGUsFo0xgEv3gHDh4rHwkBK5Ro0abdu27bHMysUjcSTGdw6SFv/nISMAoLEXXWJ7JdX7zybzW/kFWuHuNzlfyAEAkhDZxwuc6t17fJCqp6be1hYEQ+Svzcj7Pp10I52DqwSHpmIoJG/bzH/2e5J6IYLBZDl72XLmsqAzSsKC/Ga+U1XqHQDch/eTRoRwWXmZUxdkTluYO//7jPfm5HyxXDCa5O1bKp/tUt4TilbRfvOeBXc6dA4gis9dx958E3P5hKpzdat3AAhRUQDQQFONLsGBFOlJEsds9nfyC+zF7ry5gjAqV5vM8/Vpyq1CoQU6dDYilULBH4LuICKVdPD08p7hZYX8anDA/eodADQE8W+A73GXeq9+8r5PL/glG9Gowe+t7lfv4IjD72rFNFGwqbbk16/V/AxrOWX69M6bN2/Pnj1Lliw5cODAiy++GBQUlJ6evnv37itXrgQFBc2dO7e6Z/lfwFEH7rCyU3QqSiNhMR6Zq20kodb5eL6YnacVxBcsvwNAoccrGMAgiqEUOR/hpvPuRghff/316OiK9DWpSXzkxLJn3B89rg7iOSpAtIlp799I/V88wRCaIX5wby69Q717jb0bTmdF9rfsTQBAIhIABvq/ypByABjkP/K8/lQ+myslZJcN564YLzRTti5+LYtgNgsmmZp5//33J0+e/Pfff69atWrnzp0syxYflpCQMG3atE8//bTb810HjO7Xo2fPRspmzr020XrLnAAA7rQmQBZc/ECzYCIRKXuwMQwv8juyNgNAM2WrEKZeib2Twj6aHDeWFe0b0laOCX6YPWyVE05R4Yp7vtw+LdDFsqwbQs/JmZfc5NFSyf23Zzp4hqDdLRTuFQr3sqmzAIAO/vThTV9LgEgl6f4s6f4sDYB5nWg8KeiPiYZjouW6oP9H0P/DAQDBkMoOhKozqepCKFo7vYtcuHgsuErkXDySkzb7UattLJ+/1tvLkyQAwG9KGJthy/8xw7o6EyO4Ojek9fiiJWmHhr/1yqXCHTmOH4OXVJkTSpia8mIIR6a3NLJewJfTuMxcLj0bAOgAnxKJ7pWHYGR+s98r3PKn8cBx9lYaC2kAQCoVqgFPq/v1hHJGSngtl9Dngu26KejLuzl6BBNF+Y7js38QCvcCEJLQOVX7FkpFIUFQVT5zD8CPJDf6eI3O1R6z2d/OL1jupZEhlC+I4/K0KTwfRdOrvT3vMz4qE4j2oQLe49K+AAA66ENEV7Zov2oRDMew7eajxyEZ6dHnv+yAyzRVIBphDuv25Ll1KP33YLlktN+2AoC8dS3qeFdLKJOA9/LyOnLkyOTJk/fs2XP16lXn9hdffHHRokU1X2v9ZJO/NsN2wxI0rz4Q6IKdvcyyGPA6o8koit5ifnv7KR7oZfQzgAEAUnnhjS9mGQxFJisajaaWr6fwItwo4EqNg17XcgrqnlBtpCdFV0krkprF+40g0SRkzEhKmRBHyEmHw7xDw2cvTmYau7m/eM/95q/cX7Vsnpp213M6b6mv+x3FqKLcn/cZ9GvWBoeb3YrkL+dHrXSni9Ypf83auCf3NwAgETmr4dIAWXCPHj169Oix9OLczRt/Sfgt2ZB6j9Edy7IHdx4+uPOwKtht4usTJ78+xc/PDwA2Z6w+UXAEABCgmQ2XhDBF3j/bs9bvzd0JAEpK9VmDxV6SojnH6I4dyvtTSsq8aB+LYOIwRyJyQtgHhZz2suG8nHTzlviGyesDgIJSPaV5+qh2/7GCw/19h2okj/OLYpJa+Z3BeNHO7jBbdpgtwRTZTy7v78YUd6132gLbE8eCaCfkjSmf0RW+IqLcSY++pEdfAMC8VjScEAzHRcNx0RIv6I8K+qMcABByUtmeUHUhVV0IRSuXmHdR8wQEBFy6dEkQhPtL5GJjY4ODgx90oIv/DrM17mm52gSOG5WXv87b05skAQEbXpTRKtKob/N7vt4lwTIkJ8EmAgAdKCNkVZYS6CMnYkb7Ft9CB/jQAdUo4ZBUohk90GN4P/ZWmmCykO4qSVgQKpu7SnF4LZfQ+7zDojh9WgIAODU8HfSRkP8r5gson1GEW8uqnX+pyJDp+4i3o3KMtsJHPWIhShI6m1B2rMBVwilqvY/n6FztCZt9Un7BLA/3CfnamxwfRdNrfTzdy7z8IVri2MTXsFjc8UEEJAGEuOzVXPaPdydLMpIG6wmmauo1KgBms+xxLwI8ujYZACj/t2tmvaZ2ouzmUW9Li1vDL+csSQEe319lYzqpuzn4kmgRPEcFBM2r/1gmWZspa/5Mw4YN//rrr4SEhLi4uMzMzJCQkMaNG9erVzLm5qJiCCKkGHgAKLSJmZ/f5LWc34dhlAd9xs4CAAlog9EsQWgZOkRi/gjzzE2sbCmhu8mZxdfi9q9Z4zzPp59+WsvXU+ac1G+8WkrBNgCM+L2kUerASGZRjzoZqPd9P1QwC9kLb98ec7Xez83Vz3kCAKKR//TwLHv6nowfCzktAIwOehMD7M/bBQB6TgcAefacpbe/KHE2HvMAYBdtR7X7XvIb5tjoQWsUlIohGBXtrqDu9vPsVb8PegfT/5Okncm+tSP991277XZ78bMZ0sxfzvhqyexvXnjhhfHjx7ft3NnA6QQQ5ISbU6UDgCftraRUrMgiQKiYPUyc8dJNyw3Ha8d2AQuTrrxa/BL4Xj8ZDOInN95urGzxesh7zni+gdcnmeMVlNJH4udOlyPKXTE6y6SdZdIUnt9ltv5utqTxwgqDcYXB2FIi6efG9JUzjocJ2m8in7sB224BAB1WZeaxiPIkNf0dNrmYyxdN50RjjKD/WzRfLibmGcKtOansSKi7kcoOLgM8FzVDly5dVq9ePW3atEWLFjlrr5wlcn379n2803NRG1iuN+aLQihF3eL4MXnadd6ehs3Zhk9uigRKbSsPizGnDL4s+7WFspsHANgSLYl9LwgFnLyl0hZv1m7MpAOkAZ/V7cdFRNPSyIq/heLqHQAAQ/q0BMDg81YwACDKQxKxnM/bSofMqJLZPhIVkd9JeQJxWCxDcYNgPFsxAQ/3avj+2bkWjMur3gEAs+miNb60HYDtKfduAMxlweMT8Ij2IeRNRMtVJAsnVd1KHYP5fKHgTwAg1T1qdna1DvVznvV+bn5r+OWcZakAUFzDm07qHA4anqMCQpdHQR0M5lU35SuAadiwYcOGDatpKv9l3j+iS9ELAJCTYgvRcpQHTXnQAHDGbgcAG8YShL718oi48SsA/CXvDwB2gOFu8s0rlt+8ky8dFhb21ltV5lxaTbTzl8bm3NP3RsD4ej4PAO4yFKi8x6m2U2BFiuQx4DWp33CYmxg6BZXHmLRqsX8gpGdmBm0MSBh2wWt9cHi/ohzCg3l//K3d73j9rHd/X2mAjJCzIvvgMxVBIMJX6u/8sZfX8728nr9/WDNl66JM+zCAVyA/P3/9+vWrV6++ceNG8WE8z+/atWvXrl1BQUHjxo0bN25caGho8QE9vfr29Crl2X1U0JvdPZ+zChYRhKW352JcitXT/b90u2iL1Z/RcYV+0iIBvyF9xQV9DACQiJwftdK5dnBRH3NWf0JJqoOY0K6apx/2Gyk/oRT1rlr5jlp5xmbfbbEesFhjWTaWZRfoDF1l0v5ypjsjk4TNt8cPJT1fJFWVdScqFUR7kR69SY/eNMzEXL5oPCHoj4uG46L1hmiMEY0xkLkUCBmpaEuoOhOqzqSijUvMu6g+XCVyLspCgSByBDg0/Ku52p5x2mcAeBna/IFP+x2FT2/R3Rx8KWJ7CzpAmtj3ApdlV/XUBH3ZMLH/RTHTzt62Pu7pP074fDahzwWHepdGyL1GB2TMTHJoeGwTfd8PBQDSow/p0afGplQIYdctjRvLryHah1SXfqfD1puC+SIi5JTny5W5VjhFLfX0GJWntWDshtD33ppyqXcAIN2fIZTtROMZynsoHfRhqWPY1NmCdiepeqqabtxlBZGS8MW2a30wm/WgXoDsrXfA8T/u3qvG51frKFXDu9R7WSjdhd7xrB8aGiqTyZw/PoT7DbSeYKrDpbbLptwskwAAnxNik/fjFZ3cGx6ItmHcLiNbxPhIgC8G4M2XPOJ6FZKaqcHH8jGVxvNh2VmHOnXg+SLX63Xr1o0eXfGM38dI5KosHgMAPB/BfN3Lvfy5afcQozu2KuUrABgbPKnKFeD9ZNszfs/ekmXPMPGGt8M+DJcXrSD+lrVpT85vXZZ1abGtJUhRg99aKrtrAEDHFcQazriRSl+pv6Nu/Kb5xrykj2iCnhO5vHgM3MmSW7OuGi/28np+RODrFZskxvjff//94YcfduzYYbOV0nmIIIinn376tddee/HFF6XSKjYX1HOFJsEoIaTekrupj1eMF/7R7jfyBhnBvBk21RmZ//rW7CvGC47XC6JW+txZs9iaufZk4VEV5R4mrz8m6G3yTmycFVkjr1fRahqVu/+NDeNDVtsfZusJm83xYVYSxAtyZposTyYLgfKfsDJgLk80nhQMJ0XDMdESfzcHj5ASbq1JVRdC1YlUtnP1ma8l1AmX2jKSkJDgKJErvtFRItegwWOLZT0W6pALfaksP29ae8W8+2WvwCr1nOcxnqwtPGS1KQhChYhMgScEPOXj7Ba7dRYFcXZjw5d2Ggo2ZBJuJOlGcrmsqqcm6KvIpJdi2WSrW3t1/d9bkYq63WKmwvD5bELvC7Z4MwBIw5mGB6NpP2nh1v3aDWcBYwDQDPFTv+D9oMMJdVdEVX1aZUraHu+MEWUcTKi7yxrtLO8lsgXhI63uZYW8o1Q6Ji//JsdTCPEYd5ZJv/XSrDaYkjhuiadHGcvgRfMl25WexdvE3LPXmmC73AVAlDX7h5A3KfUMNYk9YYxQ8Dvl9Yqk/qoSu+7vd+MCAPT7tbeGX8Z20fd/IeoXvB+7eq8T9/fSI/BRUVEAcPr06fbt2zt/fAhl6UXnwkmGUZh70mByxqExZJuKnhUSYwxNAE66UXP+LDAoRC4C+/KkD0mm8PyJtPUDAC4oXvjR198giqNztTHLv3Wq90aNGr366qulXq72gxACjOU0+uumVULClz3cK/yBZUX216wNjte/ZW1so+7kMISrPDzmz+iOp1uTCzntC76DA2Uhju0X9DExumMAQCLSKtytDujn+0pjZXPVd+68l0m7Iku/N98h4N1pTfdibdUw4M2ZqzFgP2ngGd3xUi/tyDA/qt3X3fM553XLBUKoW7du3bp1Kygo2Lhx4+rVq69du8fSUxTFAwcOHDhwwMvLa8SIEePGjWvevHkFLlQqatpDTZe0GL2bKXAvr4W8G2eMNfIGhpT7FMs4MAlGI28w8oYce+Zg/9GqO9Yv39yec910GQBCmYhPGiyg7pSR57M5WfYMDe3lK/WnHlBbLkPoBTnzgpzRCuJfFutui+Uay/1iMj8vD4muWfUOAIj2JjUvOvyHMV8gGk4KhhOi8YRoviYaT4nGU5ABgCjCrSWp6kQoOxHKDv9l/xsXVYirRO6JYVK0YlJ0Ffd7BwAKoa89PRwa3oJEABBJtGS+3xsYd/5D33N0kvfOlghAuyFTNAsu9e6kVPUOokUa9mrAZ7xzmD3xgWegvIdKIlY+cHdFCfXvZMkkwZFAh2hE+5RInMO8AQRj0Rw0/StwiQxeiLHbz9rtniSRJ4hRNP25Rv1OfuEJm/35rNwsQZAhZMZYXTYBT7i1oLyH8Xmb2ZSZ0sjNJfZyKR8D5ijf8bVAvWMuYykiJAAUn78dC6YSHntC4T4AkWAacjk/UT6jCeY/FP58COrnPMPXNb096krOstTcFWmYx15jAkKWuWLvD6N0AT9+/HgA8PYuWhScOHFizc3oP8CFHHb/7VKioACgSLECwDkFfSLdTkYCCVCYIaYG8+Nyctab/wKAp4PHMgjJSfJLkW+9bavzwOnTp5ewIKpz/NDb4419hTsTrAjBwu4V1PB7c3do2bwwJoImJInm63/kbn/FvyJZCSbekGlPC2UipHeyl2MK/12TtszxOkxe3ymkn/Z6wV8a6E5r/KSBxRcLJISkkaI5AMCX4DU4iGlcerfVbFtGsiUJAFKtt1Ottx8yJRELZ3THX/IbXoG340Sj0bz77rvvvvvuqVOnfvzxx23btplMpuID8vPzv/nmm2+++aZNmzbjxo0bNmyYu3uN2hCoKHUHj1Iqx14L/t9g/1EGXi8lZKpiwrWRolkum6XnCk2CQcTY+RTyze25GbZUAPCgPb9s9IMzYn/bkphlT9fQXsFMuBtZ9KTrSRKjlG6jlG43OT6d51tL76r3LSazBePnGCaQqrnPF6I0pOYFUvMCFLnZnxINJwXjKdF8STSdE03nAJYBEIRbE0LZiVR1IpQdEf3AGI4LFw/CaDT27dt3wIABH3zwgatEzsVDoBCaqladsNmtGBMIMEY8AZsWBjzDyCzbcpJeiq2/syXtI+ELOJ93QlzqHQD4Qu6ueq/HNDwQTftJAQAIOaFsJxpOYsHTeKJIv8mbK6QRxYINgknQHQIAoprKpCl32vd1Lvt7AASYo4OmUD5jnDsxm2GNbet4jaTBlO/YClwhWiqZpFIuNxjzBNGXJB117+t8PAdl52UJAkJooca9XE3g6ZAZQsEfQuEeQXe4ePK5ULhX0B1GlDsd/FEF5lm1YDaLS5vt/FEo3FPqMNFyVbRcBYwlYfNramq1Hfd+3uEbmt0edQVzLvVeJkpPoXfxECqfYidiOJ/NssI9v/m3DxYa7XjFT6necSb2p6ZCJ/cFoE/C/Bw39128hdYfWa6dgJiGTIsYx/iPP/54/vyiT75HSEhOUhJN11UL66gfsjkRx7/hdzGHe+2vAguPBzRkvuzhXt4eJwVc/sfxb3Mi+2H9uTJCNivhAxKRsyOX+UkDyn6SQk676OaMbHsGADzt9fzwO1nrBl73Z852Fe0RJAtppowmy29vZjpemDYlIWBWfYennQMM+B/tgXw255GH04Sku+a5+0PZlcFoNP7yyy9r1qw5c+ZMqQMYhhk4cOC4ceO6d+9OlLNurSbBgOGOqZ6DYwWHzuiOF3D5nrT35HoznLvejxun4woAQE17LGn8k3P7NWNsHpvtKfGpJ2/oFPYiQHR6lg1jBNBKKnlBzvSWMx6P8fcgmAXjadF4SjCcEM0XQbxrT0gwDQhlR0LZkVR1RNLQh5zDRZVQJ1LsykJwcHCLFi3+/PPPxz2RWkFdT6GvPjJ5YVRefgYvKAhkEjEAKBBhwmIEQX71aZ55ew6pour/3or2kyT0ueBS7wCQ811qxoeJACAJZSKPtqF97q4Li+Yrtqs9AAhr4o6UiXYAIOVEi9y7Wp1LncVlLiUUbWRND5TmKlM1WM6FAa8HAER7yVqcc6Z02RPHCtpdjteyJvsJZbsKnFwnikNz8lN4HgAQwAwP9RCF27xC/SZTkZPfUzLZSm9Nue6mXOZSLnUWwTSUNT9e1LQFs9ZLnbEtSRK2kPJ7owLzrHLsN4YJhfsIpiHmczGno7yHFf0CMcelz8dcIaHqLhr+BkTJmh8jmCrrsPhkYDpeaI03e48LfLzqvU7c38tnYueiSiAQqKVECQGPAAFgVYZd3vqyV/0kzk625M0tAHcFVRDwHuZtAEDI6vG56wBAb7Cu+G6p89hx702uu+q9OO38JWue14zfU7ArwdrGTzKscfmy37dmrmVFewePp7Js6Tzmumh6HSs4tC1z3f/CPy51fCGnjTNeSrLEN1G2bKPu5NhoFSz5bK6MYEKY8Nbqu86rKsp9eEVL0B3Yb9usV023hl0O/6mJ+4CirCoEqLvnc5U5bWVQKpVvvPHGG2+8ceXKlTVr1mzatEmr1RYfYLVaN2/evHnz5vDw8DFjxowZMyYkpCI5/NXN/W6FXTVPl+qAMCzgtUuGs/lsnr8sEAHC9nRBfwgAzmWs5jF3E+A6pR7od7c4cAWXdpUjM7D8tj14tr3jPJ2hk1TSV870ZGTKmlfypBvp3ot070UDgGgTTecFwwnReEownRWtiaI1EXI3AACSBDjC8oSyIyGPqr7nPxdPAHPmzHn99dcvX75chVUzLp4w0nhhVG5+tiBE0nQqX2RcjgGCKfImL3z4hfdCTjTvyksacJFUUWyazaneRfNlQVum8mkkb0J5DarON1HToDshCJEVse2eFrmEWzPKeySfu472XAjwHgBAsUxybE/mslcCIEnogoyPk8xnDWE/NZEEV72VqSRsCZv0GgBgLp/LWORoaSYaYwTt70XzVHWpmHoHgGssl8LzjSX0M4zsG71xdqF+r8V6xs7SCE1Xq74zGk/YbDpR1JQrCO//Fp+7WbQm8DlrHXKdy1qJbUkE07BiaQLVgSR0nlV/VLQm0UEfcunzBd1hOmwhIpVc+gLMFRJuzRFCAED5vuZS7/ej6OKh6FKVYaonmHJE4PPy8hiGUSgUALBt27a//vqrQYMGEyZMcGba/0eo/Ar93lu2SQdKtkwDAIVFWD/nSsM/RwDxiP+UrzbYZqwsSsL38fFJTk5mGKZik6kNNF+TbRPw9df9Hfe7S7ncigumD9opG2rKscCUZI6fnzSdJug3Qj74LnkBBvx22Ec/pS2zCpbJ9WbcX25tF23vXRtjF20AEKVoOi3ibjdOVrTThKQ6HOwzPkvK+ToFkSj0+8aaYX5Vfv5KwrLs/v37N27cuHPnTqe9QnEIgujYseOoUaOGDx/u+Cqo69iTxgv5v5VlJAY0PeLaUTvwGAOABOEmpPlpGTFYHaIkarpg/r7J8aL5kmg8LRhOisYYzN9dhUGUB6FsTyg7kMqOhKJlDTvzPcHUiRX6sqDVardu3Tpr1qzXXnutTZs2/v7+JXJtHFY4/xFcEfhSeSNPe8xmj6TpVJ63YvyynNFhfNhqc0OEhkRpvDBIyoz7IL1wZy4AFI+922+MeFAWcUkQLW+XVVWdO2sDgklIfP6C5bwBAGh/aeTBaEnY3ec0zOVbz7QG0pj28Sfms61DVzbyfLXI88We8KpQ8BflPTx76Tv5azMAQBrONNjTujo0vO1Se9GaAACAaFnzEwQTYbvSSzTHAgAgUh4dD5RXhU9+wc5GSWg5QmuNpi91BgAgAZZ7abozslxBKBTFyPJHnoTCPfYbIxDlLmt5DrBoi22DBYM06tda5ejOpc7kMpcRyrYApGg8TQdOpnzHW2PbgmiRBH3Cps9FlIes5TlEVXsDXRcVo07c38skkCwWy9ixY7dt23b8+PHOnTtv2bJl2LCiZtRr1649ffr0f03DV5IoDdU+QGK+Y2KHMXAi3NRxgbl2kZVY8/wZ30wOpDmkDymAHJA7bcdcDiAJkgQAgM3OLd8a5zzbu+++W6fVOwAs6eXOieBMmG/hQ6/qXaYVuFs6PlRFkQRgwD9n/ogB9/EZ+Ffur46E6l3ZPz/v/fKv2Rs3Z6zupnn2hvmamnIfGzzJcSyNJC1VbTnMRbo1aePeqfhpJUQV27A7CfyiPsGQWfNuJU+Ik0Ywbu1qlwmZRCLp169fv3790tLS1q9fv3bt2lu3bhUfIIriiRMnTpw4MXXq1MGDB48ZM6Zz586obCY0tZU7kyfkUNobEbGAMIuwKBDMMm9/nSgesNh2m40X7NxFrLhogq9N2c/IlX3lTFeZVIrQddNlHvPeEj9fqX/NdTFEFKGIJhTRlP/bAFi0JoiGk6LxlGA4idkMoXCfULiPA6ehfUdC2YFQtkekqoam56IW4+VV9IDurMkqgavOzsVLbnIJIk7abFaMB7nJZ2ncxTu+9CAQPRlZX4U8bG1TyiuB13IhKxo5M+cpnyIBTwf8D6jSb+t81grM5VHew54k9Q4ApIJs8GerxBcuWs4buCx7wjPnGxbT8LkrLdaLL/lM2OA76SdeN8ip3gXDv0LBX0C65a4alr82g5CT0nqM9aopse+F6gJl2tgAACAASURBVNDwkgbrbZc7AWDAHJfyCanpW6TeAWjf8ZVR7wDgcJPBAJl80XKYCJAtCADgQ5I+FbJtIj36ku69BN1hLn0hiFYsGGphPzYqcCqft1U0npUEf8oaY7isFaLpAogW0nMAl78FAOjgT1zq3UUlKZOAX7JkybZt2yIjIx1CffHixZ6enj/++GNcXNwnn3yydOlSV6vYchHuTv3c/24V9PuHdX8mWRVSFJTDEhKW8c4BABrsQUKaYwB2JKxhFtuTAWD9b/bcgqInKpVKVft7vz+Sp8Medk8yslhGAX1fPcyJdPuoPwsmtlJMba/8V3sw2ZLkQXu605rblkQP2pNEVIYtVUYyftKAbHvm9qz1AKCkVCIWCUQAAIGICaEfVN+behD+H4eTHpR2XSblWXurHoKDgz/99NNPP/30/PnzP/zww+bNm81mc/EBBoNhzZo1a9asCQkJGTZs2BtvvFFHPaux8WzRK9FS6gBnLJISLcDr3Cn3VxTyVxTynXkHDljtceCfhwL2Wqx7LVZPkljrIS66OcMxvrvnc6OC3nSeJ9l6k0SkryRAUu3hekQwkQQTCb5jAQDb0wXjKdF4WjSeEi03igztAQAIQt6IUHYklO1JZXskDa7mWbmopUyZMuVxT+Ee9uzZ88cffyQmJkZEREycOLFVq1YlBkydOjU5OXn79u2PZXr/TTQkccJms2E8VOE2w0ONAIhivvTn7ew7aiWiUPDXJS21nXILCxZJyKz7zyzo/8ZcHiIVdPAnNfJWahRSSTk1PFtMw+csS834OBFRfd2fPywJylB03APwFgAAFrjk6QBguTg6d4WdkJMR25rLW6uSBsSaz+gTnjvfcE/r4mH8ykPIowiPZ8XC/QhA0B0UDCfuTF1Bh82r/PkxgKPunUboRTnzq9kyu1APAEMVpdv6lgU6dI6g/4fPWQtYBCSRhH5R+XlWLYhU0MEfs7fe5XJ+orwH8XnbBf0/QMiQJBRrdxFMJOVTJ1s+u6hVlCmFvkmTJjqdLikpiWGY3NxcPz+/Dz74YNGiRQDQvHlzhNClS5eqf6q1hSpPseu9LS+xgFdLUf/fsvv/rQ1fv0sauAkAUqgw/9DPlARib72HBQMdOpeQBHDWrMYd3rmdUXTpadOmLVy4sEqmUTsxsbjr5lw/N2Lt8xo/t3vWa3cmWKcc0b3UkJnTXTr9+lt6vvC1kHe3Za418oaGisZdNc+sSf3GjVSMCHz9h9SvKUQPD3gt2r2TkqpFUUcul7VeNqp6edbyImW9Xr9ly5Z169adPn261AEEQfTo0WPUqFEDBw6sW6n1fP52NukNAACEKM8h9y+K8wW7MJsJAMitBdPs7/vPkCUIDgFvFvEvPh6HcrfetiYWsHmdPHv39urnaHKbbEmanTgFABCgwQGje3sPcB6u5woZUl59SR/FwbxONJ0RDacE4+kSHnhI4k8oO5LKdoSyPSFvCshlj/II6kSKXZ1j4sSJq1bd7ZyMEFqyZMl7771XfEyrVq1iY2OrKS/AlUJfKmNytTF2u1O9O3H2h+8vZxZ6lh5gf1iPbszbrjwlWq7ToV/Q/pOq8Q08VgQj79DwACAJlGmG+mV/lQwAgCB8Ta405C1EKmUtzyHah8/+kU2eKpgCb76yCCgmYltzR/dZwcA7NLwkWNZwbxVreBBN1nMRWGSLb5PUX0l5Da3kiYur92WeHt0Z2TqjaaHO4PC0q4yGZ5On8dmrAYD2f4cOnf3I8Y8D0Xb1GdF0gQqYxOesBcFMBbwt5GzAglHa6DdS3fNxT8/Fw6gT9/cyuUckJye3b9/ekad9+vRpjHH37t0duxo1anT79sN6X7l4JFkmAQDUUiIwlwUAJJks0N4AEMonKwlAkkAsGJAsnPZ/i/QcsHXLOqd6l8lkJZ5snjykFAQqyIQC/pVd2jyLWOqY37O36PnC+m5RJwqOGHkDACSY4rwlPk2ULc2C6bY1qamyFY+5dHtqrVLvAJAxPTFpQGzKm3GYr9VJqmq1esKECadOnYqLi/vwww8DAkq6+ouiePjw4dGjR/v5+Y0ePfrw4cOiWPp/Vm2D8hpMMA0AADDGXA4dNrf4P0IZ7VDvgAhpg42lnsGfJMcpFdt9vff4+6hJ+mX/V6fUm/VZ5Hdz2XadMnM+LCg8YrV5SPzbu3f1lwZRiIZiwiPBfO39uHETrwyZev31WEPpjQCqEES5k+7P0iEzZU32ytukyJrspUNmkh69EeWJ2SxBu4NN/sh2pYflXJg97kUufb6gO4R5fXXPqkbBnGi+JJpjH/kPc49uDPHEk5iY+Pfff9fY5bZu3bpq1ap69ept3749Pj5+8+bNfn5+kydP/v3332tsDi5K5SMP1TyNewn1Dnf6w8/0UL+tVj7o2CKDMSxwySXdZPns1aLlOpKF076VMoitVoyimPvg1ZzbPP/IlR5HHF4erQIANsN2R72j0JWNPV4ZRro/jQUjl74A8zoufQEAZC8eXly9AwCpourvaunWTs2m2Rwm/1Xy1oogFJS/80kSAQDB1K+8egeAuYX6TSazFKHvvDTdGRkAjFEqprirMMAXhfqd5tKz3soCHTQd0d5I4k8F1a4EomIQktD5AIjP/lESMpvyHQtsHhaMpOYFl3p3USWUKcyiVqvT0orSuY8dO4YQcrraFBYWSiQuV6SKY2KxicVyGoW7UyaGAIaUtwq4hqdEZnwIAGzKp5TnQAAgPfoAAK87snjVeeexY8aM8ff3f1wzrxloAm3urxm/t/BCNptu5L3ld//YOMwCgFkwHc7/CwC0bK6OKwQAN9JNSaq3ZPzEiSwCOJT3h6P12t/a/b08+/rLgh7TWykFz9EBuj/ztJuy+HwufH1Twq22VwA2atRowYIFc+fOPXDgwPr163///XebzVZ8gNls3rBhw4YNGwIDA0eMGDFy5MimTZs+rtmWEUmDDY4iQEF/VNAdJN2fKdoh2tjUmY6XlNfLhKwcSeY0QEOaPmO37zZbd5utCgL1VIx71ofpIpNKi1Xae9BeIUy9DFuqls3LtmXAnfWleNOVpbfnqCi1vzTolYAxgbJqcP4npISyA6HsAAAAWLQmicYY0XhaNJ0RrYmC4V/B8K9jHME0JJTtCWU7QtG2aLGjzsKmfOKI2zwSRCqY6BtAlK8RxpMExnju3Lnbt28vUT5TfSxfvlwmkx08eNBRjxMZGdm4ceOuXbtOnDixZ8+eSuUDJaKL6iaKpqMe4DdGIfTIUCodNF3I/81R3U1qnndsxHwhl7EYACSh86FGUpAqxvi8ggSO+85L00lWcpLf6I3fG4yT1Mq3VY/44yyeSw/gUO9FrnV06DxB/w+fuwHb0zGvtVxsZr7Qqbh6LzqDiqr/e8ukF2PNZ/QJfS5UbRyeDp7O5/6IuQIAjAAkDdZW/pwnbfbNJrMUoW+9NF2L/epeUyoAYLHO8FmBrjsjq1hbVkR5yJqfBETUZicXQtmO9HxJ0O4QjCdp/3dsV3oCkkhCPn/c83LxhFCmT06zZs1iY2NTUlIMBsOWLVvatGnjsL3Jyso6ceJEHa19rSU4wu8BChIAVgwJMB5pIwmSBQeMy5Y2AwDMZvF5WwCA9OgNmD/y26SrSUWrvSRJ1rbaxWpCLSV+6e95eJh3K98i9c5h9vuUxZvSVwNAiuUmj3kAKOQKHN51ZsGczWYmW29m2NMwAAZwCHsRC47u7rUH5VMeDfe0prwk+n35iS9c4LXc455RmSBJsk+fPlu2bMnKylq5cmWpJtUZGRlffvlls2bNWrVqtWTJkqysrJqfZxlxFAE6XrPJ05yJ5VzmN9ieDgBAyiThy8t1Tgqh9T6e+/193lerGktok4h3W6yT8gs6ZWS/ry3ca7GaMQYAb4nvzIZfrWq+7evGa3v7vFTiJPls7hXjhTjT3QKlBHPcF4lTv02evyN7s0WoQlmFCKYB5fOqJGK5rMUZJjpRGvkzHfA/QtkeCFq0xvO569mbb9sutbOeq2+/MYzL+Fo0nASxSqNANYKzRALRPkjiX+o/IGQAAKS66MWTDsZ45syZERERyntxc3Nbv359REREjc0kPj6+U6dOxZ8oWrZs+e2332ZnZztK9lzUURDlTgcVxSRALFrz5dK+wHwBqe5Oejy2RqploY1UYsP47fyCkzZ78e0O9U4h1KJsQSyHhndrp0YkKu45TzANKL/xgAVBdxCLRO4PYyO2tyih3p1nqL+rpbyFkk2z3R5ztfJvrTh0vZWOF8jjOUJeBcvujSX0QDf59/eqdwevKRVfaNxHKhXqSrRiRbRX7feBk4TMAkIm5O+wJ44FEGn/t5Cs5r5RXTzZlCkCP2XKlAMHDjRq1Egul2u12o8//hgAfv755w8//NBisYwbN66aJ/kkk2kSAMBfQUpIJBJIqpEAgBdJaeovsl3rA4Axr0WUilR25LNXrfo5yXngwIEDa/LR6vFCERCmvvu3quMKz+pOiLgDAAQzYZ9HLo83X92Y/j1NSCeFfVQ8T94qWr69Pd8mWIYFvtZY0aJaIpmVQx6tijzaJmlArPms4Ub3s/V/byWtV2d6Cri7u0+cOHHixInXr1/fsGHDpk2b0tPTS4yJjY2NjY2dNm1ar169RowY8dJLL9XCSJqswY+OIkBsS+Zz1lD+b2E2k8tc6tgrCfocKuQ8F0JRr6sUr6sUqTx/wGLbb7VeZTlHwbwUoY4y6TOMrCcjcycIR5KIkyhFs++a/pzP5uj5wvryu61iM2ypty2JAHBRH+Mr8e+sKcrES7bePJq/10viE8yEt1C1qaT7PaK9SI8+pEcfGgAwK5ouiaYzgjFGNJ7BXM5dT3tEEfKmhLItoWhLKtsiaVhlLlozOHxAAQBzuY8YyWZiwYLIumToUDG+//772bNnq1Qqf3//xMREPz+/oKAgvV6fmJjYvn37ZcuW1dhMrFbr/aU3o0ePXrFixVdffTV+/PiQkFr3Be6ijFC+4/jcdaIljsv+ng54T7TG87kbAVF0aBU4pVUrelHUEESBKL6ZX7DM06MbIwOAZXrj9wYjAaBAqLDM9WKkkmp4IJov4Gife24odOCHQv52zOXr9zxrSwwxHMhXdivdUMB23Wy/ZQUApnkV30Ypj2dFz5dFyyVZgx+r5ITuBDFX4/6gvYPc/hPJTUgaRAe8y6UvxLbbiPahAt9/3DNy8eRQ1j7wGzdunDNnTkZGxpAhQ1atWkVR1OTJk5cuXfrmm29+8803dPl7OdZdqtbkZst1yyf/6AdHycdJQfvGtcgv6msG+Dh22RPHCtpdAEBIg6VNjyQfbhnZL4O705z777//7tatW+UnUPtJsd48XfjvpQxfd7ErAPAi/ifN7i5jGYo+lirU15A9Q2XHC44YeX2kokmEPFIjI0Y1lUvudKU7kLd7S+ZPPlL/OZHLKFRL/1C5HPbmwFjLJSPtJ62/syXTrE7KBlEUjx49unHjxh07dhiNxlLHMAzTv3//4cOH9+7du1ZV33Bp87mMLwEAEW6yVhfYlI8d/eGRNJBpVWWxjkxeOGS1HbBaL9pZx0MfCfAUI/va00Natm58GbbULHu6kTd09ujhtL7bkvnTgbzdjtcf1Pu8ibKl43Uem3PFcMFH6hfMhKkf0MOpXGB7imA8IxrPiKYzoiUOMO/chWgfQtGGULYlFe0IRSsgauM6FJe+iEufBwAIIUQHAioZ/8F8ARZMAEBIQ2WtLsKDl0LqhMlNWWjbtm18fHxCQoK/v/+oUaMKCwv/+OMPAJgzZ866desuXbrk5lZxr6ly0apVq/j4+OTkZF9f3+Lbz58/3759+x49euzfv58gCJeJXR1FMPxrj3sRSDemxTn25luC/ijlN1ESVnrzwtrDhDztvza7EhFGLEoQWubpcYnlVhqMBACBEI/xJx7qVyvhx+ZA0B0UtLtstz64OTQF20WfSSFBC0rWK5lj9EkDYgUj7zHQN+ynJoiq3ea3LhyIVmtsW8xmSCKWU94jHvdsXJSJOnF/L6uAv59bt26pVCpnC9n/DlV7g//6rHH5edP/2iiG/KPNnHnT973QwDn1Hbswm2G90BxABECEquPcpUfmrC7KPWvcuPG1a9cqf/Xaj0OZiCJ9LOabhzxMl+DHPpoeoUXaRsTCzITJGbbUIQFjn/N+sdpmWlkEI39r2BXj3wWkiqq3pbnyqSqQW48Li8Wyc+fOzZs3Hzhw4EEfE41GM2jQoOHDh3ft2pWoRB5dFWI9Xx9zWgAgVJ1EwykADACyJnvvFIpXJVpBPGy1HbBaY+wsABz296lYU1wHVsFyQX86y57BiraX/EYwZFFwY2XKorO6EwBAIWpO5Lc+Un/n+Hwu11viK6uMzBYtoumiYDwjms6KpnOYy7u7C1GEvAmhaEMo2hCKaIKpX/YPb7XC3vofn1u6GWFJECVvkwzkA5/L68QNvix4enq2a9du7969APDTTz/NmDHDkUfD83xkZOTgwYMXLFhQMzP57rvvJk2aFBkZuX79+rZt2xb/Wpg+ffqCBQtGjBixcuXKp556qmIC/saNG7t27Xr4mJycnK+//tol4KsJe8KrQsFfhLyRaLmOKI2s5XlEPTBIW0swiuL4vILLLOuGkBljEiEBYwKARIjDeIhCPtPDvQq/3QwHtDeHXb5fw7vUe91FtFwTTRcon1dryX3QxSOpE/f3igv42saoUaO2b99utVZ7WWbVCvhP/tFvuW6Z313d/ptk7caskG/rS6JHU5aSbfl4ARoPNGTkFmVqffUBM3Gwgg7+lA74X+XnUJv5I2fbvwUHW6s6CKZnzZYiTRuv5ffctPEYA4BKigL897CizUvi6ynxBgAJJbQPSyfR3T/sZEtSnOkyQ8rnR61UUerH8kbKAmbF5AlxhdtzkIQI+6GxxyDfRx9Tu8nOzv7ll182btx48eLFB40JDg4eMmTI0KFDo6Oja3Ju98MXHmJvDC6+hVR2lDbZU60XNYiiScQBVJF6FwCG5OTZMO7ByJ5mZM0kksqsbaRZk//W7su2Z2IQ3w77yO1OQvjS219cNpwHgBCm3vT686RVUeyNbbcE0znReLaU4DzlQSii7+j5NujxfQZF6w3b5S535oYk4YuKVySyKZ9gS5zj9SNjg3XiBl8WlEpl7969HW3Vjxw58vTTT5tMJrlcDgCjR4++evXq+fPnH3WOKmPChAk//PADANA0ffbs2RYtWji2cxw3cuTIrVu3yuVyURRtNlsFHl369ev3559/lnHwE/NoVKvA9mTrpQ4OnxFJ+FeUb90owHRqeBohDmMAoAB4gCpX7w7uavh3QoLmNwCXenfhomapE/f3cjT7PXPmzMaNG2NjY00m08WLF/fv3x8YGFh7LKY5jithiF0neL6+rMAm9giRFSZZAUBan7Ha0+/Pn/7rGOdU7woGjegjAcxhLrtmJ1u96LnCfwoOnNef6uvzcnv3ro6N/Xxf6ef7yv2DX23KjvyzwM5jC4fVmuMyaQEAOHqSWAD2591/BFgFS5zxUgePp6rtHVQWJCHC1zShfSS536XdHneNTbf5vhf6uCdVKRxdoCZPnhwXF/fzzz9v3rw5OTm5xJi0tLTFixcvXry4QYMGQ4cOHTJkSJMmTUo7WbVDeTzNyZthy5WinxEhjdxQ3RdVEYSqmEbHGJtFnMzzNznTjwaTF0n0ZGS9GFkHqVRSthz74gQzYSODJt6/PVrdsYDV5tgzC7l8Ad9diFyePP+qMdZPGthU2XKQ/6hyXQvJ6lGyeuD1CgCAaBVNFwXTuaLgPJst6A4JukOOgQRTvygyr2hNyJtCDRa2ELJwUtNf0O4AAADM56yTRCxzREVE/RGnekeEgvIp39uvu0RGRsbHxzteh4eHY4wvXbrUsWNHABBF8caNGzU5mRUrVnTs2HHTpk23bt0qLqFpmv755587d+68bNmypKSkh5zhIcyaNeuR3y15eXk//fRTLckJevJA0jDa700ucykhb0z5jH7c0ykrSoL40VvzQnaes58cD/CUTFod6h0AVM96RvzS/Oawy7nfpgKAxwAfl3p34cJFCcoagZ8zZ86MGTOcgzHG77zzzvLly+fNmzd9+vTqnOFdvvvuu19//fVBe+Pi4nJzc50N6gHg6NGj1TGNaqqRu1LvGJfLNknostD0w5SCGaIkgFa0FQp+p7yH8HlbX/if6ejZoojWhLHPL3njBCKVspbnEO1ThXN4jOj5wo+uv2kXbQAwwG9Yf98hjzxkwv7CQ7dtAKCS8uM7xId7ll507URGMF09e9GoFtVdP4icpSkZM26CiBuf6yCLqqEC1GpF0BmtF69x2flnbibsvHJux9FDeXmlrbIAAECzZs0cMfmat2kUbSm22NYAIgCQvuOk4V/V8AQAQAC4YGcPW21HrNa0Oz2G5Qh1lkm7M7JuMpknWTXqAgMWMF/cGGJVylcxumMAICVky5tuJlFRXsAvGWsy7WkB0uCW6raNFM3LfSE2QzSeFUznRNM50XzJaUMNAEBICbfmhFs0qWhDKFojWXhl39VDYW9P4XPWPGia9+Q3EnJ5dMJ/IYV+6tSpixcvnj59+vvvv+/p6ent7d2vX7+1a9dardaIiAiVSuWU97WE7Ozsmzdvdu7cuTpO7qqBr3ZEG5f1HanpX7d6Ui7TG1cajMhRWAUAAI56eIenXXWg/yv/1sgrmBURjTCHNa/4ha5ujEiXenfhotqpE/f3Mgn4ffv29enTJyIi4ssvv7xy5crnn3+OMT5+/PjgwYOzs7P37dv33HM10QVk8eLFU6dOLfv4CqTA6fX6s2fPPnxMcnLy66+/XuU3+Bu9zolmQfJvdP+c7J/zhjVgryJChkUb0+rC9aOjmvY97nw3Z7Y1aRKcIQmdS/m/VYUTeLzYRdvXt75Q0+7dPZ+LUjQri432q38UnMqwUwh4DBISLe6pfj6iNvpmVQzDAa3ptM5/ej1E1+0bNhZE3da/DH8cxtzdnGrsroptHrzj7Mldu3aZTKYHHdu2bduhQ4cOHjw4OLgcPdgrCZv4Bq/dDqSbvG1qGRttVh83OO6I1XbYaotjOccXAAHQTCLpyci6M9KG1eAeahUs2fYMOenmKw1wbvwg7rVCTgsAKkq9tMl65/Z/Cw4aeb2fNDBK0cytjIbtmBMt10TT+aJ/1iTHcokDRHkSilaEojXh1ppQtKryBUo+ezWbPO2eTYgi5I0xm3s3oQmRgAWCiZK1OPGQP4A6cYMvC0ajsWnTpqmpqd9///2ECRM+++yzOXPmtGzZsqCgIDU19fPPP585c+bjnmPN4RLwLu7Hod4JAAIBjwEAHPXwNabhXerdhYuapE7c38sk4J999tljx45du3atXr16DpsZx1FpaWlhYWG9evU6cOBA9U8VAGDjxo2Oqy9durTEqsFbb721e/fu4l2sAgMDy3v+wYMHPyTIX4LqqJHbYbZ8UqCbgK6/kT4YABNMfVmLs59+PHnu/KKOVh2iww8vL0SyekyLU1AXgsmlYuB1R/P3ZdrTRge9JX9wjOvhOAQ8Q6NXouTrr5ijPOm/Bj/MUhFznGi1Y6sNY0z7eZfYy6VnG/cfwzyP7axTaooWK2CMZFLP14eQHvcU7toTbhv+PAp3/gaQTIooEgiCYGSESqHq8xQqIa4w5vMKEEUiRkaU535f+FtO3vfpgXPru7WrvdX7DyH/u02mo6cBIcpTjRFCgEQ7KxpMQBA+U8ZD0/p//PHHli1b9u3b96ASGIRQ+/btBw8ePGjQoBrpIyVyqTNIr+GEvHH1X6usZAvC31bbUas9xm633/mrC6WoDT6elXG/KyMm3pBkuZFtSw+QhTRXFVkV2EXbW1eGYcAAECGP/KTBQuf425ZEDNhXGvBIVY8Fg2i64NTzJbq7IWkQ4RZNKFoRimjCrWUVNHXDvO1KN9ESh2hfzOU4tlG+4/i8n515AYhgsGiVRv1Kuvd6yJnqxA2+jOTn5//www+dO3fu1q0bx3Gvvvrq7t27BUEYOXLkd999J5NVlz4pIzNnzpw3bx7HcTVwLZeAd1GClQbjMr2RRIgA4DAe6MYkccJllnUjCLMoyhD60dszWlpdD2Om44WWyybvCUEu9e7CRY1RJ+7vZaqBv3jxYseOHevVq1die3BwcJs2ba5erbIeS49k5MiRXbp0GTly5Pjx4ydNmrRw4UKGKQq6Okx3KiDaizNkyBCdTvfwMSaT6fTp06j89ailcjmXq6+h5HeKmi6zLAC4q9uj/PrYlgiIFARhw6bfnONH9zUAgCRsYd1V77GGsyuSv+QxhwA97zMohClT0uzOBGtiIV98S4qBBwBOABmF+kYwbhL0ZYwRAJQSNK65G5Gda/jjCF+oFw0mQWcQDCZsZ53H+s54h2keWfxsxkMnDHv/edDVlc91Ze4V8JbTseaTFx40nvbzlre7J9NYt22Pbvveoh8QIuQMoZATbgypVFA+nh6v9ifua4sqWm0EI7NeNplO6RL6XAj5JsrzVf8HXbF2Yr103XT0NBAEiCKfX+yThRCIovaHXwKXfz5kyJAhQ4bo9fpdu3Zt3br10KFDJZ7UMcanT58+ffr0lClT2rVr51DyoaHV5w5A0CFzqu3kFcSPJIcq3IYq3GwYn7DZj1pt/9hsmYJgELHPHf1uxtiOsaYayncVlKqlqi2o2hbfKCVk74Z/ct10Jdue4WxcBwBaNm9O4jSHsH/O+8UhAWOdu1iRpQm6eH4NIlWkujup7u74EdvTRfN50XRBNF0QzbHYni7Y04WC3wEAgCCYBoSiFeHm+NcMKuC9hyg6dJ79+gAQDM5tfM5Pd/dLg7E9jdQ8/3D1/oTh5eX18ccfO17TNL1161ar1UoQhFQqfbwTcyCKIs/zjx7nwkU1cNBqc6p3h2udyelLTxBmUTxms1WfgFd08VB0qcMtaVy4cFFNlNXEzqmTS6DRaBITE6tuPo8mPDz8n3/+mTdv3uzZfrzbQQAAIABJREFUsw8ePLhp06YqNK8eNGjQoEGDHj7GsUJfJQI+JpMdvls7vLH8/Vxb6jvxETtaXK3HAUBTiQQhhAFEy42Df3yblpbmGK+Q0wN78KS6J+n+dOWv/riwCRYA3Frd4TnvF8uo3m08nnpEV2rCAy/iVRdL5mBHaeiWJ2KMh04U31gU/ZYzlKc77etZ4hD3l3vTwf4gYiShkaQoeE7IZYAIUiGX1CuZwu0+9AVpkwaYLZKa2GbHvACCINrsSCJhWpUM3krqBdNBfqLFKpqt2M6KZototjj3yju0ZFpEFR+v27ZHt20PoilCoZQ3a2K54p0yMU63/ZLXa1JJoDcTXVvMIx+O8fApAABRpLw8VC/0lNYPFe2s9eI1475/MS8IOqP1wjW3Tq0BQK1Wjx49evTo0fn5+b/99tuWLVv+/fdfURSLnw1jHBMTExMTM3Xq1LZt2zqUfFhY2ON4Z48NGUK9GFkvRiYC2DFmin0RDc/JT+C4KJp+ipF2k8laSCXVHZpvrmrTXNWmxEZ3WtPTq2+SOT7bnoGKNVrPtmd8njAZAPlJAwf4DWt573KAAyQNIqVBpMbR7lEUrYlF8XnzRdF8VbTeEK03IG8LAACiCCaqSM8rWhLyJmVc0yTV3UiP3kLhPodWv2cfQWF7BiCJJGRWeX4NTyAPuuO7cPFfY5xS8bFWxwEeqXSb7q5GdzztXsvTXmE5X4p8+b7FdxcuXLiobsok4Fu2bHnmzBmj0ahUKotvN5lMMTExzkYvNQZJkp999tmzzz47YsSIjh07zpw586OPPqrhOVQJSYU8AIgA+r35gpE3J1kSghEB0BgVitZEQDRgbt3qu62MBvYiFHKaDpv3+KZcEVKttzGIoUyRIVkHj24dPLqV6wwyCn3XVRZ3Ml4o1It6o2hjAeC4b9R1VYCStQ5NOeXWOVoSHuQYbOFhxUXTK02e6hPoS7jJSZWCdFeRaiV66Bo5oXRT9upU9ikhCS0vj4qWt20ub1sUk8eCiC1W0WwVzBbRaAaAEukAAEB6uhNujGi2CoWFssDjCIea41rqD9N8fozUP91/3gfShvesfdiTUiwxl0i1kvRQkZ7ulKcHqVGj6k+ufjj260kAQHl7+H/5Iaksyn9mWkTJo5tlz/4WMLbHJzkEvBMvL68JEyZMmDAhMzNz+/bt27ZtO3369P1K/syZM2fOnJk6dWp0dPTAgQNfeumlRo0a1dj7qg0QAMy9y4hPMdJ0no/nuHiO+8FgUhNEZ5m0GyPrIpNWR1j+QZCIHBH4+v3bpYTMg/bKsWemWm9dNV50Cvhce9aatGUygglkQp/2el5DOwthCIKJJJhI8B4GAIBZ0XxVNMeK5oui6aJovSFa/s/efcc1dbUPAH/uzb03OyQk7CV7CwqiCCruUVetrW2t9vd2aOtra3d9+3a93ePteO1etlatbW3rRq2j7o2gIHsom0ASAmTc3PX7IxQRUVGRoef78cMnOTm5OReE5LnnnOfJ4a05ACsBADAKl0XjinhcHo/L43FZ5GWS21MBb9nMfwl0FWDEheXuPARHFem1qH1tuZvPBx9cXWrGp59++gaNBEH6vi+bmttH705KHP/OTeuM4de0WJ9Tq3pziAiC3Hq6FMDPnz9/586d999//w8//NDWaLFY5s6dazKZrjhlfYMMHTo0Kyvr8ccff/HFF7ds2dIf67402DgA8FaI6FIrANQNoBjBEUwS4qb9NAgiVWqzqXDzX7lt/edOIQnPhbg04pJH7GOq7OU/VX2b13KawqkvYn/pSmo6AOAam0UuCrgwOBlak+e9y1n8CXAJRXi6VcvD8wCUMvKBfwwThwxo63+ihvk0o/lkLWRFRbye4IL3pY1jtjMthI4iPShMKceV8sv8+inHDleOHS44GK6phTOZOXOz9ViTaZNVPT6M0IVTA3w79Dev+9N69NQFTRgm0qgInSsV6Ov6j9kY0QvBPG+xAYBq6pi26N1JEhtGBngzZ6scFZcshejt7b1kyZIlS5ZUVlb+/vvva9euPXz4cIdIHgAyMjIyMjL+/e9/R0ZGzpo1a9asWYMHD+70gDe9p11Uj6mUJ2jHPju912Y/y7LpVlu61YYDRFHkCIkkVSLugWn5S9GQ2rcjPrdx1gaH3ktyfq+TgakvtuQLIGQ3n8QBn+01z9neyBi31q9TE66+0oBoRRyOUbhiMK74+4fL23hLdmswb8nkbcW8JZO3ZLY+iotxaRSuiMflcbg8HpdFtY/nMUkg6bmQqV6GkR6Co6a1lVALjiqMdCd8nrzh34te9cwzz1xVfxTAI7eypWqXKpa9SyHv8FHCGcOvarFMlaEZeARBelpXy8jNnz9/5cqVUqnU3d393Llz48aNO3nypNFonDRpUnp6endtCL82v/3228KFC41GI9yYxHIddGOSmwIj+01Wy5IhSmPkAa6RrV4dvpyzhabpXm98mdWvIgNe/3lj8fxH/ufs7OeB528KkA7KwIh+syHqf2VvnGo6IRXJbnOfPcV91qW6CQzjKKmgC8vshWV0YRlnNLvMmqC5d3pbB5aHcWvqKpo7Bm+dkhDYo/GKL7Na7KxweL6Hm6yvXNkxrKg+91g+qSNDtw6WhF97cThbbkvlM4Vuj/ipp59PxcfqDZZDJzmjmTOZWaOZrTdyjU3gDHcxzPezVwn3C3YNNG/bZ83IIbQakU5DuGtJdy3hrhVpVNCtv8vn7n1ScDDaR+5Vjuu4uqFm6X/p4rOSmFDPV5d08WgNDQ3p6elr167dtm3bZfbE+vv7z5w5c9q0aWlpaQTR1V1CN59ylt1ro/fb7cdph/3vP4xKHB8uEY+QiKfKpOJe/bvdXoNDX24rNTjqh2pGqAi1s3F3Q/qqqq+dt+f7Ppqmbc1aSvP2nOZMV1LnKfaRilo/Ogtci2A5xVuyeOdXW0n75Pat8bw8DpcPxOVxuCxKEFh7VqLA1GGEWmAbAQAjXQXGSAV/SrjN7cqY+0WSm05t2LChQ8tnn322Y8eOQYMG3XHHHQMGDDCZTH/++eemTZvuvffed99919e34+XCHsayLMdxPbMhHyWxQxAEucX1i/f3rn66/fHHH6dOnfrWW2/l5eUBwF9//RUcHPzmm28+/PDDvRu9A8Ds2bOTk5PXr1/fu8O4BuGuxH/HqFkDU9/I4lKR17yCfwvAf7OLDd4AAIIt/5ffdrZ1vnMCKVIMZquXtd7HSJFuNi4N65WRd9Fsr/kDVYnD1CPbPme35yirtB47Zc8ppIvPta8xhitkpI9n+54YgKtU1OS44Ol2VqA5AQNQiS8I0aUENjlEMi1UUmfl+1r0DrzA6B1FU06Gpl97DG/LaWneZ2reZ3Jb4OvzViguwQGAcNe6zBzfvpvA8ZzJzDWYMDHVIXoHAMuRLHtOYYdGjCQIN60kJlT78JxuieRxMcU5mOZtexWjkjDy/F8bprKWLqsAANLjcoUDOtDpdPPnz58/f351dfXvv//+22+/HThw4OI5+fLy8mXLli1btszT03PatGkzZswYO3Zsr2fS7nn+BDFPScxTyu2CcIJ2HLDT++32UobdbrVtt9paeP5+5XUnde8mOspdR3WsGJfqOo7AySpbeRPbGKGIbWvfql+3se4XACAw4tng10PlkQCAiRSgGt4sjVB7LcIAE7gWwXKat2S1/rOVXDA/j5G4NByTDBCYOhA4AMBIL4GpweVxhHO5/k1txowZ7e/++uuvO3fufO2111566aW2xsWLF3/11VePPPLIqFGjFixY0ONjvABBELfylTgEQRAE6aCrM/BtOI6rqqry9PSkqP6aBf06dfsVessxc8GYE4ABCIBTjrAt9zpXmpuahKCp5r+zpMHRlcqYkAtWv4q0s8Sh33XLGLpFLV21ue43X2nAJLeZV+zM2+wV//e84PweYhjl5yUODxSHBYrDAklv965Ejy/uM6/JtfqpRHvu7eZi0d2uLXr3/k9wyz5T0y4j6UFdewwvgP7LiqoXiwWalw5UBq6IkYRe9RI+3man80rYeiPbYGL1BlZvYOsNXGMzAOASsd/373Qog2f+40/H2UrCU0d6uBGeOsLTjXB1ueKPqe7NL2yZZwBAHBaovmuKONhfYBhrRk7jms1cUwsA6BbNVYxJvtrBtzEYDFu2bFm7du2ff/7pcDgu1U0qlY4dO9YZzHt4eFzzy90Eqlluv91ewLD/p5T7/x0U7bHZf7VYB1NUikQcTpF95aLXJdTYKzfpf622V7awTUuCXvSTDHC2/1bzY7r+DwqnAqTB/xywVEWcLxshcC2CNZu3nGr9Zytqv/UdMAwAB4GXRG/DlUldHEa/uELfFePGjSsrKyspKbn4oaioKG9v7507d1780M0KzcAjCILc4vrF+/tVX9UWiUQdSjEfOHAgNTW1+4Z0yzGtrQUAEIAjMQcmthcFS8JKBF6yPnOEg1nr7BM+QBSf8hAm9gcAYAxMzacAQLjd3WuDvshW/brfa1fxAjeADukQwAscJ9AOXHZBWmNcIlZNGyOwrCQmTBIZgstvYNJjAeC2n/U8Bh+O1UTpLpna6gYx/Hg+evd8eoCw2L/0vmzz1obCSSfD0gdLIq8+hsfA/VE/RbK67P4c2+nm/OFHvf8T4r6oY578y8OlEung6A6NAu1g9QZMJu1YxB6gefs+1nBBhUWMJAlPHempkyUPVozsJKM4AChGDrFlngERTheW1b3x2YVngWEUKU2M7fSJXaTVap1z8o2NjTt27Ni0adO6detaWjpWJbDZbJs3b968efOiRYuGDRs2bdq0mTNnhod3TBx4K/AmRHMUHf/L7bHb/7LZ/7LZPzCDK44nS8TDJeIUidijt5MgdspL4rvA/6mL230lA9SkayNjLLbkmxlTWwC/Rf/bXsMOd7FnsCx8RtBnOIYDb+etObzlFNe4izNtBUEA4AjdnV2P3m8mJ06cSElJ6fShkJCQffv29fB4EARBEAS5vEsG8CzLfvfdd8ePH9fr9aGhoQsWLHB+3rXZbIcOHaqtrTWbzS0tLceOHfv99997YOf5TSmj1qHbYzJ8WQkAIMLe+cbf1ZV8K88FADDc/tMP59PX3ffAv6nA1spGjtInAECkniBSj+/koL3kTHMmAKRpJ87wbL2swFtt1uPZ1mOn7afzQRB8Pn1VpG5XxQDDNPfN6PRQXaQW4wAgIy43CcxU681/bLdm5EDkXUUq75lr9Qs1piV3RBE9Ncto+LH63OLz0TsAYGI8aFVs6dxs87aGwinXGsMDyOKVEQeTKp7IN/5cW/lcoTWzyf/jCFx+XREXJqZIv85LzXu++TSdV8zUNrC19UxNPVvXwJmbmYoapqLGca764gDesv8Eb7ESnm7i8EC6oAyjSFwu5W00JsIwiuIam0EQNHOni1Tds4pbrVbfeeedd955p8Vi2bp167p167Zs2WI2mzt04zju4MGDBw8eXLp0aUxMzNSpU6dOnTps2DBRn4xUe8xStcswsfignT5E09Ust8Vq22K1AUAwSQwVi5Ml4iQxperzWUKHaUYO04y0cVZGcLRtpAcAg6OhwVHX4KjLbT6V4jrGnfIEXIIrEg87ms5Ih4xz1HtaTgAuJf1f7b2x9yZvb+9Tp05xHNfht4DjuKysLD+/q7syiCAIgiDIjdZ5AG+1WkeNGnXixIm2lq+++io9PZ0kyWnTphkMhp4a3s2srJH9+LWyJ1ZVYgIAgGmBV+5Q2UwZJZeXggBVev5IwRlnTwzD7rl3vvM2b8lm61cBRpIBb/TWyDv1WOALVs6iIbUC7bAcPm45kGE7nd+2s10cHoRLuzkFUbCGAIDIS8+o2zJy9B8uF2gHAPzvzNpv/Iav8038vFG754vcj+4ODnG/4YWOL47enTAxHrS6G2J4kUI04Nto1RjX8icLjGtqLcebAlfEyOKUV37m1SN0GmLEBVE6b7OzdQ1sXQPp3XFROtfcUv+/H87fxzHBwXAOBgAEALDYMZLQ3DtdNSWt28cpl8tnz549e/Zsh8Oxd+/e9evXb9q0qaKi4uKeOTk5OTk577zzjk6nmzx58tSpUydOnOji4nJxz5ueBMMmyaSTZFIAOMuyh+z0QTt91E6XMGwJw/7UYhEBRFLkMIl4mFg8RExRvZ335DKkIpkULthRMt/3kUnuM+voagBwp84n19hj3F5iKcgXyIdx1QD/FzHKu6fH2jekpqZ+8803zz333Pvvv99WzIXn+eeff76iomLKlCm9OzwEQRAEQTrofA/866+//vLLL2u12ieeeCI4OPjs2bPLli2TSCRisbigoGDChAmDBg2Sy+UYhul0uiFDhiQkJPT80HtLd+2RO/hNleSpfEwA5+73yt+ino8WlknKUkqmAjbgwy8rX/qhwdlz2LBhhw8fdt62507lmw4SXv+kejWAP2sr+a16hY804B7vB9saueYW4/LfrcdOOWNmwHFJdIh8aLw0MZbQdX/m/HWFtmd2N94eJv3vGPXFj7L1xqon3hBoh2JUkvquKYSHTuC4w/sKlmaLqigVBfwTQ10ejlfcuCJzhlU15xblAS/4vh3q/pj/xR14G1969ynnfviw7QnikGsvRWMvsjqX02Ni3Of1EPdH/bpWsO8Gatl3nM4rZmrq2Rp9h7X3pK+n58uPiVwvCJW5xmZMTOLS7k81JwjCyZMnN2zYsHHjxlOnTl2mJ0mSI0aMuO2226ZOnRoW1qfTQ/YADuAU7ThK04ft9CkH4/j7nWK0VPK5zrV3x9YtzKzpTHNWjb1KTii6krajg36xR64rGhoa4uPjq6qqYmJiZsyY4evrW1lZuXHjxuzsbF9f36ysLK22YwrMmxjaA48gCHKL6xfv753PwK9bt04kEu3duzc6unWX7O233x4TE8Nx3EcfffTEE0/04AhvTuZN9dKn80EAnsAoF4I1MBMiNVGehL/+FxaAcB/9+5F1AK0B/LTQ1jkQzrCObzqIkTrS59neGztsq1+/tnqFAAIrXFDKy3660LL/OGCYJDJYnpooSx7UXQukO+WMvUWXCMGbNu0SaIc8NVH3WOviBUwkGj46akNI/cvfnUz3HPje0ea9FfT/xmluUKZ6/WflwAvyISr3f3a+BhWX4v7/izgz+AhT5zD+Wuf1QuA1v5YkVBa+O7FqaVH9t5WVzxW2HGwMXBGDXXZzwY2mGDmkbV29QDuYmnqmRs/W1DM1ennyoA7Ru+NsZfWz74IgiDQupLc76e1OerkTPh6ktwfhrsVE1/UDwjAsISEhISHhtddeKysr27hx44YNG/bv339xITqGYXbv3r179+6nn346NDR08uTJkydPHjVqlFR6wxdr9EEigMFiarCYelSltAtCBu04QtOZtGNQu/SlTTz/sbnZUyRKklCxVK9VmL82LoRmuGZ0b4+i9+l0ut27dz/55JPp6ek5OTlt7TNmzHj//fdvqegdQRAEQfqFzgP40tLSqKiotugdACIiIqKiorKzs+fNm9dTY7uZ1bxXBjwIGJxZGhz7ZjFG4WJfcYgIo5sOAEBRXVBW/llnTxGGpZ4cBADA2x3lrwIA6fdvjOjNhb4llgIRJhrvNm26x5z27bJh8e7PL6QCfW/EfPvFRvqJ74yQ3R3V+cS1LSsfAFymj+nQrvFze1lTk5KV/3HCrKPV8FOuZUniDVlz7vff8OJZWZbjTef+mRfwWSRcdKGBqaWLZ50SHLx0oNLt0euttIxLcL+Pw5WjNecW5TVu1DM1NOXXVwqnYWKKGuBDDfC5VAeRWiUOC3SUVXAmM2cy288UnX8uSWgfnXupJHlXKzAwcMmSJUuWLDGZTNu2bdu8efO2bduMRuPFPYuKioqKipYtWyaVStPS0pzBfEhISLcMo9+RYFiKRJwi6bgL5rSDWdNiAQAwgwzDEsVUkkQ8RExF97dg/hYXFha2ZcuWwsLC3Nzc6upqf3//qKiooKCg3h4XgiAIgiCd6DyAN5vNbm5uHRq9vLyys7PR9fhuoZqos2Y2YwJEfn4WBKD8JJgIA4HlW44BwNr0c209RyUQIVPEAMDUfCLQ5bgshnDr5WsoD7ssqD8ZLs3kxfMv+ECPiXDZkOtKKn5VNBL8nbRLXsjgTGYAILw6qTBHerkP23/iF2XeX9EpU0Nu1OSqIkUdsj6+5PYsw8oaAOgQwzN6R9HUTHuhRRqrCN00iNB0T2589Qx3eZKL45yd8pNwLVzjOr3LZC2h6+sVH0VqldebT4EgsA0mplrP1uiZqjqmWs/U6NkGU+uOjHaYihrjyvUitYr0die9PUhfD8JDh11NIjqNRnPPPffcc889zrR2W7Zs2bx5c25u7sU9bTbb1q1bt27dCgChoaGTJk1yTsvLZNe+5eGmkSIRf6pzPWinj9F0CcPus9P77DQAKHBsMEUlisUJYiqWIsk+vGceaRMWFoZ2jiAIgiBI33fJLPT4RTmHL25Brpn3v4P2ZDRH7GggjCxG4eqZbgDAW7IErgWXhq5fv6Ot550TSe093wuOGKbqYwAgB7wNWE9MbtG8/Y2i5zSk9tEBz26uW8sL/BzvfzhKK5q27LEcyhAYlhHhLrMmiJQ3cJH89cCkErDZ+WbLxduq+RYLAGjkxP/FXpA6rs7CeVxfFvcOFMnq4HWdxPCM3lE05aQ93yKNVYRuHkxou7OyHeklJr3EAGD8qabiqQJCR/l9GKaZ1R+Kn2MY4eZKuLlCXMTlO9KFZ20nz1zwVJGI8NCSPp5UgI9qxtiu76UXiUQjR44cOXLku+++W1pa6qw2t2/fPpqmL+7snJb/5JNPxGJxamrqhAkTJkyYEBcXh92qASoGMFYqGSuVAEADxx+j6aM0fczuOMueD+YlGBZHUYliaoRUHEf19WtJtyaO43Jycmprazt9dOLEiT08HgRBEARBLuOq68Aj3QOD9fN9E5q58UdMgoOvXl3zr1myD7QH5ADn6qNOZa9w9hJh+NRRGq5xK11QBbxV5DpDpErtmQGm6/+ospdX2cufy11g4VqkmGTk8hY6qwAAAMdlQ+Ndpo/ps9E7AIhD/K3HGq1HslTTx7ZvFxjWejwbAKjQgPbt20rt//zTNNSb+k+qS6hrt/1eXBDDCxDweSTTwNy46L09zR0e5s31TbuNZfNzTL/r/T8OJ9xukvBJMWYY4ePOVNQwVXqmqpap1rN6A1OtZ6r11uOnSR93+UUJ8x3F50RurqS7Fi59ITIoKOjxxx9//PHHLRbLrl27tm3btnXr1rNnz17ck6bpXbt27dq16/nnn/fw8Bg/fvzEiRPHjx/v4dEfLpTcGDoRPkUmnSKTAoCe407QjhO04zhNlzDsUZo+StOfNzWne7kPINCbTt9SU1MzZcqUrKysS3VAZWIRBEEQpE9Bn6V6TVkTe/IOr6nBEnp1jajWMf/2Iub3AwDw0ysNbX1GjBzpET2TKX+Vt5wGXEwFvNYzY2tw6Lfp1ztvW7gWb6Ni2m9yuq4Al0oU44arJo8i3Pv6Tgrl+FTrsdOmX7aQPh7ShBhnI2+nDV+sZuuNVICPJPyCHZ6BakIrxY9WO2b83rB9jpufqtum4hXJ6uDf44tnZRlW1QiMYM1sthdapAOVoZsHEa43KnoHAEJLhmwY1PBDVdULxY0b9JyJCU0ffONerkdhmCQiWBIR3NYgMCxTXcdU6/mmFtnQ+A7dTSvXN/95AAAwQkR4uZM+HqS3B+njQfp4kL6e+EVbu+Vy+fTp06dPnw4AeXl5ziX0+/fv73Ravq6ubtWqVatWrcIwLC4ubvz48ePGjUtNTb2V19i7i0RtwbyJ50/SjmM0TQuCV7ttDq+YGk/TTLyYGiSmEinKm0C75nvHv/71r6ysrLi4uBkzZtzK/2kRBEEQpL9AAXzvYHkw2XgBAwmG0SIMOMG1gVEZj4Ictpwsbes28/aZpOcjrP5HwV5Kei3GxJ1UI7sRfq3+gRHObzwOzMW97a6qe9KUk0bg8v7xCU86KEo5cUTz9v11b38pDvanBvjydtp2Ot+5qF732Dy4cNlzuCux4263j443n9YzSqqbV0QrUtQhf8QXz8oy/lILAD0QvbfCQPcPH9U4bfV/SuRDb+YK5xhJUAE+VEDnefLkqYms3sBU1bENJqaihqmoaXsIl0q8P36R0HZSidApMjIyMjLyqaeeslgsu3fv3rp16/bt20tLSy/uKQhCVlZWVlbW+++/LxaLk5OTx44dO3bs2CFDhhC38LSzBsfbltm3V8Kw+QyTzzA/t1gAwEMkShBTg8XUYIoKo0gUzfeYHTt2hISEHDt2jEIbHBAEQRCkP7jkx8q9e/d2yGNnNpsB4OLkdgBQX1/f7SO7uXGCIAAQONDHzcAJJ8YpU0pzcHlLTaE2017g7INh2MyZMwEXi8NWcKatpNdjPTAwC9ey37Azw3wYAAiM5ARWEISjKS233f+oi6KHLh90F+1Dd5Febo2/baNLyumScmejJDJYu/Ae0tfz4v4uYvzV1GuPcjPrHG8dbn45RRXr1klk7ozhS2afEgdLQzb2SPT+N8pPMuDb1ooS5YvzGjfUe78SrHvA++LE+DcrSVSIJCoEnAXtqvVMdR1TVcdU1TJVesAxXNwxbmn8eYs1I5v0did9PFun673d5XL5tGnTpk2bBgDFxcV//vnnn3/++ddffzU1NV38ijRN79mzZ8+ePS+99JJKpRo1atS4cePGjh3bvrTHLe4HN222g8l0ODJoRybtqOO4dKst3WoDABmGxVLUIDEVT5HxYsoFpV+5YViWra6uXrhwIYreEQRBEKS/uGQAzzBMQ0PDxe2dNiJdJ9CVrH65wAtPerUQGLiMNqpGCNj9rrKS0wCQfpDmgXf2jI/xCfD3BQBcFoPLYnpgbDnNmcsrPmlkWqtqTfWYradrDpn2sMD/3rDmn4rne2AM3QnDVFPHKCeMsOeXsvUGjCTFIf6k91XsUv451/rm4aaH4uSPDFLY7exTAAAgAElEQVSIRVcId/8qp0/WOvZV0J0G8ACgSFHHFKTgUhFG9lrkjCsI1sSUP5HfsLLa/+MI2aAbUkKvz8LEFBXoSwVeoWgfXVTmKKt0lFW2eyZGaNWkj4d0cIzqtrSQkJCQkJBFixZxHJeVlbVz586dO3fu3buXYZiLj9bU1LRp06ZNmzYBgLu7+6hRo1JSUlJTUwcPHnzLZr8DAALDBompQWLqASUIAKUMe9LhyKAdWbTjHNu6bR4AMIBAkljioprQ5cSESNfxPC8Wi6urq3t7IAiCIAiCdFXnAXxLS0sPj+PWwdavYqo+AoAHnIHkbACAuTSALwDAtty6tp63DWvgbfm4rIem7Crqcz+q+o/wd0ChIbWT3GbaeOvJpqN2zpZhPnymOSta2XF3cd+HUaR0YPi1PVeEg40Rlp1oWVdoe26oanKw5DLxVqGRBYAiI3u5A6p6eSm17zuhypGaimcKrBlN+aOOu87x8H03rCeXA/QL7v961HG2kqmqba1mV1XH1ujZBhPbYLLnl6qmjGrbfyESiRISEqKlLotHjG+SUvvzc3bv3bNr167i4uJOj6zX69euXbt27VoA8PLyGj16dFpaWlpaWmhoaM+dXt+DAQSTRDBJ3CmXAYCR57NoR6bDkUU7chxMKcPus9nbAni7IPxusfqIRAPFlCuanL8+FEXNnz9/xYoVJ06cSExM7O3hIAiCIAhyZZ2HE3K5vNN25PoRujlM9cfA0+mm24zVgRMOm2w+1Ia71Y+0fO9oMe0+fj7f74wpg3BZVA8MSWCYps1/2bZsC50oORvMOkQsANzt/QCFiylcPMVt1h+1qwFgTfW3r7i/juOESKWAW2Pm8M4IWaCaeGV/U76BeWyHKf4U+XyyKsmr87WmJjsPAEYb37NjvGouU3TKUZqat0r1n1cY19Q27zL6vBHieo8X3BI/0i7BCJE4JEAc0q5OAc8zegNbrRepVR3/8/N83WufCCwHAEkYNtzN4z/zn6iVYAdryvcVnNlz4lidXt/pq9TU1Pz0008//fQTAPj4+IwePXrEiBEjRoyIjIy8cafWL7ji+BipZIxUAgCsIJSybEC7JALbrLY3TGbnbX+CGEiRAykqTkxGkqjg/LV44YUXKioqRo0atXjx4mHDhrm6unboMGrUqF4ZGIIgCIIgnbp1Uyv1FkwSSHo+ylR/7ENVvmF+e3Rdw++Jiq0y2+Pmj7YeI+106/xtkA8+eNwncOODKsvhTNPK9azeQAG4U16FojIACJFHJKqHOztMcp+5v2FHPauvtldu+OqpxBNykVopTx2inj0JV/SPhHbXI9GT2jhbtzbP+r8TzVl65p4NhrEBkueGKUM0/fh3B5eLfN4MdZ3rVfFkQcvBxrMLcuu/qfR9N0yedDMnursuOE56upGenWQAARx3ffBOW1YeU1nL1jWwegOrN7gATAGYIg0QPzqu4Y4xzppz+/bt63TDPABUVVU5U9kDgLu7e2pqqrM6/cCBA0WiWzqhG4FhYeQFK0TGSaXlKi6DduQ4HOUsW86ym602ACAxLJIkYygyliJjKSqQJNDsfFcEBgY6b7z33nuddkBl5BAEQRCkT+nHQUg/sjLH+t3p87sSpNj9X/n9FCc/Ncp368P3zNQPZ8fQBwFg5V/nN3lOnzQQVyTc0FHVVxf+lPmBX5ZjsF5OBfhw9486InwIABhg93o/hP197YArqR69QfTrbQAAu0ebY85pJfXNTZt3W49ker66hPDU3dBB9gUiDO6Okk0Pky4/Zfk6q2XXOfveCvvvt+tiLtzr7uCEtq/9gjRKEbYtwbimpuqVEsvxpoKxJ1znePr8J4T06VhWDbk85fhU5fhUABA4ntUbnEny2Go9U10nHRwTGxsbGxv7xBNPsCx78uTJ3Vu3/fntimN1lRbG0enR9Hr9H3/88ccffwCASqVKTU11zswnJiaKxehHAwoce9xFCQAcQDHDnHYwp2jHKYejlGFPOxynHa3fVTmGRVNUkoRaqFQQaGb+0pYuXdrbQ0AQBEEQ5CqgAL4n5DYwFU2c87YEp4crD+xsHHu3bs2/vN/6opo1OaS3WTbyPBw6ej5B4PRpk2jzwSVMZI7DsdnTXd3dWz1P/PnDD6qNVl/eysrHJz2kHJv88bk32SYWAEZqxw+QhTi78XZa/97XEUZR6FBdka7BLuEzX4y8g5lk+PYXOr9U//433u8/D7fGNlQZgS1OUNwTJfs0o+VwNQ0gtP1MneosPABUt3Ad2nUyXEr01fgBA9d7vdQz3Gv/e1b/Sbnx59rGDfrgtXHKtI7LaJGuwEQ46eVGerlBQidZJwmCSEpKSgiLmFtJ00ZTtkl/pL76SH3VCUONle0k+x0ANDU1paenp6enA4BEIklISEhJSUlJSRk+fLhOd/NfO7s8EUA4SYaTpHPnfAsvnGEcOQ7mNO3IdjA1HHeMpo/R9CiJJIZqvdZWwXIlDBNBkZ639rqG9t5+++3eHgKCIAiCIFcBQ6vjrlZOTk5sbCyO4xzHXbk3AADwAlQ1t3aWNnwo0791cZ/jZ7i0h5qdt9VK7NxWF0IEsz02NVAhu7zcld0aJAsM88aWeWWB9kij54L4V1w0Xnktp98vedn5qEwkwwD/uycrOByA4yChbLwVAHAMfyviczfBtfrpt5naevfnF8iGDOzGsfULVc3chF/q7WyXfndkJLbrHnd3WV+/zOE4a6t6paRxvX7A8mjNHVeRqx+5aoLA1huZmnq2Rs9U1Vkrao6fzjpclH+8oTrDrG+22a54AAzDwsPDhw8fnpqaOnz48PDwa0zTeBMzcHy2w2EThEkyadv1szl1Dc4pei2OR1NkNEVFU2T0NcXzhw8fHj58eHJy8qFDh7p14H1IUVFRVVVVWlpabw+k51zD+zuCIAhyM+kX7+9oBr4n1Fu5w9U0zwMAaLnEFAAAyLNGRcpyBR47IRqeAIe3HrS39Z+Q4kKIoEbkXSvysfF8Iy8ouzX6w0hyftBjtYQhafQ051J5G2dte9Ta7jbgABIA4IFv3ZwvCALN2TEppRg33LRqgz274BYM4BUU5iMXmWgeABheaHYIOAaCAAKACMNcxBfMt2tluLz36sZ1HTVAGrgihrfzuAQHgMKJGXSJzfP5Abr/8+nFunc3Jwwj3LWEuxbiIgDAFcAXYCbDsHUGzF1bUFR48ODBnTt37tmzp76+vtMDCIKQn5+fn5+/fPlyAFCpVElJSSkpKQkJCampqRqNpkdPp0/SivC0iyrPPaxS/NJiyXEwBp7fZ6f32Wln+6MqpXNZPtJGEIQ333xz7dq1Foult8eCIAiCIMh5KIDvCa8eaPqzrC0+j3h/wORJ6q3ltgE+h7WqsfuB4nCa33T4fIw0YpgYgF7m8rQNE8+Uy3yJ7l/tGTAwpV2KbXDwdBefKIDACA4AcCb04kydJ+W6uektfIn5gnJx/N+T8ZwgGO0XzMwb7bzRxsvJ/rFk1xm9AwDpTrUcbKx4skC/rNzrpSDXOz1RmvobCiNJ0tcTAKKjo6OjoxcsWCAIQl5e3t49e/du2HToZEZFQ+fZ7AGgqanJWYseAEQiUWRQ8LDEIcPHpCWnpoaHh9/K1eY7GCeVjJNKAKCK5c4wzBmH44yDKWCYbt+j1I8IgvDqq6+uWrVKf2G5BI7jbDZbbGxsbw0MQRAEQZBOoQC+JyR6iksaW+O9WOnhOHmuIGATXdPZQWpBwIbQR2ob+LyC1nWzIhxmDLXTILmj5Ve5Ztzrrurr/fQtCLzDsdW8aY9h+6KA5wJlnVSc9pb4B8vCOaFjDXOmpl6w2QhPN1wmdbaIRRJXUgcAXGMTALS131IC1cSjgxTOGXinqhbucKWD5QUlid8WesG8n06Keyv7R/TeXuCPsZpN9dWvldjzLWcfOMPWOdwf8+/tQd1aMAyLioqKiop6dNGjAFBVVXXgwIFDhw4d2LfvdHY2e4klvhzH5RQV5hQVfrtmNQC4SGQJQSGJMQOHJCUOGz/ed2BPVKbs+3wIkQ8hmnDRFP0t6Msvv3zttddUKpWXl1dRUZGnp6evr6/ZbC4qKho6dOiyZct6e4AIgiAIglwABfA94UQtXWJqjY1TyRwv8pzzNuHa6Lyx8yjblosgIYrQqjEAOsFxfLiSu87Ij20w6T/5YWXsiYJQCwZY+6Xy7flLA/8d+u7F7Y1Z6Y2/pstHJLot+b8LHhAEy8EMABCHBV7fAPslAodnhnZccJu6Wl/TzLlK8DdHurA8vHLArJXi98fItdL+ObmHgXq6m8ttOuNPtYYVVdJYBQDQZTZHhV2ZqgYcTer2NB8fnzlz5syZMwcAWlpajh49evDgwUOHDh0+fPhS1ekAwGy37s49vTv3NPy6CgD8vL2HDh+elJSUlJSUkJCgUCgAgLfTuATlt79FLV++XKFQ5Ofne3l5zZ8/32Qybdq0CQDeeOONH374ITo6urcHCAAwf/78tWvX2rqQHgJBEARBbnr9MoBPT0/ftGlTUVFRcHDwI488MmjQoA4dnn322bNnz65du7ZXhnexV1NVYwLEHA8AQAgP2y0/Svg6u0MhoVqyMh6MTVix8+j5uHr8MKIB1+n4BpHbPYTkusJj67HTDZ+vauFaimZaFLji4QFPRSnjruoIitHDzOt3WPafoPy8VTPGYSIcAASGMa3cYM8rEbkoZcnx1zPCfqqR5h/YYjTa+faNdRYOAKosXNpPeo6H6hYOAD4/2aKV4Mtvc43WkZ0fq2/DRJh2npd2npfz7tmHzliOmiURcs+nAjR3eqK98b1FoVCMHTt27NixAMDzfG5u7pEjRw4fPnzkyJH8/Hye5y/1xIrq6orffvvtt9/Audg+MjLGRRdh4eJ8AgZGRCn9vEhPN8JTR3q6ER5uIjXaFn7zKy0tTU1N9fLyAoC0tLSXX27NZrp06dLvv//+9ddff+edd3p1gAAADMPY7fYr90MQBEGQW0D/C+AfeeSRr776ynl7165d33zzzYcffvjEE0+077Nz586srKzeGF3nPOSiOyNkf9+T1Zx7SVKzGBMEAIgfuI4X+N3HzxeRGjVMoeMbrLj8U+XjLwFc2wy8wHGmleubtuwBQdAlxr3iv8TFxUNFqK/2OISbq/bhOQ2frzb9tLF5+z5xWKDAcXR+KdfUgolEusXzbs0l9BaHUGBkO81Cz/IXlJcTBGiw8TN/bxjtL3kwTj7Um+rBYXY/jyX+lUuL7PmWswtyq98s9VgSoJ3njffTJQY3CxzHY2JiYmJiHnroIQAwm81H2mlsbLzUEzmOy8nJyXHeydovSsdDVJpYtVusxj1W4xbpolN4eXi/vxSX34q/47cOh8PhXIgBAAMGDKiurrZarTKZjCCI1NTUHTt29EwA/9lnnzmvK3UqNzcXAEaPHt3W8tdff/XAqBAEQRCkD+pnAfwvv/zy1VdfBQUFvfvuu7GxsRkZGc8888yTTz4ZGBg4Y8aM3h5dV5WK7hBZvxkoO8XotaS7ITOPMzS2hoIaFRYWowGh/lvFwl9oRbPB9J5Wc7UxPNtgqv9wOV1YhhEizbzbVVNGwXVksVKMHiZSq4wr/mAqa9nDmc5GcUiA64N3ikMHXPNh+zUfpejwPHczfUEAf9cGg97C+SpEq6Zr2xrLm9iNxfbNxbZd5+y7ztmjdeSiwYpJQf115616urvLZDfT2traD8/Z8y0VTxXUvlPmtsjPbYGvSNXP/pjcrFxcXCZOnDhx4kQAEAShoKDg+PHjx44dO378eFZWFk1fMl0lJ/AFZkOB2fDbuXwAIDA8VKMb0lQyeMiQ+Pj4+Ph4FxcXADAuX2s9eYZw05IeOsJDS3joCA8d6a7FlfIeO0ekG4WHh+fn5ztvBwYGCoJw6tSp5ORkAOB5vqCgoGeGYbPZ9uzZc/k+V+yAIAiCILeCfvaZ+9NPP5VIJDt27AgKCgKA8PDwqKioESNGPPLII2PGjFEqr3fB57lz57Zv3375PlVVVdf5Kg024dvKF38OmUO4mgFg19Hz0+8jhsjchPoGMmCNYh4GkG61iTHszavJY2fPLa753zdbkitrx/DP+L+iCou4ztECgHRQlE98pKOihqmsxUQ46edFet/qdcJVYlx14a5hDzmut3BeCpGf6vwlFz+VKMVX/PxQ5epc66ocy5kGZvGfpqwHPBVUf118jpGY671ernd7Nm5uqP3grDWjqfrVkroPz7k97Ov2iC/phbZS9yEYhkVERERERMybNw8AWJYtKCjIyMjIyMg4ePBgZmbmZRbbswKfZ9TnrV794+rVzhYvL6+EhIQIKx9hx0JVrv5yVfv+uEwqHRTp9uQDN/SMkG43evTo//73vy+88MJTTz01YMAArVb79ddfJycn22y2Xbt2+fr69swwnnnmGQ8Pj8WLFwuC8PHHHzuvQLVZtGjRxo0bKysre2YwCIIgCNKX9bMAPj8/f/jw4c7o3Sk+Pv6TTz75xz/+8f7777/22mvXefzHH39848aNXel5mQ++V2S08bnWaNOeka5j9nCYdNfRlraHJg0TA/AegW8l86q9NjsGsM5ifVSl9OtaJTnBwVS89+nq2TXlfrQEk+BBntc8yI4wjPL3pvy9u+2ANx0lhQOAsrPI3FWKP5agWBgv31Jip1mhffTeaOdVYrz/pYTDMfV0N/V0t+Y9xtr3zzbvNdV+cLZu2TnN7R4eTwY4M94hfQ1BEM4adfPnzweApqamkydPnjhxwhnSFxcXC0Inu0La1NTUbN68efPfd901migf/yitR7jMJRyXBvM8llcKgtBhvY/hi5+YqjrCQ0u4awl3LeGhJdy0hFYNt3Dltj7l5Zdf/vXXX99+++2AgICFCxc++uijb7zxRlZWltForKmpWbhwYY+NZN68eampqfPmzXvooYcWL1787rvvSqWt2zdkMhkA+Pj49NhgEARBEKTP6mcBvM1muzhyvv/++z///PMPPvjgoYce8ve/rkpXzzzzjKfnFYJek8m0du3a6ymtbLLzcjtv/PI+9bCjNrAezTlfvG10IitSpYldp3wsCP9sMB6y00ocV3c5vMNIonFKaLnfWS3l9njgv69h0ztyzQa6k4cq6Vj3Syaro0TY7WEXbCc+pWdmr2vwlItmR0gfilPI+2FOOGWaqzLN1XKiSb+svHGj3vhrrTm9Pq4mDRWN7/tUKlVaWlpaWprzrtlszvjbiRMnSkpKLv90vcmkN5n2/H2XoqjI8IiB95cPHDgwPj4+NjbWw8MDBMGaeYYzmiH/gqNhIpFIq1akDVXfNaW7Twu5OkqlMiMj4+uvv46IiACAl19+ubCwcOPGjRzHPfDAA88//3xPDiYwMHDv3r1vvfXWa6+9tmPHjlWrViUkJPTkABAEQRCk7+tnAXxoaOiRI0fq6uo8PM4v4cYw7Isvvhg6dOiDDz64fft2/DomdkaMGDFixIjL98nJybnOAN5o592MDGtUn9s7rdBlNfN3/B4aIPL3IskBbwOABMM+07kuaTCdcTg6L/fcTob5sASXRivjAcMS71r0ZHNyoDREQaiu9DykO4kw59er+I/hLsMDVESZmV12okVG4A/H99ddxPJEVeCPMY5Ku+GHakyCAwacmT33aC4VIHV70EccIrvyIZDe5uLiMmbMmDFjxjjvNjY2ZmZmZmZmZmVlZWZm5ufnsyx7mac7HI5T2adPZZ9ua9HpdAMHDowOj4iI9Y7UeYTK1JJmC6M3sHoDZ2pi9QZbRk6HAF7guPoPv+ctVsLNlXBzJdy1hJsrodOIdBpMdJ0lNZFL0ul0L7zwgvM2SZK//PKLzWbDcVws7oUdMSKR6KWXXpowYcLcuXOTk5NfeeWVpUuXdsuRzWbz8ePHL9/n7Nmz3fJaCIIgCHLj9LMA3rmybtSoUStWrBgyZEhbrJ6QkPDss8++88478+fP/+KLL3p3kFdksvPqJgYAypui95a0n34nCI8HcVmU864Ew75yc73i0fIbT39+7l0AeLHxYR//OCrQN1Y5+MYMHOmCq7mw46UQ7bjH7Vi142AVPS30fGa7Rppvdgh+yn4WsVC+Eq8XW7e3MDV04+YG4AX9p+Wq0a66h3zV09zQtHw/olarR48e3Zb322azZWdnZ2VlnTx5MjMzMzs7+4pFuRsaGnbv3r17927nXQzDBgwYEB0dHRMTEzk8IdzTJ3pIx8lVwU7bsnIF2tHxWDhOuLqopo9VTUm7/lND2lu9enVCQoJz+r2Nc+36sWPHqqurZ86c2fOjGjp0aFZW1uOPP/7iiy9u2bLleq7Lt3nooYcuk+i+vevZIocgCIIgN1o/C+D/+c9/nj59+uuvvx42bBhJksePH4+Lay1s/tprr5WVla1evXrdunV9/N3XaOMLB0jL7tQ1T6nb+9j5AD4tyYX0vZqpBkFo2rT7C90XghwA4EvH948+60kF+moX3iMOCejuUSNXMNpfcqTKMcrv6uasMICh3lSH8nIPbDGe0jOxbuS0UOltwRJPeT+L5AFAEiGP2D+k/utK09q6pt3Gpt3GwB9jNLNu9dyH/ZdUKk1KSkpKSnLe5Tju3LlzZ86cycjIyM3NPXPmTF5e3uW30AuCUFZWVlZW1m4fPXh5eUVHR0dFRTm/xsXF+X72H8e5KrbByNUb2Xojqzew9UbWaGYbTHRBKVwYwHPm5ro3vwCeJ9w0hJuW0GlEWg2hUxNuriKNy/VU37h13HfffR9//HGHAN5p5cqVK1euvEwlwhtKoVAsX758ypQpCxcuNBqN13/AOXPmXPFcHA7Hvn37emXpAYIgCIJ0UT8L4AHg888/T05OXrVqVWlpafvPiyRJ/vTTTykpKcuWLSsuLu7FEV6RjRWsEtGOt/zGFGdnF7UukMdxGD11KUZoun4c/btf7aIONQe0XgKod2MLErHwE5W1L33s8fJiSWRw9w8dubRBHuSvM7VX7tcFk4OlpY1sdj2TXc+8c7gp0ZOaFiqdHCTRSPpT3i9ZnDLgs0jft0INq2ssx83yBBcAqP3gbPNOo3qmu2a2B6G9ZL4ApI8TiURBQUFBQUHTpk1zthiNxlOnTp0+ffr06dPZ2dm5ubkWi+WKx6mpqampqdm5c6fzLoZh/v7+4eHhkZGREREREbERkZETfT08BI7jTE0iTcdtQYKNZiprBAfjONsxPzlGiDT3z1JNHtXxKQyLkf3vja97sSz7r3/9q+3uhg0bLk7wbrfbf/31V2ftwF40e/bs5OTk9evXd8uhZs+effk+dXV1np6eajVKH4MgCIL0Xdjl50z6qdra2pKSkpSUlBtx8JycnNjYWBzHOe6Km9M7d7TaUXbGIvq4yDF4xn1v1DobB4bLT+U1AtbVT5bGFX8Yt+x+9/kqjhAwwF0pncGhl4nk/zk4tXnHQcJD5/O/l7Cu5a5H+iCaE/aU05uKbH+V03ZWAAAChxRf8TNJyihdP457S+46ZU5vAACMxFQTtNp7vVwm6TBxf7owgXQFz/NlZWXZ2dk5OTnOr4WFhZffRX8pGo3GWTE0/G9BQUEU1bpohbfamJp6rsHI1pvYBiPbYOIaTKyhkWts0twzzWXWhPaHoovP1b70EWAYoXMVadWEVk24uYq0allSnEh1daUTDh8+PHz48OTk5EOHDl3DSfUumqYlEsmV+wE8++yz77333o0eT9/hDOA9PDxqa2t7eywIgiBIL+gX7+8350SEp6fnFZPJ96Ikd1KanA18/VcKU1vjmInzux69N/6ypWnT7g2zDBwhYBj2ZOBLHmLvpfmPWDnLrml0Sr4XU1FjP5UnTYi5MWeA3HBiETYxUDIxUGJhhD/L7JuLbQcq6b3ltLdC9MbIXp4Tux5BK2Mbt9Qb19Q27TCYtzSYtzSI1IR6hrtmprsyzRXrh3n4kU7hOB4cHBwcHNy2g9rhcOTl5Z05cyYnJ8e56r6srKwrl0FNJtORI0eOHDnS1kIQREBAQFhYWHh4eNjffJPirphbFBdTuFLBGRuZ6jqmuq6tnc4r0T02/5pOtF+iKKrt+zls2LAnnnji7rvvvribQqGIiorq2aF14pVXXnnrrbcYhuntgSAIgiBIn3BzBvB9WU4NffahXG8DI08q25vRLoPd2Elder4gGL7+uXnHQZOGPRNtA4DJbrfHKAcBQKIq5bj5wJ8Nm0YMvwd+qaFLylEAfxOQk9jtYdLbw6SNdv5glWNYu93yx2ocq89YEz2p8YHi/rJPHhPjmlkemlkebL3DuLbOuKbGmtlsWFFtWFEtUhPqqW7qme6qMa4YhebkbzYURcXFxbVlLQEAu92en5+fl5eXk5OTn5+fk5NTVlbWlTiNZdmSkpKSkpKtW7e2NUql0pCQkNDQ0JB2fH1920f1pJ+X39dvCLSDrTeyBhNnaGQbTFxjk2JUUveebB+HYdjQoUOdtydNmjRq1Ki2u30Qz/PXtnYDQRAEQW5KKIDvUQInHJ6bk3CsEQCsvrm5e/7eAA94vCO6C0/nGz5daTh+tNEfNk4xAYAMl8/2ap04etj/iaycY4zg+DFg3xwA3ma/YeeB9AK1BL8t+IJVr7vO2jcX2zYX2948hKXfpQtS96dfZ8KNcl/k577Iz15gMa3TN67X23JaDKtqDKtqRC5E0M8DlSOuIh8E0h9JJJL4+Pj4+Pi2FofDUVhYmJ+fX1BQkJubW1BQkJ+f35W99PB3nvzs7OwOL9EWzAcHBwcFBQUHB/v7+5O+nqRv312l1ZPaXwQBAJ7nLRaLUqnsrfEgCIIgCHJ5/ekTf38ncMK5BbkJxxrtYpGE5k7wWW35ByLxUMOCche5i8tk3WWO0PC/H6pyj696uMGkZgUQAOAe7Pa2RwmcuMNr7s/V3xeIy2s93V1dURqem9yTQ5QRWnLnWXudhVe320aeZ2AMNj7Bk5IS/WBFuiRc7rU00GtpIF1sNf2hN63X27Kb2XoHALQcbDStrVOkqJXjXAlNP975j4Lu5y0AACAASURBVHQRRVExMTExMeeXDgmCUFFRkZ+f74zqCwsLCwsLKyoqupi9xW635+Tk5OTktG8kCMLPz68tng8KCho+fLi3t3c3n0zfptfrV69eLRaLFy1a5GyxWCzPPffc999/b7fbo6KiHnzwwSeffLJ3B4kgCIIgyMVQAN9DnNG78Zdam0RkkeASmjvccLbt0WS/oUIDXzo3O2h1rDOGr3fU7WrYMsltpppsLQUv0A7TqawVDzeYlQyOiQSBc68nQo7qIVpoq5Y0wW3Gtrr1jZxp7Wzj20GdlAVCbiYSonV1fYf2+ZuNRhtPibBET2qknzjVj4rQ9oPN5eIQmedzAzyfG8BbOVwmAgDDymrDqpr6bysxESYfotLc5em2wLe3h4n0KGdeen9//wkTzqejs9lshYWFRUVFznjeGdh3vdIYy7LOgnZtLWKxeOvWrW1F72966enp8+bNMxqNaWlpbQH8vHnz1q1bBwBubm5nzpx56qmnjh07tmbNml4dKYIgCIIgHaEAvie0Re8iJdEoE3nV0TZ3x4G8prYOcXWR8mQXyzFzWwy/vGJZQcuZekfdYwNai/1gYkr7+mOM+RUtoTE49Bhgczb72ipz9e9/o757KuXvLbCcLTN39jbXb6c1mjTsUUXuCLi15pQQp6eTlL/mWbPrmUNV9KEqGo6AmwxP9RWP8BOPD5TI+vy0vDN6BwCft0KlMQrzdkPLwcaWI+aWI2bNLHdCR7UcaeQaGOVoV7yf7PxHupdUKu2wnR4ADAZDcXFxcXFxUVFRUVGR83YXo3qapr/66qtbJIAvKSmZM2eO1WpdtGjR7be3ruE6duzYunXrVCrVrl27EhMTS0tL77zzzp9//nnBggW9/m155ZVXXnzxxd4dA4IgCIL0HSiA7wmm3+qMv9QCAOEpDmDyNS/+kR1qLprdugGeJPDJ/94jp/a73inimjk6H6sJwFIsBbEYtb6Rz2nOdOaoAwCtf+jb3JdPnXkAALSUe+WD0fl7jgqOg7D2IIhEwPMgCCADuYOwiJk1Vd+luI7BASUDu+XcHSm7O1LWaOcPVtH7Kxz7K+haC7eu0Lau0DYrXPr+6H6zt4JwJd0X+7sv9uctXPNfRkEAQkcBQMnMLK6FwyhckeyiHOWqHKmRJaqwPn9hArmhtFqtVqvtkIzNaDQ6I/mSkpLS0tLS0tKSkpLq6uqLV+BHRNwqS5Y+/fTTlpaW5cuX/+Mf/2hrXLVqFQAsXbo0MTERAIKCgtasWRMREfH555/3egBPEARBoM8qCIIgCNIKvSn2BEWKmvKROKrsdJFFO++oJm1/3Q5H26ODI3CvcUcveEITDAYAgIOE9y/V30eFDcSx1pnGDTU/cwILAA2OunWwBUZe8kXtvG17/cbJbjO7/XSQfkEtwW8Llt4WLAWAIiO7v5I+Wu2YGHg+DV6xiX37cFOQmkj2EY/yF4v6cPyLy0UuU93a7vr+N9zwQ5XleFPzXlPzXpOzgzJVrRjpqhypkcUpAO/DJ4P0IFdX16SkpKSkC5LM2+32tmC+tLS0vLx84MCBL7zwQm8Nsodt27bN19f3/vvvb9+4Y8cOALjvvvvaWsLCwkJDQwsKCnp6fAiCIAiCXBYK4G+gnHrHgm0mmgO5lX+Ob13Obk4fq71rw6FTtrZuQsjEr49Nm7KjHucEALDOdJDxPwbwTfm4aw0mA3v5m8XPP+i3xFviBwAJLsOOmw/xcFHxZEEADADOxy0ERsYo4zt2Q25Joa5EqCvxwEB5+8acemZPOb2nnF5+2vJ2mstdEbLeGt7V0t7npb3PizUxLXtNzXtNzftM9gKLebvBvN0AACI1oRyhUY7SuM71EinRnzikI4lEEhUV1RcqnPeKqqqqYcOG4TjeviU/Pz8qKsrPz699Ty8vr5MnT/b4ABEEQRAEuRz06fYG2lJK11l4AIjPaPSucRZ1E1iDRv/LrINZX7Z1E8HMYa8EtTAhej/KvcKB60v9Pm3mATvbMg7EdQBQZi0+2XTEGcBHKgf+L/qH3jgb5GYzI0zqoxQdqqLPmrkkr/Pl5VefsX6S0RylI+PdqXgPMs6ddBH3xY0YhIZUz3RXz3QHAKaWbt5nat5ratlnostsjZvqGzfVM3qH98vBrIkxrqmVhMjkQ11ELugvHnKrw3Gcoqj2LTt37gSAsWPHduhpMBh6blgIgiAIgnQN+jh7Az07VOmjxK20oMJ5WFsDvMBKRU0qrPnXtDzzZ84+OOBv7/GgGKE4XhqSZRNwkP3nExyEbC5y8OfkjudwWswDgInpaoJlBOkiDGCIFzXEi+rQbqb5eiu/t5zeW047uwWpiTgPMt6dSvah+ma1edJT7HqXp+tdngDgKLc37zVZs5pc53gCgGFlTdULRQAAOCYJlylTNN6vBov65FkgSA8IDg7Ozc0VBAH7u3zJxo0b4aIAnuf5kpISf3//XhgigiAIgiCX1hcn1m4aOMB9UfJ7bWzES0XAC6SnmLDxBIudkR3h+dY+IaS/kpGdjZKEZNkEDHI/3O2rrbBipPTncEwkGkekYYABwF7D9kr7ud48GeSWsWiw4uA8908naB6Kkyd6UmICK2lk/yiwvbzfPOXXhkY739aT5cHOdqkcd0+i/CXaeV5+H4RLwuUAoJ3r5fFkgGKYC05h9jxL/beVzXuNANC4qb749qyqF4qMv9bai6zA97kTQZAbITk5uaysrK0+3NmzZ7ds2UJRVFpaWvtun3zyic1mGzNmTC8MEUEQBEGQS0PTUDdWy6HG4tuzeAunvd/b753Q7DtPqQ40nvY43tYhOZaCAhiQaxcw+P1l8YKBK4CHvPJIv1qFdtE9dwweZq2k/jJs4wV+TdV3zwa/1ovngtw6POWiyUGiyUESAGB5yDcwmXXMKb1DLMKU7ZbT3/5HQ76BCVAR00IlSxKVvTfeyyG0pM/rIQAgOHhrVrOjwu5ymxsANO00NO0wNO1oXSQsUhLSOKUsXikbpJTFK8UhMqwvp/VDkGu1dOnS77777v7779+1a1dAQMD3339P0/Rdd93l4uLi7MDz/MqVK//1r38RBLF48eLeHS2CIAiCIB2gAP4GokttxTOzeCun+4eP/7IIwMAjzbX6QGOmrbCtT9r0avFv5XSZv12Jx416zp231Alyjw2RmrkzFKOHAcAsr/uONR6wsC15LaczzUcHuQy99AsiSPcjcIhxI2PcyHnQMctdtI4sNrFlZvbnXGv7AP7D481mOx/mSoZoiHAtoe4bW+gxCpcnuciTWqMUv/fCXKa4WTObbKearZnNjkp7ywFTywGT81Fcgksi5JIohWqcq3NlPoLcHHx9fdetWzd37tzly5c7WwICAj766CPn7U2bNv0/e/cZH0W5tgH8nrYtu+mVdEijl4CAImBBRVFAsGAHPSKIWA5FUBERUQ7YQMWCiB49B1AQEVDEdhAUXwgQSiCVhJDes8m22Zl5PyyGmIQQSrKZ5Pp/4Ld55tnZex9mZ/eaescdd9jtdoZhVqxY0Wkv9QcAANBuIcC3hVyns8LhCH63IH9xlp3E5JLiuklX9mN9A9eemr2ADcq/QU4nou1inP1RzTPDr3d18OCMtwXf9d+8j4nov/kf9/YcwDOCW94FQAOvjfRaPNwrrVz01p2N6Fan8uHBWrHeEekBBjbWR+jqzcX6CqOitEEenDuKbYjRsl43+nnd6Of601nisBwyWw6aLYeqLclmR47NcshsOWQu/2+B922BrI7NnZVmPWLWdffQ9zDqYg2GRE/OE+tPUKWbbrrpxIkTu3btOn78eHh4+B133GEwnNk8Z7Va/f39+/XrN2fOnOHDz32fUgAAAHAT/ABtRdqu+pjN/dLHH9L/uyg9uVo6bCWW2RaWaz9+5iziIM4vRG/g+h/mbzhgm/m5lpzJrH+Sl6evYPmq4LO6+SikGDlTjWQudRTvLNk6OnC8m94QQEM8Sz38/7ZFSc8zm27325vvSC93ppWL6RXOEotcYrH/nkdE9FO29pNbfF09FaIvT1g0LBPpxfcJFNx7xDofoPEc5ec56kyel8xOW0qtNaWG99OwOpaIzD+X29Jqa/ZUujpouxl6Jg8lotK1+WK+XRdr0MYadLEGtn1sngBoXkBAwIQJExq333nnnXfeeWfb1wMAAAAthADfuoxXeods7JNz66Guh60KwxS9Fp3+9sa6qf2ZvmX/7hf4+Bq/2R92Y8pEhv1a05WIysXS7cWbmpzhHxX/Q4CHdq6Hv1A/1Z82S1mVzrRyZ26189pIXV17crE479cq1+P7ehpeuvrMwe0K0a85dg1HEV58FyPnlmDPmXiPwV4eg73qWhJ+G1T7f1XWlFpbaq0tzWLoYyQixSHnPnVCqXclPyFIo43Wa6L12mi9NkqvidZro/RCiNYN7wEAAAAAOhwE+FbHHKgRRIWIGEVJ/bM8r+bPukl92Z4V397gNeYHn8jTRLRRf0stR16MwFITkaVSLFdICdLidFxQmTATF2bihoc3DLG9/YVnh3geKXHk10gD693N7rdc+yPfnblvIs9SiAfXxcR1MXLhnly4ib+xq85DcEOmZz040zW+pmt86zcyGjZmS3/zL+W2dIs93WLLsIhFDrHIQXur/vZcPRv0dGTI/K5EVPltieKQNWE6TYROCNIQi0vlAQAAAEBLIcC3rqIVp/LmpxPLFD/RxfOjghEbK1+VUuumdvPt/b/RGsOqh8JfW1zp9H/X60V/24+htu+bmaHZaW79qgHaAsfSP/p5EHk0aB8QpLm/lyG1zHmqWiqqlXLNUq5ZqpuaXWV85oozF8wrqJHe2Gc2adgwEzcxXu/pjqvlmYb7mIb7nPlDVhx5dnu21XHSas+22k9aHdlWe5bVWSbas6xEZEu3ZE06XPdcRsOGzIsOnh1FslK6Nl9xKppQHR+k0YRq+UANLoMPAAAAAA0gwLciS1J13vx0IgpfFjdgatjGYV7svb+UVNS4puoYXcbXN0rer+xeMvDq5+dbS4J0/9VOCLnpBt2oZuYZpAlpi9IB3MeoYRYOO3PsukNS8mukPLPk+rfMKt8ao6/r+XueY1Oq1fXYLinT+htdj4+WiC/tqfbUMH56NtiD8zewAQYuwMAG6NlAD07Pt1owZhlNuE4TrqOrfeo3y7USq2eJSNtV32VBN0uyWcyzOXJtYpHDWeogotok86mZJ+o/heEYPlAjdNEKwRpNFx0fqBGCNEKI1jTChzXgNHsAAACATgoBvhW5zoO1n7SW/afA9+7gCaNDRt1RSB+emerbr9+SIRH/2dMvck9gTVHX9EEelQbWxnpH6dvp/bQB2p6GY6K8+CivptdU42L1XlrmVLVUaZNvjzsb7E+UiQcKHU0+RWCZD0f71B3PX2yRN6ZaPASmi5G7JkLHtc4u/Lor2zEcEzwnqq5dERVGYIjIMMAUtizOdqJWzLeL+Xax0C4WO8QCu1hgbzCrgEfDwt+It2da0m87yPCsECDwgRohRBvwaJgu3oOIav+vitGwvL/A+wqI+gAAAAAdDAJ8K+J9hbjvE9NGJ1mSqtNvPmDc3CfZeaxuquPKgR9+tKb7v4KNRcaC3vnlH5n6G2K6a3CLOICW4li6PkrXuH1igqFPoOa0WSq1SsW1cplVLqyVSq1ySa1kdSq6envg16VY3t5/5rSUN67zHht7ZivArlz7kj+qTRrWW8t461gfLeutY311rI+e7WLkegdcns8p89fJ/AzHBE4Lrz9JERVnscNx2iYWOcR8m1giioV2qcLpPS6QiKQayVnkkG2yPbNuXkz48rjqH8oybj9UNxPWwPF+Au8rcN485yPwvgLvI3A+vBCo8bo1gDPxRCQWO1gNy3nxTV15AwAAAADaFwT4VnTaLL10uFbzz24TFqVRsvnEzUk2e1Ld1MDIvl2Xx3gWiDmxwvKpYdqUvP/c6AjWNpFGAOBCxfnycb7nX7/d29PAs1Rika1OZUiXsxfSS69wppc7z/Ws1aN9r4k8sw//x2zbsj/NJg0TYuQWXOUVYDizE7/aLu/Jc+g4xqRlTBrWpGGMAtPys/QZgRFCtUJo05evN/Q19Tk13HHa5iwVnSUOZ7noPSaAiPS9jd5jA+0nrc4yh1QuyhbJYZEcubbGcwieHdXlxW7VP5VnjD9EskJEnInnA4Tw1+M9R/mJxY7CpSdJItaT47x4zsRzRo418pyJ47wFzsSxRo41cJwnvkEAAAAA2pRaf35lZmbu3LkzJSWlrKzMYrH4+fmFhoaGhobeeuutISHt5SzxE2Xizzl2Itr/RNTCD04GHzfbrGf3wL/wQUBAoZg6wLDksQhLNSsUh20o+HRm1Dz31QvQ6fjp2ekDjI3bH+7jcUOUrsQiVdjlSptSYZMrbXK5Ta6wybJC3f3PrjlTy50ZFU4iOlgk3pFgCDCcidxv769Ze6S28Zw9BCbIg/v4Zt8IzzPHt58oE9cdtxgENtiDvaeHB/9XxnfKlFou6nlGzzMGgfEQWL5e/GcNnC7Og+L+NnMhRNv1i951f8q1krNMdFaIUoXorHBK5WceS7WS76RgIhJCNLp4g5hvl6qcktkpmZ32TAuN8jP/XF7ywenzjh6rYyM/6ukzPtB2vDZnxnGGIdbAcV485yMEPR2pjdbLtVLpZ/mMwLAaljXxrJ5ltSznxRPHcJ48IzCckWe0rOsCAQAAAABwXuoL8CdPnpw+ffr33zd9qfbHH3987Nixy5cvj4qKatu6mnB9lG7zBP9SuzRPqXzlqqgJd24Xs8/s0wvmguIKjakDDP/6MFzxYF+UhV1lrx+qKjxqPtjL1N+9ZQMAEYV7cuGe5z+H/PEBxltj9GVWmUjpH3R2H/6EeH2lXa6wyWaHYnbIZrtSK8pmh1IrKqeqndV2mejMzD8/ZvlvisX1OMqLrzs/f+ne6jWH/7YJgGXIpGF1PBPswb53o0/wX6fWJxeLG05YGKJwEze5j4fmr8vX1/DMHicZfDS8n8YgMALL6HhGyzF6nmG0DBHpexh77Bvi6ixVOuVaybXP32diEKNlnSUOyeyUqpxytSTVOKUaSTY7pWqnVOWUayTZIsl22fVc64na2j//duc8TYQueFZU+frC07PTzjuGrJ6N/qSX15gAy0FzztQURmCIJc7Esyauy4vd9D2MYqG96PUcYonRsgzPcEaeiFyH/bMGjtGyDMdwJo41cB6DPF135hPz7LJDZrUsa+AYDYPLAQAAAEDHoLIAX1ZWNmrUqMzMzJ49e44dO7ZXr15+fn6enp7V1dXl5eUnTpzYunXrxo0bk5OTd+/eHRQU5O56qXeAUCpz1XmyEsx/PD6Z3jzT3ofprkmQa9fH2JwikaLz427WXr216Mv1+Z/0iOvDMvitCaAaEZ5cRKOo38NfeP1a78adzQ6FY8lQ7zz8ZwaZevgLZrsscMzQ0LObAIaGag8WOSptisWp2JxKjUOWFKqyy1V2KrVI5Va5LsCvP25Zf/zMJoA+gZq6mSzda173V3sDBp75eoJ/jM+Zr4ANJyzvHaghomAPbuUonwAD6zM+kIj+zHd8cayWYRijhiEiPc+4tg54aRhfPTsh3uC6jb3P+EBp3+BDKRbWJvtLSriB9RrtT0Te4wPthY6qXBuJslIjMTaJcShytVNxKkytJDtkuVZSbLLiVBSZiMiebbWm1NSv0zTSV9/DWLm1pHhV7nn/I4go/I34gEfDKr4sOjn5aF0jIzDRn/TyHhdo/l/FyYeOKqJMRJyRd12DgDVywc9E+dzh/u8LAAAAgPNSWYCfP39+Zmbmq6+++uyzzzbZYeHChZ988smjjz66YMGCDz74oI3La5Inw/QVKL9mz+nUbXWNXbpErXznQ7FYjNaNLNIO25z9oU4qJqI826k/Kv53le+17qsXAFqRSdPwYnG+evaeHobGPa+N1F4b+bdz4CWZakTZ5lR4lvGrd9j5nMGmxGCNXVI4hq6odyb/7fH6GlGptsuSQhZRFmWyiopDVqyiohcYg3C2kpwqKbdaIqI8s1RikerO5P86zbots4lT6F0ivfgrQs683LIi6asSyfV4002+fT04IuJ9hNXD/T472sSpBETko2M33e5ft+3jPymWd2rIf1lCuFWeN9TTyyEzHHkM8d5x0rbGqO37eKTB7OREhZUUrV3mOdLWSDqe6W3kOFFWnIpUI9Uo9KmOr91dHeNU+vcxKWanbJNlqyTz7PdFjqqDNd5Ha7tVOVmHTERS5dlrHNT8XokADwAAAKqgsgC/a9eu+Pj4c6V3l8mTJ3/xxRe7d+++iPkfOHBgw4YNzfcpKSm5oHlqGGaVn+61yp/fP1pZ15j3pCbeqNWQtpu4v5u4n4iIMxIRx/Degu8F1w0AnQDHkpeW9Wp0YTtvHTshXt+4f2KwJjFY07i9sdmDTff3MogS6QXGv96mgeeu9Lw2UmuXlFqHQkQWpyJKikJU7VC8tcyAeqcMTIzX25yKQ1J0PNPV++w3S/8gYc9p3iErROSQyOZUXO3VdpmImHpbM/LMUlGtVMSwqUb2qXiPsL+uQfhbrn1/pXN/tEeTlW+83b9f4JmbAjzzU+U36VY6WktEGzb1q3vvT/9UuSXdSn+aieM0L8VrnAoR6WwSKytE9O5wr7ChXi0ZJQAAAAC3U1mALykp6d2793m7hYeHJycnX8T8X3zxxa1bt7akpyzLLZ+tt+D7avx7bxaucf3JcPyTo1+9pTuCOgC0F3UH5Ndn0jA3RLfo1hiDQjSDQprYWHBbrP622CY2LjQ2e7Dp4T4etaJi1DA+urMbERYO85rUw1BpO7vKFWWyiAoReeuYuvRORHOHmAZ30dglxSiwA+ptuXgi0RjuyTnrrbOr7bJrQ4K3lu050MhwuIceAAAAqIPKAvzQoUN37tyZkZERExNzrj7FxcXff//90KFDL2L+y5YtGzZsWPN9ampqFi9e7OnpeUFzZhhmzJgxmzZtIiKPxLFafRNHzAIAdGa+eta3UdjnWerpLzTVvaEgD+6u7k2sWrt6888MMl16eQAAAABup7IAP3PmzO+++27IkCELFiwYO3ZsZGRk/akFBQXbtm17+eWXi4uLp0yZchHzT0hISEhIaL5PUVHR4sWL9foW7VOq74svvli/fv3v+c6ULjfF+7Xo9ygAAAAAAACAi8oC/KhRo1auXPnkX7y8vHx9fT09PWtqasrLyysqKoiI5/n33ntv3Lhx7i62IZ1O9+CDDz7o7jIAAAAAAABAjVQW4Ilo2rRp119//erVq3fu3Hn8+PGTJ08SEcdxAQEBAwcOvP322ydPnhwcHOzuMgEAAAAAAAAuJ/UFeCKKjY1dunTp0qVLFUWxWCw2m83Hx4dl2fM/EwAAAAAAAECdVBng6zAM4+Hh4eHR9O2FAAAAAAAAADoM7LUGAAAAAAAAUAF174F3I4fD8eOPP17oszIzM/Py8oxGY2uU1BkUFRUFBATgdImLY7fba2trfX193V2IWlVUVBgMBq1W6+5CVEmWZYvFMnz4cHcXcnmkpKS4uwRoLfh+b7nS0lIvLy9B6EQ31lEUpbi4ODAwkGEYd9fSdjrn74fq6mqO4zrbcb4lJSU+Pj4834kSYoPfJ6r4fu9E/z2Xiys9VlRUjBo1yt21AACA23SqX/CdAb7fAQCA2v33OwL8BfP393/ssccyMjIu9ImKovz0009E1Nk2YV4urg3AWq22s20NvVyqq6udTqenp2en2rB6uUiSVFVVxXGcl5eXu2tRJdcFR7t27dq1a1d313LZPPDAA+4uAS4nfL9fEFEUzWazIAgmk8ndtbQd16rMYDDodDp319J2OuHvB0VRKioqGIbx8fFxdy1tx+Fw1NTUaDSaTnUwUZO/T9r797sCbcXhcBCRIAjuLkSt3nvvPSKaNm2auwtRq4EDBxLRvn373F2IKh08eJCI+vXr5+5C1OqJJ54gohUrVri7EIDLr3N+v3/33XdEdNNNN7m7kDY1Z84cInLdCKnzSExMJKL9+/e7u5C2U1lZSUReXl7uLqRNbdq0iYjGjx/v7kLa1IwZM4ho5cqV7i7kAuBcYgAAAAAAAAAVQIAHAAAAAAAAUAEEeAAAAAAAAAAVQIAHAAAAAAAAUAEEeAAAAAAAAAAVQIAHAAAAAAAAUAEEeAAAAAAAAAAVQIAHAAAAAAAAUAEEeAAAAAAAAAAVQIAHAAAAAAAAUAHe3QV0IhzH6XQ6rVbr7kLUysPDg4gMBoO7C1Er19C5hhEuFBa/S4TFDzqwzvn93jnXip1zVdYJ37VWq+V5vrMt3vhQqwWjKIq7a+hEfvvtN57nhw4d6u5CVMnhcKxfv/7mm2/28/Nzdy2qlJqaevz48XHjxrm7ELX65ptv4uPjExIS3F2IKpWXl2/duvWuu+7qbCEHOolO+P2uKMqGDRuGDBkSGRnp7lraTlVV1TfffDNx4sROFXI65++HX375xWg0Dho0yN2FtB1ZltetWzdixIjQ0FB319J2ysvLt23bduedd6ro9wkCPAAAAAAAAIAK4Bx4AAAAAAAAABVAgAcAAAAAAABQAQR4AAAAAAAAABVAgAcAAAAAAABQAQR4AAAAAAAAABVAgAcAAAAAAABQAQR4AAAAAAAAABVAgAcAAAAAAABQAQR4AAAAAAAAABVAgAcAAAAAAABQAQR4AAAAAAAAABVAgAcAAAAAAABQAQR4AAAAAAAAABVAgAcAAAAAAABQAQR4gA7i119/LSkpcXcVKoYBBACAlli+fPm7777r7ioALoNO+OOnA7xlBPg2snPnzttuuy0gIKBHjx7Tpk0rLy93d0Xt1/Dhw5mm1NbW1u+GIa3v6NGj11xzze+//97k1JaMVScfz2YGEAtkM1atWpWYmGgymQIDA4cPH75hw4bGfbD4QcfWOZfelnz2O6p///vfs2fP/uqrr9xdSFvIzc198MEHQ0JCPD09hwwZ0hn+o51O54oVlQosnQAAIABJREFUK4YMGeLl5RUZGTlu3Lh9+/a5u6jWcum/HlWn+besmjWbAq1v1apVHMdpNJoRI0bExcURUbdu3bKystxdVzvVpUsXrVab0EhtbW1dHwxpAxMnTiSizZs3N57UkrHCeDYzgFggm+R0OqdOnUpEWq12xIgR11xzjU6nI6KpU6fW74bFDzq2Trj0tvCz31GdPHnS09OTiEaOHOnuWlrd0aNHw8LCWJbt16/fTTfd5OHhQUSLFy92d12tSJKk66+/noiCgoJuueWWYcOGcRzHMMy6devcXVqruMRfj2p0rresrjUbAnyrS0tLEwTBz88vLS3N1bJkyRIiuummm9xbWPtUW1vLMMzo0aOb6YMhrfPLL7/861//SkxMdG2Pa7w+aslYdebxPO8AYoE8l08++YSI4uPjCwoKXC0ZGRldu3Ylom3btrlasPhBx9Y5l96WfPY7KqfTedVVV5lMps4Q4B0OR1hYmIeHxy+//OJqyc7ONplMLMtmZ2e7tbRWtHr1aiK6+eabLRaLq2X//v0Gg8FkMtlsNvfWdhldll+P6nLet6yuNRsCfKubM2cOEb399tv1G3v27ElEGRkZ7qqq3Tpy5AgRzZw5s5k+GNI6MTEx9Q+oabw+aslYdebxPO8AYoE8l+uuu46I9u7dW7/xyy+/JKLHHnvM9ScWP+jYOufS25LPfke1aNEihmE+/vjjzhDgP/vsMyJatGhR/caPP/54zJgx33//vbuqam0PPPAAEe3YsaN+49ixY4nojz/+cFdVl91l+fWoLud9y+pas+Ec+Fa3c+dOInJ9+Ou4/nRNgvoyMjKIKDY2tpk+GNI6v/76a3Z2dnZ29rRp05rs0JKx6szjed4BxAJ5LllZWYIgDBo0qH5j7969iSg9Pd31JxY/6Ng659Lbks9+h/Tnn38uWrRoxowZN954o7traQsffvghET344IP1G6dMmfLtt9924BEQBIGIGlzhzHXut2tSx3BZfj2qy3nfsrrWbLy7C+jgFEU5fvy4p6dnZGRk/fZevXoRUUpKipvqar8yMzOJqLS0dPTo0fv27RMEoW/fvk899dRNN93k6oAhrS80NNT1wMvLq/HUloxVJx/P5geQsECe26ZNmxiGYdm/bQVOSkoiom7duhEWP+joOu3Se97PfodUU1Nz3333xcTELF26tGNcyuu8kpOTjUZjRETEr7/+umfPnoKCgj59+owbNy4wMNDdpbWiJ554Yv369XPnzvXz87v66qurqqreeeed3377bdSoUXVHX3cAl/7rUXXO+3tPXWs2BPjWZbFYbDZbSEhIg3Y/Pz8iKisrc0dR7Zprh+dLL70UGho6ePDgwsLCnTt37tix47nnnlu8eDFhSC9ES8YK49k8LJDn0q9fvwYthw4dmjVrFsMwrsvAYPGDjq3TLr3n/ex3SDNnzszJyfnjjz/0er27a2kLFovFbDZ369btySefXLFiRV37/Pnz165dO2bMGDfW1qr69u27Z8+ea665ZvTo0XWNd95556effurGqtpY51y5qWvNhkPoW5fNZiMi1wVL63O1WCwWN9TUvmVnZ/M8v3Dhwtzc3G3btiUlJe3evdvPz+/VV1/9448/CEN6IVoyVhjP5mGBbAlFUT799NPhw4cXFBS8/vrrAwYMICx+0NFh6aVzfPY7no0bN37yyScvvvhiR9oH2zzXApyZmbl27dr333+/sLCwsLBwxYoVtbW199xzT25urrsLbC1ms/mZZ54pLy/v3r37/ffff+utt5pMpm+++cZ1hbNOAiu39r9mQ4BvXT4+PhzH1dTUNGivrq6mvzZlQX3fffedKIovvvgiwzCulqFDh7700kuyLH/++eeEIb0QLRkrjGfzsECeV1JS0pAhQx566CEPD4+vv/766aefdrVj8YOODUvvuT77HUxeXt6jjz46dOjQZ5991t21tB2eP3OI7rvvvjt16tSgoKCgoKAnnnhi/vz5ZrN55cqV7i2v9UyePPmnn35auHDhsWPHPvvssy1btqSmpsbExEyfPn3Hjh3urq6NdPKVmyrWbAjwrYtl2YCAgManS7la6s7HgOaNHDmSiA4fPkwY0gvRkrHCeF4ELJAuoijOnz9/8ODBR48eff7559PS0saNG1c3FYsfdGydeelt/rPfwWzevLm8vJxl2fvuu2/SpEmTJk2aPn06EaWkpEyaNMl10fKOx2g0sizL8/wdd9xRv911GbNDhw65qa7WVVxcvGnTpujo6AULFtRttQ8JCVm2bBkRvf/++26tru102pWbitZsOAe+1YWFhSUlJRUWFgYHB9c1njhxgjr0Z+DiyLIsiiLHcXWbfl1cf3p7e7v+xJC2XEvGCuN5LlggmyHL8gMPPLBu3brrrrtuzZo1ERERjftg8YOOrXMuvS357Hc8e/bsadBSXFy8bt06nU7nut1aB8PzfHR0dF5eXoOvP4PBQESSJLmprtZVXFysKEp8fHxdenfp3r07ERUWFrqpLjfohCs3da3ZsAe+1Y0fP15RlK1bt9Zv3Lp1K8/zt956q7uqap9Onz6t0+ka3MKBiH777Tci6tOnj+tPDGnLtWSsMJ7nggWyGe+99966devuvffeHTt2nOt7DosfdGydc+ltyWe/I3n88ccb3IH59OnT9Nd94K1Wq7sLbC0TJ0602Wyuq73UcX39Nb7cV8cQGxsrCMKxY8cabKFITk6mv67B3kl0wpWbytZsrXubeVCU/Px8nucjIyOLiopcLa4rYUyYMMG9hbVPV155JRG99tprsiy7Wg4ePBgUFGQ0GnNyclwtGNLGXOfmbd68uUF7S8YK46mcewCxQJ5LbGyswWCorq5upg8WP+jYOufS25LPfsdWP8B3YKdOneJ5vmfPnnVfdkePHg0JCdFoNEeOHHFvba3HdcrAk08+6XA4XC0ZGRkxMTEMw+zcudO9tbWGS/n1qFLnesvqWrMhwLeF999/n2XZkJCQyZMnjxo1iuf5bt26ZWVlubuu9ig9Pd11qFJ8fPztt98+ZMgQnue1Wu1nn31WvxuGtIFzrY+Ulo0VxvNcA4gFskkFBQVEpNPp+jVl1qxZdT2x+EHH1tmW3pZ/9juwThLglb8Wbw8Pj+uvv37kyJFarZZl2TfffNPddbWi0tLSmJgYIgoLC7vtttuGDx+u1WqJaN68ee4urVVc4q9HNWryLatuzYYA30a+/vrrMWPG+Pn5JSQkPPLIIwUFBe6uqP0ym81z58696qqrTCZTbGzs3XfffezYscbdMKT1NbMKVlo2Vp18PJsZQCyQjTU+HbS+iRMn1u+MxQ86tk619F7QZ7+j6jwBXlGULVu23HbbbUFBQaGhoWPGjNm9e7e7K2p1FovlpZdeuuKKK0wmU3h4+M033/zTTz+5u6jWcum/HlWnybesujUboyhKMxUDAAAAAAAAQHuAi9gBAAAAAAAAqAACPAAAAAAAAIAKIMADAAAAAAAAqAACPAAAAAAAAIAKIMADAAAAAAAAqAACPAAAAAAAAIAKIMADAAAAAAAAqAACPAAAAAAAAIAKIMADAAAAAAAAqAACPAAAAAAAAIAKIMADAAAAAAAAqAACPAAAAAAAAIAKIMADAAAAAAAAqAACPAAAAAAAAIAKIMADAAAAAAAAqAACPAAAAAAAAIAKIMADAAAAAAAAqAACPAAAAAAAAIAKIMADAAAAAAAAqAACPAAAAAAAAIAKIMADAAAAAAAAqAACPAAAAAAAAIAKIMADAAAAAAAAqAACPAAAAAAAAIAKIMADAAAAAAAAqAACPAAAAAAAAIAKIMADAAAAAAAAqAACPAAAAAAAAIAKIMADAAAAAAAAqAACPAAAAAAAAIAKIMADAAAAAAAAqAACPAAAAAAAAIAKIMADAAAAAAAAqAACPAAAAAAAAIAKIMADAAAAAAAAqAACPAAAAAAAAIAKIMADAAAAAAAAqAACPAAAAAAAAIAKIMADAAAAAAAAqAACPAAAAAAAAIAKIMADAAAAAAAAqAACPAAAAAAAAIAKIMADAAAAAAAAqAACPAAAAAAAAIAKIMADAAAAAAAAqAACPAAAAAAAAIAKIMADAAAAAAAAqAACPAAAAAAAAIAKIMADAAAAAAAAqAACPAAAAAAAAIAKIMADAAAAAAAAqAACPAAAAAAAAIAKIMADAAAAAAAAqAACPAAAAAAAAIAKIMADAAAAAAAAqAACPAAAAAAAAIAK8O4uQJUOHjyYkZHh7ioAAMCdRowYERgY6O4q4HLC9zsAALTz73dGURR316AyNTU1fn5+DofD3YUAAIA73Xzzzdu2bXN3FXDZ4PsdAACo3X+/Yw/8BautrXU4HDqd7tZbb3V3LS3lW+vlU+uVGXjK3YUAAHQEZWVlP//8c0VFhbsLgctJjd/vAABwGani+x0B/iJ5eXlt2LDB3VW0iCWpOv22g1KVM3hudJcXurq7HAAA1fvjjz+uvPJKd1cBrUJF3+8AAHB5qeL7HRex6+Dq0jsRFS49mf9ylrsrAgAAAAAAgIuBAN+RWQ6ZM8Ydkqqc3uMCoz/txQhM4dKTec/j8jwAAAAAAADqo9ZD6DMzM3fu3JmSklJWVmaxWPz8/EJDQ0NDQ2+99daQkBB3V9cuWA6ZM2496KwQvccFRq/txfAMwzMnHzpa9FYOEYUujnF3gQAAAAAAAHAB1BfgT548OX369O+//77JqY8//vjYsWOXL18eFRXVtnW1L43TOxF5jw2MXtvrTIZnKPRlZHgAAAAAAADVUFmALysrGzVqVGZmZs+ePceOHdurVy8/Pz9PT8/q6ury8vITJ05s3bp148aNycnJu3fvDgoKcne97tFkenc5m+HfzCFChgcAAAAAAFANlQX4+fPnZ2Zmvvrqq88++2yTHRYuXPjJJ588+uijCxYs+OCDD9q4vPbAeqQm/ZYDUpXT5/agqDU9XeldLHY4C+36PiYi8h4bGLWmV/bko0Vv5nBGLnhutLtLBgAAAAAAgPNT2UXsdu3aFR8ff6707jJ58uQRI0bs3r27zapqV6p/KpOqnAzPBM+NcqV3W7rlxFX/d/yq/yv58LSrj/eYAEOiJxFVfF3szloBAAAAAACgxVQW4EtKSlpyjbrw8PDi4k4aTQOnhXuN9lecSvqYg9aUGlu6Jf3mA2KBnRTK/WdqyYenFVE5+cCR2j+rOC8+8r3u7q4XAAAAAAAAWkRlAX7o0KF//PFHRkZzN0IrLi7+/vvvhw4d2mZVtSuMlu36eW+v0f7OEkfaTQfSbkgSC+ymkb5hr8USUe4/U08M/7/Kb0s4Lz722/6GAZ7urhcAAAAAAABaRGUBfubMmU6nc8iQIStWrMjJyWkwtaCgYPXq1YMGDSouLp4yZYpbKmwPXBneNNxHKhedJQ5Df1O39X0CZ0SEvxlPxFiP1LB6FukdAAAAAABAXVQW4EeNGrVy5crq6uonn3wyKirK29u7a9eu/fr1i4mJ8fX17dKlyz/+8Y/8/Pz33ntv3Lhx7i7WnRy5Nlua5a/HdnuOVREV80/lpChEjGyTa/+vyr0VAgAAAAAAwAVR2VXoiWjatGnXX3/96tWrd+7cefz48ZMnTxIRx3EBAQEDBw68/fbbJ0+eHBwc7O4y3cmeYUkbfUAstBuHerM6tvqX8owxB/V9TdU7y3gfwf/R0MJlObmz04go4LFwdxcLAAAAAAAALaK+AE9EsbGxS5cuXbp0qaIoFovFZrP5+Piw7GU4mmDXrl1ffPFF832sVisRmc3mS3+5y8h6tEbMt3ve4GfPsqbddEAstJtG+nbb0IdYJuvuw9U/lok7y3gfIWZrf0NfkxCszX0mNXd2GsOz/o+Eurt2AABoI0lJSRfUPzExsZUqAQAAgIugygBfh2EYDw8PDw+PyzXD119/fcuWLS3pabFYLteLXjrzrorMicmyReqyoBuxJBbaGZ6JeCueNXBE1HVdn5MPHrEcMHf7qq+hr4mIAh4OrdhQVPNHZfH7uQjwAACdx8CBAy+ov6IorVQJAAAAXAR1B/jLbuXKlbfcckvzffLy8hYtWnRZdvhfFnXpnYjyF2UGz402JHpakqozxh+K/W6AJkzH6thu6/uefYKsnJp5ouaPStbAhb8R77a6AQCgzc2YMcPdJQAAAMDFQ4D/m4iIiEcffbT5PkePHl20aFHb1HNeNb9XZt6RLFskvwe6eF7rm/3IscKlJ4NmRzEs1e6rTrshKe67RE2k7uwTFMp9Jq10bT5r4Lp92dc03Md9tQMAQFtbuXKlu0sAAACAi9dediPDRaj5vTJj/CG5VvJ7oEvkOwk+E4Oi1vRkeKZoWbZxpK/HIE/HKVva6CRHju3MExTKfTq1ZPXpM+l9BNI7AAA0LT09/ddff3V3FQAAAPA3atoDX1JS8vvvv7e8/9ixY1uvGLc7m94f7BK5MoFYhoh8bg8iouwpx4qWZQfNjiKi2n3VaaOT4r5L1ETocp9BegcAgPNTFOWVV1758ssva2tr3V0LAAAAnKWmAH/s2LELurt7B770TpPp3aV+hg+eHaXIZEmqTh9zwGOwV/m6QqR3AACooyjKwoULP//88+Li4vrtkiRZrdbevXu7qzAAAABokpoC/MiRI48cObJw4cKNGzcS0bRp07y8vNxdlBvU7q3KGHdItkj+k0Mj3o6vn95dfG4PUhxKztSUwmXZIQu6EZElqdp+0or0DgAA9b3//vuLFi3y9PQMCQlJT08PDg4OCwurqqpKT08fPHjwihUr3F0gAAAA/I2aAjwR9erV66uvvkpMTDxw4MDcuXMjIyPdXZEbVP9cJlskVscGPRnROL27eN8WUPSWh/VojXlnWeyW/hnjD9lSarquR3oHAICz1qxZYzQaT5w4ERIS8sADD1RUVHz77bdEtHjx4rVr1/bs2dPdBQIAAMDfqPIidnfffbe7S3CnoKciTSN8ZJucdmOSLbWJsxNli5R552Hr0RohUBOxMoHz4uN2JvY+ORzpHQAA6svKyho2bFhISAgRjRw58uDBg672Z599VlGUl19+2a3VAQAAQEOqDPCDBg2KiYnheZUdPnC51B0JLxY50m8+0CDDy1Y5887D5l/LhUBN7PYBugQPImI4htWr8v8aAABaj8PhMBqNrsdRUVH5+fkWi4WIeJ4fNmzYzp073VodAAAANKTKUDdy5Mj09PTQ0FB3F+I258rwslXOvDO5QXoHAABoUnx8/IkTJ1yPo6OjFUVJTk52/SnLcmpqqvtKAwAAgCaoMsADNZXhz6T3X5DeAQCgRa655pqjR4/Onz+/tLQ0KirKz8/vww8/JCKr1frTTz+FhYW5u0AAAAD4GwR4FTuT4a/2EYsc6bcezBh/yPxLuRCsjd2RiPQOAADntWDBgoiIiFdffXXjxo0Mw0ybNm3t2rX9+/dPSEgoKCiYNGmSuwsEAACAv+mkp5F3GKyB67axb+aEZPNvFWK+XQjWxn43QBdrcHddAACgAiaTKSkp6cMPP0xISCCiBQsWpKWlbdmyRZKkKVOmzJ07190FAgAAwN8gwKueK8NnjD9kz7QgvQMAwAXx9/efP3++67EgCOvXr7darSzLarVa9xYGAAAAjeEQ+o7Akmy2HDKLhY6q7aXurgUAANRNr9cjvQMAALRP2AOvejV/VGaMOyTXSkSU91w6EQU9GeHuoi4rxeEs3USK7fw9WT3vdzsxQuvXBADQERw7dqz5Dj179mybSgAAAKAlEODVrWZvZeb4Q3Kt5DMxyDTS99TME3nPpSsOOXh2lLtLu2yk8q2OzGkt7MwwWs5vXKvWAwDQYfTq1av5DoqitE0lAAAA0BII8CpWs7cyc9whqUbymRgU9XFPhmMYjsl5/Hj+S5lE1GEyPOt5NcN5KlI1axzIGpr+rSlbjsg1SQzvxXpd3cblAQCo14wZMxq0mM3mffv2paSkREZGzpkzxy1VNfDAAw98+eWXVqvV3YUAAAC4HwK8OkhVzsyJyWKxI2ZzP220nppK70Tkd38IEbkyvFTpDH0lxs11Xw6MEMCHzRZzXiCpShP9ryaOkFcc1uSriEgIe5bh/dxQIgCAOq1cubJxo6Ioy5cvnzNnznkPsG8boijabC04iwoAAKATQIBXAanKmX7bQUtSNRGljz4Q+90AscjeOL0TkfWwuWZvJcOT4qCit3M4Ixc8L1rMszM6lvdT8ZnhQvBUqfgz2ZruLFzNhzQ8nF4s/ECxZbD6OD7oYbeUBwDQkTAMM3v27E2bNq1aterll1/29fVt7Vd89913v/rqq3NNTUlJIaJrrrmmruWXX35p7ZIAAADaJwT49q4uvWu76oVATc3eqtTr9stmSbZKfveGRK7qTuyZ9F709inXReyIZbRd9faTtvxXslgPNv+VkwxDIc93DXw8ghh3vpeLxwhCxMv21LvFvKWc/x2M4F83RRFLnKeXE5EQuQSXrwMAuFwSExP37t0rimIbvJbVav3111+b73PeDs2z2+1Hjx5tvk95eTkRSZJ0KS8EAADQqhDg2zWp2pkx9qAlqVoTqYv9dgAfIGROTDbvqiAiVssGz42uS++kOAWfAkPfCq8b/HzvCeaDtFn3HranWsy/Wfwm6aq2lxSvyHfkBnZ5sRuj9Wc40yUWpkjV5CxvSU9GE04Md4kvR0Scz42c9/VS5Y/i6SWa6Dfq2sXcxYpUzfncyHlfd+mvAgAALllZWZ6enkFBQW3wWrNmzQoKCpoxY4aiKG+99daNN95Yf+r06dO3bNly+vTpS3mJKVOm/Oc//2lJz7Kyskt5IQAAgFaFAN9+SdXOjNsO1u6v1kTq4rYnaiJ1RNTtq76Z4w+Z91TKdjnthqS4nYlivr36xzL/e97UxX0R8ToRkVRGUhmFPnd2Vn53n3lgO0wMH6hPTLmkUK04bAf7Ky0L8HzQ5Pp5+1IIkUukqv85iz/jg6a4rmYn1x52lnxBjEaIePmyvAQAQKeSk5PTuNFms23evPm7774bNmxYm1Vy//33Dxs27P7773/kkUdmzJixdOlSvV7vmmQwGIgoNDT0UuZ//fXXp6amNt/HarW6DtcHAABotxDg26km0zsRsQau29f9XBleLLSnXrtfrpFkq+Qz5sxR5YwmUiwWZYvEcIwQrGE0rKtdscuyo5DVOBynvbTdifW4hOIYgRECFWc5sXpGONfOGVmxnyIiRnNJP7nqY/WxfNDDzsL3xez52h5biMiRM48USQiZzupjL9erAAB0HlFRUeeaxPP8ggUL2rAWio6O/t///rdkyZJFixbt3Lnz888/T0xMvFwznzx58uTJk5vvc/To0d69ezOMSk82AwCATgEBvj06V3p3qZ/hnSUOIvK83k/fb7Y1eb3iKKz45p7C1xP5AE3stv76Hsa6ZymOAuuBRCIq+Nf9hW8d6LapH+970WeMM0LUa/bj4xiG0/X6vskM7yxe68h6mtGECiHTL/ZVmiCEzZVKv5Sqf5PKt5DilKt/Z4QAPmzWZXwJAIDO47777muyPTAw8M477xw8eHAb18Nx3AsvvHDDDTfce++9Q4cOffHFF5999tk2rgEAAKA9Q4BvjwpeyardX80ZubgdiZowXeMOrIHrtqlfysA/HLl2IrIeq7GfYvmQBWLOdOOQVZqI97t9Nbh+eici8dQCIiujv02qHWg/XJ02Kinmm35NzrwlOK8RnM9oqeI7MXexpmvDuxApUo2Y+yoRaSJfJlZ/cS/RJIb3FsLnO07+05GzgEgmIiH8OYbzvIwvAQDQefz73/92dwlNGDx48KFDh2bOnPn8889v27aNZVl3VwQAANBe4EuxPfK6JYDVsVKNVPRmDilN9yn7osBx2k5EvCcvFtjTbz6QPTXeciye96ns9sWeBuldNu9zlm4kVqtLeDluxwB9T6MttTbjtkOS2XnRRWoiXyFW6yz+j1yT1GCS8/RSRSxmTVdwfuMuev7nwgc+yBp6KfYcxZ7LevTmA5refQQAAOplNBrXrFnz5Zdfpqam7tmzx93lAAAAtBfYA98emYb7dF3fN+uu5JIPTiuiEvF2AjFkSTZLlU7TCB8iKl2Tl/tMKhExWjZu9xWnHj1Ws7fKkWuTa6caXpmlOD5QbA8xuq5/zU925MwjUoSQmYw2QgimuB2JaTclWY/WZD9yrNt/+5y9lP2FYHTRQvBjYv7bjpx5up476K871Cm2LLHoIyJWE/UatcZt6xhOiFpiT7mNiDSRr12WS9wDAHQSr7/++gX1/+c//9lKlbTExIkThw4dunnzZjfWAAAA0K4gwLdTntf5ujJ86Zo8IvIeE5B1z2HZLoe/Hs8IzKknT7i6BT0Vqeuq77a5f9ZdyfZMa+Q7d7LCn87S9Y5TL2njPnX1cZb8V65JYjQhQuhTrhbOm+/63z6pw/dVbSst+Tgv4B9hF1ckHzrLWbJONu+TSjdx/hNcjY7seSTb+cAHWY/+lzQE58Z5Xq2JXEwMx3pe2UovAQDQIc2adWEXDXFvgCei0NDQxx9/3L01AAAAtB8I8O1X/Qxf9mm+IilElPvPVCJyHVfP+wlBT0UQEWfkYrcNcD1LERdJFdul8i1S1S+c1zUk1YqnXiYiIeIlYg11M9dG66M/753zWArvr7noChnOKIQ/58ia6Ti1QO87mliDVPWrVPkDwxmF8PkXPduW4EPwew4A4II13pv97rvv7ty5s3///hMmTIiKiqqoqPjhhx++/fbbe+65Z+nSpW4pEgAAAM4FAb5d87zON3hOVP7LWYqk6Hp46HsaK74sIiLOW5AqxeA50Zyp4f8gIwTyXZ4UcxeL2fO5Pr+JecsVsYg1DuT9JzboaRrh0+v4VeetQa6VbGkWQ39Tk1P5wHudxWvlmgNi/gohdJaYM5+I+LC5jBB4MW8YAABa09ixY+v/uWHDhh9//HHRokUvvPBCXeOMGTM++OCDxx57bMSIEY8++mib1wgAAADnhIurfM5vAAAgAElEQVTYtWvVP5YV/iubFGJ4xpZS60rvxDBSpcj58AGPNH2LdSHkcUYbKVtPiLkvi4WriJjmTkdXKPsfx/IWZDQ5USx2nBix78TV/1fw6slz1MhqIpcQMWL+2+Kpl2TLcUbXTQjGDz4AABX48MMPo6Oj66d3l6lTp3bv3n3Dhg1uqQoAAADOBQG+/ar+sSzr7sOyTfZ/OLTbhr6sjiWGwl6P43wFIpIqnaWf5DX9TFaniVxERGL+CpLtfMAk1ph4rldRnErV1tKiN3IqNxc3mCQWO9JvPmA7UUtEBa9kFbzWdIZnTYM5/9tJtokF75Dr6vTMxR+WDwAAbWb//v0JCQlNToqJidm/f38b1wMAAADNQ4Bvp+qn94i3Ejxv8Iv/eWDstgG6WA+pzMEHaohhcmenlbyf2+TTOd/bOM+riYg4DyG84a6V+hiBCX05hohOPZXqLHXUtdeld31PY/ib8QzHFCw+Z4bXRLzkut87530d53Pjxb5pAABoU126dElOTpYkqUG7JEmHDh0KDw93S1UAAABwLgjw7ZH5fxWZdx2WbXLgtPCItxJcB7/r+5hMw32EII0Qqg1dFBP+ehwR5c5OK12b3+RMhOhlrD5eE/kKowlu/uX8Hw41XePrLHWceirV1eIscWSMOWg7UauL94jZ0j/gH2FRa3sxPFOwOKvJY+kZTagmYhGrT9BEvXYpbxwAANrSsGHD8vLy5syZI8tyXaMsy3Pnzs3Nzb3qqvNfJwUAAADaUtMXsSspKfn9999bPpcGF8WBS1T1XalilzkjF/hkRINT1/U9jb1ThxGRVCMVv5Nrz7RUflPs/1CXxjNh9fG6vntb9HoMRb7b/fjgPys3FztLHaRQ+i0HrSk1uniP2O0DOCNnSTb7jA8k6pU9+WjBK1lEFDIvusE8+OBH+OBHLurtAgCAeyxZsmT79u1vvPHGDz/8MHbs2LCwsNOnT2/ZsuXIkSNhYWGvvPKKuwsEAACAv2k6wB87dmzcuHEtn4uiKJepnhax2+0HDx4UBCEx8cyp3Tt27Fi1alVRUVHv3r3vuuuu6667ri3ruexC5kfX/llZu6867YakuO8SNZE6V7til+0nrboED7lWyroj2Z5p0YTpwt+IJyK59qBUtqUlM2cMCbz/XQ0aNRG6bpv6Ok5aG6R3hqPUa/ZbU2q6LOgWPCeKlJ7ZU44VvJKlCdnieW1lS16O8xvPevS5kHcPAABtxN/f/+eff3766ae3b99+9OjRuvaxY8cuW7bMz8/PjbUBnJciFkkV35+5uW6zGG0k53VNG5QEANDamg7wI0eOPHLkyMKFCzdu3EhE06ZN8/LyatvCzmn9+vX/+Mc/zGYzEY0aNWrLli1r1qx5/PEzdwXfu3fv6tWr582bp+r9BpwnH/NN/4yxB2v3VaeNPpvhc+eklX6cF7O5X9HrOebfKjRhutjvBmij9UQknl4uVWxv0dwZgfebSAzXoNk41NsZ55F2Y5LrvPfYbf1JofSbD1pTaogof1EmEQXPiVKcyqnpyfq4l8T8Fm21ke052tg1F/LuAQCg7cTFxW3bti0tLS0lJSU/Pz8iIqJHjx5du3Z1d10A5yfmLnYWf96yvqx+YBbDt5dfswAAF+2c94Hv1avXV199lZiYeODAgblz50ZGRrZlWeeyb9++SZMm6fX6sWPHlpWV7dy588EHH/zmm2969uy5bNmy7t27Hzp0aNasWUuWLBk1atTIkSPdXe/Fa5zhhWCN6zZyhYuzavZX10/vRMQHPuAK8ELIEyT4NjlPZ8G7iljKB97XOL27VH1X6rrmvPf4gLO74rt7BDwcenpuel2Gr9paWrVzpNeNvzBCIB8yrclZKWKxs2AVEfGB91/qWAAAQCuLi4uLi4tzdxUAF4bzudkV4DnfsQzv02QfqXKH4ijgPK9EegeAjuGcAd7l7rvvPnDgQNuU0hIvv/wyy7K//fbbgAEDiGjevHmvvfaayWT68ccfg4ODiSgqKqpXr149e/Zcvny5qgM8NcrwIbOjpWonZ+Qap3ci4nxu5Lyvlyp/VOQaTZdFjecmVf6giKUM7yWEzT/XK/pMCCp6K8d2orZwycny/xTas6y6OI/Yb/sLwVo+UJM95Vj+okzzrgrzr+W8/31eo/YpYjFr6M15N3HCgiPzcSLifG/BEWsAAO1HamoqEUVGRup0uro/mxEfH98WZQFcFM5nNOd9nVT5EyMEaKKXNe4gW9OcJV8QwwlRr7Z9eQAAreE8AX7QoEExMTE8f55ubebgwYNXXnmlK70T0YwZM1577bVrr73Wld5dYmJirrjiivrn8rXcO++88+abbzbfx+FwUFud9s958jGb+6ffdtCSVH16XhoRSTWSJlwXu/1v6Z2IFEcBaxosVf3iLF7LCP6M5u+XtVMkMe9fRMR5Xas4chnB/692p1S+RZGq6zrGfi3nLciQrTIRma4Wgp6OZNgTzmLyHOkXtSbx5EPHzb+WMywT9uqVQtQ/xVMviTnPcV4jiPnbEiLXJjtL1hGj0US81BrDAgAAF8d11/e9e/cOHjy47s9mtPE1bgAulBC5RKoa5iz+hA+azBp6NJgq5swnReSDprCGXm4pDwDgsjtPMh85cmR6enrblNISVVVV9c/Gdz1ufH6+j4/P4cOHL2L+Bw4cyMrKaknPNvtNw3nxsVvOZHgiEkK0jdM7ETmy50jlW12PxdNNbIR2cZZ97azYbhiU5zqKXirfYk9/uEGfwKlnH0ulJJWeeSzXvEFyBBEpsmLPsfqGTHcWfy5bU51Fa/9+/XnFkT2PSBZCpjO6bhf1pgEAoFU88sgjRBQQEOD687HHHnNrOQCXitXH8UEPOQs/ErPnaXt8U3+SVPG9VPkTw3sL4ec89hAAQHXay671FkpISNi/f7/NZnMd+7dr1y4i2r9/v6IoDHPmfmuiKB44cKBHj4ZbYVvio48+ev7555vvk5aWNnr0aJZlL2L+F8eV4VOv3+8sF+N+SGyc3omI9bzqTIBnBFJE1qMfIwS6JimyQzbvIUVkOA9FquVMV9SdA8+ahhCrI9nG6BNYbYQsKpb/q5LMTkbDKg6ZiBgt65FYSXIGyfq8500Mx/g/FFq6Nq9gcRYjMAGTF9rT7hdPv8L5387wZ068d5Z+JZv/YIRAPvTpNhgcAABouY8++qj+n6tWrXJXJQCXixA2XyrdJFXvksq3cb63nGlVHI6cF4hICJvL8LifAgB0HCoL8Pfee+9TTz01YcKEJ598sqSkZO7cub6+vikpKYsWLVqwYAHDMJIkzZo1Ky8v7+L2KnAcd95L71oslouq/ZJwXnyPfUOa6SCb/zzzSBGJSK491LiPItUSkVSznxTJleHlmv0k24hIsZ6QrCeISN+z8ayJiIi16rtn+U0e5nt3sLNCrNhUVPR6TvA/x3Be10pVP4unl2qilhIRyTYxdxERCRELGM7z4t8wAAAAQAswvLcQNteRPceR87ze+zpidUQkFn6g2DJYfSwf1PBIQwAAVTt/gM/Ozq6pqUlISHCdCX/48OHPPvuspKSkZ8+et9xyS8+ejQNfK5o+ffq33367ffv27du3E5HRaNyzZ8/TTz+9cOHCzz77LC4u7tixY7m5ubGxsbNmzWrLwlpb2ecFJCl+D3Y5VwfOOEgq20xExLAMo1fkWlYXzWgjFalGrkkiYoj3IKeZiDiPgWf3wHv0IVZLsp2IWH0ccSHWwzVSpchoWU2Ezp5u4YNKNaH5RCQ7BP9HRvncEVz4enbFpiKGY8JfjyciIWqJdHiYs+hjPvBB1tBDzH9TsZ9mPfrwAZNaf1QAAOAyKCkp0ev1RqORiDZs2LBt27bY2NipU6fWHWkPUJ/iLBNzXlBk63l7MowghL/AaMNbuyQ+aIqzeK1sSREL3xe6PKWIJc7Ty4lIiFxCjNDarw4A0JaaC/Dffvvt1KlTCwoKiCg6OnrLli0lJSU33nijKIquDi+88MKyZctmzpzZFpUSEZEgCDt27Fi7du2uXbsEQXjiiSf69Onz9ddfT5kyZdOmTVlZWYIg3HnnnatWrXIdY98xOCvEU48fZ3RsMwGeD/6Hs3itbE0nRWY9B0pV/5MdhfruW+yZjxEpnPcoqfIHIoYYVohaUvcsRhslBE8T898iIkUs0fX8XtPVM/OOZPP/KoQgjS7BGfzUg66esuVRnzv6F719Kv/FTIZjIld1950UTESsPp4PfMhZtFrMnqeJeU/MX0nEaKJeI2q7UwwAAODiWCyWyZMnb9iwYffu3VddddW6desmTTqz+fWTTz7Zu3cvMjw0Jtcccpb8t4WdWa9r+YC7W7UeInJdZ96eMtaZ9zofMEnMXaJI1a4b9LT6SwMAtK1zBvikpKSxY8cS0ZAhQziOc92AXRAEb2/vF198MSEhISUlZfHixU899dSVV145cODANquY47iHH3744YfPHhDl6en51VdfFRcXFxcXx8XFaTSaNiumbdTsqlAkxZjY7BHpjCBELrafuIuIpKpdnOdVUvUe+4kJsjWNEfxl6zEiIlL4wAcbXIiVD/2ns+S/ilikOCvEvOWayFe6fdnXleH9H1zNmcxERBTsecP8ordP5T2Xfia93xNSNwchfJ5UtlGq3mU/fjvJVs5/AmsaernHAAAALr833nhjw4YN8fHxrqC+fPlyPz+/1atXp6SkPPfcc2+99dYrr7zi7hqh3eG8RrIeveXaI5zXtXzg/U32cZZ/I5VtZrQRvN+4NqrKczjnc7NUsd2R/ohU/fv/s3ee8VFUXx8/d9r2bDa72Wx6CCE0QaqIoqKIFPFPEwvSuwUfaSIgoEgVQUUURARBadK7gKKIBZDeSSgJ6bvZ3nfKfV5sDCEE2EAK4Hw/82L3zp07vwnL7vzm3nMOIIZJnFo1pxYRERGpSm5q4KdMmQIA27dv79ChAwDs37//6aef5nl+7969Tz/9NAC0adOmTZs2Dz/88IwZM9avX19lim+GXq/X6/XVraJScP5uBQDVU5pbdyPDnwuWggfAmLMAIRG8aQBAKJvy1l0AgCj1jYlYEamkEyYGLr0FAFz+Ikrfm5DXqbn24cw3toS12QMYAIFt8yDnwcKcCelAlHbvAICoCDruvUDGWMGbBoSUiZ9cgdcuIiIiIlJ5rFq1KiYm5tixYzKZzGg0Hj16dNSoUV26dOnSpcvq1au3bdsmGniRMkAkkzTbd6YD7/yLSf4cSeJK7cecXcgYAwBM4pRgRHrVwCRN89p/4R1/AABtGIqkKVV2ahGR0BFch4Of0ttCKJuQYU9Wth6R+46bGvgTJ060bNky6N4B4IknnmjevPnZs2eD7j1IvXr1WrRocWcF20RCx33IDgDKVrcx8FBUDXUfYFbwnCM17XjrLkJWm7fvL9obW3YiViryVa5gqeA6AphjM8ZJ6m4k5GT06O8FFw8A3nOp+fMfBlzG3Pu1EaIGcAVLBO8FOuadKgh1ExERERGpEDIyMtq1ayeTyQDgwIEDGOPWrVsHd9WtW3fnzp3VKU7kHoZQtSC1nXnzJjZrCpOyqNReNmcWZgsJ1aNkxP+qUhWSJNGGYWzu54iOpOIeqFxIIg8SgStjysw2fSNIEidrfKqy9Yjcd9zUwJtMpoYNG5ZsiYuLs1gspbrp9fojR45UijQRAAAQfIL3tAuRSN5IddvOhKwWZRjE5S0AAMF1lIp/DzuPgvdC8a6bHcckzvCdaQeAeftvvG03CH7B9SsgBAIYv+wPGF03944574mW2HfxxoHY7Jls9sySLXTsaDp+QnmvWkRERESkClCr1VlZWcHX+/fvRwi1aNEi+NZqtT54IWkiFQiTONVr3cUVrqOi+pcMnRO86Vz+YgCCSZoBgKpYFR33LuYdZMT/xFI4IvcslO6lgPs4IJIyDEWozK9ZzOYvBMFP6V6qanEi9wM3NfApKSmHDx8WBKG44Pno0aPNZnOpbhcuXBAz3FQq3pNOzGJZPSWhIEPpT8eO5QvXYrYQsybs/Ie37S1qv2UiVkLVnNJ15wrXAQCb+T4WWAAAjMmIV2SNwumEPyIHkbJ6u/1pecA7ADAEskK9ABwItaeIiIiISNXSoEGDvXv3ZmZmajSa1atXN2vWTKfTAUBeXt6ff/5ZxYVmRO4vEBNLR7/F5swOZIyTNthbnLyWzRwPmKWi+hOKRtUgi5AzNeZWw3lFREKGMgzmjMsF73nERNPRb93YgTOtAsGPaD0V807VyxO597mpgX/22Wfnzp07ePDg+fPnBxfXFT+VL2bRokVnz57t169fpUr8j+M56gAAebNQHyQjSk3HjQ9cGQkAvO2XYGMwPP7WB9IJUzjLDhA8gjf937FI3rZOP2h18B2XH8r5CUJWE8kbAKL5wjWI0lAx/xeichERERGRKmb06NG7d++uW7euXC43m83jx48HgJUrV44dO9bj8QwYMKC6BYrc09CxI7nCVYL7BGdaRUW+BgC8bTdv+xmRKjruvepWJyJSzQiec/6zHTBnv1kHNnMimzkRABAVLqm9ilA9CgDAu9msKQBAJ05B5O2X34r8B7lpra/JkyfXqFFjyZIlERERvXr1KrX3iy++aNGixdChQxUKxbhx4ypZ5H8a9xEHACialGMlGKXvc12qeUTTISRiRUw0Hf3mdU2YB8CErDap7RrI721c0M99fJqk7kZJ3Y2SuhuQrB4AkOFt6LixlL4PGf4cIasDiBK86bx5A1+4BgAw5/RfeJXN/lhwHwfAoV+CiIiIiEgV0LZt2+XLl8fHx/t8vgEDBgwePBgA/vnnn+zs7Ndff71kwRcRkTIgpHT8JABgr07BvBMwy2a+DwB03HuIfjDzCouIlAPsx5wjpI6cDfPu4Gs2dy4O5BOKRpSuR2WKE7mPuekMfFhY2IkTJ6ZOnbpr1y67vfSjox07dhw6dKhp06aLFi1KTU2tZJH/aTyHHQAgv3UNuVIgkqkx23emY9Az04ahhKzWrY/AbCGX/zVXsLjEGGomZSGpfhIIOQDwLqdl4z+27Ujd4VEmUQoAkpQFvlNP8/b9TNLMa4leMSt4zrM5s3nLViCkgFnBeUhwHmKzZyA6itS0I8OfI9WtgVSU648gIiIiIlJJ9O7du3fv6yqBDR8+fMKECcG19CIit4bSvcgVLBGcB7icuUBrBW86IatFGQZXty4RkeqHUDQitV158wZS112SsrjUXuy77D35GAgBAEyGPUmGtwEA7M9k874EQEzSjFvMs4r8x7nVJ0OlUs2aNev48eNbt24ttWvatGmXLl06fPhwkyZNKlPefx3ezvkueggpIatfPsdLqB4lI14AAETrqNhbJWLF/quBjHe9xxqyOZ9gzoakRY9jmJpfkpr2QfcOAPLGqoiXowSfsHrsaRPPAwChaEjpewIOBDInAcAyp6tRdt4vPh4xUYL9NwCQ1F4ha5Yhqb2KiuqPmFjMFnDG5f60Xp4jNf3nunPG5ZizlfsvIiIiIiJSySQnJ4vuXSRkEJM4DYBg8xdw2bMBgE6cdoucOyIi/ymYxI+AkPOF6wXHX6V2IWkyqe1eNNmWVFSwM5A5EQQ/Ffly0XJ6EZGyuMNHO02aNElOTq5YKSI34jnmAAyyhirElPtfikmaQapbM8nzEKUGgAUO50SLzYdLrGPHLJs13Xu8GZf/DRZ8TnU7af1dskYHqaiBlGEYGfF8qQFjP0hhZUT97fZxv2YHPTwdPwmRYbx15868bTNtDhZjCULs1Q8w7yQjOpHqZxCpJDXtmRpzZU1OSxvup+MnEqpHAHO8fW/g8v95j9T2X+jJmzeA4L2rP5OIiIiIyF1w6NCh4cOHP/HEE40bNwaAXbt2nT59urpFidw3EMomVOQrIPgx7yDD25LhbatbkYjIvQJiYuiY4QAQyBwHIJTaiwM5RS+86QDAO/bzlq1AKuj4yVWsU+T+ouwl9Deumb81arW6IsSIlCYYAC8vTwB8MYiJkdTdWPx2t8d3nmVzeP4rXYQUIcFzLnDpdcF9AoDYo+j8tXJgV23jwSolADA1PilzQDpWEjUi0TzjitPk72M0L9Nr9XQkFTuSvfpBQs5kOmrjeI32Mf6cz7QGEMMkfFDqcEL+ECF/iI4diTkzb/2JL9zAO/bx1p28dSciVaS2CxXZm1A1v4MrFRERERG5Y6ZOnTpp0iRc4vHutm3b5s+fP3369KrPcbNjx46tW7emp6fXrFlz2LBhwQcKJRkzZkxGRsbatWurWJjIraETJvGWrVjw04nTqluLiMi9BR3zDmdaKbhPcsYVlP5avBJv3SnY9yFShnlvIHOiNLwtmzEeAOiYkYgxVJ9ekfuAsud1w8tJFYv+70CqKSCQ+jnt3Q/1qU6jJ8m/ff7BJrPXst13+mnBfYKTJL2j//698Onxyvp9VbdfpZ80PrnmpceFZzQZHNfXaDby/Gpl70wqqQZ3ebGw5RWlPJAxDkCgY95E0po3GwRRWiryNUnd9bImZ5mkWYSqOeadnPF735nnfCdacnkLMFe6WqGIiIiISGXw008/TZw4MTk5ef369R988EGw8eWXXzYYDOPHj9+1a1dVihk2bNjzzz+/cOHCX375ZdGiRU2bNv3ss89K9fn555/XrVtXlapEQgHRUZL6P0nr77xtzh0Rkf8chJSJnwwAbNZHmP83p92/Iah03CRC0RAHcvxnXxA8p5EkgY5+oxrFitwX3DSJHQBIJJL69esX14EXqXoiB8VpX40OsQL8rUmiqGV6bV+jOdr6I2ebTGLOrev5smRUHkiflEq/0GkYhEIZJ1wvXWwhZuzL3vKotGu+ySIIrdWj5piHNzDN4RhScB5AtJ6KGRHKUIjWU4YhlGEI9l3kjCs40yrBez6QOR6yPqQiXqCiBorxPyIiIiKVyty5c6VS6e7du5OTk/Py8oKNrVq1OnToUFJS0pw5c9q1a1c1StasWfP1118nJyfPmjWrQYMGR44cGT169IgRI2rUqNG5c+eq0SByNxDyetUtQUTkHoXUdSeMSwTHX1zOHDrhQwBg8xZg30VClkoZBhLKxr4zHQT3cSiKmZdWt16Re52yDXzDhg1Pnjzp9/vz8/O7du3arVu3p556iiQrwEaKlJdQ3LtDEBY6XP9TyOrQZaSN2enx5vL841KJU8A/4jVK6wcI8EbN8PmyN2yC8KRUOkcb/rPX94RUogrtYY1j6pWXFmUTU2I29lADQJOoziS3gbf/GsgcDwB0wuTyVq1E0hQ6YTIdP4G37eaMy3nbz1zhOq5wHSGvS0UNJjUd2Lz58G91jVtBKujYMcGYfxERERGR23Ls2LGWLVvemNQmPj6+WbNmVRkJP3/+fKlUumfPnqCY2rVr16tX74knnhg2bNgzzzyjUonFkEVEKgvevFHwZ4bQEVHa7kgSV+mCHkSYxBm+U0+zeQspfW8g1VzOXACgE6cDoglVC1LbhTdvJMNakRH/q26lIvcBZRv4EydOXLp0acOGDevXr//qq6++/PJLnU7XuXPnbt26PfvsswzDVLHK/yaeY85LL52InZoS8fJtImEO+gNLna71bs+SSG195joPv8rl/shqRwBrKLKb+ZP6rsUAxJea8UvkPUEQWkmlc7Thoy22fV7f62Gqt9Uh3SGpWmtMi7LbzjPufF7lkxMb3N6uCVPlp58CzBVlsrkzEEVqOpKajtifzRm/44zfC55zgSsj0dWJOBT3DgAApLIZqe1yhwJERERE/nvIZLIy2yMiItLT06tMxvnz5x977LGSjxIaNWr0xRdf9O/ff/bs2VOmTLnL8cePHz9jxoxQepZMByAi8sCD2UJ/+oBQO/symORPK1XPg0qweBNn/CGQOQnRWsw7SE2HYOk4AGCSZnHSFErfq3pFitwv3HQJfc2aNceMGTNmzJicnJyNGzdu2LDhu++++/bbb8PCwjp16tS9e/f27dvL5fKq1Ppfw7bVyOb5fedv712fkUnbyqR7vL4BJnNJDx907wAwMVzZMHdckmslB9QfsZ+uI54FQQAANxZGWGx/eH0RBNFJXvY93I1sbi2XNJKlHvfOWOn69g3t6QDb26FbbXiTLviaSfr47qtWIkkcHf8+HTeWt2xl8xYIrsNF7bSBiuyBpGWUP+AtO3nbbiSJIzXt7/LsIiIiIv8dGjVqdOjQIafTWWqK2+VyHTx48OGHH64yJV6vVxBKp2ju27fvV199NWfOnEGDBiUkJNzN+KItF7H6hIE7LN1qy3vVF29fr4FoHaluzdt/Q5IEStutzD6YzedMqwGA1JXdQSQU6PiJvHkzb90JgAAxTOJHxbsQHUnHj69GbQ8Grj9tvItXt6uA3GH3OLf3WrGxsW+99dbevXvz8/MXL17cqlWrdevWde/ePTIysnv37itWrChvynqREPEEU9A3vv2sOAnwqS7iebnMIQj9TeaTgQAArHV7gu59gkbVtWBkknUlh2QjtfNHwTMOQWgukUSS5DF/4A+vT0MQS/XaZPpWCRGKWeZ0zbQ7Vr8bBQgMC/MXccqHGDqD415h3nA1zSaUTe/uokuAaFLbTfrQHulDv5CqFgCA2Xw29wuu4DsglFTka5S+X3AjtS8WBQ4lTBEDh0RERERCp0+fPoWFhX379nU4HMWNbrf7tddes1qtL774YpUpqVWr1oEDBwoKCko2IoQWLFjg9/sHDhx4o70vFzNmzMC349SpU8GT3tWViNyrnDdzJ4zsrsu+6hZyz0EnzQRE40AOqetBJ0y+cQMsAAAZ0ZkMe6K6xd7HIFpPxY4GAABMG4beIt+zyB1gXV+Q/vzRSy8eNy7Iqm4tlU45Jkt1Ot3AgQO3b99uMplWrFjRoUOHn376qVevXnq9vvL0/XfBRTXkFE1DiugmAWZpNc/LZU5BGGSyfGp3TLbYAGC8Rv2S+WO+cD0iw8ypa4/Kngr2byOT1PzXscdQZDwVkntf7nTPtDkIgN5tosNf0Atu3g49jnUAACAASURBVDkz85tIbR2azuC4fkazmb+rG6wyIZRNJPV3EspmAIAIueA+Ebg42HusMZs3H/NOAOCyZ2HWSKgeERfPi4iIiJSL3r179+7de+PGjQaDYfbs2QDQtm3bhISELVu2tG/f/o03qi4Z8qBBg3w+31NPPXXw4MGSXr1p06Zjxoz5+eef+/Tp43Q6q0yPyIMHBnEVRtkQstqUvi9gns0oo3Kk4DrMFa4FQsIkTKp6bQ8YdPQwQl4PSeKpuNHVreWBwrq+IGPgGcxhAMh+N+2B9/B3sto5LCysffv2zz///COPPAIAgUCgolWJgD/Dy9s42iChYyUhHlLSwy9yuDDAeI36Fe8WNm8+IKYgeUlPT00vFlJpGgBm2hwHfH41QWhI4kyAfbPQ4gtheeEXDgcBMCUivLtCHjulJqKReXmuNM27VK+tQ9NXOG67x3tXl31TEFPjEwACY5aOf5+Q1cGBHDZzou9offby/7EF3wAQTNJMAHHaRERERKR8LF++fM2aNampqcEs9L/++qtOp1uwYMG2bduqci76zTffHDJkyIULFx599FGpVHrixIniXVOmTHn55ZdXrFhhMBjOnz9fZZJERIoJXH7bc0DrORhx++1QDHCW6tZbbuj4CYiK4B2/89Yd1+/BgYz3ADAdPbzMGEaR8oEYaYPfZI0OIzKsuqU8OFg3GoPuPXp8cuL8uoBQ9rtpxq8eZA9fPgNvNBoXLVr03HPPRUVFDRgw4NChQ926dVuxYkUlifsvQ0XQ0toKbd+Ych1FAjSXFhl+CULNuTOBKyMAgKnxyVtsA5sgtJFJV+m1tf5NVv+qUv5DpC6SJP/2+Zc6Xbcd/3NtxKJIbXeFHAAkKXJdv1jM45xJF8MJYpleO0mj7qoINZC+vBCKh6nIVwGzguuo9OG/JHV+JMOexLyTNS4HwU/IagJx+zr2IiIiIiI38tJLLx0/ftzj8WRmZno8ngsXLgwbNqzqS8989dVXS5cubdOmTVxcXMmQdZqmV65cOW/evJiYGJ9PXP8sUg1g32UAATAOYfMKQiVNZlQiiAqn48YCQCBjAgj+4nbOtFpwHUFMNB3zTvWpe7BANCAxHXiFYdtkzOh/Oujeo8fX0PaNSfyiDiCUPfZB9vAhLZzOzc3duHHj+vXrf//9d57nlUrliy++2L17944dO4p57CoJUk3VO1LuKuhr3Z4PLTYEUI+hM30W+uIAEPxUVH9K3/slpzuf598KU46x2NJZVoEINxa+drj0JLlMr11gd7aS3j56/HuXO4BxQ4YOFpyLnlDDsjrfvqPQuc+6v7n0G4crmaZaSEJdMlBe6IRJvGULb93B2/aS4W3J8LZcwbeBK6MBQPCm+060JDXt6Zh3CFXzShIgIiIi8iDhdDo7duzYpUuXUaNGAQBJkneZKO4uIUmyX79+/fr1u3EXQRDDhw8fPnx4fn7+pUuXqlyayH8dOuVr/lhDwAKlfZGKf7+sLgH/yVZYCBBhrQkmtqr1VQRU1EDOuEzwnGXzFxTZdd7NZk0BADphMpDiNMkdIXhxiQciNwMhWvwL3wG2TcYr/a6592BjcPozc/j57LFpAKB/I746JVYOtzLwV69eDVaS+/PPPzHG4eHhPXv2fPHFF5977jlpCGZP5G7AHAYeI0k5lkisdXuK495fVSr+OTPGwOWcZR6WxExpANBHpWAxfsds3ev1RRDEUr32gC8ww2Y/nv1disQzTcKAH9ibD46YOErXPYvjLrHcIJNlcWSEiiAoHaN/OyFv2uWzE9Pe+yGeQ2CthBj4axpoPRU7kr36IZs5gVQ/CYC4/MUAQMeOwJyDM63grTt46w4i7DE6ZgQZ3kZcUS8iIiJyC1QqVUZGxq+//ho08PcFBoPBYLhNaVUREQDwcNjsue6exOQRAMDH4SwHX7KdJsGguM16E4KJpXQvcabVnGUjU2MGULpSHQLpg7AQAERJay2tCPnVASLppBn+s525nDlU5KuIjmJz5+BAPqFsRuleqm5x9yWC+7jvTHsIwcADoiR1fiTVT1e+qAeHYvceNTKx2L0HeeA9fNkGfvbs2evXrz948CAA6HS6AQMGdO/e/dlnn6Vpusz+IhVOWrsjgSveh9JaISokF1rSvfdSKnjzpobOzQEkG6+ZaSt0LY5k6tJ0SfeeStOpNP1Q/ow6ti8BbmXdi8H+y4sNI/sazScDgf4m85JIbRhBRL2dkLM4mz7qarbL8cjLse1DrkV3Z9DRb3DGHwTvBa7gO8Cc4D2PpMl07FggJHTcWC7/a67gW8Hxl9/xF6FoQMe8Q0Z0BlTVq0BFRERE7hemTp06ePDgkydPNmzYsLq1iIhUJF3WFV6ycTe2Hy0ItF5pLNX45XOa9sm3mZpian7BmbeA4PGl9ZfW21pyl+DL5MwbAYCOHgZU+N0Jr07IsCdJTQfeupPNmkrHjmLzvgJATNIMcUbkDkESEFgAAEKGiLIXqGLMAu8GzKGKW1fP5n4m2PeF0pPUtKMMwyrqvFWJfUfhv3PvNaLHl5GdQds3BnP46jvns8emETJC1/++XBdzM8o28O+++y4ASCSStm3btm7dmqKotLS0tLS0m43yf//3f5Ul8D+J4BXc/zgQCSEmTD3sDwTd+/sadU+lArMFgSujAECeOLUOlbrH6xtmsrSQSkq69+CBDXWtA+aggacRpSz78YzgwgILCJFhLQ0kuUyv7Ws0nwmwwZrzf6LAjjci+k/OH/JZYcuBtSvk8m8FYpiED/1pvdjsmQACADCJ04GQQFEJzfepmHc441Iu7yvBfcqfPhBJp9Mxb1ORr4jhRiIiIiI30qlTp88++6xt27YDBw5s1qxZdHQ0QVy38qtFixbVpU1E5G54JIYJCNfdRfl4bHILUhJFKq77kIcxREJYKM/6KSZxWuDKCMHxh+A8RKgeKd7hT+sNICAynE746BbH3xcwiVO99r2ccaXgOQuCn4p8JVgGSOQOIOR1KX0vzricVD8pqb26zD6BS29xphWkpiMR9nhFnZc3rRa8F0LpiXnHfWrgHb+YMYdJNaXtXZQvTHDzV/qc4u1c8qqGVCQDAOFd9flzMwKZPsce8wNm4BEuK/d4eRPPljnIg8rp06cbNGhAEATP87fvfUd4T7nOtTwoTVXUOxpSGHw6y71eaO6rVPRWKQHAn9aPt2wmw5+V1PmRwzDabMvheQZBJsctibzm3v8911PYfdKFlD/XP9tGJj/kD+RyfF+VQlr0GRAchxIpwZUhabQ8caOBJOIpKooiPzLbrvJ8HEXmcTzm8NcvXVXlBhqktyLkVTHd7T/XnbfvBQBS3VpSd2MZPQQ/V7iazZ2HfZcBADExdPRbVFRfIMSUDSIiIhXA33///dhjj7Vs2fKvv/6qbi13xW1/7sXfd5EHhj9z/H22WhrrmXXdtHc8iPdoAxzIRpJ4WeOTwRbOujtw4WUAYFKXUhEPQjlb9uoHbO7nAACkQvbwYcSIQSt3DmZNvuPNMO+Q1FlHhrcptVdwn/CdegYQJXv4TyRNqaiTcoXrAhcHAyGRpHwDpKqsHhb/xaGAOUntFaSmY0WdtyrhndzFzsfdh+xMvDR1ZxMqkrnY5bjrbxsAyOopa21vjBgi/YVjnqMOSYo8dWcTOjrUFF33xe972TPw8+fPr2IdIiXxpbkBQFonVLd5leNyOH6Lx9tFIVe4/uYtm4FUMMnzANAFlv3b7/dg/EeMQYaAueFezV5jWdiZpkrsIrImt1YWFaWsRVPPyKQA4L8yhhJcAhDDIuaa3J7ioxSI02CPPQBygN5qRctfa+OAgBgnvmG1GqqEtWR04lT+1JMAiE6aVXYPQkLp+1KRvXjzJjb3U8FzJpA5ns2ZQ0UPo6IGI0pd4ZJERERE7kdGjxZrEYv8V7ho4QAgw1HGuvrQkdRe7jv1DPZncXmLqOghAMBefh0ACFmdB8O9AwAVO4ozrcKskY4ZKbr3u0LwcaYVhOJh3rE/cHEIpe9Vqv4XV7gWQCAVD3OWbVTUgIqqLUfpunMFSwTn34LrCJ3wwY0dAldGA+ZI9VP3qXsHAFJFpWxuFPTwae2P0pGM+5iDiZcSStJ71pXW/iiSEN6TzvK69/uFsg38m2++WcU6REriu+AGAGlqqOkoGzFMAkWdDrCDjMalpnEAQMeMQEx0cKG7QxA6yGVhRGnvHiwd94ePmSrt2N677Xn78m+Vg2vKolpKmCeCteg4G29cBgA+Xc+pUfUKeT6f5y+x3AW/5/OcDtF8btFAeXCL7Bx0zNt0wofl/APcBkJeV1J7FQAiZKm36odIUted1HXjrbvZ3DmC8x82axqXO48yDKIMryM6smJViYiIiNx3zJ49u7oliNyPCLxlK+ast+2HyHAyouM9EsUWXFEv3N2aEkLRmAx7nHf8Gcj6gIoewGbNxqwFEGJqL68YlfcAiFRJUpfxtj10zFvVreX+hrf/xl4tugfGnIXNnVd2N9c/vOsfROkofa8KOjNikmb6Tj3N5i2g9L1Kze0L3guccRkgik6cXkGnqx6KPHynY+4jjkC2jzZIau1oQqrItPZHfefdAMAkyR5I9w4hlpETqWJ8FzwAIE0NdQZeSxLB0PQU+0pwn8JMLB39xrkAO9BkdgjCczLpxxHhJd372QA7yWo7E2ABQI7Q79Gz2175mcS+La5xsvhNxd38F3oC5oGQ6VI+fbLER+UnD4NzCADggPES15K+yJwCwkCFUUAAAMacHQAqKYccGf5syH0RqWlHatoJzgNs7me8dTeb8ymb9xUV0YWOG4ukNW4/gIiIiIiIiMi/8La9/rR+IXZman5JRfasTDlVjST1B8+RFBC8gfQ3eOtmAKDCOxLSWtWtqyIhVI8SqnIXM75n2e/zR5FEalmpuM288Lff30Euq4y7VVL9FKL1mDUSkkTBnwkETUZ0geCdM+Z5y2bg3UiahH0ZiFSRER0q8NSEoiGl78kZfwhkTpLUXllyF5sxHjBHGYYQ8noVeMZqARHoWsUuAhACRBGIQcV7H9T0i6KBvxcpWkJfuxwFIQ0kuUwrEbK/BIAvNGPbBIjhZrNdEJ6TSedoNdS/s+88wNcO5wKHi8NYT5K9lIqXlfIwguDw1MCV0di+T3D+E6yjLriP8M6/AYBJnFLyc7LT4x1jtj8dPmqWeQRNKbwNDr5mwTkcF0YQ/UdkPbLDoe0VnbiwHm/e4E8fiOhIKlhK9B6AUD0qqb1acJ/i8r7kzOu4wjWceQOl7UbFjiJkD9TvroiIiIiISOVBKJsgSoM5K6FsQsjLrl8g+NIEx19ASMiwVlUsr9Khwmn9QLZgEWdeCwCIoJlai6pbk8hNsQnCUJNZitCiSG0zyXWLQfJ5vq/RfJXjFAg9Lav4Ctm86x/MGgFA8GcCAAgsX7i2VB/sywAAzDsF+z5S260Cz07HT+LNW3jrTt72S3H4PW/Zxtv3IiqcjhtbgeeqFgQ3f+nFE66/bEyMlNRR3pOutE7HSBXpPeWS1JQjCvkuuNM7Hqu1ozFteNAm4ctRZlykihCw/6IHEEhCnoEPos2fG84XnpU0W0a16V9Yhnu/xHKvFJi+sDsFjPuoFLui9YPDlGEEAQBU1EDExABA4OKQYGd/Wl8AICQJVNSg4lNs93jHmK08QL2o7qT6acxZNfmffq/XxlGkQxCOvhuLJIR5Zb7nmClw9QMA2KwZudpLeq/PgRTAmLt5ViRPJSdMIhQNmJSF0gb7KV0PAMwVrvGdbOlPHyh4TlfqeUVERERERB4MEBVBx70HAJi1MEkzmeRPb9jmBGtf09FvI0lCdestgiIQVFA9NLrGDCCVRcPGjhFT5N7LhBNEN4Xci/EQk/mwP1DcXuzeH2LoFtJKMXiEtCb8u1IVMdEABAAilM1IVUtAJAAK3nsDACASyepW7NkRHUnFjgQANnM8YBYAAAeC9+d03ARERdzxyIKHt6zM4yyhFKGuLILu3bnfysRJa+1qkvpTU0XzsECG13vKJU1VpO5qkrqriay+0pfmTu94jM2/RbDvfYk4A3/P4c/0CV6BjpWQqnL862DfZTZvIQCBkqYTLsRjrCKISRp1Sff+YoHJh3EsRU6PCH9EUvqrSlLrO9+Z5wR/BlfwLQCB/TkAwKQsLtnnQ6udB3hbrXo9TCXQ0/mTT3D530RF9l6uT+1jLNwfxb/YL0r3dZ5z7zTV41ku2UNTqed5q/0Lu7OXSvGaUqEmCD/GzXPyGYDNhshY6roLFAD6mQr/8QUmRahfVZRj9cEdQMjrMimL6LhxbO48rnAVb97AmzeSmufomFGC+xibtyCkQWS1JHXW3EfFUQXvhUBaXyzc/lsMERKm1lJCXsG/JSIiIiIiDwZU1EDOuEzwnGXzF9IxpWsJc6bVgusIYqLp2HtlFR4A1NJQAJAQViG3vgRTY27g0lBER9GxYypiQJFK5KOIcBqh1S73YJN5YWREC4mk2L3XZejFkVp5OctvhQhiYumYt9nsjwEAs2YqsgdnWoMIOdA6wDwZ8bxg3x/sSen7VcZNFx39Omf8QfCmcQVLKcMQNvdL7LtEyGpTUf3ueEzBzV/sdtz1p01aR1FrRxNaXz0ZLnI/uOTcbyXDqVo7m0hqyAAgZXPjtHZHeAubsqVRcMq91rbG55/6x5fmzhqdlvxDg2rRWUmIBv6eo7wZ7IIErk4CHHDpXnvTEy+AIEfIKQiDCy1LI7VqggAAGoGGIJ6QSd4NVyvK+p4iVM1JVQveeTBwdTJgAQCIsCeCy+mLmaRRCxj+p5BBMOGqvjdXsJTNHB9dd+P3et1al6fhuAjLb2mKZmsAQFtj1lw6cqnDdTwQ+MLu/Nbh6qFU9FTKMYAb4//lm7aU8PACQK+CwmOBAABQVVW0CElrMMmf0nHvsnnzuYLveOsu3roL0QbM5odyuMA7AfOA7pv/RDiQH2pdUAAcyAXRwIuIiIiIlAki6cTp/nNduJxPqMhXEB11bRfvZrM+AgA64cNqnJr+7B/n0YLrZgjtfgEArti5PtssJdsZEia0DKsRXr5fc0rXg1A2IpjEu5cqUtkggEkaNQCsdrmHmSzTteGf2ZxB9158n1xJ0DEjONMq7M8CHMCsGVFa3vE7AAAhRYjBvAMAEBVOx4+rlNMjhkn80H/hNTZ7BqF+ksv9FADopOl3fO9a7N4BwHfend7xaHV5eOUT4aZF2byNs64vMIxOAgDbZpP3jBsEnDP+YtKS+ohC9l3mQLYfAFRPaqpeYaVy33iPkmRmZp47d659+/bBt1lZWfPmzTtx4gTP840aNRowYED9+vWrV+Hd4EvzQDkD4AXXUd6yQyAVfZlhwZXzY8LDBpos5wJsf5M5+N2UQFF7Y6JuPY6k9krPkVTg3QAAQEpTvyvVoZNcVvItHT+RN2/m7b/x1l0GTbvhahUAhP+wVbD7SG03Muyx5wCek0mP+AOLna59Xt8yp2uFy/2YhPnTF/Bg/EK+abMhMp6iMEB/kzno3nsqFD2UlTv9XgrERDOJ0+jYkVzeQq7gm2L3ThmG0dGDbwwzwYLff/YFzJro+HH3kXsHAFL9FKl+irfvI7VdmYRJZfYJZE3lC9cTYY/fWK1URERERESkGFL9FKnpwFt3sllTmeQvitvZnE9wIJ9QNqN0L1ajvPUXvLku/sZ2Z0D4M7v0SrRONdnyGngAeMAS1z3YlPTwowqtGKAK3DsAACGl4ycFLg4GQLztZypqCFewCAAo3auccRkAAsB03HuI0lbS+UlNR1L9NG//1X+6HeadpKYjqX7mzoYSPPylHidcf9roWEny9w2uvn3ee9qV3u5IrZ1Nqj7IPPx/+qQl9TMGnsn94BLwmI6RZr55DgRMyAjrhgIAUD0bcfWt8yDgmMk1I4fEVbG8yub+i4GfNm1acnLyl19+GXy7devW+vXrf/LJJ3v27Nm7d+/cuXObNGny+eefV6/Iu6FoBr52OR5aszkfA+A18p4ZKCIY9x5HUcv02gSKCnp4uyCENBAVQer7BF/ShkFwu/AYRGno2NEAEMgcF4x2E5z/CPaNQEh51+hAhjfYramEWaCL2GCI7CiXYYx/9/kxYALAi3HnfNNVjutnMh/y+QGgp0IxMaJ6irQjSkvHT5A2PkUnTEaEAgC4/IX+8z15xwHExCJJUvHGW7Zj1kTI6lD6vtUi9W6gk2YBonjLVsx7Sl5UcMMCy5u3ABBM4tTqVioiIiIicu/CO/7gzZtIdWtAFGdcweV8yps38eZNXP43bN58AKA0HXjzZt6+FyC0O5CKZl1X7fJOESW3V+rJAEBBE5MeDyMAuqbKgu2buus6p8puO2DVc9C2/y/rb9Wt4sEBAQwJU6oIAgMggLfUqkp37wAAQOm6E6qWABgABMdvhKoFIa8n+M4BCACYkKVSUQMqVUBwyh3zDiAkd3yDJ3j4Sy+ecP5upWMlqTubKh5R19rWWPaQ0pfuSe9wtFqCzDXdo5K+rY8olPvR5cw3zga9euqeZpSGtm4ouPpmkXs3jEmqem2VTTkeN5pMJplMplQqAeDHH3/cvn17rVq1hg4dGhlZdfW016xZ8/7776tUqq5duwJAQUFB//79WZadMmVK586d5XL5/v37J0yYMHLkyObNmz/22GNVJqwCCfre0GfgBc9p3rrbi2SLlX1LZq1TIkKBEIHQuQA7xGRZGaULpUKGpMYnPvvvgEg6KaTikJRhMGf6XvCcYwsW0dFvBjLfA8BU5NtnmuZTEeb6J1oSiqLT1qHpOVrN/6lVS5yujW5vAGMA8GLcIc8Y/G2vRvdeDCJVdMw7tL6f93gTzFkF74XApdfZ7Bl09HBK3wsIKWaNXO5ncHcLkKoRQlab0vflCr5lM8dL6m4qtTeY5oSK6k8oGlWLPBERkcpmzpw55eo/atSoSlIicv/C23/1n7suXXYga0qpPoGsj4IvmJSvKd1LVaSsBFEKMkpx3Y3PWTMLABQB4VJCABAwPB537+amzvReWpQ5FwCiJNE15bWrW86DQD7P9zOanYKgIQkrL4wqtAbj4cs1yDy7c4/X+4UuIomiLrPc335/D4WcQQgATgXY0WZrb5Wil1IhAGxwe2rTdAOGLq7KDoAFbxqTNAtRWv/FQQAEgEAnTgdURn27CoSQ1aGiBnL5X9OG1++sfHIp9y5JlgEApWNqbWuc/vwx7xlXeoej1TIPr+ke5dxvK1ycDRjCno4IevXIYfF5M64AxtK6yqgRD2aQS0gOxOPx9O/f/8cff/zjjz8ef/zx1atXv/rqq8FdS5cuPXDgQJV5+Llz5yqVytOnTyckJADApk2bzGbz/Pnz33zzzWCHlJSU5s2bN23adObMmVu2bCnv+IIgXL16VbjlfHV2dvadiQ8RbZ8YOlaqeCRUK8vmfgaANyh6tFDGfBIRHnTvPMAos/Ucy8ZTFABc4bgAxrKQUnQQ0kaHyyEXUXTCR/7zL3LZnwDmBddRxMQwif8nb3zWfdCePycjZlLNkt0TKOoDTfiwMNVCh3Od28tjHPxbd5LLqt29X4MKp5M+DlwcDKSKYKIE78VAxhg252PKMEzwnMG8k4x4gVQ/Xd0q7xA6fgJv3sjb9/HWnaTmWtFR3rabt/2MSFUwt7CIiMgDyejRo8vVXzTw9w2CN6QcpaQylKfP5mW5bGHAMCIRiLKS5sjrI1KJeRchr4+kNXjbzyD4CGUzICSC409AFBn+LGZNgusIIJpQNL6TywkN3v4rlzs/hEl+AsnrILif6matylmCAQPAiuxFE1M/eWDrWVcVJbPWLYnUfm53BuPhy+vhc3n+Isv1NZqX6bVfO51b3N79Pv88reYCyw00mZ2CUMDzAsAki2292/OIRLJMr4VgVfbIVznTCgBgs2f+mxtCIDXtqyZikUmcQoa3JdVPlmq3by/0nHAYRich5qaLEcp070EoHVNre3V6ePMPeYVLcgAAEeD41ZI/6wodI82bdQUAEzLSd851pd/pGt89hKgH7b9PSAZ+7ty5P/74Y+3atYNG/ZNPPtFqtYsXLz579uyECRM+++yzadOmVbLOIs6ePfvEE08E3TsAnDp1CgB69OhRss9DDz306KOPHjt27A7GHzp06OLFi2/fDwBXWrWziJcNES8bQuyM/dm8eRMgpmvNUYNkmuKP58c2++8+XwRBfBupjSSJkN37nUCGtyHDn+Ntu9mrHwIAkzAFCHnczNQLz/xj/OKqrl8sk1C6tKaBJCdrws+x/El/0d3Gz15fFsfFU/fKnDal684VLBGcf5Oa/nR8MzbnU8F9LJiVBxBJx7xZ3QLvHERp6Lh3AxnvBTInyNTPACEBAMAsm/k+ANBx7yFaX80SRUREKo1Nm0ovvfnyyy/37NnTuHHj7t27JyUlWa3W3bt3b926tWfPnrNmzaoWkSLlhbds96f3BVxGyHdpSIWswT4krXmLLvmzruR+dBkA/GmexAV1b/TwiNZTMSPZrCmAOUmtpZxpVeDy2ziQh6gwAKDjxtOxI/xnXwAAyjCYkFVioDhv3sLb94bU1fE7JR1eeUoqlkO2P9LcZ8IoNYmoDO+lvyy/Ph5xh3HLInC9ew/GvZfMaVcuD/+BRl3A8wd8/l7Gwo80mj+8/n1e3wCTJZ1lnYLQTi4bHqYKuncZQm+rVcUH0gmTeMtWzDswZwWwAgQzzH1UCZdbFoi58UmBZWVexrBzIGDPMWfyigZlenjM4UvdTzj3l+Heg1zn4Tsdq/1rs3JV0bobzD/kZb5xLrhOnomTZg47m/vRZSAQCDh2aoqqdcTFF47ZNhkfSA8fUuzHqlWrYmJijh07lpqaajQajx492r9//y5duowfP75Bgwbbtm2rbJXFKBSK/PxrGcINhrKNLk3THMfdwfj16tVLvh1xcZWZCEHArr9sgjfUgDHOuBwwT2n/p5MlFH8w17s9y51uBqF5uoh4ipQiFFbJQT5M0jRADAAQqkdIXTcAbva6yQAAIABJREFUUDQP03SPErxC7oeXbuyPAfqZzEH33oqRkgj5MO6cb8q6o3+1ygExSTMBCDZvISGvK22wV1J3I6LUAACY953tHLj8DvaVcWn3BVTUQEJeF/uusPlfB1u4/EWCNx1JkynDoOrVJiIiUql0vh6/3//zzz9PmTLl6NGjEyZMeO211956660tW7YsXLhw5cqVO3bsqG69IiGCg+4dkUpEhZe5FdWjFgK3noEPundEIkJOmlfkZb5+DoQyZizomDeRtKbgvcAVfEfpXyOUTXAgR/CcQ5IkOvp13ryJd/xRnCin8qCi3wjeftCJUyR1N5a5IUkSAFBRA1hQVqqYiiIgBNbmLQOAbtG9ukf3BoC1ecu9vKe6dV3DwhbuLdzB4sDtu94DBDDu/2+992X/Zq1DABM16m4KuQ/j102WKyHff0oRWqCLeFQqMfPCRKt1WoQmnCCO+P0OQWgrl82OCJ9hc6x3e6QIfaWLaCq5lp4d0fpgVfbi8sO0YQiSplTsxYZOsXsnFKR9Z+Hl107hQBnug7exrr9tAKDpGnWjew9C6Rht3xgA8J13+y97K1V2MfYdhUH3HvtRimFMUsSrhsSF9RBZ5N6j3kmUN1LV3NiIDKNsm4xZI0OqwXQfEdIzkoyMjHbt2slkMgA4cOAAxrh169bBXXXr1t25c2fl6StF27ZtV65cuW/fvqeeegoAWrVqBQCbN28ePHhwcZ/MzMwDBw48++yzdzD+iBEjRowYces+p0+fbtCgAaqcCW3LmoKMwWeixydHjw8hRgVznOkHACiZ/SKL46db7QDwgUZd8oujUkHSFDpuNJc7n0maXfzFFDslxb7NZPkxP/L1eEWzsGuqAUplrTvlD/Q0mYM57T7Waja6PS8p5E/JiubtMe/k8r4CwXd7HYSMin4DkRXzC1283imQOVlSewUO5GPOjqgIUt2as2zmjMs44/ekpi0dO5pQNquQM1YdiKITZ/jPdeFyZlORLwOQbM5sAGCSZgbvhERERP4jLFq0qEaNGhMnTizVPnTo0M8///zHH38cMmRItQgTKRdkRCci7DHB8RcVNYBO+LDMPv6zL/COPyjDYCS5aVBowaeZQfee+HU9Jkl6qctx84o8wS8kfVsfkWjWAee+q761XXUKGgFimIQP/Gm92exppK4bFTkx4OoGgJ27HrKt+Fj1v+UEA3T8RERVbvUmQlaLMgzm8r7kLdvp6LegxDrzb064TxoDD1E/9ZZleHH4xxeHnrF6AcDD4uWn3QBwOD8wfI8VACgCjXxEFa8KJVNQVbDTtMEcMCXIkltp2iCE9pl3pbvP7TBu6B7dq7qlAQBgwF9nzkl3n7Oxlm73hqRbwwI4BaEhwyyOjFCVmNAiAD6KCCcANnm8zhCTPQPwAH6MF+giXi+0HPD537NYuX8P9QrC+1bbFre32OSXOjZYlR37LgIAoiOpuDEVcHll4U/rx1s237qPNBlq76A4cwdJnQXpHY/adxZeevlk8qqGhPS6OT9KxyQtqZ8x4Ixx/lVSTUWPK8OeWNcV5IxLB4CYSTXlD6tu7FAZBLJ8IGDEELJ/zxjxqoGOZgQXr+5UFNnNJEjpKIZ3cMV5tR8YQpqYVavVWVlZwdf79+9HCLVo0SL41mq1MkzV3fFPnz49Jibm+eef/+ijj65evdq6devhw4ePHj16xYoVfr+f5/n9+/d37NjR6/UOGnRfTiR6TjgBoNR/npvBW3fhQB4hq02oWgZbBIBxFqsH4+flsq6KKi2+SseOkTXPJBQNi1uoSIY2SADD5VdPQonH932NRe69l6ooa10DCbM8UksBeDEeabbs9fqGFVoGmMwXWBYABOtuNnsmm/vZ7bfsGYJtT0VeVMIkRIbx1h28ZRubNRUA6MRpTK1vZQ8foPR9gKB56y7f6bb+c914+28VeN4qgFQ/RWraY97FZk1js6Zhzk6qnybD21a3LhERkSrl8OHDderUKXNXSkrK4cPlSYkiUq0wiTOCq8aC9qAUoUyJF3yamTPxYtC9R7xiUD4aXnNTI1JJWtcVZAw8g3n8e5b/goXLchTNVZIRnUj1M5iz+U6/nzdht3NfY/ffDTyH1VT0XoIx84Vq5y9VUXuZjhuLaL3gPMibrwsPWXrSveey80liFgDMyXl73UXZOTMHAKyAjxewAJDj5Hdc8u245NuS7v3isGv1Wc/Wi16+suIjQ8XKmn8ybgKAnrEDCUQgQK/GDkKAdpk2FfhzQxkhx3d10dW5ub6sSlJ40Lo/3X0OAH4ybTb68yrpLBWIAqG9MVGronSqG5ajBj38P7GGhiF7mTFma+vcgn/8gQW6iAYMYxcENwhNGImOJP7w+be4vSRAme4doKgqe/AlHT8BkWFl9KkIMGsMpRuiOGltj7S2otaOJnQU49hjvvzqScFX+lmGpltUsKx63rTLeTOuBBs5U9H6i+Lvh5hJNQ3vJlXcRdyGyCFxkUPjcEC41OOE/afCYKOqdUSxe+dMgYudjvnSPdLaisRv7uP64mUSklFs0KDB8ePHMzMzHQ7H6tWrmzVrptPpACAvL+/PP/9MTk6uZJHXiI+P/+mnnzQazaRJkxITE1Uq1Y4dOxwOR69evZRKpVKpfPLJJ9PS0r766qtOnTpVmaoKxH/FAwCSmiGVM+GMSwGAiupX3LLM6TriD0SS5ERNNSeEE7zC5ZdP+DO8AMDm+S/1OBFsdwjCYX+Re58Qfk1kYwmzXK+jAFgMTSQSNUH87fN3zzdNttrs6rbBqGxS/QydMLnMjVS3BgDEGMjw5yrwKhCtp2JHAIA/fQAO5BDKplTkSwCApClM8ueyRsfp6OGIVPL2X/3nuvpOtebNG0IKQbw3YBKnAmI44wrO9D0gmk4Sg11FRP5zxMTEnDhxgudLf3HxPH/8+PH4+PhqUSVSbjCLA3lkWCvAAX/64GBFt2tb4drAlTEAQEa8gL1pZQ5Qyr3zdo4tCJTy8OiGtfRF5ak8axB1FXv+p3xqQeznrytapQGA87cW9g0/O3bsq+xLR6SKjh8HAIHMiSBcm2db00W77rHViZLMy76a68y3qUi//oJnwu/2d362/XY1hOV+lcmPud/5Bd8j4a1SFUWWI0lW87GIpznMrc1bftvDMeAlWV8csP7+XfaXGCr+aURA8K/7VwaH2VAkVTtejNvlGfsbze4b0ldhgBk2e8uc/LMBNsTRkinKh/HwQst6t+fqvwvvM3lWSxSt4DCQZOObL4AlNR2pqEGU7iVK37v8lxIqTOIUAASERNb4hPxRa8nNn33+Qrv1l/vPA0wBEKRufOCqr7wePm/mlZPJ+68OP29dm18t7h0AAEH8J7Ujh8Zhv3D5tVPFHj4IZwqkP3/Me9Yle0iZurspHfWgLS8NycCPHj2a47i6desmJydnZ2f3798fAFauXPnII494PJ4BAyq3emEp6tevf/ny5eXLlz/++OMIoUuXiuKQBUGIjY0dO3bslStXhg4dWpWSKpBg6Iikxu0NPPZn8rZfgZCSuleCLZdY7nO7EwA+0qirprLlzQi6d8deCx3FRLxkAAD7T4VZ76UBQBhB9AtTvhseVtK9B2ksYVbodW1l0s+14bui9X1UCoTQjy5Pe6PnkH4sAAjec7RhCB3zDh3zzl+aYRmRbwVf01GDBe95AKATPgBSYeT5bR5vRdnoopIbmAVAwfmN4l2IMdCJU6SNT9PxkxCtF9wn/OkDvcebcfnfgHAPxardDCStSRuGAgiAecowqFLzDImIiNybtGrVKicn59133y1ZfkUQhLFjx2ZlZT3++OPVqE0kdLiC7/wXXuEdvwOA4D7uT+9/3XZxCOYKAYAzLvedaS+4Si+suObeF9aLeMXgS/ecbXbgTMO/XH9YS3r4HouuEtd7eEJWmyt8HAhB3eWC4YO3ZU3qY+88wF4yonP4SxMAwLZmGw7ZGt0xlL4PoWiMAznBEvRB4qTmmr75AJCjmdKjbtgr9eRPJ0oAIEpBPhrLAECSmnqlnrzkNuhhRYvo6iwsd8l94ZDtD4ZgXozuU7K9R3QfGSk/aj9wxnn81iP8Zfn1iicdAC66zx+0/l7hCncYN1jYwmCoAkLoiP3v20qqdhAAieCQ3z/EdJ2HxwAfWe3LnW4MwIQcGPumWtVbpfBjPNVqtwtCW7mshVRi5oXgolEJghyeH15o8d881zVTYzaT8nWIFqxccPkL/ee6+s91ZbOmIVoPgt93um2wJbi59j3Pm16Kn/Fh0ldzAXGEsteFNv4zDf+yri8on4efehkwFC7NuTLgbPW49yA38fCcKZDW8WjQvdfa3oTSVm6VvmohpBj4tm3bLl++fOrUqTk5OQMGDAgGnP/zzz/Z2dmvv/76wIEDK1lkaWia7t27d+/evQHA4/F4PB6KopRKJXXP5DC/Y7j8ACBgQjDwnHEZgEBpuyIqHAB4gPEWmx/jF0tEj1cL2C9c6X3KsddCRTK1tjWR1lF4jjt9aW7T/CyCQLHTa72rvumSoYYSZp4kIvh6XLi6p1Ix0+b4zet7A55bKWlYy3+Szf2cjhtn4vlhhRYFQositU0kDJs7BwfyCUUjStcjh+P7mgpzOF5NEE+UuX7ptvrZAt66G+DaEwAy/FkufzGhai54zwjeM8XthLwhoWyCKDUdO4KOfp0rXM3mfol9FwMZ77LZM6mogZRhMKKrqMLinUHFjeHM6wALdOz9VFxHRESkopg+ffqOHTvmzp27e/fuzp07x8XFZWdnb9my5dSpU3FxcVVWYkbkLiFULYI1pZEkHvuzgFSR4U8XOQTBx9t+BswRkjjBn41oHZKlljy2cGlOkXtfVC/iZYMv3ZPe4Sib7weAi91PpKx/WNlKU3NDo4tdjzc4aOslJeHl637XHD+laLodIMMv8rZdiNZxhWuBkDCJH6LUREmtJH96hu/sRVmjupVx1RneS4dtf74Q9ZKEkDJJM3xnOrA5cyndq0gSBwDs1cnBsq/tUju2AwCAv3L8v2b6a4ZTL9aRHcgJPKynpz1ZwcsVfRjPszuflkmblzUHu9frOx1g31SXHW2PAa/MXYwBd4jspmOuKwcTRoV30HfbkPfDqtxvp6R+RqCyw/X9gm99/g/Fb9fmLW+sbiEhKuye0MIW/mTahAAwYAKRAuYBYHXukg9TP72ZpHsBKULf63V9jIVH/YHBJvM3kVoFQhhgmtW+yuVmEPpcq0mhQ3UQCKCzXP6jy+PHmEDoBZl0pbtozoZB8JlWO9Fq3e/zDy+0fKGLkFRaBagy4UxrBPd1z1Mwa+Tt15bTExKQ/1vVERGqjDfa+S95ACBj4BkA0HSPStncOP35o4495qtvnkv6tvSyc023KOs6o22LEQCkqXJfugcwljcJM4yuvlrrCOI/qQ0Apq+zL792KnlFA0XTsLSOR33n3A+we4cQDTwAFBvmYoYPHz5hwoTgWvpqRC6Xy+VVGuxdqcR8WFPwC7cvwIBZzrgCACh9v2DDIofzZCAQQ5Fjb5jZrkqKn4HReqbW9ibSugoASPqm3vnWhwHjgnlXASB2eqgzvYkUtUAXsc/rm2VzTFe9t8T/mifnc4fm5ThFcheFfJPbM8RkXqp218z7CgAxNWbncDjo3hsxTJm/naEQyBjHmzfe2C44DwWch65rQoz8kWxANAAAIaX0/Sh9H96yg837QnAeYnNms3nzqMiedPQb1Zhl9NYgUiV9+CAArrxALBERkXsZnU63d+/eESNG7Nix4/Tp08XtnTt3nj17tlarrUZtIqFDKBpS+l6ccTkhryMgCfZdJFUtKcMwAAhcHAqYIzWdBM9JKCvyljOxAIAkBBMvLXbvqtYRTJzE/ENe0MPTBgmpogQ3H+a6Llk35ngu3+8++LCy9aFA5vuIUgNgOvqtYJ48Jjnen57BFVy3tLWi4DC7MGO2MZCPAfeI7kuoWpDazrx5E5s1hUlZJLiPc6YfATFMwuTKOPvNOBdglzpdK13u+bqIVtfPImz1eMeZrTxAV4U8nirD7v5h+eWKJ11Da9vru9y4t31klz8te3N9Wb+Zdz+j61Dm2bcWrLWxFgQouHjeypp3Gjd2Mbx6N1fEW3dw+d8EX1t9mUNZGwCQiIyRJmT5rgAC8J80njquobVIksQkz4V7sl59NEku1+v6GAuP+QODTeZFkdrPbI4VLjeD0DytplzzXmcCbH+T2Y9xIk1lstw7FpuAMQDoCKJQEP6fvTOPs7H64/j3PMvd5u5z586+z9gnayEJIQqhRCi7CoWKRLJEJNFCJGuWIjtjFypJyGAsw+z7zJ25+36f5fz+uGPMjDEbSv3m/erV6855znPueZ573ed8znebbTQtVKumG/4ZDU+p+3jslwARpO+LQIh5RyK2XUKUilD1AoTcKQ77uSJ5l9OI5AFAv+0Vx0WhpKVM1kVduDTTq+GlHVWURsDqGSbPfff4hUszTft0iEAA4LrlAAAkQI6LlswJN8K/qaTe5N+EV8NzULQmJ/21RDpA6E53/rfVO9RcwN/N3xn6/v+DZlRwTbpxhgOY0RGSJoTsCQDIYtlvLTYEsECtlP5T/34AeCefOvCy9aShrHoHAElrue+rAfpN+YiA2mp4AOgkFnUQCTfZOp5w9OrqiE9Inbkt7NsZKgUA7LE7ctJmRPNuyu+VQnHLkboS9b7az1dU119MUvGMV8BTmoFAVL43xBkPYkZHKjqWqPc7EKS6N6nuzVv/ZPKXcYZDbOF6tvB7UtWDDnyLkD9Ztyk9VBD5N+ULraeeeh5NGjRocODAgVu3bl2/fj0vLy8sLMxbUfWfnlc9tYMOncnp93DGY3TIB0zOp0zOIlLzMnale03iSBSGjfGEpBnld8cYU+zk1SIiYGqEJ9dVvDY3pe8lQkKwekbaQRm59bFkF09g4Lfk3+p3CSQkGJj0WJ81LwXGGtnStNuAcZ4iMDiRlj9n4Z1JGAAJAumgkmo+Xud5dNu2iRmGtzoIHzF6ECVyjhTt1XkKAOBY0f6O6u4BwiBB+Hyn8QhbvIPSjvBkfwzA00FvVV3x/oETJ6BbCgUJbs9bxYayGr5UvXcWi0IqU+8u3rkrfwsABIlCfy6uvHyjvzCw0J23p+CHtqqOPncV3CnyFB4r2u81j8sppYU1IYDDRbufUnetYM+vFZzxSGma3kCAwNID9uKGpa8dRg6AI4SesE+k1CNqVCur4Z/P1xVxXB3UOwB8brZYeb6nRLzYV9WnoDCD4QDgSZHgG42vNy/9frtjrZ/vSJ3+N5d7vtE8T618OBdUCax+BwAA5rniHQAAgAEQZo2ct2SVHBS3K3Rhjipa11XSXBaztyWlpiklnTsrJWPUNTpA6Ml1iRr6RGxoVmHwwqWZubNK0mQAAdnv3AyYFunzuDyl3yX9pnwA+Ic1/BcNAaBoTc7/g3qHWgn4c+fObdq06dKlSzabLSEh4ciRI8HBwc2aVfyA66kzvIMzHyxW9PIjxNUExrC67wGA0o70/vm5yeLBuL+PpK3wH4vdwm4+bdBl60kD7S+IPdRK1MCn7NGgOTGWI3o6RORMtBZ+nYVoImhuLZ6pFEIjZVJD7KeeKz93cxz6qfhkL0e7rzWqcOcfnZzHHITkst+0Rbri7Nvq/X52MSjtEFa3nrddRKJIOuSDuzvwjqts0SZAFB0+/16DELK2Qllb3pnMFqxki37kjIc44yFC2ooOnECqX6i6Bm899dRTz99PgwYNGjRoUH2/eh5VEO1HhUxhMmdx+l2ksgtnOslkf8LbrwBgWjuSKdwAAHTEQrjt6nxFx7y0u3hMc+m0drKwLxtxFta4vZB3cuJm0phdLTamueb9bkEtVBOvO55KMIOLzwoUzRoZ6hIQk46bBMgzwHe7iHQCAPSCDBcZ6YwESAIAd5rSmTiC0qgE0aEAf0hae6hQpfPyDfOeY+7rqZjjACFBZKi8V2dppyfqfLFmxnigcCcAREpi0x3JP+VtmBg5AwmC6cAJTO7n7luvYdaAaH8qqJrCwA+cSx4mwe2hEXJjPL7Y8LWvqrNYdOi2eqcQOuV0ZbFs+F0hn9esl8ys0fui6qhyG2dNtPzVTtWpQvu2vPXewuxySjEz9rOPk6fYWIu3pPy48LqXK6OD3maLtgL2nJC03y4ZhQBimfPTAnsgRFhZ87rsZSxmhnqSVdi50WdYI4bo+ggvcAJJ8nutpm+BrojjEILP1Mo6xJxOkMs6ioTDZdKjDmcmUxJued7N/OFyr9SoN1htnUSiBjS9Xuv7drFR8vcKWlIzkM/yZrknKL+hiPbFzlus8SAiRKR2BOLdTNFGb65l6y/tBcGymP0tKTUNAP7vhvN2Ln9RuifXRQcKvcHwZUcuq97VrwQAgDfFFQDE7Gnx6Gh4UkU5r9vCVzT5b6t3qLmAnz9//qxZs3CZlAzx8fHLly9fsGDB9OnTH87c/u/QrcjOm5MasriBdlxViX+xK5Uz/wKEhPQb6G25ybAygnj33rHlfwPW34yWEwYACF3SsIJ6BwDaX9DsRgegUO6HKbrlWQVLM/ynRpDS2kVMqcXBTPBkJmfhR9ZFLwm2/eV0jtB/wgOsl45dbxZgeADqHQAACEH4p65rPZi8ryi/IUgYVuEwkzEdMEcFjifElddeujOQOFYQuZQOmcEWrmUL1/K2i+7k0UgYQgW8TmmH13ut11NPPY8I9Rv0/w3ogDfYwo288xat7sWZf2ML1wMAEgRgphB4B+nbj5Q/Vdo5x8rxGHKsLAC4Ux220yZvuzvVaf/L0qKRtFWAQJbneiyjJL43UO9pkO26Ei0Jk1OdFaen+laWHwGDIOQGwA0A4E0gaQ0A4Lm1zrDBBRgDSZBqBW+1e9KyipdtdF664TdxGNTJXW5HwSYX72ylaDcsZNz0pHGXLOeuWhOayVrSwe+wRT9gTx4A0GGzUXkzdaSCUgqJFv73u7L3ZLqspwzqoYGIqjj5lkJBD4n4iMNJI8RgPFFvHC71WW+1edU7i/EQqc/d6h0A4mStBwQOc3C2at9dTEpaKtpWaLxhu3LRfNbrPN8/YKhGoO3nP3hz7ioE6Lzp92d8n28orWMZLSSKoQPeYPKXRbiuFQnz0iUDzgufbEQrX/KRKAEiGXtW/hYVdhoIzQ316DfvOwcTBvjWYg2jqF6SSrJBXfF4DjicE+QyeZ1SNWOAdRabjccIAGP43mZ/SizyqeU3sI1Q0EYoOORwTtUbMcDbCpmJ5zdZ7ZP0xq98VW/KS7waG9D0kcC6Oz7UDTpwPKvbjF2pgHnsyRJEfw0A+HofznIaEMEz+V71jj10wdJxmHPZfjcp+/gBAFvkMe0v8g7C6jzWkwavSveSvzA9/5M0RKKItU1VA/wrvKm0vTJ6R/PUAZf1m/IBofBvGv9jURQIhJFi3sUTPo9uRoYHRY3+ARw+fPijjz6KiorauXPnnDlzvI2DBg0KCAiYMWPGkSNHHuIE/59w3bQDAFnd147VbQTAlOalUgX4g1YTH+CnIf/JzPOyp1WKnhoAyJp803mtkicQEhKmvbqiFdkAEDg9qrbq3QsdNBEJw0LdN47Qx15z7eAd13hh+G75CAxAALwplz6QCAJC9jipeRF4l6dkI/MONSmlWwFEa+iQaeKWVwRRXxLiBtidw2TOcl1s6sn4ALvSKz3ld8OJzbmrWMxWerSeeuqp5wEyf/78du3aLV++/PTp05cuXQKA+Pj4uLi4hQsX/tNT++9jcvHL/7J5HtSPPRIIwucBAFu4ntQO9bZRfsNY/R4ghIKwck80ixsDQKGdc6c4bvW8yOS7ZZ3VvsODeCeXOuBydJJtc5z4nS/T5UZG2l6hHhJIe/j312U1SXO831Y6+7leSBAEAIS0Daka57zQzHGuqfNcU09GR87Yi8nv5kxowWQGAABwYtM2IyII1av9wjZ8FvrdJ2Gblmjefo3wEdt/O2/e+3MdrjLTmXrGcJJC1MuBw+SUopd2AAD8mLeWwxwQEjpsrndilN+gCicGSsm/Rvq/94RMKSQAQCGsy6rJddN+s8v5zAk30kdcxWzFTOMkwBJfVW+JmMFYgBCD8Zoy6v1lH8nMe1T5FRCC57UvDggcVu1/vbQDKuSl4zH/Y+46AMCAw8SRHdXdAKCzb48QUYQ3GP7HvDU8rphUvOawQW9bkTCKt/S2b3uKv8ADfGQwbbXZAaCBoscLbCoArJYOCnVuu3/ZZOb55WbrVL3xR5u9wqGLbs8onX6j1Z7g9tRhZG/WOm/c+zy1MoQivfHwd9eWqyKBvBeveucA3lTInhIJpysVr8l8GIwn6Y0nnXfKECYxjJGv+22vC0hQ8s+cQJz5F854GG773bAFq7x/AoD52Ai/12Mwg9OHJZr2F5VWXBM19NFODMMcznzzunFXobczU+jJ/yQNAAKmRtyt3r3IOqpCP2sAAPqNefbz5od/nZVT+FVW5vgbumVZaYMu351F/z9GjSzwS5cuFYlER48ejYqKys/P9zY+9dRT586di4iIWLJkSY8ePR7mJP9f8KQ7AapLQY89bNEPUCZ9HQD4PhzpzjBMYWFhcXFxcfGdDDQikUhzm7KdkYCI+iEu7bVE84Hi5F4JsQdaiptKgcfudKcwWgIApj269FHXgMdISPi9XqNQ/0ogRIKwOe7kUeq8TznMA8Dn8neNWCAE5AZ+st64yk/9xIOIIxCEfew0HOL0u3j/0Xdi13mXJ2s2ANChHyFKVduZU9rhlHYYZzrO5q/gzKfYglVswWpS1ZMKfIOUP13aUecp+D5nJYsZDa3tqe1//9dSTz311HMvvBv00dHRn332WWJionePftCgQTt27JgxY0arVq3qn+8PlQlHTWfz3Pk27pNO1SSgva5nG/lS1T7sSVVPUtmVM/2MeBYJwwlRFGf+GQDTgRMrOJSlmVgAyLNxuuXZTIGblFGRm5pRCgo4rN+cn/ryZVJGMQVuWSdV9PbmSEjYz5khxTG7Zo7NAAAgAElEQVTgWBG8EQSEhA6b40l5HbuzHAkjrKcshELKm+/s3SPCz3d0BgDYTzflHSL1qBflz5e4fCOKlHZqSyoVhfOWm/cclfd5BtVmDYMB/5C7BgN+1q+vvzAIAJ71e+G04Xi+K+ek/nA3TS9KMwDRakISV4WN6ukw4bZ+vk1q72TrumlPfv4io/MAAtMeXfqIq5EbmlWww5MAn/qqACDeUVKUHgF41ftctfJh2CZP6Q/nuDK85vfBQWMIRAAAgYjBwaMWp85CgLKc6b8ZjnfyfbZu4+8vOmChwocwt/oxqZFCayDkb2cCPzaaz9mSG+p/eI0rvEVFZTE3EMNdsVzQeQpMjOGlwNdQneywSoKYqVLMM5rnGc0AMFha4tF50e3xFoF7wUfcsfZ2/rLqfa04IzFz90CMhQjZzfiIgewpEZd+FU673EkeprVQ0FIoQJKmlGZAhaFK1ftkhTyHZQcVFo+Ty6YrFRjDZpv9Hb3xW426nUi4zWafazR3EAlX+/2tqUBJdS9S8QxnPgEAnswZYkUXQtKM8hvK6jZ6O7AGle/IOcIoCZCo8IvM9OFXBcFCd7pT3FQae6AlpRE4LlhsZ0z589NVL/oDAK0VqIcEGn7I1y3PknVTS9tVEtLvumXPm5cGALIuakmLfyazUuFXWbkfJgOBSDlp+dmQNuhy1LbmhOifNG0+VGok4BMSEtq3b393SpvQ0NA2bdqUzVtbz/3gznABgDCiKgHPmU5ippiQNCWkrR7GHBISEvbv33/hwoWkpKT09HSWvaddgCRJrVbbsGHDuLi4uLi45s2bN23aNGrTHQ0fs7t5/vw08xF9wLRISZw0fcRVYLEwUuxOd+bNTQtbVo3/+T3f17c/UbiWt/wOAFdFbbcJujWk6VsMA4BcGL+uM3ynfQAaHgmC6KC3mZxFnszpomYnvEGDTP5y7M4ixI0o7WvVjnCvgUlld1LZnXdcZ/NXsPodnPEgZzxISJpSAW9QmpeBEG3LW89iBgD2Ff7UXtVZQddyp6Ceeuqpp8Y8ghv0qampx44du379ul6vdzgcvr6+wcHBwcHBffr0CQwMrP78fxVWhgcAg7MaY9Hay44Ff5jbBws391FXOyYdvoAzP8UWbxXFneLtVzypE5AgkA6eXKFbqZHRb0Koab+OKfRkDL8ata15+IrGAKDfnM/bOa96J0RE1sQkd4rDIyB2dteMAwAASjOALVzHW8+Caz1Ao4APxwNJOhOus0VGQiISxv4JvImzqRwXYxBNybpXzOEqbt6IDg1ksvM96dnCmFrUoDpr/CXZfkNOKXtpX/K2UIh6OXD4soyFewt+bKfsKKXkhOKZ2b+ZQ+X2sc0rBvR5Mbv5nUlOsjFqWRt3eleyI7lXAlPokT2llD3jq1uWadqjSx9+NXJDM0RX1PCdxaIDDqf3JnudBLuIRQ9Dvds5257CrQCAAT+u7FDWVb6x9LFWinYXzWcBYHfBlseVHSRk5TekCjDgk/rDHiqgA5cfzlvtuYtzqIgI0TMZkgFXXJ6PbN9jQPGkCgEHAPG6nan2JAw4RBR+d5R+DRks9aEQmm0wzTOaOYBXpT4Jt9X78xLxArWqDppsgdG8xWYXIvSNRh2XMbmJ5Wi5wyZgbr9sC9AWAGzAAAAhpDQvVcirv8Pu8Kr3N+TSU07XbrtjpcUKAN7kyptt9j0OZybLzjWaAaC7uPIlPZP7BWfYV5OZk4qn6bCK3qBVQ0cs4K48BZjDrnSmYJXXd7X0aN6CSZhLjD3QKnheDAAUfpFZVr3nzU21nTEhGgXNuZ2pCkHEt40RAv2W/JS+l6J3NJd1LLcudac4knslMAVuaQdl9NbHkOAf0My65Vm5HyYDgrClDaWdVMnPX7T8bEgbeDlqW/Nq04r9S6lpDLz4Hl9BtVqdnJz84Obz/wvv5JlCNxISguCqxKc3QTrp+yIAWHn+gtvztFh0nz5LHMedOXNm9+7du3fvzsjIqPlZ+fn5+fn5p06d8raQJNm0adOnO3Rs2iKmUULEza5/YQ8PAAWL0hEBmAf/d8N9Xwu60fZP/cY8zZhgSfM67tIJwhc4E7tghObLP/DGvf/pdi80mXNZzg14VJH+ZR+fmSrFfd4WOmgyW/QDb7/CFm2htMOwJ5/J+xIA6MjF95+FjpA0EUQvp8PmsEWb2YI1vOOaJ20ikznTrOyeac8FJARvTtqCLSND37rP96qnnnrquReP1AZ9enr6+PHjDx8+XOnRCRMm9O3b9/PPP4+IiPg7Z/UocMvgAYACG1eTzoS4AeU/mi341pM+1RuoRYfNuVdRFQAQxUoaHGl967mLlhMlZqvwFY0pjYDRecK+bEiIyex3bhZvyCMk5KLhIdeifKweryxFgohPXYnPCJtcov4KFUSGAkKC8GAAwEyx6/JEzAOT/TLm7KRUhOhKdDId4Mdk53PGWjjcenj3xrTDFmf06Ca9xOSdK2qpaNtM1vKqNWFv4bahwWMNTn7LNYe/D3kvAX862/1TkoPhcUv/mmYIdyU70l87jAiXsrecyTUVr7mp6KlyJFgc5wuzJ+WFLG5QaodHdMAhF56mN3p1Ow9AIMRjPElv9Oa0q/n11oS9BVttrAUACES4effKzMVlj7KYIYDggbew5v2FPw0KGlnb8RGgx5VPnTYc3ytoNNF1oQeby/r2zyAVucBOMi8RYs9FcQepvC1Y/kKIsLFWr9P+fZagf9lHAgCzDaYFRnMOy+2w2b3q/TNfVR2Wdkae33xbvXcQCTntMM50FAA2ykZbCbkIEXbM95NI0ljmiocBgFCSfNGyDrMGyu/Vu6vifaJWZrGs11DUWSxaqlG/pzeutFg9GM9QKTqJRaks+7HRDADTlYqB0nsUMzL/XKFg+z3hnbUV8IS4IaUdwRauAQA2dzGp6sHmr/AeQuL2vLut64YtudfF2PhWwfNiSDll/8sSvrwRpRHkzUsrWJyBSBSxuqnyBb8yI6LwlY0BQL8lP3XA5bIa3p3iuPXcRSbfLe2gjNnV4h8JPtctz8r5IBkQhH3RSDMmGABiD7ZKfv7OD9p/UsPXSIe0aNHi3LlzVqtVJiunuGw2259//tm8efOHM7f/LzxZTsAgCBFVlb+Rd3OmwwBA+fYFgDlG80GHc5lG3a2uz4OzZ8+uW7du7969Op2ubiOUheO4K1euXLlyBQAIIBq7GjxNt+v5ZI+Qc36YB2kHZfDHMQCgfTOk8Ous3Jkpsftb1u2NcoVNPvNdwmOEJU28Weu6ikUdRMLvLLZVFiuHYavNbua5z9Qq6n7Kb5a4649msueTvv2YrDnA2Ul137JJgO4TRGvooMl04HhWv5ct+Ja3XZTrd84GIoHSnCKDMwj5acPPnX17REpqUXWvnnrqqadWPCIb9Hq9vnv37qmpqU2bNu3bt2+zZs18fX3lcrnFYjEYDElJSfHx8Tt37rx8+fLp06f9/SsPxazHCx0yjSvezlvPgjcaXPPymVx3oo4p2+eKzgMAdgavSrABgGhJo4YTb1hOGM70vvj4vpbB82PybVwuA7YpSc6NuUhMyr+PS8xiAONsC5tt8W4lNJMqBgrNW8UdL2IeI7Lkgctkf4JZM6nszlkaA1zg3R7AGBAyuHh1GY9WzmwFAMKnyrDB8hzQ7fz9+gCHI6hh69tfAN7F2c4B5l+VNfnefCpPt6VAIBdgbTuZUSpAnLnUVIgIURQSlmQI5jAAAF9NpPMdXMmO4u/eD/9yyz07JN55zZLyj7VHOELujXsPo8gslhMg5MF44kPQ8Ndtl70veMxfsVyoSU8AYArciEKUpvp6fhjwdeslALiFJBdITRuuyL949yd+21swF7s6j7qQ6CP5HLH7lwAAjPkCd46CUilpdaYz9aBuV/+AIXW+rlIN/73VBgB1Vu8AoCKIL31VQRQVJ6ChjJO5CLu+kr5LIxgrl21huR12BwjgcaFgCn3OYzAgSkmHVpKlO4AkA8g7E3lWLFriq3pPb1xrtQFABE19ZjRjAG9g/L2mRId84L7eBxApbLAJCSpxLMKs2X1zEPDuSsshVQupeo4t2gK8E3M2V2Jn4F0AAISAVD8Z/cPvxetzGZ2n8IufNCOCtOO6eF168+alFSxKv1emOq+GxxgMP9zR8I+megcAUazkP6/hayTghw0bdvz48eHDh2/YsKG00W63Dx061Gg0DhhQMT6knjrgyfT6z1f1s86ZT2HWTPjEIVF0Nst505x6f49qy+nTpz/++ONjx45V3S0wMNDPz0+j0RC3E346HI7i4mKdTmcymao4kQf+Gp90zZ208uSGECLoOfqZnr919ZmpCJ4fEzAjynLK6C1cUTeUBPGb6Fk3gIjlrjMe7z6oCKF+PuLtNkcxzwHAIYfrFlM0U6loJ6q7Oz3p258oWM1bz3qSR3Omn4EQCsJrtw9aI5CA0rxMaV4+l/OlJ39lC66oDatrw+oyCfkvZNC23FXTYhfXLZasnnrqqadqHp0N+hkzZqSmpi5cuPCDDypfsM6ZM2f9+vWvv/76rFmzVq1a9bdN7KHi4XDp/73oOE6MkKyyJNsYQRrDRtHVr9wQpaRDZ3jS38OANplnjsJo1AEjU5lgtbjxZ39ava+DRofNXpmh/tOc0O9S5qKGH/5pHbOr4Nk/DG4B8enwkKuZJZnDViTYVySUJBjzpcYfaBzvE53lStwsbjEMAHh7Ilu0CRBNh87zJG8FAOzyOC8nbSPC5p2x9IgSrXhWBQBsQbE7NRPRlCCyqrI7ZTEwxUeK9vLsTIyR83Z4nyfzI6+lUQowwduU+iYArI4GAHDfKHM+IRS3TkZkrV3/XMmO5OcvStuWLM8YnQZRNKmkWJ0H80BISOzmMYcJH5JWWzBnM2OBEwlLs9bNUiun643xDqdXw7+jNx4N1PqRD0zqPKnqsiN/IwFEZ98eFKpkZeXBnl/1R3ngn1R29rZYfzOmDrhMCInY+Jbix6q5IQhQiDjczbsA4KjosTj7L23ZzBbMtWmmuQCwV9SpgAoC6hUhIVQ7f8aABwS+FiAMXpDywZGiPR3V3e6nBH0MRQkQ8qaUay4U3M8t61E+p73XyXygfeten5eTqNhvzVZvEMvjQsEGP7n78mwAoEOmI6pG4esVNDyqTr0DACl/ilT35Qx7Of0eQUwlv2aezBnAuwlZO9K3b00vsuzpGVOAL0nBUKLeAYD3sLlLAED1QkkDVwy8dZe45e/586tU7168dniMDT8WpL18OXRpw9xZqUyBW9ZFHf3TP6OQK1XvXv7zGh7h6nItehk2bNimTZvEYrFWq83MzOzWrdvFixcNBkPPnj0PHjyI7sfO+W/j6tWrcXFxBEFwXI2c2SrAGQ960ibBXQnGsZvnnTwSICR0AHh/SQhESst57/AOzHuAECNC6MTwjfQNp/+bC9Q1dQDzUlRUNG7cuJ07d1Z6VCwW9+jRo0+fPi1btmzQoIGPzz1/gBiGycrKSkxMTExM9BreU1JS+CrzbTYiYgc+M+D1DROCg+uaxO42bow/NVm22uwihFZq1O1EwlyWG15UnMtyrYWCp2nDNoc4jycBoIdEPE0pD7zrYenC+CODqb1I+KJPJQ5O+xzOU07XPLVS7Eh0JXbxfiJ08BQ69MP7nPm9sHO26UnjbaxFhd2jhPII2znEmQDAhgROVZ+w8NmlpoN66qnnH+ePP/548skn27dvf+bMmX96LvfFpk2bhg0b1r9//w0bNmzatOmtt97CGNvt9iFDhuzbt2/58uUTJkz4e2bSuHFjjHFSUlLV3bp165afn3/t2rXajr9hw4bly5dX3cfpdF6/fh0AWrdufffRF154YdasWWVbMMYjRoy412Rq0t88dBMvVpL6jIAr7zV+K2zsY++PsQT4kMSPWk0YRZX2t3ea7GnQVdDKDQEC0eeLyN9/r3Z8AuFJ42Tpym7fZ72s2DzU0+DZsMe7tG17p/zY8XR3jpVFwAmuHShtDDILPjwUpHBSN/wKiqIjnz7r9AiINW9HpDT0AYAcK48xJpxGYN2lp7wZtm1c+CpXsWzt+RFT537sut6bt/xOBU5wnG1v2n7IgbAEIyPHvBnbrTjqKcKUrdgxPoASTdHGRgh8pF3ba8YNreH9bD8vzh5kPH/xE4dbPa37abXECQDGM6uGBl8HgCSzlEUErSQBwJFHun1iAaBDiBAAeMc1zBQRksbCuN9GjBh17do1T0xne+f3BCmnfE4tqfrzmjjwrZeOdZezshRp6jNffSUJz7uxts/Ev1LdhHt466FP723J2Th5V7X9ggW7HdFb3yGlhXPRlP3+wzBJUkePCL9dARhjgvBMeod9upMAoSia3Kz185Yuu//vDwA0nBcgDKqRNc6VzdyaU9jQEf12zkgBTwMApabLavhq5zNw+vRTKXOHmb9xETIRb0XCsInLHzsXFusZNQYAR9q3Km6dSJmvwzwOe12tbCt5XNmhbAn6Wl1vaYp4MiWZi44BAMHa1fSB+Nren3v196RPZQvX3CgKfvWxI17XVyIlWTxt6uBn9OP6GTIL6J2J42Z+VM5mU/X4gZ8uSmnYGADaCYXrtb7V9h86sMsbnbcA7xI1PUjI2pXtH+bPbJiRTSD8xuchN7OEdbjesX30r/UwAUJwW+UhSkX5vXrg4GFvXmoxpvs9o0cUl7Kxz6H4qO6GjpjE0RsfU/bVVj0+wmhkwcB25pI8XLLO6ujtJdr4gXyfa97/cUvzsXlDeMCbAnb8rrxQaf93X5rU70RXGSs9L7+8OuiHGo5vt9uTkpIe8ed7TQU8APz0008LFiy4ceOGx+MhSTI6Ovqdd94ZO3Ys+eD2Ef8V3KeAZwvWeDKmVt+vBmyTDu3Y8MuYGmzGl7J379433nijsLCwQrtSqezdu3f//v179uwpkdwzUq5qTEXG7f03nTr363mUkMKk3+urRSCic5fOw4cP7+p5SiQQ+Q4LqtvbYYB5RvOPNrsIoXkq5ZcWi1e9z5czC25N5ABahHyx0UnaeCxCaLRMOlYuFZbZacpg2V75OgwwU6UYIi23T7Hd7phtMAHA3gBtLE150iayuk1IEChufh5qnwCmhvyQu/p48QEAUFCqBY2+ESPyRMq7WlN8FG8BAACCVDxNBbxBqnrcHZFVTz31/M38ZwQ8PDIb9BqNJi4u7uTJk1V3GzlyZHx8fFFRUW3HHzJkyI8//ljX2QEAtGnT5vz582VbXC6Xv7+/xWKpSX+Lhx+2T59w4xZfZvFA+0chRGLGLRLrSQFBUJSnoy8AkCzEh2oDWNY7ftCUndIh7QgNAEDhx3OMa1bXZD7KHm+r+03nneaM95qGzj1N+0XU5DKD8+2zvriq5mUAwIm5hjvayDuVZM5rsrrAzeHC78baE+5ofqWYTtmuFfvadUdbqVs+SfktB6ywHhvnvJINJPHWXz8PCozp6B+6sGnf44HNVOa8dw/Oe8I3iCKIHMb55JZlRJkg4SruJ0ETw8/1AQTnLs5zujVPtPpILCwGgPZs/mAmmQDMIIK5nXaeZxCPxFBSKA5j1gKAkaQpij3kHxBisViUz45X95/pzkjIXdTrXp+Xdz4DtH0/5t8HAAM2hrbJCl00z2aHxwZZCvV8mzZtTi47ltrvEmfj6GChsvsG31d3Xs0SjGj5OxZJjBu/L/zowzLyiWq65Ue2/ZMKgjgUqFURRNXXe6/53N2/4YCIoLZ+AKBSqbp161b2EMdx+/fvZ5iS0IncMzr5PtVXok9EINzDHopQhbewNimr4auZz1NPCbZud3COA7reSjYfAIjI1f4NRlssloApbysnfoAAWz4YlvvDCQCQaEUv73+WEKJp0Z+U5tWr+fWWqnfnoYNZE8YpBg4KWLgIAArnzDKuX1er+1N2/DN7T7tu2WWd1QCAWZMzoRVwxvd8l50SPQMA2O22Del5YUGhXIr6TraZuBY1H185eEjAwkWIIBBCPMajZdIpSnn189nVj8n9nPBpLoo7AUCU9t+91OfZ9vTaPZ6JixyV3p9q5yMVo2u71RrFnZ8aYcOtjLhTaf+xLwq/nCp25PvmjF7GewQssGtDtq5KWl+T8Qkg5grff57qVla9Vz2f2s6/Jv1foHrOFk7hgJvhXnCc/aXS/i20cUu4uYHI/xR35j1XOble7fiP+PO9FgLeC8dxubm5AQEBAkH1kTP/Se5TwAPvdl5pj13pdOhHlP+o0ubMCTdMe3VhXzeWdTzGZM4EAEo7rGziCs70sydlDCFpSge/504eZUfSzyJ+XuzfoIZvazabJ0+eXDYIwkuXLl2mTp3arVs3urIcMzUHe/jUQVcsx/SUn6DBwVYmlfXAgQP79+8/cuSIy+Wq9BQFIe9Ndn9307RWgx6v45ve1vAIAAO0FgpW+fmuy/w0wfwnADSVtXg1/KPPTRZvJtgQivxUrWotvPO9/dFm99YpKavhverdG780TOYDAJg1MJkzSb/BpLxj3eZZLfmunFm3JnHAYQxjwyY/qeoMXpv8jXFqJq8DW9COLyIxAwBIFE1pX6W0w2tdx66eeup5cPyXBDw8Ghv0ffr0OXbs2NWrV2NiYu7VR6fTNW/e/PHHH9+3r0YJnMvicrmqtdunpqYOGjSIIIhz587dfTQ6OlqprOjylp+fn5eXV+loFfrvuOmcdrKq0DMvVENMxAAAECzEpQpJi8fldiU1FvK+AAAiO4687CE4AACxRBKiEs3tqCib9bnsfFZnKI4V+VAEbGmdN/+mJtMlLLsdY2cwy2OEwIcstw4UEvxb4sSoDxjaIYj/LL7rC707+/b0HmqypsDN4nej9e3U7rKnyO0nggULsFPIMxQpt1uPPeG8HEvIfDTjhppD/c7cKtRfzrloFB3zbxrmKF5/ZhUQhKtZtP+YQaqggJrfT0eg2UqZ5hxuqXeI5vRMkIpMh4v29HEnPcNmV3tXAQCRUnHrm/mF5ry8vDWZiqM6H62QW/7YHXtG5Z9vXn7exBR02EOpTdJef6mGHxJChoOIM6Oufn5+EonEnebULcskJDbN8K2I4gzyUUvJ1hg9PtRqqbD1FREd/RmPL7uZbf4axe0QiZp/fx5A/ysset8OToyfF8BUcVREtHFitml/EamgYve3lLSSVz1+WFTUuwwXTFEf8z+zKWNIeUdhk33e/kfInb+JAoyiZ2aYuGCuxMM0VXP1sGl3mDhyVuwSb2W7Gs6/VL0/LxG/43EV5uUBwEmxZJ1MAQCv2SzPOux1uD9hEFwwKIUpcAe8FxE0NxoA9qV91U03J4cMnaremSbwYQHmGD/q49hlRe1zqHk1H/+UWLJOpvCuG/0p8j29kb2t4au5XrnQefkJ7M4RRK+g/AZ7x7fk7g5lP+RBkkp/z4KqXP/aXG+s5jKV/x4gIWA3qXhG2HhnaX8CW2PYESSYc5yzbBNaQh4Hs32iRjaqxfg8DjEHajsFVai/8Ld+nwHQehdscAEB+ENJ9BuNK/T3ZLtuPHuey/bgpiQskUH5bBtVjH/lypVRo0Y94s/3Wgv4eu5XwANwhgPuW68iSilqcaE0wOZm1wv2P80NjrYWNzzpTh4FAFTwFEEZh21Pypts8TY6ZCZr2IUd17+Sv9stetrjwhpto5w4cWLkyJFZWVllG9u2bbt48eKOHR+MKLUc16f0uwQAUVviynrgmM3mXbt2bdmy5dSpU5XeMQSoS5cub7391gsvvFCHxWIOy/UvLLLxPAHwjUbtxyYtSZsjIsQkIu2cbWLkjBbyJ/5ye+YZzTcZpqNI+F35gpzb7Y45BhMGmKFSvCr12Wl3zDKYeID3FPIxcmntb0MdWZo296o1AQAixNEfNfi8NOL9WNH+H/PWAoAawUfK1oLibdiTCwBA+lCagZR2JOET97dNsp566inlPybgvfyzG/THjh177rnnlErlrFmz+vbtGx5errRYfn7+gQMH5s2bl5OTs3Pnzn79+j2MOdz/871qlpyz5FnLBZodSnO5OUyTTrXqSpAwNN+dA1hYpGlOxmAAwCywZxDVBCMNAAC2AnMGQfkIvClPyMa1qvxpNe2kacdNJ02ipLEVdTIAjDloPJnlElNwdUy5HFo85ufceqewwGwqbG5RciJC3D9giIAQAsDs0xaWx4ObiJuVT37Wwp+O1L3iLT3NO4LdNz8QNoiWPNmSEIsAoN/O4sQipluY4HiWJ1zEHXjMLGwQSdwjO3e1dN6iy7Zyp4ZqT5hX/aI/KsOemY5LPoQTAI6aeszNLufz3FCctCZ6FEIYAf4i/911hWPEFBKQyMliD4cJBLLyJa9EFPq+lzpWfcexkcl13+x6wZPj8n97japv5fUR7kbc+iai6x77/TCwnTGl9L/E2znfYUHhyxt5ncYxg9OHJVbQ8DWBt11AolhEKQAg2X7j05QZNEF/0vAbX8Gd1OUe3jPz5lvFHt3wkPE1L0Fv5vHTeQUejF/wES9Ql8taV2p0WaHx7SyuXXoj1y178vMJTEHJxlPAexGrJvvutFp/KHophkmmwuak+Y2fmf3bBt3LHJDGZr9FSBvWcOQdZaw+3rj3o05XWQ1f9els8TZPypuI1opaXECkDDDjutKBdyYLwj+hAsfX6hrvAruuPsvbLpDSNoIGG5DgTuyqJ2M6W/CtdwuGd3CsgRGEPODiCH8b+Z+k5S9MRyQKX91EPfDOD50ny3Xr+YueDKdPW0XM3paktBb64l/xfK+R93W1m9ZNmzatukM9ZSHVvUhlV870M5OzSBDxmbeRyXUDgCAYPFkljx82/xvaf0TJPznMePPPI+zCjuvZVOg19ejpNVDvGOP58+fPnj277E6NQCCYPXv2+++/T1H3Ww6tFFlHlbybr+W4Pvudm6KGPqJGJQZthUIxcuTIkSNH5uXlbZi1dsuGLde5m+VmCPjEyRMnTp6IjIycNGnSqFGjKuRSqoJclhtRVGzjeT+SLOK4d/TGts4TANDHfyBNCH7IXb01d10zWRdWuQQAACAASURBVMvWQsHOAL/jDmfjuxL+edOczjGYFhjNF93OIw4PD/CuXPZ3qvdLlnNXrQle0T44eEzZfHVdNc//ajiW68oyYPgRi99seYkzHmIL1nCW39jC9WzhekLahvIfSfm+CHUt1lJPPfX8P7Nt2zaNRtO1a1cAIEkyLCys7KH4+PhNmzb9PTPp3r37smXLJt1GoVCo1Wq5XG6z2QwGg9FoBACKolasWPGQ1PvfwHtPVFzN/5Zb6HZggdDQv+XFqdH9VmWe+tP0myZ7qiE/tjiQRRTQT5c8u7EV+D+IFr7lnmI+NBrarI5K+F6c0h/OcWVm2Z5LN7wABgCAKykugDuedD9edwI4y54So6IO9ZnPJT4NmJW0WSV9pkPZo04OA0CBAwOAGWhxq5quGG/omW8T7Hx5O1OxkweAab8UpLvCAcb60n5riD2T/FZiQN2VR7cUvXbRXhKdiwC/Ffg1gXgAyHGHbtYNAwAni51syYA8BrO73H6Ki0Vurtzb2c+bPTkuAOCYZwEOAwDmwhCZhWgtqXredsbkSrJTfmbpE+cxT3BmBaU2ELInEO0HjxKVqncAQDSK3Bjn1fDJfRKq1vAcwORiQwhFTVXKCWkbbyMG/GPeWgw4QDNukgktVHOhVIlSEhCCAYHDvs38vFYl6FnMez3+Q0iqguQKIkkSIRZjI1+7/bVS9S7rovZ9NTDzzesFSzIIi4171++g5qOJ+cO43M8bagatsi8mgN8sG/mtSb5XxIbXYIUc73DOMpigfNa6Z8WixWrlFL1xrdWmIonRsqrWk5RmIFu4gbeeZfO+oENnsQXf8c5kJIqiAsbU6horAwkiF7sSu3KORODvuMzwzlts4VpAJB2xAAAICSmQ/ItDoQM/jAKA/IXpmWOvA4BXw9+Pev+3UCP91qxZs6o71Jvxawsd/gln/pUtXEdpRxCSJgDAGhkkIACtxu5MQtwIiWM4QzyTPV8QvRIAOMtpzJoJcQOmcB0AfKGYNkyhrvZdOI57/fXX161bV7YxLi5u48aNLVq0eLBXhIRE9E+PpQ1NNB8qvvXcxdgDLcVNyv1mSS5Qz+3s0FP0pO01/oD8+Pr16ysEMaanp0+ePHn27Nljx459++23y64jK6Vs1rpv/XyXmiw/2uy/CV9shz3d/XoTQBwr2qfzFCxKmdlB3aWzb8/SHKTLMxYm2a5mUk39haGrIl8rLVVyyOEBgHDHnmzHTVB8VfpGi1NnZThThIQoTtaqbFX2y5YLua4sCSmJ9WkSLKpmtveCxey2vA0AgAFrBNrjxfHHi+PLdpBSJdsZ502/d9X0ilX3IdV9eGcyq1vPFf3I2y54bBeYzA9JzSDKfxQhrmlIRT311FMPALzyyisAMGnSpCVLllTwgbp06dLmzZv/NgEPAOPGjevWrduaNWuOHTt248aN9PR0ACBJ0s/Pr02bNi+++OLIkSMDAioxJv974TEPAAjQK8GjAGBg0IhLlvO+oYsXx3wSz4Suttq83YQuZD0DMgLt7H/PtNgeFgod5azzdgYDAMaQbWZTUpw5whJrs4AgZEKkc/AAwGE4mHpHnLt517aCy2IZ0vr9ybO+Hh4DAgSotaK9DyndnuTkeNwhRBAqv7N6pAl4KkRISPyEMasxbyfkHQBg83V7Qn5J6HWmkQOALDMLADYGv/dzuTiCIBnxzhPyStNDH0p1xac4KzsCf+YQAK0AQAeQjN7sKz8cIUwHgAVRny5z7il2oD/z3L3V+1v5XMRAIOCPcjM7RcgQoFebSpr50Qv/sG5PcgRJyfiXNWWH9aEJqvxUlH21mjHBxWtyDRsjpa06iqJ+s/4aIe/kwIzOuCsmd04jQooiv5sBCJv2P6/qdwSAEIQveKRS1dhOG1NevMw7OM2IoLCvG1WoVYxoFPl9s7RhV83xRSn9LsUebCVuVrngdGF81u22OV1Wnv9YrfTep1/1xzIcKXZJ9+1sQ5715HNsaBnd/YTyqZP6Qzdt12pegt6XJJeolVP1xhUWKwJ4S1GyBPrF6ZqoN7IYD5dJ+1eWe/heuFMcyb0SmAK3tIMyeutjhA9JKqikIVf6rC4OATTqiz4e13Oc8ZD7Rj+x8yZHab+Xv+HGeJHJvEJTfQr6i24PVJZzvqdEDABT9Ma/3J7R1aX5F0QsdCV2ZfK+IZU9mdzFACCIWAToAbhBET4tKM3LbPE2T9ZcYYPvvY1M5gzADOU/ipBUI+7+LVTQ8NJ2yv+8eocaCvi33nqrQovVaj1//vz169fDw8Pff//9hzCx/ziEuCHlP4ItWM1kTBc22QsAkRuaIaKQLfwKAOiIBYQoymk6xhZto7TDCVk7znjYexpmDeeF7dKl3btWV0fU4/EMHTp0x44dpS0kSU6ZMmXu3LlCYd0rq1UBEhBRW+K8Gj65V0JZDW/ap0sffhUz2H9yeKv5MU9D948//njXrl3ffvvtr7/+WnYQs9n8+eeff/XVV4MHD546deq9No/KqvfxwvQfsjeBOz8IPZ4n7HBB/NoFN99ORNtYKwCkOm7mubNLQ/gAoMhT6OAcafKhKYgeUFA0XVVu6SAlBYF0SNn3cvMuJ+dwco5k+/Wy7SszF3t4NwAoKNUXTe9k/lib/bXOna+k1a0U7doqq4lQKPboCt15pa+LPbp79cSAr1svx/o0BgBCHCsIXwChs1j9bla3nreeZwtWsQWrCHkHWjuSVPcG4qF8xPXUU89/D7FY/NVXXyUlJW3btk2hUPyzk4mNjV20aNGiRYswxg6Hw+VyqVQqorKaav86ihz86IMGi6ecydfkwgDI6Qx4bRcNoAMAO7fAwdlHJNkbdfGUdmMEGIkQeCqOWZaWGwpcbGXWFA9/sMfFNtesF3v57+tcUZN4OHj7mLF824hmDV3+vtc2PNvqi/R5ZsYIACGyxClRc/fccnI8vNrU59nISlYgpG+JZ0S+jZ/9a8XEVBYGAwDL4T3JFTW5SkiNal6JJHuzlbSpH82VL24z+biRwxAeFu8rMQ8JHiskhJ+dK1yc9/43keMwEMHkVcqw7U9DfzHhejvwK8CAEH/O2u6L1I4AbgAIl5MdQkoqkhHIm+WuShCEfdEIAIrX5OZ/OjRi5Z/yzr8ZdvRTv7xb1OALSr1CO+48rb3JFGmUvawAHkr7KiGtpIRBHcCAv8tcymJ2fMT791NKtvCLTN7BCcJFoV+UqHfeyWe/k0TKqOCFsYhCSEBEfNfkapPfWQOjW5kd/k3jsqezpkNczlIARAEcxnw6w/I6SMsiQigKAxfgzJgEYjdrAst2u8D3Bup6DXiMeRKV6AsCSAD4ufhAJ98eAcIaJS1+TiImAN7TG7+xWN2A31PIf3O5J+mNHoyHyXw+qM4pvSyVFipX9NQkfhfT/I3UlquL8sTJQbM+cZpP8M6bACAOm7lOFbHQaH5bXqN3ma6Uj5D5hFVmq+8pETcXCpQ1+O0ifFpQfoPYoh9dN/oB7ySV3Uhlt2rPqiF0+Mec8SBn2MeZT5KKLpzxCGf62Vtm8kG9xaNA4IdRgCH/0/TM169TGgFT4Ja2U0Tv+c+qd6ihgF+2bNndjRjjzz///P33369DNZd6AIAOmcEV7+Isv3LGg6TqecVzGk/Kh2yxnVT3JRVdAIAOnMDkLvVkTBfF/cwZjwAA77jKI+pzxQf9fcRVfyVZlh04cODevXtLWzQazY4dOzp16vRQL6pSDV9WvQfPL8lOJBQKBw8ePHjw4IRTF+f3mhXvOOYpszZhGGbjxo2bNm3q1avXhx9+2K5dOwBw864btiuZjjRMiL9m2+VzXBuh4Ds/39lJHxZ7CgEgDJJ9KN9kstH4YsOuAL/RYZN25G8scOc29nms7CRnxy5x8s6/GHqe0ZTEMMN1em8OvGcl4uMO53Xh8/1U5ZawM2M/c3IOF++s4AA2PnzqLft1O2eLktyxe/OYv2K5YGUtAJDuSCkV8DzmFqXOtLBmjUD7pKpze1Vnb7tWEKAVBujcBWpaEy6OqvSuWlhzquMmAtRUVt5vghBRfoMpv8G84ypbuJ4r3s5bfndbfke0htK8QmqHEeLYmn5y9dRTz/8rb775JkJo6dKl7dq1279/fxU55P5OEEI+Pj5VlDL912H18MlG1sNV0NgIADhMZFtKvYLFgMRcc/yH2wMAFItYCvMEUB2w8wweFm8oe7JGTMzqIFeKCACQCQh3+dB9jIFi8bubsh+/ZgOA1+ILEeB9XTQA0MyPtrpxhpkVUfBMeIkad3HOq7YEirLJ5ak9/fqFiaNeCnh1XfYyBMR16+UrlgtaSXiWhQ2VV7MmDpQST4cKc20lk0k1sYBBQCAPjwkCIhXlVp5iEr0QW7lBQkKhHnftFEw6BgAgl6W91aRLJ18FADweGDrz0slLXEQLMgMAZkV8GR35IpO3KpDOB0AYEeaAT95SSAFAQKLGGur3HHeBnQMAN4d/zymXjU8tJhr73pXQ946GB+Pu3uoBu8TNrrpTI4TRGUHTdwsjDwEAIRuE6G8QKaVDZ1Z9c2rOH8Zf/jT9BgC/6o/VPIb8bgJnRNn+MHsyXRmjr0Wsa4oZnDbosuWEAQAYnSdibVPM4LQhiZyJpQOEAe+EVzidTZ/OuzO9r8UATbyvmJJyxyXlbblcb+Ne1mkhKjEds5hNsiXWUMDD7bLt7+mNayy2HJY76XS5MR4m85murMUOY6Xq3cuYAWFmqSRtaGLh11kAYdo332DyviZ8mlPaoY2B2KjVVDFsWWiEKlXvXu4uYHzPccJms4Z9wNkB0XT4ghqeVRMQraWCJjHZ85mMGWTcCSbrIwCgQ6bVsMT9v4jAmVEAkP9p+v+DeocaCvhKQQhNnTp1165dK1eunDdvnlpdvUd3PWVBlJIOmebJeN+T8aEAdTLtOSVutB0IoSC8JAaeDnqXLfqBt19icr/C7kxAAsCendKhaXRs3yrdhzDGY8aMKaveIyIijh49Ghv7d8g5JCAiN8WlvXLFclyf0jtBOyk8b04KZnDA1Iig2dF392/ZudV3K75LGP/HLsGBndQBXfEdEzTGOD4+Pj4+vlu3bjNnzkwI++2K5QIAcEjs0TzRTiRcoVGLERofPvW67fLOgi0EwPKA6PVOnz12hxvjVoq2IeLwmUlvJ1j+THckR0pKLp9ApA8pfZqE+ADtFL3xuNOFASiEoimqrUrxidG8wGgGgFfL1JYTkxIxWfGePyZv85i8TYVGAhELGq3IdWUaPPoQ8Z0HIQdcsUdnZPSF7jwbay0V8LnuTJ27AAAMTLGBKa7ixmLAidaLMT6N7j5ESJoJIpdA2MesfjtbuJ63X2HylzP5ywlZO0o7jPLtB4T47rMeBhgww3sE9fb/eur590CS5OLFixs3bjx+/Pgnnnhi+/bt3pD4eh4sUUrq7DCtxV0i4DnMfZE298hfIxnWJ0pBretVsoiysvxLmUVwO2ud4wyQ0YiMwYgCeJI/87sb28oNO6ixpG2QAABGxEnO5Jaz0afrPK+uz3r8ms0lpa529W29T/dqvC5cQSW/FPBBe/nmq47Vl20qEbmse0mm6y/SlnHWiwAgp5TPaV8EgA7qZ07qD6c7kgHgh7y1YvpjqJl3+Pped9aEMavyMYC/hMi2cVKaODrovuLDvbdPSUR2VJdYKf0kxMyWnVfe3BrnyiKBF/BF/SQfif32AyAATGpHzzwR5GBsdw9V5OArbIgAwPe91U+F3PUIK9XwG16UP3NS3DjZuG+wMCpT0nIrAGC2Ja0+yTuACp6CaP/7ubpS3LxrR/5G7+taxZDfjaS1PPZgq5Q+CcZdhTzDYxtnOWmg/QW8kzfuLMQccCbGespAawWx8S2FMRWXOmTgGD7jIwBAkmZI4A8AFh5f9Xg4ADmBrDzGAO1dZwjgnZRfQ1WX8+bfAaCBT5PSdU4iJ7OB5Cl1k1pN26vh39UbDzucAFBr9Z7uvNXzojfuPfqnO6XOSlH01ESub5Y+PLHw6yxS+orvcCXl2w/gn3H2QbQ/HTyVyZpDB7zxwE0vdOAEVreJdya5bvTlncmEOJbyH/1g3+IRIXBmFKmknNftIZ81+G+rd7gfAe+ldevWZ8+eLS0yWU+toPxHsboNvOO65fhigIMAmA58Gwlvqz7Shw6d7Ukdx+QtAQDAHoZUrJSNbysSVr2rN23atO+//770z4YNGx47diw0NPQhXkl5CBERtfWxtMFXLMf0uR8mA8C91LsX32FB4dujxpwaOrnf2792+GvxksW3km6V7XD8+PHjx4/HtWva/o1W3Xt0ayx9rKnsTtbcCEnMzoLNGPNdNb3CROGzRTD7tgldKwjo5tf7sG73D3lrZsR8WsEJLd7hPOF0AUBjmk5imJUWq5YkX/CR7LM77tbwNceHlDbwaQrlT6WR4LPG3+nc+UWewiDRnc9CQakpRLGYBQCtIPBp3+7edh7469bLNKLltFIrCCQRSSKyg6pLVW9M+lDaEZR2BG9PYHWbuOIdvPWsx3qWyfiA1AygtMMIn+Z1uJxasTbrq/OmMzNiF4aL7/lx11NPPY8gY8aMiYmJeemll3r27Pn111+PGzfun57RfxCFkFDc1oaHdfusxDWxwMWwPuEKstSsPbbI5FXvQk9xfy6nW8+uAPBettEcwiMKlJ3QbKRQ35YZPgKihbbEYrwh0V7kuONuTrH43c05j1+z2cREUri4ZbzuVBtFpwvmjlvycgzsX5ExDdQUAETctodfspxLtF4kEMFjfmDQcO+eNQI0JGjMgpQPAIHOnW9jrQBVbQdfLWKWnLNWcDLw/lXk5AHAztzRzF3DhcPjaveQNTJ6AAyAnlQ9U1qZDABCxRENNS/8WpDbhc0BAJVjh/cOIUopDJ32ciMqxVguO0CqiSmw8SIStQ4sZy6W0KiB+h4ldRGELW2I3XzR2iGBU7+Rdz1mO/OEtMOfgAlB2NNM3ldIGEEHvFmry6mCA7qdJsYQKYmlEX3Lfr3mMeSVImkhi9nfMqV3gnl/EQDQfoLY+Fa8m0/pnWDaUwgUH7nufVG4GdvAeaGS0xEiMObBeQN7CujgKdrANxRuz2hdsdd74RvuAFFwGgNM9/2ScRxHAAhQiiNpSPDYMHHkHrtjpcGEAd6GWqdNlhAECSWmfgGqXRCB5Wc9U+AGBMFzoiuod1a3iS1cBwCiSIha7+AsHBIgziDlDBWLUxI+zQVRX9Zy1nWEDppIyjsS5T1GHwyESBD+sfvWcN56HgDo8AWA7qtu9KOM9q06ZqT613G/Aj4tLU0ul/v7P5gdx/87EEmHz3ffeFEYvBwQhxktHTS57HHKbxBbuJa3lfyg/qScZCaUVWfvWLVq1eLFi0v/jI6OPnnyZGBgYBWnPAwIERH1Y4mGr1q9AwAgCP+m8fUnztq2G14e0E9+nPpu+8or65OLrpQLzEs8ey3x7LWLbW7MmjWrSe/m/2PvvOObqto4/pw7stOkTZpuuluobARkg4IMZQkiKogICqIgypDlQkAREEFRkCEosmWIZSsqvIAIZSNtoYvONHsnd5z3j9RSSiktlNJivn/0k5x77rnnfpLm3N855/k9pflsU8wnLlnPSklZv6Cht7bdL2jIcePv1+ypJ01Hy0akZ7OsN2OcN9/7OY9nntFy3uPZaXcEkaSW4+YZzY8JhXF0jRn1k4gMEYWHiG4KsPejFAsarcx2ZhS58xvKmkSIo7zlmY707QXrva+7qJ4cEX4jm0i+67ofpZBRtw3QIqQtBNEtIHIOq9/Bar/nrSfZojVs0RpC2ozSvESqByOyGiFkVSfNfumY8XcA2JC3alrcvHuJ2as1WOMhLn8hYP6ONQlZazpqbi10yYePB0XXrl3/+uuvp59+ety4cRcvXhSLa2nnzn8QC2verd0KAF0aiHZfgQ7/LvnaeP5/LjcAxFK82vhJHjCxgc2DhaFNLwh/d7nIOGwHvlDB9ZVX8NFsHaj2GsUBADC84O1U8qLVJibSo6WPXrICQLfTZm6gBnZqn9+rDWktPzMoGAD8RQgAWMxuyV8LADzmo8SxpWunABArTWzr3+mE8U8AsLCmygX88XzPn9fdFR5ycRgAOB5Kd61f0nlOFXrUYnJSG7lMUKXxYmvBOoChABBaZiSdc8yy7oKdxwPlZPc9jXopKSMA8EAQwM/LenPDKbc3+h0ARBT6sKPfoEhi1s85m0EewDqWmP4iFHIm87onKw9zHBUcKKGb4c5tUHkHdAAAIFDkskbZbz7vvHJA3DCd1bfFuAEd0ofV/gAAgqi5NeU+o/MU7dfuQoCGhr4iJESz0ycd0v3SRfVksDDsziffBlGiVBAnYU+ZAUDURCaMl2AGe0vooCJhgyyMoVyGwnJgzAGrY7TfUyFjslnWu9mDAraV9kMAuCZue4RqTsti+nP7HxHKDxT//GPetw1DZs0ymHmAyUo/aTUV+BGXe7zOwAB0EQmPutyrLDYAmKSo6tOLenioZa/OvF9/dfC5crbKnPk33n7W+5oOBjoYAIC3V3TL7myIWVxbloSIkLW8T02TAf1Iv06c5Qip7FGDAfY+HiBVEifZ2dm3Frpcrp07d+7du7djx4413av/EKSiG+nfhzPuAQDONQXKb5FCggYfuS4/BQBIFL9UOEhOED1ub1+XkpIyceKNKYDg4OD9+/ffnXp38c7ZaZOVdMCU2Nl3p8QIERG7rZkn03nrjqxSHJw9xXziH9v5rupeobNicqen50y80vHo4+yzbMyIBN0p0+effHH48OGyp5w6dapfv36tWrV6//33+/bty0HJY0e0JP60+TgA8JjfW7yDx1wfzTNeG5Uocew55tTW/HUt/NqU7u7WkGR7kbCLWDxMJgGAZgLBpiD1HodzkclSwHEAEEASnlpJr6Cg/ZvSrQBusr2JlsS/Hjklw5FW4M5t5te6tPyCNWVxxmwACKDVIyLGNZHf/ueekFCBL1KBL/LOVFb7PVe8mbef82ROguyZVEA/MvAFUtGpBneL8ZjfkLfa+zrd/s9fxiOP+XeuqcbvH0zm29iTW5WanO1vOnwKUMr73SUfPh4gcXFxJ06cePbZZ7/++uv7ZHfqAwB2Fm5wcg4CEdfsVwBa7S/edT3tNADwQEhFI8XY0cL2kxY4BrPbCta9GTW9R7Tw199dDQOoXDXX+jYZZCPkZIScBADs4TNHXDQd0ntkRGqEpNVlKyElVS+EFK/MJXdqVS+G6H8sKJh9TWpkIbLkkeOQ7pdCdz4AIEBKger73G/KNU4iksMcSVkIFFyJ8dvQhhKTi7e68YZ/7HccP00u7DXA7xUj8kYBlCPDxK69YC9dz7ewpjPmCAwIADZddhz/dyLg7wIPjwEArJz8y4IJ70V8hAERwF9zxm3WDynboJvF1oy83PmrNMpGkNgzyFhk/vVg2QpMXpHz9EXLnj8008ZQav8KOk2gyK+SCua9K058VdnvoLjF30z+l5g1kH6dSf8+d7jhKrM5fy2DPe39u3ptazsFdP9Df2BT/ncTo+8ywJ538teGnHOcMlP+NGZ462+GrFcusUbGccpMqQS8LcKTHS6IzEWIpiJmVtgCk/85sBYAEEbN3m53vGcwYYBnpdJmuRNp3sEDIYyeF6C7aKAb/0oPfk6l+Mt45AQjXW8w8YDGK+TDq7mf0aveS+Pe9zuc3nh4qLKGR0IiZmPTjOEXzMm69D5n4pNbiB8p0fB02GTOsBswl//pBFbbIHx+fPnkeTzjTn0es3o6bEqdSihwLwhiv2QKltOh4x90R3zUDFUS8FFRUbc9n6Lef//9GuvOfxJB5MeOwsPOtGjJo0NuPYqxd5YTuaMXLhJoAghCVGYW8wrDzDGah8ukPSVik8n07LPPulwl+WDkcvnevXtjY+9yJ/MvRdsK3XmF7ryjhl9LI82qCyJRJepd7yl+L3WCi3cCgB+lHDLuZeNPRfZTFtEfgheGjAYA6AFP9+h37NixuXPn7tmzp+y5p0+f7t+/f8uWLYdPfUHbsBAALlrPXLSeKVtnfd63Zd8aGN0Zy8nSRfgijjvmcudzXE+xMJAkAQABPCURPyEWfWO2rbJaDRw/RKsbKZe+o/B7IL/frZUdWis7lCsMFUY0lDXOdmYYGF2hO69UwF93Zu0o3BAkDImRJLRSPEagG6sHhDhREDkXIt7njMmsdj1n/oPVbWF1W5CwARX4PBX4AhLWwI6jI4ZDOc6M0rdbC9a1ULQR1vns9FTQcOb6JwBASltgumIjD970OwCHhOE+9e7j4SM4OFguvynNkVKp3Lt374QJE775pryK81FTaN0FAMBj3sKaAcDA6LKc17yHmjhnAEDevzWL3AWlZyUZhT83u0MYMGawV7RQAVR+nLTVSbNbSDTe1kzeyV8YJ8l9N02/Pj9gaLBhc5H8y+x+TwexsWEAcNZ8suR0wKWvb6VR/BoBHxYi++R2Fb6/ZF9+poJo8woJlVHT28llAlShegeA7anOHy85yhQIAEpWjI7nuo/fXDlYWdSv9dZ028U8tzSMtwPAZwXT0sbcFDzoPHO56NPlPMcHJCgAQCPxzkQgAEyHBAa+M8qTk2f+ab8nK1c775uQz9693Tp8yKxBnqv7Wd1W99XXeNtpQBQd9WkV7/qOXLFdOG0+LiCEA4Nf9JYMDH7xpOnoecupC9aUymbtb4NXvVsPG2iNIH5PS87GXe13xri9CADoYGH8nha8g88ZO63BF+MxZjDnEkS8e2sbTO5C76s07a73RI14gIkKv1dFdpfjZwDYJn3+M3OQhr+WgIrTcOBYna2Leny6W4MBjZaLf3W6NtkcB0I0oqotwpdT73Czpx1UXcMLiJgfmpRo+KfOxCe3oFS0K80h7/SIK72/KG67ss/vwsSdsk7lZ2qYgi8xqyfECTWRjL2ugISRgqjb/uf6qHdUScAPGzaswnKNRjNkyJC2bdvWaJf+cyBRTM7Uta503ORaBTvTePMfALBF+vx2Z8O1ckFA5ac2mwAAIABJREFUmYwUVxhmpFZv4vlHhYInsejll1/OyLihoFavXn3Xyd6LPUUHiktigX4qWP+oov2tFm73Dk3QAQK1P61qrezQVtkJESj6+yaGTYWK3jf5f7Zv3z45OTklJWXOnDm7du3i+Ru7nVNSUlKGpsQ0ixowqU/Tbo8AAMN7TpqOekPKSUS2UXYsXXKnkSBJdiMIPJgkEwX0Px7m5WL92kBV4L+2AnYeH3Y5eQA/hOwYr7HYRstlijqTxEglCJwaOwcDtjAmBX1j1Lliu3DWUvLU9WqDiaUbIDFgE2Pwp1VACEnVM6TqGey+zhZvZIs3YHc2kzufyV1A+nUgA1+gVP2AuMtP2ck5dhZuLFtiZPR7tTsGBD9/dw3WGnTYVLbwW8zoMXaKGm67tQJb9L3H9CsACONW1nrvfPi47xQUFNxaSFHU119/3bdvX4OhvMWXjxphfPSMAnceAHxtFuQWQC9N/2cSnqqwpkZQvbz31sMGc7IOAMSN5aF/Gl1CYsHoiB2d/AFA80YEAOS+m2bYVCjrqLQdMQ1LLtoxMhwAOqueTLNfRgh1U/WSkBWkAed49ld9MtCWp4N6VnL1p2NFWjtXLpndpn8cgEEuIKweXkjBwISSgaZjuLB3TGWTvK80lXKAWRYAgAf+sG4fB2xeweMYkFqdIqJvBNkhwIHqU6m2bAC0jY6b4D5/yNTjlL39Tc3xvH7VFuB45eDeilaPw68mzmRFNB0063XD6m2enHxnyiXFoJ6SNs0Kpi/05OTbDp+Q9yg/h14K3eAj1pDMW/8CACr4VULS6HY1qwWP+Y35qwHgac1glaDE7c+PUvQNGrIlf+2mvDVJic1IVD13ruuTU0vU+76WogQpAMT93OJq/zOEhIxPbuEtifiyp+PvFpKWKUzuQjrwBaBumtTzZH8AvM070xGuX68KGTkiIH6UXOa+OACA50npEuW7GFCRsEM7MRWKqd+drh3uYIwgzLlvD+qUj6VxNEVXTb1beX6CzuDGeIRcVjZjXE+JmAeYojeustg6CIWPiaq0P6hEww+7YN6jS+uVgijEFnskLfzcV/vFfH9Q0vS8MPEvgF5lT8FMMZu7EB72WHEf9Z0qCfgffvjhfvfjP06DJW1ZPUP50wDA6n7CtpTSQ6x+GwBEQXE/3dxDBqKXWCxGCAAMgGeigSYirLNIOM5PvmjRorK28xMmTHj22Wfvuj+b89ewmOkQ8HixuyjNfmm3duuQkBF3f3sAHOZOmY/9rt/n4T3T4ubSSAAAfpRyTuJNGQoFDUTBU6MAgNV5KPVNU/ItW7bcvn37xYsXZ8+e/dNPP5WV8Rnnsj5/6et27drNnj07v2E6i9kkeTMxITltPo6AKBs9XhYRQusCVa8U6y96mJe0+nUalYYkDTz/SrE+nWFjaGpdoNrM82aerzvqvRQEqKx6B4An1H2CRWFZjqtm1lR2nmJL/tr9xbukpCxR1nhUxAQxKUHCCDp8Kh0+hbMc5bQ/soafOcsRznLEkzWZCuhPBg4l/TpUd2v9z0WbzawRAPwoxRPqp3YUbgCAfcU7OgY8oRZoauKO7yPCuNWufwbyjiusbgulLrcLhvXkzAAA0q8DIX/sgXTPh4+aJTU1FQAiIyNFIlHp2wqJiYmJiak4t6WPe0RIiKLEsQAwKM6TZbD2jQqMEteM34qso1LWTmE7brb+acQi4pOREe4WNxbtSzW87YgJAJiR4ZO7+AHAMeNhAMAY/6bbW3n7J4x/PPPvyvCtXChmNl52lM+UBwAAVg8PAG4WNl0uWVQ3u/jKBfyPlx3fnikbmnwjD65OV34hWsF3ntzUBACAkIVVTt0M/jdbl7nTstgiHR0cqHyuD1z17lXE0k6Pih5JCBg5qPCjL21H/lYM6kmIRcpBPYuXrHOcPFeJgEeCEDpsInN9HqL86fBpldxFtfjTcOC6MyuAVj8Z2K9seXf103/qDxa4c3/T7ekR2LdabdLBQgDgrCyT5/bKdemjfo+cb49oRPpRAIA5G3jaSVrqAAAh1nn2dj5qGAAIwD+Yp0ZGHuTNRzh7CgDQkfNamn44IXqRR/ROB9tGSCAAjEAE2Chs7sDSBiRaHaiq4qyDhCB6ScQNKPJ1P3m5Q70lYgqh7XZHVHXMibwaPr3/GdtRk7fEccaCaD9COhHgY0/2DLGiW1nzAub6HMxZSP+epNKXjMNH3aXGDLp83AvSx/4dX1mT59qrcEvoWBv7wTbeVxbwOv7LAT4ld8+NPKwgiO//+GPGjBmlldu2bVvWx666/GM7n2L+S0iIBgUPs7Cmj9ImHSre3TmgR9UTeN7Kj3krf9fvAwAF7c9jvvKQItsJU9qTKeqRoYon1fLHA8rahzZu3HjLli0Vyvjjx4/36NEjuJW61ZtJHw8dJSSE5y2njxt/f1zdu2ye9rLICWLNvxp+hFa/RO0/RW9KYxiveleThJq8afhPcXvWWm0vyKRVnPqtTQhENpG3vHVzXaQ41o9SWljTGfNfhmBdGFmyW/6qPTXLY44MeatB5FzSmMwWb+CtJ9niDWzxBiQMp9TPkurnCHFiVS6tdRf8qisJcBgUMryDf7eTpqN5rhwP79lasO71yCk1eJv3A0LRBUmbYvs5T+YkSj247OSF5+pbwNkBkcKEtQ+ugz581CQNGzYEgBMnTnh3z3nfVgKuFSuQ/yxtQwVbBtw5IXOSmpYJUPOgO68HEiKCjhDDcTMAgIzUK+kE+U3SSRQvQRTCLAaE4lvJvTnkWynaOTkHroKdZ2O/yrZw0ySiCMRVqOBvhiRATN9hSbZ9mOB0oZC/ubFjuW4M0FhDKwQ3DdA9oxVJ8pIkPsUOnsVF5Vpj8ooAQPhIHCAkohAACHhWGBMBAMKkeCAIpkALPA8EIYhtAABskb7y7tEhE4CzEX6dEVUzeZQdnN07/T007JVy2VgpRD0XOnJJ5pxdRZse8+8iv72L7a2EzorhjEzxt7lXB5+L+bGJopcaAKgy6e7Nu42kiCGVgIFEwAEAovxuDIWcDWPWu/zuLQh0nubNf7ivvgYYE+K4A1iEnSe7EjSreOWE23PS7QGASJrKZlhAwSJe3wv/T0NW1Z+fBPgk4LbRaj3Eokp8oG6H57rLfdV54z0CzPDm3/oG9NvGO/5hir6lQ0oiw3n7ebb4R0ACusHH1b2KDx+1ScUCftGiRdVqZdKkSTXRmf8ovJPXLs1WDtCIEqVAKYGQAWcFRCBBCPBOzBgYJDAQAQQCAhCLsRAYJacHgFxJ63yOP24wFo8YUZrJT6VSbd68WSCoOKLszp3B/Ma8NQDQN+hZJR2gpAM6BjxxxHBoS/7aCdEz7nj67YiXNix057ZRdmrn3+WOcdG0RohopFudp1udJ+/kH/tTM+DPYc5SWqFRBGxcOe7i271mf7pux+4jZZ8vC0/rkkf++crGVz/++OMnI/ola7dtzFs9I758ArlSymr4Zwt1HsCNKHaV6KrCxnO3VM6wO8wO5zcmOCqgB0slkfJHkKC27f2ry2P+nR/z72xgdB7eU3YKZkPeSm/IpZIO+KzRCpHmJey6xhZvYnVbsDuHyVvM5C0mpC2owCGkahCiK0vbuyF/FYsZAGggjukY8AQC9HzY6IXX3keA/jb973FVn0TZI/f7Nu8RYeJGV0pj4GxM1nulVvO8p4jVbQIAWjMKKHWlDfjwUW8YPXo0AAQGlvxTjx1bY4mvfNw/HlHT516p0l56y28G45ZCABAnSZ2X7R8vz07/6kb+bcsB/bXnz2MWixvLnRetOW9d8R8STIiJbqpe3VS9bt8qAEAWyy4wWZ6SV7DBngdYarZ4MEyN9rv8avl+xq0owBj6xop3X3MOTZLM7VzVbN6tggVrnyqvjRNXFLAYZrb3axNcvecczHMAgAgSAJ6IFE2nM5pf+xO37gEAiECIQJjjMY8RAZhhAaDiAPiyEEK6wUfV6kPl7CraZGUt8dJGrRTtbj3azO/RJvKWF6wpOwo3vBRenX9bBBGLEgGg+NvcjBcvlGp4L8Yd2qyR6Yqezwe/vZwQBAF2YcaABKGipscBgLefdl3oDgCUaiCr344EYQhRvDvbnfoc5t0IwB29dE/2EgAYH9w7g5AcdZU4C5o53ntpDotOWi6k2//xGvLVPu5rjrQ+KUyhW/aYglBS9uNmzfgGhfMzCz/NIWVvyh97g839jFIPQXQQAHiypwPm6JBxNZ6M3YePmqViAT958uRqteIT8PeC+Zfi/I8z3DmuyGWNAEAQvdhzdTRgTEd9wZv2sUWrc4ImDiFHAEArkbCY5WYVDmvF6T2EaIFy9nWW5eZ/YszJ8TZFEMT3338fGVkyCc1jYHgsJKthwfa7fl+uKytQEFS6R2tQyPBT5mPeDLFVdE9Jt/9zsHh3U79WHQNKNiC18+9aNi1N5VAaAR0k8OS4AMB6xJg34xvV0PdurRaP4IfpcH6wbM5K156jTNlVogMHDhw4cODpvk8rR5LXYlKPG/9of/urywlikcq/f2GxC2Ma0ArbXFH2lgrT4PQBKGsyayZkzpbpIVRd92kDgAC6vP4cFj7mf4bfMhzpElJKAAkASBRLR8zcTARSjsst2bww22nefsZjPwPZs0i/TqT6uQqD5C9Zz563nEYIYYxfCBvlnShJkjVt7tf6rOVvANiYv+r9+EVlE/bWQQhBCKV6htVvYwpX0OGTgAoAACZtGAAPpJSO9vm++Hh4WLnyJjcHn1PdQ4a0jULcVO48b8VuLGurgL/MHd9NdTeVCqPFlgP6jBfOYzfv/4zGvF8PAKphIeVSZFfCeQ/zm9N13OVeEagq64TPA7xvMP1kd8gJYrLS79bmCAQcBsmd1ttrEAJ5/950RUqjBgD3tWwAoAgYGkXo3Fbn+St+fR/3ZF7HLEdpVF7R7jx3BQDoBne/6/Au0LoLftPtAQADo/s4reKHcBtnBYA/DQeeUPcJE1XHfdar4REUr7hJwxt3aLNGXsQsFjcfTUj/4u1nSNUQTr+lNKbMnfYyABDCcM5yFAAEkXOQOMF9viPm3QCAlN23mv5y864m8pb/QNh7eiMP8JSY3utkTDwvQyhJQJ50Sy/JJ6wo2LkgrmHtJ5d1X3Ok9U5h8t2y9sq4Hc0JKcm7eEJEiBtJM1++mDcrKHZDR1pzlLk+TxCzhNNv5y3HEB1IhVdPBNUdWMNO3rjnzvUACEUPSn33kbY+HjgVC/idO3eWK1m2bNnBgwdbtGgxaNCgqKgoo9F44MCB3bt3v/DCC/Pnz7///XyY8eS5AMAbiQQAlHoQmzefd6YzGeMQpQSAvwWPAQdSAp12uZswF1u6/waAr/wmZfJE0LmzR75fV9rUxIkT+/S5oTGnHDb9muX69XmNqmojtJ2z7SzaBADPhY70hqkDgB+leFrz7NaCdZvy1yQl3Nk9ZVXOF95k4BhwqYCvOrydy3j2nCfHhYQEdvOElDDvUwQ8SyGSJcSJt654t3gUfnoU/j6T9vFX6QeOM2UP/bL7FyIZRXYP5cYvbdG7ze18+Aw8P15ndGEsRMiN8TY+yhvuT/p1BFTBPwiLscd+TsiZ0qiY1wqNL8ikY/zqkMtdFYmRJNwaWcBj/qTxiI2zHgCIlPeeFdid1W3hTL9y5t858+9M1rtkwNOkejDp1xkQCQA85jblrwEAjHFbZacE6Y2V9qFhoy5Yz3KYyXFmHjEc6qJ6sjbv7i4QxH/DGvcA73ClvyJqtJM3H+HspwFAEDW/BpPt+fDhw8d9hfSjEva2TO97xpFiEWKJtJWf/bQlvU9K0KTI3Knp2MP7Dw4y79Xxdk41PKTB0jsEUJSlrVCQSNOpDPNasf4bdYA3lAwDfGw0/2R30Aj1k1T8qOHdlt1AQdEEahhQG65gAWJieGNJA7+bRnBRUhwpl3kyrtv++EvWpa24TRNirch55rL14P/sx04DgKRNMwDwZOWaf9oPALLOrSts/D6h82g5zAGA3lOsh+JKavKYL/YUVU/AAwCCiIWJADc0PO/kveo9ZEZMyPRo3vqp61IvzvgzkjTCjn88mZOAs2N3LgAQsjasfjshb0uq+gMgpOiCzb8Doo3hHx2/NhMAjjlNK40ODCjCkfw714QnGwDGNoBMy1El4Weikw7S/f8y/fWYslatZG5V7wBAiAgAUPbXRK9tnPnyxeuThkavO8lq11OBQz05HwEAHTELkdWIUKhTMFfHeudW7oxul0/A12sqFvD9+/cv+3bLli2HDh2aPXv2e+/dWAh98803V6xYMXbs2C5durz22mv3t5sPNUyhB/51GfEiiP/edb49ZooxU4xI+WZIAIA3FPKFRssn+okIoJAM+VH6goDni2ZMK40DT0hImDNnTtmW0w2s1YML7VwVBfyuwo021tJI1rSl4qYf2R6BfY8YDha4cg/r93VXV+yUW0qGI11MSp5U9ytnwVIVeDt3bfA56xGjIFwUubxRxvMXhPESpjDauLNXwKBfACmEjbZXmJMzxtV6x+fS1akNdnyvOvzbjbzxPI8zD+RlHdqZM7jXmk/XRUdHlzvRwPMjtXpv3PsyVcAUg/EbeKGHdWsom0kG9KWCK/hiC9zZ6FxbAHRSM4vB+DurbZvd8YpcOkIuE1fNZLXOQiDi44ZLL1vPZTmuRUliSf+upGogZnQ27fqC/CXhnKkkSJ7WkKqBlHrwIWd+nisHAASEYHDIS2Wb0giCe6if2le8EwB2FP7YWtlBQlYvDWytQwkiZ3syJ/PmP3jr3+6ro73RfVTgbe2afPioj/hC5B56SAUVv7uFV8NDTImGvz4xFQDKqvfIZY1KlqqrxhGXO5VhRAi5MH5dZ/hGHdBWJJxtNG+y2SkABuOf7I4Z/ooKVuAJBBxupqHPjQqq1pbACqFJxLK4klz0AIAAPuxYfqM+oin/4f11X/+o+2q9+0qGtH0reZ+u5p/26VdsBABCLCL9/XTL1tuPnsIMK+3QUtyyVoO/kuTN5jVc5uJdd6wpRMIQUfjdXANBxMJEzGHdqrzMYRcwizGLQ2ZEh8yIBgBC3oZUDeD0OwhhNOdMBc7myZoMAITsUdbwCwAhiPrU+wAmSvzBlTaS0gynBKoAgdrFOXmCFPDmCM8JrbCZnQyXcsVxrr0XJc8VCx8L8ZwSMBfchL+8ouwG9w9PljOtVwpT4JZ39o/d1oyQlF98UvbXRH/XOPNlMG7rHTBkl+ufwcA7CGnTej3oE7JWnOUYABDieMAV/Y8gzDvTATApbVbBUR/1B1QVf5ru3btnZmZeu3bt1kNJSUmhoaGHDh26D32ro1y8eLFJkyYEQXDcrVHSd0PmyxeN24qiVj8S8NyNyDFX6lDeuB8A3Iru7WVLlASBALpZtsw0f4ABRgWuvyJsqd20sWBKyaMVQui3337r2rVr2Zb7bdNd0jE/D1Y/or7znHeBK/f9tLcw4A8SPo8QRZU7etZycmnmPCkp+6Th17JK3VM8vBsDvosE4GXVe/zelsJoMWdikZBg8l3pzxxt8MkYKsAkaLCaCn2m3ImWonVU5kQrEhgabm+k6HD48OFZs2YdO3asXDWapkeNGjVz5szw8JKRr6x697rWWXn+lWK92rJ/kX48UEpx81OIKm8v5E4bwRl+ptTPCeKW/+NhFpstR1xuAFCRxFg/+RCpRFCXZTyr41n7HWsRlLRs1LeZNX6UNon05LfktI9jq5zVectNhPgsEXCCDM4l7jAw9wt6ru6nlAMA5+lHMJMPhBh4JwIQNj1CSBo/6E75qIscP368ffv27dq1u/Wnpo6DqvkD9Z8ysavx8f0BwpnZknX4GAnlT9lPW+5FvQOAB+M3dYYjLrdXw4sQaicUHna5KAAWgARYoPLvLakgG273TdpME7dvSGB8QA0YJ3ddr71u435/URMhr142NS/mXYeMG34G7vZ2fQjJn+wU8PIzqDpW5/UJDDlvX9GtygOAkBnRITNuZJrAnjzn2dbAu0hFF878OwAAIkm/Tpz5d0rzsiBmcSWtWnj+lWL9JQ8TTVFrNSoNSZ52e14r1jswHiSVzA5Q1vJOtuJVudcnpgKChn+0lrS8zVMrhqsDz9qOX4/94S3SzwgAoqRkwq99xZXrBazJcToOMEepXxDELbv1uDtzEle0BoAQtbxI1HkXpwdFvRjfq/TzdOrUqQ4dKs6lERcX9+eff9Zol+6Sl156aevWrU6n885V6xhMgRsA6JCbHEdF8aucJyMx8C53LsjAgTGPmSnW+QCQImx9TtAikeNSP7+xkDJs2LBy6r26bMpfw2HucXXvW9U7ADT3a9NY3uKi9czOok3DwkrWpVnMHCr+JVn70xPqp0rlWTnr1Cpyq3oHAFJJAQBT5PF/Osa484XAV752XpgmlT9JlDXR4V3unA8ogPPy7j0UHQCgW7du//vf/5KTk99///2UlBsJ+RiGWb58+dq1a8eMGTN9+nQ/jeZlrS6dYeNp6rtAtYokAEBOECsDVSNxz+P2ju1cRx05n0pjbvLz5yxHOMPPQIjpiFkA0EhAfxuo+tvtWWy2nHF75hrNayy21xXygRIxVfdkPFu01pP5TqmRbOUIIudTISUftILyX5S0Os+Vk+24RsubijzXWd02Vv+T0lPUlc/ryublIdnfVGAKqTGgiiduJLeJX6hrCBt+77rQHXgnACDlEz717uPhwxci9x+BVFBxO5unP33Ged6K4iShH8YWLsji7ZzqpdDIrxpWV70DgAChZeqACXrj706XV8MfdrnIf9X7vNuodwAQEAgAairOTEgjALjrlXxF/+6S1k1th/7nTs/i3R5KpRQ9koAEFJNbiDmeDlKL2zSlgyvzba33IGiwuKEoTkLIKPXLN8X5I0EYHTqeyf0MszogRcC5KP9erCEZkXI6YnolTVp5foRWf6XMcggAtBIKlqkDXtcZfrI7BAi9719V88IaQTUs1PyLznJIf23wufg9LUUNb9kDiCFn4hXLIT0hkSPxNIB3SdUz9Vu9AwClJDWvcEUrWf0mqsEMQhB201HWwGnXAQCledGn3us7VRLwoaGh586d4ziOJG+a7+Q47uzZsxEREfenb9WDYRiX685bj+og/26hv9lPlZBhUgKcTeFKjWQzs6nory0fCXgHB8SKoKWBQB77ZpmrIN9blxKK5s6dey99uGQ9e8GaAgD/Mxz+y3ik4n5iBgB+1+/voX46SBgKAJ9enZnhSAOAe7Qow27+6oCztuMmQYQoYW9LQZQYABznrNqvcoLeiixanG3eqwscM8ydeUAYfVX71ftBkxehf+1wdDkfSjlzLiHbwbE/Xxx2o9FIaL+2UdAh6V9fnzdcNZcWu1yuJUuWrFq1avjrr6cNfzlBE1iq3r0oCeI7jWomM7N1Xl+q+Ds+eCQh+dfCF3NM1gwAoMMmIeGNDWythYINGvVhp2uJ2ZrKMO8bTJc9zAe1O1ZVCVJyQ73fbn6hdLWNumm0Q4DCRZHhokgAAFotkLYgGnzw1fkBjZms5lxxGLaFMbb+TGYOqTxPhZ4ngw2I9tolODg7ALCYvT+3VMMQ0lak/DHOegIQLYpb86C748NHzeMLkfvvQAXQ8b+08Gr4/A+vAcBdq3cvNEJLVf5eDe8t4QAIgHkq/363Ue8AMLaF7JyWifKrmQXtz7op822cRnL3Tx10qMb/pYHVOuWyh5ESKJKq4BacGP/t9nQQCe9mP8CDAoHmzYpD6OnQt9niDbz9Ih0+DRDJ6X4CADr8XURrKmnvmMtdTr17eUwk/EYd8LrOsNlmn6T0k9biwgYhImK3NM148YJ5ry6td0p8cgtxUpm1HwzX30nVrc4jxETs5qayTl15WytCUtcz5lQFYfSnDt0m4KxM2svCxgfLHnKnvgiYA0IsiPn8QXXPR01Rpd/Tjh07rly5curUqQsWLCD+nUTlef7dd9+9fv16Wde0+8qyZcu2bdt2u6OXL18GgG7dupWWHD58+HaV6xRMoRsA6NCbli55ZxpwNm/izQX6t/ZFbmydtxMAfpO/uCw4IbO4uPWyG3tjhr/5xj1Oo3CYJRDBY97Nuyq3v6AQWSrGhISogTjm2ZCXHpE3v5eru7OctuMmAAgcE16i3s9Y0/umcCbWsl8fNi/OckBfvLKAlE0WRr0ua/ejJ3ucMC4OALAnX6RdBwDbqDgX7wYo3/fgxwP6de2SsTf37IpUS86NreN2u335woXSFSsGvf02+fbboLwp6aiSIBaFt7UzIyjtaiZrujBpl7ec1a7jHReRMJwOGXfrXXQTi7qIRfsdzh9t9oZ1cusdpR7CXp/Hu7MRHSpudanCOs6UJtiTi4QRdwwD4zCfTQedB8EWKi4JG1ty2iasLpIzRXKmp+GfDMLvDBmYQgUCCACAr0J64TqCMHGjK30EFTgCqpNo14ePesq3334bHR1dVr17GTNmzJIlS7Zs2eIT8PWashr+HtV7SYMIaW5eyyER0lS6vN4vXtwv/rbyvro009DNNLXhhFeKG+OhWp0AYGWgqoXwprUWG49HF+vPeTyLVP59bj+FUZ8gRHTEB56rr7JFa6igUbzzChLFUcGvVn7SE2LRZyr/jiKh/y3fhMdEwo1BaiPH16Z694IERMyPTbwaPv2pMzc0PIbr76QWr8wlxETslmbybgEAQMha1WbfONMhzpB853qIJBWdyID+d655A8KbzYqznXZdfqp05gWzRt56AgAIcYI7/cYHSvr3otTPVad9H3WCKsXA63S65s2b5+XlNW7cuH///uHh4bm5uT///POFCxfCw8PPnj2rUpWPE74fLFy4cMqUKVWvfxeRe3l5efv27as8+C0vL2/27Nk1FSPHWdlzIX8QUrJ5Udey5WzhSk/WVLOsncJ2HAAMpCaA0zoJKfVoloKg3nnnncWLS4KRBEplxtWrKkXAvgyXi73plpel2PJt3BstZaGyG8MtQvBoiCBWWV5hunlXVZZJaUTf3Sb5yilckJV3L2owAAAgAElEQVT/0TVEosgVSaJEqVe9U4ECtthDBdD+/TXF3+UBQNj7i+Sdj1PqIYK4FQDgTh/F6bcj1UA+Zmnl7fM8v3vHL+/PeP9WKwe5XD5u3Ljp06crFDetmWPW5Dr7KGb1woT1ZMBTZd6uIwOq4c/3j4fRclwnsaguWJnz9nOuC10BgI6aRwe/Xu4oW7TWk/k2AIge2U/I29yxNRaz7rKOO7wTmQ5hw24w/4p4DwBgQIysuTBgsChwMKI1afbL152ZcdKGEaLoOp5YzoePyqkXMXJVQalUdujQITm5gkfJfv36/fnnnyaTqfZ79aB4mGLgy8LbOdsJs183/3tU7xig1LXO+7hAI8RgLEKo1Jf+oWS6wbTT7pAiVFbDl6r3cIrcoFEHkvVpDb5SsOvSU7z1uPeNsOEWUtnjwXboXsAe3qvhqUBBfHILcSNZiXqXkLGbm3rVe+3juvA4bz9TlZpIGCFucb667Tv+CgLsqUpNQtpU1OSP6rb/cFMvxvcqCXgASEtLe/vtt/fsuSm7YP/+/RcsWBAfH39/+lYBP/zww5tvvokx/uKLL3r27Fn20Lhx437++efc3NzSkrCwsFsauAMDBgzYtWtXFSvXiLWPK81+ueUJYazkkXPtypZ7pWly0Cex+nUN2SslV4xeKg0anp2dnZiY6HaXrDZrZr43ZfJkZTb5yXFLFS8ao6QODq12iJeB0W3MWx0iDHsmZNida1efoi+y82ZdRQQixARn55T9NVErk7JGXTLtLibEJO/kAEDW3hk+5zXgXaJH9gAQrku9gBCKm51EwiptQPB4PKtWrZo3b15eXl65Q4GBgVOnTh03bpxEciNa2zuNgoRR4mbHPTkfsYXLSb9OwqSfq3VfvQq02SybQNNvKOQ9xKIHGBnPO9M8aSN4dxbwLgCEhA3KWfpjTw5gHggRIYwUxH9HSBrd3YUwZ+ON+1nDDs50CLwZTRBJyB/bxzB/YNKMBKGiiI8Tl9Z+Slgf/3FY7Q9MXpW2DhKiKGHDrRUmkvRSLwb4qpCUlGSxWLKzs28NkYuOjlYoFBcuXHhQfat9HlYBXyOUUe+IBUwCJAjofzxMqafdQ6zhOYCZBtMuu0OM0IpAVWuhoFS9h5Dk9xp1OPXQqHcAAN6W4rrYA4AnlU8IG95262t9gXfxGUPPWw7paY1A1tnfuK2IkJCxW5rKuz4Y9Q4ArPYHT8YEQJSgwWwgK9q7gTlP9nvAO+kGH9ChE6vbPnP9EybvMwBM+ven1AM582FW+z0ACMJnIXEsAGDO5sl8BzAjiFtBqYfc8w09VNSL8b2qG30TEhKSk5PT0tIuX76cn5/foEGDpKSkmJiYO59ZowwfPrxjx47Dhw8fPXr0m2++OX/+fLG45Hvv1V13IdrLMn78+KCgoMrrGI3GrVu3VtfI93aUDYB3X3NgDxY1kgIAb/0LAHYRjfNVS38p6gXAgzBGGjQcAGbNmlWq3oPDw58eO7a1UBATTV+3sDcvwMPudJed4XtEC1Xim4aWtiE3x9tXgQvWlBXZixycPVoS/wzcFwEfNDGSKXBrl13n7Jy0pV/02saIRtHfN0nt9rfjrBUAkICwHROzxcMp1bee7FmAOQBMh75VRfUOAAKBYNy4caNHj167du2HH35YUFBQeqi4uHjKlCmfffbZpEmTJkyY4P1eUUEj2aI1vPOKJ2sqW7wJEElHzavufU1S+s0zmtMY5i2dIZGmX/eT9bhNstz7DfYU8M4rANgbmoHd2RXX4128MxV78uFuBTwiZaR6EKkehDkrZ9zL6Xdy5t94y/+eBOgBKJdSZRJu7M5Bwkhv/T3a7QzvSZAlxUuTqNtLJh8+7hHecRm7s6pUk9UDZisR8A8NdSREzkfdZ7fDuclmpwEYwCWudWJRWU+7t/TG42HBD+XeKhJgboASAHbZHWOK9V+oAr62WB9W9Q4AhKwlFfwap9tMR37yoPtSAxAiImZTU6+GrwvqHQAozYusdi1vS8GcmQ4pvx0SANjClcA7kTCKDh57F+3TEe8yhcuBs3CmZGHsl+5rbwAAoehMhZfkrmJyPgLMELJHfdng6ylVXYGvU3AcN2/evNmzZ8fFxa1fv75Vq1YA8Pzzz2/atKkWbqdmZ+iNW4syR170fyZI/WrYtUHnMItjfmzi94THmfIITynbBh0JIun93Gq2eL2w4U5CknDu3LmWLVuW5n5ft27dSy+9dLvGG68qdLL4i+7KvnH3Gpr1bc7nJ4x/tlS0HRH+hvz+xAY7zlmvPn2GNTIAgEgU+U2jgBdC9D8UZL/xD/AYAAgJwTt4KoCL2/YO8HkAgASh4uZ/A3E3Dud2u33p0qULFy40GAzlDoWEhEybNu21114TiUSc5U/35ZLoIypopCD6bpw/GIy32R3fWmyFHAcAcTQ11k/eSyKu+pif7HDqOO4luezWeaNijltjtT8nk0RV5K9TDvfl/pzlT0IUx7uuAiDhI7sIQQQA8IzOfbEnAE+I43jnVcKvvSipCtFZVQZzVs64nzPs5Ey/wr+77glpczKgL6PoMSHjA2+QfHO/NhOiZ9TgdX34KAtmtK6zrTBnE8QsJhVdK6rBui73x558usFHdOiESpqqFzP0VaGOhMjVEXwr8JWQybLjdYZrDEuWca1jMPZqeDFCT0rEnwYo79hO/aV0HZ4A4AEeVvVeDt7yP6ZodVXy1xCieDqi7o7gvIvPHH7B9j9TzIYmD1a9l/THevJ220jLhXDeZfum31xXBgEAkHLgrIBISaurQCkBALuznOceA94janywloP/6wX1Ynyv+Ik/NTUVACIjI0UiUenbSkhMTKzxnlUCSZLvvffek08++eKLL7Zr1+6DDz6YNm1abXagBvEUuAEA8/jaoHO8gwOAjGEX4rZcJ6VQIGrGA9FVLKL9Z5T+Jk6bNq1UvTdt2nTYsMoWw3GZv/fIi2GvPaF6KlZ6vz7oUvWu7K8RN5EVzMnIfv0f61GTfn0B8Dh0dpzjb7NpdzESINZAFq95IfDlBQAgiPz47tQ7AEil0unTp48bN27x4sWLFy+2WG4EIBQUFLz11lvz58+fNm3aq6++Svr34Yx7EKWgw+9yZKIRel4mHSSVbLc7VlptVxl2st74ldn6mp+sb9USzi0xW66zXDbLveevKFtby3Eva/WZLCtBaLxCfueeRH/Gne/Iu7MQKcec1ZP1nrjJ7wDApo0C4IGU8K4cAEIQeU9JDW4FkXJKPZhSDwbewRkPsIbdvOkAbz/L28/C9Y8/F0ZliGJTQBEkvRGPY2UtP+Qu9xeoEqWNm/q18q3M+7h3EK2hwiYxOR+xBd9QgS8CKm+FxRYux558JIqmg8c8kB7WPmq1+rfffvOGyF28eLG03Bsi959S7z4q5xrDetX7Z2UM22iEvlD5j9cZjrjcf7ncPMBDuQLvhQSYrlT86XIbOQ4BTFYqHnr1DgBs8Y+cfkdVanKIpMMm3vVT2f2GEBGxW5vxLp6oE5ZEQMjbkKqBnH67J+dDYfzqsoeY3HmY1ZN+ne9avQMAoXwciRKwKw04KwDQQa961TsAeLJnAe+mAl/wqff6S8XPxA0bNgSAEydOtG3btvRtJTyQZfy2bduePXt2woQJs2bNSk5OJmoqx2gtw2IAMO/RYQ+vGhFK+lHaL3PMhw4G9IeTdAsA6Fwmouz06dP79u0rffvpp5/e17s2M0YF7e99LSVl90+9M1pP+lMpnIlV9td4d84jEuV/dE3/fT4AhM2ND3qrAfbwGcMvmJN1iET6H9vIHusj76wmVdXLBHMrCoXiww8/nDBhwueff75kyRKbzVZ6KD8/f8KECfPnz582ZezwznpZxKuIVt/LtQQIDZVJB0sluxzOby22LJadYTB9abaOlMuelUlElcr4diJhgd2x0WYHgFINX6re5QRqLaxSWAQhTqQ0I9ii1SCMAMdlbD/HmvYSSMY5zgAAIYziHZepoJGE9J7SClTaAwmpGkCqBgDv4syHOcNuzriPcmcluLMSAJDzhMd+kgx4ivTrkO+6fsp8DAAOFu/uGzRkYPAL96tLPv5L0CHjWO2PvDONLVpbzloZs0Ym1zszOA/ug1VnnaWOhMj5qOMEk2QCTY/1k5XL9y5E6Et1wEyDyY1x/XwOqyo2Ho8p1hs5TowIJ+ZnGYyBpKqKg2/9hQoeyxZvBuDpiFmEKLbCOp6c97A7l9K8XGfVeyl1RL17EUR+7DTu4/Tb+aBRpSnovcMTIJKOutf4BVHSdmdKYwAAQkJHlSzMcJY/OUMykFI6onzyER/1iIoF/OjRowEgMLDE52zs2LsJwKgFZDLZmjVr+vTpM2bMmFs3QtcLJM3khJDg3XzA8BBKQfFuPvjtSGFsKgBkn48TtUNlLWEWLlxY+rpLly69e/e+T72yc7aVOV+ct5x6O+b9JvKW9+kqN+Ax8AAAiC7xNQueEoVIlD83I+zDWM34BgAACCGSAABCSgBPZr/5SsSixMAxNeNEEBAQMGfOnIkTJy5cuHDZsmVlZXxeXt74ie/NDw+fPr1o1Ci3UHivj/UUQoOkkgFSyR6Hc4XFeo1h55nMyy3WYXLpcJlMdhuL4NNuD4uBBNhos7MYfxigNHL8K8X6TJYlEVh5fNbjqaJ7EB0xk9PvwI7LSBCGPXnM1QmACMAYCUN5x2VEyunwWtnPQohI/96kf2/AHG/7mzPuZw0/Y1cGW7SKLVqFKGWUouvHqs5niIAMd15jeYvS8y7bzm8vWB8mapAkb9ZG2dHnhOejeiCBIPIjd+qLTO48Uv0Mom6sMDPX52DWQCq6kP69HmAHaxOr1dqnT58BAwZMmjQpISEhISHhQffIR92lsYDeFVyx/a0QoYUq/1ruT62BASbpjVJEpDOMN+79O436G4vVGw8/Si475nbP9lfG1sn0sfcOIW1KaYaz2nW89SQdNunWCpxxD3bnIkpJR0yv/e7Va5AglA4dz+TO92RPFzX+DRAJAEz2DMAMFTSakCTdc/thVODzbPFGQhBSUoQ5Jms6ANBhk5Eg+B7b9/EAqZcx8LeSl5e3c+dOAHjjjTfu97VqMEbOdsx0deBZ3s4FDA8BFzZsLQQAZT+/oDefAuDSnv3B+lXzzgNDvZWzs7Pj4uJYtiTT2759+8r58N/KI6sKXSxe3F3Zrzox8G7e9UHa21p3gYzymx47L0QUflc3Vz1KE7/7D9REfdcYUTe7o3M4a9Ql47YiUkHF/9zCnePKHH4BCYhGJ9qIEqQ12xO9Xv/ll1+W21TvRaPRvPPOO+PHjy/rVH8v8ACHna5vLbbzHg8APC+Tvu+vqLDmFYYZqdWbeJ4E4AD6SsSXGCaDYUkEHIYOIuEydYCwysaKbOFyT9Z0JAjDTD5gDAAIAImieVemIHIOFXLf/4luA+bt5zhDMmdM5h3/lJQRQtKvM+nfm/TviQShAHBQt3tjXslmswnRM5r73TndnQ8f5XBfGcyZfqWCXxVEfeYt4Z2prvMdAUDU5M+q5F+oFzFyVSEiIqJZs2a//PLLg+5IncAXA+/jVjwYd8ovsvA8lIl7L42HRwAYYLk6oItY9KB7er/ATLHrXGvMmoUNt5LK7jcf8zjPdcCuq4Ko+VTwaw+og/UZ3uU81wa7rwtillKa4Zxxjzv1RUQpRc1PlZ1fvof2nc5zbbA7VxC7jAp8gS1c5cma4k2uBMRD+429R+rF+F6HdpLcC2FhYW+88UYtqPcapFS9S1r5edU7KaeoANqTcRIRDG+LxWahfFSqOVnnrf/FF1+UqvcmTZo8+eST3td7M1wphRUne/TqOSldvU+Zw5yTs8dKEj+IX1Q76h0AJC3k8btbkkrKuEObNfIiLuOnX069S1r5+Q/UBL0TCQh4W80/Y6lUqg8//DA9PX3y5MlS6U2zA1qtdtq0adHR0Z999pnVar33axEAT4hFm4PUazWqgVJJT8ltf0wb0vR3GpU/QXAABKDdDmcGw1IIedX7V9VR7wBABY0mJI2wJ48Q/bvgJk7gXZlIFEMFjb7Hm7oHECFtTkfMFDU9Jm5+mo6cTcjbAs9wpoOezHecKY1dF7oyuZ8+IY54P2Hhc6Eje6j7xktvzE9vL/xx0uVRX2d9dsz4+4O7BR/1AzpyLiCaLVrDOy57S5isGYBZKmjUXWdPrKfMmTPnwIED589XO8/wfWLPnj2vv/569+7dx4wZc+ZMBXmSp0yZ8uyzPttkH7UHjVDov0kW4wV0CEUCAAnQWST0qncBgvAqmMjWXxAdSIVNhn8Xh8seYgq+wa6rhDiBChr5gHpXzyFEggYfAgBz/WPM6DzZHwAAHT69ZtQ7ABBi71Z5Jucj7L7O5H4KJQZSPvVev6nGCnxxcbFYLJbJZACwZcuW5OTk+Pj4MWPGlO60/49QIzP0tmOmqwPO8g4OCRD2YAAg5VTcruaEhLQcek7+2DGmKJ41tXOesQKJlP0DnQEo9rHFVntJ9rjViwYMH9ycVA+2EI0eXVsU4UdOa+c3/qCR46t09ccjhSt7V+bAyWKGRBSPeQSIQLU3y1O6Dq8coIle2xhR6Fb1Xlq5FpxIioqKPvvss+XLlzscjnKHVCrVW2+9NX78eKXyvpjurrParjDsEKmkRZn4utJ1+NKS9iLhMnVA5fHzFcKZ/3D/MwCRUowBgEeIxpxF2HALqexRI/2vKTBr4M1/csZ9nHEP5komTRCtJhVPkP69SGUPIEsmWb7PXf67vsQhYvEj3ymokv2cHOYAgEQPv9WQj2rhyZrKFq4k/ToLk3Zxht3utJcQ5S9qfgpRVXInrhcz9FVBr9dv3rz5o48+GjVq1KOPPhoSElLOWsVrhVM7jB07dsWKFaVvEUKff/75xIk35UBu0aLF2bNn79PmQd8KvI9b8WDcJb9IiJADYyvP95SIF6n8Dzqck/VGDiCRplIZdkVgQGfRQ62IMOM634F3pgsi51H/pj3DTLHr7KOYswgbbiOVTzzYDtZnsOtSH956AolisCuDECeKmh6t0SSm2HWpJ2/9G9FBmCnyjno11/hDSL0Y36sk4B0Ox8iRI7ds2XL06NEOHTps2rTp+eef9x6KiYk5ceLEf0rD18gAnz3msv7HAiqAZg0M/KvepW0UwLscJ0Nurf/5D+73vnZ6X4eoicvb/QQ0kKpnTKHftv9BGyQl53RWjD9odLF3/jRJBL1jxUu630F2OjnHB2kTFZT/9LhPalnDX+13hjUyygGaqFWPZI+9XKF694JZnDniojhJGjLzPvotFRUVLVy4cPny5WVj470oFIrx48dPnDixxr2ahxQVX/AwANBUIHhZLn1SIiYB9Bw/VFucy9744g2USuYEKO/u43GnDuWM+ynNS4BItug7UtFV2KhKTrMPBt7NWY5yxj2ccT/25JUUEmLSrxPp35NUdkfCBoXu/HT7ZRKR7f27lZ43K3W83lMcK0nopOrRVtnpwXTeR90DsybX2VaYNQjjv/Ncn4Nd1wTRC6mgUVU8vV4M8FUB3Wn6r9bi7DZv3jx06NCYmJj58+c3adLk9OnTkydPLigo2LlzZ//+/Uur+QS8jxoAQ/6cDOdZS4Ovk+igO7vQWXmeRiiDYV8p1pt5vplQcNHt4QDeVMjf8JMXc1wg+fDPEXPG/e7UoYhSiJqd8nr6ejLGs9r1pH9vYeKGmr8ejx1nrZIWfv8Foxveft51oRsADwD3YzaEt512XXwSgAdEipr8ee/R9Q839WJ8r9IEz+eff75ly5bExESvUF+4cKFKpVq1atXly5dnzpz5xRdfzJ1bw0mnHnpCZsZYjxo92S4AQAQqUe8AQIiAEAHvYs0Kw9anAYCQUf4jQr7Z/glAiYB/c1QXAX0GACjN8NIGH48UXhpd3o6iijHwbt61IvvzVPvF2QlLVIKSuZifizbrPFqdR/uH4UA3Ve1ZOklayGO3N7/a/4xpp/bSCTNT6CaVVPzulpIWFeRI452c5aDetEtLqejAsRG3VqgRgoKCFixY8O677y5evPirr74qGxtvNpvnzJnzxRdfjBkz5p133gkNDa2pi36jVq232Tfb7Oc9nnf0nlCz5RmJJNnhzGU5EiEOYwAgAHbYHSTAR3el4QWRc5ymw6x2PSAEiKaj5tdU5+8LhJBUPkEqn4DoRbz9PGfazxn38bYznOkAZzoAAIQ4UeXfM1DZnZQ/VvY8tUBT4Mq9bDuv82jLCnidRyshpRKyhj0UfNQXEKWkw6d5sqa6r74GmCEkjSjNiAfdqQfA5MmTH3QXSvjqq69EItHBgwe9BviJiYlJSUmdOnUaO3bs448/LpffOU2mDx9VAsP1yanFK3IBIL13SvzelnfU8HKCAIAkAb0mUDVcqzvn9gDAGwr5G35yAPgvqHcA8M6Vc6ZDTO48QfTnvP08q90ASCCInF3j18Iszhp50bhDGzAkOHJlEiIfchFPSJtSmhdZ7Q+kf6/7sZeBkLWiAoeyxRsozcs+9f5wUCUBv3HjxtDQ0DNnzojFYq1Wm5KSMmnSpAEDBgwYMGDTpk2//PKLT8BXFzpMKGkm9wp4QohEjUpUBHZlAu8CQJTC7E6PsZ9uJuug3HclI7/Q7K0gk8lGPxcD7jNkwFOkoivY73WVwMU7P706I8eZKaf8KKLk+1DozvtVl4wAYcA7Cje0UXaUkrJ7vFDVkbb2i9vV4mr/M5WrdwAg5VTkN40yX76YOy1d1FAq71qy95Wzsvr1Bcq+gYLwGtvSplar586dO3ny5KVLly5durRs1gObzbZo0aKvvvpqxIgRU6dOjY2tOM9KtVCRxFsK+Rg/2S67Y53VnsmyX1msAIAQ4jBuLqSzGM7rabfN7oC70vBIFEcHj2EKvgQMVMhrhLjeGFAT0qaEtCkdNgUzWs50kDMd4k2/8c5U3pkK+UsRKScU3UhlD1LZHQmCJ0a/Z2Ut6fbLgWUMV7WewhlX3sCYDxdHPhM8rJnfow/wdnw8KKigkWzRGt55BQDoyE9qdMtivWHBggUPugslXLlypX379mXT1zVv3vzLL78cOXLkggULZs+ueZHg47/Iv+qdEBF0kOD/7J1nfFRF24fvOXVbNr33DiFAQu9IEZCuFAsCoiDgAyICoqiIKCLy4qNYEB5BiiJFUBBRqii9B9Igvfe2m909Z099P2zYhCRAgCRA2OuXD5uZOTP3Sdmz/5m7sEnG5Kcuhe6PJj0aVMYlSxDMN7w/UnhBBHgstPsNSP9PRN0/QuEmwm0Sl/kugER6zkCKkMZdxareAaBsRwEAPA4anvRfhqkicOcxTTQ/FfAZZv8E4TS8iea30cw06DN/RkZG165dlUolAJw5c0aW5SeeeMLS1bp16/T09Kazr6WS/3Faxd5iXIkBgMhIKaNjxEoBAETDeQAwFPsAgPusTYQTMpys+GLh59YLX3xuqMb8KyCK8vuwUSwp40qymQwP2vvd0M+sYcPb8jYIstDH+ck2dlEGQb+3YHujrNVw1J21ofuiHUa5hf5xS/VuwXGMu8f8AFmQ016MNaczACDqhZSRl3MWJCUNvGhpuStkvkgo+lEo2ljvlx2/592Z/innly17d6yry01Vc8xm87p168LDw5999tlLly7d7br1okDoWY16n6db4I0EObIsIwBXnFjooLXktLNoeEuJ+LuF8JmPKE9EupPebzWKwc0MIt0I1wl06A/KTql0xO+k1xxM1VoWK8WyvVzabOZSa/ZqLz7rA5Xxcge7aF9lgPVCLWEfoWmHIyKbybikO2Ntl2Txiv5CgTnvAdyMjeYHEWTgCgAMdx6N2/d90NY8dCQnJx87dqzZlmMYRpJq53GZPHly586dV61alZWV1WyW2GixyJC9IKl4bQ4iMcye4HLMdICSTTImDbnE55vvePVfN+Len9Wo7THsgImZV1r+WMVaYMpQwmMqgGS+/rykP4VIN6K+wnL3g1W94/aE3xetcDuibEdB5rQEWWwJNbNuA8LtCI8ZiGyykGRcTbiMA+wuilLZeJhp0IGDvb19dna25fXx48cRQtasNuXl5RR15/AhG7XAHUkAkERwm+2n21tsPKtLGR0T8luUWH4OAJh9Pclhf9O+WdonDl3+pdXpnLPWC6cOywOQSM/XkKIRjnkBwEvh+0mrbxxJZwqr2n6Or4y5qr+oxFWjPZ43CJUfJM09Urq/j/OT3gq/Rlmxgag6aIN+atuQkV7vB7HXTRV7i5jYSsKZTBl12XheDxjicliLdxwdeBdvWFzG22LpHULBaYA3hsO0geSWi5+t+vyLvLxqvSeK4o4dO3bs2DFw4MCFCxcOHDjwNvM0EAygNU3kiiIny644ViHJh0zMIRMTSBJmWTbJshohD+xeAuERrlW0OwkgI6JJUvE1H4jAtb1wbS/Sb4lszhYrDosVB0Xdv5IpXjLFQ95qwFS4fW/cfgDu0B8pghWY8s2gD3iZy2LSfRT+1mlOl/+zPns1ANiTjq/5vxWqfrwSkj+G4No+yujLiKon88hjjizLy5Yt27lzp9F4L5uD90BoaOiZM2cKCwvd3d2tjQihNWvWdO3a9ZVXXjlw4AB2T290NmwA3FDv32UjEsOdCKGQAwAum6UDlOYUU9JTl8L+7EB63vIc3qreLXHv49Wql4tLD5gYAFjl7Pj4nMOT3m+JxTtkrgAASN93EV47OdH9IIty5qsJ5b8W4Voi+Jf2klEM2Rud8vTlsh0FsiAHbGhTq8awDRuPLQ0S8G3btj169GhmZqajo+O2bds6derk4uICAPn5+SdPnmzTpk0TG9mykHmx8ozLZBFhhWU7Co1nr3gu9i3/pYAv5LLnXXIaeYhwAROlyIIRHeF754k/HkjsK1+o2ndsFx3e2iNOL7ksjp3CXi0DAE6UAaCclcb8WpKhu2kj2McOt/R+d9n4y7WbDqJ7elPTo6td4t3p6shtSRa35W0AgJHuz9oTjvaEY1+nJ/8u/evn3O/nBz+sHsUCEccAACAASURBVIwYCtwSyVyppINVKaNjjOf1hAdF+ygkRmLiDXer4XH7fhYBT7iMB6z+eu9i+X6ZL9J69Hlz3oL/zHp906ZNn332WWpqas0xhw8fPnz4cIcOHRYsWDB27Fji/mrMJHMCJ8u9FPTXLk46SdpqMP5iMKXzAgAQgIyynCaI9xY1hQjHOw96pEC0L+E+hXCfApJZrDwj6Y6KFUckU7xYfkAsPwAAiA7AHfrjDgMIbe8g2luqPGP9z4mUDaOU3tlMhsmczlccFYVCS3sFX5bAFTtruwerwmhb8ZWWBaKbdWvyYUOW5SVLlvz4449FRUU120VRZBimbdsG7aI2ClOnTp01a1bfvn03bdrUuXNnq1bv2LHjggULPv3000mTJq1Zs+ae5z9x4sSWLVtuP6a8vByaMW+fjeajjnpXd7HX9nfK/zS9IRp+v4l5q4Z6B4AIilzn6jS1uOyAicEAVj42Gh4RDqTvu1z6m5i6PeH2YiPOLIty5rSEsh0FuJYI2tm+YHm6/miZ/WDnkF1RKWNiyncXAkCjaHgu8z0h/5uGjMRUbRTtjsPjkEbPxqNGg7LQHzp0aNCgQUqlUqVSlZaWfvvttzNnzty6devChQtzcnLWrFkzY8aMZrD1IeE+s9TyeV/wWbfxfpdrvFMgUZLDnxHyC6uSn3+52HvqU8b3s5f9Vvr0PSxtJchRnNBjzzivyXUj2w8W792Wt8GN9vw4fDWBSAAwioZ3EmcaxMo5ge89zKHCol5IGR1jPKfDNThgsqiXEIkUoSomwUj5KO5Gw0ts3JOS4RLps5D0ebuebuYae7U3ACjaHceUrapWF8UdO3asWLHiypUrdS/x9/efM2fO1KlT7zkP0xp9ZYEoLnKwt9Z752T5L4bdbTA54Rgjy9PsNB1omy/MLZH5QrHiiFhxRNIdk4Ub+QsQgQgHmS9pyAwcYAsVPTFctTT8S1fK/c4X2GjRPBJZahvCmjVrXnvtNa1W6+7unpyc7OHh4ePjo9PpkpOTu3btunr16i5dujSbMdOnT1+3bh0AkCR5/vz59u3bW9p5np84ceL27dtVKpUkSSzL3oPGHjly5O+//97AwTYN36Kood4JJ4Iv5FTRdqG/d8AdiMIvs3LfTUY4In0UXCZDh6jqanizLHfOLeBl2arerVzluKnFZZWStMrZcajq8XFOlsXSXzG7bohqtMS9N6n3He0LP0vXH616UtsPdvZ4KzBlTIxYITg+437/Gp5LnycUbmjISKQIVkadtwn4x41H4vneoFPBJ598cvPmzR9//HFubu7LL788bdo0ADh//nxOTs7MmTNfeaWhdXdsAACmjr7xIoqJw8VyHgAAgSJMLRSVUYHXeUkJZSrSpVQyOxy7UmZV71oNPaG/gaHaP9N9yjM3kheUs9KcwxWOCuy/AxxSywXuRowQiaNgR+KtoxXFjLSktzbIvvoXncOmH9Sv+LesvLNDzzZ2UTVtM4qG3wt3AMALXq9Y1DsAqHHNCPfxP+et/zn3+zZ27a3tDxWiXrB4ziMciQYRABShKjbZxCablBFqJsF4N+fwGOW/nI0fwud9SbhOQHTt5PZ8xiKQBcJzplW9AwCO488///xzzz134MCBFStW1AoczczMfPPNNz/88MPp06fPnj3bx8fnbm9wpra28qcQGqlSjrz5E8MVjvtaV9lLQY9Wq+xtvqY1QKQ74foC4foCyKJkvCzq/hZ1x6TK81b1jhAOhAMinIBwRDd7XkjGK7JQbqR8fdThRqGSqJHq7HjZ4WRjYpAqLNIu2oVya9ZbsmGjMdiwYYNGo7l27Zqnp+ekSZPKy8stKvfjjz/euHFjM3vYffvtt927d//xxx/T0tJqSmiSJLdu3dqzZ8/Vq1enpKTc2+SrV68ePvwOCZxyc3OXLl1qc9RvYZT8kFv8XTbCEeFM8AXV6h0A3Of4AUDuu8lcNkt60eYUU/qkuLBDHWteTiM0XatxwrDnNbWrlrSjqA2uztsMxs6P1wY6wp2facz5JDljSnz57kLcngje2b7g03T90TLSnfL9PDzrjeu6A6UAELytXeq4q+W7CxGBAta3uR9NTfq8LZb8cpvy9dYio5T/Rzb1buPhpEEn8PWSlpam1WotvvSPFfdfJ9Z8fYJYvp9wnZAy/kX2utF5ilfpD3mIRMHbEwjt+xXHe2oiEgjncir4u8mvfrj1t0TLVZNGqNYsohVt/sDsulunKjSKljrwpybWIx5G/lISX8LvHevSxqVKdUuyNDv+RUY0PeE8ZKLPdHTzG9PmnDXHSg9E2LWfH3STj4Akix8kzc1ls8Z7vTTEdfS93XXTYVXvpAclGUSLgPdeFiJVivmfpiMcKcJUTOLdncObk18RS3fjzs/QoetvWqtsjznpJUQ4KqIuIMLpVpdfvnz5v//979atW+v+kZAkOXr06Llz53bv3r3ea++HLZXGTyp0AEAjNFipGKdRd3q8PlXcJaJR1B/nUv9TfSYPAACI8sTt+2LaPrh9H5AF5kpXkDhF5EFMU9sD5aPkBemmZABQYMqvIn/E0WPiRGnj0dihbwjOzs5dunT5888/AWDDhg2LFy/OyckBAEEQwsPDx40b9+mnnz5oG2+ioKAgNTW1Z8+eTTG5rQ58i0R/tCz16RhLFrSa6t2K5RweAAHIzhM9/dfY6mw1K0yCIbHLWQDw+SS08t9y3V8lhCsVtr+DorXaUiaAL+S0A501vRzzlqQAQMTl7orQ+iMcGwif9yWftQRThinanYA651Jc+gKh8Htc24eO2HM/q9h4RHkknu/3vs0cFBT0GKr3RoEKWAYYLRT/jGtiAcD7wxD3OX4yLxtP/QsAlFpPOJdjmk5m1bA9hzKtVz0/GMNdnqmp3u8BDGG9nAaMcB9fV73nsln/lh3GEP68V22XCmvj3oLtOqH8fgxodGRBtsS9U960zEqiQVR3tAcEue+n0mEqz7cDZVFmk0yKMLUlp51QzDVkWsr/I8BUYuluSV/jv1diucwPAID0ff826h0AoqOjN2/enJCQMH36dEv5Bis8z+/cubNHjx59+vTZvXt3435MnGCn/trFqbeC5mV5r4mZWFQyrKBoY6WhvE5uZxsAALgadxxMBn4GAIApSO8FhONQRDjLXL5QvI1LfY25FMlc6QGSGddEI7IeX8HZAe+86D29m2PfXk79repdBnlV2pKlyfO35v4vw3SPp4U2bDQPHMdpNFWxVAEBAXl5eSaTCQAIgujVq9ehQ4ceqHX14OHh0UTq3UZLRRGmwp1vOBX2dKil3gHA7glHTIkByAiBpndLywvz8KNsrXGZ4g0Aue+n1FTvAKAIU4f+2YH0oPWHS/M/SgUA12k+96neAYD0nIkUIRKTJBT+UKtLYq4LRRsB4WTA8vtcxYaNpuMuBPy5c+dmz57du3fv6OhoADhw4EBcXFyTGdaSQXQA6TETQHKZ/D1SIMKR9F4W6v6GPx2cCgB0VCIAogKW7979q9Foslzi64716qihfJfc/+rPe73ytMcLqI5T0La8DZIsDnAZWm+2+Qi79u20nViJ+a3g5/u3oRGRTCITWwkAQgkvVAgOI93CDnf0XhYKkpw5PUEVZWfR8OYUEwBwuSyXc+dSMQCAKC/SazYAcJnvAFSpXz7/a9mciSlbEW4TGzJJWFjYd999l5mZ+emnn3p51ZZ/x48fHzNmjJ+f35IlS0pKGhSGfUcwgAFKxTpX54Oe7jO1dm44nsYLKyr0T+QVzi8tP2c228I6a8FlvsclTwUAkFg+d6VQvl8WSm8aIZkAQDRcYi63Ya/25DIXieV/yYLO0ulAOvV3eepVv7kveE+zXiHKYh6bnWFKOVzyx8acmzLlGAS9KNtO9mw8RISHh1+7ds3yOjAwUJZlayIPSZKuX7/+4Eyr4oMPPiDJhzF0y8ajQtnPBUIRh3CECFT8dXb+JzcVPzZdqUwZflliJMKZlGUo/L/MW81jo6lA4LsyjPJTyqIMCLw/DLaodwuKMLXn4iBASBZlylvh/UloY6xIUf5LAYDPWV7roV8VJun+CqayOWLYeHhpqID/+OOPu3Xr9vXXX584cSImJgYA9u3b17Zt2+XLbRtU9wLhPQ8wN2VEkuPI0xYp7b3UVxGSJQPCMUHvPB7TdPrxxx+t458dTNE+cxF917HTDeSq/mJ8ZQwAHCr+/eUro+v9uqq/AAD/lh7KN+c0kRn3AK4lfJaHAoBklih/ZeDGSEQi99f9PN4KkHk5bXKcpqeD/VMusiQDgPMLnrevKl8T0usNRPtKxqtC0U8AIHP5fN4XAEAGfALoLlLKu7q6Lly4MCUl5bvvvmvVqlWt3ry8vA8//NDPz2/q1KmW/6xGwZvAX7e3O+rl/rWLUx+FQpTlP0zM5KLSoflF6ysNJaLtQL6KGiVwEAAg0hPRAdYvQJRlkGWAZEoQ8teYrz/PXAxmY5/gMt8Vy/+UhYpacxKIWN7q27eCPxrj+eKzXlOs7QXm3DcTXv5P3POfpiy6ZrDtftp4KOjXr19cXNyiRYtKSkoCAgKcnZ0taeQYhjly5Mg9JOxodCRJEgThQVth4xHGZYqXMkIjizLpQSMC5X+SZtXwFvUulPOKEJVQyiMK814W8mCtfQyReTn12StcFoOpMJAhe35S5bHquDb9kbKceddBlpEC53LZ9BevykIjHEbgjk/hDgNkoYLPro4SEsv+EHVHEeFA+iy8/yVs2Gg6GiTg//rrr/fffz8oKGjXrl1LliyxND777LMeHh6LFi06cOBAExrYQkG4RpYXAIDzcz9YjvgkJgGQgEA2YSonvw/y8vKOHDliHT9h8uuk9xv3tlYRV7Do2n8s2eluBY5womGilMYU8DAd40pGMW9pGgAgHHGZTPb86xbzvBYHu7/uJ5ullDFXdAdKLbskZTsK2MQG1zTGFJTvBwDAZ38ki3o+awmIRtxpFG7f7x7sVCqV06dPT0xMPH78+PDhwxG6yQOCYZj169dHR0d36tRp8+bNPM/fwxJ1wQEGKBVrXZ0Oebm/prXzwPEMQfi/Cv0TeQX/KSkrsAV5ApBer1v2xXCHJwEA0d7K6EvK6MvK6Muk12yQOUT5WkQ+HfqDImIf6fMWpu0BiJCMV4T8b83XX2AuBLOxfbnMRWL5fquYpzC6labtMLexrTXtrGupcI2Xwo+X+CRjwvmKE9Z2XubOVhzPYFIl2+G8jWZn8eLFfn5+y5cv37VrF0Jo5syZGzdujI6ObtWqVX5+/vPPP/+gDbRh434hXKjQ/dHKNhouhyVcaUSARcNXq/cwNZtiQhQWtDnS/ilbcGhzY0416Y+UAYDXe8Gu03wkk5g6/qpFw+uPlKU9d0ViJJcp3l7vBAKA7kApl8ncYcaGQfp/AogUin6QTPEAADLHZX0AAKTPu7cPk7Rh44HTIM32+eefKxSKgwcPBgUF5efnWxp79ep17ty5gICAVatWDR48uCmNbJnwxSPEknXKVsl83pekzzuSoer09U/7mRNpz61b/88aHd2xY8eo/ivqncRBgWso5EDXnySzpw9tEoRfSz41yLkVfFm9Yyy0sYta1+6X+7ibu4DLyqv4eZ/EsJKJlc1mWRBlEytLEgAginSb/wodHlRzvDk5o/zHPQjHEE0jksDUSkytwjQqTK0kXJzo8DDciRTKeCpQweeYSzbkggx+q1sBAu9locaLlYaT5QBAhyjNKQymwjHNXaQZw12ewQr/J1We5ZKnihWHAaMp/9uUAGwQvXr16tWrV1xc3OrVq3/66SdLuKmVixcvTp48edGiRTNmzJg6daqHh8d9LmfBE8dn29u9Zm93nGF3GU3/sOajDNtXoRivud9AskceTEn6vselzJCMl4FylwwXhJKdhMt4Wajgsz8BAEwZLuoO49o+uPMoAMC0PUkAkFjRcF7SnxT1JyTDBcl4VTJeFfLXAGCYqjWm7YHbdcO0PRF5U7U5LWG/JOxzo2jIYTIDVMHW9lNlxzblfAsACkw5O/Cdmprfho2mxs7O7uLFi+vWrbP4By1evDgpKWnv3r2iKL788ssLF9qOoWy0BAgXKvSP6ORhl5l4A+lJC8Xm/E/SCj/PkFhJEaZmk4xV6n2464O29HFE0UrtMT+g4P8ycj9ICdwYKQtyyQ+5qeOveswPKFiZLjGS6zQfTS+HjFfiAcDznUA6uHE+umDKMMJ9ilCwjs9YREfs4fO/ldlUTBlOuE9ulPlt2Gg6GiTgL1++3L1796CgoFrtvr6+nTp1skXC3xtCkVD83RT/L9/l81YTrhOEkp0AoEMORa6vAsCWLVusIydOvGXEdQUrGji5mOWSjAlh6trhOgu72QX7/7q/KLeVJvI575eb5j6qEYrL+JwCPreQzyvk84qEolJRV+m2YJoyqnXNYWxMoun81XpnQGZcMrG1Gs3X09n45Fst6jJ7UtiBjslDL7HXjXSQis9nS37INSelu03DDJc0htNVEe/mFAbX4CF7oilfxd3cE6ICPmNj+4kVhwCA9JyFaP+7ufyWREZGrlu3buXKlRs3bvzyyy/T028KycvNzX3//feXLFnSr1+/V1999ZlnnsHxRkhvjgM8oVQ8oVSUSdJVM9dTUV3q9iRrzhaEgUqlC/7Y1U8iXMYLhRulyjO44yCRO8hnLSEch/E5K2ShFNN0FPX/1JPMBlPg2t64trdFzEuGi6L+hKQ/IRrOS6Z4yRQvFPwPAJAiyKLkcbtuSFH1/qnGNeGam0pzRWk7pzkNTDYmFnMFnFSdZDHZmHis9C9fZWC4uk2gqjGi/mzYqA8XF5dFixZZXpMkuX37doZhMAyjafr2F9qw8QhRW8MXmSVWUoTa1PtDgdeSYMBRwYr09ElxAd+3AYCSH3LzllZlrbPr45g+JU4WZPc3/T3frS1G7gfS5x2xZJeo/1co/EHI/RwASP9ldfPS27DxsNHQUN5ambStODk5JSffUlzZuA1CMcckhPEFg0nPv7j0BZLhPAD8bDchUmF39erVq1erJC5BEM8999ztpzIIlZ+nLVkW/o0zVfvx099lqArX9HMeQqKmKicmlFaUrttmvp4mGUy1uhBJQp06hXbDnqCCfEEGTK1AFIVIAikVCMcs4xFV+31TO+wJRUSwaGBkhpUFQTKxktEkGUyS0SRzgiIyjHCmQvd3uKHhlVw2U3mSYhNL+DIcAHCtTtQ7AIAscsyZQ8o2wzDlXWh4TN2OcHtBKPoRUR6k19y7/eHcHnt7+zlz5syePXvfvn1fffXVkSNHapZ1FEXx8OHDhw8fDg4OnjZt2ksvveTu7n6b2RqOE4Y9cfMP4eNyXYYgfFyu66lQDFcrBygVKvT41D5FVMByNnaAWHEMU0VIpgRz+hyxdC8gHBABMk94TLtdMhtMgWl7YtqeAAtBYiXjZVF/Wqo8JVWeldk0gU2D4q0AgEh3TNsTt+uK2XXFVG1qplGwJx2n+M6qO/G5ihOny/85Xf4PACwO/b8AlS0y00Yzcasn/gPhgw8+eO+99x60FTZaAoQLFbovOnnYZSbBQPspCTfKeEGHKCzox7b2Q22e8w8Yr/eDAKBgRXrG1PiA79sAgpINua7Tfex6Vat376WN/BxEhAPp8zaXsYBLfxMAcMeh9VaGt2HjYaNBAj4qKurcuXOVlZV2djclADMYDGfPnm3fvn3T2NbC4Ys4AOBK55Pe/4oVBy2Nf2snvaygP9u2zTps0KBBDZBtMidxO/I3zvRfUKvDiXQZ6vZMI5pdFy4ti7kYBwC4vR3p50V6uZPebqSXO+HhQrg4IaL20THCcUVk2F0sgBAVVE9i/JqQ7tUangpQCHksX+YCCEhXE1/kgAie0OgFo53+4Cl1r4haHgF8ToHpUjzh6kR6upHebqhOumPSbwnIAu76POBqaAIwDBs5cuTIkSMTExO//vrrLVu2VFZW1hyQmpr69ttvL168eNSoUdOnT+/fvz9qbHW9zMlhQ6XhX9b8L8v+y7JKhAYoFcNVyp4KmngMlDymjiJcxwvF24BwAkBiyS4AwO0HiLojd5fMBlNgdt0xu+4Ab4IsSqY4qfK0qD8lVZ6R+UKxdLdYuhsAAFfjmk6YXTfMrgum6Yzw+hMrjvF8MUAZnGZKqhT0rnR1MMWmnG/PVZwIUIa013Ya5DryPu/9sUIo2cHnfAYNyDWAqSPp0I038he2KFatWnVX4+fNm9dEljQQgiAI4i7yhtqwcRsI1xvn8AkGcxaDKCzop7a2uPeHhJs0/Po2nm8HGs/p0l9qKvVugXB/SSjaIJkSAaMp/4+aYgkbNhqdBj0UJ02adPjw4cmTJ2/cuNHaaDQaJ0yYUF5ePnbs2KayrkUjFPMAQDj4kF5z+JzlAIAo9999wgDg119/tQ6bMGFCoVFcebbypbbqSNfa2nJ3xlUAD57XZmaPRbArySU+TN0GAAQJAIBoLG9oSWKT0pkLsUxMoqprlMO4p2p2qjq19fxkHu7kQLg8yOqpN2n4QBVGAGZHmC4BriWCf+uoCBC5rGKEplDBoXyBmfSodg2t2P6H8fTlqm8wjHRzJn09SR8P0seDDg8kPVwR4UwFr2mGW2jduvU333yzfPnyjRs3rl27NiEhoWYvx3E7d+7cuXNnSEiI5UDezc2tsZbuQFMdaCe9JP3NsHtMzBnWvM/E7DMxWgx7QkmPVqm6KW6RaKGlQPp9KJb9IelP4Pa9Rd1xRGglNhUASJ9FiHC+lxkRjqnbY+r2hMcMAJCYZKnyjFR5Rqw8J7Mpou4fUfdP1TBla4uYx+06IzrAOoECU/Z06t/TqX/tiQFjRFOi4eo1Q2wvpwGqG/tKOWymnq8IUIWommanqQUgVZ6X2dSGjBT5IpA5QA/RQXRjMX/+/Lsa/8AFvA0bjUuVhh9+2ZxqCtxiU+8PF17vB4EgF6zKyJgW7/qqT/HaHFmQPd8JbFzP+ZtABBWwkr02hvR60xrsZsPGQw6S63g418ukSZO2bNmiVCrd3NwyMzMHDhx46dKlsrKyIUOG7N+/v9HPAx9m4uLi2rZti2GYeH9JvJOfulR5vDx0fwe7Xko2YbhkuIg7DqHDf05MTIyIqPLXJUmyqKjorzzq3X91EyNVS3rZ15yBk7jojZkspwEAFcV37jjHTxmwOHTVed2p6Xvtec45yp3u7UMPDFCEON7T8YUkMbHXjacuMedixUqDpU3ds6Pr3Cm3v+4BwueZk566ZE41YWpcMoq4PRH6e7Sqg7VUGCQNuWS6oAvcWB3txucWGo6e5vOL+JxCvrAYapRYQwTu893HuENDK881LpbMUj/++GOtRHcWcBy3RMiPGjWKoho5PiJfFPcZmX0mJulGPnxvAh+mUj6lUrZquQWZ+dzP+eyPMEUwon2BdBFLfsGUYYp2Jxo9HE7miyXDeUl/RjSclYxXQDJbuxDphmk6YXadcU0XTBMFWP2pevRCRZopmURkG7soa+PsuBeNogEB6uTQo64zjg0AkLk8JqYzSCYq+Btc26O+ESKbOEY2Z5K+75LetxO6p0+f7tGjR/fu3U+dOtVU5jYNe/bsqdXyzTffHDp0KDo6esyYMQEBAeXl5QcPHvz9999feOGFFStWPAyV5JqNxnq+23j4kc2SaBQJpxb7RHukyfsgtWBVhuV106p3KxIL2F3lSLLRYnkknu8N1XWbN28ePnz4J598kpiYCAB///13cHDwsmXLpk2b9lip90YE0RjCEe2vBEyB2/eRDBcxVSQA/Pbbb9YxAwYMcHBwMGQYAaDcLGXrhV+T2CHBCiWOAOBIyV/uriWZuU9hCK1/ymVTfnhSecn61F+Plx3BifEGk8uZXO5MLrfybGWIIzEyRDm2ldJd3SCPUKGkXP/H38bjF8QKvaWF9HRVdm6n6hSpaBV8+2sfLKQXHfZnB4uGr6veAUDT3d5wojztxVi/b1o7T/AEANLb3XHiaEuvLIh8XiGfU8DnFPDZ+YiiMLvaCqp03TZzSibl50X5e5P+3pS/F27fJAq/Y8eOa9euXb58+ebNm9etW2f517NijZB3c3ObMGHCSy+91K5do2Uv98TxaVrNNK0mief3mZh9RiZXENfpDev0hmCS2OTq4twSc92Rnq8JRVskNpV0GSPkrwEAMmB5UySzQaQr7jgUdxxalQPPGCNWnpUqz0qGCzJfJJbvF8v38wCACEzVBtN0xuw645pONU8GtIRDlLZzrWlHejx7ruJENpOu48utjaIsbs5ZAwABqpCO9t20hEOj384jBKK8LB5PQsF3hMuzdT3khcIfZHMmorxJz/88EAubgVGjRtX8dseOHYcPH166dOn7779vbZw1a9batWtnzJjRt2/fV199tdlttGGjyUE0RtAt8EHWMvD6MBgwKFiV6fl2oOeiwOZY0qbebTxSNPQE3oooirm5uR4eHo1+6PeocJ879JIpkc9bBbIoc5LESriWAABJf1rmC8GuC0559Rqz70JsiWXw2u++eXX6a28eqdiTzLiq8DJGFBv863qtgybSlfw703w4gy1nJQDAMXgyQPFOd62P3R1kfOEna5hL8QBAerure3ZU9+hA+jROPbPmgc83F36R6fyil7Ktpm5v/rK0/OXpgMBnRZjba753O3neW59xaVk1W3AHLRXoQ/l7q3tE3zFc/565/YE8AEREREyaNGnKlCmN6FpvQQa4ZOb+MDEHGUYnyb+5uwaTLTMkVSz73Zw0yfIadxxKh//UzAbIbLpoOC8ZLkiV5yRTPMiCtQsRTpim442vDreqUivJIlZDl+qE8nkJUy0V5ttpO74RWC3S9IJOjWvwlhjmfTsklrnSVTZnUUFfEG431QqSBR17pZPMl9BhG3GnUbeawMIjsUPfEAYOHJienp6aWk9kQUREhJeX1+HDh5vfqgeF7QTeho2HB8kkYqrH7All4yHgkXi+37WAr8uJEyd69erVKNY8EtznA57PXWmpL10vARGcDwAAIABJREFUuUVS+Gi95XeCYZCdesIroOfsQ+X7U6uKq6lI5KTEEIBe0JkllgCVgVUBgK8WB4AKvoyXeRKRHkqnt7ra9/enAUCU4GSueec108F0VpBgTie71zvVI2trwly9zl5NVHWLpkMap2raw0bRmuyct5IQhqJK+iHy7lxIZJ7n0nO4zFwuI4/PzOWy8iQTY+kiPV29v/qgCeytpqysbNOmTd9//32tCHkrNE0PHz584sSJTz31VKPvsokArCyrazjdTC4qLZHEJ5WKIS3Fu96cMErU/wuIUrY/hRQP1N9EYiTDZdFwTqo8LxkuynxhzU6kCMY1Har0vCryNqcHuWxWoiE2i0nrYN81StvF0pjBpH6cvAAD3FcZMM5zcitNZNPey8OEWPqrOfllRLoq2p9HRHVcEpe5SMhfg9l1U7TZD3CHt4VH4gHfEBwcHHr27PnHH3/U7Ro5cuS///5bUVHR/FY9KGwC3oYNGzYecx6J5/stj9EEQVi/fv358+eLiopCQ0NfffXV8PBwAGAY5tSpUwUFBTqdzmAwnDt3bteuXfe/C/D4gDv053NXgsQLFUEY4U560SBxouGUBHicosuZc7myXFVArms7jZd/V0mGi/k8AGAI1g1x6udPA0Cq8fonKW8TiJzounryHhlD6NgLbgCQxRiXJi9CgD4KX+1xo4QvjkEfX7qPL11kkk7mmAf4V+dvM/KyWWd0tKMQfZPSU7YLV7YLb54fyAPBbaavIlQlGcS7Ve8AgEiSDgukw6p9uoTCEi4jh8vKpwJrB4tKBlP+e58jiqQCfehAXyrIlwrwqVsqr+E4OTnNnTt37ty58fHxW7ZsWb9+fUlJSc0BZrN5165du3btcnBwGDFixLhx44YOHdooZeQBAAdQ3xwyUylLabywljes1Rt8CfxJpfJJlaIdRT26jolk4AopcQzhMe0Bq3cAwJSYtgd2I1Rb5nIlwwWp8qJovCgZYmQ2VWBToWQnAAAiMVVrTN0B00Rh6g6YqlVNz39vhZ+3orZjiJaw96L9ctnMdFNyXOVlq4DX8eV/Ff/mSnkEqUJbXuE6yXhFKNoEsoxID5kvMCcMxzSdLF2yoBPL9gAgRLpy6fMwZQThMfXBWts8eHl5XblyRRTFWu8SoijGxMT4+t61j5INGzZs2LBho0mpX8CbTKa+ffteuHDB2rJ27dr9+/eTJDlixIjS0tLmMq8FIpYfAsmSfz4NIE2sijEHDMR27Ol3DhisI0f0EiUm6f9ifQtNIgB42+EW9S6DvDXvexnkIW6jHXAngOpfh58ysJfjgH/LDm3P+2FOYO3CuW4q7Omw6qTKQkn5mF9K02TFYCZr0atdPTXVn94OpbNBjkSwQ0twkxaKy5iYBKGoDJEEHeyniGqNcBwAtAOrUovnfZAqcZL30pB7EPMWCHcXwt1F1TWqbpfM8WK5TjIyXFq2AU4DAOAY5e1BBftRQb6aPp0xdf1Zyu5ImzZtPv300yVLluzZs2fjxo2HDh2qdWRUUVGxZcuWLVu2+Pr6Pv/88xMmTGjEIHkrO91dL5jNB0zsIYbJFsQNlYYNlQZXHO+vpPsrld1oinrUcmRgylbKDvEP2op6QJQ37uSNO40iAUAWJSZRMlys+mKuS8arkvEqFAEAAEZjqraYJhpTd8A00ZgipG6wtxPpsjT8C1Ziisz5XjXk/UXd6QPFVUnOpvrN6eHYz9rFSRyFPdqRU0LBd0JxdZFOyRQnmeJqjRHLfgcAAIS7PofwOzgrtQB69er1v//976233lq5ciWGVe28SZK0cOHC7OzsoUOHPljzbNiwYaPlIepPSBVHGjIS03bHHQY1tT02Hjnqd6H/6KOPFi9e7Ozs/MYbbwQHB2dkZKxevVqhUNA0ff369UGDBkVHR6vVaoSQi4tL586dO3bs2PymPyju08VONmcxV7qCxAKAKf4JhxGjJN0JoXTXZbqjDh86utNbPF817c+bur9n+kW+kydnvWAYP6uraU77VvX2SkZTxc4/Kw+cWOvfZ6d/Fwlh0e7kL09XVVJJrRAGbSvu5EFtH31P1bMeGmSeL9u4u/LQSZCqE8sTLo7OMycoa/xkYoOP84Wc/WDnwM1tsYYl+bs7M8wcl55jTsvm0rO5tGwuJ9+a6F47vL/TS880yiq5ublbtmzZuHHj9evXbzUmMjLyhRdeeOGFF/z9Gz8yQgK4bOYOMsxRhs0Rqv6G1Qj1USr6KxV9FbQd9uieyj/0SCbJeFUyXJKMl0VDjMymAtR4Y8fVmCoSU0dh6vaYOgpThgK65d4cJ5lPlv+dZkoqNhdO8JnmqwiwtB8o3rM97wcXyj1EHT7B+1X1o6lspcrzbPxgAJn0nitWnpH0pzFVBOExTTIlCAX/QxhN+L4rFHwvm7MIl/FUyNrbTPVIuNg1hJKSkqioqNzc3MjIyFGjRvn4+OTk5Ozduzc2NtbHxycmJsbZ+dF+ENwVNhd6GzZsNANsbD/JGNOQkYj2UUbHNrU9NmrySDzf6/8Y9+uvv+I4/s8//7Rp08bS8vTTT0dGRoqi+N///veNN95oRgtbGoj2Iz1n8bn/BwDKkIu40/eSMRYAjiqfhNOOVvUeEYRvhQ/vTb0DgCwTp8tOzZJDa2eokmXD0TPlP+0R9QZAaK6nbuqT1JYCup1btcMtK8gAwAg37ezIHM/GJ/N5RYAQ5edJtw5GjeSV3VTIcvH/rTddjEMErurekQrwls2c6dxVLiuv6JM1botmKNu3tgwM/iUq5ekY3YHSpEEXg7a3o3waORMpoim6VRDdqiqFuMzzXEYul5bN5xRo+nerNdiclF6++TfC05UO9qOCfKlAH9SwwHJvb++333777bffPnXq1E8//bRjx45arvUAEBcXt2jRonfffbdbt27jx48fO3ZsIxaIwgA60lRHmnrHwf4azx9l2CMMm8Dxf5qYP00MDtDZzH2YkukIiAr0ocMC4VE7mX+owVSYXTfMrurPSRb1kuGyZLwsGS5LxhjZnCVVnpUqz94YrMTUkZiqPaaJwlTtavnbUxjdz3lIP+chtVawJxxoTFHCFZZwhX2cBlld7pONiVf0570UviGqVm60Z5Pf6f2B2XUmXMYKJTslNp0O3cTGdJJMCYh0l3TfAgDpuxip28rmLMCUpN/iB21sM+Hi4nL06NG5c+fu378/Lq7aH2HUqFErV658rNS7DRs2bDQPhNuLXHoMIJz0fgvqdW2TZT73M5BYwvXFZrfOxiNA/SfwDg4Ofn5+V69erdnYrl272NjYkpKSx/yJ3gg79BJjPNkRkfkAQHhME0zxoD81y+V73Ttbd/+y2zJk3IQOeU/9WWgQNSRiReBEGUPgbYezElMpVLndEzgb5Pfr1cTZgOS+3V6ru87coMVt7TpYv+XSskrXbTenZAKAok2o08tjKX/vuldtiTcuOa4PcSQOPFtVKd1w5HSV5r8B4eLo9Mp4Vee29/gTaHqMxy8Uf7kRt7dzXzyr+jZlufynPbrfDhOuTt5ffYCIqj0IcxqTOiaGTTYRLlTgxjZ2T9Sf37sZqDxwvPR/26u/xzHK14sK9qWD/elWQZSfVwPn4Xn+4MGDW7du3bNnj9ForHcMhmHdu3cfP378mDFjvL3r+Uu4f/JF8YiJOZCTf1mlFDFs1ZebIzJyAID0dnee/rwioqWFWD+cyEKZZLxS9WWIkc0ZN3UjClO1xtRtMVVbTN0WqSIRXn9ZREmWCs15RtEQoq52YPky/eMr+gsAgCP8s9brHMmqp4Mg86zIaogmqbB4P8hcPhPTCSQTHfG7ZIzhM98HjAbJjClDFW3/ZeOelExxpO97pPe828/zSOzQ3xVJSUkJCQl5eXl+fn4RERFBQU1fePnhw3YCb8OGjeZAFtnYvpIpnvRdTHrPrdsvFG3m0uYgyksZdR6we4y1tHFvPBLP9/pP4HU6naura61GT0/P2NjYx1y9Nw6Yki9/g3JbCICEwg0SpsYAeDL06ME/rUMSA+ebKkUA0HNVOyySDNl6EYACqPJ1x5EcpIy+CoBABgACiM7KLpSqyrWVwugQVfXnbJnj89//QjZzuJOD0+Sn1T1vGfVwMI0FgNRyYf7RitkdNQ5//13+4x4AoIL8FK2DZFFi45L4nIKiz9a5vj5J3bt2MeqHBMOxMwDg+MLImzYpEHKcMMp0MZ7PzmfjkpRRVYfwdJAy/O/O6VPi9IdKU0bFeH0Q7D7X/169H+4Lu8G9qWA/LiXTnJrFpWZxOQVcRg6XkWM4choQ8lw+v4F1AUiSHDZs2LBhwxiG2bdv3+bNmw8cOMDzfM0xkiSdPHny5MmTc+bMiYiIGDdu3IQJE0JDQxvxdjxxfOjOv3rtP2ZU0oaoiNDIMDksgIlJ5HMLp+YVXtMoe6iUfZWK3gra7SF36HiUQYQTbt8Pt6+KZpcFnWSMsUp6mU23vLAOR4pATN3OoucxVSSiqs7VMYR5Kmq7bEzwfjVY3SqbSccAqynX/y91SZIx3oF0aq/tNNmnnu3FBwWiPEnvN/jsT/iMdxSRB4XCTTKbAgCk/zKheKtkikO0fwsuAn8bwsLCwsLCHrQVNmzYsNHUSLI5+6ZAs/rBEekGGH2nYfcEwsmAT8wJo/i8VYTr84i6qVSzLBos9aoo/49s6t1GvdwyEhKrE7Bat+VBsX///t9//z05OTk4OHjGjBnR0dG1BixYsCAjI2Pnzp0PxLyG4DBimunCLyCeB1nERL0es1fEXK3QV1Ujc3HW7Js3FsMwEYCRpXkHdNfLeC2F9o69aVfFQYF9c+E6QC8a2KA0RVoQKx2LHZzTyfnVZ+uWbUcUqR3SB1GkdtRATHG796OZHTSncssA0K9JzN4UZlCu9KLKMfKV4Zq+XatGyLJuz+HyH/eUrtuuaN8a1z6M0bBcZh4AKKMjancgpIqO0GXnc5m5VgEPALgDEbKrff4n6fkr0nMXpxgv6PzXRuB2DyCNHx3iT4f4W5SQbOa4jByLmJdMLOFee/vMdPYKG59MBXhTQX6kryfCa/+TKpXKcePGjRs3rri4ePv27du2bTt16lRdv5uEhIQPP/xw6dKlHTt2HD169KhRoyIjG6GuGJuQot9/DFFk0NyXlR2q4nFkUSrf8pt9pdGI0CGGPcSwABBOkj0VdC8F3fERzHv3aIEIe9y+L27f1/KtLBpkU7xkvCqZYiVjrGRKkNk0kU0TS3+7Md4JU7dFqjaYqg2misRUrQBV+/u5UG7D3cbWXSVIFZrFpFXwZecrTr7oPd0ay7OncFsJV+RF+7bXdvJSPJgM56TnbKHoR8kUJ5RspwKWm6+Nxx0HY5rObMpMAKD8l96mJl+LRBTFuLi4goKCensHDx7czPbYsGHDRtPBpc0Rin5syEhE+yqjr9yxqui9gWv74E7DxbJ9fPZSKvjbml1CzmcyX4jZdcGdn26KpW20AB69NOMzZsxYu7Yqt9CRI0f+97//ff7557XC8g8fPhwT06DkEA8MhBQRn7GxAwBkALkEc4E/PrN2Dh06yt+BrJSkqcVlKTzvRVMAgLCqYu9WYvZuiPdIAQCVII8u7flF4NHznQ0dYhK5t1e6fziHDq5dOMpx4uiGmGZPYwAQ4oBHe1C7rxn+9Gx32LPtWKT+j0GsylSPkP3oJ9mEFOZSvOnUJbshfe7rRwFg5GVZBg3VmG+RMscDAFLUE1lkqZknm7naHRjyfC9I1dk+45W4ir3FTPz5oK1tlW0e5PYEoik6PIgOv6Uvq27PYXNSetVgiqQCfOhgP0uKe8rHA2psurm6us6aNWvWrFk5OTm7du3auXNnXSUvy/KFCxcuXLjw3nvvBQQEjBw5csSIEU888QRB3OMbheHIKQCwf3qQVb0DAMIxp8lPv/vWipf2H0t8ffIZT9czrPk6z1/n+Q2VBiVCnWm6t5LupaAD7nVdGw0H4Rpk1xWzs27P8RJzXTLGSqZY2RgvmWJloUzU/QO6f25cQGLKUEzV5oakb42oeuIvxnu9NM5rcglXRCHKqt5lkA8X7zOKBgD4u/Svz1pXZ4lLMyUJsuCj8Ffh6qa8XQAAwBSU3xJz8st89jJF+wvKqHOI8uGyPpSFUlzbG3ca2eQGPEzk5+cPHTr0Nk9MW5lYGzZstCQQ7lj1gg641RiZLwCJRbh9E6l3C5T/R0zFIaF4G+H+CqapcoyV2XS+cB0ARvkvb9LVbTzSPGKfj7dv37527dqgoKAVK1a0bdv24sWL8+fPnzt3bmBg4KhRox60dXdB6eY8/d+E1zvjxbJtAOAt5p75u9jae1jRa8RvxfmtBaNKps0oq0IAACMnT9pXZh3DmvSlnIurqwIASK02cuLsPjnksdK/Do0XX/jaXLJ6k9fnixqSZ25znPFwhrlmSwUrAUCaXnDT4BFCeY5El1OanxNMOxJNXw50fCq46mxK1bU9cynenJp1/0GuI38pYQT55ES3RnyjIlyduMxcLjNP0bp2NW8uIxcACLf6g0HsBzu3Ot4l7fmrTJzhev8L/t+2dhzj3igmiZVCzoIkZRuN2+zaeyv3jOsbLxnPxHCpWVxqFl9YYk5Kt+p5KsjP67O36l7i4+MzZ86cOXPmZGVl7dy5c+fOnefOnav7Gd1Se2L16tWurq4jR44cNWrUwIEDlUpl3QlvgzktGwBUXeqUr0NI1aW96/Y/QuKSJrUN52X5opk7wZpPsubrPP8vy/7LsgDgQ+C9FIoeCrqfgiZsx/LNAyIxVSSmigR43tIgc7mSKV6yiHlTnMSmSaYEyZQAUOXihAgHpIrAlK0xVRtMFYFUrRGuBQAEyJW66X8HAVoctirRcDWPzfZTBlrbKwX9JylvS7IEAP1dhr7o/WpT3yXu/DRWuF7Sn+RzV1L+H0tMslC43uLT2NRLP2y88847MTEx7du3HzVqlEpl89W0YcNGC4fwnieU/CzzJZT/EtypHu0gc/nMlc4AQPovbVJLEB1Aeszk877gMt5WRB60yHUu8x2QzITbJEzT4Y4z2HhsecQE/Ndff61QKA4dOmTJrxMeHh4REdG7d+8ZM2b079/fzu6hS5h0K4q+zWbiDB7z3wK0G2SutMgUn3ojZQ6GU236JAULmEqWGTCcBpmRAECQ4GROTaVNA3R2M8YCAIYAAMZ4vnih4mSKY3Fat+CgM4X5b6/0+OB1THOHD2TbEkzXy4S67aJlOcIZAEAGQCDKcLWYtwp4TKUEAJk11732bsmtFHlJFiSZxBpNpCk7RnKZuRU7/nB/b1ZNx3JzcobpYhzC8Zr+87WgA5XhRztlvX6tbFtB+ktxdIBS1VF7n/aIlULK6BjjWR0ACBWC1/uNkyCKcHO2HznA8loyMlxalsXf3pyWTTjWY7Px+AVZFKkgX9Lbw8/Pb968efPmzcvKytq9e/euXbtOnTol1Si5Z6G4uHj9+vXr169Xq9UDBw4cOnTo0KFDG5i+3vLnYflTkUyMUFyGcJzwdEM4hqmVACAzZgAgEeqmoLsp6PkAJaJ0gmVPsOZTrDlHELcZjNsMxjfttdMeykiNxwFEeeOUd3UdWomVTAmSKU4yJcimRMkULwulsv6UpK/O9YJoX0zZClNFIGU4pmqNKcMBq9r6caXcXZ2erLWEhrAb4jo60RCby2ZJcnXysHK+9MOkeRRGeSn8nnJ9OlzTBhoPyn85G9dPKFhHuE3mMxeBzBPuUzBVI0SOPFocOnQoJCTk3LlzFFVfJmQbNmzYaFkgwp70WcSlv8llvKt0GGR9PFnhsz4E0Yg7jbTmjmk6SO/5Qsk2yXBBKNlJuIwXdf+I5QcQriF9FzX10jYeaW4p4P/5559aeex0Oh0A1E1uBwDFxcV1G5uCa9eu9ejRo2Z23KioqK+++mrKlCkrV65cuvR+t8qOHDmyY8eO248pLy+H+/YqFEp4ACCcvFizG87lHD0nWOdTBEXbPenI28nOCFugsncZgP33vOFyIeeqwlb1dwAARjT9kP21STISODvLvf9BWhXoQACAGteMcB//c976/V2yZ15w5TJzRV3lHQX8D8OcUspvEvB/Z7I/xJowBBuHOel++ZNNSNGOHKiMak1gqJNH9Se8M+kGR0qjdXa8n59D02E/or/hyCk2NqlwyZf2Y4dQgb6yiTWdu1Kx80+QJO3ogbjD7TQ5psIDvm+j6eagO1RC+d9vQKxVvZOetFDMFaxIB4DG0vBWMLVS0TZc0Tb8lmaU6Yq/3Gh5jSiSCvCmAv3oIF+PIN85s2e/8cYbhYWFe/fu3bNnz5EjR1iWrXW50Wjcs2fPnj17AKB9+/YWJd+9e3f81o4euJODUFxmOh/LXLjKxCWDJAEAplSo+3SWzTwA4C61/35ccGy0WjVarZIA4jn+JMte44UeNbI2nDWbN1Ya21FkN5puR1O23HfNDabANB1qngzIfJFkipdNCZIpUTLFS8w12ZwtmrPFikPWa5DCH1O2xlThSBmBKcMwZVjNOHMEaKznpHpXk0Eq4YpKuCINrrEKeIOg35G/SYPbeSv8Ojn0oO8iZL06dxEi7AinUULpbvO1MbI5GxF2hNvE6hT9hJPFj6BlIwhCXl7e9OnTberdhg0bjw+E2yShcINkiuPzv61Vc0QyXBBKdgBGU34fNIcpuJr0fZ9L/Q+ftQR3HMJnvgMAhPcCRDaO76eNlsotBTzP83XrSANAvY3NBsMwdU8IJ0+e/O23365atWrq1Kl+fvflnPzVV19Z9MkduS8BL4NQygECwpmEnEoAOHK2WkJrh/fl7WQvAt/s6uJN4ACw8zpzuRAcFFhPHxoALugu0hUXaQDfXKpr6z49Q+yt1/Y2RR0pUxQ5sec6GwZCH9L7zv//7mrcXX2TCLpSzAOALENPH9rQ1r3kxGHq6H7PQW0QVV0sOi5T/6o+hO71n7GO4tu8rCIfOvdmzE7t/u7Mwk/XsYmp7Eff1OzS9O3q+EKDwlxdpnq7TPUGAOZqZdbc684TvVwme91tRJJkFFPHXjGe1VG+itD9HZirlekvxRWsSJfNkvfHzVpHDXeyd575AnvlmjktSygsNSdlmJMyKgEAAJGE0+Rn3If0mTZt2rRp0wwGw19//fXbb7/t37/fsmNViytXrly5cmX58uVOTk6DBg0aNmzYkCFDXFxcag1TRrc2X08r++EXkGVEEqSvp8xxfEFJ5YHjCCEAuI0fBAbQliLb1virs3CCNR9j2GMMuxoqVQh1pKlONN2ZptpSpM3N/oGASDfc3g2sJxWyKJszJFOixFyXTAkyc01iUmQ2XWTTxfL9Ny6ySPowTNkKKcMwZThShtUtX+dIOv83YmMRV1BgzglRVf+ppJmST5QdsbzON+eO9Zxoec3L3MWKMw6ko7fC346oR35zmYuF/G9qNcrmbACQhUo2tn/1TREOyg4JdU9mWhiSJNE0nZeX96ANsWHDho1mpCoJ/Eg+dxXh8iyirU6FMpfxDoBMes5CimYqpUm4PicUrpcMl8zxQyRTIlIEkZ4zmmdpG48u9Qt4g8FQb/sDJzQ09MyZM4WFhe7u1dIUIbRmzZquXbu+8sorBw4cuJ9s+d98882IESNuXwA2Nzd36dKl97OKqBNkXsa1BEAhLuoMSHPsos7aq+zbV4HQ9y7O3jeqlAdpcYDqDHY+Cn8cEZIkDP7LQQgoIZwcLO2ms1dKvto82FOz5UX2n976XsX1+ErcLeo+nXW/HeSy8gqWfOn44ig6PBgkiY29rtqyt59Dl2NuET/lEz7xxlejNADw3r+6bYmmZX0cnm39UHzqpYL8vP+7SP/nv8yFWKG4DFEkFexnN7DnbUTjrTCnMcazOuNZXcWvhX5ft6Z8G3riJxnFlGdiDCcrLOqdDlTSgcrAjZHpL8UVfpEJAM2s4e0G9LAb0AMAJBPDpWVzadnm9GwuLZvPKxL11f/1Go1m7NixY0aOqjh+7kxGyh/nz+zd/0d2dnbdCcvKyrZt27Zt2zYcxzt27Dh48ODBgwd37drVkvdO1altxbY/QJYJNyfn/0xURoTIHF955FT55t9kQcDt1JSv593ewutau/YUdZo1nzGb03jhOGs+zpoBQIlQFE11pqkuNN2WIm2p7B8YCEeKYFwRjMPwqhaZl9g02ZQoMdck0zWZSZTY9BuS/kD1dZS3RcljyjCkDMGU4Yh0wxDmQXt50F41V2ir7fBG4PtZbHopV9TVobe1/WTZ35tz1gAAAjQzYEEn+x7WrnK+1J5wQKRb1Vq0LwAOACDqZaEMIRIoL0v8ocwXgWQCwrFmsv2WCkVRkyZN2rRp04ULFzp16vSgzbFhw4aNZsKSslQs28tnf0yFfGdpFIq3S4YLiHQjvOY0oy0YFbCCjRskmRIBgPJf9jg8fWzcJ/ULeLW66fMA3xNTp06dNWtW3759N23a1LlzZ6uK7tix44IFCz799NNJkyatWbPmnuf39vZ+5ZVXbj8mLi7uPn31hRIOAAgXUmISAWB/pm9RWY6lC7OzU0W1Z2V5eYXuKxenLEHMFARnFQ4AnjfOybfn/SDKQtfyMK88k/6PY5YkbRW//FWxYz/IcqR7t9D0v5MDjYeDrk+5HysBAACRhNs7Mwo/+saclFGw+EtACGQZADCApeis/qVOB4vxUaFVcv1iASfLcDLX/JAIeADA1CqHsUMcxg65z3kcRrsFborMfvO6/khZYpezft+2dnza7Y5X1VLvAJDY9ayyjcb/u9YPUMNbwFRKRWSYIrKq7LPM84isfdZtOH5et+bn1gCtAV84YkoyBccKMw8nXD0Xd7XuJpcoiufOnTt37txHH32k1Wr79ev35JP/z959B7ZVXQ0AP/ct7S1L3tuO49iJEyckzoYQIEAKZZWyR4EmYRQ+WgJtgbJXW8pIaUpJSxJ2WSFAGwIlZJBpZ9iOdzxkW7IlW5I13v7+kOMVx5m27OT+/nCk+56ejhzJT+fde89dOK1LtgEgmhKFcQZIAAAgAElEQVRcHudjve8fAEAUKfoD4YpaZc7AKoNDoxE6X6U8X6UEgDZR3Mlyu1huB8vW8MK2MLstzAL4lQhNYpipCuZCtSqLHmOVPs5AiCZU40A1rneoTySlDx2UQlVSsFwOV0mhSplziJwDvN/2Po4yIGUWocpGqixCmYlU4whlKiAaAZqoL5yoLxzwPJP15zRY6hrDdZ28R9tnAPw37evfcfydQlSSMv5+RQrB1tPx91H22wEAZFFw/YvQzyFUWQAgC53hkkJZCjIpTwE6K+ZnPPLII42NjfPmzbv77rtnzJhhNpsH7DBv3ryoBIZhGDasmJSnQ50bhPYPKPsthG4GiAG+8Q8AQKc8ceSIsGFFaKdS1quE9g9Jw7mk6VS/smJngzH21XbZsmX79u1buXLljBkzaJreuXPnpEmTIpueeOKJurq6tWvXfvLJJ0cOsx9VuifAxzBysAQA/rOzt6vQXDTzzVj7Q56OH8LszW3tNbzQJcl3E73fRCsDpXt9uwCgxFK/79cCQDPa818AkJMFeBAQRcni1wIhAsBmbuuF4cZjrrRc7OR3NPcrRBe5KwP8rTjSJauRb7k/fLBWrm9ceHCLTuTopDjt3Gm6RXPjaTon5YgDtnL72/j8mIHZYESTX/yyJjRg/oEgAQD8vaSL7NNrihBcmKZMMYyWt6jpSrturqnhVxWdn7mcf64/ZgIv+oXqy0oCO7xMkjL7qymyBJWLdvMONlTaJQaE9NX5Uc/h+zoyewcA9bSJfH0zW32Iq28Wne50gHTQ3pY5syN5ymZ/2zYj+d8fNrnd7iMf6PP5embLx6t1CxcunB2fOo0lDb4QoikmNVF73gzO4fJ/8W34QNWJJvB9xZDkxWrVxWoVALhFaRfL7mS5HSxbzQvbWXY7y34cCH4bjyeSjT6DpPSizDZIoQopVNmdz4eqZKFD7tolde3q80AKKVIIVSahzEKqDKTMJFRZPXMFDbTppsRBRh7GKRKsjN3NuepCDe22222Nj/KNT5OWn/7g2/1J6zs2JvZSdUE+AADwjU/LgofUzyVNFw/n6x9F0tK6lwN44YUXBt0BLyOHYdgZCSkS6bilvOOP3KGHlfkb+eY/yVwroSmgrFePfDB06vNImUnZbhz5p8bGotGSHR2/FStWFBUVrVmzpra2tu8XC5qm33nnnVmzZr3yyivV1dVRjPCY+vTAVwBA6a7Onk33LFo0U6n4Z4z1hra2vSwPAJeoVQYfAQBuvu2jls+zNbkEIiVZZGUWusdxcwA9/5Ncz38piShePmKp8yM8+G3nIe8gVegB4IXt/j734sEWr750wV1TjnFVsrlLvPzf7TMTFHcWaGYnKQaMY355p/+TytCgD/zjjoETN4qd/F8vHEVF8qgYJn1tftfmDjpOAQBsXShcHjAssg46K/7Q7aWBHV4mRZn9VaEsyJHsXXOOga0Oete31964P311ftqq7hxeOU5jufGEB5MPN1KvNd92FQCAJPHNLq6ukatr4g41kXWOn6g0dzx+L52dumPHjvXr13/55ZclJSWDftFvDvr/9dnH/wJACOXl5Z177rnzz8mYO2U8zYsAIPn8Rz7k5FhI4kK16kK1CgA6JGkPyxWzXEaf7vdOSfpJaxuDoJBhChTMZAWTRdNnRR/rmIBIpEwjlWl9Ox9kvl0OVUrhSilULYcqpFC1zDXK4RoxXCNCn7H3pA4pMwlVBlJmIGUmoUxHynREGXt2mKAreGH83ziJDYoBI21mff8Tvd/yTS/41FO9fIeX79jVuTVfN0UKVQiufwIi6dRnR/S1R9Xy5cujHcKx3XTTTR9++GEoNPi5A8Mw7OTQ8fcLbWulQAnf9CLf8joAYtJeBDj5SbInDVEmOnGQpX8xbFBjL4EnSfKWW2655ZZbjtxEEMQ999xzzz33tLa21tTUjHhox4uP9MBbGSlUzvFQsbOuZ1PajPkA4Jcl7nAqFJDl8VZaTUOzvO5L1/ezc857PW+tIHen3Hxrm++zb0LFZVKIBQDKZtHOnaa7cA5iaBrRDKGAY7mzQPN1XRj6ZF4tAbHKIyCAOUn9Hq6g0MWZQ9W0D/IyACTpqY6wuNXBbnWwORb6F5M0l2Yqe9aHu6tAa9eQA/K8SFf/7ZPUVP/KApdmnGr59+Ggnd19TaHh7nL/9x36BebkV8YfWame0JAAIIcktjpYf08572C1RcaMTwq4ulDVpXu869trrt1nXGSVpcjOUThbnACCoBNj6cRYzZxpkQaZ4yNFDYuKioqKip566imn0/nv+3+7YcvmLa7GtnDwyGPIsrx///79+/e/8sorBEHkJiZPYwwL0kyLPAuOHLV7ikwEsUClXKDq95+CAEgAhyA6hNDnwRAAqBHKY5hJCnoSw0xiGCs5uv8Xzj6ItiLaSuh7p7KDxEpsnRyqksI1cqhaClfJoWpZ8MiBYilQ3O+xlAWp0onulD4NKdNpRZqRNgMAnfqMuG+24PzHxfk3zcld5eSaU1QZAMDX/xZkgYq9g1DnjuwLjaZnnx0DVyt4nj9yXQwMw7BTRWro5Me56l/yTc8BABVzLaHF1UCwMQDh0XEn6sCBA/n5+QRBDF3rrgfv+JPgWt23RfQKgocnjRSlbtm6jzv/ru6eZ7NNnbGr+jGL8fGOzi5Jnq1UlHF8hyR9E2f/3PHnHZ2bCw1Fy1IfGuQ5JEn0dSGGjiy4fUIueK+tpnPwHvgjLS/S3zHpqPURCle1drJyXgyzZrH5ndLAvw4EnQERAGxq4uZ8zV2TtUerKpbxRgsA7P+FXU2NpQzKt8F96Belgpsn1GTs/6XY7kshlL3xy5xUe/1+71ftQAKIEMneSS0JAKH9XVWX7hHcPCAAGRKeyLQ/cMRUhDGIO+Tw//cH9lDj/n37f2is3exs3NHeHBKP8e4iCCI/P3/OnDmzZ8+eM2dOfHz80PufCgmgmud3s1wJy5VwfIPQL7YEiixgmIkMM0lB59C0ApfBGyNkwSOHaqRwlRyukcJ1crhGCteAGDhyT0RZkDKNUKZJ4Wqpq5jUTGZy3kd0DACInvVs5Q2IMioLdiHKcswn3bZt28yZM4uKirZu3Xr6X9LoUFVV5XA45s+fPwLP9frrr3/00UdH21pWVuZyufpG8t133w1HGCd6fscwbOyTwwcukLp2AaFWFexCzKgbDomNsDFxfh97PfBjjhTY27u2MAAAEEpg4gEAZIAtJb0pxKzJdBXIv/Z0yDIsUqtetJhaBdEhCvEUSSDCRFtuSLhz8OcgiKFXNR/CskLtjpZ+I+3rfcK2Jo5EcPX4fv3tJIIL03q7ND+uCLUH+33FCYsyALSHxHdLAwjgxgmqsnZxVyvnCoovbvdfkKZMN55R7zf9QkvurhlND1V5PmxtfrLWvaYl8cVsw0XdS6khhkh8Nsv/vUcKSogm4p/NimTvAKDK18bcldTyTC3IoMhS25Ydo07BWMGkJljuvBYA4mV5gdPN1Td5i0v/995Hm52N2zwt+9zOQb8TS5IUWZTutddeA4D09PRIJj979uycnJzTGyEBkE3T2TT9c60GADyStI/l9nF8Mccd4LhI5/z6YAgAFAi9YTXPUB57DAsWdYgyI52Z0E3r2yjzTjlUI4Vr5HDt4Z+1suCWu9w9k+rFQHFodzYidXTS7/jWlQBAJz5yPNn72UCW5aeffvrDDz8MBAa5FHLahUKh//3vf0Pvc8wdMAzDThxi0l5iK66j4+/H2Ts2VpxRCdXoRCc9LHZ8CbKoGLeWUI8HAMfvqzs/dSW96KXtj3y9VwXQPTKwK3MxApBlYBD8Uq8jARIoMrKY3J3JD/QcUOZ5oc1Dn6bSXJdlqXrKyEd8Xh3a1sTRJHp6ruFoj/qgIvTwd52DbmrtEp/fPnBi8+9n6fpm752spGMIsn8HpzCqKw8OjophUt+aYL0tvvH/KkOlXTVX7dWfb0l4KlOVp+Xqw1WXFUtBiTLRQgdfe/XerPWTVblaAHC/3dzyXB0AEGqSrQrW/HxfxrsTkWIsjT44BoSoWCsVa1VPL7g0I6Vo5fsgywEFtUsKbqk5uKXq4EFvu3SUsT+1tbW1tbVvv/02ANhstpkzZ86aNWvGjBmFhYUq1Wle3cBMEPNVyvkqJQBIADW8sJfjSlhuH8c1CmLf+L4PhVd3BdJpKo9mJjB0Kk3hyfOjHKLtiLb3G34PIHOtMlsnhWvlcJ3Y+Y0U2AuIkEW/6PlcDtcQqnGU/ZYoxRs1siw//vjja9ascblcfdtFUQyFQvn5+SMTxoMPPmi32++++25Zll9++eULL7yw79alS5d+/vnnTU1NIxMMhmFnFUIzSTWlNNpRYNgJwAn8sCNUOZT9VqH170LLG4rczwCArfdyzYjQtIoSlOzr7dxouPwXNICFJNyidFtb+79irBlHrIAlBUKtj/+FO+SIf+EhJi1xRF9JH7Pi6HgdGZn03sMblmQAkkA6pk9qLkO8lrxhgjZyT5Dgm0Phe77pMCqIC9KUl2Wq1HT3zmVuTts/LRpnoXomzx+pk5Ve2dV1RbYq7ygV70eMdrYpZ8s5bSubWp6p9X3j9n3rsVwXGzzQxdWH1ZN0WV9MPnRnmfer9qpLirPWTw7u8tXffRAkOeGJTN0Cc8W5u3z/dbtWNNrvPxNG0R9Jt3A2ZbN2rP0MahvnATMveSKkFciTcyqz4jd+u3HDf/+7r6bqaMm8y+X69NNPP/30UwCgKCo7O3v27NmzZs0qLCzMzc1Fp3WIOwGQRVNZNHWVZpBCDztYbkuY3RJmAQIAoEIoh6FzaXo8Q09g6EyKovB4+7EAMbGIiSV0RQBAJz4U3jdHClVQsXeKbe8AAJ36LKAo/zEZeW+88cYTTzyh1+vj4uKqqqpiY2MTExO9Xm9VVdX06dNfeeWVEYvkxhtvnD179o033hhZL/b555/vuWanVqsBICEhYcSCwTAMw7BRCyfww050fywHDyJEi75N4X1zEG21/ixgmM+Crm1vmRjq6h6+bjLTd8Z9+5+u8Z/nxNzT7tkWZm8+IoeXWc757F+5uiY63k7ZojnOM0FP/XD9wHXUInPgx1voz648amxPbfWuPhAEAE9Ieq8s+F5Zb7Wz6z/rGLDzFeNUL55rhKPY1MD+a38gwMvPzz/qSIERgyhkW5pk/nls6wuH2lY2ude0IBoBQKgs0PWjN211fu21+3zfuCsv2C36RJDkxOeybMuSGx+okDmJUBGaoui/hOGjmpSjmpQjtHcIrW1AkUxKAqFSpgFceOXlANDZ2blly5bNmzdv+va73SXFLDf40gmCIJSVlZWVla1cuRIA7CbzOQWTi+bOnTFvTmFhoV5/klNIjtP9Bt1spWI/xx/guDKedwhiMcsVs92h0ghl09R4ms5l6ByaHs/QSpzPj36IplOfZ8svF1pXAgBpvoQ0nBvtmKLgrbfe0mq1Bw8ejIuLu+mmmzo6OtatWwcATz311D//+c8JEyaMZDBpaWnff//9M88888QTT2zYsGHNmjWFhYUjGQCGYRiGjX44gR92Qts7ou+HyG0peAAA6Hig4wFk2NxnAvz8ArjBv7rWd78nQXqEMTwqdBYL3FUtjT+DH5Yn3wQAsii6XnqTPVhLWU32x+4hNKd5OHEPHU0AAHWsBMTLSj62X8epJAMAcKLc6Os31TlGTSgPH+6cOEWJk5cBArzs52SzEjEkOtDGA0COlQSZ6FvGbmbCUDOQQ4IMAJ3hUVRqiDLRic9m2e5KdPyhpuMjJwDIvFR7/f70t/PS35sYyeEBoDt7f7Ci7e9NSEGkrc7XzjjqdYozBmU1UdZBFgU0Go2XXHLJJZdcAgCCIOzdu3fz5s1btmzZuHGjx+M52tGcHZ51321c993GyN1Ya8zUGdMj/fNTpkyJ9NedzuARKlIqig5PifdKUjnHl/J8OceXcXy9IJRyfCnHQwAAIIYkN8bZaJzDj3qkYR5pukjs+BoIBZP8RLTDiY7a2trZs2fHxcUBwPz58x999NFI+/Lly1etWvXkk08+99xzIxkPSZK///3vL7jgguuvv76oqOixxx47XQvdNTY2fv3110MX7nU4HKfluTAMwzBs+OAEfthR8feJnRsBEGLiZc5B2m4sExf/cYf/+fQHt5b0zuibOYn6R8vtG5yhDbUhAEAkUNNQ2KJ4mz1vmSTpEGp/dXWouIw06OyP3kNZhjHfs2kIAEjUDzXJt8ojLP6onZcG+SZU6eHnv9NvLqVRSWy63qahEQBcnKG8+IjF4SJV6E0K8kcHNz2BuWqc+sJ0pfpYlxDK2nkAqPaMogQ+gklVpa3Ks9+T7F7bIgty+z8cNTccsF4Xm/LmBOcfD6nytJbr4hofrGj7WxNSEOlr83vq3mEURRUWFhYWFt53332iKO7fv3/Lli0//vjjtm3bhl4YsrW97Ysvvvjiiy8iB5kwYcLUyVPy1MaCCRMmzZyhz0ghddrTGKeBIGYoFT0l7oKyXM7x5TxfzvEHed5MEGSf7P0qZ1u9IGTT9DiazqapLJrOoik9cQaVPBjLmJRnwqFKyn4LUqZHO5bo4DhOq+3+dKSmpjY3NweDQbVaTVHU7NmzN2zYMMIJfMT06dNLSkruvffe3/3ud+vXrydOx+fl7rvv/vzzz49nT0kagxVZMAzDsLMGTuCHHamfQ5oXi551hDpb5ByS+xPmX1fObE8w3Ne5ZW9vD3xmXuLDrhs0NDKrCAAIioHA/oA8UUfqKQGgY/Wngc27CLXK/tuldPzAseunl44hEIBROdQXJr0CZZkpP9fvW47DL0oyMCSy91/VPF5L0sfx7WtmgmKvk//Rwf3o4B79AV2YpvxptmpGgoI8SiIfKTImwihdB1E9Ra+eogcAykS3vnSo/e3mjo9d9vuTjYtjcPZ+PEiSLCgoKCgoWLZsGQB4vd6dO3du3rx59+7dmzdv7uwcvIYiHO7G37t3b/dxEEpQ67JMMQWp6ZNz86ZPnx4/IUdVmIdOXx06NUKFCqZQwQy6VYOILknew3J72N7ZAXaSzKSpbJrOoKlsms6kKRXusY8GpExTFeyOdhTRNG7cuIMHD0Zup6WlybK8d+/eoqIiAJAkqaKiIlqBabXat9566+KLL77rrruGGIxz/O6///7Y2Nih9wmFQqtXrz7tQ3gwDMMw7DTC68CfsJNYJ1Zm60N7p4PEkboZon+b9+vzWr66WLrv/mnXd1drN2jRT99Ys8k/yAzMiTHE27oq98r3EEXaf7tMmZ992l7J0e1s4ZL0ZKzmxDKcnnXgh5gDv2JP1x93DKxRP4QYNbEoXXVHgSZeOzCY323yvlsWTNKT/7tueK9onLr6JeXu1c2R24gmZF7C2fupEAThwIED27Zt27lz565du8rKyk5o0eZEtW7ypIKpF52fn58/ceLE9PR0hBDIstDmIY16xJz+MmYeSarg+Eqer+KFKl6o5vlg/z+8BEACRaZR9PU6zVy8dt1YMCbWiT0ev/71r1966aWHH374gQcesFgsMTExixcvXrVqVSgUysjI0Ov1Pel9tDgcjkgly8i1vGHldDpjY2Ptdntra+twPxeGYRg2Co2J8zvugR8JSJFCxy3jHX+SRS8gheGC73iP8d97ervfcyfEDJq9IyT7JafnrQ8BIcuS60YmeweAaXGD9yUOjUQIQFYMmfUzJCIRiMdx1YgkkJ5GbUHp7QMBVpSfmTewzFskAxpsFP+ok/LX8aSRcr3aAAAyLyEKZXwwSX+eWWjnKOvJ/KrPchRFRTrnlyxZAgA8z+/bty/SOb979+6DBw8OPQK2Kehv2vbDum3dlSkYhsnMzJxoT8j2hLL15gmp6bFpqZTdSsdaKXsMFWul422EauC8jxNiJoi+U+hlAIcgVvF8NS9U8nw1L9QIQqMgNgoiAMztMzL/2Q6vAqEMmkqnqTSKspF49TrsNHv00Uc/+OCDZ599NiUl5a677lqyZMlTTz1VUlLi8XhaWlruuuuuaAcICQkJI5C6YxiGYdhYgRP4EULHPyC0vSMFy6TgfEL9P8u1n259rDeBTw8ubJdkkUAMQSgoMSyFALqHhvul1uC1UxO16dp506MU+/GancR8VhW+MG2oVOcXkzS/mKQZ0BiZA7//F3Y1NXCofWk7/30De36a8upP3TUdAgBIsixIiETAijIAOPzilFXOvg/RMuhfl5jTjKPrvZ34bBYAuF5tQAqU/s5E/QJzyzO1Lc/UaWcYrLcnGn9qI4acs4ANgabpyMz5yN3Ozs7du3fv3Llzz549xcXFNTU1Q48z4jguUt+++/4PEK/WZelM2QZLhs6Yo7dkmmOyXljOpCefroARQCJFJlLkuYcrUYoA9bxQJwj5ffr/q3n+o0Cw7wO1BEqlqFSKSqepVIpKo6lUisIV77FTodPpdu/evXLlypycHAB49NFHKysrP//8c1EUb7vttoceeijaAcJjjz32zDPP8Dwf7UAwDMMwbFQYXUnOmYzU0EmPcTVLZK5YYA2Uybu5pHfQ78LqGUVrHH+5PoEDieMQQO8EvBBn/mLq5l9n3BiNoE+MjiEAQHHM+vVHQQyWh0yw0hOsNC/JrV2il+3pVu2//jzbr7s1JKCgMBr75ROfzVJP1ikz1OpCPQCo8rSknur60dv1o5f6TaX5+jjrbfHK7IFXN7ATZTQaFyxYsGDBgshdn8+3d+/ePXv2lJSUFBcXl5WVHTMTaA76m4P+750NPS3J29eNnzAhPz8/JycnLy9v3Lhx8N+t3n//h7SaaLuFslkpm4WyWym7hYoxkwbdicZMAqTTVDrd7w/yRIZ522bdz3G1vFArCId4oUOSDnD8Aa43fgQQS5IpNJVGUXfotXG4ix47cVar9ZFHHoncpmn6/fffD4VCBEEoFKNiNockSYIgHHs/DMMwDDs74AR+5PCtl4SrximzKrq2TXUl72ht7047VUg5QZtD7PMBwNbfaMmkJwEAIfKGhF+sblpJkeHyLrHYu32yYbT3wJ80EiFRlo9WrC6yQ661e1kuUZL9HIQFWZJlGQABxOtIAJABZBkIBFYVYRqtvdnma3pLKBl/YtOfb+n4yNn+liOwy+d6rcH1eoNutsl6e4LxJzGIGaUvYczR6/Vz5syZM2dO5C7LsgcOHCgpKdm3b9/+/fv37dvndruPeZCGpqaGpqb//Oc/PS02oymdVqfrjOlaU6belKY1Jqp1kYtQlNUU9+JDp6Xu/TQFM61PbbxOSTokCLW8cEgQ6nihThAaBLFFFFtE8UdgbSTxS333tYMqXnjZ67ORZDJFJlFUIkUmU5Qa99VjR1i7dm1hYWGk+72HSqUCgB07djQ3N19++eVRCg3DMAzDsEHgBH6EhPZ3VV1aTNtvSfnLI5qpe7d91Zue5RPjiRBiGVS0z6f6i2frS2GJkhbGLF5on1fPlmzt+A4A3mt+K18/hUKnv7zWaUQSCACOGAV/bMSxZsXzkryrlesMDzKxWQZw+PsVMHP4xR8d7E/HqUd/skKoSctN8Zab4oN7/e1vOTreb/X/0OH/oYOKYSzXx5mvjVXlnc7FzzAAUCgUfcfbA4DD4Yhk8pGUvry8/HgG67o6O1zQ8WNb76rRCorOMFtTVfo0o3ny6oRxE/OysrLsdntkq/uv73T9sJOKMVNWM2U1kVYTZTNTVjNlMZExZkQe18fGSBAFDFPA9Kb0IoBDEBoE0SWKC/tM1N/Bst+GwgMebiGIJIpKosi+P2PIIS6dYWe+G2644eWXXx6QwEesXr169erVQ6z4gGEYhmHYyMMJ/Ehga4JVl+4R3DzIub6Nc/Tnb9pxMNSzdfYFc2ETMJzM0qhgJ6V6ZsHmxzcvtl4FANfE31zs2x4Sg22cc0PbF4tsP43eizi26yeoKQRDz4E/OQoS/e8624AE/oGNnXucnIpCX10TAwDPb/dvqAsLkgwAv/7O+8J2/+xExaxExaxEhU092nuz1ZN0yX/JSXg6q+P91rZ/OEL7/M6X650v1yvHaVL/nhtZjg4bJgkJCQkJCRdddFHkLs/zFRUVZWVlpaWlkZ/V1dXHk9KzAl/maimDFqgHWLYt0qjX67OysrKyshK8bEK7P6XFkKzVWxX9FqmiYswJrzyK+o+fl1lOCoRIkx6G7DYnAZIpKpka+Jf8Wq0mg6JqBKFBEBoEsVEQmgTRLUlujivh+u3JIJROUa9azYl91tXzS5IOr1R/5hIE4eGHH+65+9lnnzU1NQ3YJxwOf/DBBwbDwOqhGIZhGIZFF07gR0Jwr19w8wAgeHjPv2/Vzdu+/UDvUmoL770o8ZLspocqFbwMAMk7k8//3tL14T+1T9ynp4yLbFd83LIGANY5P5hpmm+gTdF6FceUYaQemXkyqSZJgCABgqESFR2DdEy/+b2RyfYIQZKeBIDXFhoDvLylif2+kf2+gW3pEj+pDH1SGUIA/zddt2TyGOjKJrWk9fYE6+0JgZ0+99rmzk9c4YpAYIdXPUXP1oW8X7XrzzfjSfLDjabpvLy8vLy8nhaO4yorKyPJ/MGDB8vKyiorKzmOG+IgPXw+X6Q2ft9GtVKZGmNPNVmTVNokWp1GsoXV1WkZ6UyfrvWWR/7I1TsQSZImPWk1URYjaTJSVhNpMijGpVHWY/wdIAFmKBUzoHcOswzgFMUmQWzsrngvNAiiQxTcolTJ822i2JPA/8nr+7uvS4lQAkXGk2QcRcaTVBxFJpBkPEXaSDzPfswTRfGll17qufvdd9999913g+7561//eqSCOqrHHnvsd7/7XbSjwDAMw7DRAifwI8FwgZWOVfCtLCBEaOOd308qq/lvZBNCaPr06caFxm0rapPqBQCov6BqxiYZFXR/lb8o5vItnm+dbHNYCn3cuvbWpLuj9jKGzf3TdKwoDz32/s87/bWd/eoYVXp4AGAF+Z4NHX3bNTSx/mprW1Da3MRubmKLnXzfIcIdYWlNaTDLRE2LYyyqUeKu1TEAACAASURBVNrHqJmm10zTJ700LlwZUOVoAKD1xUPut5sBQJWnNV1hN19jZ1JVxzoMdnowDDMgpRdFsa6urqKiory8vKKiInKjvb39OA8YDIfLGuvLGut7mz54kyCIuLi4tLS01NTU1NTUGI/bFvLGSWQ8z9HtHWyfh5NGXdKbzw44ZvhAJe9sp4x60mIkjXrSoBvQdR8pdxdLklMV/VYuDMtyQJItfcbwp1CUiSA6JKmGF2r4gcXDSAAbScZTZDxJ2ilyPE1frMZvxTGGYZgff/wxcnvGjBm/+tWvrr322iN302q1ubm5IxvaICiKoo4YY4JhGIZhZy18UhwJHZ+5+FYWAECWAnv9+2ykeHgweE5OjlFv3Hf7lqR6QaQQKcjj/p0r/LxVdedPAmJXZJ/F9qvfbPgLAGx2bzzXuihVlRGdlzFsjlxYboCwIP+tOMAPtua7KMOXNQPn+l6SoZyTpMg0UbfkDzzyuurQyzv9AEAi+Psi87zkUVFmeVCIQqrc7oEDsQ+mgix3rmsLHegKHehq/eOhibVzCA0pcxIA4Ip3I4wkyczMzMzMzEsuuaSn0ePxRJL5qqqq6urqqqqqqqqqrq6u4zymJEkOh8PhcGzevLlvO0EQ8TZ7Uow9yWyJ1+hiFZpUqzV3//7k5OTe4c2y7Hx6hdwn2UYkSRp1pMVEGnWaoimaOVOP9rxKhJT9Z8FfqVFfqVEHZLlZEB2C0CKKzaLYIogtougQxDaxu2xez6CC8Qyddji/ercrsCEUjiEJG0naSTKWJG0kYSdJK+63H00iF44jty+66KJ58+b13MUwDMMwbJTDCfxIMF4S45qoC+3zcwaa8Qr7AnU9m6afM73+rjLxI5ZTIJoDANh6547i6yrBsQwcA48jI/njljUPpD82grGPCkoKfXyF5ZC3X7G6tw8Ed7awegY9Pc/Yt13LoNlJR03Lr8hW+zl5ezNX2ynomN68ZZuD+3dFcLyVnmSjJ1hp1YkvhvfsNv+be7vuKdT9atrpH66vSFel/DU3+S+S71tP56cuIBChIkCSSydvE9p53VyTfqFFv9CiSMN9oVFjNpuLioqKior6Nra0tFRWVkby+erq6tra2traWq/Xe/yHlSSpqbWlqbVlW9/W5/8AAHq9PikpKTU1NSkpyaqR7ZxsIxV2mbQLSBnmBHen4O4EAN7hHJDAy6Lkeu4NscNHmg2kQRfpsScNushdOt4GBKFBKIumsuiB5whelltFqfVwSq9EkNKnd/S7UHhbmIUjkAAWkowlyRiSiCVJC0nEkmQuQ4+jR3VhzrPBV1991feuJEmBQECnO+GlEDEMwzAMGxk4gR8JpJHK/mpK1eJi2OOTEZS0t/ZsyqxO9pS0RrJ3JMvfLPE0/KxUQw7MACVZCklBANBQZ+n3qlwrnWvt911/q4Pd2QIGJXFxxgmUzdMyaNkU7bIpA9s/rgxG5swDAElAtomeaKMn2uhJNjrbRB9PjfASFwcAe1qPa2r0yUEMYbjIarjI2tOiytF4/+v2ftXu/aodABQZav1Ci36BWTvTSBrwpzv64uLi4uLi5s2b17fR7XbX1NREkvmeG01NTZI0yDoLQ/D5fKWlpaWlpUdu0uv1CbFxiTZ7rNGUTCjiV6xITEyMjY1NTEy02WyEILJVh6SuIBwaWLoMANTT8m0P3TWgMbhrvxQIkXotadLH67WJeh3qPxQ/4i9W816WaxXFFlFsE6VWUXSJolMU3aLkEkWX2O8aHI3Q9oRY1eGh/m/7AztY1k6SFpKwEmQMSVhIwkaSZoJg8AJ4p5vL5Vq7dq1CoVi6dGmkJRAI/OY3v1m1alU4HM7Nzb399tvvv//+6AaJYRiGYdiR8Ff8kbChjn23LGC8OfFnpQdpVtpzqLeCXfqeeE5F0JyMZPmDX8X851x9Wleq0VBxtEPx0iC9W2enFD0JAHHa0zMy93cz9dPimL1Ofl8bX+Hhy918uZt/vxwAIFFH/udnMcoT75MfARn/LuCdnO8bt2+D2/+th60JttUE295oBAKpcjXaWUbtTKN2ppGOG70zBc5CFovFYrGcc845fRs5jmtoaDjUX11dXUtLiywPucriYHw+n8/nK68c5C8JQRB2uz0+NjbWZLEbjTat3sqo7UqVmaBiZMoiE0xa0oCHCC6367m/DTyOWkUadYReq79ormZ2dw+/CqEZSgUAyBwPstyT5Auy3C5JLYLoEkWXKLVJokuUYklC2Scz/yAQOHLKfYSRIHqyejNJmAlyurLfcnrYCfnyyy9vvPFGj8czf/78ngT+xhtv/OSTTwAgJiamtLT0gQce2LFjx7vvvhvVSDEMwzAMGwgn8CPh9z94ORf72BsNNCu1aWtbnN3dUCpQJqsyerL3j+fFiFVQobxu+uSjDpKnEf7O2s2qJgEg4TQl8AYFcU2O+pocAICQIJe183td/D4Xv6+N19CI6JO8/+qbzkoPn22mL85QXjAMa+adKNrOWK6Ps1wfJ4tycLfP943b/11HYI8vMlu+7W9NgCDlr7mWG+IAgG9l6ViczI9GDMNE5tUPaGdZtqGhob6+vrGxsb6+vqGhoaGhobGxsaGhIRweWP3heEiS1NLS0tLScrQdDKsN8Y/H22y2+Pj4mJiYmJiYuNhYbU6sIchZEWUSgAmykq9LCoakYAiaXQG9tieBjxDaOxz3PSmzHFIwpE5D6DSkQUfqNCk6bZpeq5ldSMfGHPm8q2IsxRzvEkWPKLlE0S1JbaLYLkpuSeqUpE5JqoHe9F7jQ7sS43ru3u/uOMjxJpIwEYSJIKwkYSZII0mYCcJ6uBF340fU1NT87Gc/CwaDS5cu/elPu5cm3bFjxyeffKLX6zdu3Dh16tTa2tqrr776vffeu/POO88999zoBoxhGIZhWF84gR8JyzIZy9NVsa1hr4k+qNwJzu728WS2kiUQyO8+aPt2uib2EEcbVPdPy74089Ooxnu2U1GoMJYpjB38Wkl1h1DhESo8wg9NbN8EvtkvAUBrUOoISyZlFKrKIRJpzjFozjHEPQJSWAru8XVt6Qz82Bnc10WZaABo/4ej4b6DlJlWT9Grp+g0042GCyxDLt6HRZ9CoYisJH/kJqfT2XiYw+Fobm6ur69vbm52OBwnl9tHeL1er9dbXl5+tB00Gk1sbKzNYjUbDBadPtZVaf/Tn6xWq9VqtdlsNpvNrFTRcTa+2SmznMBy0N5vnQjuUJPtwV/0bRE7fc5n3gBBmKjVEDoNqVUTOjWh1RBaNanTKieO62AYjyS1i2KbJHWIYrskZfefPF/B8YcE4dDg/ffd1AiZSMJMEOerVHfqx8C6ksPktdde6+rqeuutt2699daexjVr1gDA8uXLp06dCgDp6envvvtuTk7OihUrcAKPYRiGYaMKTuCHndfFGu4+EOtg28203i+WKst6NuUROQjk1lTm6/MtwQOoEwC84qZG9tJMXIps9Pr0SutBN1/m5pN1vR+fAC83dwkAUNPBT/2n06gkskxUholKN1IZRuqcOEZNj2iiTCiJyOD5vo2KLDUdr+CbWd83bt83bgDI/LhAf4ElVNbV+VmbKlejmqhTpKpwSj9W2O12u90eybgGaGtrczgcTU1NDoejpaWl52dra6vT6TyJMfl9BQKBmpqampqaIfZRqVQWi8ViNluMJotOb9ZozWqNkVGZGEWcTWXbsSMyj8BoNAKA1BXkGxyyIA56KN2CmZYl11lIoqeinuj1u19d7RQEQqsmNGpSq16t1bQadV6NyqdR++Ji3LLcKUkdouSWJI8oeSSpQ5KCshwURAeIQVk+mxP4r7/+OjEx8eabb+7buGHDBgC44YYbelqys7OzsrIqKo46nwvDMAzDsKjACfzwEtq5hkuLEyvZ9nha1yEyvHQg1FuCPpcYx6pR7CHu1082FD+SrUakkkRXjMPZ+3HJNFEKEk2wjnQVa4qAvBi62MltrGc31veWJGAoxAkyiYAkoDMs7WzhdrZ0F7Q7P1X5t4tMPXt+38AiBMl6MnVk68zp5pryK2dzjeFgsS+4x8+3supCPQC0vnio48PuYSGEhlTmaFTjNcocjXK8VpWjYZKVOKUfcyJD3wsKCo7cxPN8pOve6XQ2NTW5XK7m5man09na2trS0uJyuXieP/UAQqFQU1NTU9MgRfL6IgjCbDabzWaLyWTUaE0anVGlMihVBlqppygDQesJKj7OwDY3m0wmlar7b6PgbA/u3DfgUBoADUA8gP7S88y3XNF3k9jpc698L0iSfoPOa9DFA3hpmtCoCI2K1GkVOenoiGL7ZzCHwzFjxgyCIPq2HDx4MDc3NympX/mDuLi4PXv2jHiAGIZhGIYN5Sz61jLyRJ9QdXExXxYIJSt0bbwiJFXMVB/Y5OrZIZccxykIjoa8LQHihaqij6dkKPCiSscrP4YuuyM2Kk/9bT37+GbfoJtEGcQj+hEzzb0ftANt/G1feiK3fzFJ83CRPnJbBvihkVVSKFFH2jUkOWw5M5OkZJKUxp/YelriH81QpKiCe/2hfX7eyQV3+4K7e19dT0qvm282XxudXzh2GtE0nZiYmJiYeLQdXC6Xy+VqaWlxOp1tbW2RTvu2tjan0xm5wbKnrZSmJEnt7e3t7e1D7fT685F/FQqF8TCDUqVXqPRKpUGh0jMKHUXrSVoDpI4g47Skv75er9ebTN1XzfjGluCOfQCgA4gs49F3TL/h8oWmGy47Xa9o9CMIgulf/++bb74BgAULFgzY0+12j1xYGIZhGIYdH5zAD6NweSBU1gUA6lZe5qTvrzBsTXX6N3RP06RM5s7J6Qnbg5wKAUDOlsDdtW1/y7IlU/g/ZbQ7J44eZ6F8bL9Fv5xBSZKAQihG0y/5NirJe6b0Lv6XbabumKQpbReau8S+PfDbHOyt67sTe5pAcVoiTkvGa8nIz4szlAbFcM2rV6Sp4h/PiNwWPHy4PBA+GAiVdYUrguHyrp6U3r2mxXCJldRRh+4o9X/foUhXK9JVinSVbp5ZM00/TLFhIy8yjz0vL+9oO3R2djqdzkjiHUnsIzfa29sjGX57e/tpTPJ7sCwbuYhwjP3e/HPPTYPBoNfr9Xq9jlFqGEavUusVSg3NaClaQ1AagtKTlC3oPqelJS4ubohDnkkyMjLKyspkWUaHq/p9/vnncEQCL0lSTU1NcnJyFELEMAzDMOzocK44jDTTDZYb4txrWmRO0s0xii9n+n+5oWerYtKkF59Leu2mYm2jGgA+ujfmkBb+6Q88ajJEL2TsuGgZ4surB5bRnr3W1eIXE7Tkt9cPUmG7B0Oi5UWDpLv5Mcy1ueoqj9DkF50BscEnNvh6u/IbfOJDM7qvAjT6xSc3+7QMitWSN+ep7ZrTU4c/gjLT2llG7azeyfNCBx8uD4TLA6SJInUUAPCtHN/M8s1s1+YOACDUhwqc8wFB/V1lodIuJknJJCrpJCWToGASlEyykrIzaPhGFGAjLtIHPm7cuCH26erqcrvdkWTe3V+kxePxeDwev98/xEFOXaQg3zF2ev8t5X3Kr776av78+cMazChRVFT0+uuvv/vuu9dddx0AHDp0aP369QzDDHj5r776aigUOu+886ITJYZhGIZhR4ET+GHE1oXcH3cPmPdv9c7/qOv9jtKercpxExf8qUPbqAYEIMP573ZsvNkySxn9Zcmw49ERlrq4fpXARAkAQJDkRl+/MfQ6BhmPoyi9jkFPz+2+dsOKssMvtgZEZ0Bq9ovtIenKPpUR9rv4jfXdNcaVJLp3anc5rh0t3D3/7VDRyKYmTUrCqiasKsKsJMwqIkFLFtgZ4qSSaMpED6iHl/VZAdcQZutCbE2IrQsyad1177q2dbK1oWDJwJQMUYiyMUyCgo5V0PEKwyKr/nwLAIg+QQqIVAyDKJzen2m0Wq1Wq01JSRl6N57nPYdFsvqOwzo7Ozv6G45e/YhwOPzGG2+cJQn88uXL//GPf9x8880bN25MSUlZtWoVy7LXXHONwdD990eSpNWrVz/88MMURd19993RjRbDMAzDsAFwAj+M3tkfGM9LDEBtoiq9KQQPVrTGlvRsne1Jv/FLl0Qgh41JamUJTuZEaVs4vECFl+ke7Zr84sL32jhxkFLejoA4/x1X3xYlhb79ecwJ9ZMrSJRupNKNg388F2UoP9RY6n2ij5V+ktWb2Id42cvK7SFpwBWEiKfmGn6eq47cLmvnX9zuV1AoTkMumaK1qXuvLzT5RZoAvYJQDZFUE4hJVTGpKl3/5aXG/zg9VBbgm8Kcg+UaQpyD5R1hronlW9lIj31kN+9/3HkHZsqsVJq3VfDwgICyMpSFNl8bG/tgKgB0fubiWzkqhqZiGNrKkCaKNNJENFbmw4YbTdORWvrHs3MwGOzs7PR6vZ2H9dyOJPy+w7xeb+Tn8Ucy9ICCM0liYuInn3xy/fXXv/XWW5GWlJSUP/+5e97BunXrrr76apZlEUKvvPJKbm5u9CLFMAzDMGwQOIEfRimTdC/+MvXBlfXpTaFDicrkplBHbXXP1rvW2yQKNcYpUprCXi218t70l5OskxWDrz2OjSp6Bk2MoZ3Bfnmyo0uUJKAIFKftl2qmGigdczqTTwQwJZaZckQ5uXnJiuLb7M6A2B6SPCGpLSh5wpInJLlDYoCX+y5rv8fJb2rsTqezzVRPYr+mNPjYD905D0MigwIZFIReQRgUyKYm75uq7bkMwUvyrhZeQUKMmkzSdzcSalIzVQ9TB04QkDmpe9R9K8u1sOrJOgBANNLONgW2d/JtvNDGCW2c98v22AdTRa9Qe/3+I181oSZJE0WZaNJIU2aKMtPGK+z688wAENjhFdp50kCReorUU7hs/plKrVar1er4+Pjjf0gkq/d6vX6/3+/3+3y+zs5Ofx8dHR3BYHDy5Mm//e1vhy/y0eaiiy46ePDgpk2bysvLk5KSrr76arW6+49AKBSyWq0FBQW/+c1v5s6dG904MQzDMAw70lhN4GtqajZs2FBWVuZ2u4PBoMViSUhISEhIWLx48eipRXReiuK8Z9N9C421P9ub2hTemuYKHOjOmozIEEPZGrMVKeXhoCm84fV1r130hJXB4+fHBr2CeP9yy4DG7jnwGvLb64aaAz+sVBRKNVCpx6qicF2ueoKVbguKQV5elNH7rsuxULlW2h2SvKwUFuS2oNwW7C3UNy2O+Wl2d4f/ypLAn3Z0D5X/+yLzeSndw0Y2N7HP/+gnEegVhIZGahqpKaRTEBoaWQz0FdP0xsOT4WUCoRXjjQQy0kjpF0Q3TycqAIA0UClv5Ab3+HgXJ7RxgocXOwShg5eCohQUeUfvIOpQaZf+PDPv4irO3w1S72gI009taavzpaBYfcVewckSWpI00ISWJNUkoSVJA0WoSVJLEhqS0JKEgtDOMVFmGgDETkEKiYSKJPUknNx8A2yUiUzXj3YUo1FMTMyVV155ZPs111xzzTXXjHw8GIZhGIYdp7GXwNfV1S1duvTrr78edOuyZcsuu+yyl156KTU1dWTjOirdeebPVqZfeEeNr6p3AnwumR3IUqSUh71m6uV/ZRgtvo9a3v5lyoNRjBM7RTQBAMCMhY8UgWCynQYYuGbh1Fhm3VXWyG1WlH2s7GUlHyt5WVmU5XOTe1P9c5MVe528JywihJL1vbMDDrqFsvajriKupdGlmd2XAN4o7nppux8AKAJWL7acM14Taf+2nn2eppSzLAoSKSmkoRFFgI4haFZKlOQrY2naL4gdvNghEFP1X9eGZVGO/WWSvjYoegXRJ0hBUZGt6QhLslcI7vVLfuGYvw39AnPmZ5N5F1eav1UKHB5VQSD7fckJT2ZKQbHuxv28iyMUBFKRhJogGAKpSEJJIBoRGhJRiNRSQCLjpVZFhhoAAtu9QhtHaEgAILSUKl8bGf8v+gQpJBEKAgAIFYGGbVkBDMMwDMMw7Ew1FrKNPtxu98KFC2tqaiZMmHDZZZfl5eVZLBa9Xu/z+Twez8GDB7/44ot///vfe/fu3bx583HOqxw++9v4m77w+AmJPo8+8HqS4pbe8fNx+vG6g2GvkXrmX8lNmQqx9KUDB6rPtZSO006IYsDYqViQqly1N7Aw9QwZRqEgUYwaxagHTzJzrfTKRaYj238xSbMgReHj5C5O8nNySJCDfPdtmoT5fS4B5MfQuVbaz0kIwNgnla3y8NUdR826E7PViwq6q/E//6N/5Q8dAADp2r8vS+4ZBfD3ksBz/3QCgHJ5ptXLK1jJzEsaTlbykg3gmnjGIMlSQJSCosvF1bbz1Xl6//+8D+apNYX6cGVQCouiVwBJ3l0Ten+n39jJT/quA3HSUSLq5fvGnbVuMncoVHH+LuhTHkFzqTX2X/mCk22Ytl0ODCxPQGhIRKPIDYIhAEHMkiTbkiQpJB26o1T08EAiACCUhOWmeOPiGFmQW56pFQ5fJUEkInS9F1BIPYUiYwf6XFDwft3ON7OEkkAqEhGgnWWkrAwAhPZ3Ce1cbyQqcsA1BUJNEAqCSVZGxiOIPkFw86SORBQBCMjDiyDKvCyFxcgKBRiGYRiGYdiwGmNfuR555JGamppnn312+fLlg+7w+OOPr1q16s4773z00Uf/9re/jXB4A/CizAqyDCAHYV+WpsPWCA3dm84JpHaaqD/cmdICDMmD5AFBVL3T/I/Hsl4iEO6XG5PUFAIA5VlfUD3tKLX3BpidqJh91SD1Gu+arL04U+UNS2FRZgXZz8mSDD5OAhlkgPNTex+yOFPpCophQQaAvJje0QRJejJOSwZ5WaBRk4IAgJrDm0gEl1xmGX+4HMAH2/1/Le4CADgYvCBNee6XUyLtT27x/XN/AABgdxcAGB7JtHQKDC8xgqxgpaUTNRM0hMxKMid9dzC0qymsZCUAWHRHQhYAnaC0LUs+WOxrcvEAoOTkb1XMxlVOFSs+bFPEt3OUIGkYAoUkmZMAoKfPX+zsvmwR2OGFJUmbSrq069qIPoUSi1u5zxFpdHK3vHDoeH7Dzv+0f7g8Q+MIX3DHgb7t7TOM9S/l/EyLqmft6Dv14Gj8F1pbnsyi2rjUq0qIUO8FCMttCSmv5PAurnzqj4Jn8GEXSEFICgIAEAH8TfHC0mTkF5gb9qMWllAQjJYEgJg7E233JLNeofzyEtnJEggoAhE6Ch2+LhEW5J4oEYnQDfHklXaal+HhSuLw8yICzDfHBxdapZDE/q4SWjgAQDQiNSQAhARZHvBCSaT7eZztIgv+wGIYhmEYNraMsQR+06ZN48aNO1r2HnHrrbeuXbt28+bNJ3H8P//5z6+99trQ+3AcBwBHfB8cxJRYpuyO7lJjzeHGdN/unk2pppyZ/5v6/bjuYcNcKvfbiscaQ22bOzbONS88icgx7IyRpCOTdMcu2p9rpf943iDTmy9KV16U3m8cRJCXeUmWZJBlMKt6L5DdN007N1kRFmQCwazE3ksDv5ysTdSRnCgLEgSE7k+6l5UAQE2hgkKt/nBPdXor9589XbwEShKlz9UDAKJR4nNZZXXhd7b7OUnmRQgJsgEAFMSL96f5OVnLoPcvs+RYuq84/HO77+WtfhlAzUsrzjXkWmkmSQkAJRR6d3lmrJsjZAAAUpSrk1T+JhYADi1JeTiWzjJRACAL8sbS4M7W7o70q+KZZF13bB8nqd8pCxKS7LkwxuoVaEFmOAkANo3X7/rRl36eIeuWeLYuBAC1nUJLl6jgJFqUkw2k/nDNRVe7EAiKXyqpLzd51WHx4ViF2ScowxIhSQAQVpMpAIhGlJ0JhyUqOMjaBzIrIbZ78MJ3u/x//8Jj9gl/qg9pwpIMwLYBAIQOdAHAyk2d00p8DC+LAIMcqOeAANuklj8BaXdzr3ziIvpcgDjEyXe5JLube+VdJ3Ecf5+3VYZeahbevcwy2T5wLgmGYRiGYdioNcYS+La2tvz8/GPulpSUtHfv3pM4fllZWW1t7Uk88JjecbwpH84EtJRxx5OT8+Kroc+C2ZP153zTvv7jlrXTDLNUpHo4YsCGFU0gAKBPYLU4bISoaQSDFaanCXRO3CDrPsSoiVsnao7nyFNjmX9cbD6y/YI05QVpxzWZ4pbp+qun6ARJpgmkpnuDvG+a7pIMFdunBz7Ay4IkA4CGsRbYenPOn/Cy1cFyIqhpND1J0VN978awFHcoLEgA841dvCweznXPA7iUJuZmquhXciItSr9YfjDISaCh0CWTND0rCNKdwtrSoF6UrwUAgNIpvSuKKUl031QtAFAmOnfnjP1t/Kp9AV6S1TSxfIbOdHjNv90N4VU7/JwIABDWkLMAABRvvZlPs7Jaht9MVFvVBJOsBIApk3Wvr8gjOwUNjR4u0llEWRYBAPa0cu+VB4U++Xh7nCJPQbBm6o0Xch5JYnpmeWy3MpllIVZPrngy+7dZSpuakHlZDIglTn5ddUg8IqWvSFUl6EijAvfAYxiGYRg2lqDj6UkePRYvXrxhw4YDBw5kZmYebR+XyzVp0qRp06Z9/vnnJ3p8SZIaGhokaaj5ru3t7dOnT7fZbE6n8zgPWx+q+UPl/1V+XP/jc/tkoC0/f+GcG302665Bd/55wu0LrYtPNHIs6pwB8b3y4PUTNFYVngSBYWe4bdu2zZw5s6ioaOvWrdGOBTttnE5nbGys3W5vbW2NdiwYhmFYFIyJ8/sY64G/9957v/rqqxkzZjz66KOXXXZZSkpK360tLS3r169/8sknXS7XbbfddhLHJwjimOXrNRoNACB0Av02dkX8dOOc3JsnXXz1og37L2jwjkvVfp2qG2TWKIXoXO2kEwkZGy3sGvK+qbpoR4FhGIZhGIZh2BlrjCXwCxcufPXVV+87zGAwmM1mvV7f1dXl8Xg6OjoAgKKoFStWXH755dEOtpeSUN2V8n+R2/VNHQ3e8OX2n59/ppQrxzAMwzAMwzAMw0bAGEvgAWDJkiXnn3/+m2++uWHDrCaXkwAAF9xJREFUhvLy8rq6OgAgSTImJmbq1KlXXHHFrbfeGhsbG+0wjyoyQ/VE+u8xDMMwDMMwDMMwbAwm8ACQlZX1/PPPP//887IsB4PBcDhsMpkIYmxMPL4mR00iKIwdpHQWhmEYhmEYhmEYhh3NmEzgeyCENBpNZFL6WHFuiuLclEGWv8YwDMMwDMMwDMOwIYztBD6KOI775ptvTvRRFRUVra2tOh0udXYyZFl2uVxWq5Uk8VptJyMcDgcCAYvFEu1Axiq3263RaJRKXL3iZIiiGAwG586de0IVQEetsrKyaIeADRd8fh9h+OR+KvCZ/VTg0/pJE0UxFArNmTPnzDin9zUmzu84gT9hkbH6HR0dCxcujHYsGIZhWNSMlalb2HHC53cMwzAMRv35HSfwJ8xqtS5btqyiouJEHyjL8saNGwHAbDYPQ1xnPpZlA4GAQqEYW5MmRg+fzycIgl6vpyj8wT9hoih6vV6SJA0GQ7RjGZMi9UrS09PT09OjHctpc+utt0Y7BOx0wuf3qMAn91OBz+wnDZ/WT0UgEGBZNiMjIy0tLdqxDItRfn5HsixHO4azBc/zDMPQNM1xXLRjGZP++te/Ll26dMmSJStWrIh2LGPStGnTdu3atXPnzqlTp0Y7lrGnpKRk8uTJBQUFxcXF0Y5lTLr33ntfffXVV1555Z577ol2LBh2muHz+6nAJ/dTMXXq1N27d+/atauwsDDasYwxe/bsKSwsnDJlyu7du6Mdy9izbNmyFStWvP7660uXLo12LGejUT08AMMwDMMwDMMwDMOwCJzAYxiGYRiGYRiGYdgYgBN4DMMwDMMwDMMwDBsDcAKPYRiGYRiGYRiGYWMATuAxDMMwDMMwDMMwbAzACTyGYRiGYRiGYRiGjQE4gccwDMMwDMMwDMOwMQAn8BiGYRiGYRiGYRg2BuAEHsMwDMMwDMMwDMPGAJzAYxiGYRiGYRiGYdgYQEU7gLMISZIqlUqhUEQ7kLFKq9UCgEajiXYgY1XkVxf5NWInKvLbw2+/k4bfftgZDJ/fTwU+uZ8K/Kf1pOE33qnAb7zoQrIsRzuGs8iWLVsoipo+fXq0AxmTOI778MMPFy1aZDabox3LmFRVVVVeXv6Tn/wk2oGMVV988UV2dnZ2dna0AxmTOjs7169ff9VVV+EkBzsj4fP7ScMn91OBz+ynYt26dTk5OVlZWdEOZOzp6Oj48ssvr776aoZhoh3L2Qgn8BiGYRiGYRiGYRg2BuA58BiGYRiGYRiGYRg2BuAEHsMw7P/bu/OoqOr/j+PvGQZBcDAPCaKEC+CeS6ZimUtpgJFSaqWZqJGEaUliLuVG5gadckuPx05qyzEtF7SMOJWlpmYkbmiEiFugIkclkAhmfn/cr3PmxzIM2XC58Hz8NfczH+DN+3y4l9fcuXcAAAAADSDAAwAAAACgAQR4AAAAAAA0gAAPAAAAAIAGEOABAAAAANAAAjwAAAAAABpAgAcAAAAAQAMI8AAAAAAAaAABHgAAAAAADSDAAwAAAACgAQR4AAAAAAA0gAAPAAAAAIAGEOABAAAAANAAAjxQR+zdu/fatWtqV6FhNBAAoEUJCQmrV69WuwoANYQAX0OSk5OHDh3atGnTjh07RkdH5+XlqV1R7dWvXz9dRQoKCqyn0VJrJ0+eHDhw4M8//1zhs/b0qp7300YDWZA2rFmzpkePHkaj0cvLq1+/flu2bCk/h+WHuo3Vezfs2YfAto8//nj69OlffPGF2oVoycWLFyMiInx8fDw8PIKCglh4diopKVmxYkVQUFDjxo1btmwZHh5+5MgRtYuql8xwvDVr1jg5OTVo0KB///5t27YVEX9//8zMTLXrqqWaN2/u4uLSvpyCggLLHFpaxogRI0Rkx44d5Z+yp1f000YDWZAVKikpiYqKEhEXF5f+/fsPHDjQ1dVVRKKioqynsfxQt7F6/zU79yGw7dy5cx4eHiIyYMAAtWvRjJMnT/r6+ur1+m7duoWEhLi7u4vIwoUL1a6rtistLR00aJCIeHt7P/HEE3379nVyctLpdJs3b1a7tHqHAO9w6enpzs7Onp6e6enpysiiRYtEJCQkRN3CaqeCggKdThcaGmpjDi21+OGHH5YtW9ajRw/l9bjy+dOeXtXnflbZQBZkZT766CMRadeuXXZ2tjKSkZHRpk0bEfnqq6+UEZYf6jZW792wZx8C20pKSh5++GGj0UiAt19xcbGvr6+7u/sPP/ygjGRlZRmNRr1en5WVpWpptd369etFZMiQIYWFhcrIr7/+6ubmZjQai4qK1K2tviHAO9wbb7whIsuXL7ce7NSpk4hkZGSoVVWtdeLECRF59dVXbcyhpRYBAQHWb6gpnz/t6VV97meVDWRBVuaxxx4TkUOHDlkPbt26VURefvllZZPlh7qN1Xs37NmHwLa4uDidTvfhhx8S4O23adMmEYmLi7Me/PDDD8PCwr755hu1qtKEsWPHikhSUpL14LBhw0Tk4MGDalVVP3ENvMMlJyeLiLK+LZRN5SlYy8jIEJHAwEAbc2ipxd69e7OysrKysqKjoyucYE+v6nM/q2wgC7IymZmZzs7OPXv2tB68//77ReSPP/5QNll+qNtYvXfDnn0IbDh8+HBcXNzkyZODg4PVrkVL1q1bJyIRERHWgxMmTNi1axedtM3Z2VlEytzuV7nrh/IUaoxB7QLqOLPZfPr0aQ8Pj5YtW1qPd+7cWUTS0tJUqqv2Onv2rIjk5uaGhoYeOXLE2dm5a9euU6dODQkJUSbQUmstWrRQHjRu3Lj8s/b0qp7303YDhQVZuW3btul0Or3+/70KnJKSIiL+/v7C8kNdx+q9S1XuQ2DDX3/9NWbMmICAgKVLl3LfxGo5duxYo0aN/Pz89u7de+DAgezs7C5duoSHh3t5ealdWm03ZcqUzz//fMaMGZ6eno888sjNmzdXrVq1b9++wYMHWy5FRM0gwDtWYWFhUVGRj49PmXFPT08RuX79uhpF1WrKCc8FCxa0aNGid+/eOTk5ycnJSUlJb7755sKFC4WWVoc9vaKftrEgK9OtW7cyI6mpqbGxsTqdTrkxFcsPdRur9y5VuQ+BDa+++ur58+cPHjzYsGFDtWvRksLCwvz8fH9//9dee23FihWW8dmzZ2/YsCEsLEzF2mq/rl27HjhwYODAgaGhoZbBZ555ZuPGjSpWVT/xFnrHKioqEhHlBqHWlJHCwkIVaqrdsrKyDAbD/PnzL168+NVXX6WkpOzfv9/T03Px4sUHDx4UWlod9vSKftrGgrSH2WzeuHFjv379srOz33333QceeEBYfqjrWL3/oQr3IajMl19++dFHH82bN4/TntWl/GGePXt2w4YNa9euzcnJycnJWbFiRUFBwejRoy9evKh2gbVafn7+66+/npeX16FDhxdeeOHJJ580Go07d+5UbkiJmkSAd6wmTZo4OTn99ddfZcZv3bold16nh7U9e/b8888/8+bN0+l0ykifPn0WLFhgMpk++eQToaXVYU+v6KdtLMgqpaSkBAUFjRs3zt3dffv27TExMco4yw91G6v3v1LZPgQVunz58sSJE/v06TNz5ky1a9Eeg+F/bz1evXp1VFSUt7e3t7f3lClTZs+enZ+fv3LlSnXLq+XGjx//3XffzZ8//9SpU5s2bUpMTPz9998DAgImTZqUlJSkdnX1CwHesfR6fdOmTctfnqSMWK6/hW0DBgwQkePHjwstrQ57ekU//wUWpOKff/6ZPXt27969T548+dZbb6Wnp4eHh1ueZfmhbmP13j3b+xBUaMeOHXl5eXq9fsyYMaNGjRo1atSkSZNEJC0tbdSoUcp9wlGZRo0a6fV6g8EwcuRI63Hl3pOpqakq1aUBV69e3bZtW+vWrefOnWs5peHj4xMfHy8ia9euVbW6eocA73C+vr43btzIycmxHjxz5oxwgC/HZDL9/fffJSUlZcaVV0zvueceZZOW2s+eXtHPyrAgbTCZTGPHjl28ePGAAQNOnz799ttvK59FbI3lh7qN1Xs37NmHoDIHDhzYfEdiYqKIXL16dfPmzcrn8KEyBoOhdevWBoPBcipe4ebmJiKlpaUq1aUBV69eNZvN7dq1s6R3RYcOHUSkzG4QjkaAd7innnrKbDbv3r3benD37t0Gg+HJJ59Uq6ra6dKlS66urmU+VEZE9u3bJyJdunRRNmmp/ezpFf2sDAvShg8++GDz5s3PP/98UlKSn59fhXNYfqjbWL13w559CMp75ZVXynwi9KVLl+TO58Dfvn1b7QJruxEjRhQVFSl3sbFQDuvlb6wIi8DAQGdn51OnTpV5mePYsWNy59M3UHMc/knz9d6ff/5pMBhatmx55coVZUS52cPw4cPVLax2euihh0RkyZIlJpNJGTl69Ki3t3ejRo3Onz+vjNDS8pRr4Xbs2FFm3J5e0U9z5Q1kQVYmMDDQzc3t1q1bNuaw/FC3sXrvhj37ENjDOsCjShcuXDAYDJ06dbIcxE+ePOnj49OgQYMTJ06oW1stp1x38NprrxUXFysjGRkZAQEBOp0uOTlZ3drqGwJ8TVi7dq1er/fx8Rk/fvzgwYMNBoO/v39mZqbaddVGf/zxh/JunHbt2j399NNBQUEGg8HFxWXTpk3W02hpGZXlT7N9vaKflTWQBVmh7OxsEXF1de1WkdjYWMtMlh/qNlbvv2P/PgRVIsBXl/Jn6+7uPmjQoAEDBri4uOj1+vfee0/tumq73NzcgIAAEfH19R06dGi/fv1cXFxEZNasWWqXVu8Q4GvI9u3bw8LCPD0927dvHxkZmZ2drXZFtVd+fv6MGTMefvhho9EYGBj43HPPnTp1qvw0WmrNRoA329eret5PGw1kQZZ34MABG2/sGjFihPVklh/qNlbvv1CtfQhsI8D/C4mJiUOHDvX29m7RokVYWNj+/fvVrkgbCgsLFyxY0KtXL6PReN999w0ZMuS7775Tu6j6SGc2m//de+8BAAAAAECN4SZ2AAAAAABoAAEeAAAAAAANIMADAAAAAKABBHgAAAAAADSAAA8AAAAAgAYQ4AEAAAAA0AACPAAAAAAAGkCABwAAAABAAwjwAAAAAABoAAEeAAAAAAANIMADAAAAAKABBHgAAAAAADSAAA8AAAAAgAYQ4AEAAAAA0AACPAAAAAAAGkCABwAAAABAAwjwAAAAAABoAAEeAAAAAAANIMADAAAAAKABBHgAAAAAADSAAA8AAAAAgAYQ4AEAAAAA0AACPAAAAAAAGkCABwAAAABAAwjwAAAAAABoAAEeAAAAAAANIMADAAAAAKABBHgAAAAAADSAAA8AAAAAgAYQ4AEAAAAA0AACPAAAAAAAGkCABwAAAABAAwjwAAAAAABoAAEeAAAAAAANIMAD9UtMTIyuKnPmzBGRl156SafT/f7772qXDABAvXDlypXyB2V3d/fu3btPmzbt5s2bDvq5O3fu1Ol07777rrKp4j8AZSoBUJ5B7QIA1Ki2bdsOGjTIspmZmZmZmRkQENCqVSvLoL+/vwqVAQAAEVdX1759+yqPzWZzbm5uWlpaamrqli1bjh8/3qRJE3XLK89sNhcXFzs5ORkMJAvA4fgzA+qX6Ojo6Ohoy+aCBQvmz58fGRk5Y8aMMjNnzZoVGRnZsmXLmi0QAIB6rVmzZsnJydYjFy5cCA8PP3r06Jw5c1atWuXoAqr7D8Avv/wSFBQUGxsbHx/v0MIACG+hB1CZNm3a9O7d29XVVe1CAACo1/z8/JYvXy4i33//fQ38OP4BAGozAjyAart9+7bJZFK7CgAA6ouOHTuKSGZmZoXP5ufn12w5AFRDgAdQsejoaOt72MyaNUun06Wmpj7++OPu7u5ubm59+/bdsmWLiHz77behoaFNmzb18fEZN25cbm5umW/16aefBgcHN23a1MvLKzg4OCkpqaZ/GQAAtOzMmTMi4uvrq2xOnz5dp9Ndv35906ZNfn5+Q4YMscys8pibm5sbFRXVqVOnJk2aPP7441u3bi0zocw/ACJSXFw8b968Pn36eHh49OjRY+rUqbdu3VKeCg0NDQoKEpGEhASdTvfpp5/+h5UAKI8AD6AaRowYkZqaGhERERYWdujQoeeff37KlClDhgwpLS196qmnDAbDxo0bIyMjrb8kIiJizJgxKSkpPXv27Nix408//RQSErJo0SK1fgUAALTl8uXLU6dOFRHLze0UW7ZsiYyMvP/++4cNG6aMVHnMTU9P7969+7p1627cuNGnT5+zZ88+++yzH3zwgY2ffuPGjYceeiguLu7KlSuPPvpoSUnJ8uXLe/XqdeHCBRGJiopSahs4cOD777/fo0cPx1UCQETEDKAemz9/vogsWbKk/FMvv/yyiJw5c0bZnDlzpogEBgZeu3ZNGVm5cqWyG1m/fr0ycu3atXvuuadBgwYlJSXKiHKKPjw8/NatW8pIRkaGv7+/Xq8/fPiwY383AAA0JScnR0QaNmwYckdwcPCDDz6oXI7evHnz3NxcZWZsbKyIeHl5HTt2zPLl9hxzlXP1MTExliN1XFyccjRPSEhQRsr8AzBt2jQRef3110tLS5WRhIQEEYmIiFA2Dx06JCKxsbH/eSUAyuMMPIBqmDlz5r333qs8Dg4OFpEOHTq8+OKLysi9997bq1ev4uLiGzduKCPvvPNOw4YNN27caDQalRF/f/9ly5aZTKaPP/64xssHAKC2u3379jd3JCUlpaSkNGvWbMKECUePHvX09LSeOWHChC5dulg2qzzmpqenf/311507d05ISHByclLmzJkzp3v37pUVU1BQsHLlSj8/vyVLluj1/wsOMTEx/fv3t/G59I6oBICCj5EDUA1t2rSxPHZzcxOR9u3bW09QBhWlpaWnT5/28fEpk9WVi+RTU1MdWysAABrUqlWrc+fO2TOzZ8+elsf2HHPT0tJEZNiwYZYorhg+fPjRo0cr/BHp6enFxcWDBg1ydna2DOr1+r1791ZWlYMqAaAgwAOoBp1OV2bE8sJ5eZcvXy4uLj5//vzkyZPLP8stcwEAuBs+Pj6Wx/Ycc//880+xuhOeRatWrSr7EVlZWSLSvHlz+6tyUCUAFLyFHoCjNGvWzMnJafDgwRVewMMZeAAA7ob16Wt7jrl+fn4icunSpTLfx3LhW3lKdL969WqZcZPJVFpaWuGXOKgSAAoCPABHadCgQZs2bVJSUgoKCqzH9+/fP2XKFD5MDgCA/4o9x9wOHTqISGJioslksp5j44jcvn175Q3zZeJ6QECAt7e32WyusUoAKAjwABwoJiYmLy9v9OjRlqP4pUuXRo4cuWrVqhYtWqhbGwAAdUmVx1x/f/+hQ4eeOHFi5syZluS8bt26Xbt2VfY9GzduPH78+PT09Llz51ri+urVq8+dOxcSEmJ9YV1RUZFDKwGg4Bp4AA40ceLEnTt3JiYm+vn59e7dOy8v78iRIyaTadGiRZ07d1a7OgAA6g57jrnx8fEpKSnx8fGff/55t27dMjIy0tLSRo8e/dlnn1X2bd955539+/cvWrRo69atXbt2PX/+/JEjR7y8vOLj45UJjRo1EpGtW7fqdLoxY8b06tXLQZUAEM7AA3AoJyenPXv2rFy5snPnzocOHcrKynr00Uf37Nkza9YstUsDAKBOseeY27Zt29TU1JdeesloNP7444/NmjVbu3bt0qVLbXxbb2/v3377bfr06R4eHnv27Ll58+bEiROPHz9uuYVex44dY2NjXVxcNmzYoNydzkGVABARXYXXrgAAAAAAgFqFM/AAAAAAAGgAAR4AAAAAAA0gwAMAAAAAoAEEeAAAAAAANIAADwAAAACABhDgAQAAAADQAAI8AAAAAAAaQIAHAAAAAEADCPAAAAAAAGgAAR4AAAAAAA0gwAMAAAAAoAEEeAAAAAAANIAADwAAAACABhDgAQAAAADQAAI8AAAAAAAaQIAHAAAAAEADCPAAAAAAAGgAAR4AAAAAAA0gwAMAAAAAoAEEeAAAAAAANIAADwAAAACABhDgAQAAAADQAAI8AAAAAAAaQIAHAAAAAEADCPAAAAAAAGgAAR4AAAAAAA0gwAMAAAAAoAEEeAAAAAAANIAADwAAAACABhDgAQAAAADQAAI8AAAAAAAaQIAHAAAAAEAD/g8aMViS3EZBIQAAAABJRU5ErkJggg==" alt="SFORB pathway fit with two-component error" width="672" />
+<p class="caption">
 SFORB pathway fit with two-component error
 </p>
 </div>
-<div class="sourceCode" id="cb18"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">saem_sforb_path_1_tc_reduced</span><span class="op">)</span></span></code></pre></div>
+<pre class="r"><code>plot(saem_sforb_path_1_tc_reduced)</code></pre>
 <div class="figure" style="text-align: center">
-<img src="2022_dmta_pathway_files/figure-html/unnamed-chunk-4-1.png" alt="SFORB pathway fit with two-component error, reduced parameter model" width="700"><p class="caption">
+<img src="data:image/png;base64,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" alt="SFORB pathway fit with two-component error, reduced parameter model" width="672" />
+<p class="caption">
 SFORB pathway fit with two-component error, reduced parameter model
 </p>
 </div>
 <p>Plots of the remaining fits and listings for all successful fits are
 shown in the Appendix.</p>
-<div class="sourceCode" id="cb19"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/parallel/makeCluster.html" class="external-link">stopCluster</a></span><span class="op">(</span><span class="va">cl</span><span class="op">)</span></span></code></pre></div>
+<pre class="r"><code>stopCluster(cl)</code></pre>
 </div>
-<div class="section level2">
-<h2 id="conclusions">Conclusions<a class="anchor" aria-label="anchor" href="#conclusions"></a>
-</h2>
+<div id="conclusions" class="section level1">
+<h1>Conclusions</h1>
 <p>Pathway fits with SFO, FOMC, DFOP, SFORB and HS models for the parent
 compound could be successfully performed.</p>
 </div>
-<div class="section level2">
-<h2 id="acknowledgements">Acknowledgements<a class="anchor" aria-label="anchor" href="#acknowledgements"></a>
-</h2>
+<div id="acknowledgements" class="section level1">
+<h1>Acknowledgements</h1>
 <p>The helpful comments by Janina Wöltjen of the German Environment
 Agency on earlier versions of this document are gratefully
 acknowledged.</p>
 </div>
-<div class="section level2">
-<h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a>
-</h2>
+<div id="references" class="section level1">
+<h1>References</h1>
 <div id="refs" class="references csl-bib-body hanging-indent">
 <div id="ref-duchesne_2021" class="csl-entry">
 Duchesne, Ronan, Anissa Guillemin, Olivier Gandrillon, and Fabien
 Crauste. 2021. <span>“Practical Identifiability in the Frame of
 Nonlinear Mixed Effects Models: The Example of the in Vitro
-Erythropoiesis.”</span> <em>BMC Bioinformatics</em> 22 (478). <a href="https://doi.org/10.1186/s12859-021-04373-4" class="external-link">https://doi.org/10.1186/s12859-021-04373-4</a>.
+Erythropoiesis.”</span> <em>BMC Bioinformatics</em> 22 (478). <a href="https://doi.org/10.1186/s12859-021-04373-4">https://doi.org/10.1186/s12859-021-04373-4</a>.
 </div>
 <div id="ref-ranke2021" class="csl-entry">
 Ranke, Johannes, Janina Wöltjen, Jana Schmidt, and Emmanuelle Comets.
 2021. <span>“Taking Kinetic Evaluations of Degradation Data to the Next
 Level with Nonlinear Mixed-Effects Models.”</span> <em>Environments</em>
-8 (8). <a href="https://doi.org/10.3390/environments8080071" class="external-link">https://doi.org/10.3390/environments8080071</a>.
+8 (8). <a href="https://doi.org/10.3390/environments8080071">https://doi.org/10.3390/environments8080071</a>.
 </div>
 </div>
 </div>
-<div class="section level2">
-<h2 id="appendix">Appendix<a class="anchor" aria-label="anchor" href="#appendix"></a>
-</h2>
-<div class="section level3">
-<h3 id="plots-of-hierarchical-fits-not-selected-for-refinement">Plots of hierarchical fits not selected for refinement<a class="anchor" aria-label="anchor" href="#plots-of-hierarchical-fits-not-selected-for-refinement"></a>
-</h3>
-<div class="sourceCode" id="cb20"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">saem_1</span><span class="op">[[</span><span class="st">"sfo_path_1"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div>
+<div id="appendix" class="section level1">
+<h1>Appendix</h1>
+<div id="plots-of-hierarchical-fits-not-selected-for-refinement" class="section level2">
+<h2>Plots of hierarchical fits not selected for refinement</h2>
+<pre class="r"><code>plot(saem_1[[&quot;sfo_path_1&quot;, &quot;tc&quot;]])</code></pre>
 <div class="figure" style="text-align: center">
-<img src="2022_dmta_pathway_files/figure-html/unnamed-chunk-6-1.png" alt="SFO pathway fit with two-component error" width="700"><p class="caption">
+<img src="data:image/png;base64,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" alt="SFO pathway fit with two-component error" width="672" />
+<p class="caption">
 SFO pathway fit with two-component error
 </p>
 </div>
-<div class="sourceCode" id="cb21"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">saem_1</span><span class="op">[[</span><span class="st">"fomc_path_1"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div>
+<pre class="r"><code>plot(saem_1[[&quot;fomc_path_1&quot;, &quot;tc&quot;]])</code></pre>
 <div class="figure" style="text-align: center">
-<img src="2022_dmta_pathway_files/figure-html/unnamed-chunk-7-1.png" alt="FOMC pathway fit with two-component error" width="700"><p class="caption">
+<img src="data:image/png;base64,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" alt="FOMC pathway fit with two-component error" width="672" />
+<p class="caption">
 FOMC pathway fit with two-component error
 </p>
 </div>
-<div class="sourceCode" id="cb22"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">saem_1</span><span class="op">[[</span><span class="st">"sforb_path_1"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div>
+<pre class="r"><code>plot(saem_1[[&quot;sforb_path_1&quot;, &quot;tc&quot;]])</code></pre>
 <div class="figure" style="text-align: center">
-<img src="2022_dmta_pathway_files/figure-html/unnamed-chunk-8-1.png" alt="HS pathway fit with two-component error" width="700"><p class="caption">
+<img src="data:image/png;base64,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" alt="HS pathway fit with two-component error" width="672" />
+<p class="caption">
 HS pathway fit with two-component error
 </p>
 </div>
 </div>
-<div class="section level3">
-<h3 id="hierarchical-model-fit-listings">Hierarchical model fit listings<a class="anchor" aria-label="anchor" href="#hierarchical-model-fit-listings"></a>
-</h3>
-<div class="section level4">
-<h4 id="fits-with-random-effects-for-all-degradation-parameters">Fits with random effects for all degradation parameters<a class="anchor" aria-label="anchor" href="#fits-with-random-effects-for-all-degradation-parameters"></a>
-</h4>
+<div id="hierarchical-model-fit-listings" class="section level2">
+<h2>Hierarchical model fit listings</h2>
+<div id="fits-with-random-effects-for-all-degradation-parameters" class="section level3">
+<h3>Fits with random effects for all degradation parameters</h3>
 
 </div>
-<div class="section level4">
-<h4 id="improved-fit-of-the-sforb-pathway-model-with-two-component-error">Improved fit of the SFORB pathway model with two-component
-error<a class="anchor" aria-label="anchor" href="#improved-fit-of-the-sforb-pathway-model-with-two-component-error"></a>
-</h4>
+<div id="improved-fit-of-the-sforb-pathway-model-with-two-component-error" class="section level3">
+<h3>Improved fit of the SFORB pathway model with two-component
+error</h3>
 
 </div>
 </div>
-<div class="section level3">
-<h3 id="session-info">Session info<a class="anchor" aria-label="anchor" href="#session-info"></a>
-</h3>
-<pre><code>R version 4.3.1 (2023-06-16)
-Platform: x86_64-pc-linux-gnu (64-bit)
-Running under: Ubuntu 22.04.3 LTS
+<div id="session-info" class="section level2">
+<h2>Session info</h2>
+<pre><code>R version 4.4.2 (2024-10-31)
+Platform: x86_64-pc-linux-gnu
+Running under: Debian GNU/Linux 12 (bookworm)
 
 Matrix products: default
-BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0 
-LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
+BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.11.0 
+LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.11.0
 
 locale:
- [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
- [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
- [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
- [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
+ [1] LC_CTYPE=de_DE.UTF-8       LC_NUMERIC=C              
+ [3] LC_TIME=de_DE.UTF-8        LC_COLLATE=de_DE.UTF-8    
+ [5] LC_MONETARY=de_DE.UTF-8    LC_MESSAGES=de_DE.UTF-8   
+ [7] LC_PAPER=de_DE.UTF-8       LC_NAME=C                 
  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
-[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
+[11] LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=C       
 
-time zone: Europe/Zurich
+time zone: Europe/Berlin
 tzcode source: system (glibc)
 
 attached base packages:
@@ -2003,62 +2240,75 @@ attached base packages:
 [8] base     
 
 other attached packages:
-[1] saemix_3.2 npde_3.3   knitr_1.44 mkin_1.2.6
+[1] saemix_3.3      npde_3.5        knitr_1.49      mkin_1.2.9     
+[5] rmarkdown_2.29  nvimcom_0.9-167
 
 loaded via a namespace (and not attached):
- [1] sass_0.4.7        utf8_1.2.3        generics_0.1.3    stringi_1.7.12   
- [5] lattice_0.21-9    digest_0.6.33     magrittr_2.0.3    evaluate_0.22    
- [9] grid_4.3.1        fastmap_1.1.1     rprojroot_2.0.3   jsonlite_1.8.7   
-[13] processx_3.8.2    pkgbuild_1.4.2    deSolve_1.35      mclust_6.0.0     
-[17] ps_1.7.5          gridExtra_2.3     purrr_1.0.1       fansi_1.0.4      
-[21] scales_1.2.1      codetools_0.2-19  textshaping_0.3.6 jquerylib_0.1.4  
-[25] cli_3.6.1         crayon_1.5.2      rlang_1.1.1       munsell_0.5.0    
-[29] cachem_1.0.8      yaml_2.3.7        inline_0.3.19     tools_4.3.1      
-[33] memoise_2.0.1     dplyr_1.1.2       colorspace_2.1-0  ggplot2_3.4.2    
-[37] vctrs_0.6.3       R6_2.5.1          zoo_1.8-12        lifecycle_1.0.3  
-[41] stringr_1.5.0     fs_1.6.3          MASS_7.3-60       ragg_1.2.5       
-[45] callr_3.7.3       pkgconfig_2.0.3   desc_1.4.2        pkgdown_2.0.7    
-[49] bslib_0.5.1       pillar_1.9.0      gtable_0.3.3      glue_1.6.2       
-[53] systemfonts_1.0.4 xfun_0.40         tibble_3.2.1      lmtest_0.9-40    
-[57] tidyselect_1.2.0  rstudioapi_0.15.0 htmltools_0.5.6.1 nlme_3.1-163     
-[61] rmarkdown_2.23    compiler_4.3.1    prettyunits_1.2.0</code></pre>
+ [1] sass_0.4.9        utf8_1.2.4        generics_0.1.3    lattice_0.22-6   
+ [5] digest_0.6.37     magrittr_2.0.3    evaluate_1.0.1    grid_4.4.2       
+ [9] fastmap_1.2.0     jsonlite_1.8.9    processx_3.8.4    pkgbuild_1.4.5   
+[13] deSolve_1.40      mclust_6.1.1      ps_1.8.1          gridExtra_2.3    
+[17] fansi_1.0.6       scales_1.3.0      codetools_0.2-20  jquerylib_0.1.4  
+[21] cli_3.6.3         rlang_1.1.4       munsell_0.5.1     cachem_1.1.0     
+[25] yaml_2.3.10       tools_4.4.2       inline_0.3.20     colorout_1.3-2   
+[29] dplyr_1.1.4       colorspace_2.1-1  ggplot2_3.5.1     vctrs_0.6.5      
+[33] R6_2.5.1          zoo_1.8-12        lifecycle_1.0.4   MASS_7.3-61      
+[37] pkgconfig_2.0.3   callr_3.7.6       pillar_1.9.0      bslib_0.8.0      
+[41] gtable_0.3.6      glue_1.8.0        xfun_0.49         tibble_3.2.1     
+[45] lmtest_0.9-40     tidyselect_1.2.1  htmltools_0.5.8.1 nlme_3.1-166     
+[49] compiler_4.4.2   </code></pre>
 </div>
-<div class="section level3">
-<h3 id="hardware-info">Hardware info<a class="anchor" aria-label="anchor" href="#hardware-info"></a>
-</h3>
-<pre><code>CPU model: Intel(R) Xeon(R) Gold 6134 CPU @ 3.20GHz</code></pre>
-<pre><code>MemTotal:       247605564 kB</code></pre>
+<div id="hardware-info" class="section level2">
+<h2>Hardware info</h2>
+<pre><code>CPU model: AMD Ryzen 9 7950X 16-Core Processor</code></pre>
+<pre><code>MemTotal:       64927788 kB</code></pre>
 </div>
 </div>
-  </div>
 
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
 
-        <nav id="toc" data-toggle="toc"><h2 data-toc-skip>Contents</h2>
-    </nav>
-</div>
+
 
 </div>
 
+<script>
 
+// add bootstrap table styles to pandoc tables
+function bootstrapStylePandocTables() {
+  $('tr.odd').parent('tbody').parent('table').addClass('table table-condensed');
+}
+$(document).ready(function () {
+  bootstrapStylePandocTables();
+});
 
-      <footer><div class="copyright">
-  <p></p>
-<p>Developed by Johannes Ranke.</p>
-</div>
 
-<div class="pkgdown">
-  <p></p>
-<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
+</script>
 
-      </footer>
-</div>
+<!-- tabsets -->
+
+<script>
+$(document).ready(function () {
+  window.buildTabsets("TOC");
+});
+
+$(document).ready(function () {
+  $('.tabset-dropdown > .nav-tabs > li').click(function () {
+    $(this).parent().toggleClass('nav-tabs-open');
+  });
+});
+</script>
 
-  
+<!-- code folding -->
 
 
-  
+<!-- dynamically load mathjax for compatibility with self-contained -->
+<script>
+  (function () {
+    var script = document.createElement("script");
+    script.type = "text/javascript";
+    script.src  = "https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML";
+    document.getElementsByTagName("head")[0].appendChild(script);
+  })();
+</script>
 
-  </body>
+</body>
 </html>
diff --git a/docs/articles/prebuilt/2022_dmta_pathway_files/figure-html/saem-sforb-path-1-tc-reduced-convergence-1.png b/docs/articles/prebuilt/2022_dmta_pathway_files/figure-html/saem-sforb-path-1-tc-reduced-convergence-1.png
index f1019504..0facfbe5 100644
--- a/docs/articles/prebuilt/2022_dmta_pathway_files/figure-html/saem-sforb-path-1-tc-reduced-convergence-1.png
+++ b/docs/articles/prebuilt/2022_dmta_pathway_files/figure-html/saem-sforb-path-1-tc-reduced-convergence-1.png
diff --git a/docs/articles/prebuilt/2022_dmta_pathway_files/figure-html/unnamed-chunk-2-1.png b/docs/articles/prebuilt/2022_dmta_pathway_files/figure-html/unnamed-chunk-2-1.png
index 84cd7c7f..4e363872 100644
--- a/docs/articles/prebuilt/2022_dmta_pathway_files/figure-html/unnamed-chunk-2-1.png
+++ b/docs/articles/prebuilt/2022_dmta_pathway_files/figure-html/unnamed-chunk-2-1.png
diff --git a/docs/articles/prebuilt/2022_dmta_pathway_files/figure-html/unnamed-chunk-3-1.png b/docs/articles/prebuilt/2022_dmta_pathway_files/figure-html/unnamed-chunk-3-1.png
index de185aee..562d8d9d 100644
--- a/docs/articles/prebuilt/2022_dmta_pathway_files/figure-html/unnamed-chunk-3-1.png
+++ b/docs/articles/prebuilt/2022_dmta_pathway_files/figure-html/unnamed-chunk-3-1.png
diff --git a/docs/articles/prebuilt/2022_dmta_pathway_files/figure-html/unnamed-chunk-4-1.png b/docs/articles/prebuilt/2022_dmta_pathway_files/figure-html/unnamed-chunk-4-1.png
index e0d8cc09..7bf4aa4a 100644
--- a/docs/articles/prebuilt/2022_dmta_pathway_files/figure-html/unnamed-chunk-4-1.png
+++ b/docs/articles/prebuilt/2022_dmta_pathway_files/figure-html/unnamed-chunk-4-1.png
diff --git a/docs/articles/prebuilt/2022_dmta_pathway_files/figure-html/unnamed-chunk-6-1.png b/docs/articles/prebuilt/2022_dmta_pathway_files/figure-html/unnamed-chunk-6-1.png
index a07b7aad..602c1138 100644
--- a/docs/articles/prebuilt/2022_dmta_pathway_files/figure-html/unnamed-chunk-6-1.png
+++ b/docs/articles/prebuilt/2022_dmta_pathway_files/figure-html/unnamed-chunk-6-1.png
diff --git a/docs/articles/prebuilt/2022_dmta_pathway_files/figure-html/unnamed-chunk-7-1.png b/docs/articles/prebuilt/2022_dmta_pathway_files/figure-html/unnamed-chunk-7-1.png
index e50e0524..8db8e11c 100644
--- a/docs/articles/prebuilt/2022_dmta_pathway_files/figure-html/unnamed-chunk-7-1.png
+++ b/docs/articles/prebuilt/2022_dmta_pathway_files/figure-html/unnamed-chunk-7-1.png
diff --git a/docs/articles/prebuilt/2022_dmta_pathway_files/figure-html/unnamed-chunk-8-1.png b/docs/articles/prebuilt/2022_dmta_pathway_files/figure-html/unnamed-chunk-8-1.png
index de185aee..562d8d9d 100644
--- a/docs/articles/prebuilt/2022_dmta_pathway_files/figure-html/unnamed-chunk-8-1.png
+++ b/docs/articles/prebuilt/2022_dmta_pathway_files/figure-html/unnamed-chunk-8-1.png
diff --git a/docs/articles/prebuilt/2022_dmta_pathway_files/figure-latex/saem-sforb-path-1-tc-reduced-convergence-1.pdf b/docs/articles/prebuilt/2022_dmta_pathway_files/figure-latex/saem-sforb-path-1-tc-reduced-convergence-1.pdf
new file mode 100644
index 00000000..ec73c393
--- /dev/null
+++ b/docs/articles/prebuilt/2022_dmta_pathway_files/figure-latex/saem-sforb-path-1-tc-reduced-convergence-1.pdf
diff --git a/docs/articles/prebuilt/2022_dmta_pathway_files/figure-latex/unnamed-chunk-2-1.pdf b/docs/articles/prebuilt/2022_dmta_pathway_files/figure-latex/unnamed-chunk-2-1.pdf
new file mode 100644
index 00000000..7bdec976
--- /dev/null
+++ b/docs/articles/prebuilt/2022_dmta_pathway_files/figure-latex/unnamed-chunk-2-1.pdf
diff --git a/docs/articles/prebuilt/2022_dmta_pathway_files/figure-latex/unnamed-chunk-3-1.pdf b/docs/articles/prebuilt/2022_dmta_pathway_files/figure-latex/unnamed-chunk-3-1.pdf
new file mode 100644
index 00000000..1f8968f3
--- /dev/null
+++ b/docs/articles/prebuilt/2022_dmta_pathway_files/figure-latex/unnamed-chunk-3-1.pdf
diff --git a/docs/articles/prebuilt/2022_dmta_pathway_files/figure-latex/unnamed-chunk-4-1.pdf b/docs/articles/prebuilt/2022_dmta_pathway_files/figure-latex/unnamed-chunk-4-1.pdf
new file mode 100644
index 00000000..1a65f828
--- /dev/null
+++ b/docs/articles/prebuilt/2022_dmta_pathway_files/figure-latex/unnamed-chunk-4-1.pdf
diff --git a/docs/articles/prebuilt/2022_dmta_pathway_files/figure-latex/unnamed-chunk-5-1.pdf b/docs/articles/prebuilt/2022_dmta_pathway_files/figure-latex/unnamed-chunk-5-1.pdf
new file mode 100644
index 00000000..b8b103a8
--- /dev/null
+++ b/docs/articles/prebuilt/2022_dmta_pathway_files/figure-latex/unnamed-chunk-5-1.pdf
diff --git a/docs/articles/prebuilt/2022_dmta_pathway_files/figure-latex/unnamed-chunk-6-1.pdf b/docs/articles/prebuilt/2022_dmta_pathway_files/figure-latex/unnamed-chunk-6-1.pdf
new file mode 100644
index 00000000..e4ef5ccb
--- /dev/null
+++ b/docs/articles/prebuilt/2022_dmta_pathway_files/figure-latex/unnamed-chunk-6-1.pdf
diff --git a/docs/articles/prebuilt/2022_dmta_pathway_files/figure-latex/unnamed-chunk-7-1.pdf b/docs/articles/prebuilt/2022_dmta_pathway_files/figure-latex/unnamed-chunk-7-1.pdf
new file mode 100644
index 00000000..9b782707
--- /dev/null
+++ b/docs/articles/prebuilt/2022_dmta_pathway_files/figure-latex/unnamed-chunk-7-1.pdf
diff --git a/docs/articles/prebuilt/2022_dmta_pathway_files/figure-latex/unnamed-chunk-8-1.pdf b/docs/articles/prebuilt/2022_dmta_pathway_files/figure-latex/unnamed-chunk-8-1.pdf
new file mode 100644
index 00000000..96f2134a
--- /dev/null
+++ b/docs/articles/prebuilt/2022_dmta_pathway_files/figure-latex/unnamed-chunk-8-1.pdf
diff --git a/docs/articles/prebuilt/2023_mesotrione_parent.html b/docs/articles/prebuilt/2023_mesotrione_parent.html
index 8f166a73..8bb993dc 100644
--- a/docs/articles/prebuilt/2023_mesotrione_parent.html
+++ b/docs/articles/prebuilt/2023_mesotrione_parent.html
@@ -1,155 +1,374 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en">
+
+<html>
+
 <head>
-<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
-<meta charset="utf-8">
-<meta http-equiv="X-UA-Compatible" content="IE=edge">
-<meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione • mkin</title>
-<!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous">
-<script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../../bootstrap-toc.css">
-<script src="../../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous">
-<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous">
-<!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../../pkgdown.css" rel="stylesheet">
-<script src="../../pkgdown.js"></script><meta property="og:title" content="Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione">
-<meta property="og:description" content="mkin">
-<!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]-->
+
+<meta charset="utf-8" />
+<meta name="generator" content="pandoc" />
+<meta http-equiv="X-UA-Compatible" content="IE=EDGE" />
+
+
+<meta name="author" content="Johannes Ranke" />
+
+
+<title>Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</title>
+
+<script>// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
+// be compatible with the behavior of Pandoc < 2.8).
+document.addEventListener('DOMContentLoaded', function(e) {
+  var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
+  var i, h, a;
+  for (i = 0; i < hs.length; i++) {
+    h = hs[i];
+    if (!/^h[1-6]$/i.test(h.tagName)) continue;  // it should be a header h1-h6
+    a = h.attributes;
+    while (a.length > 0) h.removeAttribute(a[0].name);
+  }
+});
+</script>
+<script>/*! jQuery v3.6.0 | (c) OpenJS Foundation and other contributors | jquery.org/license */
+!function(e,t){"use strict";"object"==typeof module&&"object"==typeof module.exports?module.exports=e.document?t(e,!0):function(e){if(!e.document)throw new Error("jQuery requires a window with a document");return t(e)}:t(e)}("undefined"!=typeof window?window:this,function(C,e){"use strict";var t=[],r=Object.getPrototypeOf,s=t.slice,g=t.flat?function(e){return t.flat.call(e)}:function(e){return t.concat.apply([],e)},u=t.push,i=t.indexOf,n={},o=n.toString,v=n.hasOwnProperty,a=v.toString,l=a.call(Object),y={},m=function(e){return"function"==typeof e&&"number"!=typeof e.nodeType&&"function"!=typeof e.item},x=function(e){return null!=e&&e===e.window},E=C.document,c={type:!0,src:!0,nonce:!0,noModule:!0};function b(e,t,n){var r,i,o=(n=n||E).createElement("script");if(o.text=e,t)for(r in c)(i=t[r]||t.getAttribute&&t.getAttribute(r))&&o.setAttribute(r,i);n.head.appendChild(o).parentNode.removeChild(o)}function w(e){return null==e?e+"":"object"==typeof e||"function"==typeof e?n[o.call(e)]||"object":typeof e}var f="3.6.0",S=function(e,t){return new S.fn.init(e,t)};function p(e){var t=!!e&&"length"in e&&e.length,n=w(e);return!m(e)&&!x(e)&&("array"===n||0===t||"number"==typeof t&&0<t&&t-1 in e)}S.fn=S.prototype={jquery:f,constructor:S,length:0,toArray:function(){return s.call(this)},get:function(e){return null==e?s.call(this):e<0?this[e+this.length]:this[e]},pushStack:function(e){var t=S.merge(this.constructor(),e);return t.prevObject=this,t},each:function(e){return S.each(this,e)},map:function(n){return this.pushStack(S.map(this,function(e,t){return n.call(e,t,e)}))},slice:function(){return this.pushStack(s.apply(this,arguments))},first:function(){return this.eq(0)},last:function(){return this.eq(-1)},even:function(){return this.pushStack(S.grep(this,function(e,t){return(t+1)%2}))},odd:function(){return this.pushStack(S.grep(this,function(e,t){return t%2}))},eq:function(e){var t=this.length,n=+e+(e<0?t:0);return this.pushStack(0<=n&&n<t?[this[n]]:[])},end:function(){return this.prevObject||this.constructor()},push:u,sort:t.sort,splice:t.splice},S.extend=S.fn.extend=function(){var e,t,n,r,i,o,a=arguments[0]||{},s=1,u=arguments.length,l=!1;for("boolean"==typeof a&&(l=a,a=arguments[s]||{},s++),"object"==typeof a||m(a)||(a={}),s===u&&(a=this,s--);s<u;s++)if(null!=(e=arguments[s]))for(t in e)r=e[t],"__proto__"!==t&&a!==r&&(l&&r&&(S.isPlainObject(r)||(i=Array.isArray(r)))?(n=a[t],o=i&&!Array.isArray(n)?[]:i||S.isPlainObject(n)?n:{},i=!1,a[t]=S.extend(l,o,r)):void 0!==r&&(a[t]=r));return a},S.extend({expando:"jQuery"+(f+Math.random()).replace(/\D/g,""),isReady:!0,error:function(e){throw new Error(e)},noop:function(){},isPlainObject:function(e){var t,n;return!(!e||"[object Object]"!==o.call(e))&&(!(t=r(e))||"function"==typeof(n=v.call(t,"constructor")&&t.constructor)&&a.call(n)===l)},isEmptyObject:function(e){var t;for(t in e)return!1;return!0},globalEval:function(e,t,n){b(e,{nonce:t&&t.nonce},n)},each:function(e,t){var n,r=0;if(p(e)){for(n=e.length;r<n;r++)if(!1===t.call(e[r],r,e[r]))break}else for(r in e)if(!1===t.call(e[r],r,e[r]))break;return e},makeArray:function(e,t){var n=t||[];return null!=e&&(p(Object(e))?S.merge(n,"string"==typeof e?[e]:e):u.call(n,e)),n},inArray:function(e,t,n){return null==t?-1:i.call(t,e,n)},merge:function(e,t){for(var n=+t.length,r=0,i=e.length;r<n;r++)e[i++]=t[r];return e.length=i,e},grep:function(e,t,n){for(var r=[],i=0,o=e.length,a=!n;i<o;i++)!t(e[i],i)!==a&&r.push(e[i]);return r},map:function(e,t,n){var r,i,o=0,a=[];if(p(e))for(r=e.length;o<r;o++)null!=(i=t(e[o],o,n))&&a.push(i);else for(o in e)null!=(i=t(e[o],o,n))&&a.push(i);return g(a)},guid:1,support:y}),"function"==typeof Symbol&&(S.fn[Symbol.iterator]=t[Symbol.iterator]),S.each("Boolean Number String Function Array Date RegExp Object Error Symbol".split(" "),function(e,t){n["[object "+t+"]"]=t.toLowerCase()});var d=function(n){var e,d,b,o,i,h,f,g,w,u,l,T,C,a,E,v,s,c,y,S="sizzle"+1*new Date,p=n.document,k=0,r=0,m=ue(),x=ue(),A=ue(),N=ue(),j=function(e,t){return e===t&&(l=!0),0},D={}.hasOwnProperty,t=[],q=t.pop,L=t.push,H=t.push,O=t.slice,P=function(e,t){for(var n=0,r=e.length;n<r;n++)if(e[n]===t)return n;return-1},R="checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|ismap|loop|multiple|open|readonly|required|scoped",M="[\\x20\\t\\r\\n\\f]",I="(?:\\\\[\\da-fA-F]{1,6}"+M+"?|\\\\[^\\r\\n\\f]|[\\w-]|[^\0-\\x7f])+",W="\\["+M+"*("+I+")(?:"+M+"*([*^$|!~]?=)"+M+"*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|("+I+"))|)"+M+"*\\]",F=":("+I+")(?:\\((('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|((?:\\\\.|[^\\\\()[\\]]|"+W+")*)|.*)\\)|)",B=new RegExp(M+"+","g"),$=new RegExp("^"+M+"+|((?:^|[^\\\\])(?:\\\\.)*)"+M+"+$","g"),_=new RegExp("^"+M+"*,"+M+"*"),z=new RegExp("^"+M+"*([>+~]|"+M+")"+M+"*"),U=new RegExp(M+"|>"),X=new RegExp(F),V=new RegExp("^"+I+"$"),G={ID:new RegExp("^#("+I+")"),CLASS:new RegExp("^\\.("+I+")"),TAG:new RegExp("^("+I+"|[*])"),ATTR:new RegExp("^"+W),PSEUDO:new RegExp("^"+F),CHILD:new RegExp("^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\("+M+"*(even|odd|(([+-]|)(\\d*)n|)"+M+"*(?:([+-]|)"+M+"*(\\d+)|))"+M+"*\\)|)","i"),bool:new RegExp("^(?:"+R+")$","i"),needsContext:new RegExp("^"+M+"*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\("+M+"*((?:-\\d)?\\d*)"+M+"*\\)|)(?=[^-]|$)","i")},Y=/HTML$/i,Q=/^(?:input|select|textarea|button)$/i,J=/^h\d$/i,K=/^[^{]+\{\s*\[native \w/,Z=/^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/,ee=/[+~]/,te=new RegExp("\\\\[\\da-fA-F]{1,6}"+M+"?|\\\\([^\\r\\n\\f])","g"),ne=function(e,t){var n="0x"+e.slice(1)-65536;return t||(n<0?String.fromCharCode(n+65536):String.fromCharCode(n>>10|55296,1023&n|56320))},re=/([\0-\x1f\x7f]|^-?\d)|^-$|[^\0-\x1f\x7f-\uFFFF\w-]/g,ie=function(e,t){return t?"\0"===e?"\ufffd":e.slice(0,-1)+"\\"+e.charCodeAt(e.length-1).toString(16)+" ":"\\"+e},oe=function(){T()},ae=be(function(e){return!0===e.disabled&&"fieldset"===e.nodeName.toLowerCase()},{dir:"parentNode",next:"legend"});try{H.apply(t=O.call(p.childNodes),p.childNodes),t[p.childNodes.length].nodeType}catch(e){H={apply:t.length?function(e,t){L.apply(e,O.call(t))}:function(e,t){var n=e.length,r=0;while(e[n++]=t[r++]);e.length=n-1}}}function se(t,e,n,r){var i,o,a,s,u,l,c,f=e&&e.ownerDocument,p=e?e.nodeType:9;if(n=n||[],"string"!=typeof t||!t||1!==p&&9!==p&&11!==p)return n;if(!r&&(T(e),e=e||C,E)){if(11!==p&&(u=Z.exec(t)))if(i=u[1]){if(9===p){if(!(a=e.getElementById(i)))return n;if(a.id===i)return n.push(a),n}else if(f&&(a=f.getElementById(i))&&y(e,a)&&a.id===i)return n.push(a),n}else{if(u[2])return H.apply(n,e.getElementsByTagName(t)),n;if((i=u[3])&&d.getElementsByClassName&&e.getElementsByClassName)return H.apply(n,e.getElementsByClassName(i)),n}if(d.qsa&&!N[t+" "]&&(!v||!v.test(t))&&(1!==p||"object"!==e.nodeName.toLowerCase())){if(c=t,f=e,1===p&&(U.test(t)||z.test(t))){(f=ee.test(t)&&ye(e.parentNode)||e)===e&&d.scope||((s=e.getAttribute("id"))?s=s.replace(re,ie):e.setAttribute("id",s=S)),o=(l=h(t)).length;while(o--)l[o]=(s?"#"+s:":scope")+" "+xe(l[o]);c=l.join(",")}try{return H.apply(n,f.querySelectorAll(c)),n}catch(e){N(t,!0)}finally{s===S&&e.removeAttribute("id")}}}return g(t.replace($,"$1"),e,n,r)}function ue(){var r=[];return function e(t,n){return r.push(t+" ")>b.cacheLength&&delete e[r.shift()],e[t+" "]=n}}function le(e){return e[S]=!0,e}function ce(e){var t=C.createElement("fieldset");try{return!!e(t)}catch(e){return!1}finally{t.parentNode&&t.parentNode.removeChild(t),t=null}}function fe(e,t){var n=e.split("|"),r=n.length;while(r--)b.attrHandle[n[r]]=t}function pe(e,t){var n=t&&e,r=n&&1===e.nodeType&&1===t.nodeType&&e.sourceIndex-t.sourceIndex;if(r)return r;if(n)while(n=n.nextSibling)if(n===t)return-1;return e?1:-1}function de(t){return function(e){return"input"===e.nodeName.toLowerCase()&&e.type===t}}function he(n){return function(e){var t=e.nodeName.toLowerCase();return("input"===t||"button"===t)&&e.type===n}}function ge(t){return function(e){return"form"in e?e.parentNode&&!1===e.disabled?"label"in e?"label"in e.parentNode?e.parentNode.disabled===t:e.disabled===t:e.isDisabled===t||e.isDisabled!==!t&&ae(e)===t:e.disabled===t:"label"in e&&e.disabled===t}}function ve(a){return le(function(o){return o=+o,le(function(e,t){var n,r=a([],e.length,o),i=r.length;while(i--)e[n=r[i]]&&(e[n]=!(t[n]=e[n]))})})}function ye(e){return e&&"undefined"!=typeof e.getElementsByTagName&&e}for(e in d=se.support={},i=se.isXML=function(e){var t=e&&e.namespaceURI,n=e&&(e.ownerDocument||e).documentElement;return!Y.test(t||n&&n.nodeName||"HTML")},T=se.setDocument=function(e){var t,n,r=e?e.ownerDocument||e:p;return r!=C&&9===r.nodeType&&r.documentElement&&(a=(C=r).documentElement,E=!i(C),p!=C&&(n=C.defaultView)&&n.top!==n&&(n.addEventListener?n.addEventListener("unload",oe,!1):n.attachEvent&&n.attachEvent("onunload",oe)),d.scope=ce(function(e){return a.appendChild(e).appendChild(C.createElement("div")),"undefined"!=typeof e.querySelectorAll&&!e.querySelectorAll(":scope fieldset div").length}),d.attributes=ce(function(e){return e.className="i",!e.getAttribute("className")}),d.getElementsByTagName=ce(function(e){return e.appendChild(C.createComment("")),!e.getElementsByTagName("*").length}),d.getElementsByClassName=K.test(C.getElementsByClassName),d.getById=ce(function(e){return a.appendChild(e).id=S,!C.getElementsByName||!C.getElementsByName(S).length}),d.getById?(b.filter.ID=function(e){var t=e.replace(te,ne);return function(e){return e.getAttribute("id")===t}},b.find.ID=function(e,t){if("undefined"!=typeof t.getElementById&&E){var n=t.getElementById(e);return n?[n]:[]}}):(b.filter.ID=function(e){var n=e.replace(te,ne);return function(e){var t="undefined"!=typeof e.getAttributeNode&&e.getAttributeNode("id");return t&&t.value===n}},b.find.ID=function(e,t){if("undefined"!=typeof t.getElementById&&E){var n,r,i,o=t.getElementById(e);if(o){if((n=o.getAttributeNode("id"))&&n.value===e)return[o];i=t.getElementsByName(e),r=0;while(o=i[r++])if((n=o.getAttributeNode("id"))&&n.value===e)return[o]}return[]}}),b.find.TAG=d.getElementsByTagName?function(e,t){return"undefined"!=typeof t.getElementsByTagName?t.getElementsByTagName(e):d.qsa?t.querySelectorAll(e):void 0}:function(e,t){var n,r=[],i=0,o=t.getElementsByTagName(e);if("*"===e){while(n=o[i++])1===n.nodeType&&r.push(n);return r}return o},b.find.CLASS=d.getElementsByClassName&&function(e,t){if("undefined"!=typeof t.getElementsByClassName&&E)return t.getElementsByClassName(e)},s=[],v=[],(d.qsa=K.test(C.querySelectorAll))&&(ce(function(e){var t;a.appendChild(e).innerHTML="<a id='"+S+"'></a><select id='"+S+"-\r\\' msallowcapture=''><option selected=''></option></select>",e.querySelectorAll("[msallowcapture^='']").length&&v.push("[*^$]="+M+"*(?:''|\"\")"),e.querySelectorAll("[selected]").length||v.push("\\["+M+"*(?:value|"+R+")"),e.querySelectorAll("[id~="+S+"-]").length||v.push("~="),(t=C.createElement("input")).setAttribute("name",""),e.appendChild(t),e.querySelectorAll("[name='']").length||v.push("\\["+M+"*name"+M+"*="+M+"*(?:''|\"\")"),e.querySelectorAll(":checked").length||v.push(":checked"),e.querySelectorAll("a#"+S+"+*").length||v.push(".#.+[+~]"),e.querySelectorAll("\\\f"),v.push("[\\r\\n\\f]")}),ce(function(e){e.innerHTML="<a href='' disabled='disabled'></a><select disabled='disabled'><option/></select>";var t=C.createElement("input");t.setAttribute("type","hidden"),e.appendChild(t).setAttribute("name","D"),e.querySelectorAll("[name=d]").length&&v.push("name"+M+"*[*^$|!~]?="),2!==e.querySelectorAll(":enabled").length&&v.push(":enabled",":disabled"),a.appendChild(e).disabled=!0,2!==e.querySelectorAll(":disabled").length&&v.push(":enabled",":disabled"),e.querySelectorAll("*,:x"),v.push(",.*:")})),(d.matchesSelector=K.test(c=a.matches||a.webkitMatchesSelector||a.mozMatchesSelector||a.oMatchesSelector||a.msMatchesSelector))&&ce(function(e){d.disconnectedMatch=c.call(e,"*"),c.call(e,"[s!='']:x"),s.push("!=",F)}),v=v.length&&new RegExp(v.join("|")),s=s.length&&new RegExp(s.join("|")),t=K.test(a.compareDocumentPosition),y=t||K.test(a.contains)?function(e,t){var n=9===e.nodeType?e.documentElement:e,r=t&&t.parentNode;return e===r||!(!r||1!==r.nodeType||!(n.contains?n.contains(r):e.compareDocumentPosition&&16&e.compareDocumentPosition(r)))}:function(e,t){if(t)while(t=t.parentNode)if(t===e)return!0;return!1},j=t?function(e,t){if(e===t)return l=!0,0;var n=!e.compareDocumentPosition-!t.compareDocumentPosition;return n||(1&(n=(e.ownerDocument||e)==(t.ownerDocument||t)?e.compareDocumentPosition(t):1)||!d.sortDetached&&t.compareDocumentPosition(e)===n?e==C||e.ownerDocument==p&&y(p,e)?-1:t==C||t.ownerDocument==p&&y(p,t)?1:u?P(u,e)-P(u,t):0:4&n?-1:1)}:function(e,t){if(e===t)return l=!0,0;var n,r=0,i=e.parentNode,o=t.parentNode,a=[e],s=[t];if(!i||!o)return e==C?-1:t==C?1:i?-1:o?1:u?P(u,e)-P(u,t):0;if(i===o)return pe(e,t);n=e;while(n=n.parentNode)a.unshift(n);n=t;while(n=n.parentNode)s.unshift(n);while(a[r]===s[r])r++;return r?pe(a[r],s[r]):a[r]==p?-1:s[r]==p?1:0}),C},se.matches=function(e,t){return se(e,null,null,t)},se.matchesSelector=function(e,t){if(T(e),d.matchesSelector&&E&&!N[t+" "]&&(!s||!s.test(t))&&(!v||!v.test(t)))try{var n=c.call(e,t);if(n||d.disconnectedMatch||e.document&&11!==e.document.nodeType)return n}catch(e){N(t,!0)}return 0<se(t,C,null,[e]).length},se.contains=function(e,t){return(e.ownerDocument||e)!=C&&T(e),y(e,t)},se.attr=function(e,t){(e.ownerDocument||e)!=C&&T(e);var n=b.attrHandle[t.toLowerCase()],r=n&&D.call(b.attrHandle,t.toLowerCase())?n(e,t,!E):void 0;return void 0!==r?r:d.attributes||!E?e.getAttribute(t):(r=e.getAttributeNode(t))&&r.specified?r.value:null},se.escape=function(e){return(e+"").replace(re,ie)},se.error=function(e){throw new Error("Syntax error, unrecognized expression: "+e)},se.uniqueSort=function(e){var t,n=[],r=0,i=0;if(l=!d.detectDuplicates,u=!d.sortStable&&e.slice(0),e.sort(j),l){while(t=e[i++])t===e[i]&&(r=n.push(i));while(r--)e.splice(n[r],1)}return u=null,e},o=se.getText=function(e){var t,n="",r=0,i=e.nodeType;if(i){if(1===i||9===i||11===i){if("string"==typeof e.textContent)return e.textContent;for(e=e.firstChild;e;e=e.nextSibling)n+=o(e)}else if(3===i||4===i)return e.nodeValue}else while(t=e[r++])n+=o(t);return n},(b=se.selectors={cacheLength:50,createPseudo:le,match:G,attrHandle:{},find:{},relative:{">":{dir:"parentNode",first:!0}," ":{dir:"parentNode"},"+":{dir:"previousSibling",first:!0},"~":{dir:"previousSibling"}},preFilter:{ATTR:function(e){return e[1]=e[1].replace(te,ne),e[3]=(e[3]||e[4]||e[5]||"").replace(te,ne),"~="===e[2]&&(e[3]=" "+e[3]+" "),e.slice(0,4)},CHILD:function(e){return e[1]=e[1].toLowerCase(),"nth"===e[1].slice(0,3)?(e[3]||se.error(e[0]),e[4]=+(e[4]?e[5]+(e[6]||1):2*("even"===e[3]||"odd"===e[3])),e[5]=+(e[7]+e[8]||"odd"===e[3])):e[3]&&se.error(e[0]),e},PSEUDO:function(e){var t,n=!e[6]&&e[2];return G.CHILD.test(e[0])?null:(e[3]?e[2]=e[4]||e[5]||"":n&&X.test(n)&&(t=h(n,!0))&&(t=n.indexOf(")",n.length-t)-n.length)&&(e[0]=e[0].slice(0,t),e[2]=n.slice(0,t)),e.slice(0,3))}},filter:{TAG:function(e){var t=e.replace(te,ne).toLowerCase();return"*"===e?function(){return!0}:function(e){return e.nodeName&&e.nodeName.toLowerCase()===t}},CLASS:function(e){var t=m[e+" "];return t||(t=new RegExp("(^|"+M+")"+e+"("+M+"|$)"))&&m(e,function(e){return t.test("string"==typeof e.className&&e.className||"undefined"!=typeof e.getAttribute&&e.getAttribute("class")||"")})},ATTR:function(n,r,i){return function(e){var t=se.attr(e,n);return null==t?"!="===r:!r||(t+="","="===r?t===i:"!="===r?t!==i:"^="===r?i&&0===t.indexOf(i):"*="===r?i&&-1<t.indexOf(i):"$="===r?i&&t.slice(-i.length)===i:"~="===r?-1<(" "+t.replace(B," ")+" ").indexOf(i):"|="===r&&(t===i||t.slice(0,i.length+1)===i+"-"))}},CHILD:function(h,e,t,g,v){var y="nth"!==h.slice(0,3),m="last"!==h.slice(-4),x="of-type"===e;return 1===g&&0===v?function(e){return!!e.parentNode}:function(e,t,n){var r,i,o,a,s,u,l=y!==m?"nextSibling":"previousSibling",c=e.parentNode,f=x&&e.nodeName.toLowerCase(),p=!n&&!x,d=!1;if(c){if(y){while(l){a=e;while(a=a[l])if(x?a.nodeName.toLowerCase()===f:1===a.nodeType)return!1;u=l="only"===h&&!u&&"nextSibling"}return!0}if(u=[m?c.firstChild:c.lastChild],m&&p){d=(s=(r=(i=(o=(a=c)[S]||(a[S]={}))[a.uniqueID]||(o[a.uniqueID]={}))[h]||[])[0]===k&&r[1])&&r[2],a=s&&c.childNodes[s];while(a=++s&&a&&a[l]||(d=s=0)||u.pop())if(1===a.nodeType&&++d&&a===e){i[h]=[k,s,d];break}}else if(p&&(d=s=(r=(i=(o=(a=e)[S]||(a[S]={}))[a.uniqueID]||(o[a.uniqueID]={}))[h]||[])[0]===k&&r[1]),!1===d)while(a=++s&&a&&a[l]||(d=s=0)||u.pop())if((x?a.nodeName.toLowerCase()===f:1===a.nodeType)&&++d&&(p&&((i=(o=a[S]||(a[S]={}))[a.uniqueID]||(o[a.uniqueID]={}))[h]=[k,d]),a===e))break;return(d-=v)===g||d%g==0&&0<=d/g}}},PSEUDO:function(e,o){var t,a=b.pseudos[e]||b.setFilters[e.toLowerCase()]||se.error("unsupported pseudo: "+e);return a[S]?a(o):1<a.length?(t=[e,e,"",o],b.setFilters.hasOwnProperty(e.toLowerCase())?le(function(e,t){var n,r=a(e,o),i=r.length;while(i--)e[n=P(e,r[i])]=!(t[n]=r[i])}):function(e){return a(e,0,t)}):a}},pseudos:{not:le(function(e){var r=[],i=[],s=f(e.replace($,"$1"));return s[S]?le(function(e,t,n,r){var i,o=s(e,null,r,[]),a=e.length;while(a--)(i=o[a])&&(e[a]=!(t[a]=i))}):function(e,t,n){return r[0]=e,s(r,null,n,i),r[0]=null,!i.pop()}}),has:le(function(t){return function(e){return 0<se(t,e).length}}),contains:le(function(t){return t=t.replace(te,ne),function(e){return-1<(e.textContent||o(e)).indexOf(t)}}),lang:le(function(n){return V.test(n||"")||se.error("unsupported lang: "+n),n=n.replace(te,ne).toLowerCase(),function(e){var t;do{if(t=E?e.lang:e.getAttribute("xml:lang")||e.getAttribute("lang"))return(t=t.toLowerCase())===n||0===t.indexOf(n+"-")}while((e=e.parentNode)&&1===e.nodeType);return!1}}),target:function(e){var t=n.location&&n.location.hash;return t&&t.slice(1)===e.id},root:function(e){return e===a},focus:function(e){return e===C.activeElement&&(!C.hasFocus||C.hasFocus())&&!!(e.type||e.href||~e.tabIndex)},enabled:ge(!1),disabled:ge(!0),checked:function(e){var t=e.nodeName.toLowerCase();return"input"===t&&!!e.checked||"option"===t&&!!e.selected},selected:function(e){return e.parentNode&&e.parentNode.selectedIndex,!0===e.selected},empty:function(e){for(e=e.firstChild;e;e=e.nextSibling)if(e.nodeType<6)return!1;return!0},parent:function(e){return!b.pseudos.empty(e)},header:function(e){return J.test(e.nodeName)},input:function(e){return Q.test(e.nodeName)},button:function(e){var t=e.nodeName.toLowerCase();return"input"===t&&"button"===e.type||"button"===t},text:function(e){var t;return"input"===e.nodeName.toLowerCase()&&"text"===e.type&&(null==(t=e.getAttribute("type"))||"text"===t.toLowerCase())},first:ve(function(){return[0]}),last:ve(function(e,t){return[t-1]}),eq:ve(function(e,t,n){return[n<0?n+t:n]}),even:ve(function(e,t){for(var n=0;n<t;n+=2)e.push(n);return e}),odd:ve(function(e,t){for(var n=1;n<t;n+=2)e.push(n);return e}),lt:ve(function(e,t,n){for(var r=n<0?n+t:t<n?t:n;0<=--r;)e.push(r);return e}),gt:ve(function(e,t,n){for(var r=n<0?n+t:n;++r<t;)e.push(r);return e})}}).pseudos.nth=b.pseudos.eq,{radio:!0,checkbox:!0,file:!0,password:!0,image:!0})b.pseudos[e]=de(e);for(e in{submit:!0,reset:!0})b.pseudos[e]=he(e);function me(){}function xe(e){for(var t=0,n=e.length,r="";t<n;t++)r+=e[t].value;return r}function be(s,e,t){var u=e.dir,l=e.next,c=l||u,f=t&&"parentNode"===c,p=r++;return e.first?function(e,t,n){while(e=e[u])if(1===e.nodeType||f)return s(e,t,n);return!1}:function(e,t,n){var r,i,o,a=[k,p];if(n){while(e=e[u])if((1===e.nodeType||f)&&s(e,t,n))return!0}else while(e=e[u])if(1===e.nodeType||f)if(i=(o=e[S]||(e[S]={}))[e.uniqueID]||(o[e.uniqueID]={}),l&&l===e.nodeName.toLowerCase())e=e[u]||e;else{if((r=i[c])&&r[0]===k&&r[1]===p)return a[2]=r[2];if((i[c]=a)[2]=s(e,t,n))return!0}return!1}}function we(i){return 1<i.length?function(e,t,n){var r=i.length;while(r--)if(!i[r](e,t,n))return!1;return!0}:i[0]}function Te(e,t,n,r,i){for(var o,a=[],s=0,u=e.length,l=null!=t;s<u;s++)(o=e[s])&&(n&&!n(o,r,i)||(a.push(o),l&&t.push(s)));return a}function Ce(d,h,g,v,y,e){return v&&!v[S]&&(v=Ce(v)),y&&!y[S]&&(y=Ce(y,e)),le(function(e,t,n,r){var i,o,a,s=[],u=[],l=t.length,c=e||function(e,t,n){for(var r=0,i=t.length;r<i;r++)se(e,t[r],n);return n}(h||"*",n.nodeType?[n]:n,[]),f=!d||!e&&h?c:Te(c,s,d,n,r),p=g?y||(e?d:l||v)?[]:t:f;if(g&&g(f,p,n,r),v){i=Te(p,u),v(i,[],n,r),o=i.length;while(o--)(a=i[o])&&(p[u[o]]=!(f[u[o]]=a))}if(e){if(y||d){if(y){i=[],o=p.length;while(o--)(a=p[o])&&i.push(f[o]=a);y(null,p=[],i,r)}o=p.length;while(o--)(a=p[o])&&-1<(i=y?P(e,a):s[o])&&(e[i]=!(t[i]=a))}}else p=Te(p===t?p.splice(l,p.length):p),y?y(null,t,p,r):H.apply(t,p)})}function Ee(e){for(var i,t,n,r=e.length,o=b.relative[e[0].type],a=o||b.relative[" "],s=o?1:0,u=be(function(e){return e===i},a,!0),l=be(function(e){return-1<P(i,e)},a,!0),c=[function(e,t,n){var r=!o&&(n||t!==w)||((i=t).nodeType?u(e,t,n):l(e,t,n));return i=null,r}];s<r;s++)if(t=b.relative[e[s].type])c=[be(we(c),t)];else{if((t=b.filter[e[s].type].apply(null,e[s].matches))[S]){for(n=++s;n<r;n++)if(b.relative[e[n].type])break;return Ce(1<s&&we(c),1<s&&xe(e.slice(0,s-1).concat({value:" "===e[s-2].type?"*":""})).replace($,"$1"),t,s<n&&Ee(e.slice(s,n)),n<r&&Ee(e=e.slice(n)),n<r&&xe(e))}c.push(t)}return we(c)}return me.prototype=b.filters=b.pseudos,b.setFilters=new me,h=se.tokenize=function(e,t){var n,r,i,o,a,s,u,l=x[e+" "];if(l)return t?0:l.slice(0);a=e,s=[],u=b.preFilter;while(a){for(o in n&&!(r=_.exec(a))||(r&&(a=a.slice(r[0].length)||a),s.push(i=[])),n=!1,(r=z.exec(a))&&(n=r.shift(),i.push({value:n,type:r[0].replace($," ")}),a=a.slice(n.length)),b.filter)!(r=G[o].exec(a))||u[o]&&!(r=u[o](r))||(n=r.shift(),i.push({value:n,type:o,matches:r}),a=a.slice(n.length));if(!n)break}return t?a.length:a?se.error(e):x(e,s).slice(0)},f=se.compile=function(e,t){var n,v,y,m,x,r,i=[],o=[],a=A[e+" "];if(!a){t||(t=h(e)),n=t.length;while(n--)(a=Ee(t[n]))[S]?i.push(a):o.push(a);(a=A(e,(v=o,m=0<(y=i).length,x=0<v.length,r=function(e,t,n,r,i){var o,a,s,u=0,l="0",c=e&&[],f=[],p=w,d=e||x&&b.find.TAG("*",i),h=k+=null==p?1:Math.random()||.1,g=d.length;for(i&&(w=t==C||t||i);l!==g&&null!=(o=d[l]);l++){if(x&&o){a=0,t||o.ownerDocument==C||(T(o),n=!E);while(s=v[a++])if(s(o,t||C,n)){r.push(o);break}i&&(k=h)}m&&((o=!s&&o)&&u--,e&&c.push(o))}if(u+=l,m&&l!==u){a=0;while(s=y[a++])s(c,f,t,n);if(e){if(0<u)while(l--)c[l]||f[l]||(f[l]=q.call(r));f=Te(f)}H.apply(r,f),i&&!e&&0<f.length&&1<u+y.length&&se.uniqueSort(r)}return i&&(k=h,w=p),c},m?le(r):r))).selector=e}return a},g=se.select=function(e,t,n,r){var i,o,a,s,u,l="function"==typeof e&&e,c=!r&&h(e=l.selector||e);if(n=n||[],1===c.length){if(2<(o=c[0]=c[0].slice(0)).length&&"ID"===(a=o[0]).type&&9===t.nodeType&&E&&b.relative[o[1].type]){if(!(t=(b.find.ID(a.matches[0].replace(te,ne),t)||[])[0]))return n;l&&(t=t.parentNode),e=e.slice(o.shift().value.length)}i=G.needsContext.test(e)?0:o.length;while(i--){if(a=o[i],b.relative[s=a.type])break;if((u=b.find[s])&&(r=u(a.matches[0].replace(te,ne),ee.test(o[0].type)&&ye(t.parentNode)||t))){if(o.splice(i,1),!(e=r.length&&xe(o)))return H.apply(n,r),n;break}}}return(l||f(e,c))(r,t,!E,n,!t||ee.test(e)&&ye(t.parentNode)||t),n},d.sortStable=S.split("").sort(j).join("")===S,d.detectDuplicates=!!l,T(),d.sortDetached=ce(function(e){return 1&e.compareDocumentPosition(C.createElement("fieldset"))}),ce(function(e){return e.innerHTML="<a href='#'></a>","#"===e.firstChild.getAttribute("href")})||fe("type|href|height|width",function(e,t,n){if(!n)return e.getAttribute(t,"type"===t.toLowerCase()?1:2)}),d.attributes&&ce(function(e){return e.innerHTML="<input/>",e.firstChild.setAttribute("value",""),""===e.firstChild.getAttribute("value")})||fe("value",function(e,t,n){if(!n&&"input"===e.nodeName.toLowerCase())return e.defaultValue}),ce(function(e){return null==e.getAttribute("disabled")})||fe(R,function(e,t,n){var r;if(!n)return!0===e[t]?t.toLowerCase():(r=e.getAttributeNode(t))&&r.specified?r.value:null}),se}(C);S.find=d,S.expr=d.selectors,S.expr[":"]=S.expr.pseudos,S.uniqueSort=S.unique=d.uniqueSort,S.text=d.getText,S.isXMLDoc=d.isXML,S.contains=d.contains,S.escapeSelector=d.escape;var h=function(e,t,n){var r=[],i=void 0!==n;while((e=e[t])&&9!==e.nodeType)if(1===e.nodeType){if(i&&S(e).is(n))break;r.push(e)}return r},T=function(e,t){for(var n=[];e;e=e.nextSibling)1===e.nodeType&&e!==t&&n.push(e);return n},k=S.expr.match.needsContext;function A(e,t){return e.nodeName&&e.nodeName.toLowerCase()===t.toLowerCase()}var N=/^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i;function j(e,n,r){return m(n)?S.grep(e,function(e,t){return!!n.call(e,t,e)!==r}):n.nodeType?S.grep(e,function(e){return e===n!==r}):"string"!=typeof n?S.grep(e,function(e){return-1<i.call(n,e)!==r}):S.filter(n,e,r)}S.filter=function(e,t,n){var r=t[0];return n&&(e=":not("+e+")"),1===t.length&&1===r.nodeType?S.find.matchesSelector(r,e)?[r]:[]:S.find.matches(e,S.grep(t,function(e){return 1===e.nodeType}))},S.fn.extend({find:function(e){var t,n,r=this.length,i=this;if("string"!=typeof e)return this.pushStack(S(e).filter(function(){for(t=0;t<r;t++)if(S.contains(i[t],this))return!0}));for(n=this.pushStack([]),t=0;t<r;t++)S.find(e,i[t],n);return 1<r?S.uniqueSort(n):n},filter:function(e){return this.pushStack(j(this,e||[],!1))},not:function(e){return this.pushStack(j(this,e||[],!0))},is:function(e){return!!j(this,"string"==typeof e&&k.test(e)?S(e):e||[],!1).length}});var D,q=/^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]+))$/;(S.fn.init=function(e,t,n){var r,i;if(!e)return this;if(n=n||D,"string"==typeof e){if(!(r="<"===e[0]&&">"===e[e.length-1]&&3<=e.length?[null,e,null]:q.exec(e))||!r[1]&&t)return!t||t.jquery?(t||n).find(e):this.constructor(t).find(e);if(r[1]){if(t=t instanceof S?t[0]:t,S.merge(this,S.parseHTML(r[1],t&&t.nodeType?t.ownerDocument||t:E,!0)),N.test(r[1])&&S.isPlainObject(t))for(r in t)m(this[r])?this[r](t[r]):this.attr(r,t[r]);return this}return(i=E.getElementById(r[2]))&&(this[0]=i,this.length=1),this}return e.nodeType?(this[0]=e,this.length=1,this):m(e)?void 0!==n.ready?n.ready(e):e(S):S.makeArray(e,this)}).prototype=S.fn,D=S(E);var L=/^(?:parents|prev(?:Until|All))/,H={children:!0,contents:!0,next:!0,prev:!0};function O(e,t){while((e=e[t])&&1!==e.nodeType);return e}S.fn.extend({has:function(e){var t=S(e,this),n=t.length;return this.filter(function(){for(var e=0;e<n;e++)if(S.contains(this,t[e]))return!0})},closest:function(e,t){var n,r=0,i=this.length,o=[],a="string"!=typeof e&&S(e);if(!k.test(e))for(;r<i;r++)for(n=this[r];n&&n!==t;n=n.parentNode)if(n.nodeType<11&&(a?-1<a.index(n):1===n.nodeType&&S.find.matchesSelector(n,e))){o.push(n);break}return this.pushStack(1<o.length?S.uniqueSort(o):o)},index:function(e){return e?"string"==typeof e?i.call(S(e),this[0]):i.call(this,e.jquery?e[0]:e):this[0]&&this[0].parentNode?this.first().prevAll().length:-1},add:function(e,t){return this.pushStack(S.uniqueSort(S.merge(this.get(),S(e,t))))},addBack:function(e){return this.add(null==e?this.prevObject:this.prevObject.filter(e))}}),S.each({parent:function(e){var t=e.parentNode;return t&&11!==t.nodeType?t:null},parents:function(e){return h(e,"parentNode")},parentsUntil:function(e,t,n){return h(e,"parentNode",n)},next:function(e){return O(e,"nextSibling")},prev:function(e){return O(e,"previousSibling")},nextAll:function(e){return h(e,"nextSibling")},prevAll:function(e){return h(e,"previousSibling")},nextUntil:function(e,t,n){return h(e,"nextSibling",n)},prevUntil:function(e,t,n){return h(e,"previousSibling",n)},siblings:function(e){return T((e.parentNode||{}).firstChild,e)},children:function(e){return T(e.firstChild)},contents:function(e){return null!=e.contentDocument&&r(e.contentDocument)?e.contentDocument:(A(e,"template")&&(e=e.content||e),S.merge([],e.childNodes))}},function(r,i){S.fn[r]=function(e,t){var n=S.map(this,i,e);return"Until"!==r.slice(-5)&&(t=e),t&&"string"==typeof t&&(n=S.filter(t,n)),1<this.length&&(H[r]||S.uniqueSort(n),L.test(r)&&n.reverse()),this.pushStack(n)}});var P=/[^\x20\t\r\n\f]+/g;function R(e){return e}function M(e){throw e}function I(e,t,n,r){var i;try{e&&m(i=e.promise)?i.call(e).done(t).fail(n):e&&m(i=e.then)?i.call(e,t,n):t.apply(void 0,[e].slice(r))}catch(e){n.apply(void 0,[e])}}S.Callbacks=function(r){var e,n;r="string"==typeof r?(e=r,n={},S.each(e.match(P)||[],function(e,t){n[t]=!0}),n):S.extend({},r);var i,t,o,a,s=[],u=[],l=-1,c=function(){for(a=a||r.once,o=i=!0;u.length;l=-1){t=u.shift();while(++l<s.length)!1===s[l].apply(t[0],t[1])&&r.stopOnFalse&&(l=s.length,t=!1)}r.memory||(t=!1),i=!1,a&&(s=t?[]:"")},f={add:function(){return s&&(t&&!i&&(l=s.length-1,u.push(t)),function n(e){S.each(e,function(e,t){m(t)?r.unique&&f.has(t)||s.push(t):t&&t.length&&"string"!==w(t)&&n(t)})}(arguments),t&&!i&&c()),this},remove:function(){return S.each(arguments,function(e,t){var n;while(-1<(n=S.inArray(t,s,n)))s.splice(n,1),n<=l&&l--}),this},has:function(e){return e?-1<S.inArray(e,s):0<s.length},empty:function(){return s&&(s=[]),this},disable:function(){return a=u=[],s=t="",this},disabled:function(){return!s},lock:function(){return a=u=[],t||i||(s=t=""),this},locked:function(){return!!a},fireWith:function(e,t){return a||(t=[e,(t=t||[]).slice?t.slice():t],u.push(t),i||c()),this},fire:function(){return f.fireWith(this,arguments),this},fired:function(){return!!o}};return f},S.extend({Deferred:function(e){var o=[["notify","progress",S.Callbacks("memory"),S.Callbacks("memory"),2],["resolve","done",S.Callbacks("once memory"),S.Callbacks("once memory"),0,"resolved"],["reject","fail",S.Callbacks("once memory"),S.Callbacks("once memory"),1,"rejected"]],i="pending",a={state:function(){return i},always:function(){return s.done(arguments).fail(arguments),this},"catch":function(e){return a.then(null,e)},pipe:function(){var i=arguments;return S.Deferred(function(r){S.each(o,function(e,t){var n=m(i[t[4]])&&i[t[4]];s[t[1]](function(){var e=n&&n.apply(this,arguments);e&&m(e.promise)?e.promise().progress(r.notify).done(r.resolve).fail(r.reject):r[t[0]+"With"](this,n?[e]:arguments)})}),i=null}).promise()},then:function(t,n,r){var u=0;function l(i,o,a,s){return function(){var n=this,r=arguments,e=function(){var e,t;if(!(i<u)){if((e=a.apply(n,r))===o.promise())throw new TypeError("Thenable self-resolution");t=e&&("object"==typeof e||"function"==typeof e)&&e.then,m(t)?s?t.call(e,l(u,o,R,s),l(u,o,M,s)):(u++,t.call(e,l(u,o,R,s),l(u,o,M,s),l(u,o,R,o.notifyWith))):(a!==R&&(n=void 0,r=[e]),(s||o.resolveWith)(n,r))}},t=s?e:function(){try{e()}catch(e){S.Deferred.exceptionHook&&S.Deferred.exceptionHook(e,t.stackTrace),u<=i+1&&(a!==M&&(n=void 0,r=[e]),o.rejectWith(n,r))}};i?t():(S.Deferred.getStackHook&&(t.stackTrace=S.Deferred.getStackHook()),C.setTimeout(t))}}return S.Deferred(function(e){o[0][3].add(l(0,e,m(r)?r:R,e.notifyWith)),o[1][3].add(l(0,e,m(t)?t:R)),o[2][3].add(l(0,e,m(n)?n:M))}).promise()},promise:function(e){return null!=e?S.extend(e,a):a}},s={};return S.each(o,function(e,t){var n=t[2],r=t[5];a[t[1]]=n.add,r&&n.add(function(){i=r},o[3-e][2].disable,o[3-e][3].disable,o[0][2].lock,o[0][3].lock),n.add(t[3].fire),s[t[0]]=function(){return s[t[0]+"With"](this===s?void 0:this,arguments),this},s[t[0]+"With"]=n.fireWith}),a.promise(s),e&&e.call(s,s),s},when:function(e){var n=arguments.length,t=n,r=Array(t),i=s.call(arguments),o=S.Deferred(),a=function(t){return function(e){r[t]=this,i[t]=1<arguments.length?s.call(arguments):e,--n||o.resolveWith(r,i)}};if(n<=1&&(I(e,o.done(a(t)).resolve,o.reject,!n),"pending"===o.state()||m(i[t]&&i[t].then)))return o.then();while(t--)I(i[t],a(t),o.reject);return o.promise()}});var W=/^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/;S.Deferred.exceptionHook=function(e,t){C.console&&C.console.warn&&e&&W.test(e.name)&&C.console.warn("jQuery.Deferred exception: "+e.message,e.stack,t)},S.readyException=function(e){C.setTimeout(function(){throw e})};var F=S.Deferred();function B(){E.removeEventListener("DOMContentLoaded",B),C.removeEventListener("load",B),S.ready()}S.fn.ready=function(e){return F.then(e)["catch"](function(e){S.readyException(e)}),this},S.extend({isReady:!1,readyWait:1,ready:function(e){(!0===e?--S.readyWait:S.isReady)||(S.isReady=!0)!==e&&0<--S.readyWait||F.resolveWith(E,[S])}}),S.ready.then=F.then,"complete"===E.readyState||"loading"!==E.readyState&&!E.documentElement.doScroll?C.setTimeout(S.ready):(E.addEventListener("DOMContentLoaded",B),C.addEventListener("load",B));var $=function(e,t,n,r,i,o,a){var s=0,u=e.length,l=null==n;if("object"===w(n))for(s in i=!0,n)$(e,t,s,n[s],!0,o,a);else if(void 0!==r&&(i=!0,m(r)||(a=!0),l&&(a?(t.call(e,r),t=null):(l=t,t=function(e,t,n){return l.call(S(e),n)})),t))for(;s<u;s++)t(e[s],n,a?r:r.call(e[s],s,t(e[s],n)));return i?e:l?t.call(e):u?t(e[0],n):o},_=/^-ms-/,z=/-([a-z])/g;function U(e,t){return t.toUpperCase()}function X(e){return e.replace(_,"ms-").replace(z,U)}var V=function(e){return 1===e.nodeType||9===e.nodeType||!+e.nodeType};function G(){this.expando=S.expando+G.uid++}G.uid=1,G.prototype={cache:function(e){var t=e[this.expando];return t||(t={},V(e)&&(e.nodeType?e[this.expando]=t:Object.defineProperty(e,this.expando,{value:t,configurable:!0}))),t},set:function(e,t,n){var r,i=this.cache(e);if("string"==typeof t)i[X(t)]=n;else for(r in t)i[X(r)]=t[r];return i},get:function(e,t){return void 0===t?this.cache(e):e[this.expando]&&e[this.expando][X(t)]},access:function(e,t,n){return void 0===t||t&&"string"==typeof t&&void 0===n?this.get(e,t):(this.set(e,t,n),void 0!==n?n:t)},remove:function(e,t){var n,r=e[this.expando];if(void 0!==r){if(void 0!==t){n=(t=Array.isArray(t)?t.map(X):(t=X(t))in r?[t]:t.match(P)||[]).length;while(n--)delete r[t[n]]}(void 0===t||S.isEmptyObject(r))&&(e.nodeType?e[this.expando]=void 0:delete e[this.expando])}},hasData:function(e){var t=e[this.expando];return void 0!==t&&!S.isEmptyObject(t)}};var Y=new G,Q=new G,J=/^(?:\{[\w\W]*\}|\[[\w\W]*\])$/,K=/[A-Z]/g;function Z(e,t,n){var r,i;if(void 0===n&&1===e.nodeType)if(r="data-"+t.replace(K,"-$&").toLowerCase(),"string"==typeof(n=e.getAttribute(r))){try{n="true"===(i=n)||"false"!==i&&("null"===i?null:i===+i+""?+i:J.test(i)?JSON.parse(i):i)}catch(e){}Q.set(e,t,n)}else n=void 0;return n}S.extend({hasData:function(e){return Q.hasData(e)||Y.hasData(e)},data:function(e,t,n){return Q.access(e,t,n)},removeData:function(e,t){Q.remove(e,t)},_data:function(e,t,n){return Y.access(e,t,n)},_removeData:function(e,t){Y.remove(e,t)}}),S.fn.extend({data:function(n,e){var t,r,i,o=this[0],a=o&&o.attributes;if(void 0===n){if(this.length&&(i=Q.get(o),1===o.nodeType&&!Y.get(o,"hasDataAttrs"))){t=a.length;while(t--)a[t]&&0===(r=a[t].name).indexOf("data-")&&(r=X(r.slice(5)),Z(o,r,i[r]));Y.set(o,"hasDataAttrs",!0)}return i}return"object"==typeof n?this.each(function(){Q.set(this,n)}):$(this,function(e){var t;if(o&&void 0===e)return void 0!==(t=Q.get(o,n))?t:void 0!==(t=Z(o,n))?t:void 0;this.each(function(){Q.set(this,n,e)})},null,e,1<arguments.length,null,!0)},removeData:function(e){return this.each(function(){Q.remove(this,e)})}}),S.extend({queue:function(e,t,n){var r;if(e)return t=(t||"fx")+"queue",r=Y.get(e,t),n&&(!r||Array.isArray(n)?r=Y.access(e,t,S.makeArray(n)):r.push(n)),r||[]},dequeue:function(e,t){t=t||"fx";var n=S.queue(e,t),r=n.length,i=n.shift(),o=S._queueHooks(e,t);"inprogress"===i&&(i=n.shift(),r--),i&&("fx"===t&&n.unshift("inprogress"),delete o.stop,i.call(e,function(){S.dequeue(e,t)},o)),!r&&o&&o.empty.fire()},_queueHooks:function(e,t){var n=t+"queueHooks";return Y.get(e,n)||Y.access(e,n,{empty:S.Callbacks("once memory").add(function(){Y.remove(e,[t+"queue",n])})})}}),S.fn.extend({queue:function(t,n){var e=2;return"string"!=typeof t&&(n=t,t="fx",e--),arguments.length<e?S.queue(this[0],t):void 0===n?this:this.each(function(){var e=S.queue(this,t,n);S._queueHooks(this,t),"fx"===t&&"inprogress"!==e[0]&&S.dequeue(this,t)})},dequeue:function(e){return this.each(function(){S.dequeue(this,e)})},clearQueue:function(e){return this.queue(e||"fx",[])},promise:function(e,t){var n,r=1,i=S.Deferred(),o=this,a=this.length,s=function(){--r||i.resolveWith(o,[o])};"string"!=typeof e&&(t=e,e=void 0),e=e||"fx";while(a--)(n=Y.get(o[a],e+"queueHooks"))&&n.empty&&(r++,n.empty.add(s));return s(),i.promise(t)}});var ee=/[+-]?(?:\d*\.|)\d+(?:[eE][+-]?\d+|)/.source,te=new RegExp("^(?:([+-])=|)("+ee+")([a-z%]*)$","i"),ne=["Top","Right","Bottom","Left"],re=E.documentElement,ie=function(e){return S.contains(e.ownerDocument,e)},oe={composed:!0};re.getRootNode&&(ie=function(e){return S.contains(e.ownerDocument,e)||e.getRootNode(oe)===e.ownerDocument});var ae=function(e,t){return"none"===(e=t||e).style.display||""===e.style.display&&ie(e)&&"none"===S.css(e,"display")};function se(e,t,n,r){var i,o,a=20,s=r?function(){return r.cur()}:function(){return S.css(e,t,"")},u=s(),l=n&&n[3]||(S.cssNumber[t]?"":"px"),c=e.nodeType&&(S.cssNumber[t]||"px"!==l&&+u)&&te.exec(S.css(e,t));if(c&&c[3]!==l){u/=2,l=l||c[3],c=+u||1;while(a--)S.style(e,t,c+l),(1-o)*(1-(o=s()/u||.5))<=0&&(a=0),c/=o;c*=2,S.style(e,t,c+l),n=n||[]}return n&&(c=+c||+u||0,i=n[1]?c+(n[1]+1)*n[2]:+n[2],r&&(r.unit=l,r.start=c,r.end=i)),i}var ue={};function le(e,t){for(var n,r,i,o,a,s,u,l=[],c=0,f=e.length;c<f;c++)(r=e[c]).style&&(n=r.style.display,t?("none"===n&&(l[c]=Y.get(r,"display")||null,l[c]||(r.style.display="")),""===r.style.display&&ae(r)&&(l[c]=(u=a=o=void 0,a=(i=r).ownerDocument,s=i.nodeName,(u=ue[s])||(o=a.body.appendChild(a.createElement(s)),u=S.css(o,"display"),o.parentNode.removeChild(o),"none"===u&&(u="block"),ue[s]=u)))):"none"!==n&&(l[c]="none",Y.set(r,"display",n)));for(c=0;c<f;c++)null!=l[c]&&(e[c].style.display=l[c]);return e}S.fn.extend({show:function(){return le(this,!0)},hide:function(){return le(this)},toggle:function(e){return"boolean"==typeof e?e?this.show():this.hide():this.each(function(){ae(this)?S(this).show():S(this).hide()})}});var ce,fe,pe=/^(?:checkbox|radio)$/i,de=/<([a-z][^\/\0>\x20\t\r\n\f]*)/i,he=/^$|^module$|\/(?:java|ecma)script/i;ce=E.createDocumentFragment().appendChild(E.createElement("div")),(fe=E.createElement("input")).setAttribute("type","radio"),fe.setAttribute("checked","checked"),fe.setAttribute("name","t"),ce.appendChild(fe),y.checkClone=ce.cloneNode(!0).cloneNode(!0).lastChild.checked,ce.innerHTML="<textarea>x</textarea>",y.noCloneChecked=!!ce.cloneNode(!0).lastChild.defaultValue,ce.innerHTML="<option></option>",y.option=!!ce.lastChild;var ge={thead:[1,"<table>","</table>"],col:[2,"<table><colgroup>","</colgroup></table>"],tr:[2,"<table><tbody>","</tbody></table>"],td:[3,"<table><tbody><tr>","</tr></tbody></table>"],_default:[0,"",""]};function ve(e,t){var n;return n="undefined"!=typeof e.getElementsByTagName?e.getElementsByTagName(t||"*"):"undefined"!=typeof e.querySelectorAll?e.querySelectorAll(t||"*"):[],void 0===t||t&&A(e,t)?S.merge([e],n):n}function ye(e,t){for(var n=0,r=e.length;n<r;n++)Y.set(e[n],"globalEval",!t||Y.get(t[n],"globalEval"))}ge.tbody=ge.tfoot=ge.colgroup=ge.caption=ge.thead,ge.th=ge.td,y.option||(ge.optgroup=ge.option=[1,"<select multiple='multiple'>","</select>"]);var me=/<|&#?\w+;/;function xe(e,t,n,r,i){for(var o,a,s,u,l,c,f=t.createDocumentFragment(),p=[],d=0,h=e.length;d<h;d++)if((o=e[d])||0===o)if("object"===w(o))S.merge(p,o.nodeType?[o]:o);else if(me.test(o)){a=a||f.appendChild(t.createElement("div")),s=(de.exec(o)||["",""])[1].toLowerCase(),u=ge[s]||ge._default,a.innerHTML=u[1]+S.htmlPrefilter(o)+u[2],c=u[0];while(c--)a=a.lastChild;S.merge(p,a.childNodes),(a=f.firstChild).textContent=""}else p.push(t.createTextNode(o));f.textContent="",d=0;while(o=p[d++])if(r&&-1<S.inArray(o,r))i&&i.push(o);else if(l=ie(o),a=ve(f.appendChild(o),"script"),l&&ye(a),n){c=0;while(o=a[c++])he.test(o.type||"")&&n.push(o)}return f}var be=/^([^.]*)(?:\.(.+)|)/;function we(){return!0}function Te(){return!1}function Ce(e,t){return e===function(){try{return E.activeElement}catch(e){}}()==("focus"===t)}function Ee(e,t,n,r,i,o){var a,s;if("object"==typeof t){for(s in"string"!=typeof n&&(r=r||n,n=void 0),t)Ee(e,s,n,r,t[s],o);return e}if(null==r&&null==i?(i=n,r=n=void 0):null==i&&("string"==typeof n?(i=r,r=void 0):(i=r,r=n,n=void 0)),!1===i)i=Te;else if(!i)return e;return 1===o&&(a=i,(i=function(e){return S().off(e),a.apply(this,arguments)}).guid=a.guid||(a.guid=S.guid++)),e.each(function(){S.event.add(this,t,i,r,n)})}function Se(e,i,o){o?(Y.set(e,i,!1),S.event.add(e,i,{namespace:!1,handler:function(e){var t,n,r=Y.get(this,i);if(1&e.isTrigger&&this[i]){if(r.length)(S.event.special[i]||{}).delegateType&&e.stopPropagation();else if(r=s.call(arguments),Y.set(this,i,r),t=o(this,i),this[i](),r!==(n=Y.get(this,i))||t?Y.set(this,i,!1):n={},r!==n)return e.stopImmediatePropagation(),e.preventDefault(),n&&n.value}else r.length&&(Y.set(this,i,{value:S.event.trigger(S.extend(r[0],S.Event.prototype),r.slice(1),this)}),e.stopImmediatePropagation())}})):void 0===Y.get(e,i)&&S.event.add(e,i,we)}S.event={global:{},add:function(t,e,n,r,i){var o,a,s,u,l,c,f,p,d,h,g,v=Y.get(t);if(V(t)){n.handler&&(n=(o=n).handler,i=o.selector),i&&S.find.matchesSelector(re,i),n.guid||(n.guid=S.guid++),(u=v.events)||(u=v.events=Object.create(null)),(a=v.handle)||(a=v.handle=function(e){return"undefined"!=typeof S&&S.event.triggered!==e.type?S.event.dispatch.apply(t,arguments):void 0}),l=(e=(e||"").match(P)||[""]).length;while(l--)d=g=(s=be.exec(e[l])||[])[1],h=(s[2]||"").split(".").sort(),d&&(f=S.event.special[d]||{},d=(i?f.delegateType:f.bindType)||d,f=S.event.special[d]||{},c=S.extend({type:d,origType:g,data:r,handler:n,guid:n.guid,selector:i,needsContext:i&&S.expr.match.needsContext.test(i),namespace:h.join(".")},o),(p=u[d])||((p=u[d]=[]).delegateCount=0,f.setup&&!1!==f.setup.call(t,r,h,a)||t.addEventListener&&t.addEventListener(d,a)),f.add&&(f.add.call(t,c),c.handler.guid||(c.handler.guid=n.guid)),i?p.splice(p.delegateCount++,0,c):p.push(c),S.event.global[d]=!0)}},remove:function(e,t,n,r,i){var o,a,s,u,l,c,f,p,d,h,g,v=Y.hasData(e)&&Y.get(e);if(v&&(u=v.events)){l=(t=(t||"").match(P)||[""]).length;while(l--)if(d=g=(s=be.exec(t[l])||[])[1],h=(s[2]||"").split(".").sort(),d){f=S.event.special[d]||{},p=u[d=(r?f.delegateType:f.bindType)||d]||[],s=s[2]&&new RegExp("(^|\\.)"+h.join("\\.(?:.*\\.|)")+"(\\.|$)"),a=o=p.length;while(o--)c=p[o],!i&&g!==c.origType||n&&n.guid!==c.guid||s&&!s.test(c.namespace)||r&&r!==c.selector&&("**"!==r||!c.selector)||(p.splice(o,1),c.selector&&p.delegateCount--,f.remove&&f.remove.call(e,c));a&&!p.length&&(f.teardown&&!1!==f.teardown.call(e,h,v.handle)||S.removeEvent(e,d,v.handle),delete u[d])}else for(d in u)S.event.remove(e,d+t[l],n,r,!0);S.isEmptyObject(u)&&Y.remove(e,"handle events")}},dispatch:function(e){var t,n,r,i,o,a,s=new Array(arguments.length),u=S.event.fix(e),l=(Y.get(this,"events")||Object.create(null))[u.type]||[],c=S.event.special[u.type]||{};for(s[0]=u,t=1;t<arguments.length;t++)s[t]=arguments[t];if(u.delegateTarget=this,!c.preDispatch||!1!==c.preDispatch.call(this,u)){a=S.event.handlers.call(this,u,l),t=0;while((i=a[t++])&&!u.isPropagationStopped()){u.currentTarget=i.elem,n=0;while((o=i.handlers[n++])&&!u.isImmediatePropagationStopped())u.rnamespace&&!1!==o.namespace&&!u.rnamespace.test(o.namespace)||(u.handleObj=o,u.data=o.data,void 0!==(r=((S.event.special[o.origType]||{}).handle||o.handler).apply(i.elem,s))&&!1===(u.result=r)&&(u.preventDefault(),u.stopPropagation()))}return c.postDispatch&&c.postDispatch.call(this,u),u.result}},handlers:function(e,t){var n,r,i,o,a,s=[],u=t.delegateCount,l=e.target;if(u&&l.nodeType&&!("click"===e.type&&1<=e.button))for(;l!==this;l=l.parentNode||this)if(1===l.nodeType&&("click"!==e.type||!0!==l.disabled)){for(o=[],a={},n=0;n<u;n++)void 0===a[i=(r=t[n]).selector+" "]&&(a[i]=r.needsContext?-1<S(i,this).index(l):S.find(i,this,null,[l]).length),a[i]&&o.push(r);o.length&&s.push({elem:l,handlers:o})}return l=this,u<t.length&&s.push({elem:l,handlers:t.slice(u)}),s},addProp:function(t,e){Object.defineProperty(S.Event.prototype,t,{enumerable:!0,configurable:!0,get:m(e)?function(){if(this.originalEvent)return e(this.originalEvent)}:function(){if(this.originalEvent)return this.originalEvent[t]},set:function(e){Object.defineProperty(this,t,{enumerable:!0,configurable:!0,writable:!0,value:e})}})},fix:function(e){return e[S.expando]?e:new S.Event(e)},special:{load:{noBubble:!0},click:{setup:function(e){var t=this||e;return pe.test(t.type)&&t.click&&A(t,"input")&&Se(t,"click",we),!1},trigger:function(e){var t=this||e;return pe.test(t.type)&&t.click&&A(t,"input")&&Se(t,"click"),!0},_default:function(e){var t=e.target;return pe.test(t.type)&&t.click&&A(t,"input")&&Y.get(t,"click")||A(t,"a")}},beforeunload:{postDispatch:function(e){void 0!==e.result&&e.originalEvent&&(e.originalEvent.returnValue=e.result)}}}},S.removeEvent=function(e,t,n){e.removeEventListener&&e.removeEventListener(t,n)},S.Event=function(e,t){if(!(this instanceof S.Event))return new S.Event(e,t);e&&e.type?(this.originalEvent=e,this.type=e.type,this.isDefaultPrevented=e.defaultPrevented||void 0===e.defaultPrevented&&!1===e.returnValue?we:Te,this.target=e.target&&3===e.target.nodeType?e.target.parentNode:e.target,this.currentTarget=e.currentTarget,this.relatedTarget=e.relatedTarget):this.type=e,t&&S.extend(this,t),this.timeStamp=e&&e.timeStamp||Date.now(),this[S.expando]=!0},S.Event.prototype={constructor:S.Event,isDefaultPrevented:Te,isPropagationStopped:Te,isImmediatePropagationStopped:Te,isSimulated:!1,preventDefault:function(){var e=this.originalEvent;this.isDefaultPrevented=we,e&&!this.isSimulated&&e.preventDefault()},stopPropagation:function(){var e=this.originalEvent;this.isPropagationStopped=we,e&&!this.isSimulated&&e.stopPropagation()},stopImmediatePropagation:function(){var e=this.originalEvent;this.isImmediatePropagationStopped=we,e&&!this.isSimulated&&e.stopImmediatePropagation(),this.stopPropagation()}},S.each({altKey:!0,bubbles:!0,cancelable:!0,changedTouches:!0,ctrlKey:!0,detail:!0,eventPhase:!0,metaKey:!0,pageX:!0,pageY:!0,shiftKey:!0,view:!0,"char":!0,code:!0,charCode:!0,key:!0,keyCode:!0,button:!0,buttons:!0,clientX:!0,clientY:!0,offsetX:!0,offsetY:!0,pointerId:!0,pointerType:!0,screenX:!0,screenY:!0,targetTouches:!0,toElement:!0,touches:!0,which:!0},S.event.addProp),S.each({focus:"focusin",blur:"focusout"},function(e,t){S.event.special[e]={setup:function(){return Se(this,e,Ce),!1},trigger:function(){return Se(this,e),!0},_default:function(){return!0},delegateType:t}}),S.each({mouseenter:"mouseover",mouseleave:"mouseout",pointerenter:"pointerover",pointerleave:"pointerout"},function(e,i){S.event.special[e]={delegateType:i,bindType:i,handle:function(e){var t,n=e.relatedTarget,r=e.handleObj;return n&&(n===this||S.contains(this,n))||(e.type=r.origType,t=r.handler.apply(this,arguments),e.type=i),t}}}),S.fn.extend({on:function(e,t,n,r){return Ee(this,e,t,n,r)},one:function(e,t,n,r){return Ee(this,e,t,n,r,1)},off:function(e,t,n){var r,i;if(e&&e.preventDefault&&e.handleObj)return r=e.handleObj,S(e.delegateTarget).off(r.namespace?r.origType+"."+r.namespace:r.origType,r.selector,r.handler),this;if("object"==typeof e){for(i in e)this.off(i,t,e[i]);return this}return!1!==t&&"function"!=typeof t||(n=t,t=void 0),!1===n&&(n=Te),this.each(function(){S.event.remove(this,e,n,t)})}});var ke=/<script|<style|<link/i,Ae=/checked\s*(?:[^=]|=\s*.checked.)/i,Ne=/^\s*<!(?:\[CDATA\[|--)|(?:\]\]|--)>\s*$/g;function je(e,t){return A(e,"table")&&A(11!==t.nodeType?t:t.firstChild,"tr")&&S(e).children("tbody")[0]||e}function De(e){return e.type=(null!==e.getAttribute("type"))+"/"+e.type,e}function qe(e){return"true/"===(e.type||"").slice(0,5)?e.type=e.type.slice(5):e.removeAttribute("type"),e}function Le(e,t){var n,r,i,o,a,s;if(1===t.nodeType){if(Y.hasData(e)&&(s=Y.get(e).events))for(i in Y.remove(t,"handle events"),s)for(n=0,r=s[i].length;n<r;n++)S.event.add(t,i,s[i][n]);Q.hasData(e)&&(o=Q.access(e),a=S.extend({},o),Q.set(t,a))}}function He(n,r,i,o){r=g(r);var e,t,a,s,u,l,c=0,f=n.length,p=f-1,d=r[0],h=m(d);if(h||1<f&&"string"==typeof d&&!y.checkClone&&Ae.test(d))return n.each(function(e){var t=n.eq(e);h&&(r[0]=d.call(this,e,t.html())),He(t,r,i,o)});if(f&&(t=(e=xe(r,n[0].ownerDocument,!1,n,o)).firstChild,1===e.childNodes.length&&(e=t),t||o)){for(s=(a=S.map(ve(e,"script"),De)).length;c<f;c++)u=e,c!==p&&(u=S.clone(u,!0,!0),s&&S.merge(a,ve(u,"script"))),i.call(n[c],u,c);if(s)for(l=a[a.length-1].ownerDocument,S.map(a,qe),c=0;c<s;c++)u=a[c],he.test(u.type||"")&&!Y.access(u,"globalEval")&&S.contains(l,u)&&(u.src&&"module"!==(u.type||"").toLowerCase()?S._evalUrl&&!u.noModule&&S._evalUrl(u.src,{nonce:u.nonce||u.getAttribute("nonce")},l):b(u.textContent.replace(Ne,""),u,l))}return n}function Oe(e,t,n){for(var r,i=t?S.filter(t,e):e,o=0;null!=(r=i[o]);o++)n||1!==r.nodeType||S.cleanData(ve(r)),r.parentNode&&(n&&ie(r)&&ye(ve(r,"script")),r.parentNode.removeChild(r));return e}S.extend({htmlPrefilter:function(e){return e},clone:function(e,t,n){var r,i,o,a,s,u,l,c=e.cloneNode(!0),f=ie(e);if(!(y.noCloneChecked||1!==e.nodeType&&11!==e.nodeType||S.isXMLDoc(e)))for(a=ve(c),r=0,i=(o=ve(e)).length;r<i;r++)s=o[r],u=a[r],void 0,"input"===(l=u.nodeName.toLowerCase())&&pe.test(s.type)?u.checked=s.checked:"input"!==l&&"textarea"!==l||(u.defaultValue=s.defaultValue);if(t)if(n)for(o=o||ve(e),a=a||ve(c),r=0,i=o.length;r<i;r++)Le(o[r],a[r]);else Le(e,c);return 0<(a=ve(c,"script")).length&&ye(a,!f&&ve(e,"script")),c},cleanData:function(e){for(var t,n,r,i=S.event.special,o=0;void 0!==(n=e[o]);o++)if(V(n)){if(t=n[Y.expando]){if(t.events)for(r in t.events)i[r]?S.event.remove(n,r):S.removeEvent(n,r,t.handle);n[Y.expando]=void 0}n[Q.expando]&&(n[Q.expando]=void 0)}}}),S.fn.extend({detach:function(e){return Oe(this,e,!0)},remove:function(e){return Oe(this,e)},text:function(e){return $(this,function(e){return void 0===e?S.text(this):this.empty().each(function(){1!==this.nodeType&&11!==this.nodeType&&9!==this.nodeType||(this.textContent=e)})},null,e,arguments.length)},append:function(){return He(this,arguments,function(e){1!==this.nodeType&&11!==this.nodeType&&9!==this.nodeType||je(this,e).appendChild(e)})},prepend:function(){return He(this,arguments,function(e){if(1===this.nodeType||11===this.nodeType||9===this.nodeType){var t=je(this,e);t.insertBefore(e,t.firstChild)}})},before:function(){return He(this,arguments,function(e){this.parentNode&&this.parentNode.insertBefore(e,this)})},after:function(){return He(this,arguments,function(e){this.parentNode&&this.parentNode.insertBefore(e,this.nextSibling)})},empty:function(){for(var e,t=0;null!=(e=this[t]);t++)1===e.nodeType&&(S.cleanData(ve(e,!1)),e.textContent="");return this},clone:function(e,t){return e=null!=e&&e,t=null==t?e:t,this.map(function(){return S.clone(this,e,t)})},html:function(e){return $(this,function(e){var t=this[0]||{},n=0,r=this.length;if(void 0===e&&1===t.nodeType)return t.innerHTML;if("string"==typeof e&&!ke.test(e)&&!ge[(de.exec(e)||["",""])[1].toLowerCase()]){e=S.htmlPrefilter(e);try{for(;n<r;n++)1===(t=this[n]||{}).nodeType&&(S.cleanData(ve(t,!1)),t.innerHTML=e);t=0}catch(e){}}t&&this.empty().append(e)},null,e,arguments.length)},replaceWith:function(){var n=[];return He(this,arguments,function(e){var t=this.parentNode;S.inArray(this,n)<0&&(S.cleanData(ve(this)),t&&t.replaceChild(e,this))},n)}}),S.each({appendTo:"append",prependTo:"prepend",insertBefore:"before",insertAfter:"after",replaceAll:"replaceWith"},function(e,a){S.fn[e]=function(e){for(var t,n=[],r=S(e),i=r.length-1,o=0;o<=i;o++)t=o===i?this:this.clone(!0),S(r[o])[a](t),u.apply(n,t.get());return this.pushStack(n)}});var Pe=new RegExp("^("+ee+")(?!px)[a-z%]+$","i"),Re=function(e){var t=e.ownerDocument.defaultView;return t&&t.opener||(t=C),t.getComputedStyle(e)},Me=function(e,t,n){var r,i,o={};for(i in t)o[i]=e.style[i],e.style[i]=t[i];for(i in r=n.call(e),t)e.style[i]=o[i];return r},Ie=new RegExp(ne.join("|"),"i");function We(e,t,n){var r,i,o,a,s=e.style;return(n=n||Re(e))&&(""!==(a=n.getPropertyValue(t)||n[t])||ie(e)||(a=S.style(e,t)),!y.pixelBoxStyles()&&Pe.test(a)&&Ie.test(t)&&(r=s.width,i=s.minWidth,o=s.maxWidth,s.minWidth=s.maxWidth=s.width=a,a=n.width,s.width=r,s.minWidth=i,s.maxWidth=o)),void 0!==a?a+"":a}function Fe(e,t){return{get:function(){if(!e())return(this.get=t).apply(this,arguments);delete this.get}}}!function(){function e(){if(l){u.style.cssText="position:absolute;left:-11111px;width:60px;margin-top:1px;padding:0;border:0",l.style.cssText="position:relative;display:block;box-sizing:border-box;overflow:scroll;margin:auto;border:1px;padding:1px;width:60%;top:1%",re.appendChild(u).appendChild(l);var e=C.getComputedStyle(l);n="1%"!==e.top,s=12===t(e.marginLeft),l.style.right="60%",o=36===t(e.right),r=36===t(e.width),l.style.position="absolute",i=12===t(l.offsetWidth/3),re.removeChild(u),l=null}}function t(e){return Math.round(parseFloat(e))}var n,r,i,o,a,s,u=E.createElement("div"),l=E.createElement("div");l.style&&(l.style.backgroundClip="content-box",l.cloneNode(!0).style.backgroundClip="",y.clearCloneStyle="content-box"===l.style.backgroundClip,S.extend(y,{boxSizingReliable:function(){return e(),r},pixelBoxStyles:function(){return e(),o},pixelPosition:function(){return e(),n},reliableMarginLeft:function(){return e(),s},scrollboxSize:function(){return e(),i},reliableTrDimensions:function(){var e,t,n,r;return null==a&&(e=E.createElement("table"),t=E.createElement("tr"),n=E.createElement("div"),e.style.cssText="position:absolute;left:-11111px;border-collapse:separate",t.style.cssText="border:1px solid",t.style.height="1px",n.style.height="9px",n.style.display="block",re.appendChild(e).appendChild(t).appendChild(n),r=C.getComputedStyle(t),a=parseInt(r.height,10)+parseInt(r.borderTopWidth,10)+parseInt(r.borderBottomWidth,10)===t.offsetHeight,re.removeChild(e)),a}}))}();var Be=["Webkit","Moz","ms"],$e=E.createElement("div").style,_e={};function ze(e){var t=S.cssProps[e]||_e[e];return t||(e in $e?e:_e[e]=function(e){var t=e[0].toUpperCase()+e.slice(1),n=Be.length;while(n--)if((e=Be[n]+t)in $e)return e}(e)||e)}var Ue=/^(none|table(?!-c[ea]).+)/,Xe=/^--/,Ve={position:"absolute",visibility:"hidden",display:"block"},Ge={letterSpacing:"0",fontWeight:"400"};function Ye(e,t,n){var r=te.exec(t);return r?Math.max(0,r[2]-(n||0))+(r[3]||"px"):t}function Qe(e,t,n,r,i,o){var a="width"===t?1:0,s=0,u=0;if(n===(r?"border":"content"))return 0;for(;a<4;a+=2)"margin"===n&&(u+=S.css(e,n+ne[a],!0,i)),r?("content"===n&&(u-=S.css(e,"padding"+ne[a],!0,i)),"margin"!==n&&(u-=S.css(e,"border"+ne[a]+"Width",!0,i))):(u+=S.css(e,"padding"+ne[a],!0,i),"padding"!==n?u+=S.css(e,"border"+ne[a]+"Width",!0,i):s+=S.css(e,"border"+ne[a]+"Width",!0,i));return!r&&0<=o&&(u+=Math.max(0,Math.ceil(e["offset"+t[0].toUpperCase()+t.slice(1)]-o-u-s-.5))||0),u}function Je(e,t,n){var r=Re(e),i=(!y.boxSizingReliable()||n)&&"border-box"===S.css(e,"boxSizing",!1,r),o=i,a=We(e,t,r),s="offset"+t[0].toUpperCase()+t.slice(1);if(Pe.test(a)){if(!n)return a;a="auto"}return(!y.boxSizingReliable()&&i||!y.reliableTrDimensions()&&A(e,"tr")||"auto"===a||!parseFloat(a)&&"inline"===S.css(e,"display",!1,r))&&e.getClientRects().length&&(i="border-box"===S.css(e,"boxSizing",!1,r),(o=s in e)&&(a=e[s])),(a=parseFloat(a)||0)+Qe(e,t,n||(i?"border":"content"),o,r,a)+"px"}function Ke(e,t,n,r,i){return new Ke.prototype.init(e,t,n,r,i)}S.extend({cssHooks:{opacity:{get:function(e,t){if(t){var n=We(e,"opacity");return""===n?"1":n}}}},cssNumber:{animationIterationCount:!0,columnCount:!0,fillOpacity:!0,flexGrow:!0,flexShrink:!0,fontWeight:!0,gridArea:!0,gridColumn:!0,gridColumnEnd:!0,gridColumnStart:!0,gridRow:!0,gridRowEnd:!0,gridRowStart:!0,lineHeight:!0,opacity:!0,order:!0,orphans:!0,widows:!0,zIndex:!0,zoom:!0},cssProps:{},style:function(e,t,n,r){if(e&&3!==e.nodeType&&8!==e.nodeType&&e.style){var i,o,a,s=X(t),u=Xe.test(t),l=e.style;if(u||(t=ze(s)),a=S.cssHooks[t]||S.cssHooks[s],void 0===n)return a&&"get"in a&&void 0!==(i=a.get(e,!1,r))?i:l[t];"string"===(o=typeof n)&&(i=te.exec(n))&&i[1]&&(n=se(e,t,i),o="number"),null!=n&&n==n&&("number"!==o||u||(n+=i&&i[3]||(S.cssNumber[s]?"":"px")),y.clearCloneStyle||""!==n||0!==t.indexOf("background")||(l[t]="inherit"),a&&"set"in a&&void 0===(n=a.set(e,n,r))||(u?l.setProperty(t,n):l[t]=n))}},css:function(e,t,n,r){var i,o,a,s=X(t);return Xe.test(t)||(t=ze(s)),(a=S.cssHooks[t]||S.cssHooks[s])&&"get"in a&&(i=a.get(e,!0,n)),void 0===i&&(i=We(e,t,r)),"normal"===i&&t in Ge&&(i=Ge[t]),""===n||n?(o=parseFloat(i),!0===n||isFinite(o)?o||0:i):i}}),S.each(["height","width"],function(e,u){S.cssHooks[u]={get:function(e,t,n){if(t)return!Ue.test(S.css(e,"display"))||e.getClientRects().length&&e.getBoundingClientRect().width?Je(e,u,n):Me(e,Ve,function(){return Je(e,u,n)})},set:function(e,t,n){var r,i=Re(e),o=!y.scrollboxSize()&&"absolute"===i.position,a=(o||n)&&"border-box"===S.css(e,"boxSizing",!1,i),s=n?Qe(e,u,n,a,i):0;return a&&o&&(s-=Math.ceil(e["offset"+u[0].toUpperCase()+u.slice(1)]-parseFloat(i[u])-Qe(e,u,"border",!1,i)-.5)),s&&(r=te.exec(t))&&"px"!==(r[3]||"px")&&(e.style[u]=t,t=S.css(e,u)),Ye(0,t,s)}}}),S.cssHooks.marginLeft=Fe(y.reliableMarginLeft,function(e,t){if(t)return(parseFloat(We(e,"marginLeft"))||e.getBoundingClientRect().left-Me(e,{marginLeft:0},function(){return e.getBoundingClientRect().left}))+"px"}),S.each({margin:"",padding:"",border:"Width"},function(i,o){S.cssHooks[i+o]={expand:function(e){for(var t=0,n={},r="string"==typeof e?e.split(" "):[e];t<4;t++)n[i+ne[t]+o]=r[t]||r[t-2]||r[0];return n}},"margin"!==i&&(S.cssHooks[i+o].set=Ye)}),S.fn.extend({css:function(e,t){return $(this,function(e,t,n){var r,i,o={},a=0;if(Array.isArray(t)){for(r=Re(e),i=t.length;a<i;a++)o[t[a]]=S.css(e,t[a],!1,r);return o}return void 0!==n?S.style(e,t,n):S.css(e,t)},e,t,1<arguments.length)}}),((S.Tween=Ke).prototype={constructor:Ke,init:function(e,t,n,r,i,o){this.elem=e,this.prop=n,this.easing=i||S.easing._default,this.options=t,this.start=this.now=this.cur(),this.end=r,this.unit=o||(S.cssNumber[n]?"":"px")},cur:function(){var e=Ke.propHooks[this.prop];return e&&e.get?e.get(this):Ke.propHooks._default.get(this)},run:function(e){var t,n=Ke.propHooks[this.prop];return this.options.duration?this.pos=t=S.easing[this.easing](e,this.options.duration*e,0,1,this.options.duration):this.pos=t=e,this.now=(this.end-this.start)*t+this.start,this.options.step&&this.options.step.call(this.elem,this.now,this),n&&n.set?n.set(this):Ke.propHooks._default.set(this),this}}).init.prototype=Ke.prototype,(Ke.propHooks={_default:{get:function(e){var t;return 1!==e.elem.nodeType||null!=e.elem[e.prop]&&null==e.elem.style[e.prop]?e.elem[e.prop]:(t=S.css(e.elem,e.prop,""))&&"auto"!==t?t:0},set:function(e){S.fx.step[e.prop]?S.fx.step[e.prop](e):1!==e.elem.nodeType||!S.cssHooks[e.prop]&&null==e.elem.style[ze(e.prop)]?e.elem[e.prop]=e.now:S.style(e.elem,e.prop,e.now+e.unit)}}}).scrollTop=Ke.propHooks.scrollLeft={set:function(e){e.elem.nodeType&&e.elem.parentNode&&(e.elem[e.prop]=e.now)}},S.easing={linear:function(e){return e},swing:function(e){return.5-Math.cos(e*Math.PI)/2},_default:"swing"},S.fx=Ke.prototype.init,S.fx.step={};var Ze,et,tt,nt,rt=/^(?:toggle|show|hide)$/,it=/queueHooks$/;function ot(){et&&(!1===E.hidden&&C.requestAnimationFrame?C.requestAnimationFrame(ot):C.setTimeout(ot,S.fx.interval),S.fx.tick())}function at(){return C.setTimeout(function(){Ze=void 0}),Ze=Date.now()}function st(e,t){var n,r=0,i={height:e};for(t=t?1:0;r<4;r+=2-t)i["margin"+(n=ne[r])]=i["padding"+n]=e;return t&&(i.opacity=i.width=e),i}function ut(e,t,n){for(var r,i=(lt.tweeners[t]||[]).concat(lt.tweeners["*"]),o=0,a=i.length;o<a;o++)if(r=i[o].call(n,t,e))return r}function lt(o,e,t){var n,a,r=0,i=lt.prefilters.length,s=S.Deferred().always(function(){delete u.elem}),u=function(){if(a)return!1;for(var e=Ze||at(),t=Math.max(0,l.startTime+l.duration-e),n=1-(t/l.duration||0),r=0,i=l.tweens.length;r<i;r++)l.tweens[r].run(n);return s.notifyWith(o,[l,n,t]),n<1&&i?t:(i||s.notifyWith(o,[l,1,0]),s.resolveWith(o,[l]),!1)},l=s.promise({elem:o,props:S.extend({},e),opts:S.extend(!0,{specialEasing:{},easing:S.easing._default},t),originalProperties:e,originalOptions:t,startTime:Ze||at(),duration:t.duration,tweens:[],createTween:function(e,t){var n=S.Tween(o,l.opts,e,t,l.opts.specialEasing[e]||l.opts.easing);return l.tweens.push(n),n},stop:function(e){var t=0,n=e?l.tweens.length:0;if(a)return this;for(a=!0;t<n;t++)l.tweens[t].run(1);return e?(s.notifyWith(o,[l,1,0]),s.resolveWith(o,[l,e])):s.rejectWith(o,[l,e]),this}}),c=l.props;for(!function(e,t){var n,r,i,o,a;for(n in e)if(i=t[r=X(n)],o=e[n],Array.isArray(o)&&(i=o[1],o=e[n]=o[0]),n!==r&&(e[r]=o,delete e[n]),(a=S.cssHooks[r])&&"expand"in a)for(n in o=a.expand(o),delete e[r],o)n in e||(e[n]=o[n],t[n]=i);else t[r]=i}(c,l.opts.specialEasing);r<i;r++)if(n=lt.prefilters[r].call(l,o,c,l.opts))return m(n.stop)&&(S._queueHooks(l.elem,l.opts.queue).stop=n.stop.bind(n)),n;return S.map(c,ut,l),m(l.opts.start)&&l.opts.start.call(o,l),l.progress(l.opts.progress).done(l.opts.done,l.opts.complete).fail(l.opts.fail).always(l.opts.always),S.fx.timer(S.extend(u,{elem:o,anim:l,queue:l.opts.queue})),l}S.Animation=S.extend(lt,{tweeners:{"*":[function(e,t){var n=this.createTween(e,t);return se(n.elem,e,te.exec(t),n),n}]},tweener:function(e,t){m(e)?(t=e,e=["*"]):e=e.match(P);for(var n,r=0,i=e.length;r<i;r++)n=e[r],lt.tweeners[n]=lt.tweeners[n]||[],lt.tweeners[n].unshift(t)},prefilters:[function(e,t,n){var r,i,o,a,s,u,l,c,f="width"in t||"height"in t,p=this,d={},h=e.style,g=e.nodeType&&ae(e),v=Y.get(e,"fxshow");for(r in n.queue||(null==(a=S._queueHooks(e,"fx")).unqueued&&(a.unqueued=0,s=a.empty.fire,a.empty.fire=function(){a.unqueued||s()}),a.unqueued++,p.always(function(){p.always(function(){a.unqueued--,S.queue(e,"fx").length||a.empty.fire()})})),t)if(i=t[r],rt.test(i)){if(delete t[r],o=o||"toggle"===i,i===(g?"hide":"show")){if("show"!==i||!v||void 0===v[r])continue;g=!0}d[r]=v&&v[r]||S.style(e,r)}if((u=!S.isEmptyObject(t))||!S.isEmptyObject(d))for(r in f&&1===e.nodeType&&(n.overflow=[h.overflow,h.overflowX,h.overflowY],null==(l=v&&v.display)&&(l=Y.get(e,"display")),"none"===(c=S.css(e,"display"))&&(l?c=l:(le([e],!0),l=e.style.display||l,c=S.css(e,"display"),le([e]))),("inline"===c||"inline-block"===c&&null!=l)&&"none"===S.css(e,"float")&&(u||(p.done(function(){h.display=l}),null==l&&(c=h.display,l="none"===c?"":c)),h.display="inline-block")),n.overflow&&(h.overflow="hidden",p.always(function(){h.overflow=n.overflow[0],h.overflowX=n.overflow[1],h.overflowY=n.overflow[2]})),u=!1,d)u||(v?"hidden"in v&&(g=v.hidden):v=Y.access(e,"fxshow",{display:l}),o&&(v.hidden=!g),g&&le([e],!0),p.done(function(){for(r in g||le([e]),Y.remove(e,"fxshow"),d)S.style(e,r,d[r])})),u=ut(g?v[r]:0,r,p),r in v||(v[r]=u.start,g&&(u.end=u.start,u.start=0))}],prefilter:function(e,t){t?lt.prefilters.unshift(e):lt.prefilters.push(e)}}),S.speed=function(e,t,n){var r=e&&"object"==typeof e?S.extend({},e):{complete:n||!n&&t||m(e)&&e,duration:e,easing:n&&t||t&&!m(t)&&t};return S.fx.off?r.duration=0:"number"!=typeof r.duration&&(r.duration in S.fx.speeds?r.duration=S.fx.speeds[r.duration]:r.duration=S.fx.speeds._default),null!=r.queue&&!0!==r.queue||(r.queue="fx"),r.old=r.complete,r.complete=function(){m(r.old)&&r.old.call(this),r.queue&&S.dequeue(this,r.queue)},r},S.fn.extend({fadeTo:function(e,t,n,r){return this.filter(ae).css("opacity",0).show().end().animate({opacity:t},e,n,r)},animate:function(t,e,n,r){var i=S.isEmptyObject(t),o=S.speed(e,n,r),a=function(){var e=lt(this,S.extend({},t),o);(i||Y.get(this,"finish"))&&e.stop(!0)};return a.finish=a,i||!1===o.queue?this.each(a):this.queue(o.queue,a)},stop:function(i,e,o){var a=function(e){var t=e.stop;delete e.stop,t(o)};return"string"!=typeof i&&(o=e,e=i,i=void 0),e&&this.queue(i||"fx",[]),this.each(function(){var e=!0,t=null!=i&&i+"queueHooks",n=S.timers,r=Y.get(this);if(t)r[t]&&r[t].stop&&a(r[t]);else for(t in r)r[t]&&r[t].stop&&it.test(t)&&a(r[t]);for(t=n.length;t--;)n[t].elem!==this||null!=i&&n[t].queue!==i||(n[t].anim.stop(o),e=!1,n.splice(t,1));!e&&o||S.dequeue(this,i)})},finish:function(a){return!1!==a&&(a=a||"fx"),this.each(function(){var e,t=Y.get(this),n=t[a+"queue"],r=t[a+"queueHooks"],i=S.timers,o=n?n.length:0;for(t.finish=!0,S.queue(this,a,[]),r&&r.stop&&r.stop.call(this,!0),e=i.length;e--;)i[e].elem===this&&i[e].queue===a&&(i[e].anim.stop(!0),i.splice(e,1));for(e=0;e<o;e++)n[e]&&n[e].finish&&n[e].finish.call(this);delete t.finish})}}),S.each(["toggle","show","hide"],function(e,r){var i=S.fn[r];S.fn[r]=function(e,t,n){return null==e||"boolean"==typeof e?i.apply(this,arguments):this.animate(st(r,!0),e,t,n)}}),S.each({slideDown:st("show"),slideUp:st("hide"),slideToggle:st("toggle"),fadeIn:{opacity:"show"},fadeOut:{opacity:"hide"},fadeToggle:{opacity:"toggle"}},function(e,r){S.fn[e]=function(e,t,n){return this.animate(r,e,t,n)}}),S.timers=[],S.fx.tick=function(){var e,t=0,n=S.timers;for(Ze=Date.now();t<n.length;t++)(e=n[t])()||n[t]!==e||n.splice(t--,1);n.length||S.fx.stop(),Ze=void 0},S.fx.timer=function(e){S.timers.push(e),S.fx.start()},S.fx.interval=13,S.fx.start=function(){et||(et=!0,ot())},S.fx.stop=function(){et=null},S.fx.speeds={slow:600,fast:200,_default:400},S.fn.delay=function(r,e){return r=S.fx&&S.fx.speeds[r]||r,e=e||"fx",this.queue(e,function(e,t){var n=C.setTimeout(e,r);t.stop=function(){C.clearTimeout(n)}})},tt=E.createElement("input"),nt=E.createElement("select").appendChild(E.createElement("option")),tt.type="checkbox",y.checkOn=""!==tt.value,y.optSelected=nt.selected,(tt=E.createElement("input")).value="t",tt.type="radio",y.radioValue="t"===tt.value;var ct,ft=S.expr.attrHandle;S.fn.extend({attr:function(e,t){return $(this,S.attr,e,t,1<arguments.length)},removeAttr:function(e){return this.each(function(){S.removeAttr(this,e)})}}),S.extend({attr:function(e,t,n){var r,i,o=e.nodeType;if(3!==o&&8!==o&&2!==o)return"undefined"==typeof e.getAttribute?S.prop(e,t,n):(1===o&&S.isXMLDoc(e)||(i=S.attrHooks[t.toLowerCase()]||(S.expr.match.bool.test(t)?ct:void 0)),void 0!==n?null===n?void S.removeAttr(e,t):i&&"set"in i&&void 0!==(r=i.set(e,n,t))?r:(e.setAttribute(t,n+""),n):i&&"get"in i&&null!==(r=i.get(e,t))?r:null==(r=S.find.attr(e,t))?void 0:r)},attrHooks:{type:{set:function(e,t){if(!y.radioValue&&"radio"===t&&A(e,"input")){var n=e.value;return e.setAttribute("type",t),n&&(e.value=n),t}}}},removeAttr:function(e,t){var n,r=0,i=t&&t.match(P);if(i&&1===e.nodeType)while(n=i[r++])e.removeAttribute(n)}}),ct={set:function(e,t,n){return!1===t?S.removeAttr(e,n):e.setAttribute(n,n),n}},S.each(S.expr.match.bool.source.match(/\w+/g),function(e,t){var a=ft[t]||S.find.attr;ft[t]=function(e,t,n){var r,i,o=t.toLowerCase();return n||(i=ft[o],ft[o]=r,r=null!=a(e,t,n)?o:null,ft[o]=i),r}});var pt=/^(?:input|select|textarea|button)$/i,dt=/^(?:a|area)$/i;function ht(e){return(e.match(P)||[]).join(" ")}function gt(e){return e.getAttribute&&e.getAttribute("class")||""}function vt(e){return Array.isArray(e)?e:"string"==typeof e&&e.match(P)||[]}S.fn.extend({prop:function(e,t){return $(this,S.prop,e,t,1<arguments.length)},removeProp:function(e){return this.each(function(){delete this[S.propFix[e]||e]})}}),S.extend({prop:function(e,t,n){var r,i,o=e.nodeType;if(3!==o&&8!==o&&2!==o)return 1===o&&S.isXMLDoc(e)||(t=S.propFix[t]||t,i=S.propHooks[t]),void 0!==n?i&&"set"in i&&void 0!==(r=i.set(e,n,t))?r:e[t]=n:i&&"get"in i&&null!==(r=i.get(e,t))?r:e[t]},propHooks:{tabIndex:{get:function(e){var t=S.find.attr(e,"tabindex");return t?parseInt(t,10):pt.test(e.nodeName)||dt.test(e.nodeName)&&e.href?0:-1}}},propFix:{"for":"htmlFor","class":"className"}}),y.optSelected||(S.propHooks.selected={get:function(e){var t=e.parentNode;return t&&t.parentNode&&t.parentNode.selectedIndex,null},set:function(e){var t=e.parentNode;t&&(t.selectedIndex,t.parentNode&&t.parentNode.selectedIndex)}}),S.each(["tabIndex","readOnly","maxLength","cellSpacing","cellPadding","rowSpan","colSpan","useMap","frameBorder","contentEditable"],function(){S.propFix[this.toLowerCase()]=this}),S.fn.extend({addClass:function(t){var e,n,r,i,o,a,s,u=0;if(m(t))return this.each(function(e){S(this).addClass(t.call(this,e,gt(this)))});if((e=vt(t)).length)while(n=this[u++])if(i=gt(n),r=1===n.nodeType&&" "+ht(i)+" "){a=0;while(o=e[a++])r.indexOf(" "+o+" ")<0&&(r+=o+" ");i!==(s=ht(r))&&n.setAttribute("class",s)}return this},removeClass:function(t){var e,n,r,i,o,a,s,u=0;if(m(t))return this.each(function(e){S(this).removeClass(t.call(this,e,gt(this)))});if(!arguments.length)return this.attr("class","");if((e=vt(t)).length)while(n=this[u++])if(i=gt(n),r=1===n.nodeType&&" "+ht(i)+" "){a=0;while(o=e[a++])while(-1<r.indexOf(" "+o+" "))r=r.replace(" "+o+" "," ");i!==(s=ht(r))&&n.setAttribute("class",s)}return this},toggleClass:function(i,t){var o=typeof i,a="string"===o||Array.isArray(i);return"boolean"==typeof t&&a?t?this.addClass(i):this.removeClass(i):m(i)?this.each(function(e){S(this).toggleClass(i.call(this,e,gt(this),t),t)}):this.each(function(){var e,t,n,r;if(a){t=0,n=S(this),r=vt(i);while(e=r[t++])n.hasClass(e)?n.removeClass(e):n.addClass(e)}else void 0!==i&&"boolean"!==o||((e=gt(this))&&Y.set(this,"__className__",e),this.setAttribute&&this.setAttribute("class",e||!1===i?"":Y.get(this,"__className__")||""))})},hasClass:function(e){var t,n,r=0;t=" "+e+" ";while(n=this[r++])if(1===n.nodeType&&-1<(" "+ht(gt(n))+" ").indexOf(t))return!0;return!1}});var yt=/\r/g;S.fn.extend({val:function(n){var r,e,i,t=this[0];return arguments.length?(i=m(n),this.each(function(e){var t;1===this.nodeType&&(null==(t=i?n.call(this,e,S(this).val()):n)?t="":"number"==typeof t?t+="":Array.isArray(t)&&(t=S.map(t,function(e){return null==e?"":e+""})),(r=S.valHooks[this.type]||S.valHooks[this.nodeName.toLowerCase()])&&"set"in r&&void 0!==r.set(this,t,"value")||(this.value=t))})):t?(r=S.valHooks[t.type]||S.valHooks[t.nodeName.toLowerCase()])&&"get"in r&&void 0!==(e=r.get(t,"value"))?e:"string"==typeof(e=t.value)?e.replace(yt,""):null==e?"":e:void 0}}),S.extend({valHooks:{option:{get:function(e){var t=S.find.attr(e,"value");return null!=t?t:ht(S.text(e))}},select:{get:function(e){var t,n,r,i=e.options,o=e.selectedIndex,a="select-one"===e.type,s=a?null:[],u=a?o+1:i.length;for(r=o<0?u:a?o:0;r<u;r++)if(((n=i[r]).selected||r===o)&&!n.disabled&&(!n.parentNode.disabled||!A(n.parentNode,"optgroup"))){if(t=S(n).val(),a)return t;s.push(t)}return s},set:function(e,t){var n,r,i=e.options,o=S.makeArray(t),a=i.length;while(a--)((r=i[a]).selected=-1<S.inArray(S.valHooks.option.get(r),o))&&(n=!0);return n||(e.selectedIndex=-1),o}}}}),S.each(["radio","checkbox"],function(){S.valHooks[this]={set:function(e,t){if(Array.isArray(t))return e.checked=-1<S.inArray(S(e).val(),t)}},y.checkOn||(S.valHooks[this].get=function(e){return null===e.getAttribute("value")?"on":e.value})}),y.focusin="onfocusin"in C;var mt=/^(?:focusinfocus|focusoutblur)$/,xt=function(e){e.stopPropagation()};S.extend(S.event,{trigger:function(e,t,n,r){var i,o,a,s,u,l,c,f,p=[n||E],d=v.call(e,"type")?e.type:e,h=v.call(e,"namespace")?e.namespace.split("."):[];if(o=f=a=n=n||E,3!==n.nodeType&&8!==n.nodeType&&!mt.test(d+S.event.triggered)&&(-1<d.indexOf(".")&&(d=(h=d.split(".")).shift(),h.sort()),u=d.indexOf(":")<0&&"on"+d,(e=e[S.expando]?e:new S.Event(d,"object"==typeof e&&e)).isTrigger=r?2:3,e.namespace=h.join("."),e.rnamespace=e.namespace?new RegExp("(^|\\.)"+h.join("\\.(?:.*\\.|)")+"(\\.|$)"):null,e.result=void 0,e.target||(e.target=n),t=null==t?[e]:S.makeArray(t,[e]),c=S.event.special[d]||{},r||!c.trigger||!1!==c.trigger.apply(n,t))){if(!r&&!c.noBubble&&!x(n)){for(s=c.delegateType||d,mt.test(s+d)||(o=o.parentNode);o;o=o.parentNode)p.push(o),a=o;a===(n.ownerDocument||E)&&p.push(a.defaultView||a.parentWindow||C)}i=0;while((o=p[i++])&&!e.isPropagationStopped())f=o,e.type=1<i?s:c.bindType||d,(l=(Y.get(o,"events")||Object.create(null))[e.type]&&Y.get(o,"handle"))&&l.apply(o,t),(l=u&&o[u])&&l.apply&&V(o)&&(e.result=l.apply(o,t),!1===e.result&&e.preventDefault());return e.type=d,r||e.isDefaultPrevented()||c._default&&!1!==c._default.apply(p.pop(),t)||!V(n)||u&&m(n[d])&&!x(n)&&((a=n[u])&&(n[u]=null),S.event.triggered=d,e.isPropagationStopped()&&f.addEventListener(d,xt),n[d](),e.isPropagationStopped()&&f.removeEventListener(d,xt),S.event.triggered=void 0,a&&(n[u]=a)),e.result}},simulate:function(e,t,n){var r=S.extend(new S.Event,n,{type:e,isSimulated:!0});S.event.trigger(r,null,t)}}),S.fn.extend({trigger:function(e,t){return this.each(function(){S.event.trigger(e,t,this)})},triggerHandler:function(e,t){var n=this[0];if(n)return S.event.trigger(e,t,n,!0)}}),y.focusin||S.each({focus:"focusin",blur:"focusout"},function(n,r){var i=function(e){S.event.simulate(r,e.target,S.event.fix(e))};S.event.special[r]={setup:function(){var e=this.ownerDocument||this.document||this,t=Y.access(e,r);t||e.addEventListener(n,i,!0),Y.access(e,r,(t||0)+1)},teardown:function(){var e=this.ownerDocument||this.document||this,t=Y.access(e,r)-1;t?Y.access(e,r,t):(e.removeEventListener(n,i,!0),Y.remove(e,r))}}});var bt=C.location,wt={guid:Date.now()},Tt=/\?/;S.parseXML=function(e){var t,n;if(!e||"string"!=typeof e)return null;try{t=(new C.DOMParser).parseFromString(e,"text/xml")}catch(e){}return n=t&&t.getElementsByTagName("parsererror")[0],t&&!n||S.error("Invalid XML: "+(n?S.map(n.childNodes,function(e){return e.textContent}).join("\n"):e)),t};var Ct=/\[\]$/,Et=/\r?\n/g,St=/^(?:submit|button|image|reset|file)$/i,kt=/^(?:input|select|textarea|keygen)/i;function At(n,e,r,i){var t;if(Array.isArray(e))S.each(e,function(e,t){r||Ct.test(n)?i(n,t):At(n+"["+("object"==typeof t&&null!=t?e:"")+"]",t,r,i)});else if(r||"object"!==w(e))i(n,e);else for(t in e)At(n+"["+t+"]",e[t],r,i)}S.param=function(e,t){var n,r=[],i=function(e,t){var n=m(t)?t():t;r[r.length]=encodeURIComponent(e)+"="+encodeURIComponent(null==n?"":n)};if(null==e)return"";if(Array.isArray(e)||e.jquery&&!S.isPlainObject(e))S.each(e,function(){i(this.name,this.value)});else for(n in e)At(n,e[n],t,i);return r.join("&")},S.fn.extend({serialize:function(){return S.param(this.serializeArray())},serializeArray:function(){return this.map(function(){var e=S.prop(this,"elements");return e?S.makeArray(e):this}).filter(function(){var e=this.type;return this.name&&!S(this).is(":disabled")&&kt.test(this.nodeName)&&!St.test(e)&&(this.checked||!pe.test(e))}).map(function(e,t){var n=S(this).val();return null==n?null:Array.isArray(n)?S.map(n,function(e){return{name:t.name,value:e.replace(Et,"\r\n")}}):{name:t.name,value:n.replace(Et,"\r\n")}}).get()}});var Nt=/%20/g,jt=/#.*$/,Dt=/([?&])_=[^&]*/,qt=/^(.*?):[ \t]*([^\r\n]*)$/gm,Lt=/^(?:GET|HEAD)$/,Ht=/^\/\//,Ot={},Pt={},Rt="*/".concat("*"),Mt=E.createElement("a");function It(o){return function(e,t){"string"!=typeof e&&(t=e,e="*");var n,r=0,i=e.toLowerCase().match(P)||[];if(m(t))while(n=i[r++])"+"===n[0]?(n=n.slice(1)||"*",(o[n]=o[n]||[]).unshift(t)):(o[n]=o[n]||[]).push(t)}}function Wt(t,i,o,a){var s={},u=t===Pt;function l(e){var r;return s[e]=!0,S.each(t[e]||[],function(e,t){var n=t(i,o,a);return"string"!=typeof n||u||s[n]?u?!(r=n):void 0:(i.dataTypes.unshift(n),l(n),!1)}),r}return l(i.dataTypes[0])||!s["*"]&&l("*")}function Ft(e,t){var n,r,i=S.ajaxSettings.flatOptions||{};for(n in t)void 0!==t[n]&&((i[n]?e:r||(r={}))[n]=t[n]);return r&&S.extend(!0,e,r),e}Mt.href=bt.href,S.extend({active:0,lastModified:{},etag:{},ajaxSettings:{url:bt.href,type:"GET",isLocal:/^(?:about|app|app-storage|.+-extension|file|res|widget):$/.test(bt.protocol),global:!0,processData:!0,async:!0,contentType:"application/x-www-form-urlencoded; charset=UTF-8",accepts:{"*":Rt,text:"text/plain",html:"text/html",xml:"application/xml, text/xml",json:"application/json, text/javascript"},contents:{xml:/\bxml\b/,html:/\bhtml/,json:/\bjson\b/},responseFields:{xml:"responseXML",text:"responseText",json:"responseJSON"},converters:{"* text":String,"text html":!0,"text json":JSON.parse,"text xml":S.parseXML},flatOptions:{url:!0,context:!0}},ajaxSetup:function(e,t){return t?Ft(Ft(e,S.ajaxSettings),t):Ft(S.ajaxSettings,e)},ajaxPrefilter:It(Ot),ajaxTransport:It(Pt),ajax:function(e,t){"object"==typeof e&&(t=e,e=void 0),t=t||{};var c,f,p,n,d,r,h,g,i,o,v=S.ajaxSetup({},t),y=v.context||v,m=v.context&&(y.nodeType||y.jquery)?S(y):S.event,x=S.Deferred(),b=S.Callbacks("once memory"),w=v.statusCode||{},a={},s={},u="canceled",T={readyState:0,getResponseHeader:function(e){var t;if(h){if(!n){n={};while(t=qt.exec(p))n[t[1].toLowerCase()+" "]=(n[t[1].toLowerCase()+" "]||[]).concat(t[2])}t=n[e.toLowerCase()+" "]}return null==t?null:t.join(", ")},getAllResponseHeaders:function(){return h?p:null},setRequestHeader:function(e,t){return null==h&&(e=s[e.toLowerCase()]=s[e.toLowerCase()]||e,a[e]=t),this},overrideMimeType:function(e){return null==h&&(v.mimeType=e),this},statusCode:function(e){var t;if(e)if(h)T.always(e[T.status]);else for(t in e)w[t]=[w[t],e[t]];return this},abort:function(e){var t=e||u;return c&&c.abort(t),l(0,t),this}};if(x.promise(T),v.url=((e||v.url||bt.href)+"").replace(Ht,bt.protocol+"//"),v.type=t.method||t.type||v.method||v.type,v.dataTypes=(v.dataType||"*").toLowerCase().match(P)||[""],null==v.crossDomain){r=E.createElement("a");try{r.href=v.url,r.href=r.href,v.crossDomain=Mt.protocol+"//"+Mt.host!=r.protocol+"//"+r.host}catch(e){v.crossDomain=!0}}if(v.data&&v.processData&&"string"!=typeof v.data&&(v.data=S.param(v.data,v.traditional)),Wt(Ot,v,t,T),h)return T;for(i in(g=S.event&&v.global)&&0==S.active++&&S.event.trigger("ajaxStart"),v.type=v.type.toUpperCase(),v.hasContent=!Lt.test(v.type),f=v.url.replace(jt,""),v.hasContent?v.data&&v.processData&&0===(v.contentType||"").indexOf("application/x-www-form-urlencoded")&&(v.data=v.data.replace(Nt,"+")):(o=v.url.slice(f.length),v.data&&(v.processData||"string"==typeof v.data)&&(f+=(Tt.test(f)?"&":"?")+v.data,delete v.data),!1===v.cache&&(f=f.replace(Dt,"$1"),o=(Tt.test(f)?"&":"?")+"_="+wt.guid+++o),v.url=f+o),v.ifModified&&(S.lastModified[f]&&T.setRequestHeader("If-Modified-Since",S.lastModified[f]),S.etag[f]&&T.setRequestHeader("If-None-Match",S.etag[f])),(v.data&&v.hasContent&&!1!==v.contentType||t.contentType)&&T.setRequestHeader("Content-Type",v.contentType),T.setRequestHeader("Accept",v.dataTypes[0]&&v.accepts[v.dataTypes[0]]?v.accepts[v.dataTypes[0]]+("*"!==v.dataTypes[0]?", "+Rt+"; q=0.01":""):v.accepts["*"]),v.headers)T.setRequestHeader(i,v.headers[i]);if(v.beforeSend&&(!1===v.beforeSend.call(y,T,v)||h))return T.abort();if(u="abort",b.add(v.complete),T.done(v.success),T.fail(v.error),c=Wt(Pt,v,t,T)){if(T.readyState=1,g&&m.trigger("ajaxSend",[T,v]),h)return T;v.async&&0<v.timeout&&(d=C.setTimeout(function(){T.abort("timeout")},v.timeout));try{h=!1,c.send(a,l)}catch(e){if(h)throw e;l(-1,e)}}else l(-1,"No Transport");function l(e,t,n,r){var i,o,a,s,u,l=t;h||(h=!0,d&&C.clearTimeout(d),c=void 0,p=r||"",T.readyState=0<e?4:0,i=200<=e&&e<300||304===e,n&&(s=function(e,t,n){var r,i,o,a,s=e.contents,u=e.dataTypes;while("*"===u[0])u.shift(),void 0===r&&(r=e.mimeType||t.getResponseHeader("Content-Type"));if(r)for(i in s)if(s[i]&&s[i].test(r)){u.unshift(i);break}if(u[0]in n)o=u[0];else{for(i in n){if(!u[0]||e.converters[i+" "+u[0]]){o=i;break}a||(a=i)}o=o||a}if(o)return o!==u[0]&&u.unshift(o),n[o]}(v,T,n)),!i&&-1<S.inArray("script",v.dataTypes)&&S.inArray("json",v.dataTypes)<0&&(v.converters["text script"]=function(){}),s=function(e,t,n,r){var i,o,a,s,u,l={},c=e.dataTypes.slice();if(c[1])for(a in e.converters)l[a.toLowerCase()]=e.converters[a];o=c.shift();while(o)if(e.responseFields[o]&&(n[e.responseFields[o]]=t),!u&&r&&e.dataFilter&&(t=e.dataFilter(t,e.dataType)),u=o,o=c.shift())if("*"===o)o=u;else if("*"!==u&&u!==o){if(!(a=l[u+" "+o]||l["* "+o]))for(i in l)if((s=i.split(" "))[1]===o&&(a=l[u+" "+s[0]]||l["* "+s[0]])){!0===a?a=l[i]:!0!==l[i]&&(o=s[0],c.unshift(s[1]));break}if(!0!==a)if(a&&e["throws"])t=a(t);else try{t=a(t)}catch(e){return{state:"parsererror",error:a?e:"No conversion from "+u+" to "+o}}}return{state:"success",data:t}}(v,s,T,i),i?(v.ifModified&&((u=T.getResponseHeader("Last-Modified"))&&(S.lastModified[f]=u),(u=T.getResponseHeader("etag"))&&(S.etag[f]=u)),204===e||"HEAD"===v.type?l="nocontent":304===e?l="notmodified":(l=s.state,o=s.data,i=!(a=s.error))):(a=l,!e&&l||(l="error",e<0&&(e=0))),T.status=e,T.statusText=(t||l)+"",i?x.resolveWith(y,[o,l,T]):x.rejectWith(y,[T,l,a]),T.statusCode(w),w=void 0,g&&m.trigger(i?"ajaxSuccess":"ajaxError",[T,v,i?o:a]),b.fireWith(y,[T,l]),g&&(m.trigger("ajaxComplete",[T,v]),--S.active||S.event.trigger("ajaxStop")))}return T},getJSON:function(e,t,n){return S.get(e,t,n,"json")},getScript:function(e,t){return S.get(e,void 0,t,"script")}}),S.each(["get","post"],function(e,i){S[i]=function(e,t,n,r){return m(t)&&(r=r||n,n=t,t=void 0),S.ajax(S.extend({url:e,type:i,dataType:r,data:t,success:n},S.isPlainObject(e)&&e))}}),S.ajaxPrefilter(function(e){var t;for(t in e.headers)"content-type"===t.toLowerCase()&&(e.contentType=e.headers[t]||"")}),S._evalUrl=function(e,t,n){return S.ajax({url:e,type:"GET",dataType:"script",cache:!0,async:!1,global:!1,converters:{"text script":function(){}},dataFilter:function(e){S.globalEval(e,t,n)}})},S.fn.extend({wrapAll:function(e){var t;return this[0]&&(m(e)&&(e=e.call(this[0])),t=S(e,this[0].ownerDocument).eq(0).clone(!0),this[0].parentNode&&t.insertBefore(this[0]),t.map(function(){var e=this;while(e.firstElementChild)e=e.firstElementChild;return e}).append(this)),this},wrapInner:function(n){return m(n)?this.each(function(e){S(this).wrapInner(n.call(this,e))}):this.each(function(){var e=S(this),t=e.contents();t.length?t.wrapAll(n):e.append(n)})},wrap:function(t){var n=m(t);return this.each(function(e){S(this).wrapAll(n?t.call(this,e):t)})},unwrap:function(e){return this.parent(e).not("body").each(function(){S(this).replaceWith(this.childNodes)}),this}}),S.expr.pseudos.hidden=function(e){return!S.expr.pseudos.visible(e)},S.expr.pseudos.visible=function(e){return!!(e.offsetWidth||e.offsetHeight||e.getClientRects().length)},S.ajaxSettings.xhr=function(){try{return new C.XMLHttpRequest}catch(e){}};var Bt={0:200,1223:204},$t=S.ajaxSettings.xhr();y.cors=!!$t&&"withCredentials"in $t,y.ajax=$t=!!$t,S.ajaxTransport(function(i){var o,a;if(y.cors||$t&&!i.crossDomain)return{send:function(e,t){var n,r=i.xhr();if(r.open(i.type,i.url,i.async,i.username,i.password),i.xhrFields)for(n in i.xhrFields)r[n]=i.xhrFields[n];for(n in i.mimeType&&r.overrideMimeType&&r.overrideMimeType(i.mimeType),i.crossDomain||e["X-Requested-With"]||(e["X-Requested-With"]="XMLHttpRequest"),e)r.setRequestHeader(n,e[n]);o=function(e){return function(){o&&(o=a=r.onload=r.onerror=r.onabort=r.ontimeout=r.onreadystatechange=null,"abort"===e?r.abort():"error"===e?"number"!=typeof r.status?t(0,"error"):t(r.status,r.statusText):t(Bt[r.status]||r.status,r.statusText,"text"!==(r.responseType||"text")||"string"!=typeof r.responseText?{binary:r.response}:{text:r.responseText},r.getAllResponseHeaders()))}},r.onload=o(),a=r.onerror=r.ontimeout=o("error"),void 0!==r.onabort?r.onabort=a:r.onreadystatechange=function(){4===r.readyState&&C.setTimeout(function(){o&&a()})},o=o("abort");try{r.send(i.hasContent&&i.data||null)}catch(e){if(o)throw e}},abort:function(){o&&o()}}}),S.ajaxPrefilter(function(e){e.crossDomain&&(e.contents.script=!1)}),S.ajaxSetup({accepts:{script:"text/javascript, application/javascript, application/ecmascript, application/x-ecmascript"},contents:{script:/\b(?:java|ecma)script\b/},converters:{"text script":function(e){return S.globalEval(e),e}}}),S.ajaxPrefilter("script",function(e){void 0===e.cache&&(e.cache=!1),e.crossDomain&&(e.type="GET")}),S.ajaxTransport("script",function(n){var r,i;if(n.crossDomain||n.scriptAttrs)return{send:function(e,t){r=S("<script>").attr(n.scriptAttrs||{}).prop({charset:n.scriptCharset,src:n.url}).on("load error",i=function(e){r.remove(),i=null,e&&t("error"===e.type?404:200,e.type)}),E.head.appendChild(r[0])},abort:function(){i&&i()}}});var _t,zt=[],Ut=/(=)\?(?=&|$)|\?\?/;S.ajaxSetup({jsonp:"callback",jsonpCallback:function(){var e=zt.pop()||S.expando+"_"+wt.guid++;return this[e]=!0,e}}),S.ajaxPrefilter("json jsonp",function(e,t,n){var r,i,o,a=!1!==e.jsonp&&(Ut.test(e.url)?"url":"string"==typeof e.data&&0===(e.contentType||"").indexOf("application/x-www-form-urlencoded")&&Ut.test(e.data)&&"data");if(a||"jsonp"===e.dataTypes[0])return r=e.jsonpCallback=m(e.jsonpCallback)?e.jsonpCallback():e.jsonpCallback,a?e[a]=e[a].replace(Ut,"$1"+r):!1!==e.jsonp&&(e.url+=(Tt.test(e.url)?"&":"?")+e.jsonp+"="+r),e.converters["script json"]=function(){return o||S.error(r+" was not called"),o[0]},e.dataTypes[0]="json",i=C[r],C[r]=function(){o=arguments},n.always(function(){void 0===i?S(C).removeProp(r):C[r]=i,e[r]&&(e.jsonpCallback=t.jsonpCallback,zt.push(r)),o&&m(i)&&i(o[0]),o=i=void 0}),"script"}),y.createHTMLDocument=((_t=E.implementation.createHTMLDocument("").body).innerHTML="<form></form><form></form>",2===_t.childNodes.length),S.parseHTML=function(e,t,n){return"string"!=typeof e?[]:("boolean"==typeof t&&(n=t,t=!1),t||(y.createHTMLDocument?((r=(t=E.implementation.createHTMLDocument("")).createElement("base")).href=E.location.href,t.head.appendChild(r)):t=E),o=!n&&[],(i=N.exec(e))?[t.createElement(i[1])]:(i=xe([e],t,o),o&&o.length&&S(o).remove(),S.merge([],i.childNodes)));var r,i,o},S.fn.load=function(e,t,n){var r,i,o,a=this,s=e.indexOf(" ");return-1<s&&(r=ht(e.slice(s)),e=e.slice(0,s)),m(t)?(n=t,t=void 0):t&&"object"==typeof t&&(i="POST"),0<a.length&&S.ajax({url:e,type:i||"GET",dataType:"html",data:t}).done(function(e){o=arguments,a.html(r?S("<div>").append(S.parseHTML(e)).find(r):e)}).always(n&&function(e,t){a.each(function(){n.apply(this,o||[e.responseText,t,e])})}),this},S.expr.pseudos.animated=function(t){return S.grep(S.timers,function(e){return t===e.elem}).length},S.offset={setOffset:function(e,t,n){var r,i,o,a,s,u,l=S.css(e,"position"),c=S(e),f={};"static"===l&&(e.style.position="relative"),s=c.offset(),o=S.css(e,"top"),u=S.css(e,"left"),("absolute"===l||"fixed"===l)&&-1<(o+u).indexOf("auto")?(a=(r=c.position()).top,i=r.left):(a=parseFloat(o)||0,i=parseFloat(u)||0),m(t)&&(t=t.call(e,n,S.extend({},s))),null!=t.top&&(f.top=t.top-s.top+a),null!=t.left&&(f.left=t.left-s.left+i),"using"in t?t.using.call(e,f):c.css(f)}},S.fn.extend({offset:function(t){if(arguments.length)return void 0===t?this:this.each(function(e){S.offset.setOffset(this,t,e)});var e,n,r=this[0];return r?r.getClientRects().length?(e=r.getBoundingClientRect(),n=r.ownerDocument.defaultView,{top:e.top+n.pageYOffset,left:e.left+n.pageXOffset}):{top:0,left:0}:void 0},position:function(){if(this[0]){var e,t,n,r=this[0],i={top:0,left:0};if("fixed"===S.css(r,"position"))t=r.getBoundingClientRect();else{t=this.offset(),n=r.ownerDocument,e=r.offsetParent||n.documentElement;while(e&&(e===n.body||e===n.documentElement)&&"static"===S.css(e,"position"))e=e.parentNode;e&&e!==r&&1===e.nodeType&&((i=S(e).offset()).top+=S.css(e,"borderTopWidth",!0),i.left+=S.css(e,"borderLeftWidth",!0))}return{top:t.top-i.top-S.css(r,"marginTop",!0),left:t.left-i.left-S.css(r,"marginLeft",!0)}}},offsetParent:function(){return this.map(function(){var e=this.offsetParent;while(e&&"static"===S.css(e,"position"))e=e.offsetParent;return e||re})}}),S.each({scrollLeft:"pageXOffset",scrollTop:"pageYOffset"},function(t,i){var o="pageYOffset"===i;S.fn[t]=function(e){return $(this,function(e,t,n){var r;if(x(e)?r=e:9===e.nodeType&&(r=e.defaultView),void 0===n)return r?r[i]:e[t];r?r.scrollTo(o?r.pageXOffset:n,o?n:r.pageYOffset):e[t]=n},t,e,arguments.length)}}),S.each(["top","left"],function(e,n){S.cssHooks[n]=Fe(y.pixelPosition,function(e,t){if(t)return t=We(e,n),Pe.test(t)?S(e).position()[n]+"px":t})}),S.each({Height:"height",Width:"width"},function(a,s){S.each({padding:"inner"+a,content:s,"":"outer"+a},function(r,o){S.fn[o]=function(e,t){var n=arguments.length&&(r||"boolean"!=typeof e),i=r||(!0===e||!0===t?"margin":"border");return $(this,function(e,t,n){var r;return x(e)?0===o.indexOf("outer")?e["inner"+a]:e.document.documentElement["client"+a]:9===e.nodeType?(r=e.documentElement,Math.max(e.body["scroll"+a],r["scroll"+a],e.body["offset"+a],r["offset"+a],r["client"+a])):void 0===n?S.css(e,t,i):S.style(e,t,n,i)},s,n?e:void 0,n)}})}),S.each(["ajaxStart","ajaxStop","ajaxComplete","ajaxError","ajaxSuccess","ajaxSend"],function(e,t){S.fn[t]=function(e){return this.on(t,e)}}),S.fn.extend({bind:function(e,t,n){return this.on(e,null,t,n)},unbind:function(e,t){return this.off(e,null,t)},delegate:function(e,t,n,r){return this.on(t,e,n,r)},undelegate:function(e,t,n){return 1===arguments.length?this.off(e,"**"):this.off(t,e||"**",n)},hover:function(e,t){return this.mouseenter(e).mouseleave(t||e)}}),S.each("blur focus focusin focusout resize scroll click dblclick mousedown mouseup mousemove mouseover mouseout mouseenter mouseleave change select submit keydown keypress keyup contextmenu".split(" "),function(e,n){S.fn[n]=function(e,t){return 0<arguments.length?this.on(n,null,e,t):this.trigger(n)}});var Xt=/^[\s\uFEFF\xA0]+|[\s\uFEFF\xA0]+$/g;S.proxy=function(e,t){var n,r,i;if("string"==typeof t&&(n=e[t],t=e,e=n),m(e))return r=s.call(arguments,2),(i=function(){return e.apply(t||this,r.concat(s.call(arguments)))}).guid=e.guid=e.guid||S.guid++,i},S.holdReady=function(e){e?S.readyWait++:S.ready(!0)},S.isArray=Array.isArray,S.parseJSON=JSON.parse,S.nodeName=A,S.isFunction=m,S.isWindow=x,S.camelCase=X,S.type=w,S.now=Date.now,S.isNumeric=function(e){var t=S.type(e);return("number"===t||"string"===t)&&!isNaN(e-parseFloat(e))},S.trim=function(e){return null==e?"":(e+"").replace(Xt,"")},"function"==typeof define&&define.amd&&define("jquery",[],function(){return S});var Vt=C.jQuery,Gt=C.$;return S.noConflict=function(e){return C.$===S&&(C.$=Gt),e&&C.jQuery===S&&(C.jQuery=Vt),S},"undefined"==typeof e&&(C.jQuery=C.$=S),S});
+</script>
+<meta name="viewport" content="width=device-width, initial-scale=1" />
+<style type="text/css">html{font-family:sans-serif;-webkit-text-size-adjust:100%;-ms-text-size-adjust:100%}body{margin:0}article,aside,details,figcaption,figure,footer,header,hgroup,main,menu,nav,section,summary{display:block}audio,canvas,progress,video{display:inline-block;vertical-align:baseline}audio:not([controls]){display:none;height:0}[hidden],template{display:none}a{background-color:transparent}a:active,a:hover{outline:0}abbr[title]{border-bottom:1px dotted}b,strong{font-weight:700}dfn{font-style:italic}h1{margin:.67em 0;font-size:2em}mark{color:#000;background:#ff0}small{font-size:80%}sub,sup{position:relative;font-size:75%;line-height:0;vertical-align:baseline}sup{top:-.5em}sub{bottom:-.25em}img{border:0}svg:not(:root){overflow:hidden}figure{margin:1em 40px}hr{height:0;-webkit-box-sizing:content-box;-moz-box-sizing:content-box;box-sizing:content-box}pre{overflow:auto}code,kbd,pre,samp{font-family:monospace,monospace;font-size:1em}button,input,optgroup,select,textarea{margin:0;font:inherit;color:inherit}button{overflow:visible}button,select{text-transform:none}button,html input[type=button],input[type=reset],input[type=submit]{-webkit-appearance:button;cursor:pointer}button[disabled],html input[disabled]{cursor:default}button::-moz-focus-inner,input::-moz-focus-inner{padding:0;border:0}input{line-height:normal}input[type=checkbox],input[type=radio]{-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;padding:0}input[type=number]::-webkit-inner-spin-button,input[type=number]::-webkit-outer-spin-button{height:auto}input[type=search]{-webkit-box-sizing:content-box;-moz-box-sizing:content-box;box-sizing:content-box;-webkit-appearance:textfield}input[type=search]::-webkit-search-cancel-button,input[type=search]::-webkit-search-decoration{-webkit-appearance:none}fieldset{padding:.35em .625em .75em;margin:0 2px;border:1px solid silver}legend{padding:0;border:0}textarea{overflow:auto}optgroup{font-weight:700}table{border-spacing:0;border-collapse:collapse}td,th{padding:0}@media print{*,:after,:before{color:#000!important;text-shadow:none!important;background:0 0!important;-webkit-box-shadow:none!important;box-shadow:none!important}a,a:visited{text-decoration:underline}a[href]:after{content:" (" attr(href) ")"}abbr[title]:after{content:" (" attr(title) ")"}a[href^="javascript:"]:after,a[href^="#"]:after{content:""}blockquote,pre{border:1px solid #999;page-break-inside:avoid}thead{display:table-header-group}img,tr{page-break-inside:avoid}img{max-width:100%!important}h2,h3,p{orphans:3;widows:3}h2,h3{page-break-after:avoid}.navbar{display:none}.btn>.caret,.dropup>.btn>.caret{border-top-color:#000!important}.label{border:1px solid #000}.table{border-collapse:collapse!important}.table td,.table th{background-color:#fff!important}.table-bordered td,.table-bordered th{border:1px solid #ddd!important}}@font-face{font-family:'Glyphicons Halflings';src:url(data:application/vnd.ms-fontobject;base64,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);src:url(data:application/vnd.ms-fontobject;base64,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) format('embedded-opentype'),url(data:application/font-woff;base64,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) format('woff'),url(data:application/font-sfnt;base64,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) format('truetype'),url(data:image/svg+xml;base64,<?xml version="1.0" standalone="no"?>
<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN" "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd" >
<svg xmlns="http://www.w3.org/2000/svg">
<metadata></metadata>
<defs>
<font id="glyphicons_halflingsregular" horiz-adv-x="1200" >
<font-face units-per-em="1200" ascent="960" descent="-240" />
<missing-glyph horiz-adv-x="500" />
<glyph horiz-adv-x="0" />
<glyph horiz-adv-x="400" />
<glyph unicode=" " />
<glyph unicode="*" d="M600 1100q15 0 34 -1.5t30 -3.5l11 -1q10 -2 17.5 -10.5t7.5 -18.5v-224l158 158q7 7 18 8t19 -6l106 -106q7 -8 6 -19t-8 -18l-158 -158h224q10 0 18.5 -7.5t10.5 -17.5q6 -41 6 -75q0 -15 -1.5 -34t-3.5 -30l-1 -11q-2 -10 -10.5 -17.5t-18.5 -7.5h-224l158 -158 q7 -7 8 -18t-6 -19l-106 -106q-8 -7 -19 -6t-18 8l-158 158v-224q0 -10 -7.5 -18.5t-17.5 -10.5q-41 -6 -75 -6q-15 0 -34 1.5t-30 3.5l-11 1q-10 2 -17.5 10.5t-7.5 18.5v224l-158 -158q-7 -7 -18 -8t-19 6l-106 106q-7 8 -6 19t8 18l158 158h-224q-10 0 -18.5 7.5 t-10.5 17.5q-6 41 -6 75q0 15 1.5 34t3.5 30l1 11q2 10 10.5 17.5t18.5 7.5h224l-158 158q-7 7 -8 18t6 19l106 106q8 7 19 6t18 -8l158 -158v224q0 10 7.5 18.5t17.5 10.5q41 6 75 6z" />
<glyph unicode="+" d="M450 1100h200q21 0 35.5 -14.5t14.5 -35.5v-350h350q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-350v-350q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v350h-350q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5 h350v350q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xa0;" />
<glyph unicode="&#xa5;" d="M825 1100h250q10 0 12.5 -5t-5.5 -13l-364 -364q-6 -6 -11 -18h268q10 0 13 -6t-3 -14l-120 -160q-6 -8 -18 -14t-22 -6h-125v-100h275q10 0 13 -6t-3 -14l-120 -160q-6 -8 -18 -14t-22 -6h-125v-174q0 -11 -7.5 -18.5t-18.5 -7.5h-148q-11 0 -18.5 7.5t-7.5 18.5v174 h-275q-10 0 -13 6t3 14l120 160q6 8 18 14t22 6h125v100h-275q-10 0 -13 6t3 14l120 160q6 8 18 14t22 6h118q-5 12 -11 18l-364 364q-8 8 -5.5 13t12.5 5h250q25 0 43 -18l164 -164q8 -8 18 -8t18 8l164 164q18 18 43 18z" />
<glyph unicode="&#x2000;" horiz-adv-x="650" />
<glyph unicode="&#x2001;" horiz-adv-x="1300" />
<glyph unicode="&#x2002;" horiz-adv-x="650" />
<glyph unicode="&#x2003;" horiz-adv-x="1300" />
<glyph unicode="&#x2004;" horiz-adv-x="433" />
<glyph unicode="&#x2005;" horiz-adv-x="325" />
<glyph unicode="&#x2006;" horiz-adv-x="216" />
<glyph unicode="&#x2007;" horiz-adv-x="216" />
<glyph unicode="&#x2008;" horiz-adv-x="162" />
<glyph unicode="&#x2009;" horiz-adv-x="260" />
<glyph unicode="&#x200a;" horiz-adv-x="72" />
<glyph unicode="&#x202f;" horiz-adv-x="260" />
<glyph unicode="&#x205f;" horiz-adv-x="325" />
<glyph unicode="&#x20ac;" d="M744 1198q242 0 354 -189q60 -104 66 -209h-181q0 45 -17.5 82.5t-43.5 61.5t-58 40.5t-60.5 24t-51.5 7.5q-19 0 -40.5 -5.5t-49.5 -20.5t-53 -38t-49 -62.5t-39 -89.5h379l-100 -100h-300q-6 -50 -6 -100h406l-100 -100h-300q9 -74 33 -132t52.5 -91t61.5 -54.5t59 -29 t47 -7.5q22 0 50.5 7.5t60.5 24.5t58 41t43.5 61t17.5 80h174q-30 -171 -128 -278q-107 -117 -274 -117q-206 0 -324 158q-36 48 -69 133t-45 204h-217l100 100h112q1 47 6 100h-218l100 100h134q20 87 51 153.5t62 103.5q117 141 297 141z" />
<glyph unicode="&#x20bd;" d="M428 1200h350q67 0 120 -13t86 -31t57 -49.5t35 -56.5t17 -64.5t6.5 -60.5t0.5 -57v-16.5v-16.5q0 -36 -0.5 -57t-6.5 -61t-17 -65t-35 -57t-57 -50.5t-86 -31.5t-120 -13h-178l-2 -100h288q10 0 13 -6t-3 -14l-120 -160q-6 -8 -18 -14t-22 -6h-138v-175q0 -11 -5.5 -18 t-15.5 -7h-149q-10 0 -17.5 7.5t-7.5 17.5v175h-267q-10 0 -13 6t3 14l120 160q6 8 18 14t22 6h117v100h-267q-10 0 -13 6t3 14l120 160q6 8 18 14t22 6h117v475q0 10 7.5 17.5t17.5 7.5zM600 1000v-300h203q64 0 86.5 33t22.5 119q0 84 -22.5 116t-86.5 32h-203z" />
<glyph unicode="&#x2212;" d="M250 700h800q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#x231b;" d="M1000 1200v-150q0 -21 -14.5 -35.5t-35.5 -14.5h-50v-100q0 -91 -49.5 -165.5t-130.5 -109.5q81 -35 130.5 -109.5t49.5 -165.5v-150h50q21 0 35.5 -14.5t14.5 -35.5v-150h-800v150q0 21 14.5 35.5t35.5 14.5h50v150q0 91 49.5 165.5t130.5 109.5q-81 35 -130.5 109.5 t-49.5 165.5v100h-50q-21 0 -35.5 14.5t-14.5 35.5v150h800zM400 1000v-100q0 -60 32.5 -109.5t87.5 -73.5q28 -12 44 -37t16 -55t-16 -55t-44 -37q-55 -24 -87.5 -73.5t-32.5 -109.5v-150h400v150q0 60 -32.5 109.5t-87.5 73.5q-28 12 -44 37t-16 55t16 55t44 37 q55 24 87.5 73.5t32.5 109.5v100h-400z" />
<glyph unicode="&#x25fc;" horiz-adv-x="500" d="M0 0z" />
<glyph unicode="&#x2601;" d="M503 1089q110 0 200.5 -59.5t134.5 -156.5q44 14 90 14q120 0 205 -86.5t85 -206.5q0 -121 -85 -207.5t-205 -86.5h-750q-79 0 -135.5 57t-56.5 137q0 69 42.5 122.5t108.5 67.5q-2 12 -2 37q0 153 108 260.5t260 107.5z" />
<glyph unicode="&#x26fa;" d="M774 1193.5q16 -9.5 20.5 -27t-5.5 -33.5l-136 -187l467 -746h30q20 0 35 -18.5t15 -39.5v-42h-1200v42q0 21 15 39.5t35 18.5h30l468 746l-135 183q-10 16 -5.5 34t20.5 28t34 5.5t28 -20.5l111 -148l112 150q9 16 27 20.5t34 -5zM600 200h377l-182 112l-195 534v-646z " />
<glyph unicode="&#x2709;" d="M25 1100h1150q10 0 12.5 -5t-5.5 -13l-564 -567q-8 -8 -18 -8t-18 8l-564 567q-8 8 -5.5 13t12.5 5zM18 882l264 -264q8 -8 8 -18t-8 -18l-264 -264q-8 -8 -13 -5.5t-5 12.5v550q0 10 5 12.5t13 -5.5zM918 618l264 264q8 8 13 5.5t5 -12.5v-550q0 -10 -5 -12.5t-13 5.5 l-264 264q-8 8 -8 18t8 18zM818 482l364 -364q8 -8 5.5 -13t-12.5 -5h-1150q-10 0 -12.5 5t5.5 13l364 364q8 8 18 8t18 -8l164 -164q8 -8 18 -8t18 8l164 164q8 8 18 8t18 -8z" />
<glyph unicode="&#x270f;" d="M1011 1210q19 0 33 -13l153 -153q13 -14 13 -33t-13 -33l-99 -92l-214 214l95 96q13 14 32 14zM1013 800l-615 -614l-214 214l614 614zM317 96l-333 -112l110 335z" />
<glyph unicode="&#xe001;" d="M700 650v-550h250q21 0 35.5 -14.5t14.5 -35.5v-50h-800v50q0 21 14.5 35.5t35.5 14.5h250v550l-500 550h1200z" />
<glyph unicode="&#xe002;" d="M368 1017l645 163q39 15 63 0t24 -49v-831q0 -55 -41.5 -95.5t-111.5 -63.5q-79 -25 -147 -4.5t-86 75t25.5 111.5t122.5 82q72 24 138 8v521l-600 -155v-606q0 -42 -44 -90t-109 -69q-79 -26 -147 -5.5t-86 75.5t25.5 111.5t122.5 82.5q72 24 138 7v639q0 38 14.5 59 t53.5 34z" />
<glyph unicode="&#xe003;" d="M500 1191q100 0 191 -39t156.5 -104.5t104.5 -156.5t39 -191l-1 -2l1 -5q0 -141 -78 -262l275 -274q23 -26 22.5 -44.5t-22.5 -42.5l-59 -58q-26 -20 -46.5 -20t-39.5 20l-275 274q-119 -77 -261 -77l-5 1l-2 -1q-100 0 -191 39t-156.5 104.5t-104.5 156.5t-39 191 t39 191t104.5 156.5t156.5 104.5t191 39zM500 1022q-88 0 -162 -43t-117 -117t-43 -162t43 -162t117 -117t162 -43t162 43t117 117t43 162t-43 162t-117 117t-162 43z" />
<glyph unicode="&#xe005;" d="M649 949q48 68 109.5 104t121.5 38.5t118.5 -20t102.5 -64t71 -100.5t27 -123q0 -57 -33.5 -117.5t-94 -124.5t-126.5 -127.5t-150 -152.5t-146 -174q-62 85 -145.5 174t-150 152.5t-126.5 127.5t-93.5 124.5t-33.5 117.5q0 64 28 123t73 100.5t104 64t119 20 t120.5 -38.5t104.5 -104z" />
<glyph unicode="&#xe006;" d="M407 800l131 353q7 19 17.5 19t17.5 -19l129 -353h421q21 0 24 -8.5t-14 -20.5l-342 -249l130 -401q7 -20 -0.5 -25.5t-24.5 6.5l-343 246l-342 -247q-17 -12 -24.5 -6.5t-0.5 25.5l130 400l-347 251q-17 12 -14 20.5t23 8.5h429z" />
<glyph unicode="&#xe007;" d="M407 800l131 353q7 19 17.5 19t17.5 -19l129 -353h421q21 0 24 -8.5t-14 -20.5l-342 -249l130 -401q7 -20 -0.5 -25.5t-24.5 6.5l-343 246l-342 -247q-17 -12 -24.5 -6.5t-0.5 25.5l130 400l-347 251q-17 12 -14 20.5t23 8.5h429zM477 700h-240l197 -142l-74 -226 l193 139l195 -140l-74 229l192 140h-234l-78 211z" />
<glyph unicode="&#xe008;" d="M600 1200q124 0 212 -88t88 -212v-250q0 -46 -31 -98t-69 -52v-75q0 -10 6 -21.5t15 -17.5l358 -230q9 -5 15 -16.5t6 -21.5v-93q0 -10 -7.5 -17.5t-17.5 -7.5h-1150q-10 0 -17.5 7.5t-7.5 17.5v93q0 10 6 21.5t15 16.5l358 230q9 6 15 17.5t6 21.5v75q-38 0 -69 52 t-31 98v250q0 124 88 212t212 88z" />
<glyph unicode="&#xe009;" d="M25 1100h1150q10 0 17.5 -7.5t7.5 -17.5v-1050q0 -10 -7.5 -17.5t-17.5 -7.5h-1150q-10 0 -17.5 7.5t-7.5 17.5v1050q0 10 7.5 17.5t17.5 7.5zM100 1000v-100h100v100h-100zM875 1000h-550q-10 0 -17.5 -7.5t-7.5 -17.5v-350q0 -10 7.5 -17.5t17.5 -7.5h550 q10 0 17.5 7.5t7.5 17.5v350q0 10 -7.5 17.5t-17.5 7.5zM1000 1000v-100h100v100h-100zM100 800v-100h100v100h-100zM1000 800v-100h100v100h-100zM100 600v-100h100v100h-100zM1000 600v-100h100v100h-100zM875 500h-550q-10 0 -17.5 -7.5t-7.5 -17.5v-350q0 -10 7.5 -17.5 t17.5 -7.5h550q10 0 17.5 7.5t7.5 17.5v350q0 10 -7.5 17.5t-17.5 7.5zM100 400v-100h100v100h-100zM1000 400v-100h100v100h-100zM100 200v-100h100v100h-100zM1000 200v-100h100v100h-100z" />
<glyph unicode="&#xe010;" d="M50 1100h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5zM650 1100h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v400 q0 21 14.5 35.5t35.5 14.5zM50 500h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5zM650 500h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400 q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe011;" d="M50 1100h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM450 1100h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200 q0 21 14.5 35.5t35.5 14.5zM850 1100h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM50 700h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200 q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM450 700h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM850 700h200q21 0 35.5 -14.5t14.5 -35.5v-200 q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM50 300h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM450 300h200 q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM850 300h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5 t35.5 14.5z" />
<glyph unicode="&#xe012;" d="M50 1100h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM450 1100h700q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-700q-21 0 -35.5 14.5t-14.5 35.5v200 q0 21 14.5 35.5t35.5 14.5zM50 700h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM450 700h700q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-700 q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM50 300h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM450 300h700q21 0 35.5 -14.5t14.5 -35.5v-200 q0 -21 -14.5 -35.5t-35.5 -14.5h-700q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe013;" d="M465 477l571 571q8 8 18 8t17 -8l177 -177q8 -7 8 -17t-8 -18l-783 -784q-7 -8 -17.5 -8t-17.5 8l-384 384q-8 8 -8 18t8 17l177 177q7 8 17 8t18 -8l171 -171q7 -7 18 -7t18 7z" />
<glyph unicode="&#xe014;" d="M904 1083l178 -179q8 -8 8 -18.5t-8 -17.5l-267 -268l267 -268q8 -7 8 -17.5t-8 -18.5l-178 -178q-8 -8 -18.5 -8t-17.5 8l-268 267l-268 -267q-7 -8 -17.5 -8t-18.5 8l-178 178q-8 8 -8 18.5t8 17.5l267 268l-267 268q-8 7 -8 17.5t8 18.5l178 178q8 8 18.5 8t17.5 -8 l268 -267l268 268q7 7 17.5 7t18.5 -7z" />
<glyph unicode="&#xe015;" d="M507 1177q98 0 187.5 -38.5t154.5 -103.5t103.5 -154.5t38.5 -187.5q0 -141 -78 -262l300 -299q8 -8 8 -18.5t-8 -18.5l-109 -108q-7 -8 -17.5 -8t-18.5 8l-300 299q-119 -77 -261 -77q-98 0 -188 38.5t-154.5 103t-103 154.5t-38.5 188t38.5 187.5t103 154.5 t154.5 103.5t188 38.5zM506.5 1023q-89.5 0 -165.5 -44t-120 -120.5t-44 -166t44 -165.5t120 -120t165.5 -44t166 44t120.5 120t44 165.5t-44 166t-120.5 120.5t-166 44zM425 900h150q10 0 17.5 -7.5t7.5 -17.5v-75h75q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5 t-17.5 -7.5h-75v-75q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v75h-75q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5h75v75q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe016;" d="M507 1177q98 0 187.5 -38.5t154.5 -103.5t103.5 -154.5t38.5 -187.5q0 -141 -78 -262l300 -299q8 -8 8 -18.5t-8 -18.5l-109 -108q-7 -8 -17.5 -8t-18.5 8l-300 299q-119 -77 -261 -77q-98 0 -188 38.5t-154.5 103t-103 154.5t-38.5 188t38.5 187.5t103 154.5 t154.5 103.5t188 38.5zM506.5 1023q-89.5 0 -165.5 -44t-120 -120.5t-44 -166t44 -165.5t120 -120t165.5 -44t166 44t120.5 120t44 165.5t-44 166t-120.5 120.5t-166 44zM325 800h350q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-350q-10 0 -17.5 7.5 t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe017;" d="M550 1200h100q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5zM800 975v166q167 -62 272 -209.5t105 -331.5q0 -117 -45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5 t-184.5 123t-123 184.5t-45.5 224q0 184 105 331.5t272 209.5v-166q-103 -55 -165 -155t-62 -220q0 -116 57 -214.5t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5q0 120 -62 220t-165 155z" />
<glyph unicode="&#xe018;" d="M1025 1200h150q10 0 17.5 -7.5t7.5 -17.5v-1150q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v1150q0 10 7.5 17.5t17.5 7.5zM725 800h150q10 0 17.5 -7.5t7.5 -17.5v-750q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v750 q0 10 7.5 17.5t17.5 7.5zM425 500h150q10 0 17.5 -7.5t7.5 -17.5v-450q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v450q0 10 7.5 17.5t17.5 7.5zM125 300h150q10 0 17.5 -7.5t7.5 -17.5v-250q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5 v250q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe019;" d="M600 1174q33 0 74 -5l38 -152l5 -1q49 -14 94 -39l5 -2l134 80q61 -48 104 -105l-80 -134l3 -5q25 -44 39 -93l1 -6l152 -38q5 -43 5 -73q0 -34 -5 -74l-152 -38l-1 -6q-15 -49 -39 -93l-3 -5l80 -134q-48 -61 -104 -105l-134 81l-5 -3q-44 -25 -94 -39l-5 -2l-38 -151 q-43 -5 -74 -5q-33 0 -74 5l-38 151l-5 2q-49 14 -94 39l-5 3l-134 -81q-60 48 -104 105l80 134l-3 5q-25 45 -38 93l-2 6l-151 38q-6 42 -6 74q0 33 6 73l151 38l2 6q13 48 38 93l3 5l-80 134q47 61 105 105l133 -80l5 2q45 25 94 39l5 1l38 152q43 5 74 5zM600 815 q-89 0 -152 -63t-63 -151.5t63 -151.5t152 -63t152 63t63 151.5t-63 151.5t-152 63z" />
<glyph unicode="&#xe020;" d="M500 1300h300q41 0 70.5 -29.5t29.5 -70.5v-100h275q10 0 17.5 -7.5t7.5 -17.5v-75h-1100v75q0 10 7.5 17.5t17.5 7.5h275v100q0 41 29.5 70.5t70.5 29.5zM500 1200v-100h300v100h-300zM1100 900v-800q0 -41 -29.5 -70.5t-70.5 -29.5h-700q-41 0 -70.5 29.5t-29.5 70.5 v800h900zM300 800v-700h100v700h-100zM500 800v-700h100v700h-100zM700 800v-700h100v700h-100zM900 800v-700h100v700h-100z" />
<glyph unicode="&#xe021;" d="M18 618l620 608q8 7 18.5 7t17.5 -7l608 -608q8 -8 5.5 -13t-12.5 -5h-175v-575q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v375h-300v-375q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v575h-175q-10 0 -12.5 5t5.5 13z" />
<glyph unicode="&#xe022;" d="M600 1200v-400q0 -41 29.5 -70.5t70.5 -29.5h300v-650q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5v1100q0 21 14.5 35.5t35.5 14.5h450zM1000 800h-250q-21 0 -35.5 14.5t-14.5 35.5v250z" />
<glyph unicode="&#xe023;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 1027q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5 t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5t-57 214.5t-155.5 155.5t-214.5 57zM525 900h50q10 0 17.5 -7.5t7.5 -17.5v-275h175q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v350q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe024;" d="M1300 0h-538l-41 400h-242l-41 -400h-538l431 1200h209l-21 -300h162l-20 300h208zM515 800l-27 -300h224l-27 300h-170z" />
<glyph unicode="&#xe025;" d="M550 1200h200q21 0 35.5 -14.5t14.5 -35.5v-450h191q20 0 25.5 -11.5t-7.5 -27.5l-327 -400q-13 -16 -32 -16t-32 16l-327 400q-13 16 -7.5 27.5t25.5 11.5h191v450q0 21 14.5 35.5t35.5 14.5zM1125 400h50q10 0 17.5 -7.5t7.5 -17.5v-350q0 -10 -7.5 -17.5t-17.5 -7.5 h-1050q-10 0 -17.5 7.5t-7.5 17.5v350q0 10 7.5 17.5t17.5 7.5h50q10 0 17.5 -7.5t7.5 -17.5v-175h900v175q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe026;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 1027q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5 t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5t-57 214.5t-155.5 155.5t-214.5 57zM525 900h150q10 0 17.5 -7.5t7.5 -17.5v-275h137q21 0 26 -11.5t-8 -27.5l-223 -275q-13 -16 -32 -16t-32 16l-223 275q-13 16 -8 27.5t26 11.5h137v275q0 10 7.5 17.5t17.5 7.5z " />
<glyph unicode="&#xe027;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 1027q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5 t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5t-57 214.5t-155.5 155.5t-214.5 57zM632 914l223 -275q13 -16 8 -27.5t-26 -11.5h-137v-275q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v275h-137q-21 0 -26 11.5t8 27.5l223 275q13 16 32 16 t32 -16z" />
<glyph unicode="&#xe028;" d="M225 1200h750q10 0 19.5 -7t12.5 -17l186 -652q7 -24 7 -49v-425q0 -12 -4 -27t-9 -17q-12 -6 -37 -6h-1100q-12 0 -27 4t-17 8q-6 13 -6 38l1 425q0 25 7 49l185 652q3 10 12.5 17t19.5 7zM878 1000h-556q-10 0 -19 -7t-11 -18l-87 -450q-2 -11 4 -18t16 -7h150 q10 0 19.5 -7t11.5 -17l38 -152q2 -10 11.5 -17t19.5 -7h250q10 0 19.5 7t11.5 17l38 152q2 10 11.5 17t19.5 7h150q10 0 16 7t4 18l-87 450q-2 11 -11 18t-19 7z" />
<glyph unicode="&#xe029;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 1027q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5 t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5t-57 214.5t-155.5 155.5t-214.5 57zM540 820l253 -190q17 -12 17 -30t-17 -30l-253 -190q-16 -12 -28 -6.5t-12 26.5v400q0 21 12 26.5t28 -6.5z" />
<glyph unicode="&#xe030;" d="M947 1060l135 135q7 7 12.5 5t5.5 -13v-362q0 -10 -7.5 -17.5t-17.5 -7.5h-362q-11 0 -13 5.5t5 12.5l133 133q-109 76 -238 76q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5h150q0 -117 -45.5 -224 t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5q192 0 347 -117z" />
<glyph unicode="&#xe031;" d="M947 1060l135 135q7 7 12.5 5t5.5 -13v-361q0 -11 -7.5 -18.5t-18.5 -7.5h-361q-11 0 -13 5.5t5 12.5l134 134q-110 75 -239 75q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5h-150q0 117 45.5 224t123 184.5t184.5 123t224 45.5q192 0 347 -117zM1027 600h150 q0 -117 -45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5q-192 0 -348 118l-134 -134q-7 -8 -12.5 -5.5t-5.5 12.5v360q0 11 7.5 18.5t18.5 7.5h360q10 0 12.5 -5.5t-5.5 -12.5l-133 -133q110 -76 240 -76q116 0 214.5 57t155.5 155.5t57 214.5z" />
<glyph unicode="&#xe032;" d="M125 1200h1050q10 0 17.5 -7.5t7.5 -17.5v-1150q0 -10 -7.5 -17.5t-17.5 -7.5h-1050q-10 0 -17.5 7.5t-7.5 17.5v1150q0 10 7.5 17.5t17.5 7.5zM1075 1000h-850q-10 0 -17.5 -7.5t-7.5 -17.5v-850q0 -10 7.5 -17.5t17.5 -7.5h850q10 0 17.5 7.5t7.5 17.5v850 q0 10 -7.5 17.5t-17.5 7.5zM325 900h50q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-50q-10 0 -17.5 7.5t-7.5 17.5v50q0 10 7.5 17.5t17.5 7.5zM525 900h450q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-450q-10 0 -17.5 7.5t-7.5 17.5v50 q0 10 7.5 17.5t17.5 7.5zM325 700h50q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-50q-10 0 -17.5 7.5t-7.5 17.5v50q0 10 7.5 17.5t17.5 7.5zM525 700h450q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-450q-10 0 -17.5 7.5t-7.5 17.5v50 q0 10 7.5 17.5t17.5 7.5zM325 500h50q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-50q-10 0 -17.5 7.5t-7.5 17.5v50q0 10 7.5 17.5t17.5 7.5zM525 500h450q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-450q-10 0 -17.5 7.5t-7.5 17.5v50 q0 10 7.5 17.5t17.5 7.5zM325 300h50q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-50q-10 0 -17.5 7.5t-7.5 17.5v50q0 10 7.5 17.5t17.5 7.5zM525 300h450q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-450q-10 0 -17.5 7.5t-7.5 17.5v50 q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe033;" d="M900 800v200q0 83 -58.5 141.5t-141.5 58.5h-300q-82 0 -141 -59t-59 -141v-200h-100q-41 0 -70.5 -29.5t-29.5 -70.5v-600q0 -41 29.5 -70.5t70.5 -29.5h900q41 0 70.5 29.5t29.5 70.5v600q0 41 -29.5 70.5t-70.5 29.5h-100zM400 800v150q0 21 15 35.5t35 14.5h200 q20 0 35 -14.5t15 -35.5v-150h-300z" />
<glyph unicode="&#xe034;" d="M125 1100h50q10 0 17.5 -7.5t7.5 -17.5v-1075h-100v1075q0 10 7.5 17.5t17.5 7.5zM1075 1052q4 0 9 -2q16 -6 16 -23v-421q0 -6 -3 -12q-33 -59 -66.5 -99t-65.5 -58t-56.5 -24.5t-52.5 -6.5q-26 0 -57.5 6.5t-52.5 13.5t-60 21q-41 15 -63 22.5t-57.5 15t-65.5 7.5 q-85 0 -160 -57q-7 -5 -15 -5q-6 0 -11 3q-14 7 -14 22v438q22 55 82 98.5t119 46.5q23 2 43 0.5t43 -7t32.5 -8.5t38 -13t32.5 -11q41 -14 63.5 -21t57 -14t63.5 -7q103 0 183 87q7 8 18 8z" />
<glyph unicode="&#xe035;" d="M600 1175q116 0 227 -49.5t192.5 -131t131 -192.5t49.5 -227v-300q0 -10 -7.5 -17.5t-17.5 -7.5h-50q-10 0 -17.5 7.5t-7.5 17.5v300q0 127 -70.5 231.5t-184.5 161.5t-245 57t-245 -57t-184.5 -161.5t-70.5 -231.5v-300q0 -10 -7.5 -17.5t-17.5 -7.5h-50 q-10 0 -17.5 7.5t-7.5 17.5v300q0 116 49.5 227t131 192.5t192.5 131t227 49.5zM220 500h160q8 0 14 -6t6 -14v-460q0 -8 -6 -14t-14 -6h-160q-8 0 -14 6t-6 14v460q0 8 6 14t14 6zM820 500h160q8 0 14 -6t6 -14v-460q0 -8 -6 -14t-14 -6h-160q-8 0 -14 6t-6 14v460 q0 8 6 14t14 6z" />
<glyph unicode="&#xe036;" d="M321 814l258 172q9 6 15 2.5t6 -13.5v-750q0 -10 -6 -13.5t-15 2.5l-258 172q-21 14 -46 14h-250q-10 0 -17.5 7.5t-7.5 17.5v350q0 10 7.5 17.5t17.5 7.5h250q25 0 46 14zM900 668l120 120q7 7 17 7t17 -7l34 -34q7 -7 7 -17t-7 -17l-120 -120l120 -120q7 -7 7 -17 t-7 -17l-34 -34q-7 -7 -17 -7t-17 7l-120 119l-120 -119q-7 -7 -17 -7t-17 7l-34 34q-7 7 -7 17t7 17l119 120l-119 120q-7 7 -7 17t7 17l34 34q7 8 17 8t17 -8z" />
<glyph unicode="&#xe037;" d="M321 814l258 172q9 6 15 2.5t6 -13.5v-750q0 -10 -6 -13.5t-15 2.5l-258 172q-21 14 -46 14h-250q-10 0 -17.5 7.5t-7.5 17.5v350q0 10 7.5 17.5t17.5 7.5h250q25 0 46 14zM766 900h4q10 -1 16 -10q96 -129 96 -290q0 -154 -90 -281q-6 -9 -17 -10l-3 -1q-9 0 -16 6 l-29 23q-7 7 -8.5 16.5t4.5 17.5q72 103 72 229q0 132 -78 238q-6 8 -4.5 18t9.5 17l29 22q7 5 15 5z" />
<glyph unicode="&#xe038;" d="M967 1004h3q11 -1 17 -10q135 -179 135 -396q0 -105 -34 -206.5t-98 -185.5q-7 -9 -17 -10h-3q-9 0 -16 6l-42 34q-8 6 -9 16t5 18q111 150 111 328q0 90 -29.5 176t-84.5 157q-6 9 -5 19t10 16l42 33q7 5 15 5zM321 814l258 172q9 6 15 2.5t6 -13.5v-750q0 -10 -6 -13.5 t-15 2.5l-258 172q-21 14 -46 14h-250q-10 0 -17.5 7.5t-7.5 17.5v350q0 10 7.5 17.5t17.5 7.5h250q25 0 46 14zM766 900h4q10 -1 16 -10q96 -129 96 -290q0 -154 -90 -281q-6 -9 -17 -10l-3 -1q-9 0 -16 6l-29 23q-7 7 -8.5 16.5t4.5 17.5q72 103 72 229q0 132 -78 238 q-6 8 -4.5 18.5t9.5 16.5l29 22q7 5 15 5z" />
<glyph unicode="&#xe039;" d="M500 900h100v-100h-100v-100h-400v-100h-100v600h500v-300zM1200 700h-200v-100h200v-200h-300v300h-200v300h-100v200h600v-500zM100 1100v-300h300v300h-300zM800 1100v-300h300v300h-300zM300 900h-100v100h100v-100zM1000 900h-100v100h100v-100zM300 500h200v-500 h-500v500h200v100h100v-100zM800 300h200v-100h-100v-100h-200v100h-100v100h100v200h-200v100h300v-300zM100 400v-300h300v300h-300zM300 200h-100v100h100v-100zM1200 200h-100v100h100v-100zM700 0h-100v100h100v-100zM1200 0h-300v100h300v-100z" />
<glyph unicode="&#xe040;" d="M100 200h-100v1000h100v-1000zM300 200h-100v1000h100v-1000zM700 200h-200v1000h200v-1000zM900 200h-100v1000h100v-1000zM1200 200h-200v1000h200v-1000zM400 0h-300v100h300v-100zM600 0h-100v91h100v-91zM800 0h-100v91h100v-91zM1100 0h-200v91h200v-91z" />
<glyph unicode="&#xe041;" d="M500 1200l682 -682q8 -8 8 -18t-8 -18l-464 -464q-8 -8 -18 -8t-18 8l-682 682l1 475q0 10 7.5 17.5t17.5 7.5h474zM319.5 1024.5q-29.5 29.5 -71 29.5t-71 -29.5t-29.5 -71.5t29.5 -71.5t71 -29.5t71 29.5t29.5 71.5t-29.5 71.5z" />
<glyph unicode="&#xe042;" d="M500 1200l682 -682q8 -8 8 -18t-8 -18l-464 -464q-8 -8 -18 -8t-18 8l-682 682l1 475q0 10 7.5 17.5t17.5 7.5h474zM800 1200l682 -682q8 -8 8 -18t-8 -18l-464 -464q-8 -8 -18 -8t-18 8l-56 56l424 426l-700 700h150zM319.5 1024.5q-29.5 29.5 -71 29.5t-71 -29.5 t-29.5 -71.5t29.5 -71.5t71 -29.5t71 29.5t29.5 71.5t-29.5 71.5z" />
<glyph unicode="&#xe043;" d="M300 1200h825q75 0 75 -75v-900q0 -25 -18 -43l-64 -64q-8 -8 -13 -5.5t-5 12.5v950q0 10 -7.5 17.5t-17.5 7.5h-700q-25 0 -43 -18l-64 -64q-8 -8 -5.5 -13t12.5 -5h700q10 0 17.5 -7.5t7.5 -17.5v-950q0 -10 -7.5 -17.5t-17.5 -7.5h-850q-10 0 -17.5 7.5t-7.5 17.5v975 q0 25 18 43l139 139q18 18 43 18z" />
<glyph unicode="&#xe044;" d="M250 1200h800q21 0 35.5 -14.5t14.5 -35.5v-1150l-450 444l-450 -445v1151q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe045;" d="M822 1200h-444q-11 0 -19 -7.5t-9 -17.5l-78 -301q-7 -24 7 -45l57 -108q6 -9 17.5 -15t21.5 -6h450q10 0 21.5 6t17.5 15l62 108q14 21 7 45l-83 301q-1 10 -9 17.5t-19 7.5zM1175 800h-150q-10 0 -21 -6.5t-15 -15.5l-78 -156q-4 -9 -15 -15.5t-21 -6.5h-550 q-10 0 -21 6.5t-15 15.5l-78 156q-4 9 -15 15.5t-21 6.5h-150q-10 0 -17.5 -7.5t-7.5 -17.5v-650q0 -10 7.5 -17.5t17.5 -7.5h150q10 0 17.5 7.5t7.5 17.5v150q0 10 7.5 17.5t17.5 7.5h750q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 7.5 -17.5t17.5 -7.5h150q10 0 17.5 7.5 t7.5 17.5v650q0 10 -7.5 17.5t-17.5 7.5zM850 200h-500q-10 0 -19.5 -7t-11.5 -17l-38 -152q-2 -10 3.5 -17t15.5 -7h600q10 0 15.5 7t3.5 17l-38 152q-2 10 -11.5 17t-19.5 7z" />
<glyph unicode="&#xe046;" d="M500 1100h200q56 0 102.5 -20.5t72.5 -50t44 -59t25 -50.5l6 -20h150q41 0 70.5 -29.5t29.5 -70.5v-600q0 -41 -29.5 -70.5t-70.5 -29.5h-1000q-41 0 -70.5 29.5t-29.5 70.5v600q0 41 29.5 70.5t70.5 29.5h150q2 8 6.5 21.5t24 48t45 61t72 48t102.5 21.5zM900 800v-100 h100v100h-100zM600 730q-95 0 -162.5 -67.5t-67.5 -162.5t67.5 -162.5t162.5 -67.5t162.5 67.5t67.5 162.5t-67.5 162.5t-162.5 67.5zM600 603q43 0 73 -30t30 -73t-30 -73t-73 -30t-73 30t-30 73t30 73t73 30z" />
<glyph unicode="&#xe047;" d="M681 1199l385 -998q20 -50 60 -92q18 -19 36.5 -29.5t27.5 -11.5l10 -2v-66h-417v66q53 0 75 43.5t5 88.5l-82 222h-391q-58 -145 -92 -234q-11 -34 -6.5 -57t25.5 -37t46 -20t55 -6v-66h-365v66q56 24 84 52q12 12 25 30.5t20 31.5l7 13l399 1006h93zM416 521h340 l-162 457z" />
<glyph unicode="&#xe048;" d="M753 641q5 -1 14.5 -4.5t36 -15.5t50.5 -26.5t53.5 -40t50.5 -54.5t35.5 -70t14.5 -87q0 -67 -27.5 -125.5t-71.5 -97.5t-98.5 -66.5t-108.5 -40.5t-102 -13h-500v89q41 7 70.5 32.5t29.5 65.5v827q0 24 -0.5 34t-3.5 24t-8.5 19.5t-17 13.5t-28 12.5t-42.5 11.5v71 l471 -1q57 0 115.5 -20.5t108 -57t80.5 -94t31 -124.5q0 -51 -15.5 -96.5t-38 -74.5t-45 -50.5t-38.5 -30.5zM400 700h139q78 0 130.5 48.5t52.5 122.5q0 41 -8.5 70.5t-29.5 55.5t-62.5 39.5t-103.5 13.5h-118v-350zM400 200h216q80 0 121 50.5t41 130.5q0 90 -62.5 154.5 t-156.5 64.5h-159v-400z" />
<glyph unicode="&#xe049;" d="M877 1200l2 -57q-83 -19 -116 -45.5t-40 -66.5l-132 -839q-9 -49 13 -69t96 -26v-97h-500v97q186 16 200 98l173 832q3 17 3 30t-1.5 22.5t-9 17.5t-13.5 12.5t-21.5 10t-26 8.5t-33.5 10q-13 3 -19 5v57h425z" />
<glyph unicode="&#xe050;" d="M1300 900h-50q0 21 -4 37t-9.5 26.5t-18 17.5t-22 11t-28.5 5.5t-31 2t-37 0.5h-200v-850q0 -22 25 -34.5t50 -13.5l25 -2v-100h-400v100q4 0 11 0.5t24 3t30 7t24 15t11 24.5v850h-200q-25 0 -37 -0.5t-31 -2t-28.5 -5.5t-22 -11t-18 -17.5t-9.5 -26.5t-4 -37h-50v300 h1000v-300zM175 1000h-75v-800h75l-125 -167l-125 167h75v800h-75l125 167z" />
<glyph unicode="&#xe051;" d="M1100 900h-50q0 21 -4 37t-9.5 26.5t-18 17.5t-22 11t-28.5 5.5t-31 2t-37 0.5h-200v-650q0 -22 25 -34.5t50 -13.5l25 -2v-100h-400v100q4 0 11 0.5t24 3t30 7t24 15t11 24.5v650h-200q-25 0 -37 -0.5t-31 -2t-28.5 -5.5t-22 -11t-18 -17.5t-9.5 -26.5t-4 -37h-50v300 h1000v-300zM1167 50l-167 -125v75h-800v-75l-167 125l167 125v-75h800v75z" />
<glyph unicode="&#xe052;" d="M50 1100h600q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-600q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 800h1000q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1000q-21 0 -35.5 14.5t-14.5 35.5v100 q0 21 14.5 35.5t35.5 14.5zM50 500h800q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 200h1100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100 q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe053;" d="M250 1100h700q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-700q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 800h1100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5v100 q0 21 14.5 35.5t35.5 14.5zM250 500h700q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-700q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 200h1100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100 q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe054;" d="M500 950v100q0 21 14.5 35.5t35.5 14.5h600q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-600q-21 0 -35.5 14.5t-14.5 35.5zM100 650v100q0 21 14.5 35.5t35.5 14.5h1000q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1000 q-21 0 -35.5 14.5t-14.5 35.5zM300 350v100q0 21 14.5 35.5t35.5 14.5h800q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5zM0 50v100q0 21 14.5 35.5t35.5 14.5h1100q21 0 35.5 -14.5t14.5 -35.5v-100 q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5z" />
<glyph unicode="&#xe055;" d="M50 1100h1100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 800h1100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5v100 q0 21 14.5 35.5t35.5 14.5zM50 500h1100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 200h1100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100 q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe056;" d="M50 1100h100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM350 1100h800q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5v100 q0 21 14.5 35.5t35.5 14.5zM50 800h100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM350 800h800q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-800 q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 500h100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM350 500h800q21 0 35.5 -14.5t14.5 -35.5v-100 q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 200h100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM350 200h800 q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe057;" d="M400 0h-100v1100h100v-1100zM550 1100h100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM550 800h500q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-500 q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM267 550l-167 -125v75h-200v100h200v75zM550 500h300q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-300q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM550 200h600 q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-600q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe058;" d="M50 1100h100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM900 0h-100v1100h100v-1100zM50 800h500q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-500 q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM1100 600h200v-100h-200v-75l-167 125l167 125v-75zM50 500h300q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-300q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 200h600 q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-600q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe059;" d="M75 1000h750q31 0 53 -22t22 -53v-650q0 -31 -22 -53t-53 -22h-750q-31 0 -53 22t-22 53v650q0 31 22 53t53 22zM1200 300l-300 300l300 300v-600z" />
<glyph unicode="&#xe060;" d="M44 1100h1112q18 0 31 -13t13 -31v-1012q0 -18 -13 -31t-31 -13h-1112q-18 0 -31 13t-13 31v1012q0 18 13 31t31 13zM100 1000v-737l247 182l298 -131l-74 156l293 318l236 -288v500h-1000zM342 884q56 0 95 -39t39 -94.5t-39 -95t-95 -39.5t-95 39.5t-39 95t39 94.5 t95 39z" />
<glyph unicode="&#xe062;" d="M648 1169q117 0 216 -60t156.5 -161t57.5 -218q0 -115 -70 -258q-69 -109 -158 -225.5t-143 -179.5l-54 -62q-9 8 -25.5 24.5t-63.5 67.5t-91 103t-98.5 128t-95.5 148q-60 132 -60 249q0 88 34 169.5t91.5 142t137 96.5t166.5 36zM652.5 974q-91.5 0 -156.5 -65 t-65 -157t65 -156.5t156.5 -64.5t156.5 64.5t65 156.5t-65 157t-156.5 65z" />
<glyph unicode="&#xe063;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 173v854q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5 t155.5 -155.5t214.5 -57z" />
<glyph unicode="&#xe064;" d="M554 1295q21 -72 57.5 -143.5t76 -130t83 -118t82.5 -117t70 -116t49.5 -126t18.5 -136.5q0 -71 -25.5 -135t-68.5 -111t-99 -82t-118.5 -54t-125.5 -23q-84 5 -161.5 34t-139.5 78.5t-99 125t-37 164.5q0 69 18 136.5t49.5 126.5t69.5 116.5t81.5 117.5t83.5 119 t76.5 131t58.5 143zM344 710q-23 -33 -43.5 -70.5t-40.5 -102.5t-17 -123q1 -37 14.5 -69.5t30 -52t41 -37t38.5 -24.5t33 -15q21 -7 32 -1t13 22l6 34q2 10 -2.5 22t-13.5 19q-5 4 -14 12t-29.5 40.5t-32.5 73.5q-26 89 6 271q2 11 -6 11q-8 1 -15 -10z" />
<glyph unicode="&#xe065;" d="M1000 1013l108 115q2 1 5 2t13 2t20.5 -1t25 -9.5t28.5 -21.5q22 -22 27 -43t0 -32l-6 -10l-108 -115zM350 1100h400q50 0 105 -13l-187 -187h-368q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5v182l200 200v-332 q0 -165 -93.5 -257.5t-256.5 -92.5h-400q-165 0 -257.5 92.5t-92.5 257.5v400q0 165 92.5 257.5t257.5 92.5zM1009 803l-362 -362l-161 -50l55 170l355 355z" />
<glyph unicode="&#xe066;" d="M350 1100h361q-164 -146 -216 -200h-195q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5l200 153v-103q0 -165 -92.5 -257.5t-257.5 -92.5h-400q-165 0 -257.5 92.5t-92.5 257.5v400q0 165 92.5 257.5t257.5 92.5z M824 1073l339 -301q8 -7 8 -17.5t-8 -17.5l-340 -306q-7 -6 -12.5 -4t-6.5 11v203q-26 1 -54.5 0t-78.5 -7.5t-92 -17.5t-86 -35t-70 -57q10 59 33 108t51.5 81.5t65 58.5t68.5 40.5t67 24.5t56 13.5t40 4.5v210q1 10 6.5 12.5t13.5 -4.5z" />
<glyph unicode="&#xe067;" d="M350 1100h350q60 0 127 -23l-178 -177h-349q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5v69l200 200v-219q0 -165 -92.5 -257.5t-257.5 -92.5h-400q-165 0 -257.5 92.5t-92.5 257.5v400q0 165 92.5 257.5t257.5 92.5z M643 639l395 395q7 7 17.5 7t17.5 -7l101 -101q7 -7 7 -17.5t-7 -17.5l-531 -532q-7 -7 -17.5 -7t-17.5 7l-248 248q-7 7 -7 17.5t7 17.5l101 101q7 7 17.5 7t17.5 -7l111 -111q8 -7 18 -7t18 7z" />
<glyph unicode="&#xe068;" d="M318 918l264 264q8 8 18 8t18 -8l260 -264q7 -8 4.5 -13t-12.5 -5h-170v-200h200v173q0 10 5 12t13 -5l264 -260q8 -7 8 -17.5t-8 -17.5l-264 -265q-8 -7 -13 -5t-5 12v173h-200v-200h170q10 0 12.5 -5t-4.5 -13l-260 -264q-8 -8 -18 -8t-18 8l-264 264q-8 8 -5.5 13 t12.5 5h175v200h-200v-173q0 -10 -5 -12t-13 5l-264 265q-8 7 -8 17.5t8 17.5l264 260q8 7 13 5t5 -12v-173h200v200h-175q-10 0 -12.5 5t5.5 13z" />
<glyph unicode="&#xe069;" d="M250 1100h100q21 0 35.5 -14.5t14.5 -35.5v-438l464 453q15 14 25.5 10t10.5 -25v-1000q0 -21 -10.5 -25t-25.5 10l-464 453v-438q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v1000q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe070;" d="M50 1100h100q21 0 35.5 -14.5t14.5 -35.5v-438l464 453q15 14 25.5 10t10.5 -25v-438l464 453q15 14 25.5 10t10.5 -25v-1000q0 -21 -10.5 -25t-25.5 10l-464 453v-438q0 -21 -10.5 -25t-25.5 10l-464 453v-438q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5 t-14.5 35.5v1000q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe071;" d="M1200 1050v-1000q0 -21 -10.5 -25t-25.5 10l-464 453v-438q0 -21 -10.5 -25t-25.5 10l-492 480q-15 14 -15 35t15 35l492 480q15 14 25.5 10t10.5 -25v-438l464 453q15 14 25.5 10t10.5 -25z" />
<glyph unicode="&#xe072;" d="M243 1074l814 -498q18 -11 18 -26t-18 -26l-814 -498q-18 -11 -30.5 -4t-12.5 28v1000q0 21 12.5 28t30.5 -4z" />
<glyph unicode="&#xe073;" d="M250 1000h200q21 0 35.5 -14.5t14.5 -35.5v-800q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v800q0 21 14.5 35.5t35.5 14.5zM650 1000h200q21 0 35.5 -14.5t14.5 -35.5v-800q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v800 q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe074;" d="M1100 950v-800q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5v800q0 21 14.5 35.5t35.5 14.5h800q21 0 35.5 -14.5t14.5 -35.5z" />
<glyph unicode="&#xe075;" d="M500 612v438q0 21 10.5 25t25.5 -10l492 -480q15 -14 15 -35t-15 -35l-492 -480q-15 -14 -25.5 -10t-10.5 25v438l-464 -453q-15 -14 -25.5 -10t-10.5 25v1000q0 21 10.5 25t25.5 -10z" />
<glyph unicode="&#xe076;" d="M1048 1102l100 1q20 0 35 -14.5t15 -35.5l5 -1000q0 -21 -14.5 -35.5t-35.5 -14.5l-100 -1q-21 0 -35.5 14.5t-14.5 35.5l-2 437l-463 -454q-14 -15 -24.5 -10.5t-10.5 25.5l-2 437l-462 -455q-15 -14 -25.5 -9.5t-10.5 24.5l-5 1000q0 21 10.5 25.5t25.5 -10.5l466 -450 l-2 438q0 20 10.5 24.5t25.5 -9.5l466 -451l-2 438q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe077;" d="M850 1100h100q21 0 35.5 -14.5t14.5 -35.5v-1000q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v438l-464 -453q-15 -14 -25.5 -10t-10.5 25v1000q0 21 10.5 25t25.5 -10l464 -453v438q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe078;" d="M686 1081l501 -540q15 -15 10.5 -26t-26.5 -11h-1042q-22 0 -26.5 11t10.5 26l501 540q15 15 36 15t36 -15zM150 400h1000q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1000q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe079;" d="M885 900l-352 -353l352 -353l-197 -198l-552 552l552 550z" />
<glyph unicode="&#xe080;" d="M1064 547l-551 -551l-198 198l353 353l-353 353l198 198z" />
<glyph unicode="&#xe081;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM650 900h-100q-21 0 -35.5 -14.5t-14.5 -35.5v-150h-150 q-21 0 -35.5 -14.5t-14.5 -35.5v-100q0 -21 14.5 -35.5t35.5 -14.5h150v-150q0 -21 14.5 -35.5t35.5 -14.5h100q21 0 35.5 14.5t14.5 35.5v150h150q21 0 35.5 14.5t14.5 35.5v100q0 21 -14.5 35.5t-35.5 14.5h-150v150q0 21 -14.5 35.5t-35.5 14.5z" />
<glyph unicode="&#xe082;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM850 700h-500q-21 0 -35.5 -14.5t-14.5 -35.5v-100q0 -21 14.5 -35.5 t35.5 -14.5h500q21 0 35.5 14.5t14.5 35.5v100q0 21 -14.5 35.5t-35.5 14.5z" />
<glyph unicode="&#xe083;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM741.5 913q-12.5 0 -21.5 -9l-120 -120l-120 120q-9 9 -21.5 9 t-21.5 -9l-141 -141q-9 -9 -9 -21.5t9 -21.5l120 -120l-120 -120q-9 -9 -9 -21.5t9 -21.5l141 -141q9 -9 21.5 -9t21.5 9l120 120l120 -120q9 -9 21.5 -9t21.5 9l141 141q9 9 9 21.5t-9 21.5l-120 120l120 120q9 9 9 21.5t-9 21.5l-141 141q-9 9 -21.5 9z" />
<glyph unicode="&#xe084;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM546 623l-84 85q-7 7 -17.5 7t-18.5 -7l-139 -139q-7 -8 -7 -18t7 -18 l242 -241q7 -8 17.5 -8t17.5 8l375 375q7 7 7 17.5t-7 18.5l-139 139q-7 7 -17.5 7t-17.5 -7z" />
<glyph unicode="&#xe085;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM588 941q-29 0 -59 -5.5t-63 -20.5t-58 -38.5t-41.5 -63t-16.5 -89.5 q0 -25 20 -25h131q30 -5 35 11q6 20 20.5 28t45.5 8q20 0 31.5 -10.5t11.5 -28.5q0 -23 -7 -34t-26 -18q-1 0 -13.5 -4t-19.5 -7.5t-20 -10.5t-22 -17t-18.5 -24t-15.5 -35t-8 -46q-1 -8 5.5 -16.5t20.5 -8.5h173q7 0 22 8t35 28t37.5 48t29.5 74t12 100q0 47 -17 83 t-42.5 57t-59.5 34.5t-64 18t-59 4.5zM675 400h-150q-10 0 -17.5 -7.5t-7.5 -17.5v-150q0 -10 7.5 -17.5t17.5 -7.5h150q10 0 17.5 7.5t7.5 17.5v150q0 10 -7.5 17.5t-17.5 7.5z" />
<glyph unicode="&#xe086;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM675 1000h-150q-10 0 -17.5 -7.5t-7.5 -17.5v-150q0 -10 7.5 -17.5 t17.5 -7.5h150q10 0 17.5 7.5t7.5 17.5v150q0 10 -7.5 17.5t-17.5 7.5zM675 700h-250q-10 0 -17.5 -7.5t-7.5 -17.5v-50q0 -10 7.5 -17.5t17.5 -7.5h75v-200h-75q-10 0 -17.5 -7.5t-7.5 -17.5v-50q0 -10 7.5 -17.5t17.5 -7.5h350q10 0 17.5 7.5t7.5 17.5v50q0 10 -7.5 17.5 t-17.5 7.5h-75v275q0 10 -7.5 17.5t-17.5 7.5z" />
<glyph unicode="&#xe087;" d="M525 1200h150q10 0 17.5 -7.5t7.5 -17.5v-194q103 -27 178.5 -102.5t102.5 -178.5h194q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-194q-27 -103 -102.5 -178.5t-178.5 -102.5v-194q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v194 q-103 27 -178.5 102.5t-102.5 178.5h-194q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5h194q27 103 102.5 178.5t178.5 102.5v194q0 10 7.5 17.5t17.5 7.5zM700 893v-168q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v168q-68 -23 -119 -74 t-74 -119h168q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-168q23 -68 74 -119t119 -74v168q0 10 7.5 17.5t17.5 7.5h150q10 0 17.5 -7.5t7.5 -17.5v-168q68 23 119 74t74 119h-168q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5h168 q-23 68 -74 119t-119 74z" />
<glyph unicode="&#xe088;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 1027q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5 t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5t-57 214.5t-155.5 155.5t-214.5 57zM759 823l64 -64q7 -7 7 -17.5t-7 -17.5l-124 -124l124 -124q7 -7 7 -17.5t-7 -17.5l-64 -64q-7 -7 -17.5 -7t-17.5 7l-124 124l-124 -124q-7 -7 -17.5 -7t-17.5 7l-64 64 q-7 7 -7 17.5t7 17.5l124 124l-124 124q-7 7 -7 17.5t7 17.5l64 64q7 7 17.5 7t17.5 -7l124 -124l124 124q7 7 17.5 7t17.5 -7z" />
<glyph unicode="&#xe089;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 1027q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5 t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5t-57 214.5t-155.5 155.5t-214.5 57zM782 788l106 -106q7 -7 7 -17.5t-7 -17.5l-320 -321q-8 -7 -18 -7t-18 7l-202 203q-8 7 -8 17.5t8 17.5l106 106q7 8 17.5 8t17.5 -8l79 -79l197 197q7 7 17.5 7t17.5 -7z" />
<glyph unicode="&#xe090;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 1027q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5q0 -120 65 -225 l587 587q-105 65 -225 65zM965 819l-584 -584q104 -62 219 -62q116 0 214.5 57t155.5 155.5t57 214.5q0 115 -62 219z" />
<glyph unicode="&#xe091;" d="M39 582l522 427q16 13 27.5 8t11.5 -26v-291h550q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-550v-291q0 -21 -11.5 -26t-27.5 8l-522 427q-16 13 -16 32t16 32z" />
<glyph unicode="&#xe092;" d="M639 1009l522 -427q16 -13 16 -32t-16 -32l-522 -427q-16 -13 -27.5 -8t-11.5 26v291h-550q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5h550v291q0 21 11.5 26t27.5 -8z" />
<glyph unicode="&#xe093;" d="M682 1161l427 -522q13 -16 8 -27.5t-26 -11.5h-291v-550q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v550h-291q-21 0 -26 11.5t8 27.5l427 522q13 16 32 16t32 -16z" />
<glyph unicode="&#xe094;" d="M550 1200h200q21 0 35.5 -14.5t14.5 -35.5v-550h291q21 0 26 -11.5t-8 -27.5l-427 -522q-13 -16 -32 -16t-32 16l-427 522q-13 16 -8 27.5t26 11.5h291v550q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe095;" d="M639 1109l522 -427q16 -13 16 -32t-16 -32l-522 -427q-16 -13 -27.5 -8t-11.5 26v291q-94 -2 -182 -20t-170.5 -52t-147 -92.5t-100.5 -135.5q5 105 27 193.5t67.5 167t113 135t167 91.5t225.5 42v262q0 21 11.5 26t27.5 -8z" />
<glyph unicode="&#xe096;" d="M850 1200h300q21 0 35.5 -14.5t14.5 -35.5v-300q0 -21 -10.5 -25t-24.5 10l-94 94l-249 -249q-8 -7 -18 -7t-18 7l-106 106q-7 8 -7 18t7 18l249 249l-94 94q-14 14 -10 24.5t25 10.5zM350 0h-300q-21 0 -35.5 14.5t-14.5 35.5v300q0 21 10.5 25t24.5 -10l94 -94l249 249 q8 7 18 7t18 -7l106 -106q7 -8 7 -18t-7 -18l-249 -249l94 -94q14 -14 10 -24.5t-25 -10.5z" />
<glyph unicode="&#xe097;" d="M1014 1120l106 -106q7 -8 7 -18t-7 -18l-249 -249l94 -94q14 -14 10 -24.5t-25 -10.5h-300q-21 0 -35.5 14.5t-14.5 35.5v300q0 21 10.5 25t24.5 -10l94 -94l249 249q8 7 18 7t18 -7zM250 600h300q21 0 35.5 -14.5t14.5 -35.5v-300q0 -21 -10.5 -25t-24.5 10l-94 94 l-249 -249q-8 -7 -18 -7t-18 7l-106 106q-7 8 -7 18t7 18l249 249l-94 94q-14 14 -10 24.5t25 10.5z" />
<glyph unicode="&#xe101;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM704 900h-208q-20 0 -32 -14.5t-8 -34.5l58 -302q4 -20 21.5 -34.5 t37.5 -14.5h54q20 0 37.5 14.5t21.5 34.5l58 302q4 20 -8 34.5t-32 14.5zM675 400h-150q-10 0 -17.5 -7.5t-7.5 -17.5v-150q0 -10 7.5 -17.5t17.5 -7.5h150q10 0 17.5 7.5t7.5 17.5v150q0 10 -7.5 17.5t-17.5 7.5z" />
<glyph unicode="&#xe102;" d="M260 1200q9 0 19 -2t15 -4l5 -2q22 -10 44 -23l196 -118q21 -13 36 -24q29 -21 37 -12q11 13 49 35l196 118q22 13 45 23q17 7 38 7q23 0 47 -16.5t37 -33.5l13 -16q14 -21 18 -45l25 -123l8 -44q1 -9 8.5 -14.5t17.5 -5.5h61q10 0 17.5 -7.5t7.5 -17.5v-50 q0 -10 -7.5 -17.5t-17.5 -7.5h-50q-10 0 -17.5 -7.5t-7.5 -17.5v-175h-400v300h-200v-300h-400v175q0 10 -7.5 17.5t-17.5 7.5h-50q-10 0 -17.5 7.5t-7.5 17.5v50q0 10 7.5 17.5t17.5 7.5h61q11 0 18 3t7 8q0 4 9 52l25 128q5 25 19 45q2 3 5 7t13.5 15t21.5 19.5t26.5 15.5 t29.5 7zM915 1079l-166 -162q-7 -7 -5 -12t12 -5h219q10 0 15 7t2 17l-51 149q-3 10 -11 12t-15 -6zM463 917l-177 157q-8 7 -16 5t-11 -12l-51 -143q-3 -10 2 -17t15 -7h231q11 0 12.5 5t-5.5 12zM500 0h-375q-10 0 -17.5 7.5t-7.5 17.5v375h400v-400zM1100 400v-375 q0 -10 -7.5 -17.5t-17.5 -7.5h-375v400h400z" />
<glyph unicode="&#xe103;" d="M1165 1190q8 3 21 -6.5t13 -17.5q-2 -178 -24.5 -323.5t-55.5 -245.5t-87 -174.5t-102.5 -118.5t-118 -68.5t-118.5 -33t-120 -4.5t-105 9.5t-90 16.5q-61 12 -78 11q-4 1 -12.5 0t-34 -14.5t-52.5 -40.5l-153 -153q-26 -24 -37 -14.5t-11 43.5q0 64 42 102q8 8 50.5 45 t66.5 58q19 17 35 47t13 61q-9 55 -10 102.5t7 111t37 130t78 129.5q39 51 80 88t89.5 63.5t94.5 45t113.5 36t129 31t157.5 37t182 47.5zM1116 1098q-8 9 -22.5 -3t-45.5 -50q-38 -47 -119 -103.5t-142 -89.5l-62 -33q-56 -30 -102 -57t-104 -68t-102.5 -80.5t-85.5 -91 t-64 -104.5q-24 -56 -31 -86t2 -32t31.5 17.5t55.5 59.5q25 30 94 75.5t125.5 77.5t147.5 81q70 37 118.5 69t102 79.5t99 111t86.5 148.5q22 50 24 60t-6 19z" />
<glyph unicode="&#xe104;" d="M653 1231q-39 -67 -54.5 -131t-10.5 -114.5t24.5 -96.5t47.5 -80t63.5 -62.5t68.5 -46.5t65 -30q-4 7 -17.5 35t-18.5 39.5t-17 39.5t-17 43t-13 42t-9.5 44.5t-2 42t4 43t13.5 39t23 38.5q96 -42 165 -107.5t105 -138t52 -156t13 -159t-19 -149.5q-13 -55 -44 -106.5 t-68 -87t-78.5 -64.5t-72.5 -45t-53 -22q-72 -22 -127 -11q-31 6 -13 19q6 3 17 7q13 5 32.5 21t41 44t38.5 63.5t21.5 81.5t-6.5 94.5t-50 107t-104 115.5q10 -104 -0.5 -189t-37 -140.5t-65 -93t-84 -52t-93.5 -11t-95 24.5q-80 36 -131.5 114t-53.5 171q-2 23 0 49.5 t4.5 52.5t13.5 56t27.5 60t46 64.5t69.5 68.5q-8 -53 -5 -102.5t17.5 -90t34 -68.5t44.5 -39t49 -2q31 13 38.5 36t-4.5 55t-29 64.5t-36 75t-26 75.5q-15 85 2 161.5t53.5 128.5t85.5 92.5t93.5 61t81.5 25.5z" />
<glyph unicode="&#xe105;" d="M600 1094q82 0 160.5 -22.5t140 -59t116.5 -82.5t94.5 -95t68 -95t42.5 -82.5t14 -57.5t-14 -57.5t-43 -82.5t-68.5 -95t-94.5 -95t-116.5 -82.5t-140 -59t-159.5 -22.5t-159.5 22.5t-140 59t-116.5 82.5t-94.5 95t-68.5 95t-43 82.5t-14 57.5t14 57.5t42.5 82.5t68 95 t94.5 95t116.5 82.5t140 59t160.5 22.5zM888 829q-15 15 -18 12t5 -22q25 -57 25 -119q0 -124 -88 -212t-212 -88t-212 88t-88 212q0 59 23 114q8 19 4.5 22t-17.5 -12q-70 -69 -160 -184q-13 -16 -15 -40.5t9 -42.5q22 -36 47 -71t70 -82t92.5 -81t113 -58.5t133.5 -24.5 t133.5 24t113 58.5t92.5 81.5t70 81.5t47 70.5q11 18 9 42.5t-14 41.5q-90 117 -163 189zM448 727l-35 -36q-15 -15 -19.5 -38.5t4.5 -41.5q37 -68 93 -116q16 -13 38.5 -11t36.5 17l35 34q14 15 12.5 33.5t-16.5 33.5q-44 44 -89 117q-11 18 -28 20t-32 -12z" />
<glyph unicode="&#xe106;" d="M592 0h-148l31 120q-91 20 -175.5 68.5t-143.5 106.5t-103.5 119t-66.5 110t-22 76q0 21 14 57.5t42.5 82.5t68 95t94.5 95t116.5 82.5t140 59t160.5 22.5q61 0 126 -15l32 121h148zM944 770l47 181q108 -85 176.5 -192t68.5 -159q0 -26 -19.5 -71t-59.5 -102t-93 -112 t-129 -104.5t-158 -75.5l46 173q77 49 136 117t97 131q11 18 9 42.5t-14 41.5q-54 70 -107 130zM310 824q-70 -69 -160 -184q-13 -16 -15 -40.5t9 -42.5q18 -30 39 -60t57 -70.5t74 -73t90 -61t105 -41.5l41 154q-107 18 -178.5 101.5t-71.5 193.5q0 59 23 114q8 19 4.5 22 t-17.5 -12zM448 727l-35 -36q-15 -15 -19.5 -38.5t4.5 -41.5q37 -68 93 -116q16 -13 38.5 -11t36.5 17l12 11l22 86l-3 4q-44 44 -89 117q-11 18 -28 20t-32 -12z" />
<glyph unicode="&#xe107;" d="M-90 100l642 1066q20 31 48 28.5t48 -35.5l642 -1056q21 -32 7.5 -67.5t-50.5 -35.5h-1294q-37 0 -50.5 34t7.5 66zM155 200h345v75q0 10 7.5 17.5t17.5 7.5h150q10 0 17.5 -7.5t7.5 -17.5v-75h345l-445 723zM496 700h208q20 0 32 -14.5t8 -34.5l-58 -252 q-4 -20 -21.5 -34.5t-37.5 -14.5h-54q-20 0 -37.5 14.5t-21.5 34.5l-58 252q-4 20 8 34.5t32 14.5z" />
<glyph unicode="&#xe108;" d="M650 1200q62 0 106 -44t44 -106v-339l363 -325q15 -14 26 -38.5t11 -44.5v-41q0 -20 -12 -26.5t-29 5.5l-359 249v-263q100 -93 100 -113v-64q0 -21 -13 -29t-32 1l-205 128l-205 -128q-19 -9 -32 -1t-13 29v64q0 20 100 113v263l-359 -249q-17 -12 -29 -5.5t-12 26.5v41 q0 20 11 44.5t26 38.5l363 325v339q0 62 44 106t106 44z" />
<glyph unicode="&#xe109;" d="M850 1200h100q21 0 35.5 -14.5t14.5 -35.5v-50h50q21 0 35.5 -14.5t14.5 -35.5v-150h-1100v150q0 21 14.5 35.5t35.5 14.5h50v50q0 21 14.5 35.5t35.5 14.5h100q21 0 35.5 -14.5t14.5 -35.5v-50h500v50q0 21 14.5 35.5t35.5 14.5zM1100 800v-750q0 -21 -14.5 -35.5 t-35.5 -14.5h-1000q-21 0 -35.5 14.5t-14.5 35.5v750h1100zM100 600v-100h100v100h-100zM300 600v-100h100v100h-100zM500 600v-100h100v100h-100zM700 600v-100h100v100h-100zM900 600v-100h100v100h-100zM100 400v-100h100v100h-100zM300 400v-100h100v100h-100zM500 400 v-100h100v100h-100zM700 400v-100h100v100h-100zM900 400v-100h100v100h-100zM100 200v-100h100v100h-100zM300 200v-100h100v100h-100zM500 200v-100h100v100h-100zM700 200v-100h100v100h-100zM900 200v-100h100v100h-100z" />
<glyph unicode="&#xe110;" d="M1135 1165l249 -230q15 -14 15 -35t-15 -35l-249 -230q-14 -14 -24.5 -10t-10.5 25v150h-159l-600 -600h-291q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5h209l600 600h241v150q0 21 10.5 25t24.5 -10zM522 819l-141 -141l-122 122h-209q-21 0 -35.5 14.5 t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5h291zM1135 565l249 -230q15 -14 15 -35t-15 -35l-249 -230q-14 -14 -24.5 -10t-10.5 25v150h-241l-181 181l141 141l122 -122h159v150q0 21 10.5 25t24.5 -10z" />
<glyph unicode="&#xe111;" d="M100 1100h1000q41 0 70.5 -29.5t29.5 -70.5v-600q0 -41 -29.5 -70.5t-70.5 -29.5h-596l-304 -300v300h-100q-41 0 -70.5 29.5t-29.5 70.5v600q0 41 29.5 70.5t70.5 29.5z" />
<glyph unicode="&#xe112;" d="M150 1200h200q21 0 35.5 -14.5t14.5 -35.5v-250h-300v250q0 21 14.5 35.5t35.5 14.5zM850 1200h200q21 0 35.5 -14.5t14.5 -35.5v-250h-300v250q0 21 14.5 35.5t35.5 14.5zM1100 800v-300q0 -41 -3 -77.5t-15 -89.5t-32 -96t-58 -89t-89 -77t-129 -51t-174 -20t-174 20 t-129 51t-89 77t-58 89t-32 96t-15 89.5t-3 77.5v300h300v-250v-27v-42.5t1.5 -41t5 -38t10 -35t16.5 -30t25.5 -24.5t35 -19t46.5 -12t60 -4t60 4.5t46.5 12.5t35 19.5t25 25.5t17 30.5t10 35t5 38t2 40.5t-0.5 42v25v250h300z" />
<glyph unicode="&#xe113;" d="M1100 411l-198 -199l-353 353l-353 -353l-197 199l551 551z" />
<glyph unicode="&#xe114;" d="M1101 789l-550 -551l-551 551l198 199l353 -353l353 353z" />
<glyph unicode="&#xe115;" d="M404 1000h746q21 0 35.5 -14.5t14.5 -35.5v-551h150q21 0 25 -10.5t-10 -24.5l-230 -249q-14 -15 -35 -15t-35 15l-230 249q-14 14 -10 24.5t25 10.5h150v401h-381zM135 984l230 -249q14 -14 10 -24.5t-25 -10.5h-150v-400h385l215 -200h-750q-21 0 -35.5 14.5 t-14.5 35.5v550h-150q-21 0 -25 10.5t10 24.5l230 249q14 15 35 15t35 -15z" />
<glyph unicode="&#xe116;" d="M56 1200h94q17 0 31 -11t18 -27l38 -162h896q24 0 39 -18.5t10 -42.5l-100 -475q-5 -21 -27 -42.5t-55 -21.5h-633l48 -200h535q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-50v-50q0 -21 -14.5 -35.5t-35.5 -14.5t-35.5 14.5t-14.5 35.5v50h-300v-50 q0 -21 -14.5 -35.5t-35.5 -14.5t-35.5 14.5t-14.5 35.5v50h-31q-18 0 -32.5 10t-20.5 19l-5 10l-201 961h-54q-20 0 -35 14.5t-15 35.5t15 35.5t35 14.5z" />
<glyph unicode="&#xe117;" d="M1200 1000v-100h-1200v100h200q0 41 29.5 70.5t70.5 29.5h300q41 0 70.5 -29.5t29.5 -70.5h500zM0 800h1200v-800h-1200v800z" />
<glyph unicode="&#xe118;" d="M200 800l-200 -400v600h200q0 41 29.5 70.5t70.5 29.5h300q42 0 71 -29.5t29 -70.5h500v-200h-1000zM1500 700l-300 -700h-1200l300 700h1200z" />
<glyph unicode="&#xe119;" d="M635 1184l230 -249q14 -14 10 -24.5t-25 -10.5h-150v-601h150q21 0 25 -10.5t-10 -24.5l-230 -249q-14 -15 -35 -15t-35 15l-230 249q-14 14 -10 24.5t25 10.5h150v601h-150q-21 0 -25 10.5t10 24.5l230 249q14 15 35 15t35 -15z" />
<glyph unicode="&#xe120;" d="M936 864l249 -229q14 -15 14 -35.5t-14 -35.5l-249 -229q-15 -15 -25.5 -10.5t-10.5 24.5v151h-600v-151q0 -20 -10.5 -24.5t-25.5 10.5l-249 229q-14 15 -14 35.5t14 35.5l249 229q15 15 25.5 10.5t10.5 -25.5v-149h600v149q0 21 10.5 25.5t25.5 -10.5z" />
<glyph unicode="&#xe121;" d="M1169 400l-172 732q-5 23 -23 45.5t-38 22.5h-672q-20 0 -38 -20t-23 -41l-172 -739h1138zM1100 300h-1000q-41 0 -70.5 -29.5t-29.5 -70.5v-100q0 -41 29.5 -70.5t70.5 -29.5h1000q41 0 70.5 29.5t29.5 70.5v100q0 41 -29.5 70.5t-70.5 29.5zM800 100v100h100v-100h-100 zM1000 100v100h100v-100h-100z" />
<glyph unicode="&#xe122;" d="M1150 1100q21 0 35.5 -14.5t14.5 -35.5v-850q0 -21 -14.5 -35.5t-35.5 -14.5t-35.5 14.5t-14.5 35.5v850q0 21 14.5 35.5t35.5 14.5zM1000 200l-675 200h-38l47 -276q3 -16 -5.5 -20t-29.5 -4h-7h-84q-20 0 -34.5 14t-18.5 35q-55 337 -55 351v250v6q0 16 1 23.5t6.5 14 t17.5 6.5h200l675 250v-850zM0 750v-250q-4 0 -11 0.5t-24 6t-30 15t-24 30t-11 48.5v50q0 26 10.5 46t25 30t29 16t25.5 7z" />
<glyph unicode="&#xe123;" d="M553 1200h94q20 0 29 -10.5t3 -29.5l-18 -37q83 -19 144 -82.5t76 -140.5l63 -327l118 -173h17q19 0 33 -14.5t14 -35t-13 -40.5t-31 -27q-8 -4 -23 -9.5t-65 -19.5t-103 -25t-132.5 -20t-158.5 -9q-57 0 -115 5t-104 12t-88.5 15.5t-73.5 17.5t-54.5 16t-35.5 12l-11 4 q-18 8 -31 28t-13 40.5t14 35t33 14.5h17l118 173l63 327q15 77 76 140t144 83l-18 32q-6 19 3.5 32t28.5 13zM498 110q50 -6 102 -6q53 0 102 6q-12 -49 -39.5 -79.5t-62.5 -30.5t-63 30.5t-39 79.5z" />
<glyph unicode="&#xe124;" d="M800 946l224 78l-78 -224l234 -45l-180 -155l180 -155l-234 -45l78 -224l-224 78l-45 -234l-155 180l-155 -180l-45 234l-224 -78l78 224l-234 45l180 155l-180 155l234 45l-78 224l224 -78l45 234l155 -180l155 180z" />
<glyph unicode="&#xe125;" d="M650 1200h50q40 0 70 -40.5t30 -84.5v-150l-28 -125h328q40 0 70 -40.5t30 -84.5v-100q0 -45 -29 -74l-238 -344q-16 -24 -38 -40.5t-45 -16.5h-250q-7 0 -42 25t-66 50l-31 25h-61q-45 0 -72.5 18t-27.5 57v400q0 36 20 63l145 196l96 198q13 28 37.5 48t51.5 20z M650 1100l-100 -212l-150 -213v-375h100l136 -100h214l250 375v125h-450l50 225v175h-50zM50 800h100q21 0 35.5 -14.5t14.5 -35.5v-500q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v500q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe126;" d="M600 1100h250q23 0 45 -16.5t38 -40.5l238 -344q29 -29 29 -74v-100q0 -44 -30 -84.5t-70 -40.5h-328q28 -118 28 -125v-150q0 -44 -30 -84.5t-70 -40.5h-50q-27 0 -51.5 20t-37.5 48l-96 198l-145 196q-20 27 -20 63v400q0 39 27.5 57t72.5 18h61q124 100 139 100z M50 1000h100q21 0 35.5 -14.5t14.5 -35.5v-500q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v500q0 21 14.5 35.5t35.5 14.5zM636 1000l-136 -100h-100v-375l150 -213l100 -212h50v175l-50 225h450v125l-250 375h-214z" />
<glyph unicode="&#xe127;" d="M356 873l363 230q31 16 53 -6l110 -112q13 -13 13.5 -32t-11.5 -34l-84 -121h302q84 0 138 -38t54 -110t-55 -111t-139 -39h-106l-131 -339q-6 -21 -19.5 -41t-28.5 -20h-342q-7 0 -90 81t-83 94v525q0 17 14 35.5t28 28.5zM400 792v-503l100 -89h293l131 339 q6 21 19.5 41t28.5 20h203q21 0 30.5 25t0.5 50t-31 25h-456h-7h-6h-5.5t-6 0.5t-5 1.5t-5 2t-4 2.5t-4 4t-2.5 4.5q-12 25 5 47l146 183l-86 83zM50 800h100q21 0 35.5 -14.5t14.5 -35.5v-500q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v500 q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe128;" d="M475 1103l366 -230q2 -1 6 -3.5t14 -10.5t18 -16.5t14.5 -20t6.5 -22.5v-525q0 -13 -86 -94t-93 -81h-342q-15 0 -28.5 20t-19.5 41l-131 339h-106q-85 0 -139.5 39t-54.5 111t54 110t138 38h302l-85 121q-11 15 -10.5 34t13.5 32l110 112q22 22 53 6zM370 945l146 -183 q17 -22 5 -47q-2 -2 -3.5 -4.5t-4 -4t-4 -2.5t-5 -2t-5 -1.5t-6 -0.5h-6h-6.5h-6h-475v-100h221q15 0 29 -20t20 -41l130 -339h294l106 89v503l-342 236zM1050 800h100q21 0 35.5 -14.5t14.5 -35.5v-500q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5 v500q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe129;" d="M550 1294q72 0 111 -55t39 -139v-106l339 -131q21 -6 41 -19.5t20 -28.5v-342q0 -7 -81 -90t-94 -83h-525q-17 0 -35.5 14t-28.5 28l-9 14l-230 363q-16 31 6 53l112 110q13 13 32 13.5t34 -11.5l121 -84v302q0 84 38 138t110 54zM600 972v203q0 21 -25 30.5t-50 0.5 t-25 -31v-456v-7v-6v-5.5t-0.5 -6t-1.5 -5t-2 -5t-2.5 -4t-4 -4t-4.5 -2.5q-25 -12 -47 5l-183 146l-83 -86l236 -339h503l89 100v293l-339 131q-21 6 -41 19.5t-20 28.5zM450 200h500q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-500 q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe130;" d="M350 1100h500q21 0 35.5 14.5t14.5 35.5v100q0 21 -14.5 35.5t-35.5 14.5h-500q-21 0 -35.5 -14.5t-14.5 -35.5v-100q0 -21 14.5 -35.5t35.5 -14.5zM600 306v-106q0 -84 -39 -139t-111 -55t-110 54t-38 138v302l-121 -84q-15 -12 -34 -11.5t-32 13.5l-112 110 q-22 22 -6 53l230 363q1 2 3.5 6t10.5 13.5t16.5 17t20 13.5t22.5 6h525q13 0 94 -83t81 -90v-342q0 -15 -20 -28.5t-41 -19.5zM308 900l-236 -339l83 -86l183 146q22 17 47 5q2 -1 4.5 -2.5t4 -4t2.5 -4t2 -5t1.5 -5t0.5 -6v-5.5v-6v-7v-456q0 -22 25 -31t50 0.5t25 30.5 v203q0 15 20 28.5t41 19.5l339 131v293l-89 100h-503z" />
<glyph unicode="&#xe131;" d="M600 1178q118 0 225 -45.5t184.5 -123t123 -184.5t45.5 -225t-45.5 -225t-123 -184.5t-184.5 -123t-225 -45.5t-225 45.5t-184.5 123t-123 184.5t-45.5 225t45.5 225t123 184.5t184.5 123t225 45.5zM914 632l-275 223q-16 13 -27.5 8t-11.5 -26v-137h-275 q-10 0 -17.5 -7.5t-7.5 -17.5v-150q0 -10 7.5 -17.5t17.5 -7.5h275v-137q0 -21 11.5 -26t27.5 8l275 223q16 13 16 32t-16 32z" />
<glyph unicode="&#xe132;" d="M600 1178q118 0 225 -45.5t184.5 -123t123 -184.5t45.5 -225t-45.5 -225t-123 -184.5t-184.5 -123t-225 -45.5t-225 45.5t-184.5 123t-123 184.5t-45.5 225t45.5 225t123 184.5t184.5 123t225 45.5zM561 855l-275 -223q-16 -13 -16 -32t16 -32l275 -223q16 -13 27.5 -8 t11.5 26v137h275q10 0 17.5 7.5t7.5 17.5v150q0 10 -7.5 17.5t-17.5 7.5h-275v137q0 21 -11.5 26t-27.5 -8z" />
<glyph unicode="&#xe133;" d="M600 1178q118 0 225 -45.5t184.5 -123t123 -184.5t45.5 -225t-45.5 -225t-123 -184.5t-184.5 -123t-225 -45.5t-225 45.5t-184.5 123t-123 184.5t-45.5 225t45.5 225t123 184.5t184.5 123t225 45.5zM855 639l-223 275q-13 16 -32 16t-32 -16l-223 -275q-13 -16 -8 -27.5 t26 -11.5h137v-275q0 -10 7.5 -17.5t17.5 -7.5h150q10 0 17.5 7.5t7.5 17.5v275h137q21 0 26 11.5t-8 27.5z" />
<glyph unicode="&#xe134;" d="M600 1178q118 0 225 -45.5t184.5 -123t123 -184.5t45.5 -225t-45.5 -225t-123 -184.5t-184.5 -123t-225 -45.5t-225 45.5t-184.5 123t-123 184.5t-45.5 225t45.5 225t123 184.5t184.5 123t225 45.5zM675 900h-150q-10 0 -17.5 -7.5t-7.5 -17.5v-275h-137q-21 0 -26 -11.5 t8 -27.5l223 -275q13 -16 32 -16t32 16l223 275q13 16 8 27.5t-26 11.5h-137v275q0 10 -7.5 17.5t-17.5 7.5z" />
<glyph unicode="&#xe135;" d="M600 1176q116 0 222.5 -46t184 -123.5t123.5 -184t46 -222.5t-46 -222.5t-123.5 -184t-184 -123.5t-222.5 -46t-222.5 46t-184 123.5t-123.5 184t-46 222.5t46 222.5t123.5 184t184 123.5t222.5 46zM627 1101q-15 -12 -36.5 -20.5t-35.5 -12t-43 -8t-39 -6.5 q-15 -3 -45.5 0t-45.5 -2q-20 -7 -51.5 -26.5t-34.5 -34.5q-3 -11 6.5 -22.5t8.5 -18.5q-3 -34 -27.5 -91t-29.5 -79q-9 -34 5 -93t8 -87q0 -9 17 -44.5t16 -59.5q12 0 23 -5t23.5 -15t19.5 -14q16 -8 33 -15t40.5 -15t34.5 -12q21 -9 52.5 -32t60 -38t57.5 -11 q7 -15 -3 -34t-22.5 -40t-9.5 -38q13 -21 23 -34.5t27.5 -27.5t36.5 -18q0 -7 -3.5 -16t-3.5 -14t5 -17q104 -2 221 112q30 29 46.5 47t34.5 49t21 63q-13 8 -37 8.5t-36 7.5q-15 7 -49.5 15t-51.5 19q-18 0 -41 -0.5t-43 -1.5t-42 -6.5t-38 -16.5q-51 -35 -66 -12 q-4 1 -3.5 25.5t0.5 25.5q-6 13 -26.5 17.5t-24.5 6.5q1 15 -0.5 30.5t-7 28t-18.5 11.5t-31 -21q-23 -25 -42 4q-19 28 -8 58q6 16 22 22q6 -1 26 -1.5t33.5 -4t19.5 -13.5q7 -12 18 -24t21.5 -20.5t20 -15t15.5 -10.5l5 -3q2 12 7.5 30.5t8 34.5t-0.5 32q-3 18 3.5 29 t18 22.5t15.5 24.5q6 14 10.5 35t8 31t15.5 22.5t34 22.5q-6 18 10 36q8 0 24 -1.5t24.5 -1.5t20 4.5t20.5 15.5q-10 23 -31 42.5t-37.5 29.5t-49 27t-43.5 23q0 1 2 8t3 11.5t1.5 10.5t-1 9.5t-4.5 4.5q31 -13 58.5 -14.5t38.5 2.5l12 5q5 28 -9.5 46t-36.5 24t-50 15 t-41 20q-18 -4 -37 0zM613 994q0 -17 8 -42t17 -45t9 -23q-8 1 -39.5 5.5t-52.5 10t-37 16.5q3 11 16 29.5t16 25.5q10 -10 19 -10t14 6t13.5 14.5t16.5 12.5z" />
<glyph unicode="&#xe136;" d="M756 1157q164 92 306 -9l-259 -138l145 -232l251 126q6 -89 -34 -156.5t-117 -110.5q-60 -34 -127 -39.5t-126 16.5l-596 -596q-15 -16 -36.5 -16t-36.5 16l-111 110q-15 15 -15 36.5t15 37.5l600 599q-34 101 5.5 201.5t135.5 154.5z" />
<glyph unicode="&#xe137;" horiz-adv-x="1220" d="M100 1196h1000q41 0 70.5 -29.5t29.5 -70.5v-100q0 -41 -29.5 -70.5t-70.5 -29.5h-1000q-41 0 -70.5 29.5t-29.5 70.5v100q0 41 29.5 70.5t70.5 29.5zM1100 1096h-200v-100h200v100zM100 796h1000q41 0 70.5 -29.5t29.5 -70.5v-100q0 -41 -29.5 -70.5t-70.5 -29.5h-1000 q-41 0 -70.5 29.5t-29.5 70.5v100q0 41 29.5 70.5t70.5 29.5zM1100 696h-500v-100h500v100zM100 396h1000q41 0 70.5 -29.5t29.5 -70.5v-100q0 -41 -29.5 -70.5t-70.5 -29.5h-1000q-41 0 -70.5 29.5t-29.5 70.5v100q0 41 29.5 70.5t70.5 29.5zM1100 296h-300v-100h300v100z " />
<glyph unicode="&#xe138;" d="M150 1200h900q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-900q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5zM700 500v-300l-200 -200v500l-350 500h900z" />
<glyph unicode="&#xe139;" d="M500 1200h200q41 0 70.5 -29.5t29.5 -70.5v-100h300q41 0 70.5 -29.5t29.5 -70.5v-400h-500v100h-200v-100h-500v400q0 41 29.5 70.5t70.5 29.5h300v100q0 41 29.5 70.5t70.5 29.5zM500 1100v-100h200v100h-200zM1200 400v-200q0 -41 -29.5 -70.5t-70.5 -29.5h-1000 q-41 0 -70.5 29.5t-29.5 70.5v200h1200z" />
<glyph unicode="&#xe140;" d="M50 1200h300q21 0 25 -10.5t-10 -24.5l-94 -94l199 -199q7 -8 7 -18t-7 -18l-106 -106q-8 -7 -18 -7t-18 7l-199 199l-94 -94q-14 -14 -24.5 -10t-10.5 25v300q0 21 14.5 35.5t35.5 14.5zM850 1200h300q21 0 35.5 -14.5t14.5 -35.5v-300q0 -21 -10.5 -25t-24.5 10l-94 94 l-199 -199q-8 -7 -18 -7t-18 7l-106 106q-7 8 -7 18t7 18l199 199l-94 94q-14 14 -10 24.5t25 10.5zM364 470l106 -106q7 -8 7 -18t-7 -18l-199 -199l94 -94q14 -14 10 -24.5t-25 -10.5h-300q-21 0 -35.5 14.5t-14.5 35.5v300q0 21 10.5 25t24.5 -10l94 -94l199 199 q8 7 18 7t18 -7zM1071 271l94 94q14 14 24.5 10t10.5 -25v-300q0 -21 -14.5 -35.5t-35.5 -14.5h-300q-21 0 -25 10.5t10 24.5l94 94l-199 199q-7 8 -7 18t7 18l106 106q8 7 18 7t18 -7z" />
<glyph unicode="&#xe141;" d="M596 1192q121 0 231.5 -47.5t190 -127t127 -190t47.5 -231.5t-47.5 -231.5t-127 -190.5t-190 -127t-231.5 -47t-231.5 47t-190.5 127t-127 190.5t-47 231.5t47 231.5t127 190t190.5 127t231.5 47.5zM596 1010q-112 0 -207.5 -55.5t-151 -151t-55.5 -207.5t55.5 -207.5 t151 -151t207.5 -55.5t207.5 55.5t151 151t55.5 207.5t-55.5 207.5t-151 151t-207.5 55.5zM454.5 905q22.5 0 38.5 -16t16 -38.5t-16 -39t-38.5 -16.5t-38.5 16.5t-16 39t16 38.5t38.5 16zM754.5 905q22.5 0 38.5 -16t16 -38.5t-16 -39t-38 -16.5q-14 0 -29 10l-55 -145 q17 -23 17 -51q0 -36 -25.5 -61.5t-61.5 -25.5t-61.5 25.5t-25.5 61.5q0 32 20.5 56.5t51.5 29.5l122 126l1 1q-9 14 -9 28q0 23 16 39t38.5 16zM345.5 709q22.5 0 38.5 -16t16 -38.5t-16 -38.5t-38.5 -16t-38.5 16t-16 38.5t16 38.5t38.5 16zM854.5 709q22.5 0 38.5 -16 t16 -38.5t-16 -38.5t-38.5 -16t-38.5 16t-16 38.5t16 38.5t38.5 16z" />
<glyph unicode="&#xe142;" d="M546 173l469 470q91 91 99 192q7 98 -52 175.5t-154 94.5q-22 4 -47 4q-34 0 -66.5 -10t-56.5 -23t-55.5 -38t-48 -41.5t-48.5 -47.5q-376 -375 -391 -390q-30 -27 -45 -41.5t-37.5 -41t-32 -46.5t-16 -47.5t-1.5 -56.5q9 -62 53.5 -95t99.5 -33q74 0 125 51l548 548 q36 36 20 75q-7 16 -21.5 26t-32.5 10q-26 0 -50 -23q-13 -12 -39 -38l-341 -338q-15 -15 -35.5 -15.5t-34.5 13.5t-14 34.5t14 34.5q327 333 361 367q35 35 67.5 51.5t78.5 16.5q14 0 29 -1q44 -8 74.5 -35.5t43.5 -68.5q14 -47 2 -96.5t-47 -84.5q-12 -11 -32 -32 t-79.5 -81t-114.5 -115t-124.5 -123.5t-123 -119.5t-96.5 -89t-57 -45q-56 -27 -120 -27q-70 0 -129 32t-93 89q-48 78 -35 173t81 163l511 511q71 72 111 96q91 55 198 55q80 0 152 -33q78 -36 129.5 -103t66.5 -154q17 -93 -11 -183.5t-94 -156.5l-482 -476 q-15 -15 -36 -16t-37 14t-17.5 34t14.5 35z" />
<glyph unicode="&#xe143;" d="M649 949q48 68 109.5 104t121.5 38.5t118.5 -20t102.5 -64t71 -100.5t27 -123q0 -57 -33.5 -117.5t-94 -124.5t-126.5 -127.5t-150 -152.5t-146 -174q-62 85 -145.5 174t-150 152.5t-126.5 127.5t-93.5 124.5t-33.5 117.5q0 64 28 123t73 100.5t104 64t119 20 t120.5 -38.5t104.5 -104zM896 972q-33 0 -64.5 -19t-56.5 -46t-47.5 -53.5t-43.5 -45.5t-37.5 -19t-36 19t-40 45.5t-43 53.5t-54 46t-65.5 19q-67 0 -122.5 -55.5t-55.5 -132.5q0 -23 13.5 -51t46 -65t57.5 -63t76 -75l22 -22q15 -14 44 -44t50.5 -51t46 -44t41 -35t23 -12 t23.5 12t42.5 36t46 44t52.5 52t44 43q4 4 12 13q43 41 63.5 62t52 55t46 55t26 46t11.5 44q0 79 -53 133.5t-120 54.5z" />
<glyph unicode="&#xe144;" d="M776.5 1214q93.5 0 159.5 -66l141 -141q66 -66 66 -160q0 -42 -28 -95.5t-62 -87.5l-29 -29q-31 53 -77 99l-18 18l95 95l-247 248l-389 -389l212 -212l-105 -106l-19 18l-141 141q-66 66 -66 159t66 159l283 283q65 66 158.5 66zM600 706l105 105q10 -8 19 -17l141 -141 q66 -66 66 -159t-66 -159l-283 -283q-66 -66 -159 -66t-159 66l-141 141q-66 66 -66 159.5t66 159.5l55 55q29 -55 75 -102l18 -17l-95 -95l247 -248l389 389z" />
<glyph unicode="&#xe145;" d="M603 1200q85 0 162 -15t127 -38t79 -48t29 -46v-953q0 -41 -29.5 -70.5t-70.5 -29.5h-600q-41 0 -70.5 29.5t-29.5 70.5v953q0 21 30 46.5t81 48t129 37.5t163 15zM300 1000v-700h600v700h-600zM600 254q-43 0 -73.5 -30.5t-30.5 -73.5t30.5 -73.5t73.5 -30.5t73.5 30.5 t30.5 73.5t-30.5 73.5t-73.5 30.5z" />
<glyph unicode="&#xe146;" d="M902 1185l283 -282q15 -15 15 -36t-14.5 -35.5t-35.5 -14.5t-35 15l-36 35l-279 -267v-300l-212 210l-308 -307l-280 -203l203 280l307 308l-210 212h300l267 279l-35 36q-15 14 -15 35t14.5 35.5t35.5 14.5t35 -15z" />
<glyph unicode="&#xe148;" d="M700 1248v-78q38 -5 72.5 -14.5t75.5 -31.5t71 -53.5t52 -84t24 -118.5h-159q-4 36 -10.5 59t-21 45t-40 35.5t-64.5 20.5v-307l64 -13q34 -7 64 -16.5t70 -32t67.5 -52.5t47.5 -80t20 -112q0 -139 -89 -224t-244 -97v-77h-100v79q-150 16 -237 103q-40 40 -52.5 93.5 t-15.5 139.5h139q5 -77 48.5 -126t117.5 -65v335l-27 8q-46 14 -79 26.5t-72 36t-63 52t-40 72.5t-16 98q0 70 25 126t67.5 92t94.5 57t110 27v77h100zM600 754v274q-29 -4 -50 -11t-42 -21.5t-31.5 -41.5t-10.5 -65q0 -29 7 -50.5t16.5 -34t28.5 -22.5t31.5 -14t37.5 -10 q9 -3 13 -4zM700 547v-310q22 2 42.5 6.5t45 15.5t41.5 27t29 42t12 59.5t-12.5 59.5t-38 44.5t-53 31t-66.5 24.5z" />
<glyph unicode="&#xe149;" d="M561 1197q84 0 160.5 -40t123.5 -109.5t47 -147.5h-153q0 40 -19.5 71.5t-49.5 48.5t-59.5 26t-55.5 9q-37 0 -79 -14.5t-62 -35.5q-41 -44 -41 -101q0 -26 13.5 -63t26.5 -61t37 -66q6 -9 9 -14h241v-100h-197q8 -50 -2.5 -115t-31.5 -95q-45 -62 -99 -112 q34 10 83 17.5t71 7.5q32 1 102 -16t104 -17q83 0 136 30l50 -147q-31 -19 -58 -30.5t-55 -15.5t-42 -4.5t-46 -0.5q-23 0 -76 17t-111 32.5t-96 11.5q-39 -3 -82 -16t-67 -25l-23 -11l-55 145q4 3 16 11t15.5 10.5t13 9t15.5 12t14.5 14t17.5 18.5q48 55 54 126.5 t-30 142.5h-221v100h166q-23 47 -44 104q-7 20 -12 41.5t-6 55.5t6 66.5t29.5 70.5t58.5 71q97 88 263 88z" />
<glyph unicode="&#xe150;" d="M400 300h150q21 0 25 -11t-10 -25l-230 -250q-14 -15 -35 -15t-35 15l-230 250q-14 14 -10 25t25 11h150v900h200v-900zM935 1184l230 -249q14 -14 10 -24.5t-25 -10.5h-150v-900h-200v900h-150q-21 0 -25 10.5t10 24.5l230 249q14 15 35 15t35 -15z" />
<glyph unicode="&#xe151;" d="M1000 700h-100v100h-100v-100h-100v500h300v-500zM400 300h150q21 0 25 -11t-10 -25l-230 -250q-14 -15 -35 -15t-35 15l-230 250q-14 14 -10 25t25 11h150v900h200v-900zM801 1100v-200h100v200h-100zM1000 350l-200 -250h200v-100h-300v150l200 250h-200v100h300v-150z " />
<glyph unicode="&#xe152;" d="M400 300h150q21 0 25 -11t-10 -25l-230 -250q-14 -15 -35 -15t-35 15l-230 250q-14 14 -10 25t25 11h150v900h200v-900zM1000 1050l-200 -250h200v-100h-300v150l200 250h-200v100h300v-150zM1000 0h-100v100h-100v-100h-100v500h300v-500zM801 400v-200h100v200h-100z " />
<glyph unicode="&#xe153;" d="M400 300h150q21 0 25 -11t-10 -25l-230 -250q-14 -15 -35 -15t-35 15l-230 250q-14 14 -10 25t25 11h150v900h200v-900zM1000 700h-100v400h-100v100h200v-500zM1100 0h-100v100h-200v400h300v-500zM901 400v-200h100v200h-100z" />
<glyph unicode="&#xe154;" d="M400 300h150q21 0 25 -11t-10 -25l-230 -250q-14 -15 -35 -15t-35 15l-230 250q-14 14 -10 25t25 11h150v900h200v-900zM1100 700h-100v100h-200v400h300v-500zM901 1100v-200h100v200h-100zM1000 0h-100v400h-100v100h200v-500z" />
<glyph unicode="&#xe155;" d="M400 300h150q21 0 25 -11t-10 -25l-230 -250q-14 -15 -35 -15t-35 15l-230 250q-14 14 -10 25t25 11h150v900h200v-900zM900 1000h-200v200h200v-200zM1000 700h-300v200h300v-200zM1100 400h-400v200h400v-200zM1200 100h-500v200h500v-200z" />
<glyph unicode="&#xe156;" d="M400 300h150q21 0 25 -11t-10 -25l-230 -250q-14 -15 -35 -15t-35 15l-230 250q-14 14 -10 25t25 11h150v900h200v-900zM1200 1000h-500v200h500v-200zM1100 700h-400v200h400v-200zM1000 400h-300v200h300v-200zM900 100h-200v200h200v-200z" />
<glyph unicode="&#xe157;" d="M350 1100h400q162 0 256 -93.5t94 -256.5v-400q0 -165 -93.5 -257.5t-256.5 -92.5h-400q-165 0 -257.5 92.5t-92.5 257.5v400q0 165 92.5 257.5t257.5 92.5zM800 900h-500q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5 v500q0 41 -29.5 70.5t-70.5 29.5z" />
<glyph unicode="&#xe158;" d="M350 1100h400q165 0 257.5 -92.5t92.5 -257.5v-400q0 -165 -92.5 -257.5t-257.5 -92.5h-400q-163 0 -256.5 92.5t-93.5 257.5v400q0 163 94 256.5t256 93.5zM800 900h-500q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5 v500q0 41 -29.5 70.5t-70.5 29.5zM440 770l253 -190q17 -12 17 -30t-17 -30l-253 -190q-16 -12 -28 -6.5t-12 26.5v400q0 21 12 26.5t28 -6.5z" />
<glyph unicode="&#xe159;" d="M350 1100h400q163 0 256.5 -94t93.5 -256v-400q0 -165 -92.5 -257.5t-257.5 -92.5h-400q-165 0 -257.5 92.5t-92.5 257.5v400q0 163 92.5 256.5t257.5 93.5zM800 900h-500q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5 v500q0 41 -29.5 70.5t-70.5 29.5zM350 700h400q21 0 26.5 -12t-6.5 -28l-190 -253q-12 -17 -30 -17t-30 17l-190 253q-12 16 -6.5 28t26.5 12z" />
<glyph unicode="&#xe160;" d="M350 1100h400q165 0 257.5 -92.5t92.5 -257.5v-400q0 -163 -92.5 -256.5t-257.5 -93.5h-400q-163 0 -256.5 94t-93.5 256v400q0 165 92.5 257.5t257.5 92.5zM800 900h-500q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5 v500q0 41 -29.5 70.5t-70.5 29.5zM580 693l190 -253q12 -16 6.5 -28t-26.5 -12h-400q-21 0 -26.5 12t6.5 28l190 253q12 17 30 17t30 -17z" />
<glyph unicode="&#xe161;" d="M550 1100h400q165 0 257.5 -92.5t92.5 -257.5v-400q0 -165 -92.5 -257.5t-257.5 -92.5h-400q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5h450q41 0 70.5 29.5t29.5 70.5v500q0 41 -29.5 70.5t-70.5 29.5h-450q-21 0 -35.5 14.5t-14.5 35.5v100 q0 21 14.5 35.5t35.5 14.5zM338 867l324 -284q16 -14 16 -33t-16 -33l-324 -284q-16 -14 -27 -9t-11 26v150h-250q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5h250v150q0 21 11 26t27 -9z" />
<glyph unicode="&#xe162;" d="M793 1182l9 -9q8 -10 5 -27q-3 -11 -79 -225.5t-78 -221.5l300 1q24 0 32.5 -17.5t-5.5 -35.5q-1 0 -133.5 -155t-267 -312.5t-138.5 -162.5q-12 -15 -26 -15h-9l-9 8q-9 11 -4 32q2 9 42 123.5t79 224.5l39 110h-302q-23 0 -31 19q-10 21 6 41q75 86 209.5 237.5 t228 257t98.5 111.5q9 16 25 16h9z" />
<glyph unicode="&#xe163;" d="M350 1100h400q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-450q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h450q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-165 0 -257.5 92.5t-92.5 257.5v400 q0 165 92.5 257.5t257.5 92.5zM938 867l324 -284q16 -14 16 -33t-16 -33l-324 -284q-16 -14 -27 -9t-11 26v150h-250q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5h250v150q0 21 11 26t27 -9z" />
<glyph unicode="&#xe164;" d="M750 1200h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -10.5 -25t-24.5 10l-109 109l-312 -312q-15 -15 -35.5 -15t-35.5 15l-141 141q-15 15 -15 35.5t15 35.5l312 312l-109 109q-14 14 -10 24.5t25 10.5zM456 900h-156q-41 0 -70.5 -29.5t-29.5 -70.5v-500 q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5v148l200 200v-298q0 -165 -93.5 -257.5t-256.5 -92.5h-400q-165 0 -257.5 92.5t-92.5 257.5v400q0 165 92.5 257.5t257.5 92.5h300z" />
<glyph unicode="&#xe165;" d="M600 1186q119 0 227.5 -46.5t187 -125t125 -187t46.5 -227.5t-46.5 -227.5t-125 -187t-187 -125t-227.5 -46.5t-227.5 46.5t-187 125t-125 187t-46.5 227.5t46.5 227.5t125 187t187 125t227.5 46.5zM600 1022q-115 0 -212 -56.5t-153.5 -153.5t-56.5 -212t56.5 -212 t153.5 -153.5t212 -56.5t212 56.5t153.5 153.5t56.5 212t-56.5 212t-153.5 153.5t-212 56.5zM600 794q80 0 137 -57t57 -137t-57 -137t-137 -57t-137 57t-57 137t57 137t137 57z" />
<glyph unicode="&#xe166;" d="M450 1200h200q21 0 35.5 -14.5t14.5 -35.5v-350h245q20 0 25 -11t-9 -26l-383 -426q-14 -15 -33.5 -15t-32.5 15l-379 426q-13 15 -8.5 26t25.5 11h250v350q0 21 14.5 35.5t35.5 14.5zM50 300h1000q21 0 35.5 -14.5t14.5 -35.5v-250h-1100v250q0 21 14.5 35.5t35.5 14.5z M900 200v-50h100v50h-100z" />
<glyph unicode="&#xe167;" d="M583 1182l378 -435q14 -15 9 -31t-26 -16h-244v-250q0 -20 -17 -35t-39 -15h-200q-20 0 -32 14.5t-12 35.5v250h-250q-20 0 -25.5 16.5t8.5 31.5l383 431q14 16 33.5 17t33.5 -14zM50 300h1000q21 0 35.5 -14.5t14.5 -35.5v-250h-1100v250q0 21 14.5 35.5t35.5 14.5z M900 200v-50h100v50h-100z" />
<glyph unicode="&#xe168;" d="M396 723l369 369q7 7 17.5 7t17.5 -7l139 -139q7 -8 7 -18.5t-7 -17.5l-525 -525q-7 -8 -17.5 -8t-17.5 8l-292 291q-7 8 -7 18t7 18l139 139q8 7 18.5 7t17.5 -7zM50 300h1000q21 0 35.5 -14.5t14.5 -35.5v-250h-1100v250q0 21 14.5 35.5t35.5 14.5zM900 200v-50h100v50 h-100z" />
<glyph unicode="&#xe169;" d="M135 1023l142 142q14 14 35 14t35 -14l77 -77l-212 -212l-77 76q-14 15 -14 36t14 35zM655 855l210 210q14 14 24.5 10t10.5 -25l-2 -599q-1 -20 -15.5 -35t-35.5 -15l-597 -1q-21 0 -25 10.5t10 24.5l208 208l-154 155l212 212zM50 300h1000q21 0 35.5 -14.5t14.5 -35.5 v-250h-1100v250q0 21 14.5 35.5t35.5 14.5zM900 200v-50h100v50h-100z" />
<glyph unicode="&#xe170;" d="M350 1200l599 -2q20 -1 35 -15.5t15 -35.5l1 -597q0 -21 -10.5 -25t-24.5 10l-208 208l-155 -154l-212 212l155 154l-210 210q-14 14 -10 24.5t25 10.5zM524 512l-76 -77q-15 -14 -36 -14t-35 14l-142 142q-14 14 -14 35t14 35l77 77zM50 300h1000q21 0 35.5 -14.5 t14.5 -35.5v-250h-1100v250q0 21 14.5 35.5t35.5 14.5zM900 200v-50h100v50h-100z" />
<glyph unicode="&#xe171;" d="M1200 103l-483 276l-314 -399v423h-399l1196 796v-1096zM483 424v-230l683 953z" />
<glyph unicode="&#xe172;" d="M1100 1000v-850q0 -21 -14.5 -35.5t-35.5 -14.5h-150v400h-700v-400h-150q-21 0 -35.5 14.5t-14.5 35.5v1000q0 20 14.5 35t35.5 15h250v-300h500v300h100zM700 1000h-100v200h100v-200z" />
<glyph unicode="&#xe173;" d="M1100 1000l-2 -149l-299 -299l-95 95q-9 9 -21.5 9t-21.5 -9l-149 -147h-312v-400h-150q-21 0 -35.5 14.5t-14.5 35.5v1000q0 20 14.5 35t35.5 15h250v-300h500v300h100zM700 1000h-100v200h100v-200zM1132 638l106 -106q7 -7 7 -17.5t-7 -17.5l-420 -421q-8 -7 -18 -7 t-18 7l-202 203q-8 7 -8 17.5t8 17.5l106 106q7 8 17.5 8t17.5 -8l79 -79l297 297q7 7 17.5 7t17.5 -7z" />
<glyph unicode="&#xe174;" d="M1100 1000v-269l-103 -103l-134 134q-15 15 -33.5 16.5t-34.5 -12.5l-266 -266h-329v-400h-150q-21 0 -35.5 14.5t-14.5 35.5v1000q0 20 14.5 35t35.5 15h250v-300h500v300h100zM700 1000h-100v200h100v-200zM1202 572l70 -70q15 -15 15 -35.5t-15 -35.5l-131 -131 l131 -131q15 -15 15 -35.5t-15 -35.5l-70 -70q-15 -15 -35.5 -15t-35.5 15l-131 131l-131 -131q-15 -15 -35.5 -15t-35.5 15l-70 70q-15 15 -15 35.5t15 35.5l131 131l-131 131q-15 15 -15 35.5t15 35.5l70 70q15 15 35.5 15t35.5 -15l131 -131l131 131q15 15 35.5 15 t35.5 -15z" />
<glyph unicode="&#xe175;" d="M1100 1000v-300h-350q-21 0 -35.5 -14.5t-14.5 -35.5v-150h-500v-400h-150q-21 0 -35.5 14.5t-14.5 35.5v1000q0 20 14.5 35t35.5 15h250v-300h500v300h100zM700 1000h-100v200h100v-200zM850 600h100q21 0 35.5 -14.5t14.5 -35.5v-250h150q21 0 25 -10.5t-10 -24.5 l-230 -230q-14 -14 -35 -14t-35 14l-230 230q-14 14 -10 24.5t25 10.5h150v250q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe176;" d="M1100 1000v-400l-165 165q-14 15 -35 15t-35 -15l-263 -265h-402v-400h-150q-21 0 -35.5 14.5t-14.5 35.5v1000q0 20 14.5 35t35.5 15h250v-300h500v300h100zM700 1000h-100v200h100v-200zM935 565l230 -229q14 -15 10 -25.5t-25 -10.5h-150v-250q0 -20 -14.5 -35 t-35.5 -15h-100q-21 0 -35.5 15t-14.5 35v250h-150q-21 0 -25 10.5t10 25.5l230 229q14 15 35 15t35 -15z" />
<glyph unicode="&#xe177;" d="M50 1100h1100q21 0 35.5 -14.5t14.5 -35.5v-150h-1200v150q0 21 14.5 35.5t35.5 14.5zM1200 800v-550q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5v550h1200zM100 500v-200h400v200h-400z" />
<glyph unicode="&#xe178;" d="M935 1165l248 -230q14 -14 14 -35t-14 -35l-248 -230q-14 -14 -24.5 -10t-10.5 25v150h-400v200h400v150q0 21 10.5 25t24.5 -10zM200 800h-50q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5h50v-200zM400 800h-100v200h100v-200zM18 435l247 230 q14 14 24.5 10t10.5 -25v-150h400v-200h-400v-150q0 -21 -10.5 -25t-24.5 10l-247 230q-15 14 -15 35t15 35zM900 300h-100v200h100v-200zM1000 500h51q20 0 34.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-34.5 -14.5h-51v200z" />
<glyph unicode="&#xe179;" d="M862 1073l276 116q25 18 43.5 8t18.5 -41v-1106q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v397q-4 1 -11 5t-24 17.5t-30 29t-24 42t-11 56.5v359q0 31 18.5 65t43.5 52zM550 1200q22 0 34.5 -12.5t14.5 -24.5l1 -13v-450q0 -28 -10.5 -59.5 t-25 -56t-29 -45t-25.5 -31.5l-10 -11v-447q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v447q-4 4 -11 11.5t-24 30.5t-30 46t-24 55t-11 60v450q0 2 0.5 5.5t4 12t8.5 15t14.5 12t22.5 5.5q20 0 32.5 -12.5t14.5 -24.5l3 -13v-350h100v350v5.5t2.5 12 t7 15t15 12t25.5 5.5q23 0 35.5 -12.5t13.5 -24.5l1 -13v-350h100v350q0 2 0.5 5.5t3 12t7 15t15 12t24.5 5.5z" />
<glyph unicode="&#xe180;" d="M1200 1100v-56q-4 0 -11 -0.5t-24 -3t-30 -7.5t-24 -15t-11 -24v-888q0 -22 25 -34.5t50 -13.5l25 -2v-56h-400v56q75 0 87.5 6.5t12.5 43.5v394h-500v-394q0 -37 12.5 -43.5t87.5 -6.5v-56h-400v56q4 0 11 0.5t24 3t30 7.5t24 15t11 24v888q0 22 -25 34.5t-50 13.5 l-25 2v56h400v-56q-75 0 -87.5 -6.5t-12.5 -43.5v-394h500v394q0 37 -12.5 43.5t-87.5 6.5v56h400z" />
<glyph unicode="&#xe181;" d="M675 1000h375q21 0 35.5 -14.5t14.5 -35.5v-150h-105l-295 -98v98l-200 200h-400l100 100h375zM100 900h300q41 0 70.5 -29.5t29.5 -70.5v-500q0 -41 -29.5 -70.5t-70.5 -29.5h-300q-41 0 -70.5 29.5t-29.5 70.5v500q0 41 29.5 70.5t70.5 29.5zM100 800v-200h300v200 h-300zM1100 535l-400 -133v163l400 133v-163zM100 500v-200h300v200h-300zM1100 398v-248q0 -21 -14.5 -35.5t-35.5 -14.5h-375l-100 -100h-375l-100 100h400l200 200h105z" />
<glyph unicode="&#xe182;" d="M17 1007l162 162q17 17 40 14t37 -22l139 -194q14 -20 11 -44.5t-20 -41.5l-119 -118q102 -142 228 -268t267 -227l119 118q17 17 42.5 19t44.5 -12l192 -136q19 -14 22.5 -37.5t-13.5 -40.5l-163 -162q-3 -1 -9.5 -1t-29.5 2t-47.5 6t-62.5 14.5t-77.5 26.5t-90 42.5 t-101.5 60t-111 83t-119 108.5q-74 74 -133.5 150.5t-94.5 138.5t-60 119.5t-34.5 100t-15 74.5t-4.5 48z" />
<glyph unicode="&#xe183;" d="M600 1100q92 0 175 -10.5t141.5 -27t108.5 -36.5t81.5 -40t53.5 -37t31 -27l9 -10v-200q0 -21 -14.5 -33t-34.5 -9l-202 34q-20 3 -34.5 20t-14.5 38v146q-141 24 -300 24t-300 -24v-146q0 -21 -14.5 -38t-34.5 -20l-202 -34q-20 -3 -34.5 9t-14.5 33v200q3 4 9.5 10.5 t31 26t54 37.5t80.5 39.5t109 37.5t141 26.5t175 10.5zM600 795q56 0 97 -9.5t60 -23.5t30 -28t12 -24l1 -10v-50l365 -303q14 -15 24.5 -40t10.5 -45v-212q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5v212q0 20 10.5 45t24.5 40l365 303v50 q0 4 1 10.5t12 23t30 29t60 22.5t97 10z" />
<glyph unicode="&#xe184;" d="M1100 700l-200 -200h-600l-200 200v500h200v-200h200v200h200v-200h200v200h200v-500zM250 400h700q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-12l137 -100h-950l137 100h-12q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5zM50 100h1100q21 0 35.5 -14.5 t14.5 -35.5v-50h-1200v50q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe185;" d="M700 1100h-100q-41 0 -70.5 -29.5t-29.5 -70.5v-1000h300v1000q0 41 -29.5 70.5t-70.5 29.5zM1100 800h-100q-41 0 -70.5 -29.5t-29.5 -70.5v-700h300v700q0 41 -29.5 70.5t-70.5 29.5zM400 0h-300v400q0 41 29.5 70.5t70.5 29.5h100q41 0 70.5 -29.5t29.5 -70.5v-400z " />
<glyph unicode="&#xe186;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM500 700h-200v-100h200v-300h-300v100h200v100h-200v300h300v-100zM900 700v-300l-100 -100h-200v500h200z M700 700v-300h100v300h-100z" />
<glyph unicode="&#xe187;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM500 300h-100v200h-100v-200h-100v500h100v-200h100v200h100v-500zM900 700v-300l-100 -100h-200v500h200z M700 700v-300h100v300h-100z" />
<glyph unicode="&#xe188;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM500 700h-200v-300h200v-100h-300v500h300v-100zM900 700h-200v-300h200v-100h-300v500h300v-100z" />
<glyph unicode="&#xe189;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM500 400l-300 150l300 150v-300zM900 550l-300 -150v300z" />
<glyph unicode="&#xe190;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM900 300h-700v500h700v-500zM800 700h-130q-38 0 -66.5 -43t-28.5 -108t27 -107t68 -42h130v300zM300 700v-300 h130q41 0 68 42t27 107t-28.5 108t-66.5 43h-130z" />
<glyph unicode="&#xe191;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM500 700h-200v-100h200v-300h-300v100h200v100h-200v300h300v-100zM900 300h-100v400h-100v100h200v-500z M700 300h-100v100h100v-100z" />
<glyph unicode="&#xe192;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM300 700h200v-400h-300v500h100v-100zM900 300h-100v400h-100v100h200v-500zM300 600v-200h100v200h-100z M700 300h-100v100h100v-100z" />
<glyph unicode="&#xe193;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM500 500l-199 -200h-100v50l199 200v150h-200v100h300v-300zM900 300h-100v400h-100v100h200v-500zM701 300h-100 v100h100v-100z" />
<glyph unicode="&#xe194;" d="M600 1191q120 0 229.5 -47t188.5 -126t126 -188.5t47 -229.5t-47 -229.5t-126 -188.5t-188.5 -126t-229.5 -47t-229.5 47t-188.5 126t-126 188.5t-47 229.5t47 229.5t126 188.5t188.5 126t229.5 47zM600 1021q-114 0 -211 -56.5t-153.5 -153.5t-56.5 -211t56.5 -211 t153.5 -153.5t211 -56.5t211 56.5t153.5 153.5t56.5 211t-56.5 211t-153.5 153.5t-211 56.5zM800 700h-300v-200h300v-100h-300l-100 100v200l100 100h300v-100z" />
<glyph unicode="&#xe195;" d="M600 1191q120 0 229.5 -47t188.5 -126t126 -188.5t47 -229.5t-47 -229.5t-126 -188.5t-188.5 -126t-229.5 -47t-229.5 47t-188.5 126t-126 188.5t-47 229.5t47 229.5t126 188.5t188.5 126t229.5 47zM600 1021q-114 0 -211 -56.5t-153.5 -153.5t-56.5 -211t56.5 -211 t153.5 -153.5t211 -56.5t211 56.5t153.5 153.5t56.5 211t-56.5 211t-153.5 153.5t-211 56.5zM800 700v-100l-50 -50l100 -100v-50h-100l-100 100h-150v-100h-100v400h300zM500 700v-100h200v100h-200z" />
<glyph unicode="&#xe197;" d="M503 1089q110 0 200.5 -59.5t134.5 -156.5q44 14 90 14q120 0 205 -86.5t85 -207t-85 -207t-205 -86.5h-128v250q0 21 -14.5 35.5t-35.5 14.5h-300q-21 0 -35.5 -14.5t-14.5 -35.5v-250h-222q-80 0 -136 57.5t-56 136.5q0 69 43 122.5t108 67.5q-2 19 -2 37q0 100 49 185 t134 134t185 49zM525 500h150q10 0 17.5 -7.5t7.5 -17.5v-275h137q21 0 26 -11.5t-8 -27.5l-223 -244q-13 -16 -32 -16t-32 16l-223 244q-13 16 -8 27.5t26 11.5h137v275q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe198;" d="M502 1089q110 0 201 -59.5t135 -156.5q43 15 89 15q121 0 206 -86.5t86 -206.5q0 -99 -60 -181t-150 -110l-378 360q-13 16 -31.5 16t-31.5 -16l-381 -365h-9q-79 0 -135.5 57.5t-56.5 136.5q0 69 43 122.5t108 67.5q-2 19 -2 38q0 100 49 184.5t133.5 134t184.5 49.5z M632 467l223 -228q13 -16 8 -27.5t-26 -11.5h-137v-275q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v275h-137q-21 0 -26 11.5t8 27.5q199 204 223 228q19 19 31.5 19t32.5 -19z" />
<glyph unicode="&#xe199;" d="M700 100v100h400l-270 300h170l-270 300h170l-300 333l-300 -333h170l-270 -300h170l-270 -300h400v-100h-50q-21 0 -35.5 -14.5t-14.5 -35.5v-50h400v50q0 21 -14.5 35.5t-35.5 14.5h-50z" />
<glyph unicode="&#xe200;" d="M600 1179q94 0 167.5 -56.5t99.5 -145.5q89 -6 150.5 -71.5t61.5 -155.5q0 -61 -29.5 -112.5t-79.5 -82.5q9 -29 9 -55q0 -74 -52.5 -126.5t-126.5 -52.5q-55 0 -100 30v-251q21 0 35.5 -14.5t14.5 -35.5v-50h-300v50q0 21 14.5 35.5t35.5 14.5v251q-45 -30 -100 -30 q-74 0 -126.5 52.5t-52.5 126.5q0 18 4 38q-47 21 -75.5 65t-28.5 97q0 74 52.5 126.5t126.5 52.5q5 0 23 -2q0 2 -1 10t-1 13q0 116 81.5 197.5t197.5 81.5z" />
<glyph unicode="&#xe201;" d="M1010 1010q111 -111 150.5 -260.5t0 -299t-150.5 -260.5q-83 -83 -191.5 -126.5t-218.5 -43.5t-218.5 43.5t-191.5 126.5q-111 111 -150.5 260.5t0 299t150.5 260.5q83 83 191.5 126.5t218.5 43.5t218.5 -43.5t191.5 -126.5zM476 1065q-4 0 -8 -1q-121 -34 -209.5 -122.5 t-122.5 -209.5q-4 -12 2.5 -23t18.5 -14l36 -9q3 -1 7 -1q23 0 29 22q27 96 98 166q70 71 166 98q11 3 17.5 13.5t3.5 22.5l-9 35q-3 13 -14 19q-7 4 -15 4zM512 920q-4 0 -9 -2q-80 -24 -138.5 -82.5t-82.5 -138.5q-4 -13 2 -24t19 -14l34 -9q4 -1 8 -1q22 0 28 21 q18 58 58.5 98.5t97.5 58.5q12 3 18 13.5t3 21.5l-9 35q-3 12 -14 19q-7 4 -15 4zM719.5 719.5q-49.5 49.5 -119.5 49.5t-119.5 -49.5t-49.5 -119.5t49.5 -119.5t119.5 -49.5t119.5 49.5t49.5 119.5t-49.5 119.5zM855 551q-22 0 -28 -21q-18 -58 -58.5 -98.5t-98.5 -57.5 q-11 -4 -17 -14.5t-3 -21.5l9 -35q3 -12 14 -19q7 -4 15 -4q4 0 9 2q80 24 138.5 82.5t82.5 138.5q4 13 -2.5 24t-18.5 14l-34 9q-4 1 -8 1zM1000 515q-23 0 -29 -22q-27 -96 -98 -166q-70 -71 -166 -98q-11 -3 -17.5 -13.5t-3.5 -22.5l9 -35q3 -13 14 -19q7 -4 15 -4 q4 0 8 1q121 34 209.5 122.5t122.5 209.5q4 12 -2.5 23t-18.5 14l-36 9q-3 1 -7 1z" />
<glyph unicode="&#xe202;" d="M700 800h300v-380h-180v200h-340v-200h-380v755q0 10 7.5 17.5t17.5 7.5h575v-400zM1000 900h-200v200zM700 300h162l-212 -212l-212 212h162v200h100v-200zM520 0h-395q-10 0 -17.5 7.5t-7.5 17.5v395zM1000 220v-195q0 -10 -7.5 -17.5t-17.5 -7.5h-195z" />
<glyph unicode="&#xe203;" d="M700 800h300v-520l-350 350l-550 -550v1095q0 10 7.5 17.5t17.5 7.5h575v-400zM1000 900h-200v200zM862 200h-162v-200h-100v200h-162l212 212zM480 0h-355q-10 0 -17.5 7.5t-7.5 17.5v55h380v-80zM1000 80v-55q0 -10 -7.5 -17.5t-17.5 -7.5h-155v80h180z" />
<glyph unicode="&#xe204;" d="M1162 800h-162v-200h100l100 -100h-300v300h-162l212 212zM200 800h200q27 0 40 -2t29.5 -10.5t23.5 -30t7 -57.5h300v-100h-600l-200 -350v450h100q0 36 7 57.5t23.5 30t29.5 10.5t40 2zM800 400h240l-240 -400h-800l300 500h500v-100z" />
<glyph unicode="&#xe205;" d="M650 1100h100q21 0 35.5 -14.5t14.5 -35.5v-50h50q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-300q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5h50v50q0 21 14.5 35.5t35.5 14.5zM1000 850v150q41 0 70.5 -29.5t29.5 -70.5v-800 q0 -41 -29.5 -70.5t-70.5 -29.5h-600q-1 0 -20 4l246 246l-326 326v324q0 41 29.5 70.5t70.5 29.5v-150q0 -62 44 -106t106 -44h300q62 0 106 44t44 106zM412 250l-212 -212v162h-200v100h200v162z" />
<glyph unicode="&#xe206;" d="M450 1100h100q21 0 35.5 -14.5t14.5 -35.5v-50h50q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-300q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5h50v50q0 21 14.5 35.5t35.5 14.5zM800 850v150q41 0 70.5 -29.5t29.5 -70.5v-500 h-200v-300h200q0 -36 -7 -57.5t-23.5 -30t-29.5 -10.5t-40 -2h-600q-41 0 -70.5 29.5t-29.5 70.5v800q0 41 29.5 70.5t70.5 29.5v-150q0 -62 44 -106t106 -44h300q62 0 106 44t44 106zM1212 250l-212 -212v162h-200v100h200v162z" />
<glyph unicode="&#xe209;" d="M658 1197l637 -1104q23 -38 7 -65.5t-60 -27.5h-1276q-44 0 -60 27.5t7 65.5l637 1104q22 39 54 39t54 -39zM704 800h-208q-20 0 -32 -14.5t-8 -34.5l58 -302q4 -20 21.5 -34.5t37.5 -14.5h54q20 0 37.5 14.5t21.5 34.5l58 302q4 20 -8 34.5t-32 14.5zM500 300v-100h200 v100h-200z" />
<glyph unicode="&#xe210;" d="M425 1100h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5zM425 800h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5 t17.5 7.5zM825 800h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5zM25 500h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v150 q0 10 7.5 17.5t17.5 7.5zM425 500h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5zM825 500h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5 v150q0 10 7.5 17.5t17.5 7.5zM25 200h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5zM425 200h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5 t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5zM825 200h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe211;" d="M700 1200h100v-200h-100v-100h350q62 0 86.5 -39.5t-3.5 -94.5l-66 -132q-41 -83 -81 -134h-772q-40 51 -81 134l-66 132q-28 55 -3.5 94.5t86.5 39.5h350v100h-100v200h100v100h200v-100zM250 400h700q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-12l137 -100 h-950l138 100h-13q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5zM50 100h1100q21 0 35.5 -14.5t14.5 -35.5v-50h-1200v50q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe212;" d="M600 1300q40 0 68.5 -29.5t28.5 -70.5h-194q0 41 28.5 70.5t68.5 29.5zM443 1100h314q18 -37 18 -75q0 -8 -3 -25h328q41 0 44.5 -16.5t-30.5 -38.5l-175 -145h-678l-178 145q-34 22 -29 38.5t46 16.5h328q-3 17 -3 25q0 38 18 75zM250 700h700q21 0 35.5 -14.5 t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-150v-200l275 -200h-950l275 200v200h-150q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5zM50 100h1100q21 0 35.5 -14.5t14.5 -35.5v-50h-1200v50q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe213;" d="M600 1181q75 0 128 -53t53 -128t-53 -128t-128 -53t-128 53t-53 128t53 128t128 53zM602 798h46q34 0 55.5 -28.5t21.5 -86.5q0 -76 39 -183h-324q39 107 39 183q0 58 21.5 86.5t56.5 28.5h45zM250 400h700q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-13 l138 -100h-950l137 100h-12q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5zM50 100h1100q21 0 35.5 -14.5t14.5 -35.5v-50h-1200v50q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe214;" d="M600 1300q47 0 92.5 -53.5t71 -123t25.5 -123.5q0 -78 -55.5 -133.5t-133.5 -55.5t-133.5 55.5t-55.5 133.5q0 62 34 143l144 -143l111 111l-163 163q34 26 63 26zM602 798h46q34 0 55.5 -28.5t21.5 -86.5q0 -76 39 -183h-324q39 107 39 183q0 58 21.5 86.5t56.5 28.5h45 zM250 400h700q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-13l138 -100h-950l137 100h-12q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5zM50 100h1100q21 0 35.5 -14.5t14.5 -35.5v-50h-1200v50q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe215;" d="M600 1200l300 -161v-139h-300q0 -57 18.5 -108t50 -91.5t63 -72t70 -67.5t57.5 -61h-530q-60 83 -90.5 177.5t-30.5 178.5t33 164.5t87.5 139.5t126 96.5t145.5 41.5v-98zM250 400h700q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-13l138 -100h-950l137 100 h-12q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5zM50 100h1100q21 0 35.5 -14.5t14.5 -35.5v-50h-1200v50q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe216;" d="M600 1300q41 0 70.5 -29.5t29.5 -70.5v-78q46 -26 73 -72t27 -100v-50h-400v50q0 54 27 100t73 72v78q0 41 29.5 70.5t70.5 29.5zM400 800h400q54 0 100 -27t72 -73h-172v-100h200v-100h-200v-100h200v-100h-200v-100h200q0 -83 -58.5 -141.5t-141.5 -58.5h-400 q-83 0 -141.5 58.5t-58.5 141.5v400q0 83 58.5 141.5t141.5 58.5z" />
<glyph unicode="&#xe218;" d="M150 1100h900q21 0 35.5 -14.5t14.5 -35.5v-500q0 -21 -14.5 -35.5t-35.5 -14.5h-900q-21 0 -35.5 14.5t-14.5 35.5v500q0 21 14.5 35.5t35.5 14.5zM125 400h950q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-283l224 -224q13 -13 13 -31.5t-13 -32 t-31.5 -13.5t-31.5 13l-88 88h-524l-87 -88q-13 -13 -32 -13t-32 13.5t-13 32t13 31.5l224 224h-289q-10 0 -17.5 7.5t-7.5 17.5v50q0 10 7.5 17.5t17.5 7.5zM541 300l-100 -100h324l-100 100h-124z" />
<glyph unicode="&#xe219;" d="M200 1100h800q83 0 141.5 -58.5t58.5 -141.5v-200h-100q0 41 -29.5 70.5t-70.5 29.5h-250q-41 0 -70.5 -29.5t-29.5 -70.5h-100q0 41 -29.5 70.5t-70.5 29.5h-250q-41 0 -70.5 -29.5t-29.5 -70.5h-100v200q0 83 58.5 141.5t141.5 58.5zM100 600h1000q41 0 70.5 -29.5 t29.5 -70.5v-300h-1200v300q0 41 29.5 70.5t70.5 29.5zM300 100v-50q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v50h200zM1100 100v-50q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v50h200z" />
<glyph unicode="&#xe221;" d="M480 1165l682 -683q31 -31 31 -75.5t-31 -75.5l-131 -131h-481l-517 518q-32 31 -32 75.5t32 75.5l295 296q31 31 75.5 31t76.5 -31zM108 794l342 -342l303 304l-341 341zM250 100h800q21 0 35.5 -14.5t14.5 -35.5v-50h-900v50q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe223;" d="M1057 647l-189 506q-8 19 -27.5 33t-40.5 14h-400q-21 0 -40.5 -14t-27.5 -33l-189 -506q-8 -19 1.5 -33t30.5 -14h625v-150q0 -21 14.5 -35.5t35.5 -14.5t35.5 14.5t14.5 35.5v150h125q21 0 30.5 14t1.5 33zM897 0h-595v50q0 21 14.5 35.5t35.5 14.5h50v50 q0 21 14.5 35.5t35.5 14.5h48v300h200v-300h47q21 0 35.5 -14.5t14.5 -35.5v-50h50q21 0 35.5 -14.5t14.5 -35.5v-50z" />
<glyph unicode="&#xe224;" d="M900 800h300v-575q0 -10 -7.5 -17.5t-17.5 -7.5h-375v591l-300 300v84q0 10 7.5 17.5t17.5 7.5h375v-400zM1200 900h-200v200zM400 600h300v-575q0 -10 -7.5 -17.5t-17.5 -7.5h-650q-10 0 -17.5 7.5t-7.5 17.5v950q0 10 7.5 17.5t17.5 7.5h375v-400zM700 700h-200v200z " />
<glyph unicode="&#xe225;" d="M484 1095h195q75 0 146 -32.5t124 -86t89.5 -122.5t48.5 -142q18 -14 35 -20q31 -10 64.5 6.5t43.5 48.5q10 34 -15 71q-19 27 -9 43q5 8 12.5 11t19 -1t23.5 -16q41 -44 39 -105q-3 -63 -46 -106.5t-104 -43.5h-62q-7 -55 -35 -117t-56 -100l-39 -234q-3 -20 -20 -34.5 t-38 -14.5h-100q-21 0 -33 14.5t-9 34.5l12 70q-49 -14 -91 -14h-195q-24 0 -65 8l-11 -64q-3 -20 -20 -34.5t-38 -14.5h-100q-21 0 -33 14.5t-9 34.5l26 157q-84 74 -128 175l-159 53q-19 7 -33 26t-14 40v50q0 21 14.5 35.5t35.5 14.5h124q11 87 56 166l-111 95 q-16 14 -12.5 23.5t24.5 9.5h203q116 101 250 101zM675 1000h-250q-10 0 -17.5 -7.5t-7.5 -17.5v-50q0 -10 7.5 -17.5t17.5 -7.5h250q10 0 17.5 7.5t7.5 17.5v50q0 10 -7.5 17.5t-17.5 7.5z" />
<glyph unicode="&#xe226;" d="M641 900l423 247q19 8 42 2.5t37 -21.5l32 -38q14 -15 12.5 -36t-17.5 -34l-139 -120h-390zM50 1100h106q67 0 103 -17t66 -71l102 -212h823q21 0 35.5 -14.5t14.5 -35.5v-50q0 -21 -14 -40t-33 -26l-737 -132q-23 -4 -40 6t-26 25q-42 67 -100 67h-300q-62 0 -106 44 t-44 106v200q0 62 44 106t106 44zM173 928h-80q-19 0 -28 -14t-9 -35v-56q0 -51 42 -51h134q16 0 21.5 8t5.5 24q0 11 -16 45t-27 51q-18 28 -43 28zM550 727q-32 0 -54.5 -22.5t-22.5 -54.5t22.5 -54.5t54.5 -22.5t54.5 22.5t22.5 54.5t-22.5 54.5t-54.5 22.5zM130 389 l152 130q18 19 34 24t31 -3.5t24.5 -17.5t25.5 -28q28 -35 50.5 -51t48.5 -13l63 5l48 -179q13 -61 -3.5 -97.5t-67.5 -79.5l-80 -69q-47 -40 -109 -35.5t-103 51.5l-130 151q-40 47 -35.5 109.5t51.5 102.5zM380 377l-102 -88q-31 -27 2 -65l37 -43q13 -15 27.5 -19.5 t31.5 6.5l61 53q19 16 14 49q-2 20 -12 56t-17 45q-11 12 -19 14t-23 -8z" />
<glyph unicode="&#xe227;" d="M625 1200h150q10 0 17.5 -7.5t7.5 -17.5v-109q79 -33 131 -87.5t53 -128.5q1 -46 -15 -84.5t-39 -61t-46 -38t-39 -21.5l-17 -6q6 0 15 -1.5t35 -9t50 -17.5t53 -30t50 -45t35.5 -64t14.5 -84q0 -59 -11.5 -105.5t-28.5 -76.5t-44 -51t-49.5 -31.5t-54.5 -16t-49.5 -6.5 t-43.5 -1v-75q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v75h-100v-75q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v75h-175q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5h75v600h-75q-10 0 -17.5 7.5t-7.5 17.5v150 q0 10 7.5 17.5t17.5 7.5h175v75q0 10 7.5 17.5t17.5 7.5h150q10 0 17.5 -7.5t7.5 -17.5v-75h100v75q0 10 7.5 17.5t17.5 7.5zM400 900v-200h263q28 0 48.5 10.5t30 25t15 29t5.5 25.5l1 10q0 4 -0.5 11t-6 24t-15 30t-30 24t-48.5 11h-263zM400 500v-200h363q28 0 48.5 10.5 t30 25t15 29t5.5 25.5l1 10q0 4 -0.5 11t-6 24t-15 30t-30 24t-48.5 11h-363z" />
<glyph unicode="&#xe230;" d="M212 1198h780q86 0 147 -61t61 -147v-416q0 -51 -18 -142.5t-36 -157.5l-18 -66q-29 -87 -93.5 -146.5t-146.5 -59.5h-572q-82 0 -147 59t-93 147q-8 28 -20 73t-32 143.5t-20 149.5v416q0 86 61 147t147 61zM600 1045q-70 0 -132.5 -11.5t-105.5 -30.5t-78.5 -41.5 t-57 -45t-36 -41t-20.5 -30.5l-6 -12l156 -243h560l156 243q-2 5 -6 12.5t-20 29.5t-36.5 42t-57 44.5t-79 42t-105 29.5t-132.5 12zM762 703h-157l195 261z" />
<glyph unicode="&#xe231;" d="M475 1300h150q103 0 189 -86t86 -189v-500q0 -41 -42 -83t-83 -42h-450q-41 0 -83 42t-42 83v500q0 103 86 189t189 86zM700 300v-225q0 -21 -27 -48t-48 -27h-150q-21 0 -48 27t-27 48v225h300z" />
<glyph unicode="&#xe232;" d="M475 1300h96q0 -150 89.5 -239.5t239.5 -89.5v-446q0 -41 -42 -83t-83 -42h-450q-41 0 -83 42t-42 83v500q0 103 86 189t189 86zM700 300v-225q0 -21 -27 -48t-48 -27h-150q-21 0 -48 27t-27 48v225h300z" />
<glyph unicode="&#xe233;" d="M1294 767l-638 -283l-378 170l-78 -60v-224l100 -150v-199l-150 148l-150 -149v200l100 150v250q0 4 -0.5 10.5t0 9.5t1 8t3 8t6.5 6l47 40l-147 65l642 283zM1000 380l-350 -166l-350 166v147l350 -165l350 165v-147z" />
<glyph unicode="&#xe234;" d="M250 800q62 0 106 -44t44 -106t-44 -106t-106 -44t-106 44t-44 106t44 106t106 44zM650 800q62 0 106 -44t44 -106t-44 -106t-106 -44t-106 44t-44 106t44 106t106 44zM1050 800q62 0 106 -44t44 -106t-44 -106t-106 -44t-106 44t-44 106t44 106t106 44z" />
<glyph unicode="&#xe235;" d="M550 1100q62 0 106 -44t44 -106t-44 -106t-106 -44t-106 44t-44 106t44 106t106 44zM550 700q62 0 106 -44t44 -106t-44 -106t-106 -44t-106 44t-44 106t44 106t106 44zM550 300q62 0 106 -44t44 -106t-44 -106t-106 -44t-106 44t-44 106t44 106t106 44z" />
<glyph unicode="&#xe236;" d="M125 1100h950q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-950q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5zM125 700h950q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-950q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5 t17.5 7.5zM125 300h950q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-950q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe237;" d="M350 1200h500q162 0 256 -93.5t94 -256.5v-500q0 -165 -93.5 -257.5t-256.5 -92.5h-500q-165 0 -257.5 92.5t-92.5 257.5v500q0 165 92.5 257.5t257.5 92.5zM900 1000h-600q-41 0 -70.5 -29.5t-29.5 -70.5v-600q0 -41 29.5 -70.5t70.5 -29.5h600q41 0 70.5 29.5 t29.5 70.5v600q0 41 -29.5 70.5t-70.5 29.5zM350 900h500q21 0 35.5 -14.5t14.5 -35.5v-300q0 -21 -14.5 -35.5t-35.5 -14.5h-500q-21 0 -35.5 14.5t-14.5 35.5v300q0 21 14.5 35.5t35.5 14.5zM400 800v-200h400v200h-400z" />
<glyph unicode="&#xe238;" d="M150 1100h1000q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-50v-200h50q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-50v-200h50q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-50v-200h50q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5 t-35.5 -14.5h-1000q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5h50v200h-50q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5h50v200h-50q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5h50v200h-50q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe239;" d="M650 1187q87 -67 118.5 -156t0 -178t-118.5 -155q-87 66 -118.5 155t0 178t118.5 156zM300 800q124 0 212 -88t88 -212q-124 0 -212 88t-88 212zM1000 800q0 -124 -88 -212t-212 -88q0 124 88 212t212 88zM300 500q124 0 212 -88t88 -212q-124 0 -212 88t-88 212z M1000 500q0 -124 -88 -212t-212 -88q0 124 88 212t212 88zM700 199v-144q0 -21 -14.5 -35.5t-35.5 -14.5t-35.5 14.5t-14.5 35.5v142q40 -4 43 -4q17 0 57 6z" />
<glyph unicode="&#xe240;" d="M745 878l69 19q25 6 45 -12l298 -295q11 -11 15 -26.5t-2 -30.5q-5 -14 -18 -23.5t-28 -9.5h-8q1 0 1 -13q0 -29 -2 -56t-8.5 -62t-20 -63t-33 -53t-51 -39t-72.5 -14h-146q-184 0 -184 288q0 24 10 47q-20 4 -62 4t-63 -4q11 -24 11 -47q0 -288 -184 -288h-142 q-48 0 -84.5 21t-56 51t-32 71.5t-16 75t-3.5 68.5q0 13 2 13h-7q-15 0 -27.5 9.5t-18.5 23.5q-6 15 -2 30.5t15 25.5l298 296q20 18 46 11l76 -19q20 -5 30.5 -22.5t5.5 -37.5t-22.5 -31t-37.5 -5l-51 12l-182 -193h891l-182 193l-44 -12q-20 -5 -37.5 6t-22.5 31t6 37.5 t31 22.5z" />
<glyph unicode="&#xe241;" d="M1200 900h-50q0 21 -4 37t-9.5 26.5t-18 17.5t-22 11t-28.5 5.5t-31 2t-37 0.5h-200v-850q0 -22 25 -34.5t50 -13.5l25 -2v-100h-400v100q4 0 11 0.5t24 3t30 7t24 15t11 24.5v850h-200q-25 0 -37 -0.5t-31 -2t-28.5 -5.5t-22 -11t-18 -17.5t-9.5 -26.5t-4 -37h-50v300 h1000v-300zM500 450h-25q0 15 -4 24.5t-9 14.5t-17 7.5t-20 3t-25 0.5h-100v-425q0 -11 12.5 -17.5t25.5 -7.5h12v-50h-200v50q50 0 50 25v425h-100q-17 0 -25 -0.5t-20 -3t-17 -7.5t-9 -14.5t-4 -24.5h-25v150h500v-150z" />
<glyph unicode="&#xe242;" d="M1000 300v50q-25 0 -55 32q-14 14 -25 31t-16 27l-4 11l-289 747h-69l-300 -754q-18 -35 -39 -56q-9 -9 -24.5 -18.5t-26.5 -14.5l-11 -5v-50h273v50q-49 0 -78.5 21.5t-11.5 67.5l69 176h293l61 -166q13 -34 -3.5 -66.5t-55.5 -32.5v-50h312zM412 691l134 342l121 -342 h-255zM1100 150v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1000q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5h1000q21 0 35.5 -14.5t14.5 -35.5z" />
<glyph unicode="&#xe243;" d="M50 1200h1100q21 0 35.5 -14.5t14.5 -35.5v-1100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5v1100q0 21 14.5 35.5t35.5 14.5zM611 1118h-70q-13 0 -18 -12l-299 -753q-17 -32 -35 -51q-18 -18 -56 -34q-12 -5 -12 -18v-50q0 -8 5.5 -14t14.5 -6 h273q8 0 14 6t6 14v50q0 8 -6 14t-14 6q-55 0 -71 23q-10 14 0 39l63 163h266l57 -153q11 -31 -6 -55q-12 -17 -36 -17q-8 0 -14 -6t-6 -14v-50q0 -8 6 -14t14 -6h313q8 0 14 6t6 14v50q0 7 -5.5 13t-13.5 7q-17 0 -42 25q-25 27 -40 63h-1l-288 748q-5 12 -19 12zM639 611 h-197l103 264z" />
<glyph unicode="&#xe244;" d="M1200 1100h-1200v100h1200v-100zM50 1000h400q21 0 35.5 -14.5t14.5 -35.5v-900q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v900q0 21 14.5 35.5t35.5 14.5zM650 1000h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400 q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5zM700 900v-300h300v300h-300z" />
<glyph unicode="&#xe245;" d="M50 1200h400q21 0 35.5 -14.5t14.5 -35.5v-900q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v900q0 21 14.5 35.5t35.5 14.5zM650 700h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v400 q0 21 14.5 35.5t35.5 14.5zM700 600v-300h300v300h-300zM1200 0h-1200v100h1200v-100z" />
<glyph unicode="&#xe246;" d="M50 1000h400q21 0 35.5 -14.5t14.5 -35.5v-350h100v150q0 21 14.5 35.5t35.5 14.5h400q21 0 35.5 -14.5t14.5 -35.5v-150h100v-100h-100v-150q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v150h-100v-350q0 -21 -14.5 -35.5t-35.5 -14.5h-400 q-21 0 -35.5 14.5t-14.5 35.5v800q0 21 14.5 35.5t35.5 14.5zM700 700v-300h300v300h-300z" />
<glyph unicode="&#xe247;" d="M100 0h-100v1200h100v-1200zM250 1100h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5zM300 1000v-300h300v300h-300zM250 500h900q21 0 35.5 -14.5t14.5 -35.5v-400 q0 -21 -14.5 -35.5t-35.5 -14.5h-900q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe248;" d="M600 1100h150q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-150v-100h450q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-900q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5h350v100h-150q-21 0 -35.5 14.5 t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5h150v100h100v-100zM400 1000v-300h300v300h-300z" />
<glyph unicode="&#xe249;" d="M1200 0h-100v1200h100v-1200zM550 1100h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5zM600 1000v-300h300v300h-300zM50 500h900q21 0 35.5 -14.5t14.5 -35.5v-400 q0 -21 -14.5 -35.5t-35.5 -14.5h-900q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe250;" d="M865 565l-494 -494q-23 -23 -41 -23q-14 0 -22 13.5t-8 38.5v1000q0 25 8 38.5t22 13.5q18 0 41 -23l494 -494q14 -14 14 -35t-14 -35z" />
<glyph unicode="&#xe251;" d="M335 635l494 494q29 29 50 20.5t21 -49.5v-1000q0 -41 -21 -49.5t-50 20.5l-494 494q-14 14 -14 35t14 35z" />
<glyph unicode="&#xe252;" d="M100 900h1000q41 0 49.5 -21t-20.5 -50l-494 -494q-14 -14 -35 -14t-35 14l-494 494q-29 29 -20.5 50t49.5 21z" />
<glyph unicode="&#xe253;" d="M635 865l494 -494q29 -29 20.5 -50t-49.5 -21h-1000q-41 0 -49.5 21t20.5 50l494 494q14 14 35 14t35 -14z" />
<glyph unicode="&#xe254;" d="M700 741v-182l-692 -323v221l413 193l-413 193v221zM1200 0h-800v200h800v-200z" />
<glyph unicode="&#xe255;" d="M1200 900h-200v-100h200v-100h-300v300h200v100h-200v100h300v-300zM0 700h50q0 21 4 37t9.5 26.5t18 17.5t22 11t28.5 5.5t31 2t37 0.5h100v-550q0 -22 -25 -34.5t-50 -13.5l-25 -2v-100h400v100q-4 0 -11 0.5t-24 3t-30 7t-24 15t-11 24.5v550h100q25 0 37 -0.5t31 -2 t28.5 -5.5t22 -11t18 -17.5t9.5 -26.5t4 -37h50v300h-800v-300z" />
<glyph unicode="&#xe256;" d="M800 700h-50q0 21 -4 37t-9.5 26.5t-18 17.5t-22 11t-28.5 5.5t-31 2t-37 0.5h-100v-550q0 -22 25 -34.5t50 -14.5l25 -1v-100h-400v100q4 0 11 0.5t24 3t30 7t24 15t11 24.5v550h-100q-25 0 -37 -0.5t-31 -2t-28.5 -5.5t-22 -11t-18 -17.5t-9.5 -26.5t-4 -37h-50v300 h800v-300zM1100 200h-200v-100h200v-100h-300v300h200v100h-200v100h300v-300z" />
<glyph unicode="&#xe257;" d="M701 1098h160q16 0 21 -11t-7 -23l-464 -464l464 -464q12 -12 7 -23t-21 -11h-160q-13 0 -23 9l-471 471q-7 8 -7 18t7 18l471 471q10 9 23 9z" />
<glyph unicode="&#xe258;" d="M339 1098h160q13 0 23 -9l471 -471q7 -8 7 -18t-7 -18l-471 -471q-10 -9 -23 -9h-160q-16 0 -21 11t7 23l464 464l-464 464q-12 12 -7 23t21 11z" />
<glyph unicode="&#xe259;" d="M1087 882q11 -5 11 -21v-160q0 -13 -9 -23l-471 -471q-8 -7 -18 -7t-18 7l-471 471q-9 10 -9 23v160q0 16 11 21t23 -7l464 -464l464 464q12 12 23 7z" />
<glyph unicode="&#xe260;" d="M618 993l471 -471q9 -10 9 -23v-160q0 -16 -11 -21t-23 7l-464 464l-464 -464q-12 -12 -23 -7t-11 21v160q0 13 9 23l471 471q8 7 18 7t18 -7z" />
<glyph unicode="&#xf8ff;" d="M1000 1200q0 -124 -88 -212t-212 -88q0 124 88 212t212 88zM450 1000h100q21 0 40 -14t26 -33l79 -194q5 1 16 3q34 6 54 9.5t60 7t65.5 1t61 -10t56.5 -23t42.5 -42t29 -64t5 -92t-19.5 -121.5q-1 -7 -3 -19.5t-11 -50t-20.5 -73t-32.5 -81.5t-46.5 -83t-64 -70 t-82.5 -50q-13 -5 -42 -5t-65.5 2.5t-47.5 2.5q-14 0 -49.5 -3.5t-63 -3.5t-43.5 7q-57 25 -104.5 78.5t-75 111.5t-46.5 112t-26 90l-7 35q-15 63 -18 115t4.5 88.5t26 64t39.5 43.5t52 25.5t58.5 13t62.5 2t59.5 -4.5t55.5 -8l-147 192q-12 18 -5.5 30t27.5 12z" />
<glyph unicode="&#x1f511;" d="M250 1200h600q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-150v-500l-255 -178q-19 -9 -32 -1t-13 29v650h-150q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5zM400 1100v-100h300v100h-300z" />
<glyph unicode="&#x1f6aa;" d="M250 1200h750q39 0 69.5 -40.5t30.5 -84.5v-933l-700 -117v950l600 125h-700v-1000h-100v1025q0 23 15.5 49t34.5 26zM500 525v-100l100 20v100z" />
</font>
</defs></svg> ) format('svg')}.glyphicon{position:relative;top:1px;display:inline-block;font-family:'Glyphicons Halflings';font-style:normal;font-weight:400;line-height:1;-webkit-font-smoothing:antialiased;-moz-osx-font-smoothing:grayscale}.glyphicon-asterisk:before{content:"\2a"}.glyphicon-plus:before{content:"\2b"}.glyphicon-eur:before,.glyphicon-euro:before{content:"\20ac"}.glyphicon-minus:before{content:"\2212"}.glyphicon-cloud:before{content:"\2601"}.glyphicon-envelope:before{content:"\2709"}.glyphicon-pencil:before{content:"\270f"}.glyphicon-glass:before{content:"\e001"}.glyphicon-music:before{content:"\e002"}.glyphicon-search:before{content:"\e003"}.glyphicon-heart:before{content:"\e005"}.glyphicon-star:before{content:"\e006"}.glyphicon-star-empty:before{content:"\e007"}.glyphicon-user:before{content:"\e008"}.glyphicon-film:before{content:"\e009"}.glyphicon-th-large:before{content:"\e010"}.glyphicon-th:before{content:"\e011"}.glyphicon-th-list:before{content:"\e012"}.glyphicon-ok:before{content:"\e013"}.glyphicon-remove:before{content:"\e014"}.glyphicon-zoom-in:before{content:"\e015"}.glyphicon-zoom-out:before{content:"\e016"}.glyphicon-off:before{content:"\e017"}.glyphicon-signal:before{content:"\e018"}.glyphicon-cog:before{content:"\e019"}.glyphicon-trash:before{content:"\e020"}.glyphicon-home:before{content:"\e021"}.glyphicon-file:before{content:"\e022"}.glyphicon-time:before{content:"\e023"}.glyphicon-road:before{content:"\e024"}.glyphicon-download-alt:before{content:"\e025"}.glyphicon-download:before{content:"\e026"}.glyphicon-upload:before{content:"\e027"}.glyphicon-inbox:before{content:"\e028"}.glyphicon-play-circle:before{content:"\e029"}.glyphicon-repeat:before{content:"\e030"}.glyphicon-refresh:before{content:"\e031"}.glyphicon-list-alt:before{content:"\e032"}.glyphicon-lock:before{content:"\e033"}.glyphicon-flag:before{content:"\e034"}.glyphicon-headphones:before{content:"\e035"}.glyphicon-volume-off:before{content:"\e036"}.glyphicon-volume-down:before{content:"\e037"}.glyphicon-volume-up:before{content:"\e038"}.glyphicon-qrcode:before{content:"\e039"}.glyphicon-barcode:before{content:"\e040"}.glyphicon-tag:before{content:"\e041"}.glyphicon-tags:before{content:"\e042"}.glyphicon-book:before{content:"\e043"}.glyphicon-bookmark:before{content:"\e044"}.glyphicon-print:before{content:"\e045"}.glyphicon-camera:before{content:"\e046"}.glyphicon-font:before{content:"\e047"}.glyphicon-bold:before{content:"\e048"}.glyphicon-italic:before{content:"\e049"}.glyphicon-text-height:before{content:"\e050"}.glyphicon-text-width:before{content:"\e051"}.glyphicon-align-left:before{content:"\e052"}.glyphicon-align-center:before{content:"\e053"}.glyphicon-align-right:before{content:"\e054"}.glyphicon-align-justify:before{content:"\e055"}.glyphicon-list:before{content:"\e056"}.glyphicon-indent-left:before{content:"\e057"}.glyphicon-indent-right:before{content:"\e058"}.glyphicon-facetime-video:before{content:"\e059"}.glyphicon-picture:before{content:"\e060"}.glyphicon-map-marker:before{content:"\e062"}.glyphicon-adjust:before{content:"\e063"}.glyphicon-tint:before{content:"\e064"}.glyphicon-edit:before{content:"\e065"}.glyphicon-share:before{content:"\e066"}.glyphicon-check:before{content:"\e067"}.glyphicon-move:before{content:"\e068"}.glyphicon-step-backward:before{content:"\e069"}.glyphicon-fast-backward:before{content:"\e070"}.glyphicon-backward:before{content:"\e071"}.glyphicon-play:before{content:"\e072"}.glyphicon-pause:before{content:"\e073"}.glyphicon-stop:before{content:"\e074"}.glyphicon-forward:before{content:"\e075"}.glyphicon-fast-forward:before{content:"\e076"}.glyphicon-step-forward:before{content:"\e077"}.glyphicon-eject:before{content:"\e078"}.glyphicon-chevron-left:before{content:"\e079"}.glyphicon-chevron-right:before{content:"\e080"}.glyphicon-plus-sign:before{content:"\e081"}.glyphicon-minus-sign:before{content:"\e082"}.glyphicon-remove-sign:before{content:"\e083"}.glyphicon-ok-sign:before{content:"\e084"}.glyphicon-question-sign:before{content:"\e085"}.glyphicon-info-sign:before{content:"\e086"}.glyphicon-screenshot:before{content:"\e087"}.glyphicon-remove-circle:before{content:"\e088"}.glyphicon-ok-circle:before{content:"\e089"}.glyphicon-ban-circle:before{content:"\e090"}.glyphicon-arrow-left:before{content:"\e091"}.glyphicon-arrow-right:before{content:"\e092"}.glyphicon-arrow-up:before{content:"\e093"}.glyphicon-arrow-down:before{content:"\e094"}.glyphicon-share-alt:before{content:"\e095"}.glyphicon-resize-full:before{content:"\e096"}.glyphicon-resize-small:before{content:"\e097"}.glyphicon-exclamation-sign:before{content:"\e101"}.glyphicon-gift:before{content:"\e102"}.glyphicon-leaf:before{content:"\e103"}.glyphicon-fire:before{content:"\e104"}.glyphicon-eye-open:before{content:"\e105"}.glyphicon-eye-close:before{content:"\e106"}.glyphicon-warning-sign:before{content:"\e107"}.glyphicon-plane:before{content:"\e108"}.glyphicon-calendar:before{content:"\e109"}.glyphicon-random:before{content:"\e110"}.glyphicon-comment:before{content:"\e111"}.glyphicon-magnet:before{content:"\e112"}.glyphicon-chevron-up:before{content:"\e113"}.glyphicon-chevron-down:before{content:"\e114"}.glyphicon-retweet:before{content:"\e115"}.glyphicon-shopping-cart:before{content:"\e116"}.glyphicon-folder-close:before{content:"\e117"}.glyphicon-folder-open:before{content:"\e118"}.glyphicon-resize-vertical:before{content:"\e119"}.glyphicon-resize-horizontal:before{content:"\e120"}.glyphicon-hdd:before{content:"\e121"}.glyphicon-bullhorn:before{content:"\e122"}.glyphicon-bell:before{content:"\e123"}.glyphicon-certificate:before{content:"\e124"}.glyphicon-thumbs-up:before{content:"\e125"}.glyphicon-thumbs-down:before{content:"\e126"}.glyphicon-hand-right:before{content:"\e127"}.glyphicon-hand-left:before{content:"\e128"}.glyphicon-hand-up:before{content:"\e129"}.glyphicon-hand-down:before{content:"\e130"}.glyphicon-circle-arrow-right:before{content:"\e131"}.glyphicon-circle-arrow-left:before{content:"\e132"}.glyphicon-circle-arrow-up:before{content:"\e133"}.glyphicon-circle-arrow-down:before{content:"\e134"}.glyphicon-globe:before{content:"\e135"}.glyphicon-wrench:before{content:"\e136"}.glyphicon-tasks:before{content:"\e137"}.glyphicon-filter:before{content:"\e138"}.glyphicon-briefcase:before{content:"\e139"}.glyphicon-fullscreen:before{content:"\e140"}.glyphicon-dashboard:before{content:"\e141"}.glyphicon-paperclip:before{content:"\e142"}.glyphicon-heart-empty:before{content:"\e143"}.glyphicon-link:before{content:"\e144"}.glyphicon-phone:before{content:"\e145"}.glyphicon-pushpin:before{content:"\e146"}.glyphicon-usd:before{content:"\e148"}.glyphicon-gbp:before{content:"\e149"}.glyphicon-sort:before{content:"\e150"}.glyphicon-sort-by-alphabet:before{content:"\e151"}.glyphicon-sort-by-alphabet-alt:before{content:"\e152"}.glyphicon-sort-by-order:before{content:"\e153"}.glyphicon-sort-by-order-alt:before{content:"\e154"}.glyphicon-sort-by-attributes:before{content:"\e155"}.glyphicon-sort-by-attributes-alt:before{content:"\e156"}.glyphicon-unchecked:before{content:"\e157"}.glyphicon-expand:before{content:"\e158"}.glyphicon-collapse-down:before{content:"\e159"}.glyphicon-collapse-up:before{content:"\e160"}.glyphicon-log-in:before{content:"\e161"}.glyphicon-flash:before{content:"\e162"}.glyphicon-log-out:before{content:"\e163"}.glyphicon-new-window:before{content:"\e164"}.glyphicon-record:before{content:"\e165"}.glyphicon-save:before{content:"\e166"}.glyphicon-open:before{content:"\e167"}.glyphicon-saved:before{content:"\e168"}.glyphicon-import:before{content:"\e169"}.glyphicon-export:before{content:"\e170"}.glyphicon-send:before{content:"\e171"}.glyphicon-floppy-disk:before{content:"\e172"}.glyphicon-floppy-saved:before{content:"\e173"}.glyphicon-floppy-remove:before{content:"\e174"}.glyphicon-floppy-save:before{content:"\e175"}.glyphicon-floppy-open:before{content:"\e176"}.glyphicon-credit-card:before{content:"\e177"}.glyphicon-transfer:before{content:"\e178"}.glyphicon-cutlery:before{content:"\e179"}.glyphicon-header:before{content:"\e180"}.glyphicon-compressed:before{content:"\e181"}.glyphicon-earphone:before{content:"\e182"}.glyphicon-phone-alt:before{content:"\e183"}.glyphicon-tower:before{content:"\e184"}.glyphicon-stats:before{content:"\e185"}.glyphicon-sd-video:before{content:"\e186"}.glyphicon-hd-video:before{content:"\e187"}.glyphicon-subtitles:before{content:"\e188"}.glyphicon-sound-stereo:before{content:"\e189"}.glyphicon-sound-dolby:before{content:"\e190"}.glyphicon-sound-5-1:before{content:"\e191"}.glyphicon-sound-6-1:before{content:"\e192"}.glyphicon-sound-7-1:before{content:"\e193"}.glyphicon-copyright-mark:before{content:"\e194"}.glyphicon-registration-mark:before{content:"\e195"}.glyphicon-cloud-download:before{content:"\e197"}.glyphicon-cloud-upload:before{content:"\e198"}.glyphicon-tree-conifer:before{content:"\e199"}.glyphicon-tree-deciduous:before{content:"\e200"}.glyphicon-cd:before{content:"\e201"}.glyphicon-save-file:before{content:"\e202"}.glyphicon-open-file:before{content:"\e203"}.glyphicon-level-up:before{content:"\e204"}.glyphicon-copy:before{content:"\e205"}.glyphicon-paste:before{content:"\e206"}.glyphicon-alert:before{content:"\e209"}.glyphicon-equalizer:before{content:"\e210"}.glyphicon-king:before{content:"\e211"}.glyphicon-queen:before{content:"\e212"}.glyphicon-pawn:before{content:"\e213"}.glyphicon-bishop:before{content:"\e214"}.glyphicon-knight:before{content:"\e215"}.glyphicon-baby-formula:before{content:"\e216"}.glyphicon-tent:before{content:"\26fa"}.glyphicon-blackboard:before{content:"\e218"}.glyphicon-bed:before{content:"\e219"}.glyphicon-apple:before{content:"\f8ff"}.glyphicon-erase:before{content:"\e221"}.glyphicon-hourglass:before{content:"\231b"}.glyphicon-lamp:before{content:"\e223"}.glyphicon-duplicate:before{content:"\e224"}.glyphicon-piggy-bank:before{content:"\e225"}.glyphicon-scissors:before{content:"\e226"}.glyphicon-bitcoin:before{content:"\e227"}.glyphicon-btc:before{content:"\e227"}.glyphicon-xbt:before{content:"\e227"}.glyphicon-yen:before{content:"\00a5"}.glyphicon-jpy:before{content:"\00a5"}.glyphicon-ruble:before{content:"\20bd"}.glyphicon-rub:before{content:"\20bd"}.glyphicon-scale:before{content:"\e230"}.glyphicon-ice-lolly:before{content:"\e231"}.glyphicon-ice-lolly-tasted:before{content:"\e232"}.glyphicon-education:before{content:"\e233"}.glyphicon-option-horizontal:before{content:"\e234"}.glyphicon-option-vertical:before{content:"\e235"}.glyphicon-menu-hamburger:before{content:"\e236"}.glyphicon-modal-window:before{content:"\e237"}.glyphicon-oil:before{content:"\e238"}.glyphicon-grain:before{content:"\e239"}.glyphicon-sunglasses:before{content:"\e240"}.glyphicon-text-size:before{content:"\e241"}.glyphicon-text-color:before{content:"\e242"}.glyphicon-text-background:before{content:"\e243"}.glyphicon-object-align-top:before{content:"\e244"}.glyphicon-object-align-bottom:before{content:"\e245"}.glyphicon-object-align-horizontal:before{content:"\e246"}.glyphicon-object-align-left:before{content:"\e247"}.glyphicon-object-align-vertical:before{content:"\e248"}.glyphicon-object-align-right:before{content:"\e249"}.glyphicon-triangle-right:before{content:"\e250"}.glyphicon-triangle-left:before{content:"\e251"}.glyphicon-triangle-bottom:before{content:"\e252"}.glyphicon-triangle-top:before{content:"\e253"}.glyphicon-console:before{content:"\e254"}.glyphicon-superscript:before{content:"\e255"}.glyphicon-subscript:before{content:"\e256"}.glyphicon-menu-left:before{content:"\e257"}.glyphicon-menu-right:before{content:"\e258"}.glyphicon-menu-down:before{content:"\e259"}.glyphicon-menu-up:before{content:"\e260"}*{-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}:after,:before{-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}html{font-size:10px;-webkit-tap-highlight-color:rgba(0,0,0,0)}body{font-family:"Helvetica Neue",Helvetica,Arial,sans-serif;font-size:14px;line-height:1.42857143;color:#333;background-color:#fff}button,input,select,textarea{font-family:inherit;font-size:inherit;line-height:inherit}a{color:#337ab7;text-decoration:none}a:focus,a:hover{color:#23527c;text-decoration:underline}a:focus{outline:thin dotted;outline:5px auto -webkit-focus-ring-color;outline-offset:-2px}figure{margin:0}img{vertical-align:middle}.carousel-inner>.item>a>img,.carousel-inner>.item>img,.img-responsive,.thumbnail a>img,.thumbnail>img{display:block;max-width:100%;height:auto}.img-rounded{border-radius:6px}.img-thumbnail{display:inline-block;max-width:100%;height:auto;padding:4px;line-height:1.42857143;background-color:#fff;border:1px solid #ddd;border-radius:4px;-webkit-transition:all .2s ease-in-out;-o-transition:all .2s ease-in-out;transition:all .2s ease-in-out}.img-circle{border-radius:50%}hr{margin-top:20px;margin-bottom:20px;border:0;border-top:1px solid #eee}.sr-only{position:absolute;width:1px;height:1px;padding:0;margin:-1px;overflow:hidden;clip:rect(0,0,0,0);border:0}.sr-only-focusable:active,.sr-only-focusable:focus{position:static;width:auto;height:auto;margin:0;overflow:visible;clip:auto}[role=button]{cursor:pointer}.h1,.h2,.h3,.h4,.h5,.h6,h1,h2,h3,h4,h5,h6{font-family:inherit;font-weight:500;line-height:1.1;color:inherit}.h1 .small,.h1 small,.h2 .small,.h2 small,.h3 .small,.h3 small,.h4 .small,.h4 small,.h5 .small,.h5 small,.h6 .small,.h6 small,h1 .small,h1 small,h2 .small,h2 small,h3 .small,h3 small,h4 .small,h4 small,h5 .small,h5 small,h6 .small,h6 small{font-weight:400;line-height:1;color:#777}.h1,.h2,.h3,h1,h2,h3{margin-top:20px;margin-bottom:10px}.h1 .small,.h1 small,.h2 .small,.h2 small,.h3 .small,.h3 small,h1 .small,h1 small,h2 .small,h2 small,h3 .small,h3 small{font-size:65%}.h4,.h5,.h6,h4,h5,h6{margin-top:10px;margin-bottom:10px}.h4 .small,.h4 small,.h5 .small,.h5 small,.h6 .small,.h6 small,h4 .small,h4 small,h5 .small,h5 small,h6 .small,h6 small{font-size:75%}.h1,h1{font-size:36px}.h2,h2{font-size:30px}.h3,h3{font-size:24px}.h4,h4{font-size:18px}.h5,h5{font-size:14px}.h6,h6{font-size:12px}p{margin:0 0 10px}.lead{margin-bottom:20px;font-size:16px;font-weight:300;line-height:1.4}@media (min-width:768px){.lead{font-size:21px}}.small,small{font-size:85%}.mark,mark{padding:.2em;background-color:#fcf8e3}.text-left{text-align:left}.text-right{text-align:right}.text-center{text-align:center}.text-justify{text-align:justify}.text-nowrap{white-space:nowrap}.text-lowercase{text-transform:lowercase}.text-uppercase{text-transform:uppercase}.text-capitalize{text-transform:capitalize}.text-muted{color:#777}.text-primary{color:#337ab7}a.text-primary:focus,a.text-primary:hover{color:#286090}.text-success{color:#3c763d}a.text-success:focus,a.text-success:hover{color:#2b542c}.text-info{color:#31708f}a.text-info:focus,a.text-info:hover{color:#245269}.text-warning{color:#8a6d3b}a.text-warning:focus,a.text-warning:hover{color:#66512c}.text-danger{color:#a94442}a.text-danger:focus,a.text-danger:hover{color:#843534}.bg-primary{color:#fff;background-color:#337ab7}a.bg-primary:focus,a.bg-primary:hover{background-color:#286090}.bg-success{background-color:#dff0d8}a.bg-success:focus,a.bg-success:hover{background-color:#c1e2b3}.bg-info{background-color:#d9edf7}a.bg-info:focus,a.bg-info:hover{background-color:#afd9ee}.bg-warning{background-color:#fcf8e3}a.bg-warning:focus,a.bg-warning:hover{background-color:#f7ecb5}.bg-danger{background-color:#f2dede}a.bg-danger:focus,a.bg-danger:hover{background-color:#e4b9b9}.page-header{padding-bottom:9px;margin:40px 0 20px;border-bottom:1px solid #eee}ol,ul{margin-top:0;margin-bottom:10px}ol ol,ol ul,ul ol,ul ul{margin-bottom:0}.list-unstyled{padding-left:0;list-style:none}.list-inline{padding-left:0;margin-left:-5px;list-style:none}.list-inline>li{display:inline-block;padding-right:5px;padding-left:5px}dl{margin-top:0;margin-bottom:20px}dd,dt{line-height:1.42857143}dt{font-weight:700}dd{margin-left:0}@media (min-width:768px){.dl-horizontal dt{float:left;width:160px;overflow:hidden;clear:left;text-align:right;text-overflow:ellipsis;white-space:nowrap}.dl-horizontal dd{margin-left:180px}}abbr[data-original-title],abbr[title]{cursor:help;border-bottom:1px dotted #777}.initialism{font-size:90%;text-transform:uppercase}blockquote{padding:10px 20px;margin:0 0 20px;font-size:17.5px;border-left:5px solid #eee}blockquote ol:last-child,blockquote p:last-child,blockquote ul:last-child{margin-bottom:0}blockquote .small,blockquote footer,blockquote small{display:block;font-size:80%;line-height:1.42857143;color:#777}blockquote .small:before,blockquote footer:before,blockquote small:before{content:'\2014 \00A0'}.blockquote-reverse,blockquote.pull-right{padding-right:15px;padding-left:0;text-align:right;border-right:5px solid #eee;border-left:0}.blockquote-reverse .small:before,.blockquote-reverse footer:before,.blockquote-reverse small:before,blockquote.pull-right .small:before,blockquote.pull-right footer:before,blockquote.pull-right small:before{content:''}.blockquote-reverse .small:after,.blockquote-reverse footer:after,.blockquote-reverse small:after,blockquote.pull-right .small:after,blockquote.pull-right footer:after,blockquote.pull-right small:after{content:'\00A0 \2014'}address{margin-bottom:20px;font-style:normal;line-height:1.42857143}code,kbd,pre,samp{font-family:monospace}code{padding:2px 4px;font-size:90%;color:#c7254e;background-color:#f9f2f4;border-radius:4px}kbd{padding:2px 4px;font-size:90%;color:#fff;background-color:#333;border-radius:3px;-webkit-box-shadow:inset 0 -1px 0 rgba(0,0,0,.25);box-shadow:inset 0 -1px 0 rgba(0,0,0,.25)}kbd kbd{padding:0;font-size:100%;font-weight:700;-webkit-box-shadow:none;box-shadow:none}pre{display:block;padding:9.5px;margin:0 0 10px;font-size:13px;line-height:1.42857143;color:#333;word-break:break-all;word-wrap:break-word;background-color:#f5f5f5;border:1px solid #ccc;border-radius:4px}pre code{padding:0;font-size:inherit;color:inherit;white-space:pre-wrap;background-color:transparent;border-radius:0}.pre-scrollable{max-height:340px;overflow-y:scroll}.container{padding-right:15px;padding-left:15px;margin-right:auto;margin-left:auto}@media (min-width:768px){.container{width:750px}}@media (min-width:992px){.container{width:970px}}@media (min-width:1200px){.container{width:1170px}}.container-fluid{padding-right:15px;padding-left:15px;margin-right:auto;margin-left:auto}.row{margin-right:-15px;margin-left:-15px}.col-lg-1,.col-lg-10,.col-lg-11,.col-lg-12,.col-lg-2,.col-lg-3,.col-lg-4,.col-lg-5,.col-lg-6,.col-lg-7,.col-lg-8,.col-lg-9,.col-md-1,.col-md-10,.col-md-11,.col-md-12,.col-md-2,.col-md-3,.col-md-4,.col-md-5,.col-md-6,.col-md-7,.col-md-8,.col-md-9,.col-sm-1,.col-sm-10,.col-sm-11,.col-sm-12,.col-sm-2,.col-sm-3,.col-sm-4,.col-sm-5,.col-sm-6,.col-sm-7,.col-sm-8,.col-sm-9,.col-xs-1,.col-xs-10,.col-xs-11,.col-xs-12,.col-xs-2,.col-xs-3,.col-xs-4,.col-xs-5,.col-xs-6,.col-xs-7,.col-xs-8,.col-xs-9{position:relative;min-height:1px;padding-right:15px;padding-left:15px}.col-xs-1,.col-xs-10,.col-xs-11,.col-xs-12,.col-xs-2,.col-xs-3,.col-xs-4,.col-xs-5,.col-xs-6,.col-xs-7,.col-xs-8,.col-xs-9{float:left}.col-xs-12{width:100%}.col-xs-11{width:91.66666667%}.col-xs-10{width:83.33333333%}.col-xs-9{width:75%}.col-xs-8{width:66.66666667%}.col-xs-7{width:58.33333333%}.col-xs-6{width:50%}.col-xs-5{width:41.66666667%}.col-xs-4{width:33.33333333%}.col-xs-3{width:25%}.col-xs-2{width:16.66666667%}.col-xs-1{width:8.33333333%}.col-xs-pull-12{right:100%}.col-xs-pull-11{right:91.66666667%}.col-xs-pull-10{right:83.33333333%}.col-xs-pull-9{right:75%}.col-xs-pull-8{right:66.66666667%}.col-xs-pull-7{right:58.33333333%}.col-xs-pull-6{right:50%}.col-xs-pull-5{right:41.66666667%}.col-xs-pull-4{right:33.33333333%}.col-xs-pull-3{right:25%}.col-xs-pull-2{right:16.66666667%}.col-xs-pull-1{right:8.33333333%}.col-xs-pull-0{right:auto}.col-xs-push-12{left:100%}.col-xs-push-11{left:91.66666667%}.col-xs-push-10{left:83.33333333%}.col-xs-push-9{left:75%}.col-xs-push-8{left:66.66666667%}.col-xs-push-7{left:58.33333333%}.col-xs-push-6{left:50%}.col-xs-push-5{left:41.66666667%}.col-xs-push-4{left:33.33333333%}.col-xs-push-3{left:25%}.col-xs-push-2{left:16.66666667%}.col-xs-push-1{left:8.33333333%}.col-xs-push-0{left:auto}.col-xs-offset-12{margin-left:100%}.col-xs-offset-11{margin-left:91.66666667%}.col-xs-offset-10{margin-left:83.33333333%}.col-xs-offset-9{margin-left:75%}.col-xs-offset-8{margin-left:66.66666667%}.col-xs-offset-7{margin-left:58.33333333%}.col-xs-offset-6{margin-left:50%}.col-xs-offset-5{margin-left:41.66666667%}.col-xs-offset-4{margin-left:33.33333333%}.col-xs-offset-3{margin-left:25%}.col-xs-offset-2{margin-left:16.66666667%}.col-xs-offset-1{margin-left:8.33333333%}.col-xs-offset-0{margin-left:0}@media (min-width:768px){.col-sm-1,.col-sm-10,.col-sm-11,.col-sm-12,.col-sm-2,.col-sm-3,.col-sm-4,.col-sm-5,.col-sm-6,.col-sm-7,.col-sm-8,.col-sm-9{float:left}.col-sm-12{width:100%}.col-sm-11{width:91.66666667%}.col-sm-10{width:83.33333333%}.col-sm-9{width:75%}.col-sm-8{width:66.66666667%}.col-sm-7{width:58.33333333%}.col-sm-6{width:50%}.col-sm-5{width:41.66666667%}.col-sm-4{width:33.33333333%}.col-sm-3{width:25%}.col-sm-2{width:16.66666667%}.col-sm-1{width:8.33333333%}.col-sm-pull-12{right:100%}.col-sm-pull-11{right:91.66666667%}.col-sm-pull-10{right:83.33333333%}.col-sm-pull-9{right:75%}.col-sm-pull-8{right:66.66666667%}.col-sm-pull-7{right:58.33333333%}.col-sm-pull-6{right:50%}.col-sm-pull-5{right:41.66666667%}.col-sm-pull-4{right:33.33333333%}.col-sm-pull-3{right:25%}.col-sm-pull-2{right:16.66666667%}.col-sm-pull-1{right:8.33333333%}.col-sm-pull-0{right:auto}.col-sm-push-12{left:100%}.col-sm-push-11{left:91.66666667%}.col-sm-push-10{left:83.33333333%}.col-sm-push-9{left:75%}.col-sm-push-8{left:66.66666667%}.col-sm-push-7{left:58.33333333%}.col-sm-push-6{left:50%}.col-sm-push-5{left:41.66666667%}.col-sm-push-4{left:33.33333333%}.col-sm-push-3{left:25%}.col-sm-push-2{left:16.66666667%}.col-sm-push-1{left:8.33333333%}.col-sm-push-0{left:auto}.col-sm-offset-12{margin-left:100%}.col-sm-offset-11{margin-left:91.66666667%}.col-sm-offset-10{margin-left:83.33333333%}.col-sm-offset-9{margin-left:75%}.col-sm-offset-8{margin-left:66.66666667%}.col-sm-offset-7{margin-left:58.33333333%}.col-sm-offset-6{margin-left:50%}.col-sm-offset-5{margin-left:41.66666667%}.col-sm-offset-4{margin-left:33.33333333%}.col-sm-offset-3{margin-left:25%}.col-sm-offset-2{margin-left:16.66666667%}.col-sm-offset-1{margin-left:8.33333333%}.col-sm-offset-0{margin-left:0}}@media (min-width:992px){.col-md-1,.col-md-10,.col-md-11,.col-md-12,.col-md-2,.col-md-3,.col-md-4,.col-md-5,.col-md-6,.col-md-7,.col-md-8,.col-md-9{float:left}.col-md-12{width:100%}.col-md-11{width:91.66666667%}.col-md-10{width:83.33333333%}.col-md-9{width:75%}.col-md-8{width:66.66666667%}.col-md-7{width:58.33333333%}.col-md-6{width:50%}.col-md-5{width:41.66666667%}.col-md-4{width:33.33333333%}.col-md-3{width:25%}.col-md-2{width:16.66666667%}.col-md-1{width:8.33333333%}.col-md-pull-12{right:100%}.col-md-pull-11{right:91.66666667%}.col-md-pull-10{right:83.33333333%}.col-md-pull-9{right:75%}.col-md-pull-8{right:66.66666667%}.col-md-pull-7{right:58.33333333%}.col-md-pull-6{right:50%}.col-md-pull-5{right:41.66666667%}.col-md-pull-4{right:33.33333333%}.col-md-pull-3{right:25%}.col-md-pull-2{right:16.66666667%}.col-md-pull-1{right:8.33333333%}.col-md-pull-0{right:auto}.col-md-push-12{left:100%}.col-md-push-11{left:91.66666667%}.col-md-push-10{left:83.33333333%}.col-md-push-9{left:75%}.col-md-push-8{left:66.66666667%}.col-md-push-7{left:58.33333333%}.col-md-push-6{left:50%}.col-md-push-5{left:41.66666667%}.col-md-push-4{left:33.33333333%}.col-md-push-3{left:25%}.col-md-push-2{left:16.66666667%}.col-md-push-1{left:8.33333333%}.col-md-push-0{left:auto}.col-md-offset-12{margin-left:100%}.col-md-offset-11{margin-left:91.66666667%}.col-md-offset-10{margin-left:83.33333333%}.col-md-offset-9{margin-left:75%}.col-md-offset-8{margin-left:66.66666667%}.col-md-offset-7{margin-left:58.33333333%}.col-md-offset-6{margin-left:50%}.col-md-offset-5{margin-left:41.66666667%}.col-md-offset-4{margin-left:33.33333333%}.col-md-offset-3{margin-left:25%}.col-md-offset-2{margin-left:16.66666667%}.col-md-offset-1{margin-left:8.33333333%}.col-md-offset-0{margin-left:0}}@media (min-width:1200px){.col-lg-1,.col-lg-10,.col-lg-11,.col-lg-12,.col-lg-2,.col-lg-3,.col-lg-4,.col-lg-5,.col-lg-6,.col-lg-7,.col-lg-8,.col-lg-9{float:left}.col-lg-12{width:100%}.col-lg-11{width:91.66666667%}.col-lg-10{width:83.33333333%}.col-lg-9{width:75%}.col-lg-8{width:66.66666667%}.col-lg-7{width:58.33333333%}.col-lg-6{width:50%}.col-lg-5{width:41.66666667%}.col-lg-4{width:33.33333333%}.col-lg-3{width:25%}.col-lg-2{width:16.66666667%}.col-lg-1{width:8.33333333%}.col-lg-pull-12{right:100%}.col-lg-pull-11{right:91.66666667%}.col-lg-pull-10{right:83.33333333%}.col-lg-pull-9{right:75%}.col-lg-pull-8{right:66.66666667%}.col-lg-pull-7{right:58.33333333%}.col-lg-pull-6{right:50%}.col-lg-pull-5{right:41.66666667%}.col-lg-pull-4{right:33.33333333%}.col-lg-pull-3{right:25%}.col-lg-pull-2{right:16.66666667%}.col-lg-pull-1{right:8.33333333%}.col-lg-pull-0{right:auto}.col-lg-push-12{left:100%}.col-lg-push-11{left:91.66666667%}.col-lg-push-10{left:83.33333333%}.col-lg-push-9{left:75%}.col-lg-push-8{left:66.66666667%}.col-lg-push-7{left:58.33333333%}.col-lg-push-6{left:50%}.col-lg-push-5{left:41.66666667%}.col-lg-push-4{left:33.33333333%}.col-lg-push-3{left:25%}.col-lg-push-2{left:16.66666667%}.col-lg-push-1{left:8.33333333%}.col-lg-push-0{left:auto}.col-lg-offset-12{margin-left:100%}.col-lg-offset-11{margin-left:91.66666667%}.col-lg-offset-10{margin-left:83.33333333%}.col-lg-offset-9{margin-left:75%}.col-lg-offset-8{margin-left:66.66666667%}.col-lg-offset-7{margin-left:58.33333333%}.col-lg-offset-6{margin-left:50%}.col-lg-offset-5{margin-left:41.66666667%}.col-lg-offset-4{margin-left:33.33333333%}.col-lg-offset-3{margin-left:25%}.col-lg-offset-2{margin-left:16.66666667%}.col-lg-offset-1{margin-left:8.33333333%}.col-lg-offset-0{margin-left:0}}table{background-color:transparent}caption{padding-top:8px;padding-bottom:8px;color:#777;text-align:left}th{}.table{width:100%;max-width:100%;margin-bottom:20px}.table>tbody>tr>td,.table>tbody>tr>th,.table>tfoot>tr>td,.table>tfoot>tr>th,.table>thead>tr>td,.table>thead>tr>th{padding:8px;line-height:1.42857143;vertical-align:top;border-top:1px solid #ddd}.table>thead>tr>th{vertical-align:bottom;border-bottom:2px solid #ddd}.table>caption+thead>tr:first-child>td,.table>caption+thead>tr:first-child>th,.table>colgroup+thead>tr:first-child>td,.table>colgroup+thead>tr:first-child>th,.table>thead:first-child>tr:first-child>td,.table>thead:first-child>tr:first-child>th{border-top:0}.table>tbody+tbody{border-top:2px solid #ddd}.table .table{background-color:#fff}.table-condensed>tbody>tr>td,.table-condensed>tbody>tr>th,.table-condensed>tfoot>tr>td,.table-condensed>tfoot>tr>th,.table-condensed>thead>tr>td,.table-condensed>thead>tr>th{padding:5px}.table-bordered{border:1px solid #ddd}.table-bordered>tbody>tr>td,.table-bordered>tbody>tr>th,.table-bordered>tfoot>tr>td,.table-bordered>tfoot>tr>th,.table-bordered>thead>tr>td,.table-bordered>thead>tr>th{border:1px solid #ddd}.table-bordered>thead>tr>td,.table-bordered>thead>tr>th{border-bottom-width:2px}.table-striped>tbody>tr:nth-of-type(odd){background-color:#f9f9f9}.table-hover>tbody>tr:hover{background-color:#f5f5f5}table col[class*=col-]{position:static;display:table-column;float:none}table td[class*=col-],table th[class*=col-]{position:static;display:table-cell;float:none}.table>tbody>tr.active>td,.table>tbody>tr.active>th,.table>tbody>tr>td.active,.table>tbody>tr>th.active,.table>tfoot>tr.active>td,.table>tfoot>tr.active>th,.table>tfoot>tr>td.active,.table>tfoot>tr>th.active,.table>thead>tr.active>td,.table>thead>tr.active>th,.table>thead>tr>td.active,.table>thead>tr>th.active{background-color:#f5f5f5}.table-hover>tbody>tr.active:hover>td,.table-hover>tbody>tr.active:hover>th,.table-hover>tbody>tr:hover>.active,.table-hover>tbody>tr>td.active:hover,.table-hover>tbody>tr>th.active:hover{background-color:#e8e8e8}.table>tbody>tr.success>td,.table>tbody>tr.success>th,.table>tbody>tr>td.success,.table>tbody>tr>th.success,.table>tfoot>tr.success>td,.table>tfoot>tr.success>th,.table>tfoot>tr>td.success,.table>tfoot>tr>th.success,.table>thead>tr.success>td,.table>thead>tr.success>th,.table>thead>tr>td.success,.table>thead>tr>th.success{background-color:#dff0d8}.table-hover>tbody>tr.success:hover>td,.table-hover>tbody>tr.success:hover>th,.table-hover>tbody>tr:hover>.success,.table-hover>tbody>tr>td.success:hover,.table-hover>tbody>tr>th.success:hover{background-color:#d0e9c6}.table>tbody>tr.info>td,.table>tbody>tr.info>th,.table>tbody>tr>td.info,.table>tbody>tr>th.info,.table>tfoot>tr.info>td,.table>tfoot>tr.info>th,.table>tfoot>tr>td.info,.table>tfoot>tr>th.info,.table>thead>tr.info>td,.table>thead>tr.info>th,.table>thead>tr>td.info,.table>thead>tr>th.info{background-color:#d9edf7}.table-hover>tbody>tr.info:hover>td,.table-hover>tbody>tr.info:hover>th,.table-hover>tbody>tr:hover>.info,.table-hover>tbody>tr>td.info:hover,.table-hover>tbody>tr>th.info:hover{background-color:#c4e3f3}.table>tbody>tr.warning>td,.table>tbody>tr.warning>th,.table>tbody>tr>td.warning,.table>tbody>tr>th.warning,.table>tfoot>tr.warning>td,.table>tfoot>tr.warning>th,.table>tfoot>tr>td.warning,.table>tfoot>tr>th.warning,.table>thead>tr.warning>td,.table>thead>tr.warning>th,.table>thead>tr>td.warning,.table>thead>tr>th.warning{background-color:#fcf8e3}.table-hover>tbody>tr.warning:hover>td,.table-hover>tbody>tr.warning:hover>th,.table-hover>tbody>tr:hover>.warning,.table-hover>tbody>tr>td.warning:hover,.table-hover>tbody>tr>th.warning:hover{background-color:#faf2cc}.table>tbody>tr.danger>td,.table>tbody>tr.danger>th,.table>tbody>tr>td.danger,.table>tbody>tr>th.danger,.table>tfoot>tr.danger>td,.table>tfoot>tr.danger>th,.table>tfoot>tr>td.danger,.table>tfoot>tr>th.danger,.table>thead>tr.danger>td,.table>thead>tr.danger>th,.table>thead>tr>td.danger,.table>thead>tr>th.danger{background-color:#f2dede}.table-hover>tbody>tr.danger:hover>td,.table-hover>tbody>tr.danger:hover>th,.table-hover>tbody>tr:hover>.danger,.table-hover>tbody>tr>td.danger:hover,.table-hover>tbody>tr>th.danger:hover{background-color:#ebcccc}.table-responsive{min-height:.01%;overflow-x:auto}@media screen and (max-width:767px){.table-responsive{width:100%;margin-bottom:15px;overflow-y:hidden;-ms-overflow-style:-ms-autohiding-scrollbar;border:1px solid #ddd}.table-responsive>.table{margin-bottom:0}.table-responsive>.table>tbody>tr>td,.table-responsive>.table>tbody>tr>th,.table-responsive>.table>tfoot>tr>td,.table-responsive>.table>tfoot>tr>th,.table-responsive>.table>thead>tr>td,.table-responsive>.table>thead>tr>th{white-space:nowrap}.table-responsive>.table-bordered{border:0}.table-responsive>.table-bordered>tbody>tr>td:first-child,.table-responsive>.table-bordered>tbody>tr>th:first-child,.table-responsive>.table-bordered>tfoot>tr>td:first-child,.table-responsive>.table-bordered>tfoot>tr>th:first-child,.table-responsive>.table-bordered>thead>tr>td:first-child,.table-responsive>.table-bordered>thead>tr>th:first-child{border-left:0}.table-responsive>.table-bordered>tbody>tr>td:last-child,.table-responsive>.table-bordered>tbody>tr>th:last-child,.table-responsive>.table-bordered>tfoot>tr>td:last-child,.table-responsive>.table-bordered>tfoot>tr>th:last-child,.table-responsive>.table-bordered>thead>tr>td:last-child,.table-responsive>.table-bordered>thead>tr>th:last-child{border-right:0}.table-responsive>.table-bordered>tbody>tr:last-child>td,.table-responsive>.table-bordered>tbody>tr:last-child>th,.table-responsive>.table-bordered>tfoot>tr:last-child>td,.table-responsive>.table-bordered>tfoot>tr:last-child>th{border-bottom:0}}fieldset{min-width:0;padding:0;margin:0;border:0}legend{display:block;width:100%;padding:0;margin-bottom:20px;font-size:21px;line-height:inherit;color:#333;border:0;border-bottom:1px solid #e5e5e5}label{display:inline-block;max-width:100%;margin-bottom:5px;font-weight:700}input[type=search]{-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}input[type=checkbox],input[type=radio]{margin:4px 0 0;margin-top:1px\9;line-height:normal}input[type=file]{display:block}input[type=range]{display:block;width:100%}select[multiple],select[size]{height:auto}input[type=file]:focus,input[type=checkbox]:focus,input[type=radio]:focus{outline:thin dotted;outline:5px auto -webkit-focus-ring-color;outline-offset:-2px}output{display:block;padding-top:7px;font-size:14px;line-height:1.42857143;color:#555}.form-control{display:block;width:100%;height:34px;padding:6px 12px;font-size:14px;line-height:1.42857143;color:#555;background-color:#fff;background-image:none;border:1px solid #ccc;border-radius:4px;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075);box-shadow:inset 0 1px 1px rgba(0,0,0,.075);-webkit-transition:border-color ease-in-out .15s,-webkit-box-shadow ease-in-out .15s;-o-transition:border-color ease-in-out .15s,box-shadow ease-in-out .15s;transition:border-color ease-in-out .15s,box-shadow ease-in-out .15s}.form-control:focus{border-color:#66afe9;outline:0;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 8px rgba(102,175,233,.6);box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 8px rgba(102,175,233,.6)}.form-control::-moz-placeholder{color:#999;opacity:1}.form-control:-ms-input-placeholder{color:#999}.form-control::-webkit-input-placeholder{color:#999}.form-control[disabled],.form-control[readonly],fieldset[disabled] .form-control{background-color:#eee;opacity:1}.form-control[disabled],fieldset[disabled] .form-control{cursor:not-allowed}textarea.form-control{height:auto}input[type=search]{-webkit-appearance:none}@media screen and (-webkit-min-device-pixel-ratio:0){input[type=date].form-control,input[type=time].form-control,input[type=datetime-local].form-control,input[type=month].form-control{line-height:34px}.input-group-sm input[type=date],.input-group-sm input[type=time],.input-group-sm input[type=datetime-local],.input-group-sm input[type=month],input[type=date].input-sm,input[type=time].input-sm,input[type=datetime-local].input-sm,input[type=month].input-sm{line-height:30px}.input-group-lg input[type=date],.input-group-lg input[type=time],.input-group-lg input[type=datetime-local],.input-group-lg input[type=month],input[type=date].input-lg,input[type=time].input-lg,input[type=datetime-local].input-lg,input[type=month].input-lg{line-height:46px}}.form-group{margin-bottom:15px}.checkbox,.radio{position:relative;display:block;margin-top:10px;margin-bottom:10px}.checkbox label,.radio label{min-height:20px;padding-left:20px;margin-bottom:0;font-weight:400;cursor:pointer}.checkbox input[type=checkbox],.checkbox-inline input[type=checkbox],.radio input[type=radio],.radio-inline input[type=radio]{position:absolute;margin-top:4px\9;margin-left:-20px}.checkbox+.checkbox,.radio+.radio{margin-top:-5px}.checkbox-inline,.radio-inline{position:relative;display:inline-block;padding-left:20px;margin-bottom:0;font-weight:400;vertical-align:middle;cursor:pointer}.checkbox-inline+.checkbox-inline,.radio-inline+.radio-inline{margin-top:0;margin-left:10px}fieldset[disabled] input[type=checkbox],fieldset[disabled] input[type=radio],input[type=checkbox].disabled,input[type=checkbox][disabled],input[type=radio].disabled,input[type=radio][disabled]{cursor:not-allowed}.checkbox-inline.disabled,.radio-inline.disabled,fieldset[disabled] .checkbox-inline,fieldset[disabled] .radio-inline{cursor:not-allowed}.checkbox.disabled label,.radio.disabled label,fieldset[disabled] .checkbox label,fieldset[disabled] .radio label{cursor:not-allowed}.form-control-static{min-height:34px;padding-top:7px;padding-bottom:7px;margin-bottom:0}.form-control-static.input-lg,.form-control-static.input-sm{padding-right:0;padding-left:0}.input-sm{height:30px;padding:5px 10px;font-size:12px;line-height:1.5;border-radius:3px}select.input-sm{height:30px;line-height:30px}select[multiple].input-sm,textarea.input-sm{height:auto}.form-group-sm .form-control{height:30px;padding:5px 10px;font-size:12px;line-height:1.5;border-radius:3px}.form-group-sm select.form-control{height:30px;line-height:30px}.form-group-sm select[multiple].form-control,.form-group-sm textarea.form-control{height:auto}.form-group-sm .form-control-static{height:30px;min-height:32px;padding:6px 10px;font-size:12px;line-height:1.5}.input-lg{height:46px;padding:10px 16px;font-size:18px;line-height:1.3333333;border-radius:6px}select.input-lg{height:46px;line-height:46px}select[multiple].input-lg,textarea.input-lg{height:auto}.form-group-lg .form-control{height:46px;padding:10px 16px;font-size:18px;line-height:1.3333333;border-radius:6px}.form-group-lg select.form-control{height:46px;line-height:46px}.form-group-lg select[multiple].form-control,.form-group-lg textarea.form-control{height:auto}.form-group-lg .form-control-static{height:46px;min-height:38px;padding:11px 16px;font-size:18px;line-height:1.3333333}.has-feedback{position:relative}.has-feedback .form-control{padding-right:42.5px}.form-control-feedback{position:absolute;top:0;right:0;z-index:2;display:block;width:34px;height:34px;line-height:34px;text-align:center;pointer-events:none}.form-group-lg .form-control+.form-control-feedback,.input-group-lg+.form-control-feedback,.input-lg+.form-control-feedback{width:46px;height:46px;line-height:46px}.form-group-sm .form-control+.form-control-feedback,.input-group-sm+.form-control-feedback,.input-sm+.form-control-feedback{width:30px;height:30px;line-height:30px}.has-success .checkbox,.has-success .checkbox-inline,.has-success .control-label,.has-success .help-block,.has-success .radio,.has-success .radio-inline,.has-success.checkbox label,.has-success.checkbox-inline label,.has-success.radio label,.has-success.radio-inline label{color:#3c763d}.has-success .form-control{border-color:#3c763d;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075);box-shadow:inset 0 1px 1px rgba(0,0,0,.075)}.has-success .form-control:focus{border-color:#2b542c;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 6px #67b168;box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 6px #67b168}.has-success .input-group-addon{color:#3c763d;background-color:#dff0d8;border-color:#3c763d}.has-success .form-control-feedback{color:#3c763d}.has-warning .checkbox,.has-warning .checkbox-inline,.has-warning .control-label,.has-warning .help-block,.has-warning .radio,.has-warning .radio-inline,.has-warning.checkbox label,.has-warning.checkbox-inline label,.has-warning.radio label,.has-warning.radio-inline label{color:#8a6d3b}.has-warning .form-control{border-color:#8a6d3b;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075);box-shadow:inset 0 1px 1px rgba(0,0,0,.075)}.has-warning .form-control:focus{border-color:#66512c;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 6px #c0a16b;box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 6px #c0a16b}.has-warning .input-group-addon{color:#8a6d3b;background-color:#fcf8e3;border-color:#8a6d3b}.has-warning .form-control-feedback{color:#8a6d3b}.has-error .checkbox,.has-error .checkbox-inline,.has-error .control-label,.has-error .help-block,.has-error .radio,.has-error .radio-inline,.has-error.checkbox label,.has-error.checkbox-inline label,.has-error.radio label,.has-error.radio-inline label{color:#a94442}.has-error .form-control{border-color:#a94442;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075);box-shadow:inset 0 1px 1px rgba(0,0,0,.075)}.has-error .form-control:focus{border-color:#843534;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 6px #ce8483;box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 6px #ce8483}.has-error .input-group-addon{color:#a94442;background-color:#f2dede;border-color:#a94442}.has-error .form-control-feedback{color:#a94442}.has-feedback label~.form-control-feedback{top:25px}.has-feedback label.sr-only~.form-control-feedback{top:0}.help-block{display:block;margin-top:5px;margin-bottom:10px;color:#737373}@media (min-width:768px){.form-inline .form-group{display:inline-block;margin-bottom:0;vertical-align:middle}.form-inline .form-control{display:inline-block;width:auto;vertical-align:middle}.form-inline .form-control-static{display:inline-block}.form-inline .input-group{display:inline-table;vertical-align:middle}.form-inline .input-group .form-control,.form-inline .input-group .input-group-addon,.form-inline .input-group .input-group-btn{width:auto}.form-inline .input-group>.form-control{width:100%}.form-inline .control-label{margin-bottom:0;vertical-align:middle}.form-inline .checkbox,.form-inline .radio{display:inline-block;margin-top:0;margin-bottom:0;vertical-align:middle}.form-inline .checkbox label,.form-inline .radio label{padding-left:0}.form-inline .checkbox input[type=checkbox],.form-inline .radio input[type=radio]{position:relative;margin-left:0}.form-inline .has-feedback .form-control-feedback{top:0}}.form-horizontal .checkbox,.form-horizontal .checkbox-inline,.form-horizontal .radio,.form-horizontal .radio-inline{padding-top:7px;margin-top:0;margin-bottom:0}.form-horizontal .checkbox,.form-horizontal .radio{min-height:27px}.form-horizontal .form-group{margin-right:-15px;margin-left:-15px}@media (min-width:768px){.form-horizontal .control-label{padding-top:7px;margin-bottom:0;text-align:right}}.form-horizontal .has-feedback .form-control-feedback{right:15px}@media (min-width:768px){.form-horizontal .form-group-lg .control-label{padding-top:14.33px;font-size:18px}}@media (min-width:768px){.form-horizontal .form-group-sm .control-label{padding-top:6px;font-size:12px}}.btn{display:inline-block;padding:6px 12px;margin-bottom:0;font-size:14px;font-weight:400;line-height:1.42857143;text-align:center;white-space:nowrap;vertical-align:middle;-ms-touch-action:manipulation;touch-action:manipulation;cursor:pointer;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;background-image:none;border:1px solid transparent;border-radius:4px}.btn.active.focus,.btn.active:focus,.btn.focus,.btn:active.focus,.btn:active:focus,.btn:focus{outline:thin dotted;outline:5px auto -webkit-focus-ring-color;outline-offset:-2px}.btn.focus,.btn:focus,.btn:hover{color:#333;text-decoration:none}.btn.active,.btn:active{background-image:none;outline:0;-webkit-box-shadow:inset 0 3px 5px rgba(0,0,0,.125);box-shadow:inset 0 3px 5px rgba(0,0,0,.125)}.btn.disabled,.btn[disabled],fieldset[disabled] .btn{cursor:not-allowed;filter:alpha(opacity=65);-webkit-box-shadow:none;box-shadow:none;opacity:.65}a.btn.disabled,fieldset[disabled] a.btn{pointer-events:none}.btn-default{color:#333;background-color:#fff;border-color:#ccc}.btn-default.focus,.btn-default:focus{color:#333;background-color:#e6e6e6;border-color:#8c8c8c}.btn-default:hover{color:#333;background-color:#e6e6e6;border-color:#adadad}.btn-default.active,.btn-default:active,.open>.dropdown-toggle.btn-default{color:#333;background-color:#e6e6e6;border-color:#adadad}.btn-default.active.focus,.btn-default.active:focus,.btn-default.active:hover,.btn-default:active.focus,.btn-default:active:focus,.btn-default:active:hover,.open>.dropdown-toggle.btn-default.focus,.open>.dropdown-toggle.btn-default:focus,.open>.dropdown-toggle.btn-default:hover{color:#333;background-color:#d4d4d4;border-color:#8c8c8c}.btn-default.active,.btn-default:active,.open>.dropdown-toggle.btn-default{background-image:none}.btn-default.disabled,.btn-default.disabled.active,.btn-default.disabled.focus,.btn-default.disabled:active,.btn-default.disabled:focus,.btn-default.disabled:hover,.btn-default[disabled],.btn-default[disabled].active,.btn-default[disabled].focus,.btn-default[disabled]:active,.btn-default[disabled]:focus,.btn-default[disabled]:hover,fieldset[disabled] .btn-default,fieldset[disabled] .btn-default.active,fieldset[disabled] .btn-default.focus,fieldset[disabled] .btn-default:active,fieldset[disabled] .btn-default:focus,fieldset[disabled] .btn-default:hover{background-color:#fff;border-color:#ccc}.btn-default .badge{color:#fff;background-color:#333}.btn-primary{color:#fff;background-color:#337ab7;border-color:#2e6da4}.btn-primary.focus,.btn-primary:focus{color:#fff;background-color:#286090;border-color:#122b40}.btn-primary:hover{color:#fff;background-color:#286090;border-color:#204d74}.btn-primary.active,.btn-primary:active,.open>.dropdown-toggle.btn-primary{color:#fff;background-color:#286090;border-color:#204d74}.btn-primary.active.focus,.btn-primary.active:focus,.btn-primary.active:hover,.btn-primary:active.focus,.btn-primary:active:focus,.btn-primary:active:hover,.open>.dropdown-toggle.btn-primary.focus,.open>.dropdown-toggle.btn-primary:focus,.open>.dropdown-toggle.btn-primary:hover{color:#fff;background-color:#204d74;border-color:#122b40}.btn-primary.active,.btn-primary:active,.open>.dropdown-toggle.btn-primary{background-image:none}.btn-primary.disabled,.btn-primary.disabled.active,.btn-primary.disabled.focus,.btn-primary.disabled:active,.btn-primary.disabled:focus,.btn-primary.disabled:hover,.btn-primary[disabled],.btn-primary[disabled].active,.btn-primary[disabled].focus,.btn-primary[disabled]:active,.btn-primary[disabled]:focus,.btn-primary[disabled]:hover,fieldset[disabled] .btn-primary,fieldset[disabled] .btn-primary.active,fieldset[disabled] .btn-primary.focus,fieldset[disabled] .btn-primary:active,fieldset[disabled] .btn-primary:focus,fieldset[disabled] .btn-primary:hover{background-color:#337ab7;border-color:#2e6da4}.btn-primary .badge{color:#337ab7;background-color:#fff}.btn-success{color:#fff;background-color:#5cb85c;border-color:#4cae4c}.btn-success.focus,.btn-success:focus{color:#fff;background-color:#449d44;border-color:#255625}.btn-success:hover{color:#fff;background-color:#449d44;border-color:#398439}.btn-success.active,.btn-success:active,.open>.dropdown-toggle.btn-success{color:#fff;background-color:#449d44;border-color:#398439}.btn-success.active.focus,.btn-success.active:focus,.btn-success.active:hover,.btn-success:active.focus,.btn-success:active:focus,.btn-success:active:hover,.open>.dropdown-toggle.btn-success.focus,.open>.dropdown-toggle.btn-success:focus,.open>.dropdown-toggle.btn-success:hover{color:#fff;background-color:#398439;border-color:#255625}.btn-success.active,.btn-success:active,.open>.dropdown-toggle.btn-success{background-image:none}.btn-success.disabled,.btn-success.disabled.active,.btn-success.disabled.focus,.btn-success.disabled:active,.btn-success.disabled:focus,.btn-success.disabled:hover,.btn-success[disabled],.btn-success[disabled].active,.btn-success[disabled].focus,.btn-success[disabled]:active,.btn-success[disabled]:focus,.btn-success[disabled]:hover,fieldset[disabled] .btn-success,fieldset[disabled] .btn-success.active,fieldset[disabled] .btn-success.focus,fieldset[disabled] .btn-success:active,fieldset[disabled] .btn-success:focus,fieldset[disabled] .btn-success:hover{background-color:#5cb85c;border-color:#4cae4c}.btn-success .badge{color:#5cb85c;background-color:#fff}.btn-info{color:#fff;background-color:#5bc0de;border-color:#46b8da}.btn-info.focus,.btn-info:focus{color:#fff;background-color:#31b0d5;border-color:#1b6d85}.btn-info:hover{color:#fff;background-color:#31b0d5;border-color:#269abc}.btn-info.active,.btn-info:active,.open>.dropdown-toggle.btn-info{color:#fff;background-color:#31b0d5;border-color:#269abc}.btn-info.active.focus,.btn-info.active:focus,.btn-info.active:hover,.btn-info:active.focus,.btn-info:active:focus,.btn-info:active:hover,.open>.dropdown-toggle.btn-info.focus,.open>.dropdown-toggle.btn-info:focus,.open>.dropdown-toggle.btn-info:hover{color:#fff;background-color:#269abc;border-color:#1b6d85}.btn-info.active,.btn-info:active,.open>.dropdown-toggle.btn-info{background-image:none}.btn-info.disabled,.btn-info.disabled.active,.btn-info.disabled.focus,.btn-info.disabled:active,.btn-info.disabled:focus,.btn-info.disabled:hover,.btn-info[disabled],.btn-info[disabled].active,.btn-info[disabled].focus,.btn-info[disabled]:active,.btn-info[disabled]:focus,.btn-info[disabled]:hover,fieldset[disabled] .btn-info,fieldset[disabled] .btn-info.active,fieldset[disabled] .btn-info.focus,fieldset[disabled] .btn-info:active,fieldset[disabled] .btn-info:focus,fieldset[disabled] .btn-info:hover{background-color:#5bc0de;border-color:#46b8da}.btn-info .badge{color:#5bc0de;background-color:#fff}.btn-warning{color:#fff;background-color:#f0ad4e;border-color:#eea236}.btn-warning.focus,.btn-warning:focus{color:#fff;background-color:#ec971f;border-color:#985f0d}.btn-warning:hover{color:#fff;background-color:#ec971f;border-color:#d58512}.btn-warning.active,.btn-warning:active,.open>.dropdown-toggle.btn-warning{color:#fff;background-color:#ec971f;border-color:#d58512}.btn-warning.active.focus,.btn-warning.active:focus,.btn-warning.active:hover,.btn-warning:active.focus,.btn-warning:active:focus,.btn-warning:active:hover,.open>.dropdown-toggle.btn-warning.focus,.open>.dropdown-toggle.btn-warning:focus,.open>.dropdown-toggle.btn-warning:hover{color:#fff;background-color:#d58512;border-color:#985f0d}.btn-warning.active,.btn-warning:active,.open>.dropdown-toggle.btn-warning{background-image:none}.btn-warning.disabled,.btn-warning.disabled.active,.btn-warning.disabled.focus,.btn-warning.disabled:active,.btn-warning.disabled:focus,.btn-warning.disabled:hover,.btn-warning[disabled],.btn-warning[disabled].active,.btn-warning[disabled].focus,.btn-warning[disabled]:active,.btn-warning[disabled]:focus,.btn-warning[disabled]:hover,fieldset[disabled] .btn-warning,fieldset[disabled] .btn-warning.active,fieldset[disabled] .btn-warning.focus,fieldset[disabled] .btn-warning:active,fieldset[disabled] .btn-warning:focus,fieldset[disabled] .btn-warning:hover{background-color:#f0ad4e;border-color:#eea236}.btn-warning .badge{color:#f0ad4e;background-color:#fff}.btn-danger{color:#fff;background-color:#d9534f;border-color:#d43f3a}.btn-danger.focus,.btn-danger:focus{color:#fff;background-color:#c9302c;border-color:#761c19}.btn-danger:hover{color:#fff;background-color:#c9302c;border-color:#ac2925}.btn-danger.active,.btn-danger:active,.open>.dropdown-toggle.btn-danger{color:#fff;background-color:#c9302c;border-color:#ac2925}.btn-danger.active.focus,.btn-danger.active:focus,.btn-danger.active:hover,.btn-danger:active.focus,.btn-danger:active:focus,.btn-danger:active:hover,.open>.dropdown-toggle.btn-danger.focus,.open>.dropdown-toggle.btn-danger:focus,.open>.dropdown-toggle.btn-danger:hover{color:#fff;background-color:#ac2925;border-color:#761c19}.btn-danger.active,.btn-danger:active,.open>.dropdown-toggle.btn-danger{background-image:none}.btn-danger.disabled,.btn-danger.disabled.active,.btn-danger.disabled.focus,.btn-danger.disabled:active,.btn-danger.disabled:focus,.btn-danger.disabled:hover,.btn-danger[disabled],.btn-danger[disabled].active,.btn-danger[disabled].focus,.btn-danger[disabled]:active,.btn-danger[disabled]:focus,.btn-danger[disabled]:hover,fieldset[disabled] .btn-danger,fieldset[disabled] .btn-danger.active,fieldset[disabled] .btn-danger.focus,fieldset[disabled] .btn-danger:active,fieldset[disabled] .btn-danger:focus,fieldset[disabled] .btn-danger:hover{background-color:#d9534f;border-color:#d43f3a}.btn-danger .badge{color:#d9534f;background-color:#fff}.btn-link{font-weight:400;color:#337ab7;border-radius:0}.btn-link,.btn-link.active,.btn-link:active,.btn-link[disabled],fieldset[disabled] .btn-link{background-color:transparent;-webkit-box-shadow:none;box-shadow:none}.btn-link,.btn-link:active,.btn-link:focus,.btn-link:hover{border-color:transparent}.btn-link:focus,.btn-link:hover{color:#23527c;text-decoration:underline;background-color:transparent}.btn-link[disabled]:focus,.btn-link[disabled]:hover,fieldset[disabled] .btn-link:focus,fieldset[disabled] .btn-link:hover{color:#777;text-decoration:none}.btn-group-lg>.btn,.btn-lg{padding:10px 16px;font-size:18px;line-height:1.3333333;border-radius:6px}.btn-group-sm>.btn,.btn-sm{padding:5px 10px;font-size:12px;line-height:1.5;border-radius:3px}.btn-group-xs>.btn,.btn-xs{padding:1px 5px;font-size:12px;line-height:1.5;border-radius:3px}.btn-block{display:block;width:100%}.btn-block+.btn-block{margin-top:5px}input[type=button].btn-block,input[type=reset].btn-block,input[type=submit].btn-block{width:100%}.fade{opacity:0;-webkit-transition:opacity .15s linear;-o-transition:opacity .15s linear;transition:opacity .15s linear}.fade.in{opacity:1}.collapse{display:none}.collapse.in{display:block}tr.collapse.in{display:table-row}tbody.collapse.in{display:table-row-group}.collapsing{position:relative;height:0;overflow:hidden;-webkit-transition-timing-function:ease;-o-transition-timing-function:ease;transition-timing-function:ease;-webkit-transition-duration:.35s;-o-transition-duration:.35s;transition-duration:.35s;-webkit-transition-property:height,visibility;-o-transition-property:height,visibility;transition-property:height,visibility}.caret{display:inline-block;width:0;height:0;margin-left:2px;vertical-align:middle;border-top:4px dashed;border-top:4px solid\9;border-right:4px solid transparent;border-left:4px solid transparent}.dropdown,.dropup{position:relative}.dropdown-toggle:focus{outline:0}.dropdown-menu{position:absolute;top:100%;left:0;z-index:1000;display:none;float:left;min-width:160px;padding:5px 0;margin:2px 0 0;font-size:14px;text-align:left;list-style:none;background-color:#fff;-webkit-background-clip:padding-box;background-clip:padding-box;border:1px solid #ccc;border:1px solid rgba(0,0,0,.15);border-radius:4px;-webkit-box-shadow:0 6px 12px rgba(0,0,0,.175);box-shadow:0 6px 12px rgba(0,0,0,.175)}.dropdown-menu.pull-right{right:0;left:auto}.dropdown-menu .divider{height:1px;margin:9px 0;overflow:hidden;background-color:#e5e5e5}.dropdown-menu>li>a{display:block;padding:3px 20px;clear:both;font-weight:400;line-height:1.42857143;color:#333;white-space:nowrap}.dropdown-menu>li>a:focus,.dropdown-menu>li>a:hover{color:#262626;text-decoration:none;background-color:#f5f5f5}.dropdown-menu>.active>a,.dropdown-menu>.active>a:focus,.dropdown-menu>.active>a:hover{color:#fff;text-decoration:none;background-color:#337ab7;outline:0}.dropdown-menu>.disabled>a,.dropdown-menu>.disabled>a:focus,.dropdown-menu>.disabled>a:hover{color:#777}.dropdown-menu>.disabled>a:focus,.dropdown-menu>.disabled>a:hover{text-decoration:none;cursor:not-allowed;background-color:transparent;background-image:none;filter:progid:DXImageTransform.Microsoft.gradient(enabled=false)}.open>.dropdown-menu{display:block}.open>a{outline:0}.dropdown-menu-right{right:0;left:auto}.dropdown-menu-left{right:auto;left:0}.dropdown-header{display:block;padding:3px 20px;font-size:12px;line-height:1.42857143;color:#777;white-space:nowrap}.dropdown-backdrop{position:fixed;top:0;right:0;bottom:0;left:0;z-index:990}.pull-right>.dropdown-menu{right:0;left:auto}.dropup .caret,.navbar-fixed-bottom .dropdown .caret{content:"";border-top:0;border-bottom:4px dashed;border-bottom:4px solid\9}.dropup .dropdown-menu,.navbar-fixed-bottom .dropdown .dropdown-menu{top:auto;bottom:100%;margin-bottom:2px}@media (min-width:768px){.navbar-right .dropdown-menu{right:0;left:auto}.navbar-right .dropdown-menu-left{right:auto;left:0}}.btn-group,.btn-group-vertical{position:relative;display:inline-block;vertical-align:middle}.btn-group-vertical>.btn,.btn-group>.btn{position:relative;float:left}.btn-group-vertical>.btn.active,.btn-group-vertical>.btn:active,.btn-group-vertical>.btn:focus,.btn-group-vertical>.btn:hover,.btn-group>.btn.active,.btn-group>.btn:active,.btn-group>.btn:focus,.btn-group>.btn:hover{z-index:2}.btn-group .btn+.btn,.btn-group .btn+.btn-group,.btn-group .btn-group+.btn,.btn-group .btn-group+.btn-group{margin-left:-1px}.btn-toolbar{margin-left:-5px}.btn-toolbar .btn,.btn-toolbar .btn-group,.btn-toolbar .input-group{float:left}.btn-toolbar>.btn,.btn-toolbar>.btn-group,.btn-toolbar>.input-group{margin-left:5px}.btn-group>.btn:not(:first-child):not(:last-child):not(.dropdown-toggle){border-radius:0}.btn-group>.btn:first-child{margin-left:0}.btn-group>.btn:first-child:not(:last-child):not(.dropdown-toggle){border-top-right-radius:0;border-bottom-right-radius:0}.btn-group>.btn:last-child:not(:first-child),.btn-group>.dropdown-toggle:not(:first-child){border-top-left-radius:0;border-bottom-left-radius:0}.btn-group>.btn-group{float:left}.btn-group>.btn-group:not(:first-child):not(:last-child)>.btn{border-radius:0}.btn-group>.btn-group:first-child:not(:last-child)>.btn:last-child,.btn-group>.btn-group:first-child:not(:last-child)>.dropdown-toggle{border-top-right-radius:0;border-bottom-right-radius:0}.btn-group>.btn-group:last-child:not(:first-child)>.btn:first-child{border-top-left-radius:0;border-bottom-left-radius:0}.btn-group .dropdown-toggle:active,.btn-group.open .dropdown-toggle{outline:0}.btn-group>.btn+.dropdown-toggle{padding-right:8px;padding-left:8px}.btn-group>.btn-lg+.dropdown-toggle{padding-right:12px;padding-left:12px}.btn-group.open .dropdown-toggle{-webkit-box-shadow:inset 0 3px 5px rgba(0,0,0,.125);box-shadow:inset 0 3px 5px rgba(0,0,0,.125)}.btn-group.open .dropdown-toggle.btn-link{-webkit-box-shadow:none;box-shadow:none}.btn .caret{margin-left:0}.btn-lg .caret{border-width:5px 5px 0;border-bottom-width:0}.dropup .btn-lg .caret{border-width:0 5px 5px}.btn-group-vertical>.btn,.btn-group-vertical>.btn-group,.btn-group-vertical>.btn-group>.btn{display:block;float:none;width:100%;max-width:100%}.btn-group-vertical>.btn-group>.btn{float:none}.btn-group-vertical>.btn+.btn,.btn-group-vertical>.btn+.btn-group,.btn-group-vertical>.btn-group+.btn,.btn-group-vertical>.btn-group+.btn-group{margin-top:-1px;margin-left:0}.btn-group-vertical>.btn:not(:first-child):not(:last-child){border-radius:0}.btn-group-vertical>.btn:first-child:not(:last-child){border-top-right-radius:4px;border-bottom-right-radius:0;border-bottom-left-radius:0}.btn-group-vertical>.btn:last-child:not(:first-child){border-top-left-radius:0;border-top-right-radius:0;border-bottom-left-radius:4px}.btn-group-vertical>.btn-group:not(:first-child):not(:last-child)>.btn{border-radius:0}.btn-group-vertical>.btn-group:first-child:not(:last-child)>.btn:last-child,.btn-group-vertical>.btn-group:first-child:not(:last-child)>.dropdown-toggle{border-bottom-right-radius:0;border-bottom-left-radius:0}.btn-group-vertical>.btn-group:last-child:not(:first-child)>.btn:first-child{border-top-left-radius:0;border-top-right-radius:0}.btn-group-justified{display:table;width:100%;table-layout:fixed;border-collapse:separate}.btn-group-justified>.btn,.btn-group-justified>.btn-group{display:table-cell;float:none;width:1%}.btn-group-justified>.btn-group .btn{width:100%}.btn-group-justified>.btn-group .dropdown-menu{left:auto}[data-toggle=buttons]>.btn input[type=checkbox],[data-toggle=buttons]>.btn input[type=radio],[data-toggle=buttons]>.btn-group>.btn input[type=checkbox],[data-toggle=buttons]>.btn-group>.btn input[type=radio]{position:absolute;clip:rect(0,0,0,0);pointer-events:none}.input-group{position:relative;display:table;border-collapse:separate}.input-group[class*=col-]{float:none;padding-right:0;padding-left:0}.input-group .form-control{position:relative;z-index:2;float:left;width:100%;margin-bottom:0}.input-group-lg>.form-control,.input-group-lg>.input-group-addon,.input-group-lg>.input-group-btn>.btn{height:46px;padding:10px 16px;font-size:18px;line-height:1.3333333;border-radius:6px}select.input-group-lg>.form-control,select.input-group-lg>.input-group-addon,select.input-group-lg>.input-group-btn>.btn{height:46px;line-height:46px}select[multiple].input-group-lg>.form-control,select[multiple].input-group-lg>.input-group-addon,select[multiple].input-group-lg>.input-group-btn>.btn,textarea.input-group-lg>.form-control,textarea.input-group-lg>.input-group-addon,textarea.input-group-lg>.input-group-btn>.btn{height:auto}.input-group-sm>.form-control,.input-group-sm>.input-group-addon,.input-group-sm>.input-group-btn>.btn{height:30px;padding:5px 10px;font-size:12px;line-height:1.5;border-radius:3px}select.input-group-sm>.form-control,select.input-group-sm>.input-group-addon,select.input-group-sm>.input-group-btn>.btn{height:30px;line-height:30px}select[multiple].input-group-sm>.form-control,select[multiple].input-group-sm>.input-group-addon,select[multiple].input-group-sm>.input-group-btn>.btn,textarea.input-group-sm>.form-control,textarea.input-group-sm>.input-group-addon,textarea.input-group-sm>.input-group-btn>.btn{height:auto}.input-group .form-control,.input-group-addon,.input-group-btn{display:table-cell}.input-group .form-control:not(:first-child):not(:last-child),.input-group-addon:not(:first-child):not(:last-child),.input-group-btn:not(:first-child):not(:last-child){border-radius:0}.input-group-addon,.input-group-btn{width:1%;white-space:nowrap;vertical-align:middle}.input-group-addon{padding:6px 12px;font-size:14px;font-weight:400;line-height:1;color:#555;text-align:center;background-color:#eee;border:1px solid #ccc;border-radius:4px}.input-group-addon.input-sm{padding:5px 10px;font-size:12px;border-radius:3px}.input-group-addon.input-lg{padding:10px 16px;font-size:18px;border-radius:6px}.input-group-addon input[type=checkbox],.input-group-addon input[type=radio]{margin-top:0}.input-group .form-control:first-child,.input-group-addon:first-child,.input-group-btn:first-child>.btn,.input-group-btn:first-child>.btn-group>.btn,.input-group-btn:first-child>.dropdown-toggle,.input-group-btn:last-child>.btn-group:not(:last-child)>.btn,.input-group-btn:last-child>.btn:not(:last-child):not(.dropdown-toggle){border-top-right-radius:0;border-bottom-right-radius:0}.input-group-addon:first-child{border-right:0}.input-group .form-control:last-child,.input-group-addon:last-child,.input-group-btn:first-child>.btn-group:not(:first-child)>.btn,.input-group-btn:first-child>.btn:not(:first-child),.input-group-btn:last-child>.btn,.input-group-btn:last-child>.btn-group>.btn,.input-group-btn:last-child>.dropdown-toggle{border-top-left-radius:0;border-bottom-left-radius:0}.input-group-addon:last-child{border-left:0}.input-group-btn{position:relative;font-size:0;white-space:nowrap}.input-group-btn>.btn{position:relative}.input-group-btn>.btn+.btn{margin-left:-1px}.input-group-btn>.btn:active,.input-group-btn>.btn:focus,.input-group-btn>.btn:hover{z-index:2}.input-group-btn:first-child>.btn,.input-group-btn:first-child>.btn-group{margin-right:-1px}.input-group-btn:last-child>.btn,.input-group-btn:last-child>.btn-group{z-index:2;margin-left:-1px}.nav{padding-left:0;margin-bottom:0;list-style:none}.nav>li{position:relative;display:block}.nav>li>a{position:relative;display:block;padding:10px 15px}.nav>li>a:focus,.nav>li>a:hover{text-decoration:none;background-color:#eee}.nav>li.disabled>a{color:#777}.nav>li.disabled>a:focus,.nav>li.disabled>a:hover{color:#777;text-decoration:none;cursor:not-allowed;background-color:transparent}.nav .open>a,.nav .open>a:focus,.nav .open>a:hover{background-color:#eee;border-color:#337ab7}.nav .nav-divider{height:1px;margin:9px 0;overflow:hidden;background-color:#e5e5e5}.nav>li>a>img{max-width:none}.nav-tabs{border-bottom:1px solid #ddd}.nav-tabs>li{float:left;margin-bottom:-1px}.nav-tabs>li>a{margin-right:2px;line-height:1.42857143;border:1px solid transparent;border-radius:4px 4px 0 0}.nav-tabs>li>a:hover{border-color:#eee #eee #ddd}.nav-tabs>li.active>a,.nav-tabs>li.active>a:focus,.nav-tabs>li.active>a:hover{color:#555;cursor:default;background-color:#fff;border:1px solid #ddd;border-bottom-color:transparent}.nav-tabs.nav-justified{width:100%;border-bottom:0}.nav-tabs.nav-justified>li{float:none}.nav-tabs.nav-justified>li>a{margin-bottom:5px;text-align:center}.nav-tabs.nav-justified>.dropdown .dropdown-menu{top:auto;left:auto}@media (min-width:768px){.nav-tabs.nav-justified>li{display:table-cell;width:1%}.nav-tabs.nav-justified>li>a{margin-bottom:0}}.nav-tabs.nav-justified>li>a{margin-right:0;border-radius:4px}.nav-tabs.nav-justified>.active>a,.nav-tabs.nav-justified>.active>a:focus,.nav-tabs.nav-justified>.active>a:hover{border:1px solid #ddd}@media (min-width:768px){.nav-tabs.nav-justified>li>a{border-bottom:1px solid #ddd;border-radius:4px 4px 0 0}.nav-tabs.nav-justified>.active>a,.nav-tabs.nav-justified>.active>a:focus,.nav-tabs.nav-justified>.active>a:hover{border-bottom-color:#fff}}.nav-pills>li{float:left}.nav-pills>li>a{border-radius:4px}.nav-pills>li+li{margin-left:2px}.nav-pills>li.active>a,.nav-pills>li.active>a:focus,.nav-pills>li.active>a:hover{color:#fff;background-color:#337ab7}.nav-stacked>li{float:none}.nav-stacked>li+li{margin-top:2px;margin-left:0}.nav-justified{width:100%}.nav-justified>li{float:none}.nav-justified>li>a{margin-bottom:5px;text-align:center}.nav-justified>.dropdown .dropdown-menu{top:auto;left:auto}@media (min-width:768px){.nav-justified>li{display:table-cell;width:1%}.nav-justified>li>a{margin-bottom:0}}.nav-tabs-justified{border-bottom:0}.nav-tabs-justified>li>a{margin-right:0;border-radius:4px}.nav-tabs-justified>.active>a,.nav-tabs-justified>.active>a:focus,.nav-tabs-justified>.active>a:hover{border:1px solid #ddd}@media (min-width:768px){.nav-tabs-justified>li>a{border-bottom:1px solid #ddd;border-radius:4px 4px 0 0}.nav-tabs-justified>.active>a,.nav-tabs-justified>.active>a:focus,.nav-tabs-justified>.active>a:hover{border-bottom-color:#fff}}.tab-content>.tab-pane{display:none}.tab-content>.active{display:block}.nav-tabs .dropdown-menu{margin-top:-1px;border-top-left-radius:0;border-top-right-radius:0}.navbar{position:relative;min-height:50px;margin-bottom:20px;border:1px solid transparent}@media (min-width:768px){.navbar{border-radius:4px}}@media (min-width:768px){.navbar-header{float:left}}.navbar-collapse{padding-right:15px;padding-left:15px;overflow-x:visible;-webkit-overflow-scrolling:touch;border-top:1px solid transparent;-webkit-box-shadow:inset 0 1px 0 rgba(255,255,255,.1);box-shadow:inset 0 1px 0 rgba(255,255,255,.1)}.navbar-collapse.in{overflow-y:auto}@media (min-width:768px){.navbar-collapse{width:auto;border-top:0;-webkit-box-shadow:none;box-shadow:none}.navbar-collapse.collapse{display:block!important;height:auto!important;padding-bottom:0;overflow:visible!important}.navbar-collapse.in{overflow-y:visible}.navbar-fixed-bottom .navbar-collapse,.navbar-fixed-top .navbar-collapse,.navbar-static-top .navbar-collapse{padding-right:0;padding-left:0}}.navbar-fixed-bottom .navbar-collapse,.navbar-fixed-top .navbar-collapse{max-height:340px}@media (max-device-width:480px) and (orientation:landscape){.navbar-fixed-bottom .navbar-collapse,.navbar-fixed-top .navbar-collapse{max-height:200px}}.container-fluid>.navbar-collapse,.container-fluid>.navbar-header,.container>.navbar-collapse,.container>.navbar-header{margin-right:-15px;margin-left:-15px}@media (min-width:768px){.container-fluid>.navbar-collapse,.container-fluid>.navbar-header,.container>.navbar-collapse,.container>.navbar-header{margin-right:0;margin-left:0}}.navbar-static-top{z-index:1000;border-width:0 0 1px}@media (min-width:768px){.navbar-static-top{border-radius:0}}.navbar-fixed-bottom,.navbar-fixed-top{position:fixed;right:0;left:0;z-index:1030}@media (min-width:768px){.navbar-fixed-bottom,.navbar-fixed-top{border-radius:0}}.navbar-fixed-top{top:0;border-width:0 0 1px}.navbar-fixed-bottom{bottom:0;margin-bottom:0;border-width:1px 0 0}.navbar-brand{float:left;height:50px;padding:15px 15px;font-size:18px;line-height:20px}.navbar-brand:focus,.navbar-brand:hover{text-decoration:none}.navbar-brand>img{display:block}@media (min-width:768px){.navbar>.container .navbar-brand,.navbar>.container-fluid .navbar-brand{margin-left:-15px}}.navbar-toggle{position:relative;float:right;padding:9px 10px;margin-top:8px;margin-right:15px;margin-bottom:8px;background-color:transparent;background-image:none;border:1px solid transparent;border-radius:4px}.navbar-toggle:focus{outline:0}.navbar-toggle .icon-bar{display:block;width:22px;height:2px;border-radius:1px}.navbar-toggle .icon-bar+.icon-bar{margin-top:4px}@media (min-width:768px){.navbar-toggle{display:none}}.navbar-nav{margin:7.5px -15px}.navbar-nav>li>a{padding-top:10px;padding-bottom:10px;line-height:20px}@media (max-width:767px){.navbar-nav .open .dropdown-menu{position:static;float:none;width:auto;margin-top:0;background-color:transparent;border:0;-webkit-box-shadow:none;box-shadow:none}.navbar-nav .open .dropdown-menu .dropdown-header,.navbar-nav .open .dropdown-menu>li>a{padding:5px 15px 5px 25px}.navbar-nav .open .dropdown-menu>li>a{line-height:20px}.navbar-nav .open .dropdown-menu>li>a:focus,.navbar-nav .open .dropdown-menu>li>a:hover{background-image:none}}@media (min-width:768px){.navbar-nav{float:left;margin:0}.navbar-nav>li{float:left}.navbar-nav>li>a{padding-top:15px;padding-bottom:15px}}.navbar-form{padding:10px 15px;margin-top:8px;margin-right:-15px;margin-bottom:8px;margin-left:-15px;border-top:1px solid transparent;border-bottom:1px solid transparent;-webkit-box-shadow:inset 0 1px 0 rgba(255,255,255,.1),0 1px 0 rgba(255,255,255,.1);box-shadow:inset 0 1px 0 rgba(255,255,255,.1),0 1px 0 rgba(255,255,255,.1)}@media (min-width:768px){.navbar-form .form-group{display:inline-block;margin-bottom:0;vertical-align:middle}.navbar-form .form-control{display:inline-block;width:auto;vertical-align:middle}.navbar-form .form-control-static{display:inline-block}.navbar-form .input-group{display:inline-table;vertical-align:middle}.navbar-form .input-group .form-control,.navbar-form .input-group .input-group-addon,.navbar-form .input-group .input-group-btn{width:auto}.navbar-form .input-group>.form-control{width:100%}.navbar-form .control-label{margin-bottom:0;vertical-align:middle}.navbar-form .checkbox,.navbar-form .radio{display:inline-block;margin-top:0;margin-bottom:0;vertical-align:middle}.navbar-form .checkbox label,.navbar-form .radio label{padding-left:0}.navbar-form .checkbox input[type=checkbox],.navbar-form .radio input[type=radio]{position:relative;margin-left:0}.navbar-form .has-feedback .form-control-feedback{top:0}}@media (max-width:767px){.navbar-form .form-group{margin-bottom:5px}.navbar-form .form-group:last-child{margin-bottom:0}}@media (min-width:768px){.navbar-form{width:auto;padding-top:0;padding-bottom:0;margin-right:0;margin-left:0;border:0;-webkit-box-shadow:none;box-shadow:none}}.navbar-nav>li>.dropdown-menu{margin-top:0;border-top-left-radius:0;border-top-right-radius:0}.navbar-fixed-bottom .navbar-nav>li>.dropdown-menu{margin-bottom:0;border-top-left-radius:4px;border-top-right-radius:4px;border-bottom-right-radius:0;border-bottom-left-radius:0}.navbar-btn{margin-top:8px;margin-bottom:8px}.navbar-btn.btn-sm{margin-top:10px;margin-bottom:10px}.navbar-btn.btn-xs{margin-top:14px;margin-bottom:14px}.navbar-text{margin-top:15px;margin-bottom:15px}@media (min-width:768px){.navbar-text{float:left;margin-right:15px;margin-left:15px}}@media (min-width:768px){.navbar-left{float:left!important}.navbar-right{float:right!important;margin-right:-15px}.navbar-right~.navbar-right{margin-right:0}}.navbar-default{background-color:#f8f8f8;border-color:#e7e7e7}.navbar-default .navbar-brand{color:#777}.navbar-default .navbar-brand:focus,.navbar-default .navbar-brand:hover{color:#5e5e5e;background-color:transparent}.navbar-default .navbar-text{color:#777}.navbar-default .navbar-nav>li>a{color:#777}.navbar-default .navbar-nav>li>a:focus,.navbar-default .navbar-nav>li>a:hover{color:#333;background-color:transparent}.navbar-default .navbar-nav>.active>a,.navbar-default .navbar-nav>.active>a:focus,.navbar-default .navbar-nav>.active>a:hover{color:#555;background-color:#e7e7e7}.navbar-default .navbar-nav>.disabled>a,.navbar-default .navbar-nav>.disabled>a:focus,.navbar-default .navbar-nav>.disabled>a:hover{color:#ccc;background-color:transparent}.navbar-default .navbar-toggle{border-color:#ddd}.navbar-default .navbar-toggle:focus,.navbar-default .navbar-toggle:hover{background-color:#ddd}.navbar-default .navbar-toggle .icon-bar{background-color:#888}.navbar-default .navbar-collapse,.navbar-default .navbar-form{border-color:#e7e7e7}.navbar-default .navbar-nav>.open>a,.navbar-default .navbar-nav>.open>a:focus,.navbar-default .navbar-nav>.open>a:hover{color:#555;background-color:#e7e7e7}@media (max-width:767px){.navbar-default .navbar-nav .open .dropdown-menu>li>a{color:#777}.navbar-default .navbar-nav .open .dropdown-menu>li>a:focus,.navbar-default .navbar-nav .open .dropdown-menu>li>a:hover{color:#333;background-color:transparent}.navbar-default .navbar-nav .open .dropdown-menu>.active>a,.navbar-default .navbar-nav .open .dropdown-menu>.active>a:focus,.navbar-default .navbar-nav .open .dropdown-menu>.active>a:hover{color:#555;background-color:#e7e7e7}.navbar-default .navbar-nav .open .dropdown-menu>.disabled>a,.navbar-default .navbar-nav .open .dropdown-menu>.disabled>a:focus,.navbar-default .navbar-nav .open .dropdown-menu>.disabled>a:hover{color:#ccc;background-color:transparent}}.navbar-default .navbar-link{color:#777}.navbar-default .navbar-link:hover{color:#333}.navbar-default .btn-link{color:#777}.navbar-default .btn-link:focus,.navbar-default .btn-link:hover{color:#333}.navbar-default .btn-link[disabled]:focus,.navbar-default .btn-link[disabled]:hover,fieldset[disabled] .navbar-default .btn-link:focus,fieldset[disabled] .navbar-default .btn-link:hover{color:#ccc}.navbar-inverse{background-color:#222;border-color:#080808}.navbar-inverse .navbar-brand{color:#9d9d9d}.navbar-inverse .navbar-brand:focus,.navbar-inverse .navbar-brand:hover{color:#fff;background-color:transparent}.navbar-inverse .navbar-text{color:#9d9d9d}.navbar-inverse .navbar-nav>li>a{color:#9d9d9d}.navbar-inverse .navbar-nav>li>a:focus,.navbar-inverse .navbar-nav>li>a:hover{color:#fff;background-color:transparent}.navbar-inverse .navbar-nav>.active>a,.navbar-inverse .navbar-nav>.active>a:focus,.navbar-inverse .navbar-nav>.active>a:hover{color:#fff;background-color:#080808}.navbar-inverse .navbar-nav>.disabled>a,.navbar-inverse .navbar-nav>.disabled>a:focus,.navbar-inverse .navbar-nav>.disabled>a:hover{color:#444;background-color:transparent}.navbar-inverse .navbar-toggle{border-color:#333}.navbar-inverse .navbar-toggle:focus,.navbar-inverse .navbar-toggle:hover{background-color:#333}.navbar-inverse .navbar-toggle .icon-bar{background-color:#fff}.navbar-inverse .navbar-collapse,.navbar-inverse .navbar-form{border-color:#101010}.navbar-inverse .navbar-nav>.open>a,.navbar-inverse .navbar-nav>.open>a:focus,.navbar-inverse .navbar-nav>.open>a:hover{color:#fff;background-color:#080808}@media (max-width:767px){.navbar-inverse .navbar-nav .open .dropdown-menu>.dropdown-header{border-color:#080808}.navbar-inverse .navbar-nav .open .dropdown-menu .divider{background-color:#080808}.navbar-inverse .navbar-nav .open .dropdown-menu>li>a{color:#9d9d9d}.navbar-inverse .navbar-nav .open .dropdown-menu>li>a:focus,.navbar-inverse .navbar-nav .open .dropdown-menu>li>a:hover{color:#fff;background-color:transparent}.navbar-inverse .navbar-nav .open .dropdown-menu>.active>a,.navbar-inverse .navbar-nav .open .dropdown-menu>.active>a:focus,.navbar-inverse .navbar-nav .open .dropdown-menu>.active>a:hover{color:#fff;background-color:#080808}.navbar-inverse .navbar-nav .open .dropdown-menu>.disabled>a,.navbar-inverse .navbar-nav .open .dropdown-menu>.disabled>a:focus,.navbar-inverse .navbar-nav .open .dropdown-menu>.disabled>a:hover{color:#444;background-color:transparent}}.navbar-inverse .navbar-link{color:#9d9d9d}.navbar-inverse .navbar-link:hover{color:#fff}.navbar-inverse .btn-link{color:#9d9d9d}.navbar-inverse .btn-link:focus,.navbar-inverse .btn-link:hover{color:#fff}.navbar-inverse .btn-link[disabled]:focus,.navbar-inverse .btn-link[disabled]:hover,fieldset[disabled] .navbar-inverse .btn-link:focus,fieldset[disabled] .navbar-inverse .btn-link:hover{color:#444}.breadcrumb{padding:8px 15px;margin-bottom:20px;list-style:none;background-color:#f5f5f5;border-radius:4px}.breadcrumb>li{display:inline-block}.breadcrumb>li+li:before{padding:0 5px;color:#ccc;content:"/\00a0"}.breadcrumb>.active{color:#777}.pagination{display:inline-block;padding-left:0;margin:20px 0;border-radius:4px}.pagination>li{display:inline}.pagination>li>a,.pagination>li>span{position:relative;float:left;padding:6px 12px;margin-left:-1px;line-height:1.42857143;color:#337ab7;text-decoration:none;background-color:#fff;border:1px solid #ddd}.pagination>li:first-child>a,.pagination>li:first-child>span{margin-left:0;border-top-left-radius:4px;border-bottom-left-radius:4px}.pagination>li:last-child>a,.pagination>li:last-child>span{border-top-right-radius:4px;border-bottom-right-radius:4px}.pagination>li>a:focus,.pagination>li>a:hover,.pagination>li>span:focus,.pagination>li>span:hover{z-index:3;color:#23527c;background-color:#eee;border-color:#ddd}.pagination>.active>a,.pagination>.active>a:focus,.pagination>.active>a:hover,.pagination>.active>span,.pagination>.active>span:focus,.pagination>.active>span:hover{z-index:2;color:#fff;cursor:default;background-color:#337ab7;border-color:#337ab7}.pagination>.disabled>a,.pagination>.disabled>a:focus,.pagination>.disabled>a:hover,.pagination>.disabled>span,.pagination>.disabled>span:focus,.pagination>.disabled>span:hover{color:#777;cursor:not-allowed;background-color:#fff;border-color:#ddd}.pagination-lg>li>a,.pagination-lg>li>span{padding:10px 16px;font-size:18px;line-height:1.3333333}.pagination-lg>li:first-child>a,.pagination-lg>li:first-child>span{border-top-left-radius:6px;border-bottom-left-radius:6px}.pagination-lg>li:last-child>a,.pagination-lg>li:last-child>span{border-top-right-radius:6px;border-bottom-right-radius:6px}.pagination-sm>li>a,.pagination-sm>li>span{padding:5px 10px;font-size:12px;line-height:1.5}.pagination-sm>li:first-child>a,.pagination-sm>li:first-child>span{border-top-left-radius:3px;border-bottom-left-radius:3px}.pagination-sm>li:last-child>a,.pagination-sm>li:last-child>span{border-top-right-radius:3px;border-bottom-right-radius:3px}.pager{padding-left:0;margin:20px 0;text-align:center;list-style:none}.pager li{display:inline}.pager li>a,.pager li>span{display:inline-block;padding:5px 14px;background-color:#fff;border:1px solid #ddd;border-radius:15px}.pager li>a:focus,.pager li>a:hover{text-decoration:none;background-color:#eee}.pager .next>a,.pager .next>span{float:right}.pager .previous>a,.pager .previous>span{float:left}.pager .disabled>a,.pager .disabled>a:focus,.pager .disabled>a:hover,.pager .disabled>span{color:#777;cursor:not-allowed;background-color:#fff}.label{display:inline;padding:.2em .6em .3em;font-size:75%;font-weight:700;line-height:1;color:#fff;text-align:center;white-space:nowrap;vertical-align:baseline;border-radius:.25em}a.label:focus,a.label:hover{color:#fff;text-decoration:none;cursor:pointer}.label:empty{display:none}.btn .label{position:relative;top:-1px}.label-default{background-color:#777}.label-default[href]:focus,.label-default[href]:hover{background-color:#5e5e5e}.label-primary{background-color:#337ab7}.label-primary[href]:focus,.label-primary[href]:hover{background-color:#286090}.label-success{background-color:#5cb85c}.label-success[href]:focus,.label-success[href]:hover{background-color:#449d44}.label-info{background-color:#5bc0de}.label-info[href]:focus,.label-info[href]:hover{background-color:#31b0d5}.label-warning{background-color:#f0ad4e}.label-warning[href]:focus,.label-warning[href]:hover{background-color:#ec971f}.label-danger{background-color:#d9534f}.label-danger[href]:focus,.label-danger[href]:hover{background-color:#c9302c}.badge{display:inline-block;min-width:10px;padding:3px 7px;font-size:12px;font-weight:700;line-height:1;color:#fff;text-align:center;white-space:nowrap;vertical-align:middle;background-color:#777;border-radius:10px}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.btn-group-xs>.btn .badge,.btn-xs .badge{top:0;padding:1px 5px}a.badge:focus,a.badge:hover{color:#fff;text-decoration:none;cursor:pointer}.list-group-item.active>.badge,.nav-pills>.active>a>.badge{color:#337ab7;background-color:#fff}.list-group-item>.badge{float:right}.list-group-item>.badge+.badge{margin-right:5px}.nav-pills>li>a>.badge{margin-left:3px}.jumbotron{padding-top:30px;padding-bottom:30px;margin-bottom:30px;color:inherit;background-color:#eee}.jumbotron .h1,.jumbotron h1{color:inherit}.jumbotron p{margin-bottom:15px;font-size:21px;font-weight:200}.jumbotron>hr{border-top-color:#d5d5d5}.container .jumbotron,.container-fluid .jumbotron{border-radius:6px}.jumbotron .container{max-width:100%}@media screen and (min-width:768px){.jumbotron{padding-top:48px;padding-bottom:48px}.container .jumbotron,.container-fluid .jumbotron{padding-right:60px;padding-left:60px}.jumbotron .h1,.jumbotron h1{font-size:63px}}.thumbnail{display:block;padding:4px;margin-bottom:20px;line-height:1.42857143;background-color:#fff;border:1px solid #ddd;border-radius:4px;-webkit-transition:border .2s ease-in-out;-o-transition:border .2s ease-in-out;transition:border .2s ease-in-out}.thumbnail a>img,.thumbnail>img{margin-right:auto;margin-left:auto}a.thumbnail.active,a.thumbnail:focus,a.thumbnail:hover{border-color:#337ab7}.thumbnail .caption{padding:9px;color:#333}.alert{padding:15px;margin-bottom:20px;border:1px solid transparent;border-radius:4px}.alert h4{margin-top:0;color:inherit}.alert .alert-link{font-weight:700}.alert>p,.alert>ul{margin-bottom:0}.alert>p+p{margin-top:5px}.alert-dismissable,.alert-dismissible{padding-right:35px}.alert-dismissable .close,.alert-dismissible .close{position:relative;top:-2px;right:-21px;color:inherit}.alert-success{color:#3c763d;background-color:#dff0d8;border-color:#d6e9c6}.alert-success hr{border-top-color:#c9e2b3}.alert-success .alert-link{color:#2b542c}.alert-info{color:#31708f;background-color:#d9edf7;border-color:#bce8f1}.alert-info hr{border-top-color:#a6e1ec}.alert-info .alert-link{color:#245269}.alert-warning{color:#8a6d3b;background-color:#fcf8e3;border-color:#faebcc}.alert-warning hr{border-top-color:#f7e1b5}.alert-warning .alert-link{color:#66512c}.alert-danger{color:#a94442;background-color:#f2dede;border-color:#ebccd1}.alert-danger hr{border-top-color:#e4b9c0}.alert-danger .alert-link{color:#843534}@-webkit-keyframes progress-bar-stripes{from{background-position:40px 0}to{background-position:0 0}}@-o-keyframes progress-bar-stripes{from{background-position:40px 0}to{background-position:0 0}}@keyframes progress-bar-stripes{from{background-position:40px 0}to{background-position:0 0}}.progress{height:20px;margin-bottom:20px;overflow:hidden;background-color:#f5f5f5;border-radius:4px;-webkit-box-shadow:inset 0 1px 2px rgba(0,0,0,.1);box-shadow:inset 0 1px 2px rgba(0,0,0,.1)}.progress-bar{float:left;width:0;height:100%;font-size:12px;line-height:20px;color:#fff;text-align:center;background-color:#337ab7;-webkit-box-shadow:inset 0 -1px 0 rgba(0,0,0,.15);box-shadow:inset 0 -1px 0 rgba(0,0,0,.15);-webkit-transition:width .6s ease;-o-transition:width .6s ease;transition:width .6s ease}.progress-bar-striped,.progress-striped .progress-bar{background-image:-webkit-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:-o-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);-webkit-background-size:40px 40px;background-size:40px 40px}.progress-bar.active,.progress.active .progress-bar{-webkit-animation:progress-bar-stripes 2s linear infinite;-o-animation:progress-bar-stripes 2s linear infinite;animation:progress-bar-stripes 2s linear infinite}.progress-bar-success{background-color:#5cb85c}.progress-striped .progress-bar-success{background-image:-webkit-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:-o-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent)}.progress-bar-info{background-color:#5bc0de}.progress-striped .progress-bar-info{background-image:-webkit-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:-o-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent)}.progress-bar-warning{background-color:#f0ad4e}.progress-striped .progress-bar-warning{background-image:-webkit-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:-o-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent)}.progress-bar-danger{background-color:#d9534f}.progress-striped .progress-bar-danger{background-image:-webkit-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:-o-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent)}.media{margin-top:15px}.media:first-child{margin-top:0}.media,.media-body{overflow:hidden;zoom:1}.media-body{width:10000px}.media-object{display:block}.media-object.img-thumbnail{max-width:none}.media-right,.media>.pull-right{padding-left:10px}.media-left,.media>.pull-left{padding-right:10px}.media-body,.media-left,.media-right{display:table-cell;vertical-align:top}.media-middle{vertical-align:middle}.media-bottom{vertical-align:bottom}.media-heading{margin-top:0;margin-bottom:5px}.media-list{padding-left:0;list-style:none}.list-group{padding-left:0;margin-bottom:20px}.list-group-item{position:relative;display:block;padding:10px 15px;margin-bottom:-1px;background-color:#fff;border:1px solid #ddd}.list-group-item:first-child{border-top-left-radius:4px;border-top-right-radius:4px}.list-group-item:last-child{margin-bottom:0;border-bottom-right-radius:4px;border-bottom-left-radius:4px}a.list-group-item,button.list-group-item{color:#555}a.list-group-item .list-group-item-heading,button.list-group-item .list-group-item-heading{color:#333}a.list-group-item:focus,a.list-group-item:hover,button.list-group-item:focus,button.list-group-item:hover{color:#555;text-decoration:none;background-color:#f5f5f5}button.list-group-item{width:100%;text-align:left}.list-group-item.disabled,.list-group-item.disabled:focus,.list-group-item.disabled:hover{color:#777;cursor:not-allowed;background-color:#eee}.list-group-item.disabled .list-group-item-heading,.list-group-item.disabled:focus .list-group-item-heading,.list-group-item.disabled:hover .list-group-item-heading{color:inherit}.list-group-item.disabled .list-group-item-text,.list-group-item.disabled:focus .list-group-item-text,.list-group-item.disabled:hover .list-group-item-text{color:#777}.list-group-item.active,.list-group-item.active:focus,.list-group-item.active:hover{z-index:2;color:#fff;background-color:#337ab7;border-color:#337ab7}.list-group-item.active .list-group-item-heading,.list-group-item.active .list-group-item-heading>.small,.list-group-item.active .list-group-item-heading>small,.list-group-item.active:focus .list-group-item-heading,.list-group-item.active:focus .list-group-item-heading>.small,.list-group-item.active:focus .list-group-item-heading>small,.list-group-item.active:hover .list-group-item-heading,.list-group-item.active:hover .list-group-item-heading>.small,.list-group-item.active:hover .list-group-item-heading>small{color:inherit}.list-group-item.active .list-group-item-text,.list-group-item.active:focus .list-group-item-text,.list-group-item.active:hover .list-group-item-text{color:#c7ddef}.list-group-item-success{color:#3c763d;background-color:#dff0d8}a.list-group-item-success,button.list-group-item-success{color:#3c763d}a.list-group-item-success .list-group-item-heading,button.list-group-item-success .list-group-item-heading{color:inherit}a.list-group-item-success:focus,a.list-group-item-success:hover,button.list-group-item-success:focus,button.list-group-item-success:hover{color:#3c763d;background-color:#d0e9c6}a.list-group-item-success.active,a.list-group-item-success.active:focus,a.list-group-item-success.active:hover,button.list-group-item-success.active,button.list-group-item-success.active:focus,button.list-group-item-success.active:hover{color:#fff;background-color:#3c763d;border-color:#3c763d}.list-group-item-info{color:#31708f;background-color:#d9edf7}a.list-group-item-info,button.list-group-item-info{color:#31708f}a.list-group-item-info .list-group-item-heading,button.list-group-item-info .list-group-item-heading{color:inherit}a.list-group-item-info:focus,a.list-group-item-info:hover,button.list-group-item-info:focus,button.list-group-item-info:hover{color:#31708f;background-color:#c4e3f3}a.list-group-item-info.active,a.list-group-item-info.active:focus,a.list-group-item-info.active:hover,button.list-group-item-info.active,button.list-group-item-info.active:focus,button.list-group-item-info.active:hover{color:#fff;background-color:#31708f;border-color:#31708f}.list-group-item-warning{color:#8a6d3b;background-color:#fcf8e3}a.list-group-item-warning,button.list-group-item-warning{color:#8a6d3b}a.list-group-item-warning .list-group-item-heading,button.list-group-item-warning .list-group-item-heading{color:inherit}a.list-group-item-warning:focus,a.list-group-item-warning:hover,button.list-group-item-warning:focus,button.list-group-item-warning:hover{color:#8a6d3b;background-color:#faf2cc}a.list-group-item-warning.active,a.list-group-item-warning.active:focus,a.list-group-item-warning.active:hover,button.list-group-item-warning.active,button.list-group-item-warning.active:focus,button.list-group-item-warning.active:hover{color:#fff;background-color:#8a6d3b;border-color:#8a6d3b}.list-group-item-danger{color:#a94442;background-color:#f2dede}a.list-group-item-danger,button.list-group-item-danger{color:#a94442}a.list-group-item-danger .list-group-item-heading,button.list-group-item-danger .list-group-item-heading{color:inherit}a.list-group-item-danger:focus,a.list-group-item-danger:hover,button.list-group-item-danger:focus,button.list-group-item-danger:hover{color:#a94442;background-color:#ebcccc}a.list-group-item-danger.active,a.list-group-item-danger.active:focus,a.list-group-item-danger.active:hover,button.list-group-item-danger.active,button.list-group-item-danger.active:focus,button.list-group-item-danger.active:hover{color:#fff;background-color:#a94442;border-color:#a94442}.list-group-item-heading{margin-top:0;margin-bottom:5px}.list-group-item-text{margin-bottom:0;line-height:1.3}.panel{margin-bottom:20px;background-color:#fff;border:1px solid transparent;border-radius:4px;-webkit-box-shadow:0 1px 1px rgba(0,0,0,.05);box-shadow:0 1px 1px rgba(0,0,0,.05)}.panel-body{padding:15px}.panel-heading{padding:10px 15px;border-bottom:1px solid transparent;border-top-left-radius:3px;border-top-right-radius:3px}.panel-heading>.dropdown .dropdown-toggle{color:inherit}.panel-title{margin-top:0;margin-bottom:0;font-size:16px;color:inherit}.panel-title>.small,.panel-title>.small>a,.panel-title>a,.panel-title>small,.panel-title>small>a{color:inherit}.panel-footer{padding:10px 15px;background-color:#f5f5f5;border-top:1px solid #ddd;border-bottom-right-radius:3px;border-bottom-left-radius:3px}.panel>.list-group,.panel>.panel-collapse>.list-group{margin-bottom:0}.panel>.list-group .list-group-item,.panel>.panel-collapse>.list-group .list-group-item{border-width:1px 0;border-radius:0}.panel>.list-group:first-child .list-group-item:first-child,.panel>.panel-collapse>.list-group:first-child .list-group-item:first-child{border-top:0;border-top-left-radius:3px;border-top-right-radius:3px}.panel>.list-group:last-child .list-group-item:last-child,.panel>.panel-collapse>.list-group:last-child .list-group-item:last-child{border-bottom:0;border-bottom-right-radius:3px;border-bottom-left-radius:3px}.panel>.panel-heading+.panel-collapse>.list-group .list-group-item:first-child{border-top-left-radius:0;border-top-right-radius:0}.panel-heading+.list-group .list-group-item:first-child{border-top-width:0}.list-group+.panel-footer{border-top-width:0}.panel>.panel-collapse>.table,.panel>.table,.panel>.table-responsive>.table{margin-bottom:0}.panel>.panel-collapse>.table caption,.panel>.table caption,.panel>.table-responsive>.table caption{padding-right:15px;padding-left:15px}.panel>.table-responsive:first-child>.table:first-child,.panel>.table:first-child{border-top-left-radius:3px;border-top-right-radius:3px}.panel>.table-responsive:first-child>.table:first-child>tbody:first-child>tr:first-child,.panel>.table-responsive:first-child>.table:first-child>thead:first-child>tr:first-child,.panel>.table:first-child>tbody:first-child>tr:first-child,.panel>.table:first-child>thead:first-child>tr:first-child{border-top-left-radius:3px;border-top-right-radius:3px}.panel>.table-responsive:first-child>.table:first-child>tbody:first-child>tr:first-child td:first-child,.panel>.table-responsive:first-child>.table:first-child>tbody:first-child>tr:first-child th:first-child,.panel>.table-responsive:first-child>.table:first-child>thead:first-child>tr:first-child td:first-child,.panel>.table-responsive:first-child>.table:first-child>thead:first-child>tr:first-child th:first-child,.panel>.table:first-child>tbody:first-child>tr:first-child td:first-child,.panel>.table:first-child>tbody:first-child>tr:first-child th:first-child,.panel>.table:first-child>thead:first-child>tr:first-child td:first-child,.panel>.table:first-child>thead:first-child>tr:first-child th:first-child{border-top-left-radius:3px}.panel>.table-responsive:first-child>.table:first-child>tbody:first-child>tr:first-child td:last-child,.panel>.table-responsive:first-child>.table:first-child>tbody:first-child>tr:first-child th:last-child,.panel>.table-responsive:first-child>.table:first-child>thead:first-child>tr:first-child td:last-child,.panel>.table-responsive:first-child>.table:first-child>thead:first-child>tr:first-child th:last-child,.panel>.table:first-child>tbody:first-child>tr:first-child td:last-child,.panel>.table:first-child>tbody:first-child>tr:first-child th:last-child,.panel>.table:first-child>thead:first-child>tr:first-child td:last-child,.panel>.table:first-child>thead:first-child>tr:first-child th:last-child{border-top-right-radius:3px}.panel>.table-responsive:last-child>.table:last-child,.panel>.table:last-child{border-bottom-right-radius:3px;border-bottom-left-radius:3px}.panel>.table-responsive:last-child>.table:last-child>tbody:last-child>tr:last-child,.panel>.table-responsive:last-child>.table:last-child>tfoot:last-child>tr:last-child,.panel>.table:last-child>tbody:last-child>tr:last-child,.panel>.table:last-child>tfoot:last-child>tr:last-child{border-bottom-right-radius:3px;border-bottom-left-radius:3px}.panel>.table-responsive:last-child>.table:last-child>tbody:last-child>tr:last-child td:first-child,.panel>.table-responsive:last-child>.table:last-child>tbody:last-child>tr:last-child th:first-child,.panel>.table-responsive:last-child>.table:last-child>tfoot:last-child>tr:last-child td:first-child,.panel>.table-responsive:last-child>.table:last-child>tfoot:last-child>tr:last-child th:first-child,.panel>.table:last-child>tbody:last-child>tr:last-child td:first-child,.panel>.table:last-child>tbody:last-child>tr:last-child th:first-child,.panel>.table:last-child>tfoot:last-child>tr:last-child td:first-child,.panel>.table:last-child>tfoot:last-child>tr:last-child th:first-child{border-bottom-left-radius:3px}.panel>.table-responsive:last-child>.table:last-child>tbody:last-child>tr:last-child td:last-child,.panel>.table-responsive:last-child>.table:last-child>tbody:last-child>tr:last-child th:last-child,.panel>.table-responsive:last-child>.table:last-child>tfoot:last-child>tr:last-child td:last-child,.panel>.table-responsive:last-child>.table:last-child>tfoot:last-child>tr:last-child th:last-child,.panel>.table:last-child>tbody:last-child>tr:last-child td:last-child,.panel>.table:last-child>tbody:last-child>tr:last-child th:last-child,.panel>.table:last-child>tfoot:last-child>tr:last-child td:last-child,.panel>.table:last-child>tfoot:last-child>tr:last-child th:last-child{border-bottom-right-radius:3px}.panel>.panel-body+.table,.panel>.panel-body+.table-responsive,.panel>.table+.panel-body,.panel>.table-responsive+.panel-body{border-top:1px solid #ddd}.panel>.table>tbody:first-child>tr:first-child td,.panel>.table>tbody:first-child>tr:first-child th{border-top:0}.panel>.table-bordered,.panel>.table-responsive>.table-bordered{border:0}.panel>.table-bordered>tbody>tr>td:first-child,.panel>.table-bordered>tbody>tr>th:first-child,.panel>.table-bordered>tfoot>tr>td:first-child,.panel>.table-bordered>tfoot>tr>th:first-child,.panel>.table-bordered>thead>tr>td:first-child,.panel>.table-bordered>thead>tr>th:first-child,.panel>.table-responsive>.table-bordered>tbody>tr>td:first-child,.panel>.table-responsive>.table-bordered>tbody>tr>th:first-child,.panel>.table-responsive>.table-bordered>tfoot>tr>td:first-child,.panel>.table-responsive>.table-bordered>tfoot>tr>th:first-child,.panel>.table-responsive>.table-bordered>thead>tr>td:first-child,.panel>.table-responsive>.table-bordered>thead>tr>th:first-child{border-left:0}.panel>.table-bordered>tbody>tr>td:last-child,.panel>.table-bordered>tbody>tr>th:last-child,.panel>.table-bordered>tfoot>tr>td:last-child,.panel>.table-bordered>tfoot>tr>th:last-child,.panel>.table-bordered>thead>tr>td:last-child,.panel>.table-bordered>thead>tr>th:last-child,.panel>.table-responsive>.table-bordered>tbody>tr>td:last-child,.panel>.table-responsive>.table-bordered>tbody>tr>th:last-child,.panel>.table-responsive>.table-bordered>tfoot>tr>td:last-child,.panel>.table-responsive>.table-bordered>tfoot>tr>th:last-child,.panel>.table-responsive>.table-bordered>thead>tr>td:last-child,.panel>.table-responsive>.table-bordered>thead>tr>th:last-child{border-right:0}.panel>.table-bordered>tbody>tr:first-child>td,.panel>.table-bordered>tbody>tr:first-child>th,.panel>.table-bordered>thead>tr:first-child>td,.panel>.table-bordered>thead>tr:first-child>th,.panel>.table-responsive>.table-bordered>tbody>tr:first-child>td,.panel>.table-responsive>.table-bordered>tbody>tr:first-child>th,.panel>.table-responsive>.table-bordered>thead>tr:first-child>td,.panel>.table-responsive>.table-bordered>thead>tr:first-child>th{border-bottom:0}.panel>.table-bordered>tbody>tr:last-child>td,.panel>.table-bordered>tbody>tr:last-child>th,.panel>.table-bordered>tfoot>tr:last-child>td,.panel>.table-bordered>tfoot>tr:last-child>th,.panel>.table-responsive>.table-bordered>tbody>tr:last-child>td,.panel>.table-responsive>.table-bordered>tbody>tr:last-child>th,.panel>.table-responsive>.table-bordered>tfoot>tr:last-child>td,.panel>.table-responsive>.table-bordered>tfoot>tr:last-child>th{border-bottom:0}.panel>.table-responsive{margin-bottom:0;border:0}.panel-group{margin-bottom:20px}.panel-group .panel{margin-bottom:0;border-radius:4px}.panel-group .panel+.panel{margin-top:5px}.panel-group .panel-heading{border-bottom:0}.panel-group .panel-heading+.panel-collapse>.list-group,.panel-group .panel-heading+.panel-collapse>.panel-body{border-top:1px solid #ddd}.panel-group .panel-footer{border-top:0}.panel-group .panel-footer+.panel-collapse .panel-body{border-bottom:1px solid #ddd}.panel-default{border-color:#ddd}.panel-default>.panel-heading{color:#333;background-color:#f5f5f5;border-color:#ddd}.panel-default>.panel-heading+.panel-collapse>.panel-body{border-top-color:#ddd}.panel-default>.panel-heading .badge{color:#f5f5f5;background-color:#333}.panel-default>.panel-footer+.panel-collapse>.panel-body{border-bottom-color:#ddd}.panel-primary{border-color:#337ab7}.panel-primary>.panel-heading{color:#fff;background-color:#337ab7;border-color:#337ab7}.panel-primary>.panel-heading+.panel-collapse>.panel-body{border-top-color:#337ab7}.panel-primary>.panel-heading .badge{color:#337ab7;background-color:#fff}.panel-primary>.panel-footer+.panel-collapse>.panel-body{border-bottom-color:#337ab7}.panel-success{border-color:#d6e9c6}.panel-success>.panel-heading{color:#3c763d;background-color:#dff0d8;border-color:#d6e9c6}.panel-success>.panel-heading+.panel-collapse>.panel-body{border-top-color:#d6e9c6}.panel-success>.panel-heading .badge{color:#dff0d8;background-color:#3c763d}.panel-success>.panel-footer+.panel-collapse>.panel-body{border-bottom-color:#d6e9c6}.panel-info{border-color:#bce8f1}.panel-info>.panel-heading{color:#31708f;background-color:#d9edf7;border-color:#bce8f1}.panel-info>.panel-heading+.panel-collapse>.panel-body{border-top-color:#bce8f1}.panel-info>.panel-heading .badge{color:#d9edf7;background-color:#31708f}.panel-info>.panel-footer+.panel-collapse>.panel-body{border-bottom-color:#bce8f1}.panel-warning{border-color:#faebcc}.panel-warning>.panel-heading{color:#8a6d3b;background-color:#fcf8e3;border-color:#faebcc}.panel-warning>.panel-heading+.panel-collapse>.panel-body{border-top-color:#faebcc}.panel-warning>.panel-heading .badge{color:#fcf8e3;background-color:#8a6d3b}.panel-warning>.panel-footer+.panel-collapse>.panel-body{border-bottom-color:#faebcc}.panel-danger{border-color:#ebccd1}.panel-danger>.panel-heading{color:#a94442;background-color:#f2dede;border-color:#ebccd1}.panel-danger>.panel-heading+.panel-collapse>.panel-body{border-top-color:#ebccd1}.panel-danger>.panel-heading .badge{color:#f2dede;background-color:#a94442}.panel-danger>.panel-footer+.panel-collapse>.panel-body{border-bottom-color:#ebccd1}.embed-responsive{position:relative;display:block;height:0;padding:0;overflow:hidden}.embed-responsive .embed-responsive-item,.embed-responsive embed,.embed-responsive iframe,.embed-responsive object,.embed-responsive video{position:absolute;top:0;bottom:0;left:0;width:100%;height:100%;border:0}.embed-responsive-16by9{padding-bottom:56.25%}.embed-responsive-4by3{padding-bottom:75%}.well{min-height:20px;padding:19px;margin-bottom:20px;background-color:#f5f5f5;border:1px solid #e3e3e3;border-radius:4px;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.05);box-shadow:inset 0 1px 1px rgba(0,0,0,.05)}.well blockquote{border-color:#ddd;border-color:rgba(0,0,0,.15)}.well-lg{padding:24px;border-radius:6px}.well-sm{padding:9px;border-radius:3px}.close{float:right;font-size:21px;font-weight:700;line-height:1;color:#000;text-shadow:0 1px 0 #fff;filter:alpha(opacity=20);opacity:.2}.close:focus,.close:hover{color:#000;text-decoration:none;cursor:pointer;filter:alpha(opacity=50);opacity:.5}button.close{-webkit-appearance:none;padding:0;cursor:pointer;background:0 0;border:0}.modal-open{overflow:hidden}.modal{position:fixed;top:0;right:0;bottom:0;left:0;z-index:1050;display:none;overflow:hidden;-webkit-overflow-scrolling:touch;outline:0}.modal.fade .modal-dialog{-webkit-transition:-webkit-transform .3s ease-out;-o-transition:-o-transform .3s ease-out;transition:transform .3s ease-out;-webkit-transform:translate(0,-25%);-ms-transform:translate(0,-25%);-o-transform:translate(0,-25%);transform:translate(0,-25%)}.modal.in .modal-dialog{-webkit-transform:translate(0,0);-ms-transform:translate(0,0);-o-transform:translate(0,0);transform:translate(0,0)}.modal-open .modal{overflow-x:hidden;overflow-y:auto}.modal-dialog{position:relative;width:auto;margin:10px}.modal-content{position:relative;background-color:#fff;-webkit-background-clip:padding-box;background-clip:padding-box;border:1px solid #999;border:1px solid rgba(0,0,0,.2);border-radius:6px;outline:0;-webkit-box-shadow:0 3px 9px rgba(0,0,0,.5);box-shadow:0 3px 9px rgba(0,0,0,.5)}.modal-backdrop{position:fixed;top:0;right:0;bottom:0;left:0;z-index:1040;background-color:#000}.modal-backdrop.fade{filter:alpha(opacity=0);opacity:0}.modal-backdrop.in{filter:alpha(opacity=50);opacity:.5}.modal-header{min-height:16.43px;padding:15px;border-bottom:1px solid #e5e5e5}.modal-header .close{margin-top:-2px}.modal-title{margin:0;line-height:1.42857143}.modal-body{position:relative;padding:15px}.modal-footer{padding:15px;text-align:right;border-top:1px solid #e5e5e5}.modal-footer .btn+.btn{margin-bottom:0;margin-left:5px}.modal-footer .btn-group .btn+.btn{margin-left:-1px}.modal-footer .btn-block+.btn-block{margin-left:0}.modal-scrollbar-measure{position:absolute;top:-9999px;width:50px;height:50px;overflow:scroll}@media (min-width:768px){.modal-dialog{width:600px;margin:30px auto}.modal-content{-webkit-box-shadow:0 5px 15px rgba(0,0,0,.5);box-shadow:0 5px 15px rgba(0,0,0,.5)}.modal-sm{width:300px}}@media (min-width:992px){.modal-lg{width:900px}}.tooltip{position:absolute;z-index:1070;display:block;font-family:"Helvetica Neue",Helvetica,Arial,sans-serif;font-size:12px;font-style:normal;font-weight:400;line-height:1.42857143;text-align:left;text-align:start;text-decoration:none;text-shadow:none;text-transform:none;letter-spacing:normal;word-break:normal;word-spacing:normal;word-wrap:normal;white-space:normal;filter:alpha(opacity=0);opacity:0;line-break:auto}.tooltip.in{filter:alpha(opacity=90);opacity:.9}.tooltip.top{padding:5px 0;margin-top:-3px}.tooltip.right{padding:0 5px;margin-left:3px}.tooltip.bottom{padding:5px 0;margin-top:3px}.tooltip.left{padding:0 5px;margin-left:-3px}.tooltip-inner{max-width:200px;padding:3px 8px;color:#fff;text-align:center;background-color:#000;border-radius:4px}.tooltip-arrow{position:absolute;width:0;height:0;border-color:transparent;border-style:solid}.tooltip.top .tooltip-arrow{bottom:0;left:50%;margin-left:-5px;border-width:5px 5px 0;border-top-color:#000}.tooltip.top-left .tooltip-arrow{right:5px;bottom:0;margin-bottom:-5px;border-width:5px 5px 0;border-top-color:#000}.tooltip.top-right .tooltip-arrow{bottom:0;left:5px;margin-bottom:-5px;border-width:5px 5px 0;border-top-color:#000}.tooltip.right .tooltip-arrow{top:50%;left:0;margin-top:-5px;border-width:5px 5px 5px 0;border-right-color:#000}.tooltip.left .tooltip-arrow{top:50%;right:0;margin-top:-5px;border-width:5px 0 5px 5px;border-left-color:#000}.tooltip.bottom .tooltip-arrow{top:0;left:50%;margin-left:-5px;border-width:0 5px 5px;border-bottom-color:#000}.tooltip.bottom-left .tooltip-arrow{top:0;right:5px;margin-top:-5px;border-width:0 5px 5px;border-bottom-color:#000}.tooltip.bottom-right .tooltip-arrow{top:0;left:5px;margin-top:-5px;border-width:0 5px 5px;border-bottom-color:#000}.popover{position:absolute;top:0;left:0;z-index:1060;display:none;max-width:276px;padding:1px;font-family:"Helvetica Neue",Helvetica,Arial,sans-serif;font-size:14px;font-style:normal;font-weight:400;line-height:1.42857143;text-align:left;text-align:start;text-decoration:none;text-shadow:none;text-transform:none;letter-spacing:normal;word-break:normal;word-spacing:normal;word-wrap:normal;white-space:normal;background-color:#fff;-webkit-background-clip:padding-box;background-clip:padding-box;border:1px solid #ccc;border:1px solid rgba(0,0,0,.2);border-radius:6px;-webkit-box-shadow:0 5px 10px rgba(0,0,0,.2);box-shadow:0 5px 10px rgba(0,0,0,.2);line-break:auto}.popover.top{margin-top:-10px}.popover.right{margin-left:10px}.popover.bottom{margin-top:10px}.popover.left{margin-left:-10px}.popover-title{padding:8px 14px;margin:0;font-size:14px;background-color:#f7f7f7;border-bottom:1px solid #ebebeb;border-radius:5px 5px 0 0}.popover-content{padding:9px 14px}.popover>.arrow,.popover>.arrow:after{position:absolute;display:block;width:0;height:0;border-color:transparent;border-style:solid}.popover>.arrow{border-width:11px}.popover>.arrow:after{content:"";border-width:10px}.popover.top>.arrow{bottom:-11px;left:50%;margin-left:-11px;border-top-color:#999;border-top-color:rgba(0,0,0,.25);border-bottom-width:0}.popover.top>.arrow:after{bottom:1px;margin-left:-10px;content:" ";border-top-color:#fff;border-bottom-width:0}.popover.right>.arrow{top:50%;left:-11px;margin-top:-11px;border-right-color:#999;border-right-color:rgba(0,0,0,.25);border-left-width:0}.popover.right>.arrow:after{bottom:-10px;left:1px;content:" ";border-right-color:#fff;border-left-width:0}.popover.bottom>.arrow{top:-11px;left:50%;margin-left:-11px;border-top-width:0;border-bottom-color:#999;border-bottom-color:rgba(0,0,0,.25)}.popover.bottom>.arrow:after{top:1px;margin-left:-10px;content:" ";border-top-width:0;border-bottom-color:#fff}.popover.left>.arrow{top:50%;right:-11px;margin-top:-11px;border-right-width:0;border-left-color:#999;border-left-color:rgba(0,0,0,.25)}.popover.left>.arrow:after{right:1px;bottom:-10px;content:" ";border-right-width:0;border-left-color:#fff}.carousel{position:relative}.carousel-inner{position:relative;width:100%;overflow:hidden}.carousel-inner>.item{position:relative;display:none;-webkit-transition:.6s ease-in-out left;-o-transition:.6s ease-in-out left;transition:.6s ease-in-out left}.carousel-inner>.item>a>img,.carousel-inner>.item>img{line-height:1}@media all and (transform-3d),(-webkit-transform-3d){.carousel-inner>.item{-webkit-transition:-webkit-transform .6s ease-in-out;-o-transition:-o-transform .6s ease-in-out;transition:transform .6s ease-in-out;-webkit-backface-visibility:hidden;backface-visibility:hidden;-webkit-perspective:1000px;perspective:1000px}.carousel-inner>.item.active.right,.carousel-inner>.item.next{left:0;-webkit-transform:translate3d(100%,0,0);transform:translate3d(100%,0,0)}.carousel-inner>.item.active.left,.carousel-inner>.item.prev{left:0;-webkit-transform:translate3d(-100%,0,0);transform:translate3d(-100%,0,0)}.carousel-inner>.item.active,.carousel-inner>.item.next.left,.carousel-inner>.item.prev.right{left:0;-webkit-transform:translate3d(0,0,0);transform:translate3d(0,0,0)}}.carousel-inner>.active,.carousel-inner>.next,.carousel-inner>.prev{display:block}.carousel-inner>.active{left:0}.carousel-inner>.next,.carousel-inner>.prev{position:absolute;top:0;width:100%}.carousel-inner>.next{left:100%}.carousel-inner>.prev{left:-100%}.carousel-inner>.next.left,.carousel-inner>.prev.right{left:0}.carousel-inner>.active.left{left:-100%}.carousel-inner>.active.right{left:100%}.carousel-control{position:absolute;top:0;bottom:0;left:0;width:15%;font-size:20px;color:#fff;text-align:center;text-shadow:0 1px 2px rgba(0,0,0,.6);filter:alpha(opacity=50);opacity:.5}.carousel-control.left{background-image:-webkit-linear-gradient(left,rgba(0,0,0,.5) 0,rgba(0,0,0,.0001) 100%);background-image:-o-linear-gradient(left,rgba(0,0,0,.5) 0,rgba(0,0,0,.0001) 100%);background-image:-webkit-gradient(linear,left top,right top,from(rgba(0,0,0,.5)),to(rgba(0,0,0,.0001)));background-image:linear-gradient(to right,rgba(0,0,0,.5) 0,rgba(0,0,0,.0001) 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1);background-repeat:repeat-x}.carousel-control.right{right:0;left:auto;background-image:-webkit-linear-gradient(left,rgba(0,0,0,.0001) 0,rgba(0,0,0,.5) 100%);background-image:-o-linear-gradient(left,rgba(0,0,0,.0001) 0,rgba(0,0,0,.5) 100%);background-image:-webkit-gradient(linear,left top,right top,from(rgba(0,0,0,.0001)),to(rgba(0,0,0,.5)));background-image:linear-gradient(to right,rgba(0,0,0,.0001) 0,rgba(0,0,0,.5) 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1);background-repeat:repeat-x}.carousel-control:focus,.carousel-control:hover{color:#fff;text-decoration:none;filter:alpha(opacity=90);outline:0;opacity:.9}.carousel-control .glyphicon-chevron-left,.carousel-control .glyphicon-chevron-right,.carousel-control .icon-next,.carousel-control .icon-prev{position:absolute;top:50%;z-index:5;display:inline-block;margin-top:-10px}.carousel-control .glyphicon-chevron-left,.carousel-control .icon-prev{left:50%;margin-left:-10px}.carousel-control .glyphicon-chevron-right,.carousel-control .icon-next{right:50%;margin-right:-10px}.carousel-control .icon-next,.carousel-control .icon-prev{width:20px;height:20px;font-family:serif;line-height:1}.carousel-control .icon-prev:before{content:'\2039'}.carousel-control .icon-next:before{content:'\203a'}.carousel-indicators{position:absolute;bottom:10px;left:50%;z-index:15;width:60%;padding-left:0;margin-left:-30%;text-align:center;list-style:none}.carousel-indicators li{display:inline-block;width:10px;height:10px;margin:1px;text-indent:-999px;cursor:pointer;background-color:#000\9;background-color:rgba(0,0,0,0);border:1px solid #fff;border-radius:10px}.carousel-indicators .active{width:12px;height:12px;margin:0;background-color:#fff}.carousel-caption{position:absolute;right:15%;bottom:20px;left:15%;z-index:10;padding-top:20px;padding-bottom:20px;color:#fff;text-align:center;text-shadow:0 1px 2px rgba(0,0,0,.6)}.carousel-caption .btn{text-shadow:none}@media screen and (min-width:768px){.carousel-control .glyphicon-chevron-left,.carousel-control .glyphicon-chevron-right,.carousel-control .icon-next,.carousel-control .icon-prev{width:30px;height:30px;margin-top:-15px;font-size:30px}.carousel-control .glyphicon-chevron-left,.carousel-control .icon-prev{margin-left:-15px}.carousel-control .glyphicon-chevron-right,.carousel-control .icon-next{margin-right:-15px}.carousel-caption{right:20%;left:20%;padding-bottom:30px}.carousel-indicators{bottom:20px}}.btn-group-vertical>.btn-group:after,.btn-group-vertical>.btn-group:before,.btn-toolbar:after,.btn-toolbar:before,.clearfix:after,.clearfix:before,.container-fluid:after,.container-fluid:before,.container:after,.container:before,.dl-horizontal dd:after,.dl-horizontal dd:before,.form-horizontal .form-group:after,.form-horizontal .form-group:before,.modal-footer:after,.modal-footer:before,.nav:after,.nav:before,.navbar-collapse:after,.navbar-collapse:before,.navbar-header:after,.navbar-header:before,.navbar:after,.navbar:before,.pager:after,.pager:before,.panel-body:after,.panel-body:before,.row:after,.row:before{display:table;content:" "}.btn-group-vertical>.btn-group:after,.btn-toolbar:after,.clearfix:after,.container-fluid:after,.container:after,.dl-horizontal dd:after,.form-horizontal .form-group:after,.modal-footer:after,.nav:after,.navbar-collapse:after,.navbar-header:after,.navbar:after,.pager:after,.panel-body:after,.row:after{clear:both}.center-block{display:block;margin-right:auto;margin-left:auto}.pull-right{float:right!important}.pull-left{float:left!important}.hide{display:none!important}.show{display:block!important}.invisible{visibility:hidden}.text-hide{font:0/0 a;color:transparent;text-shadow:none;background-color:transparent;border:0}.hidden{display:none!important}.affix{position:fixed}@-ms-viewport{width:device-width}.visible-lg,.visible-md,.visible-sm,.visible-xs{display:none!important}.visible-lg-block,.visible-lg-inline,.visible-lg-inline-block,.visible-md-block,.visible-md-inline,.visible-md-inline-block,.visible-sm-block,.visible-sm-inline,.visible-sm-inline-block,.visible-xs-block,.visible-xs-inline,.visible-xs-inline-block{display:none!important}@media (max-width:767px){.visible-xs{display:block!important}table.visible-xs{display:table!important}tr.visible-xs{display:table-row!important}td.visible-xs,th.visible-xs{display:table-cell!important}}@media (max-width:767px){.visible-xs-block{display:block!important}}@media (max-width:767px){.visible-xs-inline{display:inline!important}}@media (max-width:767px){.visible-xs-inline-block{display:inline-block!important}}@media (min-width:768px) and (max-width:991px){.visible-sm{display:block!important}table.visible-sm{display:table!important}tr.visible-sm{display:table-row!important}td.visible-sm,th.visible-sm{display:table-cell!important}}@media (min-width:768px) and (max-width:991px){.visible-sm-block{display:block!important}}@media (min-width:768px) and (max-width:991px){.visible-sm-inline{display:inline!important}}@media (min-width:768px) and (max-width:991px){.visible-sm-inline-block{display:inline-block!important}}@media (min-width:992px) and (max-width:1199px){.visible-md{display:block!important}table.visible-md{display:table!important}tr.visible-md{display:table-row!important}td.visible-md,th.visible-md{display:table-cell!important}}@media (min-width:992px) and (max-width:1199px){.visible-md-block{display:block!important}}@media (min-width:992px) and (max-width:1199px){.visible-md-inline{display:inline!important}}@media (min-width:992px) and (max-width:1199px){.visible-md-inline-block{display:inline-block!important}}@media (min-width:1200px){.visible-lg{display:block!important}table.visible-lg{display:table!important}tr.visible-lg{display:table-row!important}td.visible-lg,th.visible-lg{display:table-cell!important}}@media (min-width:1200px){.visible-lg-block{display:block!important}}@media (min-width:1200px){.visible-lg-inline{display:inline!important}}@media (min-width:1200px){.visible-lg-inline-block{display:inline-block!important}}@media (max-width:767px){.hidden-xs{display:none!important}}@media (min-width:768px) and (max-width:991px){.hidden-sm{display:none!important}}@media (min-width:992px) and (max-width:1199px){.hidden-md{display:none!important}}@media (min-width:1200px){.hidden-lg{display:none!important}}.visible-print{display:none!important}@media print{.visible-print{display:block!important}table.visible-print{display:table!important}tr.visible-print{display:table-row!important}td.visible-print,th.visible-print{display:table-cell!important}}.visible-print-block{display:none!important}@media print{.visible-print-block{display:block!important}}.visible-print-inline{display:none!important}@media print{.visible-print-inline{display:inline!important}}.visible-print-inline-block{display:none!important}@media print{.visible-print-inline-block{display:inline-block!important}}@media print{.hidden-print{display:none!important}}
+</style>
+<script>/*!
+ * Bootstrap v3.3.5 (http://getbootstrap.com)
+ * Copyright 2011-2015 Twitter, Inc.
+ * Licensed under the MIT license
+ */
+if("undefined"==typeof jQuery)throw new Error("Bootstrap's JavaScript requires jQuery");+function(a){"use strict";var b=a.fn.jquery.split(" ")[0].split(".");if(b[0]<2&&b[1]<9||1==b[0]&&9==b[1]&&b[2]<1)throw new Error("Bootstrap's JavaScript requires jQuery version 1.9.1 or higher")}(jQuery),+function(a){"use strict";function b(){var a=document.createElement("bootstrap"),b={WebkitTransition:"webkitTransitionEnd",MozTransition:"transitionend",OTransition:"oTransitionEnd otransitionend",transition:"transitionend"};for(var c in b)if(void 0!==a.style[c])return{end:b[c]};return!1}a.fn.emulateTransitionEnd=function(b){var c=!1,d=this;a(this).one("bsTransitionEnd",function(){c=!0});var e=function(){c||a(d).trigger(a.support.transition.end)};return setTimeout(e,b),this},a(function(){a.support.transition=b(),a.support.transition&&(a.event.special.bsTransitionEnd={bindType:a.support.transition.end,delegateType:a.support.transition.end,handle:function(b){return a(b.target).is(this)?b.handleObj.handler.apply(this,arguments):void 0}})})}(jQuery),+function(a){"use strict";function b(b){return this.each(function(){var c=a(this),e=c.data("bs.alert");e||c.data("bs.alert",e=new d(this)),"string"==typeof b&&e[b].call(c)})}var c='[data-dismiss="alert"]',d=function(b){a(b).on("click",c,this.close)};d.VERSION="3.3.5",d.TRANSITION_DURATION=150,d.prototype.close=function(b){function c(){g.detach().trigger("closed.bs.alert").remove()}var e=a(this),f=e.attr("data-target");f||(f=e.attr("href"),f=f&&f.replace(/.*(?=#[^\s]*$)/,""));var g=a(f);b&&b.preventDefault(),g.length||(g=e.closest(".alert")),g.trigger(b=a.Event("close.bs.alert")),b.isDefaultPrevented()||(g.removeClass("in"),a.support.transition&&g.hasClass("fade")?g.one("bsTransitionEnd",c).emulateTransitionEnd(d.TRANSITION_DURATION):c())};var e=a.fn.alert;a.fn.alert=b,a.fn.alert.Constructor=d,a.fn.alert.noConflict=function(){return a.fn.alert=e,this},a(document).on("click.bs.alert.data-api",c,d.prototype.close)}(jQuery),+function(a){"use strict";function b(b){return this.each(function(){var d=a(this),e=d.data("bs.button"),f="object"==typeof b&&b;e||d.data("bs.button",e=new c(this,f)),"toggle"==b?e.toggle():b&&e.setState(b)})}var c=function(b,d){this.$element=a(b),this.options=a.extend({},c.DEFAULTS,d),this.isLoading=!1};c.VERSION="3.3.5",c.DEFAULTS={loadingText:"loading..."},c.prototype.setState=function(b){var c="disabled",d=this.$element,e=d.is("input")?"val":"html",f=d.data();b+="Text",null==f.resetText&&d.data("resetText",d[e]()),setTimeout(a.proxy(function(){d[e](null==f[b]?this.options[b]:f[b]),"loadingText"==b?(this.isLoading=!0,d.addClass(c).attr(c,c)):this.isLoading&&(this.isLoading=!1,d.removeClass(c).removeAttr(c))},this),0)},c.prototype.toggle=function(){var a=!0,b=this.$element.closest('[data-toggle="buttons"]');if(b.length){var c=this.$element.find("input");"radio"==c.prop("type")?(c.prop("checked")&&(a=!1),b.find(".active").removeClass("active"),this.$element.addClass("active")):"checkbox"==c.prop("type")&&(c.prop("checked")!==this.$element.hasClass("active")&&(a=!1),this.$element.toggleClass("active")),c.prop("checked",this.$element.hasClass("active")),a&&c.trigger("change")}else this.$element.attr("aria-pressed",!this.$element.hasClass("active")),this.$element.toggleClass("active")};var d=a.fn.button;a.fn.button=b,a.fn.button.Constructor=c,a.fn.button.noConflict=function(){return a.fn.button=d,this},a(document).on("click.bs.button.data-api",'[data-toggle^="button"]',function(c){var d=a(c.target);d.hasClass("btn")||(d=d.closest(".btn")),b.call(d,"toggle"),a(c.target).is('input[type="radio"]')||a(c.target).is('input[type="checkbox"]')||c.preventDefault()}).on("focus.bs.button.data-api blur.bs.button.data-api",'[data-toggle^="button"]',function(b){a(b.target).closest(".btn").toggleClass("focus",/^focus(in)?$/.test(b.type))})}(jQuery),+function(a){"use strict";function b(b){return this.each(function(){var d=a(this),e=d.data("bs.carousel"),f=a.extend({},c.DEFAULTS,d.data(),"object"==typeof b&&b),g="string"==typeof b?b:f.slide;e||d.data("bs.carousel",e=new c(this,f)),"number"==typeof b?e.to(b):g?e[g]():f.interval&&e.pause().cycle()})}var c=function(b,c){this.$element=a(b),this.$indicators=this.$element.find(".carousel-indicators"),this.options=c,this.paused=null,this.sliding=null,this.interval=null,this.$active=null,this.$items=null,this.options.keyboard&&this.$element.on("keydown.bs.carousel",a.proxy(this.keydown,this)),"hover"==this.options.pause&&!("ontouchstart"in document.documentElement)&&this.$element.on("mouseenter.bs.carousel",a.proxy(this.pause,this)).on("mouseleave.bs.carousel",a.proxy(this.cycle,this))};c.VERSION="3.3.5",c.TRANSITION_DURATION=600,c.DEFAULTS={interval:5e3,pause:"hover",wrap:!0,keyboard:!0},c.prototype.keydown=function(a){if(!/input|textarea/i.test(a.target.tagName)){switch(a.which){case 37:this.prev();break;case 39:this.next();break;default:return}a.preventDefault()}},c.prototype.cycle=function(b){return b||(this.paused=!1),this.interval&&clearInterval(this.interval),this.options.interval&&!this.paused&&(this.interval=setInterval(a.proxy(this.next,this),this.options.interval)),this},c.prototype.getItemIndex=function(a){return this.$items=a.parent().children(".item"),this.$items.index(a||this.$active)},c.prototype.getItemForDirection=function(a,b){var c=this.getItemIndex(b),d="prev"==a&&0===c||"next"==a&&c==this.$items.length-1;if(d&&!this.options.wrap)return b;var e="prev"==a?-1:1,f=(c+e)%this.$items.length;return this.$items.eq(f)},c.prototype.to=function(a){var b=this,c=this.getItemIndex(this.$active=this.$element.find(".item.active"));return a>this.$items.length-1||0>a?void 0:this.sliding?this.$element.one("slid.bs.carousel",function(){b.to(a)}):c==a?this.pause().cycle():this.slide(a>c?"next":"prev",this.$items.eq(a))},c.prototype.pause=function(b){return b||(this.paused=!0),this.$element.find(".next, .prev").length&&a.support.transition&&(this.$element.trigger(a.support.transition.end),this.cycle(!0)),this.interval=clearInterval(this.interval),this},c.prototype.next=function(){return this.sliding?void 0:this.slide("next")},c.prototype.prev=function(){return this.sliding?void 0:this.slide("prev")},c.prototype.slide=function(b,d){var e=this.$element.find(".item.active"),f=d||this.getItemForDirection(b,e),g=this.interval,h="next"==b?"left":"right",i=this;if(f.hasClass("active"))return this.sliding=!1;var j=f[0],k=a.Event("slide.bs.carousel",{relatedTarget:j,direction:h});if(this.$element.trigger(k),!k.isDefaultPrevented()){if(this.sliding=!0,g&&this.pause(),this.$indicators.length){this.$indicators.find(".active").removeClass("active");var l=a(this.$indicators.children()[this.getItemIndex(f)]);l&&l.addClass("active")}var m=a.Event("slid.bs.carousel",{relatedTarget:j,direction:h});return a.support.transition&&this.$element.hasClass("slide")?(f.addClass(b),f[0].offsetWidth,e.addClass(h),f.addClass(h),e.one("bsTransitionEnd",function(){f.removeClass([b,h].join(" ")).addClass("active"),e.removeClass(["active",h].join(" ")),i.sliding=!1,setTimeout(function(){i.$element.trigger(m)},0)}).emulateTransitionEnd(c.TRANSITION_DURATION)):(e.removeClass("active"),f.addClass("active"),this.sliding=!1,this.$element.trigger(m)),g&&this.cycle(),this}};var d=a.fn.carousel;a.fn.carousel=b,a.fn.carousel.Constructor=c,a.fn.carousel.noConflict=function(){return a.fn.carousel=d,this};var e=function(c){var d,e=a(this),f=a(e.attr("data-target")||(d=e.attr("href"))&&d.replace(/.*(?=#[^\s]+$)/,""));if(f.hasClass("carousel")){var g=a.extend({},f.data(),e.data()),h=e.attr("data-slide-to");h&&(g.interval=!1),b.call(f,g),h&&f.data("bs.carousel").to(h),c.preventDefault()}};a(document).on("click.bs.carousel.data-api","[data-slide]",e).on("click.bs.carousel.data-api","[data-slide-to]",e),a(window).on("load",function(){a('[data-ride="carousel"]').each(function(){var c=a(this);b.call(c,c.data())})})}(jQuery),+function(a){"use strict";function b(b){var c,d=b.attr("data-target")||(c=b.attr("href"))&&c.replace(/.*(?=#[^\s]+$)/,"");return a(d)}function c(b){return this.each(function(){var c=a(this),e=c.data("bs.collapse"),f=a.extend({},d.DEFAULTS,c.data(),"object"==typeof b&&b);!e&&f.toggle&&/show|hide/.test(b)&&(f.toggle=!1),e||c.data("bs.collapse",e=new d(this,f)),"string"==typeof b&&e[b]()})}var d=function(b,c){this.$element=a(b),this.options=a.extend({},d.DEFAULTS,c),this.$trigger=a('[data-toggle="collapse"][href="#'+b.id+'"],[data-toggle="collapse"][data-target="#'+b.id+'"]'),this.transitioning=null,this.options.parent?this.$parent=this.getParent():this.addAriaAndCollapsedClass(this.$element,this.$trigger),this.options.toggle&&this.toggle()};d.VERSION="3.3.5",d.TRANSITION_DURATION=350,d.DEFAULTS={toggle:!0},d.prototype.dimension=function(){var a=this.$element.hasClass("width");return a?"width":"height"},d.prototype.show=function(){if(!this.transitioning&&!this.$element.hasClass("in")){var b,e=this.$parent&&this.$parent.children(".panel").children(".in, .collapsing");if(!(e&&e.length&&(b=e.data("bs.collapse"),b&&b.transitioning))){var f=a.Event("show.bs.collapse");if(this.$element.trigger(f),!f.isDefaultPrevented()){e&&e.length&&(c.call(e,"hide"),b||e.data("bs.collapse",null));var g=this.dimension();this.$element.removeClass("collapse").addClass("collapsing")[g](0).attr("aria-expanded",!0),this.$trigger.removeClass("collapsed").attr("aria-expanded",!0),this.transitioning=1;var h=function(){this.$element.removeClass("collapsing").addClass("collapse in")[g](""),this.transitioning=0,this.$element.trigger("shown.bs.collapse")};if(!a.support.transition)return h.call(this);var i=a.camelCase(["scroll",g].join("-"));this.$element.one("bsTransitionEnd",a.proxy(h,this)).emulateTransitionEnd(d.TRANSITION_DURATION)[g](this.$element[0][i])}}}},d.prototype.hide=function(){if(!this.transitioning&&this.$element.hasClass("in")){var b=a.Event("hide.bs.collapse");if(this.$element.trigger(b),!b.isDefaultPrevented()){var c=this.dimension();this.$element[c](this.$element[c]())[0].offsetHeight,this.$element.addClass("collapsing").removeClass("collapse in").attr("aria-expanded",!1),this.$trigger.addClass("collapsed").attr("aria-expanded",!1),this.transitioning=1;var e=function(){this.transitioning=0,this.$element.removeClass("collapsing").addClass("collapse").trigger("hidden.bs.collapse")};return a.support.transition?void this.$element[c](0).one("bsTransitionEnd",a.proxy(e,this)).emulateTransitionEnd(d.TRANSITION_DURATION):e.call(this)}}},d.prototype.toggle=function(){this[this.$element.hasClass("in")?"hide":"show"]()},d.prototype.getParent=function(){return a(this.options.parent).find('[data-toggle="collapse"][data-parent="'+this.options.parent+'"]').each(a.proxy(function(c,d){var e=a(d);this.addAriaAndCollapsedClass(b(e),e)},this)).end()},d.prototype.addAriaAndCollapsedClass=function(a,b){var c=a.hasClass("in");a.attr("aria-expanded",c),b.toggleClass("collapsed",!c).attr("aria-expanded",c)};var e=a.fn.collapse;a.fn.collapse=c,a.fn.collapse.Constructor=d,a.fn.collapse.noConflict=function(){return a.fn.collapse=e,this},a(document).on("click.bs.collapse.data-api",'[data-toggle="collapse"]',function(d){var e=a(this);e.attr("data-target")||d.preventDefault();var f=b(e),g=f.data("bs.collapse"),h=g?"toggle":e.data();c.call(f,h)})}(jQuery),+function(a){"use strict";function b(b){var c=b.attr("data-target");c||(c=b.attr("href"),c=c&&/#[A-Za-z]/.test(c)&&c.replace(/.*(?=#[^\s]*$)/,""));var d=c&&a(c);return d&&d.length?d:b.parent()}function c(c){c&&3===c.which||(a(e).remove(),a(f).each(function(){var d=a(this),e=b(d),f={relatedTarget:this};e.hasClass("open")&&(c&&"click"==c.type&&/input|textarea/i.test(c.target.tagName)&&a.contains(e[0],c.target)||(e.trigger(c=a.Event("hide.bs.dropdown",f)),c.isDefaultPrevented()||(d.attr("aria-expanded","false"),e.removeClass("open").trigger("hidden.bs.dropdown",f))))}))}function d(b){return this.each(function(){var c=a(this),d=c.data("bs.dropdown");d||c.data("bs.dropdown",d=new g(this)),"string"==typeof b&&d[b].call(c)})}var e=".dropdown-backdrop",f='[data-toggle="dropdown"]',g=function(b){a(b).on("click.bs.dropdown",this.toggle)};g.VERSION="3.3.5",g.prototype.toggle=function(d){var e=a(this);if(!e.is(".disabled, :disabled")){var f=b(e),g=f.hasClass("open");if(c(),!g){"ontouchstart"in document.documentElement&&!f.closest(".navbar-nav").length&&a(document.createElement("div")).addClass("dropdown-backdrop").insertAfter(a(this)).on("click",c);var h={relatedTarget:this};if(f.trigger(d=a.Event("show.bs.dropdown",h)),d.isDefaultPrevented())return;e.trigger("focus").attr("aria-expanded","true"),f.toggleClass("open").trigger("shown.bs.dropdown",h)}return!1}},g.prototype.keydown=function(c){if(/(38|40|27|32)/.test(c.which)&&!/input|textarea/i.test(c.target.tagName)){var d=a(this);if(c.preventDefault(),c.stopPropagation(),!d.is(".disabled, :disabled")){var e=b(d),g=e.hasClass("open");if(!g&&27!=c.which||g&&27==c.which)return 27==c.which&&e.find(f).trigger("focus"),d.trigger("click");var h=" li:not(.disabled):visible a",i=e.find(".dropdown-menu"+h);if(i.length){var j=i.index(c.target);38==c.which&&j>0&&j--,40==c.which&&j<i.length-1&&j++,~j||(j=0),i.eq(j).trigger("focus")}}}};var h=a.fn.dropdown;a.fn.dropdown=d,a.fn.dropdown.Constructor=g,a.fn.dropdown.noConflict=function(){return a.fn.dropdown=h,this},a(document).on("click.bs.dropdown.data-api",c).on("click.bs.dropdown.data-api",".dropdown form",function(a){a.stopPropagation()}).on("click.bs.dropdown.data-api",f,g.prototype.toggle).on("keydown.bs.dropdown.data-api",f,g.prototype.keydown).on("keydown.bs.dropdown.data-api",".dropdown-menu",g.prototype.keydown)}(jQuery),+function(a){"use strict";function b(b,d){return this.each(function(){var e=a(this),f=e.data("bs.modal"),g=a.extend({},c.DEFAULTS,e.data(),"object"==typeof b&&b);f||e.data("bs.modal",f=new c(this,g)),"string"==typeof b?f[b](d):g.show&&f.show(d)})}var c=function(b,c){this.options=c,this.$body=a(document.body),this.$element=a(b),this.$dialog=this.$element.find(".modal-dialog"),this.$backdrop=null,this.isShown=null,this.originalBodyPad=null,this.scrollbarWidth=0,this.ignoreBackdropClick=!1,this.options.remote&&this.$element.find(".modal-content").load(this.options.remote,a.proxy(function(){this.$element.trigger("loaded.bs.modal")},this))};c.VERSION="3.3.5",c.TRANSITION_DURATION=300,c.BACKDROP_TRANSITION_DURATION=150,c.DEFAULTS={backdrop:!0,keyboard:!0,show:!0},c.prototype.toggle=function(a){return this.isShown?this.hide():this.show(a)},c.prototype.show=function(b){var d=this,e=a.Event("show.bs.modal",{relatedTarget:b});this.$element.trigger(e),this.isShown||e.isDefaultPrevented()||(this.isShown=!0,this.checkScrollbar(),this.setScrollbar(),this.$body.addClass("modal-open"),this.escape(),this.resize(),this.$element.on("click.dismiss.bs.modal",'[data-dismiss="modal"]',a.proxy(this.hide,this)),this.$dialog.on("mousedown.dismiss.bs.modal",function(){d.$element.one("mouseup.dismiss.bs.modal",function(b){a(b.target).is(d.$element)&&(d.ignoreBackdropClick=!0)})}),this.backdrop(function(){var e=a.support.transition&&d.$element.hasClass("fade");d.$element.parent().length||d.$element.appendTo(d.$body),d.$element.show().scrollTop(0),d.adjustDialog(),e&&d.$element[0].offsetWidth,d.$element.addClass("in"),d.enforceFocus();var f=a.Event("shown.bs.modal",{relatedTarget:b});e?d.$dialog.one("bsTransitionEnd",function(){d.$element.trigger("focus").trigger(f)}).emulateTransitionEnd(c.TRANSITION_DURATION):d.$element.trigger("focus").trigger(f)}))},c.prototype.hide=function(b){b&&b.preventDefault(),b=a.Event("hide.bs.modal"),this.$element.trigger(b),this.isShown&&!b.isDefaultPrevented()&&(this.isShown=!1,this.escape(),this.resize(),a(document).off("focusin.bs.modal"),this.$element.removeClass("in").off("click.dismiss.bs.modal").off("mouseup.dismiss.bs.modal"),this.$dialog.off("mousedown.dismiss.bs.modal"),a.support.transition&&this.$element.hasClass("fade")?this.$element.one("bsTransitionEnd",a.proxy(this.hideModal,this)).emulateTransitionEnd(c.TRANSITION_DURATION):this.hideModal())},c.prototype.enforceFocus=function(){a(document).off("focusin.bs.modal").on("focusin.bs.modal",a.proxy(function(a){this.$element[0]===a.target||this.$element.has(a.target).length||this.$element.trigger("focus")},this))},c.prototype.escape=function(){this.isShown&&this.options.keyboard?this.$element.on("keydown.dismiss.bs.modal",a.proxy(function(a){27==a.which&&this.hide()},this)):this.isShown||this.$element.off("keydown.dismiss.bs.modal")},c.prototype.resize=function(){this.isShown?a(window).on("resize.bs.modal",a.proxy(this.handleUpdate,this)):a(window).off("resize.bs.modal")},c.prototype.hideModal=function(){var a=this;this.$element.hide(),this.backdrop(function(){a.$body.removeClass("modal-open"),a.resetAdjustments(),a.resetScrollbar(),a.$element.trigger("hidden.bs.modal")})},c.prototype.removeBackdrop=function(){this.$backdrop&&this.$backdrop.remove(),this.$backdrop=null},c.prototype.backdrop=function(b){var d=this,e=this.$element.hasClass("fade")?"fade":"";if(this.isShown&&this.options.backdrop){var f=a.support.transition&&e;if(this.$backdrop=a(document.createElement("div")).addClass("modal-backdrop "+e).appendTo(this.$body),this.$element.on("click.dismiss.bs.modal",a.proxy(function(a){return this.ignoreBackdropClick?void(this.ignoreBackdropClick=!1):void(a.target===a.currentTarget&&("static"==this.options.backdrop?this.$element[0].focus():this.hide()))},this)),f&&this.$backdrop[0].offsetWidth,this.$backdrop.addClass("in"),!b)return;f?this.$backdrop.one("bsTransitionEnd",b).emulateTransitionEnd(c.BACKDROP_TRANSITION_DURATION):b()}else if(!this.isShown&&this.$backdrop){this.$backdrop.removeClass("in");var g=function(){d.removeBackdrop(),b&&b()};a.support.transition&&this.$element.hasClass("fade")?this.$backdrop.one("bsTransitionEnd",g).emulateTransitionEnd(c.BACKDROP_TRANSITION_DURATION):g()}else b&&b()},c.prototype.handleUpdate=function(){this.adjustDialog()},c.prototype.adjustDialog=function(){var a=this.$element[0].scrollHeight>document.documentElement.clientHeight;this.$element.css({paddingLeft:!this.bodyIsOverflowing&&a?this.scrollbarWidth:"",paddingRight:this.bodyIsOverflowing&&!a?this.scrollbarWidth:""})},c.prototype.resetAdjustments=function(){this.$element.css({paddingLeft:"",paddingRight:""})},c.prototype.checkScrollbar=function(){var a=window.innerWidth;if(!a){var b=document.documentElement.getBoundingClientRect();a=b.right-Math.abs(b.left)}this.bodyIsOverflowing=document.body.clientWidth<a,this.scrollbarWidth=this.measureScrollbar()},c.prototype.setScrollbar=function(){var a=parseInt(this.$body.css("padding-right")||0,10);this.originalBodyPad=document.body.style.paddingRight||"",this.bodyIsOverflowing&&this.$body.css("padding-right",a+this.scrollbarWidth)},c.prototype.resetScrollbar=function(){this.$body.css("padding-right",this.originalBodyPad)},c.prototype.measureScrollbar=function(){var a=document.createElement("div");a.className="modal-scrollbar-measure",this.$body.append(a);var b=a.offsetWidth-a.clientWidth;return this.$body[0].removeChild(a),b};var d=a.fn.modal;a.fn.modal=b,a.fn.modal.Constructor=c,a.fn.modal.noConflict=function(){return a.fn.modal=d,this},a(document).on("click.bs.modal.data-api",'[data-toggle="modal"]',function(c){var d=a(this),e=d.attr("href"),f=a(d.attr("data-target")||e&&e.replace(/.*(?=#[^\s]+$)/,"")),g=f.data("bs.modal")?"toggle":a.extend({remote:!/#/.test(e)&&e},f.data(),d.data());d.is("a")&&c.preventDefault(),f.one("show.bs.modal",function(a){a.isDefaultPrevented()||f.one("hidden.bs.modal",function(){d.is(":visible")&&d.trigger("focus")})}),b.call(f,g,this)})}(jQuery),+function(a){"use strict";function b(b){return this.each(function(){var d=a(this),e=d.data("bs.tooltip"),f="object"==typeof b&&b;(e||!/destroy|hide/.test(b))&&(e||d.data("bs.tooltip",e=new c(this,f)),"string"==typeof b&&e[b]())})}var c=function(a,b){this.type=null,this.options=null,this.enabled=null,this.timeout=null,this.hoverState=null,this.$element=null,this.inState=null,this.init("tooltip",a,b)};c.VERSION="3.3.5",c.TRANSITION_DURATION=150,c.DEFAULTS={animation:!0,placement:"top",selector:!1,template:'<div class="tooltip" role="tooltip"><div class="tooltip-arrow"></div><div class="tooltip-inner"></div></div>',trigger:"hover focus",title:"",delay:0,html:!1,container:!1,viewport:{selector:"body",padding:0}},c.prototype.init=function(b,c,d){if(this.enabled=!0,this.type=b,this.$element=a(c),this.options=this.getOptions(d),this.$viewport=this.options.viewport&&a(a.isFunction(this.options.viewport)?this.options.viewport.call(this,this.$element):this.options.viewport.selector||this.options.viewport),this.inState={click:!1,hover:!1,focus:!1},this.$element[0]instanceof document.constructor&&!this.options.selector)throw new Error("`selector` option must be specified when initializing "+this.type+" on the window.document object!");for(var e=this.options.trigger.split(" "),f=e.length;f--;){var g=e[f];if("click"==g)this.$element.on("click."+this.type,this.options.selector,a.proxy(this.toggle,this));else if("manual"!=g){var h="hover"==g?"mouseenter":"focusin",i="hover"==g?"mouseleave":"focusout";this.$element.on(h+"."+this.type,this.options.selector,a.proxy(this.enter,this)),this.$element.on(i+"."+this.type,this.options.selector,a.proxy(this.leave,this))}}this.options.selector?this._options=a.extend({},this.options,{trigger:"manual",selector:""}):this.fixTitle()},c.prototype.getDefaults=function(){return c.DEFAULTS},c.prototype.getOptions=function(b){return b=a.extend({},this.getDefaults(),this.$element.data(),b),b.delay&&"number"==typeof b.delay&&(b.delay={show:b.delay,hide:b.delay}),b},c.prototype.getDelegateOptions=function(){var b={},c=this.getDefaults();return this._options&&a.each(this._options,function(a,d){c[a]!=d&&(b[a]=d)}),b},c.prototype.enter=function(b){var c=b instanceof this.constructor?b:a(b.currentTarget).data("bs."+this.type);return c||(c=new this.constructor(b.currentTarget,this.getDelegateOptions()),a(b.currentTarget).data("bs."+this.type,c)),b instanceof a.Event&&(c.inState["focusin"==b.type?"focus":"hover"]=!0),c.tip().hasClass("in")||"in"==c.hoverState?void(c.hoverState="in"):(clearTimeout(c.timeout),c.hoverState="in",c.options.delay&&c.options.delay.show?void(c.timeout=setTimeout(function(){"in"==c.hoverState&&c.show()},c.options.delay.show)):c.show())},c.prototype.isInStateTrue=function(){for(var a in this.inState)if(this.inState[a])return!0;return!1},c.prototype.leave=function(b){var c=b instanceof this.constructor?b:a(b.currentTarget).data("bs."+this.type);return c||(c=new this.constructor(b.currentTarget,this.getDelegateOptions()),a(b.currentTarget).data("bs."+this.type,c)),b instanceof a.Event&&(c.inState["focusout"==b.type?"focus":"hover"]=!1),c.isInStateTrue()?void 0:(clearTimeout(c.timeout),c.hoverState="out",c.options.delay&&c.options.delay.hide?void(c.timeout=setTimeout(function(){"out"==c.hoverState&&c.hide()},c.options.delay.hide)):c.hide())},c.prototype.show=function(){var b=a.Event("show.bs."+this.type);if(this.hasContent()&&this.enabled){this.$element.trigger(b);var d=a.contains(this.$element[0].ownerDocument.documentElement,this.$element[0]);if(b.isDefaultPrevented()||!d)return;var e=this,f=this.tip(),g=this.getUID(this.type);this.setContent(),f.attr("id",g),this.$element.attr("aria-describedby",g),this.options.animation&&f.addClass("fade");var h="function"==typeof this.options.placement?this.options.placement.call(this,f[0],this.$element[0]):this.options.placement,i=/\s?auto?\s?/i,j=i.test(h);j&&(h=h.replace(i,"")||"top"),f.detach().css({top:0,left:0,display:"block"}).addClass(h).data("bs."+this.type,this),this.options.container?f.appendTo(this.options.container):f.insertAfter(this.$element),this.$element.trigger("inserted.bs."+this.type);var k=this.getPosition(),l=f[0].offsetWidth,m=f[0].offsetHeight;if(j){var n=h,o=this.getPosition(this.$viewport);h="bottom"==h&&k.bottom+m>o.bottom?"top":"top"==h&&k.top-m<o.top?"bottom":"right"==h&&k.right+l>o.width?"left":"left"==h&&k.left-l<o.left?"right":h,f.removeClass(n).addClass(h)}var p=this.getCalculatedOffset(h,k,l,m);this.applyPlacement(p,h);var q=function(){var a=e.hoverState;e.$element.trigger("shown.bs."+e.type),e.hoverState=null,"out"==a&&e.leave(e)};a.support.transition&&this.$tip.hasClass("fade")?f.one("bsTransitionEnd",q).emulateTransitionEnd(c.TRANSITION_DURATION):q()}},c.prototype.applyPlacement=function(b,c){var d=this.tip(),e=d[0].offsetWidth,f=d[0].offsetHeight,g=parseInt(d.css("margin-top"),10),h=parseInt(d.css("margin-left"),10);isNaN(g)&&(g=0),isNaN(h)&&(h=0),b.top+=g,b.left+=h,a.offset.setOffset(d[0],a.extend({using:function(a){d.css({top:Math.round(a.top),left:Math.round(a.left)})}},b),0),d.addClass("in");var i=d[0].offsetWidth,j=d[0].offsetHeight;"top"==c&&j!=f&&(b.top=b.top+f-j);var k=this.getViewportAdjustedDelta(c,b,i,j);k.left?b.left+=k.left:b.top+=k.top;var l=/top|bottom/.test(c),m=l?2*k.left-e+i:2*k.top-f+j,n=l?"offsetWidth":"offsetHeight";d.offset(b),this.replaceArrow(m,d[0][n],l)},c.prototype.replaceArrow=function(a,b,c){this.arrow().css(c?"left":"top",50*(1-a/b)+"%").css(c?"top":"left","")},c.prototype.setContent=function(){var a=this.tip(),b=this.getTitle();a.find(".tooltip-inner")[this.options.html?"html":"text"](b),a.removeClass("fade in top bottom left right")},c.prototype.hide=function(b){function d(){"in"!=e.hoverState&&f.detach(),e.$element.removeAttr("aria-describedby").trigger("hidden.bs."+e.type),b&&b()}var e=this,f=a(this.$tip),g=a.Event("hide.bs."+this.type);return this.$element.trigger(g),g.isDefaultPrevented()?void 0:(f.removeClass("in"),a.support.transition&&f.hasClass("fade")?f.one("bsTransitionEnd",d).emulateTransitionEnd(c.TRANSITION_DURATION):d(),this.hoverState=null,this)},c.prototype.fixTitle=function(){var a=this.$element;(a.attr("title")||"string"!=typeof a.attr("data-original-title"))&&a.attr("data-original-title",a.attr("title")||"").attr("title","")},c.prototype.hasContent=function(){return this.getTitle()},c.prototype.getPosition=function(b){b=b||this.$element;var c=b[0],d="BODY"==c.tagName,e=c.getBoundingClientRect();null==e.width&&(e=a.extend({},e,{width:e.right-e.left,height:e.bottom-e.top}));var f=d?{top:0,left:0}:b.offset(),g={scroll:d?document.documentElement.scrollTop||document.body.scrollTop:b.scrollTop()},h=d?{width:a(window).width(),height:a(window).height()}:null;return a.extend({},e,g,h,f)},c.prototype.getCalculatedOffset=function(a,b,c,d){return"bottom"==a?{top:b.top+b.height,left:b.left+b.width/2-c/2}:"top"==a?{top:b.top-d,left:b.left+b.width/2-c/2}:"left"==a?{top:b.top+b.height/2-d/2,left:b.left-c}:{top:b.top+b.height/2-d/2,left:b.left+b.width}},c.prototype.getViewportAdjustedDelta=function(a,b,c,d){var e={top:0,left:0};if(!this.$viewport)return e;var f=this.options.viewport&&this.options.viewport.padding||0,g=this.getPosition(this.$viewport);if(/right|left/.test(a)){var h=b.top-f-g.scroll,i=b.top+f-g.scroll+d;h<g.top?e.top=g.top-h:i>g.top+g.height&&(e.top=g.top+g.height-i)}else{var j=b.left-f,k=b.left+f+c;j<g.left?e.left=g.left-j:k>g.right&&(e.left=g.left+g.width-k)}return e},c.prototype.getTitle=function(){var a,b=this.$element,c=this.options;return a=b.attr("data-original-title")||("function"==typeof c.title?c.title.call(b[0]):c.title)},c.prototype.getUID=function(a){do a+=~~(1e6*Math.random());while(document.getElementById(a));return a},c.prototype.tip=function(){if(!this.$tip&&(this.$tip=a(this.options.template),1!=this.$tip.length))throw new Error(this.type+" `template` option must consist of exactly 1 top-level element!");return this.$tip},c.prototype.arrow=function(){return this.$arrow=this.$arrow||this.tip().find(".tooltip-arrow")},c.prototype.enable=function(){this.enabled=!0},c.prototype.disable=function(){this.enabled=!1},c.prototype.toggleEnabled=function(){this.enabled=!this.enabled},c.prototype.toggle=function(b){var c=this;b&&(c=a(b.currentTarget).data("bs."+this.type),c||(c=new this.constructor(b.currentTarget,this.getDelegateOptions()),a(b.currentTarget).data("bs."+this.type,c))),b?(c.inState.click=!c.inState.click,c.isInStateTrue()?c.enter(c):c.leave(c)):c.tip().hasClass("in")?c.leave(c):c.enter(c)},c.prototype.destroy=function(){var a=this;clearTimeout(this.timeout),this.hide(function(){a.$element.off("."+a.type).removeData("bs."+a.type),a.$tip&&a.$tip.detach(),a.$tip=null,a.$arrow=null,a.$viewport=null})};var d=a.fn.tooltip;a.fn.tooltip=b,a.fn.tooltip.Constructor=c,a.fn.tooltip.noConflict=function(){return a.fn.tooltip=d,this}}(jQuery),+function(a){"use strict";function b(b){return this.each(function(){var d=a(this),e=d.data("bs.popover"),f="object"==typeof b&&b;(e||!/destroy|hide/.test(b))&&(e||d.data("bs.popover",e=new c(this,f)),"string"==typeof b&&e[b]())})}var c=function(a,b){this.init("popover",a,b)};if(!a.fn.tooltip)throw new Error("Popover requires tooltip.js");c.VERSION="3.3.5",c.DEFAULTS=a.extend({},a.fn.tooltip.Constructor.DEFAULTS,{placement:"right",trigger:"click",content:"",template:'<div class="popover" role="tooltip"><div class="arrow"></div><h3 class="popover-title"></h3><div class="popover-content"></div></div>'}),c.prototype=a.extend({},a.fn.tooltip.Constructor.prototype),c.prototype.constructor=c,c.prototype.getDefaults=function(){return c.DEFAULTS},c.prototype.setContent=function(){var a=this.tip(),b=this.getTitle(),c=this.getContent();a.find(".popover-title")[this.options.html?"html":"text"](b),a.find(".popover-content").children().detach().end()[this.options.html?"string"==typeof c?"html":"append":"text"](c),a.removeClass("fade top bottom left right in"),a.find(".popover-title").html()||a.find(".popover-title").hide()},c.prototype.hasContent=function(){return this.getTitle()||this.getContent()},c.prototype.getContent=function(){var a=this.$element,b=this.options;return a.attr("data-content")||("function"==typeof b.content?b.content.call(a[0]):b.content)},c.prototype.arrow=function(){return this.$arrow=this.$arrow||this.tip().find(".arrow")};var d=a.fn.popover;a.fn.popover=b,a.fn.popover.Constructor=c,a.fn.popover.noConflict=function(){return a.fn.popover=d,this}}(jQuery),+function(a){"use strict";function b(c,d){this.$body=a(document.body),this.$scrollElement=a(a(c).is(document.body)?window:c),this.options=a.extend({},b.DEFAULTS,d),this.selector=(this.options.target||"")+" .nav li > a",this.offsets=[],this.targets=[],this.activeTarget=null,this.scrollHeight=0,this.$scrollElement.on("scroll.bs.scrollspy",a.proxy(this.process,this)),this.refresh(),this.process()}function c(c){return this.each(function(){var d=a(this),e=d.data("bs.scrollspy"),f="object"==typeof c&&c;e||d.data("bs.scrollspy",e=new b(this,f)),"string"==typeof c&&e[c]()})}b.VERSION="3.3.5",b.DEFAULTS={offset:10},b.prototype.getScrollHeight=function(){return this.$scrollElement[0].scrollHeight||Math.max(this.$body[0].scrollHeight,document.documentElement.scrollHeight)},b.prototype.refresh=function(){var b=this,c="offset",d=0;this.offsets=[],this.targets=[],this.scrollHeight=this.getScrollHeight(),a.isWindow(this.$scrollElement[0])||(c="position",d=this.$scrollElement.scrollTop()),this.$body.find(this.selector).map(function(){var b=a(this),e=b.data("target")||b.attr("href"),f=/^#./.test(e)&&a(e);return f&&f.length&&f.is(":visible")&&[[f[c]().top+d,e]]||null}).sort(function(a,b){return a[0]-b[0]}).each(function(){b.offsets.push(this[0]),b.targets.push(this[1])})},b.prototype.process=function(){var a,b=this.$scrollElement.scrollTop()+this.options.offset,c=this.getScrollHeight(),d=this.options.offset+c-this.$scrollElement.height(),e=this.offsets,f=this.targets,g=this.activeTarget;if(this.scrollHeight!=c&&this.refresh(),b>=d)return g!=(a=f[f.length-1])&&this.activate(a);if(g&&b<e[0])return this.activeTarget=null,this.clear();for(a=e.length;a--;)g!=f[a]&&b>=e[a]&&(void 0===e[a+1]||b<e[a+1])&&this.activate(f[a])},b.prototype.activate=function(b){this.activeTarget=b,this.clear();var c=this.selector+'[data-target="'+b+'"],'+this.selector+'[href="'+b+'"]',d=a(c).parents("li").addClass("active");d.parent(".dropdown-menu").length&&(d=d.closest("li.dropdown").addClass("active")),
+d.trigger("activate.bs.scrollspy")},b.prototype.clear=function(){a(this.selector).parentsUntil(this.options.target,".active").removeClass("active")};var d=a.fn.scrollspy;a.fn.scrollspy=c,a.fn.scrollspy.Constructor=b,a.fn.scrollspy.noConflict=function(){return a.fn.scrollspy=d,this},a(window).on("load.bs.scrollspy.data-api",function(){a('[data-spy="scroll"]').each(function(){var b=a(this);c.call(b,b.data())})})}(jQuery),+function(a){"use strict";function b(b){return this.each(function(){var d=a(this),e=d.data("bs.tab");e||d.data("bs.tab",e=new c(this)),"string"==typeof b&&e[b]()})}var c=function(b){this.element=a(b)};c.VERSION="3.3.5",c.TRANSITION_DURATION=150,c.prototype.show=function(){var b=this.element,c=b.closest("ul:not(.dropdown-menu)"),d=b.data("target");if(d||(d=b.attr("href"),d=d&&d.replace(/.*(?=#[^\s]*$)/,"")),!b.parent("li").hasClass("active")){var e=c.find(".active:last a"),f=a.Event("hide.bs.tab",{relatedTarget:b[0]}),g=a.Event("show.bs.tab",{relatedTarget:e[0]});if(e.trigger(f),b.trigger(g),!g.isDefaultPrevented()&&!f.isDefaultPrevented()){var h=a(d);this.activate(b.closest("li"),c),this.activate(h,h.parent(),function(){e.trigger({type:"hidden.bs.tab",relatedTarget:b[0]}),b.trigger({type:"shown.bs.tab",relatedTarget:e[0]})})}}},c.prototype.activate=function(b,d,e){function f(){g.removeClass("active").find("> .dropdown-menu > .active").removeClass("active").end().find('[data-toggle="tab"]').attr("aria-expanded",!1),b.addClass("active").find('[data-toggle="tab"]').attr("aria-expanded",!0),h?(b[0].offsetWidth,b.addClass("in")):b.removeClass("fade"),b.parent(".dropdown-menu").length&&b.closest("li.dropdown").addClass("active").end().find('[data-toggle="tab"]').attr("aria-expanded",!0),e&&e()}var g=d.find("> .active"),h=e&&a.support.transition&&(g.length&&g.hasClass("fade")||!!d.find("> .fade").length);g.length&&h?g.one("bsTransitionEnd",f).emulateTransitionEnd(c.TRANSITION_DURATION):f(),g.removeClass("in")};var d=a.fn.tab;a.fn.tab=b,a.fn.tab.Constructor=c,a.fn.tab.noConflict=function(){return a.fn.tab=d,this};var e=function(c){c.preventDefault(),b.call(a(this),"show")};a(document).on("click.bs.tab.data-api",'[data-toggle="tab"]',e).on("click.bs.tab.data-api",'[data-toggle="pill"]',e)}(jQuery),+function(a){"use strict";function b(b){return this.each(function(){var d=a(this),e=d.data("bs.affix"),f="object"==typeof b&&b;e||d.data("bs.affix",e=new c(this,f)),"string"==typeof b&&e[b]()})}var c=function(b,d){this.options=a.extend({},c.DEFAULTS,d),this.$target=a(this.options.target).on("scroll.bs.affix.data-api",a.proxy(this.checkPosition,this)).on("click.bs.affix.data-api",a.proxy(this.checkPositionWithEventLoop,this)),this.$element=a(b),this.affixed=null,this.unpin=null,this.pinnedOffset=null,this.checkPosition()};c.VERSION="3.3.5",c.RESET="affix affix-top affix-bottom",c.DEFAULTS={offset:0,target:window},c.prototype.getState=function(a,b,c,d){var e=this.$target.scrollTop(),f=this.$element.offset(),g=this.$target.height();if(null!=c&&"top"==this.affixed)return c>e?"top":!1;if("bottom"==this.affixed)return null!=c?e+this.unpin<=f.top?!1:"bottom":a-d>=e+g?!1:"bottom";var h=null==this.affixed,i=h?e:f.top,j=h?g:b;return null!=c&&c>=e?"top":null!=d&&i+j>=a-d?"bottom":!1},c.prototype.getPinnedOffset=function(){if(this.pinnedOffset)return this.pinnedOffset;this.$element.removeClass(c.RESET).addClass("affix");var a=this.$target.scrollTop(),b=this.$element.offset();return this.pinnedOffset=b.top-a},c.prototype.checkPositionWithEventLoop=function(){setTimeout(a.proxy(this.checkPosition,this),1)},c.prototype.checkPosition=function(){if(this.$element.is(":visible")){var b=this.$element.height(),d=this.options.offset,e=d.top,f=d.bottom,g=Math.max(a(document).height(),a(document.body).height());"object"!=typeof d&&(f=e=d),"function"==typeof e&&(e=d.top(this.$element)),"function"==typeof f&&(f=d.bottom(this.$element));var h=this.getState(g,b,e,f);if(this.affixed!=h){null!=this.unpin&&this.$element.css("top","");var i="affix"+(h?"-"+h:""),j=a.Event(i+".bs.affix");if(this.$element.trigger(j),j.isDefaultPrevented())return;this.affixed=h,this.unpin="bottom"==h?this.getPinnedOffset():null,this.$element.removeClass(c.RESET).addClass(i).trigger(i.replace("affix","affixed")+".bs.affix")}"bottom"==h&&this.$element.offset({top:g-b-f})}};var d=a.fn.affix;a.fn.affix=b,a.fn.affix.Constructor=c,a.fn.affix.noConflict=function(){return a.fn.affix=d,this},a(window).on("load",function(){a('[data-spy="affix"]').each(function(){var c=a(this),d=c.data();d.offset=d.offset||{},null!=d.offsetBottom&&(d.offset.bottom=d.offsetBottom),null!=d.offsetTop&&(d.offset.top=d.offsetTop),b.call(c,d)})})}(jQuery);</script>
+<script>/**
+* @preserve HTML5 Shiv 3.7.2 | @afarkas @jdalton @jon_neal @rem | MIT/GPL2 Licensed
+*/
+// Only run this code in IE 8
+if (!!window.navigator.userAgent.match("MSIE 8")) {
+!function(a,b){function c(a,b){var c=a.createElement("p"),d=a.getElementsByTagName("head")[0]||a.documentElement;return c.innerHTML="x<style>"+b+"</style>",d.insertBefore(c.lastChild,d.firstChild)}function d(){var a=t.elements;return"string"==typeof a?a.split(" "):a}function e(a,b){var c=t.elements;"string"!=typeof c&&(c=c.join(" ")),"string"!=typeof a&&(a=a.join(" ")),t.elements=c+" "+a,j(b)}function f(a){var b=s[a[q]];return b||(b={},r++,a[q]=r,s[r]=b),b}function g(a,c,d){if(c||(c=b),l)return c.createElement(a);d||(d=f(c));var e;return e=d.cache[a]?d.cache[a].cloneNode():p.test(a)?(d.cache[a]=d.createElem(a)).cloneNode():d.createElem(a),!e.canHaveChildren||o.test(a)||e.tagUrn?e:d.frag.appendChild(e)}function h(a,c){if(a||(a=b),l)return a.createDocumentFragment();c=c||f(a);for(var e=c.frag.cloneNode(),g=0,h=d(),i=h.length;i>g;g++)e.createElement(h[g]);return e}function i(a,b){b.cache||(b.cache={},b.createElem=a.createElement,b.createFrag=a.createDocumentFragment,b.frag=b.createFrag()),a.createElement=function(c){return t.shivMethods?g(c,a,b):b.createElem(c)},a.createDocumentFragment=Function("h,f","return function(){var n=f.cloneNode(),c=n.createElement;h.shivMethods&&("+d().join().replace(/[\w\-:]+/g,function(a){return b.createElem(a),b.frag.createElement(a),'c("'+a+'")'})+");return n}")(t,b.frag)}function j(a){a||(a=b);var d=f(a);return!t.shivCSS||k||d.hasCSS||(d.hasCSS=!!c(a,"article,aside,dialog,figcaption,figure,footer,header,hgroup,main,nav,section{display:block}mark{background:#FF0;color:#000}template{display:none}")),l||i(a,d),a}var k,l,m="3.7.2",n=a.html5||{},o=/^<|^(?:button|map|select|textarea|object|iframe|option|optgroup)$/i,p=/^(?:a|b|code|div|fieldset|h1|h2|h3|h4|h5|h6|i|label|li|ol|p|q|span|strong|style|table|tbody|td|th|tr|ul)$/i,q="_html5shiv",r=0,s={};!function(){try{var a=b.createElement("a");a.innerHTML="<xyz></xyz>",k="hidden"in a,l=1==a.childNodes.length||function(){b.createElement("a");var a=b.createDocumentFragment();return"undefined"==typeof a.cloneNode||"undefined"==typeof a.createDocumentFragment||"undefined"==typeof a.createElement}()}catch(c){k=!0,l=!0}}();var t={elements:n.elements||"abbr article aside audio bdi canvas data datalist details dialog figcaption figure footer header hgroup main mark meter nav output picture progress section summary template time video",version:m,shivCSS:n.shivCSS!==!1,supportsUnknownElements:l,shivMethods:n.shivMethods!==!1,type:"default",shivDocument:j,createElement:g,createDocumentFragment:h,addElements:e};a.html5=t,j(b)}(this,document);
+};
+</script>
+<script>/*! Respond.js v1.4.2: min/max-width media query polyfill * Copyright 2013 Scott Jehl
+ * Licensed under https://github.com/scottjehl/Respond/blob/master/LICENSE-MIT
+ *  */
+
+// Only run this code in IE 8
+if (!!window.navigator.userAgent.match("MSIE 8")) {
+!function(a){"use strict";a.matchMedia=a.matchMedia||function(a){var b,c=a.documentElement,d=c.firstElementChild||c.firstChild,e=a.createElement("body"),f=a.createElement("div");return f.id="mq-test-1",f.style.cssText="position:absolute;top:-100em",e.style.background="none",e.appendChild(f),function(a){return f.innerHTML='&shy;<style media="'+a+'"> #mq-test-1 { width: 42px; }</style>',c.insertBefore(e,d),b=42===f.offsetWidth,c.removeChild(e),{matches:b,media:a}}}(a.document)}(this),function(a){"use strict";function b(){u(!0)}var c={};a.respond=c,c.update=function(){};var d=[],e=function(){var b=!1;try{b=new a.XMLHttpRequest}catch(c){b=new a.ActiveXObject("Microsoft.XMLHTTP")}return function(){return b}}(),f=function(a,b){var c=e();c&&(c.open("GET",a,!0),c.onreadystatechange=function(){4!==c.readyState||200!==c.status&&304!==c.status||b(c.responseText)},4!==c.readyState&&c.send(null))};if(c.ajax=f,c.queue=d,c.regex={media:/@media[^\{]+\{([^\{\}]*\{[^\}\{]*\})+/gi,keyframes:/@(?:\-(?:o|moz|webkit)\-)?keyframes[^\{]+\{(?:[^\{\}]*\{[^\}\{]*\})+[^\}]*\}/gi,urls:/(url\()['"]?([^\/\)'"][^:\)'"]+)['"]?(\))/g,findStyles:/@media *([^\{]+)\{([\S\s]+?)$/,only:/(only\s+)?([a-zA-Z]+)\s?/,minw:/\([\s]*min\-width\s*:[\s]*([\s]*[0-9\.]+)(px|em)[\s]*\)/,maxw:/\([\s]*max\-width\s*:[\s]*([\s]*[0-9\.]+)(px|em)[\s]*\)/},c.mediaQueriesSupported=a.matchMedia&&null!==a.matchMedia("only all")&&a.matchMedia("only all").matches,!c.mediaQueriesSupported){var g,h,i,j=a.document,k=j.documentElement,l=[],m=[],n=[],o={},p=30,q=j.getElementsByTagName("head")[0]||k,r=j.getElementsByTagName("base")[0],s=q.getElementsByTagName("link"),t=function(){var a,b=j.createElement("div"),c=j.body,d=k.style.fontSize,e=c&&c.style.fontSize,f=!1;return b.style.cssText="position:absolute;font-size:1em;width:1em",c||(c=f=j.createElement("body"),c.style.background="none"),k.style.fontSize="100%",c.style.fontSize="100%",c.appendChild(b),f&&k.insertBefore(c,k.firstChild),a=b.offsetWidth,f?k.removeChild(c):c.removeChild(b),k.style.fontSize=d,e&&(c.style.fontSize=e),a=i=parseFloat(a)},u=function(b){var c="clientWidth",d=k[c],e="CSS1Compat"===j.compatMode&&d||j.body[c]||d,f={},o=s[s.length-1],r=(new Date).getTime();if(b&&g&&p>r-g)return a.clearTimeout(h),h=a.setTimeout(u,p),void 0;g=r;for(var v in l)if(l.hasOwnProperty(v)){var w=l[v],x=w.minw,y=w.maxw,z=null===x,A=null===y,B="em";x&&(x=parseFloat(x)*(x.indexOf(B)>-1?i||t():1)),y&&(y=parseFloat(y)*(y.indexOf(B)>-1?i||t():1)),w.hasquery&&(z&&A||!(z||e>=x)||!(A||y>=e))||(f[w.media]||(f[w.media]=[]),f[w.media].push(m[w.rules]))}for(var C in n)n.hasOwnProperty(C)&&n[C]&&n[C].parentNode===q&&q.removeChild(n[C]);n.length=0;for(var D in f)if(f.hasOwnProperty(D)){var E=j.createElement("style"),F=f[D].join("\n");E.type="text/css",E.media=D,q.insertBefore(E,o.nextSibling),E.styleSheet?E.styleSheet.cssText=F:E.appendChild(j.createTextNode(F)),n.push(E)}},v=function(a,b,d){var e=a.replace(c.regex.keyframes,"").match(c.regex.media),f=e&&e.length||0;b=b.substring(0,b.lastIndexOf("/"));var g=function(a){return a.replace(c.regex.urls,"$1"+b+"$2$3")},h=!f&&d;b.length&&(b+="/"),h&&(f=1);for(var i=0;f>i;i++){var j,k,n,o;h?(j=d,m.push(g(a))):(j=e[i].match(c.regex.findStyles)&&RegExp.$1,m.push(RegExp.$2&&g(RegExp.$2))),n=j.split(","),o=n.length;for(var p=0;o>p;p++)k=n[p],l.push({media:k.split("(")[0].match(c.regex.only)&&RegExp.$2||"all",rules:m.length-1,hasquery:k.indexOf("(")>-1,minw:k.match(c.regex.minw)&&parseFloat(RegExp.$1)+(RegExp.$2||""),maxw:k.match(c.regex.maxw)&&parseFloat(RegExp.$1)+(RegExp.$2||"")})}u()},w=function(){if(d.length){var b=d.shift();f(b.href,function(c){v(c,b.href,b.media),o[b.href]=!0,a.setTimeout(function(){w()},0)})}},x=function(){for(var b=0;b<s.length;b++){var c=s[b],e=c.href,f=c.media,g=c.rel&&"stylesheet"===c.rel.toLowerCase();e&&g&&!o[e]&&(c.styleSheet&&c.styleSheet.rawCssText?(v(c.styleSheet.rawCssText,e,f),o[e]=!0):(!/^([a-zA-Z:]*\/\/)/.test(e)&&!r||e.replace(RegExp.$1,"").split("/")[0]===a.location.host)&&("//"===e.substring(0,2)&&(e=a.location.protocol+e),d.push({href:e,media:f})))}w()};x(),c.update=x,c.getEmValue=t,a.addEventListener?a.addEventListener("resize",b,!1):a.attachEvent&&a.attachEvent("onresize",b)}}(this);
+};
+</script>
+<style>h1 {font-size: 34px;}
+       h1.title {font-size: 38px;}
+       h2 {font-size: 30px;}
+       h3 {font-size: 24px;}
+       h4 {font-size: 18px;}
+       h5 {font-size: 16px;}
+       h6 {font-size: 12px;}
+       code {color: inherit; background-color: rgba(0, 0, 0, 0.04);}
+       pre:not([class]) { background-color: white }</style>
+<script>
+
+/**
+ * jQuery Plugin: Sticky Tabs
+ *
+ * @author Aidan Lister <aidan@php.net>
+ * adapted by Ruben Arslan to activate parent tabs too
+ * http://www.aidanlister.com/2014/03/persisting-the-tab-state-in-bootstrap/
+ */
+(function($) {
+  "use strict";
+  $.fn.rmarkdownStickyTabs = function() {
+    var context = this;
+    // Show the tab corresponding with the hash in the URL, or the first tab
+    var showStuffFromHash = function() {
+      var hash = window.location.hash;
+      var selector = hash ? 'a[href="' + hash + '"]' : 'li.active > a';
+      var $selector = $(selector, context);
+      if($selector.data('toggle') === "tab") {
+        $selector.tab('show');
+        // walk up the ancestors of this element, show any hidden tabs
+        $selector.parents('.section.tabset').each(function(i, elm) {
+          var link = $('a[href="#' + $(elm).attr('id') + '"]');
+          if(link.data('toggle') === "tab") {
+            link.tab("show");
+          }
+        });
+      }
+    };
+
+
+    // Set the correct tab when the page loads
+    showStuffFromHash(context);
+
+    // Set the correct tab when a user uses their back/forward button
+    $(window).on('hashchange', function() {
+      showStuffFromHash(context);
+    });
+
+    // Change the URL when tabs are clicked
+    $('a', context).on('click', function(e) {
+      history.pushState(null, null, this.href);
+      showStuffFromHash(context);
+    });
+
+    return this;
+  };
+}(jQuery));
+
+window.buildTabsets = function(tocID) {
+
+  // build a tabset from a section div with the .tabset class
+  function buildTabset(tabset) {
+
+    // check for fade and pills options
+    var fade = tabset.hasClass("tabset-fade");
+    var pills = tabset.hasClass("tabset-pills");
+    var navClass = pills ? "nav-pills" : "nav-tabs";
+
+    // determine the heading level of the tabset and tabs
+    var match = tabset.attr('class').match(/level(\d) /);
+    if (match === null)
+      return;
+    var tabsetLevel = Number(match[1]);
+    var tabLevel = tabsetLevel + 1;
+
+    // find all subheadings immediately below
+    var tabs = tabset.find("div.section.level" + tabLevel);
+    if (!tabs.length)
+      return;
+
+    // create tablist and tab-content elements
+    var tabList = $('<ul class="nav ' + navClass + '" role="tablist"></ul>');
+    $(tabs[0]).before(tabList);
+    var tabContent = $('<div class="tab-content"></div>');
+    $(tabs[0]).before(tabContent);
+
+    // build the tabset
+    var activeTab = 0;
+    tabs.each(function(i) {
+
+      // get the tab div
+      var tab = $(tabs[i]);
+
+      // get the id then sanitize it for use with bootstrap tabs
+      var id = tab.attr('id');
+
+      // see if this is marked as the active tab
+      if (tab.hasClass('active'))
+        activeTab = i;
+
+      // remove any table of contents entries associated with
+      // this ID (since we'll be removing the heading element)
+      $("div#" + tocID + " li a[href='#" + id + "']").parent().remove();
+
+      // sanitize the id for use with bootstrap tabs
+      id = id.replace(/[.\/?&!#<>]/g, '').replace(/\s/g, '_');
+      tab.attr('id', id);
+
+      // get the heading element within it, grab it's text, then remove it
+      var heading = tab.find('h' + tabLevel + ':first');
+      var headingText = heading.html();
+      heading.remove();
+
+      // build and append the tab list item
+      var a = $('<a role="tab" data-toggle="tab">' + headingText + '</a>');
+      a.attr('href', '#' + id);
+      a.attr('aria-controls', id);
+      var li = $('<li role="presentation"></li>');
+      li.append(a);
+      tabList.append(li);
+
+      // set it's attributes
+      tab.attr('role', 'tabpanel');
+      tab.addClass('tab-pane');
+      tab.addClass('tabbed-pane');
+      if (fade)
+        tab.addClass('fade');
+
+      // move it into the tab content div
+      tab.detach().appendTo(tabContent);
+    });
+
+    // set active tab
+    $(tabList.children('li')[activeTab]).addClass('active');
+    var active = $(tabContent.children('div.section')[activeTab]);
+    active.addClass('active');
+    if (fade)
+      active.addClass('in');
+
+    if (tabset.hasClass("tabset-sticky"))
+      tabset.rmarkdownStickyTabs();
+  }
+
+  // convert section divs with the .tabset class to tabsets
+  var tabsets = $("div.section.tabset");
+  tabsets.each(function(i) {
+    buildTabset($(tabsets[i]));
+  });
+};
+
+</script>
+<style type="text/css">.hljs-literal {
+color: #990073;
+}
+.hljs-number {
+color: #099;
+}
+.hljs-comment {
+color: #998;
+font-style: italic;
+}
+.hljs-keyword {
+color: #900;
+font-weight: bold;
+}
+.hljs-string {
+color: #d14;
+}
+</style>
+<script src="data:application/javascript;base64,/*! highlight.js v9.12.0 | BSD3 License | git.io/hljslicense */
!function(e){var n="object"==typeof window&&window||"object"==typeof self&&self;"undefined"!=typeof exports?e(exports):n&&(n.hljs=e({}),"function"==typeof define&&define.amd&&define([],function(){return n.hljs}))}(function(e){function n(e){return e.replace(/&/g,"&amp;").replace(/</g,"&lt;").replace(/>/g,"&gt;")}function t(e){return e.nodeName.toLowerCase()}function r(e,n){var t=e&&e.exec(n);return t&&0===t.index}function a(e){return k.test(e)}function i(e){var n,t,r,i,o=e.className+" ";if(o+=e.parentNode?e.parentNode.className:"",t=B.exec(o))return w(t[1])?t[1]:"no-highlight";for(o=o.split(/\s+/),n=0,r=o.length;r>n;n++)if(i=o[n],a(i)||w(i))return i}function o(e){var n,t={},r=Array.prototype.slice.call(arguments,1);for(n in e)t[n]=e[n];return r.forEach(function(e){for(n in e)t[n]=e[n]}),t}function u(e){var n=[];return function r(e,a){for(var i=e.firstChild;i;i=i.nextSibling)3===i.nodeType?a+=i.nodeValue.length:1===i.nodeType&&(n.push({event:"start",offset:a,node:i}),a=r(i,a),t(i).match(/br|hr|img|input/)||n.push({event:"stop",offset:a,node:i}));return a}(e,0),n}function c(e,r,a){function i(){return e.length&&r.length?e[0].offset!==r[0].offset?e[0].offset<r[0].offset?e:r:"start"===r[0].event?e:r:e.length?e:r}function o(e){function r(e){return" "+e.nodeName+'="'+n(e.value).replace('"',"&quot;")+'"'}s+="<"+t(e)+E.map.call(e.attributes,r).join("")+">"}function u(e){s+="</"+t(e)+">"}function c(e){("start"===e.event?o:u)(e.node)}for(var l=0,s="",f=[];e.length||r.length;){var g=i();if(s+=n(a.substring(l,g[0].offset)),l=g[0].offset,g===e){f.reverse().forEach(u);do c(g.splice(0,1)[0]),g=i();while(g===e&&g.length&&g[0].offset===l);f.reverse().forEach(o)}else"start"===g[0].event?f.push(g[0].node):f.pop(),c(g.splice(0,1)[0])}return s+n(a.substr(l))}function l(e){return e.v&&!e.cached_variants&&(e.cached_variants=e.v.map(function(n){return o(e,{v:null},n)})),e.cached_variants||e.eW&&[o(e)]||[e]}function s(e){function n(e){return e&&e.source||e}function t(t,r){return new RegExp(n(t),"m"+(e.cI?"i":"")+(r?"g":""))}function r(a,i){if(!a.compiled){if(a.compiled=!0,a.k=a.k||a.bK,a.k){var o={},u=function(n,t){e.cI&&(t=t.toLowerCase()),t.split(" ").forEach(function(e){var t=e.split("|");o[t[0]]=[n,t[1]?Number(t[1]):1]})};"string"==typeof a.k?u("keyword",a.k):x(a.k).forEach(function(e){u(e,a.k[e])}),a.k=o}a.lR=t(a.l||/\w+/,!0),i&&(a.bK&&(a.b="\\b("+a.bK.split(" ").join("|")+")\\b"),a.b||(a.b=/\B|\b/),a.bR=t(a.b),a.e||a.eW||(a.e=/\B|\b/),a.e&&(a.eR=t(a.e)),a.tE=n(a.e)||"",a.eW&&i.tE&&(a.tE+=(a.e?"|":"")+i.tE)),a.i&&(a.iR=t(a.i)),null==a.r&&(a.r=1),a.c||(a.c=[]),a.c=Array.prototype.concat.apply([],a.c.map(function(e){return l("self"===e?a:e)})),a.c.forEach(function(e){r(e,a)}),a.starts&&r(a.starts,i);var c=a.c.map(function(e){return e.bK?"\\.?("+e.b+")\\.?":e.b}).concat([a.tE,a.i]).map(n).filter(Boolean);a.t=c.length?t(c.join("|"),!0):{exec:function(){return null}}}}r(e)}function f(e,t,a,i){function o(e,n){var t,a;for(t=0,a=n.c.length;a>t;t++)if(r(n.c[t].bR,e))return n.c[t]}function u(e,n){if(r(e.eR,n)){for(;e.endsParent&&e.parent;)e=e.parent;return e}return e.eW?u(e.parent,n):void 0}function c(e,n){return!a&&r(n.iR,e)}function l(e,n){var t=N.cI?n[0].toLowerCase():n[0];return e.k.hasOwnProperty(t)&&e.k[t]}function p(e,n,t,r){var a=r?"":I.classPrefix,i='<span class="'+a,o=t?"":C;return i+=e+'">',i+n+o}function h(){var e,t,r,a;if(!E.k)return n(k);for(a="",t=0,E.lR.lastIndex=0,r=E.lR.exec(k);r;)a+=n(k.substring(t,r.index)),e=l(E,r),e?(B+=e[1],a+=p(e[0],n(r[0]))):a+=n(r[0]),t=E.lR.lastIndex,r=E.lR.exec(k);return a+n(k.substr(t))}function d(){var e="string"==typeof E.sL;if(e&&!y[E.sL])return n(k);var t=e?f(E.sL,k,!0,x[E.sL]):g(k,E.sL.length?E.sL:void 0);return E.r>0&&(B+=t.r),e&&(x[E.sL]=t.top),p(t.language,t.value,!1,!0)}function b(){L+=null!=E.sL?d():h(),k=""}function v(e){L+=e.cN?p(e.cN,"",!0):"",E=Object.create(e,{parent:{value:E}})}function m(e,n){if(k+=e,null==n)return b(),0;var t=o(n,E);if(t)return t.skip?k+=n:(t.eB&&(k+=n),b(),t.rB||t.eB||(k=n)),v(t,n),t.rB?0:n.length;var r=u(E,n);if(r){var a=E;a.skip?k+=n:(a.rE||a.eE||(k+=n),b(),a.eE&&(k=n));do E.cN&&(L+=C),E.skip||(B+=E.r),E=E.parent;while(E!==r.parent);return r.starts&&v(r.starts,""),a.rE?0:n.length}if(c(n,E))throw new Error('Illegal lexeme "'+n+'" for mode "'+(E.cN||"<unnamed>")+'"');return k+=n,n.length||1}var N=w(e);if(!N)throw new Error('Unknown language: "'+e+'"');s(N);var R,E=i||N,x={},L="";for(R=E;R!==N;R=R.parent)R.cN&&(L=p(R.cN,"",!0)+L);var k="",B=0;try{for(var M,j,O=0;;){if(E.t.lastIndex=O,M=E.t.exec(t),!M)break;j=m(t.substring(O,M.index),M[0]),O=M.index+j}for(m(t.substr(O)),R=E;R.parent;R=R.parent)R.cN&&(L+=C);return{r:B,value:L,language:e,top:E}}catch(T){if(T.message&&-1!==T.message.indexOf("Illegal"))return{r:0,value:n(t)};throw T}}function g(e,t){t=t||I.languages||x(y);var r={r:0,value:n(e)},a=r;return t.filter(w).forEach(function(n){var t=f(n,e,!1);t.language=n,t.r>a.r&&(a=t),t.r>r.r&&(a=r,r=t)}),a.language&&(r.second_best=a),r}function p(e){return I.tabReplace||I.useBR?e.replace(M,function(e,n){return I.useBR&&"\n"===e?"<br>":I.tabReplace?n.replace(/\t/g,I.tabReplace):""}):e}function h(e,n,t){var r=n?L[n]:t,a=[e.trim()];return e.match(/\bhljs\b/)||a.push("hljs"),-1===e.indexOf(r)&&a.push(r),a.join(" ").trim()}function d(e){var n,t,r,o,l,s=i(e);a(s)||(I.useBR?(n=document.createElementNS("http://www.w3.org/1999/xhtml","div"),n.innerHTML=e.innerHTML.replace(/\n/g,"").replace(/<br[ \/]*>/g,"\n")):n=e,l=n.textContent,r=s?f(s,l,!0):g(l),t=u(n),t.length&&(o=document.createElementNS("http://www.w3.org/1999/xhtml","div"),o.innerHTML=r.value,r.value=c(t,u(o),l)),r.value=p(r.value),e.innerHTML=r.value,e.className=h(e.className,s,r.language),e.result={language:r.language,re:r.r},r.second_best&&(e.second_best={language:r.second_best.language,re:r.second_best.r}))}function b(e){I=o(I,e)}function v(){if(!v.called){v.called=!0;var e=document.querySelectorAll("pre code");E.forEach.call(e,d)}}function m(){addEventListener("DOMContentLoaded",v,!1),addEventListener("load",v,!1)}function N(n,t){var r=y[n]=t(e);r.aliases&&r.aliases.forEach(function(e){L[e]=n})}function R(){return x(y)}function w(e){return e=(e||"").toLowerCase(),y[e]||y[L[e]]}var E=[],x=Object.keys,y={},L={},k=/^(no-?highlight|plain|text)$/i,B=/\blang(?:uage)?-([\w-]+)\b/i,M=/((^(<[^>]+>|\t|)+|(?:\n)))/gm,C="</span>",I={classPrefix:"hljs-",tabReplace:null,useBR:!1,languages:void 0};return e.highlight=f,e.highlightAuto=g,e.fixMarkup=p,e.highlightBlock=d,e.configure=b,e.initHighlighting=v,e.initHighlightingOnLoad=m,e.registerLanguage=N,e.listLanguages=R,e.getLanguage=w,e.inherit=o,e.IR="[a-zA-Z]\\w*",e.UIR="[a-zA-Z_]\\w*",e.NR="\\b\\d+(\\.\\d+)?",e.CNR="(-?)(\\b0[xX][a-fA-F0-9]+|(\\b\\d+(\\.\\d*)?|\\.\\d+)([eE][-+]?\\d+)?)",e.BNR="\\b(0b[01]+)",e.RSR="!|!=|!==|%|%=|&|&&|&=|\\*|\\*=|\\+|\\+=|,|-|-=|/=|/|:|;|<<|<<=|<=|<|===|==|=|>>>=|>>=|>=|>>>|>>|>|\\?|\\[|\\{|\\(|\\^|\\^=|\\||\\|=|\\|\\||~",e.BE={b:"\\\\[\\s\\S]",r:0},e.ASM={cN:"string",b:"'",e:"'",i:"\\n",c:[e.BE]},e.QSM={cN:"string",b:'"',e:'"',i:"\\n",c:[e.BE]},e.PWM={b:/\b(a|an|the|are|I'm|isn't|don't|doesn't|won't|but|just|should|pretty|simply|enough|gonna|going|wtf|so|such|will|you|your|they|like|more)\b/},e.C=function(n,t,r){var a=e.inherit({cN:"comment",b:n,e:t,c:[]},r||{});return a.c.push(e.PWM),a.c.push({cN:"doctag",b:"(?:TODO|FIXME|NOTE|BUG|XXX):",r:0}),a},e.CLCM=e.C("//","$"),e.CBCM=e.C("/\\*","\\*/"),e.HCM=e.C("#","$"),e.NM={cN:"number",b:e.NR,r:0},e.CNM={cN:"number",b:e.CNR,r:0},e.BNM={cN:"number",b:e.BNR,r:0},e.CSSNM={cN:"number",b:e.NR+"(%|em|ex|ch|rem|vw|vh|vmin|vmax|cm|mm|in|pt|pc|px|deg|grad|rad|turn|s|ms|Hz|kHz|dpi|dpcm|dppx)?",r:0},e.RM={cN:"regexp",b:/\//,e:/\/[gimuy]*/,i:/\n/,c:[e.BE,{b:/\[/,e:/\]/,r:0,c:[e.BE]}]},e.TM={cN:"title",b:e.IR,r:0},e.UTM={cN:"title",b:e.UIR,r:0},e.METHOD_GUARD={b:"\\.\\s*"+e.UIR,r:0},e});hljs.registerLanguage("sql",function(e){var t=e.C("--","$");return{cI:!0,i:/[<>{}*#]/,c:[{bK:"begin end start commit rollback savepoint lock alter create drop rename call delete do handler insert load replace select truncate update set show pragma grant merge describe use explain help declare prepare execute deallocate release unlock purge reset change stop analyze cache flush optimize repair kill install uninstall checksum restore check backup revoke comment",e:/;/,eW:!0,l:/[\w\.]+/,k:{keyword:"abort abs absolute acc acce accep accept access accessed accessible account acos action activate add addtime admin administer advanced advise aes_decrypt aes_encrypt after agent aggregate ali alia alias allocate allow alter always analyze ancillary and any anydata anydataset anyschema anytype apply archive archived archivelog are as asc ascii asin assembly assertion associate asynchronous at atan atn2 attr attri attrib attribu attribut attribute attributes audit authenticated authentication authid authors auto autoallocate autodblink autoextend automatic availability avg backup badfile basicfile before begin beginning benchmark between bfile bfile_base big bigfile bin binary_double binary_float binlog bit_and bit_count bit_length bit_or bit_xor bitmap blob_base block blocksize body both bound buffer_cache buffer_pool build bulk by byte byteordermark bytes cache caching call calling cancel capacity cascade cascaded case cast catalog category ceil ceiling chain change changed char_base char_length character_length characters characterset charindex charset charsetform charsetid check checksum checksum_agg child choose chr chunk class cleanup clear client clob clob_base clone close cluster_id cluster_probability cluster_set clustering coalesce coercibility col collate collation collect colu colum column column_value columns columns_updated comment commit compact compatibility compiled complete composite_limit compound compress compute concat concat_ws concurrent confirm conn connec connect connect_by_iscycle connect_by_isleaf connect_by_root connect_time connection consider consistent constant constraint constraints constructor container content contents context contributors controlfile conv convert convert_tz corr corr_k corr_s corresponding corruption cos cost count count_big counted covar_pop covar_samp cpu_per_call cpu_per_session crc32 create creation critical cross cube cume_dist curdate current current_date current_time current_timestamp current_user cursor curtime customdatum cycle data database databases datafile datafiles datalength date_add date_cache date_format date_sub dateadd datediff datefromparts datename datepart datetime2fromparts day day_to_second dayname dayofmonth dayofweek dayofyear days db_role_change dbtimezone ddl deallocate declare decode decompose decrement decrypt deduplicate def defa defau defaul default defaults deferred defi defin define degrees delayed delegate delete delete_all delimited demand dense_rank depth dequeue des_decrypt des_encrypt des_key_file desc descr descri describ describe descriptor deterministic diagnostics difference dimension direct_load directory disable disable_all disallow disassociate discardfile disconnect diskgroup distinct distinctrow distribute distributed div do document domain dotnet double downgrade drop dumpfile duplicate duration each edition editionable editions element ellipsis else elsif elt empty enable enable_all enclosed encode encoding encrypt end end-exec endian enforced engine engines enqueue enterprise entityescaping eomonth error errors escaped evalname evaluate event eventdata events except exception exceptions exchange exclude excluding execu execut execute exempt exists exit exp expire explain export export_set extended extent external external_1 external_2 externally extract failed failed_login_attempts failover failure far fast feature_set feature_value fetch field fields file file_name_convert filesystem_like_logging final finish first first_value fixed flash_cache flashback floor flush following follows for forall force form forma format found found_rows freelist freelists freepools fresh from from_base64 from_days ftp full function general generated get get_format get_lock getdate getutcdate global global_name globally go goto grant grants greatest group group_concat group_id grouping grouping_id groups gtid_subtract guarantee guard handler hash hashkeys having hea head headi headin heading heap help hex hierarchy high high_priority hosts hour http id ident_current ident_incr ident_seed identified identity idle_time if ifnull ignore iif ilike ilm immediate import in include including increment index indexes indexing indextype indicator indices inet6_aton inet6_ntoa inet_aton inet_ntoa infile initial initialized initially initrans inmemory inner innodb input insert install instance instantiable instr interface interleaved intersect into invalidate invisible is is_free_lock is_ipv4 is_ipv4_compat is_not is_not_null is_used_lock isdate isnull isolation iterate java join json json_exists keep keep_duplicates key keys kill language large last last_day last_insert_id last_value lax lcase lead leading least leaves left len lenght length less level levels library like like2 like4 likec limit lines link list listagg little ln load load_file lob lobs local localtime localtimestamp locate locator lock locked log log10 log2 logfile logfiles logging logical logical_reads_per_call logoff logon logs long loop low low_priority lower lpad lrtrim ltrim main make_set makedate maketime managed management manual map mapping mask master master_pos_wait match matched materialized max maxextents maximize maxinstances maxlen maxlogfiles maxloghistory maxlogmembers maxsize maxtrans md5 measures median medium member memcompress memory merge microsecond mid migration min minextents minimum mining minus minute minvalue missing mod mode model modification modify module monitoring month months mount move movement multiset mutex name name_const names nan national native natural nav nchar nclob nested never new newline next nextval no no_write_to_binlog noarchivelog noaudit nobadfile nocheck nocompress nocopy nocycle nodelay nodiscardfile noentityescaping noguarantee nokeep nologfile nomapping nomaxvalue nominimize nominvalue nomonitoring none noneditionable nonschema noorder nopr nopro noprom nopromp noprompt norely noresetlogs noreverse normal norowdependencies noschemacheck noswitch not nothing notice notrim novalidate now nowait nth_value nullif nulls num numb numbe nvarchar nvarchar2 object ocicoll ocidate ocidatetime ociduration ociinterval ociloblocator ocinumber ociref ocirefcursor ocirowid ocistring ocitype oct octet_length of off offline offset oid oidindex old on online only opaque open operations operator optimal optimize option optionally or oracle oracle_date oradata ord ordaudio orddicom orddoc order ordimage ordinality ordvideo organization orlany orlvary out outer outfile outline output over overflow overriding package pad parallel parallel_enable parameters parent parse partial partition partitions pascal passing password password_grace_time password_lock_time password_reuse_max password_reuse_time password_verify_function patch path patindex pctincrease pctthreshold pctused pctversion percent percent_rank percentile_cont percentile_disc performance period period_add period_diff permanent physical pi pipe pipelined pivot pluggable plugin policy position post_transaction pow power pragma prebuilt precedes preceding precision prediction prediction_cost prediction_details prediction_probability prediction_set prepare present preserve prior priority private private_sga privileges procedural procedure procedure_analyze processlist profiles project prompt protection public publishingservername purge quarter query quick quiesce quota quotename radians raise rand range rank raw read reads readsize rebuild record records recover recovery recursive recycle redo reduced ref reference referenced references referencing refresh regexp_like register regr_avgx regr_avgy regr_count regr_intercept regr_r2 regr_slope regr_sxx regr_sxy reject rekey relational relative relaylog release release_lock relies_on relocate rely rem remainder rename repair repeat replace replicate replication required reset resetlogs resize resource respect restore restricted result result_cache resumable resume retention return returning returns reuse reverse revoke right rlike role roles rollback rolling rollup round row row_count rowdependencies rowid rownum rows rtrim rules safe salt sample save savepoint sb1 sb2 sb4 scan schema schemacheck scn scope scroll sdo_georaster sdo_topo_geometry search sec_to_time second section securefile security seed segment select self sequence sequential serializable server servererror session session_user sessions_per_user set sets settings sha sha1 sha2 share shared shared_pool short show shrink shutdown si_averagecolor si_colorhistogram si_featurelist si_positionalcolor si_stillimage si_texture siblings sid sign sin size size_t sizes skip slave sleep smalldatetimefromparts smallfile snapshot some soname sort soundex source space sparse spfile split sql sql_big_result sql_buffer_result sql_cache sql_calc_found_rows sql_small_result sql_variant_property sqlcode sqldata sqlerror sqlname sqlstate sqrt square standalone standby start starting startup statement static statistics stats_binomial_test stats_crosstab stats_ks_test stats_mode stats_mw_test stats_one_way_anova stats_t_test_ stats_t_test_indep stats_t_test_one stats_t_test_paired stats_wsr_test status std stddev stddev_pop stddev_samp stdev stop storage store stored str str_to_date straight_join strcmp strict string struct stuff style subdate subpartition subpartitions substitutable substr substring subtime subtring_index subtype success sum suspend switch switchoffset switchover sync synchronous synonym sys sys_xmlagg sysasm sysaux sysdate sysdatetimeoffset sysdba sysoper system system_user sysutcdatetime table tables tablespace tan tdo template temporary terminated tertiary_weights test than then thread through tier ties time time_format time_zone timediff timefromparts timeout timestamp timestampadd timestampdiff timezone_abbr timezone_minute timezone_region to to_base64 to_date to_days to_seconds todatetimeoffset trace tracking transaction transactional translate translation treat trigger trigger_nestlevel triggers trim truncate try_cast try_convert try_parse type ub1 ub2 ub4 ucase unarchived unbounded uncompress under undo unhex unicode uniform uninstall union unique unix_timestamp unknown unlimited unlock unpivot unrecoverable unsafe unsigned until untrusted unusable unused update updated upgrade upped upper upsert url urowid usable usage use use_stored_outlines user user_data user_resources users using utc_date utc_timestamp uuid uuid_short validate validate_password_strength validation valist value values var var_samp varcharc vari varia variab variabl variable variables variance varp varraw varrawc varray verify version versions view virtual visible void wait wallet warning warnings week weekday weekofyear wellformed when whene whenev wheneve whenever where while whitespace with within without work wrapped xdb xml xmlagg xmlattributes xmlcast xmlcolattval xmlelement xmlexists xmlforest xmlindex xmlnamespaces xmlpi xmlquery xmlroot xmlschema xmlserialize xmltable xmltype xor year year_to_month years yearweek",literal:"true false null",built_in:"array bigint binary bit blob boolean char character date dec decimal float int int8 integer interval number numeric real record serial serial8 smallint text varchar varying void"},c:[{cN:"string",b:"'",e:"'",c:[e.BE,{b:"''"}]},{cN:"string",b:'"',e:'"',c:[e.BE,{b:'""'}]},{cN:"string",b:"`",e:"`",c:[e.BE]},e.CNM,e.CBCM,t]},e.CBCM,t]}});hljs.registerLanguage("r",function(e){var r="([a-zA-Z]|\\.[a-zA-Z.])[a-zA-Z0-9._]*";return{c:[e.HCM,{b:r,l:r,k:{keyword:"function if in break next repeat else for return switch while try tryCatch stop warning require library attach detach source setMethod setGeneric setGroupGeneric setClass ...",literal:"NULL NA TRUE FALSE T F Inf NaN NA_integer_|10 NA_real_|10 NA_character_|10 NA_complex_|10"},r:0},{cN:"number",b:"0[xX][0-9a-fA-F]+[Li]?\\b",r:0},{cN:"number",b:"\\d+(?:[eE][+\\-]?\\d*)?L\\b",r:0},{cN:"number",b:"\\d+\\.(?!\\d)(?:i\\b)?",r:0},{cN:"number",b:"\\d+(?:\\.\\d*)?(?:[eE][+\\-]?\\d*)?i?\\b",r:0},{cN:"number",b:"\\.\\d+(?:[eE][+\\-]?\\d*)?i?\\b",r:0},{b:"`",e:"`",r:0},{cN:"string",c:[e.BE],v:[{b:'"',e:'"'},{b:"'",e:"'"}]}]}});hljs.registerLanguage("perl",function(e){var t="getpwent getservent quotemeta msgrcv scalar kill dbmclose undef lc ma syswrite tr send umask sysopen shmwrite vec qx utime local oct semctl localtime readpipe do return format read sprintf dbmopen pop getpgrp not getpwnam rewinddir qqfileno qw endprotoent wait sethostent bless s|0 opendir continue each sleep endgrent shutdown dump chomp connect getsockname die socketpair close flock exists index shmgetsub for endpwent redo lstat msgctl setpgrp abs exit select print ref gethostbyaddr unshift fcntl syscall goto getnetbyaddr join gmtime symlink semget splice x|0 getpeername recv log setsockopt cos last reverse gethostbyname getgrnam study formline endhostent times chop length gethostent getnetent pack getprotoent getservbyname rand mkdir pos chmod y|0 substr endnetent printf next open msgsnd readdir use unlink getsockopt getpriority rindex wantarray hex system getservbyport endservent int chr untie rmdir prototype tell listen fork shmread ucfirst setprotoent else sysseek link getgrgid shmctl waitpid unpack getnetbyname reset chdir grep split require caller lcfirst until warn while values shift telldir getpwuid my getprotobynumber delete and sort uc defined srand accept package seekdir getprotobyname semop our rename seek if q|0 chroot sysread setpwent no crypt getc chown sqrt write setnetent setpriority foreach tie sin msgget map stat getlogin unless elsif truncate exec keys glob tied closedirioctl socket readlink eval xor readline binmode setservent eof ord bind alarm pipe atan2 getgrent exp time push setgrent gt lt or ne m|0 break given say state when",r={cN:"subst",b:"[$@]\\{",e:"\\}",k:t},s={b:"->{",e:"}"},n={v:[{b:/\$\d/},{b:/[\$%@](\^\w\b|#\w+(::\w+)*|{\w+}|\w+(::\w*)*)/},{b:/[\$%@][^\s\w{]/,r:0}]},i=[e.BE,r,n],o=[n,e.HCM,e.C("^\\=\\w","\\=cut",{eW:!0}),s,{cN:"string",c:i,v:[{b:"q[qwxr]?\\s*\\(",e:"\\)",r:5},{b:"q[qwxr]?\\s*\\[",e:"\\]",r:5},{b:"q[qwxr]?\\s*\\{",e:"\\}",r:5},{b:"q[qwxr]?\\s*\\|",e:"\\|",r:5},{b:"q[qwxr]?\\s*\\<",e:"\\>",r:5},{b:"qw\\s+q",e:"q",r:5},{b:"'",e:"'",c:[e.BE]},{b:'"',e:'"'},{b:"`",e:"`",c:[e.BE]},{b:"{\\w+}",c:[],r:0},{b:"-?\\w+\\s*\\=\\>",c:[],r:0}]},{cN:"number",b:"(\\b0[0-7_]+)|(\\b0x[0-9a-fA-F_]+)|(\\b[1-9][0-9_]*(\\.[0-9_]+)?)|[0_]\\b",r:0},{b:"(\\/\\/|"+e.RSR+"|\\b(split|return|print|reverse|grep)\\b)\\s*",k:"split return print reverse grep",r:0,c:[e.HCM,{cN:"regexp",b:"(s|tr|y)/(\\\\.|[^/])*/(\\\\.|[^/])*/[a-z]*",r:10},{cN:"regexp",b:"(m|qr)?/",e:"/[a-z]*",c:[e.BE],r:0}]},{cN:"function",bK:"sub",e:"(\\s*\\(.*?\\))?[;{]",eE:!0,r:5,c:[e.TM]},{b:"-\\w\\b",r:0},{b:"^__DATA__$",e:"^__END__$",sL:"mojolicious",c:[{b:"^@@.*",e:"$",cN:"comment"}]}];return r.c=o,s.c=o,{aliases:["pl","pm"],l:/[\w\.]+/,k:t,c:o}});hljs.registerLanguage("ini",function(e){var b={cN:"string",c:[e.BE],v:[{b:"'''",e:"'''",r:10},{b:'"""',e:'"""',r:10},{b:'"',e:'"'},{b:"'",e:"'"}]};return{aliases:["toml"],cI:!0,i:/\S/,c:[e.C(";","$"),e.HCM,{cN:"section",b:/^\s*\[+/,e:/\]+/},{b:/^[a-z0-9\[\]_-]+\s*=\s*/,e:"$",rB:!0,c:[{cN:"attr",b:/[a-z0-9\[\]_-]+/},{b:/=/,eW:!0,r:0,c:[{cN:"literal",b:/\bon|off|true|false|yes|no\b/},{cN:"variable",v:[{b:/\$[\w\d"][\w\d_]*/},{b:/\$\{(.*?)}/}]},b,{cN:"number",b:/([\+\-]+)?[\d]+_[\d_]+/},e.NM]}]}]}});hljs.registerLanguage("diff",function(e){return{aliases:["patch"],c:[{cN:"meta",r:10,v:[{b:/^@@ +\-\d+,\d+ +\+\d+,\d+ +@@$/},{b:/^\*\*\* +\d+,\d+ +\*\*\*\*$/},{b:/^\-\-\- +\d+,\d+ +\-\-\-\-$/}]},{cN:"comment",v:[{b:/Index: /,e:/$/},{b:/={3,}/,e:/$/},{b:/^\-{3}/,e:/$/},{b:/^\*{3} /,e:/$/},{b:/^\+{3}/,e:/$/},{b:/\*{5}/,e:/\*{5}$/}]},{cN:"addition",b:"^\\+",e:"$"},{cN:"deletion",b:"^\\-",e:"$"},{cN:"addition",b:"^\\!",e:"$"}]}});hljs.registerLanguage("go",function(e){var t={keyword:"break default func interface select case map struct chan else goto package switch const fallthrough if range type continue for import return var go defer bool byte complex64 complex128 float32 float64 int8 int16 int32 int64 string uint8 uint16 uint32 uint64 int uint uintptr rune",literal:"true false iota nil",built_in:"append cap close complex copy imag len make new panic print println real recover delete"};return{aliases:["golang"],k:t,i:"</",c:[e.CLCM,e.CBCM,{cN:"string",v:[e.QSM,{b:"'",e:"[^\\\\]'"},{b:"`",e:"`"}]},{cN:"number",v:[{b:e.CNR+"[dflsi]",r:1},e.CNM]},{b:/:=/},{cN:"function",bK:"func",e:/\s*\{/,eE:!0,c:[e.TM,{cN:"params",b:/\(/,e:/\)/,k:t,i:/["']/}]}]}});hljs.registerLanguage("bash",function(e){var t={cN:"variable",v:[{b:/\$[\w\d#@][\w\d_]*/},{b:/\$\{(.*?)}/}]},s={cN:"string",b:/"/,e:/"/,c:[e.BE,t,{cN:"variable",b:/\$\(/,e:/\)/,c:[e.BE]}]},a={cN:"string",b:/'/,e:/'/};return{aliases:["sh","zsh"],l:/\b-?[a-z\._]+\b/,k:{keyword:"if then else elif fi for while in do done case esac function",literal:"true false",built_in:"break cd continue eval exec exit export getopts hash pwd readonly return shift test times trap umask unset alias bind builtin caller command declare echo enable help let local logout mapfile printf read readarray source type typeset ulimit unalias set shopt autoload bg bindkey bye cap chdir clone comparguments compcall compctl compdescribe compfiles compgroups compquote comptags comptry compvalues dirs disable disown echotc echoti emulate fc fg float functions getcap getln history integer jobs kill limit log noglob popd print pushd pushln rehash sched setcap setopt stat suspend ttyctl unfunction unhash unlimit unsetopt vared wait whence where which zcompile zformat zftp zle zmodload zparseopts zprof zpty zregexparse zsocket zstyle ztcp",_:"-ne -eq -lt -gt -f -d -e -s -l -a"},c:[{cN:"meta",b:/^#![^\n]+sh\s*$/,r:10},{cN:"function",b:/\w[\w\d_]*\s*\(\s*\)\s*\{/,rB:!0,c:[e.inherit(e.TM,{b:/\w[\w\d_]*/})],r:0},e.HCM,s,a,t]}});hljs.registerLanguage("python",function(e){var r={keyword:"and elif is global as in if from raise for except finally print import pass return exec else break not with class assert yield try while continue del or def lambda async await nonlocal|10 None True False",built_in:"Ellipsis NotImplemented"},b={cN:"meta",b:/^(>>>|\.\.\.) /},c={cN:"subst",b:/\{/,e:/\}/,k:r,i:/#/},a={cN:"string",c:[e.BE],v:[{b:/(u|b)?r?'''/,e:/'''/,c:[b],r:10},{b:/(u|b)?r?"""/,e:/"""/,c:[b],r:10},{b:/(fr|rf|f)'''/,e:/'''/,c:[b,c]},{b:/(fr|rf|f)"""/,e:/"""/,c:[b,c]},{b:/(u|r|ur)'/,e:/'/,r:10},{b:/(u|r|ur)"/,e:/"/,r:10},{b:/(b|br)'/,e:/'/},{b:/(b|br)"/,e:/"/},{b:/(fr|rf|f)'/,e:/'/,c:[c]},{b:/(fr|rf|f)"/,e:/"/,c:[c]},e.ASM,e.QSM]},s={cN:"number",r:0,v:[{b:e.BNR+"[lLjJ]?"},{b:"\\b(0o[0-7]+)[lLjJ]?"},{b:e.CNR+"[lLjJ]?"}]},i={cN:"params",b:/\(/,e:/\)/,c:["self",b,s,a]};return c.c=[a,s,b],{aliases:["py","gyp"],k:r,i:/(<\/|->|\?)|=>/,c:[b,s,a,e.HCM,{v:[{cN:"function",bK:"def"},{cN:"class",bK:"class"}],e:/:/,i:/[${=;\n,]/,c:[e.UTM,i,{b:/->/,eW:!0,k:"None"}]},{cN:"meta",b:/^[\t ]*@/,e:/$/},{b:/\b(print|exec)\(/}]}});hljs.registerLanguage("julia",function(e){var r={keyword:"in isa where baremodule begin break catch ccall const continue do else elseif end export false finally for function global if import importall let local macro module quote return true try using while type immutable abstract bitstype typealias ",literal:"true false ARGS C_NULL DevNull ENDIAN_BOM ENV I Inf Inf16 Inf32 Inf64 InsertionSort JULIA_HOME LOAD_PATH MergeSort NaN NaN16 NaN32 NaN64 PROGRAM_FILE QuickSort RoundDown RoundFromZero RoundNearest RoundNearestTiesAway RoundNearestTiesUp RoundToZero RoundUp STDERR STDIN STDOUT VERSION catalan e|0 eu|0 eulergamma golden im nothing pi γ π φ ",built_in:"ANY AbstractArray AbstractChannel AbstractFloat AbstractMatrix AbstractRNG AbstractSerializer AbstractSet AbstractSparseArray AbstractSparseMatrix AbstractSparseVector AbstractString AbstractUnitRange AbstractVecOrMat AbstractVector Any ArgumentError Array AssertionError Associative Base64DecodePipe Base64EncodePipe Bidiagonal BigFloat BigInt BitArray BitMatrix BitVector Bool BoundsError BufferStream CachingPool CapturedException CartesianIndex CartesianRange Cchar Cdouble Cfloat Channel Char Cint Cintmax_t Clong Clonglong ClusterManager Cmd CodeInfo Colon Complex Complex128 Complex32 Complex64 CompositeException Condition ConjArray ConjMatrix ConjVector Cptrdiff_t Cshort Csize_t Cssize_t Cstring Cuchar Cuint Cuintmax_t Culong Culonglong Cushort Cwchar_t Cwstring DataType Date DateFormat DateTime DenseArray DenseMatrix DenseVecOrMat DenseVector Diagonal Dict DimensionMismatch Dims DirectIndexString Display DivideError DomainError EOFError EachLine Enum Enumerate ErrorException Exception ExponentialBackOff Expr Factorization FileMonitor Float16 Float32 Float64 Function Future GlobalRef GotoNode HTML Hermitian IO IOBuffer IOContext IOStream IPAddr IPv4 IPv6 IndexCartesian IndexLinear IndexStyle InexactError InitError Int Int128 Int16 Int32 Int64 Int8 IntSet Integer InterruptException InvalidStateException Irrational KeyError LabelNode LinSpace LineNumberNode LoadError LowerTriangular MIME Matrix MersenneTwister Method MethodError MethodTable Module NTuple NewvarNode NullException Nullable Number ObjectIdDict OrdinalRange OutOfMemoryError OverflowError Pair ParseError PartialQuickSort PermutedDimsArray Pipe PollingFileWatcher ProcessExitedException Ptr QuoteNode RandomDevice Range RangeIndex Rational RawFD ReadOnlyMemoryError Real ReentrantLock Ref Regex RegexMatch RemoteChannel RemoteException RevString RoundingMode RowVector SSAValue SegmentationFault SerializationState Set SharedArray SharedMatrix SharedVector Signed SimpleVector Slot SlotNumber SparseMatrixCSC SparseVector StackFrame StackOverflowError StackTrace StepRange StepRangeLen StridedArray StridedMatrix StridedVecOrMat StridedVector String SubArray SubString SymTridiagonal Symbol Symmetric SystemError TCPSocket Task Text TextDisplay Timer Tridiagonal Tuple Type TypeError TypeMapEntry TypeMapLevel TypeName TypeVar TypedSlot UDPSocket UInt UInt128 UInt16 UInt32 UInt64 UInt8 UndefRefError UndefVarError UnicodeError UniformScaling Union UnionAll UnitRange Unsigned UpperTriangular Val Vararg VecElement VecOrMat Vector VersionNumber Void WeakKeyDict WeakRef WorkerConfig WorkerPool "},t="[A-Za-z_\\u00A1-\\uFFFF][A-Za-z_0-9\\u00A1-\\uFFFF]*",a={l:t,k:r,i:/<\//},n={cN:"number",b:/(\b0x[\d_]*(\.[\d_]*)?|0x\.\d[\d_]*)p[-+]?\d+|\b0[box][a-fA-F0-9][a-fA-F0-9_]*|(\b\d[\d_]*(\.[\d_]*)?|\.\d[\d_]*)([eEfF][-+]?\d+)?/,r:0},o={cN:"string",b:/'(.|\\[xXuU][a-zA-Z0-9]+)'/},i={cN:"subst",b:/\$\(/,e:/\)/,k:r},l={cN:"variable",b:"\\$"+t},c={cN:"string",c:[e.BE,i,l],v:[{b:/\w*"""/,e:/"""\w*/,r:10},{b:/\w*"/,e:/"\w*/}]},s={cN:"string",c:[e.BE,i,l],b:"`",e:"`"},d={cN:"meta",b:"@"+t},u={cN:"comment",v:[{b:"#=",e:"=#",r:10},{b:"#",e:"$"}]};return a.c=[n,o,c,s,d,u,e.HCM,{cN:"keyword",b:"\\b(((abstract|primitive)\\s+)type|(mutable\\s+)?struct)\\b"},{b:/<:/}],i.c=a.c,a});hljs.registerLanguage("coffeescript",function(e){var c={keyword:"in if for while finally new do return else break catch instanceof throw try this switch continue typeof delete debugger super yield import export from as default await then unless until loop of by when and or is isnt not",literal:"true false null undefined yes no on off",built_in:"npm require console print module global window document"},n="[A-Za-z$_][0-9A-Za-z$_]*",r={cN:"subst",b:/#\{/,e:/}/,k:c},i=[e.BNM,e.inherit(e.CNM,{starts:{e:"(\\s*/)?",r:0}}),{cN:"string",v:[{b:/'''/,e:/'''/,c:[e.BE]},{b:/'/,e:/'/,c:[e.BE]},{b:/"""/,e:/"""/,c:[e.BE,r]},{b:/"/,e:/"/,c:[e.BE,r]}]},{cN:"regexp",v:[{b:"///",e:"///",c:[r,e.HCM]},{b:"//[gim]*",r:0},{b:/\/(?![ *])(\\\/|.)*?\/[gim]*(?=\W|$)/}]},{b:"@"+n},{sL:"javascript",eB:!0,eE:!0,v:[{b:"```",e:"```"},{b:"`",e:"`"}]}];r.c=i;var s=e.inherit(e.TM,{b:n}),t="(\\(.*\\))?\\s*\\B[-=]>",o={cN:"params",b:"\\([^\\(]",rB:!0,c:[{b:/\(/,e:/\)/,k:c,c:["self"].concat(i)}]};return{aliases:["coffee","cson","iced"],k:c,i:/\/\*/,c:i.concat([e.C("###","###"),e.HCM,{cN:"function",b:"^\\s*"+n+"\\s*=\\s*"+t,e:"[-=]>",rB:!0,c:[s,o]},{b:/[:\(,=]\s*/,r:0,c:[{cN:"function",b:t,e:"[-=]>",rB:!0,c:[o]}]},{cN:"class",bK:"class",e:"$",i:/[:="\[\]]/,c:[{bK:"extends",eW:!0,i:/[:="\[\]]/,c:[s]},s]},{b:n+":",e:":",rB:!0,rE:!0,r:0}])}});hljs.registerLanguage("cpp",function(t){var e={cN:"keyword",b:"\\b[a-z\\d_]*_t\\b"},r={cN:"string",v:[{b:'(u8?|U)?L?"',e:'"',i:"\\n",c:[t.BE]},{b:'(u8?|U)?R"',e:'"',c:[t.BE]},{b:"'\\\\?.",e:"'",i:"."}]},s={cN:"number",v:[{b:"\\b(0b[01']+)"},{b:"(-?)\\b([\\d']+(\\.[\\d']*)?|\\.[\\d']+)(u|U|l|L|ul|UL|f|F|b|B)"},{b:"(-?)(\\b0[xX][a-fA-F0-9']+|(\\b[\\d']+(\\.[\\d']*)?|\\.[\\d']+)([eE][-+]?[\\d']+)?)"}],r:0},i={cN:"meta",b:/#\s*[a-z]+\b/,e:/$/,k:{"meta-keyword":"if else elif endif define undef warning error line pragma ifdef ifndef include"},c:[{b:/\\\n/,r:0},t.inherit(r,{cN:"meta-string"}),{cN:"meta-string",b:/<[^\n>]*>/,e:/$/,i:"\\n"},t.CLCM,t.CBCM]},a=t.IR+"\\s*\\(",c={keyword:"int float while private char catch import module export virtual operator sizeof dynamic_cast|10 typedef const_cast|10 const for static_cast|10 union namespace unsigned long volatile static protected bool template mutable if public friend do goto auto void enum else break extern using asm case typeid short reinterpret_cast|10 default double register explicit signed typename try this switch continue inline delete alignof constexpr decltype noexcept static_assert thread_local restrict _Bool complex _Complex _Imaginary atomic_bool atomic_char atomic_schar atomic_uchar atomic_short atomic_ushort atomic_int atomic_uint atomic_long atomic_ulong atomic_llong atomic_ullong new throw return and or not",built_in:"std string cin cout cerr clog stdin stdout stderr stringstream istringstream ostringstream auto_ptr deque list queue stack vector map set bitset multiset multimap unordered_set unordered_map unordered_multiset unordered_multimap array shared_ptr abort abs acos asin atan2 atan calloc ceil cosh cos exit exp fabs floor fmod fprintf fputs free frexp fscanf isalnum isalpha iscntrl isdigit isgraph islower isprint ispunct isspace isupper isxdigit tolower toupper labs ldexp log10 log malloc realloc memchr memcmp memcpy memset modf pow printf putchar puts scanf sinh sin snprintf sprintf sqrt sscanf strcat strchr strcmp strcpy strcspn strlen strncat strncmp strncpy strpbrk strrchr strspn strstr tanh tan vfprintf vprintf vsprintf endl initializer_list unique_ptr",literal:"true false nullptr NULL"},n=[e,t.CLCM,t.CBCM,s,r];return{aliases:["c","cc","h","c++","h++","hpp"],k:c,i:"</",c:n.concat([i,{b:"\\b(deque|list|queue|stack|vector|map|set|bitset|multiset|multimap|unordered_map|unordered_set|unordered_multiset|unordered_multimap|array)\\s*<",e:">",k:c,c:["self",e]},{b:t.IR+"::",k:c},{v:[{b:/=/,e:/;/},{b:/\(/,e:/\)/},{bK:"new throw return else",e:/;/}],k:c,c:n.concat([{b:/\(/,e:/\)/,k:c,c:n.concat(["self"]),r:0}]),r:0},{cN:"function",b:"("+t.IR+"[\\*&\\s]+)+"+a,rB:!0,e:/[{;=]/,eE:!0,k:c,i:/[^\w\s\*&]/,c:[{b:a,rB:!0,c:[t.TM],r:0},{cN:"params",b:/\(/,e:/\)/,k:c,r:0,c:[t.CLCM,t.CBCM,r,s,e]},t.CLCM,t.CBCM,i]},{cN:"class",bK:"class struct",e:/[{;:]/,c:[{b:/</,e:/>/,c:["self"]},t.TM]}]),exports:{preprocessor:i,strings:r,k:c}}});hljs.registerLanguage("ruby",function(e){var b="[a-zA-Z_]\\w*[!?=]?|[-+~]\\@|<<|>>|=~|===?|<=>|[<>]=?|\\*\\*|[-/+%^&*~`|]|\\[\\]=?",r={keyword:"and then defined module in return redo if BEGIN retry end for self when next until do begin unless END rescue else break undef not super class case require yield alias while ensure elsif or include attr_reader attr_writer attr_accessor",literal:"true false nil"},c={cN:"doctag",b:"@[A-Za-z]+"},a={b:"#<",e:">"},s=[e.C("#","$",{c:[c]}),e.C("^\\=begin","^\\=end",{c:[c],r:10}),e.C("^__END__","\\n$")],n={cN:"subst",b:"#\\{",e:"}",k:r},t={cN:"string",c:[e.BE,n],v:[{b:/'/,e:/'/},{b:/"/,e:/"/},{b:/`/,e:/`/},{b:"%[qQwWx]?\\(",e:"\\)"},{b:"%[qQwWx]?\\[",e:"\\]"},{b:"%[qQwWx]?{",e:"}"},{b:"%[qQwWx]?<",e:">"},{b:"%[qQwWx]?/",e:"/"},{b:"%[qQwWx]?%",e:"%"},{b:"%[qQwWx]?-",e:"-"},{b:"%[qQwWx]?\\|",e:"\\|"},{b:/\B\?(\\\d{1,3}|\\x[A-Fa-f0-9]{1,2}|\\u[A-Fa-f0-9]{4}|\\?\S)\b/},{b:/<<(-?)\w+$/,e:/^\s*\w+$/}]},i={cN:"params",b:"\\(",e:"\\)",endsParent:!0,k:r},d=[t,a,{cN:"class",bK:"class module",e:"$|;",i:/=/,c:[e.inherit(e.TM,{b:"[A-Za-z_]\\w*(::\\w+)*(\\?|\\!)?"}),{b:"<\\s*",c:[{b:"("+e.IR+"::)?"+e.IR}]}].concat(s)},{cN:"function",bK:"def",e:"$|;",c:[e.inherit(e.TM,{b:b}),i].concat(s)},{b:e.IR+"::"},{cN:"symbol",b:e.UIR+"(\\!|\\?)?:",r:0},{cN:"symbol",b:":(?!\\s)",c:[t,{b:b}],r:0},{cN:"number",b:"(\\b0[0-7_]+)|(\\b0x[0-9a-fA-F_]+)|(\\b[1-9][0-9_]*(\\.[0-9_]+)?)|[0_]\\b",r:0},{b:"(\\$\\W)|((\\$|\\@\\@?)(\\w+))"},{cN:"params",b:/\|/,e:/\|/,k:r},{b:"("+e.RSR+"|unless)\\s*",k:"unless",c:[a,{cN:"regexp",c:[e.BE,n],i:/\n/,v:[{b:"/",e:"/[a-z]*"},{b:"%r{",e:"}[a-z]*"},{b:"%r\\(",e:"\\)[a-z]*"},{b:"%r!",e:"![a-z]*"},{b:"%r\\[",e:"\\][a-z]*"}]}].concat(s),r:0}].concat(s);n.c=d,i.c=d;var l="[>?]>",o="[\\w#]+\\(\\w+\\):\\d+:\\d+>",u="(\\w+-)?\\d+\\.\\d+\\.\\d(p\\d+)?[^>]+>",w=[{b:/^\s*=>/,starts:{e:"$",c:d}},{cN:"meta",b:"^("+l+"|"+o+"|"+u+")",starts:{e:"$",c:d}}];return{aliases:["rb","gemspec","podspec","thor","irb"],k:r,i:/\/\*/,c:s.concat(w).concat(d)}});hljs.registerLanguage("yaml",function(e){var b="true false yes no null",a="^[ \\-]*",r="[a-zA-Z_][\\w\\-]*",t={cN:"attr",v:[{b:a+r+":"},{b:a+'"'+r+'":'},{b:a+"'"+r+"':"}]},c={cN:"template-variable",v:[{b:"{{",e:"}}"},{b:"%{",e:"}"}]},l={cN:"string",r:0,v:[{b:/'/,e:/'/},{b:/"/,e:/"/},{b:/\S+/}],c:[e.BE,c]};return{cI:!0,aliases:["yml","YAML","yaml"],c:[t,{cN:"meta",b:"^---s*$",r:10},{cN:"string",b:"[\\|>] *$",rE:!0,c:l.c,e:t.v[0].b},{b:"<%[%=-]?",e:"[%-]?%>",sL:"ruby",eB:!0,eE:!0,r:0},{cN:"type",b:"!!"+e.UIR},{cN:"meta",b:"&"+e.UIR+"$"},{cN:"meta",b:"\\*"+e.UIR+"$"},{cN:"bullet",b:"^ *-",r:0},e.HCM,{bK:b,k:{literal:b}},e.CNM,l]}});hljs.registerLanguage("css",function(e){var c="[a-zA-Z-][a-zA-Z0-9_-]*",t={b:/[A-Z\_\.\-]+\s*:/,rB:!0,e:";",eW:!0,c:[{cN:"attribute",b:/\S/,e:":",eE:!0,starts:{eW:!0,eE:!0,c:[{b:/[\w-]+\(/,rB:!0,c:[{cN:"built_in",b:/[\w-]+/},{b:/\(/,e:/\)/,c:[e.ASM,e.QSM]}]},e.CSSNM,e.QSM,e.ASM,e.CBCM,{cN:"number",b:"#[0-9A-Fa-f]+"},{cN:"meta",b:"!important"}]}}]};return{cI:!0,i:/[=\/|'\$]/,c:[e.CBCM,{cN:"selector-id",b:/#[A-Za-z0-9_-]+/},{cN:"selector-class",b:/\.[A-Za-z0-9_-]+/},{cN:"selector-attr",b:/\[/,e:/\]/,i:"$"},{cN:"selector-pseudo",b:/:(:)?[a-zA-Z0-9\_\-\+\(\)"'.]+/},{b:"@(font-face|page)",l:"[a-z-]+",k:"font-face page"},{b:"@",e:"[{;]",i:/:/,c:[{cN:"keyword",b:/\w+/},{b:/\s/,eW:!0,eE:!0,r:0,c:[e.ASM,e.QSM,e.CSSNM]}]},{cN:"selector-tag",b:c,r:0},{b:"{",e:"}",i:/\S/,c:[e.CBCM,t]}]}});hljs.registerLanguage("fortran",function(e){var t={cN:"params",b:"\\(",e:"\\)"},n={literal:".False. .True.",keyword:"kind do while private call intrinsic where elsewhere type endtype endmodule endselect endinterface end enddo endif if forall endforall only contains default return stop then public subroutine|10 function program .and. .or. .not. .le. .eq. .ge. .gt. .lt. goto save else use module select case access blank direct exist file fmt form formatted iostat name named nextrec number opened rec recl sequential status unformatted unit continue format pause cycle exit c_null_char c_alert c_backspace c_form_feed flush wait decimal round iomsg synchronous nopass non_overridable pass protected volatile abstract extends import non_intrinsic value deferred generic final enumerator class associate bind enum c_int c_short c_long c_long_long c_signed_char c_size_t c_int8_t c_int16_t c_int32_t c_int64_t c_int_least8_t c_int_least16_t c_int_least32_t c_int_least64_t c_int_fast8_t c_int_fast16_t c_int_fast32_t c_int_fast64_t c_intmax_t C_intptr_t c_float c_double c_long_double c_float_complex c_double_complex c_long_double_complex c_bool c_char c_null_ptr c_null_funptr c_new_line c_carriage_return c_horizontal_tab c_vertical_tab iso_c_binding c_loc c_funloc c_associated  c_f_pointer c_ptr c_funptr iso_fortran_env character_storage_size error_unit file_storage_size input_unit iostat_end iostat_eor numeric_storage_size output_unit c_f_procpointer ieee_arithmetic ieee_support_underflow_control ieee_get_underflow_mode ieee_set_underflow_mode newunit contiguous recursive pad position action delim readwrite eor advance nml interface procedure namelist include sequence elemental pure integer real character complex logical dimension allocatable|10 parameter external implicit|10 none double precision assign intent optional pointer target in out common equivalence data",built_in:"alog alog10 amax0 amax1 amin0 amin1 amod cabs ccos cexp clog csin csqrt dabs dacos dasin datan datan2 dcos dcosh ddim dexp dint dlog dlog10 dmax1 dmin1 dmod dnint dsign dsin dsinh dsqrt dtan dtanh float iabs idim idint idnint ifix isign max0 max1 min0 min1 sngl algama cdabs cdcos cdexp cdlog cdsin cdsqrt cqabs cqcos cqexp cqlog cqsin cqsqrt dcmplx dconjg derf derfc dfloat dgamma dimag dlgama iqint qabs qacos qasin qatan qatan2 qcmplx qconjg qcos qcosh qdim qerf qerfc qexp qgamma qimag qlgama qlog qlog10 qmax1 qmin1 qmod qnint qsign qsin qsinh qsqrt qtan qtanh abs acos aimag aint anint asin atan atan2 char cmplx conjg cos cosh exp ichar index int log log10 max min nint sign sin sinh sqrt tan tanh print write dim lge lgt lle llt mod nullify allocate deallocate adjustl adjustr all allocated any associated bit_size btest ceiling count cshift date_and_time digits dot_product eoshift epsilon exponent floor fraction huge iand ibclr ibits ibset ieor ior ishft ishftc lbound len_trim matmul maxexponent maxloc maxval merge minexponent minloc minval modulo mvbits nearest pack present product radix random_number random_seed range repeat reshape rrspacing scale scan selected_int_kind selected_real_kind set_exponent shape size spacing spread sum system_clock tiny transpose trim ubound unpack verify achar iachar transfer dble entry dprod cpu_time command_argument_count get_command get_command_argument get_environment_variable is_iostat_end ieee_arithmetic ieee_support_underflow_control ieee_get_underflow_mode ieee_set_underflow_mode is_iostat_eor move_alloc new_line selected_char_kind same_type_as extends_type_ofacosh asinh atanh bessel_j0 bessel_j1 bessel_jn bessel_y0 bessel_y1 bessel_yn erf erfc erfc_scaled gamma log_gamma hypot norm2 atomic_define atomic_ref execute_command_line leadz trailz storage_size merge_bits bge bgt ble blt dshiftl dshiftr findloc iall iany iparity image_index lcobound ucobound maskl maskr num_images parity popcnt poppar shifta shiftl shiftr this_image"};return{cI:!0,aliases:["f90","f95"],k:n,i:/\/\*/,c:[e.inherit(e.ASM,{cN:"string",r:0}),e.inherit(e.QSM,{cN:"string",r:0}),{cN:"function",bK:"subroutine function program",i:"[${=\\n]",c:[e.UTM,t]},e.C("!","$",{r:0}),{cN:"number",b:"(?=\\b|\\+|\\-|\\.)(?=\\.\\d|\\d)(?:\\d+)?(?:\\.?\\d*)(?:[de][+-]?\\d+)?\\b\\.?",r:0}]}});hljs.registerLanguage("awk",function(e){var r={cN:"variable",v:[{b:/\$[\w\d#@][\w\d_]*/},{b:/\$\{(.*?)}/}]},b="BEGIN END if else while do for in break continue delete next nextfile function func exit|10",n={cN:"string",c:[e.BE],v:[{b:/(u|b)?r?'''/,e:/'''/,r:10},{b:/(u|b)?r?"""/,e:/"""/,r:10},{b:/(u|r|ur)'/,e:/'/,r:10},{b:/(u|r|ur)"/,e:/"/,r:10},{b:/(b|br)'/,e:/'/},{b:/(b|br)"/,e:/"/},e.ASM,e.QSM]};return{k:{keyword:b},c:[r,n,e.RM,e.HCM,e.NM]}});hljs.registerLanguage("makefile",function(e){var i={cN:"variable",v:[{b:"\\$\\("+e.UIR+"\\)",c:[e.BE]},{b:/\$[@%<?\^\+\*]/}]},r={cN:"string",b:/"/,e:/"/,c:[e.BE,i]},a={cN:"variable",b:/\$\([\w-]+\s/,e:/\)/,k:{built_in:"subst patsubst strip findstring filter filter-out sort word wordlist firstword lastword dir notdir suffix basename addsuffix addprefix join wildcard realpath abspath error warning shell origin flavor foreach if or and call eval file value"},c:[i]},n={b:"^"+e.UIR+"\\s*[:+?]?=",i:"\\n",rB:!0,c:[{b:"^"+e.UIR,e:"[:+?]?=",eE:!0}]},t={cN:"meta",b:/^\.PHONY:/,e:/$/,k:{"meta-keyword":".PHONY"},l:/[\.\w]+/},l={cN:"section",b:/^[^\s]+:/,e:/$/,c:[i]};return{aliases:["mk","mak"],k:"define endef undefine ifdef ifndef ifeq ifneq else endif include -include sinclude override export unexport private vpath",l:/[\w-]+/,c:[e.HCM,i,r,a,n,t,l]}});hljs.registerLanguage("java",function(e){var a="[À-ʸa-zA-Z_$][À-ʸa-zA-Z_$0-9]*",t=a+"(<"+a+"(\\s*,\\s*"+a+")*>)?",r="false synchronized int abstract float private char boolean static null if const for true while long strictfp finally protected import native final void enum else break transient catch instanceof byte super volatile case assert short package default double public try this switch continue throws protected public private module requires exports do",s="\\b(0[bB]([01]+[01_]+[01]+|[01]+)|0[xX]([a-fA-F0-9]+[a-fA-F0-9_]+[a-fA-F0-9]+|[a-fA-F0-9]+)|(([\\d]+[\\d_]+[\\d]+|[\\d]+)(\\.([\\d]+[\\d_]+[\\d]+|[\\d]+))?|\\.([\\d]+[\\d_]+[\\d]+|[\\d]+))([eE][-+]?\\d+)?)[lLfF]?",c={cN:"number",b:s,r:0};return{aliases:["jsp"],k:r,i:/<\/|#/,c:[e.C("/\\*\\*","\\*/",{r:0,c:[{b:/\w+@/,r:0},{cN:"doctag",b:"@[A-Za-z]+"}]}),e.CLCM,e.CBCM,e.ASM,e.QSM,{cN:"class",bK:"class interface",e:/[{;=]/,eE:!0,k:"class interface",i:/[:"\[\]]/,c:[{bK:"extends implements"},e.UTM]},{bK:"new throw return else",r:0},{cN:"function",b:"("+t+"\\s+)+"+e.UIR+"\\s*\\(",rB:!0,e:/[{;=]/,eE:!0,k:r,c:[{b:e.UIR+"\\s*\\(",rB:!0,r:0,c:[e.UTM]},{cN:"params",b:/\(/,e:/\)/,k:r,r:0,c:[e.ASM,e.QSM,e.CNM,e.CBCM]},e.CLCM,e.CBCM]},c,{cN:"meta",b:"@[A-Za-z]+"}]}});hljs.registerLanguage("stan",function(e){return{c:[e.HCM,e.CLCM,e.CBCM,{b:e.UIR,l:e.UIR,k:{name:"for in while repeat until if then else",symbol:"bernoulli bernoulli_logit binomial binomial_logit beta_binomial hypergeometric categorical categorical_logit ordered_logistic neg_binomial neg_binomial_2 neg_binomial_2_log poisson poisson_log multinomial normal exp_mod_normal skew_normal student_t cauchy double_exponential logistic gumbel lognormal chi_square inv_chi_square scaled_inv_chi_square exponential inv_gamma weibull frechet rayleigh wiener pareto pareto_type_2 von_mises uniform multi_normal multi_normal_prec multi_normal_cholesky multi_gp multi_gp_cholesky multi_student_t gaussian_dlm_obs dirichlet lkj_corr lkj_corr_cholesky wishart inv_wishart","selector-tag":"int real vector simplex unit_vector ordered positive_ordered row_vector matrix cholesky_factor_corr cholesky_factor_cov corr_matrix cov_matrix",title:"functions model data parameters quantities transformed generated",literal:"true false"},r:0},{cN:"number",b:"0[xX][0-9a-fA-F]+[Li]?\\b",r:0},{cN:"number",b:"0[xX][0-9a-fA-F]+[Li]?\\b",r:0},{cN:"number",b:"\\d+(?:[eE][+\\-]?\\d*)?L\\b",r:0},{cN:"number",b:"\\d+\\.(?!\\d)(?:i\\b)?",r:0},{cN:"number",b:"\\d+(?:\\.\\d*)?(?:[eE][+\\-]?\\d*)?i?\\b",r:0},{cN:"number",b:"\\.\\d+(?:[eE][+\\-]?\\d*)?i?\\b",r:0}]}});hljs.registerLanguage("javascript",function(e){var r="[A-Za-z$_][0-9A-Za-z$_]*",t={keyword:"in of if for while finally var new function do return void else break catch instanceof with throw case default try this switch continue typeof delete let yield const export super debugger as async await static import from as",literal:"true false null undefined NaN Infinity",built_in:"eval isFinite isNaN parseFloat parseInt decodeURI decodeURIComponent encodeURI encodeURIComponent escape unescape Object Function Boolean Error EvalError InternalError RangeError ReferenceError StopIteration SyntaxError TypeError URIError Number Math Date String RegExp Array Float32Array Float64Array Int16Array Int32Array Int8Array Uint16Array Uint32Array Uint8Array Uint8ClampedArray ArrayBuffer DataView JSON Intl arguments require module console window document Symbol Set Map WeakSet WeakMap Proxy Reflect Promise"},a={cN:"number",v:[{b:"\\b(0[bB][01]+)"},{b:"\\b(0[oO][0-7]+)"},{b:e.CNR}],r:0},n={cN:"subst",b:"\\$\\{",e:"\\}",k:t,c:[]},c={cN:"string",b:"`",e:"`",c:[e.BE,n]};n.c=[e.ASM,e.QSM,c,a,e.RM];var s=n.c.concat([e.CBCM,e.CLCM]);return{aliases:["js","jsx"],k:t,c:[{cN:"meta",r:10,b:/^\s*['"]use (strict|asm)['"]/},{cN:"meta",b:/^#!/,e:/$/},e.ASM,e.QSM,c,e.CLCM,e.CBCM,a,{b:/[{,]\s*/,r:0,c:[{b:r+"\\s*:",rB:!0,r:0,c:[{cN:"attr",b:r,r:0}]}]},{b:"("+e.RSR+"|\\b(case|return|throw)\\b)\\s*",k:"return throw case",c:[e.CLCM,e.CBCM,e.RM,{cN:"function",b:"(\\(.*?\\)|"+r+")\\s*=>",rB:!0,e:"\\s*=>",c:[{cN:"params",v:[{b:r},{b:/\(\s*\)/},{b:/\(/,e:/\)/,eB:!0,eE:!0,k:t,c:s}]}]},{b:/</,e:/(\/\w+|\w+\/)>/,sL:"xml",c:[{b:/<\w+\s*\/>/,skip:!0},{b:/<\w+/,e:/(\/\w+|\w+\/)>/,skip:!0,c:[{b:/<\w+\s*\/>/,skip:!0},"self"]}]}],r:0},{cN:"function",bK:"function",e:/\{/,eE:!0,c:[e.inherit(e.TM,{b:r}),{cN:"params",b:/\(/,e:/\)/,eB:!0,eE:!0,c:s}],i:/\[|%/},{b:/\$[(.]/},e.METHOD_GUARD,{cN:"class",bK:"class",e:/[{;=]/,eE:!0,i:/[:"\[\]]/,c:[{bK:"extends"},e.UTM]},{bK:"constructor",e:/\{/,eE:!0}],i:/#(?!!)/}});hljs.registerLanguage("tex",function(c){var e={cN:"tag",b:/\\/,r:0,c:[{cN:"name",v:[{b:/[a-zA-Zа-яА-я]+[*]?/},{b:/[^a-zA-Zа-яА-я0-9]/}],starts:{eW:!0,r:0,c:[{cN:"string",v:[{b:/\[/,e:/\]/},{b:/\{/,e:/\}/}]},{b:/\s*=\s*/,eW:!0,r:0,c:[{cN:"number",b:/-?\d*\.?\d+(pt|pc|mm|cm|in|dd|cc|ex|em)?/}]}]}}]};return{c:[e,{cN:"formula",c:[e],r:0,v:[{b:/\$\$/,e:/\$\$/},{b:/\$/,e:/\$/}]},c.C("%","$",{r:0})]}});hljs.registerLanguage("xml",function(s){var e="[A-Za-z0-9\\._:-]+",t={eW:!0,i:/</,r:0,c:[{cN:"attr",b:e,r:0},{b:/=\s*/,r:0,c:[{cN:"string",endsParent:!0,v:[{b:/"/,e:/"/},{b:/'/,e:/'/},{b:/[^\s"'=<>`]+/}]}]}]};return{aliases:["html","xhtml","rss","atom","xjb","xsd","xsl","plist"],cI:!0,c:[{cN:"meta",b:"<!DOCTYPE",e:">",r:10,c:[{b:"\\[",e:"\\]"}]},s.C("<!--","-->",{r:10}),{b:"<\\!\\[CDATA\\[",e:"\\]\\]>",r:10},{b:/<\?(php)?/,e:/\?>/,sL:"php",c:[{b:"/\\*",e:"\\*/",skip:!0}]},{cN:"tag",b:"<style(?=\\s|>|$)",e:">",k:{name:"style"},c:[t],starts:{e:"</style>",rE:!0,sL:["css","xml"]}},{cN:"tag",b:"<script(?=\\s|>|$)",e:">",k:{name:"script"},c:[t],starts:{e:"</script>",rE:!0,sL:["actionscript","javascript","handlebars","xml"]}},{cN:"meta",v:[{b:/<\?xml/,e:/\?>/,r:10},{b:/<\?\w+/,e:/\?>/}]},{cN:"tag",b:"</?",e:"/?>",c:[{cN:"name",b:/[^\/><\s]+/,r:0},t]}]}});hljs.registerLanguage("markdown",function(e){return{aliases:["md","mkdown","mkd"],c:[{cN:"section",v:[{b:"^#{1,6}",e:"$"},{b:"^.+?\\n[=-]{2,}$"}]},{b:"<",e:">",sL:"xml",r:0},{cN:"bullet",b:"^([*+-]|(\\d+\\.))\\s+"},{cN:"strong",b:"[*_]{2}.+?[*_]{2}"},{cN:"emphasis",v:[{b:"\\*.+?\\*"},{b:"_.+?_",r:0}]},{cN:"quote",b:"^>\\s+",e:"$"},{cN:"code",v:[{b:"^```w*s*$",e:"^```s*$"},{b:"`.+?`"},{b:"^( {4}|	)",e:"$",r:0}]},{b:"^[-\\*]{3,}",e:"$"},{b:"\\[.+?\\][\\(\\[].*?[\\)\\]]",rB:!0,c:[{cN:"string",b:"\\[",e:"\\]",eB:!0,rE:!0,r:0},{cN:"link",b:"\\]\\(",e:"\\)",eB:!0,eE:!0},{cN:"symbol",b:"\\]\\[",e:"\\]",eB:!0,eE:!0}],r:10},{b:/^\[[^\n]+\]:/,rB:!0,c:[{cN:"symbol",b:/\[/,e:/\]/,eB:!0,eE:!0},{cN:"link",b:/:\s*/,e:/$/,eB:!0}]}]}});hljs.registerLanguage("json",function(e){var i={literal:"true false null"},n=[e.QSM,e.CNM],r={e:",",eW:!0,eE:!0,c:n,k:i},t={b:"{",e:"}",c:[{cN:"attr",b:/"/,e:/"/,c:[e.BE],i:"\\n"},e.inherit(r,{b:/:/})],i:"\\S"},c={b:"\\[",e:"\\]",c:[e.inherit(r)],i:"\\S"};return n.splice(n.length,0,t,c),{c:n,k:i,i:"\\S"}});"></script>
+
+<style type="text/css">
+  code{white-space: pre-wrap;}
+  span.smallcaps{font-variant: small-caps;}
+  span.underline{text-decoration: underline;}
+  div.column{display: inline-block; vertical-align: top; width: 50%;}
+  div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;}
+  ul.task-list{list-style: none;}
+    </style>
+
+<style type="text/css">code{white-space: pre;}</style>
+<script type="text/javascript">
+if (window.hljs) {
+  hljs.configure({languages: []});
+  hljs.initHighlightingOnLoad();
+  if (document.readyState && document.readyState === "complete") {
+    window.setTimeout(function() { hljs.initHighlighting(); }, 0);
+  }
+}
+</script>
+
+
+
+
+
+
+
+
+
+<style type="text/css">
+.main-container {
+  max-width: 940px;
+  margin-left: auto;
+  margin-right: auto;
+}
+img {
+  max-width:100%;
+}
+.tabbed-pane {
+  padding-top: 12px;
+}
+.html-widget {
+  margin-bottom: 20px;
+}
+button.code-folding-btn:focus {
+  outline: none;
+}
+summary {
+  display: list-item;
+}
+details > summary > p:only-child {
+  display: inline;
+}
+pre code {
+  padding: 0;
+}
+</style>
+
+
+
+<!-- tabsets -->
+
+<style type="text/css">
+.tabset-dropdown > .nav-tabs {
+  display: inline-table;
+  max-height: 500px;
+  min-height: 44px;
+  overflow-y: auto;
+  border: 1px solid #ddd;
+  border-radius: 4px;
+}
+
+.tabset-dropdown > .nav-tabs > li.active:before, .tabset-dropdown > .nav-tabs.nav-tabs-open:before {
+  content: "\e259";
+  font-family: 'Glyphicons Halflings';
+  display: inline-block;
+  padding: 10px;
+  border-right: 1px solid #ddd;
+}
+
+.tabset-dropdown > .nav-tabs.nav-tabs-open > li.active:before {
+  content: "\e258";
+  font-family: 'Glyphicons Halflings';
+  border: none;
+}
+
+.tabset-dropdown > .nav-tabs > li.active {
+  display: block;
+}
+
+.tabset-dropdown > .nav-tabs > li > a,
+.tabset-dropdown > .nav-tabs > li > a:focus,
+.tabset-dropdown > .nav-tabs > li > a:hover {
+  border: none;
+  display: inline-block;
+  border-radius: 4px;
+  background-color: transparent;
+}
+
+.tabset-dropdown > .nav-tabs.nav-tabs-open > li {
+  display: block;
+  float: none;
+}
+
+.tabset-dropdown > .nav-tabs > li {
+  display: none;
+}
+</style>
+
+<!-- code folding -->
+
+
+
+
 </head>
-<body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-article">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav">
-<li>
-  <a href="../../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu">
-<li>
-      <a href="../../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul>
-</li>
-<li>
-  <a href="../../news/index.html">News</a>
-</li>
-      </ul>
-<ul class="nav navbar-nav navbar-right">
-<li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul>
-</div>
-<!--/.nav-collapse -->
-  </div>
-<!--/.container -->
-</div>
-<!--/.navbar -->
 
-      
+<body>
+
+
+<div class="container-fluid main-container">
+
+
+
+
+<div id="header">
 
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header toc-ignore">
-      <h1 data-toc-skip>Testing covariate modelling in hierarchical
+
+
+<h1 class="title toc-ignore">Testing covariate modelling in hierarchical
 parent degradation kinetics with residue data on mesotrione</h1>
-                        <h4 data-toc-skip class="author">Johannes
-Ranke</h4>
-            
-            <h4 data-toc-skip class="date">Last change on 4 August 2023,
-last compiled on 13 Oktober 2023</h4>
-      
-      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/prebuilt/2023_mesotrione_parent.rmd" class="external-link"><code>vignettes/prebuilt/2023_mesotrione_parent.rmd</code></a></small>
-      <div class="hidden name"><code>2023_mesotrione_parent.rmd</code></div>
-
-    </div>
-
-    
-    
-<div class="section level2">
-<h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a>
-</h2>
+<h4 class="author">Johannes Ranke</h4>
+<h4 class="date">Last change on 4 August 2023, last compiled on 13
+Februar 2025</h4>
+
+</div>
+
+<div id="TOC">
+true
+</div>
+
+<div id="introduction" class="section level1">
+<h1>Introduction</h1>
 <p>The purpose of this document is to test demonstrate how nonlinear
 hierarchical models (NLHM) based on the parent degradation models SFO,
 FOMC, DFOP and HS can be fitted with the mkin package, also considering
@@ -158,7 +377,7 @@ parameters. Because in some other case studies, the SFORB
 parameterisation of biexponential decline has shown some advantages over
 the DFOP parameterisation, SFORB was included in the list of tested
 models as well.</p>
-<p>The mkin package is used in version 1.2.6, which is contains the
+<p>The mkin package is used in version 1.2.9, which is contains the
 functions that were used for the evaluations. The <code>saemix</code>
 package is used as a backend for fitting the NLHM, but is also loaded to
 make the convergence plot function available.</p>
@@ -166,35 +385,33 @@ make the convergence plot function available.</p>
 also provides the <code>kable</code> function that is used to improve
 the display of tabular data in R markdown documents. For parallel
 processing, the <code>parallel</code> package is used.</p>
-<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://pkgdown.jrwb.de/mkin/">mkin</a></span><span class="op">)</span></span>
-<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://yihui.org/knitr/" class="external-link">knitr</a></span><span class="op">)</span></span>
-<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va">saemix</span><span class="op">)</span></span>
-<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va">parallel</span><span class="op">)</span></span>
-<span><span class="va">n_cores</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/parallel/detectCores.html" class="external-link">detectCores</a></span><span class="op">(</span><span class="op">)</span></span>
-<span><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/Sys.info.html" class="external-link">Sys.info</a></span><span class="op">(</span><span class="op">)</span><span class="op">[</span><span class="st">"sysname"</span><span class="op">]</span> <span class="op">==</span> <span class="st">"Windows"</span><span class="op">)</span> <span class="op">{</span></span>
-<span>  <span class="va">cl</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/parallel/makeCluster.html" class="external-link">makePSOCKcluster</a></span><span class="op">(</span><span class="va">n_cores</span><span class="op">)</span></span>
-<span><span class="op">}</span> <span class="kw">else</span> <span class="op">{</span></span>
-<span>  <span class="va">cl</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/parallel/makeCluster.html" class="external-link">makeForkCluster</a></span><span class="op">(</span><span class="va">n_cores</span><span class="op">)</span></span>
-<span><span class="op">}</span></span></code></pre></div>
-<div class="section level3">
-<h3 id="test-data">Test data<a class="anchor" aria-label="anchor" href="#test-data"></a>
-</h3>
-<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">data_file</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/system.file.html" class="external-link">system.file</a></span><span class="op">(</span></span>
-<span>  <span class="st">"testdata"</span>, <span class="st">"mesotrione_soil_efsa_2016.xlsx"</span>, package <span class="op">=</span> <span class="st">"mkin"</span><span class="op">)</span></span>
-<span><span class="va">meso_ds</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/read_spreadsheet.html">read_spreadsheet</a></span><span class="op">(</span><span class="va">data_file</span>, parent_only <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
+<pre class="r"><code>library(mkin)
+library(knitr)
+library(saemix)
+library(parallel)
+n_cores &lt;- detectCores()
+if (Sys.info()[&quot;sysname&quot;] == &quot;Windows&quot;) {
+  cl &lt;- makePSOCKcluster(n_cores)
+} else {
+  cl &lt;- makeForkCluster(n_cores)
+}</code></pre>
+<div id="test-data" class="section level2">
+<h2>Test data</h2>
+<pre class="r"><code>data_file &lt;- system.file(
+  &quot;testdata&quot;, &quot;mesotrione_soil_efsa_2016.xlsx&quot;, package = &quot;mkin&quot;)
+meso_ds &lt;- read_spreadsheet(data_file, parent_only = TRUE)</code></pre>
 <p>The following tables show the covariate data and the 18 datasets that
 were read in from the spreadsheet file.</p>
-<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">pH</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/attr.html" class="external-link">attr</a></span><span class="op">(</span><span class="va">meso_ds</span>, <span class="st">"covariates"</span><span class="op">)</span></span>
-<span><span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="va">pH</span>, caption <span class="op">=</span> <span class="st">"Covariate data"</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
+<pre class="r"><code>pH &lt;- attr(meso_ds, &quot;covariates&quot;)
+kable(pH, caption = &quot;Covariate data&quot;)</code></pre>
+<table>
 <caption>Covariate data</caption>
-<thead><tr class="header">
+<thead>
+<tr class="header">
 <th align="left"></th>
 <th align="right">pH</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="left">Richmond</td>
@@ -270,19 +487,20 @@ were read in from the spreadsheet file.</p>
 </tr>
 </tbody>
 </table>
-<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="kw">for</span> <span class="op">(</span><span class="va">ds_name</span> <span class="kw">in</span> <span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">meso_ds</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
-<span>  <span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span></span>
-<span>    <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="fu"><a href="../../reference/mkin_long_to_wide.html">mkin_long_to_wide</a></span><span class="op">(</span><span class="va">meso_ds</span><span class="op">[[</span><span class="va">ds_name</span><span class="op">]</span><span class="op">]</span><span class="op">)</span>,</span>
-<span>      caption <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste</a></span><span class="op">(</span><span class="st">"Dataset"</span>, <span class="va">ds_name</span><span class="op">)</span>,</span>
-<span>      booktabs <span class="op">=</span> <span class="cn">TRUE</span>, row.names <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span><span class="op">)</span></span>
-<span><span class="op">}</span></span></code></pre></div>
-<table class="table">
+<pre class="r"><code>for (ds_name in names(meso_ds)) {
+  print(
+    kable(mkin_long_to_wide(meso_ds[[ds_name]]),
+      caption = paste(&quot;Dataset&quot;, ds_name),
+      booktabs = TRUE, row.names = FALSE))
+}</code></pre>
+<table>
 <caption>Dataset Richmond</caption>
-<thead><tr class="header">
+<thead>
+<tr class="header">
 <th align="right">time</th>
 <th align="right">meso</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="right">0.000000</td>
@@ -374,12 +592,14 @@ were read in from the spreadsheet file.</p>
 </tr>
 </tbody>
 </table>
-<table class="table">
+<table>
 <caption>Dataset Richmond 2</caption>
-<thead><tr class="header">
+<thead>
+<tr class="header">
 <th align="right">time</th>
 <th align="right">meso</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="right">0.000000</td>
@@ -415,12 +635,14 @@ were read in from the spreadsheet file.</p>
 </tr>
 </tbody>
 </table>
-<table class="table">
+<table>
 <caption>Dataset ERTC</caption>
-<thead><tr class="header">
+<thead>
+<tr class="header">
 <th align="right">time</th>
 <th align="right">meso</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="right">0.000000</td>
@@ -456,12 +678,14 @@ were read in from the spreadsheet file.</p>
 </tr>
 </tbody>
 </table>
-<table class="table">
+<table>
 <caption>Dataset Toulouse</caption>
-<thead><tr class="header">
+<thead>
+<tr class="header">
 <th align="right">time</th>
 <th align="right">meso</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="right">0.000000</td>
@@ -493,12 +717,14 @@ were read in from the spreadsheet file.</p>
 </tr>
 </tbody>
 </table>
-<table class="table">
+<table>
 <caption>Dataset Picket Piece</caption>
-<thead><tr class="header">
+<thead>
+<tr class="header">
 <th align="right">time</th>
 <th align="right">meso</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="right">0.000000</td>
@@ -526,12 +752,14 @@ were read in from the spreadsheet file.</p>
 </tr>
 </tbody>
 </table>
-<table class="table">
+<table>
 <caption>Dataset 721</caption>
-<thead><tr class="header">
+<thead>
+<tr class="header">
 <th align="right">time</th>
 <th align="right">meso</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="right">0.00000</td>
@@ -555,12 +783,14 @@ were read in from the spreadsheet file.</p>
 </tr>
 </tbody>
 </table>
-<table class="table">
+<table>
 <caption>Dataset 722</caption>
-<thead><tr class="header">
+<thead>
+<tr class="header">
 <th align="right">time</th>
 <th align="right">meso</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="right">0.00000</td>
@@ -584,12 +814,14 @@ were read in from the spreadsheet file.</p>
 </tr>
 </tbody>
 </table>
-<table class="table">
+<table>
 <caption>Dataset 723</caption>
-<thead><tr class="header">
+<thead>
+<tr class="header">
 <th align="right">time</th>
 <th align="right">meso</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="right">0.00000</td>
@@ -613,12 +845,14 @@ were read in from the spreadsheet file.</p>
 </tr>
 </tbody>
 </table>
-<table class="table">
+<table>
 <caption>Dataset 724</caption>
-<thead><tr class="header">
+<thead>
+<tr class="header">
 <th align="right">time</th>
 <th align="right">meso</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="right">0.000000</td>
@@ -642,12 +876,14 @@ were read in from the spreadsheet file.</p>
 </tr>
 </tbody>
 </table>
-<table class="table">
+<table>
 <caption>Dataset 725</caption>
-<thead><tr class="header">
+<thead>
+<tr class="header">
 <th align="right">time</th>
 <th align="right">meso</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="right">0.00000</td>
@@ -671,12 +907,14 @@ were read in from the spreadsheet file.</p>
 </tr>
 </tbody>
 </table>
-<table class="table">
+<table>
 <caption>Dataset 727</caption>
-<thead><tr class="header">
+<thead>
+<tr class="header">
 <th align="right">time</th>
 <th align="right">meso</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="right">0.00000</td>
@@ -700,12 +938,14 @@ were read in from the spreadsheet file.</p>
 </tr>
 </tbody>
 </table>
-<table class="table">
+<table>
 <caption>Dataset 728</caption>
-<thead><tr class="header">
+<thead>
+<tr class="header">
 <th align="right">time</th>
 <th align="right">meso</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="right">0.00000</td>
@@ -729,12 +969,14 @@ were read in from the spreadsheet file.</p>
 </tr>
 </tbody>
 </table>
-<table class="table">
+<table>
 <caption>Dataset 729</caption>
-<thead><tr class="header">
+<thead>
+<tr class="header">
 <th align="right">time</th>
 <th align="right">meso</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="right">0.00000</td>
@@ -758,12 +1000,14 @@ were read in from the spreadsheet file.</p>
 </tr>
 </tbody>
 </table>
-<table class="table">
+<table>
 <caption>Dataset 730</caption>
-<thead><tr class="header">
+<thead>
+<tr class="header">
 <th align="right">time</th>
 <th align="right">meso</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="right">0.00000</td>
@@ -787,12 +1031,14 @@ were read in from the spreadsheet file.</p>
 </tr>
 </tbody>
 </table>
-<table class="table">
+<table>
 <caption>Dataset 731</caption>
-<thead><tr class="header">
+<thead>
+<tr class="header">
 <th align="right">time</th>
 <th align="right">meso</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="right">0.00000</td>
@@ -816,12 +1062,14 @@ were read in from the spreadsheet file.</p>
 </tr>
 </tbody>
 </table>
-<table class="table">
+<table>
 <caption>Dataset 732</caption>
-<thead><tr class="header">
+<thead>
+<tr class="header">
 <th align="right">time</th>
 <th align="right">meso</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="right">0.00000</td>
@@ -845,12 +1093,14 @@ were read in from the spreadsheet file.</p>
 </tr>
 </tbody>
 </table>
-<table class="table">
+<table>
 <caption>Dataset 741</caption>
-<thead><tr class="header">
+<thead>
+<tr class="header">
 <th align="right">time</th>
 <th align="right">meso</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="right">0.00000</td>
@@ -874,12 +1124,14 @@ were read in from the spreadsheet file.</p>
 </tr>
 </tbody>
 </table>
-<table class="table">
+<table>
 <caption>Dataset 742</caption>
-<thead><tr class="header">
+<thead>
+<tr class="header">
 <th align="right">time</th>
 <th align="right">meso</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="right">0.00000</td>
@@ -905,33 +1157,32 @@ were read in from the spreadsheet file.</p>
 </table>
 </div>
 </div>
-<div class="section level2">
-<h2 id="separate-evaluations">Separate evaluations<a class="anchor" aria-label="anchor" href="#separate-evaluations"></a>
-</h2>
+<div id="separate-evaluations" class="section level1">
+<h1>Separate evaluations</h1>
 <p>In order to obtain suitable starting parameters for the NLHM fits,
 separate fits of the five models to the data for each soil are generated
 using the <code>mmkin</code> function from the mkin package. In a first
 step, constant variance is assumed. Convergence is checked with the
 <code>status</code> function.</p>
-<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">deg_mods</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"FOMC"</span>, <span class="st">"DFOP"</span>, <span class="st">"SFORB"</span>, <span class="st">"HS"</span><span class="op">)</span></span>
-<span><span class="va">f_sep_const</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/mmkin.html">mmkin</a></span><span class="op">(</span></span>
-<span>  <span class="va">deg_mods</span>,</span>
-<span>  <span class="va">meso_ds</span>,</span>
-<span>  error_model <span class="op">=</span> <span class="st">"const"</span>,</span>
-<span>  cluster <span class="op">=</span> <span class="va">cl</span>,</span>
-<span>  quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
-<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="../../reference/status.html">status</a></span><span class="op">(</span><span class="va">f_sep_const</span><span class="op">[</span>, <span class="fl">1</span><span class="op">:</span><span class="fl">5</span><span class="op">]</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
-<thead><tr class="header">
+<pre class="r"><code>deg_mods &lt;- c(&quot;SFO&quot;, &quot;FOMC&quot;, &quot;DFOP&quot;, &quot;SFORB&quot;, &quot;HS&quot;)
+f_sep_const &lt;- mmkin(
+  deg_mods,
+  meso_ds,
+  error_model = &quot;const&quot;,
+  cluster = cl,
+  quiet = TRUE)</code></pre>
+<pre class="r"><code>status(f_sep_const[, 1:5]) |&gt; kable()</code></pre>
+<table>
+<thead>
+<tr class="header">
 <th align="left"></th>
 <th align="left">Richmond</th>
 <th align="left">Richmond 2</th>
 <th align="left">ERTC</th>
 <th align="left">Toulouse</th>
 <th align="left">Picket Piece</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="left">SFO</td>
@@ -975,26 +1226,26 @@ step, constant variance is assumed. Convergence is checked with the
 </tr>
 </tbody>
 </table>
-<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="../../reference/status.html">status</a></span><span class="op">(</span><span class="va">f_sep_const</span><span class="op">[</span>, <span class="fl">6</span><span class="op">:</span><span class="fl">18</span><span class="op">]</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
+<pre class="r"><code>status(f_sep_const[, 6:18]) |&gt; kable()</code></pre>
+<table>
 <colgroup>
-<col width="10%">
-<col width="6%">
-<col width="6%">
-<col width="6%">
-<col width="6%">
-<col width="6%">
-<col width="6%">
-<col width="6%">
-<col width="6%">
-<col width="6%">
-<col width="6%">
-<col width="6%">
-<col width="6%">
-<col width="6%">
+<col width="10%" />
+<col width="6%" />
+<col width="6%" />
+<col width="6%" />
+<col width="6%" />
+<col width="6%" />
+<col width="6%" />
+<col width="6%" />
+<col width="6%" />
+<col width="6%" />
+<col width="6%" />
+<col width="6%" />
+<col width="6%" />
+<col width="6%" />
 </colgroup>
-<thead><tr class="header">
+<thead>
+<tr class="header">
 <th align="left"></th>
 <th align="left">721</th>
 <th align="left">722</th>
@@ -1009,7 +1260,8 @@ step, constant variance is assumed. Convergence is checked with the
 <th align="left">732</th>
 <th align="left">741</th>
 <th align="left">742</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="left">SFO</td>
@@ -1096,19 +1348,19 @@ step, constant variance is assumed. Convergence is checked with the
 <p>In the tables above, OK indicates convergence and C indicates failure
 to converge. Most separate fits with constant variance converged, with
 the exception of two FOMC fits, one SFORB fit and one HS fit.</p>
-<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">f_sep_tc</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_sep_const</span>, error_model <span class="op">=</span> <span class="st">"tc"</span><span class="op">)</span></span></code></pre></div>
-<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="../../reference/status.html">status</a></span><span class="op">(</span><span class="va">f_sep_tc</span><span class="op">[</span>, <span class="fl">1</span><span class="op">:</span><span class="fl">5</span><span class="op">]</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
-<thead><tr class="header">
+<pre class="r"><code>f_sep_tc &lt;- update(f_sep_const, error_model = &quot;tc&quot;)</code></pre>
+<pre class="r"><code>status(f_sep_tc[, 1:5]) |&gt; kable()</code></pre>
+<table>
+<thead>
+<tr class="header">
 <th align="left"></th>
 <th align="left">Richmond</th>
 <th align="left">Richmond 2</th>
 <th align="left">ERTC</th>
 <th align="left">Toulouse</th>
 <th align="left">Picket Piece</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="left">SFO</td>
@@ -1152,26 +1404,26 @@ the exception of two FOMC fits, one SFORB fit and one HS fit.</p>
 </tr>
 </tbody>
 </table>
-<div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="../../reference/status.html">status</a></span><span class="op">(</span><span class="va">f_sep_tc</span><span class="op">[</span>, <span class="fl">6</span><span class="op">:</span><span class="fl">18</span><span class="op">]</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
+<pre class="r"><code>status(f_sep_tc[, 6:18]) |&gt; kable()</code></pre>
+<table>
 <colgroup>
-<col width="10%">
-<col width="6%">
-<col width="6%">
-<col width="6%">
-<col width="6%">
-<col width="6%">
-<col width="6%">
-<col width="6%">
-<col width="6%">
-<col width="6%">
-<col width="6%">
-<col width="6%">
-<col width="6%">
-<col width="6%">
+<col width="10%" />
+<col width="6%" />
+<col width="6%" />
+<col width="6%" />
+<col width="6%" />
+<col width="6%" />
+<col width="6%" />
+<col width="6%" />
+<col width="6%" />
+<col width="6%" />
+<col width="6%" />
+<col width="6%" />
+<col width="6%" />
+<col width="6%" />
 </colgroup>
-<thead><tr class="header">
+<thead>
+<tr class="header">
 <th align="left"></th>
 <th align="left">721</th>
 <th align="left">722</th>
@@ -1186,7 +1438,8 @@ the exception of two FOMC fits, one SFORB fit and one HS fit.</p>
 <th align="left">732</th>
 <th align="left">741</th>
 <th align="left">742</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="left">SFO</td>
@@ -1274,21 +1527,21 @@ the exception of two FOMC fits, one SFORB fit and one HS fit.</p>
 converge is larger, with convergence problems appearing for a number of
 non-SFO fits.</p>
 </div>
-<div class="section level2">
-<h2 id="hierarchical-model-fits-without-covariate-effect">Hierarchical model fits without covariate effect<a class="anchor" aria-label="anchor" href="#hierarchical-model-fits-without-covariate-effect"></a>
-</h2>
+<div id="hierarchical-model-fits-without-covariate-effect" class="section level1">
+<h1>Hierarchical model fits without covariate effect</h1>
 <p>The following code fits hierarchical kinetic models for the ten
 combinations of the five different degradation models with the two
 different error models in parallel.</p>
-<div class="sourceCode" id="cb11"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">f_saem_1</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/mhmkin.html">mhmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">f_sep_const</span>, <span class="va">f_sep_tc</span><span class="op">)</span>, cluster <span class="op">=</span> <span class="va">cl</span><span class="op">)</span></span>
-<span><span class="fu"><a href="../../reference/status.html">status</a></span><span class="op">(</span><span class="va">f_saem_1</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
-<thead><tr class="header">
+<pre class="r"><code>f_saem_1 &lt;- mhmkin(list(f_sep_const, f_sep_tc), cluster = cl)
+status(f_saem_1) |&gt; kable()</code></pre>
+<table>
+<thead>
+<tr class="header">
 <th align="left"></th>
 <th align="left">const</th>
 <th align="left">tc</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="left">SFO</td>
@@ -1318,16 +1571,17 @@ different error models in parallel.</p>
 </tbody>
 </table>
 <p>All fits terminate without errors (status OK).</p>
-<div class="sourceCode" id="cb12"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_saem_1</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span>digits <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
-<thead><tr class="header">
+<pre class="r"><code>anova(f_saem_1) |&gt; kable(digits = 1)</code></pre>
+<table>
+<thead>
+<tr class="header">
 <th align="left"></th>
 <th align="right">npar</th>
 <th align="right">AIC</th>
 <th align="right">BIC</th>
 <th align="right">Lik</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="left">SFO const</td>
@@ -1406,19 +1660,20 @@ consistently preferable to the corresponding fits with two-component
 error for these data. This is confirmed by the fact that the parameter
 <code>b.1</code> (the relative standard deviation in the fits obtained
 with the saemix package), is ill-defined in all fits.</p>
-<div class="sourceCode" id="cb13"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="../../reference/illparms.html">illparms</a></span><span class="op">(</span><span class="va">f_saem_1</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
+<pre class="r"><code>illparms(f_saem_1) |&gt; kable()</code></pre>
+<table>
 <colgroup>
-<col width="6%">
-<col width="44%">
-<col width="49%">
+<col width="6%" />
+<col width="44%" />
+<col width="49%" />
 </colgroup>
-<thead><tr class="header">
+<thead>
+<tr class="header">
 <th align="left"></th>
 <th align="left">const</th>
 <th align="left">tc</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="left">SFO</td>
@@ -1450,15 +1705,16 @@ with the saemix package), is ill-defined in all fits.</p>
 <p>For obtaining fits with only well-defined random effects, we update
 the set of fits, excluding random effects that were ill-defined
 according to the <code>illparms</code> function.</p>
-<div class="sourceCode" id="cb14"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">f_saem_2</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_saem_1</span>, no_random_effect <span class="op">=</span> <span class="fu"><a href="../../reference/illparms.html">illparms</a></span><span class="op">(</span><span class="va">f_saem_1</span><span class="op">)</span><span class="op">)</span></span>
-<span><span class="fu"><a href="../../reference/status.html">status</a></span><span class="op">(</span><span class="va">f_saem_2</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
-<thead><tr class="header">
+<pre class="r"><code>f_saem_2 &lt;- update(f_saem_1, no_random_effect = illparms(f_saem_1))
+status(f_saem_2) |&gt; kable()</code></pre>
+<table>
+<thead>
+<tr class="header">
 <th align="left"></th>
 <th align="left">const</th>
 <th align="left">tc</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="left">SFO</td>
@@ -1488,14 +1744,15 @@ according to the <code>illparms</code> function.</p>
 </tbody>
 </table>
 <p>The updated fits terminate without errors.</p>
-<div class="sourceCode" id="cb15"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="../../reference/illparms.html">illparms</a></span><span class="op">(</span><span class="va">f_saem_2</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
-<thead><tr class="header">
+<pre class="r"><code>illparms(f_saem_2) |&gt; kable()</code></pre>
+<table>
+<thead>
+<tr class="header">
 <th align="left"></th>
 <th align="left">const</th>
 <th align="left">tc</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="left">SFO</td>
@@ -1520,15 +1777,14 @@ according to the <code>illparms</code> function.</p>
 <tr class="odd">
 <td align="left">HS</td>
 <td align="left"></td>
-<td align="left"></td>
+<td align="left">b.1</td>
 </tr>
 </tbody>
 </table>
 <p>No ill-defined errors remain in the fits with constant variance.</p>
 </div>
-<div class="section level2">
-<h2 id="hierarchical-model-fits-with-covariate-effect">Hierarchical model fits with covariate effect<a class="anchor" aria-label="anchor" href="#hierarchical-model-fits-with-covariate-effect"></a>
-</h2>
+<div id="hierarchical-model-fits-with-covariate-effect" class="section level1">
+<h1>Hierarchical model fits with covariate effect</h1>
 <p>In the following sections, hierarchical fits including a model for
 the influence of pH on selected degradation parameters are shown for all
 parent models. Constant variance is selected as the error model based on
@@ -1537,21 +1793,20 @@ in the fits without pH influence are excluded. A potential influence of
 the soil pH is only included for parameters with a well-defined random
 effect, because experience has shown that only for such parameters a
 significant pH effect could be found.</p>
-<div class="section level3">
-<h3 id="sfo">SFO<a class="anchor" aria-label="anchor" href="#sfo"></a>
-</h3>
-<div class="sourceCode" id="cb16"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">sfo_pH</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/saem.html">saem</a></span><span class="op">(</span><span class="va">f_sep_const</span><span class="op">[</span><span class="st">"SFO"</span>, <span class="op">]</span>, no_random_effect <span class="op">=</span> <span class="st">"meso_0"</span>, covariates <span class="op">=</span> <span class="va">pH</span>,</span>
-<span>  covariate_models <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">log_k_meso</span> <span class="op">~</span> <span class="va">pH</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
-<div class="sourceCode" id="cb17"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">sfo_pH</span><span class="op">)</span><span class="op">$</span><span class="va">confint_trans</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span>digits <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
-<thead><tr class="header">
+<div id="sfo" class="section level2">
+<h2>SFO</h2>
+<pre class="r"><code>sfo_pH &lt;- saem(f_sep_const[&quot;SFO&quot;, ], no_random_effect = &quot;meso_0&quot;, covariates = pH,
+  covariate_models = list(log_k_meso ~ pH))</code></pre>
+<pre class="r"><code>summary(sfo_pH)$confint_trans |&gt; kable(digits = 2)</code></pre>
+<table>
+<thead>
+<tr class="header">
 <th align="left"></th>
 <th align="right">est.</th>
 <th align="right">lower</th>
 <th align="right">upper</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="left">meso_0</td>
@@ -1589,26 +1844,23 @@ significant pH effect could be found.</p>
 <code>beta_pH(log_k_meso)</code>. Its confidence interval does not
 include zero, indicating that the influence of soil pH on the log of the
 degradation rate constant is significantly greater than zero.</p>
-<div class="sourceCode" id="cb18"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_saem_2</span><span class="op">[[</span><span class="st">"SFO"</span>, <span class="st">"const"</span><span class="op">]</span><span class="op">]</span>, <span class="va">sfo_pH</span>, test <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
+<pre class="r"><code>anova(f_saem_2[[&quot;SFO&quot;, &quot;const&quot;]], sfo_pH, test = TRUE)</code></pre>
 <pre><code>Data: 116 observations of 1 variable(s) grouped in 18 datasets
 
                            npar    AIC    BIC     Lik  Chisq Df Pr(&gt;Chisq)    
-f_saem_2[["SFO", "const"]]    4 797.56 801.12 -394.78                         
+f_saem_2[[&quot;SFO&quot;, &quot;const&quot;]]    4 797.56 801.12 -394.78                         
 sfo_pH                        5 783.09 787.54 -386.54 16.473  1  4.934e-05 ***
 ---
-Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</code></pre>
+Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1</code></pre>
 <p>The comparison with the SFO fit without covariate effect confirms
 that considering the soil pH improves the model, both by comparison of
 AIC and BIC and by the likelihood ratio test.</p>
-<div class="sourceCode" id="cb20"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">sfo_pH</span><span class="op">)</span></span></code></pre></div>
-<p><img src="2023_mesotrione_parent_files/figure-html/unnamed-chunk-8-1.png" width="700" style="display: block; margin: auto;"></p>
+<pre class="r"><code>plot(sfo_pH)</code></pre>
+<p><img src="data:image/png;base64,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" width="672" style="display: block; margin: auto;" /></p>
 <p>Endpoints for a model with covariates are by default calculated for
 the median of the covariate values. This quantile can be adapted, or a
 specific covariate value can be given as shown below.</p>
-<div class="sourceCode" id="cb21"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="../../reference/endpoints.html">endpoints</a></span><span class="op">(</span><span class="va">sfo_pH</span><span class="op">)</span></span></code></pre></div>
+<pre class="r"><code>endpoints(sfo_pH)</code></pre>
 <pre><code>$covariates
       pH
 50% 5.75
@@ -1616,8 +1868,7 @@ specific covariate value can be given as shown below.</p>
 $distimes
          DT50     DT90
 meso 18.52069 61.52441</code></pre>
-<div class="sourceCode" id="cb23"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="../../reference/endpoints.html">endpoints</a></span><span class="op">(</span><span class="va">sfo_pH</span>, covariate_quantile <span class="op">=</span> <span class="fl">0.9</span><span class="op">)</span></span></code></pre></div>
+<pre class="r"><code>endpoints(sfo_pH, covariate_quantile = 0.9)</code></pre>
 <pre><code>$covariates
       pH
 90% 7.13
@@ -1625,8 +1876,7 @@ meso 18.52069 61.52441</code></pre>
 $distimes
          DT50     DT90
 meso 8.237019 27.36278</code></pre>
-<div class="sourceCode" id="cb25"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="../../reference/endpoints.html">endpoints</a></span><span class="op">(</span><span class="va">sfo_pH</span>, covariates <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>pH <span class="op">=</span> <span class="fl">7.0</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
+<pre class="r"><code>endpoints(sfo_pH, covariates = c(pH = 7.0))</code></pre>
 <pre><code>$covariates
      pH
 User  7
@@ -1635,21 +1885,20 @@ $distimes
         DT50    DT90
 meso 8.89035 29.5331</code></pre>
 </div>
-<div class="section level3">
-<h3 id="fomc">FOMC<a class="anchor" aria-label="anchor" href="#fomc"></a>
-</h3>
-<div class="sourceCode" id="cb27"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">fomc_pH</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/saem.html">saem</a></span><span class="op">(</span><span class="va">f_sep_const</span><span class="op">[</span><span class="st">"FOMC"</span>, <span class="op">]</span>, no_random_effect <span class="op">=</span> <span class="st">"meso_0"</span>, covariates <span class="op">=</span> <span class="va">pH</span>,</span>
-<span>  covariate_models <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">log_alpha</span> <span class="op">~</span> <span class="va">pH</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
-<div class="sourceCode" id="cb28"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">fomc_pH</span><span class="op">)</span><span class="op">$</span><span class="va">confint_trans</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span>digits <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
-<thead><tr class="header">
+<div id="fomc" class="section level2">
+<h2>FOMC</h2>
+<pre class="r"><code>fomc_pH &lt;- saem(f_sep_const[&quot;FOMC&quot;, ], no_random_effect = &quot;meso_0&quot;, covariates = pH,
+  covariate_models = list(log_alpha ~ pH))</code></pre>
+<pre class="r"><code>summary(fomc_pH)$confint_trans |&gt; kable(digits = 2)</code></pre>
+<table>
+<thead>
+<tr class="header">
 <th align="left"></th>
 <th align="right">est.</th>
 <th align="right">lower</th>
 <th align="right">upper</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="left">meso_0</td>
@@ -1702,36 +1951,34 @@ that the model with covariate influence is preferable. However, the
 random effect for <code>alpha</code> is not well-defined any more after
 inclusion of the covariate effect (the confidence interval of
 <code>SD.log_alpha</code> includes zero).</p>
-<div class="sourceCode" id="cb29"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="../../reference/illparms.html">illparms</a></span><span class="op">(</span><span class="va">fomc_pH</span><span class="op">)</span></span></code></pre></div>
-<pre><code>[1] "sd(log_alpha)"</code></pre>
+<pre class="r"><code>illparms(fomc_pH)</code></pre>
+<pre><code>[1] &quot;sd(log_alpha)&quot;</code></pre>
 <p>Therefore, the model is updated without this random effect, and no
 ill-defined parameters remain.</p>
-<div class="sourceCode" id="cb31"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">fomc_pH_2</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">fomc_pH</span>, no_random_effect <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"meso_0"</span>, <span class="st">"log_alpha"</span><span class="op">)</span><span class="op">)</span></span>
-<span><span class="fu"><a href="../../reference/illparms.html">illparms</a></span><span class="op">(</span><span class="va">fomc_pH_2</span><span class="op">)</span></span></code></pre></div>
-<div class="sourceCode" id="cb32"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_saem_2</span><span class="op">[[</span><span class="st">"FOMC"</span>, <span class="st">"const"</span><span class="op">]</span><span class="op">]</span>, <span class="va">fomc_pH</span>, <span class="va">fomc_pH_2</span>, test <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
+<pre class="r"><code>fomc_pH_2 &lt;- update(fomc_pH, no_random_effect = c(&quot;meso_0&quot;, &quot;log_alpha&quot;))
+illparms(fomc_pH_2)</code></pre>
+<pre class="r"><code>anova(f_saem_2[[&quot;FOMC&quot;, &quot;const&quot;]], fomc_pH, fomc_pH_2, test = TRUE)</code></pre>
 <pre><code>Data: 116 observations of 1 variable(s) grouped in 18 datasets
 
                             npar    AIC    BIC     Lik  Chisq Df Pr(&gt;Chisq)    
-f_saem_2[["FOMC", "const"]]    5 783.25 787.71 -386.63                         
+f_saem_2[[&quot;FOMC&quot;, &quot;const&quot;]]    5 783.25 787.71 -386.63                         
 fomc_pH_2                      6 767.49 772.83 -377.75 17.762  1  2.503e-05 ***
 fomc_pH                        7 770.07 776.30 -378.04  0.000  1          1    
 ---
-Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</code></pre>
+Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1</code></pre>
 <p>Model comparison indicates that including pH dependence significantly
 improves the fit, and that the reduced model with covariate influence
 results in the most preferable FOMC fit.</p>
-<div class="sourceCode" id="cb34"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">fomc_pH_2</span><span class="op">)</span><span class="op">$</span><span class="va">confint_trans</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span>digits <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
-<thead><tr class="header">
+<pre class="r"><code>summary(fomc_pH_2)$confint_trans |&gt; kable(digits = 2)</code></pre>
+<table>
+<thead>
+<tr class="header">
 <th align="left"></th>
 <th align="right">est.</th>
 <th align="right">lower</th>
 <th align="right">upper</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="left">meso_0</td>
@@ -1771,11 +2018,9 @@ results in the most preferable FOMC fit.</p>
 </tr>
 </tbody>
 </table>
-<div class="sourceCode" id="cb35"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">fomc_pH_2</span><span class="op">)</span></span></code></pre></div>
-<p><img src="2023_mesotrione_parent_files/figure-html/unnamed-chunk-14-1.png" width="700" style="display: block; margin: auto;"></p>
-<div class="sourceCode" id="cb36"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="../../reference/endpoints.html">endpoints</a></span><span class="op">(</span><span class="va">fomc_pH_2</span><span class="op">)</span></span></code></pre></div>
+<pre class="r"><code>plot(fomc_pH_2)</code></pre>
+<p><img src="data:image/png;base64,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" width="672" style="display: block; margin: auto;" /></p>
+<pre class="r"><code>endpoints(fomc_pH_2)</code></pre>
 <pre><code>$covariates
       pH
 50% 5.75
@@ -1783,8 +2028,7 @@ results in the most preferable FOMC fit.</p>
 $distimes
          DT50     DT90 DT50back
 meso 17.30248 82.91343 24.95943</code></pre>
-<div class="sourceCode" id="cb38"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="../../reference/endpoints.html">endpoints</a></span><span class="op">(</span><span class="va">fomc_pH_2</span>, covariates <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>pH <span class="op">=</span> <span class="fl">7</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
+<pre class="r"><code>endpoints(fomc_pH_2, covariates = c(pH = 7))</code></pre>
 <pre><code>$covariates
      pH
 User  7
@@ -1793,21 +2037,21 @@ $distimes
          DT50     DT90 DT50back
 meso 6.986239 27.02927 8.136621</code></pre>
 </div>
-<div class="section level3">
-<h3 id="dfop">DFOP<a class="anchor" aria-label="anchor" href="#dfop"></a>
-</h3>
+<div id="dfop" class="section level2">
+<h2>DFOP</h2>
 <p>In the DFOP fits without covariate effects, random effects for two
 degradation parameters (<code>k2</code> and <code>g</code>) were
 identifiable.</p>
-<div class="sourceCode" id="cb40"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">f_saem_2</span><span class="op">[[</span><span class="st">"DFOP"</span>, <span class="st">"const"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span><span class="op">$</span><span class="va">confint_trans</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span>digits <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
-<thead><tr class="header">
+<pre class="r"><code>summary(f_saem_2[[&quot;DFOP&quot;, &quot;const&quot;]])$confint_trans |&gt; kable(digits = 2)</code></pre>
+<table>
+<thead>
+<tr class="header">
 <th align="left"></th>
 <th align="right">est.</th>
 <th align="right">lower</th>
 <th align="right">upper</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="left">meso_0</td>
@@ -1857,23 +2101,23 @@ identifiable.</p>
 excluding the same random effects as in the refined DFOP fit without
 covariate influence, and including covariate models for the two
 identifiable parameters <code>k2</code> and <code>g</code>.</p>
-<div class="sourceCode" id="cb41"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">dfop_pH</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/saem.html">saem</a></span><span class="op">(</span><span class="va">f_sep_const</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span>, no_random_effect <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"meso_0"</span>, <span class="st">"log_k1"</span><span class="op">)</span>,</span>
-<span>  covariates <span class="op">=</span> <span class="va">pH</span>,</span>
-<span>  covariate_models <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">log_k2</span> <span class="op">~</span> <span class="va">pH</span>, <span class="va">g_qlogis</span> <span class="op">~</span> <span class="va">pH</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
+<pre class="r"><code>dfop_pH &lt;- saem(f_sep_const[&quot;DFOP&quot;, ], no_random_effect = c(&quot;meso_0&quot;, &quot;log_k1&quot;),
+  covariates = pH,
+  covariate_models = list(log_k2 ~ pH, g_qlogis ~ pH))</code></pre>
 <p>The corresponding parameters for the influence of soil pH are
 <code>beta_pH(log_k2)</code> for the influence of soil pH on
 <code>k2</code>, and <code>beta_pH(g_qlogis)</code> for its influence on
 <code>g</code>.</p>
-<div class="sourceCode" id="cb42"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">dfop_pH</span><span class="op">)</span><span class="op">$</span><span class="va">confint_trans</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span>digits <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
-<thead><tr class="header">
+<pre class="r"><code>summary(dfop_pH)$confint_trans |&gt; kable(digits = 2)</code></pre>
+<table>
+<thead>
+<tr class="header">
 <th align="left"></th>
 <th align="right">est.</th>
 <th align="right">lower</th>
 <th align="right">upper</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="left">meso_0</td>
@@ -1931,55 +2175,49 @@ identifiable parameters <code>k2</code> and <code>g</code>.</p>
 </tr>
 </tbody>
 </table>
-<div class="sourceCode" id="cb43"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="../../reference/illparms.html">illparms</a></span><span class="op">(</span><span class="va">dfop_pH</span><span class="op">)</span></span></code></pre></div>
-<pre><code>[1] "sd(g_qlogis)"</code></pre>
+<pre class="r"><code>illparms(dfop_pH)</code></pre>
+<pre><code>[1] &quot;sd(g_qlogis)&quot;</code></pre>
 <p>Confidence intervals for neither of them include zero, indicating a
 significant difference from zero. However, the random effect for
 <code>g</code> is now ill-defined. The fit is updated without this
 ill-defined random effect.</p>
-<div class="sourceCode" id="cb45"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">dfop_pH_2</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">dfop_pH</span>,</span>
-<span>  no_random_effect <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"meso_0"</span>, <span class="st">"log_k1"</span>, <span class="st">"g_qlogis"</span><span class="op">)</span><span class="op">)</span></span>
-<span><span class="fu"><a href="../../reference/illparms.html">illparms</a></span><span class="op">(</span><span class="va">dfop_pH_2</span><span class="op">)</span></span></code></pre></div>
-<pre><code>[1] "beta_pH(g_qlogis)"</code></pre>
+<pre class="r"><code>dfop_pH_2 &lt;- update(dfop_pH,
+  no_random_effect = c(&quot;meso_0&quot;, &quot;log_k1&quot;, &quot;g_qlogis&quot;))
+illparms(dfop_pH_2)</code></pre>
+<pre><code>[1] &quot;beta_pH(g_qlogis)&quot;</code></pre>
 <p>Now, the slope parameter for the pH effect on <code>g</code> is
 ill-defined. Therefore, another attempt is made without the
 corresponding covariate model.</p>
-<div class="sourceCode" id="cb47"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">dfop_pH_3</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/saem.html">saem</a></span><span class="op">(</span><span class="va">f_sep_const</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span>, no_random_effect <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"meso_0"</span>, <span class="st">"log_k1"</span><span class="op">)</span>,</span>
-<span>  covariates <span class="op">=</span> <span class="va">pH</span>,</span>
-<span>  covariate_models <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">log_k2</span> <span class="op">~</span> <span class="va">pH</span><span class="op">)</span><span class="op">)</span></span>
-<span><span class="fu"><a href="../../reference/illparms.html">illparms</a></span><span class="op">(</span><span class="va">dfop_pH_3</span><span class="op">)</span></span></code></pre></div>
-<pre><code>[1] "sd(g_qlogis)"</code></pre>
+<pre class="r"><code>dfop_pH_3 &lt;- saem(f_sep_const[&quot;DFOP&quot;, ], no_random_effect = c(&quot;meso_0&quot;, &quot;log_k1&quot;),
+  covariates = pH,
+  covariate_models = list(log_k2 ~ pH))
+illparms(dfop_pH_3)</code></pre>
+<pre><code>[1] &quot;sd(g_qlogis)&quot;</code></pre>
 <p>As the random effect for <code>g</code> is again ill-defined, the fit
 is repeated without it.</p>
-<div class="sourceCode" id="cb49"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">dfop_pH_4</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">dfop_pH_3</span>, no_random_effect <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"meso_0"</span>, <span class="st">"log_k1"</span>, <span class="st">"g_qlogis"</span><span class="op">)</span><span class="op">)</span></span>
-<span><span class="fu"><a href="../../reference/illparms.html">illparms</a></span><span class="op">(</span><span class="va">dfop_pH_4</span><span class="op">)</span></span></code></pre></div>
+<pre class="r"><code>dfop_pH_4 &lt;- update(dfop_pH_3, no_random_effect = c(&quot;meso_0&quot;, &quot;log_k1&quot;, &quot;g_qlogis&quot;))
+illparms(dfop_pH_4)</code></pre>
 <p>While no ill-defined parameters remain, model comparison suggests
 that the previous model <code>dfop_pH_2</code> with two pH dependent
 parameters is preferable, based on information criteria as well as based
 on the likelihood ratio test.</p>
-<div class="sourceCode" id="cb50"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_saem_2</span><span class="op">[[</span><span class="st">"DFOP"</span>, <span class="st">"const"</span><span class="op">]</span><span class="op">]</span>, <span class="va">dfop_pH</span>, <span class="va">dfop_pH_2</span>, <span class="va">dfop_pH_3</span>, <span class="va">dfop_pH_4</span><span class="op">)</span></span></code></pre></div>
+<pre class="r"><code>anova(f_saem_2[[&quot;DFOP&quot;, &quot;const&quot;]], dfop_pH, dfop_pH_2, dfop_pH_3, dfop_pH_4)</code></pre>
 <pre><code>Data: 116 observations of 1 variable(s) grouped in 18 datasets
 
                             npar    AIC    BIC     Lik
-f_saem_2[["DFOP", "const"]]    7 782.94 789.18 -384.47
+f_saem_2[[&quot;DFOP&quot;, &quot;const&quot;]]    7 782.94 789.18 -384.47
 dfop_pH_4                      7 767.35 773.58 -376.68
 dfop_pH_2                      8 765.14 772.26 -374.57
 dfop_pH_3                      8 769.00 776.12 -376.50
 dfop_pH                        9 769.10 777.11 -375.55</code></pre>
-<div class="sourceCode" id="cb52"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">dfop_pH_2</span>, <span class="va">dfop_pH_4</span>, test <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
+<pre class="r"><code>anova(dfop_pH_2, dfop_pH_4, test = TRUE)</code></pre>
 <pre><code>Data: 116 observations of 1 variable(s) grouped in 18 datasets
 
           npar    AIC    BIC     Lik  Chisq Df Pr(&gt;Chisq)  
 dfop_pH_4    7 767.35 773.58 -376.68                       
 dfop_pH_2    8 765.14 772.26 -374.57 4.2153  1    0.04006 *
 ---
-Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</code></pre>
+Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1</code></pre>
 <p>When focussing on parameter identifiability using the test if the
 confidence interval includes zero, <code>dfop_pH_4</code> would still be
 the preferred model. However, it should be kept in mind that parameter
@@ -1988,11 +2226,9 @@ likelihood. As the confidence interval of the random effect for
 <code>g</code> only marginally includes zero, it is suggested that this
 is acceptable, and that <code>dfop_pH_2</code> can be considered the
 most preferable model.</p>
-<div class="sourceCode" id="cb54"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">dfop_pH_2</span><span class="op">)</span></span></code></pre></div>
-<p><img src="2023_mesotrione_parent_files/figure-html/unnamed-chunk-19-1.png" width="700" style="display: block; margin: auto;"></p>
-<div class="sourceCode" id="cb55"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="../../reference/endpoints.html">endpoints</a></span><span class="op">(</span><span class="va">dfop_pH_2</span><span class="op">)</span></span></code></pre></div>
+<pre class="r"><code>plot(dfop_pH_2)</code></pre>
+<p><img src="data:image/png;base64,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" width="672" style="display: block; margin: auto;" /></p>
+<pre class="r"><code>endpoints(dfop_pH_2)</code></pre>
 <pre><code>$covariates
       pH
 50% 5.75
@@ -2000,8 +2236,7 @@ most preferable model.</p>
 $distimes
          DT50     DT90 DT50back  DT50_k1  DT50_k2
 meso 18.36876 73.51841 22.13125 4.191901 23.98672</code></pre>
-<div class="sourceCode" id="cb57"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="../../reference/endpoints.html">endpoints</a></span><span class="op">(</span><span class="va">dfop_pH_2</span>, covariates <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>pH <span class="op">=</span> <span class="fl">7</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
+<pre class="r"><code>endpoints(dfop_pH_2, covariates = c(pH = 7))</code></pre>
 <pre><code>$covariates
      pH
 User  7
@@ -2010,22 +2245,21 @@ $distimes
          DT50     DT90 DT50back  DT50_k1  DT50_k2
 meso 8.346428 28.34437 8.532507 4.191901 8.753618</code></pre>
 </div>
-<div class="section level3">
-<h3 id="sforb">SFORB<a class="anchor" aria-label="anchor" href="#sforb"></a>
-</h3>
-<div class="sourceCode" id="cb59"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">sforb_pH</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/saem.html">saem</a></span><span class="op">(</span><span class="va">f_sep_const</span><span class="op">[</span><span class="st">"SFORB"</span>, <span class="op">]</span>, no_random_effect <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"meso_free_0"</span>, <span class="st">"log_k_meso_free_bound"</span><span class="op">)</span>,</span>
-<span>  covariates <span class="op">=</span> <span class="va">pH</span>,</span>
-<span>  covariate_models <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">log_k_meso_free</span> <span class="op">~</span> <span class="va">pH</span>, <span class="va">log_k_meso_bound_free</span> <span class="op">~</span> <span class="va">pH</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
-<div class="sourceCode" id="cb60"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">sforb_pH</span><span class="op">)</span><span class="op">$</span><span class="va">confint_trans</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span>digits <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
-<thead><tr class="header">
+<div id="sforb" class="section level2">
+<h2>SFORB</h2>
+<pre class="r"><code>sforb_pH &lt;- saem(f_sep_const[&quot;SFORB&quot;, ], no_random_effect = c(&quot;meso_free_0&quot;, &quot;log_k_meso_free_bound&quot;),
+  covariates = pH,
+  covariate_models = list(log_k_meso_free ~ pH, log_k_meso_bound_free ~ pH))</code></pre>
+<pre class="r"><code>summary(sforb_pH)$confint_trans |&gt; kable(digits = 2)</code></pre>
+<table>
+<thead>
+<tr class="header">
 <th align="left"></th>
 <th align="right">est.</th>
 <th align="right">lower</th>
 <th align="right">upper</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="left">meso_free_0</td>
@@ -2091,41 +2325,39 @@ effect on this parameter (<code>SD.log_k_meso_bound_free</code>)
 includes zero.</p>
 <p>Using the <code>illparms</code> function, these ill-defined
 parameters can be found more conveniently.</p>
-<div class="sourceCode" id="cb61"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="../../reference/illparms.html">illparms</a></span><span class="op">(</span><span class="va">sforb_pH</span><span class="op">)</span></span></code></pre></div>
-<pre><code>[1] "sd(log_k_meso_bound_free)"      "beta_pH(log_k_meso_bound_free)"</code></pre>
+<pre class="r"><code>illparms(sforb_pH)</code></pre>
+<pre><code>[1] &quot;sd(log_k_meso_bound_free)&quot;      &quot;beta_pH(log_k_meso_bound_free)&quot;</code></pre>
 <p>To remove the ill-defined parameters, a second variant of the SFORB
 model with pH influence is fitted. No ill-defined parameters remain.</p>
-<div class="sourceCode" id="cb63"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">sforb_pH_2</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">sforb_pH</span>,</span>
-<span>  no_random_effect <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"meso_free_0"</span>, <span class="st">"log_k_meso_free_bound"</span>, <span class="st">"log_k_meso_bound_free"</span><span class="op">)</span>,</span>
-<span>  covariate_models <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">log_k_meso_free</span> <span class="op">~</span> <span class="va">pH</span><span class="op">)</span><span class="op">)</span></span>
-<span><span class="fu"><a href="../../reference/illparms.html">illparms</a></span><span class="op">(</span><span class="va">sforb_pH_2</span><span class="op">)</span></span></code></pre></div>
+<pre class="r"><code>sforb_pH_2 &lt;- update(sforb_pH,
+  no_random_effect = c(&quot;meso_free_0&quot;, &quot;log_k_meso_free_bound&quot;, &quot;log_k_meso_bound_free&quot;),
+  covariate_models = list(log_k_meso_free ~ pH))
+illparms(sforb_pH_2)</code></pre>
 <p>The model comparison of the SFORB fits includes the refined model
 without covariate effect, and both versions of the SFORB fit with
 covariate effect.</p>
-<div class="sourceCode" id="cb64"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_saem_2</span><span class="op">[[</span><span class="st">"SFORB"</span>, <span class="st">"const"</span><span class="op">]</span><span class="op">]</span>, <span class="va">sforb_pH</span>, <span class="va">sforb_pH_2</span>, test <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
+<pre class="r"><code>anova(f_saem_2[[&quot;SFORB&quot;, &quot;const&quot;]], sforb_pH, sforb_pH_2, test = TRUE)</code></pre>
 <pre><code>Data: 116 observations of 1 variable(s) grouped in 18 datasets
 
                              npar    AIC    BIC     Lik   Chisq Df Pr(&gt;Chisq)  
-f_saem_2[["SFORB", "const"]]    7 783.40 789.63 -384.70                        
+f_saem_2[[&quot;SFORB&quot;, &quot;const&quot;]]    7 783.40 789.63 -384.70                        
 sforb_pH_2                      7 770.94 777.17 -378.47 12.4616  0             
 sforb_pH                        9 768.81 776.83 -375.41  6.1258  2    0.04675 *
 ---
-Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</code></pre>
+Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1</code></pre>
 <p>The first model including pH influence is preferable based on
 information criteria and the likelihood ratio test. However, as it is
 not fully identifiable, the second model is selected.</p>
-<div class="sourceCode" id="cb66"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">sforb_pH_2</span><span class="op">)</span><span class="op">$</span><span class="va">confint_trans</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span>digits <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
-<thead><tr class="header">
+<pre class="r"><code>summary(sforb_pH_2)$confint_trans |&gt; kable(digits = 2)</code></pre>
+<table>
+<thead>
+<tr class="header">
 <th align="left"></th>
 <th align="right">est.</th>
 <th align="right">lower</th>
 <th align="right">upper</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="left">meso_free_0</td>
@@ -2171,11 +2403,9 @@ not fully identifiable, the second model is selected.</p>
 </tr>
 </tbody>
 </table>
-<div class="sourceCode" id="cb67"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">sforb_pH_2</span><span class="op">)</span></span></code></pre></div>
-<p><img src="2023_mesotrione_parent_files/figure-html/unnamed-chunk-25-1.png" width="700" style="display: block; margin: auto;"></p>
-<div class="sourceCode" id="cb68"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="../../reference/endpoints.html">endpoints</a></span><span class="op">(</span><span class="va">sforb_pH_2</span><span class="op">)</span></span></code></pre></div>
+<pre class="r"><code>plot(sforb_pH_2)</code></pre>
+<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABUAAAAPACAIAAAB7BESOAAAACXBIWXMAAB2HAAAdhwGP5fFlAAAgAElEQVR4nOzdd1xT19sA8Ocm7I2goqIiOFDBAqKCFevCWVSUKihi3bhFbSvOWrUucHRInaXWVqtYsbaC1AFFQBEEGSoCCshS9iaE5L5/HL1vfkkICSsEn++HP+Dcc889N5zc5Ln3DIqmaUAIIYQQQgghhFD7xpJ3BRBCCCGEEEIIIdQ4DOARQgghhBBCCCEFgAE8QgghhBBCCCGkADCARwghhBBCCCGEFAAG8AghhBBCCCGEkALAAB4hhBBCCCGEEFIAGMAjhBBCCCGEEEIKAAN4hBBCCCGEEEJIAWAAjxBCCCGEEEIIKQAM4BFCCCGEEEIIIQWAATxCCCGEEEIIIaQAMIBHCCGEEEIIIYQUAAbwCCGEEEIIIYSQAsAAHiGEEEIIIYQQUgAYwCOEEEIIIYQQQgoAA3iEEEIIIYQQQkgBYACPEEIIIYQQQggpAAzgEUIIIYQQQgghBYABPEIIIYQQQgghpAAwgEcIIYQQQgghhBQABvAIIYQQQgghhJACwAAeIYQQQgghhBBSABjAI4QQQgghhBBCCgADeIQQQgghhBBCSAFgAI8QQgghhBBCCCkADOARQgghhBBCCCEFgAE8QgghhBBCCCGkADCARwghhBBCCCGEFAAG8AghhBBCCCGEkALAAB4hhBBCCCGEEFIAGMAjhBBCCCGEEEIKAAN4hBBCCCGEEEJIAWAAjxBCCCGEEEIIKQAM4BFCCCGEEEIIIQWAATxCCCGEEEIIIaQAMIBHCCGEEEIIIYQUAAbwCCGEEEIIIYSQAsAAHiGEEEIIIYQQUgAYwCOEEEIIIYQQQgoAA3jU8V2/fn3hwoX9+vXT0dHR1dW1sbH57LPPrl27Ju96ySAsLIyiKGVlZXlXpH2hadrHx+fcuXPyroic+fr6UlIbO3ZsK1UjNjaWHILD4bTSIRBCCCGEPnAYwKOOLCMjY/jw4TNnzjx//nxaWlpFRUV5eXlcXFxAQMCsWbPs7OxSU1PlXUcFlpKSIiFQXLBggeguT548mTt3rpGRkbq6+sCBA7dv315bWyuarb6+/rvvvps+fbqtre2SJUuioqLEVuD333//4osvaJpu4RNDCLV79vb2jd6usra2FtyFpukLFy5Mnjy5e/fuampq5ubmM2bMCA4OFi28CRc3hFpbE9p8bW3tnj17xo8f36NHDz09veHDh69YsSIzM1Ns+dK/QRBCckYj1EGlp6cbGRmRdj5r1qzffvstIiIiKCjou+++c3BwIOmDBg2qqKiQd00bFxsba2trO2LEiKbtvnTpUisrq19++aVla3Xjxg0J1xZ3d3eh/FeuXNHQ0BDKNnz48NzcXMFsZWVltra2gnlYLNahQ4eESuNwOCYmJgMHDqyvr2/Z81I4b9++jf9f9+7dIy/dzp07hTalpaW1UjViYmLIQWtra1vpEAgx7OzsGv2GY2VlxeSvrq4eP3682GwuLi5cLlewcFkvbgi1AVnbfHJyspmZmWgeNTW1n3/+Wahwmd4gCCH5Umr0WoCQIuJwOHPmzMnPz1dVVT116pSHhwezafLkyWvXrr148aK7u/vTp0/Xr19/9uxZOVZVGjY2No8ePWry7mlpafHx8QUFBS1YJQAg/RcsLS337NkjutXY2Fjwz7t3786ZM4emaUdHx7Vr1/bt2/fevXvbt2+Pjo52dHSMj49XUnp3OdqwYUNMTMy0adMOHTpkbGwcHBy8evVqb29vBwcHwa8vfn5+GRkZ165dY7PZLXtegg6Xlv9ZVX3CsJO1qorYDHU0vbGoJKGOe8Oosy5LPn2aOnfu3LlzZ8GUoqIi8kuvXr0++ugjeVQKKbwqXuWdwn8elz3Irs2kabqzarch2jYTO083VOki76oBABw8eLCkpETsJpqm169fn5WV5e7uziSuXr36zp07AODm5rZp06aePXumpKQcOXIkMDAwICDA3Nxc8Dom08UNobYhU5unaXrp0qXp6emampp79uz55JNPjIyMHj9+vHXr1sTExFWrVtnZ2ZmbmzMlyPQGQQjJmVxvHyDUWo4fP05a+N9//91QHi8vLwBQV1evrKxsy7q1vTFjxgCAj49Pyxa7cuVKAFi8eLE0mUkYaWtrW11dzSTeu3ePDOz38/MjKTU1NcrKyiYmJoJPca9cuQIAK1asYFLKysoMDQ3t7e1b6FQadKikzDwrxzY793EtR3Qrh89f/rbQPCvn45y8ch6vtSsjvcLCQtL+z5w502YHxSfwHUlSedyaxPmL4mcI/Sx74nKn4Ka8a9cIck925MiRvPfvyqysLHKnb9GiRXw+XzDz8uXLAYDNZmdmZjKJMl3cUAejiF+SRds8mehHSUnpv//+E8xZUFBgaGgIAGvXrmUSZX2DIITkC8fAow6Ix+P5+voCgL29/bRp0xrKtmjRIgCoqakJCgpqu8rJiInE2iHykGrAgAGN5kxISHjy5AkA7N69W11dnUkfM2bMlClTAODXX38lKS9evOByuSNHjlRVVWWyjRs3DgASExOZlMOHDxcWFh48eLBlzqRhG/V0pmuqV/LpJQVFjzh1gpu4NL2hqOS/Wk4nFutcZwNtOT1+R6jFPa9MPPZqTxWvcpD2R5vNvtFR1gOAjaa7Pu40jkfXX8g5ebvwH3nXsUF5eXmbNm3S0NDw9/dnvX9XXrt2jcfjqampHT9+nKIowfwHDhygKIrH4wnOtSH9xQ11JGQYueSUdkhsmye99j7++GNmzCBhaGg4efJkACAfyoSsbxCEkHzhN07UAcXExGRlZQHAtm3bJGSztLQMCwsLDQ21sbERTKdp+tSpUy4uLn379tXT03NwcFizZk16ejqTwcvLi6Kobt268fl80WJHjRpFUdTUqVOZlJqamn379k2ZMqVPnz7q6upmZmYTJ068fPlyfX294I7JyckURfXp0wcAnj17NmnSJA0NDdKVICIighI3C32jJS9YsICiqNDQUADYvHkzRVGOjo6CJdy9e3fGjBlkVrkBAwZ4eHjExsZKeNEESf8dl8yCo6GhQUJxQdOnTweAqKiosrIyAOjTpw+LxUpMTKQFHoMkJCQAgKmpKfkzPz//6NGjTk5OQt9LWgMb4NtO+tM11WtoeoVADM+l6fVFJfdqajuxWD93MeivaAsENNrIiVu3blEUJdQ/nzh58iRFUcOGDZPjse7evTtz5swBAwZoaGj06dNn4sSJ169fF1uH5rTzD00dn3M66xiP5k3qPGOT6deDtIZQQAFATzWTJT3XLe65lgLqj9xzbzi58q6peKtWrSotLT1w4EC/fv2YRPKJMGTIEG1tbaH8+vr6PXv2BICkpCQmEQP4D5CEQL2dx/Bi23xKSoqSklLfvn1F82tqagKAYG98Wd8gCCE5k28HACRHlZWVxe0JhyOmi3LTHDhwgDTv0tJSWffNy8sTCnEJbW1tf39/kufhw4ckMTw8XGj3zMxM8kl/5coVkhITEyN2FhkAcHV1FeyrRj4dTUxMnj9/Tnq4AcDOnTtpmr5//z4AKCkpCR5LmpK3bt1qZWWlpaUFAN27d7eysmI6onO5XG9vb5a458Z79uxp9IWqra0l+4aEhHh6eg4cOFBDQ2PIkCEeHh6RkZFCmcmkzaNHjxYt59mzZ+SgDx48ICnkmfyqVavI0Ib4+HjypeT69eskg6enJxPkt416mv6yqNg8K8f6dW50LaeOz19ZUGSelTMyOy+lrq7NqiE9yV3opWnkBLnzYmhoKFrITz/9BAC2trZMitgu9K10LPp9x05Ry5cvF8zWzHb+AbpXGLQofsY3Lzbz6XdXpw3Jny+Kn1FaV0z+9H/946L4Gf6vf5RfHRv0xx9/AMDQoUOFugGfO3fO09PzxIkTortwuVwSz/zwww8kRaaLG+owxF5PGPKuXYMaavMNqa+vHzhwIAAsWLCASZTpDYIQkrv2e0lCraeoqOjjjz+W/FnV9pSVlb29vVvkBJcsWQIA+vr6Tdh34sSJAMBms3fs2PHo0aPXr19fv36dmQaM+fZGIuf169cL7U46dRsYGJD7EfX19WSSmN69e//xxx+ZmZkFBQUxMTHLli0jBYaEhDD7kgC+R48eNjY2lpaWISEhBQUFZJNoAC9TyWLHwH/99dcAwGKx1qxZ8+DBg7y8vLt37zIdB8R+kAtKTk5m/nFC/0oWi7V582bBLxOjRo0CgNmzZ4uWw0yt9/vvv5OU7OzsHj16kPNlnsd6enqSreSpwueffy65emJV8PhlPJ40P7Ui34RKeXyvwiLzrByr17lz8gvMs3Lss/Nia2ul2beh47be7PmSA3gpGzndEgF8Kx3r999/JyV8+eWXSUlJhYWFCQkJZFAMANy9e5fJ2cx2ruiq6isr6yuk+eHy380yfezlnkXxM+4VBhVw3rzl5L/l5K9L8lgUPyO9KoX8mVDxeFH8DK/kRaL7Nnpc5qZAa+BwOL179waAW7duSb8XGTxMUVRCQgJJkenihtoVPp8v/WMDwZnVxX0rEdbQvpKP26rnK1Ob53K5cXFxpP+8trZ2UlKSNIcQfYMghOQOZ6H/EP30008RERHyroUwLpe7f//+zz//vH///s0sikQvpC+6TIKCgkJCQgDgypUrzs7OJNHY2NjR0XHMmDHR0dGbN28mL52rq+u+ffuuXr169OhRwc51ly5dAoD58+erqKgAQHp6+vPnz0k6M4O6oaHhqVOn4uLiYmJiyBzsgnXIyclhs9kpKSlqamoSqtqEkgXl5eUdPnwYAPz8/JgnmUZGRmPHjl24cOH58+e3bt3q7u4u2puOQbqYAoCBgYG3t7ednZ2ent6TJ0/27NmTmJjo4+NjZmbm6elJ8lRWVgJAp06dRMvR19enKIqmaZIHAHr06JGUlLRjx47bt2/n5uaOGjVq5cqV8+bNI1u3bdvGZrN3794t4cURy6+84ruyCikzq1NUQNfOpspKovvW0nRCXR0AlPD5898WybSvECM2O6hbF7W27ZwpfSNvz8e6efMmAMyaNYuZB8HAwODs2bP3799PTU0NCwsbO3YstEQ7V2j+r3/8r/hfKTNrK+nsG/CDlpLOi8qnAHA++yehDHtTvxT8s5RbvDbJXWjfRo/bS9306/5HpD8Fmfj5+WVmZo4ZM4bcNpJGeHj4unXrAMDZ2dnS0pIkynRxQ+3K5MmTyTVHGmZmZs+fP2cWQGmU4EeY0L4SjjtjxozAwEApDyErKdv8q1evrKysKisrybi/4cOH+/n5DR48uNHyxb5BEEJyhwH8h0hwerB2hcVikbi3mXg8HilN1h1Pnz4NAA4ODkywQairq+/du3fixImRkZFJSUkWFhZubm779u3Lzs5++PAhEz+npKTExcXB++nxAKBTp07kk1t0+VayRj0TtQry9vaWHL03uWSGn59fVVWVtbW1aD9kX1/fCxculJaWhoeHC47kF1JeXm5hYaGrq3vp0iVmUaX+/ftPnz7d0dExPDx8y5YtLi4uZCxAdXU1AOjo6IiWw2azNTU1Kysrq6qqmEQ9Pb3vv/9eNHN0dHRAQMDmzZt79eol4ezE0mGxdKRuElosSkUgrmb2raJp3vtnNZosltj16xraV5QBWw4T30nfyNvzsSoqKgCgvLxcMJGiqIcPH9bV1TETJTa/nSs0LSVtTbaWlJm1lXTZlBIAsCgWAChRyjz63VQaNNAAQEbCv0fTABpsLep/9230uLpKerKehZQqKir27dsHAN9++600+auqqvbu3Xv48GEejzdkyBDBxURlurihdqVz5876+vpSZu7SpYtMg9sFSxbaV8Jxxc7r0SJkavMsFktJSamurg4AUlNT//77bysrKwlfkyS8QRBCcocB/IdoxYoVUVFRoaGhYudgkxd1dfUvvvjCxMSk+UWR71Wis2Q16sWLFwDg5OQkumnChAlqamq1tbVpaWkWFhaDBw+2tLRMTEwMCAhgQuiLFy8CgI2NjZWVFVOTGTNmCJZTVVWVmpp69+7d27dvN1QNoUn1xGpayQzSR1TsI3pDQ0MzM7PU1NTo6GgJgc2CBQvIyHYhqqqqx48ft7GxKSsru3///syZMwGABFQN3VPgcrkA0Og9CwD46quvdHV1vb29yZ8lJSU7duy4c+dOXl6etbX1qlWrPvvss4b2na+lOV9Ls9FDNLTvHE2NdUUloTW1nVisj1RV79XU8Gnar7PBsAbWh2+R47YG6Rt5ez7W5MmTr1+/fvv27U8++WTx4sVjxowh/UiFvkM3v50rNJduHi7dPGTdy1zLMrYsyt14+ehO7143r6eLyrglRwad01XWB4CMmvRvXmwyVOl6aODJFjxuMx05cqSgoMDR0dHe3r7RzL/++uuWLVtyc3MBYPbs2SdPntTT+/87CzJd3FC7cuHChdYrvLi4WC7HbYj0bb5Pnz4lJSV8Pj8nJ+fcuXP79+/ftWtXRkbGuXPnxOaX/AZBCMkdBvAfIi0trYCAAHnXohWRx7MlJSUlJSWSb8ZfuHAhNjZWS0uLTGdFYn6xfe8piiLTyzG9K93c3BITE69everj40NSSP/5xYsXC+0bEhLy119/PX78ODU1VZpl4aR/vCxryQwSWR06dOjQoUMN5WFGp8vK2tpaT0+vtLQ0MTGRfMclXZTFfvupq6vjcDgAQGbakyAoKCg0NHT//v2kH2N2dvaIESNyc3PZbLa2tnZoaGhoaKiXl9eRIy3fQZdL0yR6N2Cxfu5iaKqstK2Yul5VvaKg6KQUMXz7IWsjb7fH8vT0fP36ta+v73///ffff/8BQNeuXR0cHKZPnz579mwNDQ2SrVXbeUdlrTs8tiwqpOCvkfpjlSgxXxJuvrlKsrV51RrE5XL9/PwAgEyAIkFWVtbSpUv//fdfADA1NT148KCLi4v0BxK9uKGOgaZpyU/jaenGybcZ6ds8g8Vi9ezZc9euXTo6Ohs3bvT399+yZYvQoMVmvkEQQm0Dl5FDHRAZ/goA4eHhknP6+voeO3aMLCjFfDw39ClORruRaBMA3NzcACAjI4PsHhcXl5KSoqqqStKJ6urqKVOmTJo06ccff0xOTra0tFyxYsXhw4fDw8MlfPkTXCm9IU0rmVFaWgoAvXr1smpYly5dGi2nISRmY0Ij8mdRkZgR49nZ2YJ5GsLn87ds2dK9e/f169eTlOXLl+fm5i5btozcqYmKiurdu/fRo0fv3LnT5GqLJRS991NWYgPs66Q3Q1NDaG259k/WRi5NUfI61r59+169enXy5MmZM2caGBi8efMmICDAw8Nj0KBBZAFkaP123iGN0BvdVbV7bu3rn19/z6N5Qltvvr0aUxapylKb3Lkdha83btx48+aNnp6eUL8kIYGBgRYWFv/++6+urq6vr++zZ8+aEJwIXdwQkgsp27xY8+fPBwCaph8/fiyY3iJvEIRQG8An8KgDsre3NzAwKCoq2r17t5OTU0PxQ3Z2dmJiIrwP+FkslpmZWXJy8suXL8XmJ+nMysAmJib29vZRUVEBAQFDhw4l/ednzpwpOM/N3r17g4ODNTQ0Tp8+PXfuXDb7/wdNkwXem6yZJffv3z87O3vDhg1eXl5NODqfzycxUv/+/cX2ccjJyQGAQYMGkT/JZDmxsbFcLldoYmcywT5FUUxmsX777beEhITTp0+Tuxvl5eXBwcEDBw708/Mj525nZ3fixIlp06Zdvnx5/PjxTTgpsUSjd5JOYngAUKzn8LI2cgnevHkj92N169Zt+fLlZHz78+fPg4ODDx48mJmZOWfOnJcvX1IU1cx2/mFiU+yVvTfvT9saVRKWXZs5ufNMPs0HgOSK+KjSsOSKeAqoRT3X6CsbyLum/4/MtuDq6iphJM7du3ddXV05HI6jo+O5c+eYwe1CZL24oQ5DwkP49vb4HRpr84WFhWTx9hs3bkyYMEFoa6dOndhsNo/Hq62tZRKlfIMghNoDfAKPOiBVVdUNGzYAwOPHj0lcLRaZnQUAmHiP9CX7559/RDOHhYWRmdgE4w3ysD0gIIB+vxarUP95Mlf2mjVr5s2bJxhjw/tng03WzJLJEnRRUVGim7hc7tGjR319fcn3VLFYLJaLi4udnd3+/ftFt6ampr59+xYAmElryQS5ZWVlon0iyKs9YsQICYMdOBzOjh07BgwYwMwOmJqaStO0jY2N4LmTcYDMwvIt4tvSchK9+wtE7wSJ4adrqNfQ9OrC4pL2NKOEBLI2cmjgITmzaFzbH4u0hx07dpAe8oS5ufmGDRvI+z0jIyMrKwua3c4/WL3UTb/qu6+zStfXNRmns45V1JcBwJnXx5Mr4jXZWqtMvhyuN0redfx/2dnZZAJwsQPXicrKytmzZ3M4nHnz5gUFBUkITmS9uKGOhKzPJDmlPWi0zRsaGqqrq9fW1oq9UD979ox8+WFW9JT+DYIQahfabsU6hNpQWVlZ3759AUBJSenIkSOiGfz9/cm9dg8PDybx77//Ju+L69evC2auqakhM9UNGzZMcAXg/Px8EkD+8MMPAGBsbMzj8QR3tLa2BgDR9e0fPHhA7ppv2bKFSSTrwANAaWmpUH7RdeBlKpmsA3/48GEmJTIykqIoFov1119/CZVA5rM1NDQkS9k3hDz21NXVffHihWB6fX09WWZ2yJAhgivlkonKJk6cKPgSJSYmampqAsCPP/4o4VhkWPuff/7JpJSVlZGH9oKlkS80S5YskVCUrE6WVUzNe5NaJ7zMNaOepncWl8zIe1vBa0dLQ0tYB16mRs6s8ZaYmCiYmbQfaGwd+NY7Vrdu3cS2/xs3bgCAsrJyVVUV3RLt/EPG5deFFt46kr57ZaLrsiezD6R5//PmamV9hbzrJezkyZMAoKmpKbQ0t6Aff/wRALp161ZZWdlogbJe3BBqY9K0eQ8PDwAwMjIS/UZBxtnp6ekxVz+Z3iAIIbnDAB51WAkJCcy6ZRMmTPD394+KioqMjDx//vykSZNIurGxcUlJieBepLOZkpLSnj17njx5kpeX988//zDTwkdGRgodheQnUej27duFtq5evRoAdHR0AgMD6+rquFzu8+fPv/zyS2YlPycnJyYElSmAl6lkEsC7urrW1dUx8RK5c89isbZs2RIREVFUVJSQkLB27Vqyu6+vr+SXNzMzk0w716NHjzNnzqSmpr58+TIwMHDYsGGk2KioKMH8JLIiAXZKSkp1dfWtW7fIzOH9+vWTEESVlZUZGBjY2dkJpU+ZMgUAPD09SagWGxtLxqYGBwdLrvmHQEIAT8vSyCsrK8mQB0tLywcPHvB4vIKCgnPnzunq6pLMkgP41jsW6eqirKz8xRdfPH78uLi4+NWrV6dPnyYrNk2bNo3J2cx2jto/Mkx3/PjxEvKMGjWK5AluWEZGBsks68UNoTYmTZtPTk4md/P79+9/8eLF58+fZ2Vl3bp1i+lyePHiRSazTG8QhJDcYQCPOrKkpCQJI2wdHR1TUlKEdsnJyRk3bpxoZm1tbX9/f9FDMIujUhSVlpYmtLWwsJDpisZisZjh387Oztu2bSO/6+vrP3nyhJYxgJep5KVLl5IUFRUVR0dHUkJxcXFD89N4eXlJ8/KGhYUJDvhndOrU6dKlS6L5v/nmGyYP0/W9W7duz549k3CUrVu3AkBYWJhQ+uvXr7t3705eFmah3XXr1klT8w5PcgAvUyP39fVlMgjOX0Ae7zQawLfSsUpLS/v16ye29Q4ePLioqIjJ2fx2jtozHo9HrkK7du2SkK1r165i24CgY8eOMfllvbgh1GakbPM0TV+6dEnsnLgqKiq7d+8WzCnrGwQhJF8YwKMOjsvlnj9/3tnZuWfPnurq6gYGBvb29gsXLhTsjy2Ez+f7+fk5Ozubmprq6OiMHDly9erV6enpYjOXlJSQh96ffPKJ2AxFRUUbN260sLAgR586dSrpTlxSUjJ+/Hg1NbUuXbo8ffqUljGAl6nk3NzcqVOnamtra2trL1iwQLCQS5cuzZkzx9zcXENDY+DAgXPnzn306FFjL+r/1OHrr78eOnSonp6enp7eJ598snHjxtzc3Iby//vvv1OnTu3cubOqqmrfvn03bdpUUFAgofzc3FwNDQ3BB6qCiouLV61aNWDAAB0dHQcHB8HnCR84yQE8LWMj/+eff8aNG8d8w1NTUzt69CiZ6KvRAL6VjkXTdHV19bFjxz7++GNjY2NVVdU+ffqQjjZi+5Q2s52jdis6Opo0lZCQkIbyVFZWNhqciMYnsl7cEGob0rR5RkpKyvr1683NzbW1tfX19e3t7VesWEEmkWE07Q2CEJIjim5/k3MghBBqh8rLyzMzM/v27SvNSocKdCyEEEIIIUWBATxCCCGEEEIIIaQAcBk5hBBCCCGEEEJIAWAAjxBCCCGEEEIIKQAM4BFCCCGEEEIIIQWAATxCCCGEEEIIIaQAMIBHCCGEEEIIIYQUAAbwCCGEEEIIIYSQAsAAHiGEEEIIIYQQUgAYwCOEEEIIIYQQQgoAA3iEEEIIIYQQQkgBYACPEEIIoQbFveHezqiVdy0QQgghBIABPPoQXL9+feHChf369dPR0dHV1bWxsfnss8+uXbsm73rJICwsjKIoZWVleVek1dE07ePjc+7cOXlXpEHlIUV5e1/ya/nyroiwlStXUuKoqqoOHjzY2dn5n3/+Eczf5EZ18OBBiqIcHR1bru6yabMzRQBQzuEvvlnsGVySXMiVd10QQs119uxZX19fmqYlZysuLo6Nja2urm6bWiGEZIIBPOrIMjIyhg8fPnPmzPPnz6elpVVUVJSXl8fFxQUEBMyaNcvOzi41NVXedVRgKSkpYuMoYsGCBYKZ6+vrv/vuu+nTp9va2i5ZsiQqKkpsmb///vsXX3zR6HcLeSn58036nCd5B169dE1ohzG8WHV1dU+fPg0MDPz000+dnJy43A4bhn04Z9pKaurp1+U8oZ/jMZXlHD4NcPBBhejWco7c3gX29vYSrj+EtYoNW6kAACAASURBVLW14C40TV+4cGHy5Mndu3dXU1MzNzefMWNGcHBw80tGHUyGy5oMlzXyroWw5rfMn3/+eenSpZs3b+bzG3nn7tq1y9bW9tmzZy16BgihlqEk7wog1Fpevnz58ccf5+fnA8CsWbNmz55tYmJSXl6empp65cqV8PDwhw8fzpw58+HDh1paWvKubCO0tbVtbW3ZbHbTdl+2bFlMTIyXl5eHh0cL1kr62x/l5eXjx4+PiYkhf8bGxvr7+x84cOCLL74QzFZXV7d9+/aBAwd+/vnnLVjPllLy55uMxcl0Pc3SZJffLnrpmmB6aQhLrX3dBu3evfvNmzcFU4qLixMSEnx9fV+/fv33338fOHBgx44d0OxG1eJkbaWKe6bt1pzAoqcNP2aPyOaM+f2tUKKWChU+v4uOavt6F4hVU1Pj5OR0584dJiUlJSUlJeWvv/5ycXG5ePGikhJ+I0IdWXp6+rp166TJWVlZefny5dauD0KoyfDjCnVMHA5nzpw5+fn5qqqqp06dEgwJJk+evHbt2osXL7q7uz99+nT9+vVnz56VY1WlYWNj8+jRoybvnpaWFh8fX1BQ0IJVgvcBvKWl5Z49e0S3GhsbM79v2LAhJiZm2rRphw4dMjY2Dg4OXr16tbe3t4ODg52dHZPNz88vIyPj2rVr7TDWKg18S6L3rht7Gyzonjrlcfntopdzn5he+oil3o6iFxUVlY8++kgocezYsYsXL7aysnr58uXevXs3b96srq7ezEbV4mRtpYp7pu2WvhoLANSVKEONd026nEOXcfiqSqDCoirqaNFN6kqUmhIll9oePHiwpKRE7CaaptevX5+VleXu7s4krl69mkTvbm5umzZt6tmzZ0pKypEjRwIDAwMCAszNzZnrmKwlow6Gefae4bLGJOAH+VZGUHNaJo/Hc3d3r6ysbPQoRUVFS5cufftW+G4dQqgdoRHqiI4fP05a+N9//91QHi8vLwBQV1evrKxsy7q1vTFjxgCAj49Pyxa7cuVKAFi8eLHkbDU1NcrKyiYmJrW1tUzilStXAGDFihVMSllZmaGhob29fctWskWUXHvzWPdOrObt7B2pJKXmRVWCWXis5u3U6Y951Tz5Vo/w9PQEABMTk4YynDp1irwp4uLimnOgAwcOAMCECROaU4go6Vtpm53ph+ZBDsfUL9fmXH5lHZ+m6ao6/tCf8039ch/kcAqqeYNP55n55T4trCOZP7tWaOqXe+ZJe7x4knuyI0eO5PHevTezsrLIbcFFixbx+XzBzMuXLwcANpudmZnZhJJRx/Nq9mrmR951kVajLXPXrl0AYGBgQK6N9fX1QhkyMzN3797t7Oysrq7OxAgxMTGtX3eEkMza0YMjhFoKj8fz9fUFAHt7+2nTpjWUbdGiRQBQU1MTFBTUdpWTUWFhobyr0CDyBH7AgAGSs7148YLL5Y4cOVJVVZVJHDduHAAkJiYyKYcPHy4sLDx48GDrVLbpSgPfvvo8iTx77/FNX5Ko1k+jX5CNspFq+Z3il65P+DUKMB5+4MCB5JeUlBT51qS1fThn2rJGdFcZ3k2llMP/NakKAPwTq0pq+UONVEZ0VzFUZ7kO0qABfoytBID/XnNi8+v01VhuAzXkXWtheXl5mzZt0tDQ8Pf3Z7Hefcm5du0aj8dTU1M7fvw4Rf1Pl4EDBw5QFMXj8RqamENyyaiDERr63g5HwotqtGVGRUXt3bu3S5cuYrvLEYmJibt27bp27VpNTU1rVhYh1ALwEwh1QDExMVlZWQCwbds2CdksLS3DwsJCQ0NtbGwE02maPnXqlIuLS9++ffX09BwcHNasWZOens5k8PLyoiiqW7duYqeBGTVqFEVRU6dOZVJqamr27ds3ZcqUPn36qKurm5mZTZw48fLly/X19YI7JicnUxTVp08fAHj27NmkSZM0NDRIV4KIiAix02g3WvKCBQsoigoNDQWAzZs3UyKTh9+9e3fGjBlGRkbq6uoDBgzw8PCIjY2V8KIJkjKA79OnD4vFSkxMpAWmpktISAAAU1NT8md+fv7Ro0ednJwcHBykPHrbEBu9EwoXw5M3BQAYGRlBw42K3P8aOnSotra2jo7O0KFDDx48KNRWRZWWlg4bNoyiKDMzM+ZARKNtrNFW2kpnKk3dGNK/LM15T8ndWlstADj9pKqghv9zYhUAeA3TJptWWGmpKVHBL2tTiutPPK4kKRrK8uk/L8GqVatKS0sPHDjQr18/JpE0iSFDhmhrawvl19fX79mzJwAkJSU1oWSE5E5yy6yoqHB3d+fxeGfOnOnSpUtDhQwdOjTgvRMnTrRmfRFCzSbvLgBIPurq6hITE2Nk8ezZM+k7DTah/FevXrXU2ZEuvgBQWloq6755eXligwdtbW1/f3+S5+HDhyQxPDxcaPfMzEzyeOfKlSskJSYmxszMTOy7z9XVVbAzJ/n6aGJi8vz5c0NDQ5Jn586dNE3fv38fAJSUlASPJU3JW7dutbKyIrP0de/e3crKium1zuVyvb29xd6t37NnT6MvVG1tLdk3JCTE09Nz4MCBGhoaQ4YM8fDwiIyMFMo8ZcoUAFi1ahUZrRAfH0++Z1y/fp1k8PT0ZIL89kO057yo9tOXXnLHch6PN2rUKPL/LSwspBtoVNnZ2WLvoYwePTo/P5/kEe1CT6J3ADA1NRXshyxlG5PQSlvvTGVq/9K8LLKW2W7NDSw09ctd/HeRqV+uy7VCwU1fh5eZ+uW6BRaZ+uXa/Pyup3278scffwDA0KFDhfrJnzt3ztPT88SJE6K7cLlcTU1NAPjhhx+aUDLqSAQ7zytKR/pGW+bChQsBYNmyZTRNBwQEkCuSaBd6QRkZGSQbdqFHqH3CAP5DlJWVRR7zysrW1raioqL1yndzc2uRE1yyZAkA6OvrN2HfiRMnAgCbzd6xY8ejR49ev359/fp1Zq4sJjQlkfP69euFdic9wA0MDDgcDk3T9fX15ubmANC7d+8//vgjMzOzoKAgJiZm2bJlpMCQkBBmXxLA9+jRw8bGxtLSMiQkpKCggGwSjUBkKlns6OKvv/4aAFgs1po1ax48eJCXl3f37l2m44DYb7qCkpOTSU7RB5ssFousUsNkzs7O7tGjBzmFzp07k2yenp5ka0pKipKS0ueffy7dv6iJ+PX86oSKqsfl5IeTVSM2nfnJP5LxWOdOrObtV8uTJeTnZNUwMXzKuEcVUaWiRQn+MOW0uIbC2uLi4rCwsAkTJpCXff78+SRdtFHx+XwSh+vo6Pz0008pKSkZGRlnzpwh/zJbW1uSTSiALy0tHT58OIhE77SMbaz5Y+ClP1OZ6iblyyLr+bYBTj0/uaAu8e27n7dVPLHpQj/nEytN/XLNfso19csNf10rmD8sq9b8VJ6ZX66pX+5PjysklyN63FY/Xw6nd+/eAHDr1i3p9yKDhymKSkhIaNmSUdvj13I46ZnMD6+iSmy62J+GonfBGF6acgSP29oabZlkMnkzMzPy5Q0DeIQ6BpyF/kN05syZV69eNWHHmJiYa9euCa3v3YLlX7x4cevWrRYWFk3YVxAZN96EmwhBQUEhISEAcOXKFWdnZ5JobGzs6Og4ZsyY6OjozZs3R0REAICrq+u+ffuuXr169OhRwRGVly5dAoD58+erqKgAQHp6+vPnz0k6M926oaHhqVOn4uLiYmJioqOjhR745+TksNnslJQUNTU1CVVtQsmC8vLyDh8+DAB+fn5kDicAMDIyGjt27MKFC8+fP79161Z3d3fR7qYMZg05AwMDb29vOzs7PT29J0+e7NmzJzEx0cfHx8zMjARaANCjR4+kpKQdO3bcvn07Nzd31KhRK1eunDdvHtm6bds2Npu9e/duCefbfDlbU9/++Pr//2ZR5uHDND7SFk4XUfxbXvFveQ3mZ1Hm4cP6/W39bGR05cOyFxNiGqnH+3Kacy4SZGRkCA3xFdSvXz8JfSMvXbr06NEjZWXliIgI5m24ZMmS/v37jx49OiYmJiIi4uOPPxbcpby8fNKkSdHR0aampvfu3evVqxezqfltrPXOVKa6SfmytPb5NsGG26W3XtUyf6qyqXvzOnfVZAuli0XTQFHQT19JtBwAUGZR7haa0pQjeNwmnYQM/Pz8MjMzx4wZQ+7DSiM8PJwsrOXs7GxpadmCJSO5yN95jJP+/0N4WFoaPc/sp5TYQulNQNfzpC+HOW5zjigNyS0zOzt7xYoVbDb7/Pnz7X+5XISQ9DCA/xCRcaGtt2+Ty1dWVmamSG0OHo8HAE2YZOj06dMA4ODgwETvhLq6+t69eydOnBgZGZmUlGRhYeHm5rZv377s7OyHDx8y8XNKSkpcXBy8nx4PADp16hQYGAgAgoulEeRVErumi7e3t+TovcklM/z8/KqqqqytrZlIg+Hr63vhwoXS0tLw8HDBkfxCysvLLSwsdHV1L126xKwY179//+nTpzs6OoaHh2/ZssXFxYUZC6Cnp/f999+LlhMdHR0QELB582bB2K81aA7X1YgsZf5kaykpG6mKphM0n+Y8q+bX8VkabLW+6sCmGspP0ksD39JcPgComKgp6YsZay163Damr6+/aNGi3bt3S/gaRyZccHd3F7qJ5uDgMHny5Ozs7KysLMEAnkTvDx8+NDY2ForeoSXaWNNIc6Yy1U3Kl0Ve5yvB8O4qOZU85k9DdRZZsF0oXQgN8LyQy6OBDaDMpoTyvyytr+bSPD6dX8WTXI7ocVtVRUXFvn37AODbb7+VJn9VVdXevXsPHz7M4/GGDBkiYTFRWUtGcqQ2xFxgrhVQ7t6FYrNE00XVvWwkLM90XW8S8EOj5Qgdt1VJbpk0TXt4eJSUlGzbtm3kyJGtXRmEUFvCAP5DtHTp0pcvX4aGhtLSfBC9x2azXVxcpJlcqmnlq6mpeXl5devWTfpdGkKCRsFp56T04sULAHBychLdNGHCBDU1tdra2rS0NAsLi8GDB1taWiYmJgYEBDAh9MWLFwHAxsbGysqKqcmMGTMEy6mqqkpNTb179+7t27cbqobQpHpiNa1kBukAL/a/aWhoaGZmlpqaGh0dLSHYWLBggdi+GKqqqsePH7exsSkrK7t///7MmTMl1+Srr77S1dX19vYmf5aUlOzYsePOnTt5eXnW1tarVq367LPPGj0daejP7qo/u6v06dwczospsZyXNcCi+v5lzYTlovkLz+W83pwCAD19B3ReYdwitW2y7t2737x5UyixS5cu0ryznj17BgCffvqp6CbRlRrKy8snT5784MED8ruSkvCnSfPbmGTNOVOZ6ibly9La59sEn1tqfm6pKX06cTahKrmAq65M1XDp6y9qFg3RZPLH5tfNCSxSYVF1fPrE40rfcXoSymljR44cKSgocHR0tLe3bzTzr7/+umXLltzcXACYPXv2yZMn9fT0WqRkJF/686frz58ufTpDytnmGy2nLUlumT4+Pvfu3bOxsSELyCGEOhIM4D9EysrKpKungpbfKPIksKSkpKSkRF9fX0LOCxcuxMbGamlpkSmmSMwvtu89RVFkejmm67ibm1tiYuLVq1d9fHxICuk/v3jxYqF9Q0JC/vrrr8ePH6empkqzLJz0z6JlLZlBblUcOnTo0KFDDeUpKCiQvkBB1tbWenp6paWliYmJkgP4oKCg0NDQ/fv3d+rUCQCys7NHjBiRm5vLZrO1tbVDQ0NDQ0O9vLyOHDnStJo0h3IP1f5BQ19Mia2Or0hziut7w1rso/XCczlZ658DQE8f+UfvAKCiosJM2SCT/Pz88vJyADAxMZEmf3R0NADY29snJiaWl5dv2LCBDLZktGobg2acKchSN+lfltY+37bB4dFnn1QBwPKPtI7HVJyMr3QbpKGm9G6owtFHFQDgYanxS2L1jdSa1TZapnrt4lsEl8v18/MDADIBigRZWVlLly79999/AcDU1PTgwYMuLi4tUjJSXFJG7xkua0wCfmjtykhJcstMTU3dvn27mprahQsXxC7AgRBSaO3ioxehljV27NhvvvkGAMLDw6dPl3Sz3NfXNz4+nsyRzvQXaGhgLXnGyOFwyJ9ubm5bt27NyMiIjY0dOnRoXFxcSkqKqqqqm5sbs0t1dfXs2bODg4MBgCw91b9//759+9rZ2fn6+pI+8KLU1dUbPcemlcwoLS0FgF69epHIWSwJ6800qk+fPnFxcZLDFT6fv2XLlu7du69fv56kLF++PDc3d9myZb6+vtra2g8ePHB1dT169Oi0adPGjx/f5Mo0WaMxfHuL3pujtvbdYGYyfYM0HBwcbt68efr06Y0bN165ciUoKIi8lYjWbmPNIX3dpH9Z2vP5Su/i0+o3VbzBhsprbbXuZdUmvOX+8ax6oaUmADzOr4vKqdNWoVbbaFVy6UtPq/3iKg+PbfDBdVu6cePGmzdv9PT0hPolCQkMDPTw8KioqNDV1d25c+eaNWsa/bdKWTJSXAqx0rsoyS0zKyurrq4OAAYNGiR2d/J9xsrKioz7QwgpFgzgUQdkb29vYGBQVFS0e/duJyenhgLy7OzsxMREABg7diwAsFgsMzOz5OTkly9fis1P0pllz01MTOzt7aOiogICAoYOHUr6z8+cOVPw6/vevXuDg4M1NDROnz49d+5cNvv/p7QhA2ubrJkl9+/fPzs7e8OGDV5eXk04Op/Pf/ToESlHbB+HnJwcaPirA/Hbb78lJCScPn2a3LAoLy8PDg4eOHCgn58fOR07O7sTJ05Mmzbt8uXLcgngQWIM35GidwDo1asXGSTy6tUr0YkkKyoqqqur1dTUdHV1SUr//v2DgoI0NTXXrVv366+/xsXFrV69Ojk5mbn91Mw21qqkr5v0L0t7Pl8p1fHoU/FVALB+mBYFsNpGa0VwyU9xlXMHaqgpUcdiKgFg8RAtHVXWahutq89rrr+oWWWt1acdPIQn05e4urpKmD3k7t27rq6uHA7H0dHx3LlzzMwdzS8ZKbT281BdJtgyEfqQtfocGwi1PVVV1Q0bNgDA48ePSVwtFpm+CACY4LB///4A8M8//4hmDgsLq66uBoEAHgDIw/aAgAD6/VqsQv3nyRjdNWvWzJs3TzDGhvfP65qsmSWTJeiioqJEN3G53KNHj/r6+pIgXCwWi+Xi4mJnZ7d//37RrampqW/fvgUACbM6czicHTt2DBgwgJnwLzU1laZpGxsbwdMhQ/vIIGR5ITG8qqk6ieHrS7ggGL23g3HvLYLFYvXr1w8AxM6h4OTkZGRkdPToUSalV69eZPVsNpt98uRJFov16tUr0vOFaGYba1XS1036l6U9n6+ULj2rflPF667F7qLBTirgGmmy++orva3mfxdTeTWlJiKbo6lMDe+mnFTALa7h2/VQ4dHwU3yVvGsN2dnZZPUQCSukVFZWzp49m8PhzJs3LygoSMroXZqSEWp7jbbMUaNGZYtz8uRJkiEjIyM7O/vWrVttV2mEUMvBAB51TOvWrevbty8ALFy4UDDqYPzyyy9kUnQPDw9m0jgyliwsLOyvv/4SzFxbW7tlyxYAGDZsmGBQOmfOHDabnZaWduLEiaysLGNjY2YNaoLMhC8UYAPAw4cPydrUTdaEkgXnFHR3d6co6urVqzdu3BDK5uPjs3HjxgMHDjALtotF5uI6deoUMykAwePxyLJMQ4YMGTFiREO7nzhxIjMzc//+/cwp9OvXj6KouLg4Pp/PZIuJiYH3N1bkSLmHar+bNqp91KvjK9Jmxr85kvkuej86oPPyjhC9E6T9nzp1Ki0tTTA9JiaGNCrSV0XUsGHDVq5cCQC+vr5JSUkksWltTKaZL5tMprpJ+bI0/z0ld78kVgNAbiVv5tXCGVcLZ1wtTCupB4CT8ZVf3isFgCouPf9GMdkU/poDANdeVJdy+JKLbW03b97k8/mamprDhw9vKM/58+dLS0u7det26tQp0ctmc0pGqO012jJVVVV7iMMs9GNsbNyjR4/2P6gHISSe3FagR6iVJSQk6OjokHY+YcIEf3//qKioyMjI8+fPT5o0iaQbGxuXlJQI7kUicCUlpT179jx58iQvL++ff/5hIvzIyEiho5D85FHk9u3bhbauXr0aAHR0dAIDA+vq6rhc7vPnz7/88ktV1XcLiTk5OfF4PJKZiXxKS0uFyiFxgpKSUtNKHjNmDAC4urrW1dXx+XySSO7cs1isLVu2REREFBUVJSQkrF27luzu6+sr+eXNzMwky3T16NHjzJkzqampL1++DAwMHDZsGCk2KiqqoX3LysoMDAzs7OyE0skIak9Pz6qqKpqmY2NjyYSCwcHBkivTNjiva5IsImI1b8dq3o7Vuv329Gt51+h/eHp6AoCJiYmU+UUbVV1dHelg0qlTp7Nnz6anp+fm5v72229kXvfRo0fX19fTNH3gwAHynhIsraysjGT7+OOPm9bGxLbSVjpTmeom5csi6/m2Q4celE8PKBD6GXQ6z9Qv19Qvt+9PudOuCG9dE1LM5cm52mQWuvHjx0vIM2rUKJInuGEZGRlNKBmhttfklhkQEEAuR8xVS6yMjAySLSYmphnVRAi1FgzgUUeWlJQk2ONdiKOjY0pKitAuOTk548aNE82sra3t7+8veghm9WCKotLS0oS2FhYWMn01WSwWMxmss7Pztm3byO/6+vpPnjyhZQzgZSp56dKlJEVFRcXR0ZGUUFxc3ND0y15eXtK8vGFhYWLn6+rUqdOlS5ck7Lh161YACAsLE0p//fp19+7dyZkyzyrXrVsnTWXaxrsYvv1F73QLhbXPnz8XO7W7mZlZVlYWySM2gKffjyIBgNOnT5MUmdqY2FbaemcqU92keVlkLVNRBKXXkADe52G5vOsiBo/HI1ehXbt2ScjWtauYpSKFHDt2rAklI9TGmtMyMYBHqGPAAB51cFwu9/z5887Ozj179lRXVzcwMLC3t1+4cOGff/7Z0C58Pt/Pz8/Z2dnU1FRHR2fkyJGrV69OT08Xm7mkpIQ89P7kk0/EZigqKtq4caOFhQU5+tSpU69fv052HD9+vJqaWpcuXZ4+fUrLGMDLVHJubu7UqVO1tbW1tbUXLFggWMilS5fmzJljbm6uoaExcODAuXPnPnr0qLEX9X/q8PXXXw8dOlRPT09PT++TTz7ZuHFjbm6uhF1yc3M1NDSmTZsmdmtxcfGqVasGDBigo6Pj4OBw8eJF6SvTNurLuNXJFfKuhRgtEtbSNF1bW/vtt99OmTKla9euhoaGDg4O3377bW1tLZOhoQCepunJkycDgL6+/ps3b5hEKduYhFbaSmcqfd1oKV6WJpSpEHh82vlqwTD//JIaeT9qF4csZwgAISEhDeWprKxsNHoXDeClKRmhtteclokBPEIdA0W3yYBDhBBCCCkiHh/qaVqVLX45D4QQQgi1JQzgEUIIIYQQQgghBYCz0COEEEIIIYQQQgoAA3iEEEIIIYQQQkgBYACPEEIIIYQQQggpAAzgEUIIIYQQQgghBYABPEIIIYQQQgghpAAwgEcIIYQQQgghhBQABvAIIYQQQgghhJACwAAeIYQQQgghhBBSABjAI4QQQgghhBBCCgADeIQQQgghhBBCSAFgAI86vuvXry9cuLBfv346Ojq6uro2NjafffbZtWvX5F0vGYSFhVEUpaysLO+KtC80Tfv4+Jw7d07eFfmwFBcXx8bGVldXy7siCCGEZHP27FlfX1+apuVdEYRQ02EAjzqyjIyM4cOHz5w58/z582lpaRUVFeXl5XFxcQEBAbNmzbKzs0tNTZV3HRWev7//7NmzTU1NNTU1Bw4c+OWXX759+1Y0W0pKCtWwBQsWCGaur6//7rvvpk+fbmtru2TJkqioKLGH/v3337/44gv8IgIA9vb2El5ewtraWnAXmqYvXLgwefLk7t27q6mpmZubz5gxIzg4uNFj7dq1y9bW9tmzZ612Ngg1rg3a/J9//vnpp5+amZlpa2vb2tquWLEiMzOz9c8MIfGa0OaF/Pzzz0uXLt28eTOfzxfd2uQPBYRQW6MR6qDS09ONjIxIO581a9Zvv/0WERERFBT03XffOTg4kPRBgwZVVFTIu6aNi42NtbW1HTFiRNN2X7p0qZWV1S+//NKytcrNzZ0wYYLoVcXQ0DAmJkYo840bNyRciNzd3ZmcZWVltra2gltZLNahQ4eECuRwOCYmJgMHDqyvr2/Z81JEdnZ2jV7traysmPzV1dXjx48Xm83FxYXL5TZ0oIqKii5dugCA6L8YdQyF9bzImlrBn6Cq6muVVUKJGQ03krbRqm2+trZ20qRJojnV1dXPnTvX5ueKEE3L3uaFpKWlaWlpkWyin5tN/lBACLU9pUavBQgpIg6HM2fOnPz8fFVV1VOnTnl4eDCbJk+evHbt2osXL7q7uz99+nT9+vVnz56VY1WlYWNj8+jRoybvnpaWFh8fX1BQ0IJVAoAFCxbcuXNHTU1t586dn376qba2dkRExJYtW7Kzs52cnNLS0jQ0NJjMpLODpaXlnj17RIsyNjZmft+wYUNMTMy0adMOHTpkbGwcHBy8evVqb29vBwcHwa8vfn5+GRkZ165dY7PZLXteiujgwYMlJSViN9E0vX79+qysLHd3dyZx9erVd+7cAQA3N7dNmzb17NkzJSXlyJEjgYGBAQEB5ubmYv9NRUVFS5cuFdvDAnUYKwuLEuu4jWZTo6ioHkZqFNUGVRKrVdv8l19+eevWLSUlpfXr17u5uRkaGiYlJX3zzTfR0dErV660tra2srJq7RNESIisbV4Qj8dzd3evrKxsqPCmfSgghORDzjcQEGodx48fJy3877//biiPl5cXAKirq1dWVrZl3dremDFjAMDHx6cFy/T39yev8J07dwTTCwsLu3XrBgB+fn6C6StXrgSAxYsXSy62pqZGWVnZxMSktraWSbxy5QoArFixgkkpKyszNDS0t7dviVMRj5OxUJcc0wAAIABJREFUtfqRiWw/cdb8uvzWq1LTkPtTI0eO5PF4JCUrK4vc9Vi0aBGfzxfMvHz5cgBgs9mZmZlMYmZm5u7du52dndXV1ZnPDnwC31GNz803z8qZ96Zg0dtC8mP5Otc8K2e+QIrF61zzrJzi9y2qvWlmmy8vL2exWACwe/duwZyVlZWmpqYAsGDBgrY5ESQvi+JnLIqfIe9ayEC0zQvZtWsXABgYGJALuNATeFk/FBBC8oVj4FEHxOPxfH19AcDe3n7atGkNZVu0aBEA1NTUBAUFtV3lZFRYWCjvKogXGBgIANOnTx83bpxguoGBAblPf/z4cVpgdDp5Aj9gwADJxb548YLL5Y4cOVJVVZVJJIdITExkUg4fPlxYWHjw4MEWOJMG0JxMur5Uth/Oa+DXtF6VmiAvL2/Tpk0aGhr+/v4kJgGAa9eu8Xg8NTW148ePU//7BPXAgQMURfF4PMF5BxITE3ft2nXt2rWamvZ1dqj1HOqkf66zAfnRY7EA4KjB/6doy+/Be6Oa3+ZjY2PJCGGhuTk0NTWdnZ0BICYmpi3OBMnJ4iczhX5p58S2eUFRUVF79+7t0qVLQ0/RZf1QQAjJFwbwqAOKiYnJysoCgG3btknIZmlpGRYWFhoaamNjI5hO0/SpU6dcXFz69u2rp6fn4OCwZs2a9PR0JoOXlxdFUd26dRM7DcyoUaMoipo6dSqTUlNTs2/fvilTpvTp00ddXd3MzGzixImXL1+ur68X3DE5OZmiqD59+gDAs2fPJk2apKGhQboSREREiJ2FvtGSFyxYQFFUaGgoAGzevJmiKEdHR8ES7t69O2PGDCMjI3V19QEDBnh4eMTGxkp40Rjx8fEAMGLECNFNo0ePBoDnz58nJCQwiVIG8H369GGxWImJiYLBPymHPPsCgPz8/KNHjzo5OTFzGbQG5Z7bgVICYKkNvqlu+0ryD0vbDgCUuiykVE1ar0pNsGrVqtLS0gMHDvTr149JJO+OIUOGaGtrC+XX19fv2bMnACQlJTGJQ4cODXjvxIkTbVJxhJqo+W2+rKyM/KKkJDzMkNxYbKgbM+oAhIJ2hYjhxbZ5RkVFhbu7O4/HO3PmDJnBRJSsHwoIIfnCMfAfqMLCwsjISA6HQ/7U0tIaNWqU6IW7zfK3LBKvAsCoUaMk5yShpqD8/HwPD49///2XSbl///79+/fPnz///fffL1y4EADc3NyOHTuWn58fGRkpdIisrKzIyEgAWLx4MUmJjY2dO3euYPz/8uXLly9f/vvvv66urr///jsl8iwrJSVl9OjR5Nm72Lvp0pfcq1cvKyurtLS0ysrK7t27d+nSxczMjGSur6/fuXPnwYMHmdsQL168ePHixa+//rpnz57t27dLfulKS0sBQOz4c01NTfJLcnLyRx99BAAcDuf169cAoKGhsXLlyrCwsMzMzL59+1pZWXl6etrb2zP7amtrT5o0KSgoaM2aNYcOHdLU1Hzy5AnpwvfZZ5+RPLt3766pqfn2228l17CZWOrmSl0X1+ef4r4+oDrouoScvJKb/IoHlJKeck/vVq2SrC5fvhwYGDh06NA1a9YIpg8ePNjT03PIkCGiu9TX1xcVFQEAMwEk+X327Nnkd5yFG7VnLdLmbWxs2Gw2j8e7evXqhg0bmJx1dXVkMs7hw4e34jkg+REbri9+MvPcR4FtXxkpNdTmGWvXrn358uWyZcucnJyuXr0qNo+sHwoIIfmiaFyB6cPz9OnTkSNHMg8ZiF69esXExHTu3Lnt87e4pUuXnj17Vl9fv7i4WNZ9J02aFBISwmazt27dOn36dCMjo8ePH+/cufPJkycAEBkZSaLNvn37pqenr1+//tixY4K7Hzp06KuvvjIwMMjNzVVRUeHxeBYWFs+fP+/du/ehQ4fs7Ow0NDQyMzNPnjx5+vRpAAgJCWEeiScnJ1tYWPTo0aNr165cLtfX19fa2trQ0BAAIiIiRo0apaSkxOW+m1xKppLHjh0bGhrq4+OzadMmpqq7d+/++uuvWSzWqlWr3N3de/fu/ezZMx8fn5s3bwLAiRMnyKj1howePTo8PNzNze33338X2vTXX3/NmDEDAHx9fTdu3AgAT58+HTx4MAAoKyszp0CwWKyNGzceOnSIuZGRk5MzYsSInJwcJSUlfX19Mveep6enn58fALx48WLw4MHu7u4///yzdP/Sd2heJb8sDOjGZ+cS2KWKm+lN8yqUu61ladk0kKm+LnMHzc1X6jyfrSdmTn4JKPUBLI2BMu0ivbq6uv79+2dmZt66dWvixIlS7nXu3LklS5ZQFPXkyRNLS0vRDJmZmSYmJgAQExMzdOjQFqwwanHFfH4sp44n46f87pKyUj5/toaGFuvdW/JyVU0NzZ+jqa5Ovbuf+EdVdS1N7+2kpyljX/pBKsq9RB5rt5QWbPM7duzYu3evmpra7t27XV1dDQwMkpOTd+7ceevWLT09vfv375MLGmpvirmFL6tfNO2brV/mYQlbV/b+ogllsiiWqUZ/fWWDJuwrjUbb/JUrV+bMmWNmZhYfH6+lpXX16lUXFxcAqK+vl3L+10Y/FBBCbQ+fwH+ILly4IBRdA0BWVtaNGzeY58Ztmb/FkWfXpC+6TIKCgkJCQgDgypUrZKwjABgbGzs6Oo4ZMyY6Onrz5s0REREA4Orqum/fvqtXrx49elTwEfqlS5cAYP78+SoqKgCQnp7+/Plzks7MoG5oaHjq1Km4uLiYmJjo6GihPu05OTlsNjslJUVNTU1CVZtQsqC8vLzDhw8DgJ+fH3m+DQBGRkZjx45duHDh+fPnt27d6u7uLqHThIODQ3h4+JUrV7Zu3WphYcGk19TUeHu/exBdVVVFfiH95wHAwMDA29vbzs5OT0/vyZMne/bsSUxM9PHxMTMz8/T0JHl69OiRlJS0Y8eO27dv5+bmjho1auXKlfPmzSNbt23bxmazd+/eLeHFEYubub3+7S+y7gU0AAXcvO/f/daw+oLf6gt+k61wSkXdNpVi68hcKyn4+fllZmaOGTNG+kgmPDx83bp1AODs7Ixf1DqALUUl4bWcpu17tbpaKOVylfAMCNuLS2UtthOLFdGjtZ7jtWCb37Nnj5GRkZeX11dfffXVV18x6R999NGlS5fMzc1btuaopRx5uTu39nVrlCw5vJfARN1sZ3/flq0MQ3Kbz87OXrFiBZvNPn/+PLOAnEzwQwGh9gkD+A+R2EcHFEUNGjRILvlbHI/HA4mdzxtCnl07ODgw0Tuhrq6+d+/eiRMnRkZGJiUlWVhYuLm57du3Lzs7++HDh0z8nJKSEhcXB++nxwOATp06kcneRJdvJb3RxK7p4u3tLTl6b3LJDD8/v6qqKmtrayZ6Z/j6+l64cKG0tDQ8PFxwJL+QLVu2XLhwISsra/To0fv27Rs7dqyysnJMTMyuXbtSUlKEMpeXl1tYWOjq6l66dIlZMa5///7Tp093dHQMDw/fsmWLi4sL6W4AAHp6et9//73oQaOjowMCAjZv3tyrVy8JZycW23AWzRO+r9Q4muaX3aN55ZSGBUtdZHghn8MvvU3TXJa2PaXSVdayWSo9KXarjCupqKjYt28fAEg50KCqqmrv3r2HDx/m8XhDhgxp/wsrImk4a2poyn4ZvFtTU0dDZzab/f6G1Vsen0/TXdgs1vublW/reXyAcepqKrI+gReZyKOltGybLygoCA8PZ7oLUdS77oqvX7+OjIzEAL7dGmcwNaWqiUO1H5VGSNg6TO/jphU7WLu1VhyU3OZpmvbw8CgpKdm2bdvIkSNlLRw/FBBqzzCA/xDNnz+/rKyMGSgOAGw2e9asWaKhYNvkb3EkDhQcHC6lFy9eAICTk5PopgkTJqipqdXW1qalpVlYWAwePNjS0jIxMTEgIIA5r4sXLwKAjY0Ns0SwoaEh6UzOqKqqSk1NvXv37u3btxuqhtCkemI1rWRGcnIyAIh9RG9oaGhmZpaamhodHS0hgNfW1v7jjz/mzp2blZW1atUqwU3bt2/39fWtqalhBsMvWLBAaD5nQlVV9fjx4zY2NmVlZffv3585s5Hpgr766itdXV3mCX9JScmOHTvu3LmTl5dnbW29atUqZpy8KLbOaLaO8JQH0uCV/8d5OoPmZKiYB1Aq//PwsO7lWprmsvWnqA4QHkcgX0eOHCkoKHB0dBScX6Ahv/7665YtW3JzcwFg9uzZJ0+e1NPTa/06olY3RUN9ioZ64/n+14S8upx63sUuhj2U3vWwHZ37poDHC+jaufP7Prcjc/JL+Py9nfT0Zb9B0EpasM0XFRWNHTs2OTm5T58+33777dixYzt16pSamnry5MkTJ04sWbIkJydnx44drXgyqKnGGU4ZZzjl/9i777imrr8P4N8MRoBAQERUXKiIsyoOUHHTWi0qohWti0dbW3FV+7SOKrXuKo62P6mjVtE+7kq1rVi3iKgobgEBRUQBWWGHkdznj9PmlyYhJuzYz/vlqw3nfu+555ID5HvvuedUbt9PWvxvRVPW1c9n4HX3+Y0bN164cKF79+5sATmD4I8CQH1Xh0vYAdSQ5cuXs+6dnZ2tO3Lfvn3z58//8ssvOY5TKBTsvveRI0e0BrO7Lt988w37kl3zbtmypTKATbH+/fffq+14+vTpgIAADw8P5R1mpUWLFinDlFO8pqamqtVw5coVIhIKhZWrWXMdeNVB7xWZNWuW7u8ex3F5eXmLFy/29PS0sbGRSCRDhw79448/2Px2RLR79+7X1sBxHPtk8PXXX+sOYw/nr127ln35/PnzJk2aEJFAIFB+tvj000/1OaKhZHEfFEZKShL+8Q2RF9wtjLQrvOagKI6viYNWWmlpaaNGjYjo4MGDuiOfPXumvIjj7OxcUc9XlZSUxOKxDvybiq0Dn1L232WiPV+kuSa/eKWycLRHSmq9Wge+evv8nDlziMje3v7Fixdqm9gCpXw+/+HDh9XWeqhP2Arwqv/qukXa6e7zjx8/NjU1NTc3f/TokWr50aNHWedXWwdeqRJ/FACg9uEOPLyBBg0a9PXXXxNReHj4yJEjdUQGBQXduXPn3XffJSLu72lvNKeFZ9iSQsqp9SdMmLBkyZKkpKRbt265ubndvn07Li7OzMxswoQJyl2Kiop8fX3DwsKIyNra2s3NzcXFpU2bNu7u7kFBQWwMvCaR6PU3zSpXsxLLsZs3b25nZ1dRTEXrzagSi8Wag/fYw/lEpOdA01atWt2+fZtNVlcRhUKxaNGiJk2azJs3j5V89NFHL1++/PDDD4OCgsRi8bVr1/z8/DZv3jxixIghQ4boc1z9mbZYVSw9W55xUNjof/hWbOY2rjRpEZHCpPEnPPM21Xu4Kjp58mR6erpEIlEbo6EmNDR0ypQp+fn5NjY2y5cvnz17Npu4AcDoVG+fP3LkCBFNmzaNXSJUNX/+/MDAwIKCgqNHjyqvFMObZPdboar34evnvXd6XZ9PTk4uLS0loooeXWSfZ7p27cqe+2PwRwHAWCCBhzeQh4dHgwYNsrKyVqxY4e3tXVFCnpKScv/+fSIaNGgQEfH5/NatWz98+PDJkyda41m5ciXzli1benh4REZGHj161M3NjY2fHz16tGpKvGrVqrCwMAsLi507d44fP1510le2wHulVbFmFxeXlJSU+fPnf/rpp1VphlaXLl0iIjMzMzbnjUKhiIqKYge1tbXVjH/x4gVV/DmD+fnnn+/du7dz5052dSMvLy8sLKx9+/bBwcHs3N3d3bdt2zZixIjDhw9XewLPM2tp4vhJ2cstpc8Wm3c8TcQrzzymyI/kmTQUNl1QvceqOjaVg5+fn46ZFM6fP+/n51dSUuLl5bV7927lxAQAzGWZTDk8voTjiOhCscz675LSerZ+TTX2eY7j2PIlyhU3VfH5fGdn53v37rGpUuGNpMzh6232Tvr1eYPgjwKAEakvT68BVCMzMzO2eG90dDTLq7Vis7MQkTLfc3FxIaLff/9dM/jSpUtFRUWkksATEbvZfvToUY7jDh06RCrLvzNs1Pfs2bMnTpyotmSLcpx55VSxZnZvPDIyUnNTWVnZ5s2bg4KCWF5dkZCQEA8PjzFjxmhu2rt3LxF5eXmxaW/5fP7YsWPd3d3Xrl2rGRwfH//q1Ssi0jHDbUlJybJly9q1a6ecHTA+Pp7jOLZcszKMPQcYExOjo9mVJmy6kGfSSJEfVZ55lBSysudfE5FJs+U1NId8paWkpLCVFLROOsAUFBT4+vqWlJRMnDjx1KlT+KAGqgTEI6Kvc3I/zcph//IUCiIKVCkp5Dgi0msRqppXvX2ex+OxJ4wePXqkubW8vJytqaGc6ATeSLvfCq3P2ftr+3y/fv1StNm+fTsLSEpKSklJOX36NPsSfxQAjAsSeHgzzZ07t02bNkQ0derUzZs3awbs3buXzXM+ZcoU5aRx06dPJ6JLly6dOHFCNVgmky1atIiIevbsqZpnvv/++wKBICEhYdu2bcnJyU5OTkOH/mMlcDYTvuZqq9evX2ePtVdaJWrmVG6aTZo0icfjHTt27OTJk2phGzduXLBgwbp16xo2bKijAZ07d7527drx48fZMH6lkJAQdl1Adc15Nhnejh07lOvJMXK5nC1R06VLl969e1d0rG3btj179mzt2rXK823bti2Px7t9+7ZCoVCG3bx5k/6+ClPteAIrk+bLiKgs+auy52u4kud8y65Ch4k1cayq+OOPPxQKhaWlZa9evSqKCQkJkUqljRs33rFjh55LAcO/xyxrq2EWItV/nUxNW5uYqBUGWIut68cMdtXe5wcMGEBEP/30E5vWVNXKlSuLi4v5fH6/fv2q3nKAynltnzczM2uqTYMGf61I7+Tk1LRpU+WDcvijAGBk6vQJfIAadO/ePWvrv+6ODh06dM+ePZGRkVevXg0JCXnnnXdYuZOTU05OjupeLAMXCoUrV668e/duamrq77//rszwr169qnYUFs+mW2eT4akKCAggImtr69DQ0NLS0rKystjY2M8//9zMzIxV6O3tLf97IijlJHZSqVStHs1J7AyqmU1i5+fnV1paqlAoWCG7cs/n8xctWhQREZGVlXXv3j02exMRBQUFvfY7zEYuSCSSH3744enTpzdu3Fi+fDn72z9p0iTVyGfPnrG78U2bNt21a1d8fPyTJ09CQ0N79uzJ2hAZGVnRUXJzcxs0aODu7q5WzmYu+PjjjwsLCzmOu3XrVqtWrYgoLCzstS2vLHnx/SGFkRL2T56n3hnqg7FjxxLRkCFDdMSw3GPIkCFhFUtKStK6Lyaxg/qm2vt8bm4u+2ViZ2e3efPmu3fvPn/+nA0wZp1/+fLltXJmANrp0+e1qmgSu6r8UQCA2ocEHt5kDx48UB3xrsbLyysuLk5tlxcvXgwePFgzWCwW79mzR/MQysVReTxeQkKC2tbMzEzlUDQ+n2/y9xrIPj4+S5cuZa9tbW3v3r3LGZjAG1TzjBkzWImpqamXlxerITs7m30I0KTnXO7p6elaHxP18fGRyWRqwZcuXdI6YZ6dnZ3uiaOXLFlCRJcuXVIrV85CLxQKlYMF5s6dq0/LK02ed70w0rYwUiJ7/D81eqDKkcvl7JscGBioI4zNXazbli1btO6LBB7qlRrq83fu3NE6Kwefz585c2ZFM3gD1AI9+7xWFSXwVfmjAAC1Dwk8vOHKyspCQkJ8fHyaNWsmEokaNGjg4eExderUX375paJdFApFcHCwj4+Ps7OztbV1nz59AgICEhMTtQbn5OSwm94DBgzQGpCVlbVgwYJOnTqxow8fPvzXX39lOw4ZMsTc3NzBwYEt9GJQAm9QzS9fvhw+fLhYLBaLxZMnT1at5ODBg++//76rq6uFhUX79u3Hjx8fFRX1um/qfxUVFX311Vc9e/a0srJycHB455132IwAFX0rvvrqKzc3N4lEIpFIBgwYsGDBgpcvX+qo/+XLlxYWFiNGjNC6NTs7e9asWe3atbO2tvb09Dxw4ID+La+0kqf/W3SrvaIkpRaOZagbN26wLvTnn39WFFNQUPDaD2o6PqshgYd6peb6fFlZ2bfffjtixIhWrVpZWlp27959ypQp6PZQ5/Tp8xXRmsBX8Y8CANQ+HlfP5pIFAAAAAAAAAE31YgYaAAAAAAAAANANCTwAAAAAAACAEUACDwAAAAAAAGAEkMADAAAAAAAAGAEk8AAAAAAAAABGAAk8AAAAAAAAgBFAAg8AAAAAAABgBJDAAwAAAAAAABgBJPAAAAAAAAAARgAJPAAAAAAAAIARQAIPAAAAAAAAYASQwMObafHixbwKmJmZubq6Dh8+/NChQxzHqe516dIlHo9nYmJi6OESExNZ5bm5udV3EvVLbm4uO8fExMS6bgsAAAAAwL8REnj41yktLY2Lizt16pSfn5+np6dMJqvrFgFUlYeHR0VXrJS6deumugvHcfv37x82bFiTJk3Mzc1dXV1HjRoVFhamtX6DggFqQU33eSLas2ePr6+vs7OzpaVl+/btP//881evXtX8mQEAAOgirOsGgBErT8ssfZaiKJIJJNZmrs58kXldt0idWCwODw9XLeE4TiqVJiQkbN68+dGjRxEREUuWLNm0aZMyvkePHgKBoC4aC/VRenp6YmJigwYNdMTIZDKFQqGWKtRzxcXF3t7e586dU5bExcXFxcWdOHFi7NixBw4cEAqFlQsGYyeLKSxNLbEebEdExFHR3XzZ40Iq50xbiix72vBMeERUdDdfkV9u1c+2jttqCIO6cWpq6pQpU86ePassiY2NjY2N/emnn8LCwtzc3Gq16QAAACrwqQsqQ/YoPmffryXxScoSnlBgOaCX7QRvgcS67tqlTiAQvPXWW5rlAwcOHD9+fJcuXZKSkrZt27ZhwwaWtHfv3j0qKqrWmwn11/nz5/Uco9G4cWNHR8eabk9F1q9fn5OTo3UTx3Hz5s1LTk6eNGmSsjAgIIBlMhMmTFi4cGGzZs3i4uI2bdoUGhp69OhRV1fXlStXVi4YjF3SjIdFd/Od1rQVSISp656WJv+3/wskwkZzmou6iJ9Ovs+Vc11fDeSZ1tk4vhrt85MnTz537py5ufny5cvfe+89sVgcERGxaNGilJQUb2/vhIQECwuLmj5BAAAArXhqzwADvFbeHxez9/xCCgXfysK8Qxu+lWV5WqYsLpHkCoGdpNHST0xbNK3rNtLixYvXrVsnkUgq+oRHRGvWrFm6dCkRxcXFubi4VOVwiYmJbdq0ISKpVGpjY1OVquqt3NxciURCRAkJCa1bt67r5tSS0NBQ1oVsbW0dHBzUtspksufPnysUCiLy9va2t7evgya+zu7du6dPn96nT5/w8HA+n09Ez58/b9WqlVwu9/f3//HHH3k8njJ45syZO3bsEAgET548ad68uaHB8AbI+jn12ScxpPjrs4FpS5FlT2ueCb/4QUHxvXwi4vGJU5DDrGZO31Tp12bNqWKf37t377Rp04jo3LlzgwcPVkZmZWV17tw5NTU1ODj4448/ru2zAgAAICI8Aw+GKrpxL/unY8RxkveHN9ux2uHzj+xnfeD49Tynb5ebd2wrz5a+WvuDPL+grpupF5ZyE1FaWlodNqO0tLSwsLA2j1hQUIArd3oyNTUlIj6fn5OTIxQK+6hwdXVNS0tTKBQsRjUlqD9SU1MXLlxoYWGxZ88elskQ0fHjx+Vyubm5+datW9WavW7dOh6PJ5fLIyMjKxEMb4AGHzS2HmLHXtv6Nup0v0+rnzq13NGh/dVeTde25fGIU5BpUzOndfU0e696nw8NDSWikSNHqmbvRNSgQQN2l37r1q34FQoAAHUFCTwYgCsrz959hDjObuoYyfvDeab/na1d2Mi+0bLZ5q6tyzNzco8ax9RWyrzdycmJvYiIiNA6C71cLg8KCnJzcxOLxdbW1m5ubuvXry8vL9dabUJCwuTJk9kkSS4uLtOmTXv69KlqAJuyvlmzZkVFRQEBAba2tlZWVhYWFl27dt29ezeLCQsLGzJkiK2trVgsdnNz27FjB7vNq4rjuB07dowdO7ZNmzYSicTT03P27NmaU8QrD0dEO3fubN68uVgsNjU1bdeu3fTp0589e6Z5CllZWbNnz27durW5ubmjo+O4ceNu3Ljx2u/nG8zNzU0gEDx8+FD5fcjOzg4LCyspKWnRooVYLK7b5ukwa9YsqVS6bt26tm3bKguTk5OJqEuXLpott7W1ZV3lwYMHlQiGN4DscWH+hWyegIjPyzmWnhaUxMrzzmWnrkjkOOKLBKUvSqQn6ul0blXv83fu3CGi3r17a1bev39/IoqNjb13717NNB8AAOA18Aw8GKD4bkx5Zo5pi6bWIwZqbuUJBXYfjX+5YE3Bxeu2U3x49XsqOI7jjhw5QkRdu3Z1dnbWEfnixYsJEyaoToYXHR0dHR39xx9/HD58uFGjRqrBly9fnjhxYkFBAZ/PVygU8fHx8fHxx48fj46OVht2LpfL33///d9//52I+Hx+cXHx3bt3p0+fnpGRoVAolixZwsoVCkV0dPTMmTNTUlK+/vpr5e5paWlTpkw5c+aMsuTKlStXrlwJCQn57rvvpk6dqnkiGzZs+Pzzz1m15eXljx8/fvz48bFjx6Kjo1W/A1evXh0/fnxKSgr7Mj09/ejRo8ePH9+8efNrv6tvqoYNGw4ZMuTcuXMPHz4kojZt2iiz94EDB/7222913UDtDh8+HBoa6ubmNnv2bNXyjh07fvzxx126dNHcpby8PCsri4iUz/MbFAxvgKz9qVw5Zz+9qaWb9bPZsS+/SiQii27WT8bfVcgU9v/T1KKLOHl+bOZPLySj1R8qqXPV0uelUikRaZ3N1NLSkr14+PCh1glWAAAAahoS+H8pRWFR4bU7yqccVfFMhJZ93XgmQs34osjbRCRwsMs/e1VrvGnzJiZOjmUpaSWxT8rjJxsfAAAgAElEQVTSMvSvvzZJpdK4uLj169dfuXLF0tJy+/btOoI5jvPx8YmKirK2tv7mm28GDRpkZmZ29uzZxYsXX758+b333lOb9G7ChAktWrT4/vvv3d3dy8vLQ0JC5s+fn5eXt3z58p9//lk1MjU19ffff58yZUpgYGCLFi2ioqImTZqUmJi4dOlSuVw+YsSIDRs2uLi43L17d+rUqQ8ePFi3bt3SpUvNzMzY7lOnTj1z5oxAIFiyZMnIkSMdHR2jo6OXL19+9+7dadOmubi4eHh4qB4uIyNj8eLFHTp0+O6771jbfv7553nz5uXm5i5duvTAgQMsLDs7+7333svJybG0tFyxYsXgwYP5fP6ZM2eWLVs2f/78avju152ioqLnz59r3WRiYtKqVSu1gbUsvri4mIiSk5Otra3btWsXGxv78OHDmJgYhULBsnflGF2ZTBYXF6d//TWttLSUXa9Zs2aN2qH9/f39/f217hUSElJYWMjj8didRkODoV6RxRcVhGufBMTEwdTmvYZa46XHXxERX8TnyrkGfo2yDqS//CqRJyBOTvb/07T5VtdyaVny/NiCyFxD669p1dXnO3fuHB4efvfuXc3gmzdvshd1++AVAAD8myGB/5fKO3lBevRUhZt5ZDXgH6MHVeOLo+4XR92vKF7Y0K4sJa3gTETBlZv6119DpFKpjqypR48eu3bt0n0X5eDBg1FRUSYmJhEREZ06dWKF06dPd3Fx6d+//82bNyMiIvr27auMl0gk165dU47SDAgISEpK2rhxo/Jjn6qxY8fu3buXvXZ3dw8KCho9erRcLu/du/eJEydYZti9e/fg4GBPT8+ysrLHjx937tyZiE6dOvXnn38S0ZEjR3x8fFgNTk5OXl5eAwcOvHHjxmeffRYREaF6rJKSEicnp+vXr1tZWbGSTz75JCEhYdOmTarXIFatWpWTkyMSiSIjI9mxiOitt97q06ePsSdp0dHR8fHxFW21sLBQu42sGq82RFyhUFhYWKhm70QUGxvLhunqWX9NCw4Ofvbs2cCBA99++209dwkPD587dy4R+fj4KN/9agmGOvF8fmz+pQpn8ex4v49ZK1FF8a++/8fVLk5Oos5Wzbe6Eo+EtiZ8S4GiUP58bkx+uFT/+mtadfV5T0/P8PDwI0eOLFmyRPlrn4iKi4sXL17MXtfyxCUAAABKSOD/pawGuyuKi7nSMs1NfAuRhZv6x3EWL3vwuPTZS7O2LU1bNq0oXi7NIyKLvt35Nlb6118npFJpRkaG7pitW7cS0aRJk1Q/xhGRp6fnsGHDUlJSkpOTVRP4RYsWqT1jyR6kzMvL06x8wYIFql8q1xZesGCBambYo0cP9kJZyc6dO1kblNk7IxKJVq1a9fbbb1+9evXBgwdqbV6yZIkye1eexaZNm3Jzc5Ulu3btIqKAgAC1lKxPnz5jx449dOiQ5lkYiw4dOgiFQs2pBIhIJBI1bKh+t5DFJyUlFRcXN2/eXCQSlZSUPH/+XC6XE1FRUdHNmzd79eqljG/VqpWlpaX+9deo/Pz81atXE9GaNWv0iS8sLFy1atWGDRvkcnmXLl1+/PHH6gqGOtR4ibN523RO20gos1YisxbmWuOlJ9LLXpXZjLA3aWRW+qIk/1wWV84R8YrvF6RtSnJc2FJRKFcUyXlm/MZLW5sfNaD+GlWNfX7RokX79+9PTk7u37//6tWrBw0aZGJicvPmzcDAwIpG2QAAANQaJPD/UkKHBnb+Yw2NL4yIzti8mwT8BjMnaA0rz8guTXrBMzMVdXG16KnlacNaJhaLVZ9dZ4qKih49erR69eqEhARvb+8bN27ouH8YExNDRO+9957mplOntAxhUBu4TkQiUYX3oJTT4KtFtm/fXrXc3Fz9c/Djx4+JyNvbW7POoUOHmpuby2SyhIQEtQRec04mtba9fPkyPz+fiMaNG6dZ88SJE406gbezs3N3dzc0Pjs7u7i4uFOnTiYmJmFhYXK5vEWLFi4uLufPn2fPwytzeBsbG92TKdSmTZs2ZWRkeHl5aXZITfv27Vu0aNHLly+JyNfXd/v27Wy9wKoHQ92y6iux6mvAu/NXvJCX8cNz87aW4kF2T/zucuWcvX9Tyx7Wz+bEvgxMJCKz5iLiyLK7tVU/iVW/+vLuV2OfF4vFhw4dGj9+fHJy8qxZs1R3/PLLL4OCgoqLi5UPwwMAANQyJPBgAFH3DgKxVUnsk8KIW5Z93dQ3c1z27qPEcZYe3XhmpnXRQHUCgUDrCHkPD48RI0a0bt26qKho7969Gzdu1Lp7Wloau+ndsmVLPY/YokUL/ZuneptdFVuWrCIcx7Gp5lu1aqW5lcfjtWzZMjY2VnO4OJtpWQd2XYCIVGdvVqo/2Wnty8vLi4qKUs5ax+fzVee0q2/KysqCg4OJaPr06bojk5OTZ8yYwaZCdHZ2Xr9+/dixFV7XMygYjFeDiY4Z25+/+iEl44fnCpnC3r9p829diUfEo2ezY18GJgpshURk90Hjum7pf1V7n3d3d3/w4MHatWuvXLly7949Ho/Xo0ePBQsW9OnTZ9WqVURka2tbA+cBAADwelhGDgzAF5lLPvAmosz/7C+4eJ1UFsJVFBVnfr+vKOoe39JC4qflfnV94+joOGLECFKZlEiTTCZjL3Rn1Ko0l6Crdsr1hyt6vF8oFBJRSUmJWnlF1wuUdJ+mnZ2dvk1841y/fl01eyeipk2bDhkyhK0tV1RUVNcN/IeTJ0+mp6dLJJJRo0bpCAsNDe3UqdOZM2dsbGyCgoJiYmJ0JOQGBYNRs+hubT2oASeTK2QKyYiGf2XvRA2mNGn6lTMRT55TbtLYtMGkepTA10SfF4vFa9asuXz5slQqzcnJOXPmzLvvvpuamsq2urq6VvM5AAAA6Ad34MEw4qF9y1LS8347n/n9vrwT58y7thdYWpSlZRRF3VMUFPFF5g6fTRfaG8etCXa3XPUJcDXNmzdnw9GfPn2qNhydiPLz84uKiszNzW1sbGq2of/E5/Nbt2798OHDJ0+eaA1g5e3atTO0ZuWN9/j4eNWnu1Wr/bcpLy8norKyMltbW3t7e7Vb7q1atXry5Ambqb7+YFMk+Pn5aT58oXT+/Hk/P7+SkhIvL6/du3c7OTnpqNCgYDB2+ReyC67+NZWd9I+M+BHRlh4SnoBX/CA/NyyLiCPilaWWZuxIcZj1mkE9taba+3xFLl26RERmZmaYuBEAAOoK7sCDweymjbGfM1lgZ1Oa/DLvxLmcAycLLlxTFBSZd3JpvGaheWeD88a6wp4A15HA8/l8ltOePXtWc6u3t7ejo2OdrI7u4uJCRGwNeTWXLl1iN4QrkcA3bNiQPQh67Ngxza1hYWEGN9T4KeeazsnJuXXr1s1/SkhIUM5apxyvUbdSUlLYCgWTJ0+uKKagoMDX17ekpGTixImnTp3SnckYFAxvgJQl8QqZouH0po6ft+KLBPmXc9LWP01d80R6IoPkXIMpTZy+aUt83ovF8Vyplikba1+19/mQkBAPD48xY8ZobmLrhnh5ealNCAoAAFBrcAceKsNqQG/Lvj1kD+NLk1IURcVCWxvzTi4mTrW6SlbVsZurulcDmj59+vz583fs2DFnzhzVOedu3rx55coVIho0aFBNt1Nrq44fP37p0qUTJ06MHDlSWS6TyRYtWkREPXv2rNwNopkzZ65fv/67776bMmVKx44dleXx8fFbtmypesuNTs+ePRMSEho0aKBjPcLCwkK5XN60adOKAmrTH3/8oVAoLC0tNcdQKIWEhEil0saNG+/YsUMgEOiu0KBgeAM4rWlbklhsP70p8ajR/Ob557OLYwtJwZm2EFkPbWDSyJSIzFqIyqVlPNN6cQ+g2vt8586dr127RkRhYWHDhg1TrSQyMpKIFi5cWE1tBwAAMBgSeKgknlAgestV9JYRPwfIhr5nZmbKZLKKBl7OmjUrODg4Li6ud+/eGzZsGDhwoEgkunDhwmeffSaXy/v379+vX7/abTUR0YgRI4YOHXr27FlfX9/AwMCRI0c6ODhER0cvW7YsOjqaiLZu3aoj4dThiy++2L59u1QqdXd3X7Vq1eDBg01MTC5evLh48eKSkhKxWMymqf/3aNOmjdpiAfUcm53L3d2dTYWg1YEDB4ioQ4cO7CKUVq6uruwZE4OC4Q0gHmQn/vuypMBaKBntoDnRvM0I+9ptlC7V3ue7devGZqmcMGHCunXr3nnnnYyMjN9++40tUzdp0qSBAwdW+1kAAADoiwN4E7Eb0RKJREfM/v372U9BaGgoK2Gf7YRCoWpYbGys1qnsW7dunZyczGISEhJYoVQqVTvKb7/9RkSOjo7KEmVwZmamamRmZiYrj42NVauElV+5ckVZ8uLFi8GDB2u2SiwW79mzR3Xfig7HcRwbFW9vb69aGBERoTm+lM/n79y5k00EkJCQoOO7CnVILpezuQYDAwN1hDVq1Oi1fxq2bNlSiWCAWlYTfZ7juPT09NatW2vG+Pj4yGSyGj8rAACAitWL8W8AdWLAgAHshb+/PxtOr1W7du2uX7++Zs2ad999t1GjRvb29p6enmvWrHn48OFrF2arOU2aNDl79mxwcLCPj4+zs7O1tXWfPn0CAgLu3LkzderUqtTcp0+f27dvz5o1y9nZ2czMrEGDBsOHD7948eKMGTOqq/FQQ27dupWdnU1Effv2rSimsLAwPT1dzwoNCgaofdXe5xkHB4f79+9/9dVXPXv2tLKycnBweOedd44ePfrLL7+YmZlVtdEAAABVwONUVgIDAAAAAAAAgPoJd+ABAAAAAAAAjAASeAAAAAAAAAAjgAQeAAAAAAAAwAgggQcAAAAAAAAwAkjgAQAAAAAAAIwAEngAAAAAAAAAI4AEHgAAAAAAAMAIIIEHAAAAAAAAMAJI4AEAAAAAAACMABJ4AAAAAAAAACOABB6qJDLn4tqExZml6XXdEHWLFy/mVcDMzMzV1XX48OGHDh3iOE51r0uXLvF4PBMTE0MPl5iYyCrPzc2tvpOoX3Jzc9k5JiYm1nVbAAAAAAD+jZDAQ+Xllefuf7EjvjDmwMvddd0WA5SWlsbFxZ06dcrPz8/T01Mmk9V1i95AHMft379/2LBhTZo0MTc3d3V1HTVqVFhYWF23qxosWLCgcePGGRkZdd2Qf/Dw8KjoipVSt27dVHcx6D2SyWQrV64cMmRI06ZNJRJJr169Zs6c+ezZs1o5OQAtKtHnDe3Gd+/eHT9+vKOjo0gkat++/Zdffom/FwAAUOeEdd0AMGK/pO4vlhcR0e3c6w/yb3cSd3vtLrVMLBaHh4erlnAcJ5VKExISNm/e/OjRo4iIiCVLlmzatEkZ36NHD4FAUBeNfXMUFxd7e3ufO3dOWRIXFxcXF3fixImxY8ceOHBAKDTi3zxXr15NS0t78uRJw4YN67otlWfQe/To0aORI0eqjryIioqKiooKCQkJDg6eNm1abbYcoHIM7cZHjx6dOnVqUVER+zI2Nnb16tVnzpwJDQ1t3LhxrTUbAABAjRF/jIa6lVz85ErOOSFP2Ndu8KWsPw+9/KmDSxc+r36lvgKB4K233tIsHzhw4Pjx47t06ZKUlLRt27YNGzawpL179+5RUVG13sw3TUBAAMsMJ0yYsHDhwmbNmsXFxW3atCk0NPTo0aOurq4rV66s6zbqKzo6+tKlS3PmzNFx0SEiIuL27dsBAQE8Hq8226Zq/fr1OTk5WjdxHDdv3rzk5ORJkyYpC/V/jziOmzFjRmJioqWl5cqVKwcMGODo6BgdHb1kyZL79+/PmjXL3d3d1dW1Fs4Raoc877JCekG1hCtJ5uQFfIsOqoU8C1eh/fjabdo/GNTnDe3G58+ff//99zmO8/LymjNnTps2bS5cuPDll1/euHHDy8vrzp07Rn0VEgAAjBsHUCnrEpb43xl18MXuMkXZophP/O+MOpvxW1036r8WLVpERBKJREfM6tWr2U9BXFxcFQ+XkJDAqpJKpVWsqt6SSqXsHBMSEnSEJScns6sh/v7+CoVCddNHH31ERAKB4NmzZzXc2Grz3nvvEdG4cePKyspYSe/evYno2rVr7MtTp06Zm5sTUWJiYt01U5cff/yRiPr06SOXy1mJQe/R8ePHiUgoFF6+fFk1MiMjw97enojmzJlTOycCtaPodo/CSIk+/xTleXXdWO00+7yh3Zhd+e3Ro0dRUZGy8MKFC2yGlODg4Fo4CwAAAK3wDDxUxg3plbiCh9ZCG+9G7wt5wnGNpxJRaNqBgvK8um6aAdq0acNepKWl1WEzSktLCwsLa/OIBQUF3D9n76tGx48fl8vl5ubmW7duVbsjvW7dOh6PJ5fLIyMja+jo1e6rr76SSCRHjhyZOHFieXm52tZTp075+PjIZLJ58+Y5OzvXSQt1S01NXbhwoYWFxZ49e/j8v37hG/QesTEpffv29fT0VI20t7cfNmwYEd29e7c2zgRqDVdKRCZN5ps0DzRpHsi37suKeebOrMSkeSDxLZSR9Y3WPm9QN7537x77csWKFSKRSFk+cODAd999l4j27dtX8+cBAACgHRJ4MFipovRoaggR+Th+YCGwJKLuNr07ibsVygt+TT9U160zgDJvd3JyYi8iIiK0zkIvl8uDgoLc3NzEYrG1tbWbm9v69es10zkmISFh8uTJbGIwFxeXadOmPX36VDWATVnfrFmzoqKigIAAW1tbKysrCwuLrl277t7913SAYWFhQ4YMsbW1FYvFbm5uO3bsUCgUagfiOG7Hjh1jx45t06aNRCLx9PScPXu25hTxysMR0c6dO5s3by4Wi01NTdu1azd9+nStEzhlZWXNnj27devW5ubmjo6O48aNu3Hjxmu/n0xycjIRdenSRSwWq22ytbVlzXjw4IGetdU5Nze38+fP29nZHTlyxM/Pr6ysTLnp9OnTY8aMkclkM2fO3Lx5cx02UodZs2ZJpdJ169a1bdtWWWjQexQXFycUCpVXu1RZWloSUUXDmMGoCRv5mzSZL2w4SVF4h4hHfBEneyIQe5g0mW/SZD6PL3p9FXVEa583qBuzqRwtLCwGDx6sFjxy5EgiioyMfIMXHAEAgPqurocAgPH5Ne2g/51RgXHz5Qq5svBFcfKMu2Om3/V5XpxUh21Teu0QeoVC0a9fPyLq2rWrsvDKlStEJBQKVSNTUlLUbtow/fv3T0tLYzHKIfQnTpywsrIiIuWdHyKytrZWHXbOghs3bjxixAgWoBq8bt26NWvWaJYvW7ZMtVWpqaleXl6arRKLxXv27FGNZIdzcnL65ptvNKu1sbFRG/sdERGhvKKhJBAIvv32W/Za9xD63bt3f/zxx9u2bdPcVFZWxj4rf//99zpqqIeio6Pt7OyIyNfXt1evXkS0ZcsWNnJ+5syZaqPQ649Dhw4RkZubm1oLq+U9Ki8vb9++PRFNnjy5OhsNda0oukthpEQhe8ZxXOmzrwojJbJYv9LnawojJbJHPn/FRLUujJQoyjLrtKVaVNTnK6K1G0+ePJn9hteMj4mJYb8GlQ/RAAAA1DIk8GCY7NLMj++N978zKjb/gdqm/Sk7/O+M+iZhmdYda5mOBD4nJ+fatWs+Pj5EZGlpef36deUmzQReoVD07NmTJeE//PBDXFxcUlLSrl272AzkPXr0YGHKBN7S0rJDhw7nz58vKirKy8v7/vvv2VxHEydOVNapDCaiKVOmJCYmlpeXR0ZGtm7dmqXKRDRixIhHjx6Vl5ffunWrU6dORGRiYiKTyZSVvP322yx42bJlUVFRz58///XXX5Uz9l29elXtcGZmZgKBoEOHDufOnSssLMzNzd22bRsba+Dn56cMzsrKsrW1ZSeycePG6OjoO3fubNiwwdzcXJn2607gdWAPpvJ4vHv37lWuhjqkzOHZf01NTet59l5SUtKiRQsiOn36tP576fMelZWV3b59mw08FovFDx6o/yoAo6ZM4BVlWUU3nAojJfL8W4ry3KKoFoWREnneVa6+JvAG9Xkd3Zhd2/X19dXcS7l+5P/93/9VZ9MBAAD0hmlU/6WySjMuZp3mSCHkmbzdcCQbCa9P+b38WyUKWU9J33ZWHdXi32k46ro0PKbg3u3c681FzgbVX0OkUqmOicF79Oixa9curdPUKx08eDAqKsrExCQiIoIl0kQ0ffp0FxeX/v3737x5MyIiom/fvsp4iURy7do15cjkgICApKSkjRs33rx5U7PysWPH7t27l712d3cPCgoaPXq0XC7v3bv3iRMnWMLcvXv34OBgT0/PsrKyx48fd+7cmYhOnTr1559/EtGRI0fYlQgicnJy8vLyGjhw4I0bNz777LOIiAjVY5WUlDg5OV2/fp0NECCiTz75JCEhYdOmTaoT769atSonJ0ckEkVGRrJjEdFbb73Vp0+f/v376/hGvVZ4ePjcuXOJyMfHR1lzbbp///7PP/9MRGKx+LPPPjMzM9NRLpfLP//886tXrxKRqalpr169BALByJEjDx48mJ2dTUSlpaWNGze2srJavHgxqycjI+PRo0cdO3Zs1arVa+uvBcHBwc+ePRs4cCC71qOP175HT58+7dq1a0FBAXugo1evXsHBwR07dqzGZkM1UuRfk+ecJiKeiYOw8cdEPH3KuXI2XSVX/vI7Tl4gsB1GXGn5i818kas8/3pZygaz9r/8FV9wU5F3Tf/6a5qeff613bigoID+vlSnxtbWlsfjcRzHYgAAAGofEvh/qcvZZ35/dZS9djBz7GM7SP9y5ax1mvGjGo3/+cXOgy9397Lt9/urY/rXXyekUqnydkpFtm7dSkSTJk1SZu+Mp6fnsGHDUlJSkpOTVRP4RYsWqT1XzCYtz8vTMr3fggULVL90c3NTlquOcu/Rowd7oaxk586drA3K7J0RiUSrVq16++23r169+uDBA7U2L1myRJm9K89i06ZNqg9z7tq1i4gCAgLU8rc+ffqMHTuWDU81VGFh4apVqzZs2CCXy7t06cLu8da+oKAg5eWSvn37Dhw4UEd5TEzMpk2blPtevnxZs8LU1NSgoCC1wmvXrulTf03Lz89niywoH8fQTf/3iM/nC4XC0tJSIoqPj//tt9+6du2q2l2h/ih7vkaeF85eC+ze5Zm11L9cURxTnr6LiEya/m9Z8lfKcnnuBUX+XxMclr/YIs+/pn/9NcqgPq+7G7O1362trTV3FAgElpaWBQUFtTzzKAAAgBIS+H+ptxuOtBKKyxRlIoFFT0lfPcvDXv2aU5Y5zGG0vamD1ngBCS9l/ZkieyYgkwlNp+tffw0Ri8Xh4eFqhUVFRY8ePVq9enVCQoK3t/eNGzd03BBmTzyytcTUnDp1SrPQw8NDrUR1EmM1ajMqKSPZM5lK7FlrVY8fPyYib29vzTqHDh1qbm4uk8kSEhLUEnh2KUFH216+fJmfn09E48aN06x54sSJlUjg9+3bt2jRopcvXxKRr6/v9u3bJRKJoZVUi5UrV3bu3Lm8vNzBwUF1NIHW8k6dOn377benT59WKBSWlpZubm48Hu/x48chISHKyQubNm36ySefKJeDzs3NZXfg27Rp89r6a9qmTZsyMjK8vLw0O6Qm/d+jVq1a5eTkKBSKFy9e7N69e+3atYGBgUlJScrJF6FeMWm1kZ8TRqTgm7VUzaJ1l5e93EzlUkXOGXb7nW/VXRnPFd4uzzpRlrKBxQtbrODnXdO//hqlf59/bTdmvxgrusfOZrLU/LUMAABQS+p6DD8YjfCss/53Ri14+D8yebGOsEf5d/3vjPrknp+0NLvW2qbptZPYpaamWlhYENHChQuVhWrPwKemprIfk1u3buk+nPKx9oyMDLVNv/32GxE5OjpqBmdm/uMJ0szMTFYeGxurVgkrv3LlCsdxCoWCfXY8cuSI1sa4uroS0TfffKN2uFevXqlFssmW7e3t2ZcXLlxgkdnZWt67+/fvs616PgP/7Nkz5TR7zs7OFbXWWISFhbFvu4ODA/19d87X17e0tLSum6autLS0UaNGRHTw4EHdkVV8j9ggBR6PFxcXV4X2Qv3CnoEvutGEPf2uukn5JHzRjRb16hl4/fu8Js1uzJ6BHzdunGZwSUkJ+3nZt29fVRsNAABQKRj3CHqRKYqPpe0nonGNp5jxdd15aG/VpZtNb2V8veXo6Mgmgdf6dDojk8nYCzZjmT40l6Crdtzf+XxFj/eze8LKD5pKrx3nrPs0tT4RWpHQ0NBOnTqdOXPGxsYmKCgoJiZm7Nix+u9e35w+fXr06NFsxbhWrVoR0ffff29nZ3fs2LEJEyaori1XH5w8eTI9PV0ikYwaNUpHWNXfow8++ICIOI6Ljo6uUouh/uHkRez2u2ohT2AtdJxJRJyiqI7apZ2efV4rzW7MfsazsrI0g1NSUlRjAAAAah+G0INewl6F5pblENH+Fzt+frFTd3AZV0ZEEdnnvRp6NzNvWQvNqxw2X7GO5XybN2/OhqM/ffpUbTg6EeXn5xcVFZmbm9vY2NRsQ/+Jz+e3bt364cOHT5480RrAytu1a2dozcplk+Pj49liaZrV6uP8+fN+fn4lJSVeXl67d+/WXJTOuKhm78HBwWyArouLy9mzZ4cOHcpy+AMHDtTCtRs9sSkS/Pz8dIzy1fM9yszMZMvCnzx5cujQoWpb7ezsBAKBXC5XXuqCNwGnYP83afq/mhuFjQPK07Zz5fVrFXTdfd7QbswmtLt161ZZWZnazzUbpcXj8Tp06FAD5wEAAPB6uAMPeilV/HU7t0heWCgv0P2PBfN4/BJ5vf5Yzx501JHA8/l8ltOePXtWc6u3t7ejo+PmzZtrroUVcXFxIaLff/9dc9OlS5fYDEyVSOAbNmzIHn4+duyY5lY23v61CgoKfH19S0pKJk6ceOrUKWPP3s+cOTNq1CiZTDZ37tzg4GDVUQ/dunU7ffq0RCI5dqiYcB8AACAASURBVOzYBx98wGa0rnMpKSlshQK2lrVW+r9H9vb2IpFIJpNpHagSExMjl8uJSPdSDmBcOHkeERFPWJowS3bHXe1fyf23OY4j4uq6mf/12j5vaDdmk9jn5uZqTqHCfuv27t2bLbcJAABQB+p2BD8YkYLyfIP+6X5Uvqa99hl4juPYAmAODg7KEs114Lds2UJE5ubm8fHxqvtGRUWxBdsvXrzIqTxnLpVK1Y5S7c/AK+skol9//VU1rLi42N3dnYh69uypXJ+8osNxGs/Acxz3xRdfEJFIJFJb3Pvx48eWln+t+af7Gfj//Oc/RNS4ceOCggIdYcaCzRQ4d+5c5feTzQV47do19mVUVBS76vH06dM6a6WK7du3E5GlpWVZWVlFMQa9R1OmTGEdWLNvjx49mv2UlZSUVLXdUG8UXWtUGCnR5x9XVpcTnSjp0+cN7cZsyNXbb78tl8uVhffv32e/Bv/zn/9U+1kAAADoCUPoQV+WAqvXBxkVNvQ9MzNTJpNVNNh41qxZwcHBcXFxvXv33rBhw8CBA0Ui0YULFz777DO5XN6/f3823VEtGzFixNChQ8+ePevr6xsYGDhy5EgHB4fo6Ohly5axxzi3bt1a0RPyun3xxRfbt2+XSqXu7u6rVq0aPHiwiYnJxYsXFy9eXFJSIhaL2TT1Ohw4cICIOnTowK6GaOXq6sqeX6j/tm7dOm3aNB8fn4q+nz169Lh27drjx49btmxZu03T7syZM0Tk7u6unB5fk0Hv0RdffHH48OG0tLRevXqtWLGiW7duFhYWMTEx33zzzblz54goODhY/0kioP4zafujIufX14bxLLuSsF7chdanzxvajdeuXevt7f3nn39+9NFHn3/+ebNmzcLDwz/66KPCwsK2bdvOmDGjFs4LAABAu7q+ggBQI/S5A79//1/T7IWGhrISzTvwHMfFxsZqHSHcunXr5ORkFlPLd+A5jnvx4sXgwYM1WyUWi/fs2aO6r0F34DmOi4iI0BxTzefzd+7cye5K6b4Dz+aC1m3Lli06aqjn1O7A1ytyuZzNNRgYGKgjzND36ODBg1pXQzQ1NV2xYkWNnxVAxfTs85zh3fjrr79WxrDxVkTUuHHjmJiYGjkTAAAA/eAZePj3GjBgAHvh7++vXNxbU7t27a5fv75mzZp33323UaNG9vb2np6ea9asefjwIZsYqU40adLk7NmzwcHBPj4+zs7O1tbWffr0CQgIuHPnztSpU6tSc58+fW7fvj1r1ixnZ2czM7MGDRoMHz784sWL+tx0KiwsTE9Pr8rR67/Ro0f37t2brdVX39y6dSs7O5uI+vbtW1FMJd6j8ePH37lzZ968ea6urmKx2NbW1sPDY+bMmQ8fPly+fHlVGw1QBfr0ecbQbrxs2bIzZ84MHz68YcOGQqGwTZs2CxcuvHfvXv382QcAgH8PHsfVo6loAAAAAAAAAEAr3IEHAAAAAAAAMAJI4AEAAAAAAACMABJ4AAAAAAAAACOABB4AAAAAAADACCCBBwAAAAAAADACSOABAAAAAAAAjAASeAAAAAAAAAAjgAQeAAAAAAAAwAgggQcAAAAAAAAwAkjgAQAAAAAAAIwAEngAAAAAAAAAI4AEHqqEI8ouVtR1K7T48MMPeXro2bOn6l6LFy+uKNLMzMzV1XX48OGHDh3iOE65S1BQkD4HYgYNGqTWzl9//XXq1Klt27a1tra2sbHp3r37uHHjjh8/XhvfI6gUrqy8PD2rrltR93788cegoCDVnwWAN1V2dvatW7eKiorquiEAAABI4KFqgq7nu4ek388oq+uG1LjS0tK4uLhTp075+fl5enrKZLIqVpiUlNSrV6/Ro0eHhIQkJCTk5+fn5eXdvn376NGjY8aMcXd3j4+Pr5aWQzVSFMuez/wyJSCw4OL1um7LP3h4eLz2+lG3bt1Ud+E4bv/+/cOGDWvSpIm5ubmrq+uoUaPCwsL0OdxPP/00Y8aMzz77TKGoj9fv4N+gEn1eJpOtXLlyyJAhTZs2lUgkvXr1mjlz5rNnz157rMDAwB49esTExNTY2QAAAOhLWNcNAOP2LK9cztHzPHnnhiZ13RYtmjVrdvLkSR0BIpFIs1AsFoeHh6uWcBwnlUoTEhI2b9786NGjiIiIJUuWbNq0iYimTJkydOhQ1eCcnBx2p3358uVjxoxR3WRlZcVePHnypG/fvmlpaUQ0ZswYX1/fli1b5uXlxcfHHzlyJDw8/Pr166NHj75+/bpyF6hzimJZ+srvFXkFRJT1w/8JxJYit0513ahKKi4u9vb2PnfunLIkLi4uLi7uxIkTY8eOPXDggFBY4V+HxMTEuXPn1kozAarNo0ePRo4cmZiYqCyJioqKiooKCQkJDg6eNm1aRTsWFBQcPny4NpoIAACgByTw8CYzMzN76623DN1LIBBo3WvgwIHjx4/v0qVLUlLStm3bNmzYIBAIGjZs2LBhQ9WwrKy/xlc3b95caz0lJSXvv/9+WlqamZnZjh07pkyZotw0bNiwOXPmHDhwYNKkSY8ePZo3b96PP/5oaPuhJrDsveRxEs9EyJWVc+XyVxt3OXw2o57k8OvXr8/JydG6ieO4efPmJScnT5o0SVkYEBDAsvcJEyYsXLiwWbNmcXFxmzZtCg0NPXr0qKur68qVK7XWJpfLJ02aVFBQUBNnAaA/g/o8x3EzZsxITEy0tLRcuXLlgAEDHB0do6OjlyxZcv/+/VmzZrm7u7u6umpWlZWVNWPGjFevXtXgmQAAABgCCTyAAcRi8Ycffrh06dKSkpLExEQXF5dKVLJ9+/Zbt24R0bFjx0aMGKEZMGHChKioqM2bNx84cODbb7+1tLSsaruhapTZu9Delm9lWZqUYjWwd8HF66827Gy4cIZFz8513UDq379/RZt2796dnJzcp0+fTz/9lJU8f/48JCSEiPz9/X/88Ucej0dEDg4Onp6eM2fO3LFjx9q1az/88MPmzZtr1rZy5cpr1641aNBAeaEK3jw//fTTa2P8/f1roSU6GNTnf/3118jISKFQeOrUKU9PT1bYpEkTd3f39u3bZ2Zmbtu27dtvv1XWkJycvGfPnjt37oSFhRUXF9foiQAAABgEz8ADGKZNmzbsBRsAbyi5XB4UFEREHh4eWrN3hn04Li4uPnXqVKWaCdVGNXt3/Ho+z0RIROJ3PG183ubK5RlBu4qi7td1GyuUmpq6cOFCCwuLPXv28Pl//cI/fvy4XC43NzffunUry96V1q1bx+Px5HJ5ZGSkZm2RkZGrVq1ycHCo6P48QJ3T2uejoqKIqG/fvsrsnbG3tx82bBgR3b17V7X8/v37gYGBx48fR/YOAAD1De7AAxhGmbc7OTlVYvebN28mJycT0dKlS3WEde7c+dKlSxzHNWvWrBJHgeqilr0LHRooN9l+MJKIco//mRG0q57ch9c0a9YsqVT67bfftm3bVlnIemCXLl3EYrFavK2tbbNmzZKTkx88eDB+/HjVTfn5+ZMmTZLL5bt27SotLa2FxgNUgtY+HxcXJxQKlZdfVbEhTmqj8d3c3I4ePcpev3r1atasWTXZZAAAAAMggQcwAMdxR44cIaKuXbs6OztXooaLFy+yF/369dMdqWOAKNQOHdk7U89z+MOHD4eGhrq5uc2ePVu1vGPHjh9//HGXLl00dykvL2dj4x0dHdU2zZkz58mTJx9++KG3t/exY8dqrtkAlVZRn1dm42rkcvnly5eJqGvXrqrljo6Ovr6+7LU+09QDAADUGiTw8CYrKSl5+PChjoAmTZrY2trqU5VUKo2Li1u/fv2VK1csLS23b99euSaxxeFsbW1tbGwqVwPUjtdm70y9zeFLS0s///xzIlqzZo3aOHl/f/+KHmAOCQkpLCzk8Xhq14+OHDmyd+/e1q1bs8UXAOohHX1eU3l5+YMHDxYvXhwTEyMWi7/44otaaSMAAEBVIYH/l4rNKgu+XajgODMB7397ixtZCtTK1eLjsstl5Vw7O6G58K9PRfmlisQceY5MTkTbbxecelKsWs4RR0RNrATKmomoomOplVej58+fd+qka5LwH374YebMmWqFUqlUx4e/Hj167Nq1qxKT2zOZmZlE1KpVq8rtDpUmexiffzqcOI5vIbKdNoYvMlcr/0e0XF50O4YrK+OZmZo2b5KzL5SI5HkF5WkZ8twCIsrZFyqw+Wv8uTyvgC8yUxSXvPpmu0kLJ9PG/12VoKJjqZXXhODg4GfPng0cOPDtt9/Wc5fw8HC2PpyPj0/nzv+9EpGSkjJz5kyBQBASEoJ1DY1IUlLS06dPicjCwqJnz57KB8KV5ZpYpD6VX7hwQUcNmsdSK68Jevb5p0+fdu3ataCgQKFQEFGvXr2Cg4M7duxYcw0DAACoRkjg/6XOJpX8lvBXyj2guZl3G5FmuaYX+XKt5Q8yyx5klmmWpxYoiP5RXtGxVMvrOalUmpGRUend5XI5EdXop1jQqvByVOHVaPbaapC7mauzZrkmrqS0KFrLIA7ZowRt0VSWlFKWlKJaVtGxVMurXX5+/urVq4lozZo1+sQXFhauWrVqw4YNcrm8S5cuqosXchw3ZcqUnJycpUuX9unTp4YaDDXh8ePHL168ICIej9exY0flxRdluSYWqU/lSUlJOmrQPJZaebUzqM/z+XyhUMimcoiPj//tt9+6du2KX8sAAGAUkMD/S33Y1dK1gbBUTpYmvH7NzDTL1aTky/NKFK4NTPh/35kuU3CxWeVnkmRPpeXTOlu5OZqolpcrSMjnWtqYiE3/eyu7omOplVejNm3asCHrBhGLxeHh4WqFRUVFjx49Wr16dUJCgre3940bN1RvUerP3t6eiBITEyuxL1SF7VQfUfcOnIITiC1VM2dluVp84bmrRXdiiMcTv+Np3rEtEXElpaXJLwvDo+Q5eZKJI03+vtMul+ZJD/+hyC8UWIttxr8rsP7vzHAVHUutvNpt2rQpIyPDy8vLw8PjtcH79u1btGjRy5cvicjX13f79u0SiUS5dePGjRcuXOjevXtgYGDNNRhqgqenZ3p6OhFZWlqqZs7Kck1qkToMGjRInxoqakO107/Pt2rVKicnR6FQvHjxYvfu3WvXrg0MDExKStq9e3fNNQ8AAKC6IIH/lzIT8Ia21DJ8t6JyrUa1pZT88qfScjdHk+GtzVXLq9KGOicQCLSOkGcLv7Vu3bqoqGjv3r0bN26sROVsbe2cnJycnBzdj9/v37//1q1bVlZWWLKrWvAtRBa9u+pfbunRLe/EueyQ4/mnw83atLAa2JuVl8QkynPyRJ1dzNq2JCJ5bn7aV98q8gtNWzRt9NUcgVhXilLRsapXWVlZcHAwEU2fPl13ZHJy8owZM86cOUNEzs7O69evHzt2rGpAfHz8l19+aW5uvn//fhMTk5prM9QEkUjUsmVL/csNomcN1XKs19K/zyvx+fxmzZoFBgZaW1svWLBgz549ixYtcnFxqclmAgAAVAMMGAMwgKOjI1u8/ebNm5WrQXnbSvMmv5qgoKAtW7bcunWrcgeCqrMeOcRuig9xXOZ/9hdcvK4ZwLL3suep+mTvtebkyZPp6ekSiWTUqFE6wkJDQzt16nTmzBkbG5ugoKCYmBi17J2IkpOTS0tLZTJZhw4deCqUkUKhkMfjdevWraZOBkAPevZ5rT744AMi4jguOrrCR2kAAADqDyTwAIZp0aIFEeXm5lZudw8PjwYNGhDRihUrOI3JApVSUlLu379PFY9ThdqhI4evn9k7Ee3cuZOI/Pz8zM0rHOFy/vx5Pz+//Px8Ly+vBw8eLFiwwNTUtBbbCFCddPf5zMxMkUgkEonOnj2rudXOzk4gEBCRTCar6XYCAABUHRJ4AMOIRCKqQgJvZmY2f/58IoqOjj5w4EBFYWw6MSIaMmRI5Q4E1UVrDq8oKPore29Zv7L3lJSUP//8k4gmT55cUUxBQYGvr29JScnEiRNPnTrl5ORUUWS/fv1StFEuo5iUlJSSknL69OlqPxEAPb22z9vb24tEIplMpnXkVExMDPtlW+m1RQAAAGoTnoEHMEx5eTkRFRYWVrqGuXPn7t27NyEhYerUqenp6Z9++qlawN69e7/77jsimjJlSvfu3avSWqgW1iOHEFF2yPHM/+xnC8Jn7Tpcnp5p2rJpo8B6lL0T0R9//KFQKCwtLXv16lVRTEhIiFQqbdy48Y4dO9i9x4qYmZk1bdpUs5yNIiEiJycn3TWAMfL396/rJhhAnz7v7e0dEhKydevWTz75xMbGRnXT8uXLiUgikWAlOQAAMApI4OFNVlJS8vChljXAVLVo0cKguZHZh7/MzEyZTKZjiLIO1tbWv/zyS79+/fLy8hYsWPDHH39MmjSpXbt2HMclJCT8/PPP7H6mk5PT1q1bK1E/1ATrkUM4hSJn/6/l6ZlEVD+zdyJiM9K5u7sLhRX+emdDPzp06HDlypWKYlxdXdnTIgD1nD59/osvvjh8+HBaWlqvXr1WrFjRrVs3CwuLmJiYb7755ty5c0QUHByMp0gAAMAoIIGHKmFrxPF5rwmrK8+fP+/UqZPumLCwsHfeeUf/Otl4Y4VCcfr06UpMmMR07tz56tWrvr6+cXFxZ8+e1Xwy08vL6/vvv1ddzQvqnM1oLyLK2f8rEZk0blgPs3eFQnH+/Hki6tevn44wtrbiuXPnWOqi1ZYtW+bNm1ftLQSoXnr2+Q4dOuzZs8ff3//x48cTJkxQ3WRqarp06VI/P7+abSgAAEA1QQIPVTKmnUWpgno2/hfduBgwYAB74e/v/+rVKx33fHTr2LHjgwcPDhw4cPz48Zs3b2ZmZlpYWLi4uLi4uIwaNcrHx6f6mgzVxma0V9nzNNmj+EZfza1v2TsR3bp1Kzs7m4j69u1bUUxhYWFFa4ADGB19+jwzfvz4bt26bdu27fTp0y9evBAKha6url26dPnss8/atGlTK40FAACoBjwd82ADAAAAAAAAQD2BWegBAAAAAAAAjAASeAAAAAAAAAAjgAQeAAAAAAAAwAgggQcAAAAAAAAwAkjgAQAAAAAAAIwAEngAAAAAAAAAI4AEHgAAAAAAAMAIIIEHAAAAAAAAMAJI4AEAAAAAAACMABJ4AAAAAAAAACOABB4AAAAAAADACCCBBwAAAAAAADACSOABAAAAAAAAjAASeAAAAAAAAAAjgAQeAAAAAAAAwAgggQcAAAAAAAAwAkjgAQAAAAAAAIwAEngAAAAAAAAAI4AEHgAAAAAAAMAIIIEHAAAAAAAAMAJI4AEAAAAAAACMABJ4AAAAAAAAACOABB4AAAAAAADACCCBBwAAAAAAADACSOABAAAAAAAAjAASeAAAAAAAAAAjgAQeAAAAAAAAwAgggQcAAAAAAAAwAkjgAQAAAAAAAIwAEngAAAAAAAAAI4AEHgAAAAAAAMAIIIEHAAAAAAAAMAJI4AEAAAAAAACMABJ4AAAAAAAAACOABB4AAAAAAADACCCBBwAAAAAAADACSOABAAAAAAAAjAASeAAAAAAAAAAjgAQeAAAAAAAAwAgggQcAAAAAAAAwAkjgAQAAAAAAAIwAEngAAAAAAAAAI4AEHgAAAAAAAMAIIIEHAAAAAAAAMAJI4AEAAAAAAACMABJ4AAAAAAAAACOABB4AAAAAAADACCCBBwAAAAAAADACSOABAAAAAAAAjAASeAAAAAAAAAAjgAQeAAAAAAAAwAgggQcAAAAAAAAwAkjgAQAAAAAAAIwAEngAAAAAAAAAI4AEHgAAAADg/9m787Co6v0P4J8z+wwzDDOsguICoqKEuJslpuaSS1YuWaal3UrbLLPbYvYzLa9p3pt1K8s9r2apqRmlmZpWoqiAC6KyCAKyDAww+3LO+f0xhibuAocZ3q+n5z5zzvnOzPtce8I333O+BwDAC6DAAwAAAAAAAHgBFHgAAAAAAAAAL4ACDwAAAAAAAOAFUOABAAAAAAAAvAAKPAAAAAAAAIAXQIEHAAAAAAAA8AIo8AAAAAAAAABeAAUeAAAAAAAAwAugwAMAAAAAAAB4ARR4AAAAAAAAAC+AAg8AAAAAAADgBVDgAQAAAAAAALwACjwAAAAAAACAF0CBBwAAAF+TkJCQkJBQWloqdBAAAIC6JBE6AAAAAEAdS0tLIyKXyyV0EAAAgLqEAg8AAABeg+d5juNucrDb7WZZtmZTLBbXTygAAIAGgkvoAQAAwGssXrxYchM8g1u1alV7JwAAgPdCgQcAAACvodVqhY4AAAAgGPw2WnhVVVWvvfZaVVWV0EEAAIA6deo0e/ZsoVPANT399NNisfill14ym82BgYFz584NCQmpPWz06NFEtHTp0sDAwDr89l27dhmNxpscbLFYPv7442bNmqnV6jrMAAAAt8F3fr7zILTvv/9e6H8LAADgksrKSqF/MsANZGVl9erVi4hCQkK2bdtWe4Dnj7KgoKAOvzQ1NVXQfzEBAOCO+MbPd8zAC8+zvk7Pnj1nzJghdBYAgCZt8uTJZrP58mXPoHGKiorav3//vHnz5s2bN3LkyH/84x+LFy+u74nu9u3bT58+vbCw8CbHp6ennzlzxt/ff9myZfUaDAAArs+Xfr4z/F+/pQahbNq0afTo0Y888sjGjRuFzgIA0KQFBgZWVFSUl5fr9Xqhs8BNOXDgwIQJE3JycqKiotasWXP33Xd79jMMQ0QFBQURERFCZZs5c+aiRYsiIiIKCgqEygAAAORbP9+xiB0AAAB4q969e6elpU2aNCk7O7tv376zZs3Cs98BAMCHocADAACAF9NoNKtWrdqwYYO/v//777/fq1evU6dOCR0KAACgXqDAAwAAgNcbO3bssWPH7rvvvqNHj3bt2lXoOAAAAPUCBR4AAAB8QfPmzXft2vXhhx/6xjJFAAAAtTWhVeirqqoyMzNzcnLMZrPFYlEoFGq1unXr1rGxsTqdTuh0AAAAcKdEItHMmTNHjRqVn59PREFBQUInAgAAqEu+X+DdbveyZcvWrFmTnJx81SX3GYbp2bPnxIkTp0yZIpPJGj4hAAAA1KG2bdu2bdtW6BQAAAB1z8cvoS8qKoqPj586deqBAwdq2jvDMGq1Wi6XezZ5nk9OTp42bVqnTp0yMzOFC3tr7Kcs5auLeBZPAQQAAAAAAGgSfLnA22y2UaNGZWRkSKXSkSNHLlu27NSpU2VlZS6Xy2Qy2e12h8ORlZW1evXqsWPHqlSqs2fP9u/f32AwCB38xiwp1acHHM57/lTesxno8AAAAAAAAE2BL19CP2/evJSUFL1ev3nz5sTExNoDZDJZVFRUVFTUxIkTjx8/Pnz48Pz8/AULFixcuLDh0948S0p11oOpbLWbGKr4ppgYavlFLCNmhM4FAAAAAAAA9ciXZ+A3btxIRO+///5V2/sV4uLili5dSkRbt26t92R3wJpqynoola126x4Kifm5q1gjqVhffG7ySd6NeXgAAAAAAABf5ssFPi8vj4j69et3k+MTExPFYvH58+frMdOdsaaazo44yla6dQ+FtFrZSd0nIPr7zmKNxLip5NwUdHgAAAAAAABf5ssFXqvVElF2dvZNjs/Ly2NZ1vOuRuiK9s5IGCLy66VFhwcAAAAAAGgKfLnA9+3bl4iWLFlis9luOJhlWc+t7z169Kj3ZLfuqu3d42KHV4vR4QEAAAAAAHyYLxf4WbNmqVSqnTt3JiYmJiUl2e32qw5zuVy7d+8eOnToihUrGIZ5+eWXGzjnDV2nvXv49dJGb0lAhwcAAAAAAPBhvrwKfXx8/KpVq8aNG5eSkjJs2DCVStWtW7fg4ODAwECNRmM2mysqKgwGw5EjR6qrqz1vmTt37oABA4SNfYUbtncPT4fPGpVq3FRCRK2Wd7zWSAAAAAAAAPBGvlzgiWjMmDF6vX7GjBnp6elWq3Xfvn3XGhkXFzd//vxhw4Y1ZLwbchucZ4cdZavdukdCb9jJ/Xppo7Z0zh6VZtxUIo9Whb/TpsFyAgAAAAAAQH3z8QJPRAMGDEhLS0tPT9+xY8eJEydyc3PNZrPValUqlRqNpnXr1h07dhw0aFBCQoLQSa9GzDByERGxJjfv5i8v8CuOWbadta0apg9QXLoPgjOxnuvnRSpfvjkCAAAAAACgCfL9Au8RHx8fHx8vdIpbJtFJY3Z0OftAavXO8pxHj7X55i7RX3V91zn78TLXGaO7RzOZZ0/1rvKc8cc4Oxc0JSLs1VaChQYAAAAAAIB6gHnaxk4R49c2KUEaJq/eVZ7z6DHOzl1+lP9rubqao0FTIiL/055w/zsAAAAAAIBvaSoz8ERUVVWVmZmZk5NjNpstFotCoVCr1a1bt46NjdXpdEKnux5Phz/7QKqnpV8+D++B9g4AAAAAAODzfL/Au93uZcuWrVmzJjk5meev8nw1hmF69uw5ceLEKVOmyGSyhk94M/7W4celt9lw6XYAtHcAAAAAAICmwMcvoS8qKoqPj586deqBAwdq2jvDMGq1Wi6XezZ5nk9OTp42bVqnTp0yMzOFC3sDl66l/7UiZ1y62MkTEbPfiPYOAAAAAADQFPhygbfZbKNGjcrIyJBKpSNHjly2bNmpU6fKyspcLpfJZLLb7Q6HIysra/Xq1WPHjlWpVGfPnu3fv7/BYBA6+DUpYvza/pAgCZZV/1rx4H9yu58wSaZlcHYu+LkWaO8AAAAAAAC+zZcvoZ83b15KSoper9+8eXNiYmLtATKZLCoqKioqauLEicePHx8+fHh+fv6CBQsWLlzY8GlvkqKDX0xSlzMPHG2TVv16WjURBT/XosXCGLR3AAAAAAAA3+bLBX7jxo1E9P7771+1vV8hLi5u6dKlQ4cO3bp16x0W+NLS0oEDBxYUFNzkeIfDQURHjhy56tHtWbZCE3vFTuVH7Vs9n6E2ubNGhKY8Fk5p5suPysTM4DaKcLX41rMDAAAA1ChUbgAAIABJREFUAABAI+XLBT4vL4+I+vXrd5PjExMTxWLx+fPn7/B7zWZzVlaWzWa7pXeVl5fX3nmuyv3yrsqrjg9+qXXrAntKJw1/yFT76EmDa1H/gFsKAAAAAAAAAI2ZLxd4rVZbWlqanZ3dvn37mxmfl5fHsmxQUNAdfm+bNm0MBoNnXv1mvPvuu5988om/v3/tQ5H+krfv9jfYuNqHfjhrO6STjmirvOpM+4NtlbeUGQAAAAAAABo5Xy7wffv23bhx45IlS/r3769U3qDQsizruXK+R48ed/7VKpVKpVLd5OCa9fBrEzE0+S6/2vs5m/3oueoiEo0JdvS5K/Q2UwIAAAAAAID38OVV6GfNmqVSqXbu3JmYmJiUlGS32686zOVy7d69e+jQoStWrGAY5uWXX27gnLfEmVtQOv+L80/+05l7nogMS9YUTJ1d/cNu3n3lffIAAAAAAADgS3x5Bj4+Pn7VqlXjxo1LSUkZNmyYSqXq1q1bcHBwYGCgRqMxm80VFRUGg+HIkSPV1dWet8ydO3fAgAHCxr4O069/Vny5gWdZRiwWKRREJPJXu/PzKlZvthxIDXnjWbG/WuiMAAAAAAAAUC98ucAT0ZgxY/R6/YwZM9LT061W6759+641Mi4ubv78+cOGDWvIeLfEmnKs/Iv1ROQ/rF/AmAdku21U5AyZMTm0NLv8qw2OM7mlH34ZNudlRozF5wEAAAAAAHyQjxd4IhowYEBaWlp6evqOHTtOnDiRm5trNputVqtSqdRoNK1bt+7YseOgQYMSEhKETno9vMNZ/tW3xPO6iQ9pR3quEbARETGMMiG22QevXXhzoSMzx/zLH5ohfQVNCgAAAAAAAPXC9wu8R3x8fHx8vNApbl/54eNnlbIOUZHaEf1rHxUHaPRPjc5dsrrg8PF7UOABAAAAAAB8UVMp8N5unpjZ8drTL18oe45hPHvKnWVEWpO7miiIiEydO0yfMbkoMGB7RWVrPZ4ADwAAAAAA4Gt8eRV6X9Izt1DE8x83C15lMhNRoT3fJT8olZrTrRtsqRk5e5Mnnr9QEKSLKiwOrDILHRYAAAAAAADqHgq8dxhYUv7ShiQR0YLK6i+rzd8UrWgV+f3d3WZm2H/ZvemrqWpVvkzSprBk3hffUOop4nmh8wIAAAAAAEAdQ4H3DrJWzQcfTH89NUNE9O+q6p2uMKVD3D1F7RT5z3lqYn5oULSxev7n6/wttop1Ww2frCGOEzoyAAAAAAAA1CUUeO/gd3cCIxYn/m/bLI5liM9XjRSVjOidF5+pfdUkC23B2P+9+4C/xaa8q51IqTDvSzGu3y50ZAAAAAAAAKhLPruIndVqPXXq1O29t2vXrnUb5s5JQgI1Q+6t/nEv/TSvTb+oHNXjuzoPOprAmHle5S6IMi5lUxQyP1XQC0+4SsuL3/24etuv6gG9pWHBQgcHAAAAAACAuuGzBf7UqVPdunW7vffyjfIect0TD1WU5O3r+keIoyQ+Nej7e4ZU8Hygw9U396vikPI/ErXje74g1geI9QHqxJ7m3Qcsew8GPDpc6NQAAAAAAABQN3z2EvrWrVu/8MILISEhQgepM4xE/Nt4iUPGtcwJPdy2G0/EEJXLpaLSvgxPyT3NVe21npF+vROIyJ6ZI2heAAAAAAAAqEs+OwOv1+s/+eSTDz74YOLEiVu2bCGizz777IEHHhA61+3Ls2X/WbmXE+n3t5pWGBQUXW0Zdq7gk7iYTX0H3Eth7or/fndh9Yut3iQicWAAEXHVeJ4cAAAAAACA7/DZAu+h0WiWLVu2Y8cOm80WEhLSsmVLoRPdJp74dYXLnIx/nu7NMt6vTWHpmoQOuo5tQyzWdyoq91PHKNWI1KofTphSO2kSWGM1EYnUfkKnBgAAAAAAgDrjs5fQ1wgMDBw8eLDQKe5UsvG3DGtRpvaVMt4vqsw4//P/yVNPEdHDfqq5+gARUbZiaKFiyDdFK1ietR46RkTydq2FTg0AAAAAAAB1xvcLPBF17txZ6Ah3xMk51pVsO+H/ilkUEiuTLi03+ltsxv9t5aw2InrYT/WePkBElK8aeYRifj293vzrHyQSqRN7CB0cAAAAAAAA6kyTKPBPPfXU9u3b+/TpI3SQ2/Rj6aY0SaJdHBIrk64MDgzv31vWqrnrQlnJe5+6iw1E9MhfHf6c6uFvLL9bpS7/IfdKm4cJHRwAAAAAAADqjI/fA+8RGRkZGRkpdIrbVOEy7CjbGiKO7qqNfy8wzF8kIqKQfz5TPGeJIyuvcPpcRXwHWcuI+1yuWS73NwkMJzH8MSZiyrCHhQ4OAAAAAAAAdalJFHiv9n3xOifn0HInq4umTy+67MA/al7l1bwKJCKi5FYFD7KGYHFoQ2UEAAAAAACAetckLqH3anKR4lbfohQp6yMJAAAAAAAACAgz8I3dhIhnJkQ8I3QKAAAAAAAAEBhm4AEAAAAAAAC8AGbgvQNnPcEaNl2+h3eV8o4CkTqBiKnZyUhDJM2exe9lAAAAAAAAfA8KvHdw5c9lK3fW3s9W77tij8i/t8jPu597DwAAAAAAALWhwHsJ3kVEkpAnGEUbIuLMR+cu/C7lJLv49bCY7i8TIyYid8ly3lFAnEvgqAAAAAAAAFAPUOC9iTjwIbH2PuJd9/XS7z1kJ6K4h8+XntEERT9DRKwxiXcUCJ0RAAAAAAAA6gUKvHdw8PymU4vp1DmilUT0+1G7Zz/P06PPffv441IiIho/rkOKYBEBAAAAAACgPmG1s8aO5eh8NZtX6bx8p1gsrnmdnZ19+aFUk6vCxjVQOAAAAAAAAGgoKPCN3Su/GvutK1Wds1++U6q/9AdXVlZ2+aFlPxp6f11ytsLdQPkAAAAAAACgQaDAN3ZauYiISpvJavbs77JV09yvZtNqtbrdl+p6WZCMIcZfzhAAAAAAAAD4EBT4xu65BLVUxNj9Lv5J7e+ylYgC2mhqBvA8v0219tIm0ej2ylA/MQEAAAAAAIAPQYFv7CI04lExSrGLp7/aOxG1uj/i8jHFhw01h8QiejZB3cAhAQAAAAAAoL6hwHuB57uotUZ3TUUnorBugVK/S08QKEouo7/qfdcQSQsNpt8BAAAAAAB8DQq8F2jhL/azsvceffDynbpo/5rXhpOVROQZ0Bl3vwMAAAAAAPgiFHjvoFEa6K+K7hHUUVfz2mlyxfzUy/Naayhv4GwAAAAAAADQAFDgvYNEZfG8qOnw2tZ/u9E968wZzwuFztGQwQAAAAAAAKBhoMB7h00nZnteTJ48efbc5kQU8PcCv339t0R0fvtog3/vho8HAAAAAAAA9Q0F3jv8kNCdiCZPnuzZnD23uX+rvxV4N88R0WruRZ511347AAAAAAAAeDsUeO+QrZQXpiYQUe4jz3v2/OvzaD+5rGZAhcM2efLkX8brDlddXMSOd7k4m73howIAAAAAAEB9kNx4CAjtt3xH9En+nU8Satq7R2v/gBNlpZ7XPFGESjPl3eJNE0KeXbXZejDNXVZBRCI/pbJzrPbBAbI2kQJEBwAAAAAAgDqCGXgv8N+j5iErrrK2fLBEefnmA82je+ysdp7g9/6Z4y6rYKRSkVLBWWyWP44U/XOhcd0PxPMNFRkAAAAAAADqGGbgG7v95x1Hip2lg4NyQ/42/f5bSf7eC3mX7zlafuHXkWxBiHytfujwBzSeKXe3wWjasb96269Vm3cQ8brHRjZoegAAAAAAAKgjmIFv7FYdtxDR+UDlFft/Lsy+Yj7d5HKOcW4jonSJ/qx/M89OSZBO9/jIkDefY8Tiqu9/cWTlEQAAAAAAAHghFPjGjee7Hj8QY7rw6673rzgiE4kv32SIXmjfjYhiTBe6GfP0OWcvP6rs3MH/wYHE89Xbfq3vyAAAAAAAAFAfcAl9o3Zh38EHUn564GqHtDL55ZsMwwwMb01Enx9cQUTlJ8TN/vfx5QM0g++t2rzDlppBPE8MU3+ZAQAAAAAAoD6gwDdq8o7RqZbKWL+A1RPK8lo7pNkucTn3+TM/1x7J8fxZizlOG1Ae6FaX0InWYZ3+PkASGCBSqzizlTNbRRq/hskPAAAAAAAAdQUFvlHTBwU9kPR1+ZcbKoK+IyJXlNQVRU+lPUhEWdsL9s86cvngVTr/8V3MGpsibuZb7aOu9tA4N0tEJMUfOgAAAAAAgPfBPfCNHsMEPjOujaUZEcUfU8v/x++ZmZI8+0R0qyCJ4m+3wRf4//bTkMquE16QX629O88VcHaHJDBApJDXPgoAAAAAAACNHAq8N2CYnt1HE1FZkPPFrGZtszSrZ60PVquHrblXn9i5ZlR5hqXleZU8q/yqz3uv/O5nIlL17Fz7EAAAAAAAADR+KPDeIc6vG2eionDnsd72pT2HxCm1RKSP0UY/eX/NGEuWMXpH54pl35n3Hvrbm3m+csOP1oNpIoVcO2pgAycHAAAAAACAOoHbob3DO2/O3pt+qP9HPX4ZVGXSuBMXfyYeRUQUEXppObrycnN1kJguUMWyDZzZIu8QxcikztwC087fHadzSCQKeuEJsT5AsHMAAAAAAACAO4AC7x3Cw8OlP/j1o2H7mJ8P9DYf6m6RsERE4uYOmc7PabQQkZvlyrVniaScw1mxevPlbxfrtEHTHlcmxAoSHgAAAAAAAO4cCnxjx9nOspU/v/yY5OXHniey3O3qvNWSe4qvcEiIiMRMpSq6hTMl0zM4rZjt569vXl0ti2zGyGS80yUJDVImxKoTezBymZCnAQAAAAAAAHcGBb6xc+W+xlbvq9kMJ5pKZOFlP2/uKwlUnuucvzcmiv4q8L+mq+K6d5qU8SdnzQt/f4lIqRAoNQAAAID32ZhpbRcojQuWCh0EAODqsIhdYyfWjyAiIpGk2VRp+HRp+HRXwX1MStuud7EFIlPMOa08pn3NYO35JGnHPP/hKWyl2PLboWt9JgAAAABcIcvo/ufeqjm/VwkdBADgmlDgGztJ6FMiVSwRx0hDpJHvSlvMrtraxrw/od2wFfrnvt4tHyFv17ZmsDm/uN3y5FLlQN4ttqWfEjA2AAAAgHdxsjwROVihcwAAXBsKfKPHiKWt5hORq3AR7yzmrDbOZheplGJ9gErE7Au22A6n1owtN5lH/7Kt0+ML9hTnObLyhAsNAAAAAAAAdQwF3guI/fuK9cOItbjOz2WkUiLiXS7i+aLU1IyHphYsWkrM38azLPv56aNspYl3OIVJDAAAAAAAAHUNBd47yFrOI5HcXbaedx6TBOt5p8uRnX981y7O7SIi4q8cn1pRbHU5rUdONHxUAAAAAAAAqA8o8N6BkbeShk0l4p15b6p6dyaiqu9+Usmu+WQ4N8ellF+w/Hm0ATMCAAAAAABAPUKB9xqSiBmMNJQzpaj7WUQqpfXICf+isuuM/7Os0Hb0JGezN1hCAAAAAAAAqD8o8F6DEaulke8QEWuYH/zyOEYsbpF65jrjk6vLeKfLeuhYQwUEAAAA8A5mJ1/l4K74x+zkiIjlqfahKgdX64ZFAAABSIQOALdAEjzeXbKSMx+RRuwMfef50LfnXWfwieICo9Ou3J+iTuzRYAkBAAAAGrll6Zb5B6qvdfR0uavLypLa+9UyJmlMcIRGXJ/RAABuADPw3kUkazmfiHFdWCJvqxiyekmYVuk5IPG78ncxHM//WX7Bduw0W3nNH1EAAAAATY1WzgTIRdpa/2hkDBGJGKb2Ia1cFKQUy8TMDT8cAKBeYQbey4g03SVBo92G75z576rCX/1tlWL9T6LlcYscaz8oPXj+isHJbtMwjrP8fsR/+H2CpAUAAABobMa0V41pr6q9P8PgGrHR0D5Q8sPooIZPBQBwMzAD732kkf9HIhVbvsVx9qnmYfyEoFHN7+/vF9289sg9eVlEZNmf0uAZAQAAAACgQfEut9tg5OwOoYNAPcIMvPdhZOHSiOmu8x/w9mybM+BQXgInLVDERBEduDiAIZ4nIiosuXCec7TIzncVlkgjQoUMDQAAAAAA9YHnzfsOmXb+4TiT66kB0vAQv77d/YfdJ1IqhA4HdQwz8F5J2uxFRt6CiPace1pcpZZzRmlMx5qjzGX3Zx1UsERk2X+4wTMCAAAAAED94szW4jmfGD752nE6h5GIJUE6Ri5zFZVWfvNj0SvvO88VCB0Q6hhm4L2TSCFv9w1nPnT4aM8+xsMyzihu36XmIMddGviHoWi0f7T9dLYAIQEAAAAAoN7wLnfJ/C8cp3PEeq3u8Qf9eicwMinxvD0jy/j1FkdWXsmcT5stmCkJCRQ6KdQZzMB7K5EqVhLyJKuRKisVcr7SqW+jCrrKFTJ7Dx/Sv/iE/qnRDZ8QAAAAAADqT/WPexyncyQhgeH/el2d2IORSYmIGEbRsW3YvFdV3eJYk7n8qw1Cx4S6hALv3TidVFWhkrFGllFpo7Q1+6XSi9dWVBqNGXJeFhkuUEAAAAAAAKgHPF+9fTcRBU19XKzXXnGQkYiDXnhC5Ke0pWa4zl8QIh/UCxR472bs5rfpuUg5W01E6uiwmv0yubzm9U8//VSd9Jvhv2t5lrvKRwAAAAAAkZ+UqflfgMbPea6QrTRJw4IVcTFXHSBSq/x6dyEiW2pGw0aDeoR74L2bUiL6vo9Bv+KgtU+yrl1ronTPfpZla8Yk/bTjOZfGceacqme8qlucQEkBAAAAGrWWWsmKB/RtAvDXY/AObkMFEUlbNLvOGGlkMyJyG4wNlAnqXxP6L1RVVVVmZmZOTo7ZbLZYLAqFQq1Wt27dOjY2VqfTCZ3uNjnzzuUPG5brsNHHo5VvjK3Z77A7JGJys0RERQajqke848w5y/4UFHgAAACAa0mMlN94EEDjwEilRMQ7XdcZ4znKyJpQ6fN5vv9n6Xa7ly1btmbNmuTkZN7zePS/YximZ8+eEydOnDJlikwma/iEd6LsQDLrsBER8XxldjkxRLxnix8/RLY2yUliWfSYV9V9uxv/t8166DhntYlUSmEzAwAAAADAHZJGhBKRI+sc73Iz0qvXOntGFhFJm19vlh68i4/fA19UVBQfHz916tQDBw7UtHeGYdRqtfyvu8R5nk9OTp42bVqnTp0yMzOFC3s7OJm75rXLzGoi/Go2m4XITmzUtlyQXhw9zOWvVcRG8y6XNeW4EDEBAAAAAKAuSYL18uiWnMVW/ePeqw5wnD1nS81gZFJl144NGw3qkS8XeJvNNmrUqIyMDKlUOnLkyGXLlp06daqsrMzlcplMJrvd7nA4srKyVq9ePXbsWJVKdfbs2f79+xsMBqGD3yyO5yrkl9I6q+y6aE3N5r7jbQL9ItqR2c2Kv8896z+iv0ipEPurhUgKAAAAAAB1LGD8cGKYyvU/WA6kXnHImV9UtmgZ8bz/8PvEGlQA3+HLl9DPmzcvJSVFr9dv3rw5MTGx9gCZTBYVFRUVFTVx4sTjx48PHz48Pz9/wYIFCxcubPi0t+G3ip2s2lmz6a406e/W5+8t9myePJ3LMtNG5xxeGy1NsfwwrutbkV8vEigpAAAAAADUMWV8B+3Dg6o27Sj7aLmle5zfPd0loYFctdl69KT51z95l1vRKSZg3DChY0Jd8uUZ+I0bNxLR+++/f9X2foW4uLilS5cS0datW+s9WV2wsObvi9cpdJdu2uedTv+2zWs2zWZziV/c/ZVHVv3+FVWl/WHcTUTusgrOYhMgLgAAAAAA1DXd+BH6KWNECrk15XjZv1dceGNhyQefm37ex7tZzf19Qt+eyojFQmeEuuTLM/B5eXlE1K9fv5scn5iYKBaLz58/X4+Z6s624g1md3V0q5i0mLsqzxwjIr8BA/xixEQ7PQN4nk87Yx3assx6ukVQduTGwLVdRZ3LXponaxnR7F8zBc0OAAAAAAB1w39ool/vLpb9KfaMs6yxWqRWydq0UN/b/fpPmAMv5csFXqvVlpaWZmdnt2/f/mbG5+XlsSwbFBRU38HuXKmzeHd5EhGZXYUh338bvnFxgCqnYvSbnGmLWC5mHRcfAl9mLeJ6H/1d1Gu5c1J04YYdAT91UykdWXnOnHxZm0hBzwAAAAAAAOqGOEDjP6K//4j+QgeBeufLl9D37duXiJYsWWKz3fiicZZlPbe+9+jRo96T3TGL28QTT0Ry++FOVfue5GKad+tOYrFV0UwXdWkdu9Jck1rv7By9l4gM5V2qyeR3bzciMu85KFBwAAAAAAAAuE2+XOBnzZqlUql27tyZmJiYlJRkt9uvOszlcu3evXvo0KErVqxgGObll19u4Jy3obWq7eLYlQs6LF3Y4dMFST0HLOo8/cgE3kV2SZi+vbZm2Pnz5yvsLcMDM8Q8W22KfjD4WXX/3kRk3p/Cu1zCxQcAAAAAAIBb5suX0MfHx69atWrcuHEpKSnDhg1TqVTdunULDg4ODAzUaDRms7miosJgMBw5cqS6utrzlrlz5w4YMEDY2DfJX6Il0hKRS2apprIQq4LsjN0vRB/ztwJf5ojXSPM6VRak61r+UeAcFhUuaxPpzMm3Hjru16eLcPEBAAAAAADg1vhygSeiMWPG6PX6GTNmpKenW63Wffv2XWtkXFzc/Pnzhw3zvqcsiDViIpLbWLKRS6PWtQ2sOXTq1KkC2yvtlM5ehqx0Xcs9eY5hUUpN/17lOfnmPQdQ4AEAAAAAALyIjxd4IhowYEBaWlp6evqOHTtOnDiRm5trNputVqtSqdRoNK1bt+7YseOgQYMSEhKETnqb8pQkITIwpXp5iZHahHSIJWYP8URElZWVa//3zfBPRyQe2ruUBuzNd7A8+d3brWL197b0TLfBKAnSCR0fAAAAAAAAborvF3iP+Pj4+Pj4hvkut9v90UcfGY3GmxzvuS7AdYs3pWcYXNuz7SzDZ/O2aUS7gg6pZUojtbEpW2jCVaZCq2dYysmsZfm9Jg1+xX9rz7y8ircCH/rXiBhVz3jL74fNe5IDxgy9pS8FAAAAAAAAoTSVAt+Q0tPT33jjjVt9l9lsvqXxX6Saf8y2E8P30IiIyF4ilXOVRGRwB+vbaWsKfEF25sIjfrbSgLQFzxLPf5T078kZGS0H9Lb8fti8+0DA6CHEMLcaFQAAAKCBsRwZHVyQ0pcXYAbBcRY2Z/wxe7Y1+vvOihg/oeMAXEWTLvAnT57ctGmTwWDo3LnzPffcExMTUycf26VLl6+//rqwsPAmxyclJe3bt8/f3/+WvuX1Xv5xITKO438+bzfwFal7dxfKLfyTj8n8muljtHm7L3iG8W7ndP9v9m+5QDxPRGxVyZ87trZ78klJWJC72GBLz1R27nBL3wsAANBI5OfnHzhw4MSJE2FhYYmJiZ06dbrqsLy8vC1bthCRVzxoBq5l9u9V352ybh8THKNv0n99hfrDWdish9LMf1YS0dmhR9v+1AUdHhoh3/8vYGpq6uLFi48cOWI0Gnv06PHBBx907NiRiN577705c+ZwHOcZJhKJZs2aNXv2bLFYfIffyDDMhAkTbn68wWDYt2/frX5vc434H/F+RPSzseQZ+4y8g+fpIOkK5ga+PVHf7m+/C+DzUtu3ujTN/v0PSU899ZSmf2/juh/Mv/6JAg8AAF7H7XbPmTNn/vz5LMt69jAM8/DDDy9fvlyr1V4xODMzc/r06YQC7+WKTCzLU7GFRYGH+lDT3mXNFfLWStN+Izo8NE4+fhnSypUre/fuvXbt2lOnThUXF2/btu3ee+/NzMxcvnz5u+++y3GcWq2+6667lEolx3Hvvffe888/L3TkW2ZzVORx5z2vLbt3VUkidDEBlw/YvqdoYC9lzeavu35xu93q+3qRWGRNOcaZrQ0aFwAA4I49+uij8+bNY1mWYZjIyEg/Pz+e5zdt2pSYmFheXi50OgDwMpyVzR6T7mnvbX/qEr2ls3ZIkKvEeWbwUfspi9DpAP7Glwv8uXPnpk2b5nA4oqKiZsyY8dZbb3Xq1MloNI4fP37mzJkSiWTx4sVVVVXp6enV1dXvvPMOEX311VdHjx4VOvitkTS7NN/uNhhYRuEX2EymltbsPHL0WHDUE0EBF2f4raaq5ORksU7rP6SvNDyUJHd6xQEAAHgXxx0QOjsR0U8//bRp0yYievbZZ8vLy/Py8iorK5cvX+7n55eenv7cc88JHRAAvAlnZbNHp5v2GT3tXd5aychFbf4Xpx0S5C5znnkAHR4aF18u8B988IHdbu/Zs2d6evqiRYvef//9I0eODBo0KC0tzWg0vvDCC6+88opIJCIiiUTy3nvvjR07luO4Tz75ROjgt0YZppP8dSsEZzbzTqfNHaZvd+kCQqPRmFU1YNB9nWv2JCUlEZH+qdHhi98SKeQNHBgAAISluANCZyci+vzzz4no4Ycf/uKLL3Q6HRFJJJLJkycnJSWJxeKNGzd66j0AwA3Vbu+e/Zd3+LPD0OGhEfHlm4iOHDlCRO+++66f38V7V2Qy2Xvvvbdz504ieuaZZ64Y/+yzz3777bcnTpxo4Jx3SCUS+4v9K9gKzyZbUWFiwwNjtcVHDDVj0tLSho18ZN33RzybP/zwwwcffOB5bUvPlEe3FPkpCQAAwBtkZmYS0SuvvHLF/r59+7722msLFix4/fXXR4wYIZPJ6vZ7zWbzM888U1ZWdpPjz549S0SN5LIFAKjtWu3dw9Phcx4/XvWz4eywo21/7KLogPvhQXi+XOBPnz5NRLGxsZfv9KxgxzBMdHT0FeM9q9B7/lrgRZQiRisPrLBeLPC8w2GiZkGxf7sN3mg0Dn1xlkT8tpvliejEiRM5OTlt2rSxHz9TMvdTv94JwTOmCBAdAACEcPjwYaEj3JH8/Hz666f2FWbNmrVmzZqcnJz//ve/tRv+HcrLy/vmm294nr+ld9lstrqNAQB14vrtnXjelpphOZCqiiqxR7Zy5AecGZgc/UNnVZdAgfICXOTLBT4kJCQ3N7e8vLxly5Y1Oz1r2/A8bzQaQ0JCLh99vT/AAAAgAElEQVTvOXTFzsZPxTCK8Y/Q8n8RkX9sC2nz5rYCV6u/F/jkP5N1AQF9ugX+dvDitPyWrdtefWW6tEUzRiy2HErXV1SJ9Vcu2wsAAD6pa9euQke4Izqdrri4uLCwsPaPbLVa/a9//WvSpElz5swZO3ZsREREHX5vx44djx49ajAYbjyUiIi+/PLL7777rvaq+AAgOM7GZY1Kq1m17or2zlZUli1eac/M9myq2uWzlt7u8uAzgw62+jw4YEx3ISIDXOTLBT42NjY3N3f9+vVdunSp2bl+/XrPiz179owbN+7y8bt376a/pui9iIph+HdfnD3qnrLczbl3Nz8nFkvVEf5qtUwjdZpcnjGHjxw25X0+rLflt4MX37X2u+9ffWW6OECj6nGX5UCqafeBgNFDBDsHAABo9I4dOyaTydq3by90ELrrrruKi4sXLVq0du1ahmGuODpx4sS1a9f+8ssvNXfF1+FXd+7c+caD/rJjxw4iqp1QQCwRx/PS60Zy8zwRSQSK7ebonX1VhWa29qGTBhcRLTpoWpZ+lbuRn+iour91o1ijAbyC5WCl53nvEe9H12rvVRfe+shtMIp1Wv9h/RSx0YxCbj1alPNkJWeXX3g/Raxzagb2ESg4gE8vYvfUU08R0X/+85+5c+fm5uaeP3/+3//+95w5c6RSKRG99dZbRUVFNYNPnz49d+5cIho0aJBQgW+PSsQElLl7nGk/ZNRgP4WViCS6ChkvC+xw6Vf+btZ99GT5iMRLS9OnHfzDM4egGXQPEZl3/UEc1+DZAQDAO3Ac169fv379+gkdhIhowoQJRLRu3bopU6aYTKbaA7766iuNRrNz586JEydaLFh66pLHSgyDLpTmut3XGlDgZocWlz5UcrP3+de5Sge36Yz1jwJH7X8q7RwRnTS4rnr051y7UJnBG6nv1ekeCSWi/BczLSnVlx8yfLrGbTAq2kdF/Odt7aj75TGtRaqgC/NZcjOy5qSIzK746ltXQbFAwQF8egb+kUceGTx48I4dO2bPnj179uya/Z999tnevXu//fbb+Pj4J598MioqKiMjY8WKFRaLJTY21useP6NkmIHrjc0+N1xoSfKYCiIykZ9YJA6K1V04dOkyv7Xfn/v3FGnHKPHJbJaIeI5N+umniU88oegUI40IdRWVspUmXEUPANBk8Ty/a9euQ4cOmc3m2kdPnjxpNBobySr0EyZMWLVq1e7du1euXLl+/fq77rrrySefnDp1as2Ali1brl69+tFHH123bt3evXs7dOggYNpGJVgsOuZ0TiotXx0S2Fpy5V8CC9zsxFLDBZa9W7gn1AQpRUljgkssV5mBX3jQdLzMNbOnJi5YWvto59A6XrMQfBsjZlqt6MhImYpvirMeTI3e0tmvh5aI7KeybcdOizR+If98RuSnIiK3wXl2WKrtpFnRVtX2py7VW8tMv/xeuWlH8MuThD4JaKJ8ucAT0datW2fOnPnf//6X4zgiEovFb7755tSpU0ePHn3ixImMjIxFixbVDG7Tps26desktX6eNXJKhmGlDBH5latlfCXDc05G6xZxgbF/a+PGSpMpYM6IgZ+ezL64St/ab7+f+MQTxDChb09zl1eivQMANFlut3vChAkbNmy4/rB77rmnYfJcH8Mw27dvnzZt2tdff2232w8dOnT5vXIeDz30UFJS0ujRo4uKii6/4K6JWxSom2aoOGB3PFFqWB0cFCW99HeeIjf7VJnhAssmyGVLgvQChozWSaJ1V/nLmOfK+dggaZ/meAIu1AFGzLRcGktEFd8UZ41K83R468F0ItLcf49I40e12rs0TK59ZLBp1x+2w8d5lmXq9A4dgJvky5fQE5FcLl+yZEllZeWePXu2bNly7tw5z3XywcHB+/bte/HFF8PCwmQyWdeuXV999dXU1NT4+HihI98yrUhkChATkbZQK+I5MWvlGUYqCbp8IXqlUjl06NByNvHhCe/U7Pxzzy92u52IJCGBig5RDZ8cAAAaiU2bNnnae/fu3YcPHy6Xy4nooYceGj169MCBAz0T72+//fbGjRsFDvoXpVK5cuXKnJycjz/+eMaMGT169Kg9ZsCAAefPn//000/79OkTGhra8CEbIQXDfBak762Ql7PcpDJDtuvitfRFbnZSmaHAzSbIZV8FB/o1pvv2AeqPp8PrHw1jq91ZD6ZZDlW5ikqISNG+DV2tvRORJEgnCQzgbHa2okrg9NBUedls8+3RaDS1b9sLDAxcsmTJkiVLeJ5vVAvM3KrhKqUkTE1EAXnaZyM+/POCggKob/CEnx0LVAFKa6WNiGw2W2lpaWlp6X33PRqun1xU4SAii8W8a9eu4cOHE5HbYCz+v4/9hyT6D79P2NMBAICGt3z5ciL6xz/+8eWXXxLRRx999Nprr7366queKffk5OR+/fqdOnWqsS2oHhkZ+dJLL11ngFqtfv75559//nkiws3wHp4O75mHn1RmWB0cpGQYtHdosv42D/9gmv5BGRGRWHzV9n7xLRIJEfHsVW70AGgAPj4DfzO8ur0TkUYkGtLKn4h4J5+gj+YtYiJySZoRQ8Gxly6BO3fuXElJCcOIRg5uV7Nz0+bvPS84q81dbKj87ife4WzY+AAAILysrCwiqrmNvH///kR08ODFJ5f06tXrpZde2rx58/bt24VKeOf8/PyEjtBYXD4P/0SZ4fFStHdo0i7Nw5vc5d+1dFfq7CcKr9XeOavNXVbBiMUSfcB1PhOIyFVYUr19d8XKjRWrN5t+/ZM14pqFutEkZuB9nuaegMhP2mv66qQiRulm7MQXugNkIpm2g4r+vDimsLDQbrdXVpx/cEjzL9Yf8+zcmrTT80IWGS6Pae04k2ven4IHYwAANDUXLlwgosjISM9m27Zt6a9W7zFlypSFCxcuXbrUc90WeDtPh59SVn7U4SSiDjIp2rvgONMB1rjz8j28o4A4G6Nse/lOkTpBrB/ZsNHqzOT0USvitwid4ioYMdPyi1jezRs3lpgO97Fm2tyVpGjv1zapizTkb+sjmnb+wbOsMr49I7vKYorgwVZUln+1wZpy/PKdFWKxZtA9ugkPMvI7WnKy+teK4n/lhr3R2n+AkKt1CAgF3ieIGM1jQZ988klJSYmy+zh7y/BcBxcmbx7Y4VzNkN27dldWVt4dx90d9WeQv9hQzRIRqwqsGeA/tG/ZmVzTT/tQ4AEAmprQ0NC8vDyr1RoYGEhEarU6MDDwzJkzNQOioqKkUmlycrJwGaGOGTmu5K9rgEtYttjNXr6mHTQ8R/aLvD37KgeMV2wzqu7nSex9V5RMTh9FjbnDS5hWyzoSzxs3lborpdJQtu2PCVe0d0dmTuW3PxKR/4P3CxTTC7iKSorfXcIaq0RKheruBFlkBLGs/XSu9fCx6p9+c5zJDf2/l0TK23ymSfUv5Tnjj3F2Lmdcepv1d/nfH3jj9/gc/JfaFyRZbWtHP7/+p5VEpApb2eKPI0fYskG8M6jjpWt7bA7bgQMHckumtQikFXO1c96Tnw5s5T923ukKdzu9hIhUvbuI12xx5hXaM84qYtte88sAAMDntG/fPi8vLzk5uUWLFp490dHRR44cYVlWLBYTEcdxbrfbarUKGhPqzAWWnVhqKHSz8XKZnGEO/XU/fGPu8CKGiMiXLxLgHUQkDZ9OEi0RsZW7uOo/iIhRRkuCH/cMcRV8SJyN550MeVmB97T3mteNt8Ov6ESiw+Y9OX4dD1UsPa0d+4CiXRtiGLai0rTrz6rvf+FdLs3gvsq72t3445ok3uUu/deXrLFKEdcuePqTYq3Gs9+fyJlfVLbwK0d2vuG/a0Nee/o2PrymvSs7qW0nzDnjjzXNDo974L1ekZudUW488svvnk1rcbkzJ9tOqkpnhbqZSqVV1ozkOO7AodOMJHhAD3qpS+zDY16RRXTYsGxP1eYdnMXKSMSa+/sQUfWPvwlzJgAAIBDPYnWvv/56zX3v3bp1q6qq+vbbbz2bSUlJPM9HR0cLFhHqjqe9e+57Xx4cuPRq69I3Qo939BvdXtUlzMef9y4JnSINny4JeYKzpBExJFLwtiyx/73S8OnS8OmM6DbnLYV1eXu/1p5GghEzrVd2b7s9QRootR07XTzr33njp+c/8dr5Z2ZVfpvEu93+D/TTTx4tdMzGy/TL766iEllkeOgbz9a0dw9ZZHjoOy+IVEprcpojM+dWP7l618X2HjQlosOfPUKmtuDsXPa4Y1VJhrqL7x1Q4L2e54Y1leiyol5ewUtU0nw5EYW0/9svpQqyso3fJFZv6zP6vSOT+qwmol8kEcZ1PxRM+z/r4eOawfcyUqk15Zi7tLxBzwEAAAQ1Y8aMqKioc+fO9erVa+vWrUQ0cuRIInr66af/+c9/vvXWW5MmTSKi++/HVaNe7/L27rnv/VrPlmts+reUL+in9ZP68hx8DXfRp8RaxLpB0mbTiMhVsEDoRLfvWl290XZ4IlJ0ion4ZLb2kcHS8BDezXI2u0il9Oud0Oz9V/WTRzNiFKhrsuxLIaKAR4df9UZ3SUig/wOJRGTed+iWPrZ6V3nOoxfbe+R/2pOIaf5hTMjUFryTy5lwvKl1ePz75/XUDENEasWlVRxEF4rcEubehaOISNfpb7/6ytq933lOTtIBxLTtrksOkruKVLrzXXtyFmvpgi8dZ3L97k4gjjP9vK+BzwIAAASkVCo3bNjQrt2li0IHDRo0ePBgq9X64Ycfzp8/v7q6ukWLFm+//baAIeHO1W7vnv3e0uGbCN5d4S5ZRkTSiJmSZs8zYjVb+QtnPix0rttx/ZbemDu8SK3SjR8RsWR2y28+jvx6UeSahcEzpshjWgudq3HjeWduATGMMqHDtYYoE2KJyJlz/uY/9cr27vnvFkNNtsOjwHs9P5GIIVIpdTV7RIZCInLwIXKzXP/3An/UUKwZOSDs/16SRjwsJvb+kJNEdLD/qICxDxDPGz752q9fDyIy/XqAszsa9jwAAEBIXbt2PXny5OnTp/v0ubiU6ebNm9988834+PiEhITnn38+NTVVp9Nd/0OgkZtWVlHgZrtc7Ylxng7fSyEvZ7lphgqhEgIRuYs+4VmzWDdYpO7KSPSSsGfIc/c7CIGRiG97xbWmhne6eJZlZFJGes0l+kUaNRFxVttNfubV27tHU+3wKPBeT0SkYhit/NKl8hK3kYgqO5r1uYGBsZfWsWOIMbudFxKiiWHEuqE8T63PbiCi7dk27dgHlAmxnNXmyMhWtI/iLFbzHiw1DADQtIjF4piYmKCgIM+mSqX64IMP0tLSjh49+umnn3oWqAevliCXDVAqvrzGE+MUDPN5kH6oStn1zh7yBHeCd1fWTL979nj1JPz1F6trnEvZwW1j5DKRQs47nGy1+VpjPDfqigP8b+YDr9feL35lU+zwKPC+oH2Gc7Sxj4pREZFMLW3WN4qIqu/nwwz+fqFKjV7tGcYTT0Q7f/mFd5W58t7JOOOfuF+td5rOV7Mny1zakQOJyJpy3H/EfURU/cNu4jjBTgkAAADq2myd9tMg/XWe965gmMWBug/0AdcaAPWNLV1VM/3u2ePtk/DXaulo7z5J3rEtEVn2X/OXTZb9KUSk6HjjJ15V/3KxvQc/1+Lq7d2DoeYfxgQ/14J3crkTj1fv8v2VvFDgvZ411TT1ydxorsVG5fKP2j7x8Ob+6jZiIqpK7NHp8UQiCokJunz85s2bL+QfZ6v3x7SlCoe1b8lpIvox2y6LjiQid4lB1f0uSWiQu7TcmpohxAkBAAAANFHusm/osul3j5pJeOK9cnmC2l0d7d1XaQbdQ0SV3yW5istqH7WlZpj3pTBSifq+Xjf8qPwXMzk75z8wsMXCmBs8QJKhFgtj/AfoOTuX/2LmbUb3Ho33aZ9wM6ypprMjjiqr2cIoeUR2cCu/HnnByS62jIj2VJ5VsruJKKijNvuyy+HPnDlTWOGnE/tLqfqU5HS/kowtLbolZdte6ywjIuJ5EokCRg8xfLGOeF6YswIAgIbVvXv3mxyZkpJSr0kAmjies1w+/e7hmYR3FS4mzi5UsDu0In5LzZJ1aO8+TNW1k6pnvPVgevGsfwc+N17VLc6zn2c50879xjVbiOcDxgyVhNz4nqzgZ5oXzs4y7amo2FCsfzTs+oMrNhSb9ho977rzs2jkUOC9mKe9s5Xu7AcCDnaUPbawVBwYoVeFljnKichEujxbNkOMNk5V8xaxSFRWVpaRkRnfd7S7bH2HB4sKftsaophQYKKjaYXBRJKwICJS39fLr09XRnbN9ScAAMCXHD7sfffWAvici7cuXjH97iFp9ry7+EueveatxY2fp8Ojvfu8oBcnltmX2dJPlf5rqSRYL2sZwbOsIyuPM1mIyP+BftqHBt3M54S+2pJEVDgrK+/ZDOJ4/WPNrjXSuKkk77kMnuXD32kT+krLOjuTxgoF3lvZT1nODjvKVrt1j4Qmfxhu3lBCRGJtcIBUJ7flMjzPSZQsz+ikAdZOl35Zy3NERJWVlbLWi2Qt53UOer1Th7WF9hP/PRNv259CRDW/J2NkUraymrM5pM2CBTg9AABoQF988UXtnTzPl5SUHD58+Mcff+R5ftKkSS+88ELDZwNoIni3iYhIJHede/PqIxgZEU83uJi4UUN7bwpECnnIW1NNO/dXb93lLqtwl118qoWsVUTAuOGq7nE3/1Gh01sSUeGsrLypp4joqh3euKnk3JSTvJsPf6dN2D+bxHP+UOC9lfW4ia12E5H+sWZ+UlGJVkxEnFOikwYyxIlcDlamCFB2jJCIK4MqQpoFl14oIyKOOCI6efIkEUMipVg33F269h+BC8YFvmnduV+kVGiGJtZ8RckHn7sKiiOWzJYE4blBAAC+7Nlnn73O0QMHDgwZMmT16tUJCQndunVrsFQATQrDO3gi4hys+To3qjBExDBYxAoaNUYs8h+a6D+kr/P8BXdpOSMSSZuH3cxl87Vdv8M3wfZOWMTOe+nHhAU/14KIch471mp3tVkrIiK2wq2TBhJRC5mdiMZGvt1S2YaIIqP/djfIr5u32NIzOZudkfdmGNXPu1J6Tx49cNeGzD4dxdpLz42XRoTxTld10t4GPC0AAGh0evfuvXjxYiJ644037HZvvQUXoJGTtvlMFDDgRv8MlLaYTWKt0GEBbgLDyCLDVd3ilF063l579wid3jJiXjTP8nlTT1Wsu1Czv2m2d8IMvBdjqMXCGCIq++L8Xc9mH347lIhYkzVAqiciNVUSBRSwbAtFSyLSai7dBi+SiHLKy87O/shfKmdkbs3QoElvl1TbWCIaP2+u4dknmb+eLqMd0d+yP8X8yx8Bo4eIVMqGP0UAAGgkRo4cSUR2uz0rK6tTp05CxwHwQZKgRyRBjwidAqAxqj0PX9Pem81qWu2dMAPv3RhqsTAm+LkWIgc3aV7xyWEuVesD/hYpEcm5MiIqcLsD0rVE5N/jUv1WB6kefrS/qfddjJ8icMoP0nCj03nxUEVh7r4//qwZKWvTQhHXjrPZTTv/aMjTAgCAxkav10ulUiJq2dL31wcCAIDGJnR6y/DZUZ55+PyXM89NPsm7+fB3o5q90bTaO6HAez2GWiyMMT/dTOrk2++Ubjx9/N1hL+XvKg4gOxHlFFisjxrELpEyUVbzDluxfWCvkWdC1fycF8S6ALGfqXObS/X+P8vXX/7xiqH3ElHyN5sa6nwAAKAxOnz4sMvlatasmUajufFoAACAuhb2eitPhzcsL+RZPvzdqLCZrYQOJQAUeO/HUM/FHSqeCv3DfuD105u2Hj+2+7WDthUlRJSbZyUzBZSp5TqpLijAM9xF7orkciIqKioS6wYT0cRH9TUftnPbRo7jajbLAzUT9m+dnbavQc8IAAAakxMnTjz99NNEFBsbK3QWAABousJebxU+J4qRMuFzmmh7JxR43yAXMQOXdDotzfZs8kTnfk4lIkO49MSoExqnk4gi2l5as7Hiz3Iiyi/Kl4RNFesGT5j0tEZ+cTUEa0XJf/7zn8s//I/SgnK3o2aT5/mVK1ceOHCgns8JAAAaTvC1+fv7x8XFnThxgohmzJghdFIAAGjSwma0ir/QL2xGK6GDCAYF3lcwFKALqNkKTq+WOviqQMnumQfsUQFEFNLh0tqPZy5k7dy1o6SwxMKGydt9o2750vAe/jVHZ86c+d13313+2YkBYfkTZtjSTvE8/8ILL0yePPnVV1+t/1MCAIAGYrg2k8lERFKpdPHixUOHDhU6KQAANHUiRZPusFiF3ncEBQRR/sXXVWylyshWhUkC1QkG62Ei0nW6dNfiIffR0nWG0uKydv2ihsQPZyTa8RM6rt+/33OU47jHHnuMiMaMGePZoxJJOLvDuG7b+199+tlnnymVynnz5jXkqQEAQL36+eefr3NUo9HEx8f7+fk1WB4AAAC4KhR4H7HHZpcHXLqVPTPurErqJpKIZW3c5gNEpElQMAzD8zwRlfHlRHTy5MlTeRn3xQ2UixT3D58c5HfQYLm4Hj0rko0fP97lcvXs2ZOIdhmLXu/cx5lz/uTv2+Vy+XfffTdgwAABThIAAOrH4MGDhY4AAAAAN9akLz/wGRzRS+XGvOCgmj12kU3FXSCiY1aDTCSTMwpGx4U3C/Mc5YlXihQlJSWOXDapdDNnOysWWfvFX5paadauM8uyEydO3LZtGxG5eG63q4qIpnfsuWnTpmHDhjXo6QEAAAD4iuNlLrOTFzqFF7DyN/5/6WbGAPgYzMD7AhGRiOi3d3rek3nP7yd/Zxgm5uFIniszU2uLKHRI8EMnzWnZltMtOrQoLLrw/+zdd1gUVxcH4N/M9gbLsnQpith770asWBJN7LFEk2iMxsSS5EtRY4kpGlua3VhiibFrLBi7sYMoNpDeYVnYztb5/liyEgQEBVb0vg9PHnbmzswZJMueufeeaz/EEx5JSImLjs9qH9dLuo+Vf3LIYK8//8mz7828f+PT//3vu2+//fjjjwEoFIr3t629GDa+hZund50GTrtPgiAIojLExcU99bHBwcGVGAlBvGzOpRgnHFF28+dtGiB7cuuX2G6dfp4y/wNXyRSXkpeutAFzlfn7dPoNHu4d+LxqDo8gnIgk8C8IMUUpZexDN/6OvnBeJ1XtZ2/JMqfmoJ2JDmjp4qay5MXpHvg09sTfhe0pUADu3bvXxlovymptBQTVomga9iXkbGbjP3eTOnfufPHiRQAqlYrFYl3jWXozvKvfrPrLt3DdeKlUOnPmTC6XW2JIBEEQxPOpbt26T30sQ/q7COIZrLquAXAuxRiRaWrlTT5BlcqXxaKBVSoNgMdzeBswR5m/V6cXUJScxXJGgAThNE+fwEdHRz98+DA2NtZoNDZo0KB+/fpNmjShKKoSgyPKT0zTOq0lb0lypyHtlcHK/Q+2iOg4AEa2+/zYGd3d+wLwaOnmaK9g58KE+IT4pi61Dpu0LRmKMaYVWQAel69cNWc96qKxWq2fHNx5IWxcgB5/rd10Oy/bvr19+/Y9evSonnskCIIgnEUikQQGBjo7CoKoMZJUljcPKfXm/zzwMtsYx5bRB5VCTvGPzWObCGe0LbnD+WXTmc9b4u72cW7eKpWGAd4vksMzwIK8/L06PZ+ifvWQ1eWQ/siXy/VM05xzqoXdXNu8rI/AnuY3/p9//pk9e/bjK4G3a9duxYoVHTt2rIzAiIoR05TnJZ3+29TMuAKfDcEAhHQKrDCyeBaKb7aZAHg1lchkMqVSCUBr0slksoz0jOaaDldFZ7aiC9fQaMbEfcs3xthPaM5OaNWu0x7/lrX3/AyApul+rw9OcZc1yNT88sb4i3XdAbi5uXXr1s1p90wQBEE8lfz8/Mc3bt68ecaMGRRFDR06dPTo0XXq1JHJZPHx8ceOHVuxYoXBYPjoo4/efvvt6o+WIGoivYXJ0FrLaGC2MSpj8fEs6WUe8rIJEwoAfJyb96NKg39zeAaYn5e/S6vnU9RqD1l7XpUPnk82xN/XRveSD6QpUjvsufBPqilGafkn1UQS+PLatGnTxIkT7d/z+fygoCA2mx0fH6/X669evdqpU6fNmzePGzeusuMknkBC0ToBDcCiMGstagoUTVlcqWwVPI20Z7Q2AkA6O8OzqUx5Vmk/ROwvUCpx9MRR9yGCq0L6rRE9xw1tcfHq2KvRFgBgbNHpKvgj4Y2prU5tz8vL27179+tr11PhUbXyDTNGjeUG+DrvdgmCIIin5+rqWmzLlStXZs6cyePxTp482alTJ8d2X1/fLl26jB8/vl27dpMnT65fv36XLl2qN1iCqJEaunO6+fPOpRgnNBN90FoM4HK66f3jeVIefWSYfNNt3fooXZda3FW93QBk62wD/8yxMZjUXOzswJ8vxXL4KS6Sas7eTTbjqoTFSrOCTbFD5f2r+nJEeTBgHP99OVXsSdLDhw+nT58OoF27duHh4Xq9/t69e7dv39bpdKdPn+7QoQOAadOmJSQkVEmwROnENKVzZQGw5ptTDIn232m2JQ1AActTbVYBsMEmaPzokY2RVQDg5rnb9pcJ+liWbODoPi6OBr75afb/M2Qy2dy5c61W66hJ72QEysEwqj1lrRhMEARB1Czff/+91WpduHBh0ezdISQkZPHixVar9bvvvqv+2AiihpreRgJg9309AFce/dstHYBJLUTeYtaUlmIRh7qQakpUWV159M57eosNYXX4IbJKHg2eOHRa5Z6w+oUJBUvc3VjAjyrN8Kyc6szeAfyVvVdpVgDYl7lda9FUwxUJ4okqlsAvX75cq9W2aNHizJkzvXr1Kjrj/ZVXXjl9+nSLFi00Gs2yZcsqO07iCcQ0rXOlAVjyLC1c29UR1gPQXOABoLV8xOw68wMFwQBef2Wo4xC+Wgwg53J+/+Q3ZwcvGOk7kWKJR4zsxaELfysStfl383MAnGzRb0dUdBsAACAASURBVP78+fYcfujP3zMsWvdPpDk1s9rvkiAIgqgSFy5cANCrV6/SGtjLnVy+fLn6YiKIGq6lF6dLLZ7WxPx2W3cpzXQ1wyTl02OaiABI+fTYJiIAP9/Q5uhtu+7pKeD9VpXc/f4CZO92YULB9+5uFBBtMrMpVFv2nmvKOZaznwLlw6uls2oPZu2shosSxBNVLIGPiooCsGDBAoFA8PhePp+/aNEiADdv3qyU4Ijyk1CU1pUNwKo0U6Bq8QMB+LEAQMeSN5I0byBuAqBeq3qsf2t1psSnhISEGAyGm1eiAqlgriW34HbooZOJtYSPOuE1ZpPj+/nz58+ZMydNq97+MBoMoz56rhrvjyAIgqhC9lnxZVSYN5lMADQa0gFFEBXwUVsxgE23dCuuaQC801wk+rdw3bvNRSIO9XdSweJ/1AYL07cOv4E7pypieAHSeAa4aiwsGGBhcMNoesIBlWR3xmaTzdhO2mVq0Kc0xTqVeyy1IKl6Lk0QZahYAh8dHQ2gZcuWpTVo1aoVgNu3bz9jWERFSWjaIKYZNmXVWBgLI+PKAcRkpAOINVoA2FP6PHZ2kyZN7IfYbLYg+AO4c+dOenq6rSDBpotsGXgvvUDjLRAD6Otbp528cKK7/d1/wYIFc+bMWXX3ym2jWtCcLAhPEATxgvD19QVw+vTp0hqcOnUKQK1ataovJoKo+Vp6cTv6cTUm5nqmScqjxzUROXZJ+fToxkIAh+MKKGBaa9L9XrKiVevedxHbx9L/oq7yh4kPdfev5V/k0tyhPuN8+f6vuPexMdadaRuq+roE8UQVS+Dd3d0BZGdnl9YgKysLgFwuf8awiIoS0RRDwSJhPbQmjBo+cuP/thUojTZjCoBEi+WO5qYnzwfA/YyoenIvx1FMghXAgwcPXF1dWZL2FNutUR3WiW09zvcbe33gxNUdw+gisyQcOfy6XTtqf/OJsF2z6r5JgiAIomqEhYUBmDt3rv1JfTF37tyZM2eOoxlBEOXnWBbuzUZC0X/XjZvcQsymYWOY9r7chlXT/W5Xc5P5YjXnP3B1ccyHr9IcngGzPX09AybM83V3rgeAId5vilmSu9pbN9VXq+66BFEeFSuV0bx58/j4+IMHD9p72h938OBBe7NKCI2oCAlFAzC40h+kfZa9TwHAP9574PKWsFAGNvNdwpLe7l1pilbxNR5FqsfnMko2zX748KFQKATFYXu9ZclcH7C7Pmi480qYJWH36quvArBkKdRHzri82pMtdyutJUEQBFEjLFiw4NChQ6mpqe3atZs2bdrQoUNDQkIAxMbG7tmz58cffzQYDLVq1frqq6+cHSlB1DBUCd8V31vSnmdSczP2okpcMa7EteUq3XnlyUT9QzeOez+PwfYtIpb4Ve8R29PW70zb2ETSkk1V4QMXwu5mtvlquvHxqV03Mk32/66J1BbbRVFo78tr7vmC/+tULIHv06fPvn37Fi1a1KJFi8GDBxfbe/DgwYULF9qbVVqARPlIaAqAUmLIZhT2LdlRSh43j22ARQID2zPVkOTJ9c40pnu29MemwqPuWWN6i1+Jdntgf8nxn8vxnwuU+qafOHRa0J8/2b/XX72l/uuMRZnvOfudqrsvgiAIohrI5fKdO3cOHDgwPz9/yZIlS5YsKdbAzc3tjz/+sA/EIwii/FZcL8wxfr+rn9xSXLQTfu1NndkGFoXL6aZ7ueaq7oR3fISrEcpY772qc/gCm2Ff5nYAI3wn8Gi+Y3uoe9jZ3BNpBckncw738xxSuRclHjf1eF6mzlra3ouppoupJVRD8BHrL4zxrMq4nK9iCfzkyZP37Nlz8uTJIUOGdOvWbeDAgXXq1GEYJiEh4ciRI2fPngXQs2fPSZMmVU20RKnE9tLxMlcWWFZYARjVJk1BTt/a/CN6QwHlmWuKqy0MyTSmCwI43u7yzFwFgAIYR1pfGx4+gS8unJRV/ke2oq5t8nYe1l+JMiWmcoPIrEiCIIiarXPnznFxcQsXLtywYUPRYnVSqXTy5Mn/+9//pFKpE8MjiJooMst0MdUo4VIhbuyILPPWaN17LQvnuucX2H6/owPQP1hw6KHhlwjtj70rZ0hjaZ/lalYOv0Or26XVCyhqrYd7Gx632N4wocAK/C8370eVpimX25VfmUXpD2buUpnz6ooatJV2Lrqdplij/N5ZGjf3YNYfHd1eceWQIahVa24Xl6hs8+Pbr2eabmSYWvtw23gX/8UA0OJF735HRRN4iqK2bt06bty48PDwc+fOnTtXvA55r169tmzZUnR5OaJ6yGgagEnKcaNcFYwSABjwNMIgNhtAHWnvIbz2CnP2DdUlnUDdtE5dewIP4KY1OuxsnrUWzGZz/jtfPvFCjnd/ltRF0qeL+tCp/D/+8vyEPLIhCIKo8WQy2fLly5cvX56enh4TE8PlckNCQjw8PJwdF0HUVCuvawG81VTUzpc79pByXZRubJPCQvTro3Q6M9MjkPdFJ5fwxIKjcQUPWlvqP/M68C/G4HkA9TicVjzuTFeX1o9l73YDhQIW8LtW589mVeJ1s02ZJxVHKFCjfN+mHpvc0EjcrIVL25vqa3szf5/g/4L8qJ9bfWvz+9bmP7595XXNjQxTZz/uh22qZALF869iRewAeHt7nzhx4q+//hoyZEjjxo35fD6fz2/cuPGQIUOOHDkSHh7u4+NTFYESZWvM5XwjkzbxFLhTMsfGNwXvBXLYAFjc4M6yUH9+EACdQB0cEOBoc9N6R3MmLzw83F6/oEJcX+tN8bj6a7fJmvAEQRAvEl9f31deeaVTp04keyeIp3Yz23w+xSjmUm81E3Xy47X14Tp63fONti3ROgAftJZ4COnhDYQM8PON4hN6K10NSu/b8Li/e8pLy97twoSCbZ5ye2dVZdmRtt7CmLvKetUWhpTYYKTf22yKc0H5d4I+thKvSxDlV+EE3i4sLGzv3r3R0dE6nU6v10dHR+/du7d///6VGxxRIYNFQnktgQ/1aNYHi2IFsFkAEi0WAAGCOgC0wvzA+vUcbW7abmvOK80mc7eD5S2q6Xj3Z0klbqMGcrzkVKWOXCIIgiAIgqjpVl3XABjfVCTl0QCmthIDWHtTpzczG2/pdGame0Bhta0prcR8NnU03hCjtDzLFWtQfv58uqu9FaW+zqcFQ7xHl9bGk+vdSz6AAbMjfQODxwqsEUTVe8oE/tHxNE1RFMMwSUlJer2+UmIinprnFP/ZCz8WCAQAunbt2qZNmxO3CwDEmywM4M71ELHEJrZR2sDHRVw45iSPUZ1SnBs5amSFLuT4C+EyMNTvp3mkED1BEEQNwuFwOBxOhw4dim0pDyeGTRA1yP1c89lkI4Cfb2iDV2cEr85464gSQF6BremGTHtn+9lko31Xxy1ZBRbGxmDDLV1VB0aS/NLYGOuOtPUAXvUaXvb89le9R7hy3B7q7l/Nv1Bd0RHEI08z5uTUqVPnzp2bOXOmi4sLgGvXrg0bNiwpKYnD4fTt23fLli1ubiSdcw5ayBrw2eDEtxOTk5NbtWpF07TeABhh5DHnVTfr8NwCBXXuam/pROqW9RucvXHNflQcEm02W+Ojm3799dcxY8YwqnDjg5Esl67pWZqEHzzq8D0/f232FR1vQVfXNxsLS7xuwb04c2qmpHfnEvcSBEEQzxWLxQLAarUW20IQRGXhsyk3Pp1XYCv/ITQFL+HTd62RzPwZnco9llaQTIFKM6ZsTv2l7MYSlovKnPdnxpaWLu25dFnj/Ami0lU4gZ80adK6desATJkyxcXFRa/X27N3AGaz+fDhw926dYuMjGRX6nQUojwuFxjTCywdVmZKurm16dFGb9Utuv8Jx6UNo+9L8fBj2vYQWtvCtf1d7S2dIK9z23aOBD53kL7B7Qb379+/f/9+0okztfv15vjNpiXt/OVpLoN+0YR7jky7FuneVcAutTZh3ua9xodJbJmroHWT6rpdgiAI4imdOXMGgETyqPzPhQukH4kgKlOQK/v6W16Pb39jr+JmthnAH4PdW5dUQ/vpVCh7r1nl6KvNTfVVAAyYi8pT5Twk15STYkgIFtWvyrgIoriKpdn79u2zZ+8hISFcLhfAn3/+mZSU5OPjs2PHDrVaPXHixOjo6B07dowdO7ZK4iVKtzBfZYvSBi1J1JzLq99DprLkZRrTJCwro+lHuTFGlldWQawfPwCAW0NpW7chi1f/bD/w7IWzI0aMuH//fnR0dDwt8ZLKhB2+AMBY8vkN52pPW5vdOHf7l55sD0FplxZ1aWN8mJT3+0FBy0agn3VeBkEQBFGlunfvXmxL585kCBVBVIcP20omHFF29edVYvYOgCTkz26c33v3tLcrdIiQJaojqvfkdgRRqSqWwP/yyy8AJkyYsGHDBvtacYcOHQLw6aef2j8NLF68eNKkSVu3biUJfPVjAXoRDcCSYwIg53pSoHS2HEbHAKA4tZmC8zKOHECSPm5Km48lEol9pd+MjIwQngRAdHR0bq9+Ocs3ecyYIOzQgmJL2T69uCEply/yX+3UwUDTX369dPzQgY9fWtKvm/rYWVNyuvbsVXGPDo83IAiCIGoihmGSk5M9PDyEwpKnUBEEUSHd/Hn7XpfXlpKRqs8dT56PJ4+spVUDNPHgCNhUE4+XtyZLxTpLY2JiAMycOdOx0vv58+cB9OvXz/6yV69eAOLj4yszRqJ8JDStcWMDsOSaAXAorgtHamOsHJMWgFTY+k2/d+uLGwtYQqVZoYe2aPmivJPn+Sx2YmJiJix6Lit72UbdhesA2B4jxd0jZkQdu/0w9mHMg7fHj0lWaB6/NMVmuY0YACBvx2HGaKqe+yUIgiAq16lTp7766iu1Wm1/ee3atdq1awcFBUml0kGDBuXl5Tk1OoJ4QTTz5Ei4pU5LJAiibD0D+dHvePcMLGGJ+JdExRL47OxsAL6+vvaXSUlJWVlZcrm8fv3CuR9SqRRAWlpapQZJlIuYovUSGhzKqrYwJhsAD64XAG92PoBMK7+nfAAN2p9fG0CSIb7oEMoIRUbnps0Zhrl9+zYrrCtsNsWvm/SXxgJ0goafpCysiWrVqz5ftbXEq4u6tOHWCbAq89XHzlX1nRIEQRCVbtKkST179pw/f77BYABQYo0bUuuOIAiCIJyrYgm8v78/gMzMTPvL06dPA+jSpYujQWJiIgBPT88SDiaqmJimGApWPx7Fphgr8G8CX4uXAyDdZrOXGw4SBgNIMsR369bNcewVrXLAG6MApJ69VG/0EOnQMLabEtRh4/2ZDVqNlUoePSc+8Ps6a4lrXlKU25uDAKj2HrdpqnwRFIIgCKISlVHj5syZMwcPHpTL5fYaN86OlCAIgiBeahVL4Bs2bAhgy5YtABiG2bhxI4D+/fs7Gmzfvh1AQEBAZcZIlI+EpgAkb29Q/+82tIAGIOd6AZAJclAAC5hMixVAgKAOgERtbAOtjc8qnIKVlpsjWUwBuJwUZ7FYpCMHiLqOtukEQIopXv7u6zzHVbTxN9f9dbnEAATNGwqaN7DpDKp9J6r2VgmCIIhK5ahx8+DBA/tasEVr3AwaNGjx4sUAtm4teRAWQRAEQRDVo2IJ/PTp0wF8//33Q4cO7dOnz/nz5/l8/qBBgwDExMR8/vnnK1euBDB8+PCqiJUom5iiASiDePwW4rlz53bt2vXkmrMAfKS5rhYawDlNzEXlqSBBMIAHWVHajbtbyB4tcJJuyw7ieuWbCi5dugRAOmIQLL0AqI+e/bR3WLv6j+aZrPh5TWkxuI0dAopSHz1nUZCpkgRBEDUGqXFDEARRgxxPKEhRW50dBeEcFUvge/bsOWnSJIZh9uzZc/LkSQCzZs3y9vYGsHXr1m+++cZsNterV+/dd9+tkmCJMolpCgDu6H4c+MPChQsvXLjwy1drU85lWmlFX28+gIPKyxtSVnEoLo/h6gUFBXxbx5atHIdHWm91YXUA8O23327atGnTpk2703vsurfseMMBB02D2vV+x9Ey8dSO6FRliTFwg/xEXVozZnP+zsNVerMEQRBEJSI1bgiCIGqKuwrz+8fz5p5XOTsQwjkqvGT3mjVrtm/fPmbMmNdee2316tWLFi1y7PLx8Zk4ceK1a9f4/Je3KqATSWgagGx7zp2TjxaxVNzNzzZlBrLZAMxsHwDKw8e9UimGQpqfpY350QIMN2xRXZguAG7duvX4yVu0aGEfVAnAaDR+sWpLaWG4jRpEsVnas1dNSeRzHkEQRM1AatwQBEHUFHoLA0BvLrEqFfHiq3ACD2DUqFFbt27dv3//5MmTHRu//PLL9PT0DRs2uLi4VF54RAWIKQqAXkK7Ua6OjQUKs9qc78diABhpHwBmvc4nnQsgw9vYytWDQ7PsLVNs6X60j4QlSE1NVSgUxU5O03TXrl0dL8N3ri/tXYPt6S7p1w0Mk7/zSGXeHkEQBFFlSI0bgiAIgqgRniaBLxGPx3tyI6IKqG22h2YLADFNA9BIWR6U3LHXrLAyYKymPABGltfb/tPrj3m7boMuADJ9zIq6fu3bt3M0jrDebuTSHqV0wnfr1o2mC39hDCl3Bv1wtLSQpG/0Y3vIKC6ntAYEQRDEc4XUuCEIgiCecza9QXv2Su7anTk/bMhdu1N79opNb3B2UE7AfrrDNBrN+fPnr169mpOT4+npOW/evLS0NA8PD/vaM0R1+jg370KBcZFM6sdmAciX0r60zLHXqDAB+D4nBvxGaVZbe7cebIpq1H4A7h9N8zXWSszu4OV/AZfsja/bbg40db+CMypVCZNqZDJZixYtIiIi7C+v7V1n/bQ/i3q8IWiJqNavCyr7RgmCIIiqYq9xs3bt2j179ti3FKtxA4DUuCEIgiCcRXPiQt7vB206fdEttFjoNuY1Sa/OTgys+j1ND/yvv/4aEBAwYMCA+fPn//LLL/v37wewc+dOPz+/ZcuWVXaExBN0E/BtwJfK/GsFJgBKKcuX8qZQmFhzODwAFLJhgw1ItVoBePP8BCyh2sWqFVtb55kdp7rMRIahy5w5c+y9Lo8LDQ11fK+NPHIoMrns2LTnrhkfJj3b/REEQRDVgdS4IQiCIJ5PeVv3567dadPp+Y1DZBOHesx6WzZhKL9RiE2rz129I2/bAWcHWK0q3AO/YMGCefPmAaBpuk6dOg8fPrRvp2laoVDMmjUrLS3thx9+qOQwidK9KRaZGea7fPVPag0AhRtLTsnG+g7fkr6LLxB4TR4MJMgteek60O5IMluC2GwKVKAg+L729tUOrl3U4LPYBVYLgBxLdionvXbt2qVdq2HDhj4+PhkZGQAYi/mblasHb15cWmOrMl+xajPLRez34zxaJKiCWycIgiAq06hRo0aNGlVs45dffrlw4UKnxEMQBEEQun8iVAdOUhyOfNpYUedHS2i5DHhFd+G64udtqv3h3OAAUceWTgyyOlWsB/769ev27H3cuHFZWVmxsbGOXdOmTfv6668BLFu27MaNG5UbJVG2tyTiT6WFtQOzpTSAjyUfXE9Mah8ZZe4zmmHJJ7o3q8NiA7hVoNietl5lyastrAsgKdCqrOvX2t3HcapddUqY/e5AUVSPHj0cLx8e+81sNpfWmCWT8hvVtaq1+XuOPdv9EQRBEE5DatwQBEEQTsMw9g522cShRbN3O1GXNrK33gCQ9/sBMC9LWf6K9cDba9gMGDBg8+bNxXZxOJzPP/88PT39559//uGHH+zlaolq85ZEDOC7fHWaKwXArDB9yuVl0ezWPLefg9YJKUoh0C3OV0XoMynFYSnHrbYwBIBNpJH3COuZk3xx01r7eQ4JHvZG4zIu1Llz5z179hiNRgDK7Izdu3ePHj26tMayt95I//R7zV9nJL06c3zJ4kMEQRDPO1LjhiBKY83/25KzrUKHUCwxJ2ABxXaropCIF9umW7pvLquttpL3Xs80Ba/OeHy7vwtr/+tyKb/SSpU7lzE20ZKdy/aSS3p1KrGBpHdn1YGTlkyF8WESLySoeqNzjor9016+fBnA7NmzS2tgL29TYg1zoqrZ++FNAtoopJkCW67a3JrHXevhLqQoAIEcNgAV5QogxZBYWxACQOWmDe73StikCY6TGB5cZBjmzJkz06ZNS0lJefwqAoGg6MrAny1aal+LskTcOv6Snp0YizV/5+HKu1GCIAiiSpAaNwRRBkv2Vmvu/gp9WbK32bQRzg6cqKkKrExp2XsZdGbG+gJ1RZuS0gEImtQDVVLpbAA0zW9SD4ApKa06A3OiivXA2zO6Bg0alNbA398fQEJCwjOGRTydtyRilc2mdmN56G0N8pm19QqzdwBBbBaAXEboBqrAZnDneriy3VSWvBxjZps2bVxcXNRqNQCrRpGWlpabm6vX6y9cuPD4ZEgAPXv2PH3qlI1hACTfi1weHvNFWP3SQpKOGlhw7yFLKqmSGyYIgiAqCalxQxBl4/hMtSoPAgwnYD7NCyi7sSVnmzX/b1rYmOX6SrVERzjH5s2bp0yZcvz48a5du1b6yae0FE9pKX58+/VM04j9uW28ubsGu1f6RZ83TIERACUsq4SqvdgWYzBWU0zOVrEeeFdXV/ybxpfo7t27AAIDA58xLOLppFmsh/QGtTsLgDbHfOjfhRa0FrUMLBYohY2aEbz4Lf+pAIJF9QDE6R+w2exu3bo5TnL37t0WLVoAiIyMLPEq3t7ebf+dNk9xePsTKVPpD/pYrhK/lXNkE4dVzh0SBEEQVYDUuCGIJ6IlbdnyoQBsukiW++AyvmiXTjbNVQCcwIWgWM4OnKhC169fNxgMUVFRzg7khcVycwFgycwpo40lPRsASyatppicrWI98O3atTt8+PC2bdvatm1bYoNDhw4BaN68eSWEVtlUKtX9+/fj4+O1Wq1Op+Pz+WKxuHbt2o0aNXJzexHmJqVZrKOyFTlWKyXnAgWuuZb5eSoAEv3xPRlbQ6xfWAS+lBicAh/b3tNp127J6ibgFdw6+nt9U26PDh0PHy4c5X7ojsK721K55wZFdmZqauobb7zRqFGjGKUlO6JfK/GND+J/6VdL2cRX4sark2Exct/6NpeSHIw1DG0gLCs4hlEfPcvx8xY0L3X4BkEQBOEspMYNQZQHJ2C+RXnEmrvf6vU2y6VLac3MSXMZq4Yle5Xl2qO0NhWl/P2g/uxVzy+mcAP9KuucREWlpKRs2rRpypQpHh4epbWJiYnZtWvXRx99JJGQ8aeVgN+kHijKEHXfqlSxZK6PN7Dk5htuPQBF8ZuEVH94TlGxHviPPvqIoqiffvpp3bp1j+89dOjQ0qVLAQwZMqRyoqsMFotl9erVnTp1cnNz69Chw+jRoydNmjRjxowpU6aMHTu2S5cu7u7uHTt2/PXXX00mk7ODfXppFuv4HEWO1QrAv64YwBBXIQPMz1NdNygYMEbWQ0YPABFrd6gO/m3OyPFN4QJIlqnUh083/ee+41Sm2H8+2JYc7NcRQGRkZGJclFV1Zvk/8WfUrwAYJDvwa3Lf1mE5i1v12N9l8CcDugPYeEtXdnhWlVa58c+cpeuteaqq+QEQBEEQT4/UuCGI8qC4Phy/jwCYEz8DYy2xjU173aL4AzSPGzCvsq6rPXtFve+ERZmftfBniyKvsk5LVNSmTZvmzZsXGhqanZ1dYoO7d+9269Zt7ty5x46RNZgqB0vqIurUkjGZFb/+zliL/0/HWK25v2xjzGZR59Ys15fliUnFEviePXt+8sknNptt0qRJrVu3/vTTTwEolcrly5cPGzZs8ODBNptt2LBhw4cPr5poKyw9Pb158+ZTpky5dOkS8+/SAhRFicVix7o4DMNcvnz5/fffb9Kkyf3790s/2fPLnr2nWaxSmgag+sy/3snWb7xW61OpCwOcMUkAcLkKRkcBSJW6iDq29Fk8q8M3y2mKle1jSWkXXMeb7yYr7EI3F+hFEZf7p7QDcDMyIjvXEHVt3sk0UXheXwZUAC8pSSvy6B7m1joOQNe7l+q6sc1PKrDBkkqE7ZrZDAX2dSAIgiCI5wqpcUMQ5cTx+YDiBdj00ZacrSXtZ0yJnwEMx2caxa9TKVfUnr2i+Kmw+r01X505byXJ4Z3Fni9ER0f37Nnz8Rz+7t27oaGhWVlZffv2fe2115wS4QvJbewQllRiiLyb9dUqU2KqY7spITVz3kpD1H2W1EU2brATI6xmFV5gYNGiRV999ZVIJIqIiPj+++8BJCcnz5w5888//2QYZuzYsWvXrq38MJ+KwWAYPHjw3bt3ORzOq6++un79+nv37uXk5JjNZo1GU1BQYDQaHz58uHnz5uHDhwuFwtjY2NDQUIVC4ezAKybZYhmTrUizWNvyuN0FfAAaIS3uIE1OSd7+xuuq3qEpB28CyGYyGC0DILtji4PDdRdl9/kckR/f3wrbjSBrWoOm/AavOM55gXu9t6EZl8dPTErOzc07lPOBjaFDvWLYLh0SEAZgTdYUWedYjo9KVD/wyDCPv4bLnxin7K03KC5He+5awb24KvpREARBEE+H1LghiPKi+dyArwCYU75mLMXHFVpydtq01ymOJ9v3w0q5mu7iDcXPv4NhuLW8AXB8PS1Zisw5yy3ZuZVyfqJC5HL5qVOnmjZtGh0d3b1794yMR6u4PXjwoFevXllZWX369Nm3bx9ZerMSseVuXl9MZcmkBffi0md/mzr1q8wvl6dO/Sr942+N9+PZcjevL99/eSbA4ykSeDabPW/evJiYmDlz5rz++utNmjSpVatWaGjo+++/f/HixS1btkilz8uPb9GiRdeuXZPJZOHh4QcOHHj77bcbNGggl8tZrMJqIlwuNzg4eNy4cbt27bp8+XJAQEBGRsZ3333n3LArakGeKtNqbcvjrvFwd6UpAHqNJWnqvZnjPwoPD8948CDhs2UGhTHLlk2ZACDR2/2i8tShrD8ABAvrA9CK80/yAq31wxznjOTf5oFXA0D8zAAAIABJREFUR96VYZg7vc4nNdrWIkE/uesIXqO/Br8yd8g9JfPQN9zc3e3Nw8K2PDYNDk3dUZgLSl9PDgDb0911cG8wTO7anY8PgCEIgiCcqF27dgC2bSt1jevnucYNQVQzlvsQ2qUzY1aY05b8Z4dVZ05ZAIATuIBiSSZGDZ4Y9Uy9grqLN3JWbobN5jZqENvXE4B0WH9evSBLjjJz3kqSwzuFh4fH33//3bRp0/v374eGhup0OgDZ2dk9evTIyMjo06fP/v37BQKBs8N8oRQwDLd2Lb8fPnMZFMqSiC1ZioL7cZYsBctF7PpqT9+ln3GDahUwL9DSeU9S4QTeztfXd8GCBXv27Ll9+3ZKSsrff//9888/d+zYsXKDe0Z//vkngK+//rp79+5PbNy0adM1a9YAOHCgho3xHioSvikWrfFwF1CUmKIBMDc1uZvTU28XdqRYzZb8OA3fmivL1ACItzICllBlyVNb8usI6wHQiPJO8BoKGnRzrK8YnXNHzWjGKNpOuFk4/sd19KwHDS5+MeneyjeiRm/IfGdf5jrFRwAunfg2JSUlMsv06p+KT04/YX6765DebG+5OSVDc/Rc1fwwCIIgiKdRE2vcEIQTcQO/AcWyZK61GR4t2WBOX8aYMmlRC7Z8mCN1f+ocvmj27vpGX/tGisf1+nIqyeGdq2gOv3//fgArV66szuydz6IA8NilrIv+YjlpKGidmrFMpaYlItn41/03LPZb8aX3ohl+K+f4r1/sNm4ILRYuU6lbp2acNBQ4O9hqUrEq9DVLUlISgFdeeaWc7bt3785iscoYQPh86icU9BMWvlNIaAqAxoUNwAOPVoa05VCALcCQeNvUVM9l/ERtEjQXbYzN3gOvESoNDJvl4sH1a2hKvQvAxtiuWiPirtwpeqHNx5ZP6/wBAEZsPBImUzNeOfms7kN31a174+yNezwWdTTeEJcvDpaW+ktFcTjuE4ZmfbM6f9cRUadWJVaSJAiCIKqfvcbNd999N2nSpNWrV/fq1Qv/1rj5559/9u7d+7zVuCEI56JFTdkeYy3Zv5mTvuA1+AMAY0wyZ/wMUNzaSyZGvV608cSowRub76/Q+UvM3gsvLRR4fTk1a9HPxpjEzHkrved/yPZ88RcDd7pNmzbZi306tGzZMi0tTalUAlCr1bVq1apVq9ZHH33kaMBmswcOHBgWFlb8XM+ssQfnu1dcW3g9zSh9k83IobkUakzyL6NpmqLWqbUMMMvVBTTNqeXNKdLgB5V6vVrLpigZ/ZQ90zXO0yTwRqMxJiZGrVaX0aZz585PG1KlcXV1zc7OjouLK6MqT1FJSUlWq1Uuf/J07ueWmKYBKN1ZALytj9a3sGYDgAcr1aZtSsvQxXvyZL+xUo7MleMmYol1XO074vBgnWy7i+vv/x6yYcCWzmhR7Pw/XfxxWucP6q7/8FcvG9Usmo7r7u+9v2EBHf/jL0O7jP/9jn5NpPb7HmXNoRC0biJs10x/9ZZy816PGRMq8d4JgiCIZ7Fo0SKBQLBkyZKIiIiIiAj8W+MGAEVRY8eOXbVqlbNjJIjnCMf/C6tynzU/3Jp/kiXtZUqaA5uR7THynbhFjzeeGDV4ROaUEs9js9ny8/MDAwM7depk31JG9m5XNIe/N/2r/FF9u742sBJvjSiGYZipU6caDIYy2qSmpm7cuLHYxkuXLlVFAk8BT1i/uRTJhvjFD//X2rXjuwEzKj2qKtKKx/1Z7vaBIm+9Wmtl8InUpejelSqNPXtf4e7Wivey1B2oWAJvs9nmzZv33Xffmc3mslsyz8E8hG7duv3555+rVq0KDQ194mgWq9W6ZMkS/DsPsIYSUxSAXCkFmvIsePQkwphpBiDlZEBDQcYkWtBN4AGAAlVbGBKtiTSKFVSGMczdw5HAp/1T8vIYP138celNKUf+kKs5TPlMmjL8aNt7bX1uxIzrp9xJ8w/EGj5sI/GTsMoIUjZhqCHqvu7iDXFoB0HzhpVz5wRBEMSzsde4effdd1evXn3nzp2YmJj8/Px69eo1aNBgzJgxz9ssOYJwOoojZ/vNNifNMSV+yg363qo8BJboPW1Gae13ef/aNaLUyuSJiYn2BF61Lzxv+0EwjKhzG7avp+5SpL2BVakCUPAg3lFISNKnqzY7R5Kvo7YcMTVrzq3tX5m3RxRBUdTx48fv3btXdGNWVtYPP/ygUhVOIPX29p41a5aLy3/Sy+fqnZMBsz1tvclmupR3tpusT31xY2dHVF7d+Pwf5W4fKPI2abTAoxx+pUqzWq2xZ+89BXynxlitKpbAr127dtGiEh4rPp++/PLLv/7668SJE927d//qq69CQ0P5/BL+ac1m8/nz57/99tvw8HCKoj78sHKqhjqFK00DUNPgBfJrpwbi35kgrhIpAH8mrkNO2tVA34dFnr8EC+tHayJVQmWeWN7G3ZdP8wpsRgC6TEN+nEYaXMKCirNb+P1keGhVHuDV2zJ5VL1/1iRA1dLl778HtRq6P8awPko3r4vL40c5sD1k0uH987buV677w3fZ5xSXU0ZjgiAIojrZa9w4OwqCqBk43pOs2VtshlhjzFgA07htym5/vtWBRR6/OF6aTKZr165ptVoAYrEYgCUjR/n7AfvgZt3F67qL14udQX3g5H8CAACIKVbWN6v9135t35iZmSmXy9nsF3mebPXr2rVr165dHS8fPHjQo0cPlUoVEBCQnJzs6+ubnp6+YcOG06dPe3t7OzHOMlzLvxiju0uBYsBsT183L2QZTdWYMeeP5/AvbfaOihaxsy8R16RJk/PnzxsMBqZ0VRNtxTRv3vy3336jKOratWsDBgxwd3fv3r370KFDJ0+ePHv27Pfee2/48OGhoaFyubxnz57h4eEAFi5c2LNnT2cH/vRc7Am8zVbveOsx1yeNHz+eoqj69eu/NXUcgAIP5o0rpwA8tFgch9QR1QOgEyqNfA4lk7WmHw2bT72YVeJVpodO1kUMYnu8CdAi/0ldRyfQPJtVqZrSUkxT2HVPn61/wrrwLgNDuUG1zJk5qr3Hn/mmCYIgiGeiUCjmzJkzb948ZwdCEDUNxeUELgIAm4HiBW5surPs5tOZr3z/JZPJbt68qdVq7am7fY0ktpc7x8sdAMVhC5s3FHVs6fiyVw6KyM28rFdy2zSxbzS6uQBgAFGXwmcHa9as8fX1nTGjxgyQrons2bu9al3//v0BfPDBB/aadj169MjMzHR2gCUw2Uy7MzYDGO33jpzrmWJIPKcMd3ZQFWPP4bkUtUmjHZmleGmzd1Q0gY+NjQWwdevWLl26lNib/bwZNmxYeHi4fdkbvV5/7ty5PXv2rF279ocfflizZs3u3btPnz5tn8zftGnTw4cPf/HFF84O+ZnYE3iVzcbx5fGDhb/99pter79//36zes0BaBtIgxR5AGI0uvDL6+bemZad/sD7dgHFUBphPkMxdMtW3T1CHGdLvVjCKPqfbO+zXDWKrVNZbmEAWJ7jWC4c2cR9HrN7IDfR5ewyxbnfN0dpyo6TYtHuk0aAolQHTjIWsqQcQRCEM7m7u//www8LFiywV2MiCKL8WNI+LGkfANzAr0HzyyhW1/3mkMjIyJs3bwIoKCg4duxYXl6eq6tr+/btHzWiab+Vc4VtmjJmizEhxXVYmMest+1fvLqBAPar0kcd3fH6/t+47wzlhQTx8tQ2htlizJaNGwJgzZo1U6ZMAdCmzRPGAhBPrWj2vn//fvtIB7FY7KhL/3zm8Eez9+aacgIEdXq4hw3zGQ9gT8ZWnVXr7Lgqxp7DsyhEmUw0XtLsHRUdQs9isVgsVrNmzaoomqrQs2fPmzdvRkVFHT9+PDo6OiEhQavV6vV6gUAgkUhq167duHHjPn36tGzZshIvGh8fn5eXV87GWVlZqKSqAVKaAqCyMborqqyVSbW+q8f35wOQc70A5NKq+rOnS3WGfJHgWlJkaoP0C7992ypC5D6FpZBbGvryAuvdf9P34OI3/g0sItess3BEj35JpqZy4D4ieBuV89svQHsAFEvM8hjFWNeo09d26bXffi8/qNIm717qwivr8RCvXm33d4Zb89QUu6wJ8wRBEERVoyiqS5cu4eHhERER9vrzBEGUH6/ebzZDDC1qbn+5sfn+x5eO29h8f6Jr4tmzZyMjIy0WS2pqqj1779evn30IvQPFZnnMfidn6Xr99dtZX63y+mo6N8DXsXfRokX/TJ905cqVFYPfHO8exACf3DilbRAIYP369e+//z6AFStWjB8/vmrv+WUVGxvbvXv3rKysPn36HDhwoGh3poeHx8mTJ0NDQ+/cudO7d+9z5865ubk5MdSi8sy5x3L2Axjt9zZN0W2lnU/nHr2vjT6U9cdI34nOjq5iIo1mKwMANjA3jCaSwD9Zq1atTp8+HRMTU8667s+P5s2b2/vhq8G9e/eaNGlisz1hGHkxZVf1LycRTbMAnc2m3JuVfzBH0s3N4z1/AG4cdzbFUVvy1XV8XDLU+bCyXJoC6dm1WQJrw7pCqQLR2pYujDo1yJeu68t9mG4CYDPbMq4pAl4pnMnTP7KrxPsGYxZTHK2ozaM/NhyfKZas9W9M2JSVZbJvyQlft+7yrFnd/cqOVtK3a9kNCIIgiOqxYsWKbt26zZo168qVKzVihB1BPEdogSN7tyuWw7+a8BaaIygoCMDZs2dv374NwJ69C4XCYgk8yszh5XL56dOnfx4+wZ69J7ett2fPz30bBK5fv37y5MkMw6xYsWL69OlVersvs507d5aYvdt5enqeOnUqNDQ0Ojr677//Hjp0qFOCfNwf6b8ZbQXtpV3riQoL143yfWd+7MyTiiNdZb38+AHODa/87PPeKdhqFRxL4/cpVtPu5VGxIfQff/wxgE8++aSi2elLxdfXt1+/fq3LzcvLCwCPx3v2S9OAiKZtgFXKBmDOLsyoKVByrgcDZndsekKaFQC/w0AuzfUL7eM1Z2qj+j0AxOoKS2t2FjyaBh90prD26dcH6TrTWuhGvau90hiAuIsUgDn1G3PK1xQvkCXtY7XZHAtK2gq0y1b9pLeUa0xB/q4jiUOnWfOfMOqeIAiCqDqNGjW6cOECm81u0KDB6tWrr169mpmZqSqJsyMliJrBMZa+aOV5b29vobBw9S9/f3/H94+z5/DCNk2tam3WV6tMyemOXa43Y9/za8AAX0aeeWf9jwBSU1NJ9l49Pvzwwx07dpSYvdt5enqeO3du586dgwcXH4XhLHG6B1fzL3Bp7lCfcY6N/oKgrrJeNsa6M7340nfPLUf2Xl+7rq3tZn3tGhrWTRrt9/mV0Alas1SsBz4sLGzx4sWff/55586dZ8yY0bBhQy635AX36tevXxnhVZrc3Fx3d/eiW2JiYv7555+oqCg3N7cWLVp06dJFJpNVyrVcXV2PHDlS/vYff/zx0qVLK6vHw5NFa2022oMLwJLzqNp8mOfr97W3vUweTKoWQA4j+bHJNg7FBVBX1BD2BJ7PBxBau97muKv2o45fsjwwnAVAhbka7nvmHyicFc/x5gGwZG5gLLks9yEcn6nTRx1Ems/5rBR7g6zw9dtuzprU5tFSdqXJ330UgHLTn2RZeIIgCGepXbs2ALPZnJaWZp9DW5rnpE4tQTz/Njbfr1QqD0QcsL+0z3vXarVCobCgoCA6OprD4bRo0aK0w4v1wxfIJBwg5dhp3q1YUFRmp8bnrhxITU0FcPfuXQCzZs3q3LnzjRs3HGfw9PT096/WteUSh04DEPTnT9V50erk4uIycuTIx7dTlKMbCzKZbMSIEdUYVFkYMNvT1zNgwjxed+d6FN31uveYa/kX72hu3lLfaObS2lkRlpM9e2dRCNGsq4v0z+sunx8zE9rVseL3X8J++AqvMEFRFIvFunz5ctm/ms/PH/gdO3YsWbKExWJdu3bNvkWv18+dO3fFihVW66Pyae7u7itWrBgzZoyTwqw0K9xl2Vari6daAZhzTI7tXWW9usp6PcyzQKsDmFizxZ69A/Di+biwXVWWPAXjKQXaCAOElEDPGACkZtlEHfMBAPlybufP6NlvAgD0URpJD7Dkb1gy11qyf+MGfT+gb8dbpxOiwnlqsxGAVaP4dc2GSW0+LTta+xs9AN3FG6JubYWtm1Tyj4MgCIIoh8TERGeHQNRINsDEMPwi2cvTtakpLEpzyqwHoraunu9XLDEuWrWuX79+WVlZ586di4yMBODr61vaURSb5TFrYvb36wyRd5l8FWgW71asjWH+d+PU7iJJsv1T99KlS5cuXVr0cB6Pl5SUZB/p+YTwomM04RcK7sVZ89QssYgb7C/u3k7UpQ1eiH+1KtW/f/9z585169bN2YGU7ILy7wR9rBvHvZ9n8REBErbLIK/hu9I3bU9f30jSnE09v+sOOlaMa6LbIjBFjQz6VMQSD/MZ/2vSkqb6TXdEb79sOXzF/qnWr1//2WefVVEoVeGjjz5auXIl/luNc8yYMfv27QPAZrODg4OtVmtcXFxubu7YsWOTkpJqeiH6YA47mMPWedp74E3F9tZ1Y3/d2nUe8uPMZgawvytToOqKGkSorjw0GdsAPC67S6s2J26cL3aswqSZhXm9Az7g226p92ZLeoDt9Y4lewtsZgBc36n/+3KCIqPer5G37e2zjq+2WGaVfxlS5bpd/EZ16ZeyFgVBEIRzRUdHOzsEokaap8w/rDeslMu68UueCai1MZMUubFm8zFvL3dWjVl0ukQWpTl2QIThtjZvd5ZVZfH5rHY5D2QYpmj2LhQK7WNe7Dm8Tqcr41iKw/H85N2oGQukmXkAGAarlQnx7sLW7q0VCkVSUpK9mVAoDAkJKfahKzAwUCqVPiE2k1nxy++6C48WnLdqtIab9ww376n/Ouv58Tss2RPO4ODolUkcOu0F7oQvJiwsLCwszNlRlKzAZtib+TuA4b5v8egSPmD3kg88m3si05h2SvFXH49Xqz3AcjmqN9iz9zeoG8kFl0JEDVu5dgDwbym+66+LW/6JVps02qZcTphQ4Ox4q0PFEvhVq1YBqFu37tKlS1u3bi0QPNc/oxMnTtiz96FDh376aWFX8M6dO+3Z++zZs+fNm2dfezM7O3vmzJm///77ggULXn311aZNmzox7ErBtifw2cUTeADDg4U/p2uyrdY0i7XWvxXg64oaRqiu3NHktQFkE6Un+hfP3h2ajfjx3L53W7/lC4AWhAjbxIPmAWDJBlGCOtPftGy8xTJarQAyUhJ37txZxqAGxxs9AICxKPKM9+IErRo/1R0TBEEQT69xY/LeSzwNXzargGE+UCh/LCmH19qYd3Jyo0wmfzZLQNfsvlxLrjl2YIThtpbrzzenGzO+jgdQzhxeo9FYrVZH9m7f6MjhY2Jiyj6c4nCaL5+b9tHX5iyF28gB3w8LA2CvWkdRFMMwQqFQr9ezWKwTJ05UbEKozZa9dL0h4g4t4LsO6S3q3Jrt6W5VaQwRd/L/+MsYm5g5d6XPtx/T4lLn6jv890Pdc2rTpk1PbDNhwoszo/Ng1h8qc16wqH47aZcSG7Ao1kjfCSsSFh3I2tnerasr+3kpm1+UP5vdnMsdJjAeS9xEgRrl9w5V2AVZWIovJXfj3MBmewoE/uXuNazpnmYd+G3btr322mu1atVyL13VRFsxy5YtAzB27Njdu3c7euB37NgBYPz48UuWLLFn7wA8PT23bdvWr18/k8lkz/lrOrYHB4A523T69OmAgAC5XL5t27ZsY0auKQdAXQ4bwD2Tfubdib8mLQEQImoIIJFNAZD1n1n2yZPVzdkB/z7GowWFv0UUi+M9xb9j6rCQR+UPpn628OhDfflCpgDwmzes0G0SBEEQBOFE77lIxklEJoaZplCeNhQU3eXI3n1YrI0ecmFNHoxtzbc8HBxpuK3lhwjrn2oT9FsTik1lfB2fsTih7APtVZ+tViuLxaJp+uTJkweLuH37tkgksrc0GAxlnIficGr9ONdv5RxpkeydYZj33nsPQNu2bYODgyMiInr37q1UKst/X+rj5w0Rd1iuEp9vZru+3pftJQdFsaQu4tCOvks/4wUHmDNzlJv2lP+EDs+Sz1syFfl//JW1+NeMT7/PWvyras9xS3buU5/tpZVtyjyZc5gCNdr3Ucb7uGYubZpKWhms+gOZO6szvPJrwuXs9JInKzZYGWt3975BgmDHLkcpvuTcDTu95E24HCfGWZ0qkMBrtdqCggIWi1V0OPrzzL5KR7FqnDdv3gTw9ttvP95+1qxZjgY1V47V+l2+OkNA0QLaprPO/GhGSkpKbm7ue++9N/fOjEUPPwFQj8MB8MBkUZnzotTXrIw1UBDMpbkKNqWnnvyrP2b8tLSS+vbZnm/SXLcRPXksqvD3Sp0a89HK7UZrCQURSnxnTxox3ZyWheemgAJBEARBEGWggP9JXcdJRGaG+TA3z5HDF83et3jKHSP+aiJrviV2UIQ+UsMPEYb81Yrjw3Mb4hm0qQnFpjIWPyGHp+nCT0RWqzUvLy/3MRpN4So8T17giaa5fl4okr2vWLHitddeA+DCkfz19r62Qa3tOXzWtXTFb+lZy5IUG9L0URrHXWT/mmJRFPn8xjDq/eEA3N8bzanlXfxqYqHHrLcpDkd77qpFkVd2aCV+qHuaHN5my9u6P+3Dhfl//GWIuGOMSzZE3MnbcSht+oL8XUfI58MK2Zm20cKYm7m0FrNdckxZZXz1lA+gQJ1ThqcYEp0ddcmi1NdvayKELNEQ79HFdr3uPUbIEtlL8TklNqeowEgDLpdrH6KTnJxsH/bznLOvrO7i8p96Bjk5OQACAkpY8NBeOf/evXvVEl1VOaov+E2j5VDoI+eaUgrylPYSdNDpdNoMvalWgcqsSUqzwgUJVnjyfLKM6ZnGND9+QJAgJEZ3J452AXKeeJX4FFPtIsMtmYKHjFVHi5qzvd+Otn47oFbdgymF48ES9y3b8e7It5qJix5exnt62ocLXQaFysa//hT3ThAEQRBENbPn8AC2aHQf5uatdHdry+O9wNm7fbvbEE+gSeKE6IzF8QB8Pi/5g7FMJhs4cCDDMCxWWT8EvV5fnlJz+G/2Pn369OPHjwNolBui/Tp7ba1l3/it6He3e1qP/3yUFTQV+3xZJ/ObBP1NjU1r9f44yL7dlJhmyc1ne7oL25Y8dZTt6S5s11R3McIQeUfS+z9jsHVmZsIRZY9A3pSW4hKPfRoMk7N8k+5SJFi0uEcHYbtmLJnUqsjTX7mpPX89f/dRS45SPm1spV3uhZZkiLupvgogSn09Sn39ie0BMAxzMGvX1KAn1J+uflbGuit9E4DXvEZK2MXL1P23FF8zdjl6Il8AFeiB53K548ePBzB//vwqi6cyBQcHA7hw4ULRjfZHD3FxcY+3t89BemK1j+ecfY5ZntVm/xvjJ39U2pSl4AK4k5d24lYBgBizubt7n0BBsH3GSx2dJ4CHtGt5rpKaZTQajY6XBfdHFtzpy5gy2N6Thw0S+AhFrH9HypnS7n2zYXdB+daEB8AA6sOnjffjy9meIAiCIAjnKtYPPzwr58XO3u3K2Q/v4eHh6elZxrRTd3d3f3//0hZmLur+/fslrvd+T/5Q1M7Vlmr+VDW1OauJilHH1k7ymhnoPs6X48Mz3NbGj7ylv6nh1RW6j3v0sdCSrQDArV1WOX1u7QAAlixFse2JKsuNTNOx+AKU2StToU549dGzukuRtEjos3CGfOoYYdtmvOAAYfvm8unjvb+aTgv42jNXtH9fKv8JX2ZSjqyuqIEH16v8X1483/ri53E1qJOKw5nGNB9erVB5/xIb9JIP9Ob5ZRszTimOVnNszlKxuf5Lly5NTk7evHmzXq+fMWNG3bp1HUODinkepsGPGDEiKipq8eLFvXr1cnS5jxgxYt68eWvXrg0NDS3W3j77vUOHDtUdaKVypWkAatu/Cbzk0Tu1LZtiAWApKK0rGCSYLaFer/XzKFxVwve6Fm0QT5fr+YXNhvj4+IYNC6es06Lm1oI4S9YGjv+X/g1Hu7bc2Fnlfy4z2b43ad+yo++PGFK/sAJK2e/mFACGyflxi+8Pn9GllLQlCIIgCOK5Ys/hzQx2aHUJFosbTb/Y2btdOfvhK4ufn98bb7zRp0+fd955p+h2I23yWxwS0+cGbAwlonc1P9Lv/YF+g+sCsOSY7nW+ak43AvD9sg7Hq8hjAvuER6bMofv2vVTxT/uVPpidsVhVu48BkE8by6tX/MfIbxzi/t6onOWb8nYdEfdoj1KyD8LBle32ed1vnR1FJVBbVIey/gAw0m8iiyr5/YRFsUb6TlyRsPB5LsVXuSr2P0D9+vUjIiIA7N69u1OnTp6envJSVE20FTN16tSgoKC4uLiuXbuuW7dOpVIBmD17dqNGjXbt2jV37lyz2WxvqVar33vvvUOHDgGYNq0GVNEsgwtFAVAzDNeXB8CX/2hSkynHCiDfmuUvZjM6WIE4s8Wx1/uykgaTTIvYgif/uU1JSSk6ioHj/R4Aa/5JABzf6fM+Yn+7ROt4uGNMic45Wd7CGPa/CJYsRf72Q+U8hCAIgiAIp9PZmLumwk9WGoaJ/fdTlhNR/1X+Ay255piwG/pIDb++KORY6xKzdzu3IZ5B6xpTLCpjcXzGN0+oafeMJBLJH3/8USx7t8tcHA8bw/HiMjrbtJQJ/Vv0BWBVWR4OjTKnG1luHAAZ3yWgSOLN9pYDMMUll5GOG2MTAXB8PEpr8MQ+9nJ2whfcibX+n73zjm+i/OP497lLLqtJk+500F3oAFqglDKlDJkiCKLIVlGGoOIPBGWLAoKIA0GUIYLIhiJ7yaalUEpL6d4rbdI0O7nc3e+P1FJKgRYLBbn3Ky9eyfM899z3jjR3n3u+Q6sjfDwe5M8v6tyO6+5CqdTm9NyGTMjy32Bf6TYDpbdl2nvIsDaS9s94Kr7+pQjYAAAgAElEQVSmpXEr8IWFhU/IjieBRCI5cODAgAED8vPzJ02aNH369IiICLlcHhYWlpqaumTJkp9++ikiIsJoNN68edOWRGTBggU9e/ZsbsP/FVIcAwA1RQvC7AAg2PduXne5s7sGCsstZUEOnCItiezgDkkGE1wAYEiSU2l219KFYvzyab/I6IyH7yUu7qqXl5darbZFHGDiSF7gr4jXAgAQzweXDWzjd+iV0MD9t9Js49fMnzNuQB+er9cjf8drrq6aI38Lo9ryQwMf5yywsLCwsLCwPEVqZ62L5vP26g22ePiegnqqTz8F6pXrtqJrDdm85Mts4y0dLuYE/hVxz6p1fchGuJJl5sJPM0qWZkuHOAtCmi4sHICq2ENpLjyo14EsRwj8MI3mjBITUH7bThbOjtAnOKb3v+6/s23etFRDgobnLwyMjUh/OcGUqtdf04giq6OIiRbuHDcna2mF/sI1UbfI+ycnC0oMCckIxx9U4nfNHwsbcggNKQtPFpUBAD8k4IEjEOIF+5PFCktRKa+VX0P2y/K8k2/MOa86CQA0Q20pXPvwwTTQAHBOdaKnY38vgc9TMK8ZaZyAz8zMfEJ2PCHatGmTkJDw+eefb9682WQyXb58T+RMRUXFiRMnbO87d+786aefDh48uDnMbEpsLvRVNO04zl0Ubd/ap3uc9XpsbGzPnj1Hj3trbcnyMnNJkAPndBUCOZNOWhlgyszFboQ74nK8UqSFnbSH1a9u/LXVxPoS9QPAwVGTX9n+U3z8tREjXq99FcQd76ad47pNV21VTnGTHExOpxkGAG6rFIPmbvxj4cRGHAnDVPz4u/uqOVgzXftZWFhYWFhYGkKdnPMeHNwOQzU57Z6+hn/IYnsDNbz0VRflb8WU1lq6PNfrm5YPLsIFAGDJNynWFQKAqKM9z/fRJdMbBVm0nDY+cFkl1Alu7JDIssQV55AwPBHBOo+FvMK5nxluhdzpEc9YaJ6/MOhIO647T/KyY8UvRfr4qhoBDwDS4f0rftiq3LCTI3fhBXjXnplSqRWrfgWKtuvXHZdKAEBn1Qhw0YPcmP8lDEkCAOI+LAMZRhAAwFia37OD5emQpLlGMzQAJGtvNHATmqFvaq+xAv4ebGnhni9cXV03bNiwbNmy2NjYa9eupaWlVVZWGgwGhmFEIpGLi0twcPDrr78eFRXV3JY2DTUCHgD4QSIA+PXXX21dFZYyKIEyc3GMA4cpYADgDkmeKI/dUbzxfe9PvHw8vfN0lztpTYIK57Kq7BHT/Hbd87h0yei3Rxv5oi6dAuJOZGZmyuVymay+IBOaVm66ZU71CXImewb6nkqvTkd3Of2a7rNH57e3wQAgAKtCWbl1v+OkNx7vVLCwsLCw1Eu9mVwbyPN4J8DyRKm3YlydvPTNtQ5fLw3R8OJuMr8/22aPvFm+oZCxMi3WtASsfhFvKTBlDLxuyTUKI8QBe8MxQROHZ3Pc3rPkfAKIILwXA1aPJ3+oH6jK7ACAaNEKEwQCZHiuSMoc0ZrWUYiDfNaHcN15AMB14wEApbpH/dr16Gi8mao/f6103mrxy91FXdtzXBzpKq0hIaXqwAlaqyd8PGSjhwBAnjFracbsEHHbD33n2bY9dXJpw4/ikYvwHCcHALAUlDxkjCW/GAA4zg4N3y/Lc00vp4H2XBn98DQN94IhrIN95ydn0jNC4wT884ujo+P48ePHjx/f3IY8cYQIcREyMIyFYUomp3JcCI/F1f5IjoQLFxFqUuXrSjFaBMCkWUiGYADgju5Wq26R3ttyMAbK7Spb3c61iwqvHHF4w5GDszats21+8OiR0T2Girt3fEvx1qJFi7Zv3963b9+6u6fp8u9/05+/hgm43AFHOyd7nEW5FEMDAG7nMCBm9u83NoZ+OoHnX08ZvzpY8otLZq/QnrgojAoXtG3VhKeIhYWF5QUnIODBfqqPooEeyCwvCA+q935/bbmnpuEbFev+ECQxDjYNX7GpCADq1fCWAlPGgOvmHKMwQhwY2w6XNv19NcdlvLVsE21IYSg91+29escQ3uUASValjDZm0XpZ4YJxtM6M8THaROdMSA483I7nK7DlseM43bvEjZDTtLG42E5z5G/NodOaQ6drdwrahTpPH4fxeQww24t+sTLWJE1CoiYuXNIRAHr1/izMmXvgtabJe8VvEwQ4Zkq6Yy2tsAXn14EsKDGlZiEuhw2ufHEQ4MJuDr2b24pnETaL438QewwBgNpkVW4rUfxQAHT1zRYC5MJzY4AR8BUcMwISKmnanh8IAEWmfLuYaD5NuJQRFIcxzh/iPPNt6Yj+IxfOqbkQXleWVAkJfljQ6NGjEUJ79uzR6/X37Jim1XvfEobP5rpbXBd8KGvtizlVfh/Vt4OTfMzIN0ZMX2DGOL+7tCtb/L05K/+RR0G0cJeOHGhzpKf1hqY9RSwsLCwsjUIsFoeFhYWFPYtFhliakSVq9U2LxZOD/35fznmbhh8jFpEM87GyUkk1YhntGcGm4TEBVrGpKH9GWs0NlQ1L4RNX7wAACOf6fAUAZPE3jKW03iGiKHvAkO6CkSx2KfhstSHRzAsQBl+NsusitT1iMN7RVx2pAABRp7rFhhCOOUwc7v71p+J+3YkW7rjEjuvhKuoW6fr5VNe5k5UYf1OSft6l5NPpXoVF/QqK+i68kvbT9aq9aQYAKDfQ62/o7n/FZhobe5S42E78UifGSpWv2UwbTXV6ab2xfM0WoGlxn65sZCULy4uyAv9CYY9hFRSt4SCOjGtVkWQFyXWpzr/ixvMoMuUryKIw55AUrQUcGAPu3cUhJkDYSn8hniFJn3xeqZvlxslt7qcVhI+HlKLD3FvcKsoDAIphjqYktkrPCQgKiImJ+fvvv8vLy7VarYuLC4ZhtrV3xMklPC0OE6y8AG+q8pOXe30ov+7/soef1zfz8wSO53aW/+XVbnh+HCz+3nX+B49ch7cf0tt4/bbpdkbF2u0u/6sn5yoLCwsLy2OgVqvvb9yyZctHH32EEBo+fPioUaP8/PwcHByys7OPHj367bffGo3GDz/88O0HpEdheWEJJwgFRS+VSd3rqxiHAOZI7UUIu22xiB7ggv6M86B1eEuhKaP/k1fvAACAS7rhDoMo1SGyYDHhX08qL64rIYkxa04SuR8sp6pEvABh0JF2XDkvYG945rBE3UV1esw1SmMVhouFbcX17oLw8XB85/X7239I0P6eYgBwAhha03gru3pZpUxPrbiqrXdCfyknxOlhAe33Ixs9xJicbs7ILZmzUjZ6iCAiBOE4Y6WMCcmVW/eTpeVcD1fpGwMbNWcdJkyY8G82Z2F5RmAF/H8QKYYBgJqmRe48q4oki821BTwAlJqLNvTv8rVas480pFnpyV7TAaD079UAEBrQ8woczPHUardUpzztK3WzCXgAOFqc/fbpq6RKtm/Xvgp1hVarPXPmTMeOHUODg22e81x5G2H7dIY8DLQJl/UNCfavvA5EgIjr7hoAMKylcNcdw+aOr879e1NZQzQ8Qk5T3yr+ZJnhaqIlp5Dw9XxSp4yFhYXlRcLe3r5Oy9WrVz/++GMej3fy5MnOne8GELq7u3ft2nXcuHEdO3Z87733WrZs2bVr16drLMszzZt2ojftRA8ZgABm2NcvGp8X7tfwlmLzg9R7jd9i0wabEN5LjOoT1vIdHNeJmF2Hut20yWHkD5rTH1JVIkzM8dvRxlb3DhPhfltbp3aNI4vNgMD1Y++HZ+O7nzFhohIyLV2fKuFIuzrEVFjK49TnOYgTJugTm0E5CbHXWtaTtM+eQC0dGqfeAQATi9wWfKBYtt6SX6xYth4RXFxiR1XpbPntCD8vl1mTMKGgsdOysPz3YAX8fxAZhgFAJU3LPHjGZB1ZbIbw6munG88dAEpNRTJXLFrC26c0pP6TzNOcmQ8AbTsOQZmxRS1ouzEDkUKDcOzNlzutmnTVNuaCoiB/fwV8kiCf6+c717egoAAA0tPTZb8fsWTkAgBZwtVfCuXItIqvPwEGAHwAwJppsFWPG84XH+w85TRHPlwsD9KWlH660mPdIo5jfZnw/oHj6uQ0fZwh7ma9AVEsLCwsLE3CihUrKIpasmRJbfVeQ2Bg4JdffjllypTly5ezAv5pQml1lLIKcXCOq+PDE3Sz1MAwTFOFwddQW8MzFlp7UW3JNYo62gfsD8cl1ffSdXZq+9hUMh7xfLjyKWTRakvuHH7YcbhXiJvS1pQuHwo0hhDQWmtaj3hJjAPXg29VWLRnVNZKEjAAGormZdpFSbkeD6xpfz+OdlW48zd+jqZPA5YGicQA4u9ydiRq4lxwgIwYNxE+K6opn85wXBzly2dpj53Tnb1qyS2yVlQCAOHnJY6JtuvTBeFPJAE+C8tzByvg/4PUrMALwiVVx5W47O7/spzvCQAl5iIACOFyASCVJAGAsZAMSSKCaydy9Bb45RqzMtsQ9jfsWg3s48Ajgld/mZqaCgBWmj6rvPISBCu3l8jn+Hp4eAiFQrVabRHdLZGqv9TmQYY5mbRDCuJ3ekdvCIz5+vo2BgNowCVWGNlaGNn6sc8GCwsLC8sjuXDhAgD07v3AdEE9e/YEgCtXrjw9m15kGEZ/MUETe9qcXQAMAwCI4Arbh9mP6E+0cG9u4557Hk9U12h45bYSAHi4eq+h4ZXnHwnXfaa1/A9ad81asZPjNLKmnbEUqffFmzIn8/1Rix8iSr/J05xUqmPv1v2x62TvvsC/aGGW/mqV8o8St0987p+cNpmN8bdMtzMprQ4T8Hn+LYSdwnGpZGfxZjNtipJ2CxJVl4J/w2NisvZGYlUcQEyTHFcdEJcjGRQjGRTDWEhKo8MldohgH12xsNwDK+D/g8hwDAAqKdr9cz/nSZ5cVwIANBoNh8OR8zwRIDWpAgAfLkeAUJGV0tC0ni4r9gP3bJLS6ILFbXKNWX9nnnCpasmLPeE3fOCwYcOWLq0uFnJSc7OPB8+Sa9THVQkixWFhYRiG+bRqpfxlp/bYecQjXD99n986yDbYeON22dK1hG8xb1ja7fgP2rSN+DSi7dFtZdcdfBK8w1756FWOQ91kKg/CnJZd9sVa6ZuDJQN6NP0pY2FhYXmxsUXFP0RpWCwWANBq6493ZWlCGAtZvmaL4WoiAGACPsfVkSGtZLFCf/mGIS7J4d3Xxb27NLeNzzoPWYT/N3K6WsO/mSQIswvY92j1XtPbNBoeF3G95luyppD5iziyQYBXRy5Y8ubZdT3nsbid45hPOM5EQDcZWWTWXVZblSQu5Yg62vN8BQAQcCBCvV8hHeR8/8T6iwmqTbsp9d2/bt2ZK6qt+yvfbBPndYHAiOHysTVdLoRbH+dBu7QJTXBEDwURXI7Tw5w0WVheWJpGwDMMk5+f7+zsLBTWEwnD8pSR4zjYCgwgsKn3b7/9dtasWVwud+PGjW/3ncHDeACwI8XA4SBGyHxZsFlVdYQeZf14ldxw6XpwlzZHFPtUUqVLIRQVFvgBvPbaazUC/kxuOn+GhFxbrtpZ+u3OxWvWrLlw4QIgZMt9oj12vmzZutoaHgAQbvfT7zlDCq+rziXzA8+/aXFbH9jr1/avvurh1oijQog2mSt/28tv5Uf4eTXZyWJhYWFhAXB3d8/NzT1z5kx4eHi9A06fPg0Anp5sLpInTsX3vxmuJmJ2Qodxw0RdOyAuBwAotUa966j22Dnl+h2YSCiKjmhuM591bJq5trRuEhUtiXFond4Vs8MR3gw5+TjOb1jLNtK6a2Txaq7X5wBAa69Syv0Yn+cydQziVbtDcj14suGudbbF7XDH0XIA2LRpUz1Tdwmq/WmggtQn3NqNH2aA6e801JG4R/YPdn39SH4GAOgpHQAb4cjC8rR5nDJyp0+fXrhwoUajsX2Mj4/39fX18fGRSqWDBw+urKxsSgNZGs8IkXCdk8NosYjWU+l9E0rX5M2dO5ckSYPBMHv27M6yl9rbRwPAyVxTVTkDADdNeitD0ogp8rCodx72M8o5iKvkKUncUs5YaZM5IiLC16taM5sslnjXmwBQuUdhJ7SjKGrdunUAYNPw4pe7MWZL2bJ1pqSz5vRxwNwBAMT37RqBxxZkIABzRu5wzR1PAaRpmD1pjSgOxwvylfTvbqsvwpgtTX3OWFhYWF5o+vfvDwDz589PTk6+vzclJWXevHk1w1ieHIb4JP3lG5hIIP/iY7uenWzqHQBwqcTx3dcdJgwHhlFt+PP+Olss9cLUoqnmxO05tdV7k8fbPxRE+HwFgMiSHxhTNgBtyZ0DwHDdP0K8plzbcJk9KefzTsXuFrEG75LgUKeXjwl6OvUHgEpSaaafja8izah2lppzqsvXWSss6v2K8nUFym0lxqRqzwKGZFR/lJJF5uazkoWlaWi0gJ80aVKvXr0WLVpkNBoBwGAwjBgxIi8vDwBIkjx06FD37t2tVmvTW8rSYDgI9RDw+QhZ8k26S+ryLUUkWZ2pLj8/32Sq/qkNlHEYDQCAHq9eUSmNtKc0OtX8H30YL5qhNXZKjb1IezNVdy6+t93dR7mHEo4IQu2sFZahPoMRQrt37652qqyl4dUHVlOqgwz5HQAgrktURNuOntUXANeJr83qKgWAHxJ0jTou2ZhXiRbuZFGZauv+xz87LCwsLCz3sXjxYk9PT51O17Fjx1mzZsXFxVVWVlZWVsbFxc2ePTsyMlKn03l6ei5cuLC5Lf2Pozl8FgBkbw7metbjpCYZ+BI/JJDS6PTnrz1ty1ieDTC7DhynEUCbLfmLrYqttP4GIty57h807V5MtDGWcxIA+py0N+w6SZvqit5wSSQAWBnrUcUzcUumj9PkTkxJ73VNe1aVMy75lv+F7NG3Cj5Jz3vvdmrnuNsdrqj/qsgZdyv33ZSSZTnNbSwLy7+lcQJ+3759GzZsAIDAwECCIABg9+7deXl5crn87NmzBw8edHJySk5O/uOPP56IsSyNxFZEhC62uLtX57xhGKawsND2PtqDx1QBAJgJ/0hpFwAoayPkB/tbKyo9/64AAJOgjEGQ8efBiu+29HP1rpn20KFDouEyALA/y+vevbter1+7du2BAwfUanWNhrfcljMmHjA3ufIKoBn1vpgAQk7hVgDQnbo0wF8wMlgY492IPKgAgLhcpw/HI4JrTsv+1+eGhYWFheUuTk5OO3bskEqlRqPx66+/joqKcnBwcHBwiIqKWrFihdFolMlkO3fudHR0bG5L/8swVsp0OxPhuKhHxweN4YcFAUDltoOFUxeWzF6h2rjLkp3/FG1kaX64LRYCJqRUB8i8eQBAeC8BrImLqx0s21lFVvqLWrajQ2m90Xjjdp0BCBAAIIAj5XsrLIqm3ftjIGwvkfRxJBWWzFduVO4pQ1xk/7Kj8/teDm+6cd15pjv67Dduqg+Wc2Rc5/fYOCCW557GCfi1a9cCwIQJE9LS0mQyGQDExsYCwOzZs3v06DF48OAvv/wSALZu3foETGVpNLiUg4lwSkd5edx1rMrLy7tSeS5DnxruruvachECWo8ch7tPEuDC24ZbipndZKNe8SuTAoDSrgwAKqQCjotjnwWf1IQ+arXa664pCEdVRyomvDkeAH7//XeVSpWUlARQvQ4v7N5NHxcMDEI4bc4uMN9RYWLS6Y1TmAA3JqaaUzK+7GE/L9qusUdEtHD3+G6+6/xpTXB2WFhYWFhq0aVLl6ysrA8//FAsvqculFQqnT17dnZ2dnR0dHPZ9oJAVWmBonGpGBPw7+9lSKty3R/qXX8BAK3XW8sqzFn5msN/F8/+WvnTdoZ85pwfNTT98JeWppvbxiagIc75m+7j3+wREXKux0cAwFBaTNwJdxz6b2arl5PlhxCgUe7vCFsHAwCZX1xngKcYdxZigY4mC23ZXfJbkxvQWBAXef/QCuNhDA2IwPx3t/XfE+61MshnQ2hoYmdBqAgYAACXaV6CsEbffLKwPGs0Loldeno6AHz88cc1AT/nz58HgH79+tk+2irQZGezC6TNzGatDgM0ViziuvHMWQYvp7uPG7MLsi87/eVIOAcIWyG81JHRVCBpCSMY5DJiV8mWP8s2Lx66JvqVnn/cHlMlMZm5RpWTxPXdcVy5y5AhQ3788UfbJAfPxn7Wb0bVEeWQLoNlMllycnJBQQGO45GRkQKBABByen9UxToo/+4CQ+IAetzB3vmjtnTVTkFkuv6cf9x3G94+vS8vL++l0dPf+mjRuNaihh+aLSWpJb/YmJgqGfhSA4uC5g6f5rP7h8acQhYWFpYXDgcHh9WrV69evbq4uDg9PZ0giMDAQGfnetJWszwJMD4BAPe7KwMA0LRi5S/GhGTgcMBqJfy8XWZOpNQaw9WbmqPntKcuWVVq1znvA/Y4uY2eBO+WKy/UeyD3EsjlHHBzaYZ0cE+RjRs3NvmcXPk0q2IbYykkfJbVqQnfJFgZMtiuDUJYkb2mSk5qsGK1MavOmB0jQGXh/5iL4tUXY5wGBIlCmmzvCqX+/DVzeg6l1eESMa+lr6hb5CMz0peuyqPNNMeRa1WSORNSAv+KEITYMSST+06yMUWPCTDaSJf/XOjyQQtMyNaTZ3m+aZyAVygUAFDjj52Xl1dWVubk5NSyZUtbi1QqBYCioqImNZKl0XxTpaUZ5i2xiHDnmbMMbb1a74A/bV3+3v7xiKuyVMRZLhAYESl0OGKkUy3ka86Dz6tOlJgKzyiP9HYaFCgOTdIk6O0qeKRX2bUkz8G9X3vttRoBv3///rVZP3qqgecreOutt3744YfExEQvL6+MjIw2bdoAgE3DU2qNMSEFEVy3hTO47i6m5A3CiGuG60FfH96fWZgJACc2rUoWRbSb/3pr58YV+azae1x/4Rqt1sjGNv2DZxYWFpYXHHd395prPctTAxMJOY5Sq1Jtyc4n/FrU7qo6dMaYkIzbiwXtQ3Wnr/BDAjiuThxXJ15LP9FLUWWLfzDeuF118JT9q32ay/g6pFhIABBjWI241NB0nRYtTWeQVgvD8J5qHrim5yFV656EegcAwPj80COMVYUJm0w21yFVl7Q4fSa4AbwDAH9B+l8PGXyobNfHfgvub9+3b9/Zs2dXrlzJ5TbsNo+mK/84pIk9xVipmjbDtVvqnUfsh/WVDu8HD6oOaKFVO0oBIPBQRPHi7KojFRkDbwQcCC9dlqM+WI7bcwIPROTPTDMkaDTHldJXXRpkDAvLs0rjntR6eXkBQGlpqe3jmTNnAKBr1641A3JzcwHAxYX9w2hm7BCiAKpomuvOA4BxkaOnTJkSHh7+1VdfxfSMceXJGWAYYPo7DwvniQEglSRxhAeIgrkUcTT3QJm6xI/bEgCMThoAqDLotFpteHh4zTpMZWXlqUunbZVF3333XQA4deqUxWK5c+cOXeMRh5Dr7Pckg3q6Lv6I6+4CAFyvOYhr3ZJ3gVtriUC594svLlQ2NjmsZEAPhONVsaeNCfUkTK5D7vBpNf+ysLCwsDwErVZ7+PDhhQsXTp06ddGiRQBQVFRkKwLP8hQQdm4HAJV/HIJajtmMlaradxwAZGOHGS4nAoCoS7uaXqKFu9P0cQBQte/4s+ZIf0zuctXDzfaywxAAnJa71rQQz7lur029ie6flHoHAABEuD059e4j8PcR+PvwfN3L+O4lXG+8RXVLvS9hQIR9VL3zLF++/Lvvvqu3tkU9MEz5mi1V+44zNGPXI8pl1iT5lzNdPnlH1LUDY7Wq//yrYu22B21quqOnNFZ+S5GgtdhvW2v7/k7Wcktaz2vV6v1ghLCDRPqKMwDoLqsf65SwsDxDNG4FPjg4OCMj47ffflu2bBnDMLYfpgEDBtQM2L59OwC0aNHigVOwPBVkOFZJ05UUzffgAQAqZ2oWzwGAwAgAEOF2bYWvvPm3FtrCbQtZZi65ojgfndSfYZjDt47qBRoIhiJekQeExhkq43bvBoDQ0NCzZ8/aJtm5c+fAgQMBoE2bNpGRkfHx8ampqW3bti0sLLz7BcAwh/GvUeoTlDoDl/bC7WMwSWel2xky0wMhZLvUWYrvnNnz2+E20wb61xPy9yB4Qb7SUYMrt+4v/+4395Wfcpzr1jipgdXtLCwsLA3kp59+mjt3rlpdfYMbHh6+YMGCHTt2LFu2bM6cOR9//HHzmvciYP9qH92ZK8Ybt5Ub/nR4e4QtTMycmklr9VxPN82BE7TRJIwK5wX61N5K0KYlz7+FOSvflJIhCA9+OqZSDAUAOPqveiMztPYKQ9eOAmAYQxoSBACqdfOMcFzcCRD3nxEMPKjQ+nPF/KBVwDDl323Rnyf5oYFui2Y0ZCuTyVRWVubtfTftMUVRNf/aMBgMKpWqJq1SbTRHz+kvJmAigeucybxWfjXtwk7h4l7RihUbdGeu8EMC7Hp2un9bq4oEANuqFSIw381hKW0vkaUWQKjFmpbC9hIAIDz4AGBVkg07Bywszy6NW4GfPn06AKxYsWL48OF9+/Y9f/48n88fPHgwAKSnp8+dO3fNmjUA8Prrrz8JW1kajhTDAEBN04Q7DwDI4rtXIBNtLDYVAkCQKERl4ChKaQBIJ61SrhOPFtjcwBjEOGDOPEpgJozIkRb/Q/fu3WvmOXDggNlcPe2kSZMA4Ny5cwCQmppaxxhzxgRz2ijGXAgAhNf8meOIc5XZtR9Uqw4uX3yq0WEX9q/0Eka2pvWG8tUbmVrXhofAinkWFhaWB7F48eIpU6ao1WoMwwICAmraMQyrqKiYOXPmzJkzm9G8FwTcXuz88duIy9Uev1A844uqvccM127pLiYAAFlSbskv5nrJnaa8df+GvJa+AEAWP6V84BbaMj9txvy0GRb6v+mdQSn3mVIGmFOH1noNs+TNMd8ZcU/j7VfIkp+a29imhywsLVu6Vn/+GibgO747soFbjR8/3t/f37aYVy+lpaUdOnQICAioceatgbFSVbuOAIDT1DG11bsNfiNxJX0AACAASURBVOuWjpPeAAD1jkNQX+5DXMYFALLUDAAMyeS+m0KWWhAXAcMU/C/DeFsHAGSJGQA4jo2L2WRheQZpnIDv1avXpEmTGIbZs2fPyZMnAWDmzJlubm4AsHXr1q+++ookyaCgIJtPNUszIvtHwNseRlqKTTVdsWU7zbQJABBCQQ5cjAIwIDPD5FLIR+SPEAIEiEF6RttG1h4APDu7Dh8+fPjw4d27d/f397e3t7fNU1VVdfToUdv7t956y8HB4caNG0VFRcXFxTWrNzY4soHAWMiS7wEAE0e5+vbbs0q49qsBIlF17jpKW5F58EeJq1fNhA0CIaepYzjODub0XPWfh+sdcr9iZzU8CwsLy/1cu3ZtwYIFADB27NiysrKMjIyarmnTpi1duhQAvvnmm4SEhGYz8YVB0Kal25IPuZ5uZGl55fZYxbL1upOXAABoWtStg3zpx5iovpphGA4AQDfocfa/51j5/hJzYYm58Fj5w8qAa2vlnGfua3k6pj4eDKkCAES44/Yv4fYv4ZJuCBEAAAjDxZ1tjYjvBwBgrWxWS58IRR9+YUxMxcV2LnMncz3dGrhVdHQ0RVFjx46tV8OXlpbGxMSkpqYGBwfbcmbVxnQ7k9LoCB8PYcc29U4u6tqeK3e2KtXm9Nz7ewWtRLiYY7qjN93R54y7ZfOcDzrSzuZLnzHwhvG2Tn2oHABEUfYNPBwWlmeWRmcrXb9+/fbt20ePHj1kyJB169Z98cUXNV1yuXzixInx8fF8fiN8oVmeBDUr8Fw5H2qtwCsspSfKD9kSlhabCuwI5CXh0GoGAFIs5BseEwBAIBAYBTqOgTBkWAAgWZsIAAqF4vjx4xRFvfTSSzV72bFjBwCQRWa8DMaOHQsAtnu74uJ7yo1wPD4GxKH0ietv6FZc1X5b/uVVl9mqVl07vX73QU/liXWiYUvW38FWXNXWft1UPMzTCbMTOk0fBzhWte+48WbdlX8WFhYWlgZic6AbOHDgli1bnJycandxudy5c+dOnToVAFatWtU89r1g8AK8Pb6Z6zLnfcmAHsIOrfkh/gBA+Ho4zxiPCeuv+G3JLQQAjotTvb1NSyWpPKzYa3t/WLG3klTeP0ZHMwDwcokiqqjU9tLTDADElJTVtJgZBgAamwTnaYLLBvCC9/GC92HS3gxjAUDA0EjU2tbIcRnT3AY+KTA7obhfd/fVcwlvecO3mjFjxvLly20avk5JaYVC0bt3b1us5YkTJ+5XCmRhKQDwgwPgQSDECwkAAEtR3dV7AEA8TDbSFRjIGJBQE/cu6iS9Gw/fK0F/tYrjTNi//DT+RlhYniiNi4G38eabb7755pt1Gj///PMlS5Y0hUksTYBNwFdSdV3odxRttDJkV4eYS5V/KyylVoYMceQUqklwh1sWSz/CAQAwhAV280s7lylWOCBXlKZLLlEUnzp+miRJX1/f995778CBA7bZYmNjdTpd7stJVCX5/l/vr1mz5vjx47Nnz/b3969tDCZoyW/99w2VZMUhLQAACADeBgC6rR6XbKc0CgBgLEbd9diktj8k3dDV3jax2LB9qOtDjpQf7C8bObBye2zFmi3yFbNrVxl50GI7W1KOhYWFpQ5XrlwBgE8++eRBA959990ff/wxKSnpKRr1YoNhwvZhwvZhAMCYLfkTP7XkFFmyCwg/r/vHWnILTSkZiMvltw5qyNw0Qydrb9zSXU/WXJdxHVuL27eXRrsQDV1o3Vm82UybOkq7MsDEqy/uKtkyqUXd/AgIATD35pxnaIYBMcJqUtdpaZp5EjXQmhzaYC35AQAI76WWvM+tis1c9xmIaISyfaaYMGHCg7qsCiWl0WFCPmfsOISg+NOVlux82dih9q/0auDks2bNAoDZs2fX3otKpRo/fnxKSkrbtm1PnjxZ5xGhDYYkAQARD/NvxwgCABhL/Us7bjN9lFtLSAWJuMjvtzBb3DsiML+tYXd6XDOm6ADAebInJvqvZm1geYF4HAFfLzwer6mmYvn3yPDqFXiOG4FwRJaTjIVONd9K1MQRGNHPeWiG/k6ZuTjbmOEvc2NyEABz02w24BYAYIDp5NLt79Bjbin+AoPYINTsvrTDjpT5+vh07969qqrK0dFRqVQCgF6v379/f5RPqDbX6HjNrlevXidPnkxOTu7UqW5+EUwY0obPyO3KS3RUL2++KeNc7F8nfFxlpsEzS7fNto3Rxe+T9JgwdkBXH3uOsrLy19s0cAiH8kSAlx9+sPZD+5pSs4w3bpev/MVtyUeI22TfahYWFpYXhIKCAgBo1arVgwbYytDk5OQ8PZsaTFVV1Z07d7Kzs3U6nV6v5/P5dnZ2vr6+ISEhMtkjakc/FyAeIenfvWr/yfJvN7stnoFLJbV7KbW2/NvNwDDi/t0xwaNdIDP1dzYV/lBiKrR9LDOX3NEl7ynd2t2hzxvuEwnsEbdzWfq0OPUFAiOGy8diCLupuXa18vxLjv3qlAEXIWRhmGNyF9k/dWcii0p0DHPa3dXuH1EfXlhivi9z+zMIWbqBIcsxcSRHPpnSxVPKfWTxGsJnWXPb1TgqTbSM/wjHW46LI8fFEQCApit+/N2SnQ8Alb/v5zg7iKIjGrij2hreltV48uTJ2dnZD1HvAGBbgLEUlDxkZlsvx6mevMUMxRTOTmfMNEKIIZmsUbckvR15fgJKa9WeUplzjLZhFesKZa+68INEDTwWFpZnk0a70Ntgy8w849S40CMciaLsiRZ8xEG7S7cCgIW2fJ72QZm5GACWZcw9q1/NVAEwTBpJfnpnLgBUWlQzUsYW0/m3Ai9KjA4AoBAWA8MIbmZgGIYQioq6Wyxk27ZtjmPkAKD8vXjRokXR0dGtW7eu1yQuht4LtwOAEj313biX/Er25usYYedRMnlg9QiGUe6cd7nIHGNfsWHFQuAQuL5iyRudH320CDl/OJ7j5mTOzFNt3GVre3isOxsJz8LCwlIbW34Tm4yvl9u3bwNA7fzSzY7Val23bl3nzp1lMlmnTp1GjRo1adKkjz76aPLkyWPGjOnataujo2N0dPRPP/30H7g/kY4YQPh6ksVlxbOWa09epHUGAKD1Bt2py8WzlpGFpYSPh2zkwEfOk6RJWJE1r8RU6MKTv+T4MgA4E25dHGIwwM4qj63ImmfLkvMgGGC2F//CANPPeagT4eLAdXrZeQgDzB9FvzLPtC/8v4A2WEt+BACu56cAwPWcDYBZFZsZy8Ok5rPG4SxT5OaynxN1jx4K1epd93ecrQgC0Ez5t5v0l288ZIusrKyEWvTq1Wv69OkURdke+WVnZwcFBa1atSovL69mzPXr10ny7lo6v01LwDFT0h1rWUW9uyALS023MxGXww8LvL9XH1+lPljOkXF9d7SxH+hE6yn1AUXZ6ryKX4rMOUaer8Bnc5ikjyOpsCi+f+CvHAvL88LjrFWyZWaefapd6GkaAAKPtAOKAQy5EG7pqWmEmBA5Cyy0hWQsBEa4SpTJFICeYewwhaEvgJ6i+HxcgAMuMto7VslLHXMrxQpAofkY1aGyChB07tz58OHqpHEnT540r2dwKUd/TRMhibp06dJDrBrZihuffvlEeftrla57j17ptdvMYHgHlxYnSqqzJZnzEm8f/6PfIZU18i0CYG5PD5lE3JDjxURCl0/eKZm7SnvioqBdqGL5z//q9LGwsLC8YHTs2PHQoUO///57ZGRkvQNiY2MBoG3btk/XrgdSXFzcp08f22OFGhBCIpGIJElbkRSGYa5cuXLlypXVq1cfPHjwIf4Fzz6IR7jOn1a+8ldTSoZy3R/KdX8gLpf5R//wQwKdZ05EPOLhk6jIivX5q6wM2dtp0Ej3CXnGrLPKY2KO5G2v6f2dh36XszTbkL6taMNErw8eNMMF1akcQ4aM69jfZaitZaDL8EuVZ/KMWRdVp7s6NNTR+jmiZvkdt48BAEzQEnccYluERw0OOmheKAZWx2sZAr6v0ilVNPGPC7mRYbJIaxCXQ6C7cQxShPpsP0T/HYd4BC/Qx5ScLuoUrr+SWL56E1gpUbcO98+fnp4eHBxMPzQrYXp6eu/eves0zpw5c+XKlbb3uNjOrkeU7vTl8jVbXOdPw/j3eILQBmP5ms1A0+J+3ep1MxFF2nt921LcVcZvJZIOdLIUmHTnK8kyCybChW3FokgJYEg6yFm5pVjS1/HRp4yF5dmm0QJ+8eLFtkS1GIb5+fllZmba2mvKzBQVFbFJbpqdmiz0AIBwBDgCgEvzkrZtPcTlcrdt2+bdR74ub2WYuN00n08jb5VpKhnMDjycukPOEQYg1C58hHDC8cTjZquZw3CNQq2ZawSxoOhSgqRLO3d3dx8fn9zcXACwWq27D+x+dUTf8g2Fyi3Fnl8HAQBJkocOHXJzc4uOjq5tFab6Y5n7jGDOhDXX5r7UQsYQOiz71Imbp2pqwgOAav+Xws9PEWInVyF6K7wRro+Ej6fT5FEVP21nrA0tKcdGwrOwsLDY+PDDD//6668ffvghLCzs/lIysbGxtvvsoUOHNod1dTEaja+++urt27e5XG7//v1feeWVLl26ODk5yWQyHMcBwGKxFBQUXLx48a+//jp06FBGRkZMTExSUtKD3HefC3CxndvC6fpL13WnL5vuZDNmC+IR/FZ+dj07ibq0B/ToWPJDZbuMlKG9ffSbHm+je2PP3fleH/rNW5D20UXV6X7Or7rz64m0N9HGvaXbAKCrrNeekt9TtDdMtDFM3K6dJOpExaHdJVvb20cLcGHtTbQ0XSfgWEvT9D+7fg6W7Blr7eV3G1zP2ZTygFWxmSOfWu9GDwkyf9KkKsn55zUW6p5TqzbRhVoKDwSrH/2bvu4i/BWTuU6LRW8YwCNc507WHjsPAMLO7bhecvWuI+Xf/wYA92t4uVz+yiuv1PHfIUkyIyPDaDQCAELI29vb0fEe5czj8Xr1uueJj2zMEFNyujk9p2TOSofRQ/jhIQjHGIoyXk+p/G0fWVLOdXeVjhxU74EjHDm/c7e2POHFdxhVN0kBxsec36un/jwLy3NH4wR87TIzq1atcnJyQv9cMKZNm2Y0Gj/77LNvvvlm1KhR7du3b3pjWRpMTRI7ANCcVuW9f1u43NmWEZQkya+++ip28H4AKDblA8CoEOEhi7EYrGX/PLsvLis6nnGcJEl/X3+dJPymNl4jrXAu9zpfktvbHAoA3bt3twl4ANi2bds7v0wo31Co3FbivtAfE+EMw2i12qqqqrCwMLH47hI6ZtceAL3hvH3L7fGbb7kCwNpeB6fswDIL6BoNT2nK1Ue/dxyxaGp7CRdrXGobUbdIUbfIhrvHsxqehYWFxUavXr1mzZq1fPnySZMmrVu3zrZWplKpVq9efenSpb1799I0PWLEiNdff725LQUA+OKLL+Lj4x0cHPbu3dujR4/7BxAE4e/v7+/vP3bs2Fu3bg0aNCg/P3/58uVff/3107e2KUFI1KW9qEt7AGBIEnEbUdGaZuhr6ksAMEz+Fqovc5wbz6OLQ8xZ5dF49cUhbm/cP+BQ2a4qslKAC2MVO2saz6tOAgCBeBqr+i/FnuHyexKzv1xSty59TElZw21udmhDSu3ldxs1i/C05kIz2lYvyeXk9dIHBIzgDAAw5UArq//3MWdAjgyjgCg+r5sXDxjmWG5hsoM9KeC5zp3MDw20CXgAkI4cCAAP0vBisXjfvn21W8rKynr16mU0GoVCocFgYBimoKBg8eLFY8Y8LG8/LrZzm/9B2bJ1ZEFJ2VfrEI/A7cWUWmPLWkf4eLp8+l79NRRZWF4wGifga5eZqdNlKzNTXFz8448/rlq1qt4KkCxPDVsSO5sLvfGmliw2G85V1fSmp6e7EHIM4QpLKclYPooU97bwXy8rV3BpAOAA5pfWhqRIX1/f7t27Q6Xppjae9jRBOVhwdO7vvwGge/fu27ZtoygKAK5evVpAFIui7PVXqyr3lEnfcv3yyy8dHBzEYnFaWlqHDnd/5TFhaImhnVyY0MnuUmzlEDdj3onD5n6dubtPU6Xl1pphmrObxJ3fOLI369bWLFtLdHT0kCFDnsaJY2FhYXlR+eKLLwQCwddff339+vXr168DQH5+vi0sDiE0ZsyY7777rrltrGb37t0AsHTp0nrVex1at269fv36/v37Hzhw4N8LeLVarVKpGj4YAKxWa3Z2dp0uDodjy+9VB5VKVROi+PDxNvXe8PHlljIdpeUg7i8Z3wFFATBmjASAImP+kpRZCCE+n6+xVgHALe31GgFfM7+aUR3VHQAAI2UQIlEHXtfr1ss6q+YV19fj1BdLzUUA6Fj5/jZ4B46OBwBROOcCds9NphkhBkBYy1MAAbRGUFRfZsR/f37+5XihoUICQOmSEEAFPo7Oz6893rYIT+sSAECtVmutTf//+3jjuziaZARTaUHvBpERDjQAXFNiGzO4jnwmJkhwgDLSSjRKIBzeUggA3+u058AEKpjioXFWUGjn8Qxvz+TuHZio1goxr87stTW8QqFgIu4JSKltj02923LOUxSVnJw8fvz4zZs3T5gwQaFQ2Lx4HmS/hsDMU15HFxNRwm2mtMKqUAIA4+6MRbWVjxyMOHUTyD+17wM7/j85/jmGaQwBAQEAcObMmZoWAAgPD6/5mJiYCAChoaGNmvYFx1a2x8PDownntDJMSH5RSH6RlWGUf5QkiE5mj7tly1Fko6SkZFHax2/fHKYlNQzDmGm6dUFxcH7Rut9+27hx48aNG0+cOKFWqzUaTZ4qZ0LikMlJb2zc9OvGf9izZ0/tgvALFy5UbitOEJ280yPuzp07ACCXy/fu3Zubm1vHsJAg10lvOoauz/L7qVja5/2aGQZ7BXmJ7qbVFQR1abE0HvGqPfF8fX0adfgFk+fnvDZVte1Ak51QFhaWFwMHBwcAUCqVzW1Is1FUVDRv3rxhw4aFhYV5enrGxMRMmTLl0qVLzW3XPdgK36SmpjZwvMFgwHGcz+f/y/1mZmbeX7/6sfnss8/qzJ+YmMh98KL6vxx/R3trQuKQhrxmJI+7f/5e30bZevv/2oVnTwDAO5eHT0gcorJUWGnr5oIfbb19f4xuoD1P+nj/5fj3XuPpL0v1l6SnN9jVO96UPkF/Waq/LF0ypf414eayX9pnit9Pxe7/iwUAQMjz81N+PxWLu4wacuBgq/yiwOPF7TaV6iw0wzAxcfmt8ou8fjqAI7SqQ++c16Z+vHVfq/wi2YSJtvkVq37NeW2q7mJCzfwzwzrlvDY1c9iUIV7VBQudZcjXA/P1wFZ++QFtyinJjw8NCQKAtm2CL5zZwScQALg7Yw72qGY8j9ug4xVyuF4iiQDnNO/5ZMf/Z8b/l67vjVuBf67LzLxQ4ADuHFxJ0QzDcD2qS8H7+/vbFlUAIDMzc0bU51pKY8cRAwCBUCsu55aFLJVIWqhUAFBQUFATziQKkej5mio7pb22OnpQr9ePHj367Nmzto/btm37PPFz7qIsq4oMbdkyNDQ0JSXFarXen694/co3Vt/gG2kBALTs98qgwC1qHb1pvyVDo5zbusvkK0dsw4zpFxnSNOSzT9qXfo0Q9Bo4slGH7zxjXOmCNVX7ThC+Xg0vfMLCwsLC4u7uvnjx4ua24hHY29srFIqsrKwG5qXLy8ujKOrfB8BLJJKwsLCGr8DbloMwDPPx8anThRAKDKybTFsmk4WEhGi12vun+vfjZVcUyAk4FBqxy1FnJhkAiys61l/tVMER7jAdVhaOeustlyjpSeVfIeI2deaXtha1eMkNACyV1qxvFR6OnsgJcTgcCkgAwBE+1nNysakwQ3/bo4tLh2FtVYl1TXr6x/svx7u7mQH0gICkBOvm2QFAp04pZO5nd2f4J3ePu5u9n1/dcOvmtL/gbzBM5fu19+v5OvAl4BEMmjKX8gQHhykA4CnG80zUthT9y778IiMHAcNX3D7Sb3SgUEIxTAVpAoARga3bvPRKOyPXkpUPANqTF0230gBAqtMFyT0yDZoAoeSbjr0jvHzPoTv7V2hwzHYqtuad+m3AB7rUbKp1IB67vNiR/36IP3Y9lfpzuahdMP7tNvNnPxhVVcxHY8T7Lrg08HjlzX4+2fHNPV7Ih75RZg5+N7MDjwtvdL9mLV0PtcpeuvE07SOCFRX1lFqod/7nmrvJwxqCq6urQqGIi4uryVKLEAoPD79xo7q2xIULF7p16xYcHFwnMSzLQ/jf//63cuVKDw+PwsLCJpy2hKLMDOPD4ZizDCltL/N8BYtD1uzcWR239scff7zxxt0IN4qGOYrKWNLYKTO7W0GuRHh3haGKrMx1SS1yym5REeidH8KhGSC4rq6uHTt2lMvlJlN1vZmLFy9Gte6IMISJ8LVr106dOrVLly4XLtQND1NXnOu5z1VDSSQcvcYqWuqxau7UdTkFencnbFqL7qv1lvK0SwCAOITPV9c4YtmfQcNbCtL4oUcwcd3a8g9Hc+i0avNeTMB3X/kpx/U5zlrEwsLyNHF0dFSpVEql0vaonqU2BQUFNE0/C5XkRowYsXv37r59++7fv18geERMLEVRkyZN2rhx4+DBgw8ePPh0LLTxhK7vj4H+fHz5d79teLusRG55W/puF++BAJBZcfPLogUexby3f3W2H9JbOmbIFxmzcgwZ77b4MFr2Us22NEMvTP+o0JQHAK+7j+/n/KqtfebttytJ5aqQX2VcRwBI06Usz/oMANz5XouDvsVQXW/n5wtL1nRr+daGjMTtu/GCn+r36pGsv6FbcVXbzo0wkMwdJbm0u/0bIcIv1VVbtfog4KZmkXwO8hRzsjESSaEjjnmfuGjb8Eagb6aX28TY0yNOX3nkXnCJnefP/zPdiGAoLeLYAy4b9b/c/afUrYMER9b7O9hzAKDr6PTrtw0Xfg9qFyIEgGXr0hb/bORysLz8Qrm87lOPBsEwZSs2MEaT24IPGpK4keU/gLV0nSV3TkNGEj5fcdzef1Dvf+n63rgV+OeuzMyLjByvvnYSXnxegJAfJOzRo4dNwCOEWrZsWXvw9wnafWUmTlu4ZOcX1qrtxI7VmefOq04eLvjd9t7gVgV5wDGTw/sP5Lq7AsCgQYNsgYgAsGXLls7rq2u2jx8/ft68eRcvXoyLi+vYsWPtHW3Na6ehtFF2V2I8qn7LCfssvl9OwaroqIiogLTVKULhuz86/DHbmnFVMGoFz97JQsGXhfO2dtrXWPUOAJJBMeaMPP3FBFNKhh0r4FlYWFgeSrdu3QBg+vTpI0aMeNCYNm3aqNXqRj33f0J8/vnnhw8fPn78eI8ePRYuXBgTE1OvZztJkufPn1+2bNmJEycQQjNmzHj6pj4L0Fq9csNOYJhewpjf4egu3a5Wlo6OhDMmFAAA18MVcVDVwVPnOyhzLBkyrmN7+3vc4M8qjxaa8jDAaKDPVhy9pDpta7cFzK/MmocjDgAwAAiAASg2FZxVHotxGvDUD7Qp4cjfp7UXGaZuhvY6IERw3Z+579WY1qJfkvS2bHZyO/y1VgIASLeQAJAOJO4PJDA5QNrkbxxFx8Xc8z9e2iPKMdAXAHQnL5qz8u16d+H5V8cPMxSlPXaeLCjBBHznDycgjiPHYyaZvxBxXfltLoyYuEvgcuj777+vSTiPCSMBrvFabhNEdKCqzs6eMNRewksqe7lORvqGwjCqzXuM8UkAUPH9VqcPxrAa/pmGYYxJd4w3blvLlIiDc9xdRFFtCb9Gx6IzlAEAMFEbTNQOAIA2WCv2AFAAOMfpNcCEAEDrr9P6JNvIF4HGCfjnq8wMiw1EYKGJ0QDwPt26qqrqypUrhtBXh191g6sl9wwTAQAgGVp7Urf2eo3/SSjATwgx0e1nV4AisJWzYxnGlbvY+saOHVsj4P/888/Vq1cLhUIAEAqFEydOXLly5ffffz9//nytVtuuXTsA0FmYzUl6AHhP/kswdnVkMLyJ/ykb9t7RTSu++mwA0XcKLpLN6f56N0+34XYyCwVCpLuub6eQtXm8vBNO08aIX+7Ga+n3WFuzsLCwvEDYHKYuXrx469atRYsWoWf7trht27abN28eOXJkfHz8wIEDhUJhhw4dnJ2dHR0dxWKxTqdTqVQVFRUJCQkajca2yZIlS+rUrHpx0J6+TBuMgoiQnt3fS8wuS9be+CJz1lse70o5DgCACfjYsPYH9QcTLLEI0FjPyUQtr1QGmANlOwCABhoAFJbSOpOXmIvv3+OBsh09nfrXm+v+eQEThvDDE5rbisdEyEHjW4u+idMCwOQIO1tNn1YE96rZ0pXPdzFhu+4YAcDFG1MKqB58Xgcez5yZZ7h680po4G1fj7AWcnFoIACYbqWZs/IFbVqKOrcDAMZKlX+9gSwowe3Frgs+IFq4AwBXPtmq+J02plvLNo0aNWnUqFH128RYydw5APDBx0u48oZWC7p3Bka1eY/mr7O2T7pzcYAhp6mjWQ3/bGLJK6r4Yasl5x7no6o9x4QdWjtOHoXbix+04YPApb25XvMAwJLzPwAKEBcYEnAJ4fs1AJAFS2h9UlMZ/+yDNWq0rcwMTdOTJk1q37797Nmz4Z8yMyNGjHj11VefqTIzLHXAMGzOnDkHDhzw617t/yZ3vRgS9AuOWQCA0QNYAPEYdJ83IsMglToUAHQBZPDQATW/lf3793dzc7O9r6qqqu2aOG3aNBzHd+7ceeHChZs3bxYVFQHAlmS92kxHuRM+4pY7/jLgiPpQvrrFqMUSiUTh8ibPtx1hruhnSnbl8IYVXwEAg5H6n9/ZFk71VKNtCIjL4YcEIBzTn483xL1Af9UsLCwsj4FNtC9ZsmTYsGE6XT1hhM8UI0aMOHHihM3jz2AwnDt3bs+ePT///POqVavWr1+/a9euM2fO2NR769atDx069Nlnnz1qyv8sppupACDu3QUBmuz9v1BxeBVZuTZ3xersRQBQaMpb1OrPhHZ6XUoWrQAAIABJREFUnEITvT5oK7mnQhgC1FIU5ky4cjECAOw4YinX0fbCEAYAEo7M9lHCkQIAAnAmXFuKwp5r9f4fwFtS7YbpbV/9hocQAETyiClSMZ0FVBa40zgARPF570jsprYLfT/QO6CwBACo1Kz7J7Spd0NCcm31DgCACMJ7EQCQhV8xVmWdreRyOYfDcXZ2tpb+TBvvIL4f17Xu+l+D+Ee9Iw4OHBwAMAFfd/Zq+bebgaYfZ0KWJ4k5Paf0s28sOYUcJ5l05ECX2ZOcZ74tGRSDCQWGa7dK5qykKqsePUt9MJZSa/lWAIwXsB4As5ZvZSz1PEP8z9O4FXh4rsrMvOBUUPSSSvUYsV0HHlH+c2Hx4qyWJzrwg0UAsL6fzDZmZfat29qbyyIH+wnafvZ3VTxpriColyN4c4N089KmAyAE8HngCglHOictwUrD5cq/+7ncda/gcDijRo365ptvbB+3bNlSE1fv7e09dOjQ3bt3X7lypVOnTklJSVIX941JegCgGZh+behtlfQN8qfOkotz8hXvHOFecewHABKqYEt0qDnDCQEgq4Xh21/LdWIAVEbaUdC4h013YZiKtdsZq9Vl1rvCyDaPOUkDsNWfZwvLs7CwPKeIRKKNGzeOHz9+//79nTt3PnDggK+vb3Mb9TB69eqVmJh48+bNY8eOJScn5+Tk6HQ6g8EgEAjEYrGvr29oaGjfvn0jIl70VKZWpRoAuF5uACDAhR/5zj+vOnm84mCJqRAALLSZwIiAO9wep+06rOl6/+ZTfGYBwLHyA38Wb/IRBHzst8DWbouBXxC00hYDv7d026GyXR2l3d7znvnUDo3lQfxyU297s+6Grqsnr3bXDwk6WxhMusoKorvt4t5d+El3AMBwJVFToZYMuFujkbFS5St/qUe9AwAALhuAS3tR6lNkwTLbcmgN27dvLy8vb+EhMSWuBADC56vaWccaTuW2gzb17vzJO+XfbGSAcpk1SbHiZ/3FBABwnjEOsMe9S2RpamijSfH1L7TJLOoW6TRlFPonM7woOsL+1d6K5T+bM3LLV292W/w4sSdk0UqgzbjTa7jjUFx1iFLuJYtW1/nWvQg0WsBzOJwFCxa8++6769atS0lJSU9PV6vVQUFBrVq1Gj16dHT0A8uHsDxl4s3m40YTB6EOPMKYpKXUVt0ltU3A1yDned7W3iww5YaKw7/pJZ1wR1kBlIKg3PleYo5Ea9V0c+jjKwwEgD5Og44o9hWac7XWKjHnbjm6CRMm1Aj448ePFxQUeHl56a9UAcBHH320e/fuPXv2dOrUqbS0ND6zWG3CACC+xAJYAHQI+CCvrR8vR2V1PJNnskWwVAgj9uoBal0XSHnE6njtT9d16/s5xHg/zo8+ICQdObDy9/0V3/3mtvTjOlcdFhYWFpYaRowYERAQMGTIkFu3bkVGRu7evbt2xdBnk7Zt27KZdx6O7QaaMZO2jxj6P3vnHV/T+cfx7xl3z9zkZkcGSZCIvRqx1SxVsyitUaWtolWKqqKtDj9FVUtLjZYaVXvvFZEgIhKRvXNv7t73rN8fNyKyJIqKnvcrf9z7nO95znOPuDmf57vQbu4vd3N/WUdo0i2pCo7Sj+tfvGweMAB4jY+FXRS9Dpbsum26cbL0UG+PgZWO3jUnH1XtRQDpo3zl6X0QljpyKseepCaUAtRBwZUCZ1yRs4MP13XI6KT3ptkwBEIV+D2CrFRpkBvgA2YLAGg37S4fZCha/d0v1vgkTCquqt5dcAK/pAxdSNUm3OtNVBhRPi4Wi8VisTNrNkPqMFl3TP7yY3wc3bZ9hr9PuNS7sF0L1yAvPNhrwfSSL35kNfzzhvHQWUpn4DUN8XjvDQR76B8Fk0s9579TOOsL+517tht3BK2b12tmxllMqrcBoBzfDwGA4z+X0vxNqrdy/GY9yQ/QEKi3gHfRINrM/MdRYCgAqCgKADi+PAAgiirXYvHnBwJAvi3H9daHwABAxaVTTLdMpJGPCoZ6l+UyDfEafVJ9kGCIvcW/j/efXj5DZGRkmzZtXLEYNE1v3bp1/vz56cNuMk66Q2qXTp06xcbGpqWlhYWF0UV3V/d5yeQoj3RiiMLbDluxCCMtFA4A1huH+4TQvSPiGYqrTvR2eNCNu8+O9udfynfQDHx63tBhlFLMfZyQPNmrvZ05BZYL11TLf/ZZPgeTih9jktpxud9dL1gnPAsLS8OldevWcXFxQ4cOjY2N7dOnz6pVq6ZPn/7o01ieYzgB3s7sfPuddG6wf8VxN457e3k0ANiT7wFFc/y9dQictVjJCmUKLTRTTFGNOTgA6u8554L25MrSlIt2x/ueMS4DK2W5rDuzr/hPkiH7KoeECMOe4SdjqZ4fEswAMK2NWO+gV8eb1yaYOwwqK7u9T2elw+gwBSfEDU83EABwxmbX3o9Cv+F0AoAoug1ciNdu2s0N8gcA04HTjsxcTCr2WjyjJi8IKgjDvd4ii9cT2fN5zfdVPETbUknVVkBwTuCXj/FZqlXvLnhNQ1gN/xxivXwdAOQjB1ZS7y4wiVg6qKdu2z7LpYT6Cvhy9zsqbAYAqCAMc3/V5YRHcOkTWXxD4TEFPMvzjxLFAEBN0QDA8eEBgLOwioAXBAJAvj3b9daTwoGBUg69rXAzAAz2HiXjlAXbc1Fee3n0Zd3Zi9ozI3zeFGBCAACGsVy+Mcy3SXl7+Z9XrPyg/xBxZ7nhaKlma+Hs2bNHjhy5e/fuBQsW5ObmDm3bViZ74L2nA/rYk/uZafmaoulijBJe+3nam6vaKs6QuiOBKw0mGycxcWSANHxkM+Huu9YbJcQ3V41LYh6cXi88po0hi9WOe9nqbzd4fTYDwZ9ke5ty9c7CwsLyAuDt7X327Nm33357y5Yt77777q1bt9asWcO5HwbJ0uAQdWptuRBvPHBK3LMTKqhSrp9h9DsPA4DopTZf6AwHrbaaZ1KC6HUAyGQgJW2JhNYBwGdpM2mGBoDeHoNG+Ex4Wp+Bpc6cyXHcUhFKITqqmZCg4bck68V8R3yxky9EAKCUT2ONIQOIDCBQGQDANYfzmsNZcQZ508buk0Zoft3lzM4HAEdmbrWR85Xg+H9Cle6hjOcp7SFM8SBGg8ieDwyJ+0xziS6z2Xz9+nWpVIphNT6JkSRpsVg6dOhg2XmkTL1/WFm9u2A1/HMHTTvziwFF+RFNajLhR4YBgDO3nrnrlKWi+93FAye853/ry4cV8C8snhgGACVlHng+ABAF9ko2/vxABJBCez7FUBiC6U0UwwFayqSRSBOud6UYuVe9x1zWnSUZYm/CNmkcdApvTp67Zk/NGIRKl2G4jSIBIFdb+teM+d0jRgF4qTfkD70xtEmTJunp6YWFhT4+PklJSV26PMivQyUdMPeh4+iNhdCmb4uYXlPiAMCRugIAXunG2bTPOWPGjGPHjqEIfNVNPnhP6R/J1ldDBW28uY9xNxAux/PjKUWffGdPydD8vN3j3XGPMUkdYZ3wLCwsDR0ej7d58+bIyMh58+b9/PPPKSkp5T1HWBocwg5RvCaBjvQc1TfrPT+agooe1KplKEr7y0578j1MJpEO7GG22QAghs/zub/Nfd5uLybprgKe9325dcpq1dCA4XLakQcAKCARkjb9PV9rKo58eh8hzXLnpvFaovEazTCtpO1aSts1FVcj51gAYN0NMwC83UrMxxE+wPhI4Q8J5rUJ5tX95ReyHPEqIsIDH9hYAAApOuJgsU1oR6e2FOFoWYSjFEUHCQW8fl0BQPPrLgBAhYJHqncAQHA5x3+uM/tjZ85CgbwXoHwAoLT7KcMZBHfj+H3kMrt48WJRUVGtM5XhfiJOkJyJYKhsWH+Ex7Xdult2gGYAwHY7Dbmf8SEfPUj/x37LpQTaZPFaMA1q3hpgedowBAk0jfC4SM3/CqiQDwCM3VmTQbVsOxMO8BUAQEosQGyFI98BACTDqGaPsd6GSv0EfE3t36ty7dq1+i+G5UkiRhEhglgZxkwzXL/qPfA8lK/keascRcWOAj9+oz1pNjocwaSMmRMy2rsDCliBPdePX9bEzYPr6cn1UTmLUgypIaIWt89eCLpbgLvLg4f3H8Y3b9u5w2W2Iy81xusKrujvzAHzUd3MmTPfe++9PXv2zJgxIyMjo1WrVmLxgwh2buBSoe7oYq+JiNYDAm9S5nhKfwpBeJ+/w+w/R1+5csWRX8zxcAtV8N5rK/5fnOmulnw8AQ8AmJvMc97UogX/M5+J5fh6yoY+TiJWVVj3OwsLy4vKnDlzmjdvPmbMmPPnz7dv395mq8U3y/IcgyDK2ROL5q+wJ6UVfLBE8nIMLzwE4eDOzDzTiYtEQQnC4Sg/nIiKBGCzAcAYsaj7fUf9ZLWmmHSMF4uj+WVlaLIJSuNwzAiaH4LobbTdl+/HQR7z73JdKHWqfsldlWZJLh85pi44pt7XRNR0csAHnjyfR86wadOmR9q89dZb/2iVzw0X8x0JxU4A+DXRsuV2WeVgBIHzeY5eW9UaOw0MqErobSlWAAAGSBNiBEamxF5vLqw0laRfV2dukeVivNfHU+pYPwj3mkiqfqOtd4jinzi+M4F2OHM/BwBOwKcIXhbDL5PJXAJeKBQGBwejDzvMCYLIyspyOBwhdwsE6cUAwFC0/s+DVa+l+nJd1UHbrVTNT9vdn6aThqV2EB4XFQlpi5XU6HF3ebU2REEJAGA1HK0jF9rsi7k+5J/M0KCpn4CPj49/SutgeRp4YFguSappKsCVA1/gAACGYU6fPm00GgcOHMjlcgP4QSpHUZ4t24/fSIgjDh0DgQC81q2k7Q+r9uwu2vpO4Ecd5GVu8w7y6IOq3aWy4pD8FipPaaiJ8l42E5OK3+bT5QL+RGGWho+KvO6S2ij1T3lv7X5r8eLF169fN5lMIpEoNze3efMHGS8I15fj+z6R/zVDlhKF/6N0RwEA95+rFPx1cdMtBJlSNOsLftMQ7yUzp7cR9wvhh8j/UcwIN8hf+cGbqm836P44gHsrRZ2fVmli1gnPwsLyYjBw4MArV64MHjw4I6OazlIsDQXc091n+ZzSVb/ZUzJcAfPlcHy9PGaM5zUJrNeEKILWRTz/Q0ochV+lf2IkDVJc1lXR54h6L8VQA72GX9ScSrekLr03Z16TL8vdDE8VUkfQZoobULavQRlJZ46dcdLcQD7uUbZ/QZY6GQfD8XusgrtPCIOjrIBBsYWqdEhjK0t0LzZX7rumd1Tfic397VHub4+qx+URjBP0lePOEKLgO9xjNKn+nbFnooKmuOcb5SauZBwej2e1WtVq9csvv1yenmOz2Y4ePepwOHg8nk4pC8xSAUWjUjGvkS+gD0og2W6nAc0IIsMeDDKMM7+Y0hkRFOW3fYqRICx1QRAVbrlyw3z6inxE/2oNzKevAICgZdM6Tlj1t/NCm31QnYb/73QUrJ8c+umnn6oOMgxTUlISHx9/6NAhhmEmTJjw3nusT/K5wBNDc0lQUVSwgocKUMpIUmZq8deLly1bBgCDBg06cOCAPz8wwXAl354DAN5iqlQHGIARC2QABJgIAG4YrpYL+Jay9gdVuwmejcQJi1RItvZwFYSLiYmJiIhITk4GAIIgjnhwxppz7Peam87rkEx69uzZ8+fPl0gkUVFRVZsScXw/IIs3MqSaKPwBGCfCD+H4TMeknRtZB9DUnzrZWHtKhunk5XQP4datWyMiI5Uxo1t58RpJHzM+Stghym3cEN3Wv0vXbMHd5bywf9QkqRb3O6vhWVhYGhBvvvkmj1e98GjevPnVq1dHjBhx5syZZ7wqlicI7uHmvXSWPemu5WoiWaRiCBL3VgpaNxd2aFltral/HZqhfshebiQNkZLW0wLnCDDhUfXfADDEa/RAz2Hrc/5303jth+zlS8NX48hTTwjNGJZovWkK2doCc8eLv8oyndMxrlp/CAjbSL1nB/JChfcG3mBsVFRuV4T7r93PgY35bcRi07082mhGeFxuI1/cy93ooEfv01hJBkXg1/7uwfKHnqAQBPwlTyzmHJN2xRQDKe0hZ9YsyngBADjBX0OVf6Dw8PCsrCyVSnX8+HGXhnepd71er1AoFApFusPheO914eYDlN4IHNzz4ynl3chyxsxinITn/GkIlwMAQNOlP2ylktJQAd/r03f/4XMdyz9HMrC75coNw97jgtbNq+4Mms/EWq8loQK+uEenOk5416apmE/vUu/lrytq+Ls2zVPsF/08Ub+vvKlTp9Zy9MqVK/369du8eXPr1q3btWv3zxbG8gRQYhXq2HnzHFk2osixc+dO19GDBw8WFxdXrGOn8LgE6i4YQek4aDZJNhO3AIB0a2r5hCHCMBnHzUDoCj0zGhU2zS4uKv9/OWnSpNmzZ7tebzqwd8Irb3H9cuw5jVXr8ub9MM/Vmqj6VaICbtBXjvTJwDgBgBu4DFAeKumEe4wkS/+U9C/WbxdnHjsT89Nyi8UCAIoh6ZE9xv/dhXFr+5jJLrIhvUmVxnTsguqbDT7L5+Aebo83DwsLC8sLQ+1hxu7u7sePH3ft0rI0aPgtwvktwms66mQYALhod6io+95aigKA8zZHAVnm0XV1t3kGXNGdK7DnevN83w2ay0Mfqr3HRwXTguasPLn4nvzuBe3JHu79AIAodBAlTlSE8oKFCKfMN2u/Z30ii5H2UljiDBljbgHFAADCRQUtRAgXtadarAnGzLFJCB9l7LRsgMe/qN7JUp1u2z7H5etc+oEnEm/cqPH4oeNbeP90w9w/RNC10VPMd3DBDVxm0590xVRiisGYtGs1Nlxu//79jxw54tLwMTExp06dcqn3vn373rhxAwAYpZv35x8Uf7bKduOO6psNFTX8A2i69Iet5vPXalLvrDfl2cNv2ljSN8Z07ELJ4tXysYMlPTsjPC4AUCazce8Jw4HTAKB4cxgmk9RxQq4js/x1RfVePlKu4Stavtg8yW+Zzp07u1qCz5s3z26vXC+N5dmjxFAAULvq2PnxAYAodEilDxot3L1719VJLs+Wfd0Qa+OdBgBMjwHAZZtJgssGeL7W072/hTK7fqyUpYWkDQBoJfkAkO3BdWTkWq8m6v44MIiR8DhlfxUyMzPjKTM/MBNQRPdnMW2ialTvAACAebyGSjoCACbrgbmVxdtwApcgmJTrt6+om8fPBaku9Q4A+kMrMjVFX+3LrHf5ygooJg4XRIVTeqN+1+FHW9fAI7Pf2fR4FhaWFwYcx9mO6y88GSQJAL+bLZ/p9K6fDIIEgC1mc/lINkkCQC5JPu3FXDNcAoABnsMqqXcXRKKj92u9hr07LDEzTrU2L7nllaSwi6kxcXfaxN4KPJ/7Xooz3160POtO6ytPZDE+C0Kkvd1d6t3tNa+o7JhmVzo2Pde+ZW5XnwXBCAKMneb68YO3/Gul9RzpOUVzv7FcjEcwVNCyqaRPtOilNphU7MjILV68eoL6xtKuss9jnkWrLYQXxPGeBgCA8riBn9dkJhKJ+vfvL5FIVCrVvn37ytU7n//gn5vj5+X9+QeYXOrS8AxBPDRFHdQ7sA9j/waKicPFPTrRdof21125E+cVzv6yYOayvEnzDftPAYIoJrwm7tW57rPd0X+oyWwB1al3F65xTWaLO/oPqzV48XjCQUeDBw8GALvdnp6eHhnJZqH8y3hW8MBzy9Lg7eHh4eW1DO7evdu1W1ceytcRmp1Fm4WCYhy3WlVCXAk/q86eMm92me0q2lJpZrPIQnAcHBDcW/GTWGUEAA5AP++gfXlpLoOtl8+2Dm7n9grHkcNDOI/cJ0J4jX8gCldx/OY8GOJ4cgIWOLPnfvrHilOxZolEYjKZAIAmnaVbZv318f7olXv7zxpac2EVhjYnMJS54gjjyEW4AYCgAKCY0tR0kBF0cAOgH2Mni/17wMLCwsLyguGHYSUk1YXP8y2vQm9zFFNUxSr0Z20OFUX5Pf1C3+mWVAC4oD0Zqz/vGqEYCgBWZi1BAMEZvL1fa887XooBinxbGgDgCg43kE8ZSEemrfS3Qs0fxYyTRjCklkvUHaLIYb6sBwSAAf1BtdsIL/krSgBw5NhKfylgGEAwxFlgt8YbhG0FjM2ByiT1SkywUOZ7lpQoSVsUeRzXGqUzqJb/TBlMgjYR7lNfLy8exhCEYe8J/a4j1s27Xv1YLuQ/o/hijt9HtCMXk3VDeEG1mIlEoh49ehw6dIgkSRzHe/bsWVG935/Kq5IfvuwATZf+8Ect6p3lXwTBMI93xwk7tjT8fcJxN8vlckM4HEHbCPmIAdyQgHrNRqV7qN/97MKlNbXYXGizr2X0Z9RaD4j+RytvKDxhAa9QKDgcDkEQgYH1q4bC8jRwhdCryjrJlRWiDwsLKze4d+8eAkiAICjdkirERCJM5CbJL9WGAYAZDxVh4mqntVIWBpgSRbZ/SXiJQuwudRO0jcTkkknNfPctW+CyOXgzfoFvROTyRhWzXxITE2maNhgMKpVqyJAhFfMtEX4TbsgaAGAIFSA8BJcBAO49mSzdNX34lVOxEMAV3gGTy9iek6g7uf7LbuObLFsfuvDtajU8pdnruDep9vvDbwGMDZyZX9HWnq6mlE8cNnaLhYXlOcRVNapt27axsbEVR+oCUckJxvICIUVRABhbsQo9qSmmqIpV6CcQGhVFYciTEca1YKdtAHDPklJpPMV0CwAAhZzvMsa8MQa3cVAe2mh9c8VQT1dVM0eWLXNUou2OBQC85wUD5PzzxWi2FNJWym2oJ6+JsPjb7KwJt4M3R/LDhff6XydKnNLe7oKWgpIV+TlvHxM1vwgACIbxI0OlA7oL6lZTbX3O/5JM10f6vtlP+epjLE+/8zClNwqiwj3nTq24cYBwOPKRA1AhX/vbX9pNewStIxD8mbRYw0S80F8faWWz2c6fP59LSmiEE0Cqz58/X7GmXTmVNDwAAwCan/4wX0yoRb1XdLSwD2P/CsJ2LYTtWrgq0iMoiisVrlj6elFiobIGKO5MMTzS8uh4BT5AobHR7oLnsajHk+UJC/j4+HiCIHx8fCSSuiY2sDw9PFFXCD0N9wU8UeQI7/Ag+a2kpAQAAgWN0y2pHWTR/TyHrrabVyWYODRiQxULmm32rm6LfUPmiiumCz55GuCCxsvNY9hrHF8vAHilT5dme7alpKQAAEFTe0uy2jZ+qDZsnz59zGbz5s2bLRZLcnJymzZtKk/NOO2JHQBXCKIuA8oHQLkh3/fr0mPeQPdxeL+5CacP5ae7DA0HvyuKenm1b/TCxau9Fs+oquEZZwkAIDx/lN8EAIChaNNVhnECgmGSToBwAIB25DD2LP3uTGvcbfnogfLh1VfLrArrfmdhYWnQkCQJAFSFZGby6UdEs7DUC2+eX6E973XfSeV15ldmLaEYalbwIgzBAEC/rwQjMBqjwQGqlTnSngrcjQMA+r9VtjsWBEEYhtEfUMOU2q5SR4ynNQDA8UjniLWy3krDSVHW+NuoEKUMpLS3u++nAtU3vwN0cxbKJW35iIBHGUy2xFRbYqqoSzuP6WPLyq3VwC1jfJLpOgAcKNn5klt3KV6/9loMQVguxAOCKKaMrtbtLx3Yw3w61plbaL+VKmgTUa/Jnx6uqnUFOusfRHcSQT+UJZTXtKtqzPHz8lr0Xsni1bYbdwBFAaB29c7y/ICKhFxR5SaFdWRHinXBOQPWhBngicVcH1JTCD0AxFwfcj2IeyzR/HO65ctuslHNHvOKDYUnKeBv3749efJkAKjYJ4zlX6SiB17S3Y3biC/qKOvRvYdMJjMYDAAQHR0NAI0EwQCQa88CgA4+XGBAakU1Yire4RwkFJhJ4xXduWhFTyEmck3b1u2lK6YLWYGGrgk6icINVyrKrzhx4sQ5c8rC4Lffu7UUoOIWfdeuXffs2XP27Nl27dqlpKRUI+ARLsILoi2JROH3HP95AIAKI2jbpHekOsaJL+3Q+4q6QOuwAQBFOEo3f3Bizv7OpWm9atDwAIApXuEGfgkARNEaVzVUYChU3J7T6DMAIAq+I/K+ELTkW+Pt+j8P4+5udSyJye7jsrCwNGjOnj0LABW32i9evPivrYblP0BsbGx2drZMJqvU97siFouFpulXX30Vx3EAaCFpU2jPy7fn9FG+4jJAAAGAZpIoHMGBgbjfjgNAwbeqJmuCrTdNcdGn8mbr3S+LFTsFgILqLbPbPoHtlumfL958/prtVjGA0Hn3KplnxnDgBbRy5AVTBloQJfH70k217HugCIRLM06O/7rlqBinLTbzuav67QctF+OBopSzJ0INMQskQ+4o3AQAElxqIo17ira9FVA/J4Ezu4C2O7hB/hwfZfUWCCLs2NKZW2hPzfgXBbydZNRWWuXA9YywwMzEHTprNDrj8UiHEwUGbkg7tYHYtBKz5vA5qVSqZ4TFdjSEYrj3kyC4jXy9Fs8oWbyaMpoBoHb1XtXRwjrhGyIiDgIAuAheXafJ+hxq0vCuInbD1pae/NONun/Wi039BLxSWcNXA4DD4XClKAPAhx/+V0oIPOd4VihiJ2gujrxTlhdy+fLl7du3R0REjB49GgCai1u6c5W+/EYA0MmPu6aPW6rUucFuTnA4BwkFF7SndhVtNlHG17zHuk4PzZdwHUiRj9Mz+557msSgPCXsEIXJJGSpbohAuQDFnDQFAJl6zem/9/ca+qC7w8KFC//666/NmzcPGzasYi29inCDvrEn9yPVv7sEvCM1U/urk3Hi/KY5gUHtPy+Ief/qcZelLfum/siq7wd+EHFxHdSs4QEAaBtZ+AMAcAI+JfKWEcXrcZ93EY6H6yC/Oc990gjNhj9Lf/oDk4rrGO3GwsLC0nDp1q1bpRHXfi4LC1SqQk/S8CSq0GdlZdntdpvN9khLtVrt4+MDAD09BpwqPXRRe6qVtH1rWcdKZknX43EVZvI2tR/f1WOAMrlXLD8XghYrOHoMUMgdp9Es295fAAAgAElEQVS1sjC5cs8T/7Rsm+Gv47o/9iNIDwChpG8fYQeF/Y7RcKGsVLM92VS0MAFFCXH3aN0JDMEBEWAAgIoE0gHd+ZFhxZ+utFy5Ibx8QxRdxWkBAACnSg8VOwp8eP7vBc/77O6si9pT3d37BgtD675C2mgGgPK892rBPNwAgDaYa7F52gzcVZptIAG8ALwgEQCUAADOsqMncogT0BYAoAigCAACIBY8ElUXxnlW0vBFHy1nGMZrwbT6+t5ZDd/gGNBYsDreLL5mkGrLYsSqavjyEvRSDdk0zmpQyvqHCJ71Qp859RPwpaWltRtwOJyvv/66f/+6hiKzPFUkKOqDYeW9RBiSAZpBuGjz5s2XLl1abubOVX7bbEP52wGN+T4OdIPdHO9wwH3//G3TjXIBT2eVhJsESZHWe32lbjss+h0H9TsOug5xAF72CzmYd8/19scf1lYU8K1aterfv//hw4dPnz69bNmymrsWrQAAuLlpTOduJV/8SNscwva+4q5/MGjia10mH87POFKQ4bIzHFlVGtn7f51Gf3luQ0nNGp4o+YUhVKi4HcdvNm26SumPk8U/cgIWPbhRfWNIrd6w55hm/Q7/n5fV8faysLCwsLC8SLgeCn83WyqNbzFXFn441M/HJRaLXf2JJBJJ+/btK+U5Z2Zm3rtX9uRQXh9HyfUa6j12V9HmtTnfvOI1orfHINe4jbKe0xy7fvPSIHiF05jvyw8Af5D+5qcdks3RY4AAPZPfa05/ADBSpSUn/lFbKdvNFN32A4ChwvbuxlMURTbG3JWFX16nLLioA5fWpdjuNdafDJV1IzlNejPULUGUpGLZPG4jX8X410rX/W48cKpaAW8kDQdKdgLAaL+JPjz/3h4Dj6r/3l746ydNvkLqfIdRsRAAKL2xFhtKZwQAVCKq+2d/4rj8onyU5jN2AEAQxIHyrCTKQwFBaDuFCjBGglMEQTAMY2M4DuDwcARHH7oP3Ea+fqsX0U6C28inpguxeY4vDBgC01qLT69XVRysqOErNoEHAN9cx/h+4vqUj2yo1E/AHz16tJajEomkZcuWItG/+e3AUom/vJXM/ddpfRNIlbP5zc6PLMragsvhI0gGQWppOlTUXIAJi+z55UcZqy3ijiAp0nonihgcPsN87qojPYc2WTCZhNesyQfDuh4cM8pleeD8mYKCAj8/P0ucofibbL8vmyxcuPDw4cNr1qypS5hGydIfaIeTHxEm6deHKMymjRdV7reWtemWoClS2a0AQFOk5rf3r84/frjNywMTjhUvXOk5dwo/4uFydOXud/95AMAJ+ITSnyCKfsa9p1e0chs9CJOIEEE1vWpYWFhYXjAyMjIe+9zGjRs/wZWwPFe8I5V4YBgN5Q8OoKXpYpJqyn2onYwvhkfVmtRdFVfkvFgsNplMN2/erNgtLCkp6d69ewiCiEQi88M7Bf09h9pp28GSXfuKdxws2UUzNAB8eGciyZDefG8AcHe6uyydh8yYFQUAYMCxy5j5ckbLLq1shL6+d6ASum37gGHcXh+MiFsYT90sWZWr/iGXUBPS3u4hO6JsCcLc6VdtGeHGC83tuekAoBjuVWkGUdf22k27HRm5lMmMSSrXBt5b/LuVskRJ27l69A72HnVFfy7dkhqnv9hRHlPHRXKD/BEux5GVT5bqcA+3aiwYxnbtFgD8u+ni09uI3z2uE6DUO9gFDBgbw1nj7AaAjsWuCBDiR6qLg4JJ+GU5x8oA/OKMLmE401qL0SpPrLi3Ry1XqV29s074BseQMME1QAAAj2ub+0bQxluWyaXW92YG/nB+9bsxM07MDtzgLZwUJQrYkg2gljDI4CYvvvsd6ivg+/bt+5TWwfKUkFdINnPm2okiB1Hk4Po/QqbiCBLF5cY5HDcczl4C/ochi129W1xgcmnjDD6f4uTaMnVNhT4t3qh4bm+AFl8tS0pKAgCCJNevX//555+bL+oNR0sRLtr5j859+vQ5ceLEqlWrHtmqgHY4AcCenGZPTgPgAvSUAwAXvmvXe8LF/a7nC3txunbvFz8OW9RKGOdn1ZV8sS7wj5UVJyl3v2PyXgCAilph8j4uJzygFUpcIIh0UE8AAIaxXk/mN2uMCv8TXwEsLCz/QZo0afLY5zIM82gjloZJBJcTwZU9vfk7d+587do1rVZ77Ngxl4ZPSkqKj49HECQmJub27dtVTxnqPSZS0vpgya475kQGGACgaTogvlGn3zsjOGJNNJM6QrOp0Pa9FlCwT0aFR1BBHpinFCVuAPFZBAA8Y2WqTobg4GAfH5/Lly+HhYXVMWGEKChxZudjUrF0UE8Ex0QdZJY4AwUgiXEL2RGF8lFRdFtRy+1Ao7asUEeGFXfnKqf6V5oE4eC4j9KZlU+V6isJ+Fxb1gXtSQzBRvu+5Rrho4Kh3mN+y1u7s/C3VtL2PLROTgWExxV1bm0+F6fdtNvzo8lVk+3NZ2IdGbmYm0zQqlldJnxK9A3hN3XnpGogVx45MIDaVezmLMLbyO19mzQCgKQs+3mNMFXadkqg/nIppyRb4i1Ch4WzT2L/dTAE3MP5AIAm27YnW1qnmvtuygYKHTj1Y4S29flfVuKERttQWHjbCgAezQT/Bfc7PEb7a5aGS1kh+gJHtUdtlJVkyjJMNidZrCoGABIcTgAIEYaFih586fMjQjESCb8rBIB4/eWqU00dN7789fr1651Op2KcDypA9QfV9ruWRYsWAcCqVasslspBepXA3Yy4u5nbpJEgKlwQFc5vKucGFmkoU4xXwOjgB1VYDGc3alMvXvBsCgD85g+njTFkRfd72X0I+AQAIYp+Zihr1YtaryervvqpeNEq2mavfXksLCws/x0kEklkZGRkJFslhOXx4XK5/fv3l8vlLg1/48aNcvVeS2RHqKjZrJBFP0T+Pr/xV28dnjqlz9tDZg3xuuYJDDBOOnPErYJF6QiG5I7TOAcjESc7oQG4II9rHV9sOKJhuIw2whwcHNy1a1eknq3vXM2rec2bIDhmT7E4MsqeGSwJxoJP7ml3FOv+KrFltnAUljW1poyk6Zyu6jwMQQIAwqnsM9te+AvN0L09Bnnz/MoHYxS9g4RNdITmqPrvui9VPnoQKhJYryaW/rCVtlQoNMAwpqPnNet3AIDbG6/WXgz/aYMAvNtGDABn7I2aRLU7pZUBwLzufu3atWvXrt3ilwMxFM5qRF7hrU9Z/ABgehsJ91ERo5WoS/A8G2Df4JD1dyc5SHiiJTpZ+/HmXKDQ20O5C3cFJY7iIzQye2t+z0Rt2C0rwUWk/RSPnu6FoDYPPBti94LB9eVZE4AoqkbAO2nnxylvBwiCP268FAD+uGPNxEi8I7jS4CvBCfDhN2/S/NbtxOZwTX+pl3hg7J97wzy8/Af3AQCGovsbQczhmgknABQXF+/atWvs2LGKMT6lvxaUrM7tsrZL754xJ09fOHbs2GuvvVbLgt0mnAHaod3GV7zxIT8iFAAcd8dYtuZCasTCll2uqAuyzXoAAIax//Zutz5jpUN6KiY8NCFtvV3R/e6i3AlPm69WvSi/aQjH18uZna/6er3XgmlInXsjs7CwsDQU9PpqQos3b948a9YsBEGGDx8+ZsyYkJAQhUKRmZl59OjR77//3mazzZw5c9KkSc9+tSwvEnw+v3///keOHNFqtVqt9pHqvRwewud9ioo2cFEBGrKvpWG/Wv1LPgCYY/UIigiXKnWSHDkAx48XcbLT7a5XeCUAwBx/3W33kKY4jkORihSIbb37chiGX1BcPi0CMFcuHVpdmyvG7gAAVMAHgOx37pAaQtJDgYkx/QG1ekO+eoMrtdAfADjeFunLTTRbirImJLUs6IZwH7jHKL2JKFQhXA7u6V5x8jj9xbvmZCkue8VrZMVxBJAxvpO/Sv/kiOqvLm693Lk1Vo+uCK5UeH40RfXNevO5OGt8krBtJO6tpE0W2807RJEaAOTD+4m7tq/LVE+V/o354Qn4XS0576zB6KC7BvDaepc1Bg+U4YMaC/bds3123nBbTXgK0eFNn5b7nQ2kb1igcuzMCHmfP3RvbyxGaDg6XrH1Ey8A+GZx8Gip+pUNpW9tKUEY5sRIhaesmu7XLyS1CXg2xO4FIJck52v1U6WSGD6P48MDAKK4Gk2OIGCjbWmWO07ayUW5LZScjAwSBeSOkzDTTNUMJMWkEU0W5gps2nzIuZF6MQchLCnJvj2jKaNJ8/MONDV7RFjLTcnXXMarV68eO3as18xAzW+F2u3FXjNiF427eeoMnDhxonfv3jWVowcATBFFGy+LXrpa8uU6r/nT+BGh3OBv5dJJFgBFS+/v9L1Gnd9L0TQA6M3670tuLxyw0mQgA2UPfqtpSyI87H534cqEp03Xql4UFQm9Fk4vWvg/++009f82Kj+aUm1XVRYWFpaGi0xWOUz66tWrs2fP5vF4J0+efOmll8rHfX19u3TpMmHChA4dOkydOjU8PLxLly7PdrEsTxd70l3L5etEXjHtJHB3GT+qqbhbh6eaRMbn84OCgm7evAkAIpHIz8/vkacAA3mz76o35KMCtPHOlpIeCmkPheWG0ZpgBACGZuybdcrmUsRbl3/otD2VB+qyAr6h1+1gBaO4vJ4vOBHESdMV577qcFYr4DG5FADIYjUAeLzlZ+8k81vSBOGhtttm/X61I8PKEAxZnIqQtzmeBr/Pu/DDxZSeqKjeAUC/+wjQtKBNVEXvt5N27i7aAgBDvceW9+gtp4moaXt5dJz+4u6iLVMD69rXid8izOerjzS/7rIn3TWff/B4g3u6K8YPFXZqVcd5nioIwPQ24g9O6s/mOgHg3bYP5RTMaCc+mG47n+cEgGltxLyn4H5naYgMEApWBKOAAEKDQ4IKX5L3RHkZKNWYxmStpQ6pjmekGAQx+DJv/meyX59kH3gAkEgkj0xsZnmWJDqJBIfzgMUaw+fJBytNF3TCdtWkt3EQri8vIN+efdmcw+M2CgjCEC24UagGo9YYjK15PA5jOVSyp5WsfaioOQCE+ft4fzil6e2vbrQw3o3bgfG6ajyk2XOXoyodMAymkH+0ZsXm3t1pmgaAuLi42NjYTp06yV/z1O0qsV692bYZ9vJLwmOXrEePHh05cmTV9bjApD1oSyIvtNB4yFmu4VFpNEAJ7nl5yM71s+a6fbfpF5fxzsvnLn2+3atd34PDPXzE93fgKGsl97uLcid8tdfFPd29P32vaNH31mtJpT9sVc4YX1P7VhYWFpYXg2+++YaiqKVLl1ZU7+WEhoZ++eWX06dP//rrr1kB/8JA6Y3qVb/Zk9LKR5yZYL2WZNh5RPH2KFHn1k/puklJSTdv3kQQRCAQmM3m8nz4Gk+oot4BwHrdaE0wIgjCMAwgQN0lfO/KAUAFDICdwSGzFyVJ4Yck2ZfOve72RXBERMS2rOw1XH5bm+XHJmUO/8NW2+c6Q02X5TVrjHA49rtZRKHK480HPW4EkWJBpBgAaJMl/93NtNXGkKBa/rPXgmmY4qFebsbD50zHLiAYJh85oOL4UfXeUqeqkSA4RtG72kuP8n0r0Rh/VX+hh0f/MFHzR93RMjj+3t6fvU8Uqe230yidARUJuSEB/PBgQJ8jP8SAxoIll4waGx3qhre77353ESTD23pz44qcQhwZ2bSaLZXaYZ3qLyr04fwRX2uBAaMYl5rIru9kduOiuBtO6kjGSQOAXoLLTeTQ/+mp1vnwyn9Ch9b2X1pfHatWrUJRFMOwUaNG7du3LykpqaCg4MKFCwsWLBCJRK4QO1cBM5bnAQ8UBQBXQ1dJd0Xza51E7co83pcvX/7oo4/27NnjehssbEIhgvcNwolqzU8cE96R0WAUAGwxW2ZptO9pHUc4g76yek1UayaqNR9otIJWzbp2nQQAqeFmudrAoEgRH0d5XEnfrr7fzm3eI6ZiN8Hvv/8eAHzmBgOK2JLMADC7xWsIgpw6dUqnqyZhzIV6nRHh7RK0OCDu1pFxOEu+XGdPvocKIwEAKIs1dvHbZl4z2YNipIKUQ3o7PfOknqIBmLLWogg/kChcVfUHuPdLxdLVtKXlBPh4f/Y+KhJYLlzT/LLzse49CwsLS4Ph4sWLANC7d/VyAgB69OgBALGxsc9uTSxPE8pgKpq/wp6UhknF8tGDvJfO8v3mY+UHE/gRoZTJrP7fRvOpK0/juhWr1g0ZMqQ8H97VZK4aqlPvAMALFbqP8wn8qbGwaQkwYG1sL+1ickQ5OB2dxUO1KYsKMoYZlm4JTB0stbSwpMbHZ9++7WpPhzEgRVHXj6DW3XmUzxP36Ag0Xbp2G+MkKh+mac36HbTVxo8K5/h7O3MKCmZ+oft9vz3pruNetvlcXPHi1dqNuwBAMXlExR63OkJzRLUXAF73nYwi1T+Hu3Hc+yqHAMAfBb8wUL+YVo6PUtInWj5ygHRgd36zxs+VegcAK8HYSAYAjE6aoisf1TtoAHDQYHBUOcbyX+XYRR1OMddjBEu+abphmDcZIWYImihxMgRNthCvH+bz+fKm8TECnGJOXKpRU7xg1OaBZ0PsXgCUGAYAarqshjxlphAMQQVoSkpKjx49nE4nAPz9999DhgwJEjah9AkkIDwEacPjAoCBpu84CQDgI0grHueu+TbN0AHC5rcJWk/TABAZ0EVs+rXU3ajoEqRN0xUFKNpNG89rEuS61owZMw4dOuR6vWfPnpycnMCmgbL+7gzBAEAYIWvduvX169ePHDkyZsyYahfvyLLZMxoLojw93m0PAOazV0u+XCdsFwkAlN5Dt03AoaFXQOMsh9n1h3/SgM6HxFh8sXNVguk9RWrZRy7dQ8GeWm4RbUutdpwb5O81f1rxkh9Mxy6gIoHbmMF1vussLCwsDQxXVnwt6W+uvxcmk+nZrYnlaVK6dhup0vBCgzznv1NeHZ0b0kgU09548LT2t780G3bwmoZw/Cr3RfsnZGRkpKamVsx7L8+HP3bsGE1X0Ww1qHcAwKR44I9Ni+av4AflAhqt54sKRuk8BcoMUk8QRCOFB5qh1nbjnJ/J/dCt3aVLlxISErSRLcDbt/IlakX++iDbzRTH3cziT1cqJo0ob8NG5BdrN+6y3bqLioTuU0ZjElHp2q3Wa0mGvccNex8E92ESsWLKSNFLD3WA31W02UHb28ujw8URUDMDPIdd0p3OtWVe1J6qyVHfEPktyWIlGAGOlFjo/em2oWEPYp7P5jrStCQfAzvF/HzTsij6oRRLA6n7On1BuDhygv/0KrOyvLDoHfTKECX9hfIn6Z1JbSWLrHT2QM+DQ9zzxs5o9Mfqwfu0KRpiaXtJEGOe0K858JFX7bQb//natHoa1O8T1iXEjqKor7/++gktj+WfosRQAFC7Njlp5k7rK3d7xQPA1atXXU9jALB7924ACBaUlTxwQ9GNSveNSvffPT04CAIAHhi6SenxNp7cyvLzPOmD3xkMwdrLXgKA4mADDxCTTFR4Pq786MsvvxwVFeV6TZLkmjVrAMB7TjAgAADijvKhQ4ciCHL27FmDofoANkl3hdtQTwAABPF4d5zLD2+9mggA9qQAhsLO2m//dOfa8o59Jnbv8+OPP86d/cGKXnIMgXXXzSrBBITri+BulX4MZkRnZACVl41wvTCvt2u6e7zwEM+PpyAc3PDXccPfJ+p/+1lYWFgaBr6+vgBw5syZmgxOnz4NAP7+lbtksTREHGlZtuvJqEjgOXdq1ebk0kE9JX2iGZLS7z76ZK9bSb3D/Zp2Lj985e2hmtW7C/O5OEd6Du6pCDs1WNpTAQBqs5ogiODg4B6vDHQb0Q8ASLU2EONHR0cjCKJWq+u7YEwi9lowHff2cGTkFs1fkT9tUfHClQUzlhTMXGa7dReTSbzmv8PxUaJioefcqT5fzJYO7M6PDOOFBgk7tnKfMspv7WeV1Hu6JfWq7gIAiDHJYdVftfycLD3oqk7/V9Hvtuqa5jRErATzW5IFAN5sIQKANQnmik74H6+bAWBClBhFYMcda4mFqnjunqJtxY7Cc5rjqebHjPNFHuaxPwXLs2RjosXKoC2Nmc1MRSObCfwkWJqWPF3oBICTBc4UDeEjxoaHC0INRZGmbBuDun7BXnjqlwPPhtg1OGQoykcQE03bGEYAQGoJothBW6mgoKBym+TkZADwFwTiCA4A5cFafARpxsFvOQkHAwAwIWD6GL/JeRQGoCo/t5NbtzOao7H680MDXr+Xl5demBdEEOWV22fMmDF58mTX6/Xr13/66aeydjLG4gkAmBvuh/u99NJLsbGxVqu1argHAOCeDyq+0Nab8pEMQAfzuTgAYGgQxzh7hN30uiroIfMawuF5NG+DIEgHH+7CaOneNBvfrYPAN9lms9lsNoVCQdFwU+Vs5cltF+BXWFhYUHDH9bQKABaLxazXy+XyqgsAAEHLZsqZb6pWbNT9vh8VCiQvs6ElLCwsLyD9+/dft27dokWL+vTpU7VXXHJy8qeffuoy+zdWx/KEcW2FS3pHY3JJtQayYf1MJy/b4pMYikKwJ1DY2WYry1YTiUTJycmuB49yEATBMIyiKAAo98OXrMwpU++7Wkq6V9MdynwmFlwd1CRC0Ug3uAoMBgiJFOTlb9++PcHXD5qEXYoKbwkAHBH0etl1VpxQ1CyvsOI8OSRZy8o5fl6+384z7D1hPn2FVGtJtRYAUJFQFNNOPrx/xRvICw/hhYfUfh8uaE+6nrLOaOq6OWIgdYnG+E5uXeto/zyzKcmis9MdfbmzOkiOZtqzDOTBDNuQUAEAnM9zJBQ7PQTo+23FuQbySKb9l0TLgpfKnPDZ1vRL2tOu19sLf/0s9H81ZR9US7Vy3TXIVt1+njE46M23LQCgVNr/ILiCA+cjULcCUIbO+QgAwuZ8BL0XROHFmw+m2DCel9IGDGxOskyMEsl4L7gTvn4Cng2xa4h4YGg+SakpqhGOcwP4jnTrF7vUafwHRV9TUlIoisIxjg8/AABc3eAZgDH7NPpgGtzATjMAgADCRXlAPfR3romoqZLrpXaW8CJEkAclnjLT1VvSLm1dR8eNG7dw4cLi4mIAMJlMGzdunDVrFi9YQBSC6ZwO2gROnDhx1KhRYnHl7X8XxmMa2SsMgiEAQOR+ThnOyUeuJgqCHOnZwvZR7tNfE906d2BNkTlHLL1G6Lb8Le7WEQDGR4rGR5bVdB0wYMC1a9cOHDhwQ9Ru7XXzzPaVn1QyMzN79OhhNpuLioq4XC5Uh2sfXbN+h2bDn6hIKIpuU60ZCwsLS8NlyZIlBw4cyM/P79Chw3vvvTd8+PDQ0FAAuHfv3p49e9asWWOz2fz9/RcvXvxvr5TlCUAUlgAAr2mNzdtwDzfcw41UaymNvlL/s8ejXJabzWaz2VyLJXo/ZxsVYQDAkAxlrE5gM4zjXg4giLB9CwDgc8sisRmccZIEADirJljXAFXdYJ4tW0eURknbAQAq4LuNecXt9UFEkZoymjCxCPfxfLwONS8rB0vwGpvvVAuOcKKkbQHAQplvGK4mma5nWdNChGEtJG3byDoJsHoUe6uqY5+lfLWSZe73GW0lGALT2og/PqNfHW8e1ESAIbA2wQwAk1uJBTjyQTvJsSz778nWyS1FXiKMAWZH4UYGmD7KVxKN8Xm27PPa493d+z2zlbP8W+xKtZmdDAAcZCIBBygCADh18otyg1Mnv+jVe8ExvRLwMJf/0eRkdt+1TYqq3NzhBaN+At7X1zc7O/vMmTOtWlXfjoINsXsOUWJYPkmpKboRDrwggSPdevem6Uq42NPLS1VSAgB2uz09PT08PDxAEAw02Gj6qNVGM5CEOZ16BnMDK9BHrWV75yUUBRX+2iGAdHLrdqBkZzJ53Y2r0IE9J+Fmi/sCnsfjvfPOO+UPfKtWrXr//fddrxnSPiLwM1RoMp5fV/iJEuGibh/5Ujd/95m9hrIE8EL6kCUbS05MtsQZxJ3lAIAr36AM54i8z7yXJRC5Zm5IAABwQ1aGE2Mh5E+H73LAHnKh55moKUe08p6TLWfPDh45rtEXcQDoxlsW4D3YLHCp99zc3J7du2MEATUIeACQ9ImmLTbdtr9Nxy+wAp6FheXFw8PDY8eOHYMGDdLr9d9+++23335bycDNzW3nzp3u7k9Ay7H86zAECQAIp7aHQFcwHVOrd7ruvPrqqyUlJUJhbWqToigEQRSKMme7cqo/qXYWLc/KeuN20C8RbiMeysZnnARDEAiPi/K4xqMXrR/lRar9EYyUtL6KSfXcIH9F286nADonpS06fsH3m7kAcNTumGswDRAKVri7uSbZZ7HO0+pD8Mr3wUKZv81cZCaNcxovaSYuSwYEBOH4enJ8Pf/JffDjNxruM/4xTjxVemhv8R9Wqiw8uNSpjtNf2lG4cbjP+G7uLz/y9JoixsvK+D8TtiZZtDY6WIaLuchtNRHqhvuKsWwD+fMNs5SHxBc7pVy0tSfntpoAgCgl56aK2HjL8kln6VXd+TTLHSkuf9Xr9VBRsx+zv9lTtK2dLFqMVx8/Uonao+Wf5R1gqS8tPTktPTlmq81qtQIAMMyld5rBsHcr2mRO843+KcXVLkooFIqFgpYVAnhfVOon4NkQu4aIsqwQPQUAvGABAHhqnAAQ1bLVyePHXDalpaXh4eHegsZgASvgszQ6AICW4AqboxgoG7lPCflgw7qTW9cDJTuvGS599NKSpFMXPVs/9Ivhajvkip3Lycn566+/hnYCABBGpqJCEyrp6D1nNKlOU63L036Zj6AxXlP3Y6Ic2p7hOt1+1+oS8JjHMEyzm9KfYIhSbkio6yjmNgD3GEmW7hS02cZvfgQAjmbaZ5/SO6j738XKniHrCgHATgMAGB00/+OzIQAx+wGgCECAfxLbhHRMzjqRN2Ge5ydThW0r/1aXI3u1NzfQF/dR1u/us7CwsDQQoqOjMzIyli5d+uuvv1aMpO0GXRIAACAASURBVJPL5VOnTp03b15NqUYsDQ7cQwEARG6hoFWzag1om51UlQKG4ve17j+Ey+UGBATU9yyfhSEAULQ8K3tyMgBU1PAIj4twOYzDWbRkreZ3qbPIH+eQyrHFHE93R4aVvpdt/vskvPUaytA8odAVYYeT1fraq2F/8Z9m0ggA2wt+XRy2sl7R2k+DHYUbj6v3A0BzcRSG4Emm61HStk7akWq+vTn/R7Wz+PE2BVw8MwW7KckCAFkGcsie0orjK+LKvm2MTnrUPk3FQ38kW99vx9tTvA0Ahvu8IcCE7WQvRUhaJZtuHlDtfN130lNfNMM4s/KdeUVAUpiblB8RivBqdPawPHHa+3D/es3j1q1bCQkJblz+0HFjsh5W7wCQNezd4Hea7dn2u8HpaBvVNiqq3t8zDZH6fSUtWbLE39/fbDZ36NDh448/jouL0+l0Op0uLi5u7ty57du3N5vNbIjd84anqxA9RQMAN1gAAF6lBAC8M226K1CtRYsW7du3B4AmgiAAQBmin1DQTygIc3Lo+9nuUVxuP6Ggr5DvQaYBgLRCVxIfnn+QoLGNsmoV6sGT3/Tq2rHi1ZVK5RtvvFH+ttyrI4hMBQBOwEJAQNLL/SJz9W/yMINgDEwHANp8w2XmavDoghe2VdDmDioIrTg/J+gbhOtLm67lJX755ptvXktOf6De6waN83QEAsCUrthoT82oxVLQujnHW0mW6krXbHGkZdXrKiwsLCzPPwqFYuXKlUajsaCg4MyZM5cuXVKpVDqdbvny5ax6f5EQtG4OAKaTlxmqek1rPnWFISl+syb/ulzxWRjiMy+YoZjsycm6XSUVD/GbhjAM4lLvqBgJP9HJ/4e3vBa9H/DrV4q3hiM4Wm5WrysW2fNPaw6jCCrjuOXbc85pjz/6nKdJrO78cfV+DsJ9N2juR42XBAtDASBEGPZx42VTAz/EEc5h1V8Jhtp6/j2yYNuzqejWO4gfqeRU/IlQcrhY2aUxFJp7cCoZ9AziH9fs0TjVgYLG0YqeLsvRvhNRBDtVerjAnvtUF2y5dL3g/SWFH39dumZL6brfS75cl/vmXO3G3bSlmvbDLE+boeOqb1nlYti4sc9sJc8D9fPAsyF2DRFXJ7mqHvgBg165fft2enp6r169XJvTSq4ngApnzIukYjeOeyJFvHZJzfUEAAjiYF8r3GiGnqw6WCqezUceiqnrrOieXZBxQXuqraxz1QXMnj37l19+ceW/xcfHn7kc0SUIUJ7TmR/pyAsFfmn2m0n/s/+UQ+f5I77EtEjq2wnhir0CDgAAtxH/wUQIjnAqh64huIzbeI0jZXjcqRWbNxsCTp/z+fR8KYVvGqiI9uO9vl+TUOwEAKU58+qcLv4f7eU27qjd/7U0eW9ubm6n0TNV3T52d5h6l6YAAO10qr5Y57lgGr/mtEAAcKRlmc/FWa8mei6cXrslCwsLS4OgtLR01apVKIp+/vnnrhFfX9/ySp8sDQIa4Fu90RfD3pDUmPxpY5jv9MYoLmdwuxYcPy+ioES7cbf75JHwsH5zpOfoth8AANmQ56J7WU1+eE5QI8tWl3pHww61FbYtyy1HcEw6sLtIVhZfLWjZtF6X21G4kWKoHu79mktars3++q+ibe3rHK39xKEZek/xVgAY5/921UesjvIYC2neVvDzrqItbWSdEHiuK6sv61pNueIdKdYF5wwAMKu9ZFrryhWRtETp/NR9CCCv+00q/3R+/EZdFX3Oao5uL/z1o5DPn9JqtRt3Gw+fBQDcw43XrAnK5RAFJfa0LOPhs7abd7wWvvtEakOw1Iuq7vfy8eA9a5/xYv5d6h0U5AqxmzlzpkTy0HeZXC6fO3duZmZm587VSLjnAYPBcPXq1e3bt2/YsOH777//6aeftm3bdunSJZ1O9+iTGzJlneRoCgB4IQIA8FSXNZBr1qzZK6+8Up6TVv7lmGlNA4BID46IUzZy2e5gAFAEbSqOBAAH7ah4ic7y7jjCuW26riUeCotyER4ePmTIkPK33/143vXCeLy1atWfqm+3C5slje0XDQBrPdaae2fe1bZMKB4GACgPJDGPDt7DZD1x77f7dka6d5C0RbjDUs8BwDcX1TdvJIz1KHB9phZBXiNGjNDs/w4A5L2m5qu0HVu3kXd8CwBeL0nAJAIAIMMCaZu9ZOlae/K9Wi4n6txa0ieatjtKlq6133nIcscd61uHtEZHXQvnsLCwsDwPuLu7r1ixYsmSJVqt9t9eC8tjYqWZ382WL/WG1YbqCwnbGGaqWvOH2fK31YZgqMe74xAOx3TsQskXPzruZQPDAABlNBv+Ol782SrG4RT36uxy1D8PVPXDMxRTspZxFvkjOOHW4wYme+hZznr1pvVyAgBcimraWiFrllfYLK/wQ40OAA5bba63zfIK52n1lS5003gtyXRdhImHeo9pK+scIWllocwHSnY+qw9amQzrXY1T7cnz6aLoVa1Bd/e+HlxPlaMo25r+jNf2RBgWLgiW4V4irLz8cEX+LNzkpB0d3WLCRA/9Kg7zGSfGJHdMiYnG+KexKuP+U8bDZxEux/2d1/3XLVF+MMF92hjvZbP8VnzCDQkgClWq5T8zBPE0Ls3CUhfq54F34QqxW7lyZWFhYVpaGpfLDQ0NVSqf09xgkiR/+eWXLVu2xMbGVpvkgyBIx44dx48fP2nSpJrqkDdoPMs88DQA8IIEgICH1onWmu+UYU1rK+uMoRDlyUkAJwpIKUXfI4gwDifarecmHXBRXkV7MS5pJW0fb7h8RXd2oOfwqhPOmTNn7969rtcnLmTdSJW0bop5TNxabjDLzmyOR2/lZ6V7fCpElxaYW6isIbJ+WlcR2krQtrtE1hyO/zxU+pJrhNvoc9p05ddPb0751PJy8Y0/A//P3nnHNXH+cfx7d9mLkIQ9ZIsDAfeqA/eso66qtc6qP7XubV24d1sX7m2tWhda90ZwggwZIjNsQiB73N3vjyAiIIIFQb33Ky9fyfM8d883MST53He1fJXP/W7kTJZLM/PeswDIW1ms9PAMTexDbVwwy72luOPY7kLBEZaFUKfYtnN63SYd6wsl/Xdt2DFsnENmQebqnVYLJ7EauJfeGgAAQcTjh5B6g/Lu46w1u23WzqHbWQFAnpZY9ahAbSD3vVTNKFXunoKCgqLWgiBI27Ztr1+//vz583I6xVLUZngosk4knJObt7NAAQDTzN77GjKp9yc6vTWGLTc3AwCmh7PlgonZm/dpQl9pQl8hTAbKZuL5SpOS53dpKxo3qEaeyIewWexC4mTGhsTECZGAQt6ZLE0kH6HrzXu/JjVv0hdsZLrXYTjZk0ZcF5tgSM1wtBRx9QYV4yPlrFAAn7drjKTxVNoBAOhrPYRHEwDAUNsxS2Nn3My93E7cxY7lWN3PsTSvlGEAYCD1m94sM41k6zIA4GHe7VhVVJHZABClDDNF139Z0FHk4iAJQUKRx6iIONWrp/IgBsoYaD2yxBQX4/WxGnwibd8J6d4GfG8aUpVFy3B5Qd5fgYAgFrPGliiNRHewsV7+a/qCjfrkNMWVe4K+ZV9VqTFIUhefrI2IIwoUCJvFdHVkedUtv1blF8SH3O9Fs7c/mym1gP/0n1r7Q+zS0tK6dOkSFRVVfBBBEC6XazAYdDodAJAkGRwcHBwcvGXLlgsXLnh6Vi7UqvZT6IHHcQBAuRjdggFZevP8D9aVJYDxUEcXqDUAYGaPAgBCkIDCXoXSj8XKIsUA+fRSn5XfiTs/zQ96ILvV03Jg6SCuVq1atWvX7t69Qt/7piOGo/5MsihZHUE4LPhtAucXf+XK3Xmnd1x5VdBPpxGivLKrIZLqSLzgPvE6ntUoCKGZAQCgTKb7ARtt+4s7lSTuPvjc4wDXju4/rlbxLJQvApG4+8xe82x/2ZO+rkfe5c02v56y7D4lRKcAAGbYGYmrgykGoUHDhuiwnrzQBOWdkPI1fKQMf9imX0OZ0TbixbnHGTInLgDcS9GpDSQA7AlVogAs2nsvAp+JDqnHwWp1dBsFBcW3y9atW9u1azdr1qyQkBAWi/XxAyhqHz04bBRgtky+s0ChJ8nZwsKQci1JTsqWmdT7IUux49ui6+xGde3++K3g3A3VoxfGrFxcp0cYdHYjT0Efvw9ewq5RbJe6AkDGhsSEUREAgND1gtaP6+xdID/z76wG+wFSfltZ2AUJEwoafd/lEYctnbqCbm1h9+fSipz/Rs6lDF2aDdPeT9zTNGLHcmwv6nI7998T0r2zXVdUz9Mqj1RtMgDk6XPz9O9Vd8vWZZiUfBFSzacnhNdsGXY2rYzfRiSQJ9L2kUD2tBwoZpThI+wk6XlPdl2qTb6Zc7mbxfelF7w7FUmWk+Rf+rmrHj4ndXpOs0ZlFjZG2SzRqAGZq3YobgbVKgGvi03I3XtK/yal+CBmbmY+oq+py/IXTcfAZxVZU9CkyWcwpjbwlVyVKRONRtOvX7+oqCg6nd6jR4++ffu2adNGIpGYm5tjGAYAer0+JSXl4cOHgYGBly5diouL8/Pze/nypUQiqWnbqwCtkTwVrdYYSC1KgghS9fjuF0oAqGfJ4GXprWX6fWFKOvreJ5oSJUAERpR7DTpcM5Wd5wAA4CgAwEWV5uLbuh2lUy8a8n1FdEmmLi1O9YqRhMbdvNe8eXOzFu/aDS5YsKBIwJ+/o7/afVMd3A4zp+N5BgBA6GgLEVoPm/AqK/b4j1ajnVxV0oUJLNTjWgGnccmOqZjoe5S/m1A8NqZtoTsuMw0iLBe603p9/GSEvnKE17pTcpVc4pIXuCnv0iYA4MfcF8++Yj56p2rHj07ZrxMt3GIZXAGiv7FjEps2KX3edn1S+uGDexkuLtCmDQCUr+FXBRWEpOnBqoeZqH1+Jgcy34tX1OHw+7Mymtx6imiNrb/CEA8KCoqvgPr16z948GD48OGenp7z589v3Lixo6Mjm80uvdLMrIxEVopaQjcOGwBmy+T7FEoAmC0UaElyYrYsRKcrod5NYHye+ch+5iP7kTo9odFhwtoePlak4TEhjed1D+PIAEFmNdhvml2xJPX33Hk0G0tWXWdAUV1cIgAgnDLexqUpMOabQuWH2o3BkHfRfwNsRjyRP4xSvgwreOotaFrlz6h8fPktnsqD3Lie/ayGmUYe5t1+lHentaBdkxwnY66cxIkrNs+TsDRf4QdFWvkKtnZyL/d6ovq1iC7pbtGvzAUogg2zHbvxzdLzGSdbmrczo5WXcfmhV6DMKxe66HgA4LTy/dDZ2N6eKJtlkGYSChX64ZITnxN1SGj21oOkwYiJhNwW3piFiFCqNc8j9InSnD+OGJLSzH/qX9M2UlQl5Ql4Op0OAE2aNAkODi4+UhEMtSAzxN/f/8mTJyKR6OzZs+3bty+9gMFguLq6urq6/vTTT+Hh4b17905OTl63bl3p4nxfItcStMsfFJju09uDloT1IQoAmMLE2gNY5Ri2PClDZNI8kPpONCezwjcGCRCUr2MBksMgAMCPxaQjCAB0ZZf0zyCAtBZ1vJT5933ZDec451QenXv9duvm3kWlcbp16+br6/vixQsAIAjioPrEjvl/2C5xien4TB1WQBoJPJOYyhw/WT3nEPbXwpsrjdsKsnalGNJ0ULrtOoIxXXfq4sahHK/3jLcYRuTfNeb8JWq0q38gHBB3UkcWBtQo0hKMAeNtpp1sPWnv4JQn8yzcACDt0uY7hv0dmtIIdU8Ac13MD/Q6dxGML/nfCChXw3d1ZoWk6Vk0ZKivJQA0vnnBKip8rvdgrZ19Y2vGuVgNA4Mf63OZNAQAwrL0wVK9DQ/z+gb6UlJQUHyhODs7A4DBYJBKpZMmTSpnJdUzuZZTXMMbAaL1hg+p9+IgTAZW0wXnK4jtUlduczNWXa4s4LHude7YV++F+k8Tr9tf75zpvuZFFAAwXSvUVups+lENrvYWNPXiv/ezg4vx+lgPPiGtlmjtj1Kf3wgBJFnzxpblIKSLACBWEQkAjCvh5rfeAEC+wJj6v0yUBKszr4mhjdEPXK0oR8PXwr9oLaE5l3ECAIbYji6Rs1mc+nzvRoKmLwuenss4Mcp+cvnnND3NohehnGeNK1QAQBN9+EolimIiM0KqxQuUtUHAG1IzsrcdIg1GQe+O5sP7Im/Fmvmw3so7IbkBf+VfuEl3sOF1bFmzdn4yghUBlVh5+s9qNaaWUJ6ANxqNAIAXazFiGvlSOH36NACsWrWqTPVeAi8vr927d/fo0eP8+fNfh4D3q8Oa3oyvMZIAQBSQAID6IgDg8JIHz/IlcsNYbx6tlCedjsIoG66IXWxCDAAwIivnmU7fj8vpzGYBQJYu/VbO7Q7i7sWbo7YTdQ7MPP1UHtTOu1vS7ZwUIUsbEcvyqmuaJUik6+hZL16MMD28gtw0jqMjDNT5UIOopsEA4PFvE98mfpcH3L106dKS5Uv27Nlju8y1zBx4AEBYLiyvW6XHGc6bCNVzQhP5RJUAYmB7fqdLeG6a0kTfV/277VmP6VZvr6x3sHSJPNzUJo4jVnABQPnQVZ90HhAMABAaRrexNKRnZa7cLp70I6998+K7DKvPCQhVZarwFjaM9o7MjDC6Vqtc/+w40WGydzOXTBX+SKoXsdD/NeGRAP3O5ADAJF9eiXgHCgoKitpDYmJiTZtAUWUUafhDCiUAfFS9f3GY9ZAAALdds0XDHpaeHRPWb7/3OWNOXkHgbQDgftfsoydM1rx5kHeThtCG2I4uPdtJ3PNe7nWpNvlGTuCHHMLVhBndvKmw9RP5wwMpf05zXoTqceW9x1APwEiwGrhjHo4nna/jNNI7nEtcfpIelmy9bBpmXrbyLFPD10L1DgDnM07mG/OcOG71+d4qvAxvUxF9rQZHKJ7fy73eUdzdkf3xfoEVeb4ojwMA+AeKQZrOYpo1raxx8o5dIPUGnl8r0c8D35tAEF7HloCiOX8czjt2ntumCfKxkhAUXwrlfZrfuXMHAIpXm3/w4EF1G1SFJCUlAUCHDh0quL59+/YYhqWkpHx86ZcAj4FMbVKyIQcA6MSOR58XPKvHO9+MV2beUZm0Y7Ge6fT3NFqTgL+QeSoo7zYb47Qy71C0RsKwqstrEK2MSOMncVBMzWUl3HpQ762Av5ao/ZvWQVLHIycpFgAMBsO6det27NjBdOOIR9jkHEzL2p7sfMRr69at169f379//4QJE9zd3V/ee+nj4yMQlIyi/yAYl+EWIAvvn+AwAADMe85Awq/npRYWQci+uBFzanzVs63pYXyjXsuViSypwVQ1XhdtpYsOLnE+0mgsuHirhIBnYshYb+7qoILfnyraOzJPencVh2Z9lxWNbt+l/23KjGbWj6S5e1+qRjbkhKTpI7INlhz0B88KhfBRUFBQ1AgRERE1bQJFVdKezXLGsDijEQC+YzG/JvVexHS7rR+aGhPWb+X+JoRKw2neiFXf7aOnOi7dR5BEV4u+1ky70rNF0doXMv5qJWxvRv94f5wqZIjt6BhlZLji+Yb4JT3uWhrVMqgHvM6ttE6dj0n3xKvThHTR8A7z9aFn9InSrA17bFbNgi/H2V4auUF2MycQABLVr6dGjKjgUWfSj85w+a1KDGC6O6mDQ9WPX3LblJ1QrY16TSjVNGsJZlbzKSeEUq1+HoHQMPORZRcC4LVvXhB4R/8mWRP2itOs0Wc2r0pInjb42LFjPj4+XtJ8++jUEh3jEgb+T+pu+9LRPDQ0dPjw4U41ZORnprwP9NKO6zZt2lSnMVWMmZlZVlZWfHx8BevSJSUl4Tj+dSTAlwPTjbNlnKPJMw8ASqVy5syZERERv/zyy6hRo0yDoQVP8g157cVdi45qx2ZuyYf7Wh0JgAC4cesG5d1+In9YXMADQFtRp2hlxIO8mz08+odFv3qjKfCQyTGREADUBhIQtNnQ6VfWFYY57d+/f8GCBQ4ODjYLXWR/ZeSdy7LP1Lu6uk6dOnXjxo3Tp08PCAiIj49XKpU9e/Ys7ynhKhIIBCv8GEW5Pn/rA1SIiJ8fpme68icdUq34Tq/TAgBBEvI942mLb4pEVg5MIhK4G5y9Nac2Tqvf1JrFM/uhG00sMp2E0OoUF28ZZXJMJLSYNbb0nj/W5+x+oQzNMlxN0B6K0qm8+l3MCWSGhWcs/6PhkiktbHkhafqjkerriVoAmNSYx6Tq11FQUNRiGjRoUNMmUFQZprz3OKNRiKIKkvxbpRagaFFNu6+DMWEf8YQvGfPM/0hLyf9KFjAvzWP5g1hVpIBm1sdq8IfW1Od7ewuahhU8/Sfj+M8O5VXDrnJEdMlMl9+2JayKVUXFNo1iaTEAuEXcvxT3LwCIGRa/Oi+WsOoQS6elzVmri01U3ntSwuvwZUFDaBKGpcJYUKmjxAzLqjKA26aJ/OQlVdBzfre2rPolkyhJg0F26CwA8DrUispw+sRUwAlmPVeMX4bTzgSnSQP9m2R9fPIXKuAvXrwYEBCwsk03e2s3EkEurt6c+uRFd1TwL1Fg16Ix8SzeLi7t4cOHAfcvCwSCdu3a1bS9n4OquSJLkmRycrKFhUVRR/HaQLt27U6fPv3777/7+fmVWYmnODiOmyLnmzf/gj/1Koh5vsFcXdirfPXq1Xv27AGA4ODg1q1bu7u7A8BfaQeydOn1+I0sGdYAoDaSMYlGSy6WgeOmZnKNzVoel+5L0SaWOHNTszbHsb3xqhhhPTEaDdmWwuyr962H9SlaUL/TDzF/b3zz5g0A6HS6NWvW7Nixg2H3NqPeGgCgoKDg2LFjQUFBoaGhHA4nMzMzLi7OZFiZaCM6kkY5q9EDhG5psvZAQiMAYl2j39fd8Epyn9567o77q8ebkkHUGkXm7rFjB8zid2wbmQaR9bq26TgHkesAeNyWvgwnewDAC5SZy343yuR0O6sPRaOxach4b97a4IIVDwsKdERbB47HhHE5Ww+qHr3IXP7HrAnjB6cJdoUqVXrSkoMO9qxFfxcUFBQUn8bLly8ZDMbX16vlK6NE1bpog3FWbl5RTbuatq7K2O99rnwNvyFkqGjVAORjif16Qn86/TAADLAZwcbK+7IeZjcuUhF2X3ajg7ibE+fjXv0qxJHtstxj6+kb/k/N4tQcHADUuEpAE7YV+fW0HMjBuACA8rnCIb1yth9V3gn5ogU8jyZY7bmjBg2gSczN+nWR/30la12A5H8jOc3fiV5jrjznz8P6Nyk0a4mgt18NGlkEodIAACr4oHovmiXeFqL+4pgxY4Yg6OUIazecJNGf+rZq0+R2QgrI9BKJpPW4kQqvUMO+M4Mtncm2PUbOmFHTxn4mSlcT/zi3bt1atmxZQUHhtbEnT544Ozs7OTkJhcI+ffrk5eVVpYH/gcWLF3M4nGvXrrVv3/7y5ctarbbMZQaD4datWz169Ni/fz+CIL/++utntvMz8Epv6JyeeV1T+ArMCkjesDkeVxgBIDm5sO8ISZI3b9403bdn1SGBjFMWRp6fiFLPvCmXKFEAuK/VAYCAJpzjumKq08ISGzFQRnNhWwAIybhqAzQSgejoKFVIGPm2qCFKoy9atKho/c6dO0tnZAkEgvT0dACYP3++ySn05MkTU8+/MkFYbqQhW/+m8D/uWKQ6V0P4WiLtrBJODjghwAqSbbtPnPuuhYwuOfzS1YA/0zhmGJGtQTz7DbW3fRcOZ1Lv+uS0ctS7ieENOSIWmqHEAWBKEx6CYZLpo7ltmxIarcXugGGIVKUnAWCiL49V4VQFCgoKitoJQRAdOnSoeFYaRY1QuuZ8VzZrk9ichiD7FMqN8sp5NWs5+73PfWgqwO6AeMLQj6p3APg3+58cfRYDZRQY8y9nnS3n9lQeZMW0IYE8mbafhM8di86j8bveEs3cbLuUvWCs46+rPLdvaXDgB5ufTOrdhMm/qo9L/My2fX0IB/fkfteUUGmy1gekzVkrO3hW/ldg1sa90qnLteGxmMjMav5ElPXB6nqfE1MYvzFbVs4aPFsGAGgtCPj/NNh3n420ccdJcnLwvz8sX4AgCI1GAwAajUaj0YauWDQx6LKBJIZYubBuhNS0sZ+JSnvgJ0yYYHLYTpo0SSAQqNXqQYMGmbLNDQbDpUuX2rVr9+LFC1otyLby9vY+ePDgkCFDnjx50qtXLw6H07RpUwsLC7FYzOfzlUqlTCbLycl59uxZ0cWIlStXdupUi5o6VhWvDAapEb+l0XZhswCARIBhIA1xamhu1rDhuy6XphLxAODG9XyW/+i1OrqNyA8AXIU0AMiXkuAG9zS6sXweALhz65W5V0u9zx24GiS7NSbEQ9qirtRGVGfTXhqPq+39I4AFAIwaNWrdunWxsbEfNTs1NfX06dMtW7ZMT09//vx5q1atylzGcN6kVTzC8/4lDVlaxGJvqBIAfm5kphXuJ2KH/yA+tT9rXJ7v9316P7146YLpkCevgmyu/QldpwDA/qzxXZiXANKgMuodADg0pL6E/iBVx2cgzWwYAIBgqMW0nxA6TXk7ePTtY68bDnxq4drHjcp+p6Cg+AIgSfLGjRuPHz9WKsuoGhUZGZmXl0e1iK/NfKhjnEnDf9F++Awcn5oj68flDOe9V/S7TD98OcK+NMF5dwFAT+jPph+t4CGxqiiZPqfM5uTVCqFUoyTYCd3qmIvLXIDyOAgNI7Q60ogjtLJrAFNUCASxmDaK6eGcf+qKPiFVn5BaNM5t00T084Dyfx9+ThguDgiToU9INaRl0m2tSi8gjbgqOBQAKlIMohaSd+Ji/pmrCIbRR30fH3Y1JS58yPJ+LTo73pNoODnsXcv6vYh6biOypY8diBy5WHDhJgCIvoGeeZWT2f/8849Jvbu7uzMYDAA4ffp0UlKSjY3NiRMnCgoKxowZExERceLEiZEjP5509BkYNGiQSCSaNWtWWFiYWq0u6kNeGi8vrzVr1vTq1etzmvfZkGAYAGS/bSiQ8lDtvgAAIABJREFUKWHUSdXq4tXQ3MzH512r9tDQUNMdD259AIhVFXrgW9gyODQkJQFnuCPP9XoFQfDRsmM3tOExrHWnLUbRsi2NBR3FggIoYNLk9hLz5GzlnRCo3xsAMAxbsGDB6NFlFHotAYIg27dvnz59+vnz52NiYtzd3cusUIAwbJj1/iE18Qjd8kykOkdDAMCvN+QATgCF9WmDcy2IzpsYz2P1adGmkfRza61t63EadsoyWPwr8OwOabhKnfP74QqqdwBQ6snwbAMAKPRksFTf0o4BAICiksnDETpNce2B/8tT/g37n4kVjPeu+S4jFBQUFOVgNBpHjBjx119/lb+sbdu2n8ceispCAJTT770rm7VBJJydm7dPoRSi6LhyA25rITKciNIbIvT5epIc/X6ubwkN39T5SKXOPMr+f+GK51AZj7oV0/bzq3cAwAQ8Y1auUSanWZYS8CRkbkvCVVrSSKAcdjWpd9KQSRQ8em/EmAeGbITtUXwQYdig/FqRH/6fQBBBj/b8zm20EbGG1AxCq6NZithedU11nWoPCIPO69BScfVe7q4TVkunIljJ/3r5qUBjVi7D0Zbl+fEq/bWNrFU71C+iEARhutXBXiWsXTrwmnuMkQ1poAQAuZnS1tVs1OCe3eLqcl8l4i4OutjEggs3jSkZlovKa4b6FVA5Ab9jxw4AGD169L59+0yRzxcvXgSAefPmmSrerV69esKECUeOHKklAh4AOnXqFBoaGhYWdvXq1YiIiISEBKVSqVar2Ww2n893dnZu0KBB165dfX19a9rSasQSQwEgGy/Me8+0YAKAPl4DAMWfeHh4OI7jGIY5sl0YKDNDK1UYC/g0AZuG7OhmrjGShxnKZzp9iE7fuVQfeAAw5uRlbdxLaHWtlA0uWIY9a6YezukRdfOe24QRqCwfORMGAIaUdADhyJEj165dGxMTU77ZO3bsoNFoQqGwQYMG4eHhQUFBffr0KbORKcr1Aa4PAFhwUCELLV5mlcSVOgLTEUyUybX6ZV/aup64Oh8AgCSyD0yxnXeZbukk0BQAgGznCUNGdgXVOwAcilDl6wh7PpaqwDc/UZyye/uFiiCR3fuFxBgGJYUsCf/ndxqprt+RQ6ei6CkoKGovZ86cMan3Zs2aWVlZXb9+XafT9e/fH8MwuVz+4MEDrVa7aNGiOXPm1LSlFGWjJsgwvb6cjnHdTb3lcvMe6XTj4AsT8PUZ9JUi4RKZfL28wEDChPcvQMyu9/fGV4MAQGB/YMKHS3mVSV1eg7q8qqngqFQqvb29/fz8TL6uKofp6aJ7naQOesHydH1vgoSU2THZu1MBgOXgLRlZXS2f9fH/w+U3K7AQYfuGIUyHajLjc4LQaWzf+mzf+jVlQOIPU5w+1thcOKSn5ulLbdTrzBV/iif+SLcpvLpEqDR5xy8qrt5DMEw0bjB8wPdWm1GHvQIAkiS1MW+ud5Y/aqQEAOcEFg2HODet+2uWgUYmOukCvWNkj9K6PDIrftTXTeUEvCnseebMmUUi6v79+wDQvXt308POnTsDgKlEWa3C29vb29v78+yVk5PTq1evnJycCq6XyWQAoFarq88kCxQDgKy3HniWOweuA5muAwArKytnZ+eEhATTlKnFCIZgLhyPaGV4vDraR9AcAL5zYAJAYoGxeDM5HaHd9GZZPV6j/tY/AoD8r0BTy5aufX7899XYSEUo2BEdhxfWdOUr+JAA+kQpoaqDcTkLFy4sKnr/ISZOnGi64+Pjk5CQkJubGxMTU379pG7OrG7O711cIA2Q+bx7l9BTapInNLf+rXnXSQ/+MRqNAICr87U7hvQdubF14lMAMGRk0x1srJdNq0hfELWRPBiuAoAVbc1m3ZY/y9CHpOlb2Bam221/rnrq3rmJk8Dl7vXvEkNPvGoxthHlhKegoKi97Nu3DwDGjx8fEBAAAJs2bZo9e/bMmTNNLvfg4OAOHTq8evXKzKy2BI5SlICHIldsLDkIIvjwz/TuHLYXg2GGogCg1JPn4jR93FhmzC/jZ/0ALgcAlsjkW/ILAN5p+Ci9YUx2br5oRzcOe6NISPtAB7XPgFQqffPmDZ1eXa22eX6tCgLvKK4/4LZrxnSrUzhKQsqc2OzdqSgTIY1GbYqzKoINpo5BVQ2JFwAAJmgHdBEAkHopoXgCAAjdAhUUtqki5LdIvIDEFZTX4r+T+MOUiizDBDzLhZMyV+3URsZJp61gutWhWYoJpUob/YbU6RE6TTJ5+BcaP78i9uk8Zx8mhv3jmBreCkg9mb85NfhBge9ET3DDVNGqF7uime3MzGbYPWqlUEUr+6Xa6XB8Xfzz/TVteXVTuU/trKwsALC1tTU9TEpKyszMlEgkdesW9voWCoUAIJVKq9TILwy5XB4WFvamwsjlcgAwScpqwhxDaQiSTxA6kgSA2QudxKNsxSNtTLMbNmzg8XgMBmP9+vVFxQtMKe5xqvcuYrVjMwHgrlZn8nDrCF28KuZq9jktoSENBvWjF4AgAY37tT2iSs9uTAI59u65xgcyTbfVqQIAuCnxbHI8r/GBzC2GzhW3n0ajtW7dGkEQjeYjJTRJXKl/MwPPv1s0gtCtLOv9OdLqKADYvDncY+XstWvXFs1mZ6a+vrERJ8gcJg+1tqigegeAw+EqmYZobM1oX4c52osLAH88U5imHqTqnmbozVloy4l906dM2dCg955QZVHfPgoKCopayOvXrwFg0qTCsEM/Pz8ACAkpLAjUsmXLadOmnT179tKlSzVlIcVHscawctS7CTsaxkMRAPgrWr30fv7RiGp0HlQ5A7iclSIhCrAlvyCgQAlF6p0gakS9Hz169M6dO+UsIEly3759wcHBVbIdw9FW0KM9aTBmrtyuDgkFeKved6UgdITfJpzfNAhhEHkXNMlTXwFRXb866A6Lme4HmO77oLDTG0oasunWE5juB5juBxCmfTXt+81SERnPqGNnu34ev0sbhE7TxSWqHj7ThEWTegPbp57Nmjnc75p9Bjurg15LZp8yJ3UopHyPAQAtyMyM1eipvkCm0wCATKd5rJUL6A3pQQIASOmH6lE4K4LeS2bXsN3VT+U88A4ODnFxcRkZGSKRCABu374N72fEJSYmAoClZZU1Y6xuTAHkV69erUKb3dzcpFJpfn5+BdevWbNm7969AkE1FpVBASQomoHjuThhS8MwIa3O9ncl6AYOHNi7d28cx4t3AXTjegLAa1V08fN40ul2NExqxCP0Bi8GXUAz8+DVj1FGhuY/aZzvQmh1DEfbAoyRr9OQae3E4kfm4qCYxN4E8e5qtAHFDDgATlTq4nBubi4ADB069KP1k0hNtDHrIC77h+V1D2E6Fj59QZupTYLbxv7o5RPOdrad2XTAs5uLT1wprMl/NfTpIqHt08E7/eywnYIKqXe1kdz/UgUAM5rxAGCUF3ffS9Ujqf5xur65DePPZ0oAmODD5dCRVh087WQ5qSmy+On77Nt6C4f0hJpzDlBQUFB8CFPvD0fHwo9NU+dOk6o3MXbs2A0bNuzevbt37941YiFF1aI1kgCgw7+wi8vF/fBZOH5Jrakp9S6TyUaOHEmn00+ePDlgwIDSC0iS/OWXX/bs2dO2bVtTvOp/x3xUfzwvX/XoRdaGvXRbS9UrH+UjLmAkz+sRxshgerlYLmz05sdXOQfTAMDxd09Aq+s1wXPPEZoYhOlAkww2SDcZUtcx61WidiDFRymu2ysSSI8J+eJfhol+HqiLS8TzFSiHzXB2wIRfauV5EwMHDhw4cGB01G25YZtIRptBdhgUvScrKwvDMACg0Wg5OTkR4eHnm0zbJL+YZ24klvRb4FUJB+GXS+UEfL169eLi4g4fPrx27VqSJPfv3w8APXv2LFpw/PhxKPYLoPZjKttmeNvkrKoQi8Vicdk1QktjCluobiwxLAPHswncFjAAMGTotK9U/I4i0yyTWbIZhhvHE0XQBPVrPaFnoAwAKNAR/kEFdd3pUsBva7ReDDoAdJX0TdemCmhmhY0o+dz1HYWLWwtIsNqc6Jqijd/V/3VTQQcAuPhau/R+vl9GxHxhpmTSjwAg/FiBiYKbMkEnEQB07tz59evXL1++dHZ2Lv8QlNcUE/XGZZf0CXOYnu+qMbEdZjaWXyJUuCFxMS64+ud85us0sydhmabZ0/cDG/TLvZkmWfEgf/l3Hw8QPRahztUQHDryIFX/IFUPAO4i2tN0/ezb+b6W9CfpeiYNydaQ60MUAMBjoFyjnp2ZKT99xZgjE08ajmBfRrwiBQXFt4OVlVVSUpJarTZ9efF4PLFYXLxdiKurK51OrypfIgXFJ1Ok4Y8pVQBQU5HzIpFo8eLF/v7+Q4cOLa3hi9Q7h8NZuXJlVW2KYJjFzDGsaw/kZ67m37TWJnEBJfnewUznfEGf3mbfd0boNLezzNcDQqtZwxMG6SYAoNvNxsT9jJn78Py7REEQKmhdDXtRVAKEyWA19Pj4ui+KWHE2ZICBAfudrjX5VWI1uRNZhw9gJDrw+h/uZENj7WdfwWkkAMRaysrLs/2KqJyQmDZtGgCsX7/+hx9+6Nq16/3791ksVp8+fQAgNjZ24cKF27ZtA4DBgwdXh62VgiRJvAKYFhuNxtKDXxkWGAoAWW/r2KXMio3r80L1+INhAmyMY89yMpKGN+rCUnOROcYzMZr4GCMA3HrbUt7XrMXWBofq870xAQ8AjDl5ACBgomZMtItFTwAIkl8xY6JmTJSFkgBAJ3BzIds0QpLlXfh/xr2ROi+WNJIA0KhRI6VSOXXq1Io8U4bL7yjXu1QQF8Kqdx5BmCRpwPNv3X5KX+7Rx5r/Tqtv7WzOpiFHI9V7wlQf3eJ2shYA1AZy9wul6fY0XQ8A0gLjpdcaANAZyf1hhVMPU3XJXPF836Eki6W8E5K1ajvx9tWjoKCgqCWYyosU1+dubm7Pnj0r+k4kCMJoNFZruRYKigriSacz3yp2TxqtpvLeV65cuWTJEoPBMHjw4BMnThSNkyQ5ZcoUk3q/cOFChw4dqnJXBOF3/Q4x+0mb5IrQEbtFbIcdgx32rRX+0B2h0wCA10bodtYH5WI5B9OSp0VXRyw9nnuOUEchTAeaxVAEE9CsJwKAQbq+yjf6ZikdNl/BfPivEqk2CQAUPGOCs1ZVDzNvxFObGQFAbWYUNuJp6tMSnLUKHl608lugcgK+U6dOEyZMIEnyzJkzN27cAIBZs2ZZW1sDwJEjR9asWWMwGDw8PMaPH18txlaGzZs30yqAabGTk1Ppwa8Mi/c7yWE8DAA0kWU0+y3Ck9cQAKKVEaaH3lZ0JobEx+NcFIkxGKTG96500O2sMDO+MTNH97rwj6e58Dsexk9Qx71Rx2ZmZCiCTnmhUgBg1XtXSIMgiDK3JnQ405WjjVblHJQCwPr1683NzQMDA8+cOUOS5O3bt4ta1pcGoZmzvO4wnDeVnKAJ6K5bTHfXHMTp+eoDLXrUs7EXiUSbN2/u2MB+g58QRWDdo4ILcR/JtF/xndm8lvy5Ld67tbYrrGDHxpDpTXklZocO9LFZNhUT8DQvY+QnqCRSCgqK2oUpG27u3LlFee9NmzbNz88/deqU6eHly5dJknRz+yIrIVF8TZjy3jUk2ZBBRwG2FShM+fA1wooVK5YsWYLj+MiRIwMDA02DU6ZM2bFjh0m9d+rUqYq3JCF1bmzWzhSEgboca2Q1rw2roUeJvnHVrOHfud8BYQAAzWYSQhOanPBVutE3yres1cvEk+ENANlhsoK12SNP2Y48KvGM5AGAZyRv5FHJyJPWqnU52eF5AFCf8TX3FCtOpUN5d+/effz48REjRnz//fe7du3y9/cvmrKxsRkzZsyTJ08+mqj8GaAq5ZbA4v1Ockx3DgDo4srzpdTlNgCAWFWk6SGHhjSzYRAEuBnpAHCzhBsZRXmdWgGAbM9fpN4AAAyU8Z24MwDczAk0GI2AQnvaa4SOsRvXDw8PP3DgwIEDBw4ePDhjxozip5k7d+7+/fsPHjsU0zEJAFJ/izPKDFZWVmvWrAGAadOmyWSy1NTU0NDQlJSUyr4INMkwTNQXAFkxwTjl8TV7Dv9y6+/jdx4xhZb0cGHNbSEgAebfyX+eoS/nPG7mtAk+vF9837v92dWcz0AAYFJj3tSm/BKzA+uyWW51rP1ncJo1YnnVrazlFBQUFNXKrFmzXF1dExMTW7Zsef78eQDo27cvAIwbN27evHlFfUO6dOlSw4ZSfNsUr1p3wlJSoqZdjVCk4efOnQsAmZmZn0m9H/Uy6yn50MLq0/BE/r0i97tphHLCfx6+WWEfHhhFEqS4nvnytbtaDZ/pkiawykEBwCoHdUkTtBo5e8maHeK6QpIgQy9F1LSxn4lP8TYPGzZs2LBhJQYXL15chUk+/51x48ZhGDZt2jSlUikWi1euXFlmmboffvgBAHbv3l3xlPUvFEvs/U5yHhwA0JYr4D14DRBA4lWxRWnw7R2ZD1J1aBYC1nBTo/2JX9gazUDqXyle1vveTxX0XBefnLHsd8nk4XR7az9xz6vZF57IH3aNsSN1IGRqRHwdQqOZOueZ8PLy8vT0jI4urJb3999/L168GEGQfG+N0l3Li2Olr05w2Ogxfvz4w4cPBwUFLV68eMqUKY8fPw4JCXFw+GiXUYLUvkFY71xGTI9DhrStHWD5r//DV154urZuG/nfV+j21tzWjQFgvA83TYkfjlCN/zfvdD+xs7ASfyBmTHRpW7PbSdqfP9wxjm5rZTlvAgCQekNB4G1OCx+67RdT8ZGCguIrhs1m//XXX8OHD4+JKUyb6tq1a7du3a5evbp+feGPcgcHh0WLFtWcjRTfOqVrzn+ot1y1kpCQsGfPnhIhhK1btw4KCgIAuVxOp9P79u17/fr169evFy0wNzefOXPmf2wyl/dPZtbOFAAoX72b4LURuhz1MuXD874zFw2x/i9bF2HMPgHF3O8maDaTjBm78Py7KMupSnb5ZvlmVXo5/NhvxOvHL5V2sn/xM9PrLbFaNJm8uQIASASsFk2m1XP6N2ElykB4UvHw/iNq2tjPRJWFi5euglbjjB49ul27diNGjAgODl62bNnevXtN6fql6dWrl52d3Wc27zNjgZo88EWt4LkAoI0tT8BzMZ4D2zlZ8+aNOsaT5wUA7R2Zq4IgJtrIsEGe6XS5OCHGUAC4mXP5VNrBH2xGdlk4KdN/hy42QTpjFdOtDt3WsqGHxUvrjFuZl3BZe5oDas2Wa0Lf6yTcqlWrgwcPtmrVypQSn5CQIJfLzc3NAUD6Q17d9TY5+1Lt17ijdHTfvn0+Pj4BAQE//viju7s7+rFmOQBgkG41pKxkOK2jWU8oGqTb/kqoI0b1PvPzQEKf0i3v8L85fx6hWYpNXVUXtxGkKIy3k3Rjr+Sd6ieWsCsRpdLfg93fg12RlZqw6LxjF/LP37BaMJFZ16XiW1BQUFBUE02aNImMjIyPjzc1mgGAs2fP+vv7X758GUXR1q1bL1++3PThTEHx+flQx7jPr+F37NixcePGchYYDIaTJ0+WHm/Tpk3xzk2fALshDxPScLkx91i6oIsYoZeX/E8aydyj6UACJqBxGlVZNXJCl1Dc/W7C5IQ3pK4lDdlVtRFFaSpSjv7rQyKRLO+yYUXs7EhF6Pr4xT86j0vhMwAghc/IcEaOxS+KV8WY0c2XdtkgpItq2tjPBFJ+IbEPoVAo7t+///jx4+zsbEtLy6VLl0qlUgsLCwaD8fGDPy9Go9Hf39/f3x/H8fHjx2/evJnHe/fhjiAIAKSmptaggJ8zZ87GjRvt7OxSU1Orb5dXesOAzOy6dPo5awsAIHVEqOUdQMAnqwPCKNSoOI7L5fLiwQg3ci6dTj+y0G2NI7tQZHY+kZ2Qb2zehx5K6JeZmw3hcQEgUhG66c0yCcNqXb1dpEKd91eg8tYjUyB9ir3+wOgstp7WWrValvCMB9pmGSpjj7YvXrxwcHAwhcG3atXqt99++/vvv01bSCSS1atX16lTRyqVNpF72bCtLCYWetp/++23lStX1q1bNzQ0tCKZGrj8ui56MKBMVoN/Ua7PuwlCo43sTqheYsJOyrsDFNeDMHMzmzWzaRJzAFAbyOEXc19mGepL6Mf7ik2B8VULacSzN+9TP36JMBkWM8dwmjSs8i0oKCg+AbFYLJPJcnNzi0QsBcUn83m+38tnzGXZ3WRdZY9qYcs43rcWRSamGfF+mdkKgujOYW8Um2OlFpxRqX+TyQmAdSLzvtwKXUn/ZDIzM48ePWo0GotGSJK8cOHCo0ePTA9RFB0yZIi3t3fxo8Ri8ZgxYyrieygfdZjidZ8XRplB2NfS+VDDD2l4EieTJkTJ/srABDS3877cZlXQq1gb2ZVQPAEAhss2muVPpXYs0L7wJo1yAGA1eohy6v/3HWsJpC5ZnzgPiEpVIEZoNpON/E56HCr+M7KC7vdvUMMDQLImYVuCf54hFwDoBMOA6k3/AoCYYTHNeZHDx6I/vqbv90/xwO/cuXPhwoVyudz00MfHZ+nSpSdPnly7du2CBQtmzpxZpRb+V2g02rJly7p16zZixIg9e/bcunXr8OHDrVt/c40uTEXsct564BEmynBi6+LV2tdqdn0eACQmJnbu3Dk+Pr5Xr17nzp0zFfPrLOndWfJey99OTsy9YUZeLgLmcE2jNQn4+nxvMcMiR5+Zq8+S8K3E4waLRvbTxSYaZXJLLvsO/UACvJHx4h7HOfrRYl/TCPvcPAAQi8UODg5BQUGPHj0aN27c+fPn9Xo9AOTk5MTHx/v6+kqlUqQz08LrXZz8okWL/v777+jo6DVr1ixfvvyjzxoTdqFZ/2LM2I3nnn9PwKNspscxbYQfLr/J6+JekOCIv07OWr3TZsN8BEM5dGR/T9HQ87lROYbRgbIjfURsWhVreISGWc4el7v7pOJmUNb6AMnEH3kdW1btFhQUFBQUFCys0t9fGAKccl27nx8FSWhJsgeHvaEs9Q4AA7kcAFgqk2cT1d5LyMrKatasWUUPTTXnHz16xGKxtFqtWCzOzc09depUnz59Siec/nc43ny3i76v+7yQX8hKGBVRpoavDvUOAKTRlP+IEQX39IoHZaygi8Aor5K9ahWE8jme929ljyKZzoNu+KQp8GtDLcSVCeekKBNHtvOKutsCM08/zLulMBYAgAHVC2hmbUR+PS0HcrHPkT5Te6i0gF+xYsXSpUsBAEVRFxeX169fm8ZRFM3JyZk1a5ZUKt20qVQB8JqmVatWoaGhU6dOPXToULt27ebPn7906dL/mIn0ZSHC0HoMuqjYpV92fa4uXq19pTIJ+N9//z0+Ph4AAgMDr1y58qF0A786rL1hqqRYnN4SCdHqZAQhQlEEkMl15kq1yRKGlWkZwmSwvAobUXaV99+dtCkTudq5+Qr0xes8CZ8f8wbYKADUrVsXAIKCgqRSad++fU+fPm06JCAgoHPnzqV3ZzKZe/bsad++/Zo1a/r16+fr60uSpEaj4XA4H3rijDr+GL8FataxxDjCtGd6HNa+6odn7Vr40uMng7NLKmJtMCAYEwDMWeiBXqLB53JfZOp3vlDObFZlsWfvQFHxxGGYuUB++t+c7UeNWbnCwT2hhnrhUFBQfFOYPu0/DVdX1yq0hKK62dGt7KyH7c+Vmx8r/teYN7N5NXzBVTV16fTHdtascr8iB3I5vTjs8tdUOSb1bqpat3379tGjR0skksmTJ69cuXLkyJEA8Pk1fDWpdwAg9bkAAIAbc858ZKUxr6o2rQ1gol4ouy6hicHEA2iWI8tfTKhCDcnLAWXe0E+LyjEAwN4w1byWH/8rq3j2+7cZSA8AXIw32PbnH2x+ytSnvVHHuXA8rJm2CHyLv5wrJ+CfPn1qUu8//fTTpk2bJBIJ8vaDcsqUKRqNZtGiRZs3b/7xxx+bNGlS9cb+N/h8/sGDB3v27Dlx4sRVq1ZduXLl6NGjNW3U5wMFOGtlYbqvS9BIF8YBBgCgfVXY9rx4/7wHDx58SMA3tWY0s2FYcFBnJnZPq7ul0f7A5QCAM8fdmeNe9iFmrU/RD6bpkoe5JGpUHuEx0Tm4HqAwAL5u3bo4joeEhLRr1+7y5cumJsN5eXm7du3q1atX6bO1bdt22rRpW7duHTFixLNnzyIiIsLDw/38/OrUqVP2M0domLh/2a8JvyXDeYs+fvLInjGDZoehBLnvYrtBgwaZZm152KHeohUPCppZV1tiCIIIh/amWYpzd5+U/33FIM2UTB2JfEvXlSgoKGqE/9IN7tMy7ygo/iMVUeY1qN4vXLhgb29vGl+xYgUA1IiGrz71DgB0hzl4xuGPr2NaY4IWVbhvzYPQ6U7rdK/6EfJrqNNqhG5VYn7No4LgtMLuRYSGBcTfCN0qXsUEIAFgb6jyfooOe98Hb8PD/ugipKOF71iqdl3FQRHUhmlvw7SvaUNqksoJ+G3btgFAr169Dh06VGKKTqcvXLgwLS1t+/btmzZtOn78eJXZWKUMHjy4devWP/300+3bt2vhVYbPgC5BE9v9mUGqQ1AEADRRhQK++Kvx+PHj4oeocCUDZdARBgBgKJz8XgwAZ1Tqe1rdNbXGJODLAUOwTpJep9MP/5t97n+N57+KiTEy3nvjYRh2v/F5AOjdu3dRz+F//vmnUaNGTZs2LX3CNWvWXLt2LSoqyt/f39TZKCgoyMrK6hP6F9IshpGa2A5Nt26Zz5+6tkCpfK8PjauQdqi3CAC0RvKN3FhfUi3SmufXChObZ2/cqwp6jhcoLeeMR6s5f4+CgoKisvD5/A9eJ6Wg+CaZNGnS7t27ORzOxYsX/fz8ijo4AMCKFStwHF+9evWoUaM4HM73339f5buX1vCAQvWpdwCgW0+mW0+u2nN+KWBm7THz7njev4aUVQyX30vM3k7SxcuLaiIUfU4WXuskAF7lGkocEp1rUBuv8hYUAAAgAElEQVRIM2ahgP82PeoUn0zlUjKCg4MBYPbs2R9aMH78eAB4+fLlfzSrWrG3t79x48b69etxvNoTpWobugRNXI/nBqmOUYdFEiQAqB4XZis1b968aNnTp0+LXhwtoZn7asKm+JIJ513YLDqCPNLp89/vpFIaQqtrLa/PBlakIjSLkA4cMtjdq0HRbExMzDrtPNN99hyNtXVhmxOCIE6cOFH8PKrH+RmbEkkDyWKx9uzZg2HYunXr5HK5ra2tVqs1tW8pH2PWEW14B1L7Xuwo3XEJJuozvLsx/a7nzyN6A4AhPduQkg4A/v7+YrG4ZcuW806H9TmdExBaXW1m2d6e1qtm0iTm2ojY9EWbjNmyjx9DQUFB8anIy2Lbtm0oimIYNmTIkPPnz4eHh0ul0vv37y9atIjL5Wo0munTp4eHh9e07RQUtQKZTBYQEFCk3ksvWLVq1cKFCw0GQ/UllnK8+W7nfTEhTX4hK+HniMQxkYXq/ULVq3cKRp3VgDKNWccI5fMSU6MbcQHAiov+1XDKXx6Dzvg9cTajAcCkxrzNfkIEgEVDjvQWnR8oOT9Q0tqOCQAD63LMmFRiPMUnUrm3jqlmuKen54cWmPpyJyQk/EezqhsURefMmRMREXHjxo0bN25IJB/ppfl1oE/SxvV+rk/V8loJ64W0tFvmCgCGDH3u4XQAcHZ2trQs7EmuVCovhJ7Zm7x1ZtTY2VETdIQuThWVY8gqfjYBijZnMowkeVPzrixnUN6dZbEzcvWFTUQMqRlZG/emjJ4nX/indwgNAM4GrtBfukNnFDYdLK7eAQClow0XvPPwREdH37hxo+hh5taktKXxmZsTAaB169YzZswwGo0BAQFt27ZlMBhJSUnFL36XCaGJIVRhupgRgKuKDaNMtwCU34xJJuuihwChzvTfnjZnbdilq0uWLJHJZCEhIX/N6UfkZ64PVpyKLq/x3n+B4Whr7T+T4WhrSM1IX7hJn5xWTRtRUFBQmJUiOjp65syZTCbz3r17J0+e7Nu3b8OGDW1tbdu2bevv7//ixQsej/fLL788eFBW5SoKim8PkUgUGBj48OHDMtW7iVWrVp07d27Xrl3VZwbHl+9+sTEmpMnPZ+WdySxU700p9V71ICxnuvUvAIQ+aX6Rd93EIE+OHR/LVBHpKrShBZZF75KQb7ThYdOa8L73YLdzZGqNZHC6vqEF3YyJhqTraChMavxtFV2jqFoqJ+BN7btNMr5MoqKiAOBLibJzd3fv1KlTp06damET+ypHn6wN7/FUn6TVtxC4/uOD8TCr2U40SwYAJE95JTuRAQDdu3c3Laaz6Wc0h4Ly7sgNuWpcQZA4CeSK2FmxqsiiEz7N0LelswDgWjEBn6h+naxJuJkTCACqoOdpc9epg0NJgmC6OraTe6MEhLsVJFy/qLzxEAByc3OLq3cTDu2t7dpYFj3ctGmTVlt4fsvJDoBA+vpEbbQKAPz9/devXz9nzhwul9umTRsACAkJkcnK813T7eejnHqEJtqQuee9CZTFrHsCYbkQqhe6uDGMJvVJI54d8K6Ja2ZqEr5vOK7OX3w3/8qbSvURqQQ0ibm1/wx2o7p4Xr7yxscDCigoKCiqClNU2sqVK8vs0uLu7r569Wocx9etW/f5baOgqJ306NHDx8en/DXff/99/frV21CtSMNjZpR6r15odnMQuhWheILnnH1vHIXxDY0AsCtjMt1p7Y7nKgD4xYfHwBAAmNaUDwCHwlVyHbH9uRInoL8Hx4FfZjsFCooKUTkBb4qyLqf228WLFwGgROtLihpHn6yN7fEMSdbFNOb8e9AV4xV+arid9TEfZEUSZNLEKNmJjE2bNg0eMci+hbXflma2YrthtmNNXRmamLUCAKVRsTF+aaQiFADeyI1Dz+VeeaTFAIK0uoK3UfStzNsDwKO8u5qo2Jxth0i9gefXymH3Kpt1cz3nzmkl8SNR8nFnHM9XAMBxi7ITfprPbojSCt+ZUql0y5Ytpvu8tubin2xJHZE8LRpIYDKZc+bM8fDwAAAnJyd3d3ccx+/evVtOZgSC8Zh1T9AsR2Dm3UtO0cQsz78RugTPu3o0ac/5lFg3Bnf0gEFFC1LiopBDPxt1mrm35cWvu5KGbFKXWKkbkB+0EOWwLRdNtpj+s9kPJS2koKCgqD5MrvUy23+Y6NixI7zNpKOgoCiNo6Nj27Zt+/Xr9/m35vjyG4S2ahDailLv1QqC8egOiwBAn/wbEO+FZPZjLbWiZ0ZrPLe+ahCebbDkoIM8C+sZ+VjSv3NgKvXkH0+V52I1GAITfbmmKUKlThm7IHXSb6S+ZJI8BUU5VE7AT58+HUGQP//8c8+ePaVnL168uHHjRgDo37/sot8UNYJJveuTtERz/voAhxTWO/nJ8eE7H2hot9KNxMmkiVFwXe+x2LrL7hY9u/byr/tHF4s+GIIBQC+rgQBAR+hG0rgraaPSqLDhYWwa8ixJ70tnGEiyyAnvzHFvJGgiYVjmBpwkcdysf1fJ5OGYsLB5Rk/LAQggz+vJ5fbl1b0TOvPrDnIqerhmzZr09HTTfftV7nQrhjJInrNfWuKoli1bmpmZyeXyJ0+elHNyhFmH4fIHyi4jDQRhuTA9DgPK7On7eF3S4xaBBzCR2cCBA4sWJLwMYZ8YN77Bu24thCpM87ye5oVvpW76N9PKsxDDuG2bYgKeIT07+ee5uTuPkwZjOespKCgo/jtyuRzKrTCv1+sBQKFQfD6bKKoTDg0BABbtW+zAVE2w2ez79++vXbu2RnanSRg0i2prmkPxFprlcJTXmNSnGdLelbIjFCFY3pkx1ocA4EC4GgAmNeYV/+P6tSkPAI5FqgwE2c+D7WRGAwBCpc5Y+geerzBmyzLX7KI0PEXFqZyA79Sp09y5cwmCmDBhQpMmTebNmwcAMplsy5YtgwYN6tevH0EQgwYNGjx4cPVYS1FpitQ7r6WZ8Iy3lotKjcXcvyRk7Ujh+PJNGv7G6fNSbbIV03ZindkM9F1agYhuYc20NZAGZ467Cldez7nIpiHtHJkkgKuGBgCBak3R4unOS2aqRxtTs+jWFsKh7/WBs2Ha+5o1N5D61MYqBOC75x8syuo7sS6PV5gdpFAoiuomYkKa/ca6ACBd/Fqf/C6U/dq1a4GBgR06dMAwLDk5+ZNfLpTfiuHyp6M1dm4TgvAZBQUFx48f79KlS9GCqODbwb9PJQjiRqL2jdyI0MwBMABAaCKE6fTRm+kkCN2igvaQBqPiZlDG0m24vOCTnxQFBQXFR7G1tQWA27dvf2jBrVu3AKCoURbFl86Q+pyNfsKfGnJr2hAKii8LlFFnDQBiSNtG6kw5xYQ+cQEAOdTLXshENQbSjIkM9nzPU+VrxWhqwzAQgCIw0ZcHJvW+/E99YgoAAIJow2MoDU9RcSpd/9Df33/ZsmVcLvf58+fr168HgOTk5JkzZ54+fZokyZEjRwYEBFS9mRSfRHH17nrO117IQADScLyoarwhTZc6Nzb51xirGXXsVrrFt3sNAG0TOzDQkhdxvfhNAMDUdPFZ/iMAmNdSMK0pb6KNgIUgj7W6jGKB69rIWADgtm2CYCUzfHpaDgSAV/yoNuFv3KNSPqThj7W7XPwa9vHjx4t+Vpr3tzTvb4krjEmToopqiIwePbp///5Pnjzp3bt3165dK/j6EIon+oRZpPG9tHma5Ae6w+KGrvDqLPPY7qkMBuPUkSM+Hu889idOnBg9ccov/+YN/Cc3osCGbjMJABC2K9v3Odv3RTk3huMSAEDoFjTbGRUxj25jYbNqJk1irotNSJ+/QZ9YMuiAgoKCoqro0aMHAPz2228RERGlZyMjI5csWVK0jOIrgEND+nuweQzKA09BUTlQfnNM3B8IrT55GQAYs44QqhcIw5ZnP5lLRwCATUOZpWJbREwEADAEJGyUUGkyV27Xv0mmW0sAAKHTMZGZNjwm0387odV97udD8QVSaQFPo9GWLl0aGxu7ZMmSAQMGNGzY0N7e3s/Pb/LkyQ8fPjx8+LBQKKwOQyk+geTp0fokLdOF7XbeF+NhLAQRYaiBJLPfim2aJQNhoLoEDaHCrWbUyWiZAQBXaRfmRf4y79Uv8179ojQqAGBl3OzH8gcA8ER+HwDStakkkI4C7NemfGsW2oHNIgCuFHPCBzNfrpkvjbWXlzbJheNRn++twdVPW6gt0vJQkvzuWd8Sa/Z7nwOAiRMn+vr6Fg1OnDhRpyv8UHPYUpdmwVDczcs9WhhaP2XKFJIkR40apdfrK/4ONOacNGbu18WOBFJffJxuN5NmNZZJ0xnihhOqcLj/fI9HKxfxu9J6h/fsFD/YUqAjfr4ki2XNKLOiSUkIrT55OQDQHRYhNLMKWshwsrdZN5fp6WLMycs7dr6CR1FQUFBUlhUrVtjb2yuVyubNm8+dO/fx48d5eXl5eXmPHz+eN29es2bNlEqlvb39smXLatpSCgoKihqGUWcloBw89yyed9WQsto0cjcVlSpxFIEMFX4v+T0dnq7E76ToAQEDAYef52eu/FP3OoluY2G5aDIAIChivexXTGSmjXqdtXonpeEpPsondiC0tbVdsWLFmTNnwsPDU1JSbt68uX379latWlWtcRT/EfOBVoCA7o0m52BhTzJ7jAYAqW+j6BE6wnRlA0Fq49TqMIUO1QGA0kKZbczM1mdm6zMJIAAgV5+db8wDAANpBAASSAPxTvH25rAB4NJbAa80FgQ6vTTQyaOcQMP7wrhwveUgAHjgnASkzs3CGhBkwOX2RbMm9Q4AGIbt3r0bRQvforGxsVu3bjXdp0kYdf7wRDDEkFn4GTdv3rwuXbpkZmYOGzasnCJ2JaDbzUEYNkRBkCFtW4kphvN6TDzg/+zdd1xT19sA8Ofem5sdSAh7LwUBB+Lee+G2ah11W/VXbV11Vq111rprW0fdey/c1lEXKoIDHCAoexNCyL7j/SMYYkDseAUr5/vJH7nnnntyEhHy3HPOc1i6SP+8r6CBvdTGdkfDzs62pbcG7u/9yTFyQ6Ge+eKMLkHyI5SX0cSSMWM9q0/BRbU5DkP/YvdMCFuJ8/ff2A3vKx0Y/v7aCIIg/4i9vf2BAwekUqlWq/3pp58aN25sZ2dnZ2fXuHHjFStWaLVamUx26NAhuVxe1T1FEAT54KisTdooX22UT+njnovmrr02yksb5aN73BwDBgD0Lwaxxhxc0oSQ9/k1uhgA2nrxAWB91FvpQjbGFBtotrkbDwC2RhcVvs4iXRycF35DyEpGdEhXRxTDI3/dPwzgK0ZRKOfWR0E+xMXrl1qAY2kz47NWvQYADw4BAKkW/0CCWmIAKLqY/7JHjEusMwAMdR73Y61NpoeYkADAvJorf6y1KVAcYrpEStpZrpBvxefZ4vhTgzHRSAHA8ax9Go4BAIoJ7a60cvY+DRSH1OQHannUvaaaei2a4QC5TrazL3QGi+jdpGHDhqNGjTIfLlq0yLy+3ba7Q92M1s7TvU2HOI7v3r3b2dn52rVrpkyK+fn5586dy8l5a+96KxjXmRdwABfVwwW1ypzEef4bCWkH1phXnDG859k9tlzejoZdpMLS5YKRO5fa39uo1DOj7zRJwnpaZTSxxBoyTfcIuF7LAPvbG4dgHMKmRzuevxdrpLK+W5O7bgf6zY4gyP+75s2bJyYmTp48WSKRWJZLpdKZM2cmJSWh2/QIglQTdOElllKwVGHpg9EBS7NUUekhAAALgHO9l99INURlGWR8fFlrW7kAf5hj/DO15KtajoY58kKLYzAnlBeqyy4iuKeCWjkv/Iawe2vGKIrhkb/uLwXwNE3Hx8f/+eefSUlJFdfU6XQnT550dHSsuBpSaeTDXL02BAKOZSxIzFr12oPDAYBUizx2wnoSAMha9ZoqMAYUBANAjPquA9fJ9MAxHADkpIMD18m0mRwA1JbUN19+JVk/7bKyLY8PAKc0mnRdyvWCSzhGBL4UAsCdgmuFxnI2Zu9w3wUAIpupwYYTFBAIGPYUDL8SK8rWXL58uXnAR61WT5o0yXwKF70VCTs5OW3fvh3DsO++++7OnTsFBQVZWVnXrl3bXqGdhx7sjxpG2HUv57PDSF7NXbikiQDP7NWfN+7OOW+xdE/TcKlIbK5yb/sP8ru/FWiZUc+WJOn8jRnrWH05KfSMKfOB0RDyPrhN83Je6C9jKcqYnq2+EZU1dzWVk/9vmkIQBCnLzs5uzZo1RUVF6enpV69evXXrVk5OjkKhWL58OVofhyBIdcOruUPQ4JWgwSvCcXhJEc7l17ljKsT4fgBA2HXBRXV/iS4GgC/rieQCfGxdMQCsjyo2XfFbdLGOYrt4cm1//nXw08sAcMijsU5SzmpKFMMjf9F7AnidTjdlyhSRSBQQENC6dWs/P7/69evfu3fPdPbx48fTpk1r06ZNaGior6+vk5OTSCTq3bu3QqH48D1H/irLGD741ywASLMYgRe3lBJiDqOmbbvY95owVESI41QPD8fv3rBhw/79+1mmdEshEicBAAPoYF8a7p5J1J5J1IpycAA4ptbsz9zBsHR7ebfBnAEYgzHAHHz6i2VnWJpW7D1lfyLBJ1mg5Rgv50bUCatPYniBg83LY2ehzA5GcrncMpvdqVOnjh1751LzLl26TJ06laKozz//XCaTOTo6qtXqf/ahlcAFvMCDuKj2vFHqxt04y1IeBds5bWvUWSwoTS56f8ci+b1N+TpiVNK+V1oX00J3S4zqPpV3FHA+1/P7f9UZAFzAd146jXRzMiSnZ878SReX8C8bRBAEKZerq2ubNm2aNWvm4PBXd81AkIqNelQFG6QjyL+CizGOFBgtnXcAAMcljYAxUNlbMY4U40hNmwqRLl/dTtffzzRI+fiQYBEADAkRygV4TLbhVpo+V8Mceq7BAD67sFv/MrmhMTeUUBXq2a2HHipPXCqKuAIALEUrT1wyPTT3HolaNMAFfN3Tl5kzf2LU71ybiVRnnArOsSzbvn3727dvWxbGxMS0b9/+1q1b2dnZPXv21Ol077oc+XjIh7kCQPLE57IlKT20DqkTXU3l2seqxH6P6GLKtou9797aGA8f4fHVL0k/jusxURFfBAANx9YO+cpXYcw/n3P8Yt4pAGABDEzpHcEO3vwT8doHzw1+zTiJRuqW1uBOiHs4DRD3FA/dE7sv6ME94kG935bW8miKS0RUVq76ZpQxMxcjiN4On6+B7RfzTnVw6B5Su3bM40cvbEifyIeipqFWnR89erRlFvqJEye2bdtWJpOV+06XLVt269atyMjICRMm7Nix48aNG+Y95N+LNeZQ2Vs5DkMxnoe5ECNseAGHdHFdlo5PJmxDjen9Qzcd2t6464i7Z9XakmX/UTt+aOnqn+befuzL7dtgWE2n27hNszcNMIbk2QAs6TIJ43n+xZ5UgHRxcFk2PXfNdm3M06wffpYN6WXbox1gKI0wgiAI8vEyRe+jHvW2WiuHIB8/Y8ZaYPSEvC/pPlP3qCmVu5t0m4JxXd+cx02r3w0UG34411REMSwAjDlXwCUwHcXygZrr1Qm8OgGAWkcCCTsKbLqe3EEyNACwFKXYU06iYmN6VuHBs3ajPquMN4n8p1QUwB88eNAUvXt7e48dO9bf3z8rK+vQoUO3bt0aM2aMQqHQ6XRCoTA8PNzLy0sgEFAUJZVK3dzc2rRpU0ndR/4ycwz/+ercUxgGC+21j1UJ3WOoAqOwroTnzQcCA4Aw26Y9ycHb40t+j8QeTwj5yndh/FQAwADzFdZM0sQ/Vj3wEwWYKrTz4tnw8Mc5xi9xQSJQObxmX0tbiDkSAGg7dG7WtUWX7KJP+8Q4bs3A3gyuk+7O8i8HegXVCE6KiVM9vJh7qkfdAc/j4opshQVxL8oG8BiGbdiwITQ01GAwAEBmZuasWbM2bdpkWYfKNdBKiucvJElyz5499evXP3LkSOfOnceMGbN9+3ZTHcvl9Cbbtm2zPKQLzhjTVlB5R/ghFzFOaaImjOvMr3VcF9eFVl4jvezsRg5tsO3ozvDBw87u12g0AMCyrG3sUbdGXe9mOI58uWubYHmtho1Na92p3P1M8QOM60K6Tf5H/27lwIUCp9njFQcilMcvKXYdNyS8lv9vCC7g/3+1jyBI9ZSUlDR37tz79+8rlcoKquXm5lZal5BPg+XYO4rhkf8W1phN5ewCwEm3abigJmHfh847akxfw/X5yVxHY2QBQEOxmqK38igbaDDQLADogJMpeGvkieXy+D06iWh9UcQVjEPYdG9XekqnU998QBdrCJmtTe/2H/btIf9NFQXwO3fuBICgoKDIyEhzSptJkyb169fv+PHjAODr63v79m0nJ6dK6Cjy78mHuTI0m/L1856rcl7rE4r2ZlIKo7SnA5VtyNmYJuvvLGpsCwDdA/uKxCJ1sRoAtHk6Vara0duhjiSsq2NfLa35MXFugTHP3CaPwDr58I8817yMf4l5OSvIOsG2b34eMKxv6xn3n03IcFG8HOoZmuJMyGz5tWvyQwIwAgeAfs5Dn6oeXcw91cG+e8duXbMexro1aVRuz4OCgmbMmLF48WLT4ZYtWwYNGmR5nyhxwCPNo+KAS2HCMBs/P78tW7YMHDhw0qRJjRqVNFg2ejcVWsbwHIeBVM4uRv3QkDiJF7DPsibG9+HVOq5/Gk7nH+PXkhYO7BR95vTWrVtHjRql1WoBoGFY/Wnd7MaezYvMcBz5ZPZp10MO7oNYutiYsggASM+FgAvh/xGOywb35AX45q3fqb4TY0hOd5g+huvp+v4LEQRBypOcnFy/fv2KQ3cE+QfKzpxHMTzyH2JMXw2MjpD3xYVBAEC6zaDzjpsG4c11DvaWZxUzVhfuidP8/qgYAFq5835oZUsXqfPW7TBm5ZLuzg5fD7O3F4tId0ZnCuA5sqG9TFfRhUVZ36+nizVcH3fn+ZNwiQgQpIyK1sAnJiYCwNSpUy0T0mIYNmPGDNPzuXPnouj9v8VhpNupxW4sBgUbUkzRu8/O2ryaQgDQPFYBQMH+rJetoxsEhJkvaZ/b95eQfeO8pnkKfALEwd/VWDHYdYxlmz39+QAQ+ZInM8SyGB6hLZ1gz8P5/Vy+AICz/s9E//tMNrSXoG4tU/QOAN5C/xBJqJ7RXcw9Jbe3D+7QBhe/M8r97rvvAgMDTc9Zlh03bpxWW7rzvLi5jDUwr0bE0kUUAAwYMGDEiBE6nW7w4MEGg6Hc6N3krVO4kBd4EJc0wfl+ZWviwiBezT2A86nsbXuvrly68/d58+YdPXp0woQJq1evnjNnjpCD/d7NvqVzYTEtKUr5laWLqPRVrDEblzTk2H+Q6U/CsBCXH2dwPV2NGTlZc1apb0d/iFdBEKQ6WLhwoVKpJEly2rRpx48fP/9uVd1T5L/kXeve0Xp45D+BpRTm4XdTiWkQHhi9MX2NuRqJYx42hNVjUphYysdxDL5tIvGwIbzdbULnjvSU8+wTnpGrfhHoylncborejWlZKHpHKlbRCHxycjIA+Pv7W5XXrFnT9CQoKOgDdQv5cLKHOG6l6DGLsqXhDj47QjASE9aV5O/O1D4uzt+VkTzxOTBsAHhfhz9N9WMiH8LI0st9hTWtGmzixpPwdSqdfWP961tcOFKsGSkRm9dkq+giDDAlpTiXe7yv8xCra3s7D4pVxVzOi+jo0N2GU1GKYx6Pt2XLltatWzMMAwDx8fELFixYsaIkcb3rAr/iWwr1/aKUr5757K4NABs2bIiMjIyLizt06FDFH8ioUaNGjix5hxjpyA8+966auE1zXo1t+vhhU/s+vnrL/cHjlxs3bjy2fRej0xEEAQACDra9d6DySS+uOladOF2bf0WE4VyvZQAfao066ezgsmx63m/71DejctdsN7xMln3RGy2JRxDk77p58yYArF+/fvz48VXdF+QTUXGUjsbhkY8fnX/ccvjdxDwIjwuDK7hWzMV2dbcr0DJB9qSphJBKnL//Juv7dYbXaVkLf3ZeMAnI0kAMRe/lYGmmOIpltO+v+QaGi3Bx/X+wZ/N/S0Uj8KYlx7a21vsc2NnZmZ4IBIIP1C3kw+klFOQMdXR80cx3b22MxABAUFcCAEVXCkzRuzDMpi5d+nvKvPX6u6TqEqWyWwAgzQ1zJIhXFPVQbzCfZVmWBRYAzuecyNFb55PzEdaoZ9tIz+giso+8t+ctWrSw/Ga5evXqW7dumZ5jJOa9LYSQcBTHc/J2ZACASCTav38/j8e7cuXKe1v+6whZV16N320l5KmVqiH9wgYMGJC9fGP6lCWayIclPQHMxncxAD4hqlOn2DMPOTNwcVjFbf5LGI/rMHmE3cjPMBxXnvpDn5T6QV8OQZBPD8uyr169AoAhQ6xvsyIIglRbdOFly+F3E/MgPKtPr/jyYHuypQfPssQUw5PuzqYYninWvHkhFL2Xg8raqIvron/W568/dHGdqKxN72/6P+79+8BjaDTv09JVKDji5ODuVJrzTFBbAhhmSNYCw7p+7xd4tUHnod38cG/T2cGDB1fQGgvs/vTfnRxvA8CVJKKXSAAARy02vWgl78jFeRhgFGs8krm7bAt9nYfgGH4t/3yeIfu9nV+2bJmnZ0kud5qmhw8fbt4ojucj8PwlEADSpr/QPikGgHr16v3+++/vbbOCN2dMX0MXnLIqJex68fw3S23IzdMTB7TJFjaqCzSTu2a77ulLUwVcVIfjOMSDm1pE23z5cNj1lMrYxtMmvI3z4imyYX14Pu6V8HIIgnxKiouLKYrCcdw0mQhB/l9UPMCOht+R/wDWQMh7Ww6/m5BuMwBw1pjzD5q0jOFzlm4EAGDZ0uh9wdcoejfDhLVMT3BRKGHbpuIHLqpnddUn7P0BPPLJKzyWbdqA3WGCh/N0b8Axv00hp4YfWsqbu0f2W2/v7lb1HxdFTX06Kl4dBwB3FNcT1M9cbDSj6ny7So4AACAASURBVPL6Bgj6iYQYwFmNVv1mR3cRIR7kOjrMtgkP50cpb8epHlq15sb3bCprQ7HUsay9ppKMxKSHm3brE8sZ+bexsdm+fbv5plJiYuK3335rPivr6yQf5sromFcjYxkNDQBDhw79xx8LSxUaUxfr40fSeUetThHyPjz/TYARxtRFombPbft2Ymmays03VyA95s8LujXAN0tLwbjzirOJlbHbIq+Gt23P9oDjxdfvJQ+Zqjx5Gd78KyAIglRAIpG4u7szDHP37t2q7gvySXlXlI6id+Rjx5omk1oPv5uUDMLDP/yWRUglzvMnkS4OhpR0AGANRmNaFtfX0/n7ryvIBlUNEbbtCFlnAMBFtXm1jlf8wEUhAEDIuhK2bau64x8cCuCru5J17wAAIKpvU1KKY0G/Nxg8fHAtQ43Ezx6pbigsL8k1ZBcaC/7IO6tndEczdwNAuFO/yY24Uxrj9piuHpfQsuzJ4kI1XWx6NJA2G+7xVTv7rgBwKHMHw1on6uztNIjEuHcVN1K0SQAQefdeDJd6vv0gS9FQRrt27b766ivz4caNG8+dK1217rGyJj9QpHuuTp+f+C8/GYwjI70WAjD6xAms3vpuAiHvy/VdD4AbUxaK2yRNViZ0m/dtQUFBybWkvSDoyNJOoWPriYwM++3VQqYSQ2mMwFmDUbH7RPayjbSquPJeGEGQ/6x58+YBwMSJE4uL0S8N5P9T2VgdRe/Ix4/VJQMAAKN73FwTKSv7MI/usFThP2ifsLN1XjiZdLYHAJZlub6ezgsm4iIUvVvjei0DnEfl7GGKK0rVzKgfUzn7AONyvX6otL5VIRTAV0dals2maTBH7wwraW8HAJpHqtJKOOb1Wy35EBdGTVvF8PVsGmGAPS56cDk3QmHMB4D96VsnxQ41PVSK7QCwNi/OXGJ6nMs5DgCp2tf3lbes+iPnOrS378YCeyhjBwDUrB0CAM9cJIozV8vt/48//mjOpMiy7JgxY8yRMy4kfHaG4CJC97xkar3VZu9lVVCBdJlIen5P2LQAjqzsWY7DYK6fKYb/4VXm0xs3bnTo0KGwsPT3OAYwq4nNsja2s5va4JW4EkXUooHT7PGERKyNjsucvtzwKq3yXhtBkP+mL7/8csWKFS9evKhVq9bGjRujo6Nzc3Pzy1PVPUX+eywjdhS9I/8JGNftr1XEMd4/XLpoiuExLpcQCVH0/i4Y34d0+hKAMSTPrmDKgyF5NgBDuozH+NbJ1z9JFWWhRz5V3+QV3NcbTlxm8r9+YVr3LqwjUf1RoLUM4KEkhgeA/L2ZiZ898jtSV9JSBgByroO30P+VJkFFF0k4NlYj6nz6xWtWp+L4AuknYqyXtXNwUsqxK9ulcKfPbhb88bT48aOiqNrB9ePj4pQAcfeimrVqSMis0ygKhcKdO3e2aNGCpmkAyMjI+Oabb3bvLllgLwgWhzxvjvNLVnKOHDly5MiRFaRyMKegLxfp+g24fvOusxyHIcAyhqTJx5aruk12jomJuXDuXCdGxAvwETaobarTydlQUFAA4Lv5YTHLwth64koI5gX1g11Wzspbu133LLH4WqSdzwfZxw5BkE+Gh4cHAHA4nLS0tAkTJlRQk0Vrc5C/b1vdEyjtPPIfwgs+WwmvQsilXvtWV8IL/adx3GdQeQcZ1T06/zgh71u2Ap13lCm6jZEOnPLWO3yS3h/At2zZ8l1ZbSo4pVAoyi1HPgY6lm1yRJE/PwsY1m1pDaevPY05BjBtBc++veUZjnn9VotloWBfZuJnj/xP1BM3lQJAI2mLV5oEhTF/XfAuy5b/TNXHZBscXOlDxZoarvNnS0tj7yKqcEfqr63lnQLE5ey6ISLEPZwH7E/fejBje0hAaNPWrc6fP//K295n/yn3/33BAqQoKS/b0h/XJk2azJgxY9myZabDPXv29OzZs3///qZDjoy0ap9l2XJj+H//TZTj+AWwBhf49vIG3R8vx/Ro1ipn2jLAcfvxg8Ttmp47d65fv35arXbw4MGxrZcVM+SzfOqndrbkhw/iOXKp0/ffaB8+5Qf6AgCVW4CLBLgQ7RyBIEg50tLQVB3kw0LRO4Ig/wBGiEmPOYakyYbkeQJZF8DfnqrA6AypCwGA9JiHETblN/HJef8UepVKVVhGBacsKyAfJx+cGPFDFjCsfKiL09eeAEA6ckknLq2k9K/L7LWIY94bawnrSRg1nTYrwVTWUNocA+xRUZSeeSs926aHxeujir2VHAA4rtZqLcLjTF36w6J7u9M2UixVbq/aybs581yz9OnX8y+4uLh4OrnQHOJJQY4+4fWWh8Xt9udefv3Wa33//fd169Y1H/7vf//Lzq4ojz1FUZ06dTI912g0LMv+3eidVlzQxw9nDda7hnCcRnO9l8ul2IAGR7m8c7LBPYFh8n7bV3Tu+vLly7VaLQDs27ePt2ekkNWcfqkddUZRbKiMISyMwIVhIbhISBcVp3/9Q/rkxbq4hEp4XQRB/nNe/mVV3VMEQRCkeuE4foGLQllDhjHjZ6tTxoy1rD7VtANUlfStSlQ0Aj9r1qxK6wdSmTx55Pnh8h5b8goOZUu7O9h2dwAAYaiN8nyeJqaI52M9SJu1KlnzUIURmNPEki3c7Eh7X1HNRPWLx0UPGkqbm2u29+JHphtuPTPUD+NG6w0RGm3/N0t6aoqD3Pie6bqUe4U3msnKyQ9JYER/l+E/v152IvtAY1mrRi2bpx05muUqS9x9NLPjGADIKH4rpx2Xy925c2ejRo0MBgMA5OXljRkz5tSpU++aLU8QxP79+xs1apSYmDhq1Kh9+/b93S0S6cJLdMEpvfYZL+gsRtpbnuI4fwkAhtezDK9niJouxnifFew4WrD1sBNbOkXl/o0rIaqB8tE7b6fb9T+Rtz3czllUSTs24SIBL9BP9+RF1sKfpX072/bvihEo/wWCIKX8/PyqugsIgiAIUi6c671cF9fFmLGW4zAY43mYSs0hPddrWbXK7FZRAG+en4x8Yrw5nJVTHdy5ROgv2UlfPPHZVVvaw8G2i73yYn7ZyefZa5MzFiZiBOb1Wy1ZfydzeSPbFonqF/cKb1oG8H1qCtbeV2Wo6P+JxdF6wz6V2hzAY4D1c/liY/JPXJz3ro6F2jYOEtd5Wvz4eNa+Ic5jgr18niQnxckFxuR0gHLSyNWtW3fBggVz5841HUZERKxatWr69OmWdViaLTyeI2kj49hz7ezsIiIimjZteuDAgZCQENOFmZmZOTk5tWvXxvH3/M8nPeczxfcZ9WNj2lKuj/WaJY7zl4CRhlfTDclzY2H4+qgrS+q3mW7j/djV/UVGydzU2IcPvFb3cvt6b3yB58AT+dvD7XyllZGHAiMI53lfFR4+V3j0QuGRc9rHzxy+HsFxtn//lQiCIG88fvyYy+UGBgZWdUcQBEGQ6gWXNCLkfej8Y4bUhTz/302FhuR5wGgIeV/cplnVdq+SVaN7FYiZN4cDAIcmOzp/680a2VfDnhSezrUf41Yvq7Wk7VsZ5rLXJi+a+UNbTZ9R9lMLm7w1u76RtIUpF72W1pgLZXz84ucOe3vKOwv49gT+3GiM0hvMZ+vZNNxY+1AD24r+jw12G0tgxLW8c/dnTbHbdFSg1hdL+HRiIgCwFk2ZzZw5s1mz0gbnzJlz584dywqqKwWvRsS+7P2Q0TIAEBgYeODAAYIg5s2bd+jQIQB49OhRdHR0ZGTkez83jLDhBR7hOA4j5H3KrcBxGsn1Ww8YESjZkeOqnRB5Tsrl7w/rHOrpY66T/CoxfkkPH1Vsmorufzz/QVY5b+qDwHHpwHCXxVM4Tvb6+NcZ3y5XXbLeDgBBEORdGIZp06ZNmzZtqrojCIIgSHXE9VoEuJDOO8qo7gCAKa0d4Hyu5/dV3bXKhgL46siDQxAA6RTlMN/XMobHhW/N6M5emxw55/qvhu1FrOph0iPzQLeJLSnzFwUaWcNj1QPLcmcRIePjJIYNFIkAYPff3IdcnsY0jpYywJ5tncV1ljfJ1zd4nmkwAAAoj180JFuvPycI4sCBA3K53HRoNBoHDhxoudGRqKmU5yfUPFSlfFOy3X3nzp2XL1/OsuyIESMePHgQFhZGEMSLFy8SE9+/dTxGOnB91xE2Ld9VgeMwhOe/mc/jHllWIAvjL0x9JBNL9oR27BzWyFwnLy83cmEf/+xrhXpmeETBlWT9X/10/jVeTR/XlbPErRoyWl3+pv05K39nijXvvwxBkGqAZdlLly4tWbJkdnl69+6tUCiUSmVVdxNBEASpjjCuK+k6EQAMr2cBS5k2liNdvzHPqK8+0DZy1REXw9w4nBSKSqFovwV+gEHWitevhj2R9nJU31cGXG5AuvCy1yanf/fSSFDsm00Xz58/T1EUh1P6M9NQ2jxB/ex+4c3G0nIC2oFi4WZV8R9aXTpFu3H+0mJvKic/e8mvrQz8J0HcFA992vdtmshaA4DobAakAKPWZi/+1fXHGYTdWxvLeXh47Ny5s0ePHqakdKmpqcOHDz99+rRpiTshJnz3137RNqpgX6aovsRhvAcATJ8+PS4ubseOHX379o2MjGzbtm1UVJRQ+Ld34GS0Cfq4LixVUPYUn4ttn5MJkAnwlKU5h8lvp2+tt3nzZtNZtVr9x6IvOkxeleA/YPyFgsWtbAcEVtL+n7iAb//1cEHDOvkb92kiH2a8TLafNIwfXKNyXh1BkI8TRVFDhw49ePBgxdVatGhROf1BEARBECuk62QqZy+jfqx/8TlTHI1x3ch3b/b8CUMj8NWUD8kBgCSKAgDX+X7OM7xZI6s4mmNI1qmuFpiid4zA2m7s4ujoaLqksLDw9u3bMdmGzDfJ5BrYNsMAe1IUYzmL3kRHsaQR6yrk0wB7itVWZ+NUDyOyD5tvDZgV7DjKqNTSOnX6+44DgIMZO0wtEzZiACBdHWmFUrGnnH1owsPDp06daj48c+bM6tWla9QFQWKvjUGAQdrsBNWNkg0ON27c2Lx585SUlJ49e9rZ2fXp08fFxeXvfIQArFH/tFu50bsVjKB49mkbN25csGCBuZCm6QurJnvf+YlmYPY15c8P/t5UhX9J1DTUdcVMXk0fKk+R9f16xd5TLE2//zIEQT5RR48eNUXvDRs27N69O4/HA4A+ffp89tlnHTp04PP5ADB37twjR45UcUcRBEGQagsXkJ7fAwBd+AcAcD0XAl4dN0hGAXw15cvhAECSsWRHN9f5fk6TvYBlASBrxWtT9O61KUg+xLVLly7mqw4ejxhwIv+byyXbBEpJu0BxbSNriFLetmp/3HlFyz054bgQAI6o1SqGsTx7Ovvwsay9dxV/WhZSeQrN/ScYjysfP7i5fbuaomAlpTidc9hcQdSyIUZyim89oMublr98+fLmzUvT6c2aNevWrdI13rI+js5TvVkj+2roE0OyDgB4PN6JEyf8/PyioqKGDBlC/5PwFccwEgAwjlTQMEXYRMEPuWQ+R3rM4de+gnFK5vbn5Wa5u7tzOJzfN222nMVwZdcaz4szCaCOx5fZwO8D4zjZOy+aIh3QDXBMefyi8vil91+DIMgnauvWrQAwduzYe/funT59esmSJQAwderUw4cPX7p06erVqzwe79mzZ7a2tu9rCUEQBEE+FI59P1zSGABwSWPCvm9Vd6dqoAC+mjKNwL+iSrdkd1vsb9ffGQB0LzWm6N3uc2cACA8PN9e5cf0qw0KRoTQabyZrAwB3FNes2pcLcA3F3nthaMLnFTPsIfVbQ/Qt7doDwIms/ZaD8Lq4+M1+7WY2GznyhnF4hOJZ0mcA+Pns08PPx116rQOAva9hZrOR39YdNOxU/rCIAvNjd6wGADgczv79+82L4SmKGjRokOVieNcFvrad5VS+MXHgI0ZNA4C9vX1ERISdnd3JkydnzpxpqqZWq+/fv69WW88aKAdGcNymAwBLFTLqhwBgTFsOAIRtOwCgMn/D+H684DNA2AKAURmZnZ09b9684CuPfm3ZXcDnm5u5dny31+mJm9vzAcDIsBRT7ot9EBiBSwd0c/lhsrBBbX4ImkWPINWXaYP3CRMmmA7btWsHAHfv3jUdNmnS5Ouvvz527FhERERV9RBBEARBADCu788c+4Fc358B/t6G0J8MFMBXU6YR+FdGyrLQc0MghgMAeP5SyxS9A0Dnzp0lEonpudzB0aqdMGlTISHK1mdalQ8NFgLAweeaL0pS2amNbGms3lTWxp3vpaKLjExpDnZKUXTOrd59wv5Wmv5Wmv5mknNGdgvAKJVoR2YxBQB0UU6oKCHP3jZSxTXVMT1OJpSMXZsWw5t3d09NTR3YtH98j2gqz8DSrOJ4DsYncB6hjS1+1uRu7pY0Rk0H+AX81mI1F+eeOXPGdFVycnJsbOz58+d1Ot17P0aMIwUAXNKcsGnBFEfRhX9ghIRb43fCpiVLKaisTbgggHSbAgAONqoDq7yFQsHtV/Ed7dx2t+gpsxjIunz21KDwdpmZmV0P5nXYn/OigHrnS34AvABfx1nj+IF+LEVnTF+etfBnKjuvMjuAIEiVy8zMBABPT0/TYY0aNeBNVG8yevRoANi0aVNV9A5BEARBSuCCGlz/jbig+o48oQC+mvItWQNvtCzERYR8uJuktUw+pHQ1uK2t7YEDBxo0aBAeHj5/+Tqrdvi4YH7NVbP9l1mV13fmhjiQBVqmIIUJJMlsmj6jKZ0ijmP4dzVWrKi12XJPeILP23j39w34413d7UyPmbVGCnGZ1CahXVAUAHRzoxwx1Qjm9lrln+Y6u7rbbelaukW81WL4PxKubry4Ob7Dg6cNI18Njy08mcPoaQDQv9KmTnkRV/t2QtcH/n+4H3XZeeHsBdMlNWrUkMvlRUVFly5dMhrf+nzeBeM6AWCm4XeO83iMIyM95gIAlfkLSykxjh0AYKS8W+P827sdm/041KZb6zBbhwvL13l5eZkbiYqK6tq1q4sIT1XRA07kXU+pvNT0pViGUWt0T15kTFumungTWOskBQiCfKqcnJwAQKMpmS0lFovlcnl8fLy5gp+fH0mSf2XHTaRaKTyVmzT4ieF1Za8CQxAEqbZQAF9NyXDcDseLGTbr7bXfnj8H1jhT32pCSrdu3e7fvx8REeHu5V22KUeusz3XemQeAEbUFgHAjieaERIRAGxTFVuGg1ycJyLElvVJLzcnnbJO7N3mbtzm7rzm7ry2HrIvPEYBAGtzkCRV9u4+NiodwyP42uSw4jRTnebuPBn/rR9jq8Xw642/331xTx+v4XoLPNYE1LrdKOh+E+9twaJGtsYcQ3GkEhcS7U518/QpGXciSbJjx44SiSQvL+/SpUsU9ZcGw98Mv4uB68JS+bikMWHTkqWUVHZJ2nlC2omQdqrhqqwjnGXbR+KxeXHDccNu375dt25dcyOPHj36sTHT3V9QbGDHnivYFfsXpvH/v8JI0nXFTFHzMEanz998IHvpbyyFMtshSLUQGBgIAJbxub+//4MHD8z5QRiGoSjKHOEjCAAojmS/+uJJ4amc+K7R1SeGz6eZXSrr5D5WMml6t0qtQ/fBEQT5AFAAX33V5JIAkFomQqOLKNMS8X+ph7/AWUQ8yzfKCnFngkgwUjd0FY0q8wN8OA52xowc1ZU75sLG0pZ1bRowmNrP+4jhWWJgTBLGsGme9gk7DzPvaM1qMTzN0rP1S5RsEU7i0p6OgjoSfi2RrI8T6cQ1VcBYlnQvXZGenp6u1Wq7du0qFouzs7P/+OOPv5Lf7s3q97bGV1N1sZ1ZfWrpIDyjBQDAOLyAfRynkcBo9fFDWePRrOzsZcuWrVu3zpxloFmzZi6O9ms7SCc3lDAsLL2toiv9Tz8uETlMGekwbTQhEWsfPacLiyq7BwiCVAXT/nAzZswwr3tv0KCBUqk8dOiQ6fDs2bMsy/r7+1dZF5GPjOJI9uvRcSzNcj34hlRd9YnhD6nVywqVI3Pzi94Rw6dR9JDsvKWFyiva96/FQxAE+btQAF99TbKRjJCIQ7ikVfnT+pHPmt/7u60xLEOxb0045+AwOFgIADsfaYZJRACwuUhV9sJ7hTfj1U8BAHBcNrQXABT8flh9J8ZcYYjbWAy4Tvb3stMviYp1QU6ugGGxHrLsbYfLtmbi4eGxfft282L4bDb3e3KlJqE4oWu0MdvAGtlXw54Uns4lbDni5lJay+SsTzHVpCgqODg4ODg4Kyurc+fOQqEwIyPj2rVrTIU32lmqwLT6neO5CBfVY3WJuqc9cUlD0yA8o7xeUg8juD6rSc8FwDKGV9MvHx63YcOGbt26zQ9ttWPE/zat//nChQsYhmEAk8LEu3rY/dZZRlRRbg5R01C39fNcV87i2MsYtbbo7HUUySPIp23atGl+fn6vX79u0qTJyZMnAaBnz54AMGbMmJkzZ86ZM2f48OEA0LFjxyruKPJxUBzPeT0mjqVZlzm+QVFNJC1lhlRdfJdo/atPP4b/TCT05HDiDMZR5cXwaRQ9LCcvk6br87jtBPxyW0AQBPk3UABffdXncWdKbQRYmRiRZfUvNfqXf2+e5Oqk72c+G69j3vrLPSRIKORg11P0DY08GY4/0Bse6A2WFXSMdmPyynWvFquoIgAQNQ+z6dGONRpzV23NWrCu6Mw19Z0Y7sU4YXxjAEhrGiPo3y7Ey8eOw9MJuDEF2erb0e/qT48ePSwXw/+pvL3FZq8uXp3QJTpx4CNT9F7jdKjbIn8AKDyVbarG4XCaNWuWlZXVpUsXnU7XqVMnHo+XkpJiuSNdWazmGQBwnMfjfC9e0ElC2gFI5yVLliaq+wEArbxqWZl0ncz1+wUwslfYn6MGBGo0GsXdh61VWPiLfF5R6Zz5Zm68tl48AJh1TdlyT86DLANULlwi4nq6AkDx9bsF2w6nT15cfP1uJfcBQZBKIxAIDh48GBAQYC7p1KlT586dNRrNihUrli1bVlRU5OHhMXfu3CrsJPKRUBzLfj0ylqVYlzm+LnN8cBHhd6SupKXMkKZL6Prpx/AOBLHX0d6P5MQZjCNy8wstYvgMih6Zm5dJ06E87mYHOb/sVywEQZB/DQXwiDVRUykAfL86tc2+HKvHiDMFAPBaSZU9FZ1lUBjzL2Vfs2xKyse/qC3CMNAb2CESEQD89vYgPB8X1LVpoKU1p7NLZmnaDe8rHzsQFwl1cQkF24/krtpasOMY95G/Su1lFGmPaY7nLPy55uUHBEVnu9nFHj6luR0D77Bs2bJQSW3z4ZasnTec7+kS1EUX83EbTo3TocL6NvyaQgDQJ+npopJ58ocOHWrQoMHLly+7du2KYVinTp24XG52dnYFnxhrzMYICcdlAgBghA0v8PDt7OnffTdv1qKjhE1LYKy/ynAcBvECD3FIyc9TslbPqf2ogRfPz9OYlZs58ydtdJxVZZplM4rpIacK9sZVzdJTcatGgrAQpliT9/Pu7CW/UnmKKukGgiAfWlhYWFxc3IsXL8xpRI4dOzZ79uy6deuGhoZ+9dVXMTExMpms4kaQT57iWPbrUXHm6N1UWN1ieHsC3+Fg70dynhmMo97E8BkUPTw3L42iQ3ncLQ5yEYreEQT5MFAAj1iTtJACgGOcKrWItnpkqWkAMNJQ9lRSRgsAuKU4b9XajMaSu8Ocwpy5w8QiCY7f0umj3x6E7+8ynINx0nSvSzvQuaX7bwvtJ30h6dxS1DRU0qUVP7DGi8QvMAaLqleU3kzqEFYnWM0AQGJN15w125QnLpX7RkiSXOOz1AErWQzPsuzs5EXxTCIAcGQk6cYHAIxHAI4BsK/HxLI0CwBisfjMmTM1a9aMjo7u2rUrj8fr169f9+7dK/7QWPloIKTmQ1P6eqPRaFoJDwDAGCz3lids2/CCTmOk07geqV9/ftNp/iBRiwaMVpf946aic9ctW17eRjouVEwx7PwbymlXCnVUZS+Lx8VCp9nj7ScNwyUibczTjClLVBduoAT1CPJJIgiiZs2a9vb2pkOhULh06dKHDx9GR0dv2LDBnFsEqbbKjd5NqnkM/9xgRNE7giCVA2PRF/Gq9u23365cudLNzS0tLa2SXzqbpu/qDOEiAWFRaNomnXDj291vYlX/dSE94my+t5TY0c36axzNUmtSx6nowrk1fvQTBkB51ilVG4tUrfi8TQ5vXZ5nyBEQQquk9Gas3jBz9Y2jsqBBdpszAmLsuY4/BKzj44Lo25G6e49dbz8BhnX4ZrioZcOy177s+/Dm+Rvjqel6Y8ldAxfC6UCNreJUPj9AVONsfbqIehp6B8MwlmXtR7l5rg80VUtLS2vdunVSUlKzZs0uXLggFpffNypjrSFlYbmnrBQavH06Pp0yZcry5cst3lqK/nl/RhuPcV14NfcWRqQXHTkPAJKOze3GDMSI0vtrZxK1s64qNRQbZE/+1lnmLiHKeY0PjC5U5f9+UBP5EAD4gX7yCYNJN6fK7waCfFByubygoCA/P9/Ozq6q+1KpOnbs6Ofnt3HjxndVYFm2c+fOLi4uO3furMyO/adV4d/3D6E0ep/r6zLbp9w6jJpO/OyR6oaC686vca4+z0dQyZ2sZHk0MyI3L9FIkRhmZFkUvSPIR+tT+vuORuCrtd+KVDMLFBc1b90mFwSLOXYkna5zUhg9bAjLh4OAVV7ZEr/124xnUVanvG15LeXtAeBa/oV3vdwIiUiEYX/q9I8Nbw3C23Md3xW9A4Dy9B8d4iM7qxLHtZvmI6yRZ8g5nLETAOo3a9Js8pfyLz8HgIIdx8pNSm/bSV4HD5pfY6a5JJPOniVZQtTi6V6oE7pFZ69JAQBJZzkuwPO2pWcsSjJVc3d3v3r1qpeX1+3bt/v06aPTlSSSLSgoSE9Pt/iwQv7if6KXiUkGg8Hd3d2yEON58oLPEzYtWEOm7ml4JHF/etQfRpZRXbqVs2IzS5cuqwv3ExzuI/e0IZ7mGXsfzbudEEq1DQAAIABJREFUXgW7xBNSieP0MY7TxxAyW93zxIzpywqPnEP7zCHIp+Hy5cvm/PPloijq2rVrJ06cqLQuIR+VvxK9Q7Uch18uk5EYGFlWgGMr5TIUvSMI8qGhAL5as8cJAHhufCt7PGAgamoLAMW3rFc7b92wOv/wgrTLu9q2bZuRkWF1to28EwbY/cKbarq47GvdStOnFdCmlfAblOWkoy8fyxZfvl1Tlbk63MnDXjzKYxIHI6/lX3iqemQ6L+nQnBfgSytVmnuPy15tN9SVdOB2SW79OdnHXHjn/p2fvH/jB4l0L9T5u9IxAnNf7O+zqzbGwbJ+fJWzoSQjvaen56VLl1xcXC5fvty7d2+9Xg8AN27cuHjxYmxsrKkOR9ZB2CRf2ESRKous0U8oalrYe35DrG7Gn4X7RU0L+y1sImyiiFUMpRmwt4WZM2dOnDjRqocYR8ardZzjNAYYbROn9RpfevCfJxQGnTbmKfP2p8RVpg7SnWvMz1bomBFnCjbFlPMhVwJhk3pua7+TtG/GUnThgTOZM37Uv0iqkp4gCPIv7dq1a9UbAJCTk7PqHVauXPnFF18Yrf5YINUGXUy/Hh3HUqzDGPcKoncTXET4Hq7L8xUY0nRp019UTg+rSgZFTykoMLLAxzAtw07MKyiscNsaBEGQf49T1R1AqpJpK/gXRsqqXNxcpjyTp7pVaDfYxbL86ZOHpidarfbAgQOWad4BwJ7rFCIJfaKKvl1wtaNDD8tTOoodcabAhoufGmS/D1ff0Okf6A1hPK7V617JO+su8KopCjaXULkFVJ6CkNnya/kDgBvfs5fzwKOZe7amrl9Yc62YIwEAUbP6+hdJ+mcvxa2sZ9HjPJzrKzDmGqZyx6fZZt7MizSV7z2yz7GRbCj0BWAJGUnISNtAkdfGoNdj49JmJxAyUj7EBQBq1Khx9erV1q1bX7hwYdCgQYcOHQoODr558+b9+/dFIpGPT+mXmICAgCtXrrRr1+7SpUu9evUyB+pHjhwZNGhjbX928NAxlpPn34JxuD4/4aJgePXtwcW5i3d5rkjL3rx4HWFna67y7Nmzhg0bqtVqPp/f69s1Ua69VtxVxeUZl7aWirmVfbMfFwnkEwaLWjXM37jPkJKR+d0a+4lfiFs3quRuIAjyL61Zs+bhw4fmw4yMjOnTp1d8ScuWLT9wp5CPESEibDrbKyNyFcey7Ue7CWq/c9KcSf6uDP0rLWAg7fspr7SyzFq33E72v7x803r4bQ5yKY5GyBAE+VDQ75dqrSbJAYB4g/WgiimPXfEN6xH4+o2amp8fPXq0bIOt5Z0B4Fr+BRbeyq3A52ANXbiFeubUM+0wsRgANpTZE15hzN+TvvnnV8tMW8qZ0EXFAMCRS+HNnLSuDn1qiGopjPk70jaYSjgOdgBAF5Yzqp/yzXP1XSUhIjgC7lLNHD/c23xq7b1fI5iLHAculWdI7PsQGNbuc2e3xTWAhZRJzw1pJXPmAwICLl68aGdnd/z48UGDBvn4+LRo0UIgEBCE9Sp0Uwzv6up66dKlhQsXAkBWVtagQYMoiurUa/r0ue9cWVryLhxH8AL2cUjJghEp25frxD5vfT2KiIgw5cDT6XQHF02odWuhCKfOJOp6Hs1LKrS+/1I5+ME1XFfNkX7WBeOSQKOJ9Ajy39OgQYM2bwCAWCxuU6ExY8Zs2bKlqnuNVAUMfHeFSHs4UAXG+K4PNNFFFdTN+SU1bWY8AHisDjDdDf8kWeWc9+QQZfPSIwiCfAhoBL5a8+Bw+BiWRdMqhpFY3C0W1JUQNhx9ktaYqSddeObyjuG9Fs+ZaspAHhkZmZaWZrWou55NAxkpz9SnxRc/DRAHW56aFCa5m5F/L8Owvq50b7E6Uqe/odO35Jc2LiPlIZLQWFXMyez9Q93GmQpxkRDehPElJRgxzmvagheTo5V3r+WfbyPvQhepDDwylk+7zFxSZ9Jo0t3ZXJnKNXDkpP/JUNKZm702ZcPxFYMTxipYJQCwwC6h1jRf08F1mYTKN7I0YDg4fePJGpniGwpCSpobqVOnzuXLl9u3b3/kyJHRo0dv27bN39/fdIphmOTkZHMmSJIkd+7c2bFjx+joaAAwDW2NGzfuyy+/TEoqmWfOoV/ZUfuFXtNwYS2rfw5C2pEXfEH/YhBTHKV70oZXczcuDrt8/rxvRGQApTZl2jPVjNizKSzhsdPozUlK+bEX2umNJRX/Q38gGJeUft5d+nlJiv7cdTuYYo3diH4ouR2C/CdYRuMYhvn7+1+9erUK+4N8zDAu7rMz5NXw2MLTuQk9Y2qcChXWtylbLeeX1LRZJdG7w1j3shU+DeXuGGfKSz8iNw+NwyMI8kGh3yzVGgHgR3JYgIS3Z9FjBCZqbFoGX2hZ7uLmwfOub3rOMMzJkyetGsQxopVdRwC4lm+9n1xTN+6+nvJlbWwlOD7ORgwAKwuLrG5QD3IbzcW5ufrSTdc5jnJcIqJy8g0ppUvu7Uj7ER5fAcD+9G2putfaqFidgJtrw4+t4fxyw05GqzPX9D1Qp3Z8C2E9CenMc19eo8uL/qevRvD5fNNZg9EwYPRAbLtt8OOmGFkywu883dv/ZCghfmuAPTQ09NSpUyKRaOfOnT///LO5fNKkSb6+vn4WOnbsaPXGN23aZFlh1ffhuPKoLrYzrbwGZeDCWvyQy7hNM1Nau6wXm3v37FWQnFonT/tz+z5CQWk63wd37zyZ33Gk6OHYeiIAyCym9XQV7yhhSErVxjzNmLa08EAEW2ZaB4IgH7OhQ4d269atqnuBfNRMMby0hwNdSCX0jCk7Dp/za7WI3jNpemhOXhpF1y+Tc96ewLc6yD05nGcG45jcfDXa6QlBkA8ABfDVXQBJAkB8mdRE4uZSACi++VYATxIgDitd3K5UKss22EreEceIB8o7RZT12cauXGcRAQBDxSIPDhFvNJ5SaywruPDcV9b6/SvvWeYSjMDFLRsAgGLnMbCYkBZm27SFXXsja9j4Ypny0RNbrbGmtw9D4DGe0oyfd5p3KccIDOO99UPevHWLXbt24W9uihcUFHTr3i27IOedH9AbLVq0OH36dEhIiLe3t7kwJCRk9uzZX375pZ+fn6+vb8Ut+Pr6+vr6Xoxyvf5ICozK8HJcudUw0p5f6wTHaTQweknBzOGDa/e6cvixIifc1uXKb1v9/PzMNbOzshaM6L5qyfdpSkObfTndDuU9zavKsNllyTRJh+YszRQeOZ8xdSmVW1CFnUEQ5G/ZvXv3kiVLqroXyMeughg+b2u6eeb8Jxy9A8AJtSaTpuvzuJvL2zHOiSB2Oco9OZw4g/FPixEFBEGQ/y8ogK/uSpbBl8ljJ2ljBwDa52rLQi9bzuKp42rVCQOAgICAUaNGlW1QRsrr2jSgWOrPgkvvelESwyba2gDAWqVKx7IAUEQpZzz7cnPKajHHhou/ldzOtl8XwlaiffQ8d+0ORl26Ic0Qt7FOjEMmZP/RrlD6WZemrVs52sn1Am4Upis8ebmCt9y/f//FixebD1+9ehUeHq5SvT8xftu2bZ88edKrVy9zyejRo2vVqtWkSZPt27ebJ8m/S2JiYmJi4rMXSV3HJXG9l5Oe899ZFSO5Piu5vuswgvfT+MRF87zWGFJ1Y/s2HvZ5dHR07969zRUZhlm0aNHgnp28IO+1kvrseP7eOM07m/3AcLFQPn6Qy6IpXE9XY1auMcV6nwIEQf5Dnj9/vnPnzrVr1168eJGiqibXBvIRKjeGz9uanjL5OVSD6B0ABotFS+2kFez37kQQex3tl9lJ2wv4ldw3BEGqAxTAV3c13zECL2pg470l2OOnmpaFGMD4ps6xMfdSU1NjY2OdnZ2hPO3l3QDgSt5Zii3nOx/NQnSWoStfEMwls2l6b7EaAI5n7c0z5EQq/nxc9MCqPmErcZwxFhcJ1Lej0/63IO+XPcpjFxX7TxfM+bnnNoygsfuN1K/a2eI43rZjBwHJVdjbRN+P0j58VsG7nj179pgxY8yH0dHRffr0Mby9O72Z8kyeIbX8m+hcLrdTp04kSb58+bKClysD4ziP4zgMqbgSx3EYP/gCxvP6vE3a2TW5NZpggGE2NjZHjx5dsGCBZRa9G39ej/mufSPVLT3Nzr+hnHO9nJkRlYYX6Ovy0yy3td8JwkJYminYelh54jJb5g4RgiAfg/379/fo0cMqt7zRaBwzZkxQUNCIESOmTJnSuXNnf39/U2oPBIE3MbxtuD1dSL3s/TB9boIpevdcG/jJR+8AYIvjfURCYYX7vdsTeG+RkIv2hEcQ5ANAAXx19yaAp8qu07Ib5FzuVjE4jru7u3M478yAWEtSx53vXWgseFR0v+zZw881/U/kr7uv+lZqAwCbiopjNa9uFJSMmR/I2EazNACcyj54LHOPqZAX4OuybLqgTgCj1hRfjVTsO6U8ekH/MtmtWBaubskCuy3tl0JjgVAobNepIw5Ysp9T3J4jVE5+BW/8t99+Cw8PNx/+8ccfw4YNY8qkjdU9UycOfPSi9X3di7cmIxiNxrZt244cOVImk3Xq1InLtd4SryzsHX/IWVpFZW1mjeVM48dFdfm1rxDS9iyVr3/e35i2HIB5+PBh1yJsX5fPXRwczTXzcnMPzhwQfGexEKNupenf25kPCiNwUypBpkilunhTsedExrSl2pinVdsrBEEsFRYWduvWbfDgwREREbGxsZanJk6cuHXrVtZi+W5ycnLz5s2PHTtW6d1EPlIYF/fdXds23J4qMGavSwEAz7WB9qPdqrpfn77j8drV91VobT2CVGcogK/u5AQux3EVw2RQ1juBsTSbsShJeTbv77aJATbAdbi/KNCZV87f8hB7EgPY/kTtaeQ05/NUDPN9zjOGZTrYhzvz3LL06ZfzIgDgWv6FiJwj9wtvma4iXZ2c5k9yW/ud3aj+tn07ST8Pd5w1zmPT4l4tpwRL6hVTRb+nrGVYxtHRsUHDBoBhzwJckjeULoYvi8PhHDx4sEmTJuaSgwcPjh8/nn37Ep6/0KaD3JhjSAiP0b8snZ1uMBiePHmyY8eOXr16SSSSCm5nWNJqtWUL6bwjhtczdU/aMuqYsmcxjh0v8DDpuQBY1pj2o/7FkIlfTbj25/VGfGlEkx4d6oaZa7Ise3rXr+SWfmvrFwFAYiF16LmGrtKNbAiZrdP8iaS7szEjJ3vJrznLNxmzcquyQwiCvDF27Nhz584BQEhIyBdffGEuj4uL27x5MwAEBwffu3dPpVKdP38+ICBAp9N9/fXX75qpVFVSUlIOHjw4b968X375xeo2hKXk5OR169atW7euMvv2yTPF8NIeDhiBoei90qy9r/rlQXGOGu3eiiDVFwrgEQjikgAQW+ZrmTFNn/Xjq5TJz+Hv3+kNkYTO8V/uxvcs55QD2c2Pr6PYVfdU06U2OMAzIoTg+vR2HjzEbSwAnMo+qKQUvZw+B4ADGdsYtjQGJd2dbbq1lg3uKf2sq7BBbYxLYoCN8fzGlpQ9LX58JHMXAASHhHh7eFIc4jVroJUVrWwXiUQRERG1apVu57Zly5bJkydb1sFIzO9gHdvOcmOWPr7TA1282nztn3/+6eLicv78+VatWll+/a3AnDlzyhYS8j64TTPWkKF/2hOYcufqY6TrZF7gAYwjoxXnvxupWPUqZvXTu7Zc3ia/xnPahlvePoh5cL9980Znz5798Y5q9jVl/xN5r5VVOX2dH1zDddVsu2F9cKFAE/UkY8oSxd5TjK6K5wggSDV35cqVI0eOAMCKFSuePHmyfv168ynT3nJcLvfYsWMNGzYUi8WdO3e+c+eOs7Nzenr6rl27qqzTb6Moat68eb6+vp9//vnixYsnTpxYp06dzz77rNzsqs+fP588ebLVr3fk38O4uO++OrUTW6DovdKYNpxh0BA8glRjKIBHIITLBYDYMsvguZ580oVnzNBrnxaXd90/920TGy6BnUzQagqMbtQjBjhq2y+FhChYUq+2pL6W1pzMOtBK3rGeTSMpaffe1mw5soleszgYeT73xK2CKwDQsk3rhnXqhQ3tT0jL2aXWklwuP3/+vKdn6Y2G9evX//DDD5Z1MB7us6eOpLXMmGNI6BGjf1Uyih4UFHT16lV3d/eoqCjz1nQVaNWqVf369cuWYxwpv9YJ0vUbQt4PcN67LiekHfm1/8TFYW3q5F7fxsmqSSR0asB1sh9r532wfX93x9Kt1xUKRd++fQd5qt0kxKMcY48jeQefVVlmOwDACMKmZ3u39fPF7ZqyFK08fjH96x+Kr0ZWMD8CQZAPat++fQDQpUuXb7/91urU2bNnAaBPnz41a5bmQJHJZKaspaZB+4+BKW6naRrDME9PT5FIxLLs0aNHW7dunZ9f0fop5P8ZBhz79y8iQxAEQf6/oAAegdolI/BldiDDwKaDHQAUXf5/3g/MQ0IMCxEyLMy+keJYfIDDGmJo20idHgCGuI3lYJzr+ReTtUlf+8yZV+MnHHv/T6mfKGCw22gA2JW28bXmJYfDCQkLlQb4vfdCAPD09Pzjjz9cXFzMJQsWLFi5cqVlHVyA+x2qK24uNabrE7pFG5JLxskDAgJu3rzp5+enUqmCg4MrfqHr16+/c6AeI0nP77m+awEqSniD8dz5QREchyFeTtSOWS/adXjsuuxrYcPa9cV2p5r06GIxnV6v19voss/2d/g8SKgxsnOuKydcUCh0VTmfnpBK7P83xGXZdF5Nb7pAmffLnoyZK3RxCVXYJQSptu7cuQMAX375pVV5ampqQkICAPTr18/qVMOGDQEgOTm5Ujr4HufOnTt69CgAjBs3Lj8/Pzk5ubCwcOvWrSKR6NGjR+PHj6/qDiIIgiDIh4ICeATqcLkcDPs/9u4zrqmrDQD4c29udkgIYS9BEEFEVNx7j2qto6627m3rqttarVr3qltrFVfVWq2r7lEHihMXIALKnllAdnLH+yEWEcLQvgql5//jA9x77s1JgNw895zzPNoS+dsAQNxZBgAFl99/NONE9qFL8tMlt3/dUGTPwxIVEo3S+wsBBQBL8/JJhnHmunVw/IQB5lDGL8y7zN1vJ+vWVtbFwpi3pKzUkAXlH1CEv7//hQsXHBzejPbPmjXLOo+0EC5k+f9RX9RMYk4zxnd9aE5/HcPXqFHj2rVrtWvXjomJKTobvxjmHUebKeVx2mAruMV5HL/NnJobAOeSuXvMyT0cv2mF9+kk5nC3+jdbOnQMl8sFgA4dOjRo0EDEwZa2kWzqLLXn4heTjN1+k/+VUslz17n+NdyWTneaMoxwlJpfpWUv3CBft5spkX8BQZAPKjs7GwD8/f2Lbb9y5QoA4DjesWPHYrusdzmTk5M/Rv/Ks23bNgDo27fv9u3bpVIpABAEMXLkyLNnz7JYrKNHj1rDewRBEASpflAAj4CMhR93cdrkaGOyuriDjCUmaE2pi6gfP348f/78EydO2NxLM/R5+fHDmbszjKnFz8zFw2o+BIDMtK+mSmv6EESihbSWlPvMZZCEkCbonj/Iu/1OT+Qrj3EBwmClWb45ebnNCnZlCAkJOXv2rEj0Ous+wzATJkywzjIthAtZficaiFrYm9ON+kdvVtd7enpev37dy8vr+fPnRe8CWO3evXv37t3h4eHh4eEV7Ayte2ZKGGmMbk8pj9tsQDgP5YVcxwXBtOGFMaZjpOFs78tHLmUlCWXS5OTk+/fvX7p0Ccdf/3d/4sc79bljEzeOwkCPOaf6ISJfb6PmwEeEYcLWjT02LpAO/hTncXWRjyilujL7gyD/PdaEmtbQt6irV68CQP369Uu+lVnraOj1lbkep1BcXBwATJs2rdj2Nm3azJgxAwBmzZpV1fLtIQiCIMj/BQrgEQAAfzbhWqSueCGWPVEnqpn/qQY2j7IWFlq6dGmfPn0OHDhQsgGO4a0dOjPA/Jnze7FdL3UvSHG4gCdX6cS30kxzpRIA2JSvyaYoPkvwmesgAPgtM9xMmwDgmvL8+GcDYzSPy34WLIw10WemlC1L0D3/PXNv0V2Zz2IL/oose9F106ZNT506VbianaKooUOH7t371nlYIlatPxvU/quxfU+nottdXFxmz54dEBCgUqmEQuHcuXN3/63sPtuEC+oQTl8ApTMljKK1D2234dfm1b1MuI4D2tQz+PcBI92/vndx6vpVOp2uUaNGhdG7lYcd60Av2YymdgSO7Y/Wf3JEfjezkj/aYhy2pF9Xj80/uK+aTbg40jq9fN3ugnPXGQqNxiPIB1ezZk0ASEtLK7qRYZjLly8DQMnhdwDIysoCAGdn55K7Pr7U1FQAKLpKv9D8+fPd3NxevXq1ZcuWj94vBEEQBPngUACPlIPtymWJbddIe/jwYeFozLp162y2+cS5L4Gx7+ffyjKlF25kgDmY+Qtg5MjGqX0C+KHOnDY8bhc+T8cwK/IKAKCtrIsP309lUVyQnwQABsBMm3albdRR5aTTExP2k33ncXDOJcXpwtrymZmZFx7cvfrgvvLAybIPb9++/e+//85ms60/UhQ1cuTIXbt2FW2DcXBhY3HJteoCgeDbb79t2LChTqdbu3ZtVFRU2Y9VFozF8dvC8V3HkrTDuDYy+b+G8zg+K7gB+3G2dHr/lOt7vPfu+N7Pzw8AzK9Sc5dvNzx9UdiWhcGEBqIT/WR1HNlpBdSXp5TLI99tocGHwLK34/h6AoAlS66LfKTa9Xvmt8v0955Wdr8QpJqzvlFYJ8wXun37tjVKtxnAWwfnAwMDP0oHy2GdO5CRkVFyl0gkWrFiBQAsWrTIZgME+VdIyiOj5ZaSXxaaAYAXSht7nystKDs9gvwXoAAeKd/LgU8Sez0quSC9adOmrL/H7R89evTgwYOSx0rZslYOHWmGPpNztHDjLdXVJH2ClC2bGNRpTQd7JwEOAPOlEjscv6A3XDMYMcAGe4zGADuTe1Rplrd16BIgrJNnUUWorpR8iGJq8P2Gek4EgAPpPyfpEwBAKpXy2Ry1o9295ETN+RtlH96zZ8/Dhw8XxvA0TY8ZM2b79u3lPi4AcDiciRMntm/f3mw2b9261fp5970RLiO4QX9gbKeym7EcevJCbuB2zer7qT6vt8WcPAcYizEuSf8wOmfJZvW+tybhB8rYx/s6TmtsR+BY+DOdzlJVLvVc/xrOc8ax3Z0tGTm5q37OXriBKZlVEUGQ/5Nu3boBwIYNG5KSkgo3Ll26FABEIlHr1q2LtX/y5Il12Xn37t0/YjdLVa9ePQBYs2aNzfQiQ4cO7dy5c35+/siRIyk0qQf5F3qYbe50WP7ZMUXJL4WeBoBR51Qld/X8XbH7qa6y+44gyAdne2QVqWYmKVQJFvIXJ5knYWOePADk0XSfbLmBYSI8XEv+TRiiteYUo+5hgbDRW1XZPDw8unfv/ueff1p//OWXXxo1alTy5D2c+0WoLt/Ju/GpywAXrruRNhzLPgAA/d2GcvE31decWKxvxHbL8/J/zMtvwuPWEgY1sm9xP+/WsewDY72njasx/bz8RJikWUWebwtpuyR9whXFmY3JyxbWWmvPd+jcvduZ039me8oe3LzV1MFe0KReGYf37dv3+PHj/fr1M5lMAMAwzMSJE00m05QpU2y2p7SUOfl1bTkcx4cMGSKVSo8fP/7rr782atRILC6nlF0FUerzjDGJcB0NGLvYLozryatz2pK+wpKxnszeQeue2rXfHr5d1YNtn3f6qqR/d5z/5nUmcPgmTNTdj6cxM0I2VmCir6aYutTkCYiyEuB/BIKwuvz6QdpLt/KOnje9eEVrdSwH+8rtEoJUV6NHj169enVSUlKzZs3GjRvn6+t78uRJa4m4AQMGCASCoo0fP348ePBgi8Xi6Og4bNiwSuryW7766quLFy8ePHiQy+Vu2LDBzs6uWIOdO3eGhIRcvHhx6NChP//88//rcWma3rVrl0pV0cos9+7dAwCSfLecLAjiJSZaeHALzDayC79QkRaKqe1AsFnFr9pCNtbApfgnBARBqh/sXZNjI/93M2fOXLNmjYeHR3p6evmt38tEheovg9GNxdrn7Fgyhs+j6RG5yjiLBQCOuTjV4RR/90+fHZ+7Jc1lWg2PJcVTFp86deqzzz6zfi8Wi5VKJUHYuCu0N33rdeXFZtK2Y72nHc3af1zxl9pukJ/ozdJ6AwMpJOVD4E/MlIZmvAi8NptF0EZV7g8EpZrnv8JPWPudnjLNUGteLYzTRvsLA2f5/UhgRFpa2tUrV2iGCXye0WDkYF5gOUXmzp0717dvX6PRWLhl6dKl8+bNK9kyeWSM6kj2k01vJeqLiorKzs7u3r27NfOT1YgRIyiK0uv1JT9ulsv4tCWtj8XtmnBr7cE4bjbbUAU3zYljGXM2xhIPX+YedeWFhMv7ct7Mkqmedu7cGRER0bNnz0zfbhsfaL3FrJXt7Zu4VYlavozJTGl0hKOUUufnLN3GqellP+ATwrF4ti0E+RBkMplKpVIqlSWzuFUz165d69evX7FY1MnJ6cGDB97er1furF279tixY3fv3qVpGsfxX3/9ddCgQZXR2eIYhunUqZN1lhOPx6tXr97w4cMnTJhQtM3x48cHDRpkNpvd3d2DgoKs6wX+4WeeBw8eWMvpvROBQKDToXFR5P+j1YHcLC0V8ZWzm8j2kAyCIDZVp+s7CuAr30cI4A0MM16uumcyubJYe51l3kVibA1Nj5Qro80WIY7paGaBVDJYJCx2uDYyL77zQ44Pv+6zFsXWfpMk6ePjY11nyGKx1Gq1zdBUaZbPjZtIMeRM/yXrXi5K4XZMFfSqSM999MfcjFcCRXVn+f34rs+6gMxfkjBDaZa3lXUZ5jkRAOLj42/duoUxEBKbHjJ5FMfbvewzXLhwoU+fPtZ0zVaLFy/+/vvvizVT/ZadMjb28U/ll0ceMWJEREREQkJCgwYN6tev/05Ph9Y+NCWLBZ/EAAAgAElEQVQMZ0zphONAjn+pU/oZUml+NY1SnaZp2HzSd9HG2PHjJ6xfvx4AaK2eISmWvd2hQ4e++OILa/tO3XtCn+WvKBmOwaAgwaxmYjtOJQ/FF7JkyTNnLGdMZozNFn/SVtKnCy4SlH8YgvwD1ekCX67k5ORvv/321q1bubm5LBarbdu2GzZsqFu3bmGDPn36WIuMODo6btq0qYpE71YGg2HixIn79++3TpIfP368dZJ/UVeuXPn888/z8vIKt/zDzzw0TW/cuNFahK8i/vrrr3v37rm4uFT8EAQpGwrgEeT9VKfr+39oCn1+fn5cXNyrV6+0Wq1Op+PxeCKRyNfXt06dOiVL6VQzfAzb7uRgjeGH5SoLY/jC6N2HIPoI+evzNY9N5pIBvKiphO3GNScb9E81gtC34nOCIH766aehQ4eaTKZ58+aVNrAs4zi1k3W5rDhzIfeEC9ctk8UDABfymbP5ibVBCt22QOQF2WQdwR8KfmAuux4LzBRw2DhfyBK5cN+KtJ9posy0KUzSvOxnLSYkX/vMWZE497ryogvHrZtzn4CAAL1O9+jx4+ggd/bG3XVmTyScyvof7tq167lz53r27KnVvk6et2DBAp1OZ82QVMhhoCvGxh7nlxrA0zR948aN2rVrA4Crq2tiYuKjR4/UanXr1q1tTliwCReF8UKuk5kbWQ49y2iGETJuwD5KecKc9O3kPkljennaB3Ww7sr6bi2pUEu/7PX06ZsscZfP/WkfGfHJlBUP3T87GKtXGektXarKvwPbzclj3Tz14T91EQ/zT17WXL4l/rSDuEf7oisCEAR5bz4+Pn/88QcA5OTkSKVSDqf4HJwmTZp4eHgEBwcPGTKksMpmFcHn88PDwxctWnTixInU1NTg4OCSbTp27JiWlrZ3795Dhw4lJibm5OT8wwfFcXzq1KkVbz9z5sx79+5V/H0eQRAEQcpV/UfgSZL85Zdf9u3bd+fOHZtPFsOwpk2bDh06dNSoUSU/vnwEH2EE3qrYOLwUxwuj973OMhVN98mWexGsi24uJY9N+/aF/Od011k+7gtszDwvKCgwmUxOTmWlWysg82Y/H2+ijXP9l1+iPDflayaK7SZJXgf8G/I12ws01AuY7yr5sq5wQI481mwBgBn24lF2xT81zogdrbYoJ/rMKjeGB4CH+ZFbk1cBwPgaMxrbtwSAyFu34uLjeXpTd+ca9v3LT8h08+bNHj16aDRvCr/Pnj27WAwPAGWUeU9ISFi+fDmXy92/f3///v0zMjKuXbtmNpulUmmnTp0+0MdixpJrfjWZUl8AAJasN8d3nXLvJe25GwDwSsrvfXi7RvtWSv82nbr5Dl/dM6zm54ECmgELzXBLrK+rLOakdPWBk4YnzwGAZScS9+4k7tYG41aJCf9INVOd7tAjxeh0OqGw+B3qD+qjXd+R/w40Ao8g76c6Xd+reRb6zMzM0NDQCRMmREZGFkbvGIaJRCIul2v9kWGYO3fuTJw4sW7dunFxcZXX2Q/OOg7fhMvNpqihucovcxWF0bszi1WLzRbhWBpJKSgbSVPsezsDgPqPXJtnFovFZUfvACAm7Ls59QaA3zLDmZIZ7QEAoJkHp1tNPgtgqYM9jmEAkE7aSCDczbkPA8yu1I0FZF7JvUUdO3bs6R9xvhnBDDA7ktZtOLomPDw8Lj4eAIwC7nFtdnh4eHh4+P79+2naxrO2at269ZkzZ4pOLli5cuXUqVPLOKQYPz+/li1bmkymgQMHWmsUf/rppxKJRK1Wnz59+r2nVtL6aGNMN1L+q829GNuZW/sQp+Z6YAkp5Qnjk2ZRslcjb/2ZbdTVVBvOtO3Xuk5I0fY3Lp//fWzTlyc3URQ15bK60Z6c8Kc6qmrc3+P4erp8/7Xbj9/y6gZQGq16/4n0CQvyT1xiLChTPYIgFfWRo3cEQRAE+RCqcwBvMBh69+4dGxvLZrN79er1yy+/PH/+XC6XWywWjUZjNBpNJlNiYuLevXutSXcTEhI6dOigUCgqu+MfkDWGD+NwcigqwUJ6sFjW6B0AWAChHA4APDSZSh4oamlPOHFMiXpDbDmV2MvQ1fkzMSF5pY/PNtquzdvCg+sswAEgkM0OYRMAcFFvMJaYN9HJsUdLhw4AYKZtdLUoa5l6jxw/D3lNGqdiat7V8zUlm5EkWTRZXUmtW7e+cOGCRCIp3LJhw4bBgwebbL1WJeE4PmrUqGHDhhEEsXTp0p49e9I03bNnT3d3d6PRePHixfj4+IqcpxjG+JLW3DW//MaUMBIYm1mOMcJ5OD/kOm7XhLHkNnNaFdxd1u3y4T2JTz15oj2BrRe37ynkv1lVrtfr58yZ07RpUzwzWm9hfrxd0O8PRayiqgTJ3MCarj9Mdpn/Nde/BlWgVR84mTFpsfZKZGX3C0GQKmfu3LlLliyp7F4gCIIgyP9fdZ5C/9133y1btszBweGPP/5o27Zt2Y2fPXvWs2fP1NTUGTNmrF69+uP00OojT7HT0PRwudI6Qd2JxTpQJKfdtgLNxnzNEDvhPHtJyQNTJ8cpdme4zfN1m1fzvR/9quLsgYyf5XbDEtlN/djsWuzXD51gIV9aLLXYhN/fBdjjzJZkkgSAEXaiWfbvWYntyJEj1ty/QpEwu0HiY909KVs2z3+FjONEkuSlS5eys7MxDGMYZvDgwTxeOSuro6KiunTpolQqC7e0a9fu+PHj9vZvVTujCsikYdG6e/l1HjRju3GL7rp58+bnn3+em5tbq1atEydOBAYG3r9/PzY2FgDat2/v4+Pzrk+QVByxJM1kKC2/YQzGcS21HUNZsjZZ0lcAbbr3wvGvuJbT+k7T7jlO5ijSjbrvs6KvRT0o2hzH8e6DRqjazc2hBCwcRoYIJze2q/Q6c28wjP7Bs7zDZ8wpGQDg8v03/NDAyu4TUk1Upyl2/2UYhjk6Osrl8srtBppCj/zfoSn0CPJ+qtP1vTqPwB89ehQAli5dWm70DgAhISE7duwAgJMnT37wnlUea9a6WLOlBkHU53DkFDUsV5n6d4naRlwuANw3mm0ea/+ZMwDknfhHn4faybq6cT2VIACAlxbLeb3B+vXSYgGABAtZuMUavWMYtlejjTRWaKC7JGsJN2dnZ51W5/zItxa/jtqiXPtqocqotEbvQqGw4okPGjZsePXqVXf3Nxn1rl271qZNm9QXKa++eKbYlQEAlIZUH8thSQheHVHqpLjMhS8Nz7QAYE4xvhz0NERd+8GDB2FhYQkJCc2aNTt16lTTpk3btGnj7Oz8fuXiCccBvNC7vJBrZUXvAICx2O5TeXWv4MLQJrUVsz87KXDY7r5qgrhHO0++aO8XY7dt21b0NgRN02cO7kr5oW0T1RVgYOcTXdfD8gtJZU1S+KgwTNC4nvuaOU7fjhR/0o7rXwMAtNfuaq/fZSgbay4QBEEQpHpwEuA8ArPjVOcP8AiClK06//+npKQAQLt27SrYvm3btiwWKy0t7QP2qVIVzTm/z1m221lmXQ9fGMOHcthcDIu3WPJtre62ayMlpGxDrNb44v3r2eIYa6T3pNpsHAC68PnrZVLrV2c+HwA+EfDWy6Tz+GIyCjgKDACaczk0wBxVXl7pC84LyDwdVdbE/pYtW7q5uRm0xhpPgz04NbJNmT/GzEqTpwqFwsI67U9OnM3+5TfGYnMi+hv16tW7f/9+vXr1Crc8e/asWZvm90/dTZ0SlzI2JiYkMnVSnPpoji4yL/+8Intt8vMWd18Nfhrf7WH+n3LNX2ovL6+IiIivvvpKo9H07dt3zpw5NWvW7NGjx3vfDsQ4rrjwzWp2hlQz5iybLXFBMK/uZY7PcsAFpPywKa61uCd5LNjZf/aEe/fu3blzZ8iQIUXbZ2ZmHP5uiNux0TWZ7EwtNfGCesQZVUp+OS/Rx4NhwhYNHUZ+jgv5DEkptv6q2LQ/Y9JizeVbjK3UCQiCIAjybxfew+HSICdRlSn4iiDIx1edA3jriuWXL19WsH1KSgpFUUXXOVcnRaN367r3ojntrDE8B8NCOGwaIMpkYxAeY2OSno4AkHfyHw3C+wlqN5Y0AwB/NtFNwLd++bEJAKjFZncT8L+QiUIojiGfAYDmPG4TLjeXouarbOerY4CZ/2Ly9y8m51vUpT0iQRCdOnVyc3Mzacy1YxvZWezzCOXzgHsdu3UoTE0Xa8iP0Ckz1uwsNy+au7v7tWvXWrduXbglKzdrPMx4REcrD2aTCrOopX2NrUEBF8P8joQ6T/DCeay803JzmlHYSOy+2A8AeDzevn37li5dimHYypUrBw0aZJ3nb6VSqRISEt57bYsptpfhcQNL5k+2V8VjBOE6nh96iyVpx1jk5pcTfF0usNhEeHh4jx49wnftOjp3sa+7R9Ejblw6GzmzdV/mjoSL30gzdT+iWH9fYyCr1tIbjGA5zxzN9nAhc5XK7YcyvvnBnGw7zwKCIAiC/HvZc3F3NHkeQf7bqnMA36ZNGwDYuHGjwWAotzFFUdal702aNPngPfvoSkbv1u0lY/jGXC4A3LMVwAOAtK8LAOgfFZTxWBEREc2bN2/dunVUVNT79ZaFweoO9iwcA4AEFbncwd4Ox68YjEe0+pKNMcB8+H55FtXWlFUUU+q4qzWGd3V1tRSQQXFN+BZRPl+5W77Rwrx+pnYCoUbMjxQw6at+ZszlxPBSqfTSpUsDBgwo3JKny59omHmJvA4A4i4y2VB3UQt7ySeOzpO9WTI2AAADplSjIeb1TAEMw+bNm3fy5EmJRHLkyJFmzZo9f/7cuuv+/fsRERHnz5+3ZuB7VyxpV6DNltRF5uS5pbXBuD7coD84ftswwqFbg5i7Bxw6tA709PSklXlh8Tlnm/Sc3Loz++9kBACg0+nObPru0iCnfrX5ZorZ/FDb9Td5mqZqjXILGtfz+Gm+07QRHG93UqE2xSdVdo8QBEEQBEEQ5P+sOgfw8+fPFwgEFy9ebNu27dmzZ0tLM26xWK5evdq9e/fdu3djGDZlypSP3M+PYIpSHW22+L4dvVvxMWyrk0NDLiebosbIVU14HAC4W0pydXFnme/euh5La5XxWF988cWdO3ciIiIGDBhAlbcgWUPavhfgZ080duMAwKUko4DEFkolALA8Lz/B1hT30d5TZRynRF2cylJOBQHrsDbXwm+U1t4OFz/XPt1wbSppNABAa/8ge5GdVsyPFOMpy7fS5a2653K5Bw8eHD9+fOEWM1i+My/9jTyZufBl9tpkADCnGeO7R1nSjfx6IozAyFxzQrco+fY3azR69ux5586doKCg6OjoJk2aXLp0CQAaNGggEAiys7MjIiLK7oNNbK/53KCjuDAU55f1awLACKdBvNDbLIee/h6G06uyz28RsCQqx8nDhPb205wDbv60vVWrVoWtRSKRjI+vam//W29ZsCM7Q0NVobn0hTBM2DLMfe1cj5/m23VuCQC5q39JG/Nd/onLtKHKLOBHEOTDa9asWVhYWGX3AkEQBEH+/4jK7sAHFBoaumfPnoEDB96/f79Hjx4CgaBRo0ZOTk4ymczOzk6r1apUKoVC8fDhw4KC12HkkiVLOnbsWLnd/hBcWawQDnuzo0Ox6N1KiGE/O8mmKlQWBhpwODwMe2G2qGlaitu4vyPt51LGAzEMU/hivnz58ujRowMHDiyt8XGt4jdFjI/Az4njao3Mz+qNL/4O0RUiCkjQWph51/O3dpVGGk3HdPppStURFycB9tbSLzEhmV9rVY4py4lTat+sOedzcnIEAoFQIJArFMFR9Z7Ujox1yJTD/dopjQvW7Gro7/2wjns+wF0AWLHde854nMct7YQAwGKxtm3b5uHhsWDBAuutAYqhV5k2pTIZ0xeOp/JJ9R+55mSDsKnE/0T9tOkvVAezGYpJmxGvvZtfY3MQLmQBQGBg4L179yZMmHDgwIFLly517tzZ2dn5s88+e/TokYtLWS91WR2TdGCFdKhIS4ztwg3YT6lOmZNm0ZpI49PWXJ+JHj/N2Pfdyl/Ct3+3eFFGRsbatWvt7Oy2b99uPSTMlXOin6NcT7kIWSQNUy6rnQWsSWEiB36VuRuIYWzP1yn9cC6bUuerD5zIP37Brmtr8SftWfZ2lds7BEE+gshIVGASQRAEqZ6qcxk5qytXrkyfPv3JkydlNwsJCVm+fHmPHj0+Tq+KqmplZsbIlRFG008yaVcB32aDnHUpxpd6742BGMtGDpWxY8fu3LnT+n1wcPDTp0/xEjcCftXqflTnV6Qz7FhMlwSLW0v61eEPyJEnWMgeAv4ambSCz+X333/XarV9+/a9ffu2Ned813btC1bveugiUMvsLNy8J7UjDYTZUe0+5qiMna0mxaKnnULzTUaBzthMYfaaOQYXCsp9lB2Lt329cBIFb6YbtCAaL+fMF2FCa/TOsiOU+7NSJsTatXXQPyqgCkh+XVHNQ/W4vm9e4ZiYGH9/fy63rFsG74ehCswJI3FRY7bHNMBsp9xnyDxL2mIyZy8AjXE8Ri2XHTp6AwC+/fbbNatW6W9HcevUImT2xY7KM9GtD+TqLYwdB/u6od2wEAHH1p9E5TLGvSw4fkn/MBoAMDYhbNFQ0q8b2925svuFVFHVqcwMUumq2vUdQRDkP6s6Xd+r8wi8VceOHR8/fvzkyZMLFy5ER0cnJSVptVq9Xs/n8+3s7Hx9fYODg7t06dKgQYPK7mlVMUYsIgGCSy+upvo92/BMK+3tLO4sK7l3xowZu3fvtk6ej4mJOXbsWP/+/Yu1GSgUeLBYRoY5m3ssxfAqQBjcTtY9maR82UTRWF+M48pazNQk9bLIgtZe3E2ODv1zFGf0hgZczpciYWndIxnSRBuFLFHhlps3b8rlcmvOecOO36ik9Ppa6RMOWwX2ITGNHwXfU0gzj7fXfBEZCMlZoTdin3UMVQNEAsbZesBt5thyX7Gvun5hXp431/Kjjnq9av02eX84PWk9d0njjr4sOwIAcAEOAIQjO/BG45eDnxqitXEt7tXYUce+l5P1kODg4KLnzMnJKRyBNxqNt27d8vDwCAx8r2rnllwq/zqVd4VSneIGHrNZcA4j7Dm+6wjn4eak6bT2wfapGaG+wT9uS3r27JnhQbR8w16My7Hv11X8aUeM/eZNw56LH+/ruCyy4HqqacWdgkPP9Yd6ObgIq1ZyHV6gH2+unzkpveDPq9qbD7TX72lv3Bc0DJYM+ITr513ZvUMQBEEQBEGQd1D9A3ir0NDQ0NDQj/NYFEVt2LAhNze3gu1v3LgBACRZVVYUN+FymziVNQ4s7etieKZVHsiyGcAHBAR88cUX+/fvt/548uTJkgE8gWHt+DwACHPv/P2LSSr1I3d7rx4SW+kDa8K9OoJDsfocHdVYzFkslUxTqlfmFYRw2PVKucWwLWV1rObJ9Jo/+Atfh7uF0TsnS6G88xgIFsjVdVX50S0CVWLHkMQWT2vdjvPXHKCfDVC4sRTqFhT3rpTIo1Vs7xrlvFgAAMD24LZkNdljv3k654fU1FTrxiQ6dajxmxVL5vfhDnCd7mN8rgMAjheP6y+ofbVRyrjneadyX331rMbWINlXbsVOuHnz5kmTJo0ePXrDhg0CgUCj0aSmpqampqalpbVq1YrPtz0zojQYz59X55Tp5SRaH0tr77IcPiutJS6sx6t7gZQfwVLmT+qbMbqngO9en+fuLWzeQBf5SH3wtPbqHenwvoJGb6rW+UuJ3Z843EwzLYssSFSTOTq6qgXwVhxfT8dJQyV9uuSfuKS7+UD/MFofFcNvGCz5tAOvbkBl9w5BEARBEARBKqT6T6H/+B48eNC4ceN3PUogEBStJVZlMSSTOuG56nAWcPCQuJaEk40oOj4+vm7duhaLBQC+//77xYsX2zxVquHVudzjDwvukLQFAMSEpKGkeQ/nfjKOU7GWSgMt+3uJ9fK8/H0anTOLdcTF0cXWkv4jmXvOy0+IWHbza606/9ulchPpAYBGkJcQ+EAHOt8k3sDfHAQyZ49NC2maZtk6v02xje4Y43TcRQ5ffP/VU0Ns4XYc8Emc0bMWzVbtyzKnGmudbmDX3gEAgIGc9SmZi1+6TPdx/75msbNdvny5V69eBoMhKCjo0KFDoaGhKSkpt27dMplMPB6vVatWXl5eFezYG7SR1j3B7ZoAlD/LnbHILak/kPJDAAzG9eH4rrSk+cQu3+xgpgGA36COw7C+hevMrSgGsvMMHlK+nmRGnVF52LGmN7Fzq5KlbkiFuuDUFc2V24zJDACCsLrOc8eXexTyH1GdptghlQ5NoUcQBKkiqtP1/T8RwKempkZGRkZHR7u6urZt27Zu3bo2m6WkpJw4cQIA/mEieoZhdu/erVCUkxG90NmzZ2/cuOHq6pqVlfVPHvcjYEgmaXh03onXkwvcFvq5zfSx2fLSpUs7duwIDAxcsGABp8RQOQPM6ZwjJ7MPM1D8z4+NcYZ7TWwubVdaHyiA0XLlHaMpmMM+4OzIw4qHozRDb05e8bjg3pceY/JvmPLzy19sTxBEh35t1736QU2pvNO4gw851tq1ruwMdsUo92amfP0cWJiZMi2323Qq+1zRvZ2Jtgs4M2ShzkERjQF/02FaR+GljFdHR0cPGjQoJiaGy+UuWrRoxowZZrM5IiLC+inQz8+vRYsWBPH+M2gYUknJD7McPsO4nqW1oTV3zEkzaH0MALCkXet/er85YzctuIkdwcFYLLuuraRffoZxX/9yZ8+evXbtWhcXl5nfLQzn9NCSOJ/AhocIxzUQ2XGq3MJ4AKA0Ws35m5oLNwhHB7cVM4FhTAnJbE9XvJTUD8h/RHW6wCOVDgXwCIIgVUR1ur5X8wCeJMlFixYtX768cBgWw7C+ffvu2rVLIpEUa3zhwoVu3brB38XGPpqqeYF/bDZTDIRx38TehdE7S0JgXBaZa+LWEgbeaGRd4/1OTuccOZ59EMdYXZx6NZO0+SFhGgBggNWxqx+jeYQBNsFnZiNJi5IHkjQAgA7oQTmKZJLszOf95OhQMvs5yZCxmid17OoRGLvo9vRx35HKfH5okMv3X5c8ucKcsypmtgLPc8vizGq0TuJYamRbkinZENv4LmOgMALzXFVrt/zX7xbOLzr474f7hI/4ufUvnSt+ToPBMHPmzK1btzIM06pVq3379vn6+sbExDx8+JCiKIlE0rp1ayen4rMVKojM2mpO+Q5wAcf7e8K19PFnhiSzf7akr2SogiwlsWCX2/lziVt7fdkU+EDR0q96S3p3AoDExMRatd5UrQsIqhP41aIYh5YMgD0Pn9hQNCS4Kua3K8oQFZOzbBvO54k6thB/0pZwtrE8BPkvqE4XeKTSVc3rO4IgyH9Qdbq+V5nKTx/GoEGDfvzxR4qiMAzz9vYWCoUMwxw7dqxt27ZKpbKye1elfS1XjZIrNTRt/ZGhmOQxMXknclliwv9kg8Drjbi+fFOCLrH3Y0rzbqv3M4ypJ3MO4xj+jc/sAW7DHDgyAGDjXAYYuSmrr+uXDDB707bqKG3JY3sfU3Q6nEuZYKuTgxjHLxmMm/M1JZsRGFFPHFYsegcAhqQXf58+t9clmx1z5LjM8FzgoGJnuZlXKZbnWVRvdTsp6f76HZrzN0oeSOWRCT2iGAPFsicYkkn7Nr7LuqZbPFdLcHFhm5d0co/dffdN3V32i8OQDGN+/Zrz+fzNmzdfvHjR09MzIiKibt26GzZsCA4O7tmzp1Qqzc/PP3v2rFZr41WqCJbTlyxZH6D15uS5jDGp1HYYQbhN5NW/TzgPcZPRO2el3Tjs2mNNc/fVs8S9OgpbNrS24vF4RZcbxD+PPfVdf8dDQ2qb4vOM9LLbBZ0Oy4/HG6gqfLeQ4+fNC6lNG4wFf15Nn7RIvmGPdYI9giAIgiAIglQd1TmAP3fu3LFjxwBg3LhxSqUyJSUlLy9v165dQqHwyZMn48ejVa9l8WezTQwTaTKDNXofHaP+PYclJvxPNRA2EnO8eLXONuR48nR38981hr8oP0UzdAfZJ/XFb7LWcXCuN79mrjlbaZEH29XXUdqbqsslj5Xx8bQCatJltTeL2OLowMaw7QWaU3pD2Y+Yb1Fbv1k4+bn1m5FPettsyX+YPnyvk7OcnWXKWJ44N9uUUbjr3oOH0faciAf3FPuOw9tzNEiF2ZJhEjWT1H3eyndPXWFTCWNhGivq7edtCeS+GZfWMNphG0ZNnjzZZDKV1tWET6KeBdzKP/9m/UWnTp2io6OHDBmi1+unTp3avXt3o9H46aef1qtXz93d/b3LzmGEhFtrNzfoGMdnBcbzKacx25lTcyMv5Apu18zXRY1nTKfzBks+cyCcHEaNGjVw4EBNQcHaydMFvLcmn9+9ceXCtx1qnv/Wm8zI0FAzrub1+F1+MclYNaN4lsTOdeEk99VzRG0aY4Dpbj6wZGQDAENSUK2nKSEIgiAIgiD/ItU5gN+2bRsA9O3bd/v27VKpFAAIghg5cuTZs2dZLNbRo0et4T1iU2seFwBuGowlo3drA44XD+PhgMG7xvDRmigACJU0lptz5OYcpVkOAAzQ/d2GsXH2deVFR44zADwreFjy2NUd7F2ErDsZ5hdKSyMuZ6FUwgAsUOU9Npc6WHpTdXla7IgjmXuKBe0lY3hLZq7q0J8iLT5sr5Mv319uzlmWMCdB9zrmb9aqJYHj2R6yW1kp2Rv3MmZL4YFcf0FIQqta58NYdizp5y61rzSqn922bnSLbkkDHuU9HT58eNFH2bRpU4sWLRITE232luPNIxXml/2fpM+Kp42vh+IlEsm+ffuOHDkik8nOnz9fv37906dPh4WFde7cmc0uPsvgnbAkHQjXcYVp7RhSTeVdAsZ22j9cWJ8XfJZbaxfG9ab10abnvY1xg65cPn/kyJG+rdp9lqq70mHAwLDmOP7mXYWm6csnD9/6tkXAjYUulCJBRU64oO73hyIyo+Syc/8AACAASURBVIoObnN8PR0nD/Pctsj1x2mcmt7AMBmTF6eNmZd3+AylLj+ZAoIgCIIgCIJ8UNU5gI+LiwOAadOmFdvepk2bGTNmAMCsWbPMpQd+/3Gt+FwAiNAZbUbvVqKmEmCAJSHKiOH1DDNflXfh70FyC21RW1QAsPblwtnPx81+Pm5RwnQA0JPata8WWmgLAFxXXgSARP2LkmdzFuD7P3VY1lYS5MgGgH5CwZcioYlhJspVaaTtsNOV60FgxHn5iZK73sTwNK279TD7u3WMTg8AEifPObWWNbZvqaU0q18uuKO+AQBubm6f9OzJY7NVzpIIRpe8eCOlyis8FeHEwYg3a7xxAYvjw2c7c3g8Xnh4+E8//VQ0k19UVFRYWNhvv/1Wsks+O4M9fvTH2Hju1rQXbe4bnr2ZId+/f//Hjx937txZLpf36dNn1KhRBQUFhXtjYmJOnz79D/MgWtKWmOIGGJ+1oTV3SmmCsWR9+aF32d4LMZYdnXfh2nbjuC9DX2nzbooYd6lshU/D66s3d+zY8a3TWiznD+18MqtZ0P3VUjr/Sa5lyGlllY3hAYDlYM8L9LN+TzjLqDxN3tFz6RMWyH/aY4ovfbkBgiAIgiAIgnxg1TmAt1bkDgiwUeR5/vz5bm5ur1692rJly0fv179DIJvtieH9ZqSVFr0DgOMYTwDAcODW4JcWw6eS5DGdfrpSfUpnAAACJzDAAMCJ42L9krKdAIABnCCcnDgubPx1oMvFbE8O97MnBgYJCmPluVJJKx5XTdMTFcq8v1fsF1VLGEQypc4OGPmkd/b369NGzZOvD6c0WpzPAwBhu6ZsjDPOe3onx54kY9mZuv5k9mEAkMlkPXr1shMINRLBbU9x7I+bTAnJZb+MVlOmTLl9+7afn1/hloKCgkGDBg0dOlSv17/VFAOXqTVqX23Eqy00xGrj2t3PWZfC/L123NPT88KFCxs3buTz+bt37+7Ro0fhcXl5eQqF4vz5848ePapIl2winL7EeL60Ptb4vDeU/qIBzmO7T+XVf0i4jHB2wNd9k5J+STZgkYv7hm9lowe0GDvs8uXLZ8+erV+/ftGD9Hr9md3r4+c2DXq8qYG90VPMAoCobPOjnKobyQOGuf4w2XXJNGHzBgzD6CIeZM1bmzVntfavO0WnYCAIgiAIgiDIx1GdA3jrtPmMjIySu0Qi0YoVKwBg0aJFNhsgDMlM/TazxZkCUsyq9aeN6B0AhI3EwkZiUk06DHPjeP+9Hl771kh4IJv9rURMAYxbtSq0Xfs1q9e48TwBYKTX5JVBO6YHbH0hmQsADC5YHbRjZdCO1UE7+SwhAHjxfSrSTxbAOpm0FptItJCji2TdK1TacvdC8z6/Tmm0bDdntocLbTCy3V3E3doAAI7hX3iMHuwxCgBO5hwOT9tMMZRYLO7Vp7e7i6uZSzwM9ojasV8XWaGAOSwsLCoqatCgQUU37t+/v3HjxtHR0cUaC+rbBUY0cRrryZjpjAWJ8R0fmFOM1l0Yhk2aNCkqKqpTp06NGjUqPKR58+YNGzbEcfyf5DrGRWH8epGcGj+yveYDVk5xAYztxPFdx6t3myXrLeSaqKz1lqTWvHpRuABv2bLl5MmTZ8+ctXP8tJpuHkWP0hQUnNmx/OrXjQ5sXmUwmr46rfr8uHLon6qH2VU3jOcF+TlNH+W5ZZGkbxeWnciUmKLYciBtzHeqPccsmTmV3TsEQRAEQRDkP6Q6B/D16tUDgDVr1tgsCzd06NDOnTvn5+ePHDmyaLmv/5qxcuUXuQp1ibg3/6zC7awaAH5d6znCwzxRobJ1NLhMqwEAyn1Zfr+HYixMdzdfsav4DZExYlHn2xHZi394ev3arFmz5Cd0AHA694iCokbKlakkCQB2OC7BcQDg4Tw+SwAArWXlV1xbfKug62/yPC0T7uRYk03EmC1j5Cr927/u3aE2Js8XtTpyoKBxPVKVZ8nIYTnYO88Zi3HeLCzv7Pjp1z5zODj3puryT0lLDJSew+F06d4tuE4dBsfi6njkXrSRl94msVh86NChLVu28Hi8wo2xsbHNmzfft29fscY4H/daV9v/ZAOOF0/3oCB3c2rRvYGBgZcuXVq/fv2b9jgeGhrav3//rl27VrA/tuFcwu1rtts3hRsYi5zKvwZgO5Ebzq/FrRXOq3uJJW7NkGpL6iLD4zA2qBITE78dPbaD3HyhWa9lrbq7yRyLHqVSqebNmzd1yuRZzezsONitdNOAE8rhZ1Q5uqr7n0g4SqVf9PL8eYnjN0O4AT60Tl/w518ZU37MXrTJ8OR5ZfcOQRAEQRAE+U+ozgH8V199BQAHDx4cNWqURmOj2NjOnTvt7OwuXrw4dOhQnU730TtYJahp+pHJPCJXWSyGt2sj5QWLAKDTiuzkLL3N2ekAYP+pE9dPYE42JA+PZiiG7ca172WjMnmt3DcDlQeWHcNURKzmyaRXW19ZLLXYbPg7i5qJNm5PWasyyz15NZpIWpXb+TwjnagmR51VsUjY5STzJFhPzOavFSpThWP4BUs8NZdv6e8/ZcwWYYuG7itnst1dirVpKGk6y2+JmJDEaB4vT5yrtigxDGvStGm7du3quHt5jB1cbj+Lmjhx4oMHD4KDgwu3aLXaYcOGDRgwoGRpQ3EHh6B7Tb3W1nae4l32afv27durV6/s7Oyii+3j4+NjY2PpUn53FWROmml63scY3ZnWPiitDS4K49Y5xQ08gguCGXPm6dXZWxf6Cd3Ef/nLBP41BrvUvNq634KWXRzt7Ysede3ateEhwhtfOk8KE4k42M0004n4cgoKVDqMzRa1a+q2bIb76jl2nVpiHLbx2Qv56l8qu18IgiAIgiDIfwJmc3S6emAYplOnTlevXgUAHo9Xr1694cOHT5gwoWib48ePDxo0yGw2u7u7BwUFXblyxXrgx+znzJkz16xZ4+Hh8U8mP783JUWPkCsSLKQfm9jj5OjIenNPR55jvNv9oXu8MduXk/1H8MhaDjbPIN+eljYjHgBoZ3bdc2G82sKSbbKysgIDAwuTrjX4rGP9RRIAiiS8P3fqeCIrnM8SfuYy8JLitNIsF7JE39Va6cr1KHmeYnQWZtBJZazC0tiNs6+nQzZDDclV5lJUSx53q6MDB8OKNi45l36jfgGZls1QFOHowK8fRDjZfoJWuebsn14tzjZlyjhOU32/9+CVE1GXTaPRjB8//uDBg0U3enh47Nq16z2G0BmG8ff3f/XqlUAgWLJkyZQpU6xV2X/99Vez2Wxvb9+8eXNXV9f36yqVf838cgJjzsYIB36jl+U1p0nF75a0ZYwpFQBwu8Zsj3nG55K8w39aMnL0pGVPTuIvsQ/UBQUAMHPmzFWrVlkPyzPR11JMnX15Qja26aH2Wa7lq7qCNl7vWSTvo6H1Bt2tKFzAE7YMo1T52Ys3ES6Odh1b8MPqYqzqfHu0GpPJZCqVSqlUOjiU9YaAIBVRudd3BEEQpFB1ur5X5wAeAAwGw8SJE/fv32+dJD9+/Hhrbbmirly58vnnn+flvcko/p8K4KGUGN66MSfHOG9Eqle8KcuXk3aszvgAWbFjaT0V3++J/qYaAC4s95o7yUbKQKtNmzZNnjy58Mfae1Y3C3uOU0pgCquYAQB482uOrzHDlesOAAwwBzN+ceG6d3LsUfKEVjk6qu8fymwd1cOPv76TfSpFDslVKCm6M5+33tFhv0b7Z5Eq8eLsMYXfP5XMcWDLxIQ9ANTlsH+Q2ts4+9u0pGZT8rIE3XMuzhvpNamxfctyDynbvn37Jk6cWHT2B4ZhY8aMWbt2rUgkKvtY2kDj/DchokKhmDZt2oEDBwCgUaNG27dvDwsLy87OvnXrlvW+ib+/f+PGjYvO3n8HtN6SvRMjJITz8Iq1N5E5uy2Z6xiLAgBY4lZsj3lTeyzoK3LxEdlrLOYbXIvHF5/16tWraM25Ql+cUt7NNANAXSf2xIaizj48HCvZqsohsxUZ05cxJjMAsKQSUfumkr5dcV5VvweBFFOdLvBIpav06zuCIAhiVZ2u79U8gLdKTU09ceJEampqcHDwiBEjSjbQarV79+49dOhQYmJiTk7Ofy2AhxIxPAZQ+KNZbv56WLLNGJ7WUwn9n+iuq41CnKejWQ3tQq83gVJiLYqimjdvfv/+feuP0i+/2rhto48l6nHB/RfaZ2ycW0dUr6GkWQNJU+zvU5ho46Tor0iGHO09pYW0fWmdj1NaBp5Uas1M7wD+qvb2CaRleK4yn6Z7CfkPTeaMt8vLNVdNBIBIh63FThLp4WpvK5gsxsKY96Ztva2+hgHWxalXf7ehOMYq3EuSZMrVmzIDJenZoYIDsLGxsUOHDn348K2i9wEBAfv27WvatGlpR2UsSJRvS3Od6esy1RvjvHmg8+fPT5gwITk5GcfxsWPHLl26VCKRPHv27OnTpxRFcbncsLCwgIAADPunATFteEHn32Q5DcBYNrIbvkbpLNk/k1mbGFINAPsu1/xuTWIHe/cp9ZrXbtvSafqo0o7TWZiDMfpdT7VyPQ0A/lJifAPRp/58osoPadManfb6Pc3lW5b0bABwnPClqGPzyu4U8m6q0wUeqXRV4fqOIAiCQPW6vv8nAvh3otPphEIbk8A/nCpygS+M4X3YBM7AK/J1ML+9QHM6NX/pqHSHOEOWL+flsaDJAY4AQOupxP5PtNfV+Y7E9p015nydxmSaax4Ise/tXNpDXI+K6tCiBW0yAYDbmnXSAQOXOtj3FgrK6NUt1dVdaRvZGGeW/xI/Qe3Smj3KMQ8/o9KamR5+/PUd7aNJ86hcpY5hhBimY5itjg7OrNdh9li5SkVTPzvJHP4O10fIlRqavu3hKq1AAP/6iSgv/prxM8mQAcLgCT4zJITUuv3x48ePHj2yV2kbaGmvScNYDuWP6gMASZJr1679/vvvLZY3lckIgpg4ceLy5csFAhuvT+6WtPQ58cAAr7bQ66fadq2lhbu0Wu2iRYs2bNhgsVhkMtmyZctGjx6t0+kiIyOtBRdkMlnLli1lsuKTKd6JKX44pTqJEQ5s70WE81dltGSoAjJrK5m1laE0eRpm70Xvuk1GfNp/WmEDKl9jik/m1w/C2G/lvTdRzO9xhp2PtekaCgA87Vhj6ov61ebziX/BcLzpxStTfLKoY3NcwFduO6h/+EzYIkzUvhnH17Oyu4aUozpd4JFKV0Wu7wiCIEh1ur5X+SGtj+4jR+9Vh4yFhzs51iSIZAv5iiR9iNfT6XsJ+QUOxE97apB1hG5JZr9+zzfGK4pG75v3+Sxp5+E1uyYAZPzwkrHYviWkpOjV7p5eh3+vOWr0tj17fhw7hgb4TpV3Qqe32d6qpUOHjo49LIx5S/JKtaV4jrdCDVw4uz9xELKxMy8NO59oQzmcn51kfAzTMQwABLDZwZzXX9borzabeLMFAAAo5h3yn7eVdZnnv0LGcYrXxSyOn/FS/8K63dvbm8/h5DmIIlz4z37caHhcoeTkBEHMnj37xo0btWrVKtxIkuTGjRsbNWp0586dkoc4f+0VcD6MFyQ0vtAldI9KGvKMlL8uwyYSiVavXh0dHd2tWzelUjlu3DhrpbouXbq0b99eKBQqlcrIyMiKP1mb2F7f4eIWDKkyJ88AKCtJHsYSsz3n8Bo8YXvMlNqLp/RL6+i12PS8H11wGwB+++23oyOn5K7ckf71D9qrb/WKy8K+rMMfQV0Ie7bJSfk0XUMtvJnf+kDu+vsaheEfpeX7CLi1a4o/7YAL+AAAOE7laQrOXsucuSJz+vKCP69S+TZyaiIIgiAIgiBIuVAAj7zlTfD99zBnCIdTgyBeSjDmeF0qSOiWZPbtH/fXZw+119VqZ2LjQZ/lzd0D2GzZMHeuv8CUqDfG2cjnr6ToYXLFSwvZoFmzhz/vGD9s2Eg70Ux7cUVi+EHuIwNFIXkW1YakpSbaWFqzMFfO7h4OQTK2rz0BAA25nJ8cpdYnsUdTfomBHxNmpRtTym1WyEfgv6DW2iBRPbVFuTJx/iXFaQBwcHDo3a+fu4urhct+HOIV+fsJ5b7jDFmhWwPNmjV78uTJ5MmTi85vf/78ecuWLceNG1eY/6+QqKV90O2mnqsCcCFLfTw3pn5k7tY0oF//AgMCAs6dO3fq1KkaNWpERUW1bNly6NChAoGgX79+TZs2bdy4ccWfqU04vxavzhlu0HFu4NGKvI1ghJTtNY/X4CnbYybGElP5V42xPYwx3c+fDv/x6p+xeQpKlafY+iutfysL/aZNm0YMG/r71uV353cT7hnolnFDbaA2P9S2OZC79t6/JgaWjRvkvnqO+JN2LDuROSVDteeP9LHzc1fu0N99UsG/DQRBEARBEASxQgE88pp1Cn0SSfoQhHUcfrhcoaBoDGCKxC6QzXZz4TU415AKEnq8NEkjNWpnYvMBn5WN3QOsdeAIzP9oaI3tdfh1i2dfK4zeA9nscGdZ4VLzCsbwLIz1tc9sV657quHV9pQ1dOlD5Y1cOX/2d+zq+zpPWxseT8rCAeCAVrsmr6DstSJKi3xZwpzCsfSKsCPE02su/MS5L8lYDmXs2pm63kybeDxel+7dGjZogGFYir/rNUX6y4U/WTJzK3JCPp+/YcOGc+fOeXl5FW6kafrnn3+uU6fO8ePHi7XH2JjzRK+gO03FnWVUPpk+Kz6h1+PCGB4APv3005iYmLlz57LZ7P379wcGBh45cqROnTouLm9K5WVlZb169er9ltKwJO1Y4jfV/qi8i8aY7qT8MJTyO8II+9dhvOdcjHCgNXfWjLkzbJzduNjLs+PvOkwZ9nrI+m8RERGF3z+7e/PWj4PwTV2944+ZzaY/XrwO9c3Uv2AREMfX02Hk5547lzrPHC1oFMIAo7//LHf1zvQx36nCj5qT0NxaBEEQBEEQpELQGvjKVxXWyJWRxK5YbTmD3PRXr4csFblil3fzOvbLylvmraHpgTmKJJIM4rDDnWSSEuvMfy7Qrs8vYAGsd3TozC81R3quKevHxNlasqCNQ+fhXl9X8Hk1zcgqoBkMMAYYBxbuymLFW0iSYWqzCeLvge4XZgsJMJ458UR98QuPMWVkvC/NHfWNPelbzLSpBt9vQo0Zzlw3AMjJybl25YreZGKRVPDzjLpjh3ADa1bwhAUFBTNnzty5c2exf8+ePXtu3ry5Ro0aJQ9R/5GTPjuBsTAhL1pi3OIvcnx8/OTJky9cuFC7du24uLiiu44cOaLT6RwcHFq1avUPF8ZbMtZY0pYCAC6sxwu+AHiZGe9pPZm7z5K5iTFnAoAFryH0Hk84DwecZzKZ7v72h++tmAtk3tjwrSXfoxydnEeMnTBj8sRMkA44oQiSsUeHCrvX/BdkubOi8jS6m/e1f90xp2Zat3C83Z1mjWG7OlVuxxCoXmvkkEpXFa7vCIIgCFSv6zsK4CtfpV/gyygjV2yjhWGmKNXXtQY7DNeygGKYUXaiGfZv5SFPHh2jjcwLjGhCSNkAEG+x9M6WB5YSvVvtLNCuyy/4UiScL5WU0c8kfcKql9+baGMvl4G9XQeX/aTMFDPqrPpRqJGqWMqz6+4uFJnlyvXASkujX6ZTkcf/EpzOBxUHuB2xTwOxUACgKCo5KUmdl8c1WgLcPDQSQVBQUMXrsV++fHn8+PEvX75Vel0kEi1ZsmTSpEksFqtYe9pAMyTNsiOgFJcuXXJxcalXr17RjRkZGZGRkRqNxt3d/T1K0BfrAqn43ZK+Csh8XoOoshLUvznCRMoPWjI3MqZkAMC4nmy3Sdt/1x5Z+lN4y09ZGHZHkbk9I+56oo1sAlwut1f/wXFBw3QOtQHARcj6MlgwvoGI9S9Icvea+VWa9tpd3a2HVL7GZd4EfsNgc2qmJSWD3zAYLzO5I/LhVKcLPFLpKv36jiAIglhVp+s7CuArX+Ve4G0G6jZ3SXBsilL9l8HogOPhzrJkkpquVJMlYviXA5/kn1G4TPH2WPo6JVsKSbqwWLwyS5e9spCeBItTXnmzxwX3NyevoBlqiOf49rJuZbTMM9Gt9+da2tAYH7Y6yhQUtSI/X08zbAyzMEzRLPQj5cqCd8xCX0xCQkJERASJW17WeJorTQcAF5W3X1oIiyoeS4tEov79+1f8zAaDYeXKlStWrDCZTEW3169ff8eOHU2aNHm/DlsdOnQoMjJy1qxZbm5uL1++dHR0/P+8ozEUMCTghfXPGVr/HOcHAFbqnQVgSFJ5nMxYRxviACA5x27iSiL5qWpUrfpf1g7FSSo6T74z88XZ+GiSKj45H8Owus3bc9uOUXm1AQw7/JmssRvn//AsPiKGoil1PuEoBYCs+etMca8wNsFvUEfUtgm/Yd1iyfmRD606XeCRSocCeARBkCqiOl3f/yVTTpEPZoJCmWAhA9jsfc5vRe/wd156Pzbx0kJOViq/Uar/MhjtcSzcWRbAZnfh89bKpASG7dJo1+S9SbHmNrcmYCDfkW5Of51wrgZBlB29X79+fcOM6X/89lu5va0vbjzUczwG2IH0HTdUl8poac/Fd3Z3sD4qpoX+IsEBJ0cZC7cwDAA44XhhFvriA9kAAHAoY9fSxNkZxtRyuwR/Vy4gaHad1CYtjV0IYOc4pMbUu+1czyEkJKRu3boSyeuZBSJR8QQBZePz+T/88MODBw9atGhRdPvjx49btGgxbdq0vLy8ss+QOjUueVSMOdlQctfWrVs3bdpUu3bthQsXurm5FX07e/Hixb179/T6spILlgpjFYnegZQfNj5taXjcmMzdWzRJ4tuHEIRjf17obW7AAdyuiY+L5uw69YEtLOeBuNvaIfYDPgn18t1Qp9WtbkOnNWorFb81TYNhmGe3rz5YPti8qk3j+F01CA0AjDuvHnhCeSrBYKH/BTcoMRZujd4BQPpFL379IIai9fee5q7+JXvhT5XbNwRBEARBEKRKQQH8f50Yx+tzOOHOb0aki5Kx8L1OjiEcdipJ3zAYAcCXzbZmrQMAmzG8oL6dtJ8LbaQzvkusSAdiYmI6deq0cePGwYMHb9y4sdz2bRw6D/IYyQCzN23rNeWFMlo28+A4CXAAqCkhACCIw/4fe+cZGEW1t/H/zPZes+k9pJJC6L1jIkWQroggCipeRSwI2EC9FhSF6wUbIIr0LhBCDb2EhJZCCOk92d53p70fNgYMIdkg7w0s8/uUnJlzZmaD+/jM+ZdNKi8GIADwskZ7C8NbmVvvrCmyFHxS+M4VY2abt+SqG8/lcgmC4N6SzPP+MJgXbkB0u1i/VQYUmq1mg8HAZDIBgM1u3By2VdXa84taW/QOOnfufPr06fXr19+Zo04QxHfffRceHr5ixQrirn3pJowHNdottXndzld/VESY/3baxo0bJ0+ebLPZPvvss4iIiNWrVzc1os/Ly8vNzd2xY0dWVpbT6XTzPluEIeqB8jpRjlJn8TzCeKrVcxGGfCQ3Lp0bf5ypnJwcjT436DJVMZwbt9L3i/7sySkOing9uPOf41745ptvwsPDm02uLC3a8u0H4WEhu/fsKdLhl2qdbx7V999Qv+KSqc7yyBR758ZGeL8/N/CnT+UzxnM6hbCD/QGANFnqPv9B8/MWe14h0DFTNDQ0NDQ0NDSPMbSBf9z5xUuxyVvZont3IWegCgZDQxByFOUgyBWHswy/bX1H8LjL5FIGwBqT+Sej2TXo/0mEq7cZ5Wi7X3dWVhb+14Jvv/32iRMn2pwyXDl6st9MCqgNVT+20hweANgMBACaAgsCmQw5AwWAOpyYUtdw0u6418RXgt/tJx/qJB2X9GfbvB8XUqk0ODjY6XRePnptjuydFNU4iiL31m3ZzV6PiexJSUlNZxYXF285lJaxfVf1d2sJk9mdxREEmT59ek5OzuTJk+8c12q18+bN69q1a0ZGRosTo451kz/jSzrI2m9K8xLPatZXN5WpDwwM3Lx587lz5/r27VtfX//qq6/GxMT8/vvvBEEMGzYsJCQEx/Fr165t27bt+vXrON7a+47W7pwbzk08z4n8leX3BkPQ1Z0pqCCJHfEDLymT6TMHGALSdMFZMg3x+fT562mTTuyauPVnjUZz8+bNXbt2Dejfv9lcs9m88L339k1U/nugJFrBarCSKy+Z+/9R/3K67mSF41HYjwcAYEjF4lGDfT9/WzFnKgDgOoMtO9eUfqr2wxUVc97XrtvuuFlCO3kaGhoaGhoamscQOge+43nIc+TsFNW7qlaAIOtUyl9N5p0W64ti4VuSv9UnO2i1va3RJXHYG1RK14j5rB7XYNLRbVfVrq2tjYmJaQoFVyqV58+fv3t/9W4yNOnF1pvTA15h3ju5elhNXRVORLJYLAQAwOAga1ECp0CEoiaSRBDEl4HW4QQB0GIOfIm10Ifjz2O0UU6spqbm4MGDvr6+I0aMyMjIKCsrY7PZI0aMOFFw9Dh7n41jZgJrEC/VfgaCg4KHDh1aU1NzOD2doCiOHYspro+aMJrfM7HN520iLS1t7ty5JSUlzcbHjx+/bNmy0NDQu6dYs4wV7960XDAAAC9W6PdhmGTU3/40O3fuXLx4satAfUxMzJIlSyZMmKDRaLKysqqrqwGAy+XGx8dHR0e7Qgn+IRRhdN6cgXBDmD4vo7zI1s7E9Xj9r3jtj5Sz1onB7pOcPWcUb8xfOmjYeADQrt1+5o9t66sL9hXlOf4KH+jRo8eFCxdcP1+odv6Raz1UYnfF0geKGJNj+ROj+UreI/buEquoMZ+6ZDmbhdeqXSNMpYzfK0nQqwsnKhTaKh5B4z6elCNH0+E85PpOQ0ND8/jgSfpOG/iO5+EX+HIc5yOokoFeczon16mlKHrMz5v3d89QguNSFG3mgSmCIk0EQ9qG5du3b99TTz1Fko3b9V27ds3MzEQehCeZXq/OdLQdAS5G0VN+3m2W0MMoJwtpoUBak4FPSUkhSdLl4VEUJUmSwUNtHn+UeQAAIABJREFU3TQXLCcBQGyR93emjB80CQAMBsOp4xkNOi0A+FZqEoRy7+fHM+StFeG/E5vNtnLlys8++8xkMt05zmazX3755U8++UQsvqsCPAW6nXVVHxW58uGlY1RhG+PvPE6S5I4dOxYvXlxYWAgAcXFxH3300YQJE2pqarKzsxsaGgBAKBQ+9dRTTYkA9w3lKLdd6Q6UEwBhh61kqqa1NcGJq3fitT+QlqsAAAiTIRvJ9HnRWaIs+vy/QpzSOmx/dvbefTCNz+evWrUqMfFvL0Q0NnL7DeumfGuFkQAAFooMD+XMjBck+zxi5e4AwHGrzHI223o2G1frXCMMuUTQM4nfO4kbHQ73W4iRpglPEniaDufh13caGhqaxwRP0nf6//Zo2iaIyXTVt0tgs5M5bD1JbjM3L28WymTevYNd9lLe9cjTtpw2osRHjRq1dOnSpl+zsrI0mtYC493nJy/FDm+v7d5e2729/lAqYws4+FmEkYnOxyXbvb1eEYtc3eBjWUx7W2+yMvVnXr0+dVvNepJqLaEaRdGBAwcKBALX+4jeXfvMiZg/L/QDESoxCrRp0s27azfhFCaRSEY+NaZ7t24MQGoCFMd5ePbn/zGmnQCy7aQDAODxeAsWLMjLy3v22WfvfNPhdDpXrlwZExOzfv16stlSCMjGe8dl9wr8JoqlYjeVGLzzzidOnJibm7t+/fqwsLDc3NxJkyYlJSWdOXNm1KhRw4cP9/LyelDv+xBOEC/xHNN7FsLyurPi3b0nsJleU7jxGdz440zVDEAYhHaPI280AtPfrc945XzalznndqUduHz58pkzZxITEwmtnvprQx4AFDx0ThfhsamqX0fKR4RySaAOFNmf2at9JErcNYMTESyfPi5g9VLfz9+WjBnKVCkIrcGYdqL2wxVV8z6j7l0NgYaGhoaGhoaGxgOgDTxN+5glEgLAryYz7oaXY/mySStROjuPcrbhSxctWvTcc8+5fu7Zs+ed1drc4awu4+Xrk46o9zcb5yJI7F/V5pN57C1DFOO9+bZ66st047Hrjn9JRL96KRQoet7hnFinLrjD8t0Nj8GnANLqdy0vWUrdq5o6AEVRp0+ftlgsCIoAwPkL59VqdYK462vixX71YSSQe+u2vF/wer75GoIgnePjn3p6nEqucHJYObH+x69drv/+d/efOiAgYMOGDRcvXmxWo766unrGjBmdO3fetm1bM8uNsFGvOQGdC/pFHevW4posFmv69Ol5eXkrVqzw8fG5du3apEmT1q9fHxAQMGrUqEmTJjVtv5MkWVlZed8l7hBuGDv0a17XAqbydl89QpeG162h8HuW1kcFSeywb3ldrrICFiAsFWkr/GNJ5fiXjNUqc+fYENc5WEVNxZwPKl5arF2/E6uuvz0Xgf6BnNVPyE49q3qrh+i1eJSJgNlJjd+pnnVAe7DY/ij5eQThdAqRTR8XsGqJ31fvSsaNYPl5A4oAAFCU5sdN6u9/t164SrkRfkJDQ0NDQ0NDQ/MIQRt4mvYxmMeNYDFrCGKPtYXOZM3wXRTGCeXZrpmqPylu/UwEQdatW7dp06ZVq1alp6e3N36eiTAxEttY9fOW6nWtuGsmCl8Mknw2QIKi8F2m6V+HdLEM1jYfr1g2qxzHp9Spd1ru2Tits6jLgvBPZSxFha2EpFp+H0FR1MmTJ4uLi9lsdmnMdY20Bsfw9PR0tVrNQbjhlfHDDeP9uIH1jpqviz76pfw7C2F2bcX369ePw2TqVRInj9uuBweAbt26nT59+tdff/Xz87tzPD8/f9KkSf369Tt58mSzKQgLQZjNP2FCj2O1jVX9OBzO66+/XlRU9OWXXyYkJAQHB9993ZKSksOHD2/bti0rK6uVMvjtwln6nrPkbVt2LFa1vJXTEJY3K+A9XvJ1TsQvPHn3Z57A01eYfnr9pCNvNKHewZBwcT8v0mw1/nms6o1Papf8x3Im684NeW8BI3fNgtf7+qhUqvlvvnH96uWMcsfcQ7rev9V/etaot7sVBPHwwA4Lkj07xn/lB/7fvY8wGBSGW05dMmdcqF/2c/nMBfVf/uS4VdbR90hDQ0NDQ0NDQ/NgoHPgO55HLkdun9X2jkYXxGTu8/FiteW0rdnGgqGXKAI67esiGiD7/7urTP2ZX8pXYJQzWdJrdtB8NtpaevPJCsfrh3UmJxWrZK16QuYtRL82GH83WQBgjIC3RCa9V+N6J+kgKKJZWbumHHgej+dy7/zuzD9tmxAKiSnprtD7stnsxMTEzMzMoKCgQUMGpTfsaQykZ8om+k3vIxsMAE6n0+FwiESi+/4ErFbrV1999eWXX9rtzcPjhw0b9uWXXyYnJ7cyPb/HBXuhRf6Mr8/bIZxQ3r1O2717N5vNTklJcTqdJ0+erKqqAoAhQ4a0aPLbC2k6j1V9TeiPoYIkbvwxd2dZruB163DNDiAsAIAwFXszkz5bfPLZ0M6jAzvxmSwAQIV84YDuwmF92UF+ubm5nTt3vnOFgE5xot6TrXFjGGLVWz1EryYLAYCggPFo1obD6zWWs5etF686CkuBogR9k73efIHCcNuVPHawP1PVvvCWxw1PypGj6XAeOX2noaGh8VQ8Sd9pA9/xPHICTwA8VVtfhOGLZZJpQkGb59d8WlzzRQk7iBtzvidD/ABqmN+LG+br35d+YSUs4fyo10MXi5h3FXK7gyI9PidNV2LAu/uyNz+lAIC9VtvHWr2NosJZzBUKeTir7Vu9ZblBAikyyg4ePMjj8Ww2G5vN7j2k1zL1Ygdp7y8fdlpzNKGsr1irYDKZOI4HBQUNHToUAKrs5esrV92y3ACAZEmvZ/1fkrEejK0qKyv78MMPN2zY0CwHHkXRKVOmLF269F4V/quXFNV9W0bhFMJEZJN8fN4J4XZqXn7fbrfz+XyKomJjY999991nnnnGYDA0NDRERkYyGIwHcv8AQDmrAeUjTGnjr7iO0B1kSIciLFVrswgzodmO1/3qKnR3KY9Y86cg/yZnw9v/FuZXOovLAQBhMgJ++qxCqw4PD29eIwCAwWTG9Rn+xuyZz04Ys+KKc+1Vy8AgzrhI3pBgDvvRtPKEzmC7VsCN68RUykxHz2pWbwQAdpAfr2tnftfOnMgQuujd3XiSwNN0OI+cvtPQ0NB4Kp6k77SB73geRYE/ZrPPVWtlKHrI11uItuFtKIwqGHbJmmWUjlWFbYhv/eR/SJW9/LuSTzTOBhXbZ17Yhz6cxqhygiJq7JVSllzIvL3FbXJSyy+aElSscZGNG86FGP6GWluC40IUWSqTpvLvuRHtYm7OM3bCNgBSyGw2ADAYjNDQ0JtYbpW93IfjlyzpdVaXoXfqgi2dSAsFAD4+Pqmpqa65FFDH1Wk7ajfYCCsH5Y7ynvCE11NMhHXn+jevXqvdfyxCKFNNHc30asfXTW5u7qJFi/bu3dtsnMVivfDCCwsXLmxxw9xZZq//vrxhbRXlIAFFJE8ofN8P4yf+LShg7dq1S5cuLSsrA4CgoKC33npr1qxZAkHjexwMw/78808ejxcXFxcYGPhAWglgFf/GqpYBwmYqxrHDvgO0jSwD0nIZr1uPa7b/tSEvZygnkLYU63nNob37VtYUTH32GRzHP/vss3vVSpTL5UkjJhQGj2YFJwOCSDhoajh3bCdeN1/2I+njAQCA0Bq0v+2yXc4lLY3JLwyRkJccx+vWmZcUg7Y/d8NT8SSBp+lwHkV9p6GhofFIPEnfaQPf8TyiAj+jXnPB4XhFLHpd0nbUt6PYdqPvRcKEB34d6fVy4P/rjRkw3YqST0ttRUKG6F+hiyyE+Yh63w1zjqt6vIyl6CHtl6p6WsxsuWebhaI+0OrTrDYAGMnnfSiTiO+9S3lcc3Bz1VqFxrdTaZI79yYUCidOnHjniA7TbKpac8lwFgBUbJ8p/rOSxN2bjm7futVksbAwIqSoLiY+Tvb0E2hb7xTu5MKFCwsXLjx+/HizcRaLNWXKlPfffz8ysoUe7M4ye+3yUs3vNZSTBBSRT/AOXh2DcG5/CBiGbdq06csvv8zLywMAmUw2Z86c1157zd/f3+l07tq1y2q1AoBUKo2NjQ0PD/+HreMpR7mz7H1CdwAoght/AhUkuDWLMBPqbXj9+sbOcwAoP67rpOK8ghoAGDx48L4NmzY9M2d3xc30spvYPXL4Fb6B4i4pzqSJnMDOABAoYoyJ5I3txAtrqzPiQwtFEI68Imt2ju3SdaymwTWIMBnc2AhechwvOY7l592xd9jheJLA03Q4j6i+09DQ0HgenqTvtIHveB5Rgb/mdE6pU3MQJM1X5eNG+LRuV33Jc9cRFtLpQLKwt9T9CzkcjiVLluTl5b366qsjRoxwawppX1329TXjJRRQEkgAQBGUpEgGMAggAEDAEL4S/E6s6G/dwnV28vNzxhGh3GEh3K1m65d6g5WivBmMT+XSftx7tjqrspen1e/yLQpTCX04XE6GJl3jrI8WxncWdXGdkKk/XWYr9ucEh+MxcXFxPj4+dy9yw5yzsernSnsZAMQKE6b6v+jPDQIAnU53/vSZWnUDAPCsjvBSdWTfnuKRgxE3wvubSEtLW7Ro0ZUrV5qNM5nMqVOnLl68OCoq6u5Zzkp73bdlmvXVpJ2MzerFjWqeK0GS5N69e7/++uszZ84AAIvFmjx58vz58+Pj42/evJmbm2s2mwGAw+FERUXFxMTw+c0D8tsF5ayi7GWo+Ha9fUKXDgw+Q9wPoLV9cdJ2g2jYgjf8QWENDgz2nyJ2ZIgGDEp947VlNe//B6uub7Bb93McOwuvX79+/V6L+EUmcHtOhl7TAWUAQKySNTqCOzKc5y96YIkD/3uw6jrrpRzbpRx7QREQjQkFnKgw30/fhAcROvGI4kkCT9PhPKL6TkNDQ+N5eJK+0wa+43l0Bf51tfawzT5dJFgobXk3uxlViwrrVpazfDjRp7qzfN3o/g0AAB999JGrSzyKoqtXr549e7Y7swgKX3jjVbWzHgC6SnsPkA//tnhppCB2iv+sXTV/XDdlsxD2gohPw/i3t6BPVzqe36cFgOmdBR/0FdcQxEKtLtPhRAAmCvkLpBJ+W67mrC7jl/LvxEzp59Grmgrd6TDNohtzHaT9rbCP40T33KgnKeKYJm137SYrYWEizCHKJ0d7TxIwhABQXV194fQZvcUMACK9JbrBEjp6hKB3F3c+h8bFSXLLli0ff/zxzZs3mx1CUXTSpEmLFy9uVtfNBVbrwKoc/K6tVRPIzs7+7rvvNm3ahOM4APTt2/eNN94YO3ZsZWVlbm5uQ0OD6yrh4eF9+/Z9IEH1AEBh9basKHC1owv5iiEd2tYEJ6FLxxs2EvojQOEAgLC8GIqJuLZ/5ras2auWswJ9hw4dSpLk5k2b1PcIrZ/15mLlmHfSS+xGBwkACMDngyQTo//Ri4mHAdJitV3Os2Xn2q7ko2Kh/7eLKZJq+PpnCid4SbG8LrEsv9aqD3gYniTwNB3Oo6vvNDQ0NB6GJ+k7XcGI5v6ZLxVLUVSAuPuvyG9phGigDKt1FE25RrrdrKuwsND1A0mSL7/88rJly9yZlW24oHbWs1E2AkiW/tzhhn2u8RBe+LywDwYrUjDKuaZi5Z0N4foFcD7oK2YzkN9yLLlqzJ/JWK9SLpRKWAiy1Wx9qrb+UqtdtZ2kY1ftHwDgIO2/V/1owHWucRlL8aTqaQDYXL3WFcbfIijCGKYc9Xn06oGKEQRFHGrY+17+y4cb/sQp3M/Pb+zECX379uWxWCapILOT6uTRo/rNzZvetwKKolOnTs3Nzf3111+bhc2TJLl58+bExMTx48dnZ2c3m8jy4dzt3imMMp3SkdbGZ0lOTv7tt99u3br11ltvicXiM2fOTJo0qXPnzpWVlaNGjRo1alRoaCgAFBUVYXf0cvuHICwVK2gJwvan7MWEZpcbE9gM+WhO1CZeci4r+BOUF01hDXjtKnBOxSM36NnO3NzclStXjhgx4srCL37snfpkcCSHxWq2Rtn1C18MklyYrvoxRTY6gqfgoXwWAgAXqp1zD+k25ll1j1oLOheogC/o1035+vOBa7/w/+59QBDAcXt+ke1ynnbd9qrXl1a++pHlTPN/GzQ0NDQ0NDQ0NP97GB9//HFH38PjzuHDh8+ePSsWi+fPn9/R99I+pCj6oljY897h5c1AUESSqtTvrrfnWQTdJXcXOW8RhUKxcePGpprhhw8fdjgcw4YNa33W2or/6DHt9IBXesoGXDdl1zgqAEDO9uovH4oA0lnU5aL+dK2jKoQf4cPxb5qV5M0eHsqNU7IGB3MRAAQgkcMezuNddTqLMHyPxWqgyJ4cDqOlbeQ9dVuuGjP9OYFW0lpmKzqtPZos6SlkigEgTNDpvO5knaNaypKH8CNauW0OykkSd+8i6VnvqKl2VOSYLp/TZfAZggBeiFKpjI6NRRFE3dBg5XOiZEpuVJg7H2ATKIomJSXNnTu3W7duN27cqK2tbTpEUVR+fv5PP/105MgRkUgUHR2N3jvzX/1LVcn0HPWaKsKIc6MFDCETAKRS6YgRI+bOnatSqQoKCoqKioRCYWpqqkAgCAkJiYyMjIqKaoqipyjKVQaPy73/2mkMUU+WzxyGdBjDawqCNv4jJK05eN0ahCFC2C2kKgAAwhAwRD2YPi8ypCMA5VGOigB53dxJaPc4ZkJc8KTRMUK/rsEm5AmuYkZYfJhIZgeqymJ0RSotXLgwOTmZgSJhUmZKGPelJGGknAUAf+Rat+Rbj5c51lyzXKzGEARiFM3N/6MFwmSIhvVlB/sjbBapMxI6A8ph83smElq95qctWHk1wmIyZRKPjLT/6quvbDbbggULeLx21JugoWmRR1ffaWhoaDwMT9J3OoS+43ncQuycFXbDAbVyht+dddFaZ//+/RMmTGhqb44gSFFRkWtft0UshPn1nOdYKPujyG+YCKvOUb2u/HsdrgnhRbwS8o7rnAzNwbT6XYMVKc8FvNzKpXESjA5SxEX+azT9YjQTAFEs1hcKafTft2c1zobFBa9hpPO9iH/LWIo/qn7KNV1d1OmLEF5jz7ZLhrOrSr8SMISfR6++sxJ+K1w1Xtpe81uVvRwA/LiB43yeSZb0QgCx2+0kSf7DlHKSJHfu3Pnpp59evXr17qOxsbHz58+fNm0ah9PC2xlHkbV0dp7lggEAEDYqG6vymhMg6Hk7jYIgiHPnziUlJQmFQtdIZWVlcXFxv379XO8Fqqur09PTAcDHx6dPnz4SiVspGO7gLJqLN2wEAFTQhRu3D9C2PiWKIAwZhHobodtPEWYAAITJkAymyFEfPffLUEVApFjeYLeeqa8M8fUb9sYc4ZDed6/hIKgDRfb9RbbTFU6MpADg7HMqbwGj3kpSFOUteITz5AEAKAqrrGV6KxE2y3rhav2yn13DqIDH7RzFS4jixkd5Uoy9J4XY0XQ4j5u+09DQ0Dy0eJK+0wa+43lsBd5wQE3aCdnTblW9zsjIGDt2rMFgAAAEQUpKSlpshOYix3xledHH7iwrZym/jv2llRPePqbfe8v2UqLw9W7CfAJ7T6Mvw3EGwLMiwb/E4qYWev8t/TLLcK6PbNCLQfNcIxjlZCHsO5f6pvjjXNOV4V6jp/rNcufeAIAC6pL+7I7aDfWOGgAI4AaP9p7UXdq3hTMpypydy/GSs4P83FzcxZEjRxYvXnzx4sW7D6lUqldeeeW1115TKpV3H7VcMNT9p9zwZwNFUADA7yLymh0om+iNclt4LzNkyJDjx4+HhITMnj37hRdeUCgUmZmZrqD6Hj16xMXFteueW4FyVmPV/yE02ynSxku6jLC83J1J2gjtAVyzjdAfAwoDgB3HqFXb2foS6qWu/SZFJeJ1am58lM9H/2plDaODPJCvPrxjQ3d/fkpq6vjjXIOdTFSxngjjPhHKDZY8qrXrb0NRtiv5tuxc29V8rLq+aZiplHE7R3Ljo3jxUQz5A3sd0yF4ksDTdDiPrb7T0NDQPGx4kr7TBr7j8QCBpwBeatDIUPRLhcz9sgrXw05h9c7Ab6O8Xgpw5/xr167NmjWrqKjovffee/fdd1s5s9xW8vHNNxkIQ85qdJ4YhekxLQthSVly1684iZkJUwg//MNO37Sy1O851k/OGAgKwqTMzwdJOnuzvjUY/zBZCAAvBmOBVDySzyu05H9xaxELZX8W9b2C3YJjLLbeXFfxfYww/pgmDQBZEvmtq8i8mxAUcVp7dE/dZj2mBYAIQfR4n+eihH8zvcfSD1VUVvpWqGMkCu/xqe218WlpacuWLbu74RwA8Pn8mTNnzps3LyKiheB/Z4Vd/UuVen01rnYCgCRFGb498e7TduzYsWDBgqKiIgBgs9njxo178cUX+/fvr9VqVSoV468uBhqNxmw2BwQEMNzoa9AaFA4UBuhfIVKk3VE4ExgCpmICQzoMkNaMNIVrCc1uXLOTNJ4DIBt0FIfDlQWMoOzD2EGp+VX1zz33XEJCwrRBw+MK6zjhQfzeXbgx4U3Thw4deuzYMdfPflFJRMwTrIQUtm8UAETImMNCuMNCuIkqFvrox57j9RrbtRv26wX26zcJo7lpnBXgw++eIJs6Cu6dhfEw40kCT9PheIC+09DQ0HgGnqTvtIHveDxA4AmAIdV19QTxukT0itit+HAA0PxWXTY3HxAkdF2cbPyD7D6NUc5/5TyHkc5vYte4HPtNS94XtxZFCmLfi/i3g7TPzXkGKCCBHOM9eazP1NZXy1NjCzIMeWoMAZgcy1/UW1wJ+FKdIdvhBIDuHLbK8JPWenGczzOjvSe1uEK24fz3pV8AAAflOkh7rCjx7bAl7X0oJ+k4qj5woH6HhTADQKK421M+U5tC9K9dvZqdnU0BoCTpV66JFMt9xgzjdApp1yWuXLmyfPnyzZs3311tDkXRIUOGzJ49++mnn77bXZN2UrejTvtHjWiQ3Ofdli9KkuSRI0dWr169b98+V736kJCQmTNnzpgxIyio8XXGzp07DQYDm80OCwuLiIjw8nJ7/7xVKExtuxwPpB0AmN6z2KFfuzerjtDswTW7SdMFABIAAOVm5MaPmnWEoqjRgZ1W9mhsaqh65yV+z0QAwDCMw+Hc/aUq8w9nxT/BjB3GDe8OKMOLjw4N5g4N4fbxZ3OZj76VpyhnWbU9p8B2/aYj7xZpswOCBKxawvSSmzMuOMuruTHh3NgIVPBolOv3JIGn6XA8QN9paGhoPANP0nfawHc8niHwFxyOWfUaCmCNl6KX22Xt6paXVX14C2EhYRsTJKktxGnfN6vLlmXqzzSluN9p4AFgfcWqE9pDABDKj3gl+F0lu40MXoyk/ptlXn3ZjJMQIGJ83E8yKJiz12L9Sm/UkiQCpI/jRCJ2hg33LDKPkZiZMBEUTgEFAK23lGsFG2E92LD7UMNeB2kHgM6iLqO8J0QK4gBAr9dfvphZVlVJASAU5V2tjaA4ASOH8LrEtqvYWHl5+YoVK3755Rej0Xj30cjIyFdfffX555+XSqVuLUcB6SDvjKuvqqpau3btunXrSkpKAABF0eHDh8+aNWvMmDF1dXVXr15Vq9WuMyUSSURERERExD9M+AcAylmFq7cRuoNM1Qym15S/hknKXopwQ1vvJE85awntHlyzizRlApC3KsjdGeSlAskXr872tUQ5K7TyFyaUmvRZWVkjRoyY8vT4wycy7rUUTywTxQ+B6GH8uMEoT8xnIgODOB/1k3jxH8nN6ruhCNJZWEoRBDeuEwBUvvYxXqsGAEAQdrC/aHhf0RP9O/gW28KTBJ6mw/EMfaehoaHxADxJ32kD3/F4jMAvNxh/Npq9GYxdPl4yt6Nnqz68Vbe8TPKkMnxrC6HX9021veLjm2/iFD7Vb9Zwr9F3Gngdpvm2+JNKeykLYWOUk4vyJvhOH6xMQVp1cQBwQ4O9l2G43oABwNNRvGWDpXqSnF97/TyhpADhkJoQ63a5s4WacHfzcvDbPaT97s6TdxMTbkxv2H1MnWYnbQDQSRAzUjUhXpyMAKLT6a5eyiqrqiQpCihK2WBMtEHQx/PafQmTae3atcuXLy8vL7/7KJfLHT169OzZs9tsB1D++g3NhhrpSKVipr94kAz+ih0nSfL48eNr1qzZtWuXqzyhUqmcNm3aSy+95OfnV1hYWFRUZLPZAABBED8/vx49erj7ysBtsMovscovELY/UzmeFbgY2vpbUM5qQrMH1+0jTRfA1REQYaCinkzZqNRpG48cO81gMA6MnH627FZadXGWpoYg79lVDmUw5TG9qKgh/PgRq6cljwzn5Wuwk+WOXv6ceC9PCLB3gVXVWc5k2XMLHTdLKQxjqhQBq5ZQBKlbvwMo4ESHc2PCH7aceU8SeJoOx2P0nYaGhuZRx5P0nTbwHY/HCDwBML1ene1wDuByfvBSuO9BDGlqXryQHXD/HcUAICcnZ//+/b179x4wYIBr5KT28PqKVRRQkYK4ToKY/fXbA7jBCeJuxzVpNsLqyw2YF/LBjtrfL+pPA0CUMO75gFfvbCnXIgQJ63Ms/8kyq/ho+uTGAO9T1vqvDMQtnAKArmxkrgiJaDUumo2wJSxZjunydyWfJom7j/ed5stxqwpAM2yE9bTu6IG6na6e8wHc4BTV2F7SASjCMJlMOVevFd4qJCgqqVjTZelb99fxC8OwrVu3rly5ssUqdwDQrVu32bNnT548WSxu3i7eRd3ysuqlRRROAQA7iKuY5qt41o8dfPtvrdPpNmzYsHbt2itXrgAAi8UqKioKDAwkSbKqqurWrVsVFRUEQSQmJiYnJ9/HI7QCYTjuLH6dclQCALfzYVTYzc2JFNZA6NII7X7CeAJIBwCcysaXbWCdumRMCYxcNfkF7Fa5xmY5Ul1yqKbkdH2lk8BbWS0qKmrJkiUZ0hGHSuwAoOChA4M4YyJ4/QPdjWR5+KEwzFF8meuuAAAgAElEQVRYypBKWH4qQm+smPM+EI1vN5g+Sm5MBDc2ghMdzvJ9MEkT/wRPEniaDsdj9J2G5qGl1kKcq3KO6cRjeMq7b5r/JzxJ32kD3/F4ksDXEsS42gY9Sb4rFc8UCds1F6tylMzIkT6tUr0S2N7rFhYWJiUlWa1WAJg1a9a3334rEokA4JLh7O+VP5jw5qHgyZKeMwP/JWAIASDLcO73yh+NuJ6JsFJV40Z5T2hzV9yCUSgCvDtc+lm7Y7/VdtBqs1IUAtCDw4lkM6NYLMHfbXMvLkf6V2xCibXwq6IPHKSdibA+ivymXWXt7sRB2jM06ekNe1wl7nw4fqmqp3vLBjERpsPh0Gq1Pt7eyD8uJ5aVlfXTTz/9/vvvrl3xZjRtyA8dOhS5600BVu3Q/F6t2VDjKLEBAKCIaKBMMdVHOs4b5d2+saysrLVr12o0mnXr1jW16NRoNAiCmM1mX19f1l+t+2pra6urqwMDAx9EkjxFmrMoRwVDMbYpkB6rWk45axjyVIa4PyCtdXSnCDOpP4xr95H6wxRhMlkoggSZwg/YKVhprPmqzZiVR5BkRm3ZkZrSk5oqrcXc4jpMJvPE1cJ0jSyj3FFpIgAAReDqLB8+EynS4w6cilZ4zrY8ADhLKm2Xc+35RY4bxaTN3jTOkIo50WHc6DBOZCg7PAj5h7UM7wtPEniaDseT9J2G5uHkneP6nQW2n1PlQ4I95603zf8HnqTvtIHveDxM4I/b7HPVWgaC/Oal6MJpR3y4Ndt4Y9AlICnVa0H+n0Ug7XmVunbt2lmzbvdmCw0N/fXXX11b8TbCekF/8pox+6YlV8KSdRZ16SHpFy6IunO6hTBvq15/SnuEAkrF8X3Of067EtSXXDNsllncOXOMgPelXNb0qwHX7a3dcst6Y17oBzKWwv0r3g1OYWe0x9Pqd9Y7awFAwpQNUj4xWJEiZrYQdl5y/hLj2k1V767czpHt2pmvq6v75Zdffv7557KyshZPiIyMfOGFF6ZPn+7r69v8GAWmkzrN79X6PfWkjQQA5Sz/oBXRrVxOq9W66tuNHTv22WefHT58OJPJBIAjR45UVFQAgFwuHz16NPqgq53bLoVRuA4AGJKBnJjdbs0hHYTxFKE7QOgOUc6qxnUwbuoruFgrHO4bOiIkMvDp1BvBin379v355595eXnNFrhy5UpiYiIA3NLhGeUOLhOZFscnSEhcW2vDKTkP7RfA6R/A6RvAfuQby98JSTpLq+z5Rfb8W44bRYTe1HQE4bBV77zES4oBAMqJIezW3qQ8QDxJ4Gk6HA/Tdxqah5DXD+v2F9lXDpeNDP9HgZw0Ho8n6Ttt4DsezxP4r/TGdSazHEU3e3sFMtthNnTb6krn5FFOUvKEImRdZ4bY3b7ZRUVFiYmJFsttF42i6Pr166dNm+b+1Qst+b9Vrq6ylwNAT2n/KX4vSFiyNmcBwDOn1ZeDneCAIIIZq2RZKTLHiUc1zDknX8VEkFAmI4TF0hBEtsM5hMf9r7K1bw0KqIv60yG8cG9O+/rAuSAp8qL+dFrDzgpbKQAwEVZPWf9hypHBvNt9zurq6g4cOIAAyBuMwWYstG8P4cCeqNt1BwGAIIi0tLTVq1cfPHiQbCnNm8lkpqamzpo168knn2zaNr893YDrdtQZ0tSK5/ykY1rbQnc6nWPGjDl06JDra0qlUo0fP37SpEldunQpKCgoKSlBEGT8+PH/tOfcXZC2QkK9jdAfRkW92CGf/zVMEcZTKDccYbeRZ0FarhH6Q4TuIGm+DEDmFxPHLxED+sR06TmGIRmGinpcuZrz49y3vC3Y0ZrSC5pqjCCmTp26cePGFlf7NtO066atynS7PmKUnNk/kNM3gNPNl833gCL2d4BV1zkKSuw3ihwFJVhNvWr+C/yeSaZDpzU/b2H5qTiRoZzIEG5sJ5b/g2xa0QxPEniaDsfz9J2G5mGDNvA0buJJ+k4b+I7H8wSeAHi1QXPS7ghlMv/wVrpf0A4ATKd0JdOu4xqMGyUI35rACXe3/Pjly5enTZt258ZmYmKiK626HXdOEYca9u6t2+Ig7RyU+1LQm8mSnm3OOmW1z9ZoES0SX8LeMlYBAC9cHes6dE6+CgDkKDqYx91hsbZp4PPM174u+hBF0B7SflP9XhQxW04sb5NSW9GRhj/P60+RFAEAIbzwYV6jXOnxBEGcO326qLjY5bx5FkdAjb5TRIQ8ZWB7M5BLS0vXrVu3bt0613743Xh5eU2dOnXatGndu3d3Z0Fbjtl8Vi9JVbIDb8twaWnpH3/8sWHDhhs3brhGfHx8XE6+X79+TdvvDodj9+7dHA4nODg4ODj4gX87E7p0R8EUAAQVJLHDvkMFCW1OobB6QpdO6A8R+mNAWl2DCEO040zI/IVZi+L7DPQJ5qAM9oyxEWNTG6c4Mbxew/L3bhYZUazHT1c6dp+4dGrfVkrkw4vpz/aNYqFIkjerjz+nXwC7iw/bo6z8HVjOXVZ//zvlcDb+jiC+X7zDCQ8i9EZncQU7LIghdbd1pTt4ksDTdDiep+80NA8btIGncRNP0nfawHc8HinwFoqaVqe+gWHxbNZaL6WwPfm7zkp78ZRr1ismhpQZ9nu8aLC7/5nZ7fZFixatWLHCtS08duzYXbt23cfNa5wNm6vXZhnOPeH11GS/mW2ef97umNmg6c7m/KyUcxhIk3t34fD9Jfsv7xHBYm729hLcO2odp7BNVWtOaY/iFDbNf84QZep93H8TamfdUfWBk9rDNsIKAAq211DFk/3kw4RMkcPhKLhxo+B6rhlzAACDIFXV2jAmL3jSaE5EcLuuQpLk4cOH165du2fPHofD0eI50dHR06ZNmzp1alhYWCtL3Rp3xXhYAwgIekhkT3vLxqlYfrdDA3Jzc7dt27Z58+aCggLXiFKpTE1NnThx4pNPPonj+J49e5qiMMRicUhISEhIiELxj3ITmqBwjbPkbUJ3CEgrO+Rzps/LdxxztlHBnnQQpnOk/iihP0LabgDA1ZvEwTNYYYXwo9cmR/QZxxD3AZT3559/Uhv3J2BMRMjnJ8XyEqN5idEMeWMSRHV1dWRkZNMDcmXerIje3Mi+3Mg+LFXo0v6SZ+P4FECNmfARMDwpYR4AKIJwllQ6bpY6CktJg1H5xgyGRFT/+Q/WrBwAYKoUnE7BnPBgdkQwOyywXeEkd+NJAk/T4XikvtPQPFTQBp7GTTxJ32kD3/F4qsCrCfLZenU5jidz2D8qFe3y8ISZKH0x17CvAWEifh+Fe88LbqvF221OnDjx3XffyeXyf//7397e9x9qq8M0EqYMRdoOH3AZ+F5czjovRTP37mJG1ObPdcZSHAcAMYo+KxQ8JxK0EpigxdT5pmvdpH04aKMa4RTOQBhtNrprEQdpP6M9dkS9r9ZRDQAshN1V2muAfESUMA4oqKyszM2+XKPVAABKkClm1Hte2+8sWkSv12/duvWHH364fPnyvc6JjY2dPn16y0nyANarprpvygwH1aSVAABAEWEviWy8t3SsiuV92yFnZ2dv3bp127ZtxcXFrhF/f/8ZM2YsXbq0tra2tLS0vLy8qdieUCgMDg7u0qXL3cH89wNpJ22FKD8WkMa4faziU6x6BSrqwZSPYfrMbr2lPABQzipCf4TQHyMNGRTxV3lFlMMQ9pjydimZY3+3c29//u0tZVaADy8hihsXmVFbOnJsC/+6AICn8B08ePD41KFVqh6/ViqkHLSbL7uXH7tfAKeT3N08lEcOW3au4c+jzlvld5bBAxRlB/iwI4I5EcGciGBWsF97K+F5ksDTdDiequ80NA8PtIGncRNP0nfawHc8HizwVTgxrV5dSxBP8nnfKNzKJ78NSVV/Wly7rBQoEI9QhPwUy1TeT8v0BwIF1PLiJQggT/tOC7kjn9yFy8B7IdYIzdv3WsHL99e9DqucgWoJEgB4CDJByH9BJPRxw12QFPnejZedpHOwMmWoYqSQeT8BwxRQ143ZxzQHckyXSYoEAB+O3wD58D7yIWKmxGg0FuTmkQ2arn36MJXt/EvdRWZm5rp167Zs2aLVals8gcFgDBw4cMKECePGjfPx8Wl2lLQQhoNq3Y5642G1q9wdwkB83gnxfb/57v2lS5dcTr60tJTFYun1ej6fDwAURdXV1ZWWlpaVlbl6EwwcOLD1zf/7Bq/9yVn2PlAYAHATL6C8SHdnUjhpziT0RwjDCdJyBShCo6e2H3Eeu4QomYFfvfAcVcqz51c3uVMtlzkmY3tVVVXrq3IU/uxOfbgRPbnhPdg+EfsneUXJmQYHWWchw2VMD+yyQ5LOylrnrTLHrTLHrTKsrJoibtcLQDhs1bsv8RJj3F/PkwSepsPxYH2noXlIoA08jZt4kr7TBr7j8WyBr8SJd7W6gVzuHHH7usq5MKRrymbn4hqM5csJWRMnGvBPveX9QVLEezdeUTvrEUB6ygaM83nGi924t08Btag6YzcR1Vv7auuLnJOv8kLQH1SKn03mdKuNAmAhSCqf+7JYFMpsbZuUAmpZ0Yc3zNcBIFnS67WQ9/7Js+gx7VldRobmoNpZDwBMhBknSuojG9xV0vvucAOCIAoOZ4gqGrx6dWlvyXqHw3Ho0KFt27Zt3769xeZzAICiaO/evSdOnDhhwgR//+bF4QgzYTjQoNtZbzyskU/0Dv4h9l7Xunr1KkEQTY3irVbrqFGjBALBmDFj+vbty2KxwsLCmmrd5eTk1NTU+Pv7BwcHCwQC95/oXlCEiTQcp3AjU/Vs0w68o3AmZS9lSAYylJNQ/j3vvHEFXE8aTxGGDMJwgrIX3T7A9Cf1/T9+60oXiXepWb+P45gyZcqRI0dOnDhhMpnuvV4jPKnXE0MHfrBo4bdVIWerHEI20s2H3dWH3d2XHe/F4npWATwXFIY5Syodt8qct8oct8rxOrXX27P43dsuWNCEJwk8TYfj2fpOQ/MwQBt4GjfxJH2nDXzHQwt862DVjpKZOeYzelTASKoddF9R5Le5cuXK/PnzMQybN2/e008/fXfT8nthJSz767cfadiPUU4UYfSSDhjpPd6XE7C+ctVuQ+UN8WvuGHhKC70ruV8MkujY5E9G8yGrjQBgAAzn854TCpJb7bpXYM49ozuWIO7aTdLH3ae9NyRF5piyMzSHrpmyXIXuvNjefWSDe8sGqji3g9sLCgrOnj0LFCXRWfz0trCoSNnAnqzAFqLfW0Gr1W7dunXTpk2nT59usWo9AKAo2qdPnwkTJowfPz4gIKDZUcpJIiy02Z8eq3XottcJ+0j5XcTNDqnV6rCwMJfFRVG0W7duTz755MiRI7t27YogSHp6enV1NQCw2eypU6c+8C50LuxXe7vS3RFuKC8p2/2JlKOSMGQQxgzScJLCGgAgK5/YedR5+irat0enr794lyHu69Dw0p577Xx91bmGykvaOhvmbGVBmUy2/PCN9QVQbry9Nc1CkXgVq6sPy2XpZdz/lw/hUcSTBJ6mw6H1nYbmgeAkqNSt6lID3t6JLyQIFve5z3rANB6GJ+k7beA7nsdN4B0UxWnPRi4AUATV8GMlgoLXy4FAAa52Mr3uM5w+Pj4+JyfH9XPv3r2XLVvWt29f96drnA176jaf02UQFIEAEiaILLIUWNmdrwpf7cXlINWz7jUxJXDjRxY90oA4LsKywdKno3gAUIrjvxjNe602jKIAII7Nmi4UpvK5LPc+nyPq/UfU+7pKeg1UPKFiNw9EdxM9pj2lPXJKe8S1IQ8A4YKo3tJBPaT9hEyR3W6/ePpMaUUFARQAoCSlqDf4Y0hwQpy4X7f2BttXVlZu2bJl48aN2dn3NLQIgnTt2nXMmDGjR49OSkpqZbXqpUW1X5UCADuAK0lVSlKVwv4ylNdoRNVq9Z49e3bv3n306NGm/X8fH58nn3xyxIgRMTExOp2OzWb37t27acG8vDwEQQICAkSiB1HVnHQQpvOk8QTCi2UqJ7jGKFzjvDkDGGKGZABDOQlhtv4BUqT1Bmk8SRhPk8ZzFK5pOoCw/W9kx29aUdpV7rupJJfTOykhIeH48ePnzp61thTscPPmzU6dOtVZiEu1zks12KVaZ4EGI/767kcAwmXMLt7syTG8Lt4dlqjykOBJAk/T4Txu+k5D8/+Ek6BGblMX69tt4GclCBbRBp4GADxL32kD3/E8VgK/VGfYZ7X9Wy4dxrvPSKeaz4prvixVzvDz+zicKW93WTJfX9/a2to7R6ZOnbpmzRoej+f+IhpnQ3rDnpPaQ07SCQBMfr9T3Gd6cTkz0Kz1lavuPn9l3O8XMdZctbY/hzPNIezrz2H8td9pcJA6IPfYbVvNFi1JAoCSgU4VCiYJBEpGG5uiO2s27KvfDgBslP11zJr7y413QQGVb7p2Tn8iS3/OTtoAgIkw48Vde8sGJoq7AY6Ul5UVXs+p1etc3xcMnPCr1HTvkiwZO/w+LldUVLR9+/bt27dfunSpldOCg4NHjx49ZsyYgQMHstnNjSVW46j9psywr8FZ2ZgljvIZogEycYpCOtKL5dtYitxqtR47dmz//v0HDhwoLy93DbJYrP79+6empj7xxBPx8fGu07Zs2eI6KhaLAwIC/Pz8fHx8Hkzpu78gbYX2a31dCfNM75ns0OVuT6VIaz5pPE0YT5PGsy4zfzwTP3IBu1yAvjaz21PjJjKEvYyXicMffnVBXX1JXZOlrTVjTgAY3KVb2o6dnGB/uCPWwOyksuucWbXOS7XOq3WYDacAIJpr3DYpkMnhfZtp5jGRJG9WF2+2yGP707WMJwk8TYfzWOk7DU2HQIfQ07iJJ+k7beA7nsdK4FcYTD8YTQjAHLHoNYmofeWhAQBAt7Ou9MU8ykkyZSzfj8KUM/2R9hTmWr9+/Ysvvojjf3uJ+/3338+dO7e9d1Jgzvmy6H0EED0zMk/8hh9V/RLz2qGGvXef+WLQPDu311y1tlkfeCdB9fytHiepsZ14M5IEVxjODSbLDQwDACaCDOdxJwv5PTicVh6vyFpwSnPETtpeDJrHRB5AvXEn6cg2XDiny8g1X3WF1vMZgu7Svj2k/aIEne02e/GtoqK8PK3NyiDIUVI/+YTWGt1RGG45fcmWnYvVaRAGyvRW8nsmCnomNjnJ0tJSl5O/ePFiK99FYrE4JSVlzJgxTzzxhFKp/Ps1wHrZaEhTGw6qrVdMQAEAMMTMhJL+CKf5G5Dr168fOHDgwIEDZ8+ebfo3EBoaevbsWR8fn7KyspKSkurq6qZmeCiKenl5+fn5xcTEcDj/qDnZ7ft11hCG46Q5m+k1BRV2cw0SxtNYyXyEF8UQD2CqpgPa+rUo0nqDNJ0lTecJ41nKWX37CMrbvzv8+E5rN4Vvkty7xma24liywpeBIKhI4Lt0XovpDzgJOWrsrddfPb51DYPBiIiOrZYmcEKTOSFJXN9O4QrOwl7igUEP5vEffjxJ4Gk6nMdK32loOgTawNO4iSfpO23gO57HTeDXmczf6I0EQDKHvUwu82O228XbC62V79w0HtEAgHKGX9D37SgxDQA3btxYuHDh7t27m0a++eab+fPnt/c2jqrT/qj6kY1yjMzoy8I5PKLO134UAJgoS2E77jpHzxvmIO2hvAiVePAms6WZgacA/nVId7DYTgEMDeb+lCoDgAsOxx8myzGb3ZWvHMxkjhPwxwp43u51w9Jhmk8L35WxFL1kA/vLhzY1omsvRlx/QXfqnC6j1NZYU03MlHaT9u4u6RcpjLWYLSRJisXNw9J0JeVIUYUwIZqpUthvFKlXrMcbmheiZwf5eb05s5mTLC8v37Fjx/bt28+fP3+vPHn4K6E9JSUlNTW1e/fujL9/Jlid05iuNh7RIhwk5Ke4Vsol6HS69PT09PT0w4cPGwyG69evh4SEuA7l5eXl5+f7+/trNBq1Wu26meTk5MTExFY/sH8Ert7uvDUbgAIAdvgqptdU9+dSjgrCdI40nSeNZ0nbTQDqUh5x9ipeVkW+OylZSiThlV7OUtzeYJyQsZMbHjR48OAlS5Zg1wqs569wwoO4CVEsP+9bt2516tTp7sVRrpATlNC9R4+5o3sLQpM+uyH1E6IJKnaCFytBxQqVemAdPE8SeJoO53HTdxqa/z20gadxE0/Sd9rAdzyPocBnOpzvanS1BCFC0SUySSq/HeHrTej/bKj9okT6lMrn3RAAIG1kU/6zO5w+fXrRokWnTp0aMGDAnj17pFJpe2/gz7qtu2o3AoCJGZEjbu7/e2tfPSdvHk4/nMddqWz+rXFLh+8vsvUN4HTzaYwSJykosGLHCPt2s7WWIACAAdCXyxkn4A/hcdmtZshrMfXHBW+aCRMApKrGTfR9vr3P1Yxqe8UF/amL+tN1jsadXglT1kXSs5ukd7SwM4rcttD19fX79+9nEKS83uDrIGWFFaiTYIcEiFMHskP9KZJyFJaa9mdgtQ0on+fz6ZvsIL+7L1dXV7dv3769e/ceOXLE1QHuXsjl8uHDh6ekpKSkpNzdi64ZeIMzr8cFhpAhGioXD5GLBsoZksaABYfDcefuekhISFlZGZfL7dWr14ABAxISEgIDAzt37uxqUAcAhYWFly9fdu3MR0ZGul8HsXUoRxlhPEPZS5m+rzTlxmMVn2A1/0UFSQzpUJbffEDaeIlD4VrSdIE0XSTMF0nzZSBv58P/Zwu1aKWRJCkAyLp4OOBokfXSdQAAFC1+dnhQXExcXJzT2VoxPABgCKTsoAROcCInKIETnCj3DYr3YnX2YsUpWfFerCDxfYTUPHR4ksDTdDiPob7T0PyPoQ08jZt4kr7TBr7jeTwF3kCS72v1R2x2AHhawH9PKhb9g2LgFfMLNL9XK2f6e88PZvm0I9YXw7D7TnIusRZ+UviOiu37etjH6y2UnqLqHNWl1iIVxyeY16kAp3wYUG/N1WO6ToIYP24gAjBeIIhnt325L84Zf75qSfZhT4jmeQWg6U7bIavdTlEAIELRVD53kkAQd+91nKTzqjEzx3R5sCIlhB/hGrQSlip7eTg/6u5ecW5Sbiu+qD+TqTvVgDWWuxMyREmSHl3EPeJESWyUY7PZjh482KDXu46iJCnTmFU2PCgkRN49kRsdBihKYZh65W+Wc5dZfiq/bxcj9w4rsNlsR44c2bt37759+5qVLWgGgiBJSUnDhg0bMmRI//79W+wMh2uwG30vNmXLIwyE31UsHiIXDpAJukvufPWzZs2a1atXX758uSkQgM/n9+nTZ9CgQYMHD+7Ro0d+fn5T6v7IkSNVKpW7n2D7waq+wSr+DUACADfxIspr3CSnsHoKq0d50dBK3gSFk9brpCmTNGcSpkzKUWayUNn5hMFCjRnIorBQZ0kXrFaVdrTwpSO7cYqMjY2lKCo/P999UWAIFaJ+z8jHLAAEBQAxB+2sZHX1Yb2SLOQ8sh3nPUngaTqcx1PfaWj+l9AGnsZNPEnfaQPf8TzOAr/FbPlCb7RTlDeD8YFMMvR+K9vVfVtW9VERkBTKRRttvO8/Tdl1OBzz58/PzMwcOHDgyy+/HB4e3uwEkiLn58004oa3w5fGChMA4ITm0PrKVQMVI54PeBUAdJjm7bwXKaBUbN+hyid7ywa5WWdub6Htg1MGs5MCAC4TGRrMHR7J0cmJPVZbnhNznRPNYo0R8FL5PB/3Qut/Kl9+XndSzJT2kQ2a6Pc80s52fI6icuO+4/brNwi9qc6PKOiO5kVb6tmNXp2NsqOFCUni7onibmwnN3/3gfLaWqNMQLl2pylKrLd0t0DQB/8CAArDq9/5AqusVc6dJhzcq81LkySZmZm5d+/etLS0K1eutP6VxWaze/XqNWTIkKFDh/bs2fNvb2dIynrZZDymNR3Tmi8YKGejP0d5aNgfCeIRijvX0Wq1J0+ePH78+PHjx3NycpouKhQK+/bt27179+jo6MjIyK5duzZ1oTtx4kRVVZVKpVKpVN7e3kqlkuHen6Z1KMJImi4C6WDIRzYN2q/2Im0FgPKZiqfY4S3UTWxhHayeNGWS5kukOZu0XKaIxjbymw86f97pvHKTVMr5Ny98Vf7f6gtnrl7R1l3V1l3R1WsdLdS0b8YLX/2Bdk7JacDqrY0f6W+j5H0DOLtv2rbesEbJWTFKZqyCFSlnsh8FV+9JAk/T4TzO+k5D87+BNvA0buJJ+k4b+I7nMRf4YgxfrNVfcToBYCiPu0AqCWx/VjwA2PMttctLdVvrKIJC2Khimq/vglCW//3b+G+++ebtt992/YyiaEpKymuvvZaSknJnyHRa/a5tNevlLOXCiM8VbK87DbyDtC8vXlJoyWej7MZi9QgzUdy9r3xwvKgro61YaBtOHSy2by+wXqx2khQAgJSLjgzn9o/jZKKOfRabjiQBAAHowmGn8nkpPF7rVeuvGS9tqf61xlGJALI8bq2kjQZmd0CS2g17jH8eA4oCAITFpLDG8m/aME7FjKgcVlGx9SYFFAAggATzwsLPO8IyndEz/6UWckrzC2p1GoKiBljRsLmN8fzm4+fV/93A79pZtfBld28DAABqa2vT09MPHjx4+PBhjUbT+slCobB///5DhgwZMGBAcnIyk3l7s5q0EKbTelOG1nxKZ8uzhP4aJx1zz430hoaGEydOZGRkHD9+PC8vr2mcw+H8+OOPzz/f+FAnTpwoLi5uOoqiqFKpdJl5X1/fB1vNHqteidf/RtmLELYfLzkH/nodg9f+QhEmVJjEEPWG1sofkKTtFmnOIs3ZpCWbtORgmJOiwBXVQZjEuCb222XmKIFCweHdMmmv6Rqu6+pzDWqT03H3WmvXrp05cyYA1FmIHDVmclCjO/EYCLx9TL/r5m3/r+Chh6Z4STlorYUoNxKdZMyHs/m8Jwk8TYfzmOs7Dc3/ANrA07iJJ+k7beA7HlrgSYBNZst3BqOZpDgIMlMknCUSCtH72ax7gALwC8gAACAASURBVDZ+wYIFX331VbPBF154Yc2aNU2/4hT+ddFHNy25YqbkKZ+pGIltrl7TXz4sQdx1Z80fNY5KBdvr/9g77/A4qqv/n+mzve+qd1nNvdsyBgwGTC8JSYCQvBBCSPilECCVFEiBEAIhL5CEkAIvJdTQMWBccJGr3NS7Vtpdbe/T5/7+WHkty90hMYj5PH70zN69c/fObDn+3nPuOT+quW8g27M++u7+VKuKVAAwk5ZF1uXN9rPLdFXHnUYgo7zWw73aw3dGJACYV0A/f7lDQmg9L7yV5dZy46H1BMACllml052nZ61H34wwzA2IqlBjqM89VJHy+PBDOIbPtSyeZZ5PYkcQmdG/vph8ax1GEuZLzzGds5T0OJGi8Pt7Ei+8zXf2YQxdeM93+DLznuT2Pckdbandgjoepm4j7TMt86eb5tTpphMKObFQn+QPjf6/nweaKgQD62H1RY31+tkNJ1VVXlXV1tbW999///3331+/fr0kScfubzAYZs+evWzZsnPPPbe5ufm4VQN9d/eN/X7YMMdkWGo1LrEaFltIGwUAY2NjGzZs2Lhx44cffrhv374HHnjgm9/8Zu4Ur9f7hz/8we12l5WV0TQdj8fzv64ul+viiy8+8as7QZAcA4zCCOP4QyXNbS/PxdsTlrOZhpdPdCBVULP71cweNbNbzexRsx2ApL4R9f0WaU+XsrzWceX8WXKkjK4pbcVMKy+9Q5Iki94giWJWlha4i1//0189l593+Kiigrb6xPaw1BGRd+7v3P/orUxydPr0pkFdbdI2jS6uL6qsayy21tvJOgdVZydr7SR1St/6j5apZOA1TjuafdfQ+E9zx9r4y13c46vsK8o/LdVSNE6NqWTfNQF/+tEMfI6woj6QSL6ayaKjJHs7cfiOjP/egdgrQVARxuCO6wrd3yhlpx1ha/QxGBwcPPPMM/OVw3NgGJZOp/P5zACAU7J/HPrtvtSuw0coZsv+X+UP3fR4frWEFNuW2LgpunaYG/fTFrGlS21nN9vPPhF/eFdUfm+An+mmlpeOm6iMhF7tz6ataC8hbuQFASEAIDFsCUOv1OvOZtnjVpLnlOxt7TfkJPdi2/Kvlk1Oxcfv6w78/GGMojw//jrbdGiWclUNP/ZMem0LVVpY/MAPcpXhJCR2JPd++PJD3TVcypzLow84RtQa6ptMc6YbZ5frqzHA5HBs5Gt3fXjBHJnAAYBQVGsk5ZahqLjYNauJaajG2ZMww5FIZO3atR988MGaNWu6u7uP259hmAULFixfvnzZsmVLly61WCyH9wk8MOi/ux8pB34eMWDrDcalVuMSq+lsO+WZXJQeAO67777vf//7uWO73b5o0aKmpqbq6mqPx1NRUTFnzpzcU4IgvPvuuwRBuFyuurq6wzP5/zsoibVK7F01u5ewrqSKvp1rRKKP33cmECbcMIcquhU3zDnOKKqgZtvUzJ5xSZ/tADSe3C7Loy98P7Npt4Jk4pmLL68yuCwCbjp/Gf6Z82+77Taz2byovmm5L83YrXRFCV1eTJUWki47AFx22WWvvXZYhUUMIx2ldFEdXVhHFzewRdPq6hvq3YY6O1ljJxcV0tbT4aKfSgZe47Sj2XcNjf80Yxll86h4aa3uk7BJS+N0MpXsuybgTz+agZ/IHlH8azK9XMdeZdAfv/cx4Tsz/nsHYi8HQUWAgf3qgoonmk5uBJ5//vnnH3nkkW3btuVaqqqqent7JyUeR4B2JVrWRVZ3ZdpkVcIwvEpXu8R21nLHyiPWZh/k+jZH17bEN6TlJADgGNFgnHGm47z5lqUnNb1/7MvcvSkJABUW8sELLAO0/FaW2yyIMkIAgANMp+lzdOwKHVtDHTXVWUQMbY9v3Jvauci6/EzHuB+VU7KbYh+U66qNv1sj7Om0XXup5YojuFiRrPi+/QspEHJ/76v6BTPz7SNf/6kUiqj3frHLNLIvtasn05krKQ8ARsLUYJpZE3E6/rjVUN4Ua57lG/EmpIPJzxlOtMXS1SpVfeuXCNsRpPWxCQQCH3744fvvv7969eqhoaETOaWqqqq5uXnevHnLli2bM2dOfk+7kpIzWxLprYn0pnh2Z0Llxjd4E1Zy5uBy7LACaslk8oknnti0adOWLVt8voO12XEcb2xsXLRo0YIFCxYsWFBVVfX666/nqtBXVlaeddZZ+Z6xWEyn07HsRxwEiMRRft9ZSAoDAOm6hq5+ZPwJlZODT2KUGzfMxtjKY5yvZjvV7D41s1/N7kPZ/ZKQ6PMqZYW4jsGQQGEsvrmjYOX/7AGARa6i55ZfMfFslaagwPmVd/65tmPfcaeK4STpKqeL6ihPdVltw8vfOq+6uvrptmxSVGttZI2NLDWRx1uV+neZSgZe47Sj2XcNDQ2NjwlTyb5rAv70oxn4Y7NXFEdl5ZzjlU87GkJfduwPw9FnAoSZnNGzDDDItqboUoZ0HsGJejS2b9/+5JNPZrPZ733ve9OmTTtaN1EUOSxrpiwnkiJORvLe5M7NsQ/2JnfKSMYAe2TGMyx+EhX1Ipz60PbUe4N8KKv+7SJ7zjMfV9U1HL+G47fwAn/g211Gkufo2LN17FyGPpEEA2vCbz49+jgA6LP4Nf90L/7lg7jhyBNLvPp+7Kl/mVY2O24+WLo88vjzqdUbTCuXOW7+PABwSrY9vacttbsttTskjuW7WWVjo2tBg3FGGVHNj4kjXT3+cFAABABuX/ScKy9nGyYnDjwpOjs7P/jgg/Xr12/YsOHYeezz2Gy2xYsXL1myZPHixQsXLsw755GEsruS6ZZEenOMtFPljzVOOjH24piaVfTzzWydASMwr9e7ZcuWLVu2tLS07Nq1a2J5NoZhZs2a1dzcfP3115eXl9ts48EXw8PDa9asAQCDweB0Op1Op8PhcDqdE0vcnTpIVrluNdtGWM7EqPHd/nL4RbH3ptwxXfUg6f7yhBNUgKMKZSQMqZl9ana/mt2vZtqQMASAPmyVt+9XuoaUb5zvmWasUGIVcsiaGlJoEQOAvbHgrVtXezPJk5o1hmE//+W9/7B9MW+lKBz7yizD7YtMANAelgCgykqyH2k1+qlk4DVOO5p919DQ0PiYMJXsuybgTz+fKgMvjYmRp3zOLxeduH6+IhDqlCQPQVxvMnzWoD+1anNKWgGECBPJ7U11LN2G0bjlQqfzy0XmFXb4KLbd9vX1XXTRRT09PYsXL7766quvuuqqkpKSEzw3o6R3J7YBQLN9Ra4FAVodepXF2dnmhVbqOL8yKoIwp7r1B2/LN9+Lrxvml5QxRRV4wqJulYTogYpoVhxvZplmllnGMq6j50jPKOk3xl5ojbWEpLEvvlN91vcfyLWn5VRAGK3QV+d3y3N7Osfu+V/drHrPXbfmT5f8Id+3f4FU1fWd/zEsnTtx5KAY2LX5n/v8LcOVYkan5NudtLveOKPeOKNQKZWCoovW2WoqJp6YTqd3vfKGPhB1F3ic9bVMfRVhMh77zkykv79/48aNmzZt2rhx48REdMemsLAw55lvbm6eO3fuxK0TE1ES8p7i9blj3EDoZxj1c836uWb9XBNboxckcdeuXdu2bduxY8f27du7u7sRQjiO+/3+fAm6zZs3t7e3y7Ks0+ny5etyGI1Gp9O5cOHCI5bH+7dQs5Lvf9XMLsQPUBW/JizjHz85/ILY+zWMKcYNM+mK+zC6+DjjKBmV61CzbWq2DWU71GwbkmO5Z4b86j0PAx+w2FT7DedXm8+xtIfZth7+vXX7uve0JwROOZ4BKigoeHbr4JZRoTsq98VlX0q5uEb30LnWoYR8zrMhBIBjUGwiqqxktZWstJKVFrLKSngMp57/fyoZeI3TzqfKvmtoaGh8nJlK9l0T8KefT4+Bl3xC9wU7hX6OrTfUvjWXcp+Qht/CC7+JJzslCQAMGHapQX+d0VB19JjwY6NmlMGvtifeDCEZAQBdylb/c6Zu5glVdzsGX/3qVx9//PH8QwzDFi9efP/99zc3N5/CaCFx7HsdN0Muqbu+epZp/kzzvAp9zQnWfvvR+sRzHdn8wzon1VBNggu1E9KgLB9sp6ickp/L0MyRohvkYGTgWz/RWR0lj92da3l44Fe7k9tonJ5pmn9z+XcJjOBa28d++ahuTqPnR1+feG7ytTXRJ18BDDNfeKb5wrNIjxMAJK8/8a/30uu3AYY5v/Pl+FxnZ3pfZ3pfV7oto6Tz5zpoV62hsUZfX2tsKGHLc1e9f//+7du35zqQkmyNZWwK5rLbPdOqDfXVVJHnxG4tAMDIyMj69etzer6trW2SYD4aJEk2NTUtWLBg/vz5c+bMmTFjxsRMeNHnAom3w5mdSXHwkLprhJEo/mWt88aDGjiRSOzcuZOm6WXLluUbi4qK/H4/ADAM09DQUFdXV1ZWVlBQYDabc7Xoli9fni9kGIvF+vv7zWazy+WyWq0nfuEniBJ7R+j9CigZAGBqnyAcV+ba1VSLPPYExlbhhrmE7fxjjIBEv5ptV7l2xHWr2Q7EdaEJ7y8APPu2+KsHqRtqZhtIWqRRsM7Y6U+0dQwMDg5OGmpJw/T3nniSKvZQhW6MInkZ0QSGYyAo6KcfJncFxKGkLKuQ2fNO8oO/AI5T7irKVWksqqqqrm2qq25w669t0luYk1j1m0oGXuO08+mx7xoaGhofc6aSfdcE/OnnU2Lg8+odIzCkoJPS8AhgIy/8LZVu4QUEgAEsYJgrDbrz9Tr2lOLqpYAQ+T9/5Emf0M9VPT3Depmb259Ob45bLnDSZaeyA/lb3/rWww8/PKnR6XT6/f6J1ctOnO3xTVvjG/anWnMl6ADATFpmmOfNNM1rMs3WE8dxxvrTynqvsH5Y2DQiZKTx77iJxq5daCiqwDfywlZe4A5891kMW8jQzSy7iKVrKSqvdZCseL98pyqIpX+6h7BbAWBnYstrY/8c4YZIjHqg8QkjaYo/90b8xXeyn51DXby0Ul87cc9/4l/vxZ55HVQVAHCDDhRV5QUAwCjKcfPnjWctyvdEgLzcYE7M92Q6Jop5HaGv0dfXGOqr2DrVi0K93mA8yqGDkhtDyBJLL1TY4ju/egr3OZ1O7969O+eZb2lpCYfDJ3giQRB1dXVNTU2NjY3z5s1bvHixy+UCADkqZVtT2V3JbGsy25oSvbz71rKSew/J/4cklFwdJj2MrsmA6wkAeOWVV5577rndu3f39vZOXFDAcbyqqqqhoWHBggUzZ86cMWNGZWXlli1burq6ch0uvfRSh2O8gr2qqhzHfTSOeqQgYRBJQdy0KB9LLw7eKQfGV6nYprdw05LxvuKoktyM62pwXR3gRw5SQIJX5boPSPpOxHUnEomuITUcV89bQo1va8eZb96P9W1kVhSU+7l0XypupujbmhYV6owAgDCMtxmN372htO6QmymrsKt3tHl6lTwhk8I4OEHai8+/7OrX/nwffsKRO1PJwGucdj4l9l1DQ0Pj489Usu+agD/9fBoMvDQqdK/aKfRz+lmmir9PH/jiPm5/mq3V1749lyo4iS2+fZL8dDrzr0w2Jz6NOLZKp7vSqJ9Nn8SG9oMgkIJiLqN47xW7k+9FAAPDAovtKo/tcvdJFZ8LBoPXXnvtmjVrJn6hcByPx+Mm06m790VV7Ezv25vasTe5MywGx4fFiGmGhhmmeU2m2aW6imO75SUV7fBL64f5tcNCb0w+t4L90wU2AJAQ2imKL4azXZjUp8j5SVtxfD5DL2SYhSxdS1GRB/+a2bTLfOGZ9hs+mx8zJSdlJNkoh5rOjn7zbjmV/u1dYQ7xNE6f7Vj1uaL/OTj/YV/yX+9ld7apmSwAEFazfuFMy+UrSbfjaBNGgHy8tyfT0Zvp6M50hCfsmccxooQtq9JPKybKjWmrPJANjo0lZREhdCZHVn7j+onjKIqS2tOhM5voyhLs6JsFDnlphDo6OlpaWjZv3rx9+/ZcTPuJnJijsrIy55mfMWPGzJkzq6urcRxX0gphICa9RdHnAoNfaQMAjMCYGr1uulE3w6hrMOqaDIJd3rN3T2tra2tr6+7du/fv3z+pQp7JZLr99tsvv/zyaDSqKEpzc3M+6d26desGBgYoirJarVar1WKx2Gw2q9VqNJ7EXoNjoWSU+Go124ZUgS75ARxYRRK6vqDE3gEAjDCzs7fn99iDyiM5itEFR9xOj6QxletCXK/KdSGuR+W6kTia5tA7m+TuIUXhmO9c7CLTFiXuUuKe4XbMjulERbngg+fX79pQWTMDAMKPPp1ev1Uw6gYszKoH7znGxJ999tnPf/7zJ3iVU8nAa5x2Pg32XUNDQ+MTwVSy75qAP/1MeQM/Ub3XvDGHtFFyWOy5uPXUNDwAZBB6J8u9lMm2CuM+tyqKvESvu1CvKzsldzcAcHtTgQeHEm+F1YwCAIBjhnkmyyqXZZVTN+NE9c/w8PCLL7740ksvtbS0qKp6xx13HF5JPsfIyMjbb79dXl6+fPnyE8w6PsoP70vu3Jva2Z3pOJjUnTTPNM29vuQWGj/+PQxzqpnG6AOFVtYNCze+FQWAEgdxebNujFW2C4JPPrgv3YLjc1V12qtrZvYOzb30HOuKJRNHU7Nc8DeP8/u72enTdn29eEtsvY/3Vhvqflhzb64DArQxuobFdTWGeouoBwI/qeJwOeJStCfT0Zvp7Ml2DHMD+QsHABbXVehrKtjqMrqqztpkow5ZFHjj5VdCiTibFcxJzoJTDpvNVVZiqqmgy4sw6gjl7g8nm822trZu3759+/btO3bs6OnpOalfS71e39TUlHObT58+febMmTkXPQDIIXH0Z33ZnUm+K4OkQ8YkbVT1S7MMCy0AIAcjic07923d3u4d6oyFOuLh9sF+n893zTXXPP3007n+Y2NjF198scFgaGhocDqdGIY5HI5JpekoiiosLFyxYgV2SuEqx0VNbpLGnkBcD2AY0/A6Ro5n/hM6rlQSawFnCPMypu45OFJFhkNQMirfg7g+le9V+R7E9SO+FykpANjfqzz9htw/AjLI/7hHbzI5MbYy9V5NehOBAciqOm/1P5LZ7NEG/v3vf//Nb37zBC9nKhl4jdPOlLfvGhoaGp8UppJ91wT86WdqG/jD1Xuu/d/U8DkGZPnlTPbVDBdSFADQYdjm4oJTC6rPoXJqcnU49tJYYnVEzY5rRbqUtZzvtFzoNC634SdWmDoUCnEcV1ZWdsRnx8bGmpqaIpEIAOh0ujPPPPPcc8/93Oc+d4J57zgluz/Vuj/V2p7eExFDAHBP3cPFbFn+WQmJZvL4W6OjnPrTjYlNI2JCUH+01HzDTAMAjMrKugz/ZpQfpaXghFhuvSBMjybnmg3zTKbpsoy39SRfWyOHY4TNUnjvHaTDCgAZJU3jNIWNR0MMcwM/6/5O7vgyz+cvKzhRF+jREFVhiOsfyPYMZHv6st0TnfMAYCLN5brqMl1Vua6qXF891DrU2d4hwyG/bwwvGZPZEhEaP3sJW39yKe7j8XguC92uXbtaW1v7+/tP9sfT6XQ2NTU1NDQ0HqDA7uHaM1xbmm9Pc21pri0jR8Sal2cbm03Rv7+UWrMZFBUAlKRFGC3HDWm2itHddIbr7On5gPCOjo7Zs2dPzHIPAFartbKysri42O122+12h8NRVFR07bXXEgciETZs2BCJRCwWS2lpaW1tLfxnkEZ/J489jsQARtp1c/flo+uFzs+pqS0YW0lYV1KlPz72IEgKIq5X5XsR36/yfYgfUPl+UMcTDby2Bq1+hxkKKDEi/vjPHQMBe79f1/au3DcSG8wk/NkUAmgsr9ywc3t+o8FxmUoGXuO0M7Xtu4aGhsYniKlk3zUBf/qZwgb+aOo9hxwWey5q5dr+LQ0PAArAJp5fneVJDH5ms+blewsv6HFsBk2fgqBXs0pqXSzxTjjxdljyC7lGXE+UPljnuLbw1OaZ54UXXrj66qsnNZrN5h07dtTW1u4IiF98PfqTZvMXGo+8nXgiQTGQldMV+pp8y0+6vjXCDzlpT62hvtbQUGtoLGJLjxFmryAIpJUiE5HvcV9L6s+70wBgMGNlFQTlgjDOhyeUnyNUtSwQrh8ancEJzRecWeNxHHFVAwF6N/Rae2rPENe3yn3l+a7Lxucs+J/x/cVK2iv1tYttyxn8FMuep+Rkf7Z7INvTn+0ezPamldTEZ3WEvlxX5cGLTVkrFUBoDCV4UcYAAFhOvLCkxnLpORP7C+mM0NGnK/ZQBS44gf3SiURi9+7du3fvzoW7d3R0TIp1PxFsNltjY2NDQ8O0adPq6+vr6uoqyytIgMDP/yB0D2AEYWiey06fFnxUjr9zUKLjJkzXaGbrDWyN3rDAzNcru3fvbmtra2tra29vb2tri8fjk16Ipunq6urp06fffffd9fX1r732Wm79iKbpa6+9Nt+ttbU1k8mYD2AymagTi1Y4BkhJAyCMOLiRROi8Wom/BwAYadHNH4ADH06h+8uI68TYCsJ+Cem69sjD5cYUA4jvV/l+JPSr/CDiB5AwiOQDV42wvz3J7tzKEpzJaSR//MwPDbVXnfiEp5KB1zjtTGH7rqGhofHJYirZd03An36mqoE/tnrP8VFp+MMZlZVz/WMAUEAQZ+vYs3TMQoY5Fec8guzuVOLtcOKdcLY1Wfj9ysIfVWX3pEZu72YbDaYzbabltpMqKQ8Ag4ODjY2NHMdNav/d7373ne9855n27F0bEtc06u9Zbjnp2QK86H9qbeRtTjkYTqwnDDWG+hp9fa2hsVJfc9xg+4G4/Miu9I6A6E0ejFcnWORxyhZdijLKPQVOZULtPQOGNdDUdJpuoqnpNFVOHqcq987ElkcG78sdf7bwS6vcV+SOc7vfXXQBjZ9KRoOIGBri+oe4vmGuf4jrj0vRic8SGFHAFHuIIqtoL1M906cttdOu/LMcx73w3D9VQGxGMKV5M0FazRab220rL2FKCkiXHY73yREEYd++fXv37t23b1/uIBQKncJVUBRVbndWEmy1u3DONVfVL5o/bdo0B26PPR/g29OpDcPiKELSIfencddidtohiev8w7792/b3hfo7uzo7Ozu7urqGh4dzufHuu+++O++8U1XVeDz+4Ycf3nvvveXl5bW1tbW1tVVVVbt3756UA0+v1+eUfHV1dWHhv7t0lQfJUcT3Y5QTYyryjdzu+YjvAwCMLtLNbTvQrApd1yEpiDHlpPMqwnbh0ceMI2EQCUMqP4iEIcQPqcIgyAm65k+E9ZyjnXU4U8nAa5x2pqp919DQ0PjEMZXsuybgTz9T0sCfiHrP8R/S8CrA7xPJ1zOcXxlXoQyGzWXoZpZZyjB1NHUK1eTVjIIbCACIPhsYvOmAwMBAP8tU+dQMplJ3rJMPZc+ePY8++ui7776br5uF4/iGDRuam5sPF/DZbPb+++/3er1z585dtGjRzJkzj+0XRYBG+eGeTEcuFVw++x0AEBhRzJZX6msq9bUVuppj58ALZtWdAXFnQGwNiG1hWVIRBrD2WrfbiLeL0l5RXJ8SOmQpjh1Shs2E40001URRTTTVSFOlJHn4re7Pdvdnu4OC/zzXpU56vALcW8GXXvQ/hWNEMVv6jYrvu+mCE7uXRyYpx4e4/iGu38sNeLnBoOhX0SHzNBDGYrasiC0tZEs8ZFHPxu50iJ9UUA5TkT7DV4/EZt12M1ngPKkJjI2N5fX8vn37Ojo6skffpH1sTCZTTU1NdXV1TU2Nu2e0wCfUzD6nYs6FQh8HCir+ZQ1GH3KPey9rTa6J4jqcqdIzVTqmSodK8CFqJERELvjcKlY//kH94x//eMstt0x6LZ1OV1JS4na7nU6n1Wp1OBxut9vhcHg8nksuuSTXR1GUTZs2KYqSE/Y2m+3UrmsyKq9yPUj0YmwVrqs/0Chwu5qQHAEAXN/Aztycbxe6PoeUNEaXkO5rCOt5H80cppaB1zjtTEn7rqGhofFJZCrZd03An36mpIHvWLqN25vSzzbVvjGXsB4neZUcFrtX7eI7MsbFlmnvz/8Ip4EA2kRpPc9v4IT9opiXZxYcX8jQi1hmIcPUUMfxGB+R7K5kcm00vT6WbkmoWaXmtTnmFfbIU/7w30cNc82GxRbjYuuJ5LHv6up67733uru7L7zwwgsuuAAADhfwk4rM63S6JUuW/OpXv1q0aNGRBz2UmBTpzXTm9LyXH5yYB+5c58XXFH/lRAYRFdQekSUFLSg86P5d8WxoKCEDAwWFxLVLdO2StF+UxhRl4ol6DKujqXqKqqeoOpqspSj9UbzZ3Zm2p0cfH+W9CKl3VN9db5yRa9+Z2LI+8q6HKaox1C+ynnEisz3C/FXRxw97+cERfmiUGxrmB9NyclIfHaF3EQU22c4mGCpK4mkaCToArGQketaN100sNS9J0p7Va2DYb7FYrAVuc3kpVejGDcdawVFVdXBwsL29vb29vaOjo62trbOzM5VKHeOUY8MwTEVFRVVVVeUBcsdWq9V/70D4zyNS8LCyagBsnaFh2yKMwAAAIbRr166Ojo7u7u7e3t7e3t7+/v5caP0kCIKoqqpavXp1ZWUlAGQymRdeeCESiVit1tLS0vPOOy9/WzZt2oTjuNFoNBqNBoMhd0CcWBWAo4GUNMq2qcIQrp+B6xvGG+U43zozl+IONy1mm94+0J4Q2lchVcKZYtzwBaQ2E2ZjrgjiCTKVDLzGaWdK2ncNDQ2NTyJTyb6fYspuDY1jQ7lpDkAOS3JMOr6Aj0pKVAIA0v2RhdDnwACm09R0mvqG2RRX1S28sIkXWgRhVFbe4/j3OB4AbDi+mGV+brOYTrhSNADo55r1c83w3QokqHJYymn1zNZ4ZmsiszUBj3kBgC5hDQvMhoUW/XyzfrYZ1x1h/Lq6urq6umO/1u7duyc+5Djugw8+uOKKK0ZGRk6kurWNciywNi+wNgOAoPLD3EAuD5yXH8ynvgMALzf48OAv6zw98AAAIABJREFULaStTFdZqqssZStKdOUsPi5KaQKb7Z7s9r9rqflfPVxHWCqQia9bxjc5BxXl+g3RMKUwDkzQoQyhtgpivl4ABlBCEtMoqpoiayiqhiQrKTK3tWGaoenn0x6SkZSR0xbqoFM3l7Fvf6p1TfjNQqa4TFeVax/lh3sy7QVMcbmuWkccJ18AjdMV+pqJyQISUmyUH/YJXh/v9fEjPn44raSGlf5h6AcTgAkAgMRIO+FKnVUaw94piBYVMEUFTLGZtI6MjOwL+UEHICZgOEH0d+ozgkGUjSRtMRjKa2sth2bshwMV3auqqi6++OJ849DQUHd3d1tbW1dXV3d3d1dX1+jo6LEvJI8gCF1dXfmC8HlsNltlZWX57PLSwtISQ1Eh7nELDlfSYRhlhf4sYSXz6ycYhs2bN2/evHnd5+7gOjJ0uY4+lxU84igbHAW/lx8ZjA0Pjg729fWNjIwMDAxEo9GcgDcYDFu3bv3jH/9IEERRUVF5eXlFRUVZWZnD4fB6vQ6Hw+FwTKytwLKs0WicPXt2aWlpvlGWZYIgTiQxPkYYMdMi3HTIchVGWtm5bSjbgUQfbpx98AkkIMGPlLjC92Z39SZeOQsASJfdePZi8yUrcN0pJlzQ0NDQ0NDQ0PiYoAl4jf8IVc/M6PvMntSGWM+qXbVvzz1GeDnfk+25qFUaE80r7BVPNE16VkLoQ15YzDJHc9sCAI/QZl5Yyh5ni7sVx1fpdav0OgAYkZWtgrCNF7YK4piivJ3lvmg0zGEOpE+X5R5JrqeoYvL4nkOMwfOe9tKH6u2fL0hviqe3JjJbE+IIL47wsVeCAIBRGNtg1M8y6mebzefamerjJ6jLc8MNN2zfvn1SYygUkiSJYY6w5OH3+5944gkcxxsaGqZPn15VVZV3gTI4W2toqDU0HH4Wp2bjUjQihvqz3eOXBpiLKShjK0t1FcVsWQlb7qQ9OHZwyeDscubs8skTcOIECkIshiCXAZ7GcDMQFjA5ATNhHIO8suKVlTUHMgDgACUkWUOR1RRZQ1LVFFlBHeIvvbb4pvmWpV5uQAW1hC3Ptz858lhPpgMAdIT+/obH9QfKkmeUdFyKupmCfD78I2KhbBbK1mialW9JyclRftgvjPh4b0AYDQijUTEclP3BpB8meOtZXOeiPewcHZ0gqBSD8QwpGGRSlxrfjKAk335v6cJZuPHgW6yqqn/3fjKaMBUW0B4n6bDmNtWXl5eXl5evXLky37Pvxw+0b9kWXbmgn0t1dXX19PT09PSclKM+FovFYrFdu3ZNatfr9RUVFaVUaclXS0pLS0tLS0tKSkpKSnK1EpSEzO1NcXtTAOAAxgEVM6ECACwXOKsHZ0mSJIpifns8EtTF8xe/Wfrm6Oio1+v1er0bN26c9HImkymn5M1mc2lp6Zlnnunz+fICPhQKvfXWW7lZLVy4sLz84Nsaj8dJktTpdMf122OECTMtnNQoj6nRJ69TxSHcwCGhhK7wKLGEHIrGn38rvWGb5/tfo0r+ra0ZGhoaGhoaGhqnF03Aa/xHwPVE9Yuzjqvh+Z5sz4W7JL9gXmGv+uesw33Ur2W5H0fjM2n6Ly77ET3kHEJfC0W3CcIPrZYvmgyHdzgiJSRRQuqvMugBYERWoqoykz4o9n4Qje8SRACw4HgjTTXRVCNFNdHUcYvMYyRmXGYzLrMBAKiI785mdiQz2xOZ7Um+PZ0TSJGn/KSLnjlwBgBEn/HLUUk3y6SfYTpGnMLXvva1JUuWrF69uqWlZevWrT6fD8OwH/zgB0dU74qirFixorOzM9/CsuzZZ5/92GOPTZRJhzPN0PhQ0z+Guf4RbmiYH/Bygz5+OCj4g4J/R2J84zGN04VMaSFbXK6rXuFcdUSFjGPw1tWuwYTcGZG7olJ3VO6KSCP9SrQPANCiYvruCyw9ktQjyb2SvC0ppCg0LMvDsvzBhKR+boKoJMlyiqggyUqSrNBNP9c4izx0geYzhddvjK7x8V4aZybmvbu/765hbgADrM44/Y7qu/Ob/BWkKEg5RoY8E2muN06vN07Pt4iqOCb6xgRfQPCNCb4APxoQRjNK2ssPAgZgBTiw1EBipBlZDLxBz7PGxuKdcqsz63bQbjNpAYD9+/fv3LMTALDhHoaXWE40IExP0Uad3mqzFs6dyVSPh0JYCj0zbC7H7IWmC5bnpxEMBnsP0Nfb2/7u2qFUPCbyx3g3DyebzeYC+A9/ymKxlJSVFNuL3DqXh3Q5FJuTs9kTVmvQaKacAEBRVD7tgppR9k/fPCNU8obraWwxHrHGg7poAA/61TF/NuATAyO+kcHBwVQqlUql8ikebr311nnz5uWOf/GLXzz99NM0TRuNRpvNtmvXroULFxYUFJSWlkqStG7dulw3lmV1Op3BYMj9NRgMlZWVx87+oMQSgbv/V4nGmZoZ9i9fxdRXAQAgxHf2R//2otjvHfvFI4X33kFYzSd16zQ0NDQ0NDQ0Pj5oAl7jP8VkDf/WXKbqEA1/XPUOAMtZpoQk9oriV0LRwzV8Xr17COKcUw2OLSGJEjjE1/dVk/EZLNsmiRFF3cILW/jxMnK59GyNFFVHU7UkWU2R9DF8/jjG1hvYeoPjukIAULMKtz+d3ZPi9qSYGj0AIEEd/FoHqONJKKhixn1Lqefb5QBg5FRcPCSf2qxZs2bNGvcV+3w+VVWPVjQ+GAxOVO8AwPP822+/ffvtt7/wwgvHvhUGwthgnNlgnJl7qCDFJ3hHuEEvPzTKDfkEb0QMDXF9Q1xfS2yDk3bPs4wHinu5wW3xjQ7a6WGKPEyRjXJUW8lqK3lR9fibkpVRX0zui8mzPVQFRdZQ5CqA7qi86l0eMMCM2Lwa6ox6pk+W+yV5SJaDihJUlK3CwbmRGFZMEKUkUUqSpSRRRpIldM3nS+p1h70FC6zNoiqGxEBUDCGE8kHaP+3+to/3WkjbPOuS64q/mu+vIAUDbGJkQR4ap0vZilK2YmJjWk6FxEBQDAQFf1AMhIRAUAzEpWgUIlE2Aix0QTcMrT0wAuOk3WbGJlXyeJYkRB0j6RlRR0sshjAACdKhuY/8Y9bv7sr1Z6dPS69tiW/YFmJwmhPNbpfO43Tare6lS5cuXQoAmQ93hEQ7XVVm+NHNAwMD/f39Awfo7+8fHBzk+ZMT9gCQSCQSiUQbtB3+FPUS5Sn1FBUVeTyeoqKigoICj8uD2Xh9hHYFHbaQ1QysGYpqoCjX37zCXtM+BwDC4fDIyIjX6x0ZGcG6leq+0kw2QXloqpDZsWPHxI/om2++mT/GMMxqtZpMJovFYjabLRZLTU3N7NnjEfIcx+W/BZFIpKWlhSRJo9E4a9Yso9EIALGn/qVE4+TMuoIffA3LS30MYxuqC39529g9j/LtPbH/e9V56xdP9hZpaGhoaGhoaHxM0JLYnX6mdpIbNavkNDxVzEx7e15ew5+Ies8RUJQvBSPDstxEU391OcwHNDyP0C3haAsvFBDEP9yO47rHT4GAorSLUpsotUtSmyiFDk3PRgCUU+SVBv2NJuMpDL52SGh7PlCwI2Hty5oHOZJXg7PNH/5qWmCAu/G2DkZCWTedqGBTJbp0EZsuZVPFjGClGh3UtU3HCr9HCC1btmzz5s2T2leuXPnuu+8esf+NN9745JNP6nS6qgOcf/75+cxkE+GUbG7TOK9wZzrOz3uzn/A+vCn6Qb4bjTMeptBDF7mZAhdd4GIK3HSBnXIeLpJbRsU9QXE0rVxUrVtUND7aOq/wlQ+imAEwI8IMgBuBNIFCAzrSaomDwMtIspQgikmykCSKCKKIJIoIggIVARDYwaWZx4bub01sk5HkYYp+Xf9orlFF6g87vx6RQjbKudR21uUFX8j3l5GEAB07Dj9PbskgJI6FxWBEDIbH/41llPQR++OA6RSDXtSxPFvBukuqZ1som41yWJE5c8cjXQU2X9l40ntCURlOZCSFRaDDCdYb8nhDzm9cZzzrCCkMEUJ+v39wcHB4eHhoaGh4eHh4eHhgYGB4ePjfSZh3DGwmm8vgsDM2J+G0I0vxrNKqi6Z5PB6Xy1VQUOByucgA1jZryyHn6GDY4gsZY3E2kWjgYvrEyMiI3+8fHR0dGxtTDv2WYRjW2tqqqqogCE1NTbfddtvq1auLiooMBoMkSTmdP3v27Pnz5zt0BuFXf1ari9tnVmAYxrJsbu/9/PnzrVYrAMjByOi37kGKWvrEr4ijf2enUpIbjdPO1LbvGhoaGp8gppJ91wT86WfKG/jDNfyJq/cch2v4/4J6P5yQorSJUockdUtyjyQNSrICUE9RrxSMVxSXELorluARqiCJSpKspMhykrQcJc/clS+H9wSl3DGOkDMqxc2USGGsoP74z0M1IzyhTP5uZlg84GHmL7Xoag3sNL15pSNX1m4S2Wz2xRdf3LFjR1tbW1tb29jYmMvl+te//pVz4U5i/fr1Z5111uHtL7300pVXXnmCdyYuRVti6/3C6JjgDwijSTl+eB8CIxyUy0m7q/TTLi+45oge7xySiv6+N9sZlYYSynBSjnAqAAAOmB4wPTRPY2qLCa+ijMjysKxIR/kFcxEHxbyHIIpIwk0QHgIj1YSBMDD4eGgAAvSbvh93p9sRoApd9U+mPZBrF1T+zo6vpuSkiTSf5bjgioJr8iNHxJCIBDvlzA9yDDglG5bGJX1UDMekSFQKh8VgQo5NKms3ET1H0LKOkvWUrKNkHS2xlMQwMps7WLhtYPojd08sTT/i9Xa//QEtSgzNGI1GndGgt1kNDhtjtxE2M8bQABCNRoeHh71e7/DwcG7veu5gZGREEISjzeTfR6fT2Rirg7DZMKtVtZh5o1U22zCLBTNbwFy6onL2X5c4HA6SJAFAVdWRvd7tn10X5ENxfSpMRordxZ9b+hnSw1BOinTTl9591foP1x/7Fc1ms8FgMJlMTU1Nl1xyyZIlS+rrx4vSjf3yUa613fXdGw1L5hzt9Klk4DVOO1PevmtoaGh8UphK9l0T8KefT4OBVzNK7xW705vjdAlb+lDd8Dc6clnrTkS955io4R9z2u+Mxv/L6v1wJIQGZcVF4NYDEj2iqGf7xyZJSguOl5NkOUmUkmQJSZSQZAlJuAliMC5v9x2h0Nc2v/hqD7fERV7F4PRglhri6WGOHuKoIY5IHeKcdH2ttPS308QR3vudLpzF6UodU6FjqvXGZis2oTReMpnU6/XkUe7S1q1bFy9efHj7Lbfc8uijjx7ezvP8dddd98Ybb5jN5pKS8XRoV1xxxTnnnJPvwynZMcE3JvoPBJmPhcRAXIoiQACAAfbbxr/YKEeu83O+v+5MbLFTTiftdlAuG+20UQ4bZbdSdjNpxQDLSGg4KQ8nFW9SCWeVLzTqyy3j1/JiF/e9LXHcAKBDy2qZIhfhUxSfrPgURT7KLxuJYW4CLyCIAoJwEUQBgTsIwoUjQolXs1b7gWz2MpIf6P9pX6ZbRtIM09zvVP1k/GbK8dvbb5KRBAArnKuuK745P/Lu5HZB5W2UvVJfe1y/vYqUmBSNSuGIGIpJkdy/uByNiuGEHJ9Y6u9wTJjRzOTuj8VEWiykzdsxnApztMxQEkPJDI7GP5CEohoT2WU2j/uGqyeOoChKrKOXUlS9w4abjaFMyuf3j46Ojo6O+v1+r9cbCARyf8Ph8LEv5KPCYrG4XC6Hw2HTWfGtqlk2mMFswUxmzGTBTGbMbAaTBTOVfbmWvMPk9/tDodDY2FggEAj6gkNr+0KZcDQdimQiMTmtHnjrbVZbe0e7x+PJb6OI/v3l5Bsf2L98pfniFUebyVQy8BqnnU+DfdfQ0ND4RDCV7Lu2B17jvwFuIGpemZ3T8H2f2QMAJ6XeASCn1b8UjLSJ0qpAMKOi06veAYDCsFrqkFd3EPgbBa59ojQgyQOyPCjLw7KcUNW9orhXnHxuEUF8q8KUy4qfg0OIxjAV4NUertJFX7HcAuCYeJYcFvmerNCd5Xuzope3XeEGAL4rm3j7EJVVdFdVwfcqhb7syJ3duJ6gS1m+lGVKWaqEpYsZ0nWItly0aNFdd931hz/8IR4/6DbHMGzVqlVHvOrnn3/+pZdeAoBQKBQKhVpbWwHgscceW7du3RlnjBdp1xH6iTXbvF7v9r3bdQaWdTJgUQvdhXn1DgAhcSwihiJiKJdSfiIkRloom51yWim7lbGXFjnPNtSX6w9W3buiVmdn8YGEHOfV60r0ngPxCM+0Z+/alsB0gOkBYwFYhOlyByAzyCcrPvmICjmuwxIFBOEg8AKCsDvunO8ijBhXSBjaRMlB4A4c1xPGOZaFQ1x/XIrgcPDTGxB8Dw/8Mne8wNp8S/kduWMJif/0/V1FioW0LbKdUcAU59pxjHDQLgftOrwiAAKUlOJBb6dvw/uh4GBaJ6XMStqgZGx4xo6nCC6F0ik+PcoPHzxnQjo9AKAUipZoQmEomaFFKq0ECkKskTSbSLORMJlIy9b3NgWDcQDAEKJEmZIURkE0glqcWOgqb/zMNeyM8ZvM87zP5/P7/QG/3+f3B4PB0dHRYDDo8/kCgUAwGJwU8X7K5Pbh9/b2Hqffo2B6ymSz2axWq81ms9lsBkFv7tcXY41GMBhxg5ExIAQqqDIoFUIJeo7Hvn1wMQvxAgBgR8r+qKGhoaGhoaHxiUAT8Br/JSZq+JNV7zkKCOLPLvvlgVBGRRQGjzhtp1G9H40ykpw0q4ii5pS8V1ZGZXlEUbyyElKUIVneJYh5Ad8mSp8PhhFCBgNOLUVbaeG+eLKQIFwE7j7wl3XSRidtXHJIlTXzOfaGLYu4jrQ4wAmDnBQQTWfaASC7J5VYHTl8hhiDUx6aLmKoQoYqZOgK3U/v/Ondd98dDAZ7O3oHRwfHxsbmz5+fV+OTOGLleVVVN2/efMRT9u7du3Tp0kwmk2+hKOoLX/jCE088kQsKuLXi+7nw8ogUjIihqBSOSdGoGIrLsbSczGn7/LkkRv1xxj/z4ff/9P9lQOwxmSwuu21zymLiLCbSbCFty6vMT9v1obQ+kEGBtBLMKmMRNZhRgllVxtB951vL3IRPUYKKstrH7UlIGAtAA2PAOIQGZHlAnnQRB5c2zDjuYr5k1eE2HO8m8IcTKRuO2wjchtkXeW5K8P2SEp5hmpvvHxQCH4Tfyh17+cFbK76fO45K4V/2fE9Bipm0XOT5zCLrwVvXntqjIMVc4DrjSz8CQZIDIZUXSKeddNogJ+/leFJOxKVoSk6mJh7I0ZScTMoJiZAkQgIYv+d+APAdmqCuBPBinJRpSqEJiaIUmpJpSqFJmSZkMrLXV1J5qYEwGgiTnjJUVVX5B4eESMyDiBJn4WJnEY3hFE4wFEXSFMgKPa0iLIuBQCAUCgUCgbGxsWAwmPsbDoc5joOPlFx+++Hh4eN3BSBvJy9ad9Gzzz6r0+kAIX5/NwDQ5UUf7ZQ0NDQ0NDQ0NP5rfOz0j8YUBjcQNa/OSa2LmlfYMebk1DsA8Aj9LJbgESIBkxD6SSwxMafdxxYHgTsIeh5ziN9bQCioqEUT6sybcKyYIIZlOYmrmA28IP89NTn/mQnH3QTuIohCgrjJbKw8sFKgm2FUpxvMABMLrdmu9DCVeqE3mytHL3p50ctLI4Ick8RhXhw+mKscIzDXzSXkOpX9Ct9IF850lVOvMwPF+4p/WUuXsSqnptZECCtJOmjSSV39mc++8847zz//vCRJ+RF0Ot2FF154xMt/4YUXJqp3AJAk6cknn7z00kuvuuoqyFWbpz0u2gPQBADhcPjKa67ctGmTwWCwO+w2h9Xqsp73hXPmrpoZlcIeunDi5vn9qV0BwXe0O48BZiItJqe5hDTVE+aZ5nln2M/lZcSSGADUK+mMnFrk1P19lPQFVFmF+86yOM14UFFDivLX7uw6PwcMYAyG0QgxgDGAMVhSVZPq0fauzwFiDhDwYhaz8mNWHLcSuBU3WQsfQkqcRGmdvnwNx5tx3IxjiqLwCDg5npTjXen9eQHfk+l4oP9nueNznBddW3wTXVECACk5+ffBXytINhKmc50XV+hr8rnxfbxXRrKJNOfjGtJyKqUk0nIqJ+xTciKlJNNyMi2nUkoyJScycppXOZHiRYqHwzby9wJA3478QxwjKBuFzSFIhSIVipSp8QOFIhWKUMiFI6PFl11cQ1ToCaMO1+sJ/bb1m7ztHYSk0IDJopjluFQmk+G4NJ8VeJE366KZdORQ/kObuWRFfvXVV//v//7vpptuSq9tkQIh0mVnaiv+E6+loaGhoaGhofFf4FMk4BOJRGdnZ39/fzqdzmQyuQTFlZWVjY2NNpvtdM/u0wKuwy2rnKdw4sSsdb91WH8YTbSJ0g2hyCdCwx8Og2Gl5CH558pI8p1Ct4TQn7syD+1LLamml9cwY4oSVJSQqgYVJaSoKVVNqWqfJANAIUH8P4spd+4Gnr8lFFUBrDjuIHAbjttw3E7gtirCWmOw4qYDYhK34bhRAMkvSAFBGhWkMUGJy7bPeACAsFCklZSjUk7qZwAsF7rsZQVjvx/y/6L/4ERx7E77zd9r+nrclBhjwiE8mqW5pTMWl3S5UT3CKAwApIBAmMhcgr189e8T5OGHH/7www/hgKN1aHAIANav3tDa2nr+rMsmdf7JtAfaA/tvv/nO/bv2kwzJmhjGTNMWqunKGtciS0pOJuV4PqNeb7bzDPu5OfWOAP2o8xtJOQEA4ACH22ggjE+FjIao0UAYDYTxbIdhGkmLkp6T2Ayvy0TKIhlDjFdDioJI+Nk5ZrMRDytKTFXfHua7MxIwgFFgN+NZDPEIBRQloCgwvsRBA7gB3G+kAFLRg7M330NhmBFTfTj1si9oJnA7SRjAjRy3y0oUU1NhesYajjfimBHD48LYtlQbgQQcSRRO57cndGfa7u39Ue748oIvXOr5HAAYSZMC8kv+pxSk6AnD+a7LynRVuT4IUF+mS0KinjBaSGtGSaflVFpJpuVURklnlFRWyaTlVFbJZJRURklnlQynZAVcgaNHnXcBQF/LxBbCShILSUIlSYUkFYpQSVwhCIUkVYNOtiwQGNeZV9A4oycMDM7SOLP7w+1D7QN8LCsms3wmk+WymSyX4bKZbFbgeElHx7OZaDQai8VisdgpSH0Mw7Lb90X+8jwA2K65FI5R/VFDQ0NDQ0ND4+PN1Bfwsiz/5S9/efLJJ1taWo74Pz8MwxYtWnT99dffeOONNH1CJaM0/sscnnM+vx/+k6vhjwiFYTYVVyNQ7iG/Yp5c6SquqiFFDSpKSlXPmFD33oYThSQRkJW4qsaP6h8eh8WwhwptZ1aNx+EHFOU3qbQugZmWMOb2+WZeNUUUfUjSyyAstWYQsl3m5rsykk+Qw5IckeSIKIdFCIMB6CooqsoVAF+rDMD+yqdm2K5wjz04NHpXLwBgDE5ayPqS4r8/9LfVW98d7Rvx9/ojfCQrcZfNv/gMcVHirTBuIggTSVhIwkwSJgKjcYI4Ql59hFBPT0++BvjBa8F1Lz/66to31k1qb1/d3dHRUdVYmVKSOUd0Rknl9p97vd7LLrustbWVIAlKT9ImijQQDZ+vnHZlORwhpSAABZkIhw8yX6v+prHQyDCMy+16PnX/WNinJ4wswS5ndY0KLcosoeiW2a1GSo/hOhkzfOin3hxEMsEIqg3RGEZhGIkwGhAFGIU5TBiHIQ6hGMJiqgwAoMCBCVQBXgU4bOcB+LzgN4D1NwCAIdSq4n8dCdhJQodjFDgjtjuRkkIqvwFqgsm0HsdYDIsJwQ+yKRxJBAoKxNbPFJTrMcyI422p1gf7786NeInn6nx2/YDge3TwPhnJRtJ0ecE1jcaZuXZRFddF3s4qGRpnKvW1CKGsmskqmaySiUmRjJyWkCSqPKdwvJrllGzuKQWTFWryPoQ8/QDg3X1IkwfAAwCAAUYqFKbihEraFYdDdREqUSrrjNXVFE4ZCCOJUVJa2bt5dzyWFpOSkBL5JCemBDHNCRmRz/ByMqOG4mlJTGOQlIUsz1+4ZNlKHxd8508AYF51puGM+UebmIaGhoaGhobGx58pLuB9Pt/KlSvb29snNmIYlqshnCuehBBqaWlpaWl58MEHX3vttXzBIY2PCUesGDcxp90U0/DHwIrjVhyflDkPAGbQ1PuFHgQQVdSYqkZVJa6iqKLEVDUn6eMKyh3EVJU/dB1rdZZ7KnVIiDvoAcoBAGAsCACYEUy/cJpwzIDhdgL/kclckUJyVJIjUiIs7vRnmLhMx2RKgbalBkYQmQoGL2NQWEZZRQqKUki86leXfelbXx65szvY5gUMgAbYCyM3dR1+gRiDn1eweOOyc9ZuWTcxO1qNvXpOXyMSVIzBlbicWhfFSAw3ELiJjA/GDh9HluXOzs6amhoLabOQh8TX/OY3v8kl3lNkRUkqfFIAgM337Pn1FQ+VTCvKKpm0kvM/55zPmd0b9z55/V8USXkOXs8PUr2i7IzfzsbwCY5cEgDgtSCokrr7z13htjipIzAco42Uw+S+7YY/eSobU6KaEOTt0m8EaczNmRicJnCWIBzdI+LefWGZtiCcnlXh1lltdEGxAExHXPVnjSpOYhRgJAAJQALCMR4Qj1BQOrBSg1XkXn1AhDfF5IEJmcB0a+5orwqP+MZyxzgUYtY/EKqMgzym0i8EwyyGGTCMV+QOWImBTKlpXyIzW03rcIwCbIzvfj+8h0QcADrDgq90XezGMQwwf7btWf8LOMriANcW37TCOb6BYkzw/dX7B1EVGJw527nKQxdllQyvciklsTuxXVQFDDA77UKABIXPqhlB5VNSUlR5QeUlVZZBkggRDl3DiQFA7NAd7wdWcnAAPYAeCIDJq12UiJEqps/gl75uR3u7cB1r/dxF5ovPPvzToqGhoaGhoaHxCWIqC3iO4y6//PL29naKolatWnXppZc2Nzc7nU6bzZbz8omi6PV6N22QtqK2AAAgAElEQVTa9Oabb77xxhs9PT0rVqzYu3ev03kqMd4a/wl4hG4ORbcJguewnPMFBPF3t+P6YLhNlG4KRf/qdhimRGTsKV8DNr7fHj+p7/XnjAYzjocUNamqKaQmVZRS1bSK0khNqSilqhxCSVVNqgCggATternGpculsn86mX4oIUM+ulpMQBBgPsDqKgCgRKRPKiQCySOwvrGiG00/mlbrjKtKUlbicjIheeMCmVaotEqnFTKhEBkFBJX20999/LnvzjZgd+zm/taThKSMlEahLnGPPzXXY17pGP1Zb/gvo/n5r0LNb+KvD6reiRdVQZUV32sWZ/N0CZtaGx17aAg3kRgGhJUSdh66WgEAAAih6GuRmfObnDoHkBhhJAGAsJCAwbuvfEWRJida7/tg+Hexh2cumc4rWU7J8irHKRyvclkl/exvX9zzePeh3Ye+8q+zenp63KVuURW2dgzxcnKYC+aeG2uNvHvLFpkff4mdufmvLDr7NwsqMJhLVzfTv+BklMqqKZnvor6bTce2/HpfeH+UYGicpjDAKZO+4rqV+jMWyxij4jqnwYDhtIyRLOkeSemjopp+/3Wucz+GKRjDYHo9zrKmiy4h3e5+BUDJRx2QoB/Pvef1j7224c387En7Uqa+HnC8XYU/jeUTCjrBdn/uaBen4u1dJAJZRoAUmfn/7d1pfBRV2vfxq7o7SWdpIIGEBGSNIAgMBCIiIogIyjogoDOKcUNhZhxG0PtWfBAQ1EEHVJyPO+L6jDooMooioriAoGyy70GWyJYFsnWS3up+Udr2pENIC0nX6fy+LzScOt35V3V11bkqtVwfbS+36mXfljZp7UnSfUlF5b4K76nDZZrNV65pnlbSsnNCe5tFGkdZ8t0H1xZ8ahWnWOTyxCv6NLrM5Su16O4TZQf+c+Jful4Wq9kuadi7pb25Ju4yr7PQXbC9+Huf7rWINdXe3KZFuX0up7fU7a045S7QdZ9PfD7N59E97mjdLXqZ3VdxaavG1/aNuyzDGnRKCwAAgHIiuYB/5JFH1q9fn5SUtHjx4n79+gV3iI6OTk9PT09Pz8rK2rZt27Bhww4fPvz444//4x//qPu0qNJSZ5lRvb9R1RPj0qzWN1KaZJ3M2+pyvV/izHLEhyXk+dWzWXT31OirWwfdW6x22DVtVHxcNR28IqU+X6FPL9V9mkj7qCj/pNHxcW5dL/D5ynW9TNeLfb5yXS/X9WKfXqHrZXa9OFrziYjPVyRyMk5O3JTS+Zcz/18oKp5fWFzpd0WX66LrrliX5LuiJrdsf2WThEJfbIl3a5lvdKN4R/8kEWk8rtmRU65dp8qiy/T4El9MRauXyv6/syivzF3iKSkp1ktE9G7Wzr5tnof25ZXa465clNP2i1+vPL9eH7q66969u9Z7Xb+eMX+5tWfzJxzZsiV49hv7qlipNNF8k5yyNDX+ArttSa5t/uEGEmeJs0qUVvHD28H9i4qKVs344rqnb4iOiXkk9pkjn2WX28rcFrfH7pn++iP+6t3v4IqjSZvSWnW5oIO7c6+KMmujKBHx2Cz/lKaLXlz34ycHK/U/uX7/6KX7EtLixCdS+HNjm7h2C1s/8eCc+f+cMblS/+Jn5wz5cKwlvoFPi/JpUV4tVherR7Prmi137Y+b7nrO5/6vE+Adl/Xo8No/fFHxIuLR4vz/9Vrsrv37srNuduf8egBFs9kSb7s95aEZhT7ZX1ZuLC+xxYujs4h48/J+mDTl7S2bNZtNi7GLiMVuT7z99kY3jtvrlldz/Z9Ua2nwoIj4ysoWT/97+eZNlrh449p1zW5PzLol/sr+Fp/PKpaYKLFrmujlJd5in8/mKbMUvv6Ga+fOGM1jFT3abr/k+ouuvqbqRyoAAAAoJ5IL+Pfee09EHn300Sqr90q6dOny4osvDh48+D//+c+5F/BHjhw5efJkDTufOHHiHH9dBBsQaz/WwDEyPq7SLd/80qzWN1OaLCpxDo6ro4q3tqU3si0a2fjs/eqKVaSBxdLAYvz4X5pYLXf/ciO9MzHq/zJd10RSAq5vv9WR0NJmK/Hp5bru0vUyXXeJXpagu0Uv9+kuEVesXnal3atLru6L0bRmiY00myYi8ZkNip5v92zB6fL/uhQg3fifxaPHOvWoCp/PKkVJFikrXzW58dyRKZmlFhHxnnLvsbm1fu+2s2ipe0t6Lj5hyT1l06Kaasnr3b7oCj2qQrd6xO70ikhckVcT7UbHLU0HtPx+x7rCvafy9ud6dI8mMjbq92lHm9554ESO1Z71+Ylrfvj1SMRI76AVstL139fTN9dSU99scOs1P+6+JP6W6T/1WnJaRKJEosTSxtUieKFpol1+d69WlgtEZJ/84G8fLNescf2wRXZX6u/z6pn/e3nBwgGladqFH+emfXRcRJL3puz9aeXOisXB7+886XR84G50U7I7ujzmpDvq+Amv1SsWccW5s1/7slL1LiLFazemfDcjpWtS8Ft98+SmwOpdRHSPp+Dllx5MGqbf0+d0VMW2ky/5NJtPokXEa4ndNfffJSu/qfQmx6c+0PySxJhWF1T4GrkLWjQq99odFl3z5Sfm5jz13KlXX63Uv/Trr9JXr7WlpflE3LqU6LpIjFhixCL5r87PnftEYOe/vPdB+xUrBgwYEBweAABAOZFcwB86dEhErrzyyhr279evn9VqPXLkyNm7VmvPnj2dOnUKvIK3JgoLC8/eqf5JtFj+erYSMTXgfuwwm5/r/6B2u6YNiYv9be85OC52cFysiJTqulfX3bo4dV1EKnS9QtdFxDgoICJOXY/S5PJ2dv+Rg8E+n6e0rELXPZc4SjNTjcZCn+6Snw8HlPh0/20ANZH/bdTg/1kvN/65scL1dF6h7vRm6zLLpmuxlhYiXz2Utu2GJLdLt5d5rW5dpNV1xy8tOLjH5yweGBUdVVRsj7Jf3eWqAwkJP2TY3D7foomNjzezWV26iESX601jJjVxOpzbt0TnFjsKvBXu0ihr9IBW1ze7oLPjl9s6HM+vOPnL9uRyyVqRvdmdnxe4QDKiu3V2X/mYt0W+Lyr6RMMrVjcQ98+z09fa+wvPqkoLMFFreNMLN8y5pkvOhTE3vXNiyGu/nqFQ6vbslYOV+lvFesd9tze1JouIK8ElFhERV6zLZ/OdPFyaLVVsM1s+c/Kt3vl7M+LGvdWx5cFj5Q7jsYWlCzcUV/EAd13v8+7aznf+rnu7XomPF55657iI6BZ91aRVL29cFXwoVHe7L/10VqOeqcYFJx5LlE+LavNtm4x3Mh4+sS638nvrmzZtooAHAACRIZIL+IYNG548eTI7O7uG96U7dOiQ1+s99wvgU1JSrrrqqoKCgrN3FRGRvLy8Q4cOpaWlnePvBeqbeE0zTquu+RkLjSyWm3/rpRY9YqLfbJ5cxYTmlf59oUjvSk3tRPrqulvXpblIbynTddcvJxBMlJ+ftFfs8xltdk1LD7hVYVuRLRUu49hEM8ns4xqZ+9NPp0pLyipcIhIVE9OqQwfNYpkmIiIx9yV1faiz/4BFe99Vtg+u2P3DD7ruc1dUVJSV2ePj+1075qf2rW9oYBUReazB9j9XiFes5b4Ylz4n5Z9pb7Zet26d1+OtKPEUeH26V64eeuvpS68+HTTftiLvAGvH7Ff+98imTbrHG+XVNdGibFF3DRmfkTV8/6Ux3XRdu+1K1+dFNs/PR0WGNemy7/nx+QVHA9/n8q7D/9ZzVpvuzcWiuaaXl10cv95Z7hFxyB8H9+64b+d4d+l/3bmgffPuAwpnWb+wiohN0y6JiY4z7n8xVm46fOP3b63SA46fOhwNRo0aVcWnBgAAoCDtNzxTVxVjx4597733Bg0atGTJktjYs/ytz+v13nXXXQsXLhw+fPiHH35YNwkN77///pgxY0aPHm2c8w8AEUzX9dOnTzudTuM5IAkJCSkpKdX0P3Xq1IEDB06d+vlxAwkJCZdcckmVjxs07Nu3b/Pmnx9TZ7FYevfuHdLh0caNGxcUFOTn5yclVXHJABCS//mf/5k7d27z5s1zcnLCnQUA6rVI2r9H8l/gp02b9sknn3z22Wf9+vWbOXPmVVddZbdXcZm02+1etWrVnDlzVqxYoWna3/72t7qPCgD1hKZpiYmJiYmJZ+8qIiKJiYk9evSo+fu3a9euXbt2vykaAACA2UVyAd+1a9fXXnvthhtuWL9+/dChQ+Pi4jIzM5OTkxs3buxwOEpKSgoKCvLy8jZu3FhU9PPDk2fPns2lkgAAAAAAE4rkAl5Exo4dm5SUdO+9927ZssXpdH7zTeW7H/t16dLl73//+9ChQ+syHgAAAAAANRThBbyIDBgwYPPmzVu2bFm+fPn27dt//PHHkpISp9MZGxvrcDjatGnTqVOnQYMGZWRkhDspAAAAAABnFPkFvKFr165du3YNdwoAAAAAAH4jS7gDAAAAnGcZGRkZGRknT54MdxAAAM6n+vIXeAAAUH8YTxN0u93hDgIAwPlEAQ8AAJSh67rP56thZ4/H4/V6/f+0Wq21EwoAgDrCKfQAAEAZTz75pK0GjM6tW7cObgQAQF0U8AAAQBkNGzYMdwQAAMKGo9EAAEAZ48ePt1qtkyZNKikpady48ezZs1NSUoK7jRkzRkRefPHFxo0b13lGAABqCwU8AABQyW233da3b99x48Z99913M2fOXLBgwfDhw6vsOXTo0ObNm9dxPAAAag+n0AMAAMWkp6evWrVqxowZ+fn5I0aMuOuuu0pKSsIdCgCAWkcBDwAA1GOz2WbOnLlq1aq2bdu+/PLL3bp1W7NmTbhDAQBQuyjgAQCAqi677LLNmzffcsst2dnZffv2nTZtGs9+BwBEMK6BN4vt27c/8MADNe+/Y8eOrVu3pqWlaZpWe6lqw/Hjx+Pi4ho0aBDuIKGpqKjIzc1NS0tT7jHCeXl5FoslKSkp3EFC4/F4jh8/npycHBMTE+4soSksLCwvL2/atGm4g4RG1/WjR48mJibGxcWFO0tonE5nSUnJyJEjo6Kizsu7nfuboI45HI7XXnttyJAhEydOfPTRR5ctW/bWW2/Vxi/Kz88fNmzYyZMna9i/oKDAeFU92b/XtvLy8ry8PBV3xLXN7XafOHEiJSUlOjo63FnMxefzHTt2LCkpKTY2NtxZTOfYsWMdOnTo1q1buIPUkUjav2u6roc7Q323bNmyIUOGhDsFAEBExGKxFBUVxcfHhzsIQpaTk5OVlfXll1/GxsaWlZUZLefxJnb79u3r3Lmzy+U6X28IAKgzEbN/p4APP6/Xu3DhQuM4fc09++yzR44c6dmzp1oPyCkuLl69erXD4ejTp0+4s4Rm+/btR44c6dSpU8uWLcOdJQQ+n2/58uUWi+Waa64Jd5bQHDx4cNeuXa1bt+7YsWO4s4Tm66+/djqdffv2VWsPkZubu2HDhuTk5MzMzHBnCc2GDRtyc3NvueWW87KqWCyWbt26DRw48NzfCmHh8/nmzZs3bdo0o8w+vwW8iOTl5RUVFdWws9frff311xMSEkL6W7qi+/c6sG3btpycnM6dO7do0SLcWcwlOzt779696enp7du3D3cWczl27NjmzZvT0tLqz9+Za6iwsHDNmjUpKSlTpkwJd5Y6ElH7dx1q6tevn4h89dVX4Q4Smq1bt4pIly5dwh0kZBMmTBCRF154IdxBQmMMYaOiosIdJGRPPvmkiEyePDncQUKWnp4uIvv37w93kNB88sknIjJ48OBwBwnZ0KFDRWTp0qXhDgIT2bt37+eff/7555+Xl5eHO0vIFN2/14E77rhDRBYsWBDuIKbz6KOPisiDDz4Y7iCm8+6774rI9ddfH+4gprNx40YR6d69e7iD4LfgGngAABBR2rVr165du3CnAADg/OMu9AAAIKJMnTp19uzZ4U4BAMD5RwEPAAAiypw5c5555plwpwAA4PyjgAcAAAAAQAEU8AAAAAAAKIACHgAAAAAABVDAAwAAAACgAB4jBwAAIkqvXr0aNmwY7hQAAJx/FPAAACCirF27NtwRAACoFZxCDwAAAACAAijgAQAAAABQAAU8AAAAAAAKoIAHAAAAAEAB3MROVQ6Hw/9fhSQkJGiaplxsEUlISBAFF7jVao2Li7Pb7eEOEjJjURuLXS0Oh8NiscTHx4c7SGgU3aSIysmBKrFKn4miO+I6oO4es7axZM6E7YzSNF3Xw50Bv0VOTs6mTZtGjBgR7iAhW758+YUXXpienh7uIKE5ffr0Z599dt1119lsih32+v777202W48ePcIdJDQul+uDDz4YMmSIcnuXPXv25OTkDBgwINxBQvbBBx/06tUrLS0t3EFCc+zYse+++27UqFHhDgKcH+ru32vbqVOnVqxYoeKOuLaVl5f/5z//GTZsmHLHjmubz+dbvHhxv379kpOTw53FdJYtW9ahQ4c2bdqEOwhCRgEPAAAAAIACuAYeAAAAAAAFUMADAAAAAKAACngAAAAAABRAAQ8AAAAAgAIo4AEAAAAAUAAFPAAAAAAACqCABwAAAABAARTwAAAAAAAogAIeAAAAAAAFUMADAAAAAKAACngAAAAAABRAAQ8AAAAAgAIo4AEAAAAAUAAFPAAAAAAACqCABwAAAABAARTwAAAAAAAogAIeAAAAAAAFUMADAAAAAKAACngAAAAAABRAAQ9U7ZVXXpk3b56u6+EOAlMoKCjYuHGj0+kMd5D64jcscL6zAOoPtnhAvUUBr54tW7bccMMNqampsbGxHTt2nDZtWnl5ebhD/UrX9bfeeuvaa69t1qyZ3W7v0KHD73//+08//fRM/c05O6+++ur48ePvu+8+n89XZQdTxS4qKvr73/+ekZHhcDhatGgxePDgFStWnKmzqZIvXrx42LBh6enpDocjMzNzwoQJhw4dOlPn8CafMWNGZmbmrl27qukTUkJTfRAmVJMFHkit7yxQpXq+lkbG+KFusMULpO4oqLYpNMpCaHQoZdGiRXFxcZU+xJ49ex49ejTc0XRd151O54ABA6pc08aMGeN2uyv1N+fs7N+/PyEhwQjj8XiCO5gq9tatW9u0aRO8wO+55x4zJy8vL7/mmmuCY8fGxi5cuNBsyYuLi1NSUkRkw4YNZ+oTUkLzfBDmVJMFHkit7yxQpXq+lkbG+KFusMULpOgoqLapNcpCqCjgVfLFF19omiYiAwcO/PDDD3fu3Pnss88mJiaKSKdOnYJ3b3XvtttuM77zf/zjHzds2HDixIlvvvlm5MiRRuO0adMCO5tzdjweT69evfwbr+Bdo6liHz9+vHnz5iLStm3bl19+ec+ePevWrbvpppuM8G+//bZpk0+aNElEbDbbvffeu2HDhoMHDy5durRnz54iEhMT88MPP5gneV5enn8dPlM9GVJCU30QJlSTBR5Ire8sUCXW0ggYP9QNtniB1B0F1TaFRln4DSjgVdK1a1cRyczMdDqd/sYvv/wyKipKRJ5//vkwZtN1/fDhw1arVURuu+02n88XOOmuu+4SEavVeujQIX+jOWdnxowZItK4ceMz7RpNFXv8+PEi0rJly8AFq+v66NGjRSQjIyOw0TzJi4qKLBaLiDz88MOB7SUlJW3bthWRm2++ObA9LMkPHTr08MMPjxo1KjY21j9UOlM9GVJC83wQphLSAg+k1ncWqFI9X0sjY/xQN9jiBVJ0FFTblBhl4VxQwCtjy5Ytxsb6448/rjRpxIgRItK7d++wBPObP3++iNjt9qKiokqTCgoKjGN777zzjtFiztlZs2aN1WpNSUl57rnnqtw1mip2Xl6ezWYTkZdeeqnSpPXr12dmZmZmZubl5Rktpkr+5ZdfGmEOHDhQadK9994rIh07dvS3hCv50qVLJUiV9WRICU31QZhKzRd4ILW+s0CVWEsjYPxQN9jiBVJ3FFTblBhl4VxwEztlGPdxiYuLu+qqqypNMr5ga9euLSwsDEOyXxw+fFhEfve73zkcjkqTEhMTW7RoISLbt283Wkw4O8XFxePGjfN6vQsWLDCuvw1mqtgfffSRx+Ox2+1jx46tNCkzM3P9+vXr16/3H6Q3VXL/LzJ2vYFiYmJE5NSpU/6WcCXv0aPHe7/wD5WqFFJCU30QplLzBe6n3HcWqBJrqerjh7rBFq8SdUdBtU2JURbOBQW8MoxdV2Zmpt1urzTp8ssvFxFd13fv3h2GZL/o1KnTxIkTb7311uBJHo8nPz9fRFJTU40WE87OX//61wMHDtx5553Dhw8/Ux9Txd64caOIZGRkNGrU6KydTZW8e/fuxtmS77//fmC7y+X66KOPRMS4TMsQruSpqamjfzFkyJBqeoaU0FQfhKnUfIH7KfedBarEWqr6+KFusMWrRN1RUG1TYpSFc0EBr4wff/xRRJKTk4MnNWnSxPjhwIEDdZrpv912223PP//8n/70p+BJb7zxRmlpqaZpffv2NVrMNjuLFi16/fXX09PTn3zyyWq6mSr2nj17RCQtLc3pdM6aNatLly7x8fGtW7ceNWrUv//970qdTZW8RYsWU6dOFZGpU6c+8cQThw8fLi0tXbdu3YgRI7Zt29aoUaPHHnvM39lUyasUUkLzz44qVPzOAlViLVV6/FA32OIFU3cUVNsibJSFYJXPrIBplZSUiEhSUlLwpMTERE3TdF03+pjNqlWrjJthjho1qkuXLkajqWYnJydnwoQJVqv1jTfe8D+apUqmil1QUCAiNputZ8+eO3bsMBoPHTp06NChJUuWvPvuu2+++ab/oSCmSi4is2fPTk1NnTx58v3333///ff727t27frOO+906NDB32K25MFCSmj+2VGCot9ZoEqspWdi/vFD3WCLVyWlR0G1LZJGWQjGX+CV4XQ6RaRBgwbBk6xWa3x8vIiUlpbWdaxqlZaWTp06tX///qWlpb/73e9eeeUV/yTzzI6u61lZWadOnXrggQd69+5dfWfzxPaHWbRo0Y4dO66//voVK1YcPHjwq6++ysrKEpHFixcb96o1YXIRyc3NXbVqldvtNv5p3KNIRI4cObJmzZrAnmZLHiykhOafHfNT9zsLVIm1NJgS44e6wRbvTJQeBdW2SBplIRgFvDKMRyud6RiY8RUNvnwljN5888327dvPmTPH6/WOHj165cqVgRcpmWd25s6d++WXX3bv3j1wQ38m5oktIrquG/+dNm3au+++e/XVV7dq1apfv36vv/76fffdJyJPPfXU3r17TZg8Pz+/f//+7777bps2bd5+++3jx49XVFTs2LFj0qRJRUVFd9xxx+zZs/2dTZW8SiElNP/smJ+631mgSqyllagyfqgbbPHORN1RUG2LsFEWglHAK8O4NatxvlAlLperoqJCRKo/sarOHD58eNCgQVlZWUePHm3btu2iRYvee+89/41ADSaZnX379k2bNs1ut7/11lvG4y6rZ5LYBuOwaJMmTaZPn15p0owZM2w2m9fr9R9nNVXyhx9+eMeOHU2aNFm9evUf/vCHpk2bRkVFXXzxxfPnz3/88cdFZObMmTt37jRh8iqFlND8s2NySn9ngSqxlvopNH6oG2zxqqHuKKi2RdgoC8Eo4JXRpk0bETHuxVpJTk5OYJ/wWrJkSefOnVesWNGwYcN58+bt2rVrzJgxwd1MMjuHDx92uVzl5eUXX3yxFsCf2WazaZqWkZFhqtgG4ykyGRkZwTv1hISE9PR0EfE/3tNUyRctWiQit956a7NmzSpNuueeexISEnw+33vvvReYyiTJqxRSQvPPjskp/Z0FqsRaalBr/FA32OJVQ91RUG2LsFEWglHAK6NTp04isnHjRv8FLX6rV68WEU3TLr744jAkC7By5co//OEPxcXFAwcO3L59+5QpU6Kjo6vsqcTsBDNV7I4dO4pIWVlZlVO9Xq+IJCYmGv80T3Jd140DvcbOtRKLxdK2bVsRycvLM1rMk/xMQkpo/tmJMCxwmB9rqdSD8UPdqFcLR9FRUG2LvFEWglHAK2PQoEEiUlhYuGrVqkqTPv74YxG59NJL/dupsCgpKRk9enRFRcWNN964bNmyCy64oJrOJpmdPn365FTlxRdfNDocPHgwJydn+fLlpoptGDt2rIhs2bKluLi40qTc3Nzs7GwR6datm9FinuSapnXu3FlE/KdvBfJ4PPv27RNTJj+TkBKaf3ZMTunvLFAl1lIVxw91gy1eNRQdBdW2yBtloQo61GF8IQcNGuT1ev2N27ZtM64CevbZZ8OYTdf1Z599VkTS0tJKSkpq0t/Ms+M/s8jj8VSaZKrYxg1pJ06cWKl93LhxIpKUlJSfn+9vNE/yyZMni0hCQsKePXsqTTKuZLNYLIGTwp784MGDxvqwYcOGKjuElDDss2N+Z13gwVT5zgJVqudraSSNH+oGWzyDoqOg2qbcKAuhooBXyUcffWRsr++44449e/Y4nc7ly5e3atVKRNq1a1dRURHeeH369BGRAQMGfHpmBw8e9Pc38+xUs2s0VewPP/zQCDNixIiVK1ceOXLkiy++GDx4sNH4xhtvmDN5YWGhcT1VUlLSU089tWXLliNHjhjnTxoJp0+fbqrkZ60nQ0oY9tkxv/NbwLPAYX71fC2NpPFD3WCLZ1B0FFTblBtlIVQU8IqZNWuW/MJqtRo/pKWl7dq1K9zR9KZNm8rZPP3004EvMe3sVLNr1E0WO/jmqyISFRU1a9YsMyffvHlzlZdUWSyWCRMmBC/28CavST0ZUkLzfBDmdH4LeJ0FDhXU57U0ksYPdYMtnp+io6DaptYoC6GigFfPihUrhgwZkpycHBMTc+GFF9577725ubnhDqWf6QGS1e+AdbPOTvW7Rt1ksb/44ouRI0empqbGxsZ279799ttv37Jly5k6mye52+1+5plnhg4d2qZNm/j4+O7du2dlZVVTsIUxeQ3ryZASmueDMKHzXsDrLHCooH6upRE2fqgbbLZK8r0AAAvDSURBVPECKToKqm0KjbIQKk3X9ZpsNwEAAAAAQBhxF3oAAAAAABRAAQ8AAAAAgAIo4AEAAAAAUAAFPAAAAAAACqCABwAAAABAARTwAAAAAAAogAIeAAAAAAAFUMADAAAAAKAACngAAAAAABRAAQ8AAAAAgAIo4AEAAAAAUAAFPAAAAAAACqCABwAAAABAARTwAAAAAAAogAIeAAAAAAAFUMADAAAAAKAACngAAAAAABRAAQ8AAAAAgAIo4AEAAAAAUAAFPAAAAAAACqCABwAAAABAARTwAAAAAAAogAIeAAAAAAAFUMADAAAAAKAACngAAAAAABRAAQ8AAAAAgAIo4AEAAAAAUAAFPAAAAAAACqCABwAAAABAARTwAAAAAAAogAIeAAAAAAAFUMADAAAAAKAACngAAAAAABRAAQ8AAAAAgAIo4AEAAAAAUAAFPAAAAAAACqCABwAAAABAARTwAAAAAAAogAIeAAAAAAAFUMADAAAAAKAACngAAAAAABRAAQ8AAAAAgAIo4AEAAAAAUAAFPFDvzJs3T6ux/v37i8jXX3+taVpUVFS4swMAEFH+9Kc/Vbn/jYmJ6dSp06hRoz7++OO6T1VYWGjEyM7ONlrMORIIzglEPAp4AAAAwFxcLtfOnTuXLFkybNiw4cOHu93ucCcCYAq2cAcAUNeysrKuvvrqwJZTp04Zf2mfPn36ddddFzgpISFBRBwOR2ZmptVqrcucAADUE82aNfvkk08CWwoKCrZu3Tpv3rwjR44sXbp0zpw5Dz30ULjiybmNBO68884NGzZMnjw5KyvrvAcD6hsKeKDeSU5OTk5ODmzJz883fmjZsmXXrl2DX9K9e/f169fXRTgAAOqf6Ojo4P1v//79b7/99m7duh04cOCRRx657777YmNjwxJPzm0ksH///s2bN+fm5p7fSED9xCn0AAAAgBk5HI4HHnhARFwu1549e8IdB0D4UcADOA9cLldpaWm4UwAAEGk6duxo/FB9AZ+Xl1cncQCEGQU8gLP79ttvK917Njs7W9O0Fi1aOJ3Ov/zlL4mJiQkJCXFxcd26dVu4cKHR59NPPx0wYEBiYqLD4ejRo8dLL73k8/mC33zlypW///3vU1NTY2NjL7rooqysrI0bN9bRjAEAYG6HDx82fkhNTTV+2LFjh6Zpbdq0EZFdu3Zdc801cXFx8+fPD3xVzfet+fn5d999d3p6ut1uT01NHTt27Lp164K7BY8EDF6vd968eT169HA4HA0aNOjRo8fjjz/u8XiMqTfffLOmaV999ZWI3HfffZqmDRw4sFZzApFPB1Dv+Q/bL1iwoMoOq1evFhGbzeZv2b9/v4ikpaUNHTrUeK3F8usBwTlz5jz22GPB7Q899FDg27rd7qlTpwZ28Js9e3btzjMAACYwceJEEWndunWVU71eb58+fYw9Y15entG4fft24yW7d+9u0qSJMXX69OnG1JD2rd9+++0FF1xQqZvVan3mmWeMn/fv32/0DB4J6Lqek5NzxRVXBP+ivn37Hj9+XNf1Bx98sFu3bsYNcZs1a9atW7cJEybUak4g4lHAA/jtBbwhKysrOzvb4/GsXbs2PT3d2KeKyNChQ3fu3OnxeDZu3Ni5c2cRiYqKKi8v97/JzJkzRcRisdx9993ffffdsWPHVq5cOWTIEONtn3vuuVqfcwAAwupMBXxBQcHXX3/tf2rMTTfd5J9kFPDNmzfv3r17ly5dPvvss9zcXP/Umu9b8/PzExMTRSQ+Pn7u3LmbNm3avHnzP/7xD7vd7q+rqyngfT7fJZdcIiINGjR44YUX9uzZc/DgwQULFhg3ys3MzPT3vPLKK0Vk7ty5gTNYSzmBiEcBD+CcCvgxY8YE9lyyZInRfumll3q9Xn/7qlWrjPatW7caLUePHo2PjxeRF198sdKvMx4z06hRo6KiovMzhwAAmJJRwFevXbt2hYWF/pcYBbyItGzZsqysLPDdQtq3Tp48WURiY2P9u2bDt99+639cXDUF/L/+9S/j0Py2bdsCX/7NN98Yr129erXRElzA115OIOJxDTyAczJlypTAf/bo0cPfHnheXGZmpvFDUVGR8cPzzz9fWlqakZFx1113VXrPefPmWSyW06dP+8t+AADqocTExClTpmzatKlBgwbBU6dOnWq32wNbQtq3LliwQET+8pe/dOnSJbBn7969x4wZc9ZsxlX348aNM06y87viiiuuvfbazp07+6/eD1aXOYEIQwEP4JxceOGFgf/0P6LWf9dcQ6URhojs2LFDRCrdzMbQpEkT41R87k8DAKgPmjVrtjnI0aNHCwoK5s2bZ1xDHqx79+6VWmq+bz169GhxcbGIjB07NrjzjTfeeNbMu3btEpFhw4YFT1q2bNm2bdv++Mc/num1dZkTiDC2cAcAoLYqbz8jItHR0dW/cO/evSLyxBNPPPHEE2fqk5ubey7ZAABQQnR0dNeuXUN9VcuWLSu11HzfavQUkXbt2gX3adu2bfW/+vjx48Ypda1btw4hcZ3nBCIPBTyA8Dh9+rSItGzZMikp6Ux9UlJS6jARAAAq8Z/15lfzfWv1x9mrebmhvLzc+OGsx+urVGc5gchDAQ8gPNq3b5+Tk3PPPfcYN6cBAADnqOb7Vv8ftPft29ezZ89KUw8cOFD9y1u2bGm328vLy3/88cdK18CLSHFxsdPptNvtDRs2DG9OIPJwDTyA8OjQoYOIrF27NniS2+1+6qmn5s2b99NPP9V5LgAAVFXzfWtycnKjRo1E5P333w/u/Omnn1b/iywWi1Faf/7558FThw8fnpqa+tRTT4U9JxB5KOABhMe4ceM0TXv//fc/+uijSpPmzp07ZcqUOXPmGM+SBQAANRHSvnXChAki8s9//tO4pZzfvn37nn766bP+rjvuuENEXnrpJf+TZQ0bNmwwnjnXv3//wHZd18OSE4gwFPAAwuOyyy4bN26cz+cbOXLk1KlT16xZU1BQsG3btkmTJj344IMiMnXq1N92ZR0AAPVTSPvW+++/v1GjRmVlZb169Zo/f/62bdt27979wgsv9OzZs6KiwuFwVP+7/vznP1900UXl5eWXXnrpwoULDxw4cOzYsX/9618jRozwer19+/bt06dPYP+NGze63W6jjK/LnECkCfeD6AGEX15enrFBWLBgQZUdjEPpNpvN3+I/3J6Xl1flW+3evbvSmxjtq1ev9rcUFBSc6QmukydPPn/zBwCASU2cOFFEWrduXfOXbN++3dhXnj59OnhqSPvWb7/99oILLqjUzWKxvPzyy8aV7fv37zd6Bo8EdF3fvXt3lTfPT09PP3z4sL/b+PHjjfbo6OiBAwfWak4g4vEXeABhk5iYuGjRonfeeef666/v0KFDXFxcx44db7jhhvXr1z/55JPhTgcAgHpC2rf27t37hx9++POf/9y2bduYmJjGjRsPGTLkq6++8pfc1bvooou+//77xx57bPDgwU2bNm3SpMkVV1zx2GOP7dixo0WLFv5us2bNGjJkiMPhiImJSU1NrfucQCTR9IDLUQAAAAAAgDnxF3gAAAAAABRAAQ8AAAAAgAIo4AEAAAAAUAAFPAAAAAAACqCABwAAAABAARTwAAAAAAAogAIeAAAAAAAFUMADAAAAAKAACngAAAAAABRAAQ8AAAAAgAIo4AEAAAAAUAAFPAAAAAAACqCABwAAAABAARTwAAAAAAAogAIeAAAAAAAFUMADAAAAAKAACngAAAAAABRAAQ8AAAAAgAIo4AEAAAAAUAAFPAAAAAAACqCABwAAAABAARTwAAAAAAAogAIeAAAAAAAFUMADAAAAAKAACngAAAAAABRAAQ8AAAAAgAIo4AEAAAAAUAAFPAAAAAAACqCABwAAAABAARTwAAAAAAAogAIeAAAAAAAFUMADAAAAAKAACngAAAAAABRAAQ8AAAAAgAIo4AEAAAAAUAAFPAAAAAAACqCABwAAAABAARTwAAAAAAAogAIeAAAAAAAFUMADAAAAAKCA/wNo1hx5Y5atqAAAAABJRU5ErkJggg==" width="672" style="display: block; margin: auto;" /></p>
+<pre class="r"><code>endpoints(sforb_pH_2)</code></pre>
 <pre><code>$covariates
       pH
 50% 5.75
@@ -2191,8 +2421,7 @@ $SFORB
 $distimes
          DT50     DT90 DT50back DT50_meso_b1 DT50_meso_b2
 meso 16.86549 73.15824 22.02282     7.119554     26.33839</code></pre>
-<div class="sourceCode" id="cb70"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="../../reference/endpoints.html">endpoints</a></span><span class="op">(</span><span class="va">sforb_pH_2</span>, covariates <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>pH <span class="op">=</span> <span class="fl">7</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
+<pre class="r"><code>endpoints(sforb_pH_2, covariates = c(pH = 7))</code></pre>
 <pre><code>$covariates
      pH
 User  7
@@ -2209,22 +2438,21 @@ $distimes
          DT50     DT90 DT50back DT50_meso_b1 DT50_meso_b2
 meso 7.932495 36.93311 11.11797     5.205671        18.26</code></pre>
 </div>
-<div class="section level3">
-<h3 id="hs">HS<a class="anchor" aria-label="anchor" href="#hs"></a>
-</h3>
-<div class="sourceCode" id="cb72"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">hs_pH</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/saem.html">saem</a></span><span class="op">(</span><span class="va">f_sep_const</span><span class="op">[</span><span class="st">"HS"</span>, <span class="op">]</span>, no_random_effect <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"meso_0"</span><span class="op">)</span>,</span>
-<span>  covariates <span class="op">=</span> <span class="va">pH</span>,</span>
-<span>  covariate_models <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">log_k1</span> <span class="op">~</span> <span class="va">pH</span>, <span class="va">log_k2</span> <span class="op">~</span> <span class="va">pH</span>, <span class="va">log_tb</span> <span class="op">~</span> <span class="va">pH</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
-<div class="sourceCode" id="cb73"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">hs_pH</span><span class="op">)</span><span class="op">$</span><span class="va">confint_trans</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span>digits <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
-<thead><tr class="header">
+<div id="hs" class="section level2">
+<h2>HS</h2>
+<pre class="r"><code>hs_pH &lt;- saem(f_sep_const[&quot;HS&quot;, ], no_random_effect = c(&quot;meso_0&quot;),
+  covariates = pH,
+  covariate_models = list(log_k1 ~ pH, log_k2 ~ pH, log_tb ~ pH))</code></pre>
+<pre class="r"><code>summary(hs_pH)$confint_trans |&gt; kable(digits = 2)</code></pre>
+<table>
+<thead>
+<tr class="header">
 <th align="left"></th>
 <th align="right">est.</th>
 <th align="right">lower</th>
 <th align="right">upper</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="left">meso_0</td>
@@ -2294,39 +2522,37 @@ meso 7.932495 36.93311 11.11797     5.205671        18.26</code></pre>
 </tr>
 </tbody>
 </table>
-<div class="sourceCode" id="cb74"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="../../reference/illparms.html">illparms</a></span><span class="op">(</span><span class="va">hs_pH</span><span class="op">)</span></span></code></pre></div>
-<pre><code>[1] "sd(log_tb)"      "beta_pH(log_tb)"</code></pre>
+<pre class="r"><code>illparms(hs_pH)</code></pre>
+<pre><code>[1] &quot;sd(log_tb)&quot;      &quot;beta_pH(log_tb)&quot;</code></pre>
 <p>According to the output of the <code>illparms</code> function, the
 random effect on the break time <code>tb</code> cannot reliably be
 quantified, neither can the influence of soil pH on <code>tb</code>. The
 fit is repeated without the corresponding covariate model, and no
 ill-defined parameters remain.</p>
-<div class="sourceCode" id="cb76"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">hs_pH_2</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">hs_pH</span>, covariate_models <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">log_k1</span> <span class="op">~</span> <span class="va">pH</span>, <span class="va">log_k2</span> <span class="op">~</span> <span class="va">pH</span><span class="op">)</span><span class="op">)</span></span>
-<span><span class="fu"><a href="../../reference/illparms.html">illparms</a></span><span class="op">(</span><span class="va">hs_pH_2</span><span class="op">)</span></span></code></pre></div>
+<pre class="r"><code>hs_pH_2 &lt;- update(hs_pH, covariate_models = list(log_k1 ~ pH, log_k2 ~ pH))
+illparms(hs_pH_2)</code></pre>
 <p>Model comparison confirms that this model is preferable to the fit
 without covariate influence, and also to the first version with
 covariate influence.</p>
-<div class="sourceCode" id="cb77"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_saem_2</span><span class="op">[[</span><span class="st">"HS"</span>, <span class="st">"const"</span><span class="op">]</span><span class="op">]</span>, <span class="va">hs_pH</span>, <span class="va">hs_pH_2</span>, test <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
+<pre class="r"><code>anova(f_saem_2[[&quot;HS&quot;, &quot;const&quot;]], hs_pH, hs_pH_2, test = TRUE)</code></pre>
 <pre><code>Data: 116 observations of 1 variable(s) grouped in 18 datasets
 
                           npar    AIC    BIC     Lik  Chisq Df Pr(&gt;Chisq)    
-f_saem_2[["HS", "const"]]    8 780.08 787.20 -382.04                         
+f_saem_2[[&quot;HS&quot;, &quot;const&quot;]]    8 780.08 787.20 -382.04                         
 hs_pH_2                     10 766.47 775.37 -373.23 17.606  2  0.0001503 ***
 hs_pH                       11 769.80 779.59 -373.90  0.000  1  1.0000000    
 ---
-Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</code></pre>
-<div class="sourceCode" id="cb79"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">hs_pH_2</span><span class="op">)</span><span class="op">$</span><span class="va">confint_trans</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span>digits <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
-<thead><tr class="header">
+Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1</code></pre>
+<pre class="r"><code>summary(hs_pH_2)$confint_trans |&gt; kable(digits = 2)</code></pre>
+<table>
+<thead>
+<tr class="header">
 <th align="left"></th>
 <th align="right">est.</th>
 <th align="right">lower</th>
 <th align="right">upper</th>
-</tr></thead>
+</tr>
+</thead>
 <tbody>
 <tr class="odd">
 <td align="left">meso_0</td>
@@ -2390,11 +2616,9 @@ Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</code></pre>
 </tr>
 </tbody>
 </table>
-<div class="sourceCode" id="cb80"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">hs_pH_2</span><span class="op">)</span></span></code></pre></div>
-<p><img src="2023_mesotrione_parent_files/figure-html/unnamed-chunk-30-1.png" width="700" style="display: block; margin: auto;"></p>
-<div class="sourceCode" id="cb81"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="../../reference/endpoints.html">endpoints</a></span><span class="op">(</span><span class="va">hs_pH_2</span><span class="op">)</span></span></code></pre></div>
+<pre class="r"><code>plot(hs_pH_2)</code></pre>
+<p><img src="data:image/png;base64,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" width="672" style="display: block; margin: auto;" /></p>
+<pre class="r"><code>endpoints(hs_pH_2)</code></pre>
 <pre><code>$covariates
       pH
 50% 5.75
@@ -2402,8 +2626,7 @@ Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</code></pre>
 $distimes
          DT50     DT90 DT50back  DT50_k1  DT50_k2
 meso 14.68725 82.45287 24.82079 14.68725 29.29299</code></pre>
-<div class="sourceCode" id="cb83"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="../../reference/endpoints.html">endpoints</a></span><span class="op">(</span><span class="va">hs_pH_2</span>, covariates <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>pH <span class="op">=</span> <span class="fl">7</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
+<pre class="r"><code>endpoints(hs_pH_2, covariates = c(pH = 7))</code></pre>
 <pre><code>$covariates
      pH
 User  7
@@ -2412,13 +2635,11 @@ $distimes
          DT50     DT90 DT50back  DT50_k1  DT50_k2
 meso 8.298536 38.85371 11.69613 8.298536 15.71561</code></pre>
 </div>
-<div class="section level3">
-<h3 id="comparison-across-parent-models">Comparison across parent models<a class="anchor" aria-label="anchor" href="#comparison-across-parent-models"></a>
-</h3>
+<div id="comparison-across-parent-models" class="section level2">
+<h2>Comparison across parent models</h2>
 <p>After model reduction for all models with pH influence, they are
 compared with each other.</p>
-<div class="sourceCode" id="cb85"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">sfo_pH</span>, <span class="va">fomc_pH_2</span>, <span class="va">dfop_pH_2</span>, <span class="va">dfop_pH_4</span>, <span class="va">sforb_pH_2</span>, <span class="va">hs_pH_2</span><span class="op">)</span></span></code></pre></div>
+<pre class="r"><code>anova(sfo_pH, fomc_pH_2, dfop_pH_2, dfop_pH_4, sforb_pH_2, hs_pH_2)</code></pre>
 <pre><code>Data: 116 observations of 1 variable(s) grouped in 18 datasets
 
            npar    AIC    BIC     Lik
@@ -2433,8 +2654,7 @@ hs_pH_2      10 766.47 775.37 -373.23</code></pre>
 selected as the best fit.</p>
 <p>The endpoints resulting from this model are listed below. Please
 refer to the Appendix for a detailed listing.</p>
-<div class="sourceCode" id="cb87"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="../../reference/endpoints.html">endpoints</a></span><span class="op">(</span><span class="va">dfop_pH_2</span><span class="op">)</span></span></code></pre></div>
+<pre class="r"><code>endpoints(dfop_pH_2)</code></pre>
 <pre><code>$covariates
       pH
 50% 5.75
@@ -2442,8 +2662,7 @@ refer to the Appendix for a detailed listing.</p>
 $distimes
          DT50     DT90 DT50back  DT50_k1  DT50_k2
 meso 18.36876 73.51841 22.13125 4.191901 23.98672</code></pre>
-<div class="sourceCode" id="cb89"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="../../reference/endpoints.html">endpoints</a></span><span class="op">(</span><span class="va">dfop_pH_2</span>, covariates <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>pH <span class="op">=</span> <span class="fl">7</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
+<pre class="r"><code>endpoints(dfop_pH_2, covariates = c(pH = 7))</code></pre>
 <pre><code>$covariates
      pH
 User  7
@@ -2453,35 +2672,29 @@ $distimes
 meso 8.346428 28.34437 8.532507 4.191901 8.753618</code></pre>
 </div>
 </div>
-<div class="section level2">
-<h2 id="conclusions">Conclusions<a class="anchor" aria-label="anchor" href="#conclusions"></a>
-</h2>
+<div id="conclusions" class="section level1">
+<h1>Conclusions</h1>
 <p>These evaluations demonstrate that covariate effects can be included
 for all types of parent degradation models. These models can then be
 further refined to make them fully identifiable.</p>
 </div>
-<div class="section level2">
-<h2 id="appendix">Appendix<a class="anchor" aria-label="anchor" href="#appendix"></a>
-</h2>
-<div class="section level3">
-<h3 id="hierarchical-fit-listings">Hierarchical fit listings<a class="anchor" aria-label="anchor" href="#hierarchical-fit-listings"></a>
-</h3>
-<div class="section level4">
-<h4 id="fits-without-covariate-effects">Fits without covariate effects<a class="anchor" aria-label="anchor" href="#fits-without-covariate-effects"></a>
-</h4>
+<div id="appendix" class="section level1">
+<h1>Appendix</h1>
+<div id="hierarchical-fit-listings" class="section level2">
+<h2>Hierarchical fit listings</h2>
+<div id="fits-without-covariate-effects" class="section level3">
+<h3>Fits without covariate effects</h3>
 
 </div>
-<div class="section level4">
-<h4 id="fits-with-covariate-effects">Fits with covariate effects<a class="anchor" aria-label="anchor" href="#fits-with-covariate-effects"></a>
-</h4>
+<div id="fits-with-covariate-effects" class="section level3">
+<h3>Fits with covariate effects</h3>
 
 </div>
 </div>
-<div class="section level3">
-<h3 id="session-info">Session info<a class="anchor" aria-label="anchor" href="#session-info"></a>
-</h3>
-<pre><code>R version 4.3.1 (2023-06-16)
-Platform: x86_64-pc-linux-gnu (64-bit)
+<div id="session-info" class="section level2">
+<h2>Session info</h2>
+<pre><code>R version 4.4.2 (2024-10-31)
+Platform: x86_64-pc-linux-gnu
 Running under: Debian GNU/Linux 12 (bookworm)
 
 Matrix products: default
@@ -2504,61 +2717,74 @@ attached base packages:
 [8] base     
 
 other attached packages:
-[1] saemix_3.2 npde_3.3   knitr_1.42 mkin_1.2.6
+[1] saemix_3.3      npde_3.5        knitr_1.49      mkin_1.2.9     
+[5] rmarkdown_2.29  nvimcom_0.9-167
 
 loaded via a namespace (and not attached):
- [1] sass_0.4.6        utf8_1.2.3        generics_0.1.3    stringi_1.7.12   
- [5] lattice_0.21-8    digest_0.6.31     magrittr_2.0.3    evaluate_0.21    
- [9] grid_4.3.1        fastmap_1.1.1     cellranger_1.1.0  rprojroot_2.0.3  
-[13] jsonlite_1.8.4    DBI_1.1.3         mclust_6.0.0      gridExtra_2.3    
-[17] purrr_1.0.1       fansi_1.0.4       scales_1.2.1      codetools_0.2-19 
-[21] textshaping_0.3.6 jquerylib_0.1.4   cli_3.6.1         rlang_1.1.1      
-[25] munsell_0.5.0     cachem_1.0.8      yaml_2.3.7        tools_4.3.1      
-[29] memoise_2.0.1     dplyr_1.1.2       colorspace_2.1-0  ggplot2_3.4.2    
-[33] vctrs_0.6.2       R6_2.5.1          zoo_1.8-12        lifecycle_1.0.3  
-[37] stringr_1.5.0     fs_1.6.2          MASS_7.3-60       ragg_1.2.5       
-[41] pkgconfig_2.0.3   desc_1.4.2        pkgdown_2.0.7     bslib_0.4.2      
-[45] pillar_1.9.0      gtable_0.3.3      glue_1.6.2        systemfonts_1.0.4
-[49] highr_0.10        xfun_0.39         tibble_3.2.1      lmtest_0.9-40    
-[53] tidyselect_1.2.0  htmltools_0.5.5   nlme_3.1-162      rmarkdown_2.21   
-[57] compiler_4.3.1    readxl_1.4.2     </code></pre>
+ [1] gtable_0.3.6      jsonlite_1.8.9    dplyr_1.1.4       compiler_4.4.2   
+ [5] tidyselect_1.2.1  colorout_1.3-2    gridExtra_2.3     jquerylib_0.1.4  
+ [9] scales_1.3.0      readxl_1.4.3      yaml_2.3.10       fastmap_1.2.0    
+[13] lattice_0.22-6    ggplot2_3.5.1     R6_2.5.1          generics_0.1.3   
+[17] lmtest_0.9-40     MASS_7.3-61       tibble_3.2.1      munsell_0.5.1    
+[21] bslib_0.8.0       pillar_1.9.0      rlang_1.1.4       utf8_1.2.4       
+[25] cachem_1.1.0      xfun_0.49         sass_0.4.9        cli_3.6.3        
+[29] magrittr_2.0.3    digest_0.6.37     grid_4.4.2        mclust_6.1.1     
+[33] lifecycle_1.0.4   nlme_3.1-166      vctrs_0.6.5       evaluate_1.0.1   
+[37] glue_1.8.0        cellranger_1.1.0  codetools_0.2-20  zoo_1.8-12       
+[41] fansi_1.0.6       colorspace_2.1-1  tools_4.4.2       pkgconfig_2.0.3  
+[45] htmltools_0.5.8.1</code></pre>
 </div>
-<div class="section level3">
-<h3 id="hardware-info">Hardware info<a class="anchor" aria-label="anchor" href="#hardware-info"></a>
-</h3>
+<div id="hardware-info" class="section level2">
+<h2>Hardware info</h2>
 <pre><code>CPU model: AMD Ryzen 9 7950X 16-Core Processor</code></pre>
-<pre><code>MemTotal:       64928100 kB</code></pre>
+<pre><code>MemTotal:       64927788 kB</code></pre>
 </div>
 </div>
-  </div>
 
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
 
-        <nav id="toc" data-toggle="toc"><h2 data-toc-skip>Contents</h2>
-    </nav>
-</div>
+
 
 </div>
 
+<script>
 
+// add bootstrap table styles to pandoc tables
+function bootstrapStylePandocTables() {
+  $('tr.odd').parent('tbody').parent('table').addClass('table table-condensed');
+}
+$(document).ready(function () {
+  bootstrapStylePandocTables();
+});
 
-      <footer><div class="copyright">
-  <p></p>
-<p>Developed by Johannes Ranke.</p>
-</div>
 
-<div class="pkgdown">
-  <p></p>
-<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
+</script>
 
-      </footer>
-</div>
+<!-- tabsets -->
+
+<script>
+$(document).ready(function () {
+  window.buildTabsets("TOC");
+});
+
+$(document).ready(function () {
+  $('.tabset-dropdown > .nav-tabs > li').click(function () {
+    $(this).parent().toggleClass('nav-tabs-open');
+  });
+});
+</script>
 
-  
+<!-- code folding -->
 
 
-  
+<!-- dynamically load mathjax for compatibility with self-contained -->
+<script>
+  (function () {
+    var script = document.createElement("script");
+    script.type = "text/javascript";
+    script.src  = "https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML";
+    document.getElementsByTagName("head")[0].appendChild(script);
+  })();
+</script>
 
-  </body>
+</body>
 </html>
diff --git a/docs/articles/prebuilt/2023_mesotrione_parent_files/figure-html/unnamed-chunk-14-1.png b/docs/articles/prebuilt/2023_mesotrione_parent_files/figure-html/unnamed-chunk-14-1.png
index 863a48bd..719bf0e9 100644
--- a/docs/articles/prebuilt/2023_mesotrione_parent_files/figure-html/unnamed-chunk-14-1.png
+++ b/docs/articles/prebuilt/2023_mesotrione_parent_files/figure-html/unnamed-chunk-14-1.png
diff --git a/docs/articles/prebuilt/2023_mesotrione_parent_files/figure-html/unnamed-chunk-19-1.png b/docs/articles/prebuilt/2023_mesotrione_parent_files/figure-html/unnamed-chunk-19-1.png
index 256b2b68..6afcac48 100644
--- a/docs/articles/prebuilt/2023_mesotrione_parent_files/figure-html/unnamed-chunk-19-1.png
+++ b/docs/articles/prebuilt/2023_mesotrione_parent_files/figure-html/unnamed-chunk-19-1.png
diff --git a/docs/articles/prebuilt/2023_mesotrione_parent_files/figure-html/unnamed-chunk-25-1.png b/docs/articles/prebuilt/2023_mesotrione_parent_files/figure-html/unnamed-chunk-25-1.png
index 59011020..50253fb1 100644
--- a/docs/articles/prebuilt/2023_mesotrione_parent_files/figure-html/unnamed-chunk-25-1.png
+++ b/docs/articles/prebuilt/2023_mesotrione_parent_files/figure-html/unnamed-chunk-25-1.png
diff --git a/docs/articles/prebuilt/2023_mesotrione_parent_files/figure-html/unnamed-chunk-30-1.png b/docs/articles/prebuilt/2023_mesotrione_parent_files/figure-html/unnamed-chunk-30-1.png
index f427bc39..c8b77724 100644
--- a/docs/articles/prebuilt/2023_mesotrione_parent_files/figure-html/unnamed-chunk-30-1.png
+++ b/docs/articles/prebuilt/2023_mesotrione_parent_files/figure-html/unnamed-chunk-30-1.png
diff --git a/docs/articles/prebuilt/2023_mesotrione_parent_files/figure-html/unnamed-chunk-8-1.png b/docs/articles/prebuilt/2023_mesotrione_parent_files/figure-html/unnamed-chunk-8-1.png
index 7c3b460b..8fa204a3 100644
--- a/docs/articles/prebuilt/2023_mesotrione_parent_files/figure-html/unnamed-chunk-8-1.png
+++ b/docs/articles/prebuilt/2023_mesotrione_parent_files/figure-html/unnamed-chunk-8-1.png
diff --git a/docs/articles/twa.html b/docs/articles/twa.html
index 4b3e11a3..19bc4761 100644
--- a/docs/articles/twa.html
+++ b/docs/articles/twa.html
@@ -4,145 +4,90 @@
 <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
 <meta charset="utf-8">
 <meta http-equiv="X-UA-Compatible" content="IE=edge">
-<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
 <title>Calculation of time weighted average concentrations with mkin • mkin</title>
-<!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous">
-<script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css">
-<script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous">
-<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous">
-<!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet">
-<script src="../pkgdown.js"></script><meta property="og:title" content="Calculation of time weighted average concentrations with mkin">
-<meta property="og:description" content="mkin">
-<!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]-->
+<script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
+<link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet">
+<script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet">
+<link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet">
+<script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Calculation of time weighted average concentrations with mkin">
 </head>
-<body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-article">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav">
-<li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu">
-<li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+<body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto">
+<li class="nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="active nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles">
+<li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul>
 </li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
       </ul>
-<ul class="nav navbar-nav navbar-right">
-<li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
+<ul class="navbar-nav">
+<li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json">
+</form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul>
 </div>
-<!--/.nav-collapse -->
+
+
   </div>
-<!--/.container -->
-</div>
-<!--/.navbar -->
+</nav><div class="container template-article">
+
+
 
-      
 
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header toc-ignore">
-      <h1 data-toc-skip>Calculation of time weighted average
-concentrations with mkin</h1>
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Calculation of time weighted average concentrations with mkin</h1>
                         <h4 data-toc-skip class="author">Johannes
 Ranke</h4>
             
             <h4 data-toc-skip class="date">Last change 18 September 2019
-(rebuilt 2023-10-30)</h4>
+(rebuilt 2025-02-13)</h4>
       
       <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/twa.rmd" class="external-link"><code>vignettes/twa.rmd</code></a></small>
-      <div class="hidden name"><code>twa.rmd</code></div>
-
+      <div class="d-none name"><code>twa.rmd</code></div>
     </div>
 
     
@@ -159,48 +104,44 @@ model are calculated using the formulas given in the FOCUS kinetics
 guidance <span class="citation">(FOCUS Work Group on Degradation
 Kinetics 2014, 251)</span>:</p>
 <p>SFO:</p>
-<p><span class="math display">\[c_\textrm{twa} = c_0 \frac{\left( 1 -
-e^{- k t} \right)}{ k t} \]</span></p>
+<p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>c</mi><mtext mathvariant="normal">twa</mtext></msub><mo>=</mo><msub><mi>c</mi><mn>0</mn></msub><mfrac><mrow><mo stretchy="true" form="prefix">(</mo><mn>1</mn><mo>−</mo><msup><mi>e</mi><mrow><mo>−</mo><mi>k</mi><mi>t</mi></mrow></msup><mo stretchy="true" form="postfix">)</mo></mrow><mrow><mi>k</mi><mi>t</mi></mrow></mfrac></mrow><annotation encoding="application/x-tex">c_\textrm{twa} = c_0 \frac{\left( 1 - e^{- k t} \right)}{ k t} </annotation></semantics></math></p>
 <p>FOMC:</p>
-<p><span class="math display">\[c_\textrm{twa} = c_0 \frac{\beta}{t (1 -
-\alpha)}
-                   \left( \left(\frac{t}{\beta} + 1 \right)^{1 - \alpha}
-- 1 \right) \]</span></p>
+<p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>c</mi><mtext mathvariant="normal">twa</mtext></msub><mo>=</mo><msub><mi>c</mi><mn>0</mn></msub><mfrac><mi>β</mi><mrow><mi>t</mi><mrow><mo stretchy="true" form="prefix">(</mo><mn>1</mn><mo>−</mo><mi>α</mi><mo stretchy="true" form="postfix">)</mo></mrow></mrow></mfrac><mrow><mo stretchy="true" form="prefix">(</mo><msup><mrow><mo stretchy="true" form="prefix">(</mo><mfrac><mi>t</mi><mi>β</mi></mfrac><mo>+</mo><mn>1</mn><mo stretchy="true" form="postfix">)</mo></mrow><mrow><mn>1</mn><mo>−</mo><mi>α</mi></mrow></msup><mo>−</mo><mn>1</mn><mo stretchy="true" form="postfix">)</mo></mrow></mrow><annotation encoding="application/x-tex">c_\textrm{twa} = c_0 \frac{\beta}{t (1 - \alpha)}
+                   \left( \left(\frac{t}{\beta} + 1 \right)^{1 - \alpha} - 1 \right) </annotation></semantics></math></p>
 <p>DFOP:</p>
-<p><span class="math display">\[c_\textrm{twa} = \frac{c_0}{t} \left(
+<p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>c</mi><mtext mathvariant="normal">twa</mtext></msub><mo>=</mo><mfrac><msub><mi>c</mi><mn>0</mn></msub><mi>t</mi></mfrac><mrow><mo stretchy="true" form="prefix">(</mo><mfrac><mi>g</mi><msub><mi>k</mi><mn>1</mn></msub></mfrac><mrow><mo stretchy="true" form="prefix">(</mo><mn>1</mn><mo>−</mo><msup><mi>e</mi><mrow><mo>−</mo><msub><mi>k</mi><mn>1</mn></msub><mi>t</mi></mrow></msup><mo stretchy="true" form="postfix">)</mo></mrow><mo>+</mo><mfrac><mrow><mn>1</mn><mo>−</mo><mi>g</mi></mrow><msub><mi>k</mi><mn>2</mn></msub></mfrac><mrow><mo stretchy="true" form="prefix">(</mo><mn>1</mn><mo>−</mo><msup><mi>e</mi><mrow><mo>−</mo><msub><mi>k</mi><mn>2</mn></msub><mi>t</mi></mrow></msup><mo stretchy="true" form="postfix">)</mo></mrow><mo stretchy="true" form="postfix">)</mo></mrow></mrow><annotation encoding="application/x-tex">c_\textrm{twa} = \frac{c_0}{t} \left(
   \frac{g}{k_1} \left( 1 - e^{- k_1 t} \right) +
-  \frac{1-g}{k_2} \left( 1 - e^{- k_2 t} \right)  \right) \]</span></p>
-<p>HS for <span class="math inline">\(t &gt; t_b\)</span>:</p>
-<p><span class="math display">\[c_\textrm{twa} = \frac{c_0}{t} \left(
+  \frac{1-g}{k_2} \left( 1 - e^{- k_2 t} \right)  \right) </annotation></semantics></math></p>
+<p>HS for
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>t</mi><mo>&gt;</mo><msub><mi>t</mi><mi>b</mi></msub></mrow><annotation encoding="application/x-tex">t &gt; t_b</annotation></semantics></math>:</p>
+<p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>c</mi><mtext mathvariant="normal">twa</mtext></msub><mo>=</mo><mfrac><msub><mi>c</mi><mn>0</mn></msub><mi>t</mi></mfrac><mrow><mo stretchy="true" form="prefix">(</mo><mfrac><mn>1</mn><msub><mi>k</mi><mn>1</mn></msub></mfrac><mrow><mo stretchy="true" form="prefix">(</mo><mn>1</mn><mo>−</mo><msup><mi>e</mi><mrow><mo>−</mo><msub><mi>k</mi><mn>1</mn></msub><msub><mi>t</mi><mi>b</mi></msub></mrow></msup><mo stretchy="true" form="postfix">)</mo></mrow><mo>+</mo><mfrac><msup><mi>e</mi><mrow><mo>−</mo><msub><mi>k</mi><mn>1</mn></msub><msub><mi>t</mi><mi>b</mi></msub></mrow></msup><msub><mi>k</mi><mn>2</mn></msub></mfrac><mrow><mo stretchy="true" form="prefix">(</mo><mn>1</mn><mo>−</mo><msup><mi>e</mi><mrow><mo>−</mo><msub><mi>k</mi><mn>2</mn></msub><mrow><mo stretchy="true" form="prefix">(</mo><mi>t</mi><mo>−</mo><msub><mi>t</mi><mi>b</mi></msub><mo stretchy="true" form="postfix">)</mo></mrow></mrow></msup><mo stretchy="true" form="postfix">)</mo></mrow><mo stretchy="true" form="postfix">)</mo></mrow></mrow><annotation encoding="application/x-tex">c_\textrm{twa} = \frac{c_0}{t} \left(
   \frac{1}{k_1} \left( 1 - e^{- k_1 t_b} \right) +
-  \frac{e^{- k_1 t_b}}{k_2} \left( 1 - e^{- k_2 (t - t_b)}
-\right)  \right) \]</span></p>
+  \frac{e^{- k_1 t_b}}{k_2} \left( 1 - e^{- k_2 (t - t_b)} \right)  \right) </annotation></semantics></math></p>
 <p>Often, the ratio between the time weighted average concentration
-<span class="math inline">\(c_\textrm{twa}\)</span> and the initial
-concentration <span class="math inline">\(c_0\)</span></p>
-<p><span class="math display">\[f_\textrm{twa} =
-\frac{c_\textrm{twa}}{c_0}\]</span></p>
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><msub><mi>c</mi><mtext mathvariant="normal">twa</mtext></msub><annotation encoding="application/x-tex">c_\textrm{twa}</annotation></semantics></math>
+and the initial concentration
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><msub><mi>c</mi><mn>0</mn></msub><annotation encoding="application/x-tex">c_0</annotation></semantics></math></p>
+<p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>f</mi><mtext mathvariant="normal">twa</mtext></msub><mo>=</mo><mfrac><msub><mi>c</mi><mtext mathvariant="normal">twa</mtext></msub><msub><mi>c</mi><mn>0</mn></msub></mfrac></mrow><annotation encoding="application/x-tex">f_\textrm{twa} = \frac{c_\textrm{twa}}{c_0}</annotation></semantics></math></p>
 <p>is needed. This can be calculated from the fitted initial
-concentration <span class="math inline">\(c_0\)</span> and the time
-weighted average concentration <span class="math inline">\(c_\textrm{twa}\)</span>, or directly from the
-model parameters using the following formulas:</p>
+concentration
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><msub><mi>c</mi><mn>0</mn></msub><annotation encoding="application/x-tex">c_0</annotation></semantics></math>
+and the time weighted average concentration
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><msub><mi>c</mi><mtext mathvariant="normal">twa</mtext></msub><annotation encoding="application/x-tex">c_\textrm{twa}</annotation></semantics></math>,
+or directly from the model parameters using the following formulas:</p>
 <p>SFO:</p>
-<p><span class="math display">\[f_\textrm{twa} = \frac{\left( 1 - e^{- k
-t} \right)}{k t} \]</span></p>
+<p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>f</mi><mtext mathvariant="normal">twa</mtext></msub><mo>=</mo><mfrac><mrow><mo stretchy="true" form="prefix">(</mo><mn>1</mn><mo>−</mo><msup><mi>e</mi><mrow><mo>−</mo><mi>k</mi><mi>t</mi></mrow></msup><mo stretchy="true" form="postfix">)</mo></mrow><mrow><mi>k</mi><mi>t</mi></mrow></mfrac></mrow><annotation encoding="application/x-tex">f_\textrm{twa} = \frac{\left( 1 - e^{- k t} \right)}{k t} </annotation></semantics></math></p>
 <p>FOMC:</p>
-<p><span class="math display">\[f_\textrm{twa} = \frac{\beta}{t (1 -
-\alpha)}
-                   \left( \left(\frac{t}{\beta} + 1 \right)^{1 - \alpha}
-- 1 \right) \]</span></p>
+<p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>f</mi><mtext mathvariant="normal">twa</mtext></msub><mo>=</mo><mfrac><mi>β</mi><mrow><mi>t</mi><mrow><mo stretchy="true" form="prefix">(</mo><mn>1</mn><mo>−</mo><mi>α</mi><mo stretchy="true" form="postfix">)</mo></mrow></mrow></mfrac><mrow><mo stretchy="true" form="prefix">(</mo><msup><mrow><mo stretchy="true" form="prefix">(</mo><mfrac><mi>t</mi><mi>β</mi></mfrac><mo>+</mo><mn>1</mn><mo stretchy="true" form="postfix">)</mo></mrow><mrow><mn>1</mn><mo>−</mo><mi>α</mi></mrow></msup><mo>−</mo><mn>1</mn><mo stretchy="true" form="postfix">)</mo></mrow></mrow><annotation encoding="application/x-tex">f_\textrm{twa} = \frac{\beta}{t (1 - \alpha)}
+                   \left( \left(\frac{t}{\beta} + 1 \right)^{1 - \alpha} - 1 \right) </annotation></semantics></math></p>
 <p>DFOP:</p>
-<p><span class="math display">\[f_\textrm{twa} = \frac{1}{t} \left(
+<p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>f</mi><mtext mathvariant="normal">twa</mtext></msub><mo>=</mo><mfrac><mn>1</mn><mi>t</mi></mfrac><mrow><mo stretchy="true" form="prefix">(</mo><mfrac><mi>g</mi><msub><mi>k</mi><mn>1</mn></msub></mfrac><mrow><mo stretchy="true" form="prefix">(</mo><mn>1</mn><mo>−</mo><msup><mi>e</mi><mrow><mo>−</mo><msub><mi>k</mi><mn>1</mn></msub><mi>t</mi></mrow></msup><mo stretchy="true" form="postfix">)</mo></mrow><mo>+</mo><mfrac><mrow><mn>1</mn><mo>−</mo><mi>g</mi></mrow><msub><mi>k</mi><mn>2</mn></msub></mfrac><mrow><mo stretchy="true" form="prefix">(</mo><mn>1</mn><mo>−</mo><msup><mi>e</mi><mrow><mo>−</mo><msub><mi>k</mi><mn>2</mn></msub><mi>t</mi></mrow></msup><mo stretchy="true" form="postfix">)</mo></mrow><mo stretchy="true" form="postfix">)</mo></mrow></mrow><annotation encoding="application/x-tex">f_\textrm{twa} = \frac{1}{t} \left(
   \frac{g}{k_1} \left( 1 - e^{- k_1 t} \right) +
-  \frac{1-g}{k_2} \left( 1 - e^{- k_2 t} \right)  \right) \]</span></p>
-<p>HS for <span class="math inline">\(t &gt; t_b\)</span>:</p>
-<p><span class="math display">\[f_\textrm{twa} = \frac{1}{t} \left(
+  \frac{1-g}{k_2} \left( 1 - e^{- k_2 t} \right)  \right) </annotation></semantics></math></p>
+<p>HS for
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>t</mi><mo>&gt;</mo><msub><mi>t</mi><mi>b</mi></msub></mrow><annotation encoding="application/x-tex">t &gt; t_b</annotation></semantics></math>:</p>
+<p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>f</mi><mtext mathvariant="normal">twa</mtext></msub><mo>=</mo><mfrac><mn>1</mn><mi>t</mi></mfrac><mrow><mo stretchy="true" form="prefix">(</mo><mfrac><mn>1</mn><msub><mi>k</mi><mn>1</mn></msub></mfrac><mrow><mo stretchy="true" form="prefix">(</mo><mn>1</mn><mo>−</mo><msup><mi>e</mi><mrow><mo>−</mo><msub><mi>k</mi><mn>1</mn></msub><msub><mi>t</mi><mi>b</mi></msub></mrow></msup><mo stretchy="true" form="postfix">)</mo></mrow><mo>+</mo><mfrac><msup><mi>e</mi><mrow><mo>−</mo><msub><mi>k</mi><mn>1</mn></msub><msub><mi>t</mi><mi>b</mi></msub></mrow></msup><msub><mi>k</mi><mn>2</mn></msub></mfrac><mrow><mo stretchy="true" form="prefix">(</mo><mn>1</mn><mo>−</mo><msup><mi>e</mi><mrow><mo>−</mo><msub><mi>k</mi><mn>2</mn></msub><mrow><mo stretchy="true" form="prefix">(</mo><mi>t</mi><mo>−</mo><msub><mi>t</mi><mi>b</mi></msub><mo stretchy="true" form="postfix">)</mo></mrow></mrow></msup><mo stretchy="true" form="postfix">)</mo></mrow><mo stretchy="true" form="postfix">)</mo></mrow></mrow><annotation encoding="application/x-tex">f_\textrm{twa} = \frac{1}{t} \left(
   \frac{1}{k_1} \left( 1 - e^{- k_1 t_b} \right) +
-  \frac{e^{- k_1 t_b}}{k_2} \left( 1 - e^{- k_2 (t - t_b)}
-\right)  \right) \]</span></p>
+  \frac{e^{- k_1 t_b}}{k_2} \left( 1 - e^{- k_2 (t - t_b)} \right)  \right) </annotation></semantics></math></p>
 <p>Note that a method for calculating maximum moving window time
 weighted average concentrations for a model fitted by ‘mkinfit’ or from
 parent decline model parameters is included in the
@@ -214,33 +155,26 @@ Estimating Persistence and Degradation Kinetics from Environmental Fate
 Studies on Pesticides in EU Registration</em>. 1.1 ed. <a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics" class="external-link">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a>.
 </div>
 </div>
-  </div>
-
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-
-      </div>
-
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside>
 </div>
 
 
 
-      <footer><div class="copyright">
-  <p></p>
-<p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p>
-<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer>
+    </footer>
 </div>
 
-  
 
 
-  
+
 
   </body>
 </html>
diff --git a/docs/articles/twa_files/accessible-code-block-0.0.1/empty-anchor.js b/docs/articles/twa_files/accessible-code-block-0.0.1/empty-anchor.js
deleted file mode 100644
index ca349fd6..00000000
--- a/docs/articles/twa_files/accessible-code-block-0.0.1/empty-anchor.js
+++ /dev/null
@@ -1,15 +0,0 @@
-// Hide empty <a> tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) -->
-// v0.0.1
-// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020.
-
-document.addEventListener('DOMContentLoaded', function() {
-  const codeList = document.getElementsByClassName("sourceCode");
-  for (var i = 0; i < codeList.length; i++) {
-    var linkList = codeList[i].getElementsByTagName('a');
-    for (var j = 0; j < linkList.length; j++) {
-      if (linkList[j].innerHTML === "") {
-        linkList[j].setAttribute('aria-hidden', 'true');
-      }
-    }
-  }
-});
diff --git a/docs/articles/twa_files/header-attrs-2.6/header-attrs.js b/docs/articles/twa_files/header-attrs-2.6/header-attrs.js
deleted file mode 100644
index dd57d92e..00000000
--- a/docs/articles/twa_files/header-attrs-2.6/header-attrs.js
+++ /dev/null
@@ -1,12 +0,0 @@
-// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
-// be compatible with the behavior of Pandoc < 2.8).
-document.addEventListener('DOMContentLoaded', function(e) {
-  var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
-  var i, h, a;
-  for (i = 0; i < hs.length; i++) {
-    h = hs[i];
-    if (!/^h[1-6]$/i.test(h.tagName)) continue;  // it should be a header h1-h6
-    a = h.attributes;
-    while (a.length > 0) h.removeAttribute(a[0].name);
-  }
-});
diff --git a/docs/articles/twa_files/header-attrs-2.7/header-attrs.js b/docs/articles/twa_files/header-attrs-2.7/header-attrs.js
deleted file mode 100644
index dd57d92e..00000000
--- a/docs/articles/twa_files/header-attrs-2.7/header-attrs.js
+++ /dev/null
@@ -1,12 +0,0 @@
-// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
-// be compatible with the behavior of Pandoc < 2.8).
-document.addEventListener('DOMContentLoaded', function(e) {
-  var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
-  var i, h, a;
-  for (i = 0; i < hs.length; i++) {
-    h = hs[i];
-    if (!/^h[1-6]$/i.test(h.tagName)) continue;  // it should be a header h1-h6
-    a = h.attributes;
-    while (a.length > 0) h.removeAttribute(a[0].name);
-  }
-});
diff --git a/docs/articles/web_only/FOCUS_Z.html b/docs/articles/web_only/FOCUS_Z.html
index f19f59ad..e91942ce 100644
--- a/docs/articles/web_only/FOCUS_Z.html
+++ b/docs/articles/web_only/FOCUS_Z.html
@@ -4,144 +4,90 @@
 <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
 <meta charset="utf-8">
 <meta http-equiv="X-UA-Compatible" content="IE=edge">
-<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
 <title>Example evaluation of FOCUS dataset Z • mkin</title>
-<!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous">
-<script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../../bootstrap-toc.css">
-<script src="../../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous">
-<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous">
-<!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../../pkgdown.css" rel="stylesheet">
-<script src="../../pkgdown.js"></script><meta property="og:title" content="Example evaluation of FOCUS dataset Z">
-<meta property="og:description" content="mkin">
-<!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]-->
+<script src="../../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
+<link href="../../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet">
+<script src="../../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet">
+<link href="../../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet">
+<script src="../../deps/headroom-0.11.0/headroom.min.js"></script><script src="../../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../../deps/search-1.0.0/fuse.min.js"></script><script src="../../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../../pkgdown.js"></script><meta property="og:title" content="Example evaluation of FOCUS dataset Z">
 </head>
-<body data-spy="scroll" data-target="#toc">
-    
+<body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
 
-    <div class="container template-article">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
 
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav">
-<li>
-  <a href="../../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu">
-<li>
-      <a href="../../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto">
+<li class="nav-item"><a class="nav-link" href="../../reference/index.html">Reference</a></li>
+<li class="active nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles">
+<li><a class="dropdown-item" href="../../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul>
 </li>
-<li>
-  <a href="../../news/index.html">News</a>
-</li>
+<li class="nav-item"><a class="nav-link" href="../../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../../news/index.html">News</a></li>
       </ul>
-<ul class="nav navbar-nav navbar-right">
-<li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
+<ul class="navbar-nav">
+<li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../../search.json">
+</form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul>
 </div>
-<!--/.nav-collapse -->
+
+
   </div>
-<!--/.container -->
-</div>
-<!--/.navbar -->
+</nav><div class="container template-article">
+
 
-      
 
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header toc-ignore">
-      <h1 data-toc-skip>Example evaluation of FOCUS dataset Z</h1>
+
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Example evaluation of FOCUS dataset Z</h1>
                         <h4 data-toc-skip class="author">Johannes
 Ranke</h4>
             
             <h4 data-toc-skip class="date">Last change 16 January 2018
-(rebuilt 2023-10-30)</h4>
+(rebuilt 2025-02-13)</h4>
       
       <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/web_only/FOCUS_Z.rmd" class="external-link"><code>vignettes/web_only/FOCUS_Z.rmd</code></a></small>
-      <div class="hidden name"><code>FOCUS_Z.rmd</code></div>
-
+      <div class="d-none name"><code>FOCUS_Z.rmd</code></div>
     </div>
 
     
@@ -336,9 +282,11 @@ not fitted very well using the SFO model, as residues at a certain low
 level remain.</p>
 <p>Therefore, an additional model is offered here, using the single
 first-order reversible binding (SFORB) model for metabolite Z3. As
-expected, the <span class="math inline">\(\chi^2\)</span> error level is
-lower for metabolite Z3 using this model and the graphical fit for Z3 is
-improved. However, the covariance matrix is not returned.</p>
+expected, the
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><msup><mi>χ</mi><mn>2</mn></msup><annotation encoding="application/x-tex">\chi^2</annotation></semantics></math>
+error level is lower for metabolite Z3 using this model and the
+graphical fit for Z3 is improved. However, the covariance matrix is not
+returned.</p>
 <div class="sourceCode" id="cb38"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">Z.mkin.1</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span>Z0 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z1"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span>
 <span>                    Z1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z2"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span>
@@ -472,35 +420,26 @@ Studies on Pesticides in EU Registration</em>. 1.1 ed. <a href="http://esdac.jrc
 </div>
 </div>
 </div>
-  </div>
-
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-
-        <nav id="toc" data-toggle="toc"><h2 data-toc-skip>Contents</h2>
-    </nav>
-</div>
-
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside>
 </div>
 
 
 
-      <footer><div class="copyright">
-  <p></p>
-<p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p>
-<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer>
+    </footer>
 </div>
 
-  
 
 
-  
+
 
   </body>
 </html>
diff --git a/docs/articles/web_only/FOCUS_Z_files/accessible-code-block-0.0.1/empty-anchor.js b/docs/articles/web_only/FOCUS_Z_files/accessible-code-block-0.0.1/empty-anchor.js
deleted file mode 100644
index ca349fd6..00000000
--- a/docs/articles/web_only/FOCUS_Z_files/accessible-code-block-0.0.1/empty-anchor.js
+++ /dev/null
@@ -1,15 +0,0 @@
-// Hide empty <a> tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) -->
-// v0.0.1
-// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020.
-
-document.addEventListener('DOMContentLoaded', function() {
-  const codeList = document.getElementsByClassName("sourceCode");
-  for (var i = 0; i < codeList.length; i++) {
-    var linkList = codeList[i].getElementsByTagName('a');
-    for (var j = 0; j < linkList.length; j++) {
-      if (linkList[j].innerHTML === "") {
-        linkList[j].setAttribute('aria-hidden', 'true');
-      }
-    }
-  }
-});
diff --git a/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_1-1.png b/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_1-1.png
index be652d31..98bc135b 100644
--- a/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_1-1.png
+++ b/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_1-1.png
diff --git a/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_10-1.png b/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_10-1.png
index 11c158fa..c1011a35 100644
--- a/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_10-1.png
+++ b/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_10-1.png
diff --git a/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11-1.png b/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11-1.png
index fb44de27..dfd2dd50 100644
--- a/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11-1.png
+++ b/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11-1.png
diff --git a/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11a-1.png b/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11a-1.png
index 4603d247..74173f36 100644
--- a/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11a-1.png
+++ b/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11a-1.png
diff --git a/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11b-1.png b/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11b-1.png
index 1454c938..1c5793cc 100644
--- a/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11b-1.png
+++ b/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11b-1.png
diff --git a/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_2-1.png b/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_2-1.png
index be652d31..98bc135b 100644
--- a/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_2-1.png
+++ b/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_2-1.png
diff --git a/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_3-1.png b/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_3-1.png
index 59524035..0380ba43 100644
--- a/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_3-1.png
+++ b/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_3-1.png
diff --git a/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_5-1.png b/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_5-1.png
index 77965455..8c594ec9 100644
--- a/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_5-1.png
+++ b/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_5-1.png
diff --git a/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_6-1.png b/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_6-1.png
index 5ea3152e..84d473d6 100644
--- a/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_6-1.png
+++ b/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_6-1.png
diff --git a/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_7-1.png b/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_7-1.png
index d87105fb..87af8874 100644
--- a/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_7-1.png
+++ b/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_7-1.png
diff --git a/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_9-1.png b/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_9-1.png
index 06e3ff57..492cdcc8 100644
--- a/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_9-1.png
+++ b/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_9-1.png
diff --git a/docs/articles/web_only/FOCUS_Z_files/header-attrs-2.6/header-attrs.js b/docs/articles/web_only/FOCUS_Z_files/header-attrs-2.6/header-attrs.js
deleted file mode 100644
index dd57d92e..00000000
--- a/docs/articles/web_only/FOCUS_Z_files/header-attrs-2.6/header-attrs.js
+++ /dev/null
@@ -1,12 +0,0 @@
-// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
-// be compatible with the behavior of Pandoc < 2.8).
-document.addEventListener('DOMContentLoaded', function(e) {
-  var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
-  var i, h, a;
-  for (i = 0; i < hs.length; i++) {
-    h = hs[i];
-    if (!/^h[1-6]$/i.test(h.tagName)) continue;  // it should be a header h1-h6
-    a = h.attributes;
-    while (a.length > 0) h.removeAttribute(a[0].name);
-  }
-});
diff --git a/docs/articles/web_only/FOCUS_Z_files/header-attrs-2.7/header-attrs.js b/docs/articles/web_only/FOCUS_Z_files/header-attrs-2.7/header-attrs.js
deleted file mode 100644
index dd57d92e..00000000
--- a/docs/articles/web_only/FOCUS_Z_files/header-attrs-2.7/header-attrs.js
+++ /dev/null
@@ -1,12 +0,0 @@
-// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
-// be compatible with the behavior of Pandoc < 2.8).
-document.addEventListener('DOMContentLoaded', function(e) {
-  var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
-  var i, h, a;
-  for (i = 0; i < hs.length; i++) {
-    h = hs[i];
-    if (!/^h[1-6]$/i.test(h.tagName)) continue;  // it should be a header h1-h6
-    a = h.attributes;
-    while (a.length > 0) h.removeAttribute(a[0].name);
-  }
-});
diff --git a/docs/articles/web_only/NAFTA_examples.html b/docs/articles/web_only/NAFTA_examples.html
index 1c5be845..88551103 100644
--- a/docs/articles/web_only/NAFTA_examples.html
+++ b/docs/articles/web_only/NAFTA_examples.html
@@ -4,145 +4,90 @@
 <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
 <meta charset="utf-8">
 <meta http-equiv="X-UA-Compatible" content="IE=edge">
-<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
 <title>Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance • mkin</title>
-<!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous">
-<script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../../bootstrap-toc.css">
-<script src="../../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous">
-<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous">
-<!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../../pkgdown.css" rel="stylesheet">
-<script src="../../pkgdown.js"></script><meta property="og:title" content="Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance">
-<meta property="og:description" content="mkin">
-<!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]-->
+<script src="../../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
+<link href="../../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet">
+<script src="../../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet">
+<link href="../../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet">
+<script src="../../deps/headroom-0.11.0/headroom.min.js"></script><script src="../../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../../deps/search-1.0.0/fuse.min.js"></script><script src="../../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../../pkgdown.js"></script><meta property="og:title" content="Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance">
 </head>
-<body data-spy="scroll" data-target="#toc">
-    
+<body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
 
-    <div class="container template-article">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
 
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav">
-<li>
-  <a href="../../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu">
-<li>
-      <a href="../../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto">
+<li class="nav-item"><a class="nav-link" href="../../reference/index.html">Reference</a></li>
+<li class="active nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles">
+<li><a class="dropdown-item" href="../../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul>
 </li>
-<li>
-  <a href="../../news/index.html">News</a>
-</li>
+<li class="nav-item"><a class="nav-link" href="../../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../../news/index.html">News</a></li>
       </ul>
-<ul class="nav navbar-nav navbar-right">
-<li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
+<ul class="navbar-nav">
+<li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../../search.json">
+</form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul>
 </div>
-<!--/.nav-collapse -->
+
+
   </div>
-<!--/.container -->
-</div>
-<!--/.navbar -->
+</nav><div class="container template-article">
 
-      
 
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header toc-ignore">
-      <h1 data-toc-skip>Evaluation of example datasets from Attachment 1
-to the US EPA SOP for the NAFTA guidance</h1>
+
+
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance</h1>
                         <h4 data-toc-skip class="author">Johannes
 Ranke</h4>
             
             <h4 data-toc-skip class="date">26 February 2019 (rebuilt
-2023-10-30)</h4>
+2025-02-13)</h4>
       
       <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/web_only/NAFTA_examples.rmd" class="external-link"><code>vignettes/web_only/NAFTA_examples.rmd</code></a></small>
-      <div class="hidden name"><code>NAFTA_examples.rmd</code></div>
-
+      <div class="d-none name"><code>NAFTA_examples.rmd</code></div>
     </div>
 
     
@@ -174,7 +119,7 @@ same.</p>
 <pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre>
 <pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre>
 <div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p5a</span><span class="op">)</span></span></code></pre></div>
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p5a</span><span class="op">)</span></span></code></pre></div>
 <p><img src="NAFTA_examples_files/figure-html/p5a-1.png" width="700"></p>
 <div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p5a</span><span class="op">)</span></span></code></pre></div>
@@ -225,7 +170,7 @@ same.</p>
 <pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre>
 <pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre>
 <div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p5b</span><span class="op">)</span></span></code></pre></div>
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p5b</span><span class="op">)</span></span></code></pre></div>
 <p><img src="NAFTA_examples_files/figure-html/p5b-1.png" width="700"></p>
 <div class="sourceCode" id="cb11"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p5b</span><span class="op">)</span></span></code></pre></div>
@@ -276,7 +221,7 @@ same.</p>
 <pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre>
 <pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre>
 <div class="sourceCode" id="cb16"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p6</span><span class="op">)</span></span></code></pre></div>
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p6</span><span class="op">)</span></span></code></pre></div>
 <p><img src="NAFTA_examples_files/figure-html/p6-1.png" width="700"></p>
 <div class="sourceCode" id="cb17"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p6</span><span class="op">)</span></span></code></pre></div>
@@ -327,7 +272,7 @@ same.</p>
 <pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre>
 <pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre>
 <div class="sourceCode" id="cb22"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p7</span><span class="op">)</span></span></code></pre></div>
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p7</span><span class="op">)</span></span></code></pre></div>
 <p><img src="NAFTA_examples_files/figure-html/p7-1.png" width="700"></p>
 <div class="sourceCode" id="cb23"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p7</span><span class="op">)</span></span></code></pre></div>
@@ -386,7 +331,7 @@ lower value for the rate constant is used here.</p>
 <pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre>
 <pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre>
 <div class="sourceCode" id="cb28"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p8</span><span class="op">)</span></span></code></pre></div>
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p8</span><span class="op">)</span></span></code></pre></div>
 <p><img src="NAFTA_examples_files/figure-html/p8-1.png" width="700"></p>
 <div class="sourceCode" id="cb29"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p8</span><span class="op">)</span></span></code></pre></div>
@@ -441,7 +386,7 @@ lower value for the rate constant is used here.</p>
 <pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre>
 <pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre>
 <div class="sourceCode" id="cb34"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p9a</span><span class="op">)</span></span></code></pre></div>
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p9a</span><span class="op">)</span></span></code></pre></div>
 <p><img src="NAFTA_examples_files/figure-html/p9a-1.png" width="700"></p>
 <div class="sourceCode" id="cb35"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p9a</span><span class="op">)</span></span></code></pre></div>
@@ -495,7 +440,7 @@ suggest a simple exponential decline.</p>
 <pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre>
 <pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre>
 <div class="sourceCode" id="cb40"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p9b</span><span class="op">)</span></span></code></pre></div>
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p9b</span><span class="op">)</span></span></code></pre></div>
 <p><img src="NAFTA_examples_files/figure-html/p9b-1.png" width="700"></p>
 <div class="sourceCode" id="cb41"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p9b</span><span class="op">)</span></span></code></pre></div>
@@ -553,7 +498,7 @@ in PestDF and g in mkin. In mkin, it is restricted to the interval from
 <pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre>
 <pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre>
 <div class="sourceCode" id="cb47"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p10</span><span class="op">)</span></span></code></pre></div>
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p10</span><span class="op">)</span></span></code></pre></div>
 <p><img src="NAFTA_examples_files/figure-html/p10-1.png" width="700"></p>
 <div class="sourceCode" id="cb48"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p10</span><span class="op">)</span></span></code></pre></div>
@@ -613,7 +558,7 @@ difference in IORE model parameters between PestDF and mkin.</p>
 <pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre>
 <pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre>
 <div class="sourceCode" id="cb53"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p11</span><span class="op">)</span></span></code></pre></div>
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p11</span><span class="op">)</span></span></code></pre></div>
 <p><img src="NAFTA_examples_files/figure-html/p11-1.png" width="700"></p>
 <div class="sourceCode" id="cb54"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p11</span><span class="op">)</span></span></code></pre></div>
@@ -679,7 +624,7 @@ overparameterisation.</p>
 <pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre>
 <pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre>
 <div class="sourceCode" id="cb61"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p12a</span><span class="op">)</span></span></code></pre></div>
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p12a</span><span class="op">)</span></span></code></pre></div>
 <p><img src="NAFTA_examples_files/figure-html/p12a-1.png" width="700"></p>
 <div class="sourceCode" id="cb62"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p12a</span><span class="op">)</span></span></code></pre></div>
@@ -731,15 +676,14 @@ overparameterisation.</p>
 <pre><code><span><span class="co">## Warning in qt(alpha/2, rdf): NaNs produced</span></span></code></pre>
 <pre><code><span><span class="co">## Warning in qt(1 - alpha/2, rdf): NaNs produced</span></span></code></pre>
 <pre><code><span><span class="co">## Warning in pt(abs(tval), rdf, lower.tail = FALSE): NaNs produced</span></span></code></pre>
-<pre><code><span><span class="co">## Warning in sqrt(1/diag(V)): NaNs produced</span></span></code></pre>
-<pre><code><span><span class="co">## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result</span></span>
-<span><span class="co">## is doubtful</span></span></code></pre>
+<pre><code><span><span class="co">## Warning in cov2cor(ans$covar): diag(V) had non-positive or NA entries; the</span></span>
+<span><span class="co">## non-finite result may be dubious</span></span></code></pre>
 <pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre>
 <pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre>
-<div class="sourceCode" id="cb73"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p12b</span><span class="op">)</span></span></code></pre></div>
+<div class="sourceCode" id="cb72"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p12b</span><span class="op">)</span></span></code></pre></div>
 <p><img src="NAFTA_examples_files/figure-html/p12b-1.png" width="700"></p>
-<div class="sourceCode" id="cb74"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb73"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p12b</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## Sums of squares:</span></span>
 <span><span class="co">##      SFO     IORE     DFOP </span></span>
@@ -783,14 +727,14 @@ overparameterisation.</p>
 <div class="section level3">
 <h3 id="example-on-page-13">Example on page 13<a class="anchor" aria-label="anchor" href="#example-on-page-13"></a>
 </h3>
-<div class="sourceCode" id="cb76"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb75"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">p13</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p13"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre>
 <pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre>
-<div class="sourceCode" id="cb79"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p13</span><span class="op">)</span></span></code></pre></div>
+<div class="sourceCode" id="cb78"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p13</span><span class="op">)</span></span></code></pre></div>
 <p><img src="NAFTA_examples_files/figure-html/p13-1.png" width="700"></p>
-<div class="sourceCode" id="cb80"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb79"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p13</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## Sums of squares:</span></span>
 <span><span class="co">##      SFO     IORE     DFOP </span></span>
@@ -835,18 +779,17 @@ overparameterisation.</p>
 <div class="section level2">
 <h2 id="dt50-not-observed-in-the-study-and-dfop-problems-in-pestdf">DT50 not observed in the study and DFOP problems in PestDF<a class="anchor" aria-label="anchor" href="#dt50-not-observed-in-the-study-and-dfop-problems-in-pestdf"></a>
 </h2>
-<div class="sourceCode" id="cb82"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb81"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">p14</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p14"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## Warning in sqrt(diag(covar)): NaNs produced</span></span></code></pre>
-<pre><code><span><span class="co">## Warning in sqrt(1/diag(V)): NaNs produced</span></span></code></pre>
-<pre><code><span><span class="co">## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result</span></span>
-<span><span class="co">## is doubtful</span></span></code></pre>
+<pre><code><span><span class="co">## Warning in cov2cor(ans$covar): diag(V) had non-positive or NA entries; the</span></span>
+<span><span class="co">## non-finite result may be dubious</span></span></code></pre>
 <pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre>
 <pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre>
-<div class="sourceCode" id="cb88"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p14</span><span class="op">)</span></span></code></pre></div>
+<div class="sourceCode" id="cb86"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p14</span><span class="op">)</span></span></code></pre></div>
 <p><img src="NAFTA_examples_files/figure-html/p14-1.png" width="700"></p>
-<div class="sourceCode" id="cb89"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb87"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p14</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## Sums of squares:</span></span>
 <span><span class="co">##      SFO     IORE     DFOP </span></span>
@@ -893,18 +836,17 @@ same results in mkin and PestDF.</p>
 <div class="section level2">
 <h2 id="n-is-less-than-1-and-dfop-fraction-parameter-is-below-zero">N is less than 1 and DFOP fraction parameter is below zero<a class="anchor" aria-label="anchor" href="#n-is-less-than-1-and-dfop-fraction-parameter-is-below-zero"></a>
 </h2>
-<div class="sourceCode" id="cb91"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb89"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">p15a</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p15a"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## Warning in sqrt(diag(covar)): NaNs produced</span></span></code></pre>
-<pre><code><span><span class="co">## Warning in sqrt(1/diag(V)): NaNs produced</span></span></code></pre>
-<pre><code><span><span class="co">## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result</span></span>
-<span><span class="co">## is doubtful</span></span></code></pre>
+<pre><code><span><span class="co">## Warning in cov2cor(ans$covar): diag(V) had non-positive or NA entries; the</span></span>
+<span><span class="co">## non-finite result may be dubious</span></span></code></pre>
 <pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre>
 <pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre>
-<div class="sourceCode" id="cb97"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p15a</span><span class="op">)</span></span></code></pre></div>
+<div class="sourceCode" id="cb94"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p15a</span><span class="op">)</span></span></code></pre></div>
 <p><img src="NAFTA_examples_files/figure-html/p15a-1.png" width="700"></p>
-<div class="sourceCode" id="cb98"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb95"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p15a</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## Sums of squares:</span></span>
 <span><span class="co">##      SFO     IORE     DFOP </span></span>
@@ -944,16 +886,16 @@ same results in mkin and PestDF.</p>
 <span><span class="co">## </span></span>
 <span><span class="co">## Representative half-life:</span></span>
 <span><span class="co">## [1] 41.33</span></span></code></pre>
-<div class="sourceCode" id="cb100"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb97"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">p15b</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p15b"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## Warning in summary.mkinfit(x): Could not calculate correlation; no covariance</span></span>
 <span><span class="co">## matrix</span></span></code></pre>
 <pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre>
 <pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre>
-<div class="sourceCode" id="cb104"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p15b</span><span class="op">)</span></span></code></pre></div>
+<div class="sourceCode" id="cb101"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p15b</span><span class="op">)</span></span></code></pre></div>
 <p><img src="NAFTA_examples_files/figure-html/p15b-1.png" width="700"></p>
-<div class="sourceCode" id="cb105"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb102"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p15b</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## Sums of squares:</span></span>
 <span><span class="co">##       SFO      IORE      DFOP </span></span>
@@ -1001,16 +943,16 @@ mkin and PestDF.</p>
 <div class="section level2">
 <h2 id="the-dfop-fraction-parameter-is-greater-than-1">The DFOP fraction parameter is greater than 1<a class="anchor" aria-label="anchor" href="#the-dfop-fraction-parameter-is-greater-than-1"></a>
 </h2>
-<div class="sourceCode" id="cb107"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb104"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">p16</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p16"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre>
 <pre><code><span><span class="co">## The representative half-life of the IORE model is longer than the one corresponding</span></span></code></pre>
 <pre><code><span><span class="co">## to the terminal degradation rate found with the DFOP model.</span></span></code></pre>
 <pre><code><span><span class="co">## The representative half-life obtained from the DFOP model may be used</span></span></code></pre>
-<div class="sourceCode" id="cb112"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p16</span><span class="op">)</span></span></code></pre></div>
+<div class="sourceCode" id="cb109"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p16</span><span class="op">)</span></span></code></pre></div>
 <p><img src="NAFTA_examples_files/figure-html/p16-1.png" width="700"></p>
-<div class="sourceCode" id="cb113"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb110"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p16</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## Sums of squares:</span></span>
 <span><span class="co">##      SFO     IORE     DFOP </span></span>
@@ -1051,10 +993,12 @@ mkin and PestDF.</p>
 <span><span class="co">## Representative half-life:</span></span>
 <span><span class="co">## [1] 8.93</span></span></code></pre>
 <p>In PestDF, the DFOP fit seems to have stuck in a local minimum, as
-mkin finds a solution with a much lower <span class="math inline">\(\chi^2\)</span> error level. As the half-life from
-the slower rate constant of the DFOP model is larger than the IORE
-derived half-life, the NAFTA recommendation obtained with mkin is to use
-the DFOP representative half-life of 8.9 days.</p>
+mkin finds a solution with a much lower
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><msup><mi>χ</mi><mn>2</mn></msup><annotation encoding="application/x-tex">\chi^2</annotation></semantics></math>
+error level. As the half-life from the slower rate constant of the DFOP
+model is larger than the IORE derived half-life, the NAFTA
+recommendation obtained with mkin is to use the DFOP representative
+half-life of 8.9 days.</p>
 </div>
 <div class="section level2">
 <h2 id="conclusions">Conclusions<a class="anchor" aria-label="anchor" href="#conclusions"></a>
@@ -1079,35 +1023,26 @@ Pesticide Degradation.”</span> <a href="https://www.epa.gov/pesticide-science-
 </div>
 </div>
 </div>
-  </div>
-
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-
-        <nav id="toc" data-toggle="toc"><h2 data-toc-skip>Contents</h2>
-    </nav>
-</div>
-
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside>
 </div>
 
 
 
-      <footer><div class="copyright">
-  <p></p>
-<p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p>
-<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer>
+    </footer>
 </div>
 
-  
 
 
-  
+
 
   </body>
 </html>
diff --git a/docs/articles/web_only/NAFTA_examples_files/accessible-code-block-0.0.1/empty-anchor.js b/docs/articles/web_only/NAFTA_examples_files/accessible-code-block-0.0.1/empty-anchor.js
deleted file mode 100644
index ca349fd6..00000000
--- a/docs/articles/web_only/NAFTA_examples_files/accessible-code-block-0.0.1/empty-anchor.js
+++ /dev/null
@@ -1,15 +0,0 @@
-// Hide empty <a> tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) -->
-// v0.0.1
-// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020.
-
-document.addEventListener('DOMContentLoaded', function() {
-  const codeList = document.getElementsByClassName("sourceCode");
-  for (var i = 0; i < codeList.length; i++) {
-    var linkList = codeList[i].getElementsByTagName('a');
-    for (var j = 0; j < linkList.length; j++) {
-      if (linkList[j].innerHTML === "") {
-        linkList[j].setAttribute('aria-hidden', 'true');
-      }
-    }
-  }
-});
diff --git a/docs/articles/web_only/NAFTA_examples_files/figure-html/p10-1.png b/docs/articles/web_only/NAFTA_examples_files/figure-html/p10-1.png
index de4a527f..1d4a25e0 100644
--- a/docs/articles/web_only/NAFTA_examples_files/figure-html/p10-1.png
+++ b/docs/articles/web_only/NAFTA_examples_files/figure-html/p10-1.png
diff --git a/docs/articles/web_only/NAFTA_examples_files/figure-html/p11-1.png b/docs/articles/web_only/NAFTA_examples_files/figure-html/p11-1.png
index 55466e47..71fc4699 100644
--- a/docs/articles/web_only/NAFTA_examples_files/figure-html/p11-1.png
+++ b/docs/articles/web_only/NAFTA_examples_files/figure-html/p11-1.png
diff --git a/docs/articles/web_only/NAFTA_examples_files/figure-html/p12a-1.png b/docs/articles/web_only/NAFTA_examples_files/figure-html/p12a-1.png
index d3143afa..a1d3a084 100644
--- a/docs/articles/web_only/NAFTA_examples_files/figure-html/p12a-1.png
+++ b/docs/articles/web_only/NAFTA_examples_files/figure-html/p12a-1.png
diff --git a/docs/articles/web_only/NAFTA_examples_files/figure-html/p12b-1.png b/docs/articles/web_only/NAFTA_examples_files/figure-html/p12b-1.png
index 3387ca69..1a6fdd03 100644
--- a/docs/articles/web_only/NAFTA_examples_files/figure-html/p12b-1.png
+++ b/docs/articles/web_only/NAFTA_examples_files/figure-html/p12b-1.png
diff --git a/docs/articles/web_only/NAFTA_examples_files/figure-html/p13-1.png b/docs/articles/web_only/NAFTA_examples_files/figure-html/p13-1.png
index 62a135f2..f9b9f637 100644
--- a/docs/articles/web_only/NAFTA_examples_files/figure-html/p13-1.png
+++ b/docs/articles/web_only/NAFTA_examples_files/figure-html/p13-1.png
diff --git a/docs/articles/web_only/NAFTA_examples_files/figure-html/p14-1.png b/docs/articles/web_only/NAFTA_examples_files/figure-html/p14-1.png
index ae4d83a4..9f7b0cc5 100644
--- a/docs/articles/web_only/NAFTA_examples_files/figure-html/p14-1.png
+++ b/docs/articles/web_only/NAFTA_examples_files/figure-html/p14-1.png
diff --git a/docs/articles/web_only/NAFTA_examples_files/figure-html/p15a-1.png b/docs/articles/web_only/NAFTA_examples_files/figure-html/p15a-1.png
index f82e6e64..aa55169e 100644
--- a/docs/articles/web_only/NAFTA_examples_files/figure-html/p15a-1.png
+++ b/docs/articles/web_only/NAFTA_examples_files/figure-html/p15a-1.png
diff --git a/docs/articles/web_only/NAFTA_examples_files/figure-html/p15b-1.png b/docs/articles/web_only/NAFTA_examples_files/figure-html/p15b-1.png
index 2cb20cf7..d17c7aae 100644
--- a/docs/articles/web_only/NAFTA_examples_files/figure-html/p15b-1.png
+++ b/docs/articles/web_only/NAFTA_examples_files/figure-html/p15b-1.png
diff --git a/docs/articles/web_only/NAFTA_examples_files/figure-html/p16-1.png b/docs/articles/web_only/NAFTA_examples_files/figure-html/p16-1.png
index 72df855b..75ac7e5b 100644
--- a/docs/articles/web_only/NAFTA_examples_files/figure-html/p16-1.png
+++ b/docs/articles/web_only/NAFTA_examples_files/figure-html/p16-1.png
diff --git a/docs/articles/web_only/NAFTA_examples_files/figure-html/p5a-1.png b/docs/articles/web_only/NAFTA_examples_files/figure-html/p5a-1.png
index 391dfb95..12a62954 100644
--- a/docs/articles/web_only/NAFTA_examples_files/figure-html/p5a-1.png
+++ b/docs/articles/web_only/NAFTA_examples_files/figure-html/p5a-1.png
diff --git a/docs/articles/web_only/NAFTA_examples_files/figure-html/p5b-1.png b/docs/articles/web_only/NAFTA_examples_files/figure-html/p5b-1.png
index 034eed46..9e38e696 100644
--- a/docs/articles/web_only/NAFTA_examples_files/figure-html/p5b-1.png
+++ b/docs/articles/web_only/NAFTA_examples_files/figure-html/p5b-1.png
diff --git a/docs/articles/web_only/NAFTA_examples_files/figure-html/p6-1.png b/docs/articles/web_only/NAFTA_examples_files/figure-html/p6-1.png
index 86cd9755..e6e3abbe 100644
--- a/docs/articles/web_only/NAFTA_examples_files/figure-html/p6-1.png
+++ b/docs/articles/web_only/NAFTA_examples_files/figure-html/p6-1.png
diff --git a/docs/articles/web_only/NAFTA_examples_files/figure-html/p7-1.png b/docs/articles/web_only/NAFTA_examples_files/figure-html/p7-1.png
index 10225504..7c5d4bab 100644
--- a/docs/articles/web_only/NAFTA_examples_files/figure-html/p7-1.png
+++ b/docs/articles/web_only/NAFTA_examples_files/figure-html/p7-1.png
diff --git a/docs/articles/web_only/NAFTA_examples_files/figure-html/p8-1.png b/docs/articles/web_only/NAFTA_examples_files/figure-html/p8-1.png
index 5cd6c806..a1e3bf25 100644
--- a/docs/articles/web_only/NAFTA_examples_files/figure-html/p8-1.png
+++ b/docs/articles/web_only/NAFTA_examples_files/figure-html/p8-1.png
diff --git a/docs/articles/web_only/NAFTA_examples_files/figure-html/p9a-1.png b/docs/articles/web_only/NAFTA_examples_files/figure-html/p9a-1.png
index 61359ea6..c247fd4e 100644
--- a/docs/articles/web_only/NAFTA_examples_files/figure-html/p9a-1.png
+++ b/docs/articles/web_only/NAFTA_examples_files/figure-html/p9a-1.png
diff --git a/docs/articles/web_only/NAFTA_examples_files/figure-html/p9b-1.png b/docs/articles/web_only/NAFTA_examples_files/figure-html/p9b-1.png
index 85790b1e..99d593fc 100644
--- a/docs/articles/web_only/NAFTA_examples_files/figure-html/p9b-1.png
+++ b/docs/articles/web_only/NAFTA_examples_files/figure-html/p9b-1.png
diff --git a/docs/articles/web_only/NAFTA_examples_files/header-attrs-2.6/header-attrs.js b/docs/articles/web_only/NAFTA_examples_files/header-attrs-2.6/header-attrs.js
deleted file mode 100644
index dd57d92e..00000000
--- a/docs/articles/web_only/NAFTA_examples_files/header-attrs-2.6/header-attrs.js
+++ /dev/null
@@ -1,12 +0,0 @@
-// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
-// be compatible with the behavior of Pandoc < 2.8).
-document.addEventListener('DOMContentLoaded', function(e) {
-  var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
-  var i, h, a;
-  for (i = 0; i < hs.length; i++) {
-    h = hs[i];
-    if (!/^h[1-6]$/i.test(h.tagName)) continue;  // it should be a header h1-h6
-    a = h.attributes;
-    while (a.length > 0) h.removeAttribute(a[0].name);
-  }
-});
diff --git a/docs/articles/web_only/NAFTA_examples_files/header-attrs-2.7/header-attrs.js b/docs/articles/web_only/NAFTA_examples_files/header-attrs-2.7/header-attrs.js
deleted file mode 100644
index dd57d92e..00000000
--- a/docs/articles/web_only/NAFTA_examples_files/header-attrs-2.7/header-attrs.js
+++ /dev/null
@@ -1,12 +0,0 @@
-// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
-// be compatible with the behavior of Pandoc < 2.8).
-document.addEventListener('DOMContentLoaded', function(e) {
-  var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
-  var i, h, a;
-  for (i = 0; i < hs.length; i++) {
-    h = hs[i];
-    if (!/^h[1-6]$/i.test(h.tagName)) continue;  // it should be a header h1-h6
-    a = h.attributes;
-    while (a.length > 0) h.removeAttribute(a[0].name);
-  }
-});
diff --git a/docs/articles/web_only/benchmarks.html b/docs/articles/web_only/benchmarks.html
index 7c4af88b..3566be42 100644
--- a/docs/articles/web_only/benchmarks.html
+++ b/docs/articles/web_only/benchmarks.html
@@ -4,144 +4,90 @@
 <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
 <meta charset="utf-8">
 <meta http-equiv="X-UA-Compatible" content="IE=edge">
-<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
 <title>Benchmark timings for mkin • mkin</title>
-<!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous">
-<script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../../bootstrap-toc.css">
-<script src="../../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous">
-<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous">
-<!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../../pkgdown.css" rel="stylesheet">
-<script src="../../pkgdown.js"></script><meta property="og:title" content="Benchmark timings for mkin">
-<meta property="og:description" content="mkin">
-<!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]-->
+<script src="../../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
+<link href="../../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet">
+<script src="../../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet">
+<link href="../../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet">
+<script src="../../deps/headroom-0.11.0/headroom.min.js"></script><script src="../../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../../deps/search-1.0.0/fuse.min.js"></script><script src="../../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../../pkgdown.js"></script><meta property="og:title" content="Benchmark timings for mkin">
 </head>
-<body data-spy="scroll" data-target="#toc">
-    
+<body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
 
-    <div class="container template-article">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
 
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav">
-<li>
-  <a href="../../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu">
-<li>
-      <a href="../../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto">
+<li class="nav-item"><a class="nav-link" href="../../reference/index.html">Reference</a></li>
+<li class="active nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles">
+<li><a class="dropdown-item" href="../../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul>
 </li>
-<li>
-  <a href="../../news/index.html">News</a>
-</li>
+<li class="nav-item"><a class="nav-link" href="../../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../../news/index.html">News</a></li>
       </ul>
-<ul class="nav navbar-nav navbar-right">
-<li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
+<ul class="navbar-nav">
+<li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../../search.json">
+</form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul>
 </div>
-<!--/.nav-collapse -->
+
+
   </div>
-<!--/.container -->
-</div>
-<!--/.navbar -->
+</nav><div class="container template-article">
+
 
-      
 
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header toc-ignore">
-      <h1 data-toc-skip>Benchmark timings for mkin</h1>
+
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Benchmark timings for mkin</h1>
                         <h4 data-toc-skip class="author">Johannes
 Ranke</h4>
             
             <h4 data-toc-skip class="date">Last change 17 February 2023
-(rebuilt 2023-11-02)</h4>
+(rebuilt 2025-02-13)</h4>
       
       <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/web_only/benchmarks.rmd" class="external-link"><code>vignettes/web_only/benchmarks.rmd</code></a></small>
-      <div class="hidden name"><code>benchmarks.rmd</code></div>
-
+      <div class="d-none name"><code>benchmarks.rmd</code></div>
     </div>
 
     
@@ -465,8 +411,16 @@ models fitted to two datasets, i.e. eight fits for each test.</p>
 <td align="left">Ryzen 9 7950X</td>
 <td align="left">4.3.2</td>
 <td align="left">1.2.6</td>
-<td align="right">1.400</td>
-<td align="right">2.012</td>
+<td align="right">1.408</td>
+<td align="right">2.041</td>
+</tr>
+<tr class="even">
+<td align="left">Linux</td>
+<td align="left">Ryzen 9 7950X</td>
+<td align="left">4.4.2</td>
+<td align="left">1.2.9</td>
+<td align="right">1.323</td>
+<td align="right">1.925</td>
 </tr>
 </tbody>
 </table>
@@ -736,9 +690,18 @@ for each test.</p>
 <td align="left">Ryzen 9 7950X</td>
 <td align="left">4.3.2</td>
 <td align="left">1.2.6</td>
-<td align="right">0.790</td>
-<td align="right">2.212</td>
-<td align="right">1.173</td>
+<td align="right">0.795</td>
+<td align="right">2.228</td>
+<td align="right">1.178</td>
+</tr>
+<tr class="even">
+<td align="left">Linux</td>
+<td align="left">Ryzen 9 7950X</td>
+<td align="left">4.4.2</td>
+<td align="left">1.2.9</td>
+<td align="right">0.754</td>
+<td align="right">2.153</td>
+<td align="right">1.139</td>
 </tr>
 </tbody>
 </table>
@@ -1092,46 +1055,49 @@ dataset, i.e. one fit for each test.</p>
 <td align="left">Ryzen 9 7950X</td>
 <td align="left">4.3.2</td>
 <td align="left">1.2.6</td>
-<td align="right">0.432</td>
-<td align="right">0.551</td>
-<td align="right">0.580</td>
-<td align="right">1.322</td>
-<td align="right">0.746</td>
-<td align="right">0.996</td>
+<td align="right">0.439</td>
+<td align="right">0.557</td>
+<td align="right">0.585</td>
+<td align="right">1.338</td>
+<td align="right">0.749</td>
+<td align="right">0.999</td>
+</tr>
+<tr class="even">
+<td align="left">Linux</td>
+<td align="left">Ryzen 9 7950X</td>
+<td align="left">4.4.2</td>
+<td align="left">1.2.9</td>
+<td align="right">0.424</td>
+<td align="right">0.534</td>
+<td align="right">0.560</td>
+<td align="right">1.298</td>
+<td align="right">0.735</td>
+<td align="right">0.981</td>
 </tr>
 </tbody>
 </table>
 </div>
 </div>
-  </div>
-
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-
-        <nav id="toc" data-toggle="toc"><h2 data-toc-skip>Contents</h2>
-    </nav>
-</div>
-
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside>
 </div>
 
 
 
-      <footer><div class="copyright">
-  <p></p>
-<p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p>
-<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer>
+    </footer>
 </div>
 
-  
 
 
-  
+
 
   </body>
 </html>
diff --git a/docs/articles/web_only/benchmarks_files/accessible-code-block-0.0.1/empty-anchor.js b/docs/articles/web_only/benchmarks_files/accessible-code-block-0.0.1/empty-anchor.js
deleted file mode 100644
index ca349fd6..00000000
--- a/docs/articles/web_only/benchmarks_files/accessible-code-block-0.0.1/empty-anchor.js
+++ /dev/null
@@ -1,15 +0,0 @@
-// Hide empty <a> tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) -->
-// v0.0.1
-// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020.
-
-document.addEventListener('DOMContentLoaded', function() {
-  const codeList = document.getElementsByClassName("sourceCode");
-  for (var i = 0; i < codeList.length; i++) {
-    var linkList = codeList[i].getElementsByTagName('a');
-    for (var j = 0; j < linkList.length; j++) {
-      if (linkList[j].innerHTML === "") {
-        linkList[j].setAttribute('aria-hidden', 'true');
-      }
-    }
-  }
-});
diff --git a/docs/articles/web_only/benchmarks_files/header-attrs-2.6/header-attrs.js b/docs/articles/web_only/benchmarks_files/header-attrs-2.6/header-attrs.js
deleted file mode 100644
index dd57d92e..00000000
--- a/docs/articles/web_only/benchmarks_files/header-attrs-2.6/header-attrs.js
+++ /dev/null
@@ -1,12 +0,0 @@
-// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
-// be compatible with the behavior of Pandoc < 2.8).
-document.addEventListener('DOMContentLoaded', function(e) {
-  var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
-  var i, h, a;
-  for (i = 0; i < hs.length; i++) {
-    h = hs[i];
-    if (!/^h[1-6]$/i.test(h.tagName)) continue;  // it should be a header h1-h6
-    a = h.attributes;
-    while (a.length > 0) h.removeAttribute(a[0].name);
-  }
-});
diff --git a/docs/articles/web_only/benchmarks_files/header-attrs-2.7/header-attrs.js b/docs/articles/web_only/benchmarks_files/header-attrs-2.7/header-attrs.js
deleted file mode 100644
index dd57d92e..00000000
--- a/docs/articles/web_only/benchmarks_files/header-attrs-2.7/header-attrs.js
+++ /dev/null
@@ -1,12 +0,0 @@
-// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
-// be compatible with the behavior of Pandoc < 2.8).
-document.addEventListener('DOMContentLoaded', function(e) {
-  var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
-  var i, h, a;
-  for (i = 0; i < hs.length; i++) {
-    h = hs[i];
-    if (!/^h[1-6]$/i.test(h.tagName)) continue;  // it should be a header h1-h6
-    a = h.attributes;
-    while (a.length > 0) h.removeAttribute(a[0].name);
-  }
-});
diff --git a/docs/articles/web_only/compiled_models.html b/docs/articles/web_only/compiled_models.html
index 1aa88e05..bbf6e897 100644
--- a/docs/articles/web_only/compiled_models.html
+++ b/docs/articles/web_only/compiled_models.html
@@ -4,144 +4,89 @@
 <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
 <meta charset="utf-8">
 <meta http-equiv="X-UA-Compatible" content="IE=edge">
-<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
 <title>Performance benefit by using compiled model definitions in mkin • mkin</title>
-<!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous">
-<script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../../bootstrap-toc.css">
-<script src="../../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous">
-<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous">
-<!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../../pkgdown.css" rel="stylesheet">
-<script src="../../pkgdown.js"></script><meta property="og:title" content="Performance benefit by using compiled model definitions in mkin">
-<meta property="og:description" content="mkin">
-<!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]-->
+<script src="../../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
+<link href="../../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet">
+<script src="../../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet">
+<link href="../../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet">
+<script src="../../deps/headroom-0.11.0/headroom.min.js"></script><script src="../../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../../deps/search-1.0.0/fuse.min.js"></script><script src="../../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../../pkgdown.js"></script><meta property="og:title" content="Performance benefit by using compiled model definitions in mkin">
 </head>
-<body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-article">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav">
-<li>
-  <a href="../../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu">
-<li>
-      <a href="../../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+<body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto">
+<li class="nav-item"><a class="nav-link" href="../../reference/index.html">Reference</a></li>
+<li class="active nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles">
+<li><a class="dropdown-item" href="../../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul>
 </li>
-<li>
-  <a href="../../news/index.html">News</a>
-</li>
+<li class="nav-item"><a class="nav-link" href="../../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../../news/index.html">News</a></li>
       </ul>
-<ul class="nav navbar-nav navbar-right">
-<li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
+<ul class="navbar-nav">
+<li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../../search.json">
+</form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul>
 </div>
-<!--/.nav-collapse -->
+
+
   </div>
-<!--/.container -->
-</div>
-<!--/.navbar -->
+</nav><div class="container template-article">
 
-      
 
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header toc-ignore">
-      <h1 data-toc-skip>Performance benefit by using compiled model
-definitions in mkin</h1>
+
+
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Performance benefit by using compiled model definitions in mkin</h1>
                         <h4 data-toc-skip class="author">Johannes
 Ranke</h4>
             
-            <h4 data-toc-skip class="date">2023-10-30</h4>
+            <h4 data-toc-skip class="date">2025-02-13</h4>
       
       <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/web_only/compiled_models.rmd" class="external-link"><code>vignettes/web_only/compiled_models.rmd</code></a></small>
-      <div class="hidden name"><code>compiled_models.rmd</code></div>
-
+      <div class="d-none name"><code>compiled_models.rmd</code></div>
     </div>
 
     
@@ -216,10 +161,10 @@ solution is also implemented, which is included in the tests below.</p>
 <span>  <span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="st">"R package rbenchmark is not available"</span><span class="op">)</span></span>
 <span><span class="op">}</span></span></code></pre></div>
 <pre><code><span><span class="co">##                    test replications relative elapsed</span></span>
-<span><span class="co">## 4            analytical            1    1.000   0.213</span></span>
-<span><span class="co">## 3     deSolve, compiled            1    1.418   0.302</span></span>
-<span><span class="co">## 2      Eigenvalue based            1    2.000   0.426</span></span>
-<span><span class="co">## 1 deSolve, not compiled            1   23.535   5.013</span></span></code></pre>
+<span><span class="co">## 4            analytical            1    1.000   0.105</span></span>
+<span><span class="co">## 3     deSolve, compiled            1    1.333   0.140</span></span>
+<span><span class="co">## 2      Eigenvalue based            1    1.667   0.175</span></span>
+<span><span class="co">## 1 deSolve, not compiled            1   22.486   2.361</span></span></code></pre>
 <p>We see that using the compiled model is by more than a factor of 10
 faster than using deSolve without compiled code.</p>
 </div>
@@ -250,45 +195,36 @@ compiled code is available.</p>
 <span><span class="op">}</span></span></code></pre></div>
 <pre><code><span><span class="co">## Temporary DLL for differentials generated and loaded</span></span></code></pre>
 <pre><code><span><span class="co">##                    test replications relative elapsed</span></span>
-<span><span class="co">## 2     deSolve, compiled            1    1.000   0.492</span></span>
-<span><span class="co">## 1 deSolve, not compiled            1   20.398  10.036</span></span></code></pre>
-<p>Here we get a performance benefit of a factor of 20 using the version
+<span><span class="co">## 2     deSolve, compiled            1    1.000   0.175</span></span>
+<span><span class="co">## 1 deSolve, not compiled            1   23.937   4.189</span></span></code></pre>
+<p>Here we get a performance benefit of a factor of 24 using the version
 of the differential equation model compiled from C code!</p>
-<p>This vignette was built with mkin 1.2.6 on</p>
-<pre><code><span><span class="co">## R version 4.3.1 (2023-06-16)</span></span>
-<span><span class="co">## Platform: x86_64-pc-linux-gnu (64-bit)</span></span>
-<span><span class="co">## Running under: Ubuntu 22.04.3 LTS</span></span></code></pre>
-<pre><code><span><span class="co">## CPU model: Intel(R) Xeon(R) Gold 6134 CPU @ 3.20GHz</span></span></code></pre>
-</div>
-  </div>
-
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-
-        <nav id="toc" data-toggle="toc"><h2 data-toc-skip>Contents</h2>
-    </nav>
+<p>This vignette was built with mkin 1.2.9 on</p>
+<pre><code><span><span class="co">## R version 4.4.2 (2024-10-31)</span></span>
+<span><span class="co">## Platform: x86_64-pc-linux-gnu</span></span>
+<span><span class="co">## Running under: Debian GNU/Linux 12 (bookworm)</span></span></code></pre>
+<pre><code><span><span class="co">## CPU model: AMD Ryzen 9 7950X 16-Core Processor</span></span></code></pre>
 </div>
-
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside>
 </div>
 
 
 
-      <footer><div class="copyright">
-  <p></p>
-<p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p>
-<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer>
+    </footer>
 </div>
 
-  
 
 
-  
+
 
   </body>
 </html>
diff --git a/docs/articles/web_only/compiled_models_files/accessible-code-block-0.0.1/empty-anchor.js b/docs/articles/web_only/compiled_models_files/accessible-code-block-0.0.1/empty-anchor.js
deleted file mode 100644
index ca349fd6..00000000
--- a/docs/articles/web_only/compiled_models_files/accessible-code-block-0.0.1/empty-anchor.js
+++ /dev/null
@@ -1,15 +0,0 @@
-// Hide empty <a> tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) -->
-// v0.0.1
-// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020.
-
-document.addEventListener('DOMContentLoaded', function() {
-  const codeList = document.getElementsByClassName("sourceCode");
-  for (var i = 0; i < codeList.length; i++) {
-    var linkList = codeList[i].getElementsByTagName('a');
-    for (var j = 0; j < linkList.length; j++) {
-      if (linkList[j].innerHTML === "") {
-        linkList[j].setAttribute('aria-hidden', 'true');
-      }
-    }
-  }
-});
diff --git a/docs/articles/web_only/compiled_models_files/header-attrs-2.6/header-attrs.js b/docs/articles/web_only/compiled_models_files/header-attrs-2.6/header-attrs.js
deleted file mode 100644
index dd57d92e..00000000
--- a/docs/articles/web_only/compiled_models_files/header-attrs-2.6/header-attrs.js
+++ /dev/null
@@ -1,12 +0,0 @@
-// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
-// be compatible with the behavior of Pandoc < 2.8).
-document.addEventListener('DOMContentLoaded', function(e) {
-  var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
-  var i, h, a;
-  for (i = 0; i < hs.length; i++) {
-    h = hs[i];
-    if (!/^h[1-6]$/i.test(h.tagName)) continue;  // it should be a header h1-h6
-    a = h.attributes;
-    while (a.length > 0) h.removeAttribute(a[0].name);
-  }
-});
diff --git a/docs/articles/web_only/dimethenamid_2018.html b/docs/articles/web_only/dimethenamid_2018.html
index 1cffd561..5d39704d 100644
--- a/docs/articles/web_only/dimethenamid_2018.html
+++ b/docs/articles/web_only/dimethenamid_2018.html
@@ -4,145 +4,90 @@
 <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
 <meta charset="utf-8">
 <meta http-equiv="X-UA-Compatible" content="IE=edge">
-<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
 <title>Example evaluations of the dimethenamid data from 2018 • mkin</title>
-<!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous">
-<script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../../bootstrap-toc.css">
-<script src="../../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous">
-<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous">
-<!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../../pkgdown.css" rel="stylesheet">
-<script src="../../pkgdown.js"></script><meta property="og:title" content="Example evaluations of the dimethenamid data from 2018">
-<meta property="og:description" content="mkin">
-<!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]-->
+<script src="../../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
+<link href="../../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet">
+<script src="../../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet">
+<link href="../../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet">
+<script src="../../deps/headroom-0.11.0/headroom.min.js"></script><script src="../../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../../deps/search-1.0.0/fuse.min.js"></script><script src="../../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../../pkgdown.js"></script><meta property="og:title" content="Example evaluations of the dimethenamid data from 2018">
 </head>
-<body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-article">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav">
-<li>
-  <a href="../../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu">
-<li>
-      <a href="../../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+<body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto">
+<li class="nav-item"><a class="nav-link" href="../../reference/index.html">Reference</a></li>
+<li class="active nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles">
+<li><a class="dropdown-item" href="../../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul>
 </li>
-<li>
-  <a href="../../news/index.html">News</a>
-</li>
+<li class="nav-item"><a class="nav-link" href="../../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../../news/index.html">News</a></li>
       </ul>
-<ul class="nav navbar-nav navbar-right">
-<li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
+<ul class="navbar-nav">
+<li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../../search.json">
+</form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul>
 </div>
-<!--/.nav-collapse -->
+
+
   </div>
-<!--/.container -->
-</div>
-<!--/.navbar -->
+</nav><div class="container template-article">
 
-      
 
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header toc-ignore">
-      <h1 data-toc-skip>Example evaluations of the dimethenamid data
-from 2018</h1>
+
+
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Example evaluations of the dimethenamid data from 2018</h1>
                         <h4 data-toc-skip class="author">Johannes
 Ranke</h4>
             
             <h4 data-toc-skip class="date">Last change 1 July 2022,
-built on 30 Oct 2023</h4>
+built on 13 Feb 2025</h4>
       
       <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/web_only/dimethenamid_2018.rmd" class="external-link"><code>vignettes/web_only/dimethenamid_2018.rmd</code></a></small>
-      <div class="hidden name"><code>dimethenamid_2018.rmd</code></div>
-
+      <div class="d-none name"><code>dimethenamid_2018.rmd</code></div>
     </div>
 
     
@@ -363,7 +308,7 @@ well for all the parent data fits shown in this vignette.</p>
 <div class="sourceCode" id="cb14"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va">saemix</span><span class="op">)</span></span></code></pre></div>
 <pre><code>Loading required package: npde</code></pre>
-<pre><code>Package saemix, version 3.2
+<pre><code>Package saemix, version 3.3, March 2024
   please direct bugs, questions and feedback to emmanuelle.comets@inserm.fr</code></pre>
 <pre><code>
 Attaching package: 'saemix'</code></pre>
@@ -463,11 +408,11 @@ DMTA_0    98.24165  96.29190 100.1914
 k1         0.06421   0.03352   0.0949
 k2         0.00866   0.00617   0.0111
 g          0.95340   0.91218   0.9946
-a.1        1.06463   0.86503   1.2642
-b.1        0.02964   0.02259   0.0367
-SD.DMTA_0  2.03611   0.40416   3.6681
+a.1        1.06463   0.87979   1.2495
+b.1        0.02964   0.02266   0.0366
+SD.DMTA_0  2.03611   0.40361   3.6686
 SD.k1      0.59534   0.25692   0.9338
-SD.k2      0.00042 -73.01372  73.0146
+SD.k2      0.00042 -73.00540  73.0062
 SD.g       1.04234   0.37189   1.7128</code></pre>
 <p>Doubling the number of iterations in the first phase of the algorithm
 leads to a slightly lower likelihood, and therefore to slightly higher
@@ -626,46 +571,45 @@ satisfactory precision.</p>
 </h2>
 <div class="sourceCode" id="cb37"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/utils/sessionInfo.html" class="external-link">sessionInfo</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
-<pre><code>R version 4.3.1 (2023-06-16)
-Platform: x86_64-pc-linux-gnu (64-bit)
-Running under: Ubuntu 22.04.3 LTS
+<pre><code>R version 4.4.2 (2024-10-31)
+Platform: x86_64-pc-linux-gnu
+Running under: Debian GNU/Linux 12 (bookworm)
 
 Matrix products: default
-BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0 
-LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
+BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.11.0 
+LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.11.0
 
 locale:
- [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
- [3] LC_TIME=C                  LC_COLLATE=en_US.UTF-8    
- [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
- [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
+ [1] LC_CTYPE=de_DE.UTF-8       LC_NUMERIC=C              
+ [3] LC_TIME=C                  LC_COLLATE=de_DE.UTF-8    
+ [5] LC_MONETARY=de_DE.UTF-8    LC_MESSAGES=de_DE.UTF-8   
+ [7] LC_PAPER=de_DE.UTF-8       LC_NAME=C                 
  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
-[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
+[11] LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=C       
 
-time zone: Europe/Zurich
+time zone: Europe/Berlin
 tzcode source: system (glibc)
 
 attached base packages:
 [1] stats     graphics  grDevices utils     datasets  methods   base     
 
 other attached packages:
-[1] saemix_3.2   npde_3.3     nlme_3.1-163 mkin_1.2.6   knitr_1.44  
+[1] saemix_3.3   npde_3.5     nlme_3.1-166 mkin_1.2.9   knitr_1.49  
 
 loaded via a namespace (and not attached):
- [1] sass_0.4.7        utf8_1.2.3        generics_0.1.3    stringi_1.7.12   
- [5] lattice_0.21-9    digest_0.6.33     magrittr_2.0.3    evaluate_0.22    
- [9] grid_4.3.1        fastmap_1.1.1     rprojroot_2.0.3   jsonlite_1.8.7   
-[13] mclust_6.0.0      gridExtra_2.3     purrr_1.0.1       fansi_1.0.4      
-[17] scales_1.2.1      codetools_0.2-19  textshaping_0.3.6 jquerylib_0.1.4  
-[21] cli_3.6.1         rlang_1.1.1       munsell_0.5.0     cachem_1.0.8     
-[25] yaml_2.3.7        tools_4.3.1       parallel_4.3.1    memoise_2.0.1    
-[29] dplyr_1.1.2       colorspace_2.1-0  ggplot2_3.4.2     vctrs_0.6.3      
-[33] R6_2.5.1          zoo_1.8-12        lifecycle_1.0.3   stringr_1.5.0    
-[37] fs_1.6.3          MASS_7.3-60       ragg_1.2.5        pkgconfig_2.0.3  
-[41] desc_1.4.2        pkgdown_2.0.7     bslib_0.5.1       pillar_1.9.0     
-[45] gtable_0.3.3      glue_1.6.2        systemfonts_1.0.4 xfun_0.40        
-[49] tibble_3.2.1      lmtest_0.9-40     tidyselect_1.2.0  rstudioapi_0.15.0
-[53] htmltools_0.5.6.1 rmarkdown_2.23    compiler_4.3.1   </code></pre>
+ [1] gtable_0.3.6      jsonlite_1.8.9    dplyr_1.1.4       compiler_4.4.2   
+ [5] tidyselect_1.2.1  parallel_4.4.2    gridExtra_2.3     jquerylib_0.1.4  
+ [9] systemfonts_1.1.0 scales_1.3.0      textshaping_0.4.1 yaml_2.3.10      
+[13] fastmap_1.2.0     lattice_0.22-6    ggplot2_3.5.1     R6_2.5.1         
+[17] generics_0.1.3    lmtest_0.9-40     MASS_7.3-61       htmlwidgets_1.6.4
+[21] tibble_3.2.1      desc_1.4.3        munsell_0.5.1     bslib_0.8.0      
+[25] pillar_1.9.0      rlang_1.1.4       utf8_1.2.4        cachem_1.1.0     
+[29] xfun_0.49         fs_1.6.5          sass_0.4.9        cli_3.6.3        
+[33] pkgdown_2.1.1     magrittr_2.0.3    digest_0.6.37     grid_4.4.2       
+[37] mclust_6.1.1      lifecycle_1.0.4   vctrs_0.6.5       evaluate_1.0.1   
+[41] glue_1.8.0        codetools_0.2-20  ragg_1.3.3        zoo_1.8-12       
+[45] fansi_1.0.6       colorspace_2.1-1  rmarkdown_2.29    pkgconfig_2.0.3  
+[49] tools_4.4.2       htmltools_0.5.8.1</code></pre>
 </div>
 <div class="section level2">
 <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a>
@@ -690,35 +634,26 @@ November 2017</span>.”</span> <a href="https://open.efsa.europa.eu/study-inven
 </div>
 </div>
 </div>
-  </div>
-
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-
-        <nav id="toc" data-toggle="toc"><h2 data-toc-skip>Contents</h2>
-    </nav>
-</div>
-
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside>
 </div>
 
 
 
-      <footer><div class="copyright">
-  <p></p>
-<p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p>
-<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer>
+    </footer>
 </div>
 
-  
 
 
-  
+
 
   </body>
 </html>
diff --git a/docs/articles/web_only/dimethenamid_2018_files/accessible-code-block-0.0.1/empty-anchor.js b/docs/articles/web_only/dimethenamid_2018_files/accessible-code-block-0.0.1/empty-anchor.js
deleted file mode 100644
index ca349fd6..00000000
--- a/docs/articles/web_only/dimethenamid_2018_files/accessible-code-block-0.0.1/empty-anchor.js
+++ /dev/null
@@ -1,15 +0,0 @@
-// Hide empty <a> tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) -->
-// v0.0.1
-// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020.
-
-document.addEventListener('DOMContentLoaded', function() {
-  const codeList = document.getElementsByClassName("sourceCode");
-  for (var i = 0; i < codeList.length; i++) {
-    var linkList = codeList[i].getElementsByTagName('a');
-    for (var j = 0; j < linkList.length; j++) {
-      if (linkList[j].innerHTML === "") {
-        linkList[j].setAttribute('aria-hidden', 'true');
-      }
-    }
-  }
-});
diff --git a/docs/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_const-1.png b/docs/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_const-1.png
index 505072ce..627e5c95 100644
--- a/docs/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_const-1.png
+++ b/docs/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_const-1.png
diff --git a/docs/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_const_test-1.png b/docs/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_const_test-1.png
index 505072ce..627e5c95 100644
--- a/docs/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_const_test-1.png
+++ b/docs/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_const_test-1.png
diff --git a/docs/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_tc_test-1.png b/docs/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_tc_test-1.png
index 0dd4da39..9f40fc35 100644
--- a/docs/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_tc_test-1.png
+++ b/docs/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_tc_test-1.png
diff --git a/docs/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_nlmixr_saem_dfop_const-1.png b/docs/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_nlmixr_saem_dfop_const-1.png
deleted file mode 100644
index 88089aaf..00000000
--- a/docs/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_nlmixr_saem_dfop_const-1.png
+++ /dev/null
diff --git a/docs/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_nlmixr_saem_dfop_tc-1.png b/docs/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_nlmixr_saem_dfop_tc-1.png
deleted file mode 100644
index efc37a5f..00000000
--- a/docs/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_nlmixr_saem_dfop_tc-1.png
+++ /dev/null
diff --git a/docs/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_nlmixr_saem_dfop_tc_10k-1.png b/docs/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_nlmixr_saem_dfop_tc_10k-1.png
deleted file mode 100644
index ab2b1b2d..00000000
--- a/docs/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_nlmixr_saem_dfop_tc_10k-1.png
+++ /dev/null
diff --git a/docs/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_nlmixr_saem_dfop_tc_1k-1.png b/docs/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_nlmixr_saem_dfop_tc_1k-1.png
deleted file mode 100644
index 70987378..00000000
--- a/docs/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_nlmixr_saem_dfop_tc_1k-1.png
+++ /dev/null
diff --git a/docs/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_nlmixr_saem_sfo_const-1.png b/docs/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_nlmixr_saem_sfo_const-1.png
deleted file mode 100644
index de0a0ded..00000000
--- a/docs/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_nlmixr_saem_sfo_const-1.png
+++ /dev/null
diff --git a/docs/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_nlmixr_saem_sfo_tc-1.png b/docs/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_nlmixr_saem_sfo_tc-1.png
deleted file mode 100644
index 0b7f5090..00000000
--- a/docs/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_nlmixr_saem_sfo_tc-1.png
+++ /dev/null
diff --git a/docs/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_saemix_dfop_tc-1.png b/docs/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_saemix_dfop_tc-1.png
index d154dc9b..fa5d34f0 100644
--- a/docs/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_saemix_dfop_tc-1.png
+++ b/docs/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_saemix_dfop_tc-1.png
diff --git a/docs/articles/web_only/dimethenamid_2018_files/figure-html/plot_parent_nlme-1.png b/docs/articles/web_only/dimethenamid_2018_files/figure-html/plot_parent_nlme-1.png
index a799b14c..6bcf3434 100644
--- a/docs/articles/web_only/dimethenamid_2018_files/figure-html/plot_parent_nlme-1.png
+++ b/docs/articles/web_only/dimethenamid_2018_files/figure-html/plot_parent_nlme-1.png
diff --git a/docs/articles/web_only/mkin_benchmarks.rda b/docs/articles/web_only/mkin_benchmarks.rda
deleted file mode 100644
index e26caf64..00000000
--- a/docs/articles/web_only/mkin_benchmarks.rda
+++ /dev/null
diff --git a/docs/articles/web_only/multistart.html b/docs/articles/web_only/multistart.html
index dff087e4..f6ab46de 100644
--- a/docs/articles/web_only/multistart.html
+++ b/docs/articles/web_only/multistart.html
@@ -4,144 +4,90 @@
 <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
 <meta charset="utf-8">
 <meta http-equiv="X-UA-Compatible" content="IE=edge">
-<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
 <title>Short demo of the multistart method • mkin</title>
-<!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous">
-<script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../../bootstrap-toc.css">
-<script src="../../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous">
-<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous">
-<!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../../pkgdown.css" rel="stylesheet">
-<script src="../../pkgdown.js"></script><meta property="og:title" content="Short demo of the multistart method">
-<meta property="og:description" content="mkin">
-<!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]-->
+<script src="../../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
+<link href="../../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet">
+<script src="../../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet">
+<link href="../../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet">
+<script src="../../deps/headroom-0.11.0/headroom.min.js"></script><script src="../../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../../deps/search-1.0.0/fuse.min.js"></script><script src="../../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../../pkgdown.js"></script><meta property="og:title" content="Short demo of the multistart method">
 </head>
-<body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-article">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav">
-<li>
-  <a href="../../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu">
-<li>
-      <a href="../../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+<body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto">
+<li class="nav-item"><a class="nav-link" href="../../reference/index.html">Reference</a></li>
+<li class="active nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles">
+<li><a class="dropdown-item" href="../../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul>
 </li>
-<li>
-  <a href="../../news/index.html">News</a>
-</li>
+<li class="nav-item"><a class="nav-link" href="../../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../../news/index.html">News</a></li>
       </ul>
-<ul class="nav navbar-nav navbar-right">
-<li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
+<ul class="navbar-nav">
+<li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../../search.json">
+</form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul>
 </div>
-<!--/.nav-collapse -->
+
+
   </div>
-<!--/.container -->
-</div>
-<!--/.navbar -->
+</nav><div class="container template-article">
+
+
 
-      
 
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header toc-ignore">
-      <h1 data-toc-skip>Short demo of the multistart method</h1>
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Short demo of the multistart method</h1>
                         <h4 data-toc-skip class="author">Johannes
 Ranke</h4>
             
             <h4 data-toc-skip class="date">Last change 20 April 2023
-(rebuilt 2023-10-30)</h4>
+(rebuilt 2025-02-13)</h4>
       
       <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/web_only/multistart.rmd" class="external-link"><code>vignettes/web_only/multistart.rmd</code></a></small>
-      <div class="hidden name"><code>multistart.rmd</code></div>
-
+      <div class="d-none name"><code>multistart.rmd</code></div>
     </div>
 
     
@@ -207,33 +153,26 @@ improvement in case of the full model, because it is less well-defined,
 which impedes convergence. For the reduced model, using multiple
 starting values only results in a small improvement of the model
 fit.</p>
-  </div>
-
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-
-      </div>
-
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside>
 </div>
 
 
 
-      <footer><div class="copyright">
-  <p></p>
-<p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p>
-<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer>
+    </footer>
 </div>
 
-  
 
 
-  
+
 
   </body>
 </html>
diff --git a/docs/articles/web_only/multistart_files/accessible-code-block-0.0.1/empty-anchor.js b/docs/articles/web_only/multistart_files/accessible-code-block-0.0.1/empty-anchor.js
deleted file mode 100644
index ca349fd6..00000000
--- a/docs/articles/web_only/multistart_files/accessible-code-block-0.0.1/empty-anchor.js
+++ /dev/null
@@ -1,15 +0,0 @@
-// Hide empty <a> tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) -->
-// v0.0.1
-// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020.
-
-document.addEventListener('DOMContentLoaded', function() {
-  const codeList = document.getElementsByClassName("sourceCode");
-  for (var i = 0; i < codeList.length; i++) {
-    var linkList = codeList[i].getElementsByTagName('a');
-    for (var j = 0; j < linkList.length; j++) {
-      if (linkList[j].innerHTML === "") {
-        linkList[j].setAttribute('aria-hidden', 'true');
-      }
-    }
-  }
-});
diff --git a/docs/articles/web_only/multistart_files/figure-html/unnamed-chunk-3-1.png b/docs/articles/web_only/multistart_files/figure-html/unnamed-chunk-3-1.png
index 8be6ba61..19b68cfe 100644
--- a/docs/articles/web_only/multistart_files/figure-html/unnamed-chunk-3-1.png
+++ b/docs/articles/web_only/multistart_files/figure-html/unnamed-chunk-3-1.png
diff --git a/docs/articles/web_only/multistart_files/figure-html/unnamed-chunk-4-1.png b/docs/articles/web_only/multistart_files/figure-html/unnamed-chunk-4-1.png
index bc518c0c..034b170c 100644
--- a/docs/articles/web_only/multistart_files/figure-html/unnamed-chunk-4-1.png
+++ b/docs/articles/web_only/multistart_files/figure-html/unnamed-chunk-4-1.png
diff --git a/docs/articles/web_only/multistart_files/figure-html/unnamed-chunk-5-1.png b/docs/articles/web_only/multistart_files/figure-html/unnamed-chunk-5-1.png
index 9ecdf4c9..c8e918cd 100644
--- a/docs/articles/web_only/multistart_files/figure-html/unnamed-chunk-5-1.png
+++ b/docs/articles/web_only/multistart_files/figure-html/unnamed-chunk-5-1.png
diff --git a/docs/articles/web_only/multistart_files/figure-html/unnamed-chunk-6-1.png b/docs/articles/web_only/multistart_files/figure-html/unnamed-chunk-6-1.png
deleted file mode 100644
index b1582557..00000000
--- a/docs/articles/web_only/multistart_files/figure-html/unnamed-chunk-6-1.png
+++ /dev/null
diff --git a/docs/articles/web_only/saem_benchmarks.html b/docs/articles/web_only/saem_benchmarks.html
index 9180773e..3116d0e6 100644
--- a/docs/articles/web_only/saem_benchmarks.html
+++ b/docs/articles/web_only/saem_benchmarks.html
@@ -4,144 +4,90 @@
 <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
 <meta charset="utf-8">
 <meta http-equiv="X-UA-Compatible" content="IE=edge">
-<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
 <title>Benchmark timings for saem.mmkin • mkin</title>
-<!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous">
-<script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../../bootstrap-toc.css">
-<script src="../../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous">
-<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous">
-<!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../../pkgdown.css" rel="stylesheet">
-<script src="../../pkgdown.js"></script><meta property="og:title" content="Benchmark timings for saem.mmkin">
-<meta property="og:description" content="mkin">
-<!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]-->
+<script src="../../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
+<link href="../../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet">
+<script src="../../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet">
+<link href="../../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet">
+<script src="../../deps/headroom-0.11.0/headroom.min.js"></script><script src="../../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../../deps/search-1.0.0/fuse.min.js"></script><script src="../../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../../pkgdown.js"></script><meta property="og:title" content="Benchmark timings for saem.mmkin">
 </head>
-<body data-spy="scroll" data-target="#toc">
-    
+<body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
 
-    <div class="container template-article">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
 
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav">
-<li>
-  <a href="../../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu">
-<li>
-      <a href="../../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto">
+<li class="nav-item"><a class="nav-link" href="../../reference/index.html">Reference</a></li>
+<li class="active nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles">
+<li><a class="dropdown-item" href="../../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul>
 </li>
-<li>
-  <a href="../../news/index.html">News</a>
-</li>
+<li class="nav-item"><a class="nav-link" href="../../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../../news/index.html">News</a></li>
       </ul>
-<ul class="nav navbar-nav navbar-right">
-<li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
+<ul class="navbar-nav">
+<li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../../search.json">
+</form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul>
 </div>
-<!--/.nav-collapse -->
+
+
   </div>
-<!--/.container -->
-</div>
-<!--/.navbar -->
+</nav><div class="container template-article">
+
 
-      
 
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header toc-ignore">
-      <h1 data-toc-skip>Benchmark timings for saem.mmkin</h1>
+
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Benchmark timings for saem.mmkin</h1>
                         <h4 data-toc-skip class="author">Johannes
 Ranke</h4>
             
             <h4 data-toc-skip class="date">Last change 17 February 2023
-(rebuilt 2023-11-02)</h4>
+(rebuilt 2025-02-13)</h4>
       
       <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/web_only/saem_benchmarks.rmd" class="external-link"><code>vignettes/web_only/saem_benchmarks.rmd</code></a></small>
-      <div class="hidden name"><code>saem_benchmarks.rmd</code></div>
-
+      <div class="d-none name"><code>saem_benchmarks.rmd</code></div>
     </div>
 
     
@@ -440,10 +386,20 @@ systems. All trademarks belong to their respective owners.</p>
 <td align="left">Linux</td>
 <td align="left">1.2.6</td>
 <td align="left">3.2</td>
-<td align="right">1.126</td>
-<td align="right">1.971</td>
-<td align="right">2.359</td>
-<td align="right">2.424</td>
+<td align="right">1.135</td>
+<td align="right">2.025</td>
+<td align="right">2.406</td>
+<td align="right">2.478</td>
+</tr>
+<tr class="even">
+<td align="left">Ryzen 9 7950X</td>
+<td align="left">Linux</td>
+<td align="left">1.2.9</td>
+<td align="left">3.3</td>
+<td align="right">1.086</td>
+<td align="right">1.991</td>
+<td align="right">1.949</td>
+<td align="right">2.411</td>
 </tr>
 </tbody>
 </table>
@@ -555,10 +511,20 @@ systems. All trademarks belong to their respective owners.</p>
 <td align="left">Linux</td>
 <td align="left">1.2.6</td>
 <td align="left">3.2</td>
-<td align="right">2.116</td>
-<td align="right">3.246</td>
-<td align="right">3.602</td>
-<td align="right">3.036</td>
+<td align="right">2.161</td>
+<td align="right">3.325</td>
+<td align="right">3.669</td>
+<td align="right">3.153</td>
+</tr>
+<tr class="even">
+<td align="left">Ryzen 9 7950X</td>
+<td align="left">Linux</td>
+<td align="left">1.2.9</td>
+<td align="left">3.3</td>
+<td align="right">2.426</td>
+<td align="right">3.196</td>
+<td align="right">3.256</td>
+<td align="right">3.322</td>
 </tr>
 </tbody>
 </table>
@@ -654,8 +620,16 @@ systems. All trademarks belong to their respective owners.</p>
 <td align="left">Linux</td>
 <td align="left">1.2.6</td>
 <td align="left">3.2</td>
-<td align="right">11.712</td>
-<td align="right">290.532</td>
+<td align="right">12.007</td>
+<td align="right">286.757</td>
+</tr>
+<tr class="even">
+<td align="left">Ryzen 9 7950X</td>
+<td align="left">Linux</td>
+<td align="left">1.2.9</td>
+<td align="left">3.3</td>
+<td align="right">12.420</td>
+<td align="right">289.338</td>
 </tr>
 </tbody>
 </table>
@@ -741,41 +715,39 @@ systems. All trademarks belong to their respective owners.</p>
 <td align="left">Linux</td>
 <td align="left">1.2.6</td>
 <td align="left">3.2</td>
-<td align="right">479.161</td>
+<td align="right">480.577</td>
+</tr>
+<tr class="even">
+<td align="left">Ryzen 9 7950X</td>
+<td align="left">Linux</td>
+<td align="left">1.2.9</td>
+<td align="left">3.3</td>
+<td align="right">485.836</td>
 </tr>
 </tbody>
 </table>
 </div>
 </div>
-  </div>
-
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-
-        <nav id="toc" data-toggle="toc"><h2 data-toc-skip>Contents</h2>
-    </nav>
-</div>
-
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside>
 </div>
 
 
 
-      <footer><div class="copyright">
-  <p></p>
-<p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p>
-<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer>
+    </footer>
 </div>
 
-  
 
 
-  
+
 
   </body>
 </html>
diff --git a/docs/articles/web_only/saem_benchmarks_files/accessible-code-block-0.0.1/empty-anchor.js b/docs/articles/web_only/saem_benchmarks_files/accessible-code-block-0.0.1/empty-anchor.js
deleted file mode 100644
index ca349fd6..00000000
--- a/docs/articles/web_only/saem_benchmarks_files/accessible-code-block-0.0.1/empty-anchor.js
+++ /dev/null
@@ -1,15 +0,0 @@
-// Hide empty <a> tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) -->
-// v0.0.1
-// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020.
-
-document.addEventListener('DOMContentLoaded', function() {
-  const codeList = document.getElementsByClassName("sourceCode");
-  for (var i = 0; i < codeList.length; i++) {
-    var linkList = codeList[i].getElementsByTagName('a');
-    for (var j = 0; j < linkList.length; j++) {
-      if (linkList[j].innerHTML === "") {
-        linkList[j].setAttribute('aria-hidden', 'true');
-      }
-    }
-  }
-});
diff --git a/docs/authors.html b/docs/authors.html
index 2e97a0dc..d3cd2a75 100644
--- a/docs/authors.html
+++ b/docs/authors.html
@@ -1,170 +1,118 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Authors and Citation • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="bootstrap-toc.css"><script src="bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="pkgdown.css" rel="stylesheet"><script src="pkgdown.js"></script><meta property="og:title" content="Authors and Citation"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-citation-authors">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Authors and Citation • mkin</title><script src="deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="deps/headroom-0.11.0/headroom.min.js"></script><script src="deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="deps/search-1.0.0/fuse.min.js"></script><script src="deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="pkgdown.js"></script><meta property="og:title" content="Authors and Citation"></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="nav-item"><a class="nav-link" href="reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-citation-authors">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Authors and Citation</h1>
     </div>
 
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="contents col-md-9">
-    <div class="section level2 authors-section">
-      <div class="page-header">
-        <h1>Authors</h1>
-      </div>
-
-      
+    <div class="section level2">
+      <h2>Authors</h2>
+
       <ul class="list-unstyled"><li>
           <p><strong>Johannes Ranke</strong>. Author, maintainer, copyright holder. <a href="https://orcid.org/0000-0003-4371-6538" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
           </p>
         </li>
         <li>
-          <p><strong>Katrin Lindenberger</strong>. Contributor. 
+          <p><strong>Katrin Lindenberger</strong>. Contributor.
           <br><small>contributed to mkinresplot()</small></p>
         </li>
         <li>
-          <p><strong>René Lehmann</strong>. Contributor. 
+          <p><strong>René Lehmann</strong>. Contributor.
           <br><small>ilr() and invilr()</small></p>
         </li>
         <li>
-          <p><strong>Eurofins Regulatory AG</strong>. Copyright holder. 
+          <p><strong>Eurofins Regulatory AG</strong>. Copyright holder.
           <br><small>copyright for some of the contributions of JR 2012-2014</small></p>
         </li>
       </ul></div>
-    <div class="section level2 citation-section">
-    <div>
-      <h1 id="citation">Citation</h1>
-      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/DESCRIPTION" class="external-link"><code>DESCRIPTION</code></a></small>
-    </div>
-    </div>
 
+    <div class="section level2">
+      <h2 id="citation">Citation</h2>
+      <p><small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/DESCRIPTION" class="external-link"><code>DESCRIPTION</code></a></small></p>
 
-    <p>Ranke J (2023).
+      <p>Ranke J (2025).
 <em>mkin: Kinetic Evaluation of Chemical Degradation Data</em>.
-R package version 1.2.6, <a href="https://pkgdown.jrwb.de/mkin/">https://pkgdown.jrwb.de/mkin/</a>. 
+R package version 1.2.9, <a href="https://pkgdown.jrwb.de/mkin/">https://pkgdown.jrwb.de/mkin/</a>.
 </p>
-    <pre>@Manual{,
+      <pre>@Manual{,
   title = {mkin: Kinetic Evaluation of Chemical Degradation Data},
   author = {Johannes Ranke},
-  year = {2023},
-  note = {R package version 1.2.6},
+  year = {2025},
+  note = {R package version 1.2.9},
   url = {https://pkgdown.jrwb.de/mkin/},
 }</pre>
+    </div>
 
-  </div>
-
-</div>
-
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/bootstrap-toc.css b/docs/bootstrap-toc.css
deleted file mode 100644
index 5a859415..00000000
--- a/docs/bootstrap-toc.css
+++ /dev/null
@@ -1,60 +0,0 @@
-/*!
- * Bootstrap Table of Contents v0.4.1 (http://afeld.github.io/bootstrap-toc/)
- * Copyright 2015 Aidan Feldman
- * Licensed under MIT (https://github.com/afeld/bootstrap-toc/blob/gh-pages/LICENSE.md) */
-
-/* modified from https://github.com/twbs/bootstrap/blob/94b4076dd2efba9af71f0b18d4ee4b163aa9e0dd/docs/assets/css/src/docs.css#L548-L601 */
-
-/* All levels of nav */
-nav[data-toggle='toc'] .nav > li > a {
-  display: block;
-  padding: 4px 20px;
-  font-size: 13px;
-  font-weight: 500;
-  color: #767676;
-}
-nav[data-toggle='toc'] .nav > li > a:hover,
-nav[data-toggle='toc'] .nav > li > a:focus {
-  padding-left: 19px;
-  color: #563d7c;
-  text-decoration: none;
-  background-color: transparent;
-  border-left: 1px solid #563d7c;
-}
-nav[data-toggle='toc'] .nav > .active > a,
-nav[data-toggle='toc'] .nav > .active:hover > a,
-nav[data-toggle='toc'] .nav > .active:focus > a {
-  padding-left: 18px;
-  font-weight: bold;
-  color: #563d7c;
-  background-color: transparent;
-  border-left: 2px solid #563d7c;
-}
-
-/* Nav: second level (shown on .active) */
-nav[data-toggle='toc'] .nav .nav {
-  display: none; /* Hide by default, but at >768px, show it */
-  padding-bottom: 10px;
-}
-nav[data-toggle='toc'] .nav .nav > li > a {
-  padding-top: 1px;
-  padding-bottom: 1px;
-  padding-left: 30px;
-  font-size: 12px;
-  font-weight: normal;
-}
-nav[data-toggle='toc'] .nav .nav > li > a:hover,
-nav[data-toggle='toc'] .nav .nav > li > a:focus {
-  padding-left: 29px;
-}
-nav[data-toggle='toc'] .nav .nav > .active > a,
-nav[data-toggle='toc'] .nav .nav > .active:hover > a,
-nav[data-toggle='toc'] .nav .nav > .active:focus > a {
-  padding-left: 28px;
-  font-weight: 500;
-}
-
-/* from https://github.com/twbs/bootstrap/blob/e38f066d8c203c3e032da0ff23cd2d6098ee2dd6/docs/assets/css/src/docs.css#L631-L634 */
-nav[data-toggle='toc'] .nav > .active > ul {
-  display: block;
-}
diff --git a/docs/bootstrap-toc.js b/docs/bootstrap-toc.js
deleted file mode 100644
index 1cdd573b..00000000
--- a/docs/bootstrap-toc.js
+++ /dev/null
@@ -1,159 +0,0 @@
-/*!
- * Bootstrap Table of Contents v0.4.1 (http://afeld.github.io/bootstrap-toc/)
- * Copyright 2015 Aidan Feldman
- * Licensed under MIT (https://github.com/afeld/bootstrap-toc/blob/gh-pages/LICENSE.md) */
-(function() {
-  'use strict';
-
-  window.Toc = {
-    helpers: {
-      // return all matching elements in the set, or their descendants
-      findOrFilter: function($el, selector) {
-        // http://danielnouri.org/notes/2011/03/14/a-jquery-find-that-also-finds-the-root-element/
-        // http://stackoverflow.com/a/12731439/358804
-        var $descendants = $el.find(selector);
-        return $el.filter(selector).add($descendants).filter(':not([data-toc-skip])');
-      },
-
-      generateUniqueIdBase: function(el) {
-        var text = $(el).text();
-        var anchor = text.trim().toLowerCase().replace(/[^A-Za-z0-9]+/g, '-');
-        return anchor || el.tagName.toLowerCase();
-      },
-
-      generateUniqueId: function(el) {
-        var anchorBase = this.generateUniqueIdBase(el);
-        for (var i = 0; ; i++) {
-          var anchor = anchorBase;
-          if (i > 0) {
-            // add suffix
-            anchor += '-' + i;
-          }
-          // check if ID already exists
-          if (!document.getElementById(anchor)) {
-            return anchor;
-          }
-        }
-      },
-
-      generateAnchor: function(el) {
-        if (el.id) {
-          return el.id;
-        } else {
-          var anchor = this.generateUniqueId(el);
-          el.id = anchor;
-          return anchor;
-        }
-      },
-
-      createNavList: function() {
-        return $('<ul class="nav"></ul>');
-      },
-
-      createChildNavList: function($parent) {
-        var $childList = this.createNavList();
-        $parent.append($childList);
-        return $childList;
-      },
-
-      generateNavEl: function(anchor, text) {
-        var $a = $('<a></a>');
-        $a.attr('href', '#' + anchor);
-        $a.text(text);
-        var $li = $('<li></li>');
-        $li.append($a);
-        return $li;
-      },
-
-      generateNavItem: function(headingEl) {
-        var anchor = this.generateAnchor(headingEl);
-        var $heading = $(headingEl);
-        var text = $heading.data('toc-text') || $heading.text();
-        return this.generateNavEl(anchor, text);
-      },
-
-      // Find the first heading level (`<h1>`, then `<h2>`, etc.) that has more than one element. Defaults to 1 (for `<h1>`).
-      getTopLevel: function($scope) {
-        for (var i = 1; i <= 6; i++) {
-          var $headings = this.findOrFilter($scope, 'h' + i);
-          if ($headings.length > 1) {
-            return i;
-          }
-        }
-
-        return 1;
-      },
-
-      // returns the elements for the top level, and the next below it
-      getHeadings: function($scope, topLevel) {
-        var topSelector = 'h' + topLevel;
-
-        var secondaryLevel = topLevel + 1;
-        var secondarySelector = 'h' + secondaryLevel;
-
-        return this.findOrFilter($scope, topSelector + ',' + secondarySelector);
-      },
-
-      getNavLevel: function(el) {
-        return parseInt(el.tagName.charAt(1), 10);
-      },
-
-      populateNav: function($topContext, topLevel, $headings) {
-        var $context = $topContext;
-        var $prevNav;
-
-        var helpers = this;
-        $headings.each(function(i, el) {
-          var $newNav = helpers.generateNavItem(el);
-          var navLevel = helpers.getNavLevel(el);
-
-          // determine the proper $context
-          if (navLevel === topLevel) {
-            // use top level
-            $context = $topContext;
-          } else if ($prevNav && $context === $topContext) {
-            // create a new level of the tree and switch to it
-            $context = helpers.createChildNavList($prevNav);
-          } // else use the current $context
-
-          $context.append($newNav);
-
-          $prevNav = $newNav;
-        });
-      },
-
-      parseOps: function(arg) {
-        var opts;
-        if (arg.jquery) {
-          opts = {
-            $nav: arg
-          };
-        } else {
-          opts = arg;
-        }
-        opts.$scope = opts.$scope || $(document.body);
-        return opts;
-      }
-    },
-
-    // accepts a jQuery object, or an options object
-    init: function(opts) {
-      opts = this.helpers.parseOps(opts);
-
-      // ensure that the data attribute is in place for styling
-      opts.$nav.attr('data-toggle', 'toc');
-
-      var $topContext = this.helpers.createChildNavList(opts.$nav);
-      var topLevel = this.helpers.getTopLevel(opts.$scope);
-      var $headings = this.helpers.getHeadings(opts.$scope, topLevel);
-      this.helpers.populateNav($topContext, topLevel, $headings);
-    }
-  };
-
-  $(function() {
-    $('nav[data-toggle="toc"]').each(function(i, el) {
-      var $nav = $(el);
-      Toc.init($nav);
-    });
-  });
-})();
diff --git a/docs/coverage/coverage.html b/docs/coverage/coverage.html
new file mode 100644
index 00000000..a910a2d3
--- /dev/null
+++ b/docs/coverage/coverage.html
@@ -0,0 +1,69921 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+<meta charset="utf-8"/>
+<style>body{background-color:white;}</style>
+<link href="lib/htmltools-fill-0.5.8.1/fill.css" rel="stylesheet" />
+<script src="lib/htmlwidgets-1.6.4/htmlwidgets.js"></script>
+<link href="lib/datatables-css-0.0.0/datatables-crosstalk.css" rel="stylesheet" />
+<script src="lib/datatables-binding-0.33/datatables.js"></script>
+<script src="lib/jquery-3.6.0/jquery-3.6.0.min.js"></script>
+<link href="lib/dt-core-1.13.6/css/jquery.dataTables.min.css" rel="stylesheet" />
+<link href="lib/dt-core-1.13.6/css/jquery.dataTables.extra.css" rel="stylesheet" />
+<script src="lib/dt-core-1.13.6/js/jquery.dataTables.min.js"></script>
+<link href="lib/crosstalk-1.2.1/css/crosstalk.min.css" rel="stylesheet" />
+<script src="lib/crosstalk-1.2.1/js/crosstalk.min.js"></script>
+<link href="lib/highlight.js-6.2/rstudio.css" rel="stylesheet" />
+<script src="lib/highlight.js-6.2/highlight.pack.js"></script>
+<meta name="viewport" content="width=device-width, initial-scale=1" />
+<link href="lib/bootstrap-3.3.5/css/bootstrap.min.css" rel="stylesheet" />
+<link href="lib/bootstrap-3.3.5/css/bootstrap-theme.min.css" rel="stylesheet" />
+<script src="lib/bootstrap-3.3.5/js/bootstrap.min.js"></script>
+<script src="lib/bootstrap-3.3.5/shim/html5shiv.min.js"></script>
+<script src="lib/bootstrap-3.3.5/shim/respond.min.js"></script>
+
+</head>
+<body>
+<div class="container-fluid">
+  <style type="text/css">table tr:hover td {
+  font-weight:bold;text-decoration:none
+}
+table tr.covered td{
+  background-color:rgba(95,151,68,0.3)
+}
+
+table tr:hover.covered .num{
+  background-color:rgba(95,151,68,0.7)
+}
+table tr.missed td{
+  background-color:rgba(185,73,71,0.3)
+}
+table tr:hover.missed .num{
+  background-color:rgba(185,73,71,0.7)
+}
+
+table tr.missed:hover td{
+  -webkit-box-shadow:0 -2px 0 0 #b94947 inset;
+  -moz-box-shadow:0 -2px 0 0 #b94947 inset;
+  box-shadow:0 -2px 0 0 #b94947 inset
+}
+table tr.covered:hover td{
+  -webkit-box-shadow:0 -2px 0 0 #5f9744 inset;
+  -moz-box-shadow:0 -2px 0 0 #5f9744 inset;
+  box-shadow:0 -2px 0 0 #5f9744 inset
+}
+
+table tr.never td{
+  background-color:transparent
+}
+
+table tbody {
+  border-style: solid;
+  border: 1px solid rgba(0,0,0,0.1)
+}
+
+table .num {
+  border-right: 1px solid rgba(0,0,0,0.1)
+}
+
+td.coverage em {
+  opacity: 0.5;
+}
+
+table td.coverage {
+  border-right: 1px solid rgba(0,0,0,0.1);
+  font-weight: bold;
+  text-align: center;
+}
+table.table-condensed pre {
+  background-color: transparent;
+  margin: 0;
+  padding: 0;
+  border: 0;
+  font-size: 11px;
+}
+div#files td {
+  padding: 0;
+  padding-left: 5px;
+}
+
+div#files td.num {
+  padding-right: 5px;
+}
+
+table.table-condensed {
+  font-size: 11px;
+}</style>
+  <div class="col-md-8 col-md-offset-2">
+    <h2>mkin coverage - 89.31%</h2>
+    <div class="tabbable">
+      <ul class="nav nav-tabs" data-tabsetid="covr">
+        <li class="active">
+          <a href="#tab-covr-1" data-toggle="tab" data-value="Files">Files</a>
+        </li>
+        <li>
+          <a href="#tab-covr-2" data-toggle="tab" data-value="Source">Source</a>
+        </li>
+      </ul>
+      <div class="tab-content" data-tabsetid="covr">
+        <div class="tab-pane active" title="Files" data-value="Files" id="tab-covr-1">
+          <div class="datatables html-widget html-fill-item" id="htmlwidget-ed9e8c4b083619214ff3" style="width:100%;height:500px;"></div>
+          <script type="application/json" data-for="htmlwidget-ed9e8c4b083619214ff3">{"x":{"filter":"none","vertical":false,"fillContainer":false,"data":[["<a href=\"#\">R/summary_listing.R<\/a>","<a href=\"#\">R/hierarchical_kinetics.R<\/a>","<a href=\"#\">R/aw.R<\/a>","<a href=\"#\">R/multistart.R<\/a>","<a href=\"#\">R/status.R<\/a>","<a href=\"#\">R/parms.R<\/a>","<a href=\"#\">R/lrtest.mkinfit.R<\/a>","<a href=\"#\">R/mkinresplot.R<\/a>","<a href=\"#\">R/mkinds.R<\/a>","<a href=\"#\">R/f_time_norm_focus.R<\/a>","<a href=\"#\">R/loftest.R<\/a>","<a href=\"#\">R/read_spreadsheet.R<\/a>","<a href=\"#\">R/mhmkin.R<\/a>","<a href=\"#\">R/plot.mixed.mmkin.R<\/a>","<a href=\"#\">R/plot.mkinfit.R<\/a>","<a href=\"#\">R/mkinpredict.R<\/a>","<a href=\"#\">R/update.mkinfit.R<\/a>","<a href=\"#\">R/anova.saem.mmkin.R<\/a>","<a href=\"#\">R/plot.mmkin.R<\/a>","<a href=\"#\">R/max_twa_parent.R<\/a>","<a href=\"#\">R/nafta.R<\/a>","<a href=\"#\">R/saem.R<\/a>","<a href=\"#\">R/llhist.R<\/a>","<a href=\"#\">R/illparms.R<\/a>","<a href=\"#\">R/mkin_wide_to_long.R<\/a>","<a href=\"#\">R/summary.saem.mmkin.R<\/a>","<a href=\"#\">R/summary.nlme.mmkin.R<\/a>","<a href=\"#\">R/summary.mkinfit.R<\/a>","<a href=\"#\">R/mean_degparms.R<\/a>","<a href=\"#\">R/transform_odeparms.R<\/a>","<a href=\"#\">R/mkinmod.R<\/a>","<a href=\"#\">R/mkinfit.R<\/a>","<a href=\"#\">R/parplot.R<\/a>","<a href=\"#\">R/endpoints.R<\/a>","<a href=\"#\">R/intervals.R<\/a>","<a href=\"#\">R/ilr.R<\/a>","<a href=\"#\">R/set_nd_nq.R<\/a>","<a href=\"#\">R/mkinparplot.R<\/a>","<a href=\"#\">R/nlme.R<\/a>","<a href=\"#\">R/mixed.mmkin.R<\/a>","<a href=\"#\">R/mkinerrplot.R<\/a>","<a href=\"#\">R/nlme.mmkin.R<\/a>","<a href=\"#\">R/mmkin.R<\/a>","<a href=\"#\">R/confint.mkinfit.R<\/a>","<a href=\"#\">R/create_deg_func.R<\/a>","<a href=\"#\">R/mkinerrmin.R<\/a>","<a href=\"#\">R/CAKE_export.R<\/a>","<a href=\"#\">R/summary.mmkin.R<\/a>","<a href=\"#\">R/AIC.mmkin.R<\/a>","<a href=\"#\">R/parent_solutions.R<\/a>","<a href=\"#\">R/residuals.mkinfit.R<\/a>","<a href=\"#\">R/add_err.R<\/a>","<a href=\"#\">R/mkin_long_to_wide.R<\/a>","<a href=\"#\">R/logLik.mkinfit.R<\/a>","<a href=\"#\">R/nobs.mkinfit.R<\/a>","<a href=\"#\">R/mkinsub.R<\/a>","<a href=\"#\">R/sigma_twocomp.R<\/a>"],[59,59,84,224,117,82,80,94,178,112,112,120,300,380,336,271,61,114,157,125,153,867,43,168,35,317,240,291,68,278,513,989,129,255,102,89,164,75,145,103,107,267,199,238,159,120,95,56,68,238,31,104,29,43,8,17,54],[28,14,27,76,59,22,20,26,44,39,28,46,110,183,109,87,23,62,47,48,58,466,18,66,11,149,116,167,28,88,225,453,69,159,53,18,56,47,62,31,37,83,51,69,95,43,41,20,17,15,13,12,6,5,1,1,1],[0,0,17,49,39,16,15,21,36,32,23,38,91,152,91,74,20,54,41,42,51,411,16,60,10,136,106,155,26,82,211,426,65,150,50,17,53,45,60,30,36,81,50,68,95,43,41,20,17,15,13,12,6,5,1,1,1],[28,14,10,27,20,6,5,5,8,7,5,8,19,31,18,13,3,8,6,6,7,55,2,6,1,13,10,12,2,6,14,27,4,9,3,1,3,2,2,1,1,2,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0],["0","0","403","168","482","20261","5","976","102","290","1","305","305","634","738","19867308","4","509","523","2","179","2273233","225","867","1025","500","323","44586","6538","30119107","4421","291933","154","29490","3125","529091","18","104","1217374","149","206","3258","1963","773","4031","72485","337","1","247","426770","1607","1285","535","166798","166810","9864","4250"],["0.00%","0.00%","62.96%","64.47%","66.10%","72.73%","75.00%","80.77%","81.82%","82.05%","82.14%","82.61%","82.73%","83.06%","83.49%","85.06%","86.96%","87.10%","87.23%","87.50%","87.93%","88.20%","88.89%","90.91%","90.91%","91.28%","91.38%","92.81%","92.86%","93.18%","93.78%","94.04%","94.20%","94.34%","94.34%","94.44%","94.64%","95.74%","96.77%","96.77%","97.30%","97.59%","98.04%","98.55%","100.00%","100.00%","100.00%","100.00%","100.00%","100.00%","100.00%","100.00%","100.00%","100.00%","100.00%","100.00%","100.00%"]],"container":"<table class=\"row-border\">\n  <thead>\n    <tr>\n      <th>File<\/th>\n      <th>Lines<\/th>\n      <th>Relevant<\/th>\n      <th>Covered<\/th>\n      <th>Missed<\/th>\n      <th>Hits / Line<\/th>\n      <th>Coverage<\/th>\n    <\/tr>\n  <\/thead>\n<\/table>","options":{"searching":false,"dom":"t","paging":false,"columnDefs":[{"targets":6,"createdCell":"function(td, cellData, rowData, row, col) {\n  var percent = cellData.replace(\"%\", \"\");\n  if (percent > 90) {\n    var grad = \"linear-gradient(90deg, #edfde7 \" + cellData + \", white \" + cellData + \")\";\n  } else if (percent > 75) {\n    var grad = \"linear-gradient(90deg, #f9ffe5 \" + cellData + \", white \" + cellData + \")\";\n  } else {\n    var grad = \"linear-gradient(90deg, #fcece9 \" + cellData + \", white \" + cellData + \")\";\n  }\n  $(td).css(\"background\", grad);\n}\n"},{"className":"dt-right","targets":[1,2,3,4]},{"name":"File","targets":0},{"name":"Lines","targets":1},{"name":"Relevant","targets":2},{"name":"Covered","targets":3},{"name":"Missed","targets":4},{"name":"Hits / Line","targets":5},{"name":"Coverage","targets":6}],"order":[],"autoWidth":false,"orderClasses":false},"callback":"function(table) {\ntable.on('click.dt', 'a', function() {\n  files = $('div#files div');\n  files.not('div.hidden').addClass('hidden');\n  id = $(this).text();\n  files.filter('div[id=\\'' + id + '\\']').removeClass('hidden');\n  $('ul.nav a[data-value=Source]').text(id).tab('show');\n});\n}"},"evals":["options.columnDefs.0.createdCell","callback"],"jsHooks":[]}</script>
+        </div>
+        <div class="tab-pane" title="Source" data-value="Source" id="tab-covr-2">
+          <div id="files">
+            <div id="R/saem.R" class="hidden">
+              <table class="table-condensed">
+                <tbody>
+                  <tr class="never">
+                    <td class="num">1</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">utils::globalVariables(c("predicted", "std"))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">2</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">3</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Fit nonlinear mixed models with SAEM</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">4</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">5</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' This function uses [saemix::saemix()] as a backend for fitting nonlinear mixed</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">6</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' effects models created from [mmkin] row objects using the Stochastic Approximation</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">7</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Expectation Maximisation algorithm (SAEM).</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">8</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">9</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' An mmkin row object is essentially a list of mkinfit objects that have been</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">10</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' obtained by fitting the same model to a list of datasets using [mkinfit].</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">11</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">12</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Starting values for the fixed effects (population mean parameters, argument</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">13</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' psi0 of [saemix::saemixModel()] are the mean values of the parameters found</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">14</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' using [mmkin].</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">15</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">16</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @importFrom utils packageVersion</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">17</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @importFrom saemix saemix</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">18</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param object An [mmkin] row object containing several fits of the same</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">19</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' [mkinmod] model to different datasets</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">20</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param verbose Should we print information about created objects of</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">21</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' type [saemix::SaemixModel] and [saemix::SaemixData]?</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">22</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param transformations Per default, all parameter transformations are done</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">23</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' in mkin. If this argument is set to 'saemix', parameter transformations</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">24</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' are done in 'saemix' for the supported cases, i.e. (as of version 1.1.2)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">25</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' SFO, FOMC, DFOP and HS without fixing `parent_0`, and SFO or DFOP with</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">26</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' one SFO metabolite.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">27</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param error_model Possibility to override the error model used in the mmkin object</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">28</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param degparms_start Parameter values given as a named numeric vector will</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">29</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' be used to override the starting values obtained from the 'mmkin' object.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">30</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param test_log_parms If TRUE, an attempt is made to use more robust starting</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">31</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' values for population parameters fitted as log parameters in mkin (like</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">32</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' rate constants) by only considering rate constants that pass the t-test</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">33</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' when calculating mean degradation parameters using [mean_degparms].</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">34</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param conf.level Possibility to adjust the required confidence level</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">35</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' for parameter that are tested if requested by 'test_log_parms'.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">36</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param solution_type Possibility to specify the solution type in case the</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">37</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' automatic choice is not desired</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">38</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param no_random_effect Character vector of degradation parameters for</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">39</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' which there should be no variability over the groups. Only used</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">40</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' if the covariance model is not explicitly specified.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">41</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param covariance.model Will be passed to [saemix::saemixModel()]. Per</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">42</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' default, uncorrelated random effects are specified for all degradation</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">43</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' parameters.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">44</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param omega.init Will be passed to [saemix::saemixModel()]. If using</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">45</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' mkin transformations and the default covariance model with optionally</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">46</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' excluded random effects, the variances of the degradation parameters</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">47</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' are estimated using [mean_degparms], with testing of untransformed</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">48</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' log parameters for significant difference from zero. If not using</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">49</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' mkin transformations or a custom covariance model, the default</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">50</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' initialisation of [saemix::saemixModel] is used for omega.init.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">51</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param covariates A data frame with covariate data for use in</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">52</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' 'covariate_models', with dataset names as row names.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">53</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param covariate_models A list containing linear model formulas with one explanatory</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">54</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' variable, i.e. of the type 'parameter ~ covariate'. Covariates must be available</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">55</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' in the 'covariates' data frame.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">56</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param error.init Will be passed to [saemix::saemixModel()].</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">57</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param quiet Should we suppress the messages saemix prints at the beginning</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">58</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' and the end of the optimisation process?</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">59</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param nbiter.saemix Convenience option to increase the number of</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">60</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' iterations</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">61</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param control Passed to [saemix::saemix].</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">62</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param \dots Further parameters passed to [saemix::saemixModel].</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">63</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @return An S3 object of class 'saem.mmkin', containing the fitted</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">64</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' [saemix::SaemixObject] as a list component named 'so'. The</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">65</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' object also inherits from 'mixed.mmkin'.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">66</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @seealso [summary.saem.mmkin] [plot.mixed.mmkin]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">67</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @examples</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">68</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' \dontrun{</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">69</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' ds &lt;- lapply(experimental_data_for_UBA_2019[6:10],</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">70</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'  function(x) subset(x$data[c("name", "time", "value")]))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">71</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' names(ds) &lt;- paste("Dataset", 6:10)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">72</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' f_mmkin_parent_p0_fixed &lt;- mmkin("FOMC", ds,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">73</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   state.ini = c(parent = 100), fixed_initials = "parent", quiet = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">74</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' f_saem_p0_fixed &lt;- saem(f_mmkin_parent_p0_fixed)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">75</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">76</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' f_mmkin_parent &lt;- mmkin(c("SFO", "FOMC", "DFOP"), ds, quiet = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">77</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' f_saem_sfo &lt;- saem(f_mmkin_parent["SFO", ])</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">78</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' f_saem_fomc &lt;- saem(f_mmkin_parent["FOMC", ])</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">79</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' f_saem_dfop &lt;- saem(f_mmkin_parent["DFOP", ])</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">80</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' anova(f_saem_sfo, f_saem_fomc, f_saem_dfop)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">81</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' anova(f_saem_sfo, f_saem_dfop, test = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">82</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' illparms(f_saem_dfop)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">83</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' f_saem_dfop_red &lt;- update(f_saem_dfop, no_random_effect = "g_qlogis")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">84</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' anova(f_saem_dfop, f_saem_dfop_red, test = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">85</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">86</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' anova(f_saem_sfo, f_saem_fomc, f_saem_dfop)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">87</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # The returned saem.mmkin object contains an SaemixObject, therefore we can use</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">88</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # functions from saemix</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">89</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' library(saemix)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">90</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' compare.saemix(f_saem_sfo$so, f_saem_fomc$so, f_saem_dfop$so)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">91</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' plot(f_saem_fomc$so, plot.type = "convergence")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">92</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' plot(f_saem_fomc$so, plot.type = "individual.fit")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">93</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' plot(f_saem_fomc$so, plot.type = "npde")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">94</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' plot(f_saem_fomc$so, plot.type = "vpc")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">95</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">96</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' f_mmkin_parent_tc &lt;- update(f_mmkin_parent, error_model = "tc")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">97</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' f_saem_fomc_tc &lt;- saem(f_mmkin_parent_tc["FOMC", ])</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">98</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' anova(f_saem_fomc, f_saem_fomc_tc, test = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">99</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">100</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' sfo_sfo &lt;- mkinmod(parent = mkinsub("SFO", "A1"),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">101</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   A1 = mkinsub("SFO"))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">102</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' fomc_sfo &lt;- mkinmod(parent = mkinsub("FOMC", "A1"),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">103</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   A1 = mkinsub("SFO"))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">104</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' dfop_sfo &lt;- mkinmod(parent = mkinsub("DFOP", "A1"),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">105</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   A1 = mkinsub("SFO"))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">106</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # The following fit uses analytical solutions for SFO-SFO and DFOP-SFO,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">107</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # and compiled ODEs for FOMC that are much slower</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">108</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' f_mmkin &lt;- mmkin(list(</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">109</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     "SFO-SFO" = sfo_sfo, "FOMC-SFO" = fomc_sfo, "DFOP-SFO" = dfop_sfo),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">110</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   ds, quiet = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">111</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # saem fits of SFO-SFO and DFOP-SFO to these data take about five seconds</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">112</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # each on this system, as we use analytical solutions written for saemix.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">113</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # When using the analytical solutions written for mkin this took around</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">114</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # four minutes</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">115</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' f_saem_sfo_sfo &lt;- saem(f_mmkin["SFO-SFO", ])</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">116</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' f_saem_dfop_sfo &lt;- saem(f_mmkin["DFOP-SFO", ])</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">117</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # We can use print, plot and summary methods to check the results</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">118</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' print(f_saem_dfop_sfo)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">119</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' plot(f_saem_dfop_sfo)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">120</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' summary(f_saem_dfop_sfo, data = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">121</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">122</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # The following takes about 6 minutes</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">123</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' f_saem_dfop_sfo_deSolve &lt;- saem(f_mmkin["DFOP-SFO", ], solution_type = "deSolve",</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">124</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   nbiter.saemix = c(200, 80))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">125</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">126</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' #anova(</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">127</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' #  f_saem_dfop_sfo,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">128</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' #  f_saem_dfop_sfo_deSolve))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">129</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">130</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # If the model supports it, we can also use eigenvalue based solutions, which</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">131</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # take a similar amount of time</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">132</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' #f_saem_sfo_sfo_eigen &lt;- saem(f_mmkin["SFO-SFO", ], solution_type = "eigen",</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">133</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' #  control = list(nbiter.saemix = c(200, 80), nbdisplay = 10))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">134</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">135</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">136</td>
+                    <td class="coverage">2411<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">saem &lt;- function(object, ...) UseMethod("saem")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">137</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">138</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @rdname saem</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">139</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">140</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">saem.mmkin &lt;- function(object,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">141</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  transformations = c("mkin", "saemix"),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">142</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  error_model = "auto",</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">143</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  degparms_start = numeric(),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">144</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  test_log_parms = TRUE,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">145</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  conf.level = 0.6,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">146</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  solution_type = "auto",</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">147</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  covariance.model = "auto",</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">148</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  omega.init = "auto",</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">149</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  covariates = NULL,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">150</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  covariate_models = NULL,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">151</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  no_random_effect = NULL,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">152</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  error.init = c(1, 1),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">153</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  nbiter.saemix = c(300, 100),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">154</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  control = list(displayProgress = FALSE, print = FALSE,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">155</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    nbiter.saemix = nbiter.saemix,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">156</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    save = FALSE, save.graphs = FALSE),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">157</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  verbose = FALSE, quiet = FALSE, ...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">158</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">{</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">159</td>
+                    <td class="coverage">2411<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  call &lt;- match.call()</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">160</td>
+                    <td class="coverage">2411<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  transformations &lt;- match.arg(transformations)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">161</td>
+                    <td class="coverage">2411<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  m_saemix &lt;- saemix_model(object, verbose = verbose,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">162</td>
+                    <td class="coverage">2411<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    error_model = error_model,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">163</td>
+                    <td class="coverage">2411<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    degparms_start = degparms_start,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">164</td>
+                    <td class="coverage">2411<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    test_log_parms = test_log_parms, conf.level = conf.level,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">165</td>
+                    <td class="coverage">2411<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    solution_type = solution_type,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">166</td>
+                    <td class="coverage">2411<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    transformations = transformations,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">167</td>
+                    <td class="coverage">2411<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    covariance.model = covariance.model,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">168</td>
+                    <td class="coverage">2411<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    omega.init = omega.init,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">169</td>
+                    <td class="coverage">2411<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    covariates = covariates,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">170</td>
+                    <td class="coverage">2411<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    covariate_models = covariate_models,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">171</td>
+                    <td class="coverage">2411<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    error.init = error.init,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">172</td>
+                    <td class="coverage">2411<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    no_random_effect = no_random_effect,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">173</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    ...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">174</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  d_saemix &lt;- saemix_data(object, covariates = covariates, verbose = verbose)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">175</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">176</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  fit_failed &lt;- FALSE</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">177</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  FIM_failed &lt;- NULL</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">178</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  fit_time &lt;- system.time({</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">179</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    utils::capture.output(f_saemix &lt;- try(saemix(m_saemix, d_saemix, control)), split = !quiet)</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">180</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (inherits(f_saemix, "try-error")) fit_failed &lt;- TRUE</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">181</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  })</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">182</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">183</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  return_data &lt;- nlme_data(object)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">184</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">185</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (!fit_failed) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">186</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (any(is.na(f_saemix@results@se.fixed))) FIM_failed &lt;- c(FIM_failed, "fixed effects")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">187</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (any(is.na(c(f_saemix@results@se.omega, f_saemix@results@se.respar)))) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">188</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      FIM_failed &lt;- c(FIM_failed, "random effects and error model parameters")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">189</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">190</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">191</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    transparms_optim &lt;- f_saemix@results@fixed.effects</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">192</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    names(transparms_optim) &lt;- f_saemix@results@name.fixed</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">193</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">194</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (transformations == "mkin") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">195</td>
+                    <td class="coverage">1413<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      bparms_optim &lt;- backtransform_odeparms(transparms_optim,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">196</td>
+                    <td class="coverage">1413<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        object[[1]]$mkinmod,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">197</td>
+                    <td class="coverage">1413<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        object[[1]]$transform_rates,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">198</td>
+                    <td class="coverage">1413<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        object[[1]]$transform_fractions)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">199</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">200</td>
+                    <td class="coverage">774<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      bparms_optim &lt;- transparms_optim</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">201</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">202</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">203</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    saemix_data_ds &lt;- f_saemix@data@data$ds</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">204</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    mkin_ds_order &lt;- as.character(unique(return_data$ds))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">205</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    saemix_ds_order &lt;- unique(saemix_data_ds)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">206</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">207</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    psi &lt;- saemix::psi(f_saemix)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">208</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    rownames(psi) &lt;- saemix_ds_order</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">209</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    return_data$predicted &lt;- f_saemix@model@model(</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">210</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      psi = psi[mkin_ds_order, ],</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">211</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      id = as.numeric(return_data$ds),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">212</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      xidep = return_data[c("time", "name")])</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">213</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">214</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    return_data &lt;- transform(return_data,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">215</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      residual = value - predicted,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">216</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      std = sigma_twocomp(predicted,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">217</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        f_saemix@results@respar[1], f_saemix@results@respar[2]))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">218</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    return_data &lt;- transform(return_data,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">219</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      standardized = residual / std)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">220</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">221</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">222</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  result &lt;- list(</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">223</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    mkinmod = object[[1]]$mkinmod,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">224</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    mmkin = object,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">225</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    solution_type = object[[1]]$solution_type,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">226</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    transformations = transformations,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">227</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    transform_rates = object[[1]]$transform_rates,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">228</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    transform_fractions = object[[1]]$transform_fractions,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">229</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    covariates = covariates,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">230</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    covariate_models = covariate_models,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">231</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    sm = m_saemix,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">232</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    so = f_saemix,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">233</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    call = call,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">234</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    time = fit_time,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">235</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    FIM_failed = FIM_failed,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">236</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    mean_dp_start = attr(m_saemix, "mean_dp_start"),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">237</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    bparms.fixed = object[[1]]$bparms.fixed,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">238</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    data = return_data,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">239</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    err_mod = object[[1]]$err_mod,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">240</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    date.fit = date(),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">241</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    saemixversion = as.character(utils::packageVersion("saemix")),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">242</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    mkinversion = as.character(utils::packageVersion("mkin")),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">243</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    Rversion = paste(R.version$major, R.version$minor, sep=".")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">244</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  )</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">245</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">246</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (!fit_failed) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">247</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    result$mkin_ds_order &lt;- mkin_ds_order</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">248</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    result$saemix_ds_order &lt;- saemix_ds_order</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">249</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    result$bparms.optim &lt;- bparms_optim</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">250</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">251</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">252</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  class(result) &lt;- c("saem.mmkin", "mixed.mmkin")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">253</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  return(result)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">254</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">255</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">256</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">257</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @rdname saem</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">258</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param x An saem.mmkin object to print</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">259</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param digits Number of digits to use for printing</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">260</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">print.saem.mmkin &lt;- function(x, digits = max(3, getOption("digits") - 3), ...) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">261</td>
+                    <td class="coverage">234<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat( "Kinetic nonlinear mixed-effects model fit by SAEM" )</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">262</td>
+                    <td class="coverage">234<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("\nStructural model:\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">263</td>
+                    <td class="coverage">234<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  diffs &lt;- x$mmkin[[1]]$mkinmod$diffs</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">264</td>
+                    <td class="coverage">234<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  nice_diffs &lt;- gsub("^(d.*) =", "\\1/dt =", diffs)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">265</td>
+                    <td class="coverage">234<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  writeLines(strwrap(nice_diffs, exdent = 11))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">266</td>
+                    <td class="coverage">234<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("\nData:\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">267</td>
+                    <td class="coverage">234<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat(nrow(x$data), "observations of",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">268</td>
+                    <td class="coverage">234<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    length(unique(x$data$name)), "variable(s) grouped in",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">269</td>
+                    <td class="coverage">234<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    length(unique(x$data$ds)), "datasets\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">270</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">271</td>
+                    <td class="coverage">234<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (inherits(x$so, "try-error")) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">272</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    cat("\nFit did not terminate successfully\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">273</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">274</td>
+                    <td class="coverage">234<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    cat("\nLikelihood computed by importance sampling\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">275</td>
+                    <td class="coverage">234<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    ll &lt;- try(logLik(x$so, type = "is"), silent = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">276</td>
+                    <td class="coverage">234<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (inherits(ll, "try-error")) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">277</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      cat("Not available\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">278</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">279</td>
+                    <td class="coverage">234<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      print(data.frame(</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">280</td>
+                    <td class="coverage">234<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          AIC = AIC(x$so, type = "is"),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">281</td>
+                    <td class="coverage">234<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          BIC = BIC(x$so, type = "is"),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">282</td>
+                    <td class="coverage">234<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          logLik = logLik(x$so, type = "is"),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">283</td>
+                    <td class="coverage">234<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          row.names = " "), digits = digits)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">284</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">285</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">286</td>
+                    <td class="coverage">234<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    cat("\nFitted parameters:\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">287</td>
+                    <td class="coverage">234<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    conf.int &lt;- parms(x, ci = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">288</td>
+                    <td class="coverage">234<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    print(conf.int, digits = digits)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">289</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">290</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">291</td>
+                    <td class="coverage">234<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  invisible(x)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">292</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">293</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">294</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @rdname saem</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">295</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @return An [saemix::SaemixModel] object.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">296</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">297</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">saemix_model &lt;- function(object, solution_type = "auto",</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">298</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  transformations = c("mkin", "saemix"), error_model = "auto",</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">299</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  degparms_start = numeric(),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">300</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  covariance.model = "auto", no_random_effect = NULL,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">301</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  omega.init = "auto",</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">302</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  covariates = NULL, covariate_models = NULL,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">303</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  error.init = numeric(),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">304</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  test_log_parms = FALSE, conf.level = 0.6, verbose = FALSE, ...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">305</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">{</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">306</td>
+                    <td class="coverage">65<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (nrow(object) &gt; 1) stop("Only row objects allowed")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">307</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">308</td>
+                    <td class="coverage">2346<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  mkin_model &lt;- object[[1]]$mkinmod</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">309</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">310</td>
+                    <td class="coverage">2346<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (length(mkin_model$spec) &gt; 1 &amp; solution_type[1] == "analytical") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">311</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    stop("mkin analytical solutions not supported for more thane one observed variable")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">312</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">313</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">314</td>
+                    <td class="coverage">2242<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  degparms_optim &lt;-  mean_degparms(object, test_log_parms = test_log_parms)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">315</td>
+                    <td class="coverage">2242<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  na_degparms &lt;- names(which(is.na(degparms_optim)))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">316</td>
+                    <td class="coverage">2242<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (length(na_degparms) &gt; 0) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">317</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    message("Did not find valid starting values for ", paste(na_degparms, collapse = ", "), "\n",</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">318</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      "Now trying with test_log_parms = FALSE")</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">319</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    degparms_optim &lt;-  mean_degparms(object, test_log_parms = FALSE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">320</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">321</td>
+                    <td class="coverage">2242<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (transformations == "saemix") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">322</td>
+                    <td class="coverage">779<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    degparms_optim &lt;- backtransform_odeparms(degparms_optim,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">323</td>
+                    <td class="coverage">779<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      object[[1]]$mkinmod,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">324</td>
+                    <td class="coverage">779<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      object[[1]]$transform_rates,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">325</td>
+                    <td class="coverage">779<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      object[[1]]$transform_fractions)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">326</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">327</td>
+                    <td class="coverage">2242<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  degparms_fixed &lt;- object[[1]]$bparms.fixed</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">328</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">329</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Transformations are done in the degradation function by default</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">330</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # (transformations = "mkin")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">331</td>
+                    <td class="coverage">2242<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  transform.par = rep(0, length(degparms_optim))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">332</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">333</td>
+                    <td class="coverage">2242<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  odeini_optim_parm_names &lt;- grep('_0$', names(degparms_optim), value = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">334</td>
+                    <td class="coverage">2242<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  odeini_fixed_parm_names &lt;- grep('_0$', names(degparms_fixed), value = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">335</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">336</td>
+                    <td class="coverage">2242<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  odeparms_fixed_names &lt;- setdiff(names(degparms_fixed), odeini_fixed_parm_names)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">337</td>
+                    <td class="coverage">2242<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  odeparms_fixed &lt;- degparms_fixed[odeparms_fixed_names]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">338</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">339</td>
+                    <td class="coverage">2242<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  odeini_fixed &lt;- degparms_fixed[odeini_fixed_parm_names]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">340</td>
+                    <td class="coverage">2242<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  names(odeini_fixed) &lt;- gsub('_0$', '', odeini_fixed_parm_names)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">341</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">342</td>
+                    <td class="coverage">2242<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  model_function &lt;- FALSE</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">343</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">344</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Model functions with analytical solutions</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">345</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Fixed parameters, use_of_ff = "min" and turning off sinks currently not supported here</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">346</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # In general, we need to consider exactly how the parameters in mkinfit were specified,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">347</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # as the parameters are currently mapped by position in these solutions</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">348</td>
+                    <td class="coverage">2242<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  sinks &lt;- sapply(mkin_model$spec, function(x) x$sink)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">349</td>
+                    <td class="coverage">2242<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (length(odeparms_fixed) == 0 &amp; mkin_model$use_of_ff == "max" &amp; all(sinks)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">350</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # Parent only</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">351</td>
+                    <td class="coverage">2242<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (length(mkin_model$spec) == 1) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">352</td>
+                    <td class="coverage">1748<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      parent_type &lt;- mkin_model$spec[[1]]$type</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">353</td>
+                    <td class="coverage">1748<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      if (length(odeini_fixed) == 1 &amp;&amp; !grepl("_bound$", names(odeini_fixed))) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">354</td>
+                    <td class="coverage">50<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        if (transformations == "saemix") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">355</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          stop("saemix transformations are not supported for parent fits with fixed initial parent value")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">356</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">357</td>
+                    <td class="coverage">50<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        if (parent_type == "SFO") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">358</td>
+                    <td class="coverage">50<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          stop("saemix needs at least two parameters to work on.")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">359</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        }</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">360</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        if (parent_type == "FOMC") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">361</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          model_function &lt;- function(psi, id, xidep) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">362</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            odeini_fixed / (xidep[, "time"]/exp(psi[id, 2]) + 1)^exp(psi[id, 1])</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">363</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">364</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        }</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">365</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        if (parent_type == "DFOP") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">366</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          model_function &lt;- function(psi, id, xidep) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">367</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            g &lt;- plogis(psi[id, 3])</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">368</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            t &lt;- xidep[, "time"]</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">369</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            odeini_fixed * (g * exp(- exp(psi[id, 1]) * t) +</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">370</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              (1 - g) * exp(- exp(psi[id, 2]) * t))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">371</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">372</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        }</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">373</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        if (parent_type == "HS") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">374</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          model_function &lt;- function(psi, id, xidep) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">375</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            tb &lt;- exp(psi[id, 3])</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">376</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            t &lt;- xidep[, "time"]</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">377</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            k1 = exp(psi[id, 1])</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">378</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            odeini_fixed * ifelse(t &lt;= tb,</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">379</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              exp(- k1 * t),</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">380</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              exp(- k1 * tb) * exp(- exp(psi[id, 2]) * (t - tb)))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">381</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">382</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">383</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">384</td>
+                    <td class="coverage">1698<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        if (length(odeini_fixed) == 2) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">385</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          stop("SFORB with fixed initial parent value is not supported")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">386</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">387</td>
+                    <td class="coverage">1698<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        if (parent_type == "SFO") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">388</td>
+                    <td class="coverage">785<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          if (transformations == "mkin") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">389</td>
+                    <td class="coverage">283<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            model_function &lt;- function(psi, id, xidep) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">390</td>
+                    <td class="coverage">2628025<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              psi[id, 1] * exp( - exp(psi[id, 2]) * xidep[, "time"])</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">391</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">392</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">393</td>
+                    <td class="coverage">502<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            model_function &lt;- function(psi, id, xidep) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">394</td>
+                    <td class="coverage">4054103<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              psi[id, 1] * exp( - psi[id, 2] * xidep[, "time"])</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">395</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">396</td>
+                    <td class="coverage">502<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            transform.par = c(0, 1)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">397</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">398</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">399</td>
+                    <td class="coverage">1698<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        if (parent_type == "FOMC") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">400</td>
+                    <td class="coverage">76<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          if (transformations == "mkin") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">401</td>
+                    <td class="coverage">41<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            model_function &lt;- function(psi, id, xidep) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">402</td>
+                    <td class="coverage">510269<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              psi[id, 1] / (xidep[, "time"]/exp(psi[id, 3]) + 1)^exp(psi[id, 2])</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">403</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">404</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">405</td>
+                    <td class="coverage">35<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            model_function &lt;- function(psi, id, xidep) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">406</td>
+                    <td class="coverage">432565<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              psi[id, 1] / (xidep[, "time"]/psi[id, 3] + 1)^psi[id, 2]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">407</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">408</td>
+                    <td class="coverage">35<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            transform.par = c(0, 1, 1)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">409</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">410</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">411</td>
+                    <td class="coverage">1698<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        if (parent_type == "DFOP") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">412</td>
+                    <td class="coverage">677<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          if (transformations == "mkin") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">413</td>
+                    <td class="coverage">637<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            model_function &lt;- function(psi, id, xidep) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">414</td>
+                    <td class="coverage">8785439<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              g &lt;- plogis(psi[id, 4])</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">415</td>
+                    <td class="coverage">8785439<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              t &lt;- xidep[, "time"]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">416</td>
+                    <td class="coverage">8785439<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              psi[id, 1] * (g * exp(- exp(psi[id, 2]) * t) +</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">417</td>
+                    <td class="coverage">8785439<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                (1 - g) * exp(- exp(psi[id, 3]) * t))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">418</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">419</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">420</td>
+                    <td class="coverage">40<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            model_function &lt;- function(psi, id, xidep) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">421</td>
+                    <td class="coverage">507885<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              g &lt;- psi[id, 4]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">422</td>
+                    <td class="coverage">507885<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              t &lt;- xidep[, "time"]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">423</td>
+                    <td class="coverage">507885<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              psi[id, 1] * (g * exp(- psi[id, 2] * t) +</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">424</td>
+                    <td class="coverage">507885<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                (1 - g) * exp(- psi[id, 3] * t))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">425</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">426</td>
+                    <td class="coverage">40<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            transform.par = c(0, 1, 1, 3)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">427</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">428</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">429</td>
+                    <td class="coverage">1698<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        if (parent_type == "SFORB") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">430</td>
+                    <td class="coverage">150<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          if (transformations == "mkin") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">431</td>
+                    <td class="coverage">130<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            model_function &lt;- function(psi, id, xidep) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">432</td>
+                    <td class="coverage">1240580<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              k_12 &lt;- exp(psi[id, 3])</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">433</td>
+                    <td class="coverage">1240580<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              k_21 &lt;- exp(psi[id, 4])</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">434</td>
+                    <td class="coverage">1240580<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              k_1output &lt;- exp(psi[id, 2])</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">435</td>
+                    <td class="coverage">1240580<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              t &lt;- xidep[, "time"]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">436</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">437</td>
+                    <td class="coverage">1240580<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              sqrt_exp = sqrt(1/4 * (k_12 + k_21 + k_1output)^2 - k_1output * k_21)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">438</td>
+                    <td class="coverage">1240580<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              b1 = 0.5 * (k_12 + k_21 + k_1output) + sqrt_exp</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">439</td>
+                    <td class="coverage">1240580<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              b2 = 0.5 * (k_12 + k_21 + k_1output) - sqrt_exp</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">440</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">441</td>
+                    <td class="coverage">1240580<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              psi[id, 1] * (((k_12 + k_21 - b1)/(b2 - b1)) * exp(-b1 * t) +</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">442</td>
+                    <td class="coverage">1240580<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                ((k_12 + k_21 - b2)/(b1 - b2)) * exp(-b2 * t))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">443</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">444</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">445</td>
+                    <td class="coverage">20<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            model_function &lt;- function(psi, id, xidep) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">446</td>
+                    <td class="coverage">290980<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              k_12 &lt;- psi[id, 3]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">447</td>
+                    <td class="coverage">290980<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              k_21 &lt;- psi[id, 4]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">448</td>
+                    <td class="coverage">290980<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              k_1output &lt;- psi[id, 2]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">449</td>
+                    <td class="coverage">290980<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              t &lt;- xidep[, "time"]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">450</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">451</td>
+                    <td class="coverage">290980<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              sqrt_exp = sqrt(1/4 * (k_12 + k_21 + k_1output)^2 - k_1output * k_21)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">452</td>
+                    <td class="coverage">290980<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              b1 = 0.5 * (k_12 + k_21 + k_1output) + sqrt_exp</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">453</td>
+                    <td class="coverage">290980<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              b2 = 0.5 * (k_12 + k_21 + k_1output) - sqrt_exp</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">454</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">455</td>
+                    <td class="coverage">290980<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              psi[id, 1] * (((k_12 + k_21 - b1)/(b2 - b1)) * exp(-b1 * t) +</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">456</td>
+                    <td class="coverage">290980<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                ((k_12 + k_21 - b2)/(b1 - b2)) * exp(-b2 * t))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">457</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">458</td>
+                    <td class="coverage">20<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            transform.par = c(0, 1, 1, 1)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">459</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">460</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">461</td>
+                    <td class="coverage">1698<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        if (parent_type == "HS") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">462</td>
+                    <td class="coverage">10<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          if (transformations == "mkin") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">463</td>
+                    <td class="coverage">10<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            model_function &lt;- function(psi, id, xidep) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">464</td>
+                    <td class="coverage">150610<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              tb &lt;- exp(psi[id, 4])</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">465</td>
+                    <td class="coverage">150610<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              t &lt;- xidep[, "time"]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">466</td>
+                    <td class="coverage">150610<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              k1 &lt;- exp(psi[id, 2])</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">467</td>
+                    <td class="coverage">150610<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              psi[id, 1] * ifelse(t &lt;= tb,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">468</td>
+                    <td class="coverage">150610<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                exp(- k1 * t),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">469</td>
+                    <td class="coverage">150610<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                exp(- k1 * tb) * exp(- exp(psi[id, 3]) * (t - tb)))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">470</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">471</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">472</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            model_function &lt;- function(psi, id, xidep) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">473</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              tb &lt;- psi[id, 4]</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">474</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              t &lt;- xidep[, "time"]</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">475</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              psi[id, 1] * ifelse(t &lt;= tb,</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">476</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                exp(- psi[id, 2] * t),</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">477</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                exp(- psi[id, 2] * tb) * exp(- psi[id, 3] * (t - tb)))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">478</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            }</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">479</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            transform.par = c(0, 1, 1, 1)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">480</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">481</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">482</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">483</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">484</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">485</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # Parent with one metabolite</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">486</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # Parameter names used in the model functions are as in</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">487</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # https://nbviewer.jupyter.org/urls/jrwb.de/nb/Symbolic%20ODE%20solutions%20for%20mkin.ipynb</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">488</td>
+                    <td class="coverage">2192<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    types &lt;- unname(sapply(mkin_model$spec, function(x) x$type))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">489</td>
+                    <td class="coverage">2192<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (length(mkin_model$spec) == 2 &amp;! "SFORB" %in% types ) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">490</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      # Initial value for the metabolite (n20) must be fixed</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">491</td>
+                    <td class="coverage">494<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      if (names(odeini_fixed) == names(mkin_model$spec)[2]) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">492</td>
+                    <td class="coverage">494<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        n20 &lt;- odeini_fixed</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">493</td>
+                    <td class="coverage">494<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        parent_name &lt;- names(mkin_model$spec)[1]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">494</td>
+                    <td class="coverage">494<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        if (identical(types, c("SFO", "SFO"))) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">495</td>
+                    <td class="coverage">208<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          if (transformations == "mkin") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">496</td>
+                    <td class="coverage">208<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            model_function &lt;- function(psi, id, xidep) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">497</td>
+                    <td class="coverage">873912<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              t &lt;- xidep[, "time"]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">498</td>
+                    <td class="coverage">873912<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              n10 &lt;- psi[id, 1]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">499</td>
+                    <td class="coverage">873912<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              k1 &lt;- exp(psi[id, 2])</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">500</td>
+                    <td class="coverage">873912<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              k2 &lt;- exp(psi[id, 3])</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">501</td>
+                    <td class="coverage">873912<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              f12 &lt;- plogis(psi[id, 4])</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">502</td>
+                    <td class="coverage">873912<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              ifelse(xidep[, "name"] == parent_name,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">503</td>
+                    <td class="coverage">873912<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                n10 * exp(- k1 * t),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">504</td>
+                    <td class="coverage">873912<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                (((k2 - k1) * n20 - f12 * k1 * n10) * exp(- k2 * t)) / (k2 - k1) +</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">505</td>
+                    <td class="coverage">873912<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                  (f12 * k1 * n10 * exp(- k1 * t)) / (k2 - k1)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">506</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              )</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">507</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">508</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">509</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            model_function &lt;- function(psi, id, xidep) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">510</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              t &lt;- xidep[, "time"]</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">511</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              n10 &lt;- psi[id, 1]</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">512</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              k1 &lt;- psi[id, 2]</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">513</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              k2 &lt;- psi[id, 3]</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">514</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              f12 &lt;- psi[id, 4]</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">515</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              ifelse(xidep[, "name"] == parent_name,</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">516</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                n10 * exp(- k1 * t),</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">517</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                (((k2 - k1) * n20 - f12 * k1 * n10) * exp(- k2 * t)) / (k2 - k1) +</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">518</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                  (f12 * k1 * n10 * exp(- k1 * t)) / (k2 - k1)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">519</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              )</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">520</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            }</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">521</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            transform.par = c(0, 1, 1, 3)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">522</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">523</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">524</td>
+                    <td class="coverage">494<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        if (identical(types, c("DFOP", "SFO"))) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">525</td>
+                    <td class="coverage">286<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          if (transformations == "mkin") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">526</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            model_function &lt;- function(psi, id, xidep) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">527</td>
+                    <td class="coverage">1821022<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              t &lt;- xidep[, "time"]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">528</td>
+                    <td class="coverage">1821022<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              n10 &lt;- psi[id, 1]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">529</td>
+                    <td class="coverage">1821022<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              k2 &lt;- exp(psi[id, 2])</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">530</td>
+                    <td class="coverage">1821022<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              f12 &lt;- plogis(psi[id, 3])</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">531</td>
+                    <td class="coverage">1821022<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              l1 &lt;- exp(psi[id, 4])</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">532</td>
+                    <td class="coverage">1821022<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              l2 &lt;- exp(psi[id, 5])</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">533</td>
+                    <td class="coverage">1821022<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              g &lt;- plogis(psi[id, 6])</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">534</td>
+                    <td class="coverage">1821022<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              ifelse(xidep[, "name"] == parent_name,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">535</td>
+                    <td class="coverage">1821022<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                n10 * (g * exp(- l1 * t) + (1 - g) * exp(- l2 * t)),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">536</td>
+                    <td class="coverage">1821022<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                ((f12 * g - f12) * l2 * n10 * exp(- l2 * t)) / (l2 - k2) -</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">537</td>
+                    <td class="coverage">1821022<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                  (f12 * g * l1 * n10 * exp(- l1 * t)) / (l1 - k2) +</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">538</td>
+                    <td class="coverage">1821022<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                  ((((l1 - k2) * l2 - k2 * l1 + k2^2) * n20 +</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">539</td>
+                    <td class="coverage">1821022<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                      ((f12 * l1 + (f12 * g - f12) * k2) * l2 -</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">540</td>
+                    <td class="coverage">1821022<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                        f12 * g * k2 * l1) * n10) * exp( - k2 * t)) /</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">541</td>
+                    <td class="coverage">1821022<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                  ((l1 - k2) * l2 - k2 * l1 + k2^2)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">542</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              )</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">543</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">544</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">545</td>
+                    <td class="coverage">182<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            model_function &lt;- function(psi, id, xidep) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">546</td>
+                    <td class="coverage">2908620<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              t &lt;- xidep[, "time"]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">547</td>
+                    <td class="coverage">2908620<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              n10 &lt;- psi[id, 1]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">548</td>
+                    <td class="coverage">2908620<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              k2 &lt;- psi[id, 2]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">549</td>
+                    <td class="coverage">2908620<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              f12 &lt;- psi[id, 3]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">550</td>
+                    <td class="coverage">2908620<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              l1 &lt;- psi[id, 4]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">551</td>
+                    <td class="coverage">2908620<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              l2 &lt;- psi[id, 5]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">552</td>
+                    <td class="coverage">2908620<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              g &lt;- psi[id, 6]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">553</td>
+                    <td class="coverage">2908620<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              ifelse(xidep[, "name"] == parent_name,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">554</td>
+                    <td class="coverage">2908620<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                n10 * (g * exp(- l1 * t) + (1 - g) * exp(- l2 * t)),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">555</td>
+                    <td class="coverage">2908620<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                ((f12 * g - f12) * l2 * n10 * exp(- l2 * t)) / (l2 - k2) -</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">556</td>
+                    <td class="coverage">2908620<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                  (f12 * g * l1 * n10 * exp(- l1 * t)) / (l1 - k2) +</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">557</td>
+                    <td class="coverage">2908620<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                  ((((l1 - k2) * l2 - k2 * l1 + k2^2) * n20 +</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">558</td>
+                    <td class="coverage">2908620<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                      ((f12 * l1 + (f12 * g - f12) * k2) * l2 -</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">559</td>
+                    <td class="coverage">2908620<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                        f12 * g * k2 * l1) * n10) * exp( - k2 * t)) /</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">560</td>
+                    <td class="coverage">2908620<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                  ((l1 - k2) * l2 - k2 * l1 + k2^2)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">561</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              )</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">562</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">563</td>
+                    <td class="coverage">182<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            transform.par = c(0, 1, 3, 1, 1, 3)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">564</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">565</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">566</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">567</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">568</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">569</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">570</td>
+                    <td class="coverage">2192<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (is.function(model_function) &amp; solution_type == "auto") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">571</td>
+                    <td class="coverage">2083<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    solution_type = "analytical saemix"</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">572</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">573</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">574</td>
+                    <td class="coverage">109<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (transformations == "saemix") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">575</td>
+                    <td class="coverage">5<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      stop("Using saemix transformations is only supported if an analytical solution is implemented for saemix")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">576</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">577</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">578</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (solution_type == "auto")</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">579</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      solution_type &lt;- object[[1]]$solution_type</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">580</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">581</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # Define some variables to avoid function calls in model function</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">582</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    transparms_optim_names &lt;- names(degparms_optim)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">583</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    odeini_optim_names &lt;- gsub('_0$', '', odeini_optim_parm_names)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">584</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    diff_names &lt;- names(mkin_model$diffs)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">585</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    ode_transparms_optim_names &lt;- setdiff(transparms_optim_names, odeini_optim_parm_names)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">586</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    transform_rates &lt;- object[[1]]$transform_rates</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">587</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    transform_fractions &lt;- object[[1]]$transform_fractions</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">588</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">589</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # Get native symbol info for speed</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">590</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    use_symbols = FALSE</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">591</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (solution_type == "deSolve" &amp; !is.null(mkin_model$cf)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">592</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      mkin_model$symbols &lt;- try(deSolve::checkDLL(</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">593</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        dllname = mkin_model$dll_info[["name"]],</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">594</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        func = "diffs", initfunc = "initpar",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">595</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        jacfunc = NULL, nout = 0, outnames = NULL))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">596</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      if (!inherits(mkin_model$symbols, "try-error")) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">597</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        use_symbols = TRUE</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">598</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">599</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">600</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">601</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # Define the model function</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">602</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    model_function &lt;- function(psi, id, xidep) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">603</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">604</td>
+                    <td class="coverage">873912<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      uid &lt;- unique(id)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">605</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">606</td>
+                    <td class="coverage">873912<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      res_list &lt;- lapply(uid, function(i) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">607</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">608</td>
+                    <td class="coverage">43671888<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        transparms_optim &lt;- as.numeric(psi[i, ]) # psi[i, ] is a dataframe when called in saemix.predict</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">609</td>
+                    <td class="coverage">43671888<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        names(transparms_optim) &lt;- transparms_optim_names</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">610</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">611</td>
+                    <td class="coverage">43671888<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        odeini_optim &lt;- transparms_optim[odeini_optim_parm_names]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">612</td>
+                    <td class="coverage">43671888<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        names(odeini_optim) &lt;- odeini_optim_names</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">613</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">614</td>
+                    <td class="coverage">43671888<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        odeini &lt;- c(odeini_optim, odeini_fixed)[diff_names]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">615</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">616</td>
+                    <td class="coverage">43671888<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        odeparms_optim &lt;- backtransform_odeparms(transparms_optim[ode_transparms_optim_names], mkin_model,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">617</td>
+                    <td class="coverage">43671888<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          transform_rates = transform_rates,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">618</td>
+                    <td class="coverage">43671888<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          transform_fractions = transform_fractions)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">619</td>
+                    <td class="coverage">43671888<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        odeparms &lt;- c(odeparms_optim, odeparms_fixed)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">620</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">621</td>
+                    <td class="coverage">43671888<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        xidep_i &lt;- xidep[which(id == i), ]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">622</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">623</td>
+                    <td class="coverage">43671888<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        if (solution_type[1] == "analytical") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">624</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          out_values &lt;- mkin_model$deg_func(xidep_i, odeini, odeparms)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">625</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">626</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">627</td>
+                    <td class="coverage">43671888<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          i_time &lt;- xidep_i$time</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">628</td>
+                    <td class="coverage">43671888<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          i_name &lt;- xidep_i$name</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">629</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">630</td>
+                    <td class="coverage">43671888<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          out_wide &lt;- mkinpredict(mkin_model,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">631</td>
+                    <td class="coverage">43671888<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            odeparms = odeparms, odeini = odeini,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">632</td>
+                    <td class="coverage">43671888<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            solution_type = solution_type,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">633</td>
+                    <td class="coverage">43671888<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            outtimes = sort(unique(i_time)),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">634</td>
+                    <td class="coverage">43671888<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            na_stop = FALSE</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">635</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          )</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">636</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">637</td>
+                    <td class="coverage">43671888<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          out_index &lt;- cbind(as.character(i_time), as.character(i_name))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">638</td>
+                    <td class="coverage">43671888<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          out_values &lt;- out_wide[out_index]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">639</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">640</td>
+                    <td class="coverage">43671888<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        return(out_values)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">641</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      })</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">642</td>
+                    <td class="coverage">873912<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      res &lt;- unlist(res_list)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">643</td>
+                    <td class="coverage">873912<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      return(res)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">644</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">645</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">646</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">647</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (identical(error_model, "auto")) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">648</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    error_model = object[[1]]$err_mod</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">649</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">650</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  error.model &lt;- switch(error_model,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">651</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    const = "constant",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">652</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    tc = "combined",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">653</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    obs = "constant")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">654</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">655</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (error_model == "obs") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">656</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    warning("The error model 'obs' (variance by variable) can currently not be transferred to an saemix model")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">657</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">658</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">659</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  degparms_psi0 &lt;- degparms_optim</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">660</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  degparms_psi0[names(degparms_start)] &lt;- degparms_start</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">661</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  psi0_matrix &lt;- matrix(degparms_psi0, nrow = 1,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">662</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    dimnames = list("(Intercept)", names(degparms_psi0)))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">663</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">664</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (covariance.model[1] == "auto") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">665</td>
+                    <td class="coverage">2062<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    covariance_diagonal &lt;- rep(1, length(degparms_optim))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">666</td>
+                    <td class="coverage">2062<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (!is.null(no_random_effect)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">667</td>
+                    <td class="coverage">766<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      degparms_no_random &lt;- which(names(degparms_psi0) %in% no_random_effect)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">668</td>
+                    <td class="coverage">766<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      covariance_diagonal[degparms_no_random] &lt;- 0</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">669</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">670</td>
+                    <td class="coverage">2062<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    covariance.model = diag(covariance_diagonal)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">671</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">672</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">673</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (omega.init[1] == "auto") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">674</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (transformations == "mkin") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">675</td>
+                    <td class="coverage">1413<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      degparms_eta_ini &lt;- as.numeric( # remove names</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">676</td>
+                    <td class="coverage">1413<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        mean_degparms(object,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">677</td>
+                    <td class="coverage">1413<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          random = TRUE, test_log_parms = TRUE)$eta)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">678</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">679</td>
+                    <td class="coverage">1413<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      omega.init &lt;- 2 * diag(degparms_eta_ini^2)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">680</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">681</td>
+                    <td class="coverage">774<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      omega.init &lt;- matrix(nrow = 0, ncol = 0)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">682</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">683</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">684</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">685</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (is.null(covariate_models)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">686</td>
+                    <td class="coverage">2027<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    covariate.model &lt;- matrix(nrow = 0, ncol = 0) # default in saemixModel()</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">687</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">688</td>
+                    <td class="coverage">160<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    degparms_dependent &lt;- sapply(covariate_models, function(x) as.character(x[[2]]))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">689</td>
+                    <td class="coverage">160<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    covariates_in_models = unique(unlist(lapply(</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">690</td>
+                    <td class="coverage">160<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      covariate_models, function(x)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">691</td>
+                    <td class="coverage">160<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        colnames(attr(terms(x), "factors"))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">692</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      )))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">693</td>
+                    <td class="coverage">160<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    covariates_not_available &lt;- setdiff(covariates_in_models, names(covariates))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">694</td>
+                    <td class="coverage">160<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (length(covariates_not_available) &gt; 0) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">695</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      stop("Covariate(s) ", paste(covariates_not_available, collapse = ", "),</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">696</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        " used in the covariate models not available in the covariate data")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">697</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">698</td>
+                    <td class="coverage">160<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    psi0_matrix &lt;- rbind(psi0_matrix,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">699</td>
+                    <td class="coverage">160<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      matrix(0, nrow = length(covariates), ncol = ncol(psi0_matrix),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">700</td>
+                    <td class="coverage">160<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        dimnames = list(names(covariates), colnames(psi0_matrix))))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">701</td>
+                    <td class="coverage">160<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    covariate.model &lt;- matrix(0, nrow = length(covariates),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">702</td>
+                    <td class="coverage">160<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      ncol = ncol(psi0_matrix),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">703</td>
+                    <td class="coverage">160<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      dimnames = list(</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">704</td>
+                    <td class="coverage">160<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        covariates = names(covariates),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">705</td>
+                    <td class="coverage">160<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        degparms = colnames(psi0_matrix)))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">706</td>
+                    <td class="coverage">160<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (transformations == "saemix") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">707</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      stop("Covariate models with saemix transformations currently not supported")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">708</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">709</td>
+                    <td class="coverage">160<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    parms_trans &lt;- as.data.frame(t(sapply(object, parms, transformed = TRUE)))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">710</td>
+                    <td class="coverage">160<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    for (covariate_model in covariate_models) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">711</td>
+                    <td class="coverage">160<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      covariate_name &lt;- as.character(covariate_model[[2]])</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">712</td>
+                    <td class="coverage">160<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      model_data &lt;- cbind(parms_trans, covariates)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">713</td>
+                    <td class="coverage">160<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      ini_model &lt;- lm(covariate_model, data = model_data)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">714</td>
+                    <td class="coverage">160<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      ini_coef &lt;- coef(ini_model)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">715</td>
+                    <td class="coverage">160<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      psi0_matrix[names(ini_coef), covariate_name] &lt;- ini_coef</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">716</td>
+                    <td class="coverage">160<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      covariate.model[names(ini_coef)[-1], covariate_name] &lt;- as.numeric(as.logical(ini_coef[-1]))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">717</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">718</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">719</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">720</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  res &lt;- saemix::saemixModel(model_function,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">721</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    psi0 = psi0_matrix,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">722</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    "Mixed model generated from mmkin object",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">723</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    transform.par = transform.par,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">724</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    error.model = error.model,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">725</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    verbose = verbose,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">726</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    covariance.model = covariance.model,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">727</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    covariate.model = covariate.model,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">728</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    omega.init = omega.init,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">729</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    error.init = error.init,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">730</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    ...</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">731</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  )</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">732</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  attr(res, "mean_dp_start") &lt;- degparms_optim</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">733</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  return(res)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">734</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">735</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">736</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @rdname saem</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">737</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @importFrom rlang !!!</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">738</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @return An [saemix::SaemixData] object.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">739</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">740</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">saemix_data &lt;- function(object, covariates = NULL, verbose = FALSE, ...) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">741</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (nrow(object) &gt; 1) stop("Only row objects allowed")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">742</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  ds_names &lt;- colnames(object)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">743</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">744</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  ds_list &lt;- lapply(object, function(x) x$data[c("time", "variable", "observed")])</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">745</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  names(ds_list) &lt;- ds_names</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">746</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  ds_saemix_all &lt;- vctrs::vec_rbind(!!!ds_list, .names_to = "ds")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">747</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  ds_saemix &lt;- data.frame(ds = ds_saemix_all$ds,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">748</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    name = as.character(ds_saemix_all$variable),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">749</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    time = ds_saemix_all$time,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">750</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    value = ds_saemix_all$observed,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">751</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    stringsAsFactors = FALSE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">752</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (!is.null(covariates)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">753</td>
+                    <td class="coverage">160<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    name.covariates &lt;- names(covariates)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">754</td>
+                    <td class="coverage">160<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    covariates$ds &lt;- rownames(covariates)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">755</td>
+                    <td class="coverage">160<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    ds_saemix &lt;- merge(ds_saemix, covariates, sort = FALSE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">756</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">757</td>
+                    <td class="coverage">2027<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    name.covariates &lt;- character(0)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">758</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">759</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">760</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  res &lt;- saemix::saemixData(ds_saemix,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">761</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    name.group = "ds",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">762</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    name.predictors = c("time", "name"),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">763</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    name.response = "value",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">764</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    name.covariates = name.covariates,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">765</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    verbose = verbose,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">766</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    ...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">767</td>
+                    <td class="coverage">2187<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  return(res)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">768</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">769</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">770</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' logLik method for saem.mmkin objects</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">771</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">772</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param object The fitted [saem.mmkin] object</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">773</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param \dots Passed to [saemix::logLik.SaemixObject]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">774</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param method Passed to [saemix::logLik.SaemixObject]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">775</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">776</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">logLik.saem.mmkin &lt;- function(object, ..., method = c("is", "lin", "gq")) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">777</td>
+                    <td class="coverage">4404<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  method &lt;- match.arg(method)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">778</td>
+                    <td class="coverage">4404<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  return(logLik(object$so, method = method))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">779</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">780</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">781</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">782</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">update.saem.mmkin &lt;- function(object, ..., evaluate = TRUE) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">783</td>
+                    <td class="coverage">507<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  call &lt;- object$call</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">784</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # For some reason we get saem.mmkin in the call when using mhmkin</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">785</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # so we need to fix this so we do not have to export saem.mmkin in</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">786</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # addition to the S3 method</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">787</td>
+                    <td class="coverage">507<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  call[[1]] &lt;- saem</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">788</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">789</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # We also need to provide the mmkin object in the call, so it</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">790</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # will also be found when called by testthat or pkgdown</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">791</td>
+                    <td class="coverage">507<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  call[[2]] &lt;- object$mmkin</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">792</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">793</td>
+                    <td class="coverage">507<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  update_arguments &lt;- match.call(expand.dots = FALSE)$...</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">794</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">795</td>
+                    <td class="coverage">507<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (length(update_arguments) &gt; 0) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">796</td>
+                    <td class="coverage">507<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    update_arguments_in_call &lt;- !is.na(match(names(update_arguments), names(call)))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">797</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">798</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">799</td>
+                    <td class="coverage">507<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  for (a in names(update_arguments)[update_arguments_in_call]) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">800</td>
+                    <td class="coverage">35<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    call[[a]] &lt;- update_arguments[[a]]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">801</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">802</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">803</td>
+                    <td class="coverage">507<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  update_arguments_not_in_call &lt;- !update_arguments_in_call</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">804</td>
+                    <td class="coverage">507<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if(any(update_arguments_not_in_call)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">805</td>
+                    <td class="coverage">472<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    call &lt;- c(as.list(call), update_arguments[update_arguments_not_in_call])</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">806</td>
+                    <td class="coverage">472<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    call &lt;- as.call(call)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">807</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">808</td>
+                    <td class="coverage">507<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if(evaluate) eval(call, parent.frame())</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">809</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  else call</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">810</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">811</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">812</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">813</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @rdname parms</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">814</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param ci Should a matrix with estimates and confidence interval boundaries</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">815</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' be returned? If FALSE (default), a vector of estimates is returned if no</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">816</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' covariates are given, otherwise a matrix of estimates is returned, with</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">817</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' each column corresponding to a row of the data frame holding the covariates</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">818</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param covariates A data frame holding covariate values for which to</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">819</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' return parameter values. Only has an effect if 'ci' is FALSE.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">820</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">parms.saem.mmkin &lt;- function(object, ci = FALSE, covariates = NULL, ...) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">821</td>
+                    <td class="coverage">2904<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cov.mod &lt;- object$sm@covariance.model</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">822</td>
+                    <td class="coverage">2904<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  n_cov_mod_parms &lt;- sum(cov.mod[upper.tri(cov.mod, diag = TRUE)])</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">823</td>
+                    <td class="coverage">2904<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  n_parms &lt;- length(object$sm@name.modpar) +</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">824</td>
+                    <td class="coverage">2904<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    n_cov_mod_parms +</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">825</td>
+                    <td class="coverage">2904<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    length(object$sm@name.sigma)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">826</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">827</td>
+                    <td class="coverage">2904<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (inherits(object$so, "try-error")) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">828</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    conf.int &lt;- matrix(rep(NA, 3 * n_parms), ncol = 3)</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">829</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    colnames(conf.int) &lt;- c("estimate", "lower", "upper")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">830</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">831</td>
+                    <td class="coverage">2904<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    conf.int &lt;- object$so@results@conf.int[c("estimate", "lower", "upper")]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">832</td>
+                    <td class="coverage">2904<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    rownames(conf.int) &lt;- object$so@results@conf.int[["name"]]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">833</td>
+                    <td class="coverage">2904<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    conf.int.var &lt;- grepl("^Var\\.", rownames(conf.int))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">834</td>
+                    <td class="coverage">2904<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    conf.int &lt;- conf.int[!conf.int.var, ]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">835</td>
+                    <td class="coverage">2904<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    conf.int.cov &lt;- grepl("^Cov\\.", rownames(conf.int))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">836</td>
+                    <td class="coverage">2904<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    conf.int &lt;- conf.int[!conf.int.cov, ]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">837</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">838</td>
+                    <td class="coverage">2904<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  estimate &lt;- conf.int[, "estimate"]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">839</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">840</td>
+                    <td class="coverage">2904<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  names(estimate) &lt;- rownames(conf.int)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">841</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">842</td>
+                    <td class="coverage">2904<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (ci) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">843</td>
+                    <td class="coverage">1034<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    return(conf.int)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">844</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">845</td>
+                    <td class="coverage">1870<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (is.null(covariates)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">846</td>
+                    <td class="coverage">1760<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      return(estimate)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">847</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">848</td>
+                    <td class="coverage">110<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      est_for_cov &lt;- matrix(NA,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">849</td>
+                    <td class="coverage">110<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        nrow = length(object$sm@name.modpar), ncol = nrow(covariates),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">850</td>
+                    <td class="coverage">110<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        dimnames = (list(object$sm@name.modpar, rownames(covariates))))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">851</td>
+                    <td class="coverage">110<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      covmods &lt;- object$covariate_models</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">852</td>
+                    <td class="coverage">110<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      names(covmods) &lt;- sapply(covmods, function(x) as.character(x[[2]]))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">853</td>
+                    <td class="coverage">110<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      for (deg_parm_name in rownames(est_for_cov)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">854</td>
+                    <td class="coverage">440<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        if (deg_parm_name %in% names(covmods)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">855</td>
+                    <td class="coverage">110<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          covariate &lt;- covmods[[deg_parm_name]][[3]]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">856</td>
+                    <td class="coverage">110<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          beta_degparm_name &lt;- paste0("beta_", covariate,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">857</td>
+                    <td class="coverage">110<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            "(", deg_parm_name, ")")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">858</td>
+                    <td class="coverage">110<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          est_for_cov[deg_parm_name, ] &lt;- estimate[deg_parm_name] +</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">859</td>
+                    <td class="coverage">110<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            covariates[[covariate]] * estimate[beta_degparm_name]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">860</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">861</td>
+                    <td class="coverage">330<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          est_for_cov[deg_parm_name, ] &lt;- estimate[deg_parm_name]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">862</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">863</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">864</td>
+                    <td class="coverage">110<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      return(est_for_cov)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">865</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">866</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">867</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                </tbody>
+              </table>
+            </div>
+            <div id="R/summary.nlme.mmkin.R" class="hidden">
+              <table class="table-condensed">
+                <tbody>
+                  <tr class="never">
+                    <td class="num">1</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Summary method for class "nlme.mmkin"</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">2</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">3</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Lists model equations, initial parameter values, optimised parameters</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">4</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' for fixed effects (population), random effects (deviations from the</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">5</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' population mean) and residual error model, as well as the resulting</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">6</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' endpoints such as formation fractions and DT50 values. Optionally</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">7</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' (default is FALSE), the data are listed in full.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">8</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">9</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param object an object of class [nlme.mmkin]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">10</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param x an object of class [summary.nlme.mmkin]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">11</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param data logical, indicating whether the full data should be included in</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">12</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   the summary.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">13</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param verbose Should the summary be verbose?</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">14</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param distimes logical, indicating whether DT50 and DT90 values should be</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">15</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   included.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">16</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param alpha error level for confidence interval estimation from the t</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">17</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   distribution</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">18</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param digits Number of digits to use for printing</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">19</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param \dots optional arguments passed to methods like \code{print}.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">20</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @return The summary function returns a list based on the [nlme] object</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">21</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   obtained in the fit, with at least the following additional components</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">22</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   \item{nlmeversion, mkinversion, Rversion}{The nlme, mkin and R versions used}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">23</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   \item{date.fit, date.summary}{The dates where the fit and the summary were</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">24</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     produced}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">25</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   \item{diffs}{The differential equations used in the degradation model}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">26</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   \item{use_of_ff}{Was maximum or minimum use made of formation fractions}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">27</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   \item{data}{The data}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">28</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   \item{confint_trans}{Transformed parameters as used in the optimisation, with confidence intervals}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">29</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   \item{confint_back}{Backtransformed parameters, with confidence intervals if available}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">30</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   \item{ff}{The estimated formation fractions derived from the fitted</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">31</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'      model.}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">32</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   \item{distimes}{The DT50 and DT90 values for each observed variable.}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">33</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   \item{SFORB}{If applicable, eigenvalues of SFORB components of the model.}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">34</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   The print method is called for its side effect, i.e. printing the summary.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">35</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @importFrom stats predict</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">36</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @author Johannes Ranke for the mkin specific parts</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">37</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   José Pinheiro and Douglas Bates for the components inherited from nlme</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">38</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @examples</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">39</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">40</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # Generate five datasets following SFO kinetics</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">41</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">42</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' dt50_sfo_in_pop &lt;- 50</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">43</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' k_in_pop &lt;- log(2) / dt50_sfo_in_pop</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">44</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' set.seed(1234)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">45</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' k_in &lt;- rlnorm(5, log(k_in_pop), 0.5)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">46</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' SFO &lt;- mkinmod(parent = mkinsub("SFO"))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">47</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">48</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' pred_sfo &lt;- function(k) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">49</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   mkinpredict(SFO,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">50</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     c(k_parent = k),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">51</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     c(parent = 100),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">52</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     sampling_times)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">53</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">54</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">55</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' ds_sfo_mean &lt;- lapply(k_in, pred_sfo)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">56</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' names(ds_sfo_mean) &lt;- paste("ds", 1:5)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">57</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">58</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' set.seed(12345)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">59</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' ds_sfo_syn &lt;- lapply(ds_sfo_mean, function(ds) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">60</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   add_err(ds,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">61</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     sdfunc = function(value) sqrt(1^2 + value^2 * 0.07^2),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">62</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     n = 1)[[1]]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">63</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' })</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">64</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">65</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' \dontrun{</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">66</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # Evaluate using mmkin and nlme</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">67</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' library(nlme)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">68</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' f_mmkin &lt;- mmkin("SFO", ds_sfo_syn, quiet = TRUE, error_model = "tc", cores = 1)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">69</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' f_nlme &lt;- nlme(f_mmkin)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">70</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' summary(f_nlme, data = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">71</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">72</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">73</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">74</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">summary.nlme.mmkin &lt;- function(object, data = FALSE, verbose = FALSE, distimes = TRUE, alpha = 0.05, ...) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">75</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">76</td>
+                    <td class="coverage">319<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  mod_vars &lt;- names(object$mkinmod$diffs)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">77</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">78</td>
+                    <td class="coverage">319<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  confint_trans &lt;- intervals(object, which = "fixed", level = 1 - alpha)$fixed</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">79</td>
+                    <td class="coverage">319<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  attr(confint_trans, "label") &lt;- NULL</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">80</td>
+                    <td class="coverage">319<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  pnames &lt;- rownames(confint_trans)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">81</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">82</td>
+                    <td class="coverage">319<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  bp &lt;- backtransform_odeparms(confint_trans[, "est."], object$mkinmod,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">83</td>
+                    <td class="coverage">319<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    object$transform_rates, object$transform_fractions)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">84</td>
+                    <td class="coverage">319<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  bpnames &lt;- names(bp)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">85</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">86</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  #  variance-covariance estimates for fixed effects (from summary.lme)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">87</td>
+                    <td class="coverage">319<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  fixed &lt;- fixef(object)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">88</td>
+                    <td class="coverage">319<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  stdFixed &lt;- sqrt(diag(as.matrix(object$varFix)))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">89</td>
+                    <td class="coverage">319<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  object$corFixed &lt;- array(</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">90</td>
+                    <td class="coverage">319<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    t(object$varFix/stdFixed)/stdFixed,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">91</td>
+                    <td class="coverage">319<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    dim(object$varFix),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">92</td>
+                    <td class="coverage">319<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    list(names(fixed), names(fixed)))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">93</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">94</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Transform boundaries of CI for one parameter at a time,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">95</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # with the exception of sets of formation fractions (single fractions are OK).</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">96</td>
+                    <td class="coverage">319<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  f_names_skip &lt;- character(0)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">97</td>
+                    <td class="coverage">319<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  for (box in mod_vars) { # Figure out sets of fractions to skip</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">98</td>
+                    <td class="coverage">436<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    f_names &lt;- grep(paste("^f", box, sep = "_"), pnames, value = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">99</td>
+                    <td class="coverage">436<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    n_paths &lt;- length(f_names)</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">100</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (n_paths &gt; 1) f_names_skip &lt;- c(f_names_skip, f_names)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">101</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">102</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">103</td>
+                    <td class="coverage">319<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  confint_back &lt;- matrix(NA, nrow = length(bp), ncol = 3,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">104</td>
+                    <td class="coverage">319<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    dimnames = list(bpnames, colnames(confint_trans)))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">105</td>
+                    <td class="coverage">319<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  confint_back[, "est."] &lt;- bp</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">106</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">107</td>
+                    <td class="coverage">319<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  for (pname in pnames) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">108</td>
+                    <td class="coverage">1410<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (!pname %in% f_names_skip) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">109</td>
+                    <td class="coverage">1410<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      par.lower &lt;- confint_trans[pname, "lower"]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">110</td>
+                    <td class="coverage">1410<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      par.upper &lt;- confint_trans[pname, "upper"]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">111</td>
+                    <td class="coverage">1410<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      names(par.lower) &lt;- names(par.upper) &lt;- pname</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">112</td>
+                    <td class="coverage">1410<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      bpl &lt;- backtransform_odeparms(par.lower, object$mkinmod,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">113</td>
+                    <td class="coverage">1410<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                                            object$transform_rates,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">114</td>
+                    <td class="coverage">1410<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                                            object$transform_fractions)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">115</td>
+                    <td class="coverage">1410<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      bpu &lt;- backtransform_odeparms(par.upper, object$mkinmod,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">116</td>
+                    <td class="coverage">1410<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                                            object$transform_rates,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">117</td>
+                    <td class="coverage">1410<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                                            object$transform_fractions)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">118</td>
+                    <td class="coverage">1410<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      confint_back[names(bpl), "lower"] &lt;- bpl</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">119</td>
+                    <td class="coverage">1410<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      confint_back[names(bpu), "upper"] &lt;- bpu</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">120</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">121</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">122</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">123</td>
+                    <td class="coverage">319<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  object$confint_trans &lt;- confint_trans</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">124</td>
+                    <td class="coverage">319<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  object$confint_back &lt;- confint_back</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">125</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">126</td>
+                    <td class="coverage">319<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  object$date.summary = date()</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">127</td>
+                    <td class="coverage">319<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  object$use_of_ff = object$mkinmod$use_of_ff</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">128</td>
+                    <td class="coverage">319<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  object$error_model_algorithm = object$mmkin[[1]]$error_model_algorithm</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">129</td>
+                    <td class="coverage">319<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  err_mod = object$mmkin[[1]]$err_mod</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">130</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">131</td>
+                    <td class="coverage">319<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  object$diffs &lt;- object$mkinmod$diffs</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">132</td>
+                    <td class="coverage">319<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  object$print_data &lt;- data</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">133</td>
+                    <td class="coverage">319<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  object$data[["observed"]] &lt;- object$data[["value"]]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">134</td>
+                    <td class="coverage">319<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  object$data[["value"]] &lt;- NULL</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">135</td>
+                    <td class="coverage">319<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  object$data[["predicted"]] &lt;- predict(object)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">136</td>
+                    <td class="coverage">319<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  object$data[["residual"]] &lt;- residuals(object, type = "response")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">137</td>
+                    <td class="coverage">319<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (is.null(object$modelStruct$varStruct)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">138</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    object$data[["std"]] &lt;- object$sigma</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">139</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">140</td>
+                    <td class="coverage">319<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    object$data[["std"]] &lt;- 1/attr(object$modelStruct$varStruct, "weights")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">141</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">142</td>
+                    <td class="coverage">319<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  object$data[["standardized"]] &lt;- residuals(object, type = "pearson")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">143</td>
+                    <td class="coverage">319<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  object$verbose &lt;- verbose</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">144</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">145</td>
+                    <td class="coverage">319<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  object$fixed &lt;- object$mmkin[[1]]$fixed</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">146</td>
+                    <td class="coverage">319<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  object$AIC = AIC(object)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">147</td>
+                    <td class="coverage">319<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  object$BIC = BIC(object)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">148</td>
+                    <td class="coverage">319<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  object$logLik = logLik(object)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">149</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">150</td>
+                    <td class="coverage">319<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  ep &lt;- endpoints(object)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">151</td>
+                    <td class="coverage">319<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (length(ep$ff) != 0)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">152</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    object$ff &lt;- ep$ff</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">153</td>
+                    <td class="coverage">319<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (distimes) object$distimes &lt;- ep$distimes</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">154</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (length(ep$SFORB) != 0) object$SFORB &lt;- ep$SFORB</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">155</td>
+                    <td class="coverage">319<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  class(object) &lt;- c("summary.nlme.mmkin", "nlme.mmkin", "nlme", "lme")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">156</td>
+                    <td class="coverage">319<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  return(object)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">157</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">158</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">159</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @rdname summary.nlme.mmkin</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">160</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">161</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">print.summary.nlme.mmkin &lt;- function(x, digits = max(3, getOption("digits") - 3), verbose = x$verbose, ...) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">162</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("nlme version used for fitting:     ", x$nlmeversion, "\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">163</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("mkin version used for pre-fitting: ", x$mkinversion, "\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">164</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("R version used for fitting:        ", x$Rversion, "\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">165</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">166</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("Date of fit:    ", x$date.fit, "\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">167</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("Date of summary:", x$date.summary, "\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">168</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">169</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("\nEquations:\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">170</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  nice_diffs &lt;- gsub("^(d.*) =", "\\1/dt =", x[["diffs"]])</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">171</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  writeLines(strwrap(nice_diffs, exdent = 11))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">172</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">173</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("\nData:\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">174</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat(nrow(x$data), "observations of",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">175</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    length(unique(x$data$name)), "variable(s) grouped in",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">176</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    length(unique(x$data$ds)), "datasets\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">177</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">178</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("\nModel predictions using solution type", x$solution_type, "\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">179</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">180</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("\nFitted in", x$time[["elapsed"]],  "s using", x$numIter, "iterations\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">181</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">182</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("\nVariance model: ")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">183</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat(switch(x$err_mod,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">184</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    const = "Constant variance",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">185</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    obs = "Variance unique to each observed variable",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">186</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    tc = "Two-component variance function"), "\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">187</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">188</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("\nMean of starting values for individual parameters:\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">189</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  print(x$mean_dp_start, digits = digits)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">190</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">191</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("\nFixed degradation parameter values:\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">192</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if(length(x$fixed$value) == 0) cat("None\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">193</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  else print(x$fixed, digits = digits)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">194</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">195</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("\nResults:\n\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">196</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  print(data.frame(AIC = x$AIC, BIC = x$BIC, logLik = x$logLik,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">197</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      row.names = " "), digits = digits, ...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">198</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">199</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("\nOptimised, transformed parameters with symmetric confidence intervals:\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">200</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  print(x$confint_trans, digits = digits, ...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">201</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">202</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (nrow(x$confint_trans) &gt; 1) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">203</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    corr &lt;- x$corFixed</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">204</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    class(corr) &lt;- "correlation"</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">205</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    print(corr, title = "\nCorrelation:", rdig = digits, ...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">206</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">207</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">208</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("\n") # Random effects</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">209</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  print(summary(x$modelStruct), sigma = x$sigma,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">210</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        reEstimates = x$coef$random, digits = digits, verbose = verbose, ...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">211</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">212</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("\nBacktransformed parameters with asymmetric confidence intervals:\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">213</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  print(x$confint_back, digits = digits, ...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">214</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">215</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">216</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  printSFORB &lt;- !is.null(x$SFORB)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">217</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if(printSFORB){</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">218</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    cat("\nEstimated Eigenvalues of SFORB model(s):\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">219</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    print(x$SFORB, digits = digits,...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">220</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">221</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">222</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  printff &lt;- !is.null(x$ff)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">223</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if(printff){</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">224</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    cat("\nResulting formation fractions:\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">225</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    print(data.frame(ff = x$ff), digits = digits, ...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">226</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">227</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">228</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  printdistimes &lt;- !is.null(x$distimes)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">229</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if(printdistimes){</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">230</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    cat("\nEstimated disappearance times:\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">231</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    print(x$distimes, digits = digits, ...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">232</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">233</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">234</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (x$print_data){</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">235</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    cat("\nData:\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">236</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    print(format(x$data, digits = digits, ...), row.names = FALSE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">237</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">238</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">239</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  invisible(x)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">240</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                </tbody>
+              </table>
+            </div>
+            <div id="R/endpoints.R" class="hidden">
+              <table class="table-condensed">
+                <tbody>
+                  <tr class="never">
+                    <td class="num">1</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Function to calculate endpoints for further use from kinetic models fitted</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">2</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' with mkinfit</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">3</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">4</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' This function calculates DT50 and DT90 values as well as formation fractions</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">5</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' from kinetic models fitted with mkinfit. If the SFORB model was specified</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">6</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' for one of the parents or metabolites, the Eigenvalues are returned. These</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">7</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' are equivalent to the rate constants of the DFOP model, but with the</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">8</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' advantage that the SFORB model can also be used for metabolites.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">9</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">10</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Additional DT50 values are calculated from the FOMC DT90 and k1 and k2 from</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">11</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' HS and DFOP, as well as from Eigenvalues b1 and b2 of any SFORB models</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">12</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">13</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param fit An object of class [mkinfit], [nlme.mmkin] or [saem.mmkin], or</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">14</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' another object that has list components mkinmod containing an [mkinmod]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">15</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' degradation model, and two numeric vectors, bparms.optim and bparms.fixed,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">16</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' that contain parameter values for that model.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">17</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param covariates Numeric vector with covariate values for all variables in</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">18</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' any covariate models in the object. If given, it overrides 'covariate_quantile'.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">19</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param covariate_quantile This argument only has an effect if the fitted</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">20</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' object has covariate models. If so, the default is to show endpoints</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">21</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' for the median of the covariate values (50th percentile).</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">22</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @importFrom stats optimize</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">23</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @return A list with a matrix of dissipation times named distimes, and, if</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">24</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' applicable, a vector of formation fractions named ff and, if the SFORB model</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">25</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' was in use, a vector of eigenvalues of these SFORB models, equivalent to</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">26</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' DFOP rate constants</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">27</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @note The function is used internally by [summary.mkinfit],</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">28</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' [summary.nlme.mmkin] and [summary.saem.mmkin].</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">29</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @author Johannes Ranke</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">30</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @examples</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">31</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">32</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   fit &lt;- mkinfit("FOMC", FOCUS_2006_C, quiet = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">33</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   endpoints(fit)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">34</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   \dontrun{</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">35</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     fit_2 &lt;- mkinfit("DFOP", FOCUS_2006_C, quiet = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">36</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     endpoints(fit_2)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">37</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     fit_3 &lt;- mkinfit("SFORB", FOCUS_2006_C, quiet = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">38</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     endpoints(fit_3)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">39</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">40</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">41</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">42</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">endpoints &lt;- function(fit, covariates = NULL, covariate_quantile = 0.5) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">43</td>
+                    <td class="coverage">56208<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  mkinmod &lt;- fit$mkinmod</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">44</td>
+                    <td class="coverage">56208<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  obs_vars &lt;- names(mkinmod$spec)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">45</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">46</td>
+                    <td class="coverage">56208<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (!is.null(fit$covariate_models)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">47</td>
+                    <td class="coverage">110<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (is.null(covariates)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">48</td>
+                    <td class="coverage">110<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      covariates = as.data.frame(</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">49</td>
+                    <td class="coverage">110<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        apply(fit$covariates, 2, quantile,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">50</td>
+                    <td class="coverage">110<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          covariate_quantile, simplify = FALSE))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">51</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">52</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      covariate_m &lt;- matrix(covariates, byrow = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">53</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      colnames(covariate_m) &lt;- names(covariates)</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">54</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      rownames(covariate_m) &lt;- "User"</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">55</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      covariates &lt;- as.data.frame(covariate_m)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">56</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">57</td>
+                    <td class="coverage">110<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    degparms_trans &lt;- parms(fit, covariates = covariates)[, 1]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">58</td>
+                    <td class="coverage">110<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (inherits(fit, "saem.mmkin") &amp; (fit$transformations == "saemix")) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">59</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      degparms &lt;- degparms_trans</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">60</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">61</td>
+                    <td class="coverage">110<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      degparms &lt;- backtransform_odeparms(degparms_trans,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">62</td>
+                    <td class="coverage">110<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        fit$mkinmod,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">63</td>
+                    <td class="coverage">110<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        transform_rates = fit$transform_rates,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">64</td>
+                    <td class="coverage">110<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        transform_fractions = fit$transform_fractions)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">65</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">66</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">67</td>
+                    <td class="coverage">56098<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    degparms &lt;- c(fit$bparms.optim, fit$bparms.fixed)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">68</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">69</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">70</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Set up object to return</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">71</td>
+                    <td class="coverage">56208<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  ep &lt;- list()</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">72</td>
+                    <td class="coverage">56208<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  ep$covariates &lt;- covariates</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">73</td>
+                    <td class="coverage">56208<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  ep$ff &lt;- vector()</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">74</td>
+                    <td class="coverage">56208<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  ep$SFORB &lt;- vector()</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">75</td>
+                    <td class="coverage">56208<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  ep$distimes &lt;- data.frame(</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">76</td>
+                    <td class="coverage">56208<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    DT50 = rep(NA, length(obs_vars)),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">77</td>
+                    <td class="coverage">56208<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    DT90 = rep(NA, length(obs_vars)),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">78</td>
+                    <td class="coverage">56208<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    row.names = obs_vars)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">79</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">80</td>
+                    <td class="coverage">56208<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  for (obs_var in obs_vars) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">81</td>
+                    <td class="coverage">73858<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    type = names(mkinmod$map[[obs_var]])[1]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">82</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">83</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # Get formation fractions if directly fitted, and calculate remaining fraction to sink</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">84</td>
+                    <td class="coverage">73858<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    f_names = grep(paste("^f", obs_var, sep = "_"), names(degparms), value=TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">85</td>
+                    <td class="coverage">73858<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (length(f_names) &gt; 0) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">86</td>
+                    <td class="coverage">15068<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      f_values = degparms[f_names]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">87</td>
+                    <td class="coverage">15068<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      f_to_sink = 1 - sum(f_values)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">88</td>
+                    <td class="coverage">15068<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      names(f_to_sink) = ifelse(type == "SFORB",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">89</td>
+                    <td class="coverage">15068<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        paste(obs_var, "free", "sink", sep = "_"),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">90</td>
+                    <td class="coverage">15068<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        paste(obs_var, "sink", sep = "_"))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">91</td>
+                    <td class="coverage">15068<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      for (f_name in f_names) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">92</td>
+                    <td class="coverage">17338<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        ep$ff[[sub("f_", "", sub("_to_", "_", f_name))]] = f_values[[f_name]]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">93</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">94</td>
+                    <td class="coverage">15068<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      ep$ff = append(ep$ff, f_to_sink)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">95</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">96</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">97</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # Get the rest</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">98</td>
+                    <td class="coverage">73858<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (type == "SFO") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">99</td>
+                    <td class="coverage">40900<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      k_names = grep(paste("^k", obs_var, sep="_"), names(degparms), value=TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">100</td>
+                    <td class="coverage">40900<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      k_tot = sum(degparms[k_names])</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">101</td>
+                    <td class="coverage">40900<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      DT50 = log(2)/k_tot</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">102</td>
+                    <td class="coverage">40900<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      DT90 = log(10)/k_tot</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">103</td>
+                    <td class="coverage">40900<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      if (mkinmod$use_of_ff == "min" &amp;&amp; length(obs_vars) &gt; 1) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">104</td>
+                    <td class="coverage">622<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        for (k_name in k_names)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">105</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">106</td>
+                    <td class="coverage">932<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          ep$ff[[sub("k_", "", k_name)]] = degparms[[k_name]] / k_tot</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">107</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">108</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">109</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">110</td>
+                    <td class="coverage">73858<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (type == "FOMC") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">111</td>
+                    <td class="coverage">1790<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      alpha = degparms["alpha"]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">112</td>
+                    <td class="coverage">1790<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      beta = degparms["beta"]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">113</td>
+                    <td class="coverage">1790<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      DT50 = beta * (2^(1/alpha) - 1)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">114</td>
+                    <td class="coverage">1790<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      DT90 = beta * (10^(1/alpha) - 1)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">115</td>
+                    <td class="coverage">1790<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      DT50_back = DT90 / (log(10)/log(2)) # Backcalculated DT50 as recommended in FOCUS 2011</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">116</td>
+                    <td class="coverage">1790<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      ep$distimes[obs_var, c("DT50back")] = DT50_back</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">117</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">118</td>
+                    <td class="coverage">73858<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (type == "IORE") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">119</td>
+                    <td class="coverage">352<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      k_names = grep(paste("^k__iore", obs_var, sep="_"), names(degparms), value=TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">120</td>
+                    <td class="coverage">352<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      k_tot = sum(degparms[k_names])</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">121</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      # From the NAFTA kinetics guidance, p. 5</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">122</td>
+                    <td class="coverage">352<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      n = degparms[paste("N", obs_var, sep = "_")]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">123</td>
+                    <td class="coverage">352<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      k = k_tot</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">124</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      # Use the initial concentration of the parent compound</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">125</td>
+                    <td class="coverage">352<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      source_name = mkinmod$map[[1]][[1]]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">126</td>
+                    <td class="coverage">352<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      c0 = degparms[paste(source_name, "0", sep = "_")]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">127</td>
+                    <td class="coverage">352<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      alpha = 1 / (n - 1)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">128</td>
+                    <td class="coverage">352<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      beta = (c0^(1 - n))/(k * (n - 1))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">129</td>
+                    <td class="coverage">352<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      DT50 = beta * (2^(1/alpha) - 1)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">130</td>
+                    <td class="coverage">352<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      DT90 = beta * (10^(1/alpha) - 1)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">131</td>
+                    <td class="coverage">352<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      DT50_back = DT90 / (log(10)/log(2)) # Backcalculated DT50 as recommended in FOCUS 2011</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">132</td>
+                    <td class="coverage">352<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      ep$distimes[obs_var, c("DT50back")] = DT50_back</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">133</td>
+                    <td class="coverage">352<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      if (mkinmod$use_of_ff == "min") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">134</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        for (k_name in k_names)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">135</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">136</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          ep$ff[[sub("k_", "", k_name)]] = degparms[[k_name]] / k_tot</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">137</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">138</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">139</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">140</td>
+                    <td class="coverage">73858<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (type == "DFOP") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">141</td>
+                    <td class="coverage">27729<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      k1 = degparms["k1"]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">142</td>
+                    <td class="coverage">27729<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      k2 = degparms["k2"]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">143</td>
+                    <td class="coverage">27729<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      g = degparms["g"]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">144</td>
+                    <td class="coverage">27729<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      f &lt;- function(log_t, x) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">145</td>
+                    <td class="coverage">684705<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        t &lt;- exp(log_t)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">146</td>
+                    <td class="coverage">684705<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        fraction &lt;- g * exp( - k1 * t) + (1 - g) * exp( - k2 * t)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">147</td>
+                    <td class="coverage">684705<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        (fraction - (1 - x/100))^2</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">148</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">149</td>
+                    <td class="coverage">27729<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      DT50_k1 = log(2)/k1</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">150</td>
+                    <td class="coverage">27729<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      DT50_k2 = log(2)/k2</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">151</td>
+                    <td class="coverage">27729<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      DT90_k1 = log(10)/k1</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">152</td>
+                    <td class="coverage">27729<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      DT90_k2 = log(10)/k2</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">153</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">154</td>
+                    <td class="coverage">27729<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      DT50 &lt;- try(exp(optimize(f, c(log(DT50_k1), log(DT50_k2)), x=50)$minimum),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">155</td>
+                    <td class="coverage">27729<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                  silent = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">156</td>
+                    <td class="coverage">27729<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      DT90 &lt;- try(exp(optimize(f, c(log(DT90_k1), log(DT90_k2)), x=90)$minimum),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">157</td>
+                    <td class="coverage">27729<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                  silent = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">158</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      if (inherits(DT50, "try-error")) DT50 = NA</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">159</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      if (inherits(DT90, "try-error")) DT90 = NA</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">160</td>
+                    <td class="coverage">27729<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      DT50_back = DT90 / (log(10)/log(2)) # Backcalculated DT50 as recommended in FOCUS 2011</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">161</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">162</td>
+                    <td class="coverage">27729<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      ep$distimes[obs_var, c("DT50back")] = DT50_back</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">163</td>
+                    <td class="coverage">27729<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      ep$distimes[obs_var, c("DT50_k1")] = DT50_k1</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">164</td>
+                    <td class="coverage">27729<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      ep$distimes[obs_var, c("DT50_k2")] = DT50_k2</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">165</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">166</td>
+                    <td class="coverage">73858<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (type == "HS") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">167</td>
+                    <td class="coverage">318<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      k1 = degparms["k1"]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">168</td>
+                    <td class="coverage">318<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      k2 = degparms["k2"]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">169</td>
+                    <td class="coverage">318<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      tb = degparms["tb"]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">170</td>
+                    <td class="coverage">318<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      DTx &lt;- function(x) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">171</td>
+                    <td class="coverage">636<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        DTx.a &lt;- (log(100/(100 - x)))/k1</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">172</td>
+                    <td class="coverage">636<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        DTx.b &lt;- tb + (log(100/(100 - x)) - k1 * tb)/k2</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">173</td>
+                    <td class="coverage">339<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        if (DTx.a &lt; tb) DTx &lt;- DTx.a</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">174</td>
+                    <td class="coverage">297<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        else DTx &lt;- DTx.b</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">175</td>
+                    <td class="coverage">636<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        return(DTx)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">176</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">177</td>
+                    <td class="coverage">318<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      DT50 &lt;- DTx(50)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">178</td>
+                    <td class="coverage">318<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      DT90 &lt;- DTx(90)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">179</td>
+                    <td class="coverage">318<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      DT50_back = DT90 / (log(10)/log(2)) # Backcalculated DT50 as recommended in FOCUS 2011</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">180</td>
+                    <td class="coverage">318<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      DT50_k1 = log(2)/k1</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">181</td>
+                    <td class="coverage">318<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      DT50_k2 = log(2)/k2</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">182</td>
+                    <td class="coverage">318<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      ep$distimes[obs_var, c("DT50back")] = DT50_back</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">183</td>
+                    <td class="coverage">318<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      ep$distimes[obs_var, c("DT50_k1")] = DT50_k1</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">184</td>
+                    <td class="coverage">318<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      ep$distimes[obs_var, c("DT50_k2")] = DT50_k2</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">185</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">186</td>
+                    <td class="coverage">73858<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (type == "SFORB") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">187</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      # FOCUS kinetics (2006), p. 60 f</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">188</td>
+                    <td class="coverage">2616<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      k_out_names = grep(paste("^k", obs_var, "free", sep="_"), names(degparms), value=TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">189</td>
+                    <td class="coverage">2616<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      k_out_names = setdiff(k_out_names, paste("k", obs_var, "free", "bound", sep="_"))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">190</td>
+                    <td class="coverage">2616<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      k_1output = sum(degparms[k_out_names])</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">191</td>
+                    <td class="coverage">2616<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      k_12 = degparms[paste("k", obs_var, "free", "bound", sep="_")]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">192</td>
+                    <td class="coverage">2616<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      k_21 = degparms[paste("k", obs_var, "bound", "free", sep="_")]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">193</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">194</td>
+                    <td class="coverage">2616<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      sqrt_exp = sqrt(1/4 * (k_12 + k_21 + k_1output)^2 - k_1output * k_21)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">195</td>
+                    <td class="coverage">2616<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      b1 = 0.5 * (k_12 + k_21 + k_1output) + sqrt_exp</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">196</td>
+                    <td class="coverage">2616<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      b2 = 0.5 * (k_12 + k_21 + k_1output) - sqrt_exp</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">197</td>
+                    <td class="coverage">2616<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      g = (k_12 + k_21 - b1)/(b2 - b1)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">198</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">199</td>
+                    <td class="coverage">2616<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      DT50_b1 = log(2)/b1</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">200</td>
+                    <td class="coverage">2616<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      DT50_b2 = log(2)/b2</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">201</td>
+                    <td class="coverage">2616<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      DT90_b1 = log(10)/b1</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">202</td>
+                    <td class="coverage">2616<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      DT90_b2 = log(10)/b2</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">203</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">204</td>
+                    <td class="coverage">2616<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      SFORB_fraction = function(t) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">205</td>
+                    <td class="coverage">60096<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        g * exp(-b1 * t) + (1 - g) * exp(-b2 * t)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">206</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">207</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">208</td>
+                    <td class="coverage">2616<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      f_50 &lt;- function(log_t) (SFORB_fraction(exp(log_t)) - 0.5)^2</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">209</td>
+                    <td class="coverage">2616<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      log_DT50 &lt;- try(optimize(f_50, c(log(DT50_b1), log(DT50_b2)))$minimum,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">210</td>
+                    <td class="coverage">2616<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                      silent = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">211</td>
+                    <td class="coverage">2616<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      f_90 &lt;- function(log_t) (SFORB_fraction(exp(log_t)) - 0.1)^2</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">212</td>
+                    <td class="coverage">2616<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      log_DT90 &lt;- try(optimize(f_90, c(log(DT90_b1), log(DT90_b2)))$minimum,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">213</td>
+                    <td class="coverage">2616<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                      silent = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">214</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">215</td>
+                    <td class="coverage">2616<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      DT50 = if (inherits(log_DT50, "try-error")) NA</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">216</td>
+                    <td class="coverage">2616<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">             else exp(log_DT50)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">217</td>
+                    <td class="coverage">2616<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      DT90 = if (inherits(log_DT90, "try-error")) NA</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">218</td>
+                    <td class="coverage">2616<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">             else exp(log_DT90)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">219</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">220</td>
+                    <td class="coverage">2616<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      DT50_back = DT90 / (log(10)/log(2)) # Backcalculated DT50 as recommended in FOCUS 2011</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">221</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">222</td>
+                    <td class="coverage">2616<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      for (k_out_name in k_out_names)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">223</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">224</td>
+                    <td class="coverage">2618<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        ep$ff[[sub("k_", "", k_out_name)]] = degparms[[k_out_name]] / k_1output</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">225</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">226</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">227</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      # Return the eigenvalues for comparison with DFOP rate constants</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">228</td>
+                    <td class="coverage">2616<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      ep$SFORB[[paste(obs_var, "b1", sep="_")]] = b1</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">229</td>
+                    <td class="coverage">2616<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      ep$SFORB[[paste(obs_var, "b2", sep="_")]] = b2</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">230</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      # Return g for comparison with DFOP</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">231</td>
+                    <td class="coverage">2616<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      ep$SFORB[[paste(obs_var, "g", sep="_")]] = g</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">232</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">233</td>
+                    <td class="coverage">2616<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      ep$distimes[obs_var, c("DT50back")] = DT50_back</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">234</td>
+                    <td class="coverage">2616<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      ep$distimes[obs_var, c(paste("DT50", obs_var, "b1", sep = "_"))] = DT50_b1</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">235</td>
+                    <td class="coverage">2616<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      ep$distimes[obs_var, c(paste("DT50", obs_var, "b2", sep = "_"))] = DT50_b2</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">236</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">237</td>
+                    <td class="coverage">73858<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (type == "logistic") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">238</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      # FOCUS kinetics (2014) p. 67</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">239</td>
+                    <td class="coverage">153<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      kmax = degparms["kmax"]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">240</td>
+                    <td class="coverage">153<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      k0 = degparms["k0"]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">241</td>
+                    <td class="coverage">153<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      r = degparms["r"]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">242</td>
+                    <td class="coverage">153<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      DT50 = (1/r) * log(1 - ((kmax/k0) * (1 - 2^(r/kmax))))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">243</td>
+                    <td class="coverage">153<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      DT90 = (1/r) * log(1 - ((kmax/k0) * (1 - 10^(r/kmax))))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">244</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">245</td>
+                    <td class="coverage">153<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      DT50_k0 = log(2)/k0</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">246</td>
+                    <td class="coverage">153<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      DT50_kmax = log(2)/kmax</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">247</td>
+                    <td class="coverage">153<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      ep$distimes[obs_var, c("DT50_k0")] = DT50_k0</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">248</td>
+                    <td class="coverage">153<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      ep$distimes[obs_var, c("DT50_kmax")] = DT50_kmax</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">249</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">250</td>
+                    <td class="coverage">73858<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    ep$distimes[obs_var, c("DT50", "DT90")] = c(DT50, DT90)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">251</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">252</td>
+                    <td class="coverage">38846<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (length(ep$ff) == 0) ep$ff &lt;- NULL</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">253</td>
+                    <td class="coverage">53592<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (length(ep$SFORB) == 0) ep$SFORB &lt;- NULL</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">254</td>
+                    <td class="coverage">56208<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  return(ep)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">255</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                </tbody>
+              </table>
+            </div>
+            <div id="R/mhmkin.R" class="hidden">
+              <table class="table-condensed">
+                <tbody>
+                  <tr class="never">
+                    <td class="num">1</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Fit nonlinear mixed-effects models built from one or more kinetic</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">2</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' degradation models and one or more error models</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">3</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">4</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' The name of the methods expresses that (**m**ultiple) **h**ierarchichal</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">5</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' (also known as multilevel) **m**ulticompartment **kin**etic models are</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">6</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' fitted. Our kinetic models are nonlinear, so we can use various nonlinear</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">7</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' mixed-effects model fitting functions.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">8</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">9</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param objects A list of [mmkin] objects containing fits of the same</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">10</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' degradation models to the same data, but using different error models.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">11</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Alternatively, a single [mmkin] object containing fits of several</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">12</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' degradation models to the same data</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">13</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param backend The backend to be used for fitting. Currently, only saemix is</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">14</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' supported</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">15</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param no_random_effect Default is NULL and will be passed to [saem]. If a</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">16</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' character vector is supplied, it will be passed to all calls to [saem],</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">17</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' which will exclude random effects for all matching parameters. Alternatively,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">18</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' a list of character vectors or an object of class [illparms.mhmkin] can be</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">19</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' specified. They have to have the same dimensions that the return object of</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">20</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' the current call will have, i.e. the number of rows must match the number</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">21</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' of degradation models in the mmkin object(s), and the number of columns must</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">22</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' match the number of error models used in the mmkin object(s).</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">23</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param algorithm The algorithm to be used for fitting (currently not used)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">24</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param \dots Further arguments that will be passed to the nonlinear mixed-effects</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">25</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' model fitting function.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">26</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param cores The number of cores to be used for multicore processing. This</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">27</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' is only used when the \code{cluster} argument is \code{NULL}. On Windows</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">28</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' machines, cores &gt; 1 is not supported, you need to use the \code{cluster}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">29</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' argument to use multiple logical processors. Per default, all cores detected</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">30</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' by [parallel::detectCores()] are used, except on Windows where the default</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">31</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' is 1.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">32</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param cluster A cluster as returned by [makeCluster] to be used for</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">33</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' parallel execution.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">34</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @importFrom parallel mclapply parLapply detectCores</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">35</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @return A two-dimensional [array] of fit objects and/or try-errors that can</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">36</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' be indexed using the degradation model names for the first index (row index)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">37</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' and the error model names for the second index (column index), with class</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">38</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' attribute 'mhmkin'.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">39</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @author Johannes Ranke</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">40</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @seealso \code{\link{[.mhmkin}} for subsetting [mhmkin] objects</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">41</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">42</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">mhmkin &lt;- function(objects, ...) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">43</td>
+                    <td class="coverage">375<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  UseMethod("mhmkin")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">44</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">45</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">46</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">47</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @rdname mhmkin</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">48</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">mhmkin.mmkin &lt;- function(objects, ...) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">49</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  mhmkin(list(objects), ...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">50</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">51</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">52</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">53</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @rdname mhmkin</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">54</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @examples</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">55</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' \dontrun{</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">56</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # We start with separate evaluations of all the first six datasets with two</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">57</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # degradation models and two error models</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">58</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' f_sep_const &lt;- mmkin(c("SFO", "FOMC"), ds_fomc[1:6], cores = 2, quiet = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">59</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' f_sep_tc &lt;- update(f_sep_const, error_model = "tc")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">60</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # The mhmkin function sets up hierarchical degradation models aka</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">61</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # nonlinear mixed-effects models for all four combinations, specifying</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">62</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # uncorrelated random effects for all degradation parameters</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">63</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' f_saem_1 &lt;- mhmkin(list(f_sep_const, f_sep_tc), cores = 2)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">64</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' status(f_saem_1)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">65</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # The 'illparms' function shows that in all hierarchical fits, at least</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">66</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # one random effect is ill-defined (the confidence interval for the</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">67</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # random effect expressed as standard deviation includes zero)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">68</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' illparms(f_saem_1)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">69</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # Therefore we repeat the fits, excluding the ill-defined random effects</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">70</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' f_saem_2 &lt;- update(f_saem_1, no_random_effect = illparms(f_saem_1))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">71</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' status(f_saem_2)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">72</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' illparms(f_saem_2)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">73</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # Model comparisons show that FOMC with two-component error is preferable,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">74</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # and confirms our reduction of the default parameter model</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">75</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' anova(f_saem_1)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">76</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' anova(f_saem_2)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">77</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # The convergence plot for the selected model looks fine</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">78</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' saemix::plot(f_saem_2[["FOMC", "tc"]]$so, plot.type = "convergence")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">79</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # The plot of predictions versus data shows that we have a pretty data-rich</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">80</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # situation with homogeneous distribution of residuals, because we used the</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">81</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # same degradation model, error model and parameter distribution model that</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">82</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # was used in the data generation.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">83</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' plot(f_saem_2[["FOMC", "tc"]])</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">84</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # We can specify the same parameter model reductions manually</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">85</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' no_ranef &lt;- list("parent_0", "log_beta", "parent_0", c("parent_0", "log_beta"))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">86</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' dim(no_ranef) &lt;- c(2, 2)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">87</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' f_saem_2m &lt;- update(f_saem_1, no_random_effect = no_ranef)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">88</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' anova(f_saem_2m)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">89</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">90</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">mhmkin.list &lt;- function(objects, backend = "saemix", algorithm = "saem",</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">91</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  no_random_effect = NULL,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">92</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  ...,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">93</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cores = if (Sys.info()["sysname"] == "Windows") 1 else parallel::detectCores(), cluster = NULL)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">94</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">{</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">95</td>
+                    <td class="coverage">375<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  call &lt;- match.call()</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">96</td>
+                    <td class="coverage">375<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  dot_args &lt;- list(...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">97</td>
+                    <td class="coverage">375<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  backend_function &lt;- switch(backend,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">98</td>
+                    <td class="coverage">375<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    saemix = "saem"</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">99</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  )</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">100</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">101</td>
+                    <td class="coverage">375<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  deg_models &lt;- lapply(objects[[1]][, 1], function(x) x$mkinmod)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">102</td>
+                    <td class="coverage">375<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  names(deg_models) &lt;- dimnames(objects[[1]])$model</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">103</td>
+                    <td class="coverage">375<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  n.deg &lt;- length(deg_models)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">104</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">105</td>
+                    <td class="coverage">375<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  ds &lt;- lapply(objects[[1]][1, ], function(x) x$data)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">106</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">107</td>
+                    <td class="coverage">375<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  for (other in objects[-1]) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">108</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # Check if the degradation models in all objects are the same</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">109</td>
+                    <td class="coverage">375<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    for (deg_model_name in names(deg_models)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">110</td>
+                    <td class="coverage">750<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      if (!all.equal(other[[deg_model_name, 1]]$mkinmod$spec,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">111</td>
+                    <td class="coverage">750<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            deg_models[[deg_model_name]]$spec))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">112</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">113</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        stop("The mmkin objects have to be based on the same degradation models")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">114</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">115</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">116</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # Check if they have been fitted to the same dataset</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">117</td>
+                    <td class="coverage">375<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    other_object_ds &lt;- lapply(other[1, ], function(x) x$data)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">118</td>
+                    <td class="coverage">375<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    for (i in 1:length(ds)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">119</td>
+                    <td class="coverage">2250<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      if (!all.equal(ds[[i]][c("time", "variable", "observed")],</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">120</td>
+                    <td class="coverage">2250<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          other_object_ds[[i]][c("time", "variable", "observed")]))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">121</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">122</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        stop("The mmkin objects have to be fitted to the same datasets")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">123</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">124</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">125</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">126</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">127</td>
+                    <td class="coverage">375<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  n.o &lt;- length(objects)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">128</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">129</td>
+                    <td class="coverage">375<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  error_models = sapply(objects, function(x) x[[1]]$err_mod)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">130</td>
+                    <td class="coverage">375<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  n.e &lt;- length(error_models)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">131</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">132</td>
+                    <td class="coverage">375<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  n.fits &lt;- n.deg * n.e</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">133</td>
+                    <td class="coverage">375<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  fit_indices &lt;- matrix(1:n.fits, ncol = n.e)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">134</td>
+                    <td class="coverage">375<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  dimnames(fit_indices) &lt;- list(degradation = names(deg_models),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">135</td>
+                    <td class="coverage">375<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                                error = error_models)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">136</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">137</td>
+                    <td class="coverage">375<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (is.null(no_random_effect) || is.null(dim(no_random_effect))) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">138</td>
+                    <td class="coverage">129<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    no_ranef &lt;- rep(list(no_random_effect), n.fits)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">139</td>
+                    <td class="coverage">129<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    dim(no_ranef) &lt;- dim(fit_indices)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">140</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">141</td>
+                    <td class="coverage">246<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (!identical(dim(no_random_effect), dim(fit_indices))) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">142</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      stop("Dimensions of argument 'no_random_effect' are not suitable")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">143</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">144</td>
+                    <td class="coverage">246<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (is(no_random_effect, "illparms.mhmkin")) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">145</td>
+                    <td class="coverage">125<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      no_ranef_dim &lt;- dim(no_random_effect)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">146</td>
+                    <td class="coverage">125<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      no_ranef &lt;- lapply(no_random_effect, function(x) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">147</td>
+                    <td class="coverage">500<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        no_ranef_split &lt;- strsplit(x, ", ")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">148</td>
+                    <td class="coverage">500<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        ret &lt;- sapply(no_ranef_split, function(y) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">149</td>
+                    <td class="coverage">500<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          gsub("sd\\((.*)\\)", "\\1", y)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">150</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        })</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">151</td>
+                    <td class="coverage">500<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        return(ret)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">152</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      })</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">153</td>
+                    <td class="coverage">125<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      dim(no_ranef) &lt;- no_ranef_dim</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">154</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">155</td>
+                    <td class="coverage">121<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      no_ranef &lt;- no_random_effect</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">156</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">157</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">158</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">159</td>
+                    <td class="coverage">375<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  fit_function &lt;- function(fit_index) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">160</td>
+                    <td class="coverage">12<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    w &lt;- which(fit_indices == fit_index, arr.ind = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">161</td>
+                    <td class="coverage">12<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    deg_index &lt;- w[1]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">162</td>
+                    <td class="coverage">12<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    error_index &lt;- w[2]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">163</td>
+                    <td class="coverage">12<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    mmkin_row &lt;- objects[[error_index]][deg_index, ]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">164</td>
+                    <td class="coverage">12<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    res &lt;- try(do.call(backend_function,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">165</td>
+                    <td class="coverage">12<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        args = c(</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">166</td>
+                    <td class="coverage">12<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          list(mmkin_row),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">167</td>
+                    <td class="coverage">12<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          dot_args,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">168</td>
+                    <td class="coverage">12<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          list(no_random_effect = no_ranef[[deg_index, error_index]]))))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">169</td>
+                    <td class="coverage">12<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    return(res)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">170</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">171</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">172</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">173</td>
+                    <td class="coverage">375<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  fit_time &lt;- system.time({</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">174</td>
+                    <td class="coverage">375<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (is.null(cluster)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">175</td>
+                    <td class="coverage">375<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      results &lt;- parallel::mclapply(as.list(1:n.fits), fit_function,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">176</td>
+                    <td class="coverage">375<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        mc.cores = cores, mc.preschedule = FALSE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">177</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">178</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      results &lt;- parallel::parLapply(cluster, as.list(1:n.fits), fit_function)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">179</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">180</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  })</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">181</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">182</td>
+                    <td class="coverage">363<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  attributes(results) &lt;- attributes(fit_indices)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">183</td>
+                    <td class="coverage">363<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  attr(results, "call") &lt;- call</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">184</td>
+                    <td class="coverage">363<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  attr(results, "time") &lt;- fit_time</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">185</td>
+                    <td class="coverage">363<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  class(results) &lt;- switch(backend,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">186</td>
+                    <td class="coverage">363<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    saemix = c("mhmkin.saem.mmkin", "mhmkin")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">187</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  )</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">188</td>
+                    <td class="coverage">363<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  return(results)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">189</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">190</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">191</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Subsetting method for mhmkin objects</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">192</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">193</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param x An [mhmkin] object.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">194</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param i Row index selecting the fits for specific models</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">195</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param j Column index selecting the fits to specific datasets</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">196</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param drop If FALSE, the method always returns an mhmkin object, otherwise</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">197</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   either a list of fit objects or a single fit object.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">198</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @return An object inheriting from \code{\link{mhmkin}}.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">199</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @rdname mhmkin</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">200</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">201</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">`[.mhmkin` &lt;- function(x, i, j, ..., drop = FALSE) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">202</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  original_class &lt;- class(x)</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">203</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  class(x) &lt;- NULL</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">204</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  x_sub &lt;- x[i, j, drop = drop]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">205</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">206</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (!drop) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">207</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    class(x_sub) &lt;- original_class</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">208</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">209</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  return(x_sub)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">210</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">211</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">212</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Print method for mhmkin objects</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">213</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">214</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @rdname mhmkin</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">215</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">216</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">print.mhmkin &lt;- function(x, ...) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">217</td>
+                    <td class="coverage">125<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("&lt;mhmkin&gt; object\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">218</td>
+                    <td class="coverage">125<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("Status of individual fits:\n\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">219</td>
+                    <td class="coverage">125<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  print(status(x))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">220</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">221</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">222</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Check if fit within an mhmkin object failed</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">223</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param x The object to be checked</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">224</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">check_failed &lt;- function(x) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">225</td>
+                    <td class="coverage">1936<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (inherits(x, "try-error")) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">226</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    return(TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">227</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">228</td>
+                    <td class="coverage">1936<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (inherits(x$so, "try-error")) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">229</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      return(TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">230</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">231</td>
+                    <td class="coverage">1936<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      return(FALSE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">232</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">233</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">234</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">235</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">236</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">237</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">AIC.mhmkin &lt;- function(object, ..., k = 2) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">238</td>
+                    <td class="coverage">125<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  res &lt;- sapply(object, function(x) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">239</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (check_failed(x)) return(NA)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">240</td>
+                    <td class="coverage">500<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    else return(AIC(x$so, k = k))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">241</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  })</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">242</td>
+                    <td class="coverage">125<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  dim(res) &lt;- dim(object)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">243</td>
+                    <td class="coverage">125<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  dimnames(res) &lt;- dimnames(object)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">244</td>
+                    <td class="coverage">125<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  return(res)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">245</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">246</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">247</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">248</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">BIC.mhmkin &lt;- function(object, ...) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">249</td>
+                    <td class="coverage">125<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  res &lt;- sapply(object, function(x) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">250</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (check_failed(x)) return(NA)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">251</td>
+                    <td class="coverage">500<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    else return(BIC(x$so))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">252</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  })</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">253</td>
+                    <td class="coverage">125<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  dim(res) &lt;- dim(object)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">254</td>
+                    <td class="coverage">125<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  dimnames(res) &lt;- dimnames(object)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">255</td>
+                    <td class="coverage">125<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  return(res)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">256</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">257</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">258</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">259</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">update.mhmkin &lt;- function(object, ..., evaluate = TRUE) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">260</td>
+                    <td class="coverage">246<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  call &lt;- attr(object, "call")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">261</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # For some reason we get mhkin.list in call[[1]] when using mhmkin from the</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">262</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # loaded package so we need to fix this so we do not have to export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">263</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # mhmkin.list in addition to the S3 method mhmkin</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">264</td>
+                    <td class="coverage">246<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  call[[1]] &lt;- mhmkin</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">265</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">266</td>
+                    <td class="coverage">246<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  update_arguments &lt;- match.call(expand.dots = FALSE)$...</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">267</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">268</td>
+                    <td class="coverage">246<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (length(update_arguments) &gt; 0) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">269</td>
+                    <td class="coverage">246<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    update_arguments_in_call &lt;- !is.na(match(names(update_arguments), names(call)))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">270</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">271</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">272</td>
+                    <td class="coverage">246<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  for (a in names(update_arguments)[update_arguments_in_call]) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">273</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    call[[a]] &lt;- update_arguments[[a]]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">274</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">275</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">276</td>
+                    <td class="coverage">246<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  update_arguments_not_in_call &lt;- !update_arguments_in_call</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">277</td>
+                    <td class="coverage">246<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if(any(update_arguments_not_in_call)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">278</td>
+                    <td class="coverage">246<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    call &lt;- c(as.list(call), update_arguments[update_arguments_not_in_call])</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">279</td>
+                    <td class="coverage">246<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    call &lt;- as.call(call)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">280</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">281</td>
+                    <td class="coverage">246<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if(evaluate) eval(call, parent.frame())</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">282</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  else call</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">283</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">284</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">285</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">286</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">anova.mhmkin &lt;- function(object, ...,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">287</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  method = c("is", "lin", "gq"), test = FALSE, model.names = "auto") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">288</td>
+                    <td class="coverage">234<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (identical(model.names, "auto")) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">289</td>
+                    <td class="coverage">234<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    model.names &lt;- outer(rownames(object), colnames(object), paste)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">290</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">291</td>
+                    <td class="coverage">234<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  failed_index &lt;- which(sapply(object, check_failed), arr.ind = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">292</td>
+                    <td class="coverage">234<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (length(failed_index &gt; 0)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">293</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    rlang::inject(anova(!!!(object[-failed_index]), method = method, test = test,</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">294</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      model.names = model.names[-failed_index]))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">295</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">296</td>
+                    <td class="coverage">234<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    rlang::inject(anova(!!!(object), method = method, test = test,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">297</td>
+                    <td class="coverage">234<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      model.names = model.names))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">298</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">299</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">300</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                </tbody>
+              </table>
+            </div>
+            <div id="R/mkinerrplot.R" class="hidden">
+              <table class="table-condensed">
+                <tbody>
+                  <tr class="never">
+                    <td class="num">1</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">utils::globalVariables(c("variable", "residual"))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">2</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">3</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Function to plot squared residuals and the error model for an mkin object</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">4</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' </pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">5</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' This function plots the squared residuals for the specified subset of the</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">6</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' observed variables from an mkinfit object. In addition, one or more dashed</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">7</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' line(s) show the fitted error model.  A combined plot of the fitted model</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">8</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' and this error model plot can be obtained with \code{\link{plot.mkinfit}}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">9</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' using the argument \code{show_errplot = TRUE}.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">10</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' </pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">11</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param object A fit represented in an \code{\link{mkinfit}} object.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">12</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param obs_vars A character vector of names of the observed variables for</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">13</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   which residuals should be plotted. Defaults to all observed variables in</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">14</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   the model</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">15</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param xlim plot range in x direction.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">16</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param xlab Label for the x axis.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">17</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param ylab Label for the y axis.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">18</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param maxy Maximum value of the residuals. This is used for the scaling of</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">19</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   the y axis and defaults to "auto".</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">20</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param legend Should a legend be plotted?</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">21</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param lpos Where should the legend be placed? Default is "topright". Will</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">22</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   be passed on to \code{\link{legend}}.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">23</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param col_obs Colors for the observed variables.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">24</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param pch_obs Symbols to be used for the observed variables.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">25</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param frame Should a frame be drawn around the plots?</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">26</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param \dots further arguments passed to \code{\link{plot}}.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">27</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @return Nothing is returned by this function, as it is called for its side</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">28</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   effect, namely to produce a plot.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">29</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @author Johannes Ranke</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">30</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @seealso \code{\link{mkinplot}}, for a way to plot the data and the fitted</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">31</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   lines of the mkinfit object.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">32</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @keywords hplot</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">33</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @examples</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">34</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' </pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">35</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' \dontrun{</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">36</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' model &lt;- mkinmod(parent = mkinsub("SFO", "m1"), m1 = mkinsub("SFO"))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">37</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' fit &lt;- mkinfit(model, FOCUS_2006_D, error_model = "tc", quiet = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">38</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' mkinerrplot(fit)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">39</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">40</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' </pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">41</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">42</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">mkinerrplot &lt;- function (object,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">43</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  obs_vars = names(object$mkinmod$map),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">44</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  xlim = c(0, 1.1 * max(object$data$predicted)),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">45</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  xlab = "Predicted", ylab = "Squared residual",</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">46</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  maxy = "auto", legend= TRUE, lpos = "topright",</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">47</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  col_obs = "auto", pch_obs = "auto",</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">48</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  frame = TRUE,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">49</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  ...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">50</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">{</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">51</td>
+                    <td class="coverage">275<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  obs_vars_all &lt;- as.character(unique(object$data$variable))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">52</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">53</td>
+                    <td class="coverage">275<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (length(obs_vars) &gt; 0){</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">54</td>
+                    <td class="coverage">275<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      obs_vars &lt;- intersect(obs_vars_all, obs_vars)</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">55</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  } else obs_vars &lt;- obs_vars_all</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">56</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">57</td>
+                    <td class="coverage">275<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  residuals &lt;- subset(object$data, variable %in% obs_vars, residual)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">58</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">59</td>
+                    <td class="coverage">275<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (maxy == "auto") maxy = max(residuals^2, na.rm = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">60</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">61</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Set colors and symbols</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">62</td>
+                    <td class="coverage">275<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (col_obs[1] == "auto") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">63</td>
+                    <td class="coverage">70<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    col_obs &lt;- 1:length(obs_vars)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">64</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">65</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">66</td>
+                    <td class="coverage">275<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (pch_obs[1] == "auto") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">67</td>
+                    <td class="coverage">70<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    pch_obs &lt;- 1:length(obs_vars)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">68</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">69</td>
+                    <td class="coverage">275<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  names(col_obs) &lt;- names(pch_obs) &lt;- obs_vars</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">70</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">71</td>
+                    <td class="coverage">275<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  plot(0, type = "n",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">72</td>
+                    <td class="coverage">275<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">       xlab = xlab, ylab = ylab,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">73</td>
+                    <td class="coverage">275<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">       xlim = xlim,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">74</td>
+                    <td class="coverage">275<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">       ylim = c(0, 1.2 * maxy), frame = frame, ...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">75</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">76</td>
+                    <td class="coverage">275<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  for(obs_var in obs_vars){</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">77</td>
+                    <td class="coverage">410<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    residuals_plot &lt;- subset(object$data, variable == obs_var, c("predicted", "residual"))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">78</td>
+                    <td class="coverage">410<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    points(residuals_plot[["predicted"]],</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">79</td>
+                    <td class="coverage">410<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">           residuals_plot[["residual"]]^2,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">80</td>
+                    <td class="coverage">410<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">           pch = pch_obs[obs_var], col = col_obs[obs_var])</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">81</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">82</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">83</td>
+                    <td class="coverage">275<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (object$err_mod == "const") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">84</td>
+                    <td class="coverage">140<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    abline(h = object$errparms^2, lty = 2, col = 1)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">85</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">86</td>
+                    <td class="coverage">275<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (object$err_mod == "obs") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">87</td>
+                    <td class="coverage">65<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    for (obs_var in obs_vars) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">88</td>
+                    <td class="coverage">130<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      sigma_name = paste0("sigma_", obs_var)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">89</td>
+                    <td class="coverage">130<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      abline(h = object$errparms[sigma_name]^2, lty = 2,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">90</td>
+                    <td class="coverage">130<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">             col = col_obs[obs_var])</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">91</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">92</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">93</td>
+                    <td class="coverage">275<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (object$err_mod == "tc") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">94</td>
+                    <td class="coverage">70<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    sigma_plot &lt;- function(predicted) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">95</td>
+                    <td class="coverage">70<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      sigma_twocomp(predicted,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">96</td>
+                    <td class="coverage">70<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                    sigma_low = object$errparms[1],</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">97</td>
+                    <td class="coverage">70<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                    rsd_high = object$errparms[2])^2</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">98</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">99</td>
+                    <td class="coverage">70<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    plot(sigma_plot, from = 0, to = max(object$data$predicted),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">100</td>
+                    <td class="coverage">70<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">         add = TRUE, lty = 2, col = 1)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">101</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">102</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">103</td>
+                    <td class="coverage">275<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (legend == TRUE) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">104</td>
+                    <td class="coverage">70<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    legend(lpos, inset = c(0.05, 0.05), legend = obs_vars,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">105</td>
+                    <td class="coverage">70<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      col = col_obs[obs_vars], pch = pch_obs[obs_vars])</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">106</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">107</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                </tbody>
+              </table>
+            </div>
+            <div id="R/mkinfit.R" class="hidden">
+              <table class="table-condensed">
+                <tbody>
+                  <tr class="never">
+                    <td class="num">1</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">utils::globalVariables(c("name", "time", "value"))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">2</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">3</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Fit a kinetic model to data with one or more state variables</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">4</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">5</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' This function maximises the likelihood of the observed data using the Port</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">6</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' algorithm [stats::nlminb()], and the specified initial or fixed</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">7</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' parameters and starting values.  In each step of the optimisation, the</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">8</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' kinetic model is solved using the function [mkinpredict()], except</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">9</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' if an analytical solution is implemented, in which case the model is solved</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">10</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' using the degradation function in the [mkinmod] object. The</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">11</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' parameters of the selected error model are fitted simultaneously with the</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">12</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' degradation model parameters, as both of them are arguments of the</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">13</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' likelihood function.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">14</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">15</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Per default, parameters in the kinetic models are internally transformed in</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">16</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' order to better satisfy the assumption of a normal distribution of their</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">17</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' estimators.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">18</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">19</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param mkinmod A list of class [mkinmod], containing the kinetic</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">20</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   model to be fitted to the data, or one of the shorthand names ("SFO",</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">21</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   "FOMC", "DFOP", "HS", "SFORB", "IORE"). If a shorthand name is given, a</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">22</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   parent only degradation model is generated for the variable with the</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">23</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   highest value in \code{observed}.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">24</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param observed A dataframe or an object coercible to a dataframe</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">25</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   (e.g. a \code{tibble}) with the observed data.  The first column called</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">26</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   "name" must contain the name of the observed variable for each data point.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">27</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   The second column must contain the times of observation, named "time".</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">28</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   The third column must be named "value" and contain the observed values.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">29</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   Zero values in the "value" column will be removed, with a warning, in</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">30</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   order to avoid problems with fitting the two-component error model. This</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">31</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   is not expected to be a problem, because in general, values of zero are</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">32</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   not observed in degradation data, because there is a lower limit of</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">33</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   detection.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">34</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param parms.ini A named vector of initial values for the parameters,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">35</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   including parameters to be optimised and potentially also fixed parameters</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">36</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   as indicated by \code{fixed_parms}.  If set to "auto", initial values for</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">37</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   rate constants are set to default values.  Using parameter names that are</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">38</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   not in the model gives an error.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">39</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">40</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   It is possible to only specify a subset of the parameters that the model</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">41</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   needs. You can use the parameter lists "bparms.ode" from a previously</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">42</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   fitted model, which contains the differential equation parameters from</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">43</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   this model.  This works nicely if the models are nested. An example is</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">44</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   given below.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">45</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param state.ini A named vector of initial values for the state variables of</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">46</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   the model. In case the observed variables are represented by more than one</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">47</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   model variable, the names will differ from the names of the observed</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">48</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   variables (see \code{map} component of [mkinmod]). The default</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">49</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   is to set the initial value of the first model variable to the mean of the</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">50</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   time zero values for the variable with the maximum observed value, and all</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">51</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   others to 0.  If this variable has no time zero observations, its initial</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">52</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   value is set to 100.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">53</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param err.ini A named vector of initial values for the error model</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">54</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   parameters to be optimised.  If set to "auto", initial values are set to</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">55</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   default values.  Otherwise, inital values for all error model parameters</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">56</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   must be given.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">57</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param fixed_parms The names of parameters that should not be optimised but</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">58</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   rather kept at the values specified in \code{parms.ini}. Alternatively,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">59</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   a named numeric vector of parameters to be fixed, regardless of the values</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">60</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   in parms.ini.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">61</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param fixed_initials The names of model variables for which the initial</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">62</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   state at time 0 should be excluded from the optimisation. Defaults to all</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">63</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   state variables except for the first one.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">64</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param from_max_mean If this is set to TRUE, and the model has only one</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">65</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   observed variable, then data before the time of the maximum observed value</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">66</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   (after averaging for each sampling time) are discarded, and this time is</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">67</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   subtracted from all remaining time values, so the time of the maximum</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">68</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   observed mean value is the new time zero.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">69</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param solution_type If set to "eigen", the solution of the system of</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">70</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   differential equations is based on the spectral decomposition of the</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">71</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   coefficient matrix in cases that this is possible. If set to "deSolve", a</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">72</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   numerical [ode solver from package deSolve][deSolve::ode()] is used. If</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">73</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   set to "analytical", an analytical solution of the model is used. This is</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">74</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   only implemented for relatively simple degradation models.  The default is</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">75</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   "auto", which uses "analytical" if possible, otherwise "deSolve" if a</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">76</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   compiler is present, and "eigen" if no compiler is present and the model</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">77</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   can be expressed using eigenvalues and eigenvectors.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">78</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param method.ode The solution method passed via [mkinpredict()]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">79</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   to [deSolve::ode()] in case the solution type is "deSolve". The default</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">80</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   "lsoda" is performant, but sometimes fails to converge.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">81</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param use_compiled If set to \code{FALSE}, no compiled version of the</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">82</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   [mkinmod] model is used in the calls to [mkinpredict()] even if a compiled</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">83</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   version is present.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">84</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param control A list of control arguments passed to [stats::nlminb()].</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">85</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param transform_rates Boolean specifying if kinetic rate constants should</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">86</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   be transformed in the model specification used in the fitting for better</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">87</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   compliance with the assumption of normal distribution of the estimator. If</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">88</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   TRUE, also alpha and beta parameters of the FOMC model are</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">89</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   log-transformed, as well as k1 and k2 rate constants for the DFOP and HS</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">90</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   models and the break point tb of the HS model.  If FALSE, zero is used as</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">91</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   a lower bound for the rates in the optimisation.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">92</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param transform_fractions Boolean specifying if formation fractions</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">93</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   should be transformed in the model specification used in the fitting for</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">94</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   better compliance with the assumption of normal distribution of the</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">95</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   estimator. The default (TRUE) is to do transformations. If TRUE,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">96</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   the g parameter of the DFOP model is also transformed. Transformations</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">97</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   are described in [transform_odeparms].</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">98</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param quiet Suppress printing out the current value of the negative</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">99</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   log-likelihood after each improvement?</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">100</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param atol Absolute error tolerance, passed to [deSolve::ode()]. Default</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">101</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   is 1e-8, which is lower than the default in the [deSolve::lsoda()]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">102</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   function which is used per default.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">103</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param rtol Absolute error tolerance, passed to [deSolve::ode()]. Default</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">104</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   is 1e-10, much lower than in [deSolve::lsoda()].</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">105</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param error_model If the error model is "const", a constant standard</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">106</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   deviation is assumed.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">107</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">108</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   If the error model is "obs", each observed variable is assumed to have its</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">109</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   own variance.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">110</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">111</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   If the error model is "tc" (two-component error model), a two component</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">112</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   error model similar to the one described by Rocke and Lorenzato (1995) is</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">113</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   used for setting up the likelihood function.  Note that this model</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">114</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   deviates from the model by Rocke and Lorenzato, as their model implies</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">115</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   that the errors follow a lognormal distribution for large values, not a</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">116</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   normal distribution as assumed by this method.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">117</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param error_model_algorithm If "auto", the selected algorithm depends on</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">118</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   the error model.  If the error model is "const", unweighted nonlinear</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">119</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   least squares fitting ("OLS") is selected. If the error model is "obs", or</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">120</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   "tc", the "d_3" algorithm is selected.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">121</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">122</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   The algorithm "d_3" will directly minimize the negative log-likelihood</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">123</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   and independently also use the three step algorithm described below.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">124</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   The fit with the higher likelihood is returned.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">125</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">126</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   The algorithm "direct" will directly minimize the negative log-likelihood.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">127</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">128</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   The algorithm "twostep" will minimize the negative log-likelihood after an</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">129</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   initial unweighted least squares optimisation step.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">130</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">131</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   The algorithm "threestep" starts with unweighted least squares, then</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">132</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   optimizes only the error model using the degradation model parameters</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">133</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   found, and then minimizes the negative log-likelihood with free</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">134</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   degradation and error model parameters.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">135</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">136</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   The algorithm "fourstep" starts with unweighted least squares, then</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">137</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   optimizes only the error model using the degradation model parameters</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">138</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   found, then optimizes the degradation model again with fixed error model</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">139</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   parameters, and finally minimizes the negative log-likelihood with free</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">140</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   degradation and error model parameters.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">141</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">142</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   The algorithm "IRLS" (Iteratively Reweighted Least Squares) starts with</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">143</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   unweighted least squares, and then iterates optimization of the error</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">144</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   model parameters and subsequent optimization of the degradation model</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">145</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   using those error model parameters, until the error model parameters</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">146</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   converge.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">147</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param reweight.tol Tolerance for the convergence criterion calculated from</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">148</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   the error model parameters in IRLS fits.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">149</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param reweight.max.iter Maximum number of iterations in IRLS fits.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">150</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param trace_parms Should a trace of the parameter values be listed?</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">151</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param test_residuals Should the residuals be tested for normal distribution?</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">152</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param \dots Further arguments that will be passed on to</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">153</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   [deSolve::ode()].</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">154</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @importFrom stats nlminb aggregate dist shapiro.test</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">155</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @return A list with "mkinfit" in the class attribute.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">156</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @note When using the "IORE" submodel for metabolites, fitting with</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">157</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   "transform_rates = TRUE" (the default) often leads to failures of the</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">158</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   numerical ODE solver. In this situation it may help to switch off the</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">159</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   internal rate transformation.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">160</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @author Johannes Ranke</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">161</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @seealso [summary.mkinfit], [plot.mkinfit], [parms] and [lrtest].</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">162</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">163</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   Comparisons of models fitted to the same data can be made using</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">164</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   \code{\link{AIC}} by virtue of the method \code{\link{logLik.mkinfit}}.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">165</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">166</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   Fitting of several models to several datasets in a single call to</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">167</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   \code{\link{mmkin}}.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">168</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @references Rocke DM and Lorenzato S (1995) A two-component model</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">169</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   for measurement error in analytical chemistry. *Technometrics* 37(2), 176-184.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">170</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">171</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   Ranke J and Meinecke S (2019) Error Models for the Kinetic Evaluation of Chemical</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">172</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   Degradation Data. *Environments* 6(12) 124</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">173</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   \doi{10.3390/environments6120124}.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">174</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @examples</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">175</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">176</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # Use shorthand notation for parent only degradation</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">177</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' fit &lt;- mkinfit("FOMC", FOCUS_2006_C, quiet = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">178</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' summary(fit)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">179</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">180</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # One parent compound, one metabolite, both single first order.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">181</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # We remove zero values from FOCUS dataset D in order to avoid warnings</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">182</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' FOCUS_D &lt;- subset(FOCUS_2006_D, value != 0)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">183</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # Use mkinsub for convenience in model formulation. Pathway to sink included per default.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">184</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' SFO_SFO &lt;- mkinmod(</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">185</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   parent = mkinsub("SFO", "m1"),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">186</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   m1 = mkinsub("SFO"))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">187</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">188</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # Fit the model quietly to the FOCUS example dataset D using defaults</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">189</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' fit &lt;- mkinfit(SFO_SFO, FOCUS_D, quiet = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">190</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' plot_sep(fit)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">191</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # As lower parent values appear to have lower variance, we try an alternative error model</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">192</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' fit.tc &lt;- mkinfit(SFO_SFO, FOCUS_D, quiet = TRUE, error_model = "tc")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">193</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # This avoids the warning, and the likelihood ratio test confirms it is preferable</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">194</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' lrtest(fit.tc, fit)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">195</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # We can also allow for different variances of parent and metabolite as error model</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">196</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' fit.obs &lt;- mkinfit(SFO_SFO, FOCUS_D, quiet = TRUE, error_model = "obs")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">197</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # The two-component error model has significantly higher likelihood</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">198</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' lrtest(fit.obs, fit.tc)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">199</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' parms(fit.tc)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">200</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' endpoints(fit.tc)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">201</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">202</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # We can show a quick (only one replication) benchmark for this case, as we</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">203</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # have several alternative solution methods for the model. We skip</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">204</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # uncompiled deSolve, as it is so slow. More benchmarks are found in the</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">205</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # benchmark vignette</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">206</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' \dontrun{</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">207</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' if(require(rbenchmark)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">208</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   benchmark(replications = 1, order = "relative", columns = c("test", "relative", "elapsed"),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">209</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     deSolve_compiled = mkinfit(SFO_SFO, FOCUS_D, quiet = TRUE, error_model = "tc",</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">210</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'       solution_type = "deSolve", use_compiled = TRUE),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">211</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     eigen = mkinfit(SFO_SFO, FOCUS_D, quiet = TRUE, error_model = "tc",</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">212</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'       solution_type = "eigen"),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">213</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     analytical = mkinfit(SFO_SFO, FOCUS_D, quiet = TRUE, error_model = "tc",</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">214</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'       solution_type = "analytical"))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">215</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">216</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">217</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">218</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # Use stepwise fitting, using optimised parameters from parent only fit, FOMC-SFO</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">219</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' \dontrun{</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">220</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' FOMC_SFO &lt;- mkinmod(</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">221</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   parent = mkinsub("FOMC", "m1"),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">222</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   m1 = mkinsub("SFO"))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">223</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' fit.FOMC_SFO &lt;- mkinfit(FOMC_SFO, FOCUS_D, quiet = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">224</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # Again, we get a warning and try a more sophisticated error model</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">225</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' fit.FOMC_SFO.tc &lt;- mkinfit(FOMC_SFO, FOCUS_D, quiet = TRUE, error_model = "tc")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">226</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # This model has a higher likelihood, but not significantly so</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">227</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' lrtest(fit.tc, fit.FOMC_SFO.tc)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">228</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # Also, the missing standard error for log_beta and the t-tests for alpha</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">229</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # and beta indicate overparameterisation</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">230</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' summary(fit.FOMC_SFO.tc, data = FALSE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">231</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">232</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # We can easily use starting parameters from the parent only fit (only for illustration)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">233</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' fit.FOMC = mkinfit("FOMC", FOCUS_2006_D, quiet = TRUE, error_model = "tc")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">234</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' fit.FOMC_SFO &lt;- mkinfit(FOMC_SFO, FOCUS_D, quiet = TRUE,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">235</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   parms.ini = fit.FOMC$bparms.ode, error_model = "tc")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">236</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">237</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">238</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">mkinfit &lt;- function(mkinmod, observed,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">239</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  parms.ini = "auto",</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">240</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  state.ini = "auto",</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">241</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  err.ini = "auto",</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">242</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  fixed_parms = NULL,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">243</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  fixed_initials = names(mkinmod$diffs)[-1],</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">244</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  from_max_mean = FALSE,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">245</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  solution_type = c("auto", "analytical", "eigen", "deSolve"),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">246</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  method.ode = "lsoda",</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">247</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  use_compiled = "auto",</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">248</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  control = list(eval.max = 300, iter.max = 200),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">249</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  transform_rates = TRUE,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">250</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  transform_fractions = TRUE,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">251</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  quiet = FALSE,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">252</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  atol = 1e-8, rtol = 1e-10,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">253</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  error_model = c("const", "obs", "tc"),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">254</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  error_model_algorithm = c("auto", "d_3", "direct", "twostep", "threestep", "fourstep", "IRLS", "OLS"),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">255</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  reweight.tol = 1e-8, reweight.max.iter = 10,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">256</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  trace_parms = FALSE,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">257</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  test_residuals = FALSE,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">258</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  ...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">259</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">{</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">260</td>
+                    <td class="coverage">9202<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  call &lt;- match.call()</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">261</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">262</td>
+                    <td class="coverage">9202<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  summary_warnings &lt;- character()</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">263</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">264</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Derive the name used for the model</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">265</td>
+                    <td class="coverage">9202<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (is.character(mkinmod)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">266</td>
+                    <td class="coverage">4009<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    mkinmod_name &lt;- mkinmod</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">267</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">268</td>
+                    <td class="coverage">5193<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (is.null(mkinmod$name)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">269</td>
+                    <td class="coverage">5071<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      mkinmod_name &lt;- deparse(substitute(mkinmod))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">270</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">271</td>
+                    <td class="coverage">18<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      mkinmod_name &lt;- mkinmod$name</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">272</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">273</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">274</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">275</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Check mkinmod and generate a model for the variable whith the highest value</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">276</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # if a suitable string is given</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">277</td>
+                    <td class="coverage">9098<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  parent_models_available = c("SFO", "FOMC", "DFOP", "HS", "SFORB", "IORE", "logistic")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">278</td>
+                    <td class="coverage">9098<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (!inherits(mkinmod, "mkinmod")) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">279</td>
+                    <td class="coverage">4009<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    presumed_parent_name = observed[which.max(observed$value), "name"]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">280</td>
+                    <td class="coverage">4009<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (mkinmod[[1]] %in% parent_models_available) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">281</td>
+                    <td class="coverage">3905<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      speclist &lt;- list(list(type = mkinmod, sink = TRUE))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">282</td>
+                    <td class="coverage">3905<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      names(speclist) &lt;- presumed_parent_name</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">283</td>
+                    <td class="coverage">3905<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      mkinmod &lt;- mkinmod(speclist = speclist, use_of_ff = "max")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">284</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">285</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      stop("Argument mkinmod must be of class mkinmod or a string containing one of\n  ",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">286</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">           paste(parent_models_available, collapse = ", "))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">287</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">288</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">289</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">290</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Get the names of the state variables in the model</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">291</td>
+                    <td class="coverage">8994<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  mod_vars &lt;- names(mkinmod$diffs)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">292</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">293</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Get the names of observed variables</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">294</td>
+                    <td class="coverage">8994<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  obs_vars &lt;- names(mkinmod$spec)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">295</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">296</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Coerce observed data to a dataframe</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">297</td>
+                    <td class="coverage">8994<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  observed &lt;- as.data.frame(observed)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">298</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">299</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Subset observed data with names of observed data in the model and remove NA values</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">300</td>
+                    <td class="coverage">8994<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  observed &lt;- subset(observed, name %in% obs_vars)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">301</td>
+                    <td class="coverage">8994<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  observed &lt;- subset(observed, !is.na(value))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">302</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">303</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Also remove zero values to avoid instabilities (e.g. of the 'tc' error model)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">304</td>
+                    <td class="coverage">8994<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (any(observed$value == 0)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">305</td>
+                    <td class="coverage">529<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    zero_warning &lt;- "Observations with value of zero were removed from the data"</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">306</td>
+                    <td class="coverage">529<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    summary_warnings &lt;- c(summary_warnings, Z = zero_warning)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">307</td>
+                    <td class="coverage">529<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    warning(zero_warning)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">308</td>
+                    <td class="coverage">529<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    observed &lt;- subset(observed, value != 0)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">309</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">310</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">311</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Sort observed values for efficient analytical predictions</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">312</td>
+                    <td class="coverage">8994<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  observed$name &lt;- ordered(observed$name, levels = obs_vars)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">313</td>
+                    <td class="coverage">8994<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  observed &lt;- observed[order(observed$name, observed$time), ]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">314</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">315</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Obtain data for decline from maximum mean value if requested</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">316</td>
+                    <td class="coverage">8994<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (from_max_mean) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">317</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # This is only used for simple decline models</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">318</td>
+                    <td class="coverage">459<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (length(obs_vars) &gt; 1)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">319</td>
+                    <td class="coverage">153<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      stop("Decline from maximum is only implemented for models with a single observed variable")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">320</td>
+                    <td class="coverage">306<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    observed$name &lt;- as.character(observed$name)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">321</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">322</td>
+                    <td class="coverage">306<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    means &lt;- aggregate(value ~ time, data = observed, mean, na.rm=TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">323</td>
+                    <td class="coverage">306<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    t_of_max &lt;- means[which.max(means$value), "time"]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">324</td>
+                    <td class="coverage">306<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    observed &lt;- subset(observed, time &gt;= t_of_max)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">325</td>
+                    <td class="coverage">306<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    observed$time &lt;- observed$time - t_of_max</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">326</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">327</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">328</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Number observations used for fitting</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">329</td>
+                    <td class="coverage">8841<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  n_observed &lt;- nrow(observed)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">330</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">331</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Define starting values for parameters where not specified by the user</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">332</td>
+                    <td class="coverage">8371<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (parms.ini[[1]] == "auto") parms.ini = vector()</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">333</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">334</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Override parms.ini for parameters given as a numeric vector in</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">335</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # fixed_parms</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">336</td>
+                    <td class="coverage">8841<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (is.numeric(fixed_parms)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">337</td>
+                    <td class="coverage">3<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    fixed_parm_names &lt;- names(fixed_parms)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">338</td>
+                    <td class="coverage">3<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    parms.ini[fixed_parm_names] &lt;- fixed_parms</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">339</td>
+                    <td class="coverage">3<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    fixed_parms &lt;- fixed_parm_names</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">340</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">341</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">342</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Warn for inital parameter specifications that are not in the model</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">343</td>
+                    <td class="coverage">8841<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  wrongpar.names &lt;- setdiff(names(parms.ini), mkinmod$parms)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">344</td>
+                    <td class="coverage">8841<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (length(wrongpar.names) &gt; 0) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">345</td>
+                    <td class="coverage">257<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    warning("Initial parameter(s) ", paste(wrongpar.names, collapse = ", "),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">346</td>
+                    <td class="coverage">257<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">         " not used in the model")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">347</td>
+                    <td class="coverage">257<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    parms.ini &lt;- parms.ini[setdiff(names(parms.ini), wrongpar.names)]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">348</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">349</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">350</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Warn that the sum of formation fractions may exceed one if they are not</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">351</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # fitted in the transformed way</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">352</td>
+                    <td class="coverage">8841<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (mkinmod$use_of_ff == "max" &amp; transform_fractions == FALSE) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">353</td>
+                    <td class="coverage">410<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    warning("The sum of formation fractions may exceed one if you do not use ",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">354</td>
+                    <td class="coverage">410<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            "transform_fractions = TRUE." )</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">355</td>
+                    <td class="coverage">410<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    for (box in mod_vars) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">356</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      # Stop if formation fractions are not transformed and we have no sink</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">357</td>
+                    <td class="coverage">716<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      if (mkinmod$spec[[box]]$sink == FALSE) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">358</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        stop("If formation fractions are not transformed during the fitting, ",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">359</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">             "it is not supported to turn off pathways to sink.\n ",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">360</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">             "Consider turning on the transformation of formation fractions or ",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">361</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">             "setting up a model with use_of_ff = 'min'.\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">362</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">363</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">364</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">365</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">366</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Do not allow fixing formation fractions if we are using the ilr transformation,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">367</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # this is not supported</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">368</td>
+                    <td class="coverage">8737<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (transform_fractions == TRUE &amp;&amp; length(fixed_parms) &gt; 0) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">369</td>
+                    <td class="coverage">107<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (any(grepl("^f_", fixed_parms))) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">370</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      stop("Fixing formation fractions is not supported when using the ilr ",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">371</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">           "transformation.")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">372</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">373</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">374</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">375</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Set initial parameter values, including a small increment (salt)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">376</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # to avoid linear dependencies (singular matrix) in Eigenvalue based solutions</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">377</td>
+                    <td class="coverage">8633<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  k_salt = 0</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">378</td>
+                    <td class="coverage">8633<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  defaultpar.names &lt;- setdiff(mkinmod$parms, names(parms.ini))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">379</td>
+                    <td class="coverage">8633<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  for (parmname in defaultpar.names) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">380</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # Default values for rate constants, depending on the parameterisation</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">381</td>
+                    <td class="coverage">20999<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (grepl("^k", parmname)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">382</td>
+                    <td class="coverage">15908<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      parms.ini[parmname] = 0.1 + k_salt</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">383</td>
+                    <td class="coverage">15908<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      k_salt = k_salt + 1e-4</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">384</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">385</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # Default values for rate constants for reversible binding</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">386</td>
+                    <td class="coverage">26<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (grepl("free_bound$", parmname)) parms.ini[parmname] = 0.1</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">387</td>
+                    <td class="coverage">26<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (grepl("bound_free$", parmname)) parms.ini[parmname] = 0.02</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">388</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # Default values for IORE exponents</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">389</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (grepl("^N", parmname)) parms.ini[parmname] = 1.1</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">390</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # Default values for the FOMC, DFOP and HS models</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">391</td>
+                    <td class="coverage">238<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (parmname == "alpha") parms.ini[parmname] = 1</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">392</td>
+                    <td class="coverage">238<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (parmname == "beta") parms.ini[parmname] = 10</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">393</td>
+                    <td class="coverage">1014<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (parmname == "k1") parms.ini[parmname] = 0.1</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">394</td>
+                    <td class="coverage">1013<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (parmname == "k2") parms.ini[parmname] = 0.01</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">395</td>
+                    <td class="coverage">30<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (parmname == "tb") parms.ini[parmname] = 5</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">396</td>
+                    <td class="coverage">984<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (parmname == "g") parms.ini[parmname] = 0.5</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">397</td>
+                    <td class="coverage">153<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (parmname == "kmax") parms.ini[parmname] = 0.1</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">398</td>
+                    <td class="coverage">153<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (parmname == "k0") parms.ini[parmname] = 0.0001</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">399</td>
+                    <td class="coverage">153<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (parmname == "r") parms.ini[parmname] = 0.2</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">400</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">401</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Default values for formation fractions in case they are present</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">402</td>
+                    <td class="coverage">8633<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  for (obs_var in obs_vars) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">403</td>
+                    <td class="coverage">13865<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    origin &lt;- mkinmod$map[[obs_var]][[1]]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">404</td>
+                    <td class="coverage">13865<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    f_names &lt;- mkinmod$parms[grep(paste0("^f_", origin), mkinmod$parms)]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">405</td>
+                    <td class="coverage">13865<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (length(f_names) &gt; 0) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">406</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      # We need to differentiate between default and specified fractions</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">407</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      # and set the unspecified to 1 - sum(specified)/n_unspecified</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">408</td>
+                    <td class="coverage">3365<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      f_default_names &lt;- intersect(f_names, defaultpar.names)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">409</td>
+                    <td class="coverage">3365<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      f_specified_names &lt;- setdiff(f_names, defaultpar.names)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">410</td>
+                    <td class="coverage">3365<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      sum_f_specified = sum(parms.ini[f_specified_names])</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">411</td>
+                    <td class="coverage">3365<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      if (sum_f_specified &gt; 1) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">412</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        stop("Starting values for the formation fractions originating from ",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">413</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">             origin, " sum up to more than 1.")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">414</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">415</td>
+                    <td class="coverage">3260<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      if (mkinmod$spec[[obs_var]]$sink) n_unspecified = length(f_default_names) + 1</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">416</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">417</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        n_unspecified = length(f_default_names)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">418</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">419</td>
+                    <td class="coverage">3261<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      parms.ini[f_default_names] &lt;- (1 - sum_f_specified) / n_unspecified</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">420</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">421</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">422</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">423</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Set default for state.ini if appropriate</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">424</td>
+                    <td class="coverage">8529<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  parent_name = names(mkinmod$spec)[[1]]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">425</td>
+                    <td class="coverage">8529<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  parent_time_0 = subset(observed, time == 0 &amp; name == parent_name)$value</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">426</td>
+                    <td class="coverage">8529<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  parent_time_0_mean = mean(parent_time_0, na.rm = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">427</td>
+                    <td class="coverage">8529<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (is.na(parent_time_0_mean)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">428</td>
+                    <td class="coverage">2<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    state.ini_auto = c(100, rep(0, length(mkinmod$diffs) - 1))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">429</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">430</td>
+                    <td class="coverage">8527<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    state.ini_auto = c(parent_time_0_mean, rep(0, length(mkinmod$diffs) - 1))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">431</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">432</td>
+                    <td class="coverage">8529<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  names(state.ini_auto) &lt;- mod_vars</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">433</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">434</td>
+                    <td class="coverage">8529<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (state.ini[1] == "auto") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">435</td>
+                    <td class="coverage">8316<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    state.ini_used &lt;- state.ini_auto</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">436</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">437</td>
+                    <td class="coverage">213<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    state.ini_used &lt;- state.ini_auto</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">438</td>
+                    <td class="coverage">213<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    state.ini_good &lt;- intersect(names(mkinmod$diffs), names(state.ini))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">439</td>
+                    <td class="coverage">213<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    state.ini_used[state.ini_good] &lt;- state.ini[state.ini_good]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">440</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">441</td>
+                    <td class="coverage">8529<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  state.ini &lt;- state.ini_used</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">442</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">443</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Name the inital state variable values if they are not named yet</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">444</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if(is.null(names(state.ini))) names(state.ini) &lt;- mod_vars</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">445</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">446</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Transform initial parameter values for fitting</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">447</td>
+                    <td class="coverage">8529<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  transparms.ini &lt;- transform_odeparms(parms.ini, mkinmod,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">448</td>
+                    <td class="coverage">8529<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                                       transform_rates = transform_rates,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">449</td>
+                    <td class="coverage">8529<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                                       transform_fractions = transform_fractions)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">450</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">451</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Parameters to be optimised:</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">452</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Kinetic parameters in parms.ini whose names are not in fixed_parms</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">453</td>
+                    <td class="coverage">8529<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  parms.fixed &lt;- parms.ini[fixed_parms]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">454</td>
+                    <td class="coverage">8529<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  parms.optim &lt;- parms.ini[setdiff(names(parms.ini), fixed_parms)]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">455</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">456</td>
+                    <td class="coverage">8529<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  transparms.fixed &lt;- transform_odeparms(parms.fixed, mkinmod,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">457</td>
+                    <td class="coverage">8529<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                                       transform_rates = transform_rates,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">458</td>
+                    <td class="coverage">8529<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                                       transform_fractions = transform_fractions)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">459</td>
+                    <td class="coverage">8529<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  transparms.optim &lt;- transform_odeparms(parms.optim, mkinmod,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">460</td>
+                    <td class="coverage">8529<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                                       transform_rates = transform_rates,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">461</td>
+                    <td class="coverage">8529<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                                       transform_fractions = transform_fractions)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">462</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">463</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Inital state variables in state.ini whose names are not in fixed_initials</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">464</td>
+                    <td class="coverage">8529<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  state.ini.fixed &lt;- state.ini[fixed_initials]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">465</td>
+                    <td class="coverage">8529<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  state.ini.optim &lt;- state.ini[setdiff(names(state.ini), fixed_initials)]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">466</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">467</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Preserve names of state variables before renaming initial state variable</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">468</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # parameters</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">469</td>
+                    <td class="coverage">8529<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  state.ini.optim.boxnames &lt;- names(state.ini.optim)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">470</td>
+                    <td class="coverage">8529<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  state.ini.fixed.boxnames &lt;- names(state.ini.fixed)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">471</td>
+                    <td class="coverage">8529<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if(length(state.ini.optim) &gt; 0) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">472</td>
+                    <td class="coverage">8529<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    names(state.ini.optim) &lt;- paste(names(state.ini.optim), "0", sep="_")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">473</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">474</td>
+                    <td class="coverage">8529<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if(length(state.ini.fixed) &gt; 0) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">475</td>
+                    <td class="coverage">4509<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    names(state.ini.fixed) &lt;- paste(names(state.ini.fixed), "0", sep="_")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">476</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">477</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">478</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Decide if the solution of the model can be based on a simple analytical</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">479</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # formula, the spectral decomposition of the matrix (fundamental system)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">480</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # or a numeric ode solver from the deSolve package</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">481</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Prefer deSolve over eigen if a compiled model is present and use_compiled</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">482</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # is not set to FALSE</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">483</td>
+                    <td class="coverage">8529<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  solution_type = match.arg(solution_type)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">484</td>
+                    <td class="coverage">8529<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (solution_type == "analytical" &amp;&amp; !is.function(mkinmod$deg_func))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">485</td>
+                    <td class="coverage">105<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">     stop("Analytical solution not implemented for this model.")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">486</td>
+                    <td class="coverage">8424<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (solution_type == "eigen" &amp;&amp; !is.matrix(mkinmod$coefmat))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">487</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">     stop("Eigenvalue based solution not possible, coefficient matrix not present.")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">488</td>
+                    <td class="coverage">8320<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (solution_type == "auto") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">489</td>
+                    <td class="coverage">6190<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (length(mkinmod$spec) == 1 || is.function(mkinmod$deg_func)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">490</td>
+                    <td class="coverage">5434<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      solution_type = "analytical"</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">491</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">492</td>
+                    <td class="coverage">756<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      if (!is.null(mkinmod$cf) &amp; use_compiled[1] != FALSE) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">493</td>
+                    <td class="coverage">756<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        solution_type = "deSolve"</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">494</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">495</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        if (is.matrix(mkinmod$coefmat)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">496</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          solution_type = "eigen"</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">497</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          if (max(observed$value, na.rm = TRUE) &lt; 0.1) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">498</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            stop("The combination of small observed values (all &lt; 0.1) and solution_type = eigen is error-prone")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">499</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">500</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">501</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          solution_type = "deSolve"</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">502</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">503</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">504</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">505</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">506</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">507</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Get native symbol before iterations info for speed</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">508</td>
+                    <td class="coverage">8320<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  use_symbols = FALSE</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">509</td>
+                    <td class="coverage">8320<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (solution_type == "deSolve" &amp; use_compiled[1] != FALSE) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">510</td>
+                    <td class="coverage">2144<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    mkinmod[["symbols"]] &lt;- try(</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">511</td>
+                    <td class="coverage">2144<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      deSolve::checkDLL(dllname = mkinmod$dll_info[["name"]],</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">512</td>
+                    <td class="coverage">2144<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        func = "diffs", initfunc = "initpar",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">513</td>
+                    <td class="coverage">2144<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        jacfunc = NULL, nout = 0, outnames = NULL))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">514</td>
+                    <td class="coverage">2144<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (!inherits(mkinmod[["symbols"]], "try-error")) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">515</td>
+                    <td class="coverage">2144<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      use_symbols = TRUE</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">516</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">517</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">518</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">519</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Get the error model and the algorithm for fitting</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">520</td>
+                    <td class="coverage">8320<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  err_mod &lt;- match.arg(error_model)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">521</td>
+                    <td class="coverage">8320<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  error_model_algorithm = match.arg(error_model_algorithm)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">522</td>
+                    <td class="coverage">8320<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (error_model_algorithm == "OLS") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">523</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (err_mod != "const") stop("OLS is only appropriate for constant variance")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">524</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">525</td>
+                    <td class="coverage">8320<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (error_model_algorithm == "auto") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">526</td>
+                    <td class="coverage">6692<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    error_model_algorithm = switch(err_mod,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">527</td>
+                    <td class="coverage">6692<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      const = "OLS", obs = "d_3", tc = "d_3")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">528</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">529</td>
+                    <td class="coverage">8320<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  errparm_names &lt;- switch(err_mod,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">530</td>
+                    <td class="coverage">8320<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    "const" = "sigma",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">531</td>
+                    <td class="coverage">8320<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    "obs" = paste0("sigma_", obs_vars),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">532</td>
+                    <td class="coverage">8320<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    "tc" = c("sigma_low", "rsd_high"))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">533</td>
+                    <td class="coverage">8320<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  errparm_names_optim &lt;- if (error_model_algorithm == "OLS") NULL else errparm_names</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">534</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">535</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Define starting values for the error model</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">536</td>
+                    <td class="coverage">8320<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (err.ini[1] != "auto") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">537</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (!identical(names(err.ini), errparm_names)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">538</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      stop("Please supply initial values for error model components ", paste(errparm_names, collapse = ", "))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">539</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">540</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      errparms = err.ini</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">541</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">542</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">543</td>
+                    <td class="coverage">8320<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (err_mod == "const") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">544</td>
+                    <td class="coverage">6410<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      errparms = 3</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">545</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">546</td>
+                    <td class="coverage">8320<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (err_mod == "obs") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">547</td>
+                    <td class="coverage">317<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      errparms = rep(3, length(obs_vars))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">548</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">549</td>
+                    <td class="coverage">8320<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (err_mod == "tc") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">550</td>
+                    <td class="coverage">1593<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      errparms &lt;- c(sigma_low = 0.1, rsd_high = 0.1)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">551</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">552</td>
+                    <td class="coverage">8320<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    names(errparms) &lt;- errparm_names</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">553</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">554</td>
+                    <td class="coverage">8320<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (error_model_algorithm == "OLS") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">555</td>
+                    <td class="coverage">6410<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    errparms_optim &lt;- NULL</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">556</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">557</td>
+                    <td class="coverage">1910<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    errparms_optim &lt;- errparms</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">558</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">559</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">560</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Unique outtimes for model solution.</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">561</td>
+                    <td class="coverage">8320<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  outtimes &lt;- sort(unique(observed$time))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">562</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">563</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Define the objective function for optimisation, including (back)transformations</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">564</td>
+                    <td class="coverage">8320<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cost_function &lt;- function(P, trans = TRUE, OLS = FALSE, fixed_degparms = FALSE, fixed_errparms = FALSE, update_data = TRUE, ...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">565</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">566</td>
+                    <td class="coverage">4086568<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    assign("calls", calls + 1, inherits = TRUE) # Increase the model solution counter</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">567</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">568</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    #browser()</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">569</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">570</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # Trace parameter values if requested and if we are actually optimising</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">571</td>
+                    <td class="coverage">3224<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if(trace_parms &amp; update_data) cat(format(P, width = 10, digits = 6), "\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">572</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">573</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # Determine local parameter values for the cost estimation</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">574</td>
+                    <td class="coverage">4086568<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (is.numeric(fixed_degparms)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">575</td>
+                    <td class="coverage">94746<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      cost_degparms &lt;- fixed_degparms</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">576</td>
+                    <td class="coverage">94746<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      cost_errparms &lt;- P</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">577</td>
+                    <td class="coverage">94746<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      degparms_fixed = TRUE</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">578</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">579</td>
+                    <td class="coverage">3991822<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      degparms_fixed = FALSE</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">580</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">581</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">582</td>
+                    <td class="coverage">4086568<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (is.numeric(fixed_errparms)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">583</td>
+                    <td class="coverage">4725<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      cost_degparms &lt;- P</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">584</td>
+                    <td class="coverage">4725<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      cost_errparms &lt;- fixed_errparms</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">585</td>
+                    <td class="coverage">4725<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      errparms_fixed = TRUE</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">586</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">587</td>
+                    <td class="coverage">4081843<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      errparms_fixed = FALSE</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">588</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">589</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">590</td>
+                    <td class="coverage">4086568<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (OLS) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">591</td>
+                    <td class="coverage">1063145<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      cost_degparms &lt;- P</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">592</td>
+                    <td class="coverage">1063145<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      cost_errparms &lt;- numeric(0)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">593</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">594</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">595</td>
+                    <td class="coverage">4086568<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (!OLS &amp; !degparms_fixed &amp; !errparms_fixed) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">596</td>
+                    <td class="coverage">2923952<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      cost_degparms &lt;- P[1:(length(P) - length(errparms))]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">597</td>
+                    <td class="coverage">2923952<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      cost_errparms &lt;- P[(length(cost_degparms) + 1):length(P)]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">598</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">599</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">600</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # Initial states for t0</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">601</td>
+                    <td class="coverage">4086568<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if(length(state.ini.optim) &gt; 0) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">602</td>
+                    <td class="coverage">4086568<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      odeini &lt;- c(cost_degparms[1:length(state.ini.optim)], state.ini.fixed)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">603</td>
+                    <td class="coverage">4086568<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      names(odeini) &lt;- c(state.ini.optim.boxnames, state.ini.fixed.boxnames)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">604</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">605</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      odeini &lt;- state.ini.fixed</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">606</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      names(odeini) &lt;- state.ini.fixed.boxnames</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">607</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">608</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">609</td>
+                    <td class="coverage">4086568<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    odeparms.optim &lt;- cost_degparms[(length(state.ini.optim) + 1):length(cost_degparms)]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">610</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">611</td>
+                    <td class="coverage">4086568<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (trans == TRUE) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">612</td>
+                    <td class="coverage">2580794<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      odeparms &lt;- c(odeparms.optim, transparms.fixed)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">613</td>
+                    <td class="coverage">2580794<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      parms &lt;- backtransform_odeparms(odeparms, mkinmod,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">614</td>
+                    <td class="coverage">2580794<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                                      transform_rates = transform_rates,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">615</td>
+                    <td class="coverage">2580794<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                                      transform_fractions = transform_fractions)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">616</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">617</td>
+                    <td class="coverage">1505774<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      parms &lt;- c(odeparms.optim, parms.fixed)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">618</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">619</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">620</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # Solve the system with current parameter values</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">621</td>
+                    <td class="coverage">4086568<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (solution_type == "analytical") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">622</td>
+                    <td class="coverage">2562380<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      observed$predicted &lt;- mkinmod$deg_func(observed, odeini, parms)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">623</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">624</td>
+                    <td class="coverage">1524188<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      out &lt;- mkinpredict(mkinmod, parms,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">625</td>
+                    <td class="coverage">1524188<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                         odeini, outtimes,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">626</td>
+                    <td class="coverage">1524188<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                         solution_type = solution_type,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">627</td>
+                    <td class="coverage">1524188<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                         use_compiled = use_compiled,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">628</td>
+                    <td class="coverage">1524188<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                         use_symbols = use_symbols,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">629</td>
+                    <td class="coverage">1524188<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                         method.ode = method.ode,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">630</td>
+                    <td class="coverage">1524188<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                         atol = atol, rtol = rtol,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">631</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                         ...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">632</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">633</td>
+                    <td class="coverage">1524188<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      observed_index &lt;- cbind(as.character(observed$time), as.character(observed$name))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">634</td>
+                    <td class="coverage">1524188<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      observed$predicted &lt;- out[observed_index]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">635</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">636</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">637</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # Define standard deviation for each observation</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">638</td>
+                    <td class="coverage">4086568<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (err_mod == "const") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">639</td>
+                    <td class="coverage">2789021<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      observed$std &lt;- if (OLS) NA else cost_errparms["sigma"]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">640</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">641</td>
+                    <td class="coverage">4086568<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (err_mod == "obs") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">642</td>
+                    <td class="coverage">366137<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      std_names &lt;- paste0("sigma_", observed$name)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">643</td>
+                    <td class="coverage">366137<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      observed$std &lt;- cost_errparms[std_names]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">644</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">645</td>
+                    <td class="coverage">4086568<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (err_mod == "tc") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">646</td>
+                    <td class="coverage">931410<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      observed$std &lt;- sqrt(cost_errparms["sigma_low"]^2 + observed$predicted^2 * cost_errparms["rsd_high"]^2)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">647</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">648</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">649</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # Calculate model cost</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">650</td>
+                    <td class="coverage">4086568<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (OLS) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">651</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      # Cost is the sum of squared residuals</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">652</td>
+                    <td class="coverage">1063145<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      cost &lt;- with(observed, sum((value - predicted)^2))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">653</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">654</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      # Cost is the negative log-likelihood</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">655</td>
+                    <td class="coverage">3023423<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      cost &lt;- - with(observed,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">656</td>
+                    <td class="coverage">3023423<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        sum(dnorm(x = value, mean = predicted, sd = std, log = TRUE)))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">657</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">658</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">659</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # We update the current cost and data during the optimisation, not</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">660</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # during hessian calculations</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">661</td>
+                    <td class="coverage">4086568<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (update_data) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">662</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">663</td>
+                    <td class="coverage">1622188<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      assign("current_data", observed, inherits = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">664</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">665</td>
+                    <td class="coverage">1622188<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      if (cost &lt; cost.current) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">666</td>
+                    <td class="coverage">594930<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        assign("cost.current", cost, inherits = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">667</td>
+                    <td class="coverage">1768<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        if (!quiet) message(ifelse(OLS, "Sum of squared residuals", "Negative log-likelihood"),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">668</td>
+                    <td class="coverage">1768<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                        " at call ", calls, ": ", signif(cost.current, 6), "\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">669</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">670</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">671</td>
+                    <td class="coverage">4086415<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    return(cost)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">672</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">673</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">674</td>
+                    <td class="coverage">8320<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  names_optim &lt;- c(names(state.ini.optim),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">675</td>
+                    <td class="coverage">8320<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                   names(transparms.optim),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">676</td>
+                    <td class="coverage">8320<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                   errparm_names_optim)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">677</td>
+                    <td class="coverage">8320<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  n_optim &lt;- length(names_optim)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">678</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">679</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Define lower and upper bounds other than -Inf and Inf for parameters</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">680</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # for which no internal transformation is requested in the call to mkinfit</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">681</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # and for optimised error model parameters</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">682</td>
+                    <td class="coverage">8320<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  lower &lt;- rep(-Inf, n_optim)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">683</td>
+                    <td class="coverage">8320<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  upper &lt;- rep(Inf, n_optim)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">684</td>
+                    <td class="coverage">8320<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  names(lower) &lt;- names(upper) &lt;- names_optim</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">685</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">686</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # IORE exponents are not transformed, but need a lower bound</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">687</td>
+                    <td class="coverage">8320<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  index_N &lt;- grep("^N", names(lower))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">688</td>
+                    <td class="coverage">8320<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  lower[index_N] &lt;- 0</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">689</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">690</td>
+                    <td class="coverage">8320<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (!transform_rates) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">691</td>
+                    <td class="coverage">553<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    index_k &lt;- grep("^k_", names(lower))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">692</td>
+                    <td class="coverage">553<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    lower[index_k] &lt;- 0</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">693</td>
+                    <td class="coverage">553<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    index_k__iore &lt;- grep("^k__iore_", names(lower))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">694</td>
+                    <td class="coverage">553<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    lower[index_k__iore] &lt;- 0</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">695</td>
+                    <td class="coverage">553<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    other_rate_parms &lt;- intersect(c("alpha", "beta", "k1", "k2", "tb", "r"), names(lower))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">696</td>
+                    <td class="coverage">553<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    lower[other_rate_parms] &lt;- 0</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">697</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">698</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">699</td>
+                    <td class="coverage">8320<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (!transform_fractions) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">700</td>
+                    <td class="coverage">306<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    index_f &lt;- grep("^f_", names(upper))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">701</td>
+                    <td class="coverage">306<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    lower[index_f] &lt;- 0</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">702</td>
+                    <td class="coverage">306<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    upper[index_f] &lt;- 1</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">703</td>
+                    <td class="coverage">306<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    other_fraction_parms &lt;- intersect(c("g"), names(upper))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">704</td>
+                    <td class="coverage">306<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    lower[other_fraction_parms] &lt;- 0</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">705</td>
+                    <td class="coverage">306<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    upper[other_fraction_parms] &lt;- 1</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">706</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">707</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">708</td>
+                    <td class="coverage">8320<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (err_mod == "const") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">709</td>
+                    <td class="coverage">6410<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (error_model_algorithm != "OLS") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">710</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      lower["sigma"] &lt;- 0</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">711</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">712</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">713</td>
+                    <td class="coverage">8320<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (err_mod == "obs") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">714</td>
+                    <td class="coverage">317<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    index_sigma &lt;- grep("^sigma_", names(lower))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">715</td>
+                    <td class="coverage">317<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    lower[index_sigma] &lt;- 0</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">716</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">717</td>
+                    <td class="coverage">8320<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (err_mod == "tc") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">718</td>
+                    <td class="coverage">1593<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    lower["sigma_low"] &lt;- 0</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">719</td>
+                    <td class="coverage">1593<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    lower["rsd_high"] &lt;- 0</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">720</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">721</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">722</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Counter for cost function evaluations</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">723</td>
+                    <td class="coverage">8320<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  calls = 0</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">724</td>
+                    <td class="coverage">8320<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cost.current &lt;- Inf</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">725</td>
+                    <td class="coverage">8320<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  out_predicted &lt;- NA</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">726</td>
+                    <td class="coverage">8320<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  current_data &lt;- NA</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">727</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">728</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Show parameter names if tracing is requested</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">729</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if(trace_parms) cat(format(names_optim, width = 10), "\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">730</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">731</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  #browser()</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">732</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">733</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Do the fit and take the time until the hessians are calculated</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">734</td>
+                    <td class="coverage">8320<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  fit_time &lt;- system.time({</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">735</td>
+                    <td class="coverage">8320<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    degparms &lt;- c(state.ini.optim, transparms.optim)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">736</td>
+                    <td class="coverage">8320<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    n_degparms &lt;- length(degparms)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">737</td>
+                    <td class="coverage">8320<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    degparms_index &lt;- seq(1, n_degparms)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">738</td>
+                    <td class="coverage">8320<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    errparms_index &lt;- seq(n_degparms + 1, length.out = length(errparms))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">739</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">740</td>
+                    <td class="coverage">8320<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (error_model_algorithm == "d_3") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">741</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      if (!quiet) message("Directly optimising the complete model")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">742</td>
+                    <td class="coverage">471<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      parms.start &lt;- c(degparms, errparms)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">743</td>
+                    <td class="coverage">471<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      fit_direct &lt;- try(nlminb(parms.start, cost_function,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">744</td>
+                    <td class="coverage">471<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        lower = lower[names(parms.start)],</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">745</td>
+                    <td class="coverage">471<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        upper = upper[names(parms.start)],</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">746</td>
+                    <td class="coverage">471<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        control = control, ...))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">747</td>
+                    <td class="coverage">471<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      if (!inherits(fit_direct, "try-error")) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">748</td>
+                    <td class="coverage">471<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        fit_direct$logLik &lt;- - cost.current</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">749</td>
+                    <td class="coverage">471<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        cost.current &lt;- Inf # reset to avoid conflict with the OLS step</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">750</td>
+                    <td class="coverage">471<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        data_direct &lt;- current_data # We need this later if it was better</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">751</td>
+                    <td class="coverage">471<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        direct_failed = FALSE</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">752</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">753</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        direct_failed = TRUE</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">754</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">755</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">756</td>
+                    <td class="coverage">8320<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (error_model_algorithm != "direct") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">757</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      if (!quiet) message("Ordinary least squares optimisation")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">758</td>
+                    <td class="coverage">7884<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      fit &lt;- nlminb(degparms, cost_function, control = control,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">759</td>
+                    <td class="coverage">7884<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        lower = lower[names(degparms)],</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">760</td>
+                    <td class="coverage">7884<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        upper = upper[names(degparms)], OLS = TRUE, ...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">761</td>
+                    <td class="coverage">7731<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      degparms &lt;- fit$par</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">762</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">763</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      # Get the maximum likelihood estimate for sigma at the optimum parameter values</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">764</td>
+                    <td class="coverage">7731<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      current_data$residual &lt;- current_data$value - current_data$predicted</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">765</td>
+                    <td class="coverage">7731<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      sigma_mle &lt;- sqrt(sum(current_data$residual^2)/nrow(current_data))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">766</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">767</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      # Use that estimate for the constant variance, or as first guess if err_mod = "obs"</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">768</td>
+                    <td class="coverage">7731<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      if (err_mod != "tc") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">769</td>
+                    <td class="coverage">6327<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        errparms[names(errparms)] &lt;- sigma_mle</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">770</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">771</td>
+                    <td class="coverage">7731<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      fit$par &lt;- c(fit$par, errparms)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">772</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">773</td>
+                    <td class="coverage">7731<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      cost.current &lt;- cost_function(c(degparms, errparms), OLS = FALSE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">774</td>
+                    <td class="coverage">7731<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      fit$logLik &lt;- - cost.current</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">775</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">776</td>
+                    <td class="coverage">8167<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (error_model_algorithm %in% c("threestep", "fourstep", "d_3")) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">777</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      if (!quiet) message("Optimising the error model")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">778</td>
+                    <td class="coverage">1096<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      fit &lt;- nlminb(errparms, cost_function, control = control,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">779</td>
+                    <td class="coverage">1096<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        lower = lower[names(errparms)],</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">780</td>
+                    <td class="coverage">1096<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        upper = upper[names(errparms)],</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">781</td>
+                    <td class="coverage">1096<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        fixed_degparms = degparms, ...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">782</td>
+                    <td class="coverage">1096<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      errparms &lt;- fit$par</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">783</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">784</td>
+                    <td class="coverage">8167<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (error_model_algorithm == "fourstep") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">785</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      if (!quiet) message("Optimising the degradation model")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">786</td>
+                    <td class="coverage">189<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      fit &lt;- nlminb(degparms, cost_function, control = control,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">787</td>
+                    <td class="coverage">189<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        lower = lower[names(degparms)],</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">788</td>
+                    <td class="coverage">189<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        upper = upper[names(degparms)],</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">789</td>
+                    <td class="coverage">189<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        fixed_errparms = errparms, ...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">790</td>
+                    <td class="coverage">189<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      degparms &lt;- fit$par</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">791</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">792</td>
+                    <td class="coverage">8167<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (error_model_algorithm %in%</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">793</td>
+                    <td class="coverage">8167<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      c("direct", "twostep", "threestep", "fourstep", "d_3")) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">794</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      if (!quiet) message("Optimising the complete model")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">795</td>
+                    <td class="coverage">1721<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      parms.start &lt;- c(degparms, errparms)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">796</td>
+                    <td class="coverage">1721<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      fit &lt;- nlminb(parms.start, cost_function,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">797</td>
+                    <td class="coverage">1721<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        lower = lower[names(parms.start)],</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">798</td>
+                    <td class="coverage">1721<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        upper = upper[names(parms.start)],</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">799</td>
+                    <td class="coverage">1721<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        control = control, ...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">800</td>
+                    <td class="coverage">1721<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      degparms &lt;- fit$par[degparms_index]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">801</td>
+                    <td class="coverage">1721<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      errparms &lt;- fit$par[errparms_index]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">802</td>
+                    <td class="coverage">1721<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      fit$logLik &lt;- - cost.current</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">803</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">804</td>
+                    <td class="coverage">1721<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      if (error_model_algorithm == "d_3") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">805</td>
+                    <td class="coverage">471<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        d_3_messages = c(</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">806</td>
+                    <td class="coverage">471<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">           direct_failed = "Direct fitting failed, results of three-step fitting are returned",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">807</td>
+                    <td class="coverage">471<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">           same = "Direct fitting and three-step fitting yield approximately the same likelihood",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">808</td>
+                    <td class="coverage">471<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">           threestep = "Three-step fitting yielded a higher likelihood than direct fitting",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">809</td>
+                    <td class="coverage">471<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">           direct = "Direct fitting yielded a higher likelihood than three-step fitting")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">810</td>
+                    <td class="coverage">471<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        if (direct_failed) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">811</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          if (!quiet) message(d_3_messages["direct_failed"])</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">812</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          fit$d_3_message &lt;- d_3_messages["direct_failed"]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">813</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">814</td>
+                    <td class="coverage">471<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          rel_diff &lt;- abs((fit_direct$logLik - fit$logLik))/-mean(c(fit_direct$logLik, fit$logLik))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">815</td>
+                    <td class="coverage">471<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          if (rel_diff &lt; 0.0001) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">816</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            if (!quiet) message(d_3_messages["same"])</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">817</td>
+                    <td class="coverage">240<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            fit$d_3_message &lt;- d_3_messages["same"]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">818</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">819</td>
+                    <td class="coverage">231<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            if (fit$logLik &gt; fit_direct$logLik) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">820</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              if (!quiet) message(d_3_messages["threestep"])</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">821</td>
+                    <td class="coverage">15<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              fit$d_3_message &lt;- d_3_messages["threestep"]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">822</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">823</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              if (!quiet) message(d_3_messages["direct"])</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">824</td>
+                    <td class="coverage">216<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              fit &lt;- fit_direct</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">825</td>
+                    <td class="coverage">216<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              fit$d_3_message &lt;- d_3_messages["direct"]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">826</td>
+                    <td class="coverage">216<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              degparms &lt;- fit$par[degparms_index]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">827</td>
+                    <td class="coverage">216<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              errparms &lt;- fit$par[errparms_index]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">828</td>
+                    <td class="coverage">216<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              current_data  &lt;- data_direct</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">829</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">830</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">831</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">832</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">833</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">834</td>
+                    <td class="coverage">8167<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (err_mod != "const" &amp; error_model_algorithm == "IRLS") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">835</td>
+                    <td class="coverage">189<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      reweight.diff &lt;- 1</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">836</td>
+                    <td class="coverage">189<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      n.iter &lt;- 0</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">837</td>
+                    <td class="coverage">189<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      errparms_last &lt;- errparms</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">838</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">839</td>
+                    <td class="coverage">189<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      while (reweight.diff &gt; reweight.tol &amp;</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">840</td>
+                    <td class="coverage">189<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">             n.iter &lt; reweight.max.iter) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">841</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">842</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        if (!quiet) message("Optimising the error model")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">843</td>
+                    <td class="coverage">756<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        fit &lt;- nlminb(errparms, cost_function, control = control,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">844</td>
+                    <td class="coverage">756<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          lower = lower[names(errparms)],</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">845</td>
+                    <td class="coverage">756<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          upper = upper[names(errparms)],</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">846</td>
+                    <td class="coverage">756<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          fixed_degparms = degparms, ...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">847</td>
+                    <td class="coverage">756<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        errparms &lt;- fit$par</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">848</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">849</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        if (!quiet) message("Optimising the degradation model")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">850</td>
+                    <td class="coverage">756<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        fit &lt;- nlminb(degparms, cost_function, control = control,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">851</td>
+                    <td class="coverage">756<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          lower = lower[names(degparms)],</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">852</td>
+                    <td class="coverage">756<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          upper = upper[names(degparms)],</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">853</td>
+                    <td class="coverage">756<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          fixed_errparms = errparms, ...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">854</td>
+                    <td class="coverage">756<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        degparms &lt;- fit$par</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">855</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">856</td>
+                    <td class="coverage">756<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        reweight.diff &lt;- dist(rbind(errparms, errparms_last))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">857</td>
+                    <td class="coverage">756<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        errparms_last &lt;- errparms</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">858</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">859</td>
+                    <td class="coverage">756<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        fit$par &lt;- c(fit$par, errparms)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">860</td>
+                    <td class="coverage">756<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        cost.current &lt;- cost_function(c(degparms, errparms), OLS = FALSE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">861</td>
+                    <td class="coverage">756<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        fit$logLik &lt;- - cost.current</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">862</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">863</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">864</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">865</td>
+                    <td class="coverage">8167<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    dim_hessian &lt;- length(c(degparms, errparms))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">866</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">867</td>
+                    <td class="coverage">8167<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    fit$hessian &lt;- try(numDeriv::hessian(cost_function, c(degparms, errparms), OLS = FALSE,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">868</td>
+                    <td class="coverage">8167<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        update_data = FALSE), silent = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">869</td>
+                    <td class="coverage">8167<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (inherits(fit$hessian, "try-error")) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">870</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      fit$hessian &lt;- matrix(NA, nrow = dim_hessian, ncol = dim_hessian)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">871</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">872</td>
+                    <td class="coverage">8167<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    dimnames(fit$hessian) &lt;- list(names(c(degparms, errparms)),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">873</td>
+                    <td class="coverage">8167<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      names(c(degparms, errparms)))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">874</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">875</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # Backtransform parameters</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">876</td>
+                    <td class="coverage">8167<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    bparms.optim = backtransform_odeparms(degparms, mkinmod,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">877</td>
+                    <td class="coverage">8167<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      transform_rates = transform_rates,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">878</td>
+                    <td class="coverage">8167<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      transform_fractions = transform_fractions)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">879</td>
+                    <td class="coverage">8167<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    bparms.fixed = c(state.ini.fixed, parms.fixed)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">880</td>
+                    <td class="coverage">8167<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    bparms.all = c(bparms.optim, parms.fixed)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">881</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">882</td>
+                    <td class="coverage">8167<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    fit$hessian_notrans &lt;- try(numDeriv::hessian(cost_function, c(bparms.optim, errparms),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">883</td>
+                    <td class="coverage">8167<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        OLS = FALSE, trans = FALSE, update_data = FALSE), silent = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">884</td>
+                    <td class="coverage">8167<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (inherits(fit$hessian_notrans, "try-error")) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">885</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      fit$hessian_notrans &lt;- matrix(NA, nrow = dim_hessian, ncol = dim_hessian)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">886</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">887</td>
+                    <td class="coverage">8167<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    dimnames(fit$hessian_notrans) &lt;- list(names(c(bparms.optim, errparms)),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">888</td>
+                    <td class="coverage">8167<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      names(c(bparms.optim, errparms)))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">889</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  })</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">890</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">891</td>
+                    <td class="coverage">8167<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  fit$call &lt;- call</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">892</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">893</td>
+                    <td class="coverage">8167<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  fit$error_model_algorithm &lt;- error_model_algorithm</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">894</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">895</td>
+                    <td class="coverage">8167<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (fit$convergence != 0) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">896</td>
+                    <td class="coverage">108<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    convergence_warning = paste0("Optimisation did not converge:\n", fit$message)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">897</td>
+                    <td class="coverage">108<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    summary_warnings &lt;- c(summary_warnings, C = convergence_warning)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">898</td>
+                    <td class="coverage">108<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    warning(convergence_warning)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">899</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">900</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if(!quiet) message("Optimisation successfully terminated.\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">901</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">902</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">903</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # We need to return some more data for summary and plotting</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">904</td>
+                    <td class="coverage">8167<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  fit$solution_type &lt;- solution_type</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">905</td>
+                    <td class="coverage">8167<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  fit$transform_rates &lt;- transform_rates</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">906</td>
+                    <td class="coverage">8167<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  fit$transform_fractions &lt;- transform_fractions</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">907</td>
+                    <td class="coverage">8167<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  fit$reweight.tol &lt;- reweight.tol</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">908</td>
+                    <td class="coverage">8167<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  fit$reweight.max.iter &lt;- reweight.max.iter</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">909</td>
+                    <td class="coverage">8167<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  fit$control &lt;- control</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">910</td>
+                    <td class="coverage">8167<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  fit$calls &lt;- calls</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">911</td>
+                    <td class="coverage">8167<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  fit$time &lt;- fit_time</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">912</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">913</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # We also need the model and a model name for summary and plotting,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">914</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # but without symbols because they could become invalid</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">915</td>
+                    <td class="coverage">8167<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  fit$symbols &lt;- NULL</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">916</td>
+                    <td class="coverage">8167<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  fit$mkinmod &lt;- mkinmod</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">917</td>
+                    <td class="coverage">8167<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  fit$mkinmod$name &lt;- mkinmod_name</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">918</td>
+                    <td class="coverage">8167<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  fit$obs_vars &lt;- obs_vars</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">919</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">920</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Residual sum of squares as a function of the fitted parameters</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">921</td>
+                    <td class="coverage">8167<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  fit$rss &lt;- function(P) cost_function(P, OLS = TRUE, update_data = FALSE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">922</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">923</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Log-likelihood with possibility to fix degparms or errparms</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">924</td>
+                    <td class="coverage">8167<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  fit$ll &lt;- function(P, fixed_degparms = FALSE, fixed_errparms = FALSE, trans = FALSE) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">925</td>
+                    <td class="coverage">547080<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    - cost_function(P, trans = trans, fixed_degparms = fixed_degparms,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">926</td>
+                    <td class="coverage">547080<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      fixed_errparms = fixed_errparms, OLS = FALSE, update_data = FALSE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">927</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">928</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">929</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Collect initial parameter values in three dataframes</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">930</td>
+                    <td class="coverage">8167<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  fit$start &lt;- data.frame(value = c(state.ini.optim,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">931</td>
+                    <td class="coverage">8167<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                                    parms.optim, errparms_optim))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">932</td>
+                    <td class="coverage">8167<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  fit$start$type = c(rep("state", length(state.ini.optim)),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">933</td>
+                    <td class="coverage">8167<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                     rep("deparm", length(parms.optim)),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">934</td>
+                    <td class="coverage">8167<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                     rep("error", length(errparms_optim)))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">935</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">936</td>
+                    <td class="coverage">8167<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  fit$start_transformed = data.frame(</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">937</td>
+                    <td class="coverage">8167<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      value = c(state.ini.optim, transparms.optim, errparms_optim),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">938</td>
+                    <td class="coverage">8167<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      lower = lower,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">939</td>
+                    <td class="coverage">8167<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      upper = upper)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">940</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">941</td>
+                    <td class="coverage">8167<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  fit$fixed &lt;- data.frame(value = c(state.ini.fixed, parms.fixed))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">942</td>
+                    <td class="coverage">8167<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  fit$fixed$type = c(rep("state", length(state.ini.fixed)),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">943</td>
+                    <td class="coverage">8167<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                     rep("deparm", length(parms.fixed)))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">944</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">945</td>
+                    <td class="coverage">8167<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  fit$data &lt;- data.frame(time = current_data$time,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">946</td>
+                    <td class="coverage">8167<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                         variable = current_data$name,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">947</td>
+                    <td class="coverage">8167<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                         observed = current_data$value,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">948</td>
+                    <td class="coverage">8167<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                         predicted = current_data$predicted)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">949</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">950</td>
+                    <td class="coverage">8167<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  fit$data$residual &lt;- fit$data$observed - fit$data$predicted</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">951</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">952</td>
+                    <td class="coverage">8167<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  fit$atol &lt;- atol</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">953</td>
+                    <td class="coverage">8167<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  fit$rtol &lt;- rtol</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">954</td>
+                    <td class="coverage">8167<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  fit$err_mod &lt;- err_mod</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">955</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">956</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Return different sets of backtransformed parameters for summary and plotting</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">957</td>
+                    <td class="coverage">8167<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  fit$bparms.optim &lt;- bparms.optim</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">958</td>
+                    <td class="coverage">8167<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  fit$bparms.fixed &lt;- bparms.fixed</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">959</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">960</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Return ode and state parameters for further fitting</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">961</td>
+                    <td class="coverage">8167<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  fit$bparms.ode &lt;- bparms.all[mkinmod$parms]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">962</td>
+                    <td class="coverage">8167<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  fit$bparms.state &lt;- c(bparms.all[setdiff(names(bparms.all), names(fit$bparms.ode))],</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">963</td>
+                    <td class="coverage">8167<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                        state.ini.fixed)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">964</td>
+                    <td class="coverage">8167<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  names(fit$bparms.state) &lt;- gsub("_0$", "", names(fit$bparms.state))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">965</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">966</td>
+                    <td class="coverage">8167<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  fit$errparms &lt;- errparms</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">967</td>
+                    <td class="coverage">8167<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  fit$df.residual &lt;- n_observed - length(c(degparms, errparms))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">968</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">969</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Assign the class here so method dispatch works for residuals</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">970</td>
+                    <td class="coverage">8167<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  class(fit) &lt;- c("mkinfit")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">971</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">972</td>
+                    <td class="coverage">8167<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (test_residuals) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">973</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # Check for normal distribution of residuals</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">974</td>
+                    <td class="coverage">153<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    fit$shapiro.p &lt;- shapiro.test(residuals(fit, standardized = TRUE))$p.value</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">975</td>
+                    <td class="coverage">153<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (fit$shapiro.p &lt; 0.05) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">976</td>
+                    <td class="coverage">153<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      shapiro_warning &lt;- paste("Shapiro-Wilk test for standardized residuals: p = ", signif(fit$shapiro.p, 3))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">977</td>
+                    <td class="coverage">153<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      warning(shapiro_warning)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">978</td>
+                    <td class="coverage">153<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      summary_warnings &lt;- c(summary_warnings, S = shapiro_warning)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">979</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">980</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">981</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">982</td>
+                    <td class="coverage">8167<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  fit$summary_warnings &lt;- summary_warnings</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">983</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">984</td>
+                    <td class="coverage">8167<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  fit$date &lt;- date()</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">985</td>
+                    <td class="coverage">8167<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  fit$version &lt;- as.character(utils::packageVersion("mkin"))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">986</td>
+                    <td class="coverage">8167<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  fit$Rversion &lt;- paste(R.version$major, R.version$minor, sep=".")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">987</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">988</td>
+                    <td class="coverage">8167<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  return(fit)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">989</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                </tbody>
+              </table>
+            </div>
+            <div id="R/mkinmod.R" class="hidden">
+              <table class="table-condensed">
+                <tbody>
+                  <tr class="never">
+                    <td class="num">1</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Function to set up a kinetic model with one or more state variables</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">2</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">3</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' This function is usually called using a call to [mkinsub()] for each observed</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">4</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' variable, specifying the corresponding submodel as well as outgoing pathways</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">5</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' (see examples).</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">6</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">7</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' For the definition of model types and their parameters, the equations given</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">8</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' in the FOCUS and NAFTA guidance documents are used.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">9</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">10</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' For kinetic models with more than one observed variable, a symbolic solution</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">11</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' of the system of differential equations is included in the resulting</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">12</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' mkinmod object in some cases, speeding up the solution.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">13</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">14</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' If a C compiler is found by [pkgbuild::has_compiler()] and there</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">15</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' is more than one observed variable in the specification, C code is generated</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">16</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' for evaluating the differential equations, compiled using</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">17</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' [inline::cfunction()] and added to the resulting mkinmod object.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">18</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">19</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param ...  For each observed variable, a list as obtained by [mkinsub()]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">20</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   has to be specified as an argument (see examples).  Currently, single</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">21</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   first order kinetics "SFO", indeterminate order rate equation kinetics</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">22</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   "IORE", or single first order with reversible binding "SFORB" are</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">23</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   implemented for all variables, while "FOMC", "DFOP", "HS" and "logistic"</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">24</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   can additionally be chosen for the first variable which is assumed to be</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">25</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   the source compartment.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">26</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   Additionally, [mkinsub()] has an argument \code{to}, specifying names of</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">27</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   variables to which a transfer is to be assumed in the model.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">28</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   If the argument \code{use_of_ff} is set to "min"</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">29</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   and the model for the compartment is "SFO" or "SFORB", an</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">30</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   additional [mkinsub()] argument can be \code{sink = FALSE}, effectively</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">31</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   fixing the flux to sink to zero.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">32</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   In print.mkinmod, this argument is currently not used.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">33</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param use_of_ff Specification of the use of formation fractions in the</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">34</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   model equations and, if applicable, the coefficient matrix.  If "max",</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">35</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   formation fractions are always used (default).  If "min", a minimum use of</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">36</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   formation fractions is made, i.e. each first-order pathway to a metabolite</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">37</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   has its own rate constant.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">38</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param speclist The specification of the observed variables and their</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">39</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   submodel types and pathways can be given as a single list using this</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">40</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   argument. Default is NULL.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">41</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param quiet Should messages be suppressed?</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">42</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param verbose If \code{TRUE}, passed to [inline::cfunction()] if</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">43</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   applicable to give detailed information about the C function being built.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">44</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param name A name for the model. Should be a valid R object name.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">45</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param dll_dir Directory where an DLL object, if generated internally by</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">46</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   [inline::cfunction()], should be saved.  The DLL will only be stored in a</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">47</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   permanent location for use in future sessions, if 'dll_dir' and 'name'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">48</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   are specified. This is helpful if fit objects are cached e.g. by knitr,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">49</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   as the cache remains functional across sessions if the DLL is stored in</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">50</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   a user defined location.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">51</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param unload If a DLL from the target location in 'dll_dir' is already</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">52</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   loaded, should that be unloaded first?</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">53</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param overwrite If a file exists at the target DLL location in 'dll_dir',</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">54</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   should this be overwritten?</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">55</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @importFrom methods signature</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">56</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @return A list of class \code{mkinmod} for use with [mkinfit()],</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">57</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   containing, among others,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">58</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   \item{diffs}{</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">59</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     A vector of string representations of differential equations, one for</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">60</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     each modelling variable.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">61</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">62</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   \item{map}{</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">63</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     A list containing named character vectors for each observed variable,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">64</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     specifying the modelling variables by which it is represented.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">65</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">66</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   \item{use_of_ff}{</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">67</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     The content of \code{use_of_ff} is passed on in this list component.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">68</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">69</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   \item{deg_func}{</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">70</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     If generated, a function containing the solution of the degradation</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">71</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     model.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">72</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">73</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   \item{coefmat}{</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">74</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     The coefficient matrix, if the system of differential equations can be</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">75</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     represented by one.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">76</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">77</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   \item{cf}{</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">78</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     If generated, a compiled function calculating the derivatives as</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">79</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     returned by cfunction.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">80</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">81</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @note The IORE submodel is not well tested for metabolites. When using this</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">82</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   model for metabolites, you may want to read the note in the help</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">83</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   page to [mkinfit].</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">84</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @author Johannes Ranke</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">85</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @references FOCUS (2006) \dQuote{Guidance Document on Estimating Persistence</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">86</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   and Degradation Kinetics from Environmental Fate Studies on Pesticides in</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">87</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   EU Registration} Report of the FOCUS Work Group on Degradation Kinetics,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">88</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   EC Document Reference Sanco/10058/2005 version 2.0, 434 pp,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">89</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   \url{http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">90</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">91</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   NAFTA Technical Working Group on Pesticides (not dated) Guidance for</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">92</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   Evaluating and Calculating Degradation Kinetics in Environmental Media</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">93</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @examples</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">94</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">95</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # Specify the SFO model (this is not needed any more, as we can now mkinfit("SFO", ...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">96</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' SFO &lt;- mkinmod(parent = mkinsub("SFO"))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">97</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">98</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # One parent compound, one metabolite, both single first order</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">99</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' SFO_SFO &lt;- mkinmod(</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">100</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   parent = mkinsub("SFO", "m1"),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">101</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   m1 = mkinsub("SFO"))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">102</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' print(SFO_SFO)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">103</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">104</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' \dontrun{</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">105</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'  fit_sfo_sfo &lt;- mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE, solution_type = "deSolve")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">106</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">107</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'  # Now supplying compound names used for plotting, and write to user defined location</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">108</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'  # We need to choose a path outside the session tempdir because this gets removed</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">109</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'  DLL_dir &lt;- "~/.local/share/mkin"</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">110</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'  if (!dir.exists(DLL_dir)) dir.create(DLL_dir)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">111</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'  SFO_SFO.2 &lt;- mkinmod(</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">112</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'    parent = mkinsub("SFO", "m1", full_name = "Test compound"),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">113</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'    m1 = mkinsub("SFO", full_name = "Metabolite M1"),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">114</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'    name = "SFO_SFO", dll_dir = DLL_dir, unload = TRUE, overwrite = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">115</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # Now we can save the model and restore it in a new session</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">116</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' saveRDS(SFO_SFO.2, file = "~/SFO_SFO.rds")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">117</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # Terminate the R session here if you would like to check, and then do</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">118</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' library(mkin)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">119</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' SFO_SFO.3 &lt;- readRDS("~/SFO_SFO.rds")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">120</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' fit_sfo_sfo &lt;- mkinfit(SFO_SFO.3, FOCUS_2006_D, quiet = TRUE, solution_type = "deSolve")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">121</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">122</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # Show details of creating the C function</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">123</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' SFO_SFO &lt;- mkinmod(</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">124</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   parent = mkinsub("SFO", "m1"),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">125</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   m1 = mkinsub("SFO"), verbose = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">126</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">127</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # The symbolic solution which is available in this case is not</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">128</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # made for human reading but for speed of computation</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">129</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' SFO_SFO$deg_func</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">130</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">131</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # If we have several parallel metabolites</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">132</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # (compare tests/testthat/test_synthetic_data_for_UBA_2014.R)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">133</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' m_synth_DFOP_par &lt;- mkinmod(</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">134</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'  parent = mkinsub("DFOP", c("M1", "M2")),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">135</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'  M1 = mkinsub("SFO"),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">136</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'  M2 = mkinsub("SFO"),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">137</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'  quiet = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">138</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">139</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' fit_DFOP_par_c &lt;- mkinfit(m_synth_DFOP_par,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">140</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   synthetic_data_for_UBA_2014[[12]]$data,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">141</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   quiet = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">142</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">143</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">144</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export mkinmod</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">145</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">mkinmod &lt;- function(..., use_of_ff = "max", name = NULL,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">146</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  speclist = NULL, quiet = FALSE, verbose = FALSE, dll_dir = NULL,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">147</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  unload = FALSE, overwrite = FALSE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">148</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">{</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">149</td>
+                    <td class="coverage">4940<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (is.null(speclist)) spec &lt;- list(...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">150</td>
+                    <td class="coverage">3905<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  else spec &lt;- speclist</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">151</td>
+                    <td class="coverage">8845<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  obs_vars &lt;- names(spec)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">152</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">153</td>
+                    <td class="coverage">8845<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  save_msg &lt;- "You need to specify both 'name' and 'dll_dir' to save a model DLL"</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">154</td>
+                    <td class="coverage">8845<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (!is.null(dll_dir)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">155</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (!dir.exists(dll_dir)) stop(dll_dir, " does not exist")</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">156</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (is.null(name)) stop(save_msg)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">157</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">158</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">159</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Check if any of the names of the observed variables contains any other</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">160</td>
+                    <td class="coverage">8845<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  for (obs_var in obs_vars) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">161</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (length(grep(obs_var, obs_vars)) &gt; 1) stop("Sorry, variable names can not contain each other")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">162</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (grepl("_to_", obs_var)) stop("Sorry, names of observed variables can not contain _to_")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">163</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (obs_var == "sink") stop("Naming a compound 'sink' is not supported")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">164</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">165</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">166</td>
+                    <td class="coverage">8533<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (!use_of_ff %in% c("min", "max"))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">167</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    stop("The use of formation fractions 'use_of_ff' can only be 'min' or 'max'")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">168</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">169</td>
+                    <td class="coverage">8429<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  parms &lt;- vector()</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">170</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # }}}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">171</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">172</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Do not return a coefficient matrix mat when FOMC, IORE, DFOP, HS or logistic is used for the parent {{{</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">173</td>
+                    <td class="coverage">8429<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if(spec[[1]]$type %in% c("FOMC", "IORE", "DFOP", "HS", "logistic")) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">174</td>
+                    <td class="coverage">2280<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    mat = FALSE</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">175</td>
+                    <td class="coverage">6149<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  } else mat = TRUE</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">176</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  #}}}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">177</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">178</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Establish a list of differential equations as well as a map from observed {{{</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">179</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # compartments to differential equations</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">180</td>
+                    <td class="coverage">8429<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  diffs &lt;- vector()</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">181</td>
+                    <td class="coverage">8429<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  map &lt;- list()</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">182</td>
+                    <td class="coverage">8429<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  for (varname in obs_vars)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">183</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  {</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">184</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # Check the type component of the compartment specification {{{</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">185</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if(is.null(spec[[varname]]$type)) stop(</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">186</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      "Every part of the model specification must be a list containing a type component")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">187</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if(!spec[[varname]]$type %in% c("SFO", "FOMC", "IORE", "DFOP", "HS", "SFORB", "logistic")) stop(</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">188</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      "Available types are SFO, FOMC, IORE, DFOP, HS, SFORB and logistic only")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">189</td>
+                    <td class="coverage">13150<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if(spec[[varname]]$type %in% c("FOMC", "DFOP", "HS", "logistic") &amp; match(varname, obs_vars) != 1) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">190</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        stop(paste("Types FOMC, DFOP, HS and logistic are only implemented for the first compartment,",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">191</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                   "which is assumed to be the source compartment"))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">192</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">193</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    #}}}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">194</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # New (sub)compartments (boxes) needed for the model type {{{</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">195</td>
+                    <td class="coverage">13046<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    new_boxes &lt;- switch(spec[[varname]]$type,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">196</td>
+                    <td class="coverage">13046<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      SFO = varname,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">197</td>
+                    <td class="coverage">13046<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      FOMC = varname,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">198</td>
+                    <td class="coverage">13046<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      IORE = varname,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">199</td>
+                    <td class="coverage">13046<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      DFOP = varname,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">200</td>
+                    <td class="coverage">13046<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      HS = varname,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">201</td>
+                    <td class="coverage">13046<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      logistic = varname,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">202</td>
+                    <td class="coverage">13046<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      SFORB = paste(varname, c("free", "bound"), sep = "_")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">203</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    )</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">204</td>
+                    <td class="coverage">13046<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    map[[varname]] &lt;- new_boxes</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">205</td>
+                    <td class="coverage">13046<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    names(map[[varname]]) &lt;- rep(spec[[varname]]$type, length(new_boxes)) #}}}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">206</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # Start a new differential equation for each new box {{{</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">207</td>
+                    <td class="coverage">13046<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    new_diffs &lt;- paste("d_", new_boxes, " =", sep = "")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">208</td>
+                    <td class="coverage">13046<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    names(new_diffs) &lt;- new_boxes</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">209</td>
+                    <td class="coverage">13046<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    diffs &lt;- c(diffs, new_diffs) #}}}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">210</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  } #}}}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">211</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">212</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Create content of differential equations and build parameter list {{{</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">213</td>
+                    <td class="coverage">8221<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  for (varname in obs_vars)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">214</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  {</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">215</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # Get the name of the box(es) we are working on for the decline term(s)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">216</td>
+                    <td class="coverage">12838<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    box_1 = map[[varname]][[1]] # This is the only box unless type is SFORB</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">217</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # Turn on sink if this is not explicitly excluded by the user by</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">218</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # specifying sink=FALSE</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">219</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if(is.null(spec[[varname]]$sink)) spec[[varname]]$sink &lt;- TRUE</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">220</td>
+                    <td class="coverage">12838<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if(spec[[varname]]$type %in% c("SFO", "IORE", "SFORB")) { # {{{ Add decline term</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">221</td>
+                    <td class="coverage">10838<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      if (use_of_ff == "min") { # Minimum use of formation fractions</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">222</td>
+                    <td class="coverage">1304<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        if(spec[[varname]]$type == "IORE" &amp;&amp; length(spec[[varname]]$to) &gt; 0) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">223</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">           stop("Transformation reactions from compounds modelled with IORE\n",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">224</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                "are only supported with formation fractions (use_of_ff = 'max')")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">225</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">226</td>
+                    <td class="coverage">1200<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        if(spec[[varname]]$sink) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">227</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          # If sink is requested, add first-order/IORE sink term</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">228</td>
+                    <td class="coverage">952<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          k_compound_sink &lt;- paste("k", box_1, "sink", sep = "_")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">229</td>
+                    <td class="coverage">952<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          if(spec[[varname]]$type == "IORE") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">230</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            k_compound_sink &lt;- paste("k__iore", box_1, "sink", sep = "_")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">231</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">232</td>
+                    <td class="coverage">952<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          parms &lt;- c(parms, k_compound_sink)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">233</td>
+                    <td class="coverage">952<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          decline_term &lt;- paste(k_compound_sink, "*", box_1)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">234</td>
+                    <td class="coverage">952<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          if(spec[[varname]]$type == "IORE") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">235</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            N &lt;- paste("N", box_1, sep = "_")</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">236</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            parms &lt;- c(parms, N)</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">237</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            decline_term &lt;- paste0(decline_term, "^", N)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">238</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">239</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        } else { # otherwise no decline term needed here</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">240</td>
+                    <td class="coverage">248<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          decline_term = "0"</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">241</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">242</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      } else { # Maximum use of formation fractions</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">243</td>
+                    <td class="coverage">9534<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        k_compound &lt;- paste("k", box_1, sep = "_")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">244</td>
+                    <td class="coverage">9534<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        if(spec[[varname]]$type == "IORE") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">245</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          k_compound &lt;- paste("k__iore", box_1, sep = "_")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">246</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">247</td>
+                    <td class="coverage">9534<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        parms &lt;- c(parms, k_compound)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">248</td>
+                    <td class="coverage">9534<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        decline_term &lt;- paste(k_compound, "*", box_1)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">249</td>
+                    <td class="coverage">9534<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        if(spec[[varname]]$type == "IORE") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">250</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          N &lt;- paste("N", box_1, sep = "_")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">251</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          parms &lt;- c(parms, N)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">252</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          decline_term &lt;- paste0(decline_term, "^", N)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">253</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">254</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">255</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    } #}}}</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">256</td>
+                    <td class="coverage">12734<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if(spec[[varname]]$type == "FOMC") { # {{{ Add FOMC decline term</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">257</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      # From p. 53 of the FOCUS kinetics report, without the power function so it works in C</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">258</td>
+                    <td class="coverage">381<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      decline_term &lt;- paste("(alpha/beta) * 1/((time/beta) + 1) *", box_1)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">259</td>
+                    <td class="coverage">381<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      parms &lt;- c(parms, "alpha", "beta")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">260</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    } #}}}</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">261</td>
+                    <td class="coverage">12734<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if(spec[[varname]]$type == "DFOP") { # {{{ Add DFOP decline term</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">262</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      # From p. 57 of the FOCUS kinetics report</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">263</td>
+                    <td class="coverage">1283<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      decline_term &lt;- paste("((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) *", box_1)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">264</td>
+                    <td class="coverage">1283<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      parms &lt;- c(parms, "k1", "k2", "g")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">265</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    } #}}}</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">266</td>
+                    <td class="coverage">12734<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    HS_decline &lt;- "ifelse(time &lt;= tb, k1, k2)" # Used below for automatic translation to C</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">267</td>
+                    <td class="coverage">12734<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if(spec[[varname]]$type == "HS") { # {{{ Add HS decline term</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">268</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      # From p. 55 of the FOCUS kinetics report</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">269</td>
+                    <td class="coverage">30<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      decline_term &lt;- paste(HS_decline, "*", box_1)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">270</td>
+                    <td class="coverage">30<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      parms &lt;- c(parms, "k1", "k2", "tb")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">271</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    } #}}}</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">272</td>
+                    <td class="coverage">12734<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if(spec[[varname]]$type == "logistic") { # {{{ Add logistic decline term</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">273</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      # From p. 67 of the FOCUS kinetics report (2014)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">274</td>
+                    <td class="coverage">306<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      decline_term &lt;- paste("(k0 * kmax)/(k0 + (kmax - k0) * exp(-r * time)) *", box_1)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">275</td>
+                    <td class="coverage">306<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      parms &lt;- c(parms, "kmax", "k0", "r")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">276</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    } #}}}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">277</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # Add origin decline term to box 1 (usually the only box, unless type is SFORB)#{{{</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">278</td>
+                    <td class="coverage">12734<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    diffs[[box_1]] &lt;- paste(diffs[[box_1]], "-", decline_term)#}}}</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">279</td>
+                    <td class="coverage">12734<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if(spec[[varname]]$type == "SFORB") { # {{{ Add SFORB reversible binding terms</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">280</td>
+                    <td class="coverage">25<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      box_2 = map[[varname]][[2]]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">281</td>
+                    <td class="coverage">25<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      k_free_bound &lt;- paste("k", varname, "free", "bound", sep = "_")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">282</td>
+                    <td class="coverage">25<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      k_bound_free &lt;- paste("k", varname, "bound", "free", sep = "_")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">283</td>
+                    <td class="coverage">25<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      parms &lt;- c(parms, k_free_bound, k_bound_free)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">284</td>
+                    <td class="coverage">25<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      reversible_binding_term_1 &lt;- paste("-", k_free_bound, "*", box_1, "+",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">285</td>
+                    <td class="coverage">25<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        k_bound_free, "*", box_2)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">286</td>
+                    <td class="coverage">25<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      reversible_binding_term_2 &lt;- paste("+", k_free_bound, "*", box_1, "-",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">287</td>
+                    <td class="coverage">25<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        k_bound_free, "*", box_2)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">288</td>
+                    <td class="coverage">25<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      diffs[[box_1]] &lt;- paste(diffs[[box_1]], reversible_binding_term_1)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">289</td>
+                    <td class="coverage">25<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      diffs[[box_2]] &lt;- paste(diffs[[box_2]], reversible_binding_term_2)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">290</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    } #}}}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">291</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">292</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # Transfer between compartments#{{{</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">293</td>
+                    <td class="coverage">12734<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    to &lt;- spec[[varname]]$to</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">294</td>
+                    <td class="coverage">12734<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if(!is.null(to)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">295</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      # Name of box from which transfer takes place</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">296</td>
+                    <td class="coverage">4174<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      origin_box &lt;- box_1</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">297</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">298</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      # Number of targets</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">299</td>
+                    <td class="coverage">4174<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      n_targets = length(to)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">300</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">301</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      # Add transfer terms to listed compartments</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">302</td>
+                    <td class="coverage">4174<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      for (target in to) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">303</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        if (!target %in% obs_vars) stop("You did not specify a submodel for target variable ", target)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">304</td>
+                    <td class="coverage">4813<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        target_box &lt;- switch(spec[[target]]$type,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">305</td>
+                    <td class="coverage">4813<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          SFO = target,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">306</td>
+                    <td class="coverage">4813<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          IORE = target,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">307</td>
+                    <td class="coverage">4813<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          SFORB = paste(target, "free", sep = "_"))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">308</td>
+                    <td class="coverage">4813<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        if (use_of_ff == "min" &amp;&amp; spec[[varname]]$type %in% c("SFO", "SFORB"))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">309</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">310</td>
+                    <td class="coverage">601<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          k_from_to &lt;- paste("k", origin_box, target_box, sep = "_")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">311</td>
+                    <td class="coverage">601<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          parms &lt;- c(parms, k_from_to)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">312</td>
+                    <td class="coverage">601<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          diffs[[origin_box]] &lt;- paste(diffs[[origin_box]], "-",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">313</td>
+                    <td class="coverage">601<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            k_from_to, "*", origin_box)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">314</td>
+                    <td class="coverage">601<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          diffs[[target_box]] &lt;- paste(diffs[[target_box]], "+",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">315</td>
+                    <td class="coverage">601<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            k_from_to, "*", origin_box)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">316</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">317</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          # Do not introduce a formation fraction if this is the only target</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">318</td>
+                    <td class="coverage">4212<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          if (spec[[varname]]$sink == FALSE &amp;&amp; n_targets == 1) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">319</td>
+                    <td class="coverage">689<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            diffs[[target_box]] &lt;- paste(diffs[[target_box]], "+",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">320</td>
+                    <td class="coverage">689<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                                         decline_term)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">321</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">322</td>
+                    <td class="coverage">3523<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            fraction_to_target = paste("f", origin_box, "to", target, sep = "_")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">323</td>
+                    <td class="coverage">3523<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            parms &lt;- c(parms, fraction_to_target)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">324</td>
+                    <td class="coverage">3523<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            diffs[[target_box]] &lt;- paste(diffs[[target_box]], "+",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">325</td>
+                    <td class="coverage">3523<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                fraction_to_target, "*", decline_term)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">326</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">327</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">328</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">329</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    } #}}}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">330</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  } #}}}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">331</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">332</td>
+                    <td class="coverage">8117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  model &lt;- list(diffs = diffs, parms = parms, map = map, spec = spec, use_of_ff = use_of_ff, name = name)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">333</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">334</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Create coefficient matrix if possible #{{{</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">335</td>
+                    <td class="coverage">8117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (mat) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">336</td>
+                    <td class="coverage">5941<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    boxes &lt;- names(diffs)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">337</td>
+                    <td class="coverage">5941<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    n &lt;- length(boxes)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">338</td>
+                    <td class="coverage">5941<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    m &lt;- matrix(nrow=n, ncol=n, dimnames=list(boxes, boxes))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">339</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">340</td>
+                    <td class="coverage">5941<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (use_of_ff == "min") { # {{{ Minimum use of formation fractions</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">341</td>
+                    <td class="coverage">600<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      for (from in boxes) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">342</td>
+                    <td class="coverage">1201<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        for (to in boxes) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">343</td>
+                    <td class="coverage">2405<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          if (from == to) { # diagonal elements</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">344</td>
+                    <td class="coverage">1201<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            k.candidate = paste("k", from, c(boxes, "sink"), sep = "_")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">345</td>
+                    <td class="coverage">1201<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            k.candidate = sub("free.*bound", "free_bound", k.candidate)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">346</td>
+                    <td class="coverage">1201<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            k.candidate = sub("bound.*free", "bound_free", k.candidate)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">347</td>
+                    <td class="coverage">1201<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            k.effective = intersect(model$parms, k.candidate)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">348</td>
+                    <td class="coverage">1201<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            m[from,to] = ifelse(length(k.effective) &gt; 0,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">349</td>
+                    <td class="coverage">1201<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                paste("-", k.effective, collapse = " "), "0")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">350</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">351</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          } else {          # off-diagonal elements</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">352</td>
+                    <td class="coverage">1204<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            k.candidate = paste("k", from, to, sep = "_")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">353</td>
+                    <td class="coverage">1204<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            if (sub("_free$", "", from) == sub("_bound$", "", to)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">354</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              k.candidate = paste("k", sub("_free$", "_free_bound", from), sep = "_")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">355</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">356</td>
+                    <td class="coverage">1204<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            if (sub("_bound$", "", from) == sub("_free$", "", to)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">357</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              k.candidate = paste("k", sub("_bound$", "_bound_free", from), sep = "_")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">358</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">359</td>
+                    <td class="coverage">1204<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            k.effective = intersect(model$parms, k.candidate)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">360</td>
+                    <td class="coverage">1204<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            m[to, from] = ifelse(length(k.effective) &gt; 0,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">361</td>
+                    <td class="coverage">1204<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                k.effective, "0")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">362</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">363</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">364</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      } # }}}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">365</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    } else { # {{{ Use formation fractions where possible</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">366</td>
+                    <td class="coverage">5341<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      for (from in boxes) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">367</td>
+                    <td class="coverage">8074<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        for (to in boxes) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">368</td>
+                    <td class="coverage">15220<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          if (from == to) { # diagonal elements</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">369</td>
+                    <td class="coverage">8074<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            k.candidate = paste("k", from, sep = "_")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">370</td>
+                    <td class="coverage">8074<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            m[from,to] = ifelse(k.candidate %in% model$parms,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">371</td>
+                    <td class="coverage">8074<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                paste("-", k.candidate), "0")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">372</td>
+                    <td class="coverage">8074<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            if(grepl("_free", from)) { # add transfer to bound compartment for SFORB</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">373</td>
+                    <td class="coverage">24<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              m[from,to] = paste(m[from,to], "-", paste("k", from, "bound", sep = "_"))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">374</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">375</td>
+                    <td class="coverage">8074<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            if(grepl("_bound", from)) { # add backtransfer to free compartment for SFORB</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">376</td>
+                    <td class="coverage">24<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              m[from,to] = paste("- k", from, "free", sep = "_")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">377</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">378</td>
+                    <td class="coverage">8074<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            m[from,to] = m[from,to]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">379</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          } else {          # off-diagonal elements</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">380</td>
+                    <td class="coverage">7146<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            f.candidate = paste("f", from, "to", to, sep = "_")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">381</td>
+                    <td class="coverage">7146<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            k.candidate = paste("k", from, to, sep = "_")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">382</td>
+                    <td class="coverage">7146<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            k.candidate = sub("free.*bound", "free_bound", k.candidate)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">383</td>
+                    <td class="coverage">7146<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            k.candidate = sub("bound.*free", "bound_free", k.candidate)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">384</td>
+                    <td class="coverage">7146<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            m[to, from] = ifelse(f.candidate %in% model$parms,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">385</td>
+                    <td class="coverage">7146<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              paste(f.candidate, " * k_", from, sep = ""),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">386</td>
+                    <td class="coverage">7146<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              ifelse(k.candidate %in% model$parms, k.candidate, "0"))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">387</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            # Special case: singular pathway and no sink</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">388</td>
+                    <td class="coverage">7146<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            if (spec[[from]]$sink == FALSE &amp;&amp; length(spec[[from]]$to) == 1 &amp;&amp; to %in% spec[[from]]$to) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">389</td>
+                    <td class="coverage">689<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              m[to, from] = paste("k", from, sep = "_")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">390</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">391</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">392</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">393</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">394</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    } # }}}</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">395</td>
+                    <td class="coverage">5941<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    model$coefmat &lt;- m</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">396</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }#}}}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">397</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">398</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Try to create a function compiled from C code if there is more than one observed variable {{{</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">399</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # and a compiler is available</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">400</td>
+                    <td class="coverage">8117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (length(obs_vars) &gt; 1 &amp; pkgbuild::has_compiler()) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">401</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">402</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # Translate the R code for the derivatives to C code</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">403</td>
+                    <td class="coverage">3728<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    diffs.C &lt;- paste(diffs, collapse = ";\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">404</td>
+                    <td class="coverage">3728<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    diffs.C &lt;- paste0(diffs.C, ";")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">405</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">406</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # HS</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">407</td>
+                    <td class="coverage">3728<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    diffs.C &lt;- gsub(HS_decline, "(time &lt;= tb ? k1 : k2)", diffs.C, fixed = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">408</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">409</td>
+                    <td class="coverage">3728<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    for (i in seq_along(diffs)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">410</td>
+                    <td class="coverage">8347<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      state_var &lt;- names(diffs)[i]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">411</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">412</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      # IORE</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">413</td>
+                    <td class="coverage">8347<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      if (state_var %in% obs_vars) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">414</td>
+                    <td class="coverage">8343<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        if (spec[[state_var]]$type == "IORE") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">415</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          diffs.C &lt;- gsub(paste0(state_var, "^N_", state_var),</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">416</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                          paste0("pow(y[", i - 1, "], N_", state_var, ")"),</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">417</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                          diffs.C, fixed = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">418</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">419</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">420</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">421</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      # Replace d_... terms by f[i-1]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">422</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      # First line</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">423</td>
+                    <td class="coverage">8347<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      pattern &lt;- paste0("^d_", state_var)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">424</td>
+                    <td class="coverage">8347<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      replacement &lt;- paste0("\nf[", i - 1, "]")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">425</td>
+                    <td class="coverage">8347<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      diffs.C &lt;- gsub(pattern, replacement, diffs.C)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">426</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      # Other lines</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">427</td>
+                    <td class="coverage">8347<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      pattern &lt;- paste0("\\nd_", state_var)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">428</td>
+                    <td class="coverage">8347<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      replacement &lt;- paste0("\nf[", i - 1, "]")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">429</td>
+                    <td class="coverage">8347<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      diffs.C &lt;- gsub(pattern, replacement, diffs.C)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">430</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">431</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      # Replace names of observed variables by y[i],</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">432</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      # making the implicit assumption that the observed variables only occur after "* "</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">433</td>
+                    <td class="coverage">8347<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      pattern &lt;- paste0("\\* ", state_var)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">434</td>
+                    <td class="coverage">8347<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      replacement &lt;- paste0("* y[", i - 1, "]")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">435</td>
+                    <td class="coverage">8347<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      diffs.C &lt;- gsub(pattern, replacement, diffs.C)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">436</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">437</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">438</td>
+                    <td class="coverage">3728<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    derivs_sig &lt;- signature(n = "integer", t = "numeric", y = "numeric",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">439</td>
+                    <td class="coverage">3728<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                            f = "numeric", rpar = "numeric", ipar = "integer")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">440</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">441</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # Declare the time variable in the body of the function if it is used</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">442</td>
+                    <td class="coverage">3728<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    derivs_code &lt;- if (spec[[1]]$type %in% c("FOMC", "DFOP", "HS")) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">443</td>
+                    <td class="coverage">1060<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      paste0("double time = *t;\n", diffs.C)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">444</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">445</td>
+                    <td class="coverage">2668<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      diffs.C</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">446</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">447</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">448</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # Define the function initializing the parameters</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">449</td>
+                    <td class="coverage">3728<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    npar &lt;- length(parms)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">450</td>
+                    <td class="coverage">3728<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    initpar_code &lt;- paste0(</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">451</td>
+                    <td class="coverage">3728<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      "static double parms [", npar, "];\n",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">452</td>
+                    <td class="coverage">3728<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      paste0("#define ", parms, " parms[", 0:(npar - 1), "]\n", collapse = ""),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">453</td>
+                    <td class="coverage">3728<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      "\n",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">454</td>
+                    <td class="coverage">3728<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      "void initpar(void (* odeparms)(int *, double *)) {\n",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">455</td>
+                    <td class="coverage">3728<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      "    int N = ", npar, ";\n",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">456</td>
+                    <td class="coverage">3728<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      "    odeparms(&amp;N, parms);\n",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">457</td>
+                    <td class="coverage">3728<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      "}\n\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">458</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">459</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # Try to build a shared library</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">460</td>
+                    <td class="coverage">3728<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    model$cf &lt;- try(inline::cfunction(derivs_sig, derivs_code,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">461</td>
+                    <td class="coverage">3728<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      otherdefs = initpar_code,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">462</td>
+                    <td class="coverage">3728<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      verbose = verbose, name = "diffs",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">463</td>
+                    <td class="coverage">3728<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      convention = ".C", language = "C"),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">464</td>
+                    <td class="coverage">3728<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      silent = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">465</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">466</td>
+                    <td class="coverage">3728<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (!inherits(model$cf, "try-error")) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">467</td>
+                    <td class="coverage">495<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      if (!quiet) message("Temporary DLL for differentials generated and loaded")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">468</td>
+                    <td class="coverage">3728<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      if (!is.null(dll_dir)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">469</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        # We suppress warnings, as we get a warning about a path "(embedding)" </pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">470</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        # under Windows, at least when using RStudio</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">471</td>
+                    <td class="coverage">247<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        suppressWarnings(inline::moveDLL(model$cf, name, dll_dir,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">472</td>
+                    <td class="coverage">247<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          unload = unload, overwrite = overwrite, verbose = !quiet))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">473</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">474</td>
+                    <td class="coverage">3728<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      model$dll_info &lt;- inline::getDynLib(model$cf)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">475</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">476</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">477</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # }}}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">478</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">479</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Attach a degradation function if an analytical solution is available</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">480</td>
+                    <td class="coverage">8117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  model$deg_func &lt;- create_deg_func(spec, use_of_ff)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">481</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">482</td>
+                    <td class="coverage">8117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  class(model) &lt;- "mkinmod"</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">483</td>
+                    <td class="coverage">8117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  return(model)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">484</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">485</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">486</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Print mkinmod objects</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">487</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">488</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Print mkinmod objects in a way that the user finds his way to get to its</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">489</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' components.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">490</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">491</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @rdname mkinmod</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">492</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param x An \code{\link{mkinmod}} object.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">493</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">494</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">print.mkinmod &lt;- function(x, ...) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">495</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("&lt;mkinmod&gt; model generated with\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">496</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("Use of formation fractions $use_of_ff:", x$use_of_ff, "\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">497</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("Specification $spec:\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">498</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  for (obs in names(x$spec)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">499</td>
+                    <td class="coverage">208<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    cat("$", obs, "\n", sep = "")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">500</td>
+                    <td class="coverage">208<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    spl &lt;- x$spec[[obs]]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">501</td>
+                    <td class="coverage">208<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    cat("$type:", spl$type)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">502</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (!is.null(spl$to) &amp;&amp; length(spl$to)) cat("; $to: ", paste(spl$to, collapse = ", "), sep = "")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">503</td>
+                    <td class="coverage">208<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    cat("; $sink: ", spl$sink, sep = "")</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">504</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (!is.null(spl$full_name)) if (!is.na(spl$full_name)) cat("; $full_name:", spl$full_name)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">505</td>
+                    <td class="coverage">208<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    cat("\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">506</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">507</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (is.matrix(x$coefmat)) cat("Coefficient matrix $coefmat available\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">508</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (!is.null(x$cf)) cat("Compiled model $cf available\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">509</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("Differential equations:\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">510</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  nice_diffs &lt;- gsub("^(d.*) =", "\\1/dt =", x[["diffs"]])</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">511</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  writeLines(strwrap(nice_diffs, exdent = 11))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">512</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">513</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"># vim: set foldmethod=marker ts=2 sw=2 expandtab:</pre>
+                    </td>
+                  </tr>
+                </tbody>
+              </table>
+            </div>
+            <div id="R/parms.R" class="hidden">
+              <table class="table-condensed">
+                <tbody>
+                  <tr class="never">
+                    <td class="num">1</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Extract model parameters</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">2</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">3</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' This function returns degradation model parameters as well as error</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">4</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' model parameters per default, in order to avoid working with a fitted model</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">5</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' without considering the error structure that was assumed for the fit.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">6</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">7</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param object A fitted model object.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">8</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param \dots Not used</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">9</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @return Depending on the object, a numeric vector of fitted model parameters,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">10</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' a matrix (e.g. for mmkin row objects), or a list of matrices (e.g. for</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">11</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' mmkin objects with more than one row).</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">12</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @seealso [saem], [multistart]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">13</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @examples</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">14</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # mkinfit objects</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">15</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' fit &lt;- mkinfit("SFO", FOCUS_2006_C, quiet = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">16</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' parms(fit)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">17</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' parms(fit, transformed = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">18</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">19</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # mmkin objects</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">20</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' ds &lt;- lapply(experimental_data_for_UBA_2019[6:10],</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">21</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'  function(x) subset(x$data[c("name", "time", "value")]))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">22</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' names(ds) &lt;- paste("Dataset", 6:10)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">23</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' \dontrun{</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">24</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' fits &lt;- mmkin(c("SFO", "FOMC", "DFOP"), ds, quiet = TRUE, cores = 1)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">25</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' parms(fits["SFO", ])</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">26</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' parms(fits[, 2])</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">27</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' parms(fits)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">28</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' parms(fits, transformed = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">29</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">30</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">31</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">parms &lt;- function(object, ...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">32</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">{</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">33</td>
+                    <td class="coverage">91384<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  UseMethod("parms", object)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">34</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">35</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">36</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param transformed Should the parameters be returned as used internally</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">37</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' during the optimisation?</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">38</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param errparms Should the error model parameters be returned</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">39</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' in addition to the degradation parameters?</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">40</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @rdname parms</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">41</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">42</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">parms.mkinfit &lt;- function(object, transformed = FALSE, errparms = TRUE, ...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">43</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">{</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">44</td>
+                    <td class="coverage">88039<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  res &lt;- if (transformed) object$par</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">45</td>
+                    <td class="coverage">88039<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    else c(object$bparms.optim, object$errparms)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">46</td>
+                    <td class="coverage">88039<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (!errparms) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">47</td>
+                    <td class="coverage">3000<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    res[setdiff(names(res), names(object$errparms))]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">48</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">49</td>
+                    <td class="coverage">85039<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  else return(res)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">50</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">51</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">52</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @rdname parms</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">53</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">54</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">parms.mmkin &lt;- function(object, transformed = FALSE, errparms = TRUE, ...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">55</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">{</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">56</td>
+                    <td class="coverage">265<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (nrow(object) == 1) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">57</td>
+                    <td class="coverage">265<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    res &lt;- sapply(object, parms, transformed = transformed,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">58</td>
+                    <td class="coverage">265<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      errparms = errparms, ...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">59</td>
+                    <td class="coverage">265<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    colnames(res) &lt;- colnames(object)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">60</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">61</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    res &lt;- list()</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">62</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    for (i in 1:nrow(object)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">63</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      res[[i]] &lt;- parms(object[i, ], transformed = transformed,</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">64</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        errparms = errparms, ...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">65</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">66</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    names(res) &lt;- rownames(object)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">67</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">68</td>
+                    <td class="coverage">265<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  return(res)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">69</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">70</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">71</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param exclude_failed For [multistart] objects, should rows for failed fits</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">72</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' be removed from the returned parameter matrix?</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">73</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @rdname parms</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">74</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">75</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">parms.multistart &lt;- function(object, exclude_failed = TRUE, ...) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">76</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  res &lt;- t(sapply(object, parms))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">77</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  successful &lt;- which(!is.na(res[, 1]))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">78</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  first_success &lt;- successful[1]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">79</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  colnames(res) &lt;- names(parms(object[[first_success]]))</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">80</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (exclude_failed[1]) res &lt;- res[successful, ]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">81</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  return(res)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">82</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                </tbody>
+              </table>
+            </div>
+            <div id="R/plot.mkinfit.R" class="hidden">
+              <table class="table-condensed">
+                <tbody>
+                  <tr class="never">
+                    <td class="num">1</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">utils::globalVariables(c("type", "variable", "observed"))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">2</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">3</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Plot the observed data and the fitted model of an mkinfit object</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">4</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">5</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Solves the differential equations with the optimised and fixed parameters</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">6</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' from a previous successful call to \code{\link{mkinfit}} and plots the</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">7</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' observed data together with the solution of the fitted model.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">8</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">9</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' If the current plot device is a \code{\link[tikzDevice]{tikz}} device, then</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">10</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' latex is being used for the formatting of the chi2 error level, if</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">11</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' \code{show_errmin = TRUE}.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">12</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">13</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @aliases plot.mkinfit plot_sep plot_res plot_err</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">14</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param x Alias for fit introduced for compatibility with the generic S3</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">15</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   method.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">16</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param fit An object of class \code{\link{mkinfit}}.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">17</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param obs_vars A character vector of names of the observed variables for</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">18</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   which the data and the model should be plotted. Defauls to all observed</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">19</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   variables in the model.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">20</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param xlab Label for the x axis.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">21</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param ylab Label for the y axis.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">22</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param xlim Plot range in x direction.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">23</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param ylim Plot range in y direction. If given as a list, plot ranges</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">24</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   for the different plot rows can be given for row layout.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">25</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param col_obs Colors used for plotting the observed data and the</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">26</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   corresponding model prediction lines.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">27</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param pch_obs Symbols to be used for plotting the data.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">28</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param lty_obs Line types to be used for the model predictions.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">29</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param add Should the plot be added to an existing plot?</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">30</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param legend Should a legend be added to the plot?</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">31</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param show_residuals Should residuals be shown? If only one plot of the</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">32</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   fits is shown, the residual plot is in the lower third of the plot.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">33</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   Otherwise, i.e. if "sep_obs" is given, the residual plots will be located</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">34</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   to the right of the plots of the fitted curves. If this is set to</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">35</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   'standardized', a plot of the residuals divided by the standard deviation</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">36</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'    given by the fitted error model will be shown.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">37</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param standardized When calling 'plot_res', should the residuals be</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">38</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   standardized in the residual plot?</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">39</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param show_errplot Should squared residuals and the error model be shown?</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">40</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   If only one plot of the fits is shown, this plot is in the lower third of</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">41</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   the plot.  Otherwise, i.e. if "sep_obs" is given, the residual plots will</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">42</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   be located to the right of the plots of the fitted curves.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">43</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param maxabs Maximum absolute value of the residuals. This is used for the</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">44</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   scaling of the y axis and defaults to "auto".</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">45</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param sep_obs Should the observed variables be shown in separate subplots?</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">46</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   If yes, residual plots requested by "show_residuals" will be shown next</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">47</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   to, not below the plot of the fits.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">48</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param rel.height.middle The relative height of the middle plot, if more</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">49</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   than two rows of plots are shown.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">50</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param row_layout Should we use a row layout where the residual plot or the</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">51</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   error model plot is shown to the right?</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">52</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param lpos Position(s) of the legend(s). Passed to \code{\link{legend}} as</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">53</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   the first argument.  If not length one, this should be of the same length</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">54</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   as the obs_var argument.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">55</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param inset Passed to \code{\link{legend}} if applicable.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">56</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param show_errmin Should the FOCUS chi2 error value be shown in the upper</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">57</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   margin of the plot?</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">58</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param errmin_digits The number of significant digits for rounding the FOCUS</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">59</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   chi2 error percentage.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">60</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param frame Should a frame be drawn around the plots?</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">61</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param \dots Further arguments passed to \code{\link{plot}}.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">62</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @import graphics</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">63</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @importFrom grDevices dev.cur</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">64</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @return The function is called for its side effect.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">65</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @author Johannes Ranke</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">66</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @examples</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">67</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">68</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # One parent compound, one metabolite, both single first order, path from</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">69</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # parent to sink included</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">70</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' \dontrun{</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">71</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' SFO_SFO &lt;- mkinmod(parent = mkinsub("SFO", "m1", full = "Parent"),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">72</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'                    m1 = mkinsub("SFO", full = "Metabolite M1" ))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">73</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' fit &lt;- mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">74</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' fit &lt;- mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE, error_model = "tc")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">75</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' plot(fit)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">76</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' plot_res(fit)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">77</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' plot_res(fit, standardized = FALSE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">78</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' plot_err(fit)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">79</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">80</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # Show the observed variables separately, with residuals</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">81</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' plot(fit, sep_obs = TRUE, show_residuals = TRUE, lpos = c("topright", "bottomright"),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">82</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'      show_errmin = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">83</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">84</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # The same can be obtained with less typing, using the convenience function plot_sep</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">85</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' plot_sep(fit, lpos = c("topright", "bottomright"))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">86</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">87</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # Show the observed variables separately, with the error model</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">88</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' plot(fit, sep_obs = TRUE, show_errplot = TRUE, lpos = c("topright", "bottomright"),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">89</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'      show_errmin = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">90</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">91</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">92</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">93</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">plot.mkinfit &lt;- function(x, fit = x,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">94</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  obs_vars = names(fit$mkinmod$map),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">95</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  xlab = "Time", ylab = "Residue",</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">96</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  xlim = range(fit$data$time),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">97</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  ylim = "default",</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">98</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  col_obs = 1:length(obs_vars),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">99</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  pch_obs = col_obs,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">100</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  lty_obs = rep(1, length(obs_vars)),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">101</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  add = FALSE, legend = !add,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">102</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  show_residuals = FALSE,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">103</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  show_errplot = FALSE,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">104</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  maxabs = "auto",</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">105</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  sep_obs = FALSE, rel.height.middle = 0.9,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">106</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  row_layout = FALSE,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">107</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  lpos = "topright", inset = c(0.05, 0.05),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">108</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  show_errmin = FALSE, errmin_digits = 3,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">109</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  frame = TRUE, ...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">110</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">{</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">111</td>
+                    <td class="coverage">1503<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (identical(show_residuals, "standardized")) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">112</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    show_residuals &lt;- TRUE</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">113</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    standardized &lt;- TRUE</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">114</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">115</td>
+                    <td class="coverage">1503<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    standardized &lt;- FALSE</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">116</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">117</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">118</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (add &amp;&amp; show_residuals) stop("If adding to an existing plot we can not show residuals")</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">119</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (add &amp;&amp; show_errplot) stop("If adding to an existing plot we can not show the error model plot")</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">120</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (show_residuals &amp;&amp; show_errplot) stop("We can either show residuals over time or the error model plot, not both")</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">121</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (add &amp;&amp; sep_obs) stop("If adding to an existing plot we can not show observed variables separately")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">122</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">123</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">124</td>
+                    <td class="coverage">1503<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  solution_type = fit$solution_type</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">125</td>
+                    <td class="coverage">1503<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  parms.all &lt;- c(fit$bparms.optim, fit$bparms.fixed)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">126</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">127</td>
+                    <td class="coverage">1503<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  ininames &lt;- c(</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">128</td>
+                    <td class="coverage">1503<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    rownames(subset(fit$start, type == "state")),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">129</td>
+                    <td class="coverage">1503<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    rownames(subset(fit$fixed, type == "state")))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">130</td>
+                    <td class="coverage">1503<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  odeini &lt;- parms.all[ininames]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">131</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">132</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Order initial state variables</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">133</td>
+                    <td class="coverage">1503<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  names(odeini) &lt;- sub("_0", "", names(odeini))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">134</td>
+                    <td class="coverage">1503<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  odeini &lt;- odeini[names(fit$mkinmod$diffs)]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">135</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">136</td>
+                    <td class="coverage">1503<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  outtimes &lt;- seq(xlim[1], xlim[2], length.out=100)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">137</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">138</td>
+                    <td class="coverage">1503<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  odenames &lt;- c(</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">139</td>
+                    <td class="coverage">1503<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    rownames(subset(fit$start, type == "deparm")),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">140</td>
+                    <td class="coverage">1503<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    rownames(subset(fit$fixed, type == "deparm")))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">141</td>
+                    <td class="coverage">1503<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  odeparms &lt;- parms.all[odenames]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">142</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">143</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">144</td>
+                    <td class="coverage">1503<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (solution_type == "deSolve" &amp; !is.null(fit$mkinmod$cf)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">145</td>
+                    <td class="coverage">140<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    fit$mkinmod[["symbols"]] &lt;- deSolve::checkDLL(dllname = fit$mkinmod$dll_info[["name"]],</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">146</td>
+                    <td class="coverage">140<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      func = "diffs", initfunc = "initpar",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">147</td>
+                    <td class="coverage">140<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      jacfunc = NULL, nout = 0, outnames = NULL)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">148</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">149</td>
+                    <td class="coverage">1503<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  out &lt;- mkinpredict(fit$mkinmod, odeparms, odeini, outtimes,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">150</td>
+                    <td class="coverage">1503<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">           solution_type = solution_type, atol = fit$atol, rtol = fit$rtol)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">151</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">152</td>
+                    <td class="coverage">1503<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  out &lt;- as.data.frame(out)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">153</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">154</td>
+                    <td class="coverage">1503<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  names(col_obs) &lt;- names(pch_obs) &lt;- names(lty_obs) &lt;- obs_vars</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">155</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">156</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Create a plot layout only if not to be added to an existing plot</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">157</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # or only a single plot is requested (e.g. by plot.mmkin)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">158</td>
+                    <td class="coverage">1503<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  do_layout = FALSE</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">159</td>
+                    <td class="coverage">485<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (show_residuals | sep_obs | show_errplot) do_layout = TRUE</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">160</td>
+                    <td class="coverage">1503<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  n_plot_rows = if (sep_obs) length(obs_vars) else 1</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">161</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">162</td>
+                    <td class="coverage">1503<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (do_layout) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">163</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # Layout should be restored afterwards</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">164</td>
+                    <td class="coverage">485<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    oldpar &lt;- par(no.readonly = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">165</td>
+                    <td class="coverage">485<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    on.exit(par(oldpar, no.readonly = TRUE))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">166</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">167</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # If the observed variables are shown separately, or if requested, do row layout</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">168</td>
+                    <td class="coverage">485<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (sep_obs | row_layout) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">169</td>
+                    <td class="coverage">415<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      row_layout &lt;- TRUE</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">170</td>
+                    <td class="coverage">415<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      n_plot_cols = if (show_residuals | show_errplot) 2 else 1</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">171</td>
+                    <td class="coverage">415<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      n_plots = n_plot_rows * n_plot_cols</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">172</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">173</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      # Set relative plot heights, so the first and the last plot are the norm</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">174</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      # and the middle plots (if n_plot_rows &gt;2) are smaller by rel.height.middle</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">175</td>
+                    <td class="coverage">415<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      rel.heights &lt;- if (n_plot_rows &gt; 2) c(1, rep(rel.height.middle, n_plot_rows - 2), 1)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">176</td>
+                    <td class="coverage">415<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                     else rep(1, n_plot_rows)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">177</td>
+                    <td class="coverage">415<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      layout_matrix = matrix(1:n_plots,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">178</td>
+                    <td class="coverage">415<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                             n_plot_rows, n_plot_cols, byrow = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">179</td>
+                    <td class="coverage">415<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      layout(layout_matrix, heights = rel.heights)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">180</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    } else { # else show residuals in the lower third to keep compatibility</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">181</td>
+                    <td class="coverage">70<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      layout(matrix(c(1, 2), 2, 1), heights = c(2, 1.3))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">182</td>
+                    <td class="coverage">70<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            par(mar = c(3, 4, 4, 2) + 0.1)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">183</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">184</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">185</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">186</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Replicate legend position argument if necessary</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">187</td>
+                    <td class="coverage">1503<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (length(lpos) == 1) lpos = rep(lpos, n_plot_rows)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">188</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">189</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Loop over plot rows</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">190</td>
+                    <td class="coverage">1503<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  for (plot_row in 1:n_plot_rows) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">191</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">192</td>
+                    <td class="coverage">1503<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    row_obs_vars = if (sep_obs) obs_vars[plot_row] else obs_vars</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">193</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">194</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # Set ylim to sensible default, or to the specified value</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">195</td>
+                    <td class="coverage">1503<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (is.list(ylim)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">196</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      ylim_row &lt;- ylim[[plot_row]]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">197</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">198</td>
+                    <td class="coverage">1503<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      if (ylim[[1]] == "default") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">199</td>
+                    <td class="coverage">1503<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        ylim_row = c(0, max(c(subset(fit$data, variable %in% row_obs_vars)$observed,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">200</td>
+                    <td class="coverage">1503<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                            unlist(out[row_obs_vars])), na.rm = TRUE))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">201</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">202</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        ylim_row = ylim</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">203</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">204</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">205</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">206</td>
+                    <td class="coverage">1503<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (row_layout) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">207</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      # Margins for top row of plots when we have more than one row</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">208</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      # Reduce bottom margin by 2.1 - hides x axis legend</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">209</td>
+                    <td class="coverage">415<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      if (plot_row == 1 &amp; n_plot_rows &gt; 1) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">210</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        par(mar = c(3.0, 4.1, 4.1, 2.1))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">211</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">212</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">213</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      # Margins for middle rows of plots, if any</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">214</td>
+                    <td class="coverage">415<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      if (plot_row &gt; 1 &amp; plot_row &lt; n_plot_rows) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">215</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        # Reduce top margin by 2 after the first plot as we have no main title,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">216</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        # reduced plot height, therefore we need rel.height.middle in the layout</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">217</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        par(mar = c(3.0, 4.1, 2.1, 2.1))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">218</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">219</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">220</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      # Margins for bottom row of plots when we have more than one row</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">221</td>
+                    <td class="coverage">415<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      if (plot_row == n_plot_rows &amp; n_plot_rows &gt; 1) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">222</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        # Restore bottom margin for last plot to show x axis legend</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">223</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        par(mar = c(5.1, 4.1, 2.1, 2.1))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">224</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">225</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">226</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">227</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # Set up the main plot if not to be added to an existing plot</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">228</td>
+                    <td class="coverage">1503<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (add == FALSE) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">229</td>
+                    <td class="coverage">1503<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      plot(0, type="n",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">230</td>
+                    <td class="coverage">1503<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        xlim = xlim, ylim = ylim_row,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">231</td>
+                    <td class="coverage">1503<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        xlab = xlab, ylab = ylab, frame = frame, ...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">232</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">233</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">234</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # Plot the data</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">235</td>
+                    <td class="coverage">1503<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    for (obs_var in row_obs_vars) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">236</td>
+                    <td class="coverage">1708<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      points(subset(fit$data, variable == obs_var, c(time, observed)),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">237</td>
+                    <td class="coverage">1708<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        pch = pch_obs[obs_var], col = col_obs[obs_var])</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">238</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">239</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">240</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # Plot the model output</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">241</td>
+                    <td class="coverage">1503<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    matlines(out$time, out[row_obs_vars], col = col_obs[row_obs_vars], lty = lty_obs[row_obs_vars])</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">242</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">243</td>
+                    <td class="coverage">1503<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (legend == TRUE) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">244</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      # Get full names from model definition if they are available</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">245</td>
+                    <td class="coverage">695<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      legend_names = lapply(row_obs_vars, function(x) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">246</td>
+                    <td class="coverage">900<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                            if (!is.null(fit$mkinmod$spec[[x]]$full_name))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">247</td>
+                    <td class="coverage">410<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                              if (is.na(fit$mkinmod$spec[[x]]$full_name)) x</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">248</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                              else fit$mkinmod$spec[[x]]$full_name</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">249</td>
+                    <td class="coverage">490<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                            else x</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">250</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        })</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">251</td>
+                    <td class="coverage">695<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      legend(lpos[plot_row], inset= inset, legend = legend_names,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">252</td>
+                    <td class="coverage">695<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        col = col_obs[row_obs_vars], pch = pch_obs[row_obs_vars], lty = lty_obs[row_obs_vars])</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">253</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">254</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">255</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # Show chi2 error value if requested</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">256</td>
+                    <td class="coverage">1503<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (show_errmin) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">257</td>
+                    <td class="coverage">70<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      if (length(row_obs_vars) == 1) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">258</td>
+                    <td class="coverage">70<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        errmin_var = row_obs_vars</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">259</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">260</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        errmin_var = "All data"</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">261</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        if (length(row_obs_vars) != length(fit$mkinmod$map)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">262</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          warning("Showing chi2 error level for all data, but only ",</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">263</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                  row_obs_vars, " were selected for plotting")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">264</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">265</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">266</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">267</td>
+                    <td class="coverage">70<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      chi2 &lt;- signif(100 * mkinerrmin(fit)[errmin_var, "err.min"], errmin_digits)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">268</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      # Use LateX if the current plotting device is tikz</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">269</td>
+                    <td class="coverage">70<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      if (names(dev.cur()) == "tikz output") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">270</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        chi2_text &lt;- paste0("$\\chi^2$ error level = ", chi2, "\\%")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">271</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">272</td>
+                    <td class="coverage">70<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        chi2_perc &lt;- paste0(chi2, "%")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">273</td>
+                    <td class="coverage">70<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        chi2_text &lt;- bquote(chi^2 ~ "error level" == .(chi2_perc))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">274</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">275</td>
+                    <td class="coverage">70<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      mtext(chi2_text, cex = 0.7, line = 0.4)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">276</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">277</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">278</td>
+                    <td class="coverage">1503<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (do_layout &amp; !row_layout) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">279</td>
+                    <td class="coverage">70<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      par(mar = c(5, 4, 0, 2) + 0.1)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">280</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">281</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">282</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # Show residuals if requested</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">283</td>
+                    <td class="coverage">1503<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (show_residuals) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">284</td>
+                    <td class="coverage">280<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      mkinresplot(fit, obs_vars = row_obs_vars, standardized = standardized,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">285</td>
+                    <td class="coverage">280<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        pch_obs = pch_obs[row_obs_vars], col_obs = col_obs[row_obs_vars],</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">286</td>
+                    <td class="coverage">280<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        legend = FALSE, frame = frame, xlab = xlab, xlim = xlim, maxabs = maxabs)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">287</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">288</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">289</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # Show error model plot if requested</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">290</td>
+                    <td class="coverage">1503<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (show_errplot) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">291</td>
+                    <td class="coverage">205<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      mkinerrplot(fit, obs_vars = row_obs_vars,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">292</td>
+                    <td class="coverage">205<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        pch_obs = pch_obs[row_obs_vars], col_obs = col_obs[row_obs_vars],</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">293</td>
+                    <td class="coverage">205<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        legend = FALSE, frame = frame)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">294</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">295</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">296</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">297</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">298</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @rdname plot.mkinfit</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">299</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">300</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">plot_sep &lt;- function(fit, show_errmin = TRUE,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">301</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  show_residuals = ifelse(identical(fit$err_mod, "const"), TRUE, "standardized"), ...) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">302</td>
+                    <td class="coverage">70<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  plot.mkinfit(fit, sep_obs = TRUE, show_residuals = show_residuals,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">303</td>
+                    <td class="coverage">70<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          show_errmin = show_errmin, ...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">304</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">305</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">306</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @rdname plot.mkinfit</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">307</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">308</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">plot_res &lt;- function(fit, sep_obs = FALSE, show_errmin = sep_obs,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">309</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  standardized = ifelse(identical(fit$err_mod, "const"), FALSE, TRUE), ...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">310</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">{</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">311</td>
+                    <td class="coverage">140<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  plot.mkinfit(fit, sep_obs = sep_obs, show_errmin = show_errmin,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">312</td>
+                    <td class="coverage">140<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    show_residuals = ifelse(standardized, "standardized", TRUE),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">313</td>
+                    <td class="coverage">140<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    row_layout = TRUE, ...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">314</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">315</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">316</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @rdname plot.mkinfit</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">317</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">318</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">plot_err &lt;- function(fit, sep_obs = FALSE, show_errmin = sep_obs, ...) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">319</td>
+                    <td class="coverage">205<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  plot.mkinfit(fit, sep_obs = sep_obs, show_errmin = show_errmin,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">320</td>
+                    <td class="coverage">205<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    show_errplot = TRUE, row_layout = TRUE, ...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">321</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">322</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">323</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Plot the observed data and the fitted model of an mkinfit object</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">324</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">325</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Deprecated function. It now only calls the plot method</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">326</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' \code{\link{plot.mkinfit}}.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">327</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">328</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param fit an object of class \code{\link{mkinfit}}.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">329</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param \dots further arguments passed to \code{\link{plot.mkinfit}}.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">330</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @return The function is called for its side effect.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">331</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @author Johannes Ranke</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">332</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">333</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">mkinplot &lt;- function(fit, ...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">334</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">{</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">335</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  plot(fit, ...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">336</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                </tbody>
+              </table>
+            </div>
+            <div id="R/illparms.R" class="hidden">
+              <table class="table-condensed">
+                <tbody>
+                  <tr class="never">
+                    <td class="num">1</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Method to get the names of ill-defined parameters</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">2</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">3</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' The method for generalised nonlinear regression fits as obtained</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">4</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' with [mkinfit] and [mmkin] checks if the degradation parameters</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">5</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' pass the Wald test (in degradation kinetics often simply called t-test) for</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">6</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' significant difference from zero. For this test, the parameterisation</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">7</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' without parameter transformations is used.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">8</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">9</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' The method for hierarchical model fits, also known as nonlinear</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">10</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' mixed-effects model fits as obtained with [saem] and [mhmkin]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">11</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' checks if any of the confidence intervals for the random</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">12</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' effects expressed as standard deviations include zero, and if</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">13</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' the confidence intervals for the error model parameters include</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">14</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' zero.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">15</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">16</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param object The object to investigate</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">17</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param x The object to be printed</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">18</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param conf.level The confidence level for checking p values</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">19</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param \dots For potential future extensions</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">20</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param random For hierarchical fits, should random effects be tested?</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">21</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param errmod For hierarchical fits, should error model parameters be</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">22</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' tested?</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">23</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param slopes For hierarchical [saem] fits using saemix as backend,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">24</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' should slope parameters in the covariate model(starting with 'beta_') be</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">25</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' tested?</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">26</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @return For [mkinfit] or [saem] objects, a character vector of parameter</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">27</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' names. For [mmkin] or [mhmkin] objects, a matrix like object of class</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">28</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' 'illparms.mmkin' or 'illparms.mhmkin'.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">29</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @note All return objects have printing methods. For the single fits, printing</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">30</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' does not output anything in the case no ill-defined parameters are found.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">31</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">32</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">illparms &lt;- function(object, ...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">33</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">{</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">34</td>
+                    <td class="coverage">2471<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  UseMethod("illparms", object)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">35</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">36</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">37</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @rdname illparms</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">38</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">39</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @examples</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">40</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' fit &lt;- mkinfit("FOMC", FOCUS_2006_A, quiet = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">41</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' illparms(fit)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">42</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">illparms.mkinfit &lt;- function(object, conf.level = 0.95, ...) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">43</td>
+                    <td class="coverage">8<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  p_values &lt;- suppressWarnings(summary(object)$bpar[, "Pr(&gt;t)"])</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">44</td>
+                    <td class="coverage">8<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  na &lt;- is.na(p_values)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">45</td>
+                    <td class="coverage">8<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  failed &lt;- p_values &gt; 1 - conf.level</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">46</td>
+                    <td class="coverage">8<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  ret &lt;- names(parms(object))[na | failed]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">47</td>
+                    <td class="coverage">8<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  class(ret) &lt;- "illparms.mkinfit"</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">48</td>
+                    <td class="coverage">8<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  return(ret)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">49</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">50</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">51</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @rdname illparms</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">52</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">53</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">print.illparms.mkinfit &lt;- function(x, ...) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">54</td>
+                    <td class="coverage">190<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  class(x) &lt;- NULL</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">55</td>
+                    <td class="coverage">190<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (length(x) &gt; 0) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">56</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    print(as.character(x))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">57</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">58</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">59</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">60</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @rdname illparms</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">61</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">62</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @examples</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">63</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' \dontrun{</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">64</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' fits &lt;- mmkin(</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">65</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   c("SFO", "FOMC"),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">66</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   list("FOCUS A" = FOCUS_2006_A,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">67</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'        "FOCUS C" = FOCUS_2006_C),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">68</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   quiet = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">69</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' illparms(fits)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">70</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">71</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">illparms.mmkin &lt;- function(object, conf.level = 0.95, ...) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">72</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  result &lt;- lapply(object,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">73</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    function(fit) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">74</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      if (inherits(fit, "try-error")) return("E")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">75</td>
+                    <td class="coverage">8<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      ill &lt;- illparms(fit, conf.level = conf.level)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">76</td>
+                    <td class="coverage">8<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      if (length(ill) &gt; 0) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">77</td>
+                    <td class="coverage">3<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        return(paste(ill, collapse = ", "))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">78</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">79</td>
+                    <td class="coverage">5<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        return("")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">80</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">81</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    })</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">82</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  result &lt;- unlist(result)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">83</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  dim(result) &lt;- dim(object)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">84</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  dimnames(result) &lt;- dimnames(object)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">85</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  class(result) &lt;- "illparms.mmkin"</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">86</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  return(result)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">87</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">88</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">89</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @rdname illparms</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">90</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">91</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">print.illparms.mmkin &lt;- function(x, ...) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">92</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  class(x) &lt;- NULL</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">93</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  print(x, quote = FALSE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">94</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">95</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">96</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @rdname illparms</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">97</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">98</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">illparms.saem.mmkin &lt;- function(object, conf.level = 0.95, random = TRUE, errmod = TRUE, slopes = TRUE, ...) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">99</td>
+                    <td class="coverage">2091<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (inherits(object$so, "try-error")) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">100</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    ill_parms &lt;- NA</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">101</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">102</td>
+                    <td class="coverage">2091<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    ints &lt;- intervals(object, conf.level = conf.level)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">103</td>
+                    <td class="coverage">2091<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    ill_parms &lt;- character(0)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">104</td>
+                    <td class="coverage">2091<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (random) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">105</td>
+                    <td class="coverage">2091<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      ill_parms_random_i &lt;- which(ints$random[, "lower"] &lt; 0)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">106</td>
+                    <td class="coverage">2091<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      ill_parms_random &lt;- rownames(ints$random)[ill_parms_random_i]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">107</td>
+                    <td class="coverage">2091<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      ill_parms &lt;- c(ill_parms, ill_parms_random)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">108</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">109</td>
+                    <td class="coverage">2091<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (errmod) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">110</td>
+                    <td class="coverage">2091<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      ill_parms_errmod_i &lt;- which(ints$errmod[, "lower"] &lt; 0 &amp; ints$errmod[, "upper"] &gt; 0)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">111</td>
+                    <td class="coverage">2091<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      ill_parms_errmod &lt;- rownames(ints$errmod)[ill_parms_errmod_i]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">112</td>
+                    <td class="coverage">2091<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      ill_parms &lt;- c(ill_parms, ill_parms_errmod)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">113</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">114</td>
+                    <td class="coverage">2091<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (slopes) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">115</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      if (is.null(object$so)) stop("Slope testing is only implemented for the saemix backend")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">116</td>
+                    <td class="coverage">2091<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      slope_names &lt;- grep("^beta_", object$so@model@name.fixed, value = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">117</td>
+                    <td class="coverage">2091<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      ci &lt;- object$so@results@conf.int</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">118</td>
+                    <td class="coverage">2091<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      rownames(ci) &lt;- ci$name</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">119</td>
+                    <td class="coverage">2091<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      slope_ci &lt;- ci[slope_names, ]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">120</td>
+                    <td class="coverage">2091<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      ill_parms_slopes &lt;- slope_ci[, "lower"] &lt; 0 &amp; slope_ci[, "upper"] &gt; 0</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">121</td>
+                    <td class="coverage">2091<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      ill_parms &lt;- c(ill_parms, slope_names[ill_parms_slopes])</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">122</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">123</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">124</td>
+                    <td class="coverage">2091<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  class(ill_parms) &lt;- "illparms.saem.mmkin"</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">125</td>
+                    <td class="coverage">2091<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  return(ill_parms)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">126</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">127</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">128</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @rdname illparms</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">129</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">130</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">print.illparms.saem.mmkin &lt;- print.illparms.mkinfit</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">131</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">132</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @rdname illparms</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">133</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">134</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">illparms.mhmkin &lt;- function(object, conf.level = 0.95, random = TRUE, errmod = TRUE, ...) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">135</td>
+                    <td class="coverage">371<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (inherits(object[[1]], "saem.mmkin")) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">136</td>
+                    <td class="coverage">371<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    check_failed &lt;- function(x) if (inherits(x$so, "try-error")) TRUE else FALSE</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">137</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">138</td>
+                    <td class="coverage">371<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  result &lt;- lapply(object,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">139</td>
+                    <td class="coverage">371<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    function(fit) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">140</td>
+                    <td class="coverage">1484<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      if (check_failed(fit)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">141</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        return("E")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">142</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">143</td>
+                    <td class="coverage">1484<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        if (!is.null(fit$FIM_failed) &amp;&amp;</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">144</td>
+                    <td class="coverage">1484<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          "random effects and error model parameters" %in% fit$FIM_failed) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">145</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          return("NA")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">146</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">147</td>
+                    <td class="coverage">1484<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          ill &lt;- illparms(fit, conf.level = conf.level, random = random, errmod = errmod)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">148</td>
+                    <td class="coverage">1484<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          if (length(ill) &gt; 0) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">149</td>
+                    <td class="coverage">1000<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            return(paste(ill, collapse = ", "))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">150</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">151</td>
+                    <td class="coverage">484<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            return("")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">152</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">153</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">154</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">155</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    })</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">156</td>
+                    <td class="coverage">371<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  result &lt;- unlist(result)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">157</td>
+                    <td class="coverage">371<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  dim(result) &lt;- dim(object)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">158</td>
+                    <td class="coverage">371<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  dimnames(result) &lt;- dimnames(object)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">159</td>
+                    <td class="coverage">371<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  class(result) &lt;- "illparms.mhmkin"</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">160</td>
+                    <td class="coverage">371<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  return(result)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">161</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">162</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">163</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @rdname illparms</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">164</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">165</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">print.illparms.mhmkin &lt;- function(x, ...) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">166</td>
+                    <td class="coverage">125<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  class(x) &lt;- NULL</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">167</td>
+                    <td class="coverage">125<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  print(x, quote = FALSE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">168</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                </tbody>
+              </table>
+            </div>
+            <div id="R/nafta.R" class="hidden">
+              <table class="table-condensed">
+                <tbody>
+                  <tr class="never">
+                    <td class="num">1</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Evaluate parent kinetics using the NAFTA guidance</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">2</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">3</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' The function fits the SFO, IORE and DFOP models using \code{\link{mmkin}}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">4</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' and returns an object of class \code{nafta} that has methods for printing</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">5</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' and plotting.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">6</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">7</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param ds A dataframe that must contain one variable called "time" with the</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">8</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   time values specified by the \code{time} argument, one column called</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">9</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   "name" with the grouping of the observed values, and finally one column of</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">10</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   observed values called "value".</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">11</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param title Optional title of the dataset</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">12</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param quiet Should the evaluation text be shown?</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">13</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param \dots Further arguments passed to \code{\link{mmkin}} (not for the</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">14</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'  printing method).</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">15</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @importFrom stats qf</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">16</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @return An list of class \code{nafta}. The list element named "mmkin" is the</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">17</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   \code{\link{mmkin}} object containing the fits of the three models. The</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">18</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   list element named "title" contains the title of the dataset used. The</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">19</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   list element "data" contains the dataset used in the fits.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">20</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @author Johannes Ranke</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">21</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @source NAFTA (2011) Guidance for evaluating and calculating degradation</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">22</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   kinetics in environmental media. NAFTA Technical Working Group on</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">23</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   Pesticides</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">24</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   \url{https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/guidance-evaluating-and-calculating-degradation}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">25</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   accessed 2019-02-22</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">26</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">27</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   US EPA (2015) Standard Operating Procedure for Using the NAFTA Guidance to</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">28</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   Calculate Representative Half-life Values and Characterizing Pesticide</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">29</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   Degradation</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">30</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   \url{https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/standard-operating-procedure-using-nafta-guidance}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">31</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @examples</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">32</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">33</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   nafta_evaluation &lt;- nafta(NAFTA_SOP_Appendix_D, cores = 1)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">34</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   print(nafta_evaluation)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">35</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   plot(nafta_evaluation)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">36</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">37</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">38</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">nafta &lt;- function(ds, title = NA, quiet = FALSE, ...) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">39</td>
+                    <td class="coverage">264<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (length(levels(ds$name)) &gt; 1) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">40</td>
+                    <td class="coverage">88<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    stop("The NAFTA procedure is only defined for decline data for a single compound")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">41</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">42</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  n &lt;- nrow(subset(ds, !is.na(value)))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">43</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  models &lt;- c("SFO", "IORE", "DFOP")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">44</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">45</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  result &lt;- list(title = title, data = ds)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">46</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  result$mmkin &lt;- mmkin(models, list(ds), quiet = TRUE, ...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">47</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">48</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  distimes &lt;- lapply(result$mmkin, function(x) as.numeric(endpoints(x)$distimes["parent", ]))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">49</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">50</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  result$distimes &lt;- matrix(NA, nrow = 3, ncol = 3,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">51</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    dimnames = list(models, c("DT50", "DT90", "DT50_rep")))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">52</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  result$distimes["SFO", ] &lt;- distimes[[1]][c(1, 2, 1)]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">53</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  result$distimes["IORE", ] &lt;- distimes[[2]][c(1, 2, 3)]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">54</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  result$distimes["DFOP", ] &lt;- distimes[[3]][c(1, 2, 5)]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">55</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">56</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Get parameters with statistics</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">57</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  result$parameters &lt;- lapply(result$mmkin, function(x) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">58</td>
+                    <td class="coverage">528<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    summary(x)$bpar[, c(1, 4:6)]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">59</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  })</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">60</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  names(result$parameters) &lt;- models</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">61</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">62</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Compare the sum of squared residuals (SSR) to the upper bound of the</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">63</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # confidence region of the SSR for the IORE model</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">64</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  result$S &lt;- sapply(result$mmkin, function(x) sum(x$data$residual^2))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">65</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  names(result$S) &lt;- c("SFO", "IORE", "DFOP")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">66</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Equation (3) on p. 3</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">67</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  p &lt;- 3</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">68</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  result$S["IORE"]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">69</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  result$S_c &lt;- result$S[["IORE"]] * (1 + p/(n - p) * qf(0.5, p, n - p))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">70</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">71</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  result$t_rep &lt;- .evaluate_nafta_results(result$S, result$S_c,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">72</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    result$distimes, quiet = quiet)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">73</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">74</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  class(result) &lt;- "nafta"</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">75</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  return(result)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">76</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">77</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">78</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Plot the results of the three models used in the NAFTA scheme.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">79</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">80</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' The plots are ordered with increasing complexity of the model in this</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">81</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' function (SFO, then IORE, then DFOP).</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">82</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">83</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Calls \code{\link{plot.mmkin}}.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">84</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">85</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param x An object of class \code{\link{nafta}}.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">86</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param legend Should a legend be added?</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">87</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param main Possibility to override the main title of the plot.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">88</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param \dots Further arguments passed to \code{\link{plot.mmkin}}.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">89</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @return The function is called for its side effect.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">90</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @author Johannes Ranke</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">91</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">92</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">plot.nafta &lt;- function(x, legend = FALSE, main = "auto", ...) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">93</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (main == "auto") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">94</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (is.na(x$title)) main = ""</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">95</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    else main = x$title</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">96</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">97</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  plot(x$mmkin, ..., legend = legend, main = main)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">98</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">99</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">100</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Print nafta objects</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">101</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">102</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Print nafta objects. The results for the three models are printed in the</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">103</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' order of increasing model complexity, i.e. SFO, then IORE, and finally DFOP.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">104</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">105</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param x An \code{\link{nafta}} object.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">106</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param digits Number of digits to be used for printing parameters and</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">107</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   dissipation times.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">108</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @rdname nafta</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">109</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">110</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">print.nafta &lt;- function(x, quiet = TRUE, digits = 3, ...) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">111</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("Sums of squares:\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">112</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  print(x$S)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">113</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("\nCritical sum of squares for checking the SFO model:\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">114</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  print(x$S_c)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">115</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("\nParameters:\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">116</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  print(x$parameters, digits = digits)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">117</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  t_rep &lt;- .evaluate_nafta_results(x$S, x$S_c, x$distimes, quiet = quiet)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">118</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("\nDTx values:\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">119</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  print(signif(x$distimes, digits = digits))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">120</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("\nRepresentative half-life:\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">121</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  print(round(t_rep, 2))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">122</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">123</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">124</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">.evaluate_nafta_results &lt;- function(S, S_c, distimes, quiet = FALSE) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">125</td>
+                    <td class="coverage">352<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  t_SFO &lt;- distimes["IORE", "DT50"]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">126</td>
+                    <td class="coverage">352<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  t_IORE &lt;- distimes["IORE", "DT50_rep"]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">127</td>
+                    <td class="coverage">352<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  t_DFOP2 &lt;- distimes["DFOP", "DT50_rep"]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">128</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">129</td>
+                    <td class="coverage">352<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (S["SFO"] &lt; S_c) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">130</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (!quiet) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">131</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      message("S_SFO is lower than the critical value S_c, use the SFO model")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">132</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">133</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    t_rep &lt;- t_SFO</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">134</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">135</td>
+                    <td class="coverage">352<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (!quiet) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">136</td>
+                    <td class="coverage">88<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      message("The SFO model is rejected as S_SFO is equal or higher than the critical value S_c")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">137</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">138</td>
+                    <td class="coverage">352<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (t_IORE &lt; t_DFOP2) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">139</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      if (!quiet) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">140</td>
+                    <td class="coverage">88<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        message("The half-life obtained from the IORE model may be used")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">141</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">142</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      t_rep &lt;- t_IORE</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">143</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">144</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      if (!quiet) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">145</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        message("The representative half-life of the IORE model is longer than the one corresponding")</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">146</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        message("to the terminal degradation rate found with the DFOP model.")</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">147</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        message("The representative half-life obtained from the DFOP model may be used")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">148</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">149</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      t_rep &lt;- t_DFOP2</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">150</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">151</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">152</td>
+                    <td class="coverage">352<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  return(t_rep)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">153</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                </tbody>
+              </table>
+            </div>
+            <div id="R/summary.mkinfit.R" class="hidden">
+              <table class="table-condensed">
+                <tbody>
+                  <tr class="never">
+                    <td class="num">1</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Summary method for class "mkinfit"</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">2</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">3</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Lists model equations, initial parameter values, optimised parameters with</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">4</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' some uncertainty statistics, the chi2 error levels calculated according to</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">5</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' FOCUS guidance (2006) as defined therein, formation fractions, DT50 values</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">6</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' and optionally the data, consisting of observed, predicted and residual</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">7</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' values.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">8</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">9</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param object an object of class [mkinfit].</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">10</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param x an object of class \code{summary.mkinfit}.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">11</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param data logical, indicating whether the data should be included in the</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">12</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' summary.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">13</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param distimes logical, indicating whether DT50 and DT90 values should be</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">14</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' included.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">15</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param alpha error level for confidence interval estimation from t</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">16</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' distribution</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">17</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param digits Number of digits to use for printing</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">18</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param \dots optional arguments passed to methods like \code{print}.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">19</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @importFrom stats qt pt cov2cor</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">20</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @return The summary function returns a list with components, among others</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">21</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   \item{version, Rversion}{The mkin and R versions used}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">22</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   \item{date.fit, date.summary}{The dates where the fit and the summary were</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">23</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     produced}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">24</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   \item{diffs}{The differential equations used in the model}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">25</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   \item{use_of_ff}{Was maximum or minimum use made of formation fractions}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">26</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   \item{bpar}{Optimised and backtransformed</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">27</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     parameters}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">28</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   \item{data}{The data (see Description above).}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">29</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   \item{start}{The starting values and bounds, if applicable, for optimised</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">30</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     parameters.}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">31</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   \item{fixed}{The values of fixed parameters.}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">32</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   \item{errmin }{The chi2 error levels for</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">33</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     each observed variable.}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">34</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   \item{bparms.ode}{All backtransformed ODE</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">35</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     parameters, for use as starting parameters for related models.}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">36</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   \item{errparms}{Error model parameters.}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">37</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   \item{ff}{The estimated formation fractions derived from the fitted</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">38</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'      model.}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">39</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   \item{distimes}{The DT50 and DT90 values for each observed variable.}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">40</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   \item{SFORB}{If applicable, eigenvalues and fractional eigenvector component</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">41</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'      g of SFORB systems in the model.}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">42</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   The print method is called for its side effect, i.e. printing the summary.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">43</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @author Johannes Ranke</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">44</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @references FOCUS (2006) \dQuote{Guidance Document on Estimating Persistence</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">45</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   and Degradation Kinetics from Environmental Fate Studies on Pesticides in</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">46</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   EU Registration} Report of the FOCUS Work Group on Degradation Kinetics,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">47</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   EC Document Reference Sanco/10058/2005 version 2.0, 434 pp,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">48</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   \url{http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">49</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @examples</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">50</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">51</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   summary(mkinfit("SFO", FOCUS_2006_A, quiet = TRUE))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">52</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">53</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">54</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">summary.mkinfit &lt;- function(object, data = TRUE, distimes = TRUE, alpha = 0.05, ...) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">55</td>
+                    <td class="coverage">52158<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  param  &lt;- object$par</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">56</td>
+                    <td class="coverage">52158<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  pnames &lt;- names(param)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">57</td>
+                    <td class="coverage">52158<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  bpnames &lt;- names(object$bparms.optim)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">58</td>
+                    <td class="coverage">52158<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  epnames &lt;- names(object$errparms)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">59</td>
+                    <td class="coverage">52158<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  p      &lt;- length(param)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">60</td>
+                    <td class="coverage">52158<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  mod_vars &lt;- names(object$mkinmod$diffs)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">61</td>
+                    <td class="coverage">52158<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  covar  &lt;- try(solve(object$hessian), silent = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">62</td>
+                    <td class="coverage">52158<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  covar_notrans  &lt;- try(solve(object$hessian_notrans), silent = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">63</td>
+                    <td class="coverage">52158<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  rdf &lt;- object$df.residual</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">64</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">65</td>
+                    <td class="coverage">52158<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (!is.numeric(covar) | is.na(covar[1])) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">66</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    covar &lt;- NULL</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">67</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    se &lt;- lci &lt;- uci &lt;- rep(NA, p)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">68</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">69</td>
+                    <td class="coverage">52158<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    rownames(covar) &lt;- colnames(covar) &lt;- pnames</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">70</td>
+                    <td class="coverage">52158<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    se     &lt;- sqrt(diag(covar))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">71</td>
+                    <td class="coverage">52158<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    lci    &lt;- param + qt(alpha/2, rdf) * se</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">72</td>
+                    <td class="coverage">52158<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    uci    &lt;- param + qt(1-alpha/2, rdf) * se</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">73</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">74</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">75</td>
+                    <td class="coverage">52158<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  beparms.optim &lt;- c(object$bparms.optim, object$par[epnames])</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">76</td>
+                    <td class="coverage">52158<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (!is.numeric(covar_notrans) | is.na(covar_notrans[1])) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">77</td>
+                    <td class="coverage">88<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    covar_notrans &lt;- NULL</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">78</td>
+                    <td class="coverage">88<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    se_notrans &lt;- tval &lt;- pval &lt;- rep(NA, p)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">79</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">80</td>
+                    <td class="coverage">52070<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    rownames(covar_notrans) &lt;- colnames(covar_notrans) &lt;- c(bpnames, epnames)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">81</td>
+                    <td class="coverage">52070<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    se_notrans &lt;- sqrt(diag(covar_notrans))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">82</td>
+                    <td class="coverage">52070<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    tval  &lt;- beparms.optim / se_notrans</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">83</td>
+                    <td class="coverage">52070<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    pval  &lt;- pt(abs(tval), rdf, lower.tail = FALSE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">84</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">85</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">86</td>
+                    <td class="coverage">52158<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  names(se) &lt;- pnames</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">87</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">88</td>
+                    <td class="coverage">52158<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  param &lt;- cbind(param, se, lci, uci)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">89</td>
+                    <td class="coverage">52158<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  dimnames(param) &lt;- list(pnames, c("Estimate", "Std. Error", "Lower", "Upper"))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">90</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">91</td>
+                    <td class="coverage">52158<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  bparam &lt;- cbind(Estimate = beparms.optim, se_notrans,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">92</td>
+                    <td class="coverage">52158<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                  "t value" = tval, "Pr(&gt;t)" = pval, Lower = NA, Upper = NA)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">93</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">94</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Transform boundaries of CI for one parameter at a time,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">95</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # with the exception of sets of formation fractions (single fractions are OK).</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">96</td>
+                    <td class="coverage">52158<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  f_names_skip &lt;- character(0)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">97</td>
+                    <td class="coverage">52158<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  for (box in mod_vars) { # Figure out sets of fractions to skip</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">98</td>
+                    <td class="coverage">70671<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    f_names &lt;- grep(paste("^f", box, sep = "_"), pnames, value = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">99</td>
+                    <td class="coverage">70671<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    n_paths &lt;- length(f_names)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">100</td>
+                    <td class="coverage">1135<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (n_paths &gt; 1) f_names_skip &lt;- c(f_names_skip, f_names)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">101</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">102</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">103</td>
+                    <td class="coverage">52158<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  for (pname in pnames) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">104</td>
+                    <td class="coverage">293621<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (!pname %in% f_names_skip) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">105</td>
+                    <td class="coverage">290217<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      par.lower &lt;- param[pname, "Lower"]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">106</td>
+                    <td class="coverage">290217<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      par.upper &lt;- param[pname, "Upper"]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">107</td>
+                    <td class="coverage">290217<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      names(par.lower) &lt;- names(par.upper) &lt;- pname</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">108</td>
+                    <td class="coverage">290217<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      bpl &lt;- backtransform_odeparms(par.lower, object$mkinmod,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">109</td>
+                    <td class="coverage">290217<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                                            object$transform_rates,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">110</td>
+                    <td class="coverage">290217<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                                            object$transform_fractions)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">111</td>
+                    <td class="coverage">290217<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      bpu &lt;- backtransform_odeparms(par.upper, object$mkinmod,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">112</td>
+                    <td class="coverage">290217<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                                            object$transform_rates,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">113</td>
+                    <td class="coverage">290217<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                                            object$transform_fractions)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">114</td>
+                    <td class="coverage">290217<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      bparam[names(bpl), "Lower"] &lt;- bpl</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">115</td>
+                    <td class="coverage">290217<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      bparam[names(bpu), "Upper"] &lt;- bpu</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">116</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">117</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">118</td>
+                    <td class="coverage">52158<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  bparam[epnames, c("Lower", "Upper")] &lt;- param[epnames, c("Lower", "Upper")]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">119</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">120</td>
+                    <td class="coverage">52158<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  ans &lt;- list(</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">121</td>
+                    <td class="coverage">52158<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    version = as.character(utils::packageVersion("mkin")),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">122</td>
+                    <td class="coverage">52158<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    Rversion = paste(R.version$major, R.version$minor, sep="."),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">123</td>
+                    <td class="coverage">52158<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    date.fit = object$date,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">124</td>
+                    <td class="coverage">52158<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    date.summary = date(),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">125</td>
+                    <td class="coverage">52158<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    solution_type = object$solution_type,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">126</td>
+                    <td class="coverage">52158<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    warnings = object$summary_warnings,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">127</td>
+                    <td class="coverage">52158<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    use_of_ff = object$mkinmod$use_of_ff,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">128</td>
+                    <td class="coverage">52158<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    error_model_algorithm = object$error_model_algorithm,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">129</td>
+                    <td class="coverage">52158<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    df = c(p, rdf),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">130</td>
+                    <td class="coverage">52158<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    covar = covar,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">131</td>
+                    <td class="coverage">52158<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    covar_notrans = covar_notrans,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">132</td>
+                    <td class="coverage">52158<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    err_mod = object$err_mod,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">133</td>
+                    <td class="coverage">52158<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    niter = object$iterations,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">134</td>
+                    <td class="coverage">52158<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    calls = object$calls,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">135</td>
+                    <td class="coverage">52158<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    time = object$time,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">136</td>
+                    <td class="coverage">52158<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    par = param,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">137</td>
+                    <td class="coverage">52158<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    bpar = bparam)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">138</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">139</td>
+                    <td class="coverage">52158<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (!is.null(object$version)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">140</td>
+                    <td class="coverage">52158<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    ans$fit_version &lt;- object$version</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">141</td>
+                    <td class="coverage">52158<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    ans$fit_Rversion &lt;- object$Rversion</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">142</td>
+                    <td class="coverage">52158<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (ans$fit_version &gt;= "0.9.49.6") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">143</td>
+                    <td class="coverage">52156<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      ans$AIC = AIC(object)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">144</td>
+                    <td class="coverage">52156<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      ans$BIC = BIC(object)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">145</td>
+                    <td class="coverage">52156<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      ans$logLik = logLik(object)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">146</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">147</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">148</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">149</td>
+                    <td class="coverage">52158<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  ans$diffs &lt;- object$mkinmod$diffs</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">150</td>
+                    <td class="coverage">52158<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if(data) ans$data &lt;- object$data</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">151</td>
+                    <td class="coverage">52158<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  ans$start &lt;- object$start</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">152</td>
+                    <td class="coverage">52158<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  ans$start_transformed &lt;- object$start_transformed</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">153</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">154</td>
+                    <td class="coverage">52158<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  ans$fixed &lt;- object$fixed</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">155</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">156</td>
+                    <td class="coverage">52158<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  ans$errmin &lt;- mkinerrmin(object, alpha = 0.05)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">157</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">158</td>
+                    <td class="coverage">52158<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (object$calls &gt; 0) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">159</td>
+                    <td class="coverage">52158<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (!is.null(ans$covar)){</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">160</td>
+                    <td class="coverage">52158<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      Corr &lt;- cov2cor(ans$covar)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">161</td>
+                    <td class="coverage">52158<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      rownames(Corr) &lt;- colnames(Corr) &lt;- rownames(ans$par)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">162</td>
+                    <td class="coverage">52158<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      ans$Corr &lt;- Corr</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">163</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">164</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      warning("Could not calculate correlation; no covariance matrix")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">165</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">166</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">167</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">168</td>
+                    <td class="coverage">52158<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  ans$bparms.ode &lt;- object$bparms.ode</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">169</td>
+                    <td class="coverage">52158<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  ans$shapiro.p &lt;- object$shapiro.p</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">170</td>
+                    <td class="coverage">52158<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  ep &lt;- endpoints(object)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">171</td>
+                    <td class="coverage">52158<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (length(ep$ff) != 0)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">172</td>
+                    <td class="coverage">15612<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    ans$ff &lt;- ep$ff</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">173</td>
+                    <td class="coverage">52158<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (distimes) ans$distimes &lt;- ep$distimes</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">174</td>
+                    <td class="coverage">2442<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (length(ep$SFORB) != 0) ans$SFORB &lt;- ep$SFORB</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">175</td>
+                    <td class="coverage">43972<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (!is.null(object$d_3_message)) ans$d_3_message &lt;- object$d_3_message</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">176</td>
+                    <td class="coverage">52158<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  class(ans) &lt;- "summary.mkinfit"</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">177</td>
+                    <td class="coverage">52158<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  return(ans)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">178</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">179</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">180</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @rdname summary.mkinfit</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">181</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">182</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">print.summary.mkinfit &lt;- function(x, digits = max(3, getOption("digits") - 3), ...) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">183</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (is.null(x$fit_version)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">184</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    cat("mkin version:   ", x$version, "\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">185</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    cat("R version:      ", x$Rversion, "\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">186</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">187</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    cat("mkin version used for fitting:   ", x$fit_version, "\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">188</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    cat("R version used for fitting:      ", x$fit_Rversion, "\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">189</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">190</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">191</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("Date of fit:    ", x$date.fit, "\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">192</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("Date of summary:", x$date.summary, "\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">193</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">194</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("\nEquations:\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">195</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  nice_diffs &lt;- gsub("^(d.*) =", "\\1/dt =", x[["diffs"]])</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">196</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  writeLines(strwrap(nice_diffs, exdent = 11))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">197</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  df  &lt;- x$df</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">198</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  rdf &lt;- df[2]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">199</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">200</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("\nModel predictions using solution type", x$solution_type, "\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">201</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">202</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("\nFitted using", x$calls, "model solutions performed in", x$time[["elapsed"]],  "s\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">203</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">204</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (!is.null(x$err_mod)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">205</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    cat("\nError model: ")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">206</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    cat(switch(x$err_mod,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">207</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">               const = "Constant variance",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">208</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">               obs = "Variance unique to each observed variable",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">209</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">               tc = "Two-component variance function"), "\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">210</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">211</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    cat("\nError model algorithm:", x$error_model_algorithm, "\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">212</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (!is.null(x$d_3_message)) cat(x$d_3_message, "\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">213</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">214</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">215</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("\nStarting values for parameters to be optimised:\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">216</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  print(x$start)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">217</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">218</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("\nStarting values for the transformed parameters actually optimised:\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">219</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  print(x$start_transformed)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">220</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">221</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("\nFixed parameter values:\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">222</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if(length(x$fixed$value) == 0) cat("None\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">223</td>
+                    <td class="coverage">3<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  else print(x$fixed)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">224</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">225</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # We used to only have one warning - show this for summarising old objects</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">226</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">   if (!is.null(x[["warning"]])) cat("\n\nWarning:", x$warning, "\n\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">227</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">228</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (length(x$warnings) &gt; 0) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">229</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    cat("\n\nWarning(s):", "\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">230</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    cat(x$warnings, sep = "\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">231</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">232</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">233</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (!is.null(x$AIC)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">234</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    cat("\nResults:\n\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">235</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    print(data.frame(AIC = x$AIC, BIC = x$BIC, logLik = x$logLik,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">236</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      row.names = " "))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">237</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">238</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">239</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("\nOptimised, transformed parameters with symmetric confidence intervals:\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">240</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  print(signif(x$par, digits = digits))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">241</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">242</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (x$calls &gt; 0) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">243</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    cat("\nParameter correlation:\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">244</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (!is.null(x$covar)){</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">245</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      print(x$Corr, digits = digits, ...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">246</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">247</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      cat("No covariance matrix")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">248</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">249</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">250</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">251</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("\nBacktransformed parameters:\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">252</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("Confidence intervals for internally transformed parameters are asymmetric.\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">253</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if ((x$version) &lt; "0.9-36") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">254</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    cat("To get the usual (questionable) t-test, upgrade mkin and repeat the fit.\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">255</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    print(signif(x$bpar, digits = digits))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">256</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">257</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    cat("t-test (unrealistically) based on the assumption of normal distribution\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">258</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    cat("for estimators of untransformed parameters.\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">259</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    print(signif(x$bpar[, c(1, 3, 4, 5, 6)], digits = digits))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">260</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">261</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">262</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("\nFOCUS Chi2 error levels in percent:\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">263</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  x$errmin$err.min &lt;- 100 * x$errmin$err.min</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">264</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  print(x$errmin, digits=digits,...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">265</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">266</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  printSFORB &lt;- !is.null(x$SFORB)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">267</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if(printSFORB){</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">268</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    cat("\nEstimated Eigenvalues and DFOP g parameter of SFORB model(s):\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">269</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    print(x$SFORB, digits=digits,...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">270</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">271</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">272</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  printff &lt;- !is.null(x$ff)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">273</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if(printff){</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">274</td>
+                    <td class="coverage">3<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    cat("\nResulting formation fractions:\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">275</td>
+                    <td class="coverage">3<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    print(data.frame(ff = x$ff), digits=digits,...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">276</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">277</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">278</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  printdistimes &lt;- !is.null(x$distimes)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">279</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if(printdistimes){</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">280</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    cat("\nEstimated disappearance times:\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">281</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    print(x$distimes, digits=digits,...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">282</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">283</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">284</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  printdata &lt;- !is.null(x$data)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">285</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (printdata){</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">286</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    cat("\nData:\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">287</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    print(format(x$data, digits = digits, ...), row.names = FALSE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">288</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">289</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">290</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  invisible(x)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">291</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                </tbody>
+              </table>
+            </div>
+            <div id="R/mixed.mmkin.R" class="hidden">
+              <table class="table-condensed">
+                <tbody>
+                  <tr class="never">
+                    <td class="num">1</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Create a mixed effects model from an mmkin row object</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">2</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">3</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @importFrom rlang !!!</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">4</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param object An [mmkin] row object</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">5</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param method The method to be used</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">6</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param \dots Currently not used</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">7</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @return An object of class 'mixed.mmkin' which has the observed data in a</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">8</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   single dataframe which is convenient for plotting</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">9</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @examples</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">10</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">11</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' n_biphasic &lt;- 8</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">12</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' err_1 = list(const = 1, prop = 0.07)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">13</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">14</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' DFOP_SFO &lt;- mkinmod(</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">15</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   parent = mkinsub("DFOP", "m1"),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">16</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   m1 = mkinsub("SFO"),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">17</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   quiet = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">18</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">19</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' set.seed(123456)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">20</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' log_sd &lt;- 0.3</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">21</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' syn_biphasic_parms &lt;- as.matrix(data.frame(</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">22</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   k1 = rlnorm(n_biphasic, log(0.05), log_sd),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">23</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   k2 = rlnorm(n_biphasic, log(0.01), log_sd),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">24</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   g = plogis(rnorm(n_biphasic, 0, log_sd)),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">25</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   f_parent_to_m1 = plogis(rnorm(n_biphasic, 0, log_sd)),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">26</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   k_m1 = rlnorm(n_biphasic, log(0.002), log_sd)))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">27</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">28</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' ds_biphasic_mean &lt;- lapply(1:n_biphasic,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">29</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   function(i) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">30</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     mkinpredict(DFOP_SFO, syn_biphasic_parms[i, ],</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">31</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'       c(parent = 100, m1 = 0), sampling_times)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">32</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">33</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' )</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">34</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">35</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' set.seed(123456L)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">36</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' ds_biphasic &lt;- lapply(ds_biphasic_mean, function(ds) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">37</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   add_err(ds,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">38</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     sdfunc = function(value) sqrt(err_1$const^2 + value^2 * err_1$prop^2),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">39</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     n = 1, secondary = "m1")[[1]]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">40</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' })</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">41</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">42</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' \dontrun{</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">43</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' f_mmkin &lt;- mmkin(list("DFOP-SFO" = DFOP_SFO), ds_biphasic, error_model = "tc", quiet = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">44</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">45</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' f_mixed &lt;- mixed(f_mmkin)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">46</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' print(f_mixed)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">47</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' plot(f_mixed)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">48</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">49</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">50</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">mixed &lt;- function(object, ...) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">51</td>
+                    <td class="coverage">182<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  UseMethod("mixed")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">52</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">53</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">54</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">55</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @rdname mixed</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">56</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">mixed.mmkin &lt;- function(object, method = c("none"), ...) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">57</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (nrow(object) &gt; 1) stop("Only row objects allowed")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">58</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">59</td>
+                    <td class="coverage">182<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  method &lt;- match.arg(method)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">60</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">61</td>
+                    <td class="coverage">182<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  ds_names &lt;- colnames(object)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">62</td>
+                    <td class="coverage">182<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  res &lt;- list(mmkin = object, mkinmod = object[[1]]$mkinmod)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">63</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">64</td>
+                    <td class="coverage">182<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (method[1] == "none") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">65</td>
+                    <td class="coverage">182<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    ds_list &lt;- lapply(object,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">66</td>
+                    <td class="coverage">182<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      function(x) x$data[c("variable", "time", "observed", "predicted", "residual")])</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">67</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">68</td>
+                    <td class="coverage">182<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    names(ds_list) &lt;- ds_names</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">69</td>
+                    <td class="coverage">182<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    res$data &lt;- vctrs::vec_rbind(!!!ds_list, .names_to = "ds")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">70</td>
+                    <td class="coverage">182<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    names(res$data)[1:4] &lt;- c("ds", "name", "time", "value")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">71</td>
+                    <td class="coverage">182<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    res$data$name &lt;- as.character(res$data$name)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">72</td>
+                    <td class="coverage">182<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    res$data$ds &lt;- ordered(res$data$ds, levels = unique(res$data$ds))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">73</td>
+                    <td class="coverage">182<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    standardized &lt;- unlist(lapply(object, residuals, standardized = TRUE))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">74</td>
+                    <td class="coverage">182<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    res$data$std &lt;- res$data$residual / standardized</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">75</td>
+                    <td class="coverage">182<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    res$data$standardized &lt;- standardized</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">76</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">77</td>
+                    <td class="coverage">182<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    class(res) &lt;- c("mixed.mmkin")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">78</td>
+                    <td class="coverage">182<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    return(res)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">79</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">80</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">81</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">82</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">83</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @rdname mixed</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">84</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param x A mixed.mmkin object to print</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">85</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param digits Number of digits to use for printing.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">86</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">print.mixed.mmkin &lt;- function(x, digits = max(3, getOption("digits") - 3), ...) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">87</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("Kinetic model fitted by nonlinear regression to each dataset" )</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">88</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("\nStructural model:\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">89</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  diffs &lt;- x$mmkin[[1]]$mkinmod$diffs</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">90</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  nice_diffs &lt;- gsub("^(d.*) =", "\\1/dt =", diffs)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">91</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  writeLines(strwrap(nice_diffs, exdent = 11))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">92</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("\nData:\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">93</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat(nrow(x$data), "observations of",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">94</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    length(unique(x$data$name)), "variable(s) grouped in",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">95</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    length(unique(x$data$ds)), "datasets\n\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">96</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">97</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  print(x$mmkin, digits = digits)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">98</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">99</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("\nMean fitted parameters:\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">100</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  print(mean_degparms(x$mmkin), digits = digits)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">101</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">102</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  invisible(x)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">103</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                </tbody>
+              </table>
+            </div>
+            <div id="R/plot.mixed.mmkin.R" class="hidden">
+              <table class="table-condensed">
+                <tbody>
+                  <tr class="never">
+                    <td class="num">1</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">utils::globalVariables("ds")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">2</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">3</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Plot predictions from a fitted nonlinear mixed model obtained via an mmkin row object</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">4</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">5</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param x An object of class [mixed.mmkin], [saem.mmkin] or [nlme.mmkin]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">6</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param i A numeric index to select datasets for which to plot the individual predictions,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">7</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' in case plots get too large</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">8</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @inheritParams plot.mkinfit</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">9</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param standardized Should the residuals be standardized? Only takes effect if</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">10</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' `resplot = "time"`.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">11</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param pop_curves Per default, one population curve is drawn in case</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">12</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' population parameters are fitted by the model, e.g. for saem objects.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">13</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' In case there is a covariate model, the behaviour depends on the value</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">14</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' of 'covariates'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">15</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param covariates Data frame with covariate values for all variables in</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">16</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' any covariate models in the object. If given, it overrides 'covariate_quantiles'.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">17</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Each line in the data frame will result in a line drawn for the population.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">18</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Rownames are used in the legend to label the lines.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">19</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param covariate_quantiles This argument only has an effect if the fitted</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">20</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' object has covariate models. If so, the default is to show three population</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">21</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' curves, for the 5th percentile, the 50th percentile and the 95th percentile</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">22</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' of the covariate values used for fitting the model.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">23</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @note Covariate models are currently only supported for saem.mmkin objects.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">24</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param pred_over Named list of alternative predictions as obtained</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">25</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' from [mkinpredict] with a compatible [mkinmod].</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">26</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param test_log_parms Passed to [mean_degparms] in the case of an</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">27</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' [mixed.mmkin] object</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">28</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param conf.level Passed to [mean_degparms] in the case of an</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">29</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' [mixed.mmkin] object</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">30</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param default_log_parms Passed to [mean_degparms] in the case of an</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">31</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' [mixed.mmkin] object</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">32</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param rel.height.legend The relative height of the legend shown on top</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">33</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param rel.height.bottom The relative height of the bottom plot row</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">34</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param ymax Vector of maximum y axis values</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">35</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param ncol.legend Number of columns to use in the legend</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">36</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param nrow.legend Number of rows to use in the legend</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">37</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param resplot Should the residuals plotted against time or against</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">38</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' predicted values?</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">39</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param col_ds Colors used for plotting the observed data and the</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">40</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' corresponding model prediction lines for the different datasets.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">41</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param pch_ds Symbols to be used for plotting the data.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">42</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param lty_ds Line types to be used for the model predictions.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">43</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @importFrom stats coefficients</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">44</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @return The function is called for its side effect.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">45</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @author Johannes Ranke</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">46</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @examples</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">47</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' ds &lt;- lapply(experimental_data_for_UBA_2019[6:10],</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">48</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'  function(x) x$data[c("name", "time", "value")])</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">49</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' names(ds) &lt;- paste0("ds ", 6:10)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">50</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' dfop_sfo &lt;- mkinmod(parent = mkinsub("DFOP", "A1"),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">51</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   A1 = mkinsub("SFO"), quiet = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">52</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' \dontrun{</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">53</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' f &lt;- mmkin(list("DFOP-SFO" = dfop_sfo), ds, quiet = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">54</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' plot(f[, 3:4], standardized = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">55</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">56</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # For this fit we need to increase pnlsMaxiter, and we increase the</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">57</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # tolerance in order to speed up the fit for this example evaluation</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">58</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # It still takes 20 seconds to run</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">59</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' f_nlme &lt;- nlme(f, control = list(pnlsMaxIter = 120, tolerance = 1e-3))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">60</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' plot(f_nlme)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">61</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">62</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' f_saem &lt;- saem(f, transformations = "saemix")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">63</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' plot(f_saem)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">64</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">65</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' f_obs &lt;- mmkin(list("DFOP-SFO" = dfop_sfo), ds, quiet = TRUE, error_model = "obs")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">66</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' f_nlmix &lt;- nlmix(f_obs)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">67</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' plot(f_nlmix)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">68</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">69</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # We can overlay the two variants if we generate predictions</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">70</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' pred_nlme &lt;- mkinpredict(dfop_sfo,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">71</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   f_nlme$bparms.optim[-1],</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">72</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   c(parent = f_nlme$bparms.optim[[1]], A1 = 0),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">73</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   seq(0, 180, by = 0.2))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">74</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' plot(f_saem, pred_over = list(nlme = pred_nlme))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">75</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">76</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">77</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">plot.mixed.mmkin &lt;- function(x,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">78</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  i = 1:ncol(x$mmkin),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">79</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  obs_vars = names(x$mkinmod$map),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">80</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  standardized = TRUE,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">81</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  covariates = NULL,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">82</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  covariate_quantiles = c(0.5, 0.05, 0.95),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">83</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  xlab = "Time",</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">84</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  xlim = range(x$data$time),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">85</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  resplot = c("predicted", "time"),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">86</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  pop_curves = "auto",</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">87</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  pred_over = NULL,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">88</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  test_log_parms = FALSE,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">89</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  conf.level = 0.6,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">90</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  default_log_parms = NA,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">91</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  ymax = "auto", maxabs = "auto",</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">92</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  ncol.legend = ifelse(length(i) &lt;= 3, length(i) + 1, ifelse(length(i) &lt;= 8, 3, 4)),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">93</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  nrow.legend = ceiling((length(i) + 1) / ncol.legend),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">94</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  rel.height.legend = 0.02 + 0.07 * nrow.legend,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">95</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  rel.height.bottom = 1.1,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">96</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  pch_ds = c(1:25, 33, 35:38, 40:41, 47:57, 60:90)[1:length(i)],</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">97</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  col_ds = pch_ds + 1,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">98</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  lty_ds = col_ds,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">99</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  frame = TRUE, ...</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">100</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">101</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">{</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">102</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Prepare parameters and data</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">103</td>
+                    <td class="coverage">283<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  fit_1 &lt;- x$mmkin[[1]]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">104</td>
+                    <td class="coverage">283<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  ds_names &lt;- colnames(x$mmkin)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">105</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">106</td>
+                    <td class="coverage">283<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  backtransform = TRUE</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">107</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">108</td>
+                    <td class="coverage">283<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (identical(class(x), "mixed.mmkin")) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">109</td>
+                    <td class="coverage">65<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (identical(pop_curves, "auto")) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">110</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      pop_curves &lt;- FALSE</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">111</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">112</td>
+                    <td class="coverage">65<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      pop_curves &lt;- TRUE</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">113</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">114</td>
+                    <td class="coverage">65<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (pop_curves) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">115</td>
+                    <td class="coverage">65<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      degparms_pop &lt;- mean_degparms(x$mmkin, test_log_parms = test_log_parms,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">116</td>
+                    <td class="coverage">65<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        conf.level = conf.level, default_log_parms = default_log_parms)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">117</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">118</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">119</td>
+                    <td class="coverage">65<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    degparms_tmp &lt;- parms(x$mmkin, transformed = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">120</td>
+                    <td class="coverage">65<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    degparms_i &lt;- as.data.frame(t(degparms_tmp[setdiff(rownames(degparms_tmp), names(fit_1$errparms)), ]))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">121</td>
+                    <td class="coverage">65<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    residual_type = ifelse(standardized, "standardized", "residual")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">122</td>
+                    <td class="coverage">65<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    residuals &lt;- x$data[[residual_type]]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">123</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">124</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">125</td>
+                    <td class="coverage">283<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (inherits(x, "nlme.mmkin")) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">126</td>
+                    <td class="coverage">65<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (identical(pop_curves, "auto")) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">127</td>
+                    <td class="coverage">65<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      pop_curves &lt;- TRUE</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">128</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">129</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      pop_curves &lt;- FALSE</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">130</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">131</td>
+                    <td class="coverage">65<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    degparms_i &lt;- coefficients(x)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">132</td>
+                    <td class="coverage">65<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    degparms_pop &lt;- nlme::fixef(x)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">133</td>
+                    <td class="coverage">65<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    residuals &lt;- residuals(x,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">134</td>
+                    <td class="coverage">65<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      type = ifelse(standardized, "pearson", "response"))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">135</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">136</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">137</td>
+                    <td class="coverage">283<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (inherits(x, "saem.mmkin")) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">138</td>
+                    <td class="coverage">65<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (x$transformations == "saemix") backtransform = FALSE</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">139</td>
+                    <td class="coverage">153<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    psi &lt;- saemix::psi(x$so)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">140</td>
+                    <td class="coverage">153<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    rownames(psi) &lt;- x$saemix_ds_order</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">141</td>
+                    <td class="coverage">153<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    degparms_i &lt;- psi[ds_names, ]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">142</td>
+                    <td class="coverage">153<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    degparms_i_names &lt;- colnames(degparms_i)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">143</td>
+                    <td class="coverage">153<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    residual_type = ifelse(standardized, "standardized", "residual")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">144</td>
+                    <td class="coverage">153<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    residuals &lt;- x$data[[residual_type]]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">145</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">146</td>
+                    <td class="coverage">153<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (identical(pop_curves, "auto")) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">147</td>
+                    <td class="coverage">153<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      if (length(x$covariate_models) == 0) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">148</td>
+                    <td class="coverage">153<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        degparms_pop &lt;- x$so@results@fixed.effects</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">149</td>
+                    <td class="coverage">153<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        names(degparms_pop) &lt;- degparms_i_names</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">150</td>
+                    <td class="coverage">153<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        pop_curves &lt;- TRUE</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">151</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">152</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        if (is.null(covariates)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">153</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          covariates = as.data.frame(</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">154</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            apply(x$covariates, 2, quantile,</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">155</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">             covariate_quantiles, simplify = FALSE))</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">156</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          rownames(covariates) &lt;- paste(</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">157</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            ifelse(length(x$covariate_models) == 1,</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">158</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              "Covariate", "Covariates"),</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">159</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              rownames(covariates))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">160</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        }</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">161</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        degparms_pop &lt;- parms(x, covariates = covariates)</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">162</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        pop_curves &lt;- TRUE</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">163</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">164</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">165</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      pop_curves &lt;- FALSE</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">166</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">167</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">168</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">169</td>
+                    <td class="coverage">283<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (pop_curves) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">170</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # Make sure degparms_pop is a matrix, columns corresponding to population curve(s)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">171</td>
+                    <td class="coverage">283<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (is.null(dim(degparms_pop))) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">172</td>
+                    <td class="coverage">283<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      degparms_pop &lt;- matrix(degparms_pop, ncol = 1,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">173</td>
+                    <td class="coverage">283<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        dimnames = list(names(degparms_pop), "Population"))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">174</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">175</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">176</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">177</td>
+                    <td class="coverage">283<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  degparms_fixed &lt;- fit_1$fixed$value</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">178</td>
+                    <td class="coverage">283<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  names(degparms_fixed) &lt;- rownames(fit_1$fixed)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">179</td>
+                    <td class="coverage">283<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  degparms_all &lt;- cbind(as.matrix(degparms_i),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">180</td>
+                    <td class="coverage">283<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    matrix(rep(degparms_fixed, nrow(degparms_i)),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">181</td>
+                    <td class="coverage">283<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      ncol = length(degparms_fixed),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">182</td>
+                    <td class="coverage">283<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      nrow = nrow(degparms_i), byrow = TRUE))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">183</td>
+                    <td class="coverage">283<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  degparms_all_names &lt;- c(names(degparms_i), names(degparms_fixed))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">184</td>
+                    <td class="coverage">283<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  colnames(degparms_all) &lt;- degparms_all_names</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">185</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">186</td>
+                    <td class="coverage">283<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  odeini_names &lt;- grep("_0$", degparms_all_names, value = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">187</td>
+                    <td class="coverage">283<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  odeparms_names &lt;- setdiff(degparms_all_names, odeini_names)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">188</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">189</td>
+                    <td class="coverage">283<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  observed &lt;- cbind(x$data[c("ds", "name", "time", "value")],</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">190</td>
+                    <td class="coverage">283<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    residual = residuals)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">191</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">192</td>
+                    <td class="coverage">283<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  solution_type = fit_1$solution_type</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">193</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">194</td>
+                    <td class="coverage">283<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  outtimes &lt;- sort(unique(c(x$data$time,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">195</td>
+                    <td class="coverage">283<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    seq(xlim[1], xlim[2], length.out = 50))))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">196</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">197</td>
+                    <td class="coverage">283<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  pred_list &lt;- lapply(i, function(ds_i)   {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">198</td>
+                    <td class="coverage">2945<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    odeparms_trans &lt;- degparms_all[ds_i, odeparms_names]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">199</td>
+                    <td class="coverage">2945<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    names(odeparms_trans) &lt;- odeparms_names # needed if only one odeparm</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">200</td>
+                    <td class="coverage">2945<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (backtransform) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">201</td>
+                    <td class="coverage">2620<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      odeparms &lt;- backtransform_odeparms(odeparms_trans,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">202</td>
+                    <td class="coverage">2620<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        x$mkinmod,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">203</td>
+                    <td class="coverage">2620<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        transform_rates = fit_1$transform_rates,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">204</td>
+                    <td class="coverage">2620<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        transform_fractions = fit_1$transform_fractions)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">205</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">206</td>
+                    <td class="coverage">325<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      odeparms &lt;- odeparms_trans</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">207</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">208</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">209</td>
+                    <td class="coverage">2945<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    odeini &lt;- degparms_all[ds_i, odeini_names]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">210</td>
+                    <td class="coverage">2945<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    names(odeini) &lt;- gsub("_0", "", odeini_names)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">211</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">212</td>
+                    <td class="coverage">2945<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    out &lt;- mkinpredict(x$mkinmod, odeparms, odeini,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">213</td>
+                    <td class="coverage">2945<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      outtimes, solution_type = solution_type,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">214</td>
+                    <td class="coverage">2945<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      atol = fit_1$atol, rtol = fit_1$rtol)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">215</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  })</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">216</td>
+                    <td class="coverage">283<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  names(pred_list) &lt;- ds_names[i]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">217</td>
+                    <td class="coverage">283<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  pred_ds &lt;- vctrs::vec_rbind(!!!pred_list, .names_to = "ds")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">218</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">219</td>
+                    <td class="coverage">283<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (pop_curves) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">220</td>
+                    <td class="coverage">283<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    pred_list_pop &lt;- lapply(1:ncol(degparms_pop), function(cov_i)   {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">221</td>
+                    <td class="coverage">283<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      degparms_all_pop_i &lt;- c(degparms_pop[, cov_i], degparms_fixed)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">222</td>
+                    <td class="coverage">283<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      odeparms_pop_trans_i &lt;- degparms_all_pop_i[odeparms_names]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">223</td>
+                    <td class="coverage">283<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      names(odeparms_pop_trans_i) &lt;- odeparms_names # needed if only one odeparm</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">224</td>
+                    <td class="coverage">283<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      if (backtransform) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">225</td>
+                    <td class="coverage">218<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        odeparms_pop_i &lt;- backtransform_odeparms(odeparms_pop_trans_i,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">226</td>
+                    <td class="coverage">218<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          x$mkinmod,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">227</td>
+                    <td class="coverage">218<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          transform_rates = fit_1$transform_rates,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">228</td>
+                    <td class="coverage">218<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          transform_fractions = fit_1$transform_fractions)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">229</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">230</td>
+                    <td class="coverage">65<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        odeparms_pop_i &lt;- odeparms_pop_trans_i</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">231</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">232</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">233</td>
+                    <td class="coverage">283<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      odeini &lt;- degparms_all_pop_i[odeini_names]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">234</td>
+                    <td class="coverage">283<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      names(odeini) &lt;- gsub("_0", "", odeini_names)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">235</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">236</td>
+                    <td class="coverage">283<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      out &lt;- mkinpredict(x$mkinmod, odeparms_pop_i, odeini,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">237</td>
+                    <td class="coverage">283<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        outtimes, solution_type = solution_type,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">238</td>
+                    <td class="coverage">283<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        atol = fit_1$atol, rtol = fit_1$rtol)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">239</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    })</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">240</td>
+                    <td class="coverage">283<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    names(pred_list_pop) &lt;- colnames(degparms_pop)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">241</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">242</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">243</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    pred_list_pop &lt;- NULL</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">244</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">245</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">246</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Start of graphical section</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">247</td>
+                    <td class="coverage">283<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  oldpar &lt;- par(no.readonly = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">248</td>
+                    <td class="coverage">283<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  on.exit(par(oldpar, no.readonly = TRUE))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">249</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">250</td>
+                    <td class="coverage">283<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  n_plot_rows = length(obs_vars)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">251</td>
+                    <td class="coverage">283<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  n_plots = n_plot_rows * 2</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">252</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">253</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Set relative plot heights, so the first plot row is the norm</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">254</td>
+                    <td class="coverage">283<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  rel.heights &lt;- if (n_plot_rows &gt; 1) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">255</td>
+                    <td class="coverage">218<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    c(rel.height.legend, c(rep(1, n_plot_rows - 1), rel.height.bottom))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">256</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">257</td>
+                    <td class="coverage">65<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    c(rel.height.legend, 1)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">258</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">259</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">260</td>
+                    <td class="coverage">283<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  layout_matrix = matrix(c(1, 1, 2:(n_plots + 1)),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">261</td>
+                    <td class="coverage">283<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    n_plot_rows + 1, 2, byrow = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">262</td>
+                    <td class="coverage">283<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  layout(layout_matrix, heights = rel.heights)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">263</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">264</td>
+                    <td class="coverage">283<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  par(mar = c(0.1, 2.1, 0.1, 2.1))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">265</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">266</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Empty plot with legend</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">267</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (!is.null(pred_over)) lty_over &lt;- seq(2, length.out = length(pred_over))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">268</td>
+                    <td class="coverage">283<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  else lty_over &lt;- NULL</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">269</td>
+                    <td class="coverage">283<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (pop_curves) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">270</td>
+                    <td class="coverage">283<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (is.null(covariates)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">271</td>
+                    <td class="coverage">283<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      lty_pop &lt;- 1</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">272</td>
+                    <td class="coverage">283<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      names(lty_pop) &lt;- "Population"</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">273</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">274</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      lty_pop &lt;- 1:nrow(covariates)</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">275</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      names(lty_pop) &lt;- rownames(covariates)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">276</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">277</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">278</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    lty_pop &lt;- NULL</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">279</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">280</td>
+                    <td class="coverage">283<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  n_pop_over &lt;- length(lty_pop) + length(lty_over)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">281</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">282</td>
+                    <td class="coverage">283<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  plot(0, type = "n", axes = FALSE, ann = FALSE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">283</td>
+                    <td class="coverage">283<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  legend("center", bty = "n", ncol = ncol.legend,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">284</td>
+                    <td class="coverage">283<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    legend = c(names(lty_pop), names(pred_over), ds_names[i]),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">285</td>
+                    <td class="coverage">283<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    lty = c(lty_pop, lty_over, lty_ds),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">286</td>
+                    <td class="coverage">283<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    lwd = c(rep(2, n_pop_over), rep(1, length(i))),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">287</td>
+                    <td class="coverage">283<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    col = c(rep(1, n_pop_over), col_ds),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">288</td>
+                    <td class="coverage">283<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    pch = c(rep(NA, n_pop_over), pch_ds))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">289</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">290</td>
+                    <td class="coverage">283<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  resplot &lt;- match.arg(resplot)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">291</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">292</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Loop plot rows</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">293</td>
+                    <td class="coverage">283<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  for (plot_row in 1:n_plot_rows) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">294</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">295</td>
+                    <td class="coverage">501<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    obs_var &lt;- obs_vars[plot_row]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">296</td>
+                    <td class="coverage">501<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    observed_row &lt;- subset(observed, name == obs_var)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">297</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">298</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # Set ylim to sensible default, or use ymax</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">299</td>
+                    <td class="coverage">501<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (identical(ymax, "auto")) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">300</td>
+                    <td class="coverage">501<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      ylim_row = c(0,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">301</td>
+                    <td class="coverage">501<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        max(c(observed_row$value, pred_ds[[obs_var]]), na.rm = TRUE))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">302</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">303</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      ylim_row = c(0, ymax[plot_row])</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">304</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">305</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">306</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # Margins for bottom row of plots when we have more than one row</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">307</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # This is the only row that needs to show the x axis legend</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">308</td>
+                    <td class="coverage">501<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (plot_row == n_plot_rows) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">309</td>
+                    <td class="coverage">283<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      par(mar = c(5.1, 4.1, 1.1, 2.1))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">310</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">311</td>
+                    <td class="coverage">218<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      par(mar = c(3.0, 4.1, 1.1, 2.1))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">312</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">313</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">314</td>
+                    <td class="coverage">501<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    plot(0, type = "n",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">315</td>
+                    <td class="coverage">501<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      xlim = xlim, ylim = ylim_row,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">316</td>
+                    <td class="coverage">501<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      xlab = xlab, ylab = paste("Residues", obs_var), frame = frame)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">317</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">318</td>
+                    <td class="coverage">501<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (!is.null(pred_over)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">319</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      for (i_over in seq_along(pred_over)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">320</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        pred_frame &lt;- as.data.frame(pred_over[[i_over]])</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">321</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        lines(pred_frame$time, pred_frame[[obs_var]],</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">322</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          lwd = 2, lty = lty_over[i_over])</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">323</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">324</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">325</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">326</td>
+                    <td class="coverage">501<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    for (ds_i in seq_along(i)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">327</td>
+                    <td class="coverage">4915<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      points(subset(observed_row, ds == ds_names[ds_i], c("time", "value")),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">328</td>
+                    <td class="coverage">4915<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        col = col_ds[ds_i], pch = pch_ds[ds_i])</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">329</td>
+                    <td class="coverage">4915<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      lines(subset(pred_ds, ds == ds_names[ds_i], c("time", obs_var)),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">330</td>
+                    <td class="coverage">4915<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        col = col_ds[ds_i], lty = lty_ds[ds_i])</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">331</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">332</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">333</td>
+                    <td class="coverage">501<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (pop_curves) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">334</td>
+                    <td class="coverage">501<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      for (cov_i in seq_along(pred_list_pop)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">335</td>
+                    <td class="coverage">501<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        cov_name &lt;- names(pred_list_pop)[cov_i]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">336</td>
+                    <td class="coverage">501<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        lines(</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">337</td>
+                    <td class="coverage">501<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          pred_list_pop[[cov_i]][, "time"],</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">338</td>
+                    <td class="coverage">501<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          pred_list_pop[[cov_i]][, obs_var],</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">339</td>
+                    <td class="coverage">501<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          type = "l", lwd = 2, lty = lty_pop[cov_i])</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">340</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">341</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">342</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">343</td>
+                    <td class="coverage">501<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (identical(maxabs, "auto")) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">344</td>
+                    <td class="coverage">283<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      maxabs = max(abs(observed_row$residual), na.rm = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">345</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">346</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">347</td>
+                    <td class="coverage">501<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (identical(resplot, "time")) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">348</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      plot(0, type = "n", xlim = xlim, xlab = "Time",</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">349</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        ylim = c(-1.2 * maxabs, 1.2 * maxabs),</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">350</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        ylab = if (standardized) "Standardized residual" else "Residual",</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">351</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        frame = frame)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">352</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">353</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      abline(h = 0, lty = 2)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">354</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">355</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      for (ds_i in seq_along(i)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">356</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        points(subset(observed_row, ds == ds_names[ds_i], c("time", "residual")),</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">357</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          col = col_ds[ds_i], pch = pch_ds[ds_i])</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">358</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">359</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">360</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">361</td>
+                    <td class="coverage">501<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (identical(resplot, "predicted")) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">362</td>
+                    <td class="coverage">501<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      plot(0, type = "n",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">363</td>
+                    <td class="coverage">501<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        xlim = c(0, max(pred_ds[[obs_var]])),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">364</td>
+                    <td class="coverage">501<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        xlab = "Predicted",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">365</td>
+                    <td class="coverage">501<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        ylim = c(-1.2 * maxabs, 1.2 * maxabs),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">366</td>
+                    <td class="coverage">501<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        ylab = if (standardized) "Standardized residual" else "Residual",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">367</td>
+                    <td class="coverage">501<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        frame = frame)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">368</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">369</td>
+                    <td class="coverage">501<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      abline(h = 0, lty = 2)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">370</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">371</td>
+                    <td class="coverage">501<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      for (ds_i in seq_along(i)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">372</td>
+                    <td class="coverage">4915<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        observed_row_ds &lt;- merge(</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">373</td>
+                    <td class="coverage">4915<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          subset(observed_row, ds == ds_names[ds_i], c("time", "residual")),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">374</td>
+                    <td class="coverage">4915<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          subset(pred_ds, ds == ds_names[ds_i], c("time", obs_var)))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">375</td>
+                    <td class="coverage">4915<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        points(observed_row_ds[c(3, 2)],</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">376</td>
+                    <td class="coverage">4915<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          col = col_ds[ds_i], pch = pch_ds[ds_i])</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">377</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">378</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">379</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">380</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                </tbody>
+              </table>
+            </div>
+            <div id="R/plot.mmkin.R" class="hidden">
+              <table class="table-condensed">
+                <tbody>
+                  <tr class="never">
+                    <td class="num">1</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Plot model fits (observed and fitted) and the residuals for a row or column</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">2</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' of an mmkin object</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">3</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">4</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' When x is a row selected from an mmkin object (\code{\link{[.mmkin}}), the</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">5</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' same model fitted for at least one dataset is shown. When it is a column,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">6</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' the fit of at least one model to the same dataset is shown.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">7</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">8</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' If the current plot device is a \code{\link[tikzDevice]{tikz}} device, then</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">9</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' latex is being used for the formatting of the chi2 error level.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">10</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">11</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param x An object of class \code{\link{mmkin}}, with either one row or one</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">12</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   column.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">13</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param main The main title placed on the outer margin of the plot.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">14</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param legends An index for the fits for which legends should be shown.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">15</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param resplot Should the residuals plotted against time, using</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">16</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   \code{\link{mkinresplot}}, or as squared residuals against predicted</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">17</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   values, with the error model, using \code{\link{mkinerrplot}}.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">18</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param ylab Label for the y axis.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">19</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param standardized Should the residuals be standardized? This option</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">20</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   is passed to \code{\link{mkinresplot}}, it only takes effect if</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">21</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   `resplot = "time"`.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">22</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param show_errmin Should the chi2 error level be shown on top of the plots</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">23</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   to the left?</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">24</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param errmin_var The variable for which the FOCUS chi2 error value should</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">25</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   be shown.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">26</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param errmin_digits The number of significant digits for rounding the FOCUS</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">27</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   chi2 error percentage.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">28</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param cex Passed to the plot functions and \code{\link{mtext}}.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">29</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param rel.height.middle The relative height of the middle plot, if more</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">30</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   than two rows of plots are shown.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">31</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param ymax Maximum y axis value for \code{\link{plot.mkinfit}}.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">32</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param \dots Further arguments passed to \code{\link{plot.mkinfit}} and</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">33</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   \code{\link{mkinresplot}}.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">34</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @return The function is called for its side effect.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">35</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @author Johannes Ranke</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">36</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @examples</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">37</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">38</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   \dontrun{</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">39</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   # Only use one core not to offend CRAN checks</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">40</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   fits &lt;- mmkin(c("FOMC", "HS"),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">41</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'                 list("FOCUS B" = FOCUS_2006_B, "FOCUS C" = FOCUS_2006_C), # named list for titles</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">42</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'                 cores = 1, quiet = TRUE, error_model = "tc")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">43</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   plot(fits[, "FOCUS C"])</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">44</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   plot(fits["FOMC", ])</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">45</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   plot(fits["FOMC", ], show_errmin = FALSE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">46</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">47</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   # We can also plot a single fit, if we like the way plot.mmkin works, but then the plot</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">48</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   # height should be smaller than the plot width (this is not possible for the html pages</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">49</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   # generated by pkgdown, as far as I know).</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">50</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   plot(fits["FOMC", "FOCUS C"]) # same as plot(fits[1, 2])</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">51</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">52</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   # Show the error models</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">53</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   plot(fits["FOMC", ], resplot = "errmod")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">54</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">55</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">56</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">57</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">plot.mmkin &lt;- function(x, main = "auto", legends = 1,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">58</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  resplot = c("time", "errmod"),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">59</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  ylab = "Residue",</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">60</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  standardized = FALSE,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">61</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  show_errmin = TRUE,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">62</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  errmin_var = "All data", errmin_digits = 3,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">63</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cex = 0.7, rel.height.middle = 0.9,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">64</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  ymax = "auto", ...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">65</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">{</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">66</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">67</td>
+                    <td class="coverage">316<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  oldpar &lt;- par(no.readonly = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">68</td>
+                    <td class="coverage">316<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  on.exit(par(oldpar, no.readonly = TRUE))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">69</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">70</td>
+                    <td class="coverage">316<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  n.m &lt;- nrow(x)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">71</td>
+                    <td class="coverage">316<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  n.d &lt;- ncol(x)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">72</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">73</td>
+                    <td class="coverage">316<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  resplot &lt;- match.arg(resplot)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">74</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">75</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # We can handle either a row (different models, same dataset)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">76</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # or a column (same model, different datasets)</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">77</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (n.m &gt; 1 &amp; n.d &gt; 1) stop("Please select fits either for one model or for one dataset")</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">78</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (n.m == 1 &amp; n.d == 1) loop_over = "none"</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">79</td>
+                    <td class="coverage">246<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (n.m &gt; 1) loop_over &lt;- "models"</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">80</td>
+                    <td class="coverage">70<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (n.d &gt; 1) loop_over &lt;- "datasets"</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">81</td>
+                    <td class="coverage">316<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  n.fits &lt;- length(x)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">82</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">83</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Set the main plot titles from the names of the models or the datasets</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">84</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Will be integer indexes if no other names are present in the mmkin object</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">85</td>
+                    <td class="coverage">316<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (main == "auto") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">86</td>
+                    <td class="coverage">140<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    main = switch(loop_over,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">87</td>
+                    <td class="coverage">140<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                  none = paste(rownames(x), colnames(x)),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">88</td>
+                    <td class="coverage">140<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                  models = colnames(x),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">89</td>
+                    <td class="coverage">140<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                  datasets = rownames(x))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">90</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">91</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">92</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Set relative plot heights, so the first and the last plot are the norm</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">93</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # and the middle plots (if n.fits &gt;2) are smaller by rel.height.middle</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">94</td>
+                    <td class="coverage">316<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  rel.heights &lt;- if (n.fits &gt; 2) c(1, rep(rel.height.middle, n.fits - 2), 1)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">95</td>
+                    <td class="coverage">316<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                 else rep(1, n.fits)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">96</td>
+                    <td class="coverage">316<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  layout(matrix(1:(2 * n.fits), n.fits, 2, byrow = TRUE), heights = rel.heights)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">97</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">98</td>
+                    <td class="coverage">316<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  par(cex = cex)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">99</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">100</td>
+                    <td class="coverage">316<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  for (i.fit in 1:n.fits) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">101</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">102</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # Margins for top row of plots when we have more than one row</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">103</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # Reduce bottom margin by 2.1 - hides x axis legend</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">104</td>
+                    <td class="coverage">948<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (i.fit == 1 &amp; n.fits &gt; 1) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">105</td>
+                    <td class="coverage">316<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      par(mar = c(3.0, 4.1, 4.1, 2.1))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">106</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">107</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">108</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # Margins for middle rows of plots, if any</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">109</td>
+                    <td class="coverage">948<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (i.fit &gt; 1 &amp; i.fit &lt; n.fits) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">110</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      # Reduce top margin by 2 after the first plot as we have no main title,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">111</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      # reduced plot height, therefore we need rel.height.middle in the layout</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">112</td>
+                    <td class="coverage">316<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      par(mar = c(3.0, 4.1, 2.1, 2.1))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">113</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">114</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">115</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # Margins for bottom row of plots when we have more than one row</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">116</td>
+                    <td class="coverage">948<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (i.fit == n.fits &amp; n.fits &gt; 1) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">117</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      # Restore bottom margin for last plot to show x axis legend</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">118</td>
+                    <td class="coverage">316<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      par(mar = c(5.1, 4.1, 2.1, 2.1))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">119</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">120</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">121</td>
+                    <td class="coverage">948<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    fit &lt;- x[[i.fit]]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">122</td>
+                    <td class="coverage">948<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (ymax == "auto") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">123</td>
+                    <td class="coverage">948<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      plot(fit, legend = legends == i.fit, ylab = ylab, ...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">124</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">125</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      plot(fit, legend = legends == i.fit, ylim = c(0, ymax), ylab = ylab, ...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">126</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">127</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">128</td>
+                    <td class="coverage">948<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    title(main, outer = TRUE, line = -2)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">129</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">130</td>
+                    <td class="coverage">948<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    fit_name &lt;- switch(loop_over,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">131</td>
+                    <td class="coverage">948<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                       models = rownames(x)[i.fit],</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">132</td>
+                    <td class="coverage">948<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                       datasets = colnames(x)[i.fit],</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">133</td>
+                    <td class="coverage">948<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                       none = "")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">134</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">135</td>
+                    <td class="coverage">948<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (show_errmin) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">136</td>
+                    <td class="coverage">948<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      chi2 &lt;- signif(100 * mkinerrmin(fit)[errmin_var, "err.min"], errmin_digits)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">137</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">138</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      # Use LateX if the current plotting device is tikz</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">139</td>
+                    <td class="coverage">948<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      if (names(dev.cur()) == "tikz output") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">140</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        chi2_text &lt;- paste0(fit_name, " $\\chi^2$ error level = ", chi2, "\\%")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">141</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">142</td>
+                    <td class="coverage">948<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        chi2_perc &lt;- paste0(chi2, "%")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">143</td>
+                    <td class="coverage">948<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        chi2_text &lt;- bquote(.(fit_name) ~ chi^2 ~ "error level" == .(chi2_perc))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">144</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">145</td>
+                    <td class="coverage">948<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      mtext(chi2_text, cex = cex, line = 0.4)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">146</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">147</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      mtext(fit_name, cex = cex, line = 0.4)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">148</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">149</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">150</td>
+                    <td class="coverage">948<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (resplot == "time") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">151</td>
+                    <td class="coverage">948<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      mkinresplot(fit, legend = FALSE, standardized = standardized, ...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">152</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">153</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      mkinerrplot(fit, legend = FALSE, ...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">154</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">155</td>
+                    <td class="coverage">948<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    mtext(paste(fit_name, "residuals"), cex = cex, line = 0.4)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">156</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">157</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                </tbody>
+              </table>
+            </div>
+            <div id="R/anova.saem.mmkin.R" class="hidden">
+              <table class="table-condensed">
+                <tbody>
+                  <tr class="never">
+                    <td class="num">1</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Anova method for saem.mmkin objects</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">2</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">3</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Generate an anova object. The method to calculate the BIC is that from the</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">4</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' saemix package. As in other prominent anova methods, models are sorted by</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">5</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' number of parameters, and the tests (if requested) are always relative to</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">6</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' the model on the previous line.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">7</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">8</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param object An [saem.mmkin] object</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">9</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param ...   further such objects</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">10</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param method Method for likelihood calculation: "is" (importance sampling),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">11</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' "lin" (linear approximation), or "gq" (Gaussian quadrature). Passed</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">12</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' to [saemix::logLik.SaemixObject]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">13</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param test Should a likelihood ratio test be performed? If TRUE,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">14</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' the alternative models are tested against the first model. Should</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">15</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' only be done for nested models.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">16</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param model.names Optional character vector of model names</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">17</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @importFrom stats anova logLik update pchisq terms</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">18</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @importFrom methods is</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">19</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @importFrom utils capture.output</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">20</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">21</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @return an "anova" data frame; the traditional (S3) result of anova()</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">22</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">anova.saem.mmkin &lt;- function(object, ...,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">23</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  method = c("is", "lin", "gq"), test = FALSE, model.names = NULL)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">24</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">{</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">25</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # The following code is heavily inspired by anova.lmer in the lme4 package</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">26</td>
+                    <td class="coverage">518<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  mCall &lt;- match.call(expand.dots = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">27</td>
+                    <td class="coverage">518<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  dots &lt;- list(...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">28</td>
+                    <td class="coverage">518<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  method &lt;- match.arg(method)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">29</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">30</td>
+                    <td class="coverage">518<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  is_model &lt;- sapply(dots, is, "saem.mmkin")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">31</td>
+                    <td class="coverage">518<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (any(is_model)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">32</td>
+                    <td class="coverage">518<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    mods &lt;- c(list(object), dots[is_model])</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">33</td>
+                    <td class="coverage">518<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    successful &lt;- sapply(mods, function(x) !inherits(x$so, "try-error"))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">34</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">35</td>
+                    <td class="coverage">518<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (is.null(model.names))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">36</td>
+                    <td class="coverage">284<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        model.names &lt;- vapply(as.list(mCall)[c(FALSE, TRUE, is_model)], deparse1, "")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">37</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">38</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # Sanitize model names</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">39</td>
+                    <td class="coverage">518<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (length(model.names) != length(mods))</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">40</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      stop("Model names vector and model list have different lengths")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">41</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">42</td>
+                    <td class="coverage">518<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (any(duplicated(model.names)))</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">43</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      stop("Duplicate model names are not allowed")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">44</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">45</td>
+                    <td class="coverage">518<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (max(nchar(model.names)) &gt; 200) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">46</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      warning("Model names longer than 200 characters, assigning generic names")</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">47</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      model.names &lt;- paste0("MODEL",seq_along(model.names))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">48</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">49</td>
+                    <td class="coverage">518<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    names(mods) &lt;- model.names</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">50</td>
+                    <td class="coverage">518<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    mods &lt;- mods[successful]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">51</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">52</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # Ensure same data, ignoring covariates</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">53</td>
+                    <td class="coverage">518<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    same_data &lt;- sapply(mods[-1], function(x) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">54</td>
+                    <td class="coverage">1182<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      identical(mods[[1]]$data[c("ds", "name", "time", "value")],</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">55</td>
+                    <td class="coverage">1182<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        x$data[c("ds", "name", "time", "value")])</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">56</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    })</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">57</td>
+                    <td class="coverage">518<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (!all(same_data)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">58</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      stop(sum(!same_data), " objects have not been fitted to the same data")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">59</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">60</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">61</td>
+                    <td class="coverage">518<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    llks &lt;- lapply(names(mods), function(x) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">62</td>
+                    <td class="coverage">1700<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      llk &lt;- try(logLik(mods[[x]], method = method), silent = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">63</td>
+                    <td class="coverage">1700<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      if (inherits(llk, "try-error")) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">64</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        warning("Could not obtain log likelihood with '", method, "' method for ", x)</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">65</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        llk &lt;- NA</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">66</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">67</td>
+                    <td class="coverage">1700<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      return(llk)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">68</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    })</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">69</td>
+                    <td class="coverage">518<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    available &lt;- !sapply(llks, is.na)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">70</td>
+                    <td class="coverage">518<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    llks &lt;- llks[available]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">71</td>
+                    <td class="coverage">518<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    mods &lt;- mods[available]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">72</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">73</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # Order models by increasing degrees of freedom:</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">74</td>
+                    <td class="coverage">518<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    npar &lt;- vapply(llks, attr, FUN.VALUE=numeric(1), "df")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">75</td>
+                    <td class="coverage">518<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    ii &lt;- order(npar)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">76</td>
+                    <td class="coverage">518<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    mods &lt;- mods[ii]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">77</td>
+                    <td class="coverage">518<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    llks &lt;- llks[ii]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">78</td>
+                    <td class="coverage">518<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    npar   &lt;- npar[ii]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">79</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">80</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # Describe data for the header, as in summary.saem.mmkin</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">81</td>
+                    <td class="coverage">518<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    header &lt;- paste("Data:", nrow(object$data), "observations of",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">82</td>
+                    <td class="coverage">518<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      length(unique(object$data$name)), "variable(s) grouped in",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">83</td>
+                    <td class="coverage">518<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      length(unique(object$data$ds)), "datasets\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">84</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">85</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # Calculate statistics</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">86</td>
+                    <td class="coverage">518<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    llk &lt;- unlist(llks)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">87</td>
+                    <td class="coverage">518<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    chisq &lt;- 2 * pmax(0, c(NA, diff(llk)))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">88</td>
+                    <td class="coverage">518<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    dfChisq &lt;- c(NA, diff(npar))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">89</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">90</td>
+                    <td class="coverage">518<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    bic &lt;- function(x, method) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">91</td>
+                    <td class="coverage">1700<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      BIC(x$so, method = method)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">92</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">93</td>
+                    <td class="coverage">518<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    val &lt;- data.frame(</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">94</td>
+                    <td class="coverage">518<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      npar = npar,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">95</td>
+                    <td class="coverage">518<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      AIC = sapply(llks, AIC),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">96</td>
+                    <td class="coverage">518<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      BIC = sapply(mods, bic, method = method), # We use the saemix method here</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">97</td>
+                    <td class="coverage">518<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      Lik = llk,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">98</td>
+                    <td class="coverage">518<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      row.names = names(mods), check.names = FALSE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">99</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">100</td>
+                    <td class="coverage">518<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (test) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">101</td>
+                    <td class="coverage">196<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      testval &lt;- data.frame(</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">102</td>
+                    <td class="coverage">196<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        Chisq = chisq,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">103</td>
+                    <td class="coverage">196<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        Df = dfChisq,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">104</td>
+                    <td class="coverage">196<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        "Pr(&gt;Chisq)" = ifelse(dfChisq == 0, NA,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">105</td>
+                    <td class="coverage">196<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          pchisq(chisq, dfChisq, lower.tail = FALSE)),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">106</td>
+                    <td class="coverage">196<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        row.names = names(mods), check.names = FALSE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">107</td>
+                    <td class="coverage">196<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      val &lt;- cbind(val, testval)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">108</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">109</td>
+                    <td class="coverage">518<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    class(val) &lt;- c("anova", class(val))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">110</td>
+                    <td class="coverage">518<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    structure(val, heading = c(header))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">111</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">112</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    stop("Currently, no anova method is implemented for the case of a single saem.mmkin object")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">113</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">114</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                </tbody>
+              </table>
+            </div>
+            <div id="R/create_deg_func.R" class="hidden">
+              <table class="table-condensed">
+                <tbody>
+                  <tr class="never">
+                    <td class="num">1</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Create degradation functions for known analytical solutions</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">2</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">3</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param spec List of model specifications as contained in mkinmod objects</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">4</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param use_of_ff Minimum or maximum use of formation fractions</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">5</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @return Degradation function to be attached to mkinmod objects</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">6</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @examples</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">7</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">8</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' SFO_SFO &lt;- mkinmod(</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">9</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   parent = mkinsub("SFO", "m1"),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">10</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   m1 = mkinsub("SFO"))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">11</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' FOCUS_D &lt;- subset(FOCUS_2006_D, value != 0) # to avoid warnings</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">12</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' fit_1 &lt;- mkinfit(SFO_SFO, FOCUS_D, solution_type = "analytical", quiet = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">13</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' \dontrun{</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">14</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' fit_2 &lt;- mkinfit(SFO_SFO, FOCUS_D, solution_type = "deSolve", quiet = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">15</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' if (require(rbenchmark))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">16</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   benchmark(</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">17</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     analytical = mkinfit(SFO_SFO, FOCUS_D, solution_type = "analytical", quiet = TRUE),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">18</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     deSolve = mkinfit(SFO_SFO, FOCUS_D, solution_type = "deSolve", quiet = TRUE),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">19</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     replications = 2)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">20</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   DFOP_SFO &lt;- mkinmod(</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">21</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     parent = mkinsub("DFOP", "m1"),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">22</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     m1 = mkinsub("SFO"))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">23</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   benchmark(</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">24</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     analytical = mkinfit(DFOP_SFO, FOCUS_D, solution_type = "analytical", quiet = TRUE),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">25</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     deSolve = mkinfit(DFOP_SFO, FOCUS_D, solution_type = "deSolve", quiet = TRUE),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">26</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     replications = 2)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">27</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">28</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">29</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">create_deg_func &lt;- function(spec, use_of_ff = c("min", "max")) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">30</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">31</td>
+                    <td class="coverage">8117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  use_of_ff &lt;- match.arg(use_of_ff)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">32</td>
+                    <td class="coverage">8117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  min_ff &lt;- use_of_ff == "min"</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">33</td>
+                    <td class="coverage">8117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  obs_vars &lt;- names(spec)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">34</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">35</td>
+                    <td class="coverage">8117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  parent &lt;- obs_vars[1]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">36</td>
+                    <td class="coverage">8117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  parent_type &lt;- spec[[1]]$type</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">37</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">38</td>
+                    <td class="coverage">8117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  supported &lt;- TRUE # This may be modified below</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">39</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">40</td>
+                    <td class="coverage">8117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  predicted_text &lt;- character(0)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">41</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">42</td>
+                    <td class="coverage">8117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (parent_type == "SFO") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">43</td>
+                    <td class="coverage">5916<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (min_ff) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">44</td>
+                    <td class="coverage">599<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      targets &lt;- c(spec[[1]]$to, if (spec[[1]]$sink) "sink" else NULL)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">45</td>
+                    <td class="coverage">599<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      k_parent &lt;- paste(paste0("k_", parent, "_", targets), collapse = " + ")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">46</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">47</td>
+                    <td class="coverage">5317<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      k_parent &lt;- paste0("k_", parent)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">48</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">49</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">50</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">51</td>
+                    <td class="coverage">8117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  predicted_text[parent] &lt;- paste0(parent_type, ".solution(t, odeini['", parent,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">52</td>
+                    <td class="coverage">8117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (parent_type == "SFORB") "_free", "'], ",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">53</td>
+                    <td class="coverage">8117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    switch(parent_type,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">54</td>
+                    <td class="coverage">8117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      SFO = k_parent,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">55</td>
+                    <td class="coverage">8117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      FOMC = "alpha, beta",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">56</td>
+                    <td class="coverage">8117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      IORE = paste0("k__iore_", parent, if (min_ff) "_sink" else "", ", N_", parent),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">57</td>
+                    <td class="coverage">8117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      DFOP = "k1, k2, g",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">58</td>
+                    <td class="coverage">8117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      SFORB = paste0("k_", parent, "_free_bound, k_", parent, "_bound_free, k_", parent, "_free", if (min_ff) "_sink" else ""),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">59</td>
+                    <td class="coverage">8117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      HS = "k1, k2, tb",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">60</td>
+                    <td class="coverage">8117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      logistic = "kmax, k0, r"</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">61</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    ),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">62</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  ")")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">63</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">64</td>
+                    <td class="coverage">3728<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (length(obs_vars) &gt;= 2) supported &lt;- FALSE</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">65</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Except for the following cases:</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">66</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">67</td>
+                    <td class="coverage">8117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (length(obs_vars) == 2) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">68</td>
+                    <td class="coverage">3036<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    n1 &lt;- obs_vars[1]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">69</td>
+                    <td class="coverage">3036<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    n2 &lt;- obs_vars[2]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">70</td>
+                    <td class="coverage">3036<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    n10 &lt;- paste0("odeini['", parent, "']")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">71</td>
+                    <td class="coverage">3036<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    n20 &lt;- paste0("odeini['", n2, "']")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">72</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">73</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # sfo_sfo</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">74</td>
+                    <td class="coverage">3036<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (all(</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">75</td>
+                    <td class="coverage">3036<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        spec[[1]]$sink == FALSE,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">76</td>
+                    <td class="coverage">3036<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        spec[[1]]$type == "SFO",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">77</td>
+                    <td class="coverage">3036<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        spec[[2]]$type == "SFO",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">78</td>
+                    <td class="coverage">3036<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        is.null(spec[[2]]$to))) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">79</td>
+                    <td class="coverage">741<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      supported &lt;- TRUE</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">80</td>
+                    <td class="coverage">741<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      k1 &lt;- k_parent</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">81</td>
+                    <td class="coverage">741<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      k2 &lt;- paste0("k_", n2, if(min_ff) "_sink" else "")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">82</td>
+                    <td class="coverage">741<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      predicted_text[n2] &lt;- paste0(</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">83</td>
+                    <td class="coverage">741<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        "(((", k2, "-", k1, ")*", n20, "-", k1, "*", n10, ")*exp(-", k2, "*t)+",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">84</td>
+                    <td class="coverage">741<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        k1, "*", n10, "*exp(-", k1, "*t))/(", k2, "-", k1, ")")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">85</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">86</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">87</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # sfo_f12_sfo</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">88</td>
+                    <td class="coverage">3036<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (all(</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">89</td>
+                    <td class="coverage">3036<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        use_of_ff == "max",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">90</td>
+                    <td class="coverage">3036<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        spec[[1]]$sink == TRUE,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">91</td>
+                    <td class="coverage">3036<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        spec[[1]]$type == "SFO",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">92</td>
+                    <td class="coverage">3036<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        spec[[2]]$type == "SFO",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">93</td>
+                    <td class="coverage">3036<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        is.null(spec[[2]]$to))) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">94</td>
+                    <td class="coverage">1129<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      supported &lt;- TRUE</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">95</td>
+                    <td class="coverage">1129<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      k1 &lt;- k_parent</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">96</td>
+                    <td class="coverage">1129<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      k2 &lt;- paste0("k_", n2)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">97</td>
+                    <td class="coverage">1129<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      f12 &lt;- paste0("f_", n1, "_to_", n2)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">98</td>
+                    <td class="coverage">1129<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      predicted_text[n2] &lt;- paste0(</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">99</td>
+                    <td class="coverage">1129<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        "(((", k2, "-", k1, ")*", n20, "-", f12, "*", k1, "*", n10, ")*exp(-", k2, "*t)+",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">100</td>
+                    <td class="coverage">1129<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        f12, "*", k1, "*", n10, "*exp(-", k1, "*t))/(", k2, "-", k1, ")")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">101</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">102</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">103</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # sfo_k120_sfo</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">104</td>
+                    <td class="coverage">3036<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (all(</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">105</td>
+                    <td class="coverage">3036<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        use_of_ff == "min",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">106</td>
+                    <td class="coverage">3036<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        spec[[1]]$sink == TRUE,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">107</td>
+                    <td class="coverage">3036<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        spec[[1]]$type == "SFO",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">108</td>
+                    <td class="coverage">3036<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        spec[[2]]$type == "SFO",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">109</td>
+                    <td class="coverage">3036<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        is.null(spec[[2]]$to))) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">110</td>
+                    <td class="coverage">351<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      supported &lt;- TRUE</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">111</td>
+                    <td class="coverage">351<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      k12 &lt;- paste0("k_", n1, "_", n2)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">112</td>
+                    <td class="coverage">351<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      k10 &lt;- paste0("k_", n1, "_sink")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">113</td>
+                    <td class="coverage">351<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      k2 &lt;- paste0("k_", n2, "_sink")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">114</td>
+                    <td class="coverage">351<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      predicted_text[n2] &lt;- paste0(</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">115</td>
+                    <td class="coverage">351<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        "(((", k2, "-", k12, "-", k10, ")*", n20, "-", k12, "*", n10, ")*exp(-", k2, "*t)+",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">116</td>
+                    <td class="coverage">351<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        k12, "*", n10, "*exp(-(", k_parent, ")*t))/(", k2, "-(", k_parent, "))")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">117</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">118</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">119</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # dfop_f12_sfo</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">120</td>
+                    <td class="coverage">3036<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (all(</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">121</td>
+                    <td class="coverage">3036<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        use_of_ff == "max",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">122</td>
+                    <td class="coverage">3036<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        spec[[1]]$sink == TRUE,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">123</td>
+                    <td class="coverage">3036<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        spec[[1]]$type == "DFOP",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">124</td>
+                    <td class="coverage">3036<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        spec[[2]]$type == "SFO",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">125</td>
+                    <td class="coverage">3036<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        is.null(spec[[2]]$to))) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">126</td>
+                    <td class="coverage">565<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      supported &lt;- TRUE</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">127</td>
+                    <td class="coverage">565<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      f12 &lt;- paste0("f_", n1, "_to_", n2)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">128</td>
+                    <td class="coverage">565<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      k2 &lt;- paste0("k_", n2)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">129</td>
+                    <td class="coverage">565<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      predicted_text[n2] &lt;- paste0(</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">130</td>
+                    <td class="coverage">565<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        "((", f12, "* g - ", f12, ") * k2 * ", n10, " * exp(- k2 * t))/(k2 - ", k2, ") - ",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">131</td>
+                    <td class="coverage">565<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        "((", f12, "* g) * k1 * ", n10, " * exp(- k1 * t))/(k1 - ", k2, ") + ",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">132</td>
+                    <td class="coverage">565<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        "(((k1 - ", k2, ") * k2 - ", k2, "* k1 + ", k2, "^2) * ", n20, "+",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">133</td>
+                    <td class="coverage">565<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        "((", f12, "* k1 + (", f12, "*g - ", f12, ") * ", k2, ") * k2 - ", f12, " * g * ", k2, " * k1) * ", n10, ") * ",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">134</td>
+                    <td class="coverage">565<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        "exp( - ", k2, " * t)/((k1 - ", k2, ") * k2 - ", k2, " * k1 + ", k2, "^2)")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">135</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">136</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">137</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">138</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">139</td>
+                    <td class="coverage">8117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (supported) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">140</td>
+                    <td class="coverage">7175<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    deg_func &lt;- function(observed, odeini, odeparms) {}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">141</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">142</td>
+                    <td class="coverage">7175<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    f_body &lt;- paste0("{\n",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">143</td>
+                    <td class="coverage">7175<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      "predicted &lt;- numeric(0)\n",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">144</td>
+                    <td class="coverage">7175<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      "with(as.list(odeparms), {\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">145</td>
+                    <td class="coverage">7175<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    for (obs_var in obs_vars) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">146</td>
+                    <td class="coverage">9961<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      f_body &lt;- paste0(f_body,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">147</td>
+                    <td class="coverage">9961<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      "t &lt;- observed[observed$name == '", obs_var, "', 'time']\n",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">148</td>
+                    <td class="coverage">9961<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      "predicted &lt;&lt;- c(predicted, ", predicted_text[obs_var], ")\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">149</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">150</td>
+                    <td class="coverage">7175<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    f_body &lt;- paste0(f_body,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">151</td>
+                    <td class="coverage">7175<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      "})\n",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">152</td>
+                    <td class="coverage">7175<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      "return(predicted)\n}\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">153</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">154</td>
+                    <td class="coverage">7175<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    body(deg_func) &lt;- parse(text = f_body)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">155</td>
+                    <td class="coverage">7175<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    return(deg_func)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">156</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">157</td>
+                    <td class="coverage">942<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    return(NULL)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">158</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">159</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                </tbody>
+              </table>
+            </div>
+            <div id="R/mkinresplot.R" class="hidden">
+              <table class="table-condensed">
+                <tbody>
+                  <tr class="never">
+                    <td class="num">1</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">utils::globalVariables(c("variable", "residual"))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">2</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">3</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Function to plot residuals stored in an mkin object</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">4</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">5</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' This function plots the residuals for the specified subset of the observed</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">6</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' variables from an mkinfit object. A combined plot of the fitted model and</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">7</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' the residuals can be obtained using \code{\link{plot.mkinfit}} using the</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">8</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' argument \code{show_residuals = TRUE}.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">9</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">10</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @importFrom stats residuals</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">11</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param object A fit represented in an \code{\link{mkinfit}} object.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">12</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param obs_vars A character vector of names of the observed variables for</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">13</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   which residuals should be plotted. Defaults to all observed variables in</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">14</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   the model</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">15</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param xlim plot range in x direction.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">16</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param xlab Label for the x axis.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">17</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param standardized Should the residuals be standardized by dividing by the</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">18</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   standard deviation given by the error model of the fit?</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">19</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param ylab Label for the y axis.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">20</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param maxabs Maximum absolute value of the residuals. This is used for the</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">21</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   scaling of the y axis and defaults to "auto".</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">22</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param legend Should a legend be plotted?</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">23</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param lpos Where should the legend be placed? Default is "topright". Will</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">24</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   be passed on to \code{\link{legend}}.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">25</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param col_obs Colors for the observed variables.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">26</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param pch_obs Symbols to be used for the observed variables.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">27</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param frame Should a frame be drawn around the plots?</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">28</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param \dots further arguments passed to \code{\link{plot}}.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">29</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @return Nothing is returned by this function, as it is called for its side</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">30</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   effect, namely to produce a plot.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">31</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @author Johannes Ranke and Katrin Lindenberger</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">32</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @seealso \code{\link{mkinplot}}, for a way to plot the data and the fitted</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">33</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   lines of the mkinfit object, and \code{\link{plot_res}} for a function</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">34</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   combining the plot of the fit and the residual plot.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">35</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @examples</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">36</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">37</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' model &lt;- mkinmod(parent = mkinsub("SFO", "m1"), m1 = mkinsub("SFO"))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">38</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' fit &lt;- mkinfit(model, FOCUS_2006_D, quiet = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">39</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' mkinresplot(fit, "m1")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">40</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">41</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">42</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">mkinresplot &lt;- function (object,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">43</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  obs_vars = names(object$mkinmod$map),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">44</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  xlim = c(0, 1.1 * max(object$data$time)),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">45</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  standardized = FALSE,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">46</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  xlab = "Time", ylab = ifelse(standardized, "Standardized residual", "Residual"),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">47</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  maxabs = "auto", legend = TRUE, lpos = "topright",</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">48</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  col_obs = "auto", pch_obs = "auto",</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">49</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  frame = TRUE,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">50</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  ...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">51</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">{</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">52</td>
+                    <td class="coverage">1228<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  obs_vars_all &lt;- as.character(unique(object$data$variable))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">53</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">54</td>
+                    <td class="coverage">1228<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (length(obs_vars) &gt; 0){</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">55</td>
+                    <td class="coverage">1228<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      obs_vars &lt;- intersect(obs_vars_all, obs_vars)</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">56</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  } else obs_vars &lt;- obs_vars_all</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">57</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">58</td>
+                    <td class="coverage">1228<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (standardized) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">59</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    res_col &lt;- "standardized"</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">60</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    object$data[[res_col]] &lt;- residuals(object, standardized = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">61</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">62</td>
+                    <td class="coverage">1228<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    res_col &lt;- "residual"</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">63</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">64</td>
+                    <td class="coverage">1228<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  res &lt;- subset(object$data, variable %in% obs_vars)[res_col]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">65</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">66</td>
+                    <td class="coverage">1228<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (maxabs == "auto") maxabs = max(abs(res), na.rm = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">67</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">68</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Set colors and symbols</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">69</td>
+                    <td class="coverage">1228<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (col_obs[1] == "auto") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">70</td>
+                    <td class="coverage">948<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    col_obs &lt;- 1:length(obs_vars)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">71</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">72</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">73</td>
+                    <td class="coverage">1228<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (pch_obs[1] == "auto") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">74</td>
+                    <td class="coverage">948<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    pch_obs &lt;- 1:length(obs_vars)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">75</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">76</td>
+                    <td class="coverage">1228<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  names(col_obs) &lt;- names(pch_obs) &lt;- obs_vars</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">77</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">78</td>
+                    <td class="coverage">1228<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  plot(0, type = "n", frame = frame,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">79</td>
+                    <td class="coverage">1228<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">       xlab = xlab, ylab = ylab,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">80</td>
+                    <td class="coverage">1228<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">       xlim = xlim,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">81</td>
+                    <td class="coverage">1228<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">       ylim = c(-1.2 * maxabs, 1.2 * maxabs), ...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">82</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">83</td>
+                    <td class="coverage">1228<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  for(obs_var in obs_vars){</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">84</td>
+                    <td class="coverage">1298<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    residuals_plot &lt;- subset(object$data, variable == obs_var, c("time", res_col))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">85</td>
+                    <td class="coverage">1298<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    points(residuals_plot, pch = pch_obs[obs_var], col = col_obs[obs_var])</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">86</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">87</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">88</td>
+                    <td class="coverage">1228<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  abline(h = 0, lty = 2)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">89</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">90</td>
+                    <td class="coverage">1228<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (legend == TRUE) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">91</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    legend(lpos, inset = c(0.05, 0.05), legend = obs_vars,</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">92</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      col = col_obs[obs_vars], pch = pch_obs[obs_vars])</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">93</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">94</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                </tbody>
+              </table>
+            </div>
+            <div id="R/hierarchical_kinetics.R" class="hidden">
+              <table class="table-condensed">
+                <tbody>
+                  <tr class="never">
+                    <td class="num">1</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Hierarchical kinetics template</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">2</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">3</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' R markdown format for setting up hierarchical kinetics based on a template</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">4</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' provided with the mkin package. This format is based on [rmarkdown::pdf_document].</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">5</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Chunk options are adapted. Echoing R code from code chunks and caching are</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">6</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' turned on per default. character for prepending output from code chunks is</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">7</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' set to the empty string, code tidying is off, figure alignment defaults to</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">8</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' centering, and positioning of figures is set to "H", which means that</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">9</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' figures will not move around in the document, but stay where the user</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">10</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' includes them.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">11</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">12</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' The latter feature (positioning the figures with "H") depends on the LaTeX</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">13</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' package 'float'. In addition, the LaTeX package 'listing' is used in the</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">14</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' template for showing model fit summaries in the Appendix. This means that</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">15</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' the LaTeX packages 'float' and 'listing' need to be installed in the TeX</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">16</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' distribution used.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">17</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">18</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' On Windows, the easiest way to achieve this (if no TeX distribution</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">19</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' is present before) is to install the 'tinytex' R package, to run</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">20</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' 'tinytex::install_tinytex()' to get the basic tiny Tex distribution,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">21</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' and then to run 'tinytex::tlmgr_install(c("float", "listing"))'.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">22</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">23</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @inheritParams rmarkdown::pdf_document</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">24</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param ... Arguments to \code{rmarkdown::pdf_document}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">25</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">26</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @return R Markdown output format to pass to</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">27</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   \code{\link[rmarkdown:render]{render}}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">28</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">29</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @examples</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">30</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">31</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' \dontrun{</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">32</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' library(rmarkdown)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">33</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # The following is now commented out after the relase of v1.2.3 for the generation</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">34</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # of online docs, as the command creates a directory and opens an editor</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">35</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' #draft("example_analysis.rmd", template = "hierarchical_kinetics", package = "mkin")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">36</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">37</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">38</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">39</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">hierarchical_kinetics &lt;- function(..., keep_tex = FALSE) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">40</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">41</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (getRversion() &lt; "4.1.0")</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">42</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    stop("You need R with version &gt; 4.1.0 to compile this document")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">43</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">44</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (!requireNamespace("knitr")) stop("Please install the knitr package to use this template")</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">45</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (!requireNamespace("rmarkdown")) stop("Please install the rmarkdown package to use this template")</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">46</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  knitr::opts_chunk$set(cache = TRUE, comment = "", tidy = FALSE, echo = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">47</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  knitr::opts_chunk$set(fig.align = "center", fig.pos = "H")</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">48</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  options(knitr.kable.NA = "")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">49</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">50</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  fmt &lt;- rmarkdown::pdf_document(...,</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">51</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    keep_tex = keep_tex,</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">52</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    toc = TRUE,</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">53</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    toc_depth = 3,</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">54</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    includes = rmarkdown::includes(in_header = "header.tex"),</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">55</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    extra_dependencies = c("float", "listing", "framed")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">56</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  )</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">57</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">58</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  return(fmt)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">59</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                </tbody>
+              </table>
+            </div>
+            <div id="R/nlme.mmkin.R" class="hidden">
+              <table class="table-condensed">
+                <tbody>
+                  <tr class="never">
+                    <td class="num">1</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"># Code inspired by nlme::nlme.nlsList and R/nlme_fit.R from nlmixr</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">2</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">3</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"># We need to assign the degradation function created in nlme.mmkin to an</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">4</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"># environment that is always accessible, also e.g. when evaluation is done by</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">5</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"># testthat or pkgdown. Therefore parent.frame() is not good enough. The</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">6</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"># following environment will be in the mkin namespace.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">7</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">.nlme_env &lt;- new.env(parent = emptyenv())</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">8</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">9</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">10</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">nlme::nlme</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">11</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">12</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Retrieve a degradation function from the mmkin namespace</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">13</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">14</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @importFrom utils getFromNamespace</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">15</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @return A function that was likely previously assigned from within</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">16</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   nlme.mmkin</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">17</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">18</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">get_deg_func &lt;- function() {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">19</td>
+                    <td class="coverage">217279<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  return(get("deg_func", getFromNamespace(".nlme_env", "mkin")))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">20</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">21</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">22</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Create an nlme model for an mmkin row object</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">23</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">24</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' This functions sets up a nonlinear mixed effects model for an mmkin row</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">25</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' object. An mmkin row object is essentially a list of mkinfit objects that</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">26</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' have been obtained by fitting the same model to a list of datasets.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">27</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">28</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Note that the convergence of the nlme algorithms depends on the quality</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">29</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' of the data. In degradation kinetics, we often only have few datasets</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">30</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' (e.g. data for few soils) and complicated degradation models, which may</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">31</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' make it impossible to obtain convergence with nlme.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">32</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">33</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param model An [mmkin] row object.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">34</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param data Ignored, data are taken from the mmkin model</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">35</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param fixed Ignored, all degradation parameters fitted in the</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">36</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   mmkin model are used as fixed parameters</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">37</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param random If not specified, no correlations between random effects are</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">38</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   set up for the optimised degradation model parameters. This is</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">39</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   achieved by using the [nlme::pdDiag] method.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">40</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param groups See the documentation of nlme</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">41</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param start If not specified, mean values of the fitted degradation</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">42</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   parameters taken from the mmkin object are used</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">43</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param correlation See the documentation of nlme</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">44</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param weights passed to nlme</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">45</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param subset passed to nlme</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">46</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param method passed to nlme</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">47</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param na.action passed to nlme</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">48</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param naPattern passed to nlme</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">49</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param control passed to nlme</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">50</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param verbose passed to nlme</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">51</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @importFrom stats na.fail as.formula</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">52</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @return Upon success, a fitted 'nlme.mmkin' object, which is an nlme object</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">53</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   with additional elements. It also inherits from 'mixed.mmkin'.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">54</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @note As the object inherits from [nlme::nlme], there is a wealth of</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">55</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   methods that will automatically work on 'nlme.mmkin' objects, such as</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">56</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   [nlme::intervals()], [nlme::anova.lme()] and [nlme::coef.lme()].</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">57</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">58</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @seealso [nlme_function()], [plot.mixed.mmkin], [summary.nlme.mmkin]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">59</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @examples</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">60</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' ds &lt;- lapply(experimental_data_for_UBA_2019[6:10],</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">61</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'  function(x) subset(x$data[c("name", "time", "value")], name == "parent"))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">62</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">63</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' \dontrun{</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">64</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   f &lt;- mmkin(c("SFO", "DFOP"), ds, quiet = TRUE, cores = 1)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">65</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   library(nlme)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">66</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   f_nlme_sfo &lt;- nlme(f["SFO", ])</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">67</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   f_nlme_dfop &lt;- nlme(f["DFOP", ])</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">68</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   anova(f_nlme_sfo, f_nlme_dfop)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">69</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   print(f_nlme_dfop)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">70</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   plot(f_nlme_dfop)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">71</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   endpoints(f_nlme_dfop)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">72</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">73</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   ds_2 &lt;- lapply(experimental_data_for_UBA_2019[6:10],</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">74</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'    function(x) x$data[c("name", "time", "value")])</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">75</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   m_sfo_sfo &lt;- mkinmod(parent = mkinsub("SFO", "A1"),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">76</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     A1 = mkinsub("SFO"), use_of_ff = "min", quiet = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">77</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   m_sfo_sfo_ff &lt;- mkinmod(parent = mkinsub("SFO", "A1"),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">78</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     A1 = mkinsub("SFO"), use_of_ff = "max", quiet = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">79</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   m_dfop_sfo &lt;- mkinmod(parent = mkinsub("DFOP", "A1"),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">80</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     A1 = mkinsub("SFO"), quiet = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">81</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">82</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   f_2 &lt;- mmkin(list("SFO-SFO" = m_sfo_sfo,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">83</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'    "SFO-SFO-ff" = m_sfo_sfo_ff,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">84</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'    "DFOP-SFO" = m_dfop_sfo),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">85</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     ds_2, quiet = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">86</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">87</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   f_nlme_sfo_sfo &lt;- nlme(f_2["SFO-SFO", ])</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">88</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   plot(f_nlme_sfo_sfo)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">89</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">90</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   # With formation fractions this does not coverge with defaults</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">91</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   # f_nlme_sfo_sfo_ff &lt;- nlme(f_2["SFO-SFO-ff", ])</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">92</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   #plot(f_nlme_sfo_sfo_ff)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">93</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">94</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   # For the following, we need to increase pnlsMaxIter and the tolerance</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">95</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   # to get convergence</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">96</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   f_nlme_dfop_sfo &lt;- nlme(f_2["DFOP-SFO", ],</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">97</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     control = list(pnlsMaxIter = 120, tolerance = 5e-4))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">98</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">99</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   plot(f_nlme_dfop_sfo)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">100</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">101</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   anova(f_nlme_dfop_sfo, f_nlme_sfo_sfo)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">102</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">103</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   endpoints(f_nlme_sfo_sfo)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">104</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   endpoints(f_nlme_dfop_sfo)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">105</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">106</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   if (length(findFunction("varConstProp")) &gt; 0) { # tc error model for nlme available</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">107</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     # Attempts to fit metabolite kinetics with the tc error model are possible,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">108</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     # but need tweeking of control values and sometimes do not converge</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">109</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">110</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     f_tc &lt;- mmkin(c("SFO", "DFOP"), ds, quiet = TRUE, error_model = "tc")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">111</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     f_nlme_sfo_tc &lt;- nlme(f_tc["SFO", ])</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">112</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     f_nlme_dfop_tc &lt;- nlme(f_tc["DFOP", ])</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">113</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     AIC(f_nlme_sfo, f_nlme_sfo_tc, f_nlme_dfop, f_nlme_dfop_tc)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">114</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     print(f_nlme_dfop_tc)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">115</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">116</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">117</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   f_2_obs &lt;- update(f_2, error_model = "obs")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">118</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   f_nlme_sfo_sfo_obs &lt;- nlme(f_2_obs["SFO-SFO", ])</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">119</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   print(f_nlme_sfo_sfo_obs)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">120</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   f_nlme_dfop_sfo_obs &lt;- nlme(f_2_obs["DFOP-SFO", ],</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">121</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     control = list(pnlsMaxIter = 120, tolerance = 5e-4))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">122</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">123</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   f_2_tc &lt;- update(f_2, error_model = "tc")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">124</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   # f_nlme_sfo_sfo_tc &lt;- nlme(f_2_tc["SFO-SFO", ]) # No convergence with 50 iterations</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">125</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   # f_nlme_dfop_sfo_tc &lt;- nlme(f_2_tc["DFOP-SFO", ],</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">126</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   #  control = list(pnlsMaxIter = 120, tolerance = 5e-4)) # Error in X[, fmap[[nm]]] &lt;- gradnm</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">127</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">128</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   anova(f_nlme_dfop_sfo, f_nlme_dfop_sfo_obs)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">129</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">130</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">131</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">nlme.mmkin &lt;- function(model, data = "auto",</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">132</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  fixed = lapply(as.list(names(mean_degparms(model))),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">133</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    function(el) eval(parse(text = paste(el, 1, sep = "~")))),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">134</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  random = pdDiag(fixed),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">135</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  groups,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">136</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  start = mean_degparms(model, random = TRUE, test_log_parms = TRUE),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">137</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  correlation = NULL, weights = NULL,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">138</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  subset, method = c("ML", "REML"),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">139</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  na.action = na.fail, naPattern,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">140</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  control = list(), verbose= FALSE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">141</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">{</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">142</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (nrow(model) &gt; 1) stop("Only row objects allowed")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">143</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">144</td>
+                    <td class="coverage">1013<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  thisCall &lt;- as.list(match.call())[-1]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">145</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">146</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Warn in case arguments were used that are overriden</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">147</td>
+                    <td class="coverage">1013<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (any(!is.na(match(names(thisCall),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">148</td>
+                    <td class="coverage">1013<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">               c("data"))))) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">149</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    warning("'nlme.mmkin' will redefine 'data'")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">150</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">151</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">152</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Get native symbol info for speed</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">153</td>
+                    <td class="coverage">1013<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (model[[1]]$solution_type == "deSolve" &amp; !is.null(model[[1]]$mkinmod$cf)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">154</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # The mkinmod stored in the first fit will be used by nlme</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">155</td>
+                    <td class="coverage">189<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    model[[1]]$mkinmod$symbols &lt;- deSolve::checkDLL(</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">156</td>
+                    <td class="coverage">189<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      dllname = model[[1]]$mkinmod$dll_info[["name"]],</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">157</td>
+                    <td class="coverage">189<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      func = "diffs", initfunc = "initpar",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">158</td>
+                    <td class="coverage">189<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      jacfunc = NULL, nout = 0, outnames = NULL)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">159</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">160</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">161</td>
+                    <td class="coverage">1013<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  deg_func &lt;- nlme_function(model)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">162</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">163</td>
+                    <td class="coverage">1013<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  assign("deg_func", deg_func, getFromNamespace(".nlme_env", "mkin"))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">164</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">165</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # For the formula, get the degradation function from the mkin namespace</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">166</td>
+                    <td class="coverage">1013<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  this_model_text &lt;- paste0("value ~ mkin::get_deg_func()(",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">167</td>
+                    <td class="coverage">1013<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    paste(names(formals(deg_func)), collapse = ", "), ")")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">168</td>
+                    <td class="coverage">1013<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  this_model &lt;- as.formula(this_model_text)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">169</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">170</td>
+                    <td class="coverage">1013<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  thisCall[["model"]] &lt;- this_model</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">171</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">172</td>
+                    <td class="coverage">1013<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  thisCall[["data"]] &lt;- nlme_data(model)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">173</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">174</td>
+                    <td class="coverage">1013<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  thisCall[["start"]] &lt;- start</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">175</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">176</td>
+                    <td class="coverage">1013<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  thisCall[["fixed"]] &lt;- fixed</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">177</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">178</td>
+                    <td class="coverage">1013<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  thisCall[["random"]] &lt;- random</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">179</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">180</td>
+                    <td class="coverage">1013<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  error_model &lt;- model[[1]]$err_mod</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">181</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">182</td>
+                    <td class="coverage">1013<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (missing(weights)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">183</td>
+                    <td class="coverage">1013<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    thisCall[["weights"]] &lt;- switch(error_model,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">184</td>
+                    <td class="coverage">1013<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      const = NULL,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">185</td>
+                    <td class="coverage">1013<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      obs = varIdent(form = ~ 1 | name),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">186</td>
+                    <td class="coverage">1013<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      tc = varConstProp())</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">187</td>
+                    <td class="coverage">1013<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    sigma &lt;- switch(error_model,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">188</td>
+                    <td class="coverage">1013<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      tc = 1,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">189</td>
+                    <td class="coverage">1013<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      NULL)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">190</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">191</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">192</td>
+                    <td class="coverage">1013<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  control &lt;- thisCall[["control"]]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">193</td>
+                    <td class="coverage">1013<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (error_model == "tc") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">194</td>
+                    <td class="coverage">928<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    control$sigma = 1</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">195</td>
+                    <td class="coverage">928<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    thisCall[["control"]] &lt;- control</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">196</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">197</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">198</td>
+                    <td class="coverage">1013<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  fit_time &lt;- system.time(val &lt;- do.call("nlme.formula", thisCall))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">199</td>
+                    <td class="coverage">1013<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  val$time &lt;- fit_time</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">200</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">201</td>
+                    <td class="coverage">1013<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  val$mkinmod &lt;- model[[1]]$mkinmod</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">202</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Don't return addresses that will become invalid</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">203</td>
+                    <td class="coverage">1013<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  val$mkinmod$symbols &lt;- NULL</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">204</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">205</td>
+                    <td class="coverage">1013<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  val$data &lt;- thisCall[["data"]]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">206</td>
+                    <td class="coverage">1013<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  val$mmkin &lt;- model</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">207</td>
+                    <td class="coverage">824<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (is.list(start)) val$mean_dp_start &lt;- start$fixed</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">208</td>
+                    <td class="coverage">189<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  else val$mean_dp_start &lt;- start</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">209</td>
+                    <td class="coverage">1013<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  val$transform_rates &lt;- model[[1]]$transform_rates</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">210</td>
+                    <td class="coverage">1013<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  val$transform_fractions &lt;- model[[1]]$transform_fractions</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">211</td>
+                    <td class="coverage">1013<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  val$solution_type &lt;- model[[1]]$solution_type</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">212</td>
+                    <td class="coverage">1013<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  val$err_mode &lt;- error_model</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">213</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">214</td>
+                    <td class="coverage">1013<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  val$bparms.optim &lt;- backtransform_odeparms(val$coefficients$fixed,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">215</td>
+                    <td class="coverage">1013<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    val$mkinmod,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">216</td>
+                    <td class="coverage">1013<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    transform_rates = val$transform_rates,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">217</td>
+                    <td class="coverage">1013<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    transform_fractions = val$transform_fractions)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">218</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">219</td>
+                    <td class="coverage">1013<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  val$bparms.fixed &lt;- model[[1]]$bparms.fixed</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">220</td>
+                    <td class="coverage">1013<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  val$date.fit &lt;- date()</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">221</td>
+                    <td class="coverage">1013<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  val$nlmeversion &lt;- as.character(utils::packageVersion("nlme"))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">222</td>
+                    <td class="coverage">1013<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  val$mkinversion &lt;- as.character(utils::packageVersion("mkin"))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">223</td>
+                    <td class="coverage">1013<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  val$Rversion &lt;- paste(R.version$major, R.version$minor, sep=".")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">224</td>
+                    <td class="coverage">1013<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  class(val) &lt;- c("nlme.mmkin", "mixed.mmkin", "nlme", "lme")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">225</td>
+                    <td class="coverage">1013<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  return(val)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">226</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">227</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">228</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">229</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @rdname nlme.mmkin</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">230</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param x An nlme.mmkin object to print</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">231</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param digits Number of digits to use for printing</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">232</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">print.nlme.mmkin &lt;- function(x, digits = max(3, getOption("digits") - 3), ...) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">233</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat( "Kinetic nonlinear mixed-effects model fit by " )</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">234</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat( if(x$method == "REML") "REML\n" else "maximum likelihood\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">235</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("\nStructural model:\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">236</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  diffs &lt;- x$mmkin[[1]]$mkinmod$diffs</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">237</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  nice_diffs &lt;- gsub("^(d.*) =", "\\1/dt =", diffs)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">238</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  writeLines(strwrap(nice_diffs, exdent = 11))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">239</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("\nData:\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">240</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat(nrow(x$data), "observations of",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">241</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    length(unique(x$data$name)), "variable(s) grouped in",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">242</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    length(unique(x$data$ds)), "datasets\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">243</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("\nLog-", if(x$method == "REML") "restricted-" else "",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">244</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      "likelihood: ", format(x$logLik, digits = digits), "\n", sep = "")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">245</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  fixF &lt;- x$call$fixed</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">246</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("\nFixed effects:\n",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">247</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      deparse(</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">248</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if(inherits(fixF, "formula") || is.call(fixF) || is.name(fixF))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">249</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    x$call$fixed</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">250</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  else</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">251</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    lapply(fixF, function(el) as.name(deparse(el)))), "\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">252</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  print(fixef(x), digits = digits, ...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">253</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">254</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  print(summary(x$modelStruct), sigma = x$sigma, digits = digits, ...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">255</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  invisible(x)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">256</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">257</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">258</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">259</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @rdname nlme.mmkin</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">260</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param object An nlme.mmkin object to update</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">261</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param ... Update specifications passed to update.nlme</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">262</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">update.nlme.mmkin &lt;- function(object, ...) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">263</td>
+                    <td class="coverage">85<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  res &lt;- NextMethod()</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">264</td>
+                    <td class="coverage">85<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  res$mmkin &lt;- object$mmkin</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">265</td>
+                    <td class="coverage">85<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  class(res) &lt;- c("nlme.mmkin", "nlme", "lme")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">266</td>
+                    <td class="coverage">85<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  return(res)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">267</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                </tbody>
+              </table>
+            </div>
+            <div id="R/mkinparplot.R" class="hidden">
+              <table class="table-condensed">
+                <tbody>
+                  <tr class="never">
+                    <td class="num">1</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Function to plot the confidence intervals obtained using mkinfit</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">2</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' </pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">3</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' This function plots the confidence intervals for the parameters fitted using</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">4</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' \code{\link{mkinfit}}.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">5</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' </pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">6</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param object A fit represented in an \code{\link{mkinfit}} object.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">7</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @return Nothing is returned by this function, as it is called for its side</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">8</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   effect, namely to produce a plot.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">9</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @author Johannes Ranke</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">10</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @examples</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">11</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' </pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">12</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' \dontrun{</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">13</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' model &lt;- mkinmod(</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">14</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   T245 = mkinsub("SFO", to = c("phenol"), sink = FALSE),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">15</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   phenol = mkinsub("SFO", to = c("anisole")),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">16</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   anisole = mkinsub("SFO"), use_of_ff = "max")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">17</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' fit &lt;- mkinfit(model, subset(mccall81_245T, soil == "Commerce"), quiet = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">18</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' mkinparplot(fit)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">19</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">20</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">21</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">mkinparplot &lt;- function(object) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">22</td>
+                    <td class="coverage">70<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  state.optim = rownames(subset(object$start, type == "state"))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">23</td>
+                    <td class="coverage">70<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  deparms.optim = rownames(subset(object$start, type == "deparm"))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">24</td>
+                    <td class="coverage">70<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  fractions.optim = grep("^f_", deparms.optim, value = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">25</td>
+                    <td class="coverage">70<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  N.optim = grep("^N_", deparms.optim, value = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">26</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if ("g" %in% deparms.optim) fractions.optim &lt;- c("g", fractions.optim)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">27</td>
+                    <td class="coverage">70<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  rates.optim.unsorted = setdiff(deparms.optim, union(fractions.optim, N.optim))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">28</td>
+                    <td class="coverage">70<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  rates.optim &lt;- rownames(object$start[rates.optim.unsorted, ])</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">29</td>
+                    <td class="coverage">70<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  n.plot &lt;- c(state.optim = length(state.optim),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">30</td>
+                    <td class="coverage">70<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              rates.optim = length(rates.optim),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">31</td>
+                    <td class="coverage">70<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">	      N.optim = length(N.optim),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">32</td>
+                    <td class="coverage">70<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              fractions.optim = length(fractions.optim))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">33</td>
+                    <td class="coverage">70<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  n.plot &lt;- n.plot[n.plot &gt; 0]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">34</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">35</td>
+                    <td class="coverage">70<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  oldpar &lt;- par(no.readonly = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">36</td>
+                    <td class="coverage">70<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  on.exit(par(oldpar, no.readonly = TRUE))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">37</td>
+                    <td class="coverage">70<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  layout(matrix(1:length(n.plot), ncol = 1), heights = n.plot + 1)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">38</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">39</td>
+                    <td class="coverage">70<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  s &lt;- summary(object)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">40</td>
+                    <td class="coverage">70<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  bpar &lt;- data.frame(t(s$bpar[, c("Estimate", "Lower", "Upper")]))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">41</td>
+                    <td class="coverage">70<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  par(mar = c(2.1, 2.1, 0.1, 2.1))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">42</td>
+                    <td class="coverage">70<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  par(cex = 1)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">43</td>
+                    <td class="coverage">70<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  for (type in names(n.plot)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">44</td>
+                    <td class="coverage">140<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    parnames &lt;- get(type)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">45</td>
+                    <td class="coverage">140<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    values &lt;- bpar[parnames]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">46</td>
+                    <td class="coverage">140<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    values_with_confints &lt;- data.frame(t(subset(data.frame(t(values)), !is.na("Lower"))))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">47</td>
+                    <td class="coverage">140<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    xlim = switch(type,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">48</td>
+                    <td class="coverage">140<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                  state.optim = range(c(0, unlist(values)),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">49</td>
+                    <td class="coverage">140<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                                      na.rm = TRUE, finite = TRUE),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">50</td>
+                    <td class="coverage">140<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                  rates.optim = range(c(0, unlist(values)),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">51</td>
+                    <td class="coverage">140<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                                      na.rm = TRUE, finite = TRUE),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">52</td>
+                    <td class="coverage">140<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                  N.optim = range(c(0, 1, unlist(values)),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">53</td>
+                    <td class="coverage">140<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                                      na.rm = TRUE, finite = TRUE),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">54</td>
+                    <td class="coverage">140<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                  fractions.optim = range(c(0, 1, unlist(values)),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">55</td>
+                    <td class="coverage">140<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                                          na.rm = TRUE, finite = TRUE))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">56</td>
+                    <td class="coverage">140<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    parname_index &lt;- length(parnames):1 # Reverse order for strip chart</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">57</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">58</td>
+                    <td class="coverage">140<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    stripchart(values["Estimate", ][parname_index],</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">59</td>
+                    <td class="coverage">140<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">               xlim = xlim,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">60</td>
+                    <td class="coverage">140<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">               ylim = c(0.5, length(get(type)) + 0.5),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">61</td>
+                    <td class="coverage">140<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">               yaxt = "n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">62</td>
+                    <td class="coverage">70<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (type %in% c("rates.optim", "fractions.optim")) abline(v = 0, lty = 2)</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">63</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (type %in% c("N.optim", "fractions.optim")) abline(v = 1, lty = 2)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">64</td>
+                    <td class="coverage">140<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    position &lt;- ifelse(values["Estimate", ] &lt; mean(xlim), "right", "left")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">65</td>
+                    <td class="coverage">140<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    text(ifelse(position == "left", min(xlim), max(xlim)),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">66</td>
+                    <td class="coverage">140<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">         parname_index, parnames,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">67</td>
+                    <td class="coverage">140<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">         pos = ifelse(position == "left", 4, 2))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">68</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">69</td>
+                    <td class="coverage">140<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    values.upper.nonInf &lt;- ifelse(values["Upper", ] == Inf, 1.5 * xlim[[2]], values["Upper", ])</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">70</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # Suppress warnings for non-existing arrow lengths</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">71</td>
+                    <td class="coverage">140<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    suppressWarnings(arrows(as.numeric(values["Lower", ]), parname_index,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">72</td>
+                    <td class="coverage">140<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">	   as.numeric(values.upper.nonInf), parname_index,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">73</td>
+                    <td class="coverage">140<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">	   code = 3, angle = 90, length = 0.05))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">74</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">75</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                </tbody>
+              </table>
+            </div>
+            <div id="R/mkinpredict.R" class="hidden">
+              <table class="table-condensed">
+                <tbody>
+                  <tr class="never">
+                    <td class="num">1</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Produce predictions from a kinetic model using specific parameters</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">2</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">3</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' This function produces a time series for all the observed variables in a</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">4</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' kinetic model as specified by [mkinmod], using a specific set of</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">5</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' kinetic parameters and initial values for the state variables.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">6</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">7</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @aliases mkinpredict mkinpredict.mkinmod mkinpredict.mkinfit</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">8</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param x A kinetic model as produced by [mkinmod], or a kinetic fit as</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">9</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' fitted by [mkinfit]. In the latter case, the fitted parameters are used for</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">10</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' the prediction.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">11</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param odeparms A numeric vector specifying the parameters used in the</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">12</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' kinetic model, which is generally defined as a set of ordinary differential</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">13</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' equations.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">14</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param odeini A numeric vector containing the initial values of the state</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">15</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' variables of the model. Note that the state variables can differ from the</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">16</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' observed variables, for example in the case of the SFORB model.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">17</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param outtimes A numeric vector specifying the time points for which model</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">18</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' predictions should be generated.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">19</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param solution_type The method that should be used for producing the</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">20</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' predictions. This should generally be "analytical" if there is only one</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">21</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' observed variable, and usually "deSolve" in the case of several observed</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">22</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' variables. The third possibility "eigen" is fast in comparison to uncompiled</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">23</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' ODE models, but not applicable to some models, e.g. using FOMC for the</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">24</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' parent compound.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">25</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param method.ode The solution method passed via [mkinpredict] to `deSolve::ode()` in</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">26</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' case the solution type is "deSolve" and we are not using compiled code.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">27</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' When using compiled code, only lsoda is supported.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">28</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param use_compiled If set to \code{FALSE}, no compiled version of the</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">29</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' [mkinmod] model is used, even if is present.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">30</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param use_symbols If set to \code{TRUE} (default), symbol info present in</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">31</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' the [mkinmod] object is used if available for accessing compiled code</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">32</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param atol Absolute error tolerance, passed to the ode solver.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">33</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param rtol Absolute error tolerance, passed to the ode solver.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">34</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param maxsteps Maximum number of steps, passed to the ode solver.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">35</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param map_output Boolean to specify if the output should list values for</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">36</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   the observed variables (default) or for all state variables (if set to</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">37</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   FALSE). Setting this to FALSE has no effect for analytical solutions,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">38</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   as these always return mapped output.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">39</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param na_stop Should it be an error if `deSolve::ode()` returns NaN values</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">40</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param \dots Further arguments passed to the ode solver in case such a</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">41</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   solver is used.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">42</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @return A matrix with the numeric solution in wide format</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">43</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @author Johannes Ranke</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">44</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @examples</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">45</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">46</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' SFO &lt;- mkinmod(degradinol = mkinsub("SFO"))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">47</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # Compare solution types</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">48</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">49</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'       solution_type = "analytical")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">50</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">51</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'       solution_type = "deSolve")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">52</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">53</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'       solution_type = "deSolve", use_compiled = FALSE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">54</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">55</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'       solution_type = "eigen")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">56</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">57</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # Compare integration methods to analytical solution</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">58</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">59</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'       solution_type = "analytical")[21,]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">60</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">61</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'       method = "lsoda", use_compiled = FALSE)[21,]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">62</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">63</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'       method = "ode45", use_compiled = FALSE)[21,]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">64</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">65</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'       method = "rk4", use_compiled = FALSE)[21,]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">66</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # rk4 is not as precise here</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">67</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">68</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # The number of output times used to make a lot of difference until the</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">69</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # default for atol was adjusted</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">70</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">71</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'       seq(0, 20, by = 0.1))[201,]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">72</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">73</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'       seq(0, 20, by = 0.01))[2001,]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">74</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">75</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # Comparison of the performance of solution types</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">76</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' SFO_SFO = mkinmod(parent = list(type = "SFO", to = "m1"),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">77</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'                   m1 = list(type = "SFO"), use_of_ff = "max")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">78</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' if(require(rbenchmark)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">79</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   benchmark(replications = 10, order = "relative", columns = c("test", "relative", "elapsed"),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">80</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     eigen = mkinpredict(SFO_SFO,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">81</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'       c(k_parent = 0.15, f_parent_to_m1 = 0.5, k_m1 = 0.01),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">82</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'       c(parent = 100, m1 = 0), seq(0, 20, by = 0.1),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">83</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'       solution_type = "eigen")[201,],</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">84</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     deSolve_compiled = mkinpredict(SFO_SFO,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">85</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'       c(k_parent = 0.15, f_parent_to_m1 = 0.5, k_m1 = 0.01),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">86</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'       c(parent = 100, m1 = 0), seq(0, 20, by = 0.1),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">87</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'       solution_type = "deSolve")[201,],</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">88</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     deSolve = mkinpredict(SFO_SFO,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">89</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'       c(k_parent = 0.15, f_parent_to_m1 = 0.5, k_m1 = 0.01),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">90</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'       c(parent = 100, m1 = 0), seq(0, 20, by = 0.1),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">91</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'       solution_type = "deSolve", use_compiled = FALSE)[201,],</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">92</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     analytical = mkinpredict(SFO_SFO,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">93</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'       c(k_parent = 0.15, f_parent_to_m1 = 0.5, k_m1 = 0.01),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">94</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'       c(parent = 100, m1 = 0), seq(0, 20, by = 0.1),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">95</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'       solution_type = "analytical", use_compiled = FALSE)[201,])</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">96</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">97</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">98</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' \dontrun{</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">99</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   # Predict from a fitted model</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">100</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   f &lt;- mkinfit(SFO_SFO, FOCUS_2006_C, quiet = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">101</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   f &lt;- mkinfit(SFO_SFO, FOCUS_2006_C, quiet = TRUE, solution_type = "deSolve")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">102</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   head(mkinpredict(f))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">103</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">104</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">105</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">106</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">mkinpredict &lt;- function(x, odeparms, odeini, outtimes, ...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">107</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">{</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">108</td>
+                    <td class="coverage">47544878<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  UseMethod("mkinpredict", x)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">109</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">110</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">111</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @rdname mkinpredict</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">112</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">113</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">mkinpredict.mkinmod &lt;- function(x,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">114</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  odeparms = c(k_parent_sink = 0.1),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">115</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  odeini = c(parent = 100),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">116</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  outtimes = seq(0, 120, by = 0.1),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">117</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  solution_type = "deSolve",</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">118</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  use_compiled = "auto",</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">119</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  use_symbols = FALSE,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">120</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  method.ode = "lsoda", atol = 1e-8, rtol = 1e-10, maxsteps = 20000L,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">121</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  map_output = TRUE,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">122</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  na_stop = TRUE,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">123</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  ...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">124</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">{</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">125</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">126</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Names of state variables and observed variables</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">127</td>
+                    <td class="coverage">47544878<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  mod_vars &lt;- names(x$diffs)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">128</td>
+                    <td class="coverage">47544878<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  obs_vars &lt;- names(x$spec)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">129</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">130</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Order the inital values for state variables if they are named</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">131</td>
+                    <td class="coverage">47544878<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (!is.null(names(odeini))) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">132</td>
+                    <td class="coverage">47544878<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    odeini &lt;- odeini[mod_vars]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">133</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">134</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">135</td>
+                    <td class="coverage">47544878<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  out_obs &lt;- matrix(NA, nrow = length(outtimes), ncol = 1 + length(obs_vars),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">136</td>
+                    <td class="coverage">47544878<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    dimnames = list(as.character(outtimes), c("time", obs_vars)))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">137</td>
+                    <td class="coverage">47544878<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  out_obs[, "time"] &lt;- outtimes</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">138</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">139</td>
+                    <td class="coverage">47544878<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  n_out_na &lt;- 0 # to check if we get NA values with deSolve</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">140</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">141</td>
+                    <td class="coverage">47544878<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (solution_type == "analytical") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">142</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # This is clumsy, as we wanted fast analytical predictions for mkinfit,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">143</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # which bypasses mkinpredict in the case of analytical solutions</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">144</td>
+                    <td class="coverage">1843695<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    pseudo_observed &lt;-</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">145</td>
+                    <td class="coverage">1843695<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      data.frame(name = rep(obs_vars, each = length(outtimes)),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">146</td>
+                    <td class="coverage">1843695<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      time = rep(outtimes, length(obs_vars)))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">147</td>
+                    <td class="coverage">1843695<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    pseudo_observed$predicted &lt;- x$deg_func(pseudo_observed, odeini, odeparms)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">148</td>
+                    <td class="coverage">1843695<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    for (obs_var in obs_vars) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">149</td>
+                    <td class="coverage">2431585<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      out_obs[, obs_var] &lt;- pseudo_observed[pseudo_observed$name == obs_var, "predicted"]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">150</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">151</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # We don't have solutions for unobserved state variables, the output of</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">152</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # analytical solutions is always mapped to observed variables</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">153</td>
+                    <td class="coverage">1843695<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    return(out_obs)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">154</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">155</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">156</td>
+                    <td class="coverage">45701183<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (solution_type == "eigen") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">157</td>
+                    <td class="coverage">97082<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    evalparse &lt;- function(string) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">158</td>
+                    <td class="coverage">392283<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      eval(parse(text=string), as.list(c(odeparms, odeini)))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">159</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">160</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">161</td>
+                    <td class="coverage">97082<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    coefmat.num &lt;- matrix(sapply(as.vector(x$coefmat), evalparse),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">162</td>
+                    <td class="coverage">97082<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      nrow = length(mod_vars))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">163</td>
+                    <td class="coverage">97082<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    e &lt;- eigen(coefmat.num)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">164</td>
+                    <td class="coverage">97082<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    c &lt;- solve(e$vectors, odeini)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">165</td>
+                    <td class="coverage">97082<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    f.out &lt;- function(t) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">166</td>
+                    <td class="coverage">1085040<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      e$vectors %*% diag(exp(e$values * t), nrow=length(mod_vars)) %*% c</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">167</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">168</td>
+                    <td class="coverage">97082<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    o &lt;- matrix(mapply(f.out, outtimes),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">169</td>
+                    <td class="coverage">97082<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      nrow = length(mod_vars), ncol = length(outtimes))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">170</td>
+                    <td class="coverage">97082<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    out &lt;- cbind(outtimes, t(o))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">171</td>
+                    <td class="coverage">97082<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    colnames(out) &lt;- c("time", mod_vars)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">172</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">173</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">174</td>
+                    <td class="coverage">45701183<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (solution_type == "deSolve") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">175</td>
+                    <td class="coverage">45604101<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (!is.null(x$cf) &amp; use_compiled[1] != FALSE) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">176</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">177</td>
+                    <td class="coverage">45603235<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      if (!is.null(x$symbols) &amp; use_symbols) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">178</td>
+                    <td class="coverage">1427314<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        lsoda_func &lt;- x$symbols</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">179</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">180</td>
+                    <td class="coverage">44175921<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        lsoda_func &lt;- "diffs"</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">181</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">182</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">183</td>
+                    <td class="coverage">45603235<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      out &lt;- deSolve::lsoda(</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">184</td>
+                    <td class="coverage">45603235<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        y = odeini,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">185</td>
+                    <td class="coverage">45603235<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        times = outtimes,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">186</td>
+                    <td class="coverage">45603235<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        func = lsoda_func,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">187</td>
+                    <td class="coverage">45603235<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        initfunc = "initpar",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">188</td>
+                    <td class="coverage">45603235<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        dllname = x$dll_info[["name"]],</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">189</td>
+                    <td class="coverage">45603235<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        parms = odeparms[x$parms], # Order matters when using compiled models</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">190</td>
+                    <td class="coverage">45603235<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        atol = atol,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">191</td>
+                    <td class="coverage">45603235<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        rtol = rtol,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">192</td>
+                    <td class="coverage">45603235<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        maxsteps = maxsteps,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">193</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        ...</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">194</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      )</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">195</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">196</td>
+                    <td class="coverage">45603235<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      colnames(out) &lt;- c("time", mod_vars)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">197</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">198</td>
+                    <td class="coverage">866<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      mkindiff &lt;- function(t, state, parms) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">199</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">200</td>
+                    <td class="coverage">145229<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        time &lt;- t</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">201</td>
+                    <td class="coverage">145229<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        diffs &lt;- vector()</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">202</td>
+                    <td class="coverage">145229<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        for (box in names(x$diffs))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">203</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">204</td>
+                    <td class="coverage">145229<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          diffname &lt;- paste("d", box, sep="_")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">205</td>
+                    <td class="coverage">145229<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          diffs[diffname] &lt;- with(as.list(c(time, state, parms)),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">206</td>
+                    <td class="coverage">145229<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            eval(parse(text=x$diffs[[box]])))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">207</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">208</td>
+                    <td class="coverage">145229<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        return(list(c(diffs)))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">209</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">210</td>
+                    <td class="coverage">866<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      out &lt;- deSolve::ode(</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">211</td>
+                    <td class="coverage">866<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        y = odeini,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">212</td>
+                    <td class="coverage">866<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        times = outtimes,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">213</td>
+                    <td class="coverage">866<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        func = mkindiff,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">214</td>
+                    <td class="coverage">866<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        parms = odeparms,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">215</td>
+                    <td class="coverage">866<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        method = method.ode,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">216</td>
+                    <td class="coverage">866<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        atol = atol,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">217</td>
+                    <td class="coverage">866<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        rtol = rtol,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">218</td>
+                    <td class="coverage">866<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        maxsteps = maxsteps,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">219</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        ...</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">220</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      )</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">221</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">222</td>
+                    <td class="coverage">45604101<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    n_out_na &lt;- sum(is.na(out))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">223</td>
+                    <td class="coverage">45604101<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (n_out_na &gt; 0 &amp; na_stop) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">224</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      cat("odeini:\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">225</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      print(odeini)</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">226</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      cat("odeparms:\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">227</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      print(odeparms)</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">228</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      cat("out:\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">229</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      print(out)</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">230</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      stop("Differential equations were not integrated for all output times because\n",</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">231</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        n_out_na, " NaN values occurred in output from ode()")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">232</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">233</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">234</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">235</td>
+                    <td class="coverage">45701183<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (map_output) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">236</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # Output transformation for models with unobserved compartments like SFORB</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">237</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # if not already mapped in analytical solution</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">238</td>
+                    <td class="coverage">45701183<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (n_out_na &gt; 0 &amp; !na_stop) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">239</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      available &lt;- c(TRUE, rep(FALSE, length(outtimes) - 1))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">240</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">241</td>
+                    <td class="coverage">45701183<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      available &lt;- rep(TRUE, length(outtimes))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">242</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">243</td>
+                    <td class="coverage">45701183<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    for (var in names(x$map)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">244</td>
+                    <td class="coverage">93237433<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      if((length(x$map[[var]]) == 1)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">245</td>
+                    <td class="coverage">93235081<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        out_obs[available, var] &lt;- out[available, var]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">246</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">247</td>
+                    <td class="coverage">2352<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        out_obs[available, var] &lt;- out[available, x$map[[var]][1]] +</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">248</td>
+                    <td class="coverage">2352<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          out[available, x$map[[var]][2]]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">249</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">250</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">251</td>
+                    <td class="coverage">45701183<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    return(out_obs)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">252</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">253</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    dimnames(out) &lt;- list(time = as.character(outtimes), c("time", mod_vars))</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">254</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    return(out)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">255</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">256</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">257</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">258</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @rdname mkinpredict</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">259</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">260</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">mkinpredict.mkinfit &lt;- function(x,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">261</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  odeparms = x$bparms.ode,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">262</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  odeini = x$bparms.state,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">263</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  outtimes = seq(0, 120, by = 0.1),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">264</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  solution_type = "deSolve",</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">265</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  use_compiled = "auto",</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">266</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  method.ode = "lsoda", atol = 1e-8, rtol = 1e-10,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">267</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  map_output = TRUE, ...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">268</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">{</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">269</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  mkinpredict(x$mkinmod, odeparms, odeini, outtimes, solution_type, use_compiled,</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">270</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              method.ode, atol, rtol, map_output, ...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">271</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                </tbody>
+              </table>
+            </div>
+            <div id="R/nlme.R" class="hidden">
+              <table class="table-condensed">
+                <tbody>
+                  <tr class="never">
+                    <td class="num">1</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Helper functions to create nlme models from mmkin row objects</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">2</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">3</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' These functions facilitate setting up a nonlinear mixed effects model for</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">4</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' an mmkin row object. An mmkin row object is essentially a list of mkinfit</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">5</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' objects that have been obtained by fitting the same model to a list of</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">6</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' datasets. They are used internally by the [nlme.mmkin()] method.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">7</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">8</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param object An mmkin row object containing several fits of the same model to different datasets</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">9</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @import nlme</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">10</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @rdname nlme</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">11</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @seealso \code{\link{nlme.mmkin}}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">12</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @examples</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">13</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">14</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' m_SFO &lt;- mkinmod(parent = mkinsub("SFO"))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">15</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' d_SFO_1 &lt;- mkinpredict(m_SFO,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">16</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   c(k_parent = 0.1),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">17</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   c(parent = 98), sampling_times)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">18</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' d_SFO_1_long &lt;- mkin_wide_to_long(d_SFO_1, time = "time")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">19</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' d_SFO_2 &lt;- mkinpredict(m_SFO,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">20</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   c(k_parent = 0.05),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">21</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   c(parent = 102), sampling_times)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">22</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' d_SFO_2_long &lt;- mkin_wide_to_long(d_SFO_2, time = "time")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">23</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' d_SFO_3 &lt;- mkinpredict(m_SFO,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">24</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   c(k_parent = 0.02),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">25</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   c(parent = 103), sampling_times)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">26</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' d_SFO_3_long &lt;- mkin_wide_to_long(d_SFO_3, time = "time")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">27</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">28</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' d1 &lt;- add_err(d_SFO_1, function(value) 3, n = 1)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">29</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' d2 &lt;- add_err(d_SFO_2, function(value) 2, n = 1)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">30</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' d3 &lt;- add_err(d_SFO_3, function(value) 4, n = 1)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">31</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' ds &lt;- c(d1 = d1, d2 = d2, d3 = d3)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">32</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">33</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' f &lt;- mmkin("SFO", ds, cores = 1, quiet = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">34</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' mean_dp &lt;- mean_degparms(f)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">35</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' grouped_data &lt;- nlme_data(f)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">36</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' nlme_f &lt;- nlme_function(f)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">37</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # These assignments are necessary for these objects to be</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">38</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # visible to nlme and augPred when evaluation is done by</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">39</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # pkgdown to generate the html docs.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">40</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' assign("nlme_f", nlme_f, globalenv())</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">41</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' assign("grouped_data", grouped_data, globalenv())</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">42</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">43</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' library(nlme)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">44</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' m_nlme &lt;- nlme(value ~ nlme_f(name, time, parent_0, log_k_parent_sink),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">45</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   data = grouped_data,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">46</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   fixed = parent_0 + log_k_parent_sink ~ 1,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">47</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   random = pdDiag(parent_0 + log_k_parent_sink ~ 1),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">48</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   start = mean_dp)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">49</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' summary(m_nlme)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">50</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' plot(augPred(m_nlme, level = 0:1), layout = c(3, 1))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">51</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # augPred does not work on fits with more than one state</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">52</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # variable</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">53</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' #</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">54</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # The procedure is greatly simplified by the nlme.mmkin function</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">55</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' f_nlme &lt;- nlme(f)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">56</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' plot(f_nlme)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">57</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @return A function that can be used with nlme</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">58</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">59</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">nlme_function &lt;- function(object) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">60</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (nrow(object) &gt; 1) stop("Only row objects allowed")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">61</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">62</td>
+                    <td class="coverage">1168<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  mkin_model &lt;- object[[1]]$mkinmod</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">63</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">64</td>
+                    <td class="coverage">1168<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  degparm_names &lt;- names(mean_degparms(object))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">65</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">66</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Inspired by https://stackoverflow.com/a/12983961/3805440</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">67</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # and https://stackoverflow.com/a/26280789/3805440</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">68</td>
+                    <td class="coverage">1168<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  model_function_alist &lt;- replicate(length(degparm_names) + 2, substitute())</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">69</td>
+                    <td class="coverage">1168<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  names(model_function_alist) &lt;- c("name", "time", degparm_names)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">70</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">71</td>
+                    <td class="coverage">1168<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  model_function_body &lt;- quote({</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">72</td>
+                    <td class="coverage">252739<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    arg_frame &lt;- as.data.frame(as.list((environment())), stringsAsFactors = FALSE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">73</td>
+                    <td class="coverage">252739<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    res_frame &lt;- arg_frame[1:2]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">74</td>
+                    <td class="coverage">252739<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    parm_frame &lt;- arg_frame[-(1:2)]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">75</td>
+                    <td class="coverage">252739<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    parms_unique &lt;- unique(parm_frame)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">76</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">77</td>
+                    <td class="coverage">252739<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    n_unique &lt;- nrow(parms_unique)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">78</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">79</td>
+                    <td class="coverage">252739<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    times_ds &lt;- list()</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">80</td>
+                    <td class="coverage">252739<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    names_ds &lt;- list()</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">81</td>
+                    <td class="coverage">252739<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    for (i in 1:n_unique) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">82</td>
+                    <td class="coverage">2342789<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      times_ds[[i]] &lt;-</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">83</td>
+                    <td class="coverage">2342789<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        arg_frame[which(arg_frame[[3]] == parms_unique[i, 1]), "time"]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">84</td>
+                    <td class="coverage">2342789<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      names_ds[[i]] &lt;-</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">85</td>
+                    <td class="coverage">2342789<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        arg_frame[which(arg_frame[[3]] == parms_unique[i, 1]), "name"]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">86</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">87</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">88</td>
+                    <td class="coverage">252739<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    res_list &lt;- lapply(1:n_unique, function(x) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">89</td>
+                    <td class="coverage">2342789<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      transparms_optim &lt;- unlist(parms_unique[x, , drop = TRUE])</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">90</td>
+                    <td class="coverage">2342789<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      parms_fixed &lt;- object[[1]]$bparms.fixed</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">91</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">92</td>
+                    <td class="coverage">2342789<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      odeini_optim_parm_names &lt;- grep('_0$', names(transparms_optim), value = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">93</td>
+                    <td class="coverage">2342789<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      odeini_optim &lt;- transparms_optim[odeini_optim_parm_names]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">94</td>
+                    <td class="coverage">2342789<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      names(odeini_optim) &lt;- gsub('_0$', '', odeini_optim_parm_names)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">95</td>
+                    <td class="coverage">2342789<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      odeini_fixed_parm_names &lt;- grep('_0$', names(parms_fixed), value = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">96</td>
+                    <td class="coverage">2342789<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      odeini_fixed &lt;- parms_fixed[odeini_fixed_parm_names]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">97</td>
+                    <td class="coverage">2342789<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      names(odeini_fixed) &lt;- gsub('_0$', '', odeini_fixed_parm_names)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">98</td>
+                    <td class="coverage">2342789<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      odeini &lt;- c(odeini_optim, odeini_fixed)[names(mkin_model$diffs)]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">99</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">100</td>
+                    <td class="coverage">2342789<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      ode_transparms_optim_names &lt;- setdiff(names(transparms_optim), odeini_optim_parm_names)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">101</td>
+                    <td class="coverage">2342789<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      odeparms_optim &lt;- backtransform_odeparms(transparms_optim[ode_transparms_optim_names], mkin_model,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">102</td>
+                    <td class="coverage">2342789<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        transform_rates = object[[1]]$transform_rates,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">103</td>
+                    <td class="coverage">2342789<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        transform_fractions = object[[1]]$transform_fractions)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">104</td>
+                    <td class="coverage">2342789<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      odeparms_fixed_names &lt;- setdiff(names(parms_fixed), odeini_fixed_parm_names)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">105</td>
+                    <td class="coverage">2342789<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      odeparms_fixed &lt;- parms_fixed[odeparms_fixed_names]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">106</td>
+                    <td class="coverage">2342789<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      odeparms &lt;- c(odeparms_optim, odeparms_fixed)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">107</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">108</td>
+                    <td class="coverage">2342789<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      out_wide &lt;- mkinpredict(mkin_model,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">109</td>
+                    <td class="coverage">2342789<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        odeparms = odeparms, odeini = odeini,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">110</td>
+                    <td class="coverage">2342789<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        solution_type = object[[1]]$solution_type,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">111</td>
+                    <td class="coverage">2342789<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        outtimes = sort(unique(times_ds[[x]])))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">112</td>
+                    <td class="coverage">2342789<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      out_array &lt;- out_wide[, -1, drop = FALSE]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">113</td>
+                    <td class="coverage">2342789<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      rownames(out_array) &lt;- as.character(unique(times_ds[[x]]))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">114</td>
+                    <td class="coverage">2342789<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      out_times &lt;- as.character(times_ds[[x]])</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">115</td>
+                    <td class="coverage">2342789<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      out_names &lt;- as.character(names_ds[[x]])</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">116</td>
+                    <td class="coverage">2342789<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      out_values &lt;- mapply(function(times, names) out_array[times, names],</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">117</td>
+                    <td class="coverage">2342789<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        out_times, out_names)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">118</td>
+                    <td class="coverage">2342789<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      return(as.numeric(out_values))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">119</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    })</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">120</td>
+                    <td class="coverage">252739<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    res &lt;- unlist(res_list)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">121</td>
+                    <td class="coverage">252739<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    return(res)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">122</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  })</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">123</td>
+                    <td class="coverage">1168<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  model_function &lt;- as.function(c(model_function_alist, model_function_body))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">124</td>
+                    <td class="coverage">1168<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  return(model_function)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">125</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">126</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">127</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @rdname nlme</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">128</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @importFrom rlang !!!</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">129</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @return A `nlme::groupedData` object</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">130</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">131</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">nlme_data &lt;- function(object) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">132</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (nrow(object) &gt; 1) stop("Only row objects allowed")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">133</td>
+                    <td class="coverage">5677<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  ds_names &lt;- colnames(object)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">134</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">135</td>
+                    <td class="coverage">5677<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  ds_list &lt;- lapply(object, function(x) x$data[c("time", "variable", "observed")])</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">136</td>
+                    <td class="coverage">5677<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  names(ds_list) &lt;- ds_names</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">137</td>
+                    <td class="coverage">5677<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  ds_nlme &lt;- vctrs::vec_rbind(!!!ds_list, .names_to = "ds")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">138</td>
+                    <td class="coverage">5677<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  ds_nlme$variable &lt;- as.character(ds_nlme$variable)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">139</td>
+                    <td class="coverage">5677<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  ds_nlme$ds &lt;- ordered(ds_nlme$ds, levels = unique(ds_nlme$ds))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">140</td>
+                    <td class="coverage">5677<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  ds_nlme_renamed &lt;- data.frame(ds = ds_nlme$ds, name = ds_nlme$variable,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">141</td>
+                    <td class="coverage">5677<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    time = ds_nlme$time, value = ds_nlme$observed,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">142</td>
+                    <td class="coverage">5677<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    stringsAsFactors = FALSE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">143</td>
+                    <td class="coverage">5677<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  ds_nlme_grouped &lt;- groupedData(value ~ time | ds, ds_nlme_renamed, order.groups = FALSE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">144</td>
+                    <td class="coverage">5677<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  return(ds_nlme_grouped)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">145</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                </tbody>
+              </table>
+            </div>
+            <div id="R/status.R" class="hidden">
+              <table class="table-condensed">
+                <tbody>
+                  <tr class="never">
+                    <td class="num">1</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Method to get status information for fit array objects</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">2</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">3</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param object The object to investigate</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">4</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param x The object to be printed</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">5</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param \dots For potential future extensions</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">6</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @return  An object with the same dimensions as the fit array</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">7</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' suitable printing method.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">8</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">9</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">status &lt;- function(object, ...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">10</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">{</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">11</td>
+                    <td class="coverage">589<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  UseMethod("status", object)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">12</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">13</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">14</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @rdname status</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">15</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">16</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @examples</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">17</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' \dontrun{</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">18</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' fits &lt;- mmkin(</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">19</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   c("SFO", "FOMC"),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">20</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   list("FOCUS A" = FOCUS_2006_A,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">21</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'        "FOCUS B" = FOCUS_2006_C),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">22</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   quiet = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">23</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' status(fits)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">24</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">25</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">status.mmkin &lt;- function(object, ...) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">26</td>
+                    <td class="coverage">376<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  all_summary_warnings &lt;- character()</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">27</td>
+                    <td class="coverage">376<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  sww &lt;- 0 # Counter for Shapiro-Wilks warnings</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">28</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">29</td>
+                    <td class="coverage">376<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  result &lt;- lapply(object,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">30</td>
+                    <td class="coverage">376<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    function(fit) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">31</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      if (inherits(fit, "try-error")) return("E")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">32</td>
+                    <td class="coverage">4391<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      sw &lt;- fit$summary_warnings</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">33</td>
+                    <td class="coverage">4391<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      swn &lt;- names(sw)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">34</td>
+                    <td class="coverage">4391<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      if (length(sw) &gt; 0) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">35</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        if (any(grepl("S", swn))) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">36</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          sww &lt;&lt;- sww + 1</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">37</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          swn &lt;- gsub("S", paste0("S", sww), swn)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">38</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        }</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">39</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        warnstring &lt;- paste(swn, collapse = ", ")</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">40</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        names(sw) &lt;- swn</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">41</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        all_summary_warnings &lt;&lt;- c(all_summary_warnings, sw)</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">42</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        return(warnstring)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">43</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">44</td>
+                    <td class="coverage">4391<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        return("OK")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">45</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">46</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    })</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">47</td>
+                    <td class="coverage">376<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  result &lt;- unlist(result)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">48</td>
+                    <td class="coverage">376<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  dim(result) &lt;- dim(object)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">49</td>
+                    <td class="coverage">376<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  dimnames(result) &lt;- dimnames(object)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">50</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">51</td>
+                    <td class="coverage">376<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  u_swn &lt;- unique(names(all_summary_warnings))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">52</td>
+                    <td class="coverage">376<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  attr(result, "unique_warnings") &lt;- all_summary_warnings[u_swn]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">53</td>
+                    <td class="coverage">376<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  class(result) &lt;- "status.mmkin"</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">54</td>
+                    <td class="coverage">376<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  return(result)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">55</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">56</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">57</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @rdname status</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">58</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">59</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">print.status.mmkin &lt;- function(x, ...) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">60</td>
+                    <td class="coverage">376<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  u_w &lt;- attr(x, "unique_warnings")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">61</td>
+                    <td class="coverage">376<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  attr(x, "unique_warnings") &lt;- NULL</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">62</td>
+                    <td class="coverage">376<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  class(x) &lt;- NULL</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">63</td>
+                    <td class="coverage">376<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  print(x, quote = FALSE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">64</td>
+                    <td class="coverage">376<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">65</td>
+                    <td class="coverage">376<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  for (i in seq_along(u_w)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">66</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    cat(names(u_w)[i], ": ", u_w[i], "\n", sep = "")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">67</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">68</td>
+                    <td class="coverage">376<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (any(x == "OK")) cat("OK: No warnings\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">69</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (any(x == "E")) cat("E: Error\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">70</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">71</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">72</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @rdname status</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">73</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">74</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">status.mhmkin &lt;- function(object, ...) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">75</td>
+                    <td class="coverage">125<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (inherits(object[[1]], "saem.mmkin")) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">76</td>
+                    <td class="coverage">125<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    test_func &lt;- function(fit) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">77</td>
+                    <td class="coverage">500<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      if (inherits(fit, "try-error")) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">78</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          return("E")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">79</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">80</td>
+                    <td class="coverage">500<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        if (inherits(fit$so, "try-error")) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">81</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          return("E")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">82</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">83</td>
+                    <td class="coverage">500<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          if (!is.null(fit$FIM_failed)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">84</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            return_values &lt;- c("fixed effects" = "Fth",</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">85</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              "random effects and error model parameters" = "FO")</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">86</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            return(paste(return_values[fit$FIM_failed], collapse = ", "))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">87</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">88</td>
+                    <td class="coverage">500<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            return("OK")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">89</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">90</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">91</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">92</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">93</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">94</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    stop("Only mhmkin objects containing saem.mmkin objects currently supported")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">95</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">96</td>
+                    <td class="coverage">125<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  result &lt;- lapply(object, test_func)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">97</td>
+                    <td class="coverage">125<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  result &lt;- unlist(result)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">98</td>
+                    <td class="coverage">125<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  dim(result) &lt;- dim(object)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">99</td>
+                    <td class="coverage">125<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  dimnames(result) &lt;- dimnames(object)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">100</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">101</td>
+                    <td class="coverage">125<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  class(result) &lt;- "status.mhmkin"</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">102</td>
+                    <td class="coverage">125<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  return(result)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">103</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">104</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">105</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @rdname status</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">106</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">107</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">print.status.mhmkin &lt;- function(x, ...) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">108</td>
+                    <td class="coverage">125<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  class(x) &lt;- NULL</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">109</td>
+                    <td class="coverage">125<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  print(x, quote = FALSE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">110</td>
+                    <td class="coverage">125<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">111</td>
+                    <td class="coverage">125<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (any(x == "OK")) cat("OK: Fit terminated successfully\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">112</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (any(x == "Fth")) cat("Fth: Could not invert FIM for fixed effects\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">113</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (any(x == "FO")) cat("FO: Could not invert FIM for random effects and error model parameters\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">114</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (any(x == "Fth, FO")) cat("Fth, FO: Could not invert FIM for fixed effects, nor for random effects and error model parameters\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">115</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (any(x == "E")) cat("E: Error\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">116</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">117</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                </tbody>
+              </table>
+            </div>
+            <div id="R/CAKE_export.R" class="hidden">
+              <table class="table-condensed">
+                <tbody>
+                  <tr class="never">
+                    <td class="num">1</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Export a list of datasets format to a CAKE study file</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">2</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' </pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">3</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' In addition to the datasets, the pathways in the degradation model can be</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">4</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' specified as well.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">5</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' </pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">6</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param ds A named list of datasets in long format as compatible with</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">7</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   \code{\link{mkinfit}}.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">8</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param map A character vector with CAKE compartment names (Parent, A1, ...),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">9</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   named with the names used in the list of datasets.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">10</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param links An optional character vector of target compartments, named with</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">11</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   the names of the source compartments. In order to make this easier, the</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">12</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   names are used as in the datasets supplied.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">13</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param filename Where to write the result. Should end in .csf in order to be</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">14</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   compatible with CAKE.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">15</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param path An optional path to the output file.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">16</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param overwrite If TRUE, existing files are overwritten.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">17</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param study The name of the study.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">18</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param description An optional description.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">19</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param time_unit The time unit for the residue data.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">20</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param res_unit The unit used for the residues.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">21</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param comment An optional comment.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">22</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param date The date of file creation.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">23</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param optimiser Can be OLS or IRLS.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">24</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @importFrom utils write.table</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">25</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @return The function is called for its side effect.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">26</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @author Johannes Ranke</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">27</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">28</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">CAKE_export &lt;- function(ds, map = c(parent = "Parent"),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">29</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  links = NA,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">30</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  filename = "CAKE_export.csf", path = ".", overwrite = FALSE,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">31</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  study = "Degradinol aerobic soil degradation",</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">32</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  description = "",</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">33</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  time_unit = "days",</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">34</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  res_unit = "% AR",</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">35</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  comment = "",</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">36</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  date = Sys.Date(),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">37</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  optimiser = "IRLS")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">38</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">{</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">39</td>
+                    <td class="coverage">741<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  file &lt;- file.path(path, filename)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">40</td>
+                    <td class="coverage">247<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (file.exists(file) &amp; !overwrite) stop(file, " already exists, stopping")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">41</td>
+                    <td class="coverage">494<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  csf &lt;- file(file, encoding = "latin1", open = "w+")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">42</td>
+                    <td class="coverage">494<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  on.exit(close(csf))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">43</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">44</td>
+                    <td class="coverage">494<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  CAKE_compartments = c("Parent", "A1", "A2", "A3", "B1", "B2", "C1")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">45</td>
+                    <td class="coverage">494<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (!all(map %in% CAKE_compartments)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">46</td>
+                    <td class="coverage">247<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    stop("The elements of map have to be CAKE compartment names")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">47</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">48</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">49</td>
+                    <td class="coverage">247<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  add &lt;- function(x) cat(paste0(x, "\r\n"), file = csf, append = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">50</td>
+                    <td class="coverage">247<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  add0 &lt;- function(x) cat(x, file = csf, append = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">51</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">52</td>
+                    <td class="coverage">247<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  add("[FileInfo]")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">53</td>
+                    <td class="coverage">247<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  add("CAKE-Version: 3.4 (Release)")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">54</td>
+                    <td class="coverage">247<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  add(paste("Name:", study))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">55</td>
+                    <td class="coverage">247<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  add(paste("Description:", description))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">56</td>
+                    <td class="coverage">247<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  add(paste("MeasurementUnits:", res_unit))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">57</td>
+                    <td class="coverage">247<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  add(paste("TimeUnits:", time_unit))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">58</td>
+                    <td class="coverage">247<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  add(paste("Comments:", comment))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">59</td>
+                    <td class="coverage">247<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  add(paste("Date:", date))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">60</td>
+                    <td class="coverage">247<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  add(paste("Optimiser:", optimiser))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">61</td>
+                    <td class="coverage">247<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  add("")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">62</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">63</td>
+                    <td class="coverage">247<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  add("[Data]")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">64</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">65</td>
+                    <td class="coverage">247<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  for (i in seq_along(ds)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">66</td>
+                    <td class="coverage">494<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    add(paste("NewDataSet:", names(ds)[i]))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">67</td>
+                    <td class="coverage">494<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    d &lt;- mkin_long_to_wide(ds[[i]])</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">68</td>
+                    <td class="coverage">494<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    names(d) &lt;- c("Time", map[names(d)[-1]])</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">69</td>
+                    <td class="coverage">494<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    write.table(d, csf,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">70</td>
+                    <td class="coverage">494<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      sep = "\t", col.names = TRUE,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">71</td>
+                    <td class="coverage">494<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      row.names = FALSE,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">72</td>
+                    <td class="coverage">494<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      quote = FALSE, eol = "\r\n", na = "")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">73</td>
+                    <td class="coverage">494<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    add("")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">74</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">75</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">76</td>
+                    <td class="coverage">247<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (!is.na(links)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">77</td>
+                    <td class="coverage">247<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    add("")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">78</td>
+                    <td class="coverage">247<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    add("[Model]")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">79</td>
+                    <td class="coverage">247<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    add(paste0("ParentCompartment: Parent\t", names(map)[1], "\t", names(map)[1]))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">80</td>
+                    <td class="coverage">247<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    for (name in names(map)[-1]) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">81</td>
+                    <td class="coverage">247<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      add(paste0("Compartment: ", map[name], "\t", name, "\t", name))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">82</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">83</td>
+                    <td class="coverage">247<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    for (li in names(links)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">84</td>
+                    <td class="coverage">247<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      add(paste0("Link: ", map[li], "\t", map[links[li]], "\t0.5\t0\t1\tFree\tExplicit"))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">85</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">86</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">87</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">88</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">89</td>
+                    <td class="coverage">247<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  add("")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">90</td>
+                    <td class="coverage">247<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  add("[ComponentNames]")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">91</td>
+                    <td class="coverage">247<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  for (name in names(map)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">92</td>
+                    <td class="coverage">494<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    add(paste0(map[name], ":", name))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">93</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">94</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">95</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                </tbody>
+              </table>
+            </div>
+            <div id="R/parplot.R" class="hidden">
+              <table class="table-condensed">
+                <tbody>
+                  <tr class="never">
+                    <td class="num">1</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Plot parameter variability of multistart objects</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">2</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">3</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Produces a boxplot with all parameters from the multiple runs, scaled</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">4</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' either by the parameters of the run with the highest likelihood,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">5</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' or by their medians as proposed in the paper by Duchesne et al. (2021).</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">6</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">7</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Starting values of degradation model parameters and error model parameters</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">8</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' are shown as green circles. The results obtained in the original run</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">9</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' are shown as red circles.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">10</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">11</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param object The [multistart] object</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">12</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param llmin The minimum likelihood of objects to be shown</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">13</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param llquant Fractional value for selecting only the fits with higher</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">14</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' likelihoods. Overrides 'llmin'.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">15</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param scale By default, scale parameters using the best</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">16</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' available fit.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">17</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' If 'median', parameters are scaled using the median parameters from all fits.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">18</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param main Title of the plot</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">19</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param lpos Positioning of the legend.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">20</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param \dots Passed to [boxplot]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">21</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @references Duchesne R, Guillemin A, Gandrillon O, Crauste F. Practical</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">22</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' identifiability in the frame of nonlinear mixed effects models: the example</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">23</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' of the in vitro erythropoiesis. BMC Bioinformatics. 2021 Oct 4;22(1):478.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">24</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' doi: 10.1186/s12859-021-04373-4.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">25</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @seealso [multistart]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">26</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @importFrom stats median quantile</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">27</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">28</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">parplot &lt;- function(object, ...) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">29</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  UseMethod("parplot")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">30</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">31</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">32</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @rdname parplot</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">33</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">34</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">parplot.multistart.saem.mmkin &lt;- function(object, llmin = -Inf, llquant = NA,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">35</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  scale = c("best", "median"),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">36</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  lpos = "bottomleft", main = "", ...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">37</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">{</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">38</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  orig &lt;- attr(object, "orig")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">39</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  orig_parms &lt;- parms(orig)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">40</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  start_degparms &lt;- orig$mean_dp_start</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">41</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  all_parms &lt;- parms(object, exclude_failed = FALSE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">42</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">43</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (inherits(object, "multistart.saem.mmkin")) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">44</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    llfunc &lt;- function(object) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">45</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      if (inherits(object$so, "try-error")) return(NA)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">46</td>
+                    <td class="coverage">1408<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      else return(logLik(object$so))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">47</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">48</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">49</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    stop("parplot is only implemented for multistart.saem.mmkin objects")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">50</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">51</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  ll &lt;- sapply(object, llfunc)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">52</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (!is.na(llquant[1])) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">53</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (llmin != -Inf) warning("Overriding 'llmin' because 'llquant' was specified")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">54</td>
+                    <td class="coverage">88<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    llmin &lt;- quantile(ll, 1 - llquant)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">55</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">56</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  selected &lt;- which(ll &gt; llmin)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">57</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  selected_parms &lt;- all_parms[selected, ]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">58</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">59</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (orig$transformations == "mkin") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">60</td>
+                    <td class="coverage">88<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    degparm_names_transformed &lt;- names(start_degparms)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">61</td>
+                    <td class="coverage">88<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    degparm_index &lt;- which(names(orig_parms) %in% degparm_names_transformed)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">62</td>
+                    <td class="coverage">88<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    orig_degparms &lt;- backtransform_odeparms(</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">63</td>
+                    <td class="coverage">88<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      orig_parms[degparm_names_transformed],</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">64</td>
+                    <td class="coverage">88<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      orig$mmkin[[1]]$mkinmod,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">65</td>
+                    <td class="coverage">88<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      transform_rates = orig$mmkin[[1]]$transform_rates,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">66</td>
+                    <td class="coverage">88<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      transform_fractions = orig$mmkin[[1]]$transform_fractions)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">67</td>
+                    <td class="coverage">88<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    start_degparms &lt;- backtransform_odeparms(start_degparms,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">68</td>
+                    <td class="coverage">88<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      orig$mmkin[[1]]$mkinmod,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">69</td>
+                    <td class="coverage">88<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      transform_rates = orig$mmkin[[1]]$transform_rates,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">70</td>
+                    <td class="coverage">88<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      transform_fractions = orig$mmkin[[1]]$transform_fractions)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">71</td>
+                    <td class="coverage">88<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    degparm_names &lt;- names(start_degparms)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">72</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">73</td>
+                    <td class="coverage">88<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    orig_parms_back &lt;- orig_parms</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">74</td>
+                    <td class="coverage">88<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    orig_parms_back[degparm_index] &lt;- orig_degparms</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">75</td>
+                    <td class="coverage">88<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    names(orig_parms_back)[degparm_index] &lt;- degparm_names</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">76</td>
+                    <td class="coverage">88<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    orig_parms &lt;- orig_parms_back</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">77</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">78</td>
+                    <td class="coverage">88<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    selected_parms[, degparm_names_transformed] &lt;-</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">79</td>
+                    <td class="coverage">88<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      t(apply(selected_parms[, degparm_names_transformed], 1, backtransform_odeparms,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">80</td>
+                    <td class="coverage">88<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      orig$mmkin[[1]]$mkinmod,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">81</td>
+                    <td class="coverage">88<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      transform_rates = orig$mmkin[[1]]$transform_rates,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">82</td>
+                    <td class="coverage">88<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      transform_fractions = orig$mmkin[[1]]$transform_fractions))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">83</td>
+                    <td class="coverage">88<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    colnames(selected_parms)[degparm_index] &lt;- degparm_names</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">84</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">85</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">86</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  start_errparms &lt;- orig$so@model@error.init</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">87</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  names(start_errparms) &lt;- orig$so@model@name.sigma</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">88</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">89</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  start_omegaparms &lt;- orig$so@model@omega.init</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">90</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">91</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  start_parms &lt;- c(start_degparms, start_errparms)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">92</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">93</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  scale &lt;- match.arg(scale)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">94</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  parm_scale &lt;- switch(scale,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">95</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    best = selected_parms[which.best(object[selected]), ],</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">96</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    median = apply(selected_parms, 2, median)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">97</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  )</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">98</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">99</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Boxplots of all scaled parameters</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">100</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  selected_scaled_parms &lt;- t(apply(selected_parms, 1, function(x) x / parm_scale))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">101</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  i_negative &lt;- selected_scaled_parms &lt;= 0</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">102</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  parms_with_negative_scaled_values &lt;- paste(names(which(apply(i_negative, 2, any))), collapse = ", ")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">103</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (any(i_negative)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">104</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    warning("There are negative values for ", parms_with_negative_scaled_values, " which are set to NA for plotting")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">105</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">106</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  selected_scaled_parms[i_negative] &lt;- NA</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">107</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  boxplot(selected_scaled_parms, log = "y", main = main, ,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">108</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    ylab = "Normalised parameters", ...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">109</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">110</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Show starting parameters</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">111</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  start_scaled_parms &lt;- rep(NA_real_, length(orig_parms))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">112</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  names(start_scaled_parms) &lt;- names(orig_parms)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">113</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  start_scaled_parms[names(start_parms)] &lt;-</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">114</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    start_parms / parm_scale[names(start_parms)]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">115</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  points(start_scaled_parms, col = 3, cex = 3)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">116</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">117</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Show parameters of original run</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">118</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  orig_scaled_parms &lt;- orig_parms / parm_scale</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">119</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  points(orig_scaled_parms, col = 2, cex = 2)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">120</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">121</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  abline(h = 1, lty = 2)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">122</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">123</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  legend(lpos, inset = c(0.05, 0.05), bty = "n",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">124</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    pch = 1, col = 3:1, lty = c(NA, NA, 1),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">125</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    legend = c(</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">126</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      "Original start",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">127</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      "Original results",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">128</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      "Multistart runs"))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">129</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                </tbody>
+              </table>
+            </div>
+            <div id="R/lrtest.mkinfit.R" class="hidden">
+              <table class="table-condensed">
+                <tbody>
+                  <tr class="never">
+                    <td class="num">1</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @importFrom lmtest lrtest</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">2</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">3</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">lmtest::lrtest</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">4</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">5</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Likelihood ratio test for mkinfit models</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">6</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">7</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Compare two mkinfit models based on their likelihood. If two fitted</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">8</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' mkinfit objects are given as arguments, it is checked if they have been</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">9</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' fitted to the same data. It is the responsibility of the user to make sure</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">10</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' that the models are nested, i.e. one of them has less degrees of freedom</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">11</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' and can be expressed by fixing the parameters of the other.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">12</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">13</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Alternatively, an argument to mkinfit can be given which is then passed</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">14</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' to \code{\link{update.mkinfit}} to obtain the alternative model.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">15</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">16</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' The comparison is then made by the \code{\link[lmtest]{lrtest.default}}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">17</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' method from the lmtest package. The model with the higher number of fitted</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">18</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' parameters (alternative hypothesis) is listed first, then the model with the</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">19</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' lower number of fitted parameters (null hypothesis).</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">20</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">21</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @importFrom stats logLik update</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">22</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param object An \code{\link{mkinfit}} object, or an \code{\link{mmkin}} column</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">23</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'  object containing two fits to the same data.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">24</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param object_2 Optionally, another mkinfit object fitted to the same data.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">25</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param \dots Argument to \code{\link{mkinfit}}, passed to</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">26</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   \code{\link{update.mkinfit}} for creating the alternative fitted object.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">27</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @examples</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">28</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' \dontrun{</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">29</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' test_data &lt;- subset(synthetic_data_for_UBA_2014[[12]]$data, name == "parent")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">30</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' sfo_fit &lt;- mkinfit("SFO", test_data, quiet = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">31</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' dfop_fit &lt;- mkinfit("DFOP", test_data, quiet = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">32</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' lrtest(dfop_fit, sfo_fit)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">33</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' lrtest(sfo_fit, dfop_fit)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">34</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">35</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # The following two examples are commented out as they fail during</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">36</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # generation of the static help pages by pkgdown</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">37</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' #lrtest(dfop_fit, error_model = "tc")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">38</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' #lrtest(dfop_fit, fixed_parms = c(k2 = 0))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">39</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">40</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # However, this equivalent syntax also works for static help pages</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">41</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' lrtest(dfop_fit, update(dfop_fit, error_model = "tc"))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">42</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' lrtest(dfop_fit, update(dfop_fit, fixed_parms = c(k2 = 0)))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">43</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">44</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">45</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">lrtest.mkinfit &lt;- function(object, object_2 = NULL, ...) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">46</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">47</td>
+                    <td class="coverage">6<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  name_function &lt;- function(x) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">48</td>
+                    <td class="coverage">10<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    object_name &lt;- paste(x$mkinmod$name, "with error model", x$err_mod)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">49</td>
+                    <td class="coverage">10<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (length(x$bparms.fixed) &gt; 0) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">50</td>
+                    <td class="coverage">7<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      object_name &lt;- paste(object_name,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">51</td>
+                    <td class="coverage">7<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        "and fixed parameter(s)",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">52</td>
+                    <td class="coverage">7<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        paste(names(x$bparms.fixed), collapse = ", "))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">53</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">54</td>
+                    <td class="coverage">10<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    return(object_name)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">55</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">56</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">57</td>
+                    <td class="coverage">6<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (is.null(object_2)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">58</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    object_2 &lt;- update(object, ...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">59</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">60</td>
+                    <td class="coverage">6<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    data_object &lt;- object$data[c("time", "variable", "observed")]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">61</td>
+                    <td class="coverage">6<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    data_object_2 &lt;- object_2$data[c("time", "variable", "observed")]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">62</td>
+                    <td class="coverage">6<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (!identical(data_object, data_object_2)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">63</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      stop("It seems that the mkinfit objects have not been fitted to the same data")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">64</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">65</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">66</td>
+                    <td class="coverage">5<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (attr(logLik(object), "df") &gt; attr(logLik(object_2), "df")) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">67</td>
+                    <td class="coverage">2<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    lmtest::lrtest.default(object, object_2, name = name_function)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">68</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">69</td>
+                    <td class="coverage">3<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    lmtest::lrtest.default(object_2, object, name = name_function)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">70</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">71</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">72</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">73</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @rdname lrtest.mkinfit</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">74</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">75</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">lrtest.mmkin &lt;- function(object, ...) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">76</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (nrow(object) != 2 | ncol(object) &gt; 1) stop("Only works for a column containing two mkinfit objects")</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">77</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  object[[1, 1]]$mkinmod$name &lt;- rownames(object)[1]</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">78</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  object[[2, 1]]$mkinmod$name &lt;- rownames(object)[2]</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">79</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  lrtest(object[[1, 1]], object[[2, 1]])</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">80</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                </tbody>
+              </table>
+            </div>
+            <div id="R/summary.saem.mmkin.R" class="hidden">
+              <table class="table-condensed">
+                <tbody>
+                  <tr class="never">
+                    <td class="num">1</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Summary method for class "saem.mmkin"</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">2</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">3</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Lists model equations, initial parameter values, optimised parameters</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">4</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' for fixed effects (population), random effects (deviations from the</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">5</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' population mean) and residual error model, as well as the resulting</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">6</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' endpoints such as formation fractions and DT50 values. Optionally</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">7</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' (default is FALSE), the data are listed in full.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">8</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">9</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param object an object of class [saem.mmkin]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">10</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param x an object of class [summary.saem.mmkin]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">11</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param data logical, indicating whether the full data should be included in</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">12</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   the summary.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">13</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param verbose Should the summary be verbose?</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">14</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param distimes logical, indicating whether DT50 and DT90 values should be</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">15</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   included.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">16</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param digits Number of digits to use for printing</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">17</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param \dots optional arguments passed to methods like \code{print}.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">18</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @inheritParams endpoints</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">19</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @return The summary function returns a list based on the [saemix::SaemixObject]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">20</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   obtained in the fit, with at least the following additional components</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">21</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   \item{saemixversion, mkinversion, Rversion}{The saemix, mkin and R versions used}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">22</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   \item{date.fit, date.summary}{The dates where the fit and the summary were</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">23</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     produced}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">24</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   \item{diffs}{The differential equations used in the degradation model}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">25</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   \item{use_of_ff}{Was maximum or minimum use made of formation fractions}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">26</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   \item{data}{The data}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">27</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   \item{confint_trans}{Transformed parameters as used in the optimisation, with confidence intervals}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">28</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   \item{confint_back}{Backtransformed parameters, with confidence intervals if available}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">29</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   \item{confint_errmod}{Error model parameters with confidence intervals}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">30</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   \item{ff}{The estimated formation fractions derived from the fitted</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">31</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'      model.}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">32</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   \item{distimes}{The DT50 and DT90 values for each observed variable.}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">33</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   \item{SFORB}{If applicable, eigenvalues of SFORB components of the model.}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">34</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   The print method is called for its side effect, i.e. printing the summary.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">35</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @importFrom stats predict vcov</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">36</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @author Johannes Ranke for the mkin specific parts</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">37</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   saemix authors for the parts inherited from saemix.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">38</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @examples</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">39</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # Generate five datasets following DFOP-SFO kinetics</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">40</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">41</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' dfop_sfo &lt;- mkinmod(parent = mkinsub("DFOP", "m1"),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">42</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'  m1 = mkinsub("SFO"), quiet = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">43</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' set.seed(1234)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">44</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' k1_in &lt;- rlnorm(5, log(0.1), 0.3)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">45</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' k2_in &lt;- rlnorm(5, log(0.02), 0.3)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">46</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' g_in &lt;- plogis(rnorm(5, qlogis(0.5), 0.3))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">47</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' f_parent_to_m1_in &lt;- plogis(rnorm(5, qlogis(0.3), 0.3))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">48</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' k_m1_in &lt;- rlnorm(5, log(0.02), 0.3)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">49</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">50</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' pred_dfop_sfo &lt;- function(k1, k2, g, f_parent_to_m1, k_m1) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">51</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   mkinpredict(dfop_sfo,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">52</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     c(k1 = k1, k2 = k2, g = g, f_parent_to_m1 = f_parent_to_m1, k_m1 = k_m1),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">53</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     c(parent = 100, m1 = 0),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">54</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     sampling_times)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">55</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">56</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">57</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' ds_mean_dfop_sfo &lt;- lapply(1:5, function(i) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">58</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   mkinpredict(dfop_sfo,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">59</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     c(k1 = k1_in[i], k2 = k2_in[i], g = g_in[i],</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">60</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'       f_parent_to_m1 = f_parent_to_m1_in[i], k_m1 = k_m1_in[i]),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">61</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     c(parent = 100, m1 = 0),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">62</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     sampling_times)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">63</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' })</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">64</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' names(ds_mean_dfop_sfo) &lt;- paste("ds", 1:5)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">65</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">66</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' ds_syn_dfop_sfo &lt;- lapply(ds_mean_dfop_sfo, function(ds) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">67</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   add_err(ds,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">68</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     sdfunc = function(value) sqrt(1^2 + value^2 * 0.07^2),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">69</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     n = 1)[[1]]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">70</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' })</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">71</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">72</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' \dontrun{</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">73</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # Evaluate using mmkin and saem</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">74</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' f_mmkin_dfop_sfo &lt;- mmkin(list(dfop_sfo), ds_syn_dfop_sfo,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">75</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   quiet = TRUE, error_model = "tc", cores = 5)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">76</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' f_saem_dfop_sfo &lt;- saem(f_mmkin_dfop_sfo)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">77</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' print(f_saem_dfop_sfo)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">78</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' illparms(f_saem_dfop_sfo)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">79</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' f_saem_dfop_sfo_2 &lt;- update(f_saem_dfop_sfo,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">80</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   no_random_effect = c("parent_0", "log_k_m1"))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">81</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' illparms(f_saem_dfop_sfo_2)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">82</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' intervals(f_saem_dfop_sfo_2)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">83</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' summary(f_saem_dfop_sfo_2, data = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">84</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # Add a correlation between random effects of g and k2</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">85</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' cov_model_3 &lt;- f_saem_dfop_sfo_2$so@model@covariance.model</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">86</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' cov_model_3["log_k2", "g_qlogis"] &lt;- 1</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">87</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' cov_model_3["g_qlogis", "log_k2"] &lt;- 1</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">88</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' f_saem_dfop_sfo_3 &lt;- update(f_saem_dfop_sfo,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">89</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   covariance.model = cov_model_3)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">90</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' intervals(f_saem_dfop_sfo_3)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">91</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # The correlation does not improve the fit judged by AIC and BIC, although</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">92</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # the likelihood is higher with the additional parameter</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">93</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' anova(f_saem_dfop_sfo, f_saem_dfop_sfo_2, f_saem_dfop_sfo_3)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">94</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">95</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">96</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">97</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">summary.saem.mmkin &lt;- function(object, data = FALSE, verbose = FALSE,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">98</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  covariates = NULL, covariate_quantile = 0.5,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">99</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  distimes = TRUE, ...) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">100</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">101</td>
+                    <td class="coverage">800<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  mod_vars &lt;- names(object$mkinmod$diffs)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">102</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">103</td>
+                    <td class="coverage">800<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  pnames &lt;- names(object$mean_dp_start)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">104</td>
+                    <td class="coverage">800<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  names_fixed_effects &lt;- object$so@results@name.fixed</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">105</td>
+                    <td class="coverage">800<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  n_fixed &lt;- length(names_fixed_effects)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">106</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">107</td>
+                    <td class="coverage">800<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  conf.int &lt;- object$so@results@conf.int</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">108</td>
+                    <td class="coverage">800<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  rownames(conf.int) &lt;- conf.int$name</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">109</td>
+                    <td class="coverage">800<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  confint_trans &lt;- as.matrix(parms(object, ci = TRUE))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">110</td>
+                    <td class="coverage">800<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  colnames(confint_trans)[1] &lt;- "est."</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">111</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">112</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # In case objects were produced by earlier versions of saem</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">113</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (is.null(object$transformations)) object$transformations &lt;- "mkin"</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">114</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">115</td>
+                    <td class="coverage">800<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (object$transformations == "mkin") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">116</td>
+                    <td class="coverage">396<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    bp &lt;- backtransform_odeparms(confint_trans[pnames, "est."], object$mkinmod,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">117</td>
+                    <td class="coverage">396<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      object$transform_rates, object$transform_fractions)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">118</td>
+                    <td class="coverage">396<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    bpnames &lt;- names(bp)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">119</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">120</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # Transform boundaries of CI for one parameter at a time,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">121</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # with the exception of sets of formation fractions (single fractions are OK).</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">122</td>
+                    <td class="coverage">396<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    f_names_skip &lt;- character(0)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">123</td>
+                    <td class="coverage">396<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    for (box in mod_vars) { # Figure out sets of fractions to skip</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">124</td>
+                    <td class="coverage">492<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      f_names &lt;- grep(paste("^f", box, sep = "_"), pnames, value = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">125</td>
+                    <td class="coverage">492<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      n_paths &lt;- length(f_names)</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">126</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      if (n_paths &gt; 1) f_names_skip &lt;- c(f_names_skip, f_names)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">127</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">128</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">129</td>
+                    <td class="coverage">396<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    confint_back &lt;- matrix(NA, nrow = length(bp), ncol = 3,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">130</td>
+                    <td class="coverage">396<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      dimnames = list(bpnames, colnames(confint_trans)))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">131</td>
+                    <td class="coverage">396<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    confint_back[, "est."] &lt;- bp</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">132</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">133</td>
+                    <td class="coverage">396<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    for (pname in pnames) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">134</td>
+                    <td class="coverage">1291<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      if (!pname %in% f_names_skip) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">135</td>
+                    <td class="coverage">1291<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        par.lower &lt;- confint_trans[pname, "lower"]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">136</td>
+                    <td class="coverage">1291<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        par.upper &lt;- confint_trans[pname, "upper"]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">137</td>
+                    <td class="coverage">1291<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        names(par.lower) &lt;- names(par.upper) &lt;- pname</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">138</td>
+                    <td class="coverage">1291<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        bpl &lt;- backtransform_odeparms(par.lower, object$mkinmod,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">139</td>
+                    <td class="coverage">1291<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                                              object$transform_rates,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">140</td>
+                    <td class="coverage">1291<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                                              object$transform_fractions)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">141</td>
+                    <td class="coverage">1291<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        bpu &lt;- backtransform_odeparms(par.upper, object$mkinmod,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">142</td>
+                    <td class="coverage">1291<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                                              object$transform_rates,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">143</td>
+                    <td class="coverage">1291<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                                              object$transform_fractions)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">144</td>
+                    <td class="coverage">1291<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        confint_back[names(bpl), "lower"] &lt;- bpl</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">145</td>
+                    <td class="coverage">1291<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        confint_back[names(bpu), "upper"] &lt;- bpu</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">146</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">147</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">148</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">149</td>
+                    <td class="coverage">404<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    confint_back &lt;- confint_trans[names_fixed_effects, ]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">150</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">151</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">152</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  #  Correlation of fixed effects (inspired by summary.nlme)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">153</td>
+                    <td class="coverage">800<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cov_so &lt;- try(solve(object$so@results@fim), silent = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">154</td>
+                    <td class="coverage">800<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (inherits(cov_so, "try-error")) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">155</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    object$corFixed &lt;- NA</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">156</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">157</td>
+                    <td class="coverage">800<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    varFix &lt;- cov_so[1:n_fixed, 1:n_fixed]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">158</td>
+                    <td class="coverage">800<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    stdFix &lt;- sqrt(diag(varFix))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">159</td>
+                    <td class="coverage">800<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    object$corFixed &lt;- array(</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">160</td>
+                    <td class="coverage">800<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      t(varFix/stdFix)/stdFix,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">161</td>
+                    <td class="coverage">800<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      dim(varFix),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">162</td>
+                    <td class="coverage">800<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      list(names_fixed_effects, names_fixed_effects))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">163</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">164</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">165</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Random effects</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">166</td>
+                    <td class="coverage">800<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  sdnames &lt;- intersect(rownames(conf.int), paste0("SD.", pnames))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">167</td>
+                    <td class="coverage">800<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  corrnames &lt;- grep("^Corr.", rownames(conf.int), value = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">168</td>
+                    <td class="coverage">800<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  confint_ranef &lt;- as.matrix(conf.int[c(sdnames, corrnames), c("estimate", "lower", "upper")])</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">169</td>
+                    <td class="coverage">800<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  colnames(confint_ranef)[1] &lt;- "est."</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">170</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">171</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Error model</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">172</td>
+                    <td class="coverage">800<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  enames &lt;- if (object$err_mod == "const") "a.1" else c("a.1", "b.1")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">173</td>
+                    <td class="coverage">800<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  confint_errmod &lt;- as.matrix(conf.int[enames, c("estimate", "lower", "upper")])</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">174</td>
+                    <td class="coverage">800<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  colnames(confint_errmod)[1] &lt;- "est."</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">175</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">176</td>
+                    <td class="coverage">800<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  object$confint_trans &lt;- confint_trans</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">177</td>
+                    <td class="coverage">800<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  object$confint_ranef &lt;- confint_ranef</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">178</td>
+                    <td class="coverage">800<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  object$confint_errmod &lt;- confint_errmod</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">179</td>
+                    <td class="coverage">800<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  object$confint_back &lt;- confint_back</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">180</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">181</td>
+                    <td class="coverage">800<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  object$date.summary = date()</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">182</td>
+                    <td class="coverage">800<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  object$use_of_ff = object$mkinmod$use_of_ff</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">183</td>
+                    <td class="coverage">800<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  object$error_model_algorithm = object$mmkin_orig[[1]]$error_model_algorithm</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">184</td>
+                    <td class="coverage">800<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  err_mod = object$mmkin_orig[[1]]$err_mod</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">185</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">186</td>
+                    <td class="coverage">800<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  object$diffs &lt;- object$mkinmod$diffs</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">187</td>
+                    <td class="coverage">800<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  object$print_data &lt;- data # boolean: Should we print the data?</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">188</td>
+                    <td class="coverage">800<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  so_pred &lt;- object$so@results@predictions</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">189</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">190</td>
+                    <td class="coverage">800<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  names(object$data)[4] &lt;- "observed" # rename value to observed</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">191</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">192</td>
+                    <td class="coverage">800<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  object$verbose &lt;- verbose</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">193</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">194</td>
+                    <td class="coverage">800<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  object$fixed &lt;- object$mmkin_orig[[1]]$fixed</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">195</td>
+                    <td class="coverage">800<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  ll &lt;-try(logLik(object$so, method = "is"), silent = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">196</td>
+                    <td class="coverage">800<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (inherits(ll, "try-error")) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">197</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    object$logLik &lt;- object$AIC &lt;- object $BIC &lt;- NA</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">198</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">199</td>
+                    <td class="coverage">800<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    object$logLik = logLik(object$so, method = "is")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">200</td>
+                    <td class="coverage">800<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    object$AIC = AIC(object$so)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">201</td>
+                    <td class="coverage">800<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    object$BIC = BIC(object$so)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">202</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">203</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">204</td>
+                    <td class="coverage">800<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  ep &lt;- endpoints(object)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">205</td>
+                    <td class="coverage">800<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  object$covariates &lt;- ep$covariates</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">206</td>
+                    <td class="coverage">800<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (length(ep$ff) != 0)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">207</td>
+                    <td class="coverage">330<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    object$ff &lt;- ep$ff</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">208</td>
+                    <td class="coverage">800<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (distimes) object$distimes &lt;- ep$distimes</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">209</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (length(ep$SFORB) != 0) object$SFORB &lt;- ep$SFORB</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">210</td>
+                    <td class="coverage">800<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  class(object) &lt;- c("summary.saem.mmkin")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">211</td>
+                    <td class="coverage">800<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  return(object)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">212</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">213</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">214</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @rdname summary.saem.mmkin</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">215</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">216</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">print.summary.saem.mmkin &lt;- function(x, digits = max(3, getOption("digits") - 3), verbose = x$verbose, ...) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">217</td>
+                    <td class="coverage">242<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("saemix version used for fitting:     ", x$saemixversion, "\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">218</td>
+                    <td class="coverage">242<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("mkin version used for pre-fitting: ", x$mkinversion, "\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">219</td>
+                    <td class="coverage">242<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("R version used for fitting:        ", x$Rversion, "\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">220</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">221</td>
+                    <td class="coverage">242<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("Date of fit:    ", x$date.fit, "\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">222</td>
+                    <td class="coverage">242<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("Date of summary:", x$date.summary, "\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">223</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">224</td>
+                    <td class="coverage">242<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("\nEquations:\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">225</td>
+                    <td class="coverage">242<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  nice_diffs &lt;- gsub("^(d.*) =", "\\1/dt =", x[["diffs"]])</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">226</td>
+                    <td class="coverage">242<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  writeLines(strwrap(nice_diffs, exdent = 11))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">227</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">228</td>
+                    <td class="coverage">242<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("\nData:\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">229</td>
+                    <td class="coverage">242<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat(nrow(x$data), "observations of",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">230</td>
+                    <td class="coverage">242<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    length(unique(x$data$name)), "variable(s) grouped in",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">231</td>
+                    <td class="coverage">242<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    length(unique(x$data$ds)), "datasets\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">232</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">233</td>
+                    <td class="coverage">242<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("\nModel predictions using solution type", x$solution_type, "\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">234</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">235</td>
+                    <td class="coverage">242<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("\nFitted in", x$time[["elapsed"]],  "s\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">236</td>
+                    <td class="coverage">242<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("Using", paste(x$so@options$nbiter.saemix, collapse = ", "),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">237</td>
+                    <td class="coverage">242<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    "iterations and", x$so@options$nb.chains, "chains\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">238</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">239</td>
+                    <td class="coverage">242<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("\nVariance model: ")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">240</td>
+                    <td class="coverage">242<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat(switch(x$err_mod,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">241</td>
+                    <td class="coverage">242<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    const = "Constant variance",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">242</td>
+                    <td class="coverage">242<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    obs = "Variance unique to each observed variable",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">243</td>
+                    <td class="coverage">242<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    tc = "Two-component variance function"), "\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">244</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">245</td>
+                    <td class="coverage">242<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("\nStarting values for degradation parameters:\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">246</td>
+                    <td class="coverage">242<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  print(x$mean_dp_start, digits = digits)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">247</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">248</td>
+                    <td class="coverage">242<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("\nFixed degradation parameter values:\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">249</td>
+                    <td class="coverage">242<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if(length(x$fixed$value) == 0) cat("None\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">250</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  else print(x$fixed, digits = digits)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">251</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">252</td>
+                    <td class="coverage">242<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("\nStarting values for random effects (square root of initial entries in omega):\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">253</td>
+                    <td class="coverage">242<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  print(sqrt(x$so@model@omega.init), digits = digits)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">254</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">255</td>
+                    <td class="coverage">242<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("\nStarting values for error model parameters:\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">256</td>
+                    <td class="coverage">242<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  errparms &lt;- x$so@model@error.init</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">257</td>
+                    <td class="coverage">242<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  names(errparms) &lt;- x$so@model@name.sigma</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">258</td>
+                    <td class="coverage">242<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  errparms &lt;- errparms[x$so@model@indx.res]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">259</td>
+                    <td class="coverage">242<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  print(errparms, digits = digits)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">260</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">261</td>
+                    <td class="coverage">242<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("\nResults:\n\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">262</td>
+                    <td class="coverage">242<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("Likelihood computed by importance sampling\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">263</td>
+                    <td class="coverage">242<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  print(data.frame(AIC = x$AIC, BIC = x$BIC, logLik = x$logLik,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">264</td>
+                    <td class="coverage">242<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      row.names = " "), digits = digits)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">265</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">266</td>
+                    <td class="coverage">242<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("\nOptimised parameters:\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">267</td>
+                    <td class="coverage">242<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  print(x$confint_trans, digits = digits)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">268</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">269</td>
+                    <td class="coverage">242<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (identical(x$corFixed, NA)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">270</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    cat("\nCorrelation is not available\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">271</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">272</td>
+                    <td class="coverage">242<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    corr &lt;- x$corFixed</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">273</td>
+                    <td class="coverage">242<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    class(corr) &lt;- "correlation"</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">274</td>
+                    <td class="coverage">242<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    print(corr, title = "\nCorrelation:", rdig = digits, ...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">275</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">276</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">277</td>
+                    <td class="coverage">242<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("\nRandom effects:\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">278</td>
+                    <td class="coverage">242<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  print(x$confint_ranef, digits = digits)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">279</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">280</td>
+                    <td class="coverage">242<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("\nVariance model:\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">281</td>
+                    <td class="coverage">242<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  print(x$confint_errmod, digits = digits)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">282</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">283</td>
+                    <td class="coverage">242<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (x$transformations == "mkin") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">284</td>
+                    <td class="coverage">125<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    cat("\nBacktransformed parameters:\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">285</td>
+                    <td class="coverage">125<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    print(x$confint_back, digits = digits)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">286</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">287</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">288</td>
+                    <td class="coverage">242<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (!is.null(x$covariates)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">289</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    cat("\nCovariates used for endpoints below:\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">290</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    print(x$covariates)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">291</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">292</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">293</td>
+                    <td class="coverage">242<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  printSFORB &lt;- !is.null(x$SFORB)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">294</td>
+                    <td class="coverage">242<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if(printSFORB){</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">295</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    cat("\nEstimated Eigenvalues of SFORB model(s):\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">296</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    print(x$SFORB, digits = digits,...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">297</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">298</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">299</td>
+                    <td class="coverage">242<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  printff &lt;- !is.null(x$ff)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">300</td>
+                    <td class="coverage">242<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if(printff){</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">301</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    cat("\nResulting formation fractions:\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">302</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    print(data.frame(ff = x$ff), digits = digits,...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">303</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">304</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">305</td>
+                    <td class="coverage">242<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  printdistimes &lt;- !is.null(x$distimes)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">306</td>
+                    <td class="coverage">242<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if(printdistimes){</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">307</td>
+                    <td class="coverage">242<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    cat("\nEstimated disappearance times:\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">308</td>
+                    <td class="coverage">242<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    print(x$distimes, digits = digits,...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">309</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">310</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">311</td>
+                    <td class="coverage">242<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (x$print_data){</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">312</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    cat("\nData:\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">313</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    print(format(x$data, digits = digits, ...), row.names = FALSE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">314</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">315</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">316</td>
+                    <td class="coverage">242<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  invisible(x)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">317</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                </tbody>
+              </table>
+            </div>
+            <div id="R/set_nd_nq.R" class="hidden">
+              <table class="table-condensed">
+                <tbody>
+                  <tr class="never">
+                    <td class="num">1</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Set non-detects and unquantified values in residue series without replicates</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">2</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">3</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' This function automates replacing unquantified values in residue time and</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">4</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' depth series. For time series, the function performs part of the residue</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">5</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' processing proposed in the FOCUS kinetics guidance for parent compounds</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">6</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' and metabolites. For two-dimensional residue series over time and depth,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">7</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' it automates the proposal of Boesten et al (2015).</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">8</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">9</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param res_raw Character vector of a residue time series, or matrix of</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">10</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' residue values with rows representing depth profiles for a specific sampling</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">11</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' time, and columns representing time series of residues at the same depth.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">12</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Values below the limit of detection (lod) have to be coded as "nd", values</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">13</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' between the limit of detection and the limit of quantification, if any, have</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">14</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' to be coded as "nq". Samples not analysed have to be coded as "na". All</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">15</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' values that are not "na", "nd" or "nq" have to be coercible to numeric</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">16</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param lod Limit of detection (numeric)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">17</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param loq Limit of quantification(numeric). Must be specified if the FOCUS rule to</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">18</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' stop after the first non-detection is to be applied</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">19</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param time_zero_presence Do we assume that residues occur at time zero?</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">20</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' This only affects samples from the first sampling time that have been</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">21</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' reported as "nd" (not detected).</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">22</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @references Boesten, J. J. T. I., van der Linden, A. M. A., Beltman, W. H.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">23</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' J. and Pol, J. W. (2015). Leaching of plant protection products and their</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">24</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' transformation products; Proposals for improving the assessment of leaching</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">25</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' to groundwater in the Netherlands — Version 2. Alterra report 2630, Alterra</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">26</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Wageningen UR (University &amp; Research centre)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">27</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @references FOCUS (2014) Generic Guidance for Estimating Persistence and Degradation</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">28</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   Kinetics from Environmental Fate Studies on Pesticides in EU Registration, Version 1.1,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">29</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   18 December 2014, p. 251</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">30</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @return A numeric vector, if a vector was supplied, or a numeric matrix otherwise</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">31</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">32</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @examples</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">33</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # FOCUS (2014) p. 75/76 and 131/132</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">34</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' parent_1 &lt;- c(.12, .09, .05, .03, "nd", "nd", "nd", "nd", "nd", "nd")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">35</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' set_nd_nq(parent_1, 0.02)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">36</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' parent_2 &lt;- c(.12, .09, .05, .03, "nd", "nd", .03, "nd", "nd", "nd")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">37</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' set_nd_nq(parent_2, 0.02)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">38</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' set_nd_nq_focus(parent_2, 0.02, loq = 0.05)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">39</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' parent_3 &lt;- c(.12, .09, .05, .03, "nd", "nd", .06, "nd", "nd", "nd")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">40</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' set_nd_nq(parent_3, 0.02)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">41</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' set_nd_nq_focus(parent_3, 0.02, loq = 0.05)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">42</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' metabolite &lt;- c("nd", "nd", "nd", 0.03, 0.06, 0.10, 0.11, 0.10, 0.09, 0.05, 0.03, "nd", "nd")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">43</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' set_nd_nq(metabolite, 0.02)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">44</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' set_nd_nq_focus(metabolite, 0.02, 0.05)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">45</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' #</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">46</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # Boesten et al. (2015), p. 57/58</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">47</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' table_8 &lt;- matrix(</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">48</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   c(10, 10, rep("nd", 4),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">49</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     10, 10, rep("nq", 2), rep("nd", 2),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">50</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     10, 10, 10, "nq", "nd", "nd",</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">51</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     "nq", 10, "nq", rep("nd", 3),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">52</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     "nd", "nq", "nq", rep("nd", 3),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">53</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     rep("nd", 6), rep("nd", 6)),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">54</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   ncol = 6, byrow = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">55</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' set_nd_nq(table_8, 0.5, 1.5, time_zero_presence = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">56</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' table_10 &lt;- matrix(</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">57</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   c(10, 10, rep("nd", 4),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">58</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     10, 10, rep("nd", 4),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">59</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     10, 10, 10, rep("nd", 3),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">60</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     "nd", 10, rep("nd", 4),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">61</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     rep("nd", 18)),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">62</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   ncol = 6, byrow = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">63</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' set_nd_nq(table_10, 0.5, time_zero_presence = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">64</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">set_nd_nq &lt;- function(res_raw, lod, loq = NA, time_zero_presence = FALSE) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">65</td>
+                    <td class="coverage">10<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (!is.character(res_raw)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">66</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    stop("Please supply a vector or a matrix of character values")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">67</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">68</td>
+                    <td class="coverage">10<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (is.vector(res_raw)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">69</td>
+                    <td class="coverage">8<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    was_vector &lt;- TRUE</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">70</td>
+                    <td class="coverage">8<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    res_raw &lt;- as.matrix(res_raw)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">71</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">72</td>
+                    <td class="coverage">2<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    was_vector &lt;- FALSE</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">73</td>
+                    <td class="coverage">2<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (!is.matrix(res_raw)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">74</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      stop("Please supply a vector or a matrix of character values")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">75</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">76</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">77</td>
+                    <td class="coverage">10<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  nq &lt;- 0.5 * (loq + lod)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">78</td>
+                    <td class="coverage">10<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  nda &lt;- 0.5 * lod # not detected but adjacent to detection</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">79</td>
+                    <td class="coverage">10<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  res_raw[res_raw == "nq"] &lt;- nq</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">80</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">81</td>
+                    <td class="coverage">10<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (!time_zero_presence) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">82</td>
+                    <td class="coverage">8<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    for (j in 1:ncol(res_raw)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">83</td>
+                    <td class="coverage">3<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      if (res_raw[1, j] == "nd") res_raw[1, j] &lt;- "na"</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">84</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">85</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">86</td>
+                    <td class="coverage">10<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  res_raw[res_raw == "na"] &lt;- NA</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">87</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">88</td>
+                    <td class="coverage">10<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  not_nd_na &lt;- function(value) !(grepl("nd", value) | is.na(value))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">89</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">90</td>
+                    <td class="coverage">10<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  for (i in 1:nrow(res_raw)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">91</td>
+                    <td class="coverage">94<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    for (j in 1:ncol(res_raw)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">92</td>
+                    <td class="coverage">164<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      if (!is.na(res_raw[i, j]) &amp;&amp; res_raw[i, j] == "nd") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">93</td>
+                    <td class="coverage">98<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        if (i &gt; 1) { # check earlier sample in same layer</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">94</td>
+                    <td class="coverage">17<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          if (not_nd_na(res_raw[i - 1, j])) res_raw[i, j] &lt;- "nda"</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">95</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">96</td>
+                    <td class="coverage">98<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        if (i &lt; nrow(res_raw)) { # check later sample</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">97</td>
+                    <td class="coverage">7<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          if (not_nd_na(res_raw[i + 1, j])) res_raw[i, j] &lt;- "nda"</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">98</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">99</td>
+                    <td class="coverage">98<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        if (j &gt; 1) { # check above sample at the same time</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">100</td>
+                    <td class="coverage">9<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          if (not_nd_na(res_raw[i, j - 1])) res_raw[i, j] &lt;- "nda"</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">101</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">102</td>
+                    <td class="coverage">98<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        if (j &lt; ncol(res_raw)) { # check sample below at the same time</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">103</td>
+                    <td class="coverage">2<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          if (not_nd_na(res_raw[i, j + 1])) res_raw[i, j] &lt;- "nda"</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">104</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">105</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">106</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">107</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">108</td>
+                    <td class="coverage">10<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  res_raw[res_raw == "nda"] &lt;- nda</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">109</td>
+                    <td class="coverage">10<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  res_raw[res_raw == "nd"] &lt;- NA</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">110</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">111</td>
+                    <td class="coverage">10<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  result &lt;- as.numeric(res_raw)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">112</td>
+                    <td class="coverage">10<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  dim(result) &lt;- dim(res_raw)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">113</td>
+                    <td class="coverage">10<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  dimnames(result) &lt;- dimnames(res_raw)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">114</td>
+                    <td class="coverage">8<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (was_vector) result &lt;- as.vector(result)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">115</td>
+                    <td class="coverage">10<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  return(result)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">116</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">117</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">118</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @describeIn set_nd_nq Set non-detects in residue time series according to FOCUS rules</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">119</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param set_first_sample_nd Should the first sample be set to "first_sample_nd_value"</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">120</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' in case it is a non-detection?</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">121</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param first_sample_nd_value Value to be used for the first sample if it is a non-detection</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">122</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param ignore_below_loq_after_first_nd Should we ignore values below the LOQ after the first</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">123</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' non-detection that occurs after the quantified values?</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">124</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">125</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">set_nd_nq_focus &lt;- function(res_raw, lod, loq = NA,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">126</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  set_first_sample_nd = TRUE, first_sample_nd_value = 0,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">127</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  ignore_below_loq_after_first_nd = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">128</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">{</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">129</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">130</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (!is.vector(res_raw)) stop("FOCUS rules are only specified for one-dimensional time series")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">131</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">132</td>
+                    <td class="coverage">5<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (ignore_below_loq_after_first_nd &amp; is.na(loq)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">133</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    stop("You need to specify an LOQ")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">134</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">135</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">136</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  n &lt;- length(res_raw)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">137</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (ignore_below_loq_after_first_nd) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">138</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    for (i in 3:n) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">139</td>
+                    <td class="coverage">35<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      if (!res_raw[i - 2] %in% c("na", "nd")) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">140</td>
+                    <td class="coverage">21<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        if (res_raw[i - 1] == "nd") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">141</td>
+                    <td class="coverage">5<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          res_remaining &lt;- res_raw[i:n]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">142</td>
+                    <td class="coverage">5<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          res_remaining_unquantified &lt;- ifelse(res_remaining == "na", TRUE,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">143</td>
+                    <td class="coverage">5<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            ifelse(res_remaining == "nd", TRUE,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">144</td>
+                    <td class="coverage">5<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">              ifelse(res_remaining == "nq", TRUE,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">145</td>
+                    <td class="coverage">5<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                ifelse(suppressWarnings(as.numeric(res_remaining)) &lt; loq, TRUE, FALSE))))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">146</td>
+                    <td class="coverage">5<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          res_remaining_numeric &lt;- suppressWarnings(as.numeric(res_remaining))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">147</td>
+                    <td class="coverage">5<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          res_remaining_below_loq &lt;- ifelse(res_remaining == "nq", TRUE,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">148</td>
+                    <td class="coverage">5<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                ifelse(!is.na(res_remaining_numeric) &amp; res_remaining_numeric &lt; loq, TRUE, FALSE))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">149</td>
+                    <td class="coverage">5<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          if (all(res_remaining_unquantified)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">150</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            res_raw[i:n] &lt;- ifelse(res_remaining_below_loq, "nd", res_remaining)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">151</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">152</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">153</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">154</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">155</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">156</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">157</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  result &lt;- set_nd_nq(res_raw, lod = lod, loq = loq)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">158</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">159</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (set_first_sample_nd) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">160</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (res_raw[1] == "nd") result[1] &lt;- first_sample_nd_value</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">161</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">162</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">163</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  return(result)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">164</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                </tbody>
+              </table>
+            </div>
+            <div id="R/aw.R" class="hidden">
+              <table class="table-condensed">
+                <tbody>
+                  <tr class="never">
+                    <td class="num">1</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Calculate Akaike weights for model averaging</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">2</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">3</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Akaike weights are calculated based on the relative</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">4</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' expected Kullback-Leibler information as specified</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">5</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' by Burnham and Anderson (2004).</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">6</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">7</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param object An [mmkin] column object, containing two or more</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">8</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   [mkinfit] models that have been fitted to the same data,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">9</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   or an mkinfit object. In the latter case, further mkinfit</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">10</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   objects fitted to the same data should be specified</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">11</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   as dots arguments.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">12</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param \dots Not used in the method for [mmkin] column objects,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">13</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   further [mkinfit] objects in the method for mkinfit objects.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">14</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @references Burnham KP and Anderson DR (2004) Multimodel</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">15</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   Inference: Understanding AIC and BIC in Model Selection.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">16</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   *Sociological Methods &amp; Research* **33**(2) 261-304</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">17</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @md</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">18</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @examples</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">19</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' \dontrun{</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">20</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' f_sfo &lt;- mkinfit("SFO", FOCUS_2006_D, quiet = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">21</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' f_dfop &lt;- mkinfit("DFOP", FOCUS_2006_D, quiet = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">22</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' aw_sfo_dfop &lt;- aw(f_sfo, f_dfop)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">23</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' sum(aw_sfo_dfop)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">24</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' aw_sfo_dfop # SFO gets more weight as it has less parameters and a similar fit</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">25</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' f &lt;- mmkin(c("SFO", "FOMC", "DFOP"), list("FOCUS D" = FOCUS_2006_D), cores = 1, quiet = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">26</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' aw(f)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">27</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' sum(aw(f))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">28</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' aw(f[c("SFO", "DFOP")])</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">29</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">30</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">31</td>
+                    <td class="coverage">1482<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">aw &lt;- function(object, ...) UseMethod("aw")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">32</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">33</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">.aw &lt;- function(all_objects) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">34</td>
+                    <td class="coverage">494<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  AIC_all &lt;- sapply(all_objects, AIC)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">35</td>
+                    <td class="coverage">494<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  delta_i &lt;- AIC_all - min(AIC_all)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">36</td>
+                    <td class="coverage">494<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  denom &lt;- sum(exp(-delta_i/2))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">37</td>
+                    <td class="coverage">494<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  w_i &lt;- exp(-delta_i/2) / denom</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">38</td>
+                    <td class="coverage">494<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  return(w_i)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">39</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">40</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">41</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">42</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @rdname aw</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">43</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">aw.mkinfit &lt;- function(object, ...) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">44</td>
+                    <td class="coverage">988<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  oo &lt;- list(...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">45</td>
+                    <td class="coverage">988<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  data_object &lt;- object$data[c("time", "variable", "observed")]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">46</td>
+                    <td class="coverage">988<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  for (i in seq_along(oo)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">47</td>
+                    <td class="coverage">247<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (!inherits(oo[[i]], "mkinfit")) stop("Please supply only mkinfit objects")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">48</td>
+                    <td class="coverage">988<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    data_other_object &lt;- oo[[i]]$data[c("time", "variable", "observed")]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">49</td>
+                    <td class="coverage">988<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (!identical(data_object, data_other_object)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">50</td>
+                    <td class="coverage">247<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      stop("It seems that the mkinfit objects have not all been fitted to the same data")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">51</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">52</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">53</td>
+                    <td class="coverage">494<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  all_objects &lt;- list(object, ...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">54</td>
+                    <td class="coverage">494<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  .aw(all_objects)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">55</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">56</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">57</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">58</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @rdname aw</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">59</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">aw.mmkin &lt;- function(object, ...) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">60</td>
+                    <td class="coverage">247<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (ncol(object) &gt; 1) stop("Please supply an mmkin column object")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">61</td>
+                    <td class="coverage">247<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  do.call(aw, object)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">62</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">63</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">64</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">65</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @rdname aw</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">66</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">aw.mixed.mmkin &lt;- function(object, ...) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">67</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  oo &lt;- list(...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">68</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  data_object &lt;- object$data[c("ds", "name", "time", "value")]</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">69</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  for (i in seq_along(oo)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">70</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (!inherits(oo[[i]], "mixed.mmkin")) stop("Please supply objects inheriting from mixed.mmkin")</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">71</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    data_other_object &lt;- oo[[i]]$data[c("ds", "name", "time", "value")]</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">72</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (!identical(data_object, data_other_object)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">73</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      stop("It seems that the mixed.mmkin objects have not all been fitted to the same data")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">74</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">75</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">76</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  all_objects &lt;- list(object, ...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">77</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  .aw(all_objects)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">78</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">79</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">80</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">81</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @rdname aw</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">82</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">aw.multistart &lt;- function(object, ...) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">83</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  do.call(aw, object)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">84</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                </tbody>
+              </table>
+            </div>
+            <div id="R/transform_odeparms.R" class="hidden">
+              <table class="table-condensed">
+                <tbody>
+                  <tr class="never">
+                    <td class="num">1</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Functions to transform and backtransform kinetic parameters for fitting</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">2</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">3</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' The transformations are intended to map parameters that should only take on</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">4</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' restricted values to the full scale of real numbers. For kinetic rate</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">5</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' constants and other parameters that can only take on positive values, a</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">6</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' simple log transformation is used. For compositional parameters, such as the</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">7</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' formations fractions that should always sum up to 1 and can not be negative,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">8</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' the [ilr] transformation is used.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">9</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">10</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' The transformation of sets of formation fractions is fragile, as it supposes</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">11</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' the same ordering of the components in forward and backward transformation.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">12</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' This is no problem for the internal use in [mkinfit].</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">13</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">14</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param parms Parameters of kinetic models as used in the differential</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">15</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   equations.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">16</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param transparms Transformed parameters of kinetic models as used in the</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">17</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   fitting procedure.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">18</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param mkinmod The kinetic model of class [mkinmod], containing</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">19</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   the names of the model variables that are needed for grouping the</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">20</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   formation fractions before [ilr] transformation, the parameter</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">21</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   names and the information if the pathway to sink is included in the model.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">22</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param transform_rates Boolean specifying if kinetic rate constants should</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">23</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   be transformed in the model specification used in the fitting for better</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">24</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   compliance with the assumption of normal distribution of the estimator. If</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">25</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   TRUE, also alpha and beta parameters of the FOMC model are</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">26</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   log-transformed, as well as k1 and k2 rate constants for the DFOP and HS</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">27</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   models and the break point tb of the HS model.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">28</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param transform_fractions Boolean specifying if formation fractions</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">29</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   constants should be transformed in the model specification used in the</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">30</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   fitting for better compliance with the assumption of normal distribution</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">31</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   of the estimator. The default (TRUE) is to do transformations.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">32</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   The g parameter of the DFOP model is also seen as a fraction.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">33</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   If a single fraction is transformed (g parameter of DFOP or only a single</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">34</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   target variable e.g. a single metabolite plus a pathway to sink), a</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">35</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   logistic transformation is used [stats::qlogis()]. In other cases, i.e. if</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">36</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   two or more formation fractions need to be transformed whose sum cannot</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">37</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   exceed one, the [ilr] transformation is used.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">38</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @return A vector of transformed or backtransformed parameters</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">39</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @importFrom stats plogis qlogis</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">40</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @author Johannes Ranke</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">41</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @examples</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">42</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">43</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' SFO_SFO &lt;- mkinmod(</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">44</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   parent = list(type = "SFO", to = "m1", sink = TRUE),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">45</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   m1 = list(type = "SFO"), use_of_ff = "min")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">46</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">47</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # Fit the model to the FOCUS example dataset D using defaults</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">48</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' FOCUS_D &lt;- subset(FOCUS_2006_D, value != 0) # remove zero values to avoid warning</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">49</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' fit &lt;- mkinfit(SFO_SFO, FOCUS_D, quiet = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">50</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' fit.s &lt;- summary(fit)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">51</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # Transformed and backtransformed parameters</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">52</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' print(fit.s$par, 3)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">53</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' print(fit.s$bpar, 3)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">54</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">55</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' \dontrun{</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">56</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # Compare to the version without transforming rate parameters (does not work</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">57</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # with analytical solution, we get NA values for m1 in predictions)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">58</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' fit.2 &lt;- mkinfit(SFO_SFO, FOCUS_D, transform_rates = FALSE,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">59</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   solution_type = "deSolve", quiet = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">60</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' fit.2.s &lt;- summary(fit.2)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">61</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' print(fit.2.s$par, 3)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">62</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' print(fit.2.s$bpar, 3)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">63</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">64</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">65</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' initials &lt;- fit$start$value</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">66</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' names(initials) &lt;- rownames(fit$start)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">67</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' transformed &lt;- fit$start_transformed$value</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">68</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' names(transformed) &lt;- rownames(fit$start_transformed)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">69</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' transform_odeparms(initials, SFO_SFO)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">70</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' backtransform_odeparms(transformed, SFO_SFO)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">71</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">72</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' \dontrun{</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">73</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # The case of formation fractions (this is now the default)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">74</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' SFO_SFO.ff &lt;- mkinmod(</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">75</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   parent = list(type = "SFO", to = "m1", sink = TRUE),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">76</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   m1 = list(type = "SFO"),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">77</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   use_of_ff = "max")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">78</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">79</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' fit.ff &lt;- mkinfit(SFO_SFO.ff, FOCUS_D, quiet = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">80</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' fit.ff.s &lt;- summary(fit.ff)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">81</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' print(fit.ff.s$par, 3)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">82</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' print(fit.ff.s$bpar, 3)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">83</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' initials &lt;- c("f_parent_to_m1" = 0.5)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">84</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' transformed &lt;- transform_odeparms(initials, SFO_SFO.ff)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">85</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' backtransform_odeparms(transformed, SFO_SFO.ff)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">86</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">87</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # And without sink</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">88</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' SFO_SFO.ff.2 &lt;- mkinmod(</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">89</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   parent = list(type = "SFO", to = "m1", sink = FALSE),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">90</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   m1 = list(type = "SFO"),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">91</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   use_of_ff = "max")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">92</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">93</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">94</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' fit.ff.2 &lt;- mkinfit(SFO_SFO.ff.2, FOCUS_D, quiet = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">95</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' fit.ff.2.s &lt;- summary(fit.ff.2)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">96</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' print(fit.ff.2.s$par, 3)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">97</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' print(fit.ff.2.s$bpar, 3)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">98</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">99</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">100</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export transform_odeparms</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">101</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">transform_odeparms &lt;- function(parms, mkinmod,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">102</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  transform_rates = TRUE, transform_fractions = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">103</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">{</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">104</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # We need the model specification for the names of the model</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">105</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # variables and the information on the sink</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">106</td>
+                    <td class="coverage">25587<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  spec = mkinmod$spec</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">107</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">108</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Set up container for transformed parameters</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">109</td>
+                    <td class="coverage">25587<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  transparms &lt;- numeric(0)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">110</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">111</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Do not transform initial values for state variables</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">112</td>
+                    <td class="coverage">25587<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  state.ini.optim &lt;- parms[grep("_0$", names(parms))]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">113</td>
+                    <td class="coverage">25587<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  transparms[names(state.ini.optim)] &lt;- state.ini.optim</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">114</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">115</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Log transformation for rate constants if requested</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">116</td>
+                    <td class="coverage">25587<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  k &lt;- parms[grep("^k_", names(parms))]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">117</td>
+                    <td class="coverage">25587<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  k__iore &lt;- parms[grep("^k__iore_", names(parms))]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">118</td>
+                    <td class="coverage">25587<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  k &lt;- c(k, k__iore)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">119</td>
+                    <td class="coverage">25587<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (length(k) &gt; 0) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">120</td>
+                    <td class="coverage">15485<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if(transform_rates) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">121</td>
+                    <td class="coverage">14379<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      transparms[paste0("log_", names(k))] &lt;- log(k)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">122</td>
+                    <td class="coverage">1106<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    } else transparms[names(k)] &lt;- k</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">123</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">124</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">125</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Do not transform exponents in IORE models</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">126</td>
+                    <td class="coverage">25587<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  N &lt;- parms[grep("^N", names(parms))]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">127</td>
+                    <td class="coverage">25587<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  transparms[names(N)] &lt;- N</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">128</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">129</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Go through state variables and transform formation fractions if requested</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">130</td>
+                    <td class="coverage">25587<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  mod_vars = names(spec)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">131</td>
+                    <td class="coverage">25587<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  for (box in mod_vars) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">132</td>
+                    <td class="coverage">41283<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    f &lt;- parms[grep(paste("^f", box, sep = "_"), names(parms))]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">133</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">134</td>
+                    <td class="coverage">41283<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (length(f) &gt; 0) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">135</td>
+                    <td class="coverage">6522<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      if(transform_fractions) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">136</td>
+                    <td class="coverage">5910<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        if (spec[[box]]$sink) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">137</td>
+                    <td class="coverage">5908<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          if (length(f) == 1) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">138</td>
+                    <td class="coverage">5894<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            trans_f_name &lt;- paste("f", box, "qlogis", sep = "_")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">139</td>
+                    <td class="coverage">5894<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            transparms[trans_f_name] &lt;- qlogis(f)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">140</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">141</td>
+                    <td class="coverage">14<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            trans_f &lt;- ilr(c(f, 1 - sum(f)))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">142</td>
+                    <td class="coverage">14<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            trans_f_names &lt;- paste("f", box, "ilr", 1:length(trans_f), sep = "_")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">143</td>
+                    <td class="coverage">14<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            transparms[trans_f_names] &lt;- trans_f</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">144</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">145</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">146</td>
+                    <td class="coverage">2<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          if (length(f) &gt; 1) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">147</td>
+                    <td class="coverage">2<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            trans_f &lt;- ilr(f)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">148</td>
+                    <td class="coverage">2<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            trans_f_names &lt;- paste("f", box, "ilr", 1:length(trans_f), sep = "_")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">149</td>
+                    <td class="coverage">2<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            transparms[trans_f_names] &lt;- trans_f</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">150</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">151</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">152</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">153</td>
+                    <td class="coverage">612<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        transparms[names(f)] &lt;- f</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">154</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">155</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">156</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">157</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">158</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Transform also FOMC parameters alpha and beta, DFOP and HS rates k1 and k2</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">159</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # and HS parameter tb as well as logistic model parameters kmax, k0 and r if</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">160</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # transformation of rates is requested</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">161</td>
+                    <td class="coverage">25587<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  for (pname in c("alpha", "beta", "k1", "k2", "tb", "kmax", "k0", "r")) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">162</td>
+                    <td class="coverage">204696<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (!is.na(parms[pname])) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">163</td>
+                    <td class="coverage">6006<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      if (transform_rates) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">164</td>
+                    <td class="coverage">6006<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        transparms[paste0("log_", pname)] &lt;- log(parms[pname])</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">165</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">166</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        transparms[pname] &lt;- parms[pname]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">167</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">168</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">169</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">170</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">171</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # DFOP parameter g is treated as a fraction</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">172</td>
+                    <td class="coverage">25587<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (!is.na(parms["g"])) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">173</td>
+                    <td class="coverage">1978<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    g &lt;- parms["g"]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">174</td>
+                    <td class="coverage">1978<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (transform_fractions) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">175</td>
+                    <td class="coverage">1978<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      transparms["g_qlogis"] &lt;- qlogis(g)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">176</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">177</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      transparms["g"] &lt;- g</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">178</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">179</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">180</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">181</td>
+                    <td class="coverage">25587<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  return(transparms)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">182</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">183</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">184</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @rdname transform_odeparms</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">185</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export backtransform_odeparms</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">186</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">backtransform_odeparms &lt;- function(transparms, mkinmod,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">187</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                                   transform_rates = TRUE,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">188</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                                   transform_fractions = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">189</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">{</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">190</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # We need the model specification for the names of the model</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">191</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # variables and the information on the sink</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">192</td>
+                    <td class="coverage">49214214<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  spec = mkinmod$spec</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">193</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">194</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Set up container for backtransformed parameters</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">195</td>
+                    <td class="coverage">49214214<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  parms &lt;- numeric(0)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">196</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">197</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Do not transform initial values for state variables</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">198</td>
+                    <td class="coverage">49214214<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  state.ini.optim &lt;- transparms[grep("_0$", names(transparms))]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">199</td>
+                    <td class="coverage">49214214<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  parms[names(state.ini.optim)] &lt;- state.ini.optim</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">200</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">201</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Exponential transformation for rate constants</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">202</td>
+                    <td class="coverage">49214214<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if(transform_rates) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">203</td>
+                    <td class="coverage">49140623<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    trans_k &lt;- transparms[grep("^log_k_", names(transparms))]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">204</td>
+                    <td class="coverage">49140623<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    trans_k__iore &lt;- transparms[grep("^log_k__iore_", names(transparms))]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">205</td>
+                    <td class="coverage">49140623<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    trans_k = c(trans_k, trans_k__iore)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">206</td>
+                    <td class="coverage">49140623<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (length(trans_k) &gt; 0) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">207</td>
+                    <td class="coverage">47598103<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      k_names &lt;- gsub("^log_k", "k", names(trans_k))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">208</td>
+                    <td class="coverage">47598103<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      parms[k_names] &lt;- exp(trans_k)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">209</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">210</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">211</td>
+                    <td class="coverage">73591<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    trans_k &lt;- transparms[grep("^k_", names(transparms))]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">212</td>
+                    <td class="coverage">73591<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    parms[names(trans_k)] &lt;- trans_k</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">213</td>
+                    <td class="coverage">73591<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    trans_k__iore &lt;- transparms[grep("^k__iore_", names(transparms))]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">214</td>
+                    <td class="coverage">73591<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    parms[names(trans_k__iore)] &lt;- trans_k__iore</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">215</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">216</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">217</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Do not transform exponents in IORE models</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">218</td>
+                    <td class="coverage">49214214<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  N &lt;- transparms[grep("^N", names(transparms))]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">219</td>
+                    <td class="coverage">49214214<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  parms[names(N)] &lt;- N</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">220</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">221</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Go through state variables and apply inverse transformations to formation fractions</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">222</td>
+                    <td class="coverage">49214214<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  mod_vars = names(spec)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">223</td>
+                    <td class="coverage">49214214<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  for (box in mod_vars) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">224</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # Get the names as used in the model</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">225</td>
+                    <td class="coverage">97593385<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    f_names = grep(paste("^f", box, sep = "_"), mkinmod$parms, value = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">226</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">227</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # Get the formation fraction parameters</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">228</td>
+                    <td class="coverage">97593385<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    trans_f = transparms[grep(paste("^f", box, sep = "_"), names(transparms))]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">229</td>
+                    <td class="coverage">97593385<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (length(trans_f) &gt; 0) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">230</td>
+                    <td class="coverage">46632823<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      if(transform_fractions) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">231</td>
+                    <td class="coverage">46588453<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        if (any(grepl("qlogis", names(trans_f)))) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">232</td>
+                    <td class="coverage">46059152<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          f_tmp  &lt;- plogis(trans_f)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">233</td>
+                    <td class="coverage">46059152<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          parms[f_names] &lt;- f_tmp</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">234</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">235</td>
+                    <td class="coverage">529301<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          f_tmp &lt;- invilr(trans_f)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">236</td>
+                    <td class="coverage">529301<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          if (spec[[box]]$sink) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">237</td>
+                    <td class="coverage">528393<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            parms[f_names] &lt;- f_tmp[1:length(f_tmp)-1]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">238</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">239</td>
+                    <td class="coverage">908<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            parms[f_names] &lt;- f_tmp</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">240</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">241</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">242</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">243</td>
+                    <td class="coverage">44370<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        parms[names(trans_f)] &lt;- trans_f</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">244</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">245</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">246</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">247</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">248</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Transform parameters also for FOMC, DFOP, HS and logistic models</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">249</td>
+                    <td class="coverage">49214214<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  for (pname in c("alpha", "beta", "k1", "k2", "tb", "kmax", "k0", "r")) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">250</td>
+                    <td class="coverage">393713712<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (transform_rates) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">251</td>
+                    <td class="coverage">393124984<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      pname_trans = paste0("log_", pname)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">252</td>
+                    <td class="coverage">393124984<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      if (!is.na(transparms[pname_trans])) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">253</td>
+                    <td class="coverage">4306142<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        parms[pname] &lt;- exp(transparms[pname_trans])</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">254</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">255</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">256</td>
+                    <td class="coverage">588728<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      if (!is.na(transparms[pname])) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">257</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        parms[pname] &lt;- transparms[pname]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">258</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">259</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">260</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">261</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">262</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # DFOP parameter g is now transformed using qlogis</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">263</td>
+                    <td class="coverage">49214214<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (!is.na(transparms["g_qlogis"])) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">264</td>
+                    <td class="coverage">2034008<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    g_qlogis &lt;- transparms["g_qlogis"]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">265</td>
+                    <td class="coverage">2034008<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    parms["g"] &lt;- plogis(g_qlogis)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">266</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">267</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # In earlier times we used ilr for g, so we keep this around</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">268</td>
+                    <td class="coverage">49214214<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (!is.na(transparms["g_ilr"])) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">269</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    g_ilr &lt;- transparms["g_ilr"]</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">270</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    parms["g"] &lt;- invilr(g_ilr)[1]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">271</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">272</td>
+                    <td class="coverage">49214214<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (!is.na(transparms["g"])) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">273</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    parms["g"] &lt;- transparms["g"]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">274</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">275</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">276</td>
+                    <td class="coverage">49214214<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  return(parms)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">277</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">278</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"># vim: set ts=2 sw=2 expandtab:</pre>
+                    </td>
+                  </tr>
+                </tbody>
+              </table>
+            </div>
+            <div id="R/mean_degparms.R" class="hidden">
+              <table class="table-condensed">
+                <tbody>
+                  <tr class="never">
+                    <td class="num">1</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Calculate mean degradation parameters for an mmkin row object</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">2</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">3</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @return If random is FALSE (default), a named vector containing mean values</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">4</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   of the fitted degradation model parameters. If random is TRUE, a list with</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">5</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   fixed and random effects, in the format required by the start argument of</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">6</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   nlme for the case of a single grouping variable ds.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">7</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param object An mmkin row object containing several fits of the same model to different datasets</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">8</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param random Should a list with fixed and random effects be returned?</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">9</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param test_log_parms If TRUE, log parameters are only considered in</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">10</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   the mean calculations if their untransformed counterparts (most likely</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">11</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   rate constants) pass the t-test for significant difference from zero.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">12</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param conf.level Possibility to adjust the required confidence level</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">13</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   for parameter that are tested if requested by 'test_log_parms'.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">14</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param default_log_parms If set to a numeric value, this is used</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">15</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   as a default value for the tested log parameters that failed the</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">16</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   t-test.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">17</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">18</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">mean_degparms &lt;- function(object, random = FALSE, test_log_parms = FALSE, conf.level = 0.6,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">19</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  default_log_parms = NA)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">20</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">{</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">21</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (nrow(object) &gt; 1) stop("Only row objects allowed")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">22</td>
+                    <td class="coverage">7271<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  parm_mat_trans &lt;- sapply(object, parms, transformed = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">23</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">24</td>
+                    <td class="coverage">7271<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (test_log_parms) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">25</td>
+                    <td class="coverage">4668<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      parm_mat_dim &lt;- dim(parm_mat_trans)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">26</td>
+                    <td class="coverage">4668<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      parm_mat_dimnames &lt;- dimnames(parm_mat_trans)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">27</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">28</td>
+                    <td class="coverage">4668<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      log_parm_trans_names &lt;- grep("^log_", rownames(parm_mat_trans), value = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">29</td>
+                    <td class="coverage">4668<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      log_parm_names &lt;- gsub("^log_", "", log_parm_trans_names)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">30</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">31</td>
+                    <td class="coverage">4668<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      t_test_back_OK &lt;- matrix(</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">32</td>
+                    <td class="coverage">4668<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        sapply(object, function(o) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">33</td>
+                    <td class="coverage">49860<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          suppressWarnings(summary(o)$bpar[log_parm_names, "Pr(&gt;t)"] &lt; (1 - conf.level))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">34</td>
+                    <td class="coverage">4668<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        }), nrow = length(log_parm_names))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">35</td>
+                    <td class="coverage">4668<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      rownames(t_test_back_OK) &lt;- log_parm_trans_names</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">36</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">37</td>
+                    <td class="coverage">4668<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      parm_mat_trans_OK &lt;- parm_mat_trans</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">38</td>
+                    <td class="coverage">4668<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      for (trans_parm in log_parm_trans_names) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">39</td>
+                    <td class="coverage">9398<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        parm_mat_trans_OK[trans_parm, ] &lt;- ifelse(t_test_back_OK[trans_parm, ],</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">40</td>
+                    <td class="coverage">9398<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          parm_mat_trans[trans_parm, ], log(default_log_parms))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">41</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">42</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">43</td>
+                    <td class="coverage">2603<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    parm_mat_trans_OK &lt;- parm_mat_trans</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">44</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">45</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">46</td>
+                    <td class="coverage">7271<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  mean_degparm_names &lt;- setdiff(rownames(parm_mat_trans), names(object[[1]]$errparms))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">47</td>
+                    <td class="coverage">7271<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  degparm_mat_trans &lt;- parm_mat_trans[mean_degparm_names, , drop = FALSE]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">48</td>
+                    <td class="coverage">7271<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  degparm_mat_trans_OK &lt;- parm_mat_trans_OK[mean_degparm_names, , drop = FALSE]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">49</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">50</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # fixed in the sense of fixed effects, as this function was</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">51</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # written to supply starting parameters for nlme</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">52</td>
+                    <td class="coverage">7271<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  fixed &lt;- apply(degparm_mat_trans_OK, 1, mean, na.rm = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">53</td>
+                    <td class="coverage">7271<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (random) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">54</td>
+                    <td class="coverage">2322<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    random &lt;- t(apply(degparm_mat_trans[mean_degparm_names, , drop = FALSE], 2, function(column) column - fixed))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">55</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # If we only have one parameter, apply returns a vector so we get a single row</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">56</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (nrow(degparm_mat_trans) == 1) random &lt;- t(random)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">57</td>
+                    <td class="coverage">2322<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    rownames(random) &lt;- levels(nlme_data(object)$ds)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">58</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">59</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # For nlmixr we can specify starting values for standard deviations eta, and</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">60</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # we ignore uncertain parameters if test_log_parms is FALSE</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">61</td>
+                    <td class="coverage">2322<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    eta &lt;- apply(degparm_mat_trans_OK, 1, stats::sd, na.rm = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">62</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">63</td>
+                    <td class="coverage">2322<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    return(list(fixed = fixed, random = list(ds = random), eta = eta))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">64</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">65</td>
+                    <td class="coverage">4949<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    return(fixed)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">66</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">67</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">68</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                </tbody>
+              </table>
+            </div>
+            <div id="R/summary_listing.R" class="hidden">
+              <table class="table-condensed">
+                <tbody>
+                  <tr class="never">
+                    <td class="num">1</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Display the output of a summary function according to the output format</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">2</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">3</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' This function is intended for use in a R markdown code chunk with the chunk</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">4</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' option `results = "asis"`.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">5</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">6</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param object The object for which the summary is to be listed</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">7</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param caption An optional caption</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">8</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param label An optional label, ignored in html output</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">9</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param clearpage Should a new page be started after the listing? Ignored in html output</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">10</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">11</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">summary_listing &lt;- function(object, caption = NULL, label = NULL,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">12</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  clearpage = TRUE) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">13</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (knitr::is_latex_output()) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">14</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    tex_listing(object = object, caption = caption, label = label,</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">15</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      clearpage = clearpage)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">16</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">17</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (knitr::is_html_output()) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">18</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    html_listing(object = object, caption = caption)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">19</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">20</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">21</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">22</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @rdname summary_listing</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">23</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">24</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">tex_listing &lt;- function(object, caption = NULL, label = NULL,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">25</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  clearpage = TRUE) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">26</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">27</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("\\begin{listing}", "\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">28</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (!is.null(caption)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">29</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    cat("\\caption{", caption, "}", "\n", sep = "")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">30</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">31</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (!is.null(label)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">32</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    cat("\\caption{", label, "}", "\n", sep = "")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">33</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">34</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("\\begin{snugshade}", "\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">35</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("\\scriptsize", "\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">36</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("\\begin{verbatim}", "\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">37</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat(capture.output(suppressWarnings(summary(object))), sep = "\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">38</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">39</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("\\end{verbatim}", "\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">40</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("\\end{snugshade}", "\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">41</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("\\end{listing}", "\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">42</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (clearpage) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">43</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    cat("\\clearpage", "\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">44</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">45</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">46</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">47</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @rdname summary_listing</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">48</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">49</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">html_listing &lt;- function(object, caption = NULL) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">50</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">51</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (!is.null(caption)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">52</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    cat("&lt;caption&gt;", caption, "&lt;/caption&gt;", "\n", sep = "")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">53</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">54</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("&lt;pre&gt;&lt;code&gt;\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">55</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat(capture.output(suppressWarnings(summary(object))), sep = "\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">56</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">57</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("&lt;/pre&gt;&lt;/code&gt;\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">58</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">59</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                </tbody>
+              </table>
+            </div>
+            <div id="R/ilr.R" class="hidden">
+              <table class="table-condensed">
+                <tbody>
+                  <tr class="never">
+                    <td class="num">1</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"># This file is part of the R package mkin</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">2</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">3</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"># mkin is free software: you can redistribute it and/or modify it under the</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">4</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"># terms of the GNU General Public License as published by the Free Software</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">5</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"># Foundation, either version 3 of the License, or (at your option) any later</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">6</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"># version.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">7</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">8</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"># This program is distributed in the hope that it will be useful, but WITHOUT</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">9</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"># ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">10</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"># FOR A PARTICULAR PURPOSE.  See the GNU General Public License for more</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">11</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"># details.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">12</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">13</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"># You should have received a copy of the GNU General Public License along with</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">14</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"># this program. If not, see &lt;http://www.gnu.org/licenses/&gt;</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">15</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">16</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Function to perform isometric log-ratio transformation</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">17</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' </pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">18</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' This implementation is a special case of the class of isometric log-ratio</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">19</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' transformations.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">20</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' </pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">21</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @aliases ilr invilr</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">22</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param x A numeric vector. Naturally, the forward transformation is only</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">23</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   sensible for vectors with all elements being greater than zero.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">24</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @return The result of the forward or backward transformation. The returned</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">25</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   components always sum to 1 for the case of the inverse log-ratio</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">26</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   transformation.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">27</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @author René Lehmann and Johannes Ranke</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">28</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @seealso Another implementation can be found in R package</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">29</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   \code{robCompositions}.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">30</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @references Peter Filzmoser, Karel Hron (2008) Outlier Detection for</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">31</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   Compositional Data Using Robust Methods. Math Geosci 40 233-248</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">32</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @keywords manip</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">33</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @examples</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">34</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' </pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">35</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # Order matters</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">36</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' ilr(c(0.1, 1, 10))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">37</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' ilr(c(10, 1, 0.1))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">38</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # Equal entries give ilr transformations with zeros as elements</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">39</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' ilr(c(3, 3, 3))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">40</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # Almost equal entries give small numbers</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">41</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' ilr(c(0.3, 0.4, 0.3))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">42</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # Only the ratio between the numbers counts, not their sum</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">43</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' invilr(ilr(c(0.7, 0.29, 0.01)))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">44</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' invilr(ilr(2.1 * c(0.7, 0.29, 0.01)))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">45</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # Inverse transformation of larger numbers gives unequal elements</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">46</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' invilr(-10)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">47</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' invilr(c(-10, 0))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">48</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # The sum of the elements of the inverse ilr is 1</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">49</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' sum(invilr(c(-10, 0)))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">50</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # This is why we do not need all elements of the inverse transformation to go back:</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">51</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' a &lt;- c(0.1, 0.3, 0.5)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">52</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' b &lt;- invilr(a)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">53</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' length(b) # Four elements</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">54</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' ilr(c(b[1:3], 1 - sum(b[1:3]))) # Gives c(0.1, 0.3, 0.5)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">55</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' </pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">56</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">57</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">ilr &lt;- function(x) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">58</td>
+                    <td class="coverage">16<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  z &lt;- vector()</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">59</td>
+                    <td class="coverage">16<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  for (i in 1:(length(x) - 1)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">60</td>
+                    <td class="coverage">44<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    z[i] &lt;- sqrt(i/(i+1)) * log((prod(x[1:i]))^(1/i) / x[i+1])</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">61</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">62</td>
+                    <td class="coverage">16<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  return(z)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">63</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">64</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">65</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @rdname ilr</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">66</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">67</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">invilr&lt;-function(x) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">68</td>
+                    <td class="coverage">529301<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  D &lt;- length(x) + 1</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">69</td>
+                    <td class="coverage">529301<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  z &lt;- c(x, 0)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">70</td>
+                    <td class="coverage">529301<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  y &lt;- rep(0, D)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">71</td>
+                    <td class="coverage">529301<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  s &lt;- sqrt(1:D*2:(D+1))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">72</td>
+                    <td class="coverage">529301<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  q &lt;- z/s</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">73</td>
+                    <td class="coverage">529301<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  y[1] &lt;- sum(q[1:D])</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">74</td>
+                    <td class="coverage">529301<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  for (i in 2:D) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">75</td>
+                    <td class="coverage">1585969<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    y[i] &lt;- sum(q[i:D]) - sqrt((i-1)/i) * z[i-1]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">76</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">77</td>
+                    <td class="coverage">529301<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  z &lt;- vector()</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">78</td>
+                    <td class="coverage">529301<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  for (i in 1:D) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">79</td>
+                    <td class="coverage">2115270<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    z[i] &lt;- exp(y[i])/sum(exp(y))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">80</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">81</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">82</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Work around a numerical problem with NaN values returned by the above</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">83</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Only works if there is only one NaN value: replace it with 1</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">84</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # if the sum of the other components is &lt; 1e-10</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">85</td>
+                    <td class="coverage">529301<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (sum(is.na(z)) == 1 &amp;&amp; sum(z[!is.na(z)]) &lt; 1e-10)</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">86</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    z = ifelse(is.na(z), 1, z)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">87</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">88</td>
+                    <td class="coverage">529301<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  return(z)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">89</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                </tbody>
+              </table>
+            </div>
+            <div id="R/read_spreadsheet.R" class="hidden">
+              <table class="table-condensed">
+                <tbody>
+                  <tr class="never">
+                    <td class="num">1</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Read datasets and relevant meta information from a spreadsheet file</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">2</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">3</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' This function imports one dataset from each sheet of a spreadsheet file.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">4</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' These sheets are selected based on the contents of a sheet 'Datasets', with</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">5</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' a column called 'Dataset Number', containing numbers identifying the dataset</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">6</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' sheets to be read in. In the second column there must be a grouping</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">7</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' variable, which will often be named 'Soil'. Optionally, time normalization</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">8</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' factors can be given in columns named 'Temperature' and 'Moisture'.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">9</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">10</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' There must be a sheet 'Compounds', with columns 'Name' and 'Acronym'.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">11</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' The first row read after the header read in from this sheet is assumed</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">12</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' to contain name and acronym of the parent compound.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">13</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">14</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' The dataset sheets should be named using the dataset numbers read in from</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">15</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' the 'Datasets' sheet, i.e. '1', '2', ... . In each dataset sheet, the name</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">16</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' of the observed variable (e.g. the acronym of the parent compound or</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">17</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' one of its transformation products) should be in the first column,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">18</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' the time values should be in the second colum, and the observed value</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">19</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' in the third column.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">20</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">21</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' In case relevant covariate data are available, they should be given</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">22</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' in a sheet 'Covariates', containing one line for each value of the grouping</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">23</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' variable specified in 'Datasets'. These values should be in the first</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">24</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' column and the column must have the same name as the second column in</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">25</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' 'Datasets'. Covariates will be read in from columns four and higher.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">26</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Their names should preferably not contain special characters like spaces,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">27</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' so they can be easily used for specifying covariate models.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">28</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">29</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' A similar data structure is defined as the R6 class [mkindsg], but</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">30</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' is probably more complicated to use.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">31</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">32</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param path Absolute or relative path to the spreadsheet file</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">33</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param valid_datasets Optional numeric index of the valid datasets, default is</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">34</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' to use all datasets</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">35</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param parent_only Should only the parent data be used?</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">36</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param normalize Should the time scale be normalized using temperature</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">37</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' and moisture normalisation factors in the sheet 'Datasets'?</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">38</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">39</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">read_spreadsheet &lt;- function(path, valid_datasets = "all",</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">40</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  parent_only = FALSE, normalize = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">41</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">{</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">42</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (!requireNamespace("readxl", quietly = TRUE))</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">43</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    stop("Please install the readxl package to use this function")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">44</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">45</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Read the compound table</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">46</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  compounds &lt;- readxl::read_excel(path, sheet = "Compounds")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">47</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  parent &lt;- compounds[1, ]$Acronym</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">48</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">49</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Read in meta information</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">50</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  ds_meta &lt;- readxl::read_excel(path, sheet = "Datasets")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">51</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  ds_meta["Dataset Number"] &lt;- as.character(ds_meta[["Dataset Number"]])</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">52</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">53</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Select valid datasets</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">54</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (valid_datasets[1] == "all") valid_datasets &lt;- 1:nrow(ds_meta)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">55</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  ds_numbers_valid &lt;- ds_meta[valid_datasets, ]$`Dataset Number`</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">56</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  grouping_factor &lt;- names(ds_meta[2]) # Often "Soil"</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">57</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">58</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Read in valid datasets</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">59</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  ds_raw &lt;- lapply(ds_numbers_valid,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">60</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    function(dsn) readxl::read_excel(path, sheet = as.character(dsn)))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">61</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">62</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Make data frames compatible with mmkin</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">63</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  ds_tmp &lt;- lapply(ds_raw, function(x) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">64</td>
+                    <td class="coverage">1287<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    ds_ret &lt;- x[1:3] |&gt;</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">65</td>
+                    <td class="coverage">1287<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      rlang::set_names(c("name", "time", "value")) |&gt;</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">66</td>
+                    <td class="coverage">1287<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      transform(value = as.numeric(value))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">67</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  })</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">68</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  names(ds_tmp) &lt;- ds_numbers_valid</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">69</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">70</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Normalize with temperature and moisture correction factors</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">71</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (normalize) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">72</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    ds_norm &lt;- lapply(ds_numbers_valid, function(ds_number) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">73</td>
+                    <td class="coverage">1287<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      f_corr &lt;- as.numeric(ds_meta[ds_number, c("Temperature", "Moisture")])</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">74</td>
+                    <td class="coverage">1287<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      ds_corr &lt;- ds_tmp[[ds_number]] |&gt;</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">75</td>
+                    <td class="coverage">1287<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        transform(time = time * f_corr[1] * f_corr[2])</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">76</td>
+                    <td class="coverage">1287<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      return(ds_corr)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">77</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    })</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">78</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">79</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    ds_norm &lt;- ds_tmp</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">80</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">81</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  names(ds_norm) &lt;- ds_numbers_valid</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">82</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">83</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Select parent data only if requested</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">84</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (parent_only) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">85</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    ds_norm &lt;- lapply(ds_norm, function(x) subset(x, name == parent))</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">86</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    compounds &lt;- compounds[1, ]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">87</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">88</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">89</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Create a single long table to combine datasets with the same group name</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">90</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  ds_all &lt;- vctrs::vec_rbind(!!!ds_norm, .names_to = "Dataset Number")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">91</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  ds_all_group &lt;- merge(ds_all, ds_meta[c("Dataset Number", grouping_factor)])</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">92</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  groups &lt;- unique(ds_meta[valid_datasets, ][[grouping_factor]])</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">93</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">94</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  ds &lt;- lapply(groups, function(x) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">95</td>
+                    <td class="coverage">819<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      ret &lt;- ds_all_group[ds_all_group[[grouping_factor]] == x, ]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">96</td>
+                    <td class="coverage">819<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      ret[c("name", "time", "value")]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">97</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">98</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  )</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">99</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  names(ds) &lt;- groups</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">100</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">101</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Get covariates</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">102</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  covariates_raw &lt;- readxl::read_excel(path, sheet = "Covariates")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">103</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  covariates &lt;- as.data.frame(covariates_raw[4:ncol(covariates_raw)])</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">104</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  nocov &lt;- setdiff(groups, covariates_raw[[1]])</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">105</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (length(nocov) &gt; 0) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">106</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    message("Did not find covariate data for ", paste(nocov, collapse = ", "))</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">107</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    message("Not returning covariate data")</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">108</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    attr(ds, "covariates") &lt;- NULL</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">109</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">110</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    rownames(covariates) &lt;- covariates_raw[[1]]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">111</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    covariates &lt;- covariates[which(colnames(covariates) != "Remarks")]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">112</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # Attach covariate data if available</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">113</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    attr(ds, "covariates") &lt;- covariates[groups, , drop = FALSE]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">114</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">115</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">116</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Attach the compound list to support automatic model building</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">117</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  attr(ds, "compounds") &lt;- as.data.frame(compounds)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">118</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">119</td>
+                    <td class="coverage">117<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  return(ds)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">120</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                </tbody>
+              </table>
+            </div>
+            <div id="R/max_twa_parent.R" class="hidden">
+              <table class="table-condensed">
+                <tbody>
+                  <tr class="never">
+                    <td class="num">1</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Function to calculate maximum time weighted average concentrations from</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">2</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' kinetic models fitted with mkinfit</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">3</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' </pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">4</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' This function calculates maximum moving window time weighted average</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">5</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' concentrations (TWAs) for kinetic models fitted with \code{\link{mkinfit}}.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">6</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Currently, only calculations for the parent are implemented for the SFO,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">7</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' FOMC, DFOP and HS models, using the analytical formulas given in the PEC</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">8</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' soil section of the FOCUS guidance.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">9</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' </pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">10</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @aliases max_twa_parent max_twa_sfo max_twa_fomc max_twa_dfop max_twa_hs</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">11</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param fit An object of class \code{\link{mkinfit}}.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">12</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param windows The width of the time windows for which the TWAs should be</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">13</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   calculated.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">14</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param M0 The initial concentration for which the maximum time weighted</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">15</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   average over the decline curve should be calculated. The default is to use</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">16</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   a value of 1, which means that a relative maximum time weighted average</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">17</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   factor (f_twa) is calculated.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">18</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param k The rate constant in the case of SFO kinetics.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">19</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param t The width of the time window.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">20</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param alpha Parameter of the FOMC model.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">21</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param beta Parameter of the FOMC model.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">22</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param k1 The first rate constant of the DFOP or the HS kinetics.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">23</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param k2 The second rate constant of the DFOP or the HS kinetics.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">24</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param g Parameter of the DFOP model.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">25</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param tb Parameter of the HS model.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">26</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @return For \code{max_twa_parent}, a numeric vector, named using the</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">27</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   \code{windows} argument.  For the other functions, a numeric vector of</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">28</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   length one (also known as 'a number').</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">29</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @author Johannes Ranke</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">30</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @references FOCUS (2006) \dQuote{Guidance Document on Estimating Persistence</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">31</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   and Degradation Kinetics from Environmental Fate Studies on Pesticides in</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">32</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   EU Registration} Report of the FOCUS Work Group on Degradation Kinetics,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">33</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   EC Document Reference Sanco/10058/2005 version 2.0, 434 pp,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">34</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   \url{http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">35</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @examples</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">36</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' </pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">37</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   fit &lt;- mkinfit("FOMC", FOCUS_2006_C, quiet = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">38</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   max_twa_parent(fit, c(7, 21))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">39</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' </pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">40</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">41</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">max_twa_parent &lt;- function(fit, windows) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">42</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  parms.all &lt;- c(fit$bparms.optim, fit$bparms.fixed)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">43</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  obs_vars &lt;- fit$obs_vars</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">44</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (length(obs_vars) &gt; 1) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">45</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    warning("Calculation of maximum time weighted average concentrations is",</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">46</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            "currently only implemented for the parent compound using",</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">47</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">            "analytical solutions")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">48</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">49</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  obs_var &lt;- obs_vars[1]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">50</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  spec = fit$mkinmod$spec</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">51</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  type = spec[[1]]$type</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">52</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">53</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  M0 &lt;- parms.all[paste0(obs_var, "_0")]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">54</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">55</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (type == "SFO") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">56</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    k_name &lt;- paste0("k_", obs_var)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">57</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (fit$mkinmod$use_of_ff == "min") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">58</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      k_name &lt;- paste0(k_name, "_sink")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">59</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">60</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    k &lt;- parms.all[k_name]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">61</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    twafunc &lt;- function(t) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">62</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      max_twa_sfo(M0, k, t)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">63</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">64</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">65</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (type == "FOMC") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">66</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    alpha &lt;- parms.all["alpha"]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">67</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    beta &lt;- parms.all["beta"]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">68</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    twafunc &lt;- function(t) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">69</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      max_twa_fomc(M0, alpha, beta, t)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">70</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">71</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">72</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (type == "DFOP") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">73</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    k1 &lt;- parms.all["k1"]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">74</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    k2 &lt;- parms.all["k2"]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">75</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    g &lt;- parms.all["g"]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">76</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    twafunc &lt;- function(t) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">77</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      max_twa_dfop(M0, k1, k2, g, t)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">78</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">79</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">80</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (type == "HS") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">81</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    k1 &lt;- parms.all["k1"]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">82</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    k2 &lt;- parms.all["k2"]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">83</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    tb &lt;- parms.all["tb"]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">84</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    twafunc &lt;- function(t) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">85</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      ifelse(t &lt;= tb,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">86</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        max_twa_sfo(M0, k1, t),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">87</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        max_twa_hs(M0, k1, k2, tb, t)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">88</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      )</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">89</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">90</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">91</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (type %in% c("IORE", "SFORB")) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">92</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    stop("Calculation of maximum time weighted average concentrations is currently ",</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">93</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">         "not implemented for the ", type, " model.")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">94</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">95</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  res &lt;- twafunc(windows)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">96</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  names(res) &lt;- windows</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">97</td>
+                    <td class="coverage">4<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  return(res)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">98</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">99</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">100</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @rdname max_twa_parent</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">101</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">102</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">max_twa_sfo &lt;- function(M0 = 1, k, t) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">103</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  M0 * (1 - exp(- k * t)) / (k * t)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">104</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">105</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">106</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @rdname max_twa_parent</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">107</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">108</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">max_twa_fomc &lt;- function(M0 = 1, alpha, beta, t) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">109</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  M0 * (beta)/(t * (1 - alpha)) * ((t/beta + 1)^(1 - alpha) - 1)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">110</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">111</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">112</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @rdname max_twa_parent</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">113</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">114</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">max_twa_dfop &lt;- function(M0 = 1, k1, k2, g, t) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">115</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  M0/t * ((g/k1) * (1 - exp(- k1 * t)) + ((1 - g)/k2) * (1 - exp(- k2 * t)))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">116</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">117</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">118</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @rdname max_twa_parent</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">119</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">120</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">max_twa_hs &lt;- function(M0 = 1, k1, k2, tb, t) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">121</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  (M0 / t) * (</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">122</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    (1/k1) * (1 - exp(- k1 * tb)) +</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">123</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    (exp(- k1 * tb) / k2) * (1 - exp(- k2 * (t - tb)))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">124</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  )</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">125</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                </tbody>
+              </table>
+            </div>
+            <div id="R/f_time_norm_focus.R" class="hidden">
+              <table class="table-condensed">
+                <tbody>
+                  <tr class="never">
+                    <td class="num">1</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">utils::globalVariables("D24_2014")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">2</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">3</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Normalisation factors for aerobic soil degradation according to FOCUS guidance</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">4</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">5</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Time step normalisation factors for aerobic soil degradation as described</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">6</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' in Appendix 8 to the FOCUS kinetics guidance (FOCUS 2014, p. 369).</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">7</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">8</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param object An object containing information used for the calculations</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">9</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param temperature Numeric vector of temperatures in °C</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">10</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param moisture Numeric vector of moisture contents in \\% w/w</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">11</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param field_moisture Numeric vector of moisture contents at field capacity</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">12</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   (pF2) in \\% w/w</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">13</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param study_moisture_ref_source Source for the reference value</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">14</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   used to calculate the study moisture. If 'auto', preference is given</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">15</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   to a reference moisture given in the meta information, otherwise</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">16</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   the focus soil moisture for the soil class is used</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">17</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param Q10 The Q10 value used for temperature normalisation</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">18</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param walker The Walker exponent used for moisture normalisation</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">19</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param f_na The factor to use for NA values. If set to NA, only factors</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">20</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'  for complete cases will be returned.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">21</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param \dots Currently not used</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">22</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @references</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">23</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' FOCUS (2006) \dQuote{Guidance Document on Estimating Persistence</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">24</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   and Degradation Kinetics from Environmental Fate Studies on Pesticides in</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">25</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   EU Registration} Report of the FOCUS Work Group on Degradation Kinetics,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">26</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   EC Document Reference Sanco/10058/2005 version 2.0, 434 pp,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">27</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   \url{http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">28</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' FOCUS (2014) \dQuote{Generic guidance for Estimating Persistence</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">29</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   and Degradation Kinetics from Environmental Fate Studies on Pesticides in</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">30</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   EU Registration} Report of the FOCUS Work Group on Degradation Kinetics,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">31</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   Version 1.1, 18 December 2014</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">32</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   \url{http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">33</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @seealso [focus_soil_moisture]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">34</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @examples</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">35</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' f_time_norm_focus(25, 20, 25) # 1.37, compare FOCUS 2014 p. 184</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">36</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">37</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' D24_2014$meta</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">38</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # No moisture normalisation in the first dataset, so we use f_na = 1 to get</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">39</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # temperature only normalisation as in the EU evaluation</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">40</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' f_time_norm_focus(D24_2014, study_moisture_ref_source = "focus", f_na = 1)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">41</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">42</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">f_time_norm_focus &lt;- function(object, ...) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">43</td>
+                    <td class="coverage">765<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  UseMethod("f_time_norm_focus")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">44</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">45</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">46</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @rdname f_time_norm_focus</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">47</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">48</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">f_time_norm_focus.numeric &lt;- function(object,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">49</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  moisture = NA, field_moisture = NA,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">50</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  temperature = object,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">51</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  Q10 = 2.58, walker = 0.7, f_na = NA, ...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">52</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">{</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">53</td>
+                    <td class="coverage">459<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  f_temp &lt;- ifelse(is.na(temperature),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">54</td>
+                    <td class="coverage">459<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    f_na,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">55</td>
+                    <td class="coverage">459<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    ifelse(temperature &lt;= 0,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">56</td>
+                    <td class="coverage">459<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      0,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">57</td>
+                    <td class="coverage">459<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      Q10^((temperature - 20)/10)))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">58</td>
+                    <td class="coverage">459<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  f_moist &lt;- ifelse(is.na(moisture),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">59</td>
+                    <td class="coverage">459<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    f_na,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">60</td>
+                    <td class="coverage">459<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    ifelse(moisture &gt;= field_moisture,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">61</td>
+                    <td class="coverage">459<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      1,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">62</td>
+                    <td class="coverage">459<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      (moisture / field_moisture)^walker))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">63</td>
+                    <td class="coverage">459<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  f_time_norm &lt;- f_temp * f_moist</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">64</td>
+                    <td class="coverage">459<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  f_time_norm</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">65</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">66</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">67</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @rdname f_time_norm_focus</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">68</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">69</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">f_time_norm_focus.mkindsg &lt;- function(object,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">70</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  study_moisture_ref_source = c("auto", "meta", "focus"),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">71</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  Q10 = 2.58, walker = 0.7, f_na = NA, ...) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">72</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">73</td>
+                    <td class="coverage">306<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  study_moisture_ref_source &lt;- match.arg(study_moisture_ref_source)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">74</td>
+                    <td class="coverage">306<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  meta &lt;- object$meta</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">75</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">76</td>
+                    <td class="coverage">306<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (is.null(meta$field_moisture)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">77</td>
+                    <td class="coverage">306<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    field_moisture &lt;- focus_soil_moisture[meta$usda_soil_type, "pF2"]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">78</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">79</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    field_moisture &lt;- ifelse(is.na(meta$field_moisture),</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">80</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      focus_soil_moisture[meta$usda_soil_type, "pF2"],</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">81</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      meta$field_moisture)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">82</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">83</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">84</td>
+                    <td class="coverage">306<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  study_moisture_ref_focus &lt;-</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">85</td>
+                    <td class="coverage">306<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    focus_soil_moisture[as.matrix(meta[c("usda_soil_type", "study_moisture_ref_type")])]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">86</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">87</td>
+                    <td class="coverage">306<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (study_moisture_ref_source == "auto") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">88</td>
+                    <td class="coverage">153<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    study_moisture_ref &lt;- ifelse (is.na(meta$study_ref_moisture),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">89</td>
+                    <td class="coverage">153<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      study_moisture_ref_focus,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">90</td>
+                    <td class="coverage">153<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      meta$study_ref_moisture)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">91</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">92</td>
+                    <td class="coverage">153<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (study_moisture_ref_source == "meta") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">93</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      study_moisture_ref &lt;- meta$study_moisture_ref</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">94</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">95</td>
+                    <td class="coverage">153<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      study_moisture_ref &lt;- study_moisture_ref_focus</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">96</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">97</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">98</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">99</td>
+                    <td class="coverage">306<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if ("study_moisture" %in% names(meta)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">100</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    study_moisture &lt;- ifelse(is.na(meta$study_moisture),</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">101</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      meta$rel_moisture * study_moisture_ref,</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">102</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      meta$study_moisture)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">103</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">104</td>
+                    <td class="coverage">306<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    study_moisture &lt;- meta$rel_moisture * study_moisture_ref</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">105</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">106</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">107</td>
+                    <td class="coverage">306<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  object$f_time_norm &lt;- f_time_norm_focus(meta$temperature,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">108</td>
+                    <td class="coverage">306<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    moisture = study_moisture, field_moisture = field_moisture,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">109</td>
+                    <td class="coverage">306<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    Q10 = Q10, walker = walker, f_na = f_na)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">110</td>
+                    <td class="coverage">306<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  message("$f_time_norm was (re)set to normalised values")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">111</td>
+                    <td class="coverage">306<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  invisible(object$f_time_norm)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">112</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                </tbody>
+              </table>
+            </div>
+            <div id="R/mkinds.R" class="hidden">
+              <table class="table-condensed">
+                <tbody>
+                  <tr class="never">
+                    <td class="num">1</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' A dataset class for mkin</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">2</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">3</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @description</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">4</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' At the moment this dataset class is hardly used in mkin. For example,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">5</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' mkinfit does not take mkinds datasets as argument, but works with dataframes</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">6</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' such as the on contained in the data field of mkinds objects. Some datasets</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">7</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' provided by this package come as mkinds objects nevertheless.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">8</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">9</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @importFrom R6 R6Class</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">10</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @examples</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">11</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">12</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' mds &lt;- mkinds$new("FOCUS A", FOCUS_2006_A)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">13</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' print(mds)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">14</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">15</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">16</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">mkinds &lt;- R6Class("mkinds",</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">17</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  public = list(</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">18</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">19</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    #' @field title A full title for the dataset</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">20</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    title = NULL,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">21</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">22</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    #' @field sampling_times The sampling times</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">23</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    sampling_times = NULL,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">24</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">25</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    #' @field time_unit The time unit</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">26</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    time_unit = NULL,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">27</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">28</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    #' @field observed Names of the observed variables</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">29</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    observed = NULL,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">30</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">31</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    #' @field unit The unit of the observations</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">32</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    unit = NULL,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">33</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">34</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    #' @field replicates The maximum number of replicates per sampling time</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">35</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    replicates = NULL,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">36</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">37</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    #' @field data A data frame with at least the columns name, time</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">38</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    #' and value in order to be compatible with mkinfit</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">39</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    data = NULL,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">40</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">41</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    #' @description</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">42</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    #' Create a new mkinds object</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">43</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    #' @param title The dataset title</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">44</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    #' @param data The data</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">45</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    #' @param time_unit The time unit</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">46</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    #' @param unit The unit of the observations</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">47</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    initialize = function(title = "", data, time_unit = NA, unit = NA) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">48</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">49</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      self$title &lt;- title</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">50</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      self$sampling_times &lt;- sort(unique(data$time))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">51</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      self$time_unit &lt;- time_unit</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">52</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      self$observed &lt;- unique(data$name)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">53</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      self$unit &lt;- unit</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">54</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      self$replicates &lt;- max(by(data, list(data$name, data$time), nrow))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">55</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      if (is.null(data$override)) data$override &lt;- NA</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">56</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      if (is.null(data$err)) data$err &lt;- 1</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">57</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      self$data &lt;- data</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">58</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">59</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">60</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  )</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">61</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">62</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">63</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Print mkinds objects</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">64</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">65</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @rdname mkinds</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">66</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param x An [mkinds] object.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">67</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param data Should the data be printed?</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">68</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param \dots Not used.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">69</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">70</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">print.mkinds &lt;- function(x, data = FALSE, ...) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">71</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("&lt;mkinds&gt; with $title: ",  x$title, "\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">72</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("Observed compounds $observed: ", paste(x$observed, collapse = ", "), "\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">73</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("Sampling times $sampling_times:\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">74</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat(paste(x$sampling_times, collapse = ", "), "\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">75</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("With a maximum of ", x$replicates, " replicates\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">76</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (!is.na(x$time_unit)) cat("Time unit: ", x$time_unit, "\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">77</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (!is.na(x$unit)) cat("Observation unit: ", x$unit, "\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">78</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (data) print(mkin_long_to_wide(x$data))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">79</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">80</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">81</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' A class for dataset groups for mkin</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">82</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">83</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @description</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">84</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' A container for working with datasets that share at least one compound,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">85</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' so that combined evaluations are desirable.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">86</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">87</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Time normalisation factors are initialised with a value of 1 for each</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">88</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' dataset if no data are supplied.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">89</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">90</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @examples</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">91</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">92</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' mdsg &lt;- mkindsg$new("Experimental X", experimental_data_for_UBA_2019[6:10])</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">93</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' print(mdsg)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">94</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' print(mdsg, verbose = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">95</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' print(mdsg, verbose = TRUE, data = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">96</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">97</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">98</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">mkindsg &lt;- R6Class("mkindsg",</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">99</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  public = list(</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">100</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">101</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    #' @field title A title for the dataset group</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">102</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    title = NULL,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">103</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">104</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    #' @field ds A list of mkinds objects</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">105</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    ds = NULL,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">106</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">107</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    #' @field observed_n Occurrence counts of compounds in datasets</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">108</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    observed_n = NULL,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">109</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">110</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    #' @field f_time_norm Time normalisation factors</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">111</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    f_time_norm = NULL,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">112</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">113</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    #' @field meta A data frame with a row for each dataset,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">114</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    #'   containing additional information in the form</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">115</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    #'   of categorical data (factors) or numerical data</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">116</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    #'   (e.g. temperature, moisture,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">117</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    #'   or covariates like soil pH).</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">118</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    meta = NULL,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">119</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">120</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    #' @description</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">121</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    #' Create a new mkindsg object</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">122</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    #' @param title The title</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">123</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    #' @param ds A list of mkinds objects</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">124</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    #' @param f_time_norm Time normalisation factors</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">125</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    #' @param meta The meta data</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">126</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    initialize = function(title = "", ds,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">127</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      f_time_norm = rep(1, length(ds)), meta)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">128</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">129</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      self$title &lt;- title</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">130</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      if (all(sapply(ds, inherits, "mkinds"))) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">131</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        self$ds &lt;- ds</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">132</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">133</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        stop("Please supply a list of mkinds objects")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">134</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">135</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">136</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      all_observed &lt;- unlist(lapply(ds, function(x) x$observed))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">137</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      observed &lt;- factor(all_observed, levels = unique(all_observed))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">138</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      self$observed_n &lt;- table(observed)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">139</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      names(dimnames(self$observed_n)) &lt;- NULL</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">140</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      self$f_time_norm &lt;- f_time_norm</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">141</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">142</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      if (!missing(meta)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">143</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        rownames(meta) &lt;- lapply(ds, function(x) x$title)</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">144</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        self$meta &lt;- meta</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">145</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">146</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">147</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  )</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">148</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">149</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">150</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Print mkindsg objects</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">151</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">152</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @rdname mkindsg</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">153</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param x An [mkindsg] object.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">154</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param verbose Should the mkinds objects be printed?</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">155</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param data Should the mkinds objects be printed with their data?</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">156</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param \dots Not used.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">157</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">158</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">print.mkindsg &lt;- function(x, data = FALSE, verbose = data, ...) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">159</td>
+                    <td class="coverage">208<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("&lt;mkindsg&gt; holding", length(x$ds), "mkinds objects\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">160</td>
+                    <td class="coverage">208<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("Title $title: ",  x$title, "\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">161</td>
+                    <td class="coverage">208<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("Occurrence of observed compounds $observed_n:\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">162</td>
+                    <td class="coverage">208<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  print(x$observed_n)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">163</td>
+                    <td class="coverage">208<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (any(x$f_time_norm != 1)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">164</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    cat("Time normalisation factors $f_time_norm:\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">165</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    print(x$f_time_norm)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">166</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">167</td>
+                    <td class="coverage">208<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (!is.null(x$meta)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">168</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    cat("Meta information $meta:\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">169</td>
+                    <td class="coverage">104<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    print(x$meta)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">170</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">171</td>
+                    <td class="coverage">208<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (verbose) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">172</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    cat("\nDatasets $ds:")</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">173</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    for (ds in x$ds) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">174</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      cat("\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">175</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      print(ds, data = data)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">176</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">177</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">178</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                </tbody>
+              </table>
+            </div>
+            <div id="R/mkinerrmin.R" class="hidden">
+              <table class="table-condensed">
+                <tbody>
+                  <tr class="never">
+                    <td class="num">1</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">utils::globalVariables(c("name", "value_mean"))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">2</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">3</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Calculate the minimum error to assume in order to pass the variance test</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">4</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">5</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' This function finds the smallest relative error still resulting in passing</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">6</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' the chi-squared test as defined in the FOCUS kinetics report from 2006.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">7</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">8</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' This function is used internally by \code{\link{summary.mkinfit}}.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">9</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">10</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param fit an object of class \code{\link{mkinfit}}.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">11</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param alpha The confidence level chosen for the chi-squared test.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">12</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @importFrom stats qchisq aggregate</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">13</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @return A dataframe with the following components: \item{err.min}{The</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">14</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' relative error, expressed as a fraction.} \item{n.optim}{The number of</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">15</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' optimised parameters attributed to the data series.} \item{df}{The number of</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">16</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' remaining degrees of freedom for the chi2 error level calculations.  Note</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">17</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' that mean values are used for the chi2 statistic and therefore every time</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">18</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' point with observed values in the series only counts one time.} The</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">19</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' dataframe has one row for the total dataset and one further row for each</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">20</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' observed state variable in the model.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">21</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @references FOCUS (2006) \dQuote{Guidance Document on Estimating Persistence</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">22</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' and Degradation Kinetics from Environmental Fate Studies on Pesticides in EU</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">23</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Registration} Report of the FOCUS Work Group on Degradation Kinetics, EC</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">24</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Document Reference Sanco/10058/2005 version 2.0, 434 pp,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">25</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' \url{http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">26</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @keywords manip</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">27</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @examples</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">28</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">29</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' SFO_SFO = mkinmod(parent = mkinsub("SFO", to = "m1"),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">30</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'                   m1 = mkinsub("SFO"),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">31</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'                   use_of_ff = "max")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">32</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">33</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' fit_FOCUS_D = mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">34</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' round(mkinerrmin(fit_FOCUS_D), 4)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">35</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' \dontrun{</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">36</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   fit_FOCUS_E = mkinfit(SFO_SFO, FOCUS_2006_E, quiet = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">37</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   round(mkinerrmin(fit_FOCUS_E), 4)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">38</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">39</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">40</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">41</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">mkinerrmin &lt;- function(fit, alpha = 0.05)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">42</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">{</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">43</td>
+                    <td class="coverage">53943<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  parms.optim &lt;- fit$par</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">44</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">45</td>
+                    <td class="coverage">53943<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  kinerrmin &lt;- function(errdata, n.parms) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">46</td>
+                    <td class="coverage">124726<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    means.mean &lt;- mean(errdata$observed, na.rm = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">47</td>
+                    <td class="coverage">124726<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    df = nrow(errdata) - n.parms</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">48</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">49</td>
+                    <td class="coverage">124726<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    err.min &lt;- sqrt((1 / qchisq(1 - alpha, df)) *</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">50</td>
+                    <td class="coverage">124726<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">               sum((errdata$observed - errdata$predicted)^2)/(means.mean^2))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">51</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">52</td>
+                    <td class="coverage">124726<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    return(list(err.min = err.min, n.optim = n.parms, df = df))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">53</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">54</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">55</td>
+                    <td class="coverage">53943<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  errdata &lt;- aggregate(cbind(observed, predicted) ~ time + variable, data = fit$data, mean, na.rm=TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">56</td>
+                    <td class="coverage">53943<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  errdata &lt;- errdata[order(errdata$time, errdata$variable), ]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">57</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">58</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Remove values at time zero for variables whose value for state.ini is fixed,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">59</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # as these will not have any effect in the optimization and should therefore not</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">60</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # be counted as degrees of freedom.</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">61</td>
+                    <td class="coverage">53943<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  fixed_initials = gsub("_0$", "", rownames(subset(fit$fixed, type == "state")))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">62</td>
+                    <td class="coverage">53943<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  errdata &lt;- subset(errdata, !(time == 0 &amp; variable %in% fixed_initials))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">63</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">64</td>
+                    <td class="coverage">53943<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  n.optim.overall &lt;- length(parms.optim) - length(fit$errparms)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">65</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">66</td>
+                    <td class="coverage">53943<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  errmin.overall &lt;- kinerrmin(errdata, n.optim.overall)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">67</td>
+                    <td class="coverage">53943<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  errmin &lt;- data.frame(err.min = errmin.overall$err.min,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">68</td>
+                    <td class="coverage">53943<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    n.optim = errmin.overall$n.optim, df = errmin.overall$df)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">69</td>
+                    <td class="coverage">53943<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  rownames(errmin) &lt;- "All data"</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">70</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">71</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # The degrees of freedom are counted according to FOCUS kinetics (2011, p. 164)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">72</td>
+                    <td class="coverage">53943<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  for (obs_var in fit$obs_vars)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">73</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">74</td>
+                    <td class="coverage">70783<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    errdata.var &lt;- subset(errdata, variable == obs_var)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">75</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">76</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # Check if initial value is optimised</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">77</td>
+                    <td class="coverage">70783<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    n.initials.optim &lt;- length(grep(paste(obs_var, ".*", "_0", sep=""), names(parms.optim)))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">78</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">79</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # Rate constants and IORE exponents are attributed to the source variable</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">80</td>
+                    <td class="coverage">70783<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    n.k.optim &lt;- length(grep(paste("^k", obs_var, sep="_"), names(parms.optim)))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">81</td>
+                    <td class="coverage">70783<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    n.k.optim &lt;- n.k.optim + length(grep(paste("^log_k", obs_var, sep="_"),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">82</td>
+                    <td class="coverage">70783<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                                         names(parms.optim)))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">83</td>
+                    <td class="coverage">70783<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    n.k__iore.optim &lt;- length(grep(paste("^k__iore", obs_var, sep="_"), names(parms.optim)))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">84</td>
+                    <td class="coverage">70783<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    n.k__iore.optim &lt;- n.k__iore.optim + length(grep(paste("^log_k__iore",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">85</td>
+                    <td class="coverage">70783<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          obs_var, sep = "_"), names(parms.optim)))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">86</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">87</td>
+                    <td class="coverage">70783<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    n.N.optim &lt;- length(grep(paste("^N", obs_var, sep="_"), names(parms.optim)))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">88</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">89</td>
+                    <td class="coverage">70783<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    n.ff.optim &lt;- 0</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">90</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # Formation fractions are attributed to the target variable, so look</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">91</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # for source compartments with formation fractions</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">92</td>
+                    <td class="coverage">70783<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    for (source_var in fit$obs_vars) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">93</td>
+                    <td class="coverage">112543<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      n.ff.source = length(grep(paste("^f", source_var, sep = "_"),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">94</td>
+                    <td class="coverage">112543<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                                 names(parms.optim)))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">95</td>
+                    <td class="coverage">112543<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      n.paths.source = length(fit$mkinmod$spec[[source_var]]$to)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">96</td>
+                    <td class="coverage">112543<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      for (target_var in fit$mkinmod$spec[[source_var]]$to) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">97</td>
+                    <td class="coverage">46296<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        if (obs_var == target_var) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">98</td>
+                    <td class="coverage">17974<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          n.ff.optim &lt;- n.ff.optim + n.ff.source/n.paths.source</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">99</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">100</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">101</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">102</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">103</td>
+                    <td class="coverage">70783<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    n.optim &lt;- sum(n.initials.optim, n.k.optim, n.k__iore.optim, n.N.optim, n.ff.optim)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">104</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">105</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # FOMC, DFOP and HS parameters are only counted if we are looking at the</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">106</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # first variable in the model which is always the source variable</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">107</td>
+                    <td class="coverage">70783<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (obs_var == fit$obs_vars[[1]]) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">108</td>
+                    <td class="coverage">53943<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      special_parms = c("alpha", "log_alpha", "beta", "log_beta",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">109</td>
+                    <td class="coverage">53943<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                        "k1", "log_k1", "k2", "log_k2",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">110</td>
+                    <td class="coverage">53943<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                        "g", "g_ilr", "g_qlogis", "tb", "log_tb")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">111</td>
+                    <td class="coverage">53943<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      n.optim &lt;- n.optim + length(intersect(special_parms, names(parms.optim)))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">112</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">113</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">114</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # Calculate and add a line to the dataframe holding the results</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">115</td>
+                    <td class="coverage">70783<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    errmin.tmp &lt;- kinerrmin(errdata.var, n.optim)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">116</td>
+                    <td class="coverage">70783<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    errmin[obs_var, c("err.min", "n.optim", "df")] &lt;- errmin.tmp</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">117</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">118</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">119</td>
+                    <td class="coverage">53943<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  return(errmin)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">120</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                </tbody>
+              </table>
+            </div>
+            <div id="R/AIC.mmkin.R" class="hidden">
+              <table class="table-condensed">
+                <tbody>
+                  <tr class="never">
+                    <td class="num">1</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Calculate the AIC for a column of an mmkin object</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">2</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">3</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Provides a convenient way to compare different kinetic models fitted to the</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">4</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' same dataset.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">5</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">6</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @importFrom stats AIC BIC</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">7</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param object An object of class \code{\link{mmkin}}, containing only one</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">8</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   column.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">9</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param \dots For compatibility with the generic method</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">10</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param k As in the generic method</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">11</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @return As in the generic method (a numeric value for single fits, or a</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">12</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   dataframe if there are several fits in the column).</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">13</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @author Johannes Ranke</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">14</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @examples</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">15</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">16</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   \dontrun{ # skip, as it takes &gt; 10 s on winbuilder</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">17</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   f &lt;- mmkin(c("SFO", "FOMC", "DFOP"),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">18</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     list("FOCUS A" = FOCUS_2006_A,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">19</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'          "FOCUS C" = FOCUS_2006_C), cores = 1, quiet = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">20</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   # We get a warning because the FOMC model does not converge for the</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">21</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   # FOCUS A dataset, as it is well described by SFO</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">22</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">23</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   AIC(f["SFO", "FOCUS A"]) # We get a single number for a single fit</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">24</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   AIC(f[["SFO", "FOCUS A"]]) # or when extracting an mkinfit object</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">25</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">26</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   # For FOCUS A, the models fit almost equally well, so the higher the number</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">27</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   # of parameters, the higher (worse) the AIC</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">28</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   AIC(f[, "FOCUS A"])</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">29</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   AIC(f[, "FOCUS A"], k = 0) # If we do not penalize additional parameters, we get nearly the same</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">30</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   BIC(f[, "FOCUS A"])        # Comparing the BIC gives a very similar picture</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">31</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">32</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   # For FOCUS C, the more complex models fit better</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">33</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   AIC(f[, "FOCUS C"])</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">34</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   BIC(f[, "FOCUS C"])</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">35</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">36</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">37</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">38</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">AIC.mmkin &lt;- function(object, ..., k = 2)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">39</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">{</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">40</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # We can only handle a single column</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">41</td>
+                    <td class="coverage">247<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (ncol(object) != 1) stop("Please provide a single column object")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">42</td>
+                    <td class="coverage">247<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  n.fits &lt;- length(object)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">43</td>
+                    <td class="coverage">247<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  model_names &lt;- rownames(object)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">44</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">45</td>
+                    <td class="coverage">247<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  code &lt;- paste0("AIC(",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">46</td>
+                    <td class="coverage">247<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    paste0("object[[", 1:n.fits, "]]", collapse = ", "),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">47</td>
+                    <td class="coverage">247<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    ", k = k)")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">48</td>
+                    <td class="coverage">247<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  res &lt;- eval(parse(text = code))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">49</td>
+                    <td class="coverage">247<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (n.fits &gt; 1) rownames(res) &lt;- model_names</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">50</td>
+                    <td class="coverage">247<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  return(res)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">51</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">52</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">53</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @rdname AIC.mmkin</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">54</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">55</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">BIC.mmkin &lt;- function(object, ...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">56</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">{</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">57</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # We can only handle a single column</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">58</td>
+                    <td class="coverage">247<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (ncol(object) != 1) stop("Please provide a single column object")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">59</td>
+                    <td class="coverage">247<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  n.fits &lt;- length(object)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">60</td>
+                    <td class="coverage">247<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  model_names &lt;- rownames(object)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">61</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">62</td>
+                    <td class="coverage">247<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  code &lt;- paste0("BIC(",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">63</td>
+                    <td class="coverage">247<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    paste0("object[[", 1:n.fits, "]]", collapse = ", "),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">64</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    ")")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">65</td>
+                    <td class="coverage">247<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  res &lt;- eval(parse(text = code))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">66</td>
+                    <td class="coverage">247<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (n.fits &gt; 1) rownames(res) &lt;- model_names</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">67</td>
+                    <td class="coverage">247<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  return(res)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">68</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                </tbody>
+              </table>
+            </div>
+            <div id="R/intervals.R" class="hidden">
+              <table class="table-condensed">
+                <tbody>
+                  <tr class="never">
+                    <td class="num">1</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @importFrom nlme intervals</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">2</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">3</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">nlme::intervals</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">4</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">5</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Confidence intervals for parameters in saem.mmkin objects</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">6</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">7</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param object The fitted saem.mmkin object</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">8</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param level The confidence level. Must be the default of 0.95 as this is what</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">9</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   is available in the saemix object</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">10</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param backtransform In case the model was fitted with mkin transformations,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">11</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'  should we backtransform the parameters where a one to one correlation</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">12</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'  between transformed and backtransformed parameters exists?</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">13</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param \dots For compatibility with the generic method</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">14</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @return An object with 'intervals.saem.mmkin' and 'intervals.lme' in the</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">15</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'  class attribute</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">16</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">17</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">intervals.saem.mmkin &lt;- function(object, level = 0.95, backtransform = TRUE, ...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">18</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">{</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">19</td>
+                    <td class="coverage">2481<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (!identical(level, 0.95)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">20</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    stop("Confidence intervals are only available for a level of 95%")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">21</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">22</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">23</td>
+                    <td class="coverage">2481<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  mod_vars &lt;- names(object$mkinmod$diffs)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">24</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">25</td>
+                    <td class="coverage">2481<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  pnames &lt;- names(object$mean_dp_start)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">26</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">27</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Confidence intervals are available in the SaemixObject, so</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">28</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # we just need to extract them and put them into a list modelled</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">29</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # after the result of nlme::intervals.lme</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">30</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">31</td>
+                    <td class="coverage">2481<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  conf.int &lt;- object$so@results@conf.int</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">32</td>
+                    <td class="coverage">2481<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  rownames(conf.int) &lt;- conf.int$name</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">33</td>
+                    <td class="coverage">2481<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  colnames(conf.int)[2] &lt;- "est."</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">34</td>
+                    <td class="coverage">2481<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  confint_trans &lt;- as.matrix(conf.int[pnames, c("lower", "est.", "upper")])</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">35</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">36</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Fixed effects</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">37</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # In case objects were produced by earlier versions of saem</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">38</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (is.null(object$transformations)) object$transformations &lt;- "mkin"</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">39</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">40</td>
+                    <td class="coverage">2481<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (object$transformations == "mkin" &amp; backtransform) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">41</td>
+                    <td class="coverage">2286<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    bp &lt;- backtransform_odeparms(confint_trans[, "est."], object$mkinmod,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">42</td>
+                    <td class="coverage">2286<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      object$transform_rates, object$transform_fractions)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">43</td>
+                    <td class="coverage">2286<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    bpnames &lt;- names(bp)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">44</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">45</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # Transform boundaries of CI for one parameter at a time,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">46</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # with the exception of sets of formation fractions (single fractions are OK).</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">47</td>
+                    <td class="coverage">2286<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    f_names_skip &lt;- character(0)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">48</td>
+                    <td class="coverage">2286<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    for (box in mod_vars) { # Figure out sets of fractions to skip</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">49</td>
+                    <td class="coverage">2396<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      f_names &lt;- grep(paste("^f", box, sep = "_"), pnames, value = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">50</td>
+                    <td class="coverage">2396<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      n_paths &lt;- length(f_names)</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">51</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      if (n_paths &gt; 1) f_names_skip &lt;- c(f_names_skip, f_names)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">52</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">53</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">54</td>
+                    <td class="coverage">2286<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    confint_back &lt;- matrix(NA, nrow = length(bp), ncol = 3,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">55</td>
+                    <td class="coverage">2286<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      dimnames = list(bpnames, colnames(confint_trans)))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">56</td>
+                    <td class="coverage">2286<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    confint_back[, "est."] &lt;- bp</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">57</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">58</td>
+                    <td class="coverage">2286<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    for (pname in pnames) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">59</td>
+                    <td class="coverage">6314<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      if (!pname %in% f_names_skip) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">60</td>
+                    <td class="coverage">6314<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        par.lower &lt;- confint_trans[pname, "lower"]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">61</td>
+                    <td class="coverage">6314<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        par.upper &lt;- confint_trans[pname, "upper"]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">62</td>
+                    <td class="coverage">6314<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        names(par.lower) &lt;- names(par.upper) &lt;- pname</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">63</td>
+                    <td class="coverage">6314<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        bpl &lt;- backtransform_odeparms(par.lower, object$mkinmod,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">64</td>
+                    <td class="coverage">6314<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                                              object$transform_rates,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">65</td>
+                    <td class="coverage">6314<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                                              object$transform_fractions)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">66</td>
+                    <td class="coverage">6314<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        bpu &lt;- backtransform_odeparms(par.upper, object$mkinmod,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">67</td>
+                    <td class="coverage">6314<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                                              object$transform_rates,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">68</td>
+                    <td class="coverage">6314<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                                              object$transform_fractions)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">69</td>
+                    <td class="coverage">6314<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        confint_back[names(bpl), "lower"] &lt;- bpl</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">70</td>
+                    <td class="coverage">6314<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        confint_back[names(bpu), "upper"] &lt;- bpu</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">71</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">72</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">73</td>
+                    <td class="coverage">2286<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    confint_ret &lt;- confint_back</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">74</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">75</td>
+                    <td class="coverage">195<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    confint_ret &lt;- confint_trans</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">76</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">77</td>
+                    <td class="coverage">2481<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  attr(confint_ret, "label") &lt;- "Fixed effects:"</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">78</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">79</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Random effects</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">80</td>
+                    <td class="coverage">2481<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  sdnames &lt;- intersect(rownames(conf.int), paste("SD", pnames, sep = "."))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">81</td>
+                    <td class="coverage">2481<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  corrnames &lt;- grep("^Corr.", rownames(conf.int), value = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">82</td>
+                    <td class="coverage">2481<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  ranef_ret &lt;- as.matrix(conf.int[c(sdnames, corrnames), c("lower", "est.", "upper")])</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">83</td>
+                    <td class="coverage">2481<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  sdnames_ret &lt;- paste0(gsub("SD\\.", "sd(", sdnames), ")")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">84</td>
+                    <td class="coverage">2481<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  corrnames_ret &lt;- gsub("Corr\\.(.*)\\.(.*)", "corr(\\1,\\2)", corrnames)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">85</td>
+                    <td class="coverage">2481<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  rownames(ranef_ret) &lt;- c(sdnames_ret, corrnames_ret)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">86</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">87</td>
+                    <td class="coverage">2481<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  attr(ranef_ret, "label") &lt;- "Random effects:"</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">88</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">89</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">90</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Error model</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">91</td>
+                    <td class="coverage">2481<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  enames &lt;- if (object$err_mod == "const") "a.1" else c("a.1", "b.1")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">92</td>
+                    <td class="coverage">2481<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  err_ret &lt;- as.matrix(conf.int[enames, c("lower", "est.", "upper")])</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">93</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">94</td>
+                    <td class="coverage">2481<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  res &lt;- list(</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">95</td>
+                    <td class="coverage">2481<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    fixed = confint_ret,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">96</td>
+                    <td class="coverage">2481<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    random = ranef_ret,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">97</td>
+                    <td class="coverage">2481<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    errmod = err_ret</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">98</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  )</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">99</td>
+                    <td class="coverage">2481<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  class(res) &lt;- c("intervals.saemix.mmkin", "intervals.lme")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">100</td>
+                    <td class="coverage">2481<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  attr(res, "level") &lt;- level</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">101</td>
+                    <td class="coverage">2481<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  return(res)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">102</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                </tbody>
+              </table>
+            </div>
+            <div id="R/confint.mkinfit.R" class="hidden">
+              <table class="table-condensed">
+                <tbody>
+                  <tr class="never">
+                    <td class="num">1</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Confidence intervals for parameters of mkinfit objects</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">2</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">3</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' The default method 'quadratic' is based on the quadratic approximation of</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">4</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' the curvature of the likelihood function at the maximum likelihood parameter</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">5</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' estimates.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">6</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' The alternative method 'profile' is based on the profile likelihood for each</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">7</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' parameter. The 'profile' method uses two nested optimisations and can take a</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">8</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' very long time, even if parallelized by specifying 'cores' on unixoid</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">9</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' platforms. The speed of the method could likely be improved by using the</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">10</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' method of Venzon and Moolgavkar (1988).</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">11</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">12</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param object An \code{\link{mkinfit}} object</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">13</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param parm A vector of names of the parameters which are to be given</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">14</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   confidence intervals. If missing, all parameters are considered.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">15</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param level The confidence level required</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">16</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param alpha The allowed error probability, overrides 'level' if specified.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">17</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param cutoff Possibility to specify an alternative cutoff for the difference</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">18</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   in the log-likelihoods at the confidence boundary. Specifying an explicit</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">19</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   cutoff value overrides arguments 'level' and 'alpha'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">20</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param method The 'quadratic' method approximates the likelihood function at</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">21</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   the optimised parameters using the second term of the Taylor expansion,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">22</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   using a second derivative (hessian) contained in the object.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">23</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   The 'profile' method searches the parameter space for the</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">24</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   cutoff of the confidence intervals by means of a likelihood ratio test.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">25</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param transformed If the quadratic approximation is used, should it be</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">26</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   applied to the likelihood based on the transformed parameters?</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">27</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param backtransform If we approximate the likelihood in terms of the</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">28</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   transformed parameters, should we backtransform the parameters with</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">29</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   their confidence intervals?</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">30</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param rel_tol If the method is 'profile', what should be the accuracy</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">31</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   of the lower and upper bounds, relative to the estimate obtained from</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">32</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   the quadratic method?</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">33</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param cores The number of cores to be used for multicore processing.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">34</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   On Windows machines, cores &gt; 1 is currently not supported.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">35</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param quiet Should we suppress the message "Profiling the likelihood"</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">36</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @return A matrix with columns giving lower and upper confidence limits for</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">37</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   each parameter.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">38</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param \dots Not used</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">39</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @importFrom stats qnorm</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">40</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @references</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">41</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   Bates DM and Watts GW (1988) Nonlinear regression analysis &amp; its applications</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">42</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">43</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   Pawitan Y (2013) In all likelihood - Statistical modelling and</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">44</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   inference using likelihood. Clarendon Press, Oxford.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">45</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">46</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   Venzon DJ and Moolgavkar SH (1988) A Method for Computing</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">47</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   Profile-Likelihood Based Confidence Intervals, Applied Statistics, 37,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">48</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   87–94.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">49</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @examples</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">50</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' f &lt;- mkinfit("SFO", FOCUS_2006_C, quiet = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">51</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' confint(f, method = "quadratic")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">52</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">53</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' \dontrun{</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">54</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' confint(f, method = "profile")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">55</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">56</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # Set the number of cores for the profiling method for further examples</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">57</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' if (identical(Sys.getenv("NOT_CRAN"), "true")) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">58</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   n_cores &lt;- parallel::detectCores() - 1</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">59</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">60</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   n_cores &lt;- 1</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">61</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">62</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' if (Sys.getenv("TRAVIS") != "") n_cores = 1</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">63</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' if (Sys.info()["sysname"] == "Windows") n_cores = 1</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">64</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">65</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' SFO_SFO &lt;- mkinmod(parent = mkinsub("SFO", "m1"), m1 = mkinsub("SFO"),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">66</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   use_of_ff = "min", quiet = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">67</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' SFO_SFO.ff &lt;- mkinmod(parent = mkinsub("SFO", "m1"), m1 = mkinsub("SFO"),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">68</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   use_of_ff = "max", quiet = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">69</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' f_d_1 &lt;- mkinfit(SFO_SFO, subset(FOCUS_2006_D, value != 0), quiet = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">70</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' system.time(ci_profile &lt;- confint(f_d_1, method = "profile", cores = 1, quiet = TRUE))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">71</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # Using more cores does not save much time here, as parent_0 takes up most of the time</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">72</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # If we additionally exclude parent_0 (the confidence of which is often of</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">73</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # minor interest), we get a nice performance improvement if we use at least 4 cores</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">74</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' system.time(ci_profile_no_parent_0 &lt;- confint(f_d_1, method = "profile",</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">75</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   c("k_parent_sink", "k_parent_m1", "k_m1_sink", "sigma"), cores = n_cores))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">76</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' ci_profile</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">77</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' ci_quadratic_transformed &lt;- confint(f_d_1, method = "quadratic")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">78</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' ci_quadratic_transformed</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">79</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' ci_quadratic_untransformed &lt;- confint(f_d_1, method = "quadratic", transformed = FALSE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">80</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' ci_quadratic_untransformed</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">81</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # Against the expectation based on Bates and Watts (1988), the confidence</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">82</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # intervals based on the internal parameter transformation are less</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">83</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # congruent with the likelihood based intervals. Note the superiority of the</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">84</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # interval based on the untransformed fit for k_m1_sink</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">85</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' rel_diffs_transformed &lt;- abs((ci_quadratic_transformed - ci_profile)/ci_profile)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">86</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' rel_diffs_untransformed &lt;- abs((ci_quadratic_untransformed - ci_profile)/ci_profile)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">87</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' rel_diffs_transformed &lt; rel_diffs_untransformed</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">88</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' signif(rel_diffs_transformed, 3)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">89</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' signif(rel_diffs_untransformed, 3)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">90</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">91</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">92</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # Investigate a case with formation fractions</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">93</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' f_d_2 &lt;- mkinfit(SFO_SFO.ff, subset(FOCUS_2006_D, value != 0), quiet = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">94</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' ci_profile_ff &lt;- confint(f_d_2, method = "profile", cores = n_cores)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">95</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' ci_profile_ff</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">96</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' ci_quadratic_transformed_ff &lt;- confint(f_d_2, method = "quadratic")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">97</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' ci_quadratic_transformed_ff</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">98</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' ci_quadratic_untransformed_ff &lt;- confint(f_d_2, method = "quadratic", transformed = FALSE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">99</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' ci_quadratic_untransformed_ff</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">100</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' rel_diffs_transformed_ff &lt;- abs((ci_quadratic_transformed_ff - ci_profile_ff)/ci_profile_ff)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">101</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' rel_diffs_untransformed_ff &lt;- abs((ci_quadratic_untransformed_ff - ci_profile_ff)/ci_profile_ff)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">102</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # While the confidence interval for the parent rate constant is closer to</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">103</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # the profile based interval when using the internal parameter</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">104</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # transformation, the interval for the metabolite rate constant is 'better</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">105</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # without internal parameter transformation.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">106</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' rel_diffs_transformed_ff &lt; rel_diffs_untransformed_ff</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">107</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' rel_diffs_transformed_ff</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">108</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' rel_diffs_untransformed_ff</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">109</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">110</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # The profiling for the following fit does not finish in a reasonable time,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">111</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # therefore we use the quadratic approximation</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">112</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' m_synth_DFOP_par &lt;- mkinmod(parent = mkinsub("DFOP", c("M1", "M2")),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">113</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   M1 = mkinsub("SFO"),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">114</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   M2 = mkinsub("SFO"),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">115</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   use_of_ff = "max", quiet = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">116</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' DFOP_par_c &lt;- synthetic_data_for_UBA_2014[[12]]$data</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">117</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' f_tc_2 &lt;- mkinfit(m_synth_DFOP_par, DFOP_par_c, error_model = "tc",</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">118</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   error_model_algorithm = "direct", quiet = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">119</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' confint(f_tc_2, method = "quadratic")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">120</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' confint(f_tc_2, "parent_0", method = "quadratic")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">121</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">122</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">123</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">confint.mkinfit &lt;- function(object, parm,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">124</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  level = 0.95, alpha = 1 - level, cutoff,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">125</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  method = c("quadratic", "profile"),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">126</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  transformed = TRUE, backtransform = TRUE,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">127</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cores = parallel::detectCores(), rel_tol = 0.01, quiet = FALSE, ...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">128</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">{</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">129</td>
+                    <td class="coverage">1260<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  tparms &lt;- parms(object, transformed = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">130</td>
+                    <td class="coverage">1260<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  bparms &lt;- parms(object, transformed = FALSE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">131</td>
+                    <td class="coverage">1260<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  tpnames &lt;- names(tparms)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">132</td>
+                    <td class="coverage">1260<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  bpnames &lt;- names(bparms)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">133</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">134</td>
+                    <td class="coverage">1260<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  return_pnames &lt;- if (missing(parm)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">135</td>
+                    <td class="coverage">210<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (backtransform) bpnames else tpnames</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">136</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">137</td>
+                    <td class="coverage">210<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    parm</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">138</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">139</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">140</td>
+                    <td class="coverage">1260<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  p &lt;- length(return_pnames)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">141</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">142</td>
+                    <td class="coverage">1260<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  method &lt;- match.arg(method)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">143</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">144</td>
+                    <td class="coverage">1260<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  a &lt;- c(alpha / 2, 1 - (alpha / 2))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">145</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">146</td>
+                    <td class="coverage">1260<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  quantiles &lt;- qt(a, object$df.residual)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">147</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">148</td>
+                    <td class="coverage">1260<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  covar_pnames &lt;- if (missing(parm)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">149</td>
+                    <td class="coverage">420<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (transformed) tpnames else bpnames</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">150</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">151</td>
+                    <td class="coverage">210<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    parm</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">152</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">153</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">154</td>
+                    <td class="coverage">1260<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  return_parms &lt;- if (backtransform) bparms[return_pnames]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">155</td>
+                    <td class="coverage">1260<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    else tparms[return_pnames]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">156</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">157</td>
+                    <td class="coverage">1260<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  covar_parms &lt;- if (transformed) tparms[covar_pnames]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">158</td>
+                    <td class="coverage">1260<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    else bparms[covar_pnames]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">159</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">160</td>
+                    <td class="coverage">1260<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (transformed) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">161</td>
+                    <td class="coverage">840<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    covar &lt;- try(solve(object$hessian), silent = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">162</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">163</td>
+                    <td class="coverage">420<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    covar &lt;- try(solve(object$hessian_notrans), silent = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">164</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">165</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">166</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # If inverting the covariance matrix failed or produced NA values</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">167</td>
+                    <td class="coverage">1260<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (!is.numeric(covar) | is.na(covar[1])) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">168</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    ses &lt;- lci &lt;- uci &lt;- rep(NA, p)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">169</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">170</td>
+                    <td class="coverage">1260<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    ses     &lt;- sqrt(diag(covar))[covar_pnames]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">171</td>
+                    <td class="coverage">1260<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    lci    &lt;- covar_parms + quantiles[1] * ses</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">172</td>
+                    <td class="coverage">1260<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    uci    &lt;- covar_parms + quantiles[2] * ses</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">173</td>
+                    <td class="coverage">1260<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (transformed &amp; backtransform) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">174</td>
+                    <td class="coverage">630<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      lci_back &lt;- backtransform_odeparms(lci,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">175</td>
+                    <td class="coverage">630<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        object$mkinmod, object$transform_rates, object$transform_fractions)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">176</td>
+                    <td class="coverage">630<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      uci_back &lt;- backtransform_odeparms(uci,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">177</td>
+                    <td class="coverage">630<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        object$mkinmod, object$transform_rates, object$transform_fractions)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">178</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">179</td>
+                    <td class="coverage">630<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      return_errparm_names &lt;- intersect(names(object$errparms), return_pnames)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">180</td>
+                    <td class="coverage">630<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      lci &lt;- c(lci_back, lci[return_errparm_names])</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">181</td>
+                    <td class="coverage">630<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      uci &lt;- c(uci_back, uci[return_errparm_names])</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">182</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">183</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">184</td>
+                    <td class="coverage">1260<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  ci &lt;- cbind(lower = lci, upper = uci)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">185</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">186</td>
+                    <td class="coverage">1260<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (method == "profile") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">187</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">188</td>
+                    <td class="coverage">210<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    ci_quadratic &lt;- ci</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">189</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">190</td>
+                    <td class="coverage">210<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (!quiet) message("Profiling the likelihood")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">191</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">192</td>
+                    <td class="coverage">210<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    lci &lt;- uci &lt;- rep(NA, p)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">193</td>
+                    <td class="coverage">210<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    names(lci) &lt;- names(uci) &lt;- return_pnames</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">194</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">195</td>
+                    <td class="coverage">210<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    profile_pnames &lt;- if(missing(parm)) names(parms(object))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">196</td>
+                    <td class="coverage">210<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      else parm</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">197</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">198</td>
+                    <td class="coverage">210<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (missing(cutoff)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">199</td>
+                    <td class="coverage">210<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      cutoff &lt;- 0.5 * qchisq(1 - alpha, 1)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">200</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">201</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">202</td>
+                    <td class="coverage">210<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    all_parms &lt;- parms(object)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">203</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">204</td>
+                    <td class="coverage">210<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    get_ci &lt;- function(pname) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">205</td>
+                    <td class="coverage">3<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      pnames_free &lt;- setdiff(names(all_parms), pname)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">206</td>
+                    <td class="coverage">3<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      profile_ll &lt;- function(x)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">207</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">208</td>
+                    <td class="coverage">80<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        pll_cost &lt;- function(P) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">209</td>
+                    <td class="coverage">3132<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          parms_cost &lt;- all_parms</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">210</td>
+                    <td class="coverage">3132<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          parms_cost[pnames_free] &lt;- P[pnames_free]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">211</td>
+                    <td class="coverage">3132<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          parms_cost[pname] &lt;- x</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">212</td>
+                    <td class="coverage">3132<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">          - object$ll(parms_cost)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">213</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">214</td>
+                    <td class="coverage">80<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        - nlminb(all_parms[pnames_free], pll_cost)$objective</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">215</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">216</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">217</td>
+                    <td class="coverage">3<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      cost &lt;- function(x) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">218</td>
+                    <td class="coverage">80<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        (cutoff - (object$logLik - profile_ll(x)))^2</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">219</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">220</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">221</td>
+                    <td class="coverage">3<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      lower_quadratic &lt;- ci_quadratic["lower"][pname]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">222</td>
+                    <td class="coverage">3<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      upper_quadratic &lt;- ci_quadratic["upper"][pname]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">223</td>
+                    <td class="coverage">3<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      ltol &lt;- if (!is.na(lower_quadratic)) rel_tol * lower_quadratic else .Machine$double.eps^0.25</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">224</td>
+                    <td class="coverage">3<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      utol &lt;- if (!is.na(upper_quadratic)) rel_tol * upper_quadratic else .Machine$double.eps^0.25</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">225</td>
+                    <td class="coverage">3<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      lci_pname &lt;- optimize(cost, lower = 0, upper = all_parms[pname], tol = ltol)$minimum</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">226</td>
+                    <td class="coverage">3<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      uci_pname &lt;- optimize(cost, lower = all_parms[pname],</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">227</td>
+                    <td class="coverage">3<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        upper = ifelse(grepl("^f_|^g$", pname), 1, 15 * all_parms[pname]),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">228</td>
+                    <td class="coverage">3<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        tol = utol)$minimum</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">229</td>
+                    <td class="coverage">3<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      return(c(lci_pname, uci_pname))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">230</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">231</td>
+                    <td class="coverage">210<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    ci &lt;- t(parallel::mcmapply(get_ci, profile_pnames, mc.cores = cores))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">232</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">233</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">234</td>
+                    <td class="coverage">1257<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  colnames(ci) &lt;- paste0(</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">235</td>
+                    <td class="coverage">1257<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    format(100 * a, trim = TRUE, scientific = FALSE, digits = 3), "%")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">236</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">237</td>
+                    <td class="coverage">1257<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  return(ci)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">238</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                </tbody>
+              </table>
+            </div>
+            <div id="R/mmkin.R" class="hidden">
+              <table class="table-condensed">
+                <tbody>
+                  <tr class="never">
+                    <td class="num">1</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Fit one or more kinetic models with one or more state variables to one or</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">2</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' more datasets</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">3</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">4</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' This function calls \code{\link{mkinfit}} on all combinations of models and</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">5</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' datasets specified in its first two arguments.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">6</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">7</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param models Either a character vector of shorthand names like</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">8</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   \code{c("SFO", "FOMC", "DFOP", "HS", "SFORB")}, or an optionally named</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">9</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   list of \code{\link{mkinmod}} objects.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">10</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param datasets An optionally named list of datasets suitable as observed</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">11</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   data for \code{\link{mkinfit}}.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">12</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param cores The number of cores to be used for multicore processing. This</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">13</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   is only used when the \code{cluster} argument is \code{NULL}. On Windows</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">14</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   machines, cores &gt; 1 is not supported, you need to use the \code{cluster}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">15</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   argument to use multiple logical processors. Per default, all cores</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">16</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   detected by [parallel::detectCores()] are used, except on Windows where</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">17</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   the default is 1.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">18</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param cluster A cluster as returned by \code{\link{makeCluster}} to be used</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">19</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   for parallel execution.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">20</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param \dots Further arguments that will be passed to \code{\link{mkinfit}}.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">21</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @importFrom parallel mclapply parLapply detectCores</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">22</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @return A two-dimensional \code{\link{array}} of \code{\link{mkinfit}}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">23</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   objects and/or try-errors that can be indexed using the model names for the</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">24</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   first index (row index) and the dataset names for the second index (column</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">25</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   index).</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">26</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @author Johannes Ranke</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">27</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @seealso \code{\link{[.mmkin}} for subsetting, \code{\link{plot.mmkin}} for</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">28</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   plotting.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">29</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @keywords optimize</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">30</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @examples</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">31</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">32</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' \dontrun{</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">33</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' m_synth_SFO_lin &lt;- mkinmod(parent = mkinsub("SFO", "M1"),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">34</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'                            M1 = mkinsub("SFO", "M2"),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">35</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'                            M2 = mkinsub("SFO"), use_of_ff = "max")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">36</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">37</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' m_synth_FOMC_lin &lt;- mkinmod(parent = mkinsub("FOMC", "M1"),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">38</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'                             M1 = mkinsub("SFO", "M2"),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">39</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'                             M2 = mkinsub("SFO"), use_of_ff = "max")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">40</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">41</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' models &lt;- list(SFO_lin = m_synth_SFO_lin, FOMC_lin = m_synth_FOMC_lin)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">42</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' datasets &lt;- lapply(synthetic_data_for_UBA_2014[1:3], function(x) x$data)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">43</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' names(datasets) &lt;- paste("Dataset", 1:3)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">44</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">45</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' time_default &lt;- system.time(fits.0 &lt;- mmkin(models, datasets, quiet = TRUE))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">46</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' time_1 &lt;- system.time(fits.4 &lt;- mmkin(models, datasets, cores = 1, quiet = TRUE))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">47</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">48</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' time_default</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">49</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' time_1</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">50</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">51</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' endpoints(fits.0[["SFO_lin", 2]])</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">52</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">53</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # plot.mkinfit handles rows or columns of mmkin result objects</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">54</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' plot(fits.0[1, ])</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">55</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' plot(fits.0[1, ], obs_var = c("M1", "M2"))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">56</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' plot(fits.0[, 1])</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">57</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # Use double brackets to extract a single mkinfit object, which will be plotted</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">58</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # by plot.mkinfit and can be plotted using plot_sep</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">59</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' plot(fits.0[[1, 1]], sep_obs = TRUE, show_residuals = TRUE, show_errmin = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">60</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' plot_sep(fits.0[[1, 1]])</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">61</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # Plotting with mmkin (single brackets, extracting an mmkin object) does not</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">62</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # allow to plot the observed variables separately</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">63</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' plot(fits.0[1, 1])</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">64</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">65</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # On Windows, we can use multiple cores by making a cluster first</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">66</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' cl &lt;- parallel::makePSOCKcluster(12)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">67</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' f &lt;- mmkin(c("SFO", "FOMC", "DFOP"),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">68</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   list(A = FOCUS_2006_A, B = FOCUS_2006_B, C = FOCUS_2006_C, D = FOCUS_2006_D),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">69</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   cluster = cl, quiet = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">70</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' print(f)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">71</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # We get false convergence for the FOMC fit to FOCUS_2006_A because this</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">72</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # dataset is really SFO, and the FOMC fit is overparameterised</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">73</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' parallel::stopCluster(cl)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">74</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">75</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">76</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export mmkin</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">77</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">mmkin &lt;- function(models = c("SFO", "FOMC", "DFOP"), datasets,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">78</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cores = if (Sys.info()["sysname"] == "Windows") 1 else parallel::detectCores(), cluster = NULL, ...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">79</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">{</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">80</td>
+                    <td class="coverage">4032<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  call &lt;- match.call()</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">81</td>
+                    <td class="coverage">4032<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  parent_models_available = c("SFO", "FOMC", "DFOP", "HS", "SFORB", "IORE", "logistic")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">82</td>
+                    <td class="coverage">4032<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  n.m &lt;- length(models)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">83</td>
+                    <td class="coverage">4032<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  n.d &lt;- length(datasets)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">84</td>
+                    <td class="coverage">4032<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  n.fits &lt;- n.m * n.d</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">85</td>
+                    <td class="coverage">4032<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  fit_indices &lt;- matrix(1:n.fits, ncol = n.d)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">86</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">87</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Check models and define their names</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">88</td>
+                    <td class="coverage">4032<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (!all(sapply(models, function(x) inherits(x, "mkinmod")))) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">89</td>
+                    <td class="coverage">2323<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (!all(models %in% parent_models_available)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">90</td>
+                    <td class="coverage">50<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      stop("Please supply models as a list of mkinmod objects or a vector combined of\n  ",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">91</td>
+                    <td class="coverage">50<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">           paste(parent_models_available, collapse = ", "))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">92</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">93</td>
+                    <td class="coverage">2273<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      names(models) &lt;- models</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">94</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">95</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">96</td>
+                    <td class="coverage">1087<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (is.null(names(models))) names(models) &lt;- as.character(1:n.m)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">97</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">98</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">99</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Check datasets and define their names</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">100</td>
+                    <td class="coverage">1575<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (is.null(names(datasets))) names(datasets) &lt;- as.character(1:n.d)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">101</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">102</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Define names for fit index</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">103</td>
+                    <td class="coverage">3982<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  dimnames(fit_indices) &lt;- list(model = names(models),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">104</td>
+                    <td class="coverage">3982<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">                                dataset = names(datasets))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">105</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">106</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">107</td>
+                    <td class="coverage">3982<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  fit_function &lt;- function(fit_index) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">108</td>
+                    <td class="coverage">793<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    w &lt;- which(fit_indices == fit_index, arr.ind = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">109</td>
+                    <td class="coverage">793<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    model_index &lt;- w[1]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">110</td>
+                    <td class="coverage">793<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    dataset_index &lt;- w[2]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">111</td>
+                    <td class="coverage">793<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    res &lt;- try(mkinfit(models[[model_index]], datasets[[dataset_index]], ...))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">112</td>
+                    <td class="coverage">793<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (!inherits(res, "try-error")) res$mkinmod$name &lt;- names(models)[model_index]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">113</td>
+                    <td class="coverage">793<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    return(res)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">114</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">115</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">116</td>
+                    <td class="coverage">3982<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  fit_time &lt;- system.time({</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">117</td>
+                    <td class="coverage">3982<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (is.null(cluster)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">118</td>
+                    <td class="coverage">2154<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      results &lt;- parallel::mclapply(as.list(1:n.fits), fit_function,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">119</td>
+                    <td class="coverage">2154<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        mc.cores = cores, mc.preschedule = FALSE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">120</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">121</td>
+                    <td class="coverage">1828<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      results &lt;- parallel::parLapply(cluster, as.list(1:n.fits), fit_function)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">122</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">123</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  })</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">124</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">125</td>
+                    <td class="coverage">3798<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  attributes(results) &lt;- attributes(fit_indices)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">126</td>
+                    <td class="coverage">3798<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  attr(results, "call") &lt;- call</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">127</td>
+                    <td class="coverage">3798<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  attr(results, "time") &lt;- fit_time</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">128</td>
+                    <td class="coverage">3798<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  class(results) &lt;- "mmkin"</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">129</td>
+                    <td class="coverage">3798<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  return(results)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">130</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">131</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">132</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Subsetting method for mmkin objects</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">133</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">134</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param x An \code{\link{mmkin} object}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">135</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param i Row index selecting the fits for specific models</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">136</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param j Column index selecting the fits to specific datasets</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">137</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param ... Not used, only there to satisfy the generic method definition</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">138</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param drop If FALSE, the method always returns an mmkin object, otherwise</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">139</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   either a list of mkinfit objects or a single mkinfit object.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">140</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @return An object of class \code{\link{mmkin}}.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">141</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @author Johannes Ranke</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">142</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @rdname Extract.mmkin</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">143</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @examples</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">144</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">145</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   # Only use one core, to pass R CMD check --as-cran</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">146</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   fits &lt;- mmkin(c("SFO", "FOMC"), list(B = FOCUS_2006_B, C = FOCUS_2006_C),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">147</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'                 cores = 1, quiet = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">148</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   fits["FOMC", ]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">149</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   fits[, "B"]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">150</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   fits["SFO", "B"]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">151</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">152</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   head(</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">153</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     # This extracts an mkinfit object with lots of components</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">154</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     fits[["FOMC", "B"]]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">155</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   )</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">156</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">157</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">`[.mmkin` &lt;- function(x, i, j, ..., drop = FALSE) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">158</td>
+                    <td class="coverage">2760<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  class(x) &lt;- NULL</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">159</td>
+                    <td class="coverage">2760<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  x_sub &lt;- x[i, j, drop = drop]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">160</td>
+                    <td class="coverage">2760<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (!drop) class(x_sub) &lt;- "mmkin"</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">161</td>
+                    <td class="coverage">2760<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  return(x_sub)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">162</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">163</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">164</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Print method for mmkin objects</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">165</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">166</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param x An [mmkin] object.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">167</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param \dots Not used.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">168</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @rdname mmkin</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">169</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">170</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">print.mmkin &lt;- function(x, ...) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">171</td>
+                    <td class="coverage">375<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("&lt;mmkin&gt; object\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">172</td>
+                    <td class="coverage">375<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("Status of individual fits:\n\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">173</td>
+                    <td class="coverage">375<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  print(status(x))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">174</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">175</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">176</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">177</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">update.mmkin &lt;- function(object, ..., evaluate = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">178</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">{</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">179</td>
+                    <td class="coverage">256<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  call &lt;- attr(object, "call")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">180</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">181</td>
+                    <td class="coverage">256<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  update_arguments &lt;- match.call(expand.dots = FALSE)$...</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">182</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">183</td>
+                    <td class="coverage">256<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (length(update_arguments) &gt; 0) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">184</td>
+                    <td class="coverage">256<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    update_arguments_in_call &lt;- !is.na(match(names(update_arguments), names(call)))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">185</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">186</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">187</td>
+                    <td class="coverage">256<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  for (a in names(update_arguments)[update_arguments_in_call]) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">188</td>
+                    <td class="coverage">115<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    call[[a]] &lt;- update_arguments[[a]]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">189</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">190</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">191</td>
+                    <td class="coverage">256<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  update_arguments_not_in_call &lt;- !update_arguments_in_call</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">192</td>
+                    <td class="coverage">256<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if(any(update_arguments_not_in_call)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">193</td>
+                    <td class="coverage">206<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    call &lt;- c(as.list(call), update_arguments[update_arguments_not_in_call])</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">194</td>
+                    <td class="coverage">206<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    call &lt;- as.call(call)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">195</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">196</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">197</td>
+                    <td class="coverage">256<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if(evaluate) eval(call, parent.frame())</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">198</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  else call</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">199</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                </tbody>
+              </table>
+            </div>
+            <div id="R/update.mkinfit.R" class="hidden">
+              <table class="table-condensed">
+                <tbody>
+                  <tr class="never">
+                    <td class="num">1</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Update an mkinfit model with different arguments</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">2</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">3</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' This function will return an updated mkinfit object. The fitted degradation</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">4</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' model parameters from the old fit are used as starting values for the</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">5</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' updated fit. Values specified as 'parms.ini' and/or 'state.ini' will</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">6</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' override these starting values.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">7</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">8</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param object An mkinfit object to be updated</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">9</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param \dots Arguments to \code{\link{mkinfit}} that should replace</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">10</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'  the arguments from the original call. Arguments set to NULL will</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">11</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'  remove arguments given in the original call</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">12</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param evaluate Should the call be evaluated or returned as a call</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">13</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @examples</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">14</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' \dontrun{</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">15</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' fit &lt;- mkinfit("SFO", subset(FOCUS_2006_D, value != 0), quiet = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">16</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' parms(fit)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">17</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' plot_err(fit)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">18</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' fit_2 &lt;- update(fit, error_model = "tc")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">19</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' parms(fit_2)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">20</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' plot_err(fit_2)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">21</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">22</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">23</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">update.mkinfit &lt;- function(object, ..., evaluate = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">24</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">{</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">25</td>
+                    <td class="coverage">5<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  call &lt;- object$call</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">26</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">27</td>
+                    <td class="coverage">5<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  update_arguments &lt;- match.call(expand.dots = FALSE)$...</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">28</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">29</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Get optimised ODE parameters and let parms.ini override them</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">30</td>
+                    <td class="coverage">5<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  ode_optim_names &lt;- intersect(names(object$bparms.optim), names(object$bparms.ode))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">31</td>
+                    <td class="coverage">5<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  ode_start &lt;- object$bparms.optim[ode_optim_names]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">32</td>
+                    <td class="coverage">5<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if ("parms.ini" %in% names(update_arguments)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">33</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    ode_start[names(update_arguments["parms.ini"])] &lt;- update_arguments["parms.ini"]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">34</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">35</td>
+                    <td class="coverage">5<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (length(ode_start)) update_arguments[["parms.ini"]] &lt;- ode_start</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">36</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">37</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Get optimised values for initial states and let state.ini override them</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">38</td>
+                    <td class="coverage">5<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  state_optim_names &lt;- intersect(names(object$bparms.optim), paste0(names(object$bparms.state), "_0"))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">39</td>
+                    <td class="coverage">5<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  state_start &lt;- object$bparms.optim[state_optim_names]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">40</td>
+                    <td class="coverage">5<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  names(state_start) &lt;- gsub("_0$", "", names(state_start))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">41</td>
+                    <td class="coverage">5<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if ("state.ini" %in% names(update_arguments)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">42</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    state_start[names(update_arguments["state.ini"])] &lt;- update_arguments["state.ini"]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">43</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">44</td>
+                    <td class="coverage">5<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (length(state_start)) update_arguments[["state.ini"]] &lt;- state_start</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">45</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">46</td>
+                    <td class="coverage">5<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (length(update_arguments) &gt; 0) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">47</td>
+                    <td class="coverage">5<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    update_arguments_in_call &lt;- !is.na(match(names(update_arguments), names(call)))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">48</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">49</td>
+                    <td class="coverage">5<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    for (a in names(update_arguments)[update_arguments_in_call]) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">50</td>
+                    <td class="coverage">3<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      call[[a]] &lt;- update_arguments[[a]]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">51</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">52</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">53</td>
+                    <td class="coverage">5<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    update_arguments_not_in_call &lt;- !update_arguments_in_call</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">54</td>
+                    <td class="coverage">5<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if(any(update_arguments_not_in_call)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">55</td>
+                    <td class="coverage">5<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      call &lt;- c(as.list(call), update_arguments[update_arguments_not_in_call])</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">56</td>
+                    <td class="coverage">5<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      call &lt;- as.call(call)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">57</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">58</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">59</td>
+                    <td class="coverage">5<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if(evaluate) eval(call, parent.frame())</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">60</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  else call</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">61</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                </tbody>
+              </table>
+            </div>
+            <div id="R/multistart.R" class="hidden">
+              <table class="table-condensed">
+                <tbody>
+                  <tr class="never">
+                    <td class="num">1</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Perform a hierarchical model fit with multiple starting values</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">2</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">3</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' The purpose of this method is to check if a certain algorithm for fitting</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">4</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' nonlinear hierarchical models (also known as nonlinear mixed-effects models)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">5</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' will reliably yield results that are sufficiently similar to each other, if</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">6</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' started with a certain range of reasonable starting parameters. It is</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">7</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' inspired by the article on practical identifiabiliy in the frame of nonlinear</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">8</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' mixed-effects models by Duchesne et al (2021).</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">9</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">10</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param object The fit object to work with</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">11</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param n How many different combinations of starting parameters should be</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">12</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' used?</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">13</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param cores How many fits should be run in parallel (only on posix platforms)?</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">14</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param cluster A cluster as returned by [parallel::makeCluster] to be used</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">15</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' for parallel execution.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">16</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param \dots Passed to the update function.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">17</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param x The multistart object to print</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">18</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @return A list of [saem.mmkin] objects, with class attributes</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">19</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' 'multistart.saem.mmkin' and 'multistart'.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">20</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @seealso [parplot], [llhist]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">21</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">22</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @references Duchesne R, Guillemin A, Gandrillon O, Crauste F. Practical</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">23</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' identifiability in the frame of nonlinear mixed effects models: the example</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">24</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' of the in vitro erythropoiesis. BMC Bioinformatics. 2021 Oct 4;22(1):478.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">25</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' doi: 10.1186/s12859-021-04373-4.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">26</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">27</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @examples</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">28</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' \dontrun{</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">29</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' library(mkin)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">30</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' dmta_ds &lt;- lapply(1:7, function(i) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">31</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   ds_i &lt;- dimethenamid_2018$ds[[i]]$data</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">32</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   ds_i[ds_i$name == "DMTAP", "name"] &lt;-  "DMTA"</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">33</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   ds_i$time &lt;- ds_i$time * dimethenamid_2018$f_time_norm[i]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">34</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   ds_i</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">35</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' })</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">36</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' names(dmta_ds) &lt;- sapply(dimethenamid_2018$ds, function(ds) ds$title)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">37</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' dmta_ds[["Elliot"]] &lt;- rbind(dmta_ds[["Elliot 1"]], dmta_ds[["Elliot 2"]])</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">38</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' dmta_ds[["Elliot 1"]] &lt;- dmta_ds[["Elliot 2"]] &lt;- NULL</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">39</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">40</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' f_mmkin &lt;- mmkin("DFOP", dmta_ds, error_model = "tc", cores = 7, quiet = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">41</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' f_saem_full &lt;- saem(f_mmkin)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">42</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' f_saem_full_multi &lt;- multistart(f_saem_full, n = 16, cores = 16)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">43</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' parplot(f_saem_full_multi, lpos = "topleft", las = 2)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">44</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' illparms(f_saem_full)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">45</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">46</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' f_saem_reduced &lt;- update(f_saem_full, no_random_effect = "log_k2")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">47</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' illparms(f_saem_reduced)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">48</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # On Windows, we need to create a PSOCK cluster first and refer to it</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">49</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # in the call to multistart()</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">50</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' library(parallel)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">51</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' cl &lt;- makePSOCKcluster(12)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">52</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' f_saem_reduced_multi &lt;- multistart(f_saem_reduced, n = 16, cluster = cl)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">53</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' parplot(f_saem_reduced_multi, lpos = "topright", ylim = c(0.5, 2), las = 2)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">54</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' stopCluster(cl)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">55</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">56</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">multistart &lt;- function(object, n = 50,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">57</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cores = if (Sys.info()["sysname"] == "Windows") 1 else parallel::detectCores(),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">58</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cluster = NULL, ...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">59</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">{</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">60</td>
+                    <td class="coverage">200<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  UseMethod("multistart", object)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">61</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">62</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">63</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @rdname multistart</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">64</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">65</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">multistart.saem.mmkin &lt;- function(object, n = 50, cores = 1,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">66</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cluster = NULL, ...) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">67</td>
+                    <td class="coverage">200<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  call &lt;- match.call()</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">68</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (n &lt;= 1) stop("Please specify an n of at least 2")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">69</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">70</td>
+                    <td class="coverage">200<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  mmkin_object &lt;- object$mmkin</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">71</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">72</td>
+                    <td class="coverage">200<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  mmkin_parms &lt;- parms(mmkin_object, errparms = FALSE,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">73</td>
+                    <td class="coverage">200<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    transformed = object$transformations == "mkin")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">74</td>
+                    <td class="coverage">200<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  start_parms &lt;- apply(</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">75</td>
+                    <td class="coverage">200<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    mmkin_parms, 1,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">76</td>
+                    <td class="coverage">200<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    function(x) stats::runif(n, min(x), max(x)))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">77</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">78</td>
+                    <td class="coverage">200<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  saem_call &lt;- object$call</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">79</td>
+                    <td class="coverage">200<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  saem_call[[1]] &lt;- saem</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">80</td>
+                    <td class="coverage">200<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  saem_call[[2]] &lt;- mmkin_object</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">81</td>
+                    <td class="coverage">200<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  i_startparms &lt;- which(names(saem_call) == "degparms_start")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">82</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">83</td>
+                    <td class="coverage">200<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  fit_function &lt;- function(x) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">84</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">85</td>
+                    <td class="coverage">16<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    new_startparms &lt;- str2lang(</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">86</td>
+                    <td class="coverage">16<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      paste0(capture.output(dput(start_parms[x, ])),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">87</td>
+                    <td class="coverage">16<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        collapse = ""))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">88</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">89</td>
+                    <td class="coverage">16<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (length(i_startparms) == 0) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">90</td>
+                    <td class="coverage">16<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      saem_call &lt;- c(as.list(saem_call), degparms_start = new_startparms)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">91</td>
+                    <td class="coverage">16<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      saem_call &lt;- as.call(saem_call)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">92</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">93</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      saem_call[i_startparms] &lt;- new_startparms</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">94</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">95</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">96</td>
+                    <td class="coverage">16<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    ret &lt;- eval(saem_call)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">97</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">98</td>
+                    <td class="coverage">16<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    return(ret)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">99</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">100</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">101</td>
+                    <td class="coverage">200<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (is.null(cluster)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">102</td>
+                    <td class="coverage">200<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    res &lt;- parallel::mclapply(1:n, fit_function,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">103</td>
+                    <td class="coverage">200<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      mc.cores = cores, mc.preschedule = FALSE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">104</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">105</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    res &lt;- parallel::parLapplyLB(cluster, 1:n, fit_function)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">106</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">107</td>
+                    <td class="coverage">184<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  attr(res, "orig") &lt;- object</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">108</td>
+                    <td class="coverage">184<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  attr(res, "start_parms") &lt;- start_parms</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">109</td>
+                    <td class="coverage">184<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  attr(res, "call") &lt;- call</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">110</td>
+                    <td class="coverage">184<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  class(res) &lt;- c("multistart.saem.mmkin", "multistart")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">111</td>
+                    <td class="coverage">184<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  return(res)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">112</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">113</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">114</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">115</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">status.multistart &lt;- function(object, ...) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">116</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  all_summary_warnings &lt;- character()</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">117</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">118</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  result &lt;- lapply(object,</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">119</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    function(fit) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">120</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      if (inherits(fit, "try-error")) return("E")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">121</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">122</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        return("OK")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">123</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">124</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  })</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">125</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  result &lt;- unlist(result)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">126</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">127</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  class(result) &lt;- "status.multistart"</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">128</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  return(result)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">129</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">130</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">131</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">132</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">status.multistart.saem.mmkin &lt;- function(object, ...) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">133</td>
+                    <td class="coverage">88<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  all_summary_warnings &lt;- character()</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">134</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">135</td>
+                    <td class="coverage">88<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  result &lt;- lapply(object,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">136</td>
+                    <td class="coverage">88<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    function(fit) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">137</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      if (inherits(fit$so, "try-error")) return("E")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">138</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">139</td>
+                    <td class="coverage">704<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        return("OK")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">140</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">141</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  })</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">142</td>
+                    <td class="coverage">88<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  result &lt;- unlist(result)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">143</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">144</td>
+                    <td class="coverage">88<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  class(result) &lt;- "status.multistart"</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">145</td>
+                    <td class="coverage">88<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  return(result)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">146</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">147</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">148</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">149</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">print.status.multistart &lt;- function(x, ...) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">150</td>
+                    <td class="coverage">88<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  class(x) &lt;- NULL</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">151</td>
+                    <td class="coverage">88<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  print(table(x, dnn = NULL))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">152</td>
+                    <td class="coverage">88<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (any(x == "OK")) cat("OK: Fit terminated successfully\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">153</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (any(x == "E")) cat("E: Error\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">154</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">155</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">156</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @rdname multistart</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">157</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">158</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">print.multistart &lt;- function(x, ...) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">159</td>
+                    <td class="coverage">88<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("&lt;multistart&gt; object with", length(x), "fits:\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">160</td>
+                    <td class="coverage">88<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  print(status(x))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">161</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">162</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">163</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @rdname multistart</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">164</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">165</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">best &lt;- function(object, ...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">166</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">{</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">167</td>
+                    <td class="coverage">184<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  UseMethod("best", object)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">168</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">169</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">170</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">171</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @return The object with the highest likelihood</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">172</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @rdname multistart</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">173</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">best.default &lt;- function(object, ...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">174</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">{</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">175</td>
+                    <td class="coverage">184<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  return(object[[which.best(object)]])</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">176</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">177</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">178</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @return The index of the object with the highest likelihood</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">179</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @rdname multistart</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">180</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">181</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">which.best &lt;- function(object, ...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">182</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">{</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">183</td>
+                    <td class="coverage">360<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  UseMethod("which.best", object)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">184</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">185</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">186</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @rdname multistart</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">187</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">188</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">which.best.default &lt;- function(object, ...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">189</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">{</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">190</td>
+                    <td class="coverage">360<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  llfunc &lt;- function(object) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">191</td>
+                    <td class="coverage">2528<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    ret &lt;- try(logLik(object))</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">192</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (inherits(ret, "try-error")) return(NA)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">193</td>
+                    <td class="coverage">2528<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    else return(ret)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">194</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">195</td>
+                    <td class="coverage">360<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  ll &lt;- sapply(object, llfunc)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">196</td>
+                    <td class="coverage">360<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  return(which.max(ll))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">197</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">198</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">199</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">200</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">update.multistart &lt;- function(object, ..., evaluate = TRUE) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">201</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  call &lt;- attr(object, "call")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">202</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # For some reason we get multistart.saem.mmkin in call[[1]] when using multistart</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">203</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # from the loaded package so we need to fix this so we do not have to export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">204</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # multistart.saem.mmkin</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">205</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  call[[1]] &lt;- multistart</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">206</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">207</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  update_arguments &lt;- match.call(expand.dots = FALSE)$...</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">208</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">209</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (length(update_arguments) &gt; 0) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">210</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    update_arguments_in_call &lt;- !is.na(match(names(update_arguments), names(call)))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">211</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">212</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">213</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  for (a in names(update_arguments)[update_arguments_in_call]) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">214</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    call[[a]] &lt;- update_arguments[[a]]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">215</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">216</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">217</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  update_arguments_not_in_call &lt;- !update_arguments_in_call</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">218</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if(any(update_arguments_not_in_call)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">219</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    call &lt;- c(as.list(call), update_arguments[update_arguments_not_in_call])</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">220</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    call &lt;- as.call(call)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">221</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">222</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if(evaluate) eval(call, parent.frame())</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">223</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  else call</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">224</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                </tbody>
+              </table>
+            </div>
+            <div id="R/residuals.mkinfit.R" class="hidden">
+              <table class="table-condensed">
+                <tbody>
+                  <tr class="never">
+                    <td class="num">1</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Extract residuals from an mkinfit model</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">2</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">3</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param object A \code{\link{mkinfit}} object</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">4</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param standardized Should the residuals be standardized by dividing by the</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">5</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   standard deviation obtained from the fitted error model?</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">6</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param \dots Not used</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">7</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">8</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @examples</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">9</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' f &lt;- mkinfit("DFOP", FOCUS_2006_C, quiet = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">10</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' residuals(f)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">11</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' residuals(f, standardized = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">12</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">residuals.mkinfit &lt;- function(object, standardized = FALSE, ...) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">13</td>
+                    <td class="coverage">2493<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  res &lt;- object$data[["residual"]]</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">14</td>
+                    <td class="coverage">2493<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (standardized) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">15</td>
+                    <td class="coverage">2428<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (object$err_mod == "const") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">16</td>
+                    <td class="coverage">543<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      sigma_fitted &lt;- object$errparms["sigma"]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">17</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">18</td>
+                    <td class="coverage">2428<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (object$err_mod == "obs") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">19</td>
+                    <td class="coverage">65<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      sigma_names = paste0("sigma_", object$data[["variable"]])</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">20</td>
+                    <td class="coverage">65<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      sigma_fitted &lt;- object$errparms[sigma_names]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">21</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">22</td>
+                    <td class="coverage">2428<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (object$err_mod == "tc") {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">23</td>
+                    <td class="coverage">1820<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      sigma_fitted &lt;- sigma_twocomp(object$data[["predicted"]],</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">24</td>
+                    <td class="coverage">1820<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        sigma_low = object$errparms[1],</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">25</td>
+                    <td class="coverage">1820<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        rsd_high = object$errparms[2])</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">26</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">27</td>
+                    <td class="coverage">2428<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    return(res / sigma_fitted)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">28</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">29</td>
+                    <td class="coverage">65<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  return(res)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">30</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">31</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                </tbody>
+              </table>
+            </div>
+            <div id="R/nobs.mkinfit.R" class="hidden">
+              <table class="table-condensed">
+                <tbody>
+                  <tr class="never">
+                    <td class="num">1</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Number of observations on which an mkinfit object was fitted</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">2</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">3</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @importFrom stats nobs</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">4</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param object An mkinfit object</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">5</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param \dots For compatibility with the generic method</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">6</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @return The number of rows in the data included in the mkinfit object</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">7</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">8</td>
+                    <td class="coverage">166810<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">nobs.mkinfit &lt;- function(object, ...) nrow(object$data)</pre>
+                    </td>
+                  </tr>
+                </tbody>
+              </table>
+            </div>
+            <div id="R/mkin_long_to_wide.R" class="hidden">
+              <table class="table-condensed">
+                <tbody>
+                  <tr class="never">
+                    <td class="num">1</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Convert a dataframe from long to wide format</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">2</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' </pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">3</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' This function takes a dataframe in the long form, i.e. with a row for each</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">4</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' observed value, and converts it into a dataframe with one independent</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">5</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' variable and several dependent variables as columns.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">6</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' </pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">7</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param long_data The dataframe must contain one variable called "time" with</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">8</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   the time values specified by the \code{time} argument, one column called</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">9</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   "name" with the grouping of the observed values, and finally one column of</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">10</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   observed values called "value".</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">11</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param time The name of the time variable in the long input data.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">12</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param outtime The name of the time variable in the wide output data.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">13</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @return Dataframe in wide format.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">14</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @author Johannes Ranke</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">15</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @examples</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">16</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' </pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">17</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' mkin_long_to_wide(FOCUS_2006_D)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">18</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' </pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">19</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export mkin_long_to_wide</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">20</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">mkin_long_to_wide &lt;- function(long_data, time = "time", outtime = "time")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">21</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">{</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">22</td>
+                    <td class="coverage">494<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  colnames &lt;- unique(long_data$name)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">23</td>
+                    <td class="coverage">494<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  wide_data &lt;- data.frame(time = subset(long_data, name == colnames[1], time))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">24</td>
+                    <td class="coverage">494<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  names(wide_data) &lt;- outtime</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">25</td>
+                    <td class="coverage">494<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  for (var in colnames) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">26</td>
+                    <td class="coverage">741<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    wide_data[var] &lt;- subset(long_data, name == var, value)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">27</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">28</td>
+                    <td class="coverage">494<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  return(wide_data)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">29</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                </tbody>
+              </table>
+            </div>
+            <div id="R/summary.mmkin.R" class="hidden">
+              <table class="table-condensed">
+                <tbody>
+                  <tr class="never">
+                    <td class="num">1</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Summary method for class "mmkin"</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">2</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">3</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Shows status information on the [mkinfit] objects contained in the object</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">4</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' and gives an overview of ill-defined parameters calculated by [illparms].</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">5</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">6</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param object an object of class [mmkin]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">7</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param x an object of class \code{summary.mmkin}.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">8</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param conf.level confidence level for testing parameters</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">9</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param digits number of digits to use for printing</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">10</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param \dots optional arguments passed to methods like \code{print}.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">11</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @examples</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">12</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">13</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' fits &lt;- mmkin(</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">14</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   c("SFO", "FOMC"),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">15</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   list("FOCUS A" = FOCUS_2006_A,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">16</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'        "FOCUS C" = FOCUS_2006_C),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">17</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   quiet = TRUE, cores = 1)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">18</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   summary(fits)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">19</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">20</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">21</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">summary.mmkin &lt;- function(object, conf.level = 0.95, ...) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">22</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">23</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  ans &lt;- list(</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">24</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    err_mod = object[[1, 1]]$err_mod,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">25</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    time = attr(object, "time"),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">26</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    illparms = illparms(object),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">27</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    status = status(object)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">28</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  )</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">29</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">30</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  class(ans) &lt;- c("summary.mmkin")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">31</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  return(ans)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">32</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">33</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">34</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @rdname summary.mmkin</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">35</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">36</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">print.summary.mmkin &lt;- function(x, digits = max(3, getOption("digits") - 3), ...) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">37</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (!is.null(x$err_mod)) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">38</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    cat("Error model: ")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">39</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    cat(switch(x$err_mod,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">40</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">               const = "Constant variance",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">41</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">               obs = "Variance unique to each observed variable",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">42</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">               tc = "Two-component variance function"), "\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">43</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">44</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("Fitted in", x$time[["elapsed"]],  "s\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">45</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">46</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  cat("\nStatus:\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">47</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  print(x$status)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">48</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">49</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (any(x$illparms != "")) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">50</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    cat("\nIll-defined parameters:\n")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">51</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    print(x$illparms)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">52</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">53</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">54</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  invisible(x)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">55</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">56</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                </tbody>
+              </table>
+            </div>
+            <div id="R/loftest.R" class="hidden">
+              <table class="table-condensed">
+                <tbody>
+                  <tr class="never">
+                    <td class="num">1</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Lack-of-fit test for models fitted to data with replicates</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">2</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">3</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' This is a generic function with a method currently only defined for mkinfit</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">4</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' objects. It fits an anova model to the data contained in the object and</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">5</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' compares the likelihoods using the likelihood ratio test</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">6</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' \code{\link[lmtest]{lrtest.default}} from the lmtest package.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">7</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">8</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' The anova model is interpreted as the simplest form of an mkinfit model,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">9</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' assuming only a constant variance about the means, but not enforcing any</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">10</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' structure of the means, so we have one model parameter for every mean</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">11</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' of replicate samples.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">12</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">13</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param object A model object with a defined loftest method</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">14</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param \dots Not used</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">15</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">16</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">loftest &lt;- function(object, ...) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">17</td>
+                    <td class="coverage">2<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  UseMethod("loftest")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">18</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">19</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">20</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @rdname loftest</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">21</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @importFrom stats logLik lm dnorm coef</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">22</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @seealso lrtest</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">23</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @examples</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">24</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' \dontrun{</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">25</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' test_data &lt;- subset(synthetic_data_for_UBA_2014[[12]]$data, name == "parent")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">26</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' sfo_fit &lt;- mkinfit("SFO", test_data, quiet = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">27</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' plot_res(sfo_fit) # We see a clear pattern in the residuals</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">28</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' loftest(sfo_fit)  # We have a clear lack of fit</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">29</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' #</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">30</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # We try a different model (the one that was used to generate the data)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">31</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' dfop_fit &lt;- mkinfit("DFOP", test_data, quiet = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">32</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' plot_res(dfop_fit) # We don't see systematic deviations, but heteroscedastic residuals</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">33</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # therefore we should consider adapting the error model, although we have</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">34</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' loftest(dfop_fit) # no lack of fit</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">35</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' #</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">36</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # This is the anova model used internally for the comparison</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">37</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' test_data_anova &lt;- test_data</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">38</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' test_data_anova$time &lt;- as.factor(test_data_anova$time)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">39</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' anova_fit &lt;- lm(value ~ time, data = test_data_anova)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">40</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' summary(anova_fit)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">41</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' logLik(anova_fit) # We get the same likelihood and degrees of freedom</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">42</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' #</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">43</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' test_data_2 &lt;- synthetic_data_for_UBA_2014[[12]]$data</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">44</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' m_synth_SFO_lin &lt;- mkinmod(parent = list(type = "SFO", to = "M1"),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">45</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   M1 = list(type = "SFO", to = "M2"),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">46</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   M2 = list(type = "SFO"), use_of_ff = "max")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">47</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' sfo_lin_fit &lt;- mkinfit(m_synth_SFO_lin, test_data_2, quiet = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">48</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' plot_res(sfo_lin_fit) # not a good model, we try parallel formation</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">49</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' loftest(sfo_lin_fit)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">50</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' #</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">51</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' m_synth_SFO_par &lt;- mkinmod(parent = list(type = "SFO", to = c("M1", "M2")),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">52</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   M1 = list(type = "SFO"),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">53</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   M2 = list(type = "SFO"), use_of_ff = "max")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">54</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' sfo_par_fit &lt;- mkinfit(m_synth_SFO_par, test_data_2, quiet = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">55</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' plot_res(sfo_par_fit) # much better for metabolites</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">56</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' loftest(sfo_par_fit)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">57</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' #</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">58</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' m_synth_DFOP_par &lt;- mkinmod(parent = list(type = "DFOP", to = c("M1", "M2")),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">59</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   M1 = list(type = "SFO"),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">60</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   M2 = list(type = "SFO"), use_of_ff = "max")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">61</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' dfop_par_fit &lt;- mkinfit(m_synth_DFOP_par, test_data_2, quiet = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">62</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' plot_res(dfop_par_fit) # No visual lack of fit</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">63</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' loftest(dfop_par_fit)  # no lack of fit found by the test</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">64</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' #</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">65</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # The anova model used for comparison in the case of transformation products</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">66</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' test_data_anova_2 &lt;- dfop_par_fit$data</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">67</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' test_data_anova_2$variable &lt;- as.factor(test_data_anova_2$variable)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">68</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' test_data_anova_2$time &lt;- as.factor(test_data_anova_2$time)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">69</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' anova_fit_2 &lt;- lm(observed ~ time:variable - 1, data = test_data_anova_2)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">70</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' summary(anova_fit_2)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">71</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">72</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">73</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">loftest.mkinfit &lt;- function(object, ...) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">74</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">75</td>
+                    <td class="coverage">2<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  name_function &lt;- function(x) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">76</td>
+                    <td class="coverage">2<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    object_name &lt;- paste(x$mkinmod$name, "with error model", x$err_mod)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">77</td>
+                    <td class="coverage">2<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    if (length(x$bparms.fixed) &gt; 0) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">78</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      object_name &lt;- paste(object_name,</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">79</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        "and fixed parameter(s)",</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">80</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        paste(names(x$bparms.fixed), collapse = ", "))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">81</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">82</td>
+                    <td class="coverage">2<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    return(object_name)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">83</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">84</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">85</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Check if we have replicates in the data</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">86</td>
+                    <td class="coverage">2<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (max(aggregate(object$data$observed,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">87</td>
+                    <td class="coverage">2<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    by = list(object$data$variable, object$data$time), length)$x) == 1) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">88</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    stop("Not defined for fits to data without replicates")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">89</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">90</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">91</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  data_anova &lt;- object$data</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">92</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  data_anova$time &lt;- as.factor(data_anova$time)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">93</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  data_anova$variable &lt;- as.factor(data_anova$variable)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">94</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (nlevels(data_anova$variable) == 1) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">95</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    object_2 &lt;- lm(observed ~ time - 1, data = data_anova)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">96</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">97</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    object_2 &lt;- lm(observed ~ variable:time - 1,</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">98</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      data = data_anova)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">99</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">100</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">101</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  object_2$mkinmod &lt;- list(name = "ANOVA")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">102</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  object_2$err_mod &lt;- "const"</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">103</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  sigma_mle &lt;- sqrt(sum(residuals(object_2)^2)/nobs(object_2))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">104</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  object_2$logLik &lt;- sum(dnorm(x = object_2$residuals,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">105</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      mean = 0, sd = sigma_mle, log = TRUE))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">106</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  object_2$data &lt;- object$data # to make the nobs.mkinfit method work</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">107</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  object_2$bparms.optim &lt;- coef(object_2)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">108</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  object_2$errparms &lt;- 1 # We have estimated one error model parameter</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">109</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  class(object_2) &lt;- "mkinfit"</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">110</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">111</td>
+                    <td class="coverage">1<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  lmtest::lrtest.default(object_2, object, name = name_function)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">112</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                </tbody>
+              </table>
+            </div>
+            <div id="R/add_err.R" class="hidden">
+              <table class="table-condensed">
+                <tbody>
+                  <tr class="never">
+                    <td class="num">1</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Add normally distributed errors to simulated kinetic degradation data</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">2</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' </pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">3</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Normally distributed errors are added to data predicted for a specific</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">4</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' degradation model using \code{\link{mkinpredict}}. The variance of the error</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">5</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' may depend on the predicted value and is specified as a standard deviation.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">6</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' </pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">7</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param prediction A prediction from a kinetic model as produced by</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">8</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   \code{\link{mkinpredict}}.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">9</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param sdfunc A function taking the predicted value as its only argument and</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">10</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   returning a standard deviation that should be used for generating the</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">11</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   random error terms for this value.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">12</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param secondary The names of state variables that should have an initial</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">13</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   value of zero</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">14</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param n The number of datasets to be generated.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">15</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param LOD The limit of detection (LOD). Values that are below the LOD after</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">16</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   adding the random error will be set to NA.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">17</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param reps The number of replicates to be generated within the datasets.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">18</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param digits The number of digits to which the values will be rounded.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">19</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param seed The seed used for the generation of random numbers. If NA, the</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">20</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   seed is not set.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">21</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @importFrom stats rnorm</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">22</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @return A list of datasets compatible with \code{\link{mmkin}}, i.e. the</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">23</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   components of the list are datasets compatible with \code{\link{mkinfit}}.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">24</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @author Johannes Ranke</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">25</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @references Ranke J and Lehmann R (2015) To t-test or not to t-test, that is</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">26</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' the question. XV Symposium on Pesticide Chemistry 2-4 September 2015,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">27</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Piacenza, Italy</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">28</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' https://jrwb.de/posters/piacenza_2015.pdf</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">29</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @examples</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">30</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' </pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">31</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # The kinetic model</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">32</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' m_SFO_SFO &lt;- mkinmod(parent = mkinsub("SFO", "M1"),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">33</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'                      M1 = mkinsub("SFO"), use_of_ff = "max")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">34</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' </pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">35</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # Generate a prediction for a specific set of parameters</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">36</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">37</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' </pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">38</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # This is the prediction used for the "Type 2 datasets" on the Piacenza poster</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">39</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # from 2015</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">40</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' d_SFO_SFO &lt;- mkinpredict(m_SFO_SFO,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">41</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'                          c(k_parent = 0.1, f_parent_to_M1 = 0.5,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">42</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'                            k_M1 = log(2)/1000),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">43</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'                          c(parent = 100, M1 = 0),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">44</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'                          sampling_times)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">45</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' </pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">46</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # Add an error term with a constant (independent of the value) standard deviation</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">47</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # of 10, and generate three datasets</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">48</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' d_SFO_SFO_err &lt;- add_err(d_SFO_SFO, function(x) 10, n = 3, seed = 123456789 )</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">49</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' </pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">50</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # Name the datasets for nicer plotting</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">51</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' names(d_SFO_SFO_err) &lt;- paste("Dataset", 1:3)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">52</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' </pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">53</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # Name the model in the list of models (with only one member in this case) for</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">54</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # nicer plotting later on.  Be quiet and use only one core not to offend CRAN</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">55</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # checks</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">56</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' \dontrun{</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">57</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' f_SFO_SFO &lt;- mmkin(list("SFO-SFO" = m_SFO_SFO),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">58</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'                    d_SFO_SFO_err, cores = 1,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">59</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'                    quiet = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">60</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' </pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">61</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' plot(f_SFO_SFO)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">62</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' </pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">63</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # We would like to inspect the fit for dataset 3 more closely</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">64</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # Using double brackets makes the returned object an mkinfit object</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">65</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # instead of a list of mkinfit objects, so plot.mkinfit is used</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">66</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' plot(f_SFO_SFO[[3]], show_residuals = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">67</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' </pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">68</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # If we use single brackets, we should give two indices (model and dataset),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">69</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' # and plot.mmkin is used</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">70</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' plot(f_SFO_SFO[1, 3])</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">71</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">72</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' </pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">73</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">74</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">add_err &lt;- function(prediction, sdfunc, secondary = c("M1", "M2"),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">75</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  n = 10, LOD = 0.1, reps = 2, digits = 1, seed = NA)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">76</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">{</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">77</td>
+                    <td class="coverage">842<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (!is.na(seed)) set.seed(seed)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">78</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">79</td>
+                    <td class="coverage">862<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  prediction &lt;- as.data.frame(prediction)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">80</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">81</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # The output of mkinpredict is in wide format</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">82</td>
+                    <td class="coverage">862<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  d_long = mkin_wide_to_long(prediction, time = "time")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">83</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">84</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Set up the list to be returned</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">85</td>
+                    <td class="coverage">862<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  d_return = list()</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">86</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">87</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Generate datasets one by one in a loop</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">88</td>
+                    <td class="coverage">862<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  for (i in 1:n) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">89</td>
+                    <td class="coverage">1712<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    d_rep = data.frame(lapply(d_long, rep, each = reps))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">90</td>
+                    <td class="coverage">1712<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    d_rep$value = rnorm(length(d_rep$value), d_rep$value, sdfunc(d_rep$value))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">91</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">92</td>
+                    <td class="coverage">1712<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    d_rep[d_rep$time == 0 &amp; d_rep$name %in% secondary, "value"] &lt;- 0</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">93</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">94</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # Set values below the LOD to NA</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">95</td>
+                    <td class="coverage">1712<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    d_NA &lt;- transform(d_rep, value = ifelse(value &lt; LOD, NA, value))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">96</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">97</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    # Round the values for convenience</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">98</td>
+                    <td class="coverage">1712<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    d_NA$value &lt;- round(d_NA$value, digits)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">99</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">100</td>
+                    <td class="coverage">1712<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    d_return[[i]] &lt;- d_NA</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">101</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">102</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">103</td>
+                    <td class="coverage">862<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  return(d_return)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">104</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                </tbody>
+              </table>
+            </div>
+            <div id="R/mkin_wide_to_long.R" class="hidden">
+              <table class="table-condensed">
+                <tbody>
+                  <tr class="never">
+                    <td class="num">1</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">utils::globalVariables(c("name", "time", "value"))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">2</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">3</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Convert a dataframe with observations over time into long format</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">4</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' </pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">5</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' This function simply takes a dataframe with one independent variable and</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">6</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' several dependent variable and converts it into the long form as required by</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">7</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' \code{\link{mkinfit}}.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">8</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' </pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">9</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param wide_data The dataframe must contain one variable with the time</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">10</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   values specified by the \code{time} argument and usually more than one</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">11</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   column of observed values.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">12</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param time The name of the time variable.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">13</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @return Dataframe in long format as needed for \code{\link{mkinfit}}.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">14</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @author Johannes Ranke</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">15</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @keywords manip</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">16</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @examples</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">17</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' </pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">18</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' wide &lt;- data.frame(t = c(1,2,3), x = c(1,4,7), y = c(3,4,5))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">19</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' mkin_wide_to_long(wide)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">20</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' </pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">21</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">22</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">mkin_wide_to_long &lt;- function(wide_data, time = "t")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">23</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">{</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">24</td>
+                    <td class="coverage">1127<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  wide_data &lt;- as.data.frame(wide_data)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">25</td>
+                    <td class="coverage">1127<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  colnames &lt;- names(wide_data)</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">26</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (!(time %in% colnames)) stop("The data in wide format have to contain a variable named ", time, ".")</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">27</td>
+                    <td class="coverage">1127<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  vars &lt;- subset(colnames, colnames != time)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">28</td>
+                    <td class="coverage">1127<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  n &lt;- length(colnames) - 1</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">29</td>
+                    <td class="coverage">1127<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  long_data &lt;- data.frame(</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">30</td>
+                    <td class="coverage">1127<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    name = rep(vars, each = length(wide_data[[time]])),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">31</td>
+                    <td class="coverage">1127<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    time = as.numeric(rep(wide_data[[time]], n)),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">32</td>
+                    <td class="coverage">1127<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    value = as.numeric(unlist(wide_data[vars])),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">33</td>
+                    <td class="coverage">1127<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    row.names = NULL)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">34</td>
+                    <td class="coverage">1127<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  return(long_data)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">35</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                </tbody>
+              </table>
+            </div>
+            <div id="R/parent_solutions.R" class="hidden">
+              <table class="table-condensed">
+                <tbody>
+                  <tr class="never">
+                    <td class="num">1</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Single First-Order kinetics</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">2</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">3</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Function describing exponential decline from a defined starting value.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">4</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">5</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @family parent solutions</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">6</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param t Time.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">7</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param parent_0 Starting value for the response variable at time zero.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">8</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param k Kinetic rate constant.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">9</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @return The value of the response variable at time \code{t}.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">10</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @references</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">11</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' FOCUS (2006) \dQuote{Guidance Document on Estimating Persistence</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">12</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   and Degradation Kinetics from Environmental Fate Studies on Pesticides in</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">13</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   EU Registration} Report of the FOCUS Work Group on Degradation Kinetics,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">14</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   EC Document Reference Sanco/10058/2005 version 2.0, 434 pp,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">15</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   \url{http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">16</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' FOCUS (2014) \dQuote{Generic guidance for Estimating Persistence</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">17</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   and Degradation Kinetics from Environmental Fate Studies on Pesticides in</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">18</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   EU Registration} Report of the FOCUS Work Group on Degradation Kinetics,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">19</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   Version 1.1, 18 December 2014</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">20</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   \url{http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">21</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @examples</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">22</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">23</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   \dontrun{plot(function(x) SFO.solution(x, 100, 3), 0, 2)}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">24</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">25</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">26</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">SFO.solution &lt;- function(t, parent_0, k)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">27</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">{</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">28</td>
+                    <td class="coverage">2338849<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  parent = parent_0 * exp(-k * t)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">29</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">30</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">31</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' First-Order Multi-Compartment kinetics</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">32</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">33</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Function describing exponential decline from a defined starting value, with</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">34</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' a decreasing rate constant.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">35</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">36</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' The form given here differs slightly from the original reference by</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">37</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Gustafson and Holden (1990). The parameter \code{beta} corresponds to 1/beta</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">38</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' in the original equation.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">39</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">40</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @family parent solutions</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">41</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @inherit SFO.solution</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">42</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param alpha Shape parameter determined by coefficient of variation of rate</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">43</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' constant values.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">44</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param beta Location parameter.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">45</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @note The solution of the FOMC kinetic model reduces to the</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">46</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' \code{\link{SFO.solution}} for large values of \code{alpha} and \code{beta}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">47</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' with \eqn{k = \frac{\beta}{\alpha}}{k = beta/alpha}.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">48</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @references</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">49</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' FOCUS (2006) \dQuote{Guidance Document on Estimating Persistence</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">50</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   and Degradation Kinetics from Environmental Fate Studies on Pesticides in</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">51</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   EU Registration} Report of the FOCUS Work Group on Degradation Kinetics,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">52</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   EC Document Reference Sanco/10058/2005 version 2.0, 434 pp,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">53</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   \url{http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">54</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">55</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' FOCUS (2014) \dQuote{Generic guidance for Estimating Persistence</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">56</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   and Degradation Kinetics from Environmental Fate Studies on Pesticides in</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">57</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   EU Registration} Report of the FOCUS Work Group on Degradation Kinetics,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">58</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   Version 1.1, 18 December 2014</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">59</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   \url{http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">60</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">61</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   Gustafson DI and Holden LR (1990) Nonlinear pesticide dissipation in soil:</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">62</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   A new model based on spatial variability. \emph{Environmental Science and</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">63</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   Technology} \bold{24}, 1032-1038</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">64</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @examples</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">65</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">66</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   plot(function(x) FOMC.solution(x, 100, 10, 2), 0, 2, ylim = c(0, 100))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">67</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">68</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">69</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">FOMC.solution &lt;- function(t, parent_0, alpha, beta)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">70</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">{</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">71</td>
+                    <td class="coverage">32626<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  parent = parent_0 / (t/beta + 1)^alpha</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">72</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">73</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">74</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Indeterminate order rate equation kinetics</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">75</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">76</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Function describing exponential decline from a defined starting value, with</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">77</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' a concentration dependent rate constant.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">78</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">79</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @family parent solutions</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">80</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @inherit SFO.solution</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">81</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param k__iore Rate constant. Note that this depends on the concentration</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">82</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' units used.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">83</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param N Exponent describing the nonlinearity of the rate equation</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">84</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @note The solution of the IORE kinetic model reduces to the</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">85</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' \code{\link{SFO.solution}} if N = 1.  The parameters of the IORE model can</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">86</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' be transformed to equivalent parameters of the FOMC mode - see the NAFTA</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">87</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' guidance for details.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">88</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @references NAFTA Technical Working Group on Pesticides (not dated) Guidance</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">89</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' for Evaluating and Calculating Degradation Kinetics in Environmental Media</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">90</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @examples</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">91</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">92</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   plot(function(x) IORE.solution(x, 100, 0.2, 1.3), 0, 2, ylim = c(0, 100))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">93</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   \dontrun{</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">94</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     fit.fomc &lt;- mkinfit("FOMC", FOCUS_2006_C, quiet = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">95</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     fit.iore &lt;- mkinfit("IORE", FOCUS_2006_C, quiet = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">96</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     fit.iore.deS &lt;- mkinfit("IORE", FOCUS_2006_C, solution_type = "deSolve", quiet = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">97</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">98</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     print(data.frame(fit.fomc$par, fit.iore$par, fit.iore.deS$par,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">99</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'                      row.names = paste("model par", 1:4)))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">100</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     print(rbind(fomc = endpoints(fit.fomc)$distimes, iore = endpoints(fit.iore)$distimes,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">101</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'                 iore.deS = endpoints(fit.iore)$distimes))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">102</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">103</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">104</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">105</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">IORE.solution &lt;- function(t, parent_0, k__iore, N)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">106</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">{</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">107</td>
+                    <td class="coverage">42328<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  parent = (parent_0^(1 - N) - (1 - N) * k__iore * t)^(1/(1 - N))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">108</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">109</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">110</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Double First-Order in Parallel kinetics</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">111</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">112</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Function describing decline from a defined starting value using the sum of</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">113</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' two exponential decline functions.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">114</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">115</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @family parent solutions</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">116</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @inherit SFO.solution</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">117</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param t Time.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">118</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param k1 First kinetic constant.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">119</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param k2 Second kinetic constant.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">120</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param g Fraction of the starting value declining according to the first</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">121</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' kinetic constant.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">122</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @examples</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">123</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">124</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   plot(function(x) DFOP.solution(x, 100, 5, 0.5, 0.3), 0, 4, ylim = c(0,100))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">125</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">126</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">127</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">DFOP.solution &lt;- function(t, parent_0, k1, k2, g)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">128</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">{</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">129</td>
+                    <td class="coverage">1904176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  parent = g * parent_0 * exp(-k1 * t) +</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">130</td>
+                    <td class="coverage">1904176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">     (1 - g) * parent_0 * exp(-k2 * t)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">131</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">132</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">133</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Hockey-Stick kinetics</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">134</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">135</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Function describing two exponential decline functions with a break point</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">136</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' between them.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">137</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">138</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @family parent solutions</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">139</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @inherit DFOP.solution</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">140</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param tb Break point. Before this time, exponential decline according to</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">141</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' \code{k1} is calculated, after this time, exponential decline proceeds</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">142</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' according to \code{k2}.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">143</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @examples</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">144</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">145</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   plot(function(x) HS.solution(x, 100, 2, 0.3, 0.5), 0, 2, ylim=c(0,100))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">146</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">147</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">148</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">HS.solution &lt;- function(t, parent_0, k1, k2, tb)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">149</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">{</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">150</td>
+                    <td class="coverage">22552<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  parent = ifelse(t &lt;= tb,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">151</td>
+                    <td class="coverage">22552<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    parent_0 * exp(-k1 * t),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">152</td>
+                    <td class="coverage">22552<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    parent_0 * exp(-k1 * tb) * exp(-k2 * (t - tb)))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">153</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">154</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">155</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Single First-Order Reversible Binding kinetics</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">156</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">157</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Function describing the solution of the differential equations describing</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">158</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' the kinetic model with first-order terms for a two-way transfer from a free</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">159</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' to a bound fraction, and a first-order degradation term for the free</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">160</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' fraction.  The initial condition is a defined amount in the free fraction</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">161</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' and no substance in the bound fraction.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">162</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">163</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @family parent solutions</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">164</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @inherit SFO.solution</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">165</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param k_12 Kinetic constant describing transfer from free to bound.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">166</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param k_21 Kinetic constant describing transfer from bound to free.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">167</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param k_1output Kinetic constant describing degradation of the free</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">168</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' fraction.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">169</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @return The value of the response variable, which is the sum of free and</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">170</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' bound fractions at time \code{t}.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">171</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @examples</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">172</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">173</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   \dontrun{plot(function(x) SFORB.solution(x, 100, 0.5, 2, 3), 0, 2)}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">174</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">175</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">176</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">SFORB.solution = function(t, parent_0, k_12, k_21, k_1output) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">177</td>
+                    <td class="coverage">9240<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  sqrt_exp = sqrt(1/4 * (k_12 + k_21 + k_1output)^2 - k_1output * k_21)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">178</td>
+                    <td class="coverage">9240<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  b1 = 0.5 * (k_12 + k_21 + k_1output) + sqrt_exp</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">179</td>
+                    <td class="coverage">9240<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  b2 = 0.5 * (k_12 + k_21 + k_1output) - sqrt_exp</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">180</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">181</td>
+                    <td class="coverage">9240<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  parent = parent_0 *</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">182</td>
+                    <td class="coverage">9240<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        (((k_12 + k_21 - b1)/(b2 - b1)) * exp(-b1 * t) +</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">183</td>
+                    <td class="coverage">9240<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">        ((k_12 + k_21 - b2)/(b1 - b2)) * exp(-b2 * t))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">184</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">185</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">186</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Logistic kinetics</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">187</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">188</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Function describing exponential decline from a defined starting value, with</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">189</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' an increasing rate constant, supposedly caused by microbial growth</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">190</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">191</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @family parent solutions</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">192</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @inherit SFO.solution</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">193</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param kmax Maximum rate constant.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">194</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param k0 Minimum rate constant effective at time zero.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">195</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param r Growth rate of the increase in the rate constant.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">196</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @note The solution of the logistic model reduces to the</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">197</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' \code{\link{SFO.solution}} if \code{k0} is equal to \code{kmax}.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">198</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @examples</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">199</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">200</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   # Reproduce the plot on page 57 of FOCUS (2014)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">201</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   plot(function(x) logistic.solution(x, 100, 0.08, 0.0001, 0.2),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">202</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'        from = 0, to = 100, ylim = c(0, 100),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">203</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'        xlab = "Time", ylab = "Residue")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">204</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   plot(function(x) logistic.solution(x, 100, 0.08, 0.0001, 0.4),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">205</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'        from = 0, to = 100, add = TRUE, lty = 2, col = 2)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">206</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   plot(function(x) logistic.solution(x, 100, 0.08, 0.0001, 0.8),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">207</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'        from = 0, to = 100, add = TRUE, lty = 3, col = 3)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">208</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   plot(function(x) logistic.solution(x, 100, 0.08, 0.001, 0.2),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">209</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'        from = 0, to = 100, add = TRUE, lty = 4, col = 4)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">210</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   plot(function(x) logistic.solution(x, 100, 0.08, 0.08, 0.2),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">211</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'        from = 0, to = 100, add = TRUE, lty = 5, col = 5)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">212</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   legend("topright", inset = 0.05,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">213</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'          legend = paste0("k0 = ", c(0.0001, 0.0001, 0.0001, 0.001, 0.08),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">214</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'                          ", r = ", c(0.2, 0.4, 0.8, 0.2, 0.2)),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">215</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'          lty = 1:5, col = 1:5)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">216</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">217</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   # Fit with synthetic data</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">218</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   logistic &lt;- mkinmod(parent = mkinsub("logistic"))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">219</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">220</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">221</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   parms_logistic &lt;- c(kmax = 0.08, k0 = 0.0001, r = 0.2)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">222</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   d_logistic &lt;- mkinpredict(logistic,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">223</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     parms_logistic, c(parent = 100),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">224</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     sampling_times)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">225</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   d_2_1 &lt;- add_err(d_logistic,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">226</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     sdfunc = function(x) sigma_twocomp(x, 0.5, 0.07),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">227</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     n = 1, reps = 2, digits = 5, LOD = 0.1, seed = 123456)[[1]]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">228</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">229</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   m &lt;- mkinfit("logistic", d_2_1, quiet = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">230</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   plot_sep(m)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">231</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   summary(m)$bpar</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">232</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   endpoints(m)$distimes</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">233</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">234</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">235</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">logistic.solution &lt;- function(t, parent_0, kmax, k0, r)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">236</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">{</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">237</td>
+                    <td class="coverage">56304<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  parent = parent_0 * (kmax / (kmax - k0 + k0 * exp (r * t))) ^(kmax/r)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">238</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                </tbody>
+              </table>
+            </div>
+            <div id="R/llhist.R" class="hidden">
+              <table class="table-condensed">
+                <tbody>
+                  <tr class="never">
+                    <td class="num">1</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Plot the distribution of log likelihoods from multistart objects</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">2</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">3</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Produces a histogram of log-likelihoods. In addition, the likelihood of the</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">4</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' original fit is shown as a red vertical line.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">5</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">6</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param object The [multistart] object</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">7</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param breaks Passed to [hist]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">8</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param lpos Positioning of the legend.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">9</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param main Title of the plot</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">10</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param \dots Passed to [hist]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">11</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @seealso [multistart]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">12</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">13</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">llhist &lt;- function(object, breaks = "Sturges", lpos = "topleft", main = "",</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">14</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  ...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">15</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">{</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">16</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  oldpar &lt;- par(no.readonly = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">17</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  on.exit(par(oldpar, no.readonly = TRUE))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">18</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">19</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  if (inherits(object, "multistart.saem.mmkin")) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">20</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    llfunc &lt;- function(object) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">21</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      if (inherits(object$so, "try-error")) return(NA)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">22</td>
+                    <td class="coverage">1408<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">      else return(logLik(object$so))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">23</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">24</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  } else {</pre>
+                    </td>
+                  </tr>
+                  <tr class="missed">
+                    <td class="num">25</td>
+                    <td class="coverage">!</td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    stop("llhist is only implemented for multistart.saem.mmkin objects")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">26</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">27</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">28</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  ll_orig &lt;- logLik(attr(object, "orig"))</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">29</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  ll &lt;- stats::na.omit(sapply(object, llfunc))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">30</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">31</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  par(las = 1)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">32</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  h &lt;- hist(ll, freq = TRUE,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">33</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    xlab = "", main = main,</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">34</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    ylab = "Frequency of log likelihoods", breaks = breaks, ...)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">35</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">36</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  freq_factor &lt;- h$counts[1] / h$density[1]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">37</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">38</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  abline(v = ll_orig, col = 2)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">39</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r"></pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">40</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  legend(lpos, inset = c(0.05, 0.05), bty = "n",</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">41</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    lty = 1, col = c(2),</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">42</td>
+                    <td class="coverage">176<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">    legend = "original fit")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">43</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                </tbody>
+              </table>
+            </div>
+            <div id="R/logLik.mkinfit.R" class="hidden">
+              <table class="table-condensed">
+                <tbody>
+                  <tr class="never">
+                    <td class="num">1</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Calculated the log-likelihood of a fitted mkinfit object</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">2</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">3</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' This function returns the product of the likelihood densities of each</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">4</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' observed value, as calculated as part of the fitting procedure using</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">5</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' \code{\link{dnorm}}, i.e. assuming normal distribution, and with the means</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">6</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' predicted by the degradation model, and the standard deviations predicted by</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">7</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' the error model.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">8</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">9</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' The total number of estimated parameters returned with the value of the</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">10</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' likelihood is calculated as the sum of fitted degradation model parameters</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">11</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' and the fitted error model parameters.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">12</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">13</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param object An object of class \code{\link{mkinfit}}.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">14</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param \dots For compatibility with the generic method</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">15</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @return An object of class \code{\link{logLik}} with the number of estimated</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">16</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   parameters (degradation model parameters plus variance model parameters)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">17</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   as attribute.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">18</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @author Johannes Ranke</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">19</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @seealso Compare the AIC of columns of \code{\link{mmkin}} objects using</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">20</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   \code{\link{AIC.mmkin}}.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">21</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @examples</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">22</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">23</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   \dontrun{</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">24</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   sfo_sfo &lt;- mkinmod(</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">25</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     parent = mkinsub("SFO", to = "m1"),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">26</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'     m1 = mkinsub("SFO")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">27</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   )</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">28</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   d_t &lt;- subset(FOCUS_2006_D, value != 0)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">29</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   f_nw &lt;- mkinfit(sfo_sfo, d_t, quiet = TRUE) # no weighting (weights are unity)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">30</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   f_obs &lt;- update(f_nw, error_model = "obs")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">31</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   f_tc &lt;- update(f_nw, error_model = "tc")</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">32</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   AIC(f_nw, f_obs, f_tc)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">33</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">34</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">35</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">36</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">logLik.mkinfit &lt;- function(object, ...) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">37</td>
+                    <td class="coverage">166798<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  val &lt;- object$logLik</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">38</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  # Number of estimated parameters</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">39</td>
+                    <td class="coverage">166798<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  attr(val, "df") &lt;- length(object$bparms.optim) + length(object$errparms)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">40</td>
+                    <td class="coverage">166798<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  attr(val, "nobs") &lt;- nobs(object)</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">41</td>
+                    <td class="coverage">166798<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  class(val) &lt;- "logLik"</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">42</td>
+                    <td class="coverage">166798<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  return(val)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">43</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                </tbody>
+              </table>
+            </div>
+            <div id="R/mkinsub.R" class="hidden">
+              <table class="table-condensed">
+                <tbody>
+                  <tr class="never">
+                    <td class="num">1</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @rdname mkinmod</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">2</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param submodel Character vector of length one to specify the submodel type.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">3</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   See \code{\link{mkinmod}} for the list of allowed submodel names.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">4</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param to Vector of the names of the state variable to which a</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">5</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   transformation shall be included in the model.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">6</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param sink Should a pathway to sink be included in the model in addition to</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">7</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   the pathways to other state variables?</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">8</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param full_name An optional name to be used e.g. for plotting fits</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">9</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   performed with the model.  You can use non-ASCII characters here, but then</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">10</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   your R code will not be portable, \emph{i.e.} may produce unintended plot</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">11</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   results on other operating systems or system configurations.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">12</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @return A list for use with \code{\link{mkinmod}}.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">13</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">14</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">mkinsub &lt;- function(submodel, to = NULL, sink = TRUE, full_name = NA)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">15</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">{</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">16</td>
+                    <td class="coverage">9864<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  return(list(type = submodel, to = to, sink = sink, full_name = full_name))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">17</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                </tbody>
+              </table>
+            </div>
+            <div id="R/sigma_twocomp.R" class="hidden">
+              <table class="table-condensed">
+                <tbody>
+                  <tr class="never">
+                    <td class="num">1</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Two-component error model</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">2</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">3</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' Function describing the standard deviation of the measurement error in</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">4</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' dependence of the measured value \eqn{y}:</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">5</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">6</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' \deqn{\sigma = \sqrt{ \sigma_{low}^2 + y^2 * {rsd}_{high}^2}} sigma =</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">7</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' sqrt(sigma_low^2 + y^2 * rsd_high^2)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">8</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">9</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' This is the error model used for example by Werner et al. (1978). The model</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">10</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' proposed by Rocke and Lorenzato (1995) can be written in this form as well,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">11</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' but assumes approximate lognormal distribution of errors for high values of</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">12</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' y.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">13</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">14</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param y The magnitude of the observed value</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">15</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param sigma_low The asymptotic minimum of the standard deviation for low</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">16</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   observed values</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">17</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @param rsd_high The coefficient describing the increase of the standard</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">18</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   deviation with the magnitude of the observed value</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">19</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @return The standard deviation of the response variable.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">20</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @references Werner, Mario, Brooks, Samuel H., and Knott, Lancaster B. (1978)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">21</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   Additive, Multiplicative, and Mixed Analytical Errors. Clinical Chemistry</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">22</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   24(11), 1895-1898.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">23</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">24</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   Rocke, David M. and Lorenzato, Stefan (1995) A two-component model for</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">25</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   measurement error in analytical chemistry. Technometrics 37(2), 176-184.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">26</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">27</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   Ranke J and Meinecke S (2019) Error Models for the Kinetic Evaluation of Chemical</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">28</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   Degradation Data. *Environments* 6(12) 124</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">29</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   \doi{10.3390/environments6120124}.</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">30</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">31</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @examples</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">32</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' times &lt;- c(0, 1, 3, 7, 14, 28, 60, 90, 120)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">33</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' d_pred &lt;- data.frame(time = times, parent = 100 * exp(- 0.03 * times))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">34</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' set.seed(123456)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">35</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' d_syn &lt;- add_err(d_pred, function(y) sigma_twocomp(y, 1, 0.07),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">36</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   reps = 2, n = 1)[[1]]</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">37</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' f_nls &lt;- nls(value ~ SSasymp(time, 0, parent_0, lrc), data = d_syn,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">38</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'  start = list(parent_0 = 100, lrc = -3))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">39</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' library(nlme)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">40</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' f_gnls &lt;- gnls(value ~ SSasymp(time, 0, parent_0, lrc),</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">41</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   data = d_syn, na.action = na.omit,</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">42</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   start = list(parent_0 = 100, lrc = -3))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">43</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' if (length(findFunction("varConstProp")) &gt; 0) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">44</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   f_gnls_tc &lt;- update(f_gnls, weights = varConstProp())</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">45</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#'   f_gnls_tc_sf &lt;- update(f_gnls_tc, control = list(sigma = 1))</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">46</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' }</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">47</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' f_mkin &lt;- mkinfit("SFO", d_syn, error_model = "const", quiet = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">48</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' f_mkin_tc &lt;- mkinfit("SFO", d_syn, error_model = "tc", quiet = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">49</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' plot_res(f_mkin_tc, standardized = TRUE)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">50</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' AIC(f_nls, f_gnls, f_gnls_tc, f_gnls_tc_sf, f_mkin, f_mkin_tc)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">51</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">#' @export</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">52</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">sigma_twocomp &lt;- function(y, sigma_low, rsd_high) {</pre>
+                    </td>
+                  </tr>
+                  <tr class="covered">
+                    <td class="num">53</td>
+                    <td class="coverage">4250<em>x</em></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">  sqrt(sigma_low^2 + y^2 * rsd_high^2)</pre>
+                    </td>
+                  </tr>
+                  <tr class="never">
+                    <td class="num">54</td>
+                    <td class="coverage"></td>
+                    <td class="col-sm-12">
+                      <pre class="language-r">}</pre>
+                    </td>
+                  </tr>
+                </tbody>
+              </table>
+            </div>
+            <script>$('div#files pre').each(function(i, block) {
+    hljs.highlightBlock(block);
+});</script>
+          </div>
+        </div>
+      </div>
+    </div>
+  </div>
+</div>
+</body>
+</html>
diff --git a/docs/coverage/lib/bootstrap-3.3.5/css/bootstrap-theme.min.css b/docs/coverage/lib/bootstrap-3.3.5/css/bootstrap-theme.min.css
new file mode 100644
index 00000000..61358b13
--- /dev/null
+++ b/docs/coverage/lib/bootstrap-3.3.5/css/bootstrap-theme.min.css
@@ -0,0 +1,5 @@
+/*!
+ * Bootstrap v3.3.5 (http://getbootstrap.com)
+ * Copyright 2011-2015 Twitter, Inc.
+ * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
+ */.btn-danger,.btn-default,.btn-info,.btn-primary,.btn-success,.btn-warning{text-shadow:0 -1px 0 rgba(0,0,0,.2);-webkit-box-shadow:inset 0 1px 0 rgba(255,255,255,.15),0 1px 1px rgba(0,0,0,.075);box-shadow:inset 0 1px 0 rgba(255,255,255,.15),0 1px 1px rgba(0,0,0,.075)}.btn-danger.active,.btn-danger:active,.btn-default.active,.btn-default:active,.btn-info.active,.btn-info:active,.btn-primary.active,.btn-primary:active,.btn-success.active,.btn-success:active,.btn-warning.active,.btn-warning:active{-webkit-box-shadow:inset 0 3px 5px rgba(0,0,0,.125);box-shadow:inset 0 3px 5px rgba(0,0,0,.125)}.btn-danger.disabled,.btn-danger[disabled],.btn-default.disabled,.btn-default[disabled],.btn-info.disabled,.btn-info[disabled],.btn-primary.disabled,.btn-primary[disabled],.btn-success.disabled,.btn-success[disabled],.btn-warning.disabled,.btn-warning[disabled],fieldset[disabled] .btn-danger,fieldset[disabled] .btn-default,fieldset[disabled] .btn-info,fieldset[disabled] .btn-primary,fieldset[disabled] .btn-success,fieldset[disabled] .btn-warning{-webkit-box-shadow:none;box-shadow:none}.btn-danger .badge,.btn-default .badge,.btn-info .badge,.btn-primary .badge,.btn-success .badge,.btn-warning .badge{text-shadow:none}.btn.active,.btn:active{background-image:none}.btn-default{text-shadow:0 1px 0 #fff;background-image:-webkit-linear-gradient(top,#fff 0,#e0e0e0 100%);background-image:-o-linear-gradient(top,#fff 0,#e0e0e0 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#fff),to(#e0e0e0));background-image:linear-gradient(to bottom,#fff 0,#e0e0e0 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffffffff', endColorstr='#ffe0e0e0', GradientType=0);filter:progid:DXImageTransform.Microsoft.gradient(enabled=false);background-repeat:repeat-x;border-color:#dbdbdb;border-color:#ccc}.btn-default:focus,.btn-default:hover{background-color:#e0e0e0;background-position:0 -15px}.btn-default.active,.btn-default:active{background-color:#e0e0e0;border-color:#dbdbdb}.btn-default.disabled,.btn-default.disabled.active,.btn-default.disabled.focus,.btn-default.disabled:active,.btn-default.disabled:focus,.btn-default.disabled:hover,.btn-default[disabled],.btn-default[disabled].active,.btn-default[disabled].focus,.btn-default[disabled]:active,.btn-default[disabled]:focus,.btn-default[disabled]:hover,fieldset[disabled] .btn-default,fieldset[disabled] .btn-default.active,fieldset[disabled] .btn-default.focus,fieldset[disabled] .btn-default:active,fieldset[disabled] .btn-default:focus,fieldset[disabled] .btn-default:hover{background-color:#e0e0e0;background-image:none}.btn-primary{background-image:-webkit-linear-gradient(top,#337ab7 0,#265a88 100%);background-image:-o-linear-gradient(top,#337ab7 0,#265a88 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#337ab7),to(#265a88));background-image:linear-gradient(to bottom,#337ab7 0,#265a88 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff265a88', GradientType=0);filter:progid:DXImageTransform.Microsoft.gradient(enabled=false);background-repeat:repeat-x;border-color:#245580}.btn-primary:focus,.btn-primary:hover{background-color:#265a88;background-position:0 -15px}.btn-primary.active,.btn-primary:active{background-color:#265a88;border-color:#245580}.btn-primary.disabled,.btn-primary.disabled.active,.btn-primary.disabled.focus,.btn-primary.disabled:active,.btn-primary.disabled:focus,.btn-primary.disabled:hover,.btn-primary[disabled],.btn-primary[disabled].active,.btn-primary[disabled].focus,.btn-primary[disabled]:active,.btn-primary[disabled]:focus,.btn-primary[disabled]:hover,fieldset[disabled] .btn-primary,fieldset[disabled] .btn-primary.active,fieldset[disabled] .btn-primary.focus,fieldset[disabled] .btn-primary:active,fieldset[disabled] .btn-primary:focus,fieldset[disabled] .btn-primary:hover{background-color:#265a88;background-image:none}.btn-success{background-image:-webkit-linear-gradient(top,#5cb85c 0,#419641 100%);background-image:-o-linear-gradient(top,#5cb85c 0,#419641 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#5cb85c),to(#419641));background-image:linear-gradient(to bottom,#5cb85c 0,#419641 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5cb85c', endColorstr='#ff419641', GradientType=0);filter:progid:DXImageTransform.Microsoft.gradient(enabled=false);background-repeat:repeat-x;border-color:#3e8f3e}.btn-success:focus,.btn-success:hover{background-color:#419641;background-position:0 -15px}.btn-success.active,.btn-success:active{background-color:#419641;border-color:#3e8f3e}.btn-success.disabled,.btn-success.disabled.active,.btn-success.disabled.focus,.btn-success.disabled:active,.btn-success.disabled:focus,.btn-success.disabled:hover,.btn-success[disabled],.btn-success[disabled].active,.btn-success[disabled].focus,.btn-success[disabled]:active,.btn-success[disabled]:focus,.btn-success[disabled]:hover,fieldset[disabled] .btn-success,fieldset[disabled] .btn-success.active,fieldset[disabled] .btn-success.focus,fieldset[disabled] .btn-success:active,fieldset[disabled] .btn-success:focus,fieldset[disabled] .btn-success:hover{background-color:#419641;background-image:none}.btn-info{background-image:-webkit-linear-gradient(top,#5bc0de 0,#2aabd2 100%);background-image:-o-linear-gradient(top,#5bc0de 0,#2aabd2 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#5bc0de),to(#2aabd2));background-image:linear-gradient(to bottom,#5bc0de 0,#2aabd2 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5bc0de', endColorstr='#ff2aabd2', GradientType=0);filter:progid:DXImageTransform.Microsoft.gradient(enabled=false);background-repeat:repeat-x;border-color:#28a4c9}.btn-info:focus,.btn-info:hover{background-color:#2aabd2;background-position:0 -15px}.btn-info.active,.btn-info:active{background-color:#2aabd2;border-color:#28a4c9}.btn-info.disabled,.btn-info.disabled.active,.btn-info.disabled.focus,.btn-info.disabled:active,.btn-info.disabled:focus,.btn-info.disabled:hover,.btn-info[disabled],.btn-info[disabled].active,.btn-info[disabled].focus,.btn-info[disabled]:active,.btn-info[disabled]:focus,.btn-info[disabled]:hover,fieldset[disabled] .btn-info,fieldset[disabled] .btn-info.active,fieldset[disabled] .btn-info.focus,fieldset[disabled] .btn-info:active,fieldset[disabled] .btn-info:focus,fieldset[disabled] .btn-info:hover{background-color:#2aabd2;background-image:none}.btn-warning{background-image:-webkit-linear-gradient(top,#f0ad4e 0,#eb9316 100%);background-image:-o-linear-gradient(top,#f0ad4e 0,#eb9316 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#f0ad4e),to(#eb9316));background-image:linear-gradient(to bottom,#f0ad4e 0,#eb9316 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff0ad4e', endColorstr='#ffeb9316', GradientType=0);filter:progid:DXImageTransform.Microsoft.gradient(enabled=false);background-repeat:repeat-x;border-color:#e38d13}.btn-warning:focus,.btn-warning:hover{background-color:#eb9316;background-position:0 -15px}.btn-warning.active,.btn-warning:active{background-color:#eb9316;border-color:#e38d13}.btn-warning.disabled,.btn-warning.disabled.active,.btn-warning.disabled.focus,.btn-warning.disabled:active,.btn-warning.disabled:focus,.btn-warning.disabled:hover,.btn-warning[disabled],.btn-warning[disabled].active,.btn-warning[disabled].focus,.btn-warning[disabled]:active,.btn-warning[disabled]:focus,.btn-warning[disabled]:hover,fieldset[disabled] .btn-warning,fieldset[disabled] .btn-warning.active,fieldset[disabled] .btn-warning.focus,fieldset[disabled] .btn-warning:active,fieldset[disabled] .btn-warning:focus,fieldset[disabled] .btn-warning:hover{background-color:#eb9316;background-image:none}.btn-danger{background-image:-webkit-linear-gradient(top,#d9534f 0,#c12e2a 100%);background-image:-o-linear-gradient(top,#d9534f 0,#c12e2a 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#d9534f),to(#c12e2a));background-image:linear-gradient(to bottom,#d9534f 0,#c12e2a 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9534f', endColorstr='#ffc12e2a', GradientType=0);filter:progid:DXImageTransform.Microsoft.gradient(enabled=false);background-repeat:repeat-x;border-color:#b92c28}.btn-danger:focus,.btn-danger:hover{background-color:#c12e2a;background-position:0 -15px}.btn-danger.active,.btn-danger:active{background-color:#c12e2a;border-color:#b92c28}.btn-danger.disabled,.btn-danger.disabled.active,.btn-danger.disabled.focus,.btn-danger.disabled:active,.btn-danger.disabled:focus,.btn-danger.disabled:hover,.btn-danger[disabled],.btn-danger[disabled].active,.btn-danger[disabled].focus,.btn-danger[disabled]:active,.btn-danger[disabled]:focus,.btn-danger[disabled]:hover,fieldset[disabled] .btn-danger,fieldset[disabled] .btn-danger.active,fieldset[disabled] .btn-danger.focus,fieldset[disabled] .btn-danger:active,fieldset[disabled] .btn-danger:focus,fieldset[disabled] .btn-danger:hover{background-color:#c12e2a;background-image:none}.img-thumbnail,.thumbnail{-webkit-box-shadow:0 1px 2px rgba(0,0,0,.075);box-shadow:0 1px 2px rgba(0,0,0,.075)}.dropdown-menu>li>a:focus,.dropdown-menu>li>a:hover{background-color:#e8e8e8;background-image:-webkit-linear-gradient(top,#f5f5f5 0,#e8e8e8 100%);background-image:-o-linear-gradient(top,#f5f5f5 0,#e8e8e8 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#f5f5f5),to(#e8e8e8));background-image:linear-gradient(to bottom,#f5f5f5 0,#e8e8e8 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff5f5f5', endColorstr='#ffe8e8e8', GradientType=0);background-repeat:repeat-x}.dropdown-menu>.active>a,.dropdown-menu>.active>a:focus,.dropdown-menu>.active>a:hover{background-color:#2e6da4;background-image:-webkit-linear-gradient(top,#337ab7 0,#2e6da4 100%);background-image:-o-linear-gradient(top,#337ab7 0,#2e6da4 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#337ab7),to(#2e6da4));background-image:linear-gradient(to bottom,#337ab7 0,#2e6da4 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2e6da4', GradientType=0);background-repeat:repeat-x}.navbar-default{background-image:-webkit-linear-gradient(top,#fff 0,#f8f8f8 100%);background-image:-o-linear-gradient(top,#fff 0,#f8f8f8 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#fff),to(#f8f8f8));background-image:linear-gradient(to bottom,#fff 0,#f8f8f8 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffffffff', endColorstr='#fff8f8f8', GradientType=0);filter:progid:DXImageTransform.Microsoft.gradient(enabled=false);background-repeat:repeat-x;border-radius:4px;-webkit-box-shadow:inset 0 1px 0 rgba(255,255,255,.15),0 1px 5px rgba(0,0,0,.075);box-shadow:inset 0 1px 0 rgba(255,255,255,.15),0 1px 5px rgba(0,0,0,.075)}.navbar-default .navbar-nav>.active>a,.navbar-default .navbar-nav>.open>a{background-image:-webkit-linear-gradient(top,#dbdbdb 0,#e2e2e2 100%);background-image:-o-linear-gradient(top,#dbdbdb 0,#e2e2e2 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#dbdbdb),to(#e2e2e2));background-image:linear-gradient(to bottom,#dbdbdb 0,#e2e2e2 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffdbdbdb', endColorstr='#ffe2e2e2', GradientType=0);background-repeat:repeat-x;-webkit-box-shadow:inset 0 3px 9px rgba(0,0,0,.075);box-shadow:inset 0 3px 9px rgba(0,0,0,.075)}.navbar-brand,.navbar-nav>li>a{text-shadow:0 1px 0 rgba(255,255,255,.25)}.navbar-inverse{background-image:-webkit-linear-gradient(top,#3c3c3c 0,#222 100%);background-image:-o-linear-gradient(top,#3c3c3c 0,#222 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#3c3c3c),to(#222));background-image:linear-gradient(to bottom,#3c3c3c 0,#222 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff3c3c3c', endColorstr='#ff222222', GradientType=0);filter:progid:DXImageTransform.Microsoft.gradient(enabled=false);background-repeat:repeat-x;border-radius:4px}.navbar-inverse .navbar-nav>.active>a,.navbar-inverse .navbar-nav>.open>a{background-image:-webkit-linear-gradient(top,#080808 0,#0f0f0f 100%);background-image:-o-linear-gradient(top,#080808 0,#0f0f0f 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#080808),to(#0f0f0f));background-image:linear-gradient(to bottom,#080808 0,#0f0f0f 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff080808', endColorstr='#ff0f0f0f', GradientType=0);background-repeat:repeat-x;-webkit-box-shadow:inset 0 3px 9px rgba(0,0,0,.25);box-shadow:inset 0 3px 9px rgba(0,0,0,.25)}.navbar-inverse .navbar-brand,.navbar-inverse .navbar-nav>li>a{text-shadow:0 -1px 0 rgba(0,0,0,.25)}.navbar-fixed-bottom,.navbar-fixed-top,.navbar-static-top{border-radius:0}@media (max-width:767px){.navbar .navbar-nav .open .dropdown-menu>.active>a,.navbar .navbar-nav .open .dropdown-menu>.active>a:focus,.navbar .navbar-nav .open .dropdown-menu>.active>a:hover{color:#fff;background-image:-webkit-linear-gradient(top,#337ab7 0,#2e6da4 100%);background-image:-o-linear-gradient(top,#337ab7 0,#2e6da4 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#337ab7),to(#2e6da4));background-image:linear-gradient(to bottom,#337ab7 0,#2e6da4 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2e6da4', GradientType=0);background-repeat:repeat-x}}.alert{text-shadow:0 1px 0 rgba(255,255,255,.2);-webkit-box-shadow:inset 0 1px 0 rgba(255,255,255,.25),0 1px 2px rgba(0,0,0,.05);box-shadow:inset 0 1px 0 rgba(255,255,255,.25),0 1px 2px rgba(0,0,0,.05)}.alert-success{background-image:-webkit-linear-gradient(top,#dff0d8 0,#c8e5bc 100%);background-image:-o-linear-gradient(top,#dff0d8 0,#c8e5bc 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#dff0d8),to(#c8e5bc));background-image:linear-gradient(to bottom,#dff0d8 0,#c8e5bc 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffdff0d8', endColorstr='#ffc8e5bc', GradientType=0);background-repeat:repeat-x;border-color:#b2dba1}.alert-info{background-image:-webkit-linear-gradient(top,#d9edf7 0,#b9def0 100%);background-image:-o-linear-gradient(top,#d9edf7 0,#b9def0 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#d9edf7),to(#b9def0));background-image:linear-gradient(to bottom,#d9edf7 0,#b9def0 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9edf7', endColorstr='#ffb9def0', GradientType=0);background-repeat:repeat-x;border-color:#9acfea}.alert-warning{background-image:-webkit-linear-gradient(top,#fcf8e3 0,#f8efc0 100%);background-image:-o-linear-gradient(top,#fcf8e3 0,#f8efc0 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#fcf8e3),to(#f8efc0));background-image:linear-gradient(to bottom,#fcf8e3 0,#f8efc0 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#fffcf8e3', endColorstr='#fff8efc0', GradientType=0);background-repeat:repeat-x;border-color:#f5e79e}.alert-danger{background-image:-webkit-linear-gradient(top,#f2dede 0,#e7c3c3 100%);background-image:-o-linear-gradient(top,#f2dede 0,#e7c3c3 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#f2dede),to(#e7c3c3));background-image:linear-gradient(to bottom,#f2dede 0,#e7c3c3 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff2dede', endColorstr='#ffe7c3c3', GradientType=0);background-repeat:repeat-x;border-color:#dca7a7}.progress{background-image:-webkit-linear-gradient(top,#ebebeb 0,#f5f5f5 100%);background-image:-o-linear-gradient(top,#ebebeb 0,#f5f5f5 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#ebebeb),to(#f5f5f5));background-image:linear-gradient(to bottom,#ebebeb 0,#f5f5f5 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffebebeb', endColorstr='#fff5f5f5', GradientType=0);background-repeat:repeat-x}.progress-bar{background-image:-webkit-linear-gradient(top,#337ab7 0,#286090 100%);background-image:-o-linear-gradient(top,#337ab7 0,#286090 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#337ab7),to(#286090));background-image:linear-gradient(to bottom,#337ab7 0,#286090 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff286090', GradientType=0);background-repeat:repeat-x}.progress-bar-success{background-image:-webkit-linear-gradient(top,#5cb85c 0,#449d44 100%);background-image:-o-linear-gradient(top,#5cb85c 0,#449d44 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#5cb85c),to(#449d44));background-image:linear-gradient(to bottom,#5cb85c 0,#449d44 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5cb85c', endColorstr='#ff449d44', GradientType=0);background-repeat:repeat-x}.progress-bar-info{background-image:-webkit-linear-gradient(top,#5bc0de 0,#31b0d5 100%);background-image:-o-linear-gradient(top,#5bc0de 0,#31b0d5 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#5bc0de),to(#31b0d5));background-image:linear-gradient(to bottom,#5bc0de 0,#31b0d5 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5bc0de', endColorstr='#ff31b0d5', GradientType=0);background-repeat:repeat-x}.progress-bar-warning{background-image:-webkit-linear-gradient(top,#f0ad4e 0,#ec971f 100%);background-image:-o-linear-gradient(top,#f0ad4e 0,#ec971f 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#f0ad4e),to(#ec971f));background-image:linear-gradient(to bottom,#f0ad4e 0,#ec971f 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff0ad4e', endColorstr='#ffec971f', GradientType=0);background-repeat:repeat-x}.progress-bar-danger{background-image:-webkit-linear-gradient(top,#d9534f 0,#c9302c 100%);background-image:-o-linear-gradient(top,#d9534f 0,#c9302c 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#d9534f),to(#c9302c));background-image:linear-gradient(to bottom,#d9534f 0,#c9302c 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9534f', endColorstr='#ffc9302c', GradientType=0);background-repeat:repeat-x}.progress-bar-striped{background-image:-webkit-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:-o-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent)}.list-group{border-radius:4px;-webkit-box-shadow:0 1px 2px rgba(0,0,0,.075);box-shadow:0 1px 2px rgba(0,0,0,.075)}.list-group-item.active,.list-group-item.active:focus,.list-group-item.active:hover{text-shadow:0 -1px 0 #286090;background-image:-webkit-linear-gradient(top,#337ab7 0,#2b669a 100%);background-image:-o-linear-gradient(top,#337ab7 0,#2b669a 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#337ab7),to(#2b669a));background-image:linear-gradient(to bottom,#337ab7 0,#2b669a 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2b669a', GradientType=0);background-repeat:repeat-x;border-color:#2b669a}.list-group-item.active .badge,.list-group-item.active:focus .badge,.list-group-item.active:hover .badge{text-shadow:none}.panel{-webkit-box-shadow:0 1px 2px rgba(0,0,0,.05);box-shadow:0 1px 2px rgba(0,0,0,.05)}.panel-default>.panel-heading{background-image:-webkit-linear-gradient(top,#f5f5f5 0,#e8e8e8 100%);background-image:-o-linear-gradient(top,#f5f5f5 0,#e8e8e8 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#f5f5f5),to(#e8e8e8));background-image:linear-gradient(to bottom,#f5f5f5 0,#e8e8e8 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff5f5f5', endColorstr='#ffe8e8e8', GradientType=0);background-repeat:repeat-x}.panel-primary>.panel-heading{background-image:-webkit-linear-gradient(top,#337ab7 0,#2e6da4 100%);background-image:-o-linear-gradient(top,#337ab7 0,#2e6da4 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#337ab7),to(#2e6da4));background-image:linear-gradient(to bottom,#337ab7 0,#2e6da4 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2e6da4', GradientType=0);background-repeat:repeat-x}.panel-success>.panel-heading{background-image:-webkit-linear-gradient(top,#dff0d8 0,#d0e9c6 100%);background-image:-o-linear-gradient(top,#dff0d8 0,#d0e9c6 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#dff0d8),to(#d0e9c6));background-image:linear-gradient(to bottom,#dff0d8 0,#d0e9c6 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffdff0d8', endColorstr='#ffd0e9c6', GradientType=0);background-repeat:repeat-x}.panel-info>.panel-heading{background-image:-webkit-linear-gradient(top,#d9edf7 0,#c4e3f3 100%);background-image:-o-linear-gradient(top,#d9edf7 0,#c4e3f3 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#d9edf7),to(#c4e3f3));background-image:linear-gradient(to bottom,#d9edf7 0,#c4e3f3 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9edf7', endColorstr='#ffc4e3f3', GradientType=0);background-repeat:repeat-x}.panel-warning>.panel-heading{background-image:-webkit-linear-gradient(top,#fcf8e3 0,#faf2cc 100%);background-image:-o-linear-gradient(top,#fcf8e3 0,#faf2cc 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#fcf8e3),to(#faf2cc));background-image:linear-gradient(to bottom,#fcf8e3 0,#faf2cc 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#fffcf8e3', endColorstr='#fffaf2cc', GradientType=0);background-repeat:repeat-x}.panel-danger>.panel-heading{background-image:-webkit-linear-gradient(top,#f2dede 0,#ebcccc 100%);background-image:-o-linear-gradient(top,#f2dede 0,#ebcccc 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#f2dede),to(#ebcccc));background-image:linear-gradient(to bottom,#f2dede 0,#ebcccc 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff2dede', endColorstr='#ffebcccc', GradientType=0);background-repeat:repeat-x}.well{background-image:-webkit-linear-gradient(top,#e8e8e8 0,#f5f5f5 100%);background-image:-o-linear-gradient(top,#e8e8e8 0,#f5f5f5 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#e8e8e8),to(#f5f5f5));background-image:linear-gradient(to bottom,#e8e8e8 0,#f5f5f5 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffe8e8e8', endColorstr='#fff5f5f5', GradientType=0);background-repeat:repeat-x;border-color:#dcdcdc;-webkit-box-shadow:inset 0 1px 3px rgba(0,0,0,.05),0 1px 0 rgba(255,255,255,.1);box-shadow:inset 0 1px 3px rgba(0,0,0,.05),0 1px 0 rgba(255,255,255,.1)}
\ No newline at end of file
diff --git a/docs/coverage/lib/bootstrap-3.3.5/css/bootstrap.min.css b/docs/coverage/lib/bootstrap-3.3.5/css/bootstrap.min.css
new file mode 100644
index 00000000..9dc0f978
--- /dev/null
+++ b/docs/coverage/lib/bootstrap-3.3.5/css/bootstrap.min.css
@@ -0,0 +1,5 @@
+/*!
+ * Bootstrap v3.3.5 (http://getbootstrap.com)
+ * Copyright 2011-2015 Twitter, Inc.
+ * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
+ *//*! normalize.css v3.0.3 | MIT License | github.com/necolas/normalize.css */html{font-family:sans-serif;-webkit-text-size-adjust:100%;-ms-text-size-adjust:100%}body{margin:0}article,aside,details,figcaption,figure,footer,header,hgroup,main,menu,nav,section,summary{display:block}audio,canvas,progress,video{display:inline-block;vertical-align:baseline}audio:not([controls]){display:none;height:0}[hidden],template{display:none}a{background-color:transparent}a:active,a:hover{outline:0}abbr[title]{border-bottom:1px dotted}b,strong{font-weight:700}dfn{font-style:italic}h1{margin:.67em 0;font-size:2em}mark{color:#000;background:#ff0}small{font-size:80%}sub,sup{position:relative;font-size:75%;line-height:0;vertical-align:baseline}sup{top:-.5em}sub{bottom:-.25em}img{border:0}svg:not(:root){overflow:hidden}figure{margin:1em 40px}hr{height:0;-webkit-box-sizing:content-box;-moz-box-sizing:content-box;box-sizing:content-box}pre{overflow:auto}code,kbd,pre,samp{font-family:monospace,monospace;font-size:1em}button,input,optgroup,select,textarea{margin:0;font:inherit;color:inherit}button{overflow:visible}button,select{text-transform:none}button,html input[type=button],input[type=reset],input[type=submit]{-webkit-appearance:button;cursor:pointer}button[disabled],html input[disabled]{cursor:default}button::-moz-focus-inner,input::-moz-focus-inner{padding:0;border:0}input{line-height:normal}input[type=checkbox],input[type=radio]{-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;padding:0}input[type=number]::-webkit-inner-spin-button,input[type=number]::-webkit-outer-spin-button{height:auto}input[type=search]{-webkit-box-sizing:content-box;-moz-box-sizing:content-box;box-sizing:content-box;-webkit-appearance:textfield}input[type=search]::-webkit-search-cancel-button,input[type=search]::-webkit-search-decoration{-webkit-appearance:none}fieldset{padding:.35em .625em .75em;margin:0 2px;border:1px solid silver}legend{padding:0;border:0}textarea{overflow:auto}optgroup{font-weight:700}table{border-spacing:0;border-collapse:collapse}td,th{padding:0}/*! Source: https://github.com/h5bp/html5-boilerplate/blob/master/src/css/main.css */@media print{*,:after,:before{color:#000!important;text-shadow:none!important;background:0 0!important;-webkit-box-shadow:none!important;box-shadow:none!important}a,a:visited{text-decoration:underline}a[href]:after{content:" (" attr(href) ")"}abbr[title]:after{content:" (" attr(title) ")"}a[href^="javascript:"]:after,a[href^="#"]:after{content:""}blockquote,pre{border:1px solid #999;page-break-inside:avoid}thead{display:table-header-group}img,tr{page-break-inside:avoid}img{max-width:100%!important}h2,h3,p{orphans:3;widows:3}h2,h3{page-break-after:avoid}.navbar{display:none}.btn>.caret,.dropup>.btn>.caret{border-top-color:#000!important}.label{border:1px solid #000}.table{border-collapse:collapse!important}.table td,.table th{background-color:#fff!important}.table-bordered td,.table-bordered th{border:1px solid #ddd!important}}@font-face{font-family:'Glyphicons Halflings';src:url(../fonts/glyphicons-halflings-regular.eot);src:url(../fonts/glyphicons-halflings-regular.eot?#iefix) format('embedded-opentype'),url(../fonts/glyphicons-halflings-regular.woff) format('woff'),url(../fonts/glyphicons-halflings-regular.ttf) format('truetype'),url(../fonts/glyphicons-halflings-regular.svg#glyphicons_halflingsregular) format('svg')}.glyphicon{position:relative;top:1px;display:inline-block;font-family:'Glyphicons Halflings';font-style:normal;font-weight:400;line-height:1;-webkit-font-smoothing:antialiased;-moz-osx-font-smoothing:grayscale}.glyphicon-asterisk:before{content:"\2a"}.glyphicon-plus:before{content:"\2b"}.glyphicon-eur:before,.glyphicon-euro:before{content:"\20ac"}.glyphicon-minus:before{content:"\2212"}.glyphicon-cloud:before{content:"\2601"}.glyphicon-envelope:before{content:"\2709"}.glyphicon-pencil:before{content:"\270f"}.glyphicon-glass:before{content:"\e001"}.glyphicon-music:before{content:"\e002"}.glyphicon-search:before{content:"\e003"}.glyphicon-heart:before{content:"\e005"}.glyphicon-star:before{content:"\e006"}.glyphicon-star-empty:before{content:"\e007"}.glyphicon-user:before{content:"\e008"}.glyphicon-film:before{content:"\e009"}.glyphicon-th-large:before{content:"\e010"}.glyphicon-th:before{content:"\e011"}.glyphicon-th-list:before{content:"\e012"}.glyphicon-ok:before{content:"\e013"}.glyphicon-remove:before{content:"\e014"}.glyphicon-zoom-in:before{content:"\e015"}.glyphicon-zoom-out:before{content:"\e016"}.glyphicon-off:before{content:"\e017"}.glyphicon-signal:before{content:"\e018"}.glyphicon-cog:before{content:"\e019"}.glyphicon-trash:before{content:"\e020"}.glyphicon-home:before{content:"\e021"}.glyphicon-file:before{content:"\e022"}.glyphicon-time:before{content:"\e023"}.glyphicon-road:before{content:"\e024"}.glyphicon-download-alt:before{content:"\e025"}.glyphicon-download:before{content:"\e026"}.glyphicon-upload:before{content:"\e027"}.glyphicon-inbox:before{content:"\e028"}.glyphicon-play-circle:before{content:"\e029"}.glyphicon-repeat:before{content:"\e030"}.glyphicon-refresh:before{content:"\e031"}.glyphicon-list-alt:before{content:"\e032"}.glyphicon-lock:before{content:"\e033"}.glyphicon-flag:before{content:"\e034"}.glyphicon-headphones:before{content:"\e035"}.glyphicon-volume-off:before{content:"\e036"}.glyphicon-volume-down:before{content:"\e037"}.glyphicon-volume-up:before{content:"\e038"}.glyphicon-qrcode:before{content:"\e039"}.glyphicon-barcode:before{content:"\e040"}.glyphicon-tag:before{content:"\e041"}.glyphicon-tags:before{content:"\e042"}.glyphicon-book:before{content:"\e043"}.glyphicon-bookmark:before{content:"\e044"}.glyphicon-print:before{content:"\e045"}.glyphicon-camera:before{content:"\e046"}.glyphicon-font:before{content:"\e047"}.glyphicon-bold:before{content:"\e048"}.glyphicon-italic:before{content:"\e049"}.glyphicon-text-height:before{content:"\e050"}.glyphicon-text-width:before{content:"\e051"}.glyphicon-align-left:before{content:"\e052"}.glyphicon-align-center:before{content:"\e053"}.glyphicon-align-right:before{content:"\e054"}.glyphicon-align-justify:before{content:"\e055"}.glyphicon-list:before{content:"\e056"}.glyphicon-indent-left:before{content:"\e057"}.glyphicon-indent-right:before{content:"\e058"}.glyphicon-facetime-video:before{content:"\e059"}.glyphicon-picture:before{content:"\e060"}.glyphicon-map-marker:before{content:"\e062"}.glyphicon-adjust:before{content:"\e063"}.glyphicon-tint:before{content:"\e064"}.glyphicon-edit:before{content:"\e065"}.glyphicon-share:before{content:"\e066"}.glyphicon-check:before{content:"\e067"}.glyphicon-move:before{content:"\e068"}.glyphicon-step-backward:before{content:"\e069"}.glyphicon-fast-backward:before{content:"\e070"}.glyphicon-backward:before{content:"\e071"}.glyphicon-play:before{content:"\e072"}.glyphicon-pause:before{content:"\e073"}.glyphicon-stop:before{content:"\e074"}.glyphicon-forward:before{content:"\e075"}.glyphicon-fast-forward:before{content:"\e076"}.glyphicon-step-forward:before{content:"\e077"}.glyphicon-eject:before{content:"\e078"}.glyphicon-chevron-left:before{content:"\e079"}.glyphicon-chevron-right:before{content:"\e080"}.glyphicon-plus-sign:before{content:"\e081"}.glyphicon-minus-sign:before{content:"\e082"}.glyphicon-remove-sign:before{content:"\e083"}.glyphicon-ok-sign:before{content:"\e084"}.glyphicon-question-sign:before{content:"\e085"}.glyphicon-info-sign:before{content:"\e086"}.glyphicon-screenshot:before{content:"\e087"}.glyphicon-remove-circle:before{content:"\e088"}.glyphicon-ok-circle:before{content:"\e089"}.glyphicon-ban-circle:before{content:"\e090"}.glyphicon-arrow-left:before{content:"\e091"}.glyphicon-arrow-right:before{content:"\e092"}.glyphicon-arrow-up:before{content:"\e093"}.glyphicon-arrow-down:before{content:"\e094"}.glyphicon-share-alt:before{content:"\e095"}.glyphicon-resize-full:before{content:"\e096"}.glyphicon-resize-small:before{content:"\e097"}.glyphicon-exclamation-sign:before{content:"\e101"}.glyphicon-gift:before{content:"\e102"}.glyphicon-leaf:before{content:"\e103"}.glyphicon-fire:before{content:"\e104"}.glyphicon-eye-open:before{content:"\e105"}.glyphicon-eye-close:before{content:"\e106"}.glyphicon-warning-sign:before{content:"\e107"}.glyphicon-plane:before{content:"\e108"}.glyphicon-calendar:before{content:"\e109"}.glyphicon-random:before{content:"\e110"}.glyphicon-comment:before{content:"\e111"}.glyphicon-magnet:before{content:"\e112"}.glyphicon-chevron-up:before{content:"\e113"}.glyphicon-chevron-down:before{content:"\e114"}.glyphicon-retweet:before{content:"\e115"}.glyphicon-shopping-cart:before{content:"\e116"}.glyphicon-folder-close:before{content:"\e117"}.glyphicon-folder-open:before{content:"\e118"}.glyphicon-resize-vertical:before{content:"\e119"}.glyphicon-resize-horizontal:before{content:"\e120"}.glyphicon-hdd:before{content:"\e121"}.glyphicon-bullhorn:before{content:"\e122"}.glyphicon-bell:before{content:"\e123"}.glyphicon-certificate:before{content:"\e124"}.glyphicon-thumbs-up:before{content:"\e125"}.glyphicon-thumbs-down:before{content:"\e126"}.glyphicon-hand-right:before{content:"\e127"}.glyphicon-hand-left:before{content:"\e128"}.glyphicon-hand-up:before{content:"\e129"}.glyphicon-hand-down:before{content:"\e130"}.glyphicon-circle-arrow-right:before{content:"\e131"}.glyphicon-circle-arrow-left:before{content:"\e132"}.glyphicon-circle-arrow-up:before{content:"\e133"}.glyphicon-circle-arrow-down:before{content:"\e134"}.glyphicon-globe:before{content:"\e135"}.glyphicon-wrench:before{content:"\e136"}.glyphicon-tasks:before{content:"\e137"}.glyphicon-filter:before{content:"\e138"}.glyphicon-briefcase:before{content:"\e139"}.glyphicon-fullscreen:before{content:"\e140"}.glyphicon-dashboard:before{content:"\e141"}.glyphicon-paperclip:before{content:"\e142"}.glyphicon-heart-empty:before{content:"\e143"}.glyphicon-link:before{content:"\e144"}.glyphicon-phone:before{content:"\e145"}.glyphicon-pushpin:before{content:"\e146"}.glyphicon-usd:before{content:"\e148"}.glyphicon-gbp:before{content:"\e149"}.glyphicon-sort:before{content:"\e150"}.glyphicon-sort-by-alphabet:before{content:"\e151"}.glyphicon-sort-by-alphabet-alt:before{content:"\e152"}.glyphicon-sort-by-order:before{content:"\e153"}.glyphicon-sort-by-order-alt:before{content:"\e154"}.glyphicon-sort-by-attributes:before{content:"\e155"}.glyphicon-sort-by-attributes-alt:before{content:"\e156"}.glyphicon-unchecked:before{content:"\e157"}.glyphicon-expand:before{content:"\e158"}.glyphicon-collapse-down:before{content:"\e159"}.glyphicon-collapse-up:before{content:"\e160"}.glyphicon-log-in:before{content:"\e161"}.glyphicon-flash:before{content:"\e162"}.glyphicon-log-out:before{content:"\e163"}.glyphicon-new-window:before{content:"\e164"}.glyphicon-record:before{content:"\e165"}.glyphicon-save:before{content:"\e166"}.glyphicon-open:before{content:"\e167"}.glyphicon-saved:before{content:"\e168"}.glyphicon-import:before{content:"\e169"}.glyphicon-export:before{content:"\e170"}.glyphicon-send:before{content:"\e171"}.glyphicon-floppy-disk:before{content:"\e172"}.glyphicon-floppy-saved:before{content:"\e173"}.glyphicon-floppy-remove:before{content:"\e174"}.glyphicon-floppy-save:before{content:"\e175"}.glyphicon-floppy-open:before{content:"\e176"}.glyphicon-credit-card:before{content:"\e177"}.glyphicon-transfer:before{content:"\e178"}.glyphicon-cutlery:before{content:"\e179"}.glyphicon-header:before{content:"\e180"}.glyphicon-compressed:before{content:"\e181"}.glyphicon-earphone:before{content:"\e182"}.glyphicon-phone-alt:before{content:"\e183"}.glyphicon-tower:before{content:"\e184"}.glyphicon-stats:before{content:"\e185"}.glyphicon-sd-video:before{content:"\e186"}.glyphicon-hd-video:before{content:"\e187"}.glyphicon-subtitles:before{content:"\e188"}.glyphicon-sound-stereo:before{content:"\e189"}.glyphicon-sound-dolby:before{content:"\e190"}.glyphicon-sound-5-1:before{content:"\e191"}.glyphicon-sound-6-1:before{content:"\e192"}.glyphicon-sound-7-1:before{content:"\e193"}.glyphicon-copyright-mark:before{content:"\e194"}.glyphicon-registration-mark:before{content:"\e195"}.glyphicon-cloud-download:before{content:"\e197"}.glyphicon-cloud-upload:before{content:"\e198"}.glyphicon-tree-conifer:before{content:"\e199"}.glyphicon-tree-deciduous:before{content:"\e200"}.glyphicon-cd:before{content:"\e201"}.glyphicon-save-file:before{content:"\e202"}.glyphicon-open-file:before{content:"\e203"}.glyphicon-level-up:before{content:"\e204"}.glyphicon-copy:before{content:"\e205"}.glyphicon-paste:before{content:"\e206"}.glyphicon-alert:before{content:"\e209"}.glyphicon-equalizer:before{content:"\e210"}.glyphicon-king:before{content:"\e211"}.glyphicon-queen:before{content:"\e212"}.glyphicon-pawn:before{content:"\e213"}.glyphicon-bishop:before{content:"\e214"}.glyphicon-knight:before{content:"\e215"}.glyphicon-baby-formula:before{content:"\e216"}.glyphicon-tent:before{content:"\26fa"}.glyphicon-blackboard:before{content:"\e218"}.glyphicon-bed:before{content:"\e219"}.glyphicon-apple:before{content:"\f8ff"}.glyphicon-erase:before{content:"\e221"}.glyphicon-hourglass:before{content:"\231b"}.glyphicon-lamp:before{content:"\e223"}.glyphicon-duplicate:before{content:"\e224"}.glyphicon-piggy-bank:before{content:"\e225"}.glyphicon-scissors:before{content:"\e226"}.glyphicon-bitcoin:before{content:"\e227"}.glyphicon-btc:before{content:"\e227"}.glyphicon-xbt:before{content:"\e227"}.glyphicon-yen:before{content:"\00a5"}.glyphicon-jpy:before{content:"\00a5"}.glyphicon-ruble:before{content:"\20bd"}.glyphicon-rub:before{content:"\20bd"}.glyphicon-scale:before{content:"\e230"}.glyphicon-ice-lolly:before{content:"\e231"}.glyphicon-ice-lolly-tasted:before{content:"\e232"}.glyphicon-education:before{content:"\e233"}.glyphicon-option-horizontal:before{content:"\e234"}.glyphicon-option-vertical:before{content:"\e235"}.glyphicon-menu-hamburger:before{content:"\e236"}.glyphicon-modal-window:before{content:"\e237"}.glyphicon-oil:before{content:"\e238"}.glyphicon-grain:before{content:"\e239"}.glyphicon-sunglasses:before{content:"\e240"}.glyphicon-text-size:before{content:"\e241"}.glyphicon-text-color:before{content:"\e242"}.glyphicon-text-background:before{content:"\e243"}.glyphicon-object-align-top:before{content:"\e244"}.glyphicon-object-align-bottom:before{content:"\e245"}.glyphicon-object-align-horizontal:before{content:"\e246"}.glyphicon-object-align-left:before{content:"\e247"}.glyphicon-object-align-vertical:before{content:"\e248"}.glyphicon-object-align-right:before{content:"\e249"}.glyphicon-triangle-right:before{content:"\e250"}.glyphicon-triangle-left:before{content:"\e251"}.glyphicon-triangle-bottom:before{content:"\e252"}.glyphicon-triangle-top:before{content:"\e253"}.glyphicon-console:before{content:"\e254"}.glyphicon-superscript:before{content:"\e255"}.glyphicon-subscript:before{content:"\e256"}.glyphicon-menu-left:before{content:"\e257"}.glyphicon-menu-right:before{content:"\e258"}.glyphicon-menu-down:before{content:"\e259"}.glyphicon-menu-up:before{content:"\e260"}*{-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}:after,:before{-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}html{font-size:10px;-webkit-tap-highlight-color:rgba(0,0,0,0)}body{font-family:"Helvetica Neue",Helvetica,Arial,sans-serif;font-size:14px;line-height:1.42857143;color:#333;background-color:#fff}button,input,select,textarea{font-family:inherit;font-size:inherit;line-height:inherit}a{color:#337ab7;text-decoration:none}a:focus,a:hover{color:#23527c;text-decoration:underline}a:focus{outline:thin dotted;outline:5px auto -webkit-focus-ring-color;outline-offset:-2px}figure{margin:0}img{vertical-align:middle}.carousel-inner>.item>a>img,.carousel-inner>.item>img,.img-responsive,.thumbnail a>img,.thumbnail>img{display:block;max-width:100%;height:auto}.img-rounded{border-radius:6px}.img-thumbnail{display:inline-block;max-width:100%;height:auto;padding:4px;line-height:1.42857143;background-color:#fff;border:1px solid #ddd;border-radius:4px;-webkit-transition:all .2s ease-in-out;-o-transition:all .2s ease-in-out;transition:all .2s ease-in-out}.img-circle{border-radius:50%}hr{margin-top:20px;margin-bottom:20px;border:0;border-top:1px solid #eee}.sr-only{position:absolute;width:1px;height:1px;padding:0;margin:-1px;overflow:hidden;clip:rect(0,0,0,0);border:0}.sr-only-focusable:active,.sr-only-focusable:focus{position:static;width:auto;height:auto;margin:0;overflow:visible;clip:auto}[role=button]{cursor:pointer}.h1,.h2,.h3,.h4,.h5,.h6,h1,h2,h3,h4,h5,h6{font-family:inherit;font-weight:500;line-height:1.1;color:inherit}.h1 .small,.h1 small,.h2 .small,.h2 small,.h3 .small,.h3 small,.h4 .small,.h4 small,.h5 .small,.h5 small,.h6 .small,.h6 small,h1 .small,h1 small,h2 .small,h2 small,h3 .small,h3 small,h4 .small,h4 small,h5 .small,h5 small,h6 .small,h6 small{font-weight:400;line-height:1;color:#777}.h1,.h2,.h3,h1,h2,h3{margin-top:20px;margin-bottom:10px}.h1 .small,.h1 small,.h2 .small,.h2 small,.h3 .small,.h3 small,h1 .small,h1 small,h2 .small,h2 small,h3 .small,h3 small{font-size:65%}.h4,.h5,.h6,h4,h5,h6{margin-top:10px;margin-bottom:10px}.h4 .small,.h4 small,.h5 .small,.h5 small,.h6 .small,.h6 small,h4 .small,h4 small,h5 .small,h5 small,h6 .small,h6 small{font-size:75%}.h1,h1{font-size:36px}.h2,h2{font-size:30px}.h3,h3{font-size:24px}.h4,h4{font-size:18px}.h5,h5{font-size:14px}.h6,h6{font-size:12px}p{margin:0 0 10px}.lead{margin-bottom:20px;font-size:16px;font-weight:300;line-height:1.4}@media (min-width:768px){.lead{font-size:21px}}.small,small{font-size:85%}.mark,mark{padding:.2em;background-color:#fcf8e3}.text-left{text-align:left}.text-right{text-align:right}.text-center{text-align:center}.text-justify{text-align:justify}.text-nowrap{white-space:nowrap}.text-lowercase{text-transform:lowercase}.text-uppercase{text-transform:uppercase}.text-capitalize{text-transform:capitalize}.text-muted{color:#777}.text-primary{color:#337ab7}a.text-primary:focus,a.text-primary:hover{color:#286090}.text-success{color:#3c763d}a.text-success:focus,a.text-success:hover{color:#2b542c}.text-info{color:#31708f}a.text-info:focus,a.text-info:hover{color:#245269}.text-warning{color:#8a6d3b}a.text-warning:focus,a.text-warning:hover{color:#66512c}.text-danger{color:#a94442}a.text-danger:focus,a.text-danger:hover{color:#843534}.bg-primary{color:#fff;background-color:#337ab7}a.bg-primary:focus,a.bg-primary:hover{background-color:#286090}.bg-success{background-color:#dff0d8}a.bg-success:focus,a.bg-success:hover{background-color:#c1e2b3}.bg-info{background-color:#d9edf7}a.bg-info:focus,a.bg-info:hover{background-color:#afd9ee}.bg-warning{background-color:#fcf8e3}a.bg-warning:focus,a.bg-warning:hover{background-color:#f7ecb5}.bg-danger{background-color:#f2dede}a.bg-danger:focus,a.bg-danger:hover{background-color:#e4b9b9}.page-header{padding-bottom:9px;margin:40px 0 20px;border-bottom:1px solid #eee}ol,ul{margin-top:0;margin-bottom:10px}ol ol,ol ul,ul ol,ul ul{margin-bottom:0}.list-unstyled{padding-left:0;list-style:none}.list-inline{padding-left:0;margin-left:-5px;list-style:none}.list-inline>li{display:inline-block;padding-right:5px;padding-left:5px}dl{margin-top:0;margin-bottom:20px}dd,dt{line-height:1.42857143}dt{font-weight:700}dd{margin-left:0}@media (min-width:768px){.dl-horizontal dt{float:left;width:160px;overflow:hidden;clear:left;text-align:right;text-overflow:ellipsis;white-space:nowrap}.dl-horizontal dd{margin-left:180px}}abbr[data-original-title],abbr[title]{cursor:help;border-bottom:1px dotted #777}.initialism{font-size:90%;text-transform:uppercase}blockquote{padding:10px 20px;margin:0 0 20px;font-size:17.5px;border-left:5px solid #eee}blockquote ol:last-child,blockquote p:last-child,blockquote ul:last-child{margin-bottom:0}blockquote .small,blockquote footer,blockquote small{display:block;font-size:80%;line-height:1.42857143;color:#777}blockquote .small:before,blockquote footer:before,blockquote small:before{content:'\2014 \00A0'}.blockquote-reverse,blockquote.pull-right{padding-right:15px;padding-left:0;text-align:right;border-right:5px solid #eee;border-left:0}.blockquote-reverse .small:before,.blockquote-reverse footer:before,.blockquote-reverse small:before,blockquote.pull-right .small:before,blockquote.pull-right footer:before,blockquote.pull-right small:before{content:''}.blockquote-reverse .small:after,.blockquote-reverse footer:after,.blockquote-reverse small:after,blockquote.pull-right .small:after,blockquote.pull-right footer:after,blockquote.pull-right small:after{content:'\00A0 \2014'}address{margin-bottom:20px;font-style:normal;line-height:1.42857143}code,kbd,pre,samp{font-family:monospace}code{padding:2px 4px;font-size:90%;color:#c7254e;background-color:#f9f2f4;border-radius:4px}kbd{padding:2px 4px;font-size:90%;color:#fff;background-color:#333;border-radius:3px;-webkit-box-shadow:inset 0 -1px 0 rgba(0,0,0,.25);box-shadow:inset 0 -1px 0 rgba(0,0,0,.25)}kbd kbd{padding:0;font-size:100%;font-weight:700;-webkit-box-shadow:none;box-shadow:none}pre{display:block;padding:9.5px;margin:0 0 10px;font-size:13px;line-height:1.42857143;color:#333;word-break:break-all;word-wrap:break-word;background-color:#f5f5f5;border:1px solid #ccc;border-radius:4px}pre code{padding:0;font-size:inherit;color:inherit;white-space:pre-wrap;background-color:transparent;border-radius:0}.pre-scrollable{max-height:340px;overflow-y:scroll}.container{padding-right:15px;padding-left:15px;margin-right:auto;margin-left:auto}@media (min-width:768px){.container{width:750px}}@media (min-width:992px){.container{width:970px}}@media (min-width:1200px){.container{width:1170px}}.container-fluid{padding-right:15px;padding-left:15px;margin-right:auto;margin-left:auto}.row{margin-right:-15px;margin-left:-15px}.col-lg-1,.col-lg-10,.col-lg-11,.col-lg-12,.col-lg-2,.col-lg-3,.col-lg-4,.col-lg-5,.col-lg-6,.col-lg-7,.col-lg-8,.col-lg-9,.col-md-1,.col-md-10,.col-md-11,.col-md-12,.col-md-2,.col-md-3,.col-md-4,.col-md-5,.col-md-6,.col-md-7,.col-md-8,.col-md-9,.col-sm-1,.col-sm-10,.col-sm-11,.col-sm-12,.col-sm-2,.col-sm-3,.col-sm-4,.col-sm-5,.col-sm-6,.col-sm-7,.col-sm-8,.col-sm-9,.col-xs-1,.col-xs-10,.col-xs-11,.col-xs-12,.col-xs-2,.col-xs-3,.col-xs-4,.col-xs-5,.col-xs-6,.col-xs-7,.col-xs-8,.col-xs-9{position:relative;min-height:1px;padding-right:15px;padding-left:15px}.col-xs-1,.col-xs-10,.col-xs-11,.col-xs-12,.col-xs-2,.col-xs-3,.col-xs-4,.col-xs-5,.col-xs-6,.col-xs-7,.col-xs-8,.col-xs-9{float:left}.col-xs-12{width:100%}.col-xs-11{width:91.66666667%}.col-xs-10{width:83.33333333%}.col-xs-9{width:75%}.col-xs-8{width:66.66666667%}.col-xs-7{width:58.33333333%}.col-xs-6{width:50%}.col-xs-5{width:41.66666667%}.col-xs-4{width:33.33333333%}.col-xs-3{width:25%}.col-xs-2{width:16.66666667%}.col-xs-1{width:8.33333333%}.col-xs-pull-12{right:100%}.col-xs-pull-11{right:91.66666667%}.col-xs-pull-10{right:83.33333333%}.col-xs-pull-9{right:75%}.col-xs-pull-8{right:66.66666667%}.col-xs-pull-7{right:58.33333333%}.col-xs-pull-6{right:50%}.col-xs-pull-5{right:41.66666667%}.col-xs-pull-4{right:33.33333333%}.col-xs-pull-3{right:25%}.col-xs-pull-2{right:16.66666667%}.col-xs-pull-1{right:8.33333333%}.col-xs-pull-0{right:auto}.col-xs-push-12{left:100%}.col-xs-push-11{left:91.66666667%}.col-xs-push-10{left:83.33333333%}.col-xs-push-9{left:75%}.col-xs-push-8{left:66.66666667%}.col-xs-push-7{left:58.33333333%}.col-xs-push-6{left:50%}.col-xs-push-5{left:41.66666667%}.col-xs-push-4{left:33.33333333%}.col-xs-push-3{left:25%}.col-xs-push-2{left:16.66666667%}.col-xs-push-1{left:8.33333333%}.col-xs-push-0{left:auto}.col-xs-offset-12{margin-left:100%}.col-xs-offset-11{margin-left:91.66666667%}.col-xs-offset-10{margin-left:83.33333333%}.col-xs-offset-9{margin-left:75%}.col-xs-offset-8{margin-left:66.66666667%}.col-xs-offset-7{margin-left:58.33333333%}.col-xs-offset-6{margin-left:50%}.col-xs-offset-5{margin-left:41.66666667%}.col-xs-offset-4{margin-left:33.33333333%}.col-xs-offset-3{margin-left:25%}.col-xs-offset-2{margin-left:16.66666667%}.col-xs-offset-1{margin-left:8.33333333%}.col-xs-offset-0{margin-left:0}@media (min-width:768px){.col-sm-1,.col-sm-10,.col-sm-11,.col-sm-12,.col-sm-2,.col-sm-3,.col-sm-4,.col-sm-5,.col-sm-6,.col-sm-7,.col-sm-8,.col-sm-9{float:left}.col-sm-12{width:100%}.col-sm-11{width:91.66666667%}.col-sm-10{width:83.33333333%}.col-sm-9{width:75%}.col-sm-8{width:66.66666667%}.col-sm-7{width:58.33333333%}.col-sm-6{width:50%}.col-sm-5{width:41.66666667%}.col-sm-4{width:33.33333333%}.col-sm-3{width:25%}.col-sm-2{width:16.66666667%}.col-sm-1{width:8.33333333%}.col-sm-pull-12{right:100%}.col-sm-pull-11{right:91.66666667%}.col-sm-pull-10{right:83.33333333%}.col-sm-pull-9{right:75%}.col-sm-pull-8{right:66.66666667%}.col-sm-pull-7{right:58.33333333%}.col-sm-pull-6{right:50%}.col-sm-pull-5{right:41.66666667%}.col-sm-pull-4{right:33.33333333%}.col-sm-pull-3{right:25%}.col-sm-pull-2{right:16.66666667%}.col-sm-pull-1{right:8.33333333%}.col-sm-pull-0{right:auto}.col-sm-push-12{left:100%}.col-sm-push-11{left:91.66666667%}.col-sm-push-10{left:83.33333333%}.col-sm-push-9{left:75%}.col-sm-push-8{left:66.66666667%}.col-sm-push-7{left:58.33333333%}.col-sm-push-6{left:50%}.col-sm-push-5{left:41.66666667%}.col-sm-push-4{left:33.33333333%}.col-sm-push-3{left:25%}.col-sm-push-2{left:16.66666667%}.col-sm-push-1{left:8.33333333%}.col-sm-push-0{left:auto}.col-sm-offset-12{margin-left:100%}.col-sm-offset-11{margin-left:91.66666667%}.col-sm-offset-10{margin-left:83.33333333%}.col-sm-offset-9{margin-left:75%}.col-sm-offset-8{margin-left:66.66666667%}.col-sm-offset-7{margin-left:58.33333333%}.col-sm-offset-6{margin-left:50%}.col-sm-offset-5{margin-left:41.66666667%}.col-sm-offset-4{margin-left:33.33333333%}.col-sm-offset-3{margin-left:25%}.col-sm-offset-2{margin-left:16.66666667%}.col-sm-offset-1{margin-left:8.33333333%}.col-sm-offset-0{margin-left:0}}@media (min-width:992px){.col-md-1,.col-md-10,.col-md-11,.col-md-12,.col-md-2,.col-md-3,.col-md-4,.col-md-5,.col-md-6,.col-md-7,.col-md-8,.col-md-9{float:left}.col-md-12{width:100%}.col-md-11{width:91.66666667%}.col-md-10{width:83.33333333%}.col-md-9{width:75%}.col-md-8{width:66.66666667%}.col-md-7{width:58.33333333%}.col-md-6{width:50%}.col-md-5{width:41.66666667%}.col-md-4{width:33.33333333%}.col-md-3{width:25%}.col-md-2{width:16.66666667%}.col-md-1{width:8.33333333%}.col-md-pull-12{right:100%}.col-md-pull-11{right:91.66666667%}.col-md-pull-10{right:83.33333333%}.col-md-pull-9{right:75%}.col-md-pull-8{right:66.66666667%}.col-md-pull-7{right:58.33333333%}.col-md-pull-6{right:50%}.col-md-pull-5{right:41.66666667%}.col-md-pull-4{right:33.33333333%}.col-md-pull-3{right:25%}.col-md-pull-2{right:16.66666667%}.col-md-pull-1{right:8.33333333%}.col-md-pull-0{right:auto}.col-md-push-12{left:100%}.col-md-push-11{left:91.66666667%}.col-md-push-10{left:83.33333333%}.col-md-push-9{left:75%}.col-md-push-8{left:66.66666667%}.col-md-push-7{left:58.33333333%}.col-md-push-6{left:50%}.col-md-push-5{left:41.66666667%}.col-md-push-4{left:33.33333333%}.col-md-push-3{left:25%}.col-md-push-2{left:16.66666667%}.col-md-push-1{left:8.33333333%}.col-md-push-0{left:auto}.col-md-offset-12{margin-left:100%}.col-md-offset-11{margin-left:91.66666667%}.col-md-offset-10{margin-left:83.33333333%}.col-md-offset-9{margin-left:75%}.col-md-offset-8{margin-left:66.66666667%}.col-md-offset-7{margin-left:58.33333333%}.col-md-offset-6{margin-left:50%}.col-md-offset-5{margin-left:41.66666667%}.col-md-offset-4{margin-left:33.33333333%}.col-md-offset-3{margin-left:25%}.col-md-offset-2{margin-left:16.66666667%}.col-md-offset-1{margin-left:8.33333333%}.col-md-offset-0{margin-left:0}}@media (min-width:1200px){.col-lg-1,.col-lg-10,.col-lg-11,.col-lg-12,.col-lg-2,.col-lg-3,.col-lg-4,.col-lg-5,.col-lg-6,.col-lg-7,.col-lg-8,.col-lg-9{float:left}.col-lg-12{width:100%}.col-lg-11{width:91.66666667%}.col-lg-10{width:83.33333333%}.col-lg-9{width:75%}.col-lg-8{width:66.66666667%}.col-lg-7{width:58.33333333%}.col-lg-6{width:50%}.col-lg-5{width:41.66666667%}.col-lg-4{width:33.33333333%}.col-lg-3{width:25%}.col-lg-2{width:16.66666667%}.col-lg-1{width:8.33333333%}.col-lg-pull-12{right:100%}.col-lg-pull-11{right:91.66666667%}.col-lg-pull-10{right:83.33333333%}.col-lg-pull-9{right:75%}.col-lg-pull-8{right:66.66666667%}.col-lg-pull-7{right:58.33333333%}.col-lg-pull-6{right:50%}.col-lg-pull-5{right:41.66666667%}.col-lg-pull-4{right:33.33333333%}.col-lg-pull-3{right:25%}.col-lg-pull-2{right:16.66666667%}.col-lg-pull-1{right:8.33333333%}.col-lg-pull-0{right:auto}.col-lg-push-12{left:100%}.col-lg-push-11{left:91.66666667%}.col-lg-push-10{left:83.33333333%}.col-lg-push-9{left:75%}.col-lg-push-8{left:66.66666667%}.col-lg-push-7{left:58.33333333%}.col-lg-push-6{left:50%}.col-lg-push-5{left:41.66666667%}.col-lg-push-4{left:33.33333333%}.col-lg-push-3{left:25%}.col-lg-push-2{left:16.66666667%}.col-lg-push-1{left:8.33333333%}.col-lg-push-0{left:auto}.col-lg-offset-12{margin-left:100%}.col-lg-offset-11{margin-left:91.66666667%}.col-lg-offset-10{margin-left:83.33333333%}.col-lg-offset-9{margin-left:75%}.col-lg-offset-8{margin-left:66.66666667%}.col-lg-offset-7{margin-left:58.33333333%}.col-lg-offset-6{margin-left:50%}.col-lg-offset-5{margin-left:41.66666667%}.col-lg-offset-4{margin-left:33.33333333%}.col-lg-offset-3{margin-left:25%}.col-lg-offset-2{margin-left:16.66666667%}.col-lg-offset-1{margin-left:8.33333333%}.col-lg-offset-0{margin-left:0}}table{background-color:transparent}caption{padding-top:8px;padding-bottom:8px;color:#777;text-align:left}th{}.table{width:100%;max-width:100%;margin-bottom:20px}.table>tbody>tr>td,.table>tbody>tr>th,.table>tfoot>tr>td,.table>tfoot>tr>th,.table>thead>tr>td,.table>thead>tr>th{padding:8px;line-height:1.42857143;vertical-align:top;border-top:1px solid #ddd}.table>thead>tr>th{vertical-align:bottom;border-bottom:2px solid #ddd}.table>caption+thead>tr:first-child>td,.table>caption+thead>tr:first-child>th,.table>colgroup+thead>tr:first-child>td,.table>colgroup+thead>tr:first-child>th,.table>thead:first-child>tr:first-child>td,.table>thead:first-child>tr:first-child>th{border-top:0}.table>tbody+tbody{border-top:2px solid #ddd}.table .table{background-color:#fff}.table-condensed>tbody>tr>td,.table-condensed>tbody>tr>th,.table-condensed>tfoot>tr>td,.table-condensed>tfoot>tr>th,.table-condensed>thead>tr>td,.table-condensed>thead>tr>th{padding:5px}.table-bordered{border:1px solid #ddd}.table-bordered>tbody>tr>td,.table-bordered>tbody>tr>th,.table-bordered>tfoot>tr>td,.table-bordered>tfoot>tr>th,.table-bordered>thead>tr>td,.table-bordered>thead>tr>th{border:1px solid #ddd}.table-bordered>thead>tr>td,.table-bordered>thead>tr>th{border-bottom-width:2px}.table-striped>tbody>tr:nth-of-type(odd){background-color:#f9f9f9}.table-hover>tbody>tr:hover{background-color:#f5f5f5}table col[class*=col-]{position:static;display:table-column;float:none}table td[class*=col-],table th[class*=col-]{position:static;display:table-cell;float:none}.table>tbody>tr.active>td,.table>tbody>tr.active>th,.table>tbody>tr>td.active,.table>tbody>tr>th.active,.table>tfoot>tr.active>td,.table>tfoot>tr.active>th,.table>tfoot>tr>td.active,.table>tfoot>tr>th.active,.table>thead>tr.active>td,.table>thead>tr.active>th,.table>thead>tr>td.active,.table>thead>tr>th.active{background-color:#f5f5f5}.table-hover>tbody>tr.active:hover>td,.table-hover>tbody>tr.active:hover>th,.table-hover>tbody>tr:hover>.active,.table-hover>tbody>tr>td.active:hover,.table-hover>tbody>tr>th.active:hover{background-color:#e8e8e8}.table>tbody>tr.success>td,.table>tbody>tr.success>th,.table>tbody>tr>td.success,.table>tbody>tr>th.success,.table>tfoot>tr.success>td,.table>tfoot>tr.success>th,.table>tfoot>tr>td.success,.table>tfoot>tr>th.success,.table>thead>tr.success>td,.table>thead>tr.success>th,.table>thead>tr>td.success,.table>thead>tr>th.success{background-color:#dff0d8}.table-hover>tbody>tr.success:hover>td,.table-hover>tbody>tr.success:hover>th,.table-hover>tbody>tr:hover>.success,.table-hover>tbody>tr>td.success:hover,.table-hover>tbody>tr>th.success:hover{background-color:#d0e9c6}.table>tbody>tr.info>td,.table>tbody>tr.info>th,.table>tbody>tr>td.info,.table>tbody>tr>th.info,.table>tfoot>tr.info>td,.table>tfoot>tr.info>th,.table>tfoot>tr>td.info,.table>tfoot>tr>th.info,.table>thead>tr.info>td,.table>thead>tr.info>th,.table>thead>tr>td.info,.table>thead>tr>th.info{background-color:#d9edf7}.table-hover>tbody>tr.info:hover>td,.table-hover>tbody>tr.info:hover>th,.table-hover>tbody>tr:hover>.info,.table-hover>tbody>tr>td.info:hover,.table-hover>tbody>tr>th.info:hover{background-color:#c4e3f3}.table>tbody>tr.warning>td,.table>tbody>tr.warning>th,.table>tbody>tr>td.warning,.table>tbody>tr>th.warning,.table>tfoot>tr.warning>td,.table>tfoot>tr.warning>th,.table>tfoot>tr>td.warning,.table>tfoot>tr>th.warning,.table>thead>tr.warning>td,.table>thead>tr.warning>th,.table>thead>tr>td.warning,.table>thead>tr>th.warning{background-color:#fcf8e3}.table-hover>tbody>tr.warning:hover>td,.table-hover>tbody>tr.warning:hover>th,.table-hover>tbody>tr:hover>.warning,.table-hover>tbody>tr>td.warning:hover,.table-hover>tbody>tr>th.warning:hover{background-color:#faf2cc}.table>tbody>tr.danger>td,.table>tbody>tr.danger>th,.table>tbody>tr>td.danger,.table>tbody>tr>th.danger,.table>tfoot>tr.danger>td,.table>tfoot>tr.danger>th,.table>tfoot>tr>td.danger,.table>tfoot>tr>th.danger,.table>thead>tr.danger>td,.table>thead>tr.danger>th,.table>thead>tr>td.danger,.table>thead>tr>th.danger{background-color:#f2dede}.table-hover>tbody>tr.danger:hover>td,.table-hover>tbody>tr.danger:hover>th,.table-hover>tbody>tr:hover>.danger,.table-hover>tbody>tr>td.danger:hover,.table-hover>tbody>tr>th.danger:hover{background-color:#ebcccc}.table-responsive{min-height:.01%;overflow-x:auto}@media screen and (max-width:767px){.table-responsive{width:100%;margin-bottom:15px;overflow-y:hidden;-ms-overflow-style:-ms-autohiding-scrollbar;border:1px solid #ddd}.table-responsive>.table{margin-bottom:0}.table-responsive>.table>tbody>tr>td,.table-responsive>.table>tbody>tr>th,.table-responsive>.table>tfoot>tr>td,.table-responsive>.table>tfoot>tr>th,.table-responsive>.table>thead>tr>td,.table-responsive>.table>thead>tr>th{white-space:nowrap}.table-responsive>.table-bordered{border:0}.table-responsive>.table-bordered>tbody>tr>td:first-child,.table-responsive>.table-bordered>tbody>tr>th:first-child,.table-responsive>.table-bordered>tfoot>tr>td:first-child,.table-responsive>.table-bordered>tfoot>tr>th:first-child,.table-responsive>.table-bordered>thead>tr>td:first-child,.table-responsive>.table-bordered>thead>tr>th:first-child{border-left:0}.table-responsive>.table-bordered>tbody>tr>td:last-child,.table-responsive>.table-bordered>tbody>tr>th:last-child,.table-responsive>.table-bordered>tfoot>tr>td:last-child,.table-responsive>.table-bordered>tfoot>tr>th:last-child,.table-responsive>.table-bordered>thead>tr>td:last-child,.table-responsive>.table-bordered>thead>tr>th:last-child{border-right:0}.table-responsive>.table-bordered>tbody>tr:last-child>td,.table-responsive>.table-bordered>tbody>tr:last-child>th,.table-responsive>.table-bordered>tfoot>tr:last-child>td,.table-responsive>.table-bordered>tfoot>tr:last-child>th{border-bottom:0}}fieldset{min-width:0;padding:0;margin:0;border:0}legend{display:block;width:100%;padding:0;margin-bottom:20px;font-size:21px;line-height:inherit;color:#333;border:0;border-bottom:1px solid #e5e5e5}label{display:inline-block;max-width:100%;margin-bottom:5px;font-weight:700}input[type=search]{-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}input[type=checkbox],input[type=radio]{margin:4px 0 0;margin-top:1px\9;line-height:normal}input[type=file]{display:block}input[type=range]{display:block;width:100%}select[multiple],select[size]{height:auto}input[type=file]:focus,input[type=checkbox]:focus,input[type=radio]:focus{outline:thin dotted;outline:5px auto -webkit-focus-ring-color;outline-offset:-2px}output{display:block;padding-top:7px;font-size:14px;line-height:1.42857143;color:#555}.form-control{display:block;width:100%;height:34px;padding:6px 12px;font-size:14px;line-height:1.42857143;color:#555;background-color:#fff;background-image:none;border:1px solid #ccc;border-radius:4px;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075);box-shadow:inset 0 1px 1px rgba(0,0,0,.075);-webkit-transition:border-color ease-in-out .15s,-webkit-box-shadow ease-in-out .15s;-o-transition:border-color ease-in-out .15s,box-shadow ease-in-out .15s;transition:border-color ease-in-out .15s,box-shadow ease-in-out .15s}.form-control:focus{border-color:#66afe9;outline:0;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 8px rgba(102,175,233,.6);box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 8px rgba(102,175,233,.6)}.form-control::-moz-placeholder{color:#999;opacity:1}.form-control:-ms-input-placeholder{color:#999}.form-control::-webkit-input-placeholder{color:#999}.form-control[disabled],.form-control[readonly],fieldset[disabled] .form-control{background-color:#eee;opacity:1}.form-control[disabled],fieldset[disabled] .form-control{cursor:not-allowed}textarea.form-control{height:auto}input[type=search]{-webkit-appearance:none}@media screen and (-webkit-min-device-pixel-ratio:0){input[type=date].form-control,input[type=time].form-control,input[type=datetime-local].form-control,input[type=month].form-control{line-height:34px}.input-group-sm input[type=date],.input-group-sm input[type=time],.input-group-sm input[type=datetime-local],.input-group-sm input[type=month],input[type=date].input-sm,input[type=time].input-sm,input[type=datetime-local].input-sm,input[type=month].input-sm{line-height:30px}.input-group-lg input[type=date],.input-group-lg input[type=time],.input-group-lg input[type=datetime-local],.input-group-lg input[type=month],input[type=date].input-lg,input[type=time].input-lg,input[type=datetime-local].input-lg,input[type=month].input-lg{line-height:46px}}.form-group{margin-bottom:15px}.checkbox,.radio{position:relative;display:block;margin-top:10px;margin-bottom:10px}.checkbox label,.radio label{min-height:20px;padding-left:20px;margin-bottom:0;font-weight:400;cursor:pointer}.checkbox input[type=checkbox],.checkbox-inline input[type=checkbox],.radio input[type=radio],.radio-inline input[type=radio]{position:absolute;margin-top:4px\9;margin-left:-20px}.checkbox+.checkbox,.radio+.radio{margin-top:-5px}.checkbox-inline,.radio-inline{position:relative;display:inline-block;padding-left:20px;margin-bottom:0;font-weight:400;vertical-align:middle;cursor:pointer}.checkbox-inline+.checkbox-inline,.radio-inline+.radio-inline{margin-top:0;margin-left:10px}fieldset[disabled] input[type=checkbox],fieldset[disabled] input[type=radio],input[type=checkbox].disabled,input[type=checkbox][disabled],input[type=radio].disabled,input[type=radio][disabled]{cursor:not-allowed}.checkbox-inline.disabled,.radio-inline.disabled,fieldset[disabled] .checkbox-inline,fieldset[disabled] .radio-inline{cursor:not-allowed}.checkbox.disabled label,.radio.disabled label,fieldset[disabled] .checkbox label,fieldset[disabled] .radio label{cursor:not-allowed}.form-control-static{min-height:34px;padding-top:7px;padding-bottom:7px;margin-bottom:0}.form-control-static.input-lg,.form-control-static.input-sm{padding-right:0;padding-left:0}.input-sm{height:30px;padding:5px 10px;font-size:12px;line-height:1.5;border-radius:3px}select.input-sm{height:30px;line-height:30px}select[multiple].input-sm,textarea.input-sm{height:auto}.form-group-sm .form-control{height:30px;padding:5px 10px;font-size:12px;line-height:1.5;border-radius:3px}.form-group-sm select.form-control{height:30px;line-height:30px}.form-group-sm select[multiple].form-control,.form-group-sm textarea.form-control{height:auto}.form-group-sm .form-control-static{height:30px;min-height:32px;padding:6px 10px;font-size:12px;line-height:1.5}.input-lg{height:46px;padding:10px 16px;font-size:18px;line-height:1.3333333;border-radius:6px}select.input-lg{height:46px;line-height:46px}select[multiple].input-lg,textarea.input-lg{height:auto}.form-group-lg .form-control{height:46px;padding:10px 16px;font-size:18px;line-height:1.3333333;border-radius:6px}.form-group-lg select.form-control{height:46px;line-height:46px}.form-group-lg select[multiple].form-control,.form-group-lg textarea.form-control{height:auto}.form-group-lg .form-control-static{height:46px;min-height:38px;padding:11px 16px;font-size:18px;line-height:1.3333333}.has-feedback{position:relative}.has-feedback .form-control{padding-right:42.5px}.form-control-feedback{position:absolute;top:0;right:0;z-index:2;display:block;width:34px;height:34px;line-height:34px;text-align:center;pointer-events:none}.form-group-lg .form-control+.form-control-feedback,.input-group-lg+.form-control-feedback,.input-lg+.form-control-feedback{width:46px;height:46px;line-height:46px}.form-group-sm .form-control+.form-control-feedback,.input-group-sm+.form-control-feedback,.input-sm+.form-control-feedback{width:30px;height:30px;line-height:30px}.has-success .checkbox,.has-success .checkbox-inline,.has-success .control-label,.has-success .help-block,.has-success .radio,.has-success .radio-inline,.has-success.checkbox label,.has-success.checkbox-inline label,.has-success.radio label,.has-success.radio-inline label{color:#3c763d}.has-success .form-control{border-color:#3c763d;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075);box-shadow:inset 0 1px 1px rgba(0,0,0,.075)}.has-success .form-control:focus{border-color:#2b542c;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 6px #67b168;box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 6px #67b168}.has-success .input-group-addon{color:#3c763d;background-color:#dff0d8;border-color:#3c763d}.has-success .form-control-feedback{color:#3c763d}.has-warning .checkbox,.has-warning .checkbox-inline,.has-warning .control-label,.has-warning .help-block,.has-warning .radio,.has-warning .radio-inline,.has-warning.checkbox label,.has-warning.checkbox-inline label,.has-warning.radio label,.has-warning.radio-inline label{color:#8a6d3b}.has-warning .form-control{border-color:#8a6d3b;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075);box-shadow:inset 0 1px 1px rgba(0,0,0,.075)}.has-warning .form-control:focus{border-color:#66512c;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 6px #c0a16b;box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 6px #c0a16b}.has-warning .input-group-addon{color:#8a6d3b;background-color:#fcf8e3;border-color:#8a6d3b}.has-warning .form-control-feedback{color:#8a6d3b}.has-error .checkbox,.has-error .checkbox-inline,.has-error .control-label,.has-error .help-block,.has-error .radio,.has-error .radio-inline,.has-error.checkbox label,.has-error.checkbox-inline label,.has-error.radio label,.has-error.radio-inline label{color:#a94442}.has-error .form-control{border-color:#a94442;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075);box-shadow:inset 0 1px 1px rgba(0,0,0,.075)}.has-error .form-control:focus{border-color:#843534;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 6px #ce8483;box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 6px #ce8483}.has-error .input-group-addon{color:#a94442;background-color:#f2dede;border-color:#a94442}.has-error .form-control-feedback{color:#a94442}.has-feedback label~.form-control-feedback{top:25px}.has-feedback label.sr-only~.form-control-feedback{top:0}.help-block{display:block;margin-top:5px;margin-bottom:10px;color:#737373}@media (min-width:768px){.form-inline .form-group{display:inline-block;margin-bottom:0;vertical-align:middle}.form-inline .form-control{display:inline-block;width:auto;vertical-align:middle}.form-inline .form-control-static{display:inline-block}.form-inline .input-group{display:inline-table;vertical-align:middle}.form-inline .input-group .form-control,.form-inline .input-group .input-group-addon,.form-inline .input-group .input-group-btn{width:auto}.form-inline .input-group>.form-control{width:100%}.form-inline .control-label{margin-bottom:0;vertical-align:middle}.form-inline .checkbox,.form-inline .radio{display:inline-block;margin-top:0;margin-bottom:0;vertical-align:middle}.form-inline .checkbox label,.form-inline .radio label{padding-left:0}.form-inline .checkbox input[type=checkbox],.form-inline .radio input[type=radio]{position:relative;margin-left:0}.form-inline .has-feedback .form-control-feedback{top:0}}.form-horizontal .checkbox,.form-horizontal .checkbox-inline,.form-horizontal .radio,.form-horizontal .radio-inline{padding-top:7px;margin-top:0;margin-bottom:0}.form-horizontal .checkbox,.form-horizontal .radio{min-height:27px}.form-horizontal .form-group{margin-right:-15px;margin-left:-15px}@media (min-width:768px){.form-horizontal .control-label{padding-top:7px;margin-bottom:0;text-align:right}}.form-horizontal .has-feedback .form-control-feedback{right:15px}@media (min-width:768px){.form-horizontal .form-group-lg .control-label{padding-top:14.33px;font-size:18px}}@media (min-width:768px){.form-horizontal .form-group-sm .control-label{padding-top:6px;font-size:12px}}.btn{display:inline-block;padding:6px 12px;margin-bottom:0;font-size:14px;font-weight:400;line-height:1.42857143;text-align:center;white-space:nowrap;vertical-align:middle;-ms-touch-action:manipulation;touch-action:manipulation;cursor:pointer;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;background-image:none;border:1px solid transparent;border-radius:4px}.btn.active.focus,.btn.active:focus,.btn.focus,.btn:active.focus,.btn:active:focus,.btn:focus{outline:thin dotted;outline:5px auto -webkit-focus-ring-color;outline-offset:-2px}.btn.focus,.btn:focus,.btn:hover{color:#333;text-decoration:none}.btn.active,.btn:active{background-image:none;outline:0;-webkit-box-shadow:inset 0 3px 5px rgba(0,0,0,.125);box-shadow:inset 0 3px 5px rgba(0,0,0,.125)}.btn.disabled,.btn[disabled],fieldset[disabled] .btn{cursor:not-allowed;filter:alpha(opacity=65);-webkit-box-shadow:none;box-shadow:none;opacity:.65}a.btn.disabled,fieldset[disabled] a.btn{pointer-events:none}.btn-default{color:#333;background-color:#fff;border-color:#ccc}.btn-default.focus,.btn-default:focus{color:#333;background-color:#e6e6e6;border-color:#8c8c8c}.btn-default:hover{color:#333;background-color:#e6e6e6;border-color:#adadad}.btn-default.active,.btn-default:active,.open>.dropdown-toggle.btn-default{color:#333;background-color:#e6e6e6;border-color:#adadad}.btn-default.active.focus,.btn-default.active:focus,.btn-default.active:hover,.btn-default:active.focus,.btn-default:active:focus,.btn-default:active:hover,.open>.dropdown-toggle.btn-default.focus,.open>.dropdown-toggle.btn-default:focus,.open>.dropdown-toggle.btn-default:hover{color:#333;background-color:#d4d4d4;border-color:#8c8c8c}.btn-default.active,.btn-default:active,.open>.dropdown-toggle.btn-default{background-image:none}.btn-default.disabled,.btn-default.disabled.active,.btn-default.disabled.focus,.btn-default.disabled:active,.btn-default.disabled:focus,.btn-default.disabled:hover,.btn-default[disabled],.btn-default[disabled].active,.btn-default[disabled].focus,.btn-default[disabled]:active,.btn-default[disabled]:focus,.btn-default[disabled]:hover,fieldset[disabled] .btn-default,fieldset[disabled] .btn-default.active,fieldset[disabled] .btn-default.focus,fieldset[disabled] .btn-default:active,fieldset[disabled] .btn-default:focus,fieldset[disabled] .btn-default:hover{background-color:#fff;border-color:#ccc}.btn-default .badge{color:#fff;background-color:#333}.btn-primary{color:#fff;background-color:#337ab7;border-color:#2e6da4}.btn-primary.focus,.btn-primary:focus{color:#fff;background-color:#286090;border-color:#122b40}.btn-primary:hover{color:#fff;background-color:#286090;border-color:#204d74}.btn-primary.active,.btn-primary:active,.open>.dropdown-toggle.btn-primary{color:#fff;background-color:#286090;border-color:#204d74}.btn-primary.active.focus,.btn-primary.active:focus,.btn-primary.active:hover,.btn-primary:active.focus,.btn-primary:active:focus,.btn-primary:active:hover,.open>.dropdown-toggle.btn-primary.focus,.open>.dropdown-toggle.btn-primary:focus,.open>.dropdown-toggle.btn-primary:hover{color:#fff;background-color:#204d74;border-color:#122b40}.btn-primary.active,.btn-primary:active,.open>.dropdown-toggle.btn-primary{background-image:none}.btn-primary.disabled,.btn-primary.disabled.active,.btn-primary.disabled.focus,.btn-primary.disabled:active,.btn-primary.disabled:focus,.btn-primary.disabled:hover,.btn-primary[disabled],.btn-primary[disabled].active,.btn-primary[disabled].focus,.btn-primary[disabled]:active,.btn-primary[disabled]:focus,.btn-primary[disabled]:hover,fieldset[disabled] .btn-primary,fieldset[disabled] .btn-primary.active,fieldset[disabled] .btn-primary.focus,fieldset[disabled] .btn-primary:active,fieldset[disabled] .btn-primary:focus,fieldset[disabled] .btn-primary:hover{background-color:#337ab7;border-color:#2e6da4}.btn-primary .badge{color:#337ab7;background-color:#fff}.btn-success{color:#fff;background-color:#5cb85c;border-color:#4cae4c}.btn-success.focus,.btn-success:focus{color:#fff;background-color:#449d44;border-color:#255625}.btn-success:hover{color:#fff;background-color:#449d44;border-color:#398439}.btn-success.active,.btn-success:active,.open>.dropdown-toggle.btn-success{color:#fff;background-color:#449d44;border-color:#398439}.btn-success.active.focus,.btn-success.active:focus,.btn-success.active:hover,.btn-success:active.focus,.btn-success:active:focus,.btn-success:active:hover,.open>.dropdown-toggle.btn-success.focus,.open>.dropdown-toggle.btn-success:focus,.open>.dropdown-toggle.btn-success:hover{color:#fff;background-color:#398439;border-color:#255625}.btn-success.active,.btn-success:active,.open>.dropdown-toggle.btn-success{background-image:none}.btn-success.disabled,.btn-success.disabled.active,.btn-success.disabled.focus,.btn-success.disabled:active,.btn-success.disabled:focus,.btn-success.disabled:hover,.btn-success[disabled],.btn-success[disabled].active,.btn-success[disabled].focus,.btn-success[disabled]:active,.btn-success[disabled]:focus,.btn-success[disabled]:hover,fieldset[disabled] .btn-success,fieldset[disabled] .btn-success.active,fieldset[disabled] .btn-success.focus,fieldset[disabled] .btn-success:active,fieldset[disabled] .btn-success:focus,fieldset[disabled] .btn-success:hover{background-color:#5cb85c;border-color:#4cae4c}.btn-success .badge{color:#5cb85c;background-color:#fff}.btn-info{color:#fff;background-color:#5bc0de;border-color:#46b8da}.btn-info.focus,.btn-info:focus{color:#fff;background-color:#31b0d5;border-color:#1b6d85}.btn-info:hover{color:#fff;background-color:#31b0d5;border-color:#269abc}.btn-info.active,.btn-info:active,.open>.dropdown-toggle.btn-info{color:#fff;background-color:#31b0d5;border-color:#269abc}.btn-info.active.focus,.btn-info.active:focus,.btn-info.active:hover,.btn-info:active.focus,.btn-info:active:focus,.btn-info:active:hover,.open>.dropdown-toggle.btn-info.focus,.open>.dropdown-toggle.btn-info:focus,.open>.dropdown-toggle.btn-info:hover{color:#fff;background-color:#269abc;border-color:#1b6d85}.btn-info.active,.btn-info:active,.open>.dropdown-toggle.btn-info{background-image:none}.btn-info.disabled,.btn-info.disabled.active,.btn-info.disabled.focus,.btn-info.disabled:active,.btn-info.disabled:focus,.btn-info.disabled:hover,.btn-info[disabled],.btn-info[disabled].active,.btn-info[disabled].focus,.btn-info[disabled]:active,.btn-info[disabled]:focus,.btn-info[disabled]:hover,fieldset[disabled] .btn-info,fieldset[disabled] .btn-info.active,fieldset[disabled] .btn-info.focus,fieldset[disabled] .btn-info:active,fieldset[disabled] .btn-info:focus,fieldset[disabled] .btn-info:hover{background-color:#5bc0de;border-color:#46b8da}.btn-info .badge{color:#5bc0de;background-color:#fff}.btn-warning{color:#fff;background-color:#f0ad4e;border-color:#eea236}.btn-warning.focus,.btn-warning:focus{color:#fff;background-color:#ec971f;border-color:#985f0d}.btn-warning:hover{color:#fff;background-color:#ec971f;border-color:#d58512}.btn-warning.active,.btn-warning:active,.open>.dropdown-toggle.btn-warning{color:#fff;background-color:#ec971f;border-color:#d58512}.btn-warning.active.focus,.btn-warning.active:focus,.btn-warning.active:hover,.btn-warning:active.focus,.btn-warning:active:focus,.btn-warning:active:hover,.open>.dropdown-toggle.btn-warning.focus,.open>.dropdown-toggle.btn-warning:focus,.open>.dropdown-toggle.btn-warning:hover{color:#fff;background-color:#d58512;border-color:#985f0d}.btn-warning.active,.btn-warning:active,.open>.dropdown-toggle.btn-warning{background-image:none}.btn-warning.disabled,.btn-warning.disabled.active,.btn-warning.disabled.focus,.btn-warning.disabled:active,.btn-warning.disabled:focus,.btn-warning.disabled:hover,.btn-warning[disabled],.btn-warning[disabled].active,.btn-warning[disabled].focus,.btn-warning[disabled]:active,.btn-warning[disabled]:focus,.btn-warning[disabled]:hover,fieldset[disabled] .btn-warning,fieldset[disabled] .btn-warning.active,fieldset[disabled] .btn-warning.focus,fieldset[disabled] .btn-warning:active,fieldset[disabled] .btn-warning:focus,fieldset[disabled] .btn-warning:hover{background-color:#f0ad4e;border-color:#eea236}.btn-warning .badge{color:#f0ad4e;background-color:#fff}.btn-danger{color:#fff;background-color:#d9534f;border-color:#d43f3a}.btn-danger.focus,.btn-danger:focus{color:#fff;background-color:#c9302c;border-color:#761c19}.btn-danger:hover{color:#fff;background-color:#c9302c;border-color:#ac2925}.btn-danger.active,.btn-danger:active,.open>.dropdown-toggle.btn-danger{color:#fff;background-color:#c9302c;border-color:#ac2925}.btn-danger.active.focus,.btn-danger.active:focus,.btn-danger.active:hover,.btn-danger:active.focus,.btn-danger:active:focus,.btn-danger:active:hover,.open>.dropdown-toggle.btn-danger.focus,.open>.dropdown-toggle.btn-danger:focus,.open>.dropdown-toggle.btn-danger:hover{color:#fff;background-color:#ac2925;border-color:#761c19}.btn-danger.active,.btn-danger:active,.open>.dropdown-toggle.btn-danger{background-image:none}.btn-danger.disabled,.btn-danger.disabled.active,.btn-danger.disabled.focus,.btn-danger.disabled:active,.btn-danger.disabled:focus,.btn-danger.disabled:hover,.btn-danger[disabled],.btn-danger[disabled].active,.btn-danger[disabled].focus,.btn-danger[disabled]:active,.btn-danger[disabled]:focus,.btn-danger[disabled]:hover,fieldset[disabled] .btn-danger,fieldset[disabled] .btn-danger.active,fieldset[disabled] .btn-danger.focus,fieldset[disabled] .btn-danger:active,fieldset[disabled] .btn-danger:focus,fieldset[disabled] .btn-danger:hover{background-color:#d9534f;border-color:#d43f3a}.btn-danger .badge{color:#d9534f;background-color:#fff}.btn-link{font-weight:400;color:#337ab7;border-radius:0}.btn-link,.btn-link.active,.btn-link:active,.btn-link[disabled],fieldset[disabled] .btn-link{background-color:transparent;-webkit-box-shadow:none;box-shadow:none}.btn-link,.btn-link:active,.btn-link:focus,.btn-link:hover{border-color:transparent}.btn-link:focus,.btn-link:hover{color:#23527c;text-decoration:underline;background-color:transparent}.btn-link[disabled]:focus,.btn-link[disabled]:hover,fieldset[disabled] .btn-link:focus,fieldset[disabled] .btn-link:hover{color:#777;text-decoration:none}.btn-group-lg>.btn,.btn-lg{padding:10px 16px;font-size:18px;line-height:1.3333333;border-radius:6px}.btn-group-sm>.btn,.btn-sm{padding:5px 10px;font-size:12px;line-height:1.5;border-radius:3px}.btn-group-xs>.btn,.btn-xs{padding:1px 5px;font-size:12px;line-height:1.5;border-radius:3px}.btn-block{display:block;width:100%}.btn-block+.btn-block{margin-top:5px}input[type=button].btn-block,input[type=reset].btn-block,input[type=submit].btn-block{width:100%}.fade{opacity:0;-webkit-transition:opacity .15s linear;-o-transition:opacity .15s linear;transition:opacity .15s linear}.fade.in{opacity:1}.collapse{display:none}.collapse.in{display:block}tr.collapse.in{display:table-row}tbody.collapse.in{display:table-row-group}.collapsing{position:relative;height:0;overflow:hidden;-webkit-transition-timing-function:ease;-o-transition-timing-function:ease;transition-timing-function:ease;-webkit-transition-duration:.35s;-o-transition-duration:.35s;transition-duration:.35s;-webkit-transition-property:height,visibility;-o-transition-property:height,visibility;transition-property:height,visibility}.caret{display:inline-block;width:0;height:0;margin-left:2px;vertical-align:middle;border-top:4px dashed;border-top:4px solid\9;border-right:4px solid transparent;border-left:4px solid transparent}.dropdown,.dropup{position:relative}.dropdown-toggle:focus{outline:0}.dropdown-menu{position:absolute;top:100%;left:0;z-index:1000;display:none;float:left;min-width:160px;padding:5px 0;margin:2px 0 0;font-size:14px;text-align:left;list-style:none;background-color:#fff;-webkit-background-clip:padding-box;background-clip:padding-box;border:1px solid #ccc;border:1px solid rgba(0,0,0,.15);border-radius:4px;-webkit-box-shadow:0 6px 12px rgba(0,0,0,.175);box-shadow:0 6px 12px rgba(0,0,0,.175)}.dropdown-menu.pull-right{right:0;left:auto}.dropdown-menu .divider{height:1px;margin:9px 0;overflow:hidden;background-color:#e5e5e5}.dropdown-menu>li>a{display:block;padding:3px 20px;clear:both;font-weight:400;line-height:1.42857143;color:#333;white-space:nowrap}.dropdown-menu>li>a:focus,.dropdown-menu>li>a:hover{color:#262626;text-decoration:none;background-color:#f5f5f5}.dropdown-menu>.active>a,.dropdown-menu>.active>a:focus,.dropdown-menu>.active>a:hover{color:#fff;text-decoration:none;background-color:#337ab7;outline:0}.dropdown-menu>.disabled>a,.dropdown-menu>.disabled>a:focus,.dropdown-menu>.disabled>a:hover{color:#777}.dropdown-menu>.disabled>a:focus,.dropdown-menu>.disabled>a:hover{text-decoration:none;cursor:not-allowed;background-color:transparent;background-image:none;filter:progid:DXImageTransform.Microsoft.gradient(enabled=false)}.open>.dropdown-menu{display:block}.open>a{outline:0}.dropdown-menu-right{right:0;left:auto}.dropdown-menu-left{right:auto;left:0}.dropdown-header{display:block;padding:3px 20px;font-size:12px;line-height:1.42857143;color:#777;white-space:nowrap}.dropdown-backdrop{position:fixed;top:0;right:0;bottom:0;left:0;z-index:990}.pull-right>.dropdown-menu{right:0;left:auto}.dropup .caret,.navbar-fixed-bottom .dropdown .caret{content:"";border-top:0;border-bottom:4px dashed;border-bottom:4px solid\9}.dropup .dropdown-menu,.navbar-fixed-bottom .dropdown .dropdown-menu{top:auto;bottom:100%;margin-bottom:2px}@media (min-width:768px){.navbar-right .dropdown-menu{right:0;left:auto}.navbar-right .dropdown-menu-left{right:auto;left:0}}.btn-group,.btn-group-vertical{position:relative;display:inline-block;vertical-align:middle}.btn-group-vertical>.btn,.btn-group>.btn{position:relative;float:left}.btn-group-vertical>.btn.active,.btn-group-vertical>.btn:active,.btn-group-vertical>.btn:focus,.btn-group-vertical>.btn:hover,.btn-group>.btn.active,.btn-group>.btn:active,.btn-group>.btn:focus,.btn-group>.btn:hover{z-index:2}.btn-group .btn+.btn,.btn-group .btn+.btn-group,.btn-group .btn-group+.btn,.btn-group .btn-group+.btn-group{margin-left:-1px}.btn-toolbar{margin-left:-5px}.btn-toolbar .btn,.btn-toolbar .btn-group,.btn-toolbar .input-group{float:left}.btn-toolbar>.btn,.btn-toolbar>.btn-group,.btn-toolbar>.input-group{margin-left:5px}.btn-group>.btn:not(:first-child):not(:last-child):not(.dropdown-toggle){border-radius:0}.btn-group>.btn:first-child{margin-left:0}.btn-group>.btn:first-child:not(:last-child):not(.dropdown-toggle){border-top-right-radius:0;border-bottom-right-radius:0}.btn-group>.btn:last-child:not(:first-child),.btn-group>.dropdown-toggle:not(:first-child){border-top-left-radius:0;border-bottom-left-radius:0}.btn-group>.btn-group{float:left}.btn-group>.btn-group:not(:first-child):not(:last-child)>.btn{border-radius:0}.btn-group>.btn-group:first-child:not(:last-child)>.btn:last-child,.btn-group>.btn-group:first-child:not(:last-child)>.dropdown-toggle{border-top-right-radius:0;border-bottom-right-radius:0}.btn-group>.btn-group:last-child:not(:first-child)>.btn:first-child{border-top-left-radius:0;border-bottom-left-radius:0}.btn-group .dropdown-toggle:active,.btn-group.open .dropdown-toggle{outline:0}.btn-group>.btn+.dropdown-toggle{padding-right:8px;padding-left:8px}.btn-group>.btn-lg+.dropdown-toggle{padding-right:12px;padding-left:12px}.btn-group.open .dropdown-toggle{-webkit-box-shadow:inset 0 3px 5px rgba(0,0,0,.125);box-shadow:inset 0 3px 5px rgba(0,0,0,.125)}.btn-group.open .dropdown-toggle.btn-link{-webkit-box-shadow:none;box-shadow:none}.btn .caret{margin-left:0}.btn-lg .caret{border-width:5px 5px 0;border-bottom-width:0}.dropup .btn-lg .caret{border-width:0 5px 5px}.btn-group-vertical>.btn,.btn-group-vertical>.btn-group,.btn-group-vertical>.btn-group>.btn{display:block;float:none;width:100%;max-width:100%}.btn-group-vertical>.btn-group>.btn{float:none}.btn-group-vertical>.btn+.btn,.btn-group-vertical>.btn+.btn-group,.btn-group-vertical>.btn-group+.btn,.btn-group-vertical>.btn-group+.btn-group{margin-top:-1px;margin-left:0}.btn-group-vertical>.btn:not(:first-child):not(:last-child){border-radius:0}.btn-group-vertical>.btn:first-child:not(:last-child){border-top-right-radius:4px;border-bottom-right-radius:0;border-bottom-left-radius:0}.btn-group-vertical>.btn:last-child:not(:first-child){border-top-left-radius:0;border-top-right-radius:0;border-bottom-left-radius:4px}.btn-group-vertical>.btn-group:not(:first-child):not(:last-child)>.btn{border-radius:0}.btn-group-vertical>.btn-group:first-child:not(:last-child)>.btn:last-child,.btn-group-vertical>.btn-group:first-child:not(:last-child)>.dropdown-toggle{border-bottom-right-radius:0;border-bottom-left-radius:0}.btn-group-vertical>.btn-group:last-child:not(:first-child)>.btn:first-child{border-top-left-radius:0;border-top-right-radius:0}.btn-group-justified{display:table;width:100%;table-layout:fixed;border-collapse:separate}.btn-group-justified>.btn,.btn-group-justified>.btn-group{display:table-cell;float:none;width:1%}.btn-group-justified>.btn-group .btn{width:100%}.btn-group-justified>.btn-group .dropdown-menu{left:auto}[data-toggle=buttons]>.btn input[type=checkbox],[data-toggle=buttons]>.btn input[type=radio],[data-toggle=buttons]>.btn-group>.btn input[type=checkbox],[data-toggle=buttons]>.btn-group>.btn input[type=radio]{position:absolute;clip:rect(0,0,0,0);pointer-events:none}.input-group{position:relative;display:table;border-collapse:separate}.input-group[class*=col-]{float:none;padding-right:0;padding-left:0}.input-group .form-control{position:relative;z-index:2;float:left;width:100%;margin-bottom:0}.input-group-lg>.form-control,.input-group-lg>.input-group-addon,.input-group-lg>.input-group-btn>.btn{height:46px;padding:10px 16px;font-size:18px;line-height:1.3333333;border-radius:6px}select.input-group-lg>.form-control,select.input-group-lg>.input-group-addon,select.input-group-lg>.input-group-btn>.btn{height:46px;line-height:46px}select[multiple].input-group-lg>.form-control,select[multiple].input-group-lg>.input-group-addon,select[multiple].input-group-lg>.input-group-btn>.btn,textarea.input-group-lg>.form-control,textarea.input-group-lg>.input-group-addon,textarea.input-group-lg>.input-group-btn>.btn{height:auto}.input-group-sm>.form-control,.input-group-sm>.input-group-addon,.input-group-sm>.input-group-btn>.btn{height:30px;padding:5px 10px;font-size:12px;line-height:1.5;border-radius:3px}select.input-group-sm>.form-control,select.input-group-sm>.input-group-addon,select.input-group-sm>.input-group-btn>.btn{height:30px;line-height:30px}select[multiple].input-group-sm>.form-control,select[multiple].input-group-sm>.input-group-addon,select[multiple].input-group-sm>.input-group-btn>.btn,textarea.input-group-sm>.form-control,textarea.input-group-sm>.input-group-addon,textarea.input-group-sm>.input-group-btn>.btn{height:auto}.input-group .form-control,.input-group-addon,.input-group-btn{display:table-cell}.input-group .form-control:not(:first-child):not(:last-child),.input-group-addon:not(:first-child):not(:last-child),.input-group-btn:not(:first-child):not(:last-child){border-radius:0}.input-group-addon,.input-group-btn{width:1%;white-space:nowrap;vertical-align:middle}.input-group-addon{padding:6px 12px;font-size:14px;font-weight:400;line-height:1;color:#555;text-align:center;background-color:#eee;border:1px solid #ccc;border-radius:4px}.input-group-addon.input-sm{padding:5px 10px;font-size:12px;border-radius:3px}.input-group-addon.input-lg{padding:10px 16px;font-size:18px;border-radius:6px}.input-group-addon input[type=checkbox],.input-group-addon input[type=radio]{margin-top:0}.input-group .form-control:first-child,.input-group-addon:first-child,.input-group-btn:first-child>.btn,.input-group-btn:first-child>.btn-group>.btn,.input-group-btn:first-child>.dropdown-toggle,.input-group-btn:last-child>.btn-group:not(:last-child)>.btn,.input-group-btn:last-child>.btn:not(:last-child):not(.dropdown-toggle){border-top-right-radius:0;border-bottom-right-radius:0}.input-group-addon:first-child{border-right:0}.input-group .form-control:last-child,.input-group-addon:last-child,.input-group-btn:first-child>.btn-group:not(:first-child)>.btn,.input-group-btn:first-child>.btn:not(:first-child),.input-group-btn:last-child>.btn,.input-group-btn:last-child>.btn-group>.btn,.input-group-btn:last-child>.dropdown-toggle{border-top-left-radius:0;border-bottom-left-radius:0}.input-group-addon:last-child{border-left:0}.input-group-btn{position:relative;font-size:0;white-space:nowrap}.input-group-btn>.btn{position:relative}.input-group-btn>.btn+.btn{margin-left:-1px}.input-group-btn>.btn:active,.input-group-btn>.btn:focus,.input-group-btn>.btn:hover{z-index:2}.input-group-btn:first-child>.btn,.input-group-btn:first-child>.btn-group{margin-right:-1px}.input-group-btn:last-child>.btn,.input-group-btn:last-child>.btn-group{z-index:2;margin-left:-1px}.nav{padding-left:0;margin-bottom:0;list-style:none}.nav>li{position:relative;display:block}.nav>li>a{position:relative;display:block;padding:10px 15px}.nav>li>a:focus,.nav>li>a:hover{text-decoration:none;background-color:#eee}.nav>li.disabled>a{color:#777}.nav>li.disabled>a:focus,.nav>li.disabled>a:hover{color:#777;text-decoration:none;cursor:not-allowed;background-color:transparent}.nav .open>a,.nav .open>a:focus,.nav .open>a:hover{background-color:#eee;border-color:#337ab7}.nav .nav-divider{height:1px;margin:9px 0;overflow:hidden;background-color:#e5e5e5}.nav>li>a>img{max-width:none}.nav-tabs{border-bottom:1px solid #ddd}.nav-tabs>li{float:left;margin-bottom:-1px}.nav-tabs>li>a{margin-right:2px;line-height:1.42857143;border:1px solid transparent;border-radius:4px 4px 0 0}.nav-tabs>li>a:hover{border-color:#eee #eee #ddd}.nav-tabs>li.active>a,.nav-tabs>li.active>a:focus,.nav-tabs>li.active>a:hover{color:#555;cursor:default;background-color:#fff;border:1px solid #ddd;border-bottom-color:transparent}.nav-tabs.nav-justified{width:100%;border-bottom:0}.nav-tabs.nav-justified>li{float:none}.nav-tabs.nav-justified>li>a{margin-bottom:5px;text-align:center}.nav-tabs.nav-justified>.dropdown .dropdown-menu{top:auto;left:auto}@media (min-width:768px){.nav-tabs.nav-justified>li{display:table-cell;width:1%}.nav-tabs.nav-justified>li>a{margin-bottom:0}}.nav-tabs.nav-justified>li>a{margin-right:0;border-radius:4px}.nav-tabs.nav-justified>.active>a,.nav-tabs.nav-justified>.active>a:focus,.nav-tabs.nav-justified>.active>a:hover{border:1px solid #ddd}@media (min-width:768px){.nav-tabs.nav-justified>li>a{border-bottom:1px solid #ddd;border-radius:4px 4px 0 0}.nav-tabs.nav-justified>.active>a,.nav-tabs.nav-justified>.active>a:focus,.nav-tabs.nav-justified>.active>a:hover{border-bottom-color:#fff}}.nav-pills>li{float:left}.nav-pills>li>a{border-radius:4px}.nav-pills>li+li{margin-left:2px}.nav-pills>li.active>a,.nav-pills>li.active>a:focus,.nav-pills>li.active>a:hover{color:#fff;background-color:#337ab7}.nav-stacked>li{float:none}.nav-stacked>li+li{margin-top:2px;margin-left:0}.nav-justified{width:100%}.nav-justified>li{float:none}.nav-justified>li>a{margin-bottom:5px;text-align:center}.nav-justified>.dropdown .dropdown-menu{top:auto;left:auto}@media (min-width:768px){.nav-justified>li{display:table-cell;width:1%}.nav-justified>li>a{margin-bottom:0}}.nav-tabs-justified{border-bottom:0}.nav-tabs-justified>li>a{margin-right:0;border-radius:4px}.nav-tabs-justified>.active>a,.nav-tabs-justified>.active>a:focus,.nav-tabs-justified>.active>a:hover{border:1px solid #ddd}@media (min-width:768px){.nav-tabs-justified>li>a{border-bottom:1px solid #ddd;border-radius:4px 4px 0 0}.nav-tabs-justified>.active>a,.nav-tabs-justified>.active>a:focus,.nav-tabs-justified>.active>a:hover{border-bottom-color:#fff}}.tab-content>.tab-pane{display:none}.tab-content>.active{display:block}.nav-tabs .dropdown-menu{margin-top:-1px;border-top-left-radius:0;border-top-right-radius:0}.navbar{position:relative;min-height:50px;margin-bottom:20px;border:1px solid transparent}@media (min-width:768px){.navbar{border-radius:4px}}@media (min-width:768px){.navbar-header{float:left}}.navbar-collapse{padding-right:15px;padding-left:15px;overflow-x:visible;-webkit-overflow-scrolling:touch;border-top:1px solid transparent;-webkit-box-shadow:inset 0 1px 0 rgba(255,255,255,.1);box-shadow:inset 0 1px 0 rgba(255,255,255,.1)}.navbar-collapse.in{overflow-y:auto}@media (min-width:768px){.navbar-collapse{width:auto;border-top:0;-webkit-box-shadow:none;box-shadow:none}.navbar-collapse.collapse{display:block!important;height:auto!important;padding-bottom:0;overflow:visible!important}.navbar-collapse.in{overflow-y:visible}.navbar-fixed-bottom .navbar-collapse,.navbar-fixed-top .navbar-collapse,.navbar-static-top .navbar-collapse{padding-right:0;padding-left:0}}.navbar-fixed-bottom .navbar-collapse,.navbar-fixed-top .navbar-collapse{max-height:340px}@media (max-device-width:480px) and (orientation:landscape){.navbar-fixed-bottom .navbar-collapse,.navbar-fixed-top .navbar-collapse{max-height:200px}}.container-fluid>.navbar-collapse,.container-fluid>.navbar-header,.container>.navbar-collapse,.container>.navbar-header{margin-right:-15px;margin-left:-15px}@media (min-width:768px){.container-fluid>.navbar-collapse,.container-fluid>.navbar-header,.container>.navbar-collapse,.container>.navbar-header{margin-right:0;margin-left:0}}.navbar-static-top{z-index:1000;border-width:0 0 1px}@media (min-width:768px){.navbar-static-top{border-radius:0}}.navbar-fixed-bottom,.navbar-fixed-top{position:fixed;right:0;left:0;z-index:1030}@media (min-width:768px){.navbar-fixed-bottom,.navbar-fixed-top{border-radius:0}}.navbar-fixed-top{top:0;border-width:0 0 1px}.navbar-fixed-bottom{bottom:0;margin-bottom:0;border-width:1px 0 0}.navbar-brand{float:left;height:50px;padding:15px 15px;font-size:18px;line-height:20px}.navbar-brand:focus,.navbar-brand:hover{text-decoration:none}.navbar-brand>img{display:block}@media (min-width:768px){.navbar>.container .navbar-brand,.navbar>.container-fluid .navbar-brand{margin-left:-15px}}.navbar-toggle{position:relative;float:right;padding:9px 10px;margin-top:8px;margin-right:15px;margin-bottom:8px;background-color:transparent;background-image:none;border:1px solid transparent;border-radius:4px}.navbar-toggle:focus{outline:0}.navbar-toggle .icon-bar{display:block;width:22px;height:2px;border-radius:1px}.navbar-toggle .icon-bar+.icon-bar{margin-top:4px}@media (min-width:768px){.navbar-toggle{display:none}}.navbar-nav{margin:7.5px -15px}.navbar-nav>li>a{padding-top:10px;padding-bottom:10px;line-height:20px}@media (max-width:767px){.navbar-nav .open .dropdown-menu{position:static;float:none;width:auto;margin-top:0;background-color:transparent;border:0;-webkit-box-shadow:none;box-shadow:none}.navbar-nav .open .dropdown-menu .dropdown-header,.navbar-nav .open .dropdown-menu>li>a{padding:5px 15px 5px 25px}.navbar-nav .open .dropdown-menu>li>a{line-height:20px}.navbar-nav .open .dropdown-menu>li>a:focus,.navbar-nav .open .dropdown-menu>li>a:hover{background-image:none}}@media (min-width:768px){.navbar-nav{float:left;margin:0}.navbar-nav>li{float:left}.navbar-nav>li>a{padding-top:15px;padding-bottom:15px}}.navbar-form{padding:10px 15px;margin-top:8px;margin-right:-15px;margin-bottom:8px;margin-left:-15px;border-top:1px solid transparent;border-bottom:1px solid transparent;-webkit-box-shadow:inset 0 1px 0 rgba(255,255,255,.1),0 1px 0 rgba(255,255,255,.1);box-shadow:inset 0 1px 0 rgba(255,255,255,.1),0 1px 0 rgba(255,255,255,.1)}@media (min-width:768px){.navbar-form .form-group{display:inline-block;margin-bottom:0;vertical-align:middle}.navbar-form .form-control{display:inline-block;width:auto;vertical-align:middle}.navbar-form .form-control-static{display:inline-block}.navbar-form .input-group{display:inline-table;vertical-align:middle}.navbar-form .input-group .form-control,.navbar-form .input-group .input-group-addon,.navbar-form .input-group .input-group-btn{width:auto}.navbar-form .input-group>.form-control{width:100%}.navbar-form .control-label{margin-bottom:0;vertical-align:middle}.navbar-form .checkbox,.navbar-form .radio{display:inline-block;margin-top:0;margin-bottom:0;vertical-align:middle}.navbar-form .checkbox label,.navbar-form .radio label{padding-left:0}.navbar-form .checkbox input[type=checkbox],.navbar-form .radio input[type=radio]{position:relative;margin-left:0}.navbar-form .has-feedback .form-control-feedback{top:0}}@media (max-width:767px){.navbar-form .form-group{margin-bottom:5px}.navbar-form .form-group:last-child{margin-bottom:0}}@media (min-width:768px){.navbar-form{width:auto;padding-top:0;padding-bottom:0;margin-right:0;margin-left:0;border:0;-webkit-box-shadow:none;box-shadow:none}}.navbar-nav>li>.dropdown-menu{margin-top:0;border-top-left-radius:0;border-top-right-radius:0}.navbar-fixed-bottom .navbar-nav>li>.dropdown-menu{margin-bottom:0;border-top-left-radius:4px;border-top-right-radius:4px;border-bottom-right-radius:0;border-bottom-left-radius:0}.navbar-btn{margin-top:8px;margin-bottom:8px}.navbar-btn.btn-sm{margin-top:10px;margin-bottom:10px}.navbar-btn.btn-xs{margin-top:14px;margin-bottom:14px}.navbar-text{margin-top:15px;margin-bottom:15px}@media (min-width:768px){.navbar-text{float:left;margin-right:15px;margin-left:15px}}@media (min-width:768px){.navbar-left{float:left!important}.navbar-right{float:right!important;margin-right:-15px}.navbar-right~.navbar-right{margin-right:0}}.navbar-default{background-color:#f8f8f8;border-color:#e7e7e7}.navbar-default .navbar-brand{color:#777}.navbar-default .navbar-brand:focus,.navbar-default .navbar-brand:hover{color:#5e5e5e;background-color:transparent}.navbar-default .navbar-text{color:#777}.navbar-default .navbar-nav>li>a{color:#777}.navbar-default .navbar-nav>li>a:focus,.navbar-default .navbar-nav>li>a:hover{color:#333;background-color:transparent}.navbar-default .navbar-nav>.active>a,.navbar-default .navbar-nav>.active>a:focus,.navbar-default .navbar-nav>.active>a:hover{color:#555;background-color:#e7e7e7}.navbar-default .navbar-nav>.disabled>a,.navbar-default .navbar-nav>.disabled>a:focus,.navbar-default .navbar-nav>.disabled>a:hover{color:#ccc;background-color:transparent}.navbar-default .navbar-toggle{border-color:#ddd}.navbar-default .navbar-toggle:focus,.navbar-default .navbar-toggle:hover{background-color:#ddd}.navbar-default .navbar-toggle .icon-bar{background-color:#888}.navbar-default .navbar-collapse,.navbar-default .navbar-form{border-color:#e7e7e7}.navbar-default .navbar-nav>.open>a,.navbar-default .navbar-nav>.open>a:focus,.navbar-default .navbar-nav>.open>a:hover{color:#555;background-color:#e7e7e7}@media (max-width:767px){.navbar-default .navbar-nav .open .dropdown-menu>li>a{color:#777}.navbar-default .navbar-nav .open .dropdown-menu>li>a:focus,.navbar-default .navbar-nav .open .dropdown-menu>li>a:hover{color:#333;background-color:transparent}.navbar-default .navbar-nav .open .dropdown-menu>.active>a,.navbar-default .navbar-nav .open .dropdown-menu>.active>a:focus,.navbar-default .navbar-nav .open .dropdown-menu>.active>a:hover{color:#555;background-color:#e7e7e7}.navbar-default .navbar-nav .open .dropdown-menu>.disabled>a,.navbar-default .navbar-nav .open .dropdown-menu>.disabled>a:focus,.navbar-default .navbar-nav .open .dropdown-menu>.disabled>a:hover{color:#ccc;background-color:transparent}}.navbar-default .navbar-link{color:#777}.navbar-default .navbar-link:hover{color:#333}.navbar-default .btn-link{color:#777}.navbar-default .btn-link:focus,.navbar-default .btn-link:hover{color:#333}.navbar-default .btn-link[disabled]:focus,.navbar-default .btn-link[disabled]:hover,fieldset[disabled] .navbar-default .btn-link:focus,fieldset[disabled] .navbar-default .btn-link:hover{color:#ccc}.navbar-inverse{background-color:#222;border-color:#080808}.navbar-inverse .navbar-brand{color:#9d9d9d}.navbar-inverse .navbar-brand:focus,.navbar-inverse .navbar-brand:hover{color:#fff;background-color:transparent}.navbar-inverse .navbar-text{color:#9d9d9d}.navbar-inverse .navbar-nav>li>a{color:#9d9d9d}.navbar-inverse .navbar-nav>li>a:focus,.navbar-inverse .navbar-nav>li>a:hover{color:#fff;background-color:transparent}.navbar-inverse .navbar-nav>.active>a,.navbar-inverse .navbar-nav>.active>a:focus,.navbar-inverse .navbar-nav>.active>a:hover{color:#fff;background-color:#080808}.navbar-inverse .navbar-nav>.disabled>a,.navbar-inverse .navbar-nav>.disabled>a:focus,.navbar-inverse .navbar-nav>.disabled>a:hover{color:#444;background-color:transparent}.navbar-inverse .navbar-toggle{border-color:#333}.navbar-inverse .navbar-toggle:focus,.navbar-inverse .navbar-toggle:hover{background-color:#333}.navbar-inverse .navbar-toggle .icon-bar{background-color:#fff}.navbar-inverse .navbar-collapse,.navbar-inverse .navbar-form{border-color:#101010}.navbar-inverse .navbar-nav>.open>a,.navbar-inverse .navbar-nav>.open>a:focus,.navbar-inverse .navbar-nav>.open>a:hover{color:#fff;background-color:#080808}@media (max-width:767px){.navbar-inverse .navbar-nav .open .dropdown-menu>.dropdown-header{border-color:#080808}.navbar-inverse .navbar-nav .open .dropdown-menu .divider{background-color:#080808}.navbar-inverse .navbar-nav .open .dropdown-menu>li>a{color:#9d9d9d}.navbar-inverse .navbar-nav .open .dropdown-menu>li>a:focus,.navbar-inverse .navbar-nav .open .dropdown-menu>li>a:hover{color:#fff;background-color:transparent}.navbar-inverse .navbar-nav .open .dropdown-menu>.active>a,.navbar-inverse .navbar-nav .open .dropdown-menu>.active>a:focus,.navbar-inverse .navbar-nav .open .dropdown-menu>.active>a:hover{color:#fff;background-color:#080808}.navbar-inverse .navbar-nav .open .dropdown-menu>.disabled>a,.navbar-inverse .navbar-nav .open .dropdown-menu>.disabled>a:focus,.navbar-inverse .navbar-nav .open .dropdown-menu>.disabled>a:hover{color:#444;background-color:transparent}}.navbar-inverse .navbar-link{color:#9d9d9d}.navbar-inverse .navbar-link:hover{color:#fff}.navbar-inverse .btn-link{color:#9d9d9d}.navbar-inverse .btn-link:focus,.navbar-inverse .btn-link:hover{color:#fff}.navbar-inverse .btn-link[disabled]:focus,.navbar-inverse .btn-link[disabled]:hover,fieldset[disabled] .navbar-inverse .btn-link:focus,fieldset[disabled] .navbar-inverse .btn-link:hover{color:#444}.breadcrumb{padding:8px 15px;margin-bottom:20px;list-style:none;background-color:#f5f5f5;border-radius:4px}.breadcrumb>li{display:inline-block}.breadcrumb>li+li:before{padding:0 5px;color:#ccc;content:"/\00a0"}.breadcrumb>.active{color:#777}.pagination{display:inline-block;padding-left:0;margin:20px 0;border-radius:4px}.pagination>li{display:inline}.pagination>li>a,.pagination>li>span{position:relative;float:left;padding:6px 12px;margin-left:-1px;line-height:1.42857143;color:#337ab7;text-decoration:none;background-color:#fff;border:1px solid #ddd}.pagination>li:first-child>a,.pagination>li:first-child>span{margin-left:0;border-top-left-radius:4px;border-bottom-left-radius:4px}.pagination>li:last-child>a,.pagination>li:last-child>span{border-top-right-radius:4px;border-bottom-right-radius:4px}.pagination>li>a:focus,.pagination>li>a:hover,.pagination>li>span:focus,.pagination>li>span:hover{z-index:3;color:#23527c;background-color:#eee;border-color:#ddd}.pagination>.active>a,.pagination>.active>a:focus,.pagination>.active>a:hover,.pagination>.active>span,.pagination>.active>span:focus,.pagination>.active>span:hover{z-index:2;color:#fff;cursor:default;background-color:#337ab7;border-color:#337ab7}.pagination>.disabled>a,.pagination>.disabled>a:focus,.pagination>.disabled>a:hover,.pagination>.disabled>span,.pagination>.disabled>span:focus,.pagination>.disabled>span:hover{color:#777;cursor:not-allowed;background-color:#fff;border-color:#ddd}.pagination-lg>li>a,.pagination-lg>li>span{padding:10px 16px;font-size:18px;line-height:1.3333333}.pagination-lg>li:first-child>a,.pagination-lg>li:first-child>span{border-top-left-radius:6px;border-bottom-left-radius:6px}.pagination-lg>li:last-child>a,.pagination-lg>li:last-child>span{border-top-right-radius:6px;border-bottom-right-radius:6px}.pagination-sm>li>a,.pagination-sm>li>span{padding:5px 10px;font-size:12px;line-height:1.5}.pagination-sm>li:first-child>a,.pagination-sm>li:first-child>span{border-top-left-radius:3px;border-bottom-left-radius:3px}.pagination-sm>li:last-child>a,.pagination-sm>li:last-child>span{border-top-right-radius:3px;border-bottom-right-radius:3px}.pager{padding-left:0;margin:20px 0;text-align:center;list-style:none}.pager li{display:inline}.pager li>a,.pager li>span{display:inline-block;padding:5px 14px;background-color:#fff;border:1px solid #ddd;border-radius:15px}.pager li>a:focus,.pager li>a:hover{text-decoration:none;background-color:#eee}.pager .next>a,.pager .next>span{float:right}.pager .previous>a,.pager .previous>span{float:left}.pager .disabled>a,.pager .disabled>a:focus,.pager .disabled>a:hover,.pager .disabled>span{color:#777;cursor:not-allowed;background-color:#fff}.label{display:inline;padding:.2em .6em .3em;font-size:75%;font-weight:700;line-height:1;color:#fff;text-align:center;white-space:nowrap;vertical-align:baseline;border-radius:.25em}a.label:focus,a.label:hover{color:#fff;text-decoration:none;cursor:pointer}.label:empty{display:none}.btn .label{position:relative;top:-1px}.label-default{background-color:#777}.label-default[href]:focus,.label-default[href]:hover{background-color:#5e5e5e}.label-primary{background-color:#337ab7}.label-primary[href]:focus,.label-primary[href]:hover{background-color:#286090}.label-success{background-color:#5cb85c}.label-success[href]:focus,.label-success[href]:hover{background-color:#449d44}.label-info{background-color:#5bc0de}.label-info[href]:focus,.label-info[href]:hover{background-color:#31b0d5}.label-warning{background-color:#f0ad4e}.label-warning[href]:focus,.label-warning[href]:hover{background-color:#ec971f}.label-danger{background-color:#d9534f}.label-danger[href]:focus,.label-danger[href]:hover{background-color:#c9302c}.badge{display:inline-block;min-width:10px;padding:3px 7px;font-size:12px;font-weight:700;line-height:1;color:#fff;text-align:center;white-space:nowrap;vertical-align:middle;background-color:#777;border-radius:10px}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.btn-group-xs>.btn .badge,.btn-xs .badge{top:0;padding:1px 5px}a.badge:focus,a.badge:hover{color:#fff;text-decoration:none;cursor:pointer}.list-group-item.active>.badge,.nav-pills>.active>a>.badge{color:#337ab7;background-color:#fff}.list-group-item>.badge{float:right}.list-group-item>.badge+.badge{margin-right:5px}.nav-pills>li>a>.badge{margin-left:3px}.jumbotron{padding-top:30px;padding-bottom:30px;margin-bottom:30px;color:inherit;background-color:#eee}.jumbotron .h1,.jumbotron h1{color:inherit}.jumbotron p{margin-bottom:15px;font-size:21px;font-weight:200}.jumbotron>hr{border-top-color:#d5d5d5}.container .jumbotron,.container-fluid .jumbotron{border-radius:6px}.jumbotron .container{max-width:100%}@media screen and (min-width:768px){.jumbotron{padding-top:48px;padding-bottom:48px}.container .jumbotron,.container-fluid .jumbotron{padding-right:60px;padding-left:60px}.jumbotron .h1,.jumbotron h1{font-size:63px}}.thumbnail{display:block;padding:4px;margin-bottom:20px;line-height:1.42857143;background-color:#fff;border:1px solid #ddd;border-radius:4px;-webkit-transition:border .2s ease-in-out;-o-transition:border .2s ease-in-out;transition:border .2s ease-in-out}.thumbnail a>img,.thumbnail>img{margin-right:auto;margin-left:auto}a.thumbnail.active,a.thumbnail:focus,a.thumbnail:hover{border-color:#337ab7}.thumbnail .caption{padding:9px;color:#333}.alert{padding:15px;margin-bottom:20px;border:1px solid transparent;border-radius:4px}.alert h4{margin-top:0;color:inherit}.alert .alert-link{font-weight:700}.alert>p,.alert>ul{margin-bottom:0}.alert>p+p{margin-top:5px}.alert-dismissable,.alert-dismissible{padding-right:35px}.alert-dismissable .close,.alert-dismissible .close{position:relative;top:-2px;right:-21px;color:inherit}.alert-success{color:#3c763d;background-color:#dff0d8;border-color:#d6e9c6}.alert-success hr{border-top-color:#c9e2b3}.alert-success .alert-link{color:#2b542c}.alert-info{color:#31708f;background-color:#d9edf7;border-color:#bce8f1}.alert-info hr{border-top-color:#a6e1ec}.alert-info .alert-link{color:#245269}.alert-warning{color:#8a6d3b;background-color:#fcf8e3;border-color:#faebcc}.alert-warning hr{border-top-color:#f7e1b5}.alert-warning .alert-link{color:#66512c}.alert-danger{color:#a94442;background-color:#f2dede;border-color:#ebccd1}.alert-danger hr{border-top-color:#e4b9c0}.alert-danger .alert-link{color:#843534}@-webkit-keyframes progress-bar-stripes{from{background-position:40px 0}to{background-position:0 0}}@-o-keyframes progress-bar-stripes{from{background-position:40px 0}to{background-position:0 0}}@keyframes progress-bar-stripes{from{background-position:40px 0}to{background-position:0 0}}.progress{height:20px;margin-bottom:20px;overflow:hidden;background-color:#f5f5f5;border-radius:4px;-webkit-box-shadow:inset 0 1px 2px rgba(0,0,0,.1);box-shadow:inset 0 1px 2px rgba(0,0,0,.1)}.progress-bar{float:left;width:0;height:100%;font-size:12px;line-height:20px;color:#fff;text-align:center;background-color:#337ab7;-webkit-box-shadow:inset 0 -1px 0 rgba(0,0,0,.15);box-shadow:inset 0 -1px 0 rgba(0,0,0,.15);-webkit-transition:width .6s ease;-o-transition:width .6s ease;transition:width .6s ease}.progress-bar-striped,.progress-striped .progress-bar{background-image:-webkit-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:-o-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);-webkit-background-size:40px 40px;background-size:40px 40px}.progress-bar.active,.progress.active .progress-bar{-webkit-animation:progress-bar-stripes 2s linear infinite;-o-animation:progress-bar-stripes 2s linear infinite;animation:progress-bar-stripes 2s linear infinite}.progress-bar-success{background-color:#5cb85c}.progress-striped .progress-bar-success{background-image:-webkit-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:-o-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent)}.progress-bar-info{background-color:#5bc0de}.progress-striped .progress-bar-info{background-image:-webkit-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:-o-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent)}.progress-bar-warning{background-color:#f0ad4e}.progress-striped .progress-bar-warning{background-image:-webkit-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:-o-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent)}.progress-bar-danger{background-color:#d9534f}.progress-striped .progress-bar-danger{background-image:-webkit-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:-o-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent)}.media{margin-top:15px}.media:first-child{margin-top:0}.media,.media-body{overflow:hidden;zoom:1}.media-body{width:10000px}.media-object{display:block}.media-object.img-thumbnail{max-width:none}.media-right,.media>.pull-right{padding-left:10px}.media-left,.media>.pull-left{padding-right:10px}.media-body,.media-left,.media-right{display:table-cell;vertical-align:top}.media-middle{vertical-align:middle}.media-bottom{vertical-align:bottom}.media-heading{margin-top:0;margin-bottom:5px}.media-list{padding-left:0;list-style:none}.list-group{padding-left:0;margin-bottom:20px}.list-group-item{position:relative;display:block;padding:10px 15px;margin-bottom:-1px;background-color:#fff;border:1px solid #ddd}.list-group-item:first-child{border-top-left-radius:4px;border-top-right-radius:4px}.list-group-item:last-child{margin-bottom:0;border-bottom-right-radius:4px;border-bottom-left-radius:4px}a.list-group-item,button.list-group-item{color:#555}a.list-group-item .list-group-item-heading,button.list-group-item .list-group-item-heading{color:#333}a.list-group-item:focus,a.list-group-item:hover,button.list-group-item:focus,button.list-group-item:hover{color:#555;text-decoration:none;background-color:#f5f5f5}button.list-group-item{width:100%;text-align:left}.list-group-item.disabled,.list-group-item.disabled:focus,.list-group-item.disabled:hover{color:#777;cursor:not-allowed;background-color:#eee}.list-group-item.disabled .list-group-item-heading,.list-group-item.disabled:focus .list-group-item-heading,.list-group-item.disabled:hover .list-group-item-heading{color:inherit}.list-group-item.disabled .list-group-item-text,.list-group-item.disabled:focus .list-group-item-text,.list-group-item.disabled:hover .list-group-item-text{color:#777}.list-group-item.active,.list-group-item.active:focus,.list-group-item.active:hover{z-index:2;color:#fff;background-color:#337ab7;border-color:#337ab7}.list-group-item.active .list-group-item-heading,.list-group-item.active .list-group-item-heading>.small,.list-group-item.active .list-group-item-heading>small,.list-group-item.active:focus .list-group-item-heading,.list-group-item.active:focus .list-group-item-heading>.small,.list-group-item.active:focus .list-group-item-heading>small,.list-group-item.active:hover .list-group-item-heading,.list-group-item.active:hover .list-group-item-heading>.small,.list-group-item.active:hover .list-group-item-heading>small{color:inherit}.list-group-item.active .list-group-item-text,.list-group-item.active:focus .list-group-item-text,.list-group-item.active:hover .list-group-item-text{color:#c7ddef}.list-group-item-success{color:#3c763d;background-color:#dff0d8}a.list-group-item-success,button.list-group-item-success{color:#3c763d}a.list-group-item-success .list-group-item-heading,button.list-group-item-success .list-group-item-heading{color:inherit}a.list-group-item-success:focus,a.list-group-item-success:hover,button.list-group-item-success:focus,button.list-group-item-success:hover{color:#3c763d;background-color:#d0e9c6}a.list-group-item-success.active,a.list-group-item-success.active:focus,a.list-group-item-success.active:hover,button.list-group-item-success.active,button.list-group-item-success.active:focus,button.list-group-item-success.active:hover{color:#fff;background-color:#3c763d;border-color:#3c763d}.list-group-item-info{color:#31708f;background-color:#d9edf7}a.list-group-item-info,button.list-group-item-info{color:#31708f}a.list-group-item-info .list-group-item-heading,button.list-group-item-info .list-group-item-heading{color:inherit}a.list-group-item-info:focus,a.list-group-item-info:hover,button.list-group-item-info:focus,button.list-group-item-info:hover{color:#31708f;background-color:#c4e3f3}a.list-group-item-info.active,a.list-group-item-info.active:focus,a.list-group-item-info.active:hover,button.list-group-item-info.active,button.list-group-item-info.active:focus,button.list-group-item-info.active:hover{color:#fff;background-color:#31708f;border-color:#31708f}.list-group-item-warning{color:#8a6d3b;background-color:#fcf8e3}a.list-group-item-warning,button.list-group-item-warning{color:#8a6d3b}a.list-group-item-warning .list-group-item-heading,button.list-group-item-warning .list-group-item-heading{color:inherit}a.list-group-item-warning:focus,a.list-group-item-warning:hover,button.list-group-item-warning:focus,button.list-group-item-warning:hover{color:#8a6d3b;background-color:#faf2cc}a.list-group-item-warning.active,a.list-group-item-warning.active:focus,a.list-group-item-warning.active:hover,button.list-group-item-warning.active,button.list-group-item-warning.active:focus,button.list-group-item-warning.active:hover{color:#fff;background-color:#8a6d3b;border-color:#8a6d3b}.list-group-item-danger{color:#a94442;background-color:#f2dede}a.list-group-item-danger,button.list-group-item-danger{color:#a94442}a.list-group-item-danger .list-group-item-heading,button.list-group-item-danger .list-group-item-heading{color:inherit}a.list-group-item-danger:focus,a.list-group-item-danger:hover,button.list-group-item-danger:focus,button.list-group-item-danger:hover{color:#a94442;background-color:#ebcccc}a.list-group-item-danger.active,a.list-group-item-danger.active:focus,a.list-group-item-danger.active:hover,button.list-group-item-danger.active,button.list-group-item-danger.active:focus,button.list-group-item-danger.active:hover{color:#fff;background-color:#a94442;border-color:#a94442}.list-group-item-heading{margin-top:0;margin-bottom:5px}.list-group-item-text{margin-bottom:0;line-height:1.3}.panel{margin-bottom:20px;background-color:#fff;border:1px solid transparent;border-radius:4px;-webkit-box-shadow:0 1px 1px rgba(0,0,0,.05);box-shadow:0 1px 1px rgba(0,0,0,.05)}.panel-body{padding:15px}.panel-heading{padding:10px 15px;border-bottom:1px solid transparent;border-top-left-radius:3px;border-top-right-radius:3px}.panel-heading>.dropdown .dropdown-toggle{color:inherit}.panel-title{margin-top:0;margin-bottom:0;font-size:16px;color:inherit}.panel-title>.small,.panel-title>.small>a,.panel-title>a,.panel-title>small,.panel-title>small>a{color:inherit}.panel-footer{padding:10px 15px;background-color:#f5f5f5;border-top:1px solid #ddd;border-bottom-right-radius:3px;border-bottom-left-radius:3px}.panel>.list-group,.panel>.panel-collapse>.list-group{margin-bottom:0}.panel>.list-group .list-group-item,.panel>.panel-collapse>.list-group .list-group-item{border-width:1px 0;border-radius:0}.panel>.list-group:first-child .list-group-item:first-child,.panel>.panel-collapse>.list-group:first-child .list-group-item:first-child{border-top:0;border-top-left-radius:3px;border-top-right-radius:3px}.panel>.list-group:last-child .list-group-item:last-child,.panel>.panel-collapse>.list-group:last-child .list-group-item:last-child{border-bottom:0;border-bottom-right-radius:3px;border-bottom-left-radius:3px}.panel>.panel-heading+.panel-collapse>.list-group .list-group-item:first-child{border-top-left-radius:0;border-top-right-radius:0}.panel-heading+.list-group .list-group-item:first-child{border-top-width:0}.list-group+.panel-footer{border-top-width:0}.panel>.panel-collapse>.table,.panel>.table,.panel>.table-responsive>.table{margin-bottom:0}.panel>.panel-collapse>.table caption,.panel>.table caption,.panel>.table-responsive>.table caption{padding-right:15px;padding-left:15px}.panel>.table-responsive:first-child>.table:first-child,.panel>.table:first-child{border-top-left-radius:3px;border-top-right-radius:3px}.panel>.table-responsive:first-child>.table:first-child>tbody:first-child>tr:first-child,.panel>.table-responsive:first-child>.table:first-child>thead:first-child>tr:first-child,.panel>.table:first-child>tbody:first-child>tr:first-child,.panel>.table:first-child>thead:first-child>tr:first-child{border-top-left-radius:3px;border-top-right-radius:3px}.panel>.table-responsive:first-child>.table:first-child>tbody:first-child>tr:first-child td:first-child,.panel>.table-responsive:first-child>.table:first-child>tbody:first-child>tr:first-child th:first-child,.panel>.table-responsive:first-child>.table:first-child>thead:first-child>tr:first-child td:first-child,.panel>.table-responsive:first-child>.table:first-child>thead:first-child>tr:first-child th:first-child,.panel>.table:first-child>tbody:first-child>tr:first-child td:first-child,.panel>.table:first-child>tbody:first-child>tr:first-child th:first-child,.panel>.table:first-child>thead:first-child>tr:first-child td:first-child,.panel>.table:first-child>thead:first-child>tr:first-child th:first-child{border-top-left-radius:3px}.panel>.table-responsive:first-child>.table:first-child>tbody:first-child>tr:first-child td:last-child,.panel>.table-responsive:first-child>.table:first-child>tbody:first-child>tr:first-child th:last-child,.panel>.table-responsive:first-child>.table:first-child>thead:first-child>tr:first-child td:last-child,.panel>.table-responsive:first-child>.table:first-child>thead:first-child>tr:first-child th:last-child,.panel>.table:first-child>tbody:first-child>tr:first-child td:last-child,.panel>.table:first-child>tbody:first-child>tr:first-child th:last-child,.panel>.table:first-child>thead:first-child>tr:first-child td:last-child,.panel>.table:first-child>thead:first-child>tr:first-child th:last-child{border-top-right-radius:3px}.panel>.table-responsive:last-child>.table:last-child,.panel>.table:last-child{border-bottom-right-radius:3px;border-bottom-left-radius:3px}.panel>.table-responsive:last-child>.table:last-child>tbody:last-child>tr:last-child,.panel>.table-responsive:last-child>.table:last-child>tfoot:last-child>tr:last-child,.panel>.table:last-child>tbody:last-child>tr:last-child,.panel>.table:last-child>tfoot:last-child>tr:last-child{border-bottom-right-radius:3px;border-bottom-left-radius:3px}.panel>.table-responsive:last-child>.table:last-child>tbody:last-child>tr:last-child td:first-child,.panel>.table-responsive:last-child>.table:last-child>tbody:last-child>tr:last-child th:first-child,.panel>.table-responsive:last-child>.table:last-child>tfoot:last-child>tr:last-child td:first-child,.panel>.table-responsive:last-child>.table:last-child>tfoot:last-child>tr:last-child th:first-child,.panel>.table:last-child>tbody:last-child>tr:last-child td:first-child,.panel>.table:last-child>tbody:last-child>tr:last-child th:first-child,.panel>.table:last-child>tfoot:last-child>tr:last-child td:first-child,.panel>.table:last-child>tfoot:last-child>tr:last-child th:first-child{border-bottom-left-radius:3px}.panel>.table-responsive:last-child>.table:last-child>tbody:last-child>tr:last-child td:last-child,.panel>.table-responsive:last-child>.table:last-child>tbody:last-child>tr:last-child th:last-child,.panel>.table-responsive:last-child>.table:last-child>tfoot:last-child>tr:last-child td:last-child,.panel>.table-responsive:last-child>.table:last-child>tfoot:last-child>tr:last-child th:last-child,.panel>.table:last-child>tbody:last-child>tr:last-child td:last-child,.panel>.table:last-child>tbody:last-child>tr:last-child th:last-child,.panel>.table:last-child>tfoot:last-child>tr:last-child td:last-child,.panel>.table:last-child>tfoot:last-child>tr:last-child th:last-child{border-bottom-right-radius:3px}.panel>.panel-body+.table,.panel>.panel-body+.table-responsive,.panel>.table+.panel-body,.panel>.table-responsive+.panel-body{border-top:1px solid #ddd}.panel>.table>tbody:first-child>tr:first-child td,.panel>.table>tbody:first-child>tr:first-child th{border-top:0}.panel>.table-bordered,.panel>.table-responsive>.table-bordered{border:0}.panel>.table-bordered>tbody>tr>td:first-child,.panel>.table-bordered>tbody>tr>th:first-child,.panel>.table-bordered>tfoot>tr>td:first-child,.panel>.table-bordered>tfoot>tr>th:first-child,.panel>.table-bordered>thead>tr>td:first-child,.panel>.table-bordered>thead>tr>th:first-child,.panel>.table-responsive>.table-bordered>tbody>tr>td:first-child,.panel>.table-responsive>.table-bordered>tbody>tr>th:first-child,.panel>.table-responsive>.table-bordered>tfoot>tr>td:first-child,.panel>.table-responsive>.table-bordered>tfoot>tr>th:first-child,.panel>.table-responsive>.table-bordered>thead>tr>td:first-child,.panel>.table-responsive>.table-bordered>thead>tr>th:first-child{border-left:0}.panel>.table-bordered>tbody>tr>td:last-child,.panel>.table-bordered>tbody>tr>th:last-child,.panel>.table-bordered>tfoot>tr>td:last-child,.panel>.table-bordered>tfoot>tr>th:last-child,.panel>.table-bordered>thead>tr>td:last-child,.panel>.table-bordered>thead>tr>th:last-child,.panel>.table-responsive>.table-bordered>tbody>tr>td:last-child,.panel>.table-responsive>.table-bordered>tbody>tr>th:last-child,.panel>.table-responsive>.table-bordered>tfoot>tr>td:last-child,.panel>.table-responsive>.table-bordered>tfoot>tr>th:last-child,.panel>.table-responsive>.table-bordered>thead>tr>td:last-child,.panel>.table-responsive>.table-bordered>thead>tr>th:last-child{border-right:0}.panel>.table-bordered>tbody>tr:first-child>td,.panel>.table-bordered>tbody>tr:first-child>th,.panel>.table-bordered>thead>tr:first-child>td,.panel>.table-bordered>thead>tr:first-child>th,.panel>.table-responsive>.table-bordered>tbody>tr:first-child>td,.panel>.table-responsive>.table-bordered>tbody>tr:first-child>th,.panel>.table-responsive>.table-bordered>thead>tr:first-child>td,.panel>.table-responsive>.table-bordered>thead>tr:first-child>th{border-bottom:0}.panel>.table-bordered>tbody>tr:last-child>td,.panel>.table-bordered>tbody>tr:last-child>th,.panel>.table-bordered>tfoot>tr:last-child>td,.panel>.table-bordered>tfoot>tr:last-child>th,.panel>.table-responsive>.table-bordered>tbody>tr:last-child>td,.panel>.table-responsive>.table-bordered>tbody>tr:last-child>th,.panel>.table-responsive>.table-bordered>tfoot>tr:last-child>td,.panel>.table-responsive>.table-bordered>tfoot>tr:last-child>th{border-bottom:0}.panel>.table-responsive{margin-bottom:0;border:0}.panel-group{margin-bottom:20px}.panel-group .panel{margin-bottom:0;border-radius:4px}.panel-group .panel+.panel{margin-top:5px}.panel-group .panel-heading{border-bottom:0}.panel-group .panel-heading+.panel-collapse>.list-group,.panel-group .panel-heading+.panel-collapse>.panel-body{border-top:1px solid #ddd}.panel-group .panel-footer{border-top:0}.panel-group .panel-footer+.panel-collapse .panel-body{border-bottom:1px solid #ddd}.panel-default{border-color:#ddd}.panel-default>.panel-heading{color:#333;background-color:#f5f5f5;border-color:#ddd}.panel-default>.panel-heading+.panel-collapse>.panel-body{border-top-color:#ddd}.panel-default>.panel-heading .badge{color:#f5f5f5;background-color:#333}.panel-default>.panel-footer+.panel-collapse>.panel-body{border-bottom-color:#ddd}.panel-primary{border-color:#337ab7}.panel-primary>.panel-heading{color:#fff;background-color:#337ab7;border-color:#337ab7}.panel-primary>.panel-heading+.panel-collapse>.panel-body{border-top-color:#337ab7}.panel-primary>.panel-heading .badge{color:#337ab7;background-color:#fff}.panel-primary>.panel-footer+.panel-collapse>.panel-body{border-bottom-color:#337ab7}.panel-success{border-color:#d6e9c6}.panel-success>.panel-heading{color:#3c763d;background-color:#dff0d8;border-color:#d6e9c6}.panel-success>.panel-heading+.panel-collapse>.panel-body{border-top-color:#d6e9c6}.panel-success>.panel-heading .badge{color:#dff0d8;background-color:#3c763d}.panel-success>.panel-footer+.panel-collapse>.panel-body{border-bottom-color:#d6e9c6}.panel-info{border-color:#bce8f1}.panel-info>.panel-heading{color:#31708f;background-color:#d9edf7;border-color:#bce8f1}.panel-info>.panel-heading+.panel-collapse>.panel-body{border-top-color:#bce8f1}.panel-info>.panel-heading .badge{color:#d9edf7;background-color:#31708f}.panel-info>.panel-footer+.panel-collapse>.panel-body{border-bottom-color:#bce8f1}.panel-warning{border-color:#faebcc}.panel-warning>.panel-heading{color:#8a6d3b;background-color:#fcf8e3;border-color:#faebcc}.panel-warning>.panel-heading+.panel-collapse>.panel-body{border-top-color:#faebcc}.panel-warning>.panel-heading .badge{color:#fcf8e3;background-color:#8a6d3b}.panel-warning>.panel-footer+.panel-collapse>.panel-body{border-bottom-color:#faebcc}.panel-danger{border-color:#ebccd1}.panel-danger>.panel-heading{color:#a94442;background-color:#f2dede;border-color:#ebccd1}.panel-danger>.panel-heading+.panel-collapse>.panel-body{border-top-color:#ebccd1}.panel-danger>.panel-heading .badge{color:#f2dede;background-color:#a94442}.panel-danger>.panel-footer+.panel-collapse>.panel-body{border-bottom-color:#ebccd1}.embed-responsive{position:relative;display:block;height:0;padding:0;overflow:hidden}.embed-responsive .embed-responsive-item,.embed-responsive embed,.embed-responsive iframe,.embed-responsive object,.embed-responsive video{position:absolute;top:0;bottom:0;left:0;width:100%;height:100%;border:0}.embed-responsive-16by9{padding-bottom:56.25%}.embed-responsive-4by3{padding-bottom:75%}.well{min-height:20px;padding:19px;margin-bottom:20px;background-color:#f5f5f5;border:1px solid #e3e3e3;border-radius:4px;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.05);box-shadow:inset 0 1px 1px rgba(0,0,0,.05)}.well blockquote{border-color:#ddd;border-color:rgba(0,0,0,.15)}.well-lg{padding:24px;border-radius:6px}.well-sm{padding:9px;border-radius:3px}.close{float:right;font-size:21px;font-weight:700;line-height:1;color:#000;text-shadow:0 1px 0 #fff;filter:alpha(opacity=20);opacity:.2}.close:focus,.close:hover{color:#000;text-decoration:none;cursor:pointer;filter:alpha(opacity=50);opacity:.5}button.close{-webkit-appearance:none;padding:0;cursor:pointer;background:0 0;border:0}.modal-open{overflow:hidden}.modal{position:fixed;top:0;right:0;bottom:0;left:0;z-index:1050;display:none;overflow:hidden;-webkit-overflow-scrolling:touch;outline:0}.modal.fade .modal-dialog{-webkit-transition:-webkit-transform .3s ease-out;-o-transition:-o-transform .3s ease-out;transition:transform .3s ease-out;-webkit-transform:translate(0,-25%);-ms-transform:translate(0,-25%);-o-transform:translate(0,-25%);transform:translate(0,-25%)}.modal.in .modal-dialog{-webkit-transform:translate(0,0);-ms-transform:translate(0,0);-o-transform:translate(0,0);transform:translate(0,0)}.modal-open .modal{overflow-x:hidden;overflow-y:auto}.modal-dialog{position:relative;width:auto;margin:10px}.modal-content{position:relative;background-color:#fff;-webkit-background-clip:padding-box;background-clip:padding-box;border:1px solid #999;border:1px solid rgba(0,0,0,.2);border-radius:6px;outline:0;-webkit-box-shadow:0 3px 9px rgba(0,0,0,.5);box-shadow:0 3px 9px rgba(0,0,0,.5)}.modal-backdrop{position:fixed;top:0;right:0;bottom:0;left:0;z-index:1040;background-color:#000}.modal-backdrop.fade{filter:alpha(opacity=0);opacity:0}.modal-backdrop.in{filter:alpha(opacity=50);opacity:.5}.modal-header{min-height:16.43px;padding:15px;border-bottom:1px solid #e5e5e5}.modal-header .close{margin-top:-2px}.modal-title{margin:0;line-height:1.42857143}.modal-body{position:relative;padding:15px}.modal-footer{padding:15px;text-align:right;border-top:1px solid #e5e5e5}.modal-footer .btn+.btn{margin-bottom:0;margin-left:5px}.modal-footer .btn-group .btn+.btn{margin-left:-1px}.modal-footer .btn-block+.btn-block{margin-left:0}.modal-scrollbar-measure{position:absolute;top:-9999px;width:50px;height:50px;overflow:scroll}@media (min-width:768px){.modal-dialog{width:600px;margin:30px auto}.modal-content{-webkit-box-shadow:0 5px 15px rgba(0,0,0,.5);box-shadow:0 5px 15px rgba(0,0,0,.5)}.modal-sm{width:300px}}@media (min-width:992px){.modal-lg{width:900px}}.tooltip{position:absolute;z-index:1070;display:block;font-family:"Helvetica Neue",Helvetica,Arial,sans-serif;font-size:12px;font-style:normal;font-weight:400;line-height:1.42857143;text-align:left;text-align:start;text-decoration:none;text-shadow:none;text-transform:none;letter-spacing:normal;word-break:normal;word-spacing:normal;word-wrap:normal;white-space:normal;filter:alpha(opacity=0);opacity:0;line-break:auto}.tooltip.in{filter:alpha(opacity=90);opacity:.9}.tooltip.top{padding:5px 0;margin-top:-3px}.tooltip.right{padding:0 5px;margin-left:3px}.tooltip.bottom{padding:5px 0;margin-top:3px}.tooltip.left{padding:0 5px;margin-left:-3px}.tooltip-inner{max-width:200px;padding:3px 8px;color:#fff;text-align:center;background-color:#000;border-radius:4px}.tooltip-arrow{position:absolute;width:0;height:0;border-color:transparent;border-style:solid}.tooltip.top .tooltip-arrow{bottom:0;left:50%;margin-left:-5px;border-width:5px 5px 0;border-top-color:#000}.tooltip.top-left .tooltip-arrow{right:5px;bottom:0;margin-bottom:-5px;border-width:5px 5px 0;border-top-color:#000}.tooltip.top-right .tooltip-arrow{bottom:0;left:5px;margin-bottom:-5px;border-width:5px 5px 0;border-top-color:#000}.tooltip.right .tooltip-arrow{top:50%;left:0;margin-top:-5px;border-width:5px 5px 5px 0;border-right-color:#000}.tooltip.left .tooltip-arrow{top:50%;right:0;margin-top:-5px;border-width:5px 0 5px 5px;border-left-color:#000}.tooltip.bottom .tooltip-arrow{top:0;left:50%;margin-left:-5px;border-width:0 5px 5px;border-bottom-color:#000}.tooltip.bottom-left .tooltip-arrow{top:0;right:5px;margin-top:-5px;border-width:0 5px 5px;border-bottom-color:#000}.tooltip.bottom-right .tooltip-arrow{top:0;left:5px;margin-top:-5px;border-width:0 5px 5px;border-bottom-color:#000}.popover{position:absolute;top:0;left:0;z-index:1060;display:none;max-width:276px;padding:1px;font-family:"Helvetica Neue",Helvetica,Arial,sans-serif;font-size:14px;font-style:normal;font-weight:400;line-height:1.42857143;text-align:left;text-align:start;text-decoration:none;text-shadow:none;text-transform:none;letter-spacing:normal;word-break:normal;word-spacing:normal;word-wrap:normal;white-space:normal;background-color:#fff;-webkit-background-clip:padding-box;background-clip:padding-box;border:1px solid #ccc;border:1px solid rgba(0,0,0,.2);border-radius:6px;-webkit-box-shadow:0 5px 10px rgba(0,0,0,.2);box-shadow:0 5px 10px rgba(0,0,0,.2);line-break:auto}.popover.top{margin-top:-10px}.popover.right{margin-left:10px}.popover.bottom{margin-top:10px}.popover.left{margin-left:-10px}.popover-title{padding:8px 14px;margin:0;font-size:14px;background-color:#f7f7f7;border-bottom:1px solid #ebebeb;border-radius:5px 5px 0 0}.popover-content{padding:9px 14px}.popover>.arrow,.popover>.arrow:after{position:absolute;display:block;width:0;height:0;border-color:transparent;border-style:solid}.popover>.arrow{border-width:11px}.popover>.arrow:after{content:"";border-width:10px}.popover.top>.arrow{bottom:-11px;left:50%;margin-left:-11px;border-top-color:#999;border-top-color:rgba(0,0,0,.25);border-bottom-width:0}.popover.top>.arrow:after{bottom:1px;margin-left:-10px;content:" ";border-top-color:#fff;border-bottom-width:0}.popover.right>.arrow{top:50%;left:-11px;margin-top:-11px;border-right-color:#999;border-right-color:rgba(0,0,0,.25);border-left-width:0}.popover.right>.arrow:after{bottom:-10px;left:1px;content:" ";border-right-color:#fff;border-left-width:0}.popover.bottom>.arrow{top:-11px;left:50%;margin-left:-11px;border-top-width:0;border-bottom-color:#999;border-bottom-color:rgba(0,0,0,.25)}.popover.bottom>.arrow:after{top:1px;margin-left:-10px;content:" ";border-top-width:0;border-bottom-color:#fff}.popover.left>.arrow{top:50%;right:-11px;margin-top:-11px;border-right-width:0;border-left-color:#999;border-left-color:rgba(0,0,0,.25)}.popover.left>.arrow:after{right:1px;bottom:-10px;content:" ";border-right-width:0;border-left-color:#fff}.carousel{position:relative}.carousel-inner{position:relative;width:100%;overflow:hidden}.carousel-inner>.item{position:relative;display:none;-webkit-transition:.6s ease-in-out left;-o-transition:.6s ease-in-out left;transition:.6s ease-in-out left}.carousel-inner>.item>a>img,.carousel-inner>.item>img{line-height:1}@media all and (transform-3d),(-webkit-transform-3d){.carousel-inner>.item{-webkit-transition:-webkit-transform .6s ease-in-out;-o-transition:-o-transform .6s ease-in-out;transition:transform .6s ease-in-out;-webkit-backface-visibility:hidden;backface-visibility:hidden;-webkit-perspective:1000px;perspective:1000px}.carousel-inner>.item.active.right,.carousel-inner>.item.next{left:0;-webkit-transform:translate3d(100%,0,0);transform:translate3d(100%,0,0)}.carousel-inner>.item.active.left,.carousel-inner>.item.prev{left:0;-webkit-transform:translate3d(-100%,0,0);transform:translate3d(-100%,0,0)}.carousel-inner>.item.active,.carousel-inner>.item.next.left,.carousel-inner>.item.prev.right{left:0;-webkit-transform:translate3d(0,0,0);transform:translate3d(0,0,0)}}.carousel-inner>.active,.carousel-inner>.next,.carousel-inner>.prev{display:block}.carousel-inner>.active{left:0}.carousel-inner>.next,.carousel-inner>.prev{position:absolute;top:0;width:100%}.carousel-inner>.next{left:100%}.carousel-inner>.prev{left:-100%}.carousel-inner>.next.left,.carousel-inner>.prev.right{left:0}.carousel-inner>.active.left{left:-100%}.carousel-inner>.active.right{left:100%}.carousel-control{position:absolute;top:0;bottom:0;left:0;width:15%;font-size:20px;color:#fff;text-align:center;text-shadow:0 1px 2px rgba(0,0,0,.6);filter:alpha(opacity=50);opacity:.5}.carousel-control.left{background-image:-webkit-linear-gradient(left,rgba(0,0,0,.5) 0,rgba(0,0,0,.0001) 100%);background-image:-o-linear-gradient(left,rgba(0,0,0,.5) 0,rgba(0,0,0,.0001) 100%);background-image:-webkit-gradient(linear,left top,right top,from(rgba(0,0,0,.5)),to(rgba(0,0,0,.0001)));background-image:linear-gradient(to right,rgba(0,0,0,.5) 0,rgba(0,0,0,.0001) 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1);background-repeat:repeat-x}.carousel-control.right{right:0;left:auto;background-image:-webkit-linear-gradient(left,rgba(0,0,0,.0001) 0,rgba(0,0,0,.5) 100%);background-image:-o-linear-gradient(left,rgba(0,0,0,.0001) 0,rgba(0,0,0,.5) 100%);background-image:-webkit-gradient(linear,left top,right top,from(rgba(0,0,0,.0001)),to(rgba(0,0,0,.5)));background-image:linear-gradient(to right,rgba(0,0,0,.0001) 0,rgba(0,0,0,.5) 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1);background-repeat:repeat-x}.carousel-control:focus,.carousel-control:hover{color:#fff;text-decoration:none;filter:alpha(opacity=90);outline:0;opacity:.9}.carousel-control .glyphicon-chevron-left,.carousel-control .glyphicon-chevron-right,.carousel-control .icon-next,.carousel-control .icon-prev{position:absolute;top:50%;z-index:5;display:inline-block;margin-top:-10px}.carousel-control .glyphicon-chevron-left,.carousel-control .icon-prev{left:50%;margin-left:-10px}.carousel-control .glyphicon-chevron-right,.carousel-control .icon-next{right:50%;margin-right:-10px}.carousel-control .icon-next,.carousel-control .icon-prev{width:20px;height:20px;font-family:serif;line-height:1}.carousel-control .icon-prev:before{content:'\2039'}.carousel-control .icon-next:before{content:'\203a'}.carousel-indicators{position:absolute;bottom:10px;left:50%;z-index:15;width:60%;padding-left:0;margin-left:-30%;text-align:center;list-style:none}.carousel-indicators li{display:inline-block;width:10px;height:10px;margin:1px;text-indent:-999px;cursor:pointer;background-color:#000\9;background-color:rgba(0,0,0,0);border:1px solid #fff;border-radius:10px}.carousel-indicators .active{width:12px;height:12px;margin:0;background-color:#fff}.carousel-caption{position:absolute;right:15%;bottom:20px;left:15%;z-index:10;padding-top:20px;padding-bottom:20px;color:#fff;text-align:center;text-shadow:0 1px 2px rgba(0,0,0,.6)}.carousel-caption .btn{text-shadow:none}@media screen and (min-width:768px){.carousel-control .glyphicon-chevron-left,.carousel-control .glyphicon-chevron-right,.carousel-control .icon-next,.carousel-control .icon-prev{width:30px;height:30px;margin-top:-15px;font-size:30px}.carousel-control .glyphicon-chevron-left,.carousel-control .icon-prev{margin-left:-15px}.carousel-control .glyphicon-chevron-right,.carousel-control .icon-next{margin-right:-15px}.carousel-caption{right:20%;left:20%;padding-bottom:30px}.carousel-indicators{bottom:20px}}.btn-group-vertical>.btn-group:after,.btn-group-vertical>.btn-group:before,.btn-toolbar:after,.btn-toolbar:before,.clearfix:after,.clearfix:before,.container-fluid:after,.container-fluid:before,.container:after,.container:before,.dl-horizontal dd:after,.dl-horizontal dd:before,.form-horizontal .form-group:after,.form-horizontal .form-group:before,.modal-footer:after,.modal-footer:before,.nav:after,.nav:before,.navbar-collapse:after,.navbar-collapse:before,.navbar-header:after,.navbar-header:before,.navbar:after,.navbar:before,.pager:after,.pager:before,.panel-body:after,.panel-body:before,.row:after,.row:before{display:table;content:" "}.btn-group-vertical>.btn-group:after,.btn-toolbar:after,.clearfix:after,.container-fluid:after,.container:after,.dl-horizontal dd:after,.form-horizontal .form-group:after,.modal-footer:after,.nav:after,.navbar-collapse:after,.navbar-header:after,.navbar:after,.pager:after,.panel-body:after,.row:after{clear:both}.center-block{display:block;margin-right:auto;margin-left:auto}.pull-right{float:right!important}.pull-left{float:left!important}.hide{display:none!important}.show{display:block!important}.invisible{visibility:hidden}.text-hide{font:0/0 a;color:transparent;text-shadow:none;background-color:transparent;border:0}.hidden{display:none!important}.affix{position:fixed}@-ms-viewport{width:device-width}.visible-lg,.visible-md,.visible-sm,.visible-xs{display:none!important}.visible-lg-block,.visible-lg-inline,.visible-lg-inline-block,.visible-md-block,.visible-md-inline,.visible-md-inline-block,.visible-sm-block,.visible-sm-inline,.visible-sm-inline-block,.visible-xs-block,.visible-xs-inline,.visible-xs-inline-block{display:none!important}@media (max-width:767px){.visible-xs{display:block!important}table.visible-xs{display:table!important}tr.visible-xs{display:table-row!important}td.visible-xs,th.visible-xs{display:table-cell!important}}@media (max-width:767px){.visible-xs-block{display:block!important}}@media (max-width:767px){.visible-xs-inline{display:inline!important}}@media (max-width:767px){.visible-xs-inline-block{display:inline-block!important}}@media (min-width:768px) and (max-width:991px){.visible-sm{display:block!important}table.visible-sm{display:table!important}tr.visible-sm{display:table-row!important}td.visible-sm,th.visible-sm{display:table-cell!important}}@media (min-width:768px) and (max-width:991px){.visible-sm-block{display:block!important}}@media (min-width:768px) and (max-width:991px){.visible-sm-inline{display:inline!important}}@media (min-width:768px) and (max-width:991px){.visible-sm-inline-block{display:inline-block!important}}@media (min-width:992px) and (max-width:1199px){.visible-md{display:block!important}table.visible-md{display:table!important}tr.visible-md{display:table-row!important}td.visible-md,th.visible-md{display:table-cell!important}}@media (min-width:992px) and (max-width:1199px){.visible-md-block{display:block!important}}@media (min-width:992px) and (max-width:1199px){.visible-md-inline{display:inline!important}}@media (min-width:992px) and (max-width:1199px){.visible-md-inline-block{display:inline-block!important}}@media (min-width:1200px){.visible-lg{display:block!important}table.visible-lg{display:table!important}tr.visible-lg{display:table-row!important}td.visible-lg,th.visible-lg{display:table-cell!important}}@media (min-width:1200px){.visible-lg-block{display:block!important}}@media (min-width:1200px){.visible-lg-inline{display:inline!important}}@media (min-width:1200px){.visible-lg-inline-block{display:inline-block!important}}@media (max-width:767px){.hidden-xs{display:none!important}}@media (min-width:768px) and (max-width:991px){.hidden-sm{display:none!important}}@media (min-width:992px) and (max-width:1199px){.hidden-md{display:none!important}}@media (min-width:1200px){.hidden-lg{display:none!important}}.visible-print{display:none!important}@media print{.visible-print{display:block!important}table.visible-print{display:table!important}tr.visible-print{display:table-row!important}td.visible-print,th.visible-print{display:table-cell!important}}.visible-print-block{display:none!important}@media print{.visible-print-block{display:block!important}}.visible-print-inline{display:none!important}@media print{.visible-print-inline{display:inline!important}}.visible-print-inline-block{display:none!important}@media print{.visible-print-inline-block{display:inline-block!important}}@media print{.hidden-print{display:none!important}}
diff --git a/docs/coverage/lib/bootstrap-3.3.5/js/bootstrap.min.js b/docs/coverage/lib/bootstrap-3.3.5/js/bootstrap.min.js
new file mode 100644
index 00000000..133aeecb
--- /dev/null
+++ b/docs/coverage/lib/bootstrap-3.3.5/js/bootstrap.min.js
@@ -0,0 +1,7 @@
+/*!
+ * Bootstrap v3.3.5 (http://getbootstrap.com)
+ * Copyright 2011-2015 Twitter, Inc.
+ * Licensed under the MIT license
+ */
+if("undefined"==typeof jQuery)throw new Error("Bootstrap's JavaScript requires jQuery");+function(a){"use strict";var b=a.fn.jquery.split(" ")[0].split(".");if(b[0]<2&&b[1]<9||1==b[0]&&9==b[1]&&b[2]<1)throw new Error("Bootstrap's JavaScript requires jQuery version 1.9.1 or higher")}(jQuery),+function(a){"use strict";function b(){var a=document.createElement("bootstrap"),b={WebkitTransition:"webkitTransitionEnd",MozTransition:"transitionend",OTransition:"oTransitionEnd otransitionend",transition:"transitionend"};for(var c in b)if(void 0!==a.style[c])return{end:b[c]};return!1}a.fn.emulateTransitionEnd=function(b){var c=!1,d=this;a(this).one("bsTransitionEnd",function(){c=!0});var e=function(){c||a(d).trigger(a.support.transition.end)};return setTimeout(e,b),this},a(function(){a.support.transition=b(),a.support.transition&&(a.event.special.bsTransitionEnd={bindType:a.support.transition.end,delegateType:a.support.transition.end,handle:function(b){return a(b.target).is(this)?b.handleObj.handler.apply(this,arguments):void 0}})})}(jQuery),+function(a){"use strict";function b(b){return this.each(function(){var c=a(this),e=c.data("bs.alert");e||c.data("bs.alert",e=new d(this)),"string"==typeof b&&e[b].call(c)})}var c='[data-dismiss="alert"]',d=function(b){a(b).on("click",c,this.close)};d.VERSION="3.3.5",d.TRANSITION_DURATION=150,d.prototype.close=function(b){function c(){g.detach().trigger("closed.bs.alert").remove()}var e=a(this),f=e.attr("data-target");f||(f=e.attr("href"),f=f&&f.replace(/.*(?=#[^\s]*$)/,""));var g=a(f);b&&b.preventDefault(),g.length||(g=e.closest(".alert")),g.trigger(b=a.Event("close.bs.alert")),b.isDefaultPrevented()||(g.removeClass("in"),a.support.transition&&g.hasClass("fade")?g.one("bsTransitionEnd",c).emulateTransitionEnd(d.TRANSITION_DURATION):c())};var e=a.fn.alert;a.fn.alert=b,a.fn.alert.Constructor=d,a.fn.alert.noConflict=function(){return a.fn.alert=e,this},a(document).on("click.bs.alert.data-api",c,d.prototype.close)}(jQuery),+function(a){"use strict";function b(b){return this.each(function(){var d=a(this),e=d.data("bs.button"),f="object"==typeof b&&b;e||d.data("bs.button",e=new c(this,f)),"toggle"==b?e.toggle():b&&e.setState(b)})}var c=function(b,d){this.$element=a(b),this.options=a.extend({},c.DEFAULTS,d),this.isLoading=!1};c.VERSION="3.3.5",c.DEFAULTS={loadingText:"loading..."},c.prototype.setState=function(b){var c="disabled",d=this.$element,e=d.is("input")?"val":"html",f=d.data();b+="Text",null==f.resetText&&d.data("resetText",d[e]()),setTimeout(a.proxy(function(){d[e](null==f[b]?this.options[b]:f[b]),"loadingText"==b?(this.isLoading=!0,d.addClass(c).attr(c,c)):this.isLoading&&(this.isLoading=!1,d.removeClass(c).removeAttr(c))},this),0)},c.prototype.toggle=function(){var a=!0,b=this.$element.closest('[data-toggle="buttons"]');if(b.length){var c=this.$element.find("input");"radio"==c.prop("type")?(c.prop("checked")&&(a=!1),b.find(".active").removeClass("active"),this.$element.addClass("active")):"checkbox"==c.prop("type")&&(c.prop("checked")!==this.$element.hasClass("active")&&(a=!1),this.$element.toggleClass("active")),c.prop("checked",this.$element.hasClass("active")),a&&c.trigger("change")}else this.$element.attr("aria-pressed",!this.$element.hasClass("active")),this.$element.toggleClass("active")};var d=a.fn.button;a.fn.button=b,a.fn.button.Constructor=c,a.fn.button.noConflict=function(){return a.fn.button=d,this},a(document).on("click.bs.button.data-api",'[data-toggle^="button"]',function(c){var d=a(c.target);d.hasClass("btn")||(d=d.closest(".btn")),b.call(d,"toggle"),a(c.target).is('input[type="radio"]')||a(c.target).is('input[type="checkbox"]')||c.preventDefault()}).on("focus.bs.button.data-api blur.bs.button.data-api",'[data-toggle^="button"]',function(b){a(b.target).closest(".btn").toggleClass("focus",/^focus(in)?$/.test(b.type))})}(jQuery),+function(a){"use strict";function b(b){return this.each(function(){var d=a(this),e=d.data("bs.carousel"),f=a.extend({},c.DEFAULTS,d.data(),"object"==typeof b&&b),g="string"==typeof b?b:f.slide;e||d.data("bs.carousel",e=new c(this,f)),"number"==typeof b?e.to(b):g?e[g]():f.interval&&e.pause().cycle()})}var c=function(b,c){this.$element=a(b),this.$indicators=this.$element.find(".carousel-indicators"),this.options=c,this.paused=null,this.sliding=null,this.interval=null,this.$active=null,this.$items=null,this.options.keyboard&&this.$element.on("keydown.bs.carousel",a.proxy(this.keydown,this)),"hover"==this.options.pause&&!("ontouchstart"in document.documentElement)&&this.$element.on("mouseenter.bs.carousel",a.proxy(this.pause,this)).on("mouseleave.bs.carousel",a.proxy(this.cycle,this))};c.VERSION="3.3.5",c.TRANSITION_DURATION=600,c.DEFAULTS={interval:5e3,pause:"hover",wrap:!0,keyboard:!0},c.prototype.keydown=function(a){if(!/input|textarea/i.test(a.target.tagName)){switch(a.which){case 37:this.prev();break;case 39:this.next();break;default:return}a.preventDefault()}},c.prototype.cycle=function(b){return b||(this.paused=!1),this.interval&&clearInterval(this.interval),this.options.interval&&!this.paused&&(this.interval=setInterval(a.proxy(this.next,this),this.options.interval)),this},c.prototype.getItemIndex=function(a){return this.$items=a.parent().children(".item"),this.$items.index(a||this.$active)},c.prototype.getItemForDirection=function(a,b){var c=this.getItemIndex(b),d="prev"==a&&0===c||"next"==a&&c==this.$items.length-1;if(d&&!this.options.wrap)return b;var e="prev"==a?-1:1,f=(c+e)%this.$items.length;return this.$items.eq(f)},c.prototype.to=function(a){var b=this,c=this.getItemIndex(this.$active=this.$element.find(".item.active"));return a>this.$items.length-1||0>a?void 0:this.sliding?this.$element.one("slid.bs.carousel",function(){b.to(a)}):c==a?this.pause().cycle():this.slide(a>c?"next":"prev",this.$items.eq(a))},c.prototype.pause=function(b){return b||(this.paused=!0),this.$element.find(".next, .prev").length&&a.support.transition&&(this.$element.trigger(a.support.transition.end),this.cycle(!0)),this.interval=clearInterval(this.interval),this},c.prototype.next=function(){return this.sliding?void 0:this.slide("next")},c.prototype.prev=function(){return this.sliding?void 0:this.slide("prev")},c.prototype.slide=function(b,d){var e=this.$element.find(".item.active"),f=d||this.getItemForDirection(b,e),g=this.interval,h="next"==b?"left":"right",i=this;if(f.hasClass("active"))return this.sliding=!1;var j=f[0],k=a.Event("slide.bs.carousel",{relatedTarget:j,direction:h});if(this.$element.trigger(k),!k.isDefaultPrevented()){if(this.sliding=!0,g&&this.pause(),this.$indicators.length){this.$indicators.find(".active").removeClass("active");var l=a(this.$indicators.children()[this.getItemIndex(f)]);l&&l.addClass("active")}var m=a.Event("slid.bs.carousel",{relatedTarget:j,direction:h});return a.support.transition&&this.$element.hasClass("slide")?(f.addClass(b),f[0].offsetWidth,e.addClass(h),f.addClass(h),e.one("bsTransitionEnd",function(){f.removeClass([b,h].join(" ")).addClass("active"),e.removeClass(["active",h].join(" ")),i.sliding=!1,setTimeout(function(){i.$element.trigger(m)},0)}).emulateTransitionEnd(c.TRANSITION_DURATION)):(e.removeClass("active"),f.addClass("active"),this.sliding=!1,this.$element.trigger(m)),g&&this.cycle(),this}};var d=a.fn.carousel;a.fn.carousel=b,a.fn.carousel.Constructor=c,a.fn.carousel.noConflict=function(){return a.fn.carousel=d,this};var e=function(c){var d,e=a(this),f=a(e.attr("data-target")||(d=e.attr("href"))&&d.replace(/.*(?=#[^\s]+$)/,""));if(f.hasClass("carousel")){var g=a.extend({},f.data(),e.data()),h=e.attr("data-slide-to");h&&(g.interval=!1),b.call(f,g),h&&f.data("bs.carousel").to(h),c.preventDefault()}};a(document).on("click.bs.carousel.data-api","[data-slide]",e).on("click.bs.carousel.data-api","[data-slide-to]",e),a(window).on("load",function(){a('[data-ride="carousel"]').each(function(){var c=a(this);b.call(c,c.data())})})}(jQuery),+function(a){"use strict";function b(b){var c,d=b.attr("data-target")||(c=b.attr("href"))&&c.replace(/.*(?=#[^\s]+$)/,"");return a(d)}function c(b){return this.each(function(){var c=a(this),e=c.data("bs.collapse"),f=a.extend({},d.DEFAULTS,c.data(),"object"==typeof b&&b);!e&&f.toggle&&/show|hide/.test(b)&&(f.toggle=!1),e||c.data("bs.collapse",e=new d(this,f)),"string"==typeof b&&e[b]()})}var d=function(b,c){this.$element=a(b),this.options=a.extend({},d.DEFAULTS,c),this.$trigger=a('[data-toggle="collapse"][href="#'+b.id+'"],[data-toggle="collapse"][data-target="#'+b.id+'"]'),this.transitioning=null,this.options.parent?this.$parent=this.getParent():this.addAriaAndCollapsedClass(this.$element,this.$trigger),this.options.toggle&&this.toggle()};d.VERSION="3.3.5",d.TRANSITION_DURATION=350,d.DEFAULTS={toggle:!0},d.prototype.dimension=function(){var a=this.$element.hasClass("width");return a?"width":"height"},d.prototype.show=function(){if(!this.transitioning&&!this.$element.hasClass("in")){var b,e=this.$parent&&this.$parent.children(".panel").children(".in, .collapsing");if(!(e&&e.length&&(b=e.data("bs.collapse"),b&&b.transitioning))){var f=a.Event("show.bs.collapse");if(this.$element.trigger(f),!f.isDefaultPrevented()){e&&e.length&&(c.call(e,"hide"),b||e.data("bs.collapse",null));var g=this.dimension();this.$element.removeClass("collapse").addClass("collapsing")[g](0).attr("aria-expanded",!0),this.$trigger.removeClass("collapsed").attr("aria-expanded",!0),this.transitioning=1;var h=function(){this.$element.removeClass("collapsing").addClass("collapse in")[g](""),this.transitioning=0,this.$element.trigger("shown.bs.collapse")};if(!a.support.transition)return h.call(this);var i=a.camelCase(["scroll",g].join("-"));this.$element.one("bsTransitionEnd",a.proxy(h,this)).emulateTransitionEnd(d.TRANSITION_DURATION)[g](this.$element[0][i])}}}},d.prototype.hide=function(){if(!this.transitioning&&this.$element.hasClass("in")){var b=a.Event("hide.bs.collapse");if(this.$element.trigger(b),!b.isDefaultPrevented()){var c=this.dimension();this.$element[c](this.$element[c]())[0].offsetHeight,this.$element.addClass("collapsing").removeClass("collapse in").attr("aria-expanded",!1),this.$trigger.addClass("collapsed").attr("aria-expanded",!1),this.transitioning=1;var e=function(){this.transitioning=0,this.$element.removeClass("collapsing").addClass("collapse").trigger("hidden.bs.collapse")};return a.support.transition?void this.$element[c](0).one("bsTransitionEnd",a.proxy(e,this)).emulateTransitionEnd(d.TRANSITION_DURATION):e.call(this)}}},d.prototype.toggle=function(){this[this.$element.hasClass("in")?"hide":"show"]()},d.prototype.getParent=function(){return a(this.options.parent).find('[data-toggle="collapse"][data-parent="'+this.options.parent+'"]').each(a.proxy(function(c,d){var e=a(d);this.addAriaAndCollapsedClass(b(e),e)},this)).end()},d.prototype.addAriaAndCollapsedClass=function(a,b){var c=a.hasClass("in");a.attr("aria-expanded",c),b.toggleClass("collapsed",!c).attr("aria-expanded",c)};var e=a.fn.collapse;a.fn.collapse=c,a.fn.collapse.Constructor=d,a.fn.collapse.noConflict=function(){return a.fn.collapse=e,this},a(document).on("click.bs.collapse.data-api",'[data-toggle="collapse"]',function(d){var e=a(this);e.attr("data-target")||d.preventDefault();var f=b(e),g=f.data("bs.collapse"),h=g?"toggle":e.data();c.call(f,h)})}(jQuery),+function(a){"use strict";function b(b){var c=b.attr("data-target");c||(c=b.attr("href"),c=c&&/#[A-Za-z]/.test(c)&&c.replace(/.*(?=#[^\s]*$)/,""));var d=c&&a(c);return d&&d.length?d:b.parent()}function c(c){c&&3===c.which||(a(e).remove(),a(f).each(function(){var d=a(this),e=b(d),f={relatedTarget:this};e.hasClass("open")&&(c&&"click"==c.type&&/input|textarea/i.test(c.target.tagName)&&a.contains(e[0],c.target)||(e.trigger(c=a.Event("hide.bs.dropdown",f)),c.isDefaultPrevented()||(d.attr("aria-expanded","false"),e.removeClass("open").trigger("hidden.bs.dropdown",f))))}))}function d(b){return this.each(function(){var c=a(this),d=c.data("bs.dropdown");d||c.data("bs.dropdown",d=new g(this)),"string"==typeof b&&d[b].call(c)})}var e=".dropdown-backdrop",f='[data-toggle="dropdown"]',g=function(b){a(b).on("click.bs.dropdown",this.toggle)};g.VERSION="3.3.5",g.prototype.toggle=function(d){var e=a(this);if(!e.is(".disabled, :disabled")){var f=b(e),g=f.hasClass("open");if(c(),!g){"ontouchstart"in document.documentElement&&!f.closest(".navbar-nav").length&&a(document.createElement("div")).addClass("dropdown-backdrop").insertAfter(a(this)).on("click",c);var h={relatedTarget:this};if(f.trigger(d=a.Event("show.bs.dropdown",h)),d.isDefaultPrevented())return;e.trigger("focus").attr("aria-expanded","true"),f.toggleClass("open").trigger("shown.bs.dropdown",h)}return!1}},g.prototype.keydown=function(c){if(/(38|40|27|32)/.test(c.which)&&!/input|textarea/i.test(c.target.tagName)){var d=a(this);if(c.preventDefault(),c.stopPropagation(),!d.is(".disabled, :disabled")){var e=b(d),g=e.hasClass("open");if(!g&&27!=c.which||g&&27==c.which)return 27==c.which&&e.find(f).trigger("focus"),d.trigger("click");var h=" li:not(.disabled):visible a",i=e.find(".dropdown-menu"+h);if(i.length){var j=i.index(c.target);38==c.which&&j>0&&j--,40==c.which&&j<i.length-1&&j++,~j||(j=0),i.eq(j).trigger("focus")}}}};var h=a.fn.dropdown;a.fn.dropdown=d,a.fn.dropdown.Constructor=g,a.fn.dropdown.noConflict=function(){return a.fn.dropdown=h,this},a(document).on("click.bs.dropdown.data-api",c).on("click.bs.dropdown.data-api",".dropdown form",function(a){a.stopPropagation()}).on("click.bs.dropdown.data-api",f,g.prototype.toggle).on("keydown.bs.dropdown.data-api",f,g.prototype.keydown).on("keydown.bs.dropdown.data-api",".dropdown-menu",g.prototype.keydown)}(jQuery),+function(a){"use strict";function b(b,d){return this.each(function(){var e=a(this),f=e.data("bs.modal"),g=a.extend({},c.DEFAULTS,e.data(),"object"==typeof b&&b);f||e.data("bs.modal",f=new c(this,g)),"string"==typeof b?f[b](d):g.show&&f.show(d)})}var c=function(b,c){this.options=c,this.$body=a(document.body),this.$element=a(b),this.$dialog=this.$element.find(".modal-dialog"),this.$backdrop=null,this.isShown=null,this.originalBodyPad=null,this.scrollbarWidth=0,this.ignoreBackdropClick=!1,this.options.remote&&this.$element.find(".modal-content").load(this.options.remote,a.proxy(function(){this.$element.trigger("loaded.bs.modal")},this))};c.VERSION="3.3.5",c.TRANSITION_DURATION=300,c.BACKDROP_TRANSITION_DURATION=150,c.DEFAULTS={backdrop:!0,keyboard:!0,show:!0},c.prototype.toggle=function(a){return this.isShown?this.hide():this.show(a)},c.prototype.show=function(b){var d=this,e=a.Event("show.bs.modal",{relatedTarget:b});this.$element.trigger(e),this.isShown||e.isDefaultPrevented()||(this.isShown=!0,this.checkScrollbar(),this.setScrollbar(),this.$body.addClass("modal-open"),this.escape(),this.resize(),this.$element.on("click.dismiss.bs.modal",'[data-dismiss="modal"]',a.proxy(this.hide,this)),this.$dialog.on("mousedown.dismiss.bs.modal",function(){d.$element.one("mouseup.dismiss.bs.modal",function(b){a(b.target).is(d.$element)&&(d.ignoreBackdropClick=!0)})}),this.backdrop(function(){var e=a.support.transition&&d.$element.hasClass("fade");d.$element.parent().length||d.$element.appendTo(d.$body),d.$element.show().scrollTop(0),d.adjustDialog(),e&&d.$element[0].offsetWidth,d.$element.addClass("in"),d.enforceFocus();var f=a.Event("shown.bs.modal",{relatedTarget:b});e?d.$dialog.one("bsTransitionEnd",function(){d.$element.trigger("focus").trigger(f)}).emulateTransitionEnd(c.TRANSITION_DURATION):d.$element.trigger("focus").trigger(f)}))},c.prototype.hide=function(b){b&&b.preventDefault(),b=a.Event("hide.bs.modal"),this.$element.trigger(b),this.isShown&&!b.isDefaultPrevented()&&(this.isShown=!1,this.escape(),this.resize(),a(document).off("focusin.bs.modal"),this.$element.removeClass("in").off("click.dismiss.bs.modal").off("mouseup.dismiss.bs.modal"),this.$dialog.off("mousedown.dismiss.bs.modal"),a.support.transition&&this.$element.hasClass("fade")?this.$element.one("bsTransitionEnd",a.proxy(this.hideModal,this)).emulateTransitionEnd(c.TRANSITION_DURATION):this.hideModal())},c.prototype.enforceFocus=function(){a(document).off("focusin.bs.modal").on("focusin.bs.modal",a.proxy(function(a){this.$element[0]===a.target||this.$element.has(a.target).length||this.$element.trigger("focus")},this))},c.prototype.escape=function(){this.isShown&&this.options.keyboard?this.$element.on("keydown.dismiss.bs.modal",a.proxy(function(a){27==a.which&&this.hide()},this)):this.isShown||this.$element.off("keydown.dismiss.bs.modal")},c.prototype.resize=function(){this.isShown?a(window).on("resize.bs.modal",a.proxy(this.handleUpdate,this)):a(window).off("resize.bs.modal")},c.prototype.hideModal=function(){var a=this;this.$element.hide(),this.backdrop(function(){a.$body.removeClass("modal-open"),a.resetAdjustments(),a.resetScrollbar(),a.$element.trigger("hidden.bs.modal")})},c.prototype.removeBackdrop=function(){this.$backdrop&&this.$backdrop.remove(),this.$backdrop=null},c.prototype.backdrop=function(b){var d=this,e=this.$element.hasClass("fade")?"fade":"";if(this.isShown&&this.options.backdrop){var f=a.support.transition&&e;if(this.$backdrop=a(document.createElement("div")).addClass("modal-backdrop "+e).appendTo(this.$body),this.$element.on("click.dismiss.bs.modal",a.proxy(function(a){return this.ignoreBackdropClick?void(this.ignoreBackdropClick=!1):void(a.target===a.currentTarget&&("static"==this.options.backdrop?this.$element[0].focus():this.hide()))},this)),f&&this.$backdrop[0].offsetWidth,this.$backdrop.addClass("in"),!b)return;f?this.$backdrop.one("bsTransitionEnd",b).emulateTransitionEnd(c.BACKDROP_TRANSITION_DURATION):b()}else if(!this.isShown&&this.$backdrop){this.$backdrop.removeClass("in");var g=function(){d.removeBackdrop(),b&&b()};a.support.transition&&this.$element.hasClass("fade")?this.$backdrop.one("bsTransitionEnd",g).emulateTransitionEnd(c.BACKDROP_TRANSITION_DURATION):g()}else b&&b()},c.prototype.handleUpdate=function(){this.adjustDialog()},c.prototype.adjustDialog=function(){var a=this.$element[0].scrollHeight>document.documentElement.clientHeight;this.$element.css({paddingLeft:!this.bodyIsOverflowing&&a?this.scrollbarWidth:"",paddingRight:this.bodyIsOverflowing&&!a?this.scrollbarWidth:""})},c.prototype.resetAdjustments=function(){this.$element.css({paddingLeft:"",paddingRight:""})},c.prototype.checkScrollbar=function(){var a=window.innerWidth;if(!a){var b=document.documentElement.getBoundingClientRect();a=b.right-Math.abs(b.left)}this.bodyIsOverflowing=document.body.clientWidth<a,this.scrollbarWidth=this.measureScrollbar()},c.prototype.setScrollbar=function(){var a=parseInt(this.$body.css("padding-right")||0,10);this.originalBodyPad=document.body.style.paddingRight||"",this.bodyIsOverflowing&&this.$body.css("padding-right",a+this.scrollbarWidth)},c.prototype.resetScrollbar=function(){this.$body.css("padding-right",this.originalBodyPad)},c.prototype.measureScrollbar=function(){var a=document.createElement("div");a.className="modal-scrollbar-measure",this.$body.append(a);var b=a.offsetWidth-a.clientWidth;return this.$body[0].removeChild(a),b};var d=a.fn.modal;a.fn.modal=b,a.fn.modal.Constructor=c,a.fn.modal.noConflict=function(){return a.fn.modal=d,this},a(document).on("click.bs.modal.data-api",'[data-toggle="modal"]',function(c){var d=a(this),e=d.attr("href"),f=a(d.attr("data-target")||e&&e.replace(/.*(?=#[^\s]+$)/,"")),g=f.data("bs.modal")?"toggle":a.extend({remote:!/#/.test(e)&&e},f.data(),d.data());d.is("a")&&c.preventDefault(),f.one("show.bs.modal",function(a){a.isDefaultPrevented()||f.one("hidden.bs.modal",function(){d.is(":visible")&&d.trigger("focus")})}),b.call(f,g,this)})}(jQuery),+function(a){"use strict";function b(b){return this.each(function(){var d=a(this),e=d.data("bs.tooltip"),f="object"==typeof b&&b;(e||!/destroy|hide/.test(b))&&(e||d.data("bs.tooltip",e=new c(this,f)),"string"==typeof b&&e[b]())})}var c=function(a,b){this.type=null,this.options=null,this.enabled=null,this.timeout=null,this.hoverState=null,this.$element=null,this.inState=null,this.init("tooltip",a,b)};c.VERSION="3.3.5",c.TRANSITION_DURATION=150,c.DEFAULTS={animation:!0,placement:"top",selector:!1,template:'<div class="tooltip" role="tooltip"><div class="tooltip-arrow"></div><div class="tooltip-inner"></div></div>',trigger:"hover focus",title:"",delay:0,html:!1,container:!1,viewport:{selector:"body",padding:0}},c.prototype.init=function(b,c,d){if(this.enabled=!0,this.type=b,this.$element=a(c),this.options=this.getOptions(d),this.$viewport=this.options.viewport&&a(a.isFunction(this.options.viewport)?this.options.viewport.call(this,this.$element):this.options.viewport.selector||this.options.viewport),this.inState={click:!1,hover:!1,focus:!1},this.$element[0]instanceof document.constructor&&!this.options.selector)throw new Error("`selector` option must be specified when initializing "+this.type+" on the window.document object!");for(var e=this.options.trigger.split(" "),f=e.length;f--;){var g=e[f];if("click"==g)this.$element.on("click."+this.type,this.options.selector,a.proxy(this.toggle,this));else if("manual"!=g){var h="hover"==g?"mouseenter":"focusin",i="hover"==g?"mouseleave":"focusout";this.$element.on(h+"."+this.type,this.options.selector,a.proxy(this.enter,this)),this.$element.on(i+"."+this.type,this.options.selector,a.proxy(this.leave,this))}}this.options.selector?this._options=a.extend({},this.options,{trigger:"manual",selector:""}):this.fixTitle()},c.prototype.getDefaults=function(){return c.DEFAULTS},c.prototype.getOptions=function(b){return b=a.extend({},this.getDefaults(),this.$element.data(),b),b.delay&&"number"==typeof b.delay&&(b.delay={show:b.delay,hide:b.delay}),b},c.prototype.getDelegateOptions=function(){var b={},c=this.getDefaults();return this._options&&a.each(this._options,function(a,d){c[a]!=d&&(b[a]=d)}),b},c.prototype.enter=function(b){var c=b instanceof this.constructor?b:a(b.currentTarget).data("bs."+this.type);return c||(c=new this.constructor(b.currentTarget,this.getDelegateOptions()),a(b.currentTarget).data("bs."+this.type,c)),b instanceof a.Event&&(c.inState["focusin"==b.type?"focus":"hover"]=!0),c.tip().hasClass("in")||"in"==c.hoverState?void(c.hoverState="in"):(clearTimeout(c.timeout),c.hoverState="in",c.options.delay&&c.options.delay.show?void(c.timeout=setTimeout(function(){"in"==c.hoverState&&c.show()},c.options.delay.show)):c.show())},c.prototype.isInStateTrue=function(){for(var a in this.inState)if(this.inState[a])return!0;return!1},c.prototype.leave=function(b){var c=b instanceof this.constructor?b:a(b.currentTarget).data("bs."+this.type);return c||(c=new this.constructor(b.currentTarget,this.getDelegateOptions()),a(b.currentTarget).data("bs."+this.type,c)),b instanceof a.Event&&(c.inState["focusout"==b.type?"focus":"hover"]=!1),c.isInStateTrue()?void 0:(clearTimeout(c.timeout),c.hoverState="out",c.options.delay&&c.options.delay.hide?void(c.timeout=setTimeout(function(){"out"==c.hoverState&&c.hide()},c.options.delay.hide)):c.hide())},c.prototype.show=function(){var b=a.Event("show.bs."+this.type);if(this.hasContent()&&this.enabled){this.$element.trigger(b);var d=a.contains(this.$element[0].ownerDocument.documentElement,this.$element[0]);if(b.isDefaultPrevented()||!d)return;var e=this,f=this.tip(),g=this.getUID(this.type);this.setContent(),f.attr("id",g),this.$element.attr("aria-describedby",g),this.options.animation&&f.addClass("fade");var h="function"==typeof this.options.placement?this.options.placement.call(this,f[0],this.$element[0]):this.options.placement,i=/\s?auto?\s?/i,j=i.test(h);j&&(h=h.replace(i,"")||"top"),f.detach().css({top:0,left:0,display:"block"}).addClass(h).data("bs."+this.type,this),this.options.container?f.appendTo(this.options.container):f.insertAfter(this.$element),this.$element.trigger("inserted.bs."+this.type);var k=this.getPosition(),l=f[0].offsetWidth,m=f[0].offsetHeight;if(j){var n=h,o=this.getPosition(this.$viewport);h="bottom"==h&&k.bottom+m>o.bottom?"top":"top"==h&&k.top-m<o.top?"bottom":"right"==h&&k.right+l>o.width?"left":"left"==h&&k.left-l<o.left?"right":h,f.removeClass(n).addClass(h)}var p=this.getCalculatedOffset(h,k,l,m);this.applyPlacement(p,h);var q=function(){var a=e.hoverState;e.$element.trigger("shown.bs."+e.type),e.hoverState=null,"out"==a&&e.leave(e)};a.support.transition&&this.$tip.hasClass("fade")?f.one("bsTransitionEnd",q).emulateTransitionEnd(c.TRANSITION_DURATION):q()}},c.prototype.applyPlacement=function(b,c){var d=this.tip(),e=d[0].offsetWidth,f=d[0].offsetHeight,g=parseInt(d.css("margin-top"),10),h=parseInt(d.css("margin-left"),10);isNaN(g)&&(g=0),isNaN(h)&&(h=0),b.top+=g,b.left+=h,a.offset.setOffset(d[0],a.extend({using:function(a){d.css({top:Math.round(a.top),left:Math.round(a.left)})}},b),0),d.addClass("in");var i=d[0].offsetWidth,j=d[0].offsetHeight;"top"==c&&j!=f&&(b.top=b.top+f-j);var k=this.getViewportAdjustedDelta(c,b,i,j);k.left?b.left+=k.left:b.top+=k.top;var l=/top|bottom/.test(c),m=l?2*k.left-e+i:2*k.top-f+j,n=l?"offsetWidth":"offsetHeight";d.offset(b),this.replaceArrow(m,d[0][n],l)},c.prototype.replaceArrow=function(a,b,c){this.arrow().css(c?"left":"top",50*(1-a/b)+"%").css(c?"top":"left","")},c.prototype.setContent=function(){var a=this.tip(),b=this.getTitle();a.find(".tooltip-inner")[this.options.html?"html":"text"](b),a.removeClass("fade in top bottom left right")},c.prototype.hide=function(b){function d(){"in"!=e.hoverState&&f.detach(),e.$element.removeAttr("aria-describedby").trigger("hidden.bs."+e.type),b&&b()}var e=this,f=a(this.$tip),g=a.Event("hide.bs."+this.type);return this.$element.trigger(g),g.isDefaultPrevented()?void 0:(f.removeClass("in"),a.support.transition&&f.hasClass("fade")?f.one("bsTransitionEnd",d).emulateTransitionEnd(c.TRANSITION_DURATION):d(),this.hoverState=null,this)},c.prototype.fixTitle=function(){var a=this.$element;(a.attr("title")||"string"!=typeof a.attr("data-original-title"))&&a.attr("data-original-title",a.attr("title")||"").attr("title","")},c.prototype.hasContent=function(){return this.getTitle()},c.prototype.getPosition=function(b){b=b||this.$element;var c=b[0],d="BODY"==c.tagName,e=c.getBoundingClientRect();null==e.width&&(e=a.extend({},e,{width:e.right-e.left,height:e.bottom-e.top}));var f=d?{top:0,left:0}:b.offset(),g={scroll:d?document.documentElement.scrollTop||document.body.scrollTop:b.scrollTop()},h=d?{width:a(window).width(),height:a(window).height()}:null;return a.extend({},e,g,h,f)},c.prototype.getCalculatedOffset=function(a,b,c,d){return"bottom"==a?{top:b.top+b.height,left:b.left+b.width/2-c/2}:"top"==a?{top:b.top-d,left:b.left+b.width/2-c/2}:"left"==a?{top:b.top+b.height/2-d/2,left:b.left-c}:{top:b.top+b.height/2-d/2,left:b.left+b.width}},c.prototype.getViewportAdjustedDelta=function(a,b,c,d){var e={top:0,left:0};if(!this.$viewport)return e;var f=this.options.viewport&&this.options.viewport.padding||0,g=this.getPosition(this.$viewport);if(/right|left/.test(a)){var h=b.top-f-g.scroll,i=b.top+f-g.scroll+d;h<g.top?e.top=g.top-h:i>g.top+g.height&&(e.top=g.top+g.height-i)}else{var j=b.left-f,k=b.left+f+c;j<g.left?e.left=g.left-j:k>g.right&&(e.left=g.left+g.width-k)}return e},c.prototype.getTitle=function(){var a,b=this.$element,c=this.options;return a=b.attr("data-original-title")||("function"==typeof c.title?c.title.call(b[0]):c.title)},c.prototype.getUID=function(a){do a+=~~(1e6*Math.random());while(document.getElementById(a));return a},c.prototype.tip=function(){if(!this.$tip&&(this.$tip=a(this.options.template),1!=this.$tip.length))throw new Error(this.type+" `template` option must consist of exactly 1 top-level element!");return this.$tip},c.prototype.arrow=function(){return this.$arrow=this.$arrow||this.tip().find(".tooltip-arrow")},c.prototype.enable=function(){this.enabled=!0},c.prototype.disable=function(){this.enabled=!1},c.prototype.toggleEnabled=function(){this.enabled=!this.enabled},c.prototype.toggle=function(b){var c=this;b&&(c=a(b.currentTarget).data("bs."+this.type),c||(c=new this.constructor(b.currentTarget,this.getDelegateOptions()),a(b.currentTarget).data("bs."+this.type,c))),b?(c.inState.click=!c.inState.click,c.isInStateTrue()?c.enter(c):c.leave(c)):c.tip().hasClass("in")?c.leave(c):c.enter(c)},c.prototype.destroy=function(){var a=this;clearTimeout(this.timeout),this.hide(function(){a.$element.off("."+a.type).removeData("bs."+a.type),a.$tip&&a.$tip.detach(),a.$tip=null,a.$arrow=null,a.$viewport=null})};var d=a.fn.tooltip;a.fn.tooltip=b,a.fn.tooltip.Constructor=c,a.fn.tooltip.noConflict=function(){return a.fn.tooltip=d,this}}(jQuery),+function(a){"use strict";function b(b){return this.each(function(){var d=a(this),e=d.data("bs.popover"),f="object"==typeof b&&b;(e||!/destroy|hide/.test(b))&&(e||d.data("bs.popover",e=new c(this,f)),"string"==typeof b&&e[b]())})}var c=function(a,b){this.init("popover",a,b)};if(!a.fn.tooltip)throw new Error("Popover requires tooltip.js");c.VERSION="3.3.5",c.DEFAULTS=a.extend({},a.fn.tooltip.Constructor.DEFAULTS,{placement:"right",trigger:"click",content:"",template:'<div class="popover" role="tooltip"><div class="arrow"></div><h3 class="popover-title"></h3><div class="popover-content"></div></div>'}),c.prototype=a.extend({},a.fn.tooltip.Constructor.prototype),c.prototype.constructor=c,c.prototype.getDefaults=function(){return c.DEFAULTS},c.prototype.setContent=function(){var a=this.tip(),b=this.getTitle(),c=this.getContent();a.find(".popover-title")[this.options.html?"html":"text"](b),a.find(".popover-content").children().detach().end()[this.options.html?"string"==typeof c?"html":"append":"text"](c),a.removeClass("fade top bottom left right in"),a.find(".popover-title").html()||a.find(".popover-title").hide()},c.prototype.hasContent=function(){return this.getTitle()||this.getContent()},c.prototype.getContent=function(){var a=this.$element,b=this.options;return a.attr("data-content")||("function"==typeof b.content?b.content.call(a[0]):b.content)},c.prototype.arrow=function(){return this.$arrow=this.$arrow||this.tip().find(".arrow")};var d=a.fn.popover;a.fn.popover=b,a.fn.popover.Constructor=c,a.fn.popover.noConflict=function(){return a.fn.popover=d,this}}(jQuery),+function(a){"use strict";function b(c,d){this.$body=a(document.body),this.$scrollElement=a(a(c).is(document.body)?window:c),this.options=a.extend({},b.DEFAULTS,d),this.selector=(this.options.target||"")+" .nav li > a",this.offsets=[],this.targets=[],this.activeTarget=null,this.scrollHeight=0,this.$scrollElement.on("scroll.bs.scrollspy",a.proxy(this.process,this)),this.refresh(),this.process()}function c(c){return this.each(function(){var d=a(this),e=d.data("bs.scrollspy"),f="object"==typeof c&&c;e||d.data("bs.scrollspy",e=new b(this,f)),"string"==typeof c&&e[c]()})}b.VERSION="3.3.5",b.DEFAULTS={offset:10},b.prototype.getScrollHeight=function(){return this.$scrollElement[0].scrollHeight||Math.max(this.$body[0].scrollHeight,document.documentElement.scrollHeight)},b.prototype.refresh=function(){var b=this,c="offset",d=0;this.offsets=[],this.targets=[],this.scrollHeight=this.getScrollHeight(),a.isWindow(this.$scrollElement[0])||(c="position",d=this.$scrollElement.scrollTop()),this.$body.find(this.selector).map(function(){var b=a(this),e=b.data("target")||b.attr("href"),f=/^#./.test(e)&&a(e);return f&&f.length&&f.is(":visible")&&[[f[c]().top+d,e]]||null}).sort(function(a,b){return a[0]-b[0]}).each(function(){b.offsets.push(this[0]),b.targets.push(this[1])})},b.prototype.process=function(){var a,b=this.$scrollElement.scrollTop()+this.options.offset,c=this.getScrollHeight(),d=this.options.offset+c-this.$scrollElement.height(),e=this.offsets,f=this.targets,g=this.activeTarget;if(this.scrollHeight!=c&&this.refresh(),b>=d)return g!=(a=f[f.length-1])&&this.activate(a);if(g&&b<e[0])return this.activeTarget=null,this.clear();for(a=e.length;a--;)g!=f[a]&&b>=e[a]&&(void 0===e[a+1]||b<e[a+1])&&this.activate(f[a])},b.prototype.activate=function(b){this.activeTarget=b,this.clear();var c=this.selector+'[data-target="'+b+'"],'+this.selector+'[href="'+b+'"]',d=a(c).parents("li").addClass("active");d.parent(".dropdown-menu").length&&(d=d.closest("li.dropdown").addClass("active")),
+d.trigger("activate.bs.scrollspy")},b.prototype.clear=function(){a(this.selector).parentsUntil(this.options.target,".active").removeClass("active")};var d=a.fn.scrollspy;a.fn.scrollspy=c,a.fn.scrollspy.Constructor=b,a.fn.scrollspy.noConflict=function(){return a.fn.scrollspy=d,this},a(window).on("load.bs.scrollspy.data-api",function(){a('[data-spy="scroll"]').each(function(){var b=a(this);c.call(b,b.data())})})}(jQuery),+function(a){"use strict";function b(b){return this.each(function(){var d=a(this),e=d.data("bs.tab");e||d.data("bs.tab",e=new c(this)),"string"==typeof b&&e[b]()})}var c=function(b){this.element=a(b)};c.VERSION="3.3.5",c.TRANSITION_DURATION=150,c.prototype.show=function(){var b=this.element,c=b.closest("ul:not(.dropdown-menu)"),d=b.data("target");if(d||(d=b.attr("href"),d=d&&d.replace(/.*(?=#[^\s]*$)/,"")),!b.parent("li").hasClass("active")){var e=c.find(".active:last a"),f=a.Event("hide.bs.tab",{relatedTarget:b[0]}),g=a.Event("show.bs.tab",{relatedTarget:e[0]});if(e.trigger(f),b.trigger(g),!g.isDefaultPrevented()&&!f.isDefaultPrevented()){var h=a(d);this.activate(b.closest("li"),c),this.activate(h,h.parent(),function(){e.trigger({type:"hidden.bs.tab",relatedTarget:b[0]}),b.trigger({type:"shown.bs.tab",relatedTarget:e[0]})})}}},c.prototype.activate=function(b,d,e){function f(){g.removeClass("active").find("> .dropdown-menu > .active").removeClass("active").end().find('[data-toggle="tab"]').attr("aria-expanded",!1),b.addClass("active").find('[data-toggle="tab"]').attr("aria-expanded",!0),h?(b[0].offsetWidth,b.addClass("in")):b.removeClass("fade"),b.parent(".dropdown-menu").length&&b.closest("li.dropdown").addClass("active").end().find('[data-toggle="tab"]').attr("aria-expanded",!0),e&&e()}var g=d.find("> .active"),h=e&&a.support.transition&&(g.length&&g.hasClass("fade")||!!d.find("> .fade").length);g.length&&h?g.one("bsTransitionEnd",f).emulateTransitionEnd(c.TRANSITION_DURATION):f(),g.removeClass("in")};var d=a.fn.tab;a.fn.tab=b,a.fn.tab.Constructor=c,a.fn.tab.noConflict=function(){return a.fn.tab=d,this};var e=function(c){c.preventDefault(),b.call(a(this),"show")};a(document).on("click.bs.tab.data-api",'[data-toggle="tab"]',e).on("click.bs.tab.data-api",'[data-toggle="pill"]',e)}(jQuery),+function(a){"use strict";function b(b){return this.each(function(){var d=a(this),e=d.data("bs.affix"),f="object"==typeof b&&b;e||d.data("bs.affix",e=new c(this,f)),"string"==typeof b&&e[b]()})}var c=function(b,d){this.options=a.extend({},c.DEFAULTS,d),this.$target=a(this.options.target).on("scroll.bs.affix.data-api",a.proxy(this.checkPosition,this)).on("click.bs.affix.data-api",a.proxy(this.checkPositionWithEventLoop,this)),this.$element=a(b),this.affixed=null,this.unpin=null,this.pinnedOffset=null,this.checkPosition()};c.VERSION="3.3.5",c.RESET="affix affix-top affix-bottom",c.DEFAULTS={offset:0,target:window},c.prototype.getState=function(a,b,c,d){var e=this.$target.scrollTop(),f=this.$element.offset(),g=this.$target.height();if(null!=c&&"top"==this.affixed)return c>e?"top":!1;if("bottom"==this.affixed)return null!=c?e+this.unpin<=f.top?!1:"bottom":a-d>=e+g?!1:"bottom";var h=null==this.affixed,i=h?e:f.top,j=h?g:b;return null!=c&&c>=e?"top":null!=d&&i+j>=a-d?"bottom":!1},c.prototype.getPinnedOffset=function(){if(this.pinnedOffset)return this.pinnedOffset;this.$element.removeClass(c.RESET).addClass("affix");var a=this.$target.scrollTop(),b=this.$element.offset();return this.pinnedOffset=b.top-a},c.prototype.checkPositionWithEventLoop=function(){setTimeout(a.proxy(this.checkPosition,this),1)},c.prototype.checkPosition=function(){if(this.$element.is(":visible")){var b=this.$element.height(),d=this.options.offset,e=d.top,f=d.bottom,g=Math.max(a(document).height(),a(document.body).height());"object"!=typeof d&&(f=e=d),"function"==typeof e&&(e=d.top(this.$element)),"function"==typeof f&&(f=d.bottom(this.$element));var h=this.getState(g,b,e,f);if(this.affixed!=h){null!=this.unpin&&this.$element.css("top","");var i="affix"+(h?"-"+h:""),j=a.Event(i+".bs.affix");if(this.$element.trigger(j),j.isDefaultPrevented())return;this.affixed=h,this.unpin="bottom"==h?this.getPinnedOffset():null,this.$element.removeClass(c.RESET).addClass(i).trigger(i.replace("affix","affixed")+".bs.affix")}"bottom"==h&&this.$element.offset({top:g-b-f})}};var d=a.fn.affix;a.fn.affix=b,a.fn.affix.Constructor=c,a.fn.affix.noConflict=function(){return a.fn.affix=d,this},a(window).on("load",function(){a('[data-spy="affix"]').each(function(){var c=a(this),d=c.data();d.offset=d.offset||{},null!=d.offsetBottom&&(d.offset.bottom=d.offsetBottom),null!=d.offsetTop&&(d.offset.top=d.offsetTop),b.call(c,d)})})}(jQuery);
\ No newline at end of file
diff --git a/docs/coverage/lib/bootstrap-3.3.5/shim/html5shiv.min.js b/docs/coverage/lib/bootstrap-3.3.5/shim/html5shiv.min.js
new file mode 100644
index 00000000..36831143
--- /dev/null
+++ b/docs/coverage/lib/bootstrap-3.3.5/shim/html5shiv.min.js
@@ -0,0 +1,7 @@
+/**
+* @preserve HTML5 Shiv 3.7.2 | @afarkas @jdalton @jon_neal @rem | MIT/GPL2 Licensed
+*/
+// Only run this code in IE 8
+if (!!window.navigator.userAgent.match("MSIE 8")) {
+!function(a,b){function c(a,b){var c=a.createElement("p"),d=a.getElementsByTagName("head")[0]||a.documentElement;return c.innerHTML="x<style>"+b+"</style>",d.insertBefore(c.lastChild,d.firstChild)}function d(){var a=t.elements;return"string"==typeof a?a.split(" "):a}function e(a,b){var c=t.elements;"string"!=typeof c&&(c=c.join(" ")),"string"!=typeof a&&(a=a.join(" ")),t.elements=c+" "+a,j(b)}function f(a){var b=s[a[q]];return b||(b={},r++,a[q]=r,s[r]=b),b}function g(a,c,d){if(c||(c=b),l)return c.createElement(a);d||(d=f(c));var e;return e=d.cache[a]?d.cache[a].cloneNode():p.test(a)?(d.cache[a]=d.createElem(a)).cloneNode():d.createElem(a),!e.canHaveChildren||o.test(a)||e.tagUrn?e:d.frag.appendChild(e)}function h(a,c){if(a||(a=b),l)return a.createDocumentFragment();c=c||f(a);for(var e=c.frag.cloneNode(),g=0,h=d(),i=h.length;i>g;g++)e.createElement(h[g]);return e}function i(a,b){b.cache||(b.cache={},b.createElem=a.createElement,b.createFrag=a.createDocumentFragment,b.frag=b.createFrag()),a.createElement=function(c){return t.shivMethods?g(c,a,b):b.createElem(c)},a.createDocumentFragment=Function("h,f","return function(){var n=f.cloneNode(),c=n.createElement;h.shivMethods&&("+d().join().replace(/[\w\-:]+/g,function(a){return b.createElem(a),b.frag.createElement(a),'c("'+a+'")'})+");return n}")(t,b.frag)}function j(a){a||(a=b);var d=f(a);return!t.shivCSS||k||d.hasCSS||(d.hasCSS=!!c(a,"article,aside,dialog,figcaption,figure,footer,header,hgroup,main,nav,section{display:block}mark{background:#FF0;color:#000}template{display:none}")),l||i(a,d),a}var k,l,m="3.7.2",n=a.html5||{},o=/^<|^(?:button|map|select|textarea|object|iframe|option|optgroup)$/i,p=/^(?:a|b|code|div|fieldset|h1|h2|h3|h4|h5|h6|i|label|li|ol|p|q|span|strong|style|table|tbody|td|th|tr|ul)$/i,q="_html5shiv",r=0,s={};!function(){try{var a=b.createElement("a");a.innerHTML="<xyz></xyz>",k="hidden"in a,l=1==a.childNodes.length||function(){b.createElement("a");var a=b.createDocumentFragment();return"undefined"==typeof a.cloneNode||"undefined"==typeof a.createDocumentFragment||"undefined"==typeof a.createElement}()}catch(c){k=!0,l=!0}}();var t={elements:n.elements||"abbr article aside audio bdi canvas data datalist details dialog figcaption figure footer header hgroup main mark meter nav output picture progress section summary template time video",version:m,shivCSS:n.shivCSS!==!1,supportsUnknownElements:l,shivMethods:n.shivMethods!==!1,type:"default",shivDocument:j,createElement:g,createDocumentFragment:h,addElements:e};a.html5=t,j(b)}(this,document);
+};
diff --git a/docs/coverage/lib/bootstrap-3.3.5/shim/respond.min.js b/docs/coverage/lib/bootstrap-3.3.5/shim/respond.min.js
new file mode 100644
index 00000000..22094690
--- /dev/null
+++ b/docs/coverage/lib/bootstrap-3.3.5/shim/respond.min.js
@@ -0,0 +1,8 @@
+/*! Respond.js v1.4.2: min/max-width media query polyfill * Copyright 2013 Scott Jehl
+ * Licensed under https://github.com/scottjehl/Respond/blob/master/LICENSE-MIT
+ *  */
+
+// Only run this code in IE 8
+if (!!window.navigator.userAgent.match("MSIE 8")) {
+!function(a){"use strict";a.matchMedia=a.matchMedia||function(a){var b,c=a.documentElement,d=c.firstElementChild||c.firstChild,e=a.createElement("body"),f=a.createElement("div");return f.id="mq-test-1",f.style.cssText="position:absolute;top:-100em",e.style.background="none",e.appendChild(f),function(a){return f.innerHTML='&shy;<style media="'+a+'"> #mq-test-1 { width: 42px; }</style>',c.insertBefore(e,d),b=42===f.offsetWidth,c.removeChild(e),{matches:b,media:a}}}(a.document)}(this),function(a){"use strict";function b(){u(!0)}var c={};a.respond=c,c.update=function(){};var d=[],e=function(){var b=!1;try{b=new a.XMLHttpRequest}catch(c){b=new a.ActiveXObject("Microsoft.XMLHTTP")}return function(){return b}}(),f=function(a,b){var c=e();c&&(c.open("GET",a,!0),c.onreadystatechange=function(){4!==c.readyState||200!==c.status&&304!==c.status||b(c.responseText)},4!==c.readyState&&c.send(null))};if(c.ajax=f,c.queue=d,c.regex={media:/@media[^\{]+\{([^\{\}]*\{[^\}\{]*\})+/gi,keyframes:/@(?:\-(?:o|moz|webkit)\-)?keyframes[^\{]+\{(?:[^\{\}]*\{[^\}\{]*\})+[^\}]*\}/gi,urls:/(url\()['"]?([^\/\)'"][^:\)'"]+)['"]?(\))/g,findStyles:/@media *([^\{]+)\{([\S\s]+?)$/,only:/(only\s+)?([a-zA-Z]+)\s?/,minw:/\([\s]*min\-width\s*:[\s]*([\s]*[0-9\.]+)(px|em)[\s]*\)/,maxw:/\([\s]*max\-width\s*:[\s]*([\s]*[0-9\.]+)(px|em)[\s]*\)/},c.mediaQueriesSupported=a.matchMedia&&null!==a.matchMedia("only all")&&a.matchMedia("only all").matches,!c.mediaQueriesSupported){var g,h,i,j=a.document,k=j.documentElement,l=[],m=[],n=[],o={},p=30,q=j.getElementsByTagName("head")[0]||k,r=j.getElementsByTagName("base")[0],s=q.getElementsByTagName("link"),t=function(){var a,b=j.createElement("div"),c=j.body,d=k.style.fontSize,e=c&&c.style.fontSize,f=!1;return b.style.cssText="position:absolute;font-size:1em;width:1em",c||(c=f=j.createElement("body"),c.style.background="none"),k.style.fontSize="100%",c.style.fontSize="100%",c.appendChild(b),f&&k.insertBefore(c,k.firstChild),a=b.offsetWidth,f?k.removeChild(c):c.removeChild(b),k.style.fontSize=d,e&&(c.style.fontSize=e),a=i=parseFloat(a)},u=function(b){var c="clientWidth",d=k[c],e="CSS1Compat"===j.compatMode&&d||j.body[c]||d,f={},o=s[s.length-1],r=(new Date).getTime();if(b&&g&&p>r-g)return a.clearTimeout(h),h=a.setTimeout(u,p),void 0;g=r;for(var v in l)if(l.hasOwnProperty(v)){var w=l[v],x=w.minw,y=w.maxw,z=null===x,A=null===y,B="em";x&&(x=parseFloat(x)*(x.indexOf(B)>-1?i||t():1)),y&&(y=parseFloat(y)*(y.indexOf(B)>-1?i||t():1)),w.hasquery&&(z&&A||!(z||e>=x)||!(A||y>=e))||(f[w.media]||(f[w.media]=[]),f[w.media].push(m[w.rules]))}for(var C in n)n.hasOwnProperty(C)&&n[C]&&n[C].parentNode===q&&q.removeChild(n[C]);n.length=0;for(var D in f)if(f.hasOwnProperty(D)){var E=j.createElement("style"),F=f[D].join("\n");E.type="text/css",E.media=D,q.insertBefore(E,o.nextSibling),E.styleSheet?E.styleSheet.cssText=F:E.appendChild(j.createTextNode(F)),n.push(E)}},v=function(a,b,d){var e=a.replace(c.regex.keyframes,"").match(c.regex.media),f=e&&e.length||0;b=b.substring(0,b.lastIndexOf("/"));var g=function(a){return a.replace(c.regex.urls,"$1"+b+"$2$3")},h=!f&&d;b.length&&(b+="/"),h&&(f=1);for(var i=0;f>i;i++){var j,k,n,o;h?(j=d,m.push(g(a))):(j=e[i].match(c.regex.findStyles)&&RegExp.$1,m.push(RegExp.$2&&g(RegExp.$2))),n=j.split(","),o=n.length;for(var p=0;o>p;p++)k=n[p],l.push({media:k.split("(")[0].match(c.regex.only)&&RegExp.$2||"all",rules:m.length-1,hasquery:k.indexOf("(")>-1,minw:k.match(c.regex.minw)&&parseFloat(RegExp.$1)+(RegExp.$2||""),maxw:k.match(c.regex.maxw)&&parseFloat(RegExp.$1)+(RegExp.$2||"")})}u()},w=function(){if(d.length){var b=d.shift();f(b.href,function(c){v(c,b.href,b.media),o[b.href]=!0,a.setTimeout(function(){w()},0)})}},x=function(){for(var b=0;b<s.length;b++){var c=s[b],e=c.href,f=c.media,g=c.rel&&"stylesheet"===c.rel.toLowerCase();e&&g&&!o[e]&&(c.styleSheet&&c.styleSheet.rawCssText?(v(c.styleSheet.rawCssText,e,f),o[e]=!0):(!/^([a-zA-Z:]*\/\/)/.test(e)&&!r||e.replace(RegExp.$1,"").split("/")[0]===a.location.host)&&("//"===e.substring(0,2)&&(e=a.location.protocol+e),d.push({href:e,media:f})))}w()};x(),c.update=x,c.getEmValue=t,a.addEventListener?a.addEventListener("resize",b,!1):a.attachEvent&&a.attachEvent("onresize",b)}}(this);
+};
diff --git a/docs/coverage/lib/crosstalk-1.2.1/css/crosstalk.min.css b/docs/coverage/lib/crosstalk-1.2.1/css/crosstalk.min.css
new file mode 100644
index 00000000..6b453828
--- /dev/null
+++ b/docs/coverage/lib/crosstalk-1.2.1/css/crosstalk.min.css
@@ -0,0 +1 @@
+.container-fluid.crosstalk-bscols{margin-left:-30px;margin-right:-30px;white-space:normal}body>.container-fluid.crosstalk-bscols{margin-left:auto;margin-right:auto}.crosstalk-input-checkboxgroup .crosstalk-options-group .crosstalk-options-column{display:inline-block;padding-right:12px;vertical-align:top}@media only screen and (max-width: 480px){.crosstalk-input-checkboxgroup .crosstalk-options-group .crosstalk-options-column{display:block;padding-right:inherit}}.crosstalk-input{margin-bottom:15px}.crosstalk-input .control-label{margin-bottom:0;vertical-align:middle}.crosstalk-input input[type="checkbox"]{margin:4px 0 0;margin-top:1px;line-height:normal}.crosstalk-input .checkbox{position:relative;display:block;margin-top:10px;margin-bottom:10px}.crosstalk-input .checkbox>label{padding-left:20px;margin-bottom:0;font-weight:400;cursor:pointer}.crosstalk-input .checkbox input[type="checkbox"],.crosstalk-input .checkbox-inline input[type="checkbox"]{position:absolute;margin-top:2px;margin-left:-20px}.crosstalk-input .checkbox+.checkbox{margin-top:-5px}.crosstalk-input .checkbox-inline{position:relative;display:inline-block;padding-left:20px;margin-bottom:0;font-weight:400;vertical-align:middle;cursor:pointer}.crosstalk-input .checkbox-inline+.checkbox-inline{margin-top:0;margin-left:10px}
diff --git a/docs/coverage/lib/crosstalk-1.2.1/js/crosstalk.js b/docs/coverage/lib/crosstalk-1.2.1/js/crosstalk.js
new file mode 100644
index 00000000..fd9eb53d
--- /dev/null
+++ b/docs/coverage/lib/crosstalk-1.2.1/js/crosstalk.js
@@ -0,0 +1,1474 @@
+(function(){function e(t,n,r){function s(o,u){if(!n[o]){if(!t[o]){var a=typeof require=="function"&&require;if(!u&&a)return a(o,!0);if(i)return i(o,!0);var f=new Error("Cannot find module '"+o+"'");throw f.code="MODULE_NOT_FOUND",f}var l=n[o]={exports:{}};t[o][0].call(l.exports,function(e){var n=t[o][1][e];return s(n?n:e)},l,l.exports,e,t,n,r)}return n[o].exports}var i=typeof require=="function"&&require;for(var o=0;o<r.length;o++)s(r[o]);return s}return e})()({1:[function(require,module,exports){
+"use strict";
+
+Object.defineProperty(exports, "__esModule", {
+  value: true
+});
+
+var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }();
+
+function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } }
+
+var Events = function () {
+  function Events() {
+    _classCallCheck(this, Events);
+
+    this._types = {};
+    this._seq = 0;
+  }
+
+  _createClass(Events, [{
+    key: "on",
+    value: function on(eventType, listener) {
+      var subs = this._types[eventType];
+      if (!subs) {
+        subs = this._types[eventType] = {};
+      }
+      var sub = "sub" + this._seq++;
+      subs[sub] = listener;
+      return sub;
+    }
+
+    // Returns false if no match, or string for sub name if matched
+
+  }, {
+    key: "off",
+    value: function off(eventType, listener) {
+      var subs = this._types[eventType];
+      if (typeof listener === "function") {
+        for (var key in subs) {
+          if (subs.hasOwnProperty(key)) {
+            if (subs[key] === listener) {
+              delete subs[key];
+              return key;
+            }
+          }
+        }
+        return false;
+      } else if (typeof listener === "string") {
+        if (subs && subs[listener]) {
+          delete subs[listener];
+          return listener;
+        }
+        return false;
+      } else {
+        throw new Error("Unexpected type for listener");
+      }
+    }
+  }, {
+    key: "trigger",
+    value: function trigger(eventType, arg, thisObj) {
+      var subs = this._types[eventType];
+      for (var key in subs) {
+        if (subs.hasOwnProperty(key)) {
+          subs[key].call(thisObj, arg);
+        }
+      }
+    }
+  }]);
+
+  return Events;
+}();
+
+exports.default = Events;
+
+},{}],2:[function(require,module,exports){
+"use strict";
+
+Object.defineProperty(exports, "__esModule", {
+  value: true
+});
+exports.FilterHandle = undefined;
+
+var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }();
+
+var _events = require("./events");
+
+var _events2 = _interopRequireDefault(_events);
+
+var _filterset = require("./filterset");
+
+var _filterset2 = _interopRequireDefault(_filterset);
+
+var _group = require("./group");
+
+var _group2 = _interopRequireDefault(_group);
+
+var _util = require("./util");
+
+var util = _interopRequireWildcard(_util);
+
+function _interopRequireWildcard(obj) { if (obj && obj.__esModule) { return obj; } else { var newObj = {}; if (obj != null) { for (var key in obj) { if (Object.prototype.hasOwnProperty.call(obj, key)) newObj[key] = obj[key]; } } newObj.default = obj; return newObj; } }
+
+function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; }
+
+function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } }
+
+function getFilterSet(group) {
+  var fsVar = group.var("filterset");
+  var result = fsVar.get();
+  if (!result) {
+    result = new _filterset2.default();
+    fsVar.set(result);
+  }
+  return result;
+}
+
+var id = 1;
+function nextId() {
+  return id++;
+}
+
+/**
+ * Use this class to contribute to, and listen for changes to, the filter set
+ * for the given group of widgets. Filter input controls should create one
+ * `FilterHandle` and only call {@link FilterHandle#set}. Output widgets that
+ * wish to displayed filtered data should create one `FilterHandle` and use
+ * the {@link FilterHandle#filteredKeys} property and listen for change
+ * events.
+ *
+ * If two (or more) `FilterHandle` instances in the same webpage share the
+ * same group name, they will contribute to a single "filter set". Each
+ * `FilterHandle` starts out with a `null` value, which means they take
+ * nothing away from the set of data that should be shown. To make a
+ * `FilterHandle` actually remove data from the filter set, set its value to
+ * an array of keys which should be displayed. Crosstalk will aggregate the
+ * various key arrays by finding their intersection; only keys that are
+ * present in all non-null filter handles are considered part of the filter
+ * set.
+ *
+ * @param {string} [group] - The name of the Crosstalk group, or if none,
+ *   null or undefined (or any other falsy value). This can be changed later
+ *   via the {@link FilterHandle#setGroup} method.
+ * @param {Object} [extraInfo] - An object whose properties will be copied to
+ *   the event object whenever an event is emitted.
+ */
+
+var FilterHandle = exports.FilterHandle = function () {
+  function FilterHandle(group, extraInfo) {
+    _classCallCheck(this, FilterHandle);
+
+    this._eventRelay = new _events2.default();
+    this._emitter = new util.SubscriptionTracker(this._eventRelay);
+
+    // Name of the group we're currently tracking, if any. Can change over time.
+    this._group = null;
+    // The filterSet that we're tracking, if any. Can change over time.
+    this._filterSet = null;
+    // The Var we're currently tracking, if any. Can change over time.
+    this._filterVar = null;
+    // The event handler subscription we currently have on var.on("change").
+    this._varOnChangeSub = null;
+
+    this._extraInfo = util.extend({ sender: this }, extraInfo);
+
+    this._id = "filter" + nextId();
+
+    this.setGroup(group);
+  }
+
+  /**
+   * Changes the Crosstalk group membership of this FilterHandle. If `set()` was
+   * previously called on this handle, switching groups will clear those keys
+   * from the old group's filter set. These keys will not be applied to the new
+   * group's filter set either. In other words, `setGroup()` effectively calls
+   * `clear()` before switching groups.
+   *
+   * @param {string} group - The name of the Crosstalk group, or null (or
+   *   undefined) to clear the group.
+   */
+
+
+  _createClass(FilterHandle, [{
+    key: "setGroup",
+    value: function setGroup(group) {
+      var _this = this;
+
+      // If group is unchanged, do nothing
+      if (this._group === group) return;
+      // Treat null, undefined, and other falsy values the same
+      if (!this._group && !group) return;
+
+      if (this._filterVar) {
+        this._filterVar.off("change", this._varOnChangeSub);
+        this.clear();
+        this._varOnChangeSub = null;
+        this._filterVar = null;
+        this._filterSet = null;
+      }
+
+      this._group = group;
+
+      if (group) {
+        group = (0, _group2.default)(group);
+        this._filterSet = getFilterSet(group);
+        this._filterVar = (0, _group2.default)(group).var("filter");
+        var sub = this._filterVar.on("change", function (e) {
+          _this._eventRelay.trigger("change", e, _this);
+        });
+        this._varOnChangeSub = sub;
+      }
+    }
+
+    /**
+     * Combine the given `extraInfo` (if any) with the handle's default
+     * `_extraInfo` (if any).
+     * @private
+     */
+
+  }, {
+    key: "_mergeExtraInfo",
+    value: function _mergeExtraInfo(extraInfo) {
+      return util.extend({}, this._extraInfo ? this._extraInfo : null, extraInfo ? extraInfo : null);
+    }
+
+    /**
+     * Close the handle. This clears this handle's contribution to the filter set,
+     * and unsubscribes all event listeners.
+     */
+
+  }, {
+    key: "close",
+    value: function close() {
+      this._emitter.removeAllListeners();
+      this.clear();
+      this.setGroup(null);
+    }
+
+    /**
+     * Clear this handle's contribution to the filter set.
+     *
+     * @param {Object} [extraInfo] - Extra properties to be included on the event
+     *   object that's passed to listeners (in addition to any options that were
+     *   passed into the `FilterHandle` constructor).
+     * 
+     * @fires FilterHandle#change
+     */
+
+  }, {
+    key: "clear",
+    value: function clear(extraInfo) {
+      if (!this._filterSet) return;
+      this._filterSet.clear(this._id);
+      this._onChange(extraInfo);
+    }
+
+    /**
+     * Set this handle's contribution to the filter set. This array should consist
+     * of the keys of the rows that _should_ be displayed; any keys that are not
+     * present in the array will be considered _filtered out_. Note that multiple
+     * `FilterHandle` instances in the group may each contribute an array of keys,
+     * and only those keys that appear in _all_ of the arrays make it through the
+     * filter.
+     *
+     * @param {string[]} keys - Empty array, or array of keys. To clear the
+     *   filter, don't pass an empty array; instead, use the
+     *   {@link FilterHandle#clear} method.
+     * @param {Object} [extraInfo] - Extra properties to be included on the event
+     *   object that's passed to listeners (in addition to any options that were
+     *   passed into the `FilterHandle` constructor).
+     * 
+     * @fires FilterHandle#change
+     */
+
+  }, {
+    key: "set",
+    value: function set(keys, extraInfo) {
+      if (!this._filterSet) return;
+      this._filterSet.update(this._id, keys);
+      this._onChange(extraInfo);
+    }
+
+    /**
+     * @return {string[]|null} - Either: 1) an array of keys that made it through
+     *   all of the `FilterHandle` instances, or, 2) `null`, which means no filter
+     *   is being applied (all data should be displayed).
+     */
+
+  }, {
+    key: "on",
+
+
+    /**
+     * Subscribe to events on this `FilterHandle`.
+     *
+     * @param {string} eventType - Indicates the type of events to listen to.
+     *   Currently, only `"change"` is supported.
+     * @param {FilterHandle~listener} listener - The callback function that
+     *   will be invoked when the event occurs.
+     * @return {string} - A token to pass to {@link FilterHandle#off} to cancel
+     *   this subscription.
+     */
+    value: function on(eventType, listener) {
+      return this._emitter.on(eventType, listener);
+    }
+
+    /**
+     * Cancel event subscriptions created by {@link FilterHandle#on}.
+     *
+     * @param {string} eventType - The type of event to unsubscribe.
+     * @param {string|FilterHandle~listener} listener - Either the callback
+     *   function previously passed into {@link FilterHandle#on}, or the
+     *   string that was returned from {@link FilterHandle#on}.
+     */
+
+  }, {
+    key: "off",
+    value: function off(eventType, listener) {
+      return this._emitter.off(eventType, listener);
+    }
+  }, {
+    key: "_onChange",
+    value: function _onChange(extraInfo) {
+      if (!this._filterSet) return;
+      this._filterVar.set(this._filterSet.value, this._mergeExtraInfo(extraInfo));
+    }
+
+    /**
+     * @callback FilterHandle~listener
+     * @param {Object} event - An object containing details of the event. For
+     *   `"change"` events, this includes the properties `value` (the new
+     *   value of the filter set, or `null` if no filter set is active),
+     *   `oldValue` (the previous value of the filter set), and `sender` (the
+     *   `FilterHandle` instance that made the change).
+     */
+
+  }, {
+    key: "filteredKeys",
+    get: function get() {
+      return this._filterSet ? this._filterSet.value : null;
+    }
+  }]);
+
+  return FilterHandle;
+}();
+
+/**
+ * @event FilterHandle#change
+ * @type {object}
+ * @property {object} value - The new value of the filter set, or `null`
+ *   if no filter set is active.
+ * @property {object} oldValue - The previous value of the filter set.
+ * @property {FilterHandle} sender - The `FilterHandle` instance that
+ *   changed the value.
+ */
+
+},{"./events":1,"./filterset":3,"./group":4,"./util":11}],3:[function(require,module,exports){
+"use strict";
+
+Object.defineProperty(exports, "__esModule", {
+  value: true
+});
+
+var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }();
+
+var _util = require("./util");
+
+function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } }
+
+function naturalComparator(a, b) {
+  if (a === b) {
+    return 0;
+  } else if (a < b) {
+    return -1;
+  } else if (a > b) {
+    return 1;
+  }
+}
+
+/**
+ * @private
+ */
+
+var FilterSet = function () {
+  function FilterSet() {
+    _classCallCheck(this, FilterSet);
+
+    this.reset();
+  }
+
+  _createClass(FilterSet, [{
+    key: "reset",
+    value: function reset() {
+      // Key: handle ID, Value: array of selected keys, or null
+      this._handles = {};
+      // Key: key string, Value: count of handles that include it
+      this._keys = {};
+      this._value = null;
+      this._activeHandles = 0;
+    }
+  }, {
+    key: "update",
+    value: function update(handleId, keys) {
+      if (keys !== null) {
+        keys = keys.slice(0); // clone before sorting
+        keys.sort(naturalComparator);
+      }
+
+      var _diffSortedLists = (0, _util.diffSortedLists)(this._handles[handleId], keys),
+          added = _diffSortedLists.added,
+          removed = _diffSortedLists.removed;
+
+      this._handles[handleId] = keys;
+
+      for (var i = 0; i < added.length; i++) {
+        this._keys[added[i]] = (this._keys[added[i]] || 0) + 1;
+      }
+      for (var _i = 0; _i < removed.length; _i++) {
+        this._keys[removed[_i]]--;
+      }
+
+      this._updateValue(keys);
+    }
+
+    /**
+     * @param {string[]} keys Sorted array of strings that indicate
+     * a superset of possible keys.
+     * @private
+     */
+
+  }, {
+    key: "_updateValue",
+    value: function _updateValue() {
+      var keys = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : this._allKeys;
+
+      var handleCount = Object.keys(this._handles).length;
+      if (handleCount === 0) {
+        this._value = null;
+      } else {
+        this._value = [];
+        for (var i = 0; i < keys.length; i++) {
+          var count = this._keys[keys[i]];
+          if (count === handleCount) {
+            this._value.push(keys[i]);
+          }
+        }
+      }
+    }
+  }, {
+    key: "clear",
+    value: function clear(handleId) {
+      if (typeof this._handles[handleId] === "undefined") {
+        return;
+      }
+
+      var keys = this._handles[handleId];
+      if (!keys) {
+        keys = [];
+      }
+
+      for (var i = 0; i < keys.length; i++) {
+        this._keys[keys[i]]--;
+      }
+      delete this._handles[handleId];
+
+      this._updateValue();
+    }
+  }, {
+    key: "value",
+    get: function get() {
+      return this._value;
+    }
+  }, {
+    key: "_allKeys",
+    get: function get() {
+      var allKeys = Object.keys(this._keys);
+      allKeys.sort(naturalComparator);
+      return allKeys;
+    }
+  }]);
+
+  return FilterSet;
+}();
+
+exports.default = FilterSet;
+
+},{"./util":11}],4:[function(require,module,exports){
+(function (global){
+"use strict";
+
+Object.defineProperty(exports, "__esModule", {
+  value: true
+});
+
+var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }();
+
+var _typeof = typeof Symbol === "function" && typeof Symbol.iterator === "symbol" ? function (obj) { return typeof obj; } : function (obj) { return obj && typeof Symbol === "function" && obj.constructor === Symbol && obj !== Symbol.prototype ? "symbol" : typeof obj; };
+
+exports.default = group;
+
+var _var2 = require("./var");
+
+var _var3 = _interopRequireDefault(_var2);
+
+function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; }
+
+function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } }
+
+// Use a global so that multiple copies of crosstalk.js can be loaded and still
+// have groups behave as singletons across all copies.
+global.__crosstalk_groups = global.__crosstalk_groups || {};
+var groups = global.__crosstalk_groups;
+
+function group(groupName) {
+  if (groupName && typeof groupName === "string") {
+    if (!groups.hasOwnProperty(groupName)) {
+      groups[groupName] = new Group(groupName);
+    }
+    return groups[groupName];
+  } else if ((typeof groupName === "undefined" ? "undefined" : _typeof(groupName)) === "object" && groupName._vars && groupName.var) {
+    // Appears to already be a group object
+    return groupName;
+  } else if (Array.isArray(groupName) && groupName.length == 1 && typeof groupName[0] === "string") {
+    return group(groupName[0]);
+  } else {
+    throw new Error("Invalid groupName argument");
+  }
+}
+
+var Group = function () {
+  function Group(name) {
+    _classCallCheck(this, Group);
+
+    this.name = name;
+    this._vars = {};
+  }
+
+  _createClass(Group, [{
+    key: "var",
+    value: function _var(name) {
+      if (!name || typeof name !== "string") {
+        throw new Error("Invalid var name");
+      }
+
+      if (!this._vars.hasOwnProperty(name)) this._vars[name] = new _var3.default(this, name);
+      return this._vars[name];
+    }
+  }, {
+    key: "has",
+    value: function has(name) {
+      if (!name || typeof name !== "string") {
+        throw new Error("Invalid var name");
+      }
+
+      return this._vars.hasOwnProperty(name);
+    }
+  }]);
+
+  return Group;
+}();
+
+}).call(this,typeof global !== "undefined" ? global : typeof self !== "undefined" ? self : typeof window !== "undefined" ? window : {})
+
+},{"./var":12}],5:[function(require,module,exports){
+(function (global){
+"use strict";
+
+Object.defineProperty(exports, "__esModule", {
+  value: true
+});
+
+var _group = require("./group");
+
+var _group2 = _interopRequireDefault(_group);
+
+var _selection = require("./selection");
+
+var _filter = require("./filter");
+
+var _input = require("./input");
+
+require("./input_selectize");
+
+require("./input_checkboxgroup");
+
+require("./input_slider");
+
+function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; }
+
+var defaultGroup = (0, _group2.default)("default");
+
+function var_(name) {
+  return defaultGroup.var(name);
+}
+
+function has(name) {
+  return defaultGroup.has(name);
+}
+
+if (global.Shiny) {
+  global.Shiny.addCustomMessageHandler("update-client-value", function (message) {
+    if (typeof message.group === "string") {
+      (0, _group2.default)(message.group).var(message.name).set(message.value);
+    } else {
+      var_(message.name).set(message.value);
+    }
+  });
+}
+
+var crosstalk = {
+  group: _group2.default,
+  var: var_,
+  has: has,
+  SelectionHandle: _selection.SelectionHandle,
+  FilterHandle: _filter.FilterHandle,
+  bind: _input.bind
+};
+
+/**
+ * @namespace crosstalk
+ */
+exports.default = crosstalk;
+
+global.crosstalk = crosstalk;
+
+}).call(this,typeof global !== "undefined" ? global : typeof self !== "undefined" ? self : typeof window !== "undefined" ? window : {})
+
+},{"./filter":2,"./group":4,"./input":6,"./input_checkboxgroup":7,"./input_selectize":8,"./input_slider":9,"./selection":10}],6:[function(require,module,exports){
+(function (global){
+"use strict";
+
+Object.defineProperty(exports, "__esModule", {
+  value: true
+});
+exports.register = register;
+exports.bind = bind;
+var $ = global.jQuery;
+
+var bindings = {};
+
+function register(reg) {
+  bindings[reg.className] = reg;
+  if (global.document && global.document.readyState !== "complete") {
+    $(function () {
+      bind();
+    });
+  } else if (global.document) {
+    setTimeout(bind, 100);
+  }
+}
+
+function bind() {
+  Object.keys(bindings).forEach(function (className) {
+    var binding = bindings[className];
+    $("." + binding.className).not(".crosstalk-input-bound").each(function (i, el) {
+      bindInstance(binding, el);
+    });
+  });
+}
+
+// Escape jQuery identifier
+function $escape(val) {
+  return val.replace(/([!"#$%&'()*+,./:;<=>?@[\\\]^`{|}~])/g, "\\$1");
+}
+
+function bindEl(el) {
+  var $el = $(el);
+  Object.keys(bindings).forEach(function (className) {
+    if ($el.hasClass(className) && !$el.hasClass("crosstalk-input-bound")) {
+      var binding = bindings[className];
+      bindInstance(binding, el);
+    }
+  });
+}
+
+function bindInstance(binding, el) {
+  var jsonEl = $(el).find("script[type='application/json'][data-for='" + $escape(el.id) + "']");
+  var data = JSON.parse(jsonEl[0].innerText);
+
+  var instance = binding.factory(el, data);
+  $(el).data("crosstalk-instance", instance);
+  $(el).addClass("crosstalk-input-bound");
+}
+
+if (global.Shiny) {
+  var inputBinding = new global.Shiny.InputBinding();
+  var _$ = global.jQuery;
+  _$.extend(inputBinding, {
+    find: function find(scope) {
+      return _$(scope).find(".crosstalk-input");
+    },
+    initialize: function initialize(el) {
+      if (!_$(el).hasClass("crosstalk-input-bound")) {
+        bindEl(el);
+      }
+    },
+    getId: function getId(el) {
+      return el.id;
+    },
+    getValue: function getValue(el) {},
+    setValue: function setValue(el, value) {},
+    receiveMessage: function receiveMessage(el, data) {},
+    subscribe: function subscribe(el, callback) {
+      _$(el).data("crosstalk-instance").resume();
+    },
+    unsubscribe: function unsubscribe(el) {
+      _$(el).data("crosstalk-instance").suspend();
+    }
+  });
+  global.Shiny.inputBindings.register(inputBinding, "crosstalk.inputBinding");
+}
+
+}).call(this,typeof global !== "undefined" ? global : typeof self !== "undefined" ? self : typeof window !== "undefined" ? window : {})
+
+},{}],7:[function(require,module,exports){
+(function (global){
+"use strict";
+
+var _input = require("./input");
+
+var input = _interopRequireWildcard(_input);
+
+var _filter = require("./filter");
+
+function _interopRequireWildcard(obj) { if (obj && obj.__esModule) { return obj; } else { var newObj = {}; if (obj != null) { for (var key in obj) { if (Object.prototype.hasOwnProperty.call(obj, key)) newObj[key] = obj[key]; } } newObj.default = obj; return newObj; } }
+
+var $ = global.jQuery;
+
+input.register({
+  className: "crosstalk-input-checkboxgroup",
+
+  factory: function factory(el, data) {
+    /*
+     * map: {"groupA": ["keyA", "keyB", ...], ...}
+     * group: "ct-groupname"
+     */
+    var ctHandle = new _filter.FilterHandle(data.group);
+
+    var lastKnownKeys = void 0;
+    var $el = $(el);
+    $el.on("change", "input[type='checkbox']", function () {
+      var checked = $el.find("input[type='checkbox']:checked");
+      if (checked.length === 0) {
+        lastKnownKeys = null;
+        ctHandle.clear();
+      } else {
+        var keys = {};
+        checked.each(function () {
+          data.map[this.value].forEach(function (key) {
+            keys[key] = true;
+          });
+        });
+        var keyArray = Object.keys(keys);
+        keyArray.sort();
+        lastKnownKeys = keyArray;
+        ctHandle.set(keyArray);
+      }
+    });
+
+    return {
+      suspend: function suspend() {
+        ctHandle.clear();
+      },
+      resume: function resume() {
+        if (lastKnownKeys) ctHandle.set(lastKnownKeys);
+      }
+    };
+  }
+});
+
+}).call(this,typeof global !== "undefined" ? global : typeof self !== "undefined" ? self : typeof window !== "undefined" ? window : {})
+
+},{"./filter":2,"./input":6}],8:[function(require,module,exports){
+(function (global){
+"use strict";
+
+var _input = require("./input");
+
+var input = _interopRequireWildcard(_input);
+
+var _util = require("./util");
+
+var util = _interopRequireWildcard(_util);
+
+var _filter = require("./filter");
+
+function _interopRequireWildcard(obj) { if (obj && obj.__esModule) { return obj; } else { var newObj = {}; if (obj != null) { for (var key in obj) { if (Object.prototype.hasOwnProperty.call(obj, key)) newObj[key] = obj[key]; } } newObj.default = obj; return newObj; } }
+
+var $ = global.jQuery;
+
+input.register({
+  className: "crosstalk-input-select",
+
+  factory: function factory(el, data) {
+    /*
+     * items: {value: [...], label: [...]}
+     * map: {"groupA": ["keyA", "keyB", ...], ...}
+     * group: "ct-groupname"
+     */
+
+    var first = [{ value: "", label: "(All)" }];
+    var items = util.dataframeToD3(data.items);
+    var opts = {
+      options: first.concat(items),
+      valueField: "value",
+      labelField: "label",
+      searchField: "label"
+    };
+
+    var select = $(el).find("select")[0];
+
+    var selectize = $(select).selectize(opts)[0].selectize;
+
+    var ctHandle = new _filter.FilterHandle(data.group);
+
+    var lastKnownKeys = void 0;
+    selectize.on("change", function () {
+      if (selectize.items.length === 0) {
+        lastKnownKeys = null;
+        ctHandle.clear();
+      } else {
+        var keys = {};
+        selectize.items.forEach(function (group) {
+          data.map[group].forEach(function (key) {
+            keys[key] = true;
+          });
+        });
+        var keyArray = Object.keys(keys);
+        keyArray.sort();
+        lastKnownKeys = keyArray;
+        ctHandle.set(keyArray);
+      }
+    });
+
+    return {
+      suspend: function suspend() {
+        ctHandle.clear();
+      },
+      resume: function resume() {
+        if (lastKnownKeys) ctHandle.set(lastKnownKeys);
+      }
+    };
+  }
+});
+
+}).call(this,typeof global !== "undefined" ? global : typeof self !== "undefined" ? self : typeof window !== "undefined" ? window : {})
+
+},{"./filter":2,"./input":6,"./util":11}],9:[function(require,module,exports){
+(function (global){
+"use strict";
+
+var _slicedToArray = function () { function sliceIterator(arr, i) { var _arr = []; var _n = true; var _d = false; var _e = undefined; try { for (var _i = arr[Symbol.iterator](), _s; !(_n = (_s = _i.next()).done); _n = true) { _arr.push(_s.value); if (i && _arr.length === i) break; } } catch (err) { _d = true; _e = err; } finally { try { if (!_n && _i["return"]) _i["return"](); } finally { if (_d) throw _e; } } return _arr; } return function (arr, i) { if (Array.isArray(arr)) { return arr; } else if (Symbol.iterator in Object(arr)) { return sliceIterator(arr, i); } else { throw new TypeError("Invalid attempt to destructure non-iterable instance"); } }; }();
+
+var _input = require("./input");
+
+var input = _interopRequireWildcard(_input);
+
+var _filter = require("./filter");
+
+function _interopRequireWildcard(obj) { if (obj && obj.__esModule) { return obj; } else { var newObj = {}; if (obj != null) { for (var key in obj) { if (Object.prototype.hasOwnProperty.call(obj, key)) newObj[key] = obj[key]; } } newObj.default = obj; return newObj; } }
+
+var $ = global.jQuery;
+var strftime = global.strftime;
+
+input.register({
+  className: "crosstalk-input-slider",
+
+  factory: function factory(el, data) {
+    /*
+     * map: {"groupA": ["keyA", "keyB", ...], ...}
+     * group: "ct-groupname"
+     */
+    var ctHandle = new _filter.FilterHandle(data.group);
+
+    var opts = {};
+    var $el = $(el).find("input");
+    var dataType = $el.data("data-type");
+    var timeFormat = $el.data("time-format");
+    var round = $el.data("round");
+    var timeFormatter = void 0;
+
+    // Set up formatting functions
+    if (dataType === "date") {
+      timeFormatter = strftime.utc();
+      opts.prettify = function (num) {
+        return timeFormatter(timeFormat, new Date(num));
+      };
+    } else if (dataType === "datetime") {
+      var timezone = $el.data("timezone");
+      if (timezone) timeFormatter = strftime.timezone(timezone);else timeFormatter = strftime;
+
+      opts.prettify = function (num) {
+        return timeFormatter(timeFormat, new Date(num));
+      };
+    } else if (dataType === "number") {
+      if (typeof round !== "undefined") opts.prettify = function (num) {
+        var factor = Math.pow(10, round);
+        return Math.round(num * factor) / factor;
+      };
+    }
+
+    $el.ionRangeSlider(opts);
+
+    function getValue() {
+      var result = $el.data("ionRangeSlider").result;
+
+      // Function for converting numeric value from slider to appropriate type.
+      var convert = void 0;
+      var dataType = $el.data("data-type");
+      if (dataType === "date") {
+        convert = function convert(val) {
+          return formatDateUTC(new Date(+val));
+        };
+      } else if (dataType === "datetime") {
+        convert = function convert(val) {
+          // Convert ms to s
+          return +val / 1000;
+        };
+      } else {
+        convert = function convert(val) {
+          return +val;
+        };
+      }
+
+      if ($el.data("ionRangeSlider").options.type === "double") {
+        return [convert(result.from), convert(result.to)];
+      } else {
+        return convert(result.from);
+      }
+    }
+
+    var lastKnownKeys = null;
+
+    $el.on("change.crosstalkSliderInput", function (event) {
+      if (!$el.data("updating") && !$el.data("animating")) {
+        var _getValue = getValue(),
+            _getValue2 = _slicedToArray(_getValue, 2),
+            from = _getValue2[0],
+            to = _getValue2[1];
+
+        var keys = [];
+        for (var i = 0; i < data.values.length; i++) {
+          var val = data.values[i];
+          if (val >= from && val <= to) {
+            keys.push(data.keys[i]);
+          }
+        }
+        keys.sort();
+        ctHandle.set(keys);
+        lastKnownKeys = keys;
+      }
+    });
+
+    // let $el = $(el);
+    // $el.on("change", "input[type="checkbox"]", function() {
+    //   let checked = $el.find("input[type="checkbox"]:checked");
+    //   if (checked.length === 0) {
+    //     ctHandle.clear();
+    //   } else {
+    //     let keys = {};
+    //     checked.each(function() {
+    //       data.map[this.value].forEach(function(key) {
+    //         keys[key] = true;
+    //       });
+    //     });
+    //     let keyArray = Object.keys(keys);
+    //     keyArray.sort();
+    //     ctHandle.set(keyArray);
+    //   }
+    // });
+
+    return {
+      suspend: function suspend() {
+        ctHandle.clear();
+      },
+      resume: function resume() {
+        if (lastKnownKeys) ctHandle.set(lastKnownKeys);
+      }
+    };
+  }
+});
+
+// Convert a number to a string with leading zeros
+function padZeros(n, digits) {
+  var str = n.toString();
+  while (str.length < digits) {
+    str = "0" + str;
+  }return str;
+}
+
+// Given a Date object, return a string in yyyy-mm-dd format, using the
+// UTC date. This may be a day off from the date in the local time zone.
+function formatDateUTC(date) {
+  if (date instanceof Date) {
+    return date.getUTCFullYear() + "-" + padZeros(date.getUTCMonth() + 1, 2) + "-" + padZeros(date.getUTCDate(), 2);
+  } else {
+    return null;
+  }
+}
+
+}).call(this,typeof global !== "undefined" ? global : typeof self !== "undefined" ? self : typeof window !== "undefined" ? window : {})
+
+},{"./filter":2,"./input":6}],10:[function(require,module,exports){
+"use strict";
+
+Object.defineProperty(exports, "__esModule", {
+  value: true
+});
+exports.SelectionHandle = undefined;
+
+var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }();
+
+var _events = require("./events");
+
+var _events2 = _interopRequireDefault(_events);
+
+var _group = require("./group");
+
+var _group2 = _interopRequireDefault(_group);
+
+var _util = require("./util");
+
+var util = _interopRequireWildcard(_util);
+
+function _interopRequireWildcard(obj) { if (obj && obj.__esModule) { return obj; } else { var newObj = {}; if (obj != null) { for (var key in obj) { if (Object.prototype.hasOwnProperty.call(obj, key)) newObj[key] = obj[key]; } } newObj.default = obj; return newObj; } }
+
+function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; }
+
+function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } }
+
+/**
+ * Use this class to read and write (and listen for changes to) the selection
+ * for a Crosstalk group. This is intended to be used for linked brushing.
+ *
+ * If two (or more) `SelectionHandle` instances in the same webpage share the
+ * same group name, they will share the same state. Setting the selection using
+ * one `SelectionHandle` instance will result in the `value` property instantly
+ * changing across the others, and `"change"` event listeners on all instances
+ * (including the one that initiated the sending) will fire.
+ *
+ * @param {string} [group] - The name of the Crosstalk group, or if none,
+ *   null or undefined (or any other falsy value). This can be changed later
+ *   via the [SelectionHandle#setGroup](#setGroup) method.
+ * @param {Object} [extraInfo] - An object whose properties will be copied to
+ *   the event object whenever an event is emitted.
+ */
+var SelectionHandle = exports.SelectionHandle = function () {
+  function SelectionHandle() {
+    var group = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : null;
+    var extraInfo = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : null;
+
+    _classCallCheck(this, SelectionHandle);
+
+    this._eventRelay = new _events2.default();
+    this._emitter = new util.SubscriptionTracker(this._eventRelay);
+
+    // Name of the group we're currently tracking, if any. Can change over time.
+    this._group = null;
+    // The Var we're currently tracking, if any. Can change over time.
+    this._var = null;
+    // The event handler subscription we currently have on var.on("change").
+    this._varOnChangeSub = null;
+
+    this._extraInfo = util.extend({ sender: this }, extraInfo);
+
+    this.setGroup(group);
+  }
+
+  /**
+   * Changes the Crosstalk group membership of this SelectionHandle. The group
+   * being switched away from (if any) will not have its selection value
+   * modified as a result of calling `setGroup`, even if this handle was the
+   * most recent handle to set the selection of the group.
+   *
+   * The group being switched to (if any) will also not have its selection value
+   * modified as a result of calling `setGroup`. If you want to set the
+   * selection value of the new group, call `set` explicitly.
+   *
+   * @param {string} group - The name of the Crosstalk group, or null (or
+   *   undefined) to clear the group.
+   */
+
+
+  _createClass(SelectionHandle, [{
+    key: "setGroup",
+    value: function setGroup(group) {
+      var _this = this;
+
+      // If group is unchanged, do nothing
+      if (this._group === group) return;
+      // Treat null, undefined, and other falsy values the same
+      if (!this._group && !group) return;
+
+      if (this._var) {
+        this._var.off("change", this._varOnChangeSub);
+        this._var = null;
+        this._varOnChangeSub = null;
+      }
+
+      this._group = group;
+
+      if (group) {
+        this._var = (0, _group2.default)(group).var("selection");
+        var sub = this._var.on("change", function (e) {
+          _this._eventRelay.trigger("change", e, _this);
+        });
+        this._varOnChangeSub = sub;
+      }
+    }
+
+    /**
+     * Retrieves the current selection for the group represented by this
+     * `SelectionHandle`.
+     *
+     * - If no selection is active, then this value will be falsy.
+     * - If a selection is active, but no data points are selected, then this
+     *   value will be an empty array.
+     * - If a selection is active, and data points are selected, then the keys
+     *   of the selected data points will be present in the array.
+     */
+
+  }, {
+    key: "_mergeExtraInfo",
+
+
+    /**
+     * Combines the given `extraInfo` (if any) with the handle's default
+     * `_extraInfo` (if any).
+     * @private
+     */
+    value: function _mergeExtraInfo(extraInfo) {
+      // Important incidental effect: shallow clone is returned
+      return util.extend({}, this._extraInfo ? this._extraInfo : null, extraInfo ? extraInfo : null);
+    }
+
+    /**
+     * Overwrites the current selection for the group, and raises the `"change"`
+     * event among all of the group's '`SelectionHandle` instances (including
+     * this one).
+     *
+     * @fires SelectionHandle#change
+     * @param {string[]} selectedKeys - Falsy, empty array, or array of keys (see
+     *   {@link SelectionHandle#value}).
+     * @param {Object} [extraInfo] - Extra properties to be included on the event
+     *   object that's passed to listeners (in addition to any options that were
+     *   passed into the `SelectionHandle` constructor).
+     */
+
+  }, {
+    key: "set",
+    value: function set(selectedKeys, extraInfo) {
+      if (this._var) this._var.set(selectedKeys, this._mergeExtraInfo(extraInfo));
+    }
+
+    /**
+     * Overwrites the current selection for the group, and raises the `"change"`
+     * event among all of the group's '`SelectionHandle` instances (including
+     * this one).
+     *
+     * @fires SelectionHandle#change
+     * @param {Object} [extraInfo] - Extra properties to be included on the event
+     *   object that's passed to listeners (in addition to any that were passed
+     *   into the `SelectionHandle` constructor).
+     */
+
+  }, {
+    key: "clear",
+    value: function clear(extraInfo) {
+      if (this._var) this.set(void 0, this._mergeExtraInfo(extraInfo));
+    }
+
+    /**
+     * Subscribes to events on this `SelectionHandle`.
+     *
+     * @param {string} eventType - Indicates the type of events to listen to.
+     *   Currently, only `"change"` is supported.
+     * @param {SelectionHandle~listener} listener - The callback function that
+     *   will be invoked when the event occurs.
+     * @return {string} - A token to pass to {@link SelectionHandle#off} to cancel
+     *   this subscription.
+     */
+
+  }, {
+    key: "on",
+    value: function on(eventType, listener) {
+      return this._emitter.on(eventType, listener);
+    }
+
+    /**
+     * Cancels event subscriptions created by {@link SelectionHandle#on}.
+     *
+     * @param {string} eventType - The type of event to unsubscribe.
+     * @param {string|SelectionHandle~listener} listener - Either the callback
+     *   function previously passed into {@link SelectionHandle#on}, or the
+     *   string that was returned from {@link SelectionHandle#on}.
+     */
+
+  }, {
+    key: "off",
+    value: function off(eventType, listener) {
+      return this._emitter.off(eventType, listener);
+    }
+
+    /**
+     * Shuts down the `SelectionHandle` object.
+     *
+     * Removes all event listeners that were added through this handle.
+     */
+
+  }, {
+    key: "close",
+    value: function close() {
+      this._emitter.removeAllListeners();
+      this.setGroup(null);
+    }
+  }, {
+    key: "value",
+    get: function get() {
+      return this._var ? this._var.get() : null;
+    }
+  }]);
+
+  return SelectionHandle;
+}();
+
+/**
+ * @callback SelectionHandle~listener
+ * @param {Object} event - An object containing details of the event. For
+ *   `"change"` events, this includes the properties `value` (the new
+ *   value of the selection, or `undefined` if no selection is active),
+ *   `oldValue` (the previous value of the selection), and `sender` (the
+ *   `SelectionHandle` instance that made the change).
+ */
+
+/**
+ * @event SelectionHandle#change
+ * @type {object}
+ * @property {object} value - The new value of the selection, or `undefined`
+ *   if no selection is active.
+ * @property {object} oldValue - The previous value of the selection.
+ * @property {SelectionHandle} sender - The `SelectionHandle` instance that
+ *   changed the value.
+ */
+
+},{"./events":1,"./group":4,"./util":11}],11:[function(require,module,exports){
+"use strict";
+
+Object.defineProperty(exports, "__esModule", {
+  value: true
+});
+
+var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }();
+
+var _typeof = typeof Symbol === "function" && typeof Symbol.iterator === "symbol" ? function (obj) { return typeof obj; } : function (obj) { return obj && typeof Symbol === "function" && obj.constructor === Symbol && obj !== Symbol.prototype ? "symbol" : typeof obj; };
+
+exports.extend = extend;
+exports.checkSorted = checkSorted;
+exports.diffSortedLists = diffSortedLists;
+exports.dataframeToD3 = dataframeToD3;
+
+function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } }
+
+function extend(target) {
+  for (var _len = arguments.length, sources = Array(_len > 1 ? _len - 1 : 0), _key = 1; _key < _len; _key++) {
+    sources[_key - 1] = arguments[_key];
+  }
+
+  for (var i = 0; i < sources.length; i++) {
+    var src = sources[i];
+    if (typeof src === "undefined" || src === null) continue;
+
+    for (var key in src) {
+      if (src.hasOwnProperty(key)) {
+        target[key] = src[key];
+      }
+    }
+  }
+  return target;
+}
+
+function checkSorted(list) {
+  for (var i = 1; i < list.length; i++) {
+    if (list[i] <= list[i - 1]) {
+      throw new Error("List is not sorted or contains duplicate");
+    }
+  }
+}
+
+function diffSortedLists(a, b) {
+  var i_a = 0;
+  var i_b = 0;
+
+  if (!a) a = [];
+  if (!b) b = [];
+
+  var a_only = [];
+  var b_only = [];
+
+  checkSorted(a);
+  checkSorted(b);
+
+  while (i_a < a.length && i_b < b.length) {
+    if (a[i_a] === b[i_b]) {
+      i_a++;
+      i_b++;
+    } else if (a[i_a] < b[i_b]) {
+      a_only.push(a[i_a++]);
+    } else {
+      b_only.push(b[i_b++]);
+    }
+  }
+
+  if (i_a < a.length) a_only = a_only.concat(a.slice(i_a));
+  if (i_b < b.length) b_only = b_only.concat(b.slice(i_b));
+  return {
+    removed: a_only,
+    added: b_only
+  };
+}
+
+// Convert from wide: { colA: [1,2,3], colB: [4,5,6], ... }
+// to long: [ {colA: 1, colB: 4}, {colA: 2, colB: 5}, ... ]
+function dataframeToD3(df) {
+  var names = [];
+  var length = void 0;
+  for (var name in df) {
+    if (df.hasOwnProperty(name)) names.push(name);
+    if (_typeof(df[name]) !== "object" || typeof df[name].length === "undefined") {
+      throw new Error("All fields must be arrays");
+    } else if (typeof length !== "undefined" && length !== df[name].length) {
+      throw new Error("All fields must be arrays of the same length");
+    }
+    length = df[name].length;
+  }
+  var results = [];
+  var item = void 0;
+  for (var row = 0; row < length; row++) {
+    item = {};
+    for (var col = 0; col < names.length; col++) {
+      item[names[col]] = df[names[col]][row];
+    }
+    results.push(item);
+  }
+  return results;
+}
+
+/**
+ * Keeps track of all event listener additions/removals and lets all active
+ * listeners be removed with a single operation.
+ *
+ * @private
+ */
+
+var SubscriptionTracker = exports.SubscriptionTracker = function () {
+  function SubscriptionTracker(emitter) {
+    _classCallCheck(this, SubscriptionTracker);
+
+    this._emitter = emitter;
+    this._subs = {};
+  }
+
+  _createClass(SubscriptionTracker, [{
+    key: "on",
+    value: function on(eventType, listener) {
+      var sub = this._emitter.on(eventType, listener);
+      this._subs[sub] = eventType;
+      return sub;
+    }
+  }, {
+    key: "off",
+    value: function off(eventType, listener) {
+      var sub = this._emitter.off(eventType, listener);
+      if (sub) {
+        delete this._subs[sub];
+      }
+      return sub;
+    }
+  }, {
+    key: "removeAllListeners",
+    value: function removeAllListeners() {
+      var _this = this;
+
+      var current_subs = this._subs;
+      this._subs = {};
+      Object.keys(current_subs).forEach(function (sub) {
+        _this._emitter.off(current_subs[sub], sub);
+      });
+    }
+  }]);
+
+  return SubscriptionTracker;
+}();
+
+},{}],12:[function(require,module,exports){
+(function (global){
+"use strict";
+
+Object.defineProperty(exports, "__esModule", {
+  value: true
+});
+
+var _typeof = typeof Symbol === "function" && typeof Symbol.iterator === "symbol" ? function (obj) { return typeof obj; } : function (obj) { return obj && typeof Symbol === "function" && obj.constructor === Symbol && obj !== Symbol.prototype ? "symbol" : typeof obj; };
+
+var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }();
+
+var _events = require("./events");
+
+var _events2 = _interopRequireDefault(_events);
+
+function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; }
+
+function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } }
+
+var Var = function () {
+  function Var(group, name, /*optional*/value) {
+    _classCallCheck(this, Var);
+
+    this._group = group;
+    this._name = name;
+    this._value = value;
+    this._events = new _events2.default();
+  }
+
+  _createClass(Var, [{
+    key: "get",
+    value: function get() {
+      return this._value;
+    }
+  }, {
+    key: "set",
+    value: function set(value, /*optional*/event) {
+      if (this._value === value) {
+        // Do nothing; the value hasn't changed
+        return;
+      }
+      var oldValue = this._value;
+      this._value = value;
+      // Alert JavaScript listeners that the value has changed
+      var evt = {};
+      if (event && (typeof event === "undefined" ? "undefined" : _typeof(event)) === "object") {
+        for (var k in event) {
+          if (event.hasOwnProperty(k)) evt[k] = event[k];
+        }
+      }
+      evt.oldValue = oldValue;
+      evt.value = value;
+      this._events.trigger("change", evt, this);
+
+      // TODO: Make this extensible, to let arbitrary back-ends know that
+      // something has changed
+      if (global.Shiny && global.Shiny.onInputChange) {
+        global.Shiny.onInputChange(".clientValue-" + (this._group.name !== null ? this._group.name + "-" : "") + this._name, typeof value === "undefined" ? null : value);
+      }
+    }
+  }, {
+    key: "on",
+    value: function on(eventType, listener) {
+      return this._events.on(eventType, listener);
+    }
+  }, {
+    key: "off",
+    value: function off(eventType, listener) {
+      return this._events.off(eventType, listener);
+    }
+  }]);
+
+  return Var;
+}();
+
+exports.default = Var;
+
+}).call(this,typeof global !== "undefined" ? global : typeof self !== "undefined" ? self : typeof window !== "undefined" ? window : {})
+
+},{"./events":1}]},{},[5])
+//# sourceMappingURL=crosstalk.js.map
diff --git a/docs/coverage/lib/crosstalk-1.2.1/js/crosstalk.js.map b/docs/coverage/lib/crosstalk-1.2.1/js/crosstalk.js.map
new file mode 100644
index 00000000..cff94f08
--- /dev/null
+++ b/docs/coverage/lib/crosstalk-1.2.1/js/crosstalk.js.map
@@ -0,0 +1,37 @@
+{
+  "version": 3,
+  "sources": [
+    "node_modules/browser-pack/_prelude.js",
+    "javascript/src/events.js",
+    "javascript/src/filter.js",
+    "javascript/src/filterset.js",
+    "javascript/src/group.js",
+    "javascript/src/index.js",
+    "javascript/src/input.js",
+    "javascript/src/input_checkboxgroup.js",
+    "javascript/src/input_selectize.js",
+    "javascript/src/input_slider.js",
+    "javascript/src/selection.js",
+    "javascript/src/util.js",
+    "javascript/src/var.js"
+  ],
+  "names": [],
+  "mappings": "AAAA;;;;;;;;;;;ICAqB,M;AACnB,oBAAc;AAAA;;AACZ,SAAK,MAAL,GAAc,EAAd;AACA,SAAK,IAAL,GAAY,CAAZ;AACD;;;;uBAEE,S,EAAW,Q,EAAU;AACtB,UAAI,OAAO,KAAK,MAAL,CAAY,SAAZ,CAAX;AACA,UAAI,CAAC,IAAL,EAAW;AACT,eAAO,KAAK,MAAL,CAAY,SAAZ,IAAyB,EAAhC;AACD;AACD,UAAI,MAAM,QAAS,KAAK,IAAL,EAAnB;AACA,WAAK,GAAL,IAAY,QAAZ;AACA,aAAO,GAAP;AACD;;AAED;;;;wBACI,S,EAAW,Q,EAAU;AACvB,UAAI,OAAO,KAAK,MAAL,CAAY,SAAZ,CAAX;AACA,UAAI,OAAO,QAAP,KAAqB,UAAzB,EAAqC;AACnC,aAAK,IAAI,GAAT,IAAgB,IAAhB,EAAsB;AACpB,cAAI,KAAK,cAAL,CAAoB,GAApB,CAAJ,EAA8B;AAC5B,gBAAI,KAAK,GAAL,MAAc,QAAlB,EAA4B;AAC1B,qBAAO,KAAK,GAAL,CAAP;AACA,qBAAO,GAAP;AACD;AACF;AACF;AACD,eAAO,KAAP;AACD,OAVD,MAUO,IAAI,OAAO,QAAP,KAAqB,QAAzB,EAAmC;AACxC,YAAI,QAAQ,KAAK,QAAL,CAAZ,EAA4B;AAC1B,iBAAO,KAAK,QAAL,CAAP;AACA,iBAAO,QAAP;AACD;AACD,eAAO,KAAP;AACD,OANM,MAMA;AACL,cAAM,IAAI,KAAJ,CAAU,8BAAV,CAAN;AACD;AACF;;;4BAEO,S,EAAW,G,EAAK,O,EAAS;AAC/B,UAAI,OAAO,KAAK,MAAL,CAAY,SAAZ,CAAX;AACA,WAAK,IAAI,GAAT,IAAgB,IAAhB,EAAsB;AACpB,YAAI,KAAK,cAAL,CAAoB,GAApB,CAAJ,EAA8B;AAC5B,eAAK,GAAL,EAAU,IAAV,CAAe,OAAf,EAAwB,GAAxB;AACD;AACF;AACF;;;;;;kBA/CkB,M;;;;;;;;;;;;ACArB;;;;AACA;;;;AACA;;;;AACA;;IAAY,I;;;;;;;;AAEZ,SAAS,YAAT,CAAsB,KAAtB,EAA6B;AAC3B,MAAI,QAAQ,MAAM,GAAN,CAAU,WAAV,CAAZ;AACA,MAAI,SAAS,MAAM,GAAN,EAAb;AACA,MAAI,CAAC,MAAL,EAAa;AACX,aAAS,yBAAT;AACA,UAAM,GAAN,CAAU,MAAV;AACD;AACD,SAAO,MAAP;AACD;;AAED,IAAI,KAAK,CAAT;AACA,SAAS,MAAT,GAAkB;AAChB,SAAO,IAAP;AACD;;AAED;;;;;;;;;;;;;;;;;;;;;;;;;IAwBa,Y,WAAA,Y;AACX,wBAAY,KAAZ,EAAmB,SAAnB,EAA8B;AAAA;;AAC5B,SAAK,WAAL,GAAmB,sBAAnB;AACA,SAAK,QAAL,GAAgB,IAAI,KAAK,mBAAT,CAA6B,KAAK,WAAlC,CAAhB;;AAEA;AACA,SAAK,MAAL,GAAc,IAAd;AACA;AACA,SAAK,UAAL,GAAkB,IAAlB;AACA;AACA,SAAK,UAAL,GAAkB,IAAlB;AACA;AACA,SAAK,eAAL,GAAuB,IAAvB;;AAEA,SAAK,UAAL,GAAkB,KAAK,MAAL,CAAY,EAAE,QAAQ,IAAV,EAAZ,EAA8B,SAA9B,CAAlB;;AAEA,SAAK,GAAL,GAAW,WAAW,QAAtB;;AAEA,SAAK,QAAL,CAAc,KAAd;AACD;;AAED;;;;;;;;;;;;;;6BAUS,K,EAAO;AAAA;;AACd;AACA,UAAI,KAAK,MAAL,KAAgB,KAApB,EACE;AACF;AACA,UAAI,CAAC,KAAK,MAAN,IAAgB,CAAC,KAArB,EACE;;AAEF,UAAI,KAAK,UAAT,EAAqB;AACnB,aAAK,UAAL,CAAgB,GAAhB,CAAoB,QAApB,EAA8B,KAAK,eAAnC;AACA,aAAK,KAAL;AACA,aAAK,eAAL,GAAuB,IAAvB;AACA,aAAK,UAAL,GAAkB,IAAlB;AACA,aAAK,UAAL,GAAkB,IAAlB;AACD;;AAED,WAAK,MAAL,GAAc,KAAd;;AAEA,UAAI,KAAJ,EAAW;AACT,gBAAQ,qBAAI,KAAJ,CAAR;AACA,aAAK,UAAL,GAAkB,aAAa,KAAb,CAAlB;AACA,aAAK,UAAL,GAAkB,qBAAI,KAAJ,EAAW,GAAX,CAAe,QAAf,CAAlB;AACA,YAAI,MAAM,KAAK,UAAL,CAAgB,EAAhB,CAAmB,QAAnB,EAA6B,UAAC,CAAD,EAAO;AAC5C,gBAAK,WAAL,CAAiB,OAAjB,CAAyB,QAAzB,EAAmC,CAAnC;AACD,SAFS,CAAV;AAGA,aAAK,eAAL,GAAuB,GAAvB;AACD;AACF;;AAED;;;;;;;;oCAKgB,S,EAAW;AACzB,aAAO,KAAK,MAAL,CAAY,EAAZ,EACL,KAAK,UAAL,GAAkB,KAAK,UAAvB,GAAoC,IAD/B,EAEL,YAAY,SAAZ,GAAwB,IAFnB,CAAP;AAGD;;AAED;;;;;;;4BAIQ;AACN,WAAK,QAAL,CAAc,kBAAd;AACA,WAAK,KAAL;AACA,WAAK,QAAL,CAAc,IAAd;AACD;;AAED;;;;;;;;;;;;0BASM,S,EAAW;AACf,UAAI,CAAC,KAAK,UAAV,EACE;AACF,WAAK,UAAL,CAAgB,KAAhB,CAAsB,KAAK,GAA3B;AACA,WAAK,SAAL,CAAe,SAAf;AACD;;AAED;;;;;;;;;;;;;;;;;;;;wBAiBI,I,EAAM,S,EAAW;AACnB,UAAI,CAAC,KAAK,UAAV,EACE;AACF,WAAK,UAAL,CAAgB,MAAhB,CAAuB,KAAK,GAA5B,EAAiC,IAAjC;AACA,WAAK,SAAL,CAAe,SAAf;AACD;;AAED;;;;;;;;;;AASA;;;;;;;;;;uBAUG,S,EAAW,Q,EAAU;AACtB,aAAO,KAAK,QAAL,CAAc,EAAd,CAAiB,SAAjB,EAA4B,QAA5B,CAAP;AACD;;AAED;;;;;;;;;;;wBAQI,S,EAAW,Q,EAAU;AACvB,aAAO,KAAK,QAAL,CAAc,GAAd,CAAkB,SAAlB,EAA6B,QAA7B,CAAP;AACD;;;8BAES,S,EAAW;AACnB,UAAI,CAAC,KAAK,UAAV,EACE;AACF,WAAK,UAAL,CAAgB,GAAhB,CAAoB,KAAK,UAAL,CAAgB,KAApC,EAA2C,KAAK,eAAL,CAAqB,SAArB,CAA3C;AACD;;AAED;;;;;;;;;;;wBApCmB;AACjB,aAAO,KAAK,UAAL,GAAkB,KAAK,UAAL,CAAgB,KAAlC,GAA0C,IAAjD;AACD;;;;;;AA6CH;;;;;;;;;;;;;;;;;;;ACzNA;;;;AAEA,SAAS,iBAAT,CAA2B,CAA3B,EAA8B,CAA9B,EAAiC;AAC/B,MAAI,MAAM,CAAV,EAAa;AACX,WAAO,CAAP;AACD,GAFD,MAEO,IAAI,IAAI,CAAR,EAAW;AAChB,WAAO,CAAC,CAAR;AACD,GAFM,MAEA,IAAI,IAAI,CAAR,EAAW;AAChB,WAAO,CAAP;AACD;AACF;;AAED;;;;IAGqB,S;AACnB,uBAAc;AAAA;;AACZ,SAAK,KAAL;AACD;;;;4BAEO;AACN;AACA,WAAK,QAAL,GAAgB,EAAhB;AACA;AACA,WAAK,KAAL,GAAa,EAAb;AACA,WAAK,MAAL,GAAc,IAAd;AACA,WAAK,cAAL,GAAsB,CAAtB;AACD;;;2BAMM,Q,EAAU,I,EAAM;AACrB,UAAI,SAAS,IAAb,EAAmB;AACjB,eAAO,KAAK,KAAL,CAAW,CAAX,CAAP,CADiB,CACK;AACtB,aAAK,IAAL,CAAU,iBAAV;AACD;;AAJoB,6BAME,2BAAgB,KAAK,QAAL,CAAc,QAAd,CAAhB,EAAyC,IAAzC,CANF;AAAA,UAMhB,KANgB,oBAMhB,KANgB;AAAA,UAMT,OANS,oBAMT,OANS;;AAOrB,WAAK,QAAL,CAAc,QAAd,IAA0B,IAA1B;;AAEA,WAAK,IAAI,IAAI,CAAb,EAAgB,IAAI,MAAM,MAA1B,EAAkC,GAAlC,EAAuC;AACrC,aAAK,KAAL,CAAW,MAAM,CAAN,CAAX,IAAuB,CAAC,KAAK,KAAL,CAAW,MAAM,CAAN,CAAX,KAAwB,CAAzB,IAA8B,CAArD;AACD;AACD,WAAK,IAAI,KAAI,CAAb,EAAgB,KAAI,QAAQ,MAA5B,EAAoC,IAApC,EAAyC;AACvC,aAAK,KAAL,CAAW,QAAQ,EAAR,CAAX;AACD;;AAED,WAAK,YAAL,CAAkB,IAAlB;AACD;;AAED;;;;;;;;mCAKmC;AAAA,UAAtB,IAAsB,uEAAf,KAAK,QAAU;;AACjC,UAAI,cAAc,OAAO,IAAP,CAAY,KAAK,QAAjB,EAA2B,MAA7C;AACA,UAAI,gBAAgB,CAApB,EAAuB;AACrB,aAAK,MAAL,GAAc,IAAd;AACD,OAFD,MAEO;AACL,aAAK,MAAL,GAAc,EAAd;AACA,aAAK,IAAI,IAAI,CAAb,EAAgB,IAAI,KAAK,MAAzB,EAAiC,GAAjC,EAAsC;AACpC,cAAI,QAAQ,KAAK,KAAL,CAAW,KAAK,CAAL,CAAX,CAAZ;AACA,cAAI,UAAU,WAAd,EAA2B;AACzB,iBAAK,MAAL,CAAY,IAAZ,CAAiB,KAAK,CAAL,CAAjB;AACD;AACF;AACF;AACF;;;0BAEK,Q,EAAU;AACd,UAAI,OAAO,KAAK,QAAL,CAAc,QAAd,CAAP,KAAoC,WAAxC,EAAqD;AACnD;AACD;;AAED,UAAI,OAAO,KAAK,QAAL,CAAc,QAAd,CAAX;AACA,UAAI,CAAC,IAAL,EAAW;AACT,eAAO,EAAP;AACD;;AAED,WAAK,IAAI,IAAI,CAAb,EAAgB,IAAI,KAAK,MAAzB,EAAiC,GAAjC,EAAsC;AACpC,aAAK,KAAL,CAAW,KAAK,CAAL,CAAX;AACD;AACD,aAAO,KAAK,QAAL,CAAc,QAAd,CAAP;;AAEA,WAAK,YAAL;AACD;;;wBA3DW;AACV,aAAO,KAAK,MAAZ;AACD;;;wBA2Dc;AACb,UAAI,UAAU,OAAO,IAAP,CAAY,KAAK,KAAjB,CAAd;AACA,cAAQ,IAAR,CAAa,iBAAb;AACA,aAAO,OAAP;AACD;;;;;;kBA/EkB,S;;;;;;;;;;;;;;kBCRG,K;;AAPxB;;;;;;;;AAEA;AACA;AACA,OAAO,kBAAP,GAA4B,OAAO,kBAAP,IAA6B,EAAzD;AACA,IAAI,SAAS,OAAO,kBAApB;;AAEe,SAAS,KAAT,CAAe,SAAf,EAA0B;AACvC,MAAI,aAAa,OAAO,SAAP,KAAsB,QAAvC,EAAiD;AAC/C,QAAI,CAAC,OAAO,cAAP,CAAsB,SAAtB,CAAL,EAAuC;AACrC,aAAO,SAAP,IAAoB,IAAI,KAAJ,CAAU,SAAV,CAApB;AACD;AACD,WAAO,OAAO,SAAP,CAAP;AACD,GALD,MAKO,IAAI,QAAO,SAAP,yCAAO,SAAP,OAAsB,QAAtB,IAAkC,UAAU,KAA5C,IAAqD,UAAU,GAAnE,EAAwE;AAC7E;AACA,WAAO,SAAP;AACD,GAHM,MAGA,IAAI,MAAM,OAAN,CAAc,SAAd,KACP,UAAU,MAAV,IAAoB,CADb,IAEP,OAAO,UAAU,CAAV,CAAP,KAAyB,QAFtB,EAEgC;AACrC,WAAO,MAAM,UAAU,CAAV,CAAN,CAAP;AACD,GAJM,MAIA;AACL,UAAM,IAAI,KAAJ,CAAU,4BAAV,CAAN;AACD;AACF;;IAEK,K;AACJ,iBAAY,IAAZ,EAAkB;AAAA;;AAChB,SAAK,IAAL,GAAY,IAAZ;AACA,SAAK,KAAL,GAAa,EAAb;AACD;;;;yBAEG,I,EAAM;AACR,UAAI,CAAC,IAAD,IAAS,OAAO,IAAP,KAAiB,QAA9B,EAAwC;AACtC,cAAM,IAAI,KAAJ,CAAU,kBAAV,CAAN;AACD;;AAED,UAAI,CAAC,KAAK,KAAL,CAAW,cAAX,CAA0B,IAA1B,CAAL,EACE,KAAK,KAAL,CAAW,IAAX,IAAmB,kBAAQ,IAAR,EAAc,IAAd,CAAnB;AACF,aAAO,KAAK,KAAL,CAAW,IAAX,CAAP;AACD;;;wBAEG,I,EAAM;AACR,UAAI,CAAC,IAAD,IAAS,OAAO,IAAP,KAAiB,QAA9B,EAAwC;AACtC,cAAM,IAAI,KAAJ,CAAU,kBAAV,CAAN;AACD;;AAED,aAAO,KAAK,KAAL,CAAW,cAAX,CAA0B,IAA1B,CAAP;AACD;;;;;;;;;;;;;;;;AC/CH;;;;AACA;;AACA;;AACA;;AACA;;AACA;;AACA;;;;AAEA,IAAM,eAAe,qBAAM,SAAN,CAArB;;AAEA,SAAS,IAAT,CAAc,IAAd,EAAoB;AAClB,SAAO,aAAa,GAAb,CAAiB,IAAjB,CAAP;AACD;;AAED,SAAS,GAAT,CAAa,IAAb,EAAmB;AACjB,SAAO,aAAa,GAAb,CAAiB,IAAjB,CAAP;AACD;;AAED,IAAI,OAAO,KAAX,EAAkB;AAChB,SAAO,KAAP,CAAa,uBAAb,CAAqC,qBAArC,EAA4D,UAAS,OAAT,EAAkB;AAC5E,QAAI,OAAO,QAAQ,KAAf,KAA0B,QAA9B,EAAwC;AACtC,2BAAM,QAAQ,KAAd,EAAqB,GAArB,CAAyB,QAAQ,IAAjC,EAAuC,GAAvC,CAA2C,QAAQ,KAAnD;AACD,KAFD,MAEO;AACL,WAAK,QAAQ,IAAb,EAAmB,GAAnB,CAAuB,QAAQ,KAA/B;AACD;AACF,GAND;AAOD;;AAED,IAAM,YAAY;AAChB,wBADgB;AAEhB,OAAK,IAFW;AAGhB,OAAK,GAHW;AAIhB,6CAJgB;AAKhB,oCALgB;AAMhB;AANgB,CAAlB;;AASA;;;kBAGe,S;;AACf,OAAO,SAAP,GAAmB,SAAnB;;;;;;;;;;;QCrCgB,Q,GAAA,Q;QAWA,I,GAAA,I;AAfhB,IAAI,IAAI,OAAO,MAAf;;AAEA,IAAI,WAAW,EAAf;;AAEO,SAAS,QAAT,CAAkB,GAAlB,EAAuB;AAC5B,WAAS,IAAI,SAAb,IAA0B,GAA1B;AACA,MAAI,OAAO,QAAP,IAAmB,OAAO,QAAP,CAAgB,UAAhB,KAA+B,UAAtD,EAAkE;AAChE,MAAE,YAAM;AACN;AACD,KAFD;AAGD,GAJD,MAIO,IAAI,OAAO,QAAX,EAAqB;AAC1B,eAAW,IAAX,EAAiB,GAAjB;AACD;AACF;;AAEM,SAAS,IAAT,GAAgB;AACrB,SAAO,IAAP,CAAY,QAAZ,EAAsB,OAAtB,CAA8B,UAAS,SAAT,EAAoB;AAChD,QAAI,UAAU,SAAS,SAAT,CAAd;AACA,MAAE,MAAM,QAAQ,SAAhB,EAA2B,GAA3B,CAA+B,wBAA/B,EAAyD,IAAzD,CAA8D,UAAS,CAAT,EAAY,EAAZ,EAAgB;AAC5E,mBAAa,OAAb,EAAsB,EAAtB;AACD,KAFD;AAGD,GALD;AAMD;;AAED;AACA,SAAS,OAAT,CAAiB,GAAjB,EAAsB;AACpB,SAAO,IAAI,OAAJ,CAAY,uCAAZ,EAAqD,MAArD,CAAP;AACD;;AAED,SAAS,MAAT,CAAgB,EAAhB,EAAoB;AAClB,MAAI,MAAM,EAAE,EAAF,CAAV;AACA,SAAO,IAAP,CAAY,QAAZ,EAAsB,OAAtB,CAA8B,UAAS,SAAT,EAAoB;AAChD,QAAI,IAAI,QAAJ,CAAa,SAAb,KAA2B,CAAC,IAAI,QAAJ,CAAa,uBAAb,CAAhC,EAAuE;AACrE,UAAI,UAAU,SAAS,SAAT,CAAd;AACA,mBAAa,OAAb,EAAsB,EAAtB;AACD;AACF,GALD;AAMD;;AAED,SAAS,YAAT,CAAsB,OAAtB,EAA+B,EAA/B,EAAmC;AACjC,MAAI,SAAS,EAAE,EAAF,EAAM,IAAN,CAAW,+CAA+C,QAAQ,GAAG,EAAX,CAA/C,GAAgE,IAA3E,CAAb;AACA,MAAI,OAAO,KAAK,KAAL,CAAW,OAAO,CAAP,EAAU,SAArB,CAAX;;AAEA,MAAI,WAAW,QAAQ,OAAR,CAAgB,EAAhB,EAAoB,IAApB,CAAf;AACA,IAAE,EAAF,EAAM,IAAN,CAAW,oBAAX,EAAiC,QAAjC;AACA,IAAE,EAAF,EAAM,QAAN,CAAe,uBAAf;AACD;;AAED,IAAI,OAAO,KAAX,EAAkB;AAChB,MAAI,eAAe,IAAI,OAAO,KAAP,CAAa,YAAjB,EAAnB;AACA,MAAI,KAAI,OAAO,MAAf;AACA,KAAE,MAAF,CAAS,YAAT,EAAuB;AACrB,UAAM,cAAS,KAAT,EAAgB;AACpB,aAAO,GAAE,KAAF,EAAS,IAAT,CAAc,kBAAd,CAAP;AACD,KAHoB;AAIrB,gBAAY,oBAAS,EAAT,EAAa;AACvB,UAAI,CAAC,GAAE,EAAF,EAAM,QAAN,CAAe,uBAAf,CAAL,EAA8C;AAC5C,eAAO,EAAP;AACD;AACF,KARoB;AASrB,WAAO,eAAS,EAAT,EAAa;AAClB,aAAO,GAAG,EAAV;AACD,KAXoB;AAYrB,cAAU,kBAAS,EAAT,EAAa,CAEtB,CAdoB;AAerB,cAAU,kBAAS,EAAT,EAAa,KAAb,EAAoB,CAE7B,CAjBoB;AAkBrB,oBAAgB,wBAAS,EAAT,EAAa,IAAb,EAAmB,CAElC,CApBoB;AAqBrB,eAAW,mBAAS,EAAT,EAAa,QAAb,EAAuB;AAChC,SAAE,EAAF,EAAM,IAAN,CAAW,oBAAX,EAAiC,MAAjC;AACD,KAvBoB;AAwBrB,iBAAa,qBAAS,EAAT,EAAa;AACxB,SAAE,EAAF,EAAM,IAAN,CAAW,oBAAX,EAAiC,OAAjC;AACD;AA1BoB,GAAvB;AA4BA,SAAO,KAAP,CAAa,aAAb,CAA2B,QAA3B,CAAoC,YAApC,EAAkD,wBAAlD;AACD;;;;;;;;AChFD;;IAAY,K;;AACZ;;;;AAEA,IAAI,IAAI,OAAO,MAAf;;AAEA,MAAM,QAAN,CAAe;AACb,aAAW,+BADE;;AAGb,WAAS,iBAAS,EAAT,EAAa,IAAb,EAAmB;AAC1B;;;;AAIA,QAAI,WAAW,yBAAiB,KAAK,KAAtB,CAAf;;AAEA,QAAI,sBAAJ;AACA,QAAI,MAAM,EAAE,EAAF,CAAV;AACA,QAAI,EAAJ,CAAO,QAAP,EAAiB,wBAAjB,EAA2C,YAAW;AACpD,UAAI,UAAU,IAAI,IAAJ,CAAS,gCAAT,CAAd;AACA,UAAI,QAAQ,MAAR,KAAmB,CAAvB,EAA0B;AACxB,wBAAgB,IAAhB;AACA,iBAAS,KAAT;AACD,OAHD,MAGO;AACL,YAAI,OAAO,EAAX;AACA,gBAAQ,IAAR,CAAa,YAAW;AACtB,eAAK,GAAL,CAAS,KAAK,KAAd,EAAqB,OAArB,CAA6B,UAAS,GAAT,EAAc;AACzC,iBAAK,GAAL,IAAY,IAAZ;AACD,WAFD;AAGD,SAJD;AAKA,YAAI,WAAW,OAAO,IAAP,CAAY,IAAZ,CAAf;AACA,iBAAS,IAAT;AACA,wBAAgB,QAAhB;AACA,iBAAS,GAAT,CAAa,QAAb;AACD;AACF,KAjBD;;AAmBA,WAAO;AACL,eAAS,mBAAW;AAClB,iBAAS,KAAT;AACD,OAHI;AAIL,cAAQ,kBAAW;AACjB,YAAI,aAAJ,EACE,SAAS,GAAT,CAAa,aAAb;AACH;AAPI,KAAP;AASD;AAxCY,CAAf;;;;;;;;ACLA;;IAAY,K;;AACZ;;IAAY,I;;AACZ;;;;AAEA,IAAI,IAAI,OAAO,MAAf;;AAEA,MAAM,QAAN,CAAe;AACb,aAAW,wBADE;;AAGb,WAAS,iBAAS,EAAT,EAAa,IAAb,EAAmB;AAC1B;;;;;;AAMA,QAAI,QAAQ,CAAC,EAAC,OAAO,EAAR,EAAY,OAAO,OAAnB,EAAD,CAAZ;AACA,QAAI,QAAQ,KAAK,aAAL,CAAmB,KAAK,KAAxB,CAAZ;AACA,QAAI,OAAO;AACT,eAAS,MAAM,MAAN,CAAa,KAAb,CADA;AAET,kBAAY,OAFH;AAGT,kBAAY,OAHH;AAIT,mBAAa;AAJJ,KAAX;;AAOA,QAAI,SAAS,EAAE,EAAF,EAAM,IAAN,CAAW,QAAX,EAAqB,CAArB,CAAb;;AAEA,QAAI,YAAY,EAAE,MAAF,EAAU,SAAV,CAAoB,IAApB,EAA0B,CAA1B,EAA6B,SAA7C;;AAEA,QAAI,WAAW,yBAAiB,KAAK,KAAtB,CAAf;;AAEA,QAAI,sBAAJ;AACA,cAAU,EAAV,CAAa,QAAb,EAAuB,YAAW;AAChC,UAAI,UAAU,KAAV,CAAgB,MAAhB,KAA2B,CAA/B,EAAkC;AAChC,wBAAgB,IAAhB;AACA,iBAAS,KAAT;AACD,OAHD,MAGO;AACL,YAAI,OAAO,EAAX;AACA,kBAAU,KAAV,CAAgB,OAAhB,CAAwB,UAAS,KAAT,EAAgB;AACtC,eAAK,GAAL,CAAS,KAAT,EAAgB,OAAhB,CAAwB,UAAS,GAAT,EAAc;AACpC,iBAAK,GAAL,IAAY,IAAZ;AACD,WAFD;AAGD,SAJD;AAKA,YAAI,WAAW,OAAO,IAAP,CAAY,IAAZ,CAAf;AACA,iBAAS,IAAT;AACA,wBAAgB,QAAhB;AACA,iBAAS,GAAT,CAAa,QAAb;AACD;AACF,KAhBD;;AAkBA,WAAO;AACL,eAAS,mBAAW;AAClB,iBAAS,KAAT;AACD,OAHI;AAIL,cAAQ,kBAAW;AACjB,YAAI,aAAJ,EACE,SAAS,GAAT,CAAa,aAAb;AACH;AAPI,KAAP;AASD;AArDY,CAAf;;;;;;;;;;ACNA;;IAAY,K;;AACZ;;;;AAEA,IAAI,IAAI,OAAO,MAAf;AACA,IAAI,WAAW,OAAO,QAAtB;;AAEA,MAAM,QAAN,CAAe;AACb,aAAW,wBADE;;AAGb,WAAS,iBAAS,EAAT,EAAa,IAAb,EAAmB;AAC1B;;;;AAIA,QAAI,WAAW,yBAAiB,KAAK,KAAtB,CAAf;;AAEA,QAAI,OAAO,EAAX;AACA,QAAI,MAAM,EAAE,EAAF,EAAM,IAAN,CAAW,OAAX,CAAV;AACA,QAAI,WAAW,IAAI,IAAJ,CAAS,WAAT,CAAf;AACA,QAAI,aAAa,IAAI,IAAJ,CAAS,aAAT,CAAjB;AACA,QAAI,QAAQ,IAAI,IAAJ,CAAS,OAAT,CAAZ;AACA,QAAI,sBAAJ;;AAEA;AACA,QAAI,aAAa,MAAjB,EAAyB;AACvB,sBAAgB,SAAS,GAAT,EAAhB;AACA,WAAK,QAAL,GAAgB,UAAS,GAAT,EAAc;AAC5B,eAAO,cAAc,UAAd,EAA0B,IAAI,IAAJ,CAAS,GAAT,CAA1B,CAAP;AACD,OAFD;AAID,KAND,MAMO,IAAI,aAAa,UAAjB,EAA6B;AAClC,UAAI,WAAW,IAAI,IAAJ,CAAS,UAAT,CAAf;AACA,UAAI,QAAJ,EACE,gBAAgB,SAAS,QAAT,CAAkB,QAAlB,CAAhB,CADF,KAGE,gBAAgB,QAAhB;;AAEF,WAAK,QAAL,GAAgB,UAAS,GAAT,EAAc;AAC5B,eAAO,cAAc,UAAd,EAA0B,IAAI,IAAJ,CAAS,GAAT,CAA1B,CAAP;AACD,OAFD;AAGD,KAVM,MAUA,IAAI,aAAa,QAAjB,EAA2B;AAChC,UAAI,OAAO,KAAP,KAAiB,WAArB,EACE,KAAK,QAAL,GAAgB,UAAS,GAAT,EAAc;AAC5B,YAAI,SAAS,KAAK,GAAL,CAAS,EAAT,EAAa,KAAb,CAAb;AACA,eAAO,KAAK,KAAL,CAAW,MAAM,MAAjB,IAA2B,MAAlC;AACD,OAHD;AAIH;;AAED,QAAI,cAAJ,CAAmB,IAAnB;;AAEA,aAAS,QAAT,GAAoB;AAClB,UAAI,SAAS,IAAI,IAAJ,CAAS,gBAAT,EAA2B,MAAxC;;AAEA;AACA,UAAI,gBAAJ;AACA,UAAI,WAAW,IAAI,IAAJ,CAAS,WAAT,CAAf;AACA,UAAI,aAAa,MAAjB,EAAyB;AACvB,kBAAU,iBAAS,GAAT,EAAc;AACtB,iBAAO,cAAc,IAAI,IAAJ,CAAS,CAAC,GAAV,CAAd,CAAP;AACD,SAFD;AAGD,OAJD,MAIO,IAAI,aAAa,UAAjB,EAA6B;AAClC,kBAAU,iBAAS,GAAT,EAAc;AACtB;AACA,iBAAO,CAAC,GAAD,GAAO,IAAd;AACD,SAHD;AAID,OALM,MAKA;AACL,kBAAU,iBAAS,GAAT,EAAc;AAAE,iBAAO,CAAC,GAAR;AAAc,SAAxC;AACD;;AAED,UAAI,IAAI,IAAJ,CAAS,gBAAT,EAA2B,OAA3B,CAAmC,IAAnC,KAA4C,QAAhD,EAA0D;AACxD,eAAO,CAAC,QAAQ,OAAO,IAAf,CAAD,EAAuB,QAAQ,OAAO,EAAf,CAAvB,CAAP;AACD,OAFD,MAEO;AACL,eAAO,QAAQ,OAAO,IAAf,CAAP;AACD;AACF;;AAED,QAAI,gBAAgB,IAApB;;AAEA,QAAI,EAAJ,CAAO,6BAAP,EAAsC,UAAS,KAAT,EAAgB;AACpD,UAAI,CAAC,IAAI,IAAJ,CAAS,UAAT,CAAD,IAAyB,CAAC,IAAI,IAAJ,CAAS,WAAT,CAA9B,EAAqD;AAAA,wBAClC,UADkC;AAAA;AAAA,YAC9C,IAD8C;AAAA,YACxC,EADwC;;AAEnD,YAAI,OAAO,EAAX;AACA,aAAK,IAAI,IAAI,CAAb,EAAgB,IAAI,KAAK,MAAL,CAAY,MAAhC,EAAwC,GAAxC,EAA6C;AAC3C,cAAI,MAAM,KAAK,MAAL,CAAY,CAAZ,CAAV;AACA,cAAI,OAAO,IAAP,IAAe,OAAO,EAA1B,EAA8B;AAC5B,iBAAK,IAAL,CAAU,KAAK,IAAL,CAAU,CAAV,CAAV;AACD;AACF;AACD,aAAK,IAAL;AACA,iBAAS,GAAT,CAAa,IAAb;AACA,wBAAgB,IAAhB;AACD;AACF,KAdD;;AAiBA;AACA;AACA;AACA;AACA;AACA;AACA;AACA;AACA;AACA;AACA;AACA;AACA;AACA;AACA;AACA;AACA;;AAEA,WAAO;AACL,eAAS,mBAAW;AAClB,iBAAS,KAAT;AACD,OAHI;AAIL,cAAQ,kBAAW;AACjB,YAAI,aAAJ,EACE,SAAS,GAAT,CAAa,aAAb;AACH;AAPI,KAAP;AASD;AApHY,CAAf;;AAwHA;AACA,SAAS,QAAT,CAAkB,CAAlB,EAAqB,MAArB,EAA6B;AAC3B,MAAI,MAAM,EAAE,QAAF,EAAV;AACA,SAAO,IAAI,MAAJ,GAAa,MAApB;AACE,UAAM,MAAM,GAAZ;AADF,GAEA,OAAO,GAAP;AACD;;AAED;AACA;AACA,SAAS,aAAT,CAAuB,IAAvB,EAA6B;AAC3B,MAAI,gBAAgB,IAApB,EAA0B;AACxB,WAAO,KAAK,cAAL,KAAwB,GAAxB,GACA,SAAS,KAAK,WAAL,KAAmB,CAA5B,EAA+B,CAA/B,CADA,GACoC,GADpC,GAEA,SAAS,KAAK,UAAL,EAAT,EAA4B,CAA5B,CAFP;AAID,GALD,MAKO;AACL,WAAO,IAAP;AACD;AACF;;;;;;;;;;;;;;ACjJD;;;;AACA;;;;AACA;;IAAY,I;;;;;;;;AAEZ;;;;;;;;;;;;;;;;IAgBa,e,WAAA,e;AAEX,6BAA4C;AAAA,QAAhC,KAAgC,uEAAxB,IAAwB;AAAA,QAAlB,SAAkB,uEAAN,IAAM;;AAAA;;AAC1C,SAAK,WAAL,GAAmB,sBAAnB;AACA,SAAK,QAAL,GAAgB,IAAI,KAAK,mBAAT,CAA6B,KAAK,WAAlC,CAAhB;;AAEA;AACA,SAAK,MAAL,GAAc,IAAd;AACA;AACA,SAAK,IAAL,GAAY,IAAZ;AACA;AACA,SAAK,eAAL,GAAuB,IAAvB;;AAEA,SAAK,UAAL,GAAkB,KAAK,MAAL,CAAY,EAAE,QAAQ,IAAV,EAAZ,EAA8B,SAA9B,CAAlB;;AAEA,SAAK,QAAL,CAAc,KAAd;AACD;;AAED;;;;;;;;;;;;;;;;;6BAaS,K,EAAO;AAAA;;AACd;AACA,UAAI,KAAK,MAAL,KAAgB,KAApB,EACE;AACF;AACA,UAAI,CAAC,KAAK,MAAN,IAAgB,CAAC,KAArB,EACE;;AAEF,UAAI,KAAK,IAAT,EAAe;AACb,aAAK,IAAL,CAAU,GAAV,CAAc,QAAd,EAAwB,KAAK,eAA7B;AACA,aAAK,IAAL,GAAY,IAAZ;AACA,aAAK,eAAL,GAAuB,IAAvB;AACD;;AAED,WAAK,MAAL,GAAc,KAAd;;AAEA,UAAI,KAAJ,EAAW;AACT,aAAK,IAAL,GAAY,qBAAI,KAAJ,EAAW,GAAX,CAAe,WAAf,CAAZ;AACA,YAAI,MAAM,KAAK,IAAL,CAAU,EAAV,CAAa,QAAb,EAAuB,UAAC,CAAD,EAAO;AACtC,gBAAK,WAAL,CAAiB,OAAjB,CAAyB,QAAzB,EAAmC,CAAnC;AACD,SAFS,CAAV;AAGA,aAAK,eAAL,GAAuB,GAAvB;AACD;AACF;;AAED;;;;;;;;;;;;;;;AAcA;;;;;oCAKgB,S,EAAW;AACzB;AACA,aAAO,KAAK,MAAL,CAAY,EAAZ,EACL,KAAK,UAAL,GAAkB,KAAK,UAAvB,GAAoC,IAD/B,EAEL,YAAY,SAAZ,GAAwB,IAFnB,CAAP;AAGD;;AAED;;;;;;;;;;;;;;;wBAYI,Y,EAAc,S,EAAW;AAC3B,UAAI,KAAK,IAAT,EACE,KAAK,IAAL,CAAU,GAAV,CAAc,YAAd,EAA4B,KAAK,eAAL,CAAqB,SAArB,CAA5B;AACH;;AAED;;;;;;;;;;;;;0BAUM,S,EAAW;AACf,UAAI,KAAK,IAAT,EACE,KAAK,GAAL,CAAS,KAAK,CAAd,EAAiB,KAAK,eAAL,CAAqB,SAArB,CAAjB;AACH;;AAED;;;;;;;;;;;;;uBAUG,S,EAAW,Q,EAAU;AACtB,aAAO,KAAK,QAAL,CAAc,EAAd,CAAiB,SAAjB,EAA4B,QAA5B,CAAP;AACD;;AAED;;;;;;;;;;;wBAQI,S,EAAW,Q,EAAU;AACvB,aAAO,KAAK,QAAL,CAAc,GAAd,CAAkB,SAAlB,EAA6B,QAA7B,CAAP;AACD;;AAED;;;;;;;;4BAKQ;AACN,WAAK,QAAL,CAAc,kBAAd;AACA,WAAK,QAAL,CAAc,IAAd;AACD;;;wBAlFW;AACV,aAAO,KAAK,IAAL,GAAY,KAAK,IAAL,CAAU,GAAV,EAAZ,GAA8B,IAArC;AACD;;;;;;AAmFH;;;;;;;;;AASA;;;;;;;;;;;;;;;;;;;;;QCpLgB,M,GAAA,M;QAeA,W,GAAA,W;QAQA,e,GAAA,e;QAoCA,a,GAAA,a;;;;AA3DT,SAAS,MAAT,CAAgB,MAAhB,EAAoC;AAAA,oCAAT,OAAS;AAAT,WAAS;AAAA;;AACzC,OAAK,IAAI,IAAI,CAAb,EAAgB,IAAI,QAAQ,MAA5B,EAAoC,GAApC,EAAyC;AACvC,QAAI,MAAM,QAAQ,CAAR,CAAV;AACA,QAAI,OAAO,GAAP,KAAgB,WAAhB,IAA+B,QAAQ,IAA3C,EACE;;AAEF,SAAK,IAAI,GAAT,IAAgB,GAAhB,EAAqB;AACnB,UAAI,IAAI,cAAJ,CAAmB,GAAnB,CAAJ,EAA6B;AAC3B,eAAO,GAAP,IAAc,IAAI,GAAJ,CAAd;AACD;AACF;AACF;AACD,SAAO,MAAP;AACD;;AAEM,SAAS,WAAT,CAAqB,IAArB,EAA2B;AAChC,OAAK,IAAI,IAAI,CAAb,EAAgB,IAAI,KAAK,MAAzB,EAAiC,GAAjC,EAAsC;AACpC,QAAI,KAAK,CAAL,KAAW,KAAK,IAAE,CAAP,CAAf,EAA0B;AACxB,YAAM,IAAI,KAAJ,CAAU,0CAAV,CAAN;AACD;AACF;AACF;;AAEM,SAAS,eAAT,CAAyB,CAAzB,EAA4B,CAA5B,EAA+B;AACpC,MAAI,MAAM,CAAV;AACA,MAAI,MAAM,CAAV;;AAEA,MAAI,CAAC,CAAL,EAAQ,IAAI,EAAJ;AACR,MAAI,CAAC,CAAL,EAAQ,IAAI,EAAJ;;AAER,MAAI,SAAS,EAAb;AACA,MAAI,SAAS,EAAb;;AAEA,cAAY,CAAZ;AACA,cAAY,CAAZ;;AAEA,SAAO,MAAM,EAAE,MAAR,IAAkB,MAAM,EAAE,MAAjC,EAAyC;AACvC,QAAI,EAAE,GAAF,MAAW,EAAE,GAAF,CAAf,EAAuB;AACrB;AACA;AACD,KAHD,MAGO,IAAI,EAAE,GAAF,IAAS,EAAE,GAAF,CAAb,EAAqB;AAC1B,aAAO,IAAP,CAAY,EAAE,KAAF,CAAZ;AACD,KAFM,MAEA;AACL,aAAO,IAAP,CAAY,EAAE,KAAF,CAAZ;AACD;AACF;;AAED,MAAI,MAAM,EAAE,MAAZ,EACE,SAAS,OAAO,MAAP,CAAc,EAAE,KAAF,CAAQ,GAAR,CAAd,CAAT;AACF,MAAI,MAAM,EAAE,MAAZ,EACE,SAAS,OAAO,MAAP,CAAc,EAAE,KAAF,CAAQ,GAAR,CAAd,CAAT;AACF,SAAO;AACL,aAAS,MADJ;AAEL,WAAO;AAFF,GAAP;AAID;;AAED;AACA;AACO,SAAS,aAAT,CAAuB,EAAvB,EAA2B;AAChC,MAAI,QAAQ,EAAZ;AACA,MAAI,eAAJ;AACA,OAAK,IAAI,IAAT,IAAiB,EAAjB,EAAqB;AACnB,QAAI,GAAG,cAAH,CAAkB,IAAlB,CAAJ,EACE,MAAM,IAAN,CAAW,IAAX;AACF,QAAI,QAAO,GAAG,IAAH,CAAP,MAAqB,QAArB,IAAiC,OAAO,GAAG,IAAH,EAAS,MAAhB,KAA4B,WAAjE,EAA8E;AAC5E,YAAM,IAAI,KAAJ,CAAU,2BAAV,CAAN;AACD,KAFD,MAEO,IAAI,OAAO,MAAP,KAAmB,WAAnB,IAAkC,WAAW,GAAG,IAAH,EAAS,MAA1D,EAAkE;AACvE,YAAM,IAAI,KAAJ,CAAU,8CAAV,CAAN;AACD;AACD,aAAS,GAAG,IAAH,EAAS,MAAlB;AACD;AACD,MAAI,UAAU,EAAd;AACA,MAAI,aAAJ;AACA,OAAK,IAAI,MAAM,CAAf,EAAkB,MAAM,MAAxB,EAAgC,KAAhC,EAAuC;AACrC,WAAO,EAAP;AACA,SAAK,IAAI,MAAM,CAAf,EAAkB,MAAM,MAAM,MAA9B,EAAsC,KAAtC,EAA6C;AAC3C,WAAK,MAAM,GAAN,CAAL,IAAmB,GAAG,MAAM,GAAN,CAAH,EAAe,GAAf,CAAnB;AACD;AACD,YAAQ,IAAR,CAAa,IAAb;AACD;AACD,SAAO,OAAP;AACD;;AAED;;;;;;;IAMa,mB,WAAA,mB;AACX,+BAAY,OAAZ,EAAqB;AAAA;;AACnB,SAAK,QAAL,GAAgB,OAAhB;AACA,SAAK,KAAL,GAAa,EAAb;AACD;;;;uBAEE,S,EAAW,Q,EAAU;AACtB,UAAI,MAAM,KAAK,QAAL,CAAc,EAAd,CAAiB,SAAjB,EAA4B,QAA5B,CAAV;AACA,WAAK,KAAL,CAAW,GAAX,IAAkB,SAAlB;AACA,aAAO,GAAP;AACD;;;wBAEG,S,EAAW,Q,EAAU;AACvB,UAAI,MAAM,KAAK,QAAL,CAAc,GAAd,CAAkB,SAAlB,EAA6B,QAA7B,CAAV;AACA,UAAI,GAAJ,EAAS;AACP,eAAO,KAAK,KAAL,CAAW,GAAX,CAAP;AACD;AACD,aAAO,GAAP;AACD;;;yCAEoB;AAAA;;AACnB,UAAI,eAAe,KAAK,KAAxB;AACA,WAAK,KAAL,GAAa,EAAb;AACA,aAAO,IAAP,CAAY,YAAZ,EAA0B,OAA1B,CAAkC,UAAC,GAAD,EAAS;AACzC,cAAK,QAAL,CAAc,GAAd,CAAkB,aAAa,GAAb,CAAlB,EAAqC,GAArC;AACD,OAFD;AAGD;;;;;;;;;;;;;;;;;;ACpHH;;;;;;;;IAEqB,G;AACnB,eAAY,KAAZ,EAAmB,IAAnB,EAAyB,YAAa,KAAtC,EAA6C;AAAA;;AAC3C,SAAK,MAAL,GAAc,KAAd;AACA,SAAK,KAAL,GAAa,IAAb;AACA,SAAK,MAAL,GAAc,KAAd;AACA,SAAK,OAAL,GAAe,sBAAf;AACD;;;;0BAEK;AACJ,aAAO,KAAK,MAAZ;AACD;;;wBAEG,K,EAAO,YAAa,K,EAAO;AAC7B,UAAI,KAAK,MAAL,KAAgB,KAApB,EAA2B;AACzB;AACA;AACD;AACD,UAAI,WAAW,KAAK,MAApB;AACA,WAAK,MAAL,GAAc,KAAd;AACA;AACA,UAAI,MAAM,EAAV;AACA,UAAI,SAAS,QAAO,KAAP,yCAAO,KAAP,OAAkB,QAA/B,EAAyC;AACvC,aAAK,IAAI,CAAT,IAAc,KAAd,EAAqB;AACnB,cAAI,MAAM,cAAN,CAAqB,CAArB,CAAJ,EACE,IAAI,CAAJ,IAAS,MAAM,CAAN,CAAT;AACH;AACF;AACD,UAAI,QAAJ,GAAe,QAAf;AACA,UAAI,KAAJ,GAAY,KAAZ;AACA,WAAK,OAAL,CAAa,OAAb,CAAqB,QAArB,EAA+B,GAA/B,EAAoC,IAApC;;AAEA;AACA;AACA,UAAI,OAAO,KAAP,IAAgB,OAAO,KAAP,CAAa,aAAjC,EAAgD;AAC9C,eAAO,KAAP,CAAa,aAAb,CACE,mBACG,KAAK,MAAL,CAAY,IAAZ,KAAqB,IAArB,GAA4B,KAAK,MAAL,CAAY,IAAZ,GAAmB,GAA/C,GAAqD,EADxD,IAEE,KAAK,KAHT,EAIE,OAAO,KAAP,KAAkB,WAAlB,GAAgC,IAAhC,GAAuC,KAJzC;AAMD;AACF;;;uBAEE,S,EAAW,Q,EAAU;AACtB,aAAO,KAAK,OAAL,CAAa,EAAb,CAAgB,SAAhB,EAA2B,QAA3B,CAAP;AACD;;;wBAEG,S,EAAW,Q,EAAU;AACvB,aAAO,KAAK,OAAL,CAAa,GAAb,CAAiB,SAAjB,EAA4B,QAA5B,CAAP;AACD;;;;;;kBAjDkB,G",
+  "file": "generated.js",
+  "sourceRoot": "",
+  "sourcesContent": [
+    "(function(){function e(t,n,r){function s(o,u){if(!n[o]){if(!t[o]){var a=typeof require==\"function\"&&require;if(!u&&a)return a(o,!0);if(i)return i(o,!0);var f=new Error(\"Cannot find module '\"+o+\"'\");throw f.code=\"MODULE_NOT_FOUND\",f}var l=n[o]={exports:{}};t[o][0].call(l.exports,function(e){var n=t[o][1][e];return s(n?n:e)},l,l.exports,e,t,n,r)}return n[o].exports}var i=typeof require==\"function\"&&require;for(var o=0;o<r.length;o++)s(r[o]);return s}return e})()",
+    "export default class Events {\n  constructor() {\n    this._types = {};\n    this._seq = 0;\n  }\n\n  on(eventType, listener) {\n    let subs = this._types[eventType];\n    if (!subs) {\n      subs = this._types[eventType] = {};\n    }\n    let sub = \"sub\" + (this._seq++);\n    subs[sub] = listener;\n    return sub;\n  }\n\n  // Returns false if no match, or string for sub name if matched\n  off(eventType, listener) {\n    let subs = this._types[eventType];\n    if (typeof(listener) === \"function\") {\n      for (let key in subs) {\n        if (subs.hasOwnProperty(key)) {\n          if (subs[key] === listener) {\n            delete subs[key];\n            return key;\n          }\n        }\n      }\n      return false;\n    } else if (typeof(listener) === \"string\") {\n      if (subs && subs[listener]) {\n        delete subs[listener];\n        return listener;\n      }\n      return false;\n    } else {\n      throw new Error(\"Unexpected type for listener\");\n    }\n  }\n\n  trigger(eventType, arg, thisObj) {\n    let subs = this._types[eventType];\n    for (let key in subs) {\n      if (subs.hasOwnProperty(key)) {\n        subs[key].call(thisObj, arg);\n      }\n    }\n  }\n}\n",
+    "import Events from \"./events\";\nimport FilterSet from \"./filterset\";\nimport grp from \"./group\";\nimport * as util from \"./util\";\n\nfunction getFilterSet(group) {\n  let fsVar = group.var(\"filterset\");\n  let result = fsVar.get();\n  if (!result) {\n    result = new FilterSet();\n    fsVar.set(result);\n  }\n  return result;\n}\n\nlet id = 1;\nfunction nextId() {\n  return id++;\n}\n\n/**\n * Use this class to contribute to, and listen for changes to, the filter set\n * for the given group of widgets. Filter input controls should create one\n * `FilterHandle` and only call {@link FilterHandle#set}. Output widgets that\n * wish to displayed filtered data should create one `FilterHandle` and use\n * the {@link FilterHandle#filteredKeys} property and listen for change\n * events.\n *\n * If two (or more) `FilterHandle` instances in the same webpage share the\n * same group name, they will contribute to a single \"filter set\". Each\n * `FilterHandle` starts out with a `null` value, which means they take\n * nothing away from the set of data that should be shown. To make a\n * `FilterHandle` actually remove data from the filter set, set its value to\n * an array of keys which should be displayed. Crosstalk will aggregate the\n * various key arrays by finding their intersection; only keys that are\n * present in all non-null filter handles are considered part of the filter\n * set.\n *\n * @param {string} [group] - The name of the Crosstalk group, or if none,\n *   null or undefined (or any other falsy value). This can be changed later\n *   via the {@link FilterHandle#setGroup} method.\n * @param {Object} [extraInfo] - An object whose properties will be copied to\n *   the event object whenever an event is emitted.\n */\nexport class FilterHandle {\n  constructor(group, extraInfo) {\n    this._eventRelay = new Events();\n    this._emitter = new util.SubscriptionTracker(this._eventRelay);\n\n    // Name of the group we're currently tracking, if any. Can change over time.\n    this._group = null;\n    // The filterSet that we're tracking, if any. Can change over time.\n    this._filterSet = null;\n    // The Var we're currently tracking, if any. Can change over time.\n    this._filterVar = null;\n    // The event handler subscription we currently have on var.on(\"change\").\n    this._varOnChangeSub = null;\n\n    this._extraInfo = util.extend({ sender: this }, extraInfo);\n\n    this._id = \"filter\" + nextId();\n\n    this.setGroup(group);\n  }\n\n  /**\n   * Changes the Crosstalk group membership of this FilterHandle. If `set()` was\n   * previously called on this handle, switching groups will clear those keys\n   * from the old group's filter set. These keys will not be applied to the new\n   * group's filter set either. In other words, `setGroup()` effectively calls\n   * `clear()` before switching groups.\n   *\n   * @param {string} group - The name of the Crosstalk group, or null (or\n   *   undefined) to clear the group.\n   */\n  setGroup(group) {\n    // If group is unchanged, do nothing\n    if (this._group === group)\n      return;\n    // Treat null, undefined, and other falsy values the same\n    if (!this._group && !group)\n      return;\n\n    if (this._filterVar) {\n      this._filterVar.off(\"change\", this._varOnChangeSub);\n      this.clear();\n      this._varOnChangeSub = null;\n      this._filterVar = null;\n      this._filterSet = null;\n    }\n\n    this._group = group;\n\n    if (group) {\n      group = grp(group);\n      this._filterSet = getFilterSet(group);\n      this._filterVar = grp(group).var(\"filter\");\n      let sub = this._filterVar.on(\"change\", (e) => {\n        this._eventRelay.trigger(\"change\", e, this);\n      });\n      this._varOnChangeSub = sub;\n    }\n  }\n\n  /**\n   * Combine the given `extraInfo` (if any) with the handle's default\n   * `_extraInfo` (if any).\n   * @private\n   */\n  _mergeExtraInfo(extraInfo) {\n    return util.extend({},\n      this._extraInfo ? this._extraInfo : null,\n      extraInfo ? extraInfo : null);\n  }\n\n  /**\n   * Close the handle. This clears this handle's contribution to the filter set,\n   * and unsubscribes all event listeners.\n   */\n  close() {\n    this._emitter.removeAllListeners();\n    this.clear();\n    this.setGroup(null);\n  }\n\n  /**\n   * Clear this handle's contribution to the filter set.\n   *\n   * @param {Object} [extraInfo] - Extra properties to be included on the event\n   *   object that's passed to listeners (in addition to any options that were\n   *   passed into the `FilterHandle` constructor).\n   * \n   * @fires FilterHandle#change\n   */\n  clear(extraInfo) {\n    if (!this._filterSet)\n      return;\n    this._filterSet.clear(this._id);\n    this._onChange(extraInfo);\n  }\n\n  /**\n   * Set this handle's contribution to the filter set. This array should consist\n   * of the keys of the rows that _should_ be displayed; any keys that are not\n   * present in the array will be considered _filtered out_. Note that multiple\n   * `FilterHandle` instances in the group may each contribute an array of keys,\n   * and only those keys that appear in _all_ of the arrays make it through the\n   * filter.\n   *\n   * @param {string[]} keys - Empty array, or array of keys. To clear the\n   *   filter, don't pass an empty array; instead, use the\n   *   {@link FilterHandle#clear} method.\n   * @param {Object} [extraInfo] - Extra properties to be included on the event\n   *   object that's passed to listeners (in addition to any options that were\n   *   passed into the `FilterHandle` constructor).\n   * \n   * @fires FilterHandle#change\n   */\n  set(keys, extraInfo) {\n    if (!this._filterSet)\n      return;\n    this._filterSet.update(this._id, keys);\n    this._onChange(extraInfo);\n  }\n\n  /**\n   * @return {string[]|null} - Either: 1) an array of keys that made it through\n   *   all of the `FilterHandle` instances, or, 2) `null`, which means no filter\n   *   is being applied (all data should be displayed).\n   */\n  get filteredKeys() {\n    return this._filterSet ? this._filterSet.value : null;\n  }\n\n  /**\n   * Subscribe to events on this `FilterHandle`.\n   *\n   * @param {string} eventType - Indicates the type of events to listen to.\n   *   Currently, only `\"change\"` is supported.\n   * @param {FilterHandle~listener} listener - The callback function that\n   *   will be invoked when the event occurs.\n   * @return {string} - A token to pass to {@link FilterHandle#off} to cancel\n   *   this subscription.\n   */\n  on(eventType, listener) {\n    return this._emitter.on(eventType, listener);\n  }\n\n  /**\n   * Cancel event subscriptions created by {@link FilterHandle#on}.\n   *\n   * @param {string} eventType - The type of event to unsubscribe.\n   * @param {string|FilterHandle~listener} listener - Either the callback\n   *   function previously passed into {@link FilterHandle#on}, or the\n   *   string that was returned from {@link FilterHandle#on}.\n   */\n  off(eventType, listener) {\n    return this._emitter.off(eventType, listener);\n  }\n\n  _onChange(extraInfo) {\n    if (!this._filterSet)\n      return;\n    this._filterVar.set(this._filterSet.value, this._mergeExtraInfo(extraInfo));\n  }\n\n  /**\n   * @callback FilterHandle~listener\n   * @param {Object} event - An object containing details of the event. For\n   *   `\"change\"` events, this includes the properties `value` (the new\n   *   value of the filter set, or `null` if no filter set is active),\n   *   `oldValue` (the previous value of the filter set), and `sender` (the\n   *   `FilterHandle` instance that made the change).\n   */\n\n}\n\n/**\n * @event FilterHandle#change\n * @type {object}\n * @property {object} value - The new value of the filter set, or `null`\n *   if no filter set is active.\n * @property {object} oldValue - The previous value of the filter set.\n * @property {FilterHandle} sender - The `FilterHandle` instance that\n *   changed the value.\n */\n",
+    "import { diffSortedLists } from \"./util\";\n\nfunction naturalComparator(a, b) {\n  if (a === b) {\n    return 0;\n  } else if (a < b) {\n    return -1;\n  } else if (a > b) {\n    return 1;\n  }\n}\n\n/**\n * @private\n */\nexport default class FilterSet {\n  constructor() {\n    this.reset();\n  }\n\n  reset() {\n    // Key: handle ID, Value: array of selected keys, or null\n    this._handles = {};\n    // Key: key string, Value: count of handles that include it\n    this._keys = {};\n    this._value = null;\n    this._activeHandles = 0;\n  }\n\n  get value() {\n    return this._value;\n  }\n\n  update(handleId, keys) {\n    if (keys !== null) {\n      keys = keys.slice(0); // clone before sorting\n      keys.sort(naturalComparator);\n    }\n\n    let {added, removed} = diffSortedLists(this._handles[handleId], keys);\n    this._handles[handleId] = keys;\n\n    for (let i = 0; i < added.length; i++) {\n      this._keys[added[i]] = (this._keys[added[i]] || 0) + 1;\n    }\n    for (let i = 0; i < removed.length; i++) {\n      this._keys[removed[i]]--;\n    }\n\n    this._updateValue(keys);\n  }\n\n  /**\n   * @param {string[]} keys Sorted array of strings that indicate\n   * a superset of possible keys.\n   * @private\n   */\n  _updateValue(keys = this._allKeys) {\n    let handleCount = Object.keys(this._handles).length;\n    if (handleCount === 0) {\n      this._value = null;\n    } else {\n      this._value = [];\n      for (let i = 0; i < keys.length; i++) {\n        let count = this._keys[keys[i]];\n        if (count === handleCount) {\n          this._value.push(keys[i]);\n        }\n      }\n    }\n  }\n\n  clear(handleId) {\n    if (typeof(this._handles[handleId]) === \"undefined\") {\n      return;\n    }\n\n    let keys = this._handles[handleId];\n    if (!keys) {\n      keys = [];\n    }\n\n    for (let i = 0; i < keys.length; i++) {\n      this._keys[keys[i]]--;\n    }\n    delete this._handles[handleId];\n\n    this._updateValue();\n  }\n\n  get _allKeys() {\n    let allKeys = Object.keys(this._keys);\n    allKeys.sort(naturalComparator);\n    return allKeys;\n  }\n}\n",
+    "import Var from \"./var\";\n\n// Use a global so that multiple copies of crosstalk.js can be loaded and still\n// have groups behave as singletons across all copies.\nglobal.__crosstalk_groups = global.__crosstalk_groups || {};\nlet groups = global.__crosstalk_groups;\n\nexport default function group(groupName) {\n  if (groupName && typeof(groupName) === \"string\") {\n    if (!groups.hasOwnProperty(groupName)) {\n      groups[groupName] = new Group(groupName);\n    }\n    return groups[groupName];\n  } else if (typeof(groupName) === \"object\" && groupName._vars && groupName.var) {\n    // Appears to already be a group object\n    return groupName;\n  } else if (Array.isArray(groupName) &&\n      groupName.length == 1 &&\n      typeof(groupName[0]) === \"string\") {\n    return group(groupName[0]);\n  } else {\n    throw new Error(\"Invalid groupName argument\");\n  }\n}\n\nclass Group {\n  constructor(name) {\n    this.name = name;\n    this._vars = {};\n  }\n\n  var(name) {\n    if (!name || typeof(name) !== \"string\") {\n      throw new Error(\"Invalid var name\");\n    }\n\n    if (!this._vars.hasOwnProperty(name))\n      this._vars[name] = new Var(this, name);\n    return this._vars[name];\n  }\n\n  has(name) {\n    if (!name || typeof(name) !== \"string\") {\n      throw new Error(\"Invalid var name\");\n    }\n\n    return this._vars.hasOwnProperty(name);\n  }\n}\n",
+    "import group from \"./group\";\nimport { SelectionHandle } from \"./selection\";\nimport { FilterHandle } from \"./filter\";\nimport { bind } from \"./input\";\nimport \"./input_selectize\";\nimport \"./input_checkboxgroup\";\nimport \"./input_slider\";\n\nconst defaultGroup = group(\"default\");\n\nfunction var_(name) {\n  return defaultGroup.var(name);\n}\n\nfunction has(name) {\n  return defaultGroup.has(name);\n}\n\nif (global.Shiny) {\n  global.Shiny.addCustomMessageHandler(\"update-client-value\", function(message) {\n    if (typeof(message.group) === \"string\") {\n      group(message.group).var(message.name).set(message.value);\n    } else {\n      var_(message.name).set(message.value);\n    }\n  });\n}\n\nconst crosstalk = {\n  group: group,\n  var: var_,\n  has: has,\n  SelectionHandle: SelectionHandle,\n  FilterHandle: FilterHandle,\n  bind: bind\n};\n\n/**\n * @namespace crosstalk\n */\nexport default crosstalk;\nglobal.crosstalk = crosstalk;\n",
+    "let $ = global.jQuery;\n\nlet bindings = {};\n\nexport function register(reg) {\n  bindings[reg.className] = reg;\n  if (global.document && global.document.readyState !== \"complete\") {\n    $(() => {\n      bind();\n    });\n  } else if (global.document) {\n    setTimeout(bind, 100);\n  }\n}\n\nexport function bind() {\n  Object.keys(bindings).forEach(function(className) {\n    let binding = bindings[className];\n    $(\".\" + binding.className).not(\".crosstalk-input-bound\").each(function(i, el) {\n      bindInstance(binding, el);\n    });\n  });\n}\n\n// Escape jQuery identifier\nfunction $escape(val) {\n  return val.replace(/([!\"#$%&'()*+,./:;<=>?@[\\\\\\]^`{|}~])/g, \"\\\\$1\");\n}\n\nfunction bindEl(el) {\n  let $el = $(el);\n  Object.keys(bindings).forEach(function(className) {\n    if ($el.hasClass(className) && !$el.hasClass(\"crosstalk-input-bound\")) {\n      let binding = bindings[className];\n      bindInstance(binding, el);\n    }\n  });\n}\n\nfunction bindInstance(binding, el) {\n  let jsonEl = $(el).find(\"script[type='application/json'][data-for='\" + $escape(el.id) + \"']\");\n  let data = JSON.parse(jsonEl[0].innerText);\n\n  let instance = binding.factory(el, data);\n  $(el).data(\"crosstalk-instance\", instance);\n  $(el).addClass(\"crosstalk-input-bound\");\n}\n\nif (global.Shiny) {\n  let inputBinding = new global.Shiny.InputBinding();\n  let $ = global.jQuery;\n  $.extend(inputBinding, {\n    find: function(scope) {\n      return $(scope).find(\".crosstalk-input\");\n    },\n    initialize: function(el) {\n      if (!$(el).hasClass(\"crosstalk-input-bound\")) {\n        bindEl(el);\n      }\n    },\n    getId: function(el) {\n      return el.id;\n    },\n    getValue: function(el) {\n\n    },\n    setValue: function(el, value) {\n\n    },\n    receiveMessage: function(el, data) {\n\n    },\n    subscribe: function(el, callback) {\n      $(el).data(\"crosstalk-instance\").resume();\n    },\n    unsubscribe: function(el) {\n      $(el).data(\"crosstalk-instance\").suspend();\n    }\n  });\n  global.Shiny.inputBindings.register(inputBinding, \"crosstalk.inputBinding\");\n}\n",
+    "import * as input from \"./input\";\nimport { FilterHandle } from \"./filter\";\n\nlet $ = global.jQuery;\n\ninput.register({\n  className: \"crosstalk-input-checkboxgroup\",\n\n  factory: function(el, data) {\n    /*\n     * map: {\"groupA\": [\"keyA\", \"keyB\", ...], ...}\n     * group: \"ct-groupname\"\n     */\n    let ctHandle = new FilterHandle(data.group);\n\n    let lastKnownKeys;\n    let $el = $(el);\n    $el.on(\"change\", \"input[type='checkbox']\", function() {\n      let checked = $el.find(\"input[type='checkbox']:checked\");\n      if (checked.length === 0) {\n        lastKnownKeys = null;\n        ctHandle.clear();\n      } else {\n        let keys = {};\n        checked.each(function() {\n          data.map[this.value].forEach(function(key) {\n            keys[key] = true;\n          });\n        });\n        let keyArray = Object.keys(keys);\n        keyArray.sort();\n        lastKnownKeys = keyArray;\n        ctHandle.set(keyArray);\n      }\n    });\n\n    return {\n      suspend: function() {\n        ctHandle.clear();\n      },\n      resume: function() {\n        if (lastKnownKeys)\n          ctHandle.set(lastKnownKeys);\n      }\n    };\n  }\n});\n",
+    "import * as input from \"./input\";\nimport * as util from \"./util\";\nimport { FilterHandle } from \"./filter\";\n\nlet $ = global.jQuery;\n\ninput.register({\n  className: \"crosstalk-input-select\",\n\n  factory: function(el, data) {\n    /*\n     * items: {value: [...], label: [...]}\n     * map: {\"groupA\": [\"keyA\", \"keyB\", ...], ...}\n     * group: \"ct-groupname\"\n     */\n\n    let first = [{value: \"\", label: \"(All)\"}];\n    let items = util.dataframeToD3(data.items);\n    let opts = {\n      options: first.concat(items),\n      valueField: \"value\",\n      labelField: \"label\",\n      searchField: \"label\"\n    };\n\n    let select = $(el).find(\"select\")[0];\n\n    let selectize = $(select).selectize(opts)[0].selectize;\n\n    let ctHandle = new FilterHandle(data.group);\n\n    let lastKnownKeys;\n    selectize.on(\"change\", function() {\n      if (selectize.items.length === 0) {\n        lastKnownKeys = null;\n        ctHandle.clear();\n      } else {\n        let keys = {};\n        selectize.items.forEach(function(group) {\n          data.map[group].forEach(function(key) {\n            keys[key] = true;\n          });\n        });\n        let keyArray = Object.keys(keys);\n        keyArray.sort();\n        lastKnownKeys = keyArray;\n        ctHandle.set(keyArray);\n      }\n    });\n\n    return {\n      suspend: function() {\n        ctHandle.clear();\n      },\n      resume: function() {\n        if (lastKnownKeys)\n          ctHandle.set(lastKnownKeys);\n      }\n    };\n  }\n});\n",
+    "import * as input from \"./input\";\nimport { FilterHandle } from \"./filter\";\n\nlet $ = global.jQuery;\nlet strftime = global.strftime;\n\ninput.register({\n  className: \"crosstalk-input-slider\",\n\n  factory: function(el, data) {\n    /*\n     * map: {\"groupA\": [\"keyA\", \"keyB\", ...], ...}\n     * group: \"ct-groupname\"\n     */\n    let ctHandle = new FilterHandle(data.group);\n\n    let opts = {};\n    let $el = $(el).find(\"input\");\n    let dataType = $el.data(\"data-type\");\n    let timeFormat = $el.data(\"time-format\");\n    let round = $el.data(\"round\");\n    let timeFormatter;\n\n    // Set up formatting functions\n    if (dataType === \"date\") {\n      timeFormatter = strftime.utc();\n      opts.prettify = function(num) {\n        return timeFormatter(timeFormat, new Date(num));\n      };\n\n    } else if (dataType === \"datetime\") {\n      let timezone = $el.data(\"timezone\");\n      if (timezone)\n        timeFormatter = strftime.timezone(timezone);\n      else\n        timeFormatter = strftime;\n\n      opts.prettify = function(num) {\n        return timeFormatter(timeFormat, new Date(num));\n      };\n    } else if (dataType === \"number\") {\n      if (typeof round !== \"undefined\")\n        opts.prettify = function(num) {\n          let factor = Math.pow(10, round);\n          return Math.round(num * factor) / factor;\n        };\n    }\n\n    $el.ionRangeSlider(opts);\n\n    function getValue() {\n      let result = $el.data(\"ionRangeSlider\").result;\n\n      // Function for converting numeric value from slider to appropriate type.\n      let convert;\n      let dataType = $el.data(\"data-type\");\n      if (dataType === \"date\") {\n        convert = function(val) {\n          return formatDateUTC(new Date(+val));\n        };\n      } else if (dataType === \"datetime\") {\n        convert = function(val) {\n          // Convert ms to s\n          return +val / 1000;\n        };\n      } else {\n        convert = function(val) { return +val; };\n      }\n\n      if ($el.data(\"ionRangeSlider\").options.type === \"double\") {\n        return [convert(result.from), convert(result.to)];\n      } else {\n        return convert(result.from);\n      }\n    }\n\n    let lastKnownKeys = null;\n\n    $el.on(\"change.crosstalkSliderInput\", function(event) {\n      if (!$el.data(\"updating\") && !$el.data(\"animating\")) {\n        let [from, to] = getValue();\n        let keys = [];\n        for (let i = 0; i < data.values.length; i++) {\n          let val = data.values[i];\n          if (val >= from && val <= to) {\n            keys.push(data.keys[i]);\n          }\n        }\n        keys.sort();\n        ctHandle.set(keys);\n        lastKnownKeys = keys;\n      }\n    });\n\n\n    // let $el = $(el);\n    // $el.on(\"change\", \"input[type=\"checkbox\"]\", function() {\n    //   let checked = $el.find(\"input[type=\"checkbox\"]:checked\");\n    //   if (checked.length === 0) {\n    //     ctHandle.clear();\n    //   } else {\n    //     let keys = {};\n    //     checked.each(function() {\n    //       data.map[this.value].forEach(function(key) {\n    //         keys[key] = true;\n    //       });\n    //     });\n    //     let keyArray = Object.keys(keys);\n    //     keyArray.sort();\n    //     ctHandle.set(keyArray);\n    //   }\n    // });\n\n    return {\n      suspend: function() {\n        ctHandle.clear();\n      },\n      resume: function() {\n        if (lastKnownKeys)\n          ctHandle.set(lastKnownKeys);\n      }\n    };\n  }\n});\n\n\n// Convert a number to a string with leading zeros\nfunction padZeros(n, digits) {\n  let str = n.toString();\n  while (str.length < digits)\n    str = \"0\" + str;\n  return str;\n}\n\n// Given a Date object, return a string in yyyy-mm-dd format, using the\n// UTC date. This may be a day off from the date in the local time zone.\nfunction formatDateUTC(date) {\n  if (date instanceof Date) {\n    return date.getUTCFullYear() + \"-\" +\n           padZeros(date.getUTCMonth()+1, 2) + \"-\" +\n           padZeros(date.getUTCDate(), 2);\n\n  } else {\n    return null;\n  }\n}\n",
+    "import Events from \"./events\";\nimport grp from \"./group\";\nimport * as util from \"./util\";\n\n/**\n * Use this class to read and write (and listen for changes to) the selection\n * for a Crosstalk group. This is intended to be used for linked brushing.\n *\n * If two (or more) `SelectionHandle` instances in the same webpage share the\n * same group name, they will share the same state. Setting the selection using\n * one `SelectionHandle` instance will result in the `value` property instantly\n * changing across the others, and `\"change\"` event listeners on all instances\n * (including the one that initiated the sending) will fire.\n *\n * @param {string} [group] - The name of the Crosstalk group, or if none,\n *   null or undefined (or any other falsy value). This can be changed later\n *   via the [SelectionHandle#setGroup](#setGroup) method.\n * @param {Object} [extraInfo] - An object whose properties will be copied to\n *   the event object whenever an event is emitted.\n */\nexport class SelectionHandle {\n\n  constructor(group = null, extraInfo = null) {\n    this._eventRelay = new Events();\n    this._emitter = new util.SubscriptionTracker(this._eventRelay);\n\n    // Name of the group we're currently tracking, if any. Can change over time.\n    this._group = null;\n    // The Var we're currently tracking, if any. Can change over time.\n    this._var = null;\n    // The event handler subscription we currently have on var.on(\"change\").\n    this._varOnChangeSub = null;\n\n    this._extraInfo = util.extend({ sender: this }, extraInfo);\n\n    this.setGroup(group);\n  }\n\n  /**\n   * Changes the Crosstalk group membership of this SelectionHandle. The group\n   * being switched away from (if any) will not have its selection value\n   * modified as a result of calling `setGroup`, even if this handle was the\n   * most recent handle to set the selection of the group.\n   *\n   * The group being switched to (if any) will also not have its selection value\n   * modified as a result of calling `setGroup`. If you want to set the\n   * selection value of the new group, call `set` explicitly.\n   *\n   * @param {string} group - The name of the Crosstalk group, or null (or\n   *   undefined) to clear the group.\n   */\n  setGroup(group) {\n    // If group is unchanged, do nothing\n    if (this._group === group)\n      return;\n    // Treat null, undefined, and other falsy values the same\n    if (!this._group && !group)\n      return;\n\n    if (this._var) {\n      this._var.off(\"change\", this._varOnChangeSub);\n      this._var = null;\n      this._varOnChangeSub = null;\n    }\n\n    this._group = group;\n\n    if (group) {\n      this._var = grp(group).var(\"selection\");\n      let sub = this._var.on(\"change\", (e) => {\n        this._eventRelay.trigger(\"change\", e, this);\n      });\n      this._varOnChangeSub = sub;\n    }\n  }\n\n  /**\n   * Retrieves the current selection for the group represented by this\n   * `SelectionHandle`.\n   *\n   * - If no selection is active, then this value will be falsy.\n   * - If a selection is active, but no data points are selected, then this\n   *   value will be an empty array.\n   * - If a selection is active, and data points are selected, then the keys\n   *   of the selected data points will be present in the array.\n   */\n  get value() {\n    return this._var ? this._var.get() : null;\n  }\n\n  /**\n   * Combines the given `extraInfo` (if any) with the handle's default\n   * `_extraInfo` (if any).\n   * @private\n   */\n  _mergeExtraInfo(extraInfo) {\n    // Important incidental effect: shallow clone is returned\n    return util.extend({},\n      this._extraInfo ? this._extraInfo : null,\n      extraInfo ? extraInfo : null);\n  }\n\n  /**\n   * Overwrites the current selection for the group, and raises the `\"change\"`\n   * event among all of the group's '`SelectionHandle` instances (including\n   * this one).\n   *\n   * @fires SelectionHandle#change\n   * @param {string[]} selectedKeys - Falsy, empty array, or array of keys (see\n   *   {@link SelectionHandle#value}).\n   * @param {Object} [extraInfo] - Extra properties to be included on the event\n   *   object that's passed to listeners (in addition to any options that were\n   *   passed into the `SelectionHandle` constructor).\n   */\n  set(selectedKeys, extraInfo) {\n    if (this._var)\n      this._var.set(selectedKeys, this._mergeExtraInfo(extraInfo));\n  }\n\n  /**\n   * Overwrites the current selection for the group, and raises the `\"change\"`\n   * event among all of the group's '`SelectionHandle` instances (including\n   * this one).\n   *\n   * @fires SelectionHandle#change\n   * @param {Object} [extraInfo] - Extra properties to be included on the event\n   *   object that's passed to listeners (in addition to any that were passed\n   *   into the `SelectionHandle` constructor).\n   */\n  clear(extraInfo) {\n    if (this._var)\n      this.set(void 0, this._mergeExtraInfo(extraInfo));\n  }\n\n  /**\n   * Subscribes to events on this `SelectionHandle`.\n   *\n   * @param {string} eventType - Indicates the type of events to listen to.\n   *   Currently, only `\"change\"` is supported.\n   * @param {SelectionHandle~listener} listener - The callback function that\n   *   will be invoked when the event occurs.\n   * @return {string} - A token to pass to {@link SelectionHandle#off} to cancel\n   *   this subscription.\n   */\n  on(eventType, listener) {\n    return this._emitter.on(eventType, listener);\n  }\n\n  /**\n   * Cancels event subscriptions created by {@link SelectionHandle#on}.\n   *\n   * @param {string} eventType - The type of event to unsubscribe.\n   * @param {string|SelectionHandle~listener} listener - Either the callback\n   *   function previously passed into {@link SelectionHandle#on}, or the\n   *   string that was returned from {@link SelectionHandle#on}.\n   */\n  off(eventType, listener) {\n    return this._emitter.off(eventType, listener);\n  }\n\n  /**\n   * Shuts down the `SelectionHandle` object.\n   *\n   * Removes all event listeners that were added through this handle.\n   */\n  close() {\n    this._emitter.removeAllListeners();\n    this.setGroup(null);\n  }\n}\n\n/**\n * @callback SelectionHandle~listener\n * @param {Object} event - An object containing details of the event. For\n *   `\"change\"` events, this includes the properties `value` (the new\n *   value of the selection, or `undefined` if no selection is active),\n *   `oldValue` (the previous value of the selection), and `sender` (the\n *   `SelectionHandle` instance that made the change).\n */\n\n/**\n * @event SelectionHandle#change\n * @type {object}\n * @property {object} value - The new value of the selection, or `undefined`\n *   if no selection is active.\n * @property {object} oldValue - The previous value of the selection.\n * @property {SelectionHandle} sender - The `SelectionHandle` instance that\n *   changed the value.\n */\n",
+    "export function extend(target, ...sources) {\n  for (let i = 0; i < sources.length; i++) {\n    let src = sources[i];\n    if (typeof(src) === \"undefined\" || src === null)\n      continue;\n\n    for (let key in src) {\n      if (src.hasOwnProperty(key)) {\n        target[key] = src[key];\n      }\n    }\n  }\n  return target;\n}\n\nexport function checkSorted(list) {\n  for (let i = 1; i < list.length; i++) {\n    if (list[i] <= list[i-1]) {\n      throw new Error(\"List is not sorted or contains duplicate\");\n    }\n  }\n}\n\nexport function diffSortedLists(a, b) {\n  let i_a = 0;\n  let i_b = 0;\n\n  if (!a) a = [];\n  if (!b) b = [];\n\n  let a_only = [];\n  let b_only = [];\n\n  checkSorted(a);\n  checkSorted(b);\n\n  while (i_a < a.length && i_b < b.length) {\n    if (a[i_a] === b[i_b]) {\n      i_a++;\n      i_b++;\n    } else if (a[i_a] < b[i_b]) {\n      a_only.push(a[i_a++]);\n    } else {\n      b_only.push(b[i_b++]);\n    }\n  }\n\n  if (i_a < a.length)\n    a_only = a_only.concat(a.slice(i_a));\n  if (i_b < b.length)\n    b_only = b_only.concat(b.slice(i_b));\n  return {\n    removed: a_only,\n    added: b_only\n  };\n}\n\n// Convert from wide: { colA: [1,2,3], colB: [4,5,6], ... }\n// to long: [ {colA: 1, colB: 4}, {colA: 2, colB: 5}, ... ]\nexport function dataframeToD3(df) {\n  let names = [];\n  let length;\n  for (let name in df) {\n    if (df.hasOwnProperty(name))\n      names.push(name);\n    if (typeof(df[name]) !== \"object\" || typeof(df[name].length) === \"undefined\") {\n      throw new Error(\"All fields must be arrays\");\n    } else if (typeof(length) !== \"undefined\" && length !== df[name].length) {\n      throw new Error(\"All fields must be arrays of the same length\");\n    }\n    length = df[name].length;\n  }\n  let results = [];\n  let item;\n  for (let row = 0; row < length; row++) {\n    item = {};\n    for (let col = 0; col < names.length; col++) {\n      item[names[col]] = df[names[col]][row];\n    }\n    results.push(item);\n  }\n  return results;\n}\n\n/**\n * Keeps track of all event listener additions/removals and lets all active\n * listeners be removed with a single operation.\n *\n * @private\n */\nexport class SubscriptionTracker {\n  constructor(emitter) {\n    this._emitter = emitter;\n    this._subs = {};\n  }\n\n  on(eventType, listener) {\n    let sub = this._emitter.on(eventType, listener);\n    this._subs[sub] = eventType;\n    return sub;\n  }\n\n  off(eventType, listener) {\n    let sub = this._emitter.off(eventType, listener);\n    if (sub) {\n      delete this._subs[sub];\n    }\n    return sub;\n  }\n\n  removeAllListeners() {\n    let current_subs = this._subs;\n    this._subs = {};\n    Object.keys(current_subs).forEach((sub) => {\n      this._emitter.off(current_subs[sub], sub);\n    });\n  }\n}\n",
+    "import Events from \"./events\";\n\nexport default class Var {\n  constructor(group, name, /*optional*/ value) {\n    this._group = group;\n    this._name = name;\n    this._value = value;\n    this._events = new Events();\n  }\n\n  get() {\n    return this._value;\n  }\n\n  set(value, /*optional*/ event) {\n    if (this._value === value) {\n      // Do nothing; the value hasn't changed\n      return;\n    }\n    let oldValue = this._value;\n    this._value = value;\n    // Alert JavaScript listeners that the value has changed\n    let evt = {};\n    if (event && typeof(event) === \"object\") {\n      for (let k in event) {\n        if (event.hasOwnProperty(k))\n          evt[k] = event[k];\n      }\n    }\n    evt.oldValue = oldValue;\n    evt.value = value;\n    this._events.trigger(\"change\", evt, this);\n\n    // TODO: Make this extensible, to let arbitrary back-ends know that\n    // something has changed\n    if (global.Shiny && global.Shiny.onInputChange) {\n      global.Shiny.onInputChange(\n        \".clientValue-\" +\n          (this._group.name !== null ? this._group.name + \"-\" : \"\") +\n          this._name,\n        typeof(value) === \"undefined\" ? null : value\n      );\n    }\n  }\n\n  on(eventType, listener) {\n    return this._events.on(eventType, listener);\n  }\n\n  off(eventType, listener) {\n    return this._events.off(eventType, listener);\n  }\n}\n"
+  ]
+}
\ No newline at end of file
diff --git a/docs/coverage/lib/crosstalk-1.2.1/js/crosstalk.min.js b/docs/coverage/lib/crosstalk-1.2.1/js/crosstalk.min.js
new file mode 100644
index 00000000..b7ec0ac9
--- /dev/null
+++ b/docs/coverage/lib/crosstalk-1.2.1/js/crosstalk.min.js
@@ -0,0 +1,2 @@
+!function o(u,a,l){function s(n,e){if(!a[n]){if(!u[n]){var t="function"==typeof require&&require;if(!e&&t)return t(n,!0);if(f)return f(n,!0);var r=new Error("Cannot find module '"+n+"'");throw r.code="MODULE_NOT_FOUND",r}var i=a[n]={exports:{}};u[n][0].call(i.exports,function(e){var t=u[n][1][e];return s(t||e)},i,i.exports,o,u,a,l)}return a[n].exports}for(var f="function"==typeof require&&require,e=0;e<l.length;e++)s(l[e]);return s}({1:[function(e,t,n){"use strict";Object.defineProperty(n,"__esModule",{value:!0});var r=function(){function r(e,t){for(var n=0;n<t.length;n++){var r=t[n];r.enumerable=r.enumerable||!1,r.configurable=!0,"value"in r&&(r.writable=!0),Object.defineProperty(e,r.key,r)}}return function(e,t,n){return t&&r(e.prototype,t),n&&r(e,n),e}}();var i=function(){function e(){!function(e,t){if(!(e instanceof t))throw new TypeError("Cannot call a class as a function")}(this,e),this._types={},this._seq=0}return r(e,[{key:"on",value:function(e,t){var n=this._types[e];n||(n=this._types[e]={});var r="sub"+this._seq++;return n[r]=t,r}},{key:"off",value:function(e,t){var n=this._types[e];if("function"==typeof t){for(var r in n)if(n.hasOwnProperty(r)&&n[r]===t)return delete n[r],r;return!1}if("string"==typeof t)return!(!n||!n[t])&&(delete n[t],t);throw new Error("Unexpected type for listener")}},{key:"trigger",value:function(e,t,n){var r=this._types[e];for(var i in r)r.hasOwnProperty(i)&&r[i].call(n,t)}}]),e}();n.default=i},{}],2:[function(e,t,n){"use strict";Object.defineProperty(n,"__esModule",{value:!0}),n.FilterHandle=void 0;var r=function(){function r(e,t){for(var n=0;n<t.length;n++){var r=t[n];r.enumerable=r.enumerable||!1,r.configurable=!0,"value"in r&&(r.writable=!0),Object.defineProperty(e,r.key,r)}}return function(e,t,n){return t&&r(e.prototype,t),n&&r(e,n),e}}(),i=l(e("./events")),o=l(e("./filterset")),u=l(e("./group")),a=function(e){{if(e&&e.__esModule)return e;var t={};if(null!=e)for(var n in e)Object.prototype.hasOwnProperty.call(e,n)&&(t[n]=e[n]);return t.default=e,t}}(e("./util"));function l(e){return e&&e.__esModule?e:{default:e}}var s=1;n.FilterHandle=function(){function n(e,t){!function(e,t){if(!(e instanceof t))throw new TypeError("Cannot call a class as a function")}(this,n),this._eventRelay=new i.default,this._emitter=new a.SubscriptionTracker(this._eventRelay),this._group=null,this._filterSet=null,this._filterVar=null,this._varOnChangeSub=null,this._extraInfo=a.extend({sender:this},t),this._id="filter"+s++,this.setGroup(e)}return r(n,[{key:"setGroup",value:function(e){var t,n,r=this;if(this._group!==e&&((this._group||e)&&(this._filterVar&&(this._filterVar.off("change",this._varOnChangeSub),this.clear(),this._varOnChangeSub=null,this._filterVar=null,this._filterSet=null),this._group=e))){e=(0,u.default)(e),this._filterSet=(t=e.var("filterset"),(n=t.get())||(n=new o.default,t.set(n)),n),this._filterVar=(0,u.default)(e).var("filter");var i=this._filterVar.on("change",function(e){r._eventRelay.trigger("change",e,r)});this._varOnChangeSub=i}}},{key:"_mergeExtraInfo",value:function(e){return a.extend({},this._extraInfo?this._extraInfo:null,e||null)}},{key:"close",value:function(){this._emitter.removeAllListeners(),this.clear(),this.setGroup(null)}},{key:"clear",value:function(e){this._filterSet&&(this._filterSet.clear(this._id),this._onChange(e))}},{key:"set",value:function(e,t){this._filterSet&&(this._filterSet.update(this._id,e),this._onChange(t))}},{key:"on",value:function(e,t){return this._emitter.on(e,t)}},{key:"off",value:function(e,t){return this._emitter.off(e,t)}},{key:"_onChange",value:function(e){this._filterSet&&this._filterVar.set(this._filterSet.value,this._mergeExtraInfo(e))}},{key:"filteredKeys",get:function(){return this._filterSet?this._filterSet.value:null}}]),n}()},{"./events":1,"./filterset":3,"./group":4,"./util":11}],3:[function(e,t,n){"use strict";Object.defineProperty(n,"__esModule",{value:!0});var r=function(){function r(e,t){for(var n=0;n<t.length;n++){var r=t[n];r.enumerable=r.enumerable||!1,r.configurable=!0,"value"in r&&(r.writable=!0),Object.defineProperty(e,r.key,r)}}return function(e,t,n){return t&&r(e.prototype,t),n&&r(e,n),e}}(),a=e("./util");function l(e,t){return e===t?0:e<t?-1:t<e?1:void 0}var i=function(){function e(){!function(e,t){if(!(e instanceof t))throw new TypeError("Cannot call a class as a function")}(this,e),this.reset()}return r(e,[{key:"reset",value:function(){this._handles={},this._keys={},this._value=null,this._activeHandles=0}},{key:"update",value:function(e,t){null!==t&&(t=t.slice(0)).sort(l);var n=(0,a.diffSortedLists)(this._handles[e],t),r=n.added,i=n.removed;this._handles[e]=t;for(var o=0;o<r.length;o++)this._keys[r[o]]=(this._keys[r[o]]||0)+1;for(var u=0;u<i.length;u++)this._keys[i[u]]--;this._updateValue(t)}},{key:"_updateValue",value:function(){var e=0<arguments.length&&void 0!==arguments[0]?arguments[0]:this._allKeys,t=Object.keys(this._handles).length;if(0===t)this._value=null;else{this._value=[];for(var n=0;n<e.length;n++){this._keys[e[n]]===t&&this._value.push(e[n])}}}},{key:"clear",value:function(e){if(void 0!==this._handles[e]){var t=this._handles[e];t||(t=[]);for(var n=0;n<t.length;n++)this._keys[t[n]]--;delete this._handles[e],this._updateValue()}}},{key:"value",get:function(){return this._value}},{key:"_allKeys",get:function(){var e=Object.keys(this._keys);return e.sort(l),e}}]),e}();n.default=i},{"./util":11}],4:[function(l,e,s){(function(e){"use strict";Object.defineProperty(s,"__esModule",{value:!0});var n=function(){function r(e,t){for(var n=0;n<t.length;n++){var r=t[n];r.enumerable=r.enumerable||!1,r.configurable=!0,"value"in r&&(r.writable=!0),Object.defineProperty(e,r.key,r)}}return function(e,t,n){return t&&r(e.prototype,t),n&&r(e,n),e}}(),r="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(e){return typeof e}:function(e){return e&&"function"==typeof Symbol&&e.constructor===Symbol&&e!==Symbol.prototype?"symbol":typeof e};s.default=function e(t){{if(t&&"string"==typeof t)return u.hasOwnProperty(t)||(u[t]=new a(t)),u[t];if("object"===(void 0===t?"undefined":r(t))&&t._vars&&t.var)return t;if(Array.isArray(t)&&1==t.length&&"string"==typeof t[0])return e(t[0]);throw new Error("Invalid groupName argument")}};var t,i=l("./var"),o=(t=i)&&t.__esModule?t:{default:t};e.__crosstalk_groups=e.__crosstalk_groups||{};var u=e.__crosstalk_groups;var a=function(){function t(e){!function(e,t){if(!(e instanceof t))throw new TypeError("Cannot call a class as a function")}(this,t),this.name=e,this._vars={}}return n(t,[{key:"var",value:function(e){if(!e||"string"!=typeof e)throw new Error("Invalid var name");return this._vars.hasOwnProperty(e)||(this._vars[e]=new o.default(this,e)),this._vars[e]}},{key:"has",value:function(e){if(!e||"string"!=typeof e)throw new Error("Invalid var name");return this._vars.hasOwnProperty(e)}}]),t}()}).call(this,"undefined"!=typeof global?global:"undefined"!=typeof self?self:"undefined"!=typeof window?window:{})},{"./var":12}],5:[function(f,e,c){(function(e){"use strict";Object.defineProperty(c,"__esModule",{value:!0});var t,n=f("./group"),r=(t=n)&&t.__esModule?t:{default:t},i=f("./selection"),o=f("./filter"),u=f("./input");f("./input_selectize"),f("./input_checkboxgroup"),f("./input_slider");var a=(0,r.default)("default");function l(e){return a.var(e)}e.Shiny&&e.Shiny.addCustomMessageHandler("update-client-value",function(e){"string"==typeof e.group?(0,r.default)(e.group).var(e.name).set(e.value):l(e.name).set(e.value)});var s={group:r.default,var:l,has:function(e){return a.has(e)},SelectionHandle:i.SelectionHandle,FilterHandle:o.FilterHandle,bind:u.bind};c.default=s,e.crosstalk=s}).call(this,"undefined"!=typeof global?global:"undefined"!=typeof self?self:"undefined"!=typeof window?window:{})},{"./filter":2,"./group":4,"./input":6,"./input_checkboxgroup":7,"./input_selectize":8,"./input_slider":9,"./selection":10}],6:[function(e,t,a){(function(t){"use strict";Object.defineProperty(a,"__esModule",{value:!0}),a.register=function(e){r[e.className]=e,t.document&&"complete"!==t.document.readyState?o(function(){n()}):t.document&&setTimeout(n,100)},a.bind=n;var o=t.jQuery,r={};function n(){Object.keys(r).forEach(function(e){var n=r[e];o("."+n.className).not(".crosstalk-input-bound").each(function(e,t){i(n,t)})})}function i(e,t){var n=o(t).find("script[type='application/json'][data-for='"+t.id.replace(/([!"#$%&'()*+,./:;<=>?@[\\\]^`{|}~])/g,"\\$1")+"']"),r=JSON.parse(n[0].innerText),i=e.factory(t,r);o(t).data("crosstalk-instance",i),o(t).addClass("crosstalk-input-bound")}if(t.Shiny){var e=new t.Shiny.InputBinding,u=t.jQuery;u.extend(e,{find:function(e){return u(e).find(".crosstalk-input")},initialize:function(e){var t,n;u(e).hasClass("crosstalk-input-bound")||(n=o(t=e),Object.keys(r).forEach(function(e){n.hasClass(e)&&!n.hasClass("crosstalk-input-bound")&&i(r[e],t)}))},getId:function(e){return e.id},getValue:function(e){},setValue:function(e,t){},receiveMessage:function(e,t){},subscribe:function(e,t){u(e).data("crosstalk-instance").resume()},unsubscribe:function(e){u(e).data("crosstalk-instance").suspend()}}),t.Shiny.inputBindings.register(e,"crosstalk.inputBinding")}}).call(this,"undefined"!=typeof global?global:"undefined"!=typeof self?self:"undefined"!=typeof window?window:{})},{}],7:[function(r,e,t){(function(e){"use strict";var t=function(e){{if(e&&e.__esModule)return e;var t={};if(null!=e)for(var n in e)Object.prototype.hasOwnProperty.call(e,n)&&(t[n]=e[n]);return t.default=e,t}}(r("./input")),n=r("./filter");var a=e.jQuery;t.register({className:"crosstalk-input-checkboxgroup",factory:function(e,r){var i=new n.FilterHandle(r.group),o=void 0,u=a(e);return u.on("change","input[type='checkbox']",function(){var e=u.find("input[type='checkbox']:checked");if(0===e.length)o=null,i.clear();else{var t={};e.each(function(){r.map[this.value].forEach(function(e){t[e]=!0})});var n=Object.keys(t);n.sort(),o=n,i.set(n)}}),{suspend:function(){i.clear()},resume:function(){o&&i.set(o)}}}})}).call(this,"undefined"!=typeof global?global:"undefined"!=typeof self?self:"undefined"!=typeof window?window:{})},{"./filter":2,"./input":6}],8:[function(r,e,t){(function(e){"use strict";var t=n(r("./input")),l=n(r("./util")),s=r("./filter");function n(e){if(e&&e.__esModule)return e;var t={};if(null!=e)for(var n in e)Object.prototype.hasOwnProperty.call(e,n)&&(t[n]=e[n]);return t.default=e,t}var f=e.jQuery;t.register({className:"crosstalk-input-select",factory:function(e,n){var t=l.dataframeToD3(n.items),r={options:[{value:"",label:"(All)"}].concat(t),valueField:"value",labelField:"label",searchField:"label"},i=f(e).find("select")[0],o=f(i).selectize(r)[0].selectize,u=new s.FilterHandle(n.group),a=void 0;return o.on("change",function(){if(0===o.items.length)a=null,u.clear();else{var t={};o.items.forEach(function(e){n.map[e].forEach(function(e){t[e]=!0})});var e=Object.keys(t);e.sort(),a=e,u.set(e)}}),{suspend:function(){u.clear()},resume:function(){a&&u.set(a)}}}})}).call(this,"undefined"!=typeof global?global:"undefined"!=typeof self?self:"undefined"!=typeof window?window:{})},{"./filter":2,"./input":6,"./util":11}],9:[function(n,e,t){(function(e){"use strict";var d=function(e,t){if(Array.isArray(e))return e;if(Symbol.iterator in Object(e))return function(e,t){var n=[],r=!0,i=!1,o=void 0;try{for(var u,a=e[Symbol.iterator]();!(r=(u=a.next()).done)&&(n.push(u.value),!t||n.length!==t);r=!0);}catch(e){i=!0,o=e}finally{try{!r&&a.return&&a.return()}finally{if(i)throw o}}return n}(e,t);throw new TypeError("Invalid attempt to destructure non-iterable instance")},t=function(e){{if(e&&e.__esModule)return e;var t={};if(null!=e)for(var n in e)Object.prototype.hasOwnProperty.call(e,n)&&(t[n]=e[n]);return t.default=e,t}}(n("./input")),a=n("./filter");var v=e.jQuery,p=e.strftime;function y(e,t){for(var n=e.toString();n.length<t;)n="0"+n;return n}t.register({className:"crosstalk-input-slider",factory:function(e,l){var s=new a.FilterHandle(l.group),t={},f=v(e).find("input"),n=f.data("data-type"),r=f.data("time-format"),i=f.data("round"),o=void 0;if("date"===n)o=p.utc(),t.prettify=function(e){return o(r,new Date(e))};else if("datetime"===n){var u=f.data("timezone");o=u?p.timezone(u):p,t.prettify=function(e){return o(r,new Date(e))}}else"number"===n&&void 0!==i&&(t.prettify=function(e){var t=Math.pow(10,i);return Math.round(e*t)/t});function c(){var e=f.data("ionRangeSlider").result,t=void 0,n=f.data("data-type");return t="date"===n?function(e){return(t=new Date(+e))instanceof Date?t.getUTCFullYear()+"-"+y(t.getUTCMonth()+1,2)+"-"+y(t.getUTCDate(),2):null;var t}:"datetime"===n?function(e){return+e/1e3}:function(e){return+e},"double"===f.data("ionRangeSlider").options.type?[t(e.from),t(e.to)]:t(e.from)}f.ionRangeSlider(t);var h=null;return f.on("change.crosstalkSliderInput",function(e){if(!f.data("updating")&&!f.data("animating")){for(var t=c(),n=d(t,2),r=n[0],i=n[1],o=[],u=0;u<l.values.length;u++){var a=l.values[u];r<=a&&a<=i&&o.push(l.keys[u])}o.sort(),s.set(o),h=o}}),{suspend:function(){s.clear()},resume:function(){h&&s.set(h)}}}})}).call(this,"undefined"!=typeof global?global:"undefined"!=typeof self?self:"undefined"!=typeof window?window:{})},{"./filter":2,"./input":6}],10:[function(e,t,n){"use strict";Object.defineProperty(n,"__esModule",{value:!0}),n.SelectionHandle=void 0;var r=function(){function r(e,t){for(var n=0;n<t.length;n++){var r=t[n];r.enumerable=r.enumerable||!1,r.configurable=!0,"value"in r&&(r.writable=!0),Object.defineProperty(e,r.key,r)}}return function(e,t,n){return t&&r(e.prototype,t),n&&r(e,n),e}}(),i=a(e("./events")),o=a(e("./group")),u=function(e){{if(e&&e.__esModule)return e;var t={};if(null!=e)for(var n in e)Object.prototype.hasOwnProperty.call(e,n)&&(t[n]=e[n]);return t.default=e,t}}(e("./util"));function a(e){return e&&e.__esModule?e:{default:e}}n.SelectionHandle=function(){function n(){var e=0<arguments.length&&void 0!==arguments[0]?arguments[0]:null,t=1<arguments.length&&void 0!==arguments[1]?arguments[1]:null;!function(e,t){if(!(e instanceof t))throw new TypeError("Cannot call a class as a function")}(this,n),this._eventRelay=new i.default,this._emitter=new u.SubscriptionTracker(this._eventRelay),this._group=null,this._var=null,this._varOnChangeSub=null,this._extraInfo=u.extend({sender:this},t),this.setGroup(e)}return r(n,[{key:"setGroup",value:function(e){var t=this;if(this._group!==e&&(this._group||e)&&(this._var&&(this._var.off("change",this._varOnChangeSub),this._var=null,this._varOnChangeSub=null),this._group=e)){this._var=(0,o.default)(e).var("selection");var n=this._var.on("change",function(e){t._eventRelay.trigger("change",e,t)});this._varOnChangeSub=n}}},{key:"_mergeExtraInfo",value:function(e){return u.extend({},this._extraInfo?this._extraInfo:null,e||null)}},{key:"set",value:function(e,t){this._var&&this._var.set(e,this._mergeExtraInfo(t))}},{key:"clear",value:function(e){this._var&&this.set(void 0,this._mergeExtraInfo(e))}},{key:"on",value:function(e,t){return this._emitter.on(e,t)}},{key:"off",value:function(e,t){return this._emitter.off(e,t)}},{key:"close",value:function(){this._emitter.removeAllListeners(),this.setGroup(null)}},{key:"value",get:function(){return this._var?this._var.get():null}}]),n}()},{"./events":1,"./group":4,"./util":11}],11:[function(e,t,n){"use strict";Object.defineProperty(n,"__esModule",{value:!0});var r=function(){function r(e,t){for(var n=0;n<t.length;n++){var r=t[n];r.enumerable=r.enumerable||!1,r.configurable=!0,"value"in r&&(r.writable=!0),Object.defineProperty(e,r.key,r)}}return function(e,t,n){return t&&r(e.prototype,t),n&&r(e,n),e}}(),l="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(e){return typeof e}:function(e){return e&&"function"==typeof Symbol&&e.constructor===Symbol&&e!==Symbol.prototype?"symbol":typeof e};function u(e){for(var t=1;t<e.length;t++)if(e[t]<=e[t-1])throw new Error("List is not sorted or contains duplicate")}n.extend=function(e){for(var t=arguments.length,n=Array(1<t?t-1:0),r=1;r<t;r++)n[r-1]=arguments[r];for(var i=0;i<n.length;i++){var o=n[i];if(null!=o)for(var u in o)o.hasOwnProperty(u)&&(e[u]=o[u])}return e},n.checkSorted=u,n.diffSortedLists=function(e,t){var n=0,r=0;e||(e=[]);t||(t=[]);var i=[],o=[];u(e),u(t);for(;n<e.length&&r<t.length;)e[n]===t[r]?(n++,r++):e[n]<t[r]?i.push(e[n++]):o.push(t[r++]);n<e.length&&(i=i.concat(e.slice(n)));r<t.length&&(o=o.concat(t.slice(r)));return{removed:i,added:o}},n.dataframeToD3=function(e){var t=[],n=void 0;for(var r in e){if(e.hasOwnProperty(r)&&t.push(r),"object"!==l(e[r])||void 0===e[r].length)throw new Error("All fields must be arrays");if(void 0!==n&&n!==e[r].length)throw new Error("All fields must be arrays of the same length");n=e[r].length}for(var i=[],o=void 0,u=0;u<n;u++){o={};for(var a=0;a<t.length;a++)o[t[a]]=e[t[a]][u];i.push(o)}return i};n.SubscriptionTracker=function(){function t(e){!function(e,t){if(!(e instanceof t))throw new TypeError("Cannot call a class as a function")}(this,t),this._emitter=e,this._subs={}}return r(t,[{key:"on",value:function(e,t){var n=this._emitter.on(e,t);return this._subs[n]=e,n}},{key:"off",value:function(e,t){var n=this._emitter.off(e,t);return n&&delete this._subs[n],n}},{key:"removeAllListeners",value:function(){var t=this,n=this._subs;this._subs={},Object.keys(n).forEach(function(e){t._emitter.off(n[e],e)})}}]),t}()},{}],12:[function(a,e,l){(function(o){"use strict";Object.defineProperty(l,"__esModule",{value:!0});var e,u="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(e){return typeof e}:function(e){return e&&"function"==typeof Symbol&&e.constructor===Symbol&&e!==Symbol.prototype?"symbol":typeof e},t=function(){function r(e,t){for(var n=0;n<t.length;n++){var r=t[n];r.enumerable=r.enumerable||!1,r.configurable=!0,"value"in r&&(r.writable=!0),Object.defineProperty(e,r.key,r)}}return function(e,t,n){return t&&r(e.prototype,t),n&&r(e,n),e}}(),n=a("./events"),i=(e=n)&&e.__esModule?e:{default:e};var r=function(){function r(e,t,n){!function(e,t){if(!(e instanceof t))throw new TypeError("Cannot call a class as a function")}(this,r),this._group=e,this._name=t,this._value=n,this._events=new i.default}return t(r,[{key:"get",value:function(){return this._value}},{key:"set",value:function(e,t){if(this._value!==e){var n=this._value;this._value=e;var r={};if(t&&"object"===(void 0===t?"undefined":u(t)))for(var i in t)t.hasOwnProperty(i)&&(r[i]=t[i]);r.oldValue=n,r.value=e,this._events.trigger("change",r,this),o.Shiny&&o.Shiny.onInputChange&&o.Shiny.onInputChange(".clientValue-"+(null!==this._group.name?this._group.name+"-":"")+this._name,void 0===e?null:e)}}},{key:"on",value:function(e,t){return this._events.on(e,t)}},{key:"off",value:function(e,t){return this._events.off(e,t)}}]),r}();l.default=r}).call(this,"undefined"!=typeof global?global:"undefined"!=typeof self?self:"undefined"!=typeof window?window:{})},{"./events":1}]},{},[5]);
+//# sourceMappingURL=crosstalk.min.js.map
\ No newline at end of file
diff --git a/docs/coverage/lib/crosstalk-1.2.1/js/crosstalk.min.js.map b/docs/coverage/lib/crosstalk-1.2.1/js/crosstalk.min.js.map
new file mode 100644
index 00000000..886ebeef
--- /dev/null
+++ b/docs/coverage/lib/crosstalk-1.2.1/js/crosstalk.min.js.map
@@ -0,0 +1 @@
+{"version":3,"sources":["node_modules/browser-pack/_prelude.js","javascript/src/events.js","javascript/src/filter.js","javascript/src/filterset.js","javascript/src/group.js","javascript/src/index.js","javascript/src/input.js","javascript/src/input_checkboxgroup.js","javascript/src/input_selectize.js","javascript/src/input_slider.js","javascript/src/selection.js","javascript/src/util.js","javascript/src/var.js"],"names":["e","t","n","r","s","o","u","a","require","i","f","Error","code","l","exports","call","length","1","module","Events","_classCallCheck","this","_types","_seq","eventType","listener","subs","sub","key","hasOwnProperty","arg","thisObj","util","id","FilterHandle","group","extraInfo","_eventRelay","_events2","default","_emitter","SubscriptionTracker","_group","_filterSet","_filterVar","_varOnChangeSub","_extraInfo","extend","sender","_id","setGroup","fsVar","result","_this","off","clear","_group2","var","get","_filterset2","set","on","trigger","removeAllListeners","_onChange","keys","update","value","_mergeExtraInfo","_util","naturalComparator","b","FilterSet","reset","_handles","_keys","_value","_activeHandles","handleId","slice","sort","_diffSortedLists","diffSortedLists","added","removed","_i","_updateValue","arguments","undefined","_allKeys","handleCount","Object","push","allKeys","groupName","groups","Group","_typeof","_vars","Array","isArray","_var2","global","__crosstalk_groups","name","_var3","_selection","_filter","_input","defaultGroup","var_","Shiny","addCustomMessageHandler","message","crosstalk","has","SelectionHandle","bind","register","reg","bindings","className","document","readyState","$","setTimeout","jQuery","forEach","binding","not","each","el","bindInstance","jsonEl","find","replace","data","JSON","parse","innerText","instance","factory","addClass","inputBinding","InputBinding","_$","scope","initialize","$el","hasClass","getId","getValue","setValue","receiveMessage","subscribe","callback","resume","unsubscribe","suspend","inputBindings","input","ctHandle","lastKnownKeys","checked","map","keyArray","items","dataframeToD3","opts","options","label","concat","valueField","labelField","searchField","select","selectize","strftime","padZeros","digits","str","toString","dataType","timeFormat","round","timeFormatter","utc","prettify","num","Date","timezone","factor","Math","pow","convert","val","date","getUTCFullYear","getUTCMonth","getUTCDate","type","from","to","ionRangeSlider","event","_getValue","_getValue2","_slicedToArray","values","_var","selectedKeys","checkSorted","list","target","_len","sources","_key","src","i_a","i_b","a_only","b_only","df","names","results","item","row","col","emitter","_subs","current_subs","_events","Var","_name","oldValue","evt","k","onInputChange"],"mappings":"CAAA,SAAAA,EAAAC,EAAAC,EAAAC,GAAA,SAAAC,EAAAC,EAAAC,GAAA,IAAAJ,EAAAG,GAAA,CAAA,IAAAJ,EAAAI,GAAA,CAAA,IAAAE,EAAA,mBAAAC,SAAAA,QAAA,IAAAF,GAAAC,EAAA,OAAAA,EAAAF,GAAA,GAAA,GAAAI,EAAA,OAAAA,EAAAJ,GAAA,GAAA,IAAAK,EAAA,IAAAC,MAAA,uBAAAN,EAAA,KAAA,MAAAK,EAAAE,KAAA,mBAAAF,EAAA,IAAAG,EAAAX,EAAAG,IAAAS,YAAAb,EAAAI,GAAA,GAAAU,KAAAF,EAAAC,QAAA,SAAAd,GAAA,IAAAE,EAAAD,EAAAI,GAAA,GAAAL,GAAA,OAAAI,EAAAF,GAAAF,IAAAa,EAAAA,EAAAC,QAAAd,EAAAC,EAAAC,EAAAC,GAAA,OAAAD,EAAAG,GAAAS,QAAA,IAAA,IAAAL,EAAA,mBAAAD,SAAAA,QAAAH,EAAA,EAAAA,EAAAF,EAAAa,OAAAX,IAAAD,EAAAD,EAAAE,IAAA,OAAAD,EAAA,EAAAa,GAAA,SAAAT,EAAAU,EAAAJ,8TCAqBK,aACnB,SAAAA,iGAAcC,CAAAC,KAAAF,GACZE,KAAKC,UACLD,KAAKE,KAAO,uCAGXC,EAAWC,GACZ,IAAIC,EAAOL,KAAKC,OAAOE,GAClBE,IACHA,EAAOL,KAAKC,OAAOE,OAErB,IAAIG,EAAM,MAASN,KAAKE,OAExB,OADAG,EAAKC,GAAOF,EACLE,8BAILH,EAAWC,GACb,IAAIC,EAAOL,KAAKC,OAAOE,GACvB,GAAyB,mBAAdC,EAA0B,CACnC,IAAK,IAAIG,KAAOF,EACd,GAAIA,EAAKG,eAAeD,IAClBF,EAAKE,KAASH,EAEhB,cADOC,EAAKE,GACLA,EAIb,OAAO,EACF,GAAyB,iBAAdH,EAChB,SAAIC,IAAQA,EAAKD,aACRC,EAAKD,GACLA,GAIT,MAAM,IAAId,MAAM,gEAIZa,EAAWM,EAAKC,GACtB,IAAIL,EAAOL,KAAKC,OAAOE,GACvB,IAAK,IAAII,KAAOF,EACVA,EAAKG,eAAeD,IACtBF,EAAKE,GAAKb,KAAKgB,EAASD,sBA5CXX,2WCArBX,EAAA,iBACAA,EAAA,oBACAA,EAAA,YACYwB,4JAAZxB,EAAA,8DAYA,IAAIyB,EAAK,IA6BIC,wBACX,SAAAA,EAAYC,EAAOC,gGAAWhB,CAAAC,KAAAa,GAC5Bb,KAAKgB,YAAc,IAAAC,EAAAC,QACnBlB,KAAKmB,SAAW,IAAIR,EAAKS,oBAAoBpB,KAAKgB,aAGlDhB,KAAKqB,OAAS,KAEdrB,KAAKsB,WAAa,KAElBtB,KAAKuB,WAAa,KAElBvB,KAAKwB,gBAAkB,KAEvBxB,KAAKyB,WAAad,EAAKe,QAASC,OAAQ3B,MAAQe,GAEhDf,KAAK4B,IAAM,SA3CNhB,IA6CLZ,KAAK6B,SAASf,8CAaPA,GAAO,IArEZgB,EACAC,EAoEYC,EAAAhC,KAEd,GAAIA,KAAKqB,SAAWP,KAGfd,KAAKqB,QAAWP,KAGjBd,KAAKuB,aACPvB,KAAKuB,WAAWU,IAAI,SAAUjC,KAAKwB,iBACnCxB,KAAKkC,QACLlC,KAAKwB,gBAAkB,KACvBxB,KAAKuB,WAAa,KAClBvB,KAAKsB,WAAa,MAGpBtB,KAAKqB,OAASP,IAEH,CACTA,GAAQ,EAAAqB,EAAAjB,SAAIJ,GACZd,KAAKsB,YAzFLQ,EAyF+BhB,EAzFjBsB,IAAI,cAClBL,EAASD,EAAMO,SAEjBN,EAAS,IAAAO,EAAApB,QACTY,EAAMS,IAAIR,IAELA,GAoFH/B,KAAKuB,YAAa,EAAAY,EAAAjB,SAAIJ,GAAOsB,IAAI,UACjC,IAAI9B,EAAMN,KAAKuB,WAAWiB,GAAG,SAAU,SAAC7D,GACtCqD,EAAKhB,YAAYyB,QAAQ,SAAU9D,EAAnCqD,KAEFhC,KAAKwB,gBAAkBlB,2CASXS,GACd,OAAOJ,EAAKe,UACV1B,KAAKyB,WAAazB,KAAKyB,WAAa,KACpCV,GAAwB,sCAQ1Bf,KAAKmB,SAASuB,qBACd1C,KAAKkC,QACLlC,KAAK6B,SAAS,oCAYVd,GACCf,KAAKsB,aAEVtB,KAAKsB,WAAWY,MAAMlC,KAAK4B,KAC3B5B,KAAK2C,UAAU5B,gCAoBb6B,EAAM7B,GACHf,KAAKsB,aAEVtB,KAAKsB,WAAWuB,OAAO7C,KAAK4B,IAAKgB,GACjC5C,KAAK2C,UAAU5B,+BAsBdZ,EAAWC,GACZ,OAAOJ,KAAKmB,SAASqB,GAAGrC,EAAWC,+BAWjCD,EAAWC,GACb,OAAOJ,KAAKmB,SAASc,IAAI9B,EAAWC,qCAG5BW,GACHf,KAAKsB,YAEVtB,KAAKuB,WAAWgB,IAAIvC,KAAKsB,WAAWwB,MAAO9C,KAAK+C,gBAAgBhC,yCAhChE,OAAOf,KAAKsB,WAAatB,KAAKsB,WAAWwB,MAAQ,iZC3KrDE,EAAA7D,EAAA,UAEA,SAAS8D,EAAkB/D,EAAGgE,GAC5B,OAAIhE,IAAMgE,EACD,EACEhE,EAAIgE,GACL,EACKA,EAAJhE,EACF,OADF,MAQYiE,aACnB,SAAAA,iGAAcpD,CAAAC,KAAAmD,GACZnD,KAAKoD,kDAKLpD,KAAKqD,YAELrD,KAAKsD,SACLtD,KAAKuD,OAAS,KACdvD,KAAKwD,eAAiB,iCAOjBC,EAAUb,GACF,OAATA,IACFA,EAAOA,EAAKc,MAAM,IACbC,KAAKV,GAHS,IAAAW,GAME,EAAAZ,EAAAa,iBAAgB7D,KAAKqD,SAASI,GAAWb,GAA3DkB,EANgBF,EAMhBE,MAAOC,EANSH,EAMTG,QACZ/D,KAAKqD,SAASI,GAAYb,EAE1B,IAAK,IAAIxD,EAAI,EAAGA,EAAI0E,EAAMnE,OAAQP,IAChCY,KAAKsD,MAAMQ,EAAM1E,KAAOY,KAAKsD,MAAMQ,EAAM1E,KAAO,GAAK,EAEvD,IAAK,IAAI4E,EAAI,EAAGA,EAAID,EAAQpE,OAAQqE,IAClChE,KAAKsD,MAAMS,EAAQC,MAGrBhE,KAAKiE,aAAarB,0CAQe,IAAtBA,EAAsB,EAAAsB,UAAAvE,aAAAwE,IAAAD,UAAA,GAAAA,UAAA,GAAflE,KAAKoE,SACnBC,EAAcC,OAAO1B,KAAK5C,KAAKqD,UAAU1D,OAC7C,GAAoB,IAAhB0E,EACFrE,KAAKuD,OAAS,SACT,CACLvD,KAAKuD,UACL,IAAK,IAAInE,EAAI,EAAGA,EAAIwD,EAAKjD,OAAQP,IAAK,CACxBY,KAAKsD,MAAMV,EAAKxD,MACdiF,GACZrE,KAAKuD,OAAOgB,KAAK3B,EAAKxD,oCAMxBqE,GACJ,QAAwC,IAA7BzD,KAAKqD,SAASI,GAAzB,CAIA,IAAIb,EAAO5C,KAAKqD,SAASI,GACpBb,IACHA,MAGF,IAAK,IAAIxD,EAAI,EAAGA,EAAIwD,EAAKjD,OAAQP,IAC/BY,KAAKsD,MAAMV,EAAKxD,aAEXY,KAAKqD,SAASI,GAErBzD,KAAKiE,8CAzDL,OAAOjE,KAAKuD,wCA6DZ,IAAIiB,EAAUF,OAAO1B,KAAK5C,KAAKsD,OAE/B,OADAkB,EAAQb,KAAKV,GACNuB,qBA9EUrB,+jBCRN,SAASrC,EAAM2D,GAC5B,CAAA,GAAIA,GAAmC,iBAAfA,EAItB,OAHKC,EAAOlE,eAAeiE,KACzBC,EAAOD,GAAa,IAAIE,EAAMF,IAEzBC,EAAOD,GACT,GAA0B,iBAAtB,IAAOA,EAAP,YAAAG,EAAOH,KAA2BA,EAAUI,OAASJ,EAAUrC,IAExE,OAAOqC,EACF,GAAIK,MAAMC,QAAQN,IACD,GAApBA,EAAU9E,QACe,iBAAlB8E,EAAU,GACnB,OAAO3D,EAAM2D,EAAU,IAEvB,MAAM,IAAInF,MAAM,gCArBpB,MAAA0F,EAAA7F,EAAA,6CAIA8F,EAAOC,mBAAqBD,EAAOC,uBACnC,IAAIR,EAASO,EAAOC,uBAoBdP,aACJ,SAAAA,EAAYQ,gGAAMpF,CAAAC,KAAA2E,GAChB3E,KAAKmF,KAAOA,EACZnF,KAAK6E,+CAGHM,GACF,IAAKA,GAAyB,iBAAVA,EAClB,MAAM,IAAI7F,MAAM,oBAKlB,OAFKU,KAAK6E,MAAMrE,eAAe2E,KAC7BnF,KAAK6E,MAAMM,GAAQ,IAAAC,EAAAlE,QAAQlB,KAAMmF,IAC5BnF,KAAK6E,MAAMM,+BAGhBA,GACF,IAAKA,GAAyB,iBAAVA,EAClB,MAAM,IAAI7F,MAAM,oBAGlB,OAAOU,KAAK6E,MAAMrE,eAAe2E,2OC9CrC,MAAA9D,EAAAlC,EAAA,+CACAkG,EAAAlG,EAAA,eACAmG,EAAAnG,EAAA,YACAoG,EAAApG,EAAA,WACAA,EAAA,qBACAA,EAAA,yBACAA,EAAA,kBAEA,IAAMqG,GAAe,EAAArD,EAAAjB,SAAM,WAE3B,SAASuE,EAAKN,GACZ,OAAOK,EAAapD,IAAI+C,GAOtBF,EAAOS,OACTT,EAAOS,MAAMC,wBAAwB,sBAAuB,SAASC,GACrC,iBAAnBA,EAAQ9E,OACjB,EAAAqB,EAAAjB,SAAM0E,EAAQ9E,OAAOsB,IAAIwD,EAAQT,MAAM5C,IAAIqD,EAAQ9C,OAEnD2C,EAAKG,EAAQT,MAAM5C,IAAIqD,EAAQ9C,SAKrC,IAAM+C,GACJ/E,MAAAqB,EAAAjB,QACAkB,IAAKqD,EACLK,IAjBF,SAAaX,GACX,OAAOK,EAAaM,IAAIX,IAiBxBY,gBAAAV,EAAAU,gBACAlF,aAAAyE,EAAAzE,aACAmF,KAAAT,EAAAS,gBAMaH,EACfZ,EAAOY,UAAYA,iVCrCHI,SAAT,SAAkBC,GACvBC,EAASD,EAAIE,WAAaF,EACtBjB,EAAOoB,UAA2C,aAA/BpB,EAAOoB,SAASC,WACrCC,EAAE,WACAP,MAEOf,EAAOoB,UAChBG,WAAWR,EAAM,QAILA,KAAAA,EAfhB,IAAIO,EAAItB,EAAOwB,OAEXN,KAaG,SAASH,IACd1B,OAAO1B,KAAKuD,GAAUO,QAAQ,SAASN,GACrC,IAAIO,EAAUR,EAASC,GACvBG,EAAE,IAAMI,EAAQP,WAAWQ,IAAI,0BAA0BC,KAAK,SAASzH,EAAG0H,GACxEC,EAAaJ,EAASG,OAoB5B,SAASC,EAAaJ,EAASG,GAC7B,IAAIE,EAAST,EAAEO,GAAIG,KAAK,6CAAuDH,EAAGlG,GAdvEsG,QAAQ,wCAAyC,QAc4B,MACpFC,EAAOC,KAAKC,MAAML,EAAO,GAAGM,WAE5BC,EAAWZ,EAAQa,QAAQV,EAAIK,GACnCZ,EAAEO,GAAIK,KAAK,qBAAsBI,GACjChB,EAAEO,GAAIW,SAAS,yBAGjB,GAAIxC,EAAOS,MAAO,CAChB,IAAIgC,EAAe,IAAIzC,EAAOS,MAAMiC,aAChCC,EAAI3C,EAAOwB,OACfmB,EAAElG,OAAOgG,GACPT,KAAM,SAASY,GACb,OAAOD,EAAEC,GAAOZ,KAAK,qBAEvBa,WAAY,SAAShB,GA1BzB,IAAgBA,EACViB,EA0BKH,EAAEd,GAAIkB,SAAS,2BA1BpBD,EAAMxB,EADIO,EA4BDA,GA1BbxC,OAAO1B,KAAKuD,GAAUO,QAAQ,SAASN,GACjC2B,EAAIC,SAAS5B,KAAe2B,EAAIC,SAAS,0BAE3CjB,EADcZ,EAASC,GACDU,OA0BxBmB,MAAO,SAASnB,GACd,OAAOA,EAAGlG,IAEZsH,SAAU,SAASpB,KAGnBqB,SAAU,SAASrB,EAAIhE,KAGvBsF,eAAgB,SAAStB,EAAIK,KAG7BkB,UAAW,SAASvB,EAAIwB,GACtBV,EAAEd,GAAIK,KAAK,sBAAsBoB,UAEnCC,YAAa,SAAS1B,GACpBc,EAAEd,GAAIK,KAAK,sBAAsBsB,aAGrCxD,EAAOS,MAAMgD,cAAczC,SAASyB,EAAc,+LC/EpD,IAAYiB,4JAAZxJ,EAAA,YACAmG,EAAAnG,EAAA,YAEA,IAAIoH,EAAItB,EAAOwB,OAEfkC,EAAM1C,UACJG,UAAW,gCAEXoB,QAAS,SAASV,EAAIK,GAKpB,IAAIyB,EAAW,IAAAtD,EAAAzE,aAAiBsG,EAAKrG,OAEjC+H,OAAA,EACAd,EAAMxB,EAAEO,GAoBZ,OAnBAiB,EAAIvF,GAAG,SAAU,yBAA0B,WACzC,IAAIsG,EAAUf,EAAId,KAAK,kCACvB,GAAuB,IAAnB6B,EAAQnJ,OACVkJ,EAAgB,KAChBD,EAAS1G,YACJ,CACL,IAAIU,KACJkG,EAAQjC,KAAK,WACXM,EAAK4B,IAAI/I,KAAK8C,OAAO4D,QAAQ,SAASnG,GACpCqC,EAAKrC,IAAO,MAGhB,IAAIyI,EAAW1E,OAAO1B,KAAKA,GAC3BoG,EAASrF,OACTkF,EAAgBG,EAChBJ,EAASrG,IAAIyG,OAKfP,QAAS,WACPG,EAAS1G,SAEXqG,OAAQ,WACFM,GACFD,EAASrG,IAAIsG,oMC1CvB,IAAYF,IAAZxJ,EAAA,YACYwB,IAAZxB,EAAA,WACAmG,EAAAnG,EAAA,qKAEA,IAAIoH,EAAItB,EAAOwB,OAEfkC,EAAM1C,UACJG,UAAW,yBAEXoB,QAAS,SAASV,EAAIK,GAOpB,IACI8B,EAAQtI,EAAKuI,cAAc/B,EAAK8B,OAChCE,GACFC,UAHYtG,MAAO,GAAIuG,MAAO,UAGfC,OAAOL,GACtBM,WAAY,QACZC,WAAY,QACZC,YAAa,SAGXC,EAASnD,EAAEO,GAAIG,KAAK,UAAU,GAE9B0C,EAAYpD,EAAEmD,GAAQC,UAAUR,GAAM,GAAGQ,UAEzCf,EAAW,IAAAtD,EAAAzE,aAAiBsG,EAAKrG,OAEjC+H,OAAA,EAmBJ,OAlBAc,EAAUnH,GAAG,SAAU,WACrB,GAA+B,IAA3BmH,EAAUV,MAAMtJ,OAClBkJ,EAAgB,KAChBD,EAAS1G,YACJ,CACL,IAAIU,KACJ+G,EAAUV,MAAMvC,QAAQ,SAAS5F,GAC/BqG,EAAK4B,IAAIjI,GAAO4F,QAAQ,SAASnG,GAC/BqC,EAAKrC,IAAO,MAGhB,IAAIyI,EAAW1E,OAAO1B,KAAKA,GAC3BoG,EAASrF,OACTkF,EAAgBG,EAChBJ,EAASrG,IAAIyG,OAKfP,QAAS,WACPG,EAAS1G,SAEXqG,OAAQ,WACFM,GACFD,EAASrG,IAAIsG,kmBCxDXF,4JAAZxJ,EAAA,YACAmG,EAAAnG,EAAA,YAEA,IAAIoH,EAAItB,EAAOwB,OACXmD,EAAW3E,EAAO2E,SA2HtB,SAASC,EAAShL,EAAGiL,GAEnB,IADA,IAAIC,EAAMlL,EAAEmL,WACLD,EAAIpK,OAASmK,GAClBC,EAAM,IAAMA,EACd,OAAOA,EA7HTpB,EAAM1C,UACJG,UAAW,yBAEXoB,QAAS,SAASV,EAAIK,GAKpB,IAAIyB,EAAW,IAAAtD,EAAAzE,aAAiBsG,EAAKrG,OAEjCqI,KACApB,EAAMxB,EAAEO,GAAIG,KAAK,SACjBgD,EAAWlC,EAAIZ,KAAK,aACpB+C,EAAanC,EAAIZ,KAAK,eACtBgD,EAAQpC,EAAIZ,KAAK,SACjBiD,OAAA,EAGJ,GAAiB,SAAbH,EACFG,EAAgBR,EAASS,MACzBlB,EAAKmB,SAAW,SAASC,GACvB,OAAOH,EAAcF,EAAY,IAAIM,KAAKD,UAGvC,GAAiB,aAAbN,EAAyB,CAClC,IAAIQ,EAAW1C,EAAIZ,KAAK,YAEtBiD,EADEK,EACcb,EAASa,SAASA,GAElBb,EAElBT,EAAKmB,SAAW,SAASC,GACvB,OAAOH,EAAcF,EAAY,IAAIM,KAAKD,SAEtB,WAAbN,QACY,IAAVE,IACThB,EAAKmB,SAAW,SAASC,GACvB,IAAIG,EAASC,KAAKC,IAAI,GAAIT,GAC1B,OAAOQ,KAAKR,MAAMI,EAAMG,GAAUA,IAMxC,SAASxC,IACP,IAAInG,EAASgG,EAAIZ,KAAK,kBAAkBpF,OAGpC8I,OAAA,EACAZ,EAAWlC,EAAIZ,KAAK,aAcxB,OAZE0D,EADe,SAAbZ,EACQ,SAASa,GACjB,OA8EaC,EA9EQ,IAAIP,MAAMM,cA+EnBN,KACXO,EAAKC,iBAAmB,IACxBnB,EAASkB,EAAKE,cAAc,EAAG,GAAK,IACpCpB,EAASkB,EAAKG,aAAc,GAG5B,KAPX,IAAuBH,GA5EO,aAAbd,EACC,SAASa,GAEjB,OAAQA,EAAM,KAGN,SAASA,GAAO,OAAQA,GAGY,WAA5C/C,EAAIZ,KAAK,kBAAkBiC,QAAQ+B,MAC7BN,EAAQ9I,EAAOqJ,MAAOP,EAAQ9I,EAAOsJ,KAEtCR,EAAQ9I,EAAOqJ,MAxB1BrD,EAAIuD,eAAenC,GA4BnB,IAAIN,EAAgB,KAqCpB,OAnCAd,EAAIvF,GAAG,8BAA+B,SAAS+I,GAC7C,IAAKxD,EAAIZ,KAAK,cAAgBY,EAAIZ,KAAK,aAAc,CAGnD,IAHmD,IAAAqE,EAClCtD,IADkCuD,EAAAC,EAAAF,EAAA,GAC9CJ,EAD8CK,EAAA,GACxCJ,EADwCI,EAAA,GAE/C7I,KACKxD,EAAI,EAAGA,EAAI+H,EAAKwE,OAAOhM,OAAQP,IAAK,CAC3C,IAAI0L,EAAM3D,EAAKwE,OAAOvM,GACXgM,GAAPN,GAAeA,GAAOO,GACxBzI,EAAK2B,KAAK4C,EAAKvE,KAAKxD,IAGxBwD,EAAKe,OACLiF,EAASrG,IAAIK,GACbiG,EAAgBjG,MAwBlB6F,QAAS,WACPG,EAAS1G,SAEXqG,OAAQ,WACFM,GACFD,EAASrG,IAAIsG,+fCvHvB1J,EAAA,iBACAA,EAAA,YACYwB,4JAAZxB,EAAA,gEAkBa4G,2BAEX,SAAAA,IAA4C,IAAhCjF,EAAgC,EAAAoD,UAAAvE,aAAAwE,IAAAD,UAAA,GAAAA,UAAA,GAAxB,KAAMnD,EAAkB,EAAAmD,UAAAvE,aAAAwE,IAAAD,UAAA,GAAAA,UAAA,GAAN,kGAAMnE,CAAAC,KAAA+F,GAC1C/F,KAAKgB,YAAc,IAAAC,EAAAC,QACnBlB,KAAKmB,SAAW,IAAIR,EAAKS,oBAAoBpB,KAAKgB,aAGlDhB,KAAKqB,OAAS,KAEdrB,KAAK4L,KAAO,KAEZ5L,KAAKwB,gBAAkB,KAEvBxB,KAAKyB,WAAad,EAAKe,QAASC,OAAQ3B,MAAQe,GAEhDf,KAAK6B,SAASf,8CAgBPA,GAAO,IAAAkB,EAAAhC,KAEd,GAAIA,KAAKqB,SAAWP,IAGfd,KAAKqB,QAAWP,KAGjBd,KAAK4L,OACP5L,KAAK4L,KAAK3J,IAAI,SAAUjC,KAAKwB,iBAC7BxB,KAAK4L,KAAO,KACZ5L,KAAKwB,gBAAkB,MAGzBxB,KAAKqB,OAASP,GAEH,CACTd,KAAK4L,MAAO,EAAAzJ,EAAAjB,SAAIJ,GAAOsB,IAAI,aAC3B,IAAI9B,EAAMN,KAAK4L,KAAKpJ,GAAG,SAAU,SAAC7D,GAChCqD,EAAKhB,YAAYyB,QAAQ,SAAU9D,EAAnCqD,KAEFhC,KAAKwB,gBAAkBlB,2CAuBXS,GAEd,OAAOJ,EAAKe,UACV1B,KAAKyB,WAAazB,KAAKyB,WAAa,KACpCV,GAAwB,kCAexB8K,EAAc9K,GACZf,KAAK4L,MACP5L,KAAK4L,KAAKrJ,IAAIsJ,EAAc7L,KAAK+C,gBAAgBhC,kCAa/CA,GACAf,KAAK4L,MACP5L,KAAKuC,SAAI,EAAQvC,KAAK+C,gBAAgBhC,+BAavCZ,EAAWC,GACZ,OAAOJ,KAAKmB,SAASqB,GAAGrC,EAAWC,+BAWjCD,EAAWC,GACb,OAAOJ,KAAKmB,SAASc,IAAI9B,EAAWC,mCASpCJ,KAAKmB,SAASuB,qBACd1C,KAAK6B,SAAS,oCAhFd,OAAO7B,KAAK4L,KAAO5L,KAAK4L,KAAKvJ,MAAQ,8kBCxElC,SAASyJ,EAAYC,GAC1B,IAAK,IAAI3M,EAAI,EAAGA,EAAI2M,EAAKpM,OAAQP,IAC/B,GAAI2M,EAAK3M,IAAM2M,EAAK3M,EAAE,GACpB,MAAM,IAAIE,MAAM,8CAlBNoC,OAAT,SAAgBsK,GAAoB,IAAA,IAAAC,EAAA/H,UAAAvE,OAATuM,EAASpH,MAAA,EAAAmH,EAAAA,EAAA,EAAA,GAAAE,EAAA,EAAAA,EAAAF,EAAAE,IAATD,EAASC,EAAA,GAAAjI,UAAAiI,GACzC,IAAK,IAAI/M,EAAI,EAAGA,EAAI8M,EAAQvM,OAAQP,IAAK,CACvC,IAAIgN,EAAMF,EAAQ9M,GAClB,GAAI,MAAOgN,EAGX,IAAK,IAAI7L,KAAO6L,EACVA,EAAI5L,eAAeD,KACrByL,EAAOzL,GAAO6L,EAAI7L,IAIxB,OAAOyL,KAGOF,YAAAA,IAQAjI,gBAAT,SAAyB3E,EAAGgE,GACjC,IAAImJ,EAAM,EACNC,EAAM,EAELpN,IAAGA,MACHgE,IAAGA,MAER,IAAIqJ,KACAC,KAEJV,EAAY5M,GACZ4M,EAAY5I,GAEZ,KAAOmJ,EAAMnN,EAAES,QAAU2M,EAAMpJ,EAAEvD,QAC3BT,EAAEmN,KAASnJ,EAAEoJ,IACfD,IACAC,KACSpN,EAAEmN,GAAOnJ,EAAEoJ,GACpBC,EAAOhI,KAAKrF,EAAEmN,MAEdG,EAAOjI,KAAKrB,EAAEoJ,MAIdD,EAAMnN,EAAES,SACV4M,EAASA,EAAOjD,OAAOpK,EAAEwE,MAAM2I,KAC7BC,EAAMpJ,EAAEvD,SACV6M,EAASA,EAAOlD,OAAOpG,EAAEQ,MAAM4I,KACjC,OACEvI,QAASwI,EACTzI,MAAO0I,MAMKtD,cAAT,SAAuBuD,GAC5B,IAAIC,KACA/M,OAAA,EACJ,IAAK,IAAIwF,KAAQsH,EAAI,CAGnB,GAFIA,EAAGjM,eAAe2E,IACpBuH,EAAMnI,KAAKY,GACY,WAArBP,EAAO6H,EAAGtH,UAAmD,IAArBsH,EAAGtH,GAAMxF,OACnD,MAAM,IAAIL,MAAM,6BACX,QAAuB,IAAZK,GAA2BA,IAAW8M,EAAGtH,GAAMxF,OAC/D,MAAM,IAAIL,MAAM,gDAElBK,EAAS8M,EAAGtH,GAAMxF,OAIpB,IAFA,IAAIgN,KACAC,OAAA,EACKC,EAAM,EAAGA,EAAMlN,EAAQkN,IAAO,CACrCD,KACA,IAAK,IAAIE,EAAM,EAAGA,EAAMJ,EAAM/M,OAAQmN,IACpCF,EAAKF,EAAMI,IAAQL,EAAGC,EAAMI,IAAMD,GAEpCF,EAAQpI,KAAKqI,GAEf,OAAOD,KASIvL,+BACX,SAAAA,EAAY2L,gGAAShN,CAAAC,KAAAoB,GACnBpB,KAAKmB,SAAW4L,EAChB/M,KAAKgN,8CAGJ7M,EAAWC,GACZ,IAAIE,EAAMN,KAAKmB,SAASqB,GAAGrC,EAAWC,GAEtC,OADAJ,KAAKgN,MAAM1M,GAAOH,EACXG,8BAGLH,EAAWC,GACb,IAAIE,EAAMN,KAAKmB,SAASc,IAAI9B,EAAWC,GAIvC,OAHIE,UACKN,KAAKgN,MAAM1M,GAEbA,+CAGY,IAAA0B,EAAAhC,KACfiN,EAAejN,KAAKgN,MACxBhN,KAAKgN,SACL1I,OAAO1B,KAAKqK,GAAcvG,QAAQ,SAACpG,GACjC0B,EAAKb,SAASc,IAAIgL,EAAa3M,GAAMA,yjBClH3C4M,EAAA/N,EAAA,oDAEqBgO,aACnB,SAAAA,EAAYrM,EAAOqE,EAAmBrC,gGAAO/C,CAAAC,KAAAmN,GAC3CnN,KAAKqB,OAASP,EACdd,KAAKoN,MAAQjI,EACbnF,KAAKuD,OAAST,EACd9C,KAAKkN,QAAU,IAAAjM,EAAAC,gDAIf,OAAOlB,KAAKuD,mCAGVT,EAAoByI,GACtB,GAAIvL,KAAKuD,SAAWT,EAApB,CAIA,IAAIuK,EAAWrN,KAAKuD,OACpBvD,KAAKuD,OAAST,EAEd,IAAIwK,KACJ,GAAI/B,GAA2B,iBAAlB,IAAOA,EAAP,YAAA3G,EAAO2G,IAClB,IAAK,IAAIgC,KAAKhC,EACRA,EAAM/K,eAAe+M,KACvBD,EAAIC,GAAKhC,EAAMgC,IAGrBD,EAAID,SAAWA,EACfC,EAAIxK,MAAQA,EACZ9C,KAAKkN,QAAQzK,QAAQ,SAAU6K,EAAKtN,MAIhCiF,EAAOS,OAAST,EAAOS,MAAM8H,eAC/BvI,EAAOS,MAAM8H,cACX,iBACwB,OAArBxN,KAAKqB,OAAO8D,KAAgBnF,KAAKqB,OAAO8D,KAAO,IAAM,IACtDnF,KAAKoN,WACW,IAAXtK,EAAyB,KAAOA,+BAK1C3C,EAAWC,GACZ,OAAOJ,KAAKkN,QAAQ1K,GAAGrC,EAAWC,+BAGhCD,EAAWC,GACb,OAAOJ,KAAKkN,QAAQjL,IAAI9B,EAAWC,sBAhDlB+M","file":"crosstalk.min.js","sourcesContent":["(function(){function e(t,n,r){function s(o,u){if(!n[o]){if(!t[o]){var a=typeof require==\"function\"&&require;if(!u&&a)return a(o,!0);if(i)return i(o,!0);var f=new Error(\"Cannot find module '\"+o+\"'\");throw f.code=\"MODULE_NOT_FOUND\",f}var l=n[o]={exports:{}};t[o][0].call(l.exports,function(e){var n=t[o][1][e];return s(n?n:e)},l,l.exports,e,t,n,r)}return n[o].exports}var i=typeof require==\"function\"&&require;for(var o=0;o<r.length;o++)s(r[o]);return s}return e})()","export default class Events {\n  constructor() {\n    this._types = {};\n    this._seq = 0;\n  }\n\n  on(eventType, listener) {\n    let subs = this._types[eventType];\n    if (!subs) {\n      subs = this._types[eventType] = {};\n    }\n    let sub = \"sub\" + (this._seq++);\n    subs[sub] = listener;\n    return sub;\n  }\n\n  // Returns false if no match, or string for sub name if matched\n  off(eventType, listener) {\n    let subs = this._types[eventType];\n    if (typeof(listener) === \"function\") {\n      for (let key in subs) {\n        if (subs.hasOwnProperty(key)) {\n          if (subs[key] === listener) {\n            delete subs[key];\n            return key;\n          }\n        }\n      }\n      return false;\n    } else if (typeof(listener) === \"string\") {\n      if (subs && subs[listener]) {\n        delete subs[listener];\n        return listener;\n      }\n      return false;\n    } else {\n      throw new Error(\"Unexpected type for listener\");\n    }\n  }\n\n  trigger(eventType, arg, thisObj) {\n    let subs = this._types[eventType];\n    for (let key in subs) {\n      if (subs.hasOwnProperty(key)) {\n        subs[key].call(thisObj, arg);\n      }\n    }\n  }\n}\n","import Events from \"./events\";\nimport FilterSet from \"./filterset\";\nimport grp from \"./group\";\nimport * as util from \"./util\";\n\nfunction getFilterSet(group) {\n  let fsVar = group.var(\"filterset\");\n  let result = fsVar.get();\n  if (!result) {\n    result = new FilterSet();\n    fsVar.set(result);\n  }\n  return result;\n}\n\nlet id = 1;\nfunction nextId() {\n  return id++;\n}\n\n/**\n * Use this class to contribute to, and listen for changes to, the filter set\n * for the given group of widgets. Filter input controls should create one\n * `FilterHandle` and only call {@link FilterHandle#set}. Output widgets that\n * wish to displayed filtered data should create one `FilterHandle` and use\n * the {@link FilterHandle#filteredKeys} property and listen for change\n * events.\n *\n * If two (or more) `FilterHandle` instances in the same webpage share the\n * same group name, they will contribute to a single \"filter set\". Each\n * `FilterHandle` starts out with a `null` value, which means they take\n * nothing away from the set of data that should be shown. To make a\n * `FilterHandle` actually remove data from the filter set, set its value to\n * an array of keys which should be displayed. Crosstalk will aggregate the\n * various key arrays by finding their intersection; only keys that are\n * present in all non-null filter handles are considered part of the filter\n * set.\n *\n * @param {string} [group] - The name of the Crosstalk group, or if none,\n *   null or undefined (or any other falsy value). This can be changed later\n *   via the {@link FilterHandle#setGroup} method.\n * @param {Object} [extraInfo] - An object whose properties will be copied to\n *   the event object whenever an event is emitted.\n */\nexport class FilterHandle {\n  constructor(group, extraInfo) {\n    this._eventRelay = new Events();\n    this._emitter = new util.SubscriptionTracker(this._eventRelay);\n\n    // Name of the group we're currently tracking, if any. Can change over time.\n    this._group = null;\n    // The filterSet that we're tracking, if any. Can change over time.\n    this._filterSet = null;\n    // The Var we're currently tracking, if any. Can change over time.\n    this._filterVar = null;\n    // The event handler subscription we currently have on var.on(\"change\").\n    this._varOnChangeSub = null;\n\n    this._extraInfo = util.extend({ sender: this }, extraInfo);\n\n    this._id = \"filter\" + nextId();\n\n    this.setGroup(group);\n  }\n\n  /**\n   * Changes the Crosstalk group membership of this FilterHandle. If `set()` was\n   * previously called on this handle, switching groups will clear those keys\n   * from the old group's filter set. These keys will not be applied to the new\n   * group's filter set either. In other words, `setGroup()` effectively calls\n   * `clear()` before switching groups.\n   *\n   * @param {string} group - The name of the Crosstalk group, or null (or\n   *   undefined) to clear the group.\n   */\n  setGroup(group) {\n    // If group is unchanged, do nothing\n    if (this._group === group)\n      return;\n    // Treat null, undefined, and other falsy values the same\n    if (!this._group && !group)\n      return;\n\n    if (this._filterVar) {\n      this._filterVar.off(\"change\", this._varOnChangeSub);\n      this.clear();\n      this._varOnChangeSub = null;\n      this._filterVar = null;\n      this._filterSet = null;\n    }\n\n    this._group = group;\n\n    if (group) {\n      group = grp(group);\n      this._filterSet = getFilterSet(group);\n      this._filterVar = grp(group).var(\"filter\");\n      let sub = this._filterVar.on(\"change\", (e) => {\n        this._eventRelay.trigger(\"change\", e, this);\n      });\n      this._varOnChangeSub = sub;\n    }\n  }\n\n  /**\n   * Combine the given `extraInfo` (if any) with the handle's default\n   * `_extraInfo` (if any).\n   * @private\n   */\n  _mergeExtraInfo(extraInfo) {\n    return util.extend({},\n      this._extraInfo ? this._extraInfo : null,\n      extraInfo ? extraInfo : null);\n  }\n\n  /**\n   * Close the handle. This clears this handle's contribution to the filter set,\n   * and unsubscribes all event listeners.\n   */\n  close() {\n    this._emitter.removeAllListeners();\n    this.clear();\n    this.setGroup(null);\n  }\n\n  /**\n   * Clear this handle's contribution to the filter set.\n   *\n   * @param {Object} [extraInfo] - Extra properties to be included on the event\n   *   object that's passed to listeners (in addition to any options that were\n   *   passed into the `FilterHandle` constructor).\n   * \n   * @fires FilterHandle#change\n   */\n  clear(extraInfo) {\n    if (!this._filterSet)\n      return;\n    this._filterSet.clear(this._id);\n    this._onChange(extraInfo);\n  }\n\n  /**\n   * Set this handle's contribution to the filter set. This array should consist\n   * of the keys of the rows that _should_ be displayed; any keys that are not\n   * present in the array will be considered _filtered out_. Note that multiple\n   * `FilterHandle` instances in the group may each contribute an array of keys,\n   * and only those keys that appear in _all_ of the arrays make it through the\n   * filter.\n   *\n   * @param {string[]} keys - Empty array, or array of keys. To clear the\n   *   filter, don't pass an empty array; instead, use the\n   *   {@link FilterHandle#clear} method.\n   * @param {Object} [extraInfo] - Extra properties to be included on the event\n   *   object that's passed to listeners (in addition to any options that were\n   *   passed into the `FilterHandle` constructor).\n   * \n   * @fires FilterHandle#change\n   */\n  set(keys, extraInfo) {\n    if (!this._filterSet)\n      return;\n    this._filterSet.update(this._id, keys);\n    this._onChange(extraInfo);\n  }\n\n  /**\n   * @return {string[]|null} - Either: 1) an array of keys that made it through\n   *   all of the `FilterHandle` instances, or, 2) `null`, which means no filter\n   *   is being applied (all data should be displayed).\n   */\n  get filteredKeys() {\n    return this._filterSet ? this._filterSet.value : null;\n  }\n\n  /**\n   * Subscribe to events on this `FilterHandle`.\n   *\n   * @param {string} eventType - Indicates the type of events to listen to.\n   *   Currently, only `\"change\"` is supported.\n   * @param {FilterHandle~listener} listener - The callback function that\n   *   will be invoked when the event occurs.\n   * @return {string} - A token to pass to {@link FilterHandle#off} to cancel\n   *   this subscription.\n   */\n  on(eventType, listener) {\n    return this._emitter.on(eventType, listener);\n  }\n\n  /**\n   * Cancel event subscriptions created by {@link FilterHandle#on}.\n   *\n   * @param {string} eventType - The type of event to unsubscribe.\n   * @param {string|FilterHandle~listener} listener - Either the callback\n   *   function previously passed into {@link FilterHandle#on}, or the\n   *   string that was returned from {@link FilterHandle#on}.\n   */\n  off(eventType, listener) {\n    return this._emitter.off(eventType, listener);\n  }\n\n  _onChange(extraInfo) {\n    if (!this._filterSet)\n      return;\n    this._filterVar.set(this._filterSet.value, this._mergeExtraInfo(extraInfo));\n  }\n\n  /**\n   * @callback FilterHandle~listener\n   * @param {Object} event - An object containing details of the event. For\n   *   `\"change\"` events, this includes the properties `value` (the new\n   *   value of the filter set, or `null` if no filter set is active),\n   *   `oldValue` (the previous value of the filter set), and `sender` (the\n   *   `FilterHandle` instance that made the change).\n   */\n\n}\n\n/**\n * @event FilterHandle#change\n * @type {object}\n * @property {object} value - The new value of the filter set, or `null`\n *   if no filter set is active.\n * @property {object} oldValue - The previous value of the filter set.\n * @property {FilterHandle} sender - The `FilterHandle` instance that\n *   changed the value.\n */\n","import { diffSortedLists } from \"./util\";\n\nfunction naturalComparator(a, b) {\n  if (a === b) {\n    return 0;\n  } else if (a < b) {\n    return -1;\n  } else if (a > b) {\n    return 1;\n  }\n}\n\n/**\n * @private\n */\nexport default class FilterSet {\n  constructor() {\n    this.reset();\n  }\n\n  reset() {\n    // Key: handle ID, Value: array of selected keys, or null\n    this._handles = {};\n    // Key: key string, Value: count of handles that include it\n    this._keys = {};\n    this._value = null;\n    this._activeHandles = 0;\n  }\n\n  get value() {\n    return this._value;\n  }\n\n  update(handleId, keys) {\n    if (keys !== null) {\n      keys = keys.slice(0); // clone before sorting\n      keys.sort(naturalComparator);\n    }\n\n    let {added, removed} = diffSortedLists(this._handles[handleId], keys);\n    this._handles[handleId] = keys;\n\n    for (let i = 0; i < added.length; i++) {\n      this._keys[added[i]] = (this._keys[added[i]] || 0) + 1;\n    }\n    for (let i = 0; i < removed.length; i++) {\n      this._keys[removed[i]]--;\n    }\n\n    this._updateValue(keys);\n  }\n\n  /**\n   * @param {string[]} keys Sorted array of strings that indicate\n   * a superset of possible keys.\n   * @private\n   */\n  _updateValue(keys = this._allKeys) {\n    let handleCount = Object.keys(this._handles).length;\n    if (handleCount === 0) {\n      this._value = null;\n    } else {\n      this._value = [];\n      for (let i = 0; i < keys.length; i++) {\n        let count = this._keys[keys[i]];\n        if (count === handleCount) {\n          this._value.push(keys[i]);\n        }\n      }\n    }\n  }\n\n  clear(handleId) {\n    if (typeof(this._handles[handleId]) === \"undefined\") {\n      return;\n    }\n\n    let keys = this._handles[handleId];\n    if (!keys) {\n      keys = [];\n    }\n\n    for (let i = 0; i < keys.length; i++) {\n      this._keys[keys[i]]--;\n    }\n    delete this._handles[handleId];\n\n    this._updateValue();\n  }\n\n  get _allKeys() {\n    let allKeys = Object.keys(this._keys);\n    allKeys.sort(naturalComparator);\n    return allKeys;\n  }\n}\n","import Var from \"./var\";\n\n// Use a global so that multiple copies of crosstalk.js can be loaded and still\n// have groups behave as singletons across all copies.\nglobal.__crosstalk_groups = global.__crosstalk_groups || {};\nlet groups = global.__crosstalk_groups;\n\nexport default function group(groupName) {\n  if (groupName && typeof(groupName) === \"string\") {\n    if (!groups.hasOwnProperty(groupName)) {\n      groups[groupName] = new Group(groupName);\n    }\n    return groups[groupName];\n  } else if (typeof(groupName) === \"object\" && groupName._vars && groupName.var) {\n    // Appears to already be a group object\n    return groupName;\n  } else if (Array.isArray(groupName) &&\n      groupName.length == 1 &&\n      typeof(groupName[0]) === \"string\") {\n    return group(groupName[0]);\n  } else {\n    throw new Error(\"Invalid groupName argument\");\n  }\n}\n\nclass Group {\n  constructor(name) {\n    this.name = name;\n    this._vars = {};\n  }\n\n  var(name) {\n    if (!name || typeof(name) !== \"string\") {\n      throw new Error(\"Invalid var name\");\n    }\n\n    if (!this._vars.hasOwnProperty(name))\n      this._vars[name] = new Var(this, name);\n    return this._vars[name];\n  }\n\n  has(name) {\n    if (!name || typeof(name) !== \"string\") {\n      throw new Error(\"Invalid var name\");\n    }\n\n    return this._vars.hasOwnProperty(name);\n  }\n}\n","import group from \"./group\";\nimport { SelectionHandle } from \"./selection\";\nimport { FilterHandle } from \"./filter\";\nimport { bind } from \"./input\";\nimport \"./input_selectize\";\nimport \"./input_checkboxgroup\";\nimport \"./input_slider\";\n\nconst defaultGroup = group(\"default\");\n\nfunction var_(name) {\n  return defaultGroup.var(name);\n}\n\nfunction has(name) {\n  return defaultGroup.has(name);\n}\n\nif (global.Shiny) {\n  global.Shiny.addCustomMessageHandler(\"update-client-value\", function(message) {\n    if (typeof(message.group) === \"string\") {\n      group(message.group).var(message.name).set(message.value);\n    } else {\n      var_(message.name).set(message.value);\n    }\n  });\n}\n\nconst crosstalk = {\n  group: group,\n  var: var_,\n  has: has,\n  SelectionHandle: SelectionHandle,\n  FilterHandle: FilterHandle,\n  bind: bind\n};\n\n/**\n * @namespace crosstalk\n */\nexport default crosstalk;\nglobal.crosstalk = crosstalk;\n","let $ = global.jQuery;\n\nlet bindings = {};\n\nexport function register(reg) {\n  bindings[reg.className] = reg;\n  if (global.document && global.document.readyState !== \"complete\") {\n    $(() => {\n      bind();\n    });\n  } else if (global.document) {\n    setTimeout(bind, 100);\n  }\n}\n\nexport function bind() {\n  Object.keys(bindings).forEach(function(className) {\n    let binding = bindings[className];\n    $(\".\" + binding.className).not(\".crosstalk-input-bound\").each(function(i, el) {\n      bindInstance(binding, el);\n    });\n  });\n}\n\n// Escape jQuery identifier\nfunction $escape(val) {\n  return val.replace(/([!\"#$%&'()*+,./:;<=>?@[\\\\\\]^`{|}~])/g, \"\\\\$1\");\n}\n\nfunction bindEl(el) {\n  let $el = $(el);\n  Object.keys(bindings).forEach(function(className) {\n    if ($el.hasClass(className) && !$el.hasClass(\"crosstalk-input-bound\")) {\n      let binding = bindings[className];\n      bindInstance(binding, el);\n    }\n  });\n}\n\nfunction bindInstance(binding, el) {\n  let jsonEl = $(el).find(\"script[type='application/json'][data-for='\" + $escape(el.id) + \"']\");\n  let data = JSON.parse(jsonEl[0].innerText);\n\n  let instance = binding.factory(el, data);\n  $(el).data(\"crosstalk-instance\", instance);\n  $(el).addClass(\"crosstalk-input-bound\");\n}\n\nif (global.Shiny) {\n  let inputBinding = new global.Shiny.InputBinding();\n  let $ = global.jQuery;\n  $.extend(inputBinding, {\n    find: function(scope) {\n      return $(scope).find(\".crosstalk-input\");\n    },\n    initialize: function(el) {\n      if (!$(el).hasClass(\"crosstalk-input-bound\")) {\n        bindEl(el);\n      }\n    },\n    getId: function(el) {\n      return el.id;\n    },\n    getValue: function(el) {\n\n    },\n    setValue: function(el, value) {\n\n    },\n    receiveMessage: function(el, data) {\n\n    },\n    subscribe: function(el, callback) {\n      $(el).data(\"crosstalk-instance\").resume();\n    },\n    unsubscribe: function(el) {\n      $(el).data(\"crosstalk-instance\").suspend();\n    }\n  });\n  global.Shiny.inputBindings.register(inputBinding, \"crosstalk.inputBinding\");\n}\n","import * as input from \"./input\";\nimport { FilterHandle } from \"./filter\";\n\nlet $ = global.jQuery;\n\ninput.register({\n  className: \"crosstalk-input-checkboxgroup\",\n\n  factory: function(el, data) {\n    /*\n     * map: {\"groupA\": [\"keyA\", \"keyB\", ...], ...}\n     * group: \"ct-groupname\"\n     */\n    let ctHandle = new FilterHandle(data.group);\n\n    let lastKnownKeys;\n    let $el = $(el);\n    $el.on(\"change\", \"input[type='checkbox']\", function() {\n      let checked = $el.find(\"input[type='checkbox']:checked\");\n      if (checked.length === 0) {\n        lastKnownKeys = null;\n        ctHandle.clear();\n      } else {\n        let keys = {};\n        checked.each(function() {\n          data.map[this.value].forEach(function(key) {\n            keys[key] = true;\n          });\n        });\n        let keyArray = Object.keys(keys);\n        keyArray.sort();\n        lastKnownKeys = keyArray;\n        ctHandle.set(keyArray);\n      }\n    });\n\n    return {\n      suspend: function() {\n        ctHandle.clear();\n      },\n      resume: function() {\n        if (lastKnownKeys)\n          ctHandle.set(lastKnownKeys);\n      }\n    };\n  }\n});\n","import * as input from \"./input\";\nimport * as util from \"./util\";\nimport { FilterHandle } from \"./filter\";\n\nlet $ = global.jQuery;\n\ninput.register({\n  className: \"crosstalk-input-select\",\n\n  factory: function(el, data) {\n    /*\n     * items: {value: [...], label: [...]}\n     * map: {\"groupA\": [\"keyA\", \"keyB\", ...], ...}\n     * group: \"ct-groupname\"\n     */\n\n    let first = [{value: \"\", label: \"(All)\"}];\n    let items = util.dataframeToD3(data.items);\n    let opts = {\n      options: first.concat(items),\n      valueField: \"value\",\n      labelField: \"label\",\n      searchField: \"label\"\n    };\n\n    let select = $(el).find(\"select\")[0];\n\n    let selectize = $(select).selectize(opts)[0].selectize;\n\n    let ctHandle = new FilterHandle(data.group);\n\n    let lastKnownKeys;\n    selectize.on(\"change\", function() {\n      if (selectize.items.length === 0) {\n        lastKnownKeys = null;\n        ctHandle.clear();\n      } else {\n        let keys = {};\n        selectize.items.forEach(function(group) {\n          data.map[group].forEach(function(key) {\n            keys[key] = true;\n          });\n        });\n        let keyArray = Object.keys(keys);\n        keyArray.sort();\n        lastKnownKeys = keyArray;\n        ctHandle.set(keyArray);\n      }\n    });\n\n    return {\n      suspend: function() {\n        ctHandle.clear();\n      },\n      resume: function() {\n        if (lastKnownKeys)\n          ctHandle.set(lastKnownKeys);\n      }\n    };\n  }\n});\n","import * as input from \"./input\";\nimport { FilterHandle } from \"./filter\";\n\nlet $ = global.jQuery;\nlet strftime = global.strftime;\n\ninput.register({\n  className: \"crosstalk-input-slider\",\n\n  factory: function(el, data) {\n    /*\n     * map: {\"groupA\": [\"keyA\", \"keyB\", ...], ...}\n     * group: \"ct-groupname\"\n     */\n    let ctHandle = new FilterHandle(data.group);\n\n    let opts = {};\n    let $el = $(el).find(\"input\");\n    let dataType = $el.data(\"data-type\");\n    let timeFormat = $el.data(\"time-format\");\n    let round = $el.data(\"round\");\n    let timeFormatter;\n\n    // Set up formatting functions\n    if (dataType === \"date\") {\n      timeFormatter = strftime.utc();\n      opts.prettify = function(num) {\n        return timeFormatter(timeFormat, new Date(num));\n      };\n\n    } else if (dataType === \"datetime\") {\n      let timezone = $el.data(\"timezone\");\n      if (timezone)\n        timeFormatter = strftime.timezone(timezone);\n      else\n        timeFormatter = strftime;\n\n      opts.prettify = function(num) {\n        return timeFormatter(timeFormat, new Date(num));\n      };\n    } else if (dataType === \"number\") {\n      if (typeof round !== \"undefined\")\n        opts.prettify = function(num) {\n          let factor = Math.pow(10, round);\n          return Math.round(num * factor) / factor;\n        };\n    }\n\n    $el.ionRangeSlider(opts);\n\n    function getValue() {\n      let result = $el.data(\"ionRangeSlider\").result;\n\n      // Function for converting numeric value from slider to appropriate type.\n      let convert;\n      let dataType = $el.data(\"data-type\");\n      if (dataType === \"date\") {\n        convert = function(val) {\n          return formatDateUTC(new Date(+val));\n        };\n      } else if (dataType === \"datetime\") {\n        convert = function(val) {\n          // Convert ms to s\n          return +val / 1000;\n        };\n      } else {\n        convert = function(val) { return +val; };\n      }\n\n      if ($el.data(\"ionRangeSlider\").options.type === \"double\") {\n        return [convert(result.from), convert(result.to)];\n      } else {\n        return convert(result.from);\n      }\n    }\n\n    let lastKnownKeys = null;\n\n    $el.on(\"change.crosstalkSliderInput\", function(event) {\n      if (!$el.data(\"updating\") && !$el.data(\"animating\")) {\n        let [from, to] = getValue();\n        let keys = [];\n        for (let i = 0; i < data.values.length; i++) {\n          let val = data.values[i];\n          if (val >= from && val <= to) {\n            keys.push(data.keys[i]);\n          }\n        }\n        keys.sort();\n        ctHandle.set(keys);\n        lastKnownKeys = keys;\n      }\n    });\n\n\n    // let $el = $(el);\n    // $el.on(\"change\", \"input[type=\"checkbox\"]\", function() {\n    //   let checked = $el.find(\"input[type=\"checkbox\"]:checked\");\n    //   if (checked.length === 0) {\n    //     ctHandle.clear();\n    //   } else {\n    //     let keys = {};\n    //     checked.each(function() {\n    //       data.map[this.value].forEach(function(key) {\n    //         keys[key] = true;\n    //       });\n    //     });\n    //     let keyArray = Object.keys(keys);\n    //     keyArray.sort();\n    //     ctHandle.set(keyArray);\n    //   }\n    // });\n\n    return {\n      suspend: function() {\n        ctHandle.clear();\n      },\n      resume: function() {\n        if (lastKnownKeys)\n          ctHandle.set(lastKnownKeys);\n      }\n    };\n  }\n});\n\n\n// Convert a number to a string with leading zeros\nfunction padZeros(n, digits) {\n  let str = n.toString();\n  while (str.length < digits)\n    str = \"0\" + str;\n  return str;\n}\n\n// Given a Date object, return a string in yyyy-mm-dd format, using the\n// UTC date. This may be a day off from the date in the local time zone.\nfunction formatDateUTC(date) {\n  if (date instanceof Date) {\n    return date.getUTCFullYear() + \"-\" +\n           padZeros(date.getUTCMonth()+1, 2) + \"-\" +\n           padZeros(date.getUTCDate(), 2);\n\n  } else {\n    return null;\n  }\n}\n","import Events from \"./events\";\nimport grp from \"./group\";\nimport * as util from \"./util\";\n\n/**\n * Use this class to read and write (and listen for changes to) the selection\n * for a Crosstalk group. This is intended to be used for linked brushing.\n *\n * If two (or more) `SelectionHandle` instances in the same webpage share the\n * same group name, they will share the same state. Setting the selection using\n * one `SelectionHandle` instance will result in the `value` property instantly\n * changing across the others, and `\"change\"` event listeners on all instances\n * (including the one that initiated the sending) will fire.\n *\n * @param {string} [group] - The name of the Crosstalk group, or if none,\n *   null or undefined (or any other falsy value). This can be changed later\n *   via the [SelectionHandle#setGroup](#setGroup) method.\n * @param {Object} [extraInfo] - An object whose properties will be copied to\n *   the event object whenever an event is emitted.\n */\nexport class SelectionHandle {\n\n  constructor(group = null, extraInfo = null) {\n    this._eventRelay = new Events();\n    this._emitter = new util.SubscriptionTracker(this._eventRelay);\n\n    // Name of the group we're currently tracking, if any. Can change over time.\n    this._group = null;\n    // The Var we're currently tracking, if any. Can change over time.\n    this._var = null;\n    // The event handler subscription we currently have on var.on(\"change\").\n    this._varOnChangeSub = null;\n\n    this._extraInfo = util.extend({ sender: this }, extraInfo);\n\n    this.setGroup(group);\n  }\n\n  /**\n   * Changes the Crosstalk group membership of this SelectionHandle. The group\n   * being switched away from (if any) will not have its selection value\n   * modified as a result of calling `setGroup`, even if this handle was the\n   * most recent handle to set the selection of the group.\n   *\n   * The group being switched to (if any) will also not have its selection value\n   * modified as a result of calling `setGroup`. If you want to set the\n   * selection value of the new group, call `set` explicitly.\n   *\n   * @param {string} group - The name of the Crosstalk group, or null (or\n   *   undefined) to clear the group.\n   */\n  setGroup(group) {\n    // If group is unchanged, do nothing\n    if (this._group === group)\n      return;\n    // Treat null, undefined, and other falsy values the same\n    if (!this._group && !group)\n      return;\n\n    if (this._var) {\n      this._var.off(\"change\", this._varOnChangeSub);\n      this._var = null;\n      this._varOnChangeSub = null;\n    }\n\n    this._group = group;\n\n    if (group) {\n      this._var = grp(group).var(\"selection\");\n      let sub = this._var.on(\"change\", (e) => {\n        this._eventRelay.trigger(\"change\", e, this);\n      });\n      this._varOnChangeSub = sub;\n    }\n  }\n\n  /**\n   * Retrieves the current selection for the group represented by this\n   * `SelectionHandle`.\n   *\n   * - If no selection is active, then this value will be falsy.\n   * - If a selection is active, but no data points are selected, then this\n   *   value will be an empty array.\n   * - If a selection is active, and data points are selected, then the keys\n   *   of the selected data points will be present in the array.\n   */\n  get value() {\n    return this._var ? this._var.get() : null;\n  }\n\n  /**\n   * Combines the given `extraInfo` (if any) with the handle's default\n   * `_extraInfo` (if any).\n   * @private\n   */\n  _mergeExtraInfo(extraInfo) {\n    // Important incidental effect: shallow clone is returned\n    return util.extend({},\n      this._extraInfo ? this._extraInfo : null,\n      extraInfo ? extraInfo : null);\n  }\n\n  /**\n   * Overwrites the current selection for the group, and raises the `\"change\"`\n   * event among all of the group's '`SelectionHandle` instances (including\n   * this one).\n   *\n   * @fires SelectionHandle#change\n   * @param {string[]} selectedKeys - Falsy, empty array, or array of keys (see\n   *   {@link SelectionHandle#value}).\n   * @param {Object} [extraInfo] - Extra properties to be included on the event\n   *   object that's passed to listeners (in addition to any options that were\n   *   passed into the `SelectionHandle` constructor).\n   */\n  set(selectedKeys, extraInfo) {\n    if (this._var)\n      this._var.set(selectedKeys, this._mergeExtraInfo(extraInfo));\n  }\n\n  /**\n   * Overwrites the current selection for the group, and raises the `\"change\"`\n   * event among all of the group's '`SelectionHandle` instances (including\n   * this one).\n   *\n   * @fires SelectionHandle#change\n   * @param {Object} [extraInfo] - Extra properties to be included on the event\n   *   object that's passed to listeners (in addition to any that were passed\n   *   into the `SelectionHandle` constructor).\n   */\n  clear(extraInfo) {\n    if (this._var)\n      this.set(void 0, this._mergeExtraInfo(extraInfo));\n  }\n\n  /**\n   * Subscribes to events on this `SelectionHandle`.\n   *\n   * @param {string} eventType - Indicates the type of events to listen to.\n   *   Currently, only `\"change\"` is supported.\n   * @param {SelectionHandle~listener} listener - The callback function that\n   *   will be invoked when the event occurs.\n   * @return {string} - A token to pass to {@link SelectionHandle#off} to cancel\n   *   this subscription.\n   */\n  on(eventType, listener) {\n    return this._emitter.on(eventType, listener);\n  }\n\n  /**\n   * Cancels event subscriptions created by {@link SelectionHandle#on}.\n   *\n   * @param {string} eventType - The type of event to unsubscribe.\n   * @param {string|SelectionHandle~listener} listener - Either the callback\n   *   function previously passed into {@link SelectionHandle#on}, or the\n   *   string that was returned from {@link SelectionHandle#on}.\n   */\n  off(eventType, listener) {\n    return this._emitter.off(eventType, listener);\n  }\n\n  /**\n   * Shuts down the `SelectionHandle` object.\n   *\n   * Removes all event listeners that were added through this handle.\n   */\n  close() {\n    this._emitter.removeAllListeners();\n    this.setGroup(null);\n  }\n}\n\n/**\n * @callback SelectionHandle~listener\n * @param {Object} event - An object containing details of the event. For\n *   `\"change\"` events, this includes the properties `value` (the new\n *   value of the selection, or `undefined` if no selection is active),\n *   `oldValue` (the previous value of the selection), and `sender` (the\n *   `SelectionHandle` instance that made the change).\n */\n\n/**\n * @event SelectionHandle#change\n * @type {object}\n * @property {object} value - The new value of the selection, or `undefined`\n *   if no selection is active.\n * @property {object} oldValue - The previous value of the selection.\n * @property {SelectionHandle} sender - The `SelectionHandle` instance that\n *   changed the value.\n */\n","export function extend(target, ...sources) {\n  for (let i = 0; i < sources.length; i++) {\n    let src = sources[i];\n    if (typeof(src) === \"undefined\" || src === null)\n      continue;\n\n    for (let key in src) {\n      if (src.hasOwnProperty(key)) {\n        target[key] = src[key];\n      }\n    }\n  }\n  return target;\n}\n\nexport function checkSorted(list) {\n  for (let i = 1; i < list.length; i++) {\n    if (list[i] <= list[i-1]) {\n      throw new Error(\"List is not sorted or contains duplicate\");\n    }\n  }\n}\n\nexport function diffSortedLists(a, b) {\n  let i_a = 0;\n  let i_b = 0;\n\n  if (!a) a = [];\n  if (!b) b = [];\n\n  let a_only = [];\n  let b_only = [];\n\n  checkSorted(a);\n  checkSorted(b);\n\n  while (i_a < a.length && i_b < b.length) {\n    if (a[i_a] === b[i_b]) {\n      i_a++;\n      i_b++;\n    } else if (a[i_a] < b[i_b]) {\n      a_only.push(a[i_a++]);\n    } else {\n      b_only.push(b[i_b++]);\n    }\n  }\n\n  if (i_a < a.length)\n    a_only = a_only.concat(a.slice(i_a));\n  if (i_b < b.length)\n    b_only = b_only.concat(b.slice(i_b));\n  return {\n    removed: a_only,\n    added: b_only\n  };\n}\n\n// Convert from wide: { colA: [1,2,3], colB: [4,5,6], ... }\n// to long: [ {colA: 1, colB: 4}, {colA: 2, colB: 5}, ... ]\nexport function dataframeToD3(df) {\n  let names = [];\n  let length;\n  for (let name in df) {\n    if (df.hasOwnProperty(name))\n      names.push(name);\n    if (typeof(df[name]) !== \"object\" || typeof(df[name].length) === \"undefined\") {\n      throw new Error(\"All fields must be arrays\");\n    } else if (typeof(length) !== \"undefined\" && length !== df[name].length) {\n      throw new Error(\"All fields must be arrays of the same length\");\n    }\n    length = df[name].length;\n  }\n  let results = [];\n  let item;\n  for (let row = 0; row < length; row++) {\n    item = {};\n    for (let col = 0; col < names.length; col++) {\n      item[names[col]] = df[names[col]][row];\n    }\n    results.push(item);\n  }\n  return results;\n}\n\n/**\n * Keeps track of all event listener additions/removals and lets all active\n * listeners be removed with a single operation.\n *\n * @private\n */\nexport class SubscriptionTracker {\n  constructor(emitter) {\n    this._emitter = emitter;\n    this._subs = {};\n  }\n\n  on(eventType, listener) {\n    let sub = this._emitter.on(eventType, listener);\n    this._subs[sub] = eventType;\n    return sub;\n  }\n\n  off(eventType, listener) {\n    let sub = this._emitter.off(eventType, listener);\n    if (sub) {\n      delete this._subs[sub];\n    }\n    return sub;\n  }\n\n  removeAllListeners() {\n    let current_subs = this._subs;\n    this._subs = {};\n    Object.keys(current_subs).forEach((sub) => {\n      this._emitter.off(current_subs[sub], sub);\n    });\n  }\n}\n","import Events from \"./events\";\n\nexport default class Var {\n  constructor(group, name, /*optional*/ value) {\n    this._group = group;\n    this._name = name;\n    this._value = value;\n    this._events = new Events();\n  }\n\n  get() {\n    return this._value;\n  }\n\n  set(value, /*optional*/ event) {\n    if (this._value === value) {\n      // Do nothing; the value hasn't changed\n      return;\n    }\n    let oldValue = this._value;\n    this._value = value;\n    // Alert JavaScript listeners that the value has changed\n    let evt = {};\n    if (event && typeof(event) === \"object\") {\n      for (let k in event) {\n        if (event.hasOwnProperty(k))\n          evt[k] = event[k];\n      }\n    }\n    evt.oldValue = oldValue;\n    evt.value = value;\n    this._events.trigger(\"change\", evt, this);\n\n    // TODO: Make this extensible, to let arbitrary back-ends know that\n    // something has changed\n    if (global.Shiny && global.Shiny.onInputChange) {\n      global.Shiny.onInputChange(\n        \".clientValue-\" +\n          (this._group.name !== null ? this._group.name + \"-\" : \"\") +\n          this._name,\n        typeof(value) === \"undefined\" ? null : value\n      );\n    }\n  }\n\n  on(eventType, listener) {\n    return this._events.on(eventType, listener);\n  }\n\n  off(eventType, listener) {\n    return this._events.off(eventType, listener);\n  }\n}\n"]}
\ No newline at end of file
diff --git a/docs/coverage/lib/crosstalk-1.2.1/scss/crosstalk.scss b/docs/coverage/lib/crosstalk-1.2.1/scss/crosstalk.scss
new file mode 100644
index 00000000..35665616
--- /dev/null
+++ b/docs/coverage/lib/crosstalk-1.2.1/scss/crosstalk.scss
@@ -0,0 +1,75 @@
+/* Adjust margins outwards, so column contents line up with the edges of the
+   parent of container-fluid. */
+.container-fluid.crosstalk-bscols {
+  margin-left: -30px;
+  margin-right: -30px;
+  white-space: normal;
+}
+
+/* But don't adjust the margins outwards if we're directly under the body,
+   i.e. we were the top-level of something at the console. */
+body > .container-fluid.crosstalk-bscols {
+  margin-left: auto;
+  margin-right: auto;
+}
+
+.crosstalk-input-checkboxgroup .crosstalk-options-group .crosstalk-options-column {
+  display: inline-block;
+  padding-right: 12px;
+  vertical-align: top;
+}
+
+@media only screen and (max-width:480px) {
+  .crosstalk-input-checkboxgroup .crosstalk-options-group .crosstalk-options-column {
+    display: block;
+    padding-right: inherit;
+  }
+}
+
+/* Relevant BS3 styles to make filter_checkbox() look reasonable without Bootstrap */
+.crosstalk-input {
+  margin-bottom: 15px; /* a la .form-group */
+  .control-label {
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  input[type="checkbox"] {
+    margin: 4px 0 0;
+    margin-top: 1px;
+    line-height: normal;
+  }
+  .checkbox {
+    position: relative;
+    display: block;
+    margin-top: 10px;
+    margin-bottom: 10px;
+  }
+  .checkbox > label{
+    padding-left: 20px;
+    margin-bottom: 0;
+    font-weight: 400;
+    cursor: pointer;
+  }
+  .checkbox input[type="checkbox"],
+  .checkbox-inline input[type="checkbox"] {
+    position: absolute;
+    margin-top: 2px;
+    margin-left: -20px;
+  }
+  .checkbox + .checkbox {
+    margin-top: -5px;
+  }
+  .checkbox-inline {
+    position: relative;
+    display: inline-block;
+    padding-left: 20px;
+    margin-bottom: 0;
+    font-weight: 400;
+    vertical-align: middle;
+    cursor: pointer;
+  }
+  .checkbox-inline + .checkbox-inline {
+    margin-top: 0;
+    margin-left: 10px;
+  }
+}
diff --git a/docs/coverage/lib/datatables-binding-0.33/datatables.js b/docs/coverage/lib/datatables-binding-0.33/datatables.js
new file mode 100644
index 00000000..765b53cb
--- /dev/null
+++ b/docs/coverage/lib/datatables-binding-0.33/datatables.js
@@ -0,0 +1,1539 @@
+(function() {
+
+// some helper functions: using a global object DTWidget so that it can be used
+// in JS() code, e.g. datatable(options = list(foo = JS('code'))); unlike R's
+// dynamic scoping, when 'code' is eval'ed, JavaScript does not know objects
+// from the "parent frame", e.g. JS('DTWidget') will not work unless it was made
+// a global object
+var DTWidget = {};
+
+// 123456666.7890 -> 123,456,666.7890
+var markInterval = function(d, digits, interval, mark, decMark, precision) {
+  x = precision ? d.toPrecision(digits) : d.toFixed(digits);
+  if (!/^-?[\d.]+$/.test(x)) return x;
+  var xv = x.split('.');
+  if (xv.length > 2) return x;  // should have at most one decimal point
+  xv[0] = xv[0].replace(new RegExp('\\B(?=(\\d{' + interval + '})+(?!\\d))', 'g'), mark);
+  return xv.join(decMark);
+};
+
+DTWidget.formatCurrency = function(data, currency, digits, interval, mark, decMark, before, zeroPrint) {
+  var d = parseFloat(data);
+  if (isNaN(d)) return '';
+  if (zeroPrint !== null && d === 0.0) return zeroPrint;
+  var res = markInterval(d, digits, interval, mark, decMark);
+  res = before ? (/^-/.test(res) ? '-' + currency + res.replace(/^-/, '') : currency + res) :
+    res + currency;
+  return res;
+};
+
+DTWidget.formatString = function(data, prefix, suffix) {
+  var d = data;
+  if (d === null) return '';
+  return prefix + d + suffix;
+};
+
+DTWidget.formatPercentage = function(data, digits, interval, mark, decMark, zeroPrint) {
+  var d = parseFloat(data);
+  if (isNaN(d)) return '';
+  if (zeroPrint !== null && d === 0.0) return zeroPrint;
+  return markInterval(d * 100, digits, interval, mark, decMark) + '%';
+};
+
+DTWidget.formatRound = function(data, digits, interval, mark, decMark, zeroPrint) {
+  var d = parseFloat(data);
+  if (isNaN(d)) return '';
+  if (zeroPrint !== null && d === 0.0) return zeroPrint;
+  return markInterval(d, digits, interval, mark, decMark);
+};
+
+DTWidget.formatSignif = function(data, digits, interval, mark, decMark, zeroPrint) {
+  var d = parseFloat(data);
+  if (isNaN(d)) return '';
+  if (zeroPrint !== null && d === 0.0) return zeroPrint;
+  return markInterval(d, digits, interval, mark, decMark, true);
+};
+
+DTWidget.formatDate = function(data, method, params) {
+  var d = data;
+  if (d === null) return '';
+  // (new Date('2015-10-28')).toDateString() may return 2015-10-27 because the
+  // actual time created could be like 'Tue Oct 27 2015 19:00:00 GMT-0500 (CDT)',
+  // i.e. the date-only string is treated as UTC time instead of local time
+  if ((method === 'toDateString' || method === 'toLocaleDateString') && /^\d{4,}\D\d{2}\D\d{2}$/.test(d)) {
+    d = d.split(/\D/);
+    d = new Date(d[0], d[1] - 1, d[2]);
+  } else {
+    d = new Date(d);
+  }
+  return d[method].apply(d, params);
+};
+
+window.DTWidget = DTWidget;
+
+// A helper function to update the properties of existing filters
+var setFilterProps = function(td, props) {
+  // Update enabled/disabled state
+  var $input = $(td).find('input').first();
+  var searchable = $input.data('searchable');
+  $input.prop('disabled', !searchable || props.disabled);
+
+  // Based on the filter type, set its new values
+  var type = td.getAttribute('data-type');
+  if (['factor', 'logical'].includes(type)) {
+    // Reformat the new dropdown options for use with selectize
+    var new_vals = props.params.options.map(function(item) {
+      return { text: item, value: item };
+    });
+
+    // Find the selectize object
+    var dropdown = $(td).find('.selectized').eq(0)[0].selectize;
+
+    // Note the current values
+    var old_vals = dropdown.getValue();
+
+    // Remove the existing values
+    dropdown.clearOptions();
+
+    // Add the new options
+    dropdown.addOption(new_vals);
+
+    // Preserve the existing values
+    dropdown.setValue(old_vals);
+
+  } else if (['number', 'integer', 'date', 'time'].includes(type)) {
+    // Apply internal scaling to new limits. Updating scale not yet implemented.
+    var slider = $(td).find('.noUi-target').eq(0);
+    var scale = Math.pow(10, Math.max(0, +slider.data('scale') || 0));
+    var new_vals = [props.params.min * scale, props.params.max * scale];
+
+    // Note what the new limits will be just for this filter
+    var new_lims = new_vals.slice();
+
+    // Determine the current values and limits
+    var old_vals = slider.val().map(Number);
+    var old_lims = slider.noUiSlider('options').range;
+    old_lims = [old_lims.min, old_lims.max];
+
+    // Preserve the current values if filters have been applied; otherwise, apply no filtering
+    if (old_vals[0] != old_lims[0]) {
+      new_vals[0] = Math.max(old_vals[0], new_vals[0]);
+    }
+
+    if (old_vals[1] != old_lims[1]) {
+      new_vals[1] = Math.min(old_vals[1], new_vals[1]);
+    }
+
+    // Update the endpoints of the slider
+    slider.noUiSlider({
+      start: new_vals,
+      range: {'min': new_lims[0], 'max': new_lims[1]}
+    }, true);
+  }
+};
+
+var transposeArray2D = function(a) {
+  return a.length === 0 ? a : HTMLWidgets.transposeArray2D(a);
+};
+
+var crosstalkPluginsInstalled = false;
+
+function maybeInstallCrosstalkPlugins() {
+  if (crosstalkPluginsInstalled)
+    return;
+  crosstalkPluginsInstalled = true;
+
+  $.fn.dataTable.ext.afnFiltering.push(
+    function(oSettings, aData, iDataIndex) {
+      var ctfilter = oSettings.nTable.ctfilter;
+      if (ctfilter && !ctfilter[iDataIndex])
+        return false;
+
+      var ctselect = oSettings.nTable.ctselect;
+      if (ctselect && !ctselect[iDataIndex])
+        return false;
+
+      return true;
+    }
+  );
+}
+
+HTMLWidgets.widget({
+  name: "datatables",
+  type: "output",
+  renderOnNullValue: true,
+  initialize: function(el, width, height) {
+    // in order that the type=number inputs return a number
+    $.valHooks.number = {
+      get: function(el) {
+        var value = parseFloat(el.value);
+        return isNaN(value) ? "" : value;
+      }
+    };
+    $(el).html('&nbsp;');
+    return {
+      data: null,
+      ctfilterHandle: new crosstalk.FilterHandle(),
+      ctfilterSubscription: null,
+      ctselectHandle: new crosstalk.SelectionHandle(),
+      ctselectSubscription: null
+    };
+  },
+  renderValue: function(el, data, instance) {
+    if (el.offsetWidth === 0 || el.offsetHeight === 0) {
+      instance.data = data;
+      return;
+    }
+    instance.data = null;
+    var $el = $(el);
+    $el.empty();
+
+    if (data === null) {
+      $el.append('&nbsp;');
+      // clear previous Shiny inputs (if any)
+      for (var i in instance.clearInputs) instance.clearInputs[i]();
+      instance.clearInputs = {};
+      return;
+    }
+
+    var crosstalkOptions = data.crosstalkOptions;
+    if (!crosstalkOptions) crosstalkOptions = {
+      'key': null, 'group': null
+    };
+    if (crosstalkOptions.group) {
+      maybeInstallCrosstalkPlugins();
+      instance.ctfilterHandle.setGroup(crosstalkOptions.group);
+      instance.ctselectHandle.setGroup(crosstalkOptions.group);
+    }
+
+    // if we are in the viewer then we always want to fillContainer and
+    // and autoHideNavigation (unless the user has explicitly set these)
+    if (window.HTMLWidgets.viewerMode) {
+      if (!data.hasOwnProperty("fillContainer"))
+        data.fillContainer = true;
+      if (!data.hasOwnProperty("autoHideNavigation"))
+        data.autoHideNavigation = true;
+    }
+
+    // propagate fillContainer to instance (so we have it in resize)
+    instance.fillContainer = data.fillContainer;
+
+    var cells = data.data;
+
+    if (cells instanceof Array) cells = transposeArray2D(cells);
+
+    $el.append(data.container);
+    var $table = $el.find('table');
+    if (data.class) $table.addClass(data.class);
+    if (data.caption) $table.prepend(data.caption);
+
+    if (!data.selection) data.selection = {
+      mode: 'none', selected: null, target: 'row', selectable: null
+    };
+    if (HTMLWidgets.shinyMode && data.selection.mode !== 'none' &&
+        data.selection.target === 'row+column') {
+      if ($table.children('tfoot').length === 0) {
+        $table.append($('<tfoot>'));
+        $table.find('thead tr').clone().appendTo($table.find('tfoot'));
+      }
+    }
+
+    // column filters
+    var filterRow;
+    switch (data.filter) {
+      case 'top':
+        $table.children('thead').append(data.filterHTML);
+        filterRow = $table.find('thead tr:last td');
+        break;
+      case 'bottom':
+        if ($table.children('tfoot').length === 0) {
+          $table.append($('<tfoot>'));
+        }
+        $table.children('tfoot').prepend(data.filterHTML);
+        filterRow = $table.find('tfoot tr:first td');
+        break;
+    }
+
+    var options = { searchDelay: 1000 };
+    if (cells !== null) $.extend(options, {
+      data: cells
+    });
+
+    // options for fillContainer
+    var bootstrapActive = typeof($.fn.popover) != 'undefined';
+    if (instance.fillContainer) {
+
+      // force scrollX/scrollY and turn off autoWidth
+      options.scrollX = true;
+      options.scrollY = "100px"; // can be any value, we'll adjust below
+
+      // if we aren't paginating then move around the info/filter controls
+      // to save space at the bottom and rephrase the info callback
+      if (data.options.paging === false) {
+
+        // we know how to do this cleanly for bootstrap, not so much
+        // for other themes/layouts
+        if (bootstrapActive) {
+          options.dom = "<'row'<'col-sm-4'i><'col-sm-8'f>>" +
+                        "<'row'<'col-sm-12'tr>>";
+        }
+
+        options.fnInfoCallback = function(oSettings, iStart, iEnd,
+                                           iMax, iTotal, sPre) {
+          return Number(iTotal).toLocaleString() + " records";
+        };
+      }
+    }
+
+    // auto hide navigation if requested
+    // Note, this only works on client-side processing mode as on server-side,
+    // cells (data.data) is null; In addition, we require the pageLength option
+    // being provided explicitly to enable this. Despite we may be able to deduce
+    // the default value of pageLength, it may complicate things so we'd rather
+    // put this responsiblity to users and warn them on the R side.
+    if (data.autoHideNavigation === true && data.options.paging !== false) {
+      // strip all nav if length >= cells
+      if ((cells instanceof Array) && data.options.pageLength >= cells.length)
+        options.dom = bootstrapActive ? "<'row'<'col-sm-12'tr>>" : "t";
+      // alternatively lean things out for flexdashboard mobile portrait
+      else if (bootstrapActive && window.FlexDashboard && window.FlexDashboard.isMobilePhone())
+        options.dom = "<'row'<'col-sm-12'f>>" +
+                      "<'row'<'col-sm-12'tr>>"  +
+                      "<'row'<'col-sm-12'p>>";
+    }
+
+    $.extend(true, options, data.options || {});
+
+    var searchCols = options.searchCols;
+    if (searchCols) {
+      searchCols = searchCols.map(function(x) {
+        return x === null ? '' : x.search;
+      });
+      // FIXME: this means I don't respect the escapeRegex setting
+      delete options.searchCols;
+    }
+
+    // server-side processing?
+    var server = options.serverSide === true;
+
+    // use the dataSrc function to pre-process JSON data returned from R
+    var DT_rows_all = [], DT_rows_current = [];
+    if (server && HTMLWidgets.shinyMode && typeof options.ajax === 'object' &&
+        /^session\/[\da-z]+\/dataobj/.test(options.ajax.url) && !options.ajax.dataSrc) {
+      options.ajax.dataSrc = function(json) {
+        DT_rows_all = $.makeArray(json.DT_rows_all);
+        DT_rows_current = $.makeArray(json.DT_rows_current);
+        var data = json.data;
+        if (!colReorderEnabled()) return data;
+        var table = $table.DataTable(), order = table.colReorder.order(), flag = true, i, j, row;
+        for (i = 0; i < order.length; ++i) if (order[i] !== i) flag = false;
+        if (flag) return data;
+        for (i = 0; i < data.length; ++i) {
+          row = data[i].slice();
+          for (j = 0; j < order.length; ++j) data[i][j] = row[order[j]];
+        }
+        return data;
+      };
+    }
+
+    var thiz = this;
+    if (instance.fillContainer) $table.on('init.dt', function(e) {
+      thiz.fillAvailableHeight(el, $(el).innerHeight());
+    });
+    // If the page contains serveral datatables and one of which enables colReorder,
+    // the table.colReorder.order() function will exist but throws error when called.
+    // So it seems like the only way to know if colReorder is enabled or not is to
+    // check the options.
+    var colReorderEnabled = function() { return "colReorder" in options; };
+    var table = $table.DataTable(options);
+    $el.data('datatable', table);
+
+    if ('rowGroup' in options) {
+      // Maintain RowGroup dataSrc when columns are reordered (#1109)
+      table.on('column-reorder', function(e, settings, details) {
+        var oldDataSrc = table.rowGroup().dataSrc();
+        var newDataSrc = details.mapping[oldDataSrc];
+        table.rowGroup().dataSrc(newDataSrc);
+      });
+    }
+
+    // Unregister previous Crosstalk event subscriptions, if they exist
+    if (instance.ctfilterSubscription) {
+      instance.ctfilterHandle.off("change", instance.ctfilterSubscription);
+      instance.ctfilterSubscription = null;
+    }
+    if (instance.ctselectSubscription) {
+      instance.ctselectHandle.off("change", instance.ctselectSubscription);
+      instance.ctselectSubscription = null;
+    }
+
+    if (!crosstalkOptions.group) {
+      $table[0].ctfilter = null;
+      $table[0].ctselect = null;
+    } else {
+      var key = crosstalkOptions.key;
+      function keysToMatches(keys) {
+        if (!keys) {
+          return null;
+        } else {
+          var selectedKeys = {};
+          for (var i = 0; i < keys.length; i++) {
+            selectedKeys[keys[i]] = true;
+          }
+          var matches = {};
+          for (var j = 0; j < key.length; j++) {
+            if (selectedKeys[key[j]])
+              matches[j] = true;
+          }
+          return matches;
+        }
+      }
+
+      function applyCrosstalkFilter(e) {
+        $table[0].ctfilter = keysToMatches(e.value);
+        table.draw();
+      }
+      instance.ctfilterSubscription = instance.ctfilterHandle.on("change", applyCrosstalkFilter);
+      applyCrosstalkFilter({value: instance.ctfilterHandle.filteredKeys});
+
+      function applyCrosstalkSelection(e) {
+        if (e.sender !== instance.ctselectHandle) {
+          table
+            .rows('.' + selClass, {search: 'applied'})
+            .nodes()
+            .to$()
+            .removeClass(selClass);
+          if (selectedRows)
+            changeInput('rows_selected', selectedRows(), void 0, true);
+        }
+
+        if (e.sender !== instance.ctselectHandle && e.value && e.value.length) {
+          var matches = keysToMatches(e.value);
+
+          // persistent selection with plotly (& leaflet)
+          var ctOpts = crosstalk.var("plotlyCrosstalkOpts").get() || {};
+          if (ctOpts.persistent === true) {
+            var matches = $.extend(matches, $table[0].ctselect);
+          }
+
+          $table[0].ctselect = matches;
+          table.draw();
+        } else {
+          if ($table[0].ctselect) {
+            $table[0].ctselect = null;
+            table.draw();
+          }
+        }
+      }
+      instance.ctselectSubscription = instance.ctselectHandle.on("change", applyCrosstalkSelection);
+      // TODO: This next line doesn't seem to work when renderDataTable is used
+      applyCrosstalkSelection({value: instance.ctselectHandle.value});
+    }
+
+    var inArray = function(val, array) {
+      return $.inArray(val, $.makeArray(array)) > -1;
+    };
+
+    // search the i-th column
+    var searchColumn = function(i, value) {
+      var regex = false, ci = true;
+      if (options.search) {
+        regex = options.search.regex,
+        ci = options.search.caseInsensitive !== false;
+      }
+      // need to transpose the column index when colReorder is enabled
+      if (table.colReorder) i = table.colReorder.transpose(i);
+      return table.column(i).search(value, regex, !regex, ci);
+    };
+
+    if (data.filter !== 'none') {
+      if (!data.hasOwnProperty('filterSettings')) data.filterSettings = {};
+
+      filterRow.each(function(i, td) {
+
+        var $td = $(td), type = $td.data('type'), filter;
+        var $input = $td.children('div').first().children('input');
+        var disabled = $input.prop('disabled');
+        var searchable = table.settings()[0].aoColumns[i].bSearchable;
+        $input.prop('disabled', !searchable || disabled);
+        $input.data('searchable', searchable); // for updating later
+        $input.on('input blur', function() {
+          $input.next('span').toggle(Boolean($input.val()));
+        });
+        // Bootstrap sets pointer-events to none and we won't be able to click
+        // the clear button
+        $input.next('span').css('pointer-events', 'auto').hide().click(function() {
+          $(this).hide().prev('input').val('').trigger('input').focus();
+        });
+        var searchCol;  // search string for this column
+        if (searchCols && searchCols[i]) {
+          searchCol = searchCols[i];
+          $input.val(searchCol).trigger('input');
+        }
+        var $x = $td.children('div').last();
+
+        // remove the overflow: hidden attribute of the scrollHead
+        // (otherwise the scrolling table body obscures the filters)
+        // The workaround and the discussion from
+        // https://github.com/rstudio/DT/issues/554#issuecomment-518007347
+        // Otherwise the filter selection will not be anchored to the values
+        // when the columns number is many and scrollX is enabled.
+        var scrollHead = $(el).find('.dataTables_scrollHead,.dataTables_scrollFoot');
+        var cssOverflowHead = scrollHead.css('overflow');
+        var scrollBody = $(el).find('.dataTables_scrollBody');
+        var cssOverflowBody = scrollBody.css('overflow');
+        var scrollTable = $(el).find('.dataTables_scroll');
+        var cssOverflowTable = scrollTable.css('overflow');
+        if (cssOverflowHead === 'hidden') {
+          $x.on('show hide', function(e) {
+            if (e.type === 'show') {
+              scrollHead.css('overflow', 'visible');
+              scrollBody.css('overflow', 'visible');
+              scrollTable.css('overflow-x', 'scroll');
+            } else {
+              scrollHead.css('overflow', cssOverflowHead);
+              scrollBody.css('overflow', cssOverflowBody);
+              scrollTable.css('overflow-x', cssOverflowTable);
+            }
+          });
+          $x.css('z-index', 25);
+        }
+
+        if (inArray(type, ['factor', 'logical'])) {
+          $input.on({
+            click: function() {
+              $input.parent().hide(); $x.show().trigger('show'); filter[0].selectize.focus();
+            },
+            input: function() {
+              var v1 = JSON.stringify(filter[0].selectize.getValue()), v2 = $input.val();
+              if (v1 === '[]') v1 = '';
+              if (v1 !== v2) filter[0].selectize.setValue(v2 === '' ? [] : JSON.parse(v2));
+            }
+          });
+          var $input2 = $x.children('select');
+          filter = $input2.selectize($.extend({
+            options: $input2.data('options').map(function(v, i) {
+              return ({text: v, value: v});
+            }),
+            plugins: ['remove_button'],
+            hideSelected: true,
+            onChange: function(value) {
+              if (value === null) value = []; // compatibility with jQuery 3.0
+              $input.val(value.length ? JSON.stringify(value) : '');
+              if (value.length) $input.trigger('input');
+              $input.attr('title', $input.val());
+              if (server) {
+                searchColumn(i, value.length ? JSON.stringify(value) : '').draw();
+                return;
+              }
+              // turn off filter if nothing selected
+              $td.data('filter', value.length > 0);
+              table.draw();  // redraw table, and filters will be applied
+            }
+          }, data.filterSettings.select));
+          filter[0].selectize.on('blur', function() {
+            $x.hide().trigger('hide'); $input.parent().show(); $input.trigger('blur');
+          });
+          filter.next('div').css('margin-bottom', 'auto');
+        } else if (type === 'character') {
+          var fun = function() {
+            searchColumn(i, $input.val()).draw();
+          };
+          // throttle searching for server-side processing
+          var throttledFun = $.fn.dataTable.util.throttle(fun, options.searchDelay);
+          $input.on('input', function(e, immediate) {
+            // always bypass throttling when immediate = true (via the updateSearch method)
+            (immediate || !server) ? fun() : throttledFun();
+          });
+        } else if (inArray(type, ['number', 'integer', 'date', 'time'])) {
+          var $x0 = $x;
+          $x = $x0.children('div').first();
+          $x0.css({
+            'background-color': '#fff',
+            'border': '1px #ddd solid',
+            'border-radius': '4px',
+            'padding': data.vertical ? '35px 20px': '20px 20px 10px 20px'
+          });
+          var $spans = $x0.children('span').css({
+            'margin-top': data.vertical ? '0' : '10px',
+            'white-space': 'nowrap'
+          });
+          var $span1 = $spans.first(), $span2 = $spans.last();
+          var r1 = +$x.data('min'), r2 = +$x.data('max');
+          // when the numbers are too small or have many decimal places, the
+          // slider may have numeric precision problems (#150)
+          var scale = Math.pow(10, Math.max(0, +$x.data('scale') || 0));
+          r1 = Math.round(r1 * scale); r2 = Math.round(r2 * scale);
+          var scaleBack = function(x, scale) {
+            if (scale === 1) return x;
+            var d = Math.round(Math.log(scale) / Math.log(10));
+            // to avoid problems like 3.423/100 -> 0.034230000000000003
+            return (x / scale).toFixed(d);
+          };
+          var slider_min = function() {
+            return filter.noUiSlider('options').range.min;
+          };
+          var slider_max = function() {
+            return filter.noUiSlider('options').range.max;
+          };
+          $input.on({
+            focus: function() {
+              $x0.show().trigger('show');
+              // first, make sure the slider div leaves at least 20px between
+              // the two (slider value) span's
+              $x0.width(Math.max(160, $span1.outerWidth() + $span2.outerWidth() + 20));
+              // then, if the input is really wide or slider is vertical,
+              // make the slider the same width as the input
+              if ($x0.outerWidth() < $input.outerWidth() || data.vertical) {
+                $x0.outerWidth($input.outerWidth());
+              }
+              // make sure the slider div does not reach beyond the right margin
+              if ($(window).width() < $x0.offset().left + $x0.width()) {
+                $x0.offset({
+                  'left': $input.offset().left + $input.outerWidth() - $x0.outerWidth()
+                });
+              }
+            },
+            blur: function() {
+              $x0.hide().trigger('hide');
+            },
+            input: function() {
+              if ($input.val() === '') filter.val([slider_min(), slider_max()]);
+            },
+            change: function() {
+              var v = $input.val().replace(/\s/g, '');
+              if (v === '') return;
+              v = v.split('...');
+              if (v.length !== 2) {
+                $input.parent().addClass('has-error');
+                return;
+              }
+              if (v[0] === '') v[0] = slider_min();
+              if (v[1] === '') v[1] = slider_max();
+              $input.parent().removeClass('has-error');
+              // treat date as UTC time at midnight
+              var strTime = function(x) {
+                var s = type === 'date' ? 'T00:00:00Z' : '';
+                var t = new Date(x + s).getTime();
+                // add 10 minutes to date since it does not hurt the date, and
+                // it helps avoid the tricky floating point arithmetic problems,
+                // e.g. sometimes the date may be a few milliseconds earlier
+                // than the midnight due to precision problems in noUiSlider
+                return type === 'date' ? t + 3600000 : t;
+              };
+              if (inArray(type, ['date', 'time'])) {
+                v[0] = strTime(v[0]);
+                v[1] = strTime(v[1]);
+              }
+              if (v[0] != slider_min()) v[0] *= scale;
+              if (v[1] != slider_max()) v[1] *= scale;
+              filter.val(v);
+            }
+          });
+          var formatDate = function(d) {
+            d = scaleBack(d, scale);
+            if (type === 'number') return d;
+            if (type === 'integer') return parseInt(d);
+            var x = new Date(+d);
+            if (type === 'date') {
+              var pad0 = function(x) {
+                return ('0' + x).substr(-2, 2);
+              };
+              return x.getUTCFullYear() + '-' + pad0(1 + x.getUTCMonth())
+                      + '-' + pad0(x.getUTCDate());
+            } else {
+              return x.toISOString();
+            }
+          };
+          var opts = type === 'date' ? { step: 60 * 60 * 1000 } :
+                     type === 'integer' ? { step: 1 } : {};
+
+          opts.orientation = data.vertical ? 'vertical': 'horizontal';
+          opts.direction = data.vertical ? 'rtl': 'ltr';
+
+          filter = $x.noUiSlider($.extend({
+            start: [r1, r2],
+            range: {min: r1, max: r2},
+            connect: true
+          }, opts, data.filterSettings.slider));
+          if (scale > 1) (function() {
+            var t1 = r1, t2 = r2;
+            var val = filter.val();
+            while (val[0] > r1 || val[1] < r2) {
+              if (val[0] > r1) {
+                t1 -= val[0] - r1;
+              }
+              if (val[1] < r2) {
+                t2 += r2 - val[1];
+              }
+              filter = $x.noUiSlider($.extend({
+                start: [t1, t2],
+                range: {min: t1, max: t2},
+                connect: true
+              }, opts, data.filterSettings.slider), true);
+              val = filter.val();
+            }
+            r1  = t1; r2 = t2;
+          })();
+          // format with active column renderer, if defined
+          var colDef = data.options.columnDefs.find(function(def) {
+            return (def.targets === i || inArray(i, def.targets)) && 'render' in def;
+          });
+          var updateSliderText = function(v1, v2) {
+            // we only know how to use function renderers
+            if (colDef && typeof colDef.render === 'function') {
+              var restore = function(v) {
+                v = scaleBack(v, scale);
+                return inArray(type, ['date', 'time']) ? new Date(+v) : v;
+              }
+              $span1.text(colDef.render(restore(v1), 'display'));
+              $span2.text(colDef.render(restore(v2), 'display'));
+            } else {
+              $span1.text(formatDate(v1));
+              $span2.text(formatDate(v2));
+            }
+          };
+          updateSliderText(r1, r2);
+          var updateSlider = function(e) {
+            var val = filter.val();
+            // turn off filter if in full range
+            $td.data('filter', val[0] > slider_min() || val[1] < slider_max());
+            var v1 = formatDate(val[0]), v2 = formatDate(val[1]), ival;
+            if ($td.data('filter')) {
+              ival = v1 + ' ... ' + v2;
+              $input.attr('title', ival).val(ival).trigger('input');
+            } else {
+              $input.attr('title', '').val('');
+            }
+            updateSliderText(val[0], val[1]);
+            if (e.type === 'slide') return;  // no searching when sliding only
+            if (server) {
+              searchColumn(i, $td.data('filter') ? ival : '').draw();
+              return;
+            }
+            table.draw();
+          };
+          filter.on({
+            set: updateSlider,
+            slide: updateSlider
+          });
+        }
+
+        // server-side processing will be handled by R (or whatever server
+        // language you use); the following code is only needed for client-side
+        // processing
+        if (server) {
+          // if a search string has been pre-set, search now
+          if (searchCol) $input.trigger('input').trigger('change');
+          return;
+        }
+
+        var customFilter = function(settings, data, dataIndex) {
+          // there is no way to attach a search function to a specific table,
+          // and we need to make sure a global search function is not applied to
+          // all tables (i.e. a range filter in a previous table should not be
+          // applied to the current table); we use the settings object to
+          // determine if we want to perform searching on the current table,
+          // since settings.sTableId will be different to different tables
+          if (table.settings()[0] !== settings) return true;
+          // no filter on this column or no need to filter this column
+          if (typeof filter === 'undefined' || !$td.data('filter')) return true;
+
+          var r = filter.val(), v, r0, r1;
+          var i_data = function(i) {
+            if (!colReorderEnabled()) return i;
+            var order = table.colReorder.order(), k;
+            for (k = 0; k < order.length; ++k) if (order[k] === i) return k;
+            return i; // in theory it will never be here...
+          }
+          v = data[i_data(i)];
+          if (type === 'number' || type === 'integer') {
+            v = parseFloat(v);
+            // how to handle NaN? currently exclude these rows
+            if (isNaN(v)) return(false);
+            r0 = parseFloat(scaleBack(r[0], scale))
+            r1 = parseFloat(scaleBack(r[1], scale));
+            if (v >= r0 && v <= r1) return true;
+          } else if (type === 'date' || type === 'time') {
+            v = new Date(v);
+            r0 = new Date(r[0] / scale); r1 = new Date(r[1] / scale);
+            if (v >= r0 && v <= r1) return true;
+          } else if (type === 'factor') {
+            if (r.length === 0 || inArray(v, r)) return true;
+          } else if (type === 'logical') {
+            if (r.length === 0) return true;
+            if (inArray(v === '' ? 'na' : v, r)) return true;
+          }
+          return false;
+        };
+
+        $.fn.dataTable.ext.search.push(customFilter);
+
+        // search for the preset search strings if it is non-empty
+        if (searchCol) $input.trigger('input').trigger('change');
+
+      });
+
+    }
+
+    // highlight search keywords
+    var highlight = function() {
+      var body = $(table.table().body());
+      // removing the old highlighting first
+      body.unhighlight();
+
+      // don't highlight the "not found" row, so we get the rows using the api
+      if (table.rows({ filter: 'applied' }).data().length === 0) return;
+      // highlight global search keywords
+      body.highlight($.trim(table.search()).split(/\s+/));
+      // then highlight keywords from individual column filters
+      if (filterRow) filterRow.each(function(i, td) {
+        var $td = $(td), type = $td.data('type');
+        if (type !== 'character') return;
+        var $input = $td.children('div').first().children('input');
+        var column = table.column(i).nodes().to$(),
+            val = $.trim($input.val());
+        if (type !== 'character' || val === '') return;
+        column.highlight(val.split(/\s+/));
+      });
+    };
+
+    if (options.searchHighlight) {
+      table
+      .on('draw.dt.dth column-visibility.dt.dth column-reorder.dt.dth', highlight)
+      .on('destroy', function() {
+        // remove event handler
+        table.off('draw.dt.dth column-visibility.dt.dth column-reorder.dt.dth');
+      });
+
+      // Set the option for escaping regex characters in our search string.  This will be used
+      // for all future matching.
+      jQuery.fn.highlight.options.escapeRegex = (!options.search || !options.search.regex);
+
+      // initial highlight for state saved conditions and initial states
+      highlight();
+    }
+
+    // run the callback function on the table instance
+    if (typeof data.callback === 'function') data.callback(table);
+
+    // double click to edit the cell, row, column, or all cells
+    if (data.editable) table.on('dblclick.dt', 'tbody td', function(e) {
+      // only bring up the editor when the cell itself is dbclicked, and ignore
+      // other dbclick events bubbled up (e.g. from the <input>)
+      if (e.target !== this) return;
+      var target = [], immediate = false;
+      switch (data.editable.target) {
+        case 'cell':
+          target = [this];
+          immediate = true;  // edit will take effect immediately
+          break;
+        case 'row':
+          target = table.cells(table.cell(this).index().row, '*').nodes();
+          break;
+        case 'column':
+          target = table.cells('*', table.cell(this).index().column).nodes();
+          break;
+        case 'all':
+          target = table.cells().nodes();
+          break;
+        default:
+          throw 'The editable parameter must be "cell", "row", "column", or "all"';
+      }
+      var disableCols = data.editable.disable ? data.editable.disable.columns : null;
+      var numericCols = data.editable.numeric;
+      var areaCols = data.editable.area;
+      var dateCols = data.editable.date;
+      for (var i = 0; i < target.length; i++) {
+        (function(cell, current) {
+          var $cell = $(cell), html = $cell.html();
+          var _cell = table.cell(cell), value = _cell.data(), index = _cell.index().column;
+          var $input;
+          if (inArray(index, numericCols)) {
+            $input = $('<input type="number">');
+          } else if (inArray(index, areaCols)) {
+            $input = $('<textarea></textarea>');
+          } else if (inArray(index, dateCols)) {
+            $input = $('<input type="date">');
+          } else {
+            $input = $('<input type="text">');
+          }
+          if (!immediate) {
+            $cell.data('input', $input).data('html', html);
+            $input.attr('title', 'Hit Ctrl+Enter to finish editing, or Esc to cancel');
+          }
+          $input.val(value);
+          if (inArray(index, disableCols)) {
+            $input.attr('readonly', '').css('filter', 'invert(25%)');
+          }
+          $cell.empty().append($input);
+          if (cell === current) $input.focus();
+          $input.css('width', '100%');
+
+          if (immediate) $input.on('blur', function(e) {
+            var valueNew = $input.val();
+            if (valueNew !== value) {
+              _cell.data(valueNew);
+              if (HTMLWidgets.shinyMode) {
+                changeInput('cell_edit', [cellInfo(cell)], 'DT.cellInfo', null, {priority: 'event'});
+              }
+              // for server-side processing, users have to call replaceData() to update the table
+              if (!server) table.draw(false);
+            } else {
+              $cell.html(html);
+            }
+          }).on('keyup', function(e) {
+            // hit Escape to cancel editing
+            if (e.keyCode === 27) $input.trigger('blur');
+          });
+
+          // bulk edit (row, column, or all)
+          if (!immediate) $input.on('keyup', function(e) {
+            var removeInput = function($cell, restore) {
+              $cell.data('input').remove();
+              if (restore) $cell.html($cell.data('html'));
+            }
+            if (e.keyCode === 27) {
+              for (var i = 0; i < target.length; i++) {
+                removeInput($(target[i]), true);
+              }
+            } else if (e.keyCode === 13 && e.ctrlKey) {
+              // Ctrl + Enter
+              var cell, $cell, _cell, cellData = [];
+              for (var i = 0; i < target.length; i++) {
+                cell = target[i]; $cell = $(cell); _cell = table.cell(cell);
+                _cell.data($cell.data('input').val());
+                HTMLWidgets.shinyMode && cellData.push(cellInfo(cell));
+                removeInput($cell, false);
+              }
+              if (HTMLWidgets.shinyMode) {
+                changeInput('cell_edit', cellData, 'DT.cellInfo', null, {priority: "event"});
+              }
+              if (!server) table.draw(false);
+            }
+          });
+        })(target[i], this);
+      }
+    });
+
+    // interaction with shiny
+    if (!HTMLWidgets.shinyMode && !crosstalkOptions.group) return;
+
+    var methods = {};
+    var shinyData = {};
+
+    methods.updateCaption = function(caption) {
+      if (!caption) return;
+      $table.children('caption').replaceWith(caption);
+    }
+
+    // register clear functions to remove input values when the table is removed
+    instance.clearInputs = {};
+
+    var changeInput = function(id, value, type, noCrosstalk, opts) {
+      var event = id;
+      id = el.id + '_' + id;
+      if (type) id = id + ':' + type;
+      // do not update if the new value is the same as old value
+      if (event !== 'cell_edit' && !/_clicked$/.test(event) && shinyData.hasOwnProperty(id) && shinyData[id] === JSON.stringify(value))
+        return;
+      shinyData[id] = JSON.stringify(value);
+      if (HTMLWidgets.shinyMode && Shiny.setInputValue) {
+        Shiny.setInputValue(id, value, opts);
+        if (!instance.clearInputs[id]) instance.clearInputs[id] = function() {
+          Shiny.setInputValue(id, null);
+        }
+      }
+
+      // HACK
+      if (event === "rows_selected" && !noCrosstalk) {
+        if (crosstalkOptions.group) {
+          var keys = crosstalkOptions.key;
+          var selectedKeys = null;
+          if (value) {
+            selectedKeys = [];
+            for (var i = 0; i < value.length; i++) {
+              // The value array's contents use 1-based row numbers, so we must
+              // convert to 0-based before indexing into the keys array.
+              selectedKeys.push(keys[value[i] - 1]);
+            }
+          }
+          instance.ctselectHandle.set(selectedKeys);
+        }
+      }
+    };
+
+    var addOne = function(x) {
+      return x.map(function(i) { return 1 + i; });
+    };
+
+    var unique = function(x) {
+      var ux = [];
+      $.each(x, function(i, el){
+        if ($.inArray(el, ux) === -1) ux.push(el);
+      });
+      return ux;
+    }
+
+    // change the row index of a cell
+    var tweakCellIndex = function(cell) {
+      var info = cell.index();
+      // some cell may not be valid. e.g, #759
+      // when using the RowGroup extension, datatables will
+      // generate the row label and the cells are not part of
+      // the data thus contain no row/col info
+      if (info === undefined)
+        return {row: null, col: null};
+      if (server) {
+        info.row = DT_rows_current[info.row];
+      } else {
+        info.row += 1;
+      }
+      return {row: info.row, col: info.column};
+    }
+
+    var cleanSelectedValues = function() {
+      changeInput('rows_selected', []);
+      changeInput('columns_selected', []);
+      changeInput('cells_selected', transposeArray2D([]), 'shiny.matrix');
+    }
+    // #828 we should clean the selection on the server-side when the table reloads
+    cleanSelectedValues();
+
+    // a flag to indicates if select extension is initialized or not
+    var flagSelectExt = table.settings()[0]._select !== undefined;
+    // the Select extension should only be used in the client mode and
+    // when the selection.mode is set to none
+    if (data.selection.mode === 'none' && !server && flagSelectExt) {
+      var updateRowsSelected = function() {
+        var rows = table.rows({selected: true});
+        var selected = [];
+        $.each(rows.indexes().toArray(), function(i, v) {
+          selected.push(v + 1);
+        });
+        changeInput('rows_selected', selected);
+      }
+      var updateColsSelected = function() {
+        var columns = table.columns({selected: true});
+        changeInput('columns_selected', columns.indexes().toArray());
+      }
+      var updateCellsSelected = function() {
+        var cells = table.cells({selected: true});
+        var selected = [];
+        cells.every(function() {
+          var row = this.index().row;
+          var col = this.index().column;
+          selected = selected.concat([[row + 1, col]]);
+        });
+        changeInput('cells_selected', transposeArray2D(selected), 'shiny.matrix');
+      }
+      table.on('select deselect', function(e, dt, type, indexes) {
+        updateRowsSelected();
+        updateColsSelected();
+        updateCellsSelected();
+      })
+      updateRowsSelected();
+      updateColsSelected();
+      updateCellsSelected();
+    }
+
+    var selMode = data.selection.mode, selTarget = data.selection.target;
+    var selDisable = data.selection.selectable === false;
+    if (inArray(selMode, ['single', 'multiple'])) {
+      var selClass = inArray(data.style, ['bootstrap', 'bootstrap4']) ? 'active' : 'selected';
+      // selected1: row indices; selected2: column indices
+      var initSel = function(x) {
+        if (x === null || typeof x === 'boolean' || selTarget === 'cell') {
+          return {rows: [], cols: []};
+        } else if (selTarget === 'row') {
+          return {rows: $.makeArray(x), cols: []};
+        } else if (selTarget === 'column') {
+          return {rows: [], cols: $.makeArray(x)};
+        } else if (selTarget === 'row+column') {
+          return {rows: $.makeArray(x.rows), cols: $.makeArray(x.cols)};
+        }
+      }
+      var selected = data.selection.selected;
+      var selected1 = initSel(selected).rows, selected2 = initSel(selected).cols;
+      // selectable should contain either all positive or all non-positive values, not both
+      // positive values indicate "selectable" while non-positive values means "nonselectable"
+      // the assertion is performed on R side. (only column indicides could be zero which indicates
+      // the row name)
+      var selectable = data.selection.selectable;
+      var selectable1 = initSel(selectable).rows, selectable2 = initSel(selectable).cols;
+
+      // After users reorder the rows or filter the table, we cannot use the table index
+      // directly. Instead, we need this function to find out the rows between the two clicks.
+      // If user filter the table again between the start click and the end click, the behavior
+      // would be undefined, but it should not be a problem.
+      var shiftSelRowsIndex = function(start, end) {
+        var indexes = server ? DT_rows_all : table.rows({ search: 'applied' }).indexes().toArray();
+        start = indexes.indexOf(start); end = indexes.indexOf(end);
+        // if start is larger than end, we need to swap
+        if (start > end) {
+          var tmp = end; end = start; start = tmp;
+        }
+        return indexes.slice(start, end + 1);
+      }
+
+      var serverRowIndex = function(clientRowIndex) {
+        return server ? DT_rows_current[clientRowIndex] : clientRowIndex + 1;
+      }
+
+      // row, column, or cell selection
+      var lastClickedRow;
+      if (inArray(selTarget, ['row', 'row+column'])) {
+        // Get the current selected rows. It will also
+        // update the selected1's value based on the current row selection state
+        // Note we can't put this function inside selectRows() directly,
+        // the reason is method.selectRows() will override selected1's value but this
+        // function will add rows to selected1 (keep the existing selection), which is
+        // inconsistent with column and cell selection.
+        var selectedRows = function() {
+          var rows = table.rows('.' + selClass);
+          var idx = rows.indexes().toArray();
+          if (!server) {
+            selected1 = addOne(idx);
+            return selected1;
+          }
+          idx = idx.map(function(i) {
+            return DT_rows_current[i];
+          });
+          selected1 = selMode === 'multiple' ? unique(selected1.concat(idx)) : idx;
+          return selected1;
+        }
+        // Change selected1's value based on selectable1, then refresh the row state
+        var onlyKeepSelectableRows = function() {
+          if (selDisable) { // users can't select; useful when only want backend select
+            selected1 = [];
+            return;
+          }
+          if (selectable1.length === 0) return;
+          var nonselectable = selectable1[0] <= 0;
+          if (nonselectable) {
+            // should make selectable1 positive
+            selected1 = $(selected1).not(selectable1.map(function(i) { return -i; })).get();
+          } else {
+            selected1 = $(selected1).filter(selectable1).get();
+          }
+        }
+        // Change selected1's value based on selectable1, then
+        // refresh the row selection state according to values in selected1
+        var selectRows = function(ignoreSelectable) {
+          if (!ignoreSelectable) onlyKeepSelectableRows();
+          table.$('tr.' + selClass).removeClass(selClass);
+          if (selected1.length === 0) return;
+          if (server) {
+            table.rows({page: 'current'}).every(function() {
+              if (inArray(DT_rows_current[this.index()], selected1)) {
+                $(this.node()).addClass(selClass);
+              }
+            });
+          } else {
+            var selected0 = selected1.map(function(i) { return i - 1; });
+            $(table.rows(selected0).nodes()).addClass(selClass);
+          }
+        }
+        table.on('mousedown.dt', 'tbody tr', function(e) {
+          var $this = $(this), thisRow = table.row(this);
+          if (selMode === 'multiple') {
+            if (e.shiftKey && lastClickedRow !== undefined) {
+              // select or de-select depends on the last clicked row's status
+              var flagSel = !$this.hasClass(selClass);
+              var crtClickedRow = serverRowIndex(thisRow.index());
+              if (server) {
+                var rowsIndex = shiftSelRowsIndex(lastClickedRow, crtClickedRow);
+                // update current page's selClass
+                rowsIndex.map(function(i) {
+                  var rowIndex = DT_rows_current.indexOf(i);
+                  if (rowIndex >= 0) {
+                    var row = table.row(rowIndex).nodes().to$();
+                    var flagRowSel = !row.hasClass(selClass);
+                    if (flagSel === flagRowSel) row.toggleClass(selClass);
+                  }
+                });
+                // update selected1
+                if (flagSel) {
+                  selected1 = unique(selected1.concat(rowsIndex));
+                } else {
+                  selected1 = selected1.filter(function(index) {
+                    return !inArray(index, rowsIndex);
+                  });
+                }
+              } else {
+                // js starts from 0
+                shiftSelRowsIndex(lastClickedRow - 1, crtClickedRow - 1).map(function(value) {
+                  var row = table.row(value).nodes().to$();
+                  var flagRowSel = !row.hasClass(selClass);
+                  if (flagSel === flagRowSel) row.toggleClass(selClass);
+                });
+              }
+              e.preventDefault();
+            } else {
+              $this.toggleClass(selClass);
+            }
+          } else {
+            if ($this.hasClass(selClass)) {
+              $this.removeClass(selClass);
+            } else {
+              table.$('tr.' + selClass).removeClass(selClass);
+              $this.addClass(selClass);
+            }
+          }
+          if (server && !$this.hasClass(selClass)) {
+            var id = DT_rows_current[thisRow.index()];
+            // remove id from selected1 since its class .selected has been removed
+            if (inArray(id, selected1)) selected1.splice($.inArray(id, selected1), 1);
+          }
+          selectedRows(); // update selected1's value based on selClass
+          selectRows(false); // only keep the selectable rows
+          changeInput('rows_selected', selected1);
+          changeInput('row_last_clicked', serverRowIndex(thisRow.index()), null, null, {priority: 'event'});
+          lastClickedRow = serverRowIndex(thisRow.index());
+        });
+        selectRows(false);  // in case users have specified pre-selected rows
+        // restore selected rows after the table is redrawn (e.g. sort/search/page);
+        // client-side tables will preserve the selections automatically; for
+        // server-side tables, we have to *real* row indices are in `selected1`
+        changeInput('rows_selected', selected1);
+        if (server) table.on('draw.dt', function(e) { selectRows(false); });
+        methods.selectRows = function(selected, ignoreSelectable) {
+          selected1 = $.makeArray(selected);
+          selectRows(ignoreSelectable);
+          changeInput('rows_selected', selected1);
+        }
+      }
+
+      if (inArray(selTarget, ['column', 'row+column'])) {
+        if (selTarget === 'row+column') {
+          $(table.columns().footer()).css('cursor', 'pointer');
+        }
+        // update selected2's value based on selectable2
+        var onlyKeepSelectableCols = function() {
+          if (selDisable) { // users can't select; useful when only want backend select
+            selected2 = [];
+            return;
+          }
+          if (selectable2.length === 0) return;
+          var nonselectable = selectable2[0] <= 0;
+          if (nonselectable) {
+            // need to make selectable2 positive
+            selected2 = $(selected2).not(selectable2.map(function(i) { return -i; })).get();
+          } else {
+            selected2 = $(selected2).filter(selectable2).get();
+          }
+        }
+        // update selected2 and then
+        // refresh the col selection state according to values in selected2
+        var selectCols = function(ignoreSelectable) {
+          if (!ignoreSelectable) onlyKeepSelectableCols();
+          // if selected2 is not a valide index (e.g., larger than the column number)
+          // table.columns(selected2) will fail and result in a blank table
+          // this is different from the table.rows(), where the out-of-range indexes
+          // doesn't affect at all
+          selected2 = $(selected2).filter(table.columns().indexes()).get();
+          table.columns().nodes().flatten().to$().removeClass(selClass);
+          if (selected2.length > 0)
+            table.columns(selected2).nodes().flatten().to$().addClass(selClass);
+        }
+        var callback = function() {
+          var colIdx = selTarget === 'column' ? table.cell(this).index().column :
+              $.inArray(this, table.columns().footer()),
+              thisCol = $(table.column(colIdx).nodes());
+          if (colIdx === -1) return;
+          if (thisCol.hasClass(selClass)) {
+            thisCol.removeClass(selClass);
+            selected2.splice($.inArray(colIdx, selected2), 1);
+          } else {
+            if (selMode === 'single') $(table.cells().nodes()).removeClass(selClass);
+            thisCol.addClass(selClass);
+            selected2 = selMode === 'single' ? [colIdx] : unique(selected2.concat([colIdx]));
+          }
+          selectCols(false); // update selected2 based on selectable
+          changeInput('columns_selected', selected2);
+        }
+        if (selTarget === 'column') {
+          $(table.table().body()).on('click.dt', 'td', callback);
+        } else {
+          $(table.table().footer()).on('click.dt', 'tr th', callback);
+        }
+        selectCols(false);  // in case users have specified pre-selected columns
+        changeInput('columns_selected', selected2);
+        if (server) table.on('draw.dt', function(e) { selectCols(false); });
+        methods.selectColumns = function(selected, ignoreSelectable) {
+          selected2 = $.makeArray(selected);
+          selectCols(ignoreSelectable);
+          changeInput('columns_selected', selected2);
+        }
+      }
+
+      if (selTarget === 'cell') {
+        var selected3 = [], selectable3 = [];
+        if (selected !== null) selected3 = selected;
+        if (selectable !== null && typeof selectable !== 'boolean') selectable3 = selectable;
+        var findIndex = function(ij, sel) {
+          for (var i = 0; i < sel.length; i++) {
+            if (ij[0] === sel[i][0] && ij[1] === sel[i][1]) return i;
+          }
+          return -1;
+        }
+         // Change selected3's value based on selectable3, then refresh the cell state
+        var onlyKeepSelectableCells = function() {
+          if (selDisable) { // users can't select; useful when only want backend select
+            selected3 = [];
+            return;
+          }
+          if (selectable3.length === 0) return;
+          var nonselectable = selectable3[0][0] <= 0;
+          var out = [];
+          if (nonselectable) {
+            selected3.map(function(ij) {
+              // should make selectable3 positive
+              if (findIndex([-ij[0], -ij[1]], selectable3) === -1) { out.push(ij); }
+            });
+          } else {
+            selected3.map(function(ij) {
+              if (findIndex(ij, selectable3) > -1) { out.push(ij); }
+            });
+          }
+          selected3 = out;
+        }
+        // Change selected3's value based on selectable3, then
+        // refresh the cell selection state according to values in selected3
+        var selectCells = function(ignoreSelectable) {
+          if (!ignoreSelectable) onlyKeepSelectableCells();
+          table.$('td.' + selClass).removeClass(selClass);
+          if (selected3.length === 0) return;
+          if (server) {
+            table.cells({page: 'current'}).every(function() {
+              var info = tweakCellIndex(this);
+              if (findIndex([info.row, info.col], selected3) > -1)
+                $(this.node()).addClass(selClass);
+            });
+          } else {
+            selected3.map(function(ij) {
+              $(table.cell(ij[0] - 1, ij[1]).node()).addClass(selClass);
+            });
+          }
+        };
+        table.on('click.dt', 'tbody td', function() {
+          var $this = $(this), info = tweakCellIndex(table.cell(this));
+          if ($this.hasClass(selClass)) {
+            $this.removeClass(selClass);
+            selected3.splice(findIndex([info.row, info.col], selected3), 1);
+          } else {
+            if (selMode === 'single') $(table.cells().nodes()).removeClass(selClass);
+            $this.addClass(selClass);
+            selected3 = selMode === 'single' ? [[info.row, info.col]] :
+              unique(selected3.concat([[info.row, info.col]]));
+          }
+          selectCells(false); // must call this to update selected3 based on selectable3
+          changeInput('cells_selected', transposeArray2D(selected3), 'shiny.matrix');
+        });
+        selectCells(false);  // in case users have specified pre-selected columns
+        changeInput('cells_selected', transposeArray2D(selected3), 'shiny.matrix');
+
+        if (server) table.on('draw.dt', function(e) { selectCells(false); });
+        methods.selectCells = function(selected, ignoreSelectable) {
+          selected3 = selected ? selected : [];
+          selectCells(ignoreSelectable);
+          changeInput('cells_selected', transposeArray2D(selected3), 'shiny.matrix');
+        }
+      }
+    }
+
+    // expose some table info to Shiny
+    var updateTableInfo = function(e, settings) {
+      // TODO: is anyone interested in the page info?
+      // changeInput('page_info', table.page.info());
+      var updateRowInfo = function(id, modifier) {
+        var idx;
+        if (server) {
+          idx = modifier.page === 'current' ? DT_rows_current : DT_rows_all;
+        } else {
+          var rows = table.rows($.extend({
+            search: 'applied',
+            page: 'all'
+          }, modifier));
+          idx = addOne(rows.indexes().toArray());
+        }
+        changeInput('rows' + '_' + id, idx);
+      };
+      updateRowInfo('current', {page: 'current'});
+      updateRowInfo('all', {});
+    }
+    table.on('draw.dt', updateTableInfo);
+    updateTableInfo();
+
+    // state info
+    table.on('draw.dt column-visibility.dt', function() {
+      changeInput('state', table.state());
+    });
+    changeInput('state', table.state());
+
+    // search info
+    var updateSearchInfo = function() {
+      changeInput('search', table.search());
+      if (filterRow) changeInput('search_columns', filterRow.toArray().map(function(td) {
+        return $(td).find('input').first().val();
+      }));
+    }
+    table.on('draw.dt', updateSearchInfo);
+    updateSearchInfo();
+
+    var cellInfo = function(thiz) {
+      var info = tweakCellIndex(table.cell(thiz));
+      info.value = table.cell(thiz).data();
+      return info;
+    }
+    // the current cell clicked on
+    table.on('click.dt', 'tbody td', function() {
+      changeInput('cell_clicked', cellInfo(this), null, null, {priority: 'event'});
+    })
+    changeInput('cell_clicked', {});
+
+    // do not trigger table selection when clicking on links unless they have classes
+    table.on('mousedown.dt', 'tbody td a', function(e) {
+      if (this.className === '') e.stopPropagation();
+    });
+
+    methods.addRow = function(data, rowname, resetPaging) {
+      var n = table.columns().indexes().length, d = n - data.length;
+      if (d === 1) {
+        data = rowname.concat(data)
+      } else if (d !== 0) {
+        console.log(data);
+        console.log(table.columns().indexes());
+        throw 'New data must be of the same length as current data (' + n + ')';
+      };
+      table.row.add(data).draw(resetPaging);
+    }
+
+    methods.updateSearch = function(keywords) {
+      if (keywords.global !== null)
+        $(table.table().container()).find('input[type=search]').first()
+             .val(keywords.global).trigger('input');
+      var columns = keywords.columns;
+      if (!filterRow || columns === null) return;
+      filterRow.toArray().map(function(td, i) {
+        var v = typeof columns === 'string' ? columns : columns[i];
+        if (typeof v === 'undefined') {
+          console.log('The search keyword for column ' + i + ' is undefined')
+          return;
+        }
+        // Update column search string and values on linked filter widgets.
+        // 'input' for factor and char filters, 'change' for numeric filters.
+        $(td).find('input').first().val(v).trigger('input', [true]).trigger('change');
+      });
+      table.draw();
+    }
+
+    methods.hideCols = function(hide, reset) {
+      if (reset) table.columns().visible(true, false);
+      table.columns(hide).visible(false);
+    }
+
+    methods.showCols = function(show, reset) {
+      if (reset) table.columns().visible(false, false);
+      table.columns(show).visible(true);
+    }
+
+    methods.colReorder = function(order, origOrder) {
+      table.colReorder.order(order, origOrder);
+    }
+
+    methods.selectPage = function(page) {
+      if (table.page.info().pages < page || page < 1) {
+        throw 'Selected page is out of range';
+      };
+      table.page(page - 1).draw(false);
+    }
+
+    methods.reloadData = function(resetPaging, clearSelection) {
+      // empty selections first if necessary
+      if (methods.selectRows && inArray('row', clearSelection)) methods.selectRows([]);
+      if (methods.selectColumns && inArray('column', clearSelection)) methods.selectColumns([]);
+      if (methods.selectCells && inArray('cell', clearSelection)) methods.selectCells([]);
+      table.ajax.reload(null, resetPaging);
+    }
+
+    // update table filters (set new limits of sliders)
+    methods.updateFilters = function(newProps) {
+      // loop through each filter in the filter row
+      filterRow.each(function(i, td) {
+        var k = i;
+        if (filterRow.length > newProps.length) {
+          if (i === 0) return;  // first column is row names
+          k = i - 1;
+        }
+        // Update the filters to reflect the updated data.
+        // Allow "falsy" (e.g. NULL) to signify a no-op.
+        if (newProps[k]) {
+          setFilterProps(td, newProps[k]);
+        }
+      });
+    };
+
+    table.shinyMethods = methods;
+  },
+  resize: function(el, width, height, instance) {
+    if (instance.data) this.renderValue(el, instance.data, instance);
+
+    // dynamically adjust height if fillContainer = TRUE
+    if (instance.fillContainer)
+      this.fillAvailableHeight(el, height);
+
+    this.adjustWidth(el);
+  },
+
+  // dynamically set the scroll body to fill available height
+  // (used with fillContainer = TRUE)
+  fillAvailableHeight: function(el, availableHeight) {
+
+    // see how much of the table is occupied by header/footer elements
+    // and use that to compute a target scroll body height
+    var dtWrapper = $(el).find('div.dataTables_wrapper');
+    var dtScrollBody = $(el).find($('div.dataTables_scrollBody'));
+    var framingHeight = dtWrapper.innerHeight() - dtScrollBody.innerHeight();
+    var scrollBodyHeight = availableHeight - framingHeight;
+
+    // we need to set `max-height` to none as datatables library now sets this
+    // to a fixed height, disabling the ability to resize to fill the window,
+    // as it will be set to a fixed 100px under such circumstances, e.g., RStudio IDE,
+    // or FlexDashboard
+    // see https://github.com/rstudio/DT/issues/951#issuecomment-1026464509
+    dtScrollBody.css('max-height', 'none');
+    // set the height
+    dtScrollBody.height(scrollBodyHeight + 'px');
+  },
+
+  // adjust the width of columns; remove the hard-coded widths on table and the
+  // scroll header when scrollX/Y are enabled
+  adjustWidth: function(el) {
+    var $el = $(el), table = $el.data('datatable');
+    if (table) table.columns.adjust();
+    $el.find('.dataTables_scrollHeadInner').css('width', '')
+        .children('table').css('margin-left', '');
+  }
+});
+
+  if (!HTMLWidgets.shinyMode) return;
+
+  Shiny.addCustomMessageHandler('datatable-calls', function(data) {
+    var id = data.id;
+    var el = document.getElementById(id);
+    var table = el ? $(el).data('datatable') : null;
+    if (!table) {
+      console.log("Couldn't find table with id " + id);
+      return;
+    }
+
+    var methods = table.shinyMethods, call = data.call;
+    if (methods[call.method]) {
+      methods[call.method].apply(table, call.args);
+    } else {
+      console.log("Unknown method " + call.method);
+    }
+  });
+
+})();
diff --git a/docs/coverage/lib/datatables-css-0.0.0/datatables-crosstalk.css b/docs/coverage/lib/datatables-css-0.0.0/datatables-crosstalk.css
new file mode 100644
index 00000000..bd1159c8
--- /dev/null
+++ b/docs/coverage/lib/datatables-css-0.0.0/datatables-crosstalk.css
@@ -0,0 +1,32 @@
+.dt-crosstalk-fade {
+  opacity: 0.2;
+}
+
+html body div.DTS div.dataTables_scrollBody {
+  background: none;
+}
+
+
+/*
+Fix https://github.com/rstudio/DT/issues/563
+If the `table.display` is set to "block" (e.g., pkgdown), the browser will display
+datatable objects strangely. The search panel and the page buttons will still be
+in full-width but the table body will be "compact" and shorter.
+In therory, having this attributes will affect `dom="t"`
+with `display: block` users. But in reality, there should be no one.
+We may remove the below lines in the future if the upstream agree to have this there.
+See https://github.com/DataTables/DataTablesSrc/issues/160
+*/
+
+table.dataTable {
+  display: table;
+}
+
+
+/*
+When DTOutput(fill = TRUE), it receives a .html-fill-item class (via htmltools::bindFillRole()), which effectively amounts to `flex: 1 1 auto`. That's mostly fine, but the case where `fillContainer=TRUE`+`height:auto`+`flex-basis:auto` and the container (e.g., a bslib::card()) doesn't have a defined height is a bit problematic since the table wants to fit the parent but the parent wants to fit the table, which results pretty small table height (maybe because there is a minimum height somewhere?). It seems better in this case to impose a 400px height default for the table, which we can do by setting `flex-basis` to 400px (the table is still allowed to grow/shrink when the container has an opinionated height).
+*/
+
+.html-fill-container > .html-fill-item.datatables {
+  flex-basis: 400px;
+}
diff --git a/docs/coverage/lib/dt-core-1.13.6/css/jquery.dataTables.extra.css b/docs/coverage/lib/dt-core-1.13.6/css/jquery.dataTables.extra.css
new file mode 100644
index 00000000..b2dd141f
--- /dev/null
+++ b/docs/coverage/lib/dt-core-1.13.6/css/jquery.dataTables.extra.css
@@ -0,0 +1,28 @@
+/* Selected rows/cells */
+table.dataTable tr.selected td, table.dataTable td.selected {
+  background-color: #b0bed9 !important;
+}
+/* In case of scrollX/Y or FixedHeader */
+.dataTables_scrollBody .dataTables_sizing {
+  visibility: hidden;
+}
+
+/* The datatables' theme CSS file doesn't define
+the color but with white background. It leads to an issue that
+when the HTML's body color is set to 'white', the user can't
+see the text since the background is white. One case happens in the
+RStudio's IDE when inline viewing the DT table inside an Rmd file,
+if the IDE theme is set to "Cobalt".
+
+See https://github.com/rstudio/DT/issues/447 for more info
+
+This fixes should have little side-effects because all the other elements
+of the default theme use the #333 font color.
+
+TODO: The upstream may use relative colors for both the table background
+and the color. It means the table can display well without this patch
+then. At that time, we need to remove the below CSS attributes.
+*/
+div.datatables {
+  color: #333;
+}
diff --git a/docs/coverage/lib/dt-core-1.13.6/css/jquery.dataTables.min.css b/docs/coverage/lib/dt-core-1.13.6/css/jquery.dataTables.min.css
new file mode 100644
index 00000000..ad59f843
--- /dev/null
+++ b/docs/coverage/lib/dt-core-1.13.6/css/jquery.dataTables.min.css
@@ -0,0 +1 @@
+:root{--dt-row-selected: 13, 110, 253;--dt-row-selected-text: 255, 255, 255;--dt-row-selected-link: 9, 10, 11;--dt-row-stripe: 0, 0, 0;--dt-row-hover: 0, 0, 0;--dt-column-ordering: 0, 0, 0;--dt-html-background: white}:root.dark{--dt-html-background: rgb(33, 37, 41)}table.dataTable td.dt-control{text-align:center;cursor:pointer}table.dataTable td.dt-control:before{display:inline-block;color:rgba(0, 0, 0, 0.5);content:"►"}table.dataTable tr.dt-hasChild td.dt-control:before{content:"▼"}html.dark table.dataTable td.dt-control:before{color:rgba(255, 255, 255, 0.5)}html.dark table.dataTable tr.dt-hasChild td.dt-control:before{color:rgba(255, 255, 255, 0.5)}table.dataTable thead>tr>th.sorting,table.dataTable thead>tr>th.sorting_asc,table.dataTable thead>tr>th.sorting_desc,table.dataTable thead>tr>th.sorting_asc_disabled,table.dataTable thead>tr>th.sorting_desc_disabled,table.dataTable thead>tr>td.sorting,table.dataTable thead>tr>td.sorting_asc,table.dataTable thead>tr>td.sorting_desc,table.dataTable thead>tr>td.sorting_asc_disabled,table.dataTable thead>tr>td.sorting_desc_disabled{cursor:pointer;position:relative;padding-right:26px}table.dataTable thead>tr>th.sorting:before,table.dataTable thead>tr>th.sorting:after,table.dataTable thead>tr>th.sorting_asc:before,table.dataTable thead>tr>th.sorting_asc:after,table.dataTable thead>tr>th.sorting_desc:before,table.dataTable thead>tr>th.sorting_desc:after,table.dataTable thead>tr>th.sorting_asc_disabled:before,table.dataTable thead>tr>th.sorting_asc_disabled:after,table.dataTable thead>tr>th.sorting_desc_disabled:before,table.dataTable thead>tr>th.sorting_desc_disabled:after,table.dataTable thead>tr>td.sorting:before,table.dataTable thead>tr>td.sorting:after,table.dataTable thead>tr>td.sorting_asc:before,table.dataTable thead>tr>td.sorting_asc:after,table.dataTable thead>tr>td.sorting_desc:before,table.dataTable thead>tr>td.sorting_desc:after,table.dataTable thead>tr>td.sorting_asc_disabled:before,table.dataTable thead>tr>td.sorting_asc_disabled:after,table.dataTable thead>tr>td.sorting_desc_disabled:before,table.dataTable thead>tr>td.sorting_desc_disabled:after{position:absolute;display:block;opacity:.125;right:10px;line-height:9px;font-size:.8em}table.dataTable thead>tr>th.sorting:before,table.dataTable thead>tr>th.sorting_asc:before,table.dataTable thead>tr>th.sorting_desc:before,table.dataTable thead>tr>th.sorting_asc_disabled:before,table.dataTable thead>tr>th.sorting_desc_disabled:before,table.dataTable thead>tr>td.sorting:before,table.dataTable thead>tr>td.sorting_asc:before,table.dataTable thead>tr>td.sorting_desc:before,table.dataTable thead>tr>td.sorting_asc_disabled:before,table.dataTable thead>tr>td.sorting_desc_disabled:before{bottom:50%;content:"▲";content:"▲"/""}table.dataTable thead>tr>th.sorting:after,table.dataTable thead>tr>th.sorting_asc:after,table.dataTable thead>tr>th.sorting_desc:after,table.dataTable thead>tr>th.sorting_asc_disabled:after,table.dataTable thead>tr>th.sorting_desc_disabled:after,table.dataTable thead>tr>td.sorting:after,table.dataTable thead>tr>td.sorting_asc:after,table.dataTable thead>tr>td.sorting_desc:after,table.dataTable thead>tr>td.sorting_asc_disabled:after,table.dataTable thead>tr>td.sorting_desc_disabled:after{top:50%;content:"▼";content:"▼"/""}table.dataTable thead>tr>th.sorting_asc:before,table.dataTable thead>tr>th.sorting_desc:after,table.dataTable thead>tr>td.sorting_asc:before,table.dataTable thead>tr>td.sorting_desc:after{opacity:.6}table.dataTable thead>tr>th.sorting_desc_disabled:after,table.dataTable thead>tr>th.sorting_asc_disabled:before,table.dataTable thead>tr>td.sorting_desc_disabled:after,table.dataTable thead>tr>td.sorting_asc_disabled:before{display:none}table.dataTable thead>tr>th:active,table.dataTable thead>tr>td:active{outline:none}div.dataTables_scrollBody>table.dataTable>thead>tr>th:before,div.dataTables_scrollBody>table.dataTable>thead>tr>th:after,div.dataTables_scrollBody>table.dataTable>thead>tr>td:before,div.dataTables_scrollBody>table.dataTable>thead>tr>td:after{display:none}div.dataTables_processing{position:absolute;top:50%;left:50%;width:200px;margin-left:-100px;margin-top:-26px;text-align:center;padding:2px}div.dataTables_processing>div:last-child{position:relative;width:80px;height:15px;margin:1em auto}div.dataTables_processing>div:last-child>div{position:absolute;top:0;width:13px;height:13px;border-radius:50%;background:rgb(13, 110, 253);background:rgb(var(--dt-row-selected));animation-timing-function:cubic-bezier(0, 1, 1, 0)}div.dataTables_processing>div:last-child>div:nth-child(1){left:8px;animation:datatables-loader-1 .6s infinite}div.dataTables_processing>div:last-child>div:nth-child(2){left:8px;animation:datatables-loader-2 .6s infinite}div.dataTables_processing>div:last-child>div:nth-child(3){left:32px;animation:datatables-loader-2 .6s infinite}div.dataTables_processing>div:last-child>div:nth-child(4){left:56px;animation:datatables-loader-3 .6s infinite}@keyframes datatables-loader-1{0%{transform:scale(0)}100%{transform:scale(1)}}@keyframes datatables-loader-3{0%{transform:scale(1)}100%{transform:scale(0)}}@keyframes datatables-loader-2{0%{transform:translate(0, 0)}100%{transform:translate(24px, 0)}}table.dataTable.nowrap th,table.dataTable.nowrap td{white-space:nowrap}table.dataTable th.dt-left,table.dataTable td.dt-left{text-align:left}table.dataTable th.dt-center,table.dataTable td.dt-center,table.dataTable td.dataTables_empty{text-align:center}table.dataTable th.dt-right,table.dataTable td.dt-right{text-align:right}table.dataTable th.dt-justify,table.dataTable td.dt-justify{text-align:justify}table.dataTable th.dt-nowrap,table.dataTable td.dt-nowrap{white-space:nowrap}table.dataTable thead th,table.dataTable thead td,table.dataTable tfoot th,table.dataTable tfoot td{text-align:left}table.dataTable thead th.dt-head-left,table.dataTable thead td.dt-head-left,table.dataTable tfoot th.dt-head-left,table.dataTable tfoot td.dt-head-left{text-align:left}table.dataTable thead th.dt-head-center,table.dataTable thead td.dt-head-center,table.dataTable tfoot th.dt-head-center,table.dataTable tfoot td.dt-head-center{text-align:center}table.dataTable thead th.dt-head-right,table.dataTable thead td.dt-head-right,table.dataTable tfoot th.dt-head-right,table.dataTable tfoot td.dt-head-right{text-align:right}table.dataTable thead th.dt-head-justify,table.dataTable thead td.dt-head-justify,table.dataTable tfoot th.dt-head-justify,table.dataTable tfoot td.dt-head-justify{text-align:justify}table.dataTable thead th.dt-head-nowrap,table.dataTable thead td.dt-head-nowrap,table.dataTable tfoot th.dt-head-nowrap,table.dataTable tfoot td.dt-head-nowrap{white-space:nowrap}table.dataTable tbody th.dt-body-left,table.dataTable tbody td.dt-body-left{text-align:left}table.dataTable tbody th.dt-body-center,table.dataTable tbody td.dt-body-center{text-align:center}table.dataTable tbody th.dt-body-right,table.dataTable tbody td.dt-body-right{text-align:right}table.dataTable tbody th.dt-body-justify,table.dataTable tbody td.dt-body-justify{text-align:justify}table.dataTable tbody th.dt-body-nowrap,table.dataTable tbody td.dt-body-nowrap{white-space:nowrap}table.dataTable{width:100%;margin:0 auto;clear:both;border-collapse:separate;border-spacing:0}table.dataTable thead th,table.dataTable tfoot th{font-weight:bold}table.dataTable>thead>tr>th,table.dataTable>thead>tr>td{padding:10px;border-bottom:1px solid rgba(0, 0, 0, 0.3)}table.dataTable>thead>tr>th:active,table.dataTable>thead>tr>td:active{outline:none}table.dataTable>tfoot>tr>th,table.dataTable>tfoot>tr>td{padding:10px 10px 6px 10px;border-top:1px solid rgba(0, 0, 0, 0.3)}table.dataTable tbody tr{background-color:transparent}table.dataTable tbody tr.selected>*{box-shadow:inset 0 0 0 9999px rgba(13, 110, 253, 0.9);box-shadow:inset 0 0 0 9999px rgba(var(--dt-row-selected), 0.9);color:rgb(255, 255, 255);color:rgb(var(--dt-row-selected-text))}table.dataTable tbody tr.selected a{color:rgb(9, 10, 11);color:rgb(var(--dt-row-selected-link))}table.dataTable tbody th,table.dataTable tbody td{padding:8px 10px}table.dataTable.row-border>tbody>tr>th,table.dataTable.row-border>tbody>tr>td,table.dataTable.display>tbody>tr>th,table.dataTable.display>tbody>tr>td{border-top:1px solid rgba(0, 0, 0, 0.15)}table.dataTable.row-border>tbody>tr:first-child>th,table.dataTable.row-border>tbody>tr:first-child>td,table.dataTable.display>tbody>tr:first-child>th,table.dataTable.display>tbody>tr:first-child>td{border-top:none}table.dataTable.row-border>tbody>tr.selected+tr.selected>td,table.dataTable.display>tbody>tr.selected+tr.selected>td{border-top-color:#0262ef}table.dataTable.cell-border>tbody>tr>th,table.dataTable.cell-border>tbody>tr>td{border-top:1px solid rgba(0, 0, 0, 0.15);border-right:1px solid rgba(0, 0, 0, 0.15)}table.dataTable.cell-border>tbody>tr>th:first-child,table.dataTable.cell-border>tbody>tr>td:first-child{border-left:1px solid rgba(0, 0, 0, 0.15)}table.dataTable.cell-border>tbody>tr:first-child>th,table.dataTable.cell-border>tbody>tr:first-child>td{border-top:none}table.dataTable.stripe>tbody>tr.odd>*,table.dataTable.display>tbody>tr.odd>*{box-shadow:inset 0 0 0 9999px rgba(0, 0, 0, 0.023);box-shadow:inset 0 0 0 9999px rgba(var(--dt-row-stripe), 0.023)}table.dataTable.stripe>tbody>tr.odd.selected>*,table.dataTable.display>tbody>tr.odd.selected>*{box-shadow:inset 0 0 0 9999px rgba(13, 110, 253, 0.923);box-shadow:inset 0 0 0 9999px rgba(var(--dt-row-selected), 0.923)}table.dataTable.hover>tbody>tr:hover>*,table.dataTable.display>tbody>tr:hover>*{box-shadow:inset 0 0 0 9999px rgba(0, 0, 0, 0.035);box-shadow:inset 0 0 0 9999px rgba(var(--dt-row-hover), 0.035)}table.dataTable.hover>tbody>tr.selected:hover>*,table.dataTable.display>tbody>tr.selected:hover>*{box-shadow:inset 0 0 0 9999px #0d6efd !important;box-shadow:inset 0 0 0 9999px rgba(var(--dt-row-selected), 1) !important}table.dataTable.order-column>tbody tr>.sorting_1,table.dataTable.order-column>tbody tr>.sorting_2,table.dataTable.order-column>tbody tr>.sorting_3,table.dataTable.display>tbody tr>.sorting_1,table.dataTable.display>tbody tr>.sorting_2,table.dataTable.display>tbody tr>.sorting_3{box-shadow:inset 0 0 0 9999px rgba(0, 0, 0, 0.019);box-shadow:inset 0 0 0 9999px rgba(var(--dt-column-ordering), 0.019)}table.dataTable.order-column>tbody tr.selected>.sorting_1,table.dataTable.order-column>tbody tr.selected>.sorting_2,table.dataTable.order-column>tbody tr.selected>.sorting_3,table.dataTable.display>tbody tr.selected>.sorting_1,table.dataTable.display>tbody tr.selected>.sorting_2,table.dataTable.display>tbody tr.selected>.sorting_3{box-shadow:inset 0 0 0 9999px rgba(13, 110, 253, 0.919);box-shadow:inset 0 0 0 9999px rgba(var(--dt-row-selected), 0.919)}table.dataTable.display>tbody>tr.odd>.sorting_1,table.dataTable.order-column.stripe>tbody>tr.odd>.sorting_1{box-shadow:inset 0 0 0 9999px rgba(0, 0, 0, 0.054);box-shadow:inset 0 0 0 9999px rgba(var(--dt-column-ordering), 0.054)}table.dataTable.display>tbody>tr.odd>.sorting_2,table.dataTable.order-column.stripe>tbody>tr.odd>.sorting_2{box-shadow:inset 0 0 0 9999px rgba(0, 0, 0, 0.047);box-shadow:inset 0 0 0 9999px rgba(var(--dt-column-ordering), 0.047)}table.dataTable.display>tbody>tr.odd>.sorting_3,table.dataTable.order-column.stripe>tbody>tr.odd>.sorting_3{box-shadow:inset 0 0 0 9999px rgba(0, 0, 0, 0.039);box-shadow:inset 0 0 0 9999px rgba(var(--dt-column-ordering), 0.039)}table.dataTable.display>tbody>tr.odd.selected>.sorting_1,table.dataTable.order-column.stripe>tbody>tr.odd.selected>.sorting_1{box-shadow:inset 0 0 0 9999px rgba(13, 110, 253, 0.954);box-shadow:inset 0 0 0 9999px rgba(var(--dt-row-selected), 0.954)}table.dataTable.display>tbody>tr.odd.selected>.sorting_2,table.dataTable.order-column.stripe>tbody>tr.odd.selected>.sorting_2{box-shadow:inset 0 0 0 9999px rgba(13, 110, 253, 0.947);box-shadow:inset 0 0 0 9999px rgba(var(--dt-row-selected), 0.947)}table.dataTable.display>tbody>tr.odd.selected>.sorting_3,table.dataTable.order-column.stripe>tbody>tr.odd.selected>.sorting_3{box-shadow:inset 0 0 0 9999px rgba(13, 110, 253, 0.939);box-shadow:inset 0 0 0 9999px rgba(var(--dt-row-selected), 0.939)}table.dataTable.display>tbody>tr.even>.sorting_1,table.dataTable.order-column.stripe>tbody>tr.even>.sorting_1{box-shadow:inset 0 0 0 9999px rgba(0, 0, 0, 0.019);box-shadow:inset 0 0 0 9999px rgba(var(--dt-row-selected), 0.019)}table.dataTable.display>tbody>tr.even>.sorting_2,table.dataTable.order-column.stripe>tbody>tr.even>.sorting_2{box-shadow:inset 0 0 0 9999px rgba(0, 0, 0, 0.011);box-shadow:inset 0 0 0 9999px rgba(var(--dt-row-selected), 0.011)}table.dataTable.display>tbody>tr.even>.sorting_3,table.dataTable.order-column.stripe>tbody>tr.even>.sorting_3{box-shadow:inset 0 0 0 9999px rgba(0, 0, 0, 0.003);box-shadow:inset 0 0 0 9999px rgba(var(--dt-row-selected), 0.003)}table.dataTable.display>tbody>tr.even.selected>.sorting_1,table.dataTable.order-column.stripe>tbody>tr.even.selected>.sorting_1{box-shadow:inset 0 0 0 9999px rgba(13, 110, 253, 0.919);box-shadow:inset 0 0 0 9999px rgba(var(--dt-row-selected), 0.919)}table.dataTable.display>tbody>tr.even.selected>.sorting_2,table.dataTable.order-column.stripe>tbody>tr.even.selected>.sorting_2{box-shadow:inset 0 0 0 9999px rgba(13, 110, 253, 0.911);box-shadow:inset 0 0 0 9999px rgba(var(--dt-row-selected), 0.911)}table.dataTable.display>tbody>tr.even.selected>.sorting_3,table.dataTable.order-column.stripe>tbody>tr.even.selected>.sorting_3{box-shadow:inset 0 0 0 9999px rgba(13, 110, 253, 0.903);box-shadow:inset 0 0 0 9999px rgba(var(--dt-row-selected), 0.903)}table.dataTable.display tbody tr:hover>.sorting_1,table.dataTable.order-column.hover tbody tr:hover>.sorting_1{box-shadow:inset 0 0 0 9999px rgba(0, 0, 0, 0.082);box-shadow:inset 0 0 0 9999px rgba(var(--dt-row-hover), 0.082)}table.dataTable.display tbody tr:hover>.sorting_2,table.dataTable.order-column.hover tbody tr:hover>.sorting_2{box-shadow:inset 0 0 0 9999px rgba(0, 0, 0, 0.074);box-shadow:inset 0 0 0 9999px rgba(var(--dt-row-hover), 0.074)}table.dataTable.display tbody tr:hover>.sorting_3,table.dataTable.order-column.hover tbody tr:hover>.sorting_3{box-shadow:inset 0 0 0 9999px rgba(0, 0, 0, 0.062);box-shadow:inset 0 0 0 9999px rgba(var(--dt-row-hover), 0.062)}table.dataTable.display tbody tr:hover.selected>.sorting_1,table.dataTable.order-column.hover tbody tr:hover.selected>.sorting_1{box-shadow:inset 0 0 0 9999px rgba(13, 110, 253, 0.982);box-shadow:inset 0 0 0 9999px rgba(var(--dt-row-selected), 0.982)}table.dataTable.display tbody tr:hover.selected>.sorting_2,table.dataTable.order-column.hover tbody tr:hover.selected>.sorting_2{box-shadow:inset 0 0 0 9999px rgba(13, 110, 253, 0.974);box-shadow:inset 0 0 0 9999px rgba(var(--dt-row-selected), 0.974)}table.dataTable.display tbody tr:hover.selected>.sorting_3,table.dataTable.order-column.hover tbody tr:hover.selected>.sorting_3{box-shadow:inset 0 0 0 9999px rgba(13, 110, 253, 0.962);box-shadow:inset 0 0 0 9999px rgba(var(--dt-row-selected), 0.962)}table.dataTable.no-footer{border-bottom:1px solid rgba(0, 0, 0, 0.3)}table.dataTable.compact thead th,table.dataTable.compact thead td,table.dataTable.compact tfoot th,table.dataTable.compact tfoot td,table.dataTable.compact tbody th,table.dataTable.compact tbody td{padding:4px}table.dataTable th,table.dataTable td{box-sizing:content-box}.dataTables_wrapper{position:relative;clear:both}.dataTables_wrapper .dataTables_length{float:left}.dataTables_wrapper .dataTables_length select{border:1px solid #aaa;border-radius:3px;padding:5px;background-color:transparent;color:inherit;padding:4px}.dataTables_wrapper .dataTables_filter{float:right;text-align:right}.dataTables_wrapper .dataTables_filter input{border:1px solid #aaa;border-radius:3px;padding:5px;background-color:transparent;color:inherit;margin-left:3px}.dataTables_wrapper .dataTables_info{clear:both;float:left;padding-top:.755em}.dataTables_wrapper .dataTables_paginate{float:right;text-align:right;padding-top:.25em}.dataTables_wrapper .dataTables_paginate .paginate_button{box-sizing:border-box;display:inline-block;min-width:1.5em;padding:.5em 1em;margin-left:2px;text-align:center;text-decoration:none !important;cursor:pointer;color:inherit !important;border:1px solid transparent;border-radius:2px;background:transparent}.dataTables_wrapper .dataTables_paginate .paginate_button.current,.dataTables_wrapper .dataTables_paginate .paginate_button.current:hover{color:inherit !important;border:1px solid rgba(0, 0, 0, 0.3);background-color:rgba(0, 0, 0, 0.05);background:-webkit-gradient(linear, left top, left bottom, color-stop(0%, rgba(230, 230, 230, 0.05)), color-stop(100%, rgba(0, 0, 0, 0.05)));background:-webkit-linear-gradient(top, rgba(230, 230, 230, 0.05) 0%, rgba(0, 0, 0, 0.05) 100%);background:-moz-linear-gradient(top, rgba(230, 230, 230, 0.05) 0%, rgba(0, 0, 0, 0.05) 100%);background:-ms-linear-gradient(top, rgba(230, 230, 230, 0.05) 0%, rgba(0, 0, 0, 0.05) 100%);background:-o-linear-gradient(top, rgba(230, 230, 230, 0.05) 0%, rgba(0, 0, 0, 0.05) 100%);background:linear-gradient(to bottom, rgba(230, 230, 230, 0.05) 0%, rgba(0, 0, 0, 0.05) 100%)}.dataTables_wrapper .dataTables_paginate .paginate_button.disabled,.dataTables_wrapper .dataTables_paginate .paginate_button.disabled:hover,.dataTables_wrapper .dataTables_paginate .paginate_button.disabled:active{cursor:default;color:#666 !important;border:1px solid transparent;background:transparent;box-shadow:none}.dataTables_wrapper .dataTables_paginate .paginate_button:hover{color:white !important;border:1px solid #111;background-color:#111;background:-webkit-gradient(linear, left top, left bottom, color-stop(0%, #585858), color-stop(100%, #111));background:-webkit-linear-gradient(top, #585858 0%, #111 100%);background:-moz-linear-gradient(top, #585858 0%, #111 100%);background:-ms-linear-gradient(top, #585858 0%, #111 100%);background:-o-linear-gradient(top, #585858 0%, #111 100%);background:linear-gradient(to bottom, #585858 0%, #111 100%)}.dataTables_wrapper .dataTables_paginate .paginate_button:active{outline:none;background-color:#0c0c0c;background:-webkit-gradient(linear, left top, left bottom, color-stop(0%, #2b2b2b), color-stop(100%, #0c0c0c));background:-webkit-linear-gradient(top, #2b2b2b 0%, #0c0c0c 100%);background:-moz-linear-gradient(top, #2b2b2b 0%, #0c0c0c 100%);background:-ms-linear-gradient(top, #2b2b2b 0%, #0c0c0c 100%);background:-o-linear-gradient(top, #2b2b2b 0%, #0c0c0c 100%);background:linear-gradient(to bottom, #2b2b2b 0%, #0c0c0c 100%);box-shadow:inset 0 0 3px #111}.dataTables_wrapper .dataTables_paginate .ellipsis{padding:0 1em}.dataTables_wrapper .dataTables_length,.dataTables_wrapper .dataTables_filter,.dataTables_wrapper .dataTables_info,.dataTables_wrapper .dataTables_processing,.dataTables_wrapper .dataTables_paginate{color:inherit}.dataTables_wrapper .dataTables_scroll{clear:both}.dataTables_wrapper .dataTables_scroll div.dataTables_scrollBody{-webkit-overflow-scrolling:touch}.dataTables_wrapper .dataTables_scroll div.dataTables_scrollBody>table>thead>tr>th,.dataTables_wrapper .dataTables_scroll div.dataTables_scrollBody>table>thead>tr>td,.dataTables_wrapper .dataTables_scroll div.dataTables_scrollBody>table>tbody>tr>th,.dataTables_wrapper .dataTables_scroll div.dataTables_scrollBody>table>tbody>tr>td{vertical-align:middle}.dataTables_wrapper .dataTables_scroll div.dataTables_scrollBody>table>thead>tr>th>div.dataTables_sizing,.dataTables_wrapper .dataTables_scroll div.dataTables_scrollBody>table>thead>tr>td>div.dataTables_sizing,.dataTables_wrapper .dataTables_scroll div.dataTables_scrollBody>table>tbody>tr>th>div.dataTables_sizing,.dataTables_wrapper .dataTables_scroll div.dataTables_scrollBody>table>tbody>tr>td>div.dataTables_sizing{height:0;overflow:hidden;margin:0 !important;padding:0 !important}.dataTables_wrapper.no-footer .dataTables_scrollBody{border-bottom:1px solid rgba(0, 0, 0, 0.3)}.dataTables_wrapper.no-footer div.dataTables_scrollHead table.dataTable,.dataTables_wrapper.no-footer div.dataTables_scrollBody>table{border-bottom:none}.dataTables_wrapper:after{visibility:hidden;display:block;content:"";clear:both;height:0}@media screen and (max-width: 767px){.dataTables_wrapper .dataTables_info,.dataTables_wrapper .dataTables_paginate{float:none;text-align:center}.dataTables_wrapper .dataTables_paginate{margin-top:.5em}}@media screen and (max-width: 640px){.dataTables_wrapper .dataTables_length,.dataTables_wrapper .dataTables_filter{float:none;text-align:center}.dataTables_wrapper .dataTables_filter{margin-top:.5em}}html.dark{--dt-row-hover: 255, 255, 255;--dt-row-stripe: 255, 255, 255;--dt-column-ordering: 255, 255, 255}html.dark table.dataTable>thead>tr>th,html.dark table.dataTable>thead>tr>td{border-bottom:1px solid rgb(89, 91, 94)}html.dark table.dataTable>thead>tr>th:active,html.dark table.dataTable>thead>tr>td:active{outline:none}html.dark table.dataTable>tfoot>tr>th,html.dark table.dataTable>tfoot>tr>td{border-top:1px solid rgb(89, 91, 94)}html.dark table.dataTable.row-border>tbody>tr>th,html.dark table.dataTable.row-border>tbody>tr>td,html.dark table.dataTable.display>tbody>tr>th,html.dark table.dataTable.display>tbody>tr>td{border-top:1px solid rgb(64, 67, 70)}html.dark table.dataTable.row-border>tbody>tr.selected+tr.selected>td,html.dark table.dataTable.display>tbody>tr.selected+tr.selected>td{border-top-color:#0257d5}html.dark table.dataTable.cell-border>tbody>tr>th,html.dark table.dataTable.cell-border>tbody>tr>td{border-top:1px solid rgb(64, 67, 70);border-right:1px solid rgb(64, 67, 70)}html.dark table.dataTable.cell-border>tbody>tr>th:first-child,html.dark table.dataTable.cell-border>tbody>tr>td:first-child{border-left:1px solid rgb(64, 67, 70)}html.dark .dataTables_wrapper .dataTables_filter input,html.dark .dataTables_wrapper .dataTables_length select{border:1px solid rgba(255, 255, 255, 0.2);background-color:var(--dt-html-background)}html.dark .dataTables_wrapper .dataTables_paginate .paginate_button.current,html.dark .dataTables_wrapper .dataTables_paginate .paginate_button.current:hover{border:1px solid rgb(89, 91, 94);background:rgba(255, 255, 255, 0.15)}html.dark .dataTables_wrapper .dataTables_paginate .paginate_button.disabled,html.dark .dataTables_wrapper .dataTables_paginate .paginate_button.disabled:hover,html.dark .dataTables_wrapper .dataTables_paginate .paginate_button.disabled:active{color:#666 !important}html.dark .dataTables_wrapper .dataTables_paginate .paginate_button:hover{border:1px solid rgb(53, 53, 53);background:rgb(53, 53, 53)}html.dark .dataTables_wrapper .dataTables_paginate .paginate_button:active{background:#3a3a3a}
diff --git a/docs/coverage/lib/dt-core-1.13.6/js/jquery.dataTables.min.js b/docs/coverage/lib/dt-core-1.13.6/js/jquery.dataTables.min.js
new file mode 100644
index 00000000..f786b0da
--- /dev/null
+++ b/docs/coverage/lib/dt-core-1.13.6/js/jquery.dataTables.min.js
@@ -0,0 +1,4 @@
+/*! DataTables 1.13.6
+ * ©2008-2023 SpryMedia Ltd - datatables.net/license
+ */
+!function(n){"use strict";var a;"function"==typeof define&&define.amd?define(["jquery"],function(t){return n(t,window,document)}):"object"==typeof exports?(a=require("jquery"),"undefined"==typeof window?module.exports=function(t,e){return t=t||window,e=e||a(t),n(e,t,t.document)}:n(a,window,window.document)):window.DataTable=n(jQuery,window,document)}(function(P,j,v,H){"use strict";function d(t){var e=parseInt(t,10);return!isNaN(e)&&isFinite(t)?e:null}function l(t,e,n){var a=typeof t,r="string"==a;return"number"==a||"bigint"==a||!!h(t)||(e&&r&&(t=$(t,e)),n&&r&&(t=t.replace(q,"")),!isNaN(parseFloat(t))&&isFinite(t))}function a(t,e,n){var a;return!!h(t)||(h(a=t)||"string"==typeof a)&&!!l(t.replace(V,"").replace(/<script/i,""),e,n)||null}function m(t,e,n,a){var r=[],o=0,i=e.length;if(a!==H)for(;o<i;o++)t[e[o]][n]&&r.push(t[e[o]][n][a]);else for(;o<i;o++)r.push(t[e[o]][n]);return r}function f(t,e){var n,a=[];e===H?(e=0,n=t):(n=e,e=t);for(var r=e;r<n;r++)a.push(r);return a}function _(t){for(var e=[],n=0,a=t.length;n<a;n++)t[n]&&e.push(t[n]);return e}function s(t,e){return-1!==this.indexOf(t,e=e===H?0:e)}var p,e,t,w=function(t,v){if(w.factory(t,v))return w;if(this instanceof w)return P(t).DataTable(v);v=t,this.$=function(t,e){return this.api(!0).$(t,e)},this._=function(t,e){return this.api(!0).rows(t,e).data()},this.api=function(t){return new B(t?ge(this[p.iApiIndex]):this)},this.fnAddData=function(t,e){var n=this.api(!0),t=(Array.isArray(t)&&(Array.isArray(t[0])||P.isPlainObject(t[0]))?n.rows:n.row).add(t);return e!==H&&!e||n.draw(),t.flatten().toArray()},this.fnAdjustColumnSizing=function(t){var e=this.api(!0).columns.adjust(),n=e.settings()[0],a=n.oScroll;t===H||t?e.draw(!1):""===a.sX&&""===a.sY||Qt(n)},this.fnClearTable=function(t){var e=this.api(!0).clear();t!==H&&!t||e.draw()},this.fnClose=function(t){this.api(!0).row(t).child.hide()},this.fnDeleteRow=function(t,e,n){var a=this.api(!0),t=a.rows(t),r=t.settings()[0],o=r.aoData[t[0][0]];return t.remove(),e&&e.call(this,r,o),n!==H&&!n||a.draw(),o},this.fnDestroy=function(t){this.api(!0).destroy(t)},this.fnDraw=function(t){this.api(!0).draw(t)},this.fnFilter=function(t,e,n,a,r,o){var i=this.api(!0);(null===e||e===H?i:i.column(e)).search(t,n,a,o),i.draw()},this.fnGetData=function(t,e){var n,a=this.api(!0);return t!==H?(n=t.nodeName?t.nodeName.toLowerCase():"",e!==H||"td"==n||"th"==n?a.cell(t,e).data():a.row(t).data()||null):a.data().toArray()},this.fnGetNodes=function(t){var e=this.api(!0);return t!==H?e.row(t).node():e.rows().nodes().flatten().toArray()},this.fnGetPosition=function(t){var e=this.api(!0),n=t.nodeName.toUpperCase();return"TR"==n?e.row(t).index():"TD"==n||"TH"==n?[(n=e.cell(t).index()).row,n.columnVisible,n.column]:null},this.fnIsOpen=function(t){return this.api(!0).row(t).child.isShown()},this.fnOpen=function(t,e,n){return this.api(!0).row(t).child(e,n).show().child()[0]},this.fnPageChange=function(t,e){t=this.api(!0).page(t);e!==H&&!e||t.draw(!1)},this.fnSetColumnVis=function(t,e,n){t=this.api(!0).column(t).visible(e);n!==H&&!n||t.columns.adjust().draw()},this.fnSettings=function(){return ge(this[p.iApiIndex])},this.fnSort=function(t){this.api(!0).order(t).draw()},this.fnSortListener=function(t,e,n){this.api(!0).order.listener(t,e,n)},this.fnUpdate=function(t,e,n,a,r){var o=this.api(!0);return(n===H||null===n?o.row(e):o.cell(e,n)).data(t),r!==H&&!r||o.columns.adjust(),a!==H&&!a||o.draw(),0},this.fnVersionCheck=p.fnVersionCheck;var e,y=this,D=v===H,_=this.length;for(e in D&&(v={}),this.oApi=this.internal=p.internal,w.ext.internal)e&&(this[e]=$e(e));return this.each(function(){var r=1<_?be({},v,!0):v,o=0,t=this.getAttribute("id"),i=!1,e=w.defaults,l=P(this);if("table"!=this.nodeName.toLowerCase())W(null,0,"Non-table node initialisation ("+this.nodeName+")",2);else{K(e),Q(e.column),C(e,e,!0),C(e.column,e.column,!0),C(e,P.extend(r,l.data()),!0);for(var n=w.settings,o=0,s=n.length;o<s;o++){var a=n[o];if(a.nTable==this||a.nTHead&&a.nTHead.parentNode==this||a.nTFoot&&a.nTFoot.parentNode==this){var u=(r.bRetrieve!==H?r:e).bRetrieve,c=(r.bDestroy!==H?r:e).bDestroy;if(D||u)return a.oInstance;if(c){a.oInstance.fnDestroy();break}return void W(a,0,"Cannot reinitialise DataTable",3)}if(a.sTableId==this.id){n.splice(o,1);break}}null!==t&&""!==t||(t="DataTables_Table_"+w.ext._unique++,this.id=t);var f,d,h=P.extend(!0,{},w.models.oSettings,{sDestroyWidth:l[0].style.width,sInstance:t,sTableId:t}),p=(h.nTable=this,h.oApi=y.internal,h.oInit=r,n.push(h),h.oInstance=1===y.length?y:l.dataTable(),K(r),Z(r.oLanguage),r.aLengthMenu&&!r.iDisplayLength&&(r.iDisplayLength=(Array.isArray(r.aLengthMenu[0])?r.aLengthMenu[0]:r.aLengthMenu)[0]),r=be(P.extend(!0,{},e),r),F(h.oFeatures,r,["bPaginate","bLengthChange","bFilter","bSort","bSortMulti","bInfo","bProcessing","bAutoWidth","bSortClasses","bServerSide","bDeferRender"]),F(h,r,["asStripeClasses","ajax","fnServerData","fnFormatNumber","sServerMethod","aaSorting","aaSortingFixed","aLengthMenu","sPaginationType","sAjaxSource","sAjaxDataProp","iStateDuration","sDom","bSortCellsTop","iTabIndex","fnStateLoadCallback","fnStateSaveCallback","renderer","searchDelay","rowId",["iCookieDuration","iStateDuration"],["oSearch","oPreviousSearch"],["aoSearchCols","aoPreSearchCols"],["iDisplayLength","_iDisplayLength"]]),F(h.oScroll,r,[["sScrollX","sX"],["sScrollXInner","sXInner"],["sScrollY","sY"],["bScrollCollapse","bCollapse"]]),F(h.oLanguage,r,"fnInfoCallback"),L(h,"aoDrawCallback",r.fnDrawCallback,"user"),L(h,"aoServerParams",r.fnServerParams,"user"),L(h,"aoStateSaveParams",r.fnStateSaveParams,"user"),L(h,"aoStateLoadParams",r.fnStateLoadParams,"user"),L(h,"aoStateLoaded",r.fnStateLoaded,"user"),L(h,"aoRowCallback",r.fnRowCallback,"user"),L(h,"aoRowCreatedCallback",r.fnCreatedRow,"user"),L(h,"aoHeaderCallback",r.fnHeaderCallback,"user"),L(h,"aoFooterCallback",r.fnFooterCallback,"user"),L(h,"aoInitComplete",r.fnInitComplete,"user"),L(h,"aoPreDrawCallback",r.fnPreDrawCallback,"user"),h.rowIdFn=A(r.rowId),tt(h),h.oClasses),g=(P.extend(p,w.ext.classes,r.oClasses),l.addClass(p.sTable),h.iInitDisplayStart===H&&(h.iInitDisplayStart=r.iDisplayStart,h._iDisplayStart=r.iDisplayStart),null!==r.iDeferLoading&&(h.bDeferLoading=!0,t=Array.isArray(r.iDeferLoading),h._iRecordsDisplay=t?r.iDeferLoading[0]:r.iDeferLoading,h._iRecordsTotal=t?r.iDeferLoading[1]:r.iDeferLoading),h.oLanguage),t=(P.extend(!0,g,r.oLanguage),g.sUrl?(P.ajax({dataType:"json",url:g.sUrl,success:function(t){C(e.oLanguage,t),Z(t),P.extend(!0,g,t,h.oInit.oLanguage),R(h,null,"i18n",[h]),Jt(h)},error:function(){Jt(h)}}),i=!0):R(h,null,"i18n",[h]),null===r.asStripeClasses&&(h.asStripeClasses=[p.sStripeOdd,p.sStripeEven]),h.asStripeClasses),b=l.children("tbody").find("tr").eq(0),m=(-1!==P.inArray(!0,P.map(t,function(t,e){return b.hasClass(t)}))&&(P("tbody tr",this).removeClass(t.join(" ")),h.asDestroyStripes=t.slice()),[]),t=this.getElementsByTagName("thead");if(0!==t.length&&(wt(h.aoHeader,t[0]),m=Ct(h)),null===r.aoColumns)for(f=[],o=0,s=m.length;o<s;o++)f.push(null);else f=r.aoColumns;for(o=0,s=f.length;o<s;o++)nt(h,m?m[o]:null);st(h,r.aoColumnDefs,f,function(t,e){at(h,t,e)}),b.length&&(d=function(t,e){return null!==t.getAttribute("data-"+e)?e:null},P(b[0]).children("th, td").each(function(t,e){var n,a=h.aoColumns[t];a||W(h,0,"Incorrect column count",18),a.mData===t&&(n=d(e,"sort")||d(e,"order"),e=d(e,"filter")||d(e,"search"),null===n&&null===e||(a.mData={_:t+".display",sort:null!==n?t+".@data-"+n:H,type:null!==n?t+".@data-"+n:H,filter:null!==e?t+".@data-"+e:H},a._isArrayHost=!0,at(h,t)))}));var S=h.oFeatures,t=function(){if(r.aaSorting===H){var t=h.aaSorting;for(o=0,s=t.length;o<s;o++)t[o][1]=h.aoColumns[o].asSorting[0]}ce(h),S.bSort&&L(h,"aoDrawCallback",function(){var t,n;h.bSorted&&(t=I(h),n={},P.each(t,function(t,e){n[e.src]=e.dir}),R(h,null,"order",[h,t,n]),le(h))}),L(h,"aoDrawCallback",function(){(h.bSorted||"ssp"===E(h)||S.bDeferRender)&&ce(h)},"sc");var e=l.children("caption").each(function(){this._captionSide=P(this).css("caption-side")}),n=l.children("thead"),a=(0===n.length&&(n=P("<thead/>").appendTo(l)),h.nTHead=n[0],l.children("tbody")),n=(0===a.length&&(a=P("<tbody/>").insertAfter(n)),h.nTBody=a[0],l.children("tfoot"));if(0===(n=0===n.length&&0<e.length&&(""!==h.oScroll.sX||""!==h.oScroll.sY)?P("<tfoot/>").appendTo(l):n).length||0===n.children().length?l.addClass(p.sNoFooter):0<n.length&&(h.nTFoot=n[0],wt(h.aoFooter,h.nTFoot)),r.aaData)for(o=0;o<r.aaData.length;o++)x(h,r.aaData[o]);else!h.bDeferLoading&&"dom"!=E(h)||ut(h,P(h.nTBody).children("tr"));h.aiDisplay=h.aiDisplayMaster.slice(),!(h.bInitialised=!0)===i&&Jt(h)};L(h,"aoDrawCallback",de,"state_save"),r.bStateSave?(S.bStateSave=!0,he(h,0,t)):t()}}),y=null,this},c={},U=/[\r\n\u2028]/g,V=/<.*?>/g,X=/^\d{2,4}[\.\/\-]\d{1,2}[\.\/\-]\d{1,2}([T ]{1}\d{1,2}[:\.]\d{2}([\.:]\d{2})?)?$/,J=new RegExp("(\\"+["/",".","*","+","?","|","(",")","[","]","{","}","\\","$","^","-"].join("|\\")+")","g"),q=/['\u00A0,$£€¥%\u2009\u202F\u20BD\u20a9\u20BArfkɃΞ]/gi,h=function(t){return!t||!0===t||"-"===t},$=function(t,e){return c[e]||(c[e]=new RegExp(Ot(e),"g")),"string"==typeof t&&"."!==e?t.replace(/\./g,"").replace(c[e],"."):t},N=function(t,e,n){var a=[],r=0,o=t.length;if(n!==H)for(;r<o;r++)t[r]&&t[r][e]&&a.push(t[r][e][n]);else for(;r<o;r++)t[r]&&a.push(t[r][e]);return a},G=function(t){if(!(t.length<2))for(var e=t.slice().sort(),n=e[0],a=1,r=e.length;a<r;a++){if(e[a]===n)return!1;n=e[a]}return!0},z=function(t){if(G(t))return t.slice();var e,n,a,r=[],o=t.length,i=0;t:for(n=0;n<o;n++){for(e=t[n],a=0;a<i;a++)if(r[a]===e)continue t;r.push(e),i++}return r},Y=function(t,e){if(Array.isArray(e))for(var n=0;n<e.length;n++)Y(t,e[n]);else t.push(e);return t};function i(n){var a,r,o={};P.each(n,function(t,e){(a=t.match(/^([^A-Z]+?)([A-Z])/))&&-1!=="a aa ai ao as b fn i m o s ".indexOf(a[1]+" ")&&(r=t.replace(a[0],a[2].toLowerCase()),o[r]=t,"o"===a[1])&&i(n[t])}),n._hungarianMap=o}function C(n,a,r){var o;n._hungarianMap||i(n),P.each(a,function(t,e){(o=n._hungarianMap[t])===H||!r&&a[o]!==H||("o"===o.charAt(0)?(a[o]||(a[o]={}),P.extend(!0,a[o],a[t]),C(n[o],a[o],r)):a[o]=a[t])})}function Z(t){var e,n=w.defaults.oLanguage,a=n.sDecimal;a&&Me(a),t&&(e=t.sZeroRecords,!t.sEmptyTable&&e&&"No data available in table"===n.sEmptyTable&&F(t,t,"sZeroRecords","sEmptyTable"),!t.sLoadingRecords&&e&&"Loading..."===n.sLoadingRecords&&F(t,t,"sZeroRecords","sLoadingRecords"),t.sInfoThousands&&(t.sThousands=t.sInfoThousands),e=t.sDecimal)&&a!==e&&Me(e)}Array.isArray||(Array.isArray=function(t){return"[object Array]"===Object.prototype.toString.call(t)}),Array.prototype.includes||(Array.prototype.includes=s),String.prototype.trim||(String.prototype.trim=function(){return this.replace(/^[\s\uFEFF\xA0]+|[\s\uFEFF\xA0]+$/g,"")}),String.prototype.includes||(String.prototype.includes=s),w.util={throttle:function(a,t){var r,o,i=t!==H?t:200;return function(){var t=this,e=+new Date,n=arguments;r&&e<r+i?(clearTimeout(o),o=setTimeout(function(){r=H,a.apply(t,n)},i)):(r=e,a.apply(t,n))}},escapeRegex:function(t){return t.replace(J,"\\$1")},set:function(a){var d;return P.isPlainObject(a)?w.util.set(a._):null===a?function(){}:"function"==typeof a?function(t,e,n){a(t,"set",e,n)}:"string"!=typeof a||-1===a.indexOf(".")&&-1===a.indexOf("[")&&-1===a.indexOf("(")?function(t,e){t[a]=e}:(d=function(t,e,n){for(var a,r,o,i,l=dt(n),n=l[l.length-1],s=0,u=l.length-1;s<u;s++){if("__proto__"===l[s]||"constructor"===l[s])throw new Error("Cannot set prototype values");if(a=l[s].match(ft),r=l[s].match(g),a){if(l[s]=l[s].replace(ft,""),t[l[s]]=[],(a=l.slice()).splice(0,s+1),i=a.join("."),Array.isArray(e))for(var c=0,f=e.length;c<f;c++)d(o={},e[c],i),t[l[s]].push(o);else t[l[s]]=e;return}r&&(l[s]=l[s].replace(g,""),t=t[l[s]](e)),null!==t[l[s]]&&t[l[s]]!==H||(t[l[s]]={}),t=t[l[s]]}n.match(g)?t[n.replace(g,"")](e):t[n.replace(ft,"")]=e},function(t,e){return d(t,e,a)})},get:function(r){var o,d;return P.isPlainObject(r)?(o={},P.each(r,function(t,e){e&&(o[t]=w.util.get(e))}),function(t,e,n,a){var r=o[e]||o._;return r!==H?r(t,e,n,a):t}):null===r?function(t){return t}:"function"==typeof r?function(t,e,n,a){return r(t,e,n,a)}:"string"!=typeof r||-1===r.indexOf(".")&&-1===r.indexOf("[")&&-1===r.indexOf("(")?function(t,e){return t[r]}:(d=function(t,e,n){var a,r,o;if(""!==n)for(var i=dt(n),l=0,s=i.length;l<s;l++){if(f=i[l].match(ft),a=i[l].match(g),f){if(i[l]=i[l].replace(ft,""),""!==i[l]&&(t=t[i[l]]),r=[],i.splice(0,l+1),o=i.join("."),Array.isArray(t))for(var u=0,c=t.length;u<c;u++)r.push(d(t[u],e,o));var f=f[0].substring(1,f[0].length-1);t=""===f?r:r.join(f);break}if(a)i[l]=i[l].replace(g,""),t=t[i[l]]();else{if(null===t||null===t[i[l]])return null;if(t===H||t[i[l]]===H)return H;t=t[i[l]]}}return t},function(t,e){return d(t,e,r)})}};var r=function(t,e,n){t[e]!==H&&(t[n]=t[e])};function K(t){r(t,"ordering","bSort"),r(t,"orderMulti","bSortMulti"),r(t,"orderClasses","bSortClasses"),r(t,"orderCellsTop","bSortCellsTop"),r(t,"order","aaSorting"),r(t,"orderFixed","aaSortingFixed"),r(t,"paging","bPaginate"),r(t,"pagingType","sPaginationType"),r(t,"pageLength","iDisplayLength"),r(t,"searching","bFilter"),"boolean"==typeof t.sScrollX&&(t.sScrollX=t.sScrollX?"100%":""),"boolean"==typeof t.scrollX&&(t.scrollX=t.scrollX?"100%":"");var e=t.aoSearchCols;if(e)for(var n=0,a=e.length;n<a;n++)e[n]&&C(w.models.oSearch,e[n])}function Q(t){r(t,"orderable","bSortable"),r(t,"orderData","aDataSort"),r(t,"orderSequence","asSorting"),r(t,"orderDataType","sortDataType");var e=t.aDataSort;"number"!=typeof e||Array.isArray(e)||(t.aDataSort=[e])}function tt(t){var e,n,a,r;w.__browser||(w.__browser=e={},r=(a=(n=P("<div/>").css({position:"fixed",top:0,left:-1*P(j).scrollLeft(),height:1,width:1,overflow:"hidden"}).append(P("<div/>").css({position:"absolute",top:1,left:1,width:100,overflow:"scroll"}).append(P("<div/>").css({width:"100%",height:10}))).appendTo("body")).children()).children(),e.barWidth=a[0].offsetWidth-a[0].clientWidth,e.bScrollOversize=100===r[0].offsetWidth&&100!==a[0].clientWidth,e.bScrollbarLeft=1!==Math.round(r.offset().left),e.bBounding=!!n[0].getBoundingClientRect().width,n.remove()),P.extend(t.oBrowser,w.__browser),t.oScroll.iBarWidth=w.__browser.barWidth}function et(t,e,n,a,r,o){var i,l=a,s=!1;for(n!==H&&(i=n,s=!0);l!==r;)t.hasOwnProperty(l)&&(i=s?e(i,t[l],l,t):t[l],s=!0,l+=o);return i}function nt(t,e){var n=w.defaults.column,a=t.aoColumns.length,n=P.extend({},w.models.oColumn,n,{nTh:e||v.createElement("th"),sTitle:n.sTitle||(e?e.innerHTML:""),aDataSort:n.aDataSort||[a],mData:n.mData||a,idx:a}),n=(t.aoColumns.push(n),t.aoPreSearchCols);n[a]=P.extend({},w.models.oSearch,n[a]),at(t,a,P(e).data())}function at(t,e,n){function a(t){return"string"==typeof t&&-1!==t.indexOf("@")}var e=t.aoColumns[e],r=t.oClasses,o=P(e.nTh),i=(!e.sWidthOrig&&(e.sWidthOrig=o.attr("width")||null,u=(o.attr("style")||"").match(/width:\s*(\d+[pxem%]+)/))&&(e.sWidthOrig=u[1]),n!==H&&null!==n&&(Q(n),C(w.defaults.column,n,!0),n.mDataProp===H||n.mData||(n.mData=n.mDataProp),n.sType&&(e._sManualType=n.sType),n.className&&!n.sClass&&(n.sClass=n.className),n.sClass&&o.addClass(n.sClass),u=e.sClass,P.extend(e,n),F(e,n,"sWidth","sWidthOrig"),u!==e.sClass&&(e.sClass=u+" "+e.sClass),n.iDataSort!==H&&(e.aDataSort=[n.iDataSort]),F(e,n,"aDataSort"),e.ariaTitle||(e.ariaTitle=o.attr("aria-label"))),e.mData),l=A(i),s=e.mRender?A(e.mRender):null,u=(e._bAttrSrc=P.isPlainObject(i)&&(a(i.sort)||a(i.type)||a(i.filter)),e._setter=null,e.fnGetData=function(t,e,n){var a=l(t,e,H,n);return s&&e?s(a,e,t,n):a},e.fnSetData=function(t,e,n){return b(i)(t,e,n)},"number"==typeof i||e._isArrayHost||(t._rowReadObject=!0),t.oFeatures.bSort||(e.bSortable=!1,o.addClass(r.sSortableNone)),-1!==P.inArray("asc",e.asSorting)),n=-1!==P.inArray("desc",e.asSorting);e.bSortable&&(u||n)?u&&!n?(e.sSortingClass=r.sSortableAsc,e.sSortingClassJUI=r.sSortJUIAscAllowed):!u&&n?(e.sSortingClass=r.sSortableDesc,e.sSortingClassJUI=r.sSortJUIDescAllowed):(e.sSortingClass=r.sSortable,e.sSortingClassJUI=r.sSortJUI):(e.sSortingClass=r.sSortableNone,e.sSortingClassJUI="")}function O(t){if(!1!==t.oFeatures.bAutoWidth){var e=t.aoColumns;ee(t);for(var n=0,a=e.length;n<a;n++)e[n].nTh.style.width=e[n].sWidth}var r=t.oScroll;""===r.sY&&""===r.sX||Qt(t),R(t,null,"column-sizing",[t])}function rt(t,e){t=it(t,"bVisible");return"number"==typeof t[e]?t[e]:null}function ot(t,e){t=it(t,"bVisible"),e=P.inArray(e,t);return-1!==e?e:null}function T(t){var n=0;return P.each(t.aoColumns,function(t,e){e.bVisible&&"none"!==P(e.nTh).css("display")&&n++}),n}function it(t,n){var a=[];return P.map(t.aoColumns,function(t,e){t[n]&&a.push(e)}),a}function lt(t){for(var e,n,a,r,o,i,l,s=t.aoColumns,u=t.aoData,c=w.ext.type.detect,f=0,d=s.length;f<d;f++)if(l=[],!(o=s[f]).sType&&o._sManualType)o.sType=o._sManualType;else if(!o.sType){for(e=0,n=c.length;e<n;e++){for(a=0,r=u.length;a<r&&(l[a]===H&&(l[a]=S(t,a,f,"type")),(i=c[e](l[a],t))||e===c.length-1)&&("html"!==i||h(l[a]));a++);if(i){o.sType=i;break}}o.sType||(o.sType="string")}}function st(t,e,n,a){var r,o,i,l,s=t.aoColumns;if(e)for(r=e.length-1;0<=r;r--)for(var u,c=(u=e[r]).target!==H?u.target:u.targets!==H?u.targets:u.aTargets,f=0,d=(c=Array.isArray(c)?c:[c]).length;f<d;f++)if("number"==typeof c[f]&&0<=c[f]){for(;s.length<=c[f];)nt(t);a(c[f],u)}else if("number"==typeof c[f]&&c[f]<0)a(s.length+c[f],u);else if("string"==typeof c[f])for(i=0,l=s.length;i<l;i++)"_all"!=c[f]&&!P(s[i].nTh).hasClass(c[f])||a(i,u);if(n)for(r=0,o=n.length;r<o;r++)a(r,n[r])}function x(t,e,n,a){for(var r=t.aoData.length,o=P.extend(!0,{},w.models.oRow,{src:n?"dom":"data",idx:r}),i=(o._aData=e,t.aoData.push(o),t.aoColumns),l=0,s=i.length;l<s;l++)i[l].sType=null;t.aiDisplayMaster.push(r);e=t.rowIdFn(e);return e!==H&&(t.aIds[e]=o),!n&&t.oFeatures.bDeferRender||St(t,r,n,a),r}function ut(n,t){var a;return(t=t instanceof P?t:P(t)).map(function(t,e){return a=mt(n,e),x(n,a.data,e,a.cells)})}function S(t,e,n,a){"search"===a?a="filter":"order"===a&&(a="sort");var r=t.iDraw,o=t.aoColumns[n],i=t.aoData[e]._aData,l=o.sDefaultContent,s=o.fnGetData(i,a,{settings:t,row:e,col:n});if(s===H)return t.iDrawError!=r&&null===l&&(W(t,0,"Requested unknown parameter "+("function"==typeof o.mData?"{function}":"'"+o.mData+"'")+" for row "+e+", column "+n,4),t.iDrawError=r),l;if(s!==i&&null!==s||null===l||a===H){if("function"==typeof s)return s.call(i)}else s=l;return null===s&&"display"===a?"":"filter"===a&&(e=w.ext.type.search)[o.sType]?e[o.sType](s):s}function ct(t,e,n,a){var r=t.aoColumns[n],o=t.aoData[e]._aData;r.fnSetData(o,a,{settings:t,row:e,col:n})}var ft=/\[.*?\]$/,g=/\(\)$/;function dt(t){return P.map(t.match(/(\\.|[^\.])+/g)||[""],function(t){return t.replace(/\\\./g,".")})}var A=w.util.get,b=w.util.set;function ht(t){return N(t.aoData,"_aData")}function pt(t){t.aoData.length=0,t.aiDisplayMaster.length=0,t.aiDisplay.length=0,t.aIds={}}function gt(t,e,n){for(var a=-1,r=0,o=t.length;r<o;r++)t[r]==e?a=r:t[r]>e&&t[r]--;-1!=a&&n===H&&t.splice(a,1)}function bt(n,a,t,e){function r(t,e){for(;t.childNodes.length;)t.removeChild(t.firstChild);t.innerHTML=S(n,a,e,"display")}var o,i,l=n.aoData[a];if("dom"!==t&&(t&&"auto"!==t||"dom"!==l.src)){var s=l.anCells;if(s)if(e!==H)r(s[e],e);else for(o=0,i=s.length;o<i;o++)r(s[o],o)}else l._aData=mt(n,l,e,e===H?H:l._aData).data;l._aSortData=null,l._aFilterData=null;var u=n.aoColumns;if(e!==H)u[e].sType=null;else{for(o=0,i=u.length;o<i;o++)u[o].sType=null;vt(n,l)}}function mt(t,e,n,a){function r(t,e){var n;"string"==typeof t&&-1!==(n=t.indexOf("@"))&&(n=t.substring(n+1),b(t)(a,e.getAttribute(n)))}function o(t){n!==H&&n!==f||(l=d[f],s=t.innerHTML.trim(),l&&l._bAttrSrc?(b(l.mData._)(a,s),r(l.mData.sort,t),r(l.mData.type,t),r(l.mData.filter,t)):h?(l._setter||(l._setter=b(l.mData)),l._setter(a,s)):a[f]=s),f++}var i,l,s,u=[],c=e.firstChild,f=0,d=t.aoColumns,h=t._rowReadObject;a=a!==H?a:h?{}:[];if(c)for(;c;)"TD"!=(i=c.nodeName.toUpperCase())&&"TH"!=i||(o(c),u.push(c)),c=c.nextSibling;else for(var p=0,g=(u=e.anCells).length;p<g;p++)o(u[p]);var e=e.firstChild?e:e.nTr;return e&&(e=e.getAttribute("id"))&&b(t.rowId)(a,e),{data:a,cells:u}}function St(t,e,n,a){var r,o,i,l,s,u,c=t.aoData[e],f=c._aData,d=[];if(null===c.nTr){for(r=n||v.createElement("tr"),c.nTr=r,c.anCells=d,r._DT_RowIndex=e,vt(t,c),l=0,s=t.aoColumns.length;l<s;l++)i=t.aoColumns[l],(o=(u=!n)?v.createElement(i.sCellType):a[l])||W(t,0,"Incorrect column count",18),o._DT_CellIndex={row:e,column:l},d.push(o),!u&&(!i.mRender&&i.mData===l||P.isPlainObject(i.mData)&&i.mData._===l+".display")||(o.innerHTML=S(t,e,l,"display")),i.sClass&&(o.className+=" "+i.sClass),i.bVisible&&!n?r.appendChild(o):!i.bVisible&&n&&o.parentNode.removeChild(o),i.fnCreatedCell&&i.fnCreatedCell.call(t.oInstance,o,S(t,e,l),f,e,l);R(t,"aoRowCreatedCallback",null,[r,f,e,d])}}function vt(t,e){var n=e.nTr,a=e._aData;n&&((t=t.rowIdFn(a))&&(n.id=t),a.DT_RowClass&&(t=a.DT_RowClass.split(" "),e.__rowc=e.__rowc?z(e.__rowc.concat(t)):t,P(n).removeClass(e.__rowc.join(" ")).addClass(a.DT_RowClass)),a.DT_RowAttr&&P(n).attr(a.DT_RowAttr),a.DT_RowData)&&P(n).data(a.DT_RowData)}function yt(t){var e,n,a,r=t.nTHead,o=t.nTFoot,i=0===P("th, td",r).length,l=t.oClasses,s=t.aoColumns;for(i&&(n=P("<tr/>").appendTo(r)),c=0,f=s.length;c<f;c++)a=s[c],e=P(a.nTh).addClass(a.sClass),i&&e.appendTo(n),t.oFeatures.bSort&&(e.addClass(a.sSortingClass),!1!==a.bSortable)&&(e.attr("tabindex",t.iTabIndex).attr("aria-controls",t.sTableId),ue(t,a.nTh,c)),a.sTitle!=e[0].innerHTML&&e.html(a.sTitle),ve(t,"header")(t,e,a,l);if(i&&wt(t.aoHeader,r),P(r).children("tr").children("th, td").addClass(l.sHeaderTH),P(o).children("tr").children("th, td").addClass(l.sFooterTH),null!==o)for(var u=t.aoFooter[0],c=0,f=u.length;c<f;c++)(a=s[c])?(a.nTf=u[c].cell,a.sClass&&P(a.nTf).addClass(a.sClass)):W(t,0,"Incorrect column count",18)}function Dt(t,e,n){var a,r,o,i,l,s,u,c,f,d=[],h=[],p=t.aoColumns.length;if(e){for(n===H&&(n=!1),a=0,r=e.length;a<r;a++){for(d[a]=e[a].slice(),d[a].nTr=e[a].nTr,o=p-1;0<=o;o--)t.aoColumns[o].bVisible||n||d[a].splice(o,1);h.push([])}for(a=0,r=d.length;a<r;a++){if(u=d[a].nTr)for(;s=u.firstChild;)u.removeChild(s);for(o=0,i=d[a].length;o<i;o++)if(f=c=1,h[a][o]===H){for(u.appendChild(d[a][o].cell),h[a][o]=1;d[a+c]!==H&&d[a][o].cell==d[a+c][o].cell;)h[a+c][o]=1,c++;for(;d[a][o+f]!==H&&d[a][o].cell==d[a][o+f].cell;){for(l=0;l<c;l++)h[a+l][o+f]=1;f++}P(d[a][o].cell).attr("rowspan",c).attr("colspan",f)}}}}function y(t,e){n="ssp"==E(s=t),(l=s.iInitDisplayStart)!==H&&-1!==l&&(s._iDisplayStart=!n&&l>=s.fnRecordsDisplay()?0:l,s.iInitDisplayStart=-1);var n=R(t,"aoPreDrawCallback","preDraw",[t]);if(-1!==P.inArray(!1,n))D(t,!1);else{var a=[],r=0,o=t.asStripeClasses,i=o.length,l=t.oLanguage,s="ssp"==E(t),u=t.aiDisplay,n=t._iDisplayStart,c=t.fnDisplayEnd();if(t.bDrawing=!0,t.bDeferLoading)t.bDeferLoading=!1,t.iDraw++,D(t,!1);else if(s){if(!t.bDestroying&&!e)return void xt(t)}else t.iDraw++;if(0!==u.length)for(var f=s?t.aoData.length:c,d=s?0:n;d<f;d++){var h,p=u[d],g=t.aoData[p],b=(null===g.nTr&&St(t,p),g.nTr);0!==i&&(h=o[r%i],g._sRowStripe!=h)&&(P(b).removeClass(g._sRowStripe).addClass(h),g._sRowStripe=h),R(t,"aoRowCallback",null,[b,g._aData,r,d,p]),a.push(b),r++}else{e=l.sZeroRecords;1==t.iDraw&&"ajax"==E(t)?e=l.sLoadingRecords:l.sEmptyTable&&0===t.fnRecordsTotal()&&(e=l.sEmptyTable),a[0]=P("<tr/>",{class:i?o[0]:""}).append(P("<td />",{valign:"top",colSpan:T(t),class:t.oClasses.sRowEmpty}).html(e))[0]}R(t,"aoHeaderCallback","header",[P(t.nTHead).children("tr")[0],ht(t),n,c,u]),R(t,"aoFooterCallback","footer",[P(t.nTFoot).children("tr")[0],ht(t),n,c,u]);s=P(t.nTBody);s.children().detach(),s.append(P(a)),R(t,"aoDrawCallback","draw",[t]),t.bSorted=!1,t.bFiltered=!1,t.bDrawing=!1}}function u(t,e){var n=t.oFeatures,a=n.bSort,n=n.bFilter;a&&ie(t),n?Rt(t,t.oPreviousSearch):t.aiDisplay=t.aiDisplayMaster.slice(),!0!==e&&(t._iDisplayStart=0),t._drawHold=e,y(t),t._drawHold=!1}function _t(t){for(var e,n,a,r,o,i,l,s=t.oClasses,u=P(t.nTable),u=P("<div/>").insertBefore(u),c=t.oFeatures,f=P("<div/>",{id:t.sTableId+"_wrapper",class:s.sWrapper+(t.nTFoot?"":" "+s.sNoFooter)}),d=(t.nHolding=u[0],t.nTableWrapper=f[0],t.nTableReinsertBefore=t.nTable.nextSibling,t.sDom.split("")),h=0;h<d.length;h++){if(e=null,"<"==(n=d[h])){if(a=P("<div/>")[0],"'"==(r=d[h+1])||'"'==r){for(o="",i=2;d[h+i]!=r;)o+=d[h+i],i++;"H"==o?o=s.sJUIHeader:"F"==o&&(o=s.sJUIFooter),-1!=o.indexOf(".")?(l=o.split("."),a.id=l[0].substr(1,l[0].length-1),a.className=l[1]):"#"==o.charAt(0)?a.id=o.substr(1,o.length-1):a.className=o,h+=i}f.append(a),f=P(a)}else if(">"==n)f=f.parent();else if("l"==n&&c.bPaginate&&c.bLengthChange)e=Gt(t);else if("f"==n&&c.bFilter)e=Lt(t);else if("r"==n&&c.bProcessing)e=Zt(t);else if("t"==n)e=Kt(t);else if("i"==n&&c.bInfo)e=Ut(t);else if("p"==n&&c.bPaginate)e=zt(t);else if(0!==w.ext.feature.length)for(var p=w.ext.feature,g=0,b=p.length;g<b;g++)if(n==p[g].cFeature){e=p[g].fnInit(t);break}e&&((l=t.aanFeatures)[n]||(l[n]=[]),l[n].push(e),f.append(e))}u.replaceWith(f),t.nHolding=null}function wt(t,e){var n,a,r,o,i,l,s,u,c,f,d=P(e).children("tr");for(t.splice(0,t.length),r=0,l=d.length;r<l;r++)t.push([]);for(r=0,l=d.length;r<l;r++)for(a=(n=d[r]).firstChild;a;){if("TD"==a.nodeName.toUpperCase()||"TH"==a.nodeName.toUpperCase())for(u=(u=+a.getAttribute("colspan"))&&0!=u&&1!=u?u:1,c=(c=+a.getAttribute("rowspan"))&&0!=c&&1!=c?c:1,s=function(t,e,n){for(var a=t[e];a[n];)n++;return n}(t,r,0),f=1==u,i=0;i<u;i++)for(o=0;o<c;o++)t[r+o][s+i]={cell:a,unique:f},t[r+o].nTr=n;a=a.nextSibling}}function Ct(t,e,n){var a=[];n||(n=t.aoHeader,e&&wt(n=[],e));for(var r=0,o=n.length;r<o;r++)for(var i=0,l=n[r].length;i<l;i++)!n[r][i].unique||a[i]&&t.bSortCellsTop||(a[i]=n[r][i].cell);return a}function Tt(r,t,n){function e(t){var e=r.jqXHR?r.jqXHR.status:null;(null===t||"number"==typeof e&&204==e)&&Ft(r,t={},[]),(e=t.error||t.sError)&&W(r,0,e),r.json=t,R(r,null,"xhr",[r,t,r.jqXHR]),n(t)}R(r,"aoServerParams","serverParams",[t]),t&&Array.isArray(t)&&(a={},o=/(.*?)\[\]$/,P.each(t,function(t,e){var n=e.name.match(o);n?(n=n[0],a[n]||(a[n]=[]),a[n].push(e.value)):a[e.name]=e.value}),t=a);var a,o,i,l=r.ajax,s=r.oInstance,u=(P.isPlainObject(l)&&l.data&&(u="function"==typeof(i=l.data)?i(t,r):i,t="function"==typeof i&&u?u:P.extend(!0,t,u),delete l.data),{data:t,success:e,dataType:"json",cache:!1,type:r.sServerMethod,error:function(t,e,n){var a=R(r,null,"xhr",[r,null,r.jqXHR]);-1===P.inArray(!0,a)&&("parsererror"==e?W(r,0,"Invalid JSON response",1):4===t.readyState&&W(r,0,"Ajax error",7)),D(r,!1)}});r.oAjaxData=t,R(r,null,"preXhr",[r,t]),r.fnServerData?r.fnServerData.call(s,r.sAjaxSource,P.map(t,function(t,e){return{name:e,value:t}}),e,r):r.sAjaxSource||"string"==typeof l?r.jqXHR=P.ajax(P.extend(u,{url:l||r.sAjaxSource})):"function"==typeof l?r.jqXHR=l.call(s,t,e,r):(r.jqXHR=P.ajax(P.extend(u,l)),l.data=i)}function xt(e){e.iDraw++,D(e,!0);var n=e._drawHold;Tt(e,At(e),function(t){e._drawHold=n,It(e,t),e._drawHold=!1})}function At(t){for(var e,n,a,r=t.aoColumns,o=r.length,i=t.oFeatures,l=t.oPreviousSearch,s=t.aoPreSearchCols,u=[],c=I(t),f=t._iDisplayStart,d=!1!==i.bPaginate?t._iDisplayLength:-1,h=function(t,e){u.push({name:t,value:e})},p=(h("sEcho",t.iDraw),h("iColumns",o),h("sColumns",N(r,"sName").join(",")),h("iDisplayStart",f),h("iDisplayLength",d),{draw:t.iDraw,columns:[],order:[],start:f,length:d,search:{value:l.sSearch,regex:l.bRegex}}),g=0;g<o;g++)n=r[g],a=s[g],e="function"==typeof n.mData?"function":n.mData,p.columns.push({data:e,name:n.sName,searchable:n.bSearchable,orderable:n.bSortable,search:{value:a.sSearch,regex:a.bRegex}}),h("mDataProp_"+g,e),i.bFilter&&(h("sSearch_"+g,a.sSearch),h("bRegex_"+g,a.bRegex),h("bSearchable_"+g,n.bSearchable)),i.bSort&&h("bSortable_"+g,n.bSortable);i.bFilter&&(h("sSearch",l.sSearch),h("bRegex",l.bRegex)),i.bSort&&(P.each(c,function(t,e){p.order.push({column:e.col,dir:e.dir}),h("iSortCol_"+t,e.col),h("sSortDir_"+t,e.dir)}),h("iSortingCols",c.length));f=w.ext.legacy.ajax;return null===f?t.sAjaxSource?u:p:f?u:p}function It(t,n){function e(t,e){return n[t]!==H?n[t]:n[e]}var a=Ft(t,n),r=e("sEcho","draw"),o=e("iTotalRecords","recordsTotal"),i=e("iTotalDisplayRecords","recordsFiltered");if(r!==H){if(+r<t.iDraw)return;t.iDraw=+r}a=a||[],pt(t),t._iRecordsTotal=parseInt(o,10),t._iRecordsDisplay=parseInt(i,10);for(var l=0,s=a.length;l<s;l++)x(t,a[l]);t.aiDisplay=t.aiDisplayMaster.slice(),y(t,!0),t._bInitComplete||qt(t,n),D(t,!1)}function Ft(t,e,n){t=P.isPlainObject(t.ajax)&&t.ajax.dataSrc!==H?t.ajax.dataSrc:t.sAjaxDataProp;if(!n)return"data"===t?e.aaData||e[t]:""!==t?A(t)(e):e;b(t)(e,n)}function Lt(n){function e(t){i.f;var e=this.value||"";o.return&&"Enter"!==t.key||e!=o.sSearch&&(Rt(n,{sSearch:e,bRegex:o.bRegex,bSmart:o.bSmart,bCaseInsensitive:o.bCaseInsensitive,return:o.return}),n._iDisplayStart=0,y(n))}var t=n.oClasses,a=n.sTableId,r=n.oLanguage,o=n.oPreviousSearch,i=n.aanFeatures,l='<input type="search" class="'+t.sFilterInput+'"/>',s=(s=r.sSearch).match(/_INPUT_/)?s.replace("_INPUT_",l):s+l,l=P("<div/>",{id:i.f?null:a+"_filter",class:t.sFilter}).append(P("<label/>").append(s)),t=null!==n.searchDelay?n.searchDelay:"ssp"===E(n)?400:0,u=P("input",l).val(o.sSearch).attr("placeholder",r.sSearchPlaceholder).on("keyup.DT search.DT input.DT paste.DT cut.DT",t?ne(e,t):e).on("mouseup.DT",function(t){setTimeout(function(){e.call(u[0],t)},10)}).on("keypress.DT",function(t){if(13==t.keyCode)return!1}).attr("aria-controls",a);return P(n.nTable).on("search.dt.DT",function(t,e){if(n===e)try{u[0]!==v.activeElement&&u.val(o.sSearch)}catch(t){}}),l[0]}function Rt(t,e,n){function a(t){o.sSearch=t.sSearch,o.bRegex=t.bRegex,o.bSmart=t.bSmart,o.bCaseInsensitive=t.bCaseInsensitive,o.return=t.return}function r(t){return t.bEscapeRegex!==H?!t.bEscapeRegex:t.bRegex}var o=t.oPreviousSearch,i=t.aoPreSearchCols;if(lt(t),"ssp"!=E(t)){Ht(t,e.sSearch,n,r(e),e.bSmart,e.bCaseInsensitive),a(e);for(var l=0;l<i.length;l++)jt(t,i[l].sSearch,l,r(i[l]),i[l].bSmart,i[l].bCaseInsensitive);Pt(t)}else a(e);t.bFiltered=!0,R(t,null,"search",[t])}function Pt(t){for(var e,n,a=w.ext.search,r=t.aiDisplay,o=0,i=a.length;o<i;o++){for(var l=[],s=0,u=r.length;s<u;s++)n=r[s],e=t.aoData[n],a[o](t,e._aFilterData,n,e._aData,s)&&l.push(n);r.length=0,P.merge(r,l)}}function jt(t,e,n,a,r,o){if(""!==e){for(var i,l=[],s=t.aiDisplay,u=Nt(e,a,r,o),c=0;c<s.length;c++)i=t.aoData[s[c]]._aFilterData[n],u.test(i)&&l.push(s[c]);t.aiDisplay=l}}function Ht(t,e,n,a,r,o){var i,l,s,u=Nt(e,a,r,o),r=t.oPreviousSearch.sSearch,o=t.aiDisplayMaster,c=[];if(0!==w.ext.search.length&&(n=!0),l=Wt(t),e.length<=0)t.aiDisplay=o.slice();else{for((l||n||a||r.length>e.length||0!==e.indexOf(r)||t.bSorted)&&(t.aiDisplay=o.slice()),i=t.aiDisplay,s=0;s<i.length;s++)u.test(t.aoData[i[s]]._sFilterRow)&&c.push(i[s]);t.aiDisplay=c}}function Nt(t,e,n,a){return t=e?t:Ot(t),n&&(t="^(?=.*?"+P.map(t.match(/["\u201C][^"\u201D]+["\u201D]|[^ ]+/g)||[""],function(t){var e;return'"'===t.charAt(0)?t=(e=t.match(/^"(.*)"$/))?e[1]:t:"“"===t.charAt(0)&&(t=(e=t.match(/^\u201C(.*)\u201D$/))?e[1]:t),t.replace('"',"")}).join(")(?=.*?")+").*$"),new RegExp(t,a?"i":"")}var Ot=w.util.escapeRegex,kt=P("<div>")[0],Mt=kt.textContent!==H;function Wt(t){for(var e,n,a,r,o,i=t.aoColumns,l=!1,s=0,u=t.aoData.length;s<u;s++)if(!(o=t.aoData[s])._aFilterData){for(a=[],e=0,n=i.length;e<n;e++)i[e].bSearchable?"string"!=typeof(r=null===(r=S(t,s,e,"filter"))?"":r)&&r.toString&&(r=r.toString()):r="",r.indexOf&&-1!==r.indexOf("&")&&(kt.innerHTML=r,r=Mt?kt.textContent:kt.innerText),r.replace&&(r=r.replace(/[\r\n\u2028]/g,"")),a.push(r);o._aFilterData=a,o._sFilterRow=a.join("  "),l=!0}return l}function Et(t){return{search:t.sSearch,smart:t.bSmart,regex:t.bRegex,caseInsensitive:t.bCaseInsensitive}}function Bt(t){return{sSearch:t.search,bSmart:t.smart,bRegex:t.regex,bCaseInsensitive:t.caseInsensitive}}function Ut(t){var e=t.sTableId,n=t.aanFeatures.i,a=P("<div/>",{class:t.oClasses.sInfo,id:n?null:e+"_info"});return n||(t.aoDrawCallback.push({fn:Vt,sName:"information"}),a.attr("role","status").attr("aria-live","polite"),P(t.nTable).attr("aria-describedby",e+"_info")),a[0]}function Vt(t){var e,n,a,r,o,i,l=t.aanFeatures.i;0!==l.length&&(i=t.oLanguage,e=t._iDisplayStart+1,n=t.fnDisplayEnd(),a=t.fnRecordsTotal(),o=(r=t.fnRecordsDisplay())?i.sInfo:i.sInfoEmpty,r!==a&&(o+=" "+i.sInfoFiltered),o=Xt(t,o+=i.sInfoPostFix),null!==(i=i.fnInfoCallback)&&(o=i.call(t.oInstance,t,e,n,a,r,o)),P(l).html(o))}function Xt(t,e){var n=t.fnFormatNumber,a=t._iDisplayStart+1,r=t._iDisplayLength,o=t.fnRecordsDisplay(),i=-1===r;return e.replace(/_START_/g,n.call(t,a)).replace(/_END_/g,n.call(t,t.fnDisplayEnd())).replace(/_MAX_/g,n.call(t,t.fnRecordsTotal())).replace(/_TOTAL_/g,n.call(t,o)).replace(/_PAGE_/g,n.call(t,i?1:Math.ceil(a/r))).replace(/_PAGES_/g,n.call(t,i?1:Math.ceil(o/r)))}function Jt(n){var a,t,e,r=n.iInitDisplayStart,o=n.aoColumns,i=n.oFeatures,l=n.bDeferLoading;if(n.bInitialised){for(_t(n),yt(n),Dt(n,n.aoHeader),Dt(n,n.aoFooter),D(n,!0),i.bAutoWidth&&ee(n),a=0,t=o.length;a<t;a++)(e=o[a]).sWidth&&(e.nTh.style.width=M(e.sWidth));R(n,null,"preInit",[n]),u(n);i=E(n);"ssp"==i&&!l||("ajax"==i?Tt(n,[],function(t){var e=Ft(n,t);for(a=0;a<e.length;a++)x(n,e[a]);n.iInitDisplayStart=r,u(n),D(n,!1),qt(n,t)}):(D(n,!1),qt(n)))}else setTimeout(function(){Jt(n)},200)}function qt(t,e){t._bInitComplete=!0,(e||t.oInit.aaData)&&O(t),R(t,null,"plugin-init",[t,e]),R(t,"aoInitComplete","init",[t,e])}function $t(t,e){e=parseInt(e,10);t._iDisplayLength=e,Se(t),R(t,null,"length",[t,e])}function Gt(a){for(var t=a.oClasses,e=a.sTableId,n=a.aLengthMenu,r=Array.isArray(n[0]),o=r?n[0]:n,i=r?n[1]:n,l=P("<select/>",{name:e+"_length","aria-controls":e,class:t.sLengthSelect}),s=0,u=o.length;s<u;s++)l[0][s]=new Option("number"==typeof i[s]?a.fnFormatNumber(i[s]):i[s],o[s]);var c=P("<div><label/></div>").addClass(t.sLength);return a.aanFeatures.l||(c[0].id=e+"_length"),c.children().append(a.oLanguage.sLengthMenu.replace("_MENU_",l[0].outerHTML)),P("select",c).val(a._iDisplayLength).on("change.DT",function(t){$t(a,P(this).val()),y(a)}),P(a.nTable).on("length.dt.DT",function(t,e,n){a===e&&P("select",c).val(n)}),c[0]}function zt(t){function c(t){y(t)}var e=t.sPaginationType,f=w.ext.pager[e],d="function"==typeof f,e=P("<div/>").addClass(t.oClasses.sPaging+e)[0],h=t.aanFeatures;return d||f.fnInit(t,e,c),h.p||(e.id=t.sTableId+"_paginate",t.aoDrawCallback.push({fn:function(t){if(d)for(var e=t._iDisplayStart,n=t._iDisplayLength,a=t.fnRecordsDisplay(),r=-1===n,o=r?0:Math.ceil(e/n),i=r?1:Math.ceil(a/n),l=f(o,i),s=0,u=h.p.length;s<u;s++)ve(t,"pageButton")(t,h.p[s],s,l,o,i);else f.fnUpdate(t,c)},sName:"pagination"})),e}function Yt(t,e,n){var a=t._iDisplayStart,r=t._iDisplayLength,o=t.fnRecordsDisplay(),o=(0===o||-1===r?a=0:"number"==typeof e?o<(a=e*r)&&(a=0):"first"==e?a=0:"previous"==e?(a=0<=r?a-r:0)<0&&(a=0):"next"==e?a+r<o&&(a+=r):"last"==e?a=Math.floor((o-1)/r)*r:W(t,0,"Unknown paging action: "+e,5),t._iDisplayStart!==a);return t._iDisplayStart=a,o?(R(t,null,"page",[t]),n&&y(t)):R(t,null,"page-nc",[t]),o}function Zt(t){return P("<div/>",{id:t.aanFeatures.r?null:t.sTableId+"_processing",class:t.oClasses.sProcessing,role:"status"}).html(t.oLanguage.sProcessing).append("<div><div></div><div></div><div></div><div></div></div>").insertBefore(t.nTable)[0]}function D(t,e){t.oFeatures.bProcessing&&P(t.aanFeatures.r).css("display",e?"block":"none"),R(t,null,"processing",[t,e])}function Kt(t){var e,n,a,r,o,i,l,s,u,c,f,d,h=P(t.nTable),p=t.oScroll;return""===p.sX&&""===p.sY?t.nTable:(e=p.sX,n=p.sY,a=t.oClasses,o=(r=h.children("caption")).length?r[0]._captionSide:null,s=P(h[0].cloneNode(!1)),i=P(h[0].cloneNode(!1)),u=function(t){return t?M(t):null},(l=h.children("tfoot")).length||(l=null),s=P(f="<div/>",{class:a.sScrollWrapper}).append(P(f,{class:a.sScrollHead}).css({overflow:"hidden",position:"relative",border:0,width:e?u(e):"100%"}).append(P(f,{class:a.sScrollHeadInner}).css({"box-sizing":"content-box",width:p.sXInner||"100%"}).append(s.removeAttr("id").css("margin-left",0).append("top"===o?r:null).append(h.children("thead"))))).append(P(f,{class:a.sScrollBody}).css({position:"relative",overflow:"auto",width:u(e)}).append(h)),l&&s.append(P(f,{class:a.sScrollFoot}).css({overflow:"hidden",border:0,width:e?u(e):"100%"}).append(P(f,{class:a.sScrollFootInner}).append(i.removeAttr("id").css("margin-left",0).append("bottom"===o?r:null).append(h.children("tfoot"))))),u=s.children(),c=u[0],f=u[1],d=l?u[2]:null,e&&P(f).on("scroll.DT",function(t){var e=this.scrollLeft;c.scrollLeft=e,l&&(d.scrollLeft=e)}),P(f).css("max-height",n),p.bCollapse||P(f).css("height",n),t.nScrollHead=c,t.nScrollBody=f,t.nScrollFoot=d,t.aoDrawCallback.push({fn:Qt,sName:"scrolling"}),s[0])}function Qt(n){function t(t){(t=t.style).paddingTop="0",t.paddingBottom="0",t.borderTopWidth="0",t.borderBottomWidth="0",t.height=0}var e,a,r,o,i,l=n.oScroll,s=l.sX,u=l.sXInner,c=l.sY,l=l.iBarWidth,f=P(n.nScrollHead),d=f[0].style,h=f.children("div"),p=h[0].style,h=h.children("table"),g=n.nScrollBody,b=P(g),m=g.style,S=P(n.nScrollFoot).children("div"),v=S.children("table"),y=P(n.nTHead),D=P(n.nTable),_=D[0],w=_.style,C=n.nTFoot?P(n.nTFoot):null,T=n.oBrowser,x=T.bScrollOversize,A=(N(n.aoColumns,"nTh"),[]),I=[],F=[],L=[],R=g.scrollHeight>g.clientHeight;n.scrollBarVis!==R&&n.scrollBarVis!==H?(n.scrollBarVis=R,O(n)):(n.scrollBarVis=R,D.children("thead, tfoot").remove(),C&&(R=C.clone().prependTo(D),i=C.find("tr"),a=R.find("tr"),R.find("[id]").removeAttr("id")),R=y.clone().prependTo(D),y=y.find("tr"),e=R.find("tr"),R.find("th, td").removeAttr("tabindex"),R.find("[id]").removeAttr("id"),s||(m.width="100%",f[0].style.width="100%"),P.each(Ct(n,R),function(t,e){r=rt(n,t),e.style.width=n.aoColumns[r].sWidth}),C&&k(function(t){t.style.width=""},a),f=D.outerWidth(),""===s?(w.width="100%",x&&(D.find("tbody").height()>g.offsetHeight||"scroll"==b.css("overflow-y"))&&(w.width=M(D.outerWidth()-l)),f=D.outerWidth()):""!==u&&(w.width=M(u),f=D.outerWidth()),k(t,e),k(function(t){var e=j.getComputedStyle?j.getComputedStyle(t).width:M(P(t).width());F.push(t.innerHTML),A.push(e)},e),k(function(t,e){t.style.width=A[e]},y),P(e).css("height",0),C&&(k(t,a),k(function(t){L.push(t.innerHTML),I.push(M(P(t).css("width")))},a),k(function(t,e){t.style.width=I[e]},i),P(a).height(0)),k(function(t,e){t.innerHTML='<div class="dataTables_sizing">'+F[e]+"</div>",t.childNodes[0].style.height="0",t.childNodes[0].style.overflow="hidden",t.style.width=A[e]},e),C&&k(function(t,e){t.innerHTML='<div class="dataTables_sizing">'+L[e]+"</div>",t.childNodes[0].style.height="0",t.childNodes[0].style.overflow="hidden",t.style.width=I[e]},a),Math.round(D.outerWidth())<Math.round(f)?(o=g.scrollHeight>g.offsetHeight||"scroll"==b.css("overflow-y")?f+l:f,x&&(g.scrollHeight>g.offsetHeight||"scroll"==b.css("overflow-y"))&&(w.width=M(o-l)),""!==s&&""===u||W(n,1,"Possible column misalignment",6)):o="100%",m.width=M(o),d.width=M(o),C&&(n.nScrollFoot.style.width=M(o)),c||x&&(m.height=M(_.offsetHeight+l)),R=D.outerWidth(),h[0].style.width=M(R),p.width=M(R),y=D.height()>g.clientHeight||"scroll"==b.css("overflow-y"),p[i="padding"+(T.bScrollbarLeft?"Left":"Right")]=y?l+"px":"0px",C&&(v[0].style.width=M(R),S[0].style.width=M(R),S[0].style[i]=y?l+"px":"0px"),D.children("colgroup").insertBefore(D.children("thead")),b.trigger("scroll"),!n.bSorted&&!n.bFiltered||n._drawHold||(g.scrollTop=0))}function k(t,e,n){for(var a,r,o=0,i=0,l=e.length;i<l;){for(a=e[i].firstChild,r=n?n[i].firstChild:null;a;)1===a.nodeType&&(n?t(a,r,o):t(a,o),o++),a=a.nextSibling,r=n?r.nextSibling:null;i++}}var te=/<.*?>/g;function ee(t){var e,n,a=t.nTable,r=t.aoColumns,o=t.oScroll,i=o.sY,l=o.sX,o=o.sXInner,s=r.length,u=it(t,"bVisible"),c=P("th",t.nTHead),f=a.getAttribute("width"),d=a.parentNode,h=!1,p=t.oBrowser,g=p.bScrollOversize,b=a.style.width;for(b&&-1!==b.indexOf("%")&&(f=b),D=0;D<u.length;D++)null!==(e=r[u[D]]).sWidth&&(e.sWidth=ae(e.sWidthOrig,d),h=!0);if(g||!h&&!l&&!i&&s==T(t)&&s==c.length)for(D=0;D<s;D++){var m=rt(t,D);null!==m&&(r[m].sWidth=M(c.eq(D).width()))}else{var b=P(a).clone().css("visibility","hidden").removeAttr("id"),S=(b.find("tbody tr").remove(),P("<tr/>").appendTo(b.find("tbody")));for(b.find("thead, tfoot").remove(),b.append(P(t.nTHead).clone()).append(P(t.nTFoot).clone()),b.find("tfoot th, tfoot td").css("width",""),c=Ct(t,b.find("thead")[0]),D=0;D<u.length;D++)e=r[u[D]],c[D].style.width=null!==e.sWidthOrig&&""!==e.sWidthOrig?M(e.sWidthOrig):"",e.sWidthOrig&&l&&P(c[D]).append(P("<div/>").css({width:e.sWidthOrig,margin:0,padding:0,border:0,height:1}));if(t.aoData.length)for(D=0;D<u.length;D++)e=r[n=u[D]],P(re(t,n)).clone(!1).append(e.sContentPadding).appendTo(S);P("[name]",b).removeAttr("name");for(var v=P("<div/>").css(l||i?{position:"absolute",top:0,left:0,height:1,right:0,overflow:"hidden"}:{}).append(b).appendTo(d),y=(l&&o?b.width(o):l?(b.css("width","auto"),b.removeAttr("width"),b.width()<d.clientWidth&&f&&b.width(d.clientWidth)):i?b.width(d.clientWidth):f&&b.width(f),0),D=0;D<u.length;D++){var _=P(c[D]),w=_.outerWidth()-_.width(),_=p.bBounding?Math.ceil(c[D].getBoundingClientRect().width):_.outerWidth();y+=_,r[u[D]].sWidth=M(_-w)}a.style.width=M(y),v.remove()}f&&(a.style.width=M(f)),!f&&!l||t._reszEvt||(o=function(){P(j).on("resize.DT-"+t.sInstance,ne(function(){O(t)}))},g?setTimeout(o,1e3):o(),t._reszEvt=!0)}var ne=w.util.throttle;function ae(t,e){return t?(e=(t=P("<div/>").css("width",M(t)).appendTo(e||v.body))[0].offsetWidth,t.remove(),e):0}function re(t,e){var n,a=oe(t,e);return a<0?null:(n=t.aoData[a]).nTr?n.anCells[e]:P("<td/>").html(S(t,a,e,"display"))[0]}function oe(t,e){for(var n,a=-1,r=-1,o=0,i=t.aoData.length;o<i;o++)(n=(n=(n=S(t,o,e,"display")+"").replace(te,"")).replace(/&nbsp;/g," ")).length>a&&(a=n.length,r=o);return r}function M(t){return null===t?"0px":"number"==typeof t?t<0?"0px":t+"px":t.match(/\d$/)?t+"px":t}function I(t){function e(t){t.length&&!Array.isArray(t[0])?h.push(t):P.merge(h,t)}var n,a,r,o,i,l,s,u=[],c=t.aoColumns,f=t.aaSortingFixed,d=P.isPlainObject(f),h=[];for(Array.isArray(f)&&e(f),d&&f.pre&&e(f.pre),e(t.aaSorting),d&&f.post&&e(f.post),n=0;n<h.length;n++)for(r=(o=c[s=h[n][a=0]].aDataSort).length;a<r;a++)l=c[i=o[a]].sType||"string",h[n]._idx===H&&(h[n]._idx=P.inArray(h[n][1],c[i].asSorting)),u.push({src:s,col:i,dir:h[n][1],index:h[n]._idx,type:l,formatter:w.ext.type.order[l+"-pre"]});return u}function ie(t){var e,n,a,r,c,f=[],u=w.ext.type.order,d=t.aoData,o=(t.aoColumns,0),i=t.aiDisplayMaster;for(lt(t),e=0,n=(c=I(t)).length;e<n;e++)(r=c[e]).formatter&&o++,fe(t,r.col);if("ssp"!=E(t)&&0!==c.length){for(e=0,a=i.length;e<a;e++)f[i[e]]=e;o===c.length?i.sort(function(t,e){for(var n,a,r,o,i=c.length,l=d[t]._aSortData,s=d[e]._aSortData,u=0;u<i;u++)if(0!=(r=(n=l[(o=c[u]).col])<(a=s[o.col])?-1:a<n?1:0))return"asc"===o.dir?r:-r;return(n=f[t])<(a=f[e])?-1:a<n?1:0}):i.sort(function(t,e){for(var n,a,r,o=c.length,i=d[t]._aSortData,l=d[e]._aSortData,s=0;s<o;s++)if(n=i[(r=c[s]).col],a=l[r.col],0!==(r=(u[r.type+"-"+r.dir]||u["string-"+r.dir])(n,a)))return r;return(n=f[t])<(a=f[e])?-1:a<n?1:0})}t.bSorted=!0}function le(t){for(var e=t.aoColumns,n=I(t),a=t.oLanguage.oAria,r=0,o=e.length;r<o;r++){var i=e[r],l=i.asSorting,s=i.ariaTitle||i.sTitle.replace(/<.*?>/g,""),u=i.nTh;u.removeAttribute("aria-sort"),i=i.bSortable?s+("asc"===(0<n.length&&n[0].col==r&&(u.setAttribute("aria-sort","asc"==n[0].dir?"ascending":"descending"),l[n[0].index+1])||l[0])?a.sSortAscending:a.sSortDescending):s,u.setAttribute("aria-label",i)}}function se(t,e,n,a){function r(t,e){var n=t._idx;return(n=n===H?P.inArray(t[1],s):n)+1<s.length?n+1:e?null:0}var o,i=t.aoColumns[e],l=t.aaSorting,s=i.asSorting;"number"==typeof l[0]&&(l=t.aaSorting=[l]),n&&t.oFeatures.bSortMulti?-1!==(i=P.inArray(e,N(l,"0")))?null===(o=null===(o=r(l[i],!0))&&1===l.length?0:o)?l.splice(i,1):(l[i][1]=s[o],l[i]._idx=o):(l.push([e,s[0],0]),l[l.length-1]._idx=0):l.length&&l[0][0]==e?(o=r(l[0]),l.length=1,l[0][1]=s[o],l[0]._idx=o):(l.length=0,l.push([e,s[0]]),l[0]._idx=0),u(t),"function"==typeof a&&a(t)}function ue(e,t,n,a){var r=e.aoColumns[n];me(t,{},function(t){!1!==r.bSortable&&(e.oFeatures.bProcessing?(D(e,!0),setTimeout(function(){se(e,n,t.shiftKey,a),"ssp"!==E(e)&&D(e,!1)},0)):se(e,n,t.shiftKey,a))})}function ce(t){var e,n,a,r=t.aLastSort,o=t.oClasses.sSortColumn,i=I(t),l=t.oFeatures;if(l.bSort&&l.bSortClasses){for(e=0,n=r.length;e<n;e++)a=r[e].src,P(N(t.aoData,"anCells",a)).removeClass(o+(e<2?e+1:3));for(e=0,n=i.length;e<n;e++)a=i[e].src,P(N(t.aoData,"anCells",a)).addClass(o+(e<2?e+1:3))}t.aLastSort=i}function fe(t,e){for(var n,a,r,o=t.aoColumns[e],i=w.ext.order[o.sSortDataType],l=(i&&(n=i.call(t.oInstance,t,e,ot(t,e))),w.ext.type.order[o.sType+"-pre"]),s=0,u=t.aoData.length;s<u;s++)(a=t.aoData[s])._aSortData||(a._aSortData=[]),a._aSortData[e]&&!i||(r=i?n[s]:S(t,s,e,"sort"),a._aSortData[e]=l?l(r):r)}function de(n){var t;n._bLoadingState||(t={time:+new Date,start:n._iDisplayStart,length:n._iDisplayLength,order:P.extend(!0,[],n.aaSorting),search:Et(n.oPreviousSearch),columns:P.map(n.aoColumns,function(t,e){return{visible:t.bVisible,search:Et(n.aoPreSearchCols[e])}})},n.oSavedState=t,R(n,"aoStateSaveParams","stateSaveParams",[n,t]),n.oFeatures.bStateSave&&!n.bDestroying&&n.fnStateSaveCallback.call(n.oInstance,n,t))}function he(e,t,n){var a;if(e.oFeatures.bStateSave)return(a=e.fnStateLoadCallback.call(e.oInstance,e,function(t){pe(e,t,n)}))!==H&&pe(e,a,n),!0;n()}function pe(n,t,e){var a,r,o=n.aoColumns,i=(n._bLoadingState=!0,n._bInitComplete?new w.Api(n):null);if(t&&t.time){var l=R(n,"aoStateLoadParams","stateLoadParams",[n,t]);if(-1!==P.inArray(!1,l))n._bLoadingState=!1;else{l=n.iStateDuration;if(0<l&&t.time<+new Date-1e3*l)n._bLoadingState=!1;else if(t.columns&&o.length!==t.columns.length)n._bLoadingState=!1;else{if(n.oLoadedState=P.extend(!0,{},t),t.length!==H&&(i?i.page.len(t.length):n._iDisplayLength=t.length),t.start!==H&&(null===i?(n._iDisplayStart=t.start,n.iInitDisplayStart=t.start):Yt(n,t.start/n._iDisplayLength)),t.order!==H&&(n.aaSorting=[],P.each(t.order,function(t,e){n.aaSorting.push(e[0]>=o.length?[0,e[1]]:e)})),t.search!==H&&P.extend(n.oPreviousSearch,Bt(t.search)),t.columns){for(a=0,r=t.columns.length;a<r;a++){var s=t.columns[a];s.visible!==H&&(i?i.column(a).visible(s.visible,!1):o[a].bVisible=s.visible),s.search!==H&&P.extend(n.aoPreSearchCols[a],Bt(s.search))}i&&i.columns.adjust()}n._bLoadingState=!1,R(n,"aoStateLoaded","stateLoaded",[n,t])}}}else n._bLoadingState=!1;e()}function ge(t){var e=w.settings,t=P.inArray(t,N(e,"nTable"));return-1!==t?e[t]:null}function W(t,e,n,a){if(n="DataTables warning: "+(t?"table id="+t.sTableId+" - ":"")+n,a&&(n+=". For more information about this error, please see http://datatables.net/tn/"+a),e)j.console&&console.log&&console.log(n);else{e=w.ext,e=e.sErrMode||e.errMode;if(t&&R(t,null,"error",[t,a,n]),"alert"==e)alert(n);else{if("throw"==e)throw new Error(n);"function"==typeof e&&e(t,a,n)}}}function F(n,a,t,e){Array.isArray(t)?P.each(t,function(t,e){Array.isArray(e)?F(n,a,e[0],e[1]):F(n,a,e)}):(e===H&&(e=t),a[t]!==H&&(n[e]=a[t]))}function be(t,e,n){var a,r;for(r in e)e.hasOwnProperty(r)&&(a=e[r],P.isPlainObject(a)?(P.isPlainObject(t[r])||(t[r]={}),P.extend(!0,t[r],a)):n&&"data"!==r&&"aaData"!==r&&Array.isArray(a)?t[r]=a.slice():t[r]=a);return t}function me(e,t,n){P(e).on("click.DT",t,function(t){P(e).trigger("blur"),n(t)}).on("keypress.DT",t,function(t){13===t.which&&(t.preventDefault(),n(t))}).on("selectstart.DT",function(){return!1})}function L(t,e,n,a){n&&t[e].push({fn:n,sName:a})}function R(n,t,e,a){var r=[];return t&&(r=P.map(n[t].slice().reverse(),function(t,e){return t.fn.apply(n.oInstance,a)})),null!==e&&(t=P.Event(e+".dt"),(e=P(n.nTable)).trigger(t,a),0===e.parents("body").length&&P("body").trigger(t,a),r.push(t.result)),r}function Se(t){var e=t._iDisplayStart,n=t.fnDisplayEnd(),a=t._iDisplayLength;n<=e&&(e=n-a),e-=e%a,t._iDisplayStart=e=-1===a||e<0?0:e}function ve(t,e){var t=t.renderer,n=w.ext.renderer[e];return P.isPlainObject(t)&&t[e]?n[t[e]]||n._:"string"==typeof t&&n[t]||n._}function E(t){return t.oFeatures.bServerSide?"ssp":t.ajax||t.sAjaxSource?"ajax":"dom"}function ye(t,n){var a;return Array.isArray(t)?P.map(t,function(t){return ye(t,n)}):"number"==typeof t?[n[t]]:(a=P.map(n,function(t,e){return t.nTable}),P(a).filter(t).map(function(t){var e=P.inArray(this,a);return n[e]}).toArray())}function De(r,o,t){var e,n;t&&(e=new B(r)).one("draw",function(){t(e.ajax.json())}),"ssp"==E(r)?u(r,o):(D(r,!0),(n=r.jqXHR)&&4!==n.readyState&&n.abort(),Tt(r,[],function(t){pt(r);for(var e=Ft(r,t),n=0,a=e.length;n<a;n++)x(r,e[n]);u(r,o),D(r,!1)}))}function _e(t,e,n,a,r){for(var o,i,l,s,u=[],c=typeof e,f=0,d=(e=e&&"string"!=c&&"function"!=c&&e.length!==H?e:[e]).length;f<d;f++)for(l=0,s=(i=e[f]&&e[f].split&&!e[f].match(/[\[\(:]/)?e[f].split(","):[e[f]]).length;l<s;l++)(o=n("string"==typeof i[l]?i[l].trim():i[l]))&&o.length&&(u=u.concat(o));var h=p.selector[t];if(h.length)for(f=0,d=h.length;f<d;f++)u=h[f](a,r,u);return z(u)}function we(t){return(t=t||{}).filter&&t.search===H&&(t.search=t.filter),P.extend({search:"none",order:"current",page:"all"},t)}function Ce(t){for(var e=0,n=t.length;e<n;e++)if(0<t[e].length)return t[0]=t[e],t[0].length=1,t.length=1,t.context=[t.context[e]],t;return t.length=0,t}function Te(o,t,e,n){function i(t,e){var n;if(Array.isArray(t)||t instanceof P)for(var a=0,r=t.length;a<r;a++)i(t[a],e);else t.nodeName&&"tr"===t.nodeName.toLowerCase()?l.push(t):(n=P("<tr><td></td></tr>").addClass(e),P("td",n).addClass(e).html(t)[0].colSpan=T(o),l.push(n[0]))}var l=[];i(e,n),t._details&&t._details.detach(),t._details=P(l),t._detailsShow&&t._details.insertAfter(t.nTr)}function xe(t,e){var n=t.context;if(n.length&&t.length){var a=n[0].aoData[t[0]];if(a._details){(a._detailsShow=e)?(a._details.insertAfter(a.nTr),P(a.nTr).addClass("dt-hasChild")):(a._details.detach(),P(a.nTr).removeClass("dt-hasChild")),R(n[0],null,"childRow",[e,t.row(t[0])]);var s=n[0],r=new B(s),a=".dt.DT_details",e="draw"+a,t="column-sizing"+a,a="destroy"+a,u=s.aoData;if(r.off(e+" "+t+" "+a),N(u,"_details").length>0){r.on(e,function(t,e){if(s!==e)return;r.rows({page:"current"}).eq(0).each(function(t){var e=u[t];if(e._detailsShow)e._details.insertAfter(e.nTr)})});r.on(t,function(t,e,n,a){if(s!==e)return;var r,o=T(e);for(var i=0,l=u.length;i<l;i++){r=u[i];if(r._details)r._details.children("td[colspan]").attr("colspan",o)}});r.on(a,function(t,e){if(s!==e)return;for(var n=0,a=u.length;n<a;n++)if(u[n]._details)Re(r,n)})}Le(n)}}}function Ae(t,e,n,a,r){for(var o=[],i=0,l=r.length;i<l;i++)o.push(S(t,r[i],e));return o}var Ie=[],o=Array.prototype,B=function(t,e){if(!(this instanceof B))return new B(t,e);function n(t){var e,n,a,r;t=t,a=w.settings,r=P.map(a,function(t,e){return t.nTable}),(t=t?t.nTable&&t.oApi?[t]:t.nodeName&&"table"===t.nodeName.toLowerCase()?-1!==(e=P.inArray(t,r))?[a[e]]:null:t&&"function"==typeof t.settings?t.settings().toArray():("string"==typeof t?n=P(t):t instanceof P&&(n=t),n?n.map(function(t){return-1!==(e=P.inArray(this,r))?a[e]:null}).toArray():void 0):[])&&o.push.apply(o,t)}var o=[];if(Array.isArray(t))for(var a=0,r=t.length;a<r;a++)n(t[a]);else n(t);this.context=z(o),e&&P.merge(this,e),this.selector={rows:null,cols:null,opts:null},B.extend(this,this,Ie)},Fe=(w.Api=B,P.extend(B.prototype,{any:function(){return 0!==this.count()},concat:o.concat,context:[],count:function(){return this.flatten().length},each:function(t){for(var e=0,n=this.length;e<n;e++)t.call(this,this[e],e,this);return this},eq:function(t){var e=this.context;return e.length>t?new B(e[t],this[t]):null},filter:function(t){var e=[];if(o.filter)e=o.filter.call(this,t,this);else for(var n=0,a=this.length;n<a;n++)t.call(this,this[n],n,this)&&e.push(this[n]);return new B(this.context,e)},flatten:function(){var t=[];return new B(this.context,t.concat.apply(t,this.toArray()))},join:o.join,indexOf:o.indexOf||function(t,e){for(var n=e||0,a=this.length;n<a;n++)if(this[n]===t)return n;return-1},iterator:function(t,e,n,a){var r,o,i,l,s,u,c,f,d=[],h=this.context,p=this.selector;for("string"==typeof t&&(a=n,n=e,e=t,t=!1),o=0,i=h.length;o<i;o++){var g=new B(h[o]);if("table"===e)(r=n.call(g,h[o],o))!==H&&d.push(r);else if("columns"===e||"rows"===e)(r=n.call(g,h[o],this[o],o))!==H&&d.push(r);else if("column"===e||"column-rows"===e||"row"===e||"cell"===e)for(c=this[o],"column-rows"===e&&(u=Fe(h[o],p.opts)),l=0,s=c.length;l<s;l++)f=c[l],(r="cell"===e?n.call(g,h[o],f.row,f.column,o,l):n.call(g,h[o],f,o,l,u))!==H&&d.push(r)}return d.length||a?((t=(a=new B(h,t?d.concat.apply([],d):d)).selector).rows=p.rows,t.cols=p.cols,t.opts=p.opts,a):this},lastIndexOf:o.lastIndexOf||function(t,e){return this.indexOf.apply(this.toArray.reverse(),arguments)},length:0,map:function(t){var e=[];if(o.map)e=o.map.call(this,t,this);else for(var n=0,a=this.length;n<a;n++)e.push(t.call(this,this[n],n));return new B(this.context,e)},pluck:function(t){var e=w.util.get(t);return this.map(function(t){return e(t)})},pop:o.pop,push:o.push,reduce:o.reduce||function(t,e){return et(this,t,e,0,this.length,1)},reduceRight:o.reduceRight||function(t,e){return et(this,t,e,this.length-1,-1,-1)},reverse:o.reverse,selector:null,shift:o.shift,slice:function(){return new B(this.context,this)},sort:o.sort,splice:o.splice,toArray:function(){return o.slice.call(this)},to$:function(){return P(this)},toJQuery:function(){return P(this)},unique:function(){return new B(this.context,z(this))},unshift:o.unshift}),B.extend=function(t,e,n){if(n.length&&e&&(e instanceof B||e.__dt_wrapper))for(var a,r=0,o=n.length;r<o;r++)e[(a=n[r]).name]="function"===a.type?function(e,n,a){return function(){var t=n.apply(e,arguments);return B.extend(t,t,a.methodExt),t}}(t,a.val,a):"object"===a.type?{}:a.val,e[a.name].__dt_wrapper=!0,B.extend(t,e[a.name],a.propExt)},B.register=e=function(t,e){if(Array.isArray(t))for(var n=0,a=t.length;n<a;n++)B.register(t[n],e);else for(var r=t.split("."),o=Ie,i=0,l=r.length;i<l;i++){var s,u,c=function(t,e){for(var n=0,a=t.length;n<a;n++)if(t[n].name===e)return t[n];return null}(o,u=(s=-1!==r[i].indexOf("()"))?r[i].replace("()",""):r[i]);c||o.push(c={name:u,val:{},methodExt:[],propExt:[],type:"object"}),i===l-1?(c.val=e,c.type="function"==typeof e?"function":P.isPlainObject(e)?"object":"other"):o=s?c.methodExt:c.propExt}},B.registerPlural=t=function(t,e,n){B.register(t,n),B.register(e,function(){var t=n.apply(this,arguments);return t===this?this:t instanceof B?t.length?Array.isArray(t[0])?new B(t.context,t[0]):t[0]:H:t})},e("tables()",function(t){return t!==H&&null!==t?new B(ye(t,this.context)):this}),e("table()",function(t){var t=this.tables(t),e=t.context;return e.length?new B(e[0]):t}),t("tables().nodes()","table().node()",function(){return this.iterator("table",function(t){return t.nTable},1)}),t("tables().body()","table().body()",function(){return this.iterator("table",function(t){return t.nTBody},1)}),t("tables().header()","table().header()",function(){return this.iterator("table",function(t){return t.nTHead},1)}),t("tables().footer()","table().footer()",function(){return this.iterator("table",function(t){return t.nTFoot},1)}),t("tables().containers()","table().container()",function(){return this.iterator("table",function(t){return t.nTableWrapper},1)}),e("draw()",function(e){return this.iterator("table",function(t){"page"===e?y(t):u(t,!1===(e="string"==typeof e?"full-hold"!==e:e))})}),e("page()",function(e){return e===H?this.page.info().page:this.iterator("table",function(t){Yt(t,e)})}),e("page.info()",function(t){var e,n,a,r,o;return 0===this.context.length?H:(n=(e=this.context[0])._iDisplayStart,a=e.oFeatures.bPaginate?e._iDisplayLength:-1,r=e.fnRecordsDisplay(),{page:(o=-1===a)?0:Math.floor(n/a),pages:o?1:Math.ceil(r/a),start:n,end:e.fnDisplayEnd(),length:a,recordsTotal:e.fnRecordsTotal(),recordsDisplay:r,serverSide:"ssp"===E(e)})}),e("page.len()",function(e){return e===H?0!==this.context.length?this.context[0]._iDisplayLength:H:this.iterator("table",function(t){$t(t,e)})}),e("ajax.json()",function(){var t=this.context;if(0<t.length)return t[0].json}),e("ajax.params()",function(){var t=this.context;if(0<t.length)return t[0].oAjaxData}),e("ajax.reload()",function(e,n){return this.iterator("table",function(t){De(t,!1===n,e)})}),e("ajax.url()",function(e){var t=this.context;return e===H?0===t.length?H:(t=t[0]).ajax?P.isPlainObject(t.ajax)?t.ajax.url:t.ajax:t.sAjaxSource:this.iterator("table",function(t){P.isPlainObject(t.ajax)?t.ajax.url=e:t.ajax=e})}),e("ajax.url().load()",function(e,n){return this.iterator("table",function(t){De(t,!1===n,e)})}),function(t,e){var n,a=[],r=t.aiDisplay,o=t.aiDisplayMaster,i=e.search,l=e.order,e=e.page;if("ssp"==E(t))return"removed"===i?[]:f(0,o.length);if("current"==e)for(u=t._iDisplayStart,c=t.fnDisplayEnd();u<c;u++)a.push(r[u]);else if("current"==l||"applied"==l){if("none"==i)a=o.slice();else if("applied"==i)a=r.slice();else if("removed"==i){for(var s={},u=0,c=r.length;u<c;u++)s[r[u]]=null;a=P.map(o,function(t){return s.hasOwnProperty(t)?null:t})}}else if("index"==l||"original"==l)for(u=0,c=t.aoData.length;u<c;u++)("none"==i||-1===(n=P.inArray(u,r))&&"removed"==i||0<=n&&"applied"==i)&&a.push(u);return a}),Le=(e("rows()",function(e,n){e===H?e="":P.isPlainObject(e)&&(n=e,e=""),n=we(n);var t=this.iterator("table",function(t){return _e("row",e,function(n){var t=d(n),a=r.aoData;if(null!==t&&!o)return[t];if(i=i||Fe(r,o),null!==t&&-1!==P.inArray(t,i))return[t];if(null===n||n===H||""===n)return i;if("function"==typeof n)return P.map(i,function(t){var e=a[t];return n(t,e._aData,e.nTr)?t:null});if(n.nodeName)return t=n._DT_RowIndex,e=n._DT_CellIndex,t!==H?a[t]&&a[t].nTr===n?[t]:[]:e?a[e.row]&&a[e.row].nTr===n.parentNode?[e.row]:[]:(t=P(n).closest("*[data-dt-row]")).length?[t.data("dt-row")]:[];if("string"==typeof n&&"#"===n.charAt(0)){var e=r.aIds[n.replace(/^#/,"")];if(e!==H)return[e.idx]}t=_(m(r.aoData,i,"nTr"));return P(t).filter(n).map(function(){return this._DT_RowIndex}).toArray()},r=t,o=n);var r,o,i},1);return t.selector.rows=e,t.selector.opts=n,t}),e("rows().nodes()",function(){return this.iterator("row",function(t,e){return t.aoData[e].nTr||H},1)}),e("rows().data()",function(){return this.iterator(!0,"rows",function(t,e){return m(t.aoData,e,"_aData")},1)}),t("rows().cache()","row().cache()",function(n){return this.iterator("row",function(t,e){t=t.aoData[e];return"search"===n?t._aFilterData:t._aSortData},1)}),t("rows().invalidate()","row().invalidate()",function(n){return this.iterator("row",function(t,e){bt(t,e,n)})}),t("rows().indexes()","row().index()",function(){return this.iterator("row",function(t,e){return e},1)}),t("rows().ids()","row().id()",function(t){for(var e=[],n=this.context,a=0,r=n.length;a<r;a++)for(var o=0,i=this[a].length;o<i;o++){var l=n[a].rowIdFn(n[a].aoData[this[a][o]]._aData);e.push((!0===t?"#":"")+l)}return new B(n,e)}),t("rows().remove()","row().remove()",function(){var f=this;return this.iterator("row",function(t,e,n){var a,r,o,i,l,s,u=t.aoData,c=u[e];for(u.splice(e,1),a=0,r=u.length;a<r;a++)if(s=(l=u[a]).anCells,null!==l.nTr&&(l.nTr._DT_RowIndex=a),null!==s)for(o=0,i=s.length;o<i;o++)s[o]._DT_CellIndex.row=a;gt(t.aiDisplayMaster,e),gt(t.aiDisplay,e),gt(f[n],e,!1),0<t._iRecordsDisplay&&t._iRecordsDisplay--,Se(t);n=t.rowIdFn(c._aData);n!==H&&delete t.aIds[n]}),this.iterator("table",function(t){for(var e=0,n=t.aoData.length;e<n;e++)t.aoData[e].idx=e}),this}),e("rows.add()",function(o){var t=this.iterator("table",function(t){for(var e,n=[],a=0,r=o.length;a<r;a++)(e=o[a]).nodeName&&"TR"===e.nodeName.toUpperCase()?n.push(ut(t,e)[0]):n.push(x(t,e));return n},1),e=this.rows(-1);return e.pop(),P.merge(e,t),e}),e("row()",function(t,e){return Ce(this.rows(t,e))}),e("row().data()",function(t){var e,n=this.context;return t===H?n.length&&this.length?n[0].aoData[this[0]]._aData:H:((e=n[0].aoData[this[0]])._aData=t,Array.isArray(t)&&e.nTr&&e.nTr.id&&b(n[0].rowId)(t,e.nTr.id),bt(n[0],this[0],"data"),this)}),e("row().node()",function(){var t=this.context;return t.length&&this.length&&t[0].aoData[this[0]].nTr||null}),e("row.add()",function(e){e instanceof P&&e.length&&(e=e[0]);var t=this.iterator("table",function(t){return e.nodeName&&"TR"===e.nodeName.toUpperCase()?ut(t,e)[0]:x(t,e)});return this.row(t[0])}),P(v).on("plugin-init.dt",function(t,e){var n=new B(e),a="on-plugin-init",r="stateSaveParams."+a,o="destroy. "+a,a=(n.on(r,function(t,e,n){for(var a=e.rowIdFn,r=e.aoData,o=[],i=0;i<r.length;i++)r[i]._detailsShow&&o.push("#"+a(r[i]._aData));n.childRows=o}),n.on(o,function(){n.off(r+" "+o)}),n.state.loaded());a&&a.childRows&&n.rows(P.map(a.childRows,function(t){return t.replace(/:/g,"\\:")})).every(function(){R(e,null,"requestChild",[this])})}),w.util.throttle(function(t){de(t[0])},500)),Re=function(t,e){var n=t.context;n.length&&(e=n[0].aoData[e!==H?e:t[0]])&&e._details&&(e._details.remove(),e._detailsShow=H,e._details=H,P(e.nTr).removeClass("dt-hasChild"),Le(n))},Pe="row().child",je=Pe+"()",He=(e(je,function(t,e){var n=this.context;return t===H?n.length&&this.length?n[0].aoData[this[0]]._details:H:(!0===t?this.child.show():!1===t?Re(this):n.length&&this.length&&Te(n[0],n[0].aoData[this[0]],t,e),this)}),e([Pe+".show()",je+".show()"],function(t){return xe(this,!0),this}),e([Pe+".hide()",je+".hide()"],function(){return xe(this,!1),this}),e([Pe+".remove()",je+".remove()"],function(){return Re(this),this}),e(Pe+".isShown()",function(){var t=this.context;return t.length&&this.length&&t[0].aoData[this[0]]._detailsShow||!1}),/^([^:]+):(name|visIdx|visible)$/),Ne=(e("columns()",function(n,a){n===H?n="":P.isPlainObject(n)&&(a=n,n=""),a=we(a);var t=this.iterator("table",function(t){return e=n,l=a,s=(i=t).aoColumns,u=N(s,"sName"),c=N(s,"nTh"),_e("column",e,function(n){var a,t=d(n);if(""===n)return f(s.length);if(null!==t)return[0<=t?t:s.length+t];if("function"==typeof n)return a=Fe(i,l),P.map(s,function(t,e){return n(e,Ae(i,e,0,0,a),c[e])?e:null});var r="string"==typeof n?n.match(He):"";if(r)switch(r[2]){case"visIdx":case"visible":var e,o=parseInt(r[1],10);return o<0?[(e=P.map(s,function(t,e){return t.bVisible?e:null}))[e.length+o]]:[rt(i,o)];case"name":return P.map(u,function(t,e){return t===r[1]?e:null});default:return[]}return n.nodeName&&n._DT_CellIndex?[n._DT_CellIndex.column]:(t=P(c).filter(n).map(function(){return P.inArray(this,c)}).toArray()).length||!n.nodeName?t:(t=P(n).closest("*[data-dt-column]")).length?[t.data("dt-column")]:[]},i,l);var i,e,l,s,u,c},1);return t.selector.cols=n,t.selector.opts=a,t}),t("columns().header()","column().header()",function(t,e){return this.iterator("column",function(t,e){return t.aoColumns[e].nTh},1)}),t("columns().footer()","column().footer()",function(t,e){return this.iterator("column",function(t,e){return t.aoColumns[e].nTf},1)}),t("columns().data()","column().data()",function(){return this.iterator("column-rows",Ae,1)}),t("columns().dataSrc()","column().dataSrc()",function(){return this.iterator("column",function(t,e){return t.aoColumns[e].mData},1)}),t("columns().cache()","column().cache()",function(o){return this.iterator("column-rows",function(t,e,n,a,r){return m(t.aoData,r,"search"===o?"_aFilterData":"_aSortData",e)},1)}),t("columns().nodes()","column().nodes()",function(){return this.iterator("column-rows",function(t,e,n,a,r){return m(t.aoData,r,"anCells",e)},1)}),t("columns().visible()","column().visible()",function(f,n){var e=this,t=this.iterator("column",function(t,e){if(f===H)return t.aoColumns[e].bVisible;var n,a,r=e,e=f,o=t.aoColumns,i=o[r],l=t.aoData;if(e===H)i.bVisible;else if(i.bVisible!==e){if(e)for(var s=P.inArray(!0,N(o,"bVisible"),r+1),u=0,c=l.length;u<c;u++)a=l[u].nTr,n=l[u].anCells,a&&a.insertBefore(n[r],n[s]||null);else P(N(t.aoData,"anCells",r)).detach();i.bVisible=e}});return f!==H&&this.iterator("table",function(t){Dt(t,t.aoHeader),Dt(t,t.aoFooter),t.aiDisplay.length||P(t.nTBody).find("td[colspan]").attr("colspan",T(t)),de(t),e.iterator("column",function(t,e){R(t,null,"column-visibility",[t,e,f,n])}),n!==H&&!n||e.columns.adjust()}),t}),t("columns().indexes()","column().index()",function(n){return this.iterator("column",function(t,e){return"visible"===n?ot(t,e):e},1)}),e("columns.adjust()",function(){return this.iterator("table",function(t){O(t)},1)}),e("column.index()",function(t,e){var n;if(0!==this.context.length)return n=this.context[0],"fromVisible"===t||"toData"===t?rt(n,e):"fromData"===t||"toVisible"===t?ot(n,e):void 0}),e("column()",function(t,e){return Ce(this.columns(t,e))}),e("cells()",function(g,t,b){var a,r,o,i,l,s,e;return P.isPlainObject(g)&&(g.row===H?(b=g,g=null):(b=t,t=null)),P.isPlainObject(t)&&(b=t,t=null),null===t||t===H?this.iterator("table",function(t){return a=t,t=g,e=we(b),f=a.aoData,d=Fe(a,e),n=_(m(f,d,"anCells")),h=P(Y([],n)),p=a.aoColumns.length,_e("cell",t,function(t){var e,n="function"==typeof t;if(null===t||t===H||n){for(o=[],i=0,l=d.length;i<l;i++)for(r=d[i],s=0;s<p;s++)u={row:r,column:s},(!n||(c=f[r],t(u,S(a,r,s),c.anCells?c.anCells[s]:null)))&&o.push(u);return o}return P.isPlainObject(t)?t.column!==H&&t.row!==H&&-1!==P.inArray(t.row,d)?[t]:[]:(e=h.filter(t).map(function(t,e){return{row:e._DT_CellIndex.row,column:e._DT_CellIndex.column}}).toArray()).length||!t.nodeName?e:(c=P(t).closest("*[data-dt-row]")).length?[{row:c.data("dt-row"),column:c.data("dt-column")}]:[]},a,e);var a,e,r,o,i,l,s,u,c,f,d,n,h,p}):(e=b?{page:b.page,order:b.order,search:b.search}:{},a=this.columns(t,e),r=this.rows(g,e),e=this.iterator("table",function(t,e){var n=[];for(o=0,i=r[e].length;o<i;o++)for(l=0,s=a[e].length;l<s;l++)n.push({row:r[e][o],column:a[e][l]});return n},1),e=b&&b.selected?this.cells(e,b):e,P.extend(e.selector,{cols:t,rows:g,opts:b}),e)}),t("cells().nodes()","cell().node()",function(){return this.iterator("cell",function(t,e,n){t=t.aoData[e];return t&&t.anCells?t.anCells[n]:H},1)}),e("cells().data()",function(){return this.iterator("cell",function(t,e,n){return S(t,e,n)},1)}),t("cells().cache()","cell().cache()",function(a){return a="search"===a?"_aFilterData":"_aSortData",this.iterator("cell",function(t,e,n){return t.aoData[e][a][n]},1)}),t("cells().render()","cell().render()",function(a){return this.iterator("cell",function(t,e,n){return S(t,e,n,a)},1)}),t("cells().indexes()","cell().index()",function(){return this.iterator("cell",function(t,e,n){return{row:e,column:n,columnVisible:ot(t,n)}},1)}),t("cells().invalidate()","cell().invalidate()",function(a){return this.iterator("cell",function(t,e,n){bt(t,e,a,n)})}),e("cell()",function(t,e,n){return Ce(this.cells(t,e,n))}),e("cell().data()",function(t){var e=this.context,n=this[0];return t===H?e.length&&n.length?S(e[0],n[0].row,n[0].column):H:(ct(e[0],n[0].row,n[0].column,t),bt(e[0],n[0].row,"data",n[0].column),this)}),e("order()",function(e,t){var n=this.context;return e===H?0!==n.length?n[0].aaSorting:H:("number"==typeof e?e=[[e,t]]:e.length&&!Array.isArray(e[0])&&(e=Array.prototype.slice.call(arguments)),this.iterator("table",function(t){t.aaSorting=e.slice()}))}),e("order.listener()",function(e,n,a){return this.iterator("table",function(t){ue(t,e,n,a)})}),e("order.fixed()",function(e){var t;return e?this.iterator("table",function(t){t.aaSortingFixed=P.extend(!0,{},e)}):(t=(t=this.context).length?t[0].aaSortingFixed:H,Array.isArray(t)?{pre:t}:t)}),e(["columns().order()","column().order()"],function(a){var r=this;return this.iterator("table",function(t,e){var n=[];P.each(r[e],function(t,e){n.push([e,a])}),t.aaSorting=n})}),e("search()",function(e,n,a,r){var t=this.context;return e===H?0!==t.length?t[0].oPreviousSearch.sSearch:H:this.iterator("table",function(t){t.oFeatures.bFilter&&Rt(t,P.extend({},t.oPreviousSearch,{sSearch:e+"",bRegex:null!==n&&n,bSmart:null===a||a,bCaseInsensitive:null===r||r}),1)})}),t("columns().search()","column().search()",function(a,r,o,i){return this.iterator("column",function(t,e){var n=t.aoPreSearchCols;if(a===H)return n[e].sSearch;t.oFeatures.bFilter&&(P.extend(n[e],{sSearch:a+"",bRegex:null!==r&&r,bSmart:null===o||o,bCaseInsensitive:null===i||i}),Rt(t,t.oPreviousSearch,1))})}),e("state()",function(){return this.context.length?this.context[0].oSavedState:null}),e("state.clear()",function(){return this.iterator("table",function(t){t.fnStateSaveCallback.call(t.oInstance,t,{})})}),e("state.loaded()",function(){return this.context.length?this.context[0].oLoadedState:null}),e("state.save()",function(){return this.iterator("table",function(t){de(t)})}),w.use=function(t,e){"lib"===e||t.fn?P=t:"win"==e||t.document?v=(j=t).document:"datetime"!==e&&"DateTime"!==t.type||(w.DateTime=t)},w.factory=function(t,e){var n=!1;return t&&t.document&&(v=(j=t).document),e&&e.fn&&e.fn.jquery&&(P=e,n=!0),n},w.versionCheck=w.fnVersionCheck=function(t){for(var e,n,a=w.version.split("."),r=t.split("."),o=0,i=r.length;o<i;o++)if((e=parseInt(a[o],10)||0)!==(n=parseInt(r[o],10)||0))return n<e;return!0},w.isDataTable=w.fnIsDataTable=function(t){var r=P(t).get(0),o=!1;return t instanceof w.Api||(P.each(w.settings,function(t,e){var n=e.nScrollHead?P("table",e.nScrollHead)[0]:null,a=e.nScrollFoot?P("table",e.nScrollFoot)[0]:null;e.nTable!==r&&n!==r&&a!==r||(o=!0)}),o)},w.tables=w.fnTables=function(e){var t=!1,n=(P.isPlainObject(e)&&(t=e.api,e=e.visible),P.map(w.settings,function(t){if(!e||P(t.nTable).is(":visible"))return t.nTable}));return t?new B(n):n},w.camelToHungarian=C,e("$()",function(t,e){e=this.rows(e).nodes(),e=P(e);return P([].concat(e.filter(t).toArray(),e.find(t).toArray()))}),P.each(["on","one","off"],function(t,n){e(n+"()",function(){var t=Array.prototype.slice.call(arguments),e=(t[0]=P.map(t[0].split(/\s/),function(t){return t.match(/\.dt\b/)?t:t+".dt"}).join(" "),P(this.tables().nodes()));return e[n].apply(e,t),this})}),e("clear()",function(){return this.iterator("table",function(t){pt(t)})}),e("settings()",function(){return new B(this.context,this.context)}),e("init()",function(){var t=this.context;return t.length?t[0].oInit:null}),e("data()",function(){return this.iterator("table",function(t){return N(t.aoData,"_aData")}).flatten()}),e("destroy()",function(c){return c=c||!1,this.iterator("table",function(e){var n,t=e.oClasses,a=e.nTable,r=e.nTBody,o=e.nTHead,i=e.nTFoot,l=P(a),r=P(r),s=P(e.nTableWrapper),u=P.map(e.aoData,function(t){return t.nTr}),i=(e.bDestroying=!0,R(e,"aoDestroyCallback","destroy",[e]),c||new B(e).columns().visible(!0),s.off(".DT").find(":not(tbody *)").off(".DT"),P(j).off(".DT-"+e.sInstance),a!=o.parentNode&&(l.children("thead").detach(),l.append(o)),i&&a!=i.parentNode&&(l.children("tfoot").detach(),l.append(i)),e.aaSorting=[],e.aaSortingFixed=[],ce(e),P(u).removeClass(e.asStripeClasses.join(" ")),P("th, td",o).removeClass(t.sSortable+" "+t.sSortableAsc+" "+t.sSortableDesc+" "+t.sSortableNone),r.children().detach(),r.append(u),e.nTableWrapper.parentNode),o=c?"remove":"detach",u=(l[o](),s[o](),!c&&i&&(i.insertBefore(a,e.nTableReinsertBefore),l.css("width",e.sDestroyWidth).removeClass(t.sTable),n=e.asDestroyStripes.length)&&r.children().each(function(t){P(this).addClass(e.asDestroyStripes[t%n])}),P.inArray(e,w.settings));-1!==u&&w.settings.splice(u,1)})}),P.each(["column","row","cell"],function(t,s){e(s+"s().every()",function(o){var i=this.selector.opts,l=this;return this.iterator(s,function(t,e,n,a,r){o.call(l[s](e,"cell"===s?n:i,"cell"===s?i:H),e,n,a,r)})})}),e("i18n()",function(t,e,n){var a=this.context[0],t=A(t)(a.oLanguage);return t===H&&(t=e),"string"==typeof(t=n!==H&&P.isPlainObject(t)?t[n]!==H?t[n]:t._:t)?t.replace("%d",n):t}),w.version="1.13.6",w.settings=[],w.models={},w.models.oSearch={bCaseInsensitive:!0,sSearch:"",bRegex:!1,bSmart:!0,return:!1},w.models.oRow={nTr:null,anCells:null,_aData:[],_aSortData:null,_aFilterData:null,_sFilterRow:null,_sRowStripe:"",src:null,idx:-1},w.models.oColumn={idx:null,aDataSort:null,asSorting:null,bSearchable:null,bSortable:null,bVisible:null,_sManualType:null,_bAttrSrc:!1,fnCreatedCell:null,fnGetData:null,fnSetData:null,mData:null,mRender:null,nTh:null,nTf:null,sClass:null,sContentPadding:null,sDefaultContent:null,sName:null,sSortDataType:"std",sSortingClass:null,sSortingClassJUI:null,sTitle:null,sType:null,sWidth:null,sWidthOrig:null},w.defaults={aaData:null,aaSorting:[[0,"asc"]],aaSortingFixed:[],ajax:null,aLengthMenu:[10,25,50,100],aoColumns:null,aoColumnDefs:null,aoSearchCols:[],asStripeClasses:null,bAutoWidth:!0,bDeferRender:!1,bDestroy:!1,bFilter:!0,bInfo:!0,bLengthChange:!0,bPaginate:!0,bProcessing:!1,bRetrieve:!1,bScrollCollapse:!1,bServerSide:!1,bSort:!0,bSortMulti:!0,bSortCellsTop:!1,bSortClasses:!0,bStateSave:!1,fnCreatedRow:null,fnDrawCallback:null,fnFooterCallback:null,fnFormatNumber:function(t){return t.toString().replace(/\B(?=(\d{3})+(?!\d))/g,this.oLanguage.sThousands)},fnHeaderCallback:null,fnInfoCallback:null,fnInitComplete:null,fnPreDrawCallback:null,fnRowCallback:null,fnServerData:null,fnServerParams:null,fnStateLoadCallback:function(t){try{return JSON.parse((-1===t.iStateDuration?sessionStorage:localStorage).getItem("DataTables_"+t.sInstance+"_"+location.pathname))}catch(t){return{}}},fnStateLoadParams:null,fnStateLoaded:null,fnStateSaveCallback:function(t,e){try{(-1===t.iStateDuration?sessionStorage:localStorage).setItem("DataTables_"+t.sInstance+"_"+location.pathname,JSON.stringify(e))}catch(t){}},fnStateSaveParams:null,iStateDuration:7200,iDeferLoading:null,iDisplayLength:10,iDisplayStart:0,iTabIndex:0,oClasses:{},oLanguage:{oAria:{sSortAscending:": activate to sort column ascending",sSortDescending:": activate to sort column descending"},oPaginate:{sFirst:"First",sLast:"Last",sNext:"Next",sPrevious:"Previous"},sEmptyTable:"No data available in table",sInfo:"Showing _START_ to _END_ of _TOTAL_ entries",sInfoEmpty:"Showing 0 to 0 of 0 entries",sInfoFiltered:"(filtered from _MAX_ total entries)",sInfoPostFix:"",sDecimal:"",sThousands:",",sLengthMenu:"Show _MENU_ entries",sLoadingRecords:"Loading...",sProcessing:"",sSearch:"Search:",sSearchPlaceholder:"",sUrl:"",sZeroRecords:"No matching records found"},oSearch:P.extend({},w.models.oSearch),sAjaxDataProp:"data",sAjaxSource:null,sDom:"lfrtip",searchDelay:null,sPaginationType:"simple_numbers",sScrollX:"",sScrollXInner:"",sScrollY:"",sServerMethod:"GET",renderer:null,rowId:"DT_RowId"},i(w.defaults),w.defaults.column={aDataSort:null,iDataSort:-1,asSorting:["asc","desc"],bSearchable:!0,bSortable:!0,bVisible:!0,fnCreatedCell:null,mData:null,mRender:null,sCellType:"td",sClass:"",sContentPadding:"",sDefaultContent:null,sName:"",sSortDataType:"std",sTitle:null,sType:null,sWidth:null},i(w.defaults.column),w.models.oSettings={oFeatures:{bAutoWidth:null,bDeferRender:null,bFilter:null,bInfo:null,bLengthChange:null,bPaginate:null,bProcessing:null,bServerSide:null,bSort:null,bSortMulti:null,bSortClasses:null,bStateSave:null},oScroll:{bCollapse:null,iBarWidth:0,sX:null,sXInner:null,sY:null},oLanguage:{fnInfoCallback:null},oBrowser:{bScrollOversize:!1,bScrollbarLeft:!1,bBounding:!1,barWidth:0},ajax:null,aanFeatures:[],aoData:[],aiDisplay:[],aiDisplayMaster:[],aIds:{},aoColumns:[],aoHeader:[],aoFooter:[],oPreviousSearch:{},aoPreSearchCols:[],aaSorting:null,aaSortingFixed:[],asStripeClasses:null,asDestroyStripes:[],sDestroyWidth:0,aoRowCallback:[],aoHeaderCallback:[],aoFooterCallback:[],aoDrawCallback:[],aoRowCreatedCallback:[],aoPreDrawCallback:[],aoInitComplete:[],aoStateSaveParams:[],aoStateLoadParams:[],aoStateLoaded:[],sTableId:"",nTable:null,nTHead:null,nTFoot:null,nTBody:null,nTableWrapper:null,bDeferLoading:!1,bInitialised:!1,aoOpenRows:[],sDom:null,searchDelay:null,sPaginationType:"two_button",iStateDuration:0,aoStateSave:[],aoStateLoad:[],oSavedState:null,oLoadedState:null,sAjaxSource:null,sAjaxDataProp:null,jqXHR:null,json:H,oAjaxData:H,fnServerData:null,aoServerParams:[],sServerMethod:null,fnFormatNumber:null,aLengthMenu:null,iDraw:0,bDrawing:!1,iDrawError:-1,_iDisplayLength:10,_iDisplayStart:0,_iRecordsTotal:0,_iRecordsDisplay:0,oClasses:{},bFiltered:!1,bSorted:!1,bSortCellsTop:null,oInit:null,aoDestroyCallback:[],fnRecordsTotal:function(){return"ssp"==E(this)?+this._iRecordsTotal:this.aiDisplayMaster.length},fnRecordsDisplay:function(){return"ssp"==E(this)?+this._iRecordsDisplay:this.aiDisplay.length},fnDisplayEnd:function(){var t=this._iDisplayLength,e=this._iDisplayStart,n=e+t,a=this.aiDisplay.length,r=this.oFeatures,o=r.bPaginate;return r.bServerSide?!1===o||-1===t?e+a:Math.min(e+t,this._iRecordsDisplay):!o||a<n||-1===t?a:n},oInstance:null,sInstance:null,iTabIndex:0,nScrollHead:null,nScrollFoot:null,aLastSort:[],oPlugins:{},rowIdFn:null,rowId:null},w.ext=p={buttons:{},classes:{},builder:"-source-",errMode:"alert",feature:[],search:[],selector:{cell:[],column:[],row:[]},internal:{},legacy:{ajax:null},pager:{},renderer:{pageButton:{},header:{}},order:{},type:{detect:[],search:{},order:{}},_unique:0,fnVersionCheck:w.fnVersionCheck,iApiIndex:0,oJUIClasses:{},sVersion:w.version},P.extend(p,{afnFiltering:p.search,aTypes:p.type.detect,ofnSearch:p.type.search,oSort:p.type.order,afnSortData:p.order,aoFeatures:p.feature,oApi:p.internal,oStdClasses:p.classes,oPagination:p.pager}),P.extend(w.ext.classes,{sTable:"dataTable",sNoFooter:"no-footer",sPageButton:"paginate_button",sPageButtonActive:"current",sPageButtonDisabled:"disabled",sStripeOdd:"odd",sStripeEven:"even",sRowEmpty:"dataTables_empty",sWrapper:"dataTables_wrapper",sFilter:"dataTables_filter",sInfo:"dataTables_info",sPaging:"dataTables_paginate paging_",sLength:"dataTables_length",sProcessing:"dataTables_processing",sSortAsc:"sorting_asc",sSortDesc:"sorting_desc",sSortable:"sorting",sSortableAsc:"sorting_desc_disabled",sSortableDesc:"sorting_asc_disabled",sSortableNone:"sorting_disabled",sSortColumn:"sorting_",sFilterInput:"",sLengthSelect:"",sScrollWrapper:"dataTables_scroll",sScrollHead:"dataTables_scrollHead",sScrollHeadInner:"dataTables_scrollHeadInner",sScrollBody:"dataTables_scrollBody",sScrollFoot:"dataTables_scrollFoot",sScrollFootInner:"dataTables_scrollFootInner",sHeaderTH:"",sFooterTH:"",sSortJUIAsc:"",sSortJUIDesc:"",sSortJUI:"",sSortJUIAscAllowed:"",sSortJUIDescAllowed:"",sSortJUIWrapper:"",sSortIcon:"",sJUIHeader:"",sJUIFooter:""}),w.ext.pager);function Oe(t,e){var n=[],a=Ne.numbers_length,r=Math.floor(a/2);return e<=a?n=f(0,e):t<=r?((n=f(0,a-2)).push("ellipsis"),n.push(e-1)):((e-1-r<=t?n=f(e-(a-2),e):((n=f(t-r+2,t+r-1)).push("ellipsis"),n.push(e-1),n)).splice(0,0,"ellipsis"),n.splice(0,0,0)),n.DT_el="span",n}P.extend(Ne,{simple:function(t,e){return["previous","next"]},full:function(t,e){return["first","previous","next","last"]},numbers:function(t,e){return[Oe(t,e)]},simple_numbers:function(t,e){return["previous",Oe(t,e),"next"]},full_numbers:function(t,e){return["first","previous",Oe(t,e),"next","last"]},first_last_numbers:function(t,e){return["first",Oe(t,e),"last"]},_numbers:Oe,numbers_length:7}),P.extend(!0,w.ext.renderer,{pageButton:{_:function(u,t,c,e,f,d){function h(t,e){for(var n,a=b.sPageButtonDisabled,r=function(t){Yt(u,t.data.action,!0)},o=0,i=e.length;o<i;o++)if(n=e[o],Array.isArray(n)){var l=P("<"+(n.DT_el||"div")+"/>").appendTo(t);h(l,n)}else{var s=!1;switch(p=null,g=n){case"ellipsis":t.append('<span class="ellipsis">&#x2026;</span>');break;case"first":p=m.sFirst,0===f&&(s=!0);break;case"previous":p=m.sPrevious,0===f&&(s=!0);break;case"next":p=m.sNext,0!==d&&f!==d-1||(s=!0);break;case"last":p=m.sLast,0!==d&&f!==d-1||(s=!0);break;default:p=u.fnFormatNumber(n+1),g=f===n?b.sPageButtonActive:""}null!==p&&(l=u.oInit.pagingTag||"a",s&&(g+=" "+a),me(P("<"+l+">",{class:b.sPageButton+" "+g,"aria-controls":u.sTableId,"aria-disabled":s?"true":null,"aria-label":S[n],role:"link","aria-current":g===b.sPageButtonActive?"page":null,"data-dt-idx":n,tabindex:s?-1:u.iTabIndex,id:0===c&&"string"==typeof n?u.sTableId+"_"+n:null}).html(p).appendTo(t),{action:n},r))}}var p,g,n,b=u.oClasses,m=u.oLanguage.oPaginate,S=u.oLanguage.oAria.paginate||{};try{n=P(t).find(v.activeElement).data("dt-idx")}catch(t){}h(P(t).empty(),e),n!==H&&P(t).find("[data-dt-idx="+n+"]").trigger("focus")}}}),P.extend(w.ext.type.detect,[function(t,e){e=e.oLanguage.sDecimal;return l(t,e)?"num"+e:null},function(t,e){var n;return(!t||t instanceof Date||X.test(t))&&(null!==(n=Date.parse(t))&&!isNaN(n)||h(t))?"date":null},function(t,e){e=e.oLanguage.sDecimal;return l(t,e,!0)?"num-fmt"+e:null},function(t,e){e=e.oLanguage.sDecimal;return a(t,e)?"html-num"+e:null},function(t,e){e=e.oLanguage.sDecimal;return a(t,e,!0)?"html-num-fmt"+e:null},function(t,e){return h(t)||"string"==typeof t&&-1!==t.indexOf("<")?"html":null}]),P.extend(w.ext.type.search,{html:function(t){return h(t)?t:"string"==typeof t?t.replace(U," ").replace(V,""):""},string:function(t){return!h(t)&&"string"==typeof t?t.replace(U," "):t}});function ke(t,e,n,a){var r;return 0===t||t&&"-"!==t?"number"==(r=typeof t)||"bigint"==r?t:+(t=(t=e?$(t,e):t).replace&&(n&&(t=t.replace(n,"")),a)?t.replace(a,""):t):-1/0}function Me(n){P.each({num:function(t){return ke(t,n)},"num-fmt":function(t){return ke(t,n,q)},"html-num":function(t){return ke(t,n,V)},"html-num-fmt":function(t){return ke(t,n,V,q)}},function(t,e){p.type.order[t+n+"-pre"]=e,t.match(/^html\-/)&&(p.type.search[t+n]=p.type.search.html)})}P.extend(p.type.order,{"date-pre":function(t){t=Date.parse(t);return isNaN(t)?-1/0:t},"html-pre":function(t){return h(t)?"":t.replace?t.replace(/<.*?>/g,"").toLowerCase():t+""},"string-pre":function(t){return h(t)?"":"string"==typeof t?t.toLowerCase():t.toString?t.toString():""},"string-asc":function(t,e){return t<e?-1:e<t?1:0},"string-desc":function(t,e){return t<e?1:e<t?-1:0}}),Me(""),P.extend(!0,w.ext.renderer,{header:{_:function(r,o,i,l){P(r.nTable).on("order.dt.DT",function(t,e,n,a){r===e&&(e=i.idx,o.removeClass(l.sSortAsc+" "+l.sSortDesc).addClass("asc"==a[e]?l.sSortAsc:"desc"==a[e]?l.sSortDesc:i.sSortingClass))})},jqueryui:function(r,o,i,l){P("<div/>").addClass(l.sSortJUIWrapper).append(o.contents()).append(P("<span/>").addClass(l.sSortIcon+" "+i.sSortingClassJUI)).appendTo(o),P(r.nTable).on("order.dt.DT",function(t,e,n,a){r===e&&(e=i.idx,o.removeClass(l.sSortAsc+" "+l.sSortDesc).addClass("asc"==a[e]?l.sSortAsc:"desc"==a[e]?l.sSortDesc:i.sSortingClass),o.find("span."+l.sSortIcon).removeClass(l.sSortJUIAsc+" "+l.sSortJUIDesc+" "+l.sSortJUI+" "+l.sSortJUIAscAllowed+" "+l.sSortJUIDescAllowed).addClass("asc"==a[e]?l.sSortJUIAsc:"desc"==a[e]?l.sSortJUIDesc:i.sSortingClassJUI))})}}});function We(t){return"string"==typeof(t=Array.isArray(t)?t.join(","):t)?t.replace(/&/g,"&amp;").replace(/</g,"&lt;").replace(/>/g,"&gt;").replace(/"/g,"&quot;"):t}function Ee(t,e,n,a,r){return j.moment?t[e](r):j.luxon?t[n](r):a?t[a](r):t}var Be=!1;function Ue(t,e,n){var a;if(j.moment){if(!(a=j.moment.utc(t,e,n,!0)).isValid())return null}else if(j.luxon){if(!(a=e&&"string"==typeof t?j.luxon.DateTime.fromFormat(t,e):j.luxon.DateTime.fromISO(t)).isValid)return null;a.setLocale(n)}else e?(Be||alert("DataTables warning: Formatted date without Moment.js or Luxon - https://datatables.net/tn/17"),Be=!0):a=new Date(t);return a}function Ve(s){return function(a,r,o,i){0===arguments.length?(o="en",a=r=null):1===arguments.length?(o="en",r=a,a=null):2===arguments.length&&(o=r,r=a,a=null);var l="datetime-"+r;return w.ext.type.order[l]||(w.ext.type.detect.unshift(function(t){return t===l&&l}),w.ext.type.order[l+"-asc"]=function(t,e){t=t.valueOf(),e=e.valueOf();return t===e?0:t<e?-1:1},w.ext.type.order[l+"-desc"]=function(t,e){t=t.valueOf(),e=e.valueOf();return t===e?0:e<t?-1:1}),function(t,e){var n;return null!==t&&t!==H||(t="--now"===i?(n=new Date,new Date(Date.UTC(n.getFullYear(),n.getMonth(),n.getDate(),n.getHours(),n.getMinutes(),n.getSeconds()))):""),"type"===e?l:""===t?"sort"!==e?"":Ue("0000-01-01 00:00:00",null,o):!(null===r||a!==r||"sort"===e||"type"===e||t instanceof Date)||null===(n=Ue(t,a,o))?t:"sort"===e?n:(t=null===r?Ee(n,"toDate","toJSDate","")[s]():Ee(n,"format","toFormat","toISOString",r),"display"===e?We(t):t)}}}var Xe=",",Je=".";if(j.Intl!==H)try{for(var qe=(new Intl.NumberFormat).formatToParts(100000.1),n=0;n<qe.length;n++)"group"===qe[n].type?Xe=qe[n].value:"decimal"===qe[n].type&&(Je=qe[n].value)}catch(t){}function $e(e){return function(){var t=[ge(this[w.ext.iApiIndex])].concat(Array.prototype.slice.call(arguments));return w.ext.internal[e].apply(this,t)}}return w.datetime=function(n,a){var r="datetime-detect-"+n;a=a||"en",w.ext.type.order[r]||(w.ext.type.detect.unshift(function(t){var e=Ue(t,n,a);return!(""!==t&&!e)&&r}),w.ext.type.order[r+"-pre"]=function(t){return Ue(t,n,a)||0})},w.render={date:Ve("toLocaleDateString"),datetime:Ve("toLocaleString"),time:Ve("toLocaleTimeString"),number:function(a,r,o,i,l){return null!==a&&a!==H||(a=Xe),null!==r&&r!==H||(r=Je),{display:function(t){if("number"!=typeof t&&"string"!=typeof t)return t;if(""===t||null===t)return t;var e=t<0?"-":"",n=parseFloat(t);if(isNaN(n))return We(t);n=n.toFixed(o),t=Math.abs(n);n=parseInt(t,10),t=o?r+(t-n).toFixed(o).substring(2):"";return(e=0===n&&0===parseFloat(t)?"":e)+(i||"")+n.toString().replace(/\B(?=(\d{3})+(?!\d))/g,a)+t+(l||"")}}},text:function(){return{display:We,filter:We}}},P.extend(w.ext.internal,{_fnExternApiFunc:$e,_fnBuildAjax:Tt,_fnAjaxUpdate:xt,_fnAjaxParameters:At,_fnAjaxUpdateDraw:It,_fnAjaxDataSrc:Ft,_fnAddColumn:nt,_fnColumnOptions:at,_fnAdjustColumnSizing:O,_fnVisibleToColumnIndex:rt,_fnColumnIndexToVisible:ot,_fnVisbleColumns:T,_fnGetColumns:it,_fnColumnTypes:lt,_fnApplyColumnDefs:st,_fnHungarianMap:i,_fnCamelToHungarian:C,_fnLanguageCompat:Z,_fnBrowserDetect:tt,_fnAddData:x,_fnAddTr:ut,_fnNodeToDataIndex:function(t,e){return e._DT_RowIndex!==H?e._DT_RowIndex:null},_fnNodeToColumnIndex:function(t,e,n){return P.inArray(n,t.aoData[e].anCells)},_fnGetCellData:S,_fnSetCellData:ct,_fnSplitObjNotation:dt,_fnGetObjectDataFn:A,_fnSetObjectDataFn:b,_fnGetDataMaster:ht,_fnClearTable:pt,_fnDeleteIndex:gt,_fnInvalidate:bt,_fnGetRowElements:mt,_fnCreateTr:St,_fnBuildHead:yt,_fnDrawHead:Dt,_fnDraw:y,_fnReDraw:u,_fnAddOptionsHtml:_t,_fnDetectHeader:wt,_fnGetUniqueThs:Ct,_fnFeatureHtmlFilter:Lt,_fnFilterComplete:Rt,_fnFilterCustom:Pt,_fnFilterColumn:jt,_fnFilter:Ht,_fnFilterCreateSearch:Nt,_fnEscapeRegex:Ot,_fnFilterData:Wt,_fnFeatureHtmlInfo:Ut,_fnUpdateInfo:Vt,_fnInfoMacros:Xt,_fnInitialise:Jt,_fnInitComplete:qt,_fnLengthChange:$t,_fnFeatureHtmlLength:Gt,_fnFeatureHtmlPaginate:zt,_fnPageChange:Yt,_fnFeatureHtmlProcessing:Zt,_fnProcessingDisplay:D,_fnFeatureHtmlTable:Kt,_fnScrollDraw:Qt,_fnApplyToChildren:k,_fnCalculateColumnWidths:ee,_fnThrottle:ne,_fnConvertToWidth:ae,_fnGetWidestNode:re,_fnGetMaxLenString:oe,_fnStringToCss:M,_fnSortFlatten:I,_fnSort:ie,_fnSortAria:le,_fnSortListener:se,_fnSortAttachListener:ue,_fnSortingClasses:ce,_fnSortData:fe,_fnSaveState:de,_fnLoadState:he,_fnImplementState:pe,_fnSettingsFromNode:ge,_fnLog:W,_fnMap:F,_fnBindAction:me,_fnCallbackReg:L,_fnCallbackFire:R,_fnLengthOverflow:Se,_fnRenderer:ve,_fnDataSource:E,_fnRowAttributes:vt,_fnExtend:be,_fnCalculateEnd:function(){}}),((P.fn.dataTable=w).$=P).fn.dataTableSettings=w.settings,P.fn.dataTableExt=w.ext,P.fn.DataTable=function(t){return P(this).dataTable(t).api()},P.each(w,function(t,e){P.fn.DataTable[t]=e}),w});
\ No newline at end of file
diff --git a/docs/coverage/lib/highlight.js-6.2/LICENSE b/docs/coverage/lib/highlight.js-6.2/LICENSE
new file mode 100644
index 00000000..fe2f67b1
--- /dev/null
+++ b/docs/coverage/lib/highlight.js-6.2/LICENSE
@@ -0,0 +1,24 @@
+Copyright (c) 2006, Ivan Sagalaev
+All rights reserved.
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+    * Redistributions of source code must retain the above copyright
+      notice, this list of conditions and the following disclaimer.
+    * Redistributions in binary form must reproduce the above copyright
+      notice, this list of conditions and the following disclaimer in the
+      documentation and/or other materials provided with the distribution.
+    * Neither the name of highlight.js nor the names of its contributors
+      may be used to endorse or promote products derived from this software
+      without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE REGENTS AND CONTRIBUTORS ``AS IS'' AND ANY
+EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE REGENTS AND CONTRIBUTORS BE LIABLE FOR ANY
+DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
+ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
diff --git a/docs/coverage/lib/highlight.js-6.2/highlight.pack.js b/docs/coverage/lib/highlight.js-6.2/highlight.pack.js
new file mode 100644
index 00000000..6687e557
--- /dev/null
+++ b/docs/coverage/lib/highlight.js-6.2/highlight.pack.js
@@ -0,0 +1 @@
+var hljs=new function(){function m(p){return p.replace(/&/gm,"&amp;").replace(/</gm,"&lt;")}function f(r,q,p){return RegExp(q,"m"+(r.cI?"i":"")+(p?"g":""))}function b(r){for(var p=0;p<r.childNodes.length;p++){var q=r.childNodes[p];if(q.nodeName=="CODE"){return q}if(!(q.nodeType==3&&q.nodeValue.match(/\s+/))){break}}}function h(t,s){var p="";for(var r=0;r<t.childNodes.length;r++){if(t.childNodes[r].nodeType==3){var q=t.childNodes[r].nodeValue;if(s){q=q.replace(/\n/g,"")}p+=q}else{if(t.childNodes[r].nodeName=="BR"){p+="\n"}else{p+=h(t.childNodes[r])}}}if(/MSIE [678]/.test(navigator.userAgent)){p=p.replace(/\r/g,"\n")}return p}function a(s){var r=s.className.split(/\s+/);r=r.concat(s.parentNode.className.split(/\s+/));for(var q=0;q<r.length;q++){var p=r[q].replace(/^language-/,"");if(e[p]){return p}}}function c(q){var p=[];(function(s,t){for(var r=0;r<s.childNodes.length;r++){if(s.childNodes[r].nodeType==3){t+=s.childNodes[r].nodeValue.length}else{if(s.childNodes[r].nodeName=="BR"){t+=1}else{if(s.childNodes[r].nodeType==1){p.push({event:"start",offset:t,node:s.childNodes[r]});t=arguments.callee(s.childNodes[r],t);p.push({event:"stop",offset:t,node:s.childNodes[r]})}}}}return t})(q,0);return p}function k(y,w,x){var q=0;var z="";var s=[];function u(){if(y.length&&w.length){if(y[0].offset!=w[0].offset){return(y[0].offset<w[0].offset)?y:w}else{return w[0].event=="start"?y:w}}else{return y.length?y:w}}function t(D){var A="<"+D.nodeName.toLowerCase();for(var B=0;B<D.attributes.length;B++){var C=D.attributes[B];A+=" "+C.nodeName.toLowerCase();if(C.value!==undefined&&C.value!==false&&C.value!==null){A+='="'+m(C.value)+'"'}}return A+">"}while(y.length||w.length){var v=u().splice(0,1)[0];z+=m(x.substr(q,v.offset-q));q=v.offset;if(v.event=="start"){z+=t(v.node);s.push(v.node)}else{if(v.event=="stop"){var p,r=s.length;do{r--;p=s[r];z+=("</"+p.nodeName.toLowerCase()+">")}while(p!=v.node);s.splice(r,1);while(r<s.length){z+=t(s[r]);r++}}}}return z+m(x.substr(q))}function j(){function q(x,y,v){if(x.compiled){return}var u;var s=[];if(x.k){x.lR=f(y,x.l||hljs.IR,true);for(var w in x.k){if(!x.k.hasOwnProperty(w)){continue}if(x.k[w] instanceof Object){u=x.k[w]}else{u=x.k;w="keyword"}for(var r in u){if(!u.hasOwnProperty(r)){continue}x.k[r]=[w,u[r]];s.push(r)}}}if(!v){if(x.bWK){x.b="\\b("+s.join("|")+")\\s"}x.bR=f(y,x.b?x.b:"\\B|\\b");if(!x.e&&!x.eW){x.e="\\B|\\b"}if(x.e){x.eR=f(y,x.e)}}if(x.i){x.iR=f(y,x.i)}if(x.r===undefined){x.r=1}if(!x.c){x.c=[]}x.compiled=true;for(var t=0;t<x.c.length;t++){if(x.c[t]=="self"){x.c[t]=x}q(x.c[t],y,false)}if(x.starts){q(x.starts,y,false)}}for(var p in e){if(!e.hasOwnProperty(p)){continue}q(e[p].dM,e[p],true)}}function d(B,C){if(!j.called){j();j.called=true}function q(r,M){for(var L=0;L<M.c.length;L++){if((M.c[L].bR.exec(r)||[null])[0]==r){return M.c[L]}}}function v(L,r){if(D[L].e&&D[L].eR.test(r)){return 1}if(D[L].eW){var M=v(L-1,r);return M?M+1:0}return 0}function w(r,L){return L.i&&L.iR.test(r)}function K(N,O){var M=[];for(var L=0;L<N.c.length;L++){M.push(N.c[L].b)}var r=D.length-1;do{if(D[r].e){M.push(D[r].e)}r--}while(D[r+1].eW);if(N.i){M.push(N.i)}return f(O,M.join("|"),true)}function p(M,L){var N=D[D.length-1];if(!N.t){N.t=K(N,E)}N.t.lastIndex=L;var r=N.t.exec(M);return r?[M.substr(L,r.index-L),r[0],false]:[M.substr(L),"",true]}function z(N,r){var L=E.cI?r[0].toLowerCase():r[0];var M=N.k[L];if(M&&M instanceof Array){return M}return false}function F(L,P){L=m(L);if(!P.k){return L}var r="";var O=0;P.lR.lastIndex=0;var M=P.lR.exec(L);while(M){r+=L.substr(O,M.index-O);var N=z(P,M);if(N){x+=N[1];r+='<span class="'+N[0]+'">'+M[0]+"</span>"}else{r+=M[0]}O=P.lR.lastIndex;M=P.lR.exec(L)}return r+L.substr(O,L.length-O)}function J(L,M){if(M.sL&&e[M.sL]){var r=d(M.sL,L);x+=r.keyword_count;return r.value}else{return F(L,M)}}function I(M,r){var L=M.cN?'<span class="'+M.cN+'">':"";if(M.rB){y+=L;M.buffer=""}else{if(M.eB){y+=m(r)+L;M.buffer=""}else{y+=L;M.buffer=r}}D.push(M);A+=M.r}function G(N,M,Q){var R=D[D.length-1];if(Q){y+=J(R.buffer+N,R);return false}var P=q(M,R);if(P){y+=J(R.buffer+N,R);I(P,M);return P.rB}var L=v(D.length-1,M);if(L){var O=R.cN?"</span>":"";if(R.rE){y+=J(R.buffer+N,R)+O}else{if(R.eE){y+=J(R.buffer+N,R)+O+m(M)}else{y+=J(R.buffer+N+M,R)+O}}while(L>1){O=D[D.length-2].cN?"</span>":"";y+=O;L--;D.length--}var r=D[D.length-1];D.length--;D[D.length-1].buffer="";if(r.starts){I(r.starts,"")}return R.rE}if(w(M,R)){throw"Illegal"}}var E=e[B];var D=[E.dM];var A=0;var x=0;var y="";try{var s,u=0;E.dM.buffer="";do{s=p(C,u);var t=G(s[0],s[1],s[2]);u+=s[0].length;if(!t){u+=s[1].length}}while(!s[2]);if(D.length>1){throw"Illegal"}return{r:A,keyword_count:x,value:y}}catch(H){if(H=="Illegal"){return{r:0,keyword_count:0,value:m(C)}}else{throw H}}}function g(t){var p={keyword_count:0,r:0,value:m(t)};var r=p;for(var q in e){if(!e.hasOwnProperty(q)){continue}var s=d(q,t);s.language=q;if(s.keyword_count+s.r>r.keyword_count+r.r){r=s}if(s.keyword_count+s.r>p.keyword_count+p.r){r=p;p=s}}if(r.language){p.second_best=r}return p}function i(r,q,p){if(q){r=r.replace(/^((<[^>]+>|\t)+)/gm,function(t,w,v,u){return w.replace(/\t/g,q)})}if(p){r=r.replace(/\n/g,"<br>")}return r}function n(t,w,r){var x=h(t,r);var v=a(t);var y,s;if(v){y=d(v,x)}else{return}var q=c(t);if(q.length){s=document.createElement("pre");s.innerHTML=y.value;y.value=k(q,c(s),x)}y.value=i(y.value,w,r);var u=t.className;if(!u.match("(\\s|^)(language-)?"+v+"(\\s|$)")){u=u?(u+" "+v):v}if(/MSIE [678]/.test(navigator.userAgent)&&t.tagName=="CODE"&&t.parentNode.tagName=="PRE"){s=t.parentNode;var p=document.createElement("div");p.innerHTML="<pre><code>"+y.value+"</code></pre>";t=p.firstChild.firstChild;p.firstChild.cN=s.cN;s.parentNode.replaceChild(p.firstChild,s)}else{t.innerHTML=y.value}t.className=u;t.result={language:v,kw:y.keyword_count,re:y.r};if(y.second_best){t.second_best={language:y.second_best.language,kw:y.second_best.keyword_count,re:y.second_best.r}}}function o(){if(o.called){return}o.called=true;var r=document.getElementsByTagName("pre");for(var p=0;p<r.length;p++){var q=b(r[p]);if(q){n(q,hljs.tabReplace)}}}function l(){if(window.addEventListener){window.addEventListener("DOMContentLoaded",o,false);window.addEventListener("load",o,false)}else{if(window.attachEvent){window.attachEvent("onload",o)}else{window.onload=o}}}var e={};this.LANGUAGES=e;this.highlight=d;this.highlightAuto=g;this.fixMarkup=i;this.highlightBlock=n;this.initHighlighting=o;this.initHighlightingOnLoad=l;this.IR="[a-zA-Z][a-zA-Z0-9_]*";this.UIR="[a-zA-Z_][a-zA-Z0-9_]*";this.NR="\\b\\d+(\\.\\d+)?";this.CNR="\\b(0[xX][a-fA-F0-9]+|(\\d+(\\.\\d*)?|\\.\\d+)([eE][-+]?\\d+)?)";this.BNR="\\b(0b[01]+)";this.RSR="!|!=|!==|%|%=|&|&&|&=|\\*|\\*=|\\+|\\+=|,|\\.|-|-=|/|/=|:|;|<|<<|<<=|<=|=|==|===|>|>=|>>|>>=|>>>|>>>=|\\?|\\[|\\{|\\(|\\^|\\^=|\\||\\|=|\\|\\||~";this.ER="(?![\\s\\S])";this.BE={b:"\\\\.",r:0};this.ASM={cN:"string",b:"'",e:"'",i:"\\n",c:[this.BE],r:0};this.QSM={cN:"string",b:'"',e:'"',i:"\\n",c:[this.BE],r:0};this.CLCM={cN:"comment",b:"//",e:"$"};this.CBLCLM={cN:"comment",b:"/\\*",e:"\\*/"};this.HCM={cN:"comment",b:"#",e:"$"};this.NM={cN:"number",b:this.NR,r:0};this.CNM={cN:"number",b:this.CNR,r:0};this.BNM={cN:"number",b:this.BNR,r:0};this.inherit=function(r,s){var p={};for(var q in r){p[q]=r[q]}if(s){for(var q in s){p[q]=s[q]}}return p}}();hljs.LANGUAGES.css=function(){var a={cN:"function",b:hljs.IR+"\\(",e:"\\)",c:[{eW:true,eE:true,c:[hljs.NM,hljs.ASM,hljs.QSM]}]};return{cI:true,dM:{i:"[=/|']",c:[hljs.CBLCLM,{cN:"id",b:"\\#[A-Za-z0-9_-]+"},{cN:"class",b:"\\.[A-Za-z0-9_-]+",r:0},{cN:"attr_selector",b:"\\[",e:"\\]",i:"$"},{cN:"pseudo",b:":(:)?[a-zA-Z0-9\\_\\-\\+\\(\\)\\\"\\']+"},{cN:"at_rule",b:"@(font-face|page)",l:"[a-z-]+",k:{"font-face":1,page:1}},{cN:"at_rule",b:"@",e:"[{;]",eE:true,k:{"import":1,page:1,media:1,charset:1},c:[a,hljs.ASM,hljs.QSM,hljs.NM]},{cN:"tag",b:hljs.IR,r:0},{cN:"rules",b:"{",e:"}",i:"[^\\s]",r:0,c:[hljs.CBLCLM,{cN:"rule",b:"[^\\s]",rB:true,e:";",eW:true,c:[{cN:"attribute",b:"[A-Z\\_\\.\\-]+",e:":",eE:true,i:"[^\\s]",starts:{cN:"value",eW:true,eE:true,c:[a,hljs.NM,hljs.QSM,hljs.ASM,hljs.CBLCLM,{cN:"hexcolor",b:"\\#[0-9A-F]+"},{cN:"important",b:"!important"}]}}]}]}]}}}();hljs.LANGUAGES.javascript={dM:{k:{keyword:{"in":1,"if":1,"for":1,"while":1,"finally":1,"var":1,"new":1,"function":1,"do":1,"return":1,"void":1,"else":1,"break":1,"catch":1,"instanceof":1,"with":1,"throw":1,"case":1,"default":1,"try":1,"this":1,"switch":1,"continue":1,"typeof":1,"delete":1},literal:{"true":1,"false":1,"null":1}},c:[hljs.ASM,hljs.QSM,hljs.CLCM,hljs.CBLCLM,hljs.CNM,{b:"("+hljs.RSR+"|\\b(case|return|throw)\\b)\\s*",k:{"return":1,"throw":1,"case":1},c:[hljs.CLCM,hljs.CBLCLM,{cN:"regexp",b:"/",e:"/[gim]*",c:[{b:"\\\\/"}]}],r:0},{cN:"function",bWK:true,e:"{",k:{"function":1},c:[{cN:"title",b:"[A-Za-z$_][0-9A-Za-z$_]*"},{cN:"params",b:"\\(",e:"\\)",c:[hljs.ASM,hljs.QSM,hljs.CLCM,hljs.CBLCLM]}]}]}};hljs.LANGUAGES.r={dM:{c:[hljs.HCM,{cN:"number",b:"\\b0[xX][0-9a-fA-F]+[Li]?\\b",e:hljs.IMMEDIATE_RE,r:0},{cN:"number",b:"\\b\\d+(?:[eE][+\\-]?\\d*)?L\\b",e:hljs.IMMEDIATE_RE,r:0},{cN:"number",b:"\\b\\d+\\.(?!\\d)(?:i\\b)?",e:hljs.IMMEDIATE_RE,r:1},{cN:"number",b:"\\b\\d+(?:\\.\\d*)?(?:[eE][+\\-]?\\d*)?i?\\b",e:hljs.IMMEDIATE_RE,r:0},{cN:"number",b:"\\.\\d+(?:[eE][+\\-]?\\d*)?i?\\b",e:hljs.IMMEDIATE_RE,r:1},{cN:"keyword",b:"(?:tryCatch|library|setGeneric|setGroupGeneric)\\b",e:hljs.IMMEDIATE_RE,r:10},{cN:"keyword",b:"\\.\\.\\.",e:hljs.IMMEDIATE_RE,r:10},{cN:"keyword",b:"\\.\\.\\d+(?![\\w.])",e:hljs.IMMEDIATE_RE,r:10},{cN:"keyword",b:"\\b(?:function)",e:hljs.IMMEDIATE_RE,r:2},{cN:"keyword",b:"(?:if|in|break|next|repeat|else|for|return|switch|while|try|stop|warning|require|attach|detach|source|setMethod|setClass)\\b",e:hljs.IMMEDIATE_RE,r:1},{cN:"literal",b:"(?:NA|NA_integer_|NA_real_|NA_character_|NA_complex_)\\b",e:hljs.IMMEDIATE_RE,r:10},{cN:"literal",b:"(?:NULL|TRUE|FALSE|T|F|Inf|NaN)\\b",e:hljs.IMMEDIATE_RE,r:1},{cN:"identifier",b:"[a-zA-Z.][a-zA-Z0-9._]*\\b",e:hljs.IMMEDIATE_RE,r:0},{cN:"operator",b:"<\\-(?!\\s*\\d)",e:hljs.IMMEDIATE_RE,r:2},{cN:"operator",b:"\\->|<\\-",e:hljs.IMMEDIATE_RE,r:1},{cN:"operator",b:"%%|~",e:hljs.IMMEDIATE_RE},{cN:"operator",b:">=|<=|==|!=|\\|\\||&&|=|\\+|\\-|\\*|/|\\^|>|<|!|&|\\||\\$|:",e:hljs.IMMEDIATE_RE,r:0},{cN:"operator",b:"%",e:"%",i:"\\n",r:1},{cN:"identifier",b:"`",e:"`",r:0},{cN:"string",b:'"',e:'"',c:[hljs.BE],r:0},{cN:"string",b:"'",e:"'",c:[hljs.BE],r:0},{cN:"paren",b:"[[({\\])}]",e:hljs.IMMEDIATE_RE,r:0}]}};hljs.LANGUAGES.xml=function(){var b="[A-Za-z0-9\\._:-]+";var a={eW:true,c:[{cN:"attribute",b:b,r:0},{b:'="',rB:true,e:'"',c:[{cN:"value",b:'"',eW:true}]},{b:"='",rB:true,e:"'",c:[{cN:"value",b:"'",eW:true}]},{b:"=",c:[{cN:"value",b:"[^\\s/>]+"}]}]};return{cI:true,dM:{c:[{cN:"pi",b:"<\\?",e:"\\?>",r:10},{cN:"doctype",b:"<!DOCTYPE",e:">",r:10,c:[{b:"\\[",e:"\\]"}]},{cN:"comment",b:"<!--",e:"-->",r:10},{cN:"cdata",b:"<\\!\\[CDATA\\[",e:"\\]\\]>",r:10},{cN:"tag",b:"<style(?=\\s|>|$)",e:">",k:{title:{style:1}},c:[a],starts:{cN:"css",e:"</style>",rE:true,sL:"css"}},{cN:"tag",b:"<script(?=\\s|>|$)",e:">",k:{title:{script:1}},c:[a],starts:{cN:"javascript",e:"<\/script>",rE:true,sL:"javascript"}},{cN:"vbscript",b:"<%",e:"%>",sL:"vbscript"},{cN:"tag",b:"</?",e:"/?>",c:[{cN:"title",b:"[^ />]+"},a]}]}}}();
diff --git a/docs/coverage/lib/highlight.js-6.2/rstudio.css b/docs/coverage/lib/highlight.js-6.2/rstudio.css
new file mode 100644
index 00000000..58de9a9a
--- /dev/null
+++ b/docs/coverage/lib/highlight.js-6.2/rstudio.css
@@ -0,0 +1,81 @@
+code {
+  line-height: 150%;
+}
+
+pre .operator,
+pre .paren {
+  color: rgb(104, 118, 135)
+}
+
+pre .literal {
+  color: rgb(88, 72, 246)
+}
+
+pre .number {
+  color: rgb(0, 0, 205);
+}
+
+pre .comment {
+  color: rgb(76, 136, 107);
+  font-style: italic;
+}
+
+pre .keyword,
+pre .id {
+  color: rgb(0, 0, 255);
+}
+
+pre .identifier {
+  color: rgb(0, 0, 0);
+}
+
+pre .string,
+pre .attribute {
+  color: rgb(3, 106, 7);
+}
+
+pre .doctype {
+  color: rgb(104, 104, 92);
+}
+
+pre .tag,
+pre .title {
+  color: rgb(4, 29, 140);
+}
+
+pre .value {
+  color: rgb(13, 105, 18);
+}
+
+.language-xml .attribute {
+  color: rgb(0, 0, 0);
+}
+
+.language-css .attribute {
+  color: rgb(110, 124, 219);
+}
+
+.language-css .value {
+  color: rgb(23, 149, 30);
+}
+
+.language-css .number,
+.language-css .hexcolor {
+  color: rgb(7, 27, 201);
+}
+
+.language-css .function {
+  color: rgb(61, 77, 113);
+}
+
+.language-css .tag {
+  color: rgb(195, 13, 25);
+}
+
+.language-css .class {
+  color: rgb(53, 132, 148);
+}
+
+.language-css .pseudo {
+  color: rgb(13, 105, 18);
+}
diff --git a/docs/coverage/lib/htmltools-fill-0.5.8.1/fill.css b/docs/coverage/lib/htmltools-fill-0.5.8.1/fill.css
new file mode 100644
index 00000000..841ea9d5
--- /dev/null
+++ b/docs/coverage/lib/htmltools-fill-0.5.8.1/fill.css
@@ -0,0 +1,21 @@
+@layer htmltools {
+  .html-fill-container {
+    display: flex;
+    flex-direction: column;
+    /* Prevent the container from expanding vertically or horizontally beyond its
+    parent's constraints. */
+    min-height: 0;
+    min-width: 0;
+  }
+  .html-fill-container > .html-fill-item {
+    /* Fill items can grow and shrink freely within
+    available vertical space in fillable container */
+    flex: 1 1 auto;
+    min-height: 0;
+    min-width: 0;
+  }
+  .html-fill-container > :not(.html-fill-item) {
+    /* Prevent shrinking or growing of non-fill items */
+    flex: 0 0 auto;
+  }
+}
diff --git a/docs/coverage/lib/htmlwidgets-1.6.4/htmlwidgets.js b/docs/coverage/lib/htmlwidgets-1.6.4/htmlwidgets.js
new file mode 100644
index 00000000..1067d029
--- /dev/null
+++ b/docs/coverage/lib/htmlwidgets-1.6.4/htmlwidgets.js
@@ -0,0 +1,901 @@
+(function() {
+  // If window.HTMLWidgets is already defined, then use it; otherwise create a
+  // new object. This allows preceding code to set options that affect the
+  // initialization process (though none currently exist).
+  window.HTMLWidgets = window.HTMLWidgets || {};
+
+  // See if we're running in a viewer pane. If not, we're in a web browser.
+  var viewerMode = window.HTMLWidgets.viewerMode =
+      /\bviewer_pane=1\b/.test(window.location);
+
+  // See if we're running in Shiny mode. If not, it's a static document.
+  // Note that static widgets can appear in both Shiny and static modes, but
+  // obviously, Shiny widgets can only appear in Shiny apps/documents.
+  var shinyMode = window.HTMLWidgets.shinyMode =
+      typeof(window.Shiny) !== "undefined" && !!window.Shiny.outputBindings;
+
+  // We can't count on jQuery being available, so we implement our own
+  // version if necessary.
+  function querySelectorAll(scope, selector) {
+    if (typeof(jQuery) !== "undefined" && scope instanceof jQuery) {
+      return scope.find(selector);
+    }
+    if (scope.querySelectorAll) {
+      return scope.querySelectorAll(selector);
+    }
+  }
+
+  function asArray(value) {
+    if (value === null)
+      return [];
+    if ($.isArray(value))
+      return value;
+    return [value];
+  }
+
+  // Implement jQuery's extend
+  function extend(target /*, ... */) {
+    if (arguments.length == 1) {
+      return target;
+    }
+    for (var i = 1; i < arguments.length; i++) {
+      var source = arguments[i];
+      for (var prop in source) {
+        if (source.hasOwnProperty(prop)) {
+          target[prop] = source[prop];
+        }
+      }
+    }
+    return target;
+  }
+
+  // IE8 doesn't support Array.forEach.
+  function forEach(values, callback, thisArg) {
+    if (values.forEach) {
+      values.forEach(callback, thisArg);
+    } else {
+      for (var i = 0; i < values.length; i++) {
+        callback.call(thisArg, values[i], i, values);
+      }
+    }
+  }
+
+  // Replaces the specified method with the return value of funcSource.
+  //
+  // Note that funcSource should not BE the new method, it should be a function
+  // that RETURNS the new method. funcSource receives a single argument that is
+  // the overridden method, it can be called from the new method. The overridden
+  // method can be called like a regular function, it has the target permanently
+  // bound to it so "this" will work correctly.
+  function overrideMethod(target, methodName, funcSource) {
+    var superFunc = target[methodName] || function() {};
+    var superFuncBound = function() {
+      return superFunc.apply(target, arguments);
+    };
+    target[methodName] = funcSource(superFuncBound);
+  }
+
+  // Add a method to delegator that, when invoked, calls
+  // delegatee.methodName. If there is no such method on
+  // the delegatee, but there was one on delegator before
+  // delegateMethod was called, then the original version
+  // is invoked instead.
+  // For example:
+  //
+  // var a = {
+  //   method1: function() { console.log('a1'); }
+  //   method2: function() { console.log('a2'); }
+  // };
+  // var b = {
+  //   method1: function() { console.log('b1'); }
+  // };
+  // delegateMethod(a, b, "method1");
+  // delegateMethod(a, b, "method2");
+  // a.method1();
+  // a.method2();
+  //
+  // The output would be "b1", "a2".
+  function delegateMethod(delegator, delegatee, methodName) {
+    var inherited = delegator[methodName];
+    delegator[methodName] = function() {
+      var target = delegatee;
+      var method = delegatee[methodName];
+
+      // The method doesn't exist on the delegatee. Instead,
+      // call the method on the delegator, if it exists.
+      if (!method) {
+        target = delegator;
+        method = inherited;
+      }
+
+      if (method) {
+        return method.apply(target, arguments);
+      }
+    };
+  }
+
+  // Implement a vague facsimilie of jQuery's data method
+  function elementData(el, name, value) {
+    if (arguments.length == 2) {
+      return el["htmlwidget_data_" + name];
+    } else if (arguments.length == 3) {
+      el["htmlwidget_data_" + name] = value;
+      return el;
+    } else {
+      throw new Error("Wrong number of arguments for elementData: " +
+        arguments.length);
+    }
+  }
+
+  // http://stackoverflow.com/questions/3446170/escape-string-for-use-in-javascript-regex
+  function escapeRegExp(str) {
+    return str.replace(/[\-\[\]\/\{\}\(\)\*\+\?\.\\\^\$\|]/g, "\\$&");
+  }
+
+  function hasClass(el, className) {
+    var re = new RegExp("\\b" + escapeRegExp(className) + "\\b");
+    return re.test(el.className);
+  }
+
+  // elements - array (or array-like object) of HTML elements
+  // className - class name to test for
+  // include - if true, only return elements with given className;
+  //   if false, only return elements *without* given className
+  function filterByClass(elements, className, include) {
+    var results = [];
+    for (var i = 0; i < elements.length; i++) {
+      if (hasClass(elements[i], className) == include)
+        results.push(elements[i]);
+    }
+    return results;
+  }
+
+  function on(obj, eventName, func) {
+    if (obj.addEventListener) {
+      obj.addEventListener(eventName, func, false);
+    } else if (obj.attachEvent) {
+      obj.attachEvent(eventName, func);
+    }
+  }
+
+  function off(obj, eventName, func) {
+    if (obj.removeEventListener)
+      obj.removeEventListener(eventName, func, false);
+    else if (obj.detachEvent) {
+      obj.detachEvent(eventName, func);
+    }
+  }
+
+  // Translate array of values to top/right/bottom/left, as usual with
+  // the "padding" CSS property
+  // https://developer.mozilla.org/en-US/docs/Web/CSS/padding
+  function unpackPadding(value) {
+    if (typeof(value) === "number")
+      value = [value];
+    if (value.length === 1) {
+      return {top: value[0], right: value[0], bottom: value[0], left: value[0]};
+    }
+    if (value.length === 2) {
+      return {top: value[0], right: value[1], bottom: value[0], left: value[1]};
+    }
+    if (value.length === 3) {
+      return {top: value[0], right: value[1], bottom: value[2], left: value[1]};
+    }
+    if (value.length === 4) {
+      return {top: value[0], right: value[1], bottom: value[2], left: value[3]};
+    }
+  }
+
+  // Convert an unpacked padding object to a CSS value
+  function paddingToCss(paddingObj) {
+    return paddingObj.top + "px " + paddingObj.right + "px " + paddingObj.bottom + "px " + paddingObj.left + "px";
+  }
+
+  // Makes a number suitable for CSS
+  function px(x) {
+    if (typeof(x) === "number")
+      return x + "px";
+    else
+      return x;
+  }
+
+  // Retrieves runtime widget sizing information for an element.
+  // The return value is either null, or an object with fill, padding,
+  // defaultWidth, defaultHeight fields.
+  function sizingPolicy(el) {
+    var sizingEl = document.querySelector("script[data-for='" + el.id + "'][type='application/htmlwidget-sizing']");
+    if (!sizingEl)
+      return null;
+    var sp = JSON.parse(sizingEl.textContent || sizingEl.text || "{}");
+    if (viewerMode) {
+      return sp.viewer;
+    } else {
+      return sp.browser;
+    }
+  }
+
+  // @param tasks Array of strings (or falsy value, in which case no-op).
+  //   Each element must be a valid JavaScript expression that yields a
+  //   function. Or, can be an array of objects with "code" and "data"
+  //   properties; in this case, the "code" property should be a string
+  //   of JS that's an expr that yields a function, and "data" should be
+  //   an object that will be added as an additional argument when that
+  //   function is called.
+  // @param target The object that will be "this" for each function
+  //   execution.
+  // @param args Array of arguments to be passed to the functions. (The
+  //   same arguments will be passed to all functions.)
+  function evalAndRun(tasks, target, args) {
+    if (tasks) {
+      forEach(tasks, function(task) {
+        var theseArgs = args;
+        if (typeof(task) === "object") {
+          theseArgs = theseArgs.concat([task.data]);
+          task = task.code;
+        }
+        var taskFunc = tryEval(task);
+        if (typeof(taskFunc) !== "function") {
+          throw new Error("Task must be a function! Source:\n" + task);
+        }
+        taskFunc.apply(target, theseArgs);
+      });
+    }
+  }
+
+  // Attempt eval() both with and without enclosing in parentheses.
+  // Note that enclosing coerces a function declaration into
+  // an expression that eval() can parse
+  // (otherwise, a SyntaxError is thrown)
+  function tryEval(code) {
+    var result = null;
+    try {
+      result = eval("(" + code + ")");
+    } catch(error) {
+      if (!(error instanceof SyntaxError)) {
+        throw error;
+      }
+      try {
+        result = eval(code);
+      } catch(e) {
+        if (e instanceof SyntaxError) {
+          throw error;
+        } else {
+          throw e;
+        }
+      }
+    }
+    return result;
+  }
+
+  function initSizing(el) {
+    var sizing = sizingPolicy(el);
+    if (!sizing)
+      return;
+
+    var cel = document.getElementById("htmlwidget_container");
+    if (!cel)
+      return;
+
+    if (typeof(sizing.padding) !== "undefined") {
+      document.body.style.margin = "0";
+      document.body.style.padding = paddingToCss(unpackPadding(sizing.padding));
+    }
+
+    if (sizing.fill) {
+      document.body.style.overflow = "hidden";
+      document.body.style.width = "100%";
+      document.body.style.height = "100%";
+      document.documentElement.style.width = "100%";
+      document.documentElement.style.height = "100%";
+      cel.style.position = "absolute";
+      var pad = unpackPadding(sizing.padding);
+      cel.style.top = pad.top + "px";
+      cel.style.right = pad.right + "px";
+      cel.style.bottom = pad.bottom + "px";
+      cel.style.left = pad.left + "px";
+      el.style.width = "100%";
+      el.style.height = "100%";
+
+      return {
+        getWidth: function() { return cel.getBoundingClientRect().width; },
+        getHeight: function() { return cel.getBoundingClientRect().height; }
+      };
+
+    } else {
+      el.style.width = px(sizing.width);
+      el.style.height = px(sizing.height);
+
+      return {
+        getWidth: function() { return cel.getBoundingClientRect().width; },
+        getHeight: function() { return cel.getBoundingClientRect().height; }
+      };
+    }
+  }
+
+  // Default implementations for methods
+  var defaults = {
+    find: function(scope) {
+      return querySelectorAll(scope, "." + this.name);
+    },
+    renderError: function(el, err) {
+      var $el = $(el);
+
+      this.clearError(el);
+
+      // Add all these error classes, as Shiny does
+      var errClass = "shiny-output-error";
+      if (err.type !== null) {
+        // use the classes of the error condition as CSS class names
+        errClass = errClass + " " + $.map(asArray(err.type), function(type) {
+          return errClass + "-" + type;
+        }).join(" ");
+      }
+      errClass = errClass + " htmlwidgets-error";
+
+      // Is el inline or block? If inline or inline-block, just display:none it
+      // and add an inline error.
+      var display = $el.css("display");
+      $el.data("restore-display-mode", display);
+
+      if (display === "inline" || display === "inline-block") {
+        $el.hide();
+        if (err.message !== "") {
+          var errorSpan = $("<span>").addClass(errClass);
+          errorSpan.text(err.message);
+          $el.after(errorSpan);
+        }
+      } else if (display === "block") {
+        // If block, add an error just after the el, set visibility:none on the
+        // el, and position the error to be on top of the el.
+        // Mark it with a unique ID and CSS class so we can remove it later.
+        $el.css("visibility", "hidden");
+        if (err.message !== "") {
+          var errorDiv = $("<div>").addClass(errClass).css("position", "absolute")
+            .css("top", el.offsetTop)
+            .css("left", el.offsetLeft)
+            // setting width can push out the page size, forcing otherwise
+            // unnecessary scrollbars to appear and making it impossible for
+            // the element to shrink; so use max-width instead
+            .css("maxWidth", el.offsetWidth)
+            .css("height", el.offsetHeight);
+          errorDiv.text(err.message);
+          $el.after(errorDiv);
+
+          // Really dumb way to keep the size/position of the error in sync with
+          // the parent element as the window is resized or whatever.
+          var intId = setInterval(function() {
+            if (!errorDiv[0].parentElement) {
+              clearInterval(intId);
+              return;
+            }
+            errorDiv
+              .css("top", el.offsetTop)
+              .css("left", el.offsetLeft)
+              .css("maxWidth", el.offsetWidth)
+              .css("height", el.offsetHeight);
+          }, 500);
+        }
+      }
+    },
+    clearError: function(el) {
+      var $el = $(el);
+      var display = $el.data("restore-display-mode");
+      $el.data("restore-display-mode", null);
+
+      if (display === "inline" || display === "inline-block") {
+        if (display)
+          $el.css("display", display);
+        $(el.nextSibling).filter(".htmlwidgets-error").remove();
+      } else if (display === "block"){
+        $el.css("visibility", "inherit");
+        $(el.nextSibling).filter(".htmlwidgets-error").remove();
+      }
+    },
+    sizing: {}
+  };
+
+  // Called by widget bindings to register a new type of widget. The definition
+  // object can contain the following properties:
+  // - name (required) - A string indicating the binding name, which will be
+  //   used by default as the CSS classname to look for.
+  // - initialize (optional) - A function(el) that will be called once per
+  //   widget element; if a value is returned, it will be passed as the third
+  //   value to renderValue.
+  // - renderValue (required) - A function(el, data, initValue) that will be
+  //   called with data. Static contexts will cause this to be called once per
+  //   element; Shiny apps will cause this to be called multiple times per
+  //   element, as the data changes.
+  window.HTMLWidgets.widget = function(definition) {
+    if (!definition.name) {
+      throw new Error("Widget must have a name");
+    }
+    if (!definition.type) {
+      throw new Error("Widget must have a type");
+    }
+    // Currently we only support output widgets
+    if (definition.type !== "output") {
+      throw new Error("Unrecognized widget type '" + definition.type + "'");
+    }
+    // TODO: Verify that .name is a valid CSS classname
+
+    // Support new-style instance-bound definitions. Old-style class-bound
+    // definitions have one widget "object" per widget per type/class of
+    // widget; the renderValue and resize methods on such widget objects
+    // take el and instance arguments, because the widget object can't
+    // store them. New-style instance-bound definitions have one widget
+    // object per widget instance; the definition that's passed in doesn't
+    // provide renderValue or resize methods at all, just the single method
+    //   factory(el, width, height)
+    // which returns an object that has renderValue(x) and resize(w, h).
+    // This enables a far more natural programming style for the widget
+    // author, who can store per-instance state using either OO-style
+    // instance fields or functional-style closure variables (I guess this
+    // is in contrast to what can only be called C-style pseudo-OO which is
+    // what we required before).
+    if (definition.factory) {
+      definition = createLegacyDefinitionAdapter(definition);
+    }
+
+    if (!definition.renderValue) {
+      throw new Error("Widget must have a renderValue function");
+    }
+
+    // For static rendering (non-Shiny), use a simple widget registration
+    // scheme. We also use this scheme for Shiny apps/documents that also
+    // contain static widgets.
+    window.HTMLWidgets.widgets = window.HTMLWidgets.widgets || [];
+    // Merge defaults into the definition; don't mutate the original definition.
+    var staticBinding = extend({}, defaults, definition);
+    overrideMethod(staticBinding, "find", function(superfunc) {
+      return function(scope) {
+        var results = superfunc(scope);
+        // Filter out Shiny outputs, we only want the static kind
+        return filterByClass(results, "html-widget-output", false);
+      };
+    });
+    window.HTMLWidgets.widgets.push(staticBinding);
+
+    if (shinyMode) {
+      // Shiny is running. Register the definition with an output binding.
+      // The definition itself will not be the output binding, instead
+      // we will make an output binding object that delegates to the
+      // definition. This is because we foolishly used the same method
+      // name (renderValue) for htmlwidgets definition and Shiny bindings
+      // but they actually have quite different semantics (the Shiny
+      // bindings receive data that includes lots of metadata that it
+      // strips off before calling htmlwidgets renderValue). We can't
+      // just ignore the difference because in some widgets it's helpful
+      // to call this.renderValue() from inside of resize(), and if
+      // we're not delegating, then that call will go to the Shiny
+      // version instead of the htmlwidgets version.
+
+      // Merge defaults with definition, without mutating either.
+      var bindingDef = extend({}, defaults, definition);
+
+      // This object will be our actual Shiny binding.
+      var shinyBinding = new Shiny.OutputBinding();
+
+      // With a few exceptions, we'll want to simply use the bindingDef's
+      // version of methods if they are available, otherwise fall back to
+      // Shiny's defaults. NOTE: If Shiny's output bindings gain additional
+      // methods in the future, and we want them to be overrideable by
+      // HTMLWidget binding definitions, then we'll need to add them to this
+      // list.
+      delegateMethod(shinyBinding, bindingDef, "getId");
+      delegateMethod(shinyBinding, bindingDef, "onValueChange");
+      delegateMethod(shinyBinding, bindingDef, "onValueError");
+      delegateMethod(shinyBinding, bindingDef, "renderError");
+      delegateMethod(shinyBinding, bindingDef, "clearError");
+      delegateMethod(shinyBinding, bindingDef, "showProgress");
+
+      // The find, renderValue, and resize are handled differently, because we
+      // want to actually decorate the behavior of the bindingDef methods.
+
+      shinyBinding.find = function(scope) {
+        var results = bindingDef.find(scope);
+
+        // Only return elements that are Shiny outputs, not static ones
+        var dynamicResults = results.filter(".html-widget-output");
+
+        // It's possible that whatever caused Shiny to think there might be
+        // new dynamic outputs, also caused there to be new static outputs.
+        // Since there might be lots of different htmlwidgets bindings, we
+        // schedule execution for later--no need to staticRender multiple
+        // times.
+        if (results.length !== dynamicResults.length)
+          scheduleStaticRender();
+
+        return dynamicResults;
+      };
+
+      // Wrap renderValue to handle initialization, which unfortunately isn't
+      // supported natively by Shiny at the time of this writing.
+
+      shinyBinding.renderValue = function(el, data) {
+        Shiny.renderDependencies(data.deps);
+        // Resolve strings marked as javascript literals to objects
+        if (!(data.evals instanceof Array)) data.evals = [data.evals];
+        for (var i = 0; data.evals && i < data.evals.length; i++) {
+          window.HTMLWidgets.evaluateStringMember(data.x, data.evals[i]);
+        }
+        if (!bindingDef.renderOnNullValue) {
+          if (data.x === null) {
+            el.style.visibility = "hidden";
+            return;
+          } else {
+            el.style.visibility = "inherit";
+          }
+        }
+        if (!elementData(el, "initialized")) {
+          initSizing(el);
+
+          elementData(el, "initialized", true);
+          if (bindingDef.initialize) {
+            var rect = el.getBoundingClientRect();
+            var result = bindingDef.initialize(el, rect.width, rect.height);
+            elementData(el, "init_result", result);
+          }
+        }
+        bindingDef.renderValue(el, data.x, elementData(el, "init_result"));
+        evalAndRun(data.jsHooks.render, elementData(el, "init_result"), [el, data.x]);
+      };
+
+      // Only override resize if bindingDef implements it
+      if (bindingDef.resize) {
+        shinyBinding.resize = function(el, width, height) {
+          // Shiny can call resize before initialize/renderValue have been
+          // called, which doesn't make sense for widgets.
+          if (elementData(el, "initialized")) {
+            bindingDef.resize(el, width, height, elementData(el, "init_result"));
+          }
+        };
+      }
+
+      Shiny.outputBindings.register(shinyBinding, bindingDef.name);
+    }
+  };
+
+  var scheduleStaticRenderTimerId = null;
+  function scheduleStaticRender() {
+    if (!scheduleStaticRenderTimerId) {
+      scheduleStaticRenderTimerId = setTimeout(function() {
+        scheduleStaticRenderTimerId = null;
+        window.HTMLWidgets.staticRender();
+      }, 1);
+    }
+  }
+
+  // Render static widgets after the document finishes loading
+  // Statically render all elements that are of this widget's class
+  window.HTMLWidgets.staticRender = function() {
+    var bindings = window.HTMLWidgets.widgets || [];
+    forEach(bindings, function(binding) {
+      var matches = binding.find(document.documentElement);
+      forEach(matches, function(el) {
+        var sizeObj = initSizing(el, binding);
+
+        var getSize = function(el) {
+          if (sizeObj) {
+            return {w: sizeObj.getWidth(), h: sizeObj.getHeight()}
+          } else {
+            var rect = el.getBoundingClientRect();
+            return {w: rect.width, h: rect.height}
+          }
+        };
+
+        if (hasClass(el, "html-widget-static-bound"))
+          return;
+        el.className = el.className + " html-widget-static-bound";
+
+        var initResult;
+        if (binding.initialize) {
+          var size = getSize(el);
+          initResult = binding.initialize(el, size.w, size.h);
+          elementData(el, "init_result", initResult);
+        }
+
+        if (binding.resize) {
+          var lastSize = getSize(el);
+          var resizeHandler = function(e) {
+            var size = getSize(el);
+            if (size.w === 0 && size.h === 0)
+              return;
+            if (size.w === lastSize.w && size.h === lastSize.h)
+              return;
+            lastSize = size;
+            binding.resize(el, size.w, size.h, initResult);
+          };
+
+          on(window, "resize", resizeHandler);
+
+          // This is needed for cases where we're running in a Shiny
+          // app, but the widget itself is not a Shiny output, but
+          // rather a simple static widget. One example of this is
+          // an rmarkdown document that has runtime:shiny and widget
+          // that isn't in a render function. Shiny only knows to
+          // call resize handlers for Shiny outputs, not for static
+          // widgets, so we do it ourselves.
+          if (window.jQuery) {
+            window.jQuery(document).on(
+              "shown.htmlwidgets shown.bs.tab.htmlwidgets shown.bs.collapse.htmlwidgets",
+              resizeHandler
+            );
+            window.jQuery(document).on(
+              "hidden.htmlwidgets hidden.bs.tab.htmlwidgets hidden.bs.collapse.htmlwidgets",
+              resizeHandler
+            );
+          }
+
+          // This is needed for the specific case of ioslides, which
+          // flips slides between display:none and display:block.
+          // Ideally we would not have to have ioslide-specific code
+          // here, but rather have ioslides raise a generic event,
+          // but the rmarkdown package just went to CRAN so the
+          // window to getting that fixed may be long.
+          if (window.addEventListener) {
+            // It's OK to limit this to window.addEventListener
+            // browsers because ioslides itself only supports
+            // such browsers.
+            on(document, "slideenter", resizeHandler);
+            on(document, "slideleave", resizeHandler);
+          }
+        }
+
+        var scriptData = document.querySelector("script[data-for='" + el.id + "'][type='application/json']");
+        if (scriptData) {
+          var data = JSON.parse(scriptData.textContent || scriptData.text);
+          // Resolve strings marked as javascript literals to objects
+          if (!(data.evals instanceof Array)) data.evals = [data.evals];
+          for (var k = 0; data.evals && k < data.evals.length; k++) {
+            window.HTMLWidgets.evaluateStringMember(data.x, data.evals[k]);
+          }
+          binding.renderValue(el, data.x, initResult);
+          evalAndRun(data.jsHooks.render, initResult, [el, data.x]);
+        }
+      });
+    });
+
+    invokePostRenderHandlers();
+  }
+
+
+  function has_jQuery3() {
+    if (!window.jQuery) {
+      return false;
+    }
+    var $version = window.jQuery.fn.jquery;
+    var $major_version = parseInt($version.split(".")[0]);
+    return $major_version >= 3;
+  }
+
+  /*
+  / Shiny 1.4 bumped jQuery from 1.x to 3.x which means jQuery's
+  / on-ready handler (i.e., $(fn)) is now asyncronous (i.e., it now
+  / really means $(setTimeout(fn)).
+  / https://jquery.com/upgrade-guide/3.0/#breaking-change-document-ready-handlers-are-now-asynchronous
+  /
+  / Since Shiny uses $() to schedule initShiny, shiny>=1.4 calls initShiny
+  / one tick later than it did before, which means staticRender() is
+  / called renderValue() earlier than (advanced) widget authors might be expecting.
+  / https://github.com/rstudio/shiny/issues/2630
+  /
+  / For a concrete example, leaflet has some methods (e.g., updateBounds)
+  / which reference Shiny methods registered in initShiny (e.g., setInputValue).
+  / Since leaflet is privy to this life-cycle, it knows to use setTimeout() to
+  / delay execution of those methods (until Shiny methods are ready)
+  / https://github.com/rstudio/leaflet/blob/18ec981/javascript/src/index.js#L266-L268
+  /
+  / Ideally widget authors wouldn't need to use this setTimeout() hack that
+  / leaflet uses to call Shiny methods on a staticRender(). In the long run,
+  / the logic initShiny should be broken up so that method registration happens
+  / right away, but binding happens later.
+  */
+  function maybeStaticRenderLater() {
+    if (shinyMode && has_jQuery3()) {
+      window.jQuery(window.HTMLWidgets.staticRender);
+    } else {
+      window.HTMLWidgets.staticRender();
+    }
+  }
+
+  if (document.addEventListener) {
+    document.addEventListener("DOMContentLoaded", function() {
+      document.removeEventListener("DOMContentLoaded", arguments.callee, false);
+      maybeStaticRenderLater();
+    }, false);
+  } else if (document.attachEvent) {
+    document.attachEvent("onreadystatechange", function() {
+      if (document.readyState === "complete") {
+        document.detachEvent("onreadystatechange", arguments.callee);
+        maybeStaticRenderLater();
+      }
+    });
+  }
+
+
+  window.HTMLWidgets.getAttachmentUrl = function(depname, key) {
+    // If no key, default to the first item
+    if (typeof(key) === "undefined")
+      key = 1;
+
+    var link = document.getElementById(depname + "-" + key + "-attachment");
+    if (!link) {
+      throw new Error("Attachment " + depname + "/" + key + " not found in document");
+    }
+    return link.getAttribute("href");
+  };
+
+  window.HTMLWidgets.dataframeToD3 = function(df) {
+    var names = [];
+    var length;
+    for (var name in df) {
+        if (df.hasOwnProperty(name))
+            names.push(name);
+        if (typeof(df[name]) !== "object" || typeof(df[name].length) === "undefined") {
+            throw new Error("All fields must be arrays");
+        } else if (typeof(length) !== "undefined" && length !== df[name].length) {
+            throw new Error("All fields must be arrays of the same length");
+        }
+        length = df[name].length;
+    }
+    var results = [];
+    var item;
+    for (var row = 0; row < length; row++) {
+        item = {};
+        for (var col = 0; col < names.length; col++) {
+            item[names[col]] = df[names[col]][row];
+        }
+        results.push(item);
+    }
+    return results;
+  };
+
+  window.HTMLWidgets.transposeArray2D = function(array) {
+      if (array.length === 0) return array;
+      var newArray = array[0].map(function(col, i) {
+          return array.map(function(row) {
+              return row[i]
+          })
+      });
+      return newArray;
+  };
+  // Split value at splitChar, but allow splitChar to be escaped
+  // using escapeChar. Any other characters escaped by escapeChar
+  // will be included as usual (including escapeChar itself).
+  function splitWithEscape(value, splitChar, escapeChar) {
+    var results = [];
+    var escapeMode = false;
+    var currentResult = "";
+    for (var pos = 0; pos < value.length; pos++) {
+      if (!escapeMode) {
+        if (value[pos] === splitChar) {
+          results.push(currentResult);
+          currentResult = "";
+        } else if (value[pos] === escapeChar) {
+          escapeMode = true;
+        } else {
+          currentResult += value[pos];
+        }
+      } else {
+        currentResult += value[pos];
+        escapeMode = false;
+      }
+    }
+    if (currentResult !== "") {
+      results.push(currentResult);
+    }
+    return results;
+  }
+  // Function authored by Yihui/JJ Allaire
+  window.HTMLWidgets.evaluateStringMember = function(o, member) {
+    var parts = splitWithEscape(member, '.', '\\');
+    for (var i = 0, l = parts.length; i < l; i++) {
+      var part = parts[i];
+      // part may be a character or 'numeric' member name
+      if (o !== null && typeof o === "object" && part in o) {
+        if (i == (l - 1)) { // if we are at the end of the line then evalulate
+          if (typeof o[part] === "string")
+            o[part] = tryEval(o[part]);
+        } else { // otherwise continue to next embedded object
+          o = o[part];
+        }
+      }
+    }
+  };
+
+  // Retrieve the HTMLWidget instance (i.e. the return value of an
+  // HTMLWidget binding's initialize() or factory() function)
+  // associated with an element, or null if none.
+  window.HTMLWidgets.getInstance = function(el) {
+    return elementData(el, "init_result");
+  };
+
+  // Finds the first element in the scope that matches the selector,
+  // and returns the HTMLWidget instance (i.e. the return value of
+  // an HTMLWidget binding's initialize() or factory() function)
+  // associated with that element, if any. If no element matches the
+  // selector, or the first matching element has no HTMLWidget
+  // instance associated with it, then null is returned.
+  //
+  // The scope argument is optional, and defaults to window.document.
+  window.HTMLWidgets.find = function(scope, selector) {
+    if (arguments.length == 1) {
+      selector = scope;
+      scope = document;
+    }
+
+    var el = scope.querySelector(selector);
+    if (el === null) {
+      return null;
+    } else {
+      return window.HTMLWidgets.getInstance(el);
+    }
+  };
+
+  // Finds all elements in the scope that match the selector, and
+  // returns the HTMLWidget instances (i.e. the return values of
+  // an HTMLWidget binding's initialize() or factory() function)
+  // associated with the elements, in an array. If elements that
+  // match the selector don't have an associated HTMLWidget
+  // instance, the returned array will contain nulls.
+  //
+  // The scope argument is optional, and defaults to window.document.
+  window.HTMLWidgets.findAll = function(scope, selector) {
+    if (arguments.length == 1) {
+      selector = scope;
+      scope = document;
+    }
+
+    var nodes = scope.querySelectorAll(selector);
+    var results = [];
+    for (var i = 0; i < nodes.length; i++) {
+      results.push(window.HTMLWidgets.getInstance(nodes[i]));
+    }
+    return results;
+  };
+
+  var postRenderHandlers = [];
+  function invokePostRenderHandlers() {
+    while (postRenderHandlers.length) {
+      var handler = postRenderHandlers.shift();
+      if (handler) {
+        handler();
+      }
+    }
+  }
+
+  // Register the given callback function to be invoked after the
+  // next time static widgets are rendered.
+  window.HTMLWidgets.addPostRenderHandler = function(callback) {
+    postRenderHandlers.push(callback);
+  };
+
+  // Takes a new-style instance-bound definition, and returns an
+  // old-style class-bound definition. This saves us from having
+  // to rewrite all the logic in this file to accomodate both
+  // types of definitions.
+  function createLegacyDefinitionAdapter(defn) {
+    var result = {
+      name: defn.name,
+      type: defn.type,
+      initialize: function(el, width, height) {
+        return defn.factory(el, width, height);
+      },
+      renderValue: function(el, x, instance) {
+        return instance.renderValue(x);
+      },
+      resize: function(el, width, height, instance) {
+        return instance.resize(width, height);
+      }
+    };
+
+    if (defn.find)
+      result.find = defn.find;
+    if (defn.renderError)
+      result.renderError = defn.renderError;
+    if (defn.clearError)
+      result.clearError = defn.clearError;
+
+    return result;
+  }
+})();
diff --git a/docs/coverage/lib/jquery-3.6.0/jquery-3.6.0.js b/docs/coverage/lib/jquery-3.6.0/jquery-3.6.0.js
new file mode 100644
index 00000000..fc6c299b
--- /dev/null
+++ b/docs/coverage/lib/jquery-3.6.0/jquery-3.6.0.js
@@ -0,0 +1,10881 @@
+/*!
+ * jQuery JavaScript Library v3.6.0
+ * https://jquery.com/
+ *
+ * Includes Sizzle.js
+ * https://sizzlejs.com/
+ *
+ * Copyright OpenJS Foundation and other contributors
+ * Released under the MIT license
+ * https://jquery.org/license
+ *
+ * Date: 2021-03-02T17:08Z
+ */
+( function( global, factory ) {
+
+	"use strict";
+
+	if ( typeof module === "object" && typeof module.exports === "object" ) {
+
+		// For CommonJS and CommonJS-like environments where a proper `window`
+		// is present, execute the factory and get jQuery.
+		// For environments that do not have a `window` with a `document`
+		// (such as Node.js), expose a factory as module.exports.
+		// This accentuates the need for the creation of a real `window`.
+		// e.g. var jQuery = require("jquery")(window);
+		// See ticket #14549 for more info.
+		module.exports = global.document ?
+			factory( global, true ) :
+			function( w ) {
+				if ( !w.document ) {
+					throw new Error( "jQuery requires a window with a document" );
+				}
+				return factory( w );
+			};
+	} else {
+		factory( global );
+	}
+
+// Pass this if window is not defined yet
+} )( typeof window !== "undefined" ? window : this, function( window, noGlobal ) {
+
+// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1
+// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode
+// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common
+// enough that all such attempts are guarded in a try block.
+"use strict";
+
+var arr = [];
+
+var getProto = Object.getPrototypeOf;
+
+var slice = arr.slice;
+
+var flat = arr.flat ? function( array ) {
+	return arr.flat.call( array );
+} : function( array ) {
+	return arr.concat.apply( [], array );
+};
+
+
+var push = arr.push;
+
+var indexOf = arr.indexOf;
+
+var class2type = {};
+
+var toString = class2type.toString;
+
+var hasOwn = class2type.hasOwnProperty;
+
+var fnToString = hasOwn.toString;
+
+var ObjectFunctionString = fnToString.call( Object );
+
+var support = {};
+
+var isFunction = function isFunction( obj ) {
+
+		// Support: Chrome <=57, Firefox <=52
+		// In some browsers, typeof returns "function" for HTML <object> elements
+		// (i.e., `typeof document.createElement( "object" ) === "function"`).
+		// We don't want to classify *any* DOM node as a function.
+		// Support: QtWeb <=3.8.5, WebKit <=534.34, wkhtmltopdf tool <=0.12.5
+		// Plus for old WebKit, typeof returns "function" for HTML collections
+		// (e.g., `typeof document.getElementsByTagName("div") === "function"`). (gh-4756)
+		return typeof obj === "function" && typeof obj.nodeType !== "number" &&
+			typeof obj.item !== "function";
+	};
+
+
+var isWindow = function isWindow( obj ) {
+		return obj != null && obj === obj.window;
+	};
+
+
+var document = window.document;
+
+
+
+	var preservedScriptAttributes = {
+		type: true,
+		src: true,
+		nonce: true,
+		noModule: true
+	};
+
+	function DOMEval( code, node, doc ) {
+		doc = doc || document;
+
+		var i, val,
+			script = doc.createElement( "script" );
+
+		script.text = code;
+		if ( node ) {
+			for ( i in preservedScriptAttributes ) {
+
+				// Support: Firefox 64+, Edge 18+
+				// Some browsers don't support the "nonce" property on scripts.
+				// On the other hand, just using `getAttribute` is not enough as
+				// the `nonce` attribute is reset to an empty string whenever it
+				// becomes browsing-context connected.
+				// See https://github.com/whatwg/html/issues/2369
+				// See https://html.spec.whatwg.org/#nonce-attributes
+				// The `node.getAttribute` check was added for the sake of
+				// `jQuery.globalEval` so that it can fake a nonce-containing node
+				// via an object.
+				val = node[ i ] || node.getAttribute && node.getAttribute( i );
+				if ( val ) {
+					script.setAttribute( i, val );
+				}
+			}
+		}
+		doc.head.appendChild( script ).parentNode.removeChild( script );
+	}
+
+
+function toType( obj ) {
+	if ( obj == null ) {
+		return obj + "";
+	}
+
+	// Support: Android <=2.3 only (functionish RegExp)
+	return typeof obj === "object" || typeof obj === "function" ?
+		class2type[ toString.call( obj ) ] || "object" :
+		typeof obj;
+}
+/* global Symbol */
+// Defining this global in .eslintrc.json would create a danger of using the global
+// unguarded in another place, it seems safer to define global only for this module
+
+
+
+var
+	version = "3.6.0",
+
+	// Define a local copy of jQuery
+	jQuery = function( selector, context ) {
+
+		// The jQuery object is actually just the init constructor 'enhanced'
+		// Need init if jQuery is called (just allow error to be thrown if not included)
+		return new jQuery.fn.init( selector, context );
+	};
+
+jQuery.fn = jQuery.prototype = {
+
+	// The current version of jQuery being used
+	jquery: version,
+
+	constructor: jQuery,
+
+	// The default length of a jQuery object is 0
+	length: 0,
+
+	toArray: function() {
+		return slice.call( this );
+	},
+
+	// Get the Nth element in the matched element set OR
+	// Get the whole matched element set as a clean array
+	get: function( num ) {
+
+		// Return all the elements in a clean array
+		if ( num == null ) {
+			return slice.call( this );
+		}
+
+		// Return just the one element from the set
+		return num < 0 ? this[ num + this.length ] : this[ num ];
+	},
+
+	// Take an array of elements and push it onto the stack
+	// (returning the new matched element set)
+	pushStack: function( elems ) {
+
+		// Build a new jQuery matched element set
+		var ret = jQuery.merge( this.constructor(), elems );
+
+		// Add the old object onto the stack (as a reference)
+		ret.prevObject = this;
+
+		// Return the newly-formed element set
+		return ret;
+	},
+
+	// Execute a callback for every element in the matched set.
+	each: function( callback ) {
+		return jQuery.each( this, callback );
+	},
+
+	map: function( callback ) {
+		return this.pushStack( jQuery.map( this, function( elem, i ) {
+			return callback.call( elem, i, elem );
+		} ) );
+	},
+
+	slice: function() {
+		return this.pushStack( slice.apply( this, arguments ) );
+	},
+
+	first: function() {
+		return this.eq( 0 );
+	},
+
+	last: function() {
+		return this.eq( -1 );
+	},
+
+	even: function() {
+		return this.pushStack( jQuery.grep( this, function( _elem, i ) {
+			return ( i + 1 ) % 2;
+		} ) );
+	},
+
+	odd: function() {
+		return this.pushStack( jQuery.grep( this, function( _elem, i ) {
+			return i % 2;
+		} ) );
+	},
+
+	eq: function( i ) {
+		var len = this.length,
+			j = +i + ( i < 0 ? len : 0 );
+		return this.pushStack( j >= 0 && j < len ? [ this[ j ] ] : [] );
+	},
+
+	end: function() {
+		return this.prevObject || this.constructor();
+	},
+
+	// For internal use only.
+	// Behaves like an Array's method, not like a jQuery method.
+	push: push,
+	sort: arr.sort,
+	splice: arr.splice
+};
+
+jQuery.extend = jQuery.fn.extend = function() {
+	var options, name, src, copy, copyIsArray, clone,
+		target = arguments[ 0 ] || {},
+		i = 1,
+		length = arguments.length,
+		deep = false;
+
+	// Handle a deep copy situation
+	if ( typeof target === "boolean" ) {
+		deep = target;
+
+		// Skip the boolean and the target
+		target = arguments[ i ] || {};
+		i++;
+	}
+
+	// Handle case when target is a string or something (possible in deep copy)
+	if ( typeof target !== "object" && !isFunction( target ) ) {
+		target = {};
+	}
+
+	// Extend jQuery itself if only one argument is passed
+	if ( i === length ) {
+		target = this;
+		i--;
+	}
+
+	for ( ; i < length; i++ ) {
+
+		// Only deal with non-null/undefined values
+		if ( ( options = arguments[ i ] ) != null ) {
+
+			// Extend the base object
+			for ( name in options ) {
+				copy = options[ name ];
+
+				// Prevent Object.prototype pollution
+				// Prevent never-ending loop
+				if ( name === "__proto__" || target === copy ) {
+					continue;
+				}
+
+				// Recurse if we're merging plain objects or arrays
+				if ( deep && copy && ( jQuery.isPlainObject( copy ) ||
+					( copyIsArray = Array.isArray( copy ) ) ) ) {
+					src = target[ name ];
+
+					// Ensure proper type for the source value
+					if ( copyIsArray && !Array.isArray( src ) ) {
+						clone = [];
+					} else if ( !copyIsArray && !jQuery.isPlainObject( src ) ) {
+						clone = {};
+					} else {
+						clone = src;
+					}
+					copyIsArray = false;
+
+					// Never move original objects, clone them
+					target[ name ] = jQuery.extend( deep, clone, copy );
+
+				// Don't bring in undefined values
+				} else if ( copy !== undefined ) {
+					target[ name ] = copy;
+				}
+			}
+		}
+	}
+
+	// Return the modified object
+	return target;
+};
+
+jQuery.extend( {
+
+	// Unique for each copy of jQuery on the page
+	expando: "jQuery" + ( version + Math.random() ).replace( /\D/g, "" ),
+
+	// Assume jQuery is ready without the ready module
+	isReady: true,
+
+	error: function( msg ) {
+		throw new Error( msg );
+	},
+
+	noop: function() {},
+
+	isPlainObject: function( obj ) {
+		var proto, Ctor;
+
+		// Detect obvious negatives
+		// Use toString instead of jQuery.type to catch host objects
+		if ( !obj || toString.call( obj ) !== "[object Object]" ) {
+			return false;
+		}
+
+		proto = getProto( obj );
+
+		// Objects with no prototype (e.g., `Object.create( null )`) are plain
+		if ( !proto ) {
+			return true;
+		}
+
+		// Objects with prototype are plain iff they were constructed by a global Object function
+		Ctor = hasOwn.call( proto, "constructor" ) && proto.constructor;
+		return typeof Ctor === "function" && fnToString.call( Ctor ) === ObjectFunctionString;
+	},
+
+	isEmptyObject: function( obj ) {
+		var name;
+
+		for ( name in obj ) {
+			return false;
+		}
+		return true;
+	},
+
+	// Evaluates a script in a provided context; falls back to the global one
+	// if not specified.
+	globalEval: function( code, options, doc ) {
+		DOMEval( code, { nonce: options && options.nonce }, doc );
+	},
+
+	each: function( obj, callback ) {
+		var length, i = 0;
+
+		if ( isArrayLike( obj ) ) {
+			length = obj.length;
+			for ( ; i < length; i++ ) {
+				if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {
+					break;
+				}
+			}
+		} else {
+			for ( i in obj ) {
+				if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {
+					break;
+				}
+			}
+		}
+
+		return obj;
+	},
+
+	// results is for internal usage only
+	makeArray: function( arr, results ) {
+		var ret = results || [];
+
+		if ( arr != null ) {
+			if ( isArrayLike( Object( arr ) ) ) {
+				jQuery.merge( ret,
+					typeof arr === "string" ?
+						[ arr ] : arr
+				);
+			} else {
+				push.call( ret, arr );
+			}
+		}
+
+		return ret;
+	},
+
+	inArray: function( elem, arr, i ) {
+		return arr == null ? -1 : indexOf.call( arr, elem, i );
+	},
+
+	// Support: Android <=4.0 only, PhantomJS 1 only
+	// push.apply(_, arraylike) throws on ancient WebKit
+	merge: function( first, second ) {
+		var len = +second.length,
+			j = 0,
+			i = first.length;
+
+		for ( ; j < len; j++ ) {
+			first[ i++ ] = second[ j ];
+		}
+
+		first.length = i;
+
+		return first;
+	},
+
+	grep: function( elems, callback, invert ) {
+		var callbackInverse,
+			matches = [],
+			i = 0,
+			length = elems.length,
+			callbackExpect = !invert;
+
+		// Go through the array, only saving the items
+		// that pass the validator function
+		for ( ; i < length; i++ ) {
+			callbackInverse = !callback( elems[ i ], i );
+			if ( callbackInverse !== callbackExpect ) {
+				matches.push( elems[ i ] );
+			}
+		}
+
+		return matches;
+	},
+
+	// arg is for internal usage only
+	map: function( elems, callback, arg ) {
+		var length, value,
+			i = 0,
+			ret = [];
+
+		// Go through the array, translating each of the items to their new values
+		if ( isArrayLike( elems ) ) {
+			length = elems.length;
+			for ( ; i < length; i++ ) {
+				value = callback( elems[ i ], i, arg );
+
+				if ( value != null ) {
+					ret.push( value );
+				}
+			}
+
+		// Go through every key on the object,
+		} else {
+			for ( i in elems ) {
+				value = callback( elems[ i ], i, arg );
+
+				if ( value != null ) {
+					ret.push( value );
+				}
+			}
+		}
+
+		// Flatten any nested arrays
+		return flat( ret );
+	},
+
+	// A global GUID counter for objects
+	guid: 1,
+
+	// jQuery.support is not used in Core but other projects attach their
+	// properties to it so it needs to exist.
+	support: support
+} );
+
+if ( typeof Symbol === "function" ) {
+	jQuery.fn[ Symbol.iterator ] = arr[ Symbol.iterator ];
+}
+
+// Populate the class2type map
+jQuery.each( "Boolean Number String Function Array Date RegExp Object Error Symbol".split( " " ),
+	function( _i, name ) {
+		class2type[ "[object " + name + "]" ] = name.toLowerCase();
+	} );
+
+function isArrayLike( obj ) {
+
+	// Support: real iOS 8.2 only (not reproducible in simulator)
+	// `in` check used to prevent JIT error (gh-2145)
+	// hasOwn isn't used here due to false negatives
+	// regarding Nodelist length in IE
+	var length = !!obj && "length" in obj && obj.length,
+		type = toType( obj );
+
+	if ( isFunction( obj ) || isWindow( obj ) ) {
+		return false;
+	}
+
+	return type === "array" || length === 0 ||
+		typeof length === "number" && length > 0 && ( length - 1 ) in obj;
+}
+var Sizzle =
+/*!
+ * Sizzle CSS Selector Engine v2.3.6
+ * https://sizzlejs.com/
+ *
+ * Copyright JS Foundation and other contributors
+ * Released under the MIT license
+ * https://js.foundation/
+ *
+ * Date: 2021-02-16
+ */
+( function( window ) {
+var i,
+	support,
+	Expr,
+	getText,
+	isXML,
+	tokenize,
+	compile,
+	select,
+	outermostContext,
+	sortInput,
+	hasDuplicate,
+
+	// Local document vars
+	setDocument,
+	document,
+	docElem,
+	documentIsHTML,
+	rbuggyQSA,
+	rbuggyMatches,
+	matches,
+	contains,
+
+	// Instance-specific data
+	expando = "sizzle" + 1 * new Date(),
+	preferredDoc = window.document,
+	dirruns = 0,
+	done = 0,
+	classCache = createCache(),
+	tokenCache = createCache(),
+	compilerCache = createCache(),
+	nonnativeSelectorCache = createCache(),
+	sortOrder = function( a, b ) {
+		if ( a === b ) {
+			hasDuplicate = true;
+		}
+		return 0;
+	},
+
+	// Instance methods
+	hasOwn = ( {} ).hasOwnProperty,
+	arr = [],
+	pop = arr.pop,
+	pushNative = arr.push,
+	push = arr.push,
+	slice = arr.slice,
+
+	// Use a stripped-down indexOf as it's faster than native
+	// https://jsperf.com/thor-indexof-vs-for/5
+	indexOf = function( list, elem ) {
+		var i = 0,
+			len = list.length;
+		for ( ; i < len; i++ ) {
+			if ( list[ i ] === elem ) {
+				return i;
+			}
+		}
+		return -1;
+	},
+
+	booleans = "checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|" +
+		"ismap|loop|multiple|open|readonly|required|scoped",
+
+	// Regular expressions
+
+	// http://www.w3.org/TR/css3-selectors/#whitespace
+	whitespace = "[\\x20\\t\\r\\n\\f]",
+
+	// https://www.w3.org/TR/css-syntax-3/#ident-token-diagram
+	identifier = "(?:\\\\[\\da-fA-F]{1,6}" + whitespace +
+		"?|\\\\[^\\r\\n\\f]|[\\w-]|[^\0-\\x7f])+",
+
+	// Attribute selectors: http://www.w3.org/TR/selectors/#attribute-selectors
+	attributes = "\\[" + whitespace + "*(" + identifier + ")(?:" + whitespace +
+
+		// Operator (capture 2)
+		"*([*^$|!~]?=)" + whitespace +
+
+		// "Attribute values must be CSS identifiers [capture 5]
+		// or strings [capture 3 or capture 4]"
+		"*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" +
+		whitespace + "*\\]",
+
+	pseudos = ":(" + identifier + ")(?:\\((" +
+
+		// To reduce the number of selectors needing tokenize in the preFilter, prefer arguments:
+		// 1. quoted (capture 3; capture 4 or capture 5)
+		"('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|" +
+
+		// 2. simple (capture 6)
+		"((?:\\\\.|[^\\\\()[\\]]|" + attributes + ")*)|" +
+
+		// 3. anything else (capture 2)
+		".*" +
+		")\\)|)",
+
+	// Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter
+	rwhitespace = new RegExp( whitespace + "+", "g" ),
+	rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" +
+		whitespace + "+$", "g" ),
+
+	rcomma = new RegExp( "^" + whitespace + "*," + whitespace + "*" ),
+	rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace +
+		"*" ),
+	rdescend = new RegExp( whitespace + "|>" ),
+
+	rpseudo = new RegExp( pseudos ),
+	ridentifier = new RegExp( "^" + identifier + "$" ),
+
+	matchExpr = {
+		"ID": new RegExp( "^#(" + identifier + ")" ),
+		"CLASS": new RegExp( "^\\.(" + identifier + ")" ),
+		"TAG": new RegExp( "^(" + identifier + "|[*])" ),
+		"ATTR": new RegExp( "^" + attributes ),
+		"PSEUDO": new RegExp( "^" + pseudos ),
+		"CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" +
+			whitespace + "*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" +
+			whitespace + "*(\\d+)|))" + whitespace + "*\\)|)", "i" ),
+		"bool": new RegExp( "^(?:" + booleans + ")$", "i" ),
+
+		// For use in libraries implementing .is()
+		// We use this for POS matching in `select`
+		"needsContext": new RegExp( "^" + whitespace +
+			"*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\(" + whitespace +
+			"*((?:-\\d)?\\d*)" + whitespace + "*\\)|)(?=[^-]|$)", "i" )
+	},
+
+	rhtml = /HTML$/i,
+	rinputs = /^(?:input|select|textarea|button)$/i,
+	rheader = /^h\d$/i,
+
+	rnative = /^[^{]+\{\s*\[native \w/,
+
+	// Easily-parseable/retrievable ID or TAG or CLASS selectors
+	rquickExpr = /^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/,
+
+	rsibling = /[+~]/,
+
+	// CSS escapes
+	// http://www.w3.org/TR/CSS21/syndata.html#escaped-characters
+	runescape = new RegExp( "\\\\[\\da-fA-F]{1,6}" + whitespace + "?|\\\\([^\\r\\n\\f])", "g" ),
+	funescape = function( escape, nonHex ) {
+		var high = "0x" + escape.slice( 1 ) - 0x10000;
+
+		return nonHex ?
+
+			// Strip the backslash prefix from a non-hex escape sequence
+			nonHex :
+
+			// Replace a hexadecimal escape sequence with the encoded Unicode code point
+			// Support: IE <=11+
+			// For values outside the Basic Multilingual Plane (BMP), manually construct a
+			// surrogate pair
+			high < 0 ?
+				String.fromCharCode( high + 0x10000 ) :
+				String.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 );
+	},
+
+	// CSS string/identifier serialization
+	// https://drafts.csswg.org/cssom/#common-serializing-idioms
+	rcssescape = /([\0-\x1f\x7f]|^-?\d)|^-$|[^\0-\x1f\x7f-\uFFFF\w-]/g,
+	fcssescape = function( ch, asCodePoint ) {
+		if ( asCodePoint ) {
+
+			// U+0000 NULL becomes U+FFFD REPLACEMENT CHARACTER
+			if ( ch === "\0" ) {
+				return "\uFFFD";
+			}
+
+			// Control characters and (dependent upon position) numbers get escaped as code points
+			return ch.slice( 0, -1 ) + "\\" +
+				ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " ";
+		}
+
+		// Other potentially-special ASCII characters get backslash-escaped
+		return "\\" + ch;
+	},
+
+	// Used for iframes
+	// See setDocument()
+	// Removing the function wrapper causes a "Permission Denied"
+	// error in IE
+	unloadHandler = function() {
+		setDocument();
+	},
+
+	inDisabledFieldset = addCombinator(
+		function( elem ) {
+			return elem.disabled === true && elem.nodeName.toLowerCase() === "fieldset";
+		},
+		{ dir: "parentNode", next: "legend" }
+	);
+
+// Optimize for push.apply( _, NodeList )
+try {
+	push.apply(
+		( arr = slice.call( preferredDoc.childNodes ) ),
+		preferredDoc.childNodes
+	);
+
+	// Support: Android<4.0
+	// Detect silently failing push.apply
+	// eslint-disable-next-line no-unused-expressions
+	arr[ preferredDoc.childNodes.length ].nodeType;
+} catch ( e ) {
+	push = { apply: arr.length ?
+
+		// Leverage slice if possible
+		function( target, els ) {
+			pushNative.apply( target, slice.call( els ) );
+		} :
+
+		// Support: IE<9
+		// Otherwise append directly
+		function( target, els ) {
+			var j = target.length,
+				i = 0;
+
+			// Can't trust NodeList.length
+			while ( ( target[ j++ ] = els[ i++ ] ) ) {}
+			target.length = j - 1;
+		}
+	};
+}
+
+function Sizzle( selector, context, results, seed ) {
+	var m, i, elem, nid, match, groups, newSelector,
+		newContext = context && context.ownerDocument,
+
+		// nodeType defaults to 9, since context defaults to document
+		nodeType = context ? context.nodeType : 9;
+
+	results = results || [];
+
+	// Return early from calls with invalid selector or context
+	if ( typeof selector !== "string" || !selector ||
+		nodeType !== 1 && nodeType !== 9 && nodeType !== 11 ) {
+
+		return results;
+	}
+
+	// Try to shortcut find operations (as opposed to filters) in HTML documents
+	if ( !seed ) {
+		setDocument( context );
+		context = context || document;
+
+		if ( documentIsHTML ) {
+
+			// If the selector is sufficiently simple, try using a "get*By*" DOM method
+			// (excepting DocumentFragment context, where the methods don't exist)
+			if ( nodeType !== 11 && ( match = rquickExpr.exec( selector ) ) ) {
+
+				// ID selector
+				if ( ( m = match[ 1 ] ) ) {
+
+					// Document context
+					if ( nodeType === 9 ) {
+						if ( ( elem = context.getElementById( m ) ) ) {
+
+							// Support: IE, Opera, Webkit
+							// TODO: identify versions
+							// getElementById can match elements by name instead of ID
+							if ( elem.id === m ) {
+								results.push( elem );
+								return results;
+							}
+						} else {
+							return results;
+						}
+
+					// Element context
+					} else {
+
+						// Support: IE, Opera, Webkit
+						// TODO: identify versions
+						// getElementById can match elements by name instead of ID
+						if ( newContext && ( elem = newContext.getElementById( m ) ) &&
+							contains( context, elem ) &&
+							elem.id === m ) {
+
+							results.push( elem );
+							return results;
+						}
+					}
+
+				// Type selector
+				} else if ( match[ 2 ] ) {
+					push.apply( results, context.getElementsByTagName( selector ) );
+					return results;
+
+				// Class selector
+				} else if ( ( m = match[ 3 ] ) && support.getElementsByClassName &&
+					context.getElementsByClassName ) {
+
+					push.apply( results, context.getElementsByClassName( m ) );
+					return results;
+				}
+			}
+
+			// Take advantage of querySelectorAll
+			if ( support.qsa &&
+				!nonnativeSelectorCache[ selector + " " ] &&
+				( !rbuggyQSA || !rbuggyQSA.test( selector ) ) &&
+
+				// Support: IE 8 only
+				// Exclude object elements
+				( nodeType !== 1 || context.nodeName.toLowerCase() !== "object" ) ) {
+
+				newSelector = selector;
+				newContext = context;
+
+				// qSA considers elements outside a scoping root when evaluating child or
+				// descendant combinators, which is not what we want.
+				// In such cases, we work around the behavior by prefixing every selector in the
+				// list with an ID selector referencing the scope context.
+				// The technique has to be used as well when a leading combinator is used
+				// as such selectors are not recognized by querySelectorAll.
+				// Thanks to Andrew Dupont for this technique.
+				if ( nodeType === 1 &&
+					( rdescend.test( selector ) || rcombinators.test( selector ) ) ) {
+
+					// Expand context for sibling selectors
+					newContext = rsibling.test( selector ) && testContext( context.parentNode ) ||
+						context;
+
+					// We can use :scope instead of the ID hack if the browser
+					// supports it & if we're not changing the context.
+					if ( newContext !== context || !support.scope ) {
+
+						// Capture the context ID, setting it first if necessary
+						if ( ( nid = context.getAttribute( "id" ) ) ) {
+							nid = nid.replace( rcssescape, fcssescape );
+						} else {
+							context.setAttribute( "id", ( nid = expando ) );
+						}
+					}
+
+					// Prefix every selector in the list
+					groups = tokenize( selector );
+					i = groups.length;
+					while ( i-- ) {
+						groups[ i ] = ( nid ? "#" + nid : ":scope" ) + " " +
+							toSelector( groups[ i ] );
+					}
+					newSelector = groups.join( "," );
+				}
+
+				try {
+					push.apply( results,
+						newContext.querySelectorAll( newSelector )
+					);
+					return results;
+				} catch ( qsaError ) {
+					nonnativeSelectorCache( selector, true );
+				} finally {
+					if ( nid === expando ) {
+						context.removeAttribute( "id" );
+					}
+				}
+			}
+		}
+	}
+
+	// All others
+	return select( selector.replace( rtrim, "$1" ), context, results, seed );
+}
+
+/**
+ * Create key-value caches of limited size
+ * @returns {function(string, object)} Returns the Object data after storing it on itself with
+ *	property name the (space-suffixed) string and (if the cache is larger than Expr.cacheLength)
+ *	deleting the oldest entry
+ */
+function createCache() {
+	var keys = [];
+
+	function cache( key, value ) {
+
+		// Use (key + " ") to avoid collision with native prototype properties (see Issue #157)
+		if ( keys.push( key + " " ) > Expr.cacheLength ) {
+
+			// Only keep the most recent entries
+			delete cache[ keys.shift() ];
+		}
+		return ( cache[ key + " " ] = value );
+	}
+	return cache;
+}
+
+/**
+ * Mark a function for special use by Sizzle
+ * @param {Function} fn The function to mark
+ */
+function markFunction( fn ) {
+	fn[ expando ] = true;
+	return fn;
+}
+
+/**
+ * Support testing using an element
+ * @param {Function} fn Passed the created element and returns a boolean result
+ */
+function assert( fn ) {
+	var el = document.createElement( "fieldset" );
+
+	try {
+		return !!fn( el );
+	} catch ( e ) {
+		return false;
+	} finally {
+
+		// Remove from its parent by default
+		if ( el.parentNode ) {
+			el.parentNode.removeChild( el );
+		}
+
+		// release memory in IE
+		el = null;
+	}
+}
+
+/**
+ * Adds the same handler for all of the specified attrs
+ * @param {String} attrs Pipe-separated list of attributes
+ * @param {Function} handler The method that will be applied
+ */
+function addHandle( attrs, handler ) {
+	var arr = attrs.split( "|" ),
+		i = arr.length;
+
+	while ( i-- ) {
+		Expr.attrHandle[ arr[ i ] ] = handler;
+	}
+}
+
+/**
+ * Checks document order of two siblings
+ * @param {Element} a
+ * @param {Element} b
+ * @returns {Number} Returns less than 0 if a precedes b, greater than 0 if a follows b
+ */
+function siblingCheck( a, b ) {
+	var cur = b && a,
+		diff = cur && a.nodeType === 1 && b.nodeType === 1 &&
+			a.sourceIndex - b.sourceIndex;
+
+	// Use IE sourceIndex if available on both nodes
+	if ( diff ) {
+		return diff;
+	}
+
+	// Check if b follows a
+	if ( cur ) {
+		while ( ( cur = cur.nextSibling ) ) {
+			if ( cur === b ) {
+				return -1;
+			}
+		}
+	}
+
+	return a ? 1 : -1;
+}
+
+/**
+ * Returns a function to use in pseudos for input types
+ * @param {String} type
+ */
+function createInputPseudo( type ) {
+	return function( elem ) {
+		var name = elem.nodeName.toLowerCase();
+		return name === "input" && elem.type === type;
+	};
+}
+
+/**
+ * Returns a function to use in pseudos for buttons
+ * @param {String} type
+ */
+function createButtonPseudo( type ) {
+	return function( elem ) {
+		var name = elem.nodeName.toLowerCase();
+		return ( name === "input" || name === "button" ) && elem.type === type;
+	};
+}
+
+/**
+ * Returns a function to use in pseudos for :enabled/:disabled
+ * @param {Boolean} disabled true for :disabled; false for :enabled
+ */
+function createDisabledPseudo( disabled ) {
+
+	// Known :disabled false positives: fieldset[disabled] > legend:nth-of-type(n+2) :can-disable
+	return function( elem ) {
+
+		// Only certain elements can match :enabled or :disabled
+		// https://html.spec.whatwg.org/multipage/scripting.html#selector-enabled
+		// https://html.spec.whatwg.org/multipage/scripting.html#selector-disabled
+		if ( "form" in elem ) {
+
+			// Check for inherited disabledness on relevant non-disabled elements:
+			// * listed form-associated elements in a disabled fieldset
+			//   https://html.spec.whatwg.org/multipage/forms.html#category-listed
+			//   https://html.spec.whatwg.org/multipage/forms.html#concept-fe-disabled
+			// * option elements in a disabled optgroup
+			//   https://html.spec.whatwg.org/multipage/forms.html#concept-option-disabled
+			// All such elements have a "form" property.
+			if ( elem.parentNode && elem.disabled === false ) {
+
+				// Option elements defer to a parent optgroup if present
+				if ( "label" in elem ) {
+					if ( "label" in elem.parentNode ) {
+						return elem.parentNode.disabled === disabled;
+					} else {
+						return elem.disabled === disabled;
+					}
+				}
+
+				// Support: IE 6 - 11
+				// Use the isDisabled shortcut property to check for disabled fieldset ancestors
+				return elem.isDisabled === disabled ||
+
+					// Where there is no isDisabled, check manually
+					/* jshint -W018 */
+					elem.isDisabled !== !disabled &&
+					inDisabledFieldset( elem ) === disabled;
+			}
+
+			return elem.disabled === disabled;
+
+		// Try to winnow out elements that can't be disabled before trusting the disabled property.
+		// Some victims get caught in our net (label, legend, menu, track), but it shouldn't
+		// even exist on them, let alone have a boolean value.
+		} else if ( "label" in elem ) {
+			return elem.disabled === disabled;
+		}
+
+		// Remaining elements are neither :enabled nor :disabled
+		return false;
+	};
+}
+
+/**
+ * Returns a function to use in pseudos for positionals
+ * @param {Function} fn
+ */
+function createPositionalPseudo( fn ) {
+	return markFunction( function( argument ) {
+		argument = +argument;
+		return markFunction( function( seed, matches ) {
+			var j,
+				matchIndexes = fn( [], seed.length, argument ),
+				i = matchIndexes.length;
+
+			// Match elements found at the specified indexes
+			while ( i-- ) {
+				if ( seed[ ( j = matchIndexes[ i ] ) ] ) {
+					seed[ j ] = !( matches[ j ] = seed[ j ] );
+				}
+			}
+		} );
+	} );
+}
+
+/**
+ * Checks a node for validity as a Sizzle context
+ * @param {Element|Object=} context
+ * @returns {Element|Object|Boolean} The input node if acceptable, otherwise a falsy value
+ */
+function testContext( context ) {
+	return context && typeof context.getElementsByTagName !== "undefined" && context;
+}
+
+// Expose support vars for convenience
+support = Sizzle.support = {};
+
+/**
+ * Detects XML nodes
+ * @param {Element|Object} elem An element or a document
+ * @returns {Boolean} True iff elem is a non-HTML XML node
+ */
+isXML = Sizzle.isXML = function( elem ) {
+	var namespace = elem && elem.namespaceURI,
+		docElem = elem && ( elem.ownerDocument || elem ).documentElement;
+
+	// Support: IE <=8
+	// Assume HTML when documentElement doesn't yet exist, such as inside loading iframes
+	// https://bugs.jquery.com/ticket/4833
+	return !rhtml.test( namespace || docElem && docElem.nodeName || "HTML" );
+};
+
+/**
+ * Sets document-related variables once based on the current document
+ * @param {Element|Object} [doc] An element or document object to use to set the document
+ * @returns {Object} Returns the current document
+ */
+setDocument = Sizzle.setDocument = function( node ) {
+	var hasCompare, subWindow,
+		doc = node ? node.ownerDocument || node : preferredDoc;
+
+	// Return early if doc is invalid or already selected
+	// Support: IE 11+, Edge 17 - 18+
+	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+	// two documents; shallow comparisons work.
+	// eslint-disable-next-line eqeqeq
+	if ( doc == document || doc.nodeType !== 9 || !doc.documentElement ) {
+		return document;
+	}
+
+	// Update global variables
+	document = doc;
+	docElem = document.documentElement;
+	documentIsHTML = !isXML( document );
+
+	// Support: IE 9 - 11+, Edge 12 - 18+
+	// Accessing iframe documents after unload throws "permission denied" errors (jQuery #13936)
+	// Support: IE 11+, Edge 17 - 18+
+	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+	// two documents; shallow comparisons work.
+	// eslint-disable-next-line eqeqeq
+	if ( preferredDoc != document &&
+		( subWindow = document.defaultView ) && subWindow.top !== subWindow ) {
+
+		// Support: IE 11, Edge
+		if ( subWindow.addEventListener ) {
+			subWindow.addEventListener( "unload", unloadHandler, false );
+
+		// Support: IE 9 - 10 only
+		} else if ( subWindow.attachEvent ) {
+			subWindow.attachEvent( "onunload", unloadHandler );
+		}
+	}
+
+	// Support: IE 8 - 11+, Edge 12 - 18+, Chrome <=16 - 25 only, Firefox <=3.6 - 31 only,
+	// Safari 4 - 5 only, Opera <=11.6 - 12.x only
+	// IE/Edge & older browsers don't support the :scope pseudo-class.
+	// Support: Safari 6.0 only
+	// Safari 6.0 supports :scope but it's an alias of :root there.
+	support.scope = assert( function( el ) {
+		docElem.appendChild( el ).appendChild( document.createElement( "div" ) );
+		return typeof el.querySelectorAll !== "undefined" &&
+			!el.querySelectorAll( ":scope fieldset div" ).length;
+	} );
+
+	/* Attributes
+	---------------------------------------------------------------------- */
+
+	// Support: IE<8
+	// Verify that getAttribute really returns attributes and not properties
+	// (excepting IE8 booleans)
+	support.attributes = assert( function( el ) {
+		el.className = "i";
+		return !el.getAttribute( "className" );
+	} );
+
+	/* getElement(s)By*
+	---------------------------------------------------------------------- */
+
+	// Check if getElementsByTagName("*") returns only elements
+	support.getElementsByTagName = assert( function( el ) {
+		el.appendChild( document.createComment( "" ) );
+		return !el.getElementsByTagName( "*" ).length;
+	} );
+
+	// Support: IE<9
+	support.getElementsByClassName = rnative.test( document.getElementsByClassName );
+
+	// Support: IE<10
+	// Check if getElementById returns elements by name
+	// The broken getElementById methods don't pick up programmatically-set names,
+	// so use a roundabout getElementsByName test
+	support.getById = assert( function( el ) {
+		docElem.appendChild( el ).id = expando;
+		return !document.getElementsByName || !document.getElementsByName( expando ).length;
+	} );
+
+	// ID filter and find
+	if ( support.getById ) {
+		Expr.filter[ "ID" ] = function( id ) {
+			var attrId = id.replace( runescape, funescape );
+			return function( elem ) {
+				return elem.getAttribute( "id" ) === attrId;
+			};
+		};
+		Expr.find[ "ID" ] = function( id, context ) {
+			if ( typeof context.getElementById !== "undefined" && documentIsHTML ) {
+				var elem = context.getElementById( id );
+				return elem ? [ elem ] : [];
+			}
+		};
+	} else {
+		Expr.filter[ "ID" ] =  function( id ) {
+			var attrId = id.replace( runescape, funescape );
+			return function( elem ) {
+				var node = typeof elem.getAttributeNode !== "undefined" &&
+					elem.getAttributeNode( "id" );
+				return node && node.value === attrId;
+			};
+		};
+
+		// Support: IE 6 - 7 only
+		// getElementById is not reliable as a find shortcut
+		Expr.find[ "ID" ] = function( id, context ) {
+			if ( typeof context.getElementById !== "undefined" && documentIsHTML ) {
+				var node, i, elems,
+					elem = context.getElementById( id );
+
+				if ( elem ) {
+
+					// Verify the id attribute
+					node = elem.getAttributeNode( "id" );
+					if ( node && node.value === id ) {
+						return [ elem ];
+					}
+
+					// Fall back on getElementsByName
+					elems = context.getElementsByName( id );
+					i = 0;
+					while ( ( elem = elems[ i++ ] ) ) {
+						node = elem.getAttributeNode( "id" );
+						if ( node && node.value === id ) {
+							return [ elem ];
+						}
+					}
+				}
+
+				return [];
+			}
+		};
+	}
+
+	// Tag
+	Expr.find[ "TAG" ] = support.getElementsByTagName ?
+		function( tag, context ) {
+			if ( typeof context.getElementsByTagName !== "undefined" ) {
+				return context.getElementsByTagName( tag );
+
+			// DocumentFragment nodes don't have gEBTN
+			} else if ( support.qsa ) {
+				return context.querySelectorAll( tag );
+			}
+		} :
+
+		function( tag, context ) {
+			var elem,
+				tmp = [],
+				i = 0,
+
+				// By happy coincidence, a (broken) gEBTN appears on DocumentFragment nodes too
+				results = context.getElementsByTagName( tag );
+
+			// Filter out possible comments
+			if ( tag === "*" ) {
+				while ( ( elem = results[ i++ ] ) ) {
+					if ( elem.nodeType === 1 ) {
+						tmp.push( elem );
+					}
+				}
+
+				return tmp;
+			}
+			return results;
+		};
+
+	// Class
+	Expr.find[ "CLASS" ] = support.getElementsByClassName && function( className, context ) {
+		if ( typeof context.getElementsByClassName !== "undefined" && documentIsHTML ) {
+			return context.getElementsByClassName( className );
+		}
+	};
+
+	/* QSA/matchesSelector
+	---------------------------------------------------------------------- */
+
+	// QSA and matchesSelector support
+
+	// matchesSelector(:active) reports false when true (IE9/Opera 11.5)
+	rbuggyMatches = [];
+
+	// qSa(:focus) reports false when true (Chrome 21)
+	// We allow this because of a bug in IE8/9 that throws an error
+	// whenever `document.activeElement` is accessed on an iframe
+	// So, we allow :focus to pass through QSA all the time to avoid the IE error
+	// See https://bugs.jquery.com/ticket/13378
+	rbuggyQSA = [];
+
+	if ( ( support.qsa = rnative.test( document.querySelectorAll ) ) ) {
+
+		// Build QSA regex
+		// Regex strategy adopted from Diego Perini
+		assert( function( el ) {
+
+			var input;
+
+			// Select is set to empty string on purpose
+			// This is to test IE's treatment of not explicitly
+			// setting a boolean content attribute,
+			// since its presence should be enough
+			// https://bugs.jquery.com/ticket/12359
+			docElem.appendChild( el ).innerHTML = "<a id='" + expando + "'></a>" +
+				"<select id='" + expando + "-\r\\' msallowcapture=''>" +
+				"<option selected=''></option></select>";
+
+			// Support: IE8, Opera 11-12.16
+			// Nothing should be selected when empty strings follow ^= or $= or *=
+			// The test attribute must be unknown in Opera but "safe" for WinRT
+			// https://msdn.microsoft.com/en-us/library/ie/hh465388.aspx#attribute_section
+			if ( el.querySelectorAll( "[msallowcapture^='']" ).length ) {
+				rbuggyQSA.push( "[*^$]=" + whitespace + "*(?:''|\"\")" );
+			}
+
+			// Support: IE8
+			// Boolean attributes and "value" are not treated correctly
+			if ( !el.querySelectorAll( "[selected]" ).length ) {
+				rbuggyQSA.push( "\\[" + whitespace + "*(?:value|" + booleans + ")" );
+			}
+
+			// Support: Chrome<29, Android<4.4, Safari<7.0+, iOS<7.0+, PhantomJS<1.9.8+
+			if ( !el.querySelectorAll( "[id~=" + expando + "-]" ).length ) {
+				rbuggyQSA.push( "~=" );
+			}
+
+			// Support: IE 11+, Edge 15 - 18+
+			// IE 11/Edge don't find elements on a `[name='']` query in some cases.
+			// Adding a temporary attribute to the document before the selection works
+			// around the issue.
+			// Interestingly, IE 10 & older don't seem to have the issue.
+			input = document.createElement( "input" );
+			input.setAttribute( "name", "" );
+			el.appendChild( input );
+			if ( !el.querySelectorAll( "[name='']" ).length ) {
+				rbuggyQSA.push( "\\[" + whitespace + "*name" + whitespace + "*=" +
+					whitespace + "*(?:''|\"\")" );
+			}
+
+			// Webkit/Opera - :checked should return selected option elements
+			// http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked
+			// IE8 throws error here and will not see later tests
+			if ( !el.querySelectorAll( ":checked" ).length ) {
+				rbuggyQSA.push( ":checked" );
+			}
+
+			// Support: Safari 8+, iOS 8+
+			// https://bugs.webkit.org/show_bug.cgi?id=136851
+			// In-page `selector#id sibling-combinator selector` fails
+			if ( !el.querySelectorAll( "a#" + expando + "+*" ).length ) {
+				rbuggyQSA.push( ".#.+[+~]" );
+			}
+
+			// Support: Firefox <=3.6 - 5 only
+			// Old Firefox doesn't throw on a badly-escaped identifier.
+			el.querySelectorAll( "\\\f" );
+			rbuggyQSA.push( "[\\r\\n\\f]" );
+		} );
+
+		assert( function( el ) {
+			el.innerHTML = "<a href='' disabled='disabled'></a>" +
+				"<select disabled='disabled'><option/></select>";
+
+			// Support: Windows 8 Native Apps
+			// The type and name attributes are restricted during .innerHTML assignment
+			var input = document.createElement( "input" );
+			input.setAttribute( "type", "hidden" );
+			el.appendChild( input ).setAttribute( "name", "D" );
+
+			// Support: IE8
+			// Enforce case-sensitivity of name attribute
+			if ( el.querySelectorAll( "[name=d]" ).length ) {
+				rbuggyQSA.push( "name" + whitespace + "*[*^$|!~]?=" );
+			}
+
+			// FF 3.5 - :enabled/:disabled and hidden elements (hidden elements are still enabled)
+			// IE8 throws error here and will not see later tests
+			if ( el.querySelectorAll( ":enabled" ).length !== 2 ) {
+				rbuggyQSA.push( ":enabled", ":disabled" );
+			}
+
+			// Support: IE9-11+
+			// IE's :disabled selector does not pick up the children of disabled fieldsets
+			docElem.appendChild( el ).disabled = true;
+			if ( el.querySelectorAll( ":disabled" ).length !== 2 ) {
+				rbuggyQSA.push( ":enabled", ":disabled" );
+			}
+
+			// Support: Opera 10 - 11 only
+			// Opera 10-11 does not throw on post-comma invalid pseudos
+			el.querySelectorAll( "*,:x" );
+			rbuggyQSA.push( ",.*:" );
+		} );
+	}
+
+	if ( ( support.matchesSelector = rnative.test( ( matches = docElem.matches ||
+		docElem.webkitMatchesSelector ||
+		docElem.mozMatchesSelector ||
+		docElem.oMatchesSelector ||
+		docElem.msMatchesSelector ) ) ) ) {
+
+		assert( function( el ) {
+
+			// Check to see if it's possible to do matchesSelector
+			// on a disconnected node (IE 9)
+			support.disconnectedMatch = matches.call( el, "*" );
+
+			// This should fail with an exception
+			// Gecko does not error, returns false instead
+			matches.call( el, "[s!='']:x" );
+			rbuggyMatches.push( "!=", pseudos );
+		} );
+	}
+
+	rbuggyQSA = rbuggyQSA.length && new RegExp( rbuggyQSA.join( "|" ) );
+	rbuggyMatches = rbuggyMatches.length && new RegExp( rbuggyMatches.join( "|" ) );
+
+	/* Contains
+	---------------------------------------------------------------------- */
+	hasCompare = rnative.test( docElem.compareDocumentPosition );
+
+	// Element contains another
+	// Purposefully self-exclusive
+	// As in, an element does not contain itself
+	contains = hasCompare || rnative.test( docElem.contains ) ?
+		function( a, b ) {
+			var adown = a.nodeType === 9 ? a.documentElement : a,
+				bup = b && b.parentNode;
+			return a === bup || !!( bup && bup.nodeType === 1 && (
+				adown.contains ?
+					adown.contains( bup ) :
+					a.compareDocumentPosition && a.compareDocumentPosition( bup ) & 16
+			) );
+		} :
+		function( a, b ) {
+			if ( b ) {
+				while ( ( b = b.parentNode ) ) {
+					if ( b === a ) {
+						return true;
+					}
+				}
+			}
+			return false;
+		};
+
+	/* Sorting
+	---------------------------------------------------------------------- */
+
+	// Document order sorting
+	sortOrder = hasCompare ?
+	function( a, b ) {
+
+		// Flag for duplicate removal
+		if ( a === b ) {
+			hasDuplicate = true;
+			return 0;
+		}
+
+		// Sort on method existence if only one input has compareDocumentPosition
+		var compare = !a.compareDocumentPosition - !b.compareDocumentPosition;
+		if ( compare ) {
+			return compare;
+		}
+
+		// Calculate position if both inputs belong to the same document
+		// Support: IE 11+, Edge 17 - 18+
+		// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+		// two documents; shallow comparisons work.
+		// eslint-disable-next-line eqeqeq
+		compare = ( a.ownerDocument || a ) == ( b.ownerDocument || b ) ?
+			a.compareDocumentPosition( b ) :
+
+			// Otherwise we know they are disconnected
+			1;
+
+		// Disconnected nodes
+		if ( compare & 1 ||
+			( !support.sortDetached && b.compareDocumentPosition( a ) === compare ) ) {
+
+			// Choose the first element that is related to our preferred document
+			// Support: IE 11+, Edge 17 - 18+
+			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+			// two documents; shallow comparisons work.
+			// eslint-disable-next-line eqeqeq
+			if ( a == document || a.ownerDocument == preferredDoc &&
+				contains( preferredDoc, a ) ) {
+				return -1;
+			}
+
+			// Support: IE 11+, Edge 17 - 18+
+			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+			// two documents; shallow comparisons work.
+			// eslint-disable-next-line eqeqeq
+			if ( b == document || b.ownerDocument == preferredDoc &&
+				contains( preferredDoc, b ) ) {
+				return 1;
+			}
+
+			// Maintain original order
+			return sortInput ?
+				( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) :
+				0;
+		}
+
+		return compare & 4 ? -1 : 1;
+	} :
+	function( a, b ) {
+
+		// Exit early if the nodes are identical
+		if ( a === b ) {
+			hasDuplicate = true;
+			return 0;
+		}
+
+		var cur,
+			i = 0,
+			aup = a.parentNode,
+			bup = b.parentNode,
+			ap = [ a ],
+			bp = [ b ];
+
+		// Parentless nodes are either documents or disconnected
+		if ( !aup || !bup ) {
+
+			// Support: IE 11+, Edge 17 - 18+
+			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+			// two documents; shallow comparisons work.
+			/* eslint-disable eqeqeq */
+			return a == document ? -1 :
+				b == document ? 1 :
+				/* eslint-enable eqeqeq */
+				aup ? -1 :
+				bup ? 1 :
+				sortInput ?
+				( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) :
+				0;
+
+		// If the nodes are siblings, we can do a quick check
+		} else if ( aup === bup ) {
+			return siblingCheck( a, b );
+		}
+
+		// Otherwise we need full lists of their ancestors for comparison
+		cur = a;
+		while ( ( cur = cur.parentNode ) ) {
+			ap.unshift( cur );
+		}
+		cur = b;
+		while ( ( cur = cur.parentNode ) ) {
+			bp.unshift( cur );
+		}
+
+		// Walk down the tree looking for a discrepancy
+		while ( ap[ i ] === bp[ i ] ) {
+			i++;
+		}
+
+		return i ?
+
+			// Do a sibling check if the nodes have a common ancestor
+			siblingCheck( ap[ i ], bp[ i ] ) :
+
+			// Otherwise nodes in our document sort first
+			// Support: IE 11+, Edge 17 - 18+
+			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+			// two documents; shallow comparisons work.
+			/* eslint-disable eqeqeq */
+			ap[ i ] == preferredDoc ? -1 :
+			bp[ i ] == preferredDoc ? 1 :
+			/* eslint-enable eqeqeq */
+			0;
+	};
+
+	return document;
+};
+
+Sizzle.matches = function( expr, elements ) {
+	return Sizzle( expr, null, null, elements );
+};
+
+Sizzle.matchesSelector = function( elem, expr ) {
+	setDocument( elem );
+
+	if ( support.matchesSelector && documentIsHTML &&
+		!nonnativeSelectorCache[ expr + " " ] &&
+		( !rbuggyMatches || !rbuggyMatches.test( expr ) ) &&
+		( !rbuggyQSA     || !rbuggyQSA.test( expr ) ) ) {
+
+		try {
+			var ret = matches.call( elem, expr );
+
+			// IE 9's matchesSelector returns false on disconnected nodes
+			if ( ret || support.disconnectedMatch ||
+
+				// As well, disconnected nodes are said to be in a document
+				// fragment in IE 9
+				elem.document && elem.document.nodeType !== 11 ) {
+				return ret;
+			}
+		} catch ( e ) {
+			nonnativeSelectorCache( expr, true );
+		}
+	}
+
+	return Sizzle( expr, document, null, [ elem ] ).length > 0;
+};
+
+Sizzle.contains = function( context, elem ) {
+
+	// Set document vars if needed
+	// Support: IE 11+, Edge 17 - 18+
+	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+	// two documents; shallow comparisons work.
+	// eslint-disable-next-line eqeqeq
+	if ( ( context.ownerDocument || context ) != document ) {
+		setDocument( context );
+	}
+	return contains( context, elem );
+};
+
+Sizzle.attr = function( elem, name ) {
+
+	// Set document vars if needed
+	// Support: IE 11+, Edge 17 - 18+
+	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+	// two documents; shallow comparisons work.
+	// eslint-disable-next-line eqeqeq
+	if ( ( elem.ownerDocument || elem ) != document ) {
+		setDocument( elem );
+	}
+
+	var fn = Expr.attrHandle[ name.toLowerCase() ],
+
+		// Don't get fooled by Object.prototype properties (jQuery #13807)
+		val = fn && hasOwn.call( Expr.attrHandle, name.toLowerCase() ) ?
+			fn( elem, name, !documentIsHTML ) :
+			undefined;
+
+	return val !== undefined ?
+		val :
+		support.attributes || !documentIsHTML ?
+			elem.getAttribute( name ) :
+			( val = elem.getAttributeNode( name ) ) && val.specified ?
+				val.value :
+				null;
+};
+
+Sizzle.escape = function( sel ) {
+	return ( sel + "" ).replace( rcssescape, fcssescape );
+};
+
+Sizzle.error = function( msg ) {
+	throw new Error( "Syntax error, unrecognized expression: " + msg );
+};
+
+/**
+ * Document sorting and removing duplicates
+ * @param {ArrayLike} results
+ */
+Sizzle.uniqueSort = function( results ) {
+	var elem,
+		duplicates = [],
+		j = 0,
+		i = 0;
+
+	// Unless we *know* we can detect duplicates, assume their presence
+	hasDuplicate = !support.detectDuplicates;
+	sortInput = !support.sortStable && results.slice( 0 );
+	results.sort( sortOrder );
+
+	if ( hasDuplicate ) {
+		while ( ( elem = results[ i++ ] ) ) {
+			if ( elem === results[ i ] ) {
+				j = duplicates.push( i );
+			}
+		}
+		while ( j-- ) {
+			results.splice( duplicates[ j ], 1 );
+		}
+	}
+
+	// Clear input after sorting to release objects
+	// See https://github.com/jquery/sizzle/pull/225
+	sortInput = null;
+
+	return results;
+};
+
+/**
+ * Utility function for retrieving the text value of an array of DOM nodes
+ * @param {Array|Element} elem
+ */
+getText = Sizzle.getText = function( elem ) {
+	var node,
+		ret = "",
+		i = 0,
+		nodeType = elem.nodeType;
+
+	if ( !nodeType ) {
+
+		// If no nodeType, this is expected to be an array
+		while ( ( node = elem[ i++ ] ) ) {
+
+			// Do not traverse comment nodes
+			ret += getText( node );
+		}
+	} else if ( nodeType === 1 || nodeType === 9 || nodeType === 11 ) {
+
+		// Use textContent for elements
+		// innerText usage removed for consistency of new lines (jQuery #11153)
+		if ( typeof elem.textContent === "string" ) {
+			return elem.textContent;
+		} else {
+
+			// Traverse its children
+			for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) {
+				ret += getText( elem );
+			}
+		}
+	} else if ( nodeType === 3 || nodeType === 4 ) {
+		return elem.nodeValue;
+	}
+
+	// Do not include comment or processing instruction nodes
+
+	return ret;
+};
+
+Expr = Sizzle.selectors = {
+
+	// Can be adjusted by the user
+	cacheLength: 50,
+
+	createPseudo: markFunction,
+
+	match: matchExpr,
+
+	attrHandle: {},
+
+	find: {},
+
+	relative: {
+		">": { dir: "parentNode", first: true },
+		" ": { dir: "parentNode" },
+		"+": { dir: "previousSibling", first: true },
+		"~": { dir: "previousSibling" }
+	},
+
+	preFilter: {
+		"ATTR": function( match ) {
+			match[ 1 ] = match[ 1 ].replace( runescape, funescape );
+
+			// Move the given value to match[3] whether quoted or unquoted
+			match[ 3 ] = ( match[ 3 ] || match[ 4 ] ||
+				match[ 5 ] || "" ).replace( runescape, funescape );
+
+			if ( match[ 2 ] === "~=" ) {
+				match[ 3 ] = " " + match[ 3 ] + " ";
+			}
+
+			return match.slice( 0, 4 );
+		},
+
+		"CHILD": function( match ) {
+
+			/* matches from matchExpr["CHILD"]
+				1 type (only|nth|...)
+				2 what (child|of-type)
+				3 argument (even|odd|\d*|\d*n([+-]\d+)?|...)
+				4 xn-component of xn+y argument ([+-]?\d*n|)
+				5 sign of xn-component
+				6 x of xn-component
+				7 sign of y-component
+				8 y of y-component
+			*/
+			match[ 1 ] = match[ 1 ].toLowerCase();
+
+			if ( match[ 1 ].slice( 0, 3 ) === "nth" ) {
+
+				// nth-* requires argument
+				if ( !match[ 3 ] ) {
+					Sizzle.error( match[ 0 ] );
+				}
+
+				// numeric x and y parameters for Expr.filter.CHILD
+				// remember that false/true cast respectively to 0/1
+				match[ 4 ] = +( match[ 4 ] ?
+					match[ 5 ] + ( match[ 6 ] || 1 ) :
+					2 * ( match[ 3 ] === "even" || match[ 3 ] === "odd" ) );
+				match[ 5 ] = +( ( match[ 7 ] + match[ 8 ] ) || match[ 3 ] === "odd" );
+
+				// other types prohibit arguments
+			} else if ( match[ 3 ] ) {
+				Sizzle.error( match[ 0 ] );
+			}
+
+			return match;
+		},
+
+		"PSEUDO": function( match ) {
+			var excess,
+				unquoted = !match[ 6 ] && match[ 2 ];
+
+			if ( matchExpr[ "CHILD" ].test( match[ 0 ] ) ) {
+				return null;
+			}
+
+			// Accept quoted arguments as-is
+			if ( match[ 3 ] ) {
+				match[ 2 ] = match[ 4 ] || match[ 5 ] || "";
+
+			// Strip excess characters from unquoted arguments
+			} else if ( unquoted && rpseudo.test( unquoted ) &&
+
+				// Get excess from tokenize (recursively)
+				( excess = tokenize( unquoted, true ) ) &&
+
+				// advance to the next closing parenthesis
+				( excess = unquoted.indexOf( ")", unquoted.length - excess ) - unquoted.length ) ) {
+
+				// excess is a negative index
+				match[ 0 ] = match[ 0 ].slice( 0, excess );
+				match[ 2 ] = unquoted.slice( 0, excess );
+			}
+
+			// Return only captures needed by the pseudo filter method (type and argument)
+			return match.slice( 0, 3 );
+		}
+	},
+
+	filter: {
+
+		"TAG": function( nodeNameSelector ) {
+			var nodeName = nodeNameSelector.replace( runescape, funescape ).toLowerCase();
+			return nodeNameSelector === "*" ?
+				function() {
+					return true;
+				} :
+				function( elem ) {
+					return elem.nodeName && elem.nodeName.toLowerCase() === nodeName;
+				};
+		},
+
+		"CLASS": function( className ) {
+			var pattern = classCache[ className + " " ];
+
+			return pattern ||
+				( pattern = new RegExp( "(^|" + whitespace +
+					")" + className + "(" + whitespace + "|$)" ) ) && classCache(
+						className, function( elem ) {
+							return pattern.test(
+								typeof elem.className === "string" && elem.className ||
+								typeof elem.getAttribute !== "undefined" &&
+									elem.getAttribute( "class" ) ||
+								""
+							);
+				} );
+		},
+
+		"ATTR": function( name, operator, check ) {
+			return function( elem ) {
+				var result = Sizzle.attr( elem, name );
+
+				if ( result == null ) {
+					return operator === "!=";
+				}
+				if ( !operator ) {
+					return true;
+				}
+
+				result += "";
+
+				/* eslint-disable max-len */
+
+				return operator === "=" ? result === check :
+					operator === "!=" ? result !== check :
+					operator === "^=" ? check && result.indexOf( check ) === 0 :
+					operator === "*=" ? check && result.indexOf( check ) > -1 :
+					operator === "$=" ? check && result.slice( -check.length ) === check :
+					operator === "~=" ? ( " " + result.replace( rwhitespace, " " ) + " " ).indexOf( check ) > -1 :
+					operator === "|=" ? result === check || result.slice( 0, check.length + 1 ) === check + "-" :
+					false;
+				/* eslint-enable max-len */
+
+			};
+		},
+
+		"CHILD": function( type, what, _argument, first, last ) {
+			var simple = type.slice( 0, 3 ) !== "nth",
+				forward = type.slice( -4 ) !== "last",
+				ofType = what === "of-type";
+
+			return first === 1 && last === 0 ?
+
+				// Shortcut for :nth-*(n)
+				function( elem ) {
+					return !!elem.parentNode;
+				} :
+
+				function( elem, _context, xml ) {
+					var cache, uniqueCache, outerCache, node, nodeIndex, start,
+						dir = simple !== forward ? "nextSibling" : "previousSibling",
+						parent = elem.parentNode,
+						name = ofType && elem.nodeName.toLowerCase(),
+						useCache = !xml && !ofType,
+						diff = false;
+
+					if ( parent ) {
+
+						// :(first|last|only)-(child|of-type)
+						if ( simple ) {
+							while ( dir ) {
+								node = elem;
+								while ( ( node = node[ dir ] ) ) {
+									if ( ofType ?
+										node.nodeName.toLowerCase() === name :
+										node.nodeType === 1 ) {
+
+										return false;
+									}
+								}
+
+								// Reverse direction for :only-* (if we haven't yet done so)
+								start = dir = type === "only" && !start && "nextSibling";
+							}
+							return true;
+						}
+
+						start = [ forward ? parent.firstChild : parent.lastChild ];
+
+						// non-xml :nth-child(...) stores cache data on `parent`
+						if ( forward && useCache ) {
+
+							// Seek `elem` from a previously-cached index
+
+							// ...in a gzip-friendly way
+							node = parent;
+							outerCache = node[ expando ] || ( node[ expando ] = {} );
+
+							// Support: IE <9 only
+							// Defend against cloned attroperties (jQuery gh-1709)
+							uniqueCache = outerCache[ node.uniqueID ] ||
+								( outerCache[ node.uniqueID ] = {} );
+
+							cache = uniqueCache[ type ] || [];
+							nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];
+							diff = nodeIndex && cache[ 2 ];
+							node = nodeIndex && parent.childNodes[ nodeIndex ];
+
+							while ( ( node = ++nodeIndex && node && node[ dir ] ||
+
+								// Fallback to seeking `elem` from the start
+								( diff = nodeIndex = 0 ) || start.pop() ) ) {
+
+								// When found, cache indexes on `parent` and break
+								if ( node.nodeType === 1 && ++diff && node === elem ) {
+									uniqueCache[ type ] = [ dirruns, nodeIndex, diff ];
+									break;
+								}
+							}
+
+						} else {
+
+							// Use previously-cached element index if available
+							if ( useCache ) {
+
+								// ...in a gzip-friendly way
+								node = elem;
+								outerCache = node[ expando ] || ( node[ expando ] = {} );
+
+								// Support: IE <9 only
+								// Defend against cloned attroperties (jQuery gh-1709)
+								uniqueCache = outerCache[ node.uniqueID ] ||
+									( outerCache[ node.uniqueID ] = {} );
+
+								cache = uniqueCache[ type ] || [];
+								nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];
+								diff = nodeIndex;
+							}
+
+							// xml :nth-child(...)
+							// or :nth-last-child(...) or :nth(-last)?-of-type(...)
+							if ( diff === false ) {
+
+								// Use the same loop as above to seek `elem` from the start
+								while ( ( node = ++nodeIndex && node && node[ dir ] ||
+									( diff = nodeIndex = 0 ) || start.pop() ) ) {
+
+									if ( ( ofType ?
+										node.nodeName.toLowerCase() === name :
+										node.nodeType === 1 ) &&
+										++diff ) {
+
+										// Cache the index of each encountered element
+										if ( useCache ) {
+											outerCache = node[ expando ] ||
+												( node[ expando ] = {} );
+
+											// Support: IE <9 only
+											// Defend against cloned attroperties (jQuery gh-1709)
+											uniqueCache = outerCache[ node.uniqueID ] ||
+												( outerCache[ node.uniqueID ] = {} );
+
+											uniqueCache[ type ] = [ dirruns, diff ];
+										}
+
+										if ( node === elem ) {
+											break;
+										}
+									}
+								}
+							}
+						}
+
+						// Incorporate the offset, then check against cycle size
+						diff -= last;
+						return diff === first || ( diff % first === 0 && diff / first >= 0 );
+					}
+				};
+		},
+
+		"PSEUDO": function( pseudo, argument ) {
+
+			// pseudo-class names are case-insensitive
+			// http://www.w3.org/TR/selectors/#pseudo-classes
+			// Prioritize by case sensitivity in case custom pseudos are added with uppercase letters
+			// Remember that setFilters inherits from pseudos
+			var args,
+				fn = Expr.pseudos[ pseudo ] || Expr.setFilters[ pseudo.toLowerCase() ] ||
+					Sizzle.error( "unsupported pseudo: " + pseudo );
+
+			// The user may use createPseudo to indicate that
+			// arguments are needed to create the filter function
+			// just as Sizzle does
+			if ( fn[ expando ] ) {
+				return fn( argument );
+			}
+
+			// But maintain support for old signatures
+			if ( fn.length > 1 ) {
+				args = [ pseudo, pseudo, "", argument ];
+				return Expr.setFilters.hasOwnProperty( pseudo.toLowerCase() ) ?
+					markFunction( function( seed, matches ) {
+						var idx,
+							matched = fn( seed, argument ),
+							i = matched.length;
+						while ( i-- ) {
+							idx = indexOf( seed, matched[ i ] );
+							seed[ idx ] = !( matches[ idx ] = matched[ i ] );
+						}
+					} ) :
+					function( elem ) {
+						return fn( elem, 0, args );
+					};
+			}
+
+			return fn;
+		}
+	},
+
+	pseudos: {
+
+		// Potentially complex pseudos
+		"not": markFunction( function( selector ) {
+
+			// Trim the selector passed to compile
+			// to avoid treating leading and trailing
+			// spaces as combinators
+			var input = [],
+				results = [],
+				matcher = compile( selector.replace( rtrim, "$1" ) );
+
+			return matcher[ expando ] ?
+				markFunction( function( seed, matches, _context, xml ) {
+					var elem,
+						unmatched = matcher( seed, null, xml, [] ),
+						i = seed.length;
+
+					// Match elements unmatched by `matcher`
+					while ( i-- ) {
+						if ( ( elem = unmatched[ i ] ) ) {
+							seed[ i ] = !( matches[ i ] = elem );
+						}
+					}
+				} ) :
+				function( elem, _context, xml ) {
+					input[ 0 ] = elem;
+					matcher( input, null, xml, results );
+
+					// Don't keep the element (issue #299)
+					input[ 0 ] = null;
+					return !results.pop();
+				};
+		} ),
+
+		"has": markFunction( function( selector ) {
+			return function( elem ) {
+				return Sizzle( selector, elem ).length > 0;
+			};
+		} ),
+
+		"contains": markFunction( function( text ) {
+			text = text.replace( runescape, funescape );
+			return function( elem ) {
+				return ( elem.textContent || getText( elem ) ).indexOf( text ) > -1;
+			};
+		} ),
+
+		// "Whether an element is represented by a :lang() selector
+		// is based solely on the element's language value
+		// being equal to the identifier C,
+		// or beginning with the identifier C immediately followed by "-".
+		// The matching of C against the element's language value is performed case-insensitively.
+		// The identifier C does not have to be a valid language name."
+		// http://www.w3.org/TR/selectors/#lang-pseudo
+		"lang": markFunction( function( lang ) {
+
+			// lang value must be a valid identifier
+			if ( !ridentifier.test( lang || "" ) ) {
+				Sizzle.error( "unsupported lang: " + lang );
+			}
+			lang = lang.replace( runescape, funescape ).toLowerCase();
+			return function( elem ) {
+				var elemLang;
+				do {
+					if ( ( elemLang = documentIsHTML ?
+						elem.lang :
+						elem.getAttribute( "xml:lang" ) || elem.getAttribute( "lang" ) ) ) {
+
+						elemLang = elemLang.toLowerCase();
+						return elemLang === lang || elemLang.indexOf( lang + "-" ) === 0;
+					}
+				} while ( ( elem = elem.parentNode ) && elem.nodeType === 1 );
+				return false;
+			};
+		} ),
+
+		// Miscellaneous
+		"target": function( elem ) {
+			var hash = window.location && window.location.hash;
+			return hash && hash.slice( 1 ) === elem.id;
+		},
+
+		"root": function( elem ) {
+			return elem === docElem;
+		},
+
+		"focus": function( elem ) {
+			return elem === document.activeElement &&
+				( !document.hasFocus || document.hasFocus() ) &&
+				!!( elem.type || elem.href || ~elem.tabIndex );
+		},
+
+		// Boolean properties
+		"enabled": createDisabledPseudo( false ),
+		"disabled": createDisabledPseudo( true ),
+
+		"checked": function( elem ) {
+
+			// In CSS3, :checked should return both checked and selected elements
+			// http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked
+			var nodeName = elem.nodeName.toLowerCase();
+			return ( nodeName === "input" && !!elem.checked ) ||
+				( nodeName === "option" && !!elem.selected );
+		},
+
+		"selected": function( elem ) {
+
+			// Accessing this property makes selected-by-default
+			// options in Safari work properly
+			if ( elem.parentNode ) {
+				// eslint-disable-next-line no-unused-expressions
+				elem.parentNode.selectedIndex;
+			}
+
+			return elem.selected === true;
+		},
+
+		// Contents
+		"empty": function( elem ) {
+
+			// http://www.w3.org/TR/selectors/#empty-pseudo
+			// :empty is negated by element (1) or content nodes (text: 3; cdata: 4; entity ref: 5),
+			//   but not by others (comment: 8; processing instruction: 7; etc.)
+			// nodeType < 6 works because attributes (2) do not appear as children
+			for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) {
+				if ( elem.nodeType < 6 ) {
+					return false;
+				}
+			}
+			return true;
+		},
+
+		"parent": function( elem ) {
+			return !Expr.pseudos[ "empty" ]( elem );
+		},
+
+		// Element/input types
+		"header": function( elem ) {
+			return rheader.test( elem.nodeName );
+		},
+
+		"input": function( elem ) {
+			return rinputs.test( elem.nodeName );
+		},
+
+		"button": function( elem ) {
+			var name = elem.nodeName.toLowerCase();
+			return name === "input" && elem.type === "button" || name === "button";
+		},
+
+		"text": function( elem ) {
+			var attr;
+			return elem.nodeName.toLowerCase() === "input" &&
+				elem.type === "text" &&
+
+				// Support: IE<8
+				// New HTML5 attribute values (e.g., "search") appear with elem.type === "text"
+				( ( attr = elem.getAttribute( "type" ) ) == null ||
+					attr.toLowerCase() === "text" );
+		},
+
+		// Position-in-collection
+		"first": createPositionalPseudo( function() {
+			return [ 0 ];
+		} ),
+
+		"last": createPositionalPseudo( function( _matchIndexes, length ) {
+			return [ length - 1 ];
+		} ),
+
+		"eq": createPositionalPseudo( function( _matchIndexes, length, argument ) {
+			return [ argument < 0 ? argument + length : argument ];
+		} ),
+
+		"even": createPositionalPseudo( function( matchIndexes, length ) {
+			var i = 0;
+			for ( ; i < length; i += 2 ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		} ),
+
+		"odd": createPositionalPseudo( function( matchIndexes, length ) {
+			var i = 1;
+			for ( ; i < length; i += 2 ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		} ),
+
+		"lt": createPositionalPseudo( function( matchIndexes, length, argument ) {
+			var i = argument < 0 ?
+				argument + length :
+				argument > length ?
+					length :
+					argument;
+			for ( ; --i >= 0; ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		} ),
+
+		"gt": createPositionalPseudo( function( matchIndexes, length, argument ) {
+			var i = argument < 0 ? argument + length : argument;
+			for ( ; ++i < length; ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		} )
+	}
+};
+
+Expr.pseudos[ "nth" ] = Expr.pseudos[ "eq" ];
+
+// Add button/input type pseudos
+for ( i in { radio: true, checkbox: true, file: true, password: true, image: true } ) {
+	Expr.pseudos[ i ] = createInputPseudo( i );
+}
+for ( i in { submit: true, reset: true } ) {
+	Expr.pseudos[ i ] = createButtonPseudo( i );
+}
+
+// Easy API for creating new setFilters
+function setFilters() {}
+setFilters.prototype = Expr.filters = Expr.pseudos;
+Expr.setFilters = new setFilters();
+
+tokenize = Sizzle.tokenize = function( selector, parseOnly ) {
+	var matched, match, tokens, type,
+		soFar, groups, preFilters,
+		cached = tokenCache[ selector + " " ];
+
+	if ( cached ) {
+		return parseOnly ? 0 : cached.slice( 0 );
+	}
+
+	soFar = selector;
+	groups = [];
+	preFilters = Expr.preFilter;
+
+	while ( soFar ) {
+
+		// Comma and first run
+		if ( !matched || ( match = rcomma.exec( soFar ) ) ) {
+			if ( match ) {
+
+				// Don't consume trailing commas as valid
+				soFar = soFar.slice( match[ 0 ].length ) || soFar;
+			}
+			groups.push( ( tokens = [] ) );
+		}
+
+		matched = false;
+
+		// Combinators
+		if ( ( match = rcombinators.exec( soFar ) ) ) {
+			matched = match.shift();
+			tokens.push( {
+				value: matched,
+
+				// Cast descendant combinators to space
+				type: match[ 0 ].replace( rtrim, " " )
+			} );
+			soFar = soFar.slice( matched.length );
+		}
+
+		// Filters
+		for ( type in Expr.filter ) {
+			if ( ( match = matchExpr[ type ].exec( soFar ) ) && ( !preFilters[ type ] ||
+				( match = preFilters[ type ]( match ) ) ) ) {
+				matched = match.shift();
+				tokens.push( {
+					value: matched,
+					type: type,
+					matches: match
+				} );
+				soFar = soFar.slice( matched.length );
+			}
+		}
+
+		if ( !matched ) {
+			break;
+		}
+	}
+
+	// Return the length of the invalid excess
+	// if we're just parsing
+	// Otherwise, throw an error or return tokens
+	return parseOnly ?
+		soFar.length :
+		soFar ?
+			Sizzle.error( selector ) :
+
+			// Cache the tokens
+			tokenCache( selector, groups ).slice( 0 );
+};
+
+function toSelector( tokens ) {
+	var i = 0,
+		len = tokens.length,
+		selector = "";
+	for ( ; i < len; i++ ) {
+		selector += tokens[ i ].value;
+	}
+	return selector;
+}
+
+function addCombinator( matcher, combinator, base ) {
+	var dir = combinator.dir,
+		skip = combinator.next,
+		key = skip || dir,
+		checkNonElements = base && key === "parentNode",
+		doneName = done++;
+
+	return combinator.first ?
+
+		// Check against closest ancestor/preceding element
+		function( elem, context, xml ) {
+			while ( ( elem = elem[ dir ] ) ) {
+				if ( elem.nodeType === 1 || checkNonElements ) {
+					return matcher( elem, context, xml );
+				}
+			}
+			return false;
+		} :
+
+		// Check against all ancestor/preceding elements
+		function( elem, context, xml ) {
+			var oldCache, uniqueCache, outerCache,
+				newCache = [ dirruns, doneName ];
+
+			// We can't set arbitrary data on XML nodes, so they don't benefit from combinator caching
+			if ( xml ) {
+				while ( ( elem = elem[ dir ] ) ) {
+					if ( elem.nodeType === 1 || checkNonElements ) {
+						if ( matcher( elem, context, xml ) ) {
+							return true;
+						}
+					}
+				}
+			} else {
+				while ( ( elem = elem[ dir ] ) ) {
+					if ( elem.nodeType === 1 || checkNonElements ) {
+						outerCache = elem[ expando ] || ( elem[ expando ] = {} );
+
+						// Support: IE <9 only
+						// Defend against cloned attroperties (jQuery gh-1709)
+						uniqueCache = outerCache[ elem.uniqueID ] ||
+							( outerCache[ elem.uniqueID ] = {} );
+
+						if ( skip && skip === elem.nodeName.toLowerCase() ) {
+							elem = elem[ dir ] || elem;
+						} else if ( ( oldCache = uniqueCache[ key ] ) &&
+							oldCache[ 0 ] === dirruns && oldCache[ 1 ] === doneName ) {
+
+							// Assign to newCache so results back-propagate to previous elements
+							return ( newCache[ 2 ] = oldCache[ 2 ] );
+						} else {
+
+							// Reuse newcache so results back-propagate to previous elements
+							uniqueCache[ key ] = newCache;
+
+							// A match means we're done; a fail means we have to keep checking
+							if ( ( newCache[ 2 ] = matcher( elem, context, xml ) ) ) {
+								return true;
+							}
+						}
+					}
+				}
+			}
+			return false;
+		};
+}
+
+function elementMatcher( matchers ) {
+	return matchers.length > 1 ?
+		function( elem, context, xml ) {
+			var i = matchers.length;
+			while ( i-- ) {
+				if ( !matchers[ i ]( elem, context, xml ) ) {
+					return false;
+				}
+			}
+			return true;
+		} :
+		matchers[ 0 ];
+}
+
+function multipleContexts( selector, contexts, results ) {
+	var i = 0,
+		len = contexts.length;
+	for ( ; i < len; i++ ) {
+		Sizzle( selector, contexts[ i ], results );
+	}
+	return results;
+}
+
+function condense( unmatched, map, filter, context, xml ) {
+	var elem,
+		newUnmatched = [],
+		i = 0,
+		len = unmatched.length,
+		mapped = map != null;
+
+	for ( ; i < len; i++ ) {
+		if ( ( elem = unmatched[ i ] ) ) {
+			if ( !filter || filter( elem, context, xml ) ) {
+				newUnmatched.push( elem );
+				if ( mapped ) {
+					map.push( i );
+				}
+			}
+		}
+	}
+
+	return newUnmatched;
+}
+
+function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postSelector ) {
+	if ( postFilter && !postFilter[ expando ] ) {
+		postFilter = setMatcher( postFilter );
+	}
+	if ( postFinder && !postFinder[ expando ] ) {
+		postFinder = setMatcher( postFinder, postSelector );
+	}
+	return markFunction( function( seed, results, context, xml ) {
+		var temp, i, elem,
+			preMap = [],
+			postMap = [],
+			preexisting = results.length,
+
+			// Get initial elements from seed or context
+			elems = seed || multipleContexts(
+				selector || "*",
+				context.nodeType ? [ context ] : context,
+				[]
+			),
+
+			// Prefilter to get matcher input, preserving a map for seed-results synchronization
+			matcherIn = preFilter && ( seed || !selector ) ?
+				condense( elems, preMap, preFilter, context, xml ) :
+				elems,
+
+			matcherOut = matcher ?
+
+				// If we have a postFinder, or filtered seed, or non-seed postFilter or preexisting results,
+				postFinder || ( seed ? preFilter : preexisting || postFilter ) ?
+
+					// ...intermediate processing is necessary
+					[] :
+
+					// ...otherwise use results directly
+					results :
+				matcherIn;
+
+		// Find primary matches
+		if ( matcher ) {
+			matcher( matcherIn, matcherOut, context, xml );
+		}
+
+		// Apply postFilter
+		if ( postFilter ) {
+			temp = condense( matcherOut, postMap );
+			postFilter( temp, [], context, xml );
+
+			// Un-match failing elements by moving them back to matcherIn
+			i = temp.length;
+			while ( i-- ) {
+				if ( ( elem = temp[ i ] ) ) {
+					matcherOut[ postMap[ i ] ] = !( matcherIn[ postMap[ i ] ] = elem );
+				}
+			}
+		}
+
+		if ( seed ) {
+			if ( postFinder || preFilter ) {
+				if ( postFinder ) {
+
+					// Get the final matcherOut by condensing this intermediate into postFinder contexts
+					temp = [];
+					i = matcherOut.length;
+					while ( i-- ) {
+						if ( ( elem = matcherOut[ i ] ) ) {
+
+							// Restore matcherIn since elem is not yet a final match
+							temp.push( ( matcherIn[ i ] = elem ) );
+						}
+					}
+					postFinder( null, ( matcherOut = [] ), temp, xml );
+				}
+
+				// Move matched elements from seed to results to keep them synchronized
+				i = matcherOut.length;
+				while ( i-- ) {
+					if ( ( elem = matcherOut[ i ] ) &&
+						( temp = postFinder ? indexOf( seed, elem ) : preMap[ i ] ) > -1 ) {
+
+						seed[ temp ] = !( results[ temp ] = elem );
+					}
+				}
+			}
+
+		// Add elements to results, through postFinder if defined
+		} else {
+			matcherOut = condense(
+				matcherOut === results ?
+					matcherOut.splice( preexisting, matcherOut.length ) :
+					matcherOut
+			);
+			if ( postFinder ) {
+				postFinder( null, results, matcherOut, xml );
+			} else {
+				push.apply( results, matcherOut );
+			}
+		}
+	} );
+}
+
+function matcherFromTokens( tokens ) {
+	var checkContext, matcher, j,
+		len = tokens.length,
+		leadingRelative = Expr.relative[ tokens[ 0 ].type ],
+		implicitRelative = leadingRelative || Expr.relative[ " " ],
+		i = leadingRelative ? 1 : 0,
+
+		// The foundational matcher ensures that elements are reachable from top-level context(s)
+		matchContext = addCombinator( function( elem ) {
+			return elem === checkContext;
+		}, implicitRelative, true ),
+		matchAnyContext = addCombinator( function( elem ) {
+			return indexOf( checkContext, elem ) > -1;
+		}, implicitRelative, true ),
+		matchers = [ function( elem, context, xml ) {
+			var ret = ( !leadingRelative && ( xml || context !== outermostContext ) ) || (
+				( checkContext = context ).nodeType ?
+					matchContext( elem, context, xml ) :
+					matchAnyContext( elem, context, xml ) );
+
+			// Avoid hanging onto element (issue #299)
+			checkContext = null;
+			return ret;
+		} ];
+
+	for ( ; i < len; i++ ) {
+		if ( ( matcher = Expr.relative[ tokens[ i ].type ] ) ) {
+			matchers = [ addCombinator( elementMatcher( matchers ), matcher ) ];
+		} else {
+			matcher = Expr.filter[ tokens[ i ].type ].apply( null, tokens[ i ].matches );
+
+			// Return special upon seeing a positional matcher
+			if ( matcher[ expando ] ) {
+
+				// Find the next relative operator (if any) for proper handling
+				j = ++i;
+				for ( ; j < len; j++ ) {
+					if ( Expr.relative[ tokens[ j ].type ] ) {
+						break;
+					}
+				}
+				return setMatcher(
+					i > 1 && elementMatcher( matchers ),
+					i > 1 && toSelector(
+
+					// If the preceding token was a descendant combinator, insert an implicit any-element `*`
+					tokens
+						.slice( 0, i - 1 )
+						.concat( { value: tokens[ i - 2 ].type === " " ? "*" : "" } )
+					).replace( rtrim, "$1" ),
+					matcher,
+					i < j && matcherFromTokens( tokens.slice( i, j ) ),
+					j < len && matcherFromTokens( ( tokens = tokens.slice( j ) ) ),
+					j < len && toSelector( tokens )
+				);
+			}
+			matchers.push( matcher );
+		}
+	}
+
+	return elementMatcher( matchers );
+}
+
+function matcherFromGroupMatchers( elementMatchers, setMatchers ) {
+	var bySet = setMatchers.length > 0,
+		byElement = elementMatchers.length > 0,
+		superMatcher = function( seed, context, xml, results, outermost ) {
+			var elem, j, matcher,
+				matchedCount = 0,
+				i = "0",
+				unmatched = seed && [],
+				setMatched = [],
+				contextBackup = outermostContext,
+
+				// We must always have either seed elements or outermost context
+				elems = seed || byElement && Expr.find[ "TAG" ]( "*", outermost ),
+
+				// Use integer dirruns iff this is the outermost matcher
+				dirrunsUnique = ( dirruns += contextBackup == null ? 1 : Math.random() || 0.1 ),
+				len = elems.length;
+
+			if ( outermost ) {
+
+				// Support: IE 11+, Edge 17 - 18+
+				// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+				// two documents; shallow comparisons work.
+				// eslint-disable-next-line eqeqeq
+				outermostContext = context == document || context || outermost;
+			}
+
+			// Add elements passing elementMatchers directly to results
+			// Support: IE<9, Safari
+			// Tolerate NodeList properties (IE: "length"; Safari: <number>) matching elements by id
+			for ( ; i !== len && ( elem = elems[ i ] ) != null; i++ ) {
+				if ( byElement && elem ) {
+					j = 0;
+
+					// Support: IE 11+, Edge 17 - 18+
+					// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+					// two documents; shallow comparisons work.
+					// eslint-disable-next-line eqeqeq
+					if ( !context && elem.ownerDocument != document ) {
+						setDocument( elem );
+						xml = !documentIsHTML;
+					}
+					while ( ( matcher = elementMatchers[ j++ ] ) ) {
+						if ( matcher( elem, context || document, xml ) ) {
+							results.push( elem );
+							break;
+						}
+					}
+					if ( outermost ) {
+						dirruns = dirrunsUnique;
+					}
+				}
+
+				// Track unmatched elements for set filters
+				if ( bySet ) {
+
+					// They will have gone through all possible matchers
+					if ( ( elem = !matcher && elem ) ) {
+						matchedCount--;
+					}
+
+					// Lengthen the array for every element, matched or not
+					if ( seed ) {
+						unmatched.push( elem );
+					}
+				}
+			}
+
+			// `i` is now the count of elements visited above, and adding it to `matchedCount`
+			// makes the latter nonnegative.
+			matchedCount += i;
+
+			// Apply set filters to unmatched elements
+			// NOTE: This can be skipped if there are no unmatched elements (i.e., `matchedCount`
+			// equals `i`), unless we didn't visit _any_ elements in the above loop because we have
+			// no element matchers and no seed.
+			// Incrementing an initially-string "0" `i` allows `i` to remain a string only in that
+			// case, which will result in a "00" `matchedCount` that differs from `i` but is also
+			// numerically zero.
+			if ( bySet && i !== matchedCount ) {
+				j = 0;
+				while ( ( matcher = setMatchers[ j++ ] ) ) {
+					matcher( unmatched, setMatched, context, xml );
+				}
+
+				if ( seed ) {
+
+					// Reintegrate element matches to eliminate the need for sorting
+					if ( matchedCount > 0 ) {
+						while ( i-- ) {
+							if ( !( unmatched[ i ] || setMatched[ i ] ) ) {
+								setMatched[ i ] = pop.call( results );
+							}
+						}
+					}
+
+					// Discard index placeholder values to get only actual matches
+					setMatched = condense( setMatched );
+				}
+
+				// Add matches to results
+				push.apply( results, setMatched );
+
+				// Seedless set matches succeeding multiple successful matchers stipulate sorting
+				if ( outermost && !seed && setMatched.length > 0 &&
+					( matchedCount + setMatchers.length ) > 1 ) {
+
+					Sizzle.uniqueSort( results );
+				}
+			}
+
+			// Override manipulation of globals by nested matchers
+			if ( outermost ) {
+				dirruns = dirrunsUnique;
+				outermostContext = contextBackup;
+			}
+
+			return unmatched;
+		};
+
+	return bySet ?
+		markFunction( superMatcher ) :
+		superMatcher;
+}
+
+compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) {
+	var i,
+		setMatchers = [],
+		elementMatchers = [],
+		cached = compilerCache[ selector + " " ];
+
+	if ( !cached ) {
+
+		// Generate a function of recursive functions that can be used to check each element
+		if ( !match ) {
+			match = tokenize( selector );
+		}
+		i = match.length;
+		while ( i-- ) {
+			cached = matcherFromTokens( match[ i ] );
+			if ( cached[ expando ] ) {
+				setMatchers.push( cached );
+			} else {
+				elementMatchers.push( cached );
+			}
+		}
+
+		// Cache the compiled function
+		cached = compilerCache(
+			selector,
+			matcherFromGroupMatchers( elementMatchers, setMatchers )
+		);
+
+		// Save selector and tokenization
+		cached.selector = selector;
+	}
+	return cached;
+};
+
+/**
+ * A low-level selection function that works with Sizzle's compiled
+ *  selector functions
+ * @param {String|Function} selector A selector or a pre-compiled
+ *  selector function built with Sizzle.compile
+ * @param {Element} context
+ * @param {Array} [results]
+ * @param {Array} [seed] A set of elements to match against
+ */
+select = Sizzle.select = function( selector, context, results, seed ) {
+	var i, tokens, token, type, find,
+		compiled = typeof selector === "function" && selector,
+		match = !seed && tokenize( ( selector = compiled.selector || selector ) );
+
+	results = results || [];
+
+	// Try to minimize operations if there is only one selector in the list and no seed
+	// (the latter of which guarantees us context)
+	if ( match.length === 1 ) {
+
+		// Reduce context if the leading compound selector is an ID
+		tokens = match[ 0 ] = match[ 0 ].slice( 0 );
+		if ( tokens.length > 2 && ( token = tokens[ 0 ] ).type === "ID" &&
+			context.nodeType === 9 && documentIsHTML && Expr.relative[ tokens[ 1 ].type ] ) {
+
+			context = ( Expr.find[ "ID" ]( token.matches[ 0 ]
+				.replace( runescape, funescape ), context ) || [] )[ 0 ];
+			if ( !context ) {
+				return results;
+
+			// Precompiled matchers will still verify ancestry, so step up a level
+			} else if ( compiled ) {
+				context = context.parentNode;
+			}
+
+			selector = selector.slice( tokens.shift().value.length );
+		}
+
+		// Fetch a seed set for right-to-left matching
+		i = matchExpr[ "needsContext" ].test( selector ) ? 0 : tokens.length;
+		while ( i-- ) {
+			token = tokens[ i ];
+
+			// Abort if we hit a combinator
+			if ( Expr.relative[ ( type = token.type ) ] ) {
+				break;
+			}
+			if ( ( find = Expr.find[ type ] ) ) {
+
+				// Search, expanding context for leading sibling combinators
+				if ( ( seed = find(
+					token.matches[ 0 ].replace( runescape, funescape ),
+					rsibling.test( tokens[ 0 ].type ) && testContext( context.parentNode ) ||
+						context
+				) ) ) {
+
+					// If seed is empty or no tokens remain, we can return early
+					tokens.splice( i, 1 );
+					selector = seed.length && toSelector( tokens );
+					if ( !selector ) {
+						push.apply( results, seed );
+						return results;
+					}
+
+					break;
+				}
+			}
+		}
+	}
+
+	// Compile and execute a filtering function if one is not provided
+	// Provide `match` to avoid retokenization if we modified the selector above
+	( compiled || compile( selector, match ) )(
+		seed,
+		context,
+		!documentIsHTML,
+		results,
+		!context || rsibling.test( selector ) && testContext( context.parentNode ) || context
+	);
+	return results;
+};
+
+// One-time assignments
+
+// Sort stability
+support.sortStable = expando.split( "" ).sort( sortOrder ).join( "" ) === expando;
+
+// Support: Chrome 14-35+
+// Always assume duplicates if they aren't passed to the comparison function
+support.detectDuplicates = !!hasDuplicate;
+
+// Initialize against the default document
+setDocument();
+
+// Support: Webkit<537.32 - Safari 6.0.3/Chrome 25 (fixed in Chrome 27)
+// Detached nodes confoundingly follow *each other*
+support.sortDetached = assert( function( el ) {
+
+	// Should return 1, but returns 4 (following)
+	return el.compareDocumentPosition( document.createElement( "fieldset" ) ) & 1;
+} );
+
+// Support: IE<8
+// Prevent attribute/property "interpolation"
+// https://msdn.microsoft.com/en-us/library/ms536429%28VS.85%29.aspx
+if ( !assert( function( el ) {
+	el.innerHTML = "<a href='#'></a>";
+	return el.firstChild.getAttribute( "href" ) === "#";
+} ) ) {
+	addHandle( "type|href|height|width", function( elem, name, isXML ) {
+		if ( !isXML ) {
+			return elem.getAttribute( name, name.toLowerCase() === "type" ? 1 : 2 );
+		}
+	} );
+}
+
+// Support: IE<9
+// Use defaultValue in place of getAttribute("value")
+if ( !support.attributes || !assert( function( el ) {
+	el.innerHTML = "<input/>";
+	el.firstChild.setAttribute( "value", "" );
+	return el.firstChild.getAttribute( "value" ) === "";
+} ) ) {
+	addHandle( "value", function( elem, _name, isXML ) {
+		if ( !isXML && elem.nodeName.toLowerCase() === "input" ) {
+			return elem.defaultValue;
+		}
+	} );
+}
+
+// Support: IE<9
+// Use getAttributeNode to fetch booleans when getAttribute lies
+if ( !assert( function( el ) {
+	return el.getAttribute( "disabled" ) == null;
+} ) ) {
+	addHandle( booleans, function( elem, name, isXML ) {
+		var val;
+		if ( !isXML ) {
+			return elem[ name ] === true ? name.toLowerCase() :
+				( val = elem.getAttributeNode( name ) ) && val.specified ?
+					val.value :
+					null;
+		}
+	} );
+}
+
+return Sizzle;
+
+} )( window );
+
+
+
+jQuery.find = Sizzle;
+jQuery.expr = Sizzle.selectors;
+
+// Deprecated
+jQuery.expr[ ":" ] = jQuery.expr.pseudos;
+jQuery.uniqueSort = jQuery.unique = Sizzle.uniqueSort;
+jQuery.text = Sizzle.getText;
+jQuery.isXMLDoc = Sizzle.isXML;
+jQuery.contains = Sizzle.contains;
+jQuery.escapeSelector = Sizzle.escape;
+
+
+
+
+var dir = function( elem, dir, until ) {
+	var matched = [],
+		truncate = until !== undefined;
+
+	while ( ( elem = elem[ dir ] ) && elem.nodeType !== 9 ) {
+		if ( elem.nodeType === 1 ) {
+			if ( truncate && jQuery( elem ).is( until ) ) {
+				break;
+			}
+			matched.push( elem );
+		}
+	}
+	return matched;
+};
+
+
+var siblings = function( n, elem ) {
+	var matched = [];
+
+	for ( ; n; n = n.nextSibling ) {
+		if ( n.nodeType === 1 && n !== elem ) {
+			matched.push( n );
+		}
+	}
+
+	return matched;
+};
+
+
+var rneedsContext = jQuery.expr.match.needsContext;
+
+
+
+function nodeName( elem, name ) {
+
+	return elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase();
+
+}
+var rsingleTag = ( /^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i );
+
+
+
+// Implement the identical functionality for filter and not
+function winnow( elements, qualifier, not ) {
+	if ( isFunction( qualifier ) ) {
+		return jQuery.grep( elements, function( elem, i ) {
+			return !!qualifier.call( elem, i, elem ) !== not;
+		} );
+	}
+
+	// Single element
+	if ( qualifier.nodeType ) {
+		return jQuery.grep( elements, function( elem ) {
+			return ( elem === qualifier ) !== not;
+		} );
+	}
+
+	// Arraylike of elements (jQuery, arguments, Array)
+	if ( typeof qualifier !== "string" ) {
+		return jQuery.grep( elements, function( elem ) {
+			return ( indexOf.call( qualifier, elem ) > -1 ) !== not;
+		} );
+	}
+
+	// Filtered directly for both simple and complex selectors
+	return jQuery.filter( qualifier, elements, not );
+}
+
+jQuery.filter = function( expr, elems, not ) {
+	var elem = elems[ 0 ];
+
+	if ( not ) {
+		expr = ":not(" + expr + ")";
+	}
+
+	if ( elems.length === 1 && elem.nodeType === 1 ) {
+		return jQuery.find.matchesSelector( elem, expr ) ? [ elem ] : [];
+	}
+
+	return jQuery.find.matches( expr, jQuery.grep( elems, function( elem ) {
+		return elem.nodeType === 1;
+	} ) );
+};
+
+jQuery.fn.extend( {
+	find: function( selector ) {
+		var i, ret,
+			len = this.length,
+			self = this;
+
+		if ( typeof selector !== "string" ) {
+			return this.pushStack( jQuery( selector ).filter( function() {
+				for ( i = 0; i < len; i++ ) {
+					if ( jQuery.contains( self[ i ], this ) ) {
+						return true;
+					}
+				}
+			} ) );
+		}
+
+		ret = this.pushStack( [] );
+
+		for ( i = 0; i < len; i++ ) {
+			jQuery.find( selector, self[ i ], ret );
+		}
+
+		return len > 1 ? jQuery.uniqueSort( ret ) : ret;
+	},
+	filter: function( selector ) {
+		return this.pushStack( winnow( this, selector || [], false ) );
+	},
+	not: function( selector ) {
+		return this.pushStack( winnow( this, selector || [], true ) );
+	},
+	is: function( selector ) {
+		return !!winnow(
+			this,
+
+			// If this is a positional/relative selector, check membership in the returned set
+			// so $("p:first").is("p:last") won't return true for a doc with two "p".
+			typeof selector === "string" && rneedsContext.test( selector ) ?
+				jQuery( selector ) :
+				selector || [],
+			false
+		).length;
+	}
+} );
+
+
+// Initialize a jQuery object
+
+
+// A central reference to the root jQuery(document)
+var rootjQuery,
+
+	// A simple way to check for HTML strings
+	// Prioritize #id over <tag> to avoid XSS via location.hash (#9521)
+	// Strict HTML recognition (#11290: must start with <)
+	// Shortcut simple #id case for speed
+	rquickExpr = /^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]+))$/,
+
+	init = jQuery.fn.init = function( selector, context, root ) {
+		var match, elem;
+
+		// HANDLE: $(""), $(null), $(undefined), $(false)
+		if ( !selector ) {
+			return this;
+		}
+
+		// Method init() accepts an alternate rootjQuery
+		// so migrate can support jQuery.sub (gh-2101)
+		root = root || rootjQuery;
+
+		// Handle HTML strings
+		if ( typeof selector === "string" ) {
+			if ( selector[ 0 ] === "<" &&
+				selector[ selector.length - 1 ] === ">" &&
+				selector.length >= 3 ) {
+
+				// Assume that strings that start and end with <> are HTML and skip the regex check
+				match = [ null, selector, null ];
+
+			} else {
+				match = rquickExpr.exec( selector );
+			}
+
+			// Match html or make sure no context is specified for #id
+			if ( match && ( match[ 1 ] || !context ) ) {
+
+				// HANDLE: $(html) -> $(array)
+				if ( match[ 1 ] ) {
+					context = context instanceof jQuery ? context[ 0 ] : context;
+
+					// Option to run scripts is true for back-compat
+					// Intentionally let the error be thrown if parseHTML is not present
+					jQuery.merge( this, jQuery.parseHTML(
+						match[ 1 ],
+						context && context.nodeType ? context.ownerDocument || context : document,
+						true
+					) );
+
+					// HANDLE: $(html, props)
+					if ( rsingleTag.test( match[ 1 ] ) && jQuery.isPlainObject( context ) ) {
+						for ( match in context ) {
+
+							// Properties of context are called as methods if possible
+							if ( isFunction( this[ match ] ) ) {
+								this[ match ]( context[ match ] );
+
+							// ...and otherwise set as attributes
+							} else {
+								this.attr( match, context[ match ] );
+							}
+						}
+					}
+
+					return this;
+
+				// HANDLE: $(#id)
+				} else {
+					elem = document.getElementById( match[ 2 ] );
+
+					if ( elem ) {
+
+						// Inject the element directly into the jQuery object
+						this[ 0 ] = elem;
+						this.length = 1;
+					}
+					return this;
+				}
+
+			// HANDLE: $(expr, $(...))
+			} else if ( !context || context.jquery ) {
+				return ( context || root ).find( selector );
+
+			// HANDLE: $(expr, context)
+			// (which is just equivalent to: $(context).find(expr)
+			} else {
+				return this.constructor( context ).find( selector );
+			}
+
+		// HANDLE: $(DOMElement)
+		} else if ( selector.nodeType ) {
+			this[ 0 ] = selector;
+			this.length = 1;
+			return this;
+
+		// HANDLE: $(function)
+		// Shortcut for document ready
+		} else if ( isFunction( selector ) ) {
+			return root.ready !== undefined ?
+				root.ready( selector ) :
+
+				// Execute immediately if ready is not present
+				selector( jQuery );
+		}
+
+		return jQuery.makeArray( selector, this );
+	};
+
+// Give the init function the jQuery prototype for later instantiation
+init.prototype = jQuery.fn;
+
+// Initialize central reference
+rootjQuery = jQuery( document );
+
+
+var rparentsprev = /^(?:parents|prev(?:Until|All))/,
+
+	// Methods guaranteed to produce a unique set when starting from a unique set
+	guaranteedUnique = {
+		children: true,
+		contents: true,
+		next: true,
+		prev: true
+	};
+
+jQuery.fn.extend( {
+	has: function( target ) {
+		var targets = jQuery( target, this ),
+			l = targets.length;
+
+		return this.filter( function() {
+			var i = 0;
+			for ( ; i < l; i++ ) {
+				if ( jQuery.contains( this, targets[ i ] ) ) {
+					return true;
+				}
+			}
+		} );
+	},
+
+	closest: function( selectors, context ) {
+		var cur,
+			i = 0,
+			l = this.length,
+			matched = [],
+			targets = typeof selectors !== "string" && jQuery( selectors );
+
+		// Positional selectors never match, since there's no _selection_ context
+		if ( !rneedsContext.test( selectors ) ) {
+			for ( ; i < l; i++ ) {
+				for ( cur = this[ i ]; cur && cur !== context; cur = cur.parentNode ) {
+
+					// Always skip document fragments
+					if ( cur.nodeType < 11 && ( targets ?
+						targets.index( cur ) > -1 :
+
+						// Don't pass non-elements to Sizzle
+						cur.nodeType === 1 &&
+							jQuery.find.matchesSelector( cur, selectors ) ) ) {
+
+						matched.push( cur );
+						break;
+					}
+				}
+			}
+		}
+
+		return this.pushStack( matched.length > 1 ? jQuery.uniqueSort( matched ) : matched );
+	},
+
+	// Determine the position of an element within the set
+	index: function( elem ) {
+
+		// No argument, return index in parent
+		if ( !elem ) {
+			return ( this[ 0 ] && this[ 0 ].parentNode ) ? this.first().prevAll().length : -1;
+		}
+
+		// Index in selector
+		if ( typeof elem === "string" ) {
+			return indexOf.call( jQuery( elem ), this[ 0 ] );
+		}
+
+		// Locate the position of the desired element
+		return indexOf.call( this,
+
+			// If it receives a jQuery object, the first element is used
+			elem.jquery ? elem[ 0 ] : elem
+		);
+	},
+
+	add: function( selector, context ) {
+		return this.pushStack(
+			jQuery.uniqueSort(
+				jQuery.merge( this.get(), jQuery( selector, context ) )
+			)
+		);
+	},
+
+	addBack: function( selector ) {
+		return this.add( selector == null ?
+			this.prevObject : this.prevObject.filter( selector )
+		);
+	}
+} );
+
+function sibling( cur, dir ) {
+	while ( ( cur = cur[ dir ] ) && cur.nodeType !== 1 ) {}
+	return cur;
+}
+
+jQuery.each( {
+	parent: function( elem ) {
+		var parent = elem.parentNode;
+		return parent && parent.nodeType !== 11 ? parent : null;
+	},
+	parents: function( elem ) {
+		return dir( elem, "parentNode" );
+	},
+	parentsUntil: function( elem, _i, until ) {
+		return dir( elem, "parentNode", until );
+	},
+	next: function( elem ) {
+		return sibling( elem, "nextSibling" );
+	},
+	prev: function( elem ) {
+		return sibling( elem, "previousSibling" );
+	},
+	nextAll: function( elem ) {
+		return dir( elem, "nextSibling" );
+	},
+	prevAll: function( elem ) {
+		return dir( elem, "previousSibling" );
+	},
+	nextUntil: function( elem, _i, until ) {
+		return dir( elem, "nextSibling", until );
+	},
+	prevUntil: function( elem, _i, until ) {
+		return dir( elem, "previousSibling", until );
+	},
+	siblings: function( elem ) {
+		return siblings( ( elem.parentNode || {} ).firstChild, elem );
+	},
+	children: function( elem ) {
+		return siblings( elem.firstChild );
+	},
+	contents: function( elem ) {
+		if ( elem.contentDocument != null &&
+
+			// Support: IE 11+
+			// <object> elements with no `data` attribute has an object
+			// `contentDocument` with a `null` prototype.
+			getProto( elem.contentDocument ) ) {
+
+			return elem.contentDocument;
+		}
+
+		// Support: IE 9 - 11 only, iOS 7 only, Android Browser <=4.3 only
+		// Treat the template element as a regular one in browsers that
+		// don't support it.
+		if ( nodeName( elem, "template" ) ) {
+			elem = elem.content || elem;
+		}
+
+		return jQuery.merge( [], elem.childNodes );
+	}
+}, function( name, fn ) {
+	jQuery.fn[ name ] = function( until, selector ) {
+		var matched = jQuery.map( this, fn, until );
+
+		if ( name.slice( -5 ) !== "Until" ) {
+			selector = until;
+		}
+
+		if ( selector && typeof selector === "string" ) {
+			matched = jQuery.filter( selector, matched );
+		}
+
+		if ( this.length > 1 ) {
+
+			// Remove duplicates
+			if ( !guaranteedUnique[ name ] ) {
+				jQuery.uniqueSort( matched );
+			}
+
+			// Reverse order for parents* and prev-derivatives
+			if ( rparentsprev.test( name ) ) {
+				matched.reverse();
+			}
+		}
+
+		return this.pushStack( matched );
+	};
+} );
+var rnothtmlwhite = ( /[^\x20\t\r\n\f]+/g );
+
+
+
+// Convert String-formatted options into Object-formatted ones
+function createOptions( options ) {
+	var object = {};
+	jQuery.each( options.match( rnothtmlwhite ) || [], function( _, flag ) {
+		object[ flag ] = true;
+	} );
+	return object;
+}
+
+/*
+ * Create a callback list using the following parameters:
+ *
+ *	options: an optional list of space-separated options that will change how
+ *			the callback list behaves or a more traditional option object
+ *
+ * By default a callback list will act like an event callback list and can be
+ * "fired" multiple times.
+ *
+ * Possible options:
+ *
+ *	once:			will ensure the callback list can only be fired once (like a Deferred)
+ *
+ *	memory:			will keep track of previous values and will call any callback added
+ *					after the list has been fired right away with the latest "memorized"
+ *					values (like a Deferred)
+ *
+ *	unique:			will ensure a callback can only be added once (no duplicate in the list)
+ *
+ *	stopOnFalse:	interrupt callings when a callback returns false
+ *
+ */
+jQuery.Callbacks = function( options ) {
+
+	// Convert options from String-formatted to Object-formatted if needed
+	// (we check in cache first)
+	options = typeof options === "string" ?
+		createOptions( options ) :
+		jQuery.extend( {}, options );
+
+	var // Flag to know if list is currently firing
+		firing,
+
+		// Last fire value for non-forgettable lists
+		memory,
+
+		// Flag to know if list was already fired
+		fired,
+
+		// Flag to prevent firing
+		locked,
+
+		// Actual callback list
+		list = [],
+
+		// Queue of execution data for repeatable lists
+		queue = [],
+
+		// Index of currently firing callback (modified by add/remove as needed)
+		firingIndex = -1,
+
+		// Fire callbacks
+		fire = function() {
+
+			// Enforce single-firing
+			locked = locked || options.once;
+
+			// Execute callbacks for all pending executions,
+			// respecting firingIndex overrides and runtime changes
+			fired = firing = true;
+			for ( ; queue.length; firingIndex = -1 ) {
+				memory = queue.shift();
+				while ( ++firingIndex < list.length ) {
+
+					// Run callback and check for early termination
+					if ( list[ firingIndex ].apply( memory[ 0 ], memory[ 1 ] ) === false &&
+						options.stopOnFalse ) {
+
+						// Jump to end and forget the data so .add doesn't re-fire
+						firingIndex = list.length;
+						memory = false;
+					}
+				}
+			}
+
+			// Forget the data if we're done with it
+			if ( !options.memory ) {
+				memory = false;
+			}
+
+			firing = false;
+
+			// Clean up if we're done firing for good
+			if ( locked ) {
+
+				// Keep an empty list if we have data for future add calls
+				if ( memory ) {
+					list = [];
+
+				// Otherwise, this object is spent
+				} else {
+					list = "";
+				}
+			}
+		},
+
+		// Actual Callbacks object
+		self = {
+
+			// Add a callback or a collection of callbacks to the list
+			add: function() {
+				if ( list ) {
+
+					// If we have memory from a past run, we should fire after adding
+					if ( memory && !firing ) {
+						firingIndex = list.length - 1;
+						queue.push( memory );
+					}
+
+					( function add( args ) {
+						jQuery.each( args, function( _, arg ) {
+							if ( isFunction( arg ) ) {
+								if ( !options.unique || !self.has( arg ) ) {
+									list.push( arg );
+								}
+							} else if ( arg && arg.length && toType( arg ) !== "string" ) {
+
+								// Inspect recursively
+								add( arg );
+							}
+						} );
+					} )( arguments );
+
+					if ( memory && !firing ) {
+						fire();
+					}
+				}
+				return this;
+			},
+
+			// Remove a callback from the list
+			remove: function() {
+				jQuery.each( arguments, function( _, arg ) {
+					var index;
+					while ( ( index = jQuery.inArray( arg, list, index ) ) > -1 ) {
+						list.splice( index, 1 );
+
+						// Handle firing indexes
+						if ( index <= firingIndex ) {
+							firingIndex--;
+						}
+					}
+				} );
+				return this;
+			},
+
+			// Check if a given callback is in the list.
+			// If no argument is given, return whether or not list has callbacks attached.
+			has: function( fn ) {
+				return fn ?
+					jQuery.inArray( fn, list ) > -1 :
+					list.length > 0;
+			},
+
+			// Remove all callbacks from the list
+			empty: function() {
+				if ( list ) {
+					list = [];
+				}
+				return this;
+			},
+
+			// Disable .fire and .add
+			// Abort any current/pending executions
+			// Clear all callbacks and values
+			disable: function() {
+				locked = queue = [];
+				list = memory = "";
+				return this;
+			},
+			disabled: function() {
+				return !list;
+			},
+
+			// Disable .fire
+			// Also disable .add unless we have memory (since it would have no effect)
+			// Abort any pending executions
+			lock: function() {
+				locked = queue = [];
+				if ( !memory && !firing ) {
+					list = memory = "";
+				}
+				return this;
+			},
+			locked: function() {
+				return !!locked;
+			},
+
+			// Call all callbacks with the given context and arguments
+			fireWith: function( context, args ) {
+				if ( !locked ) {
+					args = args || [];
+					args = [ context, args.slice ? args.slice() : args ];
+					queue.push( args );
+					if ( !firing ) {
+						fire();
+					}
+				}
+				return this;
+			},
+
+			// Call all the callbacks with the given arguments
+			fire: function() {
+				self.fireWith( this, arguments );
+				return this;
+			},
+
+			// To know if the callbacks have already been called at least once
+			fired: function() {
+				return !!fired;
+			}
+		};
+
+	return self;
+};
+
+
+function Identity( v ) {
+	return v;
+}
+function Thrower( ex ) {
+	throw ex;
+}
+
+function adoptValue( value, resolve, reject, noValue ) {
+	var method;
+
+	try {
+
+		// Check for promise aspect first to privilege synchronous behavior
+		if ( value && isFunction( ( method = value.promise ) ) ) {
+			method.call( value ).done( resolve ).fail( reject );
+
+		// Other thenables
+		} else if ( value && isFunction( ( method = value.then ) ) ) {
+			method.call( value, resolve, reject );
+
+		// Other non-thenables
+		} else {
+
+			// Control `resolve` arguments by letting Array#slice cast boolean `noValue` to integer:
+			// * false: [ value ].slice( 0 ) => resolve( value )
+			// * true: [ value ].slice( 1 ) => resolve()
+			resolve.apply( undefined, [ value ].slice( noValue ) );
+		}
+
+	// For Promises/A+, convert exceptions into rejections
+	// Since jQuery.when doesn't unwrap thenables, we can skip the extra checks appearing in
+	// Deferred#then to conditionally suppress rejection.
+	} catch ( value ) {
+
+		// Support: Android 4.0 only
+		// Strict mode functions invoked without .call/.apply get global-object context
+		reject.apply( undefined, [ value ] );
+	}
+}
+
+jQuery.extend( {
+
+	Deferred: function( func ) {
+		var tuples = [
+
+				// action, add listener, callbacks,
+				// ... .then handlers, argument index, [final state]
+				[ "notify", "progress", jQuery.Callbacks( "memory" ),
+					jQuery.Callbacks( "memory" ), 2 ],
+				[ "resolve", "done", jQuery.Callbacks( "once memory" ),
+					jQuery.Callbacks( "once memory" ), 0, "resolved" ],
+				[ "reject", "fail", jQuery.Callbacks( "once memory" ),
+					jQuery.Callbacks( "once memory" ), 1, "rejected" ]
+			],
+			state = "pending",
+			promise = {
+				state: function() {
+					return state;
+				},
+				always: function() {
+					deferred.done( arguments ).fail( arguments );
+					return this;
+				},
+				"catch": function( fn ) {
+					return promise.then( null, fn );
+				},
+
+				// Keep pipe for back-compat
+				pipe: function( /* fnDone, fnFail, fnProgress */ ) {
+					var fns = arguments;
+
+					return jQuery.Deferred( function( newDefer ) {
+						jQuery.each( tuples, function( _i, tuple ) {
+
+							// Map tuples (progress, done, fail) to arguments (done, fail, progress)
+							var fn = isFunction( fns[ tuple[ 4 ] ] ) && fns[ tuple[ 4 ] ];
+
+							// deferred.progress(function() { bind to newDefer or newDefer.notify })
+							// deferred.done(function() { bind to newDefer or newDefer.resolve })
+							// deferred.fail(function() { bind to newDefer or newDefer.reject })
+							deferred[ tuple[ 1 ] ]( function() {
+								var returned = fn && fn.apply( this, arguments );
+								if ( returned && isFunction( returned.promise ) ) {
+									returned.promise()
+										.progress( newDefer.notify )
+										.done( newDefer.resolve )
+										.fail( newDefer.reject );
+								} else {
+									newDefer[ tuple[ 0 ] + "With" ](
+										this,
+										fn ? [ returned ] : arguments
+									);
+								}
+							} );
+						} );
+						fns = null;
+					} ).promise();
+				},
+				then: function( onFulfilled, onRejected, onProgress ) {
+					var maxDepth = 0;
+					function resolve( depth, deferred, handler, special ) {
+						return function() {
+							var that = this,
+								args = arguments,
+								mightThrow = function() {
+									var returned, then;
+
+									// Support: Promises/A+ section 2.3.3.3.3
+									// https://promisesaplus.com/#point-59
+									// Ignore double-resolution attempts
+									if ( depth < maxDepth ) {
+										return;
+									}
+
+									returned = handler.apply( that, args );
+
+									// Support: Promises/A+ section 2.3.1
+									// https://promisesaplus.com/#point-48
+									if ( returned === deferred.promise() ) {
+										throw new TypeError( "Thenable self-resolution" );
+									}
+
+									// Support: Promises/A+ sections 2.3.3.1, 3.5
+									// https://promisesaplus.com/#point-54
+									// https://promisesaplus.com/#point-75
+									// Retrieve `then` only once
+									then = returned &&
+
+										// Support: Promises/A+ section 2.3.4
+										// https://promisesaplus.com/#point-64
+										// Only check objects and functions for thenability
+										( typeof returned === "object" ||
+											typeof returned === "function" ) &&
+										returned.then;
+
+									// Handle a returned thenable
+									if ( isFunction( then ) ) {
+
+										// Special processors (notify) just wait for resolution
+										if ( special ) {
+											then.call(
+												returned,
+												resolve( maxDepth, deferred, Identity, special ),
+												resolve( maxDepth, deferred, Thrower, special )
+											);
+
+										// Normal processors (resolve) also hook into progress
+										} else {
+
+											// ...and disregard older resolution values
+											maxDepth++;
+
+											then.call(
+												returned,
+												resolve( maxDepth, deferred, Identity, special ),
+												resolve( maxDepth, deferred, Thrower, special ),
+												resolve( maxDepth, deferred, Identity,
+													deferred.notifyWith )
+											);
+										}
+
+									// Handle all other returned values
+									} else {
+
+										// Only substitute handlers pass on context
+										// and multiple values (non-spec behavior)
+										if ( handler !== Identity ) {
+											that = undefined;
+											args = [ returned ];
+										}
+
+										// Process the value(s)
+										// Default process is resolve
+										( special || deferred.resolveWith )( that, args );
+									}
+								},
+
+								// Only normal processors (resolve) catch and reject exceptions
+								process = special ?
+									mightThrow :
+									function() {
+										try {
+											mightThrow();
+										} catch ( e ) {
+
+											if ( jQuery.Deferred.exceptionHook ) {
+												jQuery.Deferred.exceptionHook( e,
+													process.stackTrace );
+											}
+
+											// Support: Promises/A+ section 2.3.3.3.4.1
+											// https://promisesaplus.com/#point-61
+											// Ignore post-resolution exceptions
+											if ( depth + 1 >= maxDepth ) {
+
+												// Only substitute handlers pass on context
+												// and multiple values (non-spec behavior)
+												if ( handler !== Thrower ) {
+													that = undefined;
+													args = [ e ];
+												}
+
+												deferred.rejectWith( that, args );
+											}
+										}
+									};
+
+							// Support: Promises/A+ section 2.3.3.3.1
+							// https://promisesaplus.com/#point-57
+							// Re-resolve promises immediately to dodge false rejection from
+							// subsequent errors
+							if ( depth ) {
+								process();
+							} else {
+
+								// Call an optional hook to record the stack, in case of exception
+								// since it's otherwise lost when execution goes async
+								if ( jQuery.Deferred.getStackHook ) {
+									process.stackTrace = jQuery.Deferred.getStackHook();
+								}
+								window.setTimeout( process );
+							}
+						};
+					}
+
+					return jQuery.Deferred( function( newDefer ) {
+
+						// progress_handlers.add( ... )
+						tuples[ 0 ][ 3 ].add(
+							resolve(
+								0,
+								newDefer,
+								isFunction( onProgress ) ?
+									onProgress :
+									Identity,
+								newDefer.notifyWith
+							)
+						);
+
+						// fulfilled_handlers.add( ... )
+						tuples[ 1 ][ 3 ].add(
+							resolve(
+								0,
+								newDefer,
+								isFunction( onFulfilled ) ?
+									onFulfilled :
+									Identity
+							)
+						);
+
+						// rejected_handlers.add( ... )
+						tuples[ 2 ][ 3 ].add(
+							resolve(
+								0,
+								newDefer,
+								isFunction( onRejected ) ?
+									onRejected :
+									Thrower
+							)
+						);
+					} ).promise();
+				},
+
+				// Get a promise for this deferred
+				// If obj is provided, the promise aspect is added to the object
+				promise: function( obj ) {
+					return obj != null ? jQuery.extend( obj, promise ) : promise;
+				}
+			},
+			deferred = {};
+
+		// Add list-specific methods
+		jQuery.each( tuples, function( i, tuple ) {
+			var list = tuple[ 2 ],
+				stateString = tuple[ 5 ];
+
+			// promise.progress = list.add
+			// promise.done = list.add
+			// promise.fail = list.add
+			promise[ tuple[ 1 ] ] = list.add;
+
+			// Handle state
+			if ( stateString ) {
+				list.add(
+					function() {
+
+						// state = "resolved" (i.e., fulfilled)
+						// state = "rejected"
+						state = stateString;
+					},
+
+					// rejected_callbacks.disable
+					// fulfilled_callbacks.disable
+					tuples[ 3 - i ][ 2 ].disable,
+
+					// rejected_handlers.disable
+					// fulfilled_handlers.disable
+					tuples[ 3 - i ][ 3 ].disable,
+
+					// progress_callbacks.lock
+					tuples[ 0 ][ 2 ].lock,
+
+					// progress_handlers.lock
+					tuples[ 0 ][ 3 ].lock
+				);
+			}
+
+			// progress_handlers.fire
+			// fulfilled_handlers.fire
+			// rejected_handlers.fire
+			list.add( tuple[ 3 ].fire );
+
+			// deferred.notify = function() { deferred.notifyWith(...) }
+			// deferred.resolve = function() { deferred.resolveWith(...) }
+			// deferred.reject = function() { deferred.rejectWith(...) }
+			deferred[ tuple[ 0 ] ] = function() {
+				deferred[ tuple[ 0 ] + "With" ]( this === deferred ? undefined : this, arguments );
+				return this;
+			};
+
+			// deferred.notifyWith = list.fireWith
+			// deferred.resolveWith = list.fireWith
+			// deferred.rejectWith = list.fireWith
+			deferred[ tuple[ 0 ] + "With" ] = list.fireWith;
+		} );
+
+		// Make the deferred a promise
+		promise.promise( deferred );
+
+		// Call given func if any
+		if ( func ) {
+			func.call( deferred, deferred );
+		}
+
+		// All done!
+		return deferred;
+	},
+
+	// Deferred helper
+	when: function( singleValue ) {
+		var
+
+			// count of uncompleted subordinates
+			remaining = arguments.length,
+
+			// count of unprocessed arguments
+			i = remaining,
+
+			// subordinate fulfillment data
+			resolveContexts = Array( i ),
+			resolveValues = slice.call( arguments ),
+
+			// the primary Deferred
+			primary = jQuery.Deferred(),
+
+			// subordinate callback factory
+			updateFunc = function( i ) {
+				return function( value ) {
+					resolveContexts[ i ] = this;
+					resolveValues[ i ] = arguments.length > 1 ? slice.call( arguments ) : value;
+					if ( !( --remaining ) ) {
+						primary.resolveWith( resolveContexts, resolveValues );
+					}
+				};
+			};
+
+		// Single- and empty arguments are adopted like Promise.resolve
+		if ( remaining <= 1 ) {
+			adoptValue( singleValue, primary.done( updateFunc( i ) ).resolve, primary.reject,
+				!remaining );
+
+			// Use .then() to unwrap secondary thenables (cf. gh-3000)
+			if ( primary.state() === "pending" ||
+				isFunction( resolveValues[ i ] && resolveValues[ i ].then ) ) {
+
+				return primary.then();
+			}
+		}
+
+		// Multiple arguments are aggregated like Promise.all array elements
+		while ( i-- ) {
+			adoptValue( resolveValues[ i ], updateFunc( i ), primary.reject );
+		}
+
+		return primary.promise();
+	}
+} );
+
+
+// These usually indicate a programmer mistake during development,
+// warn about them ASAP rather than swallowing them by default.
+var rerrorNames = /^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/;
+
+jQuery.Deferred.exceptionHook = function( error, stack ) {
+
+	// Support: IE 8 - 9 only
+	// Console exists when dev tools are open, which can happen at any time
+	if ( window.console && window.console.warn && error && rerrorNames.test( error.name ) ) {
+		window.console.warn( "jQuery.Deferred exception: " + error.message, error.stack, stack );
+	}
+};
+
+
+
+
+jQuery.readyException = function( error ) {
+	window.setTimeout( function() {
+		throw error;
+	} );
+};
+
+
+
+
+// The deferred used on DOM ready
+var readyList = jQuery.Deferred();
+
+jQuery.fn.ready = function( fn ) {
+
+	readyList
+		.then( fn )
+
+		// Wrap jQuery.readyException in a function so that the lookup
+		// happens at the time of error handling instead of callback
+		// registration.
+		.catch( function( error ) {
+			jQuery.readyException( error );
+		} );
+
+	return this;
+};
+
+jQuery.extend( {
+
+	// Is the DOM ready to be used? Set to true once it occurs.
+	isReady: false,
+
+	// A counter to track how many items to wait for before
+	// the ready event fires. See #6781
+	readyWait: 1,
+
+	// Handle when the DOM is ready
+	ready: function( wait ) {
+
+		// Abort if there are pending holds or we're already ready
+		if ( wait === true ? --jQuery.readyWait : jQuery.isReady ) {
+			return;
+		}
+
+		// Remember that the DOM is ready
+		jQuery.isReady = true;
+
+		// If a normal DOM Ready event fired, decrement, and wait if need be
+		if ( wait !== true && --jQuery.readyWait > 0 ) {
+			return;
+		}
+
+		// If there are functions bound, to execute
+		readyList.resolveWith( document, [ jQuery ] );
+	}
+} );
+
+jQuery.ready.then = readyList.then;
+
+// The ready event handler and self cleanup method
+function completed() {
+	document.removeEventListener( "DOMContentLoaded", completed );
+	window.removeEventListener( "load", completed );
+	jQuery.ready();
+}
+
+// Catch cases where $(document).ready() is called
+// after the browser event has already occurred.
+// Support: IE <=9 - 10 only
+// Older IE sometimes signals "interactive" too soon
+if ( document.readyState === "complete" ||
+	( document.readyState !== "loading" && !document.documentElement.doScroll ) ) {
+
+	// Handle it asynchronously to allow scripts the opportunity to delay ready
+	window.setTimeout( jQuery.ready );
+
+} else {
+
+	// Use the handy event callback
+	document.addEventListener( "DOMContentLoaded", completed );
+
+	// A fallback to window.onload, that will always work
+	window.addEventListener( "load", completed );
+}
+
+
+
+
+// Multifunctional method to get and set values of a collection
+// The value/s can optionally be executed if it's a function
+var access = function( elems, fn, key, value, chainable, emptyGet, raw ) {
+	var i = 0,
+		len = elems.length,
+		bulk = key == null;
+
+	// Sets many values
+	if ( toType( key ) === "object" ) {
+		chainable = true;
+		for ( i in key ) {
+			access( elems, fn, i, key[ i ], true, emptyGet, raw );
+		}
+
+	// Sets one value
+	} else if ( value !== undefined ) {
+		chainable = true;
+
+		if ( !isFunction( value ) ) {
+			raw = true;
+		}
+
+		if ( bulk ) {
+
+			// Bulk operations run against the entire set
+			if ( raw ) {
+				fn.call( elems, value );
+				fn = null;
+
+			// ...except when executing function values
+			} else {
+				bulk = fn;
+				fn = function( elem, _key, value ) {
+					return bulk.call( jQuery( elem ), value );
+				};
+			}
+		}
+
+		if ( fn ) {
+			for ( ; i < len; i++ ) {
+				fn(
+					elems[ i ], key, raw ?
+						value :
+						value.call( elems[ i ], i, fn( elems[ i ], key ) )
+				);
+			}
+		}
+	}
+
+	if ( chainable ) {
+		return elems;
+	}
+
+	// Gets
+	if ( bulk ) {
+		return fn.call( elems );
+	}
+
+	return len ? fn( elems[ 0 ], key ) : emptyGet;
+};
+
+
+// Matches dashed string for camelizing
+var rmsPrefix = /^-ms-/,
+	rdashAlpha = /-([a-z])/g;
+
+// Used by camelCase as callback to replace()
+function fcamelCase( _all, letter ) {
+	return letter.toUpperCase();
+}
+
+// Convert dashed to camelCase; used by the css and data modules
+// Support: IE <=9 - 11, Edge 12 - 15
+// Microsoft forgot to hump their vendor prefix (#9572)
+function camelCase( string ) {
+	return string.replace( rmsPrefix, "ms-" ).replace( rdashAlpha, fcamelCase );
+}
+var acceptData = function( owner ) {
+
+	// Accepts only:
+	//  - Node
+	//    - Node.ELEMENT_NODE
+	//    - Node.DOCUMENT_NODE
+	//  - Object
+	//    - Any
+	return owner.nodeType === 1 || owner.nodeType === 9 || !( +owner.nodeType );
+};
+
+
+
+
+function Data() {
+	this.expando = jQuery.expando + Data.uid++;
+}
+
+Data.uid = 1;
+
+Data.prototype = {
+
+	cache: function( owner ) {
+
+		// Check if the owner object already has a cache
+		var value = owner[ this.expando ];
+
+		// If not, create one
+		if ( !value ) {
+			value = {};
+
+			// We can accept data for non-element nodes in modern browsers,
+			// but we should not, see #8335.
+			// Always return an empty object.
+			if ( acceptData( owner ) ) {
+
+				// If it is a node unlikely to be stringify-ed or looped over
+				// use plain assignment
+				if ( owner.nodeType ) {
+					owner[ this.expando ] = value;
+
+				// Otherwise secure it in a non-enumerable property
+				// configurable must be true to allow the property to be
+				// deleted when data is removed
+				} else {
+					Object.defineProperty( owner, this.expando, {
+						value: value,
+						configurable: true
+					} );
+				}
+			}
+		}
+
+		return value;
+	},
+	set: function( owner, data, value ) {
+		var prop,
+			cache = this.cache( owner );
+
+		// Handle: [ owner, key, value ] args
+		// Always use camelCase key (gh-2257)
+		if ( typeof data === "string" ) {
+			cache[ camelCase( data ) ] = value;
+
+		// Handle: [ owner, { properties } ] args
+		} else {
+
+			// Copy the properties one-by-one to the cache object
+			for ( prop in data ) {
+				cache[ camelCase( prop ) ] = data[ prop ];
+			}
+		}
+		return cache;
+	},
+	get: function( owner, key ) {
+		return key === undefined ?
+			this.cache( owner ) :
+
+			// Always use camelCase key (gh-2257)
+			owner[ this.expando ] && owner[ this.expando ][ camelCase( key ) ];
+	},
+	access: function( owner, key, value ) {
+
+		// In cases where either:
+		//
+		//   1. No key was specified
+		//   2. A string key was specified, but no value provided
+		//
+		// Take the "read" path and allow the get method to determine
+		// which value to return, respectively either:
+		//
+		//   1. The entire cache object
+		//   2. The data stored at the key
+		//
+		if ( key === undefined ||
+				( ( key && typeof key === "string" ) && value === undefined ) ) {
+
+			return this.get( owner, key );
+		}
+
+		// When the key is not a string, or both a key and value
+		// are specified, set or extend (existing objects) with either:
+		//
+		//   1. An object of properties
+		//   2. A key and value
+		//
+		this.set( owner, key, value );
+
+		// Since the "set" path can have two possible entry points
+		// return the expected data based on which path was taken[*]
+		return value !== undefined ? value : key;
+	},
+	remove: function( owner, key ) {
+		var i,
+			cache = owner[ this.expando ];
+
+		if ( cache === undefined ) {
+			return;
+		}
+
+		if ( key !== undefined ) {
+
+			// Support array or space separated string of keys
+			if ( Array.isArray( key ) ) {
+
+				// If key is an array of keys...
+				// We always set camelCase keys, so remove that.
+				key = key.map( camelCase );
+			} else {
+				key = camelCase( key );
+
+				// If a key with the spaces exists, use it.
+				// Otherwise, create an array by matching non-whitespace
+				key = key in cache ?
+					[ key ] :
+					( key.match( rnothtmlwhite ) || [] );
+			}
+
+			i = key.length;
+
+			while ( i-- ) {
+				delete cache[ key[ i ] ];
+			}
+		}
+
+		// Remove the expando if there's no more data
+		if ( key === undefined || jQuery.isEmptyObject( cache ) ) {
+
+			// Support: Chrome <=35 - 45
+			// Webkit & Blink performance suffers when deleting properties
+			// from DOM nodes, so set to undefined instead
+			// https://bugs.chromium.org/p/chromium/issues/detail?id=378607 (bug restricted)
+			if ( owner.nodeType ) {
+				owner[ this.expando ] = undefined;
+			} else {
+				delete owner[ this.expando ];
+			}
+		}
+	},
+	hasData: function( owner ) {
+		var cache = owner[ this.expando ];
+		return cache !== undefined && !jQuery.isEmptyObject( cache );
+	}
+};
+var dataPriv = new Data();
+
+var dataUser = new Data();
+
+
+
+//	Implementation Summary
+//
+//	1. Enforce API surface and semantic compatibility with 1.9.x branch
+//	2. Improve the module's maintainability by reducing the storage
+//		paths to a single mechanism.
+//	3. Use the same single mechanism to support "private" and "user" data.
+//	4. _Never_ expose "private" data to user code (TODO: Drop _data, _removeData)
+//	5. Avoid exposing implementation details on user objects (eg. expando properties)
+//	6. Provide a clear path for implementation upgrade to WeakMap in 2014
+
+var rbrace = /^(?:\{[\w\W]*\}|\[[\w\W]*\])$/,
+	rmultiDash = /[A-Z]/g;
+
+function getData( data ) {
+	if ( data === "true" ) {
+		return true;
+	}
+
+	if ( data === "false" ) {
+		return false;
+	}
+
+	if ( data === "null" ) {
+		return null;
+	}
+
+	// Only convert to a number if it doesn't change the string
+	if ( data === +data + "" ) {
+		return +data;
+	}
+
+	if ( rbrace.test( data ) ) {
+		return JSON.parse( data );
+	}
+
+	return data;
+}
+
+function dataAttr( elem, key, data ) {
+	var name;
+
+	// If nothing was found internally, try to fetch any
+	// data from the HTML5 data-* attribute
+	if ( data === undefined && elem.nodeType === 1 ) {
+		name = "data-" + key.replace( rmultiDash, "-$&" ).toLowerCase();
+		data = elem.getAttribute( name );
+
+		if ( typeof data === "string" ) {
+			try {
+				data = getData( data );
+			} catch ( e ) {}
+
+			// Make sure we set the data so it isn't changed later
+			dataUser.set( elem, key, data );
+		} else {
+			data = undefined;
+		}
+	}
+	return data;
+}
+
+jQuery.extend( {
+	hasData: function( elem ) {
+		return dataUser.hasData( elem ) || dataPriv.hasData( elem );
+	},
+
+	data: function( elem, name, data ) {
+		return dataUser.access( elem, name, data );
+	},
+
+	removeData: function( elem, name ) {
+		dataUser.remove( elem, name );
+	},
+
+	// TODO: Now that all calls to _data and _removeData have been replaced
+	// with direct calls to dataPriv methods, these can be deprecated.
+	_data: function( elem, name, data ) {
+		return dataPriv.access( elem, name, data );
+	},
+
+	_removeData: function( elem, name ) {
+		dataPriv.remove( elem, name );
+	}
+} );
+
+jQuery.fn.extend( {
+	data: function( key, value ) {
+		var i, name, data,
+			elem = this[ 0 ],
+			attrs = elem && elem.attributes;
+
+		// Gets all values
+		if ( key === undefined ) {
+			if ( this.length ) {
+				data = dataUser.get( elem );
+
+				if ( elem.nodeType === 1 && !dataPriv.get( elem, "hasDataAttrs" ) ) {
+					i = attrs.length;
+					while ( i-- ) {
+
+						// Support: IE 11 only
+						// The attrs elements can be null (#14894)
+						if ( attrs[ i ] ) {
+							name = attrs[ i ].name;
+							if ( name.indexOf( "data-" ) === 0 ) {
+								name = camelCase( name.slice( 5 ) );
+								dataAttr( elem, name, data[ name ] );
+							}
+						}
+					}
+					dataPriv.set( elem, "hasDataAttrs", true );
+				}
+			}
+
+			return data;
+		}
+
+		// Sets multiple values
+		if ( typeof key === "object" ) {
+			return this.each( function() {
+				dataUser.set( this, key );
+			} );
+		}
+
+		return access( this, function( value ) {
+			var data;
+
+			// The calling jQuery object (element matches) is not empty
+			// (and therefore has an element appears at this[ 0 ]) and the
+			// `value` parameter was not undefined. An empty jQuery object
+			// will result in `undefined` for elem = this[ 0 ] which will
+			// throw an exception if an attempt to read a data cache is made.
+			if ( elem && value === undefined ) {
+
+				// Attempt to get data from the cache
+				// The key will always be camelCased in Data
+				data = dataUser.get( elem, key );
+				if ( data !== undefined ) {
+					return data;
+				}
+
+				// Attempt to "discover" the data in
+				// HTML5 custom data-* attrs
+				data = dataAttr( elem, key );
+				if ( data !== undefined ) {
+					return data;
+				}
+
+				// We tried really hard, but the data doesn't exist.
+				return;
+			}
+
+			// Set the data...
+			this.each( function() {
+
+				// We always store the camelCased key
+				dataUser.set( this, key, value );
+			} );
+		}, null, value, arguments.length > 1, null, true );
+	},
+
+	removeData: function( key ) {
+		return this.each( function() {
+			dataUser.remove( this, key );
+		} );
+	}
+} );
+
+
+jQuery.extend( {
+	queue: function( elem, type, data ) {
+		var queue;
+
+		if ( elem ) {
+			type = ( type || "fx" ) + "queue";
+			queue = dataPriv.get( elem, type );
+
+			// Speed up dequeue by getting out quickly if this is just a lookup
+			if ( data ) {
+				if ( !queue || Array.isArray( data ) ) {
+					queue = dataPriv.access( elem, type, jQuery.makeArray( data ) );
+				} else {
+					queue.push( data );
+				}
+			}
+			return queue || [];
+		}
+	},
+
+	dequeue: function( elem, type ) {
+		type = type || "fx";
+
+		var queue = jQuery.queue( elem, type ),
+			startLength = queue.length,
+			fn = queue.shift(),
+			hooks = jQuery._queueHooks( elem, type ),
+			next = function() {
+				jQuery.dequeue( elem, type );
+			};
+
+		// If the fx queue is dequeued, always remove the progress sentinel
+		if ( fn === "inprogress" ) {
+			fn = queue.shift();
+			startLength--;
+		}
+
+		if ( fn ) {
+
+			// Add a progress sentinel to prevent the fx queue from being
+			// automatically dequeued
+			if ( type === "fx" ) {
+				queue.unshift( "inprogress" );
+			}
+
+			// Clear up the last queue stop function
+			delete hooks.stop;
+			fn.call( elem, next, hooks );
+		}
+
+		if ( !startLength && hooks ) {
+			hooks.empty.fire();
+		}
+	},
+
+	// Not public - generate a queueHooks object, or return the current one
+	_queueHooks: function( elem, type ) {
+		var key = type + "queueHooks";
+		return dataPriv.get( elem, key ) || dataPriv.access( elem, key, {
+			empty: jQuery.Callbacks( "once memory" ).add( function() {
+				dataPriv.remove( elem, [ type + "queue", key ] );
+			} )
+		} );
+	}
+} );
+
+jQuery.fn.extend( {
+	queue: function( type, data ) {
+		var setter = 2;
+
+		if ( typeof type !== "string" ) {
+			data = type;
+			type = "fx";
+			setter--;
+		}
+
+		if ( arguments.length < setter ) {
+			return jQuery.queue( this[ 0 ], type );
+		}
+
+		return data === undefined ?
+			this :
+			this.each( function() {
+				var queue = jQuery.queue( this, type, data );
+
+				// Ensure a hooks for this queue
+				jQuery._queueHooks( this, type );
+
+				if ( type === "fx" && queue[ 0 ] !== "inprogress" ) {
+					jQuery.dequeue( this, type );
+				}
+			} );
+	},
+	dequeue: function( type ) {
+		return this.each( function() {
+			jQuery.dequeue( this, type );
+		} );
+	},
+	clearQueue: function( type ) {
+		return this.queue( type || "fx", [] );
+	},
+
+	// Get a promise resolved when queues of a certain type
+	// are emptied (fx is the type by default)
+	promise: function( type, obj ) {
+		var tmp,
+			count = 1,
+			defer = jQuery.Deferred(),
+			elements = this,
+			i = this.length,
+			resolve = function() {
+				if ( !( --count ) ) {
+					defer.resolveWith( elements, [ elements ] );
+				}
+			};
+
+		if ( typeof type !== "string" ) {
+			obj = type;
+			type = undefined;
+		}
+		type = type || "fx";
+
+		while ( i-- ) {
+			tmp = dataPriv.get( elements[ i ], type + "queueHooks" );
+			if ( tmp && tmp.empty ) {
+				count++;
+				tmp.empty.add( resolve );
+			}
+		}
+		resolve();
+		return defer.promise( obj );
+	}
+} );
+var pnum = ( /[+-]?(?:\d*\.|)\d+(?:[eE][+-]?\d+|)/ ).source;
+
+var rcssNum = new RegExp( "^(?:([+-])=|)(" + pnum + ")([a-z%]*)$", "i" );
+
+
+var cssExpand = [ "Top", "Right", "Bottom", "Left" ];
+
+var documentElement = document.documentElement;
+
+
+
+	var isAttached = function( elem ) {
+			return jQuery.contains( elem.ownerDocument, elem );
+		},
+		composed = { composed: true };
+
+	// Support: IE 9 - 11+, Edge 12 - 18+, iOS 10.0 - 10.2 only
+	// Check attachment across shadow DOM boundaries when possible (gh-3504)
+	// Support: iOS 10.0-10.2 only
+	// Early iOS 10 versions support `attachShadow` but not `getRootNode`,
+	// leading to errors. We need to check for `getRootNode`.
+	if ( documentElement.getRootNode ) {
+		isAttached = function( elem ) {
+			return jQuery.contains( elem.ownerDocument, elem ) ||
+				elem.getRootNode( composed ) === elem.ownerDocument;
+		};
+	}
+var isHiddenWithinTree = function( elem, el ) {
+
+		// isHiddenWithinTree might be called from jQuery#filter function;
+		// in that case, element will be second argument
+		elem = el || elem;
+
+		// Inline style trumps all
+		return elem.style.display === "none" ||
+			elem.style.display === "" &&
+
+			// Otherwise, check computed style
+			// Support: Firefox <=43 - 45
+			// Disconnected elements can have computed display: none, so first confirm that elem is
+			// in the document.
+			isAttached( elem ) &&
+
+			jQuery.css( elem, "display" ) === "none";
+	};
+
+
+
+function adjustCSS( elem, prop, valueParts, tween ) {
+	var adjusted, scale,
+		maxIterations = 20,
+		currentValue = tween ?
+			function() {
+				return tween.cur();
+			} :
+			function() {
+				return jQuery.css( elem, prop, "" );
+			},
+		initial = currentValue(),
+		unit = valueParts && valueParts[ 3 ] || ( jQuery.cssNumber[ prop ] ? "" : "px" ),
+
+		// Starting value computation is required for potential unit mismatches
+		initialInUnit = elem.nodeType &&
+			( jQuery.cssNumber[ prop ] || unit !== "px" && +initial ) &&
+			rcssNum.exec( jQuery.css( elem, prop ) );
+
+	if ( initialInUnit && initialInUnit[ 3 ] !== unit ) {
+
+		// Support: Firefox <=54
+		// Halve the iteration target value to prevent interference from CSS upper bounds (gh-2144)
+		initial = initial / 2;
+
+		// Trust units reported by jQuery.css
+		unit = unit || initialInUnit[ 3 ];
+
+		// Iteratively approximate from a nonzero starting point
+		initialInUnit = +initial || 1;
+
+		while ( maxIterations-- ) {
+
+			// Evaluate and update our best guess (doubling guesses that zero out).
+			// Finish if the scale equals or crosses 1 (making the old*new product non-positive).
+			jQuery.style( elem, prop, initialInUnit + unit );
+			if ( ( 1 - scale ) * ( 1 - ( scale = currentValue() / initial || 0.5 ) ) <= 0 ) {
+				maxIterations = 0;
+			}
+			initialInUnit = initialInUnit / scale;
+
+		}
+
+		initialInUnit = initialInUnit * 2;
+		jQuery.style( elem, prop, initialInUnit + unit );
+
+		// Make sure we update the tween properties later on
+		valueParts = valueParts || [];
+	}
+
+	if ( valueParts ) {
+		initialInUnit = +initialInUnit || +initial || 0;
+
+		// Apply relative offset (+=/-=) if specified
+		adjusted = valueParts[ 1 ] ?
+			initialInUnit + ( valueParts[ 1 ] + 1 ) * valueParts[ 2 ] :
+			+valueParts[ 2 ];
+		if ( tween ) {
+			tween.unit = unit;
+			tween.start = initialInUnit;
+			tween.end = adjusted;
+		}
+	}
+	return adjusted;
+}
+
+
+var defaultDisplayMap = {};
+
+function getDefaultDisplay( elem ) {
+	var temp,
+		doc = elem.ownerDocument,
+		nodeName = elem.nodeName,
+		display = defaultDisplayMap[ nodeName ];
+
+	if ( display ) {
+		return display;
+	}
+
+	temp = doc.body.appendChild( doc.createElement( nodeName ) );
+	display = jQuery.css( temp, "display" );
+
+	temp.parentNode.removeChild( temp );
+
+	if ( display === "none" ) {
+		display = "block";
+	}
+	defaultDisplayMap[ nodeName ] = display;
+
+	return display;
+}
+
+function showHide( elements, show ) {
+	var display, elem,
+		values = [],
+		index = 0,
+		length = elements.length;
+
+	// Determine new display value for elements that need to change
+	for ( ; index < length; index++ ) {
+		elem = elements[ index ];
+		if ( !elem.style ) {
+			continue;
+		}
+
+		display = elem.style.display;
+		if ( show ) {
+
+			// Since we force visibility upon cascade-hidden elements, an immediate (and slow)
+			// check is required in this first loop unless we have a nonempty display value (either
+			// inline or about-to-be-restored)
+			if ( display === "none" ) {
+				values[ index ] = dataPriv.get( elem, "display" ) || null;
+				if ( !values[ index ] ) {
+					elem.style.display = "";
+				}
+			}
+			if ( elem.style.display === "" && isHiddenWithinTree( elem ) ) {
+				values[ index ] = getDefaultDisplay( elem );
+			}
+		} else {
+			if ( display !== "none" ) {
+				values[ index ] = "none";
+
+				// Remember what we're overwriting
+				dataPriv.set( elem, "display", display );
+			}
+		}
+	}
+
+	// Set the display of the elements in a second loop to avoid constant reflow
+	for ( index = 0; index < length; index++ ) {
+		if ( values[ index ] != null ) {
+			elements[ index ].style.display = values[ index ];
+		}
+	}
+
+	return elements;
+}
+
+jQuery.fn.extend( {
+	show: function() {
+		return showHide( this, true );
+	},
+	hide: function() {
+		return showHide( this );
+	},
+	toggle: function( state ) {
+		if ( typeof state === "boolean" ) {
+			return state ? this.show() : this.hide();
+		}
+
+		return this.each( function() {
+			if ( isHiddenWithinTree( this ) ) {
+				jQuery( this ).show();
+			} else {
+				jQuery( this ).hide();
+			}
+		} );
+	}
+} );
+var rcheckableType = ( /^(?:checkbox|radio)$/i );
+
+var rtagName = ( /<([a-z][^\/\0>\x20\t\r\n\f]*)/i );
+
+var rscriptType = ( /^$|^module$|\/(?:java|ecma)script/i );
+
+
+
+( function() {
+	var fragment = document.createDocumentFragment(),
+		div = fragment.appendChild( document.createElement( "div" ) ),
+		input = document.createElement( "input" );
+
+	// Support: Android 4.0 - 4.3 only
+	// Check state lost if the name is set (#11217)
+	// Support: Windows Web Apps (WWA)
+	// `name` and `type` must use .setAttribute for WWA (#14901)
+	input.setAttribute( "type", "radio" );
+	input.setAttribute( "checked", "checked" );
+	input.setAttribute( "name", "t" );
+
+	div.appendChild( input );
+
+	// Support: Android <=4.1 only
+	// Older WebKit doesn't clone checked state correctly in fragments
+	support.checkClone = div.cloneNode( true ).cloneNode( true ).lastChild.checked;
+
+	// Support: IE <=11 only
+	// Make sure textarea (and checkbox) defaultValue is properly cloned
+	div.innerHTML = "<textarea>x</textarea>";
+	support.noCloneChecked = !!div.cloneNode( true ).lastChild.defaultValue;
+
+	// Support: IE <=9 only
+	// IE <=9 replaces <option> tags with their contents when inserted outside of
+	// the select element.
+	div.innerHTML = "<option></option>";
+	support.option = !!div.lastChild;
+} )();
+
+
+// We have to close these tags to support XHTML (#13200)
+var wrapMap = {
+
+	// XHTML parsers do not magically insert elements in the
+	// same way that tag soup parsers do. So we cannot shorten
+	// this by omitting <tbody> or other required elements.
+	thead: [ 1, "<table>", "</table>" ],
+	col: [ 2, "<table><colgroup>", "</colgroup></table>" ],
+	tr: [ 2, "<table><tbody>", "</tbody></table>" ],
+	td: [ 3, "<table><tbody><tr>", "</tr></tbody></table>" ],
+
+	_default: [ 0, "", "" ]
+};
+
+wrapMap.tbody = wrapMap.tfoot = wrapMap.colgroup = wrapMap.caption = wrapMap.thead;
+wrapMap.th = wrapMap.td;
+
+// Support: IE <=9 only
+if ( !support.option ) {
+	wrapMap.optgroup = wrapMap.option = [ 1, "<select multiple='multiple'>", "</select>" ];
+}
+
+
+function getAll( context, tag ) {
+
+	// Support: IE <=9 - 11 only
+	// Use typeof to avoid zero-argument method invocation on host objects (#15151)
+	var ret;
+
+	if ( typeof context.getElementsByTagName !== "undefined" ) {
+		ret = context.getElementsByTagName( tag || "*" );
+
+	} else if ( typeof context.querySelectorAll !== "undefined" ) {
+		ret = context.querySelectorAll( tag || "*" );
+
+	} else {
+		ret = [];
+	}
+
+	if ( tag === undefined || tag && nodeName( context, tag ) ) {
+		return jQuery.merge( [ context ], ret );
+	}
+
+	return ret;
+}
+
+
+// Mark scripts as having already been evaluated
+function setGlobalEval( elems, refElements ) {
+	var i = 0,
+		l = elems.length;
+
+	for ( ; i < l; i++ ) {
+		dataPriv.set(
+			elems[ i ],
+			"globalEval",
+			!refElements || dataPriv.get( refElements[ i ], "globalEval" )
+		);
+	}
+}
+
+
+var rhtml = /<|&#?\w+;/;
+
+function buildFragment( elems, context, scripts, selection, ignored ) {
+	var elem, tmp, tag, wrap, attached, j,
+		fragment = context.createDocumentFragment(),
+		nodes = [],
+		i = 0,
+		l = elems.length;
+
+	for ( ; i < l; i++ ) {
+		elem = elems[ i ];
+
+		if ( elem || elem === 0 ) {
+
+			// Add nodes directly
+			if ( toType( elem ) === "object" ) {
+
+				// Support: Android <=4.0 only, PhantomJS 1 only
+				// push.apply(_, arraylike) throws on ancient WebKit
+				jQuery.merge( nodes, elem.nodeType ? [ elem ] : elem );
+
+			// Convert non-html into a text node
+			} else if ( !rhtml.test( elem ) ) {
+				nodes.push( context.createTextNode( elem ) );
+
+			// Convert html into DOM nodes
+			} else {
+				tmp = tmp || fragment.appendChild( context.createElement( "div" ) );
+
+				// Deserialize a standard representation
+				tag = ( rtagName.exec( elem ) || [ "", "" ] )[ 1 ].toLowerCase();
+				wrap = wrapMap[ tag ] || wrapMap._default;
+				tmp.innerHTML = wrap[ 1 ] + jQuery.htmlPrefilter( elem ) + wrap[ 2 ];
+
+				// Descend through wrappers to the right content
+				j = wrap[ 0 ];
+				while ( j-- ) {
+					tmp = tmp.lastChild;
+				}
+
+				// Support: Android <=4.0 only, PhantomJS 1 only
+				// push.apply(_, arraylike) throws on ancient WebKit
+				jQuery.merge( nodes, tmp.childNodes );
+
+				// Remember the top-level container
+				tmp = fragment.firstChild;
+
+				// Ensure the created nodes are orphaned (#12392)
+				tmp.textContent = "";
+			}
+		}
+	}
+
+	// Remove wrapper from fragment
+	fragment.textContent = "";
+
+	i = 0;
+	while ( ( elem = nodes[ i++ ] ) ) {
+
+		// Skip elements already in the context collection (trac-4087)
+		if ( selection && jQuery.inArray( elem, selection ) > -1 ) {
+			if ( ignored ) {
+				ignored.push( elem );
+			}
+			continue;
+		}
+
+		attached = isAttached( elem );
+
+		// Append to fragment
+		tmp = getAll( fragment.appendChild( elem ), "script" );
+
+		// Preserve script evaluation history
+		if ( attached ) {
+			setGlobalEval( tmp );
+		}
+
+		// Capture executables
+		if ( scripts ) {
+			j = 0;
+			while ( ( elem = tmp[ j++ ] ) ) {
+				if ( rscriptType.test( elem.type || "" ) ) {
+					scripts.push( elem );
+				}
+			}
+		}
+	}
+
+	return fragment;
+}
+
+
+var rtypenamespace = /^([^.]*)(?:\.(.+)|)/;
+
+function returnTrue() {
+	return true;
+}
+
+function returnFalse() {
+	return false;
+}
+
+// Support: IE <=9 - 11+
+// focus() and blur() are asynchronous, except when they are no-op.
+// So expect focus to be synchronous when the element is already active,
+// and blur to be synchronous when the element is not already active.
+// (focus and blur are always synchronous in other supported browsers,
+// this just defines when we can count on it).
+function expectSync( elem, type ) {
+	return ( elem === safeActiveElement() ) === ( type === "focus" );
+}
+
+// Support: IE <=9 only
+// Accessing document.activeElement can throw unexpectedly
+// https://bugs.jquery.com/ticket/13393
+function safeActiveElement() {
+	try {
+		return document.activeElement;
+	} catch ( err ) { }
+}
+
+function on( elem, types, selector, data, fn, one ) {
+	var origFn, type;
+
+	// Types can be a map of types/handlers
+	if ( typeof types === "object" ) {
+
+		// ( types-Object, selector, data )
+		if ( typeof selector !== "string" ) {
+
+			// ( types-Object, data )
+			data = data || selector;
+			selector = undefined;
+		}
+		for ( type in types ) {
+			on( elem, type, selector, data, types[ type ], one );
+		}
+		return elem;
+	}
+
+	if ( data == null && fn == null ) {
+
+		// ( types, fn )
+		fn = selector;
+		data = selector = undefined;
+	} else if ( fn == null ) {
+		if ( typeof selector === "string" ) {
+
+			// ( types, selector, fn )
+			fn = data;
+			data = undefined;
+		} else {
+
+			// ( types, data, fn )
+			fn = data;
+			data = selector;
+			selector = undefined;
+		}
+	}
+	if ( fn === false ) {
+		fn = returnFalse;
+	} else if ( !fn ) {
+		return elem;
+	}
+
+	if ( one === 1 ) {
+		origFn = fn;
+		fn = function( event ) {
+
+			// Can use an empty set, since event contains the info
+			jQuery().off( event );
+			return origFn.apply( this, arguments );
+		};
+
+		// Use same guid so caller can remove using origFn
+		fn.guid = origFn.guid || ( origFn.guid = jQuery.guid++ );
+	}
+	return elem.each( function() {
+		jQuery.event.add( this, types, fn, data, selector );
+	} );
+}
+
+/*
+ * Helper functions for managing events -- not part of the public interface.
+ * Props to Dean Edwards' addEvent library for many of the ideas.
+ */
+jQuery.event = {
+
+	global: {},
+
+	add: function( elem, types, handler, data, selector ) {
+
+		var handleObjIn, eventHandle, tmp,
+			events, t, handleObj,
+			special, handlers, type, namespaces, origType,
+			elemData = dataPriv.get( elem );
+
+		// Only attach events to objects that accept data
+		if ( !acceptData( elem ) ) {
+			return;
+		}
+
+		// Caller can pass in an object of custom data in lieu of the handler
+		if ( handler.handler ) {
+			handleObjIn = handler;
+			handler = handleObjIn.handler;
+			selector = handleObjIn.selector;
+		}
+
+		// Ensure that invalid selectors throw exceptions at attach time
+		// Evaluate against documentElement in case elem is a non-element node (e.g., document)
+		if ( selector ) {
+			jQuery.find.matchesSelector( documentElement, selector );
+		}
+
+		// Make sure that the handler has a unique ID, used to find/remove it later
+		if ( !handler.guid ) {
+			handler.guid = jQuery.guid++;
+		}
+
+		// Init the element's event structure and main handler, if this is the first
+		if ( !( events = elemData.events ) ) {
+			events = elemData.events = Object.create( null );
+		}
+		if ( !( eventHandle = elemData.handle ) ) {
+			eventHandle = elemData.handle = function( e ) {
+
+				// Discard the second event of a jQuery.event.trigger() and
+				// when an event is called after a page has unloaded
+				return typeof jQuery !== "undefined" && jQuery.event.triggered !== e.type ?
+					jQuery.event.dispatch.apply( elem, arguments ) : undefined;
+			};
+		}
+
+		// Handle multiple events separated by a space
+		types = ( types || "" ).match( rnothtmlwhite ) || [ "" ];
+		t = types.length;
+		while ( t-- ) {
+			tmp = rtypenamespace.exec( types[ t ] ) || [];
+			type = origType = tmp[ 1 ];
+			namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort();
+
+			// There *must* be a type, no attaching namespace-only handlers
+			if ( !type ) {
+				continue;
+			}
+
+			// If event changes its type, use the special event handlers for the changed type
+			special = jQuery.event.special[ type ] || {};
+
+			// If selector defined, determine special event api type, otherwise given type
+			type = ( selector ? special.delegateType : special.bindType ) || type;
+
+			// Update special based on newly reset type
+			special = jQuery.event.special[ type ] || {};
+
+			// handleObj is passed to all event handlers
+			handleObj = jQuery.extend( {
+				type: type,
+				origType: origType,
+				data: data,
+				handler: handler,
+				guid: handler.guid,
+				selector: selector,
+				needsContext: selector && jQuery.expr.match.needsContext.test( selector ),
+				namespace: namespaces.join( "." )
+			}, handleObjIn );
+
+			// Init the event handler queue if we're the first
+			if ( !( handlers = events[ type ] ) ) {
+				handlers = events[ type ] = [];
+				handlers.delegateCount = 0;
+
+				// Only use addEventListener if the special events handler returns false
+				if ( !special.setup ||
+					special.setup.call( elem, data, namespaces, eventHandle ) === false ) {
+
+					if ( elem.addEventListener ) {
+						elem.addEventListener( type, eventHandle );
+					}
+				}
+			}
+
+			if ( special.add ) {
+				special.add.call( elem, handleObj );
+
+				if ( !handleObj.handler.guid ) {
+					handleObj.handler.guid = handler.guid;
+				}
+			}
+
+			// Add to the element's handler list, delegates in front
+			if ( selector ) {
+				handlers.splice( handlers.delegateCount++, 0, handleObj );
+			} else {
+				handlers.push( handleObj );
+			}
+
+			// Keep track of which events have ever been used, for event optimization
+			jQuery.event.global[ type ] = true;
+		}
+
+	},
+
+	// Detach an event or set of events from an element
+	remove: function( elem, types, handler, selector, mappedTypes ) {
+
+		var j, origCount, tmp,
+			events, t, handleObj,
+			special, handlers, type, namespaces, origType,
+			elemData = dataPriv.hasData( elem ) && dataPriv.get( elem );
+
+		if ( !elemData || !( events = elemData.events ) ) {
+			return;
+		}
+
+		// Once for each type.namespace in types; type may be omitted
+		types = ( types || "" ).match( rnothtmlwhite ) || [ "" ];
+		t = types.length;
+		while ( t-- ) {
+			tmp = rtypenamespace.exec( types[ t ] ) || [];
+			type = origType = tmp[ 1 ];
+			namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort();
+
+			// Unbind all events (on this namespace, if provided) for the element
+			if ( !type ) {
+				for ( type in events ) {
+					jQuery.event.remove( elem, type + types[ t ], handler, selector, true );
+				}
+				continue;
+			}
+
+			special = jQuery.event.special[ type ] || {};
+			type = ( selector ? special.delegateType : special.bindType ) || type;
+			handlers = events[ type ] || [];
+			tmp = tmp[ 2 ] &&
+				new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" );
+
+			// Remove matching events
+			origCount = j = handlers.length;
+			while ( j-- ) {
+				handleObj = handlers[ j ];
+
+				if ( ( mappedTypes || origType === handleObj.origType ) &&
+					( !handler || handler.guid === handleObj.guid ) &&
+					( !tmp || tmp.test( handleObj.namespace ) ) &&
+					( !selector || selector === handleObj.selector ||
+						selector === "**" && handleObj.selector ) ) {
+					handlers.splice( j, 1 );
+
+					if ( handleObj.selector ) {
+						handlers.delegateCount--;
+					}
+					if ( special.remove ) {
+						special.remove.call( elem, handleObj );
+					}
+				}
+			}
+
+			// Remove generic event handler if we removed something and no more handlers exist
+			// (avoids potential for endless recursion during removal of special event handlers)
+			if ( origCount && !handlers.length ) {
+				if ( !special.teardown ||
+					special.teardown.call( elem, namespaces, elemData.handle ) === false ) {
+
+					jQuery.removeEvent( elem, type, elemData.handle );
+				}
+
+				delete events[ type ];
+			}
+		}
+
+		// Remove data and the expando if it's no longer used
+		if ( jQuery.isEmptyObject( events ) ) {
+			dataPriv.remove( elem, "handle events" );
+		}
+	},
+
+	dispatch: function( nativeEvent ) {
+
+		var i, j, ret, matched, handleObj, handlerQueue,
+			args = new Array( arguments.length ),
+
+			// Make a writable jQuery.Event from the native event object
+			event = jQuery.event.fix( nativeEvent ),
+
+			handlers = (
+				dataPriv.get( this, "events" ) || Object.create( null )
+			)[ event.type ] || [],
+			special = jQuery.event.special[ event.type ] || {};
+
+		// Use the fix-ed jQuery.Event rather than the (read-only) native event
+		args[ 0 ] = event;
+
+		for ( i = 1; i < arguments.length; i++ ) {
+			args[ i ] = arguments[ i ];
+		}
+
+		event.delegateTarget = this;
+
+		// Call the preDispatch hook for the mapped type, and let it bail if desired
+		if ( special.preDispatch && special.preDispatch.call( this, event ) === false ) {
+			return;
+		}
+
+		// Determine handlers
+		handlerQueue = jQuery.event.handlers.call( this, event, handlers );
+
+		// Run delegates first; they may want to stop propagation beneath us
+		i = 0;
+		while ( ( matched = handlerQueue[ i++ ] ) && !event.isPropagationStopped() ) {
+			event.currentTarget = matched.elem;
+
+			j = 0;
+			while ( ( handleObj = matched.handlers[ j++ ] ) &&
+				!event.isImmediatePropagationStopped() ) {
+
+				// If the event is namespaced, then each handler is only invoked if it is
+				// specially universal or its namespaces are a superset of the event's.
+				if ( !event.rnamespace || handleObj.namespace === false ||
+					event.rnamespace.test( handleObj.namespace ) ) {
+
+					event.handleObj = handleObj;
+					event.data = handleObj.data;
+
+					ret = ( ( jQuery.event.special[ handleObj.origType ] || {} ).handle ||
+						handleObj.handler ).apply( matched.elem, args );
+
+					if ( ret !== undefined ) {
+						if ( ( event.result = ret ) === false ) {
+							event.preventDefault();
+							event.stopPropagation();
+						}
+					}
+				}
+			}
+		}
+
+		// Call the postDispatch hook for the mapped type
+		if ( special.postDispatch ) {
+			special.postDispatch.call( this, event );
+		}
+
+		return event.result;
+	},
+
+	handlers: function( event, handlers ) {
+		var i, handleObj, sel, matchedHandlers, matchedSelectors,
+			handlerQueue = [],
+			delegateCount = handlers.delegateCount,
+			cur = event.target;
+
+		// Find delegate handlers
+		if ( delegateCount &&
+
+			// Support: IE <=9
+			// Black-hole SVG <use> instance trees (trac-13180)
+			cur.nodeType &&
+
+			// Support: Firefox <=42
+			// Suppress spec-violating clicks indicating a non-primary pointer button (trac-3861)
+			// https://www.w3.org/TR/DOM-Level-3-Events/#event-type-click
+			// Support: IE 11 only
+			// ...but not arrow key "clicks" of radio inputs, which can have `button` -1 (gh-2343)
+			!( event.type === "click" && event.button >= 1 ) ) {
+
+			for ( ; cur !== this; cur = cur.parentNode || this ) {
+
+				// Don't check non-elements (#13208)
+				// Don't process clicks on disabled elements (#6911, #8165, #11382, #11764)
+				if ( cur.nodeType === 1 && !( event.type === "click" && cur.disabled === true ) ) {
+					matchedHandlers = [];
+					matchedSelectors = {};
+					for ( i = 0; i < delegateCount; i++ ) {
+						handleObj = handlers[ i ];
+
+						// Don't conflict with Object.prototype properties (#13203)
+						sel = handleObj.selector + " ";
+
+						if ( matchedSelectors[ sel ] === undefined ) {
+							matchedSelectors[ sel ] = handleObj.needsContext ?
+								jQuery( sel, this ).index( cur ) > -1 :
+								jQuery.find( sel, this, null, [ cur ] ).length;
+						}
+						if ( matchedSelectors[ sel ] ) {
+							matchedHandlers.push( handleObj );
+						}
+					}
+					if ( matchedHandlers.length ) {
+						handlerQueue.push( { elem: cur, handlers: matchedHandlers } );
+					}
+				}
+			}
+		}
+
+		// Add the remaining (directly-bound) handlers
+		cur = this;
+		if ( delegateCount < handlers.length ) {
+			handlerQueue.push( { elem: cur, handlers: handlers.slice( delegateCount ) } );
+		}
+
+		return handlerQueue;
+	},
+
+	addProp: function( name, hook ) {
+		Object.defineProperty( jQuery.Event.prototype, name, {
+			enumerable: true,
+			configurable: true,
+
+			get: isFunction( hook ) ?
+				function() {
+					if ( this.originalEvent ) {
+						return hook( this.originalEvent );
+					}
+				} :
+				function() {
+					if ( this.originalEvent ) {
+						return this.originalEvent[ name ];
+					}
+				},
+
+			set: function( value ) {
+				Object.defineProperty( this, name, {
+					enumerable: true,
+					configurable: true,
+					writable: true,
+					value: value
+				} );
+			}
+		} );
+	},
+
+	fix: function( originalEvent ) {
+		return originalEvent[ jQuery.expando ] ?
+			originalEvent :
+			new jQuery.Event( originalEvent );
+	},
+
+	special: {
+		load: {
+
+			// Prevent triggered image.load events from bubbling to window.load
+			noBubble: true
+		},
+		click: {
+
+			// Utilize native event to ensure correct state for checkable inputs
+			setup: function( data ) {
+
+				// For mutual compressibility with _default, replace `this` access with a local var.
+				// `|| data` is dead code meant only to preserve the variable through minification.
+				var el = this || data;
+
+				// Claim the first handler
+				if ( rcheckableType.test( el.type ) &&
+					el.click && nodeName( el, "input" ) ) {
+
+					// dataPriv.set( el, "click", ... )
+					leverageNative( el, "click", returnTrue );
+				}
+
+				// Return false to allow normal processing in the caller
+				return false;
+			},
+			trigger: function( data ) {
+
+				// For mutual compressibility with _default, replace `this` access with a local var.
+				// `|| data` is dead code meant only to preserve the variable through minification.
+				var el = this || data;
+
+				// Force setup before triggering a click
+				if ( rcheckableType.test( el.type ) &&
+					el.click && nodeName( el, "input" ) ) {
+
+					leverageNative( el, "click" );
+				}
+
+				// Return non-false to allow normal event-path propagation
+				return true;
+			},
+
+			// For cross-browser consistency, suppress native .click() on links
+			// Also prevent it if we're currently inside a leveraged native-event stack
+			_default: function( event ) {
+				var target = event.target;
+				return rcheckableType.test( target.type ) &&
+					target.click && nodeName( target, "input" ) &&
+					dataPriv.get( target, "click" ) ||
+					nodeName( target, "a" );
+			}
+		},
+
+		beforeunload: {
+			postDispatch: function( event ) {
+
+				// Support: Firefox 20+
+				// Firefox doesn't alert if the returnValue field is not set.
+				if ( event.result !== undefined && event.originalEvent ) {
+					event.originalEvent.returnValue = event.result;
+				}
+			}
+		}
+	}
+};
+
+// Ensure the presence of an event listener that handles manually-triggered
+// synthetic events by interrupting progress until reinvoked in response to
+// *native* events that it fires directly, ensuring that state changes have
+// already occurred before other listeners are invoked.
+function leverageNative( el, type, expectSync ) {
+
+	// Missing expectSync indicates a trigger call, which must force setup through jQuery.event.add
+	if ( !expectSync ) {
+		if ( dataPriv.get( el, type ) === undefined ) {
+			jQuery.event.add( el, type, returnTrue );
+		}
+		return;
+	}
+
+	// Register the controller as a special universal handler for all event namespaces
+	dataPriv.set( el, type, false );
+	jQuery.event.add( el, type, {
+		namespace: false,
+		handler: function( event ) {
+			var notAsync, result,
+				saved = dataPriv.get( this, type );
+
+			if ( ( event.isTrigger & 1 ) && this[ type ] ) {
+
+				// Interrupt processing of the outer synthetic .trigger()ed event
+				// Saved data should be false in such cases, but might be a leftover capture object
+				// from an async native handler (gh-4350)
+				if ( !saved.length ) {
+
+					// Store arguments for use when handling the inner native event
+					// There will always be at least one argument (an event object), so this array
+					// will not be confused with a leftover capture object.
+					saved = slice.call( arguments );
+					dataPriv.set( this, type, saved );
+
+					// Trigger the native event and capture its result
+					// Support: IE <=9 - 11+
+					// focus() and blur() are asynchronous
+					notAsync = expectSync( this, type );
+					this[ type ]();
+					result = dataPriv.get( this, type );
+					if ( saved !== result || notAsync ) {
+						dataPriv.set( this, type, false );
+					} else {
+						result = {};
+					}
+					if ( saved !== result ) {
+
+						// Cancel the outer synthetic event
+						event.stopImmediatePropagation();
+						event.preventDefault();
+
+						// Support: Chrome 86+
+						// In Chrome, if an element having a focusout handler is blurred by
+						// clicking outside of it, it invokes the handler synchronously. If
+						// that handler calls `.remove()` on the element, the data is cleared,
+						// leaving `result` undefined. We need to guard against this.
+						return result && result.value;
+					}
+
+				// If this is an inner synthetic event for an event with a bubbling surrogate
+				// (focus or blur), assume that the surrogate already propagated from triggering the
+				// native event and prevent that from happening again here.
+				// This technically gets the ordering wrong w.r.t. to `.trigger()` (in which the
+				// bubbling surrogate propagates *after* the non-bubbling base), but that seems
+				// less bad than duplication.
+				} else if ( ( jQuery.event.special[ type ] || {} ).delegateType ) {
+					event.stopPropagation();
+				}
+
+			// If this is a native event triggered above, everything is now in order
+			// Fire an inner synthetic event with the original arguments
+			} else if ( saved.length ) {
+
+				// ...and capture the result
+				dataPriv.set( this, type, {
+					value: jQuery.event.trigger(
+
+						// Support: IE <=9 - 11+
+						// Extend with the prototype to reset the above stopImmediatePropagation()
+						jQuery.extend( saved[ 0 ], jQuery.Event.prototype ),
+						saved.slice( 1 ),
+						this
+					)
+				} );
+
+				// Abort handling of the native event
+				event.stopImmediatePropagation();
+			}
+		}
+	} );
+}
+
+jQuery.removeEvent = function( elem, type, handle ) {
+
+	// This "if" is needed for plain objects
+	if ( elem.removeEventListener ) {
+		elem.removeEventListener( type, handle );
+	}
+};
+
+jQuery.Event = function( src, props ) {
+
+	// Allow instantiation without the 'new' keyword
+	if ( !( this instanceof jQuery.Event ) ) {
+		return new jQuery.Event( src, props );
+	}
+
+	// Event object
+	if ( src && src.type ) {
+		this.originalEvent = src;
+		this.type = src.type;
+
+		// Events bubbling up the document may have been marked as prevented
+		// by a handler lower down the tree; reflect the correct value.
+		this.isDefaultPrevented = src.defaultPrevented ||
+				src.defaultPrevented === undefined &&
+
+				// Support: Android <=2.3 only
+				src.returnValue === false ?
+			returnTrue :
+			returnFalse;
+
+		// Create target properties
+		// Support: Safari <=6 - 7 only
+		// Target should not be a text node (#504, #13143)
+		this.target = ( src.target && src.target.nodeType === 3 ) ?
+			src.target.parentNode :
+			src.target;
+
+		this.currentTarget = src.currentTarget;
+		this.relatedTarget = src.relatedTarget;
+
+	// Event type
+	} else {
+		this.type = src;
+	}
+
+	// Put explicitly provided properties onto the event object
+	if ( props ) {
+		jQuery.extend( this, props );
+	}
+
+	// Create a timestamp if incoming event doesn't have one
+	this.timeStamp = src && src.timeStamp || Date.now();
+
+	// Mark it as fixed
+	this[ jQuery.expando ] = true;
+};
+
+// jQuery.Event is based on DOM3 Events as specified by the ECMAScript Language Binding
+// https://www.w3.org/TR/2003/WD-DOM-Level-3-Events-20030331/ecma-script-binding.html
+jQuery.Event.prototype = {
+	constructor: jQuery.Event,
+	isDefaultPrevented: returnFalse,
+	isPropagationStopped: returnFalse,
+	isImmediatePropagationStopped: returnFalse,
+	isSimulated: false,
+
+	preventDefault: function() {
+		var e = this.originalEvent;
+
+		this.isDefaultPrevented = returnTrue;
+
+		if ( e && !this.isSimulated ) {
+			e.preventDefault();
+		}
+	},
+	stopPropagation: function() {
+		var e = this.originalEvent;
+
+		this.isPropagationStopped = returnTrue;
+
+		if ( e && !this.isSimulated ) {
+			e.stopPropagation();
+		}
+	},
+	stopImmediatePropagation: function() {
+		var e = this.originalEvent;
+
+		this.isImmediatePropagationStopped = returnTrue;
+
+		if ( e && !this.isSimulated ) {
+			e.stopImmediatePropagation();
+		}
+
+		this.stopPropagation();
+	}
+};
+
+// Includes all common event props including KeyEvent and MouseEvent specific props
+jQuery.each( {
+	altKey: true,
+	bubbles: true,
+	cancelable: true,
+	changedTouches: true,
+	ctrlKey: true,
+	detail: true,
+	eventPhase: true,
+	metaKey: true,
+	pageX: true,
+	pageY: true,
+	shiftKey: true,
+	view: true,
+	"char": true,
+	code: true,
+	charCode: true,
+	key: true,
+	keyCode: true,
+	button: true,
+	buttons: true,
+	clientX: true,
+	clientY: true,
+	offsetX: true,
+	offsetY: true,
+	pointerId: true,
+	pointerType: true,
+	screenX: true,
+	screenY: true,
+	targetTouches: true,
+	toElement: true,
+	touches: true,
+	which: true
+}, jQuery.event.addProp );
+
+jQuery.each( { focus: "focusin", blur: "focusout" }, function( type, delegateType ) {
+	jQuery.event.special[ type ] = {
+
+		// Utilize native event if possible so blur/focus sequence is correct
+		setup: function() {
+
+			// Claim the first handler
+			// dataPriv.set( this, "focus", ... )
+			// dataPriv.set( this, "blur", ... )
+			leverageNative( this, type, expectSync );
+
+			// Return false to allow normal processing in the caller
+			return false;
+		},
+		trigger: function() {
+
+			// Force setup before trigger
+			leverageNative( this, type );
+
+			// Return non-false to allow normal event-path propagation
+			return true;
+		},
+
+		// Suppress native focus or blur as it's already being fired
+		// in leverageNative.
+		_default: function() {
+			return true;
+		},
+
+		delegateType: delegateType
+	};
+} );
+
+// Create mouseenter/leave events using mouseover/out and event-time checks
+// so that event delegation works in jQuery.
+// Do the same for pointerenter/pointerleave and pointerover/pointerout
+//
+// Support: Safari 7 only
+// Safari sends mouseenter too often; see:
+// https://bugs.chromium.org/p/chromium/issues/detail?id=470258
+// for the description of the bug (it existed in older Chrome versions as well).
+jQuery.each( {
+	mouseenter: "mouseover",
+	mouseleave: "mouseout",
+	pointerenter: "pointerover",
+	pointerleave: "pointerout"
+}, function( orig, fix ) {
+	jQuery.event.special[ orig ] = {
+		delegateType: fix,
+		bindType: fix,
+
+		handle: function( event ) {
+			var ret,
+				target = this,
+				related = event.relatedTarget,
+				handleObj = event.handleObj;
+
+			// For mouseenter/leave call the handler if related is outside the target.
+			// NB: No relatedTarget if the mouse left/entered the browser window
+			if ( !related || ( related !== target && !jQuery.contains( target, related ) ) ) {
+				event.type = handleObj.origType;
+				ret = handleObj.handler.apply( this, arguments );
+				event.type = fix;
+			}
+			return ret;
+		}
+	};
+} );
+
+jQuery.fn.extend( {
+
+	on: function( types, selector, data, fn ) {
+		return on( this, types, selector, data, fn );
+	},
+	one: function( types, selector, data, fn ) {
+		return on( this, types, selector, data, fn, 1 );
+	},
+	off: function( types, selector, fn ) {
+		var handleObj, type;
+		if ( types && types.preventDefault && types.handleObj ) {
+
+			// ( event )  dispatched jQuery.Event
+			handleObj = types.handleObj;
+			jQuery( types.delegateTarget ).off(
+				handleObj.namespace ?
+					handleObj.origType + "." + handleObj.namespace :
+					handleObj.origType,
+				handleObj.selector,
+				handleObj.handler
+			);
+			return this;
+		}
+		if ( typeof types === "object" ) {
+
+			// ( types-object [, selector] )
+			for ( type in types ) {
+				this.off( type, selector, types[ type ] );
+			}
+			return this;
+		}
+		if ( selector === false || typeof selector === "function" ) {
+
+			// ( types [, fn] )
+			fn = selector;
+			selector = undefined;
+		}
+		if ( fn === false ) {
+			fn = returnFalse;
+		}
+		return this.each( function() {
+			jQuery.event.remove( this, types, fn, selector );
+		} );
+	}
+} );
+
+
+var
+
+	// Support: IE <=10 - 11, Edge 12 - 13 only
+	// In IE/Edge using regex groups here causes severe slowdowns.
+	// See https://connect.microsoft.com/IE/feedback/details/1736512/
+	rnoInnerhtml = /<script|<style|<link/i,
+
+	// checked="checked" or checked
+	rchecked = /checked\s*(?:[^=]|=\s*.checked.)/i,
+	rcleanScript = /^\s*<!(?:\[CDATA\[|--)|(?:\]\]|--)>\s*$/g;
+
+// Prefer a tbody over its parent table for containing new rows
+function manipulationTarget( elem, content ) {
+	if ( nodeName( elem, "table" ) &&
+		nodeName( content.nodeType !== 11 ? content : content.firstChild, "tr" ) ) {
+
+		return jQuery( elem ).children( "tbody" )[ 0 ] || elem;
+	}
+
+	return elem;
+}
+
+// Replace/restore the type attribute of script elements for safe DOM manipulation
+function disableScript( elem ) {
+	elem.type = ( elem.getAttribute( "type" ) !== null ) + "/" + elem.type;
+	return elem;
+}
+function restoreScript( elem ) {
+	if ( ( elem.type || "" ).slice( 0, 5 ) === "true/" ) {
+		elem.type = elem.type.slice( 5 );
+	} else {
+		elem.removeAttribute( "type" );
+	}
+
+	return elem;
+}
+
+function cloneCopyEvent( src, dest ) {
+	var i, l, type, pdataOld, udataOld, udataCur, events;
+
+	if ( dest.nodeType !== 1 ) {
+		return;
+	}
+
+	// 1. Copy private data: events, handlers, etc.
+	if ( dataPriv.hasData( src ) ) {
+		pdataOld = dataPriv.get( src );
+		events = pdataOld.events;
+
+		if ( events ) {
+			dataPriv.remove( dest, "handle events" );
+
+			for ( type in events ) {
+				for ( i = 0, l = events[ type ].length; i < l; i++ ) {
+					jQuery.event.add( dest, type, events[ type ][ i ] );
+				}
+			}
+		}
+	}
+
+	// 2. Copy user data
+	if ( dataUser.hasData( src ) ) {
+		udataOld = dataUser.access( src );
+		udataCur = jQuery.extend( {}, udataOld );
+
+		dataUser.set( dest, udataCur );
+	}
+}
+
+// Fix IE bugs, see support tests
+function fixInput( src, dest ) {
+	var nodeName = dest.nodeName.toLowerCase();
+
+	// Fails to persist the checked state of a cloned checkbox or radio button.
+	if ( nodeName === "input" && rcheckableType.test( src.type ) ) {
+		dest.checked = src.checked;
+
+	// Fails to return the selected option to the default selected state when cloning options
+	} else if ( nodeName === "input" || nodeName === "textarea" ) {
+		dest.defaultValue = src.defaultValue;
+	}
+}
+
+function domManip( collection, args, callback, ignored ) {
+
+	// Flatten any nested arrays
+	args = flat( args );
+
+	var fragment, first, scripts, hasScripts, node, doc,
+		i = 0,
+		l = collection.length,
+		iNoClone = l - 1,
+		value = args[ 0 ],
+		valueIsFunction = isFunction( value );
+
+	// We can't cloneNode fragments that contain checked, in WebKit
+	if ( valueIsFunction ||
+			( l > 1 && typeof value === "string" &&
+				!support.checkClone && rchecked.test( value ) ) ) {
+		return collection.each( function( index ) {
+			var self = collection.eq( index );
+			if ( valueIsFunction ) {
+				args[ 0 ] = value.call( this, index, self.html() );
+			}
+			domManip( self, args, callback, ignored );
+		} );
+	}
+
+	if ( l ) {
+		fragment = buildFragment( args, collection[ 0 ].ownerDocument, false, collection, ignored );
+		first = fragment.firstChild;
+
+		if ( fragment.childNodes.length === 1 ) {
+			fragment = first;
+		}
+
+		// Require either new content or an interest in ignored elements to invoke the callback
+		if ( first || ignored ) {
+			scripts = jQuery.map( getAll( fragment, "script" ), disableScript );
+			hasScripts = scripts.length;
+
+			// Use the original fragment for the last item
+			// instead of the first because it can end up
+			// being emptied incorrectly in certain situations (#8070).
+			for ( ; i < l; i++ ) {
+				node = fragment;
+
+				if ( i !== iNoClone ) {
+					node = jQuery.clone( node, true, true );
+
+					// Keep references to cloned scripts for later restoration
+					if ( hasScripts ) {
+
+						// Support: Android <=4.0 only, PhantomJS 1 only
+						// push.apply(_, arraylike) throws on ancient WebKit
+						jQuery.merge( scripts, getAll( node, "script" ) );
+					}
+				}
+
+				callback.call( collection[ i ], node, i );
+			}
+
+			if ( hasScripts ) {
+				doc = scripts[ scripts.length - 1 ].ownerDocument;
+
+				// Reenable scripts
+				jQuery.map( scripts, restoreScript );
+
+				// Evaluate executable scripts on first document insertion
+				for ( i = 0; i < hasScripts; i++ ) {
+					node = scripts[ i ];
+					if ( rscriptType.test( node.type || "" ) &&
+						!dataPriv.access( node, "globalEval" ) &&
+						jQuery.contains( doc, node ) ) {
+
+						if ( node.src && ( node.type || "" ).toLowerCase()  !== "module" ) {
+
+							// Optional AJAX dependency, but won't run scripts if not present
+							if ( jQuery._evalUrl && !node.noModule ) {
+								jQuery._evalUrl( node.src, {
+									nonce: node.nonce || node.getAttribute( "nonce" )
+								}, doc );
+							}
+						} else {
+							DOMEval( node.textContent.replace( rcleanScript, "" ), node, doc );
+						}
+					}
+				}
+			}
+		}
+	}
+
+	return collection;
+}
+
+function remove( elem, selector, keepData ) {
+	var node,
+		nodes = selector ? jQuery.filter( selector, elem ) : elem,
+		i = 0;
+
+	for ( ; ( node = nodes[ i ] ) != null; i++ ) {
+		if ( !keepData && node.nodeType === 1 ) {
+			jQuery.cleanData( getAll( node ) );
+		}
+
+		if ( node.parentNode ) {
+			if ( keepData && isAttached( node ) ) {
+				setGlobalEval( getAll( node, "script" ) );
+			}
+			node.parentNode.removeChild( node );
+		}
+	}
+
+	return elem;
+}
+
+jQuery.extend( {
+	htmlPrefilter: function( html ) {
+		return html;
+	},
+
+	clone: function( elem, dataAndEvents, deepDataAndEvents ) {
+		var i, l, srcElements, destElements,
+			clone = elem.cloneNode( true ),
+			inPage = isAttached( elem );
+
+		// Fix IE cloning issues
+		if ( !support.noCloneChecked && ( elem.nodeType === 1 || elem.nodeType === 11 ) &&
+				!jQuery.isXMLDoc( elem ) ) {
+
+			// We eschew Sizzle here for performance reasons: https://jsperf.com/getall-vs-sizzle/2
+			destElements = getAll( clone );
+			srcElements = getAll( elem );
+
+			for ( i = 0, l = srcElements.length; i < l; i++ ) {
+				fixInput( srcElements[ i ], destElements[ i ] );
+			}
+		}
+
+		// Copy the events from the original to the clone
+		if ( dataAndEvents ) {
+			if ( deepDataAndEvents ) {
+				srcElements = srcElements || getAll( elem );
+				destElements = destElements || getAll( clone );
+
+				for ( i = 0, l = srcElements.length; i < l; i++ ) {
+					cloneCopyEvent( srcElements[ i ], destElements[ i ] );
+				}
+			} else {
+				cloneCopyEvent( elem, clone );
+			}
+		}
+
+		// Preserve script evaluation history
+		destElements = getAll( clone, "script" );
+		if ( destElements.length > 0 ) {
+			setGlobalEval( destElements, !inPage && getAll( elem, "script" ) );
+		}
+
+		// Return the cloned set
+		return clone;
+	},
+
+	cleanData: function( elems ) {
+		var data, elem, type,
+			special = jQuery.event.special,
+			i = 0;
+
+		for ( ; ( elem = elems[ i ] ) !== undefined; i++ ) {
+			if ( acceptData( elem ) ) {
+				if ( ( data = elem[ dataPriv.expando ] ) ) {
+					if ( data.events ) {
+						for ( type in data.events ) {
+							if ( special[ type ] ) {
+								jQuery.event.remove( elem, type );
+
+							// This is a shortcut to avoid jQuery.event.remove's overhead
+							} else {
+								jQuery.removeEvent( elem, type, data.handle );
+							}
+						}
+					}
+
+					// Support: Chrome <=35 - 45+
+					// Assign undefined instead of using delete, see Data#remove
+					elem[ dataPriv.expando ] = undefined;
+				}
+				if ( elem[ dataUser.expando ] ) {
+
+					// Support: Chrome <=35 - 45+
+					// Assign undefined instead of using delete, see Data#remove
+					elem[ dataUser.expando ] = undefined;
+				}
+			}
+		}
+	}
+} );
+
+jQuery.fn.extend( {
+	detach: function( selector ) {
+		return remove( this, selector, true );
+	},
+
+	remove: function( selector ) {
+		return remove( this, selector );
+	},
+
+	text: function( value ) {
+		return access( this, function( value ) {
+			return value === undefined ?
+				jQuery.text( this ) :
+				this.empty().each( function() {
+					if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
+						this.textContent = value;
+					}
+				} );
+		}, null, value, arguments.length );
+	},
+
+	append: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
+				var target = manipulationTarget( this, elem );
+				target.appendChild( elem );
+			}
+		} );
+	},
+
+	prepend: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
+				var target = manipulationTarget( this, elem );
+				target.insertBefore( elem, target.firstChild );
+			}
+		} );
+	},
+
+	before: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.parentNode ) {
+				this.parentNode.insertBefore( elem, this );
+			}
+		} );
+	},
+
+	after: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.parentNode ) {
+				this.parentNode.insertBefore( elem, this.nextSibling );
+			}
+		} );
+	},
+
+	empty: function() {
+		var elem,
+			i = 0;
+
+		for ( ; ( elem = this[ i ] ) != null; i++ ) {
+			if ( elem.nodeType === 1 ) {
+
+				// Prevent memory leaks
+				jQuery.cleanData( getAll( elem, false ) );
+
+				// Remove any remaining nodes
+				elem.textContent = "";
+			}
+		}
+
+		return this;
+	},
+
+	clone: function( dataAndEvents, deepDataAndEvents ) {
+		dataAndEvents = dataAndEvents == null ? false : dataAndEvents;
+		deepDataAndEvents = deepDataAndEvents == null ? dataAndEvents : deepDataAndEvents;
+
+		return this.map( function() {
+			return jQuery.clone( this, dataAndEvents, deepDataAndEvents );
+		} );
+	},
+
+	html: function( value ) {
+		return access( this, function( value ) {
+			var elem = this[ 0 ] || {},
+				i = 0,
+				l = this.length;
+
+			if ( value === undefined && elem.nodeType === 1 ) {
+				return elem.innerHTML;
+			}
+
+			// See if we can take a shortcut and just use innerHTML
+			if ( typeof value === "string" && !rnoInnerhtml.test( value ) &&
+				!wrapMap[ ( rtagName.exec( value ) || [ "", "" ] )[ 1 ].toLowerCase() ] ) {
+
+				value = jQuery.htmlPrefilter( value );
+
+				try {
+					for ( ; i < l; i++ ) {
+						elem = this[ i ] || {};
+
+						// Remove element nodes and prevent memory leaks
+						if ( elem.nodeType === 1 ) {
+							jQuery.cleanData( getAll( elem, false ) );
+							elem.innerHTML = value;
+						}
+					}
+
+					elem = 0;
+
+				// If using innerHTML throws an exception, use the fallback method
+				} catch ( e ) {}
+			}
+
+			if ( elem ) {
+				this.empty().append( value );
+			}
+		}, null, value, arguments.length );
+	},
+
+	replaceWith: function() {
+		var ignored = [];
+
+		// Make the changes, replacing each non-ignored context element with the new content
+		return domManip( this, arguments, function( elem ) {
+			var parent = this.parentNode;
+
+			if ( jQuery.inArray( this, ignored ) < 0 ) {
+				jQuery.cleanData( getAll( this ) );
+				if ( parent ) {
+					parent.replaceChild( elem, this );
+				}
+			}
+
+		// Force callback invocation
+		}, ignored );
+	}
+} );
+
+jQuery.each( {
+	appendTo: "append",
+	prependTo: "prepend",
+	insertBefore: "before",
+	insertAfter: "after",
+	replaceAll: "replaceWith"
+}, function( name, original ) {
+	jQuery.fn[ name ] = function( selector ) {
+		var elems,
+			ret = [],
+			insert = jQuery( selector ),
+			last = insert.length - 1,
+			i = 0;
+
+		for ( ; i <= last; i++ ) {
+			elems = i === last ? this : this.clone( true );
+			jQuery( insert[ i ] )[ original ]( elems );
+
+			// Support: Android <=4.0 only, PhantomJS 1 only
+			// .get() because push.apply(_, arraylike) throws on ancient WebKit
+			push.apply( ret, elems.get() );
+		}
+
+		return this.pushStack( ret );
+	};
+} );
+var rnumnonpx = new RegExp( "^(" + pnum + ")(?!px)[a-z%]+$", "i" );
+
+var getStyles = function( elem ) {
+
+		// Support: IE <=11 only, Firefox <=30 (#15098, #14150)
+		// IE throws on elements created in popups
+		// FF meanwhile throws on frame elements through "defaultView.getComputedStyle"
+		var view = elem.ownerDocument.defaultView;
+
+		if ( !view || !view.opener ) {
+			view = window;
+		}
+
+		return view.getComputedStyle( elem );
+	};
+
+var swap = function( elem, options, callback ) {
+	var ret, name,
+		old = {};
+
+	// Remember the old values, and insert the new ones
+	for ( name in options ) {
+		old[ name ] = elem.style[ name ];
+		elem.style[ name ] = options[ name ];
+	}
+
+	ret = callback.call( elem );
+
+	// Revert the old values
+	for ( name in options ) {
+		elem.style[ name ] = old[ name ];
+	}
+
+	return ret;
+};
+
+
+var rboxStyle = new RegExp( cssExpand.join( "|" ), "i" );
+
+
+
+( function() {
+
+	// Executing both pixelPosition & boxSizingReliable tests require only one layout
+	// so they're executed at the same time to save the second computation.
+	function computeStyleTests() {
+
+		// This is a singleton, we need to execute it only once
+		if ( !div ) {
+			return;
+		}
+
+		container.style.cssText = "position:absolute;left:-11111px;width:60px;" +
+			"margin-top:1px;padding:0;border:0";
+		div.style.cssText =
+			"position:relative;display:block;box-sizing:border-box;overflow:scroll;" +
+			"margin:auto;border:1px;padding:1px;" +
+			"width:60%;top:1%";
+		documentElement.appendChild( container ).appendChild( div );
+
+		var divStyle = window.getComputedStyle( div );
+		pixelPositionVal = divStyle.top !== "1%";
+
+		// Support: Android 4.0 - 4.3 only, Firefox <=3 - 44
+		reliableMarginLeftVal = roundPixelMeasures( divStyle.marginLeft ) === 12;
+
+		// Support: Android 4.0 - 4.3 only, Safari <=9.1 - 10.1, iOS <=7.0 - 9.3
+		// Some styles come back with percentage values, even though they shouldn't
+		div.style.right = "60%";
+		pixelBoxStylesVal = roundPixelMeasures( divStyle.right ) === 36;
+
+		// Support: IE 9 - 11 only
+		// Detect misreporting of content dimensions for box-sizing:border-box elements
+		boxSizingReliableVal = roundPixelMeasures( divStyle.width ) === 36;
+
+		// Support: IE 9 only
+		// Detect overflow:scroll screwiness (gh-3699)
+		// Support: Chrome <=64
+		// Don't get tricked when zoom affects offsetWidth (gh-4029)
+		div.style.position = "absolute";
+		scrollboxSizeVal = roundPixelMeasures( div.offsetWidth / 3 ) === 12;
+
+		documentElement.removeChild( container );
+
+		// Nullify the div so it wouldn't be stored in the memory and
+		// it will also be a sign that checks already performed
+		div = null;
+	}
+
+	function roundPixelMeasures( measure ) {
+		return Math.round( parseFloat( measure ) );
+	}
+
+	var pixelPositionVal, boxSizingReliableVal, scrollboxSizeVal, pixelBoxStylesVal,
+		reliableTrDimensionsVal, reliableMarginLeftVal,
+		container = document.createElement( "div" ),
+		div = document.createElement( "div" );
+
+	// Finish early in limited (non-browser) environments
+	if ( !div.style ) {
+		return;
+	}
+
+	// Support: IE <=9 - 11 only
+	// Style of cloned element affects source element cloned (#8908)
+	div.style.backgroundClip = "content-box";
+	div.cloneNode( true ).style.backgroundClip = "";
+	support.clearCloneStyle = div.style.backgroundClip === "content-box";
+
+	jQuery.extend( support, {
+		boxSizingReliable: function() {
+			computeStyleTests();
+			return boxSizingReliableVal;
+		},
+		pixelBoxStyles: function() {
+			computeStyleTests();
+			return pixelBoxStylesVal;
+		},
+		pixelPosition: function() {
+			computeStyleTests();
+			return pixelPositionVal;
+		},
+		reliableMarginLeft: function() {
+			computeStyleTests();
+			return reliableMarginLeftVal;
+		},
+		scrollboxSize: function() {
+			computeStyleTests();
+			return scrollboxSizeVal;
+		},
+
+		// Support: IE 9 - 11+, Edge 15 - 18+
+		// IE/Edge misreport `getComputedStyle` of table rows with width/height
+		// set in CSS while `offset*` properties report correct values.
+		// Behavior in IE 9 is more subtle than in newer versions & it passes
+		// some versions of this test; make sure not to make it pass there!
+		//
+		// Support: Firefox 70+
+		// Only Firefox includes border widths
+		// in computed dimensions. (gh-4529)
+		reliableTrDimensions: function() {
+			var table, tr, trChild, trStyle;
+			if ( reliableTrDimensionsVal == null ) {
+				table = document.createElement( "table" );
+				tr = document.createElement( "tr" );
+				trChild = document.createElement( "div" );
+
+				table.style.cssText = "position:absolute;left:-11111px;border-collapse:separate";
+				tr.style.cssText = "border:1px solid";
+
+				// Support: Chrome 86+
+				// Height set through cssText does not get applied.
+				// Computed height then comes back as 0.
+				tr.style.height = "1px";
+				trChild.style.height = "9px";
+
+				// Support: Android 8 Chrome 86+
+				// In our bodyBackground.html iframe,
+				// display for all div elements is set to "inline",
+				// which causes a problem only in Android 8 Chrome 86.
+				// Ensuring the div is display: block
+				// gets around this issue.
+				trChild.style.display = "block";
+
+				documentElement
+					.appendChild( table )
+					.appendChild( tr )
+					.appendChild( trChild );
+
+				trStyle = window.getComputedStyle( tr );
+				reliableTrDimensionsVal = ( parseInt( trStyle.height, 10 ) +
+					parseInt( trStyle.borderTopWidth, 10 ) +
+					parseInt( trStyle.borderBottomWidth, 10 ) ) === tr.offsetHeight;
+
+				documentElement.removeChild( table );
+			}
+			return reliableTrDimensionsVal;
+		}
+	} );
+} )();
+
+
+function curCSS( elem, name, computed ) {
+	var width, minWidth, maxWidth, ret,
+
+		// Support: Firefox 51+
+		// Retrieving style before computed somehow
+		// fixes an issue with getting wrong values
+		// on detached elements
+		style = elem.style;
+
+	computed = computed || getStyles( elem );
+
+	// getPropertyValue is needed for:
+	//   .css('filter') (IE 9 only, #12537)
+	//   .css('--customProperty) (#3144)
+	if ( computed ) {
+		ret = computed.getPropertyValue( name ) || computed[ name ];
+
+		if ( ret === "" && !isAttached( elem ) ) {
+			ret = jQuery.style( elem, name );
+		}
+
+		// A tribute to the "awesome hack by Dean Edwards"
+		// Android Browser returns percentage for some values,
+		// but width seems to be reliably pixels.
+		// This is against the CSSOM draft spec:
+		// https://drafts.csswg.org/cssom/#resolved-values
+		if ( !support.pixelBoxStyles() && rnumnonpx.test( ret ) && rboxStyle.test( name ) ) {
+
+			// Remember the original values
+			width = style.width;
+			minWidth = style.minWidth;
+			maxWidth = style.maxWidth;
+
+			// Put in the new values to get a computed value out
+			style.minWidth = style.maxWidth = style.width = ret;
+			ret = computed.width;
+
+			// Revert the changed values
+			style.width = width;
+			style.minWidth = minWidth;
+			style.maxWidth = maxWidth;
+		}
+	}
+
+	return ret !== undefined ?
+
+		// Support: IE <=9 - 11 only
+		// IE returns zIndex value as an integer.
+		ret + "" :
+		ret;
+}
+
+
+function addGetHookIf( conditionFn, hookFn ) {
+
+	// Define the hook, we'll check on the first run if it's really needed.
+	return {
+		get: function() {
+			if ( conditionFn() ) {
+
+				// Hook not needed (or it's not possible to use it due
+				// to missing dependency), remove it.
+				delete this.get;
+				return;
+			}
+
+			// Hook needed; redefine it so that the support test is not executed again.
+			return ( this.get = hookFn ).apply( this, arguments );
+		}
+	};
+}
+
+
+var cssPrefixes = [ "Webkit", "Moz", "ms" ],
+	emptyStyle = document.createElement( "div" ).style,
+	vendorProps = {};
+
+// Return a vendor-prefixed property or undefined
+function vendorPropName( name ) {
+
+	// Check for vendor prefixed names
+	var capName = name[ 0 ].toUpperCase() + name.slice( 1 ),
+		i = cssPrefixes.length;
+
+	while ( i-- ) {
+		name = cssPrefixes[ i ] + capName;
+		if ( name in emptyStyle ) {
+			return name;
+		}
+	}
+}
+
+// Return a potentially-mapped jQuery.cssProps or vendor prefixed property
+function finalPropName( name ) {
+	var final = jQuery.cssProps[ name ] || vendorProps[ name ];
+
+	if ( final ) {
+		return final;
+	}
+	if ( name in emptyStyle ) {
+		return name;
+	}
+	return vendorProps[ name ] = vendorPropName( name ) || name;
+}
+
+
+var
+
+	// Swappable if display is none or starts with table
+	// except "table", "table-cell", or "table-caption"
+	// See here for display values: https://developer.mozilla.org/en-US/docs/CSS/display
+	rdisplayswap = /^(none|table(?!-c[ea]).+)/,
+	rcustomProp = /^--/,
+	cssShow = { position: "absolute", visibility: "hidden", display: "block" },
+	cssNormalTransform = {
+		letterSpacing: "0",
+		fontWeight: "400"
+	};
+
+function setPositiveNumber( _elem, value, subtract ) {
+
+	// Any relative (+/-) values have already been
+	// normalized at this point
+	var matches = rcssNum.exec( value );
+	return matches ?
+
+		// Guard against undefined "subtract", e.g., when used as in cssHooks
+		Math.max( 0, matches[ 2 ] - ( subtract || 0 ) ) + ( matches[ 3 ] || "px" ) :
+		value;
+}
+
+function boxModelAdjustment( elem, dimension, box, isBorderBox, styles, computedVal ) {
+	var i = dimension === "width" ? 1 : 0,
+		extra = 0,
+		delta = 0;
+
+	// Adjustment may not be necessary
+	if ( box === ( isBorderBox ? "border" : "content" ) ) {
+		return 0;
+	}
+
+	for ( ; i < 4; i += 2 ) {
+
+		// Both box models exclude margin
+		if ( box === "margin" ) {
+			delta += jQuery.css( elem, box + cssExpand[ i ], true, styles );
+		}
+
+		// If we get here with a content-box, we're seeking "padding" or "border" or "margin"
+		if ( !isBorderBox ) {
+
+			// Add padding
+			delta += jQuery.css( elem, "padding" + cssExpand[ i ], true, styles );
+
+			// For "border" or "margin", add border
+			if ( box !== "padding" ) {
+				delta += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
+
+			// But still keep track of it otherwise
+			} else {
+				extra += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
+			}
+
+		// If we get here with a border-box (content + padding + border), we're seeking "content" or
+		// "padding" or "margin"
+		} else {
+
+			// For "content", subtract padding
+			if ( box === "content" ) {
+				delta -= jQuery.css( elem, "padding" + cssExpand[ i ], true, styles );
+			}
+
+			// For "content" or "padding", subtract border
+			if ( box !== "margin" ) {
+				delta -= jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
+			}
+		}
+	}
+
+	// Account for positive content-box scroll gutter when requested by providing computedVal
+	if ( !isBorderBox && computedVal >= 0 ) {
+
+		// offsetWidth/offsetHeight is a rounded sum of content, padding, scroll gutter, and border
+		// Assuming integer scroll gutter, subtract the rest and round down
+		delta += Math.max( 0, Math.ceil(
+			elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] -
+			computedVal -
+			delta -
+			extra -
+			0.5
+
+		// If offsetWidth/offsetHeight is unknown, then we can't determine content-box scroll gutter
+		// Use an explicit zero to avoid NaN (gh-3964)
+		) ) || 0;
+	}
+
+	return delta;
+}
+
+function getWidthOrHeight( elem, dimension, extra ) {
+
+	// Start with computed style
+	var styles = getStyles( elem ),
+
+		// To avoid forcing a reflow, only fetch boxSizing if we need it (gh-4322).
+		// Fake content-box until we know it's needed to know the true value.
+		boxSizingNeeded = !support.boxSizingReliable() || extra,
+		isBorderBox = boxSizingNeeded &&
+			jQuery.css( elem, "boxSizing", false, styles ) === "border-box",
+		valueIsBorderBox = isBorderBox,
+
+		val = curCSS( elem, dimension, styles ),
+		offsetProp = "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 );
+
+	// Support: Firefox <=54
+	// Return a confounding non-pixel value or feign ignorance, as appropriate.
+	if ( rnumnonpx.test( val ) ) {
+		if ( !extra ) {
+			return val;
+		}
+		val = "auto";
+	}
+
+
+	// Support: IE 9 - 11 only
+	// Use offsetWidth/offsetHeight for when box sizing is unreliable.
+	// In those cases, the computed value can be trusted to be border-box.
+	if ( ( !support.boxSizingReliable() && isBorderBox ||
+
+		// Support: IE 10 - 11+, Edge 15 - 18+
+		// IE/Edge misreport `getComputedStyle` of table rows with width/height
+		// set in CSS while `offset*` properties report correct values.
+		// Interestingly, in some cases IE 9 doesn't suffer from this issue.
+		!support.reliableTrDimensions() && nodeName( elem, "tr" ) ||
+
+		// Fall back to offsetWidth/offsetHeight when value is "auto"
+		// This happens for inline elements with no explicit setting (gh-3571)
+		val === "auto" ||
+
+		// Support: Android <=4.1 - 4.3 only
+		// Also use offsetWidth/offsetHeight for misreported inline dimensions (gh-3602)
+		!parseFloat( val ) && jQuery.css( elem, "display", false, styles ) === "inline" ) &&
+
+		// Make sure the element is visible & connected
+		elem.getClientRects().length ) {
+
+		isBorderBox = jQuery.css( elem, "boxSizing", false, styles ) === "border-box";
+
+		// Where available, offsetWidth/offsetHeight approximate border box dimensions.
+		// Where not available (e.g., SVG), assume unreliable box-sizing and interpret the
+		// retrieved value as a content box dimension.
+		valueIsBorderBox = offsetProp in elem;
+		if ( valueIsBorderBox ) {
+			val = elem[ offsetProp ];
+		}
+	}
+
+	// Normalize "" and auto
+	val = parseFloat( val ) || 0;
+
+	// Adjust for the element's box model
+	return ( val +
+		boxModelAdjustment(
+			elem,
+			dimension,
+			extra || ( isBorderBox ? "border" : "content" ),
+			valueIsBorderBox,
+			styles,
+
+			// Provide the current computed size to request scroll gutter calculation (gh-3589)
+			val
+		)
+	) + "px";
+}
+
+jQuery.extend( {
+
+	// Add in style property hooks for overriding the default
+	// behavior of getting and setting a style property
+	cssHooks: {
+		opacity: {
+			get: function( elem, computed ) {
+				if ( computed ) {
+
+					// We should always get a number back from opacity
+					var ret = curCSS( elem, "opacity" );
+					return ret === "" ? "1" : ret;
+				}
+			}
+		}
+	},
+
+	// Don't automatically add "px" to these possibly-unitless properties
+	cssNumber: {
+		"animationIterationCount": true,
+		"columnCount": true,
+		"fillOpacity": true,
+		"flexGrow": true,
+		"flexShrink": true,
+		"fontWeight": true,
+		"gridArea": true,
+		"gridColumn": true,
+		"gridColumnEnd": true,
+		"gridColumnStart": true,
+		"gridRow": true,
+		"gridRowEnd": true,
+		"gridRowStart": true,
+		"lineHeight": true,
+		"opacity": true,
+		"order": true,
+		"orphans": true,
+		"widows": true,
+		"zIndex": true,
+		"zoom": true
+	},
+
+	// Add in properties whose names you wish to fix before
+	// setting or getting the value
+	cssProps: {},
+
+	// Get and set the style property on a DOM Node
+	style: function( elem, name, value, extra ) {
+
+		// Don't set styles on text and comment nodes
+		if ( !elem || elem.nodeType === 3 || elem.nodeType === 8 || !elem.style ) {
+			return;
+		}
+
+		// Make sure that we're working with the right name
+		var ret, type, hooks,
+			origName = camelCase( name ),
+			isCustomProp = rcustomProp.test( name ),
+			style = elem.style;
+
+		// Make sure that we're working with the right name. We don't
+		// want to query the value if it is a CSS custom property
+		// since they are user-defined.
+		if ( !isCustomProp ) {
+			name = finalPropName( origName );
+		}
+
+		// Gets hook for the prefixed version, then unprefixed version
+		hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ];
+
+		// Check if we're setting a value
+		if ( value !== undefined ) {
+			type = typeof value;
+
+			// Convert "+=" or "-=" to relative numbers (#7345)
+			if ( type === "string" && ( ret = rcssNum.exec( value ) ) && ret[ 1 ] ) {
+				value = adjustCSS( elem, name, ret );
+
+				// Fixes bug #9237
+				type = "number";
+			}
+
+			// Make sure that null and NaN values aren't set (#7116)
+			if ( value == null || value !== value ) {
+				return;
+			}
+
+			// If a number was passed in, add the unit (except for certain CSS properties)
+			// The isCustomProp check can be removed in jQuery 4.0 when we only auto-append
+			// "px" to a few hardcoded values.
+			if ( type === "number" && !isCustomProp ) {
+				value += ret && ret[ 3 ] || ( jQuery.cssNumber[ origName ] ? "" : "px" );
+			}
+
+			// background-* props affect original clone's values
+			if ( !support.clearCloneStyle && value === "" && name.indexOf( "background" ) === 0 ) {
+				style[ name ] = "inherit";
+			}
+
+			// If a hook was provided, use that value, otherwise just set the specified value
+			if ( !hooks || !( "set" in hooks ) ||
+				( value = hooks.set( elem, value, extra ) ) !== undefined ) {
+
+				if ( isCustomProp ) {
+					style.setProperty( name, value );
+				} else {
+					style[ name ] = value;
+				}
+			}
+
+		} else {
+
+			// If a hook was provided get the non-computed value from there
+			if ( hooks && "get" in hooks &&
+				( ret = hooks.get( elem, false, extra ) ) !== undefined ) {
+
+				return ret;
+			}
+
+			// Otherwise just get the value from the style object
+			return style[ name ];
+		}
+	},
+
+	css: function( elem, name, extra, styles ) {
+		var val, num, hooks,
+			origName = camelCase( name ),
+			isCustomProp = rcustomProp.test( name );
+
+		// Make sure that we're working with the right name. We don't
+		// want to modify the value if it is a CSS custom property
+		// since they are user-defined.
+		if ( !isCustomProp ) {
+			name = finalPropName( origName );
+		}
+
+		// Try prefixed name followed by the unprefixed name
+		hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ];
+
+		// If a hook was provided get the computed value from there
+		if ( hooks && "get" in hooks ) {
+			val = hooks.get( elem, true, extra );
+		}
+
+		// Otherwise, if a way to get the computed value exists, use that
+		if ( val === undefined ) {
+			val = curCSS( elem, name, styles );
+		}
+
+		// Convert "normal" to computed value
+		if ( val === "normal" && name in cssNormalTransform ) {
+			val = cssNormalTransform[ name ];
+		}
+
+		// Make numeric if forced or a qualifier was provided and val looks numeric
+		if ( extra === "" || extra ) {
+			num = parseFloat( val );
+			return extra === true || isFinite( num ) ? num || 0 : val;
+		}
+
+		return val;
+	}
+} );
+
+jQuery.each( [ "height", "width" ], function( _i, dimension ) {
+	jQuery.cssHooks[ dimension ] = {
+		get: function( elem, computed, extra ) {
+			if ( computed ) {
+
+				// Certain elements can have dimension info if we invisibly show them
+				// but it must have a current display style that would benefit
+				return rdisplayswap.test( jQuery.css( elem, "display" ) ) &&
+
+					// Support: Safari 8+
+					// Table columns in Safari have non-zero offsetWidth & zero
+					// getBoundingClientRect().width unless display is changed.
+					// Support: IE <=11 only
+					// Running getBoundingClientRect on a disconnected node
+					// in IE throws an error.
+					( !elem.getClientRects().length || !elem.getBoundingClientRect().width ) ?
+					swap( elem, cssShow, function() {
+						return getWidthOrHeight( elem, dimension, extra );
+					} ) :
+					getWidthOrHeight( elem, dimension, extra );
+			}
+		},
+
+		set: function( elem, value, extra ) {
+			var matches,
+				styles = getStyles( elem ),
+
+				// Only read styles.position if the test has a chance to fail
+				// to avoid forcing a reflow.
+				scrollboxSizeBuggy = !support.scrollboxSize() &&
+					styles.position === "absolute",
+
+				// To avoid forcing a reflow, only fetch boxSizing if we need it (gh-3991)
+				boxSizingNeeded = scrollboxSizeBuggy || extra,
+				isBorderBox = boxSizingNeeded &&
+					jQuery.css( elem, "boxSizing", false, styles ) === "border-box",
+				subtract = extra ?
+					boxModelAdjustment(
+						elem,
+						dimension,
+						extra,
+						isBorderBox,
+						styles
+					) :
+					0;
+
+			// Account for unreliable border-box dimensions by comparing offset* to computed and
+			// faking a content-box to get border and padding (gh-3699)
+			if ( isBorderBox && scrollboxSizeBuggy ) {
+				subtract -= Math.ceil(
+					elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] -
+					parseFloat( styles[ dimension ] ) -
+					boxModelAdjustment( elem, dimension, "border", false, styles ) -
+					0.5
+				);
+			}
+
+			// Convert to pixels if value adjustment is needed
+			if ( subtract && ( matches = rcssNum.exec( value ) ) &&
+				( matches[ 3 ] || "px" ) !== "px" ) {
+
+				elem.style[ dimension ] = value;
+				value = jQuery.css( elem, dimension );
+			}
+
+			return setPositiveNumber( elem, value, subtract );
+		}
+	};
+} );
+
+jQuery.cssHooks.marginLeft = addGetHookIf( support.reliableMarginLeft,
+	function( elem, computed ) {
+		if ( computed ) {
+			return ( parseFloat( curCSS( elem, "marginLeft" ) ) ||
+				elem.getBoundingClientRect().left -
+					swap( elem, { marginLeft: 0 }, function() {
+						return elem.getBoundingClientRect().left;
+					} )
+			) + "px";
+		}
+	}
+);
+
+// These hooks are used by animate to expand properties
+jQuery.each( {
+	margin: "",
+	padding: "",
+	border: "Width"
+}, function( prefix, suffix ) {
+	jQuery.cssHooks[ prefix + suffix ] = {
+		expand: function( value ) {
+			var i = 0,
+				expanded = {},
+
+				// Assumes a single number if not a string
+				parts = typeof value === "string" ? value.split( " " ) : [ value ];
+
+			for ( ; i < 4; i++ ) {
+				expanded[ prefix + cssExpand[ i ] + suffix ] =
+					parts[ i ] || parts[ i - 2 ] || parts[ 0 ];
+			}
+
+			return expanded;
+		}
+	};
+
+	if ( prefix !== "margin" ) {
+		jQuery.cssHooks[ prefix + suffix ].set = setPositiveNumber;
+	}
+} );
+
+jQuery.fn.extend( {
+	css: function( name, value ) {
+		return access( this, function( elem, name, value ) {
+			var styles, len,
+				map = {},
+				i = 0;
+
+			if ( Array.isArray( name ) ) {
+				styles = getStyles( elem );
+				len = name.length;
+
+				for ( ; i < len; i++ ) {
+					map[ name[ i ] ] = jQuery.css( elem, name[ i ], false, styles );
+				}
+
+				return map;
+			}
+
+			return value !== undefined ?
+				jQuery.style( elem, name, value ) :
+				jQuery.css( elem, name );
+		}, name, value, arguments.length > 1 );
+	}
+} );
+
+
+function Tween( elem, options, prop, end, easing ) {
+	return new Tween.prototype.init( elem, options, prop, end, easing );
+}
+jQuery.Tween = Tween;
+
+Tween.prototype = {
+	constructor: Tween,
+	init: function( elem, options, prop, end, easing, unit ) {
+		this.elem = elem;
+		this.prop = prop;
+		this.easing = easing || jQuery.easing._default;
+		this.options = options;
+		this.start = this.now = this.cur();
+		this.end = end;
+		this.unit = unit || ( jQuery.cssNumber[ prop ] ? "" : "px" );
+	},
+	cur: function() {
+		var hooks = Tween.propHooks[ this.prop ];
+
+		return hooks && hooks.get ?
+			hooks.get( this ) :
+			Tween.propHooks._default.get( this );
+	},
+	run: function( percent ) {
+		var eased,
+			hooks = Tween.propHooks[ this.prop ];
+
+		if ( this.options.duration ) {
+			this.pos = eased = jQuery.easing[ this.easing ](
+				percent, this.options.duration * percent, 0, 1, this.options.duration
+			);
+		} else {
+			this.pos = eased = percent;
+		}
+		this.now = ( this.end - this.start ) * eased + this.start;
+
+		if ( this.options.step ) {
+			this.options.step.call( this.elem, this.now, this );
+		}
+
+		if ( hooks && hooks.set ) {
+			hooks.set( this );
+		} else {
+			Tween.propHooks._default.set( this );
+		}
+		return this;
+	}
+};
+
+Tween.prototype.init.prototype = Tween.prototype;
+
+Tween.propHooks = {
+	_default: {
+		get: function( tween ) {
+			var result;
+
+			// Use a property on the element directly when it is not a DOM element,
+			// or when there is no matching style property that exists.
+			if ( tween.elem.nodeType !== 1 ||
+				tween.elem[ tween.prop ] != null && tween.elem.style[ tween.prop ] == null ) {
+				return tween.elem[ tween.prop ];
+			}
+
+			// Passing an empty string as a 3rd parameter to .css will automatically
+			// attempt a parseFloat and fallback to a string if the parse fails.
+			// Simple values such as "10px" are parsed to Float;
+			// complex values such as "rotate(1rad)" are returned as-is.
+			result = jQuery.css( tween.elem, tween.prop, "" );
+
+			// Empty strings, null, undefined and "auto" are converted to 0.
+			return !result || result === "auto" ? 0 : result;
+		},
+		set: function( tween ) {
+
+			// Use step hook for back compat.
+			// Use cssHook if its there.
+			// Use .style if available and use plain properties where available.
+			if ( jQuery.fx.step[ tween.prop ] ) {
+				jQuery.fx.step[ tween.prop ]( tween );
+			} else if ( tween.elem.nodeType === 1 && (
+				jQuery.cssHooks[ tween.prop ] ||
+					tween.elem.style[ finalPropName( tween.prop ) ] != null ) ) {
+				jQuery.style( tween.elem, tween.prop, tween.now + tween.unit );
+			} else {
+				tween.elem[ tween.prop ] = tween.now;
+			}
+		}
+	}
+};
+
+// Support: IE <=9 only
+// Panic based approach to setting things on disconnected nodes
+Tween.propHooks.scrollTop = Tween.propHooks.scrollLeft = {
+	set: function( tween ) {
+		if ( tween.elem.nodeType && tween.elem.parentNode ) {
+			tween.elem[ tween.prop ] = tween.now;
+		}
+	}
+};
+
+jQuery.easing = {
+	linear: function( p ) {
+		return p;
+	},
+	swing: function( p ) {
+		return 0.5 - Math.cos( p * Math.PI ) / 2;
+	},
+	_default: "swing"
+};
+
+jQuery.fx = Tween.prototype.init;
+
+// Back compat <1.8 extension point
+jQuery.fx.step = {};
+
+
+
+
+var
+	fxNow, inProgress,
+	rfxtypes = /^(?:toggle|show|hide)$/,
+	rrun = /queueHooks$/;
+
+function schedule() {
+	if ( inProgress ) {
+		if ( document.hidden === false && window.requestAnimationFrame ) {
+			window.requestAnimationFrame( schedule );
+		} else {
+			window.setTimeout( schedule, jQuery.fx.interval );
+		}
+
+		jQuery.fx.tick();
+	}
+}
+
+// Animations created synchronously will run synchronously
+function createFxNow() {
+	window.setTimeout( function() {
+		fxNow = undefined;
+	} );
+	return ( fxNow = Date.now() );
+}
+
+// Generate parameters to create a standard animation
+function genFx( type, includeWidth ) {
+	var which,
+		i = 0,
+		attrs = { height: type };
+
+	// If we include width, step value is 1 to do all cssExpand values,
+	// otherwise step value is 2 to skip over Left and Right
+	includeWidth = includeWidth ? 1 : 0;
+	for ( ; i < 4; i += 2 - includeWidth ) {
+		which = cssExpand[ i ];
+		attrs[ "margin" + which ] = attrs[ "padding" + which ] = type;
+	}
+
+	if ( includeWidth ) {
+		attrs.opacity = attrs.width = type;
+	}
+
+	return attrs;
+}
+
+function createTween( value, prop, animation ) {
+	var tween,
+		collection = ( Animation.tweeners[ prop ] || [] ).concat( Animation.tweeners[ "*" ] ),
+		index = 0,
+		length = collection.length;
+	for ( ; index < length; index++ ) {
+		if ( ( tween = collection[ index ].call( animation, prop, value ) ) ) {
+
+			// We're done with this property
+			return tween;
+		}
+	}
+}
+
+function defaultPrefilter( elem, props, opts ) {
+	var prop, value, toggle, hooks, oldfire, propTween, restoreDisplay, display,
+		isBox = "width" in props || "height" in props,
+		anim = this,
+		orig = {},
+		style = elem.style,
+		hidden = elem.nodeType && isHiddenWithinTree( elem ),
+		dataShow = dataPriv.get( elem, "fxshow" );
+
+	// Queue-skipping animations hijack the fx hooks
+	if ( !opts.queue ) {
+		hooks = jQuery._queueHooks( elem, "fx" );
+		if ( hooks.unqueued == null ) {
+			hooks.unqueued = 0;
+			oldfire = hooks.empty.fire;
+			hooks.empty.fire = function() {
+				if ( !hooks.unqueued ) {
+					oldfire();
+				}
+			};
+		}
+		hooks.unqueued++;
+
+		anim.always( function() {
+
+			// Ensure the complete handler is called before this completes
+			anim.always( function() {
+				hooks.unqueued--;
+				if ( !jQuery.queue( elem, "fx" ).length ) {
+					hooks.empty.fire();
+				}
+			} );
+		} );
+	}
+
+	// Detect show/hide animations
+	for ( prop in props ) {
+		value = props[ prop ];
+		if ( rfxtypes.test( value ) ) {
+			delete props[ prop ];
+			toggle = toggle || value === "toggle";
+			if ( value === ( hidden ? "hide" : "show" ) ) {
+
+				// Pretend to be hidden if this is a "show" and
+				// there is still data from a stopped show/hide
+				if ( value === "show" && dataShow && dataShow[ prop ] !== undefined ) {
+					hidden = true;
+
+				// Ignore all other no-op show/hide data
+				} else {
+					continue;
+				}
+			}
+			orig[ prop ] = dataShow && dataShow[ prop ] || jQuery.style( elem, prop );
+		}
+	}
+
+	// Bail out if this is a no-op like .hide().hide()
+	propTween = !jQuery.isEmptyObject( props );
+	if ( !propTween && jQuery.isEmptyObject( orig ) ) {
+		return;
+	}
+
+	// Restrict "overflow" and "display" styles during box animations
+	if ( isBox && elem.nodeType === 1 ) {
+
+		// Support: IE <=9 - 11, Edge 12 - 15
+		// Record all 3 overflow attributes because IE does not infer the shorthand
+		// from identically-valued overflowX and overflowY and Edge just mirrors
+		// the overflowX value there.
+		opts.overflow = [ style.overflow, style.overflowX, style.overflowY ];
+
+		// Identify a display type, preferring old show/hide data over the CSS cascade
+		restoreDisplay = dataShow && dataShow.display;
+		if ( restoreDisplay == null ) {
+			restoreDisplay = dataPriv.get( elem, "display" );
+		}
+		display = jQuery.css( elem, "display" );
+		if ( display === "none" ) {
+			if ( restoreDisplay ) {
+				display = restoreDisplay;
+			} else {
+
+				// Get nonempty value(s) by temporarily forcing visibility
+				showHide( [ elem ], true );
+				restoreDisplay = elem.style.display || restoreDisplay;
+				display = jQuery.css( elem, "display" );
+				showHide( [ elem ] );
+			}
+		}
+
+		// Animate inline elements as inline-block
+		if ( display === "inline" || display === "inline-block" && restoreDisplay != null ) {
+			if ( jQuery.css( elem, "float" ) === "none" ) {
+
+				// Restore the original display value at the end of pure show/hide animations
+				if ( !propTween ) {
+					anim.done( function() {
+						style.display = restoreDisplay;
+					} );
+					if ( restoreDisplay == null ) {
+						display = style.display;
+						restoreDisplay = display === "none" ? "" : display;
+					}
+				}
+				style.display = "inline-block";
+			}
+		}
+	}
+
+	if ( opts.overflow ) {
+		style.overflow = "hidden";
+		anim.always( function() {
+			style.overflow = opts.overflow[ 0 ];
+			style.overflowX = opts.overflow[ 1 ];
+			style.overflowY = opts.overflow[ 2 ];
+		} );
+	}
+
+	// Implement show/hide animations
+	propTween = false;
+	for ( prop in orig ) {
+
+		// General show/hide setup for this element animation
+		if ( !propTween ) {
+			if ( dataShow ) {
+				if ( "hidden" in dataShow ) {
+					hidden = dataShow.hidden;
+				}
+			} else {
+				dataShow = dataPriv.access( elem, "fxshow", { display: restoreDisplay } );
+			}
+
+			// Store hidden/visible for toggle so `.stop().toggle()` "reverses"
+			if ( toggle ) {
+				dataShow.hidden = !hidden;
+			}
+
+			// Show elements before animating them
+			if ( hidden ) {
+				showHide( [ elem ], true );
+			}
+
+			/* eslint-disable no-loop-func */
+
+			anim.done( function() {
+
+				/* eslint-enable no-loop-func */
+
+				// The final step of a "hide" animation is actually hiding the element
+				if ( !hidden ) {
+					showHide( [ elem ] );
+				}
+				dataPriv.remove( elem, "fxshow" );
+				for ( prop in orig ) {
+					jQuery.style( elem, prop, orig[ prop ] );
+				}
+			} );
+		}
+
+		// Per-property setup
+		propTween = createTween( hidden ? dataShow[ prop ] : 0, prop, anim );
+		if ( !( prop in dataShow ) ) {
+			dataShow[ prop ] = propTween.start;
+			if ( hidden ) {
+				propTween.end = propTween.start;
+				propTween.start = 0;
+			}
+		}
+	}
+}
+
+function propFilter( props, specialEasing ) {
+	var index, name, easing, value, hooks;
+
+	// camelCase, specialEasing and expand cssHook pass
+	for ( index in props ) {
+		name = camelCase( index );
+		easing = specialEasing[ name ];
+		value = props[ index ];
+		if ( Array.isArray( value ) ) {
+			easing = value[ 1 ];
+			value = props[ index ] = value[ 0 ];
+		}
+
+		if ( index !== name ) {
+			props[ name ] = value;
+			delete props[ index ];
+		}
+
+		hooks = jQuery.cssHooks[ name ];
+		if ( hooks && "expand" in hooks ) {
+			value = hooks.expand( value );
+			delete props[ name ];
+
+			// Not quite $.extend, this won't overwrite existing keys.
+			// Reusing 'index' because we have the correct "name"
+			for ( index in value ) {
+				if ( !( index in props ) ) {
+					props[ index ] = value[ index ];
+					specialEasing[ index ] = easing;
+				}
+			}
+		} else {
+			specialEasing[ name ] = easing;
+		}
+	}
+}
+
+function Animation( elem, properties, options ) {
+	var result,
+		stopped,
+		index = 0,
+		length = Animation.prefilters.length,
+		deferred = jQuery.Deferred().always( function() {
+
+			// Don't match elem in the :animated selector
+			delete tick.elem;
+		} ),
+		tick = function() {
+			if ( stopped ) {
+				return false;
+			}
+			var currentTime = fxNow || createFxNow(),
+				remaining = Math.max( 0, animation.startTime + animation.duration - currentTime ),
+
+				// Support: Android 2.3 only
+				// Archaic crash bug won't allow us to use `1 - ( 0.5 || 0 )` (#12497)
+				temp = remaining / animation.duration || 0,
+				percent = 1 - temp,
+				index = 0,
+				length = animation.tweens.length;
+
+			for ( ; index < length; index++ ) {
+				animation.tweens[ index ].run( percent );
+			}
+
+			deferred.notifyWith( elem, [ animation, percent, remaining ] );
+
+			// If there's more to do, yield
+			if ( percent < 1 && length ) {
+				return remaining;
+			}
+
+			// If this was an empty animation, synthesize a final progress notification
+			if ( !length ) {
+				deferred.notifyWith( elem, [ animation, 1, 0 ] );
+			}
+
+			// Resolve the animation and report its conclusion
+			deferred.resolveWith( elem, [ animation ] );
+			return false;
+		},
+		animation = deferred.promise( {
+			elem: elem,
+			props: jQuery.extend( {}, properties ),
+			opts: jQuery.extend( true, {
+				specialEasing: {},
+				easing: jQuery.easing._default
+			}, options ),
+			originalProperties: properties,
+			originalOptions: options,
+			startTime: fxNow || createFxNow(),
+			duration: options.duration,
+			tweens: [],
+			createTween: function( prop, end ) {
+				var tween = jQuery.Tween( elem, animation.opts, prop, end,
+					animation.opts.specialEasing[ prop ] || animation.opts.easing );
+				animation.tweens.push( tween );
+				return tween;
+			},
+			stop: function( gotoEnd ) {
+				var index = 0,
+
+					// If we are going to the end, we want to run all the tweens
+					// otherwise we skip this part
+					length = gotoEnd ? animation.tweens.length : 0;
+				if ( stopped ) {
+					return this;
+				}
+				stopped = true;
+				for ( ; index < length; index++ ) {
+					animation.tweens[ index ].run( 1 );
+				}
+
+				// Resolve when we played the last frame; otherwise, reject
+				if ( gotoEnd ) {
+					deferred.notifyWith( elem, [ animation, 1, 0 ] );
+					deferred.resolveWith( elem, [ animation, gotoEnd ] );
+				} else {
+					deferred.rejectWith( elem, [ animation, gotoEnd ] );
+				}
+				return this;
+			}
+		} ),
+		props = animation.props;
+
+	propFilter( props, animation.opts.specialEasing );
+
+	for ( ; index < length; index++ ) {
+		result = Animation.prefilters[ index ].call( animation, elem, props, animation.opts );
+		if ( result ) {
+			if ( isFunction( result.stop ) ) {
+				jQuery._queueHooks( animation.elem, animation.opts.queue ).stop =
+					result.stop.bind( result );
+			}
+			return result;
+		}
+	}
+
+	jQuery.map( props, createTween, animation );
+
+	if ( isFunction( animation.opts.start ) ) {
+		animation.opts.start.call( elem, animation );
+	}
+
+	// Attach callbacks from options
+	animation
+		.progress( animation.opts.progress )
+		.done( animation.opts.done, animation.opts.complete )
+		.fail( animation.opts.fail )
+		.always( animation.opts.always );
+
+	jQuery.fx.timer(
+		jQuery.extend( tick, {
+			elem: elem,
+			anim: animation,
+			queue: animation.opts.queue
+		} )
+	);
+
+	return animation;
+}
+
+jQuery.Animation = jQuery.extend( Animation, {
+
+	tweeners: {
+		"*": [ function( prop, value ) {
+			var tween = this.createTween( prop, value );
+			adjustCSS( tween.elem, prop, rcssNum.exec( value ), tween );
+			return tween;
+		} ]
+	},
+
+	tweener: function( props, callback ) {
+		if ( isFunction( props ) ) {
+			callback = props;
+			props = [ "*" ];
+		} else {
+			props = props.match( rnothtmlwhite );
+		}
+
+		var prop,
+			index = 0,
+			length = props.length;
+
+		for ( ; index < length; index++ ) {
+			prop = props[ index ];
+			Animation.tweeners[ prop ] = Animation.tweeners[ prop ] || [];
+			Animation.tweeners[ prop ].unshift( callback );
+		}
+	},
+
+	prefilters: [ defaultPrefilter ],
+
+	prefilter: function( callback, prepend ) {
+		if ( prepend ) {
+			Animation.prefilters.unshift( callback );
+		} else {
+			Animation.prefilters.push( callback );
+		}
+	}
+} );
+
+jQuery.speed = function( speed, easing, fn ) {
+	var opt = speed && typeof speed === "object" ? jQuery.extend( {}, speed ) : {
+		complete: fn || !fn && easing ||
+			isFunction( speed ) && speed,
+		duration: speed,
+		easing: fn && easing || easing && !isFunction( easing ) && easing
+	};
+
+	// Go to the end state if fx are off
+	if ( jQuery.fx.off ) {
+		opt.duration = 0;
+
+	} else {
+		if ( typeof opt.duration !== "number" ) {
+			if ( opt.duration in jQuery.fx.speeds ) {
+				opt.duration = jQuery.fx.speeds[ opt.duration ];
+
+			} else {
+				opt.duration = jQuery.fx.speeds._default;
+			}
+		}
+	}
+
+	// Normalize opt.queue - true/undefined/null -> "fx"
+	if ( opt.queue == null || opt.queue === true ) {
+		opt.queue = "fx";
+	}
+
+	// Queueing
+	opt.old = opt.complete;
+
+	opt.complete = function() {
+		if ( isFunction( opt.old ) ) {
+			opt.old.call( this );
+		}
+
+		if ( opt.queue ) {
+			jQuery.dequeue( this, opt.queue );
+		}
+	};
+
+	return opt;
+};
+
+jQuery.fn.extend( {
+	fadeTo: function( speed, to, easing, callback ) {
+
+		// Show any hidden elements after setting opacity to 0
+		return this.filter( isHiddenWithinTree ).css( "opacity", 0 ).show()
+
+			// Animate to the value specified
+			.end().animate( { opacity: to }, speed, easing, callback );
+	},
+	animate: function( prop, speed, easing, callback ) {
+		var empty = jQuery.isEmptyObject( prop ),
+			optall = jQuery.speed( speed, easing, callback ),
+			doAnimation = function() {
+
+				// Operate on a copy of prop so per-property easing won't be lost
+				var anim = Animation( this, jQuery.extend( {}, prop ), optall );
+
+				// Empty animations, or finishing resolves immediately
+				if ( empty || dataPriv.get( this, "finish" ) ) {
+					anim.stop( true );
+				}
+			};
+
+		doAnimation.finish = doAnimation;
+
+		return empty || optall.queue === false ?
+			this.each( doAnimation ) :
+			this.queue( optall.queue, doAnimation );
+	},
+	stop: function( type, clearQueue, gotoEnd ) {
+		var stopQueue = function( hooks ) {
+			var stop = hooks.stop;
+			delete hooks.stop;
+			stop( gotoEnd );
+		};
+
+		if ( typeof type !== "string" ) {
+			gotoEnd = clearQueue;
+			clearQueue = type;
+			type = undefined;
+		}
+		if ( clearQueue ) {
+			this.queue( type || "fx", [] );
+		}
+
+		return this.each( function() {
+			var dequeue = true,
+				index = type != null && type + "queueHooks",
+				timers = jQuery.timers,
+				data = dataPriv.get( this );
+
+			if ( index ) {
+				if ( data[ index ] && data[ index ].stop ) {
+					stopQueue( data[ index ] );
+				}
+			} else {
+				for ( index in data ) {
+					if ( data[ index ] && data[ index ].stop && rrun.test( index ) ) {
+						stopQueue( data[ index ] );
+					}
+				}
+			}
+
+			for ( index = timers.length; index--; ) {
+				if ( timers[ index ].elem === this &&
+					( type == null || timers[ index ].queue === type ) ) {
+
+					timers[ index ].anim.stop( gotoEnd );
+					dequeue = false;
+					timers.splice( index, 1 );
+				}
+			}
+
+			// Start the next in the queue if the last step wasn't forced.
+			// Timers currently will call their complete callbacks, which
+			// will dequeue but only if they were gotoEnd.
+			if ( dequeue || !gotoEnd ) {
+				jQuery.dequeue( this, type );
+			}
+		} );
+	},
+	finish: function( type ) {
+		if ( type !== false ) {
+			type = type || "fx";
+		}
+		return this.each( function() {
+			var index,
+				data = dataPriv.get( this ),
+				queue = data[ type + "queue" ],
+				hooks = data[ type + "queueHooks" ],
+				timers = jQuery.timers,
+				length = queue ? queue.length : 0;
+
+			// Enable finishing flag on private data
+			data.finish = true;
+
+			// Empty the queue first
+			jQuery.queue( this, type, [] );
+
+			if ( hooks && hooks.stop ) {
+				hooks.stop.call( this, true );
+			}
+
+			// Look for any active animations, and finish them
+			for ( index = timers.length; index--; ) {
+				if ( timers[ index ].elem === this && timers[ index ].queue === type ) {
+					timers[ index ].anim.stop( true );
+					timers.splice( index, 1 );
+				}
+			}
+
+			// Look for any animations in the old queue and finish them
+			for ( index = 0; index < length; index++ ) {
+				if ( queue[ index ] && queue[ index ].finish ) {
+					queue[ index ].finish.call( this );
+				}
+			}
+
+			// Turn off finishing flag
+			delete data.finish;
+		} );
+	}
+} );
+
+jQuery.each( [ "toggle", "show", "hide" ], function( _i, name ) {
+	var cssFn = jQuery.fn[ name ];
+	jQuery.fn[ name ] = function( speed, easing, callback ) {
+		return speed == null || typeof speed === "boolean" ?
+			cssFn.apply( this, arguments ) :
+			this.animate( genFx( name, true ), speed, easing, callback );
+	};
+} );
+
+// Generate shortcuts for custom animations
+jQuery.each( {
+	slideDown: genFx( "show" ),
+	slideUp: genFx( "hide" ),
+	slideToggle: genFx( "toggle" ),
+	fadeIn: { opacity: "show" },
+	fadeOut: { opacity: "hide" },
+	fadeToggle: { opacity: "toggle" }
+}, function( name, props ) {
+	jQuery.fn[ name ] = function( speed, easing, callback ) {
+		return this.animate( props, speed, easing, callback );
+	};
+} );
+
+jQuery.timers = [];
+jQuery.fx.tick = function() {
+	var timer,
+		i = 0,
+		timers = jQuery.timers;
+
+	fxNow = Date.now();
+
+	for ( ; i < timers.length; i++ ) {
+		timer = timers[ i ];
+
+		// Run the timer and safely remove it when done (allowing for external removal)
+		if ( !timer() && timers[ i ] === timer ) {
+			timers.splice( i--, 1 );
+		}
+	}
+
+	if ( !timers.length ) {
+		jQuery.fx.stop();
+	}
+	fxNow = undefined;
+};
+
+jQuery.fx.timer = function( timer ) {
+	jQuery.timers.push( timer );
+	jQuery.fx.start();
+};
+
+jQuery.fx.interval = 13;
+jQuery.fx.start = function() {
+	if ( inProgress ) {
+		return;
+	}
+
+	inProgress = true;
+	schedule();
+};
+
+jQuery.fx.stop = function() {
+	inProgress = null;
+};
+
+jQuery.fx.speeds = {
+	slow: 600,
+	fast: 200,
+
+	// Default speed
+	_default: 400
+};
+
+
+// Based off of the plugin by Clint Helfers, with permission.
+// https://web.archive.org/web/20100324014747/http://blindsignals.com/index.php/2009/07/jquery-delay/
+jQuery.fn.delay = function( time, type ) {
+	time = jQuery.fx ? jQuery.fx.speeds[ time ] || time : time;
+	type = type || "fx";
+
+	return this.queue( type, function( next, hooks ) {
+		var timeout = window.setTimeout( next, time );
+		hooks.stop = function() {
+			window.clearTimeout( timeout );
+		};
+	} );
+};
+
+
+( function() {
+	var input = document.createElement( "input" ),
+		select = document.createElement( "select" ),
+		opt = select.appendChild( document.createElement( "option" ) );
+
+	input.type = "checkbox";
+
+	// Support: Android <=4.3 only
+	// Default value for a checkbox should be "on"
+	support.checkOn = input.value !== "";
+
+	// Support: IE <=11 only
+	// Must access selectedIndex to make default options select
+	support.optSelected = opt.selected;
+
+	// Support: IE <=11 only
+	// An input loses its value after becoming a radio
+	input = document.createElement( "input" );
+	input.value = "t";
+	input.type = "radio";
+	support.radioValue = input.value === "t";
+} )();
+
+
+var boolHook,
+	attrHandle = jQuery.expr.attrHandle;
+
+jQuery.fn.extend( {
+	attr: function( name, value ) {
+		return access( this, jQuery.attr, name, value, arguments.length > 1 );
+	},
+
+	removeAttr: function( name ) {
+		return this.each( function() {
+			jQuery.removeAttr( this, name );
+		} );
+	}
+} );
+
+jQuery.extend( {
+	attr: function( elem, name, value ) {
+		var ret, hooks,
+			nType = elem.nodeType;
+
+		// Don't get/set attributes on text, comment and attribute nodes
+		if ( nType === 3 || nType === 8 || nType === 2 ) {
+			return;
+		}
+
+		// Fallback to prop when attributes are not supported
+		if ( typeof elem.getAttribute === "undefined" ) {
+			return jQuery.prop( elem, name, value );
+		}
+
+		// Attribute hooks are determined by the lowercase version
+		// Grab necessary hook if one is defined
+		if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) {
+			hooks = jQuery.attrHooks[ name.toLowerCase() ] ||
+				( jQuery.expr.match.bool.test( name ) ? boolHook : undefined );
+		}
+
+		if ( value !== undefined ) {
+			if ( value === null ) {
+				jQuery.removeAttr( elem, name );
+				return;
+			}
+
+			if ( hooks && "set" in hooks &&
+				( ret = hooks.set( elem, value, name ) ) !== undefined ) {
+				return ret;
+			}
+
+			elem.setAttribute( name, value + "" );
+			return value;
+		}
+
+		if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) {
+			return ret;
+		}
+
+		ret = jQuery.find.attr( elem, name );
+
+		// Non-existent attributes return null, we normalize to undefined
+		return ret == null ? undefined : ret;
+	},
+
+	attrHooks: {
+		type: {
+			set: function( elem, value ) {
+				if ( !support.radioValue && value === "radio" &&
+					nodeName( elem, "input" ) ) {
+					var val = elem.value;
+					elem.setAttribute( "type", value );
+					if ( val ) {
+						elem.value = val;
+					}
+					return value;
+				}
+			}
+		}
+	},
+
+	removeAttr: function( elem, value ) {
+		var name,
+			i = 0,
+
+			// Attribute names can contain non-HTML whitespace characters
+			// https://html.spec.whatwg.org/multipage/syntax.html#attributes-2
+			attrNames = value && value.match( rnothtmlwhite );
+
+		if ( attrNames && elem.nodeType === 1 ) {
+			while ( ( name = attrNames[ i++ ] ) ) {
+				elem.removeAttribute( name );
+			}
+		}
+	}
+} );
+
+// Hooks for boolean attributes
+boolHook = {
+	set: function( elem, value, name ) {
+		if ( value === false ) {
+
+			// Remove boolean attributes when set to false
+			jQuery.removeAttr( elem, name );
+		} else {
+			elem.setAttribute( name, name );
+		}
+		return name;
+	}
+};
+
+jQuery.each( jQuery.expr.match.bool.source.match( /\w+/g ), function( _i, name ) {
+	var getter = attrHandle[ name ] || jQuery.find.attr;
+
+	attrHandle[ name ] = function( elem, name, isXML ) {
+		var ret, handle,
+			lowercaseName = name.toLowerCase();
+
+		if ( !isXML ) {
+
+			// Avoid an infinite loop by temporarily removing this function from the getter
+			handle = attrHandle[ lowercaseName ];
+			attrHandle[ lowercaseName ] = ret;
+			ret = getter( elem, name, isXML ) != null ?
+				lowercaseName :
+				null;
+			attrHandle[ lowercaseName ] = handle;
+		}
+		return ret;
+	};
+} );
+
+
+
+
+var rfocusable = /^(?:input|select|textarea|button)$/i,
+	rclickable = /^(?:a|area)$/i;
+
+jQuery.fn.extend( {
+	prop: function( name, value ) {
+		return access( this, jQuery.prop, name, value, arguments.length > 1 );
+	},
+
+	removeProp: function( name ) {
+		return this.each( function() {
+			delete this[ jQuery.propFix[ name ] || name ];
+		} );
+	}
+} );
+
+jQuery.extend( {
+	prop: function( elem, name, value ) {
+		var ret, hooks,
+			nType = elem.nodeType;
+
+		// Don't get/set properties on text, comment and attribute nodes
+		if ( nType === 3 || nType === 8 || nType === 2 ) {
+			return;
+		}
+
+		if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) {
+
+			// Fix name and attach hooks
+			name = jQuery.propFix[ name ] || name;
+			hooks = jQuery.propHooks[ name ];
+		}
+
+		if ( value !== undefined ) {
+			if ( hooks && "set" in hooks &&
+				( ret = hooks.set( elem, value, name ) ) !== undefined ) {
+				return ret;
+			}
+
+			return ( elem[ name ] = value );
+		}
+
+		if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) {
+			return ret;
+		}
+
+		return elem[ name ];
+	},
+
+	propHooks: {
+		tabIndex: {
+			get: function( elem ) {
+
+				// Support: IE <=9 - 11 only
+				// elem.tabIndex doesn't always return the
+				// correct value when it hasn't been explicitly set
+				// https://web.archive.org/web/20141116233347/http://fluidproject.org/blog/2008/01/09/getting-setting-and-removing-tabindex-values-with-javascript/
+				// Use proper attribute retrieval(#12072)
+				var tabindex = jQuery.find.attr( elem, "tabindex" );
+
+				if ( tabindex ) {
+					return parseInt( tabindex, 10 );
+				}
+
+				if (
+					rfocusable.test( elem.nodeName ) ||
+					rclickable.test( elem.nodeName ) &&
+					elem.href
+				) {
+					return 0;
+				}
+
+				return -1;
+			}
+		}
+	},
+
+	propFix: {
+		"for": "htmlFor",
+		"class": "className"
+	}
+} );
+
+// Support: IE <=11 only
+// Accessing the selectedIndex property
+// forces the browser to respect setting selected
+// on the option
+// The getter ensures a default option is selected
+// when in an optgroup
+// eslint rule "no-unused-expressions" is disabled for this code
+// since it considers such accessions noop
+if ( !support.optSelected ) {
+	jQuery.propHooks.selected = {
+		get: function( elem ) {
+
+			/* eslint no-unused-expressions: "off" */
+
+			var parent = elem.parentNode;
+			if ( parent && parent.parentNode ) {
+				parent.parentNode.selectedIndex;
+			}
+			return null;
+		},
+		set: function( elem ) {
+
+			/* eslint no-unused-expressions: "off" */
+
+			var parent = elem.parentNode;
+			if ( parent ) {
+				parent.selectedIndex;
+
+				if ( parent.parentNode ) {
+					parent.parentNode.selectedIndex;
+				}
+			}
+		}
+	};
+}
+
+jQuery.each( [
+	"tabIndex",
+	"readOnly",
+	"maxLength",
+	"cellSpacing",
+	"cellPadding",
+	"rowSpan",
+	"colSpan",
+	"useMap",
+	"frameBorder",
+	"contentEditable"
+], function() {
+	jQuery.propFix[ this.toLowerCase() ] = this;
+} );
+
+
+
+
+	// Strip and collapse whitespace according to HTML spec
+	// https://infra.spec.whatwg.org/#strip-and-collapse-ascii-whitespace
+	function stripAndCollapse( value ) {
+		var tokens = value.match( rnothtmlwhite ) || [];
+		return tokens.join( " " );
+	}
+
+
+function getClass( elem ) {
+	return elem.getAttribute && elem.getAttribute( "class" ) || "";
+}
+
+function classesToArray( value ) {
+	if ( Array.isArray( value ) ) {
+		return value;
+	}
+	if ( typeof value === "string" ) {
+		return value.match( rnothtmlwhite ) || [];
+	}
+	return [];
+}
+
+jQuery.fn.extend( {
+	addClass: function( value ) {
+		var classes, elem, cur, curValue, clazz, j, finalValue,
+			i = 0;
+
+		if ( isFunction( value ) ) {
+			return this.each( function( j ) {
+				jQuery( this ).addClass( value.call( this, j, getClass( this ) ) );
+			} );
+		}
+
+		classes = classesToArray( value );
+
+		if ( classes.length ) {
+			while ( ( elem = this[ i++ ] ) ) {
+				curValue = getClass( elem );
+				cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " );
+
+				if ( cur ) {
+					j = 0;
+					while ( ( clazz = classes[ j++ ] ) ) {
+						if ( cur.indexOf( " " + clazz + " " ) < 0 ) {
+							cur += clazz + " ";
+						}
+					}
+
+					// Only assign if different to avoid unneeded rendering.
+					finalValue = stripAndCollapse( cur );
+					if ( curValue !== finalValue ) {
+						elem.setAttribute( "class", finalValue );
+					}
+				}
+			}
+		}
+
+		return this;
+	},
+
+	removeClass: function( value ) {
+		var classes, elem, cur, curValue, clazz, j, finalValue,
+			i = 0;
+
+		if ( isFunction( value ) ) {
+			return this.each( function( j ) {
+				jQuery( this ).removeClass( value.call( this, j, getClass( this ) ) );
+			} );
+		}
+
+		if ( !arguments.length ) {
+			return this.attr( "class", "" );
+		}
+
+		classes = classesToArray( value );
+
+		if ( classes.length ) {
+			while ( ( elem = this[ i++ ] ) ) {
+				curValue = getClass( elem );
+
+				// This expression is here for better compressibility (see addClass)
+				cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " );
+
+				if ( cur ) {
+					j = 0;
+					while ( ( clazz = classes[ j++ ] ) ) {
+
+						// Remove *all* instances
+						while ( cur.indexOf( " " + clazz + " " ) > -1 ) {
+							cur = cur.replace( " " + clazz + " ", " " );
+						}
+					}
+
+					// Only assign if different to avoid unneeded rendering.
+					finalValue = stripAndCollapse( cur );
+					if ( curValue !== finalValue ) {
+						elem.setAttribute( "class", finalValue );
+					}
+				}
+			}
+		}
+
+		return this;
+	},
+
+	toggleClass: function( value, stateVal ) {
+		var type = typeof value,
+			isValidValue = type === "string" || Array.isArray( value );
+
+		if ( typeof stateVal === "boolean" && isValidValue ) {
+			return stateVal ? this.addClass( value ) : this.removeClass( value );
+		}
+
+		if ( isFunction( value ) ) {
+			return this.each( function( i ) {
+				jQuery( this ).toggleClass(
+					value.call( this, i, getClass( this ), stateVal ),
+					stateVal
+				);
+			} );
+		}
+
+		return this.each( function() {
+			var className, i, self, classNames;
+
+			if ( isValidValue ) {
+
+				// Toggle individual class names
+				i = 0;
+				self = jQuery( this );
+				classNames = classesToArray( value );
+
+				while ( ( className = classNames[ i++ ] ) ) {
+
+					// Check each className given, space separated list
+					if ( self.hasClass( className ) ) {
+						self.removeClass( className );
+					} else {
+						self.addClass( className );
+					}
+				}
+
+			// Toggle whole class name
+			} else if ( value === undefined || type === "boolean" ) {
+				className = getClass( this );
+				if ( className ) {
+
+					// Store className if set
+					dataPriv.set( this, "__className__", className );
+				}
+
+				// If the element has a class name or if we're passed `false`,
+				// then remove the whole classname (if there was one, the above saved it).
+				// Otherwise bring back whatever was previously saved (if anything),
+				// falling back to the empty string if nothing was stored.
+				if ( this.setAttribute ) {
+					this.setAttribute( "class",
+						className || value === false ?
+							"" :
+							dataPriv.get( this, "__className__" ) || ""
+					);
+				}
+			}
+		} );
+	},
+
+	hasClass: function( selector ) {
+		var className, elem,
+			i = 0;
+
+		className = " " + selector + " ";
+		while ( ( elem = this[ i++ ] ) ) {
+			if ( elem.nodeType === 1 &&
+				( " " + stripAndCollapse( getClass( elem ) ) + " " ).indexOf( className ) > -1 ) {
+				return true;
+			}
+		}
+
+		return false;
+	}
+} );
+
+
+
+
+var rreturn = /\r/g;
+
+jQuery.fn.extend( {
+	val: function( value ) {
+		var hooks, ret, valueIsFunction,
+			elem = this[ 0 ];
+
+		if ( !arguments.length ) {
+			if ( elem ) {
+				hooks = jQuery.valHooks[ elem.type ] ||
+					jQuery.valHooks[ elem.nodeName.toLowerCase() ];
+
+				if ( hooks &&
+					"get" in hooks &&
+					( ret = hooks.get( elem, "value" ) ) !== undefined
+				) {
+					return ret;
+				}
+
+				ret = elem.value;
+
+				// Handle most common string cases
+				if ( typeof ret === "string" ) {
+					return ret.replace( rreturn, "" );
+				}
+
+				// Handle cases where value is null/undef or number
+				return ret == null ? "" : ret;
+			}
+
+			return;
+		}
+
+		valueIsFunction = isFunction( value );
+
+		return this.each( function( i ) {
+			var val;
+
+			if ( this.nodeType !== 1 ) {
+				return;
+			}
+
+			if ( valueIsFunction ) {
+				val = value.call( this, i, jQuery( this ).val() );
+			} else {
+				val = value;
+			}
+
+			// Treat null/undefined as ""; convert numbers to string
+			if ( val == null ) {
+				val = "";
+
+			} else if ( typeof val === "number" ) {
+				val += "";
+
+			} else if ( Array.isArray( val ) ) {
+				val = jQuery.map( val, function( value ) {
+					return value == null ? "" : value + "";
+				} );
+			}
+
+			hooks = jQuery.valHooks[ this.type ] || jQuery.valHooks[ this.nodeName.toLowerCase() ];
+
+			// If set returns undefined, fall back to normal setting
+			if ( !hooks || !( "set" in hooks ) || hooks.set( this, val, "value" ) === undefined ) {
+				this.value = val;
+			}
+		} );
+	}
+} );
+
+jQuery.extend( {
+	valHooks: {
+		option: {
+			get: function( elem ) {
+
+				var val = jQuery.find.attr( elem, "value" );
+				return val != null ?
+					val :
+
+					// Support: IE <=10 - 11 only
+					// option.text throws exceptions (#14686, #14858)
+					// Strip and collapse whitespace
+					// https://html.spec.whatwg.org/#strip-and-collapse-whitespace
+					stripAndCollapse( jQuery.text( elem ) );
+			}
+		},
+		select: {
+			get: function( elem ) {
+				var value, option, i,
+					options = elem.options,
+					index = elem.selectedIndex,
+					one = elem.type === "select-one",
+					values = one ? null : [],
+					max = one ? index + 1 : options.length;
+
+				if ( index < 0 ) {
+					i = max;
+
+				} else {
+					i = one ? index : 0;
+				}
+
+				// Loop through all the selected options
+				for ( ; i < max; i++ ) {
+					option = options[ i ];
+
+					// Support: IE <=9 only
+					// IE8-9 doesn't update selected after form reset (#2551)
+					if ( ( option.selected || i === index ) &&
+
+							// Don't return options that are disabled or in a disabled optgroup
+							!option.disabled &&
+							( !option.parentNode.disabled ||
+								!nodeName( option.parentNode, "optgroup" ) ) ) {
+
+						// Get the specific value for the option
+						value = jQuery( option ).val();
+
+						// We don't need an array for one selects
+						if ( one ) {
+							return value;
+						}
+
+						// Multi-Selects return an array
+						values.push( value );
+					}
+				}
+
+				return values;
+			},
+
+			set: function( elem, value ) {
+				var optionSet, option,
+					options = elem.options,
+					values = jQuery.makeArray( value ),
+					i = options.length;
+
+				while ( i-- ) {
+					option = options[ i ];
+
+					/* eslint-disable no-cond-assign */
+
+					if ( option.selected =
+						jQuery.inArray( jQuery.valHooks.option.get( option ), values ) > -1
+					) {
+						optionSet = true;
+					}
+
+					/* eslint-enable no-cond-assign */
+				}
+
+				// Force browsers to behave consistently when non-matching value is set
+				if ( !optionSet ) {
+					elem.selectedIndex = -1;
+				}
+				return values;
+			}
+		}
+	}
+} );
+
+// Radios and checkboxes getter/setter
+jQuery.each( [ "radio", "checkbox" ], function() {
+	jQuery.valHooks[ this ] = {
+		set: function( elem, value ) {
+			if ( Array.isArray( value ) ) {
+				return ( elem.checked = jQuery.inArray( jQuery( elem ).val(), value ) > -1 );
+			}
+		}
+	};
+	if ( !support.checkOn ) {
+		jQuery.valHooks[ this ].get = function( elem ) {
+			return elem.getAttribute( "value" ) === null ? "on" : elem.value;
+		};
+	}
+} );
+
+
+
+
+// Return jQuery for attributes-only inclusion
+
+
+support.focusin = "onfocusin" in window;
+
+
+var rfocusMorph = /^(?:focusinfocus|focusoutblur)$/,
+	stopPropagationCallback = function( e ) {
+		e.stopPropagation();
+	};
+
+jQuery.extend( jQuery.event, {
+
+	trigger: function( event, data, elem, onlyHandlers ) {
+
+		var i, cur, tmp, bubbleType, ontype, handle, special, lastElement,
+			eventPath = [ elem || document ],
+			type = hasOwn.call( event, "type" ) ? event.type : event,
+			namespaces = hasOwn.call( event, "namespace" ) ? event.namespace.split( "." ) : [];
+
+		cur = lastElement = tmp = elem = elem || document;
+
+		// Don't do events on text and comment nodes
+		if ( elem.nodeType === 3 || elem.nodeType === 8 ) {
+			return;
+		}
+
+		// focus/blur morphs to focusin/out; ensure we're not firing them right now
+		if ( rfocusMorph.test( type + jQuery.event.triggered ) ) {
+			return;
+		}
+
+		if ( type.indexOf( "." ) > -1 ) {
+
+			// Namespaced trigger; create a regexp to match event type in handle()
+			namespaces = type.split( "." );
+			type = namespaces.shift();
+			namespaces.sort();
+		}
+		ontype = type.indexOf( ":" ) < 0 && "on" + type;
+
+		// Caller can pass in a jQuery.Event object, Object, or just an event type string
+		event = event[ jQuery.expando ] ?
+			event :
+			new jQuery.Event( type, typeof event === "object" && event );
+
+		// Trigger bitmask: & 1 for native handlers; & 2 for jQuery (always true)
+		event.isTrigger = onlyHandlers ? 2 : 3;
+		event.namespace = namespaces.join( "." );
+		event.rnamespace = event.namespace ?
+			new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ) :
+			null;
+
+		// Clean up the event in case it is being reused
+		event.result = undefined;
+		if ( !event.target ) {
+			event.target = elem;
+		}
+
+		// Clone any incoming data and prepend the event, creating the handler arg list
+		data = data == null ?
+			[ event ] :
+			jQuery.makeArray( data, [ event ] );
+
+		// Allow special events to draw outside the lines
+		special = jQuery.event.special[ type ] || {};
+		if ( !onlyHandlers && special.trigger && special.trigger.apply( elem, data ) === false ) {
+			return;
+		}
+
+		// Determine event propagation path in advance, per W3C events spec (#9951)
+		// Bubble up to document, then to window; watch for a global ownerDocument var (#9724)
+		if ( !onlyHandlers && !special.noBubble && !isWindow( elem ) ) {
+
+			bubbleType = special.delegateType || type;
+			if ( !rfocusMorph.test( bubbleType + type ) ) {
+				cur = cur.parentNode;
+			}
+			for ( ; cur; cur = cur.parentNode ) {
+				eventPath.push( cur );
+				tmp = cur;
+			}
+
+			// Only add window if we got to document (e.g., not plain obj or detached DOM)
+			if ( tmp === ( elem.ownerDocument || document ) ) {
+				eventPath.push( tmp.defaultView || tmp.parentWindow || window );
+			}
+		}
+
+		// Fire handlers on the event path
+		i = 0;
+		while ( ( cur = eventPath[ i++ ] ) && !event.isPropagationStopped() ) {
+			lastElement = cur;
+			event.type = i > 1 ?
+				bubbleType :
+				special.bindType || type;
+
+			// jQuery handler
+			handle = ( dataPriv.get( cur, "events" ) || Object.create( null ) )[ event.type ] &&
+				dataPriv.get( cur, "handle" );
+			if ( handle ) {
+				handle.apply( cur, data );
+			}
+
+			// Native handler
+			handle = ontype && cur[ ontype ];
+			if ( handle && handle.apply && acceptData( cur ) ) {
+				event.result = handle.apply( cur, data );
+				if ( event.result === false ) {
+					event.preventDefault();
+				}
+			}
+		}
+		event.type = type;
+
+		// If nobody prevented the default action, do it now
+		if ( !onlyHandlers && !event.isDefaultPrevented() ) {
+
+			if ( ( !special._default ||
+				special._default.apply( eventPath.pop(), data ) === false ) &&
+				acceptData( elem ) ) {
+
+				// Call a native DOM method on the target with the same name as the event.
+				// Don't do default actions on window, that's where global variables be (#6170)
+				if ( ontype && isFunction( elem[ type ] ) && !isWindow( elem ) ) {
+
+					// Don't re-trigger an onFOO event when we call its FOO() method
+					tmp = elem[ ontype ];
+
+					if ( tmp ) {
+						elem[ ontype ] = null;
+					}
+
+					// Prevent re-triggering of the same event, since we already bubbled it above
+					jQuery.event.triggered = type;
+
+					if ( event.isPropagationStopped() ) {
+						lastElement.addEventListener( type, stopPropagationCallback );
+					}
+
+					elem[ type ]();
+
+					if ( event.isPropagationStopped() ) {
+						lastElement.removeEventListener( type, stopPropagationCallback );
+					}
+
+					jQuery.event.triggered = undefined;
+
+					if ( tmp ) {
+						elem[ ontype ] = tmp;
+					}
+				}
+			}
+		}
+
+		return event.result;
+	},
+
+	// Piggyback on a donor event to simulate a different one
+	// Used only for `focus(in | out)` events
+	simulate: function( type, elem, event ) {
+		var e = jQuery.extend(
+			new jQuery.Event(),
+			event,
+			{
+				type: type,
+				isSimulated: true
+			}
+		);
+
+		jQuery.event.trigger( e, null, elem );
+	}
+
+} );
+
+jQuery.fn.extend( {
+
+	trigger: function( type, data ) {
+		return this.each( function() {
+			jQuery.event.trigger( type, data, this );
+		} );
+	},
+	triggerHandler: function( type, data ) {
+		var elem = this[ 0 ];
+		if ( elem ) {
+			return jQuery.event.trigger( type, data, elem, true );
+		}
+	}
+} );
+
+
+// Support: Firefox <=44
+// Firefox doesn't have focus(in | out) events
+// Related ticket - https://bugzilla.mozilla.org/show_bug.cgi?id=687787
+//
+// Support: Chrome <=48 - 49, Safari <=9.0 - 9.1
+// focus(in | out) events fire after focus & blur events,
+// which is spec violation - http://www.w3.org/TR/DOM-Level-3-Events/#events-focusevent-event-order
+// Related ticket - https://bugs.chromium.org/p/chromium/issues/detail?id=449857
+if ( !support.focusin ) {
+	jQuery.each( { focus: "focusin", blur: "focusout" }, function( orig, fix ) {
+
+		// Attach a single capturing handler on the document while someone wants focusin/focusout
+		var handler = function( event ) {
+			jQuery.event.simulate( fix, event.target, jQuery.event.fix( event ) );
+		};
+
+		jQuery.event.special[ fix ] = {
+			setup: function() {
+
+				// Handle: regular nodes (via `this.ownerDocument`), window
+				// (via `this.document`) & document (via `this`).
+				var doc = this.ownerDocument || this.document || this,
+					attaches = dataPriv.access( doc, fix );
+
+				if ( !attaches ) {
+					doc.addEventListener( orig, handler, true );
+				}
+				dataPriv.access( doc, fix, ( attaches || 0 ) + 1 );
+			},
+			teardown: function() {
+				var doc = this.ownerDocument || this.document || this,
+					attaches = dataPriv.access( doc, fix ) - 1;
+
+				if ( !attaches ) {
+					doc.removeEventListener( orig, handler, true );
+					dataPriv.remove( doc, fix );
+
+				} else {
+					dataPriv.access( doc, fix, attaches );
+				}
+			}
+		};
+	} );
+}
+var location = window.location;
+
+var nonce = { guid: Date.now() };
+
+var rquery = ( /\?/ );
+
+
+
+// Cross-browser xml parsing
+jQuery.parseXML = function( data ) {
+	var xml, parserErrorElem;
+	if ( !data || typeof data !== "string" ) {
+		return null;
+	}
+
+	// Support: IE 9 - 11 only
+	// IE throws on parseFromString with invalid input.
+	try {
+		xml = ( new window.DOMParser() ).parseFromString( data, "text/xml" );
+	} catch ( e ) {}
+
+	parserErrorElem = xml && xml.getElementsByTagName( "parsererror" )[ 0 ];
+	if ( !xml || parserErrorElem ) {
+		jQuery.error( "Invalid XML: " + (
+			parserErrorElem ?
+				jQuery.map( parserErrorElem.childNodes, function( el ) {
+					return el.textContent;
+				} ).join( "\n" ) :
+				data
+		) );
+	}
+	return xml;
+};
+
+
+var
+	rbracket = /\[\]$/,
+	rCRLF = /\r?\n/g,
+	rsubmitterTypes = /^(?:submit|button|image|reset|file)$/i,
+	rsubmittable = /^(?:input|select|textarea|keygen)/i;
+
+function buildParams( prefix, obj, traditional, add ) {
+	var name;
+
+	if ( Array.isArray( obj ) ) {
+
+		// Serialize array item.
+		jQuery.each( obj, function( i, v ) {
+			if ( traditional || rbracket.test( prefix ) ) {
+
+				// Treat each array item as a scalar.
+				add( prefix, v );
+
+			} else {
+
+				// Item is non-scalar (array or object), encode its numeric index.
+				buildParams(
+					prefix + "[" + ( typeof v === "object" && v != null ? i : "" ) + "]",
+					v,
+					traditional,
+					add
+				);
+			}
+		} );
+
+	} else if ( !traditional && toType( obj ) === "object" ) {
+
+		// Serialize object item.
+		for ( name in obj ) {
+			buildParams( prefix + "[" + name + "]", obj[ name ], traditional, add );
+		}
+
+	} else {
+
+		// Serialize scalar item.
+		add( prefix, obj );
+	}
+}
+
+// Serialize an array of form elements or a set of
+// key/values into a query string
+jQuery.param = function( a, traditional ) {
+	var prefix,
+		s = [],
+		add = function( key, valueOrFunction ) {
+
+			// If value is a function, invoke it and use its return value
+			var value = isFunction( valueOrFunction ) ?
+				valueOrFunction() :
+				valueOrFunction;
+
+			s[ s.length ] = encodeURIComponent( key ) + "=" +
+				encodeURIComponent( value == null ? "" : value );
+		};
+
+	if ( a == null ) {
+		return "";
+	}
+
+	// If an array was passed in, assume that it is an array of form elements.
+	if ( Array.isArray( a ) || ( a.jquery && !jQuery.isPlainObject( a ) ) ) {
+
+		// Serialize the form elements
+		jQuery.each( a, function() {
+			add( this.name, this.value );
+		} );
+
+	} else {
+
+		// If traditional, encode the "old" way (the way 1.3.2 or older
+		// did it), otherwise encode params recursively.
+		for ( prefix in a ) {
+			buildParams( prefix, a[ prefix ], traditional, add );
+		}
+	}
+
+	// Return the resulting serialization
+	return s.join( "&" );
+};
+
+jQuery.fn.extend( {
+	serialize: function() {
+		return jQuery.param( this.serializeArray() );
+	},
+	serializeArray: function() {
+		return this.map( function() {
+
+			// Can add propHook for "elements" to filter or add form elements
+			var elements = jQuery.prop( this, "elements" );
+			return elements ? jQuery.makeArray( elements ) : this;
+		} ).filter( function() {
+			var type = this.type;
+
+			// Use .is( ":disabled" ) so that fieldset[disabled] works
+			return this.name && !jQuery( this ).is( ":disabled" ) &&
+				rsubmittable.test( this.nodeName ) && !rsubmitterTypes.test( type ) &&
+				( this.checked || !rcheckableType.test( type ) );
+		} ).map( function( _i, elem ) {
+			var val = jQuery( this ).val();
+
+			if ( val == null ) {
+				return null;
+			}
+
+			if ( Array.isArray( val ) ) {
+				return jQuery.map( val, function( val ) {
+					return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) };
+				} );
+			}
+
+			return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) };
+		} ).get();
+	}
+} );
+
+
+var
+	r20 = /%20/g,
+	rhash = /#.*$/,
+	rantiCache = /([?&])_=[^&]*/,
+	rheaders = /^(.*?):[ \t]*([^\r\n]*)$/mg,
+
+	// #7653, #8125, #8152: local protocol detection
+	rlocalProtocol = /^(?:about|app|app-storage|.+-extension|file|res|widget):$/,
+	rnoContent = /^(?:GET|HEAD)$/,
+	rprotocol = /^\/\//,
+
+	/* Prefilters
+	 * 1) They are useful to introduce custom dataTypes (see ajax/jsonp.js for an example)
+	 * 2) These are called:
+	 *    - BEFORE asking for a transport
+	 *    - AFTER param serialization (s.data is a string if s.processData is true)
+	 * 3) key is the dataType
+	 * 4) the catchall symbol "*" can be used
+	 * 5) execution will start with transport dataType and THEN continue down to "*" if needed
+	 */
+	prefilters = {},
+
+	/* Transports bindings
+	 * 1) key is the dataType
+	 * 2) the catchall symbol "*" can be used
+	 * 3) selection will start with transport dataType and THEN go to "*" if needed
+	 */
+	transports = {},
+
+	// Avoid comment-prolog char sequence (#10098); must appease lint and evade compression
+	allTypes = "*/".concat( "*" ),
+
+	// Anchor tag for parsing the document origin
+	originAnchor = document.createElement( "a" );
+
+originAnchor.href = location.href;
+
+// Base "constructor" for jQuery.ajaxPrefilter and jQuery.ajaxTransport
+function addToPrefiltersOrTransports( structure ) {
+
+	// dataTypeExpression is optional and defaults to "*"
+	return function( dataTypeExpression, func ) {
+
+		if ( typeof dataTypeExpression !== "string" ) {
+			func = dataTypeExpression;
+			dataTypeExpression = "*";
+		}
+
+		var dataType,
+			i = 0,
+			dataTypes = dataTypeExpression.toLowerCase().match( rnothtmlwhite ) || [];
+
+		if ( isFunction( func ) ) {
+
+			// For each dataType in the dataTypeExpression
+			while ( ( dataType = dataTypes[ i++ ] ) ) {
+
+				// Prepend if requested
+				if ( dataType[ 0 ] === "+" ) {
+					dataType = dataType.slice( 1 ) || "*";
+					( structure[ dataType ] = structure[ dataType ] || [] ).unshift( func );
+
+				// Otherwise append
+				} else {
+					( structure[ dataType ] = structure[ dataType ] || [] ).push( func );
+				}
+			}
+		}
+	};
+}
+
+// Base inspection function for prefilters and transports
+function inspectPrefiltersOrTransports( structure, options, originalOptions, jqXHR ) {
+
+	var inspected = {},
+		seekingTransport = ( structure === transports );
+
+	function inspect( dataType ) {
+		var selected;
+		inspected[ dataType ] = true;
+		jQuery.each( structure[ dataType ] || [], function( _, prefilterOrFactory ) {
+			var dataTypeOrTransport = prefilterOrFactory( options, originalOptions, jqXHR );
+			if ( typeof dataTypeOrTransport === "string" &&
+				!seekingTransport && !inspected[ dataTypeOrTransport ] ) {
+
+				options.dataTypes.unshift( dataTypeOrTransport );
+				inspect( dataTypeOrTransport );
+				return false;
+			} else if ( seekingTransport ) {
+				return !( selected = dataTypeOrTransport );
+			}
+		} );
+		return selected;
+	}
+
+	return inspect( options.dataTypes[ 0 ] ) || !inspected[ "*" ] && inspect( "*" );
+}
+
+// A special extend for ajax options
+// that takes "flat" options (not to be deep extended)
+// Fixes #9887
+function ajaxExtend( target, src ) {
+	var key, deep,
+		flatOptions = jQuery.ajaxSettings.flatOptions || {};
+
+	for ( key in src ) {
+		if ( src[ key ] !== undefined ) {
+			( flatOptions[ key ] ? target : ( deep || ( deep = {} ) ) )[ key ] = src[ key ];
+		}
+	}
+	if ( deep ) {
+		jQuery.extend( true, target, deep );
+	}
+
+	return target;
+}
+
+/* Handles responses to an ajax request:
+ * - finds the right dataType (mediates between content-type and expected dataType)
+ * - returns the corresponding response
+ */
+function ajaxHandleResponses( s, jqXHR, responses ) {
+
+	var ct, type, finalDataType, firstDataType,
+		contents = s.contents,
+		dataTypes = s.dataTypes;
+
+	// Remove auto dataType and get content-type in the process
+	while ( dataTypes[ 0 ] === "*" ) {
+		dataTypes.shift();
+		if ( ct === undefined ) {
+			ct = s.mimeType || jqXHR.getResponseHeader( "Content-Type" );
+		}
+	}
+
+	// Check if we're dealing with a known content-type
+	if ( ct ) {
+		for ( type in contents ) {
+			if ( contents[ type ] && contents[ type ].test( ct ) ) {
+				dataTypes.unshift( type );
+				break;
+			}
+		}
+	}
+
+	// Check to see if we have a response for the expected dataType
+	if ( dataTypes[ 0 ] in responses ) {
+		finalDataType = dataTypes[ 0 ];
+	} else {
+
+		// Try convertible dataTypes
+		for ( type in responses ) {
+			if ( !dataTypes[ 0 ] || s.converters[ type + " " + dataTypes[ 0 ] ] ) {
+				finalDataType = type;
+				break;
+			}
+			if ( !firstDataType ) {
+				firstDataType = type;
+			}
+		}
+
+		// Or just use first one
+		finalDataType = finalDataType || firstDataType;
+	}
+
+	// If we found a dataType
+	// We add the dataType to the list if needed
+	// and return the corresponding response
+	if ( finalDataType ) {
+		if ( finalDataType !== dataTypes[ 0 ] ) {
+			dataTypes.unshift( finalDataType );
+		}
+		return responses[ finalDataType ];
+	}
+}
+
+/* Chain conversions given the request and the original response
+ * Also sets the responseXXX fields on the jqXHR instance
+ */
+function ajaxConvert( s, response, jqXHR, isSuccess ) {
+	var conv2, current, conv, tmp, prev,
+		converters = {},
+
+		// Work with a copy of dataTypes in case we need to modify it for conversion
+		dataTypes = s.dataTypes.slice();
+
+	// Create converters map with lowercased keys
+	if ( dataTypes[ 1 ] ) {
+		for ( conv in s.converters ) {
+			converters[ conv.toLowerCase() ] = s.converters[ conv ];
+		}
+	}
+
+	current = dataTypes.shift();
+
+	// Convert to each sequential dataType
+	while ( current ) {
+
+		if ( s.responseFields[ current ] ) {
+			jqXHR[ s.responseFields[ current ] ] = response;
+		}
+
+		// Apply the dataFilter if provided
+		if ( !prev && isSuccess && s.dataFilter ) {
+			response = s.dataFilter( response, s.dataType );
+		}
+
+		prev = current;
+		current = dataTypes.shift();
+
+		if ( current ) {
+
+			// There's only work to do if current dataType is non-auto
+			if ( current === "*" ) {
+
+				current = prev;
+
+			// Convert response if prev dataType is non-auto and differs from current
+			} else if ( prev !== "*" && prev !== current ) {
+
+				// Seek a direct converter
+				conv = converters[ prev + " " + current ] || converters[ "* " + current ];
+
+				// If none found, seek a pair
+				if ( !conv ) {
+					for ( conv2 in converters ) {
+
+						// If conv2 outputs current
+						tmp = conv2.split( " " );
+						if ( tmp[ 1 ] === current ) {
+
+							// If prev can be converted to accepted input
+							conv = converters[ prev + " " + tmp[ 0 ] ] ||
+								converters[ "* " + tmp[ 0 ] ];
+							if ( conv ) {
+
+								// Condense equivalence converters
+								if ( conv === true ) {
+									conv = converters[ conv2 ];
+
+								// Otherwise, insert the intermediate dataType
+								} else if ( converters[ conv2 ] !== true ) {
+									current = tmp[ 0 ];
+									dataTypes.unshift( tmp[ 1 ] );
+								}
+								break;
+							}
+						}
+					}
+				}
+
+				// Apply converter (if not an equivalence)
+				if ( conv !== true ) {
+
+					// Unless errors are allowed to bubble, catch and return them
+					if ( conv && s.throws ) {
+						response = conv( response );
+					} else {
+						try {
+							response = conv( response );
+						} catch ( e ) {
+							return {
+								state: "parsererror",
+								error: conv ? e : "No conversion from " + prev + " to " + current
+							};
+						}
+					}
+				}
+			}
+		}
+	}
+
+	return { state: "success", data: response };
+}
+
+jQuery.extend( {
+
+	// Counter for holding the number of active queries
+	active: 0,
+
+	// Last-Modified header cache for next request
+	lastModified: {},
+	etag: {},
+
+	ajaxSettings: {
+		url: location.href,
+		type: "GET",
+		isLocal: rlocalProtocol.test( location.protocol ),
+		global: true,
+		processData: true,
+		async: true,
+		contentType: "application/x-www-form-urlencoded; charset=UTF-8",
+
+		/*
+		timeout: 0,
+		data: null,
+		dataType: null,
+		username: null,
+		password: null,
+		cache: null,
+		throws: false,
+		traditional: false,
+		headers: {},
+		*/
+
+		accepts: {
+			"*": allTypes,
+			text: "text/plain",
+			html: "text/html",
+			xml: "application/xml, text/xml",
+			json: "application/json, text/javascript"
+		},
+
+		contents: {
+			xml: /\bxml\b/,
+			html: /\bhtml/,
+			json: /\bjson\b/
+		},
+
+		responseFields: {
+			xml: "responseXML",
+			text: "responseText",
+			json: "responseJSON"
+		},
+
+		// Data converters
+		// Keys separate source (or catchall "*") and destination types with a single space
+		converters: {
+
+			// Convert anything to text
+			"* text": String,
+
+			// Text to html (true = no transformation)
+			"text html": true,
+
+			// Evaluate text as a json expression
+			"text json": JSON.parse,
+
+			// Parse text as xml
+			"text xml": jQuery.parseXML
+		},
+
+		// For options that shouldn't be deep extended:
+		// you can add your own custom options here if
+		// and when you create one that shouldn't be
+		// deep extended (see ajaxExtend)
+		flatOptions: {
+			url: true,
+			context: true
+		}
+	},
+
+	// Creates a full fledged settings object into target
+	// with both ajaxSettings and settings fields.
+	// If target is omitted, writes into ajaxSettings.
+	ajaxSetup: function( target, settings ) {
+		return settings ?
+
+			// Building a settings object
+			ajaxExtend( ajaxExtend( target, jQuery.ajaxSettings ), settings ) :
+
+			// Extending ajaxSettings
+			ajaxExtend( jQuery.ajaxSettings, target );
+	},
+
+	ajaxPrefilter: addToPrefiltersOrTransports( prefilters ),
+	ajaxTransport: addToPrefiltersOrTransports( transports ),
+
+	// Main method
+	ajax: function( url, options ) {
+
+		// If url is an object, simulate pre-1.5 signature
+		if ( typeof url === "object" ) {
+			options = url;
+			url = undefined;
+		}
+
+		// Force options to be an object
+		options = options || {};
+
+		var transport,
+
+			// URL without anti-cache param
+			cacheURL,
+
+			// Response headers
+			responseHeadersString,
+			responseHeaders,
+
+			// timeout handle
+			timeoutTimer,
+
+			// Url cleanup var
+			urlAnchor,
+
+			// Request state (becomes false upon send and true upon completion)
+			completed,
+
+			// To know if global events are to be dispatched
+			fireGlobals,
+
+			// Loop variable
+			i,
+
+			// uncached part of the url
+			uncached,
+
+			// Create the final options object
+			s = jQuery.ajaxSetup( {}, options ),
+
+			// Callbacks context
+			callbackContext = s.context || s,
+
+			// Context for global events is callbackContext if it is a DOM node or jQuery collection
+			globalEventContext = s.context &&
+				( callbackContext.nodeType || callbackContext.jquery ) ?
+				jQuery( callbackContext ) :
+				jQuery.event,
+
+			// Deferreds
+			deferred = jQuery.Deferred(),
+			completeDeferred = jQuery.Callbacks( "once memory" ),
+
+			// Status-dependent callbacks
+			statusCode = s.statusCode || {},
+
+			// Headers (they are sent all at once)
+			requestHeaders = {},
+			requestHeadersNames = {},
+
+			// Default abort message
+			strAbort = "canceled",
+
+			// Fake xhr
+			jqXHR = {
+				readyState: 0,
+
+				// Builds headers hashtable if needed
+				getResponseHeader: function( key ) {
+					var match;
+					if ( completed ) {
+						if ( !responseHeaders ) {
+							responseHeaders = {};
+							while ( ( match = rheaders.exec( responseHeadersString ) ) ) {
+								responseHeaders[ match[ 1 ].toLowerCase() + " " ] =
+									( responseHeaders[ match[ 1 ].toLowerCase() + " " ] || [] )
+										.concat( match[ 2 ] );
+							}
+						}
+						match = responseHeaders[ key.toLowerCase() + " " ];
+					}
+					return match == null ? null : match.join( ", " );
+				},
+
+				// Raw string
+				getAllResponseHeaders: function() {
+					return completed ? responseHeadersString : null;
+				},
+
+				// Caches the header
+				setRequestHeader: function( name, value ) {
+					if ( completed == null ) {
+						name = requestHeadersNames[ name.toLowerCase() ] =
+							requestHeadersNames[ name.toLowerCase() ] || name;
+						requestHeaders[ name ] = value;
+					}
+					return this;
+				},
+
+				// Overrides response content-type header
+				overrideMimeType: function( type ) {
+					if ( completed == null ) {
+						s.mimeType = type;
+					}
+					return this;
+				},
+
+				// Status-dependent callbacks
+				statusCode: function( map ) {
+					var code;
+					if ( map ) {
+						if ( completed ) {
+
+							// Execute the appropriate callbacks
+							jqXHR.always( map[ jqXHR.status ] );
+						} else {
+
+							// Lazy-add the new callbacks in a way that preserves old ones
+							for ( code in map ) {
+								statusCode[ code ] = [ statusCode[ code ], map[ code ] ];
+							}
+						}
+					}
+					return this;
+				},
+
+				// Cancel the request
+				abort: function( statusText ) {
+					var finalText = statusText || strAbort;
+					if ( transport ) {
+						transport.abort( finalText );
+					}
+					done( 0, finalText );
+					return this;
+				}
+			};
+
+		// Attach deferreds
+		deferred.promise( jqXHR );
+
+		// Add protocol if not provided (prefilters might expect it)
+		// Handle falsy url in the settings object (#10093: consistency with old signature)
+		// We also use the url parameter if available
+		s.url = ( ( url || s.url || location.href ) + "" )
+			.replace( rprotocol, location.protocol + "//" );
+
+		// Alias method option to type as per ticket #12004
+		s.type = options.method || options.type || s.method || s.type;
+
+		// Extract dataTypes list
+		s.dataTypes = ( s.dataType || "*" ).toLowerCase().match( rnothtmlwhite ) || [ "" ];
+
+		// A cross-domain request is in order when the origin doesn't match the current origin.
+		if ( s.crossDomain == null ) {
+			urlAnchor = document.createElement( "a" );
+
+			// Support: IE <=8 - 11, Edge 12 - 15
+			// IE throws exception on accessing the href property if url is malformed,
+			// e.g. http://example.com:80x/
+			try {
+				urlAnchor.href = s.url;
+
+				// Support: IE <=8 - 11 only
+				// Anchor's host property isn't correctly set when s.url is relative
+				urlAnchor.href = urlAnchor.href;
+				s.crossDomain = originAnchor.protocol + "//" + originAnchor.host !==
+					urlAnchor.protocol + "//" + urlAnchor.host;
+			} catch ( e ) {
+
+				// If there is an error parsing the URL, assume it is crossDomain,
+				// it can be rejected by the transport if it is invalid
+				s.crossDomain = true;
+			}
+		}
+
+		// Convert data if not already a string
+		if ( s.data && s.processData && typeof s.data !== "string" ) {
+			s.data = jQuery.param( s.data, s.traditional );
+		}
+
+		// Apply prefilters
+		inspectPrefiltersOrTransports( prefilters, s, options, jqXHR );
+
+		// If request was aborted inside a prefilter, stop there
+		if ( completed ) {
+			return jqXHR;
+		}
+
+		// We can fire global events as of now if asked to
+		// Don't fire events if jQuery.event is undefined in an AMD-usage scenario (#15118)
+		fireGlobals = jQuery.event && s.global;
+
+		// Watch for a new set of requests
+		if ( fireGlobals && jQuery.active++ === 0 ) {
+			jQuery.event.trigger( "ajaxStart" );
+		}
+
+		// Uppercase the type
+		s.type = s.type.toUpperCase();
+
+		// Determine if request has content
+		s.hasContent = !rnoContent.test( s.type );
+
+		// Save the URL in case we're toying with the If-Modified-Since
+		// and/or If-None-Match header later on
+		// Remove hash to simplify url manipulation
+		cacheURL = s.url.replace( rhash, "" );
+
+		// More options handling for requests with no content
+		if ( !s.hasContent ) {
+
+			// Remember the hash so we can put it back
+			uncached = s.url.slice( cacheURL.length );
+
+			// If data is available and should be processed, append data to url
+			if ( s.data && ( s.processData || typeof s.data === "string" ) ) {
+				cacheURL += ( rquery.test( cacheURL ) ? "&" : "?" ) + s.data;
+
+				// #9682: remove data so that it's not used in an eventual retry
+				delete s.data;
+			}
+
+			// Add or update anti-cache param if needed
+			if ( s.cache === false ) {
+				cacheURL = cacheURL.replace( rantiCache, "$1" );
+				uncached = ( rquery.test( cacheURL ) ? "&" : "?" ) + "_=" + ( nonce.guid++ ) +
+					uncached;
+			}
+
+			// Put hash and anti-cache on the URL that will be requested (gh-1732)
+			s.url = cacheURL + uncached;
+
+		// Change '%20' to '+' if this is encoded form body content (gh-2658)
+		} else if ( s.data && s.processData &&
+			( s.contentType || "" ).indexOf( "application/x-www-form-urlencoded" ) === 0 ) {
+			s.data = s.data.replace( r20, "+" );
+		}
+
+		// Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode.
+		if ( s.ifModified ) {
+			if ( jQuery.lastModified[ cacheURL ] ) {
+				jqXHR.setRequestHeader( "If-Modified-Since", jQuery.lastModified[ cacheURL ] );
+			}
+			if ( jQuery.etag[ cacheURL ] ) {
+				jqXHR.setRequestHeader( "If-None-Match", jQuery.etag[ cacheURL ] );
+			}
+		}
+
+		// Set the correct header, if data is being sent
+		if ( s.data && s.hasContent && s.contentType !== false || options.contentType ) {
+			jqXHR.setRequestHeader( "Content-Type", s.contentType );
+		}
+
+		// Set the Accepts header for the server, depending on the dataType
+		jqXHR.setRequestHeader(
+			"Accept",
+			s.dataTypes[ 0 ] && s.accepts[ s.dataTypes[ 0 ] ] ?
+				s.accepts[ s.dataTypes[ 0 ] ] +
+					( s.dataTypes[ 0 ] !== "*" ? ", " + allTypes + "; q=0.01" : "" ) :
+				s.accepts[ "*" ]
+		);
+
+		// Check for headers option
+		for ( i in s.headers ) {
+			jqXHR.setRequestHeader( i, s.headers[ i ] );
+		}
+
+		// Allow custom headers/mimetypes and early abort
+		if ( s.beforeSend &&
+			( s.beforeSend.call( callbackContext, jqXHR, s ) === false || completed ) ) {
+
+			// Abort if not done already and return
+			return jqXHR.abort();
+		}
+
+		// Aborting is no longer a cancellation
+		strAbort = "abort";
+
+		// Install callbacks on deferreds
+		completeDeferred.add( s.complete );
+		jqXHR.done( s.success );
+		jqXHR.fail( s.error );
+
+		// Get transport
+		transport = inspectPrefiltersOrTransports( transports, s, options, jqXHR );
+
+		// If no transport, we auto-abort
+		if ( !transport ) {
+			done( -1, "No Transport" );
+		} else {
+			jqXHR.readyState = 1;
+
+			// Send global event
+			if ( fireGlobals ) {
+				globalEventContext.trigger( "ajaxSend", [ jqXHR, s ] );
+			}
+
+			// If request was aborted inside ajaxSend, stop there
+			if ( completed ) {
+				return jqXHR;
+			}
+
+			// Timeout
+			if ( s.async && s.timeout > 0 ) {
+				timeoutTimer = window.setTimeout( function() {
+					jqXHR.abort( "timeout" );
+				}, s.timeout );
+			}
+
+			try {
+				completed = false;
+				transport.send( requestHeaders, done );
+			} catch ( e ) {
+
+				// Rethrow post-completion exceptions
+				if ( completed ) {
+					throw e;
+				}
+
+				// Propagate others as results
+				done( -1, e );
+			}
+		}
+
+		// Callback for when everything is done
+		function done( status, nativeStatusText, responses, headers ) {
+			var isSuccess, success, error, response, modified,
+				statusText = nativeStatusText;
+
+			// Ignore repeat invocations
+			if ( completed ) {
+				return;
+			}
+
+			completed = true;
+
+			// Clear timeout if it exists
+			if ( timeoutTimer ) {
+				window.clearTimeout( timeoutTimer );
+			}
+
+			// Dereference transport for early garbage collection
+			// (no matter how long the jqXHR object will be used)
+			transport = undefined;
+
+			// Cache response headers
+			responseHeadersString = headers || "";
+
+			// Set readyState
+			jqXHR.readyState = status > 0 ? 4 : 0;
+
+			// Determine if successful
+			isSuccess = status >= 200 && status < 300 || status === 304;
+
+			// Get response data
+			if ( responses ) {
+				response = ajaxHandleResponses( s, jqXHR, responses );
+			}
+
+			// Use a noop converter for missing script but not if jsonp
+			if ( !isSuccess &&
+				jQuery.inArray( "script", s.dataTypes ) > -1 &&
+				jQuery.inArray( "json", s.dataTypes ) < 0 ) {
+				s.converters[ "text script" ] = function() {};
+			}
+
+			// Convert no matter what (that way responseXXX fields are always set)
+			response = ajaxConvert( s, response, jqXHR, isSuccess );
+
+			// If successful, handle type chaining
+			if ( isSuccess ) {
+
+				// Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode.
+				if ( s.ifModified ) {
+					modified = jqXHR.getResponseHeader( "Last-Modified" );
+					if ( modified ) {
+						jQuery.lastModified[ cacheURL ] = modified;
+					}
+					modified = jqXHR.getResponseHeader( "etag" );
+					if ( modified ) {
+						jQuery.etag[ cacheURL ] = modified;
+					}
+				}
+
+				// if no content
+				if ( status === 204 || s.type === "HEAD" ) {
+					statusText = "nocontent";
+
+				// if not modified
+				} else if ( status === 304 ) {
+					statusText = "notmodified";
+
+				// If we have data, let's convert it
+				} else {
+					statusText = response.state;
+					success = response.data;
+					error = response.error;
+					isSuccess = !error;
+				}
+			} else {
+
+				// Extract error from statusText and normalize for non-aborts
+				error = statusText;
+				if ( status || !statusText ) {
+					statusText = "error";
+					if ( status < 0 ) {
+						status = 0;
+					}
+				}
+			}
+
+			// Set data for the fake xhr object
+			jqXHR.status = status;
+			jqXHR.statusText = ( nativeStatusText || statusText ) + "";
+
+			// Success/Error
+			if ( isSuccess ) {
+				deferred.resolveWith( callbackContext, [ success, statusText, jqXHR ] );
+			} else {
+				deferred.rejectWith( callbackContext, [ jqXHR, statusText, error ] );
+			}
+
+			// Status-dependent callbacks
+			jqXHR.statusCode( statusCode );
+			statusCode = undefined;
+
+			if ( fireGlobals ) {
+				globalEventContext.trigger( isSuccess ? "ajaxSuccess" : "ajaxError",
+					[ jqXHR, s, isSuccess ? success : error ] );
+			}
+
+			// Complete
+			completeDeferred.fireWith( callbackContext, [ jqXHR, statusText ] );
+
+			if ( fireGlobals ) {
+				globalEventContext.trigger( "ajaxComplete", [ jqXHR, s ] );
+
+				// Handle the global AJAX counter
+				if ( !( --jQuery.active ) ) {
+					jQuery.event.trigger( "ajaxStop" );
+				}
+			}
+		}
+
+		return jqXHR;
+	},
+
+	getJSON: function( url, data, callback ) {
+		return jQuery.get( url, data, callback, "json" );
+	},
+
+	getScript: function( url, callback ) {
+		return jQuery.get( url, undefined, callback, "script" );
+	}
+} );
+
+jQuery.each( [ "get", "post" ], function( _i, method ) {
+	jQuery[ method ] = function( url, data, callback, type ) {
+
+		// Shift arguments if data argument was omitted
+		if ( isFunction( data ) ) {
+			type = type || callback;
+			callback = data;
+			data = undefined;
+		}
+
+		// The url can be an options object (which then must have .url)
+		return jQuery.ajax( jQuery.extend( {
+			url: url,
+			type: method,
+			dataType: type,
+			data: data,
+			success: callback
+		}, jQuery.isPlainObject( url ) && url ) );
+	};
+} );
+
+jQuery.ajaxPrefilter( function( s ) {
+	var i;
+	for ( i in s.headers ) {
+		if ( i.toLowerCase() === "content-type" ) {
+			s.contentType = s.headers[ i ] || "";
+		}
+	}
+} );
+
+
+jQuery._evalUrl = function( url, options, doc ) {
+	return jQuery.ajax( {
+		url: url,
+
+		// Make this explicit, since user can override this through ajaxSetup (#11264)
+		type: "GET",
+		dataType: "script",
+		cache: true,
+		async: false,
+		global: false,
+
+		// Only evaluate the response if it is successful (gh-4126)
+		// dataFilter is not invoked for failure responses, so using it instead
+		// of the default converter is kludgy but it works.
+		converters: {
+			"text script": function() {}
+		},
+		dataFilter: function( response ) {
+			jQuery.globalEval( response, options, doc );
+		}
+	} );
+};
+
+
+jQuery.fn.extend( {
+	wrapAll: function( html ) {
+		var wrap;
+
+		if ( this[ 0 ] ) {
+			if ( isFunction( html ) ) {
+				html = html.call( this[ 0 ] );
+			}
+
+			// The elements to wrap the target around
+			wrap = jQuery( html, this[ 0 ].ownerDocument ).eq( 0 ).clone( true );
+
+			if ( this[ 0 ].parentNode ) {
+				wrap.insertBefore( this[ 0 ] );
+			}
+
+			wrap.map( function() {
+				var elem = this;
+
+				while ( elem.firstElementChild ) {
+					elem = elem.firstElementChild;
+				}
+
+				return elem;
+			} ).append( this );
+		}
+
+		return this;
+	},
+
+	wrapInner: function( html ) {
+		if ( isFunction( html ) ) {
+			return this.each( function( i ) {
+				jQuery( this ).wrapInner( html.call( this, i ) );
+			} );
+		}
+
+		return this.each( function() {
+			var self = jQuery( this ),
+				contents = self.contents();
+
+			if ( contents.length ) {
+				contents.wrapAll( html );
+
+			} else {
+				self.append( html );
+			}
+		} );
+	},
+
+	wrap: function( html ) {
+		var htmlIsFunction = isFunction( html );
+
+		return this.each( function( i ) {
+			jQuery( this ).wrapAll( htmlIsFunction ? html.call( this, i ) : html );
+		} );
+	},
+
+	unwrap: function( selector ) {
+		this.parent( selector ).not( "body" ).each( function() {
+			jQuery( this ).replaceWith( this.childNodes );
+		} );
+		return this;
+	}
+} );
+
+
+jQuery.expr.pseudos.hidden = function( elem ) {
+	return !jQuery.expr.pseudos.visible( elem );
+};
+jQuery.expr.pseudos.visible = function( elem ) {
+	return !!( elem.offsetWidth || elem.offsetHeight || elem.getClientRects().length );
+};
+
+
+
+
+jQuery.ajaxSettings.xhr = function() {
+	try {
+		return new window.XMLHttpRequest();
+	} catch ( e ) {}
+};
+
+var xhrSuccessStatus = {
+
+		// File protocol always yields status code 0, assume 200
+		0: 200,
+
+		// Support: IE <=9 only
+		// #1450: sometimes IE returns 1223 when it should be 204
+		1223: 204
+	},
+	xhrSupported = jQuery.ajaxSettings.xhr();
+
+support.cors = !!xhrSupported && ( "withCredentials" in xhrSupported );
+support.ajax = xhrSupported = !!xhrSupported;
+
+jQuery.ajaxTransport( function( options ) {
+	var callback, errorCallback;
+
+	// Cross domain only allowed if supported through XMLHttpRequest
+	if ( support.cors || xhrSupported && !options.crossDomain ) {
+		return {
+			send: function( headers, complete ) {
+				var i,
+					xhr = options.xhr();
+
+				xhr.open(
+					options.type,
+					options.url,
+					options.async,
+					options.username,
+					options.password
+				);
+
+				// Apply custom fields if provided
+				if ( options.xhrFields ) {
+					for ( i in options.xhrFields ) {
+						xhr[ i ] = options.xhrFields[ i ];
+					}
+				}
+
+				// Override mime type if needed
+				if ( options.mimeType && xhr.overrideMimeType ) {
+					xhr.overrideMimeType( options.mimeType );
+				}
+
+				// X-Requested-With header
+				// For cross-domain requests, seeing as conditions for a preflight are
+				// akin to a jigsaw puzzle, we simply never set it to be sure.
+				// (it can always be set on a per-request basis or even using ajaxSetup)
+				// For same-domain requests, won't change header if already provided.
+				if ( !options.crossDomain && !headers[ "X-Requested-With" ] ) {
+					headers[ "X-Requested-With" ] = "XMLHttpRequest";
+				}
+
+				// Set headers
+				for ( i in headers ) {
+					xhr.setRequestHeader( i, headers[ i ] );
+				}
+
+				// Callback
+				callback = function( type ) {
+					return function() {
+						if ( callback ) {
+							callback = errorCallback = xhr.onload =
+								xhr.onerror = xhr.onabort = xhr.ontimeout =
+									xhr.onreadystatechange = null;
+
+							if ( type === "abort" ) {
+								xhr.abort();
+							} else if ( type === "error" ) {
+
+								// Support: IE <=9 only
+								// On a manual native abort, IE9 throws
+								// errors on any property access that is not readyState
+								if ( typeof xhr.status !== "number" ) {
+									complete( 0, "error" );
+								} else {
+									complete(
+
+										// File: protocol always yields status 0; see #8605, #14207
+										xhr.status,
+										xhr.statusText
+									);
+								}
+							} else {
+								complete(
+									xhrSuccessStatus[ xhr.status ] || xhr.status,
+									xhr.statusText,
+
+									// Support: IE <=9 only
+									// IE9 has no XHR2 but throws on binary (trac-11426)
+									// For XHR2 non-text, let the caller handle it (gh-2498)
+									( xhr.responseType || "text" ) !== "text"  ||
+									typeof xhr.responseText !== "string" ?
+										{ binary: xhr.response } :
+										{ text: xhr.responseText },
+									xhr.getAllResponseHeaders()
+								);
+							}
+						}
+					};
+				};
+
+				// Listen to events
+				xhr.onload = callback();
+				errorCallback = xhr.onerror = xhr.ontimeout = callback( "error" );
+
+				// Support: IE 9 only
+				// Use onreadystatechange to replace onabort
+				// to handle uncaught aborts
+				if ( xhr.onabort !== undefined ) {
+					xhr.onabort = errorCallback;
+				} else {
+					xhr.onreadystatechange = function() {
+
+						// Check readyState before timeout as it changes
+						if ( xhr.readyState === 4 ) {
+
+							// Allow onerror to be called first,
+							// but that will not handle a native abort
+							// Also, save errorCallback to a variable
+							// as xhr.onerror cannot be accessed
+							window.setTimeout( function() {
+								if ( callback ) {
+									errorCallback();
+								}
+							} );
+						}
+					};
+				}
+
+				// Create the abort callback
+				callback = callback( "abort" );
+
+				try {
+
+					// Do send the request (this may raise an exception)
+					xhr.send( options.hasContent && options.data || null );
+				} catch ( e ) {
+
+					// #14683: Only rethrow if this hasn't been notified as an error yet
+					if ( callback ) {
+						throw e;
+					}
+				}
+			},
+
+			abort: function() {
+				if ( callback ) {
+					callback();
+				}
+			}
+		};
+	}
+} );
+
+
+
+
+// Prevent auto-execution of scripts when no explicit dataType was provided (See gh-2432)
+jQuery.ajaxPrefilter( function( s ) {
+	if ( s.crossDomain ) {
+		s.contents.script = false;
+	}
+} );
+
+// Install script dataType
+jQuery.ajaxSetup( {
+	accepts: {
+		script: "text/javascript, application/javascript, " +
+			"application/ecmascript, application/x-ecmascript"
+	},
+	contents: {
+		script: /\b(?:java|ecma)script\b/
+	},
+	converters: {
+		"text script": function( text ) {
+			jQuery.globalEval( text );
+			return text;
+		}
+	}
+} );
+
+// Handle cache's special case and crossDomain
+jQuery.ajaxPrefilter( "script", function( s ) {
+	if ( s.cache === undefined ) {
+		s.cache = false;
+	}
+	if ( s.crossDomain ) {
+		s.type = "GET";
+	}
+} );
+
+// Bind script tag hack transport
+jQuery.ajaxTransport( "script", function( s ) {
+
+	// This transport only deals with cross domain or forced-by-attrs requests
+	if ( s.crossDomain || s.scriptAttrs ) {
+		var script, callback;
+		return {
+			send: function( _, complete ) {
+				script = jQuery( "<script>" )
+					.attr( s.scriptAttrs || {} )
+					.prop( { charset: s.scriptCharset, src: s.url } )
+					.on( "load error", callback = function( evt ) {
+						script.remove();
+						callback = null;
+						if ( evt ) {
+							complete( evt.type === "error" ? 404 : 200, evt.type );
+						}
+					} );
+
+				// Use native DOM manipulation to avoid our domManip AJAX trickery
+				document.head.appendChild( script[ 0 ] );
+			},
+			abort: function() {
+				if ( callback ) {
+					callback();
+				}
+			}
+		};
+	}
+} );
+
+
+
+
+var oldCallbacks = [],
+	rjsonp = /(=)\?(?=&|$)|\?\?/;
+
+// Default jsonp settings
+jQuery.ajaxSetup( {
+	jsonp: "callback",
+	jsonpCallback: function() {
+		var callback = oldCallbacks.pop() || ( jQuery.expando + "_" + ( nonce.guid++ ) );
+		this[ callback ] = true;
+		return callback;
+	}
+} );
+
+// Detect, normalize options and install callbacks for jsonp requests
+jQuery.ajaxPrefilter( "json jsonp", function( s, originalSettings, jqXHR ) {
+
+	var callbackName, overwritten, responseContainer,
+		jsonProp = s.jsonp !== false && ( rjsonp.test( s.url ) ?
+			"url" :
+			typeof s.data === "string" &&
+				( s.contentType || "" )
+					.indexOf( "application/x-www-form-urlencoded" ) === 0 &&
+				rjsonp.test( s.data ) && "data"
+		);
+
+	// Handle iff the expected data type is "jsonp" or we have a parameter to set
+	if ( jsonProp || s.dataTypes[ 0 ] === "jsonp" ) {
+
+		// Get callback name, remembering preexisting value associated with it
+		callbackName = s.jsonpCallback = isFunction( s.jsonpCallback ) ?
+			s.jsonpCallback() :
+			s.jsonpCallback;
+
+		// Insert callback into url or form data
+		if ( jsonProp ) {
+			s[ jsonProp ] = s[ jsonProp ].replace( rjsonp, "$1" + callbackName );
+		} else if ( s.jsonp !== false ) {
+			s.url += ( rquery.test( s.url ) ? "&" : "?" ) + s.jsonp + "=" + callbackName;
+		}
+
+		// Use data converter to retrieve json after script execution
+		s.converters[ "script json" ] = function() {
+			if ( !responseContainer ) {
+				jQuery.error( callbackName + " was not called" );
+			}
+			return responseContainer[ 0 ];
+		};
+
+		// Force json dataType
+		s.dataTypes[ 0 ] = "json";
+
+		// Install callback
+		overwritten = window[ callbackName ];
+		window[ callbackName ] = function() {
+			responseContainer = arguments;
+		};
+
+		// Clean-up function (fires after converters)
+		jqXHR.always( function() {
+
+			// If previous value didn't exist - remove it
+			if ( overwritten === undefined ) {
+				jQuery( window ).removeProp( callbackName );
+
+			// Otherwise restore preexisting value
+			} else {
+				window[ callbackName ] = overwritten;
+			}
+
+			// Save back as free
+			if ( s[ callbackName ] ) {
+
+				// Make sure that re-using the options doesn't screw things around
+				s.jsonpCallback = originalSettings.jsonpCallback;
+
+				// Save the callback name for future use
+				oldCallbacks.push( callbackName );
+			}
+
+			// Call if it was a function and we have a response
+			if ( responseContainer && isFunction( overwritten ) ) {
+				overwritten( responseContainer[ 0 ] );
+			}
+
+			responseContainer = overwritten = undefined;
+		} );
+
+		// Delegate to script
+		return "script";
+	}
+} );
+
+
+
+
+// Support: Safari 8 only
+// In Safari 8 documents created via document.implementation.createHTMLDocument
+// collapse sibling forms: the second one becomes a child of the first one.
+// Because of that, this security measure has to be disabled in Safari 8.
+// https://bugs.webkit.org/show_bug.cgi?id=137337
+support.createHTMLDocument = ( function() {
+	var body = document.implementation.createHTMLDocument( "" ).body;
+	body.innerHTML = "<form></form><form></form>";
+	return body.childNodes.length === 2;
+} )();
+
+
+// Argument "data" should be string of html
+// context (optional): If specified, the fragment will be created in this context,
+// defaults to document
+// keepScripts (optional): If true, will include scripts passed in the html string
+jQuery.parseHTML = function( data, context, keepScripts ) {
+	if ( typeof data !== "string" ) {
+		return [];
+	}
+	if ( typeof context === "boolean" ) {
+		keepScripts = context;
+		context = false;
+	}
+
+	var base, parsed, scripts;
+
+	if ( !context ) {
+
+		// Stop scripts or inline event handlers from being executed immediately
+		// by using document.implementation
+		if ( support.createHTMLDocument ) {
+			context = document.implementation.createHTMLDocument( "" );
+
+			// Set the base href for the created document
+			// so any parsed elements with URLs
+			// are based on the document's URL (gh-2965)
+			base = context.createElement( "base" );
+			base.href = document.location.href;
+			context.head.appendChild( base );
+		} else {
+			context = document;
+		}
+	}
+
+	parsed = rsingleTag.exec( data );
+	scripts = !keepScripts && [];
+
+	// Single tag
+	if ( parsed ) {
+		return [ context.createElement( parsed[ 1 ] ) ];
+	}
+
+	parsed = buildFragment( [ data ], context, scripts );
+
+	if ( scripts && scripts.length ) {
+		jQuery( scripts ).remove();
+	}
+
+	return jQuery.merge( [], parsed.childNodes );
+};
+
+
+/**
+ * Load a url into a page
+ */
+jQuery.fn.load = function( url, params, callback ) {
+	var selector, type, response,
+		self = this,
+		off = url.indexOf( " " );
+
+	if ( off > -1 ) {
+		selector = stripAndCollapse( url.slice( off ) );
+		url = url.slice( 0, off );
+	}
+
+	// If it's a function
+	if ( isFunction( params ) ) {
+
+		// We assume that it's the callback
+		callback = params;
+		params = undefined;
+
+	// Otherwise, build a param string
+	} else if ( params && typeof params === "object" ) {
+		type = "POST";
+	}
+
+	// If we have elements to modify, make the request
+	if ( self.length > 0 ) {
+		jQuery.ajax( {
+			url: url,
+
+			// If "type" variable is undefined, then "GET" method will be used.
+			// Make value of this field explicit since
+			// user can override it through ajaxSetup method
+			type: type || "GET",
+			dataType: "html",
+			data: params
+		} ).done( function( responseText ) {
+
+			// Save response for use in complete callback
+			response = arguments;
+
+			self.html( selector ?
+
+				// If a selector was specified, locate the right elements in a dummy div
+				// Exclude scripts to avoid IE 'Permission Denied' errors
+				jQuery( "<div>" ).append( jQuery.parseHTML( responseText ) ).find( selector ) :
+
+				// Otherwise use the full result
+				responseText );
+
+		// If the request succeeds, this function gets "data", "status", "jqXHR"
+		// but they are ignored because response was set above.
+		// If it fails, this function gets "jqXHR", "status", "error"
+		} ).always( callback && function( jqXHR, status ) {
+			self.each( function() {
+				callback.apply( this, response || [ jqXHR.responseText, status, jqXHR ] );
+			} );
+		} );
+	}
+
+	return this;
+};
+
+
+
+
+jQuery.expr.pseudos.animated = function( elem ) {
+	return jQuery.grep( jQuery.timers, function( fn ) {
+		return elem === fn.elem;
+	} ).length;
+};
+
+
+
+
+jQuery.offset = {
+	setOffset: function( elem, options, i ) {
+		var curPosition, curLeft, curCSSTop, curTop, curOffset, curCSSLeft, calculatePosition,
+			position = jQuery.css( elem, "position" ),
+			curElem = jQuery( elem ),
+			props = {};
+
+		// Set position first, in-case top/left are set even on static elem
+		if ( position === "static" ) {
+			elem.style.position = "relative";
+		}
+
+		curOffset = curElem.offset();
+		curCSSTop = jQuery.css( elem, "top" );
+		curCSSLeft = jQuery.css( elem, "left" );
+		calculatePosition = ( position === "absolute" || position === "fixed" ) &&
+			( curCSSTop + curCSSLeft ).indexOf( "auto" ) > -1;
+
+		// Need to be able to calculate position if either
+		// top or left is auto and position is either absolute or fixed
+		if ( calculatePosition ) {
+			curPosition = curElem.position();
+			curTop = curPosition.top;
+			curLeft = curPosition.left;
+
+		} else {
+			curTop = parseFloat( curCSSTop ) || 0;
+			curLeft = parseFloat( curCSSLeft ) || 0;
+		}
+
+		if ( isFunction( options ) ) {
+
+			// Use jQuery.extend here to allow modification of coordinates argument (gh-1848)
+			options = options.call( elem, i, jQuery.extend( {}, curOffset ) );
+		}
+
+		if ( options.top != null ) {
+			props.top = ( options.top - curOffset.top ) + curTop;
+		}
+		if ( options.left != null ) {
+			props.left = ( options.left - curOffset.left ) + curLeft;
+		}
+
+		if ( "using" in options ) {
+			options.using.call( elem, props );
+
+		} else {
+			curElem.css( props );
+		}
+	}
+};
+
+jQuery.fn.extend( {
+
+	// offset() relates an element's border box to the document origin
+	offset: function( options ) {
+
+		// Preserve chaining for setter
+		if ( arguments.length ) {
+			return options === undefined ?
+				this :
+				this.each( function( i ) {
+					jQuery.offset.setOffset( this, options, i );
+				} );
+		}
+
+		var rect, win,
+			elem = this[ 0 ];
+
+		if ( !elem ) {
+			return;
+		}
+
+		// Return zeros for disconnected and hidden (display: none) elements (gh-2310)
+		// Support: IE <=11 only
+		// Running getBoundingClientRect on a
+		// disconnected node in IE throws an error
+		if ( !elem.getClientRects().length ) {
+			return { top: 0, left: 0 };
+		}
+
+		// Get document-relative position by adding viewport scroll to viewport-relative gBCR
+		rect = elem.getBoundingClientRect();
+		win = elem.ownerDocument.defaultView;
+		return {
+			top: rect.top + win.pageYOffset,
+			left: rect.left + win.pageXOffset
+		};
+	},
+
+	// position() relates an element's margin box to its offset parent's padding box
+	// This corresponds to the behavior of CSS absolute positioning
+	position: function() {
+		if ( !this[ 0 ] ) {
+			return;
+		}
+
+		var offsetParent, offset, doc,
+			elem = this[ 0 ],
+			parentOffset = { top: 0, left: 0 };
+
+		// position:fixed elements are offset from the viewport, which itself always has zero offset
+		if ( jQuery.css( elem, "position" ) === "fixed" ) {
+
+			// Assume position:fixed implies availability of getBoundingClientRect
+			offset = elem.getBoundingClientRect();
+
+		} else {
+			offset = this.offset();
+
+			// Account for the *real* offset parent, which can be the document or its root element
+			// when a statically positioned element is identified
+			doc = elem.ownerDocument;
+			offsetParent = elem.offsetParent || doc.documentElement;
+			while ( offsetParent &&
+				( offsetParent === doc.body || offsetParent === doc.documentElement ) &&
+				jQuery.css( offsetParent, "position" ) === "static" ) {
+
+				offsetParent = offsetParent.parentNode;
+			}
+			if ( offsetParent && offsetParent !== elem && offsetParent.nodeType === 1 ) {
+
+				// Incorporate borders into its offset, since they are outside its content origin
+				parentOffset = jQuery( offsetParent ).offset();
+				parentOffset.top += jQuery.css( offsetParent, "borderTopWidth", true );
+				parentOffset.left += jQuery.css( offsetParent, "borderLeftWidth", true );
+			}
+		}
+
+		// Subtract parent offsets and element margins
+		return {
+			top: offset.top - parentOffset.top - jQuery.css( elem, "marginTop", true ),
+			left: offset.left - parentOffset.left - jQuery.css( elem, "marginLeft", true )
+		};
+	},
+
+	// This method will return documentElement in the following cases:
+	// 1) For the element inside the iframe without offsetParent, this method will return
+	//    documentElement of the parent window
+	// 2) For the hidden or detached element
+	// 3) For body or html element, i.e. in case of the html node - it will return itself
+	//
+	// but those exceptions were never presented as a real life use-cases
+	// and might be considered as more preferable results.
+	//
+	// This logic, however, is not guaranteed and can change at any point in the future
+	offsetParent: function() {
+		return this.map( function() {
+			var offsetParent = this.offsetParent;
+
+			while ( offsetParent && jQuery.css( offsetParent, "position" ) === "static" ) {
+				offsetParent = offsetParent.offsetParent;
+			}
+
+			return offsetParent || documentElement;
+		} );
+	}
+} );
+
+// Create scrollLeft and scrollTop methods
+jQuery.each( { scrollLeft: "pageXOffset", scrollTop: "pageYOffset" }, function( method, prop ) {
+	var top = "pageYOffset" === prop;
+
+	jQuery.fn[ method ] = function( val ) {
+		return access( this, function( elem, method, val ) {
+
+			// Coalesce documents and windows
+			var win;
+			if ( isWindow( elem ) ) {
+				win = elem;
+			} else if ( elem.nodeType === 9 ) {
+				win = elem.defaultView;
+			}
+
+			if ( val === undefined ) {
+				return win ? win[ prop ] : elem[ method ];
+			}
+
+			if ( win ) {
+				win.scrollTo(
+					!top ? val : win.pageXOffset,
+					top ? val : win.pageYOffset
+				);
+
+			} else {
+				elem[ method ] = val;
+			}
+		}, method, val, arguments.length );
+	};
+} );
+
+// Support: Safari <=7 - 9.1, Chrome <=37 - 49
+// Add the top/left cssHooks using jQuery.fn.position
+// Webkit bug: https://bugs.webkit.org/show_bug.cgi?id=29084
+// Blink bug: https://bugs.chromium.org/p/chromium/issues/detail?id=589347
+// getComputedStyle returns percent when specified for top/left/bottom/right;
+// rather than make the css module depend on the offset module, just check for it here
+jQuery.each( [ "top", "left" ], function( _i, prop ) {
+	jQuery.cssHooks[ prop ] = addGetHookIf( support.pixelPosition,
+		function( elem, computed ) {
+			if ( computed ) {
+				computed = curCSS( elem, prop );
+
+				// If curCSS returns percentage, fallback to offset
+				return rnumnonpx.test( computed ) ?
+					jQuery( elem ).position()[ prop ] + "px" :
+					computed;
+			}
+		}
+	);
+} );
+
+
+// Create innerHeight, innerWidth, height, width, outerHeight and outerWidth methods
+jQuery.each( { Height: "height", Width: "width" }, function( name, type ) {
+	jQuery.each( {
+		padding: "inner" + name,
+		content: type,
+		"": "outer" + name
+	}, function( defaultExtra, funcName ) {
+
+		// Margin is only for outerHeight, outerWidth
+		jQuery.fn[ funcName ] = function( margin, value ) {
+			var chainable = arguments.length && ( defaultExtra || typeof margin !== "boolean" ),
+				extra = defaultExtra || ( margin === true || value === true ? "margin" : "border" );
+
+			return access( this, function( elem, type, value ) {
+				var doc;
+
+				if ( isWindow( elem ) ) {
+
+					// $( window ).outerWidth/Height return w/h including scrollbars (gh-1729)
+					return funcName.indexOf( "outer" ) === 0 ?
+						elem[ "inner" + name ] :
+						elem.document.documentElement[ "client" + name ];
+				}
+
+				// Get document width or height
+				if ( elem.nodeType === 9 ) {
+					doc = elem.documentElement;
+
+					// Either scroll[Width/Height] or offset[Width/Height] or client[Width/Height],
+					// whichever is greatest
+					return Math.max(
+						elem.body[ "scroll" + name ], doc[ "scroll" + name ],
+						elem.body[ "offset" + name ], doc[ "offset" + name ],
+						doc[ "client" + name ]
+					);
+				}
+
+				return value === undefined ?
+
+					// Get width or height on the element, requesting but not forcing parseFloat
+					jQuery.css( elem, type, extra ) :
+
+					// Set width or height on the element
+					jQuery.style( elem, type, value, extra );
+			}, type, chainable ? margin : undefined, chainable );
+		};
+	} );
+} );
+
+
+jQuery.each( [
+	"ajaxStart",
+	"ajaxStop",
+	"ajaxComplete",
+	"ajaxError",
+	"ajaxSuccess",
+	"ajaxSend"
+], function( _i, type ) {
+	jQuery.fn[ type ] = function( fn ) {
+		return this.on( type, fn );
+	};
+} );
+
+
+
+
+jQuery.fn.extend( {
+
+	bind: function( types, data, fn ) {
+		return this.on( types, null, data, fn );
+	},
+	unbind: function( types, fn ) {
+		return this.off( types, null, fn );
+	},
+
+	delegate: function( selector, types, data, fn ) {
+		return this.on( types, selector, data, fn );
+	},
+	undelegate: function( selector, types, fn ) {
+
+		// ( namespace ) or ( selector, types [, fn] )
+		return arguments.length === 1 ?
+			this.off( selector, "**" ) :
+			this.off( types, selector || "**", fn );
+	},
+
+	hover: function( fnOver, fnOut ) {
+		return this.mouseenter( fnOver ).mouseleave( fnOut || fnOver );
+	}
+} );
+
+jQuery.each(
+	( "blur focus focusin focusout resize scroll click dblclick " +
+	"mousedown mouseup mousemove mouseover mouseout mouseenter mouseleave " +
+	"change select submit keydown keypress keyup contextmenu" ).split( " " ),
+	function( _i, name ) {
+
+		// Handle event binding
+		jQuery.fn[ name ] = function( data, fn ) {
+			return arguments.length > 0 ?
+				this.on( name, null, data, fn ) :
+				this.trigger( name );
+		};
+	}
+);
+
+
+
+
+// Support: Android <=4.0 only
+// Make sure we trim BOM and NBSP
+var rtrim = /^[\s\uFEFF\xA0]+|[\s\uFEFF\xA0]+$/g;
+
+// Bind a function to a context, optionally partially applying any
+// arguments.
+// jQuery.proxy is deprecated to promote standards (specifically Function#bind)
+// However, it is not slated for removal any time soon
+jQuery.proxy = function( fn, context ) {
+	var tmp, args, proxy;
+
+	if ( typeof context === "string" ) {
+		tmp = fn[ context ];
+		context = fn;
+		fn = tmp;
+	}
+
+	// Quick check to determine if target is callable, in the spec
+	// this throws a TypeError, but we will just return undefined.
+	if ( !isFunction( fn ) ) {
+		return undefined;
+	}
+
+	// Simulated bind
+	args = slice.call( arguments, 2 );
+	proxy = function() {
+		return fn.apply( context || this, args.concat( slice.call( arguments ) ) );
+	};
+
+	// Set the guid of unique handler to the same of original handler, so it can be removed
+	proxy.guid = fn.guid = fn.guid || jQuery.guid++;
+
+	return proxy;
+};
+
+jQuery.holdReady = function( hold ) {
+	if ( hold ) {
+		jQuery.readyWait++;
+	} else {
+		jQuery.ready( true );
+	}
+};
+jQuery.isArray = Array.isArray;
+jQuery.parseJSON = JSON.parse;
+jQuery.nodeName = nodeName;
+jQuery.isFunction = isFunction;
+jQuery.isWindow = isWindow;
+jQuery.camelCase = camelCase;
+jQuery.type = toType;
+
+jQuery.now = Date.now;
+
+jQuery.isNumeric = function( obj ) {
+
+	// As of jQuery 3.0, isNumeric is limited to
+	// strings and numbers (primitives or objects)
+	// that can be coerced to finite numbers (gh-2662)
+	var type = jQuery.type( obj );
+	return ( type === "number" || type === "string" ) &&
+
+		// parseFloat NaNs numeric-cast false positives ("")
+		// ...but misinterprets leading-number strings, particularly hex literals ("0x...")
+		// subtraction forces infinities to NaN
+		!isNaN( obj - parseFloat( obj ) );
+};
+
+jQuery.trim = function( text ) {
+	return text == null ?
+		"" :
+		( text + "" ).replace( rtrim, "" );
+};
+
+
+
+// Register as a named AMD module, since jQuery can be concatenated with other
+// files that may use define, but not via a proper concatenation script that
+// understands anonymous AMD modules. A named AMD is safest and most robust
+// way to register. Lowercase jquery is used because AMD module names are
+// derived from file names, and jQuery is normally delivered in a lowercase
+// file name. Do this after creating the global so that if an AMD module wants
+// to call noConflict to hide this version of jQuery, it will work.
+
+// Note that for maximum portability, libraries that are not jQuery should
+// declare themselves as anonymous modules, and avoid setting a global if an
+// AMD loader is present. jQuery is a special case. For more information, see
+// https://github.com/jrburke/requirejs/wiki/Updating-existing-libraries#wiki-anon
+
+if ( typeof define === "function" && define.amd ) {
+	define( "jquery", [], function() {
+		return jQuery;
+	} );
+}
+
+
+
+
+var
+
+	// Map over jQuery in case of overwrite
+	_jQuery = window.jQuery,
+
+	// Map over the $ in case of overwrite
+	_$ = window.$;
+
+jQuery.noConflict = function( deep ) {
+	if ( window.$ === jQuery ) {
+		window.$ = _$;
+	}
+
+	if ( deep && window.jQuery === jQuery ) {
+		window.jQuery = _jQuery;
+	}
+
+	return jQuery;
+};
+
+// Expose jQuery and $ identifiers, even in AMD
+// (#7102#comment:10, https://github.com/jquery/jquery/pull/557)
+// and CommonJS for browser emulators (#13566)
+if ( typeof noGlobal === "undefined" ) {
+	window.jQuery = window.$ = jQuery;
+}
+
+
+
+
+return jQuery;
+} );
diff --git a/docs/coverage/lib/jquery-3.6.0/jquery-3.6.0.min.js b/docs/coverage/lib/jquery-3.6.0/jquery-3.6.0.min.js
new file mode 100644
index 00000000..c4c6022f
--- /dev/null
+++ b/docs/coverage/lib/jquery-3.6.0/jquery-3.6.0.min.js
@@ -0,0 +1,2 @@
+/*! jQuery v3.6.0 | (c) OpenJS Foundation and other contributors | jquery.org/license */
+!function(e,t){"use strict";"object"==typeof module&&"object"==typeof module.exports?module.exports=e.document?t(e,!0):function(e){if(!e.document)throw new Error("jQuery requires a window with a document");return t(e)}:t(e)}("undefined"!=typeof window?window:this,function(C,e){"use strict";var t=[],r=Object.getPrototypeOf,s=t.slice,g=t.flat?function(e){return t.flat.call(e)}:function(e){return t.concat.apply([],e)},u=t.push,i=t.indexOf,n={},o=n.toString,v=n.hasOwnProperty,a=v.toString,l=a.call(Object),y={},m=function(e){return"function"==typeof e&&"number"!=typeof e.nodeType&&"function"!=typeof e.item},x=function(e){return null!=e&&e===e.window},E=C.document,c={type:!0,src:!0,nonce:!0,noModule:!0};function b(e,t,n){var r,i,o=(n=n||E).createElement("script");if(o.text=e,t)for(r in c)(i=t[r]||t.getAttribute&&t.getAttribute(r))&&o.setAttribute(r,i);n.head.appendChild(o).parentNode.removeChild(o)}function w(e){return null==e?e+"":"object"==typeof e||"function"==typeof e?n[o.call(e)]||"object":typeof e}var f="3.6.0",S=function(e,t){return new S.fn.init(e,t)};function p(e){var t=!!e&&"length"in e&&e.length,n=w(e);return!m(e)&&!x(e)&&("array"===n||0===t||"number"==typeof t&&0<t&&t-1 in e)}S.fn=S.prototype={jquery:f,constructor:S,length:0,toArray:function(){return s.call(this)},get:function(e){return null==e?s.call(this):e<0?this[e+this.length]:this[e]},pushStack:function(e){var t=S.merge(this.constructor(),e);return t.prevObject=this,t},each:function(e){return S.each(this,e)},map:function(n){return this.pushStack(S.map(this,function(e,t){return n.call(e,t,e)}))},slice:function(){return this.pushStack(s.apply(this,arguments))},first:function(){return this.eq(0)},last:function(){return this.eq(-1)},even:function(){return this.pushStack(S.grep(this,function(e,t){return(t+1)%2}))},odd:function(){return this.pushStack(S.grep(this,function(e,t){return t%2}))},eq:function(e){var t=this.length,n=+e+(e<0?t:0);return this.pushStack(0<=n&&n<t?[this[n]]:[])},end:function(){return this.prevObject||this.constructor()},push:u,sort:t.sort,splice:t.splice},S.extend=S.fn.extend=function(){var e,t,n,r,i,o,a=arguments[0]||{},s=1,u=arguments.length,l=!1;for("boolean"==typeof a&&(l=a,a=arguments[s]||{},s++),"object"==typeof a||m(a)||(a={}),s===u&&(a=this,s--);s<u;s++)if(null!=(e=arguments[s]))for(t in e)r=e[t],"__proto__"!==t&&a!==r&&(l&&r&&(S.isPlainObject(r)||(i=Array.isArray(r)))?(n=a[t],o=i&&!Array.isArray(n)?[]:i||S.isPlainObject(n)?n:{},i=!1,a[t]=S.extend(l,o,r)):void 0!==r&&(a[t]=r));return a},S.extend({expando:"jQuery"+(f+Math.random()).replace(/\D/g,""),isReady:!0,error:function(e){throw new Error(e)},noop:function(){},isPlainObject:function(e){var t,n;return!(!e||"[object Object]"!==o.call(e))&&(!(t=r(e))||"function"==typeof(n=v.call(t,"constructor")&&t.constructor)&&a.call(n)===l)},isEmptyObject:function(e){var t;for(t in e)return!1;return!0},globalEval:function(e,t,n){b(e,{nonce:t&&t.nonce},n)},each:function(e,t){var n,r=0;if(p(e)){for(n=e.length;r<n;r++)if(!1===t.call(e[r],r,e[r]))break}else for(r in e)if(!1===t.call(e[r],r,e[r]))break;return e},makeArray:function(e,t){var n=t||[];return null!=e&&(p(Object(e))?S.merge(n,"string"==typeof e?[e]:e):u.call(n,e)),n},inArray:function(e,t,n){return null==t?-1:i.call(t,e,n)},merge:function(e,t){for(var n=+t.length,r=0,i=e.length;r<n;r++)e[i++]=t[r];return e.length=i,e},grep:function(e,t,n){for(var r=[],i=0,o=e.length,a=!n;i<o;i++)!t(e[i],i)!==a&&r.push(e[i]);return r},map:function(e,t,n){var r,i,o=0,a=[];if(p(e))for(r=e.length;o<r;o++)null!=(i=t(e[o],o,n))&&a.push(i);else for(o in e)null!=(i=t(e[o],o,n))&&a.push(i);return g(a)},guid:1,support:y}),"function"==typeof Symbol&&(S.fn[Symbol.iterator]=t[Symbol.iterator]),S.each("Boolean Number String Function Array Date RegExp Object Error Symbol".split(" "),function(e,t){n["[object "+t+"]"]=t.toLowerCase()});var d=function(n){var e,d,b,o,i,h,f,g,w,u,l,T,C,a,E,v,s,c,y,S="sizzle"+1*new Date,p=n.document,k=0,r=0,m=ue(),x=ue(),A=ue(),N=ue(),j=function(e,t){return e===t&&(l=!0),0},D={}.hasOwnProperty,t=[],q=t.pop,L=t.push,H=t.push,O=t.slice,P=function(e,t){for(var n=0,r=e.length;n<r;n++)if(e[n]===t)return n;return-1},R="checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|ismap|loop|multiple|open|readonly|required|scoped",M="[\\x20\\t\\r\\n\\f]",I="(?:\\\\[\\da-fA-F]{1,6}"+M+"?|\\\\[^\\r\\n\\f]|[\\w-]|[^\0-\\x7f])+",W="\\["+M+"*("+I+")(?:"+M+"*([*^$|!~]?=)"+M+"*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|("+I+"))|)"+M+"*\\]",F=":("+I+")(?:\\((('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|((?:\\\\.|[^\\\\()[\\]]|"+W+")*)|.*)\\)|)",B=new RegExp(M+"+","g"),$=new RegExp("^"+M+"+|((?:^|[^\\\\])(?:\\\\.)*)"+M+"+$","g"),_=new RegExp("^"+M+"*,"+M+"*"),z=new RegExp("^"+M+"*([>+~]|"+M+")"+M+"*"),U=new RegExp(M+"|>"),X=new RegExp(F),V=new RegExp("^"+I+"$"),G={ID:new RegExp("^#("+I+")"),CLASS:new RegExp("^\\.("+I+")"),TAG:new RegExp("^("+I+"|[*])"),ATTR:new RegExp("^"+W),PSEUDO:new RegExp("^"+F),CHILD:new RegExp("^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\("+M+"*(even|odd|(([+-]|)(\\d*)n|)"+M+"*(?:([+-]|)"+M+"*(\\d+)|))"+M+"*\\)|)","i"),bool:new RegExp("^(?:"+R+")$","i"),needsContext:new RegExp("^"+M+"*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\("+M+"*((?:-\\d)?\\d*)"+M+"*\\)|)(?=[^-]|$)","i")},Y=/HTML$/i,Q=/^(?:input|select|textarea|button)$/i,J=/^h\d$/i,K=/^[^{]+\{\s*\[native \w/,Z=/^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/,ee=/[+~]/,te=new RegExp("\\\\[\\da-fA-F]{1,6}"+M+"?|\\\\([^\\r\\n\\f])","g"),ne=function(e,t){var n="0x"+e.slice(1)-65536;return t||(n<0?String.fromCharCode(n+65536):String.fromCharCode(n>>10|55296,1023&n|56320))},re=/([\0-\x1f\x7f]|^-?\d)|^-$|[^\0-\x1f\x7f-\uFFFF\w-]/g,ie=function(e,t){return t?"\0"===e?"\ufffd":e.slice(0,-1)+"\\"+e.charCodeAt(e.length-1).toString(16)+" ":"\\"+e},oe=function(){T()},ae=be(function(e){return!0===e.disabled&&"fieldset"===e.nodeName.toLowerCase()},{dir:"parentNode",next:"legend"});try{H.apply(t=O.call(p.childNodes),p.childNodes),t[p.childNodes.length].nodeType}catch(e){H={apply:t.length?function(e,t){L.apply(e,O.call(t))}:function(e,t){var n=e.length,r=0;while(e[n++]=t[r++]);e.length=n-1}}}function se(t,e,n,r){var i,o,a,s,u,l,c,f=e&&e.ownerDocument,p=e?e.nodeType:9;if(n=n||[],"string"!=typeof t||!t||1!==p&&9!==p&&11!==p)return n;if(!r&&(T(e),e=e||C,E)){if(11!==p&&(u=Z.exec(t)))if(i=u[1]){if(9===p){if(!(a=e.getElementById(i)))return n;if(a.id===i)return n.push(a),n}else if(f&&(a=f.getElementById(i))&&y(e,a)&&a.id===i)return n.push(a),n}else{if(u[2])return H.apply(n,e.getElementsByTagName(t)),n;if((i=u[3])&&d.getElementsByClassName&&e.getElementsByClassName)return H.apply(n,e.getElementsByClassName(i)),n}if(d.qsa&&!N[t+" "]&&(!v||!v.test(t))&&(1!==p||"object"!==e.nodeName.toLowerCase())){if(c=t,f=e,1===p&&(U.test(t)||z.test(t))){(f=ee.test(t)&&ye(e.parentNode)||e)===e&&d.scope||((s=e.getAttribute("id"))?s=s.replace(re,ie):e.setAttribute("id",s=S)),o=(l=h(t)).length;while(o--)l[o]=(s?"#"+s:":scope")+" "+xe(l[o]);c=l.join(",")}try{return H.apply(n,f.querySelectorAll(c)),n}catch(e){N(t,!0)}finally{s===S&&e.removeAttribute("id")}}}return g(t.replace($,"$1"),e,n,r)}function ue(){var r=[];return function e(t,n){return r.push(t+" ")>b.cacheLength&&delete e[r.shift()],e[t+" "]=n}}function le(e){return e[S]=!0,e}function ce(e){var t=C.createElement("fieldset");try{return!!e(t)}catch(e){return!1}finally{t.parentNode&&t.parentNode.removeChild(t),t=null}}function fe(e,t){var n=e.split("|"),r=n.length;while(r--)b.attrHandle[n[r]]=t}function pe(e,t){var n=t&&e,r=n&&1===e.nodeType&&1===t.nodeType&&e.sourceIndex-t.sourceIndex;if(r)return r;if(n)while(n=n.nextSibling)if(n===t)return-1;return e?1:-1}function de(t){return function(e){return"input"===e.nodeName.toLowerCase()&&e.type===t}}function he(n){return function(e){var t=e.nodeName.toLowerCase();return("input"===t||"button"===t)&&e.type===n}}function ge(t){return function(e){return"form"in e?e.parentNode&&!1===e.disabled?"label"in e?"label"in e.parentNode?e.parentNode.disabled===t:e.disabled===t:e.isDisabled===t||e.isDisabled!==!t&&ae(e)===t:e.disabled===t:"label"in e&&e.disabled===t}}function ve(a){return le(function(o){return o=+o,le(function(e,t){var n,r=a([],e.length,o),i=r.length;while(i--)e[n=r[i]]&&(e[n]=!(t[n]=e[n]))})})}function ye(e){return e&&"undefined"!=typeof e.getElementsByTagName&&e}for(e in d=se.support={},i=se.isXML=function(e){var t=e&&e.namespaceURI,n=e&&(e.ownerDocument||e).documentElement;return!Y.test(t||n&&n.nodeName||"HTML")},T=se.setDocument=function(e){var t,n,r=e?e.ownerDocument||e:p;return r!=C&&9===r.nodeType&&r.documentElement&&(a=(C=r).documentElement,E=!i(C),p!=C&&(n=C.defaultView)&&n.top!==n&&(n.addEventListener?n.addEventListener("unload",oe,!1):n.attachEvent&&n.attachEvent("onunload",oe)),d.scope=ce(function(e){return a.appendChild(e).appendChild(C.createElement("div")),"undefined"!=typeof e.querySelectorAll&&!e.querySelectorAll(":scope fieldset div").length}),d.attributes=ce(function(e){return e.className="i",!e.getAttribute("className")}),d.getElementsByTagName=ce(function(e){return e.appendChild(C.createComment("")),!e.getElementsByTagName("*").length}),d.getElementsByClassName=K.test(C.getElementsByClassName),d.getById=ce(function(e){return a.appendChild(e).id=S,!C.getElementsByName||!C.getElementsByName(S).length}),d.getById?(b.filter.ID=function(e){var t=e.replace(te,ne);return function(e){return e.getAttribute("id")===t}},b.find.ID=function(e,t){if("undefined"!=typeof t.getElementById&&E){var n=t.getElementById(e);return n?[n]:[]}}):(b.filter.ID=function(e){var n=e.replace(te,ne);return function(e){var t="undefined"!=typeof e.getAttributeNode&&e.getAttributeNode("id");return t&&t.value===n}},b.find.ID=function(e,t){if("undefined"!=typeof t.getElementById&&E){var n,r,i,o=t.getElementById(e);if(o){if((n=o.getAttributeNode("id"))&&n.value===e)return[o];i=t.getElementsByName(e),r=0;while(o=i[r++])if((n=o.getAttributeNode("id"))&&n.value===e)return[o]}return[]}}),b.find.TAG=d.getElementsByTagName?function(e,t){return"undefined"!=typeof t.getElementsByTagName?t.getElementsByTagName(e):d.qsa?t.querySelectorAll(e):void 0}:function(e,t){var n,r=[],i=0,o=t.getElementsByTagName(e);if("*"===e){while(n=o[i++])1===n.nodeType&&r.push(n);return r}return o},b.find.CLASS=d.getElementsByClassName&&function(e,t){if("undefined"!=typeof t.getElementsByClassName&&E)return t.getElementsByClassName(e)},s=[],v=[],(d.qsa=K.test(C.querySelectorAll))&&(ce(function(e){var t;a.appendChild(e).innerHTML="<a id='"+S+"'></a><select id='"+S+"-\r\\' msallowcapture=''><option selected=''></option></select>",e.querySelectorAll("[msallowcapture^='']").length&&v.push("[*^$]="+M+"*(?:''|\"\")"),e.querySelectorAll("[selected]").length||v.push("\\["+M+"*(?:value|"+R+")"),e.querySelectorAll("[id~="+S+"-]").length||v.push("~="),(t=C.createElement("input")).setAttribute("name",""),e.appendChild(t),e.querySelectorAll("[name='']").length||v.push("\\["+M+"*name"+M+"*="+M+"*(?:''|\"\")"),e.querySelectorAll(":checked").length||v.push(":checked"),e.querySelectorAll("a#"+S+"+*").length||v.push(".#.+[+~]"),e.querySelectorAll("\\\f"),v.push("[\\r\\n\\f]")}),ce(function(e){e.innerHTML="<a href='' disabled='disabled'></a><select disabled='disabled'><option/></select>";var t=C.createElement("input");t.setAttribute("type","hidden"),e.appendChild(t).setAttribute("name","D"),e.querySelectorAll("[name=d]").length&&v.push("name"+M+"*[*^$|!~]?="),2!==e.querySelectorAll(":enabled").length&&v.push(":enabled",":disabled"),a.appendChild(e).disabled=!0,2!==e.querySelectorAll(":disabled").length&&v.push(":enabled",":disabled"),e.querySelectorAll("*,:x"),v.push(",.*:")})),(d.matchesSelector=K.test(c=a.matches||a.webkitMatchesSelector||a.mozMatchesSelector||a.oMatchesSelector||a.msMatchesSelector))&&ce(function(e){d.disconnectedMatch=c.call(e,"*"),c.call(e,"[s!='']:x"),s.push("!=",F)}),v=v.length&&new RegExp(v.join("|")),s=s.length&&new RegExp(s.join("|")),t=K.test(a.compareDocumentPosition),y=t||K.test(a.contains)?function(e,t){var n=9===e.nodeType?e.documentElement:e,r=t&&t.parentNode;return e===r||!(!r||1!==r.nodeType||!(n.contains?n.contains(r):e.compareDocumentPosition&&16&e.compareDocumentPosition(r)))}:function(e,t){if(t)while(t=t.parentNode)if(t===e)return!0;return!1},j=t?function(e,t){if(e===t)return l=!0,0;var n=!e.compareDocumentPosition-!t.compareDocumentPosition;return n||(1&(n=(e.ownerDocument||e)==(t.ownerDocument||t)?e.compareDocumentPosition(t):1)||!d.sortDetached&&t.compareDocumentPosition(e)===n?e==C||e.ownerDocument==p&&y(p,e)?-1:t==C||t.ownerDocument==p&&y(p,t)?1:u?P(u,e)-P(u,t):0:4&n?-1:1)}:function(e,t){if(e===t)return l=!0,0;var n,r=0,i=e.parentNode,o=t.parentNode,a=[e],s=[t];if(!i||!o)return e==C?-1:t==C?1:i?-1:o?1:u?P(u,e)-P(u,t):0;if(i===o)return pe(e,t);n=e;while(n=n.parentNode)a.unshift(n);n=t;while(n=n.parentNode)s.unshift(n);while(a[r]===s[r])r++;return r?pe(a[r],s[r]):a[r]==p?-1:s[r]==p?1:0}),C},se.matches=function(e,t){return se(e,null,null,t)},se.matchesSelector=function(e,t){if(T(e),d.matchesSelector&&E&&!N[t+" "]&&(!s||!s.test(t))&&(!v||!v.test(t)))try{var n=c.call(e,t);if(n||d.disconnectedMatch||e.document&&11!==e.document.nodeType)return n}catch(e){N(t,!0)}return 0<se(t,C,null,[e]).length},se.contains=function(e,t){return(e.ownerDocument||e)!=C&&T(e),y(e,t)},se.attr=function(e,t){(e.ownerDocument||e)!=C&&T(e);var n=b.attrHandle[t.toLowerCase()],r=n&&D.call(b.attrHandle,t.toLowerCase())?n(e,t,!E):void 0;return void 0!==r?r:d.attributes||!E?e.getAttribute(t):(r=e.getAttributeNode(t))&&r.specified?r.value:null},se.escape=function(e){return(e+"").replace(re,ie)},se.error=function(e){throw new Error("Syntax error, unrecognized expression: "+e)},se.uniqueSort=function(e){var t,n=[],r=0,i=0;if(l=!d.detectDuplicates,u=!d.sortStable&&e.slice(0),e.sort(j),l){while(t=e[i++])t===e[i]&&(r=n.push(i));while(r--)e.splice(n[r],1)}return u=null,e},o=se.getText=function(e){var t,n="",r=0,i=e.nodeType;if(i){if(1===i||9===i||11===i){if("string"==typeof e.textContent)return e.textContent;for(e=e.firstChild;e;e=e.nextSibling)n+=o(e)}else if(3===i||4===i)return e.nodeValue}else while(t=e[r++])n+=o(t);return n},(b=se.selectors={cacheLength:50,createPseudo:le,match:G,attrHandle:{},find:{},relative:{">":{dir:"parentNode",first:!0}," ":{dir:"parentNode"},"+":{dir:"previousSibling",first:!0},"~":{dir:"previousSibling"}},preFilter:{ATTR:function(e){return e[1]=e[1].replace(te,ne),e[3]=(e[3]||e[4]||e[5]||"").replace(te,ne),"~="===e[2]&&(e[3]=" "+e[3]+" "),e.slice(0,4)},CHILD:function(e){return e[1]=e[1].toLowerCase(),"nth"===e[1].slice(0,3)?(e[3]||se.error(e[0]),e[4]=+(e[4]?e[5]+(e[6]||1):2*("even"===e[3]||"odd"===e[3])),e[5]=+(e[7]+e[8]||"odd"===e[3])):e[3]&&se.error(e[0]),e},PSEUDO:function(e){var t,n=!e[6]&&e[2];return G.CHILD.test(e[0])?null:(e[3]?e[2]=e[4]||e[5]||"":n&&X.test(n)&&(t=h(n,!0))&&(t=n.indexOf(")",n.length-t)-n.length)&&(e[0]=e[0].slice(0,t),e[2]=n.slice(0,t)),e.slice(0,3))}},filter:{TAG:function(e){var t=e.replace(te,ne).toLowerCase();return"*"===e?function(){return!0}:function(e){return e.nodeName&&e.nodeName.toLowerCase()===t}},CLASS:function(e){var t=m[e+" "];return t||(t=new RegExp("(^|"+M+")"+e+"("+M+"|$)"))&&m(e,function(e){return t.test("string"==typeof e.className&&e.className||"undefined"!=typeof e.getAttribute&&e.getAttribute("class")||"")})},ATTR:function(n,r,i){return function(e){var t=se.attr(e,n);return null==t?"!="===r:!r||(t+="","="===r?t===i:"!="===r?t!==i:"^="===r?i&&0===t.indexOf(i):"*="===r?i&&-1<t.indexOf(i):"$="===r?i&&t.slice(-i.length)===i:"~="===r?-1<(" "+t.replace(B," ")+" ").indexOf(i):"|="===r&&(t===i||t.slice(0,i.length+1)===i+"-"))}},CHILD:function(h,e,t,g,v){var y="nth"!==h.slice(0,3),m="last"!==h.slice(-4),x="of-type"===e;return 1===g&&0===v?function(e){return!!e.parentNode}:function(e,t,n){var r,i,o,a,s,u,l=y!==m?"nextSibling":"previousSibling",c=e.parentNode,f=x&&e.nodeName.toLowerCase(),p=!n&&!x,d=!1;if(c){if(y){while(l){a=e;while(a=a[l])if(x?a.nodeName.toLowerCase()===f:1===a.nodeType)return!1;u=l="only"===h&&!u&&"nextSibling"}return!0}if(u=[m?c.firstChild:c.lastChild],m&&p){d=(s=(r=(i=(o=(a=c)[S]||(a[S]={}))[a.uniqueID]||(o[a.uniqueID]={}))[h]||[])[0]===k&&r[1])&&r[2],a=s&&c.childNodes[s];while(a=++s&&a&&a[l]||(d=s=0)||u.pop())if(1===a.nodeType&&++d&&a===e){i[h]=[k,s,d];break}}else if(p&&(d=s=(r=(i=(o=(a=e)[S]||(a[S]={}))[a.uniqueID]||(o[a.uniqueID]={}))[h]||[])[0]===k&&r[1]),!1===d)while(a=++s&&a&&a[l]||(d=s=0)||u.pop())if((x?a.nodeName.toLowerCase()===f:1===a.nodeType)&&++d&&(p&&((i=(o=a[S]||(a[S]={}))[a.uniqueID]||(o[a.uniqueID]={}))[h]=[k,d]),a===e))break;return(d-=v)===g||d%g==0&&0<=d/g}}},PSEUDO:function(e,o){var t,a=b.pseudos[e]||b.setFilters[e.toLowerCase()]||se.error("unsupported pseudo: "+e);return a[S]?a(o):1<a.length?(t=[e,e,"",o],b.setFilters.hasOwnProperty(e.toLowerCase())?le(function(e,t){var n,r=a(e,o),i=r.length;while(i--)e[n=P(e,r[i])]=!(t[n]=r[i])}):function(e){return a(e,0,t)}):a}},pseudos:{not:le(function(e){var r=[],i=[],s=f(e.replace($,"$1"));return s[S]?le(function(e,t,n,r){var i,o=s(e,null,r,[]),a=e.length;while(a--)(i=o[a])&&(e[a]=!(t[a]=i))}):function(e,t,n){return r[0]=e,s(r,null,n,i),r[0]=null,!i.pop()}}),has:le(function(t){return function(e){return 0<se(t,e).length}}),contains:le(function(t){return t=t.replace(te,ne),function(e){return-1<(e.textContent||o(e)).indexOf(t)}}),lang:le(function(n){return V.test(n||"")||se.error("unsupported lang: "+n),n=n.replace(te,ne).toLowerCase(),function(e){var t;do{if(t=E?e.lang:e.getAttribute("xml:lang")||e.getAttribute("lang"))return(t=t.toLowerCase())===n||0===t.indexOf(n+"-")}while((e=e.parentNode)&&1===e.nodeType);return!1}}),target:function(e){var t=n.location&&n.location.hash;return t&&t.slice(1)===e.id},root:function(e){return e===a},focus:function(e){return e===C.activeElement&&(!C.hasFocus||C.hasFocus())&&!!(e.type||e.href||~e.tabIndex)},enabled:ge(!1),disabled:ge(!0),checked:function(e){var t=e.nodeName.toLowerCase();return"input"===t&&!!e.checked||"option"===t&&!!e.selected},selected:function(e){return e.parentNode&&e.parentNode.selectedIndex,!0===e.selected},empty:function(e){for(e=e.firstChild;e;e=e.nextSibling)if(e.nodeType<6)return!1;return!0},parent:function(e){return!b.pseudos.empty(e)},header:function(e){return J.test(e.nodeName)},input:function(e){return Q.test(e.nodeName)},button:function(e){var t=e.nodeName.toLowerCase();return"input"===t&&"button"===e.type||"button"===t},text:function(e){var t;return"input"===e.nodeName.toLowerCase()&&"text"===e.type&&(null==(t=e.getAttribute("type"))||"text"===t.toLowerCase())},first:ve(function(){return[0]}),last:ve(function(e,t){return[t-1]}),eq:ve(function(e,t,n){return[n<0?n+t:n]}),even:ve(function(e,t){for(var n=0;n<t;n+=2)e.push(n);return e}),odd:ve(function(e,t){for(var n=1;n<t;n+=2)e.push(n);return e}),lt:ve(function(e,t,n){for(var r=n<0?n+t:t<n?t:n;0<=--r;)e.push(r);return e}),gt:ve(function(e,t,n){for(var r=n<0?n+t:n;++r<t;)e.push(r);return e})}}).pseudos.nth=b.pseudos.eq,{radio:!0,checkbox:!0,file:!0,password:!0,image:!0})b.pseudos[e]=de(e);for(e in{submit:!0,reset:!0})b.pseudos[e]=he(e);function me(){}function xe(e){for(var t=0,n=e.length,r="";t<n;t++)r+=e[t].value;return r}function be(s,e,t){var u=e.dir,l=e.next,c=l||u,f=t&&"parentNode"===c,p=r++;return e.first?function(e,t,n){while(e=e[u])if(1===e.nodeType||f)return s(e,t,n);return!1}:function(e,t,n){var r,i,o,a=[k,p];if(n){while(e=e[u])if((1===e.nodeType||f)&&s(e,t,n))return!0}else while(e=e[u])if(1===e.nodeType||f)if(i=(o=e[S]||(e[S]={}))[e.uniqueID]||(o[e.uniqueID]={}),l&&l===e.nodeName.toLowerCase())e=e[u]||e;else{if((r=i[c])&&r[0]===k&&r[1]===p)return a[2]=r[2];if((i[c]=a)[2]=s(e,t,n))return!0}return!1}}function we(i){return 1<i.length?function(e,t,n){var r=i.length;while(r--)if(!i[r](e,t,n))return!1;return!0}:i[0]}function Te(e,t,n,r,i){for(var o,a=[],s=0,u=e.length,l=null!=t;s<u;s++)(o=e[s])&&(n&&!n(o,r,i)||(a.push(o),l&&t.push(s)));return a}function Ce(d,h,g,v,y,e){return v&&!v[S]&&(v=Ce(v)),y&&!y[S]&&(y=Ce(y,e)),le(function(e,t,n,r){var i,o,a,s=[],u=[],l=t.length,c=e||function(e,t,n){for(var r=0,i=t.length;r<i;r++)se(e,t[r],n);return n}(h||"*",n.nodeType?[n]:n,[]),f=!d||!e&&h?c:Te(c,s,d,n,r),p=g?y||(e?d:l||v)?[]:t:f;if(g&&g(f,p,n,r),v){i=Te(p,u),v(i,[],n,r),o=i.length;while(o--)(a=i[o])&&(p[u[o]]=!(f[u[o]]=a))}if(e){if(y||d){if(y){i=[],o=p.length;while(o--)(a=p[o])&&i.push(f[o]=a);y(null,p=[],i,r)}o=p.length;while(o--)(a=p[o])&&-1<(i=y?P(e,a):s[o])&&(e[i]=!(t[i]=a))}}else p=Te(p===t?p.splice(l,p.length):p),y?y(null,t,p,r):H.apply(t,p)})}function Ee(e){for(var i,t,n,r=e.length,o=b.relative[e[0].type],a=o||b.relative[" "],s=o?1:0,u=be(function(e){return e===i},a,!0),l=be(function(e){return-1<P(i,e)},a,!0),c=[function(e,t,n){var r=!o&&(n||t!==w)||((i=t).nodeType?u(e,t,n):l(e,t,n));return i=null,r}];s<r;s++)if(t=b.relative[e[s].type])c=[be(we(c),t)];else{if((t=b.filter[e[s].type].apply(null,e[s].matches))[S]){for(n=++s;n<r;n++)if(b.relative[e[n].type])break;return Ce(1<s&&we(c),1<s&&xe(e.slice(0,s-1).concat({value:" "===e[s-2].type?"*":""})).replace($,"$1"),t,s<n&&Ee(e.slice(s,n)),n<r&&Ee(e=e.slice(n)),n<r&&xe(e))}c.push(t)}return we(c)}return me.prototype=b.filters=b.pseudos,b.setFilters=new me,h=se.tokenize=function(e,t){var n,r,i,o,a,s,u,l=x[e+" "];if(l)return t?0:l.slice(0);a=e,s=[],u=b.preFilter;while(a){for(o in n&&!(r=_.exec(a))||(r&&(a=a.slice(r[0].length)||a),s.push(i=[])),n=!1,(r=z.exec(a))&&(n=r.shift(),i.push({value:n,type:r[0].replace($," ")}),a=a.slice(n.length)),b.filter)!(r=G[o].exec(a))||u[o]&&!(r=u[o](r))||(n=r.shift(),i.push({value:n,type:o,matches:r}),a=a.slice(n.length));if(!n)break}return t?a.length:a?se.error(e):x(e,s).slice(0)},f=se.compile=function(e,t){var n,v,y,m,x,r,i=[],o=[],a=A[e+" "];if(!a){t||(t=h(e)),n=t.length;while(n--)(a=Ee(t[n]))[S]?i.push(a):o.push(a);(a=A(e,(v=o,m=0<(y=i).length,x=0<v.length,r=function(e,t,n,r,i){var o,a,s,u=0,l="0",c=e&&[],f=[],p=w,d=e||x&&b.find.TAG("*",i),h=k+=null==p?1:Math.random()||.1,g=d.length;for(i&&(w=t==C||t||i);l!==g&&null!=(o=d[l]);l++){if(x&&o){a=0,t||o.ownerDocument==C||(T(o),n=!E);while(s=v[a++])if(s(o,t||C,n)){r.push(o);break}i&&(k=h)}m&&((o=!s&&o)&&u--,e&&c.push(o))}if(u+=l,m&&l!==u){a=0;while(s=y[a++])s(c,f,t,n);if(e){if(0<u)while(l--)c[l]||f[l]||(f[l]=q.call(r));f=Te(f)}H.apply(r,f),i&&!e&&0<f.length&&1<u+y.length&&se.uniqueSort(r)}return i&&(k=h,w=p),c},m?le(r):r))).selector=e}return a},g=se.select=function(e,t,n,r){var i,o,a,s,u,l="function"==typeof e&&e,c=!r&&h(e=l.selector||e);if(n=n||[],1===c.length){if(2<(o=c[0]=c[0].slice(0)).length&&"ID"===(a=o[0]).type&&9===t.nodeType&&E&&b.relative[o[1].type]){if(!(t=(b.find.ID(a.matches[0].replace(te,ne),t)||[])[0]))return n;l&&(t=t.parentNode),e=e.slice(o.shift().value.length)}i=G.needsContext.test(e)?0:o.length;while(i--){if(a=o[i],b.relative[s=a.type])break;if((u=b.find[s])&&(r=u(a.matches[0].replace(te,ne),ee.test(o[0].type)&&ye(t.parentNode)||t))){if(o.splice(i,1),!(e=r.length&&xe(o)))return H.apply(n,r),n;break}}}return(l||f(e,c))(r,t,!E,n,!t||ee.test(e)&&ye(t.parentNode)||t),n},d.sortStable=S.split("").sort(j).join("")===S,d.detectDuplicates=!!l,T(),d.sortDetached=ce(function(e){return 1&e.compareDocumentPosition(C.createElement("fieldset"))}),ce(function(e){return e.innerHTML="<a href='#'></a>","#"===e.firstChild.getAttribute("href")})||fe("type|href|height|width",function(e,t,n){if(!n)return e.getAttribute(t,"type"===t.toLowerCase()?1:2)}),d.attributes&&ce(function(e){return e.innerHTML="<input/>",e.firstChild.setAttribute("value",""),""===e.firstChild.getAttribute("value")})||fe("value",function(e,t,n){if(!n&&"input"===e.nodeName.toLowerCase())return e.defaultValue}),ce(function(e){return null==e.getAttribute("disabled")})||fe(R,function(e,t,n){var r;if(!n)return!0===e[t]?t.toLowerCase():(r=e.getAttributeNode(t))&&r.specified?r.value:null}),se}(C);S.find=d,S.expr=d.selectors,S.expr[":"]=S.expr.pseudos,S.uniqueSort=S.unique=d.uniqueSort,S.text=d.getText,S.isXMLDoc=d.isXML,S.contains=d.contains,S.escapeSelector=d.escape;var h=function(e,t,n){var r=[],i=void 0!==n;while((e=e[t])&&9!==e.nodeType)if(1===e.nodeType){if(i&&S(e).is(n))break;r.push(e)}return r},T=function(e,t){for(var n=[];e;e=e.nextSibling)1===e.nodeType&&e!==t&&n.push(e);return n},k=S.expr.match.needsContext;function A(e,t){return e.nodeName&&e.nodeName.toLowerCase()===t.toLowerCase()}var N=/^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i;function j(e,n,r){return m(n)?S.grep(e,function(e,t){return!!n.call(e,t,e)!==r}):n.nodeType?S.grep(e,function(e){return e===n!==r}):"string"!=typeof n?S.grep(e,function(e){return-1<i.call(n,e)!==r}):S.filter(n,e,r)}S.filter=function(e,t,n){var r=t[0];return n&&(e=":not("+e+")"),1===t.length&&1===r.nodeType?S.find.matchesSelector(r,e)?[r]:[]:S.find.matches(e,S.grep(t,function(e){return 1===e.nodeType}))},S.fn.extend({find:function(e){var t,n,r=this.length,i=this;if("string"!=typeof e)return this.pushStack(S(e).filter(function(){for(t=0;t<r;t++)if(S.contains(i[t],this))return!0}));for(n=this.pushStack([]),t=0;t<r;t++)S.find(e,i[t],n);return 1<r?S.uniqueSort(n):n},filter:function(e){return this.pushStack(j(this,e||[],!1))},not:function(e){return this.pushStack(j(this,e||[],!0))},is:function(e){return!!j(this,"string"==typeof e&&k.test(e)?S(e):e||[],!1).length}});var D,q=/^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]+))$/;(S.fn.init=function(e,t,n){var r,i;if(!e)return this;if(n=n||D,"string"==typeof e){if(!(r="<"===e[0]&&">"===e[e.length-1]&&3<=e.length?[null,e,null]:q.exec(e))||!r[1]&&t)return!t||t.jquery?(t||n).find(e):this.constructor(t).find(e);if(r[1]){if(t=t instanceof S?t[0]:t,S.merge(this,S.parseHTML(r[1],t&&t.nodeType?t.ownerDocument||t:E,!0)),N.test(r[1])&&S.isPlainObject(t))for(r in t)m(this[r])?this[r](t[r]):this.attr(r,t[r]);return this}return(i=E.getElementById(r[2]))&&(this[0]=i,this.length=1),this}return e.nodeType?(this[0]=e,this.length=1,this):m(e)?void 0!==n.ready?n.ready(e):e(S):S.makeArray(e,this)}).prototype=S.fn,D=S(E);var L=/^(?:parents|prev(?:Until|All))/,H={children:!0,contents:!0,next:!0,prev:!0};function O(e,t){while((e=e[t])&&1!==e.nodeType);return e}S.fn.extend({has:function(e){var t=S(e,this),n=t.length;return this.filter(function(){for(var e=0;e<n;e++)if(S.contains(this,t[e]))return!0})},closest:function(e,t){var n,r=0,i=this.length,o=[],a="string"!=typeof e&&S(e);if(!k.test(e))for(;r<i;r++)for(n=this[r];n&&n!==t;n=n.parentNode)if(n.nodeType<11&&(a?-1<a.index(n):1===n.nodeType&&S.find.matchesSelector(n,e))){o.push(n);break}return this.pushStack(1<o.length?S.uniqueSort(o):o)},index:function(e){return e?"string"==typeof e?i.call(S(e),this[0]):i.call(this,e.jquery?e[0]:e):this[0]&&this[0].parentNode?this.first().prevAll().length:-1},add:function(e,t){return this.pushStack(S.uniqueSort(S.merge(this.get(),S(e,t))))},addBack:function(e){return this.add(null==e?this.prevObject:this.prevObject.filter(e))}}),S.each({parent:function(e){var t=e.parentNode;return t&&11!==t.nodeType?t:null},parents:function(e){return h(e,"parentNode")},parentsUntil:function(e,t,n){return h(e,"parentNode",n)},next:function(e){return O(e,"nextSibling")},prev:function(e){return O(e,"previousSibling")},nextAll:function(e){return h(e,"nextSibling")},prevAll:function(e){return h(e,"previousSibling")},nextUntil:function(e,t,n){return h(e,"nextSibling",n)},prevUntil:function(e,t,n){return h(e,"previousSibling",n)},siblings:function(e){return T((e.parentNode||{}).firstChild,e)},children:function(e){return T(e.firstChild)},contents:function(e){return null!=e.contentDocument&&r(e.contentDocument)?e.contentDocument:(A(e,"template")&&(e=e.content||e),S.merge([],e.childNodes))}},function(r,i){S.fn[r]=function(e,t){var n=S.map(this,i,e);return"Until"!==r.slice(-5)&&(t=e),t&&"string"==typeof t&&(n=S.filter(t,n)),1<this.length&&(H[r]||S.uniqueSort(n),L.test(r)&&n.reverse()),this.pushStack(n)}});var P=/[^\x20\t\r\n\f]+/g;function R(e){return e}function M(e){throw e}function I(e,t,n,r){var i;try{e&&m(i=e.promise)?i.call(e).done(t).fail(n):e&&m(i=e.then)?i.call(e,t,n):t.apply(void 0,[e].slice(r))}catch(e){n.apply(void 0,[e])}}S.Callbacks=function(r){var e,n;r="string"==typeof r?(e=r,n={},S.each(e.match(P)||[],function(e,t){n[t]=!0}),n):S.extend({},r);var i,t,o,a,s=[],u=[],l=-1,c=function(){for(a=a||r.once,o=i=!0;u.length;l=-1){t=u.shift();while(++l<s.length)!1===s[l].apply(t[0],t[1])&&r.stopOnFalse&&(l=s.length,t=!1)}r.memory||(t=!1),i=!1,a&&(s=t?[]:"")},f={add:function(){return s&&(t&&!i&&(l=s.length-1,u.push(t)),function n(e){S.each(e,function(e,t){m(t)?r.unique&&f.has(t)||s.push(t):t&&t.length&&"string"!==w(t)&&n(t)})}(arguments),t&&!i&&c()),this},remove:function(){return S.each(arguments,function(e,t){var n;while(-1<(n=S.inArray(t,s,n)))s.splice(n,1),n<=l&&l--}),this},has:function(e){return e?-1<S.inArray(e,s):0<s.length},empty:function(){return s&&(s=[]),this},disable:function(){return a=u=[],s=t="",this},disabled:function(){return!s},lock:function(){return a=u=[],t||i||(s=t=""),this},locked:function(){return!!a},fireWith:function(e,t){return a||(t=[e,(t=t||[]).slice?t.slice():t],u.push(t),i||c()),this},fire:function(){return f.fireWith(this,arguments),this},fired:function(){return!!o}};return f},S.extend({Deferred:function(e){var o=[["notify","progress",S.Callbacks("memory"),S.Callbacks("memory"),2],["resolve","done",S.Callbacks("once memory"),S.Callbacks("once memory"),0,"resolved"],["reject","fail",S.Callbacks("once memory"),S.Callbacks("once memory"),1,"rejected"]],i="pending",a={state:function(){return i},always:function(){return s.done(arguments).fail(arguments),this},"catch":function(e){return a.then(null,e)},pipe:function(){var i=arguments;return S.Deferred(function(r){S.each(o,function(e,t){var n=m(i[t[4]])&&i[t[4]];s[t[1]](function(){var e=n&&n.apply(this,arguments);e&&m(e.promise)?e.promise().progress(r.notify).done(r.resolve).fail(r.reject):r[t[0]+"With"](this,n?[e]:arguments)})}),i=null}).promise()},then:function(t,n,r){var u=0;function l(i,o,a,s){return function(){var n=this,r=arguments,e=function(){var e,t;if(!(i<u)){if((e=a.apply(n,r))===o.promise())throw new TypeError("Thenable self-resolution");t=e&&("object"==typeof e||"function"==typeof e)&&e.then,m(t)?s?t.call(e,l(u,o,R,s),l(u,o,M,s)):(u++,t.call(e,l(u,o,R,s),l(u,o,M,s),l(u,o,R,o.notifyWith))):(a!==R&&(n=void 0,r=[e]),(s||o.resolveWith)(n,r))}},t=s?e:function(){try{e()}catch(e){S.Deferred.exceptionHook&&S.Deferred.exceptionHook(e,t.stackTrace),u<=i+1&&(a!==M&&(n=void 0,r=[e]),o.rejectWith(n,r))}};i?t():(S.Deferred.getStackHook&&(t.stackTrace=S.Deferred.getStackHook()),C.setTimeout(t))}}return S.Deferred(function(e){o[0][3].add(l(0,e,m(r)?r:R,e.notifyWith)),o[1][3].add(l(0,e,m(t)?t:R)),o[2][3].add(l(0,e,m(n)?n:M))}).promise()},promise:function(e){return null!=e?S.extend(e,a):a}},s={};return S.each(o,function(e,t){var n=t[2],r=t[5];a[t[1]]=n.add,r&&n.add(function(){i=r},o[3-e][2].disable,o[3-e][3].disable,o[0][2].lock,o[0][3].lock),n.add(t[3].fire),s[t[0]]=function(){return s[t[0]+"With"](this===s?void 0:this,arguments),this},s[t[0]+"With"]=n.fireWith}),a.promise(s),e&&e.call(s,s),s},when:function(e){var n=arguments.length,t=n,r=Array(t),i=s.call(arguments),o=S.Deferred(),a=function(t){return function(e){r[t]=this,i[t]=1<arguments.length?s.call(arguments):e,--n||o.resolveWith(r,i)}};if(n<=1&&(I(e,o.done(a(t)).resolve,o.reject,!n),"pending"===o.state()||m(i[t]&&i[t].then)))return o.then();while(t--)I(i[t],a(t),o.reject);return o.promise()}});var W=/^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/;S.Deferred.exceptionHook=function(e,t){C.console&&C.console.warn&&e&&W.test(e.name)&&C.console.warn("jQuery.Deferred exception: "+e.message,e.stack,t)},S.readyException=function(e){C.setTimeout(function(){throw e})};var F=S.Deferred();function B(){E.removeEventListener("DOMContentLoaded",B),C.removeEventListener("load",B),S.ready()}S.fn.ready=function(e){return F.then(e)["catch"](function(e){S.readyException(e)}),this},S.extend({isReady:!1,readyWait:1,ready:function(e){(!0===e?--S.readyWait:S.isReady)||(S.isReady=!0)!==e&&0<--S.readyWait||F.resolveWith(E,[S])}}),S.ready.then=F.then,"complete"===E.readyState||"loading"!==E.readyState&&!E.documentElement.doScroll?C.setTimeout(S.ready):(E.addEventListener("DOMContentLoaded",B),C.addEventListener("load",B));var $=function(e,t,n,r,i,o,a){var s=0,u=e.length,l=null==n;if("object"===w(n))for(s in i=!0,n)$(e,t,s,n[s],!0,o,a);else if(void 0!==r&&(i=!0,m(r)||(a=!0),l&&(a?(t.call(e,r),t=null):(l=t,t=function(e,t,n){return l.call(S(e),n)})),t))for(;s<u;s++)t(e[s],n,a?r:r.call(e[s],s,t(e[s],n)));return i?e:l?t.call(e):u?t(e[0],n):o},_=/^-ms-/,z=/-([a-z])/g;function U(e,t){return t.toUpperCase()}function X(e){return e.replace(_,"ms-").replace(z,U)}var V=function(e){return 1===e.nodeType||9===e.nodeType||!+e.nodeType};function G(){this.expando=S.expando+G.uid++}G.uid=1,G.prototype={cache:function(e){var t=e[this.expando];return t||(t={},V(e)&&(e.nodeType?e[this.expando]=t:Object.defineProperty(e,this.expando,{value:t,configurable:!0}))),t},set:function(e,t,n){var r,i=this.cache(e);if("string"==typeof t)i[X(t)]=n;else for(r in t)i[X(r)]=t[r];return i},get:function(e,t){return void 0===t?this.cache(e):e[this.expando]&&e[this.expando][X(t)]},access:function(e,t,n){return void 0===t||t&&"string"==typeof t&&void 0===n?this.get(e,t):(this.set(e,t,n),void 0!==n?n:t)},remove:function(e,t){var n,r=e[this.expando];if(void 0!==r){if(void 0!==t){n=(t=Array.isArray(t)?t.map(X):(t=X(t))in r?[t]:t.match(P)||[]).length;while(n--)delete r[t[n]]}(void 0===t||S.isEmptyObject(r))&&(e.nodeType?e[this.expando]=void 0:delete e[this.expando])}},hasData:function(e){var t=e[this.expando];return void 0!==t&&!S.isEmptyObject(t)}};var Y=new G,Q=new G,J=/^(?:\{[\w\W]*\}|\[[\w\W]*\])$/,K=/[A-Z]/g;function Z(e,t,n){var r,i;if(void 0===n&&1===e.nodeType)if(r="data-"+t.replace(K,"-$&").toLowerCase(),"string"==typeof(n=e.getAttribute(r))){try{n="true"===(i=n)||"false"!==i&&("null"===i?null:i===+i+""?+i:J.test(i)?JSON.parse(i):i)}catch(e){}Q.set(e,t,n)}else n=void 0;return n}S.extend({hasData:function(e){return Q.hasData(e)||Y.hasData(e)},data:function(e,t,n){return Q.access(e,t,n)},removeData:function(e,t){Q.remove(e,t)},_data:function(e,t,n){return Y.access(e,t,n)},_removeData:function(e,t){Y.remove(e,t)}}),S.fn.extend({data:function(n,e){var t,r,i,o=this[0],a=o&&o.attributes;if(void 0===n){if(this.length&&(i=Q.get(o),1===o.nodeType&&!Y.get(o,"hasDataAttrs"))){t=a.length;while(t--)a[t]&&0===(r=a[t].name).indexOf("data-")&&(r=X(r.slice(5)),Z(o,r,i[r]));Y.set(o,"hasDataAttrs",!0)}return i}return"object"==typeof n?this.each(function(){Q.set(this,n)}):$(this,function(e){var t;if(o&&void 0===e)return void 0!==(t=Q.get(o,n))?t:void 0!==(t=Z(o,n))?t:void 0;this.each(function(){Q.set(this,n,e)})},null,e,1<arguments.length,null,!0)},removeData:function(e){return this.each(function(){Q.remove(this,e)})}}),S.extend({queue:function(e,t,n){var r;if(e)return t=(t||"fx")+"queue",r=Y.get(e,t),n&&(!r||Array.isArray(n)?r=Y.access(e,t,S.makeArray(n)):r.push(n)),r||[]},dequeue:function(e,t){t=t||"fx";var n=S.queue(e,t),r=n.length,i=n.shift(),o=S._queueHooks(e,t);"inprogress"===i&&(i=n.shift(),r--),i&&("fx"===t&&n.unshift("inprogress"),delete o.stop,i.call(e,function(){S.dequeue(e,t)},o)),!r&&o&&o.empty.fire()},_queueHooks:function(e,t){var n=t+"queueHooks";return Y.get(e,n)||Y.access(e,n,{empty:S.Callbacks("once memory").add(function(){Y.remove(e,[t+"queue",n])})})}}),S.fn.extend({queue:function(t,n){var e=2;return"string"!=typeof t&&(n=t,t="fx",e--),arguments.length<e?S.queue(this[0],t):void 0===n?this:this.each(function(){var e=S.queue(this,t,n);S._queueHooks(this,t),"fx"===t&&"inprogress"!==e[0]&&S.dequeue(this,t)})},dequeue:function(e){return this.each(function(){S.dequeue(this,e)})},clearQueue:function(e){return this.queue(e||"fx",[])},promise:function(e,t){var n,r=1,i=S.Deferred(),o=this,a=this.length,s=function(){--r||i.resolveWith(o,[o])};"string"!=typeof e&&(t=e,e=void 0),e=e||"fx";while(a--)(n=Y.get(o[a],e+"queueHooks"))&&n.empty&&(r++,n.empty.add(s));return s(),i.promise(t)}});var ee=/[+-]?(?:\d*\.|)\d+(?:[eE][+-]?\d+|)/.source,te=new RegExp("^(?:([+-])=|)("+ee+")([a-z%]*)$","i"),ne=["Top","Right","Bottom","Left"],re=E.documentElement,ie=function(e){return S.contains(e.ownerDocument,e)},oe={composed:!0};re.getRootNode&&(ie=function(e){return S.contains(e.ownerDocument,e)||e.getRootNode(oe)===e.ownerDocument});var ae=function(e,t){return"none"===(e=t||e).style.display||""===e.style.display&&ie(e)&&"none"===S.css(e,"display")};function se(e,t,n,r){var i,o,a=20,s=r?function(){return r.cur()}:function(){return S.css(e,t,"")},u=s(),l=n&&n[3]||(S.cssNumber[t]?"":"px"),c=e.nodeType&&(S.cssNumber[t]||"px"!==l&&+u)&&te.exec(S.css(e,t));if(c&&c[3]!==l){u/=2,l=l||c[3],c=+u||1;while(a--)S.style(e,t,c+l),(1-o)*(1-(o=s()/u||.5))<=0&&(a=0),c/=o;c*=2,S.style(e,t,c+l),n=n||[]}return n&&(c=+c||+u||0,i=n[1]?c+(n[1]+1)*n[2]:+n[2],r&&(r.unit=l,r.start=c,r.end=i)),i}var ue={};function le(e,t){for(var n,r,i,o,a,s,u,l=[],c=0,f=e.length;c<f;c++)(r=e[c]).style&&(n=r.style.display,t?("none"===n&&(l[c]=Y.get(r,"display")||null,l[c]||(r.style.display="")),""===r.style.display&&ae(r)&&(l[c]=(u=a=o=void 0,a=(i=r).ownerDocument,s=i.nodeName,(u=ue[s])||(o=a.body.appendChild(a.createElement(s)),u=S.css(o,"display"),o.parentNode.removeChild(o),"none"===u&&(u="block"),ue[s]=u)))):"none"!==n&&(l[c]="none",Y.set(r,"display",n)));for(c=0;c<f;c++)null!=l[c]&&(e[c].style.display=l[c]);return e}S.fn.extend({show:function(){return le(this,!0)},hide:function(){return le(this)},toggle:function(e){return"boolean"==typeof e?e?this.show():this.hide():this.each(function(){ae(this)?S(this).show():S(this).hide()})}});var ce,fe,pe=/^(?:checkbox|radio)$/i,de=/<([a-z][^\/\0>\x20\t\r\n\f]*)/i,he=/^$|^module$|\/(?:java|ecma)script/i;ce=E.createDocumentFragment().appendChild(E.createElement("div")),(fe=E.createElement("input")).setAttribute("type","radio"),fe.setAttribute("checked","checked"),fe.setAttribute("name","t"),ce.appendChild(fe),y.checkClone=ce.cloneNode(!0).cloneNode(!0).lastChild.checked,ce.innerHTML="<textarea>x</textarea>",y.noCloneChecked=!!ce.cloneNode(!0).lastChild.defaultValue,ce.innerHTML="<option></option>",y.option=!!ce.lastChild;var ge={thead:[1,"<table>","</table>"],col:[2,"<table><colgroup>","</colgroup></table>"],tr:[2,"<table><tbody>","</tbody></table>"],td:[3,"<table><tbody><tr>","</tr></tbody></table>"],_default:[0,"",""]};function ve(e,t){var n;return n="undefined"!=typeof e.getElementsByTagName?e.getElementsByTagName(t||"*"):"undefined"!=typeof e.querySelectorAll?e.querySelectorAll(t||"*"):[],void 0===t||t&&A(e,t)?S.merge([e],n):n}function ye(e,t){for(var n=0,r=e.length;n<r;n++)Y.set(e[n],"globalEval",!t||Y.get(t[n],"globalEval"))}ge.tbody=ge.tfoot=ge.colgroup=ge.caption=ge.thead,ge.th=ge.td,y.option||(ge.optgroup=ge.option=[1,"<select multiple='multiple'>","</select>"]);var me=/<|&#?\w+;/;function xe(e,t,n,r,i){for(var o,a,s,u,l,c,f=t.createDocumentFragment(),p=[],d=0,h=e.length;d<h;d++)if((o=e[d])||0===o)if("object"===w(o))S.merge(p,o.nodeType?[o]:o);else if(me.test(o)){a=a||f.appendChild(t.createElement("div")),s=(de.exec(o)||["",""])[1].toLowerCase(),u=ge[s]||ge._default,a.innerHTML=u[1]+S.htmlPrefilter(o)+u[2],c=u[0];while(c--)a=a.lastChild;S.merge(p,a.childNodes),(a=f.firstChild).textContent=""}else p.push(t.createTextNode(o));f.textContent="",d=0;while(o=p[d++])if(r&&-1<S.inArray(o,r))i&&i.push(o);else if(l=ie(o),a=ve(f.appendChild(o),"script"),l&&ye(a),n){c=0;while(o=a[c++])he.test(o.type||"")&&n.push(o)}return f}var be=/^([^.]*)(?:\.(.+)|)/;function we(){return!0}function Te(){return!1}function Ce(e,t){return e===function(){try{return E.activeElement}catch(e){}}()==("focus"===t)}function Ee(e,t,n,r,i,o){var a,s;if("object"==typeof t){for(s in"string"!=typeof n&&(r=r||n,n=void 0),t)Ee(e,s,n,r,t[s],o);return e}if(null==r&&null==i?(i=n,r=n=void 0):null==i&&("string"==typeof n?(i=r,r=void 0):(i=r,r=n,n=void 0)),!1===i)i=Te;else if(!i)return e;return 1===o&&(a=i,(i=function(e){return S().off(e),a.apply(this,arguments)}).guid=a.guid||(a.guid=S.guid++)),e.each(function(){S.event.add(this,t,i,r,n)})}function Se(e,i,o){o?(Y.set(e,i,!1),S.event.add(e,i,{namespace:!1,handler:function(e){var t,n,r=Y.get(this,i);if(1&e.isTrigger&&this[i]){if(r.length)(S.event.special[i]||{}).delegateType&&e.stopPropagation();else if(r=s.call(arguments),Y.set(this,i,r),t=o(this,i),this[i](),r!==(n=Y.get(this,i))||t?Y.set(this,i,!1):n={},r!==n)return e.stopImmediatePropagation(),e.preventDefault(),n&&n.value}else r.length&&(Y.set(this,i,{value:S.event.trigger(S.extend(r[0],S.Event.prototype),r.slice(1),this)}),e.stopImmediatePropagation())}})):void 0===Y.get(e,i)&&S.event.add(e,i,we)}S.event={global:{},add:function(t,e,n,r,i){var o,a,s,u,l,c,f,p,d,h,g,v=Y.get(t);if(V(t)){n.handler&&(n=(o=n).handler,i=o.selector),i&&S.find.matchesSelector(re,i),n.guid||(n.guid=S.guid++),(u=v.events)||(u=v.events=Object.create(null)),(a=v.handle)||(a=v.handle=function(e){return"undefined"!=typeof S&&S.event.triggered!==e.type?S.event.dispatch.apply(t,arguments):void 0}),l=(e=(e||"").match(P)||[""]).length;while(l--)d=g=(s=be.exec(e[l])||[])[1],h=(s[2]||"").split(".").sort(),d&&(f=S.event.special[d]||{},d=(i?f.delegateType:f.bindType)||d,f=S.event.special[d]||{},c=S.extend({type:d,origType:g,data:r,handler:n,guid:n.guid,selector:i,needsContext:i&&S.expr.match.needsContext.test(i),namespace:h.join(".")},o),(p=u[d])||((p=u[d]=[]).delegateCount=0,f.setup&&!1!==f.setup.call(t,r,h,a)||t.addEventListener&&t.addEventListener(d,a)),f.add&&(f.add.call(t,c),c.handler.guid||(c.handler.guid=n.guid)),i?p.splice(p.delegateCount++,0,c):p.push(c),S.event.global[d]=!0)}},remove:function(e,t,n,r,i){var o,a,s,u,l,c,f,p,d,h,g,v=Y.hasData(e)&&Y.get(e);if(v&&(u=v.events)){l=(t=(t||"").match(P)||[""]).length;while(l--)if(d=g=(s=be.exec(t[l])||[])[1],h=(s[2]||"").split(".").sort(),d){f=S.event.special[d]||{},p=u[d=(r?f.delegateType:f.bindType)||d]||[],s=s[2]&&new RegExp("(^|\\.)"+h.join("\\.(?:.*\\.|)")+"(\\.|$)"),a=o=p.length;while(o--)c=p[o],!i&&g!==c.origType||n&&n.guid!==c.guid||s&&!s.test(c.namespace)||r&&r!==c.selector&&("**"!==r||!c.selector)||(p.splice(o,1),c.selector&&p.delegateCount--,f.remove&&f.remove.call(e,c));a&&!p.length&&(f.teardown&&!1!==f.teardown.call(e,h,v.handle)||S.removeEvent(e,d,v.handle),delete u[d])}else for(d in u)S.event.remove(e,d+t[l],n,r,!0);S.isEmptyObject(u)&&Y.remove(e,"handle events")}},dispatch:function(e){var t,n,r,i,o,a,s=new Array(arguments.length),u=S.event.fix(e),l=(Y.get(this,"events")||Object.create(null))[u.type]||[],c=S.event.special[u.type]||{};for(s[0]=u,t=1;t<arguments.length;t++)s[t]=arguments[t];if(u.delegateTarget=this,!c.preDispatch||!1!==c.preDispatch.call(this,u)){a=S.event.handlers.call(this,u,l),t=0;while((i=a[t++])&&!u.isPropagationStopped()){u.currentTarget=i.elem,n=0;while((o=i.handlers[n++])&&!u.isImmediatePropagationStopped())u.rnamespace&&!1!==o.namespace&&!u.rnamespace.test(o.namespace)||(u.handleObj=o,u.data=o.data,void 0!==(r=((S.event.special[o.origType]||{}).handle||o.handler).apply(i.elem,s))&&!1===(u.result=r)&&(u.preventDefault(),u.stopPropagation()))}return c.postDispatch&&c.postDispatch.call(this,u),u.result}},handlers:function(e,t){var n,r,i,o,a,s=[],u=t.delegateCount,l=e.target;if(u&&l.nodeType&&!("click"===e.type&&1<=e.button))for(;l!==this;l=l.parentNode||this)if(1===l.nodeType&&("click"!==e.type||!0!==l.disabled)){for(o=[],a={},n=0;n<u;n++)void 0===a[i=(r=t[n]).selector+" "]&&(a[i]=r.needsContext?-1<S(i,this).index(l):S.find(i,this,null,[l]).length),a[i]&&o.push(r);o.length&&s.push({elem:l,handlers:o})}return l=this,u<t.length&&s.push({elem:l,handlers:t.slice(u)}),s},addProp:function(t,e){Object.defineProperty(S.Event.prototype,t,{enumerable:!0,configurable:!0,get:m(e)?function(){if(this.originalEvent)return e(this.originalEvent)}:function(){if(this.originalEvent)return this.originalEvent[t]},set:function(e){Object.defineProperty(this,t,{enumerable:!0,configurable:!0,writable:!0,value:e})}})},fix:function(e){return e[S.expando]?e:new S.Event(e)},special:{load:{noBubble:!0},click:{setup:function(e){var t=this||e;return pe.test(t.type)&&t.click&&A(t,"input")&&Se(t,"click",we),!1},trigger:function(e){var t=this||e;return pe.test(t.type)&&t.click&&A(t,"input")&&Se(t,"click"),!0},_default:function(e){var t=e.target;return pe.test(t.type)&&t.click&&A(t,"input")&&Y.get(t,"click")||A(t,"a")}},beforeunload:{postDispatch:function(e){void 0!==e.result&&e.originalEvent&&(e.originalEvent.returnValue=e.result)}}}},S.removeEvent=function(e,t,n){e.removeEventListener&&e.removeEventListener(t,n)},S.Event=function(e,t){if(!(this instanceof S.Event))return new S.Event(e,t);e&&e.type?(this.originalEvent=e,this.type=e.type,this.isDefaultPrevented=e.defaultPrevented||void 0===e.defaultPrevented&&!1===e.returnValue?we:Te,this.target=e.target&&3===e.target.nodeType?e.target.parentNode:e.target,this.currentTarget=e.currentTarget,this.relatedTarget=e.relatedTarget):this.type=e,t&&S.extend(this,t),this.timeStamp=e&&e.timeStamp||Date.now(),this[S.expando]=!0},S.Event.prototype={constructor:S.Event,isDefaultPrevented:Te,isPropagationStopped:Te,isImmediatePropagationStopped:Te,isSimulated:!1,preventDefault:function(){var e=this.originalEvent;this.isDefaultPrevented=we,e&&!this.isSimulated&&e.preventDefault()},stopPropagation:function(){var e=this.originalEvent;this.isPropagationStopped=we,e&&!this.isSimulated&&e.stopPropagation()},stopImmediatePropagation:function(){var e=this.originalEvent;this.isImmediatePropagationStopped=we,e&&!this.isSimulated&&e.stopImmediatePropagation(),this.stopPropagation()}},S.each({altKey:!0,bubbles:!0,cancelable:!0,changedTouches:!0,ctrlKey:!0,detail:!0,eventPhase:!0,metaKey:!0,pageX:!0,pageY:!0,shiftKey:!0,view:!0,"char":!0,code:!0,charCode:!0,key:!0,keyCode:!0,button:!0,buttons:!0,clientX:!0,clientY:!0,offsetX:!0,offsetY:!0,pointerId:!0,pointerType:!0,screenX:!0,screenY:!0,targetTouches:!0,toElement:!0,touches:!0,which:!0},S.event.addProp),S.each({focus:"focusin",blur:"focusout"},function(e,t){S.event.special[e]={setup:function(){return Se(this,e,Ce),!1},trigger:function(){return Se(this,e),!0},_default:function(){return!0},delegateType:t}}),S.each({mouseenter:"mouseover",mouseleave:"mouseout",pointerenter:"pointerover",pointerleave:"pointerout"},function(e,i){S.event.special[e]={delegateType:i,bindType:i,handle:function(e){var t,n=e.relatedTarget,r=e.handleObj;return n&&(n===this||S.contains(this,n))||(e.type=r.origType,t=r.handler.apply(this,arguments),e.type=i),t}}}),S.fn.extend({on:function(e,t,n,r){return Ee(this,e,t,n,r)},one:function(e,t,n,r){return Ee(this,e,t,n,r,1)},off:function(e,t,n){var r,i;if(e&&e.preventDefault&&e.handleObj)return r=e.handleObj,S(e.delegateTarget).off(r.namespace?r.origType+"."+r.namespace:r.origType,r.selector,r.handler),this;if("object"==typeof e){for(i in e)this.off(i,t,e[i]);return this}return!1!==t&&"function"!=typeof t||(n=t,t=void 0),!1===n&&(n=Te),this.each(function(){S.event.remove(this,e,n,t)})}});var ke=/<script|<style|<link/i,Ae=/checked\s*(?:[^=]|=\s*.checked.)/i,Ne=/^\s*<!(?:\[CDATA\[|--)|(?:\]\]|--)>\s*$/g;function je(e,t){return A(e,"table")&&A(11!==t.nodeType?t:t.firstChild,"tr")&&S(e).children("tbody")[0]||e}function De(e){return e.type=(null!==e.getAttribute("type"))+"/"+e.type,e}function qe(e){return"true/"===(e.type||"").slice(0,5)?e.type=e.type.slice(5):e.removeAttribute("type"),e}function Le(e,t){var n,r,i,o,a,s;if(1===t.nodeType){if(Y.hasData(e)&&(s=Y.get(e).events))for(i in Y.remove(t,"handle events"),s)for(n=0,r=s[i].length;n<r;n++)S.event.add(t,i,s[i][n]);Q.hasData(e)&&(o=Q.access(e),a=S.extend({},o),Q.set(t,a))}}function He(n,r,i,o){r=g(r);var e,t,a,s,u,l,c=0,f=n.length,p=f-1,d=r[0],h=m(d);if(h||1<f&&"string"==typeof d&&!y.checkClone&&Ae.test(d))return n.each(function(e){var t=n.eq(e);h&&(r[0]=d.call(this,e,t.html())),He(t,r,i,o)});if(f&&(t=(e=xe(r,n[0].ownerDocument,!1,n,o)).firstChild,1===e.childNodes.length&&(e=t),t||o)){for(s=(a=S.map(ve(e,"script"),De)).length;c<f;c++)u=e,c!==p&&(u=S.clone(u,!0,!0),s&&S.merge(a,ve(u,"script"))),i.call(n[c],u,c);if(s)for(l=a[a.length-1].ownerDocument,S.map(a,qe),c=0;c<s;c++)u=a[c],he.test(u.type||"")&&!Y.access(u,"globalEval")&&S.contains(l,u)&&(u.src&&"module"!==(u.type||"").toLowerCase()?S._evalUrl&&!u.noModule&&S._evalUrl(u.src,{nonce:u.nonce||u.getAttribute("nonce")},l):b(u.textContent.replace(Ne,""),u,l))}return n}function Oe(e,t,n){for(var r,i=t?S.filter(t,e):e,o=0;null!=(r=i[o]);o++)n||1!==r.nodeType||S.cleanData(ve(r)),r.parentNode&&(n&&ie(r)&&ye(ve(r,"script")),r.parentNode.removeChild(r));return e}S.extend({htmlPrefilter:function(e){return e},clone:function(e,t,n){var r,i,o,a,s,u,l,c=e.cloneNode(!0),f=ie(e);if(!(y.noCloneChecked||1!==e.nodeType&&11!==e.nodeType||S.isXMLDoc(e)))for(a=ve(c),r=0,i=(o=ve(e)).length;r<i;r++)s=o[r],u=a[r],void 0,"input"===(l=u.nodeName.toLowerCase())&&pe.test(s.type)?u.checked=s.checked:"input"!==l&&"textarea"!==l||(u.defaultValue=s.defaultValue);if(t)if(n)for(o=o||ve(e),a=a||ve(c),r=0,i=o.length;r<i;r++)Le(o[r],a[r]);else Le(e,c);return 0<(a=ve(c,"script")).length&&ye(a,!f&&ve(e,"script")),c},cleanData:function(e){for(var t,n,r,i=S.event.special,o=0;void 0!==(n=e[o]);o++)if(V(n)){if(t=n[Y.expando]){if(t.events)for(r in t.events)i[r]?S.event.remove(n,r):S.removeEvent(n,r,t.handle);n[Y.expando]=void 0}n[Q.expando]&&(n[Q.expando]=void 0)}}}),S.fn.extend({detach:function(e){return Oe(this,e,!0)},remove:function(e){return Oe(this,e)},text:function(e){return $(this,function(e){return void 0===e?S.text(this):this.empty().each(function(){1!==this.nodeType&&11!==this.nodeType&&9!==this.nodeType||(this.textContent=e)})},null,e,arguments.length)},append:function(){return He(this,arguments,function(e){1!==this.nodeType&&11!==this.nodeType&&9!==this.nodeType||je(this,e).appendChild(e)})},prepend:function(){return He(this,arguments,function(e){if(1===this.nodeType||11===this.nodeType||9===this.nodeType){var t=je(this,e);t.insertBefore(e,t.firstChild)}})},before:function(){return He(this,arguments,function(e){this.parentNode&&this.parentNode.insertBefore(e,this)})},after:function(){return He(this,arguments,function(e){this.parentNode&&this.parentNode.insertBefore(e,this.nextSibling)})},empty:function(){for(var e,t=0;null!=(e=this[t]);t++)1===e.nodeType&&(S.cleanData(ve(e,!1)),e.textContent="");return this},clone:function(e,t){return e=null!=e&&e,t=null==t?e:t,this.map(function(){return S.clone(this,e,t)})},html:function(e){return $(this,function(e){var t=this[0]||{},n=0,r=this.length;if(void 0===e&&1===t.nodeType)return t.innerHTML;if("string"==typeof e&&!ke.test(e)&&!ge[(de.exec(e)||["",""])[1].toLowerCase()]){e=S.htmlPrefilter(e);try{for(;n<r;n++)1===(t=this[n]||{}).nodeType&&(S.cleanData(ve(t,!1)),t.innerHTML=e);t=0}catch(e){}}t&&this.empty().append(e)},null,e,arguments.length)},replaceWith:function(){var n=[];return He(this,arguments,function(e){var t=this.parentNode;S.inArray(this,n)<0&&(S.cleanData(ve(this)),t&&t.replaceChild(e,this))},n)}}),S.each({appendTo:"append",prependTo:"prepend",insertBefore:"before",insertAfter:"after",replaceAll:"replaceWith"},function(e,a){S.fn[e]=function(e){for(var t,n=[],r=S(e),i=r.length-1,o=0;o<=i;o++)t=o===i?this:this.clone(!0),S(r[o])[a](t),u.apply(n,t.get());return this.pushStack(n)}});var Pe=new RegExp("^("+ee+")(?!px)[a-z%]+$","i"),Re=function(e){var t=e.ownerDocument.defaultView;return t&&t.opener||(t=C),t.getComputedStyle(e)},Me=function(e,t,n){var r,i,o={};for(i in t)o[i]=e.style[i],e.style[i]=t[i];for(i in r=n.call(e),t)e.style[i]=o[i];return r},Ie=new RegExp(ne.join("|"),"i");function We(e,t,n){var r,i,o,a,s=e.style;return(n=n||Re(e))&&(""!==(a=n.getPropertyValue(t)||n[t])||ie(e)||(a=S.style(e,t)),!y.pixelBoxStyles()&&Pe.test(a)&&Ie.test(t)&&(r=s.width,i=s.minWidth,o=s.maxWidth,s.minWidth=s.maxWidth=s.width=a,a=n.width,s.width=r,s.minWidth=i,s.maxWidth=o)),void 0!==a?a+"":a}function Fe(e,t){return{get:function(){if(!e())return(this.get=t).apply(this,arguments);delete this.get}}}!function(){function e(){if(l){u.style.cssText="position:absolute;left:-11111px;width:60px;margin-top:1px;padding:0;border:0",l.style.cssText="position:relative;display:block;box-sizing:border-box;overflow:scroll;margin:auto;border:1px;padding:1px;width:60%;top:1%",re.appendChild(u).appendChild(l);var e=C.getComputedStyle(l);n="1%"!==e.top,s=12===t(e.marginLeft),l.style.right="60%",o=36===t(e.right),r=36===t(e.width),l.style.position="absolute",i=12===t(l.offsetWidth/3),re.removeChild(u),l=null}}function t(e){return Math.round(parseFloat(e))}var n,r,i,o,a,s,u=E.createElement("div"),l=E.createElement("div");l.style&&(l.style.backgroundClip="content-box",l.cloneNode(!0).style.backgroundClip="",y.clearCloneStyle="content-box"===l.style.backgroundClip,S.extend(y,{boxSizingReliable:function(){return e(),r},pixelBoxStyles:function(){return e(),o},pixelPosition:function(){return e(),n},reliableMarginLeft:function(){return e(),s},scrollboxSize:function(){return e(),i},reliableTrDimensions:function(){var e,t,n,r;return null==a&&(e=E.createElement("table"),t=E.createElement("tr"),n=E.createElement("div"),e.style.cssText="position:absolute;left:-11111px;border-collapse:separate",t.style.cssText="border:1px solid",t.style.height="1px",n.style.height="9px",n.style.display="block",re.appendChild(e).appendChild(t).appendChild(n),r=C.getComputedStyle(t),a=parseInt(r.height,10)+parseInt(r.borderTopWidth,10)+parseInt(r.borderBottomWidth,10)===t.offsetHeight,re.removeChild(e)),a}}))}();var Be=["Webkit","Moz","ms"],$e=E.createElement("div").style,_e={};function ze(e){var t=S.cssProps[e]||_e[e];return t||(e in $e?e:_e[e]=function(e){var t=e[0].toUpperCase()+e.slice(1),n=Be.length;while(n--)if((e=Be[n]+t)in $e)return e}(e)||e)}var Ue=/^(none|table(?!-c[ea]).+)/,Xe=/^--/,Ve={position:"absolute",visibility:"hidden",display:"block"},Ge={letterSpacing:"0",fontWeight:"400"};function Ye(e,t,n){var r=te.exec(t);return r?Math.max(0,r[2]-(n||0))+(r[3]||"px"):t}function Qe(e,t,n,r,i,o){var a="width"===t?1:0,s=0,u=0;if(n===(r?"border":"content"))return 0;for(;a<4;a+=2)"margin"===n&&(u+=S.css(e,n+ne[a],!0,i)),r?("content"===n&&(u-=S.css(e,"padding"+ne[a],!0,i)),"margin"!==n&&(u-=S.css(e,"border"+ne[a]+"Width",!0,i))):(u+=S.css(e,"padding"+ne[a],!0,i),"padding"!==n?u+=S.css(e,"border"+ne[a]+"Width",!0,i):s+=S.css(e,"border"+ne[a]+"Width",!0,i));return!r&&0<=o&&(u+=Math.max(0,Math.ceil(e["offset"+t[0].toUpperCase()+t.slice(1)]-o-u-s-.5))||0),u}function Je(e,t,n){var r=Re(e),i=(!y.boxSizingReliable()||n)&&"border-box"===S.css(e,"boxSizing",!1,r),o=i,a=We(e,t,r),s="offset"+t[0].toUpperCase()+t.slice(1);if(Pe.test(a)){if(!n)return a;a="auto"}return(!y.boxSizingReliable()&&i||!y.reliableTrDimensions()&&A(e,"tr")||"auto"===a||!parseFloat(a)&&"inline"===S.css(e,"display",!1,r))&&e.getClientRects().length&&(i="border-box"===S.css(e,"boxSizing",!1,r),(o=s in e)&&(a=e[s])),(a=parseFloat(a)||0)+Qe(e,t,n||(i?"border":"content"),o,r,a)+"px"}function Ke(e,t,n,r,i){return new Ke.prototype.init(e,t,n,r,i)}S.extend({cssHooks:{opacity:{get:function(e,t){if(t){var n=We(e,"opacity");return""===n?"1":n}}}},cssNumber:{animationIterationCount:!0,columnCount:!0,fillOpacity:!0,flexGrow:!0,flexShrink:!0,fontWeight:!0,gridArea:!0,gridColumn:!0,gridColumnEnd:!0,gridColumnStart:!0,gridRow:!0,gridRowEnd:!0,gridRowStart:!0,lineHeight:!0,opacity:!0,order:!0,orphans:!0,widows:!0,zIndex:!0,zoom:!0},cssProps:{},style:function(e,t,n,r){if(e&&3!==e.nodeType&&8!==e.nodeType&&e.style){var i,o,a,s=X(t),u=Xe.test(t),l=e.style;if(u||(t=ze(s)),a=S.cssHooks[t]||S.cssHooks[s],void 0===n)return a&&"get"in a&&void 0!==(i=a.get(e,!1,r))?i:l[t];"string"===(o=typeof n)&&(i=te.exec(n))&&i[1]&&(n=se(e,t,i),o="number"),null!=n&&n==n&&("number"!==o||u||(n+=i&&i[3]||(S.cssNumber[s]?"":"px")),y.clearCloneStyle||""!==n||0!==t.indexOf("background")||(l[t]="inherit"),a&&"set"in a&&void 0===(n=a.set(e,n,r))||(u?l.setProperty(t,n):l[t]=n))}},css:function(e,t,n,r){var i,o,a,s=X(t);return Xe.test(t)||(t=ze(s)),(a=S.cssHooks[t]||S.cssHooks[s])&&"get"in a&&(i=a.get(e,!0,n)),void 0===i&&(i=We(e,t,r)),"normal"===i&&t in Ge&&(i=Ge[t]),""===n||n?(o=parseFloat(i),!0===n||isFinite(o)?o||0:i):i}}),S.each(["height","width"],function(e,u){S.cssHooks[u]={get:function(e,t,n){if(t)return!Ue.test(S.css(e,"display"))||e.getClientRects().length&&e.getBoundingClientRect().width?Je(e,u,n):Me(e,Ve,function(){return Je(e,u,n)})},set:function(e,t,n){var r,i=Re(e),o=!y.scrollboxSize()&&"absolute"===i.position,a=(o||n)&&"border-box"===S.css(e,"boxSizing",!1,i),s=n?Qe(e,u,n,a,i):0;return a&&o&&(s-=Math.ceil(e["offset"+u[0].toUpperCase()+u.slice(1)]-parseFloat(i[u])-Qe(e,u,"border",!1,i)-.5)),s&&(r=te.exec(t))&&"px"!==(r[3]||"px")&&(e.style[u]=t,t=S.css(e,u)),Ye(0,t,s)}}}),S.cssHooks.marginLeft=Fe(y.reliableMarginLeft,function(e,t){if(t)return(parseFloat(We(e,"marginLeft"))||e.getBoundingClientRect().left-Me(e,{marginLeft:0},function(){return e.getBoundingClientRect().left}))+"px"}),S.each({margin:"",padding:"",border:"Width"},function(i,o){S.cssHooks[i+o]={expand:function(e){for(var t=0,n={},r="string"==typeof e?e.split(" "):[e];t<4;t++)n[i+ne[t]+o]=r[t]||r[t-2]||r[0];return n}},"margin"!==i&&(S.cssHooks[i+o].set=Ye)}),S.fn.extend({css:function(e,t){return $(this,function(e,t,n){var r,i,o={},a=0;if(Array.isArray(t)){for(r=Re(e),i=t.length;a<i;a++)o[t[a]]=S.css(e,t[a],!1,r);return o}return void 0!==n?S.style(e,t,n):S.css(e,t)},e,t,1<arguments.length)}}),((S.Tween=Ke).prototype={constructor:Ke,init:function(e,t,n,r,i,o){this.elem=e,this.prop=n,this.easing=i||S.easing._default,this.options=t,this.start=this.now=this.cur(),this.end=r,this.unit=o||(S.cssNumber[n]?"":"px")},cur:function(){var e=Ke.propHooks[this.prop];return e&&e.get?e.get(this):Ke.propHooks._default.get(this)},run:function(e){var t,n=Ke.propHooks[this.prop];return this.options.duration?this.pos=t=S.easing[this.easing](e,this.options.duration*e,0,1,this.options.duration):this.pos=t=e,this.now=(this.end-this.start)*t+this.start,this.options.step&&this.options.step.call(this.elem,this.now,this),n&&n.set?n.set(this):Ke.propHooks._default.set(this),this}}).init.prototype=Ke.prototype,(Ke.propHooks={_default:{get:function(e){var t;return 1!==e.elem.nodeType||null!=e.elem[e.prop]&&null==e.elem.style[e.prop]?e.elem[e.prop]:(t=S.css(e.elem,e.prop,""))&&"auto"!==t?t:0},set:function(e){S.fx.step[e.prop]?S.fx.step[e.prop](e):1!==e.elem.nodeType||!S.cssHooks[e.prop]&&null==e.elem.style[ze(e.prop)]?e.elem[e.prop]=e.now:S.style(e.elem,e.prop,e.now+e.unit)}}}).scrollTop=Ke.propHooks.scrollLeft={set:function(e){e.elem.nodeType&&e.elem.parentNode&&(e.elem[e.prop]=e.now)}},S.easing={linear:function(e){return e},swing:function(e){return.5-Math.cos(e*Math.PI)/2},_default:"swing"},S.fx=Ke.prototype.init,S.fx.step={};var Ze,et,tt,nt,rt=/^(?:toggle|show|hide)$/,it=/queueHooks$/;function ot(){et&&(!1===E.hidden&&C.requestAnimationFrame?C.requestAnimationFrame(ot):C.setTimeout(ot,S.fx.interval),S.fx.tick())}function at(){return C.setTimeout(function(){Ze=void 0}),Ze=Date.now()}function st(e,t){var n,r=0,i={height:e};for(t=t?1:0;r<4;r+=2-t)i["margin"+(n=ne[r])]=i["padding"+n]=e;return t&&(i.opacity=i.width=e),i}function ut(e,t,n){for(var r,i=(lt.tweeners[t]||[]).concat(lt.tweeners["*"]),o=0,a=i.length;o<a;o++)if(r=i[o].call(n,t,e))return r}function lt(o,e,t){var n,a,r=0,i=lt.prefilters.length,s=S.Deferred().always(function(){delete u.elem}),u=function(){if(a)return!1;for(var e=Ze||at(),t=Math.max(0,l.startTime+l.duration-e),n=1-(t/l.duration||0),r=0,i=l.tweens.length;r<i;r++)l.tweens[r].run(n);return s.notifyWith(o,[l,n,t]),n<1&&i?t:(i||s.notifyWith(o,[l,1,0]),s.resolveWith(o,[l]),!1)},l=s.promise({elem:o,props:S.extend({},e),opts:S.extend(!0,{specialEasing:{},easing:S.easing._default},t),originalProperties:e,originalOptions:t,startTime:Ze||at(),duration:t.duration,tweens:[],createTween:function(e,t){var n=S.Tween(o,l.opts,e,t,l.opts.specialEasing[e]||l.opts.easing);return l.tweens.push(n),n},stop:function(e){var t=0,n=e?l.tweens.length:0;if(a)return this;for(a=!0;t<n;t++)l.tweens[t].run(1);return e?(s.notifyWith(o,[l,1,0]),s.resolveWith(o,[l,e])):s.rejectWith(o,[l,e]),this}}),c=l.props;for(!function(e,t){var n,r,i,o,a;for(n in e)if(i=t[r=X(n)],o=e[n],Array.isArray(o)&&(i=o[1],o=e[n]=o[0]),n!==r&&(e[r]=o,delete e[n]),(a=S.cssHooks[r])&&"expand"in a)for(n in o=a.expand(o),delete e[r],o)n in e||(e[n]=o[n],t[n]=i);else t[r]=i}(c,l.opts.specialEasing);r<i;r++)if(n=lt.prefilters[r].call(l,o,c,l.opts))return m(n.stop)&&(S._queueHooks(l.elem,l.opts.queue).stop=n.stop.bind(n)),n;return S.map(c,ut,l),m(l.opts.start)&&l.opts.start.call(o,l),l.progress(l.opts.progress).done(l.opts.done,l.opts.complete).fail(l.opts.fail).always(l.opts.always),S.fx.timer(S.extend(u,{elem:o,anim:l,queue:l.opts.queue})),l}S.Animation=S.extend(lt,{tweeners:{"*":[function(e,t){var n=this.createTween(e,t);return se(n.elem,e,te.exec(t),n),n}]},tweener:function(e,t){m(e)?(t=e,e=["*"]):e=e.match(P);for(var n,r=0,i=e.length;r<i;r++)n=e[r],lt.tweeners[n]=lt.tweeners[n]||[],lt.tweeners[n].unshift(t)},prefilters:[function(e,t,n){var r,i,o,a,s,u,l,c,f="width"in t||"height"in t,p=this,d={},h=e.style,g=e.nodeType&&ae(e),v=Y.get(e,"fxshow");for(r in n.queue||(null==(a=S._queueHooks(e,"fx")).unqueued&&(a.unqueued=0,s=a.empty.fire,a.empty.fire=function(){a.unqueued||s()}),a.unqueued++,p.always(function(){p.always(function(){a.unqueued--,S.queue(e,"fx").length||a.empty.fire()})})),t)if(i=t[r],rt.test(i)){if(delete t[r],o=o||"toggle"===i,i===(g?"hide":"show")){if("show"!==i||!v||void 0===v[r])continue;g=!0}d[r]=v&&v[r]||S.style(e,r)}if((u=!S.isEmptyObject(t))||!S.isEmptyObject(d))for(r in f&&1===e.nodeType&&(n.overflow=[h.overflow,h.overflowX,h.overflowY],null==(l=v&&v.display)&&(l=Y.get(e,"display")),"none"===(c=S.css(e,"display"))&&(l?c=l:(le([e],!0),l=e.style.display||l,c=S.css(e,"display"),le([e]))),("inline"===c||"inline-block"===c&&null!=l)&&"none"===S.css(e,"float")&&(u||(p.done(function(){h.display=l}),null==l&&(c=h.display,l="none"===c?"":c)),h.display="inline-block")),n.overflow&&(h.overflow="hidden",p.always(function(){h.overflow=n.overflow[0],h.overflowX=n.overflow[1],h.overflowY=n.overflow[2]})),u=!1,d)u||(v?"hidden"in v&&(g=v.hidden):v=Y.access(e,"fxshow",{display:l}),o&&(v.hidden=!g),g&&le([e],!0),p.done(function(){for(r in g||le([e]),Y.remove(e,"fxshow"),d)S.style(e,r,d[r])})),u=ut(g?v[r]:0,r,p),r in v||(v[r]=u.start,g&&(u.end=u.start,u.start=0))}],prefilter:function(e,t){t?lt.prefilters.unshift(e):lt.prefilters.push(e)}}),S.speed=function(e,t,n){var r=e&&"object"==typeof e?S.extend({},e):{complete:n||!n&&t||m(e)&&e,duration:e,easing:n&&t||t&&!m(t)&&t};return S.fx.off?r.duration=0:"number"!=typeof r.duration&&(r.duration in S.fx.speeds?r.duration=S.fx.speeds[r.duration]:r.duration=S.fx.speeds._default),null!=r.queue&&!0!==r.queue||(r.queue="fx"),r.old=r.complete,r.complete=function(){m(r.old)&&r.old.call(this),r.queue&&S.dequeue(this,r.queue)},r},S.fn.extend({fadeTo:function(e,t,n,r){return this.filter(ae).css("opacity",0).show().end().animate({opacity:t},e,n,r)},animate:function(t,e,n,r){var i=S.isEmptyObject(t),o=S.speed(e,n,r),a=function(){var e=lt(this,S.extend({},t),o);(i||Y.get(this,"finish"))&&e.stop(!0)};return a.finish=a,i||!1===o.queue?this.each(a):this.queue(o.queue,a)},stop:function(i,e,o){var a=function(e){var t=e.stop;delete e.stop,t(o)};return"string"!=typeof i&&(o=e,e=i,i=void 0),e&&this.queue(i||"fx",[]),this.each(function(){var e=!0,t=null!=i&&i+"queueHooks",n=S.timers,r=Y.get(this);if(t)r[t]&&r[t].stop&&a(r[t]);else for(t in r)r[t]&&r[t].stop&&it.test(t)&&a(r[t]);for(t=n.length;t--;)n[t].elem!==this||null!=i&&n[t].queue!==i||(n[t].anim.stop(o),e=!1,n.splice(t,1));!e&&o||S.dequeue(this,i)})},finish:function(a){return!1!==a&&(a=a||"fx"),this.each(function(){var e,t=Y.get(this),n=t[a+"queue"],r=t[a+"queueHooks"],i=S.timers,o=n?n.length:0;for(t.finish=!0,S.queue(this,a,[]),r&&r.stop&&r.stop.call(this,!0),e=i.length;e--;)i[e].elem===this&&i[e].queue===a&&(i[e].anim.stop(!0),i.splice(e,1));for(e=0;e<o;e++)n[e]&&n[e].finish&&n[e].finish.call(this);delete t.finish})}}),S.each(["toggle","show","hide"],function(e,r){var i=S.fn[r];S.fn[r]=function(e,t,n){return null==e||"boolean"==typeof e?i.apply(this,arguments):this.animate(st(r,!0),e,t,n)}}),S.each({slideDown:st("show"),slideUp:st("hide"),slideToggle:st("toggle"),fadeIn:{opacity:"show"},fadeOut:{opacity:"hide"},fadeToggle:{opacity:"toggle"}},function(e,r){S.fn[e]=function(e,t,n){return this.animate(r,e,t,n)}}),S.timers=[],S.fx.tick=function(){var e,t=0,n=S.timers;for(Ze=Date.now();t<n.length;t++)(e=n[t])()||n[t]!==e||n.splice(t--,1);n.length||S.fx.stop(),Ze=void 0},S.fx.timer=function(e){S.timers.push(e),S.fx.start()},S.fx.interval=13,S.fx.start=function(){et||(et=!0,ot())},S.fx.stop=function(){et=null},S.fx.speeds={slow:600,fast:200,_default:400},S.fn.delay=function(r,e){return r=S.fx&&S.fx.speeds[r]||r,e=e||"fx",this.queue(e,function(e,t){var n=C.setTimeout(e,r);t.stop=function(){C.clearTimeout(n)}})},tt=E.createElement("input"),nt=E.createElement("select").appendChild(E.createElement("option")),tt.type="checkbox",y.checkOn=""!==tt.value,y.optSelected=nt.selected,(tt=E.createElement("input")).value="t",tt.type="radio",y.radioValue="t"===tt.value;var ct,ft=S.expr.attrHandle;S.fn.extend({attr:function(e,t){return $(this,S.attr,e,t,1<arguments.length)},removeAttr:function(e){return this.each(function(){S.removeAttr(this,e)})}}),S.extend({attr:function(e,t,n){var r,i,o=e.nodeType;if(3!==o&&8!==o&&2!==o)return"undefined"==typeof e.getAttribute?S.prop(e,t,n):(1===o&&S.isXMLDoc(e)||(i=S.attrHooks[t.toLowerCase()]||(S.expr.match.bool.test(t)?ct:void 0)),void 0!==n?null===n?void S.removeAttr(e,t):i&&"set"in i&&void 0!==(r=i.set(e,n,t))?r:(e.setAttribute(t,n+""),n):i&&"get"in i&&null!==(r=i.get(e,t))?r:null==(r=S.find.attr(e,t))?void 0:r)},attrHooks:{type:{set:function(e,t){if(!y.radioValue&&"radio"===t&&A(e,"input")){var n=e.value;return e.setAttribute("type",t),n&&(e.value=n),t}}}},removeAttr:function(e,t){var n,r=0,i=t&&t.match(P);if(i&&1===e.nodeType)while(n=i[r++])e.removeAttribute(n)}}),ct={set:function(e,t,n){return!1===t?S.removeAttr(e,n):e.setAttribute(n,n),n}},S.each(S.expr.match.bool.source.match(/\w+/g),function(e,t){var a=ft[t]||S.find.attr;ft[t]=function(e,t,n){var r,i,o=t.toLowerCase();return n||(i=ft[o],ft[o]=r,r=null!=a(e,t,n)?o:null,ft[o]=i),r}});var pt=/^(?:input|select|textarea|button)$/i,dt=/^(?:a|area)$/i;function ht(e){return(e.match(P)||[]).join(" ")}function gt(e){return e.getAttribute&&e.getAttribute("class")||""}function vt(e){return Array.isArray(e)?e:"string"==typeof e&&e.match(P)||[]}S.fn.extend({prop:function(e,t){return $(this,S.prop,e,t,1<arguments.length)},removeProp:function(e){return this.each(function(){delete this[S.propFix[e]||e]})}}),S.extend({prop:function(e,t,n){var r,i,o=e.nodeType;if(3!==o&&8!==o&&2!==o)return 1===o&&S.isXMLDoc(e)||(t=S.propFix[t]||t,i=S.propHooks[t]),void 0!==n?i&&"set"in i&&void 0!==(r=i.set(e,n,t))?r:e[t]=n:i&&"get"in i&&null!==(r=i.get(e,t))?r:e[t]},propHooks:{tabIndex:{get:function(e){var t=S.find.attr(e,"tabindex");return t?parseInt(t,10):pt.test(e.nodeName)||dt.test(e.nodeName)&&e.href?0:-1}}},propFix:{"for":"htmlFor","class":"className"}}),y.optSelected||(S.propHooks.selected={get:function(e){var t=e.parentNode;return t&&t.parentNode&&t.parentNode.selectedIndex,null},set:function(e){var t=e.parentNode;t&&(t.selectedIndex,t.parentNode&&t.parentNode.selectedIndex)}}),S.each(["tabIndex","readOnly","maxLength","cellSpacing","cellPadding","rowSpan","colSpan","useMap","frameBorder","contentEditable"],function(){S.propFix[this.toLowerCase()]=this}),S.fn.extend({addClass:function(t){var e,n,r,i,o,a,s,u=0;if(m(t))return this.each(function(e){S(this).addClass(t.call(this,e,gt(this)))});if((e=vt(t)).length)while(n=this[u++])if(i=gt(n),r=1===n.nodeType&&" "+ht(i)+" "){a=0;while(o=e[a++])r.indexOf(" "+o+" ")<0&&(r+=o+" ");i!==(s=ht(r))&&n.setAttribute("class",s)}return this},removeClass:function(t){var e,n,r,i,o,a,s,u=0;if(m(t))return this.each(function(e){S(this).removeClass(t.call(this,e,gt(this)))});if(!arguments.length)return this.attr("class","");if((e=vt(t)).length)while(n=this[u++])if(i=gt(n),r=1===n.nodeType&&" "+ht(i)+" "){a=0;while(o=e[a++])while(-1<r.indexOf(" "+o+" "))r=r.replace(" "+o+" "," ");i!==(s=ht(r))&&n.setAttribute("class",s)}return this},toggleClass:function(i,t){var o=typeof i,a="string"===o||Array.isArray(i);return"boolean"==typeof t&&a?t?this.addClass(i):this.removeClass(i):m(i)?this.each(function(e){S(this).toggleClass(i.call(this,e,gt(this),t),t)}):this.each(function(){var e,t,n,r;if(a){t=0,n=S(this),r=vt(i);while(e=r[t++])n.hasClass(e)?n.removeClass(e):n.addClass(e)}else void 0!==i&&"boolean"!==o||((e=gt(this))&&Y.set(this,"__className__",e),this.setAttribute&&this.setAttribute("class",e||!1===i?"":Y.get(this,"__className__")||""))})},hasClass:function(e){var t,n,r=0;t=" "+e+" ";while(n=this[r++])if(1===n.nodeType&&-1<(" "+ht(gt(n))+" ").indexOf(t))return!0;return!1}});var yt=/\r/g;S.fn.extend({val:function(n){var r,e,i,t=this[0];return arguments.length?(i=m(n),this.each(function(e){var t;1===this.nodeType&&(null==(t=i?n.call(this,e,S(this).val()):n)?t="":"number"==typeof t?t+="":Array.isArray(t)&&(t=S.map(t,function(e){return null==e?"":e+""})),(r=S.valHooks[this.type]||S.valHooks[this.nodeName.toLowerCase()])&&"set"in r&&void 0!==r.set(this,t,"value")||(this.value=t))})):t?(r=S.valHooks[t.type]||S.valHooks[t.nodeName.toLowerCase()])&&"get"in r&&void 0!==(e=r.get(t,"value"))?e:"string"==typeof(e=t.value)?e.replace(yt,""):null==e?"":e:void 0}}),S.extend({valHooks:{option:{get:function(e){var t=S.find.attr(e,"value");return null!=t?t:ht(S.text(e))}},select:{get:function(e){var t,n,r,i=e.options,o=e.selectedIndex,a="select-one"===e.type,s=a?null:[],u=a?o+1:i.length;for(r=o<0?u:a?o:0;r<u;r++)if(((n=i[r]).selected||r===o)&&!n.disabled&&(!n.parentNode.disabled||!A(n.parentNode,"optgroup"))){if(t=S(n).val(),a)return t;s.push(t)}return s},set:function(e,t){var n,r,i=e.options,o=S.makeArray(t),a=i.length;while(a--)((r=i[a]).selected=-1<S.inArray(S.valHooks.option.get(r),o))&&(n=!0);return n||(e.selectedIndex=-1),o}}}}),S.each(["radio","checkbox"],function(){S.valHooks[this]={set:function(e,t){if(Array.isArray(t))return e.checked=-1<S.inArray(S(e).val(),t)}},y.checkOn||(S.valHooks[this].get=function(e){return null===e.getAttribute("value")?"on":e.value})}),y.focusin="onfocusin"in C;var mt=/^(?:focusinfocus|focusoutblur)$/,xt=function(e){e.stopPropagation()};S.extend(S.event,{trigger:function(e,t,n,r){var i,o,a,s,u,l,c,f,p=[n||E],d=v.call(e,"type")?e.type:e,h=v.call(e,"namespace")?e.namespace.split("."):[];if(o=f=a=n=n||E,3!==n.nodeType&&8!==n.nodeType&&!mt.test(d+S.event.triggered)&&(-1<d.indexOf(".")&&(d=(h=d.split(".")).shift(),h.sort()),u=d.indexOf(":")<0&&"on"+d,(e=e[S.expando]?e:new S.Event(d,"object"==typeof e&&e)).isTrigger=r?2:3,e.namespace=h.join("."),e.rnamespace=e.namespace?new RegExp("(^|\\.)"+h.join("\\.(?:.*\\.|)")+"(\\.|$)"):null,e.result=void 0,e.target||(e.target=n),t=null==t?[e]:S.makeArray(t,[e]),c=S.event.special[d]||{},r||!c.trigger||!1!==c.trigger.apply(n,t))){if(!r&&!c.noBubble&&!x(n)){for(s=c.delegateType||d,mt.test(s+d)||(o=o.parentNode);o;o=o.parentNode)p.push(o),a=o;a===(n.ownerDocument||E)&&p.push(a.defaultView||a.parentWindow||C)}i=0;while((o=p[i++])&&!e.isPropagationStopped())f=o,e.type=1<i?s:c.bindType||d,(l=(Y.get(o,"events")||Object.create(null))[e.type]&&Y.get(o,"handle"))&&l.apply(o,t),(l=u&&o[u])&&l.apply&&V(o)&&(e.result=l.apply(o,t),!1===e.result&&e.preventDefault());return e.type=d,r||e.isDefaultPrevented()||c._default&&!1!==c._default.apply(p.pop(),t)||!V(n)||u&&m(n[d])&&!x(n)&&((a=n[u])&&(n[u]=null),S.event.triggered=d,e.isPropagationStopped()&&f.addEventListener(d,xt),n[d](),e.isPropagationStopped()&&f.removeEventListener(d,xt),S.event.triggered=void 0,a&&(n[u]=a)),e.result}},simulate:function(e,t,n){var r=S.extend(new S.Event,n,{type:e,isSimulated:!0});S.event.trigger(r,null,t)}}),S.fn.extend({trigger:function(e,t){return this.each(function(){S.event.trigger(e,t,this)})},triggerHandler:function(e,t){var n=this[0];if(n)return S.event.trigger(e,t,n,!0)}}),y.focusin||S.each({focus:"focusin",blur:"focusout"},function(n,r){var i=function(e){S.event.simulate(r,e.target,S.event.fix(e))};S.event.special[r]={setup:function(){var e=this.ownerDocument||this.document||this,t=Y.access(e,r);t||e.addEventListener(n,i,!0),Y.access(e,r,(t||0)+1)},teardown:function(){var e=this.ownerDocument||this.document||this,t=Y.access(e,r)-1;t?Y.access(e,r,t):(e.removeEventListener(n,i,!0),Y.remove(e,r))}}});var bt=C.location,wt={guid:Date.now()},Tt=/\?/;S.parseXML=function(e){var t,n;if(!e||"string"!=typeof e)return null;try{t=(new C.DOMParser).parseFromString(e,"text/xml")}catch(e){}return n=t&&t.getElementsByTagName("parsererror")[0],t&&!n||S.error("Invalid XML: "+(n?S.map(n.childNodes,function(e){return e.textContent}).join("\n"):e)),t};var Ct=/\[\]$/,Et=/\r?\n/g,St=/^(?:submit|button|image|reset|file)$/i,kt=/^(?:input|select|textarea|keygen)/i;function At(n,e,r,i){var t;if(Array.isArray(e))S.each(e,function(e,t){r||Ct.test(n)?i(n,t):At(n+"["+("object"==typeof t&&null!=t?e:"")+"]",t,r,i)});else if(r||"object"!==w(e))i(n,e);else for(t in e)At(n+"["+t+"]",e[t],r,i)}S.param=function(e,t){var n,r=[],i=function(e,t){var n=m(t)?t():t;r[r.length]=encodeURIComponent(e)+"="+encodeURIComponent(null==n?"":n)};if(null==e)return"";if(Array.isArray(e)||e.jquery&&!S.isPlainObject(e))S.each(e,function(){i(this.name,this.value)});else for(n in e)At(n,e[n],t,i);return r.join("&")},S.fn.extend({serialize:function(){return S.param(this.serializeArray())},serializeArray:function(){return this.map(function(){var e=S.prop(this,"elements");return e?S.makeArray(e):this}).filter(function(){var e=this.type;return this.name&&!S(this).is(":disabled")&&kt.test(this.nodeName)&&!St.test(e)&&(this.checked||!pe.test(e))}).map(function(e,t){var n=S(this).val();return null==n?null:Array.isArray(n)?S.map(n,function(e){return{name:t.name,value:e.replace(Et,"\r\n")}}):{name:t.name,value:n.replace(Et,"\r\n")}}).get()}});var Nt=/%20/g,jt=/#.*$/,Dt=/([?&])_=[^&]*/,qt=/^(.*?):[ \t]*([^\r\n]*)$/gm,Lt=/^(?:GET|HEAD)$/,Ht=/^\/\//,Ot={},Pt={},Rt="*/".concat("*"),Mt=E.createElement("a");function It(o){return function(e,t){"string"!=typeof e&&(t=e,e="*");var n,r=0,i=e.toLowerCase().match(P)||[];if(m(t))while(n=i[r++])"+"===n[0]?(n=n.slice(1)||"*",(o[n]=o[n]||[]).unshift(t)):(o[n]=o[n]||[]).push(t)}}function Wt(t,i,o,a){var s={},u=t===Pt;function l(e){var r;return s[e]=!0,S.each(t[e]||[],function(e,t){var n=t(i,o,a);return"string"!=typeof n||u||s[n]?u?!(r=n):void 0:(i.dataTypes.unshift(n),l(n),!1)}),r}return l(i.dataTypes[0])||!s["*"]&&l("*")}function Ft(e,t){var n,r,i=S.ajaxSettings.flatOptions||{};for(n in t)void 0!==t[n]&&((i[n]?e:r||(r={}))[n]=t[n]);return r&&S.extend(!0,e,r),e}Mt.href=bt.href,S.extend({active:0,lastModified:{},etag:{},ajaxSettings:{url:bt.href,type:"GET",isLocal:/^(?:about|app|app-storage|.+-extension|file|res|widget):$/.test(bt.protocol),global:!0,processData:!0,async:!0,contentType:"application/x-www-form-urlencoded; charset=UTF-8",accepts:{"*":Rt,text:"text/plain",html:"text/html",xml:"application/xml, text/xml",json:"application/json, text/javascript"},contents:{xml:/\bxml\b/,html:/\bhtml/,json:/\bjson\b/},responseFields:{xml:"responseXML",text:"responseText",json:"responseJSON"},converters:{"* text":String,"text html":!0,"text json":JSON.parse,"text xml":S.parseXML},flatOptions:{url:!0,context:!0}},ajaxSetup:function(e,t){return t?Ft(Ft(e,S.ajaxSettings),t):Ft(S.ajaxSettings,e)},ajaxPrefilter:It(Ot),ajaxTransport:It(Pt),ajax:function(e,t){"object"==typeof e&&(t=e,e=void 0),t=t||{};var c,f,p,n,d,r,h,g,i,o,v=S.ajaxSetup({},t),y=v.context||v,m=v.context&&(y.nodeType||y.jquery)?S(y):S.event,x=S.Deferred(),b=S.Callbacks("once memory"),w=v.statusCode||{},a={},s={},u="canceled",T={readyState:0,getResponseHeader:function(e){var t;if(h){if(!n){n={};while(t=qt.exec(p))n[t[1].toLowerCase()+" "]=(n[t[1].toLowerCase()+" "]||[]).concat(t[2])}t=n[e.toLowerCase()+" "]}return null==t?null:t.join(", ")},getAllResponseHeaders:function(){return h?p:null},setRequestHeader:function(e,t){return null==h&&(e=s[e.toLowerCase()]=s[e.toLowerCase()]||e,a[e]=t),this},overrideMimeType:function(e){return null==h&&(v.mimeType=e),this},statusCode:function(e){var t;if(e)if(h)T.always(e[T.status]);else for(t in e)w[t]=[w[t],e[t]];return this},abort:function(e){var t=e||u;return c&&c.abort(t),l(0,t),this}};if(x.promise(T),v.url=((e||v.url||bt.href)+"").replace(Ht,bt.protocol+"//"),v.type=t.method||t.type||v.method||v.type,v.dataTypes=(v.dataType||"*").toLowerCase().match(P)||[""],null==v.crossDomain){r=E.createElement("a");try{r.href=v.url,r.href=r.href,v.crossDomain=Mt.protocol+"//"+Mt.host!=r.protocol+"//"+r.host}catch(e){v.crossDomain=!0}}if(v.data&&v.processData&&"string"!=typeof v.data&&(v.data=S.param(v.data,v.traditional)),Wt(Ot,v,t,T),h)return T;for(i in(g=S.event&&v.global)&&0==S.active++&&S.event.trigger("ajaxStart"),v.type=v.type.toUpperCase(),v.hasContent=!Lt.test(v.type),f=v.url.replace(jt,""),v.hasContent?v.data&&v.processData&&0===(v.contentType||"").indexOf("application/x-www-form-urlencoded")&&(v.data=v.data.replace(Nt,"+")):(o=v.url.slice(f.length),v.data&&(v.processData||"string"==typeof v.data)&&(f+=(Tt.test(f)?"&":"?")+v.data,delete v.data),!1===v.cache&&(f=f.replace(Dt,"$1"),o=(Tt.test(f)?"&":"?")+"_="+wt.guid+++o),v.url=f+o),v.ifModified&&(S.lastModified[f]&&T.setRequestHeader("If-Modified-Since",S.lastModified[f]),S.etag[f]&&T.setRequestHeader("If-None-Match",S.etag[f])),(v.data&&v.hasContent&&!1!==v.contentType||t.contentType)&&T.setRequestHeader("Content-Type",v.contentType),T.setRequestHeader("Accept",v.dataTypes[0]&&v.accepts[v.dataTypes[0]]?v.accepts[v.dataTypes[0]]+("*"!==v.dataTypes[0]?", "+Rt+"; q=0.01":""):v.accepts["*"]),v.headers)T.setRequestHeader(i,v.headers[i]);if(v.beforeSend&&(!1===v.beforeSend.call(y,T,v)||h))return T.abort();if(u="abort",b.add(v.complete),T.done(v.success),T.fail(v.error),c=Wt(Pt,v,t,T)){if(T.readyState=1,g&&m.trigger("ajaxSend",[T,v]),h)return T;v.async&&0<v.timeout&&(d=C.setTimeout(function(){T.abort("timeout")},v.timeout));try{h=!1,c.send(a,l)}catch(e){if(h)throw e;l(-1,e)}}else l(-1,"No Transport");function l(e,t,n,r){var i,o,a,s,u,l=t;h||(h=!0,d&&C.clearTimeout(d),c=void 0,p=r||"",T.readyState=0<e?4:0,i=200<=e&&e<300||304===e,n&&(s=function(e,t,n){var r,i,o,a,s=e.contents,u=e.dataTypes;while("*"===u[0])u.shift(),void 0===r&&(r=e.mimeType||t.getResponseHeader("Content-Type"));if(r)for(i in s)if(s[i]&&s[i].test(r)){u.unshift(i);break}if(u[0]in n)o=u[0];else{for(i in n){if(!u[0]||e.converters[i+" "+u[0]]){o=i;break}a||(a=i)}o=o||a}if(o)return o!==u[0]&&u.unshift(o),n[o]}(v,T,n)),!i&&-1<S.inArray("script",v.dataTypes)&&S.inArray("json",v.dataTypes)<0&&(v.converters["text script"]=function(){}),s=function(e,t,n,r){var i,o,a,s,u,l={},c=e.dataTypes.slice();if(c[1])for(a in e.converters)l[a.toLowerCase()]=e.converters[a];o=c.shift();while(o)if(e.responseFields[o]&&(n[e.responseFields[o]]=t),!u&&r&&e.dataFilter&&(t=e.dataFilter(t,e.dataType)),u=o,o=c.shift())if("*"===o)o=u;else if("*"!==u&&u!==o){if(!(a=l[u+" "+o]||l["* "+o]))for(i in l)if((s=i.split(" "))[1]===o&&(a=l[u+" "+s[0]]||l["* "+s[0]])){!0===a?a=l[i]:!0!==l[i]&&(o=s[0],c.unshift(s[1]));break}if(!0!==a)if(a&&e["throws"])t=a(t);else try{t=a(t)}catch(e){return{state:"parsererror",error:a?e:"No conversion from "+u+" to "+o}}}return{state:"success",data:t}}(v,s,T,i),i?(v.ifModified&&((u=T.getResponseHeader("Last-Modified"))&&(S.lastModified[f]=u),(u=T.getResponseHeader("etag"))&&(S.etag[f]=u)),204===e||"HEAD"===v.type?l="nocontent":304===e?l="notmodified":(l=s.state,o=s.data,i=!(a=s.error))):(a=l,!e&&l||(l="error",e<0&&(e=0))),T.status=e,T.statusText=(t||l)+"",i?x.resolveWith(y,[o,l,T]):x.rejectWith(y,[T,l,a]),T.statusCode(w),w=void 0,g&&m.trigger(i?"ajaxSuccess":"ajaxError",[T,v,i?o:a]),b.fireWith(y,[T,l]),g&&(m.trigger("ajaxComplete",[T,v]),--S.active||S.event.trigger("ajaxStop")))}return T},getJSON:function(e,t,n){return S.get(e,t,n,"json")},getScript:function(e,t){return S.get(e,void 0,t,"script")}}),S.each(["get","post"],function(e,i){S[i]=function(e,t,n,r){return m(t)&&(r=r||n,n=t,t=void 0),S.ajax(S.extend({url:e,type:i,dataType:r,data:t,success:n},S.isPlainObject(e)&&e))}}),S.ajaxPrefilter(function(e){var t;for(t in e.headers)"content-type"===t.toLowerCase()&&(e.contentType=e.headers[t]||"")}),S._evalUrl=function(e,t,n){return S.ajax({url:e,type:"GET",dataType:"script",cache:!0,async:!1,global:!1,converters:{"text script":function(){}},dataFilter:function(e){S.globalEval(e,t,n)}})},S.fn.extend({wrapAll:function(e){var t;return this[0]&&(m(e)&&(e=e.call(this[0])),t=S(e,this[0].ownerDocument).eq(0).clone(!0),this[0].parentNode&&t.insertBefore(this[0]),t.map(function(){var e=this;while(e.firstElementChild)e=e.firstElementChild;return e}).append(this)),this},wrapInner:function(n){return m(n)?this.each(function(e){S(this).wrapInner(n.call(this,e))}):this.each(function(){var e=S(this),t=e.contents();t.length?t.wrapAll(n):e.append(n)})},wrap:function(t){var n=m(t);return this.each(function(e){S(this).wrapAll(n?t.call(this,e):t)})},unwrap:function(e){return this.parent(e).not("body").each(function(){S(this).replaceWith(this.childNodes)}),this}}),S.expr.pseudos.hidden=function(e){return!S.expr.pseudos.visible(e)},S.expr.pseudos.visible=function(e){return!!(e.offsetWidth||e.offsetHeight||e.getClientRects().length)},S.ajaxSettings.xhr=function(){try{return new C.XMLHttpRequest}catch(e){}};var Bt={0:200,1223:204},$t=S.ajaxSettings.xhr();y.cors=!!$t&&"withCredentials"in $t,y.ajax=$t=!!$t,S.ajaxTransport(function(i){var o,a;if(y.cors||$t&&!i.crossDomain)return{send:function(e,t){var n,r=i.xhr();if(r.open(i.type,i.url,i.async,i.username,i.password),i.xhrFields)for(n in i.xhrFields)r[n]=i.xhrFields[n];for(n in i.mimeType&&r.overrideMimeType&&r.overrideMimeType(i.mimeType),i.crossDomain||e["X-Requested-With"]||(e["X-Requested-With"]="XMLHttpRequest"),e)r.setRequestHeader(n,e[n]);o=function(e){return function(){o&&(o=a=r.onload=r.onerror=r.onabort=r.ontimeout=r.onreadystatechange=null,"abort"===e?r.abort():"error"===e?"number"!=typeof r.status?t(0,"error"):t(r.status,r.statusText):t(Bt[r.status]||r.status,r.statusText,"text"!==(r.responseType||"text")||"string"!=typeof r.responseText?{binary:r.response}:{text:r.responseText},r.getAllResponseHeaders()))}},r.onload=o(),a=r.onerror=r.ontimeout=o("error"),void 0!==r.onabort?r.onabort=a:r.onreadystatechange=function(){4===r.readyState&&C.setTimeout(function(){o&&a()})},o=o("abort");try{r.send(i.hasContent&&i.data||null)}catch(e){if(o)throw e}},abort:function(){o&&o()}}}),S.ajaxPrefilter(function(e){e.crossDomain&&(e.contents.script=!1)}),S.ajaxSetup({accepts:{script:"text/javascript, application/javascript, application/ecmascript, application/x-ecmascript"},contents:{script:/\b(?:java|ecma)script\b/},converters:{"text script":function(e){return S.globalEval(e),e}}}),S.ajaxPrefilter("script",function(e){void 0===e.cache&&(e.cache=!1),e.crossDomain&&(e.type="GET")}),S.ajaxTransport("script",function(n){var r,i;if(n.crossDomain||n.scriptAttrs)return{send:function(e,t){r=S("<script>").attr(n.scriptAttrs||{}).prop({charset:n.scriptCharset,src:n.url}).on("load error",i=function(e){r.remove(),i=null,e&&t("error"===e.type?404:200,e.type)}),E.head.appendChild(r[0])},abort:function(){i&&i()}}});var _t,zt=[],Ut=/(=)\?(?=&|$)|\?\?/;S.ajaxSetup({jsonp:"callback",jsonpCallback:function(){var e=zt.pop()||S.expando+"_"+wt.guid++;return this[e]=!0,e}}),S.ajaxPrefilter("json jsonp",function(e,t,n){var r,i,o,a=!1!==e.jsonp&&(Ut.test(e.url)?"url":"string"==typeof e.data&&0===(e.contentType||"").indexOf("application/x-www-form-urlencoded")&&Ut.test(e.data)&&"data");if(a||"jsonp"===e.dataTypes[0])return r=e.jsonpCallback=m(e.jsonpCallback)?e.jsonpCallback():e.jsonpCallback,a?e[a]=e[a].replace(Ut,"$1"+r):!1!==e.jsonp&&(e.url+=(Tt.test(e.url)?"&":"?")+e.jsonp+"="+r),e.converters["script json"]=function(){return o||S.error(r+" was not called"),o[0]},e.dataTypes[0]="json",i=C[r],C[r]=function(){o=arguments},n.always(function(){void 0===i?S(C).removeProp(r):C[r]=i,e[r]&&(e.jsonpCallback=t.jsonpCallback,zt.push(r)),o&&m(i)&&i(o[0]),o=i=void 0}),"script"}),y.createHTMLDocument=((_t=E.implementation.createHTMLDocument("").body).innerHTML="<form></form><form></form>",2===_t.childNodes.length),S.parseHTML=function(e,t,n){return"string"!=typeof e?[]:("boolean"==typeof t&&(n=t,t=!1),t||(y.createHTMLDocument?((r=(t=E.implementation.createHTMLDocument("")).createElement("base")).href=E.location.href,t.head.appendChild(r)):t=E),o=!n&&[],(i=N.exec(e))?[t.createElement(i[1])]:(i=xe([e],t,o),o&&o.length&&S(o).remove(),S.merge([],i.childNodes)));var r,i,o},S.fn.load=function(e,t,n){var r,i,o,a=this,s=e.indexOf(" ");return-1<s&&(r=ht(e.slice(s)),e=e.slice(0,s)),m(t)?(n=t,t=void 0):t&&"object"==typeof t&&(i="POST"),0<a.length&&S.ajax({url:e,type:i||"GET",dataType:"html",data:t}).done(function(e){o=arguments,a.html(r?S("<div>").append(S.parseHTML(e)).find(r):e)}).always(n&&function(e,t){a.each(function(){n.apply(this,o||[e.responseText,t,e])})}),this},S.expr.pseudos.animated=function(t){return S.grep(S.timers,function(e){return t===e.elem}).length},S.offset={setOffset:function(e,t,n){var r,i,o,a,s,u,l=S.css(e,"position"),c=S(e),f={};"static"===l&&(e.style.position="relative"),s=c.offset(),o=S.css(e,"top"),u=S.css(e,"left"),("absolute"===l||"fixed"===l)&&-1<(o+u).indexOf("auto")?(a=(r=c.position()).top,i=r.left):(a=parseFloat(o)||0,i=parseFloat(u)||0),m(t)&&(t=t.call(e,n,S.extend({},s))),null!=t.top&&(f.top=t.top-s.top+a),null!=t.left&&(f.left=t.left-s.left+i),"using"in t?t.using.call(e,f):c.css(f)}},S.fn.extend({offset:function(t){if(arguments.length)return void 0===t?this:this.each(function(e){S.offset.setOffset(this,t,e)});var e,n,r=this[0];return r?r.getClientRects().length?(e=r.getBoundingClientRect(),n=r.ownerDocument.defaultView,{top:e.top+n.pageYOffset,left:e.left+n.pageXOffset}):{top:0,left:0}:void 0},position:function(){if(this[0]){var e,t,n,r=this[0],i={top:0,left:0};if("fixed"===S.css(r,"position"))t=r.getBoundingClientRect();else{t=this.offset(),n=r.ownerDocument,e=r.offsetParent||n.documentElement;while(e&&(e===n.body||e===n.documentElement)&&"static"===S.css(e,"position"))e=e.parentNode;e&&e!==r&&1===e.nodeType&&((i=S(e).offset()).top+=S.css(e,"borderTopWidth",!0),i.left+=S.css(e,"borderLeftWidth",!0))}return{top:t.top-i.top-S.css(r,"marginTop",!0),left:t.left-i.left-S.css(r,"marginLeft",!0)}}},offsetParent:function(){return this.map(function(){var e=this.offsetParent;while(e&&"static"===S.css(e,"position"))e=e.offsetParent;return e||re})}}),S.each({scrollLeft:"pageXOffset",scrollTop:"pageYOffset"},function(t,i){var o="pageYOffset"===i;S.fn[t]=function(e){return $(this,function(e,t,n){var r;if(x(e)?r=e:9===e.nodeType&&(r=e.defaultView),void 0===n)return r?r[i]:e[t];r?r.scrollTo(o?r.pageXOffset:n,o?n:r.pageYOffset):e[t]=n},t,e,arguments.length)}}),S.each(["top","left"],function(e,n){S.cssHooks[n]=Fe(y.pixelPosition,function(e,t){if(t)return t=We(e,n),Pe.test(t)?S(e).position()[n]+"px":t})}),S.each({Height:"height",Width:"width"},function(a,s){S.each({padding:"inner"+a,content:s,"":"outer"+a},function(r,o){S.fn[o]=function(e,t){var n=arguments.length&&(r||"boolean"!=typeof e),i=r||(!0===e||!0===t?"margin":"border");return $(this,function(e,t,n){var r;return x(e)?0===o.indexOf("outer")?e["inner"+a]:e.document.documentElement["client"+a]:9===e.nodeType?(r=e.documentElement,Math.max(e.body["scroll"+a],r["scroll"+a],e.body["offset"+a],r["offset"+a],r["client"+a])):void 0===n?S.css(e,t,i):S.style(e,t,n,i)},s,n?e:void 0,n)}})}),S.each(["ajaxStart","ajaxStop","ajaxComplete","ajaxError","ajaxSuccess","ajaxSend"],function(e,t){S.fn[t]=function(e){return this.on(t,e)}}),S.fn.extend({bind:function(e,t,n){return this.on(e,null,t,n)},unbind:function(e,t){return this.off(e,null,t)},delegate:function(e,t,n,r){return this.on(t,e,n,r)},undelegate:function(e,t,n){return 1===arguments.length?this.off(e,"**"):this.off(t,e||"**",n)},hover:function(e,t){return this.mouseenter(e).mouseleave(t||e)}}),S.each("blur focus focusin focusout resize scroll click dblclick mousedown mouseup mousemove mouseover mouseout mouseenter mouseleave change select submit keydown keypress keyup contextmenu".split(" "),function(e,n){S.fn[n]=function(e,t){return 0<arguments.length?this.on(n,null,e,t):this.trigger(n)}});var Xt=/^[\s\uFEFF\xA0]+|[\s\uFEFF\xA0]+$/g;S.proxy=function(e,t){var n,r,i;if("string"==typeof t&&(n=e[t],t=e,e=n),m(e))return r=s.call(arguments,2),(i=function(){return e.apply(t||this,r.concat(s.call(arguments)))}).guid=e.guid=e.guid||S.guid++,i},S.holdReady=function(e){e?S.readyWait++:S.ready(!0)},S.isArray=Array.isArray,S.parseJSON=JSON.parse,S.nodeName=A,S.isFunction=m,S.isWindow=x,S.camelCase=X,S.type=w,S.now=Date.now,S.isNumeric=function(e){var t=S.type(e);return("number"===t||"string"===t)&&!isNaN(e-parseFloat(e))},S.trim=function(e){return null==e?"":(e+"").replace(Xt,"")},"function"==typeof define&&define.amd&&define("jquery",[],function(){return S});var Vt=C.jQuery,Gt=C.$;return S.noConflict=function(e){return C.$===S&&(C.$=Gt),e&&C.jQuery===S&&(C.jQuery=Vt),S},"undefined"==typeof e&&(C.jQuery=C.$=S),S});
diff --git a/docs/coverage/lib/jquery-3.6.0/jquery-3.6.0.min.map b/docs/coverage/lib/jquery-3.6.0/jquery-3.6.0.min.map
new file mode 100644
index 00000000..7d86eb16
--- /dev/null
+++ b/docs/coverage/lib/jquery-3.6.0/jquery-3.6.0.min.map
@@ -0,0 +1 @@
+{"version":3,"sources":["jquery-3.6.0.js"],"names":["global","factory","module","exports","document","w","Error","window","this","noGlobal","arr","getProto","Object","getPrototypeOf","slice","flat","array","call","concat","apply","push","indexOf","class2type","toString","hasOwn","hasOwnProperty","fnToString","ObjectFunctionString","support","isFunction","obj","nodeType","item","isWindow","preservedScriptAttributes","type","src","nonce","noModule","DOMEval","code","node","doc","i","val","script","createElement","text","getAttribute","setAttribute","head","appendChild","parentNode","removeChild","toType","version","jQuery","selector","context","fn","init","isArrayLike","length","prototype","jquery","constructor","toArray","get","num","pushStack","elems","ret","merge","prevObject","each","callback","map","elem","arguments","first","eq","last","even","grep","_elem","odd","len","j","end","sort","splice","extend","options","name","copy","copyIsArray","clone","target","deep","isPlainObject","Array","isArray","undefined","expando","Math","random","replace","isReady","error","msg","noop","proto","Ctor","isEmptyObject","globalEval","makeArray","results","inArray","second","invert","matches","callbackExpect","arg","value","guid","Symbol","iterator","split","_i","toLowerCase","Sizzle","Expr","getText","isXML","tokenize","compile","select","outermostContext","sortInput","hasDuplicate","setDocument","docElem","documentIsHTML","rbuggyQSA","rbuggyMatches","contains","Date","preferredDoc","dirruns","done","classCache","createCache","tokenCache","compilerCache","nonnativeSelectorCache","sortOrder","a","b","pop","pushNative","list","booleans","whitespace","identifier","attributes","pseudos","rwhitespace","RegExp","rtrim","rcomma","rcombinators","rdescend","rpseudo","ridentifier","matchExpr","ID","CLASS","TAG","ATTR","PSEUDO","CHILD","bool","needsContext","rhtml","rinputs","rheader","rnative","rquickExpr","rsibling","runescape","funescape","escape","nonHex","high","String","fromCharCode","rcssescape","fcssescape","ch","asCodePoint","charCodeAt","unloadHandler","inDisabledFieldset","addCombinator","disabled","nodeName","dir","next","childNodes","e","els","seed","m","nid","match","groups","newSelector","newContext","ownerDocument","exec","getElementById","id","getElementsByTagName","getElementsByClassName","qsa","test","testContext","scope","toSelector","join","querySelectorAll","qsaError","removeAttribute","keys","cache","key","cacheLength","shift","markFunction","assert","el","addHandle","attrs","handler","attrHandle","siblingCheck","cur","diff","sourceIndex","nextSibling","createInputPseudo","createButtonPseudo","createDisabledPseudo","isDisabled","createPositionalPseudo","argument","matchIndexes","namespace","namespaceURI","documentElement","hasCompare","subWindow","defaultView","top","addEventListener","attachEvent","className","createComment","getById","getElementsByName","filter","attrId","find","getAttributeNode","tag","tmp","input","innerHTML","matchesSelector","webkitMatchesSelector","mozMatchesSelector","oMatchesSelector","msMatchesSelector","disconnectedMatch","compareDocumentPosition","adown","bup","compare","sortDetached","aup","ap","bp","unshift","expr","elements","attr","specified","sel","uniqueSort","duplicates","detectDuplicates","sortStable","textContent","firstChild","nodeValue","selectors","createPseudo","relative",">"," ","+","~","preFilter","excess","unquoted","nodeNameSelector","pattern","operator","check","result","what","_argument","simple","forward","ofType","_context","xml","uniqueCache","outerCache","nodeIndex","start","parent","useCache","lastChild","uniqueID","pseudo","args","setFilters","idx","matched","not","matcher","unmatched","has","lang","elemLang","hash","location","root","focus","activeElement","hasFocus","href","tabIndex","enabled","checked","selected","selectedIndex","empty","header","button","_matchIndexes","lt","gt","radio","checkbox","file","password","image","submit","reset","tokens","combinator","base","skip","checkNonElements","doneName","oldCache","newCache","elementMatcher","matchers","condense","newUnmatched","mapped","setMatcher","postFilter","postFinder","postSelector","temp","preMap","postMap","preexisting","contexts","multipleContexts","matcherIn","matcherOut","matcherFromTokens","checkContext","leadingRelative","implicitRelative","matchContext","matchAnyContext","filters","parseOnly","soFar","preFilters","cached","elementMatchers","setMatchers","bySet","byElement","superMatcher","outermost","matchedCount","setMatched","contextBackup","dirrunsUnique","token","compiled","_name","defaultValue","unique","isXMLDoc","escapeSelector","until","truncate","is","siblings","n","rneedsContext","rsingleTag","winnow","qualifier","self","rootjQuery","parseHTML","ready","rparentsprev","guaranteedUnique","children","contents","prev","sibling","targets","l","closest","index","prevAll","add","addBack","parents","parentsUntil","nextAll","nextUntil","prevUntil","contentDocument","content","reverse","rnothtmlwhite","Identity","v","Thrower","ex","adoptValue","resolve","reject","noValue","method","promise","fail","then","Callbacks","object","_","flag","firing","memory","fired","locked","queue","firingIndex","fire","once","stopOnFalse","remove","disable","lock","fireWith","Deferred","func","tuples","state","always","deferred","catch","pipe","fns","newDefer","tuple","returned","progress","notify","onFulfilled","onRejected","onProgress","maxDepth","depth","special","that","mightThrow","TypeError","notifyWith","resolveWith","process","exceptionHook","stackTrace","rejectWith","getStackHook","setTimeout","stateString","when","singleValue","remaining","resolveContexts","resolveValues","primary","updateFunc","rerrorNames","stack","console","warn","message","readyException","readyList","completed","removeEventListener","readyWait","wait","readyState","doScroll","access","chainable","emptyGet","raw","bulk","_key","rmsPrefix","rdashAlpha","fcamelCase","_all","letter","toUpperCase","camelCase","string","acceptData","owner","Data","uid","defineProperty","configurable","set","data","prop","hasData","dataPriv","dataUser","rbrace","rmultiDash","dataAttr","JSON","parse","removeData","_data","_removeData","dequeue","startLength","hooks","_queueHooks","stop","setter","clearQueue","count","defer","pnum","source","rcssNum","cssExpand","isAttached","composed","getRootNode","isHiddenWithinTree","style","display","css","adjustCSS","valueParts","tween","adjusted","scale","maxIterations","currentValue","initial","unit","cssNumber","initialInUnit","defaultDisplayMap","showHide","show","values","body","hide","toggle","div","rcheckableType","rtagName","rscriptType","createDocumentFragment","checkClone","cloneNode","noCloneChecked","option","wrapMap","thead","col","tr","td","_default","getAll","setGlobalEval","refElements","tbody","tfoot","colgroup","caption","th","optgroup","buildFragment","scripts","selection","ignored","wrap","attached","fragment","nodes","htmlPrefilter","createTextNode","rtypenamespace","returnTrue","returnFalse","expectSync","err","safeActiveElement","on","types","one","origFn","event","off","leverageNative","notAsync","saved","isTrigger","delegateType","stopPropagation","stopImmediatePropagation","preventDefault","trigger","Event","handleObjIn","eventHandle","events","t","handleObj","handlers","namespaces","origType","elemData","create","handle","triggered","dispatch","bindType","delegateCount","setup","mappedTypes","origCount","teardown","removeEvent","nativeEvent","handlerQueue","fix","delegateTarget","preDispatch","isPropagationStopped","currentTarget","isImmediatePropagationStopped","rnamespace","postDispatch","matchedHandlers","matchedSelectors","addProp","hook","enumerable","originalEvent","writable","load","noBubble","click","beforeunload","returnValue","props","isDefaultPrevented","defaultPrevented","relatedTarget","timeStamp","now","isSimulated","altKey","bubbles","cancelable","changedTouches","ctrlKey","detail","eventPhase","metaKey","pageX","pageY","shiftKey","view","char","charCode","keyCode","buttons","clientX","clientY","offsetX","offsetY","pointerId","pointerType","screenX","screenY","targetTouches","toElement","touches","which","blur","mouseenter","mouseleave","pointerenter","pointerleave","orig","related","rnoInnerhtml","rchecked","rcleanScript","manipulationTarget","disableScript","restoreScript","cloneCopyEvent","dest","udataOld","udataCur","domManip","collection","hasScripts","iNoClone","valueIsFunction","html","_evalUrl","keepData","cleanData","dataAndEvents","deepDataAndEvents","srcElements","destElements","inPage","detach","append","prepend","insertBefore","before","after","replaceWith","replaceChild","appendTo","prependTo","insertAfter","replaceAll","original","insert","rnumnonpx","getStyles","opener","getComputedStyle","swap","old","rboxStyle","curCSS","computed","width","minWidth","maxWidth","getPropertyValue","pixelBoxStyles","addGetHookIf","conditionFn","hookFn","computeStyleTests","container","cssText","divStyle","pixelPositionVal","reliableMarginLeftVal","roundPixelMeasures","marginLeft","right","pixelBoxStylesVal","boxSizingReliableVal","position","scrollboxSizeVal","offsetWidth","measure","round","parseFloat","reliableTrDimensionsVal","backgroundClip","clearCloneStyle","boxSizingReliable","pixelPosition","reliableMarginLeft","scrollboxSize","reliableTrDimensions","table","trChild","trStyle","height","parseInt","borderTopWidth","borderBottomWidth","offsetHeight","cssPrefixes","emptyStyle","vendorProps","finalPropName","final","cssProps","capName","vendorPropName","rdisplayswap","rcustomProp","cssShow","visibility","cssNormalTransform","letterSpacing","fontWeight","setPositiveNumber","subtract","max","boxModelAdjustment","dimension","box","isBorderBox","styles","computedVal","extra","delta","ceil","getWidthOrHeight","valueIsBorderBox","offsetProp","getClientRects","Tween","easing","cssHooks","opacity","animationIterationCount","columnCount","fillOpacity","flexGrow","flexShrink","gridArea","gridColumn","gridColumnEnd","gridColumnStart","gridRow","gridRowEnd","gridRowStart","lineHeight","order","orphans","widows","zIndex","zoom","origName","isCustomProp","setProperty","isFinite","getBoundingClientRect","scrollboxSizeBuggy","left","margin","padding","border","prefix","suffix","expand","expanded","parts","propHooks","run","percent","eased","duration","pos","step","fx","scrollTop","scrollLeft","linear","p","swing","cos","PI","fxNow","inProgress","opt","rfxtypes","rrun","schedule","hidden","requestAnimationFrame","interval","tick","createFxNow","genFx","includeWidth","createTween","animation","Animation","tweeners","properties","stopped","prefilters","currentTime","startTime","tweens","opts","specialEasing","originalProperties","originalOptions","gotoEnd","propFilter","bind","complete","timer","anim","*","tweener","oldfire","propTween","restoreDisplay","isBox","dataShow","unqueued","overflow","overflowX","overflowY","prefilter","speed","speeds","fadeTo","to","animate","optall","doAnimation","finish","stopQueue","timers","cssFn","slideDown","slideUp","slideToggle","fadeIn","fadeOut","fadeToggle","slow","fast","delay","time","timeout","clearTimeout","checkOn","optSelected","radioValue","boolHook","removeAttr","nType","attrHooks","attrNames","getter","lowercaseName","rfocusable","rclickable","stripAndCollapse","getClass","classesToArray","removeProp","propFix","tabindex","for","class","addClass","classes","curValue","clazz","finalValue","removeClass","toggleClass","stateVal","isValidValue","classNames","hasClass","rreturn","valHooks","optionSet","focusin","rfocusMorph","stopPropagationCallback","onlyHandlers","bubbleType","ontype","lastElement","eventPath","parentWindow","simulate","triggerHandler","attaches","rquery","parseXML","parserErrorElem","DOMParser","parseFromString","rbracket","rCRLF","rsubmitterTypes","rsubmittable","buildParams","traditional","param","s","valueOrFunction","encodeURIComponent","serialize","serializeArray","r20","rhash","rantiCache","rheaders","rnoContent","rprotocol","transports","allTypes","originAnchor","addToPrefiltersOrTransports","structure","dataTypeExpression","dataType","dataTypes","inspectPrefiltersOrTransports","jqXHR","inspected","seekingTransport","inspect","prefilterOrFactory","dataTypeOrTransport","ajaxExtend","flatOptions","ajaxSettings","active","lastModified","etag","url","isLocal","protocol","processData","async","contentType","accepts","json","responseFields","converters","* text","text html","text json","text xml","ajaxSetup","settings","ajaxPrefilter","ajaxTransport","ajax","transport","cacheURL","responseHeadersString","responseHeaders","timeoutTimer","urlAnchor","fireGlobals","uncached","callbackContext","globalEventContext","completeDeferred","statusCode","requestHeaders","requestHeadersNames","strAbort","getResponseHeader","getAllResponseHeaders","setRequestHeader","overrideMimeType","mimeType","status","abort","statusText","finalText","crossDomain","host","hasContent","ifModified","headers","beforeSend","success","send","nativeStatusText","responses","isSuccess","response","modified","ct","finalDataType","firstDataType","ajaxHandleResponses","conv2","current","conv","dataFilter","throws","ajaxConvert","getJSON","getScript","text script","wrapAll","firstElementChild","wrapInner","htmlIsFunction","unwrap","visible","xhr","XMLHttpRequest","xhrSuccessStatus","0","1223","xhrSupported","cors","errorCallback","open","username","xhrFields","onload","onerror","onabort","ontimeout","onreadystatechange","responseType","responseText","binary","scriptAttrs","charset","scriptCharset","evt","oldCallbacks","rjsonp","jsonp","jsonpCallback","originalSettings","callbackName","overwritten","responseContainer","jsonProp","createHTMLDocument","implementation","keepScripts","parsed","params","animated","offset","setOffset","curPosition","curLeft","curCSSTop","curTop","curOffset","curCSSLeft","curElem","using","rect","win","pageYOffset","pageXOffset","offsetParent","parentOffset","scrollTo","Height","Width","","defaultExtra","funcName","unbind","delegate","undelegate","hover","fnOver","fnOut","proxy","holdReady","hold","parseJSON","isNumeric","isNaN","trim","define","amd","_jQuery","_$","$","noConflict"],"mappings":";CAaA,SAAYA,EAAQC,GAEnB,aAEuB,iBAAXC,QAAiD,iBAAnBA,OAAOC,QAShDD,OAAOC,QAAUH,EAAOI,SACvBH,EAASD,GAAQ,GACjB,SAAUK,GACT,IAAMA,EAAED,SACP,MAAM,IAAIE,MAAO,4CAElB,OAAOL,EAASI,IAGlBJ,EAASD,GAtBX,CA0BuB,oBAAXO,OAAyBA,OAASC,KAAM,SAAUD,EAAQE,GAMtE,aAEA,IAAIC,EAAM,GAENC,EAAWC,OAAOC,eAElBC,EAAQJ,EAAII,MAEZC,EAAOL,EAAIK,KAAO,SAAUC,GAC/B,OAAON,EAAIK,KAAKE,KAAMD,IACnB,SAAUA,GACb,OAAON,EAAIQ,OAAOC,MAAO,GAAIH,IAI1BI,EAAOV,EAAIU,KAEXC,EAAUX,EAAIW,QAEdC,EAAa,GAEbC,EAAWD,EAAWC,SAEtBC,EAASF,EAAWG,eAEpBC,EAAaF,EAAOD,SAEpBI,EAAuBD,EAAWT,KAAML,QAExCgB,EAAU,GAEVC,EAAa,SAAqBC,GASpC,MAAsB,mBAARA,GAA8C,iBAAjBA,EAAIC,UAC1B,mBAAbD,EAAIE,MAIVC,EAAW,SAAmBH,GAChC,OAAc,MAAPA,GAAeA,IAAQA,EAAIvB,QAIhCH,EAAWG,EAAOH,SAIjB8B,EAA4B,CAC/BC,MAAM,EACNC,KAAK,EACLC,OAAO,EACPC,UAAU,GAGX,SAASC,EAASC,EAAMC,EAAMC,GAG7B,IAAIC,EAAGC,EACNC,GAHDH,EAAMA,GAAOtC,GAGC0C,cAAe,UAG7B,GADAD,EAAOE,KAAOP,EACTC,EACJ,IAAME,KAAKT,GAYVU,EAAMH,EAAME,IAAOF,EAAKO,cAAgBP,EAAKO,aAAcL,KAE1DE,EAAOI,aAAcN,EAAGC,GAI3BF,EAAIQ,KAAKC,YAAaN,GAASO,WAAWC,YAAaR,GAIzD,SAASS,EAAQxB,GAChB,OAAY,MAAPA,EACGA,EAAM,GAIQ,iBAARA,GAAmC,mBAARA,EACxCR,EAAYC,EAASN,KAAMa,KAAW,gBAC/BA,EAQT,IACCyB,EAAU,QAGVC,EAAS,SAAUC,EAAUC,GAI5B,OAAO,IAAIF,EAAOG,GAAGC,KAAMH,EAAUC,IA0VvC,SAASG,EAAa/B,GAMrB,IAAIgC,IAAWhC,GAAO,WAAYA,GAAOA,EAAIgC,OAC5C3B,EAAOmB,EAAQxB,GAEhB,OAAKD,EAAYC,KAASG,EAAUH,KAIpB,UAATK,GAA+B,IAAX2B,GACR,iBAAXA,GAAgC,EAATA,GAAgBA,EAAS,KAAOhC,GArWhE0B,EAAOG,GAAKH,EAAOO,UAAY,CAG9BC,OAAQT,EAERU,YAAaT,EAGbM,OAAQ,EAERI,QAAS,WACR,OAAOpD,EAAMG,KAAMT,OAKpB2D,IAAK,SAAUC,GAGd,OAAY,MAAPA,EACGtD,EAAMG,KAAMT,MAIb4D,EAAM,EAAI5D,KAAM4D,EAAM5D,KAAKsD,QAAWtD,KAAM4D,IAKpDC,UAAW,SAAUC,GAGpB,IAAIC,EAAMf,EAAOgB,MAAOhE,KAAKyD,cAAeK,GAM5C,OAHAC,EAAIE,WAAajE,KAGV+D,GAIRG,KAAM,SAAUC,GACf,OAAOnB,EAAOkB,KAAMlE,KAAMmE,IAG3BC,IAAK,SAAUD,GACd,OAAOnE,KAAK6D,UAAWb,EAAOoB,IAAKpE,KAAM,SAAUqE,EAAMlC,GACxD,OAAOgC,EAAS1D,KAAM4D,EAAMlC,EAAGkC,OAIjC/D,MAAO,WACN,OAAON,KAAK6D,UAAWvD,EAAMK,MAAOX,KAAMsE,aAG3CC,MAAO,WACN,OAAOvE,KAAKwE,GAAI,IAGjBC,KAAM,WACL,OAAOzE,KAAKwE,IAAK,IAGlBE,KAAM,WACL,OAAO1E,KAAK6D,UAAWb,EAAO2B,KAAM3E,KAAM,SAAU4E,EAAOzC,GAC1D,OAASA,EAAI,GAAM,MAIrB0C,IAAK,WACJ,OAAO7E,KAAK6D,UAAWb,EAAO2B,KAAM3E,KAAM,SAAU4E,EAAOzC,GAC1D,OAAOA,EAAI,MAIbqC,GAAI,SAAUrC,GACb,IAAI2C,EAAM9E,KAAKsD,OACdyB,GAAK5C,GAAMA,EAAI,EAAI2C,EAAM,GAC1B,OAAO9E,KAAK6D,UAAgB,GAALkB,GAAUA,EAAID,EAAM,CAAE9E,KAAM+E,IAAQ,KAG5DC,IAAK,WACJ,OAAOhF,KAAKiE,YAAcjE,KAAKyD,eAKhC7C,KAAMA,EACNqE,KAAM/E,EAAI+E,KACVC,OAAQhF,EAAIgF,QAGblC,EAAOmC,OAASnC,EAAOG,GAAGgC,OAAS,WAClC,IAAIC,EAASC,EAAMzD,EAAK0D,EAAMC,EAAaC,EAC1CC,EAASnB,UAAW,IAAO,GAC3BnC,EAAI,EACJmB,EAASgB,UAAUhB,OACnBoC,GAAO,EAsBR,IAnBuB,kBAAXD,IACXC,EAAOD,EAGPA,EAASnB,UAAWnC,IAAO,GAC3BA,KAIsB,iBAAXsD,GAAwBpE,EAAYoE,KAC/CA,EAAS,IAILtD,IAAMmB,IACVmC,EAASzF,KACTmC,KAGOA,EAAImB,EAAQnB,IAGnB,GAAqC,OAA9BiD,EAAUd,UAAWnC,IAG3B,IAAMkD,KAAQD,EACbE,EAAOF,EAASC,GAIF,cAATA,GAAwBI,IAAWH,IAKnCI,GAAQJ,IAAUtC,EAAO2C,cAAeL,KAC1CC,EAAcK,MAAMC,QAASP,MAC/B1D,EAAM6D,EAAQJ,GAIbG,EADID,IAAgBK,MAAMC,QAASjE,GAC3B,GACI2D,GAAgBvC,EAAO2C,cAAe/D,GAG1CA,EAFA,GAIT2D,GAAc,EAGdE,EAAQJ,GAASrC,EAAOmC,OAAQO,EAAMF,EAAOF,SAGzBQ,IAATR,IACXG,EAAQJ,GAASC,IAOrB,OAAOG,GAGRzC,EAAOmC,OAAQ,CAGdY,QAAS,UAAahD,EAAUiD,KAAKC,UAAWC,QAAS,MAAO,IAGhEC,SAAS,EAETC,MAAO,SAAUC,GAChB,MAAM,IAAIvG,MAAOuG,IAGlBC,KAAM,aAENX,cAAe,SAAUrE,GACxB,IAAIiF,EAAOC,EAIX,SAAMlF,GAAgC,oBAAzBP,EAASN,KAAMa,QAI5BiF,EAAQpG,EAAUmB,KASK,mBADvBkF,EAAOxF,EAAOP,KAAM8F,EAAO,gBAAmBA,EAAM9C,cACfvC,EAAWT,KAAM+F,KAAWrF,IAGlEsF,cAAe,SAAUnF,GACxB,IAAI+D,EAEJ,IAAMA,KAAQ/D,EACb,OAAO,EAER,OAAO,GAKRoF,WAAY,SAAU1E,EAAMoD,EAASlD,GACpCH,EAASC,EAAM,CAAEH,MAAOuD,GAAWA,EAAQvD,OAASK,IAGrDgC,KAAM,SAAU5C,EAAK6C,GACpB,IAAIb,EAAQnB,EAAI,EAEhB,GAAKkB,EAAa/B,IAEjB,IADAgC,EAAShC,EAAIgC,OACLnB,EAAImB,EAAQnB,IACnB,IAAgD,IAA3CgC,EAAS1D,KAAMa,EAAKa,GAAKA,EAAGb,EAAKa,IACrC,WAIF,IAAMA,KAAKb,EACV,IAAgD,IAA3C6C,EAAS1D,KAAMa,EAAKa,GAAKA,EAAGb,EAAKa,IACrC,MAKH,OAAOb,GAIRqF,UAAW,SAAUzG,EAAK0G,GACzB,IAAI7C,EAAM6C,GAAW,GAarB,OAXY,MAAP1G,IACCmD,EAAajD,OAAQF,IACzB8C,EAAOgB,MAAOD,EACE,iBAAR7D,EACN,CAAEA,GAAQA,GAGZU,EAAKH,KAAMsD,EAAK7D,IAIX6D,GAGR8C,QAAS,SAAUxC,EAAMnE,EAAKiC,GAC7B,OAAc,MAAPjC,GAAe,EAAIW,EAAQJ,KAAMP,EAAKmE,EAAMlC,IAKpD6B,MAAO,SAAUO,EAAOuC,GAKvB,IAJA,IAAIhC,GAAOgC,EAAOxD,OACjByB,EAAI,EACJ5C,EAAIoC,EAAMjB,OAEHyB,EAAID,EAAKC,IAChBR,EAAOpC,KAAQ2E,EAAQ/B,GAKxB,OAFAR,EAAMjB,OAASnB,EAERoC,GAGRI,KAAM,SAAUb,EAAOK,EAAU4C,GAShC,IARA,IACCC,EAAU,GACV7E,EAAI,EACJmB,EAASQ,EAAMR,OACf2D,GAAkBF,EAIX5E,EAAImB,EAAQnB,KACAgC,EAAUL,EAAO3B,GAAKA,KAChB8E,GACxBD,EAAQpG,KAAMkD,EAAO3B,IAIvB,OAAO6E,GAIR5C,IAAK,SAAUN,EAAOK,EAAU+C,GAC/B,IAAI5D,EAAQ6D,EACXhF,EAAI,EACJ4B,EAAM,GAGP,GAAKV,EAAaS,GAEjB,IADAR,EAASQ,EAAMR,OACPnB,EAAImB,EAAQnB,IAGL,OAFdgF,EAAQhD,EAAUL,EAAO3B,GAAKA,EAAG+E,KAGhCnD,EAAInD,KAAMuG,QAMZ,IAAMhF,KAAK2B,EAGI,OAFdqD,EAAQhD,EAAUL,EAAO3B,GAAKA,EAAG+E,KAGhCnD,EAAInD,KAAMuG,GAMb,OAAO5G,EAAMwD,IAIdqD,KAAM,EAINhG,QAASA,IAGa,mBAAXiG,SACXrE,EAAOG,GAAIkE,OAAOC,UAAapH,EAAKmH,OAAOC,WAI5CtE,EAAOkB,KAAM,uEAAuEqD,MAAO,KAC1F,SAAUC,EAAInC,GACbvE,EAAY,WAAauE,EAAO,KAAQA,EAAKoC,gBAmB/C,IAAIC,EAWJ,SAAY3H,GACZ,IAAIoC,EACHf,EACAuG,EACAC,EACAC,EACAC,EACAC,EACAC,EACAC,EACAC,EACAC,EAGAC,EACAxI,EACAyI,EACAC,EACAC,EACAC,EACAxB,EACAyB,EAGA1C,EAAU,SAAW,EAAI,IAAI2C,KAC7BC,EAAe5I,EAAOH,SACtBgJ,EAAU,EACVC,EAAO,EACPC,EAAaC,KACbC,EAAaD,KACbE,EAAgBF,KAChBG,EAAyBH,KACzBI,EAAY,SAAUC,EAAGC,GAIxB,OAHKD,IAAMC,IACVlB,GAAe,GAET,GAIRnH,EAAS,GAAOC,eAChBf,EAAM,GACNoJ,EAAMpJ,EAAIoJ,IACVC,EAAarJ,EAAIU,KACjBA,EAAOV,EAAIU,KACXN,EAAQJ,EAAII,MAIZO,EAAU,SAAU2I,EAAMnF,GAGzB,IAFA,IAAIlC,EAAI,EACP2C,EAAM0E,EAAKlG,OACJnB,EAAI2C,EAAK3C,IAChB,GAAKqH,EAAMrH,KAAQkC,EAClB,OAAOlC,EAGT,OAAQ,GAGTsH,EAAW,6HAMXC,EAAa,sBAGbC,EAAa,0BAA4BD,EACxC,0CAGDE,EAAa,MAAQF,EAAa,KAAOC,EAAa,OAASD,EAG9D,gBAAkBA,EAIlB,2DAA6DC,EAAa,OAC1ED,EAAa,OAEdG,EAAU,KAAOF,EAAa,wFAOAC,EAAa,eAO3CE,EAAc,IAAIC,OAAQL,EAAa,IAAK,KAC5CM,EAAQ,IAAID,OAAQ,IAAML,EAAa,8BACtCA,EAAa,KAAM,KAEpBO,EAAS,IAAIF,OAAQ,IAAML,EAAa,KAAOA,EAAa,KAC5DQ,EAAe,IAAIH,OAAQ,IAAML,EAAa,WAAaA,EAAa,IAAMA,EAC7E,KACDS,EAAW,IAAIJ,OAAQL,EAAa,MAEpCU,EAAU,IAAIL,OAAQF,GACtBQ,EAAc,IAAIN,OAAQ,IAAMJ,EAAa,KAE7CW,EAAY,CACXC,GAAM,IAAIR,OAAQ,MAAQJ,EAAa,KACvCa,MAAS,IAAIT,OAAQ,QAAUJ,EAAa,KAC5Cc,IAAO,IAAIV,OAAQ,KAAOJ,EAAa,SACvCe,KAAQ,IAAIX,OAAQ,IAAMH,GAC1Be,OAAU,IAAIZ,OAAQ,IAAMF,GAC5Be,MAAS,IAAIb,OAAQ,yDACpBL,EAAa,+BAAiCA,EAAa,cAC3DA,EAAa,aAAeA,EAAa,SAAU,KACpDmB,KAAQ,IAAId,OAAQ,OAASN,EAAW,KAAM,KAI9CqB,aAAgB,IAAIf,OAAQ,IAAML,EACjC,mDAAqDA,EACrD,mBAAqBA,EAAa,mBAAoB,MAGxDqB,EAAQ,SACRC,EAAU,sCACVC,EAAU,SAEVC,EAAU,yBAGVC,EAAa,mCAEbC,GAAW,OAIXC,GAAY,IAAItB,OAAQ,uBAAyBL,EAAa,uBAAwB,KACtF4B,GAAY,SAAUC,EAAQC,GAC7B,IAAIC,EAAO,KAAOF,EAAOjL,MAAO,GAAM,MAEtC,OAAOkL,IASNC,EAAO,EACNC,OAAOC,aAAcF,EAAO,OAC5BC,OAAOC,aAAcF,GAAQ,GAAK,MAAe,KAAPA,EAAe,SAK5DG,GAAa,sDACbC,GAAa,SAAUC,EAAIC,GAC1B,OAAKA,EAGQ,OAAPD,EACG,SAIDA,EAAGxL,MAAO,GAAI,GAAM,KAC1BwL,EAAGE,WAAYF,EAAGxI,OAAS,GAAIvC,SAAU,IAAO,IAI3C,KAAO+K,GAOfG,GAAgB,WACf7D,KAGD8D,GAAqBC,GACpB,SAAU9H,GACT,OAAyB,IAAlBA,EAAK+H,UAAqD,aAAhC/H,EAAKgI,SAAS5E,eAEhD,CAAE6E,IAAK,aAAcC,KAAM,WAI7B,IACC3L,EAAKD,MACFT,EAAMI,EAAMG,KAAMkI,EAAa6D,YACjC7D,EAAa6D,YAMdtM,EAAKyI,EAAa6D,WAAWlJ,QAAS/B,SACrC,MAAQkL,GACT7L,EAAO,CAAED,MAAOT,EAAIoD,OAGnB,SAAUmC,EAAQiH,GACjBnD,EAAW5I,MAAO8E,EAAQnF,EAAMG,KAAMiM,KAKvC,SAAUjH,EAAQiH,GACjB,IAAI3H,EAAIU,EAAOnC,OACdnB,EAAI,EAGL,MAAUsD,EAAQV,KAAQ2H,EAAKvK,MAC/BsD,EAAOnC,OAASyB,EAAI,IAKvB,SAAS2C,GAAQzE,EAAUC,EAAS0D,EAAS+F,GAC5C,IAAIC,EAAGzK,EAAGkC,EAAMwI,EAAKC,EAAOC,EAAQC,EACnCC,EAAa/J,GAAWA,EAAQgK,cAGhC3L,EAAW2B,EAAUA,EAAQ3B,SAAW,EAKzC,GAHAqF,EAAUA,GAAW,GAGI,iBAAb3D,IAA0BA,GACxB,IAAb1B,GAA+B,IAAbA,GAA+B,KAAbA,EAEpC,OAAOqF,EAIR,IAAM+F,IACLvE,EAAalF,GACbA,EAAUA,GAAWtD,EAEhB0I,GAAiB,CAIrB,GAAkB,KAAb/G,IAAqBuL,EAAQ3B,EAAWgC,KAAMlK,IAGlD,GAAO2J,EAAIE,EAAO,IAGjB,GAAkB,IAAbvL,EAAiB,CACrB,KAAO8C,EAAOnB,EAAQkK,eAAgBR,IAUrC,OAAOhG,EALP,GAAKvC,EAAKgJ,KAAOT,EAEhB,OADAhG,EAAQhG,KAAMyD,GACPuC,OAYT,GAAKqG,IAAgB5I,EAAO4I,EAAWG,eAAgBR,KACtDnE,EAAUvF,EAASmB,IACnBA,EAAKgJ,KAAOT,EAGZ,OADAhG,EAAQhG,KAAMyD,GACPuC,MAKH,CAAA,GAAKkG,EAAO,GAElB,OADAlM,EAAKD,MAAOiG,EAAS1D,EAAQoK,qBAAsBrK,IAC5C2D,EAGD,IAAOgG,EAAIE,EAAO,KAAS1L,EAAQmM,wBACzCrK,EAAQqK,uBAGR,OADA3M,EAAKD,MAAOiG,EAAS1D,EAAQqK,uBAAwBX,IAC9ChG,EAKT,GAAKxF,EAAQoM,MACXtE,EAAwBjG,EAAW,QACjCsF,IAAcA,EAAUkF,KAAMxK,MAIlB,IAAb1B,GAAqD,WAAnC2B,EAAQmJ,SAAS5E,eAA+B,CAYpE,GAVAuF,EAAc/J,EACdgK,EAAa/J,EASK,IAAb3B,IACF4I,EAASsD,KAAMxK,IAAciH,EAAauD,KAAMxK,IAAe,EAGjEgK,EAAa7B,GAASqC,KAAMxK,IAAcyK,GAAaxK,EAAQN,aAC9DM,KAImBA,GAAY9B,EAAQuM,SAGhCd,EAAM3J,EAAQV,aAAc,OAClCqK,EAAMA,EAAI3G,QAAS0F,GAAYC,IAE/B3I,EAAQT,aAAc,KAAQoK,EAAM9G,IAMtC5D,GADA4K,EAASjF,EAAU7E,IACRK,OACX,MAAQnB,IACP4K,EAAQ5K,IAAQ0K,EAAM,IAAMA,EAAM,UAAa,IAC9Ce,GAAYb,EAAQ5K,IAEtB6K,EAAcD,EAAOc,KAAM,KAG5B,IAIC,OAHAjN,EAAKD,MAAOiG,EACXqG,EAAWa,iBAAkBd,IAEvBpG,EACN,MAAQmH,GACT7E,EAAwBjG,GAAU,GACjC,QACI4J,IAAQ9G,GACZ7C,EAAQ8K,gBAAiB,QAQ9B,OAAOhG,EAAQ/E,EAASiD,QAAS8D,EAAO,MAAQ9G,EAAS0D,EAAS+F,GASnE,SAAS5D,KACR,IAAIkF,EAAO,GAYX,OAVA,SAASC,EAAOC,EAAKhH,GAQpB,OALK8G,EAAKrN,KAAMuN,EAAM,KAAQxG,EAAKyG,oBAG3BF,EAAOD,EAAKI,SAEXH,EAAOC,EAAM,KAAQhH,GAShC,SAASmH,GAAcnL,GAEtB,OADAA,EAAI4C,IAAY,EACT5C,EAOR,SAASoL,GAAQpL,GAChB,IAAIqL,EAAK5O,EAAS0C,cAAe,YAEjC,IACC,QAASa,EAAIqL,GACZ,MAAQ/B,GACT,OAAO,EACN,QAGI+B,EAAG5L,YACP4L,EAAG5L,WAAWC,YAAa2L,GAI5BA,EAAK,MASP,SAASC,GAAWC,EAAOC,GAC1B,IAAIzO,EAAMwO,EAAMnH,MAAO,KACtBpF,EAAIjC,EAAIoD,OAET,MAAQnB,IACPwF,EAAKiH,WAAY1O,EAAKiC,IAAQwM,EAUhC,SAASE,GAAczF,EAAGC,GACzB,IAAIyF,EAAMzF,GAAKD,EACd2F,EAAOD,GAAsB,IAAf1F,EAAE7H,UAAiC,IAAf8H,EAAE9H,UACnC6H,EAAE4F,YAAc3F,EAAE2F,YAGpB,GAAKD,EACJ,OAAOA,EAIR,GAAKD,EACJ,MAAUA,EAAMA,EAAIG,YACnB,GAAKH,IAAQzF,EACZ,OAAQ,EAKX,OAAOD,EAAI,GAAK,EAOjB,SAAS8F,GAAmBvN,GAC3B,OAAO,SAAU0C,GAEhB,MAAgB,UADLA,EAAKgI,SAAS5E,eACEpD,EAAK1C,OAASA,GAQ3C,SAASwN,GAAoBxN,GAC5B,OAAO,SAAU0C,GAChB,IAAIgB,EAAOhB,EAAKgI,SAAS5E,cACzB,OAAkB,UAATpC,GAA6B,WAATA,IAAuBhB,EAAK1C,OAASA,GAQpE,SAASyN,GAAsBhD,GAG9B,OAAO,SAAU/H,GAKhB,MAAK,SAAUA,EASTA,EAAKzB,aAAgC,IAAlByB,EAAK+H,SAGvB,UAAW/H,EACV,UAAWA,EAAKzB,WACbyB,EAAKzB,WAAWwJ,WAAaA,EAE7B/H,EAAK+H,WAAaA,EAMpB/H,EAAKgL,aAAejD,GAI1B/H,EAAKgL,cAAgBjD,GACrBF,GAAoB7H,KAAW+H,EAG1B/H,EAAK+H,WAAaA,EAKd,UAAW/H,GACfA,EAAK+H,WAAaA,GAY5B,SAASkD,GAAwBnM,GAChC,OAAOmL,GAAc,SAAUiB,GAE9B,OADAA,GAAYA,EACLjB,GAAc,SAAU3B,EAAM3F,GACpC,IAAIjC,EACHyK,EAAerM,EAAI,GAAIwJ,EAAKrJ,OAAQiM,GACpCpN,EAAIqN,EAAalM,OAGlB,MAAQnB,IACFwK,EAAQ5H,EAAIyK,EAAcrN,MAC9BwK,EAAM5H,KAASiC,EAASjC,GAAM4H,EAAM5H,SAYzC,SAAS2I,GAAaxK,GACrB,OAAOA,GAAmD,oBAAjCA,EAAQoK,sBAAwCpK,EAkrC1E,IAAMf,KA9qCNf,EAAUsG,GAAOtG,QAAU,GAO3ByG,EAAQH,GAAOG,MAAQ,SAAUxD,GAChC,IAAIoL,EAAYpL,GAAQA,EAAKqL,aAC5BrH,EAAUhE,IAAUA,EAAK6I,eAAiB7I,GAAOsL,gBAKlD,OAAQ5E,EAAM0C,KAAMgC,GAAapH,GAAWA,EAAQgE,UAAY,SAQjEjE,EAAcV,GAAOU,YAAc,SAAUnG,GAC5C,IAAI2N,EAAYC,EACf3N,EAAMD,EAAOA,EAAKiL,eAAiBjL,EAAO0G,EAO3C,OAAKzG,GAAOtC,GAA6B,IAAjBsC,EAAIX,UAAmBW,EAAIyN,kBAMnDtH,GADAzI,EAAWsC,GACQyN,gBACnBrH,GAAkBT,EAAOjI,GAQpB+I,GAAgB/I,IAClBiQ,EAAYjQ,EAASkQ,cAAiBD,EAAUE,MAAQF,IAGrDA,EAAUG,iBACdH,EAAUG,iBAAkB,SAAU/D,IAAe,GAG1C4D,EAAUI,aACrBJ,EAAUI,YAAa,WAAYhE,KASrC7K,EAAQuM,MAAQY,GAAQ,SAAUC,GAEjC,OADAnG,EAAQ1F,YAAa6L,GAAK7L,YAAa/C,EAAS0C,cAAe,QACzB,oBAAxBkM,EAAGV,mBACfU,EAAGV,iBAAkB,uBAAwBxK,SAShDlC,EAAQwI,WAAa2E,GAAQ,SAAUC,GAEtC,OADAA,EAAG0B,UAAY,KACP1B,EAAGhM,aAAc,eAO1BpB,EAAQkM,qBAAuBiB,GAAQ,SAAUC,GAEhD,OADAA,EAAG7L,YAAa/C,EAASuQ,cAAe,MAChC3B,EAAGlB,qBAAsB,KAAMhK,SAIxClC,EAAQmM,uBAAyBrC,EAAQuC,KAAM7N,EAAS2N,wBAMxDnM,EAAQgP,QAAU7B,GAAQ,SAAUC,GAEnC,OADAnG,EAAQ1F,YAAa6L,GAAKnB,GAAKtH,GACvBnG,EAASyQ,oBAAsBzQ,EAASyQ,kBAAmBtK,GAAUzC,SAIzElC,EAAQgP,SACZzI,EAAK2I,OAAa,GAAI,SAAUjD,GAC/B,IAAIkD,EAASlD,EAAGnH,QAASmF,GAAWC,IACpC,OAAO,SAAUjH,GAChB,OAAOA,EAAK7B,aAAc,QAAW+N,IAGvC5I,EAAK6I,KAAW,GAAI,SAAUnD,EAAInK,GACjC,GAAuC,oBAA3BA,EAAQkK,gBAAkC9E,EAAiB,CACtE,IAAIjE,EAAOnB,EAAQkK,eAAgBC,GACnC,OAAOhJ,EAAO,CAAEA,GAAS,OAI3BsD,EAAK2I,OAAa,GAAK,SAAUjD,GAChC,IAAIkD,EAASlD,EAAGnH,QAASmF,GAAWC,IACpC,OAAO,SAAUjH,GAChB,IAAIpC,EAAwC,oBAA1BoC,EAAKoM,kBACtBpM,EAAKoM,iBAAkB,MACxB,OAAOxO,GAAQA,EAAKkF,QAAUoJ,IAMhC5I,EAAK6I,KAAW,GAAI,SAAUnD,EAAInK,GACjC,GAAuC,oBAA3BA,EAAQkK,gBAAkC9E,EAAiB,CACtE,IAAIrG,EAAME,EAAG2B,EACZO,EAAOnB,EAAQkK,eAAgBC,GAEhC,GAAKhJ,EAAO,CAIX,IADApC,EAAOoC,EAAKoM,iBAAkB,QACjBxO,EAAKkF,QAAUkG,EAC3B,MAAO,CAAEhJ,GAIVP,EAAQZ,EAAQmN,kBAAmBhD,GACnClL,EAAI,EACJ,MAAUkC,EAAOP,EAAO3B,KAEvB,IADAF,EAAOoC,EAAKoM,iBAAkB,QACjBxO,EAAKkF,QAAUkG,EAC3B,MAAO,CAAEhJ,GAKZ,MAAO,MAMVsD,EAAK6I,KAAY,IAAIpP,EAAQkM,qBAC5B,SAAUoD,EAAKxN,GACd,MAA6C,oBAAjCA,EAAQoK,qBACZpK,EAAQoK,qBAAsBoD,GAG1BtP,EAAQoM,IACZtK,EAAQ4K,iBAAkB4C,QAD3B,GAKR,SAAUA,EAAKxN,GACd,IAAImB,EACHsM,EAAM,GACNxO,EAAI,EAGJyE,EAAU1D,EAAQoK,qBAAsBoD,GAGzC,GAAa,MAARA,EAAc,CAClB,MAAUrM,EAAOuC,EAASzE,KACF,IAAlBkC,EAAK9C,UACToP,EAAI/P,KAAMyD,GAIZ,OAAOsM,EAER,OAAO/J,GAITe,EAAK6I,KAAc,MAAIpP,EAAQmM,wBAA0B,SAAU2C,EAAWhN,GAC7E,GAA+C,oBAAnCA,EAAQqK,wBAA0CjF,EAC7D,OAAOpF,EAAQqK,uBAAwB2C,IAUzC1H,EAAgB,GAOhBD,EAAY,IAELnH,EAAQoM,IAAMtC,EAAQuC,KAAM7N,EAASkO,qBAI3CS,GAAQ,SAAUC,GAEjB,IAAIoC,EAOJvI,EAAQ1F,YAAa6L,GAAKqC,UAAY,UAAY9K,EAAU,qBAC1CA,EAAU,kEAOvByI,EAAGV,iBAAkB,wBAAyBxK,QAClDiF,EAAU3H,KAAM,SAAW8I,EAAa,gBAKnC8E,EAAGV,iBAAkB,cAAexK,QACzCiF,EAAU3H,KAAM,MAAQ8I,EAAa,aAAeD,EAAW,KAI1D+E,EAAGV,iBAAkB,QAAU/H,EAAU,MAAOzC,QACrDiF,EAAU3H,KAAM,OAQjBgQ,EAAQhR,EAAS0C,cAAe,UAC1BG,aAAc,OAAQ,IAC5B+L,EAAG7L,YAAaiO,GACVpC,EAAGV,iBAAkB,aAAcxK,QACxCiF,EAAU3H,KAAM,MAAQ8I,EAAa,QAAUA,EAAa,KAC3DA,EAAa,gBAMT8E,EAAGV,iBAAkB,YAAaxK,QACvCiF,EAAU3H,KAAM,YAMX4N,EAAGV,iBAAkB,KAAO/H,EAAU,MAAOzC,QAClDiF,EAAU3H,KAAM,YAKjB4N,EAAGV,iBAAkB,QACrBvF,EAAU3H,KAAM,iBAGjB2N,GAAQ,SAAUC,GACjBA,EAAGqC,UAAY,oFAKf,IAAID,EAAQhR,EAAS0C,cAAe,SACpCsO,EAAMnO,aAAc,OAAQ,UAC5B+L,EAAG7L,YAAaiO,GAAQnO,aAAc,OAAQ,KAIzC+L,EAAGV,iBAAkB,YAAaxK,QACtCiF,EAAU3H,KAAM,OAAS8I,EAAa,eAKW,IAA7C8E,EAAGV,iBAAkB,YAAaxK,QACtCiF,EAAU3H,KAAM,WAAY,aAK7ByH,EAAQ1F,YAAa6L,GAAKpC,UAAW,EACc,IAA9CoC,EAAGV,iBAAkB,aAAcxK,QACvCiF,EAAU3H,KAAM,WAAY,aAK7B4N,EAAGV,iBAAkB,QACrBvF,EAAU3H,KAAM,YAIXQ,EAAQ0P,gBAAkB5F,EAAQuC,KAAQzG,EAAUqB,EAAQrB,SAClEqB,EAAQ0I,uBACR1I,EAAQ2I,oBACR3I,EAAQ4I,kBACR5I,EAAQ6I,qBAER3C,GAAQ,SAAUC,GAIjBpN,EAAQ+P,kBAAoBnK,EAAQvG,KAAM+N,EAAI,KAI9CxH,EAAQvG,KAAM+N,EAAI,aAClBhG,EAAc5H,KAAM,KAAMiJ,KAI5BtB,EAAYA,EAAUjF,QAAU,IAAIyG,OAAQxB,EAAUsF,KAAM,MAC5DrF,EAAgBA,EAAclF,QAAU,IAAIyG,OAAQvB,EAAcqF,KAAM,MAIxE+B,EAAa1E,EAAQuC,KAAMpF,EAAQ+I,yBAKnC3I,EAAWmH,GAAc1E,EAAQuC,KAAMpF,EAAQI,UAC9C,SAAUW,EAAGC,GACZ,IAAIgI,EAAuB,IAAfjI,EAAE7H,SAAiB6H,EAAEuG,gBAAkBvG,EAClDkI,EAAMjI,GAAKA,EAAEzG,WACd,OAAOwG,IAAMkI,MAAWA,GAAwB,IAAjBA,EAAI/P,YAClC8P,EAAM5I,SACL4I,EAAM5I,SAAU6I,GAChBlI,EAAEgI,yBAA8D,GAAnChI,EAAEgI,wBAAyBE,MAG3D,SAAUlI,EAAGC,GACZ,GAAKA,EACJ,MAAUA,EAAIA,EAAEzG,WACf,GAAKyG,IAAMD,EACV,OAAO,EAIV,OAAO,GAOTD,EAAYyG,EACZ,SAAUxG,EAAGC,GAGZ,GAAKD,IAAMC,EAEV,OADAlB,GAAe,EACR,EAIR,IAAIoJ,GAAWnI,EAAEgI,yBAA2B/H,EAAE+H,wBAC9C,OAAKG,IAgBU,GAPfA,GAAYnI,EAAE8D,eAAiB9D,KAASC,EAAE6D,eAAiB7D,GAC1DD,EAAEgI,wBAAyB/H,GAG3B,KAIGjI,EAAQoQ,cAAgBnI,EAAE+H,wBAAyBhI,KAAQmI,EAOzDnI,GAAKxJ,GAAYwJ,EAAE8D,eAAiBvE,GACxCF,EAAUE,EAAcS,IAChB,EAOJC,GAAKzJ,GAAYyJ,EAAE6D,eAAiBvE,GACxCF,EAAUE,EAAcU,GACjB,EAIDnB,EACJrH,EAASqH,EAAWkB,GAAMvI,EAASqH,EAAWmB,GAChD,EAGe,EAAVkI,GAAe,EAAI,IAE3B,SAAUnI,EAAGC,GAGZ,GAAKD,IAAMC,EAEV,OADAlB,GAAe,EACR,EAGR,IAAI2G,EACH3M,EAAI,EACJsP,EAAMrI,EAAExG,WACR0O,EAAMjI,EAAEzG,WACR8O,EAAK,CAAEtI,GACPuI,EAAK,CAAEtI,GAGR,IAAMoI,IAAQH,EAMb,OAAOlI,GAAKxJ,GAAY,EACvByJ,GAAKzJ,EAAW,EAEhB6R,GAAO,EACPH,EAAM,EACNpJ,EACErH,EAASqH,EAAWkB,GAAMvI,EAASqH,EAAWmB,GAChD,EAGK,GAAKoI,IAAQH,EACnB,OAAOzC,GAAczF,EAAGC,GAIzByF,EAAM1F,EACN,MAAU0F,EAAMA,EAAIlM,WACnB8O,EAAGE,QAAS9C,GAEbA,EAAMzF,EACN,MAAUyF,EAAMA,EAAIlM,WACnB+O,EAAGC,QAAS9C,GAIb,MAAQ4C,EAAIvP,KAAQwP,EAAIxP,GACvBA,IAGD,OAAOA,EAGN0M,GAAc6C,EAAIvP,GAAKwP,EAAIxP,IAO3BuP,EAAIvP,IAAOwG,GAAgB,EAC3BgJ,EAAIxP,IAAOwG,EAAe,EAE1B,IAGK/I,GAGR8H,GAAOV,QAAU,SAAU6K,EAAMC,GAChC,OAAOpK,GAAQmK,EAAM,KAAM,KAAMC,IAGlCpK,GAAOoJ,gBAAkB,SAAUzM,EAAMwN,GAGxC,GAFAzJ,EAAa/D,GAERjD,EAAQ0P,iBAAmBxI,IAC9BY,EAAwB2I,EAAO,QAC7BrJ,IAAkBA,EAAciF,KAAMoE,OACtCtJ,IAAkBA,EAAUkF,KAAMoE,IAErC,IACC,IAAI9N,EAAMiD,EAAQvG,KAAM4D,EAAMwN,GAG9B,GAAK9N,GAAO3C,EAAQ+P,mBAInB9M,EAAKzE,UAAuC,KAA3ByE,EAAKzE,SAAS2B,SAC/B,OAAOwC,EAEP,MAAQ0I,GACTvD,EAAwB2I,GAAM,GAIhC,OAAyD,EAAlDnK,GAAQmK,EAAMjS,EAAU,KAAM,CAAEyE,IAASf,QAGjDoE,GAAOe,SAAW,SAAUvF,EAASmB,GAUpC,OAHOnB,EAAQgK,eAAiBhK,IAAatD,GAC5CwI,EAAalF,GAEPuF,EAAUvF,EAASmB,IAG3BqD,GAAOqK,KAAO,SAAU1N,EAAMgB,IAOtBhB,EAAK6I,eAAiB7I,IAAUzE,GACtCwI,EAAa/D,GAGd,IAAIlB,EAAKwE,EAAKiH,WAAYvJ,EAAKoC,eAG9BrF,EAAMe,GAAMnC,EAAOP,KAAMkH,EAAKiH,WAAYvJ,EAAKoC,eAC9CtE,EAAIkB,EAAMgB,GAAOiD,QACjBxC,EAEF,YAAeA,IAAR1D,EACNA,EACAhB,EAAQwI,aAAetB,EACtBjE,EAAK7B,aAAc6C,IACjBjD,EAAMiC,EAAKoM,iBAAkBpL,KAAYjD,EAAI4P,UAC9C5P,EAAI+E,MACJ,MAGJO,GAAO6D,OAAS,SAAU0G,GACzB,OAASA,EAAM,IAAK/L,QAAS0F,GAAYC,KAG1CnE,GAAOtB,MAAQ,SAAUC,GACxB,MAAM,IAAIvG,MAAO,0CAA4CuG,IAO9DqB,GAAOwK,WAAa,SAAUtL,GAC7B,IAAIvC,EACH8N,EAAa,GACbpN,EAAI,EACJ5C,EAAI,EAOL,GAJAgG,GAAgB/G,EAAQgR,iBACxBlK,GAAa9G,EAAQiR,YAAczL,EAAQtG,MAAO,GAClDsG,EAAQ3B,KAAMkE,GAEThB,EAAe,CACnB,MAAU9D,EAAOuC,EAASzE,KACpBkC,IAASuC,EAASzE,KACtB4C,EAAIoN,EAAWvR,KAAMuB,IAGvB,MAAQ4C,IACP6B,EAAQ1B,OAAQiN,EAAYpN,GAAK,GAQnC,OAFAmD,EAAY,KAELtB,GAORgB,EAAUF,GAAOE,QAAU,SAAUvD,GACpC,IAAIpC,EACH8B,EAAM,GACN5B,EAAI,EACJZ,EAAW8C,EAAK9C,SAEjB,GAAMA,GAQC,GAAkB,IAAbA,GAA+B,IAAbA,GAA+B,KAAbA,EAAkB,CAIjE,GAAiC,iBAArB8C,EAAKiO,YAChB,OAAOjO,EAAKiO,YAIZ,IAAMjO,EAAOA,EAAKkO,WAAYlO,EAAMA,EAAOA,EAAK4K,YAC/ClL,GAAO6D,EAASvD,QAGZ,GAAkB,IAAb9C,GAA+B,IAAbA,EAC7B,OAAO8C,EAAKmO,eAnBZ,MAAUvQ,EAAOoC,EAAMlC,KAGtB4B,GAAO6D,EAAS3F,GAqBlB,OAAO8B,IAGR4D,EAAOD,GAAO+K,UAAY,CAGzBrE,YAAa,GAEbsE,aAAcpE,GAEdxB,MAAOxC,EAEPsE,WAAY,GAEZ4B,KAAM,GAENmC,SAAU,CACTC,IAAK,CAAEtG,IAAK,aAAc/H,OAAO,GACjCsO,IAAK,CAAEvG,IAAK,cACZwG,IAAK,CAAExG,IAAK,kBAAmB/H,OAAO,GACtCwO,IAAK,CAAEzG,IAAK,oBAGb0G,UAAW,CACVtI,KAAQ,SAAUoC,GAWjB,OAVAA,EAAO,GAAMA,EAAO,GAAI5G,QAASmF,GAAWC,IAG5CwB,EAAO,IAAQA,EAAO,IAAOA,EAAO,IACnCA,EAAO,IAAO,IAAK5G,QAASmF,GAAWC,IAEpB,OAAfwB,EAAO,KACXA,EAAO,GAAM,IAAMA,EAAO,GAAM,KAG1BA,EAAMxM,MAAO,EAAG,IAGxBsK,MAAS,SAAUkC,GAiClB,OArBAA,EAAO,GAAMA,EAAO,GAAIrF,cAEU,QAA7BqF,EAAO,GAAIxM,MAAO,EAAG,IAGnBwM,EAAO,IACZpF,GAAOtB,MAAO0G,EAAO,IAKtBA,EAAO,KAASA,EAAO,GACtBA,EAAO,IAAQA,EAAO,IAAO,GAC7B,GAAqB,SAAfA,EAAO,IAAiC,QAAfA,EAAO,KACvCA,EAAO,KAAWA,EAAO,GAAMA,EAAO,IAAwB,QAAfA,EAAO,KAG3CA,EAAO,IAClBpF,GAAOtB,MAAO0G,EAAO,IAGfA,GAGRnC,OAAU,SAAUmC,GACnB,IAAImG,EACHC,GAAYpG,EAAO,IAAOA,EAAO,GAElC,OAAKxC,EAAmB,MAAEmD,KAAMX,EAAO,IAC/B,MAIHA,EAAO,GACXA,EAAO,GAAMA,EAAO,IAAOA,EAAO,IAAO,GAG9BoG,GAAY9I,EAAQqD,KAAMyF,KAGnCD,EAASnL,EAAUoL,GAAU,MAG7BD,EAASC,EAASrS,QAAS,IAAKqS,EAAS5P,OAAS2P,GAAWC,EAAS5P,UAGxEwJ,EAAO,GAAMA,EAAO,GAAIxM,MAAO,EAAG2S,GAClCnG,EAAO,GAAMoG,EAAS5S,MAAO,EAAG2S,IAI1BnG,EAAMxM,MAAO,EAAG,MAIzBgQ,OAAQ,CAEP7F,IAAO,SAAU0I,GAChB,IAAI9G,EAAW8G,EAAiBjN,QAASmF,GAAWC,IAAY7D,cAChE,MAA4B,MAArB0L,EACN,WACC,OAAO,GAER,SAAU9O,GACT,OAAOA,EAAKgI,UAAYhI,EAAKgI,SAAS5E,gBAAkB4E,IAI3D7B,MAAS,SAAU0F,GAClB,IAAIkD,EAAUtK,EAAYoH,EAAY,KAEtC,OAAOkD,IACJA,EAAU,IAAIrJ,OAAQ,MAAQL,EAC/B,IAAMwG,EAAY,IAAMxG,EAAa,SAAaZ,EACjDoH,EAAW,SAAU7L,GACpB,OAAO+O,EAAQ3F,KACY,iBAAnBpJ,EAAK6L,WAA0B7L,EAAK6L,WACd,oBAAtB7L,EAAK7B,cACX6B,EAAK7B,aAAc,UACpB,OAKNkI,KAAQ,SAAUrF,EAAMgO,EAAUC,GACjC,OAAO,SAAUjP,GAChB,IAAIkP,EAAS7L,GAAOqK,KAAM1N,EAAMgB,GAEhC,OAAe,MAAVkO,EACgB,OAAbF,GAEFA,IAINE,GAAU,GAIU,MAAbF,EAAmBE,IAAWD,EACvB,OAAbD,EAAoBE,IAAWD,EAClB,OAAbD,EAAoBC,GAAqC,IAA5BC,EAAO1S,QAASyS,GAChC,OAAbD,EAAoBC,IAAoC,EAA3BC,EAAO1S,QAASyS,GAChC,OAAbD,EAAoBC,GAASC,EAAOjT,OAAQgT,EAAMhQ,UAAagQ,EAClD,OAAbD,GAA2F,GAArE,IAAME,EAAOrN,QAAS4D,EAAa,KAAQ,KAAMjJ,QAASyS,GACnE,OAAbD,IAAoBE,IAAWD,GAASC,EAAOjT,MAAO,EAAGgT,EAAMhQ,OAAS,KAAQgQ,EAAQ,QAO3F1I,MAAS,SAAUjJ,EAAM6R,EAAMC,EAAWlP,EAAOE,GAChD,IAAIiP,EAAgC,QAAvB/R,EAAKrB,MAAO,EAAG,GAC3BqT,EAA+B,SAArBhS,EAAKrB,OAAQ,GACvBsT,EAAkB,YAATJ,EAEV,OAAiB,IAAVjP,GAAwB,IAATE,EAGrB,SAAUJ,GACT,QAASA,EAAKzB,YAGf,SAAUyB,EAAMwP,EAAUC,GACzB,IAAI5F,EAAO6F,EAAaC,EAAY/R,EAAMgS,EAAWC,EACpD5H,EAAMoH,IAAWC,EAAU,cAAgB,kBAC3CQ,EAAS9P,EAAKzB,WACdyC,EAAOuO,GAAUvP,EAAKgI,SAAS5E,cAC/B2M,GAAYN,IAAQF,EACpB7E,GAAO,EAER,GAAKoF,EAAS,CAGb,GAAKT,EAAS,CACb,MAAQpH,EAAM,CACbrK,EAAOoC,EACP,MAAUpC,EAAOA,EAAMqK,GACtB,GAAKsH,EACJ3R,EAAKoK,SAAS5E,gBAAkBpC,EACd,IAAlBpD,EAAKV,SAEL,OAAO,EAKT2S,EAAQ5H,EAAe,SAAT3K,IAAoBuS,GAAS,cAE5C,OAAO,EAMR,GAHAA,EAAQ,CAAEP,EAAUQ,EAAO5B,WAAa4B,EAAOE,WAG1CV,GAAWS,EAAW,CAe1BrF,GADAkF,GADA/F,GAHA6F,GAJAC,GADA/R,EAAOkS,GACYpO,KAAe9D,EAAM8D,GAAY,KAI1B9D,EAAKqS,YAC5BN,EAAY/R,EAAKqS,UAAa,KAEZ3S,IAAU,IACZ,KAAQiH,GAAWsF,EAAO,KACzBA,EAAO,GAC3BjM,EAAOgS,GAAaE,EAAO3H,WAAYyH,GAEvC,MAAUhS,IAASgS,GAAahS,GAAQA,EAAMqK,KAG3CyC,EAAOkF,EAAY,IAAOC,EAAM5K,MAGlC,GAAuB,IAAlBrH,EAAKV,YAAoBwN,GAAQ9M,IAASoC,EAAO,CACrD0P,EAAapS,GAAS,CAAEiH,EAASqL,EAAWlF,GAC5C,YAyBF,GAlBKqF,IAaJrF,EADAkF,GADA/F,GAHA6F,GAJAC,GADA/R,EAAOoC,GACY0B,KAAe9D,EAAM8D,GAAY,KAI1B9D,EAAKqS,YAC5BN,EAAY/R,EAAKqS,UAAa,KAEZ3S,IAAU,IACZ,KAAQiH,GAAWsF,EAAO,KAMhC,IAATa,EAGJ,MAAU9M,IAASgS,GAAahS,GAAQA,EAAMqK,KAC3CyC,EAAOkF,EAAY,IAAOC,EAAM5K,MAElC,IAAOsK,EACN3R,EAAKoK,SAAS5E,gBAAkBpC,EACd,IAAlBpD,EAAKV,aACHwN,IAGGqF,KAMJL,GALAC,EAAa/R,EAAM8D,KAChB9D,EAAM8D,GAAY,KAIK9D,EAAKqS,YAC5BN,EAAY/R,EAAKqS,UAAa,KAEpB3S,GAAS,CAAEiH,EAASmG,IAG7B9M,IAASoC,GACb,MASL,OADA0K,GAAQtK,KACQF,GAAWwK,EAAOxK,GAAU,GAAqB,GAAhBwK,EAAOxK,KAK5DoG,OAAU,SAAU4J,EAAQhF,GAM3B,IAAIiF,EACHrR,EAAKwE,EAAKkC,QAAS0K,IAAY5M,EAAK8M,WAAYF,EAAO9M,gBACtDC,GAAOtB,MAAO,uBAAyBmO,GAKzC,OAAKpR,EAAI4C,GACD5C,EAAIoM,GAIK,EAAZpM,EAAGG,QACPkR,EAAO,CAAED,EAAQA,EAAQ,GAAIhF,GACtB5H,EAAK8M,WAAWxT,eAAgBsT,EAAO9M,eAC7C6G,GAAc,SAAU3B,EAAM3F,GAC7B,IAAI0N,EACHC,EAAUxR,EAAIwJ,EAAM4C,GACpBpN,EAAIwS,EAAQrR,OACb,MAAQnB,IAEPwK,EADA+H,EAAM7T,EAAS8L,EAAMgI,EAASxS,OACb6E,EAAS0N,GAAQC,EAASxS,MAG7C,SAAUkC,GACT,OAAOlB,EAAIkB,EAAM,EAAGmQ,KAIhBrR,IAIT0G,QAAS,CAGR+K,IAAOtG,GAAc,SAAUrL,GAK9B,IAAI2N,EAAQ,GACXhK,EAAU,GACViO,EAAU9M,EAAS9E,EAASiD,QAAS8D,EAAO,OAE7C,OAAO6K,EAAS9O,GACfuI,GAAc,SAAU3B,EAAM3F,EAAS6M,EAAUC,GAChD,IAAIzP,EACHyQ,EAAYD,EAASlI,EAAM,KAAMmH,EAAK,IACtC3R,EAAIwK,EAAKrJ,OAGV,MAAQnB,KACAkC,EAAOyQ,EAAW3S,MACxBwK,EAAMxK,KAAS6E,EAAS7E,GAAMkC,MAIjC,SAAUA,EAAMwP,EAAUC,GAMzB,OALAlD,EAAO,GAAMvM,EACbwQ,EAASjE,EAAO,KAAMkD,EAAKlN,GAG3BgK,EAAO,GAAM,MACLhK,EAAQ0C,SAInByL,IAAOzG,GAAc,SAAUrL,GAC9B,OAAO,SAAUoB,GAChB,OAAyC,EAAlCqD,GAAQzE,EAAUoB,GAAOf,UAIlCmF,SAAY6F,GAAc,SAAU/L,GAEnC,OADAA,EAAOA,EAAK2D,QAASmF,GAAWC,IACzB,SAAUjH,GAChB,OAAkE,GAAzDA,EAAKiO,aAAe1K,EAASvD,IAASxD,QAAS0B,MAW1DyS,KAAQ1G,GAAc,SAAU0G,GAO/B,OAJM3K,EAAYoD,KAAMuH,GAAQ,KAC/BtN,GAAOtB,MAAO,qBAAuB4O,GAEtCA,EAAOA,EAAK9O,QAASmF,GAAWC,IAAY7D,cACrC,SAAUpD,GAChB,IAAI4Q,EACJ,GACC,GAAOA,EAAW3M,EACjBjE,EAAK2Q,KACL3Q,EAAK7B,aAAc,aAAgB6B,EAAK7B,aAAc,QAGtD,OADAyS,EAAWA,EAASxN,iBACAuN,GAA2C,IAAnCC,EAASpU,QAASmU,EAAO,YAE3C3Q,EAAOA,EAAKzB,aAAkC,IAAlByB,EAAK9C,UAC7C,OAAO,KAKTkE,OAAU,SAAUpB,GACnB,IAAI6Q,EAAOnV,EAAOoV,UAAYpV,EAAOoV,SAASD,KAC9C,OAAOA,GAAQA,EAAK5U,MAAO,KAAQ+D,EAAKgJ,IAGzC+H,KAAQ,SAAU/Q,GACjB,OAAOA,IAASgE,GAGjBgN,MAAS,SAAUhR,GAClB,OAAOA,IAASzE,EAAS0V,iBACrB1V,EAAS2V,UAAY3V,EAAS2V,gBAC7BlR,EAAK1C,MAAQ0C,EAAKmR,OAASnR,EAAKoR,WAItCC,QAAWtG,IAAsB,GACjChD,SAAYgD,IAAsB,GAElCuG,QAAW,SAAUtR,GAIpB,IAAIgI,EAAWhI,EAAKgI,SAAS5E,cAC7B,MAAsB,UAAb4E,KAA0BhI,EAAKsR,SACxB,WAAbtJ,KAA2BhI,EAAKuR,UAGpCA,SAAY,SAAUvR,GASrB,OALKA,EAAKzB,YAETyB,EAAKzB,WAAWiT,eAGQ,IAAlBxR,EAAKuR,UAIbE,MAAS,SAAUzR,GAMlB,IAAMA,EAAOA,EAAKkO,WAAYlO,EAAMA,EAAOA,EAAK4K,YAC/C,GAAK5K,EAAK9C,SAAW,EACpB,OAAO,EAGT,OAAO,GAGR4S,OAAU,SAAU9P,GACnB,OAAQsD,EAAKkC,QAAiB,MAAGxF,IAIlC0R,OAAU,SAAU1R,GACnB,OAAO4G,EAAQwC,KAAMpJ,EAAKgI,WAG3BuE,MAAS,SAAUvM,GAClB,OAAO2G,EAAQyC,KAAMpJ,EAAKgI,WAG3B2J,OAAU,SAAU3R,GACnB,IAAIgB,EAAOhB,EAAKgI,SAAS5E,cACzB,MAAgB,UAATpC,GAAkC,WAAdhB,EAAK1C,MAA8B,WAAT0D,GAGtD9C,KAAQ,SAAU8B,GACjB,IAAI0N,EACJ,MAAuC,UAAhC1N,EAAKgI,SAAS5E,eACN,SAAdpD,EAAK1C,OAIuC,OAAxCoQ,EAAO1N,EAAK7B,aAAc,UACN,SAAvBuP,EAAKtK,gBAIRlD,MAAS+K,GAAwB,WAChC,MAAO,CAAE,KAGV7K,KAAQ6K,GAAwB,SAAU2G,EAAe3S,GACxD,MAAO,CAAEA,EAAS,KAGnBkB,GAAM8K,GAAwB,SAAU2G,EAAe3S,EAAQiM,GAC9D,MAAO,CAAEA,EAAW,EAAIA,EAAWjM,EAASiM,KAG7C7K,KAAQ4K,GAAwB,SAAUE,EAAclM,GAEvD,IADA,IAAInB,EAAI,EACAA,EAAImB,EAAQnB,GAAK,EACxBqN,EAAa5O,KAAMuB,GAEpB,OAAOqN,IAGR3K,IAAOyK,GAAwB,SAAUE,EAAclM,GAEtD,IADA,IAAInB,EAAI,EACAA,EAAImB,EAAQnB,GAAK,EACxBqN,EAAa5O,KAAMuB,GAEpB,OAAOqN,IAGR0G,GAAM5G,GAAwB,SAAUE,EAAclM,EAAQiM,GAM7D,IALA,IAAIpN,EAAIoN,EAAW,EAClBA,EAAWjM,EACAA,EAAXiM,EACCjM,EACAiM,EACa,KAALpN,GACTqN,EAAa5O,KAAMuB,GAEpB,OAAOqN,IAGR2G,GAAM7G,GAAwB,SAAUE,EAAclM,EAAQiM,GAE7D,IADA,IAAIpN,EAAIoN,EAAW,EAAIA,EAAWjM,EAASiM,IACjCpN,EAAImB,GACbkM,EAAa5O,KAAMuB,GAEpB,OAAOqN,OAKL3F,QAAe,IAAIlC,EAAKkC,QAAc,GAGhC,CAAEuM,OAAO,EAAMC,UAAU,EAAMC,MAAM,EAAMC,UAAU,EAAMC,OAAO,GAC5E7O,EAAKkC,QAAS1H,GAAM+M,GAAmB/M,GAExC,IAAMA,IAAK,CAAEsU,QAAQ,EAAMC,OAAO,GACjC/O,EAAKkC,QAAS1H,GAAMgN,GAAoBhN,GAIzC,SAASsS,MA0ET,SAAS7G,GAAY+I,GAIpB,IAHA,IAAIxU,EAAI,EACP2C,EAAM6R,EAAOrT,OACbL,EAAW,GACJd,EAAI2C,EAAK3C,IAChBc,GAAY0T,EAAQxU,GAAIgF,MAEzB,OAAOlE,EAGR,SAASkJ,GAAe0I,EAAS+B,EAAYC,GAC5C,IAAIvK,EAAMsK,EAAWtK,IACpBwK,EAAOF,EAAWrK,KAClB4B,EAAM2I,GAAQxK,EACdyK,EAAmBF,GAAgB,eAAR1I,EAC3B6I,EAAWnO,IAEZ,OAAO+N,EAAWrS,MAGjB,SAAUF,EAAMnB,EAAS4Q,GACxB,MAAUzP,EAAOA,EAAMiI,GACtB,GAAuB,IAAlBjI,EAAK9C,UAAkBwV,EAC3B,OAAOlC,EAASxQ,EAAMnB,EAAS4Q,GAGjC,OAAO,GAIR,SAAUzP,EAAMnB,EAAS4Q,GACxB,IAAImD,EAAUlD,EAAaC,EAC1BkD,EAAW,CAAEtO,EAASoO,GAGvB,GAAKlD,GACJ,MAAUzP,EAAOA,EAAMiI,GACtB,IAAuB,IAAlBjI,EAAK9C,UAAkBwV,IACtBlC,EAASxQ,EAAMnB,EAAS4Q,GAC5B,OAAO,OAKV,MAAUzP,EAAOA,EAAMiI,GACtB,GAAuB,IAAlBjI,EAAK9C,UAAkBwV,EAQ3B,GAHAhD,GAJAC,EAAa3P,EAAM0B,KAAe1B,EAAM0B,GAAY,KAI1B1B,EAAKiQ,YAC5BN,EAAY3P,EAAKiQ,UAAa,IAE5BwC,GAAQA,IAASzS,EAAKgI,SAAS5E,cACnCpD,EAAOA,EAAMiI,IAASjI,MAChB,CAAA,IAAO4S,EAAWlD,EAAa5F,KACrC8I,EAAU,KAAQrO,GAAWqO,EAAU,KAAQD,EAG/C,OAASE,EAAU,GAAMD,EAAU,GAOnC,IAHAlD,EAAa5F,GAAQ+I,GAGJ,GAAMrC,EAASxQ,EAAMnB,EAAS4Q,GAC9C,OAAO,EAMZ,OAAO,GAIV,SAASqD,GAAgBC,GACxB,OAAyB,EAAlBA,EAAS9T,OACf,SAAUe,EAAMnB,EAAS4Q,GACxB,IAAI3R,EAAIiV,EAAS9T,OACjB,MAAQnB,IACP,IAAMiV,EAAUjV,GAAKkC,EAAMnB,EAAS4Q,GACnC,OAAO,EAGT,OAAO,GAERsD,EAAU,GAYZ,SAASC,GAAUvC,EAAW1Q,EAAKkM,EAAQpN,EAAS4Q,GAOnD,IANA,IAAIzP,EACHiT,EAAe,GACfnV,EAAI,EACJ2C,EAAMgQ,EAAUxR,OAChBiU,EAAgB,MAAPnT,EAEFjC,EAAI2C,EAAK3C,KACTkC,EAAOyQ,EAAW3S,MAClBmO,IAAUA,EAAQjM,EAAMnB,EAAS4Q,KACtCwD,EAAa1W,KAAMyD,GACdkT,GACJnT,EAAIxD,KAAMuB,KAMd,OAAOmV,EAGR,SAASE,GAAYxE,EAAW/P,EAAU4R,EAAS4C,EAAYC,EAAYC,GAO1E,OANKF,IAAeA,EAAY1R,KAC/B0R,EAAaD,GAAYC,IAErBC,IAAeA,EAAY3R,KAC/B2R,EAAaF,GAAYE,EAAYC,IAE/BrJ,GAAc,SAAU3B,EAAM/F,EAAS1D,EAAS4Q,GACtD,IAAI8D,EAAMzV,EAAGkC,EACZwT,EAAS,GACTC,EAAU,GACVC,EAAcnR,EAAQtD,OAGtBQ,EAAQ6I,GA5CX,SAA2B1J,EAAU+U,EAAUpR,GAG9C,IAFA,IAAIzE,EAAI,EACP2C,EAAMkT,EAAS1U,OACRnB,EAAI2C,EAAK3C,IAChBuF,GAAQzE,EAAU+U,EAAU7V,GAAKyE,GAElC,OAAOA,EAsCWqR,CACfhV,GAAY,IACZC,EAAQ3B,SAAW,CAAE2B,GAAYA,EACjC,IAIDgV,GAAYlF,IAAerG,GAAS1J,EAEnCa,EADAuT,GAAUvT,EAAO+T,EAAQ7E,EAAW9P,EAAS4Q,GAG9CqE,EAAatD,EAGZ6C,IAAgB/K,EAAOqG,EAAY+E,GAAeN,GAGjD,GAGA7Q,EACDsR,EAQF,GALKrD,GACJA,EAASqD,EAAWC,EAAYjV,EAAS4Q,GAIrC2D,EAAa,CACjBG,EAAOP,GAAUc,EAAYL,GAC7BL,EAAYG,EAAM,GAAI1U,EAAS4Q,GAG/B3R,EAAIyV,EAAKtU,OACT,MAAQnB,KACAkC,EAAOuT,EAAMzV,MACnBgW,EAAYL,EAAS3V,MAAW+V,EAAWJ,EAAS3V,IAAQkC,IAK/D,GAAKsI,GACJ,GAAK+K,GAAc1E,EAAY,CAC9B,GAAK0E,EAAa,CAGjBE,EAAO,GACPzV,EAAIgW,EAAW7U,OACf,MAAQnB,KACAkC,EAAO8T,EAAYhW,KAGzByV,EAAKhX,KAAQsX,EAAW/V,GAAMkC,GAGhCqT,EAAY,KAAQS,EAAa,GAAMP,EAAM9D,GAI9C3R,EAAIgW,EAAW7U,OACf,MAAQnB,KACAkC,EAAO8T,EAAYhW,MACsC,GAA7DyV,EAAOF,EAAa7W,EAAS8L,EAAMtI,GAASwT,EAAQ1V,MAEtDwK,EAAMiL,KAAYhR,EAASgR,GAASvT,UAOvC8T,EAAad,GACZc,IAAevR,EACduR,EAAWjT,OAAQ6S,EAAaI,EAAW7U,QAC3C6U,GAEGT,EACJA,EAAY,KAAM9Q,EAASuR,EAAYrE,GAEvClT,EAAKD,MAAOiG,EAASuR,KAMzB,SAASC,GAAmBzB,GAyB3B,IAxBA,IAAI0B,EAAcxD,EAAS9P,EAC1BD,EAAM6R,EAAOrT,OACbgV,EAAkB3Q,EAAKgL,SAAUgE,EAAQ,GAAIhV,MAC7C4W,EAAmBD,GAAmB3Q,EAAKgL,SAAU,KACrDxQ,EAAImW,EAAkB,EAAI,EAG1BE,EAAerM,GAAe,SAAU9H,GACvC,OAAOA,IAASgU,GACdE,GAAkB,GACrBE,EAAkBtM,GAAe,SAAU9H,GAC1C,OAAwC,EAAjCxD,EAASwX,EAAchU,IAC5BkU,GAAkB,GACrBnB,EAAW,CAAE,SAAU/S,EAAMnB,EAAS4Q,GACrC,IAAI/P,GAASuU,IAAqBxE,GAAO5Q,IAAY+E,MAClDoQ,EAAenV,GAAU3B,SAC1BiX,EAAcnU,EAAMnB,EAAS4Q,GAC7B2E,EAAiBpU,EAAMnB,EAAS4Q,IAIlC,OADAuE,EAAe,KACRtU,IAGD5B,EAAI2C,EAAK3C,IAChB,GAAO0S,EAAUlN,EAAKgL,SAAUgE,EAAQxU,GAAIR,MAC3CyV,EAAW,CAAEjL,GAAegL,GAAgBC,GAAYvC,QAClD,CAIN,IAHAA,EAAUlN,EAAK2I,OAAQqG,EAAQxU,GAAIR,MAAOhB,MAAO,KAAMgW,EAAQxU,GAAI6E,UAGrDjB,GAAY,CAIzB,IADAhB,IAAM5C,EACE4C,EAAID,EAAKC,IAChB,GAAK4C,EAAKgL,SAAUgE,EAAQ5R,GAAIpD,MAC/B,MAGF,OAAO6V,GACF,EAAJrV,GAASgV,GAAgBC,GACrB,EAAJjV,GAASyL,GAGT+I,EACErW,MAAO,EAAG6B,EAAI,GACdzB,OAAQ,CAAEyG,MAAgC,MAAzBwP,EAAQxU,EAAI,GAAIR,KAAe,IAAM,MACtDuE,QAAS8D,EAAO,MAClB6K,EACA1S,EAAI4C,GAAKqT,GAAmBzB,EAAOrW,MAAO6B,EAAG4C,IAC7CA,EAAID,GAAOsT,GAAqBzB,EAASA,EAAOrW,MAAOyE,IACvDA,EAAID,GAAO8I,GAAY+I,IAGzBS,EAASxW,KAAMiU,GAIjB,OAAOsC,GAAgBC,GAoTxB,OAtpBA3C,GAAWlR,UAAYoE,EAAK+Q,QAAU/Q,EAAKkC,QAC3ClC,EAAK8M,WAAa,IAAIA,GAEtB3M,EAAWJ,GAAOI,SAAW,SAAU7E,EAAU0V,GAChD,IAAIhE,EAAS7H,EAAO6J,EAAQhV,EAC3BiX,EAAO7L,EAAQ8L,EACfC,EAAS9P,EAAY/F,EAAW,KAEjC,GAAK6V,EACJ,OAAOH,EAAY,EAAIG,EAAOxY,MAAO,GAGtCsY,EAAQ3V,EACR8J,EAAS,GACT8L,EAAalR,EAAKqL,UAElB,MAAQ4F,EAAQ,CA2Bf,IAAMjX,KAxBAgT,KAAa7H,EAAQ7C,EAAOkD,KAAMyL,MAClC9L,IAGJ8L,EAAQA,EAAMtY,MAAOwM,EAAO,GAAIxJ,SAAYsV,GAE7C7L,EAAOnM,KAAQ+V,EAAS,KAGzBhC,GAAU,GAGH7H,EAAQ5C,EAAaiD,KAAMyL,MACjCjE,EAAU7H,EAAMuB,QAChBsI,EAAO/V,KAAM,CACZuG,MAAOwN,EAGPhT,KAAMmL,EAAO,GAAI5G,QAAS8D,EAAO,OAElC4O,EAAQA,EAAMtY,MAAOqU,EAAQrR,SAIhBqE,EAAK2I,SACXxD,EAAQxC,EAAW3I,GAAOwL,KAAMyL,KAAgBC,EAAYlX,MAChEmL,EAAQ+L,EAAYlX,GAAQmL,MAC9B6H,EAAU7H,EAAMuB,QAChBsI,EAAO/V,KAAM,CACZuG,MAAOwN,EACPhT,KAAMA,EACNqF,QAAS8F,IAEV8L,EAAQA,EAAMtY,MAAOqU,EAAQrR,SAI/B,IAAMqR,EACL,MAOF,OAAOgE,EACNC,EAAMtV,OACNsV,EACClR,GAAOtB,MAAOnD,GAGd+F,EAAY/F,EAAU8J,GAASzM,MAAO,IA4ZzCyH,EAAUL,GAAOK,QAAU,SAAU9E,EAAU6J,GAC9C,IAAI3K,EA9H8B4W,EAAiBC,EAC/CC,EACHC,EACAC,EA4HAH,EAAc,GACdD,EAAkB,GAClBD,EAAS7P,EAAehG,EAAW,KAEpC,IAAM6V,EAAS,CAGRhM,IACLA,EAAQhF,EAAU7E,IAEnBd,EAAI2K,EAAMxJ,OACV,MAAQnB,KACP2W,EAASV,GAAmBtL,EAAO3K,KACtB4D,GACZiT,EAAYpY,KAAMkY,GAElBC,EAAgBnY,KAAMkY,IAKxBA,EAAS7P,EACRhG,GArJgC8V,EAsJNA,EArJxBE,EAA6B,GADkBD,EAsJNA,GArJrB1V,OACvB4V,EAAqC,EAAzBH,EAAgBzV,OAC5B6V,EAAe,SAAUxM,EAAMzJ,EAAS4Q,EAAKlN,EAASwS,GACrD,IAAI/U,EAAMU,EAAG8P,EACZwE,EAAe,EACflX,EAAI,IACJ2S,EAAYnI,GAAQ,GACpB2M,EAAa,GACbC,EAAgBtR,EAGhBnE,EAAQ6I,GAAQuM,GAAavR,EAAK6I,KAAY,IAAG,IAAK4I,GAGtDI,EAAkB5Q,GAA4B,MAAjB2Q,EAAwB,EAAIvT,KAAKC,UAAY,GAC1EnB,EAAMhB,EAAMR,OAcb,IAZK8V,IAMJnR,EAAmB/E,GAAWtD,GAAYsD,GAAWkW,GAM9CjX,IAAM2C,GAAgC,OAAvBT,EAAOP,EAAO3B,IAAeA,IAAM,CACzD,GAAK+W,GAAa7U,EAAO,CACxBU,EAAI,EAME7B,GAAWmB,EAAK6I,eAAiBtN,IACtCwI,EAAa/D,GACbyP,GAAOxL,GAER,MAAUuM,EAAUkE,EAAiBhU,KACpC,GAAK8P,EAASxQ,EAAMnB,GAAWtD,EAAUkU,GAAQ,CAChDlN,EAAQhG,KAAMyD,GACd,MAGG+U,IACJxQ,EAAU4Q,GAKPP,KAGG5U,GAAQwQ,GAAWxQ,IACzBgV,IAII1M,GACJmI,EAAUlU,KAAMyD,IAgBnB,GATAgV,GAAgBlX,EASX8W,GAAS9W,IAAMkX,EAAe,CAClCtU,EAAI,EACJ,MAAU8P,EAAUmE,EAAajU,KAChC8P,EAASC,EAAWwE,EAAYpW,EAAS4Q,GAG1C,GAAKnH,EAAO,CAGX,GAAoB,EAAf0M,EACJ,MAAQlX,IACC2S,EAAW3S,IAAOmX,EAAYnX,KACrCmX,EAAYnX,GAAMmH,EAAI7I,KAAMmG,IAM/B0S,EAAajC,GAAUiC,GAIxB1Y,EAAKD,MAAOiG,EAAS0S,GAGhBF,IAAczM,GAA4B,EAApB2M,EAAWhW,QACG,EAAtC+V,EAAeL,EAAY1V,QAE7BoE,GAAOwK,WAAYtL,GAUrB,OALKwS,IACJxQ,EAAU4Q,EACVvR,EAAmBsR,GAGbzE,GAGFmE,EACN3K,GAAc6K,GACdA,KAgCOlW,SAAWA,EAEnB,OAAO6V,GAYR9Q,EAASN,GAAOM,OAAS,SAAU/E,EAAUC,EAAS0D,EAAS+F,GAC9D,IAAIxK,EAAGwU,EAAQ8C,EAAO9X,EAAM6O,EAC3BkJ,EAA+B,mBAAbzW,GAA2BA,EAC7C6J,GAASH,GAAQ7E,EAAY7E,EAAWyW,EAASzW,UAAYA,GAM9D,GAJA2D,EAAUA,GAAW,GAIC,IAAjBkG,EAAMxJ,OAAe,CAIzB,GAAqB,GADrBqT,EAAS7J,EAAO,GAAMA,EAAO,GAAIxM,MAAO,IAC5BgD,QAA+C,QAA/BmW,EAAQ9C,EAAQ,IAAMhV,MAC5B,IAArBuB,EAAQ3B,UAAkB+G,GAAkBX,EAAKgL,SAAUgE,EAAQ,GAAIhV,MAAS,CAIhF,KAFAuB,GAAYyE,EAAK6I,KAAW,GAAGiJ,EAAMzS,QAAS,GAC5Cd,QAASmF,GAAWC,IAAapI,IAAa,IAAM,IAErD,OAAO0D,EAGI8S,IACXxW,EAAUA,EAAQN,YAGnBK,EAAWA,EAAS3C,MAAOqW,EAAOtI,QAAQlH,MAAM7D,QAIjDnB,EAAImI,EAA0B,aAAEmD,KAAMxK,GAAa,EAAI0T,EAAOrT,OAC9D,MAAQnB,IAAM,CAIb,GAHAsX,EAAQ9C,EAAQxU,GAGXwF,EAAKgL,SAAYhR,EAAO8X,EAAM9X,MAClC,MAED,IAAO6O,EAAO7I,EAAK6I,KAAM7O,MAGjBgL,EAAO6D,EACbiJ,EAAMzS,QAAS,GAAId,QAASmF,GAAWC,IACvCF,GAASqC,KAAMkJ,EAAQ,GAAIhV,OAAU+L,GAAaxK,EAAQN,aACzDM,IACI,CAKL,GAFAyT,EAAOzR,OAAQ/C,EAAG,KAClBc,EAAW0J,EAAKrJ,QAAUsK,GAAY+I,IAGrC,OADA/V,EAAKD,MAAOiG,EAAS+F,GACd/F,EAGR,QAeJ,OAPE8S,GAAY3R,EAAS9E,EAAU6J,IAChCH,EACAzJ,GACCoF,EACD1B,GACC1D,GAAWkI,GAASqC,KAAMxK,IAAcyK,GAAaxK,EAAQN,aAAgBM,GAExE0D,GAMRxF,EAAQiR,WAAatM,EAAQwB,MAAO,IAAKtC,KAAMkE,GAAY0E,KAAM,MAAS9H,EAI1E3E,EAAQgR,mBAAqBjK,EAG7BC,IAIAhH,EAAQoQ,aAAejD,GAAQ,SAAUC,GAGxC,OAA4E,EAArEA,EAAG4C,wBAAyBxR,EAAS0C,cAAe,eAMtDiM,GAAQ,SAAUC,GAEvB,OADAA,EAAGqC,UAAY,mBACiC,MAAzCrC,EAAG+D,WAAW/P,aAAc,WAEnCiM,GAAW,yBAA0B,SAAUpK,EAAMgB,EAAMwC,GAC1D,IAAMA,EACL,OAAOxD,EAAK7B,aAAc6C,EAA6B,SAAvBA,EAAKoC,cAA2B,EAAI,KAOjErG,EAAQwI,YAAe2E,GAAQ,SAAUC,GAG9C,OAFAA,EAAGqC,UAAY,WACfrC,EAAG+D,WAAW9P,aAAc,QAAS,IACY,KAA1C+L,EAAG+D,WAAW/P,aAAc,YAEnCiM,GAAW,QAAS,SAAUpK,EAAMsV,EAAO9R,GAC1C,IAAMA,GAAyC,UAAhCxD,EAAKgI,SAAS5E,cAC5B,OAAOpD,EAAKuV,eAOTrL,GAAQ,SAAUC,GACvB,OAAwC,MAAjCA,EAAGhM,aAAc,eAExBiM,GAAWhF,EAAU,SAAUpF,EAAMgB,EAAMwC,GAC1C,IAAIzF,EACJ,IAAMyF,EACL,OAAwB,IAAjBxD,EAAMgB,GAAkBA,EAAKoC,eACjCrF,EAAMiC,EAAKoM,iBAAkBpL,KAAYjD,EAAI4P,UAC9C5P,EAAI+E,MACJ,OAKEO,GA14EP,CA44EK3H,GAILiD,EAAOwN,KAAO9I,EACd1E,EAAO6O,KAAOnK,EAAO+K,UAGrBzP,EAAO6O,KAAM,KAAQ7O,EAAO6O,KAAKhI,QACjC7G,EAAOkP,WAAalP,EAAO6W,OAASnS,EAAOwK,WAC3ClP,EAAOT,KAAOmF,EAAOE,QACrB5E,EAAO8W,SAAWpS,EAAOG,MACzB7E,EAAOyF,SAAWf,EAAOe,SACzBzF,EAAO+W,eAAiBrS,EAAO6D,OAK/B,IAAIe,EAAM,SAAUjI,EAAMiI,EAAK0N,GAC9B,IAAIrF,EAAU,GACbsF,OAAqBnU,IAAVkU,EAEZ,OAAU3V,EAAOA,EAAMiI,KAA6B,IAAlBjI,EAAK9C,SACtC,GAAuB,IAAlB8C,EAAK9C,SAAiB,CAC1B,GAAK0Y,GAAYjX,EAAQqB,GAAO6V,GAAIF,GACnC,MAEDrF,EAAQ/T,KAAMyD,GAGhB,OAAOsQ,GAIJwF,EAAW,SAAUC,EAAG/V,GAG3B,IAFA,IAAIsQ,EAAU,GAENyF,EAAGA,EAAIA,EAAEnL,YACI,IAAfmL,EAAE7Y,UAAkB6Y,IAAM/V,GAC9BsQ,EAAQ/T,KAAMwZ,GAIhB,OAAOzF,GAIJ0F,EAAgBrX,EAAO6O,KAAK/E,MAAMhC,aAItC,SAASuB,EAAUhI,EAAMgB,GAExB,OAAOhB,EAAKgI,UAAYhI,EAAKgI,SAAS5E,gBAAkBpC,EAAKoC,cAG9D,IAAI6S,EAAa,kEAKjB,SAASC,EAAQzI,EAAU0I,EAAW5F,GACrC,OAAKvT,EAAYmZ,GACTxX,EAAO2B,KAAMmN,EAAU,SAAUzN,EAAMlC,GAC7C,QAASqY,EAAU/Z,KAAM4D,EAAMlC,EAAGkC,KAAWuQ,IAK1C4F,EAAUjZ,SACPyB,EAAO2B,KAAMmN,EAAU,SAAUzN,GACvC,OAASA,IAASmW,IAAgB5F,IAKV,iBAAd4F,EACJxX,EAAO2B,KAAMmN,EAAU,SAAUzN,GACvC,OAA4C,EAAnCxD,EAAQJ,KAAM+Z,EAAWnW,KAAkBuQ,IAK/C5R,EAAOsN,OAAQkK,EAAW1I,EAAU8C,GAG5C5R,EAAOsN,OAAS,SAAUuB,EAAM/N,EAAO8Q,GACtC,IAAIvQ,EAAOP,EAAO,GAMlB,OAJK8Q,IACJ/C,EAAO,QAAUA,EAAO,KAGH,IAAjB/N,EAAMR,QAAkC,IAAlBe,EAAK9C,SACxByB,EAAOwN,KAAKM,gBAAiBzM,EAAMwN,GAAS,CAAExN,GAAS,GAGxDrB,EAAOwN,KAAKxJ,QAAS6K,EAAM7O,EAAO2B,KAAMb,EAAO,SAAUO,GAC/D,OAAyB,IAAlBA,EAAK9C,aAIdyB,EAAOG,GAAGgC,OAAQ,CACjBqL,KAAM,SAAUvN,GACf,IAAId,EAAG4B,EACNe,EAAM9E,KAAKsD,OACXmX,EAAOza,KAER,GAAyB,iBAAbiD,EACX,OAAOjD,KAAK6D,UAAWb,EAAQC,GAAWqN,OAAQ,WACjD,IAAMnO,EAAI,EAAGA,EAAI2C,EAAK3C,IACrB,GAAKa,EAAOyF,SAAUgS,EAAMtY,GAAKnC,MAChC,OAAO,KAQX,IAFA+D,EAAM/D,KAAK6D,UAAW,IAEhB1B,EAAI,EAAGA,EAAI2C,EAAK3C,IACrBa,EAAOwN,KAAMvN,EAAUwX,EAAMtY,GAAK4B,GAGnC,OAAa,EAANe,EAAU9B,EAAOkP,WAAYnO,GAAQA,GAE7CuM,OAAQ,SAAUrN,GACjB,OAAOjD,KAAK6D,UAAW0W,EAAQva,KAAMiD,GAAY,IAAI,KAEtD2R,IAAK,SAAU3R,GACd,OAAOjD,KAAK6D,UAAW0W,EAAQva,KAAMiD,GAAY,IAAI,KAEtDiX,GAAI,SAAUjX,GACb,QAASsX,EACRva,KAIoB,iBAAbiD,GAAyBoX,EAAc5M,KAAMxK,GACnDD,EAAQC,GACRA,GAAY,IACb,GACCK,UASJ,IAAIoX,EAMHvP,EAAa,uCAENnI,EAAOG,GAAGC,KAAO,SAAUH,EAAUC,EAASkS,GACpD,IAAItI,EAAOzI,EAGX,IAAMpB,EACL,OAAOjD,KAQR,GAHAoV,EAAOA,GAAQsF,EAGU,iBAAbzX,EAAwB,CAanC,KAPC6J,EALsB,MAAlB7J,EAAU,IACsB,MAApCA,EAAUA,EAASK,OAAS,IACT,GAAnBL,EAASK,OAGD,CAAE,KAAML,EAAU,MAGlBkI,EAAWgC,KAAMlK,MAIV6J,EAAO,IAAQ5J,EA6CxB,OAAMA,GAAWA,EAAQM,QACtBN,GAAWkS,GAAO5E,KAAMvN,GAK1BjD,KAAKyD,YAAaP,GAAUsN,KAAMvN,GAhDzC,GAAK6J,EAAO,GAAM,CAYjB,GAXA5J,EAAUA,aAAmBF,EAASE,EAAS,GAAMA,EAIrDF,EAAOgB,MAAOhE,KAAMgD,EAAO2X,UAC1B7N,EAAO,GACP5J,GAAWA,EAAQ3B,SAAW2B,EAAQgK,eAAiBhK,EAAUtD,GACjE,IAII0a,EAAW7M,KAAMX,EAAO,KAAS9J,EAAO2C,cAAezC,GAC3D,IAAM4J,KAAS5J,EAGT7B,EAAYrB,KAAM8M,IACtB9M,KAAM8M,GAAS5J,EAAS4J,IAIxB9M,KAAK+R,KAAMjF,EAAO5J,EAAS4J,IAK9B,OAAO9M,KAYP,OARAqE,EAAOzE,EAASwN,eAAgBN,EAAO,OAKtC9M,KAAM,GAAMqE,EACZrE,KAAKsD,OAAS,GAERtD,KAcH,OAAKiD,EAAS1B,UACpBvB,KAAM,GAAMiD,EACZjD,KAAKsD,OAAS,EACPtD,MAIIqB,EAAY4B,QACD6C,IAAfsP,EAAKwF,MACXxF,EAAKwF,MAAO3X,GAGZA,EAAUD,GAGLA,EAAO2D,UAAW1D,EAAUjD,QAIhCuD,UAAYP,EAAOG,GAGxBuX,EAAa1X,EAAQpD,GAGrB,IAAIib,EAAe,iCAGlBC,EAAmB,CAClBC,UAAU,EACVC,UAAU,EACVzO,MAAM,EACN0O,MAAM,GAoFR,SAASC,EAASpM,EAAKxC,GACtB,OAAUwC,EAAMA,EAAKxC,KAA4B,IAAjBwC,EAAIvN,UACpC,OAAOuN,EAnFR9L,EAAOG,GAAGgC,OAAQ,CACjB4P,IAAK,SAAUtP,GACd,IAAI0V,EAAUnY,EAAQyC,EAAQzF,MAC7Bob,EAAID,EAAQ7X,OAEb,OAAOtD,KAAKsQ,OAAQ,WAEnB,IADA,IAAInO,EAAI,EACAA,EAAIiZ,EAAGjZ,IACd,GAAKa,EAAOyF,SAAUzI,KAAMmb,EAAShZ,IACpC,OAAO,KAMXkZ,QAAS,SAAU5I,EAAWvP,GAC7B,IAAI4L,EACH3M,EAAI,EACJiZ,EAAIpb,KAAKsD,OACTqR,EAAU,GACVwG,EAA+B,iBAAd1I,GAA0BzP,EAAQyP,GAGpD,IAAM4H,EAAc5M,KAAMgF,GACzB,KAAQtQ,EAAIiZ,EAAGjZ,IACd,IAAM2M,EAAM9O,KAAMmC,GAAK2M,GAAOA,IAAQ5L,EAAS4L,EAAMA,EAAIlM,WAGxD,GAAKkM,EAAIvN,SAAW,KAAQ4Z,GACH,EAAxBA,EAAQG,MAAOxM,GAGE,IAAjBA,EAAIvN,UACHyB,EAAOwN,KAAKM,gBAAiBhC,EAAK2D,IAAgB,CAEnDkC,EAAQ/T,KAAMkO,GACd,MAMJ,OAAO9O,KAAK6D,UAA4B,EAAjB8Q,EAAQrR,OAAaN,EAAOkP,WAAYyC,GAAYA,IAI5E2G,MAAO,SAAUjX,GAGhB,OAAMA,EAKe,iBAATA,EACJxD,EAAQJ,KAAMuC,EAAQqB,GAAQrE,KAAM,IAIrCa,EAAQJ,KAAMT,KAGpBqE,EAAKb,OAASa,EAAM,GAAMA,GAZjBrE,KAAM,IAAOA,KAAM,GAAI4C,WAAe5C,KAAKuE,QAAQgX,UAAUjY,QAAU,GAgBlFkY,IAAK,SAAUvY,EAAUC,GACxB,OAAOlD,KAAK6D,UACXb,EAAOkP,WACNlP,EAAOgB,MAAOhE,KAAK2D,MAAOX,EAAQC,EAAUC,OAK/CuY,QAAS,SAAUxY,GAClB,OAAOjD,KAAKwb,IAAiB,MAAZvY,EAChBjD,KAAKiE,WAAajE,KAAKiE,WAAWqM,OAAQrN,OAU7CD,EAAOkB,KAAM,CACZiQ,OAAQ,SAAU9P,GACjB,IAAI8P,EAAS9P,EAAKzB,WAClB,OAAOuR,GAA8B,KAApBA,EAAO5S,SAAkB4S,EAAS,MAEpDuH,QAAS,SAAUrX,GAClB,OAAOiI,EAAKjI,EAAM,eAEnBsX,aAAc,SAAUtX,EAAMmD,EAAIwS,GACjC,OAAO1N,EAAKjI,EAAM,aAAc2V,IAEjCzN,KAAM,SAAUlI,GACf,OAAO6W,EAAS7W,EAAM,gBAEvB4W,KAAM,SAAU5W,GACf,OAAO6W,EAAS7W,EAAM,oBAEvBuX,QAAS,SAAUvX,GAClB,OAAOiI,EAAKjI,EAAM,gBAEnBkX,QAAS,SAAUlX,GAClB,OAAOiI,EAAKjI,EAAM,oBAEnBwX,UAAW,SAAUxX,EAAMmD,EAAIwS,GAC9B,OAAO1N,EAAKjI,EAAM,cAAe2V,IAElC8B,UAAW,SAAUzX,EAAMmD,EAAIwS,GAC9B,OAAO1N,EAAKjI,EAAM,kBAAmB2V,IAEtCG,SAAU,SAAU9V,GACnB,OAAO8V,GAAY9V,EAAKzB,YAAc,IAAK2P,WAAYlO,IAExD0W,SAAU,SAAU1W,GACnB,OAAO8V,EAAU9V,EAAKkO,aAEvByI,SAAU,SAAU3W,GACnB,OAA6B,MAAxBA,EAAK0X,iBAKT5b,EAAUkE,EAAK0X,iBAER1X,EAAK0X,iBAMR1P,EAAUhI,EAAM,cACpBA,EAAOA,EAAK2X,SAAW3X,GAGjBrB,EAAOgB,MAAO,GAAIK,EAAKmI,eAE7B,SAAUnH,EAAMlC,GAClBH,EAAOG,GAAIkC,GAAS,SAAU2U,EAAO/W,GACpC,IAAI0R,EAAU3R,EAAOoB,IAAKpE,KAAMmD,EAAI6W,GAuBpC,MArB0B,UAArB3U,EAAK/E,OAAQ,KACjB2C,EAAW+W,GAGP/W,GAAgC,iBAAbA,IACvB0R,EAAU3R,EAAOsN,OAAQrN,EAAU0R,IAGjB,EAAd3U,KAAKsD,SAGHwX,EAAkBzV,IACvBrC,EAAOkP,WAAYyC,GAIfkG,EAAapN,KAAMpI,IACvBsP,EAAQsH,WAIHjc,KAAK6D,UAAW8Q,MAGzB,IAAIuH,EAAgB,oBAsOpB,SAASC,EAAUC,GAClB,OAAOA,EAER,SAASC,EAASC,GACjB,MAAMA,EAGP,SAASC,EAAYpV,EAAOqV,EAASC,EAAQC,GAC5C,IAAIC,EAEJ,IAGMxV,GAAS9F,EAAcsb,EAASxV,EAAMyV,SAC1CD,EAAOlc,KAAM0G,GAAQ0B,KAAM2T,GAAUK,KAAMJ,GAGhCtV,GAAS9F,EAAcsb,EAASxV,EAAM2V,MACjDH,EAAOlc,KAAM0G,EAAOqV,EAASC,GAQ7BD,EAAQ7b,WAAOmF,EAAW,CAAEqB,GAAQ7G,MAAOoc,IAM3C,MAAQvV,GAITsV,EAAO9b,WAAOmF,EAAW,CAAEqB,KAvO7BnE,EAAO+Z,UAAY,SAAU3X,GA9B7B,IAAwBA,EACnB4X,EAiCJ5X,EAA6B,iBAAZA,GAlCMA,EAmCPA,EAlCZ4X,EAAS,GACbha,EAAOkB,KAAMkB,EAAQ0H,MAAOoP,IAAmB,GAAI,SAAUe,EAAGC,GAC/DF,EAAQE,IAAS,IAEXF,GA+BNha,EAAOmC,OAAQ,GAAIC,GAEpB,IACC+X,EAGAC,EAGAC,EAGAC,EAGA9T,EAAO,GAGP+T,EAAQ,GAGRC,GAAe,EAGfC,EAAO,WAQN,IALAH,EAASA,GAAUlY,EAAQsY,KAI3BL,EAAQF,GAAS,EACTI,EAAMja,OAAQka,GAAe,EAAI,CACxCJ,EAASG,EAAMlP,QACf,QAAUmP,EAAchU,EAAKlG,QAGmC,IAA1DkG,EAAMgU,GAAc7c,MAAOyc,EAAQ,GAAKA,EAAQ,KACpDhY,EAAQuY,cAGRH,EAAchU,EAAKlG,OACnB8Z,GAAS,GAMNhY,EAAQgY,SACbA,GAAS,GAGVD,GAAS,EAGJG,IAIH9T,EADI4T,EACG,GAIA,KAMV3C,EAAO,CAGNe,IAAK,WA2BJ,OA1BKhS,IAGC4T,IAAWD,IACfK,EAAchU,EAAKlG,OAAS,EAC5Bia,EAAM3c,KAAMwc,IAGb,SAAW5B,EAAKhH,GACfxR,EAAOkB,KAAMsQ,EAAM,SAAUyI,EAAG/V,GAC1B7F,EAAY6F,GACV9B,EAAQyU,QAAWY,EAAK1F,IAAK7N,IAClCsC,EAAK5I,KAAMsG,GAEDA,GAAOA,EAAI5D,QAA4B,WAAlBR,EAAQoE,IAGxCsU,EAAKtU,KATR,CAYK5C,WAEA8Y,IAAWD,GACfM,KAGKzd,MAIR4d,OAAQ,WAYP,OAXA5a,EAAOkB,KAAMI,UAAW,SAAU2Y,EAAG/V,GACpC,IAAIoU,EACJ,OAA0D,GAAhDA,EAAQtY,EAAO6D,QAASK,EAAKsC,EAAM8R,IAC5C9R,EAAKtE,OAAQoW,EAAO,GAGfA,GAASkC,GACbA,MAIIxd,MAKR+U,IAAK,SAAU5R,GACd,OAAOA,GACwB,EAA9BH,EAAO6D,QAAS1D,EAAIqG,GACN,EAAdA,EAAKlG,QAIPwS,MAAO,WAIN,OAHKtM,IACJA,EAAO,IAEDxJ,MAMR6d,QAAS,WAGR,OAFAP,EAASC,EAAQ,GACjB/T,EAAO4T,EAAS,GACTpd,MAERoM,SAAU,WACT,OAAQ5C,GAMTsU,KAAM,WAKL,OAJAR,EAASC,EAAQ,GACXH,GAAWD,IAChB3T,EAAO4T,EAAS,IAEVpd,MAERsd,OAAQ,WACP,QAASA,GAIVS,SAAU,SAAU7a,EAASsR,GAS5B,OARM8I,IAEL9I,EAAO,CAAEtR,GADTsR,EAAOA,GAAQ,IACQlU,MAAQkU,EAAKlU,QAAUkU,GAC9C+I,EAAM3c,KAAM4T,GACN2I,GACLM,KAGKzd,MAIRyd,KAAM,WAEL,OADAhD,EAAKsD,SAAU/d,KAAMsE,WACdtE,MAIRqd,MAAO,WACN,QAASA,IAIZ,OAAO5C,GA4CRzX,EAAOmC,OAAQ,CAEd6Y,SAAU,SAAUC,GACnB,IAAIC,EAAS,CAIX,CAAE,SAAU,WAAYlb,EAAO+Z,UAAW,UACzC/Z,EAAO+Z,UAAW,UAAY,GAC/B,CAAE,UAAW,OAAQ/Z,EAAO+Z,UAAW,eACtC/Z,EAAO+Z,UAAW,eAAiB,EAAG,YACvC,CAAE,SAAU,OAAQ/Z,EAAO+Z,UAAW,eACrC/Z,EAAO+Z,UAAW,eAAiB,EAAG,aAExCoB,EAAQ,UACRvB,EAAU,CACTuB,MAAO,WACN,OAAOA,GAERC,OAAQ,WAEP,OADAC,EAASxV,KAAMvE,WAAYuY,KAAMvY,WAC1BtE,MAERse,QAAS,SAAUnb,GAClB,OAAOyZ,EAAQE,KAAM,KAAM3Z,IAI5Bob,KAAM,WACL,IAAIC,EAAMla,UAEV,OAAOtB,EAAOgb,SAAU,SAAUS,GACjCzb,EAAOkB,KAAMga,EAAQ,SAAU1W,EAAIkX,GAGlC,IAAIvb,EAAK9B,EAAYmd,EAAKE,EAAO,MAAWF,EAAKE,EAAO,IAKxDL,EAAUK,EAAO,IAAO,WACvB,IAAIC,EAAWxb,GAAMA,EAAGxC,MAAOX,KAAMsE,WAChCqa,GAAYtd,EAAYsd,EAAS/B,SACrC+B,EAAS/B,UACPgC,SAAUH,EAASI,QACnBhW,KAAM4V,EAASjC,SACfK,KAAM4B,EAAShC,QAEjBgC,EAAUC,EAAO,GAAM,QACtB1e,KACAmD,EAAK,CAAEwb,GAAara,eAKxBka,EAAM,OACH5B,WAELE,KAAM,SAAUgC,EAAaC,EAAYC,GACxC,IAAIC,EAAW,EACf,SAASzC,EAAS0C,EAAOb,EAAU1P,EAASwQ,GAC3C,OAAO,WACN,IAAIC,EAAOpf,KACVwU,EAAOlQ,UACP+a,EAAa,WACZ,IAAIV,EAAU7B,EAKd,KAAKoC,EAAQD,GAAb,CAQA,IAJAN,EAAWhQ,EAAQhO,MAAOye,EAAM5K,MAId6J,EAASzB,UAC1B,MAAM,IAAI0C,UAAW,4BAOtBxC,EAAO6B,IAKgB,iBAAbA,GACY,mBAAbA,IACRA,EAAS7B,KAGLzb,EAAYyb,GAGXqC,EACJrC,EAAKrc,KACJke,EACAnC,EAASyC,EAAUZ,EAAUlC,EAAUgD,GACvC3C,EAASyC,EAAUZ,EAAUhC,EAAS8C,KAOvCF,IAEAnC,EAAKrc,KACJke,EACAnC,EAASyC,EAAUZ,EAAUlC,EAAUgD,GACvC3C,EAASyC,EAAUZ,EAAUhC,EAAS8C,GACtC3C,EAASyC,EAAUZ,EAAUlC,EAC5BkC,EAASkB,eASP5Q,IAAYwN,IAChBiD,OAAOtZ,EACP0O,EAAO,CAAEmK,KAKRQ,GAAWd,EAASmB,aAAeJ,EAAM5K,MAK7CiL,EAAUN,EACTE,EACA,WACC,IACCA,IACC,MAAQ5S,GAEJzJ,EAAOgb,SAAS0B,eACpB1c,EAAOgb,SAAS0B,cAAejT,EAC9BgT,EAAQE,YAMQV,GAAbC,EAAQ,IAIPvQ,IAAY0N,IAChB+C,OAAOtZ,EACP0O,EAAO,CAAE/H,IAGV4R,EAASuB,WAAYR,EAAM5K,MAS3B0K,EACJO,KAKKzc,EAAOgb,SAAS6B,eACpBJ,EAAQE,WAAa3c,EAAOgb,SAAS6B,gBAEtC9f,EAAO+f,WAAYL,KAKtB,OAAOzc,EAAOgb,SAAU,SAAUS,GAGjCP,EAAQ,GAAK,GAAI1C,IAChBgB,EACC,EACAiC,EACApd,EAAY2d,GACXA,EACA7C,EACDsC,EAASc,aAKXrB,EAAQ,GAAK,GAAI1C,IAChBgB,EACC,EACAiC,EACApd,EAAYyd,GACXA,EACA3C,IAKH+B,EAAQ,GAAK,GAAI1C,IAChBgB,EACC,EACAiC,EACApd,EAAY0d,GACXA,EACA1C,MAGAO,WAKLA,QAAS,SAAUtb,GAClB,OAAc,MAAPA,EAAc0B,EAAOmC,OAAQ7D,EAAKsb,GAAYA,IAGvDyB,EAAW,GAkEZ,OA/DArb,EAAOkB,KAAMga,EAAQ,SAAU/b,EAAGuc,GACjC,IAAIlV,EAAOkV,EAAO,GACjBqB,EAAcrB,EAAO,GAKtB9B,EAAS8B,EAAO,IAAQlV,EAAKgS,IAGxBuE,GACJvW,EAAKgS,IACJ,WAIC2C,EAAQ4B,GAKT7B,EAAQ,EAAI/b,GAAK,GAAI0b,QAIrBK,EAAQ,EAAI/b,GAAK,GAAI0b,QAGrBK,EAAQ,GAAK,GAAIJ,KAGjBI,EAAQ,GAAK,GAAIJ,MAOnBtU,EAAKgS,IAAKkD,EAAO,GAAIjB,MAKrBY,EAAUK,EAAO,IAAQ,WAExB,OADAL,EAAUK,EAAO,GAAM,QAAU1e,OAASqe,OAAWvY,EAAY9F,KAAMsE,WAChEtE,MAMRqe,EAAUK,EAAO,GAAM,QAAWlV,EAAKuU,WAIxCnB,EAAQA,QAASyB,GAGZJ,GACJA,EAAKxd,KAAM4d,EAAUA,GAIfA,GAIR2B,KAAM,SAAUC,GACf,IAGCC,EAAY5b,UAAUhB,OAGtBnB,EAAI+d,EAGJC,EAAkBva,MAAOzD,GACzBie,EAAgB9f,EAAMG,KAAM6D,WAG5B+b,EAAUrd,EAAOgb,WAGjBsC,EAAa,SAAUne,GACtB,OAAO,SAAUgF,GAChBgZ,EAAiBhe,GAAMnC,KACvBogB,EAAeje,GAAyB,EAAnBmC,UAAUhB,OAAahD,EAAMG,KAAM6D,WAAc6C,IAC5D+Y,GACTG,EAAQb,YAAaW,EAAiBC,KAM1C,GAAKF,GAAa,IACjB3D,EAAY0D,EAAaI,EAAQxX,KAAMyX,EAAYne,IAAMqa,QAAS6D,EAAQ5D,QACxEyD,GAGuB,YAApBG,EAAQlC,SACZ9c,EAAY+e,EAAeje,IAAOie,EAAeje,GAAI2a,OAErD,OAAOuD,EAAQvD,OAKjB,MAAQ3a,IACPoa,EAAY6D,EAAeje,GAAKme,EAAYne,GAAKke,EAAQ5D,QAG1D,OAAO4D,EAAQzD,aAOjB,IAAI2D,EAAc,yDAElBvd,EAAOgb,SAAS0B,cAAgB,SAAUtZ,EAAOoa,GAI3CzgB,EAAO0gB,SAAW1gB,EAAO0gB,QAAQC,MAAQta,GAASma,EAAY9S,KAAMrH,EAAMf,OAC9EtF,EAAO0gB,QAAQC,KAAM,8BAAgCta,EAAMua,QAASva,EAAMoa,MAAOA,IAOnFxd,EAAO4d,eAAiB,SAAUxa,GACjCrG,EAAO+f,WAAY,WAClB,MAAM1Z,KAQR,IAAIya,EAAY7d,EAAOgb,WAkDvB,SAAS8C,IACRlhB,EAASmhB,oBAAqB,mBAAoBD,GAClD/gB,EAAOghB,oBAAqB,OAAQD,GACpC9d,EAAO4X,QAnDR5X,EAAOG,GAAGyX,MAAQ,SAAUzX,GAY3B,OAVA0d,EACE/D,KAAM3Z,GAKNmb,SAAO,SAAUlY,GACjBpD,EAAO4d,eAAgBxa,KAGlBpG,MAGRgD,EAAOmC,OAAQ,CAGdgB,SAAS,EAIT6a,UAAW,EAGXpG,MAAO,SAAUqG,KAGF,IAATA,IAAkBje,EAAOge,UAAYhe,EAAOmD,WAKjDnD,EAAOmD,SAAU,KAGZ8a,GAAsC,IAAnBje,EAAOge,WAK/BH,EAAUrB,YAAa5f,EAAU,CAAEoD,OAIrCA,EAAO4X,MAAMkC,KAAO+D,EAAU/D,KAaD,aAAxBld,EAASshB,YACa,YAAxBthB,EAASshB,aAA6BthB,EAAS+P,gBAAgBwR,SAGjEphB,EAAO+f,WAAY9c,EAAO4X,QAK1Bhb,EAASoQ,iBAAkB,mBAAoB8Q,GAG/C/gB,EAAOiQ,iBAAkB,OAAQ8Q,IAQlC,IAAIM,EAAS,SAAUtd,EAAOX,EAAIgL,EAAKhH,EAAOka,EAAWC,EAAUC,GAClE,IAAIpf,EAAI,EACP2C,EAAMhB,EAAMR,OACZke,EAAc,MAAPrT,EAGR,GAAuB,WAAlBrL,EAAQqL,GAEZ,IAAMhM,KADNkf,GAAY,EACDlT,EACViT,EAAQtd,EAAOX,EAAIhB,EAAGgM,EAAKhM,IAAK,EAAMmf,EAAUC,QAI3C,QAAezb,IAAVqB,IACXka,GAAY,EAENhgB,EAAY8F,KACjBoa,GAAM,GAGFC,IAGCD,GACJpe,EAAG1C,KAAMqD,EAAOqD,GAChBhE,EAAK,OAILqe,EAAOre,EACPA,EAAK,SAAUkB,EAAMod,EAAMta,GAC1B,OAAOqa,EAAK/gB,KAAMuC,EAAQqB,GAAQ8C,MAKhChE,GACJ,KAAQhB,EAAI2C,EAAK3C,IAChBgB,EACCW,EAAO3B,GAAKgM,EAAKoT,EAChBpa,EACAA,EAAM1G,KAAMqD,EAAO3B,GAAKA,EAAGgB,EAAIW,EAAO3B,GAAKgM,KAMhD,OAAKkT,EACGvd,EAIH0d,EACGre,EAAG1C,KAAMqD,GAGVgB,EAAM3B,EAAIW,EAAO,GAAKqK,GAAQmT,GAKlCI,EAAY,QACfC,EAAa,YAGd,SAASC,EAAYC,EAAMC,GAC1B,OAAOA,EAAOC,cAMf,SAASC,EAAWC,GACnB,OAAOA,EAAO/b,QAASwb,EAAW,OAAQxb,QAASyb,EAAYC,GAEhE,IAAIM,EAAa,SAAUC,GAQ1B,OAA0B,IAAnBA,EAAM5gB,UAAqC,IAAnB4gB,EAAM5gB,YAAsB4gB,EAAM5gB,UAMlE,SAAS6gB,IACRpiB,KAAK+F,QAAU/C,EAAO+C,QAAUqc,EAAKC,MAGtCD,EAAKC,IAAM,EAEXD,EAAK7e,UAAY,CAEhB2K,MAAO,SAAUiU,GAGhB,IAAIhb,EAAQgb,EAAOniB,KAAK+F,SA4BxB,OAzBMoB,IACLA,EAAQ,GAKH+a,EAAYC,KAIXA,EAAM5gB,SACV4gB,EAAOniB,KAAK+F,SAAYoB,EAMxB/G,OAAOkiB,eAAgBH,EAAOniB,KAAK+F,QAAS,CAC3CoB,MAAOA,EACPob,cAAc,MAMXpb,GAERqb,IAAK,SAAUL,EAAOM,EAAMtb,GAC3B,IAAIub,EACHxU,EAAQlO,KAAKkO,MAAOiU,GAIrB,GAAqB,iBAATM,EACXvU,EAAO8T,EAAWS,IAAWtb,OAM7B,IAAMub,KAAQD,EACbvU,EAAO8T,EAAWU,IAAWD,EAAMC,GAGrC,OAAOxU,GAERvK,IAAK,SAAUwe,EAAOhU,GACrB,YAAerI,IAARqI,EACNnO,KAAKkO,MAAOiU,GAGZA,EAAOniB,KAAK+F,UAAaoc,EAAOniB,KAAK+F,SAAWic,EAAW7T,KAE7DiT,OAAQ,SAAUe,EAAOhU,EAAKhH,GAa7B,YAAarB,IAARqI,GACCA,GAAsB,iBAARA,QAAgCrI,IAAVqB,EAElCnH,KAAK2D,IAAKwe,EAAOhU,IASzBnO,KAAKwiB,IAAKL,EAAOhU,EAAKhH,QAILrB,IAAVqB,EAAsBA,EAAQgH,IAEtCyP,OAAQ,SAAUuE,EAAOhU,GACxB,IAAIhM,EACH+L,EAAQiU,EAAOniB,KAAK+F,SAErB,QAAeD,IAAVoI,EAAL,CAIA,QAAapI,IAARqI,EAAoB,CAkBxBhM,GAXCgM,EAJIvI,MAAMC,QAASsI,GAIbA,EAAI/J,IAAK4d,IAEf7T,EAAM6T,EAAW7T,MAIJD,EACZ,CAAEC,GACAA,EAAIrB,MAAOoP,IAAmB,IAG1B5Y,OAER,MAAQnB,WACA+L,EAAOC,EAAKhM,UAKR2D,IAARqI,GAAqBnL,EAAOyD,cAAeyH,MAM1CiU,EAAM5gB,SACV4gB,EAAOniB,KAAK+F,cAAYD,SAEjBqc,EAAOniB,KAAK+F,YAItB4c,QAAS,SAAUR,GAClB,IAAIjU,EAAQiU,EAAOniB,KAAK+F,SACxB,YAAiBD,IAAVoI,IAAwBlL,EAAOyD,cAAeyH,KAGvD,IAAI0U,EAAW,IAAIR,EAEfS,EAAW,IAAIT,EAcfU,EAAS,gCACZC,EAAa,SA2Bd,SAASC,EAAU3e,EAAM8J,EAAKsU,GAC7B,IAAIpd,EA1Baod,EA8BjB,QAAc3c,IAAT2c,GAAwC,IAAlBpe,EAAK9C,SAI/B,GAHA8D,EAAO,QAAU8I,EAAIjI,QAAS6c,EAAY,OAAQtb,cAG7B,iBAFrBgb,EAAOpe,EAAK7B,aAAc6C,IAEM,CAC/B,IACCod,EAnCW,UADGA,EAoCEA,IA/BL,UAATA,IAIS,SAATA,EACG,KAIHA,KAAUA,EAAO,IACbA,EAGJK,EAAOrV,KAAMgV,GACVQ,KAAKC,MAAOT,GAGbA,GAeH,MAAQhW,IAGVoW,EAASL,IAAKne,EAAM8J,EAAKsU,QAEzBA,OAAO3c,EAGT,OAAO2c,EAGRzf,EAAOmC,OAAQ,CACdwd,QAAS,SAAUte,GAClB,OAAOwe,EAASF,QAASte,IAAUue,EAASD,QAASte,IAGtDoe,KAAM,SAAUpe,EAAMgB,EAAMod,GAC3B,OAAOI,EAASzB,OAAQ/c,EAAMgB,EAAMod,IAGrCU,WAAY,SAAU9e,EAAMgB,GAC3Bwd,EAASjF,OAAQvZ,EAAMgB,IAKxB+d,MAAO,SAAU/e,EAAMgB,EAAMod,GAC5B,OAAOG,EAASxB,OAAQ/c,EAAMgB,EAAMod,IAGrCY,YAAa,SAAUhf,EAAMgB,GAC5Bud,EAAShF,OAAQvZ,EAAMgB,MAIzBrC,EAAOG,GAAGgC,OAAQ,CACjBsd,KAAM,SAAUtU,EAAKhH,GACpB,IAAIhF,EAAGkD,EAAMod,EACZpe,EAAOrE,KAAM,GACb0O,EAAQrK,GAAQA,EAAKuF,WAGtB,QAAa9D,IAARqI,EAAoB,CACxB,GAAKnO,KAAKsD,SACTmf,EAAOI,EAASlf,IAAKU,GAEE,IAAlBA,EAAK9C,WAAmBqhB,EAASjf,IAAKU,EAAM,iBAAmB,CACnElC,EAAIuM,EAAMpL,OACV,MAAQnB,IAIFuM,EAAOvM,IAEsB,KADjCkD,EAAOqJ,EAAOvM,GAAIkD,MACRxE,QAAS,WAClBwE,EAAO2c,EAAW3c,EAAK/E,MAAO,IAC9B0iB,EAAU3e,EAAMgB,EAAMod,EAAMpd,KAI/Bud,EAASJ,IAAKne,EAAM,gBAAgB,GAItC,OAAOoe,EAIR,MAAoB,iBAARtU,EACJnO,KAAKkE,KAAM,WACjB2e,EAASL,IAAKxiB,KAAMmO,KAIfiT,EAAQphB,KAAM,SAAUmH,GAC9B,IAAIsb,EAOJ,GAAKpe,QAAkByB,IAAVqB,EAKZ,YAAcrB,KADd2c,EAAOI,EAASlf,IAAKU,EAAM8J,IAEnBsU,OAMM3c,KADd2c,EAAOO,EAAU3e,EAAM8J,IAEfsU,OAIR,EAIDziB,KAAKkE,KAAM,WAGV2e,EAASL,IAAKxiB,KAAMmO,EAAKhH,MAExB,KAAMA,EAA0B,EAAnB7C,UAAUhB,OAAY,MAAM,IAG7C6f,WAAY,SAAUhV,GACrB,OAAOnO,KAAKkE,KAAM,WACjB2e,EAASjF,OAAQ5d,KAAMmO,QAM1BnL,EAAOmC,OAAQ,CACdoY,MAAO,SAAUlZ,EAAM1C,EAAM8gB,GAC5B,IAAIlF,EAEJ,GAAKlZ,EAYJ,OAXA1C,GAASA,GAAQ,MAAS,QAC1B4b,EAAQqF,EAASjf,IAAKU,EAAM1C,GAGvB8gB,KACElF,GAAS3X,MAAMC,QAAS4c,GAC7BlF,EAAQqF,EAASxB,OAAQ/c,EAAM1C,EAAMqB,EAAO2D,UAAW8b,IAEvDlF,EAAM3c,KAAM6hB,IAGPlF,GAAS,IAIlB+F,QAAS,SAAUjf,EAAM1C,GACxBA,EAAOA,GAAQ,KAEf,IAAI4b,EAAQva,EAAOua,MAAOlZ,EAAM1C,GAC/B4hB,EAAchG,EAAMja,OACpBH,EAAKoa,EAAMlP,QACXmV,EAAQxgB,EAAOygB,YAAapf,EAAM1C,GAMvB,eAAPwB,IACJA,EAAKoa,EAAMlP,QACXkV,KAGIpgB,IAIU,OAATxB,GACJ4b,EAAM3L,QAAS,qBAIT4R,EAAME,KACbvgB,EAAG1C,KAAM4D,EApBF,WACNrB,EAAOsgB,QAASjf,EAAM1C,IAmBF6hB,KAGhBD,GAAeC,GACpBA,EAAM1N,MAAM2H,QAKdgG,YAAa,SAAUpf,EAAM1C,GAC5B,IAAIwM,EAAMxM,EAAO,aACjB,OAAOihB,EAASjf,IAAKU,EAAM8J,IAASyU,EAASxB,OAAQ/c,EAAM8J,EAAK,CAC/D2H,MAAO9S,EAAO+Z,UAAW,eAAgBvB,IAAK,WAC7CoH,EAAShF,OAAQvZ,EAAM,CAAE1C,EAAO,QAASwM,WAM7CnL,EAAOG,GAAGgC,OAAQ,CACjBoY,MAAO,SAAU5b,EAAM8gB,GACtB,IAAIkB,EAAS,EAQb,MANqB,iBAAThiB,IACX8gB,EAAO9gB,EACPA,EAAO,KACPgiB,KAGIrf,UAAUhB,OAASqgB,EAChB3gB,EAAOua,MAAOvd,KAAM,GAAK2B,QAGjBmE,IAAT2c,EACNziB,KACAA,KAAKkE,KAAM,WACV,IAAIqZ,EAAQva,EAAOua,MAAOvd,KAAM2B,EAAM8gB,GAGtCzf,EAAOygB,YAAazjB,KAAM2B,GAEZ,OAATA,GAAgC,eAAf4b,EAAO,IAC5Bva,EAAOsgB,QAAStjB,KAAM2B,MAI1B2hB,QAAS,SAAU3hB,GAClB,OAAO3B,KAAKkE,KAAM,WACjBlB,EAAOsgB,QAAStjB,KAAM2B,MAGxBiiB,WAAY,SAAUjiB,GACrB,OAAO3B,KAAKud,MAAO5b,GAAQ,KAAM,KAKlCib,QAAS,SAAUjb,EAAML,GACxB,IAAIqP,EACHkT,EAAQ,EACRC,EAAQ9gB,EAAOgb,WACflM,EAAW9R,KACXmC,EAAInC,KAAKsD,OACTkZ,EAAU,aACCqH,GACTC,EAAMtE,YAAa1N,EAAU,CAAEA,KAIb,iBAATnQ,IACXL,EAAMK,EACNA,OAAOmE,GAERnE,EAAOA,GAAQ,KAEf,MAAQQ,KACPwO,EAAMiS,EAASjf,IAAKmO,EAAU3P,GAAKR,EAAO,gBAC9BgP,EAAImF,QACf+N,IACAlT,EAAImF,MAAM0F,IAAKgB,IAIjB,OADAA,IACOsH,EAAMlH,QAAStb,MAGxB,IAAIyiB,GAAO,sCAA0CC,OAEjDC,GAAU,IAAIla,OAAQ,iBAAmBga,GAAO,cAAe,KAG/DG,GAAY,CAAE,MAAO,QAAS,SAAU,QAExCvU,GAAkB/P,EAAS+P,gBAI1BwU,GAAa,SAAU9f,GACzB,OAAOrB,EAAOyF,SAAUpE,EAAK6I,cAAe7I,IAE7C+f,GAAW,CAAEA,UAAU,GAOnBzU,GAAgB0U,cACpBF,GAAa,SAAU9f,GACtB,OAAOrB,EAAOyF,SAAUpE,EAAK6I,cAAe7I,IAC3CA,EAAKggB,YAAaD,MAAe/f,EAAK6I,gBAG1C,IAAIoX,GAAqB,SAAUjgB,EAAMmK,GAOvC,MAA8B,UAH9BnK,EAAOmK,GAAMnK,GAGDkgB,MAAMC,SACM,KAAvBngB,EAAKkgB,MAAMC,SAMXL,GAAY9f,IAEsB,SAAlCrB,EAAOyhB,IAAKpgB,EAAM,YAKrB,SAASqgB,GAAWrgB,EAAMqe,EAAMiC,EAAYC,GAC3C,IAAIC,EAAUC,EACbC,EAAgB,GAChBC,EAAeJ,EACd,WACC,OAAOA,EAAM9V,OAEd,WACC,OAAO9L,EAAOyhB,IAAKpgB,EAAMqe,EAAM,KAEjCuC,EAAUD,IACVE,EAAOP,GAAcA,EAAY,KAAS3hB,EAAOmiB,UAAWzC,GAAS,GAAK,MAG1E0C,EAAgB/gB,EAAK9C,WAClByB,EAAOmiB,UAAWzC,IAAmB,OAATwC,IAAkBD,IAChDhB,GAAQ9W,KAAMnK,EAAOyhB,IAAKpgB,EAAMqe,IAElC,GAAK0C,GAAiBA,EAAe,KAAQF,EAAO,CAInDD,GAAoB,EAGpBC,EAAOA,GAAQE,EAAe,GAG9BA,GAAiBH,GAAW,EAE5B,MAAQF,IAIP/hB,EAAOuhB,MAAOlgB,EAAMqe,EAAM0C,EAAgBF,IACnC,EAAIJ,IAAY,GAAMA,EAAQE,IAAiBC,GAAW,MAAW,IAC3EF,EAAgB,GAEjBK,GAAgCN,EAIjCM,GAAgC,EAChCpiB,EAAOuhB,MAAOlgB,EAAMqe,EAAM0C,EAAgBF,GAG1CP,EAAaA,GAAc,GAgB5B,OAbKA,IACJS,GAAiBA,IAAkBH,GAAW,EAG9CJ,EAAWF,EAAY,GACtBS,GAAkBT,EAAY,GAAM,GAAMA,EAAY,IACrDA,EAAY,GACTC,IACJA,EAAMM,KAAOA,EACbN,EAAM1Q,MAAQkR,EACdR,EAAM5f,IAAM6f,IAGPA,EAIR,IAAIQ,GAAoB,GAyBxB,SAASC,GAAUxT,EAAUyT,GAO5B,IANA,IAAIf,EAASngB,EAxBcA,EACvBuT,EACH1V,EACAmK,EACAmY,EAqBAgB,EAAS,GACTlK,EAAQ,EACRhY,EAASwO,EAASxO,OAGXgY,EAAQhY,EAAQgY,KACvBjX,EAAOyN,EAAUwJ,IACNiJ,QAIXC,EAAUngB,EAAKkgB,MAAMC,QAChBe,GAKa,SAAZf,IACJgB,EAAQlK,GAAUsH,EAASjf,IAAKU,EAAM,YAAe,KAC/CmhB,EAAQlK,KACbjX,EAAKkgB,MAAMC,QAAU,KAGK,KAAvBngB,EAAKkgB,MAAMC,SAAkBF,GAAoBjgB,KACrDmhB,EAAQlK,IA7CVkJ,EAFAtiB,EADG0V,OAAAA,EACH1V,GAF0BmC,EAiDaA,GA/C5B6I,cACXb,EAAWhI,EAAKgI,UAChBmY,EAAUa,GAAmBhZ,MAM9BuL,EAAO1V,EAAIujB,KAAK9iB,YAAaT,EAAII,cAAe+J,IAChDmY,EAAUxhB,EAAOyhB,IAAK7M,EAAM,WAE5BA,EAAKhV,WAAWC,YAAa+U,GAEZ,SAAZ4M,IACJA,EAAU,SAEXa,GAAmBhZ,GAAamY,MAkCb,SAAZA,IACJgB,EAAQlK,GAAU,OAGlBsH,EAASJ,IAAKne,EAAM,UAAWmgB,KAMlC,IAAMlJ,EAAQ,EAAGA,EAAQhY,EAAQgY,IACR,MAAnBkK,EAAQlK,KACZxJ,EAAUwJ,GAAQiJ,MAAMC,QAAUgB,EAAQlK,IAI5C,OAAOxJ,EAGR9O,EAAOG,GAAGgC,OAAQ,CACjBogB,KAAM,WACL,OAAOD,GAAUtlB,MAAM,IAExB0lB,KAAM,WACL,OAAOJ,GAAUtlB,OAElB2lB,OAAQ,SAAUxH,GACjB,MAAsB,kBAAVA,EACJA,EAAQne,KAAKulB,OAASvlB,KAAK0lB,OAG5B1lB,KAAKkE,KAAM,WACZogB,GAAoBtkB,MACxBgD,EAAQhD,MAAOulB,OAEfviB,EAAQhD,MAAO0lB,YAKnB,IAUEE,GACAhV,GAXEiV,GAAiB,wBAEjBC,GAAW,iCAEXC,GAAc,qCAMhBH,GADchmB,EAASomB,yBACRrjB,YAAa/C,EAAS0C,cAAe,SACpDsO,GAAQhR,EAAS0C,cAAe,UAM3BG,aAAc,OAAQ,SAC5BmO,GAAMnO,aAAc,UAAW,WAC/BmO,GAAMnO,aAAc,OAAQ,KAE5BmjB,GAAIjjB,YAAaiO,IAIjBxP,EAAQ6kB,WAAaL,GAAIM,WAAW,GAAOA,WAAW,GAAO7R,UAAUsB,QAIvEiQ,GAAI/U,UAAY,yBAChBzP,EAAQ+kB,iBAAmBP,GAAIM,WAAW,GAAO7R,UAAUuF,aAK3DgM,GAAI/U,UAAY,oBAChBzP,EAAQglB,SAAWR,GAAIvR,UAKxB,IAAIgS,GAAU,CAKbC,MAAO,CAAE,EAAG,UAAW,YACvBC,IAAK,CAAE,EAAG,oBAAqB,uBAC/BC,GAAI,CAAE,EAAG,iBAAkB,oBAC3BC,GAAI,CAAE,EAAG,qBAAsB,yBAE/BC,SAAU,CAAE,EAAG,GAAI,KAYpB,SAASC,GAAQzjB,EAASwN,GAIzB,IAAI3M,EAYJ,OATCA,EAD4C,oBAAjCb,EAAQoK,qBACbpK,EAAQoK,qBAAsBoD,GAAO,KAEI,oBAA7BxN,EAAQ4K,iBACpB5K,EAAQ4K,iBAAkB4C,GAAO,KAGjC,QAGM5K,IAAR4K,GAAqBA,GAAOrE,EAAUnJ,EAASwN,GAC5C1N,EAAOgB,MAAO,CAAEd,GAAWa,GAG5BA,EAKR,SAAS6iB,GAAe9iB,EAAO+iB,GAI9B,IAHA,IAAI1kB,EAAI,EACPiZ,EAAItX,EAAMR,OAEHnB,EAAIiZ,EAAGjZ,IACdygB,EAASJ,IACR1e,EAAO3B,GACP,cACC0kB,GAAejE,EAASjf,IAAKkjB,EAAa1kB,GAAK,eA1CnDkkB,GAAQS,MAAQT,GAAQU,MAAQV,GAAQW,SAAWX,GAAQY,QAAUZ,GAAQC,MAC7ED,GAAQa,GAAKb,GAAQI,GAGfrlB,EAAQglB,SACbC,GAAQc,SAAWd,GAAQD,OAAS,CAAE,EAAG,+BAAgC,cA2C1E,IAAIrb,GAAQ,YAEZ,SAASqc,GAAetjB,EAAOZ,EAASmkB,EAASC,EAAWC,GAO3D,IANA,IAAIljB,EAAMsM,EAAKD,EAAK8W,EAAMC,EAAU1iB,EACnC2iB,EAAWxkB,EAAQ8iB,yBACnB2B,EAAQ,GACRxlB,EAAI,EACJiZ,EAAItX,EAAMR,OAEHnB,EAAIiZ,EAAGjZ,IAGd,IAFAkC,EAAOP,EAAO3B,KAEQ,IAATkC,EAGZ,GAAwB,WAAnBvB,EAAQuB,GAIZrB,EAAOgB,MAAO2jB,EAAOtjB,EAAK9C,SAAW,CAAE8C,GAASA,QAG1C,GAAM0G,GAAM0C,KAAMpJ,GAIlB,CACNsM,EAAMA,GAAO+W,EAAS/kB,YAAaO,EAAQZ,cAAe,QAG1DoO,GAAQoV,GAAS3Y,KAAM9I,IAAU,CAAE,GAAI,KAAQ,GAAIoD,cACnD+f,EAAOnB,GAAS3V,IAAS2V,GAAQK,SACjC/V,EAAIE,UAAY2W,EAAM,GAAMxkB,EAAO4kB,cAAevjB,GAASmjB,EAAM,GAGjEziB,EAAIyiB,EAAM,GACV,MAAQziB,IACP4L,EAAMA,EAAI0D,UAKXrR,EAAOgB,MAAO2jB,EAAOhX,EAAInE,aAGzBmE,EAAM+W,EAASnV,YAGXD,YAAc,QAzBlBqV,EAAM/mB,KAAMsC,EAAQ2kB,eAAgBxjB,IA+BvCqjB,EAASpV,YAAc,GAEvBnQ,EAAI,EACJ,MAAUkC,EAAOsjB,EAAOxlB,KAGvB,GAAKmlB,IAAkD,EAArCtkB,EAAO6D,QAASxC,EAAMijB,GAClCC,GACJA,EAAQ3mB,KAAMyD,QAgBhB,GAXAojB,EAAWtD,GAAY9f,GAGvBsM,EAAMgW,GAAQe,EAAS/kB,YAAa0B,GAAQ,UAGvCojB,GACJb,GAAejW,GAIX0W,EAAU,CACdtiB,EAAI,EACJ,MAAUV,EAAOsM,EAAK5L,KAChBghB,GAAYtY,KAAMpJ,EAAK1C,MAAQ,KACnC0lB,EAAQzmB,KAAMyD,GAMlB,OAAOqjB,EAIR,IAAII,GAAiB,sBAErB,SAASC,KACR,OAAO,EAGR,SAASC,KACR,OAAO,EASR,SAASC,GAAY5jB,EAAM1C,GAC1B,OAAS0C,IAMV,WACC,IACC,OAAOzE,EAAS0V,cACf,MAAQ4S,KATQC,KAAqC,UAATxmB,GAY/C,SAASymB,GAAI/jB,EAAMgkB,EAAOplB,EAAUwf,EAAMtf,EAAImlB,GAC7C,IAAIC,EAAQ5mB,EAGZ,GAAsB,iBAAV0mB,EAAqB,CAShC,IAAM1mB,IANmB,iBAAbsB,IAGXwf,EAAOA,GAAQxf,EACfA,OAAW6C,GAEEuiB,EACbD,GAAI/jB,EAAM1C,EAAMsB,EAAUwf,EAAM4F,EAAO1mB,GAAQ2mB,GAEhD,OAAOjkB,EAsBR,GAnBa,MAARoe,GAAsB,MAANtf,GAGpBA,EAAKF,EACLwf,EAAOxf,OAAW6C,GACD,MAAN3C,IACc,iBAAbF,GAGXE,EAAKsf,EACLA,OAAO3c,IAIP3C,EAAKsf,EACLA,EAAOxf,EACPA,OAAW6C,KAGD,IAAP3C,EACJA,EAAK6kB,QACC,IAAM7kB,EACZ,OAAOkB,EAeR,OAZa,IAARikB,IACJC,EAASplB,GACTA,EAAK,SAAUqlB,GAId,OADAxlB,IAASylB,IAAKD,GACPD,EAAO5nB,MAAOX,KAAMsE,aAIzB8C,KAAOmhB,EAAOnhB,OAAUmhB,EAAOnhB,KAAOpE,EAAOoE,SAE1C/C,EAAKH,KAAM,WACjBlB,EAAOwlB,MAAMhN,IAAKxb,KAAMqoB,EAAOllB,EAAIsf,EAAMxf,KA+a3C,SAASylB,GAAgBla,EAAI7M,EAAMsmB,GAG5BA,GAQNrF,EAASJ,IAAKhU,EAAI7M,GAAM,GACxBqB,EAAOwlB,MAAMhN,IAAKhN,EAAI7M,EAAM,CAC3B8N,WAAW,EACXd,QAAS,SAAU6Z,GAClB,IAAIG,EAAUpV,EACbqV,EAAQhG,EAASjf,IAAK3D,KAAM2B,GAE7B,GAAyB,EAAlB6mB,EAAMK,WAAmB7oB,KAAM2B,IAKrC,GAAMinB,EAAMtlB,QAuCEN,EAAOwlB,MAAMrJ,QAASxd,IAAU,IAAKmnB,cAClDN,EAAMO,uBArBN,GAdAH,EAAQtoB,EAAMG,KAAM6D,WACpBse,EAASJ,IAAKxiB,KAAM2B,EAAMinB,GAK1BD,EAAWV,EAAYjoB,KAAM2B,GAC7B3B,KAAM2B,KAEDinB,KADLrV,EAASqP,EAASjf,IAAK3D,KAAM2B,KACJgnB,EACxB/F,EAASJ,IAAKxiB,KAAM2B,GAAM,GAE1B4R,EAAS,GAELqV,IAAUrV,EAWd,OARAiV,EAAMQ,2BACNR,EAAMS,iBAOC1V,GAAUA,EAAOpM,WAefyhB,EAAMtlB,SAGjBsf,EAASJ,IAAKxiB,KAAM2B,EAAM,CACzBwF,MAAOnE,EAAOwlB,MAAMU,QAInBlmB,EAAOmC,OAAQyjB,EAAO,GAAK5lB,EAAOmmB,MAAM5lB,WACxCqlB,EAAMtoB,MAAO,GACbN,QAKFwoB,EAAMQ,qCA/E0BljB,IAA7B8c,EAASjf,IAAK6K,EAAI7M,IACtBqB,EAAOwlB,MAAMhN,IAAKhN,EAAI7M,EAAMomB,IA5a/B/kB,EAAOwlB,MAAQ,CAEdhpB,OAAQ,GAERgc,IAAK,SAAUnX,EAAMgkB,EAAO1Z,EAAS8T,EAAMxf,GAE1C,IAAImmB,EAAaC,EAAa1Y,EAC7B2Y,EAAQC,EAAGC,EACXrK,EAASsK,EAAU9nB,EAAM+nB,EAAYC,EACrCC,EAAWhH,EAASjf,IAAKU,GAG1B,GAAM6d,EAAY7d,GAAlB,CAKKsK,EAAQA,UAEZA,GADAya,EAAcza,GACQA,QACtB1L,EAAWmmB,EAAYnmB,UAKnBA,GACJD,EAAOwN,KAAKM,gBAAiBnB,GAAiB1M,GAIzC0L,EAAQvH,OACbuH,EAAQvH,KAAOpE,EAAOoE,SAIfkiB,EAASM,EAASN,UACzBA,EAASM,EAASN,OAASlpB,OAAOypB,OAAQ,QAEnCR,EAAcO,EAASE,UAC9BT,EAAcO,EAASE,OAAS,SAAUrd,GAIzC,MAAyB,oBAAXzJ,GAA0BA,EAAOwlB,MAAMuB,YAActd,EAAE9K,KACpEqB,EAAOwlB,MAAMwB,SAASrpB,MAAO0D,EAAMC,gBAAcwB,IAMpDyjB,GADAlB,GAAUA,GAAS,IAAKvb,MAAOoP,IAAmB,CAAE,KAC1C5Y,OACV,MAAQimB,IAEP5nB,EAAOgoB,GADPhZ,EAAMmX,GAAe3a,KAAMkb,EAAOkB,KAAS,IACpB,GACvBG,GAAe/Y,EAAK,IAAO,IAAKpJ,MAAO,KAAMtC,OAGvCtD,IAKNwd,EAAUnc,EAAOwlB,MAAMrJ,QAASxd,IAAU,GAG1CA,GAASsB,EAAWkc,EAAQ2J,aAAe3J,EAAQ8K,WAActoB,EAGjEwd,EAAUnc,EAAOwlB,MAAMrJ,QAASxd,IAAU,GAG1C6nB,EAAYxmB,EAAOmC,OAAQ,CAC1BxD,KAAMA,EACNgoB,SAAUA,EACVlH,KAAMA,EACN9T,QAASA,EACTvH,KAAMuH,EAAQvH,KACdnE,SAAUA,EACV6H,aAAc7H,GAAYD,EAAO6O,KAAK/E,MAAMhC,aAAa2C,KAAMxK,GAC/DwM,UAAWia,EAAW7b,KAAM,MAC1Bub,IAGKK,EAAWH,EAAQ3nB,OAC1B8nB,EAAWH,EAAQ3nB,GAAS,IACnBuoB,cAAgB,EAGnB/K,EAAQgL,QACiD,IAA9DhL,EAAQgL,MAAM1pB,KAAM4D,EAAMoe,EAAMiH,EAAYL,IAEvChlB,EAAK2L,kBACT3L,EAAK2L,iBAAkBrO,EAAM0nB,IAK3BlK,EAAQ3D,MACZ2D,EAAQ3D,IAAI/a,KAAM4D,EAAMmlB,GAElBA,EAAU7a,QAAQvH,OACvBoiB,EAAU7a,QAAQvH,KAAOuH,EAAQvH,OAK9BnE,EACJwmB,EAASvkB,OAAQukB,EAASS,gBAAiB,EAAGV,GAE9CC,EAAS7oB,KAAM4oB,GAIhBxmB,EAAOwlB,MAAMhpB,OAAQmC,IAAS,KAMhCic,OAAQ,SAAUvZ,EAAMgkB,EAAO1Z,EAAS1L,EAAUmnB,GAEjD,IAAIrlB,EAAGslB,EAAW1Z,EACjB2Y,EAAQC,EAAGC,EACXrK,EAASsK,EAAU9nB,EAAM+nB,EAAYC,EACrCC,EAAWhH,EAASD,QAASte,IAAUue,EAASjf,IAAKU,GAEtD,GAAMulB,IAAeN,EAASM,EAASN,QAAvC,CAMAC,GADAlB,GAAUA,GAAS,IAAKvb,MAAOoP,IAAmB,CAAE,KAC1C5Y,OACV,MAAQimB,IAMP,GAJA5nB,EAAOgoB,GADPhZ,EAAMmX,GAAe3a,KAAMkb,EAAOkB,KAAS,IACpB,GACvBG,GAAe/Y,EAAK,IAAO,IAAKpJ,MAAO,KAAMtC,OAGvCtD,EAAN,CAOAwd,EAAUnc,EAAOwlB,MAAMrJ,QAASxd,IAAU,GAE1C8nB,EAAWH,EADX3nB,GAASsB,EAAWkc,EAAQ2J,aAAe3J,EAAQ8K,WAActoB,IACpC,GAC7BgP,EAAMA,EAAK,IACV,IAAI5G,OAAQ,UAAY2f,EAAW7b,KAAM,iBAAoB,WAG9Dwc,EAAYtlB,EAAI0kB,EAASnmB,OACzB,MAAQyB,IACPykB,EAAYC,EAAU1kB,IAEfqlB,GAAeT,IAAaH,EAAUG,UACzChb,GAAWA,EAAQvH,OAASoiB,EAAUpiB,MACtCuJ,IAAOA,EAAIlD,KAAM+b,EAAU/Z,YAC3BxM,GAAYA,IAAaumB,EAAUvmB,WACxB,OAAbA,IAAqBumB,EAAUvmB,YAChCwmB,EAASvkB,OAAQH,EAAG,GAEfykB,EAAUvmB,UACdwmB,EAASS,gBAEL/K,EAAQvB,QACZuB,EAAQvB,OAAOnd,KAAM4D,EAAMmlB,IAOzBa,IAAcZ,EAASnmB,SACrB6b,EAAQmL,WACkD,IAA/DnL,EAAQmL,SAAS7pB,KAAM4D,EAAMqlB,EAAYE,EAASE,SAElD9mB,EAAOunB,YAAalmB,EAAM1C,EAAMioB,EAASE,eAGnCR,EAAQ3nB,SA1Cf,IAAMA,KAAQ2nB,EACbtmB,EAAOwlB,MAAM5K,OAAQvZ,EAAM1C,EAAO0mB,EAAOkB,GAAK5a,EAAS1L,GAAU,GA8C/DD,EAAOyD,cAAe6iB,IAC1B1G,EAAShF,OAAQvZ,EAAM,mBAIzB2lB,SAAU,SAAUQ,GAEnB,IAAIroB,EAAG4C,EAAGhB,EAAK4Q,EAAS6U,EAAWiB,EAClCjW,EAAO,IAAI5O,MAAOtB,UAAUhB,QAG5BklB,EAAQxlB,EAAOwlB,MAAMkC,IAAKF,GAE1Bf,GACC7G,EAASjf,IAAK3D,KAAM,WAAcI,OAAOypB,OAAQ,OAC/CrB,EAAM7mB,OAAU,GACnBwd,EAAUnc,EAAOwlB,MAAMrJ,QAASqJ,EAAM7mB,OAAU,GAKjD,IAFA6S,EAAM,GAAMgU,EAENrmB,EAAI,EAAGA,EAAImC,UAAUhB,OAAQnB,IAClCqS,EAAMrS,GAAMmC,UAAWnC,GAMxB,GAHAqmB,EAAMmC,eAAiB3qB,MAGlBmf,EAAQyL,cAA2D,IAA5CzL,EAAQyL,YAAYnqB,KAAMT,KAAMwoB,GAA5D,CAKAiC,EAAeznB,EAAOwlB,MAAMiB,SAAShpB,KAAMT,KAAMwoB,EAAOiB,GAGxDtnB,EAAI,EACJ,OAAUwS,EAAU8V,EAActoB,QAAYqmB,EAAMqC,uBAAyB,CAC5ErC,EAAMsC,cAAgBnW,EAAQtQ,KAE9BU,EAAI,EACJ,OAAUykB,EAAY7U,EAAQ8U,SAAU1kB,QACtCyjB,EAAMuC,gCAIDvC,EAAMwC,aAAsC,IAAxBxB,EAAU/Z,YACnC+Y,EAAMwC,WAAWvd,KAAM+b,EAAU/Z,aAEjC+Y,EAAMgB,UAAYA,EAClBhB,EAAM/F,KAAO+G,EAAU/G,UAKV3c,KAHb/B,IAAUf,EAAOwlB,MAAMrJ,QAASqK,EAAUG,WAAc,IAAKG,QAC5DN,EAAU7a,SAAUhO,MAAOgU,EAAQtQ,KAAMmQ,MAGT,KAAzBgU,EAAMjV,OAASxP,KACrBykB,EAAMS,iBACNT,EAAMO,oBAYX,OAJK5J,EAAQ8L,cACZ9L,EAAQ8L,aAAaxqB,KAAMT,KAAMwoB,GAG3BA,EAAMjV,SAGdkW,SAAU,SAAUjB,EAAOiB,GAC1B,IAAItnB,EAAGqnB,EAAWvX,EAAKiZ,EAAiBC,EACvCV,EAAe,GACfP,EAAgBT,EAASS,cACzBpb,EAAM0Z,EAAM/iB,OAGb,GAAKykB,GAIJpb,EAAIvN,YAOc,UAAfinB,EAAM7mB,MAAoC,GAAhB6mB,EAAMxS,QAEnC,KAAQlH,IAAQ9O,KAAM8O,EAAMA,EAAIlM,YAAc5C,KAI7C,GAAsB,IAAjB8O,EAAIvN,WAAoC,UAAfinB,EAAM7mB,OAAqC,IAAjBmN,EAAI1C,UAAsB,CAGjF,IAFA8e,EAAkB,GAClBC,EAAmB,GACbhpB,EAAI,EAAGA,EAAI+nB,EAAe/nB,SAME2D,IAA5BqlB,EAFLlZ,GAHAuX,EAAYC,EAAUtnB,IAGNc,SAAW,OAG1BkoB,EAAkBlZ,GAAQuX,EAAU1e,cACC,EAApC9H,EAAQiP,EAAKjS,MAAOsb,MAAOxM,GAC3B9L,EAAOwN,KAAMyB,EAAKjS,KAAM,KAAM,CAAE8O,IAAQxL,QAErC6nB,EAAkBlZ,IACtBiZ,EAAgBtqB,KAAM4oB,GAGnB0B,EAAgB5nB,QACpBmnB,EAAa7pB,KAAM,CAAEyD,KAAMyK,EAAK2a,SAAUyB,IAY9C,OALApc,EAAM9O,KACDkqB,EAAgBT,EAASnmB,QAC7BmnB,EAAa7pB,KAAM,CAAEyD,KAAMyK,EAAK2a,SAAUA,EAASnpB,MAAO4pB,KAGpDO,GAGRW,QAAS,SAAU/lB,EAAMgmB,GACxBjrB,OAAOkiB,eAAgBtf,EAAOmmB,MAAM5lB,UAAW8B,EAAM,CACpDimB,YAAY,EACZ/I,cAAc,EAEd5e,IAAKtC,EAAYgqB,GAChB,WACC,GAAKrrB,KAAKurB,cACT,OAAOF,EAAMrrB,KAAKurB,gBAGpB,WACC,GAAKvrB,KAAKurB,cACT,OAAOvrB,KAAKurB,cAAelmB,IAI9Bmd,IAAK,SAAUrb,GACd/G,OAAOkiB,eAAgBtiB,KAAMqF,EAAM,CAClCimB,YAAY,EACZ/I,cAAc,EACdiJ,UAAU,EACVrkB,MAAOA,QAMXujB,IAAK,SAAUa,GACd,OAAOA,EAAevoB,EAAO+C,SAC5BwlB,EACA,IAAIvoB,EAAOmmB,MAAOoC,IAGpBpM,QAAS,CACRsM,KAAM,CAGLC,UAAU,GAEXC,MAAO,CAGNxB,MAAO,SAAU1H,GAIhB,IAAIjU,EAAKxO,MAAQyiB,EAWjB,OARKoD,GAAepY,KAAMe,EAAG7M,OAC5B6M,EAAGmd,OAAStf,EAAUmC,EAAI,UAG1Bka,GAAgBla,EAAI,QAASuZ,KAIvB,GAERmB,QAAS,SAAUzG,GAIlB,IAAIjU,EAAKxO,MAAQyiB,EAUjB,OAPKoD,GAAepY,KAAMe,EAAG7M,OAC5B6M,EAAGmd,OAAStf,EAAUmC,EAAI,UAE1Bka,GAAgBla,EAAI,UAId,GAKRkY,SAAU,SAAU8B,GACnB,IAAI/iB,EAAS+iB,EAAM/iB,OACnB,OAAOogB,GAAepY,KAAMhI,EAAO9D,OAClC8D,EAAOkmB,OAAStf,EAAU5G,EAAQ,UAClCmd,EAASjf,IAAK8B,EAAQ,UACtB4G,EAAU5G,EAAQ,OAIrBmmB,aAAc,CACbX,aAAc,SAAUzC,QAID1iB,IAAjB0iB,EAAMjV,QAAwBiV,EAAM+C,gBACxC/C,EAAM+C,cAAcM,YAAcrD,EAAMjV,YAoG7CvQ,EAAOunB,YAAc,SAAUlmB,EAAM1C,EAAMmoB,GAGrCzlB,EAAK0c,qBACT1c,EAAK0c,oBAAqBpf,EAAMmoB,IAIlC9mB,EAAOmmB,MAAQ,SAAUvnB,EAAKkqB,GAG7B,KAAQ9rB,gBAAgBgD,EAAOmmB,OAC9B,OAAO,IAAInmB,EAAOmmB,MAAOvnB,EAAKkqB,GAI1BlqB,GAAOA,EAAID,MACf3B,KAAKurB,cAAgB3pB,EACrB5B,KAAK2B,KAAOC,EAAID,KAIhB3B,KAAK+rB,mBAAqBnqB,EAAIoqB,uBACHlmB,IAAzBlE,EAAIoqB,mBAGgB,IAApBpqB,EAAIiqB,YACL9D,GACAC,GAKDhoB,KAAKyF,OAAW7D,EAAI6D,QAAkC,IAAxB7D,EAAI6D,OAAOlE,SACxCK,EAAI6D,OAAO7C,WACXhB,EAAI6D,OAELzF,KAAK8qB,cAAgBlpB,EAAIkpB,cACzB9qB,KAAKisB,cAAgBrqB,EAAIqqB,eAIzBjsB,KAAK2B,KAAOC,EAIRkqB,GACJ9oB,EAAOmC,OAAQnF,KAAM8rB,GAItB9rB,KAAKksB,UAAYtqB,GAAOA,EAAIsqB,WAAaxjB,KAAKyjB,MAG9CnsB,KAAMgD,EAAO+C,UAAY,GAK1B/C,EAAOmmB,MAAM5lB,UAAY,CACxBE,YAAaT,EAAOmmB,MACpB4C,mBAAoB/D,GACpB6C,qBAAsB7C,GACtB+C,8BAA+B/C,GAC/BoE,aAAa,EAEbnD,eAAgB,WACf,IAAIxc,EAAIzM,KAAKurB,cAEbvrB,KAAK+rB,mBAAqBhE,GAErBtb,IAAMzM,KAAKosB,aACf3f,EAAEwc,kBAGJF,gBAAiB,WAChB,IAAItc,EAAIzM,KAAKurB,cAEbvrB,KAAK6qB,qBAAuB9C,GAEvBtb,IAAMzM,KAAKosB,aACf3f,EAAEsc,mBAGJC,yBAA0B,WACzB,IAAIvc,EAAIzM,KAAKurB,cAEbvrB,KAAK+qB,8BAAgChD,GAEhCtb,IAAMzM,KAAKosB,aACf3f,EAAEuc,2BAGHhpB,KAAK+oB,oBAKP/lB,EAAOkB,KAAM,CACZmoB,QAAQ,EACRC,SAAS,EACTC,YAAY,EACZC,gBAAgB,EAChBC,SAAS,EACTC,QAAQ,EACRC,YAAY,EACZC,SAAS,EACTC,OAAO,EACPC,OAAO,EACPC,UAAU,EACVC,MAAM,EACNC,QAAQ,EACRjrB,MAAM,EACNkrB,UAAU,EACV/e,KAAK,EACLgf,SAAS,EACTnX,QAAQ,EACRoX,SAAS,EACTC,SAAS,EACTC,SAAS,EACTC,SAAS,EACTC,SAAS,EACTC,WAAW,EACXC,aAAa,EACbC,SAAS,EACTC,SAAS,EACTC,eAAe,EACfC,WAAW,EACXC,SAAS,EACTC,OAAO,GACLhrB,EAAOwlB,MAAM4C,SAEhBpoB,EAAOkB,KAAM,CAAEmR,MAAO,UAAW4Y,KAAM,YAAc,SAAUtsB,EAAMmnB,GACpE9lB,EAAOwlB,MAAMrJ,QAASxd,GAAS,CAG9BwoB,MAAO,WAQN,OAHAzB,GAAgB1oB,KAAM2B,EAAMsmB,KAGrB,GAERiB,QAAS,WAMR,OAHAR,GAAgB1oB,KAAM2B,IAGf,GAKR+kB,SAAU,WACT,OAAO,GAGRoC,aAAcA,KAYhB9lB,EAAOkB,KAAM,CACZgqB,WAAY,YACZC,WAAY,WACZC,aAAc,cACdC,aAAc,cACZ,SAAUC,EAAM5D,GAClB1nB,EAAOwlB,MAAMrJ,QAASmP,GAAS,CAC9BxF,aAAc4B,EACdT,SAAUS,EAEVZ,OAAQ,SAAUtB,GACjB,IAAIzkB,EAEHwqB,EAAU/F,EAAMyD,cAChBzC,EAAYhB,EAAMgB,UASnB,OALM+E,IAAaA,IANTvuB,MAMgCgD,EAAOyF,SANvCzI,KAMyDuuB,MAClE/F,EAAM7mB,KAAO6nB,EAAUG,SACvB5lB,EAAMylB,EAAU7a,QAAQhO,MAAOX,KAAMsE,WACrCkkB,EAAM7mB,KAAO+oB,GAEP3mB,MAKVf,EAAOG,GAAGgC,OAAQ,CAEjBijB,GAAI,SAAUC,EAAOplB,EAAUwf,EAAMtf,GACpC,OAAOilB,GAAIpoB,KAAMqoB,EAAOplB,EAAUwf,EAAMtf,IAEzCmlB,IAAK,SAAUD,EAAOplB,EAAUwf,EAAMtf,GACrC,OAAOilB,GAAIpoB,KAAMqoB,EAAOplB,EAAUwf,EAAMtf,EAAI,IAE7CslB,IAAK,SAAUJ,EAAOplB,EAAUE,GAC/B,IAAIqmB,EAAW7nB,EACf,GAAK0mB,GAASA,EAAMY,gBAAkBZ,EAAMmB,UAW3C,OARAA,EAAYnB,EAAMmB,UAClBxmB,EAAQqlB,EAAMsC,gBAAiBlC,IAC9Be,EAAU/Z,UACT+Z,EAAUG,SAAW,IAAMH,EAAU/Z,UACrC+Z,EAAUG,SACXH,EAAUvmB,SACVumB,EAAU7a,SAEJ3O,KAER,GAAsB,iBAAVqoB,EAAqB,CAGhC,IAAM1mB,KAAQ0mB,EACbroB,KAAKyoB,IAAK9mB,EAAMsB,EAAUolB,EAAO1mB,IAElC,OAAO3B,KAWR,OATkB,IAAbiD,GAA0C,mBAAbA,IAGjCE,EAAKF,EACLA,OAAW6C,IAEA,IAAP3C,IACJA,EAAK6kB,IAEChoB,KAAKkE,KAAM,WACjBlB,EAAOwlB,MAAM5K,OAAQ5d,KAAMqoB,EAAOllB,EAAIF,QAMzC,IAKCurB,GAAe,wBAGfC,GAAW,oCACXC,GAAe,2CAGhB,SAASC,GAAoBtqB,EAAM2X,GAClC,OAAK3P,EAAUhI,EAAM,UACpBgI,EAA+B,KAArB2P,EAAQza,SAAkBya,EAAUA,EAAQzJ,WAAY,OAE3DvP,EAAQqB,GAAO0W,SAAU,SAAW,IAGrC1W,EAIR,SAASuqB,GAAevqB,GAEvB,OADAA,EAAK1C,MAAyC,OAAhC0C,EAAK7B,aAAc,SAAsB,IAAM6B,EAAK1C,KAC3D0C,EAER,SAASwqB,GAAexqB,GAOvB,MAN2C,WAApCA,EAAK1C,MAAQ,IAAKrB,MAAO,EAAG,GAClC+D,EAAK1C,KAAO0C,EAAK1C,KAAKrB,MAAO,GAE7B+D,EAAK2J,gBAAiB,QAGhB3J,EAGR,SAASyqB,GAAgBltB,EAAKmtB,GAC7B,IAAI5sB,EAAGiZ,EAAGzZ,EAAgBqtB,EAAUC,EAAU3F,EAE9C,GAAuB,IAAlByF,EAAKxtB,SAAV,CAKA,GAAKqhB,EAASD,QAAS/gB,KAEtB0nB,EADW1G,EAASjf,IAAK/B,GACP0nB,QAKjB,IAAM3nB,KAFNihB,EAAShF,OAAQmR,EAAM,iBAETzF,EACb,IAAMnnB,EAAI,EAAGiZ,EAAIkO,EAAQ3nB,GAAO2B,OAAQnB,EAAIiZ,EAAGjZ,IAC9Ca,EAAOwlB,MAAMhN,IAAKuT,EAAMptB,EAAM2nB,EAAQ3nB,GAAQQ,IAO7C0gB,EAASF,QAAS/gB,KACtBotB,EAAWnM,EAASzB,OAAQxf,GAC5BqtB,EAAWjsB,EAAOmC,OAAQ,GAAI6pB,GAE9BnM,EAASL,IAAKuM,EAAME,KAkBtB,SAASC,GAAUC,EAAY3a,EAAMrQ,EAAUojB,GAG9C/S,EAAOjU,EAAMiU,GAEb,IAAIkT,EAAUnjB,EAAO8iB,EAAS+H,EAAYntB,EAAMC,EAC/CC,EAAI,EACJiZ,EAAI+T,EAAW7rB,OACf+rB,EAAWjU,EAAI,EACfjU,EAAQqN,EAAM,GACd8a,EAAkBjuB,EAAY8F,GAG/B,GAAKmoB,GACG,EAAJlU,GAA0B,iBAAVjU,IAChB/F,EAAQ6kB,YAAcwI,GAAShhB,KAAMtG,GACxC,OAAOgoB,EAAWjrB,KAAM,SAAUoX,GACjC,IAAIb,EAAO0U,EAAW3qB,GAAI8W,GACrBgU,IACJ9a,EAAM,GAAMrN,EAAM1G,KAAMT,KAAMsb,EAAOb,EAAK8U,SAE3CL,GAAUzU,EAAMjG,EAAMrQ,EAAUojB,KAIlC,GAAKnM,IAEJ7W,GADAmjB,EAAWN,GAAe5S,EAAM2a,EAAY,GAAIjiB,eAAe,EAAOiiB,EAAY5H,IACjEhV,WAEmB,IAA/BmV,EAASlb,WAAWlJ,SACxBokB,EAAWnjB,GAIPA,GAASgjB,GAAU,CAOvB,IALA6H,GADA/H,EAAUrkB,EAAOoB,IAAKuiB,GAAQe,EAAU,UAAYkH,KAC/BtrB,OAKbnB,EAAIiZ,EAAGjZ,IACdF,EAAOylB,EAEFvlB,IAAMktB,IACVptB,EAAOe,EAAOwC,MAAOvD,GAAM,GAAM,GAG5BmtB,GAIJpsB,EAAOgB,MAAOqjB,EAASV,GAAQ1kB,EAAM,YAIvCkC,EAAS1D,KAAM0uB,EAAYhtB,GAAKF,EAAME,GAGvC,GAAKitB,EAOJ,IANAltB,EAAMmlB,EAASA,EAAQ/jB,OAAS,GAAI4J,cAGpClK,EAAOoB,IAAKijB,EAASwH,IAGf1sB,EAAI,EAAGA,EAAIitB,EAAYjtB,IAC5BF,EAAOolB,EAASllB,GACX4jB,GAAYtY,KAAMxL,EAAKN,MAAQ,MAClCihB,EAASxB,OAAQnf,EAAM,eACxBe,EAAOyF,SAAUvG,EAAKD,KAEjBA,EAAKL,KAA8C,YAArCK,EAAKN,MAAQ,IAAK8F,cAG/BzE,EAAOwsB,WAAavtB,EAAKH,UAC7BkB,EAAOwsB,SAAUvtB,EAAKL,IAAK,CAC1BC,MAAOI,EAAKJ,OAASI,EAAKO,aAAc,UACtCN,GAGJH,EAASE,EAAKqQ,YAAYpM,QAASwoB,GAAc,IAAMzsB,EAAMC,IAQnE,OAAOitB,EAGR,SAASvR,GAAQvZ,EAAMpB,EAAUwsB,GAKhC,IAJA,IAAIxtB,EACH0lB,EAAQ1kB,EAAWD,EAAOsN,OAAQrN,EAAUoB,GAASA,EACrDlC,EAAI,EAE4B,OAAvBF,EAAO0lB,EAAOxlB,IAAeA,IAChCstB,GAA8B,IAAlBxtB,EAAKV,UACtByB,EAAO0sB,UAAW/I,GAAQ1kB,IAGtBA,EAAKW,aACJ6sB,GAAYtL,GAAYliB,IAC5B2kB,GAAeD,GAAQ1kB,EAAM,WAE9BA,EAAKW,WAAWC,YAAaZ,IAI/B,OAAOoC,EAGRrB,EAAOmC,OAAQ,CACdyiB,cAAe,SAAU2H,GACxB,OAAOA,GAGR/pB,MAAO,SAAUnB,EAAMsrB,EAAeC,GACrC,IAAIztB,EAAGiZ,EAAGyU,EAAaC,EApINluB,EAAKmtB,EACnB1iB,EAoIF7G,EAAQnB,EAAK6hB,WAAW,GACxB6J,EAAS5L,GAAY9f,GAGtB,KAAMjD,EAAQ+kB,gBAAsC,IAAlB9hB,EAAK9C,UAAoC,KAAlB8C,EAAK9C,UAC3DyB,EAAO8W,SAAUzV,IAMnB,IAHAyrB,EAAenJ,GAAQnhB,GAGjBrD,EAAI,EAAGiZ,GAFbyU,EAAclJ,GAAQtiB,IAEOf,OAAQnB,EAAIiZ,EAAGjZ,IAhJ5BP,EAiJLiuB,EAAa1tB,GAjJH4sB,EAiJQe,EAAc3tB,QAhJzCkK,EAGc,WAHdA,EAAW0iB,EAAK1iB,SAAS5E,gBAGAoe,GAAepY,KAAM7L,EAAID,MACrDotB,EAAKpZ,QAAU/T,EAAI+T,QAGK,UAAbtJ,GAAqC,aAAbA,IACnC0iB,EAAKnV,aAAehY,EAAIgY,cA6IxB,GAAK+V,EACJ,GAAKC,EAIJ,IAHAC,EAAcA,GAAelJ,GAAQtiB,GACrCyrB,EAAeA,GAAgBnJ,GAAQnhB,GAEjCrD,EAAI,EAAGiZ,EAAIyU,EAAYvsB,OAAQnB,EAAIiZ,EAAGjZ,IAC3C2sB,GAAgBe,EAAa1tB,GAAK2tB,EAAc3tB,SAGjD2sB,GAAgBzqB,EAAMmB,GAWxB,OAL2B,GAD3BsqB,EAAenJ,GAAQnhB,EAAO,WACZlC,QACjBsjB,GAAekJ,GAAeC,GAAUpJ,GAAQtiB,EAAM,WAIhDmB,GAGRkqB,UAAW,SAAU5rB,GAKpB,IAJA,IAAI2e,EAAMpe,EAAM1C,EACfwd,EAAUnc,EAAOwlB,MAAMrJ,QACvBhd,EAAI,OAE6B2D,KAAxBzB,EAAOP,EAAO3B,IAAqBA,IAC5C,GAAK+f,EAAY7d,GAAS,CACzB,GAAOoe,EAAOpe,EAAMue,EAAS7c,SAAc,CAC1C,GAAK0c,EAAK6G,OACT,IAAM3nB,KAAQ8gB,EAAK6G,OACbnK,EAASxd,GACbqB,EAAOwlB,MAAM5K,OAAQvZ,EAAM1C,GAI3BqB,EAAOunB,YAAalmB,EAAM1C,EAAM8gB,EAAKqH,QAOxCzlB,EAAMue,EAAS7c,cAAYD,EAEvBzB,EAAMwe,EAAS9c,WAInB1B,EAAMwe,EAAS9c,cAAYD,OAOhC9C,EAAOG,GAAGgC,OAAQ,CACjB6qB,OAAQ,SAAU/sB,GACjB,OAAO2a,GAAQ5d,KAAMiD,GAAU,IAGhC2a,OAAQ,SAAU3a,GACjB,OAAO2a,GAAQ5d,KAAMiD,IAGtBV,KAAM,SAAU4E,GACf,OAAOia,EAAQphB,KAAM,SAAUmH,GAC9B,YAAiBrB,IAAVqB,EACNnE,EAAOT,KAAMvC,MACbA,KAAK8V,QAAQ5R,KAAM,WACK,IAAlBlE,KAAKuB,UAAoC,KAAlBvB,KAAKuB,UAAqC,IAAlBvB,KAAKuB,WACxDvB,KAAKsS,YAAcnL,MAGpB,KAAMA,EAAO7C,UAAUhB,SAG3B2sB,OAAQ,WACP,OAAOf,GAAUlvB,KAAMsE,UAAW,SAAUD,GACpB,IAAlBrE,KAAKuB,UAAoC,KAAlBvB,KAAKuB,UAAqC,IAAlBvB,KAAKuB,UAC3CotB,GAAoB3uB,KAAMqE,GAChC1B,YAAa0B,MAKvB6rB,QAAS,WACR,OAAOhB,GAAUlvB,KAAMsE,UAAW,SAAUD,GAC3C,GAAuB,IAAlBrE,KAAKuB,UAAoC,KAAlBvB,KAAKuB,UAAqC,IAAlBvB,KAAKuB,SAAiB,CACzE,IAAIkE,EAASkpB,GAAoB3uB,KAAMqE,GACvCoB,EAAO0qB,aAAc9rB,EAAMoB,EAAO8M,gBAKrC6d,OAAQ,WACP,OAAOlB,GAAUlvB,KAAMsE,UAAW,SAAUD,GACtCrE,KAAK4C,YACT5C,KAAK4C,WAAWutB,aAAc9rB,EAAMrE,SAKvCqwB,MAAO,WACN,OAAOnB,GAAUlvB,KAAMsE,UAAW,SAAUD,GACtCrE,KAAK4C,YACT5C,KAAK4C,WAAWutB,aAAc9rB,EAAMrE,KAAKiP,gBAK5C6G,MAAO,WAIN,IAHA,IAAIzR,EACHlC,EAAI,EAE2B,OAAtBkC,EAAOrE,KAAMmC,IAAeA,IACd,IAAlBkC,EAAK9C,WAGTyB,EAAO0sB,UAAW/I,GAAQtiB,GAAM,IAGhCA,EAAKiO,YAAc,IAIrB,OAAOtS,MAGRwF,MAAO,SAAUmqB,EAAeC,GAI/B,OAHAD,EAAiC,MAAjBA,GAAgCA,EAChDC,EAAyC,MAArBA,EAA4BD,EAAgBC,EAEzD5vB,KAAKoE,IAAK,WAChB,OAAOpB,EAAOwC,MAAOxF,KAAM2vB,EAAeC,MAI5CL,KAAM,SAAUpoB,GACf,OAAOia,EAAQphB,KAAM,SAAUmH,GAC9B,IAAI9C,EAAOrE,KAAM,IAAO,GACvBmC,EAAI,EACJiZ,EAAIpb,KAAKsD,OAEV,QAAewC,IAAVqB,GAAyC,IAAlB9C,EAAK9C,SAChC,OAAO8C,EAAKwM,UAIb,GAAsB,iBAAV1J,IAAuBqnB,GAAa/gB,KAAMtG,KACpDkf,IAAWP,GAAS3Y,KAAMhG,IAAW,CAAE,GAAI,KAAQ,GAAIM,eAAkB,CAE1EN,EAAQnE,EAAO4kB,cAAezgB,GAE9B,IACC,KAAQhF,EAAIiZ,EAAGjZ,IAIS,KAHvBkC,EAAOrE,KAAMmC,IAAO,IAGVZ,WACTyB,EAAO0sB,UAAW/I,GAAQtiB,GAAM,IAChCA,EAAKwM,UAAY1J,GAInB9C,EAAO,EAGN,MAAQoI,KAGNpI,GACJrE,KAAK8V,QAAQma,OAAQ9oB,IAEpB,KAAMA,EAAO7C,UAAUhB,SAG3BgtB,YAAa,WACZ,IAAI/I,EAAU,GAGd,OAAO2H,GAAUlvB,KAAMsE,UAAW,SAAUD,GAC3C,IAAI8P,EAASnU,KAAK4C,WAEbI,EAAO6D,QAAS7G,KAAMunB,GAAY,IACtCvkB,EAAO0sB,UAAW/I,GAAQ3mB,OACrBmU,GACJA,EAAOoc,aAAclsB,EAAMrE,QAK3BunB,MAILvkB,EAAOkB,KAAM,CACZssB,SAAU,SACVC,UAAW,UACXN,aAAc,SACdO,YAAa,QACbC,WAAY,eACV,SAAUtrB,EAAMurB,GAClB5tB,EAAOG,GAAIkC,GAAS,SAAUpC,GAO7B,IANA,IAAIa,EACHC,EAAM,GACN8sB,EAAS7tB,EAAQC,GACjBwB,EAAOosB,EAAOvtB,OAAS,EACvBnB,EAAI,EAEGA,GAAKsC,EAAMtC,IAClB2B,EAAQ3B,IAAMsC,EAAOzE,KAAOA,KAAKwF,OAAO,GACxCxC,EAAQ6tB,EAAQ1uB,IAAOyuB,GAAY9sB,GAInClD,EAAKD,MAAOoD,EAAKD,EAAMH,OAGxB,OAAO3D,KAAK6D,UAAWE,MAGzB,IAAI+sB,GAAY,IAAI/mB,OAAQ,KAAOga,GAAO,kBAAmB,KAEzDgN,GAAY,SAAU1sB,GAKxB,IAAI2oB,EAAO3oB,EAAK6I,cAAc4C,YAM9B,OAJMkd,GAASA,EAAKgE,SACnBhE,EAAOjtB,GAGDitB,EAAKiE,iBAAkB5sB,IAG5B6sB,GAAO,SAAU7sB,EAAMe,EAASjB,GACnC,IAAIJ,EAAKsB,EACR8rB,EAAM,GAGP,IAAM9rB,KAAQD,EACb+rB,EAAK9rB,GAAShB,EAAKkgB,MAAOlf,GAC1BhB,EAAKkgB,MAAOlf,GAASD,EAASC,GAM/B,IAAMA,KAHNtB,EAAMI,EAAS1D,KAAM4D,GAGPe,EACbf,EAAKkgB,MAAOlf,GAAS8rB,EAAK9rB,GAG3B,OAAOtB,GAIJqtB,GAAY,IAAIrnB,OAAQma,GAAUrW,KAAM,KAAO,KAiJnD,SAASwjB,GAAQhtB,EAAMgB,EAAMisB,GAC5B,IAAIC,EAAOC,EAAUC,EAAU1tB,EAM9BwgB,EAAQlgB,EAAKkgB,MAqCd,OAnCA+M,EAAWA,GAAYP,GAAW1sB,MAQpB,MAFbN,EAAMutB,EAASI,iBAAkBrsB,IAAUisB,EAAUjsB,KAEjC8e,GAAY9f,KAC/BN,EAAMf,EAAOuhB,MAAOlgB,EAAMgB,KAQrBjE,EAAQuwB,kBAAoBb,GAAUrjB,KAAM1J,IAASqtB,GAAU3jB,KAAMpI,KAG1EksB,EAAQhN,EAAMgN,MACdC,EAAWjN,EAAMiN,SACjBC,EAAWlN,EAAMkN,SAGjBlN,EAAMiN,SAAWjN,EAAMkN,SAAWlN,EAAMgN,MAAQxtB,EAChDA,EAAMutB,EAASC,MAGfhN,EAAMgN,MAAQA,EACdhN,EAAMiN,SAAWA,EACjBjN,EAAMkN,SAAWA,SAIJ3rB,IAAR/B,EAINA,EAAM,GACNA,EAIF,SAAS6tB,GAAcC,EAAaC,GAGnC,MAAO,CACNnuB,IAAK,WACJ,IAAKkuB,IASL,OAAS7xB,KAAK2D,IAAMmuB,GAASnxB,MAAOX,KAAMsE,kBALlCtE,KAAK2D,OA3MhB,WAIC,SAASouB,IAGR,GAAMnM,EAAN,CAIAoM,EAAUzN,MAAM0N,QAAU,+EAE1BrM,EAAIrB,MAAM0N,QACT,4HAGDtiB,GAAgBhN,YAAaqvB,GAAYrvB,YAAaijB,GAEtD,IAAIsM,EAAWnyB,EAAOkxB,iBAAkBrL,GACxCuM,EAAoC,OAAjBD,EAASniB,IAG5BqiB,EAAsE,KAA9CC,EAAoBH,EAASI,YAIrD1M,EAAIrB,MAAMgO,MAAQ,MAClBC,EAA6D,KAAzCH,EAAoBH,EAASK,OAIjDE,EAAgE,KAAzCJ,EAAoBH,EAASX,OAMpD3L,EAAIrB,MAAMmO,SAAW,WACrBC,EAAiE,KAA9CN,EAAoBzM,EAAIgN,YAAc,GAEzDjjB,GAAgB9M,YAAamvB,GAI7BpM,EAAM,MAGP,SAASyM,EAAoBQ,GAC5B,OAAO7sB,KAAK8sB,MAAOC,WAAYF,IAGhC,IAAIV,EAAkBM,EAAsBE,EAAkBH,EAC7DQ,EAAyBZ,EACzBJ,EAAYpyB,EAAS0C,cAAe,OACpCsjB,EAAMhmB,EAAS0C,cAAe,OAGzBsjB,EAAIrB,QAMVqB,EAAIrB,MAAM0O,eAAiB,cAC3BrN,EAAIM,WAAW,GAAO3B,MAAM0O,eAAiB,GAC7C7xB,EAAQ8xB,gBAA+C,gBAA7BtN,EAAIrB,MAAM0O,eAEpCjwB,EAAOmC,OAAQ/D,EAAS,CACvB+xB,kBAAmB,WAElB,OADApB,IACOU,GAERd,eAAgB,WAEf,OADAI,IACOS,GAERY,cAAe,WAEd,OADArB,IACOI,GAERkB,mBAAoB,WAEnB,OADAtB,IACOK,GAERkB,cAAe,WAEd,OADAvB,IACOY,GAYRY,qBAAsB,WACrB,IAAIC,EAAOhN,EAAIiN,EAASC,EAmCxB,OAlCgC,MAA3BV,IACJQ,EAAQ5zB,EAAS0C,cAAe,SAChCkkB,EAAK5mB,EAAS0C,cAAe,MAC7BmxB,EAAU7zB,EAAS0C,cAAe,OAElCkxB,EAAMjP,MAAM0N,QAAU,2DACtBzL,EAAGjC,MAAM0N,QAAU,mBAKnBzL,EAAGjC,MAAMoP,OAAS,MAClBF,EAAQlP,MAAMoP,OAAS,MAQvBF,EAAQlP,MAAMC,QAAU,QAExB7U,GACEhN,YAAa6wB,GACb7wB,YAAa6jB,GACb7jB,YAAa8wB,GAEfC,EAAU3zB,EAAOkxB,iBAAkBzK,GACnCwM,EAA4BY,SAAUF,EAAQC,OAAQ,IACrDC,SAAUF,EAAQG,eAAgB,IAClCD,SAAUF,EAAQI,kBAAmB,MAAWtN,EAAGuN,aAEpDpkB,GAAgB9M,YAAa2wB,IAEvBR,MAvIV,GAsNA,IAAIgB,GAAc,CAAE,SAAU,MAAO,MACpCC,GAAar0B,EAAS0C,cAAe,OAAQiiB,MAC7C2P,GAAc,GAkBf,SAASC,GAAe9uB,GACvB,IAAI+uB,EAAQpxB,EAAOqxB,SAAUhvB,IAAU6uB,GAAa7uB,GAEpD,OAAK+uB,IAGA/uB,KAAQ4uB,GACL5uB,EAED6uB,GAAa7uB,GAxBrB,SAAyBA,GAGxB,IAAIivB,EAAUjvB,EAAM,GAAI0c,cAAgB1c,EAAK/E,MAAO,GACnD6B,EAAI6xB,GAAY1wB,OAEjB,MAAQnB,IAEP,IADAkD,EAAO2uB,GAAa7xB,GAAMmyB,KACbL,GACZ,OAAO5uB,EAeoBkvB,CAAgBlvB,IAAUA,GAIxD,IAKCmvB,GAAe,4BACfC,GAAc,MACdC,GAAU,CAAEhC,SAAU,WAAYiC,WAAY,SAAUnQ,QAAS,SACjEoQ,GAAqB,CACpBC,cAAe,IACfC,WAAY,OAGd,SAASC,GAAmBnwB,EAAOuC,EAAO6tB,GAIzC,IAAIhuB,EAAUid,GAAQ9W,KAAMhG,GAC5B,OAAOH,EAGNhB,KAAKivB,IAAK,EAAGjuB,EAAS,IAAQguB,GAAY,KAAUhuB,EAAS,IAAO,MACpEG,EAGF,SAAS+tB,GAAoB7wB,EAAM8wB,EAAWC,EAAKC,EAAaC,EAAQC,GACvE,IAAIpzB,EAAkB,UAAdgzB,EAAwB,EAAI,EACnCK,EAAQ,EACRC,EAAQ,EAGT,GAAKL,KAAUC,EAAc,SAAW,WACvC,OAAO,EAGR,KAAQlzB,EAAI,EAAGA,GAAK,EAGN,WAARizB,IACJK,GAASzyB,EAAOyhB,IAAKpgB,EAAM+wB,EAAMlR,GAAW/hB,IAAK,EAAMmzB,IAIlDD,GAmBQ,YAARD,IACJK,GAASzyB,EAAOyhB,IAAKpgB,EAAM,UAAY6f,GAAW/hB,IAAK,EAAMmzB,IAIjD,WAARF,IACJK,GAASzyB,EAAOyhB,IAAKpgB,EAAM,SAAW6f,GAAW/hB,GAAM,SAAS,EAAMmzB,MAtBvEG,GAASzyB,EAAOyhB,IAAKpgB,EAAM,UAAY6f,GAAW/hB,IAAK,EAAMmzB,GAGhD,YAARF,EACJK,GAASzyB,EAAOyhB,IAAKpgB,EAAM,SAAW6f,GAAW/hB,GAAM,SAAS,EAAMmzB,GAItEE,GAASxyB,EAAOyhB,IAAKpgB,EAAM,SAAW6f,GAAW/hB,GAAM,SAAS,EAAMmzB,IAoCzE,OAhBMD,GAA8B,GAAfE,IAIpBE,GAASzvB,KAAKivB,IAAK,EAAGjvB,KAAK0vB,KAC1BrxB,EAAM,SAAW8wB,EAAW,GAAIpT,cAAgBoT,EAAU70B,MAAO,IACjEi1B,EACAE,EACAD,EACA,MAIM,GAGDC,EAGR,SAASE,GAAkBtxB,EAAM8wB,EAAWK,GAG3C,IAAIF,EAASvE,GAAW1sB,GAKvBgxB,IADmBj0B,EAAQ+xB,qBAAuBqC,IAEE,eAAnDxyB,EAAOyhB,IAAKpgB,EAAM,aAAa,EAAOixB,GACvCM,EAAmBP,EAEnBjzB,EAAMivB,GAAQhtB,EAAM8wB,EAAWG,GAC/BO,EAAa,SAAWV,EAAW,GAAIpT,cAAgBoT,EAAU70B,MAAO,GAIzE,GAAKwwB,GAAUrjB,KAAMrL,GAAQ,CAC5B,IAAMozB,EACL,OAAOpzB,EAERA,EAAM,OAyCP,QAlCQhB,EAAQ+xB,qBAAuBkC,IAMrCj0B,EAAQmyB,wBAA0BlnB,EAAUhI,EAAM,OAI3C,SAARjC,IAIC2wB,WAAY3wB,IAA0D,WAAjDY,EAAOyhB,IAAKpgB,EAAM,WAAW,EAAOixB,KAG1DjxB,EAAKyxB,iBAAiBxyB,SAEtB+xB,EAAiE,eAAnDryB,EAAOyhB,IAAKpgB,EAAM,aAAa,EAAOixB,IAKpDM,EAAmBC,KAAcxxB,KAEhCjC,EAAMiC,EAAMwxB,MAKdzzB,EAAM2wB,WAAY3wB,IAAS,GAI1B8yB,GACC7wB,EACA8wB,EACAK,IAAWH,EAAc,SAAW,WACpCO,EACAN,EAGAlzB,GAEE,KA+SL,SAAS2zB,GAAO1xB,EAAMe,EAASsd,EAAM1d,EAAKgxB,GACzC,OAAO,IAAID,GAAMxyB,UAAUH,KAAMiB,EAAMe,EAASsd,EAAM1d,EAAKgxB,GA7S5DhzB,EAAOmC,OAAQ,CAId8wB,SAAU,CACTC,QAAS,CACRvyB,IAAK,SAAUU,EAAMitB,GACpB,GAAKA,EAAW,CAGf,IAAIvtB,EAAMstB,GAAQhtB,EAAM,WACxB,MAAe,KAARN,EAAa,IAAMA,MAO9BohB,UAAW,CACVgR,yBAA2B,EAC3BC,aAAe,EACfC,aAAe,EACfC,UAAY,EACZC,YAAc,EACdzB,YAAc,EACd0B,UAAY,EACZC,YAAc,EACdC,eAAiB,EACjBC,iBAAmB,EACnBC,SAAW,EACXC,YAAc,EACdC,cAAgB,EAChBC,YAAc,EACdb,SAAW,EACXc,OAAS,EACTC,SAAW,EACXC,QAAU,EACVC,QAAU,EACVC,MAAQ,GAKT/C,SAAU,GAGV9P,MAAO,SAAUlgB,EAAMgB,EAAM8B,EAAOquB,GAGnC,GAAMnxB,GAA0B,IAAlBA,EAAK9C,UAAoC,IAAlB8C,EAAK9C,UAAmB8C,EAAKkgB,MAAlE,CAKA,IAAIxgB,EAAKpC,EAAM6hB,EACd6T,EAAWrV,EAAW3c,GACtBiyB,EAAe7C,GAAYhnB,KAAMpI,GACjCkf,EAAQlgB,EAAKkgB,MAad,GARM+S,IACLjyB,EAAO8uB,GAAekD,IAIvB7T,EAAQxgB,EAAOizB,SAAU5wB,IAAUrC,EAAOizB,SAAUoB,QAGrCvxB,IAAVqB,EA0CJ,OAAKqc,GAAS,QAASA,QACwB1d,KAA5C/B,EAAMyf,EAAM7f,IAAKU,GAAM,EAAOmxB,IAEzBzxB,EAIDwgB,EAAOlf,GA7CA,YAHd1D,SAAcwF,KAGcpD,EAAMkgB,GAAQ9W,KAAMhG,KAAapD,EAAK,KACjEoD,EAAQud,GAAWrgB,EAAMgB,EAAMtB,GAG/BpC,EAAO,UAIM,MAATwF,GAAiBA,GAAUA,IAOlB,WAATxF,GAAsB21B,IAC1BnwB,GAASpD,GAAOA,EAAK,KAASf,EAAOmiB,UAAWkS,GAAa,GAAK,OAI7Dj2B,EAAQ8xB,iBAA6B,KAAV/rB,GAAiD,IAAjC9B,EAAKxE,QAAS,gBAC9D0jB,EAAOlf,GAAS,WAIXme,GAAY,QAASA,QACsB1d,KAA9CqB,EAAQqc,EAAMhB,IAAKne,EAAM8C,EAAOquB,MAE7B8B,EACJ/S,EAAMgT,YAAalyB,EAAM8B,GAEzBod,EAAOlf,GAAS8B,MAkBpBsd,IAAK,SAAUpgB,EAAMgB,EAAMmwB,EAAOF,GACjC,IAAIlzB,EAAKwB,EAAK4f,EACb6T,EAAWrV,EAAW3c,GA6BvB,OA5BgBovB,GAAYhnB,KAAMpI,KAMjCA,EAAO8uB,GAAekD,KAIvB7T,EAAQxgB,EAAOizB,SAAU5wB,IAAUrC,EAAOizB,SAAUoB,KAGtC,QAAS7T,IACtBphB,EAAMohB,EAAM7f,IAAKU,GAAM,EAAMmxB,SAIjB1vB,IAAR1D,IACJA,EAAMivB,GAAQhtB,EAAMgB,EAAMiwB,IAId,WAARlzB,GAAoBiD,KAAQuvB,KAChCxyB,EAAMwyB,GAAoBvvB,IAIZ,KAAVmwB,GAAgBA,GACpB5xB,EAAMmvB,WAAY3wB,IACD,IAAVozB,GAAkBgC,SAAU5zB,GAAQA,GAAO,EAAIxB,GAGhDA,KAITY,EAAOkB,KAAM,CAAE,SAAU,SAAW,SAAUsD,EAAI2tB,GACjDnyB,EAAOizB,SAAUd,GAAc,CAC9BxxB,IAAK,SAAUU,EAAMitB,EAAUkE,GAC9B,GAAKlE,EAIJ,OAAOkD,GAAa/mB,KAAMzK,EAAOyhB,IAAKpgB,EAAM,aAQxCA,EAAKyxB,iBAAiBxyB,QAAWe,EAAKozB,wBAAwBlG,MAIjEoE,GAAkBtxB,EAAM8wB,EAAWK,GAHnCtE,GAAM7sB,EAAMqwB,GAAS,WACpB,OAAOiB,GAAkBtxB,EAAM8wB,EAAWK,MAM9ChT,IAAK,SAAUne,EAAM8C,EAAOquB,GAC3B,IAAIxuB,EACHsuB,EAASvE,GAAW1sB,GAIpBqzB,GAAsBt2B,EAAQkyB,iBACT,aAApBgC,EAAO5C,SAIR2C,GADkBqC,GAAsBlC,IAEY,eAAnDxyB,EAAOyhB,IAAKpgB,EAAM,aAAa,EAAOixB,GACvCN,EAAWQ,EACVN,GACC7wB,EACA8wB,EACAK,EACAH,EACAC,GAED,EAqBF,OAjBKD,GAAeqC,IACnB1C,GAAYhvB,KAAK0vB,KAChBrxB,EAAM,SAAW8wB,EAAW,GAAIpT,cAAgBoT,EAAU70B,MAAO,IACjEyyB,WAAYuC,EAAQH,IACpBD,GAAoB7wB,EAAM8wB,EAAW,UAAU,EAAOG,GACtD,KAKGN,IAAchuB,EAAUid,GAAQ9W,KAAMhG,KACb,QAA3BH,EAAS,IAAO,QAElB3C,EAAKkgB,MAAO4Q,GAAchuB,EAC1BA,EAAQnE,EAAOyhB,IAAKpgB,EAAM8wB,IAGpBJ,GAAmB1wB,EAAM8C,EAAO6tB,OAK1ChyB,EAAOizB,SAAS3D,WAAaV,GAAcxwB,EAAQiyB,mBAClD,SAAUhvB,EAAMitB,GACf,GAAKA,EACJ,OAASyB,WAAY1B,GAAQhtB,EAAM,gBAClCA,EAAKozB,wBAAwBE,KAC5BzG,GAAM7sB,EAAM,CAAEiuB,WAAY,GAAK,WAC9B,OAAOjuB,EAAKozB,wBAAwBE,QAEnC,OAMP30B,EAAOkB,KAAM,CACZ0zB,OAAQ,GACRC,QAAS,GACTC,OAAQ,SACN,SAAUC,EAAQC,GACpBh1B,EAAOizB,SAAU8B,EAASC,GAAW,CACpCC,OAAQ,SAAU9wB,GAOjB,IANA,IAAIhF,EAAI,EACP+1B,EAAW,GAGXC,EAAyB,iBAAVhxB,EAAqBA,EAAMI,MAAO,KAAQ,CAAEJ,GAEpDhF,EAAI,EAAGA,IACd+1B,EAAUH,EAAS7T,GAAW/hB,GAAM61B,GACnCG,EAAOh2B,IAAOg2B,EAAOh2B,EAAI,IAAOg2B,EAAO,GAGzC,OAAOD,IAIO,WAAXH,IACJ/0B,EAAOizB,SAAU8B,EAASC,GAASxV,IAAMuS,MAI3C/xB,EAAOG,GAAGgC,OAAQ,CACjBsf,IAAK,SAAUpf,EAAM8B,GACpB,OAAOia,EAAQphB,KAAM,SAAUqE,EAAMgB,EAAM8B,GAC1C,IAAImuB,EAAQxwB,EACXV,EAAM,GACNjC,EAAI,EAEL,GAAKyD,MAAMC,QAASR,GAAS,CAI5B,IAHAiwB,EAASvE,GAAW1sB,GACpBS,EAAMO,EAAK/B,OAEHnB,EAAI2C,EAAK3C,IAChBiC,EAAKiB,EAAMlD,IAAQa,EAAOyhB,IAAKpgB,EAAMgB,EAAMlD,IAAK,EAAOmzB,GAGxD,OAAOlxB,EAGR,YAAiB0B,IAAVqB,EACNnE,EAAOuhB,MAAOlgB,EAAMgB,EAAM8B,GAC1BnE,EAAOyhB,IAAKpgB,EAAMgB,IACjBA,EAAM8B,EAA0B,EAAnB7C,UAAUhB,aAQ5BN,EAAO+yB,MAAQA,IAETxyB,UAAY,CACjBE,YAAasyB,GACb3yB,KAAM,SAAUiB,EAAMe,EAASsd,EAAM1d,EAAKgxB,EAAQ9Q,GACjDllB,KAAKqE,KAAOA,EACZrE,KAAK0iB,KAAOA,EACZ1iB,KAAKg2B,OAASA,GAAUhzB,EAAOgzB,OAAOtP,SACtC1mB,KAAKoF,QAAUA,EACfpF,KAAKkU,MAAQlU,KAAKmsB,IAAMnsB,KAAK8O,MAC7B9O,KAAKgF,IAAMA,EACXhF,KAAKklB,KAAOA,IAAUliB,EAAOmiB,UAAWzC,GAAS,GAAK,OAEvD5T,IAAK,WACJ,IAAI0U,EAAQuS,GAAMqC,UAAWp4B,KAAK0iB,MAElC,OAAOc,GAASA,EAAM7f,IACrB6f,EAAM7f,IAAK3D,MACX+1B,GAAMqC,UAAU1R,SAAS/iB,IAAK3D,OAEhCq4B,IAAK,SAAUC,GACd,IAAIC,EACH/U,EAAQuS,GAAMqC,UAAWp4B,KAAK0iB,MAoB/B,OAlBK1iB,KAAKoF,QAAQozB,SACjBx4B,KAAKy4B,IAAMF,EAAQv1B,EAAOgzB,OAAQh2B,KAAKg2B,QACtCsC,EAASt4B,KAAKoF,QAAQozB,SAAWF,EAAS,EAAG,EAAGt4B,KAAKoF,QAAQozB,UAG9Dx4B,KAAKy4B,IAAMF,EAAQD,EAEpBt4B,KAAKmsB,KAAQnsB,KAAKgF,IAAMhF,KAAKkU,OAAUqkB,EAAQv4B,KAAKkU,MAE/ClU,KAAKoF,QAAQszB,MACjB14B,KAAKoF,QAAQszB,KAAKj4B,KAAMT,KAAKqE,KAAMrE,KAAKmsB,IAAKnsB,MAGzCwjB,GAASA,EAAMhB,IACnBgB,EAAMhB,IAAKxiB,MAEX+1B,GAAMqC,UAAU1R,SAASlE,IAAKxiB,MAExBA,QAIOoD,KAAKG,UAAYwyB,GAAMxyB,WAEvCwyB,GAAMqC,UAAY,CACjB1R,SAAU,CACT/iB,IAAK,SAAUihB,GACd,IAAIrR,EAIJ,OAA6B,IAAxBqR,EAAMvgB,KAAK9C,UACa,MAA5BqjB,EAAMvgB,KAAMugB,EAAMlC,OAAoD,MAAlCkC,EAAMvgB,KAAKkgB,MAAOK,EAAMlC,MACrDkC,EAAMvgB,KAAMugB,EAAMlC,OAO1BnP,EAASvQ,EAAOyhB,IAAKG,EAAMvgB,KAAMugB,EAAMlC,KAAM,MAGhB,SAAXnP,EAAwBA,EAAJ,GAEvCiP,IAAK,SAAUoC,GAKT5hB,EAAO21B,GAAGD,KAAM9T,EAAMlC,MAC1B1f,EAAO21B,GAAGD,KAAM9T,EAAMlC,MAAQkC,GACK,IAAxBA,EAAMvgB,KAAK9C,WACtByB,EAAOizB,SAAUrR,EAAMlC,OAC6B,MAAnDkC,EAAMvgB,KAAKkgB,MAAO4P,GAAevP,EAAMlC,OAGxCkC,EAAMvgB,KAAMugB,EAAMlC,MAASkC,EAAMuH,IAFjCnpB,EAAOuhB,MAAOK,EAAMvgB,KAAMugB,EAAMlC,KAAMkC,EAAMuH,IAAMvH,EAAMM,UAU5C0T,UAAY7C,GAAMqC,UAAUS,WAAa,CACxDrW,IAAK,SAAUoC,GACTA,EAAMvgB,KAAK9C,UAAYqjB,EAAMvgB,KAAKzB,aACtCgiB,EAAMvgB,KAAMugB,EAAMlC,MAASkC,EAAMuH,OAKpCnpB,EAAOgzB,OAAS,CACf8C,OAAQ,SAAUC,GACjB,OAAOA,GAERC,MAAO,SAAUD,GAChB,MAAO,GAAM/yB,KAAKizB,IAAKF,EAAI/yB,KAAKkzB,IAAO,GAExCxS,SAAU,SAGX1jB,EAAO21B,GAAK5C,GAAMxyB,UAAUH,KAG5BJ,EAAO21B,GAAGD,KAAO,GAKjB,IACCS,GAAOC,GAmrBHxoB,GAEHyoB,GAprBDC,GAAW,yBACXC,GAAO,cAER,SAASC,KACHJ,MACqB,IAApBx5B,EAAS65B,QAAoB15B,EAAO25B,sBACxC35B,EAAO25B,sBAAuBF,IAE9Bz5B,EAAO+f,WAAY0Z,GAAUx2B,EAAO21B,GAAGgB,UAGxC32B,EAAO21B,GAAGiB,QAKZ,SAASC,KAIR,OAHA95B,EAAO+f,WAAY,WAClBqZ,QAAQrzB,IAEAqzB,GAAQzwB,KAAKyjB,MAIvB,SAAS2N,GAAOn4B,EAAMo4B,GACrB,IAAI/L,EACH7rB,EAAI,EACJuM,EAAQ,CAAEilB,OAAQhyB,GAKnB,IADAo4B,EAAeA,EAAe,EAAI,EAC1B53B,EAAI,EAAGA,GAAK,EAAI43B,EAEvBrrB,EAAO,UADPsf,EAAQ9J,GAAW/hB,KACSuM,EAAO,UAAYsf,GAAUrsB,EAO1D,OAJKo4B,IACJrrB,EAAMwnB,QAAUxnB,EAAM6iB,MAAQ5vB,GAGxB+M,EAGR,SAASsrB,GAAa7yB,EAAOub,EAAMuX,GAKlC,IAJA,IAAIrV,EACHuK,GAAe+K,GAAUC,SAAUzX,IAAU,IAAKhiB,OAAQw5B,GAAUC,SAAU,MAC9E7e,EAAQ,EACRhY,EAAS6rB,EAAW7rB,OACbgY,EAAQhY,EAAQgY,IACvB,GAAOsJ,EAAQuK,EAAY7T,GAAQ7a,KAAMw5B,EAAWvX,EAAMvb,GAGzD,OAAOyd,EAsNV,SAASsV,GAAW71B,EAAM+1B,EAAYh1B,GACrC,IAAImO,EACH8mB,EACA/e,EAAQ,EACRhY,EAAS42B,GAAUI,WAAWh3B,OAC9B+a,EAAWrb,EAAOgb,WAAWI,OAAQ,kBAG7Bwb,EAAKv1B,OAEbu1B,EAAO,WACN,GAAKS,EACJ,OAAO,EAYR,IAVA,IAAIE,EAAcpB,IAASU,KAC1B3Z,EAAYla,KAAKivB,IAAK,EAAGgF,EAAUO,UAAYP,EAAUzB,SAAW+B,GAKpEjC,EAAU,GADHpY,EAAY+Z,EAAUzB,UAAY,GAEzCld,EAAQ,EACRhY,EAAS22B,EAAUQ,OAAOn3B,OAEnBgY,EAAQhY,EAAQgY,IACvB2e,EAAUQ,OAAQnf,GAAQ+c,IAAKC,GAMhC,OAHAja,EAASkB,WAAYlb,EAAM,CAAE41B,EAAW3B,EAASpY,IAG5CoY,EAAU,GAAKh1B,EACZ4c,GAIF5c,GACL+a,EAASkB,WAAYlb,EAAM,CAAE41B,EAAW,EAAG,IAI5C5b,EAASmB,YAAanb,EAAM,CAAE41B,KACvB,IAERA,EAAY5b,EAASzB,QAAS,CAC7BvY,KAAMA,EACNynB,MAAO9oB,EAAOmC,OAAQ,GAAIi1B,GAC1BM,KAAM13B,EAAOmC,QAAQ,EAAM,CAC1Bw1B,cAAe,GACf3E,OAAQhzB,EAAOgzB,OAAOtP,UACpBthB,GACHw1B,mBAAoBR,EACpBS,gBAAiBz1B,EACjBo1B,UAAWrB,IAASU,KACpBrB,SAAUpzB,EAAQozB,SAClBiC,OAAQ,GACRT,YAAa,SAAUtX,EAAM1d,GAC5B,IAAI4f,EAAQ5hB,EAAO+yB,MAAO1xB,EAAM41B,EAAUS,KAAMhY,EAAM1d,EACrDi1B,EAAUS,KAAKC,cAAejY,IAAUuX,EAAUS,KAAK1E,QAExD,OADAiE,EAAUQ,OAAO75B,KAAMgkB,GAChBA,GAERlB,KAAM,SAAUoX,GACf,IAAIxf,EAAQ,EAIXhY,EAASw3B,EAAUb,EAAUQ,OAAOn3B,OAAS,EAC9C,GAAK+2B,EACJ,OAAOr6B,KAGR,IADAq6B,GAAU,EACF/e,EAAQhY,EAAQgY,IACvB2e,EAAUQ,OAAQnf,GAAQ+c,IAAK,GAUhC,OANKyC,GACJzc,EAASkB,WAAYlb,EAAM,CAAE41B,EAAW,EAAG,IAC3C5b,EAASmB,YAAanb,EAAM,CAAE41B,EAAWa,KAEzCzc,EAASuB,WAAYvb,EAAM,CAAE41B,EAAWa,IAElC96B,QAGT8rB,EAAQmO,EAAUnO,MAInB,KA/HD,SAAqBA,EAAO6O,GAC3B,IAAIrf,EAAOjW,EAAM2wB,EAAQ7uB,EAAOqc,EAGhC,IAAMlI,KAASwQ,EAed,GAbAkK,EAAS2E,EADTt1B,EAAO2c,EAAW1G,IAElBnU,EAAQ2kB,EAAOxQ,GACV1V,MAAMC,QAASsB,KACnB6uB,EAAS7uB,EAAO,GAChBA,EAAQ2kB,EAAOxQ,GAAUnU,EAAO,IAG5BmU,IAAUjW,IACdymB,EAAOzmB,GAAS8B,SACT2kB,EAAOxQ,KAGfkI,EAAQxgB,EAAOizB,SAAU5wB,KACX,WAAYme,EAMzB,IAAMlI,KALNnU,EAAQqc,EAAMyU,OAAQ9wB,UACf2kB,EAAOzmB,GAIC8B,EACNmU,KAASwQ,IAChBA,EAAOxQ,GAAUnU,EAAOmU,GACxBqf,EAAerf,GAAU0a,QAI3B2E,EAAet1B,GAAS2wB,EA6F1B+E,CAAYjP,EAAOmO,EAAUS,KAAKC,eAE1Brf,EAAQhY,EAAQgY,IAEvB,GADA/H,EAAS2mB,GAAUI,WAAYhf,GAAQ7a,KAAMw5B,EAAW51B,EAAMynB,EAAOmO,EAAUS,MAM9E,OAJKr5B,EAAYkS,EAAOmQ,QACvB1gB,EAAOygB,YAAawW,EAAU51B,KAAM41B,EAAUS,KAAKnd,OAAQmG,KAC1DnQ,EAAOmQ,KAAKsX,KAAMznB,IAEbA,EAyBT,OArBAvQ,EAAOoB,IAAK0nB,EAAOkO,GAAaC,GAE3B54B,EAAY44B,EAAUS,KAAKxmB,QAC/B+lB,EAAUS,KAAKxmB,MAAMzT,KAAM4D,EAAM41B,GAIlCA,EACErb,SAAUqb,EAAUS,KAAK9b,UACzB/V,KAAMoxB,EAAUS,KAAK7xB,KAAMoxB,EAAUS,KAAKO,UAC1Cpe,KAAMod,EAAUS,KAAK7d,MACrBuB,OAAQ6b,EAAUS,KAAKtc,QAEzBpb,EAAO21B,GAAGuC,MACTl4B,EAAOmC,OAAQy0B,EAAM,CACpBv1B,KAAMA,EACN82B,KAAMlB,EACN1c,MAAO0c,EAAUS,KAAKnd,SAIjB0c,EAGRj3B,EAAOk3B,UAAYl3B,EAAOmC,OAAQ+0B,GAAW,CAE5CC,SAAU,CACTiB,IAAK,CAAE,SAAU1Y,EAAMvb,GACtB,IAAIyd,EAAQ5kB,KAAKg6B,YAAatX,EAAMvb,GAEpC,OADAud,GAAWE,EAAMvgB,KAAMqe,EAAMuB,GAAQ9W,KAAMhG,GAASyd,GAC7CA,KAITyW,QAAS,SAAUvP,EAAO3nB,GACpB9C,EAAYyqB,IAChB3nB,EAAW2nB,EACXA,EAAQ,CAAE,MAEVA,EAAQA,EAAMhf,MAAOoP,GAOtB,IAJA,IAAIwG,EACHpH,EAAQ,EACRhY,EAASwoB,EAAMxoB,OAERgY,EAAQhY,EAAQgY,IACvBoH,EAAOoJ,EAAOxQ,GACd4e,GAAUC,SAAUzX,GAASwX,GAAUC,SAAUzX,IAAU,GAC3DwX,GAAUC,SAAUzX,GAAO9Q,QAASzN,IAItCm2B,WAAY,CA3Wb,SAA2Bj2B,EAAMynB,EAAO4O,GACvC,IAAIhY,EAAMvb,EAAOwe,EAAQnC,EAAO8X,EAASC,EAAWC,EAAgBhX,EACnEiX,EAAQ,UAAW3P,GAAS,WAAYA,EACxCqP,EAAOn7B,KACPsuB,EAAO,GACP/J,EAAQlgB,EAAKkgB,MACbkV,EAASp1B,EAAK9C,UAAY+iB,GAAoBjgB,GAC9Cq3B,EAAW9Y,EAASjf,IAAKU,EAAM,UA6BhC,IAAMqe,KA1BAgY,EAAKnd,QAEa,OADvBiG,EAAQxgB,EAAOygB,YAAapf,EAAM,OACvBs3B,WACVnY,EAAMmY,SAAW,EACjBL,EAAU9X,EAAM1N,MAAM2H,KACtB+F,EAAM1N,MAAM2H,KAAO,WACZ+F,EAAMmY,UACXL,MAIH9X,EAAMmY,WAENR,EAAK/c,OAAQ,WAGZ+c,EAAK/c,OAAQ,WACZoF,EAAMmY,WACA34B,EAAOua,MAAOlZ,EAAM,MAAOf,QAChCkgB,EAAM1N,MAAM2H,YAOFqO,EAEb,GADA3kB,EAAQ2kB,EAAOpJ,GACV4W,GAAS7rB,KAAMtG,GAAU,CAG7B,UAFO2kB,EAAOpJ,GACdiD,EAASA,GAAoB,WAAVxe,EACdA,KAAYsyB,EAAS,OAAS,QAAW,CAI7C,GAAe,SAAVtyB,IAAoBu0B,QAAiC51B,IAArB41B,EAAUhZ,GAK9C,SAJA+W,GAAS,EAOXnL,EAAM5L,GAASgZ,GAAYA,EAAUhZ,IAAU1f,EAAOuhB,MAAOlgB,EAAMqe,GAMrE,IADA6Y,GAAav4B,EAAOyD,cAAeqlB,MAChB9oB,EAAOyD,cAAe6nB,GA8DzC,IAAM5L,KAzDD+Y,GAA2B,IAAlBp3B,EAAK9C,WAMlBm5B,EAAKkB,SAAW,CAAErX,EAAMqX,SAAUrX,EAAMsX,UAAWtX,EAAMuX,WAIlC,OADvBN,EAAiBE,GAAYA,EAASlX,WAErCgX,EAAiB5Y,EAASjf,IAAKU,EAAM,YAGrB,UADjBmgB,EAAUxhB,EAAOyhB,IAAKpgB,EAAM,cAEtBm3B,EACJhX,EAAUgX,GAIVlW,GAAU,CAAEjhB,IAAQ,GACpBm3B,EAAiBn3B,EAAKkgB,MAAMC,SAAWgX,EACvChX,EAAUxhB,EAAOyhB,IAAKpgB,EAAM,WAC5BihB,GAAU,CAAEjhB,OAKG,WAAZmgB,GAAoC,iBAAZA,GAAgD,MAAlBgX,IACrB,SAAhCx4B,EAAOyhB,IAAKpgB,EAAM,WAGhBk3B,IACLJ,EAAKtyB,KAAM,WACV0b,EAAMC,QAAUgX,IAEM,MAAlBA,IACJhX,EAAUD,EAAMC,QAChBgX,EAA6B,SAAZhX,EAAqB,GAAKA,IAG7CD,EAAMC,QAAU,iBAKdkW,EAAKkB,WACTrX,EAAMqX,SAAW,SACjBT,EAAK/c,OAAQ,WACZmG,EAAMqX,SAAWlB,EAAKkB,SAAU,GAChCrX,EAAMsX,UAAYnB,EAAKkB,SAAU,GACjCrX,EAAMuX,UAAYpB,EAAKkB,SAAU,MAKnCL,GAAY,EACEjN,EAGPiN,IACAG,EACC,WAAYA,IAChBjC,EAASiC,EAASjC,QAGnBiC,EAAW9Y,EAASxB,OAAQ/c,EAAM,SAAU,CAAEmgB,QAASgX,IAInD7V,IACJ+V,EAASjC,QAAUA,GAIfA,GACJnU,GAAU,CAAEjhB,IAAQ,GAKrB82B,EAAKtyB,KAAM,WASV,IAAM6Z,KAJA+W,GACLnU,GAAU,CAAEjhB,IAEbue,EAAShF,OAAQvZ,EAAM,UACTiqB,EACbtrB,EAAOuhB,MAAOlgB,EAAMqe,EAAM4L,EAAM5L,OAMnC6Y,EAAYvB,GAAaP,EAASiC,EAAUhZ,GAAS,EAAGA,EAAMyY,GACtDzY,KAAQgZ,IACfA,EAAUhZ,GAAS6Y,EAAUrnB,MACxBulB,IACJ8B,EAAUv2B,IAAMu2B,EAAUrnB,MAC1BqnB,EAAUrnB,MAAQ,MAuMrB6nB,UAAW,SAAU53B,EAAU+rB,GACzBA,EACJgK,GAAUI,WAAW1oB,QAASzN,GAE9B+1B,GAAUI,WAAW15B,KAAMuD,MAK9BnB,EAAOg5B,MAAQ,SAAUA,EAAOhG,EAAQ7yB,GACvC,IAAIk2B,EAAM2C,GAA0B,iBAAVA,EAAqBh5B,EAAOmC,OAAQ,GAAI62B,GAAU,CAC3Ef,SAAU93B,IAAOA,GAAM6yB,GACtB30B,EAAY26B,IAAWA,EACxBxD,SAAUwD,EACVhG,OAAQ7yB,GAAM6yB,GAAUA,IAAW30B,EAAY20B,IAAYA,GAoC5D,OAhCKhzB,EAAO21B,GAAGlQ,IACd4Q,EAAIb,SAAW,EAGc,iBAAjBa,EAAIb,WACVa,EAAIb,YAAYx1B,EAAO21B,GAAGsD,OAC9B5C,EAAIb,SAAWx1B,EAAO21B,GAAGsD,OAAQ5C,EAAIb,UAGrCa,EAAIb,SAAWx1B,EAAO21B,GAAGsD,OAAOvV,UAMjB,MAAb2S,EAAI9b,QAA+B,IAAd8b,EAAI9b,QAC7B8b,EAAI9b,MAAQ,MAIb8b,EAAIlI,IAAMkI,EAAI4B,SAEd5B,EAAI4B,SAAW,WACT55B,EAAYg4B,EAAIlI,MACpBkI,EAAIlI,IAAI1wB,KAAMT,MAGVq5B,EAAI9b,OACRva,EAAOsgB,QAAStjB,KAAMq5B,EAAI9b,QAIrB8b,GAGRr2B,EAAOG,GAAGgC,OAAQ,CACjB+2B,OAAQ,SAAUF,EAAOG,EAAInG,EAAQ7xB,GAGpC,OAAOnE,KAAKsQ,OAAQgU,IAAqBG,IAAK,UAAW,GAAIc,OAG3DvgB,MAAMo3B,QAAS,CAAElG,QAASiG,GAAMH,EAAOhG,EAAQ7xB,IAElDi4B,QAAS,SAAU1Z,EAAMsZ,EAAOhG,EAAQ7xB,GACvC,IAAI2R,EAAQ9S,EAAOyD,cAAeic,GACjC2Z,EAASr5B,EAAOg5B,MAAOA,EAAOhG,EAAQ7xB,GACtCm4B,EAAc,WAGb,IAAInB,EAAOjB,GAAWl6B,KAAMgD,EAAOmC,OAAQ,GAAIud,GAAQ2Z,IAGlDvmB,GAAS8M,EAASjf,IAAK3D,KAAM,YACjCm7B,EAAKzX,MAAM,IAMd,OAFA4Y,EAAYC,OAASD,EAEdxmB,IAA0B,IAAjBumB,EAAO9e,MACtBvd,KAAKkE,KAAMo4B,GACXt8B,KAAKud,MAAO8e,EAAO9e,MAAO+e,IAE5B5Y,KAAM,SAAU/hB,EAAMiiB,EAAYkX,GACjC,IAAI0B,EAAY,SAAUhZ,GACzB,IAAIE,EAAOF,EAAME,YACVF,EAAME,KACbA,EAAMoX,IAYP,MATqB,iBAATn5B,IACXm5B,EAAUlX,EACVA,EAAajiB,EACbA,OAAOmE,GAEH8d,GACJ5jB,KAAKud,MAAO5b,GAAQ,KAAM,IAGpB3B,KAAKkE,KAAM,WACjB,IAAIof,GAAU,EACbhI,EAAgB,MAAR3Z,GAAgBA,EAAO,aAC/B86B,EAASz5B,EAAOy5B,OAChBha,EAAOG,EAASjf,IAAK3D,MAEtB,GAAKsb,EACCmH,EAAMnH,IAAWmH,EAAMnH,GAAQoI,MACnC8Y,EAAW/Z,EAAMnH,SAGlB,IAAMA,KAASmH,EACTA,EAAMnH,IAAWmH,EAAMnH,GAAQoI,MAAQ6V,GAAK9rB,KAAM6N,IACtDkhB,EAAW/Z,EAAMnH,IAKpB,IAAMA,EAAQmhB,EAAOn5B,OAAQgY,KACvBmhB,EAAQnhB,GAAQjX,OAASrE,MACnB,MAAR2B,GAAgB86B,EAAQnhB,GAAQiC,QAAU5b,IAE5C86B,EAAQnhB,GAAQ6f,KAAKzX,KAAMoX,GAC3BxX,GAAU,EACVmZ,EAAOv3B,OAAQoW,EAAO,KAOnBgI,GAAYwX,GAChB93B,EAAOsgB,QAAStjB,KAAM2B,MAIzB46B,OAAQ,SAAU56B,GAIjB,OAHc,IAATA,IACJA,EAAOA,GAAQ,MAET3B,KAAKkE,KAAM,WACjB,IAAIoX,EACHmH,EAAOG,EAASjf,IAAK3D,MACrBud,EAAQkF,EAAM9gB,EAAO,SACrB6hB,EAAQf,EAAM9gB,EAAO,cACrB86B,EAASz5B,EAAOy5B,OAChBn5B,EAASia,EAAQA,EAAMja,OAAS,EAajC,IAVAmf,EAAK8Z,QAAS,EAGdv5B,EAAOua,MAAOvd,KAAM2B,EAAM,IAErB6hB,GAASA,EAAME,MACnBF,EAAME,KAAKjjB,KAAMT,MAAM,GAIlBsb,EAAQmhB,EAAOn5B,OAAQgY,KACvBmhB,EAAQnhB,GAAQjX,OAASrE,MAAQy8B,EAAQnhB,GAAQiC,QAAU5b,IAC/D86B,EAAQnhB,GAAQ6f,KAAKzX,MAAM,GAC3B+Y,EAAOv3B,OAAQoW,EAAO,IAKxB,IAAMA,EAAQ,EAAGA,EAAQhY,EAAQgY,IAC3BiC,EAAOjC,IAAWiC,EAAOjC,GAAQihB,QACrChf,EAAOjC,GAAQihB,OAAO97B,KAAMT,aAKvByiB,EAAK8Z,YAKfv5B,EAAOkB,KAAM,CAAE,SAAU,OAAQ,QAAU,SAAUsD,EAAInC,GACxD,IAAIq3B,EAAQ15B,EAAOG,GAAIkC,GACvBrC,EAAOG,GAAIkC,GAAS,SAAU22B,EAAOhG,EAAQ7xB,GAC5C,OAAgB,MAAT63B,GAAkC,kBAAVA,EAC9BU,EAAM/7B,MAAOX,KAAMsE,WACnBtE,KAAKo8B,QAAStC,GAAOz0B,GAAM,GAAQ22B,EAAOhG,EAAQ7xB,MAKrDnB,EAAOkB,KAAM,CACZy4B,UAAW7C,GAAO,QAClB8C,QAAS9C,GAAO,QAChB+C,YAAa/C,GAAO,UACpBgD,OAAQ,CAAE5G,QAAS,QACnB6G,QAAS,CAAE7G,QAAS,QACpB8G,WAAY,CAAE9G,QAAS,WACrB,SAAU7wB,EAAMymB,GAClB9oB,EAAOG,GAAIkC,GAAS,SAAU22B,EAAOhG,EAAQ7xB,GAC5C,OAAOnE,KAAKo8B,QAAStQ,EAAOkQ,EAAOhG,EAAQ7xB,MAI7CnB,EAAOy5B,OAAS,GAChBz5B,EAAO21B,GAAGiB,KAAO,WAChB,IAAIsB,EACH/4B,EAAI,EACJs6B,EAASz5B,EAAOy5B,OAIjB,IAFAtD,GAAQzwB,KAAKyjB,MAELhqB,EAAIs6B,EAAOn5B,OAAQnB,KAC1B+4B,EAAQuB,EAAQt6B,OAGCs6B,EAAQt6B,KAAQ+4B,GAChCuB,EAAOv3B,OAAQ/C,IAAK,GAIhBs6B,EAAOn5B,QACZN,EAAO21B,GAAGjV,OAEXyV,QAAQrzB,GAGT9C,EAAO21B,GAAGuC,MAAQ,SAAUA,GAC3Bl4B,EAAOy5B,OAAO77B,KAAMs6B,GACpBl4B,EAAO21B,GAAGzkB,SAGXlR,EAAO21B,GAAGgB,SAAW,GACrB32B,EAAO21B,GAAGzkB,MAAQ,WACZklB,KAILA,IAAa,EACbI,OAGDx2B,EAAO21B,GAAGjV,KAAO,WAChB0V,GAAa,MAGdp2B,EAAO21B,GAAGsD,OAAS,CAClBgB,KAAM,IACNC,KAAM,IAGNxW,SAAU,KAMX1jB,EAAOG,GAAGg6B,MAAQ,SAAUC,EAAMz7B,GAIjC,OAHAy7B,EAAOp6B,EAAO21B,IAAK31B,EAAO21B,GAAGsD,OAAQmB,IAAiBA,EACtDz7B,EAAOA,GAAQ,KAER3B,KAAKud,MAAO5b,EAAM,SAAU4K,EAAMiX,GACxC,IAAI6Z,EAAUt9B,EAAO+f,WAAYvT,EAAM6wB,GACvC5Z,EAAME,KAAO,WACZ3jB,EAAOu9B,aAAcD,OAOnBzsB,GAAQhR,EAAS0C,cAAe,SAEnC+2B,GADSz5B,EAAS0C,cAAe,UACpBK,YAAa/C,EAAS0C,cAAe,WAEnDsO,GAAMjP,KAAO,WAIbP,EAAQm8B,QAA0B,KAAhB3sB,GAAMzJ,MAIxB/F,EAAQo8B,YAAcnE,GAAIzjB,UAI1BhF,GAAQhR,EAAS0C,cAAe,UAC1B6E,MAAQ,IACdyJ,GAAMjP,KAAO,QACbP,EAAQq8B,WAA6B,MAAhB7sB,GAAMzJ,MAI5B,IAAIu2B,GACH9uB,GAAa5L,EAAO6O,KAAKjD,WAE1B5L,EAAOG,GAAGgC,OAAQ,CACjB4M,KAAM,SAAU1M,EAAM8B,GACrB,OAAOia,EAAQphB,KAAMgD,EAAO+O,KAAM1M,EAAM8B,EAA0B,EAAnB7C,UAAUhB,SAG1Dq6B,WAAY,SAAUt4B,GACrB,OAAOrF,KAAKkE,KAAM,WACjBlB,EAAO26B,WAAY39B,KAAMqF,QAK5BrC,EAAOmC,OAAQ,CACd4M,KAAM,SAAU1N,EAAMgB,EAAM8B,GAC3B,IAAIpD,EAAKyf,EACRoa,EAAQv5B,EAAK9C,SAGd,GAAe,IAAVq8B,GAAyB,IAAVA,GAAyB,IAAVA,EAKnC,MAAkC,oBAAtBv5B,EAAK7B,aACTQ,EAAO0f,KAAMre,EAAMgB,EAAM8B,IAKlB,IAAVy2B,GAAgB56B,EAAO8W,SAAUzV,KACrCmf,EAAQxgB,EAAO66B,UAAWx4B,EAAKoC,iBAC5BzE,EAAO6O,KAAK/E,MAAMjC,KAAK4C,KAAMpI,GAASq4B,QAAW53B,SAGtCA,IAAVqB,EACW,OAAVA,OACJnE,EAAO26B,WAAYt5B,EAAMgB,GAIrBme,GAAS,QAASA,QACuB1d,KAA3C/B,EAAMyf,EAAMhB,IAAKne,EAAM8C,EAAO9B,IACzBtB,GAGRM,EAAK5B,aAAc4C,EAAM8B,EAAQ,IAC1BA,GAGHqc,GAAS,QAASA,GAA+C,QAApCzf,EAAMyf,EAAM7f,IAAKU,EAAMgB,IACjDtB,EAMM,OAHdA,EAAMf,EAAOwN,KAAKuB,KAAM1N,EAAMgB,SAGTS,EAAY/B,IAGlC85B,UAAW,CACVl8B,KAAM,CACL6gB,IAAK,SAAUne,EAAM8C,GACpB,IAAM/F,EAAQq8B,YAAwB,UAAVt2B,GAC3BkF,EAAUhI,EAAM,SAAY,CAC5B,IAAIjC,EAAMiC,EAAK8C,MAKf,OAJA9C,EAAK5B,aAAc,OAAQ0E,GACtB/E,IACJiC,EAAK8C,MAAQ/E,GAEP+E,MAMXw2B,WAAY,SAAUt5B,EAAM8C,GAC3B,IAAI9B,EACHlD,EAAI,EAIJ27B,EAAY32B,GAASA,EAAM2F,MAAOoP,GAEnC,GAAK4hB,GAA+B,IAAlBz5B,EAAK9C,SACtB,MAAU8D,EAAOy4B,EAAW37B,KAC3BkC,EAAK2J,gBAAiB3I,MAO1Bq4B,GAAW,CACVlb,IAAK,SAAUne,EAAM8C,EAAO9B,GAQ3B,OAPe,IAAV8B,EAGJnE,EAAO26B,WAAYt5B,EAAMgB,GAEzBhB,EAAK5B,aAAc4C,EAAMA,GAEnBA,IAITrC,EAAOkB,KAAMlB,EAAO6O,KAAK/E,MAAMjC,KAAKmZ,OAAOlX,MAAO,QAAU,SAAUtF,EAAInC,GACzE,IAAI04B,EAASnvB,GAAYvJ,IAAUrC,EAAOwN,KAAKuB,KAE/CnD,GAAYvJ,GAAS,SAAUhB,EAAMgB,EAAMwC,GAC1C,IAAI9D,EAAK+lB,EACRkU,EAAgB34B,EAAKoC,cAYtB,OAVMI,IAGLiiB,EAASlb,GAAYovB,GACrBpvB,GAAYovB,GAAkBj6B,EAC9BA,EAAqC,MAA/Bg6B,EAAQ15B,EAAMgB,EAAMwC,GACzBm2B,EACA,KACDpvB,GAAYovB,GAAkBlU,GAExB/lB,KAOT,IAAIk6B,GAAa,sCAChBC,GAAa,gBAyIb,SAASC,GAAkBh3B,GAE1B,OADaA,EAAM2F,MAAOoP,IAAmB,IAC/BrO,KAAM,KAItB,SAASuwB,GAAU/5B,GAClB,OAAOA,EAAK7B,cAAgB6B,EAAK7B,aAAc,UAAa,GAG7D,SAAS67B,GAAgBl3B,GACxB,OAAKvB,MAAMC,QAASsB,GACZA,EAEc,iBAAVA,GACJA,EAAM2F,MAAOoP,IAEd,GAxJRlZ,EAAOG,GAAGgC,OAAQ,CACjBud,KAAM,SAAUrd,EAAM8B,GACrB,OAAOia,EAAQphB,KAAMgD,EAAO0f,KAAMrd,EAAM8B,EAA0B,EAAnB7C,UAAUhB,SAG1Dg7B,WAAY,SAAUj5B,GACrB,OAAOrF,KAAKkE,KAAM,kBACVlE,KAAMgD,EAAOu7B,QAASl5B,IAAUA,QAK1CrC,EAAOmC,OAAQ,CACdud,KAAM,SAAUre,EAAMgB,EAAM8B,GAC3B,IAAIpD,EAAKyf,EACRoa,EAAQv5B,EAAK9C,SAGd,GAAe,IAAVq8B,GAAyB,IAAVA,GAAyB,IAAVA,EAWnC,OAPe,IAAVA,GAAgB56B,EAAO8W,SAAUzV,KAGrCgB,EAAOrC,EAAOu7B,QAASl5B,IAAUA,EACjCme,EAAQxgB,EAAOo1B,UAAW/yB,SAGZS,IAAVqB,EACCqc,GAAS,QAASA,QACuB1d,KAA3C/B,EAAMyf,EAAMhB,IAAKne,EAAM8C,EAAO9B,IACzBtB,EAGCM,EAAMgB,GAAS8B,EAGpBqc,GAAS,QAASA,GAA+C,QAApCzf,EAAMyf,EAAM7f,IAAKU,EAAMgB,IACjDtB,EAGDM,EAAMgB,IAGd+yB,UAAW,CACV3iB,SAAU,CACT9R,IAAK,SAAUU,GAOd,IAAIm6B,EAAWx7B,EAAOwN,KAAKuB,KAAM1N,EAAM,YAEvC,OAAKm6B,EACG5K,SAAU4K,EAAU,IAI3BP,GAAWxwB,KAAMpJ,EAAKgI,WACtB6xB,GAAWzwB,KAAMpJ,EAAKgI,WACtBhI,EAAKmR,KAEE,GAGA,KAKX+oB,QAAS,CACRE,MAAO,UACPC,QAAS,eAYLt9B,EAAQo8B,cACbx6B,EAAOo1B,UAAUxiB,SAAW,CAC3BjS,IAAK,SAAUU,GAId,IAAI8P,EAAS9P,EAAKzB,WAIlB,OAHKuR,GAAUA,EAAOvR,YACrBuR,EAAOvR,WAAWiT,cAEZ,MAER2M,IAAK,SAAUne,GAId,IAAI8P,EAAS9P,EAAKzB,WACbuR,IACJA,EAAO0B,cAEF1B,EAAOvR,YACXuR,EAAOvR,WAAWiT,kBAOvB7S,EAAOkB,KAAM,CACZ,WACA,WACA,YACA,cACA,cACA,UACA,UACA,SACA,cACA,mBACE,WACFlB,EAAOu7B,QAASv+B,KAAKyH,eAAkBzH,OA4BxCgD,EAAOG,GAAGgC,OAAQ,CACjBw5B,SAAU,SAAUx3B,GACnB,IAAIy3B,EAASv6B,EAAMyK,EAAK+vB,EAAUC,EAAO/5B,EAAGg6B,EAC3C58B,EAAI,EAEL,GAAKd,EAAY8F,GAChB,OAAOnH,KAAKkE,KAAM,SAAUa,GAC3B/B,EAAQhD,MAAO2+B,SAAUx3B,EAAM1G,KAAMT,KAAM+E,EAAGq5B,GAAUp+B,UAM1D,IAFA4+B,EAAUP,GAAgBl3B,IAEb7D,OACZ,MAAUe,EAAOrE,KAAMmC,KAItB,GAHA08B,EAAWT,GAAU/5B,GACrByK,EAAwB,IAAlBzK,EAAK9C,UAAoB,IAAM48B,GAAkBU,GAAa,IAEzD,CACV95B,EAAI,EACJ,MAAU+5B,EAAQF,EAAS75B,KACrB+J,EAAIjO,QAAS,IAAMi+B,EAAQ,KAAQ,IACvChwB,GAAOgwB,EAAQ,KAMZD,KADLE,EAAaZ,GAAkBrvB,KAE9BzK,EAAK5B,aAAc,QAASs8B,GAMhC,OAAO/+B,MAGRg/B,YAAa,SAAU73B,GACtB,IAAIy3B,EAASv6B,EAAMyK,EAAK+vB,EAAUC,EAAO/5B,EAAGg6B,EAC3C58B,EAAI,EAEL,GAAKd,EAAY8F,GAChB,OAAOnH,KAAKkE,KAAM,SAAUa,GAC3B/B,EAAQhD,MAAOg/B,YAAa73B,EAAM1G,KAAMT,KAAM+E,EAAGq5B,GAAUp+B,UAI7D,IAAMsE,UAAUhB,OACf,OAAOtD,KAAK+R,KAAM,QAAS,IAK5B,IAFA6sB,EAAUP,GAAgBl3B,IAEb7D,OACZ,MAAUe,EAAOrE,KAAMmC,KAMtB,GALA08B,EAAWT,GAAU/5B,GAGrByK,EAAwB,IAAlBzK,EAAK9C,UAAoB,IAAM48B,GAAkBU,GAAa,IAEzD,CACV95B,EAAI,EACJ,MAAU+5B,EAAQF,EAAS75B,KAG1B,OAA4C,EAApC+J,EAAIjO,QAAS,IAAMi+B,EAAQ,KAClChwB,EAAMA,EAAI5I,QAAS,IAAM44B,EAAQ,IAAK,KAMnCD,KADLE,EAAaZ,GAAkBrvB,KAE9BzK,EAAK5B,aAAc,QAASs8B,GAMhC,OAAO/+B,MAGRi/B,YAAa,SAAU93B,EAAO+3B,GAC7B,IAAIv9B,SAAcwF,EACjBg4B,EAAwB,WAATx9B,GAAqBiE,MAAMC,QAASsB,GAEpD,MAAyB,kBAAb+3B,GAA0BC,EAC9BD,EAAWl/B,KAAK2+B,SAAUx3B,GAAUnH,KAAKg/B,YAAa73B,GAGzD9F,EAAY8F,GACTnH,KAAKkE,KAAM,SAAU/B,GAC3Ba,EAAQhD,MAAOi/B,YACd93B,EAAM1G,KAAMT,KAAMmC,EAAGi8B,GAAUp+B,MAAQk/B,GACvCA,KAKIl/B,KAAKkE,KAAM,WACjB,IAAIgM,EAAW/N,EAAGsY,EAAM2kB,EAExB,GAAKD,EAAe,CAGnBh9B,EAAI,EACJsY,EAAOzX,EAAQhD,MACfo/B,EAAaf,GAAgBl3B,GAE7B,MAAU+I,EAAYkvB,EAAYj9B,KAG5BsY,EAAK4kB,SAAUnvB,GACnBuK,EAAKukB,YAAa9uB,GAElBuK,EAAKkkB,SAAUzuB,aAKIpK,IAAVqB,GAAgC,YAATxF,KAClCuO,EAAYkuB,GAAUp+B,QAIrB4iB,EAASJ,IAAKxiB,KAAM,gBAAiBkQ,GAOjClQ,KAAKyC,cACTzC,KAAKyC,aAAc,QAClByN,IAAuB,IAAV/I,EACZ,GACAyb,EAASjf,IAAK3D,KAAM,kBAAqB,QAO/Cq/B,SAAU,SAAUp8B,GACnB,IAAIiN,EAAW7L,EACdlC,EAAI,EAEL+N,EAAY,IAAMjN,EAAW,IAC7B,MAAUoB,EAAOrE,KAAMmC,KACtB,GAAuB,IAAlBkC,EAAK9C,WACoE,GAA3E,IAAM48B,GAAkBC,GAAU/5B,IAAW,KAAMxD,QAASqP,GAC9D,OAAO,EAIT,OAAO,KAOT,IAAIovB,GAAU,MAEdt8B,EAAOG,GAAGgC,OAAQ,CACjB/C,IAAK,SAAU+E,GACd,IAAIqc,EAAOzf,EAAKurB,EACfjrB,EAAOrE,KAAM,GAEd,OAAMsE,UAAUhB,QA0BhBgsB,EAAkBjuB,EAAY8F,GAEvBnH,KAAKkE,KAAM,SAAU/B,GAC3B,IAAIC,EAEmB,IAAlBpC,KAAKuB,WAWE,OANXa,EADIktB,EACEnoB,EAAM1G,KAAMT,KAAMmC,EAAGa,EAAQhD,MAAOoC,OAEpC+E,GAKN/E,EAAM,GAEoB,iBAARA,EAClBA,GAAO,GAEIwD,MAAMC,QAASzD,KAC1BA,EAAMY,EAAOoB,IAAKhC,EAAK,SAAU+E,GAChC,OAAgB,MAATA,EAAgB,GAAKA,EAAQ,OAItCqc,EAAQxgB,EAAOu8B,SAAUv/B,KAAK2B,OAAUqB,EAAOu8B,SAAUv/B,KAAKqM,SAAS5E,iBAGrD,QAAS+b,QAA+C1d,IAApC0d,EAAMhB,IAAKxiB,KAAMoC,EAAK,WAC3DpC,KAAKmH,MAAQ/E,OAzDTiC,GACJmf,EAAQxgB,EAAOu8B,SAAUl7B,EAAK1C,OAC7BqB,EAAOu8B,SAAUl7B,EAAKgI,SAAS5E,iBAG/B,QAAS+b,QACgC1d,KAAvC/B,EAAMyf,EAAM7f,IAAKU,EAAM,UAElBN,EAMY,iBAHpBA,EAAMM,EAAK8C,OAIHpD,EAAImC,QAASo5B,GAAS,IAIhB,MAAPv7B,EAAc,GAAKA,OAG3B,KAyCHf,EAAOmC,OAAQ,CACdo6B,SAAU,CACTnZ,OAAQ,CACPziB,IAAK,SAAUU,GAEd,IAAIjC,EAAMY,EAAOwN,KAAKuB,KAAM1N,EAAM,SAClC,OAAc,MAAPjC,EACNA,EAMA+7B,GAAkBn7B,EAAOT,KAAM8B,MAGlC2D,OAAQ,CACPrE,IAAK,SAAUU,GACd,IAAI8C,EAAOif,EAAQjkB,EAClBiD,EAAUf,EAAKe,QACfkW,EAAQjX,EAAKwR,cACbyS,EAAoB,eAAdjkB,EAAK1C,KACX6jB,EAAS8C,EAAM,KAAO,GACtB2M,EAAM3M,EAAMhN,EAAQ,EAAIlW,EAAQ9B,OAUjC,IAPCnB,EADImZ,EAAQ,EACR2Z,EAGA3M,EAAMhN,EAAQ,EAIXnZ,EAAI8yB,EAAK9yB,IAKhB,KAJAikB,EAAShhB,EAASjD,IAIJyT,UAAYzT,IAAMmZ,KAG7B8K,EAAOha,YACLga,EAAOxjB,WAAWwJ,WACnBC,EAAU+Z,EAAOxjB,WAAY,aAAiB,CAMjD,GAHAuE,EAAQnE,EAAQojB,GAAShkB,MAGpBkmB,EACJ,OAAOnhB,EAIRqe,EAAO5kB,KAAMuG,GAIf,OAAOqe,GAGRhD,IAAK,SAAUne,EAAM8C,GACpB,IAAIq4B,EAAWpZ,EACdhhB,EAAUf,EAAKe,QACfogB,EAASxiB,EAAO2D,UAAWQ,GAC3BhF,EAAIiD,EAAQ9B,OAEb,MAAQnB,MACPikB,EAAShhB,EAASjD,IAINyT,UACuD,EAAlE5S,EAAO6D,QAAS7D,EAAOu8B,SAASnZ,OAAOziB,IAAKyiB,GAAUZ,MAEtDga,GAAY,GAUd,OAHMA,IACLn7B,EAAKwR,eAAiB,GAEhB2P,OAOXxiB,EAAOkB,KAAM,CAAE,QAAS,YAAc,WACrClB,EAAOu8B,SAAUv/B,MAAS,CACzBwiB,IAAK,SAAUne,EAAM8C,GACpB,GAAKvB,MAAMC,QAASsB,GACnB,OAAS9C,EAAKsR,SAA2D,EAAjD3S,EAAO6D,QAAS7D,EAAQqB,GAAOjC,MAAO+E,KAI3D/F,EAAQm8B,UACbv6B,EAAOu8B,SAAUv/B,MAAO2D,IAAM,SAAUU,GACvC,OAAwC,OAAjCA,EAAK7B,aAAc,SAAqB,KAAO6B,EAAK8C,UAW9D/F,EAAQq+B,QAAU,cAAe1/B,EAGjC,IAAI2/B,GAAc,kCACjBC,GAA0B,SAAUlzB,GACnCA,EAAEsc,mBAGJ/lB,EAAOmC,OAAQnC,EAAOwlB,MAAO,CAE5BU,QAAS,SAAUV,EAAO/F,EAAMpe,EAAMu7B,GAErC,IAAIz9B,EAAG2M,EAAK6B,EAAKkvB,EAAYC,EAAQhW,EAAQ3K,EAAS4gB,EACrDC,EAAY,CAAE37B,GAAQzE,GACtB+B,EAAOX,EAAOP,KAAM+nB,EAAO,QAAWA,EAAM7mB,KAAO6mB,EACnDkB,EAAa1oB,EAAOP,KAAM+nB,EAAO,aAAgBA,EAAM/Y,UAAUlI,MAAO,KAAQ,GAKjF,GAHAuH,EAAMixB,EAAcpvB,EAAMtM,EAAOA,GAAQzE,EAGlB,IAAlByE,EAAK9C,UAAoC,IAAlB8C,EAAK9C,WAK5Bm+B,GAAYjyB,KAAM9L,EAAOqB,EAAOwlB,MAAMuB,cAIf,EAAvBpoB,EAAKd,QAAS,OAIlBc,GADA+nB,EAAa/nB,EAAK4F,MAAO,MACP8G,QAClBqb,EAAWzkB,QAEZ66B,EAASn+B,EAAKd,QAAS,KAAQ,GAAK,KAAOc,GAG3C6mB,EAAQA,EAAOxlB,EAAO+C,SACrByiB,EACA,IAAIxlB,EAAOmmB,MAAOxnB,EAAuB,iBAAV6mB,GAAsBA,IAGhDK,UAAY+W,EAAe,EAAI,EACrCpX,EAAM/Y,UAAYia,EAAW7b,KAAM,KACnC2a,EAAMwC,WAAaxC,EAAM/Y,UACxB,IAAI1F,OAAQ,UAAY2f,EAAW7b,KAAM,iBAAoB,WAC7D,KAGD2a,EAAMjV,YAASzN,EACT0iB,EAAM/iB,SACX+iB,EAAM/iB,OAASpB,GAIhBoe,EAAe,MAARA,EACN,CAAE+F,GACFxlB,EAAO2D,UAAW8b,EAAM,CAAE+F,IAG3BrJ,EAAUnc,EAAOwlB,MAAMrJ,QAASxd,IAAU,GACpCi+B,IAAgBzgB,EAAQ+J,UAAmD,IAAxC/J,EAAQ+J,QAAQvoB,MAAO0D,EAAMoe,IAAtE,CAMA,IAAMmd,IAAiBzgB,EAAQuM,WAAajqB,EAAU4C,GAAS,CAM9D,IAJAw7B,EAAa1gB,EAAQ2J,cAAgBnnB,EAC/B+9B,GAAYjyB,KAAMoyB,EAAal+B,KACpCmN,EAAMA,EAAIlM,YAEHkM,EAAKA,EAAMA,EAAIlM,WACtBo9B,EAAUp/B,KAAMkO,GAChB6B,EAAM7B,EAIF6B,KAAUtM,EAAK6I,eAAiBtN,IACpCogC,EAAUp/B,KAAM+P,EAAIb,aAAea,EAAIsvB,cAAgBlgC,GAKzDoC,EAAI,EACJ,OAAU2M,EAAMkxB,EAAW79B,QAAYqmB,EAAMqC,uBAC5CkV,EAAcjxB,EACd0Z,EAAM7mB,KAAW,EAAJQ,EACZ09B,EACA1gB,EAAQ8K,UAAYtoB,GAGrBmoB,GAAWlH,EAASjf,IAAKmL,EAAK,WAAc1O,OAAOypB,OAAQ,OAAUrB,EAAM7mB,OAC1EihB,EAASjf,IAAKmL,EAAK,YAEnBgb,EAAOnpB,MAAOmO,EAAK2T,IAIpBqH,EAASgW,GAAUhxB,EAAKgxB,KACThW,EAAOnpB,OAASuhB,EAAYpT,KAC1C0Z,EAAMjV,OAASuW,EAAOnpB,MAAOmO,EAAK2T,IACZ,IAAjB+F,EAAMjV,QACViV,EAAMS,kBA8CT,OA1CAT,EAAM7mB,KAAOA,EAGPi+B,GAAiBpX,EAAMuD,sBAEpB5M,EAAQuH,WACqC,IAApDvH,EAAQuH,SAAS/lB,MAAOq/B,EAAU12B,MAAOmZ,KACzCP,EAAY7d,IAIPy7B,GAAUz+B,EAAYgD,EAAM1C,MAAaF,EAAU4C,MAGvDsM,EAAMtM,EAAMy7B,MAGXz7B,EAAMy7B,GAAW,MAIlB98B,EAAOwlB,MAAMuB,UAAYpoB,EAEpB6mB,EAAMqC,wBACVkV,EAAY/vB,iBAAkBrO,EAAMg+B,IAGrCt7B,EAAM1C,KAED6mB,EAAMqC,wBACVkV,EAAYhf,oBAAqBpf,EAAMg+B,IAGxC38B,EAAOwlB,MAAMuB,eAAYjkB,EAEpB6K,IACJtM,EAAMy7B,GAAWnvB,IAMd6X,EAAMjV,SAKd2sB,SAAU,SAAUv+B,EAAM0C,EAAMmkB,GAC/B,IAAI/b,EAAIzJ,EAAOmC,OACd,IAAInC,EAAOmmB,MACXX,EACA,CACC7mB,KAAMA,EACNyqB,aAAa,IAIfppB,EAAOwlB,MAAMU,QAASzc,EAAG,KAAMpI,MAKjCrB,EAAOG,GAAGgC,OAAQ,CAEjB+jB,QAAS,SAAUvnB,EAAM8gB,GACxB,OAAOziB,KAAKkE,KAAM,WACjBlB,EAAOwlB,MAAMU,QAASvnB,EAAM8gB,EAAMziB,SAGpCmgC,eAAgB,SAAUx+B,EAAM8gB,GAC/B,IAAIpe,EAAOrE,KAAM,GACjB,GAAKqE,EACJ,OAAOrB,EAAOwlB,MAAMU,QAASvnB,EAAM8gB,EAAMpe,GAAM,MAc5CjD,EAAQq+B,SACbz8B,EAAOkB,KAAM,CAAEmR,MAAO,UAAW4Y,KAAM,YAAc,SAAUK,EAAM5D,GAGpE,IAAI/b,EAAU,SAAU6Z,GACvBxlB,EAAOwlB,MAAM0X,SAAUxV,EAAKlC,EAAM/iB,OAAQzC,EAAOwlB,MAAMkC,IAAKlC,KAG7DxlB,EAAOwlB,MAAMrJ,QAASuL,GAAQ,CAC7BP,MAAO,WAIN,IAAIjoB,EAAMlC,KAAKkN,eAAiBlN,KAAKJ,UAAYI,KAChDogC,EAAWxd,EAASxB,OAAQlf,EAAKwoB,GAE5B0V,GACLl+B,EAAI8N,iBAAkBse,EAAM3f,GAAS,GAEtCiU,EAASxB,OAAQlf,EAAKwoB,GAAO0V,GAAY,GAAM,IAEhD9V,SAAU,WACT,IAAIpoB,EAAMlC,KAAKkN,eAAiBlN,KAAKJ,UAAYI,KAChDogC,EAAWxd,EAASxB,OAAQlf,EAAKwoB,GAAQ,EAEpC0V,EAKLxd,EAASxB,OAAQlf,EAAKwoB,EAAK0V,IAJ3Bl+B,EAAI6e,oBAAqBuN,EAAM3f,GAAS,GACxCiU,EAAShF,OAAQ1b,EAAKwoB,QAS3B,IAAIvV,GAAWpV,EAAOoV,SAElBtT,GAAQ,CAAEuF,KAAMsB,KAAKyjB,OAErBkU,GAAS,KAKbr9B,EAAOs9B,SAAW,SAAU7d,GAC3B,IAAI3O,EAAKysB,EACT,IAAM9d,GAAwB,iBAATA,EACpB,OAAO,KAKR,IACC3O,GAAM,IAAM/T,EAAOygC,WAAcC,gBAAiBhe,EAAM,YACvD,MAAQhW,IAYV,OAVA8zB,EAAkBzsB,GAAOA,EAAIxG,qBAAsB,eAAiB,GAC9DwG,IAAOysB,GACZv9B,EAAOoD,MAAO,iBACbm6B,EACCv9B,EAAOoB,IAAKm8B,EAAgB/zB,WAAY,SAAUgC,GACjD,OAAOA,EAAG8D,cACPzE,KAAM,MACV4U,IAGI3O,GAIR,IACC4sB,GAAW,QACXC,GAAQ,SACRC,GAAkB,wCAClBC,GAAe,qCAEhB,SAASC,GAAa/I,EAAQz2B,EAAKy/B,EAAavlB,GAC/C,IAAInW,EAEJ,GAAKO,MAAMC,QAASvE,GAGnB0B,EAAOkB,KAAM5C,EAAK,SAAUa,EAAGia,GACzB2kB,GAAeL,GAASjzB,KAAMsqB,GAGlCvc,EAAKuc,EAAQ3b,GAKb0kB,GACC/I,EAAS,KAAqB,iBAAN3b,GAAuB,MAALA,EAAYja,EAAI,IAAO,IACjEia,EACA2kB,EACAvlB,UAKG,GAAMulB,GAAiC,WAAlBj+B,EAAQxB,GAUnCka,EAAKuc,EAAQz2B,QAPb,IAAM+D,KAAQ/D,EACbw/B,GAAa/I,EAAS,IAAM1yB,EAAO,IAAK/D,EAAK+D,GAAQ07B,EAAavlB,GAYrExY,EAAOg+B,MAAQ,SAAU53B,EAAG23B,GAC3B,IAAIhJ,EACHkJ,EAAI,GACJzlB,EAAM,SAAUrN,EAAK+yB,GAGpB,IAAI/5B,EAAQ9F,EAAY6/B,GACvBA,IACAA,EAEDD,EAAGA,EAAE39B,QAAW69B,mBAAoBhzB,GAAQ,IAC3CgzB,mBAA6B,MAATh6B,EAAgB,GAAKA,IAG5C,GAAU,MAALiC,EACJ,MAAO,GAIR,GAAKxD,MAAMC,QAASuD,IAASA,EAAE5F,SAAWR,EAAO2C,cAAeyD,GAG/DpG,EAAOkB,KAAMkF,EAAG,WACfoS,EAAKxb,KAAKqF,KAAMrF,KAAKmH,cAOtB,IAAM4wB,KAAU3uB,EACf03B,GAAa/I,EAAQ3uB,EAAG2uB,GAAUgJ,EAAavlB,GAKjD,OAAOylB,EAAEpzB,KAAM,MAGhB7K,EAAOG,GAAGgC,OAAQ,CACjBi8B,UAAW,WACV,OAAOp+B,EAAOg+B,MAAOhhC,KAAKqhC,mBAE3BA,eAAgB,WACf,OAAOrhC,KAAKoE,IAAK,WAGhB,IAAI0N,EAAW9O,EAAO0f,KAAM1iB,KAAM,YAClC,OAAO8R,EAAW9O,EAAO2D,UAAWmL,GAAa9R,OAC9CsQ,OAAQ,WACX,IAAI3O,EAAO3B,KAAK2B,KAGhB,OAAO3B,KAAKqF,OAASrC,EAAQhD,MAAOka,GAAI,cACvC2mB,GAAapzB,KAAMzN,KAAKqM,YAAeu0B,GAAgBnzB,KAAM9L,KAC3D3B,KAAK2V,UAAYkQ,GAAepY,KAAM9L,MACtCyC,IAAK,SAAUoD,EAAInD,GACtB,IAAIjC,EAAMY,EAAQhD,MAAOoC,MAEzB,OAAY,MAAPA,EACG,KAGHwD,MAAMC,QAASzD,GACZY,EAAOoB,IAAKhC,EAAK,SAAUA,GACjC,MAAO,CAAEiD,KAAMhB,EAAKgB,KAAM8B,MAAO/E,EAAI8D,QAASy6B,GAAO,WAIhD,CAAEt7B,KAAMhB,EAAKgB,KAAM8B,MAAO/E,EAAI8D,QAASy6B,GAAO,WAClDh9B,SAKN,IACC29B,GAAM,OACNC,GAAQ,OACRC,GAAa,gBACbC,GAAW,6BAIXC,GAAa,iBACbC,GAAY,QAWZrH,GAAa,GAObsH,GAAa,GAGbC,GAAW,KAAKnhC,OAAQ,KAGxBohC,GAAeliC,EAAS0C,cAAe,KAKxC,SAASy/B,GAA6BC,GAGrC,OAAO,SAAUC,EAAoBhkB,GAED,iBAAvBgkB,IACXhkB,EAAOgkB,EACPA,EAAqB,KAGtB,IAAIC,EACH//B,EAAI,EACJggC,EAAYF,EAAmBx6B,cAAcqF,MAAOoP,IAAmB,GAExE,GAAK7a,EAAY4c,GAGhB,MAAUikB,EAAWC,EAAWhgC,KAGR,MAAlB+/B,EAAU,IACdA,EAAWA,EAAS5hC,MAAO,IAAO,KAChC0hC,EAAWE,GAAaF,EAAWE,IAAc,IAAKtwB,QAASqM,KAI/D+jB,EAAWE,GAAaF,EAAWE,IAAc,IAAKthC,KAAMqd,IAQnE,SAASmkB,GAA+BJ,EAAW58B,EAASy1B,EAAiBwH,GAE5E,IAAIC,EAAY,GACfC,EAAqBP,IAAcJ,GAEpC,SAASY,EAASN,GACjB,IAAItsB,EAcJ,OAbA0sB,EAAWJ,IAAa,EACxBl/B,EAAOkB,KAAM89B,EAAWE,IAAc,GAAI,SAAUjlB,EAAGwlB,GACtD,IAAIC,EAAsBD,EAAoBr9B,EAASy1B,EAAiBwH,GACxE,MAAoC,iBAAxBK,GACVH,GAAqBD,EAAWI,GAKtBH,IACD3sB,EAAW8sB,QADf,GAHNt9B,EAAQ+8B,UAAUvwB,QAAS8wB,GAC3BF,EAASE,IACF,KAKF9sB,EAGR,OAAO4sB,EAASp9B,EAAQ+8B,UAAW,MAAUG,EAAW,MAASE,EAAS,KAM3E,SAASG,GAAYl9B,EAAQ7D,GAC5B,IAAIuM,EAAKzI,EACRk9B,EAAc5/B,EAAO6/B,aAAaD,aAAe,GAElD,IAAMz0B,KAAOvM,OACQkE,IAAflE,EAAKuM,MACPy0B,EAAaz0B,GAAQ1I,EAAWC,IAAUA,EAAO,KAAUyI,GAAQvM,EAAKuM,IAO5E,OAJKzI,GACJ1C,EAAOmC,QAAQ,EAAMM,EAAQC,GAGvBD,EA/ERq8B,GAAatsB,KAAOL,GAASK,KAgP7BxS,EAAOmC,OAAQ,CAGd29B,OAAQ,EAGRC,aAAc,GACdC,KAAM,GAENH,aAAc,CACbI,IAAK9tB,GAASK,KACd7T,KAAM,MACNuhC,QAxRgB,4DAwRQz1B,KAAM0H,GAASguB,UACvC3jC,QAAQ,EACR4jC,aAAa,EACbC,OAAO,EACPC,YAAa,mDAcbC,QAAS,CACRnI,IAAKyG,GACLt/B,KAAM,aACNgtB,KAAM,YACNzb,IAAK,4BACL0vB,KAAM,qCAGPxoB,SAAU,CACTlH,IAAK,UACLyb,KAAM,SACNiU,KAAM,YAGPC,eAAgB,CACf3vB,IAAK,cACLvR,KAAM,eACNihC,KAAM,gBAKPE,WAAY,CAGXC,SAAUj4B,OAGVk4B,aAAa,EAGbC,YAAa5gB,KAAKC,MAGlB4gB,WAAY9gC,EAAOs9B,UAOpBsC,YAAa,CACZK,KAAK,EACL//B,SAAS,IAOX6gC,UAAW,SAAUt+B,EAAQu+B,GAC5B,OAAOA,EAGNrB,GAAYA,GAAYl9B,EAAQzC,EAAO6/B,cAAgBmB,GAGvDrB,GAAY3/B,EAAO6/B,aAAcp9B,IAGnCw+B,cAAelC,GAA6BzH,IAC5C4J,cAAenC,GAA6BH,IAG5CuC,KAAM,SAAUlB,EAAK79B,GAGA,iBAAR69B,IACX79B,EAAU69B,EACVA,OAAMn9B,GAIPV,EAAUA,GAAW,GAErB,IAAIg/B,EAGHC,EAGAC,EACAC,EAGAC,EAGAC,EAGA3jB,EAGA4jB,EAGAviC,EAGAwiC,EAGA1D,EAAIj+B,EAAO+gC,UAAW,GAAI3+B,GAG1Bw/B,EAAkB3D,EAAE/9B,SAAW+9B,EAG/B4D,EAAqB5D,EAAE/9B,UACpB0hC,EAAgBrjC,UAAYqjC,EAAgBphC,QAC9CR,EAAQ4hC,GACR5hC,EAAOwlB,MAGRnK,EAAWrb,EAAOgb,WAClB8mB,EAAmB9hC,EAAO+Z,UAAW,eAGrCgoB,EAAa9D,EAAE8D,YAAc,GAG7BC,EAAiB,GACjBC,EAAsB,GAGtBC,EAAW,WAGX7C,EAAQ,CACPnhB,WAAY,EAGZikB,kBAAmB,SAAUh3B,GAC5B,IAAIrB,EACJ,GAAKgU,EAAY,CAChB,IAAMyjB,EAAkB,CACvBA,EAAkB,GAClB,MAAUz3B,EAAQ20B,GAASt0B,KAAMm3B,GAChCC,EAAiBz3B,EAAO,GAAIrF,cAAgB,MACzC88B,EAAiBz3B,EAAO,GAAIrF,cAAgB,MAAS,IACrD/G,OAAQoM,EAAO,IAGpBA,EAAQy3B,EAAiBp2B,EAAI1G,cAAgB,KAE9C,OAAgB,MAATqF,EAAgB,KAAOA,EAAMe,KAAM,OAI3Cu3B,sBAAuB,WACtB,OAAOtkB,EAAYwjB,EAAwB,MAI5Ce,iBAAkB,SAAUhgC,EAAM8B,GAMjC,OALkB,MAAb2Z,IACJzb,EAAO4/B,EAAqB5/B,EAAKoC,eAChCw9B,EAAqB5/B,EAAKoC,gBAAmBpC,EAC9C2/B,EAAgB3/B,GAAS8B,GAEnBnH,MAIRslC,iBAAkB,SAAU3jC,GAI3B,OAHkB,MAAbmf,IACJmgB,EAAEsE,SAAW5jC,GAEP3B,MAIR+kC,WAAY,SAAU3gC,GACrB,IAAIpC,EACJ,GAAKoC,EACJ,GAAK0c,EAGJuhB,EAAMjkB,OAAQha,EAAKi+B,EAAMmD,cAIzB,IAAMxjC,KAAQoC,EACb2gC,EAAY/iC,GAAS,CAAE+iC,EAAY/iC,GAAQoC,EAAKpC,IAInD,OAAOhC,MAIRylC,MAAO,SAAUC,GAChB,IAAIC,EAAYD,GAAcR,EAK9B,OAJKd,GACJA,EAAUqB,MAAOE,GAElB98B,EAAM,EAAG88B,GACF3lC,OAoBV,GAfAqe,EAASzB,QAASylB,GAKlBpB,EAAEgC,MAAUA,GAAOhC,EAAEgC,KAAO9tB,GAASK,MAAS,IAC5CtP,QAASy7B,GAAWxsB,GAASguB,SAAW,MAG1ClC,EAAEt/B,KAAOyD,EAAQuX,QAAUvX,EAAQzD,MAAQs/B,EAAEtkB,QAAUskB,EAAEt/B,KAGzDs/B,EAAEkB,WAAclB,EAAEiB,UAAY,KAAMz6B,cAAcqF,MAAOoP,IAAmB,CAAE,IAGxD,MAAjB+kB,EAAE2E,YAAsB,CAC5BnB,EAAY7kC,EAAS0C,cAAe,KAKpC,IACCmiC,EAAUjvB,KAAOyrB,EAAEgC,IAInBwB,EAAUjvB,KAAOivB,EAAUjvB,KAC3ByrB,EAAE2E,YAAc9D,GAAaqB,SAAW,KAAOrB,GAAa+D,MAC3DpB,EAAUtB,SAAW,KAAOsB,EAAUoB,KACtC,MAAQp5B,GAITw0B,EAAE2E,aAAc,GAalB,GARK3E,EAAExe,MAAQwe,EAAEmC,aAAiC,iBAAXnC,EAAExe,OACxCwe,EAAExe,KAAOzf,EAAOg+B,MAAOC,EAAExe,KAAMwe,EAAEF,cAIlCqB,GAA+B9H,GAAY2G,EAAG77B,EAASi9B,GAGlDvhB,EACJ,OAAOuhB,EA8ER,IAAMlgC,KAzENuiC,EAAc1hC,EAAOwlB,OAASyY,EAAEzhC,SAGQ,GAApBwD,EAAO8/B,UAC1B9/B,EAAOwlB,MAAMU,QAAS,aAIvB+X,EAAEt/B,KAAOs/B,EAAEt/B,KAAKogB,cAGhBkf,EAAE6E,YAAcpE,GAAWj0B,KAAMwzB,EAAEt/B,MAKnC0iC,EAAWpD,EAAEgC,IAAI/8B,QAASq7B,GAAO,IAG3BN,EAAE6E,WAwBI7E,EAAExe,MAAQwe,EAAEmC,aACoD,KAAzEnC,EAAEqC,aAAe,IAAKziC,QAAS,uCACjCogC,EAAExe,KAAOwe,EAAExe,KAAKvc,QAASo7B,GAAK,OAvB9BqD,EAAW1D,EAAEgC,IAAI3iC,MAAO+jC,EAAS/gC,QAG5B29B,EAAExe,OAAUwe,EAAEmC,aAAiC,iBAAXnC,EAAExe,QAC1C4hB,IAAchE,GAAO5yB,KAAM42B,GAAa,IAAM,KAAQpD,EAAExe,YAGjDwe,EAAExe,OAIO,IAAZwe,EAAE/yB,QACNm2B,EAAWA,EAASn+B,QAASs7B,GAAY,MACzCmD,GAAatE,GAAO5yB,KAAM42B,GAAa,IAAM,KAAQ,KAASxiC,GAAMuF,OACnEu9B,GAIF1D,EAAEgC,IAAMoB,EAAWM,GASf1D,EAAE8E,aACD/iC,EAAO+/B,aAAcsB,IACzBhC,EAAMgD,iBAAkB,oBAAqBriC,EAAO+/B,aAAcsB,IAE9DrhC,EAAOggC,KAAMqB,IACjBhC,EAAMgD,iBAAkB,gBAAiBriC,EAAOggC,KAAMqB,MAKnDpD,EAAExe,MAAQwe,EAAE6E,aAAgC,IAAlB7E,EAAEqC,aAAyBl+B,EAAQk+B,cACjEjB,EAAMgD,iBAAkB,eAAgBpE,EAAEqC,aAI3CjB,EAAMgD,iBACL,SACApE,EAAEkB,UAAW,IAAOlB,EAAEsC,QAAStC,EAAEkB,UAAW,IAC3ClB,EAAEsC,QAAStC,EAAEkB,UAAW,KACA,MAArBlB,EAAEkB,UAAW,GAAc,KAAON,GAAW,WAAa,IAC7DZ,EAAEsC,QAAS,MAIFtC,EAAE+E,QACZ3D,EAAMgD,iBAAkBljC,EAAG8+B,EAAE+E,QAAS7jC,IAIvC,GAAK8+B,EAAEgF,cAC+C,IAAnDhF,EAAEgF,WAAWxlC,KAAMmkC,EAAiBvC,EAAOpB,IAAiBngB,GAG9D,OAAOuhB,EAAMoD,QAed,GAXAP,EAAW,QAGXJ,EAAiBtpB,IAAKylB,EAAEhG,UACxBoH,EAAMx5B,KAAMo4B,EAAEiF,SACd7D,EAAMxlB,KAAMokB,EAAE76B,OAGdg+B,EAAYhC,GAA+BR,GAAYX,EAAG77B,EAASi9B,GAK5D,CASN,GARAA,EAAMnhB,WAAa,EAGdwjB,GACJG,EAAmB3b,QAAS,WAAY,CAAEmZ,EAAOpB,IAI7CngB,EACJ,OAAOuhB,EAIHpB,EAAEoC,OAAqB,EAAZpC,EAAE5D,UACjBmH,EAAezkC,EAAO+f,WAAY,WACjCuiB,EAAMoD,MAAO,YACXxE,EAAE5D,UAGN,IACCvc,GAAY,EACZsjB,EAAU+B,KAAMnB,EAAgBn8B,GAC/B,MAAQ4D,GAGT,GAAKqU,EACJ,MAAMrU,EAIP5D,GAAO,EAAG4D,SAhCX5D,GAAO,EAAG,gBAqCX,SAASA,EAAM28B,EAAQY,EAAkBC,EAAWL,GACnD,IAAIM,EAAWJ,EAAS9/B,EAAOmgC,EAAUC,EACxCd,EAAaU,EAGTtlB,IAILA,GAAY,EAGP0jB,GACJzkC,EAAOu9B,aAAckH,GAKtBJ,OAAYt+B,EAGZw+B,EAAwB0B,GAAW,GAGnC3D,EAAMnhB,WAAsB,EAATskB,EAAa,EAAI,EAGpCc,EAAsB,KAAVd,GAAiBA,EAAS,KAAkB,MAAXA,EAGxCa,IACJE,EA7lBJ,SAA8BtF,EAAGoB,EAAOgE,GAEvC,IAAII,EAAI9kC,EAAM+kC,EAAeC,EAC5B3rB,EAAWimB,EAAEjmB,SACbmnB,EAAYlB,EAAEkB,UAGf,MAA2B,MAAnBA,EAAW,GAClBA,EAAU9zB,aACEvI,IAAP2gC,IACJA,EAAKxF,EAAEsE,UAAYlD,EAAM8C,kBAAmB,iBAK9C,GAAKsB,EACJ,IAAM9kC,KAAQqZ,EACb,GAAKA,EAAUrZ,IAAUqZ,EAAUrZ,GAAO8L,KAAMg5B,GAAO,CACtDtE,EAAUvwB,QAASjQ,GACnB,MAMH,GAAKwgC,EAAW,KAAOkE,EACtBK,EAAgBvE,EAAW,OACrB,CAGN,IAAMxgC,KAAQ0kC,EAAY,CACzB,IAAMlE,EAAW,IAAOlB,EAAEyC,WAAY/hC,EAAO,IAAMwgC,EAAW,IAAQ,CACrEuE,EAAgB/kC,EAChB,MAEKglC,IACLA,EAAgBhlC,GAKlB+kC,EAAgBA,GAAiBC,EAMlC,GAAKD,EAIJ,OAHKA,IAAkBvE,EAAW,IACjCA,EAAUvwB,QAAS80B,GAEbL,EAAWK,GA0iBLE,CAAqB3F,EAAGoB,EAAOgE,KAIrCC,IACsC,EAA3CtjC,EAAO6D,QAAS,SAAUo6B,EAAEkB,YAC5Bn/B,EAAO6D,QAAS,OAAQo6B,EAAEkB,WAAc,IACxClB,EAAEyC,WAAY,eAAkB,cAIjC6C,EA9iBH,SAAsBtF,EAAGsF,EAAUlE,EAAOiE,GACzC,IAAIO,EAAOC,EAASC,EAAMp2B,EAAKsK,EAC9ByoB,EAAa,GAGbvB,EAAYlB,EAAEkB,UAAU7hC,QAGzB,GAAK6hC,EAAW,GACf,IAAM4E,KAAQ9F,EAAEyC,WACfA,EAAYqD,EAAKt/B,eAAkBw5B,EAAEyC,WAAYqD,GAInDD,EAAU3E,EAAU9zB,QAGpB,MAAQy4B,EAcP,GAZK7F,EAAEwC,eAAgBqD,KACtBzE,EAAOpB,EAAEwC,eAAgBqD,IAAcP,IAIlCtrB,GAAQqrB,GAAarF,EAAE+F,aAC5BT,EAAWtF,EAAE+F,WAAYT,EAAUtF,EAAEiB,WAGtCjnB,EAAO6rB,EACPA,EAAU3E,EAAU9zB,QAKnB,GAAiB,MAAZy4B,EAEJA,EAAU7rB,OAGJ,GAAc,MAATA,GAAgBA,IAAS6rB,EAAU,CAM9C,KAHAC,EAAOrD,EAAYzoB,EAAO,IAAM6rB,IAAapD,EAAY,KAAOoD,IAI/D,IAAMD,KAASnD,EAId,IADA/yB,EAAMk2B,EAAMt/B,MAAO,MACT,KAAQu/B,IAGjBC,EAAOrD,EAAYzoB,EAAO,IAAMtK,EAAK,KACpC+yB,EAAY,KAAO/yB,EAAK,KACb,EAGG,IAATo2B,EACJA,EAAOrD,EAAYmD,IAGgB,IAAxBnD,EAAYmD,KACvBC,EAAUn2B,EAAK,GACfwxB,EAAUvwB,QAASjB,EAAK,KAEzB,MAOJ,IAAc,IAATo2B,EAGJ,GAAKA,GAAQ9F,EAAEgG,UACdV,EAAWQ,EAAMR,QAEjB,IACCA,EAAWQ,EAAMR,GAChB,MAAQ95B,GACT,MAAO,CACN0R,MAAO,cACP/X,MAAO2gC,EAAOt6B,EAAI,sBAAwBwO,EAAO,OAAS6rB,IASjE,MAAO,CAAE3oB,MAAO,UAAWsE,KAAM8jB,GAidpBW,CAAajG,EAAGsF,EAAUlE,EAAOiE,GAGvCA,GAGCrF,EAAE8E,cACNS,EAAWnE,EAAM8C,kBAAmB,oBAEnCniC,EAAO+/B,aAAcsB,GAAamC,IAEnCA,EAAWnE,EAAM8C,kBAAmB,WAEnCniC,EAAOggC,KAAMqB,GAAamC,IAKZ,MAAXhB,GAA6B,SAAXvE,EAAEt/B,KACxB+jC,EAAa,YAGS,MAAXF,EACXE,EAAa,eAIbA,EAAaa,EAASpoB,MACtB+nB,EAAUK,EAAS9jB,KAEnB6jB,IADAlgC,EAAQmgC,EAASngC,UAMlBA,EAAQs/B,GACHF,GAAWE,IACfA,EAAa,QACRF,EAAS,IACbA,EAAS,KAMZnD,EAAMmD,OAASA,EACfnD,EAAMqD,YAAeU,GAAoBV,GAAe,GAGnDY,EACJjoB,EAASmB,YAAaolB,EAAiB,CAAEsB,EAASR,EAAYrD,IAE9DhkB,EAASuB,WAAYglB,EAAiB,CAAEvC,EAAOqD,EAAYt/B,IAI5Di8B,EAAM0C,WAAYA,GAClBA,OAAaj/B,EAER4+B,GACJG,EAAmB3b,QAASod,EAAY,cAAgB,YACvD,CAAEjE,EAAOpB,EAAGqF,EAAYJ,EAAU9/B,IAIpC0+B,EAAiB/mB,SAAU6mB,EAAiB,CAAEvC,EAAOqD,IAEhDhB,IACJG,EAAmB3b,QAAS,eAAgB,CAAEmZ,EAAOpB,MAG3Cj+B,EAAO8/B,QAChB9/B,EAAOwlB,MAAMU,QAAS,cAKzB,OAAOmZ,GAGR8E,QAAS,SAAUlE,EAAKxgB,EAAMte,GAC7B,OAAOnB,EAAOW,IAAKs/B,EAAKxgB,EAAMte,EAAU,SAGzCijC,UAAW,SAAUnE,EAAK9+B,GACzB,OAAOnB,EAAOW,IAAKs/B,OAAKn9B,EAAW3B,EAAU,aAI/CnB,EAAOkB,KAAM,CAAE,MAAO,QAAU,SAAUsD,EAAImV,GAC7C3Z,EAAQ2Z,GAAW,SAAUsmB,EAAKxgB,EAAMte,EAAUxC,GAUjD,OAPKN,EAAYohB,KAChB9gB,EAAOA,GAAQwC,EACfA,EAAWse,EACXA,OAAO3c,GAID9C,EAAOmhC,KAAMnhC,EAAOmC,OAAQ,CAClC89B,IAAKA,EACLthC,KAAMgb,EACNulB,SAAUvgC,EACV8gB,KAAMA,EACNyjB,QAAS/hC,GACPnB,EAAO2C,cAAes9B,IAASA,OAIpCjgC,EAAOihC,cAAe,SAAUhD,GAC/B,IAAI9+B,EACJ,IAAMA,KAAK8+B,EAAE+E,QACa,iBAApB7jC,EAAEsF,gBACNw5B,EAAEqC,YAAcrC,EAAE+E,QAAS7jC,IAAO,MAMrCa,EAAOwsB,SAAW,SAAUyT,EAAK79B,EAASlD,GACzC,OAAOc,EAAOmhC,KAAM,CACnBlB,IAAKA,EAGLthC,KAAM,MACNugC,SAAU,SACVh0B,OAAO,EACPm1B,OAAO,EACP7jC,QAAQ,EAKRkkC,WAAY,CACX2D,cAAe,cAEhBL,WAAY,SAAUT,GACrBvjC,EAAO0D,WAAY6/B,EAAUnhC,EAASlD,OAMzCc,EAAOG,GAAGgC,OAAQ,CACjBmiC,QAAS,SAAU/X,GAClB,IAAI/H,EAyBJ,OAvBKxnB,KAAM,KACLqB,EAAYkuB,KAChBA,EAAOA,EAAK9uB,KAAMT,KAAM,KAIzBwnB,EAAOxkB,EAAQusB,EAAMvvB,KAAM,GAAIkN,eAAgB1I,GAAI,GAAIgB,OAAO,GAEzDxF,KAAM,GAAI4C,YACd4kB,EAAK2I,aAAcnwB,KAAM,IAG1BwnB,EAAKpjB,IAAK,WACT,IAAIC,EAAOrE,KAEX,MAAQqE,EAAKkjC,kBACZljC,EAAOA,EAAKkjC,kBAGb,OAAOljC,IACJ4rB,OAAQjwB,OAGNA,MAGRwnC,UAAW,SAAUjY,GACpB,OAAKluB,EAAYkuB,GACTvvB,KAAKkE,KAAM,SAAU/B,GAC3Ba,EAAQhD,MAAOwnC,UAAWjY,EAAK9uB,KAAMT,KAAMmC,MAItCnC,KAAKkE,KAAM,WACjB,IAAIuW,EAAOzX,EAAQhD,MAClBgb,EAAWP,EAAKO,WAEZA,EAAS1X,OACb0X,EAASssB,QAAS/X,GAGlB9U,EAAKwV,OAAQV,MAKhB/H,KAAM,SAAU+H,GACf,IAAIkY,EAAiBpmC,EAAYkuB,GAEjC,OAAOvvB,KAAKkE,KAAM,SAAU/B,GAC3Ba,EAAQhD,MAAOsnC,QAASG,EAAiBlY,EAAK9uB,KAAMT,KAAMmC,GAAMotB,MAIlEmY,OAAQ,SAAUzkC,GAIjB,OAHAjD,KAAKmU,OAAQlR,GAAW2R,IAAK,QAAS1Q,KAAM,WAC3ClB,EAAQhD,MAAOswB,YAAatwB,KAAKwM,cAE3BxM,QAKTgD,EAAO6O,KAAKhI,QAAQ4vB,OAAS,SAAUp1B,GACtC,OAAQrB,EAAO6O,KAAKhI,QAAQ89B,QAAStjC,IAEtCrB,EAAO6O,KAAKhI,QAAQ89B,QAAU,SAAUtjC,GACvC,SAAWA,EAAKuuB,aAAevuB,EAAK0vB,cAAgB1vB,EAAKyxB,iBAAiBxyB,SAM3EN,EAAO6/B,aAAa+E,IAAM,WACzB,IACC,OAAO,IAAI7nC,EAAO8nC,eACjB,MAAQp7B,MAGX,IAAIq7B,GAAmB,CAGrBC,EAAG,IAIHC,KAAM,KAEPC,GAAejlC,EAAO6/B,aAAa+E,MAEpCxmC,EAAQ8mC,OAASD,IAAkB,oBAAqBA,GACxD7mC,EAAQ+iC,KAAO8D,KAAiBA,GAEhCjlC,EAAOkhC,cAAe,SAAU9+B,GAC/B,IAAIjB,EAAUgkC,EAGd,GAAK/mC,EAAQ8mC,MAAQD,KAAiB7iC,EAAQwgC,YAC7C,MAAO,CACNO,KAAM,SAAUH,EAAS/K,GACxB,IAAI94B,EACHylC,EAAMxiC,EAAQwiC,MAWf,GATAA,EAAIQ,KACHhjC,EAAQzD,KACRyD,EAAQ69B,IACR79B,EAAQi+B,MACRj+B,EAAQijC,SACRjjC,EAAQmR,UAIJnR,EAAQkjC,UACZ,IAAMnmC,KAAKiD,EAAQkjC,UAClBV,EAAKzlC,GAAMiD,EAAQkjC,UAAWnmC,GAmBhC,IAAMA,KAdDiD,EAAQmgC,UAAYqC,EAAItC,kBAC5BsC,EAAItC,iBAAkBlgC,EAAQmgC,UAQzBngC,EAAQwgC,aAAgBI,EAAS,sBACtCA,EAAS,oBAAuB,kBAItBA,EACV4B,EAAIvC,iBAAkBljC,EAAG6jC,EAAS7jC,IAInCgC,EAAW,SAAUxC,GACpB,OAAO,WACDwC,IACJA,EAAWgkC,EAAgBP,EAAIW,OAC9BX,EAAIY,QAAUZ,EAAIa,QAAUb,EAAIc,UAC/Bd,EAAIe,mBAAqB,KAEb,UAAThnC,EACJimC,EAAInC,QACgB,UAAT9jC,EAKgB,iBAAfimC,EAAIpC,OACfvK,EAAU,EAAG,SAEbA,EAGC2M,EAAIpC,OACJoC,EAAIlC,YAINzK,EACC6M,GAAkBF,EAAIpC,SAAYoC,EAAIpC,OACtCoC,EAAIlC,WAK+B,UAAjCkC,EAAIgB,cAAgB,SACM,iBAArBhB,EAAIiB,aACV,CAAEC,OAAQlB,EAAIrB,UACd,CAAEhkC,KAAMqlC,EAAIiB,cACbjB,EAAIxC,4BAQTwC,EAAIW,OAASpkC,IACbgkC,EAAgBP,EAAIY,QAAUZ,EAAIc,UAAYvkC,EAAU,cAKnC2B,IAAhB8hC,EAAIa,QACRb,EAAIa,QAAUN,EAEdP,EAAIe,mBAAqB,WAGA,IAAnBf,EAAI1mB,YAMRnhB,EAAO+f,WAAY,WACb3b,GACJgkC,OAQLhkC,EAAWA,EAAU,SAErB,IAGCyjC,EAAIzB,KAAM/gC,EAAQ0gC,YAAc1gC,EAAQqd,MAAQ,MAC/C,MAAQhW,GAGT,GAAKtI,EACJ,MAAMsI,IAKTg5B,MAAO,WACDthC,GACJA,QAWLnB,EAAOihC,cAAe,SAAUhD,GAC1BA,EAAE2E,cACN3E,EAAEjmB,SAAS3Y,QAAS,KAKtBW,EAAO+gC,UAAW,CACjBR,QAAS,CACRlhC,OAAQ,6FAGT2Y,SAAU,CACT3Y,OAAQ,2BAETqhC,WAAY,CACX2D,cAAe,SAAU9kC,GAExB,OADAS,EAAO0D,WAAYnE,GACZA,MAMVS,EAAOihC,cAAe,SAAU,SAAUhD,QACxBn7B,IAAZm7B,EAAE/yB,QACN+yB,EAAE/yB,OAAQ,GAEN+yB,EAAE2E,cACN3E,EAAEt/B,KAAO,SAKXqB,EAAOkhC,cAAe,SAAU,SAAUjD,GAIxC,IAAI5+B,EAAQ8B,EADb,GAAK88B,EAAE2E,aAAe3E,EAAE8H,YAEvB,MAAO,CACN5C,KAAM,SAAUlpB,EAAGge,GAClB54B,EAASW,EAAQ,YACf+O,KAAMkvB,EAAE8H,aAAe,IACvBrmB,KAAM,CAAEsmB,QAAS/H,EAAEgI,cAAernC,IAAKq/B,EAAEgC,MACzC7a,GAAI,aAAcjkB,EAAW,SAAU+kC,GACvC7mC,EAAOub,SACPzZ,EAAW,KACN+kC,GACJjO,EAAuB,UAAbiO,EAAIvnC,KAAmB,IAAM,IAAKunC,EAAIvnC,QAKnD/B,EAAS8C,KAAKC,YAAaN,EAAQ,KAEpCojC,MAAO,WACDthC,GACJA,QAUL,IAqGKshB,GArGD0jB,GAAe,GAClBC,GAAS,oBAGVpmC,EAAO+gC,UAAW,CACjBsF,MAAO,WACPC,cAAe,WACd,IAAInlC,EAAWglC,GAAa7/B,OAAWtG,EAAO+C,QAAU,IAAQlE,GAAMuF,OAEtE,OADApH,KAAMmE,IAAa,EACZA,KAKTnB,EAAOihC,cAAe,aAAc,SAAUhD,EAAGsI,EAAkBlH,GAElE,IAAImH,EAAcC,EAAaC,EAC9BC,GAAuB,IAAZ1I,EAAEoI,QAAqBD,GAAO37B,KAAMwzB,EAAEgC,KAChD,MACkB,iBAAXhC,EAAExe,MAE6C,KADnDwe,EAAEqC,aAAe,IACjBziC,QAAS,sCACXuoC,GAAO37B,KAAMwzB,EAAExe,OAAU,QAI5B,GAAKknB,GAAiC,UAArB1I,EAAEkB,UAAW,GA8D7B,OA3DAqH,EAAevI,EAAEqI,cAAgBjoC,EAAY4/B,EAAEqI,eAC9CrI,EAAEqI,gBACFrI,EAAEqI,cAGEK,EACJ1I,EAAG0I,GAAa1I,EAAG0I,GAAWzjC,QAASkjC,GAAQ,KAAOI,IAC/B,IAAZvI,EAAEoI,QACbpI,EAAEgC,MAAS5C,GAAO5yB,KAAMwzB,EAAEgC,KAAQ,IAAM,KAAQhC,EAAEoI,MAAQ,IAAMG,GAIjEvI,EAAEyC,WAAY,eAAkB,WAI/B,OAHMgG,GACL1mC,EAAOoD,MAAOojC,EAAe,mBAEvBE,EAAmB,IAI3BzI,EAAEkB,UAAW,GAAM,OAGnBsH,EAAc1pC,EAAQypC,GACtBzpC,EAAQypC,GAAiB,WACxBE,EAAoBplC,WAIrB+9B,EAAMjkB,OAAQ,gBAGQtY,IAAhB2jC,EACJzmC,EAAQjD,GAASu+B,WAAYkL,GAI7BzpC,EAAQypC,GAAiBC,EAIrBxI,EAAGuI,KAGPvI,EAAEqI,cAAgBC,EAAiBD,cAGnCH,GAAavoC,KAAM4oC,IAIfE,GAAqBroC,EAAYooC,IACrCA,EAAaC,EAAmB,IAGjCA,EAAoBD,OAAc3jC,IAI5B,WAYT1E,EAAQwoC,qBACHnkB,GAAO7lB,EAASiqC,eAAeD,mBAAoB,IAAKnkB,MACvD5U,UAAY,6BACiB,IAA3B4U,GAAKjZ,WAAWlJ,QAQxBN,EAAO2X,UAAY,SAAU8H,EAAMvf,EAAS4mC,GAC3C,MAAqB,iBAATrnB,EACJ,IAEgB,kBAAZvf,IACX4mC,EAAc5mC,EACdA,GAAU,GAKLA,IAIA9B,EAAQwoC,qBAMZ/yB,GALA3T,EAAUtD,EAASiqC,eAAeD,mBAAoB,KAKvCtnC,cAAe,SACzBkT,KAAO5V,EAASuV,SAASK,KAC9BtS,EAAQR,KAAKC,YAAakU,IAE1B3T,EAAUtD,GAKZynB,GAAWyiB,GAAe,IAD1BC,EAASzvB,EAAWnN,KAAMsV,IAKlB,CAAEvf,EAAQZ,cAAeynC,EAAQ,MAGzCA,EAAS3iB,GAAe,CAAE3E,GAAQvf,EAASmkB,GAEtCA,GAAWA,EAAQ/jB,QACvBN,EAAQqkB,GAAUzJ,SAGZ5a,EAAOgB,MAAO,GAAI+lC,EAAOv9B,cAlChC,IAAIqK,EAAMkzB,EAAQ1iB,GAyCnBrkB,EAAOG,GAAGsoB,KAAO,SAAUwX,EAAK+G,EAAQ7lC,GACvC,IAAIlB,EAAUtB,EAAM4kC,EACnB9rB,EAAOza,KACPyoB,EAAMwa,EAAIpiC,QAAS,KAsDpB,OApDY,EAAP4nB,IACJxlB,EAAWk7B,GAAkB8E,EAAI3iC,MAAOmoB,IACxCwa,EAAMA,EAAI3iC,MAAO,EAAGmoB,IAIhBpnB,EAAY2oC,IAGhB7lC,EAAW6lC,EACXA,OAASlkC,GAGEkkC,GAA4B,iBAAXA,IAC5BroC,EAAO,QAIW,EAAd8Y,EAAKnX,QACTN,EAAOmhC,KAAM,CACZlB,IAAKA,EAKLthC,KAAMA,GAAQ,MACdugC,SAAU,OACVzf,KAAMunB,IACHnhC,KAAM,SAAUggC,GAGnBtC,EAAWjiC,UAEXmW,EAAK8U,KAAMtsB,EAIVD,EAAQ,SAAUitB,OAAQjtB,EAAO2X,UAAWkuB,IAAiBr4B,KAAMvN,GAGnE4lC,KAKEzqB,OAAQja,GAAY,SAAUk+B,EAAOmD,GACxC/qB,EAAKvW,KAAM,WACVC,EAASxD,MAAOX,KAAMumC,GAAY,CAAElE,EAAMwG,aAAcrD,EAAQnD,QAK5DriC,MAMRgD,EAAO6O,KAAKhI,QAAQogC,SAAW,SAAU5lC,GACxC,OAAOrB,EAAO2B,KAAM3B,EAAOy5B,OAAQ,SAAUt5B,GAC5C,OAAOkB,IAASlB,EAAGkB,OAChBf,QAMLN,EAAOknC,OAAS,CACfC,UAAW,SAAU9lC,EAAMe,EAASjD,GACnC,IAAIioC,EAAaC,EAASC,EAAWC,EAAQC,EAAWC,EACvD/X,EAAW1vB,EAAOyhB,IAAKpgB,EAAM,YAC7BqmC,EAAU1nC,EAAQqB,GAClBynB,EAAQ,GAGS,WAAb4G,IACJruB,EAAKkgB,MAAMmO,SAAW,YAGvB8X,EAAYE,EAAQR,SACpBI,EAAYtnC,EAAOyhB,IAAKpgB,EAAM,OAC9BomC,EAAaznC,EAAOyhB,IAAKpgB,EAAM,SACI,aAAbquB,GAAwC,UAAbA,KACA,GAA9C4X,EAAYG,GAAa5pC,QAAS,SAMpC0pC,GADAH,EAAcM,EAAQhY,YACD3iB,IACrBs6B,EAAUD,EAAYzS,OAGtB4S,EAASxX,WAAYuX,IAAe,EACpCD,EAAUtX,WAAY0X,IAAgB,GAGlCppC,EAAY+D,KAGhBA,EAAUA,EAAQ3E,KAAM4D,EAAMlC,EAAGa,EAAOmC,OAAQ,GAAIqlC,KAGjC,MAAfplC,EAAQ2K,MACZ+b,EAAM/b,IAAQ3K,EAAQ2K,IAAMy6B,EAAUz6B,IAAQw6B,GAE1B,MAAhBnlC,EAAQuyB,OACZ7L,EAAM6L,KAASvyB,EAAQuyB,KAAO6S,EAAU7S,KAAS0S,GAG7C,UAAWjlC,EACfA,EAAQulC,MAAMlqC,KAAM4D,EAAMynB,GAG1B4e,EAAQjmB,IAAKqH,KAKhB9oB,EAAOG,GAAGgC,OAAQ,CAGjB+kC,OAAQ,SAAU9kC,GAGjB,GAAKd,UAAUhB,OACd,YAAmBwC,IAAZV,EACNpF,KACAA,KAAKkE,KAAM,SAAU/B,GACpBa,EAAOknC,OAAOC,UAAWnqC,KAAMoF,EAASjD,KAI3C,IAAIyoC,EAAMC,EACTxmC,EAAOrE,KAAM,GAEd,OAAMqE,EAQAA,EAAKyxB,iBAAiBxyB,QAK5BsnC,EAAOvmC,EAAKozB,wBACZoT,EAAMxmC,EAAK6I,cAAc4C,YAClB,CACNC,IAAK66B,EAAK76B,IAAM86B,EAAIC,YACpBnT,KAAMiT,EAAKjT,KAAOkT,EAAIE,cARf,CAAEh7B,IAAK,EAAG4nB,KAAM,QATxB,GAuBDjF,SAAU,WACT,GAAM1yB,KAAM,GAAZ,CAIA,IAAIgrC,EAAcd,EAAQhoC,EACzBmC,EAAOrE,KAAM,GACbirC,EAAe,CAAEl7B,IAAK,EAAG4nB,KAAM,GAGhC,GAAwC,UAAnC30B,EAAOyhB,IAAKpgB,EAAM,YAGtB6lC,EAAS7lC,EAAKozB,4BAER,CACNyS,EAASlqC,KAAKkqC,SAIdhoC,EAAMmC,EAAK6I,cACX89B,EAAe3mC,EAAK2mC,cAAgB9oC,EAAIyN,gBACxC,MAAQq7B,IACLA,IAAiB9oC,EAAIujB,MAAQulB,IAAiB9oC,EAAIyN,kBACT,WAA3C3M,EAAOyhB,IAAKumB,EAAc,YAE1BA,EAAeA,EAAapoC,WAExBooC,GAAgBA,IAAiB3mC,GAAkC,IAA1B2mC,EAAazpC,YAG1D0pC,EAAejoC,EAAQgoC,GAAed,UACzBn6B,KAAO/M,EAAOyhB,IAAKumB,EAAc,kBAAkB,GAChEC,EAAatT,MAAQ30B,EAAOyhB,IAAKumB,EAAc,mBAAmB,IAKpE,MAAO,CACNj7B,IAAKm6B,EAAOn6B,IAAMk7B,EAAal7B,IAAM/M,EAAOyhB,IAAKpgB,EAAM,aAAa,GACpEszB,KAAMuS,EAAOvS,KAAOsT,EAAatT,KAAO30B,EAAOyhB,IAAKpgB,EAAM,cAAc,MAc1E2mC,aAAc,WACb,OAAOhrC,KAAKoE,IAAK,WAChB,IAAI4mC,EAAehrC,KAAKgrC,aAExB,MAAQA,GAA2D,WAA3ChoC,EAAOyhB,IAAKumB,EAAc,YACjDA,EAAeA,EAAaA,aAG7B,OAAOA,GAAgBr7B,QAM1B3M,EAAOkB,KAAM,CAAE20B,WAAY,cAAeD,UAAW,eAAiB,SAAUjc,EAAQ+F,GACvF,IAAI3S,EAAM,gBAAkB2S,EAE5B1f,EAAOG,GAAIwZ,GAAW,SAAUva,GAC/B,OAAOgf,EAAQphB,KAAM,SAAUqE,EAAMsY,EAAQva,GAG5C,IAAIyoC,EAOJ,GANKppC,EAAU4C,GACdwmC,EAAMxmC,EACuB,IAAlBA,EAAK9C,WAChBspC,EAAMxmC,EAAKyL,kBAGChK,IAAR1D,EACJ,OAAOyoC,EAAMA,EAAKnoB,GAASre,EAAMsY,GAG7BkuB,EACJA,EAAIK,SACFn7B,EAAY86B,EAAIE,YAAV3oC,EACP2N,EAAM3N,EAAMyoC,EAAIC,aAIjBzmC,EAAMsY,GAAWva,GAEhBua,EAAQva,EAAKkC,UAAUhB,WAU5BN,EAAOkB,KAAM,CAAE,MAAO,QAAU,SAAUsD,EAAIkb,GAC7C1f,EAAOizB,SAAUvT,GAASkP,GAAcxwB,EAAQgyB,cAC/C,SAAU/uB,EAAMitB,GACf,GAAKA,EAIJ,OAHAA,EAAWD,GAAQhtB,EAAMqe,GAGlBoO,GAAUrjB,KAAM6jB,GACtBtuB,EAAQqB,GAAOquB,WAAYhQ,GAAS,KACpC4O,MAQLtuB,EAAOkB,KAAM,CAAEinC,OAAQ,SAAUC,MAAO,SAAW,SAAU/lC,EAAM1D,GAClEqB,EAAOkB,KAAM,CACZ2zB,QAAS,QAAUxyB,EACnB2W,QAASra,EACT0pC,GAAI,QAAUhmC,GACZ,SAAUimC,EAAcC,GAG1BvoC,EAAOG,GAAIooC,GAAa,SAAU3T,EAAQzwB,GACzC,IAAIka,EAAY/c,UAAUhB,SAAYgoC,GAAkC,kBAAX1T,GAC5DpC,EAAQ8V,KAA6B,IAAX1T,IAA6B,IAAVzwB,EAAiB,SAAW,UAE1E,OAAOia,EAAQphB,KAAM,SAAUqE,EAAM1C,EAAMwF,GAC1C,IAAIjF,EAEJ,OAAKT,EAAU4C,GAGyB,IAAhCknC,EAAS1qC,QAAS,SACxBwD,EAAM,QAAUgB,GAChBhB,EAAKzE,SAAS+P,gBAAiB,SAAWtK,GAIrB,IAAlBhB,EAAK9C,UACTW,EAAMmC,EAAKsL,gBAIJ3J,KAAKivB,IACX5wB,EAAKohB,KAAM,SAAWpgB,GAAQnD,EAAK,SAAWmD,GAC9ChB,EAAKohB,KAAM,SAAWpgB,GAAQnD,EAAK,SAAWmD,GAC9CnD,EAAK,SAAWmD,UAIDS,IAAVqB,EAGNnE,EAAOyhB,IAAKpgB,EAAM1C,EAAM6zB,GAGxBxyB,EAAOuhB,MAAOlgB,EAAM1C,EAAMwF,EAAOquB,IAChC7zB,EAAM0f,EAAYuW,OAAS9xB,EAAWub,QAM5Cre,EAAOkB,KAAM,CACZ,YACA,WACA,eACA,YACA,cACA,YACE,SAAUsD,EAAI7F,GAChBqB,EAAOG,GAAIxB,GAAS,SAAUwB,GAC7B,OAAOnD,KAAKooB,GAAIzmB,EAAMwB,MAOxBH,EAAOG,GAAGgC,OAAQ,CAEjB61B,KAAM,SAAU3S,EAAO5F,EAAMtf,GAC5B,OAAOnD,KAAKooB,GAAIC,EAAO,KAAM5F,EAAMtf,IAEpCqoC,OAAQ,SAAUnjB,EAAOllB,GACxB,OAAOnD,KAAKyoB,IAAKJ,EAAO,KAAMllB,IAG/BsoC,SAAU,SAAUxoC,EAAUolB,EAAO5F,EAAMtf,GAC1C,OAAOnD,KAAKooB,GAAIC,EAAOplB,EAAUwf,EAAMtf,IAExCuoC,WAAY,SAAUzoC,EAAUolB,EAAOllB,GAGtC,OAA4B,IAArBmB,UAAUhB,OAChBtD,KAAKyoB,IAAKxlB,EAAU,MACpBjD,KAAKyoB,IAAKJ,EAAOplB,GAAY,KAAME,IAGrCwoC,MAAO,SAAUC,EAAQC,GACxB,OAAO7rC,KAAKkuB,WAAY0d,GAASzd,WAAY0d,GAASD,MAIxD5oC,EAAOkB,KACN,wLAE4DqD,MAAO,KACnE,SAAUC,EAAInC,GAGbrC,EAAOG,GAAIkC,GAAS,SAAUod,EAAMtf,GACnC,OAA0B,EAAnBmB,UAAUhB,OAChBtD,KAAKooB,GAAI/iB,EAAM,KAAMod,EAAMtf,GAC3BnD,KAAKkpB,QAAS7jB,MAUlB,IAAI2E,GAAQ,qCAMZhH,EAAO8oC,MAAQ,SAAU3oC,EAAID,GAC5B,IAAIyN,EAAK6D,EAAMs3B,EAUf,GARwB,iBAAZ5oC,IACXyN,EAAMxN,EAAID,GACVA,EAAUC,EACVA,EAAKwN,GAKAtP,EAAY8B,GAalB,OARAqR,EAAOlU,EAAMG,KAAM6D,UAAW,IAC9BwnC,EAAQ,WACP,OAAO3oC,EAAGxC,MAAOuC,GAAWlD,KAAMwU,EAAK9T,OAAQJ,EAAMG,KAAM6D,eAItD8C,KAAOjE,EAAGiE,KAAOjE,EAAGiE,MAAQpE,EAAOoE,OAElC0kC,GAGR9oC,EAAO+oC,UAAY,SAAUC,GACvBA,EACJhpC,EAAOge,YAEPhe,EAAO4X,OAAO,IAGhB5X,EAAO6C,QAAUD,MAAMC,QACvB7C,EAAOipC,UAAYhpB,KAAKC,MACxBlgB,EAAOqJ,SAAWA,EAClBrJ,EAAO3B,WAAaA,EACpB2B,EAAOvB,SAAWA,EAClBuB,EAAOgf,UAAYA,EACnBhf,EAAOrB,KAAOmB,EAEdE,EAAOmpB,IAAMzjB,KAAKyjB,IAElBnpB,EAAOkpC,UAAY,SAAU5qC,GAK5B,IAAIK,EAAOqB,EAAOrB,KAAML,GACxB,OAAkB,WAATK,GAA8B,WAATA,KAK5BwqC,MAAO7qC,EAAMyxB,WAAYzxB,KAG5B0B,EAAOopC,KAAO,SAAU7pC,GACvB,OAAe,MAARA,EACN,IACEA,EAAO,IAAK2D,QAAS8D,GAAO,KAkBT,mBAAXqiC,QAAyBA,OAAOC,KAC3CD,OAAQ,SAAU,GAAI,WACrB,OAAOrpC,IAOT,IAGCupC,GAAUxsC,EAAOiD,OAGjBwpC,GAAKzsC,EAAO0sC,EAwBb,OAtBAzpC,EAAO0pC,WAAa,SAAUhnC,GAS7B,OARK3F,EAAO0sC,IAAMzpC,IACjBjD,EAAO0sC,EAAID,IAGP9mC,GAAQ3F,EAAOiD,SAAWA,IAC9BjD,EAAOiD,OAASupC,IAGVvpC,GAMiB,oBAAb/C,IACXF,EAAOiD,OAASjD,EAAO0sC,EAAIzpC,GAMrBA","file":"jquery-3.6.0.min.js"}
\ No newline at end of file
diff --git a/docs/deps/bootstrap-5.3.1/bootstrap.bundle.min.js b/docs/deps/bootstrap-5.3.1/bootstrap.bundle.min.js
new file mode 100644
index 00000000..e8f21f70
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/bootstrap.bundle.min.js
@@ -0,0 +1,7 @@
+/*!
+  * Bootstrap v5.3.1 (https://getbootstrap.com/)
+  * Copyright 2011-2023 The Bootstrap Authors (https://github.com/twbs/bootstrap/graphs/contributors)
+  * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)
+  */
+!function(t,e){"object"==typeof exports&&"undefined"!=typeof module?module.exports=e():"function"==typeof define&&define.amd?define(e):(t="undefined"!=typeof globalThis?globalThis:t||self).bootstrap=e()}(this,(function(){"use strict";const t=new Map,e={set(e,i,n){t.has(e)||t.set(e,new Map);const s=t.get(e);s.has(i)||0===s.size?s.set(i,n):console.error(`Bootstrap doesn't allow more than one instance per element. Bound instance: ${Array.from(s.keys())[0]}.`)},get:(e,i)=>t.has(e)&&t.get(e).get(i)||null,remove(e,i){if(!t.has(e))return;const n=t.get(e);n.delete(i),0===n.size&&t.delete(e)}},i="transitionend",n=t=>(t&&window.CSS&&window.CSS.escape&&(t=t.replace(/#([^\s"#']+)/g,((t,e)=>`#${CSS.escape(e)}`))),t),s=t=>{t.dispatchEvent(new Event(i))},o=t=>!(!t||"object"!=typeof t)&&(void 0!==t.jquery&&(t=t[0]),void 0!==t.nodeType),r=t=>o(t)?t.jquery?t[0]:t:"string"==typeof t&&t.length>0?document.querySelector(n(t)):null,a=t=>{if(!o(t)||0===t.getClientRects().length)return!1;const e="visible"===getComputedStyle(t).getPropertyValue("visibility"),i=t.closest("details:not([open])");if(!i)return e;if(i!==t){const e=t.closest("summary");if(e&&e.parentNode!==i)return!1;if(null===e)return!1}return e},l=t=>!t||t.nodeType!==Node.ELEMENT_NODE||!!t.classList.contains("disabled")||(void 0!==t.disabled?t.disabled:t.hasAttribute("disabled")&&"false"!==t.getAttribute("disabled")),c=t=>{if(!document.documentElement.attachShadow)return null;if("function"==typeof t.getRootNode){const e=t.getRootNode();return e instanceof ShadowRoot?e:null}return t instanceof ShadowRoot?t:t.parentNode?c(t.parentNode):null},h=()=>{},d=t=>{t.offsetHeight},u=()=>window.jQuery&&!document.body.hasAttribute("data-bs-no-jquery")?window.jQuery:null,f=[],p=()=>"rtl"===document.documentElement.dir,m=t=>{var e;e=()=>{const e=u();if(e){const i=t.NAME,n=e.fn[i];e.fn[i]=t.jQueryInterface,e.fn[i].Constructor=t,e.fn[i].noConflict=()=>(e.fn[i]=n,t.jQueryInterface)}},"loading"===document.readyState?(f.length||document.addEventListener("DOMContentLoaded",(()=>{for(const t of f)t()})),f.push(e)):e()},g=(t,e=[],i=t)=>"function"==typeof t?t(...e):i,_=(t,e,n=!0)=>{if(!n)return void g(t);const o=(t=>{if(!t)return 0;let{transitionDuration:e,transitionDelay:i}=window.getComputedStyle(t);const n=Number.parseFloat(e),s=Number.parseFloat(i);return n||s?(e=e.split(",")[0],i=i.split(",")[0],1e3*(Number.parseFloat(e)+Number.parseFloat(i))):0})(e)+5;let r=!1;const a=({target:n})=>{n===e&&(r=!0,e.removeEventListener(i,a),g(t))};e.addEventListener(i,a),setTimeout((()=>{r||s(e)}),o)},b=(t,e,i,n)=>{const s=t.length;let o=t.indexOf(e);return-1===o?!i&&n?t[s-1]:t[0]:(o+=i?1:-1,n&&(o=(o+s)%s),t[Math.max(0,Math.min(o,s-1))])},v=/[^.]*(?=\..*)\.|.*/,y=/\..*/,w=/::\d+$/,A={};let E=1;const T={mouseenter:"mouseover",mouseleave:"mouseout"},C=new Set(["click","dblclick","mouseup","mousedown","contextmenu","mousewheel","DOMMouseScroll","mouseover","mouseout","mousemove","selectstart","selectend","keydown","keypress","keyup","orientationchange","touchstart","touchmove","touchend","touchcancel","pointerdown","pointermove","pointerup","pointerleave","pointercancel","gesturestart","gesturechange","gestureend","focus","blur","change","reset","select","submit","focusin","focusout","load","unload","beforeunload","resize","move","DOMContentLoaded","readystatechange","error","abort","scroll"]);function O(t,e){return e&&`${e}::${E++}`||t.uidEvent||E++}function x(t){const e=O(t);return t.uidEvent=e,A[e]=A[e]||{},A[e]}function k(t,e,i=null){return Object.values(t).find((t=>t.callable===e&&t.delegationSelector===i))}function L(t,e,i){const n="string"==typeof e,s=n?i:e||i;let o=I(t);return C.has(o)||(o=t),[n,s,o]}function S(t,e,i,n,s){if("string"!=typeof e||!t)return;let[o,r,a]=L(e,i,n);if(e in T){const t=t=>function(e){if(!e.relatedTarget||e.relatedTarget!==e.delegateTarget&&!e.delegateTarget.contains(e.relatedTarget))return t.call(this,e)};r=t(r)}const l=x(t),c=l[a]||(l[a]={}),h=k(c,r,o?i:null);if(h)return void(h.oneOff=h.oneOff&&s);const d=O(r,e.replace(v,"")),u=o?function(t,e,i){return function n(s){const o=t.querySelectorAll(e);for(let{target:r}=s;r&&r!==this;r=r.parentNode)for(const a of o)if(a===r)return P(s,{delegateTarget:r}),n.oneOff&&N.off(t,s.type,e,i),i.apply(r,[s])}}(t,i,r):function(t,e){return function i(n){return P(n,{delegateTarget:t}),i.oneOff&&N.off(t,n.type,e),e.apply(t,[n])}}(t,r);u.delegationSelector=o?i:null,u.callable=r,u.oneOff=s,u.uidEvent=d,c[d]=u,t.addEventListener(a,u,o)}function D(t,e,i,n,s){const o=k(e[i],n,s);o&&(t.removeEventListener(i,o,Boolean(s)),delete e[i][o.uidEvent])}function $(t,e,i,n){const s=e[i]||{};for(const[o,r]of Object.entries(s))o.includes(n)&&D(t,e,i,r.callable,r.delegationSelector)}function I(t){return t=t.replace(y,""),T[t]||t}const N={on(t,e,i,n){S(t,e,i,n,!1)},one(t,e,i,n){S(t,e,i,n,!0)},off(t,e,i,n){if("string"!=typeof e||!t)return;const[s,o,r]=L(e,i,n),a=r!==e,l=x(t),c=l[r]||{},h=e.startsWith(".");if(void 0===o){if(h)for(const i of Object.keys(l))$(t,l,i,e.slice(1));for(const[i,n]of Object.entries(c)){const s=i.replace(w,"");a&&!e.includes(s)||D(t,l,r,n.callable,n.delegationSelector)}}else{if(!Object.keys(c).length)return;D(t,l,r,o,s?i:null)}},trigger(t,e,i){if("string"!=typeof e||!t)return null;const n=u();let s=null,o=!0,r=!0,a=!1;e!==I(e)&&n&&(s=n.Event(e,i),n(t).trigger(s),o=!s.isPropagationStopped(),r=!s.isImmediatePropagationStopped(),a=s.isDefaultPrevented());const l=P(new Event(e,{bubbles:o,cancelable:!0}),i);return a&&l.preventDefault(),r&&t.dispatchEvent(l),l.defaultPrevented&&s&&s.preventDefault(),l}};function P(t,e={}){for(const[i,n]of Object.entries(e))try{t[i]=n}catch(e){Object.defineProperty(t,i,{configurable:!0,get:()=>n})}return t}function M(t){if("true"===t)return!0;if("false"===t)return!1;if(t===Number(t).toString())return Number(t);if(""===t||"null"===t)return null;if("string"!=typeof t)return t;try{return JSON.parse(decodeURIComponent(t))}catch(e){return t}}function j(t){return t.replace(/[A-Z]/g,(t=>`-${t.toLowerCase()}`))}const F={setDataAttribute(t,e,i){t.setAttribute(`data-bs-${j(e)}`,i)},removeDataAttribute(t,e){t.removeAttribute(`data-bs-${j(e)}`)},getDataAttributes(t){if(!t)return{};const e={},i=Object.keys(t.dataset).filter((t=>t.startsWith("bs")&&!t.startsWith("bsConfig")));for(const n of i){let i=n.replace(/^bs/,"");i=i.charAt(0).toLowerCase()+i.slice(1,i.length),e[i]=M(t.dataset[n])}return e},getDataAttribute:(t,e)=>M(t.getAttribute(`data-bs-${j(e)}`))};class H{static get Default(){return{}}static get DefaultType(){return{}}static get NAME(){throw new Error('You have to implement the static method "NAME", for each component!')}_getConfig(t){return t=this._mergeConfigObj(t),t=this._configAfterMerge(t),this._typeCheckConfig(t),t}_configAfterMerge(t){return t}_mergeConfigObj(t,e){const i=o(e)?F.getDataAttribute(e,"config"):{};return{...this.constructor.Default,..."object"==typeof i?i:{},...o(e)?F.getDataAttributes(e):{},..."object"==typeof t?t:{}}}_typeCheckConfig(t,e=this.constructor.DefaultType){for(const[n,s]of Object.entries(e)){const e=t[n],r=o(e)?"element":null==(i=e)?`${i}`:Object.prototype.toString.call(i).match(/\s([a-z]+)/i)[1].toLowerCase();if(!new RegExp(s).test(r))throw new TypeError(`${this.constructor.NAME.toUpperCase()}: Option "${n}" provided type "${r}" but expected type "${s}".`)}var i}}class W extends H{constructor(t,i){super(),(t=r(t))&&(this._element=t,this._config=this._getConfig(i),e.set(this._element,this.constructor.DATA_KEY,this))}dispose(){e.remove(this._element,this.constructor.DATA_KEY),N.off(this._element,this.constructor.EVENT_KEY);for(const t of Object.getOwnPropertyNames(this))this[t]=null}_queueCallback(t,e,i=!0){_(t,e,i)}_getConfig(t){return t=this._mergeConfigObj(t,this._element),t=this._configAfterMerge(t),this._typeCheckConfig(t),t}static getInstance(t){return e.get(r(t),this.DATA_KEY)}static getOrCreateInstance(t,e={}){return this.getInstance(t)||new this(t,"object"==typeof e?e:null)}static get VERSION(){return"5.3.1"}static get DATA_KEY(){return`bs.${this.NAME}`}static get EVENT_KEY(){return`.${this.DATA_KEY}`}static eventName(t){return`${t}${this.EVENT_KEY}`}}const B=t=>{let e=t.getAttribute("data-bs-target");if(!e||"#"===e){let i=t.getAttribute("href");if(!i||!i.includes("#")&&!i.startsWith("."))return null;i.includes("#")&&!i.startsWith("#")&&(i=`#${i.split("#")[1]}`),e=i&&"#"!==i?i.trim():null}return n(e)},z={find:(t,e=document.documentElement)=>[].concat(...Element.prototype.querySelectorAll.call(e,t)),findOne:(t,e=document.documentElement)=>Element.prototype.querySelector.call(e,t),children:(t,e)=>[].concat(...t.children).filter((t=>t.matches(e))),parents(t,e){const i=[];let n=t.parentNode.closest(e);for(;n;)i.push(n),n=n.parentNode.closest(e);return i},prev(t,e){let i=t.previousElementSibling;for(;i;){if(i.matches(e))return[i];i=i.previousElementSibling}return[]},next(t,e){let i=t.nextElementSibling;for(;i;){if(i.matches(e))return[i];i=i.nextElementSibling}return[]},focusableChildren(t){const e=["a","button","input","textarea","select","details","[tabindex]",'[contenteditable="true"]'].map((t=>`${t}:not([tabindex^="-"])`)).join(",");return this.find(e,t).filter((t=>!l(t)&&a(t)))},getSelectorFromElement(t){const e=B(t);return e&&z.findOne(e)?e:null},getElementFromSelector(t){const e=B(t);return e?z.findOne(e):null},getMultipleElementsFromSelector(t){const e=B(t);return e?z.find(e):[]}},R=(t,e="hide")=>{const i=`click.dismiss${t.EVENT_KEY}`,n=t.NAME;N.on(document,i,`[data-bs-dismiss="${n}"]`,(function(i){if(["A","AREA"].includes(this.tagName)&&i.preventDefault(),l(this))return;const s=z.getElementFromSelector(this)||this.closest(`.${n}`);t.getOrCreateInstance(s)[e]()}))},q=".bs.alert",V=`close${q}`,K=`closed${q}`;class Q extends W{static get NAME(){return"alert"}close(){if(N.trigger(this._element,V).defaultPrevented)return;this._element.classList.remove("show");const t=this._element.classList.contains("fade");this._queueCallback((()=>this._destroyElement()),this._element,t)}_destroyElement(){this._element.remove(),N.trigger(this._element,K),this.dispose()}static jQueryInterface(t){return this.each((function(){const e=Q.getOrCreateInstance(this);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}R(Q,"close"),m(Q);const X='[data-bs-toggle="button"]';class Y extends W{static get NAME(){return"button"}toggle(){this._element.setAttribute("aria-pressed",this._element.classList.toggle("active"))}static jQueryInterface(t){return this.each((function(){const e=Y.getOrCreateInstance(this);"toggle"===t&&e[t]()}))}}N.on(document,"click.bs.button.data-api",X,(t=>{t.preventDefault();const e=t.target.closest(X);Y.getOrCreateInstance(e).toggle()})),m(Y);const U=".bs.swipe",G=`touchstart${U}`,J=`touchmove${U}`,Z=`touchend${U}`,tt=`pointerdown${U}`,et=`pointerup${U}`,it={endCallback:null,leftCallback:null,rightCallback:null},nt={endCallback:"(function|null)",leftCallback:"(function|null)",rightCallback:"(function|null)"};class st extends H{constructor(t,e){super(),this._element=t,t&&st.isSupported()&&(this._config=this._getConfig(e),this._deltaX=0,this._supportPointerEvents=Boolean(window.PointerEvent),this._initEvents())}static get Default(){return it}static get DefaultType(){return nt}static get NAME(){return"swipe"}dispose(){N.off(this._element,U)}_start(t){this._supportPointerEvents?this._eventIsPointerPenTouch(t)&&(this._deltaX=t.clientX):this._deltaX=t.touches[0].clientX}_end(t){this._eventIsPointerPenTouch(t)&&(this._deltaX=t.clientX-this._deltaX),this._handleSwipe(),g(this._config.endCallback)}_move(t){this._deltaX=t.touches&&t.touches.length>1?0:t.touches[0].clientX-this._deltaX}_handleSwipe(){const t=Math.abs(this._deltaX);if(t<=40)return;const e=t/this._deltaX;this._deltaX=0,e&&g(e>0?this._config.rightCallback:this._config.leftCallback)}_initEvents(){this._supportPointerEvents?(N.on(this._element,tt,(t=>this._start(t))),N.on(this._element,et,(t=>this._end(t))),this._element.classList.add("pointer-event")):(N.on(this._element,G,(t=>this._start(t))),N.on(this._element,J,(t=>this._move(t))),N.on(this._element,Z,(t=>this._end(t))))}_eventIsPointerPenTouch(t){return this._supportPointerEvents&&("pen"===t.pointerType||"touch"===t.pointerType)}static isSupported(){return"ontouchstart"in document.documentElement||navigator.maxTouchPoints>0}}const ot=".bs.carousel",rt=".data-api",at="next",lt="prev",ct="left",ht="right",dt=`slide${ot}`,ut=`slid${ot}`,ft=`keydown${ot}`,pt=`mouseenter${ot}`,mt=`mouseleave${ot}`,gt=`dragstart${ot}`,_t=`load${ot}${rt}`,bt=`click${ot}${rt}`,vt="carousel",yt="active",wt=".active",At=".carousel-item",Et=wt+At,Tt={ArrowLeft:ht,ArrowRight:ct},Ct={interval:5e3,keyboard:!0,pause:"hover",ride:!1,touch:!0,wrap:!0},Ot={interval:"(number|boolean)",keyboard:"boolean",pause:"(string|boolean)",ride:"(boolean|string)",touch:"boolean",wrap:"boolean"};class xt extends W{constructor(t,e){super(t,e),this._interval=null,this._activeElement=null,this._isSliding=!1,this.touchTimeout=null,this._swipeHelper=null,this._indicatorsElement=z.findOne(".carousel-indicators",this._element),this._addEventListeners(),this._config.ride===vt&&this.cycle()}static get Default(){return Ct}static get DefaultType(){return Ot}static get NAME(){return"carousel"}next(){this._slide(at)}nextWhenVisible(){!document.hidden&&a(this._element)&&this.next()}prev(){this._slide(lt)}pause(){this._isSliding&&s(this._element),this._clearInterval()}cycle(){this._clearInterval(),this._updateInterval(),this._interval=setInterval((()=>this.nextWhenVisible()),this._config.interval)}_maybeEnableCycle(){this._config.ride&&(this._isSliding?N.one(this._element,ut,(()=>this.cycle())):this.cycle())}to(t){const e=this._getItems();if(t>e.length-1||t<0)return;if(this._isSliding)return void N.one(this._element,ut,(()=>this.to(t)));const i=this._getItemIndex(this._getActive());if(i===t)return;const n=t>i?at:lt;this._slide(n,e[t])}dispose(){this._swipeHelper&&this._swipeHelper.dispose(),super.dispose()}_configAfterMerge(t){return t.defaultInterval=t.interval,t}_addEventListeners(){this._config.keyboard&&N.on(this._element,ft,(t=>this._keydown(t))),"hover"===this._config.pause&&(N.on(this._element,pt,(()=>this.pause())),N.on(this._element,mt,(()=>this._maybeEnableCycle()))),this._config.touch&&st.isSupported()&&this._addTouchEventListeners()}_addTouchEventListeners(){for(const t of z.find(".carousel-item img",this._element))N.on(t,gt,(t=>t.preventDefault()));const t={leftCallback:()=>this._slide(this._directionToOrder(ct)),rightCallback:()=>this._slide(this._directionToOrder(ht)),endCallback:()=>{"hover"===this._config.pause&&(this.pause(),this.touchTimeout&&clearTimeout(this.touchTimeout),this.touchTimeout=setTimeout((()=>this._maybeEnableCycle()),500+this._config.interval))}};this._swipeHelper=new st(this._element,t)}_keydown(t){if(/input|textarea/i.test(t.target.tagName))return;const e=Tt[t.key];e&&(t.preventDefault(),this._slide(this._directionToOrder(e)))}_getItemIndex(t){return this._getItems().indexOf(t)}_setActiveIndicatorElement(t){if(!this._indicatorsElement)return;const e=z.findOne(wt,this._indicatorsElement);e.classList.remove(yt),e.removeAttribute("aria-current");const i=z.findOne(`[data-bs-slide-to="${t}"]`,this._indicatorsElement);i&&(i.classList.add(yt),i.setAttribute("aria-current","true"))}_updateInterval(){const t=this._activeElement||this._getActive();if(!t)return;const e=Number.parseInt(t.getAttribute("data-bs-interval"),10);this._config.interval=e||this._config.defaultInterval}_slide(t,e=null){if(this._isSliding)return;const i=this._getActive(),n=t===at,s=e||b(this._getItems(),i,n,this._config.wrap);if(s===i)return;const o=this._getItemIndex(s),r=e=>N.trigger(this._element,e,{relatedTarget:s,direction:this._orderToDirection(t),from:this._getItemIndex(i),to:o});if(r(dt).defaultPrevented)return;if(!i||!s)return;const a=Boolean(this._interval);this.pause(),this._isSliding=!0,this._setActiveIndicatorElement(o),this._activeElement=s;const l=n?"carousel-item-start":"carousel-item-end",c=n?"carousel-item-next":"carousel-item-prev";s.classList.add(c),d(s),i.classList.add(l),s.classList.add(l),this._queueCallback((()=>{s.classList.remove(l,c),s.classList.add(yt),i.classList.remove(yt,c,l),this._isSliding=!1,r(ut)}),i,this._isAnimated()),a&&this.cycle()}_isAnimated(){return this._element.classList.contains("slide")}_getActive(){return z.findOne(Et,this._element)}_getItems(){return z.find(At,this._element)}_clearInterval(){this._interval&&(clearInterval(this._interval),this._interval=null)}_directionToOrder(t){return p()?t===ct?lt:at:t===ct?at:lt}_orderToDirection(t){return p()?t===lt?ct:ht:t===lt?ht:ct}static jQueryInterface(t){return this.each((function(){const e=xt.getOrCreateInstance(this,t);if("number"!=typeof t){if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}else e.to(t)}))}}N.on(document,bt,"[data-bs-slide], [data-bs-slide-to]",(function(t){const e=z.getElementFromSelector(this);if(!e||!e.classList.contains(vt))return;t.preventDefault();const i=xt.getOrCreateInstance(e),n=this.getAttribute("data-bs-slide-to");return n?(i.to(n),void i._maybeEnableCycle()):"next"===F.getDataAttribute(this,"slide")?(i.next(),void i._maybeEnableCycle()):(i.prev(),void i._maybeEnableCycle())})),N.on(window,_t,(()=>{const t=z.find('[data-bs-ride="carousel"]');for(const e of t)xt.getOrCreateInstance(e)})),m(xt);const kt=".bs.collapse",Lt=`show${kt}`,St=`shown${kt}`,Dt=`hide${kt}`,$t=`hidden${kt}`,It=`click${kt}.data-api`,Nt="show",Pt="collapse",Mt="collapsing",jt=`:scope .${Pt} .${Pt}`,Ft='[data-bs-toggle="collapse"]',Ht={parent:null,toggle:!0},Wt={parent:"(null|element)",toggle:"boolean"};class Bt extends W{constructor(t,e){super(t,e),this._isTransitioning=!1,this._triggerArray=[];const i=z.find(Ft);for(const t of i){const e=z.getSelectorFromElement(t),i=z.find(e).filter((t=>t===this._element));null!==e&&i.length&&this._triggerArray.push(t)}this._initializeChildren(),this._config.parent||this._addAriaAndCollapsedClass(this._triggerArray,this._isShown()),this._config.toggle&&this.toggle()}static get Default(){return Ht}static get DefaultType(){return Wt}static get NAME(){return"collapse"}toggle(){this._isShown()?this.hide():this.show()}show(){if(this._isTransitioning||this._isShown())return;let t=[];if(this._config.parent&&(t=this._getFirstLevelChildren(".collapse.show, .collapse.collapsing").filter((t=>t!==this._element)).map((t=>Bt.getOrCreateInstance(t,{toggle:!1})))),t.length&&t[0]._isTransitioning)return;if(N.trigger(this._element,Lt).defaultPrevented)return;for(const e of t)e.hide();const e=this._getDimension();this._element.classList.remove(Pt),this._element.classList.add(Mt),this._element.style[e]=0,this._addAriaAndCollapsedClass(this._triggerArray,!0),this._isTransitioning=!0;const i=`scroll${e[0].toUpperCase()+e.slice(1)}`;this._queueCallback((()=>{this._isTransitioning=!1,this._element.classList.remove(Mt),this._element.classList.add(Pt,Nt),this._element.style[e]="",N.trigger(this._element,St)}),this._element,!0),this._element.style[e]=`${this._element[i]}px`}hide(){if(this._isTransitioning||!this._isShown())return;if(N.trigger(this._element,Dt).defaultPrevented)return;const t=this._getDimension();this._element.style[t]=`${this._element.getBoundingClientRect()[t]}px`,d(this._element),this._element.classList.add(Mt),this._element.classList.remove(Pt,Nt);for(const t of this._triggerArray){const e=z.getElementFromSelector(t);e&&!this._isShown(e)&&this._addAriaAndCollapsedClass([t],!1)}this._isTransitioning=!0,this._element.style[t]="",this._queueCallback((()=>{this._isTransitioning=!1,this._element.classList.remove(Mt),this._element.classList.add(Pt),N.trigger(this._element,$t)}),this._element,!0)}_isShown(t=this._element){return t.classList.contains(Nt)}_configAfterMerge(t){return t.toggle=Boolean(t.toggle),t.parent=r(t.parent),t}_getDimension(){return this._element.classList.contains("collapse-horizontal")?"width":"height"}_initializeChildren(){if(!this._config.parent)return;const t=this._getFirstLevelChildren(Ft);for(const e of t){const t=z.getElementFromSelector(e);t&&this._addAriaAndCollapsedClass([e],this._isShown(t))}}_getFirstLevelChildren(t){const e=z.find(jt,this._config.parent);return z.find(t,this._config.parent).filter((t=>!e.includes(t)))}_addAriaAndCollapsedClass(t,e){if(t.length)for(const i of t)i.classList.toggle("collapsed",!e),i.setAttribute("aria-expanded",e)}static jQueryInterface(t){const e={};return"string"==typeof t&&/show|hide/.test(t)&&(e.toggle=!1),this.each((function(){const i=Bt.getOrCreateInstance(this,e);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t]()}}))}}N.on(document,It,Ft,(function(t){("A"===t.target.tagName||t.delegateTarget&&"A"===t.delegateTarget.tagName)&&t.preventDefault();for(const t of z.getMultipleElementsFromSelector(this))Bt.getOrCreateInstance(t,{toggle:!1}).toggle()})),m(Bt);var zt="top",Rt="bottom",qt="right",Vt="left",Kt="auto",Qt=[zt,Rt,qt,Vt],Xt="start",Yt="end",Ut="clippingParents",Gt="viewport",Jt="popper",Zt="reference",te=Qt.reduce((function(t,e){return t.concat([e+"-"+Xt,e+"-"+Yt])}),[]),ee=[].concat(Qt,[Kt]).reduce((function(t,e){return t.concat([e,e+"-"+Xt,e+"-"+Yt])}),[]),ie="beforeRead",ne="read",se="afterRead",oe="beforeMain",re="main",ae="afterMain",le="beforeWrite",ce="write",he="afterWrite",de=[ie,ne,se,oe,re,ae,le,ce,he];function ue(t){return t?(t.nodeName||"").toLowerCase():null}function fe(t){if(null==t)return window;if("[object Window]"!==t.toString()){var e=t.ownerDocument;return e&&e.defaultView||window}return t}function pe(t){return t instanceof fe(t).Element||t instanceof Element}function me(t){return t instanceof fe(t).HTMLElement||t instanceof HTMLElement}function ge(t){return"undefined"!=typeof ShadowRoot&&(t instanceof fe(t).ShadowRoot||t instanceof ShadowRoot)}const _e={name:"applyStyles",enabled:!0,phase:"write",fn:function(t){var e=t.state;Object.keys(e.elements).forEach((function(t){var i=e.styles[t]||{},n=e.attributes[t]||{},s=e.elements[t];me(s)&&ue(s)&&(Object.assign(s.style,i),Object.keys(n).forEach((function(t){var e=n[t];!1===e?s.removeAttribute(t):s.setAttribute(t,!0===e?"":e)})))}))},effect:function(t){var e=t.state,i={popper:{position:e.options.strategy,left:"0",top:"0",margin:"0"},arrow:{position:"absolute"},reference:{}};return Object.assign(e.elements.popper.style,i.popper),e.styles=i,e.elements.arrow&&Object.assign(e.elements.arrow.style,i.arrow),function(){Object.keys(e.elements).forEach((function(t){var n=e.elements[t],s=e.attributes[t]||{},o=Object.keys(e.styles.hasOwnProperty(t)?e.styles[t]:i[t]).reduce((function(t,e){return t[e]="",t}),{});me(n)&&ue(n)&&(Object.assign(n.style,o),Object.keys(s).forEach((function(t){n.removeAttribute(t)})))}))}},requires:["computeStyles"]};function be(t){return t.split("-")[0]}var ve=Math.max,ye=Math.min,we=Math.round;function Ae(){var t=navigator.userAgentData;return null!=t&&t.brands&&Array.isArray(t.brands)?t.brands.map((function(t){return t.brand+"/"+t.version})).join(" "):navigator.userAgent}function Ee(){return!/^((?!chrome|android).)*safari/i.test(Ae())}function Te(t,e,i){void 0===e&&(e=!1),void 0===i&&(i=!1);var n=t.getBoundingClientRect(),s=1,o=1;e&&me(t)&&(s=t.offsetWidth>0&&we(n.width)/t.offsetWidth||1,o=t.offsetHeight>0&&we(n.height)/t.offsetHeight||1);var r=(pe(t)?fe(t):window).visualViewport,a=!Ee()&&i,l=(n.left+(a&&r?r.offsetLeft:0))/s,c=(n.top+(a&&r?r.offsetTop:0))/o,h=n.width/s,d=n.height/o;return{width:h,height:d,top:c,right:l+h,bottom:c+d,left:l,x:l,y:c}}function Ce(t){var e=Te(t),i=t.offsetWidth,n=t.offsetHeight;return Math.abs(e.width-i)<=1&&(i=e.width),Math.abs(e.height-n)<=1&&(n=e.height),{x:t.offsetLeft,y:t.offsetTop,width:i,height:n}}function Oe(t,e){var i=e.getRootNode&&e.getRootNode();if(t.contains(e))return!0;if(i&&ge(i)){var n=e;do{if(n&&t.isSameNode(n))return!0;n=n.parentNode||n.host}while(n)}return!1}function xe(t){return fe(t).getComputedStyle(t)}function ke(t){return["table","td","th"].indexOf(ue(t))>=0}function Le(t){return((pe(t)?t.ownerDocument:t.document)||window.document).documentElement}function Se(t){return"html"===ue(t)?t:t.assignedSlot||t.parentNode||(ge(t)?t.host:null)||Le(t)}function De(t){return me(t)&&"fixed"!==xe(t).position?t.offsetParent:null}function $e(t){for(var e=fe(t),i=De(t);i&&ke(i)&&"static"===xe(i).position;)i=De(i);return i&&("html"===ue(i)||"body"===ue(i)&&"static"===xe(i).position)?e:i||function(t){var e=/firefox/i.test(Ae());if(/Trident/i.test(Ae())&&me(t)&&"fixed"===xe(t).position)return null;var i=Se(t);for(ge(i)&&(i=i.host);me(i)&&["html","body"].indexOf(ue(i))<0;){var n=xe(i);if("none"!==n.transform||"none"!==n.perspective||"paint"===n.contain||-1!==["transform","perspective"].indexOf(n.willChange)||e&&"filter"===n.willChange||e&&n.filter&&"none"!==n.filter)return i;i=i.parentNode}return null}(t)||e}function Ie(t){return["top","bottom"].indexOf(t)>=0?"x":"y"}function Ne(t,e,i){return ve(t,ye(e,i))}function Pe(t){return Object.assign({},{top:0,right:0,bottom:0,left:0},t)}function Me(t,e){return e.reduce((function(e,i){return e[i]=t,e}),{})}const je={name:"arrow",enabled:!0,phase:"main",fn:function(t){var e,i=t.state,n=t.name,s=t.options,o=i.elements.arrow,r=i.modifiersData.popperOffsets,a=be(i.placement),l=Ie(a),c=[Vt,qt].indexOf(a)>=0?"height":"width";if(o&&r){var h=function(t,e){return Pe("number"!=typeof(t="function"==typeof t?t(Object.assign({},e.rects,{placement:e.placement})):t)?t:Me(t,Qt))}(s.padding,i),d=Ce(o),u="y"===l?zt:Vt,f="y"===l?Rt:qt,p=i.rects.reference[c]+i.rects.reference[l]-r[l]-i.rects.popper[c],m=r[l]-i.rects.reference[l],g=$e(o),_=g?"y"===l?g.clientHeight||0:g.clientWidth||0:0,b=p/2-m/2,v=h[u],y=_-d[c]-h[f],w=_/2-d[c]/2+b,A=Ne(v,w,y),E=l;i.modifiersData[n]=((e={})[E]=A,e.centerOffset=A-w,e)}},effect:function(t){var e=t.state,i=t.options.element,n=void 0===i?"[data-popper-arrow]":i;null!=n&&("string"!=typeof n||(n=e.elements.popper.querySelector(n)))&&Oe(e.elements.popper,n)&&(e.elements.arrow=n)},requires:["popperOffsets"],requiresIfExists:["preventOverflow"]};function Fe(t){return t.split("-")[1]}var He={top:"auto",right:"auto",bottom:"auto",left:"auto"};function We(t){var e,i=t.popper,n=t.popperRect,s=t.placement,o=t.variation,r=t.offsets,a=t.position,l=t.gpuAcceleration,c=t.adaptive,h=t.roundOffsets,d=t.isFixed,u=r.x,f=void 0===u?0:u,p=r.y,m=void 0===p?0:p,g="function"==typeof h?h({x:f,y:m}):{x:f,y:m};f=g.x,m=g.y;var _=r.hasOwnProperty("x"),b=r.hasOwnProperty("y"),v=Vt,y=zt,w=window;if(c){var A=$e(i),E="clientHeight",T="clientWidth";A===fe(i)&&"static"!==xe(A=Le(i)).position&&"absolute"===a&&(E="scrollHeight",T="scrollWidth"),(s===zt||(s===Vt||s===qt)&&o===Yt)&&(y=Rt,m-=(d&&A===w&&w.visualViewport?w.visualViewport.height:A[E])-n.height,m*=l?1:-1),s!==Vt&&(s!==zt&&s!==Rt||o!==Yt)||(v=qt,f-=(d&&A===w&&w.visualViewport?w.visualViewport.width:A[T])-n.width,f*=l?1:-1)}var C,O=Object.assign({position:a},c&&He),x=!0===h?function(t,e){var i=t.x,n=t.y,s=e.devicePixelRatio||1;return{x:we(i*s)/s||0,y:we(n*s)/s||0}}({x:f,y:m},fe(i)):{x:f,y:m};return f=x.x,m=x.y,l?Object.assign({},O,((C={})[y]=b?"0":"",C[v]=_?"0":"",C.transform=(w.devicePixelRatio||1)<=1?"translate("+f+"px, "+m+"px)":"translate3d("+f+"px, "+m+"px, 0)",C)):Object.assign({},O,((e={})[y]=b?m+"px":"",e[v]=_?f+"px":"",e.transform="",e))}const Be={name:"computeStyles",enabled:!0,phase:"beforeWrite",fn:function(t){var e=t.state,i=t.options,n=i.gpuAcceleration,s=void 0===n||n,o=i.adaptive,r=void 0===o||o,a=i.roundOffsets,l=void 0===a||a,c={placement:be(e.placement),variation:Fe(e.placement),popper:e.elements.popper,popperRect:e.rects.popper,gpuAcceleration:s,isFixed:"fixed"===e.options.strategy};null!=e.modifiersData.popperOffsets&&(e.styles.popper=Object.assign({},e.styles.popper,We(Object.assign({},c,{offsets:e.modifiersData.popperOffsets,position:e.options.strategy,adaptive:r,roundOffsets:l})))),null!=e.modifiersData.arrow&&(e.styles.arrow=Object.assign({},e.styles.arrow,We(Object.assign({},c,{offsets:e.modifiersData.arrow,position:"absolute",adaptive:!1,roundOffsets:l})))),e.attributes.popper=Object.assign({},e.attributes.popper,{"data-popper-placement":e.placement})},data:{}};var ze={passive:!0};const Re={name:"eventListeners",enabled:!0,phase:"write",fn:function(){},effect:function(t){var e=t.state,i=t.instance,n=t.options,s=n.scroll,o=void 0===s||s,r=n.resize,a=void 0===r||r,l=fe(e.elements.popper),c=[].concat(e.scrollParents.reference,e.scrollParents.popper);return o&&c.forEach((function(t){t.addEventListener("scroll",i.update,ze)})),a&&l.addEventListener("resize",i.update,ze),function(){o&&c.forEach((function(t){t.removeEventListener("scroll",i.update,ze)})),a&&l.removeEventListener("resize",i.update,ze)}},data:{}};var qe={left:"right",right:"left",bottom:"top",top:"bottom"};function Ve(t){return t.replace(/left|right|bottom|top/g,(function(t){return qe[t]}))}var Ke={start:"end",end:"start"};function Qe(t){return t.replace(/start|end/g,(function(t){return Ke[t]}))}function Xe(t){var e=fe(t);return{scrollLeft:e.pageXOffset,scrollTop:e.pageYOffset}}function Ye(t){return Te(Le(t)).left+Xe(t).scrollLeft}function Ue(t){var e=xe(t),i=e.overflow,n=e.overflowX,s=e.overflowY;return/auto|scroll|overlay|hidden/.test(i+s+n)}function Ge(t){return["html","body","#document"].indexOf(ue(t))>=0?t.ownerDocument.body:me(t)&&Ue(t)?t:Ge(Se(t))}function Je(t,e){var i;void 0===e&&(e=[]);var n=Ge(t),s=n===(null==(i=t.ownerDocument)?void 0:i.body),o=fe(n),r=s?[o].concat(o.visualViewport||[],Ue(n)?n:[]):n,a=e.concat(r);return s?a:a.concat(Je(Se(r)))}function Ze(t){return Object.assign({},t,{left:t.x,top:t.y,right:t.x+t.width,bottom:t.y+t.height})}function ti(t,e,i){return e===Gt?Ze(function(t,e){var i=fe(t),n=Le(t),s=i.visualViewport,o=n.clientWidth,r=n.clientHeight,a=0,l=0;if(s){o=s.width,r=s.height;var c=Ee();(c||!c&&"fixed"===e)&&(a=s.offsetLeft,l=s.offsetTop)}return{width:o,height:r,x:a+Ye(t),y:l}}(t,i)):pe(e)?function(t,e){var i=Te(t,!1,"fixed"===e);return i.top=i.top+t.clientTop,i.left=i.left+t.clientLeft,i.bottom=i.top+t.clientHeight,i.right=i.left+t.clientWidth,i.width=t.clientWidth,i.height=t.clientHeight,i.x=i.left,i.y=i.top,i}(e,i):Ze(function(t){var e,i=Le(t),n=Xe(t),s=null==(e=t.ownerDocument)?void 0:e.body,o=ve(i.scrollWidth,i.clientWidth,s?s.scrollWidth:0,s?s.clientWidth:0),r=ve(i.scrollHeight,i.clientHeight,s?s.scrollHeight:0,s?s.clientHeight:0),a=-n.scrollLeft+Ye(t),l=-n.scrollTop;return"rtl"===xe(s||i).direction&&(a+=ve(i.clientWidth,s?s.clientWidth:0)-o),{width:o,height:r,x:a,y:l}}(Le(t)))}function ei(t){var e,i=t.reference,n=t.element,s=t.placement,o=s?be(s):null,r=s?Fe(s):null,a=i.x+i.width/2-n.width/2,l=i.y+i.height/2-n.height/2;switch(o){case zt:e={x:a,y:i.y-n.height};break;case Rt:e={x:a,y:i.y+i.height};break;case qt:e={x:i.x+i.width,y:l};break;case Vt:e={x:i.x-n.width,y:l};break;default:e={x:i.x,y:i.y}}var c=o?Ie(o):null;if(null!=c){var h="y"===c?"height":"width";switch(r){case Xt:e[c]=e[c]-(i[h]/2-n[h]/2);break;case Yt:e[c]=e[c]+(i[h]/2-n[h]/2)}}return e}function ii(t,e){void 0===e&&(e={});var i=e,n=i.placement,s=void 0===n?t.placement:n,o=i.strategy,r=void 0===o?t.strategy:o,a=i.boundary,l=void 0===a?Ut:a,c=i.rootBoundary,h=void 0===c?Gt:c,d=i.elementContext,u=void 0===d?Jt:d,f=i.altBoundary,p=void 0!==f&&f,m=i.padding,g=void 0===m?0:m,_=Pe("number"!=typeof g?g:Me(g,Qt)),b=u===Jt?Zt:Jt,v=t.rects.popper,y=t.elements[p?b:u],w=function(t,e,i,n){var s="clippingParents"===e?function(t){var e=Je(Se(t)),i=["absolute","fixed"].indexOf(xe(t).position)>=0&&me(t)?$e(t):t;return pe(i)?e.filter((function(t){return pe(t)&&Oe(t,i)&&"body"!==ue(t)})):[]}(t):[].concat(e),o=[].concat(s,[i]),r=o[0],a=o.reduce((function(e,i){var s=ti(t,i,n);return e.top=ve(s.top,e.top),e.right=ye(s.right,e.right),e.bottom=ye(s.bottom,e.bottom),e.left=ve(s.left,e.left),e}),ti(t,r,n));return a.width=a.right-a.left,a.height=a.bottom-a.top,a.x=a.left,a.y=a.top,a}(pe(y)?y:y.contextElement||Le(t.elements.popper),l,h,r),A=Te(t.elements.reference),E=ei({reference:A,element:v,strategy:"absolute",placement:s}),T=Ze(Object.assign({},v,E)),C=u===Jt?T:A,O={top:w.top-C.top+_.top,bottom:C.bottom-w.bottom+_.bottom,left:w.left-C.left+_.left,right:C.right-w.right+_.right},x=t.modifiersData.offset;if(u===Jt&&x){var k=x[s];Object.keys(O).forEach((function(t){var e=[qt,Rt].indexOf(t)>=0?1:-1,i=[zt,Rt].indexOf(t)>=0?"y":"x";O[t]+=k[i]*e}))}return O}function ni(t,e){void 0===e&&(e={});var i=e,n=i.placement,s=i.boundary,o=i.rootBoundary,r=i.padding,a=i.flipVariations,l=i.allowedAutoPlacements,c=void 0===l?ee:l,h=Fe(n),d=h?a?te:te.filter((function(t){return Fe(t)===h})):Qt,u=d.filter((function(t){return c.indexOf(t)>=0}));0===u.length&&(u=d);var f=u.reduce((function(e,i){return e[i]=ii(t,{placement:i,boundary:s,rootBoundary:o,padding:r})[be(i)],e}),{});return Object.keys(f).sort((function(t,e){return f[t]-f[e]}))}const si={name:"flip",enabled:!0,phase:"main",fn:function(t){var e=t.state,i=t.options,n=t.name;if(!e.modifiersData[n]._skip){for(var s=i.mainAxis,o=void 0===s||s,r=i.altAxis,a=void 0===r||r,l=i.fallbackPlacements,c=i.padding,h=i.boundary,d=i.rootBoundary,u=i.altBoundary,f=i.flipVariations,p=void 0===f||f,m=i.allowedAutoPlacements,g=e.options.placement,_=be(g),b=l||(_!==g&&p?function(t){if(be(t)===Kt)return[];var e=Ve(t);return[Qe(t),e,Qe(e)]}(g):[Ve(g)]),v=[g].concat(b).reduce((function(t,i){return t.concat(be(i)===Kt?ni(e,{placement:i,boundary:h,rootBoundary:d,padding:c,flipVariations:p,allowedAutoPlacements:m}):i)}),[]),y=e.rects.reference,w=e.rects.popper,A=new Map,E=!0,T=v[0],C=0;C<v.length;C++){var O=v[C],x=be(O),k=Fe(O)===Xt,L=[zt,Rt].indexOf(x)>=0,S=L?"width":"height",D=ii(e,{placement:O,boundary:h,rootBoundary:d,altBoundary:u,padding:c}),$=L?k?qt:Vt:k?Rt:zt;y[S]>w[S]&&($=Ve($));var I=Ve($),N=[];if(o&&N.push(D[x]<=0),a&&N.push(D[$]<=0,D[I]<=0),N.every((function(t){return t}))){T=O,E=!1;break}A.set(O,N)}if(E)for(var P=function(t){var e=v.find((function(e){var i=A.get(e);if(i)return i.slice(0,t).every((function(t){return t}))}));if(e)return T=e,"break"},M=p?3:1;M>0&&"break"!==P(M);M--);e.placement!==T&&(e.modifiersData[n]._skip=!0,e.placement=T,e.reset=!0)}},requiresIfExists:["offset"],data:{_skip:!1}};function oi(t,e,i){return void 0===i&&(i={x:0,y:0}),{top:t.top-e.height-i.y,right:t.right-e.width+i.x,bottom:t.bottom-e.height+i.y,left:t.left-e.width-i.x}}function ri(t){return[zt,qt,Rt,Vt].some((function(e){return t[e]>=0}))}const ai={name:"hide",enabled:!0,phase:"main",requiresIfExists:["preventOverflow"],fn:function(t){var e=t.state,i=t.name,n=e.rects.reference,s=e.rects.popper,o=e.modifiersData.preventOverflow,r=ii(e,{elementContext:"reference"}),a=ii(e,{altBoundary:!0}),l=oi(r,n),c=oi(a,s,o),h=ri(l),d=ri(c);e.modifiersData[i]={referenceClippingOffsets:l,popperEscapeOffsets:c,isReferenceHidden:h,hasPopperEscaped:d},e.attributes.popper=Object.assign({},e.attributes.popper,{"data-popper-reference-hidden":h,"data-popper-escaped":d})}},li={name:"offset",enabled:!0,phase:"main",requires:["popperOffsets"],fn:function(t){var e=t.state,i=t.options,n=t.name,s=i.offset,o=void 0===s?[0,0]:s,r=ee.reduce((function(t,i){return t[i]=function(t,e,i){var n=be(t),s=[Vt,zt].indexOf(n)>=0?-1:1,o="function"==typeof i?i(Object.assign({},e,{placement:t})):i,r=o[0],a=o[1];return r=r||0,a=(a||0)*s,[Vt,qt].indexOf(n)>=0?{x:a,y:r}:{x:r,y:a}}(i,e.rects,o),t}),{}),a=r[e.placement],l=a.x,c=a.y;null!=e.modifiersData.popperOffsets&&(e.modifiersData.popperOffsets.x+=l,e.modifiersData.popperOffsets.y+=c),e.modifiersData[n]=r}},ci={name:"popperOffsets",enabled:!0,phase:"read",fn:function(t){var e=t.state,i=t.name;e.modifiersData[i]=ei({reference:e.rects.reference,element:e.rects.popper,strategy:"absolute",placement:e.placement})},data:{}},hi={name:"preventOverflow",enabled:!0,phase:"main",fn:function(t){var e=t.state,i=t.options,n=t.name,s=i.mainAxis,o=void 0===s||s,r=i.altAxis,a=void 0!==r&&r,l=i.boundary,c=i.rootBoundary,h=i.altBoundary,d=i.padding,u=i.tether,f=void 0===u||u,p=i.tetherOffset,m=void 0===p?0:p,g=ii(e,{boundary:l,rootBoundary:c,padding:d,altBoundary:h}),_=be(e.placement),b=Fe(e.placement),v=!b,y=Ie(_),w="x"===y?"y":"x",A=e.modifiersData.popperOffsets,E=e.rects.reference,T=e.rects.popper,C="function"==typeof m?m(Object.assign({},e.rects,{placement:e.placement})):m,O="number"==typeof C?{mainAxis:C,altAxis:C}:Object.assign({mainAxis:0,altAxis:0},C),x=e.modifiersData.offset?e.modifiersData.offset[e.placement]:null,k={x:0,y:0};if(A){if(o){var L,S="y"===y?zt:Vt,D="y"===y?Rt:qt,$="y"===y?"height":"width",I=A[y],N=I+g[S],P=I-g[D],M=f?-T[$]/2:0,j=b===Xt?E[$]:T[$],F=b===Xt?-T[$]:-E[$],H=e.elements.arrow,W=f&&H?Ce(H):{width:0,height:0},B=e.modifiersData["arrow#persistent"]?e.modifiersData["arrow#persistent"].padding:{top:0,right:0,bottom:0,left:0},z=B[S],R=B[D],q=Ne(0,E[$],W[$]),V=v?E[$]/2-M-q-z-O.mainAxis:j-q-z-O.mainAxis,K=v?-E[$]/2+M+q+R+O.mainAxis:F+q+R+O.mainAxis,Q=e.elements.arrow&&$e(e.elements.arrow),X=Q?"y"===y?Q.clientTop||0:Q.clientLeft||0:0,Y=null!=(L=null==x?void 0:x[y])?L:0,U=I+K-Y,G=Ne(f?ye(N,I+V-Y-X):N,I,f?ve(P,U):P);A[y]=G,k[y]=G-I}if(a){var J,Z="x"===y?zt:Vt,tt="x"===y?Rt:qt,et=A[w],it="y"===w?"height":"width",nt=et+g[Z],st=et-g[tt],ot=-1!==[zt,Vt].indexOf(_),rt=null!=(J=null==x?void 0:x[w])?J:0,at=ot?nt:et-E[it]-T[it]-rt+O.altAxis,lt=ot?et+E[it]+T[it]-rt-O.altAxis:st,ct=f&&ot?function(t,e,i){var n=Ne(t,e,i);return n>i?i:n}(at,et,lt):Ne(f?at:nt,et,f?lt:st);A[w]=ct,k[w]=ct-et}e.modifiersData[n]=k}},requiresIfExists:["offset"]};function di(t,e,i){void 0===i&&(i=!1);var n,s,o=me(e),r=me(e)&&function(t){var e=t.getBoundingClientRect(),i=we(e.width)/t.offsetWidth||1,n=we(e.height)/t.offsetHeight||1;return 1!==i||1!==n}(e),a=Le(e),l=Te(t,r,i),c={scrollLeft:0,scrollTop:0},h={x:0,y:0};return(o||!o&&!i)&&(("body"!==ue(e)||Ue(a))&&(c=(n=e)!==fe(n)&&me(n)?{scrollLeft:(s=n).scrollLeft,scrollTop:s.scrollTop}:Xe(n)),me(e)?((h=Te(e,!0)).x+=e.clientLeft,h.y+=e.clientTop):a&&(h.x=Ye(a))),{x:l.left+c.scrollLeft-h.x,y:l.top+c.scrollTop-h.y,width:l.width,height:l.height}}function ui(t){var e=new Map,i=new Set,n=[];function s(t){i.add(t.name),[].concat(t.requires||[],t.requiresIfExists||[]).forEach((function(t){if(!i.has(t)){var n=e.get(t);n&&s(n)}})),n.push(t)}return t.forEach((function(t){e.set(t.name,t)})),t.forEach((function(t){i.has(t.name)||s(t)})),n}var fi={placement:"bottom",modifiers:[],strategy:"absolute"};function pi(){for(var t=arguments.length,e=new Array(t),i=0;i<t;i++)e[i]=arguments[i];return!e.some((function(t){return!(t&&"function"==typeof t.getBoundingClientRect)}))}function mi(t){void 0===t&&(t={});var e=t,i=e.defaultModifiers,n=void 0===i?[]:i,s=e.defaultOptions,o=void 0===s?fi:s;return function(t,e,i){void 0===i&&(i=o);var s,r,a={placement:"bottom",orderedModifiers:[],options:Object.assign({},fi,o),modifiersData:{},elements:{reference:t,popper:e},attributes:{},styles:{}},l=[],c=!1,h={state:a,setOptions:function(i){var s="function"==typeof i?i(a.options):i;d(),a.options=Object.assign({},o,a.options,s),a.scrollParents={reference:pe(t)?Je(t):t.contextElement?Je(t.contextElement):[],popper:Je(e)};var r,c,u=function(t){var e=ui(t);return de.reduce((function(t,i){return t.concat(e.filter((function(t){return t.phase===i})))}),[])}((r=[].concat(n,a.options.modifiers),c=r.reduce((function(t,e){var i=t[e.name];return t[e.name]=i?Object.assign({},i,e,{options:Object.assign({},i.options,e.options),data:Object.assign({},i.data,e.data)}):e,t}),{}),Object.keys(c).map((function(t){return c[t]}))));return a.orderedModifiers=u.filter((function(t){return t.enabled})),a.orderedModifiers.forEach((function(t){var e=t.name,i=t.options,n=void 0===i?{}:i,s=t.effect;if("function"==typeof s){var o=s({state:a,name:e,instance:h,options:n});l.push(o||function(){})}})),h.update()},forceUpdate:function(){if(!c){var t=a.elements,e=t.reference,i=t.popper;if(pi(e,i)){a.rects={reference:di(e,$e(i),"fixed"===a.options.strategy),popper:Ce(i)},a.reset=!1,a.placement=a.options.placement,a.orderedModifiers.forEach((function(t){return a.modifiersData[t.name]=Object.assign({},t.data)}));for(var n=0;n<a.orderedModifiers.length;n++)if(!0!==a.reset){var s=a.orderedModifiers[n],o=s.fn,r=s.options,l=void 0===r?{}:r,d=s.name;"function"==typeof o&&(a=o({state:a,options:l,name:d,instance:h})||a)}else a.reset=!1,n=-1}}},update:(s=function(){return new Promise((function(t){h.forceUpdate(),t(a)}))},function(){return r||(r=new Promise((function(t){Promise.resolve().then((function(){r=void 0,t(s())}))}))),r}),destroy:function(){d(),c=!0}};if(!pi(t,e))return h;function d(){l.forEach((function(t){return t()})),l=[]}return h.setOptions(i).then((function(t){!c&&i.onFirstUpdate&&i.onFirstUpdate(t)})),h}}var gi=mi(),_i=mi({defaultModifiers:[Re,ci,Be,_e]}),bi=mi({defaultModifiers:[Re,ci,Be,_e,li,si,hi,je,ai]});const vi=Object.freeze(Object.defineProperty({__proto__:null,afterMain:ae,afterRead:se,afterWrite:he,applyStyles:_e,arrow:je,auto:Kt,basePlacements:Qt,beforeMain:oe,beforeRead:ie,beforeWrite:le,bottom:Rt,clippingParents:Ut,computeStyles:Be,createPopper:bi,createPopperBase:gi,createPopperLite:_i,detectOverflow:ii,end:Yt,eventListeners:Re,flip:si,hide:ai,left:Vt,main:re,modifierPhases:de,offset:li,placements:ee,popper:Jt,popperGenerator:mi,popperOffsets:ci,preventOverflow:hi,read:ne,reference:Zt,right:qt,start:Xt,top:zt,variationPlacements:te,viewport:Gt,write:ce},Symbol.toStringTag,{value:"Module"})),yi="dropdown",wi=".bs.dropdown",Ai=".data-api",Ei="ArrowUp",Ti="ArrowDown",Ci=`hide${wi}`,Oi=`hidden${wi}`,xi=`show${wi}`,ki=`shown${wi}`,Li=`click${wi}${Ai}`,Si=`keydown${wi}${Ai}`,Di=`keyup${wi}${Ai}`,$i="show",Ii='[data-bs-toggle="dropdown"]:not(.disabled):not(:disabled)',Ni=`${Ii}.${$i}`,Pi=".dropdown-menu",Mi=p()?"top-end":"top-start",ji=p()?"top-start":"top-end",Fi=p()?"bottom-end":"bottom-start",Hi=p()?"bottom-start":"bottom-end",Wi=p()?"left-start":"right-start",Bi=p()?"right-start":"left-start",zi={autoClose:!0,boundary:"clippingParents",display:"dynamic",offset:[0,2],popperConfig:null,reference:"toggle"},Ri={autoClose:"(boolean|string)",boundary:"(string|element)",display:"string",offset:"(array|string|function)",popperConfig:"(null|object|function)",reference:"(string|element|object)"};class qi extends W{constructor(t,e){super(t,e),this._popper=null,this._parent=this._element.parentNode,this._menu=z.next(this._element,Pi)[0]||z.prev(this._element,Pi)[0]||z.findOne(Pi,this._parent),this._inNavbar=this._detectNavbar()}static get Default(){return zi}static get DefaultType(){return Ri}static get NAME(){return yi}toggle(){return this._isShown()?this.hide():this.show()}show(){if(l(this._element)||this._isShown())return;const t={relatedTarget:this._element};if(!N.trigger(this._element,xi,t).defaultPrevented){if(this._createPopper(),"ontouchstart"in document.documentElement&&!this._parent.closest(".navbar-nav"))for(const t of[].concat(...document.body.children))N.on(t,"mouseover",h);this._element.focus(),this._element.setAttribute("aria-expanded",!0),this._menu.classList.add($i),this._element.classList.add($i),N.trigger(this._element,ki,t)}}hide(){if(l(this._element)||!this._isShown())return;const t={relatedTarget:this._element};this._completeHide(t)}dispose(){this._popper&&this._popper.destroy(),super.dispose()}update(){this._inNavbar=this._detectNavbar(),this._popper&&this._popper.update()}_completeHide(t){if(!N.trigger(this._element,Ci,t).defaultPrevented){if("ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.off(t,"mouseover",h);this._popper&&this._popper.destroy(),this._menu.classList.remove($i),this._element.classList.remove($i),this._element.setAttribute("aria-expanded","false"),F.removeDataAttribute(this._menu,"popper"),N.trigger(this._element,Oi,t)}}_getConfig(t){if("object"==typeof(t=super._getConfig(t)).reference&&!o(t.reference)&&"function"!=typeof t.reference.getBoundingClientRect)throw new TypeError(`${yi.toUpperCase()}: Option "reference" provided type "object" without a required "getBoundingClientRect" method.`);return t}_createPopper(){if(void 0===vi)throw new TypeError("Bootstrap's dropdowns require Popper (https://popper.js.org)");let t=this._element;"parent"===this._config.reference?t=this._parent:o(this._config.reference)?t=r(this._config.reference):"object"==typeof this._config.reference&&(t=this._config.reference);const e=this._getPopperConfig();this._popper=bi(t,this._menu,e)}_isShown(){return this._menu.classList.contains($i)}_getPlacement(){const t=this._parent;if(t.classList.contains("dropend"))return Wi;if(t.classList.contains("dropstart"))return Bi;if(t.classList.contains("dropup-center"))return"top";if(t.classList.contains("dropdown-center"))return"bottom";const e="end"===getComputedStyle(this._menu).getPropertyValue("--bs-position").trim();return t.classList.contains("dropup")?e?ji:Mi:e?Hi:Fi}_detectNavbar(){return null!==this._element.closest(".navbar")}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return(this._inNavbar||"static"===this._config.display)&&(F.setDataAttribute(this._menu,"popper","static"),t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,...g(this._config.popperConfig,[t])}}_selectMenuItem({key:t,target:e}){const i=z.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter((t=>a(t)));i.length&&b(i,e,t===Ti,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=qi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(2===t.button||"keyup"===t.type&&"Tab"!==t.key)return;const e=z.find(Ni);for(const i of e){const e=qi.getInstance(i);if(!e||!1===e._config.autoClose)continue;const n=t.composedPath(),s=n.includes(e._menu);if(n.includes(e._element)||"inside"===e._config.autoClose&&!s||"outside"===e._config.autoClose&&s)continue;if(e._menu.contains(t.target)&&("keyup"===t.type&&"Tab"===t.key||/input|select|option|textarea|form/i.test(t.target.tagName)))continue;const o={relatedTarget:e._element};"click"===t.type&&(o.clickEvent=t),e._completeHide(o)}}static dataApiKeydownHandler(t){const e=/input|textarea/i.test(t.target.tagName),i="Escape"===t.key,n=[Ei,Ti].includes(t.key);if(!n&&!i)return;if(e&&!i)return;t.preventDefault();const s=this.matches(Ii)?this:z.prev(this,Ii)[0]||z.next(this,Ii)[0]||z.findOne(Ii,t.delegateTarget.parentNode),o=qi.getOrCreateInstance(s);if(n)return t.stopPropagation(),o.show(),void o._selectMenuItem(t);o._isShown()&&(t.stopPropagation(),o.hide(),s.focus())}}N.on(document,Si,Ii,qi.dataApiKeydownHandler),N.on(document,Si,Pi,qi.dataApiKeydownHandler),N.on(document,Li,qi.clearMenus),N.on(document,Di,qi.clearMenus),N.on(document,Li,Ii,(function(t){t.preventDefault(),qi.getOrCreateInstance(this).toggle()})),m(qi);const Vi="backdrop",Ki="show",Qi=`mousedown.bs.${Vi}`,Xi={className:"modal-backdrop",clickCallback:null,isAnimated:!1,isVisible:!0,rootElement:"body"},Yi={className:"string",clickCallback:"(function|null)",isAnimated:"boolean",isVisible:"boolean",rootElement:"(element|string)"};class Ui extends H{constructor(t){super(),this._config=this._getConfig(t),this._isAppended=!1,this._element=null}static get Default(){return Xi}static get DefaultType(){return Yi}static get NAME(){return Vi}show(t){if(!this._config.isVisible)return void g(t);this._append();const e=this._getElement();this._config.isAnimated&&d(e),e.classList.add(Ki),this._emulateAnimation((()=>{g(t)}))}hide(t){this._config.isVisible?(this._getElement().classList.remove(Ki),this._emulateAnimation((()=>{this.dispose(),g(t)}))):g(t)}dispose(){this._isAppended&&(N.off(this._element,Qi),this._element.remove(),this._isAppended=!1)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_configAfterMerge(t){return t.rootElement=r(t.rootElement),t}_append(){if(this._isAppended)return;const t=this._getElement();this._config.rootElement.append(t),N.on(t,Qi,(()=>{g(this._config.clickCallback)})),this._isAppended=!0}_emulateAnimation(t){_(t,this._getElement(),this._config.isAnimated)}}const Gi=".bs.focustrap",Ji=`focusin${Gi}`,Zi=`keydown.tab${Gi}`,tn="backward",en={autofocus:!0,trapElement:null},nn={autofocus:"boolean",trapElement:"element"};class sn extends H{constructor(t){super(),this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}static get Default(){return en}static get DefaultType(){return nn}static get NAME(){return"focustrap"}activate(){this._isActive||(this._config.autofocus&&this._config.trapElement.focus(),N.off(document,Gi),N.on(document,Ji,(t=>this._handleFocusin(t))),N.on(document,Zi,(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,N.off(document,Gi))}_handleFocusin(t){const{trapElement:e}=this._config;if(t.target===document||t.target===e||e.contains(t.target))return;const i=z.focusableChildren(e);0===i.length?e.focus():this._lastTabNavDirection===tn?i[i.length-1].focus():i[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?tn:"forward")}}const on=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",rn=".sticky-top",an="padding-right",ln="margin-right";class cn{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,an,(e=>e+t)),this._setElementAttributes(on,an,(e=>e+t)),this._setElementAttributes(rn,ln,(e=>e-t))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,an),this._resetElementAttributes(on,an),this._resetElementAttributes(rn,ln)}isOverflowing(){return this.getWidth()>0}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t).getPropertyValue(e);t.style.setProperty(e,`${i(Number.parseFloat(s))}px`)}))}_saveInitialAttribute(t,e){const i=t.style.getPropertyValue(e);i&&F.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=F.getDataAttribute(t,e);null!==i?(F.removeDataAttribute(t,e),t.style.setProperty(e,i)):t.style.removeProperty(e)}))}_applyManipulationCallback(t,e){if(o(t))e(t);else for(const i of z.find(t,this._element))e(i)}}const hn=".bs.modal",dn=`hide${hn}`,un=`hidePrevented${hn}`,fn=`hidden${hn}`,pn=`show${hn}`,mn=`shown${hn}`,gn=`resize${hn}`,_n=`click.dismiss${hn}`,bn=`mousedown.dismiss${hn}`,vn=`keydown.dismiss${hn}`,yn=`click${hn}.data-api`,wn="modal-open",An="show",En="modal-static",Tn={backdrop:!0,focus:!0,keyboard:!0},Cn={backdrop:"(boolean|string)",focus:"boolean",keyboard:"boolean"};class On extends W{constructor(t,e){super(t,e),this._dialog=z.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._isTransitioning=!1,this._scrollBar=new cn,this._addEventListeners()}static get Default(){return Tn}static get DefaultType(){return Cn}static get NAME(){return"modal"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||N.trigger(this._element,pn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isTransitioning=!0,this._scrollBar.hide(),document.body.classList.add(wn),this._adjustDialog(),this._backdrop.show((()=>this._showElement(t))))}hide(){this._isShown&&!this._isTransitioning&&(N.trigger(this._element,dn).defaultPrevented||(this._isShown=!1,this._isTransitioning=!0,this._focustrap.deactivate(),this._element.classList.remove(An),this._queueCallback((()=>this._hideModal()),this._element,this._isAnimated())))}dispose(){N.off(window,hn),N.off(this._dialog,hn),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new Ui({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_showElement(t){document.body.contains(this._element)||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0;const e=z.findOne(".modal-body",this._dialog);e&&(e.scrollTop=0),d(this._element),this._element.classList.add(An),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,N.trigger(this._element,mn,{relatedTarget:t})}),this._dialog,this._isAnimated())}_addEventListeners(){N.on(this._element,vn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():this._triggerBackdropTransition())})),N.on(window,gn,(()=>{this._isShown&&!this._isTransitioning&&this._adjustDialog()})),N.on(this._element,bn,(t=>{N.one(this._element,_n,(e=>{this._element===t.target&&this._element===e.target&&("static"!==this._config.backdrop?this._config.backdrop&&this.hide():this._triggerBackdropTransition())}))}))}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(wn),this._resetAdjustments(),this._scrollBar.reset(),N.trigger(this._element,fn)}))}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(N.trigger(this._element,un).defaultPrevented)return;const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._element.style.overflowY;"hidden"===e||this._element.classList.contains(En)||(t||(this._element.style.overflowY="hidden"),this._element.classList.add(En),this._queueCallback((()=>{this._element.classList.remove(En),this._queueCallback((()=>{this._element.style.overflowY=e}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;if(i&&!t){const t=p()?"paddingLeft":"paddingRight";this._element.style[t]=`${e}px`}if(!i&&t){const t=p()?"paddingRight":"paddingLeft";this._element.style[t]=`${e}px`}}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=On.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}N.on(document,yn,'[data-bs-toggle="modal"]',(function(t){const e=z.getElementFromSelector(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),N.one(e,pn,(t=>{t.defaultPrevented||N.one(e,fn,(()=>{a(this)&&this.focus()}))}));const i=z.findOne(".modal.show");i&&On.getInstance(i).hide(),On.getOrCreateInstance(e).toggle(this)})),R(On),m(On);const xn=".bs.offcanvas",kn=".data-api",Ln=`load${xn}${kn}`,Sn="show",Dn="showing",$n="hiding",In=".offcanvas.show",Nn=`show${xn}`,Pn=`shown${xn}`,Mn=`hide${xn}`,jn=`hidePrevented${xn}`,Fn=`hidden${xn}`,Hn=`resize${xn}`,Wn=`click${xn}${kn}`,Bn=`keydown.dismiss${xn}`,zn={backdrop:!0,keyboard:!0,scroll:!1},Rn={backdrop:"(boolean|string)",keyboard:"boolean",scroll:"boolean"};class qn extends W{constructor(t,e){super(t,e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get Default(){return zn}static get DefaultType(){return Rn}static get NAME(){return"offcanvas"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||N.trigger(this._element,Nn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._backdrop.show(),this._config.scroll||(new cn).hide(),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add(Dn),this._queueCallback((()=>{this._config.scroll&&!this._config.backdrop||this._focustrap.activate(),this._element.classList.add(Sn),this._element.classList.remove(Dn),N.trigger(this._element,Pn,{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(N.trigger(this._element,Mn).defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.add($n),this._backdrop.hide(),this._queueCallback((()=>{this._element.classList.remove(Sn,$n),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._config.scroll||(new cn).reset(),N.trigger(this._element,Fn)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_initializeBackDrop(){const t=Boolean(this._config.backdrop);return new Ui({className:"offcanvas-backdrop",isVisible:t,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:t?()=>{"static"!==this._config.backdrop?this.hide():N.trigger(this._element,jn)}:null})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_addEventListeners(){N.on(this._element,Bn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():N.trigger(this._element,jn))}))}static jQueryInterface(t){return this.each((function(){const e=qn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}N.on(document,Wn,'[data-bs-toggle="offcanvas"]',(function(t){const e=z.getElementFromSelector(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),l(this))return;N.one(e,Fn,(()=>{a(this)&&this.focus()}));const i=z.findOne(In);i&&i!==e&&qn.getInstance(i).hide(),qn.getOrCreateInstance(e).toggle(this)})),N.on(window,Ln,(()=>{for(const t of z.find(In))qn.getOrCreateInstance(t).show()})),N.on(window,Hn,(()=>{for(const t of z.find("[aria-modal][class*=show][class*=offcanvas-]"))"fixed"!==getComputedStyle(t).position&&qn.getOrCreateInstance(t).hide()})),R(qn),m(qn);const Vn={"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},Kn=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Qn=/^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i,Xn=(t,e)=>{const i=t.nodeName.toLowerCase();return e.includes(i)?!Kn.has(i)||Boolean(Qn.test(t.nodeValue)):e.filter((t=>t instanceof RegExp)).some((t=>t.test(i)))},Yn={allowList:Vn,content:{},extraClass:"",html:!1,sanitize:!0,sanitizeFn:null,template:"<div></div>"},Un={allowList:"object",content:"object",extraClass:"(string|function)",html:"boolean",sanitize:"boolean",sanitizeFn:"(null|function)",template:"string"},Gn={entry:"(string|element|function|null)",selector:"(string|element)"};class Jn extends H{constructor(t){super(),this._config=this._getConfig(t)}static get Default(){return Yn}static get DefaultType(){return Un}static get NAME(){return"TemplateFactory"}getContent(){return Object.values(this._config.content).map((t=>this._resolvePossibleFunction(t))).filter(Boolean)}hasContent(){return this.getContent().length>0}changeContent(t){return this._checkContent(t),this._config.content={...this._config.content,...t},this}toHtml(){const t=document.createElement("div");t.innerHTML=this._maybeSanitize(this._config.template);for(const[e,i]of Object.entries(this._config.content))this._setContent(t,i,e);const e=t.children[0],i=this._resolvePossibleFunction(this._config.extraClass);return i&&e.classList.add(...i.split(" ")),e}_typeCheckConfig(t){super._typeCheckConfig(t),this._checkContent(t.content)}_checkContent(t){for(const[e,i]of Object.entries(t))super._typeCheckConfig({selector:e,entry:i},Gn)}_setContent(t,e,i){const n=z.findOne(i,t);n&&((e=this._resolvePossibleFunction(e))?o(e)?this._putElementInTemplate(r(e),n):this._config.html?n.innerHTML=this._maybeSanitize(e):n.textContent=e:n.remove())}_maybeSanitize(t){return this._config.sanitize?function(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(const t of s){const i=t.nodeName.toLowerCase();if(!Object.keys(e).includes(i)){t.remove();continue}const n=[].concat(...t.attributes),s=[].concat(e["*"]||[],e[i]||[]);for(const e of n)Xn(e,s)||t.removeAttribute(e.nodeName)}return n.body.innerHTML}(t,this._config.allowList,this._config.sanitizeFn):t}_resolvePossibleFunction(t){return g(t,[this])}_putElementInTemplate(t,e){if(this._config.html)return e.innerHTML="",void e.append(t);e.textContent=t.textContent}}const Zn=new Set(["sanitize","allowList","sanitizeFn"]),ts="fade",es="show",is=".modal",ns="hide.bs.modal",ss="hover",os="focus",rs={AUTO:"auto",TOP:"top",RIGHT:p()?"left":"right",BOTTOM:"bottom",LEFT:p()?"right":"left"},as={allowList:Vn,animation:!0,boundary:"clippingParents",container:!1,customClass:"",delay:0,fallbackPlacements:["top","right","bottom","left"],html:!1,offset:[0,6],placement:"top",popperConfig:null,sanitize:!0,sanitizeFn:null,selector:!1,template:'<div class="tooltip" role="tooltip"><div class="tooltip-arrow"></div><div class="tooltip-inner"></div></div>',title:"",trigger:"hover focus"},ls={allowList:"object",animation:"boolean",boundary:"(string|element)",container:"(string|element|boolean)",customClass:"(string|function)",delay:"(number|object)",fallbackPlacements:"array",html:"boolean",offset:"(array|string|function)",placement:"(string|function)",popperConfig:"(null|object|function)",sanitize:"boolean",sanitizeFn:"(null|function)",selector:"(string|boolean)",template:"string",title:"(string|element|function)",trigger:"string"};class cs extends W{constructor(t,e){if(void 0===vi)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t,e),this._isEnabled=!0,this._timeout=0,this._isHovered=null,this._activeTrigger={},this._popper=null,this._templateFactory=null,this._newContent=null,this.tip=null,this._setListeners(),this._config.selector||this._fixTitle()}static get Default(){return as}static get DefaultType(){return ls}static get NAME(){return"tooltip"}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(){this._isEnabled&&(this._activeTrigger.click=!this._activeTrigger.click,this._isShown()?this._leave():this._enter())}dispose(){clearTimeout(this._timeout),N.off(this._element.closest(is),ns,this._hideModalHandler),this._element.getAttribute("data-bs-original-title")&&this._element.setAttribute("title",this._element.getAttribute("data-bs-original-title")),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this._isWithContent()||!this._isEnabled)return;const t=N.trigger(this._element,this.constructor.eventName("show")),e=(c(this._element)||this._element.ownerDocument.documentElement).contains(this._element);if(t.defaultPrevented||!e)return;this._disposePopper();const i=this._getTipElement();this._element.setAttribute("aria-describedby",i.getAttribute("id"));const{container:n}=this._config;if(this._element.ownerDocument.documentElement.contains(this.tip)||(n.append(i),N.trigger(this._element,this.constructor.eventName("inserted"))),this._popper=this._createPopper(i),i.classList.add(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.on(t,"mouseover",h);this._queueCallback((()=>{N.trigger(this._element,this.constructor.eventName("shown")),!1===this._isHovered&&this._leave(),this._isHovered=!1}),this.tip,this._isAnimated())}hide(){if(this._isShown()&&!N.trigger(this._element,this.constructor.eventName("hide")).defaultPrevented){if(this._getTipElement().classList.remove(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.off(t,"mouseover",h);this._activeTrigger.click=!1,this._activeTrigger[os]=!1,this._activeTrigger[ss]=!1,this._isHovered=null,this._queueCallback((()=>{this._isWithActiveTrigger()||(this._isHovered||this._disposePopper(),this._element.removeAttribute("aria-describedby"),N.trigger(this._element,this.constructor.eventName("hidden")))}),this.tip,this._isAnimated())}}update(){this._popper&&this._popper.update()}_isWithContent(){return Boolean(this._getTitle())}_getTipElement(){return this.tip||(this.tip=this._createTipElement(this._newContent||this._getContentForTemplate())),this.tip}_createTipElement(t){const e=this._getTemplateFactory(t).toHtml();if(!e)return null;e.classList.remove(ts,es),e.classList.add(`bs-${this.constructor.NAME}-auto`);const i=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME).toString();return e.setAttribute("id",i),this._isAnimated()&&e.classList.add(ts),e}setContent(t){this._newContent=t,this._isShown()&&(this._disposePopper(),this.show())}_getTemplateFactory(t){return this._templateFactory?this._templateFactory.changeContent(t):this._templateFactory=new Jn({...this._config,content:t,extraClass:this._resolvePossibleFunction(this._config.customClass)}),this._templateFactory}_getContentForTemplate(){return{".tooltip-inner":this._getTitle()}}_getTitle(){return this._resolvePossibleFunction(this._config.title)||this._element.getAttribute("data-bs-original-title")}_initializeOnDelegatedTarget(t){return this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_isAnimated(){return this._config.animation||this.tip&&this.tip.classList.contains(ts)}_isShown(){return this.tip&&this.tip.classList.contains(es)}_createPopper(t){const e=g(this._config.placement,[this,t,this._element]),i=rs[e.toUpperCase()];return bi(this._element,t,this._getPopperConfig(i))}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return g(t,[this._element])}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"preSetPlacement",enabled:!0,phase:"beforeMain",fn:t=>{this._getTipElement().setAttribute("data-popper-placement",t.state.placement)}}]};return{...e,...g(this._config.popperConfig,[e])}}_setListeners(){const t=this._config.trigger.split(" ");for(const e of t)if("click"===e)N.on(this._element,this.constructor.eventName("click"),this._config.selector,(t=>{this._initializeOnDelegatedTarget(t).toggle()}));else if("manual"!==e){const t=e===ss?this.constructor.eventName("mouseenter"):this.constructor.eventName("focusin"),i=e===ss?this.constructor.eventName("mouseleave"):this.constructor.eventName("focusout");N.on(this._element,t,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusin"===t.type?os:ss]=!0,e._enter()})),N.on(this._element,i,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusout"===t.type?os:ss]=e._element.contains(t.relatedTarget),e._leave()}))}this._hideModalHandler=()=>{this._element&&this.hide()},N.on(this._element.closest(is),ns,this._hideModalHandler)}_fixTitle(){const t=this._element.getAttribute("title");t&&(this._element.getAttribute("aria-label")||this._element.textContent.trim()||this._element.setAttribute("aria-label",t),this._element.setAttribute("data-bs-original-title",t),this._element.removeAttribute("title"))}_enter(){this._isShown()||this._isHovered?this._isHovered=!0:(this._isHovered=!0,this._setTimeout((()=>{this._isHovered&&this.show()}),this._config.delay.show))}_leave(){this._isWithActiveTrigger()||(this._isHovered=!1,this._setTimeout((()=>{this._isHovered||this.hide()}),this._config.delay.hide))}_setTimeout(t,e){clearTimeout(this._timeout),this._timeout=setTimeout(t,e)}_isWithActiveTrigger(){return Object.values(this._activeTrigger).includes(!0)}_getConfig(t){const e=F.getDataAttributes(this._element);for(const t of Object.keys(e))Zn.has(t)&&delete e[t];return t={...e,..."object"==typeof t&&t?t:{}},t=this._mergeConfigObj(t),t=this._configAfterMerge(t),this._typeCheckConfig(t),t}_configAfterMerge(t){return t.container=!1===t.container?document.body:r(t.container),"number"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),"number"==typeof t.title&&(t.title=t.title.toString()),"number"==typeof t.content&&(t.content=t.content.toString()),t}_getDelegateConfig(){const t={};for(const[e,i]of Object.entries(this._config))this.constructor.Default[e]!==i&&(t[e]=i);return t.selector=!1,t.trigger="manual",t}_disposePopper(){this._popper&&(this._popper.destroy(),this._popper=null),this.tip&&(this.tip.remove(),this.tip=null)}static jQueryInterface(t){return this.each((function(){const e=cs.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}m(cs);const hs={...cs.Default,content:"",offset:[0,8],placement:"right",template:'<div class="popover" role="tooltip"><div class="popover-arrow"></div><h3 class="popover-header"></h3><div class="popover-body"></div></div>',trigger:"click"},ds={...cs.DefaultType,content:"(null|string|element|function)"};class us extends cs{static get Default(){return hs}static get DefaultType(){return ds}static get NAME(){return"popover"}_isWithContent(){return this._getTitle()||this._getContent()}_getContentForTemplate(){return{".popover-header":this._getTitle(),".popover-body":this._getContent()}}_getContent(){return this._resolvePossibleFunction(this._config.content)}static jQueryInterface(t){return this.each((function(){const e=us.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}m(us);const fs=".bs.scrollspy",ps=`activate${fs}`,ms=`click${fs}`,gs=`load${fs}.data-api`,_s="active",bs="[href]",vs=".nav-link",ys=`${vs}, .nav-item > ${vs}, .list-group-item`,ws={offset:null,rootMargin:"0px 0px -25%",smoothScroll:!1,target:null,threshold:[.1,.5,1]},As={offset:"(number|null)",rootMargin:"string",smoothScroll:"boolean",target:"element",threshold:"array"};class Es extends W{constructor(t,e){super(t,e),this._targetLinks=new Map,this._observableSections=new Map,this._rootElement="visible"===getComputedStyle(this._element).overflowY?null:this._element,this._activeTarget=null,this._observer=null,this._previousScrollData={visibleEntryTop:0,parentScrollTop:0},this.refresh()}static get Default(){return ws}static get DefaultType(){return As}static get NAME(){return"scrollspy"}refresh(){this._initializeTargetsAndObservables(),this._maybeEnableSmoothScroll(),this._observer?this._observer.disconnect():this._observer=this._getNewObserver();for(const t of this._observableSections.values())this._observer.observe(t)}dispose(){this._observer.disconnect(),super.dispose()}_configAfterMerge(t){return t.target=r(t.target)||document.body,t.rootMargin=t.offset?`${t.offset}px 0px -30%`:t.rootMargin,"string"==typeof t.threshold&&(t.threshold=t.threshold.split(",").map((t=>Number.parseFloat(t)))),t}_maybeEnableSmoothScroll(){this._config.smoothScroll&&(N.off(this._config.target,ms),N.on(this._config.target,ms,bs,(t=>{const e=this._observableSections.get(t.target.hash);if(e){t.preventDefault();const i=this._rootElement||window,n=e.offsetTop-this._element.offsetTop;if(i.scrollTo)return void i.scrollTo({top:n,behavior:"smooth"});i.scrollTop=n}})))}_getNewObserver(){const t={root:this._rootElement,threshold:this._config.threshold,rootMargin:this._config.rootMargin};return new IntersectionObserver((t=>this._observerCallback(t)),t)}_observerCallback(t){const e=t=>this._targetLinks.get(`#${t.target.id}`),i=t=>{this._previousScrollData.visibleEntryTop=t.target.offsetTop,this._process(e(t))},n=(this._rootElement||document.documentElement).scrollTop,s=n>=this._previousScrollData.parentScrollTop;this._previousScrollData.parentScrollTop=n;for(const o of t){if(!o.isIntersecting){this._activeTarget=null,this._clearActiveClass(e(o));continue}const t=o.target.offsetTop>=this._previousScrollData.visibleEntryTop;if(s&&t){if(i(o),!n)return}else s||t||i(o)}}_initializeTargetsAndObservables(){this._targetLinks=new Map,this._observableSections=new Map;const t=z.find(bs,this._config.target);for(const e of t){if(!e.hash||l(e))continue;const t=z.findOne(decodeURI(e.hash),this._element);a(t)&&(this._targetLinks.set(decodeURI(e.hash),e),this._observableSections.set(e.hash,t))}}_process(t){this._activeTarget!==t&&(this._clearActiveClass(this._config.target),this._activeTarget=t,t.classList.add(_s),this._activateParents(t),N.trigger(this._element,ps,{relatedTarget:t}))}_activateParents(t){if(t.classList.contains("dropdown-item"))z.findOne(".dropdown-toggle",t.closest(".dropdown")).classList.add(_s);else for(const e of z.parents(t,".nav, .list-group"))for(const t of z.prev(e,ys))t.classList.add(_s)}_clearActiveClass(t){t.classList.remove(_s);const e=z.find(`${bs}.${_s}`,t);for(const t of e)t.classList.remove(_s)}static jQueryInterface(t){return this.each((function(){const e=Es.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}))}}N.on(window,gs,(()=>{for(const t of z.find('[data-bs-spy="scroll"]'))Es.getOrCreateInstance(t)})),m(Es);const Ts=".bs.tab",Cs=`hide${Ts}`,Os=`hidden${Ts}`,xs=`show${Ts}`,ks=`shown${Ts}`,Ls=`click${Ts}`,Ss=`keydown${Ts}`,Ds=`load${Ts}`,$s="ArrowLeft",Is="ArrowRight",Ns="ArrowUp",Ps="ArrowDown",Ms="Home",js="End",Fs="active",Hs="fade",Ws="show",Bs=":not(.dropdown-toggle)",zs='[data-bs-toggle="tab"], [data-bs-toggle="pill"], [data-bs-toggle="list"]',Rs=`.nav-link${Bs}, .list-group-item${Bs}, [role="tab"]${Bs}, ${zs}`,qs=`.${Fs}[data-bs-toggle="tab"], .${Fs}[data-bs-toggle="pill"], .${Fs}[data-bs-toggle="list"]`;class Vs extends W{constructor(t){super(t),this._parent=this._element.closest('.list-group, .nav, [role="tablist"]'),this._parent&&(this._setInitialAttributes(this._parent,this._getChildren()),N.on(this._element,Ss,(t=>this._keydown(t))))}static get NAME(){return"tab"}show(){const t=this._element;if(this._elemIsActive(t))return;const e=this._getActiveElem(),i=e?N.trigger(e,Cs,{relatedTarget:t}):null;N.trigger(t,xs,{relatedTarget:e}).defaultPrevented||i&&i.defaultPrevented||(this._deactivate(e,t),this._activate(t,e))}_activate(t,e){t&&(t.classList.add(Fs),this._activate(z.getElementFromSelector(t)),this._queueCallback((()=>{"tab"===t.getAttribute("role")?(t.removeAttribute("tabindex"),t.setAttribute("aria-selected",!0),this._toggleDropDown(t,!0),N.trigger(t,ks,{relatedTarget:e})):t.classList.add(Ws)}),t,t.classList.contains(Hs)))}_deactivate(t,e){t&&(t.classList.remove(Fs),t.blur(),this._deactivate(z.getElementFromSelector(t)),this._queueCallback((()=>{"tab"===t.getAttribute("role")?(t.setAttribute("aria-selected",!1),t.setAttribute("tabindex","-1"),this._toggleDropDown(t,!1),N.trigger(t,Os,{relatedTarget:e})):t.classList.remove(Ws)}),t,t.classList.contains(Hs)))}_keydown(t){if(![$s,Is,Ns,Ps,Ms,js].includes(t.key))return;t.stopPropagation(),t.preventDefault();const e=this._getChildren().filter((t=>!l(t)));let i;if([Ms,js].includes(t.key))i=e[t.key===Ms?0:e.length-1];else{const n=[Is,Ps].includes(t.key);i=b(e,t.target,n,!0)}i&&(i.focus({preventScroll:!0}),Vs.getOrCreateInstance(i).show())}_getChildren(){return z.find(Rs,this._parent)}_getActiveElem(){return this._getChildren().find((t=>this._elemIsActive(t)))||null}_setInitialAttributes(t,e){this._setAttributeIfNotExists(t,"role","tablist");for(const t of e)this._setInitialAttributesOnChild(t)}_setInitialAttributesOnChild(t){t=this._getInnerElement(t);const e=this._elemIsActive(t),i=this._getOuterElement(t);t.setAttribute("aria-selected",e),i!==t&&this._setAttributeIfNotExists(i,"role","presentation"),e||t.setAttribute("tabindex","-1"),this._setAttributeIfNotExists(t,"role","tab"),this._setInitialAttributesOnTargetPanel(t)}_setInitialAttributesOnTargetPanel(t){const e=z.getElementFromSelector(t);e&&(this._setAttributeIfNotExists(e,"role","tabpanel"),t.id&&this._setAttributeIfNotExists(e,"aria-labelledby",`${t.id}`))}_toggleDropDown(t,e){const i=this._getOuterElement(t);if(!i.classList.contains("dropdown"))return;const n=(t,n)=>{const s=z.findOne(t,i);s&&s.classList.toggle(n,e)};n(".dropdown-toggle",Fs),n(".dropdown-menu",Ws),i.setAttribute("aria-expanded",e)}_setAttributeIfNotExists(t,e,i){t.hasAttribute(e)||t.setAttribute(e,i)}_elemIsActive(t){return t.classList.contains(Fs)}_getInnerElement(t){return t.matches(Rs)?t:z.findOne(Rs,t)}_getOuterElement(t){return t.closest(".nav-item, .list-group-item")||t}static jQueryInterface(t){return this.each((function(){const e=Vs.getOrCreateInstance(this);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}))}}N.on(document,Ls,zs,(function(t){["A","AREA"].includes(this.tagName)&&t.preventDefault(),l(this)||Vs.getOrCreateInstance(this).show()})),N.on(window,Ds,(()=>{for(const t of z.find(qs))Vs.getOrCreateInstance(t)})),m(Vs);const Ks=".bs.toast",Qs=`mouseover${Ks}`,Xs=`mouseout${Ks}`,Ys=`focusin${Ks}`,Us=`focusout${Ks}`,Gs=`hide${Ks}`,Js=`hidden${Ks}`,Zs=`show${Ks}`,to=`shown${Ks}`,eo="hide",io="show",no="showing",so={animation:"boolean",autohide:"boolean",delay:"number"},oo={animation:!0,autohide:!0,delay:5e3};class ro extends W{constructor(t,e){super(t,e),this._timeout=null,this._hasMouseInteraction=!1,this._hasKeyboardInteraction=!1,this._setListeners()}static get Default(){return oo}static get DefaultType(){return so}static get NAME(){return"toast"}show(){N.trigger(this._element,Zs).defaultPrevented||(this._clearTimeout(),this._config.animation&&this._element.classList.add("fade"),this._element.classList.remove(eo),d(this._element),this._element.classList.add(io,no),this._queueCallback((()=>{this._element.classList.remove(no),N.trigger(this._element,to),this._maybeScheduleHide()}),this._element,this._config.animation))}hide(){this.isShown()&&(N.trigger(this._element,Gs).defaultPrevented||(this._element.classList.add(no),this._queueCallback((()=>{this._element.classList.add(eo),this._element.classList.remove(no,io),N.trigger(this._element,Js)}),this._element,this._config.animation)))}dispose(){this._clearTimeout(),this.isShown()&&this._element.classList.remove(io),super.dispose()}isShown(){return this._element.classList.contains(io)}_maybeScheduleHide(){this._config.autohide&&(this._hasMouseInteraction||this._hasKeyboardInteraction||(this._timeout=setTimeout((()=>{this.hide()}),this._config.delay)))}_onInteraction(t,e){switch(t.type){case"mouseover":case"mouseout":this._hasMouseInteraction=e;break;case"focusin":case"focusout":this._hasKeyboardInteraction=e}if(e)return void this._clearTimeout();const i=t.relatedTarget;this._element===i||this._element.contains(i)||this._maybeScheduleHide()}_setListeners(){N.on(this._element,Qs,(t=>this._onInteraction(t,!0))),N.on(this._element,Xs,(t=>this._onInteraction(t,!1))),N.on(this._element,Ys,(t=>this._onInteraction(t,!0))),N.on(this._element,Us,(t=>this._onInteraction(t,!1)))}_clearTimeout(){clearTimeout(this._timeout),this._timeout=null}static jQueryInterface(t){return this.each((function(){const e=ro.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}return R(ro),m(ro),{Alert:Q,Button:Y,Carousel:xt,Collapse:Bt,Dropdown:qi,Modal:On,Offcanvas:qn,Popover:us,ScrollSpy:Es,Tab:Vs,Toast:ro,Tooltip:cs}}));
+//# sourceMappingURL=bootstrap.bundle.min.js.map
\ No newline at end of file
diff --git a/docs/deps/bootstrap-5.3.1/bootstrap.bundle.min.js.map b/docs/deps/bootstrap-5.3.1/bootstrap.bundle.min.js.map
new file mode 100644
index 00000000..3863da8b
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/bootstrap.bundle.min.js.map
@@ -0,0 +1 @@
+{"version":3,"names":["elementMap","Map","Data","set","element","key","instance","has","instanceMap","get","size","console","error","Array","from","keys","remove","delete","TRANSITION_END","parseSelector","selector","window","CSS","escape","replace","match","id","triggerTransitionEnd","dispatchEvent","Event","isElement","object","jquery","nodeType","getElement","length","document","querySelector","isVisible","getClientRects","elementIsVisible","getComputedStyle","getPropertyValue","closedDetails","closest","summary","parentNode","isDisabled","Node","ELEMENT_NODE","classList","contains","disabled","hasAttribute","getAttribute","findShadowRoot","documentElement","attachShadow","getRootNode","root","ShadowRoot","noop","reflow","offsetHeight","getjQuery","jQuery","body","DOMContentLoadedCallbacks","isRTL","dir","defineJQueryPlugin","plugin","callback","$","name","NAME","JQUERY_NO_CONFLICT","fn","jQueryInterface","Constructor","noConflict","readyState","addEventListener","push","execute","possibleCallback","args","defaultValue","executeAfterTransition","transitionElement","waitForTransition","emulatedDuration","transitionDuration","transitionDelay","floatTransitionDuration","Number","parseFloat","floatTransitionDelay","split","getTransitionDurationFromElement","called","handler","target","removeEventListener","setTimeout","getNextActiveElement","list","activeElement","shouldGetNext","isCycleAllowed","listLength","index","indexOf","Math","max","min","namespaceRegex","stripNameRegex","stripUidRegex","eventRegistry","uidEvent","customEvents","mouseenter","mouseleave","nativeEvents","Set","makeEventUid","uid","getElementEvents","findHandler","events","callable","delegationSelector","Object","values","find","event","normalizeParameters","originalTypeEvent","delegationFunction","isDelegated","typeEvent","getTypeEvent","addHandler","oneOff","wrapFunction","relatedTarget","delegateTarget","call","this","handlers","previousFunction","domElements","querySelectorAll","domElement","hydrateObj","EventHandler","off","type","apply","bootstrapDelegationHandler","bootstrapHandler","removeHandler","Boolean","removeNamespacedHandlers","namespace","storeElementEvent","handlerKey","entries","includes","on","one","inNamespace","isNamespace","startsWith","elementEvent","slice","keyHandlers","trigger","jQueryEvent","bubbles","nativeDispatch","defaultPrevented","isPropagationStopped","isImmediatePropagationStopped","isDefaultPrevented","evt","cancelable","preventDefault","obj","meta","value","_unused","defineProperty","configurable","normalizeData","toString","JSON","parse","decodeURIComponent","normalizeDataKey","chr","toLowerCase","Manipulator","setDataAttribute","setAttribute","removeDataAttribute","removeAttribute","getDataAttributes","attributes","bsKeys","dataset","filter","pureKey","charAt","getDataAttribute","Config","Default","DefaultType","Error","_getConfig","config","_mergeConfigObj","_configAfterMerge","_typeCheckConfig","jsonConfig","constructor","configTypes","property","expectedTypes","valueType","prototype","RegExp","test","TypeError","toUpperCase","BaseComponent","super","_element","_config","DATA_KEY","dispose","EVENT_KEY","propertyName","getOwnPropertyNames","_queueCallback","isAnimated","getInstance","getOrCreateInstance","VERSION","eventName","getSelector","hrefAttribute","trim","SelectorEngine","concat","Element","findOne","children","child","matches","parents","ancestor","prev","previous","previousElementSibling","next","nextElementSibling","focusableChildren","focusables","map","join","el","getSelectorFromElement","getElementFromSelector","getMultipleElementsFromSelector","enableDismissTrigger","component","method","clickEvent","tagName","EVENT_CLOSE","EVENT_CLOSED","Alert","close","_destroyElement","each","data","undefined","SELECTOR_DATA_TOGGLE","Button","toggle","button","EVENT_TOUCHSTART","EVENT_TOUCHMOVE","EVENT_TOUCHEND","EVENT_POINTERDOWN","EVENT_POINTERUP","endCallback","leftCallback","rightCallback","Swipe","isSupported","_deltaX","_supportPointerEvents","PointerEvent","_initEvents","_start","_eventIsPointerPenTouch","clientX","touches","_end","_handleSwipe","_move","absDeltaX","abs","direction","add","pointerType","navigator","maxTouchPoints","DATA_API_KEY","ORDER_NEXT","ORDER_PREV","DIRECTION_LEFT","DIRECTION_RIGHT","EVENT_SLIDE","EVENT_SLID","EVENT_KEYDOWN","EVENT_MOUSEENTER","EVENT_MOUSELEAVE","EVENT_DRAG_START","EVENT_LOAD_DATA_API","EVENT_CLICK_DATA_API","CLASS_NAME_CAROUSEL","CLASS_NAME_ACTIVE","SELECTOR_ACTIVE","SELECTOR_ITEM","SELECTOR_ACTIVE_ITEM","KEY_TO_DIRECTION","ArrowLeft","ArrowRight","interval","keyboard","pause","ride","touch","wrap","Carousel","_interval","_activeElement","_isSliding","touchTimeout","_swipeHelper","_indicatorsElement","_addEventListeners","cycle","_slide","nextWhenVisible","hidden","_clearInterval","_updateInterval","setInterval","_maybeEnableCycle","to","items","_getItems","activeIndex","_getItemIndex","_getActive","order","defaultInterval","_keydown","_addTouchEventListeners","img","swipeConfig","_directionToOrder","endCallBack","clearTimeout","_setActiveIndicatorElement","activeIndicator","newActiveIndicator","elementInterval","parseInt","isNext","nextElement","nextElementIndex","triggerEvent","_orderToDirection","isCycling","directionalClassName","orderClassName","completeCallBack","_isAnimated","clearInterval","carousel","slideIndex","carousels","EVENT_SHOW","EVENT_SHOWN","EVENT_HIDE","EVENT_HIDDEN","CLASS_NAME_SHOW","CLASS_NAME_COLLAPSE","CLASS_NAME_COLLAPSING","CLASS_NAME_DEEPER_CHILDREN","parent","Collapse","_isTransitioning","_triggerArray","toggleList","elem","filterElement","foundElement","_initializeChildren","_addAriaAndCollapsedClass","_isShown","hide","show","activeChildren","_getFirstLevelChildren","activeInstance","dimension","_getDimension","style","scrollSize","complete","getBoundingClientRect","selected","triggerArray","isOpen","top","bottom","right","left","auto","basePlacements","start","end","clippingParents","viewport","popper","reference","variationPlacements","reduce","acc","placement","placements","beforeRead","read","afterRead","beforeMain","main","afterMain","beforeWrite","write","afterWrite","modifierPhases","getNodeName","nodeName","getWindow","node","ownerDocument","defaultView","isHTMLElement","HTMLElement","isShadowRoot","applyStyles$1","enabled","phase","_ref","state","elements","forEach","styles","assign","effect","_ref2","initialStyles","position","options","strategy","margin","arrow","hasOwnProperty","attribute","requires","getBasePlacement","round","getUAString","uaData","userAgentData","brands","isArray","item","brand","version","userAgent","isLayoutViewport","includeScale","isFixedStrategy","clientRect","scaleX","scaleY","offsetWidth","width","height","visualViewport","addVisualOffsets","x","offsetLeft","y","offsetTop","getLayoutRect","rootNode","isSameNode","host","isTableElement","getDocumentElement","getParentNode","assignedSlot","getTrueOffsetParent","offsetParent","getOffsetParent","isFirefox","currentNode","css","transform","perspective","contain","willChange","getContainingBlock","getMainAxisFromPlacement","within","mathMax","mathMin","mergePaddingObject","paddingObject","expandToHashMap","hashMap","arrow$1","_state$modifiersData$","arrowElement","popperOffsets","modifiersData","basePlacement","axis","len","padding","rects","toPaddingObject","arrowRect","minProp","maxProp","endDiff","startDiff","arrowOffsetParent","clientSize","clientHeight","clientWidth","centerToReference","center","offset","axisProp","centerOffset","_options$element","requiresIfExists","getVariation","unsetSides","mapToStyles","_Object$assign2","popperRect","variation","offsets","gpuAcceleration","adaptive","roundOffsets","isFixed","_offsets$x","_offsets$y","_ref3","hasX","hasY","sideX","sideY","win","heightProp","widthProp","_Object$assign","commonStyles","_ref4","dpr","devicePixelRatio","roundOffsetsByDPR","computeStyles$1","_ref5","_options$gpuAccelerat","_options$adaptive","_options$roundOffsets","passive","eventListeners","_options$scroll","scroll","_options$resize","resize","scrollParents","scrollParent","update","hash","getOppositePlacement","matched","getOppositeVariationPlacement","getWindowScroll","scrollLeft","pageXOffset","scrollTop","pageYOffset","getWindowScrollBarX","isScrollParent","_getComputedStyle","overflow","overflowX","overflowY","getScrollParent","listScrollParents","_element$ownerDocumen","isBody","updatedList","rectToClientRect","rect","getClientRectFromMixedType","clippingParent","html","layoutViewport","getViewportRect","clientTop","clientLeft","getInnerBoundingClientRect","winScroll","scrollWidth","scrollHeight","getDocumentRect","computeOffsets","commonX","commonY","mainAxis","detectOverflow","_options","_options$placement","_options$strategy","_options$boundary","boundary","_options$rootBoundary","rootBoundary","_options$elementConte","elementContext","_options$altBoundary","altBoundary","_options$padding","altContext","clippingClientRect","mainClippingParents","clipperElement","getClippingParents","firstClippingParent","clippingRect","accRect","getClippingRect","contextElement","referenceClientRect","popperClientRect","elementClientRect","overflowOffsets","offsetData","multiply","computeAutoPlacement","flipVariations","_options$allowedAutoP","allowedAutoPlacements","allPlacements","allowedPlacements","overflows","sort","a","b","flip$1","_skip","_options$mainAxis","checkMainAxis","_options$altAxis","altAxis","checkAltAxis","specifiedFallbackPlacements","fallbackPlacements","_options$flipVariatio","preferredPlacement","oppositePlacement","getExpandedFallbackPlacements","referenceRect","checksMap","makeFallbackChecks","firstFittingPlacement","i","_basePlacement","isStartVariation","isVertical","mainVariationSide","altVariationSide","checks","every","check","_loop","_i","fittingPlacement","reset","getSideOffsets","preventedOffsets","isAnySideFullyClipped","some","side","hide$1","preventOverflow","referenceOverflow","popperAltOverflow","referenceClippingOffsets","popperEscapeOffsets","isReferenceHidden","hasPopperEscaped","offset$1","_options$offset","invertDistance","skidding","distance","distanceAndSkiddingToXY","_data$state$placement","popperOffsets$1","preventOverflow$1","_options$tether","tether","_options$tetherOffset","tetherOffset","isBasePlacement","tetherOffsetValue","normalizedTetherOffsetValue","offsetModifierState","_offsetModifierState$","mainSide","altSide","additive","minLen","maxLen","arrowPaddingObject","arrowPaddingMin","arrowPaddingMax","arrowLen","minOffset","maxOffset","clientOffset","offsetModifierValue","tetherMax","preventedOffset","_offsetModifierState$2","_mainSide","_altSide","_offset","_len","_min","_max","isOriginSide","_offsetModifierValue","_tetherMin","_tetherMax","_preventedOffset","v","withinMaxClamp","getCompositeRect","elementOrVirtualElement","isOffsetParentAnElement","offsetParentIsScaled","isElementScaled","modifiers","visited","result","modifier","dep","depModifier","DEFAULT_OPTIONS","areValidElements","arguments","_key","popperGenerator","generatorOptions","_generatorOptions","_generatorOptions$def","defaultModifiers","_generatorOptions$def2","defaultOptions","pending","orderedModifiers","effectCleanupFns","isDestroyed","setOptions","setOptionsAction","cleanupModifierEffects","merged","orderModifiers","current","existing","m","_ref$options","cleanupFn","forceUpdate","_state$elements","_state$orderedModifie","_state$orderedModifie2","Promise","resolve","then","destroy","onFirstUpdate","createPopper","computeStyles","applyStyles","flip","ARROW_UP_KEY","ARROW_DOWN_KEY","EVENT_KEYDOWN_DATA_API","EVENT_KEYUP_DATA_API","SELECTOR_DATA_TOGGLE_SHOWN","SELECTOR_MENU","PLACEMENT_TOP","PLACEMENT_TOPEND","PLACEMENT_BOTTOM","PLACEMENT_BOTTOMEND","PLACEMENT_RIGHT","PLACEMENT_LEFT","autoClose","display","popperConfig","Dropdown","_popper","_parent","_menu","_inNavbar","_detectNavbar","_createPopper","focus","_completeHide","Popper","referenceElement","_getPopperConfig","_getPlacement","parentDropdown","isEnd","_getOffset","popperData","defaultBsPopperConfig","_selectMenuItem","clearMenus","openToggles","context","composedPath","isMenuTarget","dataApiKeydownHandler","isInput","isEscapeEvent","isUpOrDownEvent","getToggleButton","stopPropagation","EVENT_MOUSEDOWN","className","clickCallback","rootElement","Backdrop","_isAppended","_append","_getElement","_emulateAnimation","backdrop","createElement","append","EVENT_FOCUSIN","EVENT_KEYDOWN_TAB","TAB_NAV_BACKWARD","autofocus","trapElement","FocusTrap","_isActive","_lastTabNavDirection","activate","_handleFocusin","_handleKeydown","deactivate","shiftKey","SELECTOR_FIXED_CONTENT","SELECTOR_STICKY_CONTENT","PROPERTY_PADDING","PROPERTY_MARGIN","ScrollBarHelper","getWidth","documentWidth","innerWidth","_disableOverFlow","_setElementAttributes","calculatedValue","_resetElementAttributes","isOverflowing","_saveInitialAttribute","styleProperty","scrollbarWidth","_applyManipulationCallback","setProperty","actualValue","removeProperty","callBack","sel","EVENT_HIDE_PREVENTED","EVENT_RESIZE","EVENT_CLICK_DISMISS","EVENT_MOUSEDOWN_DISMISS","EVENT_KEYDOWN_DISMISS","CLASS_NAME_OPEN","CLASS_NAME_STATIC","Modal","_dialog","_backdrop","_initializeBackDrop","_focustrap","_initializeFocusTrap","_scrollBar","_adjustDialog","_showElement","_hideModal","handleUpdate","modalBody","transitionComplete","_triggerBackdropTransition","event2","_resetAdjustments","isModalOverflowing","initialOverflowY","isBodyOverflowing","paddingLeft","paddingRight","showEvent","alreadyOpen","CLASS_NAME_SHOWING","CLASS_NAME_HIDING","OPEN_SELECTOR","Offcanvas","blur","completeCallback","DefaultAllowlist","area","br","col","code","div","em","hr","h1","h2","h3","h4","h5","h6","li","ol","p","pre","s","small","span","sub","sup","strong","u","ul","uriAttributes","SAFE_URL_PATTERN","allowedAttribute","allowedAttributeList","attributeName","nodeValue","attributeRegex","regex","allowList","content","extraClass","sanitize","sanitizeFn","template","DefaultContentType","entry","TemplateFactory","getContent","_resolvePossibleFunction","hasContent","changeContent","_checkContent","toHtml","templateWrapper","innerHTML","_maybeSanitize","text","_setContent","arg","templateElement","_putElementInTemplate","textContent","unsafeHtml","sanitizeFunction","createdDocument","DOMParser","parseFromString","elementName","attributeList","allowedAttributes","sanitizeHtml","DISALLOWED_ATTRIBUTES","CLASS_NAME_FADE","SELECTOR_MODAL","EVENT_MODAL_HIDE","TRIGGER_HOVER","TRIGGER_FOCUS","AttachmentMap","AUTO","TOP","RIGHT","BOTTOM","LEFT","animation","container","customClass","delay","title","Tooltip","_isEnabled","_timeout","_isHovered","_activeTrigger","_templateFactory","_newContent","tip","_setListeners","_fixTitle","enable","disable","toggleEnabled","click","_leave","_enter","_hideModalHandler","_disposePopper","_isWithContent","isInTheDom","_getTipElement","_isWithActiveTrigger","_getTitle","_createTipElement","_getContentForTemplate","_getTemplateFactory","tipId","prefix","floor","random","getElementById","getUID","setContent","_initializeOnDelegatedTarget","_getDelegateConfig","attachment","triggers","eventIn","eventOut","_setTimeout","timeout","dataAttributes","dataAttribute","Popover","_getContent","EVENT_ACTIVATE","EVENT_CLICK","SELECTOR_TARGET_LINKS","SELECTOR_NAV_LINKS","SELECTOR_LINK_ITEMS","rootMargin","smoothScroll","threshold","ScrollSpy","_targetLinks","_observableSections","_rootElement","_activeTarget","_observer","_previousScrollData","visibleEntryTop","parentScrollTop","refresh","_initializeTargetsAndObservables","_maybeEnableSmoothScroll","disconnect","_getNewObserver","section","observe","observableSection","scrollTo","behavior","IntersectionObserver","_observerCallback","targetElement","_process","userScrollsDown","isIntersecting","_clearActiveClass","entryIsLowerThanPrevious","targetLinks","anchor","decodeURI","_activateParents","listGroup","activeNodes","spy","ARROW_LEFT_KEY","ARROW_RIGHT_KEY","HOME_KEY","END_KEY","NOT_SELECTOR_DROPDOWN_TOGGLE","SELECTOR_INNER_ELEM","SELECTOR_DATA_TOGGLE_ACTIVE","Tab","_setInitialAttributes","_getChildren","innerElem","_elemIsActive","active","_getActiveElem","hideEvent","_deactivate","_activate","relatedElem","_toggleDropDown","nextActiveElement","preventScroll","_setAttributeIfNotExists","_setInitialAttributesOnChild","_getInnerElement","isActive","outerElem","_getOuterElement","_setInitialAttributesOnTargetPanel","open","EVENT_MOUSEOVER","EVENT_MOUSEOUT","EVENT_FOCUSOUT","CLASS_NAME_HIDE","autohide","Toast","_hasMouseInteraction","_hasKeyboardInteraction","_clearTimeout","_maybeScheduleHide","isShown","_onInteraction","isInteracting"],"sources":["../../js/src/dom/data.js","../../js/src/util/index.js","../../js/src/dom/event-handler.js","../../js/src/dom/manipulator.js","../../js/src/util/config.js","../../js/src/base-component.js","../../js/src/dom/selector-engine.js","../../js/src/util/component-functions.js","../../js/src/alert.js","../../js/src/button.js","../../js/src/util/swipe.js","../../js/src/carousel.js","../../js/src/collapse.js","../../node_modules/@popperjs/core/lib/enums.js","../../node_modules/@popperjs/core/lib/dom-utils/getNodeName.js","../../node_modules/@popperjs/core/lib/dom-utils/getWindow.js","../../node_modules/@popperjs/core/lib/dom-utils/instanceOf.js","../../node_modules/@popperjs/core/lib/modifiers/applyStyles.js","../../node_modules/@popperjs/core/lib/utils/getBasePlacement.js","../../node_modules/@popperjs/core/lib/utils/math.js","../../node_modules/@popperjs/core/lib/utils/userAgent.js","../../node_modules/@popperjs/core/lib/dom-utils/isLayoutViewport.js","../../node_modules/@popperjs/core/lib/dom-utils/getBoundingClientRect.js","../../node_modules/@popperjs/core/lib/dom-utils/getLayoutRect.js","../../node_modules/@popperjs/core/lib/dom-utils/contains.js","../../node_modules/@popperjs/core/lib/dom-utils/getComputedStyle.js","../../node_modules/@popperjs/core/lib/dom-utils/isTableElement.js","../../node_modules/@popperjs/core/lib/dom-utils/getDocumentElement.js","../../node_modules/@popperjs/core/lib/dom-utils/getParentNode.js","../../node_modules/@popperjs/core/lib/dom-utils/getOffsetParent.js","../../node_modules/@popperjs/core/lib/utils/getMainAxisFromPlacement.js","../../node_modules/@popperjs/core/lib/utils/within.js","../../node_modules/@popperjs/core/lib/utils/mergePaddingObject.js","../../node_modules/@popperjs/core/lib/utils/getFreshSideObject.js","../../node_modules/@popperjs/core/lib/utils/expandToHashMap.js","../../node_modules/@popperjs/core/lib/modifiers/arrow.js","../../node_modules/@popperjs/core/lib/utils/getVariation.js","../../node_modules/@popperjs/core/lib/modifiers/computeStyles.js","../../node_modules/@popperjs/core/lib/modifiers/eventListeners.js","../../node_modules/@popperjs/core/lib/utils/getOppositePlacement.js","../../node_modules/@popperjs/core/lib/utils/getOppositeVariationPlacement.js","../../node_modules/@popperjs/core/lib/dom-utils/getWindowScroll.js","../../node_modules/@popperjs/core/lib/dom-utils/getWindowScrollBarX.js","../../node_modules/@popperjs/core/lib/dom-utils/isScrollParent.js","../../node_modules/@popperjs/core/lib/dom-utils/getScrollParent.js","../../node_modules/@popperjs/core/lib/dom-utils/listScrollParents.js","../../node_modules/@popperjs/core/lib/utils/rectToClientRect.js","../../node_modules/@popperjs/core/lib/dom-utils/getClippingRect.js","../../node_modules/@popperjs/core/lib/dom-utils/getViewportRect.js","../../node_modules/@popperjs/core/lib/dom-utils/getDocumentRect.js","../../node_modules/@popperjs/core/lib/utils/computeOffsets.js","../../node_modules/@popperjs/core/lib/utils/detectOverflow.js","../../node_modules/@popperjs/core/lib/utils/computeAutoPlacement.js","../../node_modules/@popperjs/core/lib/modifiers/flip.js","../../node_modules/@popperjs/core/lib/modifiers/hide.js","../../node_modules/@popperjs/core/lib/modifiers/offset.js","../../node_modules/@popperjs/core/lib/modifiers/popperOffsets.js","../../node_modules/@popperjs/core/lib/modifiers/preventOverflow.js","../../node_modules/@popperjs/core/lib/utils/getAltAxis.js","../../node_modules/@popperjs/core/lib/dom-utils/getCompositeRect.js","../../node_modules/@popperjs/core/lib/dom-utils/getNodeScroll.js","../../node_modules/@popperjs/core/lib/dom-utils/getHTMLElementScroll.js","../../node_modules/@popperjs/core/lib/utils/orderModifiers.js","../../node_modules/@popperjs/core/lib/createPopper.js","../../node_modules/@popperjs/core/lib/utils/debounce.js","../../node_modules/@popperjs/core/lib/utils/mergeByName.js","../../node_modules/@popperjs/core/lib/popper-lite.js","../../node_modules/@popperjs/core/lib/popper.js","../../js/src/dropdown.js","../../js/src/util/backdrop.js","../../js/src/util/focustrap.js","../../js/src/util/scrollbar.js","../../js/src/modal.js","../../js/src/offcanvas.js","../../js/src/util/sanitizer.js","../../js/src/util/template-factory.js","../../js/src/tooltip.js","../../js/src/popover.js","../../js/src/scrollspy.js","../../js/src/tab.js","../../js/src/toast.js","../../js/index.umd.js"],"sourcesContent":["/**\n * --------------------------------------------------------------------------\n * Bootstrap dom/data.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n/**\n * Constants\n */\n\nconst elementMap = new Map()\n\nexport default {\n  set(element, key, instance) {\n    if (!elementMap.has(element)) {\n      elementMap.set(element, new Map())\n    }\n\n    const instanceMap = elementMap.get(element)\n\n    // make it clear we only want one instance per element\n    // can be removed later when multiple key/instances are fine to be used\n    if (!instanceMap.has(key) && instanceMap.size !== 0) {\n      // eslint-disable-next-line no-console\n      console.error(`Bootstrap doesn't allow more than one instance per element. Bound instance: ${Array.from(instanceMap.keys())[0]}.`)\n      return\n    }\n\n    instanceMap.set(key, instance)\n  },\n\n  get(element, key) {\n    if (elementMap.has(element)) {\n      return elementMap.get(element).get(key) || null\n    }\n\n    return null\n  },\n\n  remove(element, key) {\n    if (!elementMap.has(element)) {\n      return\n    }\n\n    const instanceMap = elementMap.get(element)\n\n    instanceMap.delete(key)\n\n    // free up element references if there are no instances left for an element\n    if (instanceMap.size === 0) {\n      elementMap.delete(element)\n    }\n  }\n}\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/index.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nconst MAX_UID = 1_000_000\nconst MILLISECONDS_MULTIPLIER = 1000\nconst TRANSITION_END = 'transitionend'\n\n/**\n * Properly escape IDs selectors to handle weird IDs\n * @param {string} selector\n * @returns {string}\n */\nconst parseSelector = selector => {\n  if (selector && window.CSS && window.CSS.escape) {\n    // document.querySelector needs escaping to handle IDs (html5+) containing for instance /\n    selector = selector.replace(/#([^\\s\"#']+)/g, (match, id) => `#${CSS.escape(id)}`)\n  }\n\n  return selector\n}\n\n// Shout-out Angus Croll (https://goo.gl/pxwQGp)\nconst toType = object => {\n  if (object === null || object === undefined) {\n    return `${object}`\n  }\n\n  return Object.prototype.toString.call(object).match(/\\s([a-z]+)/i)[1].toLowerCase()\n}\n\n/**\n * Public Util API\n */\n\nconst getUID = prefix => {\n  do {\n    prefix += Math.floor(Math.random() * MAX_UID)\n  } while (document.getElementById(prefix))\n\n  return prefix\n}\n\nconst getTransitionDurationFromElement = element => {\n  if (!element) {\n    return 0\n  }\n\n  // Get transition-duration of the element\n  let { transitionDuration, transitionDelay } = window.getComputedStyle(element)\n\n  const floatTransitionDuration = Number.parseFloat(transitionDuration)\n  const floatTransitionDelay = Number.parseFloat(transitionDelay)\n\n  // Return 0 if element or transition duration is not found\n  if (!floatTransitionDuration && !floatTransitionDelay) {\n    return 0\n  }\n\n  // If multiple durations are defined, take the first\n  transitionDuration = transitionDuration.split(',')[0]\n  transitionDelay = transitionDelay.split(',')[0]\n\n  return (Number.parseFloat(transitionDuration) + Number.parseFloat(transitionDelay)) * MILLISECONDS_MULTIPLIER\n}\n\nconst triggerTransitionEnd = element => {\n  element.dispatchEvent(new Event(TRANSITION_END))\n}\n\nconst isElement = object => {\n  if (!object || typeof object !== 'object') {\n    return false\n  }\n\n  if (typeof object.jquery !== 'undefined') {\n    object = object[0]\n  }\n\n  return typeof object.nodeType !== 'undefined'\n}\n\nconst getElement = object => {\n  // it's a jQuery object or a node element\n  if (isElement(object)) {\n    return object.jquery ? object[0] : object\n  }\n\n  if (typeof object === 'string' && object.length > 0) {\n    return document.querySelector(parseSelector(object))\n  }\n\n  return null\n}\n\nconst isVisible = element => {\n  if (!isElement(element) || element.getClientRects().length === 0) {\n    return false\n  }\n\n  const elementIsVisible = getComputedStyle(element).getPropertyValue('visibility') === 'visible'\n  // Handle `details` element as its content may falsie appear visible when it is closed\n  const closedDetails = element.closest('details:not([open])')\n\n  if (!closedDetails) {\n    return elementIsVisible\n  }\n\n  if (closedDetails !== element) {\n    const summary = element.closest('summary')\n    if (summary && summary.parentNode !== closedDetails) {\n      return false\n    }\n\n    if (summary === null) {\n      return false\n    }\n  }\n\n  return elementIsVisible\n}\n\nconst isDisabled = element => {\n  if (!element || element.nodeType !== Node.ELEMENT_NODE) {\n    return true\n  }\n\n  if (element.classList.contains('disabled')) {\n    return true\n  }\n\n  if (typeof element.disabled !== 'undefined') {\n    return element.disabled\n  }\n\n  return element.hasAttribute('disabled') && element.getAttribute('disabled') !== 'false'\n}\n\nconst findShadowRoot = element => {\n  if (!document.documentElement.attachShadow) {\n    return null\n  }\n\n  // Can find the shadow root otherwise it'll return the document\n  if (typeof element.getRootNode === 'function') {\n    const root = element.getRootNode()\n    return root instanceof ShadowRoot ? root : null\n  }\n\n  if (element instanceof ShadowRoot) {\n    return element\n  }\n\n  // when we don't find a shadow root\n  if (!element.parentNode) {\n    return null\n  }\n\n  return findShadowRoot(element.parentNode)\n}\n\nconst noop = () => {}\n\n/**\n * Trick to restart an element's animation\n *\n * @param {HTMLElement} element\n * @return void\n *\n * @see https://www.charistheo.io/blog/2021/02/restart-a-css-animation-with-javascript/#restarting-a-css-animation\n */\nconst reflow = element => {\n  element.offsetHeight // eslint-disable-line no-unused-expressions\n}\n\nconst getjQuery = () => {\n  if (window.jQuery && !document.body.hasAttribute('data-bs-no-jquery')) {\n    return window.jQuery\n  }\n\n  return null\n}\n\nconst DOMContentLoadedCallbacks = []\n\nconst onDOMContentLoaded = callback => {\n  if (document.readyState === 'loading') {\n    // add listener on the first call when the document is in loading state\n    if (!DOMContentLoadedCallbacks.length) {\n      document.addEventListener('DOMContentLoaded', () => {\n        for (const callback of DOMContentLoadedCallbacks) {\n          callback()\n        }\n      })\n    }\n\n    DOMContentLoadedCallbacks.push(callback)\n  } else {\n    callback()\n  }\n}\n\nconst isRTL = () => document.documentElement.dir === 'rtl'\n\nconst defineJQueryPlugin = plugin => {\n  onDOMContentLoaded(() => {\n    const $ = getjQuery()\n    /* istanbul ignore if */\n    if ($) {\n      const name = plugin.NAME\n      const JQUERY_NO_CONFLICT = $.fn[name]\n      $.fn[name] = plugin.jQueryInterface\n      $.fn[name].Constructor = plugin\n      $.fn[name].noConflict = () => {\n        $.fn[name] = JQUERY_NO_CONFLICT\n        return plugin.jQueryInterface\n      }\n    }\n  })\n}\n\nconst execute = (possibleCallback, args = [], defaultValue = possibleCallback) => {\n  return typeof possibleCallback === 'function' ? possibleCallback(...args) : defaultValue\n}\n\nconst executeAfterTransition = (callback, transitionElement, waitForTransition = true) => {\n  if (!waitForTransition) {\n    execute(callback)\n    return\n  }\n\n  const durationPadding = 5\n  const emulatedDuration = getTransitionDurationFromElement(transitionElement) + durationPadding\n\n  let called = false\n\n  const handler = ({ target }) => {\n    if (target !== transitionElement) {\n      return\n    }\n\n    called = true\n    transitionElement.removeEventListener(TRANSITION_END, handler)\n    execute(callback)\n  }\n\n  transitionElement.addEventListener(TRANSITION_END, handler)\n  setTimeout(() => {\n    if (!called) {\n      triggerTransitionEnd(transitionElement)\n    }\n  }, emulatedDuration)\n}\n\n/**\n * Return the previous/next element of a list.\n *\n * @param {array} list    The list of elements\n * @param activeElement   The active element\n * @param shouldGetNext   Choose to get next or previous element\n * @param isCycleAllowed\n * @return {Element|elem} The proper element\n */\nconst getNextActiveElement = (list, activeElement, shouldGetNext, isCycleAllowed) => {\n  const listLength = list.length\n  let index = list.indexOf(activeElement)\n\n  // if the element does not exist in the list return an element\n  // depending on the direction and if cycle is allowed\n  if (index === -1) {\n    return !shouldGetNext && isCycleAllowed ? list[listLength - 1] : list[0]\n  }\n\n  index += shouldGetNext ? 1 : -1\n\n  if (isCycleAllowed) {\n    index = (index + listLength) % listLength\n  }\n\n  return list[Math.max(0, Math.min(index, listLength - 1))]\n}\n\nexport {\n  defineJQueryPlugin,\n  execute,\n  executeAfterTransition,\n  findShadowRoot,\n  getElement,\n  getjQuery,\n  getNextActiveElement,\n  getTransitionDurationFromElement,\n  getUID,\n  isDisabled,\n  isElement,\n  isRTL,\n  isVisible,\n  noop,\n  onDOMContentLoaded,\n  parseSelector,\n  reflow,\n  triggerTransitionEnd,\n  toType\n}\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap dom/event-handler.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport { getjQuery } from '../util/index.js'\n\n/**\n * Constants\n */\n\nconst namespaceRegex = /[^.]*(?=\\..*)\\.|.*/\nconst stripNameRegex = /\\..*/\nconst stripUidRegex = /::\\d+$/\nconst eventRegistry = {} // Events storage\nlet uidEvent = 1\nconst customEvents = {\n  mouseenter: 'mouseover',\n  mouseleave: 'mouseout'\n}\n\nconst nativeEvents = new Set([\n  'click',\n  'dblclick',\n  'mouseup',\n  'mousedown',\n  'contextmenu',\n  'mousewheel',\n  'DOMMouseScroll',\n  'mouseover',\n  'mouseout',\n  'mousemove',\n  'selectstart',\n  'selectend',\n  'keydown',\n  'keypress',\n  'keyup',\n  'orientationchange',\n  'touchstart',\n  'touchmove',\n  'touchend',\n  'touchcancel',\n  'pointerdown',\n  'pointermove',\n  'pointerup',\n  'pointerleave',\n  'pointercancel',\n  'gesturestart',\n  'gesturechange',\n  'gestureend',\n  'focus',\n  'blur',\n  'change',\n  'reset',\n  'select',\n  'submit',\n  'focusin',\n  'focusout',\n  'load',\n  'unload',\n  'beforeunload',\n  'resize',\n  'move',\n  'DOMContentLoaded',\n  'readystatechange',\n  'error',\n  'abort',\n  'scroll'\n])\n\n/**\n * Private methods\n */\n\nfunction makeEventUid(element, uid) {\n  return (uid && `${uid}::${uidEvent++}`) || element.uidEvent || uidEvent++\n}\n\nfunction getElementEvents(element) {\n  const uid = makeEventUid(element)\n\n  element.uidEvent = uid\n  eventRegistry[uid] = eventRegistry[uid] || {}\n\n  return eventRegistry[uid]\n}\n\nfunction bootstrapHandler(element, fn) {\n  return function handler(event) {\n    hydrateObj(event, { delegateTarget: element })\n\n    if (handler.oneOff) {\n      EventHandler.off(element, event.type, fn)\n    }\n\n    return fn.apply(element, [event])\n  }\n}\n\nfunction bootstrapDelegationHandler(element, selector, fn) {\n  return function handler(event) {\n    const domElements = element.querySelectorAll(selector)\n\n    for (let { target } = event; target && target !== this; target = target.parentNode) {\n      for (const domElement of domElements) {\n        if (domElement !== target) {\n          continue\n        }\n\n        hydrateObj(event, { delegateTarget: target })\n\n        if (handler.oneOff) {\n          EventHandler.off(element, event.type, selector, fn)\n        }\n\n        return fn.apply(target, [event])\n      }\n    }\n  }\n}\n\nfunction findHandler(events, callable, delegationSelector = null) {\n  return Object.values(events)\n    .find(event => event.callable === callable && event.delegationSelector === delegationSelector)\n}\n\nfunction normalizeParameters(originalTypeEvent, handler, delegationFunction) {\n  const isDelegated = typeof handler === 'string'\n  // TODO: tooltip passes `false` instead of selector, so we need to check\n  const callable = isDelegated ? delegationFunction : (handler || delegationFunction)\n  let typeEvent = getTypeEvent(originalTypeEvent)\n\n  if (!nativeEvents.has(typeEvent)) {\n    typeEvent = originalTypeEvent\n  }\n\n  return [isDelegated, callable, typeEvent]\n}\n\nfunction addHandler(element, originalTypeEvent, handler, delegationFunction, oneOff) {\n  if (typeof originalTypeEvent !== 'string' || !element) {\n    return\n  }\n\n  let [isDelegated, callable, typeEvent] = normalizeParameters(originalTypeEvent, handler, delegationFunction)\n\n  // in case of mouseenter or mouseleave wrap the handler within a function that checks for its DOM position\n  // this prevents the handler from being dispatched the same way as mouseover or mouseout does\n  if (originalTypeEvent in customEvents) {\n    const wrapFunction = fn => {\n      return function (event) {\n        if (!event.relatedTarget || (event.relatedTarget !== event.delegateTarget && !event.delegateTarget.contains(event.relatedTarget))) {\n          return fn.call(this, event)\n        }\n      }\n    }\n\n    callable = wrapFunction(callable)\n  }\n\n  const events = getElementEvents(element)\n  const handlers = events[typeEvent] || (events[typeEvent] = {})\n  const previousFunction = findHandler(handlers, callable, isDelegated ? handler : null)\n\n  if (previousFunction) {\n    previousFunction.oneOff = previousFunction.oneOff && oneOff\n\n    return\n  }\n\n  const uid = makeEventUid(callable, originalTypeEvent.replace(namespaceRegex, ''))\n  const fn = isDelegated ?\n    bootstrapDelegationHandler(element, handler, callable) :\n    bootstrapHandler(element, callable)\n\n  fn.delegationSelector = isDelegated ? handler : null\n  fn.callable = callable\n  fn.oneOff = oneOff\n  fn.uidEvent = uid\n  handlers[uid] = fn\n\n  element.addEventListener(typeEvent, fn, isDelegated)\n}\n\nfunction removeHandler(element, events, typeEvent, handler, delegationSelector) {\n  const fn = findHandler(events[typeEvent], handler, delegationSelector)\n\n  if (!fn) {\n    return\n  }\n\n  element.removeEventListener(typeEvent, fn, Boolean(delegationSelector))\n  delete events[typeEvent][fn.uidEvent]\n}\n\nfunction removeNamespacedHandlers(element, events, typeEvent, namespace) {\n  const storeElementEvent = events[typeEvent] || {}\n\n  for (const [handlerKey, event] of Object.entries(storeElementEvent)) {\n    if (handlerKey.includes(namespace)) {\n      removeHandler(element, events, typeEvent, event.callable, event.delegationSelector)\n    }\n  }\n}\n\nfunction getTypeEvent(event) {\n  // allow to get the native events from namespaced events ('click.bs.button' --> 'click')\n  event = event.replace(stripNameRegex, '')\n  return customEvents[event] || event\n}\n\nconst EventHandler = {\n  on(element, event, handler, delegationFunction) {\n    addHandler(element, event, handler, delegationFunction, false)\n  },\n\n  one(element, event, handler, delegationFunction) {\n    addHandler(element, event, handler, delegationFunction, true)\n  },\n\n  off(element, originalTypeEvent, handler, delegationFunction) {\n    if (typeof originalTypeEvent !== 'string' || !element) {\n      return\n    }\n\n    const [isDelegated, callable, typeEvent] = normalizeParameters(originalTypeEvent, handler, delegationFunction)\n    const inNamespace = typeEvent !== originalTypeEvent\n    const events = getElementEvents(element)\n    const storeElementEvent = events[typeEvent] || {}\n    const isNamespace = originalTypeEvent.startsWith('.')\n\n    if (typeof callable !== 'undefined') {\n      // Simplest case: handler is passed, remove that listener ONLY.\n      if (!Object.keys(storeElementEvent).length) {\n        return\n      }\n\n      removeHandler(element, events, typeEvent, callable, isDelegated ? handler : null)\n      return\n    }\n\n    if (isNamespace) {\n      for (const elementEvent of Object.keys(events)) {\n        removeNamespacedHandlers(element, events, elementEvent, originalTypeEvent.slice(1))\n      }\n    }\n\n    for (const [keyHandlers, event] of Object.entries(storeElementEvent)) {\n      const handlerKey = keyHandlers.replace(stripUidRegex, '')\n\n      if (!inNamespace || originalTypeEvent.includes(handlerKey)) {\n        removeHandler(element, events, typeEvent, event.callable, event.delegationSelector)\n      }\n    }\n  },\n\n  trigger(element, event, args) {\n    if (typeof event !== 'string' || !element) {\n      return null\n    }\n\n    const $ = getjQuery()\n    const typeEvent = getTypeEvent(event)\n    const inNamespace = event !== typeEvent\n\n    let jQueryEvent = null\n    let bubbles = true\n    let nativeDispatch = true\n    let defaultPrevented = false\n\n    if (inNamespace && $) {\n      jQueryEvent = $.Event(event, args)\n\n      $(element).trigger(jQueryEvent)\n      bubbles = !jQueryEvent.isPropagationStopped()\n      nativeDispatch = !jQueryEvent.isImmediatePropagationStopped()\n      defaultPrevented = jQueryEvent.isDefaultPrevented()\n    }\n\n    const evt = hydrateObj(new Event(event, { bubbles, cancelable: true }), args)\n\n    if (defaultPrevented) {\n      evt.preventDefault()\n    }\n\n    if (nativeDispatch) {\n      element.dispatchEvent(evt)\n    }\n\n    if (evt.defaultPrevented && jQueryEvent) {\n      jQueryEvent.preventDefault()\n    }\n\n    return evt\n  }\n}\n\nfunction hydrateObj(obj, meta = {}) {\n  for (const [key, value] of Object.entries(meta)) {\n    try {\n      obj[key] = value\n    } catch {\n      Object.defineProperty(obj, key, {\n        configurable: true,\n        get() {\n          return value\n        }\n      })\n    }\n  }\n\n  return obj\n}\n\nexport default EventHandler\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap dom/manipulator.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nfunction normalizeData(value) {\n  if (value === 'true') {\n    return true\n  }\n\n  if (value === 'false') {\n    return false\n  }\n\n  if (value === Number(value).toString()) {\n    return Number(value)\n  }\n\n  if (value === '' || value === 'null') {\n    return null\n  }\n\n  if (typeof value !== 'string') {\n    return value\n  }\n\n  try {\n    return JSON.parse(decodeURIComponent(value))\n  } catch {\n    return value\n  }\n}\n\nfunction normalizeDataKey(key) {\n  return key.replace(/[A-Z]/g, chr => `-${chr.toLowerCase()}`)\n}\n\nconst Manipulator = {\n  setDataAttribute(element, key, value) {\n    element.setAttribute(`data-bs-${normalizeDataKey(key)}`, value)\n  },\n\n  removeDataAttribute(element, key) {\n    element.removeAttribute(`data-bs-${normalizeDataKey(key)}`)\n  },\n\n  getDataAttributes(element) {\n    if (!element) {\n      return {}\n    }\n\n    const attributes = {}\n    const bsKeys = Object.keys(element.dataset).filter(key => key.startsWith('bs') && !key.startsWith('bsConfig'))\n\n    for (const key of bsKeys) {\n      let pureKey = key.replace(/^bs/, '')\n      pureKey = pureKey.charAt(0).toLowerCase() + pureKey.slice(1, pureKey.length)\n      attributes[pureKey] = normalizeData(element.dataset[key])\n    }\n\n    return attributes\n  },\n\n  getDataAttribute(element, key) {\n    return normalizeData(element.getAttribute(`data-bs-${normalizeDataKey(key)}`))\n  }\n}\n\nexport default Manipulator\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/config.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport Manipulator from '../dom/manipulator.js'\nimport { isElement, toType } from './index.js'\n\n/**\n * Class definition\n */\n\nclass Config {\n  // Getters\n  static get Default() {\n    return {}\n  }\n\n  static get DefaultType() {\n    return {}\n  }\n\n  static get NAME() {\n    throw new Error('You have to implement the static method \"NAME\", for each component!')\n  }\n\n  _getConfig(config) {\n    config = this._mergeConfigObj(config)\n    config = this._configAfterMerge(config)\n    this._typeCheckConfig(config)\n    return config\n  }\n\n  _configAfterMerge(config) {\n    return config\n  }\n\n  _mergeConfigObj(config, element) {\n    const jsonConfig = isElement(element) ? Manipulator.getDataAttribute(element, 'config') : {} // try to parse\n\n    return {\n      ...this.constructor.Default,\n      ...(typeof jsonConfig === 'object' ? jsonConfig : {}),\n      ...(isElement(element) ? Manipulator.getDataAttributes(element) : {}),\n      ...(typeof config === 'object' ? config : {})\n    }\n  }\n\n  _typeCheckConfig(config, configTypes = this.constructor.DefaultType) {\n    for (const [property, expectedTypes] of Object.entries(configTypes)) {\n      const value = config[property]\n      const valueType = isElement(value) ? 'element' : toType(value)\n\n      if (!new RegExp(expectedTypes).test(valueType)) {\n        throw new TypeError(\n          `${this.constructor.NAME.toUpperCase()}: Option \"${property}\" provided type \"${valueType}\" but expected type \"${expectedTypes}\".`\n        )\n      }\n    }\n  }\n}\n\nexport default Config\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap base-component.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport Data from './dom/data.js'\nimport EventHandler from './dom/event-handler.js'\nimport Config from './util/config.js'\nimport { executeAfterTransition, getElement } from './util/index.js'\n\n/**\n * Constants\n */\n\nconst VERSION = '5.3.1'\n\n/**\n * Class definition\n */\n\nclass BaseComponent extends Config {\n  constructor(element, config) {\n    super()\n\n    element = getElement(element)\n    if (!element) {\n      return\n    }\n\n    this._element = element\n    this._config = this._getConfig(config)\n\n    Data.set(this._element, this.constructor.DATA_KEY, this)\n  }\n\n  // Public\n  dispose() {\n    Data.remove(this._element, this.constructor.DATA_KEY)\n    EventHandler.off(this._element, this.constructor.EVENT_KEY)\n\n    for (const propertyName of Object.getOwnPropertyNames(this)) {\n      this[propertyName] = null\n    }\n  }\n\n  _queueCallback(callback, element, isAnimated = true) {\n    executeAfterTransition(callback, element, isAnimated)\n  }\n\n  _getConfig(config) {\n    config = this._mergeConfigObj(config, this._element)\n    config = this._configAfterMerge(config)\n    this._typeCheckConfig(config)\n    return config\n  }\n\n  // Static\n  static getInstance(element) {\n    return Data.get(getElement(element), this.DATA_KEY)\n  }\n\n  static getOrCreateInstance(element, config = {}) {\n    return this.getInstance(element) || new this(element, typeof config === 'object' ? config : null)\n  }\n\n  static get VERSION() {\n    return VERSION\n  }\n\n  static get DATA_KEY() {\n    return `bs.${this.NAME}`\n  }\n\n  static get EVENT_KEY() {\n    return `.${this.DATA_KEY}`\n  }\n\n  static eventName(name) {\n    return `${name}${this.EVENT_KEY}`\n  }\n}\n\nexport default BaseComponent\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap dom/selector-engine.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport { isDisabled, isVisible, parseSelector } from '../util/index.js'\n\nconst getSelector = element => {\n  let selector = element.getAttribute('data-bs-target')\n\n  if (!selector || selector === '#') {\n    let hrefAttribute = element.getAttribute('href')\n\n    // The only valid content that could double as a selector are IDs or classes,\n    // so everything starting with `#` or `.`. If a \"real\" URL is used as the selector,\n    // `document.querySelector` will rightfully complain it is invalid.\n    // See https://github.com/twbs/bootstrap/issues/32273\n    if (!hrefAttribute || (!hrefAttribute.includes('#') && !hrefAttribute.startsWith('.'))) {\n      return null\n    }\n\n    // Just in case some CMS puts out a full URL with the anchor appended\n    if (hrefAttribute.includes('#') && !hrefAttribute.startsWith('#')) {\n      hrefAttribute = `#${hrefAttribute.split('#')[1]}`\n    }\n\n    selector = hrefAttribute && hrefAttribute !== '#' ? hrefAttribute.trim() : null\n  }\n\n  return parseSelector(selector)\n}\n\nconst SelectorEngine = {\n  find(selector, element = document.documentElement) {\n    return [].concat(...Element.prototype.querySelectorAll.call(element, selector))\n  },\n\n  findOne(selector, element = document.documentElement) {\n    return Element.prototype.querySelector.call(element, selector)\n  },\n\n  children(element, selector) {\n    return [].concat(...element.children).filter(child => child.matches(selector))\n  },\n\n  parents(element, selector) {\n    const parents = []\n    let ancestor = element.parentNode.closest(selector)\n\n    while (ancestor) {\n      parents.push(ancestor)\n      ancestor = ancestor.parentNode.closest(selector)\n    }\n\n    return parents\n  },\n\n  prev(element, selector) {\n    let previous = element.previousElementSibling\n\n    while (previous) {\n      if (previous.matches(selector)) {\n        return [previous]\n      }\n\n      previous = previous.previousElementSibling\n    }\n\n    return []\n  },\n  // TODO: this is now unused; remove later along with prev()\n  next(element, selector) {\n    let next = element.nextElementSibling\n\n    while (next) {\n      if (next.matches(selector)) {\n        return [next]\n      }\n\n      next = next.nextElementSibling\n    }\n\n    return []\n  },\n\n  focusableChildren(element) {\n    const focusables = [\n      'a',\n      'button',\n      'input',\n      'textarea',\n      'select',\n      'details',\n      '[tabindex]',\n      '[contenteditable=\"true\"]'\n    ].map(selector => `${selector}:not([tabindex^=\"-\"])`).join(',')\n\n    return this.find(focusables, element).filter(el => !isDisabled(el) && isVisible(el))\n  },\n\n  getSelectorFromElement(element) {\n    const selector = getSelector(element)\n\n    if (selector) {\n      return SelectorEngine.findOne(selector) ? selector : null\n    }\n\n    return null\n  },\n\n  getElementFromSelector(element) {\n    const selector = getSelector(element)\n\n    return selector ? SelectorEngine.findOne(selector) : null\n  },\n\n  getMultipleElementsFromSelector(element) {\n    const selector = getSelector(element)\n\n    return selector ? SelectorEngine.find(selector) : []\n  }\n}\n\nexport default SelectorEngine\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/component-functions.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport EventHandler from '../dom/event-handler.js'\nimport SelectorEngine from '../dom/selector-engine.js'\nimport { isDisabled } from './index.js'\n\nconst enableDismissTrigger = (component, method = 'hide') => {\n  const clickEvent = `click.dismiss${component.EVENT_KEY}`\n  const name = component.NAME\n\n  EventHandler.on(document, clickEvent, `[data-bs-dismiss=\"${name}\"]`, function (event) {\n    if (['A', 'AREA'].includes(this.tagName)) {\n      event.preventDefault()\n    }\n\n    if (isDisabled(this)) {\n      return\n    }\n\n    const target = SelectorEngine.getElementFromSelector(this) || this.closest(`.${name}`)\n    const instance = component.getOrCreateInstance(target)\n\n    // Method argument is left, for Alert and only, as it doesn't implement the 'hide' method\n    instance[method]()\n  })\n}\n\nexport {\n  enableDismissTrigger\n}\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap alert.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport { enableDismissTrigger } from './util/component-functions.js'\nimport { defineJQueryPlugin } from './util/index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'alert'\nconst DATA_KEY = 'bs.alert'\nconst EVENT_KEY = `.${DATA_KEY}`\n\nconst EVENT_CLOSE = `close${EVENT_KEY}`\nconst EVENT_CLOSED = `closed${EVENT_KEY}`\nconst CLASS_NAME_FADE = 'fade'\nconst CLASS_NAME_SHOW = 'show'\n\n/**\n * Class definition\n */\n\nclass Alert extends BaseComponent {\n  // Getters\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n  close() {\n    const closeEvent = EventHandler.trigger(this._element, EVENT_CLOSE)\n\n    if (closeEvent.defaultPrevented) {\n      return\n    }\n\n    this._element.classList.remove(CLASS_NAME_SHOW)\n\n    const isAnimated = this._element.classList.contains(CLASS_NAME_FADE)\n    this._queueCallback(() => this._destroyElement(), this._element, isAnimated)\n  }\n\n  // Private\n  _destroyElement() {\n    this._element.remove()\n    EventHandler.trigger(this._element, EVENT_CLOSED)\n    this.dispose()\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Alert.getOrCreateInstance(this)\n\n      if (typeof config !== 'string') {\n        return\n      }\n\n      if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n        throw new TypeError(`No method named \"${config}\"`)\n      }\n\n      data[config](this)\n    })\n  }\n}\n\n/**\n * Data API implementation\n */\n\nenableDismissTrigger(Alert, 'close')\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Alert)\n\nexport default Alert\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap button.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport { defineJQueryPlugin } from './util/index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'button'\nconst DATA_KEY = 'bs.button'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\n\nconst CLASS_NAME_ACTIVE = 'active'\nconst SELECTOR_DATA_TOGGLE = '[data-bs-toggle=\"button\"]'\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}${DATA_API_KEY}`\n\n/**\n * Class definition\n */\n\nclass Button extends BaseComponent {\n  // Getters\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n  toggle() {\n    // Toggle class and sync the `aria-pressed` attribute with the return value of the `.toggle()` method\n    this._element.setAttribute('aria-pressed', this._element.classList.toggle(CLASS_NAME_ACTIVE))\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Button.getOrCreateInstance(this)\n\n      if (config === 'toggle') {\n        data[config]()\n      }\n    })\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_TOGGLE, event => {\n  event.preventDefault()\n\n  const button = event.target.closest(SELECTOR_DATA_TOGGLE)\n  const data = Button.getOrCreateInstance(button)\n\n  data.toggle()\n})\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Button)\n\nexport default Button\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/swipe.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport EventHandler from '../dom/event-handler.js'\nimport Config from './config.js'\nimport { execute } from './index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'swipe'\nconst EVENT_KEY = '.bs.swipe'\nconst EVENT_TOUCHSTART = `touchstart${EVENT_KEY}`\nconst EVENT_TOUCHMOVE = `touchmove${EVENT_KEY}`\nconst EVENT_TOUCHEND = `touchend${EVENT_KEY}`\nconst EVENT_POINTERDOWN = `pointerdown${EVENT_KEY}`\nconst EVENT_POINTERUP = `pointerup${EVENT_KEY}`\nconst POINTER_TYPE_TOUCH = 'touch'\nconst POINTER_TYPE_PEN = 'pen'\nconst CLASS_NAME_POINTER_EVENT = 'pointer-event'\nconst SWIPE_THRESHOLD = 40\n\nconst Default = {\n  endCallback: null,\n  leftCallback: null,\n  rightCallback: null\n}\n\nconst DefaultType = {\n  endCallback: '(function|null)',\n  leftCallback: '(function|null)',\n  rightCallback: '(function|null)'\n}\n\n/**\n * Class definition\n */\n\nclass Swipe extends Config {\n  constructor(element, config) {\n    super()\n    this._element = element\n\n    if (!element || !Swipe.isSupported()) {\n      return\n    }\n\n    this._config = this._getConfig(config)\n    this._deltaX = 0\n    this._supportPointerEvents = Boolean(window.PointerEvent)\n    this._initEvents()\n  }\n\n  // Getters\n  static get Default() {\n    return Default\n  }\n\n  static get DefaultType() {\n    return DefaultType\n  }\n\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n  dispose() {\n    EventHandler.off(this._element, EVENT_KEY)\n  }\n\n  // Private\n  _start(event) {\n    if (!this._supportPointerEvents) {\n      this._deltaX = event.touches[0].clientX\n\n      return\n    }\n\n    if (this._eventIsPointerPenTouch(event)) {\n      this._deltaX = event.clientX\n    }\n  }\n\n  _end(event) {\n    if (this._eventIsPointerPenTouch(event)) {\n      this._deltaX = event.clientX - this._deltaX\n    }\n\n    this._handleSwipe()\n    execute(this._config.endCallback)\n  }\n\n  _move(event) {\n    this._deltaX = event.touches && event.touches.length > 1 ?\n      0 :\n      event.touches[0].clientX - this._deltaX\n  }\n\n  _handleSwipe() {\n    const absDeltaX = Math.abs(this._deltaX)\n\n    if (absDeltaX <= SWIPE_THRESHOLD) {\n      return\n    }\n\n    const direction = absDeltaX / this._deltaX\n\n    this._deltaX = 0\n\n    if (!direction) {\n      return\n    }\n\n    execute(direction > 0 ? this._config.rightCallback : this._config.leftCallback)\n  }\n\n  _initEvents() {\n    if (this._supportPointerEvents) {\n      EventHandler.on(this._element, EVENT_POINTERDOWN, event => this._start(event))\n      EventHandler.on(this._element, EVENT_POINTERUP, event => this._end(event))\n\n      this._element.classList.add(CLASS_NAME_POINTER_EVENT)\n    } else {\n      EventHandler.on(this._element, EVENT_TOUCHSTART, event => this._start(event))\n      EventHandler.on(this._element, EVENT_TOUCHMOVE, event => this._move(event))\n      EventHandler.on(this._element, EVENT_TOUCHEND, event => this._end(event))\n    }\n  }\n\n  _eventIsPointerPenTouch(event) {\n    return this._supportPointerEvents && (event.pointerType === POINTER_TYPE_PEN || event.pointerType === POINTER_TYPE_TOUCH)\n  }\n\n  // Static\n  static isSupported() {\n    return 'ontouchstart' in document.documentElement || navigator.maxTouchPoints > 0\n  }\n}\n\nexport default Swipe\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap carousel.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport Manipulator from './dom/manipulator.js'\nimport SelectorEngine from './dom/selector-engine.js'\nimport {\n  defineJQueryPlugin,\n  getNextActiveElement,\n  isRTL,\n  isVisible,\n  reflow,\n  triggerTransitionEnd\n} from './util/index.js'\nimport Swipe from './util/swipe.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'carousel'\nconst DATA_KEY = 'bs.carousel'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\n\nconst ARROW_LEFT_KEY = 'ArrowLeft'\nconst ARROW_RIGHT_KEY = 'ArrowRight'\nconst TOUCHEVENT_COMPAT_WAIT = 500 // Time for mouse compat events to fire after touch\n\nconst ORDER_NEXT = 'next'\nconst ORDER_PREV = 'prev'\nconst DIRECTION_LEFT = 'left'\nconst DIRECTION_RIGHT = 'right'\n\nconst EVENT_SLIDE = `slide${EVENT_KEY}`\nconst EVENT_SLID = `slid${EVENT_KEY}`\nconst EVENT_KEYDOWN = `keydown${EVENT_KEY}`\nconst EVENT_MOUSEENTER = `mouseenter${EVENT_KEY}`\nconst EVENT_MOUSELEAVE = `mouseleave${EVENT_KEY}`\nconst EVENT_DRAG_START = `dragstart${EVENT_KEY}`\nconst EVENT_LOAD_DATA_API = `load${EVENT_KEY}${DATA_API_KEY}`\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}${DATA_API_KEY}`\n\nconst CLASS_NAME_CAROUSEL = 'carousel'\nconst CLASS_NAME_ACTIVE = 'active'\nconst CLASS_NAME_SLIDE = 'slide'\nconst CLASS_NAME_END = 'carousel-item-end'\nconst CLASS_NAME_START = 'carousel-item-start'\nconst CLASS_NAME_NEXT = 'carousel-item-next'\nconst CLASS_NAME_PREV = 'carousel-item-prev'\n\nconst SELECTOR_ACTIVE = '.active'\nconst SELECTOR_ITEM = '.carousel-item'\nconst SELECTOR_ACTIVE_ITEM = SELECTOR_ACTIVE + SELECTOR_ITEM\nconst SELECTOR_ITEM_IMG = '.carousel-item img'\nconst SELECTOR_INDICATORS = '.carousel-indicators'\nconst SELECTOR_DATA_SLIDE = '[data-bs-slide], [data-bs-slide-to]'\nconst SELECTOR_DATA_RIDE = '[data-bs-ride=\"carousel\"]'\n\nconst KEY_TO_DIRECTION = {\n  [ARROW_LEFT_KEY]: DIRECTION_RIGHT,\n  [ARROW_RIGHT_KEY]: DIRECTION_LEFT\n}\n\nconst Default = {\n  interval: 5000,\n  keyboard: true,\n  pause: 'hover',\n  ride: false,\n  touch: true,\n  wrap: true\n}\n\nconst DefaultType = {\n  interval: '(number|boolean)', // TODO:v6 remove boolean support\n  keyboard: 'boolean',\n  pause: '(string|boolean)',\n  ride: '(boolean|string)',\n  touch: 'boolean',\n  wrap: 'boolean'\n}\n\n/**\n * Class definition\n */\n\nclass Carousel extends BaseComponent {\n  constructor(element, config) {\n    super(element, config)\n\n    this._interval = null\n    this._activeElement = null\n    this._isSliding = false\n    this.touchTimeout = null\n    this._swipeHelper = null\n\n    this._indicatorsElement = SelectorEngine.findOne(SELECTOR_INDICATORS, this._element)\n    this._addEventListeners()\n\n    if (this._config.ride === CLASS_NAME_CAROUSEL) {\n      this.cycle()\n    }\n  }\n\n  // Getters\n  static get Default() {\n    return Default\n  }\n\n  static get DefaultType() {\n    return DefaultType\n  }\n\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n  next() {\n    this._slide(ORDER_NEXT)\n  }\n\n  nextWhenVisible() {\n    // FIXME TODO use `document.visibilityState`\n    // Don't call next when the page isn't visible\n    // or the carousel or its parent isn't visible\n    if (!document.hidden && isVisible(this._element)) {\n      this.next()\n    }\n  }\n\n  prev() {\n    this._slide(ORDER_PREV)\n  }\n\n  pause() {\n    if (this._isSliding) {\n      triggerTransitionEnd(this._element)\n    }\n\n    this._clearInterval()\n  }\n\n  cycle() {\n    this._clearInterval()\n    this._updateInterval()\n\n    this._interval = setInterval(() => this.nextWhenVisible(), this._config.interval)\n  }\n\n  _maybeEnableCycle() {\n    if (!this._config.ride) {\n      return\n    }\n\n    if (this._isSliding) {\n      EventHandler.one(this._element, EVENT_SLID, () => this.cycle())\n      return\n    }\n\n    this.cycle()\n  }\n\n  to(index) {\n    const items = this._getItems()\n    if (index > items.length - 1 || index < 0) {\n      return\n    }\n\n    if (this._isSliding) {\n      EventHandler.one(this._element, EVENT_SLID, () => this.to(index))\n      return\n    }\n\n    const activeIndex = this._getItemIndex(this._getActive())\n    if (activeIndex === index) {\n      return\n    }\n\n    const order = index > activeIndex ? ORDER_NEXT : ORDER_PREV\n\n    this._slide(order, items[index])\n  }\n\n  dispose() {\n    if (this._swipeHelper) {\n      this._swipeHelper.dispose()\n    }\n\n    super.dispose()\n  }\n\n  // Private\n  _configAfterMerge(config) {\n    config.defaultInterval = config.interval\n    return config\n  }\n\n  _addEventListeners() {\n    if (this._config.keyboard) {\n      EventHandler.on(this._element, EVENT_KEYDOWN, event => this._keydown(event))\n    }\n\n    if (this._config.pause === 'hover') {\n      EventHandler.on(this._element, EVENT_MOUSEENTER, () => this.pause())\n      EventHandler.on(this._element, EVENT_MOUSELEAVE, () => this._maybeEnableCycle())\n    }\n\n    if (this._config.touch && Swipe.isSupported()) {\n      this._addTouchEventListeners()\n    }\n  }\n\n  _addTouchEventListeners() {\n    for (const img of SelectorEngine.find(SELECTOR_ITEM_IMG, this._element)) {\n      EventHandler.on(img, EVENT_DRAG_START, event => event.preventDefault())\n    }\n\n    const endCallBack = () => {\n      if (this._config.pause !== 'hover') {\n        return\n      }\n\n      // If it's a touch-enabled device, mouseenter/leave are fired as\n      // part of the mouse compatibility events on first tap - the carousel\n      // would stop cycling until user tapped out of it;\n      // here, we listen for touchend, explicitly pause the carousel\n      // (as if it's the second time we tap on it, mouseenter compat event\n      // is NOT fired) and after a timeout (to allow for mouse compatibility\n      // events to fire) we explicitly restart cycling\n\n      this.pause()\n      if (this.touchTimeout) {\n        clearTimeout(this.touchTimeout)\n      }\n\n      this.touchTimeout = setTimeout(() => this._maybeEnableCycle(), TOUCHEVENT_COMPAT_WAIT + this._config.interval)\n    }\n\n    const swipeConfig = {\n      leftCallback: () => this._slide(this._directionToOrder(DIRECTION_LEFT)),\n      rightCallback: () => this._slide(this._directionToOrder(DIRECTION_RIGHT)),\n      endCallback: endCallBack\n    }\n\n    this._swipeHelper = new Swipe(this._element, swipeConfig)\n  }\n\n  _keydown(event) {\n    if (/input|textarea/i.test(event.target.tagName)) {\n      return\n    }\n\n    const direction = KEY_TO_DIRECTION[event.key]\n    if (direction) {\n      event.preventDefault()\n      this._slide(this._directionToOrder(direction))\n    }\n  }\n\n  _getItemIndex(element) {\n    return this._getItems().indexOf(element)\n  }\n\n  _setActiveIndicatorElement(index) {\n    if (!this._indicatorsElement) {\n      return\n    }\n\n    const activeIndicator = SelectorEngine.findOne(SELECTOR_ACTIVE, this._indicatorsElement)\n\n    activeIndicator.classList.remove(CLASS_NAME_ACTIVE)\n    activeIndicator.removeAttribute('aria-current')\n\n    const newActiveIndicator = SelectorEngine.findOne(`[data-bs-slide-to=\"${index}\"]`, this._indicatorsElement)\n\n    if (newActiveIndicator) {\n      newActiveIndicator.classList.add(CLASS_NAME_ACTIVE)\n      newActiveIndicator.setAttribute('aria-current', 'true')\n    }\n  }\n\n  _updateInterval() {\n    const element = this._activeElement || this._getActive()\n\n    if (!element) {\n      return\n    }\n\n    const elementInterval = Number.parseInt(element.getAttribute('data-bs-interval'), 10)\n\n    this._config.interval = elementInterval || this._config.defaultInterval\n  }\n\n  _slide(order, element = null) {\n    if (this._isSliding) {\n      return\n    }\n\n    const activeElement = this._getActive()\n    const isNext = order === ORDER_NEXT\n    const nextElement = element || getNextActiveElement(this._getItems(), activeElement, isNext, this._config.wrap)\n\n    if (nextElement === activeElement) {\n      return\n    }\n\n    const nextElementIndex = this._getItemIndex(nextElement)\n\n    const triggerEvent = eventName => {\n      return EventHandler.trigger(this._element, eventName, {\n        relatedTarget: nextElement,\n        direction: this._orderToDirection(order),\n        from: this._getItemIndex(activeElement),\n        to: nextElementIndex\n      })\n    }\n\n    const slideEvent = triggerEvent(EVENT_SLIDE)\n\n    if (slideEvent.defaultPrevented) {\n      return\n    }\n\n    if (!activeElement || !nextElement) {\n      // Some weirdness is happening, so we bail\n      // TODO: change tests that use empty divs to avoid this check\n      return\n    }\n\n    const isCycling = Boolean(this._interval)\n    this.pause()\n\n    this._isSliding = true\n\n    this._setActiveIndicatorElement(nextElementIndex)\n    this._activeElement = nextElement\n\n    const directionalClassName = isNext ? CLASS_NAME_START : CLASS_NAME_END\n    const orderClassName = isNext ? CLASS_NAME_NEXT : CLASS_NAME_PREV\n\n    nextElement.classList.add(orderClassName)\n\n    reflow(nextElement)\n\n    activeElement.classList.add(directionalClassName)\n    nextElement.classList.add(directionalClassName)\n\n    const completeCallBack = () => {\n      nextElement.classList.remove(directionalClassName, orderClassName)\n      nextElement.classList.add(CLASS_NAME_ACTIVE)\n\n      activeElement.classList.remove(CLASS_NAME_ACTIVE, orderClassName, directionalClassName)\n\n      this._isSliding = false\n\n      triggerEvent(EVENT_SLID)\n    }\n\n    this._queueCallback(completeCallBack, activeElement, this._isAnimated())\n\n    if (isCycling) {\n      this.cycle()\n    }\n  }\n\n  _isAnimated() {\n    return this._element.classList.contains(CLASS_NAME_SLIDE)\n  }\n\n  _getActive() {\n    return SelectorEngine.findOne(SELECTOR_ACTIVE_ITEM, this._element)\n  }\n\n  _getItems() {\n    return SelectorEngine.find(SELECTOR_ITEM, this._element)\n  }\n\n  _clearInterval() {\n    if (this._interval) {\n      clearInterval(this._interval)\n      this._interval = null\n    }\n  }\n\n  _directionToOrder(direction) {\n    if (isRTL()) {\n      return direction === DIRECTION_LEFT ? ORDER_PREV : ORDER_NEXT\n    }\n\n    return direction === DIRECTION_LEFT ? ORDER_NEXT : ORDER_PREV\n  }\n\n  _orderToDirection(order) {\n    if (isRTL()) {\n      return order === ORDER_PREV ? DIRECTION_LEFT : DIRECTION_RIGHT\n    }\n\n    return order === ORDER_PREV ? DIRECTION_RIGHT : DIRECTION_LEFT\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Carousel.getOrCreateInstance(this, config)\n\n      if (typeof config === 'number') {\n        data.to(config)\n        return\n      }\n\n      if (typeof config === 'string') {\n        if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n          throw new TypeError(`No method named \"${config}\"`)\n        }\n\n        data[config]()\n      }\n    })\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_SLIDE, function (event) {\n  const target = SelectorEngine.getElementFromSelector(this)\n\n  if (!target || !target.classList.contains(CLASS_NAME_CAROUSEL)) {\n    return\n  }\n\n  event.preventDefault()\n\n  const carousel = Carousel.getOrCreateInstance(target)\n  const slideIndex = this.getAttribute('data-bs-slide-to')\n\n  if (slideIndex) {\n    carousel.to(slideIndex)\n    carousel._maybeEnableCycle()\n    return\n  }\n\n  if (Manipulator.getDataAttribute(this, 'slide') === 'next') {\n    carousel.next()\n    carousel._maybeEnableCycle()\n    return\n  }\n\n  carousel.prev()\n  carousel._maybeEnableCycle()\n})\n\nEventHandler.on(window, EVENT_LOAD_DATA_API, () => {\n  const carousels = SelectorEngine.find(SELECTOR_DATA_RIDE)\n\n  for (const carousel of carousels) {\n    Carousel.getOrCreateInstance(carousel)\n  }\n})\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Carousel)\n\nexport default Carousel\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap collapse.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport SelectorEngine from './dom/selector-engine.js'\nimport {\n  defineJQueryPlugin,\n  getElement,\n  reflow\n} from './util/index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'collapse'\nconst DATA_KEY = 'bs.collapse'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\n\nconst EVENT_SHOW = `show${EVENT_KEY}`\nconst EVENT_SHOWN = `shown${EVENT_KEY}`\nconst EVENT_HIDE = `hide${EVENT_KEY}`\nconst EVENT_HIDDEN = `hidden${EVENT_KEY}`\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}${DATA_API_KEY}`\n\nconst CLASS_NAME_SHOW = 'show'\nconst CLASS_NAME_COLLAPSE = 'collapse'\nconst CLASS_NAME_COLLAPSING = 'collapsing'\nconst CLASS_NAME_COLLAPSED = 'collapsed'\nconst CLASS_NAME_DEEPER_CHILDREN = `:scope .${CLASS_NAME_COLLAPSE} .${CLASS_NAME_COLLAPSE}`\nconst CLASS_NAME_HORIZONTAL = 'collapse-horizontal'\n\nconst WIDTH = 'width'\nconst HEIGHT = 'height'\n\nconst SELECTOR_ACTIVES = '.collapse.show, .collapse.collapsing'\nconst SELECTOR_DATA_TOGGLE = '[data-bs-toggle=\"collapse\"]'\n\nconst Default = {\n  parent: null,\n  toggle: true\n}\n\nconst DefaultType = {\n  parent: '(null|element)',\n  toggle: 'boolean'\n}\n\n/**\n * Class definition\n */\n\nclass Collapse extends BaseComponent {\n  constructor(element, config) {\n    super(element, config)\n\n    this._isTransitioning = false\n    this._triggerArray = []\n\n    const toggleList = SelectorEngine.find(SELECTOR_DATA_TOGGLE)\n\n    for (const elem of toggleList) {\n      const selector = SelectorEngine.getSelectorFromElement(elem)\n      const filterElement = SelectorEngine.find(selector)\n        .filter(foundElement => foundElement === this._element)\n\n      if (selector !== null && filterElement.length) {\n        this._triggerArray.push(elem)\n      }\n    }\n\n    this._initializeChildren()\n\n    if (!this._config.parent) {\n      this._addAriaAndCollapsedClass(this._triggerArray, this._isShown())\n    }\n\n    if (this._config.toggle) {\n      this.toggle()\n    }\n  }\n\n  // Getters\n  static get Default() {\n    return Default\n  }\n\n  static get DefaultType() {\n    return DefaultType\n  }\n\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n  toggle() {\n    if (this._isShown()) {\n      this.hide()\n    } else {\n      this.show()\n    }\n  }\n\n  show() {\n    if (this._isTransitioning || this._isShown()) {\n      return\n    }\n\n    let activeChildren = []\n\n    // find active children\n    if (this._config.parent) {\n      activeChildren = this._getFirstLevelChildren(SELECTOR_ACTIVES)\n        .filter(element => element !== this._element)\n        .map(element => Collapse.getOrCreateInstance(element, { toggle: false }))\n    }\n\n    if (activeChildren.length && activeChildren[0]._isTransitioning) {\n      return\n    }\n\n    const startEvent = EventHandler.trigger(this._element, EVENT_SHOW)\n    if (startEvent.defaultPrevented) {\n      return\n    }\n\n    for (const activeInstance of activeChildren) {\n      activeInstance.hide()\n    }\n\n    const dimension = this._getDimension()\n\n    this._element.classList.remove(CLASS_NAME_COLLAPSE)\n    this._element.classList.add(CLASS_NAME_COLLAPSING)\n\n    this._element.style[dimension] = 0\n\n    this._addAriaAndCollapsedClass(this._triggerArray, true)\n    this._isTransitioning = true\n\n    const complete = () => {\n      this._isTransitioning = false\n\n      this._element.classList.remove(CLASS_NAME_COLLAPSING)\n      this._element.classList.add(CLASS_NAME_COLLAPSE, CLASS_NAME_SHOW)\n\n      this._element.style[dimension] = ''\n\n      EventHandler.trigger(this._element, EVENT_SHOWN)\n    }\n\n    const capitalizedDimension = dimension[0].toUpperCase() + dimension.slice(1)\n    const scrollSize = `scroll${capitalizedDimension}`\n\n    this._queueCallback(complete, this._element, true)\n    this._element.style[dimension] = `${this._element[scrollSize]}px`\n  }\n\n  hide() {\n    if (this._isTransitioning || !this._isShown()) {\n      return\n    }\n\n    const startEvent = EventHandler.trigger(this._element, EVENT_HIDE)\n    if (startEvent.defaultPrevented) {\n      return\n    }\n\n    const dimension = this._getDimension()\n\n    this._element.style[dimension] = `${this._element.getBoundingClientRect()[dimension]}px`\n\n    reflow(this._element)\n\n    this._element.classList.add(CLASS_NAME_COLLAPSING)\n    this._element.classList.remove(CLASS_NAME_COLLAPSE, CLASS_NAME_SHOW)\n\n    for (const trigger of this._triggerArray) {\n      const element = SelectorEngine.getElementFromSelector(trigger)\n\n      if (element && !this._isShown(element)) {\n        this._addAriaAndCollapsedClass([trigger], false)\n      }\n    }\n\n    this._isTransitioning = true\n\n    const complete = () => {\n      this._isTransitioning = false\n      this._element.classList.remove(CLASS_NAME_COLLAPSING)\n      this._element.classList.add(CLASS_NAME_COLLAPSE)\n      EventHandler.trigger(this._element, EVENT_HIDDEN)\n    }\n\n    this._element.style[dimension] = ''\n\n    this._queueCallback(complete, this._element, true)\n  }\n\n  _isShown(element = this._element) {\n    return element.classList.contains(CLASS_NAME_SHOW)\n  }\n\n  // Private\n  _configAfterMerge(config) {\n    config.toggle = Boolean(config.toggle) // Coerce string values\n    config.parent = getElement(config.parent)\n    return config\n  }\n\n  _getDimension() {\n    return this._element.classList.contains(CLASS_NAME_HORIZONTAL) ? WIDTH : HEIGHT\n  }\n\n  _initializeChildren() {\n    if (!this._config.parent) {\n      return\n    }\n\n    const children = this._getFirstLevelChildren(SELECTOR_DATA_TOGGLE)\n\n    for (const element of children) {\n      const selected = SelectorEngine.getElementFromSelector(element)\n\n      if (selected) {\n        this._addAriaAndCollapsedClass([element], this._isShown(selected))\n      }\n    }\n  }\n\n  _getFirstLevelChildren(selector) {\n    const children = SelectorEngine.find(CLASS_NAME_DEEPER_CHILDREN, this._config.parent)\n    // remove children if greater depth\n    return SelectorEngine.find(selector, this._config.parent).filter(element => !children.includes(element))\n  }\n\n  _addAriaAndCollapsedClass(triggerArray, isOpen) {\n    if (!triggerArray.length) {\n      return\n    }\n\n    for (const element of triggerArray) {\n      element.classList.toggle(CLASS_NAME_COLLAPSED, !isOpen)\n      element.setAttribute('aria-expanded', isOpen)\n    }\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    const _config = {}\n    if (typeof config === 'string' && /show|hide/.test(config)) {\n      _config.toggle = false\n    }\n\n    return this.each(function () {\n      const data = Collapse.getOrCreateInstance(this, _config)\n\n      if (typeof config === 'string') {\n        if (typeof data[config] === 'undefined') {\n          throw new TypeError(`No method named \"${config}\"`)\n        }\n\n        data[config]()\n      }\n    })\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_TOGGLE, function (event) {\n  // preventDefault only for <a> elements (which change the URL) not inside the collapsible element\n  if (event.target.tagName === 'A' || (event.delegateTarget && event.delegateTarget.tagName === 'A')) {\n    event.preventDefault()\n  }\n\n  for (const element of SelectorEngine.getMultipleElementsFromSelector(this)) {\n    Collapse.getOrCreateInstance(element, { toggle: false }).toggle()\n  }\n})\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Collapse)\n\nexport default Collapse\n","export var top = 'top';\nexport var bottom = 'bottom';\nexport var right = 'right';\nexport var left = 'left';\nexport var auto = 'auto';\nexport var basePlacements = [top, bottom, right, left];\nexport var start = 'start';\nexport var end = 'end';\nexport var clippingParents = 'clippingParents';\nexport var viewport = 'viewport';\nexport var popper = 'popper';\nexport var reference = 'reference';\nexport var variationPlacements = /*#__PURE__*/basePlacements.reduce(function (acc, placement) {\n  return acc.concat([placement + \"-\" + start, placement + \"-\" + end]);\n}, []);\nexport var placements = /*#__PURE__*/[].concat(basePlacements, [auto]).reduce(function (acc, placement) {\n  return acc.concat([placement, placement + \"-\" + start, placement + \"-\" + end]);\n}, []); // modifiers that need to read the DOM\n\nexport var beforeRead = 'beforeRead';\nexport var read = 'read';\nexport var afterRead = 'afterRead'; // pure-logic modifiers\n\nexport var beforeMain = 'beforeMain';\nexport var main = 'main';\nexport var afterMain = 'afterMain'; // modifier with the purpose to write to the DOM (or write into a framework state)\n\nexport var beforeWrite = 'beforeWrite';\nexport var write = 'write';\nexport var afterWrite = 'afterWrite';\nexport var modifierPhases = [beforeRead, read, afterRead, beforeMain, main, afterMain, beforeWrite, write, afterWrite];","export default function getNodeName(element) {\n  return element ? (element.nodeName || '').toLowerCase() : null;\n}","export default function getWindow(node) {\n  if (node == null) {\n    return window;\n  }\n\n  if (node.toString() !== '[object Window]') {\n    var ownerDocument = node.ownerDocument;\n    return ownerDocument ? ownerDocument.defaultView || window : window;\n  }\n\n  return node;\n}","import getWindow from \"./getWindow.js\";\n\nfunction isElement(node) {\n  var OwnElement = getWindow(node).Element;\n  return node instanceof OwnElement || node instanceof Element;\n}\n\nfunction isHTMLElement(node) {\n  var OwnElement = getWindow(node).HTMLElement;\n  return node instanceof OwnElement || node instanceof HTMLElement;\n}\n\nfunction isShadowRoot(node) {\n  // IE 11 has no ShadowRoot\n  if (typeof ShadowRoot === 'undefined') {\n    return false;\n  }\n\n  var OwnElement = getWindow(node).ShadowRoot;\n  return node instanceof OwnElement || node instanceof ShadowRoot;\n}\n\nexport { isElement, isHTMLElement, isShadowRoot };","import getNodeName from \"../dom-utils/getNodeName.js\";\nimport { isHTMLElement } from \"../dom-utils/instanceOf.js\"; // This modifier takes the styles prepared by the `computeStyles` modifier\n// and applies them to the HTMLElements such as popper and arrow\n\nfunction applyStyles(_ref) {\n  var state = _ref.state;\n  Object.keys(state.elements).forEach(function (name) {\n    var style = state.styles[name] || {};\n    var attributes = state.attributes[name] || {};\n    var element = state.elements[name]; // arrow is optional + virtual elements\n\n    if (!isHTMLElement(element) || !getNodeName(element)) {\n      return;\n    } // Flow doesn't support to extend this property, but it's the most\n    // effective way to apply styles to an HTMLElement\n    // $FlowFixMe[cannot-write]\n\n\n    Object.assign(element.style, style);\n    Object.keys(attributes).forEach(function (name) {\n      var value = attributes[name];\n\n      if (value === false) {\n        element.removeAttribute(name);\n      } else {\n        element.setAttribute(name, value === true ? '' : value);\n      }\n    });\n  });\n}\n\nfunction effect(_ref2) {\n  var state = _ref2.state;\n  var initialStyles = {\n    popper: {\n      position: state.options.strategy,\n      left: '0',\n      top: '0',\n      margin: '0'\n    },\n    arrow: {\n      position: 'absolute'\n    },\n    reference: {}\n  };\n  Object.assign(state.elements.popper.style, initialStyles.popper);\n  state.styles = initialStyles;\n\n  if (state.elements.arrow) {\n    Object.assign(state.elements.arrow.style, initialStyles.arrow);\n  }\n\n  return function () {\n    Object.keys(state.elements).forEach(function (name) {\n      var element = state.elements[name];\n      var attributes = state.attributes[name] || {};\n      var styleProperties = Object.keys(state.styles.hasOwnProperty(name) ? state.styles[name] : initialStyles[name]); // Set all values to an empty string to unset them\n\n      var style = styleProperties.reduce(function (style, property) {\n        style[property] = '';\n        return style;\n      }, {}); // arrow is optional + virtual elements\n\n      if (!isHTMLElement(element) || !getNodeName(element)) {\n        return;\n      }\n\n      Object.assign(element.style, style);\n      Object.keys(attributes).forEach(function (attribute) {\n        element.removeAttribute(attribute);\n      });\n    });\n  };\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'applyStyles',\n  enabled: true,\n  phase: 'write',\n  fn: applyStyles,\n  effect: effect,\n  requires: ['computeStyles']\n};","import { auto } from \"../enums.js\";\nexport default function getBasePlacement(placement) {\n  return placement.split('-')[0];\n}","export var max = Math.max;\nexport var min = Math.min;\nexport var round = Math.round;","export default function getUAString() {\n  var uaData = navigator.userAgentData;\n\n  if (uaData != null && uaData.brands && Array.isArray(uaData.brands)) {\n    return uaData.brands.map(function (item) {\n      return item.brand + \"/\" + item.version;\n    }).join(' ');\n  }\n\n  return navigator.userAgent;\n}","import getUAString from \"../utils/userAgent.js\";\nexport default function isLayoutViewport() {\n  return !/^((?!chrome|android).)*safari/i.test(getUAString());\n}","import { isElement, isHTMLElement } from \"./instanceOf.js\";\nimport { round } from \"../utils/math.js\";\nimport getWindow from \"./getWindow.js\";\nimport isLayoutViewport from \"./isLayoutViewport.js\";\nexport default function getBoundingClientRect(element, includeScale, isFixedStrategy) {\n  if (includeScale === void 0) {\n    includeScale = false;\n  }\n\n  if (isFixedStrategy === void 0) {\n    isFixedStrategy = false;\n  }\n\n  var clientRect = element.getBoundingClientRect();\n  var scaleX = 1;\n  var scaleY = 1;\n\n  if (includeScale && isHTMLElement(element)) {\n    scaleX = element.offsetWidth > 0 ? round(clientRect.width) / element.offsetWidth || 1 : 1;\n    scaleY = element.offsetHeight > 0 ? round(clientRect.height) / element.offsetHeight || 1 : 1;\n  }\n\n  var _ref = isElement(element) ? getWindow(element) : window,\n      visualViewport = _ref.visualViewport;\n\n  var addVisualOffsets = !isLayoutViewport() && isFixedStrategy;\n  var x = (clientRect.left + (addVisualOffsets && visualViewport ? visualViewport.offsetLeft : 0)) / scaleX;\n  var y = (clientRect.top + (addVisualOffsets && visualViewport ? visualViewport.offsetTop : 0)) / scaleY;\n  var width = clientRect.width / scaleX;\n  var height = clientRect.height / scaleY;\n  return {\n    width: width,\n    height: height,\n    top: y,\n    right: x + width,\n    bottom: y + height,\n    left: x,\n    x: x,\n    y: y\n  };\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\"; // Returns the layout rect of an element relative to its offsetParent. Layout\n// means it doesn't take into account transforms.\n\nexport default function getLayoutRect(element) {\n  var clientRect = getBoundingClientRect(element); // Use the clientRect sizes if it's not been transformed.\n  // Fixes https://github.com/popperjs/popper-core/issues/1223\n\n  var width = element.offsetWidth;\n  var height = element.offsetHeight;\n\n  if (Math.abs(clientRect.width - width) <= 1) {\n    width = clientRect.width;\n  }\n\n  if (Math.abs(clientRect.height - height) <= 1) {\n    height = clientRect.height;\n  }\n\n  return {\n    x: element.offsetLeft,\n    y: element.offsetTop,\n    width: width,\n    height: height\n  };\n}","import { isShadowRoot } from \"./instanceOf.js\";\nexport default function contains(parent, child) {\n  var rootNode = child.getRootNode && child.getRootNode(); // First, attempt with faster native method\n\n  if (parent.contains(child)) {\n    return true;\n  } // then fallback to custom implementation with Shadow DOM support\n  else if (rootNode && isShadowRoot(rootNode)) {\n      var next = child;\n\n      do {\n        if (next && parent.isSameNode(next)) {\n          return true;\n        } // $FlowFixMe[prop-missing]: need a better way to handle this...\n\n\n        next = next.parentNode || next.host;\n      } while (next);\n    } // Give up, the result is false\n\n\n  return false;\n}","import getWindow from \"./getWindow.js\";\nexport default function getComputedStyle(element) {\n  return getWindow(element).getComputedStyle(element);\n}","import getNodeName from \"./getNodeName.js\";\nexport default function isTableElement(element) {\n  return ['table', 'td', 'th'].indexOf(getNodeName(element)) >= 0;\n}","import { isElement } from \"./instanceOf.js\";\nexport default function getDocumentElement(element) {\n  // $FlowFixMe[incompatible-return]: assume body is always available\n  return ((isElement(element) ? element.ownerDocument : // $FlowFixMe[prop-missing]\n  element.document) || window.document).documentElement;\n}","import getNodeName from \"./getNodeName.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport { isShadowRoot } from \"./instanceOf.js\";\nexport default function getParentNode(element) {\n  if (getNodeName(element) === 'html') {\n    return element;\n  }\n\n  return (// this is a quicker (but less type safe) way to save quite some bytes from the bundle\n    // $FlowFixMe[incompatible-return]\n    // $FlowFixMe[prop-missing]\n    element.assignedSlot || // step into the shadow DOM of the parent of a slotted node\n    element.parentNode || ( // DOM Element detected\n    isShadowRoot(element) ? element.host : null) || // ShadowRoot detected\n    // $FlowFixMe[incompatible-call]: HTMLElement is a Node\n    getDocumentElement(element) // fallback\n\n  );\n}","import getWindow from \"./getWindow.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport { isHTMLElement, isShadowRoot } from \"./instanceOf.js\";\nimport isTableElement from \"./isTableElement.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport getUAString from \"../utils/userAgent.js\";\n\nfunction getTrueOffsetParent(element) {\n  if (!isHTMLElement(element) || // https://github.com/popperjs/popper-core/issues/837\n  getComputedStyle(element).position === 'fixed') {\n    return null;\n  }\n\n  return element.offsetParent;\n} // `.offsetParent` reports `null` for fixed elements, while absolute elements\n// return the containing block\n\n\nfunction getContainingBlock(element) {\n  var isFirefox = /firefox/i.test(getUAString());\n  var isIE = /Trident/i.test(getUAString());\n\n  if (isIE && isHTMLElement(element)) {\n    // In IE 9, 10 and 11 fixed elements containing block is always established by the viewport\n    var elementCss = getComputedStyle(element);\n\n    if (elementCss.position === 'fixed') {\n      return null;\n    }\n  }\n\n  var currentNode = getParentNode(element);\n\n  if (isShadowRoot(currentNode)) {\n    currentNode = currentNode.host;\n  }\n\n  while (isHTMLElement(currentNode) && ['html', 'body'].indexOf(getNodeName(currentNode)) < 0) {\n    var css = getComputedStyle(currentNode); // This is non-exhaustive but covers the most common CSS properties that\n    // create a containing block.\n    // https://developer.mozilla.org/en-US/docs/Web/CSS/Containing_block#identifying_the_containing_block\n\n    if (css.transform !== 'none' || css.perspective !== 'none' || css.contain === 'paint' || ['transform', 'perspective'].indexOf(css.willChange) !== -1 || isFirefox && css.willChange === 'filter' || isFirefox && css.filter && css.filter !== 'none') {\n      return currentNode;\n    } else {\n      currentNode = currentNode.parentNode;\n    }\n  }\n\n  return null;\n} // Gets the closest ancestor positioned element. Handles some edge cases,\n// such as table ancestors and cross browser bugs.\n\n\nexport default function getOffsetParent(element) {\n  var window = getWindow(element);\n  var offsetParent = getTrueOffsetParent(element);\n\n  while (offsetParent && isTableElement(offsetParent) && getComputedStyle(offsetParent).position === 'static') {\n    offsetParent = getTrueOffsetParent(offsetParent);\n  }\n\n  if (offsetParent && (getNodeName(offsetParent) === 'html' || getNodeName(offsetParent) === 'body' && getComputedStyle(offsetParent).position === 'static')) {\n    return window;\n  }\n\n  return offsetParent || getContainingBlock(element) || window;\n}","export default function getMainAxisFromPlacement(placement) {\n  return ['top', 'bottom'].indexOf(placement) >= 0 ? 'x' : 'y';\n}","import { max as mathMax, min as mathMin } from \"./math.js\";\nexport function within(min, value, max) {\n  return mathMax(min, mathMin(value, max));\n}\nexport function withinMaxClamp(min, value, max) {\n  var v = within(min, value, max);\n  return v > max ? max : v;\n}","import getFreshSideObject from \"./getFreshSideObject.js\";\nexport default function mergePaddingObject(paddingObject) {\n  return Object.assign({}, getFreshSideObject(), paddingObject);\n}","export default function getFreshSideObject() {\n  return {\n    top: 0,\n    right: 0,\n    bottom: 0,\n    left: 0\n  };\n}","export default function expandToHashMap(value, keys) {\n  return keys.reduce(function (hashMap, key) {\n    hashMap[key] = value;\n    return hashMap;\n  }, {});\n}","import getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getLayoutRect from \"../dom-utils/getLayoutRect.js\";\nimport contains from \"../dom-utils/contains.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport getMainAxisFromPlacement from \"../utils/getMainAxisFromPlacement.js\";\nimport { within } from \"../utils/within.js\";\nimport mergePaddingObject from \"../utils/mergePaddingObject.js\";\nimport expandToHashMap from \"../utils/expandToHashMap.js\";\nimport { left, right, basePlacements, top, bottom } from \"../enums.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar toPaddingObject = function toPaddingObject(padding, state) {\n  padding = typeof padding === 'function' ? padding(Object.assign({}, state.rects, {\n    placement: state.placement\n  })) : padding;\n  return mergePaddingObject(typeof padding !== 'number' ? padding : expandToHashMap(padding, basePlacements));\n};\n\nfunction arrow(_ref) {\n  var _state$modifiersData$;\n\n  var state = _ref.state,\n      name = _ref.name,\n      options = _ref.options;\n  var arrowElement = state.elements.arrow;\n  var popperOffsets = state.modifiersData.popperOffsets;\n  var basePlacement = getBasePlacement(state.placement);\n  var axis = getMainAxisFromPlacement(basePlacement);\n  var isVertical = [left, right].indexOf(basePlacement) >= 0;\n  var len = isVertical ? 'height' : 'width';\n\n  if (!arrowElement || !popperOffsets) {\n    return;\n  }\n\n  var paddingObject = toPaddingObject(options.padding, state);\n  var arrowRect = getLayoutRect(arrowElement);\n  var minProp = axis === 'y' ? top : left;\n  var maxProp = axis === 'y' ? bottom : right;\n  var endDiff = state.rects.reference[len] + state.rects.reference[axis] - popperOffsets[axis] - state.rects.popper[len];\n  var startDiff = popperOffsets[axis] - state.rects.reference[axis];\n  var arrowOffsetParent = getOffsetParent(arrowElement);\n  var clientSize = arrowOffsetParent ? axis === 'y' ? arrowOffsetParent.clientHeight || 0 : arrowOffsetParent.clientWidth || 0 : 0;\n  var centerToReference = endDiff / 2 - startDiff / 2; // Make sure the arrow doesn't overflow the popper if the center point is\n  // outside of the popper bounds\n\n  var min = paddingObject[minProp];\n  var max = clientSize - arrowRect[len] - paddingObject[maxProp];\n  var center = clientSize / 2 - arrowRect[len] / 2 + centerToReference;\n  var offset = within(min, center, max); // Prevents breaking syntax highlighting...\n\n  var axisProp = axis;\n  state.modifiersData[name] = (_state$modifiersData$ = {}, _state$modifiersData$[axisProp] = offset, _state$modifiersData$.centerOffset = offset - center, _state$modifiersData$);\n}\n\nfunction effect(_ref2) {\n  var state = _ref2.state,\n      options = _ref2.options;\n  var _options$element = options.element,\n      arrowElement = _options$element === void 0 ? '[data-popper-arrow]' : _options$element;\n\n  if (arrowElement == null) {\n    return;\n  } // CSS selector\n\n\n  if (typeof arrowElement === 'string') {\n    arrowElement = state.elements.popper.querySelector(arrowElement);\n\n    if (!arrowElement) {\n      return;\n    }\n  }\n\n  if (!contains(state.elements.popper, arrowElement)) {\n    return;\n  }\n\n  state.elements.arrow = arrowElement;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'arrow',\n  enabled: true,\n  phase: 'main',\n  fn: arrow,\n  effect: effect,\n  requires: ['popperOffsets'],\n  requiresIfExists: ['preventOverflow']\n};","export default function getVariation(placement) {\n  return placement.split('-')[1];\n}","import { top, left, right, bottom, end } from \"../enums.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport getWindow from \"../dom-utils/getWindow.js\";\nimport getDocumentElement from \"../dom-utils/getDocumentElement.js\";\nimport getComputedStyle from \"../dom-utils/getComputedStyle.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getVariation from \"../utils/getVariation.js\";\nimport { round } from \"../utils/math.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar unsetSides = {\n  top: 'auto',\n  right: 'auto',\n  bottom: 'auto',\n  left: 'auto'\n}; // Round the offsets to the nearest suitable subpixel based on the DPR.\n// Zooming can change the DPR, but it seems to report a value that will\n// cleanly divide the values into the appropriate subpixels.\n\nfunction roundOffsetsByDPR(_ref, win) {\n  var x = _ref.x,\n      y = _ref.y;\n  var dpr = win.devicePixelRatio || 1;\n  return {\n    x: round(x * dpr) / dpr || 0,\n    y: round(y * dpr) / dpr || 0\n  };\n}\n\nexport function mapToStyles(_ref2) {\n  var _Object$assign2;\n\n  var popper = _ref2.popper,\n      popperRect = _ref2.popperRect,\n      placement = _ref2.placement,\n      variation = _ref2.variation,\n      offsets = _ref2.offsets,\n      position = _ref2.position,\n      gpuAcceleration = _ref2.gpuAcceleration,\n      adaptive = _ref2.adaptive,\n      roundOffsets = _ref2.roundOffsets,\n      isFixed = _ref2.isFixed;\n  var _offsets$x = offsets.x,\n      x = _offsets$x === void 0 ? 0 : _offsets$x,\n      _offsets$y = offsets.y,\n      y = _offsets$y === void 0 ? 0 : _offsets$y;\n\n  var _ref3 = typeof roundOffsets === 'function' ? roundOffsets({\n    x: x,\n    y: y\n  }) : {\n    x: x,\n    y: y\n  };\n\n  x = _ref3.x;\n  y = _ref3.y;\n  var hasX = offsets.hasOwnProperty('x');\n  var hasY = offsets.hasOwnProperty('y');\n  var sideX = left;\n  var sideY = top;\n  var win = window;\n\n  if (adaptive) {\n    var offsetParent = getOffsetParent(popper);\n    var heightProp = 'clientHeight';\n    var widthProp = 'clientWidth';\n\n    if (offsetParent === getWindow(popper)) {\n      offsetParent = getDocumentElement(popper);\n\n      if (getComputedStyle(offsetParent).position !== 'static' && position === 'absolute') {\n        heightProp = 'scrollHeight';\n        widthProp = 'scrollWidth';\n      }\n    } // $FlowFixMe[incompatible-cast]: force type refinement, we compare offsetParent with window above, but Flow doesn't detect it\n\n\n    offsetParent = offsetParent;\n\n    if (placement === top || (placement === left || placement === right) && variation === end) {\n      sideY = bottom;\n      var offsetY = isFixed && offsetParent === win && win.visualViewport ? win.visualViewport.height : // $FlowFixMe[prop-missing]\n      offsetParent[heightProp];\n      y -= offsetY - popperRect.height;\n      y *= gpuAcceleration ? 1 : -1;\n    }\n\n    if (placement === left || (placement === top || placement === bottom) && variation === end) {\n      sideX = right;\n      var offsetX = isFixed && offsetParent === win && win.visualViewport ? win.visualViewport.width : // $FlowFixMe[prop-missing]\n      offsetParent[widthProp];\n      x -= offsetX - popperRect.width;\n      x *= gpuAcceleration ? 1 : -1;\n    }\n  }\n\n  var commonStyles = Object.assign({\n    position: position\n  }, adaptive && unsetSides);\n\n  var _ref4 = roundOffsets === true ? roundOffsetsByDPR({\n    x: x,\n    y: y\n  }, getWindow(popper)) : {\n    x: x,\n    y: y\n  };\n\n  x = _ref4.x;\n  y = _ref4.y;\n\n  if (gpuAcceleration) {\n    var _Object$assign;\n\n    return Object.assign({}, commonStyles, (_Object$assign = {}, _Object$assign[sideY] = hasY ? '0' : '', _Object$assign[sideX] = hasX ? '0' : '', _Object$assign.transform = (win.devicePixelRatio || 1) <= 1 ? \"translate(\" + x + \"px, \" + y + \"px)\" : \"translate3d(\" + x + \"px, \" + y + \"px, 0)\", _Object$assign));\n  }\n\n  return Object.assign({}, commonStyles, (_Object$assign2 = {}, _Object$assign2[sideY] = hasY ? y + \"px\" : '', _Object$assign2[sideX] = hasX ? x + \"px\" : '', _Object$assign2.transform = '', _Object$assign2));\n}\n\nfunction computeStyles(_ref5) {\n  var state = _ref5.state,\n      options = _ref5.options;\n  var _options$gpuAccelerat = options.gpuAcceleration,\n      gpuAcceleration = _options$gpuAccelerat === void 0 ? true : _options$gpuAccelerat,\n      _options$adaptive = options.adaptive,\n      adaptive = _options$adaptive === void 0 ? true : _options$adaptive,\n      _options$roundOffsets = options.roundOffsets,\n      roundOffsets = _options$roundOffsets === void 0 ? true : _options$roundOffsets;\n  var commonStyles = {\n    placement: getBasePlacement(state.placement),\n    variation: getVariation(state.placement),\n    popper: state.elements.popper,\n    popperRect: state.rects.popper,\n    gpuAcceleration: gpuAcceleration,\n    isFixed: state.options.strategy === 'fixed'\n  };\n\n  if (state.modifiersData.popperOffsets != null) {\n    state.styles.popper = Object.assign({}, state.styles.popper, mapToStyles(Object.assign({}, commonStyles, {\n      offsets: state.modifiersData.popperOffsets,\n      position: state.options.strategy,\n      adaptive: adaptive,\n      roundOffsets: roundOffsets\n    })));\n  }\n\n  if (state.modifiersData.arrow != null) {\n    state.styles.arrow = Object.assign({}, state.styles.arrow, mapToStyles(Object.assign({}, commonStyles, {\n      offsets: state.modifiersData.arrow,\n      position: 'absolute',\n      adaptive: false,\n      roundOffsets: roundOffsets\n    })));\n  }\n\n  state.attributes.popper = Object.assign({}, state.attributes.popper, {\n    'data-popper-placement': state.placement\n  });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'computeStyles',\n  enabled: true,\n  phase: 'beforeWrite',\n  fn: computeStyles,\n  data: {}\n};","import getWindow from \"../dom-utils/getWindow.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar passive = {\n  passive: true\n};\n\nfunction effect(_ref) {\n  var state = _ref.state,\n      instance = _ref.instance,\n      options = _ref.options;\n  var _options$scroll = options.scroll,\n      scroll = _options$scroll === void 0 ? true : _options$scroll,\n      _options$resize = options.resize,\n      resize = _options$resize === void 0 ? true : _options$resize;\n  var window = getWindow(state.elements.popper);\n  var scrollParents = [].concat(state.scrollParents.reference, state.scrollParents.popper);\n\n  if (scroll) {\n    scrollParents.forEach(function (scrollParent) {\n      scrollParent.addEventListener('scroll', instance.update, passive);\n    });\n  }\n\n  if (resize) {\n    window.addEventListener('resize', instance.update, passive);\n  }\n\n  return function () {\n    if (scroll) {\n      scrollParents.forEach(function (scrollParent) {\n        scrollParent.removeEventListener('scroll', instance.update, passive);\n      });\n    }\n\n    if (resize) {\n      window.removeEventListener('resize', instance.update, passive);\n    }\n  };\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'eventListeners',\n  enabled: true,\n  phase: 'write',\n  fn: function fn() {},\n  effect: effect,\n  data: {}\n};","var hash = {\n  left: 'right',\n  right: 'left',\n  bottom: 'top',\n  top: 'bottom'\n};\nexport default function getOppositePlacement(placement) {\n  return placement.replace(/left|right|bottom|top/g, function (matched) {\n    return hash[matched];\n  });\n}","var hash = {\n  start: 'end',\n  end: 'start'\n};\nexport default function getOppositeVariationPlacement(placement) {\n  return placement.replace(/start|end/g, function (matched) {\n    return hash[matched];\n  });\n}","import getWindow from \"./getWindow.js\";\nexport default function getWindowScroll(node) {\n  var win = getWindow(node);\n  var scrollLeft = win.pageXOffset;\n  var scrollTop = win.pageYOffset;\n  return {\n    scrollLeft: scrollLeft,\n    scrollTop: scrollTop\n  };\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getWindowScroll from \"./getWindowScroll.js\";\nexport default function getWindowScrollBarX(element) {\n  // If <html> has a CSS width greater than the viewport, then this will be\n  // incorrect for RTL.\n  // Popper 1 is broken in this case and never had a bug report so let's assume\n  // it's not an issue. I don't think anyone ever specifies width on <html>\n  // anyway.\n  // Browsers where the left scrollbar doesn't cause an issue report `0` for\n  // this (e.g. Edge 2019, IE11, Safari)\n  return getBoundingClientRect(getDocumentElement(element)).left + getWindowScroll(element).scrollLeft;\n}","import getComputedStyle from \"./getComputedStyle.js\";\nexport default function isScrollParent(element) {\n  // Firefox wants us to check `-x` and `-y` variations as well\n  var _getComputedStyle = getComputedStyle(element),\n      overflow = _getComputedStyle.overflow,\n      overflowX = _getComputedStyle.overflowX,\n      overflowY = _getComputedStyle.overflowY;\n\n  return /auto|scroll|overlay|hidden/.test(overflow + overflowY + overflowX);\n}","import getParentNode from \"./getParentNode.js\";\nimport isScrollParent from \"./isScrollParent.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nexport default function getScrollParent(node) {\n  if (['html', 'body', '#document'].indexOf(getNodeName(node)) >= 0) {\n    // $FlowFixMe[incompatible-return]: assume body is always available\n    return node.ownerDocument.body;\n  }\n\n  if (isHTMLElement(node) && isScrollParent(node)) {\n    return node;\n  }\n\n  return getScrollParent(getParentNode(node));\n}","import getScrollParent from \"./getScrollParent.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport getWindow from \"./getWindow.js\";\nimport isScrollParent from \"./isScrollParent.js\";\n/*\ngiven a DOM element, return the list of all scroll parents, up the list of ancesors\nuntil we get to the top window object. This list is what we attach scroll listeners\nto, because if any of these parent elements scroll, we'll need to re-calculate the\nreference element's position.\n*/\n\nexport default function listScrollParents(element, list) {\n  var _element$ownerDocumen;\n\n  if (list === void 0) {\n    list = [];\n  }\n\n  var scrollParent = getScrollParent(element);\n  var isBody = scrollParent === ((_element$ownerDocumen = element.ownerDocument) == null ? void 0 : _element$ownerDocumen.body);\n  var win = getWindow(scrollParent);\n  var target = isBody ? [win].concat(win.visualViewport || [], isScrollParent(scrollParent) ? scrollParent : []) : scrollParent;\n  var updatedList = list.concat(target);\n  return isBody ? updatedList : // $FlowFixMe[incompatible-call]: isBody tells us target will be an HTMLElement here\n  updatedList.concat(listScrollParents(getParentNode(target)));\n}","export default function rectToClientRect(rect) {\n  return Object.assign({}, rect, {\n    left: rect.x,\n    top: rect.y,\n    right: rect.x + rect.width,\n    bottom: rect.y + rect.height\n  });\n}","import { viewport } from \"../enums.js\";\nimport getViewportRect from \"./getViewportRect.js\";\nimport getDocumentRect from \"./getDocumentRect.js\";\nimport listScrollParents from \"./listScrollParents.js\";\nimport getOffsetParent from \"./getOffsetParent.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport { isElement, isHTMLElement } from \"./instanceOf.js\";\nimport getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport contains from \"./contains.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport rectToClientRect from \"../utils/rectToClientRect.js\";\nimport { max, min } from \"../utils/math.js\";\n\nfunction getInnerBoundingClientRect(element, strategy) {\n  var rect = getBoundingClientRect(element, false, strategy === 'fixed');\n  rect.top = rect.top + element.clientTop;\n  rect.left = rect.left + element.clientLeft;\n  rect.bottom = rect.top + element.clientHeight;\n  rect.right = rect.left + element.clientWidth;\n  rect.width = element.clientWidth;\n  rect.height = element.clientHeight;\n  rect.x = rect.left;\n  rect.y = rect.top;\n  return rect;\n}\n\nfunction getClientRectFromMixedType(element, clippingParent, strategy) {\n  return clippingParent === viewport ? rectToClientRect(getViewportRect(element, strategy)) : isElement(clippingParent) ? getInnerBoundingClientRect(clippingParent, strategy) : rectToClientRect(getDocumentRect(getDocumentElement(element)));\n} // A \"clipping parent\" is an overflowable container with the characteristic of\n// clipping (or hiding) overflowing elements with a position different from\n// `initial`\n\n\nfunction getClippingParents(element) {\n  var clippingParents = listScrollParents(getParentNode(element));\n  var canEscapeClipping = ['absolute', 'fixed'].indexOf(getComputedStyle(element).position) >= 0;\n  var clipperElement = canEscapeClipping && isHTMLElement(element) ? getOffsetParent(element) : element;\n\n  if (!isElement(clipperElement)) {\n    return [];\n  } // $FlowFixMe[incompatible-return]: https://github.com/facebook/flow/issues/1414\n\n\n  return clippingParents.filter(function (clippingParent) {\n    return isElement(clippingParent) && contains(clippingParent, clipperElement) && getNodeName(clippingParent) !== 'body';\n  });\n} // Gets the maximum area that the element is visible in due to any number of\n// clipping parents\n\n\nexport default function getClippingRect(element, boundary, rootBoundary, strategy) {\n  var mainClippingParents = boundary === 'clippingParents' ? getClippingParents(element) : [].concat(boundary);\n  var clippingParents = [].concat(mainClippingParents, [rootBoundary]);\n  var firstClippingParent = clippingParents[0];\n  var clippingRect = clippingParents.reduce(function (accRect, clippingParent) {\n    var rect = getClientRectFromMixedType(element, clippingParent, strategy);\n    accRect.top = max(rect.top, accRect.top);\n    accRect.right = min(rect.right, accRect.right);\n    accRect.bottom = min(rect.bottom, accRect.bottom);\n    accRect.left = max(rect.left, accRect.left);\n    return accRect;\n  }, getClientRectFromMixedType(element, firstClippingParent, strategy));\n  clippingRect.width = clippingRect.right - clippingRect.left;\n  clippingRect.height = clippingRect.bottom - clippingRect.top;\n  clippingRect.x = clippingRect.left;\n  clippingRect.y = clippingRect.top;\n  return clippingRect;\n}","import getWindow from \"./getWindow.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport isLayoutViewport from \"./isLayoutViewport.js\";\nexport default function getViewportRect(element, strategy) {\n  var win = getWindow(element);\n  var html = getDocumentElement(element);\n  var visualViewport = win.visualViewport;\n  var width = html.clientWidth;\n  var height = html.clientHeight;\n  var x = 0;\n  var y = 0;\n\n  if (visualViewport) {\n    width = visualViewport.width;\n    height = visualViewport.height;\n    var layoutViewport = isLayoutViewport();\n\n    if (layoutViewport || !layoutViewport && strategy === 'fixed') {\n      x = visualViewport.offsetLeft;\n      y = visualViewport.offsetTop;\n    }\n  }\n\n  return {\n    width: width,\n    height: height,\n    x: x + getWindowScrollBarX(element),\n    y: y\n  };\n}","import getDocumentElement from \"./getDocumentElement.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport getWindowScroll from \"./getWindowScroll.js\";\nimport { max } from \"../utils/math.js\"; // Gets the entire size of the scrollable document area, even extending outside\n// of the `<html>` and `<body>` rect bounds if horizontally scrollable\n\nexport default function getDocumentRect(element) {\n  var _element$ownerDocumen;\n\n  var html = getDocumentElement(element);\n  var winScroll = getWindowScroll(element);\n  var body = (_element$ownerDocumen = element.ownerDocument) == null ? void 0 : _element$ownerDocumen.body;\n  var width = max(html.scrollWidth, html.clientWidth, body ? body.scrollWidth : 0, body ? body.clientWidth : 0);\n  var height = max(html.scrollHeight, html.clientHeight, body ? body.scrollHeight : 0, body ? body.clientHeight : 0);\n  var x = -winScroll.scrollLeft + getWindowScrollBarX(element);\n  var y = -winScroll.scrollTop;\n\n  if (getComputedStyle(body || html).direction === 'rtl') {\n    x += max(html.clientWidth, body ? body.clientWidth : 0) - width;\n  }\n\n  return {\n    width: width,\n    height: height,\n    x: x,\n    y: y\n  };\n}","import getBasePlacement from \"./getBasePlacement.js\";\nimport getVariation from \"./getVariation.js\";\nimport getMainAxisFromPlacement from \"./getMainAxisFromPlacement.js\";\nimport { top, right, bottom, left, start, end } from \"../enums.js\";\nexport default function computeOffsets(_ref) {\n  var reference = _ref.reference,\n      element = _ref.element,\n      placement = _ref.placement;\n  var basePlacement = placement ? getBasePlacement(placement) : null;\n  var variation = placement ? getVariation(placement) : null;\n  var commonX = reference.x + reference.width / 2 - element.width / 2;\n  var commonY = reference.y + reference.height / 2 - element.height / 2;\n  var offsets;\n\n  switch (basePlacement) {\n    case top:\n      offsets = {\n        x: commonX,\n        y: reference.y - element.height\n      };\n      break;\n\n    case bottom:\n      offsets = {\n        x: commonX,\n        y: reference.y + reference.height\n      };\n      break;\n\n    case right:\n      offsets = {\n        x: reference.x + reference.width,\n        y: commonY\n      };\n      break;\n\n    case left:\n      offsets = {\n        x: reference.x - element.width,\n        y: commonY\n      };\n      break;\n\n    default:\n      offsets = {\n        x: reference.x,\n        y: reference.y\n      };\n  }\n\n  var mainAxis = basePlacement ? getMainAxisFromPlacement(basePlacement) : null;\n\n  if (mainAxis != null) {\n    var len = mainAxis === 'y' ? 'height' : 'width';\n\n    switch (variation) {\n      case start:\n        offsets[mainAxis] = offsets[mainAxis] - (reference[len] / 2 - element[len] / 2);\n        break;\n\n      case end:\n        offsets[mainAxis] = offsets[mainAxis] + (reference[len] / 2 - element[len] / 2);\n        break;\n\n      default:\n    }\n  }\n\n  return offsets;\n}","import getClippingRect from \"../dom-utils/getClippingRect.js\";\nimport getDocumentElement from \"../dom-utils/getDocumentElement.js\";\nimport getBoundingClientRect from \"../dom-utils/getBoundingClientRect.js\";\nimport computeOffsets from \"./computeOffsets.js\";\nimport rectToClientRect from \"./rectToClientRect.js\";\nimport { clippingParents, reference, popper, bottom, top, right, basePlacements, viewport } from \"../enums.js\";\nimport { isElement } from \"../dom-utils/instanceOf.js\";\nimport mergePaddingObject from \"./mergePaddingObject.js\";\nimport expandToHashMap from \"./expandToHashMap.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport default function detectOverflow(state, options) {\n  if (options === void 0) {\n    options = {};\n  }\n\n  var _options = options,\n      _options$placement = _options.placement,\n      placement = _options$placement === void 0 ? state.placement : _options$placement,\n      _options$strategy = _options.strategy,\n      strategy = _options$strategy === void 0 ? state.strategy : _options$strategy,\n      _options$boundary = _options.boundary,\n      boundary = _options$boundary === void 0 ? clippingParents : _options$boundary,\n      _options$rootBoundary = _options.rootBoundary,\n      rootBoundary = _options$rootBoundary === void 0 ? viewport : _options$rootBoundary,\n      _options$elementConte = _options.elementContext,\n      elementContext = _options$elementConte === void 0 ? popper : _options$elementConte,\n      _options$altBoundary = _options.altBoundary,\n      altBoundary = _options$altBoundary === void 0 ? false : _options$altBoundary,\n      _options$padding = _options.padding,\n      padding = _options$padding === void 0 ? 0 : _options$padding;\n  var paddingObject = mergePaddingObject(typeof padding !== 'number' ? padding : expandToHashMap(padding, basePlacements));\n  var altContext = elementContext === popper ? reference : popper;\n  var popperRect = state.rects.popper;\n  var element = state.elements[altBoundary ? altContext : elementContext];\n  var clippingClientRect = getClippingRect(isElement(element) ? element : element.contextElement || getDocumentElement(state.elements.popper), boundary, rootBoundary, strategy);\n  var referenceClientRect = getBoundingClientRect(state.elements.reference);\n  var popperOffsets = computeOffsets({\n    reference: referenceClientRect,\n    element: popperRect,\n    strategy: 'absolute',\n    placement: placement\n  });\n  var popperClientRect = rectToClientRect(Object.assign({}, popperRect, popperOffsets));\n  var elementClientRect = elementContext === popper ? popperClientRect : referenceClientRect; // positive = overflowing the clipping rect\n  // 0 or negative = within the clipping rect\n\n  var overflowOffsets = {\n    top: clippingClientRect.top - elementClientRect.top + paddingObject.top,\n    bottom: elementClientRect.bottom - clippingClientRect.bottom + paddingObject.bottom,\n    left: clippingClientRect.left - elementClientRect.left + paddingObject.left,\n    right: elementClientRect.right - clippingClientRect.right + paddingObject.right\n  };\n  var offsetData = state.modifiersData.offset; // Offsets can be applied only to the popper element\n\n  if (elementContext === popper && offsetData) {\n    var offset = offsetData[placement];\n    Object.keys(overflowOffsets).forEach(function (key) {\n      var multiply = [right, bottom].indexOf(key) >= 0 ? 1 : -1;\n      var axis = [top, bottom].indexOf(key) >= 0 ? 'y' : 'x';\n      overflowOffsets[key] += offset[axis] * multiply;\n    });\n  }\n\n  return overflowOffsets;\n}","import getVariation from \"./getVariation.js\";\nimport { variationPlacements, basePlacements, placements as allPlacements } from \"../enums.js\";\nimport detectOverflow from \"./detectOverflow.js\";\nimport getBasePlacement from \"./getBasePlacement.js\";\nexport default function computeAutoPlacement(state, options) {\n  if (options === void 0) {\n    options = {};\n  }\n\n  var _options = options,\n      placement = _options.placement,\n      boundary = _options.boundary,\n      rootBoundary = _options.rootBoundary,\n      padding = _options.padding,\n      flipVariations = _options.flipVariations,\n      _options$allowedAutoP = _options.allowedAutoPlacements,\n      allowedAutoPlacements = _options$allowedAutoP === void 0 ? allPlacements : _options$allowedAutoP;\n  var variation = getVariation(placement);\n  var placements = variation ? flipVariations ? variationPlacements : variationPlacements.filter(function (placement) {\n    return getVariation(placement) === variation;\n  }) : basePlacements;\n  var allowedPlacements = placements.filter(function (placement) {\n    return allowedAutoPlacements.indexOf(placement) >= 0;\n  });\n\n  if (allowedPlacements.length === 0) {\n    allowedPlacements = placements;\n  } // $FlowFixMe[incompatible-type]: Flow seems to have problems with two array unions...\n\n\n  var overflows = allowedPlacements.reduce(function (acc, placement) {\n    acc[placement] = detectOverflow(state, {\n      placement: placement,\n      boundary: boundary,\n      rootBoundary: rootBoundary,\n      padding: padding\n    })[getBasePlacement(placement)];\n    return acc;\n  }, {});\n  return Object.keys(overflows).sort(function (a, b) {\n    return overflows[a] - overflows[b];\n  });\n}","import getOppositePlacement from \"../utils/getOppositePlacement.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getOppositeVariationPlacement from \"../utils/getOppositeVariationPlacement.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\nimport computeAutoPlacement from \"../utils/computeAutoPlacement.js\";\nimport { bottom, top, start, right, left, auto } from \"../enums.js\";\nimport getVariation from \"../utils/getVariation.js\"; // eslint-disable-next-line import/no-unused-modules\n\nfunction getExpandedFallbackPlacements(placement) {\n  if (getBasePlacement(placement) === auto) {\n    return [];\n  }\n\n  var oppositePlacement = getOppositePlacement(placement);\n  return [getOppositeVariationPlacement(placement), oppositePlacement, getOppositeVariationPlacement(oppositePlacement)];\n}\n\nfunction flip(_ref) {\n  var state = _ref.state,\n      options = _ref.options,\n      name = _ref.name;\n\n  if (state.modifiersData[name]._skip) {\n    return;\n  }\n\n  var _options$mainAxis = options.mainAxis,\n      checkMainAxis = _options$mainAxis === void 0 ? true : _options$mainAxis,\n      _options$altAxis = options.altAxis,\n      checkAltAxis = _options$altAxis === void 0 ? true : _options$altAxis,\n      specifiedFallbackPlacements = options.fallbackPlacements,\n      padding = options.padding,\n      boundary = options.boundary,\n      rootBoundary = options.rootBoundary,\n      altBoundary = options.altBoundary,\n      _options$flipVariatio = options.flipVariations,\n      flipVariations = _options$flipVariatio === void 0 ? true : _options$flipVariatio,\n      allowedAutoPlacements = options.allowedAutoPlacements;\n  var preferredPlacement = state.options.placement;\n  var basePlacement = getBasePlacement(preferredPlacement);\n  var isBasePlacement = basePlacement === preferredPlacement;\n  var fallbackPlacements = specifiedFallbackPlacements || (isBasePlacement || !flipVariations ? [getOppositePlacement(preferredPlacement)] : getExpandedFallbackPlacements(preferredPlacement));\n  var placements = [preferredPlacement].concat(fallbackPlacements).reduce(function (acc, placement) {\n    return acc.concat(getBasePlacement(placement) === auto ? computeAutoPlacement(state, {\n      placement: placement,\n      boundary: boundary,\n      rootBoundary: rootBoundary,\n      padding: padding,\n      flipVariations: flipVariations,\n      allowedAutoPlacements: allowedAutoPlacements\n    }) : placement);\n  }, []);\n  var referenceRect = state.rects.reference;\n  var popperRect = state.rects.popper;\n  var checksMap = new Map();\n  var makeFallbackChecks = true;\n  var firstFittingPlacement = placements[0];\n\n  for (var i = 0; i < placements.length; i++) {\n    var placement = placements[i];\n\n    var _basePlacement = getBasePlacement(placement);\n\n    var isStartVariation = getVariation(placement) === start;\n    var isVertical = [top, bottom].indexOf(_basePlacement) >= 0;\n    var len = isVertical ? 'width' : 'height';\n    var overflow = detectOverflow(state, {\n      placement: placement,\n      boundary: boundary,\n      rootBoundary: rootBoundary,\n      altBoundary: altBoundary,\n      padding: padding\n    });\n    var mainVariationSide = isVertical ? isStartVariation ? right : left : isStartVariation ? bottom : top;\n\n    if (referenceRect[len] > popperRect[len]) {\n      mainVariationSide = getOppositePlacement(mainVariationSide);\n    }\n\n    var altVariationSide = getOppositePlacement(mainVariationSide);\n    var checks = [];\n\n    if (checkMainAxis) {\n      checks.push(overflow[_basePlacement] <= 0);\n    }\n\n    if (checkAltAxis) {\n      checks.push(overflow[mainVariationSide] <= 0, overflow[altVariationSide] <= 0);\n    }\n\n    if (checks.every(function (check) {\n      return check;\n    })) {\n      firstFittingPlacement = placement;\n      makeFallbackChecks = false;\n      break;\n    }\n\n    checksMap.set(placement, checks);\n  }\n\n  if (makeFallbackChecks) {\n    // `2` may be desired in some cases – research later\n    var numberOfChecks = flipVariations ? 3 : 1;\n\n    var _loop = function _loop(_i) {\n      var fittingPlacement = placements.find(function (placement) {\n        var checks = checksMap.get(placement);\n\n        if (checks) {\n          return checks.slice(0, _i).every(function (check) {\n            return check;\n          });\n        }\n      });\n\n      if (fittingPlacement) {\n        firstFittingPlacement = fittingPlacement;\n        return \"break\";\n      }\n    };\n\n    for (var _i = numberOfChecks; _i > 0; _i--) {\n      var _ret = _loop(_i);\n\n      if (_ret === \"break\") break;\n    }\n  }\n\n  if (state.placement !== firstFittingPlacement) {\n    state.modifiersData[name]._skip = true;\n    state.placement = firstFittingPlacement;\n    state.reset = true;\n  }\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'flip',\n  enabled: true,\n  phase: 'main',\n  fn: flip,\n  requiresIfExists: ['offset'],\n  data: {\n    _skip: false\n  }\n};","import { top, bottom, left, right } from \"../enums.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\n\nfunction getSideOffsets(overflow, rect, preventedOffsets) {\n  if (preventedOffsets === void 0) {\n    preventedOffsets = {\n      x: 0,\n      y: 0\n    };\n  }\n\n  return {\n    top: overflow.top - rect.height - preventedOffsets.y,\n    right: overflow.right - rect.width + preventedOffsets.x,\n    bottom: overflow.bottom - rect.height + preventedOffsets.y,\n    left: overflow.left - rect.width - preventedOffsets.x\n  };\n}\n\nfunction isAnySideFullyClipped(overflow) {\n  return [top, right, bottom, left].some(function (side) {\n    return overflow[side] >= 0;\n  });\n}\n\nfunction hide(_ref) {\n  var state = _ref.state,\n      name = _ref.name;\n  var referenceRect = state.rects.reference;\n  var popperRect = state.rects.popper;\n  var preventedOffsets = state.modifiersData.preventOverflow;\n  var referenceOverflow = detectOverflow(state, {\n    elementContext: 'reference'\n  });\n  var popperAltOverflow = detectOverflow(state, {\n    altBoundary: true\n  });\n  var referenceClippingOffsets = getSideOffsets(referenceOverflow, referenceRect);\n  var popperEscapeOffsets = getSideOffsets(popperAltOverflow, popperRect, preventedOffsets);\n  var isReferenceHidden = isAnySideFullyClipped(referenceClippingOffsets);\n  var hasPopperEscaped = isAnySideFullyClipped(popperEscapeOffsets);\n  state.modifiersData[name] = {\n    referenceClippingOffsets: referenceClippingOffsets,\n    popperEscapeOffsets: popperEscapeOffsets,\n    isReferenceHidden: isReferenceHidden,\n    hasPopperEscaped: hasPopperEscaped\n  };\n  state.attributes.popper = Object.assign({}, state.attributes.popper, {\n    'data-popper-reference-hidden': isReferenceHidden,\n    'data-popper-escaped': hasPopperEscaped\n  });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'hide',\n  enabled: true,\n  phase: 'main',\n  requiresIfExists: ['preventOverflow'],\n  fn: hide\n};","import getBasePlacement from \"../utils/getBasePlacement.js\";\nimport { top, left, right, placements } from \"../enums.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport function distanceAndSkiddingToXY(placement, rects, offset) {\n  var basePlacement = getBasePlacement(placement);\n  var invertDistance = [left, top].indexOf(basePlacement) >= 0 ? -1 : 1;\n\n  var _ref = typeof offset === 'function' ? offset(Object.assign({}, rects, {\n    placement: placement\n  })) : offset,\n      skidding = _ref[0],\n      distance = _ref[1];\n\n  skidding = skidding || 0;\n  distance = (distance || 0) * invertDistance;\n  return [left, right].indexOf(basePlacement) >= 0 ? {\n    x: distance,\n    y: skidding\n  } : {\n    x: skidding,\n    y: distance\n  };\n}\n\nfunction offset(_ref2) {\n  var state = _ref2.state,\n      options = _ref2.options,\n      name = _ref2.name;\n  var _options$offset = options.offset,\n      offset = _options$offset === void 0 ? [0, 0] : _options$offset;\n  var data = placements.reduce(function (acc, placement) {\n    acc[placement] = distanceAndSkiddingToXY(placement, state.rects, offset);\n    return acc;\n  }, {});\n  var _data$state$placement = data[state.placement],\n      x = _data$state$placement.x,\n      y = _data$state$placement.y;\n\n  if (state.modifiersData.popperOffsets != null) {\n    state.modifiersData.popperOffsets.x += x;\n    state.modifiersData.popperOffsets.y += y;\n  }\n\n  state.modifiersData[name] = data;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'offset',\n  enabled: true,\n  phase: 'main',\n  requires: ['popperOffsets'],\n  fn: offset\n};","import computeOffsets from \"../utils/computeOffsets.js\";\n\nfunction popperOffsets(_ref) {\n  var state = _ref.state,\n      name = _ref.name;\n  // Offsets are the actual position the popper needs to have to be\n  // properly positioned near its reference element\n  // This is the most basic placement, and will be adjusted by\n  // the modifiers in the next step\n  state.modifiersData[name] = computeOffsets({\n    reference: state.rects.reference,\n    element: state.rects.popper,\n    strategy: 'absolute',\n    placement: state.placement\n  });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'popperOffsets',\n  enabled: true,\n  phase: 'read',\n  fn: popperOffsets,\n  data: {}\n};","import { top, left, right, bottom, start } from \"../enums.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getMainAxisFromPlacement from \"../utils/getMainAxisFromPlacement.js\";\nimport getAltAxis from \"../utils/getAltAxis.js\";\nimport { within, withinMaxClamp } from \"../utils/within.js\";\nimport getLayoutRect from \"../dom-utils/getLayoutRect.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\nimport getVariation from \"../utils/getVariation.js\";\nimport getFreshSideObject from \"../utils/getFreshSideObject.js\";\nimport { min as mathMin, max as mathMax } from \"../utils/math.js\";\n\nfunction preventOverflow(_ref) {\n  var state = _ref.state,\n      options = _ref.options,\n      name = _ref.name;\n  var _options$mainAxis = options.mainAxis,\n      checkMainAxis = _options$mainAxis === void 0 ? true : _options$mainAxis,\n      _options$altAxis = options.altAxis,\n      checkAltAxis = _options$altAxis === void 0 ? false : _options$altAxis,\n      boundary = options.boundary,\n      rootBoundary = options.rootBoundary,\n      altBoundary = options.altBoundary,\n      padding = options.padding,\n      _options$tether = options.tether,\n      tether = _options$tether === void 0 ? true : _options$tether,\n      _options$tetherOffset = options.tetherOffset,\n      tetherOffset = _options$tetherOffset === void 0 ? 0 : _options$tetherOffset;\n  var overflow = detectOverflow(state, {\n    boundary: boundary,\n    rootBoundary: rootBoundary,\n    padding: padding,\n    altBoundary: altBoundary\n  });\n  var basePlacement = getBasePlacement(state.placement);\n  var variation = getVariation(state.placement);\n  var isBasePlacement = !variation;\n  var mainAxis = getMainAxisFromPlacement(basePlacement);\n  var altAxis = getAltAxis(mainAxis);\n  var popperOffsets = state.modifiersData.popperOffsets;\n  var referenceRect = state.rects.reference;\n  var popperRect = state.rects.popper;\n  var tetherOffsetValue = typeof tetherOffset === 'function' ? tetherOffset(Object.assign({}, state.rects, {\n    placement: state.placement\n  })) : tetherOffset;\n  var normalizedTetherOffsetValue = typeof tetherOffsetValue === 'number' ? {\n    mainAxis: tetherOffsetValue,\n    altAxis: tetherOffsetValue\n  } : Object.assign({\n    mainAxis: 0,\n    altAxis: 0\n  }, tetherOffsetValue);\n  var offsetModifierState = state.modifiersData.offset ? state.modifiersData.offset[state.placement] : null;\n  var data = {\n    x: 0,\n    y: 0\n  };\n\n  if (!popperOffsets) {\n    return;\n  }\n\n  if (checkMainAxis) {\n    var _offsetModifierState$;\n\n    var mainSide = mainAxis === 'y' ? top : left;\n    var altSide = mainAxis === 'y' ? bottom : right;\n    var len = mainAxis === 'y' ? 'height' : 'width';\n    var offset = popperOffsets[mainAxis];\n    var min = offset + overflow[mainSide];\n    var max = offset - overflow[altSide];\n    var additive = tether ? -popperRect[len] / 2 : 0;\n    var minLen = variation === start ? referenceRect[len] : popperRect[len];\n    var maxLen = variation === start ? -popperRect[len] : -referenceRect[len]; // We need to include the arrow in the calculation so the arrow doesn't go\n    // outside the reference bounds\n\n    var arrowElement = state.elements.arrow;\n    var arrowRect = tether && arrowElement ? getLayoutRect(arrowElement) : {\n      width: 0,\n      height: 0\n    };\n    var arrowPaddingObject = state.modifiersData['arrow#persistent'] ? state.modifiersData['arrow#persistent'].padding : getFreshSideObject();\n    var arrowPaddingMin = arrowPaddingObject[mainSide];\n    var arrowPaddingMax = arrowPaddingObject[altSide]; // If the reference length is smaller than the arrow length, we don't want\n    // to include its full size in the calculation. If the reference is small\n    // and near the edge of a boundary, the popper can overflow even if the\n    // reference is not overflowing as well (e.g. virtual elements with no\n    // width or height)\n\n    var arrowLen = within(0, referenceRect[len], arrowRect[len]);\n    var minOffset = isBasePlacement ? referenceRect[len] / 2 - additive - arrowLen - arrowPaddingMin - normalizedTetherOffsetValue.mainAxis : minLen - arrowLen - arrowPaddingMin - normalizedTetherOffsetValue.mainAxis;\n    var maxOffset = isBasePlacement ? -referenceRect[len] / 2 + additive + arrowLen + arrowPaddingMax + normalizedTetherOffsetValue.mainAxis : maxLen + arrowLen + arrowPaddingMax + normalizedTetherOffsetValue.mainAxis;\n    var arrowOffsetParent = state.elements.arrow && getOffsetParent(state.elements.arrow);\n    var clientOffset = arrowOffsetParent ? mainAxis === 'y' ? arrowOffsetParent.clientTop || 0 : arrowOffsetParent.clientLeft || 0 : 0;\n    var offsetModifierValue = (_offsetModifierState$ = offsetModifierState == null ? void 0 : offsetModifierState[mainAxis]) != null ? _offsetModifierState$ : 0;\n    var tetherMin = offset + minOffset - offsetModifierValue - clientOffset;\n    var tetherMax = offset + maxOffset - offsetModifierValue;\n    var preventedOffset = within(tether ? mathMin(min, tetherMin) : min, offset, tether ? mathMax(max, tetherMax) : max);\n    popperOffsets[mainAxis] = preventedOffset;\n    data[mainAxis] = preventedOffset - offset;\n  }\n\n  if (checkAltAxis) {\n    var _offsetModifierState$2;\n\n    var _mainSide = mainAxis === 'x' ? top : left;\n\n    var _altSide = mainAxis === 'x' ? bottom : right;\n\n    var _offset = popperOffsets[altAxis];\n\n    var _len = altAxis === 'y' ? 'height' : 'width';\n\n    var _min = _offset + overflow[_mainSide];\n\n    var _max = _offset - overflow[_altSide];\n\n    var isOriginSide = [top, left].indexOf(basePlacement) !== -1;\n\n    var _offsetModifierValue = (_offsetModifierState$2 = offsetModifierState == null ? void 0 : offsetModifierState[altAxis]) != null ? _offsetModifierState$2 : 0;\n\n    var _tetherMin = isOriginSide ? _min : _offset - referenceRect[_len] - popperRect[_len] - _offsetModifierValue + normalizedTetherOffsetValue.altAxis;\n\n    var _tetherMax = isOriginSide ? _offset + referenceRect[_len] + popperRect[_len] - _offsetModifierValue - normalizedTetherOffsetValue.altAxis : _max;\n\n    var _preventedOffset = tether && isOriginSide ? withinMaxClamp(_tetherMin, _offset, _tetherMax) : within(tether ? _tetherMin : _min, _offset, tether ? _tetherMax : _max);\n\n    popperOffsets[altAxis] = _preventedOffset;\n    data[altAxis] = _preventedOffset - _offset;\n  }\n\n  state.modifiersData[name] = data;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'preventOverflow',\n  enabled: true,\n  phase: 'main',\n  fn: preventOverflow,\n  requiresIfExists: ['offset']\n};","export default function getAltAxis(axis) {\n  return axis === 'x' ? 'y' : 'x';\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getNodeScroll from \"./getNodeScroll.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport isScrollParent from \"./isScrollParent.js\";\nimport { round } from \"../utils/math.js\";\n\nfunction isElementScaled(element) {\n  var rect = element.getBoundingClientRect();\n  var scaleX = round(rect.width) / element.offsetWidth || 1;\n  var scaleY = round(rect.height) / element.offsetHeight || 1;\n  return scaleX !== 1 || scaleY !== 1;\n} // Returns the composite rect of an element relative to its offsetParent.\n// Composite means it takes into account transforms as well as layout.\n\n\nexport default function getCompositeRect(elementOrVirtualElement, offsetParent, isFixed) {\n  if (isFixed === void 0) {\n    isFixed = false;\n  }\n\n  var isOffsetParentAnElement = isHTMLElement(offsetParent);\n  var offsetParentIsScaled = isHTMLElement(offsetParent) && isElementScaled(offsetParent);\n  var documentElement = getDocumentElement(offsetParent);\n  var rect = getBoundingClientRect(elementOrVirtualElement, offsetParentIsScaled, isFixed);\n  var scroll = {\n    scrollLeft: 0,\n    scrollTop: 0\n  };\n  var offsets = {\n    x: 0,\n    y: 0\n  };\n\n  if (isOffsetParentAnElement || !isOffsetParentAnElement && !isFixed) {\n    if (getNodeName(offsetParent) !== 'body' || // https://github.com/popperjs/popper-core/issues/1078\n    isScrollParent(documentElement)) {\n      scroll = getNodeScroll(offsetParent);\n    }\n\n    if (isHTMLElement(offsetParent)) {\n      offsets = getBoundingClientRect(offsetParent, true);\n      offsets.x += offsetParent.clientLeft;\n      offsets.y += offsetParent.clientTop;\n    } else if (documentElement) {\n      offsets.x = getWindowScrollBarX(documentElement);\n    }\n  }\n\n  return {\n    x: rect.left + scroll.scrollLeft - offsets.x,\n    y: rect.top + scroll.scrollTop - offsets.y,\n    width: rect.width,\n    height: rect.height\n  };\n}","import getWindowScroll from \"./getWindowScroll.js\";\nimport getWindow from \"./getWindow.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nimport getHTMLElementScroll from \"./getHTMLElementScroll.js\";\nexport default function getNodeScroll(node) {\n  if (node === getWindow(node) || !isHTMLElement(node)) {\n    return getWindowScroll(node);\n  } else {\n    return getHTMLElementScroll(node);\n  }\n}","export default function getHTMLElementScroll(element) {\n  return {\n    scrollLeft: element.scrollLeft,\n    scrollTop: element.scrollTop\n  };\n}","import { modifierPhases } from \"../enums.js\"; // source: https://stackoverflow.com/questions/49875255\n\nfunction order(modifiers) {\n  var map = new Map();\n  var visited = new Set();\n  var result = [];\n  modifiers.forEach(function (modifier) {\n    map.set(modifier.name, modifier);\n  }); // On visiting object, check for its dependencies and visit them recursively\n\n  function sort(modifier) {\n    visited.add(modifier.name);\n    var requires = [].concat(modifier.requires || [], modifier.requiresIfExists || []);\n    requires.forEach(function (dep) {\n      if (!visited.has(dep)) {\n        var depModifier = map.get(dep);\n\n        if (depModifier) {\n          sort(depModifier);\n        }\n      }\n    });\n    result.push(modifier);\n  }\n\n  modifiers.forEach(function (modifier) {\n    if (!visited.has(modifier.name)) {\n      // check for visited object\n      sort(modifier);\n    }\n  });\n  return result;\n}\n\nexport default function orderModifiers(modifiers) {\n  // order based on dependencies\n  var orderedModifiers = order(modifiers); // order based on phase\n\n  return modifierPhases.reduce(function (acc, phase) {\n    return acc.concat(orderedModifiers.filter(function (modifier) {\n      return modifier.phase === phase;\n    }));\n  }, []);\n}","import getCompositeRect from \"./dom-utils/getCompositeRect.js\";\nimport getLayoutRect from \"./dom-utils/getLayoutRect.js\";\nimport listScrollParents from \"./dom-utils/listScrollParents.js\";\nimport getOffsetParent from \"./dom-utils/getOffsetParent.js\";\nimport orderModifiers from \"./utils/orderModifiers.js\";\nimport debounce from \"./utils/debounce.js\";\nimport mergeByName from \"./utils/mergeByName.js\";\nimport detectOverflow from \"./utils/detectOverflow.js\";\nimport { isElement } from \"./dom-utils/instanceOf.js\";\nvar DEFAULT_OPTIONS = {\n  placement: 'bottom',\n  modifiers: [],\n  strategy: 'absolute'\n};\n\nfunction areValidElements() {\n  for (var _len = arguments.length, args = new Array(_len), _key = 0; _key < _len; _key++) {\n    args[_key] = arguments[_key];\n  }\n\n  return !args.some(function (element) {\n    return !(element && typeof element.getBoundingClientRect === 'function');\n  });\n}\n\nexport function popperGenerator(generatorOptions) {\n  if (generatorOptions === void 0) {\n    generatorOptions = {};\n  }\n\n  var _generatorOptions = generatorOptions,\n      _generatorOptions$def = _generatorOptions.defaultModifiers,\n      defaultModifiers = _generatorOptions$def === void 0 ? [] : _generatorOptions$def,\n      _generatorOptions$def2 = _generatorOptions.defaultOptions,\n      defaultOptions = _generatorOptions$def2 === void 0 ? DEFAULT_OPTIONS : _generatorOptions$def2;\n  return function createPopper(reference, popper, options) {\n    if (options === void 0) {\n      options = defaultOptions;\n    }\n\n    var state = {\n      placement: 'bottom',\n      orderedModifiers: [],\n      options: Object.assign({}, DEFAULT_OPTIONS, defaultOptions),\n      modifiersData: {},\n      elements: {\n        reference: reference,\n        popper: popper\n      },\n      attributes: {},\n      styles: {}\n    };\n    var effectCleanupFns = [];\n    var isDestroyed = false;\n    var instance = {\n      state: state,\n      setOptions: function setOptions(setOptionsAction) {\n        var options = typeof setOptionsAction === 'function' ? setOptionsAction(state.options) : setOptionsAction;\n        cleanupModifierEffects();\n        state.options = Object.assign({}, defaultOptions, state.options, options);\n        state.scrollParents = {\n          reference: isElement(reference) ? listScrollParents(reference) : reference.contextElement ? listScrollParents(reference.contextElement) : [],\n          popper: listScrollParents(popper)\n        }; // Orders the modifiers based on their dependencies and `phase`\n        // properties\n\n        var orderedModifiers = orderModifiers(mergeByName([].concat(defaultModifiers, state.options.modifiers))); // Strip out disabled modifiers\n\n        state.orderedModifiers = orderedModifiers.filter(function (m) {\n          return m.enabled;\n        });\n        runModifierEffects();\n        return instance.update();\n      },\n      // Sync update – it will always be executed, even if not necessary. This\n      // is useful for low frequency updates where sync behavior simplifies the\n      // logic.\n      // For high frequency updates (e.g. `resize` and `scroll` events), always\n      // prefer the async Popper#update method\n      forceUpdate: function forceUpdate() {\n        if (isDestroyed) {\n          return;\n        }\n\n        var _state$elements = state.elements,\n            reference = _state$elements.reference,\n            popper = _state$elements.popper; // Don't proceed if `reference` or `popper` are not valid elements\n        // anymore\n\n        if (!areValidElements(reference, popper)) {\n          return;\n        } // Store the reference and popper rects to be read by modifiers\n\n\n        state.rects = {\n          reference: getCompositeRect(reference, getOffsetParent(popper), state.options.strategy === 'fixed'),\n          popper: getLayoutRect(popper)\n        }; // Modifiers have the ability to reset the current update cycle. The\n        // most common use case for this is the `flip` modifier changing the\n        // placement, which then needs to re-run all the modifiers, because the\n        // logic was previously ran for the previous placement and is therefore\n        // stale/incorrect\n\n        state.reset = false;\n        state.placement = state.options.placement; // On each update cycle, the `modifiersData` property for each modifier\n        // is filled with the initial data specified by the modifier. This means\n        // it doesn't persist and is fresh on each update.\n        // To ensure persistent data, use `${name}#persistent`\n\n        state.orderedModifiers.forEach(function (modifier) {\n          return state.modifiersData[modifier.name] = Object.assign({}, modifier.data);\n        });\n\n        for (var index = 0; index < state.orderedModifiers.length; index++) {\n          if (state.reset === true) {\n            state.reset = false;\n            index = -1;\n            continue;\n          }\n\n          var _state$orderedModifie = state.orderedModifiers[index],\n              fn = _state$orderedModifie.fn,\n              _state$orderedModifie2 = _state$orderedModifie.options,\n              _options = _state$orderedModifie2 === void 0 ? {} : _state$orderedModifie2,\n              name = _state$orderedModifie.name;\n\n          if (typeof fn === 'function') {\n            state = fn({\n              state: state,\n              options: _options,\n              name: name,\n              instance: instance\n            }) || state;\n          }\n        }\n      },\n      // Async and optimistically optimized update – it will not be executed if\n      // not necessary (debounced to run at most once-per-tick)\n      update: debounce(function () {\n        return new Promise(function (resolve) {\n          instance.forceUpdate();\n          resolve(state);\n        });\n      }),\n      destroy: function destroy() {\n        cleanupModifierEffects();\n        isDestroyed = true;\n      }\n    };\n\n    if (!areValidElements(reference, popper)) {\n      return instance;\n    }\n\n    instance.setOptions(options).then(function (state) {\n      if (!isDestroyed && options.onFirstUpdate) {\n        options.onFirstUpdate(state);\n      }\n    }); // Modifiers have the ability to execute arbitrary code before the first\n    // update cycle runs. They will be executed in the same order as the update\n    // cycle. This is useful when a modifier adds some persistent data that\n    // other modifiers need to use, but the modifier is run after the dependent\n    // one.\n\n    function runModifierEffects() {\n      state.orderedModifiers.forEach(function (_ref) {\n        var name = _ref.name,\n            _ref$options = _ref.options,\n            options = _ref$options === void 0 ? {} : _ref$options,\n            effect = _ref.effect;\n\n        if (typeof effect === 'function') {\n          var cleanupFn = effect({\n            state: state,\n            name: name,\n            instance: instance,\n            options: options\n          });\n\n          var noopFn = function noopFn() {};\n\n          effectCleanupFns.push(cleanupFn || noopFn);\n        }\n      });\n    }\n\n    function cleanupModifierEffects() {\n      effectCleanupFns.forEach(function (fn) {\n        return fn();\n      });\n      effectCleanupFns = [];\n    }\n\n    return instance;\n  };\n}\nexport var createPopper = /*#__PURE__*/popperGenerator(); // eslint-disable-next-line import/no-unused-modules\n\nexport { detectOverflow };","export default function debounce(fn) {\n  var pending;\n  return function () {\n    if (!pending) {\n      pending = new Promise(function (resolve) {\n        Promise.resolve().then(function () {\n          pending = undefined;\n          resolve(fn());\n        });\n      });\n    }\n\n    return pending;\n  };\n}","export default function mergeByName(modifiers) {\n  var merged = modifiers.reduce(function (merged, current) {\n    var existing = merged[current.name];\n    merged[current.name] = existing ? Object.assign({}, existing, current, {\n      options: Object.assign({}, existing.options, current.options),\n      data: Object.assign({}, existing.data, current.data)\n    }) : current;\n    return merged;\n  }, {}); // IE11 does not support Object.values\n\n  return Object.keys(merged).map(function (key) {\n    return merged[key];\n  });\n}","import { popperGenerator, detectOverflow } from \"./createPopper.js\";\nimport eventListeners from \"./modifiers/eventListeners.js\";\nimport popperOffsets from \"./modifiers/popperOffsets.js\";\nimport computeStyles from \"./modifiers/computeStyles.js\";\nimport applyStyles from \"./modifiers/applyStyles.js\";\nvar defaultModifiers = [eventListeners, popperOffsets, computeStyles, applyStyles];\nvar createPopper = /*#__PURE__*/popperGenerator({\n  defaultModifiers: defaultModifiers\n}); // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper, popperGenerator, defaultModifiers, detectOverflow };","import { popperGenerator, detectOverflow } from \"./createPopper.js\";\nimport eventListeners from \"./modifiers/eventListeners.js\";\nimport popperOffsets from \"./modifiers/popperOffsets.js\";\nimport computeStyles from \"./modifiers/computeStyles.js\";\nimport applyStyles from \"./modifiers/applyStyles.js\";\nimport offset from \"./modifiers/offset.js\";\nimport flip from \"./modifiers/flip.js\";\nimport preventOverflow from \"./modifiers/preventOverflow.js\";\nimport arrow from \"./modifiers/arrow.js\";\nimport hide from \"./modifiers/hide.js\";\nvar defaultModifiers = [eventListeners, popperOffsets, computeStyles, applyStyles, offset, flip, preventOverflow, arrow, hide];\nvar createPopper = /*#__PURE__*/popperGenerator({\n  defaultModifiers: defaultModifiers\n}); // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper, popperGenerator, defaultModifiers, detectOverflow }; // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper as createPopperLite } from \"./popper-lite.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport * from \"./modifiers/index.js\";","/**\n * --------------------------------------------------------------------------\n * Bootstrap dropdown.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport * as Popper from '@popperjs/core'\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport Manipulator from './dom/manipulator.js'\nimport SelectorEngine from './dom/selector-engine.js'\nimport {\n  defineJQueryPlugin,\n  execute,\n  getElement,\n  getNextActiveElement,\n  isDisabled,\n  isElement,\n  isRTL,\n  isVisible,\n  noop\n} from './util/index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'dropdown'\nconst DATA_KEY = 'bs.dropdown'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\n\nconst ESCAPE_KEY = 'Escape'\nconst TAB_KEY = 'Tab'\nconst ARROW_UP_KEY = 'ArrowUp'\nconst ARROW_DOWN_KEY = 'ArrowDown'\nconst RIGHT_MOUSE_BUTTON = 2 // MouseEvent.button value for the secondary button, usually the right button\n\nconst EVENT_HIDE = `hide${EVENT_KEY}`\nconst EVENT_HIDDEN = `hidden${EVENT_KEY}`\nconst EVENT_SHOW = `show${EVENT_KEY}`\nconst EVENT_SHOWN = `shown${EVENT_KEY}`\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}${DATA_API_KEY}`\nconst EVENT_KEYDOWN_DATA_API = `keydown${EVENT_KEY}${DATA_API_KEY}`\nconst EVENT_KEYUP_DATA_API = `keyup${EVENT_KEY}${DATA_API_KEY}`\n\nconst CLASS_NAME_SHOW = 'show'\nconst CLASS_NAME_DROPUP = 'dropup'\nconst CLASS_NAME_DROPEND = 'dropend'\nconst CLASS_NAME_DROPSTART = 'dropstart'\nconst CLASS_NAME_DROPUP_CENTER = 'dropup-center'\nconst CLASS_NAME_DROPDOWN_CENTER = 'dropdown-center'\n\nconst SELECTOR_DATA_TOGGLE = '[data-bs-toggle=\"dropdown\"]:not(.disabled):not(:disabled)'\nconst SELECTOR_DATA_TOGGLE_SHOWN = `${SELECTOR_DATA_TOGGLE}.${CLASS_NAME_SHOW}`\nconst SELECTOR_MENU = '.dropdown-menu'\nconst SELECTOR_NAVBAR = '.navbar'\nconst SELECTOR_NAVBAR_NAV = '.navbar-nav'\nconst SELECTOR_VISIBLE_ITEMS = '.dropdown-menu .dropdown-item:not(.disabled):not(:disabled)'\n\nconst PLACEMENT_TOP = isRTL() ? 'top-end' : 'top-start'\nconst PLACEMENT_TOPEND = isRTL() ? 'top-start' : 'top-end'\nconst PLACEMENT_BOTTOM = isRTL() ? 'bottom-end' : 'bottom-start'\nconst PLACEMENT_BOTTOMEND = isRTL() ? 'bottom-start' : 'bottom-end'\nconst PLACEMENT_RIGHT = isRTL() ? 'left-start' : 'right-start'\nconst PLACEMENT_LEFT = isRTL() ? 'right-start' : 'left-start'\nconst PLACEMENT_TOPCENTER = 'top'\nconst PLACEMENT_BOTTOMCENTER = 'bottom'\n\nconst Default = {\n  autoClose: true,\n  boundary: 'clippingParents',\n  display: 'dynamic',\n  offset: [0, 2],\n  popperConfig: null,\n  reference: 'toggle'\n}\n\nconst DefaultType = {\n  autoClose: '(boolean|string)',\n  boundary: '(string|element)',\n  display: 'string',\n  offset: '(array|string|function)',\n  popperConfig: '(null|object|function)',\n  reference: '(string|element|object)'\n}\n\n/**\n * Class definition\n */\n\nclass Dropdown extends BaseComponent {\n  constructor(element, config) {\n    super(element, config)\n\n    this._popper = null\n    this._parent = this._element.parentNode // dropdown wrapper\n    // TODO: v6 revert #37011 & change markup https://getbootstrap.com/docs/5.3/forms/input-group/\n    this._menu = SelectorEngine.next(this._element, SELECTOR_MENU)[0] ||\n      SelectorEngine.prev(this._element, SELECTOR_MENU)[0] ||\n      SelectorEngine.findOne(SELECTOR_MENU, this._parent)\n    this._inNavbar = this._detectNavbar()\n  }\n\n  // Getters\n  static get Default() {\n    return Default\n  }\n\n  static get DefaultType() {\n    return DefaultType\n  }\n\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n  toggle() {\n    return this._isShown() ? this.hide() : this.show()\n  }\n\n  show() {\n    if (isDisabled(this._element) || this._isShown()) {\n      return\n    }\n\n    const relatedTarget = {\n      relatedTarget: this._element\n    }\n\n    const showEvent = EventHandler.trigger(this._element, EVENT_SHOW, relatedTarget)\n\n    if (showEvent.defaultPrevented) {\n      return\n    }\n\n    this._createPopper()\n\n    // If this is a touch-enabled device we add extra\n    // empty mouseover listeners to the body's immediate children;\n    // only needed because of broken event delegation on iOS\n    // https://www.quirksmode.org/blog/archives/2014/02/mouse_event_bub.html\n    if ('ontouchstart' in document.documentElement && !this._parent.closest(SELECTOR_NAVBAR_NAV)) {\n      for (const element of [].concat(...document.body.children)) {\n        EventHandler.on(element, 'mouseover', noop)\n      }\n    }\n\n    this._element.focus()\n    this._element.setAttribute('aria-expanded', true)\n\n    this._menu.classList.add(CLASS_NAME_SHOW)\n    this._element.classList.add(CLASS_NAME_SHOW)\n    EventHandler.trigger(this._element, EVENT_SHOWN, relatedTarget)\n  }\n\n  hide() {\n    if (isDisabled(this._element) || !this._isShown()) {\n      return\n    }\n\n    const relatedTarget = {\n      relatedTarget: this._element\n    }\n\n    this._completeHide(relatedTarget)\n  }\n\n  dispose() {\n    if (this._popper) {\n      this._popper.destroy()\n    }\n\n    super.dispose()\n  }\n\n  update() {\n    this._inNavbar = this._detectNavbar()\n    if (this._popper) {\n      this._popper.update()\n    }\n  }\n\n  // Private\n  _completeHide(relatedTarget) {\n    const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE, relatedTarget)\n    if (hideEvent.defaultPrevented) {\n      return\n    }\n\n    // If this is a touch-enabled device we remove the extra\n    // empty mouseover listeners we added for iOS support\n    if ('ontouchstart' in document.documentElement) {\n      for (const element of [].concat(...document.body.children)) {\n        EventHandler.off(element, 'mouseover', noop)\n      }\n    }\n\n    if (this._popper) {\n      this._popper.destroy()\n    }\n\n    this._menu.classList.remove(CLASS_NAME_SHOW)\n    this._element.classList.remove(CLASS_NAME_SHOW)\n    this._element.setAttribute('aria-expanded', 'false')\n    Manipulator.removeDataAttribute(this._menu, 'popper')\n    EventHandler.trigger(this._element, EVENT_HIDDEN, relatedTarget)\n  }\n\n  _getConfig(config) {\n    config = super._getConfig(config)\n\n    if (typeof config.reference === 'object' && !isElement(config.reference) &&\n      typeof config.reference.getBoundingClientRect !== 'function'\n    ) {\n      // Popper virtual elements require a getBoundingClientRect method\n      throw new TypeError(`${NAME.toUpperCase()}: Option \"reference\" provided type \"object\" without a required \"getBoundingClientRect\" method.`)\n    }\n\n    return config\n  }\n\n  _createPopper() {\n    if (typeof Popper === 'undefined') {\n      throw new TypeError('Bootstrap\\'s dropdowns require Popper (https://popper.js.org)')\n    }\n\n    let referenceElement = this._element\n\n    if (this._config.reference === 'parent') {\n      referenceElement = this._parent\n    } else if (isElement(this._config.reference)) {\n      referenceElement = getElement(this._config.reference)\n    } else if (typeof this._config.reference === 'object') {\n      referenceElement = this._config.reference\n    }\n\n    const popperConfig = this._getPopperConfig()\n    this._popper = Popper.createPopper(referenceElement, this._menu, popperConfig)\n  }\n\n  _isShown() {\n    return this._menu.classList.contains(CLASS_NAME_SHOW)\n  }\n\n  _getPlacement() {\n    const parentDropdown = this._parent\n\n    if (parentDropdown.classList.contains(CLASS_NAME_DROPEND)) {\n      return PLACEMENT_RIGHT\n    }\n\n    if (parentDropdown.classList.contains(CLASS_NAME_DROPSTART)) {\n      return PLACEMENT_LEFT\n    }\n\n    if (parentDropdown.classList.contains(CLASS_NAME_DROPUP_CENTER)) {\n      return PLACEMENT_TOPCENTER\n    }\n\n    if (parentDropdown.classList.contains(CLASS_NAME_DROPDOWN_CENTER)) {\n      return PLACEMENT_BOTTOMCENTER\n    }\n\n    // We need to trim the value because custom properties can also include spaces\n    const isEnd = getComputedStyle(this._menu).getPropertyValue('--bs-position').trim() === 'end'\n\n    if (parentDropdown.classList.contains(CLASS_NAME_DROPUP)) {\n      return isEnd ? PLACEMENT_TOPEND : PLACEMENT_TOP\n    }\n\n    return isEnd ? PLACEMENT_BOTTOMEND : PLACEMENT_BOTTOM\n  }\n\n  _detectNavbar() {\n    return this._element.closest(SELECTOR_NAVBAR) !== null\n  }\n\n  _getOffset() {\n    const { offset } = this._config\n\n    if (typeof offset === 'string') {\n      return offset.split(',').map(value => Number.parseInt(value, 10))\n    }\n\n    if (typeof offset === 'function') {\n      return popperData => offset(popperData, this._element)\n    }\n\n    return offset\n  }\n\n  _getPopperConfig() {\n    const defaultBsPopperConfig = {\n      placement: this._getPlacement(),\n      modifiers: [{\n        name: 'preventOverflow',\n        options: {\n          boundary: this._config.boundary\n        }\n      },\n      {\n        name: 'offset',\n        options: {\n          offset: this._getOffset()\n        }\n      }]\n    }\n\n    // Disable Popper if we have a static display or Dropdown is in Navbar\n    if (this._inNavbar || this._config.display === 'static') {\n      Manipulator.setDataAttribute(this._menu, 'popper', 'static') // TODO: v6 remove\n      defaultBsPopperConfig.modifiers = [{\n        name: 'applyStyles',\n        enabled: false\n      }]\n    }\n\n    return {\n      ...defaultBsPopperConfig,\n      ...execute(this._config.popperConfig, [defaultBsPopperConfig])\n    }\n  }\n\n  _selectMenuItem({ key, target }) {\n    const items = SelectorEngine.find(SELECTOR_VISIBLE_ITEMS, this._menu).filter(element => isVisible(element))\n\n    if (!items.length) {\n      return\n    }\n\n    // if target isn't included in items (e.g. when expanding the dropdown)\n    // allow cycling to get the last item in case key equals ARROW_UP_KEY\n    getNextActiveElement(items, target, key === ARROW_DOWN_KEY, !items.includes(target)).focus()\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Dropdown.getOrCreateInstance(this, config)\n\n      if (typeof config !== 'string') {\n        return\n      }\n\n      if (typeof data[config] === 'undefined') {\n        throw new TypeError(`No method named \"${config}\"`)\n      }\n\n      data[config]()\n    })\n  }\n\n  static clearMenus(event) {\n    if (event.button === RIGHT_MOUSE_BUTTON || (event.type === 'keyup' && event.key !== TAB_KEY)) {\n      return\n    }\n\n    const openToggles = SelectorEngine.find(SELECTOR_DATA_TOGGLE_SHOWN)\n\n    for (const toggle of openToggles) {\n      const context = Dropdown.getInstance(toggle)\n      if (!context || context._config.autoClose === false) {\n        continue\n      }\n\n      const composedPath = event.composedPath()\n      const isMenuTarget = composedPath.includes(context._menu)\n      if (\n        composedPath.includes(context._element) ||\n        (context._config.autoClose === 'inside' && !isMenuTarget) ||\n        (context._config.autoClose === 'outside' && isMenuTarget)\n      ) {\n        continue\n      }\n\n      // Tab navigation through the dropdown menu or events from contained inputs shouldn't close the menu\n      if (context._menu.contains(event.target) && ((event.type === 'keyup' && event.key === TAB_KEY) || /input|select|option|textarea|form/i.test(event.target.tagName))) {\n        continue\n      }\n\n      const relatedTarget = { relatedTarget: context._element }\n\n      if (event.type === 'click') {\n        relatedTarget.clickEvent = event\n      }\n\n      context._completeHide(relatedTarget)\n    }\n  }\n\n  static dataApiKeydownHandler(event) {\n    // If not an UP | DOWN | ESCAPE key => not a dropdown command\n    // If input/textarea && if key is other than ESCAPE => not a dropdown command\n\n    const isInput = /input|textarea/i.test(event.target.tagName)\n    const isEscapeEvent = event.key === ESCAPE_KEY\n    const isUpOrDownEvent = [ARROW_UP_KEY, ARROW_DOWN_KEY].includes(event.key)\n\n    if (!isUpOrDownEvent && !isEscapeEvent) {\n      return\n    }\n\n    if (isInput && !isEscapeEvent) {\n      return\n    }\n\n    event.preventDefault()\n\n    // TODO: v6 revert #37011 & change markup https://getbootstrap.com/docs/5.3/forms/input-group/\n    const getToggleButton = this.matches(SELECTOR_DATA_TOGGLE) ?\n      this :\n      (SelectorEngine.prev(this, SELECTOR_DATA_TOGGLE)[0] ||\n        SelectorEngine.next(this, SELECTOR_DATA_TOGGLE)[0] ||\n        SelectorEngine.findOne(SELECTOR_DATA_TOGGLE, event.delegateTarget.parentNode))\n\n    const instance = Dropdown.getOrCreateInstance(getToggleButton)\n\n    if (isUpOrDownEvent) {\n      event.stopPropagation()\n      instance.show()\n      instance._selectMenuItem(event)\n      return\n    }\n\n    if (instance._isShown()) { // else is escape and we check if it is shown\n      event.stopPropagation()\n      instance.hide()\n      getToggleButton.focus()\n    }\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_KEYDOWN_DATA_API, SELECTOR_DATA_TOGGLE, Dropdown.dataApiKeydownHandler)\nEventHandler.on(document, EVENT_KEYDOWN_DATA_API, SELECTOR_MENU, Dropdown.dataApiKeydownHandler)\nEventHandler.on(document, EVENT_CLICK_DATA_API, Dropdown.clearMenus)\nEventHandler.on(document, EVENT_KEYUP_DATA_API, Dropdown.clearMenus)\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_TOGGLE, function (event) {\n  event.preventDefault()\n  Dropdown.getOrCreateInstance(this).toggle()\n})\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Dropdown)\n\nexport default Dropdown\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/backdrop.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport EventHandler from '../dom/event-handler.js'\nimport Config from './config.js'\nimport { execute, executeAfterTransition, getElement, reflow } from './index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'backdrop'\nconst CLASS_NAME_FADE = 'fade'\nconst CLASS_NAME_SHOW = 'show'\nconst EVENT_MOUSEDOWN = `mousedown.bs.${NAME}`\n\nconst Default = {\n  className: 'modal-backdrop',\n  clickCallback: null,\n  isAnimated: false,\n  isVisible: true, // if false, we use the backdrop helper without adding any element to the dom\n  rootElement: 'body' // give the choice to place backdrop under different elements\n}\n\nconst DefaultType = {\n  className: 'string',\n  clickCallback: '(function|null)',\n  isAnimated: 'boolean',\n  isVisible: 'boolean',\n  rootElement: '(element|string)'\n}\n\n/**\n * Class definition\n */\n\nclass Backdrop extends Config {\n  constructor(config) {\n    super()\n    this._config = this._getConfig(config)\n    this._isAppended = false\n    this._element = null\n  }\n\n  // Getters\n  static get Default() {\n    return Default\n  }\n\n  static get DefaultType() {\n    return DefaultType\n  }\n\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n  show(callback) {\n    if (!this._config.isVisible) {\n      execute(callback)\n      return\n    }\n\n    this._append()\n\n    const element = this._getElement()\n    if (this._config.isAnimated) {\n      reflow(element)\n    }\n\n    element.classList.add(CLASS_NAME_SHOW)\n\n    this._emulateAnimation(() => {\n      execute(callback)\n    })\n  }\n\n  hide(callback) {\n    if (!this._config.isVisible) {\n      execute(callback)\n      return\n    }\n\n    this._getElement().classList.remove(CLASS_NAME_SHOW)\n\n    this._emulateAnimation(() => {\n      this.dispose()\n      execute(callback)\n    })\n  }\n\n  dispose() {\n    if (!this._isAppended) {\n      return\n    }\n\n    EventHandler.off(this._element, EVENT_MOUSEDOWN)\n\n    this._element.remove()\n    this._isAppended = false\n  }\n\n  // Private\n  _getElement() {\n    if (!this._element) {\n      const backdrop = document.createElement('div')\n      backdrop.className = this._config.className\n      if (this._config.isAnimated) {\n        backdrop.classList.add(CLASS_NAME_FADE)\n      }\n\n      this._element = backdrop\n    }\n\n    return this._element\n  }\n\n  _configAfterMerge(config) {\n    // use getElement() with the default \"body\" to get a fresh Element on each instantiation\n    config.rootElement = getElement(config.rootElement)\n    return config\n  }\n\n  _append() {\n    if (this._isAppended) {\n      return\n    }\n\n    const element = this._getElement()\n    this._config.rootElement.append(element)\n\n    EventHandler.on(element, EVENT_MOUSEDOWN, () => {\n      execute(this._config.clickCallback)\n    })\n\n    this._isAppended = true\n  }\n\n  _emulateAnimation(callback) {\n    executeAfterTransition(callback, this._getElement(), this._config.isAnimated)\n  }\n}\n\nexport default Backdrop\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/focustrap.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport EventHandler from '../dom/event-handler.js'\nimport SelectorEngine from '../dom/selector-engine.js'\nimport Config from './config.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'focustrap'\nconst DATA_KEY = 'bs.focustrap'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst EVENT_FOCUSIN = `focusin${EVENT_KEY}`\nconst EVENT_KEYDOWN_TAB = `keydown.tab${EVENT_KEY}`\n\nconst TAB_KEY = 'Tab'\nconst TAB_NAV_FORWARD = 'forward'\nconst TAB_NAV_BACKWARD = 'backward'\n\nconst Default = {\n  autofocus: true,\n  trapElement: null // The element to trap focus inside of\n}\n\nconst DefaultType = {\n  autofocus: 'boolean',\n  trapElement: 'element'\n}\n\n/**\n * Class definition\n */\n\nclass FocusTrap extends Config {\n  constructor(config) {\n    super()\n    this._config = this._getConfig(config)\n    this._isActive = false\n    this._lastTabNavDirection = null\n  }\n\n  // Getters\n  static get Default() {\n    return Default\n  }\n\n  static get DefaultType() {\n    return DefaultType\n  }\n\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n  activate() {\n    if (this._isActive) {\n      return\n    }\n\n    if (this._config.autofocus) {\n      this._config.trapElement.focus()\n    }\n\n    EventHandler.off(document, EVENT_KEY) // guard against infinite focus loop\n    EventHandler.on(document, EVENT_FOCUSIN, event => this._handleFocusin(event))\n    EventHandler.on(document, EVENT_KEYDOWN_TAB, event => this._handleKeydown(event))\n\n    this._isActive = true\n  }\n\n  deactivate() {\n    if (!this._isActive) {\n      return\n    }\n\n    this._isActive = false\n    EventHandler.off(document, EVENT_KEY)\n  }\n\n  // Private\n  _handleFocusin(event) {\n    const { trapElement } = this._config\n\n    if (event.target === document || event.target === trapElement || trapElement.contains(event.target)) {\n      return\n    }\n\n    const elements = SelectorEngine.focusableChildren(trapElement)\n\n    if (elements.length === 0) {\n      trapElement.focus()\n    } else if (this._lastTabNavDirection === TAB_NAV_BACKWARD) {\n      elements[elements.length - 1].focus()\n    } else {\n      elements[0].focus()\n    }\n  }\n\n  _handleKeydown(event) {\n    if (event.key !== TAB_KEY) {\n      return\n    }\n\n    this._lastTabNavDirection = event.shiftKey ? TAB_NAV_BACKWARD : TAB_NAV_FORWARD\n  }\n}\n\nexport default FocusTrap\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/scrollBar.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport Manipulator from '../dom/manipulator.js'\nimport SelectorEngine from '../dom/selector-engine.js'\nimport { isElement } from './index.js'\n\n/**\n * Constants\n */\n\nconst SELECTOR_FIXED_CONTENT = '.fixed-top, .fixed-bottom, .is-fixed, .sticky-top'\nconst SELECTOR_STICKY_CONTENT = '.sticky-top'\nconst PROPERTY_PADDING = 'padding-right'\nconst PROPERTY_MARGIN = 'margin-right'\n\n/**\n * Class definition\n */\n\nclass ScrollBarHelper {\n  constructor() {\n    this._element = document.body\n  }\n\n  // Public\n  getWidth() {\n    // https://developer.mozilla.org/en-US/docs/Web/API/Window/innerWidth#usage_notes\n    const documentWidth = document.documentElement.clientWidth\n    return Math.abs(window.innerWidth - documentWidth)\n  }\n\n  hide() {\n    const width = this.getWidth()\n    this._disableOverFlow()\n    // give padding to element to balance the hidden scrollbar width\n    this._setElementAttributes(this._element, PROPERTY_PADDING, calculatedValue => calculatedValue + width)\n    // trick: We adjust positive paddingRight and negative marginRight to sticky-top elements to keep showing fullwidth\n    this._setElementAttributes(SELECTOR_FIXED_CONTENT, PROPERTY_PADDING, calculatedValue => calculatedValue + width)\n    this._setElementAttributes(SELECTOR_STICKY_CONTENT, PROPERTY_MARGIN, calculatedValue => calculatedValue - width)\n  }\n\n  reset() {\n    this._resetElementAttributes(this._element, 'overflow')\n    this._resetElementAttributes(this._element, PROPERTY_PADDING)\n    this._resetElementAttributes(SELECTOR_FIXED_CONTENT, PROPERTY_PADDING)\n    this._resetElementAttributes(SELECTOR_STICKY_CONTENT, PROPERTY_MARGIN)\n  }\n\n  isOverflowing() {\n    return this.getWidth() > 0\n  }\n\n  // Private\n  _disableOverFlow() {\n    this._saveInitialAttribute(this._element, 'overflow')\n    this._element.style.overflow = 'hidden'\n  }\n\n  _setElementAttributes(selector, styleProperty, callback) {\n    const scrollbarWidth = this.getWidth()\n    const manipulationCallBack = element => {\n      if (element !== this._element && window.innerWidth > element.clientWidth + scrollbarWidth) {\n        return\n      }\n\n      this._saveInitialAttribute(element, styleProperty)\n      const calculatedValue = window.getComputedStyle(element).getPropertyValue(styleProperty)\n      element.style.setProperty(styleProperty, `${callback(Number.parseFloat(calculatedValue))}px`)\n    }\n\n    this._applyManipulationCallback(selector, manipulationCallBack)\n  }\n\n  _saveInitialAttribute(element, styleProperty) {\n    const actualValue = element.style.getPropertyValue(styleProperty)\n    if (actualValue) {\n      Manipulator.setDataAttribute(element, styleProperty, actualValue)\n    }\n  }\n\n  _resetElementAttributes(selector, styleProperty) {\n    const manipulationCallBack = element => {\n      const value = Manipulator.getDataAttribute(element, styleProperty)\n      // We only want to remove the property if the value is `null`; the value can also be zero\n      if (value === null) {\n        element.style.removeProperty(styleProperty)\n        return\n      }\n\n      Manipulator.removeDataAttribute(element, styleProperty)\n      element.style.setProperty(styleProperty, value)\n    }\n\n    this._applyManipulationCallback(selector, manipulationCallBack)\n  }\n\n  _applyManipulationCallback(selector, callBack) {\n    if (isElement(selector)) {\n      callBack(selector)\n      return\n    }\n\n    for (const sel of SelectorEngine.find(selector, this._element)) {\n      callBack(sel)\n    }\n  }\n}\n\nexport default ScrollBarHelper\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap modal.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport SelectorEngine from './dom/selector-engine.js'\nimport Backdrop from './util/backdrop.js'\nimport { enableDismissTrigger } from './util/component-functions.js'\nimport FocusTrap from './util/focustrap.js'\nimport { defineJQueryPlugin, isRTL, isVisible, reflow } from './util/index.js'\nimport ScrollBarHelper from './util/scrollbar.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'modal'\nconst DATA_KEY = 'bs.modal'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\nconst ESCAPE_KEY = 'Escape'\n\nconst EVENT_HIDE = `hide${EVENT_KEY}`\nconst EVENT_HIDE_PREVENTED = `hidePrevented${EVENT_KEY}`\nconst EVENT_HIDDEN = `hidden${EVENT_KEY}`\nconst EVENT_SHOW = `show${EVENT_KEY}`\nconst EVENT_SHOWN = `shown${EVENT_KEY}`\nconst EVENT_RESIZE = `resize${EVENT_KEY}`\nconst EVENT_CLICK_DISMISS = `click.dismiss${EVENT_KEY}`\nconst EVENT_MOUSEDOWN_DISMISS = `mousedown.dismiss${EVENT_KEY}`\nconst EVENT_KEYDOWN_DISMISS = `keydown.dismiss${EVENT_KEY}`\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}${DATA_API_KEY}`\n\nconst CLASS_NAME_OPEN = 'modal-open'\nconst CLASS_NAME_FADE = 'fade'\nconst CLASS_NAME_SHOW = 'show'\nconst CLASS_NAME_STATIC = 'modal-static'\n\nconst OPEN_SELECTOR = '.modal.show'\nconst SELECTOR_DIALOG = '.modal-dialog'\nconst SELECTOR_MODAL_BODY = '.modal-body'\nconst SELECTOR_DATA_TOGGLE = '[data-bs-toggle=\"modal\"]'\n\nconst Default = {\n  backdrop: true,\n  focus: true,\n  keyboard: true\n}\n\nconst DefaultType = {\n  backdrop: '(boolean|string)',\n  focus: 'boolean',\n  keyboard: 'boolean'\n}\n\n/**\n * Class definition\n */\n\nclass Modal extends BaseComponent {\n  constructor(element, config) {\n    super(element, config)\n\n    this._dialog = SelectorEngine.findOne(SELECTOR_DIALOG, this._element)\n    this._backdrop = this._initializeBackDrop()\n    this._focustrap = this._initializeFocusTrap()\n    this._isShown = false\n    this._isTransitioning = false\n    this._scrollBar = new ScrollBarHelper()\n\n    this._addEventListeners()\n  }\n\n  // Getters\n  static get Default() {\n    return Default\n  }\n\n  static get DefaultType() {\n    return DefaultType\n  }\n\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n  toggle(relatedTarget) {\n    return this._isShown ? this.hide() : this.show(relatedTarget)\n  }\n\n  show(relatedTarget) {\n    if (this._isShown || this._isTransitioning) {\n      return\n    }\n\n    const showEvent = EventHandler.trigger(this._element, EVENT_SHOW, {\n      relatedTarget\n    })\n\n    if (showEvent.defaultPrevented) {\n      return\n    }\n\n    this._isShown = true\n    this._isTransitioning = true\n\n    this._scrollBar.hide()\n\n    document.body.classList.add(CLASS_NAME_OPEN)\n\n    this._adjustDialog()\n\n    this._backdrop.show(() => this._showElement(relatedTarget))\n  }\n\n  hide() {\n    if (!this._isShown || this._isTransitioning) {\n      return\n    }\n\n    const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE)\n\n    if (hideEvent.defaultPrevented) {\n      return\n    }\n\n    this._isShown = false\n    this._isTransitioning = true\n    this._focustrap.deactivate()\n\n    this._element.classList.remove(CLASS_NAME_SHOW)\n\n    this._queueCallback(() => this._hideModal(), this._element, this._isAnimated())\n  }\n\n  dispose() {\n    EventHandler.off(window, EVENT_KEY)\n    EventHandler.off(this._dialog, EVENT_KEY)\n\n    this._backdrop.dispose()\n    this._focustrap.deactivate()\n\n    super.dispose()\n  }\n\n  handleUpdate() {\n    this._adjustDialog()\n  }\n\n  // Private\n  _initializeBackDrop() {\n    return new Backdrop({\n      isVisible: Boolean(this._config.backdrop), // 'static' option will be translated to true, and booleans will keep their value,\n      isAnimated: this._isAnimated()\n    })\n  }\n\n  _initializeFocusTrap() {\n    return new FocusTrap({\n      trapElement: this._element\n    })\n  }\n\n  _showElement(relatedTarget) {\n    // try to append dynamic modal\n    if (!document.body.contains(this._element)) {\n      document.body.append(this._element)\n    }\n\n    this._element.style.display = 'block'\n    this._element.removeAttribute('aria-hidden')\n    this._element.setAttribute('aria-modal', true)\n    this._element.setAttribute('role', 'dialog')\n    this._element.scrollTop = 0\n\n    const modalBody = SelectorEngine.findOne(SELECTOR_MODAL_BODY, this._dialog)\n    if (modalBody) {\n      modalBody.scrollTop = 0\n    }\n\n    reflow(this._element)\n\n    this._element.classList.add(CLASS_NAME_SHOW)\n\n    const transitionComplete = () => {\n      if (this._config.focus) {\n        this._focustrap.activate()\n      }\n\n      this._isTransitioning = false\n      EventHandler.trigger(this._element, EVENT_SHOWN, {\n        relatedTarget\n      })\n    }\n\n    this._queueCallback(transitionComplete, this._dialog, this._isAnimated())\n  }\n\n  _addEventListeners() {\n    EventHandler.on(this._element, EVENT_KEYDOWN_DISMISS, event => {\n      if (event.key !== ESCAPE_KEY) {\n        return\n      }\n\n      if (this._config.keyboard) {\n        this.hide()\n        return\n      }\n\n      this._triggerBackdropTransition()\n    })\n\n    EventHandler.on(window, EVENT_RESIZE, () => {\n      if (this._isShown && !this._isTransitioning) {\n        this._adjustDialog()\n      }\n    })\n\n    EventHandler.on(this._element, EVENT_MOUSEDOWN_DISMISS, event => {\n      // a bad trick to segregate clicks that may start inside dialog but end outside, and avoid listen to scrollbar clicks\n      EventHandler.one(this._element, EVENT_CLICK_DISMISS, event2 => {\n        if (this._element !== event.target || this._element !== event2.target) {\n          return\n        }\n\n        if (this._config.backdrop === 'static') {\n          this._triggerBackdropTransition()\n          return\n        }\n\n        if (this._config.backdrop) {\n          this.hide()\n        }\n      })\n    })\n  }\n\n  _hideModal() {\n    this._element.style.display = 'none'\n    this._element.setAttribute('aria-hidden', true)\n    this._element.removeAttribute('aria-modal')\n    this._element.removeAttribute('role')\n    this._isTransitioning = false\n\n    this._backdrop.hide(() => {\n      document.body.classList.remove(CLASS_NAME_OPEN)\n      this._resetAdjustments()\n      this._scrollBar.reset()\n      EventHandler.trigger(this._element, EVENT_HIDDEN)\n    })\n  }\n\n  _isAnimated() {\n    return this._element.classList.contains(CLASS_NAME_FADE)\n  }\n\n  _triggerBackdropTransition() {\n    const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED)\n    if (hideEvent.defaultPrevented) {\n      return\n    }\n\n    const isModalOverflowing = this._element.scrollHeight > document.documentElement.clientHeight\n    const initialOverflowY = this._element.style.overflowY\n    // return if the following background transition hasn't yet completed\n    if (initialOverflowY === 'hidden' || this._element.classList.contains(CLASS_NAME_STATIC)) {\n      return\n    }\n\n    if (!isModalOverflowing) {\n      this._element.style.overflowY = 'hidden'\n    }\n\n    this._element.classList.add(CLASS_NAME_STATIC)\n    this._queueCallback(() => {\n      this._element.classList.remove(CLASS_NAME_STATIC)\n      this._queueCallback(() => {\n        this._element.style.overflowY = initialOverflowY\n      }, this._dialog)\n    }, this._dialog)\n\n    this._element.focus()\n  }\n\n  /**\n   * The following methods are used to handle overflowing modals\n   */\n\n  _adjustDialog() {\n    const isModalOverflowing = this._element.scrollHeight > document.documentElement.clientHeight\n    const scrollbarWidth = this._scrollBar.getWidth()\n    const isBodyOverflowing = scrollbarWidth > 0\n\n    if (isBodyOverflowing && !isModalOverflowing) {\n      const property = isRTL() ? 'paddingLeft' : 'paddingRight'\n      this._element.style[property] = `${scrollbarWidth}px`\n    }\n\n    if (!isBodyOverflowing && isModalOverflowing) {\n      const property = isRTL() ? 'paddingRight' : 'paddingLeft'\n      this._element.style[property] = `${scrollbarWidth}px`\n    }\n  }\n\n  _resetAdjustments() {\n    this._element.style.paddingLeft = ''\n    this._element.style.paddingRight = ''\n  }\n\n  // Static\n  static jQueryInterface(config, relatedTarget) {\n    return this.each(function () {\n      const data = Modal.getOrCreateInstance(this, config)\n\n      if (typeof config !== 'string') {\n        return\n      }\n\n      if (typeof data[config] === 'undefined') {\n        throw new TypeError(`No method named \"${config}\"`)\n      }\n\n      data[config](relatedTarget)\n    })\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_TOGGLE, function (event) {\n  const target = SelectorEngine.getElementFromSelector(this)\n\n  if (['A', 'AREA'].includes(this.tagName)) {\n    event.preventDefault()\n  }\n\n  EventHandler.one(target, EVENT_SHOW, showEvent => {\n    if (showEvent.defaultPrevented) {\n      // only register focus restorer if modal will actually get shown\n      return\n    }\n\n    EventHandler.one(target, EVENT_HIDDEN, () => {\n      if (isVisible(this)) {\n        this.focus()\n      }\n    })\n  })\n\n  // avoid conflict when clicking modal toggler while another one is open\n  const alreadyOpen = SelectorEngine.findOne(OPEN_SELECTOR)\n  if (alreadyOpen) {\n    Modal.getInstance(alreadyOpen).hide()\n  }\n\n  const data = Modal.getOrCreateInstance(target)\n\n  data.toggle(this)\n})\n\nenableDismissTrigger(Modal)\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Modal)\n\nexport default Modal\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap offcanvas.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport SelectorEngine from './dom/selector-engine.js'\nimport Backdrop from './util/backdrop.js'\nimport { enableDismissTrigger } from './util/component-functions.js'\nimport FocusTrap from './util/focustrap.js'\nimport {\n  defineJQueryPlugin,\n  isDisabled,\n  isVisible\n} from './util/index.js'\nimport ScrollBarHelper from './util/scrollbar.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'offcanvas'\nconst DATA_KEY = 'bs.offcanvas'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\nconst EVENT_LOAD_DATA_API = `load${EVENT_KEY}${DATA_API_KEY}`\nconst ESCAPE_KEY = 'Escape'\n\nconst CLASS_NAME_SHOW = 'show'\nconst CLASS_NAME_SHOWING = 'showing'\nconst CLASS_NAME_HIDING = 'hiding'\nconst CLASS_NAME_BACKDROP = 'offcanvas-backdrop'\nconst OPEN_SELECTOR = '.offcanvas.show'\n\nconst EVENT_SHOW = `show${EVENT_KEY}`\nconst EVENT_SHOWN = `shown${EVENT_KEY}`\nconst EVENT_HIDE = `hide${EVENT_KEY}`\nconst EVENT_HIDE_PREVENTED = `hidePrevented${EVENT_KEY}`\nconst EVENT_HIDDEN = `hidden${EVENT_KEY}`\nconst EVENT_RESIZE = `resize${EVENT_KEY}`\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}${DATA_API_KEY}`\nconst EVENT_KEYDOWN_DISMISS = `keydown.dismiss${EVENT_KEY}`\n\nconst SELECTOR_DATA_TOGGLE = '[data-bs-toggle=\"offcanvas\"]'\n\nconst Default = {\n  backdrop: true,\n  keyboard: true,\n  scroll: false\n}\n\nconst DefaultType = {\n  backdrop: '(boolean|string)',\n  keyboard: 'boolean',\n  scroll: 'boolean'\n}\n\n/**\n * Class definition\n */\n\nclass Offcanvas extends BaseComponent {\n  constructor(element, config) {\n    super(element, config)\n\n    this._isShown = false\n    this._backdrop = this._initializeBackDrop()\n    this._focustrap = this._initializeFocusTrap()\n    this._addEventListeners()\n  }\n\n  // Getters\n  static get Default() {\n    return Default\n  }\n\n  static get DefaultType() {\n    return DefaultType\n  }\n\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n  toggle(relatedTarget) {\n    return this._isShown ? this.hide() : this.show(relatedTarget)\n  }\n\n  show(relatedTarget) {\n    if (this._isShown) {\n      return\n    }\n\n    const showEvent = EventHandler.trigger(this._element, EVENT_SHOW, { relatedTarget })\n\n    if (showEvent.defaultPrevented) {\n      return\n    }\n\n    this._isShown = true\n    this._backdrop.show()\n\n    if (!this._config.scroll) {\n      new ScrollBarHelper().hide()\n    }\n\n    this._element.setAttribute('aria-modal', true)\n    this._element.setAttribute('role', 'dialog')\n    this._element.classList.add(CLASS_NAME_SHOWING)\n\n    const completeCallBack = () => {\n      if (!this._config.scroll || this._config.backdrop) {\n        this._focustrap.activate()\n      }\n\n      this._element.classList.add(CLASS_NAME_SHOW)\n      this._element.classList.remove(CLASS_NAME_SHOWING)\n      EventHandler.trigger(this._element, EVENT_SHOWN, { relatedTarget })\n    }\n\n    this._queueCallback(completeCallBack, this._element, true)\n  }\n\n  hide() {\n    if (!this._isShown) {\n      return\n    }\n\n    const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE)\n\n    if (hideEvent.defaultPrevented) {\n      return\n    }\n\n    this._focustrap.deactivate()\n    this._element.blur()\n    this._isShown = false\n    this._element.classList.add(CLASS_NAME_HIDING)\n    this._backdrop.hide()\n\n    const completeCallback = () => {\n      this._element.classList.remove(CLASS_NAME_SHOW, CLASS_NAME_HIDING)\n      this._element.removeAttribute('aria-modal')\n      this._element.removeAttribute('role')\n\n      if (!this._config.scroll) {\n        new ScrollBarHelper().reset()\n      }\n\n      EventHandler.trigger(this._element, EVENT_HIDDEN)\n    }\n\n    this._queueCallback(completeCallback, this._element, true)\n  }\n\n  dispose() {\n    this._backdrop.dispose()\n    this._focustrap.deactivate()\n    super.dispose()\n  }\n\n  // Private\n  _initializeBackDrop() {\n    const clickCallback = () => {\n      if (this._config.backdrop === 'static') {\n        EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED)\n        return\n      }\n\n      this.hide()\n    }\n\n    // 'static' option will be translated to true, and booleans will keep their value\n    const isVisible = Boolean(this._config.backdrop)\n\n    return new Backdrop({\n      className: CLASS_NAME_BACKDROP,\n      isVisible,\n      isAnimated: true,\n      rootElement: this._element.parentNode,\n      clickCallback: isVisible ? clickCallback : null\n    })\n  }\n\n  _initializeFocusTrap() {\n    return new FocusTrap({\n      trapElement: this._element\n    })\n  }\n\n  _addEventListeners() {\n    EventHandler.on(this._element, EVENT_KEYDOWN_DISMISS, event => {\n      if (event.key !== ESCAPE_KEY) {\n        return\n      }\n\n      if (this._config.keyboard) {\n        this.hide()\n        return\n      }\n\n      EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED)\n    })\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Offcanvas.getOrCreateInstance(this, config)\n\n      if (typeof config !== 'string') {\n        return\n      }\n\n      if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n        throw new TypeError(`No method named \"${config}\"`)\n      }\n\n      data[config](this)\n    })\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_TOGGLE, function (event) {\n  const target = SelectorEngine.getElementFromSelector(this)\n\n  if (['A', 'AREA'].includes(this.tagName)) {\n    event.preventDefault()\n  }\n\n  if (isDisabled(this)) {\n    return\n  }\n\n  EventHandler.one(target, EVENT_HIDDEN, () => {\n    // focus on trigger when it is closed\n    if (isVisible(this)) {\n      this.focus()\n    }\n  })\n\n  // avoid conflict when clicking a toggler of an offcanvas, while another is open\n  const alreadyOpen = SelectorEngine.findOne(OPEN_SELECTOR)\n  if (alreadyOpen && alreadyOpen !== target) {\n    Offcanvas.getInstance(alreadyOpen).hide()\n  }\n\n  const data = Offcanvas.getOrCreateInstance(target)\n  data.toggle(this)\n})\n\nEventHandler.on(window, EVENT_LOAD_DATA_API, () => {\n  for (const selector of SelectorEngine.find(OPEN_SELECTOR)) {\n    Offcanvas.getOrCreateInstance(selector).show()\n  }\n})\n\nEventHandler.on(window, EVENT_RESIZE, () => {\n  for (const element of SelectorEngine.find('[aria-modal][class*=show][class*=offcanvas-]')) {\n    if (getComputedStyle(element).position !== 'fixed') {\n      Offcanvas.getOrCreateInstance(element).hide()\n    }\n  }\n})\n\nenableDismissTrigger(Offcanvas)\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Offcanvas)\n\nexport default Offcanvas\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/sanitizer.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n// js-docs-start allow-list\nconst ARIA_ATTRIBUTE_PATTERN = /^aria-[\\w-]*$/i\n\nexport const DefaultAllowlist = {\n  // Global attributes allowed on any supplied element below.\n  '*': ['class', 'dir', 'id', 'lang', 'role', ARIA_ATTRIBUTE_PATTERN],\n  a: ['target', 'href', 'title', 'rel'],\n  area: [],\n  b: [],\n  br: [],\n  col: [],\n  code: [],\n  div: [],\n  em: [],\n  hr: [],\n  h1: [],\n  h2: [],\n  h3: [],\n  h4: [],\n  h5: [],\n  h6: [],\n  i: [],\n  img: ['src', 'srcset', 'alt', 'title', 'width', 'height'],\n  li: [],\n  ol: [],\n  p: [],\n  pre: [],\n  s: [],\n  small: [],\n  span: [],\n  sub: [],\n  sup: [],\n  strong: [],\n  u: [],\n  ul: []\n}\n// js-docs-end allow-list\n\nconst uriAttributes = new Set([\n  'background',\n  'cite',\n  'href',\n  'itemtype',\n  'longdesc',\n  'poster',\n  'src',\n  'xlink:href'\n])\n\n/**\n * A pattern that recognizes URLs that are safe wrt. XSS in URL navigation\n * contexts.\n *\n * Shout-out to Angular https://github.com/angular/angular/blob/15.2.8/packages/core/src/sanitization/url_sanitizer.ts#L38\n */\n// eslint-disable-next-line unicorn/better-regex\nconst SAFE_URL_PATTERN = /^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i\n\nconst allowedAttribute = (attribute, allowedAttributeList) => {\n  const attributeName = attribute.nodeName.toLowerCase()\n\n  if (allowedAttributeList.includes(attributeName)) {\n    if (uriAttributes.has(attributeName)) {\n      return Boolean(SAFE_URL_PATTERN.test(attribute.nodeValue))\n    }\n\n    return true\n  }\n\n  // Check if a regular expression validates the attribute.\n  return allowedAttributeList.filter(attributeRegex => attributeRegex instanceof RegExp)\n    .some(regex => regex.test(attributeName))\n}\n\nexport function sanitizeHtml(unsafeHtml, allowList, sanitizeFunction) {\n  if (!unsafeHtml.length) {\n    return unsafeHtml\n  }\n\n  if (sanitizeFunction && typeof sanitizeFunction === 'function') {\n    return sanitizeFunction(unsafeHtml)\n  }\n\n  const domParser = new window.DOMParser()\n  const createdDocument = domParser.parseFromString(unsafeHtml, 'text/html')\n  const elements = [].concat(...createdDocument.body.querySelectorAll('*'))\n\n  for (const element of elements) {\n    const elementName = element.nodeName.toLowerCase()\n\n    if (!Object.keys(allowList).includes(elementName)) {\n      element.remove()\n      continue\n    }\n\n    const attributeList = [].concat(...element.attributes)\n    const allowedAttributes = [].concat(allowList['*'] || [], allowList[elementName] || [])\n\n    for (const attribute of attributeList) {\n      if (!allowedAttribute(attribute, allowedAttributes)) {\n        element.removeAttribute(attribute.nodeName)\n      }\n    }\n  }\n\n  return createdDocument.body.innerHTML\n}\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/template-factory.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport SelectorEngine from '../dom/selector-engine.js'\nimport Config from './config.js'\nimport { DefaultAllowlist, sanitizeHtml } from './sanitizer.js'\nimport { execute, getElement, isElement } from './index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'TemplateFactory'\n\nconst Default = {\n  allowList: DefaultAllowlist,\n  content: {}, // { selector : text ,  selector2 : text2 , }\n  extraClass: '',\n  html: false,\n  sanitize: true,\n  sanitizeFn: null,\n  template: '<div></div>'\n}\n\nconst DefaultType = {\n  allowList: 'object',\n  content: 'object',\n  extraClass: '(string|function)',\n  html: 'boolean',\n  sanitize: 'boolean',\n  sanitizeFn: '(null|function)',\n  template: 'string'\n}\n\nconst DefaultContentType = {\n  entry: '(string|element|function|null)',\n  selector: '(string|element)'\n}\n\n/**\n * Class definition\n */\n\nclass TemplateFactory extends Config {\n  constructor(config) {\n    super()\n    this._config = this._getConfig(config)\n  }\n\n  // Getters\n  static get Default() {\n    return Default\n  }\n\n  static get DefaultType() {\n    return DefaultType\n  }\n\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n  getContent() {\n    return Object.values(this._config.content)\n      .map(config => this._resolvePossibleFunction(config))\n      .filter(Boolean)\n  }\n\n  hasContent() {\n    return this.getContent().length > 0\n  }\n\n  changeContent(content) {\n    this._checkContent(content)\n    this._config.content = { ...this._config.content, ...content }\n    return this\n  }\n\n  toHtml() {\n    const templateWrapper = document.createElement('div')\n    templateWrapper.innerHTML = this._maybeSanitize(this._config.template)\n\n    for (const [selector, text] of Object.entries(this._config.content)) {\n      this._setContent(templateWrapper, text, selector)\n    }\n\n    const template = templateWrapper.children[0]\n    const extraClass = this._resolvePossibleFunction(this._config.extraClass)\n\n    if (extraClass) {\n      template.classList.add(...extraClass.split(' '))\n    }\n\n    return template\n  }\n\n  // Private\n  _typeCheckConfig(config) {\n    super._typeCheckConfig(config)\n    this._checkContent(config.content)\n  }\n\n  _checkContent(arg) {\n    for (const [selector, content] of Object.entries(arg)) {\n      super._typeCheckConfig({ selector, entry: content }, DefaultContentType)\n    }\n  }\n\n  _setContent(template, content, selector) {\n    const templateElement = SelectorEngine.findOne(selector, template)\n\n    if (!templateElement) {\n      return\n    }\n\n    content = this._resolvePossibleFunction(content)\n\n    if (!content) {\n      templateElement.remove()\n      return\n    }\n\n    if (isElement(content)) {\n      this._putElementInTemplate(getElement(content), templateElement)\n      return\n    }\n\n    if (this._config.html) {\n      templateElement.innerHTML = this._maybeSanitize(content)\n      return\n    }\n\n    templateElement.textContent = content\n  }\n\n  _maybeSanitize(arg) {\n    return this._config.sanitize ? sanitizeHtml(arg, this._config.allowList, this._config.sanitizeFn) : arg\n  }\n\n  _resolvePossibleFunction(arg) {\n    return execute(arg, [this])\n  }\n\n  _putElementInTemplate(element, templateElement) {\n    if (this._config.html) {\n      templateElement.innerHTML = ''\n      templateElement.append(element)\n      return\n    }\n\n    templateElement.textContent = element.textContent\n  }\n}\n\nexport default TemplateFactory\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap tooltip.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport * as Popper from '@popperjs/core'\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport Manipulator from './dom/manipulator.js'\nimport { defineJQueryPlugin, execute, findShadowRoot, getElement, getUID, isRTL, noop } from './util/index.js'\nimport { DefaultAllowlist } from './util/sanitizer.js'\nimport TemplateFactory from './util/template-factory.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'tooltip'\nconst DISALLOWED_ATTRIBUTES = new Set(['sanitize', 'allowList', 'sanitizeFn'])\n\nconst CLASS_NAME_FADE = 'fade'\nconst CLASS_NAME_MODAL = 'modal'\nconst CLASS_NAME_SHOW = 'show'\n\nconst SELECTOR_TOOLTIP_INNER = '.tooltip-inner'\nconst SELECTOR_MODAL = `.${CLASS_NAME_MODAL}`\n\nconst EVENT_MODAL_HIDE = 'hide.bs.modal'\n\nconst TRIGGER_HOVER = 'hover'\nconst TRIGGER_FOCUS = 'focus'\nconst TRIGGER_CLICK = 'click'\nconst TRIGGER_MANUAL = 'manual'\n\nconst EVENT_HIDE = 'hide'\nconst EVENT_HIDDEN = 'hidden'\nconst EVENT_SHOW = 'show'\nconst EVENT_SHOWN = 'shown'\nconst EVENT_INSERTED = 'inserted'\nconst EVENT_CLICK = 'click'\nconst EVENT_FOCUSIN = 'focusin'\nconst EVENT_FOCUSOUT = 'focusout'\nconst EVENT_MOUSEENTER = 'mouseenter'\nconst EVENT_MOUSELEAVE = 'mouseleave'\n\nconst AttachmentMap = {\n  AUTO: 'auto',\n  TOP: 'top',\n  RIGHT: isRTL() ? 'left' : 'right',\n  BOTTOM: 'bottom',\n  LEFT: isRTL() ? 'right' : 'left'\n}\n\nconst Default = {\n  allowList: DefaultAllowlist,\n  animation: true,\n  boundary: 'clippingParents',\n  container: false,\n  customClass: '',\n  delay: 0,\n  fallbackPlacements: ['top', 'right', 'bottom', 'left'],\n  html: false,\n  offset: [0, 6],\n  placement: 'top',\n  popperConfig: null,\n  sanitize: true,\n  sanitizeFn: null,\n  selector: false,\n  template: '<div class=\"tooltip\" role=\"tooltip\">' +\n            '<div class=\"tooltip-arrow\"></div>' +\n            '<div class=\"tooltip-inner\"></div>' +\n            '</div>',\n  title: '',\n  trigger: 'hover focus'\n}\n\nconst DefaultType = {\n  allowList: 'object',\n  animation: 'boolean',\n  boundary: '(string|element)',\n  container: '(string|element|boolean)',\n  customClass: '(string|function)',\n  delay: '(number|object)',\n  fallbackPlacements: 'array',\n  html: 'boolean',\n  offset: '(array|string|function)',\n  placement: '(string|function)',\n  popperConfig: '(null|object|function)',\n  sanitize: 'boolean',\n  sanitizeFn: '(null|function)',\n  selector: '(string|boolean)',\n  template: 'string',\n  title: '(string|element|function)',\n  trigger: 'string'\n}\n\n/**\n * Class definition\n */\n\nclass Tooltip extends BaseComponent {\n  constructor(element, config) {\n    if (typeof Popper === 'undefined') {\n      throw new TypeError('Bootstrap\\'s tooltips require Popper (https://popper.js.org)')\n    }\n\n    super(element, config)\n\n    // Private\n    this._isEnabled = true\n    this._timeout = 0\n    this._isHovered = null\n    this._activeTrigger = {}\n    this._popper = null\n    this._templateFactory = null\n    this._newContent = null\n\n    // Protected\n    this.tip = null\n\n    this._setListeners()\n\n    if (!this._config.selector) {\n      this._fixTitle()\n    }\n  }\n\n  // Getters\n  static get Default() {\n    return Default\n  }\n\n  static get DefaultType() {\n    return DefaultType\n  }\n\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n  enable() {\n    this._isEnabled = true\n  }\n\n  disable() {\n    this._isEnabled = false\n  }\n\n  toggleEnabled() {\n    this._isEnabled = !this._isEnabled\n  }\n\n  toggle() {\n    if (!this._isEnabled) {\n      return\n    }\n\n    this._activeTrigger.click = !this._activeTrigger.click\n    if (this._isShown()) {\n      this._leave()\n      return\n    }\n\n    this._enter()\n  }\n\n  dispose() {\n    clearTimeout(this._timeout)\n\n    EventHandler.off(this._element.closest(SELECTOR_MODAL), EVENT_MODAL_HIDE, this._hideModalHandler)\n\n    if (this._element.getAttribute('data-bs-original-title')) {\n      this._element.setAttribute('title', this._element.getAttribute('data-bs-original-title'))\n    }\n\n    this._disposePopper()\n    super.dispose()\n  }\n\n  show() {\n    if (this._element.style.display === 'none') {\n      throw new Error('Please use show on visible elements')\n    }\n\n    if (!(this._isWithContent() && this._isEnabled)) {\n      return\n    }\n\n    const showEvent = EventHandler.trigger(this._element, this.constructor.eventName(EVENT_SHOW))\n    const shadowRoot = findShadowRoot(this._element)\n    const isInTheDom = (shadowRoot || this._element.ownerDocument.documentElement).contains(this._element)\n\n    if (showEvent.defaultPrevented || !isInTheDom) {\n      return\n    }\n\n    // TODO: v6 remove this or make it optional\n    this._disposePopper()\n\n    const tip = this._getTipElement()\n\n    this._element.setAttribute('aria-describedby', tip.getAttribute('id'))\n\n    const { container } = this._config\n\n    if (!this._element.ownerDocument.documentElement.contains(this.tip)) {\n      container.append(tip)\n      EventHandler.trigger(this._element, this.constructor.eventName(EVENT_INSERTED))\n    }\n\n    this._popper = this._createPopper(tip)\n\n    tip.classList.add(CLASS_NAME_SHOW)\n\n    // If this is a touch-enabled device we add extra\n    // empty mouseover listeners to the body's immediate children;\n    // only needed because of broken event delegation on iOS\n    // https://www.quirksmode.org/blog/archives/2014/02/mouse_event_bub.html\n    if ('ontouchstart' in document.documentElement) {\n      for (const element of [].concat(...document.body.children)) {\n        EventHandler.on(element, 'mouseover', noop)\n      }\n    }\n\n    const complete = () => {\n      EventHandler.trigger(this._element, this.constructor.eventName(EVENT_SHOWN))\n\n      if (this._isHovered === false) {\n        this._leave()\n      }\n\n      this._isHovered = false\n    }\n\n    this._queueCallback(complete, this.tip, this._isAnimated())\n  }\n\n  hide() {\n    if (!this._isShown()) {\n      return\n    }\n\n    const hideEvent = EventHandler.trigger(this._element, this.constructor.eventName(EVENT_HIDE))\n    if (hideEvent.defaultPrevented) {\n      return\n    }\n\n    const tip = this._getTipElement()\n    tip.classList.remove(CLASS_NAME_SHOW)\n\n    // If this is a touch-enabled device we remove the extra\n    // empty mouseover listeners we added for iOS support\n    if ('ontouchstart' in document.documentElement) {\n      for (const element of [].concat(...document.body.children)) {\n        EventHandler.off(element, 'mouseover', noop)\n      }\n    }\n\n    this._activeTrigger[TRIGGER_CLICK] = false\n    this._activeTrigger[TRIGGER_FOCUS] = false\n    this._activeTrigger[TRIGGER_HOVER] = false\n    this._isHovered = null // it is a trick to support manual triggering\n\n    const complete = () => {\n      if (this._isWithActiveTrigger()) {\n        return\n      }\n\n      if (!this._isHovered) {\n        this._disposePopper()\n      }\n\n      this._element.removeAttribute('aria-describedby')\n      EventHandler.trigger(this._element, this.constructor.eventName(EVENT_HIDDEN))\n    }\n\n    this._queueCallback(complete, this.tip, this._isAnimated())\n  }\n\n  update() {\n    if (this._popper) {\n      this._popper.update()\n    }\n  }\n\n  // Protected\n  _isWithContent() {\n    return Boolean(this._getTitle())\n  }\n\n  _getTipElement() {\n    if (!this.tip) {\n      this.tip = this._createTipElement(this._newContent || this._getContentForTemplate())\n    }\n\n    return this.tip\n  }\n\n  _createTipElement(content) {\n    const tip = this._getTemplateFactory(content).toHtml()\n\n    // TODO: remove this check in v6\n    if (!tip) {\n      return null\n    }\n\n    tip.classList.remove(CLASS_NAME_FADE, CLASS_NAME_SHOW)\n    // TODO: v6 the following can be achieved with CSS only\n    tip.classList.add(`bs-${this.constructor.NAME}-auto`)\n\n    const tipId = getUID(this.constructor.NAME).toString()\n\n    tip.setAttribute('id', tipId)\n\n    if (this._isAnimated()) {\n      tip.classList.add(CLASS_NAME_FADE)\n    }\n\n    return tip\n  }\n\n  setContent(content) {\n    this._newContent = content\n    if (this._isShown()) {\n      this._disposePopper()\n      this.show()\n    }\n  }\n\n  _getTemplateFactory(content) {\n    if (this._templateFactory) {\n      this._templateFactory.changeContent(content)\n    } else {\n      this._templateFactory = new TemplateFactory({\n        ...this._config,\n        // the `content` var has to be after `this._config`\n        // to override config.content in case of popover\n        content,\n        extraClass: this._resolvePossibleFunction(this._config.customClass)\n      })\n    }\n\n    return this._templateFactory\n  }\n\n  _getContentForTemplate() {\n    return {\n      [SELECTOR_TOOLTIP_INNER]: this._getTitle()\n    }\n  }\n\n  _getTitle() {\n    return this._resolvePossibleFunction(this._config.title) || this._element.getAttribute('data-bs-original-title')\n  }\n\n  // Private\n  _initializeOnDelegatedTarget(event) {\n    return this.constructor.getOrCreateInstance(event.delegateTarget, this._getDelegateConfig())\n  }\n\n  _isAnimated() {\n    return this._config.animation || (this.tip && this.tip.classList.contains(CLASS_NAME_FADE))\n  }\n\n  _isShown() {\n    return this.tip && this.tip.classList.contains(CLASS_NAME_SHOW)\n  }\n\n  _createPopper(tip) {\n    const placement = execute(this._config.placement, [this, tip, this._element])\n    const attachment = AttachmentMap[placement.toUpperCase()]\n    return Popper.createPopper(this._element, tip, this._getPopperConfig(attachment))\n  }\n\n  _getOffset() {\n    const { offset } = this._config\n\n    if (typeof offset === 'string') {\n      return offset.split(',').map(value => Number.parseInt(value, 10))\n    }\n\n    if (typeof offset === 'function') {\n      return popperData => offset(popperData, this._element)\n    }\n\n    return offset\n  }\n\n  _resolvePossibleFunction(arg) {\n    return execute(arg, [this._element])\n  }\n\n  _getPopperConfig(attachment) {\n    const defaultBsPopperConfig = {\n      placement: attachment,\n      modifiers: [\n        {\n          name: 'flip',\n          options: {\n            fallbackPlacements: this._config.fallbackPlacements\n          }\n        },\n        {\n          name: 'offset',\n          options: {\n            offset: this._getOffset()\n          }\n        },\n        {\n          name: 'preventOverflow',\n          options: {\n            boundary: this._config.boundary\n          }\n        },\n        {\n          name: 'arrow',\n          options: {\n            element: `.${this.constructor.NAME}-arrow`\n          }\n        },\n        {\n          name: 'preSetPlacement',\n          enabled: true,\n          phase: 'beforeMain',\n          fn: data => {\n            // Pre-set Popper's placement attribute in order to read the arrow sizes properly.\n            // Otherwise, Popper mixes up the width and height dimensions since the initial arrow style is for top placement\n            this._getTipElement().setAttribute('data-popper-placement', data.state.placement)\n          }\n        }\n      ]\n    }\n\n    return {\n      ...defaultBsPopperConfig,\n      ...execute(this._config.popperConfig, [defaultBsPopperConfig])\n    }\n  }\n\n  _setListeners() {\n    const triggers = this._config.trigger.split(' ')\n\n    for (const trigger of triggers) {\n      if (trigger === 'click') {\n        EventHandler.on(this._element, this.constructor.eventName(EVENT_CLICK), this._config.selector, event => {\n          const context = this._initializeOnDelegatedTarget(event)\n          context.toggle()\n        })\n      } else if (trigger !== TRIGGER_MANUAL) {\n        const eventIn = trigger === TRIGGER_HOVER ?\n          this.constructor.eventName(EVENT_MOUSEENTER) :\n          this.constructor.eventName(EVENT_FOCUSIN)\n        const eventOut = trigger === TRIGGER_HOVER ?\n          this.constructor.eventName(EVENT_MOUSELEAVE) :\n          this.constructor.eventName(EVENT_FOCUSOUT)\n\n        EventHandler.on(this._element, eventIn, this._config.selector, event => {\n          const context = this._initializeOnDelegatedTarget(event)\n          context._activeTrigger[event.type === 'focusin' ? TRIGGER_FOCUS : TRIGGER_HOVER] = true\n          context._enter()\n        })\n        EventHandler.on(this._element, eventOut, this._config.selector, event => {\n          const context = this._initializeOnDelegatedTarget(event)\n          context._activeTrigger[event.type === 'focusout' ? TRIGGER_FOCUS : TRIGGER_HOVER] =\n            context._element.contains(event.relatedTarget)\n\n          context._leave()\n        })\n      }\n    }\n\n    this._hideModalHandler = () => {\n      if (this._element) {\n        this.hide()\n      }\n    }\n\n    EventHandler.on(this._element.closest(SELECTOR_MODAL), EVENT_MODAL_HIDE, this._hideModalHandler)\n  }\n\n  _fixTitle() {\n    const title = this._element.getAttribute('title')\n\n    if (!title) {\n      return\n    }\n\n    if (!this._element.getAttribute('aria-label') && !this._element.textContent.trim()) {\n      this._element.setAttribute('aria-label', title)\n    }\n\n    this._element.setAttribute('data-bs-original-title', title) // DO NOT USE IT. Is only for backwards compatibility\n    this._element.removeAttribute('title')\n  }\n\n  _enter() {\n    if (this._isShown() || this._isHovered) {\n      this._isHovered = true\n      return\n    }\n\n    this._isHovered = true\n\n    this._setTimeout(() => {\n      if (this._isHovered) {\n        this.show()\n      }\n    }, this._config.delay.show)\n  }\n\n  _leave() {\n    if (this._isWithActiveTrigger()) {\n      return\n    }\n\n    this._isHovered = false\n\n    this._setTimeout(() => {\n      if (!this._isHovered) {\n        this.hide()\n      }\n    }, this._config.delay.hide)\n  }\n\n  _setTimeout(handler, timeout) {\n    clearTimeout(this._timeout)\n    this._timeout = setTimeout(handler, timeout)\n  }\n\n  _isWithActiveTrigger() {\n    return Object.values(this._activeTrigger).includes(true)\n  }\n\n  _getConfig(config) {\n    const dataAttributes = Manipulator.getDataAttributes(this._element)\n\n    for (const dataAttribute of Object.keys(dataAttributes)) {\n      if (DISALLOWED_ATTRIBUTES.has(dataAttribute)) {\n        delete dataAttributes[dataAttribute]\n      }\n    }\n\n    config = {\n      ...dataAttributes,\n      ...(typeof config === 'object' && config ? config : {})\n    }\n    config = this._mergeConfigObj(config)\n    config = this._configAfterMerge(config)\n    this._typeCheckConfig(config)\n    return config\n  }\n\n  _configAfterMerge(config) {\n    config.container = config.container === false ? document.body : getElement(config.container)\n\n    if (typeof config.delay === 'number') {\n      config.delay = {\n        show: config.delay,\n        hide: config.delay\n      }\n    }\n\n    if (typeof config.title === 'number') {\n      config.title = config.title.toString()\n    }\n\n    if (typeof config.content === 'number') {\n      config.content = config.content.toString()\n    }\n\n    return config\n  }\n\n  _getDelegateConfig() {\n    const config = {}\n\n    for (const [key, value] of Object.entries(this._config)) {\n      if (this.constructor.Default[key] !== value) {\n        config[key] = value\n      }\n    }\n\n    config.selector = false\n    config.trigger = 'manual'\n\n    // In the future can be replaced with:\n    // const keysWithDifferentValues = Object.entries(this._config).filter(entry => this.constructor.Default[entry[0]] !== this._config[entry[0]])\n    // `Object.fromEntries(keysWithDifferentValues)`\n    return config\n  }\n\n  _disposePopper() {\n    if (this._popper) {\n      this._popper.destroy()\n      this._popper = null\n    }\n\n    if (this.tip) {\n      this.tip.remove()\n      this.tip = null\n    }\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Tooltip.getOrCreateInstance(this, config)\n\n      if (typeof config !== 'string') {\n        return\n      }\n\n      if (typeof data[config] === 'undefined') {\n        throw new TypeError(`No method named \"${config}\"`)\n      }\n\n      data[config]()\n    })\n  }\n}\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Tooltip)\n\nexport default Tooltip\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap popover.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport Tooltip from './tooltip.js'\nimport { defineJQueryPlugin } from './util/index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'popover'\n\nconst SELECTOR_TITLE = '.popover-header'\nconst SELECTOR_CONTENT = '.popover-body'\n\nconst Default = {\n  ...Tooltip.Default,\n  content: '',\n  offset: [0, 8],\n  placement: 'right',\n  template: '<div class=\"popover\" role=\"tooltip\">' +\n    '<div class=\"popover-arrow\"></div>' +\n    '<h3 class=\"popover-header\"></h3>' +\n    '<div class=\"popover-body\"></div>' +\n    '</div>',\n  trigger: 'click'\n}\n\nconst DefaultType = {\n  ...Tooltip.DefaultType,\n  content: '(null|string|element|function)'\n}\n\n/**\n * Class definition\n */\n\nclass Popover extends Tooltip {\n  // Getters\n  static get Default() {\n    return Default\n  }\n\n  static get DefaultType() {\n    return DefaultType\n  }\n\n  static get NAME() {\n    return NAME\n  }\n\n  // Overrides\n  _isWithContent() {\n    return this._getTitle() || this._getContent()\n  }\n\n  // Private\n  _getContentForTemplate() {\n    return {\n      [SELECTOR_TITLE]: this._getTitle(),\n      [SELECTOR_CONTENT]: this._getContent()\n    }\n  }\n\n  _getContent() {\n    return this._resolvePossibleFunction(this._config.content)\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Popover.getOrCreateInstance(this, config)\n\n      if (typeof config !== 'string') {\n        return\n      }\n\n      if (typeof data[config] === 'undefined') {\n        throw new TypeError(`No method named \"${config}\"`)\n      }\n\n      data[config]()\n    })\n  }\n}\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Popover)\n\nexport default Popover\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap scrollspy.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport SelectorEngine from './dom/selector-engine.js'\nimport { defineJQueryPlugin, getElement, isDisabled, isVisible } from './util/index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'scrollspy'\nconst DATA_KEY = 'bs.scrollspy'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\n\nconst EVENT_ACTIVATE = `activate${EVENT_KEY}`\nconst EVENT_CLICK = `click${EVENT_KEY}`\nconst EVENT_LOAD_DATA_API = `load${EVENT_KEY}${DATA_API_KEY}`\n\nconst CLASS_NAME_DROPDOWN_ITEM = 'dropdown-item'\nconst CLASS_NAME_ACTIVE = 'active'\n\nconst SELECTOR_DATA_SPY = '[data-bs-spy=\"scroll\"]'\nconst SELECTOR_TARGET_LINKS = '[href]'\nconst SELECTOR_NAV_LIST_GROUP = '.nav, .list-group'\nconst SELECTOR_NAV_LINKS = '.nav-link'\nconst SELECTOR_NAV_ITEMS = '.nav-item'\nconst SELECTOR_LIST_ITEMS = '.list-group-item'\nconst SELECTOR_LINK_ITEMS = `${SELECTOR_NAV_LINKS}, ${SELECTOR_NAV_ITEMS} > ${SELECTOR_NAV_LINKS}, ${SELECTOR_LIST_ITEMS}`\nconst SELECTOR_DROPDOWN = '.dropdown'\nconst SELECTOR_DROPDOWN_TOGGLE = '.dropdown-toggle'\n\nconst Default = {\n  offset: null, // TODO: v6 @deprecated, keep it for backwards compatibility reasons\n  rootMargin: '0px 0px -25%',\n  smoothScroll: false,\n  target: null,\n  threshold: [0.1, 0.5, 1]\n}\n\nconst DefaultType = {\n  offset: '(number|null)', // TODO v6 @deprecated, keep it for backwards compatibility reasons\n  rootMargin: 'string',\n  smoothScroll: 'boolean',\n  target: 'element',\n  threshold: 'array'\n}\n\n/**\n * Class definition\n */\n\nclass ScrollSpy extends BaseComponent {\n  constructor(element, config) {\n    super(element, config)\n\n    // this._element is the observablesContainer and config.target the menu links wrapper\n    this._targetLinks = new Map()\n    this._observableSections = new Map()\n    this._rootElement = getComputedStyle(this._element).overflowY === 'visible' ? null : this._element\n    this._activeTarget = null\n    this._observer = null\n    this._previousScrollData = {\n      visibleEntryTop: 0,\n      parentScrollTop: 0\n    }\n    this.refresh() // initialize\n  }\n\n  // Getters\n  static get Default() {\n    return Default\n  }\n\n  static get DefaultType() {\n    return DefaultType\n  }\n\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n  refresh() {\n    this._initializeTargetsAndObservables()\n    this._maybeEnableSmoothScroll()\n\n    if (this._observer) {\n      this._observer.disconnect()\n    } else {\n      this._observer = this._getNewObserver()\n    }\n\n    for (const section of this._observableSections.values()) {\n      this._observer.observe(section)\n    }\n  }\n\n  dispose() {\n    this._observer.disconnect()\n    super.dispose()\n  }\n\n  // Private\n  _configAfterMerge(config) {\n    // TODO: on v6 target should be given explicitly & remove the {target: 'ss-target'} case\n    config.target = getElement(config.target) || document.body\n\n    // TODO: v6 Only for backwards compatibility reasons. Use rootMargin only\n    config.rootMargin = config.offset ? `${config.offset}px 0px -30%` : config.rootMargin\n\n    if (typeof config.threshold === 'string') {\n      config.threshold = config.threshold.split(',').map(value => Number.parseFloat(value))\n    }\n\n    return config\n  }\n\n  _maybeEnableSmoothScroll() {\n    if (!this._config.smoothScroll) {\n      return\n    }\n\n    // unregister any previous listeners\n    EventHandler.off(this._config.target, EVENT_CLICK)\n\n    EventHandler.on(this._config.target, EVENT_CLICK, SELECTOR_TARGET_LINKS, event => {\n      const observableSection = this._observableSections.get(event.target.hash)\n      if (observableSection) {\n        event.preventDefault()\n        const root = this._rootElement || window\n        const height = observableSection.offsetTop - this._element.offsetTop\n        if (root.scrollTo) {\n          root.scrollTo({ top: height, behavior: 'smooth' })\n          return\n        }\n\n        // Chrome 60 doesn't support `scrollTo`\n        root.scrollTop = height\n      }\n    })\n  }\n\n  _getNewObserver() {\n    const options = {\n      root: this._rootElement,\n      threshold: this._config.threshold,\n      rootMargin: this._config.rootMargin\n    }\n\n    return new IntersectionObserver(entries => this._observerCallback(entries), options)\n  }\n\n  // The logic of selection\n  _observerCallback(entries) {\n    const targetElement = entry => this._targetLinks.get(`#${entry.target.id}`)\n    const activate = entry => {\n      this._previousScrollData.visibleEntryTop = entry.target.offsetTop\n      this._process(targetElement(entry))\n    }\n\n    const parentScrollTop = (this._rootElement || document.documentElement).scrollTop\n    const userScrollsDown = parentScrollTop >= this._previousScrollData.parentScrollTop\n    this._previousScrollData.parentScrollTop = parentScrollTop\n\n    for (const entry of entries) {\n      if (!entry.isIntersecting) {\n        this._activeTarget = null\n        this._clearActiveClass(targetElement(entry))\n\n        continue\n      }\n\n      const entryIsLowerThanPrevious = entry.target.offsetTop >= this._previousScrollData.visibleEntryTop\n      // if we are scrolling down, pick the bigger offsetTop\n      if (userScrollsDown && entryIsLowerThanPrevious) {\n        activate(entry)\n        // if parent isn't scrolled, let's keep the first visible item, breaking the iteration\n        if (!parentScrollTop) {\n          return\n        }\n\n        continue\n      }\n\n      // if we are scrolling up, pick the smallest offsetTop\n      if (!userScrollsDown && !entryIsLowerThanPrevious) {\n        activate(entry)\n      }\n    }\n  }\n\n  _initializeTargetsAndObservables() {\n    this._targetLinks = new Map()\n    this._observableSections = new Map()\n\n    const targetLinks = SelectorEngine.find(SELECTOR_TARGET_LINKS, this._config.target)\n\n    for (const anchor of targetLinks) {\n      // ensure that the anchor has an id and is not disabled\n      if (!anchor.hash || isDisabled(anchor)) {\n        continue\n      }\n\n      const observableSection = SelectorEngine.findOne(decodeURI(anchor.hash), this._element)\n\n      // ensure that the observableSection exists & is visible\n      if (isVisible(observableSection)) {\n        this._targetLinks.set(decodeURI(anchor.hash), anchor)\n        this._observableSections.set(anchor.hash, observableSection)\n      }\n    }\n  }\n\n  _process(target) {\n    if (this._activeTarget === target) {\n      return\n    }\n\n    this._clearActiveClass(this._config.target)\n    this._activeTarget = target\n    target.classList.add(CLASS_NAME_ACTIVE)\n    this._activateParents(target)\n\n    EventHandler.trigger(this._element, EVENT_ACTIVATE, { relatedTarget: target })\n  }\n\n  _activateParents(target) {\n    // Activate dropdown parents\n    if (target.classList.contains(CLASS_NAME_DROPDOWN_ITEM)) {\n      SelectorEngine.findOne(SELECTOR_DROPDOWN_TOGGLE, target.closest(SELECTOR_DROPDOWN))\n        .classList.add(CLASS_NAME_ACTIVE)\n      return\n    }\n\n    for (const listGroup of SelectorEngine.parents(target, SELECTOR_NAV_LIST_GROUP)) {\n      // Set triggered links parents as active\n      // With both <ul> and <nav> markup a parent is the previous sibling of any nav ancestor\n      for (const item of SelectorEngine.prev(listGroup, SELECTOR_LINK_ITEMS)) {\n        item.classList.add(CLASS_NAME_ACTIVE)\n      }\n    }\n  }\n\n  _clearActiveClass(parent) {\n    parent.classList.remove(CLASS_NAME_ACTIVE)\n\n    const activeNodes = SelectorEngine.find(`${SELECTOR_TARGET_LINKS}.${CLASS_NAME_ACTIVE}`, parent)\n    for (const node of activeNodes) {\n      node.classList.remove(CLASS_NAME_ACTIVE)\n    }\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = ScrollSpy.getOrCreateInstance(this, config)\n\n      if (typeof config !== 'string') {\n        return\n      }\n\n      if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n        throw new TypeError(`No method named \"${config}\"`)\n      }\n\n      data[config]()\n    })\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(window, EVENT_LOAD_DATA_API, () => {\n  for (const spy of SelectorEngine.find(SELECTOR_DATA_SPY)) {\n    ScrollSpy.getOrCreateInstance(spy)\n  }\n})\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(ScrollSpy)\n\nexport default ScrollSpy\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap tab.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport SelectorEngine from './dom/selector-engine.js'\nimport { defineJQueryPlugin, getNextActiveElement, isDisabled } from './util/index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'tab'\nconst DATA_KEY = 'bs.tab'\nconst EVENT_KEY = `.${DATA_KEY}`\n\nconst EVENT_HIDE = `hide${EVENT_KEY}`\nconst EVENT_HIDDEN = `hidden${EVENT_KEY}`\nconst EVENT_SHOW = `show${EVENT_KEY}`\nconst EVENT_SHOWN = `shown${EVENT_KEY}`\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}`\nconst EVENT_KEYDOWN = `keydown${EVENT_KEY}`\nconst EVENT_LOAD_DATA_API = `load${EVENT_KEY}`\n\nconst ARROW_LEFT_KEY = 'ArrowLeft'\nconst ARROW_RIGHT_KEY = 'ArrowRight'\nconst ARROW_UP_KEY = 'ArrowUp'\nconst ARROW_DOWN_KEY = 'ArrowDown'\nconst HOME_KEY = 'Home'\nconst END_KEY = 'End'\n\nconst CLASS_NAME_ACTIVE = 'active'\nconst CLASS_NAME_FADE = 'fade'\nconst CLASS_NAME_SHOW = 'show'\nconst CLASS_DROPDOWN = 'dropdown'\n\nconst SELECTOR_DROPDOWN_TOGGLE = '.dropdown-toggle'\nconst SELECTOR_DROPDOWN_MENU = '.dropdown-menu'\nconst NOT_SELECTOR_DROPDOWN_TOGGLE = ':not(.dropdown-toggle)'\n\nconst SELECTOR_TAB_PANEL = '.list-group, .nav, [role=\"tablist\"]'\nconst SELECTOR_OUTER = '.nav-item, .list-group-item'\nconst SELECTOR_INNER = `.nav-link${NOT_SELECTOR_DROPDOWN_TOGGLE}, .list-group-item${NOT_SELECTOR_DROPDOWN_TOGGLE}, [role=\"tab\"]${NOT_SELECTOR_DROPDOWN_TOGGLE}`\nconst SELECTOR_DATA_TOGGLE = '[data-bs-toggle=\"tab\"], [data-bs-toggle=\"pill\"], [data-bs-toggle=\"list\"]' // TODO: could only be `tab` in v6\nconst SELECTOR_INNER_ELEM = `${SELECTOR_INNER}, ${SELECTOR_DATA_TOGGLE}`\n\nconst SELECTOR_DATA_TOGGLE_ACTIVE = `.${CLASS_NAME_ACTIVE}[data-bs-toggle=\"tab\"], .${CLASS_NAME_ACTIVE}[data-bs-toggle=\"pill\"], .${CLASS_NAME_ACTIVE}[data-bs-toggle=\"list\"]`\n\n/**\n * Class definition\n */\n\nclass Tab extends BaseComponent {\n  constructor(element) {\n    super(element)\n    this._parent = this._element.closest(SELECTOR_TAB_PANEL)\n\n    if (!this._parent) {\n      return\n      // TODO: should throw exception in v6\n      // throw new TypeError(`${element.outerHTML} has not a valid parent ${SELECTOR_INNER_ELEM}`)\n    }\n\n    // Set up initial aria attributes\n    this._setInitialAttributes(this._parent, this._getChildren())\n\n    EventHandler.on(this._element, EVENT_KEYDOWN, event => this._keydown(event))\n  }\n\n  // Getters\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n  show() { // Shows this elem and deactivate the active sibling if exists\n    const innerElem = this._element\n    if (this._elemIsActive(innerElem)) {\n      return\n    }\n\n    // Search for active tab on same parent to deactivate it\n    const active = this._getActiveElem()\n\n    const hideEvent = active ?\n      EventHandler.trigger(active, EVENT_HIDE, { relatedTarget: innerElem }) :\n      null\n\n    const showEvent = EventHandler.trigger(innerElem, EVENT_SHOW, { relatedTarget: active })\n\n    if (showEvent.defaultPrevented || (hideEvent && hideEvent.defaultPrevented)) {\n      return\n    }\n\n    this._deactivate(active, innerElem)\n    this._activate(innerElem, active)\n  }\n\n  // Private\n  _activate(element, relatedElem) {\n    if (!element) {\n      return\n    }\n\n    element.classList.add(CLASS_NAME_ACTIVE)\n\n    this._activate(SelectorEngine.getElementFromSelector(element)) // Search and activate/show the proper section\n\n    const complete = () => {\n      if (element.getAttribute('role') !== 'tab') {\n        element.classList.add(CLASS_NAME_SHOW)\n        return\n      }\n\n      element.removeAttribute('tabindex')\n      element.setAttribute('aria-selected', true)\n      this._toggleDropDown(element, true)\n      EventHandler.trigger(element, EVENT_SHOWN, {\n        relatedTarget: relatedElem\n      })\n    }\n\n    this._queueCallback(complete, element, element.classList.contains(CLASS_NAME_FADE))\n  }\n\n  _deactivate(element, relatedElem) {\n    if (!element) {\n      return\n    }\n\n    element.classList.remove(CLASS_NAME_ACTIVE)\n    element.blur()\n\n    this._deactivate(SelectorEngine.getElementFromSelector(element)) // Search and deactivate the shown section too\n\n    const complete = () => {\n      if (element.getAttribute('role') !== 'tab') {\n        element.classList.remove(CLASS_NAME_SHOW)\n        return\n      }\n\n      element.setAttribute('aria-selected', false)\n      element.setAttribute('tabindex', '-1')\n      this._toggleDropDown(element, false)\n      EventHandler.trigger(element, EVENT_HIDDEN, { relatedTarget: relatedElem })\n    }\n\n    this._queueCallback(complete, element, element.classList.contains(CLASS_NAME_FADE))\n  }\n\n  _keydown(event) {\n    if (!([ARROW_LEFT_KEY, ARROW_RIGHT_KEY, ARROW_UP_KEY, ARROW_DOWN_KEY, HOME_KEY, END_KEY].includes(event.key))) {\n      return\n    }\n\n    event.stopPropagation()// stopPropagation/preventDefault both added to support up/down keys without scrolling the page\n    event.preventDefault()\n\n    const children = this._getChildren().filter(element => !isDisabled(element))\n    let nextActiveElement\n\n    if ([HOME_KEY, END_KEY].includes(event.key)) {\n      nextActiveElement = children[event.key === HOME_KEY ? 0 : children.length - 1]\n    } else {\n      const isNext = [ARROW_RIGHT_KEY, ARROW_DOWN_KEY].includes(event.key)\n      nextActiveElement = getNextActiveElement(children, event.target, isNext, true)\n    }\n\n    if (nextActiveElement) {\n      nextActiveElement.focus({ preventScroll: true })\n      Tab.getOrCreateInstance(nextActiveElement).show()\n    }\n  }\n\n  _getChildren() { // collection of inner elements\n    return SelectorEngine.find(SELECTOR_INNER_ELEM, this._parent)\n  }\n\n  _getActiveElem() {\n    return this._getChildren().find(child => this._elemIsActive(child)) || null\n  }\n\n  _setInitialAttributes(parent, children) {\n    this._setAttributeIfNotExists(parent, 'role', 'tablist')\n\n    for (const child of children) {\n      this._setInitialAttributesOnChild(child)\n    }\n  }\n\n  _setInitialAttributesOnChild(child) {\n    child = this._getInnerElement(child)\n    const isActive = this._elemIsActive(child)\n    const outerElem = this._getOuterElement(child)\n    child.setAttribute('aria-selected', isActive)\n\n    if (outerElem !== child) {\n      this._setAttributeIfNotExists(outerElem, 'role', 'presentation')\n    }\n\n    if (!isActive) {\n      child.setAttribute('tabindex', '-1')\n    }\n\n    this._setAttributeIfNotExists(child, 'role', 'tab')\n\n    // set attributes to the related panel too\n    this._setInitialAttributesOnTargetPanel(child)\n  }\n\n  _setInitialAttributesOnTargetPanel(child) {\n    const target = SelectorEngine.getElementFromSelector(child)\n\n    if (!target) {\n      return\n    }\n\n    this._setAttributeIfNotExists(target, 'role', 'tabpanel')\n\n    if (child.id) {\n      this._setAttributeIfNotExists(target, 'aria-labelledby', `${child.id}`)\n    }\n  }\n\n  _toggleDropDown(element, open) {\n    const outerElem = this._getOuterElement(element)\n    if (!outerElem.classList.contains(CLASS_DROPDOWN)) {\n      return\n    }\n\n    const toggle = (selector, className) => {\n      const element = SelectorEngine.findOne(selector, outerElem)\n      if (element) {\n        element.classList.toggle(className, open)\n      }\n    }\n\n    toggle(SELECTOR_DROPDOWN_TOGGLE, CLASS_NAME_ACTIVE)\n    toggle(SELECTOR_DROPDOWN_MENU, CLASS_NAME_SHOW)\n    outerElem.setAttribute('aria-expanded', open)\n  }\n\n  _setAttributeIfNotExists(element, attribute, value) {\n    if (!element.hasAttribute(attribute)) {\n      element.setAttribute(attribute, value)\n    }\n  }\n\n  _elemIsActive(elem) {\n    return elem.classList.contains(CLASS_NAME_ACTIVE)\n  }\n\n  // Try to get the inner element (usually the .nav-link)\n  _getInnerElement(elem) {\n    return elem.matches(SELECTOR_INNER_ELEM) ? elem : SelectorEngine.findOne(SELECTOR_INNER_ELEM, elem)\n  }\n\n  // Try to get the outer element (usually the .nav-item)\n  _getOuterElement(elem) {\n    return elem.closest(SELECTOR_OUTER) || elem\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Tab.getOrCreateInstance(this)\n\n      if (typeof config !== 'string') {\n        return\n      }\n\n      if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n        throw new TypeError(`No method named \"${config}\"`)\n      }\n\n      data[config]()\n    })\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_TOGGLE, function (event) {\n  if (['A', 'AREA'].includes(this.tagName)) {\n    event.preventDefault()\n  }\n\n  if (isDisabled(this)) {\n    return\n  }\n\n  Tab.getOrCreateInstance(this).show()\n})\n\n/**\n * Initialize on focus\n */\nEventHandler.on(window, EVENT_LOAD_DATA_API, () => {\n  for (const element of SelectorEngine.find(SELECTOR_DATA_TOGGLE_ACTIVE)) {\n    Tab.getOrCreateInstance(element)\n  }\n})\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Tab)\n\nexport default Tab\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap toast.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport { enableDismissTrigger } from './util/component-functions.js'\nimport { defineJQueryPlugin, reflow } from './util/index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'toast'\nconst DATA_KEY = 'bs.toast'\nconst EVENT_KEY = `.${DATA_KEY}`\n\nconst EVENT_MOUSEOVER = `mouseover${EVENT_KEY}`\nconst EVENT_MOUSEOUT = `mouseout${EVENT_KEY}`\nconst EVENT_FOCUSIN = `focusin${EVENT_KEY}`\nconst EVENT_FOCUSOUT = `focusout${EVENT_KEY}`\nconst EVENT_HIDE = `hide${EVENT_KEY}`\nconst EVENT_HIDDEN = `hidden${EVENT_KEY}`\nconst EVENT_SHOW = `show${EVENT_KEY}`\nconst EVENT_SHOWN = `shown${EVENT_KEY}`\n\nconst CLASS_NAME_FADE = 'fade'\nconst CLASS_NAME_HIDE = 'hide' // @deprecated - kept here only for backwards compatibility\nconst CLASS_NAME_SHOW = 'show'\nconst CLASS_NAME_SHOWING = 'showing'\n\nconst DefaultType = {\n  animation: 'boolean',\n  autohide: 'boolean',\n  delay: 'number'\n}\n\nconst Default = {\n  animation: true,\n  autohide: true,\n  delay: 5000\n}\n\n/**\n * Class definition\n */\n\nclass Toast extends BaseComponent {\n  constructor(element, config) {\n    super(element, config)\n\n    this._timeout = null\n    this._hasMouseInteraction = false\n    this._hasKeyboardInteraction = false\n    this._setListeners()\n  }\n\n  // Getters\n  static get Default() {\n    return Default\n  }\n\n  static get DefaultType() {\n    return DefaultType\n  }\n\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n  show() {\n    const showEvent = EventHandler.trigger(this._element, EVENT_SHOW)\n\n    if (showEvent.defaultPrevented) {\n      return\n    }\n\n    this._clearTimeout()\n\n    if (this._config.animation) {\n      this._element.classList.add(CLASS_NAME_FADE)\n    }\n\n    const complete = () => {\n      this._element.classList.remove(CLASS_NAME_SHOWING)\n      EventHandler.trigger(this._element, EVENT_SHOWN)\n\n      this._maybeScheduleHide()\n    }\n\n    this._element.classList.remove(CLASS_NAME_HIDE) // @deprecated\n    reflow(this._element)\n    this._element.classList.add(CLASS_NAME_SHOW, CLASS_NAME_SHOWING)\n\n    this._queueCallback(complete, this._element, this._config.animation)\n  }\n\n  hide() {\n    if (!this.isShown()) {\n      return\n    }\n\n    const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE)\n\n    if (hideEvent.defaultPrevented) {\n      return\n    }\n\n    const complete = () => {\n      this._element.classList.add(CLASS_NAME_HIDE) // @deprecated\n      this._element.classList.remove(CLASS_NAME_SHOWING, CLASS_NAME_SHOW)\n      EventHandler.trigger(this._element, EVENT_HIDDEN)\n    }\n\n    this._element.classList.add(CLASS_NAME_SHOWING)\n    this._queueCallback(complete, this._element, this._config.animation)\n  }\n\n  dispose() {\n    this._clearTimeout()\n\n    if (this.isShown()) {\n      this._element.classList.remove(CLASS_NAME_SHOW)\n    }\n\n    super.dispose()\n  }\n\n  isShown() {\n    return this._element.classList.contains(CLASS_NAME_SHOW)\n  }\n\n  // Private\n\n  _maybeScheduleHide() {\n    if (!this._config.autohide) {\n      return\n    }\n\n    if (this._hasMouseInteraction || this._hasKeyboardInteraction) {\n      return\n    }\n\n    this._timeout = setTimeout(() => {\n      this.hide()\n    }, this._config.delay)\n  }\n\n  _onInteraction(event, isInteracting) {\n    switch (event.type) {\n      case 'mouseover':\n      case 'mouseout': {\n        this._hasMouseInteraction = isInteracting\n        break\n      }\n\n      case 'focusin':\n      case 'focusout': {\n        this._hasKeyboardInteraction = isInteracting\n        break\n      }\n\n      default: {\n        break\n      }\n    }\n\n    if (isInteracting) {\n      this._clearTimeout()\n      return\n    }\n\n    const nextElement = event.relatedTarget\n    if (this._element === nextElement || this._element.contains(nextElement)) {\n      return\n    }\n\n    this._maybeScheduleHide()\n  }\n\n  _setListeners() {\n    EventHandler.on(this._element, EVENT_MOUSEOVER, event => this._onInteraction(event, true))\n    EventHandler.on(this._element, EVENT_MOUSEOUT, event => this._onInteraction(event, false))\n    EventHandler.on(this._element, EVENT_FOCUSIN, event => this._onInteraction(event, true))\n    EventHandler.on(this._element, EVENT_FOCUSOUT, event => this._onInteraction(event, false))\n  }\n\n  _clearTimeout() {\n    clearTimeout(this._timeout)\n    this._timeout = null\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Toast.getOrCreateInstance(this, config)\n\n      if (typeof config === 'string') {\n        if (typeof data[config] === 'undefined') {\n          throw new TypeError(`No method named \"${config}\"`)\n        }\n\n        data[config](this)\n      }\n    })\n  }\n}\n\n/**\n * Data API implementation\n */\n\nenableDismissTrigger(Toast)\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Toast)\n\nexport default Toast\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap index.umd.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport Alert from './src/alert.js'\nimport Button from './src/button.js'\nimport Carousel from './src/carousel.js'\nimport Collapse from './src/collapse.js'\nimport Dropdown from './src/dropdown.js'\nimport Modal from './src/modal.js'\nimport Offcanvas from './src/offcanvas.js'\nimport Popover from './src/popover.js'\nimport ScrollSpy from './src/scrollspy.js'\nimport Tab from './src/tab.js'\nimport Toast from './src/toast.js'\nimport Tooltip from './src/tooltip.js'\n\nexport default {\n  Alert,\n  Button,\n  Carousel,\n  Collapse,\n  Dropdown,\n  Modal,\n  Offcanvas,\n  Popover,\n  ScrollSpy,\n  Tab,\n  Toast,\n  Tooltip\n}\n"],"mappings":";;;;;0OAWA,MAAMA,EAAa,IAAIC,IAEvBC,EAAe,CACbC,IAAIC,EAASC,EAAKC,GACXN,EAAWO,IAAIH,IAClBJ,EAAWG,IAAIC,EAAS,IAAIH,KAG9B,MAAMO,EAAcR,EAAWS,IAAIL,GAI9BI,EAAYD,IAAIF,IAA6B,IAArBG,EAAYE,KAMzCF,EAAYL,IAAIE,EAAKC,GAJnBK,QAAQC,MAAO,+EAA8EC,MAAMC,KAAKN,EAAYO,QAAQ,M,EAOhIN,IAAGA,CAACL,EAASC,IACPL,EAAWO,IAAIH,IACVJ,EAAWS,IAAIL,GAASK,IAAIJ,IAG9B,KAGTW,OAAOZ,EAASC,GACd,IAAKL,EAAWO,IAAIH,GAClB,OAGF,MAAMI,EAAcR,EAAWS,IAAIL,GAEnCI,EAAYS,OAAOZ,GAGM,IAArBG,EAAYE,MACdV,EAAWiB,OAAOb,EAEtB,GC5CIc,EAAiB,gBAOjBC,EAAgBC,IAChBA,GAAYC,OAAOC,KAAOD,OAAOC,IAAIC,SAEvCH,EAAWA,EAASI,QAAQ,iBAAiB,CAACC,EAAOC,IAAQ,IAAGJ,IAAIC,OAAOG,QAGtEN,GA+CHO,EAAuBvB,IAC3BA,EAAQwB,cAAc,IAAIC,MAAMX,GAAgB,EAG5CY,EAAYC,MACXA,GAA4B,iBAAXA,UAIO,IAAlBA,EAAOC,SAChBD,EAASA,EAAO,SAGgB,IAApBA,EAAOE,UAGjBC,EAAaH,GAEbD,EAAUC,GACLA,EAAOC,OAASD,EAAO,GAAKA,EAGf,iBAAXA,GAAuBA,EAAOI,OAAS,EACzCC,SAASC,cAAclB,EAAcY,IAGvC,KAGHO,EAAYlC,IAChB,IAAK0B,EAAU1B,IAAgD,IAApCA,EAAQmC,iBAAiBJ,OAClD,OAAO,EAGT,MAAMK,EAAgF,YAA7DC,iBAAiBrC,GAASsC,iBAAiB,cAE9DC,EAAgBvC,EAAQwC,QAAQ,uBAEtC,IAAKD,EACH,OAAOH,EAGT,GAAIG,IAAkBvC,EAAS,CAC7B,MAAMyC,EAAUzC,EAAQwC,QAAQ,WAChC,GAAIC,GAAWA,EAAQC,aAAeH,EACpC,OAAO,EAGT,GAAgB,OAAZE,EACF,OAAO,CAEX,CAEA,OAAOL,CAAgB,EAGnBO,EAAa3C,IACZA,GAAWA,EAAQ6B,WAAae,KAAKC,gBAItC7C,EAAQ8C,UAAUC,SAAS,mBAIC,IAArB/C,EAAQgD,SACVhD,EAAQgD,SAGVhD,EAAQiD,aAAa,aAAoD,UAArCjD,EAAQkD,aAAa,aAG5DC,EAAiBnD,IACrB,IAAKgC,SAASoB,gBAAgBC,aAC5B,OAAO,KAIT,GAAmC,mBAAxBrD,EAAQsD,YAA4B,CAC7C,MAAMC,EAAOvD,EAAQsD,cACrB,OAAOC,aAAgBC,WAAaD,EAAO,IAC7C,CAEA,OAAIvD,aAAmBwD,WACdxD,EAIJA,EAAQ0C,WAINS,EAAenD,EAAQ0C,YAHrB,IAGgC,EAGrCe,EAAOA,OAUPC,EAAS1D,IACbA,EAAQ2D,YAAY,EAGhBC,EAAYA,IACZ3C,OAAO4C,SAAW7B,SAAS8B,KAAKb,aAAa,qBACxChC,OAAO4C,OAGT,KAGHE,EAA4B,GAmB5BC,EAAQA,IAAuC,QAAjChC,SAASoB,gBAAgBa,IAEvCC,EAAqBC,IAnBAC,QAoBN,KACjB,MAAMC,EAAIT,IAEV,GAAIS,EAAG,CACL,MAAMC,EAAOH,EAAOI,KACdC,EAAqBH,EAAEI,GAAGH,GAChCD,EAAEI,GAAGH,GAAQH,EAAOO,gBACpBL,EAAEI,GAAGH,GAAMK,YAAcR,EACzBE,EAAEI,GAAGH,GAAMM,WAAa,KACtBP,EAAEI,GAAGH,GAAQE,EACNL,EAAOO,gBAElB,GA/B0B,YAAxB1C,SAAS6C,YAENd,EAA0BhC,QAC7BC,SAAS8C,iBAAiB,oBAAoB,KAC5C,IAAK,MAAMV,KAAYL,EACrBK,GACF,IAIJL,EAA0BgB,KAAKX,IAE/BA,GAoBA,EAGEY,EAAUA,CAACC,EAAkBC,EAAO,GAAIC,EAAeF,IACxB,mBAArBA,EAAkCA,KAAoBC,GAAQC,EAGxEC,EAAyBA,CAAChB,EAAUiB,EAAmBC,GAAoB,KAC/E,IAAKA,EAEH,YADAN,EAAQZ,GAIV,MACMmB,EA7LiCvF,KACvC,IAAKA,EACH,OAAO,EAIT,IAAIwF,mBAAEA,EAAkBC,gBAAEA,GAAoBxE,OAAOoB,iBAAiBrC,GAEtE,MAAM0F,EAA0BC,OAAOC,WAAWJ,GAC5CK,EAAuBF,OAAOC,WAAWH,GAG/C,OAAKC,GAA4BG,GAKjCL,EAAqBA,EAAmBM,MAAM,KAAK,GACnDL,EAAkBA,EAAgBK,MAAM,KAAK,GAxDf,KA0DtBH,OAAOC,WAAWJ,GAAsBG,OAAOC,WAAWH,KAPzD,CAOoG,EAyKpFM,CAAiCV,GADlC,EAGxB,IAAIW,GAAS,EAEb,MAAMC,EAAUA,EAAGC,aACbA,IAAWb,IAIfW,GAAS,EACTX,EAAkBc,oBAAoBrF,EAAgBmF,GACtDjB,EAAQZ,GAAS,EAGnBiB,EAAkBP,iBAAiBhE,EAAgBmF,GACnDG,YAAW,KACJJ,GACHzE,EAAqB8D,EACvB,GACCE,EAAiB,EAYhBc,EAAuBA,CAACC,EAAMC,EAAeC,EAAeC,KAChE,MAAMC,EAAaJ,EAAKvE,OACxB,IAAI4E,EAAQL,EAAKM,QAAQL,GAIzB,OAAe,IAAXI,GACMH,GAAiBC,EAAiBH,EAAKI,EAAa,GAAKJ,EAAK,IAGxEK,GAASH,EAAgB,GAAK,EAE1BC,IACFE,GAASA,EAAQD,GAAcA,GAG1BJ,EAAKO,KAAKC,IAAI,EAAGD,KAAKE,IAAIJ,EAAOD,EAAa,KAAI,EC7QrDM,EAAiB,qBACjBC,EAAiB,OACjBC,EAAgB,SAChBC,EAAgB,GACtB,IAAIC,EAAW,EACf,MAAMC,EAAe,CACnBC,WAAY,YACZC,WAAY,YAGRC,EAAe,IAAIC,IAAI,CAC3B,QACA,WACA,UACA,YACA,cACA,aACA,iBACA,YACA,WACA,YACA,cACA,YACA,UACA,WACA,QACA,oBACA,aACA,YACA,WACA,cACA,cACA,cACA,YACA,eACA,gBACA,eACA,gBACA,aACA,QACA,OACA,SACA,QACA,SACA,SACA,UACA,WACA,OACA,SACA,eACA,SACA,OACA,mBACA,mBACA,QACA,QACA,WAOF,SAASC,EAAa1H,EAAS2H,GAC7B,OAAQA,GAAQ,GAAEA,MAAQP,OAAiBpH,EAAQoH,UAAYA,GACjE,CAEA,SAASQ,EAAiB5H,GACxB,MAAM2H,EAAMD,EAAa1H,GAKzB,OAHAA,EAAQoH,SAAWO,EACnBR,EAAcQ,GAAOR,EAAcQ,IAAQ,GAEpCR,EAAcQ,EACvB,CAoCA,SAASE,EAAYC,EAAQC,EAAUC,EAAqB,MAC1D,OAAOC,OAAOC,OAAOJ,GAClBK,MAAKC,GAASA,EAAML,WAAaA,GAAYK,EAAMJ,qBAAuBA,GAC/E,CAEA,SAASK,EAAoBC,EAAmBrC,EAASsC,GACvD,MAAMC,EAAiC,iBAAZvC,EAErB8B,EAAWS,EAAcD,EAAsBtC,GAAWsC,EAChE,IAAIE,EAAYC,EAAaJ,GAM7B,OAJKd,EAAarH,IAAIsI,KACpBA,EAAYH,GAGP,CAACE,EAAaT,EAAUU,EACjC,CAEA,SAASE,EAAW3I,EAASsI,EAAmBrC,EAASsC,EAAoBK,GAC3E,GAAiC,iBAAtBN,IAAmCtI,EAC5C,OAGF,IAAKwI,EAAaT,EAAUU,GAAaJ,EAAoBC,EAAmBrC,EAASsC,GAIzF,GAAID,KAAqBjB,EAAc,CACrC,MAAMwB,EAAepE,GACZ,SAAU2D,GACf,IAAKA,EAAMU,eAAkBV,EAAMU,gBAAkBV,EAAMW,iBAAmBX,EAAMW,eAAehG,SAASqF,EAAMU,eAChH,OAAOrE,EAAGuE,KAAKC,KAAMb,E,EAK3BL,EAAWc,EAAad,EAC1B,CAEA,MAAMD,EAASF,EAAiB5H,GAC1BkJ,EAAWpB,EAAOW,KAAeX,EAAOW,GAAa,IACrDU,EAAmBtB,EAAYqB,EAAUnB,EAAUS,EAAcvC,EAAU,MAEjF,GAAIkD,EAGF,YAFAA,EAAiBP,OAASO,EAAiBP,QAAUA,GAKvD,MAAMjB,EAAMD,EAAaK,EAAUO,EAAkBlH,QAAQ4F,EAAgB,KACvEvC,EAAK+D,EAxEb,SAAoCxI,EAASgB,EAAUyD,GACrD,OAAO,SAASwB,EAAQmC,GACtB,MAAMgB,EAAcpJ,EAAQqJ,iBAAiBrI,GAE7C,IAAK,IAAIkF,OAAEA,GAAWkC,EAAOlC,GAAUA,IAAW+C,KAAM/C,EAASA,EAAOxD,WACtE,IAAK,MAAM4G,KAAcF,EACvB,GAAIE,IAAepD,EAUnB,OANAqD,EAAWnB,EAAO,CAAEW,eAAgB7C,IAEhCD,EAAQ2C,QACVY,EAAaC,IAAIzJ,EAASoI,EAAMsB,KAAM1I,EAAUyD,GAG3CA,EAAGkF,MAAMzD,EAAQ,CAACkC,G,CAIjC,CAqDIwB,CAA2B5J,EAASiG,EAAS8B,GArFjD,SAA0B/H,EAASyE,GACjC,OAAO,SAASwB,EAAQmC,GAOtB,OANAmB,EAAWnB,EAAO,CAAEW,eAAgB/I,IAEhCiG,EAAQ2C,QACVY,EAAaC,IAAIzJ,EAASoI,EAAMsB,KAAMjF,GAGjCA,EAAGkF,MAAM3J,EAAS,CAACoI,G,CAE9B,CA4EIyB,CAAiB7J,EAAS+H,GAE5BtD,EAAGuD,mBAAqBQ,EAAcvC,EAAU,KAChDxB,EAAGsD,SAAWA,EACdtD,EAAGmE,OAASA,EACZnE,EAAG2C,SAAWO,EACduB,EAASvB,GAAOlD,EAEhBzE,EAAQ8E,iBAAiB2D,EAAWhE,EAAI+D,EAC1C,CAEA,SAASsB,EAAc9J,EAAS8H,EAAQW,EAAWxC,EAAS+B,GAC1D,MAAMvD,EAAKoD,EAAYC,EAAOW,GAAYxC,EAAS+B,GAE9CvD,IAILzE,EAAQmG,oBAAoBsC,EAAWhE,EAAIsF,QAAQ/B,WAC5CF,EAAOW,GAAWhE,EAAG2C,UAC9B,CAEA,SAAS4C,EAAyBhK,EAAS8H,EAAQW,EAAWwB,GAC5D,MAAMC,EAAoBpC,EAAOW,IAAc,GAE/C,IAAK,MAAO0B,EAAY/B,KAAUH,OAAOmC,QAAQF,GAC3CC,EAAWE,SAASJ,IACtBH,EAAc9J,EAAS8H,EAAQW,EAAWL,EAAML,SAAUK,EAAMJ,mBAGtE,CAEA,SAASU,EAAaN,GAGpB,OADAA,EAAQA,EAAMhH,QAAQ6F,EAAgB,IAC/BI,EAAae,IAAUA,CAChC,CAEA,MAAMoB,EAAe,CACnBc,GAAGtK,EAASoI,EAAOnC,EAASsC,GAC1BI,EAAW3I,EAASoI,EAAOnC,EAASsC,GAAoB,E,EAG1DgC,IAAIvK,EAASoI,EAAOnC,EAASsC,GAC3BI,EAAW3I,EAASoI,EAAOnC,EAASsC,GAAoB,E,EAG1DkB,IAAIzJ,EAASsI,EAAmBrC,EAASsC,GACvC,GAAiC,iBAAtBD,IAAmCtI,EAC5C,OAGF,MAAOwI,EAAaT,EAAUU,GAAaJ,EAAoBC,EAAmBrC,EAASsC,GACrFiC,EAAc/B,IAAcH,EAC5BR,EAASF,EAAiB5H,GAC1BkK,EAAoBpC,EAAOW,IAAc,GACzCgC,EAAcnC,EAAkBoC,WAAW,KAEjD,QAAwB,IAAb3C,EAAX,CAUA,GAAI0C,EACF,IAAK,MAAME,KAAgB1C,OAAOtH,KAAKmH,GACrCkC,EAAyBhK,EAAS8H,EAAQ6C,EAAcrC,EAAkBsC,MAAM,IAIpF,IAAK,MAAOC,EAAazC,KAAUH,OAAOmC,QAAQF,GAAoB,CACpE,MAAMC,EAAaU,EAAYzJ,QAAQ8F,EAAe,IAEjDsD,IAAelC,EAAkB+B,SAASF,IAC7CL,EAAc9J,EAAS8H,EAAQW,EAAWL,EAAML,SAAUK,EAAMJ,mBAEpE,CAdA,KARA,CAEE,IAAKC,OAAOtH,KAAKuJ,GAAmBnI,OAClC,OAGF+H,EAAc9J,EAAS8H,EAAQW,EAAWV,EAAUS,EAAcvC,EAAU,KAE9E,C,EAiBF6E,QAAQ9K,EAASoI,EAAOlD,GACtB,GAAqB,iBAAVkD,IAAuBpI,EAChC,OAAO,KAGT,MAAMqE,EAAIT,IAIV,IAAImH,EAAc,KACdC,GAAU,EACVC,GAAiB,EACjBC,GAAmB,EALH9C,IADFM,EAAaN,IAQZ/D,IACjB0G,EAAc1G,EAAE5C,MAAM2G,EAAOlD,GAE7Bb,EAAErE,GAAS8K,QAAQC,GACnBC,GAAWD,EAAYI,uBACvBF,GAAkBF,EAAYK,gCAC9BF,EAAmBH,EAAYM,sBAGjC,MAAMC,EAAM/B,EAAW,IAAI9H,MAAM2G,EAAO,CAAE4C,UAASO,YAAY,IAASrG,GAcxE,OAZIgG,GACFI,EAAIE,iBAGFP,GACFjL,EAAQwB,cAAc8J,GAGpBA,EAAIJ,kBAAoBH,GAC1BA,EAAYS,iBAGPF,CACT,GAGF,SAAS/B,EAAWkC,EAAKC,EAAO,IAC9B,IAAK,MAAOzL,EAAK0L,KAAU1D,OAAOmC,QAAQsB,GACxC,IACED,EAAIxL,GAAO0L,C,CACX,MAAAC,GACA3D,OAAO4D,eAAeJ,EAAKxL,EAAK,CAC9B6L,cAAc,EACdzL,IAAGA,IACMsL,GAGb,CAGF,OAAOF,CACT,CCnTA,SAASM,EAAcJ,GACrB,GAAc,SAAVA,EACF,OAAO,EAGT,GAAc,UAAVA,EACF,OAAO,EAGT,GAAIA,IAAUhG,OAAOgG,GAAOK,WAC1B,OAAOrG,OAAOgG,GAGhB,GAAc,KAAVA,GAA0B,SAAVA,EAClB,OAAO,KAGT,GAAqB,iBAAVA,EACT,OAAOA,EAGT,IACE,OAAOM,KAAKC,MAAMC,mBAAmBR,G,CACrC,MAAAC,GACA,OAAOD,CACT,CACF,CAEA,SAASS,EAAiBnM,GACxB,OAAOA,EAAImB,QAAQ,UAAUiL,GAAQ,IAAGA,EAAIC,iBAC9C,CAEA,MAAMC,EAAc,CAClBC,iBAAiBxM,EAASC,EAAK0L,GAC7B3L,EAAQyM,aAAc,WAAUL,EAAiBnM,KAAQ0L,E,EAG3De,oBAAoB1M,EAASC,GAC3BD,EAAQ2M,gBAAiB,WAAUP,EAAiBnM,K,EAGtD2M,kBAAkB5M,GAChB,IAAKA,EACH,MAAO,GAGT,MAAM6M,EAAa,GACbC,EAAS7E,OAAOtH,KAAKX,EAAQ+M,SAASC,QAAO/M,GAAOA,EAAIyK,WAAW,QAAUzK,EAAIyK,WAAW,cAElG,IAAK,MAAMzK,KAAO6M,EAAQ,CACxB,IAAIG,EAAUhN,EAAImB,QAAQ,MAAO,IACjC6L,EAAUA,EAAQC,OAAO,GAAGZ,cAAgBW,EAAQrC,MAAM,EAAGqC,EAAQlL,QACrE8K,EAAWI,GAAWlB,EAAc/L,EAAQ+M,QAAQ9M,GACtD,CAEA,OAAO4M,C,EAGTM,iBAAgBA,CAACnN,EAASC,IACjB8L,EAAc/L,EAAQkD,aAAc,WAAUkJ,EAAiBnM,QCpD1E,MAAMmN,EAEJ,kBAAWC,GACT,MAAO,EACT,CAEA,sBAAWC,GACT,MAAO,EACT,CAEA,eAAW/I,GACT,MAAM,IAAIgJ,MAAM,sEAClB,CAEAC,WAAWC,GAIT,OAHAA,EAASxE,KAAKyE,gBAAgBD,GAC9BA,EAASxE,KAAK0E,kBAAkBF,GAChCxE,KAAK2E,iBAAiBH,GACfA,CACT,CAEAE,kBAAkBF,GAChB,OAAOA,CACT,CAEAC,gBAAgBD,EAAQzN,GACtB,MAAM6N,EAAanM,EAAU1B,GAAWuM,EAAYY,iBAAiBnN,EAAS,UAAY,GAE1F,MAAO,IACFiJ,KAAK6E,YAAYT,WACM,iBAAfQ,EAA0BA,EAAa,MAC9CnM,EAAU1B,GAAWuM,EAAYK,kBAAkB5M,GAAW,MAC5C,iBAAXyN,EAAsBA,EAAS,GAE9C,CAEAG,iBAAiBH,EAAQM,EAAc9E,KAAK6E,YAAYR,aACtD,IAAK,MAAOU,EAAUC,KAAkBhG,OAAOmC,QAAQ2D,GAAc,CACnE,MAAMpC,EAAQ8B,EAAOO,GACfE,EAAYxM,EAAUiK,GAAS,UH1BrChK,OADSA,EG2B+CgK,GHzBlD,GAAEhK,IAGLsG,OAAOkG,UAAUnC,SAAShD,KAAKrH,GAAQN,MAAM,eAAe,GAAGiL,cGwBlE,IAAK,IAAI8B,OAAOH,GAAeI,KAAKH,GAClC,MAAM,IAAII,UACP,GAAErF,KAAK6E,YAAYvJ,KAAKgK,0BAA0BP,qBAA4BE,yBAAiCD,MAGtH,CHlCWtM,KGmCb,ECvCF,MAAM6M,UAAsBpB,EAC1BU,YAAY9N,EAASyN,GACnBgB,SAEAzO,EAAU8B,EAAW9B,MAKrBiJ,KAAKyF,SAAW1O,EAChBiJ,KAAK0F,QAAU1F,KAAKuE,WAAWC,GAE/B3N,EAAKC,IAAIkJ,KAAKyF,SAAUzF,KAAK6E,YAAYc,SAAU3F,MACrD,CAGA4F,UACE/O,EAAKc,OAAOqI,KAAKyF,SAAUzF,KAAK6E,YAAYc,UAC5CpF,EAAaC,IAAIR,KAAKyF,SAAUzF,KAAK6E,YAAYgB,WAEjD,IAAK,MAAMC,KAAgB9G,OAAO+G,oBAAoB/F,MACpDA,KAAK8F,GAAgB,IAEzB,CAEAE,eAAe7K,EAAUpE,EAASkP,GAAa,GAC7C9J,EAAuBhB,EAAUpE,EAASkP,EAC5C,CAEA1B,WAAWC,GAIT,OAHAA,EAASxE,KAAKyE,gBAAgBD,EAAQxE,KAAKyF,UAC3CjB,EAASxE,KAAK0E,kBAAkBF,GAChCxE,KAAK2E,iBAAiBH,GACfA,CACT,CAGA,kBAAO0B,CAAYnP,GACjB,OAAOF,EAAKO,IAAIyB,EAAW9B,GAAUiJ,KAAK2F,SAC5C,CAEA,0BAAOQ,CAAoBpP,EAASyN,EAAS,IAC3C,OAAOxE,KAAKkG,YAAYnP,IAAY,IAAIiJ,KAAKjJ,EAA2B,iBAAXyN,EAAsBA,EAAS,KAC9F,CAEA,kBAAW4B,GACT,MApDY,OAqDd,CAEA,mBAAWT,GACT,MAAQ,MAAK3F,KAAK1E,MACpB,CAEA,oBAAWuK,GACT,MAAQ,IAAG7F,KAAK2F,UAClB,CAEA,gBAAOU,CAAUhL,GACf,MAAQ,GAAEA,IAAO2E,KAAK6F,WACxB,ECxEF,MAAMS,EAAcvP,IAClB,IAAIgB,EAAWhB,EAAQkD,aAAa,kBAEpC,IAAKlC,GAAyB,MAAbA,EAAkB,CACjC,IAAIwO,EAAgBxP,EAAQkD,aAAa,QAMzC,IAAKsM,IAAmBA,EAAcnF,SAAS,OAASmF,EAAc9E,WAAW,KAC/E,OAAO,KAIL8E,EAAcnF,SAAS,OAASmF,EAAc9E,WAAW,OAC3D8E,EAAiB,IAAGA,EAAc1J,MAAM,KAAK,MAG/C9E,EAAWwO,GAAmC,MAAlBA,EAAwBA,EAAcC,OAAS,IAC7E,CAEA,OAAO1O,EAAcC,EAAS,EAG1B0O,EAAiB,CACrBvH,KAAIA,CAACnH,EAAUhB,EAAUgC,SAASoB,kBACzB,GAAGuM,UAAUC,QAAQzB,UAAU9E,iBAAiBL,KAAKhJ,EAASgB,IAGvE6O,QAAOA,CAAC7O,EAAUhB,EAAUgC,SAASoB,kBAC5BwM,QAAQzB,UAAUlM,cAAc+G,KAAKhJ,EAASgB,GAGvD8O,SAAQA,CAAC9P,EAASgB,IACT,GAAG2O,UAAU3P,EAAQ8P,UAAU9C,QAAO+C,GAASA,EAAMC,QAAQhP,KAGtEiP,QAAQjQ,EAASgB,GACf,MAAMiP,EAAU,GAChB,IAAIC,EAAWlQ,EAAQ0C,WAAWF,QAAQxB,GAE1C,KAAOkP,GACLD,EAAQlL,KAAKmL,GACbA,EAAWA,EAASxN,WAAWF,QAAQxB,GAGzC,OAAOiP,C,EAGTE,KAAKnQ,EAASgB,GACZ,IAAIoP,EAAWpQ,EAAQqQ,uBAEvB,KAAOD,GAAU,CACf,GAAIA,EAASJ,QAAQhP,GACnB,MAAO,CAACoP,GAGVA,EAAWA,EAASC,sBACtB,CAEA,MAAO,E,EAGTC,KAAKtQ,EAASgB,GACZ,IAAIsP,EAAOtQ,EAAQuQ,mBAEnB,KAAOD,GAAM,CACX,GAAIA,EAAKN,QAAQhP,GACf,MAAO,CAACsP,GAGVA,EAAOA,EAAKC,kBACd,CAEA,MAAO,E,EAGTC,kBAAkBxQ,GAChB,MAAMyQ,EAAa,CACjB,IACA,SACA,QACA,WACA,SACA,UACA,aACA,4BACAC,KAAI1P,GAAa,GAAEA,2BAAiC2P,KAAK,KAE3D,OAAO1H,KAAKd,KAAKsI,EAAYzQ,GAASgN,QAAO4D,IAAOjO,EAAWiO,IAAO1O,EAAU0O,I,EAGlFC,uBAAuB7Q,GACrB,MAAMgB,EAAWuO,EAAYvP,GAE7B,OAAIgB,GACK0O,EAAeG,QAAQ7O,GAAYA,EAGrC,I,EAGT8P,uBAAuB9Q,GACrB,MAAMgB,EAAWuO,EAAYvP,GAE7B,OAAOgB,EAAW0O,EAAeG,QAAQ7O,GAAY,I,EAGvD+P,gCAAgC/Q,GAC9B,MAAMgB,EAAWuO,EAAYvP,GAE7B,OAAOgB,EAAW0O,EAAevH,KAAKnH,GAAY,EACpD,GC/GIgQ,EAAuBA,CAACC,EAAWC,EAAS,UAChD,MAAMC,EAAc,gBAAeF,EAAUnC,YACvCxK,EAAO2M,EAAU1M,KAEvBiF,EAAac,GAAGtI,SAAUmP,EAAa,qBAAoB7M,OAAU,SAAU8D,GAK7E,GAJI,CAAC,IAAK,QAAQiC,SAASpB,KAAKmI,UAC9BhJ,EAAMoD,iBAGJ7I,EAAWsG,MACb,OAGF,MAAM/C,EAASwJ,EAAeoB,uBAAuB7H,OAASA,KAAKzG,QAAS,IAAG8B,KAC9D2M,EAAU7B,oBAAoBlJ,GAGtCgL,IACX,GAAE,ECXEpC,EAAa,YAEbuC,EAAe,QAAOvC,IACtBwC,EAAgB,SAAQxC,IAQ9B,MAAMyC,UAAc/C,EAElB,eAAWjK,GACT,MAhBS,OAiBX,CAGAiN,QAGE,GAFmBhI,EAAasB,QAAQ7B,KAAKyF,SAAU2C,GAExCnG,iBACb,OAGFjC,KAAKyF,SAAS5L,UAAUlC,OApBJ,QAsBpB,MAAMsO,EAAajG,KAAKyF,SAAS5L,UAAUC,SAvBvB,QAwBpBkG,KAAKgG,gBAAe,IAAMhG,KAAKwI,mBAAmBxI,KAAKyF,SAAUQ,EACnE,CAGAuC,kBACExI,KAAKyF,SAAS9N,SACd4I,EAAasB,QAAQ7B,KAAKyF,SAAU4C,GACpCrI,KAAK4F,SACP,CAGA,sBAAOnK,CAAgB+I,GACrB,OAAOxE,KAAKyI,MAAK,WACf,MAAMC,EAAOJ,EAAMnC,oBAAoBnG,MAEvC,GAAsB,iBAAXwE,EAAX,CAIA,QAAqBmE,IAAjBD,EAAKlE,IAAyBA,EAAO/C,WAAW,MAAmB,gBAAX+C,EAC1D,MAAM,IAAIa,UAAW,oBAAmBb,MAG1CkE,EAAKlE,GAAQxE,KANb,CAOF,GACF,EAOF+H,EAAqBO,EAAO,SAM5BrN,EAAmBqN,GCrEnB,MAMMM,EAAuB,4BAO7B,MAAMC,UAAetD,EAEnB,eAAWjK,GACT,MAhBS,QAiBX,CAGAwN,SAEE9I,KAAKyF,SAASjC,aAAa,eAAgBxD,KAAKyF,SAAS5L,UAAUiP,OAjB7C,UAkBxB,CAGA,sBAAOrN,CAAgB+I,GACrB,OAAOxE,KAAKyI,MAAK,WACf,MAAMC,EAAOG,EAAO1C,oBAAoBnG,MAEzB,WAAXwE,GACFkE,EAAKlE,IAET,GACF,EAOFjE,EAAac,GAAGtI,SAlCc,2BAkCkB6P,GAAsBzJ,IACpEA,EAAMoD,iBAEN,MAAMwG,EAAS5J,EAAMlC,OAAO1D,QAAQqP,GACvBC,EAAO1C,oBAAoB4C,GAEnCD,QAAQ,IAOf7N,EAAmB4N,GCtDnB,MACMhD,EAAY,YACZmD,EAAoB,aAAYnD,IAChCoD,EAAmB,YAAWpD,IAC9BqD,EAAkB,WAAUrD,IAC5BsD,GAAqB,cAAatD,IAClCuD,GAAmB,YAAWvD,IAM9BzB,GAAU,CACdiF,YAAa,KACbC,aAAc,KACdC,cAAe,MAGXlF,GAAc,CAClBgF,YAAa,kBACbC,aAAc,kBACdC,cAAe,mBAOjB,MAAMC,WAAcrF,EAClBU,YAAY9N,EAASyN,GACnBgB,QACAxF,KAAKyF,SAAW1O,EAEXA,GAAYyS,GAAMC,gBAIvBzJ,KAAK0F,QAAU1F,KAAKuE,WAAWC,GAC/BxE,KAAK0J,QAAU,EACf1J,KAAK2J,sBAAwB7I,QAAQ9I,OAAO4R,cAC5C5J,KAAK6J,cACP,CAGA,kBAAWzF,GACT,OAAOA,EACT,CAEA,sBAAWC,GACT,OAAOA,EACT,CAEA,eAAW/I,GACT,MArDS,OAsDX,CAGAsK,UACErF,EAAaC,IAAIR,KAAKyF,SAAUI,EAClC,CAGAiE,OAAO3K,GACAa,KAAK2J,sBAMN3J,KAAK+J,wBAAwB5K,KAC/Ba,KAAK0J,QAAUvK,EAAM6K,SANrBhK,KAAK0J,QAAUvK,EAAM8K,QAAQ,GAAGD,OAQpC,CAEAE,KAAK/K,GACCa,KAAK+J,wBAAwB5K,KAC/Ba,KAAK0J,QAAUvK,EAAM6K,QAAUhK,KAAK0J,SAGtC1J,KAAKmK,eACLpO,EAAQiE,KAAK0F,QAAQ2D,YACvB,CAEAe,MAAMjL,GACJa,KAAK0J,QAAUvK,EAAM8K,SAAW9K,EAAM8K,QAAQnR,OAAS,EACrD,EACAqG,EAAM8K,QAAQ,GAAGD,QAAUhK,KAAK0J,OACpC,CAEAS,eACE,MAAME,EAAYzM,KAAK0M,IAAItK,KAAK0J,SAEhC,GAAIW,GAlFgB,GAmFlB,OAGF,MAAME,EAAYF,EAAYrK,KAAK0J,QAEnC1J,KAAK0J,QAAU,EAEVa,GAILxO,EAAQwO,EAAY,EAAIvK,KAAK0F,QAAQ6D,cAAgBvJ,KAAK0F,QAAQ4D,aACpE,CAEAO,cACM7J,KAAK2J,uBACPpJ,EAAac,GAAGrB,KAAKyF,SAAU0D,IAAmBhK,GAASa,KAAK8J,OAAO3K,KACvEoB,EAAac,GAAGrB,KAAKyF,SAAU2D,IAAiBjK,GAASa,KAAKkK,KAAK/K,KAEnEa,KAAKyF,SAAS5L,UAAU2Q,IAvGG,mBAyG3BjK,EAAac,GAAGrB,KAAKyF,SAAUuD,GAAkB7J,GAASa,KAAK8J,OAAO3K,KACtEoB,EAAac,GAAGrB,KAAKyF,SAAUwD,GAAiB9J,GAASa,KAAKoK,MAAMjL,KACpEoB,EAAac,GAAGrB,KAAKyF,SAAUyD,GAAgB/J,GAASa,KAAKkK,KAAK/K,KAEtE,CAEA4K,wBAAwB5K,GACtB,OAAOa,KAAK2J,wBAjHS,QAiHiBxK,EAAMsL,aAlHrB,UAkHyDtL,EAAMsL,YACxF,CAGA,kBAAOhB,GACL,MAAO,iBAAkB1Q,SAASoB,iBAAmBuQ,UAAUC,eAAiB,CAClF,ECrHF,MAEM9E,GAAa,eACb+E,GAAe,YAMfC,GAAa,OACbC,GAAa,OACbC,GAAiB,OACjBC,GAAkB,QAElBC,GAAe,QAAOpF,KACtBqF,GAAc,OAAMrF,KACpBsF,GAAiB,UAAStF,KAC1BuF,GAAoB,aAAYvF,KAChCwF,GAAoB,aAAYxF,KAChCyF,GAAoB,YAAWzF,KAC/B0F,GAAuB,OAAM1F,KAAY+E,KACzCY,GAAwB,QAAO3F,KAAY+E,KAE3Ca,GAAsB,WACtBC,GAAoB,SAOpBC,GAAkB,UAClBC,GAAgB,iBAChBC,GAAuBF,GAAkBC,GAMzCE,GAAmB,CACvBC,UAAkBf,GAClBgB,WAAmBjB,IAGf3G,GAAU,CACd6H,SAAU,IACVC,UAAU,EACVC,MAAO,QACPC,MAAM,EACNC,OAAO,EACPC,MAAM,GAGFjI,GAAc,CAClB4H,SAAU,mBACVC,SAAU,UACVC,MAAO,mBACPC,KAAM,mBACNC,MAAO,UACPC,KAAM,WAOR,MAAMC,WAAiBhH,EACrBV,YAAY9N,EAASyN,GACnBgB,MAAMzO,EAASyN,GAEfxE,KAAKwM,UAAY,KACjBxM,KAAKyM,eAAiB,KACtBzM,KAAK0M,YAAa,EAClB1M,KAAK2M,aAAe,KACpB3M,KAAK4M,aAAe,KAEpB5M,KAAK6M,mBAAqBpG,EAAeG,QAzCjB,uBAyC8C5G,KAAKyF,UAC3EzF,KAAK8M,qBAED9M,KAAK0F,QAAQ0G,OAASX,IACxBzL,KAAK+M,OAET,CAGA,kBAAW3I,GACT,OAAOA,EACT,CAEA,sBAAWC,GACT,OAAOA,EACT,CAEA,eAAW/I,GACT,MA9FS,UA+FX,CAGA+L,OACErH,KAAKgN,OAAOnC,GACd,CAEAoC,mBAIOlU,SAASmU,QAAUjU,EAAU+G,KAAKyF,WACrCzF,KAAKqH,MAET,CAEAH,OACElH,KAAKgN,OAAOlC,GACd,CAEAqB,QACMnM,KAAK0M,YACPpU,EAAqB0H,KAAKyF,UAG5BzF,KAAKmN,gBACP,CAEAJ,QACE/M,KAAKmN,iBACLnN,KAAKoN,kBAELpN,KAAKwM,UAAYa,aAAY,IAAMrN,KAAKiN,mBAAmBjN,KAAK0F,QAAQuG,SAC1E,CAEAqB,oBACOtN,KAAK0F,QAAQ0G,OAIdpM,KAAK0M,WACPnM,EAAae,IAAItB,KAAKyF,SAAUyF,IAAY,IAAMlL,KAAK+M,UAIzD/M,KAAK+M,QACP,CAEAQ,GAAG7P,GACD,MAAM8P,EAAQxN,KAAKyN,YACnB,GAAI/P,EAAQ8P,EAAM1U,OAAS,GAAK4E,EAAQ,EACtC,OAGF,GAAIsC,KAAK0M,WAEP,YADAnM,EAAae,IAAItB,KAAKyF,SAAUyF,IAAY,IAAMlL,KAAKuN,GAAG7P,KAI5D,MAAMgQ,EAAc1N,KAAK2N,cAAc3N,KAAK4N,cAC5C,GAAIF,IAAgBhQ,EAClB,OAGF,MAAMmQ,EAAQnQ,EAAQgQ,EAAc7C,GAAaC,GAEjD9K,KAAKgN,OAAOa,EAAOL,EAAM9P,GAC3B,CAEAkI,UACM5F,KAAK4M,cACP5M,KAAK4M,aAAahH,UAGpBJ,MAAMI,SACR,CAGAlB,kBAAkBF,GAEhB,OADAA,EAAOsJ,gBAAkBtJ,EAAOyH,SACzBzH,CACT,CAEAsI,qBACM9M,KAAK0F,QAAQwG,UACf3L,EAAac,GAAGrB,KAAKyF,SAAU0F,IAAehM,GAASa,KAAK+N,SAAS5O,KAG5C,UAAvBa,KAAK0F,QAAQyG,QACf5L,EAAac,GAAGrB,KAAKyF,SAAU2F,IAAkB,IAAMpL,KAAKmM,UAC5D5L,EAAac,GAAGrB,KAAKyF,SAAU4F,IAAkB,IAAMrL,KAAKsN,uBAG1DtN,KAAK0F,QAAQ2G,OAAS7C,GAAMC,eAC9BzJ,KAAKgO,yBAET,CAEAA,0BACE,IAAK,MAAMC,KAAOxH,EAAevH,KAhKX,qBAgKmCc,KAAKyF,UAC5DlF,EAAac,GAAG4M,EAAK3C,IAAkBnM,GAASA,EAAMoD,mBAGxD,MAqBM2L,EAAc,CAClB5E,aAAcA,IAAMtJ,KAAKgN,OAAOhN,KAAKmO,kBAAkBpD,KACvDxB,cAAeA,IAAMvJ,KAAKgN,OAAOhN,KAAKmO,kBAAkBnD,KACxD3B,YAxBkB+E,KACS,UAAvBpO,KAAK0F,QAAQyG,QAYjBnM,KAAKmM,QACDnM,KAAK2M,cACP0B,aAAarO,KAAK2M,cAGpB3M,KAAK2M,aAAexP,YAAW,IAAM6C,KAAKsN,qBAjNjB,IAiN+DtN,KAAK0F,QAAQuG,UAAS,GAShHjM,KAAK4M,aAAe,IAAIpD,GAAMxJ,KAAKyF,SAAUyI,EAC/C,CAEAH,SAAS5O,GACP,GAAI,kBAAkBiG,KAAKjG,EAAMlC,OAAOkL,SACtC,OAGF,MAAMoC,EAAYuB,GAAiB3M,EAAMnI,KACrCuT,IACFpL,EAAMoD,iBACNvC,KAAKgN,OAAOhN,KAAKmO,kBAAkB5D,IAEvC,CAEAoD,cAAc5W,GACZ,OAAOiJ,KAAKyN,YAAY9P,QAAQ5G,EAClC,CAEAuX,2BAA2B5Q,GACzB,IAAKsC,KAAK6M,mBACR,OAGF,MAAM0B,EAAkB9H,EAAeG,QAAQ+E,GAAiB3L,KAAK6M,oBAErE0B,EAAgB1U,UAAUlC,OAAO+T,IACjC6C,EAAgB7K,gBAAgB,gBAEhC,MAAM8K,EAAqB/H,EAAeG,QAAS,sBAAqBlJ,MAAWsC,KAAK6M,oBAEpF2B,IACFA,EAAmB3U,UAAU2Q,IAAIkB,IACjC8C,EAAmBhL,aAAa,eAAgB,QAEpD,CAEA4J,kBACE,MAAMrW,EAAUiJ,KAAKyM,gBAAkBzM,KAAK4N,aAE5C,IAAK7W,EACH,OAGF,MAAM0X,EAAkB/R,OAAOgS,SAAS3X,EAAQkD,aAAa,oBAAqB,IAElF+F,KAAK0F,QAAQuG,SAAWwC,GAAmBzO,KAAK0F,QAAQoI,eAC1D,CAEAd,OAAOa,EAAO9W,EAAU,MACtB,GAAIiJ,KAAK0M,WACP,OAGF,MAAMpP,EAAgB0C,KAAK4N,aACrBe,EAASd,IAAUhD,GACnB+D,EAAc7X,GAAWqG,EAAqB4C,KAAKyN,YAAanQ,EAAeqR,EAAQ3O,KAAK0F,QAAQ4G,MAE1G,GAAIsC,IAAgBtR,EAClB,OAGF,MAAMuR,EAAmB7O,KAAK2N,cAAciB,GAEtCE,EAAezI,GACZ9F,EAAasB,QAAQ7B,KAAKyF,SAAUY,EAAW,CACpDxG,cAAe+O,EACfrE,UAAWvK,KAAK+O,kBAAkBlB,GAClCpW,KAAMuI,KAAK2N,cAAcrQ,GACzBiQ,GAAIsB,IAMR,GAFmBC,EAAa7D,IAEjBhJ,iBACb,OAGF,IAAK3E,IAAkBsR,EAGrB,OAGF,MAAMI,EAAYlO,QAAQd,KAAKwM,WAC/BxM,KAAKmM,QAELnM,KAAK0M,YAAa,EAElB1M,KAAKsO,2BAA2BO,GAChC7O,KAAKyM,eAAiBmC,EAEtB,MAAMK,EAAuBN,EAnSR,sBADF,oBAqSbO,EAAiBP,EAnSH,qBACA,qBAoSpBC,EAAY/U,UAAU2Q,IAAI0E,GAE1BzU,EAAOmU,GAEPtR,EAAczD,UAAU2Q,IAAIyE,GAC5BL,EAAY/U,UAAU2Q,IAAIyE,GAa1BjP,KAAKgG,gBAXoBmJ,KACvBP,EAAY/U,UAAUlC,OAAOsX,EAAsBC,GACnDN,EAAY/U,UAAU2Q,IAAIkB,IAE1BpO,EAAczD,UAAUlC,OAAO+T,GAAmBwD,EAAgBD,GAElEjP,KAAK0M,YAAa,EAElBoC,EAAa5D,GAAW,GAGY5N,EAAe0C,KAAKoP,eAEtDJ,GACFhP,KAAK+M,OAET,CAEAqC,cACE,OAAOpP,KAAKyF,SAAS5L,UAAUC,SAlUV,QAmUvB,CAEA8T,aACE,OAAOnH,EAAeG,QAAQiF,GAAsB7L,KAAKyF,SAC3D,CAEAgI,YACE,OAAOhH,EAAevH,KAAK0M,GAAe5L,KAAKyF,SACjD,CAEA0H,iBACMnN,KAAKwM,YACP6C,cAAcrP,KAAKwM,WACnBxM,KAAKwM,UAAY,KAErB,CAEA2B,kBAAkB5D,GAChB,OAAIxP,IACKwP,IAAcQ,GAAiBD,GAAaD,GAG9CN,IAAcQ,GAAiBF,GAAaC,EACrD,CAEAiE,kBAAkBlB,GAChB,OAAI9S,IACK8S,IAAU/C,GAAaC,GAAiBC,GAG1C6C,IAAU/C,GAAaE,GAAkBD,EAClD,CAGA,sBAAOtP,CAAgB+I,GACrB,OAAOxE,KAAKyI,MAAK,WACf,MAAMC,EAAO6D,GAASpG,oBAAoBnG,KAAMwE,GAEhD,GAAsB,iBAAXA,GAKX,GAAsB,iBAAXA,EAAqB,CAC9B,QAAqBmE,IAAjBD,EAAKlE,IAAyBA,EAAO/C,WAAW,MAAmB,gBAAX+C,EAC1D,MAAM,IAAIa,UAAW,oBAAmBb,MAG1CkE,EAAKlE,IACP,OAVEkE,EAAK6E,GAAG/I,EAWZ,GACF,EAOFjE,EAAac,GAAGtI,SAAUyS,GAlXE,uCAkXyC,SAAUrM,GAC7E,MAAMlC,EAASwJ,EAAeoB,uBAAuB7H,MAErD,IAAK/C,IAAWA,EAAOpD,UAAUC,SAAS2R,IACxC,OAGFtM,EAAMoD,iBAEN,MAAM+M,EAAW/C,GAASpG,oBAAoBlJ,GACxCsS,EAAavP,KAAK/F,aAAa,oBAErC,OAAIsV,GACFD,EAAS/B,GAAGgC,QACZD,EAAShC,qBAIyC,SAAhDhK,EAAYY,iBAAiBlE,KAAM,UACrCsP,EAASjI,YACTiI,EAAShC,sBAIXgC,EAASpI,YACToI,EAAShC,oBACX,IAEA/M,EAAac,GAAGrJ,OAAQuT,IAAqB,KAC3C,MAAMiE,EAAY/I,EAAevH,KA9YR,6BAgZzB,IAAK,MAAMoQ,KAAYE,EACrBjD,GAASpG,oBAAoBmJ,EAC/B,IAOFrU,EAAmBsR,ICncnB,MAEM1G,GAAa,eAGb4J,GAAc,OAAM5J,KACpB6J,GAAe,QAAO7J,KACtB8J,GAAc,OAAM9J,KACpB+J,GAAgB,SAAQ/J,KACxB2F,GAAwB,QAAO3F,cAE/BgK,GAAkB,OAClBC,GAAsB,WACtBC,GAAwB,aAExBC,GAA8B,WAAUF,OAAwBA,KAOhElH,GAAuB,8BAEvBxE,GAAU,CACd6L,OAAQ,KACRnH,QAAQ,GAGJzE,GAAc,CAClB4L,OAAQ,iBACRnH,OAAQ,WAOV,MAAMoH,WAAiB3K,EACrBV,YAAY9N,EAASyN,GACnBgB,MAAMzO,EAASyN,GAEfxE,KAAKmQ,kBAAmB,EACxBnQ,KAAKoQ,cAAgB,GAErB,MAAMC,EAAa5J,EAAevH,KAAK0J,IAEvC,IAAK,MAAM0H,KAAQD,EAAY,CAC7B,MAAMtY,EAAW0O,EAAemB,uBAAuB0I,GACjDC,EAAgB9J,EAAevH,KAAKnH,GACvCgM,QAAOyM,GAAgBA,IAAiBxQ,KAAKyF,WAE/B,OAAb1N,GAAqBwY,EAAczX,QACrCkH,KAAKoQ,cAActU,KAAKwU,EAE5B,CAEAtQ,KAAKyQ,sBAEAzQ,KAAK0F,QAAQuK,QAChBjQ,KAAK0Q,0BAA0B1Q,KAAKoQ,cAAepQ,KAAK2Q,YAGtD3Q,KAAK0F,QAAQoD,QACf9I,KAAK8I,QAET,CAGA,kBAAW1E,GACT,OAAOA,EACT,CAEA,sBAAWC,GACT,OAAOA,EACT,CAEA,eAAW/I,GACT,MA9ES,UA+EX,CAGAwN,SACM9I,KAAK2Q,WACP3Q,KAAK4Q,OAEL5Q,KAAK6Q,MAET,CAEAA,OACE,GAAI7Q,KAAKmQ,kBAAoBnQ,KAAK2Q,WAChC,OAGF,IAAIG,EAAiB,GASrB,GANI9Q,KAAK0F,QAAQuK,SACfa,EAAiB9Q,KAAK+Q,uBA9EH,wCA+EhBhN,QAAOhN,GAAWA,IAAYiJ,KAAKyF,WACnCgC,KAAI1Q,GAAWmZ,GAAS/J,oBAAoBpP,EAAS,CAAE+R,QAAQ,OAGhEgI,EAAehY,QAAUgY,EAAe,GAAGX,iBAC7C,OAIF,GADmB5P,EAAasB,QAAQ7B,KAAKyF,SAAUgK,IACxCxN,iBACb,OAGF,IAAK,MAAM+O,KAAkBF,EAC3BE,EAAeJ,OAGjB,MAAMK,EAAYjR,KAAKkR,gBAEvBlR,KAAKyF,SAAS5L,UAAUlC,OAAOmY,IAC/B9P,KAAKyF,SAAS5L,UAAU2Q,IAAIuF,IAE5B/P,KAAKyF,SAAS0L,MAAMF,GAAa,EAEjCjR,KAAK0Q,0BAA0B1Q,KAAKoQ,eAAe,GACnDpQ,KAAKmQ,kBAAmB,EAExB,MAYMiB,EAAc,SADSH,EAAU,GAAG3L,cAAgB2L,EAAUtP,MAAM,KAG1E3B,KAAKgG,gBAdYqL,KACfrR,KAAKmQ,kBAAmB,EAExBnQ,KAAKyF,SAAS5L,UAAUlC,OAAOoY,IAC/B/P,KAAKyF,SAAS5L,UAAU2Q,IAAIsF,GAAqBD,IAEjD7P,KAAKyF,SAAS0L,MAAMF,GAAa,GAEjC1Q,EAAasB,QAAQ7B,KAAKyF,SAAUiK,GAAY,GAMpB1P,KAAKyF,UAAU,GAC7CzF,KAAKyF,SAAS0L,MAAMF,GAAc,GAAEjR,KAAKyF,SAAS2L,MACpD,CAEAR,OACE,GAAI5Q,KAAKmQ,mBAAqBnQ,KAAK2Q,WACjC,OAIF,GADmBpQ,EAAasB,QAAQ7B,KAAKyF,SAAUkK,IACxC1N,iBACb,OAGF,MAAMgP,EAAYjR,KAAKkR,gBAEvBlR,KAAKyF,SAAS0L,MAAMF,GAAc,GAAEjR,KAAKyF,SAAS6L,wBAAwBL,OAE1ExW,EAAOuF,KAAKyF,UAEZzF,KAAKyF,SAAS5L,UAAU2Q,IAAIuF,IAC5B/P,KAAKyF,SAAS5L,UAAUlC,OAAOmY,GAAqBD,IAEpD,IAAK,MAAMhO,KAAW7B,KAAKoQ,cAAe,CACxC,MAAMrZ,EAAU0P,EAAeoB,uBAAuBhG,GAElD9K,IAAYiJ,KAAK2Q,SAAS5Z,IAC5BiJ,KAAK0Q,0BAA0B,CAAC7O,IAAU,EAE9C,CAEA7B,KAAKmQ,kBAAmB,EASxBnQ,KAAKyF,SAAS0L,MAAMF,GAAa,GAEjCjR,KAAKgG,gBATYqL,KACfrR,KAAKmQ,kBAAmB,EACxBnQ,KAAKyF,SAAS5L,UAAUlC,OAAOoY,IAC/B/P,KAAKyF,SAAS5L,UAAU2Q,IAAIsF,IAC5BvP,EAAasB,QAAQ7B,KAAKyF,SAAUmK,GAAa,GAKrB5P,KAAKyF,UAAU,EAC/C,CAEAkL,SAAS5Z,EAAUiJ,KAAKyF,UACtB,OAAO1O,EAAQ8C,UAAUC,SAAS+V,GACpC,CAGAnL,kBAAkBF,GAGhB,OAFAA,EAAOsE,OAAShI,QAAQ0D,EAAOsE,QAC/BtE,EAAOyL,OAASpX,EAAW2L,EAAOyL,QAC3BzL,CACT,CAEA0M,gBACE,OAAOlR,KAAKyF,SAAS5L,UAAUC,SAtLL,uBAEhB,QACC,QAoLb,CAEA2W,sBACE,IAAKzQ,KAAK0F,QAAQuK,OAChB,OAGF,MAAMpJ,EAAW7G,KAAK+Q,uBAAuBnI,IAE7C,IAAK,MAAM7R,KAAW8P,EAAU,CAC9B,MAAM0K,EAAW9K,EAAeoB,uBAAuB9Q,GAEnDwa,GACFvR,KAAK0Q,0BAA0B,CAAC3Z,GAAUiJ,KAAK2Q,SAASY,GAE5D,CACF,CAEAR,uBAAuBhZ,GACrB,MAAM8O,EAAWJ,EAAevH,KAAK8Q,GAA4BhQ,KAAK0F,QAAQuK,QAE9E,OAAOxJ,EAAevH,KAAKnH,EAAUiI,KAAK0F,QAAQuK,QAAQlM,QAAOhN,IAAY8P,EAASzF,SAASrK,IACjG,CAEA2Z,0BAA0Bc,EAAcC,GACtC,GAAKD,EAAa1Y,OAIlB,IAAK,MAAM/B,KAAWya,EACpBza,EAAQ8C,UAAUiP,OAvNK,aAuNyB2I,GAChD1a,EAAQyM,aAAa,gBAAiBiO,EAE1C,CAGA,sBAAOhW,CAAgB+I,GACrB,MAAMkB,EAAU,GAKhB,MAJsB,iBAAXlB,GAAuB,YAAYY,KAAKZ,KACjDkB,EAAQoD,QAAS,GAGZ9I,KAAKyI,MAAK,WACf,MAAMC,EAAOwH,GAAS/J,oBAAoBnG,KAAM0F,GAEhD,GAAsB,iBAAXlB,EAAqB,CAC9B,QAA4B,IAAjBkE,EAAKlE,GACd,MAAM,IAAIa,UAAW,oBAAmBb,MAG1CkE,EAAKlE,IACP,CACF,GACF,EAOFjE,EAAac,GAAGtI,SAAUyS,GAAsB5C,IAAsB,SAAUzJ,IAEjD,MAAzBA,EAAMlC,OAAOkL,SAAoBhJ,EAAMW,gBAAmD,MAAjCX,EAAMW,eAAeqI,UAChFhJ,EAAMoD,iBAGR,IAAK,MAAMxL,KAAW0P,EAAeqB,gCAAgC9H,MACnEkQ,GAAS/J,oBAAoBpP,EAAS,CAAE+R,QAAQ,IAASA,QAE7D,IAMA7N,EAAmBiV,ICtSZ,IAAIwB,GAAM,MACNC,GAAS,SACTC,GAAQ,QACRC,GAAO,OACPC,GAAO,OACPC,GAAiB,CAACL,GAAKC,GAAQC,GAAOC,IACtCG,GAAQ,QACRC,GAAM,MACNC,GAAkB,kBAClBC,GAAW,WACXC,GAAS,SACTC,GAAY,YACZC,GAAmCP,GAAeQ,QAAO,SAAUC,EAAKC,GACjF,OAAOD,EAAI9L,OAAO,CAAC+L,EAAY,IAAMT,GAAOS,EAAY,IAAMR,IAChE,GAAG,IACQS,GAA0B,GAAGhM,OAAOqL,GAAgB,CAACD,KAAOS,QAAO,SAAUC,EAAKC,GAC3F,OAAOD,EAAI9L,OAAO,CAAC+L,EAAWA,EAAY,IAAMT,GAAOS,EAAY,IAAMR,IAC3E,GAAG,IAEQU,GAAa,aACbC,GAAO,OACPC,GAAY,YAEZC,GAAa,aACbC,GAAO,OACPC,GAAY,YAEZC,GAAc,cACdC,GAAQ,QACRC,GAAa,aACbC,GAAiB,CAACT,GAAYC,GAAMC,GAAWC,GAAYC,GAAMC,GAAWC,GAAaC,GAAOC,IC9B5F,SAASE,GAAYtc,GAClC,OAAOA,GAAWA,EAAQuc,UAAY,IAAIjQ,cAAgB,IAC5D,CCFe,SAASkQ,GAAUC,GAChC,GAAY,MAARA,EACF,OAAOxb,OAGT,GAAwB,oBAApBwb,EAAKzQ,WAAkC,CACzC,IAAI0Q,EAAgBD,EAAKC,cACzB,OAAOA,GAAgBA,EAAcC,aAAwB1b,MACjE,CAEE,OAAOwb,CACT,CCTA,SAAS/a,GAAU+a,GAEjB,OAAOA,aADUD,GAAUC,GAAM7M,SACI6M,aAAgB7M,OACvD,CAEA,SAASgN,GAAcH,GAErB,OAAOA,aADUD,GAAUC,GAAMI,aACIJ,aAAgBI,WACvD,CAEA,SAASC,GAAaL,GAEpB,MAA0B,oBAAfjZ,aAKJiZ,aADUD,GAAUC,GAAMjZ,YACIiZ,aAAgBjZ,WACvD,CCwDA,MAAAuZ,GAAe,CACbzY,KAAM,cACN0Y,SAAS,EACTC,MAAO,QACPxY,GA5EF,SAAqByY,GACnB,IAAIC,EAAQD,EAAKC,MACjBlV,OAAOtH,KAAKwc,EAAMC,UAAUC,SAAQ,SAAU/Y,GAC5C,IAAI8V,EAAQ+C,EAAMG,OAAOhZ,IAAS,GAC9BuI,EAAasQ,EAAMtQ,WAAWvI,IAAS,GACvCtE,EAAUmd,EAAMC,SAAS9Y,GAExBsY,GAAc5c,IAAasc,GAAYtc,KAO5CiI,OAAOsV,OAAOvd,EAAQoa,MAAOA,GAC7BnS,OAAOtH,KAAKkM,GAAYwQ,SAAQ,SAAU/Y,GACxC,IAAIqH,EAAQkB,EAAWvI,IAET,IAAVqH,EACF3L,EAAQ2M,gBAAgBrI,GAExBtE,EAAQyM,aAAanI,GAAgB,IAAVqH,EAAiB,GAAKA,EAEzD,IACA,GACA,EAoDE6R,OAlDF,SAAgBC,GACd,IAAIN,EAAQM,EAAMN,MACdO,EAAgB,CAClBrC,OAAQ,CACNsC,SAAUR,EAAMS,QAAQC,SACxB/C,KAAM,IACNH,IAAK,IACLmD,OAAQ,KAEVC,MAAO,CACLJ,SAAU,YAEZrC,UAAW,IASb,OAPArT,OAAOsV,OAAOJ,EAAMC,SAAS/B,OAAOjB,MAAOsD,EAAcrC,QACzD8B,EAAMG,OAASI,EAEXP,EAAMC,SAASW,OACjB9V,OAAOsV,OAAOJ,EAAMC,SAASW,MAAM3D,MAAOsD,EAAcK,OAGnD,WACL9V,OAAOtH,KAAKwc,EAAMC,UAAUC,SAAQ,SAAU/Y,GAC5C,IAAItE,EAAUmd,EAAMC,SAAS9Y,GACzBuI,EAAasQ,EAAMtQ,WAAWvI,IAAS,GAGvC8V,EAFkBnS,OAAOtH,KAAKwc,EAAMG,OAAOU,eAAe1Z,GAAQ6Y,EAAMG,OAAOhZ,GAAQoZ,EAAcpZ,IAE7EkX,QAAO,SAAUpB,EAAOpM,GAElD,OADAoM,EAAMpM,GAAY,GACXoM,CACf,GAAS,IAEEwC,GAAc5c,IAAasc,GAAYtc,KAI5CiI,OAAOsV,OAAOvd,EAAQoa,MAAOA,GAC7BnS,OAAOtH,KAAKkM,GAAYwQ,SAAQ,SAAUY,GACxCje,EAAQ2M,gBAAgBsR,EAChC,IACA,GACA,CACA,EASEC,SAAU,CAAC,kBCjFE,SAASC,GAAiBzC,GACvC,OAAOA,EAAU5V,MAAM,KAAK,EAC9B,CCHO,IAAIgB,GAAMD,KAAKC,IACXC,GAAMF,KAAKE,IACXqX,GAAQvX,KAAKuX,MCFT,SAASC,KACtB,IAAIC,EAAS3K,UAAU4K,cAEvB,OAAc,MAAVD,GAAkBA,EAAOE,QAAU/d,MAAMge,QAAQH,EAAOE,QACnDF,EAAOE,OAAO9N,KAAI,SAAUgO,GACjC,OAAOA,EAAKC,MAAQ,IAAMD,EAAKE,OACrC,IAAOjO,KAAK,KAGHgD,UAAUkL,SACnB,CCTe,SAASC,KACtB,OAAQ,iCAAiCzQ,KAAKgQ,KAChD,CCCe,SAAS9D,GAAsBva,EAAS+e,EAAcC,QAC9C,IAAjBD,IACFA,GAAe,QAGO,IAApBC,IACFA,GAAkB,GAGpB,IAAIC,EAAajf,EAAQua,wBACrB2E,EAAS,EACTC,EAAS,EAETJ,GAAgBnC,GAAc5c,KAChCkf,EAASlf,EAAQof,YAAc,GAAIhB,GAAMa,EAAWI,OAASrf,EAAQof,aAAmB,EACxFD,EAASnf,EAAQ2D,aAAe,GAAIya,GAAMa,EAAWK,QAAUtf,EAAQ2D,cAAoB,GAG7F,IACI4b,GADO7d,GAAU1B,GAAWwc,GAAUxc,GAAWiB,QAC3Bse,eAEtBC,GAAoBV,MAAsBE,EAC1CS,GAAKR,EAAWnE,MAAQ0E,GAAoBD,EAAiBA,EAAeG,WAAa,IAAMR,EAC/FS,GAAKV,EAAWtE,KAAO6E,GAAoBD,EAAiBA,EAAeK,UAAY,IAAMT,EAC7FE,EAAQJ,EAAWI,MAAQH,EAC3BI,EAASL,EAAWK,OAASH,EACjC,MAAO,CACLE,MAAOA,EACPC,OAAQA,EACR3E,IAAKgF,EACL9E,MAAO4E,EAAIJ,EACXzE,OAAQ+E,EAAIL,EACZxE,KAAM2E,EACNA,EAAGA,EACHE,EAAGA,EAEP,CCrCe,SAASE,GAAc7f,GACpC,IAAIif,EAAa1E,GAAsBva,GAGnCqf,EAAQrf,EAAQof,YAChBE,EAAStf,EAAQ2D,aAUrB,OARIkD,KAAK0M,IAAI0L,EAAWI,MAAQA,IAAU,IACxCA,EAAQJ,EAAWI,OAGjBxY,KAAK0M,IAAI0L,EAAWK,OAASA,IAAW,IAC1CA,EAASL,EAAWK,QAGf,CACLG,EAAGzf,EAAQ0f,WACXC,EAAG3f,EAAQ4f,UACXP,MAAOA,EACPC,OAAQA,EAEZ,CCvBe,SAASvc,GAASmW,EAAQnJ,GACvC,IAAI+P,EAAW/P,EAAMzM,aAAeyM,EAAMzM,cAE1C,GAAI4V,EAAOnW,SAASgN,GAClB,OAAO,EAEJ,GAAI+P,GAAYhD,GAAagD,GAAW,CACzC,IAAIxP,EAAOP,EAEX,EAAG,CACD,GAAIO,GAAQ4I,EAAO6G,WAAWzP,GAC5B,OAAO,EAITA,EAAOA,EAAK5N,YAAc4N,EAAK0P,IACvC,OAAe1P,EACf,CAGE,OAAO,CACT,CCrBe,SAASjO,GAAiBrC,GACvC,OAAOwc,GAAUxc,GAASqC,iBAAiBrC,EAC7C,CCFe,SAASigB,GAAejgB,GACrC,MAAO,CAAC,QAAS,KAAM,MAAM4G,QAAQ0V,GAAYtc,KAAa,CAChE,CCFe,SAASkgB,GAAmBlgB,GAEzC,QAAS0B,GAAU1B,GAAWA,EAAQ0c,cACtC1c,EAAQgC,WAAaf,OAAOe,UAAUoB,eACxC,CCFe,SAAS+c,GAAcngB,GACpC,MAA6B,SAAzBsc,GAAYtc,GACPA,EAMPA,EAAQogB,cACRpgB,EAAQ0C,aACRoa,GAAa9c,GAAWA,EAAQggB,KAAO,OAEvCE,GAAmBlgB,EAGvB,CCVA,SAASqgB,GAAoBrgB,GAC3B,OAAK4c,GAAc5c,IACoB,UAAvCqC,GAAiBrC,GAAS2d,SAInB3d,EAAQsgB,aAHN,IAIX,CAwCe,SAASC,GAAgBvgB,GAItC,IAHA,IAAIiB,EAASub,GAAUxc,GACnBsgB,EAAeD,GAAoBrgB,GAEhCsgB,GAAgBL,GAAeK,IAA6D,WAA5Cje,GAAiBie,GAAc3C,UACpF2C,EAAeD,GAAoBC,GAGrC,OAAIA,IAA+C,SAA9BhE,GAAYgE,IAA0D,SAA9BhE,GAAYgE,IAAwE,WAA5Cje,GAAiBie,GAAc3C,UAC3H1c,EAGFqf,GAhDT,SAA4BtgB,GAC1B,IAAIwgB,EAAY,WAAWnS,KAAKgQ,MAGhC,GAFW,WAAWhQ,KAAKgQ,OAEfzB,GAAc5c,IAII,UAFXqC,GAAiBrC,GAEnB2d,SACb,OAAO,KAIX,IAAI8C,EAAcN,GAAcngB,GAMhC,IAJI8c,GAAa2D,KACfA,EAAcA,EAAYT,MAGrBpD,GAAc6D,IAAgB,CAAC,OAAQ,QAAQ7Z,QAAQ0V,GAAYmE,IAAgB,GAAG,CAC3F,IAAIC,EAAMre,GAAiBoe,GAI3B,GAAsB,SAAlBC,EAAIC,WAA4C,SAApBD,EAAIE,aAA0C,UAAhBF,EAAIG,UAAiF,IAA1D,CAAC,YAAa,eAAeja,QAAQ8Z,EAAII,aAAsBN,GAAgC,WAAnBE,EAAII,YAA2BN,GAAaE,EAAI1T,QAAyB,SAAf0T,EAAI1T,OACjO,OAAOyT,EAEPA,EAAcA,EAAY/d,UAEhC,CAEE,OAAO,IACT,CAgByBqe,CAAmB/gB,IAAYiB,CACxD,CCpEe,SAAS+f,GAAyBtF,GAC/C,MAAO,CAAC,MAAO,UAAU9U,QAAQ8U,IAAc,EAAI,IAAM,GAC3D,CCDO,SAASuF,GAAOla,EAAK4E,EAAO7E,GACjC,OAAOoa,GAAQna,EAAKoa,GAAQxV,EAAO7E,GACrC,CCFe,SAASsa,GAAmBC,GACzC,OAAOpZ,OAAOsV,OAAO,GCDd,CACL5C,IAAK,EACLE,MAAO,EACPD,OAAQ,EACRE,KAAM,GDHuCuG,EACjD,CEHe,SAASC,GAAgB3V,EAAOhL,GAC7C,OAAOA,EAAK6a,QAAO,SAAU+F,EAASthB,GAEpC,OADAshB,EAAQthB,GAAO0L,EACR4V,CACX,GAAK,GACL,CC4EA,MAAAC,GAAe,CACbld,KAAM,QACN0Y,SAAS,EACTC,MAAO,OACPxY,GApEF,SAAeyY,GACb,IAAIuE,EAEAtE,EAAQD,EAAKC,MACb7Y,EAAO4Y,EAAK5Y,KACZsZ,EAAUV,EAAKU,QACf8D,EAAevE,EAAMC,SAASW,MAC9B4D,EAAgBxE,EAAMyE,cAAcD,cACpCE,EAAgB1D,GAAiBhB,EAAMzB,WACvCoG,EAAOd,GAAyBa,GAEhCE,EADa,CAACjH,GAAMD,IAAOjU,QAAQib,IAAkB,EAClC,SAAW,QAElC,GAAKH,GAAiBC,EAAtB,CAIA,IAAIN,EAxBgB,SAAyBW,EAAS7E,GAItD,OAAOiE,GAAsC,iBAH7CY,EAA6B,mBAAZA,EAAyBA,EAAQ/Z,OAAOsV,OAAO,GAAIJ,EAAM8E,MAAO,CAC/EvG,UAAWyB,EAAMzB,aACbsG,GACkDA,EAAUV,GAAgBU,EAAShH,IAC7F,CAmBsBkH,CAAgBtE,EAAQoE,QAAS7E,GACjDgF,EAAYtC,GAAc6B,GAC1BU,EAAmB,MAATN,EAAenH,GAAMG,GAC/BuH,EAAmB,MAATP,EAAelH,GAASC,GAClCyH,EAAUnF,EAAM8E,MAAM3G,UAAUyG,GAAO5E,EAAM8E,MAAM3G,UAAUwG,GAAQH,EAAcG,GAAQ3E,EAAM8E,MAAM5G,OAAO0G,GAC9GQ,EAAYZ,EAAcG,GAAQ3E,EAAM8E,MAAM3G,UAAUwG,GACxDU,EAAoBjC,GAAgBmB,GACpCe,EAAaD,EAA6B,MAATV,EAAeU,EAAkBE,cAAgB,EAAIF,EAAkBG,aAAe,EAAI,EAC3HC,EAAoBN,EAAU,EAAIC,EAAY,EAG9Cxb,EAAMsa,EAAce,GACpBtb,EAAM2b,EAAaN,EAAUJ,GAAOV,EAAcgB,GAClDQ,EAASJ,EAAa,EAAIN,EAAUJ,GAAO,EAAIa,EAC/CE,EAAS7B,GAAOla,EAAK8b,EAAQ/b,GAE7Bic,EAAWjB,EACf3E,EAAMyE,cAActd,KAASmd,EAAwB,IAA0BsB,GAAYD,EAAQrB,EAAsBuB,aAAeF,EAASD,EAAQpB,EAnB3J,CAoBA,EAkCEjE,OAhCF,SAAgBC,GACd,IAAIN,EAAQM,EAAMN,MAEd8F,EADUxF,EAAMG,QACW5d,QAC3B0hB,OAAoC,IAArBuB,EAA8B,sBAAwBA,EAErD,MAAhBvB,IAKwB,iBAAjBA,IACTA,EAAevE,EAAMC,SAAS/B,OAAOpZ,cAAcyf,MAOhD3e,GAASoa,EAAMC,SAAS/B,OAAQqG,KAIrCvE,EAAMC,SAASW,MAAQ2D,EACzB,EASExD,SAAU,CAAC,iBACXgF,iBAAkB,CAAC,oBCxFN,SAASC,GAAazH,GACnC,OAAOA,EAAU5V,MAAM,KAAK,EAC9B,CCOA,IAAIsd,GAAa,CACfzI,IAAK,OACLE,MAAO,OACPD,OAAQ,OACRE,KAAM,QAeD,SAASuI,GAAY5F,GAC1B,IAAI6F,EAEAjI,EAASoC,EAAMpC,OACfkI,EAAa9F,EAAM8F,WACnB7H,EAAY+B,EAAM/B,UAClB8H,EAAY/F,EAAM+F,UAClBC,EAAUhG,EAAMgG,QAChB9F,EAAWF,EAAME,SACjB+F,EAAkBjG,EAAMiG,gBACxBC,EAAWlG,EAAMkG,SACjBC,EAAenG,EAAMmG,aACrBC,EAAUpG,EAAMoG,QAChBC,EAAaL,EAAQhE,EACrBA,OAAmB,IAAfqE,EAAwB,EAAIA,EAChCC,EAAaN,EAAQ9D,EACrBA,OAAmB,IAAfoE,EAAwB,EAAIA,EAEhCC,EAAgC,mBAAjBJ,EAA8BA,EAAa,CAC5DnE,EAAGA,EACHE,EAAGA,IACA,CACHF,EAAGA,EACHE,EAAGA,GAGLF,EAAIuE,EAAMvE,EACVE,EAAIqE,EAAMrE,EACV,IAAIsE,EAAOR,EAAQzF,eAAe,KAC9BkG,EAAOT,EAAQzF,eAAe,KAC9BmG,EAAQrJ,GACRsJ,EAAQzJ,GACR0J,EAAMpjB,OAEV,GAAI0iB,EAAU,CACZ,IAAIrD,EAAeC,GAAgBlF,GAC/BiJ,EAAa,eACbC,EAAY,cAEZjE,IAAiB9D,GAAUnB,IAGmB,WAA5ChZ,GAFJie,EAAeJ,GAAmB7E,IAECsC,UAAsC,aAAbA,IAC1D2G,EAAa,eACbC,EAAY,gBAOZ7I,IAAcf,KAAQe,IAAcZ,IAAQY,IAAcb,KAAU2I,IAActI,MACpFkJ,EAAQxJ,GAGR+E,IAFckE,GAAWvD,IAAiB+D,GAAOA,EAAI9E,eAAiB8E,EAAI9E,eAAeD,OACzFgB,EAAagE,IACEf,EAAWjE,OAC1BK,GAAK+D,EAAkB,GAAK,GAG1BhI,IAAcZ,KAASY,IAAcf,IAAOe,IAAcd,IAAW4I,IAActI,MACrFiJ,EAAQtJ,GAGR4E,IAFcoE,GAAWvD,IAAiB+D,GAAOA,EAAI9E,eAAiB8E,EAAI9E,eAAeF,MACzFiB,EAAaiE,IACEhB,EAAWlE,MAC1BI,GAAKiE,EAAkB,GAAK,EAElC,CAEE,IAgBMc,EAhBFC,EAAexc,OAAOsV,OAAO,CAC/BI,SAAUA,GACTgG,GAAYP,IAEXsB,GAAyB,IAAjBd,EAlFd,SAA2B1G,EAAMmH,GAC/B,IAAI5E,EAAIvC,EAAKuC,EACTE,EAAIzC,EAAKyC,EACTgF,EAAMN,EAAIO,kBAAoB,EAClC,MAAO,CACLnF,EAAGrB,GAAMqB,EAAIkF,GAAOA,GAAO,EAC3BhF,EAAGvB,GAAMuB,EAAIgF,GAAOA,GAAO,EAE/B,CA0EsCE,CAAkB,CACpDpF,EAAGA,EACHE,EAAGA,GACFnD,GAAUnB,IAAW,CACtBoE,EAAGA,EACHE,EAAGA,GAML,OAHAF,EAAIiF,EAAMjF,EACVE,EAAI+E,EAAM/E,EAEN+D,EAGKzb,OAAOsV,OAAO,GAAIkH,IAAeD,EAAiB,IAAmBJ,GAASF,EAAO,IAAM,GAAIM,EAAeL,GAASF,EAAO,IAAM,GAAIO,EAAe7D,WAAa0D,EAAIO,kBAAoB,IAAM,EAAI,aAAenF,EAAI,OAASE,EAAI,MAAQ,eAAiBF,EAAI,OAASE,EAAI,SAAU6E,IAG5Rvc,OAAOsV,OAAO,GAAIkH,IAAenB,EAAkB,IAAoBc,GAASF,EAAOvE,EAAI,KAAO,GAAI2D,EAAgBa,GAASF,EAAOxE,EAAI,KAAO,GAAI6D,EAAgB3C,UAAY,GAAI2C,GAC9L,CA4CA,MAAAwB,GAAe,CACbxgB,KAAM,gBACN0Y,SAAS,EACTC,MAAO,cACPxY,GA9CF,SAAuBsgB,GACrB,IAAI5H,EAAQ4H,EAAM5H,MACdS,EAAUmH,EAAMnH,QAChBoH,EAAwBpH,EAAQ8F,gBAChCA,OAA4C,IAA1BsB,GAA0CA,EAC5DC,EAAoBrH,EAAQ+F,SAC5BA,OAAiC,IAAtBsB,GAAsCA,EACjDC,EAAwBtH,EAAQgG,aAChCA,OAAyC,IAA1BsB,GAA0CA,EACzDT,EAAe,CACjB/I,UAAWyC,GAAiBhB,EAAMzB,WAClC8H,UAAWL,GAAahG,EAAMzB,WAC9BL,OAAQ8B,EAAMC,SAAS/B,OACvBkI,WAAYpG,EAAM8E,MAAM5G,OACxBqI,gBAAiBA,EACjBG,QAAoC,UAA3B1G,EAAMS,QAAQC,UAGgB,MAArCV,EAAMyE,cAAcD,gBACtBxE,EAAMG,OAAOjC,OAASpT,OAAOsV,OAAO,GAAIJ,EAAMG,OAAOjC,OAAQgI,GAAYpb,OAAOsV,OAAO,GAAIkH,EAAc,CACvGhB,QAAStG,EAAMyE,cAAcD,cAC7BhE,SAAUR,EAAMS,QAAQC,SACxB8F,SAAUA,EACVC,aAAcA,OAIe,MAA7BzG,EAAMyE,cAAc7D,QACtBZ,EAAMG,OAAOS,MAAQ9V,OAAOsV,OAAO,GAAIJ,EAAMG,OAAOS,MAAOsF,GAAYpb,OAAOsV,OAAO,GAAIkH,EAAc,CACrGhB,QAAStG,EAAMyE,cAAc7D,MAC7BJ,SAAU,WACVgG,UAAU,EACVC,aAAcA,OAIlBzG,EAAMtQ,WAAWwO,OAASpT,OAAOsV,OAAO,GAAIJ,EAAMtQ,WAAWwO,OAAQ,CACnE,wBAAyB8B,EAAMzB,WAEnC,EAQE/J,KAAM,ICrKR,IAAIwT,GAAU,CACZA,SAAS,GAsCX,MAAAC,GAAe,CACb9gB,KAAM,iBACN0Y,SAAS,EACTC,MAAO,QACPxY,GAAI,WAAc,EAClB+Y,OAxCF,SAAgBN,GACd,IAAIC,EAAQD,EAAKC,MACbjd,EAAWgd,EAAKhd,SAChB0d,EAAUV,EAAKU,QACfyH,EAAkBzH,EAAQ0H,OAC1BA,OAA6B,IAApBD,GAAoCA,EAC7CE,EAAkB3H,EAAQ4H,OAC1BA,OAA6B,IAApBD,GAAoCA,EAC7CtkB,EAASub,GAAUW,EAAMC,SAAS/B,QAClCoK,EAAgB,GAAG9V,OAAOwN,EAAMsI,cAAcnK,UAAW6B,EAAMsI,cAAcpK,QAYjF,OAVIiK,GACFG,EAAcpI,SAAQ,SAAUqI,GAC9BA,EAAa5gB,iBAAiB,SAAU5E,EAASylB,OAAQR,GAC/D,IAGMK,GACFvkB,EAAO6D,iBAAiB,SAAU5E,EAASylB,OAAQR,IAG9C,WACDG,GACFG,EAAcpI,SAAQ,SAAUqI,GAC9BA,EAAavf,oBAAoB,SAAUjG,EAASylB,OAAQR,GACpE,IAGQK,GACFvkB,EAAOkF,oBAAoB,SAAUjG,EAASylB,OAAQR,GAE5D,CACA,EASExT,KAAM,IC/CR,IAAIiU,GAAO,CACT9K,KAAM,QACND,MAAO,OACPD,OAAQ,MACRD,IAAK,UAEQ,SAASkL,GAAqBnK,GAC3C,OAAOA,EAAUta,QAAQ,0BAA0B,SAAU0kB,GAC3D,OAAOF,GAAKE,EAChB,GACA,CCVA,IAAIF,GAAO,CACT3K,MAAO,MACPC,IAAK,SAEQ,SAAS6K,GAA8BrK,GACpD,OAAOA,EAAUta,QAAQ,cAAc,SAAU0kB,GAC/C,OAAOF,GAAKE,EAChB,GACA,CCPe,SAASE,GAAgBvJ,GACtC,IAAI4H,EAAM7H,GAAUC,GAGpB,MAAO,CACLwJ,WAHe5B,EAAI6B,YAInBC,UAHc9B,EAAI+B,YAKtB,CCNe,SAASC,GAAoBrmB,GAQ1C,OAAOua,GAAsB2F,GAAmBlgB,IAAU8a,KAAOkL,GAAgBhmB,GAASimB,UAC5F,CCXe,SAASK,GAAetmB,GAErC,IAAIumB,EAAoBlkB,GAAiBrC,GACrCwmB,EAAWD,EAAkBC,SAC7BC,EAAYF,EAAkBE,UAC9BC,EAAYH,EAAkBG,UAElC,MAAO,6BAA6BrY,KAAKmY,EAAWE,EAAYD,EAClE,CCLe,SAASE,GAAgBlK,GACtC,MAAI,CAAC,OAAQ,OAAQ,aAAa7V,QAAQ0V,GAAYG,KAAU,EAEvDA,EAAKC,cAAc5Y,KAGxB8Y,GAAcH,IAAS6J,GAAe7J,GACjCA,EAGFkK,GAAgBxG,GAAc1D,GACvC,CCJe,SAASmK,GAAkB5mB,EAASsG,GACjD,IAAIugB,OAES,IAATvgB,IACFA,EAAO,IAGT,IAAIof,EAAeiB,GAAgB3mB,GAC/B8mB,EAASpB,KAAqE,OAAlDmB,EAAwB7mB,EAAQ0c,oBAAyB,EAASmK,EAAsB/iB,MACpHugB,EAAM7H,GAAUkJ,GAChBxf,EAAS4gB,EAAS,CAACzC,GAAK1U,OAAO0U,EAAI9E,gBAAkB,GAAI+G,GAAeZ,GAAgBA,EAAe,IAAMA,EAC7GqB,EAAczgB,EAAKqJ,OAAOzJ,GAC9B,OAAO4gB,EAASC,EAChBA,EAAYpX,OAAOiX,GAAkBzG,GAAcja,IACrD,CCzBe,SAAS8gB,GAAiBC,GACvC,OAAOhf,OAAOsV,OAAO,GAAI0J,EAAM,CAC7BnM,KAAMmM,EAAKxH,EACX9E,IAAKsM,EAAKtH,EACV9E,MAAOoM,EAAKxH,EAAIwH,EAAK5H,MACrBzE,OAAQqM,EAAKtH,EAAIsH,EAAK3H,QAE1B,CCqBA,SAAS4H,GAA2BlnB,EAASmnB,EAAgBtJ,GAC3D,OAAOsJ,IAAmB/L,GAAW4L,GCzBxB,SAAyBhnB,EAAS6d,GAC/C,IAAIwG,EAAM7H,GAAUxc,GAChBonB,EAAOlH,GAAmBlgB,GAC1Buf,EAAiB8E,EAAI9E,eACrBF,EAAQ+H,EAAKzE,YACbrD,EAAS8H,EAAK1E,aACdjD,EAAI,EACJE,EAAI,EAER,GAAIJ,EAAgB,CAClBF,EAAQE,EAAeF,MACvBC,EAASC,EAAeD,OACxB,IAAI+H,EAAiBvI,MAEjBuI,IAAmBA,GAA+B,UAAbxJ,KACvC4B,EAAIF,EAAeG,WACnBC,EAAIJ,EAAeK,UAEzB,CAEE,MAAO,CACLP,MAAOA,EACPC,OAAQA,EACRG,EAAGA,EAAI4G,GAAoBrmB,GAC3B2f,EAAGA,EAEP,CDDwD2H,CAAgBtnB,EAAS6d,IAAanc,GAAUylB,GAdxG,SAAoCnnB,EAAS6d,GAC3C,IAAIoJ,EAAO1M,GAAsBva,GAAS,EAAoB,UAAb6d,GASjD,OARAoJ,EAAKtM,IAAMsM,EAAKtM,IAAM3a,EAAQunB,UAC9BN,EAAKnM,KAAOmM,EAAKnM,KAAO9a,EAAQwnB,WAChCP,EAAKrM,OAASqM,EAAKtM,IAAM3a,EAAQ0iB,aACjCuE,EAAKpM,MAAQoM,EAAKnM,KAAO9a,EAAQ2iB,YACjCsE,EAAK5H,MAAQrf,EAAQ2iB,YACrBsE,EAAK3H,OAAStf,EAAQ0iB,aACtBuE,EAAKxH,EAAIwH,EAAKnM,KACdmM,EAAKtH,EAAIsH,EAAKtM,IACPsM,CACT,CAG0HQ,CAA2BN,EAAgBtJ,GAAYmJ,GEtBlK,SAAyBhnB,GACtC,IAAI6mB,EAEAO,EAAOlH,GAAmBlgB,GAC1B0nB,EAAY1B,GAAgBhmB,GAC5B8D,EAA0D,OAAlD+iB,EAAwB7mB,EAAQ0c,oBAAyB,EAASmK,EAAsB/iB,KAChGub,EAAQvY,GAAIsgB,EAAKO,YAAaP,EAAKzE,YAAa7e,EAAOA,EAAK6jB,YAAc,EAAG7jB,EAAOA,EAAK6e,YAAc,GACvGrD,EAASxY,GAAIsgB,EAAKQ,aAAcR,EAAK1E,aAAc5e,EAAOA,EAAK8jB,aAAe,EAAG9jB,EAAOA,EAAK4e,aAAe,GAC5GjD,GAAKiI,EAAUzB,WAAaI,GAAoBrmB,GAChD2f,GAAK+H,EAAUvB,UAMnB,MAJiD,QAA7C9jB,GAAiByB,GAAQsjB,GAAM5T,YACjCiM,GAAK3Y,GAAIsgB,EAAKzE,YAAa7e,EAAOA,EAAK6e,YAAc,GAAKtD,GAGrD,CACLA,MAAOA,EACPC,OAAQA,EACRG,EAAGA,EACHE,EAAGA,EAEP,CFCkMkI,CAAgB3H,GAAmBlgB,IACrO,CG1Be,SAAS8nB,GAAe5K,GACrC,IAOIuG,EAPAnI,EAAY4B,EAAK5B,UACjBtb,EAAUkd,EAAKld,QACf0b,EAAYwB,EAAKxB,UACjBmG,EAAgBnG,EAAYyC,GAAiBzC,GAAa,KAC1D8H,EAAY9H,EAAYyH,GAAazH,GAAa,KAClDqM,EAAUzM,EAAUmE,EAAInE,EAAU+D,MAAQ,EAAIrf,EAAQqf,MAAQ,EAC9D2I,EAAU1M,EAAUqE,EAAIrE,EAAUgE,OAAS,EAAItf,EAAQsf,OAAS,EAGpE,OAAQuC,GACN,KAAKlH,GACH8I,EAAU,CACRhE,EAAGsI,EACHpI,EAAGrE,EAAUqE,EAAI3f,EAAQsf,QAE3B,MAEF,KAAK1E,GACH6I,EAAU,CACRhE,EAAGsI,EACHpI,EAAGrE,EAAUqE,EAAIrE,EAAUgE,QAE7B,MAEF,KAAKzE,GACH4I,EAAU,CACRhE,EAAGnE,EAAUmE,EAAInE,EAAU+D,MAC3BM,EAAGqI,GAEL,MAEF,KAAKlN,GACH2I,EAAU,CACRhE,EAAGnE,EAAUmE,EAAIzf,EAAQqf,MACzBM,EAAGqI,GAEL,MAEF,QACEvE,EAAU,CACRhE,EAAGnE,EAAUmE,EACbE,EAAGrE,EAAUqE,GAInB,IAAIsI,EAAWpG,EAAgBb,GAAyBa,GAAiB,KAEzE,GAAgB,MAAZoG,EAAkB,CACpB,IAAIlG,EAAmB,MAAbkG,EAAmB,SAAW,QAExC,OAAQzE,GACN,KAAKvI,GACHwI,EAAQwE,GAAYxE,EAAQwE,IAAa3M,EAAUyG,GAAO,EAAI/hB,EAAQ+hB,GAAO,GAC7E,MAEF,KAAK7G,GACHuI,EAAQwE,GAAYxE,EAAQwE,IAAa3M,EAAUyG,GAAO,EAAI/hB,EAAQ+hB,GAAO,GAKrF,CAEE,OAAO0B,CACT,CC3De,SAASyE,GAAe/K,EAAOS,QAC5B,IAAZA,IACFA,EAAU,IAGZ,IAAIuK,EAAWvK,EACXwK,EAAqBD,EAASzM,UAC9BA,OAAmC,IAAvB0M,EAAgCjL,EAAMzB,UAAY0M,EAC9DC,EAAoBF,EAAStK,SAC7BA,OAAiC,IAAtBwK,EAA+BlL,EAAMU,SAAWwK,EAC3DC,EAAoBH,EAASI,SAC7BA,OAAiC,IAAtBD,EAA+BnN,GAAkBmN,EAC5DE,EAAwBL,EAASM,aACjCA,OAAyC,IAA1BD,EAAmCpN,GAAWoN,EAC7DE,EAAwBP,EAASQ,eACjCA,OAA2C,IAA1BD,EAAmCrN,GAASqN,EAC7DE,EAAuBT,EAASU,YAChCA,OAAuC,IAAzBD,GAA0CA,EACxDE,EAAmBX,EAASnG,QAC5BA,OAA+B,IAArB8G,EAA8B,EAAIA,EAC5CzH,EAAgBD,GAAsC,iBAAZY,EAAuBA,EAAUV,GAAgBU,EAAShH,KACpG+N,EAAaJ,IAAmBtN,GAASC,GAAYD,GACrDkI,EAAapG,EAAM8E,MAAM5G,OACzBrb,EAAUmd,EAAMC,SAASyL,EAAcE,EAAaJ,GACpDK,EJkBS,SAAyBhpB,EAASuoB,EAAUE,EAAc5K,GACvE,IAAIoL,EAAmC,oBAAbV,EAlB5B,SAA4BvoB,GAC1B,IAAImb,EAAkByL,GAAkBzG,GAAcngB,IAElDkpB,EADoB,CAAC,WAAY,SAAStiB,QAAQvE,GAAiBrC,GAAS2d,WAAa,GACnDf,GAAc5c,GAAWugB,GAAgBvgB,GAAWA,EAE9F,OAAK0B,GAAUwnB,GAKR/N,EAAgBnO,QAAO,SAAUma,GACtC,OAAOzlB,GAAUylB,IAAmBpkB,GAASokB,EAAgB+B,IAAmD,SAAhC5M,GAAY6K,EAChG,IANW,EAOX,CAK6DgC,CAAmBnpB,GAAW,GAAG2P,OAAO4Y,GAC/FpN,EAAkB,GAAGxL,OAAOsZ,EAAqB,CAACR,IAClDW,EAAsBjO,EAAgB,GACtCkO,EAAelO,EAAgBK,QAAO,SAAU8N,EAASnC,GAC3D,IAAIF,EAAOC,GAA2BlnB,EAASmnB,EAAgBtJ,GAK/D,OAJAyL,EAAQ3O,IAAM7T,GAAImgB,EAAKtM,IAAK2O,EAAQ3O,KACpC2O,EAAQzO,MAAQ9T,GAAIkgB,EAAKpM,MAAOyO,EAAQzO,OACxCyO,EAAQ1O,OAAS7T,GAAIkgB,EAAKrM,OAAQ0O,EAAQ1O,QAC1C0O,EAAQxO,KAAOhU,GAAImgB,EAAKnM,KAAMwO,EAAQxO,MAC/BwO,CACX,GAAKpC,GAA2BlnB,EAASopB,EAAqBvL,IAK5D,OAJAwL,EAAahK,MAAQgK,EAAaxO,MAAQwO,EAAavO,KACvDuO,EAAa/J,OAAS+J,EAAazO,OAASyO,EAAa1O,IACzD0O,EAAa5J,EAAI4J,EAAavO,KAC9BuO,EAAa1J,EAAI0J,EAAa1O,IACvB0O,CACT,CInC2BE,CAAgB7nB,GAAU1B,GAAWA,EAAUA,EAAQwpB,gBAAkBtJ,GAAmB/C,EAAMC,SAAS/B,QAASkN,EAAUE,EAAc5K,GACjK4L,EAAsBlP,GAAsB4C,EAAMC,SAAS9B,WAC3DqG,EAAgBmG,GAAe,CACjCxM,UAAWmO,EACXzpB,QAASujB,EACT1F,SAAU,WACVnC,UAAWA,IAETgO,EAAmB1C,GAAiB/e,OAAOsV,OAAO,GAAIgG,EAAY5B,IAClEgI,EAAoBhB,IAAmBtN,GAASqO,EAAmBD,EAGnEG,EAAkB,CACpBjP,IAAKqO,EAAmBrO,IAAMgP,EAAkBhP,IAAM0G,EAAc1G,IACpEC,OAAQ+O,EAAkB/O,OAASoO,EAAmBpO,OAASyG,EAAczG,OAC7EE,KAAMkO,EAAmBlO,KAAO6O,EAAkB7O,KAAOuG,EAAcvG,KACvED,MAAO8O,EAAkB9O,MAAQmO,EAAmBnO,MAAQwG,EAAcxG,OAExEgP,EAAa1M,EAAMyE,cAAckB,OAErC,GAAI6F,IAAmBtN,IAAUwO,EAAY,CAC3C,IAAI/G,EAAS+G,EAAWnO,GACxBzT,OAAOtH,KAAKipB,GAAiBvM,SAAQ,SAAUpd,GAC7C,IAAI6pB,EAAW,CAACjP,GAAOD,IAAQhU,QAAQ3G,IAAQ,EAAI,GAAK,EACpD6hB,EAAO,CAACnH,GAAKC,IAAQhU,QAAQ3G,IAAQ,EAAI,IAAM,IACnD2pB,EAAgB3pB,IAAQ6iB,EAAOhB,GAAQgI,CAC7C,GACA,CAEE,OAAOF,CACT,CC5De,SAASG,GAAqB5M,EAAOS,QAClC,IAAZA,IACFA,EAAU,IAGZ,IAAIuK,EAAWvK,EACXlC,EAAYyM,EAASzM,UACrB6M,EAAWJ,EAASI,SACpBE,EAAeN,EAASM,aACxBzG,EAAUmG,EAASnG,QACnBgI,EAAiB7B,EAAS6B,eAC1BC,EAAwB9B,EAAS+B,sBACjCA,OAAkD,IAA1BD,EAAmCE,GAAgBF,EAC3EzG,EAAYL,GAAazH,GACzBC,EAAa6H,EAAYwG,EAAiBzO,GAAsBA,GAAoBvO,QAAO,SAAU0O,GACvG,OAAOyH,GAAazH,KAAe8H,CACvC,IAAOxI,GACDoP,EAAoBzO,EAAW3O,QAAO,SAAU0O,GAClD,OAAOwO,EAAsBtjB,QAAQ8U,IAAc,CACvD,IAEmC,IAA7B0O,EAAkBroB,SACpBqoB,EAAoBzO,GAItB,IAAI0O,EAAYD,EAAkB5O,QAAO,SAAUC,EAAKC,GAOtD,OANAD,EAAIC,GAAawM,GAAe/K,EAAO,CACrCzB,UAAWA,EACX6M,SAAUA,EACVE,aAAcA,EACdzG,QAASA,IACR7D,GAAiBzC,IACbD,CACX,GAAK,IACH,OAAOxT,OAAOtH,KAAK0pB,GAAWC,MAAK,SAAUC,EAAGC,GAC9C,OAAOH,EAAUE,GAAKF,EAAUG,EACpC,GACA,CC+FA,MAAAC,GAAe,CACbnmB,KAAM,OACN0Y,SAAS,EACTC,MAAO,OACPxY,GA5HF,SAAcyY,GACZ,IAAIC,EAAQD,EAAKC,MACbS,EAAUV,EAAKU,QACftZ,EAAO4Y,EAAK5Y,KAEhB,IAAI6Y,EAAMyE,cAActd,GAAMomB,MAA9B,CAoCA,IAhCA,IAAIC,EAAoB/M,EAAQqK,SAC5B2C,OAAsC,IAAtBD,GAAsCA,EACtDE,EAAmBjN,EAAQkN,QAC3BC,OAAoC,IAArBF,GAAqCA,EACpDG,EAA8BpN,EAAQqN,mBACtCjJ,EAAUpE,EAAQoE,QAClBuG,EAAW3K,EAAQ2K,SACnBE,EAAe7K,EAAQ6K,aACvBI,EAAcjL,EAAQiL,YACtBqC,EAAwBtN,EAAQoM,eAChCA,OAA2C,IAA1BkB,GAA0CA,EAC3DhB,EAAwBtM,EAAQsM,sBAChCiB,EAAqBhO,EAAMS,QAAQlC,UACnCmG,EAAgB1D,GAAiBgN,GAEjCF,EAAqBD,IADHnJ,IAAkBsJ,GACqCnB,EAjC/E,SAAuCtO,GACrC,GAAIyC,GAAiBzC,KAAeX,GAClC,MAAO,GAGT,IAAIqQ,EAAoBvF,GAAqBnK,GAC7C,MAAO,CAACqK,GAA8BrK,GAAY0P,EAAmBrF,GAA8BqF,GACrG,CA0B6IC,CAA8BF,GAA3E,CAACtF,GAAqBsF,KAChHxP,EAAa,CAACwP,GAAoBxb,OAAOsb,GAAoBzP,QAAO,SAAUC,EAAKC,GACrF,OAAOD,EAAI9L,OAAOwO,GAAiBzC,KAAeX,GAAOgP,GAAqB5M,EAAO,CACnFzB,UAAWA,EACX6M,SAAUA,EACVE,aAAcA,EACdzG,QAASA,EACTgI,eAAgBA,EAChBE,sBAAuBA,IACpBxO,EACT,GAAK,IACC4P,EAAgBnO,EAAM8E,MAAM3G,UAC5BiI,EAAapG,EAAM8E,MAAM5G,OACzBkQ,EAAY,IAAI1rB,IAChB2rB,GAAqB,EACrBC,EAAwB9P,EAAW,GAE9B+P,EAAI,EAAGA,EAAI/P,EAAW5Z,OAAQ2pB,IAAK,CAC1C,IAAIhQ,EAAYC,EAAW+P,GAEvBC,EAAiBxN,GAAiBzC,GAElCkQ,EAAmBzI,GAAazH,KAAeT,GAC/C4Q,EAAa,CAAClR,GAAKC,IAAQhU,QAAQ+kB,IAAmB,EACtD5J,EAAM8J,EAAa,QAAU,SAC7BrF,EAAW0B,GAAe/K,EAAO,CACnCzB,UAAWA,EACX6M,SAAUA,EACVE,aAAcA,EACdI,YAAaA,EACb7G,QAASA,IAEP8J,EAAoBD,EAAaD,EAAmB/Q,GAAQC,GAAO8Q,EAAmBhR,GAASD,GAE/F2Q,EAAcvJ,GAAOwB,EAAWxB,KAClC+J,EAAoBjG,GAAqBiG,IAG3C,IAAIC,EAAmBlG,GAAqBiG,GACxCE,EAAS,GAUb,GARIpB,GACFoB,EAAOjnB,KAAKyhB,EAASmF,IAAmB,GAGtCZ,GACFiB,EAAOjnB,KAAKyhB,EAASsF,IAAsB,EAAGtF,EAASuF,IAAqB,GAG1EC,EAAOC,OAAM,SAAUC,GACzB,OAAOA,CACb,IAAQ,CACFT,EAAwB/P,EACxB8P,GAAqB,EACrB,KACN,CAEID,EAAUxrB,IAAI2b,EAAWsQ,EAC7B,CAEE,GAAIR,EAqBF,IAnBA,IAEIW,EAAQ,SAAeC,GACzB,IAAIC,EAAmB1Q,EAAWxT,MAAK,SAAUuT,GAC/C,IAAIsQ,EAAST,EAAUlrB,IAAIqb,GAE3B,GAAIsQ,EACF,OAAOA,EAAOphB,MAAM,EAAGwhB,GAAIH,OAAM,SAAUC,GACzC,OAAOA,CACnB,GAEA,IAEM,GAAIG,EAEF,OADAZ,EAAwBY,EACjB,OAEf,EAEaD,EAnBYpC,EAAiB,EAAI,EAmBZoC,EAAK,GAGpB,UAFFD,EAAMC,GADmBA,KAOpCjP,EAAMzB,YAAc+P,IACtBtO,EAAMyE,cAActd,GAAMomB,OAAQ,EAClCvN,EAAMzB,UAAY+P,EAClBtO,EAAMmP,OAAQ,EA5GlB,CA8GA,EAQEpJ,iBAAkB,CAAC,UACnBvR,KAAM,CACJ+Y,OAAO,IC7IX,SAAS6B,GAAe/F,EAAUS,EAAMuF,GAQtC,YAPyB,IAArBA,IACFA,EAAmB,CACjB/M,EAAG,EACHE,EAAG,IAIA,CACLhF,IAAK6L,EAAS7L,IAAMsM,EAAK3H,OAASkN,EAAiB7M,EACnD9E,MAAO2L,EAAS3L,MAAQoM,EAAK5H,MAAQmN,EAAiB/M,EACtD7E,OAAQ4L,EAAS5L,OAASqM,EAAK3H,OAASkN,EAAiB7M,EACzD7E,KAAM0L,EAAS1L,KAAOmM,EAAK5H,MAAQmN,EAAiB/M,EAExD,CAEA,SAASgN,GAAsBjG,GAC7B,MAAO,CAAC7L,GAAKE,GAAOD,GAAQE,IAAM4R,MAAK,SAAUC,GAC/C,OAAOnG,EAASmG,IAAS,CAC7B,GACA,CA+BA,MAAAC,GAAe,CACbtoB,KAAM,OACN0Y,SAAS,EACTC,MAAO,OACPiG,iBAAkB,CAAC,mBACnBze,GAlCF,SAAcyY,GACZ,IAAIC,EAAQD,EAAKC,MACb7Y,EAAO4Y,EAAK5Y,KACZgnB,EAAgBnO,EAAM8E,MAAM3G,UAC5BiI,EAAapG,EAAM8E,MAAM5G,OACzBmR,EAAmBrP,EAAMyE,cAAciL,gBACvCC,EAAoB5E,GAAe/K,EAAO,CAC5CwL,eAAgB,cAEdoE,EAAoB7E,GAAe/K,EAAO,CAC5C0L,aAAa,IAEXmE,EAA2BT,GAAeO,EAAmBxB,GAC7D2B,EAAsBV,GAAeQ,EAAmBxJ,EAAYiJ,GACpEU,EAAoBT,GAAsBO,GAC1CG,EAAmBV,GAAsBQ,GAC7C9P,EAAMyE,cAActd,GAAQ,CAC1B0oB,yBAA0BA,EAC1BC,oBAAqBA,EACrBC,kBAAmBA,EACnBC,iBAAkBA,GAEpBhQ,EAAMtQ,WAAWwO,OAASpT,OAAOsV,OAAO,GAAIJ,EAAMtQ,WAAWwO,OAAQ,CACnE,+BAAgC6R,EAChC,sBAAuBC,GAE3B,GCJAC,GAAe,CACb9oB,KAAM,SACN0Y,SAAS,EACTC,MAAO,OACPiB,SAAU,CAAC,iBACXzZ,GA5BF,SAAgBgZ,GACd,IAAIN,EAAQM,EAAMN,MACdS,EAAUH,EAAMG,QAChBtZ,EAAOmZ,EAAMnZ,KACb+oB,EAAkBzP,EAAQkF,OAC1BA,OAA6B,IAApBuK,EAA6B,CAAC,EAAG,GAAKA,EAC/C1b,EAAOgK,GAAWH,QAAO,SAAUC,EAAKC,GAE1C,OADAD,EAAIC,GA5BD,SAAiCA,EAAWuG,EAAOa,GACxD,IAAIjB,EAAgB1D,GAAiBzC,GACjC4R,EAAiB,CAACxS,GAAMH,IAAK/T,QAAQib,IAAkB,GAAK,EAAI,EAEhE3E,EAAyB,mBAAX4F,EAAwBA,EAAO7a,OAAOsV,OAAO,GAAI0E,EAAO,CACxEvG,UAAWA,KACPoH,EACFyK,EAAWrQ,EAAK,GAChBsQ,EAAWtQ,EAAK,GAIpB,OAFAqQ,EAAWA,GAAY,EACvBC,GAAYA,GAAY,GAAKF,EACtB,CAACxS,GAAMD,IAAOjU,QAAQib,IAAkB,EAAI,CACjDpC,EAAG+N,EACH7N,EAAG4N,GACD,CACF9N,EAAG8N,EACH5N,EAAG6N,EAEP,CASqBC,CAAwB/R,EAAWyB,EAAM8E,MAAOa,GAC1DrH,CACX,GAAK,IACCiS,EAAwB/b,EAAKwL,EAAMzB,WACnC+D,EAAIiO,EAAsBjO,EAC1BE,EAAI+N,EAAsB/N,EAEW,MAArCxC,EAAMyE,cAAcD,gBACtBxE,EAAMyE,cAAcD,cAAclC,GAAKA,EACvCtC,EAAMyE,cAAcD,cAAchC,GAAKA,GAGzCxC,EAAMyE,cAActd,GAAQqN,CAC9B,GC1BAgc,GAAe,CACbrpB,KAAM,gBACN0Y,SAAS,EACTC,MAAO,OACPxY,GApBF,SAAuByY,GACrB,IAAIC,EAAQD,EAAKC,MACb7Y,EAAO4Y,EAAK5Y,KAKhB6Y,EAAMyE,cAActd,GAAQwjB,GAAe,CACzCxM,UAAW6B,EAAM8E,MAAM3G,UACvBtb,QAASmd,EAAM8E,MAAM5G,OACrBwC,SAAU,WACVnC,UAAWyB,EAAMzB,WAErB,EAQE/J,KAAM,ICgHRic,GAAe,CACbtpB,KAAM,kBACN0Y,SAAS,EACTC,MAAO,OACPxY,GA/HF,SAAyByY,GACvB,IAAIC,EAAQD,EAAKC,MACbS,EAAUV,EAAKU,QACftZ,EAAO4Y,EAAK5Y,KACZqmB,EAAoB/M,EAAQqK,SAC5B2C,OAAsC,IAAtBD,GAAsCA,EACtDE,EAAmBjN,EAAQkN,QAC3BC,OAAoC,IAArBF,GAAsCA,EACrDtC,EAAW3K,EAAQ2K,SACnBE,EAAe7K,EAAQ6K,aACvBI,EAAcjL,EAAQiL,YACtB7G,EAAUpE,EAAQoE,QAClB6L,EAAkBjQ,EAAQkQ,OAC1BA,OAA6B,IAApBD,GAAoCA,EAC7CE,EAAwBnQ,EAAQoQ,aAChCA,OAAyC,IAA1BD,EAAmC,EAAIA,EACtDvH,EAAW0B,GAAe/K,EAAO,CACnCoL,SAAUA,EACVE,aAAcA,EACdzG,QAASA,EACT6G,YAAaA,IAEXhH,EAAgB1D,GAAiBhB,EAAMzB,WACvC8H,EAAYL,GAAahG,EAAMzB,WAC/BuS,GAAmBzK,EACnByE,EAAWjH,GAAyBa,GACpCiJ,ECrCY,MDqCS7C,ECrCH,IAAM,IDsCxBtG,EAAgBxE,EAAMyE,cAAcD,cACpC2J,EAAgBnO,EAAM8E,MAAM3G,UAC5BiI,EAAapG,EAAM8E,MAAM5G,OACzB6S,EAA4C,mBAAjBF,EAA8BA,EAAa/lB,OAAOsV,OAAO,GAAIJ,EAAM8E,MAAO,CACvGvG,UAAWyB,EAAMzB,aACbsS,EACFG,EAA2D,iBAAtBD,EAAiC,CACxEjG,SAAUiG,EACVpD,QAASoD,GACPjmB,OAAOsV,OAAO,CAChB0K,SAAU,EACV6C,QAAS,GACRoD,GACCE,EAAsBjR,EAAMyE,cAAckB,OAAS3F,EAAMyE,cAAckB,OAAO3F,EAAMzB,WAAa,KACjG/J,EAAO,CACT8N,EAAG,EACHE,EAAG,GAGL,GAAKgC,EAAL,CAIA,GAAIiJ,EAAe,CACjB,IAAIyD,EAEAC,EAAwB,MAAbrG,EAAmBtN,GAAMG,GACpCyT,EAAuB,MAAbtG,EAAmBrN,GAASC,GACtCkH,EAAmB,MAAbkG,EAAmB,SAAW,QACpCnF,EAASnB,EAAcsG,GACvBlhB,EAAM+b,EAAS0D,EAAS8H,GACxBxnB,EAAMgc,EAAS0D,EAAS+H,GACxBC,EAAWV,GAAUvK,EAAWxB,GAAO,EAAI,EAC3C0M,EAASjL,IAAcvI,GAAQqQ,EAAcvJ,GAAOwB,EAAWxB,GAC/D2M,EAASlL,IAAcvI,IAASsI,EAAWxB,IAAQuJ,EAAcvJ,GAGjEL,EAAevE,EAAMC,SAASW,MAC9BoE,EAAY2L,GAAUpM,EAAe7B,GAAc6B,GAAgB,CACrErC,MAAO,EACPC,OAAQ,GAENqP,EAAqBxR,EAAMyE,cAAc,oBAAsBzE,EAAMyE,cAAc,oBAAoBI,QxBhFtG,CACLrH,IAAK,EACLE,MAAO,EACPD,OAAQ,EACRE,KAAM,GwB6EF8T,EAAkBD,EAAmBL,GACrCO,EAAkBF,EAAmBJ,GAMrCO,EAAW7N,GAAO,EAAGqK,EAAcvJ,GAAMI,EAAUJ,IACnDgN,EAAYd,EAAkB3C,EAAcvJ,GAAO,EAAIyM,EAAWM,EAAWF,EAAkBT,EAA4BlG,SAAWwG,EAASK,EAAWF,EAAkBT,EAA4BlG,SACxM+G,EAAYf,GAAmB3C,EAAcvJ,GAAO,EAAIyM,EAAWM,EAAWD,EAAkBV,EAA4BlG,SAAWyG,EAASI,EAAWD,EAAkBV,EAA4BlG,SACzMzF,EAAoBrF,EAAMC,SAASW,OAASwC,GAAgBpD,EAAMC,SAASW,OAC3EkR,EAAezM,EAAiC,MAAbyF,EAAmBzF,EAAkB+E,WAAa,EAAI/E,EAAkBgF,YAAc,EAAI,EAC7H0H,EAAwH,OAAjGb,EAA+C,MAAvBD,OAA8B,EAASA,EAAoBnG,IAAqBoG,EAAwB,EAEvJc,EAAYrM,EAASkM,EAAYE,EACjCE,EAAkBnO,GAAO6M,EAAS3M,GAAQpa,EAF9B+b,EAASiM,EAAYG,EAAsBD,GAEKloB,EAAK+b,EAAQgL,EAAS5M,GAAQpa,EAAKqoB,GAAaroB,GAChH6a,EAAcsG,GAAYmH,EAC1Bzd,EAAKsW,GAAYmH,EAAkBtM,CACvC,CAEE,GAAIiI,EAAc,CAChB,IAAIsE,EAEAC,EAAyB,MAAbrH,EAAmBtN,GAAMG,GAErCyU,GAAwB,MAAbtH,EAAmBrN,GAASC,GAEvC2U,GAAU7N,EAAcmJ,GAExB2E,GAAmB,MAAZ3E,EAAkB,SAAW,QAEpC4E,GAAOF,GAAUhJ,EAAS8I,GAE1BK,GAAOH,GAAUhJ,EAAS+I,IAE1BK,IAAuD,IAAxC,CAACjV,GAAKG,IAAMlU,QAAQib,GAEnCgO,GAAyH,OAAjGR,EAAgD,MAAvBjB,OAA8B,EAASA,EAAoBtD,IAAoBuE,EAAyB,EAEzJS,GAAaF,GAAeF,GAAOF,GAAUlE,EAAcmE,IAAQlM,EAAWkM,IAAQI,GAAuB1B,EAA4BrD,QAEzIiF,GAAaH,GAAeJ,GAAUlE,EAAcmE,IAAQlM,EAAWkM,IAAQI,GAAuB1B,EAA4BrD,QAAU6E,GAE5IK,GAAmBlC,GAAU8B,G1BzH9B,SAAwB7oB,EAAK4E,EAAO7E,GACzC,IAAImpB,EAAIhP,GAAOla,EAAK4E,EAAO7E,GAC3B,OAAOmpB,EAAInpB,EAAMA,EAAMmpB,CACzB,C0BsHoDC,CAAeJ,GAAYN,GAASO,IAAc9O,GAAO6M,EAASgC,GAAaJ,GAAMF,GAAS1B,EAASiC,GAAaJ,IAEpKhO,EAAcmJ,GAAWkF,GACzBre,EAAKmZ,GAAWkF,GAAmBR,EACvC,CAEErS,EAAMyE,cAActd,GAAQqN,CAvE9B,CAwEA,EAQEuR,iBAAkB,CAAC,WE1HN,SAASiN,GAAiBC,EAAyB9P,EAAcuD,QAC9D,IAAZA,IACFA,GAAU,GAGZ,ICnBoCpH,ECJOzc,EFuBvCqwB,EAA0BzT,GAAc0D,GACxCgQ,EAAuB1T,GAAc0D,IAf3C,SAAyBtgB,GACvB,IAAIinB,EAAOjnB,EAAQua,wBACf2E,EAASd,GAAM6I,EAAK5H,OAASrf,EAAQof,aAAe,EACpDD,EAASf,GAAM6I,EAAK3H,QAAUtf,EAAQ2D,cAAgB,EAC1D,OAAkB,IAAXub,GAA2B,IAAXC,CACzB,CAU4DoR,CAAgBjQ,GACtEld,EAAkB8c,GAAmBI,GACrC2G,EAAO1M,GAAsB6V,EAAyBE,EAAsBzM,GAC5EyB,EAAS,CACXW,WAAY,EACZE,UAAW,GAET1C,EAAU,CACZhE,EAAG,EACHE,EAAG,GAkBL,OAfI0Q,IAA4BA,IAA4BxM,MACxB,SAA9BvH,GAAYgE,IAChBgG,GAAeljB,MACbkiB,GCnCgC7I,EDmCT6D,KClCd9D,GAAUC,IAAUG,GAAcH,GCJxC,CACLwJ,YAFyCjmB,EDQbyc,GCNRwJ,WACpBE,UAAWnmB,EAAQmmB,WDGZH,GAAgBvJ,IDoCnBG,GAAc0D,KAChBmD,EAAUlJ,GAAsB+F,GAAc,IACtCb,GAAKa,EAAakH,WAC1B/D,EAAQ9D,GAAKW,EAAaiH,WACjBnkB,IACTqgB,EAAQhE,EAAI4G,GAAoBjjB,KAI7B,CACLqc,EAAGwH,EAAKnM,KAAOwK,EAAOW,WAAaxC,EAAQhE,EAC3CE,EAAGsH,EAAKtM,IAAM2K,EAAOa,UAAY1C,EAAQ9D,EACzCN,MAAO4H,EAAK5H,MACZC,OAAQ2H,EAAK3H,OAEjB,CGvDA,SAASxI,GAAM0Z,GACb,IAAI9f,EAAM,IAAI7Q,IACV4wB,EAAU,IAAIhpB,IACdipB,EAAS,GAKb,SAASpG,EAAKqG,GACZF,EAAQhd,IAAIkd,EAASrsB,MACN,GAAGqL,OAAOghB,EAASzS,UAAY,GAAIyS,EAASzN,kBAAoB,IACtE7F,SAAQ,SAAUuT,GACzB,IAAKH,EAAQtwB,IAAIywB,GAAM,CACrB,IAAIC,EAAcngB,EAAIrQ,IAAIuwB,GAEtBC,GACFvG,EAAKuG,EAEf,CACA,IACIH,EAAO3rB,KAAK4rB,EAChB,CAQE,OAzBAH,EAAUnT,SAAQ,SAAUsT,GAC1BjgB,EAAI3Q,IAAI4wB,EAASrsB,KAAMqsB,EAC3B,IAiBEH,EAAUnT,SAAQ,SAAUsT,GACrBF,EAAQtwB,IAAIwwB,EAASrsB,OAExBgmB,EAAKqG,EAEX,IACSD,CACT,CCvBA,IAAII,GAAkB,CACpBpV,UAAW,SACX8U,UAAW,GACX3S,SAAU,YAGZ,SAASkT,KACP,IAAK,IAAItB,EAAOuB,UAAUjvB,OAAQmD,EAAO,IAAIzE,MAAMgvB,GAAOwB,EAAO,EAAGA,EAAOxB,EAAMwB,IAC/E/rB,EAAK+rB,GAAQD,UAAUC,GAGzB,OAAQ/rB,EAAKwnB,MAAK,SAAU1sB,GAC1B,QAASA,GAAoD,mBAAlCA,EAAQua,sBACvC,GACA,CAEO,SAAS2W,GAAgBC,QACL,IAArBA,IACFA,EAAmB,IAGrB,IAAIC,EAAoBD,EACpBE,EAAwBD,EAAkBE,iBAC1CA,OAA6C,IAA1BD,EAAmC,GAAKA,EAC3DE,EAAyBH,EAAkBI,eAC3CA,OAA4C,IAA3BD,EAAoCT,GAAkBS,EAC3E,OAAO,SAAsBjW,EAAWD,EAAQuC,QAC9B,IAAZA,IACFA,EAAU4T,GAGZ,ICxC6B/sB,EAC3BgtB,EDuCEtU,EAAQ,CACVzB,UAAW,SACXgW,iBAAkB,GAClB9T,QAAS3V,OAAOsV,OAAO,GAAIuT,GAAiBU,GAC5C5P,cAAe,GACfxE,SAAU,CACR9B,UAAWA,EACXD,OAAQA,GAEVxO,WAAY,GACZyQ,OAAQ,IAENqU,EAAmB,GACnBC,GAAc,EACd1xB,EAAW,CACbid,MAAOA,EACP0U,WAAY,SAAoBC,GAC9B,IAAIlU,EAAsC,mBAArBkU,EAAkCA,EAAiB3U,EAAMS,SAAWkU,EACzFC,IACA5U,EAAMS,QAAU3V,OAAOsV,OAAO,GAAIiU,EAAgBrU,EAAMS,QAASA,GACjET,EAAMsI,cAAgB,CACpBnK,UAAW5Z,GAAU4Z,GAAasL,GAAkBtL,GAAaA,EAAUkO,eAAiB5C,GAAkBtL,EAAUkO,gBAAkB,GAC1InO,OAAQuL,GAAkBvL,IAI5B,IElE4BmV,EAC9BwB,EFiEMN,EDhCG,SAAwBlB,GAErC,IAAIkB,EAAmB5a,GAAM0Z,GAE7B,OAAOnU,GAAeb,QAAO,SAAUC,EAAKwB,GAC1C,OAAOxB,EAAI9L,OAAO+hB,EAAiB1kB,QAAO,SAAU2jB,GAClD,OAAOA,EAAS1T,QAAUA,CAChC,IACA,GAAK,GACL,CCuB+BgV,EElEKzB,EFkEsB,GAAG7gB,OAAO2hB,EAAkBnU,EAAMS,QAAQ4S,WEjE9FwB,EAASxB,EAAUhV,QAAO,SAAUwW,EAAQE,GAC9C,IAAIC,EAAWH,EAAOE,EAAQ5tB,MAK9B,OAJA0tB,EAAOE,EAAQ5tB,MAAQ6tB,EAAWlqB,OAAOsV,OAAO,GAAI4U,EAAUD,EAAS,CACrEtU,QAAS3V,OAAOsV,OAAO,GAAI4U,EAASvU,QAASsU,EAAQtU,SACrDjM,KAAM1J,OAAOsV,OAAO,GAAI4U,EAASxgB,KAAMugB,EAAQvgB,QAC5CugB,EACEF,CACX,GAAK,IAEI/pB,OAAOtH,KAAKqxB,GAAQthB,KAAI,SAAUzQ,GACvC,OAAO+xB,EAAO/xB,EAClB,MF4DQ,OAJAkd,EAAMuU,iBAAmBA,EAAiB1kB,QAAO,SAAUolB,GACzD,OAAOA,EAAEpV,OACnB,IA+FMG,EAAMuU,iBAAiBrU,SAAQ,SAAUH,GACvC,IAAI5Y,EAAO4Y,EAAK5Y,KACZ+tB,EAAenV,EAAKU,QACpBA,OAA2B,IAAjByU,EAA0B,GAAKA,EACzC7U,EAASN,EAAKM,OAElB,GAAsB,mBAAXA,EAAuB,CAChC,IAAI8U,EAAY9U,EAAO,CACrBL,MAAOA,EACP7Y,KAAMA,EACNpE,SAAUA,EACV0d,QAASA,IAKX+T,EAAiB5sB,KAAKutB,GAFT,WAAkB,EAGzC,CACA,IA/GepyB,EAASylB,QACxB,EAMM4M,YAAa,WACX,IAAIX,EAAJ,CAIA,IAAIY,EAAkBrV,EAAMC,SACxB9B,EAAYkX,EAAgBlX,UAC5BD,EAASmX,EAAgBnX,OAG7B,GAAK0V,GAAiBzV,EAAWD,GAAjC,CAKA8B,EAAM8E,MAAQ,CACZ3G,UAAW6U,GAAiB7U,EAAWiF,GAAgBlF,GAAoC,UAA3B8B,EAAMS,QAAQC,UAC9ExC,OAAQwE,GAAcxE,IAOxB8B,EAAMmP,OAAQ,EACdnP,EAAMzB,UAAYyB,EAAMS,QAAQlC,UAKhCyB,EAAMuU,iBAAiBrU,SAAQ,SAAUsT,GACvC,OAAOxT,EAAMyE,cAAc+O,EAASrsB,MAAQ2D,OAAOsV,OAAO,GAAIoT,EAAShf,KACjF,IAEQ,IAAK,IAAIhL,EAAQ,EAAGA,EAAQwW,EAAMuU,iBAAiB3vB,OAAQ4E,IACzD,IAAoB,IAAhBwW,EAAMmP,MAAV,CAMA,IAAImG,EAAwBtV,EAAMuU,iBAAiB/qB,GAC/ClC,EAAKguB,EAAsBhuB,GAC3BiuB,EAAyBD,EAAsB7U,QAC/CuK,OAAsC,IAA3BuK,EAAoC,GAAKA,EACpDpuB,EAAOmuB,EAAsBnuB,KAEf,mBAAPG,IACT0Y,EAAQ1Y,EAAG,CACT0Y,MAAOA,EACPS,QAASuK,EACT7jB,KAAMA,EACNpE,SAAUA,KACNid,EAdlB,MAHYA,EAAMmP,OAAQ,EACd3lB,GAAS,CAzBrB,CATA,CAqDA,EAGMgf,QC1I2BlhB,ED0IV,WACf,OAAO,IAAIkuB,SAAQ,SAAUC,GAC3B1yB,EAASqyB,cACTK,EAAQzV,EAClB,GACA,EC7IS,WAUL,OATKsU,IACHA,EAAU,IAAIkB,SAAQ,SAAUC,GAC9BD,QAAQC,UAAUC,MAAK,WACrBpB,OAAU7f,EACVghB,EAAQnuB,IAClB,GACA,KAGWgtB,CACX,GDmIMqB,QAAS,WACPf,IACAH,GAAc,CACtB,GAGI,IAAKb,GAAiBzV,EAAWD,GAC/B,OAAOnb,EAmCT,SAAS6xB,IACPJ,EAAiBtU,SAAQ,SAAU5Y,GACjC,OAAOA,GACf,IACMktB,EAAmB,EACzB,CAEI,OAvCAzxB,EAAS2xB,WAAWjU,GAASiV,MAAK,SAAU1V,IACrCyU,GAAehU,EAAQmV,eAC1BnV,EAAQmV,cAAc5V,EAE9B,IAmCWjd,CACX,CACA,CACO,IAAI8yB,GAA4B9B,KG9LnC8B,GAA4B9B,GAAgB,CAC9CI,iBAFqB,CAAClM,GAAgBzD,GAAesR,GAAeC,MCMlEF,GAA4B9B,GAAgB,CAC9CI,iBAFqB,CAAClM,GAAgBzD,GAAesR,GAAeC,GAAapQ,GAAQqQ,GAAMtG,GAAiB9O,GAAOlE,M,+lBCkBnHtV,GAAO,WAEPuK,GAAa,eACb+E,GAAe,YAIfuf,GAAe,UACfC,GAAiB,YAGjBza,GAAc,OAAM9J,KACpB+J,GAAgB,SAAQ/J,KACxB4J,GAAc,OAAM5J,KACpB6J,GAAe,QAAO7J,KACtB2F,GAAwB,QAAO3F,KAAY+E,KAC3Cyf,GAA0B,UAASxkB,KAAY+E,KAC/C0f,GAAwB,QAAOzkB,KAAY+E,KAE3CiF,GAAkB,OAOlBjH,GAAuB,4DACvB2hB,GAA8B,GAAE3hB,MAAwBiH,KACxD2a,GAAgB,iBAKhBC,GAAgB1vB,IAAU,UAAY,YACtC2vB,GAAmB3vB,IAAU,YAAc,UAC3C4vB,GAAmB5vB,IAAU,aAAe,eAC5C6vB,GAAsB7vB,IAAU,eAAiB,aACjD8vB,GAAkB9vB,IAAU,aAAe,cAC3C+vB,GAAiB/vB,IAAU,cAAgB,aAI3CqJ,GAAU,CACd2mB,WAAW,EACXzL,SAAU,kBACV0L,QAAS,UACTnR,OAAQ,CAAC,EAAG,GACZoR,aAAc,KACd5Y,UAAW,UAGPhO,GAAc,CAClB0mB,UAAW,mBACXzL,SAAU,mBACV0L,QAAS,SACTnR,OAAQ,0BACRoR,aAAc,yBACd5Y,UAAW,2BAOb,MAAM6Y,WAAiB3lB,EACrBV,YAAY9N,EAASyN,GACnBgB,MAAMzO,EAASyN,GAEfxE,KAAKmrB,QAAU,KACfnrB,KAAKorB,QAAUprB,KAAKyF,SAAShM,WAE7BuG,KAAKqrB,MAAQ5kB,EAAeY,KAAKrH,KAAKyF,SAAU+kB,IAAe,IAC7D/jB,EAAeS,KAAKlH,KAAKyF,SAAU+kB,IAAe,IAClD/jB,EAAeG,QAAQ4jB,GAAexqB,KAAKorB,SAC7CprB,KAAKsrB,UAAYtrB,KAAKurB,eACxB,CAGA,kBAAWnnB,GACT,OAAOA,EACT,CAEA,sBAAWC,GACT,OAAOA,EACT,CAEA,eAAW/I,GACT,OAAOA,EACT,CAGAwN,SACE,OAAO9I,KAAK2Q,WAAa3Q,KAAK4Q,OAAS5Q,KAAK6Q,MAC9C,CAEAA,OACE,GAAInX,EAAWsG,KAAKyF,WAAazF,KAAK2Q,WACpC,OAGF,MAAM9Q,EAAgB,CACpBA,cAAeG,KAAKyF,UAKtB,IAFkBlF,EAAasB,QAAQ7B,KAAKyF,SAAUgK,GAAY5P,GAEpDoC,iBAAd,CAUA,GANAjC,KAAKwrB,gBAMD,iBAAkBzyB,SAASoB,kBAAoB6F,KAAKorB,QAAQ7xB,QAtFxC,eAuFtB,IAAK,MAAMxC,IAAW,GAAG2P,UAAU3N,SAAS8B,KAAKgM,UAC/CtG,EAAac,GAAGtK,EAAS,YAAayD,GAI1CwF,KAAKyF,SAASgmB,QACdzrB,KAAKyF,SAASjC,aAAa,iBAAiB,GAE5CxD,KAAKqrB,MAAMxxB,UAAU2Q,IAAIqF,IACzB7P,KAAKyF,SAAS5L,UAAU2Q,IAAIqF,IAC5BtP,EAAasB,QAAQ7B,KAAKyF,SAAUiK,GAAa7P,EAnBjD,CAoBF,CAEA+Q,OACE,GAAIlX,EAAWsG,KAAKyF,YAAczF,KAAK2Q,WACrC,OAGF,MAAM9Q,EAAgB,CACpBA,cAAeG,KAAKyF,UAGtBzF,KAAK0rB,cAAc7rB,EACrB,CAEA+F,UACM5F,KAAKmrB,SACPnrB,KAAKmrB,QAAQtB,UAGfrkB,MAAMI,SACR,CAEA8W,SACE1c,KAAKsrB,UAAYtrB,KAAKurB,gBAClBvrB,KAAKmrB,SACPnrB,KAAKmrB,QAAQzO,QAEjB,CAGAgP,cAAc7rB,GAEZ,IADkBU,EAAasB,QAAQ7B,KAAKyF,SAAUkK,GAAY9P,GACpDoC,iBAAd,CAMA,GAAI,iBAAkBlJ,SAASoB,gBAC7B,IAAK,MAAMpD,IAAW,GAAG2P,UAAU3N,SAAS8B,KAAKgM,UAC/CtG,EAAaC,IAAIzJ,EAAS,YAAayD,GAIvCwF,KAAKmrB,SACPnrB,KAAKmrB,QAAQtB,UAGf7pB,KAAKqrB,MAAMxxB,UAAUlC,OAAOkY,IAC5B7P,KAAKyF,SAAS5L,UAAUlC,OAAOkY,IAC/B7P,KAAKyF,SAASjC,aAAa,gBAAiB,SAC5CF,EAAYG,oBAAoBzD,KAAKqrB,MAAO,UAC5C9qB,EAAasB,QAAQ7B,KAAKyF,SAAUmK,GAAc/P,EAlBlD,CAmBF,CAEA0E,WAAWC,GAGT,GAAgC,iBAFhCA,EAASgB,MAAMjB,WAAWC,IAER6N,YAA2B5Z,EAAU+L,EAAO6N,YACV,mBAA3C7N,EAAO6N,UAAUf,sBAGxB,MAAM,IAAIjM,UAAW,GAAE/J,GAAKgK,+GAG9B,OAAOd,CACT,CAEAgnB,gBACE,QAAsB,IAAXG,GACT,MAAM,IAAItmB,UAAU,gEAGtB,IAAIumB,EAAmB5rB,KAAKyF,SAEG,WAA3BzF,KAAK0F,QAAQ2M,UACfuZ,EAAmB5rB,KAAKorB,QACf3yB,EAAUuH,KAAK0F,QAAQ2M,WAChCuZ,EAAmB/yB,EAAWmH,KAAK0F,QAAQ2M,WACA,iBAA3BrS,KAAK0F,QAAQ2M,YAC7BuZ,EAAmB5rB,KAAK0F,QAAQ2M,WAGlC,MAAM4Y,EAAejrB,KAAK6rB,mBAC1B7rB,KAAKmrB,QAAUQ,GAAoBC,EAAkB5rB,KAAKqrB,MAAOJ,EACnE,CAEAta,WACE,OAAO3Q,KAAKqrB,MAAMxxB,UAAUC,SAAS+V,GACvC,CAEAic,gBACE,MAAMC,EAAiB/rB,KAAKorB,QAE5B,GAAIW,EAAelyB,UAAUC,SAzMN,WA0MrB,OAAO+wB,GAGT,GAAIkB,EAAelyB,UAAUC,SA5MJ,aA6MvB,OAAOgxB,GAGT,GAAIiB,EAAelyB,UAAUC,SA/MA,iBAgN3B,MAhMsB,MAmMxB,GAAIiyB,EAAelyB,UAAUC,SAlNE,mBAmN7B,MAnMyB,SAuM3B,MAAMkyB,EAAkF,QAA1E5yB,iBAAiB4G,KAAKqrB,OAAOhyB,iBAAiB,iBAAiBmN,OAE7E,OAAIulB,EAAelyB,UAAUC,SA7NP,UA8NbkyB,EAAQtB,GAAmBD,GAG7BuB,EAAQpB,GAAsBD,EACvC,CAEAY,gBACE,OAAkD,OAA3CvrB,KAAKyF,SAASlM,QA5ND,UA6NtB,CAEA0yB,aACE,MAAMpS,OAAEA,GAAW7Z,KAAK0F,QAExB,MAAsB,iBAAXmU,EACFA,EAAOhd,MAAM,KAAK4K,KAAI/E,GAAShG,OAAOgS,SAAShM,EAAO,MAGzC,mBAAXmX,EACFqS,GAAcrS,EAAOqS,EAAYlsB,KAAKyF,UAGxCoU,CACT,CAEAgS,mBACE,MAAMM,EAAwB,CAC5B1Z,UAAWzS,KAAK8rB,gBAChBvE,UAAW,CAAC,CACVlsB,KAAM,kBACNsZ,QAAS,CACP2K,SAAUtf,KAAK0F,QAAQ4Z,WAG3B,CACEjkB,KAAM,SACNsZ,QAAS,CACPkF,OAAQ7Z,KAAKisB,iBAcnB,OARIjsB,KAAKsrB,WAAsC,WAAzBtrB,KAAK0F,QAAQslB,WACjC1nB,EAAYC,iBAAiBvD,KAAKqrB,MAAO,SAAU,UACnDc,EAAsB5E,UAAY,CAAC,CACjClsB,KAAM,cACN0Y,SAAS,KAIN,IACFoY,KACApwB,EAAQiE,KAAK0F,QAAQulB,aAAc,CAACkB,IAE3C,CAEAC,iBAAgBp1B,IAAEA,EAAGiG,OAAEA,IACrB,MAAMuQ,EAAQ/G,EAAevH,KA5QF,8DA4Q+Bc,KAAKqrB,OAAOtnB,QAAOhN,GAAWkC,EAAUlC,KAE7FyW,EAAM1U,QAMXsE,EAAqBoQ,EAAOvQ,EAAQjG,IAAQozB,IAAiB5c,EAAMpM,SAASnE,IAASwuB,OACvF,CAGA,sBAAOhwB,CAAgB+I,GACrB,OAAOxE,KAAKyI,MAAK,WACf,MAAMC,EAAOwiB,GAAS/kB,oBAAoBnG,KAAMwE,GAEhD,GAAsB,iBAAXA,EAAX,CAIA,QAA4B,IAAjBkE,EAAKlE,GACd,MAAM,IAAIa,UAAW,oBAAmBb,MAG1CkE,EAAKlE,IANL,CAOF,GACF,CAEA,iBAAO6nB,CAAWltB,GAChB,GA/TuB,IA+TnBA,EAAM4J,QAAiD,UAAf5J,EAAMsB,MAlUtC,QAkU0DtB,EAAMnI,IAC1E,OAGF,MAAMs1B,EAAc7lB,EAAevH,KAAKqrB,IAExC,IAAK,MAAMzhB,KAAUwjB,EAAa,CAChC,MAAMC,EAAUrB,GAAShlB,YAAY4C,GACrC,IAAKyjB,IAAyC,IAA9BA,EAAQ7mB,QAAQqlB,UAC9B,SAGF,MAAMyB,EAAertB,EAAMqtB,eACrBC,EAAeD,EAAaprB,SAASmrB,EAAQlB,OACnD,GACEmB,EAAaprB,SAASmrB,EAAQ9mB,WACC,WAA9B8mB,EAAQ7mB,QAAQqlB,YAA2B0B,GACb,YAA9BF,EAAQ7mB,QAAQqlB,WAA2B0B,EAE5C,SAIF,GAAIF,EAAQlB,MAAMvxB,SAASqF,EAAMlC,UAA4B,UAAfkC,EAAMsB,MAzV1C,QAyV8DtB,EAAMnI,KAAoB,qCAAqCoO,KAAKjG,EAAMlC,OAAOkL,UACvJ,SAGF,MAAMtI,EAAgB,CAAEA,cAAe0sB,EAAQ9mB,UAE5B,UAAftG,EAAMsB,OACRZ,EAAcqI,WAAa/I,GAG7BotB,EAAQb,cAAc7rB,EACxB,CACF,CAEA,4BAAO6sB,CAAsBvtB,GAI3B,MAAMwtB,EAAU,kBAAkBvnB,KAAKjG,EAAMlC,OAAOkL,SAC9CykB,EA7WS,WA6WOztB,EAAMnI,IACtB61B,EAAkB,CAAC1C,GAAcC,IAAgBhpB,SAASjC,EAAMnI,KAEtE,IAAK61B,IAAoBD,EACvB,OAGF,GAAID,IAAYC,EACd,OAGFztB,EAAMoD,iBAGN,MAAMuqB,EAAkB9sB,KAAK+G,QAAQ6B,IACnC5I,KACCyG,EAAeS,KAAKlH,KAAM4I,IAAsB,IAC/CnC,EAAeY,KAAKrH,KAAM4I,IAAsB,IAChDnC,EAAeG,QAAQgC,GAAsBzJ,EAAMW,eAAerG,YAEhExC,EAAWi0B,GAAS/kB,oBAAoB2mB,GAE9C,GAAID,EAIF,OAHA1tB,EAAM4tB,kBACN91B,EAAS4Z,YACT5Z,EAASm1B,gBAAgBjtB,GAIvBlI,EAAS0Z,aACXxR,EAAM4tB,kBACN91B,EAAS2Z,OACTkc,EAAgBrB,QAEpB,EAOFlrB,EAAac,GAAGtI,SAAUsxB,GAAwBzhB,GAAsBsiB,GAASwB,uBACjFnsB,EAAac,GAAGtI,SAAUsxB,GAAwBG,GAAeU,GAASwB,uBAC1EnsB,EAAac,GAAGtI,SAAUyS,GAAsB0f,GAASmB,YACzD9rB,EAAac,GAAGtI,SAAUuxB,GAAsBY,GAASmB,YACzD9rB,EAAac,GAAGtI,SAAUyS,GAAsB5C,IAAsB,SAAUzJ,GAC9EA,EAAMoD,iBACN2oB,GAAS/kB,oBAAoBnG,MAAM8I,QACrC,IAMA7N,EAAmBiwB,ICrbnB,MAAM5vB,GAAO,WAEPuU,GAAkB,OAClBmd,GAAmB,gBAAe1xB,KAElC8I,GAAU,CACd6oB,UAAW,iBACXC,cAAe,KACfjnB,YAAY,EACZhN,WAAW,EACXk0B,YAAa,QAGT9oB,GAAc,CAClB4oB,UAAW,SACXC,cAAe,kBACfjnB,WAAY,UACZhN,UAAW,UACXk0B,YAAa,oBAOf,MAAMC,WAAiBjpB,EACrBU,YAAYL,GACVgB,QACAxF,KAAK0F,QAAU1F,KAAKuE,WAAWC,GAC/BxE,KAAKqtB,aAAc,EACnBrtB,KAAKyF,SAAW,IAClB,CAGA,kBAAWrB,GACT,OAAOA,EACT,CAEA,sBAAWC,GACT,OAAOA,EACT,CAEA,eAAW/I,GACT,OAAOA,EACT,CAGAuV,KAAK1V,GACH,IAAK6E,KAAK0F,QAAQzM,UAEhB,YADA8C,EAAQZ,GAIV6E,KAAKstB,UAEL,MAAMv2B,EAAUiJ,KAAKutB,cACjBvtB,KAAK0F,QAAQO,YACfxL,EAAO1D,GAGTA,EAAQ8C,UAAU2Q,IAAIqF,IAEtB7P,KAAKwtB,mBAAkB,KACrBzxB,EAAQZ,EAAS,GAErB,CAEAyV,KAAKzV,GACE6E,KAAK0F,QAAQzM,WAKlB+G,KAAKutB,cAAc1zB,UAAUlC,OAAOkY,IAEpC7P,KAAKwtB,mBAAkB,KACrBxtB,KAAK4F,UACL7J,EAAQZ,EAAS,KARjBY,EAAQZ,EAUZ,CAEAyK,UACO5F,KAAKqtB,cAIV9sB,EAAaC,IAAIR,KAAKyF,SAAUunB,IAEhChtB,KAAKyF,SAAS9N,SACdqI,KAAKqtB,aAAc,EACrB,CAGAE,cACE,IAAKvtB,KAAKyF,SAAU,CAClB,MAAMgoB,EAAW10B,SAAS20B,cAAc,OACxCD,EAASR,UAAYjtB,KAAK0F,QAAQunB,UAC9BjtB,KAAK0F,QAAQO,YACfwnB,EAAS5zB,UAAU2Q,IAjGH,QAoGlBxK,KAAKyF,SAAWgoB,CAClB,CAEA,OAAOztB,KAAKyF,QACd,CAEAf,kBAAkBF,GAGhB,OADAA,EAAO2oB,YAAct0B,EAAW2L,EAAO2oB,aAChC3oB,CACT,CAEA8oB,UACE,GAAIttB,KAAKqtB,YACP,OAGF,MAAMt2B,EAAUiJ,KAAKutB,cACrBvtB,KAAK0F,QAAQynB,YAAYQ,OAAO52B,GAEhCwJ,EAAac,GAAGtK,EAASi2B,IAAiB,KACxCjxB,EAAQiE,KAAK0F,QAAQwnB,cAAc,IAGrCltB,KAAKqtB,aAAc,CACrB,CAEAG,kBAAkBryB,GAChBgB,EAAuBhB,EAAU6E,KAAKutB,cAAevtB,KAAK0F,QAAQO,WACpE,EClIF,MAEMJ,GAAa,gBACb+nB,GAAiB,UAAS/nB,KAC1BgoB,GAAqB,cAAahoB,KAIlCioB,GAAmB,WAEnB1pB,GAAU,CACd2pB,WAAW,EACXC,YAAa,MAGT3pB,GAAc,CAClB0pB,UAAW,UACXC,YAAa,WAOf,MAAMC,WAAkB9pB,EACtBU,YAAYL,GACVgB,QACAxF,KAAK0F,QAAU1F,KAAKuE,WAAWC,GAC/BxE,KAAKkuB,WAAY,EACjBluB,KAAKmuB,qBAAuB,IAC9B,CAGA,kBAAW/pB,GACT,OAAOA,EACT,CAEA,sBAAWC,GACT,OAAOA,EACT,CAEA,eAAW/I,GACT,MA1CS,WA2CX,CAGA8yB,WACMpuB,KAAKkuB,YAILluB,KAAK0F,QAAQqoB,WACf/tB,KAAK0F,QAAQsoB,YAAYvC,QAG3BlrB,EAAaC,IAAIzH,SAAU8M,IAC3BtF,EAAac,GAAGtI,SAAU60B,IAAezuB,GAASa,KAAKquB,eAAelvB,KACtEoB,EAAac,GAAGtI,SAAU80B,IAAmB1uB,GAASa,KAAKsuB,eAAenvB,KAE1Ea,KAAKkuB,WAAY,EACnB,CAEAK,aACOvuB,KAAKkuB,YAIVluB,KAAKkuB,WAAY,EACjB3tB,EAAaC,IAAIzH,SAAU8M,IAC7B,CAGAwoB,eAAelvB,GACb,MAAM6uB,YAAEA,GAAgBhuB,KAAK0F,QAE7B,GAAIvG,EAAMlC,SAAWlE,UAAYoG,EAAMlC,SAAW+wB,GAAeA,EAAYl0B,SAASqF,EAAMlC,QAC1F,OAGF,MAAMkX,EAAW1N,EAAec,kBAAkBymB,GAE1B,IAApB7Z,EAASrb,OACXk1B,EAAYvC,QACHzrB,KAAKmuB,uBAAyBL,GACvC3Z,EAASA,EAASrb,OAAS,GAAG2yB,QAE9BtX,EAAS,GAAGsX,OAEhB,CAEA6C,eAAenvB,GApFD,QAqFRA,EAAMnI,MAIVgJ,KAAKmuB,qBAAuBhvB,EAAMqvB,SAAWV,GAxFzB,UAyFtB,EChGF,MAAMW,GAAyB,oDACzBC,GAA0B,cAC1BC,GAAmB,gBACnBC,GAAkB,eAMxB,MAAMC,GACJhqB,cACE7E,KAAKyF,SAAW1M,SAAS8B,IAC3B,CAGAi0B,WAEE,MAAMC,EAAgBh2B,SAASoB,gBAAgBuf,YAC/C,OAAO9b,KAAK0M,IAAItS,OAAOg3B,WAAaD,EACtC,CAEAne,OACE,MAAMwF,EAAQpW,KAAK8uB,WACnB9uB,KAAKivB,mBAELjvB,KAAKkvB,sBAAsBlvB,KAAKyF,SAAUkpB,IAAkBQ,GAAmBA,EAAkB/Y,IAEjGpW,KAAKkvB,sBAAsBT,GAAwBE,IAAkBQ,GAAmBA,EAAkB/Y,IAC1GpW,KAAKkvB,sBAAsBR,GAAyBE,IAAiBO,GAAmBA,EAAkB/Y,GAC5G,CAEAiN,QACErjB,KAAKovB,wBAAwBpvB,KAAKyF,SAAU,YAC5CzF,KAAKovB,wBAAwBpvB,KAAKyF,SAAUkpB,IAC5C3uB,KAAKovB,wBAAwBX,GAAwBE,IACrD3uB,KAAKovB,wBAAwBV,GAAyBE,GACxD,CAEAS,gBACE,OAAOrvB,KAAK8uB,WAAa,CAC3B,CAGAG,mBACEjvB,KAAKsvB,sBAAsBtvB,KAAKyF,SAAU,YAC1CzF,KAAKyF,SAAS0L,MAAMoM,SAAW,QACjC,CAEA2R,sBAAsBn3B,EAAUw3B,EAAep0B,GAC7C,MAAMq0B,EAAiBxvB,KAAK8uB,WAW5B9uB,KAAKyvB,2BAA2B13B,GAVHhB,IAC3B,GAAIA,IAAYiJ,KAAKyF,UAAYzN,OAAOg3B,WAAaj4B,EAAQ2iB,YAAc8V,EACzE,OAGFxvB,KAAKsvB,sBAAsBv4B,EAASw4B,GACpC,MAAMJ,EAAkBn3B,OAAOoB,iBAAiBrC,GAASsC,iBAAiBk2B,GAC1Ex4B,EAAQoa,MAAMue,YAAYH,EAAgB,GAAEp0B,EAASuB,OAAOC,WAAWwyB,QAAsB,GAIjG,CAEAG,sBAAsBv4B,EAASw4B,GAC7B,MAAMI,EAAc54B,EAAQoa,MAAM9X,iBAAiBk2B,GAC/CI,GACFrsB,EAAYC,iBAAiBxM,EAASw4B,EAAeI,EAEzD,CAEAP,wBAAwBr3B,EAAUw3B,GAahCvvB,KAAKyvB,2BAA2B13B,GAZHhB,IAC3B,MAAM2L,EAAQY,EAAYY,iBAAiBnN,EAASw4B,GAEtC,OAAV7sB,GAKJY,EAAYG,oBAAoB1M,EAASw4B,GACzCx4B,EAAQoa,MAAMue,YAAYH,EAAe7sB,IALvC3L,EAAQoa,MAAMye,eAAeL,EAKgB,GAInD,CAEAE,2BAA2B13B,EAAU83B,GACnC,GAAIp3B,EAAUV,GACZ83B,EAAS93B,QAIX,IAAK,MAAM+3B,KAAOrpB,EAAevH,KAAKnH,EAAUiI,KAAKyF,UACnDoqB,EAASC,EAEb,EC1FF,MAEMjqB,GAAa,YAIb8J,GAAc,OAAM9J,KACpBkqB,GAAwB,gBAAelqB,KACvC+J,GAAgB,SAAQ/J,KACxB4J,GAAc,OAAM5J,KACpB6J,GAAe,QAAO7J,KACtBmqB,GAAgB,SAAQnqB,KACxBoqB,GAAuB,gBAAepqB,KACtCqqB,GAA2B,oBAAmBrqB,KAC9CsqB,GAAyB,kBAAiBtqB,KAC1C2F,GAAwB,QAAO3F,cAE/BuqB,GAAkB,aAElBvgB,GAAkB,OAClBwgB,GAAoB,eAOpBjsB,GAAU,CACdqpB,UAAU,EACVhC,OAAO,EACPvf,UAAU,GAGN7H,GAAc,CAClBopB,SAAU,mBACVhC,MAAO,UACPvf,SAAU,WAOZ,MAAMokB,WAAc/qB,EAClBV,YAAY9N,EAASyN,GACnBgB,MAAMzO,EAASyN,GAEfxE,KAAKuwB,QAAU9pB,EAAeG,QAxBV,gBAwBmC5G,KAAKyF,UAC5DzF,KAAKwwB,UAAYxwB,KAAKywB,sBACtBzwB,KAAK0wB,WAAa1wB,KAAK2wB,uBACvB3wB,KAAK2Q,UAAW,EAChB3Q,KAAKmQ,kBAAmB,EACxBnQ,KAAK4wB,WAAa,IAAI/B,GAEtB7uB,KAAK8M,oBACP,CAGA,kBAAW1I,GACT,OAAOA,EACT,CAEA,sBAAWC,GACT,OAAOA,EACT,CAEA,eAAW/I,GACT,MAnES,OAoEX,CAGAwN,OAAOjJ,GACL,OAAOG,KAAK2Q,SAAW3Q,KAAK4Q,OAAS5Q,KAAK6Q,KAAKhR,EACjD,CAEAgR,KAAKhR,GACCG,KAAK2Q,UAAY3Q,KAAKmQ,kBAIR5P,EAAasB,QAAQ7B,KAAKyF,SAAUgK,GAAY,CAChE5P,kBAGYoC,mBAIdjC,KAAK2Q,UAAW,EAChB3Q,KAAKmQ,kBAAmB,EAExBnQ,KAAK4wB,WAAWhgB,OAEhB7X,SAAS8B,KAAKhB,UAAU2Q,IAAI4lB,IAE5BpwB,KAAK6wB,gBAEL7wB,KAAKwwB,UAAU3f,MAAK,IAAM7Q,KAAK8wB,aAAajxB,KAC9C,CAEA+Q,OACO5Q,KAAK2Q,WAAY3Q,KAAKmQ,mBAIT5P,EAAasB,QAAQ7B,KAAKyF,SAAUkK,IAExC1N,mBAIdjC,KAAK2Q,UAAW,EAChB3Q,KAAKmQ,kBAAmB,EACxBnQ,KAAK0wB,WAAWnC,aAEhBvuB,KAAKyF,SAAS5L,UAAUlC,OAAOkY,IAE/B7P,KAAKgG,gBAAe,IAAMhG,KAAK+wB,cAAc/wB,KAAKyF,SAAUzF,KAAKoP,gBACnE,CAEAxJ,UACErF,EAAaC,IAAIxI,OAAQ6N,IACzBtF,EAAaC,IAAIR,KAAKuwB,QAAS1qB,IAE/B7F,KAAKwwB,UAAU5qB,UACf5F,KAAK0wB,WAAWnC,aAEhB/oB,MAAMI,SACR,CAEAorB,eACEhxB,KAAK6wB,eACP,CAGAJ,sBACE,OAAO,IAAIrD,GAAS,CAClBn0B,UAAW6H,QAAQd,KAAK0F,QAAQ+nB,UAChCxnB,WAAYjG,KAAKoP,eAErB,CAEAuhB,uBACE,OAAO,IAAI1C,GAAU,CACnBD,YAAahuB,KAAKyF,UAEtB,CAEAqrB,aAAajxB,GAEN9G,SAAS8B,KAAKf,SAASkG,KAAKyF,WAC/B1M,SAAS8B,KAAK8yB,OAAO3tB,KAAKyF,UAG5BzF,KAAKyF,SAAS0L,MAAM6Z,QAAU,QAC9BhrB,KAAKyF,SAAS/B,gBAAgB,eAC9B1D,KAAKyF,SAASjC,aAAa,cAAc,GACzCxD,KAAKyF,SAASjC,aAAa,OAAQ,UACnCxD,KAAKyF,SAASyX,UAAY,EAE1B,MAAM+T,EAAYxqB,EAAeG,QAxIT,cAwIsC5G,KAAKuwB,SAC/DU,IACFA,EAAU/T,UAAY,GAGxBziB,EAAOuF,KAAKyF,UAEZzF,KAAKyF,SAAS5L,UAAU2Q,IAAIqF,IAa5B7P,KAAKgG,gBAXsBkrB,KACrBlxB,KAAK0F,QAAQ+lB,OACfzrB,KAAK0wB,WAAWtC,WAGlBpuB,KAAKmQ,kBAAmB,EACxB5P,EAAasB,QAAQ7B,KAAKyF,SAAUiK,GAAa,CAC/C7P,iBACA,GAGoCG,KAAKuwB,QAASvwB,KAAKoP,cAC7D,CAEAtC,qBACEvM,EAAac,GAAGrB,KAAKyF,SAAU0qB,IAAuBhxB,IApLvC,WAqLTA,EAAMnI,MAINgJ,KAAK0F,QAAQwG,SACflM,KAAK4Q,OAIP5Q,KAAKmxB,6BAA4B,IAGnC5wB,EAAac,GAAGrJ,OAAQg4B,IAAc,KAChChwB,KAAK2Q,WAAa3Q,KAAKmQ,kBACzBnQ,KAAK6wB,eACP,IAGFtwB,EAAac,GAAGrB,KAAKyF,SAAUyqB,IAAyB/wB,IAEtDoB,EAAae,IAAItB,KAAKyF,SAAUwqB,IAAqBmB,IAC/CpxB,KAAKyF,WAAatG,EAAMlC,QAAU+C,KAAKyF,WAAa2rB,EAAOn0B,SAIjC,WAA1B+C,KAAK0F,QAAQ+nB,SAKbztB,KAAK0F,QAAQ+nB,UACfztB,KAAK4Q,OALL5Q,KAAKmxB,6BAMP,GACA,GAEN,CAEAJ,aACE/wB,KAAKyF,SAAS0L,MAAM6Z,QAAU,OAC9BhrB,KAAKyF,SAASjC,aAAa,eAAe,GAC1CxD,KAAKyF,SAAS/B,gBAAgB,cAC9B1D,KAAKyF,SAAS/B,gBAAgB,QAC9B1D,KAAKmQ,kBAAmB,EAExBnQ,KAAKwwB,UAAU5f,MAAK,KAClB7X,SAAS8B,KAAKhB,UAAUlC,OAAOy4B,IAC/BpwB,KAAKqxB,oBACLrxB,KAAK4wB,WAAWvN,QAChB9iB,EAAasB,QAAQ7B,KAAKyF,SAAUmK,GAAa,GAErD,CAEAR,cACE,OAAOpP,KAAKyF,SAAS5L,UAAUC,SA5NX,OA6NtB,CAEAq3B,6BAEE,GADkB5wB,EAAasB,QAAQ7B,KAAKyF,SAAUsqB,IACxC9tB,iBACZ,OAGF,MAAMqvB,EAAqBtxB,KAAKyF,SAASkZ,aAAe5lB,SAASoB,gBAAgBsf,aAC3E8X,EAAmBvxB,KAAKyF,SAAS0L,MAAMsM,UAEpB,WAArB8T,GAAiCvxB,KAAKyF,SAAS5L,UAAUC,SAASu2B,MAIjEiB,IACHtxB,KAAKyF,SAAS0L,MAAMsM,UAAY,UAGlCzd,KAAKyF,SAAS5L,UAAU2Q,IAAI6lB,IAC5BrwB,KAAKgG,gBAAe,KAClBhG,KAAKyF,SAAS5L,UAAUlC,OAAO04B,IAC/BrwB,KAAKgG,gBAAe,KAClBhG,KAAKyF,SAAS0L,MAAMsM,UAAY8T,CAAgB,GAC/CvxB,KAAKuwB,QAAQ,GACfvwB,KAAKuwB,SAERvwB,KAAKyF,SAASgmB,QAChB,CAMAoF,gBACE,MAAMS,EAAqBtxB,KAAKyF,SAASkZ,aAAe5lB,SAASoB,gBAAgBsf,aAC3E+V,EAAiBxvB,KAAK4wB,WAAW9B,WACjC0C,EAAoBhC,EAAiB,EAE3C,GAAIgC,IAAsBF,EAAoB,CAC5C,MAAMvsB,EAAWhK,IAAU,cAAgB,eAC3CiF,KAAKyF,SAAS0L,MAAMpM,GAAa,GAAEyqB,KACrC,CAEA,IAAKgC,GAAqBF,EAAoB,CAC5C,MAAMvsB,EAAWhK,IAAU,eAAiB,cAC5CiF,KAAKyF,SAAS0L,MAAMpM,GAAa,GAAEyqB,KACrC,CACF,CAEA6B,oBACErxB,KAAKyF,SAAS0L,MAAMsgB,YAAc,GAClCzxB,KAAKyF,SAAS0L,MAAMugB,aAAe,EACrC,CAGA,sBAAOj2B,CAAgB+I,EAAQ3E,GAC7B,OAAOG,KAAKyI,MAAK,WACf,MAAMC,EAAO4nB,GAAMnqB,oBAAoBnG,KAAMwE,GAE7C,GAAsB,iBAAXA,EAAX,CAIA,QAA4B,IAAjBkE,EAAKlE,GACd,MAAM,IAAIa,UAAW,oBAAmBb,MAG1CkE,EAAKlE,GAAQ3E,EANb,CAOF,GACF,EAOFU,EAAac,GAAGtI,SAAUyS,GAnSG,4BAmSyC,SAAUrM,GAC9E,MAAMlC,EAASwJ,EAAeoB,uBAAuB7H,MAEjD,CAAC,IAAK,QAAQoB,SAASpB,KAAKmI,UAC9BhJ,EAAMoD,iBAGRhC,EAAae,IAAIrE,EAAQwS,IAAYkiB,IAC/BA,EAAU1vB,kBAKd1B,EAAae,IAAIrE,EAAQ2S,IAAc,KACjC3W,EAAU+G,OACZA,KAAKyrB,OACP,GACA,IAIJ,MAAMmG,EAAcnrB,EAAeG,QA3Tf,eA4ThBgrB,GACFtB,GAAMpqB,YAAY0rB,GAAahhB,OAGpB0f,GAAMnqB,oBAAoBlJ,GAElC6L,OAAO9I,KACd,IAEA+H,EAAqBuoB,IAMrBr1B,EAAmBq1B,IC7VnB,MAEMzqB,GAAa,gBACb+E,GAAe,YACfW,GAAuB,OAAM1F,KAAY+E,KAGzCiF,GAAkB,OAClBgiB,GAAqB,UACrBC,GAAoB,SAEpBC,GAAgB,kBAEhBtiB,GAAc,OAAM5J,KACpB6J,GAAe,QAAO7J,KACtB8J,GAAc,OAAM9J,KACpBkqB,GAAwB,gBAAelqB,KACvC+J,GAAgB,SAAQ/J,KACxBmqB,GAAgB,SAAQnqB,KACxB2F,GAAwB,QAAO3F,KAAY+E,KAC3CulB,GAAyB,kBAAiBtqB,KAI1CzB,GAAU,CACdqpB,UAAU,EACVvhB,UAAU,EACVmQ,QAAQ,GAGJhY,GAAc,CAClBopB,SAAU,mBACVvhB,SAAU,UACVmQ,OAAQ,WAOV,MAAM2V,WAAkBzsB,EACtBV,YAAY9N,EAASyN,GACnBgB,MAAMzO,EAASyN,GAEfxE,KAAK2Q,UAAW,EAChB3Q,KAAKwwB,UAAYxwB,KAAKywB,sBACtBzwB,KAAK0wB,WAAa1wB,KAAK2wB,uBACvB3wB,KAAK8M,oBACP,CAGA,kBAAW1I,GACT,OAAOA,EACT,CAEA,sBAAWC,GACT,OAAOA,EACT,CAEA,eAAW/I,GACT,MA5DS,WA6DX,CAGAwN,OAAOjJ,GACL,OAAOG,KAAK2Q,SAAW3Q,KAAK4Q,OAAS5Q,KAAK6Q,KAAKhR,EACjD,CAEAgR,KAAKhR,GACCG,KAAK2Q,UAISpQ,EAAasB,QAAQ7B,KAAKyF,SAAUgK,GAAY,CAAE5P,kBAEtDoC,mBAIdjC,KAAK2Q,UAAW,EAChB3Q,KAAKwwB,UAAU3f,OAEV7Q,KAAK0F,QAAQ2W,SAChB,IAAIwS,IAAkBje,OAGxB5Q,KAAKyF,SAASjC,aAAa,cAAc,GACzCxD,KAAKyF,SAASjC,aAAa,OAAQ,UACnCxD,KAAKyF,SAAS5L,UAAU2Q,IAAIqnB,IAY5B7xB,KAAKgG,gBAVoBmJ,KAClBnP,KAAK0F,QAAQ2W,SAAUrc,KAAK0F,QAAQ+nB,UACvCztB,KAAK0wB,WAAWtC,WAGlBpuB,KAAKyF,SAAS5L,UAAU2Q,IAAIqF,IAC5B7P,KAAKyF,SAAS5L,UAAUlC,OAAOk6B,IAC/BtxB,EAAasB,QAAQ7B,KAAKyF,SAAUiK,GAAa,CAAE7P,iBAAgB,GAG/BG,KAAKyF,UAAU,GACvD,CAEAmL,OACO5Q,KAAK2Q,WAIQpQ,EAAasB,QAAQ7B,KAAKyF,SAAUkK,IAExC1N,mBAIdjC,KAAK0wB,WAAWnC,aAChBvuB,KAAKyF,SAASwsB,OACdjyB,KAAK2Q,UAAW,EAChB3Q,KAAKyF,SAAS5L,UAAU2Q,IAAIsnB,IAC5B9xB,KAAKwwB,UAAU5f,OAcf5Q,KAAKgG,gBAZoBksB,KACvBlyB,KAAKyF,SAAS5L,UAAUlC,OAAOkY,GAAiBiiB,IAChD9xB,KAAKyF,SAAS/B,gBAAgB,cAC9B1D,KAAKyF,SAAS/B,gBAAgB,QAEzB1D,KAAK0F,QAAQ2W,SAChB,IAAIwS,IAAkBxL,QAGxB9iB,EAAasB,QAAQ7B,KAAKyF,SAAUmK,GAAa,GAGb5P,KAAKyF,UAAU,IACvD,CAEAG,UACE5F,KAAKwwB,UAAU5qB,UACf5F,KAAK0wB,WAAWnC,aAChB/oB,MAAMI,SACR,CAGA6qB,sBACE,MAUMx3B,EAAY6H,QAAQd,KAAK0F,QAAQ+nB,UAEvC,OAAO,IAAIL,GAAS,CAClBH,UAlJsB,qBAmJtBh0B,YACAgN,YAAY,EACZknB,YAAantB,KAAKyF,SAAShM,WAC3ByzB,cAAej0B,EAjBKi0B,KACU,WAA1BltB,KAAK0F,QAAQ+nB,SAKjBztB,KAAK4Q,OAJHrQ,EAAasB,QAAQ7B,KAAKyF,SAAUsqB,GAI3B,EAWgC,MAE/C,CAEAY,uBACE,OAAO,IAAI1C,GAAU,CACnBD,YAAahuB,KAAKyF,UAEtB,CAEAqH,qBACEvM,EAAac,GAAGrB,KAAKyF,SAAU0qB,IAAuBhxB,IAtKvC,WAuKTA,EAAMnI,MAINgJ,KAAK0F,QAAQwG,SACflM,KAAK4Q,OAIPrQ,EAAasB,QAAQ7B,KAAKyF,SAAUsqB,IAAqB,GAE7D,CAGA,sBAAOt0B,CAAgB+I,GACrB,OAAOxE,KAAKyI,MAAK,WACf,MAAMC,EAAOspB,GAAU7rB,oBAAoBnG,KAAMwE,GAEjD,GAAsB,iBAAXA,EAAX,CAIA,QAAqBmE,IAAjBD,EAAKlE,IAAyBA,EAAO/C,WAAW,MAAmB,gBAAX+C,EAC1D,MAAM,IAAIa,UAAW,oBAAmBb,MAG1CkE,EAAKlE,GAAQxE,KANb,CAOF,GACF,EAOFO,EAAac,GAAGtI,SAAUyS,GAzLG,gCAyLyC,SAAUrM,GAC9E,MAAMlC,EAASwJ,EAAeoB,uBAAuB7H,MAMrD,GAJI,CAAC,IAAK,QAAQoB,SAASpB,KAAKmI,UAC9BhJ,EAAMoD,iBAGJ7I,EAAWsG,MACb,OAGFO,EAAae,IAAIrE,EAAQ2S,IAAc,KAEjC3W,EAAU+G,OACZA,KAAKyrB,OACP,IAIF,MAAMmG,EAAcnrB,EAAeG,QAAQmrB,IACvCH,GAAeA,IAAgB30B,GACjC+0B,GAAU9rB,YAAY0rB,GAAahhB,OAGxBohB,GAAU7rB,oBAAoBlJ,GACtC6L,OAAO9I,KACd,IAEAO,EAAac,GAAGrJ,OAAQuT,IAAqB,KAC3C,IAAK,MAAMxT,KAAY0O,EAAevH,KAAK6yB,IACzCC,GAAU7rB,oBAAoBpO,GAAU8Y,MAC1C,IAGFtQ,EAAac,GAAGrJ,OAAQg4B,IAAc,KACpC,IAAK,MAAMj5B,KAAW0P,EAAevH,KAAK,gDACG,UAAvC9F,iBAAiBrC,GAAS2d,UAC5Bsd,GAAU7rB,oBAAoBpP,GAAS6Z,MAE3C,IAGF7I,EAAqBiqB,IAMrB/2B,EAAmB+2B,IC/QnB,MAEaG,GAAmB,CAE9B,IAAK,CAAC,QAAS,MAAO,KAAM,OAAQ,OAJP,kBAK7B7Q,EAAG,CAAC,SAAU,OAAQ,QAAS,OAC/B8Q,KAAM,GACN7Q,EAAG,GACH8Q,GAAI,GACJC,IAAK,GACLC,KAAM,GACNC,IAAK,GACLC,GAAI,GACJC,GAAI,GACJC,GAAI,GACJC,GAAI,GACJC,GAAI,GACJC,GAAI,GACJC,GAAI,GACJC,GAAI,GACJvQ,EAAG,GACHxU,IAAK,CAAC,MAAO,SAAU,MAAO,QAAS,QAAS,UAChDglB,GAAI,GACJC,GAAI,GACJC,EAAG,GACHC,IAAK,GACLC,EAAG,GACHC,MAAO,GACPC,KAAM,GACNC,IAAK,GACLC,IAAK,GACLC,OAAQ,GACRC,EAAG,GACHC,GAAI,IAIAC,GAAgB,IAAIr1B,IAAI,CAC5B,aACA,OACA,OACA,WACA,WACA,SACA,MACA,eAUIs1B,GAAmB,0DAEnBC,GAAmBA,CAAC/e,EAAWgf,KACnC,MAAMC,EAAgBjf,EAAU1B,SAASjQ,cAEzC,OAAI2wB,EAAqB5yB,SAAS6yB,IAC5BJ,GAAc38B,IAAI+8B,IACbnzB,QAAQgzB,GAAiB1uB,KAAK4P,EAAUkf,YAO5CF,EAAqBjwB,QAAOowB,GAAkBA,aAA0BhvB,SAC5Ese,MAAK2Q,GAASA,EAAMhvB,KAAK6uB,IAAe,EC5DvC7vB,GAAU,CACdiwB,UAAWlC,GACXmC,QAAS,GACTC,WAAY,GACZpW,MAAM,EACNqW,UAAU,EACVC,WAAY,KACZC,SAAU,eAGNrwB,GAAc,CAClBgwB,UAAW,SACXC,QAAS,SACTC,WAAY,oBACZpW,KAAM,UACNqW,SAAU,UACVC,WAAY,kBACZC,SAAU,UAGNC,GAAqB,CACzBC,MAAO,iCACP78B,SAAU,oBAOZ,MAAM88B,WAAwB1wB,EAC5BU,YAAYL,GACVgB,QACAxF,KAAK0F,QAAU1F,KAAKuE,WAAWC,EACjC,CAGA,kBAAWJ,GACT,OAAOA,EACT,CAEA,sBAAWC,GACT,OAAOA,EACT,CAEA,eAAW/I,GACT,MA/CS,iBAgDX,CAGAw5B,aACE,OAAO91B,OAAOC,OAAOe,KAAK0F,QAAQ4uB,SAC/B7sB,KAAIjD,GAAUxE,KAAK+0B,yBAAyBvwB,KAC5CT,OAAOjD,QACZ,CAEAk0B,aACE,OAAOh1B,KAAK80B,aAAah8B,OAAS,CACpC,CAEAm8B,cAAcX,GAGZ,OAFAt0B,KAAKk1B,cAAcZ,GACnBt0B,KAAK0F,QAAQ4uB,QAAU,IAAKt0B,KAAK0F,QAAQ4uB,WAAYA,GAC9Ct0B,IACT,CAEAm1B,SACE,MAAMC,EAAkBr8B,SAAS20B,cAAc,OAC/C0H,EAAgBC,UAAYr1B,KAAKs1B,eAAet1B,KAAK0F,QAAQgvB,UAE7D,IAAK,MAAO38B,EAAUw9B,KAASv2B,OAAOmC,QAAQnB,KAAK0F,QAAQ4uB,SACzDt0B,KAAKw1B,YAAYJ,EAAiBG,EAAMx9B,GAG1C,MAAM28B,EAAWU,EAAgBvuB,SAAS,GACpC0tB,EAAav0B,KAAK+0B,yBAAyB/0B,KAAK0F,QAAQ6uB,YAM9D,OAJIA,GACFG,EAAS76B,UAAU2Q,OAAO+pB,EAAW13B,MAAM,MAGtC63B,CACT,CAGA/vB,iBAAiBH,GACfgB,MAAMb,iBAAiBH,GACvBxE,KAAKk1B,cAAc1wB,EAAO8vB,QAC5B,CAEAY,cAAcO,GACZ,IAAK,MAAO19B,EAAUu8B,KAAYt1B,OAAOmC,QAAQs0B,GAC/CjwB,MAAMb,iBAAiB,CAAE5M,WAAU68B,MAAON,GAAWK,GAEzD,CAEAa,YAAYd,EAAUJ,EAASv8B,GAC7B,MAAM29B,EAAkBjvB,EAAeG,QAAQ7O,EAAU28B,GAEpDgB,KAILpB,EAAUt0B,KAAK+0B,yBAAyBT,IAOpC77B,EAAU67B,GACZt0B,KAAK21B,sBAAsB98B,EAAWy7B,GAAUoB,GAI9C11B,KAAK0F,QAAQyY,KACfuX,EAAgBL,UAAYr1B,KAAKs1B,eAAehB,GAIlDoB,EAAgBE,YAActB,EAd5BoB,EAAgB/9B,SAepB,CAEA29B,eAAeG,GACb,OAAOz1B,KAAK0F,QAAQ8uB,SD5DjB,SAAsBqB,EAAYxB,EAAWyB,GAClD,IAAKD,EAAW/8B,OACd,OAAO+8B,EAGT,GAAIC,GAAgD,mBAArBA,EAC7B,OAAOA,EAAiBD,GAG1B,MACME,GADY,IAAI/9B,OAAOg+B,WACKC,gBAAgBJ,EAAY,aACxD1hB,EAAW,GAAGzN,UAAUqvB,EAAgBl7B,KAAKuF,iBAAiB,MAEpE,IAAK,MAAMrJ,KAAWod,EAAU,CAC9B,MAAM+hB,EAAcn/B,EAAQuc,SAASjQ,cAErC,IAAKrE,OAAOtH,KAAK28B,GAAWjzB,SAAS80B,GAAc,CACjDn/B,EAAQY,SACR,QACF,CAEA,MAAMw+B,EAAgB,GAAGzvB,UAAU3P,EAAQ6M,YACrCwyB,EAAoB,GAAG1vB,OAAO2tB,EAAU,MAAQ,GAAIA,EAAU6B,IAAgB,IAEpF,IAAK,MAAMlhB,KAAamhB,EACjBpC,GAAiB/e,EAAWohB,IAC/Br/B,EAAQ2M,gBAAgBsR,EAAU1B,SAGxC,CAEA,OAAOyiB,EAAgBl7B,KAAKw6B,SAC9B,CC4BmCgB,CAAaZ,EAAKz1B,KAAK0F,QAAQ2uB,UAAWr0B,KAAK0F,QAAQ+uB,YAAcgB,CACtG,CAEAV,yBAAyBU,GACvB,OAAO15B,EAAQ05B,EAAK,CAACz1B,MACvB,CAEA21B,sBAAsB5+B,EAAS2+B,GAC7B,GAAI11B,KAAK0F,QAAQyY,KAGf,OAFAuX,EAAgBL,UAAY,QAC5BK,EAAgB/H,OAAO52B,GAIzB2+B,EAAgBE,YAAc7+B,EAAQ6+B,WACxC,ECzIF,MACMU,GAAwB,IAAI93B,IAAI,CAAC,WAAY,YAAa,eAE1D+3B,GAAkB,OAElB1mB,GAAkB,OAGlB2mB,GAAkB,SAElBC,GAAmB,gBAEnBC,GAAgB,QAChBC,GAAgB,QAehBC,GAAgB,CACpBC,KAAM,OACNC,IAAK,MACLC,MAAOh8B,IAAU,OAAS,QAC1Bi8B,OAAQ,SACRC,KAAMl8B,IAAU,QAAU,QAGtBqJ,GAAU,CACdiwB,UAAWlC,GACX+E,WAAW,EACX5X,SAAU,kBACV6X,WAAW,EACXC,YAAa,GACbC,MAAO,EACPrV,mBAAoB,CAAC,MAAO,QAAS,SAAU,QAC/C7D,MAAM,EACNtE,OAAQ,CAAC,EAAG,GACZpH,UAAW,MACXwY,aAAc,KACduJ,UAAU,EACVC,WAAY,KACZ18B,UAAU,EACV28B,SAAU,+GAIV4C,MAAO,GACPz1B,QAAS,eAGLwC,GAAc,CAClBgwB,UAAW,SACX6C,UAAW,UACX5X,SAAU,mBACV6X,UAAW,2BACXC,YAAa,oBACbC,MAAO,kBACPrV,mBAAoB,QACpB7D,KAAM,UACNtE,OAAQ,0BACRpH,UAAW,oBACXwY,aAAc,yBACduJ,SAAU,UACVC,WAAY,kBACZ18B,SAAU,mBACV28B,SAAU,SACV4C,MAAO,4BACPz1B,QAAS,UAOX,MAAM01B,WAAgBhyB,EACpBV,YAAY9N,EAASyN,GACnB,QAAsB,IAAXmnB,GACT,MAAM,IAAItmB,UAAU,+DAGtBG,MAAMzO,EAASyN,GAGfxE,KAAKw3B,YAAa,EAClBx3B,KAAKy3B,SAAW,EAChBz3B,KAAK03B,WAAa,KAClB13B,KAAK23B,eAAiB,GACtB33B,KAAKmrB,QAAU,KACfnrB,KAAK43B,iBAAmB,KACxB53B,KAAK63B,YAAc,KAGnB73B,KAAK83B,IAAM,KAEX93B,KAAK+3B,gBAEA/3B,KAAK0F,QAAQ3N,UAChBiI,KAAKg4B,WAET,CAGA,kBAAW5zB,GACT,OAAOA,EACT,CAEA,sBAAWC,GACT,OAAOA,EACT,CAEA,eAAW/I,GACT,MAxHS,SAyHX,CAGA28B,SACEj4B,KAAKw3B,YAAa,CACpB,CAEAU,UACEl4B,KAAKw3B,YAAa,CACpB,CAEAW,gBACEn4B,KAAKw3B,YAAcx3B,KAAKw3B,UAC1B,CAEA1uB,SACO9I,KAAKw3B,aAIVx3B,KAAK23B,eAAeS,OAASp4B,KAAK23B,eAAeS,MAC7Cp4B,KAAK2Q,WACP3Q,KAAKq4B,SAIPr4B,KAAKs4B,SACP,CAEA1yB,UACEyI,aAAarO,KAAKy3B,UAElBl3B,EAAaC,IAAIR,KAAKyF,SAASlM,QAAQi9B,IAAiBC,GAAkBz2B,KAAKu4B,mBAE3Ev4B,KAAKyF,SAASxL,aAAa,2BAC7B+F,KAAKyF,SAASjC,aAAa,QAASxD,KAAKyF,SAASxL,aAAa,2BAGjE+F,KAAKw4B,iBACLhzB,MAAMI,SACR,CAEAiL,OACE,GAAoC,SAAhC7Q,KAAKyF,SAAS0L,MAAM6Z,QACtB,MAAM,IAAI1mB,MAAM,uCAGlB,IAAMtE,KAAKy4B,mBAAoBz4B,KAAKw3B,WAClC,OAGF,MAAM7F,EAAYpxB,EAAasB,QAAQ7B,KAAKyF,SAAUzF,KAAK6E,YAAYwB,UAzJxD,SA2JTqyB,GADax+B,EAAe8F,KAAKyF,WACLzF,KAAKyF,SAASgO,cAActZ,iBAAiBL,SAASkG,KAAKyF,UAE7F,GAAIksB,EAAU1vB,mBAAqBy2B,EACjC,OAIF14B,KAAKw4B,iBAEL,MAAMV,EAAM93B,KAAK24B,iBAEjB34B,KAAKyF,SAASjC,aAAa,mBAAoBs0B,EAAI79B,aAAa,OAEhE,MAAMk9B,UAAEA,GAAcn3B,KAAK0F,QAe3B,GAbK1F,KAAKyF,SAASgO,cAActZ,gBAAgBL,SAASkG,KAAK83B,OAC7DX,EAAUxJ,OAAOmK,GACjBv3B,EAAasB,QAAQ7B,KAAKyF,SAAUzF,KAAK6E,YAAYwB,UA1KpC,cA6KnBrG,KAAKmrB,QAAUnrB,KAAKwrB,cAAcsM,GAElCA,EAAIj+B,UAAU2Q,IAAIqF,IAMd,iBAAkB9W,SAASoB,gBAC7B,IAAK,MAAMpD,IAAW,GAAG2P,UAAU3N,SAAS8B,KAAKgM,UAC/CtG,EAAac,GAAGtK,EAAS,YAAayD,GAc1CwF,KAAKgG,gBAVYqL,KACf9Q,EAAasB,QAAQ7B,KAAKyF,SAAUzF,KAAK6E,YAAYwB,UA7LvC,WA+LU,IAApBrG,KAAK03B,YACP13B,KAAKq4B,SAGPr4B,KAAK03B,YAAa,CAAK,GAGK13B,KAAK83B,IAAK93B,KAAKoP,cAC/C,CAEAwB,OACE,GAAK5Q,KAAK2Q,aAIQpQ,EAAasB,QAAQ7B,KAAKyF,SAAUzF,KAAK6E,YAAYwB,UAjNxD,SAkNDpE,iBAAd,CASA,GALYjC,KAAK24B,iBACb9+B,UAAUlC,OAAOkY,IAIjB,iBAAkB9W,SAASoB,gBAC7B,IAAK,MAAMpD,IAAW,GAAG2P,UAAU3N,SAAS8B,KAAKgM,UAC/CtG,EAAaC,IAAIzJ,EAAS,YAAayD,GAI3CwF,KAAK23B,eAA4B,OAAI,EACrC33B,KAAK23B,eAAehB,KAAiB,EACrC32B,KAAK23B,eAAejB,KAAiB,EACrC12B,KAAK03B,WAAa,KAelB13B,KAAKgG,gBAbYqL,KACXrR,KAAK44B,yBAIJ54B,KAAK03B,YACR13B,KAAKw4B,iBAGPx4B,KAAKyF,SAAS/B,gBAAgB,oBAC9BnD,EAAasB,QAAQ7B,KAAKyF,SAAUzF,KAAK6E,YAAYwB,UA/OtC,WA+O8D,GAGjDrG,KAAK83B,IAAK93B,KAAKoP,cA/B7C,CAgCF,CAEAsN,SACM1c,KAAKmrB,SACPnrB,KAAKmrB,QAAQzO,QAEjB,CAGA+b,iBACE,OAAO33B,QAAQd,KAAK64B,YACtB,CAEAF,iBAKE,OAJK34B,KAAK83B,MACR93B,KAAK83B,IAAM93B,KAAK84B,kBAAkB94B,KAAK63B,aAAe73B,KAAK+4B,2BAGtD/4B,KAAK83B,GACd,CAEAgB,kBAAkBxE,GAChB,MAAMwD,EAAM93B,KAAKg5B,oBAAoB1E,GAASa,SAG9C,IAAK2C,EACH,OAAO,KAGTA,EAAIj+B,UAAUlC,OAAO4+B,GAAiB1mB,IAEtCioB,EAAIj+B,UAAU2Q,IAAK,MAAKxK,KAAK6E,YAAYvJ,aAEzC,MAAM29B,E3EnRKC,KACb,GACEA,GAAUt7B,KAAKu7B,MAjCH,IAiCSv7B,KAAKw7B,gBACnBrgC,SAASsgC,eAAeH,IAEjC,OAAOA,CAAM,E2E8QGI,CAAOt5B,KAAK6E,YAAYvJ,MAAMyH,WAQ5C,OANA+0B,EAAIt0B,aAAa,KAAMy1B,GAEnBj5B,KAAKoP,eACP0oB,EAAIj+B,UAAU2Q,IAAI+rB,IAGbuB,CACT,CAEAyB,WAAWjF,GACTt0B,KAAK63B,YAAcvD,EACft0B,KAAK2Q,aACP3Q,KAAKw4B,iBACLx4B,KAAK6Q,OAET,CAEAmoB,oBAAoB1E,GAalB,OAZIt0B,KAAK43B,iBACP53B,KAAK43B,iBAAiB3C,cAAcX,GAEpCt0B,KAAK43B,iBAAmB,IAAI/C,GAAgB,IACvC70B,KAAK0F,QAGR4uB,UACAC,WAAYv0B,KAAK+0B,yBAAyB/0B,KAAK0F,QAAQ0xB,eAIpDp3B,KAAK43B,gBACd,CAEAmB,yBACE,MAAO,CACL,iBAA0B/4B,KAAK64B,YAEnC,CAEAA,YACE,OAAO74B,KAAK+0B,yBAAyB/0B,KAAK0F,QAAQ4xB,QAAUt3B,KAAKyF,SAASxL,aAAa,yBACzF,CAGAu/B,6BAA6Br6B,GAC3B,OAAOa,KAAK6E,YAAYsB,oBAAoBhH,EAAMW,eAAgBE,KAAKy5B,qBACzE,CAEArqB,cACE,OAAOpP,KAAK0F,QAAQwxB,WAAcl3B,KAAK83B,KAAO93B,KAAK83B,IAAIj+B,UAAUC,SAASy8B,GAC5E,CAEA5lB,WACE,OAAO3Q,KAAK83B,KAAO93B,KAAK83B,IAAIj+B,UAAUC,SAAS+V,GACjD,CAEA2b,cAAcsM,GACZ,MAAMrlB,EAAY1W,EAAQiE,KAAK0F,QAAQ+M,UAAW,CAACzS,KAAM83B,EAAK93B,KAAKyF,WAC7Di0B,EAAa9C,GAAcnkB,EAAUnN,eAC3C,OAAOqmB,GAAoB3rB,KAAKyF,SAAUqyB,EAAK93B,KAAK6rB,iBAAiB6N,GACvE,CAEAzN,aACE,MAAMpS,OAAEA,GAAW7Z,KAAK0F,QAExB,MAAsB,iBAAXmU,EACFA,EAAOhd,MAAM,KAAK4K,KAAI/E,GAAShG,OAAOgS,SAAShM,EAAO,MAGzC,mBAAXmX,EACFqS,GAAcrS,EAAOqS,EAAYlsB,KAAKyF,UAGxCoU,CACT,CAEAkb,yBAAyBU,GACvB,OAAO15B,EAAQ05B,EAAK,CAACz1B,KAAKyF,UAC5B,CAEAomB,iBAAiB6N,GACf,MAAMvN,EAAwB,CAC5B1Z,UAAWinB,EACXnS,UAAW,CACT,CACElsB,KAAM,OACNsZ,QAAS,CACPqN,mBAAoBhiB,KAAK0F,QAAQsc,qBAGrC,CACE3mB,KAAM,SACNsZ,QAAS,CACPkF,OAAQ7Z,KAAKisB,eAGjB,CACE5wB,KAAM,kBACNsZ,QAAS,CACP2K,SAAUtf,KAAK0F,QAAQ4Z,WAG3B,CACEjkB,KAAM,QACNsZ,QAAS,CACP5d,QAAU,IAAGiJ,KAAK6E,YAAYvJ,eAGlC,CACED,KAAM,kBACN0Y,SAAS,EACTC,MAAO,aACPxY,GAAIkN,IAGF1I,KAAK24B,iBAAiBn1B,aAAa,wBAAyBkF,EAAKwL,MAAMzB,UAAU,KAMzF,MAAO,IACF0Z,KACApwB,EAAQiE,KAAK0F,QAAQulB,aAAc,CAACkB,IAE3C,CAEA4L,gBACE,MAAM4B,EAAW35B,KAAK0F,QAAQ7D,QAAQhF,MAAM,KAE5C,IAAK,MAAMgF,KAAW83B,EACpB,GAAgB,UAAZ93B,EACFtB,EAAac,GAAGrB,KAAKyF,SAAUzF,KAAK6E,YAAYwB,UAtZpC,SAsZ4DrG,KAAK0F,QAAQ3N,UAAUoH,IAC7Ea,KAAKw5B,6BAA6Br6B,GAC1C2J,QAAQ,SAEb,GAjaU,WAiaNjH,EAA4B,CACrC,MAAM+3B,EAAU/3B,IAAY60B,GAC1B12B,KAAK6E,YAAYwB,UAzZF,cA0ZfrG,KAAK6E,YAAYwB,UA5ZL,WA6ZRwzB,EAAWh4B,IAAY60B,GAC3B12B,KAAK6E,YAAYwB,UA3ZF,cA4ZfrG,KAAK6E,YAAYwB,UA9ZJ,YAgaf9F,EAAac,GAAGrB,KAAKyF,SAAUm0B,EAAS55B,KAAK0F,QAAQ3N,UAAUoH,IAC7D,MAAMotB,EAAUvsB,KAAKw5B,6BAA6Br6B,GAClDotB,EAAQoL,eAA8B,YAAfx4B,EAAMsB,KAAqBk2B,GAAgBD,KAAiB,EACnFnK,EAAQ+L,QAAQ,IAElB/3B,EAAac,GAAGrB,KAAKyF,SAAUo0B,EAAU75B,KAAK0F,QAAQ3N,UAAUoH,IAC9D,MAAMotB,EAAUvsB,KAAKw5B,6BAA6Br6B,GAClDotB,EAAQoL,eAA8B,aAAfx4B,EAAMsB,KAAsBk2B,GAAgBD,IACjEnK,EAAQ9mB,SAAS3L,SAASqF,EAAMU,eAElC0sB,EAAQ8L,QAAQ,GAEpB,CAGFr4B,KAAKu4B,kBAAoB,KACnBv4B,KAAKyF,UACPzF,KAAK4Q,MACP,EAGFrQ,EAAac,GAAGrB,KAAKyF,SAASlM,QAAQi9B,IAAiBC,GAAkBz2B,KAAKu4B,kBAChF,CAEAP,YACE,MAAMV,EAAQt3B,KAAKyF,SAASxL,aAAa,SAEpCq9B,IAIAt3B,KAAKyF,SAASxL,aAAa,eAAkB+F,KAAKyF,SAASmwB,YAAYpvB,QAC1ExG,KAAKyF,SAASjC,aAAa,aAAc8zB,GAG3Ct3B,KAAKyF,SAASjC,aAAa,yBAA0B8zB,GACrDt3B,KAAKyF,SAAS/B,gBAAgB,SAChC,CAEA40B,SACMt4B,KAAK2Q,YAAc3Q,KAAK03B,WAC1B13B,KAAK03B,YAAa,GAIpB13B,KAAK03B,YAAa,EAElB13B,KAAK85B,aAAY,KACX95B,KAAK03B,YACP13B,KAAK6Q,MACP,GACC7Q,KAAK0F,QAAQ2xB,MAAMxmB,MACxB,CAEAwnB,SACMr4B,KAAK44B,yBAIT54B,KAAK03B,YAAa,EAElB13B,KAAK85B,aAAY,KACV95B,KAAK03B,YACR13B,KAAK4Q,MACP,GACC5Q,KAAK0F,QAAQ2xB,MAAMzmB,MACxB,CAEAkpB,YAAY98B,EAAS+8B,GACnB1rB,aAAarO,KAAKy3B,UAClBz3B,KAAKy3B,SAAWt6B,WAAWH,EAAS+8B,EACtC,CAEAnB,uBACE,OAAO55B,OAAOC,OAAOe,KAAK23B,gBAAgBv2B,UAAS,EACrD,CAEAmD,WAAWC,GACT,MAAMw1B,EAAiB12B,EAAYK,kBAAkB3D,KAAKyF,UAE1D,IAAK,MAAMw0B,KAAiBj7B,OAAOtH,KAAKsiC,GAClC1D,GAAsBp/B,IAAI+iC,WACrBD,EAAeC,GAW1B,OAPAz1B,EAAS,IACJw1B,KACmB,iBAAXx1B,GAAuBA,EAASA,EAAS,IAEtDA,EAASxE,KAAKyE,gBAAgBD,GAC9BA,EAASxE,KAAK0E,kBAAkBF,GAChCxE,KAAK2E,iBAAiBH,GACfA,CACT,CAEAE,kBAAkBF,GAkBhB,OAjBAA,EAAO2yB,WAAiC,IAArB3yB,EAAO2yB,UAAsBp+B,SAAS8B,KAAOhC,EAAW2L,EAAO2yB,WAEtD,iBAAjB3yB,EAAO6yB,QAChB7yB,EAAO6yB,MAAQ,CACbxmB,KAAMrM,EAAO6yB,MACbzmB,KAAMpM,EAAO6yB,QAIW,iBAAjB7yB,EAAO8yB,QAChB9yB,EAAO8yB,MAAQ9yB,EAAO8yB,MAAMv0B,YAGA,iBAAnByB,EAAO8vB,UAChB9vB,EAAO8vB,QAAU9vB,EAAO8vB,QAAQvxB,YAG3ByB,CACT,CAEAi1B,qBACE,MAAMj1B,EAAS,GAEf,IAAK,MAAOxN,EAAK0L,KAAU1D,OAAOmC,QAAQnB,KAAK0F,SACzC1F,KAAK6E,YAAYT,QAAQpN,KAAS0L,IACpC8B,EAAOxN,GAAO0L,GAUlB,OANA8B,EAAOzM,UAAW,EAClByM,EAAO3C,QAAU,SAKV2C,CACT,CAEAg0B,iBACMx4B,KAAKmrB,UACPnrB,KAAKmrB,QAAQtB,UACb7pB,KAAKmrB,QAAU,MAGbnrB,KAAK83B,MACP93B,KAAK83B,IAAIngC,SACTqI,KAAK83B,IAAM,KAEf,CAGA,sBAAOr8B,CAAgB+I,GACrB,OAAOxE,KAAKyI,MAAK,WACf,MAAMC,EAAO6uB,GAAQpxB,oBAAoBnG,KAAMwE,GAE/C,GAAsB,iBAAXA,EAAX,CAIA,QAA4B,IAAjBkE,EAAKlE,GACd,MAAM,IAAIa,UAAW,oBAAmBb,MAG1CkE,EAAKlE,IANL,CAOF,GACF,EAOFvJ,EAAmBs8B,ICtmBnB,MAKMnzB,GAAU,IACXmzB,GAAQnzB,QACXkwB,QAAS,GACTza,OAAQ,CAAC,EAAG,GACZpH,UAAW,QACXiiB,SAAU,8IAKV7yB,QAAS,SAGLwC,GAAc,IACfkzB,GAAQlzB,YACXiwB,QAAS,kCAOX,MAAM4F,WAAgB3C,GAEpB,kBAAWnzB,GACT,OAAOA,EACT,CAEA,sBAAWC,GACT,OAAOA,EACT,CAEA,eAAW/I,GACT,MAtCS,SAuCX,CAGAm9B,iBACE,OAAOz4B,KAAK64B,aAAe74B,KAAKm6B,aAClC,CAGApB,yBACE,MAAO,CACL,kBAAkB/4B,KAAK64B,YACvB,gBAAoB74B,KAAKm6B,cAE7B,CAEAA,cACE,OAAOn6B,KAAK+0B,yBAAyB/0B,KAAK0F,QAAQ4uB,QACpD,CAGA,sBAAO74B,CAAgB+I,GACrB,OAAOxE,KAAKyI,MAAK,WACf,MAAMC,EAAOwxB,GAAQ/zB,oBAAoBnG,KAAMwE,GAE/C,GAAsB,iBAAXA,EAAX,CAIA,QAA4B,IAAjBkE,EAAKlE,GACd,MAAM,IAAIa,UAAW,oBAAmBb,MAG1CkE,EAAKlE,IANL,CAOF,GACF,EAOFvJ,EAAmBi/B,IC9EnB,MAEMr0B,GAAa,gBAGbu0B,GAAkB,WAAUv0B,KAC5Bw0B,GAAe,QAAOx0B,KACtB0F,GAAuB,OAAM1F,cAG7B6F,GAAoB,SAGpB4uB,GAAwB,SAExBC,GAAqB,YAGrBC,GAAuB,GAAED,mBAA+CA,uBAIxEn2B,GAAU,CACdyV,OAAQ,KACR4gB,WAAY,eACZC,cAAc,EACdz9B,OAAQ,KACR09B,UAAW,CAAC,GAAK,GAAK,IAGlBt2B,GAAc,CAClBwV,OAAQ,gBACR4gB,WAAY,SACZC,aAAc,UACdz9B,OAAQ,UACR09B,UAAW,SAOb,MAAMC,WAAkBr1B,EACtBV,YAAY9N,EAASyN,GACnBgB,MAAMzO,EAASyN,GAGfxE,KAAK66B,aAAe,IAAIjkC,IACxBoJ,KAAK86B,oBAAsB,IAAIlkC,IAC/BoJ,KAAK+6B,aAA6D,YAA9C3hC,iBAAiB4G,KAAKyF,UAAUgY,UAA0B,KAAOzd,KAAKyF,SAC1FzF,KAAKg7B,cAAgB,KACrBh7B,KAAKi7B,UAAY,KACjBj7B,KAAKk7B,oBAAsB,CACzBC,gBAAiB,EACjBC,gBAAiB,GAEnBp7B,KAAKq7B,SACP,CAGA,kBAAWj3B,GACT,OAAOA,EACT,CAEA,sBAAWC,GACT,OAAOA,EACT,CAEA,eAAW/I,GACT,MArES,WAsEX,CAGA+/B,UACEr7B,KAAKs7B,mCACLt7B,KAAKu7B,2BAEDv7B,KAAKi7B,UACPj7B,KAAKi7B,UAAUO,aAEfx7B,KAAKi7B,UAAYj7B,KAAKy7B,kBAGxB,IAAK,MAAMC,KAAW17B,KAAK86B,oBAAoB77B,SAC7Ce,KAAKi7B,UAAUU,QAAQD,EAE3B,CAEA91B,UACE5F,KAAKi7B,UAAUO,aACfh2B,MAAMI,SACR,CAGAlB,kBAAkBF,GAWhB,OATAA,EAAOvH,OAASpE,EAAW2L,EAAOvH,SAAWlE,SAAS8B,KAGtD2J,EAAOi2B,WAAaj2B,EAAOqV,OAAU,GAAErV,EAAOqV,oBAAsBrV,EAAOi2B,WAE3C,iBAArBj2B,EAAOm2B,YAChBn2B,EAAOm2B,UAAYn2B,EAAOm2B,UAAU99B,MAAM,KAAK4K,KAAI/E,GAAShG,OAAOC,WAAW+F,MAGzE8B,CACT,CAEA+2B,2BACOv7B,KAAK0F,QAAQg1B,eAKlBn6B,EAAaC,IAAIR,KAAK0F,QAAQzI,OAAQo9B,IAEtC95B,EAAac,GAAGrB,KAAK0F,QAAQzI,OAAQo9B,GAAaC,IAAuBn7B,IACvE,MAAMy8B,EAAoB57B,KAAK86B,oBAAoB1jC,IAAI+H,EAAMlC,OAAO0f,MACpE,GAAIif,EAAmB,CACrBz8B,EAAMoD,iBACN,MAAMjI,EAAO0F,KAAK+6B,cAAgB/iC,OAC5Bqe,EAASulB,EAAkBjlB,UAAY3W,KAAKyF,SAASkR,UAC3D,GAAIrc,EAAKuhC,SAEP,YADAvhC,EAAKuhC,SAAS,CAAEnqB,IAAK2E,EAAQylB,SAAU,WAKzCxhC,EAAK4iB,UAAY7G,CACnB,KAEJ,CAEAolB,kBACE,MAAM9mB,EAAU,CACdra,KAAM0F,KAAK+6B,aACXJ,UAAW36B,KAAK0F,QAAQi1B,UACxBF,WAAYz6B,KAAK0F,QAAQ+0B,YAG3B,OAAO,IAAIsB,sBAAqB56B,GAAWnB,KAAKg8B,kBAAkB76B,IAAUwT,EAC9E,CAGAqnB,kBAAkB76B,GAChB,MAAM86B,EAAgBrH,GAAS50B,KAAK66B,aAAazjC,IAAK,IAAGw9B,EAAM33B,OAAO5E,MAChE+1B,EAAWwG,IACf50B,KAAKk7B,oBAAoBC,gBAAkBvG,EAAM33B,OAAO0Z,UACxD3W,KAAKk8B,SAASD,EAAcrH,GAAO,EAG/BwG,GAAmBp7B,KAAK+6B,cAAgBhiC,SAASoB,iBAAiB+iB,UAClEif,EAAkBf,GAAmBp7B,KAAKk7B,oBAAoBE,gBACpEp7B,KAAKk7B,oBAAoBE,gBAAkBA,EAE3C,IAAK,MAAMxG,KAASzzB,EAAS,CAC3B,IAAKyzB,EAAMwH,eAAgB,CACzBp8B,KAAKg7B,cAAgB,KACrBh7B,KAAKq8B,kBAAkBJ,EAAcrH,IAErC,QACF,CAEA,MAAM0H,EAA2B1H,EAAM33B,OAAO0Z,WAAa3W,KAAKk7B,oBAAoBC,gBAEpF,GAAIgB,GAAmBG,GAGrB,GAFAlO,EAASwG,IAEJwG,EACH,YAOCe,GAAoBG,GACvBlO,EAASwG,EAEb,CACF,CAEA0G,mCACEt7B,KAAK66B,aAAe,IAAIjkC,IACxBoJ,KAAK86B,oBAAsB,IAAIlkC,IAE/B,MAAM2lC,EAAc91B,EAAevH,KAAKo7B,GAAuBt6B,KAAK0F,QAAQzI,QAE5E,IAAK,MAAMu/B,KAAUD,EAAa,CAEhC,IAAKC,EAAO7f,MAAQjjB,EAAW8iC,GAC7B,SAGF,MAAMZ,EAAoBn1B,EAAeG,QAAQ61B,UAAUD,EAAO7f,MAAO3c,KAAKyF,UAG1ExM,EAAU2iC,KACZ57B,KAAK66B,aAAa/jC,IAAI2lC,UAAUD,EAAO7f,MAAO6f,GAC9Cx8B,KAAK86B,oBAAoBhkC,IAAI0lC,EAAO7f,KAAMif,GAE9C,CACF,CAEAM,SAASj/B,GACH+C,KAAKg7B,gBAAkB/9B,IAI3B+C,KAAKq8B,kBAAkBr8B,KAAK0F,QAAQzI,QACpC+C,KAAKg7B,cAAgB/9B,EACrBA,EAAOpD,UAAU2Q,IAAIkB,IACrB1L,KAAK08B,iBAAiBz/B,GAEtBsD,EAAasB,QAAQ7B,KAAKyF,SAAU20B,GAAgB,CAAEv6B,cAAe5C,IACvE,CAEAy/B,iBAAiBz/B,GAEf,GAAIA,EAAOpD,UAAUC,SAlNQ,iBAmN3B2M,EAAeG,QAxMY,mBAwMsB3J,EAAO1D,QAzMpC,cA0MjBM,UAAU2Q,IAAIkB,SAInB,IAAK,MAAMixB,KAAal2B,EAAeO,QAAQ/J,EAnNnB,qBAsN1B,IAAK,MAAMwY,KAAQhP,EAAeS,KAAKy1B,EAAWnC,IAChD/kB,EAAK5b,UAAU2Q,IAAIkB,GAGzB,CAEA2wB,kBAAkBpsB,GAChBA,EAAOpW,UAAUlC,OAAO+T,IAExB,MAAMkxB,EAAcn2B,EAAevH,KAAM,GAAEo7B,MAAyB5uB,KAAqBuE,GACzF,IAAK,MAAMuD,KAAQopB,EACjBppB,EAAK3Z,UAAUlC,OAAO+T,GAE1B,CAGA,sBAAOjQ,CAAgB+I,GACrB,OAAOxE,KAAKyI,MAAK,WACf,MAAMC,EAAOkyB,GAAUz0B,oBAAoBnG,KAAMwE,GAEjD,GAAsB,iBAAXA,EAAX,CAIA,QAAqBmE,IAAjBD,EAAKlE,IAAyBA,EAAO/C,WAAW,MAAmB,gBAAX+C,EAC1D,MAAM,IAAIa,UAAW,oBAAmBb,MAG1CkE,EAAKlE,IANL,CAOF,GACF,EAOFjE,EAAac,GAAGrJ,OAAQuT,IAAqB,KAC3C,IAAK,MAAMsxB,KAAOp2B,EAAevH,KA9PT,0BA+PtB07B,GAAUz0B,oBAAoB02B,EAChC,IAOF5hC,EAAmB2/B,ICnRnB,MAEM/0B,GAAa,UAEb8J,GAAc,OAAM9J,KACpB+J,GAAgB,SAAQ/J,KACxB4J,GAAc,OAAM5J,KACpB6J,GAAe,QAAO7J,KACtB2F,GAAwB,QAAO3F,KAC/BsF,GAAiB,UAAStF,KAC1B0F,GAAuB,OAAM1F,KAE7Bi3B,GAAiB,YACjBC,GAAkB,aAClB5S,GAAe,UACfC,GAAiB,YACjB4S,GAAW,OACXC,GAAU,MAEVvxB,GAAoB,SACpB6qB,GAAkB,OAClB1mB,GAAkB,OAKlBqtB,GAA+B,yBAK/Bt0B,GAAuB,2EACvBu0B,GAAuB,YAFMD,uBAAiDA,mBAA6CA,OAE/Et0B,KAE5Cw0B,GAA+B,IAAG1xB,8BAA6CA,+BAA8CA,4BAMnI,MAAM2xB,WAAY93B,EAChBV,YAAY9N,GACVyO,MAAMzO,GACNiJ,KAAKorB,QAAUprB,KAAKyF,SAASlM,QAfN,uCAiBlByG,KAAKorB,UAOVprB,KAAKs9B,sBAAsBt9B,KAAKorB,QAASprB,KAAKu9B,gBAE9Ch9B,EAAac,GAAGrB,KAAKyF,SAAU0F,IAAehM,GAASa,KAAK+N,SAAS5O,KACvE,CAGA,eAAW7D,GACT,MA3DS,KA4DX,CAGAuV,OACE,MAAM2sB,EAAYx9B,KAAKyF,SACvB,GAAIzF,KAAKy9B,cAAcD,GACrB,OAIF,MAAME,EAAS19B,KAAK29B,iBAEdC,EAAYF,EAChBn9B,EAAasB,QAAQ67B,EAAQ/tB,GAAY,CAAE9P,cAAe29B,IAC1D,KAEgBj9B,EAAasB,QAAQ27B,EAAW/tB,GAAY,CAAE5P,cAAe69B,IAEjEz7B,kBAAqB27B,GAAaA,EAAU37B,mBAI1DjC,KAAK69B,YAAYH,EAAQF,GACzBx9B,KAAK89B,UAAUN,EAAWE,GAC5B,CAGAI,UAAU/mC,EAASgnC,GACZhnC,IAILA,EAAQ8C,UAAU2Q,IAAIkB,IAEtB1L,KAAK89B,UAAUr3B,EAAeoB,uBAAuB9Q,IAgBrDiJ,KAAKgG,gBAdYqL,KACsB,QAAjCta,EAAQkD,aAAa,SAKzBlD,EAAQ2M,gBAAgB,YACxB3M,EAAQyM,aAAa,iBAAiB,GACtCxD,KAAKg+B,gBAAgBjnC,GAAS,GAC9BwJ,EAAasB,QAAQ9K,EAAS2Y,GAAa,CACzC7P,cAAek+B,KARfhnC,EAAQ8C,UAAU2Q,IAAIqF,GAStB,GAG0B9Y,EAASA,EAAQ8C,UAAUC,SAASy8B,KACpE,CAEAsH,YAAY9mC,EAASgnC,GACdhnC,IAILA,EAAQ8C,UAAUlC,OAAO+T,IACzB3U,EAAQk7B,OAERjyB,KAAK69B,YAAYp3B,EAAeoB,uBAAuB9Q,IAcvDiJ,KAAKgG,gBAZYqL,KACsB,QAAjCta,EAAQkD,aAAa,SAKzBlD,EAAQyM,aAAa,iBAAiB,GACtCzM,EAAQyM,aAAa,WAAY,MACjCxD,KAAKg+B,gBAAgBjnC,GAAS,GAC9BwJ,EAAasB,QAAQ9K,EAAS6Y,GAAc,CAAE/P,cAAek+B,KAP3DhnC,EAAQ8C,UAAUlC,OAAOkY,GAOgD,GAG/C9Y,EAASA,EAAQ8C,UAAUC,SAASy8B,KACpE,CAEAxoB,SAAS5O,GACP,IAAM,CAAC29B,GAAgBC,GAAiB5S,GAAcC,GAAgB4S,GAAUC,IAAS77B,SAASjC,EAAMnI,KACtG,OAGFmI,EAAM4tB,kBACN5tB,EAAMoD,iBAEN,MAAMsE,EAAW7G,KAAKu9B,eAAex5B,QAAOhN,IAAY2C,EAAW3C,KACnE,IAAIknC,EAEJ,GAAI,CAACjB,GAAUC,IAAS77B,SAASjC,EAAMnI,KACrCinC,EAAoBp3B,EAAS1H,EAAMnI,MAAQgmC,GAAW,EAAIn2B,EAAS/N,OAAS,OACvE,CACL,MAAM6V,EAAS,CAACouB,GAAiB3S,IAAgBhpB,SAASjC,EAAMnI,KAChEinC,EAAoB7gC,EAAqByJ,EAAU1H,EAAMlC,OAAQ0R,GAAQ,EAC3E,CAEIsvB,IACFA,EAAkBxS,MAAM,CAAEyS,eAAe,IACzCb,GAAIl3B,oBAAoB83B,GAAmBptB,OAE/C,CAEA0sB,eACE,OAAO92B,EAAevH,KAAKi+B,GAAqBn9B,KAAKorB,QACvD,CAEAuS,iBACE,OAAO39B,KAAKu9B,eAAer+B,MAAK4H,GAAS9G,KAAKy9B,cAAc32B,MAAW,IACzE,CAEAw2B,sBAAsBrtB,EAAQpJ,GAC5B7G,KAAKm+B,yBAAyBluB,EAAQ,OAAQ,WAE9C,IAAK,MAAMnJ,KAASD,EAClB7G,KAAKo+B,6BAA6Bt3B,EAEtC,CAEAs3B,6BAA6Bt3B,GAC3BA,EAAQ9G,KAAKq+B,iBAAiBv3B,GAC9B,MAAMw3B,EAAWt+B,KAAKy9B,cAAc32B,GAC9By3B,EAAYv+B,KAAKw+B,iBAAiB13B,GACxCA,EAAMtD,aAAa,gBAAiB86B,GAEhCC,IAAcz3B,GAChB9G,KAAKm+B,yBAAyBI,EAAW,OAAQ,gBAG9CD,GACHx3B,EAAMtD,aAAa,WAAY,MAGjCxD,KAAKm+B,yBAAyBr3B,EAAO,OAAQ,OAG7C9G,KAAKy+B,mCAAmC33B,EAC1C,CAEA23B,mCAAmC33B,GACjC,MAAM7J,EAASwJ,EAAeoB,uBAAuBf,GAEhD7J,IAIL+C,KAAKm+B,yBAAyBlhC,EAAQ,OAAQ,YAE1C6J,EAAMzO,IACR2H,KAAKm+B,yBAAyBlhC,EAAQ,kBAAoB,GAAE6J,EAAMzO,MAEtE,CAEA2lC,gBAAgBjnC,EAAS2nC,GACvB,MAAMH,EAAYv+B,KAAKw+B,iBAAiBznC,GACxC,IAAKwnC,EAAU1kC,UAAUC,SAhMN,YAiMjB,OAGF,MAAMgP,EAASA,CAAC/Q,EAAUk1B,KACxB,MAAMl2B,EAAU0P,EAAeG,QAAQ7O,EAAUwmC,GAC7CxnC,GACFA,EAAQ8C,UAAUiP,OAAOmkB,EAAWyR,EACtC,EAGF51B,EAzM6B,mBAyMI4C,IACjC5C,EAzM2B,iBAyMI+G,IAC/B0uB,EAAU/6B,aAAa,gBAAiBk7B,EAC1C,CAEAP,yBAAyBpnC,EAASie,EAAWtS,GACtC3L,EAAQiD,aAAagb,IACxBje,EAAQyM,aAAawR,EAAWtS,EAEpC,CAEA+6B,cAAcntB,GACZ,OAAOA,EAAKzW,UAAUC,SAAS4R,GACjC,CAGA2yB,iBAAiB/tB,GACf,OAAOA,EAAKvJ,QAAQo2B,IAAuB7sB,EAAO7J,EAAeG,QAAQu2B,GAAqB7sB,EAChG,CAGAkuB,iBAAiBluB,GACf,OAAOA,EAAK/W,QA1NO,gCA0NoB+W,CACzC,CAGA,sBAAO7U,CAAgB+I,GACrB,OAAOxE,KAAKyI,MAAK,WACf,MAAMC,EAAO20B,GAAIl3B,oBAAoBnG,MAErC,GAAsB,iBAAXwE,EAAX,CAIA,QAAqBmE,IAAjBD,EAAKlE,IAAyBA,EAAO/C,WAAW,MAAmB,gBAAX+C,EAC1D,MAAM,IAAIa,UAAW,oBAAmBb,MAG1CkE,EAAKlE,IANL,CAOF,GACF,EAOFjE,EAAac,GAAGtI,SAAUyS,GAAsB5C,IAAsB,SAAUzJ,GAC1E,CAAC,IAAK,QAAQiC,SAASpB,KAAKmI,UAC9BhJ,EAAMoD,iBAGJ7I,EAAWsG,OAIfq9B,GAAIl3B,oBAAoBnG,MAAM6Q,MAChC,IAKAtQ,EAAac,GAAGrJ,OAAQuT,IAAqB,KAC3C,IAAK,MAAMxU,KAAW0P,EAAevH,KAAKk+B,IACxCC,GAAIl3B,oBAAoBpP,EAC1B,IAMFkE,EAAmBoiC,ICxSnB,MAEMx3B,GAAa,YAEb84B,GAAmB,YAAW94B,KAC9B+4B,GAAkB,WAAU/4B,KAC5B+nB,GAAiB,UAAS/nB,KAC1Bg5B,GAAkB,WAAUh5B,KAC5B8J,GAAc,OAAM9J,KACpB+J,GAAgB,SAAQ/J,KACxB4J,GAAc,OAAM5J,KACpB6J,GAAe,QAAO7J,KAGtBi5B,GAAkB,OAClBjvB,GAAkB,OAClBgiB,GAAqB,UAErBxtB,GAAc,CAClB6yB,UAAW,UACX6H,SAAU,UACV1H,MAAO,UAGHjzB,GAAU,CACd8yB,WAAW,EACX6H,UAAU,EACV1H,MAAO,KAOT,MAAM2H,WAAcz5B,EAClBV,YAAY9N,EAASyN,GACnBgB,MAAMzO,EAASyN,GAEfxE,KAAKy3B,SAAW,KAChBz3B,KAAKi/B,sBAAuB,EAC5Bj/B,KAAKk/B,yBAA0B,EAC/Bl/B,KAAK+3B,eACP,CAGA,kBAAW3zB,GACT,OAAOA,EACT,CAEA,sBAAWC,GACT,OAAOA,EACT,CAEA,eAAW/I,GACT,MAtDS,OAuDX,CAGAuV,OACoBtQ,EAAasB,QAAQ7B,KAAKyF,SAAUgK,IAExCxN,mBAIdjC,KAAKm/B,gBAEDn/B,KAAK0F,QAAQwxB,WACfl3B,KAAKyF,SAAS5L,UAAU2Q,IAvDN,QAiEpBxK,KAAKyF,SAAS5L,UAAUlC,OAAOmnC,IAC/BrkC,EAAOuF,KAAKyF,UACZzF,KAAKyF,SAAS5L,UAAU2Q,IAAIqF,GAAiBgiB,IAE7C7xB,KAAKgG,gBAXYqL,KACfrR,KAAKyF,SAAS5L,UAAUlC,OAAOk6B,IAC/BtxB,EAAasB,QAAQ7B,KAAKyF,SAAUiK,IAEpC1P,KAAKo/B,oBAAoB,GAOGp/B,KAAKyF,SAAUzF,KAAK0F,QAAQwxB,WAC5D,CAEAtmB,OACO5Q,KAAKq/B,YAIQ9+B,EAAasB,QAAQ7B,KAAKyF,SAAUkK,IAExC1N,mBAUdjC,KAAKyF,SAAS5L,UAAU2Q,IAAIqnB,IAC5B7xB,KAAKgG,gBAPYqL,KACfrR,KAAKyF,SAAS5L,UAAU2Q,IAAIs0B,IAC5B9+B,KAAKyF,SAAS5L,UAAUlC,OAAOk6B,GAAoBhiB,IACnDtP,EAAasB,QAAQ7B,KAAKyF,SAAUmK,GAAa,GAIrB5P,KAAKyF,SAAUzF,KAAK0F,QAAQwxB,YAC5D,CAEAtxB,UACE5F,KAAKm/B,gBAEDn/B,KAAKq/B,WACPr/B,KAAKyF,SAAS5L,UAAUlC,OAAOkY,IAGjCrK,MAAMI,SACR,CAEAy5B,UACE,OAAOr/B,KAAKyF,SAAS5L,UAAUC,SAAS+V,GAC1C,CAIAuvB,qBACOp/B,KAAK0F,QAAQq5B,WAId/+B,KAAKi/B,sBAAwBj/B,KAAKk/B,0BAItCl/B,KAAKy3B,SAAWt6B,YAAW,KACzB6C,KAAK4Q,MAAM,GACV5Q,KAAK0F,QAAQ2xB,QAClB,CAEAiI,eAAengC,EAAOogC,GACpB,OAAQpgC,EAAMsB,MACZ,IAAK,YACL,IAAK,WACHT,KAAKi/B,qBAAuBM,EAC5B,MAGF,IAAK,UACL,IAAK,WACHv/B,KAAKk/B,wBAA0BK,EASnC,GAAIA,EAEF,YADAv/B,KAAKm/B,gBAIP,MAAMvwB,EAAczP,EAAMU,cACtBG,KAAKyF,WAAamJ,GAAe5O,KAAKyF,SAAS3L,SAAS8U,IAI5D5O,KAAKo/B,oBACP,CAEArH,gBACEx3B,EAAac,GAAGrB,KAAKyF,SAAUk5B,IAAiBx/B,GAASa,KAAKs/B,eAAengC,GAAO,KACpFoB,EAAac,GAAGrB,KAAKyF,SAAUm5B,IAAgBz/B,GAASa,KAAKs/B,eAAengC,GAAO,KACnFoB,EAAac,GAAGrB,KAAKyF,SAAUmoB,IAAezuB,GAASa,KAAKs/B,eAAengC,GAAO,KAClFoB,EAAac,GAAGrB,KAAKyF,SAAUo5B,IAAgB1/B,GAASa,KAAKs/B,eAAengC,GAAO,IACrF,CAEAggC,gBACE9wB,aAAarO,KAAKy3B,UAClBz3B,KAAKy3B,SAAW,IAClB,CAGA,sBAAOh8B,CAAgB+I,GACrB,OAAOxE,KAAKyI,MAAK,WACf,MAAMC,EAAOs2B,GAAM74B,oBAAoBnG,KAAMwE,GAE7C,GAAsB,iBAAXA,EAAqB,CAC9B,QAA4B,IAAjBkE,EAAKlE,GACd,MAAM,IAAIa,UAAW,oBAAmBb,MAG1CkE,EAAKlE,GAAQxE,KACf,CACF,GACF,E,OAOF+H,EAAqBi3B,IAMrB/jC,EAAmB+jC,IC1MJ,CACb12B,QACAO,SACA0D,YACA2D,YACAgb,YACAoF,SACA0B,aACAkI,WACAU,aACAyC,OACA2B,SACAzH,W"}
\ No newline at end of file
diff --git a/docs/deps/bootstrap-5.3.1/bootstrap.min.css b/docs/deps/bootstrap-5.3.1/bootstrap.min.css
new file mode 100644
index 00000000..5dccca08
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/bootstrap.min.css
@@ -0,0 +1,5 @@
+@import url("font.css");:root{--bslib-bootstrap-version: 5;--bslib-preset-name: spacelab;--bslib-preset-type: bootswatch}/*!
+   * Bootstrap  v5.3.1 (https://getbootstrap.com/)
+   * Copyright 2011-2023 The Bootstrap Authors
+   * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)
+   */:root,[data-bs-theme="light"]{--bs-blue: #446e9b;--bs-indigo: #6610f2;--bs-purple: #6f42c1;--bs-pink: #e83e8c;--bs-red: #cd0200;--bs-orange: #fd7e14;--bs-yellow: #d47500;--bs-green: #3cb521;--bs-teal: #20c997;--bs-cyan: #3399f3;--bs-black: #000;--bs-white: #fff;--bs-gray: #777;--bs-gray-dark: #333;--bs-gray-100: #f8f9fa;--bs-gray-200: #eee;--bs-gray-300: #dee2e6;--bs-gray-400: #ced4da;--bs-gray-500: #999;--bs-gray-600: #777;--bs-gray-700: #495057;--bs-gray-800: #333;--bs-gray-900: #2d2d2d;--bs-default: #999;--bs-primary: #446e9b;--bs-secondary: #999;--bs-success: #3cb521;--bs-info: #3399f3;--bs-warning: #d47500;--bs-danger: #cd0200;--bs-light: #eee;--bs-dark: #333;--bs-default-rgb: 153,153,153;--bs-primary-rgb: 68,110,155;--bs-secondary-rgb: 153,153,153;--bs-success-rgb: 60,181,33;--bs-info-rgb: 51,153,243;--bs-warning-rgb: 212,117,0;--bs-danger-rgb: 205,2,0;--bs-light-rgb: 238,238,238;--bs-dark-rgb: 51,51,51;--bs-primary-text-emphasis: #1b2c3e;--bs-secondary-text-emphasis: #3d3d3d;--bs-success-text-emphasis: #18480d;--bs-info-text-emphasis: #143d61;--bs-warning-text-emphasis: #552f00;--bs-danger-text-emphasis: #520100;--bs-light-text-emphasis: #495057;--bs-dark-text-emphasis: #495057;--bs-primary-bg-subtle: #dae2eb;--bs-secondary-bg-subtle: #ebebeb;--bs-success-bg-subtle: #d8f0d3;--bs-info-bg-subtle: #d6ebfd;--bs-warning-bg-subtle: #f6e3cc;--bs-danger-bg-subtle: #f5cccc;--bs-light-bg-subtle: #fcfcfd;--bs-dark-bg-subtle: #ced4da;--bs-primary-border-subtle: #b4c5d7;--bs-secondary-border-subtle: #d6d6d6;--bs-success-border-subtle: #b1e1a6;--bs-info-border-subtle: #add6fa;--bs-warning-border-subtle: #eec899;--bs-danger-border-subtle: #eb9a99;--bs-light-border-subtle: #eee;--bs-dark-border-subtle: #999;--bs-white-rgb: 255,255,255;--bs-black-rgb: 0,0,0;--bs-font-sans-serif: "Open Sans", -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "Helvetica Neue", Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--bs-font-monospace: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace;--bs-gradient: linear-gradient(180deg, rgba(255,255,255,0.15), rgba(255,255,255,0));--bs-body-font-family: var(--bs-font-sans-serif);--bs-body-font-size:1rem;--bs-body-font-weight: 400;--bs-body-line-height: 1.5;--bs-body-color: #777;--bs-body-color-rgb: 119,119,119;--bs-body-bg: #fff;--bs-body-bg-rgb: 255,255,255;--bs-emphasis-color: #000;--bs-emphasis-color-rgb: 0,0,0;--bs-secondary-color: rgba(119,119,119,0.75);--bs-secondary-color-rgb: 119,119,119;--bs-secondary-bg: #eee;--bs-secondary-bg-rgb: 238,238,238;--bs-tertiary-color: rgba(119,119,119,0.5);--bs-tertiary-color-rgb: 119,119,119;--bs-tertiary-bg: #f8f9fa;--bs-tertiary-bg-rgb: 248,249,250;--bs-heading-color: #2d2d2d;--bs-link-color: #3399f3;--bs-link-color-rgb: 51,153,243;--bs-link-decoration: underline;--bs-link-hover-color: #297ac2;--bs-link-hover-color-rgb: 41,122,194;--bs-code-color: RGB(var(--bs-emphasis-color-rgb, 0, 0, 0));--bs-highlight-bg: #f6e3cc;--bs-border-width: 1px;--bs-border-style: solid;--bs-border-color: #dee2e6;--bs-border-color-translucent: rgba(0,0,0,0.175);--bs-border-radius: .375rem;--bs-border-radius-sm: .25rem;--bs-border-radius-lg: .5rem;--bs-border-radius-xl: 1rem;--bs-border-radius-xxl: 2rem;--bs-border-radius-2xl: var(--bs-border-radius-xxl);--bs-border-radius-pill: 50rem;--bs-box-shadow: 0 0.5rem 1rem rgba(0,0,0,0.15);--bs-box-shadow-sm: 0 0.125rem 0.25rem rgba(0,0,0,0.075);--bs-box-shadow-lg: 0 1rem 3rem rgba(0,0,0,0.175);--bs-box-shadow-inset: inset 0 1px 2px rgba(0,0,0,0.075);--bs-focus-ring-width: .25rem;--bs-focus-ring-opacity: .25;--bs-focus-ring-color: rgba(68,110,155,0.25);--bs-form-valid-color: #3cb521;--bs-form-valid-border-color: #3cb521;--bs-form-invalid-color: #cd0200;--bs-form-invalid-border-color: #cd0200}[data-bs-theme="dark"]{color-scheme:dark;--bs-body-color: #dee2e6;--bs-body-color-rgb: 222,226,230;--bs-body-bg: #2d2d2d;--bs-body-bg-rgb: 45,45,45;--bs-emphasis-color: #fff;--bs-emphasis-color-rgb: 255,255,255;--bs-secondary-color: rgba(222,226,230,0.75);--bs-secondary-color-rgb: 222,226,230;--bs-secondary-bg: #333;--bs-secondary-bg-rgb: 51,51,51;--bs-tertiary-color: rgba(222,226,230,0.5);--bs-tertiary-color-rgb: 222,226,230;--bs-tertiary-bg: #303030;--bs-tertiary-bg-rgb: 48,48,48;--bs-primary-text-emphasis: #8fa8c3;--bs-secondary-text-emphasis: #c2c2c2;--bs-success-text-emphasis: #8ad37a;--bs-info-text-emphasis: #85c2f8;--bs-warning-text-emphasis: #e5ac66;--bs-danger-text-emphasis: #e16766;--bs-light-text-emphasis: #f8f9fa;--bs-dark-text-emphasis: #dee2e6;--bs-primary-bg-subtle: #0e161f;--bs-secondary-bg-subtle: #1f1f1f;--bs-success-bg-subtle: #0c2407;--bs-info-bg-subtle: #0a1f31;--bs-warning-bg-subtle: #2a1700;--bs-danger-bg-subtle: #290000;--bs-light-bg-subtle: #333;--bs-dark-bg-subtle: #1a1a1a;--bs-primary-border-subtle: #29425d;--bs-secondary-border-subtle: #5c5c5c;--bs-success-border-subtle: #246d14;--bs-info-border-subtle: #1f5c92;--bs-warning-border-subtle: #7f4600;--bs-danger-border-subtle: #7b0100;--bs-light-border-subtle: #495057;--bs-dark-border-subtle: #333;--bs-heading-color: inherit;--bs-link-color: #8fa8c3;--bs-link-hover-color: #a5b9cf;--bs-link-color-rgb: 143,168,195;--bs-link-hover-color-rgb: 165,185,207;--bs-code-color: RGB(var(--bs-emphasis-color-rgb, 0, 0, 0));--bs-border-color: #495057;--bs-border-color-translucent: rgba(255,255,255,0.15);--bs-form-valid-color: #8ad37a;--bs-form-valid-border-color: #8ad37a;--bs-form-invalid-color: #e16766;--bs-form-invalid-border-color: #e16766}*,*::before,*::after{box-sizing:border-box}@media (prefers-reduced-motion: no-preference){:root{scroll-behavior:smooth}}body{margin:0;font-family:var(--bs-body-font-family);font-size:var(--bs-body-font-size);font-weight:var(--bs-body-font-weight);line-height:var(--bs-body-line-height);color:var(--bs-body-color);text-align:var(--bs-body-text-align);background-color:var(--bs-body-bg);-webkit-text-size-adjust:100%;-webkit-tap-highlight-color:rgba(0,0,0,0)}hr{margin:1rem 0;color:inherit;border:0;border-top:var(--bs-border-width) solid;opacity:.25}h6,.h6,h5,.h5,h4,.h4,h3,.h3,h2,.h2,h1,.h1{margin-top:0;margin-bottom:.5rem;font-weight:500;line-height:1.2;color:var(--bs-heading-color)}h1,.h1{font-size:calc(1.375rem + 1.5vw)}@media (min-width: 1200px){h1,.h1{font-size:2.5rem}}h2,.h2{font-size:calc(1.325rem + .9vw)}@media (min-width: 1200px){h2,.h2{font-size:2rem}}h3,.h3{font-size:calc(1.3rem + .6vw)}@media (min-width: 1200px){h3,.h3{font-size:1.75rem}}h4,.h4{font-size:calc(1.275rem + .3vw)}@media (min-width: 1200px){h4,.h4{font-size:1.5rem}}h5,.h5{font-size:1.25rem}h6,.h6{font-size:1rem}p{margin-top:0;margin-bottom:1rem}abbr[title]{text-decoration:underline dotted;-webkit-text-decoration:underline dotted;-moz-text-decoration:underline dotted;-ms-text-decoration:underline dotted;-o-text-decoration:underline dotted;cursor:help;text-decoration-skip-ink:none}address{margin-bottom:1rem;font-style:normal;line-height:inherit}ol,ul{padding-left:2rem}ol,ul,dl{margin-top:0;margin-bottom:1rem}ol ol,ul ul,ol ul,ul ol{margin-bottom:0}dt{font-weight:700}dd{margin-bottom:.5rem;margin-left:0}blockquote{margin:0 0 1rem;padding:.625rem 1.25rem;border-left:.25rem solid #eee}blockquote p:last-child,blockquote ul:last-child,blockquote ol:last-child{margin-bottom:0}b,strong{font-weight:bolder}small,.small{font-size:.875em}mark,.mark{padding:.1875em;background-color:var(--bs-highlight-bg)}sub,sup{position:relative;font-size:.75em;line-height:0;vertical-align:baseline}sub{bottom:-.25em}sup{top:-.5em}a{color:rgba(var(--bs-link-color-rgb), var(--bs-link-opacity, 1));text-decoration:underline;-webkit-text-decoration:underline;-moz-text-decoration:underline;-ms-text-decoration:underline;-o-text-decoration:underline}a:hover{--bs-link-color-rgb: var(--bs-link-hover-color-rgb)}a:not([href]):not([class]),a:not([href]):not([class]):hover{color:inherit;text-decoration:none}pre,code,kbd,samp{font-family:var(--bs-font-monospace);font-size:1em}pre{display:block;margin-top:0;margin-bottom:1rem;overflow:auto;font-size:.875em;color:RGB(var(--bs-emphasis-color-rgb, 0, 0, 0));background-color:RGBA(var(--bs-emphasis-color-rgb, 0, 0, 0), 0.04);padding:.5rem;border:1px solid var(--bs-border-color, #dee2e6);border-radius:.375rem}pre code{background-color:transparent;font-size:inherit;color:inherit;word-break:normal}code{font-size:.875em;color:var(--bs-code-color);background-color:RGBA(var(--bs-emphasis-color-rgb, 0, 0, 0), 0.04);border-radius:.375rem;padding:.125rem .25rem;word-wrap:break-word}a>code{color:inherit}kbd{padding:.1875rem .375rem;font-size:.875em;color:var(--bs-body-bg);background-color:var(--bs-body-color);border-radius:.25rem}kbd kbd{padding:0;font-size:1em}figure{margin:0 0 1rem}img,svg{vertical-align:middle}table{caption-side:bottom;border-collapse:collapse}caption{padding-top:.5rem;padding-bottom:.5rem;color:var(--bs-secondary-color);text-align:left}th{text-align:inherit;text-align:-webkit-match-parent}thead,tbody,tfoot,tr,td,th{border-color:inherit;border-style:solid;border-width:0}label{display:inline-block}button{border-radius:0}button:focus:not(:focus-visible){outline:0}input,button,select,optgroup,textarea{margin:0;font-family:inherit;font-size:inherit;line-height:inherit}button,select{text-transform:none}[role="button"]{cursor:pointer}select{word-wrap:normal}select:disabled{opacity:1}[list]:not([type="date"]):not([type="datetime-local"]):not([type="month"]):not([type="week"]):not([type="time"])::-webkit-calendar-picker-indicator{display:none !important}button,[type="button"],[type="reset"],[type="submit"]{-webkit-appearance:button}button:not(:disabled),[type="button"]:not(:disabled),[type="reset"]:not(:disabled),[type="submit"]:not(:disabled){cursor:pointer}::-moz-focus-inner{padding:0;border-style:none}textarea{resize:vertical}fieldset{min-width:0;padding:0;margin:0;border:0}legend{float:left;width:100%;padding:0;margin-bottom:.5rem;font-size:calc(1.275rem + .3vw);line-height:inherit}@media (min-width: 1200px){legend{font-size:1.5rem}}legend+*{clear:left}::-webkit-datetime-edit-fields-wrapper,::-webkit-datetime-edit-text,::-webkit-datetime-edit-minute,::-webkit-datetime-edit-hour-field,::-webkit-datetime-edit-day-field,::-webkit-datetime-edit-month-field,::-webkit-datetime-edit-year-field{padding:0}::-webkit-inner-spin-button{height:auto}[type="search"]{-webkit-appearance:textfield;outline-offset:-2px}::-webkit-search-decoration{-webkit-appearance:none}::-webkit-color-swatch-wrapper{padding:0}::file-selector-button{font:inherit;-webkit-appearance:button}output{display:inline-block}iframe{border:0}summary{display:list-item;cursor:pointer}progress{vertical-align:baseline}[hidden]{display:none !important}.lead{font-size:1.25rem;font-weight:300}.display-1{font-size:calc(1.625rem + 4.5vw);font-weight:300;line-height:1.2}@media (min-width: 1200px){.display-1{font-size:5rem}}.display-2{font-size:calc(1.575rem + 3.9vw);font-weight:300;line-height:1.2}@media (min-width: 1200px){.display-2{font-size:4.5rem}}.display-3{font-size:calc(1.525rem + 3.3vw);font-weight:300;line-height:1.2}@media (min-width: 1200px){.display-3{font-size:4rem}}.display-4{font-size:calc(1.475rem + 2.7vw);font-weight:300;line-height:1.2}@media (min-width: 1200px){.display-4{font-size:3.5rem}}.display-5{font-size:calc(1.425rem + 2.1vw);font-weight:300;line-height:1.2}@media (min-width: 1200px){.display-5{font-size:3rem}}.display-6{font-size:calc(1.375rem + 1.5vw);font-weight:300;line-height:1.2}@media (min-width: 1200px){.display-6{font-size:2.5rem}}.list-unstyled{padding-left:0;list-style:none}.list-inline{padding-left:0;list-style:none}.list-inline-item{display:inline-block}.list-inline-item:not(:last-child){margin-right:.5rem}.initialism{font-size:.875em;text-transform:uppercase}.blockquote{margin-bottom:1rem;font-size:1.25rem}.blockquote>:last-child{margin-bottom:0}.blockquote-footer{margin-top:-1rem;margin-bottom:1rem;font-size:.875em;color:#777}.blockquote-footer::before{content:"\2014\00A0"}.img-fluid{max-width:100%;height:auto}.img-thumbnail{padding:.25rem;background-color:var(--bs-body-bg);border:var(--bs-border-width) solid var(--bs-border-color);border-radius:var(--bs-border-radius);max-width:100%;height:auto}.figure{display:inline-block}.figure-img{margin-bottom:.5rem;line-height:1}.figure-caption{font-size:.875em;color:var(--bs-secondary-color)}.container,.container-fluid,.container-xxl,.container-xl,.container-lg,.container-md,.container-sm{--bs-gutter-x: 1.5rem;--bs-gutter-y: 0;width:100%;padding-right:calc(var(--bs-gutter-x) * .5);padding-left:calc(var(--bs-gutter-x) * .5);margin-right:auto;margin-left:auto}@media (min-width: 576px){.container-sm,.container{max-width:540px}}@media (min-width: 768px){.container-md,.container-sm,.container{max-width:720px}}@media (min-width: 992px){.container-lg,.container-md,.container-sm,.container{max-width:960px}}@media (min-width: 1200px){.container-xl,.container-lg,.container-md,.container-sm,.container{max-width:1140px}}@media (min-width: 1400px){.container-xxl,.container-xl,.container-lg,.container-md,.container-sm,.container{max-width:1320px}}:root{--bs-breakpoint-xs: 0;--bs-breakpoint-sm: 576px;--bs-breakpoint-md: 768px;--bs-breakpoint-lg: 992px;--bs-breakpoint-xl: 1200px;--bs-breakpoint-xxl: 1400px}.row{--bs-gutter-x: 1.5rem;--bs-gutter-y: 0;display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;margin-top:calc(-1 * var(--bs-gutter-y));margin-right:calc(-.5 * var(--bs-gutter-x));margin-left:calc(-.5 * var(--bs-gutter-x))}.row>*{flex-shrink:0;-webkit-flex-shrink:0;width:100%;max-width:100%;padding-right:calc(var(--bs-gutter-x) * .5);padding-left:calc(var(--bs-gutter-x) * .5);margin-top:var(--bs-gutter-y)}.grid{display:grid;grid-template-rows:repeat(var(--bs-rows, 1), 1fr);grid-template-columns:repeat(var(--bs-columns, 12), 1fr);gap:var(--bs-gap, 1.5rem)}.grid .g-col-1{grid-column:auto/span 1}.grid .g-col-2{grid-column:auto/span 2}.grid .g-col-3{grid-column:auto/span 3}.grid .g-col-4{grid-column:auto/span 4}.grid .g-col-5{grid-column:auto/span 5}.grid .g-col-6{grid-column:auto/span 6}.grid .g-col-7{grid-column:auto/span 7}.grid .g-col-8{grid-column:auto/span 8}.grid .g-col-9{grid-column:auto/span 9}.grid .g-col-10{grid-column:auto/span 10}.grid .g-col-11{grid-column:auto/span 11}.grid .g-col-12{grid-column:auto/span 12}.grid .g-start-1{grid-column-start:1}.grid .g-start-2{grid-column-start:2}.grid .g-start-3{grid-column-start:3}.grid .g-start-4{grid-column-start:4}.grid .g-start-5{grid-column-start:5}.grid .g-start-6{grid-column-start:6}.grid .g-start-7{grid-column-start:7}.grid .g-start-8{grid-column-start:8}.grid .g-start-9{grid-column-start:9}.grid .g-start-10{grid-column-start:10}.grid .g-start-11{grid-column-start:11}@media (min-width: 576px){.grid .g-col-sm-1{grid-column:auto/span 1}.grid .g-col-sm-2{grid-column:auto/span 2}.grid .g-col-sm-3{grid-column:auto/span 3}.grid .g-col-sm-4{grid-column:auto/span 4}.grid .g-col-sm-5{grid-column:auto/span 5}.grid .g-col-sm-6{grid-column:auto/span 6}.grid .g-col-sm-7{grid-column:auto/span 7}.grid .g-col-sm-8{grid-column:auto/span 8}.grid .g-col-sm-9{grid-column:auto/span 9}.grid .g-col-sm-10{grid-column:auto/span 10}.grid .g-col-sm-11{grid-column:auto/span 11}.grid .g-col-sm-12{grid-column:auto/span 12}.grid .g-start-sm-1{grid-column-start:1}.grid .g-start-sm-2{grid-column-start:2}.grid .g-start-sm-3{grid-column-start:3}.grid .g-start-sm-4{grid-column-start:4}.grid .g-start-sm-5{grid-column-start:5}.grid .g-start-sm-6{grid-column-start:6}.grid .g-start-sm-7{grid-column-start:7}.grid .g-start-sm-8{grid-column-start:8}.grid .g-start-sm-9{grid-column-start:9}.grid .g-start-sm-10{grid-column-start:10}.grid .g-start-sm-11{grid-column-start:11}}@media (min-width: 768px){.grid .g-col-md-1{grid-column:auto/span 1}.grid .g-col-md-2{grid-column:auto/span 2}.grid .g-col-md-3{grid-column:auto/span 3}.grid .g-col-md-4{grid-column:auto/span 4}.grid .g-col-md-5{grid-column:auto/span 5}.grid .g-col-md-6{grid-column:auto/span 6}.grid .g-col-md-7{grid-column:auto/span 7}.grid .g-col-md-8{grid-column:auto/span 8}.grid .g-col-md-9{grid-column:auto/span 9}.grid .g-col-md-10{grid-column:auto/span 10}.grid .g-col-md-11{grid-column:auto/span 11}.grid .g-col-md-12{grid-column:auto/span 12}.grid .g-start-md-1{grid-column-start:1}.grid .g-start-md-2{grid-column-start:2}.grid .g-start-md-3{grid-column-start:3}.grid .g-start-md-4{grid-column-start:4}.grid .g-start-md-5{grid-column-start:5}.grid .g-start-md-6{grid-column-start:6}.grid .g-start-md-7{grid-column-start:7}.grid .g-start-md-8{grid-column-start:8}.grid .g-start-md-9{grid-column-start:9}.grid .g-start-md-10{grid-column-start:10}.grid .g-start-md-11{grid-column-start:11}}@media (min-width: 992px){.grid .g-col-lg-1{grid-column:auto/span 1}.grid .g-col-lg-2{grid-column:auto/span 2}.grid .g-col-lg-3{grid-column:auto/span 3}.grid .g-col-lg-4{grid-column:auto/span 4}.grid .g-col-lg-5{grid-column:auto/span 5}.grid .g-col-lg-6{grid-column:auto/span 6}.grid .g-col-lg-7{grid-column:auto/span 7}.grid .g-col-lg-8{grid-column:auto/span 8}.grid .g-col-lg-9{grid-column:auto/span 9}.grid .g-col-lg-10{grid-column:auto/span 10}.grid .g-col-lg-11{grid-column:auto/span 11}.grid .g-col-lg-12{grid-column:auto/span 12}.grid .g-start-lg-1{grid-column-start:1}.grid .g-start-lg-2{grid-column-start:2}.grid .g-start-lg-3{grid-column-start:3}.grid .g-start-lg-4{grid-column-start:4}.grid .g-start-lg-5{grid-column-start:5}.grid .g-start-lg-6{grid-column-start:6}.grid .g-start-lg-7{grid-column-start:7}.grid .g-start-lg-8{grid-column-start:8}.grid .g-start-lg-9{grid-column-start:9}.grid .g-start-lg-10{grid-column-start:10}.grid .g-start-lg-11{grid-column-start:11}}@media (min-width: 1200px){.grid .g-col-xl-1{grid-column:auto/span 1}.grid .g-col-xl-2{grid-column:auto/span 2}.grid .g-col-xl-3{grid-column:auto/span 3}.grid .g-col-xl-4{grid-column:auto/span 4}.grid .g-col-xl-5{grid-column:auto/span 5}.grid .g-col-xl-6{grid-column:auto/span 6}.grid .g-col-xl-7{grid-column:auto/span 7}.grid .g-col-xl-8{grid-column:auto/span 8}.grid .g-col-xl-9{grid-column:auto/span 9}.grid .g-col-xl-10{grid-column:auto/span 10}.grid .g-col-xl-11{grid-column:auto/span 11}.grid .g-col-xl-12{grid-column:auto/span 12}.grid .g-start-xl-1{grid-column-start:1}.grid .g-start-xl-2{grid-column-start:2}.grid .g-start-xl-3{grid-column-start:3}.grid .g-start-xl-4{grid-column-start:4}.grid .g-start-xl-5{grid-column-start:5}.grid .g-start-xl-6{grid-column-start:6}.grid .g-start-xl-7{grid-column-start:7}.grid .g-start-xl-8{grid-column-start:8}.grid .g-start-xl-9{grid-column-start:9}.grid .g-start-xl-10{grid-column-start:10}.grid .g-start-xl-11{grid-column-start:11}}@media (min-width: 1400px){.grid .g-col-xxl-1{grid-column:auto/span 1}.grid .g-col-xxl-2{grid-column:auto/span 2}.grid .g-col-xxl-3{grid-column:auto/span 3}.grid .g-col-xxl-4{grid-column:auto/span 4}.grid .g-col-xxl-5{grid-column:auto/span 5}.grid .g-col-xxl-6{grid-column:auto/span 6}.grid .g-col-xxl-7{grid-column:auto/span 7}.grid .g-col-xxl-8{grid-column:auto/span 8}.grid .g-col-xxl-9{grid-column:auto/span 9}.grid .g-col-xxl-10{grid-column:auto/span 10}.grid .g-col-xxl-11{grid-column:auto/span 11}.grid .g-col-xxl-12{grid-column:auto/span 12}.grid .g-start-xxl-1{grid-column-start:1}.grid .g-start-xxl-2{grid-column-start:2}.grid .g-start-xxl-3{grid-column-start:3}.grid .g-start-xxl-4{grid-column-start:4}.grid .g-start-xxl-5{grid-column-start:5}.grid .g-start-xxl-6{grid-column-start:6}.grid .g-start-xxl-7{grid-column-start:7}.grid .g-start-xxl-8{grid-column-start:8}.grid .g-start-xxl-9{grid-column-start:9}.grid .g-start-xxl-10{grid-column-start:10}.grid .g-start-xxl-11{grid-column-start:11}}.col{flex:1 0 0%;-webkit-flex:1 0 0%}.row-cols-auto>*{flex:0 0 auto;-webkit-flex:0 0 auto;width:auto}.row-cols-1>*{flex:0 0 auto;-webkit-flex:0 0 auto;width:100%}.row-cols-2>*{flex:0 0 auto;-webkit-flex:0 0 auto;width:50%}.row-cols-3>*{flex:0 0 auto;-webkit-flex:0 0 auto;width:33.33333%}.row-cols-4>*{flex:0 0 auto;-webkit-flex:0 0 auto;width:25%}.row-cols-5>*{flex:0 0 auto;-webkit-flex:0 0 auto;width:20%}.row-cols-6>*{flex:0 0 auto;-webkit-flex:0 0 auto;width:16.66667%}.col-auto{flex:0 0 auto;-webkit-flex:0 0 auto;width:auto}.col-1{flex:0 0 auto;-webkit-flex:0 0 auto;width:8.33333%}.col-2{flex:0 0 auto;-webkit-flex:0 0 auto;width:16.66667%}.col-3{flex:0 0 auto;-webkit-flex:0 0 auto;width:25%}.col-4{flex:0 0 auto;-webkit-flex:0 0 auto;width:33.33333%}.col-5{flex:0 0 auto;-webkit-flex:0 0 auto;width:41.66667%}.col-6{flex:0 0 auto;-webkit-flex:0 0 auto;width:50%}.col-7{flex:0 0 auto;-webkit-flex:0 0 auto;width:58.33333%}.col-8{flex:0 0 auto;-webkit-flex:0 0 auto;width:66.66667%}.col-9{flex:0 0 auto;-webkit-flex:0 0 auto;width:75%}.col-10{flex:0 0 auto;-webkit-flex:0 0 auto;width:83.33333%}.col-11{flex:0 0 auto;-webkit-flex:0 0 auto;width:91.66667%}.col-12{flex:0 0 auto;-webkit-flex:0 0 auto;width:100%}.offset-1{margin-left:8.33333%}.offset-2{margin-left:16.66667%}.offset-3{margin-left:25%}.offset-4{margin-left:33.33333%}.offset-5{margin-left:41.66667%}.offset-6{margin-left:50%}.offset-7{margin-left:58.33333%}.offset-8{margin-left:66.66667%}.offset-9{margin-left:75%}.offset-10{margin-left:83.33333%}.offset-11{margin-left:91.66667%}.g-0,.gx-0{--bs-gutter-x: 0}.g-0,.gy-0{--bs-gutter-y: 0}.g-1,.gx-1{--bs-gutter-x: .25rem}.g-1,.gy-1{--bs-gutter-y: .25rem}.g-2,.gx-2{--bs-gutter-x: .5rem}.g-2,.gy-2{--bs-gutter-y: .5rem}.g-3,.gx-3{--bs-gutter-x: 1rem}.g-3,.gy-3{--bs-gutter-y: 1rem}.g-4,.gx-4{--bs-gutter-x: 1.5rem}.g-4,.gy-4{--bs-gutter-y: 1.5rem}.g-5,.gx-5{--bs-gutter-x: 3rem}.g-5,.gy-5{--bs-gutter-y: 3rem}@media (min-width: 576px){.col-sm{flex:1 0 0%;-webkit-flex:1 0 0%}.row-cols-sm-auto>*{flex:0 0 auto;-webkit-flex:0 0 auto;width:auto}.row-cols-sm-1>*{flex:0 0 auto;-webkit-flex:0 0 auto;width:100%}.row-cols-sm-2>*{flex:0 0 auto;-webkit-flex:0 0 auto;width:50%}.row-cols-sm-3>*{flex:0 0 auto;-webkit-flex:0 0 auto;width:33.33333%}.row-cols-sm-4>*{flex:0 0 auto;-webkit-flex:0 0 auto;width:25%}.row-cols-sm-5>*{flex:0 0 auto;-webkit-flex:0 0 auto;width:20%}.row-cols-sm-6>*{flex:0 0 auto;-webkit-flex:0 0 auto;width:16.66667%}.col-sm-auto{flex:0 0 auto;-webkit-flex:0 0 auto;width:auto}.col-sm-1{flex:0 0 auto;-webkit-flex:0 0 auto;width:8.33333%}.col-sm-2{flex:0 0 auto;-webkit-flex:0 0 auto;width:16.66667%}.col-sm-3{flex:0 0 auto;-webkit-flex:0 0 auto;width:25%}.col-sm-4{flex:0 0 auto;-webkit-flex:0 0 auto;width:33.33333%}.col-sm-5{flex:0 0 auto;-webkit-flex:0 0 auto;width:41.66667%}.col-sm-6{flex:0 0 auto;-webkit-flex:0 0 auto;width:50%}.col-sm-7{flex:0 0 auto;-webkit-flex:0 0 auto;width:58.33333%}.col-sm-8{flex:0 0 auto;-webkit-flex:0 0 auto;width:66.66667%}.col-sm-9{flex:0 0 auto;-webkit-flex:0 0 auto;width:75%}.col-sm-10{flex:0 0 auto;-webkit-flex:0 0 auto;width:83.33333%}.col-sm-11{flex:0 0 auto;-webkit-flex:0 0 auto;width:91.66667%}.col-sm-12{flex:0 0 auto;-webkit-flex:0 0 auto;width:100%}.offset-sm-0{margin-left:0}.offset-sm-1{margin-left:8.33333%}.offset-sm-2{margin-left:16.66667%}.offset-sm-3{margin-left:25%}.offset-sm-4{margin-left:33.33333%}.offset-sm-5{margin-left:41.66667%}.offset-sm-6{margin-left:50%}.offset-sm-7{margin-left:58.33333%}.offset-sm-8{margin-left:66.66667%}.offset-sm-9{margin-left:75%}.offset-sm-10{margin-left:83.33333%}.offset-sm-11{margin-left:91.66667%}.g-sm-0,.gx-sm-0{--bs-gutter-x: 0}.g-sm-0,.gy-sm-0{--bs-gutter-y: 0}.g-sm-1,.gx-sm-1{--bs-gutter-x: .25rem}.g-sm-1,.gy-sm-1{--bs-gutter-y: .25rem}.g-sm-2,.gx-sm-2{--bs-gutter-x: .5rem}.g-sm-2,.gy-sm-2{--bs-gutter-y: .5rem}.g-sm-3,.gx-sm-3{--bs-gutter-x: 1rem}.g-sm-3,.gy-sm-3{--bs-gutter-y: 1rem}.g-sm-4,.gx-sm-4{--bs-gutter-x: 1.5rem}.g-sm-4,.gy-sm-4{--bs-gutter-y: 1.5rem}.g-sm-5,.gx-sm-5{--bs-gutter-x: 3rem}.g-sm-5,.gy-sm-5{--bs-gutter-y: 3rem}}@media (min-width: 768px){.col-md{flex:1 0 0%;-webkit-flex:1 0 0%}.row-cols-md-auto>*{flex:0 0 auto;-webkit-flex:0 0 auto;width:auto}.row-cols-md-1>*{flex:0 0 auto;-webkit-flex:0 0 auto;width:100%}.row-cols-md-2>*{flex:0 0 auto;-webkit-flex:0 0 auto;width:50%}.row-cols-md-3>*{flex:0 0 auto;-webkit-flex:0 0 auto;width:33.33333%}.row-cols-md-4>*{flex:0 0 auto;-webkit-flex:0 0 auto;width:25%}.row-cols-md-5>*{flex:0 0 auto;-webkit-flex:0 0 auto;width:20%}.row-cols-md-6>*{flex:0 0 auto;-webkit-flex:0 0 auto;width:16.66667%}.col-md-auto{flex:0 0 auto;-webkit-flex:0 0 auto;width:auto}.col-md-1{flex:0 0 auto;-webkit-flex:0 0 auto;width:8.33333%}.col-md-2{flex:0 0 auto;-webkit-flex:0 0 auto;width:16.66667%}.col-md-3{flex:0 0 auto;-webkit-flex:0 0 auto;width:25%}.col-md-4{flex:0 0 auto;-webkit-flex:0 0 auto;width:33.33333%}.col-md-5{flex:0 0 auto;-webkit-flex:0 0 auto;width:41.66667%}.col-md-6{flex:0 0 auto;-webkit-flex:0 0 auto;width:50%}.col-md-7{flex:0 0 auto;-webkit-flex:0 0 auto;width:58.33333%}.col-md-8{flex:0 0 auto;-webkit-flex:0 0 auto;width:66.66667%}.col-md-9{flex:0 0 auto;-webkit-flex:0 0 auto;width:75%}.col-md-10{flex:0 0 auto;-webkit-flex:0 0 auto;width:83.33333%}.col-md-11{flex:0 0 auto;-webkit-flex:0 0 auto;width:91.66667%}.col-md-12{flex:0 0 auto;-webkit-flex:0 0 auto;width:100%}.offset-md-0{margin-left:0}.offset-md-1{margin-left:8.33333%}.offset-md-2{margin-left:16.66667%}.offset-md-3{margin-left:25%}.offset-md-4{margin-left:33.33333%}.offset-md-5{margin-left:41.66667%}.offset-md-6{margin-left:50%}.offset-md-7{margin-left:58.33333%}.offset-md-8{margin-left:66.66667%}.offset-md-9{margin-left:75%}.offset-md-10{margin-left:83.33333%}.offset-md-11{margin-left:91.66667%}.g-md-0,.gx-md-0{--bs-gutter-x: 0}.g-md-0,.gy-md-0{--bs-gutter-y: 0}.g-md-1,.gx-md-1{--bs-gutter-x: .25rem}.g-md-1,.gy-md-1{--bs-gutter-y: .25rem}.g-md-2,.gx-md-2{--bs-gutter-x: .5rem}.g-md-2,.gy-md-2{--bs-gutter-y: .5rem}.g-md-3,.gx-md-3{--bs-gutter-x: 1rem}.g-md-3,.gy-md-3{--bs-gutter-y: 1rem}.g-md-4,.gx-md-4{--bs-gutter-x: 1.5rem}.g-md-4,.gy-md-4{--bs-gutter-y: 1.5rem}.g-md-5,.gx-md-5{--bs-gutter-x: 3rem}.g-md-5,.gy-md-5{--bs-gutter-y: 3rem}}@media (min-width: 992px){.col-lg{flex:1 0 0%;-webkit-flex:1 0 0%}.row-cols-lg-auto>*{flex:0 0 auto;-webkit-flex:0 0 auto;width:auto}.row-cols-lg-1>*{flex:0 0 auto;-webkit-flex:0 0 auto;width:100%}.row-cols-lg-2>*{flex:0 0 auto;-webkit-flex:0 0 auto;width:50%}.row-cols-lg-3>*{flex:0 0 auto;-webkit-flex:0 0 auto;width:33.33333%}.row-cols-lg-4>*{flex:0 0 auto;-webkit-flex:0 0 auto;width:25%}.row-cols-lg-5>*{flex:0 0 auto;-webkit-flex:0 0 auto;width:20%}.row-cols-lg-6>*{flex:0 0 auto;-webkit-flex:0 0 auto;width:16.66667%}.col-lg-auto{flex:0 0 auto;-webkit-flex:0 0 auto;width:auto}.col-lg-1{flex:0 0 auto;-webkit-flex:0 0 auto;width:8.33333%}.col-lg-2{flex:0 0 auto;-webkit-flex:0 0 auto;width:16.66667%}.col-lg-3{flex:0 0 auto;-webkit-flex:0 0 auto;width:25%}.col-lg-4{flex:0 0 auto;-webkit-flex:0 0 auto;width:33.33333%}.col-lg-5{flex:0 0 auto;-webkit-flex:0 0 auto;width:41.66667%}.col-lg-6{flex:0 0 auto;-webkit-flex:0 0 auto;width:50%}.col-lg-7{flex:0 0 auto;-webkit-flex:0 0 auto;width:58.33333%}.col-lg-8{flex:0 0 auto;-webkit-flex:0 0 auto;width:66.66667%}.col-lg-9{flex:0 0 auto;-webkit-flex:0 0 auto;width:75%}.col-lg-10{flex:0 0 auto;-webkit-flex:0 0 auto;width:83.33333%}.col-lg-11{flex:0 0 auto;-webkit-flex:0 0 auto;width:91.66667%}.col-lg-12{flex:0 0 auto;-webkit-flex:0 0 auto;width:100%}.offset-lg-0{margin-left:0}.offset-lg-1{margin-left:8.33333%}.offset-lg-2{margin-left:16.66667%}.offset-lg-3{margin-left:25%}.offset-lg-4{margin-left:33.33333%}.offset-lg-5{margin-left:41.66667%}.offset-lg-6{margin-left:50%}.offset-lg-7{margin-left:58.33333%}.offset-lg-8{margin-left:66.66667%}.offset-lg-9{margin-left:75%}.offset-lg-10{margin-left:83.33333%}.offset-lg-11{margin-left:91.66667%}.g-lg-0,.gx-lg-0{--bs-gutter-x: 0}.g-lg-0,.gy-lg-0{--bs-gutter-y: 0}.g-lg-1,.gx-lg-1{--bs-gutter-x: .25rem}.g-lg-1,.gy-lg-1{--bs-gutter-y: .25rem}.g-lg-2,.gx-lg-2{--bs-gutter-x: .5rem}.g-lg-2,.gy-lg-2{--bs-gutter-y: .5rem}.g-lg-3,.gx-lg-3{--bs-gutter-x: 1rem}.g-lg-3,.gy-lg-3{--bs-gutter-y: 1rem}.g-lg-4,.gx-lg-4{--bs-gutter-x: 1.5rem}.g-lg-4,.gy-lg-4{--bs-gutter-y: 1.5rem}.g-lg-5,.gx-lg-5{--bs-gutter-x: 3rem}.g-lg-5,.gy-lg-5{--bs-gutter-y: 3rem}}@media (min-width: 1200px){.col-xl{flex:1 0 0%;-webkit-flex:1 0 0%}.row-cols-xl-auto>*{flex:0 0 auto;-webkit-flex:0 0 auto;width:auto}.row-cols-xl-1>*{flex:0 0 auto;-webkit-flex:0 0 auto;width:100%}.row-cols-xl-2>*{flex:0 0 auto;-webkit-flex:0 0 auto;width:50%}.row-cols-xl-3>*{flex:0 0 auto;-webkit-flex:0 0 auto;width:33.33333%}.row-cols-xl-4>*{flex:0 0 auto;-webkit-flex:0 0 auto;width:25%}.row-cols-xl-5>*{flex:0 0 auto;-webkit-flex:0 0 auto;width:20%}.row-cols-xl-6>*{flex:0 0 auto;-webkit-flex:0 0 auto;width:16.66667%}.col-xl-auto{flex:0 0 auto;-webkit-flex:0 0 auto;width:auto}.col-xl-1{flex:0 0 auto;-webkit-flex:0 0 auto;width:8.33333%}.col-xl-2{flex:0 0 auto;-webkit-flex:0 0 auto;width:16.66667%}.col-xl-3{flex:0 0 auto;-webkit-flex:0 0 auto;width:25%}.col-xl-4{flex:0 0 auto;-webkit-flex:0 0 auto;width:33.33333%}.col-xl-5{flex:0 0 auto;-webkit-flex:0 0 auto;width:41.66667%}.col-xl-6{flex:0 0 auto;-webkit-flex:0 0 auto;width:50%}.col-xl-7{flex:0 0 auto;-webkit-flex:0 0 auto;width:58.33333%}.col-xl-8{flex:0 0 auto;-webkit-flex:0 0 auto;width:66.66667%}.col-xl-9{flex:0 0 auto;-webkit-flex:0 0 auto;width:75%}.col-xl-10{flex:0 0 auto;-webkit-flex:0 0 auto;width:83.33333%}.col-xl-11{flex:0 0 auto;-webkit-flex:0 0 auto;width:91.66667%}.col-xl-12{flex:0 0 auto;-webkit-flex:0 0 auto;width:100%}.offset-xl-0{margin-left:0}.offset-xl-1{margin-left:8.33333%}.offset-xl-2{margin-left:16.66667%}.offset-xl-3{margin-left:25%}.offset-xl-4{margin-left:33.33333%}.offset-xl-5{margin-left:41.66667%}.offset-xl-6{margin-left:50%}.offset-xl-7{margin-left:58.33333%}.offset-xl-8{margin-left:66.66667%}.offset-xl-9{margin-left:75%}.offset-xl-10{margin-left:83.33333%}.offset-xl-11{margin-left:91.66667%}.g-xl-0,.gx-xl-0{--bs-gutter-x: 0}.g-xl-0,.gy-xl-0{--bs-gutter-y: 0}.g-xl-1,.gx-xl-1{--bs-gutter-x: .25rem}.g-xl-1,.gy-xl-1{--bs-gutter-y: .25rem}.g-xl-2,.gx-xl-2{--bs-gutter-x: .5rem}.g-xl-2,.gy-xl-2{--bs-gutter-y: .5rem}.g-xl-3,.gx-xl-3{--bs-gutter-x: 1rem}.g-xl-3,.gy-xl-3{--bs-gutter-y: 1rem}.g-xl-4,.gx-xl-4{--bs-gutter-x: 1.5rem}.g-xl-4,.gy-xl-4{--bs-gutter-y: 1.5rem}.g-xl-5,.gx-xl-5{--bs-gutter-x: 3rem}.g-xl-5,.gy-xl-5{--bs-gutter-y: 3rem}}@media (min-width: 1400px){.col-xxl{flex:1 0 0%;-webkit-flex:1 0 0%}.row-cols-xxl-auto>*{flex:0 0 auto;-webkit-flex:0 0 auto;width:auto}.row-cols-xxl-1>*{flex:0 0 auto;-webkit-flex:0 0 auto;width:100%}.row-cols-xxl-2>*{flex:0 0 auto;-webkit-flex:0 0 auto;width:50%}.row-cols-xxl-3>*{flex:0 0 auto;-webkit-flex:0 0 auto;width:33.33333%}.row-cols-xxl-4>*{flex:0 0 auto;-webkit-flex:0 0 auto;width:25%}.row-cols-xxl-5>*{flex:0 0 auto;-webkit-flex:0 0 auto;width:20%}.row-cols-xxl-6>*{flex:0 0 auto;-webkit-flex:0 0 auto;width:16.66667%}.col-xxl-auto{flex:0 0 auto;-webkit-flex:0 0 auto;width:auto}.col-xxl-1{flex:0 0 auto;-webkit-flex:0 0 auto;width:8.33333%}.col-xxl-2{flex:0 0 auto;-webkit-flex:0 0 auto;width:16.66667%}.col-xxl-3{flex:0 0 auto;-webkit-flex:0 0 auto;width:25%}.col-xxl-4{flex:0 0 auto;-webkit-flex:0 0 auto;width:33.33333%}.col-xxl-5{flex:0 0 auto;-webkit-flex:0 0 auto;width:41.66667%}.col-xxl-6{flex:0 0 auto;-webkit-flex:0 0 auto;width:50%}.col-xxl-7{flex:0 0 auto;-webkit-flex:0 0 auto;width:58.33333%}.col-xxl-8{flex:0 0 auto;-webkit-flex:0 0 auto;width:66.66667%}.col-xxl-9{flex:0 0 auto;-webkit-flex:0 0 auto;width:75%}.col-xxl-10{flex:0 0 auto;-webkit-flex:0 0 auto;width:83.33333%}.col-xxl-11{flex:0 0 auto;-webkit-flex:0 0 auto;width:91.66667%}.col-xxl-12{flex:0 0 auto;-webkit-flex:0 0 auto;width:100%}.offset-xxl-0{margin-left:0}.offset-xxl-1{margin-left:8.33333%}.offset-xxl-2{margin-left:16.66667%}.offset-xxl-3{margin-left:25%}.offset-xxl-4{margin-left:33.33333%}.offset-xxl-5{margin-left:41.66667%}.offset-xxl-6{margin-left:50%}.offset-xxl-7{margin-left:58.33333%}.offset-xxl-8{margin-left:66.66667%}.offset-xxl-9{margin-left:75%}.offset-xxl-10{margin-left:83.33333%}.offset-xxl-11{margin-left:91.66667%}.g-xxl-0,.gx-xxl-0{--bs-gutter-x: 0}.g-xxl-0,.gy-xxl-0{--bs-gutter-y: 0}.g-xxl-1,.gx-xxl-1{--bs-gutter-x: .25rem}.g-xxl-1,.gy-xxl-1{--bs-gutter-y: .25rem}.g-xxl-2,.gx-xxl-2{--bs-gutter-x: .5rem}.g-xxl-2,.gy-xxl-2{--bs-gutter-y: .5rem}.g-xxl-3,.gx-xxl-3{--bs-gutter-x: 1rem}.g-xxl-3,.gy-xxl-3{--bs-gutter-y: 1rem}.g-xxl-4,.gx-xxl-4{--bs-gutter-x: 1.5rem}.g-xxl-4,.gy-xxl-4{--bs-gutter-y: 1.5rem}.g-xxl-5,.gx-xxl-5{--bs-gutter-x: 3rem}.g-xxl-5,.gy-xxl-5{--bs-gutter-y: 3rem}}.table{--bs-table-color-type: initial;--bs-table-bg-type: initial;--bs-table-color-state: initial;--bs-table-bg-state: initial;--bs-table-color: var(--bs-body-color);--bs-table-bg: var(--bs-body-bg);--bs-table-border-color: var(--bs-border-color);--bs-table-accent-bg: rgba(0,0,0,0);--bs-table-striped-color: var(--bs-body-color);--bs-table-striped-bg: rgba(0,0,0,0.05);--bs-table-active-color: var(--bs-body-color);--bs-table-active-bg: rgba(0,0,0,0.1);--bs-table-hover-color: var(--bs-body-color);--bs-table-hover-bg: rgba(0,0,0,0.075);width:100%;margin-bottom:1rem;vertical-align:top;border-color:var(--bs-table-border-color)}.table>:not(caption)>*>*{padding:.5rem .5rem;color:var(--bs-table-color-state, var(--bs-table-color-type, var(--bs-table-color)));background-color:var(--bs-table-bg);border-bottom-width:var(--bs-border-width);box-shadow:inset 0 0 0 9999px var(--bs-table-bg-state, var(--bs-table-bg-type, var(--bs-table-accent-bg)))}.table>tbody{vertical-align:inherit}.table>thead{vertical-align:bottom}.table-group-divider{border-top:calc(var(--bs-border-width) * 2) solid currentcolor}.caption-top{caption-side:top}.table-sm>:not(caption)>*>*{padding:.25rem .25rem}.table-bordered>:not(caption)>*{border-width:var(--bs-border-width) 0}.table-bordered>:not(caption)>*>*{border-width:0 var(--bs-border-width)}.table-borderless>:not(caption)>*>*{border-bottom-width:0}.table-borderless>:not(:first-child){border-top-width:0}.table-striped>tbody>tr:nth-of-type(odd)>*{--bs-table-color-type: var(--bs-table-striped-color);--bs-table-bg-type: var(--bs-table-striped-bg)}.table-striped-columns>:not(caption)>tr>:nth-child(even){--bs-table-color-type: var(--bs-table-striped-color);--bs-table-bg-type: var(--bs-table-striped-bg)}.table-active{--bs-table-color-state: var(--bs-table-active-color);--bs-table-bg-state: var(--bs-table-active-bg)}.table-hover>tbody>tr:hover>*{--bs-table-color-state: var(--bs-table-hover-color);--bs-table-bg-state: var(--bs-table-hover-bg)}.table-primary{--bs-table-color: #000;--bs-table-bg: #dae2eb;--bs-table-border-color: #c4cbd4;--bs-table-striped-bg: #cfd7df;--bs-table-striped-color: #000;--bs-table-active-bg: #c4cbd4;--bs-table-active-color: #000;--bs-table-hover-bg: #cad1d9;--bs-table-hover-color: #000;color:var(--bs-table-color);border-color:var(--bs-table-border-color)}.table-secondary{--bs-table-color: #000;--bs-table-bg: #ebebeb;--bs-table-border-color: #d4d4d4;--bs-table-striped-bg: #dfdfdf;--bs-table-striped-color: #000;--bs-table-active-bg: #d4d4d4;--bs-table-active-color: #000;--bs-table-hover-bg: #d9d9d9;--bs-table-hover-color: #000;color:var(--bs-table-color);border-color:var(--bs-table-border-color)}.table-success{--bs-table-color: #000;--bs-table-bg: #d8f0d3;--bs-table-border-color: #c2d8be;--bs-table-striped-bg: #cde4c8;--bs-table-striped-color: #000;--bs-table-active-bg: #c2d8be;--bs-table-active-color: #000;--bs-table-hover-bg: #c8dec3;--bs-table-hover-color: #000;color:var(--bs-table-color);border-color:var(--bs-table-border-color)}.table-info{--bs-table-color: #000;--bs-table-bg: #d6ebfd;--bs-table-border-color: #c1d4e4;--bs-table-striped-bg: #cbdff0;--bs-table-striped-color: #000;--bs-table-active-bg: #c1d4e4;--bs-table-active-color: #000;--bs-table-hover-bg: #c6d9ea;--bs-table-hover-color: #000;color:var(--bs-table-color);border-color:var(--bs-table-border-color)}.table-warning{--bs-table-color: #000;--bs-table-bg: #f6e3cc;--bs-table-border-color: #ddccb8;--bs-table-striped-bg: #ead8c2;--bs-table-striped-color: #000;--bs-table-active-bg: #ddccb8;--bs-table-active-color: #000;--bs-table-hover-bg: #e4d2bd;--bs-table-hover-color: #000;color:var(--bs-table-color);border-color:var(--bs-table-border-color)}.table-danger{--bs-table-color: #000;--bs-table-bg: #f5cccc;--bs-table-border-color: #ddb8b8;--bs-table-striped-bg: #e9c2c2;--bs-table-striped-color: #000;--bs-table-active-bg: #ddb8b8;--bs-table-active-color: #000;--bs-table-hover-bg: #e3bdbd;--bs-table-hover-color: #000;color:var(--bs-table-color);border-color:var(--bs-table-border-color)}.table-light{--bs-table-color: #000;--bs-table-bg: #eee;--bs-table-border-color: #d6d6d6;--bs-table-striped-bg: #e2e2e2;--bs-table-striped-color: #000;--bs-table-active-bg: #d6d6d6;--bs-table-active-color: #000;--bs-table-hover-bg: #dcdcdc;--bs-table-hover-color: #000;color:var(--bs-table-color);border-color:var(--bs-table-border-color)}.table-dark{--bs-table-color: #fff;--bs-table-bg: #333;--bs-table-border-color: #474747;--bs-table-striped-bg: #3d3d3d;--bs-table-striped-color: #fff;--bs-table-active-bg: #474747;--bs-table-active-color: #fff;--bs-table-hover-bg: #424242;--bs-table-hover-color: #fff;color:var(--bs-table-color);border-color:var(--bs-table-border-color)}.table-responsive{overflow-x:auto;-webkit-overflow-scrolling:touch}@media (max-width: 575.98px){.table-responsive-sm{overflow-x:auto;-webkit-overflow-scrolling:touch}}@media (max-width: 767.98px){.table-responsive-md{overflow-x:auto;-webkit-overflow-scrolling:touch}}@media (max-width: 991.98px){.table-responsive-lg{overflow-x:auto;-webkit-overflow-scrolling:touch}}@media (max-width: 1199.98px){.table-responsive-xl{overflow-x:auto;-webkit-overflow-scrolling:touch}}@media (max-width: 1399.98px){.table-responsive-xxl{overflow-x:auto;-webkit-overflow-scrolling:touch}}.form-label,.shiny-input-container .control-label{margin-bottom:.5rem}.col-form-label{padding-top:calc(.375rem + var(--bs-border-width));padding-bottom:calc(.375rem + var(--bs-border-width));margin-bottom:0;font-size:inherit;line-height:1.5}.col-form-label-lg{padding-top:calc(.5rem + var(--bs-border-width));padding-bottom:calc(.5rem + var(--bs-border-width));font-size:1.25rem}.col-form-label-sm{padding-top:calc(.25rem + var(--bs-border-width));padding-bottom:calc(.25rem + var(--bs-border-width));font-size:.875rem}.form-text{margin-top:.25rem;font-size:.875em;color:var(--bs-secondary-color)}.form-control{display:block;width:100%;padding:.375rem .75rem;font-size:1rem;font-weight:400;line-height:1.5;color:var(--bs-body-color);appearance:none;-webkit-appearance:none;-moz-appearance:none;-ms-appearance:none;-o-appearance:none;background-color:var(--bs-body-bg);background-clip:padding-box;border:var(--bs-border-width) solid var(--bs-border-color);border-radius:var(--bs-border-radius);transition:border-color 0.15s ease-in-out,box-shadow 0.15s ease-in-out}@media (prefers-reduced-motion: reduce){.form-control{transition:none}}.form-control[type="file"]{overflow:hidden}.form-control[type="file"]:not(:disabled):not([readonly]){cursor:pointer}.form-control:focus{color:var(--bs-body-color);background-color:var(--bs-body-bg);border-color:#a2b7cd;outline:0;box-shadow:0 0 0 .25rem rgba(68,110,155,0.25)}.form-control::-webkit-date-and-time-value{min-width:85px;height:1.5em;margin:0}.form-control::-webkit-datetime-edit{display:block;padding:0}.form-control::placeholder{color:var(--bs-secondary-color);opacity:1}.form-control:disabled{background-color:var(--bs-secondary-bg);opacity:1}.form-control::file-selector-button{padding:.375rem .75rem;margin:-.375rem -.75rem;margin-inline-end:.75rem;color:var(--bs-body-color);background-color:var(--bs-tertiary-bg);pointer-events:none;border-color:inherit;border-style:solid;border-width:0;border-inline-end-width:var(--bs-border-width);border-radius:0;transition:color 0.15s ease-in-out,background-color 0.15s ease-in-out,border-color 0.15s ease-in-out,box-shadow 0.15s ease-in-out}@media (prefers-reduced-motion: reduce){.form-control::file-selector-button{transition:none}}.form-control:hover:not(:disabled):not([readonly])::file-selector-button{background-color:var(--bs-secondary-bg)}.form-control-plaintext{display:block;width:100%;padding:.375rem 0;margin-bottom:0;line-height:1.5;color:var(--bs-body-color);background-color:transparent;border:solid transparent;border-width:var(--bs-border-width) 0}.form-control-plaintext:focus{outline:0}.form-control-plaintext.form-control-sm,.form-control-plaintext.form-control-lg{padding-right:0;padding-left:0}.form-control-sm{min-height:calc(1.5em + .5rem + calc(var(--bs-border-width) * 2));padding:.25rem .5rem;font-size:.875rem;border-radius:var(--bs-border-radius-sm)}.form-control-sm::file-selector-button{padding:.25rem .5rem;margin:-.25rem -.5rem;margin-inline-end:.5rem}.form-control-lg{min-height:calc(1.5em + 1rem + calc(var(--bs-border-width) * 2));padding:.5rem 1rem;font-size:1.25rem;border-radius:var(--bs-border-radius-lg)}.form-control-lg::file-selector-button{padding:.5rem 1rem;margin:-.5rem -1rem;margin-inline-end:1rem}textarea.form-control{min-height:calc(1.5em + .75rem + calc(var(--bs-border-width) * 2))}textarea.form-control-sm{min-height:calc(1.5em + .5rem + calc(var(--bs-border-width) * 2))}textarea.form-control-lg{min-height:calc(1.5em + 1rem + calc(var(--bs-border-width) * 2))}.form-control-color{width:3rem;height:calc(1.5em + .75rem + calc(var(--bs-border-width) * 2));padding:.375rem}.form-control-color:not(:disabled):not([readonly]){cursor:pointer}.form-control-color::-moz-color-swatch{border:0 !important;border-radius:var(--bs-border-radius)}.form-control-color::-webkit-color-swatch{border:0 !important;border-radius:var(--bs-border-radius)}.form-control-color.form-control-sm{height:calc(1.5em + .5rem + calc(var(--bs-border-width) * 2))}.form-control-color.form-control-lg{height:calc(1.5em + 1rem + calc(var(--bs-border-width) * 2))}.form-select{--bs-form-select-bg-img: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16'%3e%3cpath fill='none' stroke='%23333' stroke-linecap='round' stroke-linejoin='round' stroke-width='2' d='m2 5 6 6 6-6'/%3e%3c/svg%3e");display:block;width:100%;padding:.375rem 2.25rem .375rem .75rem;font-size:1rem;font-weight:400;line-height:1.5;color:var(--bs-body-color);appearance:none;-webkit-appearance:none;-moz-appearance:none;-ms-appearance:none;-o-appearance:none;background-color:var(--bs-body-bg);background-image:var(--bs-form-select-bg-img),var(--bs-form-select-bg-icon, none);background-repeat:no-repeat;background-position:right .75rem center;background-size:16px 12px;border:var(--bs-border-width) solid var(--bs-border-color);border-radius:var(--bs-border-radius);transition:border-color 0.15s ease-in-out,box-shadow 0.15s ease-in-out}@media (prefers-reduced-motion: reduce){.form-select{transition:none}}.form-select:focus{border-color:#a2b7cd;outline:0;box-shadow:0 0 0 .25rem rgba(68,110,155,0.25)}.form-select[multiple],.form-select[size]:not([size="1"]){padding-right:.75rem;background-image:none}.form-select:disabled{background-color:var(--bs-secondary-bg)}.form-select:-moz-focusring{color:transparent;text-shadow:0 0 0 var(--bs-body-color)}.form-select-sm{padding-top:.25rem;padding-bottom:.25rem;padding-left:.5rem;font-size:.875rem;border-radius:var(--bs-border-radius-sm)}.form-select-lg{padding-top:.5rem;padding-bottom:.5rem;padding-left:1rem;font-size:1.25rem;border-radius:var(--bs-border-radius-lg)}[data-bs-theme="dark"] .form-select{--bs-form-select-bg-img: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16'%3e%3cpath fill='none' stroke='%23dee2e6' stroke-linecap='round' stroke-linejoin='round' stroke-width='2' d='m2 5 6 6 6-6'/%3e%3c/svg%3e")}.form-check,.shiny-input-container .checkbox,.shiny-input-container .radio{display:block;min-height:1.5rem;padding-left:0;margin-bottom:.125rem}.form-check .form-check-input,.form-check .shiny-input-container .checkbox input,.form-check .shiny-input-container .radio input,.shiny-input-container .checkbox .form-check-input,.shiny-input-container .checkbox .shiny-input-container .checkbox input,.shiny-input-container .checkbox .shiny-input-container .radio input,.shiny-input-container .radio .form-check-input,.shiny-input-container .radio .shiny-input-container .checkbox input,.shiny-input-container .radio .shiny-input-container .radio input{float:left;margin-left:0}.form-check-reverse{padding-right:0;padding-left:0;text-align:right}.form-check-reverse .form-check-input{float:right;margin-right:0;margin-left:0}.form-check-input,.shiny-input-container .checkbox input,.shiny-input-container .checkbox-inline input,.shiny-input-container .radio input,.shiny-input-container .radio-inline input{--bs-form-check-bg: var(--bs-body-bg);width:1em;height:1em;margin-top:.25em;vertical-align:top;appearance:none;-webkit-appearance:none;-moz-appearance:none;-ms-appearance:none;-o-appearance:none;background-color:var(--bs-form-check-bg);background-image:var(--bs-form-check-bg-image);background-repeat:no-repeat;background-position:center;background-size:contain;border:var(--bs-border-width) solid var(--bs-border-color);print-color-adjust:exact}.form-check-input[type="checkbox"],.shiny-input-container .checkbox input[type="checkbox"],.shiny-input-container .checkbox-inline input[type="checkbox"],.shiny-input-container .radio input[type="checkbox"],.shiny-input-container .radio-inline input[type="checkbox"]{border-radius:.25em}.form-check-input[type="radio"],.shiny-input-container .checkbox input[type="radio"],.shiny-input-container .checkbox-inline input[type="radio"],.shiny-input-container .radio input[type="radio"],.shiny-input-container .radio-inline input[type="radio"]{border-radius:50%}.form-check-input:active,.shiny-input-container .checkbox input:active,.shiny-input-container .checkbox-inline input:active,.shiny-input-container .radio input:active,.shiny-input-container .radio-inline input:active{filter:brightness(90%)}.form-check-input:focus,.shiny-input-container .checkbox input:focus,.shiny-input-container .checkbox-inline input:focus,.shiny-input-container .radio input:focus,.shiny-input-container .radio-inline input:focus{border-color:#a2b7cd;outline:0;box-shadow:0 0 0 .25rem rgba(68,110,155,0.25)}.form-check-input:checked,.shiny-input-container .checkbox input:checked,.shiny-input-container .checkbox-inline input:checked,.shiny-input-container .radio input:checked,.shiny-input-container .radio-inline input:checked{background-color:#446e9b;border-color:#446e9b}.form-check-input:checked[type="checkbox"],.shiny-input-container .checkbox input:checked[type="checkbox"],.shiny-input-container .checkbox-inline input:checked[type="checkbox"],.shiny-input-container .radio input:checked[type="checkbox"],.shiny-input-container .radio-inline input:checked[type="checkbox"]{--bs-form-check-bg-image: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 20 20'%3e%3cpath fill='none' stroke='%23fff' stroke-linecap='round' stroke-linejoin='round' stroke-width='3' d='m6 10 3 3 6-6'/%3e%3c/svg%3e")}.form-check-input:checked[type="radio"],.shiny-input-container .checkbox input:checked[type="radio"],.shiny-input-container .checkbox-inline input:checked[type="radio"],.shiny-input-container .radio input:checked[type="radio"],.shiny-input-container .radio-inline input:checked[type="radio"]{--bs-form-check-bg-image: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='-4 -4 8 8'%3e%3ccircle r='2' fill='%23fff'/%3e%3c/svg%3e")}.form-check-input[type="checkbox"]:indeterminate,.shiny-input-container .checkbox input[type="checkbox"]:indeterminate,.shiny-input-container .checkbox-inline input[type="checkbox"]:indeterminate,.shiny-input-container .radio input[type="checkbox"]:indeterminate,.shiny-input-container .radio-inline input[type="checkbox"]:indeterminate{background-color:#446e9b;border-color:#446e9b;--bs-form-check-bg-image: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 20 20'%3e%3cpath fill='none' stroke='%23fff' stroke-linecap='round' stroke-linejoin='round' stroke-width='3' d='M6 10h8'/%3e%3c/svg%3e")}.form-check-input:disabled,.shiny-input-container .checkbox input:disabled,.shiny-input-container .checkbox-inline input:disabled,.shiny-input-container .radio input:disabled,.shiny-input-container .radio-inline input:disabled{pointer-events:none;filter:none;opacity:.5}.form-check-input[disabled]~.form-check-label,.form-check-input[disabled]~span,.form-check-input:disabled~.form-check-label,.form-check-input:disabled~span,.shiny-input-container .checkbox input[disabled]~.form-check-label,.shiny-input-container .checkbox input[disabled]~span,.shiny-input-container .checkbox input:disabled~.form-check-label,.shiny-input-container .checkbox input:disabled~span,.shiny-input-container .checkbox-inline input[disabled]~.form-check-label,.shiny-input-container .checkbox-inline input[disabled]~span,.shiny-input-container .checkbox-inline input:disabled~.form-check-label,.shiny-input-container .checkbox-inline input:disabled~span,.shiny-input-container .radio input[disabled]~.form-check-label,.shiny-input-container .radio input[disabled]~span,.shiny-input-container .radio input:disabled~.form-check-label,.shiny-input-container .radio input:disabled~span,.shiny-input-container .radio-inline input[disabled]~.form-check-label,.shiny-input-container .radio-inline input[disabled]~span,.shiny-input-container .radio-inline input:disabled~.form-check-label,.shiny-input-container .radio-inline input:disabled~span{cursor:default;opacity:.5}.form-check-label,.shiny-input-container .checkbox label,.shiny-input-container .checkbox-inline label,.shiny-input-container .radio label,.shiny-input-container .radio-inline label{cursor:pointer}.form-switch{padding-left:2.5em}.form-switch .form-check-input{--bs-form-switch-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='-4 -4 8 8'%3e%3ccircle r='3' fill='rgba%280,0,0,0.25%29'/%3e%3c/svg%3e");width:2em;margin-left:-2.5em;background-image:var(--bs-form-switch-bg);background-position:left center;border-radius:2em;transition:background-position 0.15s ease-in-out}@media (prefers-reduced-motion: reduce){.form-switch .form-check-input{transition:none}}.form-switch .form-check-input:focus{--bs-form-switch-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='-4 -4 8 8'%3e%3ccircle r='3' fill='%23a2b7cd'/%3e%3c/svg%3e")}.form-switch .form-check-input:checked{background-position:right center;--bs-form-switch-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='-4 -4 8 8'%3e%3ccircle r='3' fill='%23fff'/%3e%3c/svg%3e")}.form-switch.form-check-reverse{padding-right:2.5em;padding-left:0}.form-switch.form-check-reverse .form-check-input{margin-right:-2.5em;margin-left:0}.form-check-inline{display:inline-block;margin-right:1rem}.btn-check{position:absolute;clip:rect(0, 0, 0, 0);pointer-events:none}.btn-check[disabled]+.btn,.btn-check:disabled+.btn{pointer-events:none;filter:none;opacity:.65}[data-bs-theme="dark"] .form-switch .form-check-input:not(:checked):not(:focus){--bs-form-switch-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='-4 -4 8 8'%3e%3ccircle r='3' fill='rgba%28255,255,255,0.25%29'/%3e%3c/svg%3e")}.form-range{width:100%;height:1.5rem;padding:0;appearance:none;-webkit-appearance:none;-moz-appearance:none;-ms-appearance:none;-o-appearance:none;background-color:transparent}.form-range:focus{outline:0}.form-range:focus::-webkit-slider-thumb{box-shadow:0 0 0 1px #fff,0 0 0 .25rem rgba(68,110,155,0.25)}.form-range:focus::-moz-range-thumb{box-shadow:0 0 0 1px #fff,0 0 0 .25rem rgba(68,110,155,0.25)}.form-range::-moz-focus-outer{border:0}.form-range::-webkit-slider-thumb{width:1rem;height:1rem;margin-top:-.25rem;appearance:none;-webkit-appearance:none;-moz-appearance:none;-ms-appearance:none;-o-appearance:none;background-color:#446e9b;border:0;border-radius:1rem;transition:background-color 0.15s ease-in-out,border-color 0.15s ease-in-out,box-shadow 0.15s ease-in-out}@media (prefers-reduced-motion: reduce){.form-range::-webkit-slider-thumb{transition:none}}.form-range::-webkit-slider-thumb:active{background-color:#c7d4e1}.form-range::-webkit-slider-runnable-track{width:100%;height:.5rem;color:transparent;cursor:pointer;background-color:var(--bs-tertiary-bg);border-color:transparent;border-radius:1rem}.form-range::-moz-range-thumb{width:1rem;height:1rem;appearance:none;-webkit-appearance:none;-moz-appearance:none;-ms-appearance:none;-o-appearance:none;background-color:#446e9b;border:0;border-radius:1rem;transition:background-color 0.15s ease-in-out,border-color 0.15s ease-in-out,box-shadow 0.15s ease-in-out}@media (prefers-reduced-motion: reduce){.form-range::-moz-range-thumb{transition:none}}.form-range::-moz-range-thumb:active{background-color:#c7d4e1}.form-range::-moz-range-track{width:100%;height:.5rem;color:transparent;cursor:pointer;background-color:var(--bs-tertiary-bg);border-color:transparent;border-radius:1rem}.form-range:disabled{pointer-events:none}.form-range:disabled::-webkit-slider-thumb{background-color:var(--bs-secondary-color)}.form-range:disabled::-moz-range-thumb{background-color:var(--bs-secondary-color)}.form-floating{position:relative}.form-floating>.form-control,.form-floating>.form-control-plaintext,.form-floating>.form-select{height:calc(3.5rem + calc(var(--bs-border-width) * 2));min-height:calc(3.5rem + calc(var(--bs-border-width) * 2));line-height:1.25}.form-floating>label{position:absolute;top:0;left:0;z-index:2;height:100%;padding:1rem .75rem;overflow:hidden;text-align:start;text-overflow:ellipsis;white-space:nowrap;pointer-events:none;border:var(--bs-border-width) solid transparent;transform-origin:0 0;transition:opacity 0.1s ease-in-out,transform 0.1s ease-in-out}@media (prefers-reduced-motion: reduce){.form-floating>label{transition:none}}.form-floating>.form-control,.form-floating>.form-control-plaintext{padding:1rem .75rem}.form-floating>.form-control::placeholder,.form-floating>.form-control-plaintext::placeholder{color:transparent}.form-floating>.form-control:focus,.form-floating>.form-control:not(:placeholder-shown),.form-floating>.form-control-plaintext:focus,.form-floating>.form-control-plaintext:not(:placeholder-shown){padding-top:1.625rem;padding-bottom:.625rem}.form-floating>.form-control:-webkit-autofill,.form-floating>.form-control-plaintext:-webkit-autofill{padding-top:1.625rem;padding-bottom:.625rem}.form-floating>.form-select{padding-top:1.625rem;padding-bottom:.625rem}.form-floating>.form-control:focus~label,.form-floating>.form-control:not(:placeholder-shown)~label,.form-floating>.form-control-plaintext~label,.form-floating>.form-select~label{color:rgba(var(--bs-body-color-rgb), .65);transform:scale(0.85) translateY(-0.5rem) translateX(0.15rem)}.form-floating>.form-control:focus~label::after,.form-floating>.form-control:not(:placeholder-shown)~label::after,.form-floating>.form-control-plaintext~label::after,.form-floating>.form-select~label::after{position:absolute;inset:1rem .375rem;z-index:-1;height:1.5em;content:"";background-color:var(--bs-body-bg);border-radius:var(--bs-border-radius)}.form-floating>.form-control:-webkit-autofill~label{color:rgba(var(--bs-body-color-rgb), .65);transform:scale(0.85) translateY(-0.5rem) translateX(0.15rem)}.form-floating>.form-control-plaintext~label{border-width:var(--bs-border-width) 0}.form-floating>:disabled~label,.form-floating>.form-control:disabled~label{color:#777}.form-floating>:disabled~label::after,.form-floating>.form-control:disabled~label::after{background-color:var(--bs-secondary-bg)}.input-group{position:relative;display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;align-items:stretch;-webkit-align-items:stretch;width:100%}.input-group>.form-control,.input-group>.form-select,.input-group>.form-floating{position:relative;flex:1 1 auto;-webkit-flex:1 1 auto;width:1%;min-width:0}.input-group>.form-control:focus,.input-group>.form-select:focus,.input-group>.form-floating:focus-within{z-index:5}.input-group .btn{position:relative;z-index:2}.input-group .btn:focus{z-index:5}.input-group-text{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;padding:.375rem .75rem;font-size:1rem;font-weight:400;line-height:1.5;color:var(--bs-body-color);text-align:center;white-space:nowrap;background-color:var(--bs-tertiary-bg);border:var(--bs-border-width) solid var(--bs-border-color);border-radius:var(--bs-border-radius)}.input-group-lg>.form-control,.input-group-lg>.form-select,.input-group-lg>.input-group-text,.input-group-lg>.btn{padding:.5rem 1rem;font-size:1.25rem;border-radius:var(--bs-border-radius-lg)}.input-group-sm>.form-control,.input-group-sm>.form-select,.input-group-sm>.input-group-text,.input-group-sm>.btn{padding:.25rem .5rem;font-size:.875rem;border-radius:var(--bs-border-radius-sm)}.input-group-lg>.form-select,.input-group-sm>.form-select{padding-right:3rem}.input-group:not(.has-validation)>:not(:last-child):not(.dropdown-toggle):not(.dropdown-menu):not(.form-floating),.input-group:not(.has-validation)>.dropdown-toggle:nth-last-child(n + 3),.input-group:not(.has-validation)>.form-floating:not(:last-child)>.form-control,.input-group:not(.has-validation)>.form-floating:not(:last-child)>.form-select{border-top-right-radius:0;border-bottom-right-radius:0}.input-group.has-validation>:nth-last-child(n + 3):not(.dropdown-toggle):not(.dropdown-menu):not(.form-floating),.input-group.has-validation>.dropdown-toggle:nth-last-child(n + 4),.input-group.has-validation>.form-floating:nth-last-child(n + 3)>.form-control,.input-group.has-validation>.form-floating:nth-last-child(n + 3)>.form-select{border-top-right-radius:0;border-bottom-right-radius:0}.input-group>:not(:first-child):not(.dropdown-menu):not(.valid-tooltip):not(.valid-feedback):not(.invalid-tooltip):not(.invalid-feedback){margin-left:calc(var(--bs-border-width) * -1);border-top-left-radius:0;border-bottom-left-radius:0}.input-group>.form-floating:not(:first-child)>.form-control,.input-group>.form-floating:not(:first-child)>.form-select{border-top-left-radius:0;border-bottom-left-radius:0}.valid-feedback{display:none;width:100%;margin-top:.25rem;font-size:.875em;color:var(--bs-form-valid-color)}.valid-tooltip{position:absolute;top:100%;z-index:5;display:none;max-width:100%;padding:.25rem .5rem;margin-top:.1rem;font-size:.875rem;color:#fff;background-color:var(--bs-success);border-radius:var(--bs-border-radius)}.was-validated :valid~.valid-feedback,.was-validated :valid~.valid-tooltip,.is-valid~.valid-feedback,.is-valid~.valid-tooltip{display:block}.was-validated .form-control:valid,.form-control.is-valid{border-color:var(--bs-form-valid-border-color);padding-right:calc(1.5em + .75rem);background-image:url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 8 8'%3e%3cpath fill='%233cb521' d='M2.3 6.73.6 4.53c-.4-1.04.46-1.4 1.1-.8l1.1 1.4 3.4-3.8c.6-.63 1.6-.27 1.2.7l-4 4.6c-.43.5-.8.4-1.1.1z'/%3e%3c/svg%3e");background-repeat:no-repeat;background-position:right calc(.375em + .1875rem) center;background-size:calc(.75em + .375rem) calc(.75em + .375rem)}.was-validated .form-control:valid:focus,.form-control.is-valid:focus{border-color:var(--bs-form-valid-border-color);box-shadow:0 0 0 .25rem rgba(var(--bs-success-rgb), 0.25)}.was-validated textarea.form-control:valid,textarea.form-control.is-valid{padding-right:calc(1.5em + .75rem);background-position:top calc(.375em + .1875rem) right calc(.375em + .1875rem)}.was-validated .form-select:valid,.form-select.is-valid{border-color:var(--bs-form-valid-border-color)}.was-validated .form-select:valid:not([multiple]):not([size]),.was-validated .form-select:valid:not([multiple])[size="1"],.form-select.is-valid:not([multiple]):not([size]),.form-select.is-valid:not([multiple])[size="1"]{--bs-form-select-bg-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 8 8'%3e%3cpath fill='%233cb521' d='M2.3 6.73.6 4.53c-.4-1.04.46-1.4 1.1-.8l1.1 1.4 3.4-3.8c.6-.63 1.6-.27 1.2.7l-4 4.6c-.43.5-.8.4-1.1.1z'/%3e%3c/svg%3e");padding-right:4.125rem;background-position:right .75rem center,center right 2.25rem;background-size:16px 12px,calc(.75em + .375rem) calc(.75em + .375rem)}.was-validated .form-select:valid:focus,.form-select.is-valid:focus{border-color:var(--bs-form-valid-border-color);box-shadow:0 0 0 .25rem rgba(var(--bs-success-rgb), 0.25)}.was-validated .form-control-color:valid,.form-control-color.is-valid{width:calc(3rem + calc(1.5em + .75rem))}.was-validated .form-check-input:valid,.form-check-input.is-valid{border-color:var(--bs-form-valid-border-color)}.was-validated .form-check-input:valid:checked,.form-check-input.is-valid:checked{background-color:var(--bs-form-valid-color)}.was-validated .form-check-input:valid:focus,.form-check-input.is-valid:focus{box-shadow:0 0 0 .25rem rgba(var(--bs-success-rgb), 0.25)}.was-validated .form-check-input:valid~.form-check-label,.form-check-input.is-valid~.form-check-label{color:var(--bs-form-valid-color)}.form-check-inline .form-check-input~.valid-feedback{margin-left:.5em}.was-validated .input-group>.form-control:not(:focus):valid,.input-group>.form-control:not(:focus).is-valid,.was-validated .input-group>.form-select:not(:focus):valid,.input-group>.form-select:not(:focus).is-valid,.was-validated .input-group>.form-floating:not(:focus-within):valid,.input-group>.form-floating:not(:focus-within).is-valid{z-index:3}.invalid-feedback{display:none;width:100%;margin-top:.25rem;font-size:.875em;color:var(--bs-form-invalid-color)}.invalid-tooltip{position:absolute;top:100%;z-index:5;display:none;max-width:100%;padding:.25rem .5rem;margin-top:.1rem;font-size:.875rem;color:#fff;background-color:var(--bs-danger);border-radius:var(--bs-border-radius)}.was-validated :invalid~.invalid-feedback,.was-validated :invalid~.invalid-tooltip,.is-invalid~.invalid-feedback,.is-invalid~.invalid-tooltip{display:block}.was-validated .form-control:invalid,.form-control.is-invalid{border-color:var(--bs-form-invalid-border-color);padding-right:calc(1.5em + .75rem);background-image:url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 12 12' width='12' height='12' fill='none' stroke='%23cd0200'%3e%3ccircle cx='6' cy='6' r='4.5'/%3e%3cpath stroke-linejoin='round' d='M5.8 3.6h.4L6 6.5z'/%3e%3ccircle cx='6' cy='8.2' r='.6' fill='%23cd0200' stroke='none'/%3e%3c/svg%3e");background-repeat:no-repeat;background-position:right calc(.375em + .1875rem) center;background-size:calc(.75em + .375rem) calc(.75em + .375rem)}.was-validated .form-control:invalid:focus,.form-control.is-invalid:focus{border-color:var(--bs-form-invalid-border-color);box-shadow:0 0 0 .25rem rgba(var(--bs-danger-rgb), 0.25)}.was-validated textarea.form-control:invalid,textarea.form-control.is-invalid{padding-right:calc(1.5em + .75rem);background-position:top calc(.375em + .1875rem) right calc(.375em + .1875rem)}.was-validated .form-select:invalid,.form-select.is-invalid{border-color:var(--bs-form-invalid-border-color)}.was-validated .form-select:invalid:not([multiple]):not([size]),.was-validated .form-select:invalid:not([multiple])[size="1"],.form-select.is-invalid:not([multiple]):not([size]),.form-select.is-invalid:not([multiple])[size="1"]{--bs-form-select-bg-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 12 12' width='12' height='12' fill='none' stroke='%23cd0200'%3e%3ccircle cx='6' cy='6' r='4.5'/%3e%3cpath stroke-linejoin='round' d='M5.8 3.6h.4L6 6.5z'/%3e%3ccircle cx='6' cy='8.2' r='.6' fill='%23cd0200' stroke='none'/%3e%3c/svg%3e");padding-right:4.125rem;background-position:right .75rem center,center right 2.25rem;background-size:16px 12px,calc(.75em + .375rem) calc(.75em + .375rem)}.was-validated .form-select:invalid:focus,.form-select.is-invalid:focus{border-color:var(--bs-form-invalid-border-color);box-shadow:0 0 0 .25rem rgba(var(--bs-danger-rgb), 0.25)}.was-validated .form-control-color:invalid,.form-control-color.is-invalid{width:calc(3rem + calc(1.5em + .75rem))}.was-validated .form-check-input:invalid,.form-check-input.is-invalid{border-color:var(--bs-form-invalid-border-color)}.was-validated .form-check-input:invalid:checked,.form-check-input.is-invalid:checked{background-color:var(--bs-form-invalid-color)}.was-validated .form-check-input:invalid:focus,.form-check-input.is-invalid:focus{box-shadow:0 0 0 .25rem rgba(var(--bs-danger-rgb), 0.25)}.was-validated .form-check-input:invalid~.form-check-label,.form-check-input.is-invalid~.form-check-label{color:var(--bs-form-invalid-color)}.form-check-inline .form-check-input~.invalid-feedback{margin-left:.5em}.was-validated .input-group>.form-control:not(:focus):invalid,.input-group>.form-control:not(:focus).is-invalid,.was-validated .input-group>.form-select:not(:focus):invalid,.input-group>.form-select:not(:focus).is-invalid,.was-validated .input-group>.form-floating:not(:focus-within):invalid,.input-group>.form-floating:not(:focus-within).is-invalid{z-index:4}.btn{--bs-btn-padding-x: .75rem;--bs-btn-padding-y: .375rem;--bs-btn-font-family: ;--bs-btn-font-size:1rem;--bs-btn-font-weight: 400;--bs-btn-line-height: 1.5;--bs-btn-color: var(--bs-body-color);--bs-btn-bg: transparent;--bs-btn-border-width: var(--bs-border-width);--bs-btn-border-color: transparent;--bs-btn-border-radius: var(--bs-border-radius);--bs-btn-hover-border-color: transparent;--bs-btn-box-shadow: inset 0 1px 0 rgba(255,255,255,0.15),0 1px 1px rgba(0,0,0,0.075);--bs-btn-disabled-opacity: .65;--bs-btn-focus-box-shadow: 0 0 0 .25rem rgba(var(--bs-btn-focus-shadow-rgb), .5);display:inline-block;padding:var(--bs-btn-padding-y) var(--bs-btn-padding-x);font-family:var(--bs-btn-font-family);font-size:var(--bs-btn-font-size);font-weight:var(--bs-btn-font-weight);line-height:var(--bs-btn-line-height);color:var(--bs-btn-color);text-align:center;text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;vertical-align:middle;cursor:pointer;user-select:none;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;-o-user-select:none;border:var(--bs-btn-border-width) solid var(--bs-btn-border-color);border-radius:var(--bs-btn-border-radius);background-color:var(--bs-btn-bg);transition:color 0.15s ease-in-out,background-color 0.15s ease-in-out,border-color 0.15s ease-in-out,box-shadow 0.15s ease-in-out}@media (prefers-reduced-motion: reduce){.btn{transition:none}}.btn:hover{color:var(--bs-btn-hover-color);background-color:var(--bs-btn-hover-bg);border-color:var(--bs-btn-hover-border-color)}.btn-check+.btn:hover{color:var(--bs-btn-color);background-color:var(--bs-btn-bg);border-color:var(--bs-btn-border-color)}.btn:focus-visible{color:var(--bs-btn-hover-color);background-color:var(--bs-btn-hover-bg);border-color:var(--bs-btn-hover-border-color);outline:0;box-shadow:var(--bs-btn-focus-box-shadow)}.btn-check:focus-visible+.btn{border-color:var(--bs-btn-hover-border-color);outline:0;box-shadow:var(--bs-btn-focus-box-shadow)}.btn-check:checked+.btn,:not(.btn-check)+.btn:active,.btn:first-child:active,.btn.active,.btn.show{color:var(--bs-btn-active-color);background-color:var(--bs-btn-active-bg);border-color:var(--bs-btn-active-border-color)}.btn-check:checked+.btn:focus-visible,:not(.btn-check)+.btn:active:focus-visible,.btn:first-child:active:focus-visible,.btn.active:focus-visible,.btn.show:focus-visible{box-shadow:var(--bs-btn-focus-box-shadow)}.btn:disabled,.btn.disabled,fieldset:disabled .btn{color:var(--bs-btn-disabled-color);pointer-events:none;background-color:var(--bs-btn-disabled-bg);border-color:var(--bs-btn-disabled-border-color);opacity:var(--bs-btn-disabled-opacity)}.btn-default{--bs-btn-color: #fff;--bs-btn-bg: #999;--bs-btn-border-color: #999;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #828282;--bs-btn-hover-border-color: #7a7a7a;--bs-btn-focus-shadow-rgb: 168,168,168;--bs-btn-active-color: #fff;--bs-btn-active-bg: #7a7a7a;--bs-btn-active-border-color: #737373;--bs-btn-active-shadow: inset 0 3px 5px rgba(0,0,0,0.125);--bs-btn-disabled-color: #fff;--bs-btn-disabled-bg: #999;--bs-btn-disabled-border-color: #999}.btn-primary{--bs-btn-color: #fff;--bs-btn-bg: #446e9b;--bs-btn-border-color: #446e9b;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #3a5e84;--bs-btn-hover-border-color: #36587c;--bs-btn-focus-shadow-rgb: 96,132,170;--bs-btn-active-color: #fff;--bs-btn-active-bg: #36587c;--bs-btn-active-border-color: #335374;--bs-btn-active-shadow: inset 0 3px 5px rgba(0,0,0,0.125);--bs-btn-disabled-color: #fff;--bs-btn-disabled-bg: #446e9b;--bs-btn-disabled-border-color: #446e9b}.btn-secondary,.btn-default:not(.btn-primary):not(.btn-info):not(.btn-success):not(.btn-warning):not(.btn-danger):not(.btn-dark):not(.btn-light):not([class*='btn-outline-']){--bs-btn-color: #fff;--bs-btn-bg: #999;--bs-btn-border-color: #999;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #828282;--bs-btn-hover-border-color: #7a7a7a;--bs-btn-focus-shadow-rgb: 168,168,168;--bs-btn-active-color: #fff;--bs-btn-active-bg: #7a7a7a;--bs-btn-active-border-color: #737373;--bs-btn-active-shadow: inset 0 3px 5px rgba(0,0,0,0.125);--bs-btn-disabled-color: #fff;--bs-btn-disabled-bg: #999;--bs-btn-disabled-border-color: #999}.btn-success{--bs-btn-color: #fff;--bs-btn-bg: #3cb521;--bs-btn-border-color: #3cb521;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #339a1c;--bs-btn-hover-border-color: #30911a;--bs-btn-focus-shadow-rgb: 89,192,66;--bs-btn-active-color: #fff;--bs-btn-active-bg: #30911a;--bs-btn-active-border-color: #2d8819;--bs-btn-active-shadow: inset 0 3px 5px rgba(0,0,0,0.125);--bs-btn-disabled-color: #fff;--bs-btn-disabled-bg: #3cb521;--bs-btn-disabled-border-color: #3cb521}.btn-info{--bs-btn-color: #fff;--bs-btn-bg: #3399f3;--bs-btn-border-color: #3399f3;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #2b82cf;--bs-btn-hover-border-color: #297ac2;--bs-btn-focus-shadow-rgb: 82,168,245;--bs-btn-active-color: #fff;--bs-btn-active-bg: #297ac2;--bs-btn-active-border-color: #2673b6;--bs-btn-active-shadow: inset 0 3px 5px rgba(0,0,0,0.125);--bs-btn-disabled-color: #fff;--bs-btn-disabled-bg: #3399f3;--bs-btn-disabled-border-color: #3399f3}.btn-warning{--bs-btn-color: #fff;--bs-btn-bg: #d47500;--bs-btn-border-color: #d47500;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #b46300;--bs-btn-hover-border-color: #aa5e00;--bs-btn-focus-shadow-rgb: 218,138,38;--bs-btn-active-color: #fff;--bs-btn-active-bg: #aa5e00;--bs-btn-active-border-color: #9f5800;--bs-btn-active-shadow: inset 0 3px 5px rgba(0,0,0,0.125);--bs-btn-disabled-color: #fff;--bs-btn-disabled-bg: #d47500;--bs-btn-disabled-border-color: #d47500}.btn-danger{--bs-btn-color: #fff;--bs-btn-bg: #cd0200;--bs-btn-border-color: #cd0200;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #ae0200;--bs-btn-hover-border-color: #a40200;--bs-btn-focus-shadow-rgb: 213,40,38;--bs-btn-active-color: #fff;--bs-btn-active-bg: #a40200;--bs-btn-active-border-color: #9a0200;--bs-btn-active-shadow: inset 0 3px 5px rgba(0,0,0,0.125);--bs-btn-disabled-color: #fff;--bs-btn-disabled-bg: #cd0200;--bs-btn-disabled-border-color: #cd0200}.btn-light{--bs-btn-color: #000;--bs-btn-bg: #eee;--bs-btn-border-color: #eee;--bs-btn-hover-color: #000;--bs-btn-hover-bg: #cacaca;--bs-btn-hover-border-color: #bebebe;--bs-btn-focus-shadow-rgb: 202,202,202;--bs-btn-active-color: #000;--bs-btn-active-bg: #bebebe;--bs-btn-active-border-color: #b3b3b3;--bs-btn-active-shadow: inset 0 3px 5px rgba(0,0,0,0.125);--bs-btn-disabled-color: #000;--bs-btn-disabled-bg: #eee;--bs-btn-disabled-border-color: #eee}.btn-dark{--bs-btn-color: #fff;--bs-btn-bg: #333;--bs-btn-border-color: #333;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #525252;--bs-btn-hover-border-color: #474747;--bs-btn-focus-shadow-rgb: 82,82,82;--bs-btn-active-color: #fff;--bs-btn-active-bg: #5c5c5c;--bs-btn-active-border-color: #474747;--bs-btn-active-shadow: inset 0 3px 5px rgba(0,0,0,0.125);--bs-btn-disabled-color: #fff;--bs-btn-disabled-bg: #333;--bs-btn-disabled-border-color: #333}.btn-outline-default{--bs-btn-color: #999;--bs-btn-border-color: #999;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #999;--bs-btn-hover-border-color: #999;--bs-btn-focus-shadow-rgb: 153,153,153;--bs-btn-active-color: #fff;--bs-btn-active-bg: #999;--bs-btn-active-border-color: #999;--bs-btn-active-shadow: inset 0 3px 5px rgba(0,0,0,0.125);--bs-btn-disabled-color: #999;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #999;--bs-btn-bg: transparent;--bs-gradient: none}.btn-outline-primary{--bs-btn-color: #446e9b;--bs-btn-border-color: #446e9b;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #446e9b;--bs-btn-hover-border-color: #446e9b;--bs-btn-focus-shadow-rgb: 68,110,155;--bs-btn-active-color: #fff;--bs-btn-active-bg: #446e9b;--bs-btn-active-border-color: #446e9b;--bs-btn-active-shadow: inset 0 3px 5px rgba(0,0,0,0.125);--bs-btn-disabled-color: #446e9b;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #446e9b;--bs-btn-bg: transparent;--bs-gradient: none}.btn-outline-secondary{--bs-btn-color: #999;--bs-btn-border-color: #999;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #999;--bs-btn-hover-border-color: #999;--bs-btn-focus-shadow-rgb: 153,153,153;--bs-btn-active-color: #fff;--bs-btn-active-bg: #999;--bs-btn-active-border-color: #999;--bs-btn-active-shadow: inset 0 3px 5px rgba(0,0,0,0.125);--bs-btn-disabled-color: #999;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #999;--bs-btn-bg: transparent;--bs-gradient: none}.btn-outline-success{--bs-btn-color: #3cb521;--bs-btn-border-color: #3cb521;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #3cb521;--bs-btn-hover-border-color: #3cb521;--bs-btn-focus-shadow-rgb: 60,181,33;--bs-btn-active-color: #fff;--bs-btn-active-bg: #3cb521;--bs-btn-active-border-color: #3cb521;--bs-btn-active-shadow: inset 0 3px 5px rgba(0,0,0,0.125);--bs-btn-disabled-color: #3cb521;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #3cb521;--bs-btn-bg: transparent;--bs-gradient: none}.btn-outline-info{--bs-btn-color: #3399f3;--bs-btn-border-color: #3399f3;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #3399f3;--bs-btn-hover-border-color: #3399f3;--bs-btn-focus-shadow-rgb: 51,153,243;--bs-btn-active-color: #fff;--bs-btn-active-bg: #3399f3;--bs-btn-active-border-color: #3399f3;--bs-btn-active-shadow: inset 0 3px 5px rgba(0,0,0,0.125);--bs-btn-disabled-color: #3399f3;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #3399f3;--bs-btn-bg: transparent;--bs-gradient: none}.btn-outline-warning{--bs-btn-color: #d47500;--bs-btn-border-color: #d47500;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #d47500;--bs-btn-hover-border-color: #d47500;--bs-btn-focus-shadow-rgb: 212,117,0;--bs-btn-active-color: #fff;--bs-btn-active-bg: #d47500;--bs-btn-active-border-color: #d47500;--bs-btn-active-shadow: inset 0 3px 5px rgba(0,0,0,0.125);--bs-btn-disabled-color: #d47500;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #d47500;--bs-btn-bg: transparent;--bs-gradient: none}.btn-outline-danger{--bs-btn-color: #cd0200;--bs-btn-border-color: #cd0200;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #cd0200;--bs-btn-hover-border-color: #cd0200;--bs-btn-focus-shadow-rgb: 205,2,0;--bs-btn-active-color: #fff;--bs-btn-active-bg: #cd0200;--bs-btn-active-border-color: #cd0200;--bs-btn-active-shadow: inset 0 3px 5px rgba(0,0,0,0.125);--bs-btn-disabled-color: #cd0200;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #cd0200;--bs-btn-bg: transparent;--bs-gradient: none}.btn-outline-light{--bs-btn-color: #eee;--bs-btn-border-color: #eee;--bs-btn-hover-color: #000;--bs-btn-hover-bg: #eee;--bs-btn-hover-border-color: #eee;--bs-btn-focus-shadow-rgb: 238,238,238;--bs-btn-active-color: #000;--bs-btn-active-bg: #eee;--bs-btn-active-border-color: #eee;--bs-btn-active-shadow: inset 0 3px 5px rgba(0,0,0,0.125);--bs-btn-disabled-color: #eee;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #eee;--bs-btn-bg: transparent;--bs-gradient: none}.btn-outline-dark{--bs-btn-color: #333;--bs-btn-border-color: #333;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #333;--bs-btn-hover-border-color: #333;--bs-btn-focus-shadow-rgb: 51,51,51;--bs-btn-active-color: #fff;--bs-btn-active-bg: #333;--bs-btn-active-border-color: #333;--bs-btn-active-shadow: inset 0 3px 5px rgba(0,0,0,0.125);--bs-btn-disabled-color: #333;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #333;--bs-btn-bg: transparent;--bs-gradient: none}.btn-link{--bs-btn-font-weight: 400;--bs-btn-color: var(--bs-link-color);--bs-btn-bg: transparent;--bs-btn-border-color: transparent;--bs-btn-hover-color: var(--bs-link-hover-color);--bs-btn-hover-border-color: transparent;--bs-btn-active-color: var(--bs-link-hover-color);--bs-btn-active-border-color: transparent;--bs-btn-disabled-color: #777;--bs-btn-disabled-border-color: transparent;--bs-btn-box-shadow: 0 0 0 #000;--bs-btn-focus-shadow-rgb: 82,168,245;text-decoration:underline;-webkit-text-decoration:underline;-moz-text-decoration:underline;-ms-text-decoration:underline;-o-text-decoration:underline}.btn-link:focus-visible{color:var(--bs-btn-color)}.btn-link:hover{color:var(--bs-btn-hover-color)}.btn-lg,.btn-group-lg>.btn{--bs-btn-padding-y: .5rem;--bs-btn-padding-x: 1rem;--bs-btn-font-size:1.25rem;--bs-btn-border-radius: var(--bs-border-radius-lg)}.btn-sm,.btn-group-sm>.btn{--bs-btn-padding-y: .25rem;--bs-btn-padding-x: .5rem;--bs-btn-font-size:.875rem;--bs-btn-border-radius: var(--bs-border-radius-sm)}.fade{transition:opacity 0.15s linear}@media (prefers-reduced-motion: reduce){.fade{transition:none}}.fade:not(.show){opacity:0}.collapse:not(.show){display:none}.collapsing{height:0;overflow:hidden;transition:height 0.35s ease}@media (prefers-reduced-motion: reduce){.collapsing{transition:none}}.collapsing.collapse-horizontal{width:0;height:auto;transition:width 0.35s ease}@media (prefers-reduced-motion: reduce){.collapsing.collapse-horizontal{transition:none}}.dropup,.dropend,.dropdown,.dropstart,.dropup-center,.dropdown-center{position:relative}.dropdown-toggle{white-space:nowrap}.dropdown-toggle::after{display:inline-block;margin-left:.255em;vertical-align:.255em;content:"";border-top:.3em solid;border-right:.3em solid transparent;border-bottom:0;border-left:.3em solid transparent}.dropdown-toggle:empty::after{margin-left:0}.dropdown-menu{--bs-dropdown-zindex: 1000;--bs-dropdown-min-width: 10rem;--bs-dropdown-padding-x: 0;--bs-dropdown-padding-y: .5rem;--bs-dropdown-spacer: .125rem;--bs-dropdown-font-size:1rem;--bs-dropdown-color: var(--bs-body-color);--bs-dropdown-bg: var(--bs-body-bg);--bs-dropdown-border-color: var(--bs-border-color-translucent);--bs-dropdown-border-radius: var(--bs-border-radius);--bs-dropdown-border-width: var(--bs-border-width);--bs-dropdown-inner-border-radius: calc(var(--bs-border-radius) - var(--bs-border-width));--bs-dropdown-divider-bg: var(--bs-border-color-translucent);--bs-dropdown-divider-margin-y: .5rem;--bs-dropdown-box-shadow: 0 0.5rem 1rem rgba(0,0,0,0.15);--bs-dropdown-link-color: var(--bs-body-color);--bs-dropdown-link-hover-color: var(--bs-body-color);--bs-dropdown-link-hover-bg: var(--bs-tertiary-bg);--bs-dropdown-link-active-color: #fff;--bs-dropdown-link-active-bg: #446e9b;--bs-dropdown-link-disabled-color: var(--bs-tertiary-color);--bs-dropdown-item-padding-x: 1rem;--bs-dropdown-item-padding-y: .25rem;--bs-dropdown-header-color: #777;--bs-dropdown-header-padding-x: 1rem;--bs-dropdown-header-padding-y: .5rem;position:absolute;z-index:var(--bs-dropdown-zindex);display:none;min-width:var(--bs-dropdown-min-width);padding:var(--bs-dropdown-padding-y) var(--bs-dropdown-padding-x);margin:0;font-size:var(--bs-dropdown-font-size);color:var(--bs-dropdown-color);text-align:left;list-style:none;background-color:var(--bs-dropdown-bg);background-clip:padding-box;border:var(--bs-dropdown-border-width) solid var(--bs-dropdown-border-color);border-radius:var(--bs-dropdown-border-radius)}.dropdown-menu[data-bs-popper]{top:100%;left:0;margin-top:var(--bs-dropdown-spacer)}.dropdown-menu-start{--bs-position: start}.dropdown-menu-start[data-bs-popper]{right:auto;left:0}.dropdown-menu-end{--bs-position: end}.dropdown-menu-end[data-bs-popper]{right:0;left:auto}@media (min-width: 576px){.dropdown-menu-sm-start{--bs-position: start}.dropdown-menu-sm-start[data-bs-popper]{right:auto;left:0}.dropdown-menu-sm-end{--bs-position: end}.dropdown-menu-sm-end[data-bs-popper]{right:0;left:auto}}@media (min-width: 768px){.dropdown-menu-md-start{--bs-position: start}.dropdown-menu-md-start[data-bs-popper]{right:auto;left:0}.dropdown-menu-md-end{--bs-position: end}.dropdown-menu-md-end[data-bs-popper]{right:0;left:auto}}@media (min-width: 992px){.dropdown-menu-lg-start{--bs-position: start}.dropdown-menu-lg-start[data-bs-popper]{right:auto;left:0}.dropdown-menu-lg-end{--bs-position: end}.dropdown-menu-lg-end[data-bs-popper]{right:0;left:auto}}@media (min-width: 1200px){.dropdown-menu-xl-start{--bs-position: start}.dropdown-menu-xl-start[data-bs-popper]{right:auto;left:0}.dropdown-menu-xl-end{--bs-position: end}.dropdown-menu-xl-end[data-bs-popper]{right:0;left:auto}}@media (min-width: 1400px){.dropdown-menu-xxl-start{--bs-position: start}.dropdown-menu-xxl-start[data-bs-popper]{right:auto;left:0}.dropdown-menu-xxl-end{--bs-position: end}.dropdown-menu-xxl-end[data-bs-popper]{right:0;left:auto}}.dropup .dropdown-menu[data-bs-popper]{top:auto;bottom:100%;margin-top:0;margin-bottom:var(--bs-dropdown-spacer)}.dropup .dropdown-toggle::after{display:inline-block;margin-left:.255em;vertical-align:.255em;content:"";border-top:0;border-right:.3em solid transparent;border-bottom:.3em solid;border-left:.3em solid transparent}.dropup .dropdown-toggle:empty::after{margin-left:0}.dropend .dropdown-menu[data-bs-popper]{top:0;right:auto;left:100%;margin-top:0;margin-left:var(--bs-dropdown-spacer)}.dropend .dropdown-toggle::after{display:inline-block;margin-left:.255em;vertical-align:.255em;content:"";border-top:.3em solid transparent;border-right:0;border-bottom:.3em solid transparent;border-left:.3em solid}.dropend .dropdown-toggle:empty::after{margin-left:0}.dropend .dropdown-toggle::after{vertical-align:0}.dropstart .dropdown-menu[data-bs-popper]{top:0;right:100%;left:auto;margin-top:0;margin-right:var(--bs-dropdown-spacer)}.dropstart .dropdown-toggle::after{display:inline-block;margin-left:.255em;vertical-align:.255em;content:""}.dropstart .dropdown-toggle::after{display:none}.dropstart .dropdown-toggle::before{display:inline-block;margin-right:.255em;vertical-align:.255em;content:"";border-top:.3em solid transparent;border-right:.3em solid;border-bottom:.3em solid transparent}.dropstart .dropdown-toggle:empty::after{margin-left:0}.dropstart .dropdown-toggle::before{vertical-align:0}.dropdown-divider{height:0;margin:var(--bs-dropdown-divider-margin-y) 0;overflow:hidden;border-top:1px solid var(--bs-dropdown-divider-bg);opacity:1}.dropdown-item{display:block;width:100%;padding:var(--bs-dropdown-item-padding-y) var(--bs-dropdown-item-padding-x);clear:both;font-weight:400;color:var(--bs-dropdown-link-color);text-align:inherit;text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;white-space:nowrap;background-color:transparent;border:0;border-radius:var(--bs-dropdown-item-border-radius, 0)}.dropdown-item:hover,.dropdown-item:focus{color:var(--bs-dropdown-link-hover-color);background-color:var(--bs-dropdown-link-hover-bg)}.dropdown-item.active,.dropdown-item:active{color:var(--bs-dropdown-link-active-color);text-decoration:none;background-color:var(--bs-dropdown-link-active-bg)}.dropdown-item.disabled,.dropdown-item:disabled{color:var(--bs-dropdown-link-disabled-color);pointer-events:none;background-color:transparent}.dropdown-menu.show{display:block}.dropdown-header{display:block;padding:var(--bs-dropdown-header-padding-y) var(--bs-dropdown-header-padding-x);margin-bottom:0;font-size:.875rem;color:var(--bs-dropdown-header-color);white-space:nowrap}.dropdown-item-text{display:block;padding:var(--bs-dropdown-item-padding-y) var(--bs-dropdown-item-padding-x);color:var(--bs-dropdown-link-color)}.dropdown-menu-dark{--bs-dropdown-color: #dee2e6;--bs-dropdown-bg: #333;--bs-dropdown-border-color: var(--bs-border-color-translucent);--bs-dropdown-box-shadow: ;--bs-dropdown-link-color: #dee2e6;--bs-dropdown-link-hover-color: #fff;--bs-dropdown-divider-bg: var(--bs-border-color-translucent);--bs-dropdown-link-hover-bg: rgba(255,255,255,0.15);--bs-dropdown-link-active-color: #fff;--bs-dropdown-link-active-bg: #446e9b;--bs-dropdown-link-disabled-color: #999;--bs-dropdown-header-color: #999}.btn-group,.btn-group-vertical{position:relative;display:inline-flex;vertical-align:middle}.btn-group>.btn,.btn-group-vertical>.btn{position:relative;flex:1 1 auto;-webkit-flex:1 1 auto}.btn-group>.btn-check:checked+.btn,.btn-group>.btn-check:focus+.btn,.btn-group>.btn:hover,.btn-group>.btn:focus,.btn-group>.btn:active,.btn-group>.btn.active,.btn-group-vertical>.btn-check:checked+.btn,.btn-group-vertical>.btn-check:focus+.btn,.btn-group-vertical>.btn:hover,.btn-group-vertical>.btn:focus,.btn-group-vertical>.btn:active,.btn-group-vertical>.btn.active{z-index:1}.btn-toolbar{display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;justify-content:flex-start;-webkit-justify-content:flex-start}.btn-toolbar .input-group{width:auto}.btn-group{border-radius:var(--bs-border-radius)}.btn-group>:not(.btn-check:first-child)+.btn,.btn-group>.btn-group:not(:first-child){margin-left:calc(var(--bs-border-width) * -1)}.btn-group>.btn:not(:last-child):not(.dropdown-toggle),.btn-group>.btn.dropdown-toggle-split:first-child,.btn-group>.btn-group:not(:last-child)>.btn{border-top-right-radius:0;border-bottom-right-radius:0}.btn-group>.btn:nth-child(n + 3),.btn-group>:not(.btn-check)+.btn,.btn-group>.btn-group:not(:first-child)>.btn{border-top-left-radius:0;border-bottom-left-radius:0}.dropdown-toggle-split{padding-right:.5625rem;padding-left:.5625rem}.dropdown-toggle-split::after,.dropup .dropdown-toggle-split::after,.dropend .dropdown-toggle-split::after{margin-left:0}.dropstart .dropdown-toggle-split::before{margin-right:0}.btn-sm+.dropdown-toggle-split,.btn-group-sm>.btn+.dropdown-toggle-split{padding-right:.375rem;padding-left:.375rem}.btn-lg+.dropdown-toggle-split,.btn-group-lg>.btn+.dropdown-toggle-split{padding-right:.75rem;padding-left:.75rem}.btn-group-vertical{flex-direction:column;-webkit-flex-direction:column;align-items:flex-start;-webkit-align-items:flex-start;justify-content:center;-webkit-justify-content:center}.btn-group-vertical>.btn,.btn-group-vertical>.btn-group{width:100%}.btn-group-vertical>.btn:not(:first-child),.btn-group-vertical>.btn-group:not(:first-child){margin-top:calc(var(--bs-border-width) * -1)}.btn-group-vertical>.btn:not(:last-child):not(.dropdown-toggle),.btn-group-vertical>.btn-group:not(:last-child)>.btn{border-bottom-right-radius:0;border-bottom-left-radius:0}.btn-group-vertical>.btn~.btn,.btn-group-vertical>.btn-group:not(:first-child)>.btn{border-top-left-radius:0;border-top-right-radius:0}.nav{--bs-nav-link-padding-x: 1rem;--bs-nav-link-padding-y: .5rem;--bs-nav-link-font-weight: ;--bs-nav-link-color: var(--bs-link-color);--bs-nav-link-hover-color: var(--bs-link-hover-color);--bs-nav-link-disabled-color: var(--bs-secondary-color);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding-left:0;margin-bottom:0;list-style:none}.nav-link{display:block;padding:var(--bs-nav-link-padding-y) var(--bs-nav-link-padding-x);font-size:var(--bs-nav-link-font-size);font-weight:var(--bs-nav-link-font-weight);color:var(--bs-nav-link-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background:none;border:0;transition:color 0.15s ease-in-out,background-color 0.15s ease-in-out,border-color 0.15s ease-in-out}@media (prefers-reduced-motion: reduce){.nav-link{transition:none}}.nav-link:hover,.nav-link:focus{color:var(--bs-nav-link-hover-color)}.nav-link:focus-visible{outline:0;box-shadow:0 0 0 .25rem rgba(68,110,155,0.25)}.nav-link.disabled,.nav-link:disabled{color:var(--bs-nav-link-disabled-color);pointer-events:none;cursor:default}.nav-tabs{--bs-nav-tabs-border-width: var(--bs-border-width);--bs-nav-tabs-border-color: var(--bs-border-color);--bs-nav-tabs-border-radius: var(--bs-border-radius);--bs-nav-tabs-link-hover-border-color: var(--bs-secondary-bg) var(--bs-secondary-bg) var(--bs-border-color);--bs-nav-tabs-link-active-color: var(--bs-emphasis-color);--bs-nav-tabs-link-active-bg: var(--bs-body-bg);--bs-nav-tabs-link-active-border-color: var(--bs-border-color) var(--bs-border-color) var(--bs-body-bg);border-bottom:var(--bs-nav-tabs-border-width) solid var(--bs-nav-tabs-border-color)}.nav-tabs .nav-link{margin-bottom:calc(-1 * var(--bs-nav-tabs-border-width));border:var(--bs-nav-tabs-border-width) solid transparent;border-top-left-radius:var(--bs-nav-tabs-border-radius);border-top-right-radius:var(--bs-nav-tabs-border-radius)}.nav-tabs .nav-link:hover,.nav-tabs .nav-link:focus{isolation:isolate;border-color:var(--bs-nav-tabs-link-hover-border-color)}.nav-tabs .nav-link.active,.nav-tabs .nav-item.show .nav-link{color:var(--bs-nav-tabs-link-active-color);background-color:var(--bs-nav-tabs-link-active-bg);border-color:var(--bs-nav-tabs-link-active-border-color)}.nav-tabs .dropdown-menu{margin-top:calc(-1 * var(--bs-nav-tabs-border-width));border-top-left-radius:0;border-top-right-radius:0}.nav-pills{--bs-nav-pills-border-radius: var(--bs-border-radius);--bs-nav-pills-link-active-color: #fff;--bs-nav-pills-link-active-bg: #446e9b}.nav-pills .nav-link{border-radius:var(--bs-nav-pills-border-radius)}.nav-pills .nav-link.active,.nav-pills .show>.nav-link{color:var(--bs-nav-pills-link-active-color);background-color:var(--bs-nav-pills-link-active-bg)}.nav-underline{--bs-nav-underline-gap: 1rem;--bs-nav-underline-border-width: .125rem;--bs-nav-underline-link-active-color: var(--bs-emphasis-color);gap:var(--bs-nav-underline-gap)}.nav-underline .nav-link{padding-right:0;padding-left:0;border-bottom:var(--bs-nav-underline-border-width) solid transparent}.nav-underline .nav-link:hover,.nav-underline .nav-link:focus{border-bottom-color:currentcolor}.nav-underline .nav-link.active,.nav-underline .show>.nav-link{font-weight:700;color:var(--bs-nav-underline-link-active-color);border-bottom-color:currentcolor}.nav-fill>.nav-link,.nav-fill .nav-item{flex:1 1 auto;-webkit-flex:1 1 auto;text-align:center}.nav-justified>.nav-link,.nav-justified .nav-item{flex-basis:0;-webkit-flex-basis:0;flex-grow:1;-webkit-flex-grow:1;text-align:center}.nav-fill .nav-item .nav-link,.nav-justified .nav-item .nav-link{width:100%}.tab-content>.tab-pane{display:none}.tab-content>.active{display:block}.navbar{--bs-navbar-padding-x: 0;--bs-navbar-padding-y: .5rem;--bs-navbar-color: rgba(0,0,0,0.65);--bs-navbar-hover-color: #3399f3;--bs-navbar-disabled-color: rgba(0,0,0,0.3);--bs-navbar-active-color: #3399f3;--bs-navbar-brand-padding-y: .3125rem;--bs-navbar-brand-margin-end: 1rem;--bs-navbar-brand-font-size: 1.25rem;--bs-navbar-brand-color: #3399f3;--bs-navbar-brand-hover-color: #3399f3;--bs-navbar-nav-link-padding-x: .5rem;--bs-navbar-toggler-padding-y: .25rem;--bs-navbar-toggler-padding-x: .75rem;--bs-navbar-toggler-font-size: 1.25rem;--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='rgba%280,0,0,0.75%29' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e");--bs-navbar-toggler-border-color: rgba(0,0,0,0.15);--bs-navbar-toggler-border-radius: var(--bs-border-radius);--bs-navbar-toggler-focus-width: .25rem;--bs-navbar-toggler-transition: box-shadow 0.15s ease-in-out;position:relative;display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;align-items:center;-webkit-align-items:center;justify-content:space-between;-webkit-justify-content:space-between;padding:var(--bs-navbar-padding-y) var(--bs-navbar-padding-x)}.navbar>.container,.navbar>.container-fluid,.navbar>.container-sm,.navbar>.container-md,.navbar>.container-lg,.navbar>.container-xl,.navbar>.container-xxl{display:flex;display:-webkit-flex;flex-wrap:inherit;-webkit-flex-wrap:inherit;align-items:center;-webkit-align-items:center;justify-content:space-between;-webkit-justify-content:space-between}.navbar-brand{padding-top:var(--bs-navbar-brand-padding-y);padding-bottom:var(--bs-navbar-brand-padding-y);margin-right:var(--bs-navbar-brand-margin-end);font-size:var(--bs-navbar-brand-font-size);color:var(--bs-navbar-brand-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;white-space:nowrap}.navbar-brand:hover,.navbar-brand:focus{color:var(--bs-navbar-brand-hover-color)}.navbar-nav{--bs-nav-link-padding-x: 0;--bs-nav-link-padding-y: .5rem;--bs-nav-link-font-weight: ;--bs-nav-link-color: var(--bs-navbar-color);--bs-nav-link-hover-color: var(--bs-navbar-hover-color);--bs-nav-link-disabled-color: var(--bs-navbar-disabled-color);display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0;list-style:none}.navbar-nav .nav-link.active,.navbar-nav .nav-link.show{color:var(--bs-navbar-active-color)}.navbar-nav .dropdown-menu{position:static}.navbar-text{padding-top:.5rem;padding-bottom:.5rem;color:var(--bs-navbar-color)}.navbar-text a,.navbar-text a:hover,.navbar-text a:focus{color:var(--bs-navbar-active-color)}.navbar-collapse{flex-basis:100%;-webkit-flex-basis:100%;flex-grow:1;-webkit-flex-grow:1;align-items:center;-webkit-align-items:center}.navbar-toggler{padding:var(--bs-navbar-toggler-padding-y) var(--bs-navbar-toggler-padding-x);font-size:var(--bs-navbar-toggler-font-size);line-height:1;color:var(--bs-navbar-color);background-color:transparent;border:var(--bs-border-width) solid var(--bs-navbar-toggler-border-color);border-radius:var(--bs-navbar-toggler-border-radius);transition:var(--bs-navbar-toggler-transition)}@media (prefers-reduced-motion: reduce){.navbar-toggler{transition:none}}.navbar-toggler:hover{text-decoration:none}.navbar-toggler:focus{text-decoration:none;outline:0;box-shadow:0 0 0 var(--bs-navbar-toggler-focus-width)}.navbar-toggler-icon{display:inline-block;width:1.5em;height:1.5em;vertical-align:middle;background-image:var(--bs-navbar-toggler-icon-bg);background-repeat:no-repeat;background-position:center;background-size:100%}.navbar-nav-scroll{max-height:var(--bs-scroll-height, 75vh);overflow-y:auto}@media (min-width: 576px){.navbar-expand-sm{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-sm .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-sm .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-sm .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-sm .navbar-nav-scroll{overflow:visible}.navbar-expand-sm .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-sm .navbar-toggler{display:none}.navbar-expand-sm .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:transparent !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-sm .offcanvas .offcanvas-header{display:none}.navbar-expand-sm .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}@media (min-width: 768px){.navbar-expand-md{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-md .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-md .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-md .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-md .navbar-nav-scroll{overflow:visible}.navbar-expand-md .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-md .navbar-toggler{display:none}.navbar-expand-md .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:transparent !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-md .offcanvas .offcanvas-header{display:none}.navbar-expand-md .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}@media (min-width: 992px){.navbar-expand-lg{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-lg .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-lg .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-lg .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-lg .navbar-nav-scroll{overflow:visible}.navbar-expand-lg .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-lg .navbar-toggler{display:none}.navbar-expand-lg .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:transparent !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-lg .offcanvas .offcanvas-header{display:none}.navbar-expand-lg .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}@media (min-width: 1200px){.navbar-expand-xl{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-xl .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-xl .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-xl .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-xl .navbar-nav-scroll{overflow:visible}.navbar-expand-xl .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-xl .navbar-toggler{display:none}.navbar-expand-xl .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:transparent !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-xl .offcanvas .offcanvas-header{display:none}.navbar-expand-xl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}@media (min-width: 1400px){.navbar-expand-xxl{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-xxl .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-xxl .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-xxl .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-xxl .navbar-nav-scroll{overflow:visible}.navbar-expand-xxl .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-xxl .navbar-toggler{display:none}.navbar-expand-xxl .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:transparent !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-xxl .offcanvas .offcanvas-header{display:none}.navbar-expand-xxl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}.navbar-expand{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand .navbar-nav .dropdown-menu{position:absolute}.navbar-expand .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand .navbar-nav-scroll{overflow:visible}.navbar-expand .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand .navbar-toggler{display:none}.navbar-expand .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:transparent !important;border:0 !important;transform:none !important;transition:none}.navbar-expand .offcanvas .offcanvas-header{display:none}.navbar-expand .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}.navbar-dark,.navbar[data-bs-theme="dark"]{--bs-navbar-color: rgba(255,255,255,0.55);--bs-navbar-hover-color: rgba(255,255,255,0.75);--bs-navbar-disabled-color: rgba(255,255,255,0.25);--bs-navbar-active-color: #fff;--bs-navbar-brand-color: #fff;--bs-navbar-brand-hover-color: #fff;--bs-navbar-toggler-border-color: rgba(255,255,255,0.1);--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='rgba%28255,255,255,0.75%29' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}[data-bs-theme="dark"] .navbar-toggler-icon{--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='rgba%28255,255,255,0.75%29' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}.card{--bs-card-spacer-y: 1rem;--bs-card-spacer-x: 1rem;--bs-card-title-spacer-y: .5rem;--bs-card-title-color: ;--bs-card-subtitle-color: ;--bs-card-border-width: var(--bs-border-width);--bs-card-border-color: var(--bs-border-color-translucent);--bs-card-border-radius: var(--bs-border-radius);--bs-card-box-shadow: ;--bs-card-inner-border-radius: calc(var(--bs-border-radius) - (var(--bs-border-width)));--bs-card-cap-padding-y: .5rem;--bs-card-cap-padding-x: 1rem;--bs-card-cap-bg: rgba(var(--bs-body-color-rgb), 0.03);--bs-card-cap-color: ;--bs-card-height: ;--bs-card-color: ;--bs-card-bg: var(--bs-body-bg);--bs-card-img-overlay-padding: 1rem;--bs-card-group-margin: .75rem;position:relative;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;min-width:0;height:var(--bs-card-height);color:var(--bs-body-color);word-wrap:break-word;background-color:var(--bs-card-bg);background-clip:border-box;border:var(--bs-card-border-width) solid var(--bs-card-border-color);border-radius:var(--bs-card-border-radius)}.card>hr{margin-right:0;margin-left:0}.card>.list-group{border-top:inherit;border-bottom:inherit}.card>.list-group:first-child{border-top-width:0;border-top-left-radius:var(--bs-card-inner-border-radius);border-top-right-radius:var(--bs-card-inner-border-radius)}.card>.list-group:last-child{border-bottom-width:0;border-bottom-right-radius:var(--bs-card-inner-border-radius);border-bottom-left-radius:var(--bs-card-inner-border-radius)}.card>.card-header+.list-group,.card>.list-group+.card-footer{border-top:0}.card-body{flex:1 1 auto;-webkit-flex:1 1 auto;padding:var(--bs-card-spacer-y) var(--bs-card-spacer-x);color:var(--bs-card-color)}.card-title{margin-bottom:var(--bs-card-title-spacer-y);color:var(--bs-card-title-color)}.card-subtitle{margin-top:calc(-.5 * var(--bs-card-title-spacer-y));margin-bottom:0;color:var(--bs-card-subtitle-color)}.card-text:last-child{margin-bottom:0}.card-link+.card-link{margin-left:var(--bs-card-spacer-x)}.card-header{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);margin-bottom:0;color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-bottom:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-header:first-child{border-radius:var(--bs-card-inner-border-radius) var(--bs-card-inner-border-radius) 0 0}.card-footer{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-top:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-footer:last-child{border-radius:0 0 var(--bs-card-inner-border-radius) var(--bs-card-inner-border-radius)}.card-header-tabs{margin-right:calc(-.5 * var(--bs-card-cap-padding-x));margin-bottom:calc(-1 * var(--bs-card-cap-padding-y));margin-left:calc(-.5 * var(--bs-card-cap-padding-x));border-bottom:0}.card-header-tabs .nav-link.active{background-color:var(--bs-card-bg);border-bottom-color:var(--bs-card-bg)}.card-header-pills{margin-right:calc(-.5 * var(--bs-card-cap-padding-x));margin-left:calc(-.5 * var(--bs-card-cap-padding-x))}.card-img-overlay{position:absolute;top:0;right:0;bottom:0;left:0;padding:var(--bs-card-img-overlay-padding);border-radius:var(--bs-card-inner-border-radius)}.card-img,.card-img-top,.card-img-bottom{width:100%}.card-img,.card-img-top{border-top-left-radius:var(--bs-card-inner-border-radius);border-top-right-radius:var(--bs-card-inner-border-radius)}.card-img,.card-img-bottom{border-bottom-right-radius:var(--bs-card-inner-border-radius);border-bottom-left-radius:var(--bs-card-inner-border-radius)}.card-group>.card{margin-bottom:var(--bs-card-group-margin)}@media (min-width: 576px){.card-group{display:flex;display:-webkit-flex;flex-flow:row wrap;-webkit-flex-flow:row wrap}.card-group>.card{flex:1 0 0%;-webkit-flex:1 0 0%;margin-bottom:0}.card-group>.card+.card{margin-left:0;border-left:0}.card-group>.card:not(:last-child){border-top-right-radius:0;border-bottom-right-radius:0}.card-group>.card:not(:last-child) .card-img-top,.card-group>.card:not(:last-child) .card-header{border-top-right-radius:0}.card-group>.card:not(:last-child) .card-img-bottom,.card-group>.card:not(:last-child) .card-footer{border-bottom-right-radius:0}.card-group>.card:not(:first-child){border-top-left-radius:0;border-bottom-left-radius:0}.card-group>.card:not(:first-child) .card-img-top,.card-group>.card:not(:first-child) .card-header{border-top-left-radius:0}.card-group>.card:not(:first-child) .card-img-bottom,.card-group>.card:not(:first-child) .card-footer{border-bottom-left-radius:0}}.accordion{--bs-accordion-color: var(--bs-body-color);--bs-accordion-bg: var(--bs-body-bg);--bs-accordion-transition: color 0.15s ease-in-out,background-color 0.15s ease-in-out,border-color 0.15s ease-in-out,box-shadow 0.15s ease-in-out,border-radius 0.15s ease;--bs-accordion-border-color: var(--bs-border-color);--bs-accordion-border-width: var(--bs-border-width);--bs-accordion-border-radius: var(--bs-border-radius);--bs-accordion-inner-border-radius: calc(var(--bs-border-radius) - (var(--bs-border-width)));--bs-accordion-btn-padding-x: 1.25rem;--bs-accordion-btn-padding-y: 1rem;--bs-accordion-btn-color: var(--bs-body-color);--bs-accordion-btn-bg: var(--bs-accordion-bg);--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23777'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-icon-width: 1.25rem;--bs-accordion-btn-icon-transform: rotate(-180deg);--bs-accordion-btn-icon-transition: transform 0.2s ease-in-out;--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%231b2c3e'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-focus-border-color: #a2b7cd;--bs-accordion-btn-focus-box-shadow: 0 0 0 .25rem rgba(68,110,155,0.25);--bs-accordion-body-padding-x: 1.25rem;--bs-accordion-body-padding-y: 1rem;--bs-accordion-active-color: var(--bs-primary-text-emphasis);--bs-accordion-active-bg: var(--bs-primary-bg-subtle)}.accordion-button{position:relative;display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;width:100%;padding:var(--bs-accordion-btn-padding-y) var(--bs-accordion-btn-padding-x);font-size:1rem;color:var(--bs-accordion-btn-color);text-align:left;background-color:var(--bs-accordion-btn-bg);border:0;border-radius:0;overflow-anchor:none;transition:var(--bs-accordion-transition)}@media (prefers-reduced-motion: reduce){.accordion-button{transition:none}}.accordion-button:not(.collapsed){color:var(--bs-accordion-active-color);background-color:var(--bs-accordion-active-bg);box-shadow:inset 0 calc(-1 * var(--bs-accordion-border-width)) 0 var(--bs-accordion-border-color)}.accordion-button:not(.collapsed)::after{background-image:var(--bs-accordion-btn-active-icon);transform:var(--bs-accordion-btn-icon-transform)}.accordion-button::after{flex-shrink:0;-webkit-flex-shrink:0;width:var(--bs-accordion-btn-icon-width);height:var(--bs-accordion-btn-icon-width);margin-left:auto;content:"";background-image:var(--bs-accordion-btn-icon);background-repeat:no-repeat;background-size:var(--bs-accordion-btn-icon-width);transition:var(--bs-accordion-btn-icon-transition)}@media (prefers-reduced-motion: reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:first-of-type{border-top-left-radius:var(--bs-accordion-border-radius);border-top-right-radius:var(--bs-accordion-border-radius)}.accordion-item:first-of-type .accordion-button{border-top-left-radius:var(--bs-accordion-inner-border-radius);border-top-right-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:not(:first-of-type){border-top:0}.accordion-item:last-of-type{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-item:last-of-type .accordion-button.collapsed{border-bottom-right-radius:var(--bs-accordion-inner-border-radius);border-bottom-left-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:last-of-type .accordion-collapse{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0;border-radius:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}.accordion-flush .accordion-item .accordion-button,.accordion-flush .accordion-item .accordion-button.collapsed{border-radius:0}[data-bs-theme="dark"] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%238fa8c3'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%238fa8c3'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e")}.breadcrumb{--bs-breadcrumb-padding-x: 0;--bs-breadcrumb-padding-y: 0;--bs-breadcrumb-margin-bottom: 1rem;--bs-breadcrumb-bg: ;--bs-breadcrumb-border-radius: ;--bs-breadcrumb-divider-color: var(--bs-secondary-color);--bs-breadcrumb-item-padding-x: .5rem;--bs-breadcrumb-item-active-color: var(--bs-secondary-color);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg);border-radius:var(--bs-breadcrumb-border-radius)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, "/") /* rtl: var(--bs-breadcrumb-divider, "/") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: .75rem;--bs-pagination-padding-y: .375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: var(--bs-link-color);--bs-pagination-bg: var(--bs-body-bg);--bs-pagination-border-width: var(--bs-border-width);--bs-pagination-border-color: var(--bs-border-color);--bs-pagination-border-radius: var(--bs-border-radius);--bs-pagination-hover-color: var(--bs-link-hover-color);--bs-pagination-hover-bg: var(--bs-tertiary-bg);--bs-pagination-hover-border-color: var(--bs-border-color);--bs-pagination-focus-color: var(--bs-link-hover-color);--bs-pagination-focus-bg: var(--bs-secondary-bg);--bs-pagination-focus-box-shadow: 0 0 0 .25rem rgba(68,110,155,0.25);--bs-pagination-active-color: #fff;--bs-pagination-active-bg: #446e9b;--bs-pagination-active-border-color: #446e9b;--bs-pagination-disabled-color: var(--bs-secondary-color);--bs-pagination-disabled-bg: var(--bs-secondary-bg);--bs-pagination-disabled-border-color: var(--bs-border-color);display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color 0.15s ease-in-out,background-color 0.15s ease-in-out,border-color 0.15s ease-in-out,box-shadow 0.15s ease-in-out}@media (prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(var(--bs-border-width) * -1)}.page-item:first-child .page-link{border-top-left-radius:var(--bs-pagination-border-radius);border-bottom-left-radius:var(--bs-pagination-border-radius)}.page-item:last-child .page-link{border-top-right-radius:var(--bs-pagination-border-radius);border-bottom-right-radius:var(--bs-pagination-border-radius)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: .75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: var(--bs-border-radius-lg)}.pagination-sm{--bs-pagination-padding-x: .5rem;--bs-pagination-padding-y: .25rem;--bs-pagination-font-size:.875rem;--bs-pagination-border-radius: var(--bs-border-radius-sm)}.badge{--bs-badge-padding-x: .65em;--bs-badge-padding-y: .35em;--bs-badge-font-size:.75em;--bs-badge-font-weight: 700;--bs-badge-color: #fff;--bs-badge-border-radius: var(--bs-border-radius);display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline;border-radius:var(--bs-badge-border-radius)}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: var(--bs-border-width) solid var(--bs-alert-border-color);--bs-alert-border-radius: var(--bs-border-radius);--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border);border-radius:var(--bs-alert-border-radius)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:1rem}}.progress,.progress-stacked{--bs-progress-height: 1rem;--bs-progress-font-size:.75rem;--bs-progress-bg: var(--bs-secondary-bg);--bs-progress-border-radius: var(--bs-border-radius);--bs-progress-box-shadow: var(--bs-box-shadow-inset);--bs-progress-bar-color: #fff;--bs-progress-bar-bg: #446e9b;--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg);border-radius:var(--bs-progress-border-radius)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media (prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media (prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: var(--bs-body-color);--bs-list-group-bg: var(--bs-body-bg);--bs-list-group-border-color: var(--bs-border-color);--bs-list-group-border-width: var(--bs-border-width);--bs-list-group-border-radius: var(--bs-border-radius);--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: .5rem;--bs-list-group-action-color: var(--bs-secondary-color);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-tertiary-bg);--bs-list-group-action-active-color: var(--bs-body-color);--bs-list-group-action-active-bg: var(--bs-secondary-bg);--bs-list-group-disabled-color: var(--bs-secondary-color);--bs-list-group-disabled-bg: var(--bs-body-bg);--bs-list-group-active-color: #fff;--bs-list-group-active-bg: #446e9b;--bs-list-group-active-border-color: #446e9b;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0;border-radius:var(--bs-list-group-border-radius)}.list-group-numbered{list-style-type:none;counter-reset:section}.list-group-numbered>.list-group-item::before{content:counters(section, ".") ". ";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:hover,.list-group-item-action:focus{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid var(--bs-list-group-border-color)}.list-group-item:first-child{border-top-left-radius:inherit;border-top-right-radius:inherit}.list-group-item:last-child{border-bottom-right-radius:inherit;border-bottom-left-radius:inherit}.list-group-item.disabled,.list-group-item:disabled{color:var(--bs-list-group-disabled-color);pointer-events:none;background-color:var(--bs-list-group-disabled-bg)}.list-group-item.active{z-index:2;color:var(--bs-list-group-active-color);background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color)}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{margin-top:calc(-1 * var(--bs-list-group-border-width));border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}@media (min-width: 576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-sm>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 768px){.list-group-horizontal-md{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-md>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-md>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-lg>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 1400px){.list-group-horizontal-xxl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xxl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xxl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xxl>.list-group-item.active{margin-top:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}.list-group-flush{border-radius:0}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{--bs-list-group-color: var(--bs-default-text-emphasis);--bs-list-group-bg: var(--bs-default-bg-subtle);--bs-list-group-border-color: var(--bs-default-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-default-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #000;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23000'%3e%3cpath d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/%3e%3c/svg%3e");--bs-btn-close-opacity: .5;--bs-btn-close-hover-opacity: .75;--bs-btn-close-focus-shadow: 0 0 0 .25rem rgba(68,110,155,0.25);--bs-btn-close-focus-opacity: 1;--bs-btn-close-disabled-opacity: .25;--bs-btn-close-white-filter: invert(1) grayscale(100%) brightness(200%);box-sizing:content-box;width:1em;height:1em;padding:.25em .25em;color:var(--bs-btn-close-color);background:transparent var(--bs-btn-close-bg) center/1em auto no-repeat;border:0;border-radius:.375rem;opacity:var(--bs-btn-close-opacity)}.btn-close:hover{color:var(--bs-btn-close-color);text-decoration:none;opacity:var(--bs-btn-close-hover-opacity)}.btn-close:focus{outline:0;box-shadow:var(--bs-btn-close-focus-shadow);opacity:var(--bs-btn-close-focus-opacity)}.btn-close:disabled,.btn-close.disabled{pointer-events:none;user-select:none;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;-o-user-select:none;opacity:var(--bs-btn-close-disabled-opacity)}.btn-close-white{filter:var(--bs-btn-close-white-filter)}[data-bs-theme="dark"] .btn-close{filter:var(--bs-btn-close-white-filter)}.toast{--bs-toast-zindex: 1090;--bs-toast-padding-x: .75rem;--bs-toast-padding-y: .5rem;--bs-toast-spacing: 1.5rem;--bs-toast-max-width: 350px;--bs-toast-font-size:.875rem;--bs-toast-color: ;--bs-toast-bg: rgba(var(--bs-body-bg-rgb), 0.85);--bs-toast-border-width: var(--bs-border-width);--bs-toast-border-color: var(--bs-border-color-translucent);--bs-toast-border-radius: var(--bs-border-radius);--bs-toast-box-shadow: var(--bs-box-shadow);--bs-toast-header-color: var(--bs-secondary-color);--bs-toast-header-bg: rgba(var(--bs-body-bg-rgb), 0.85);--bs-toast-header-border-color: var(--bs-border-color-translucent);width:var(--bs-toast-max-width);max-width:100%;font-size:var(--bs-toast-font-size);color:var(--bs-toast-color);pointer-events:auto;background-color:var(--bs-toast-bg);background-clip:padding-box;border:var(--bs-toast-border-width) solid var(--bs-toast-border-color);box-shadow:var(--bs-toast-box-shadow);border-radius:var(--bs-toast-border-radius)}.toast.showing{opacity:0}.toast:not(.show){display:none}.toast-container{--bs-toast-zindex: 1090;position:absolute;z-index:var(--bs-toast-zindex);width:max-content;width:-webkit-max-content;width:-moz-max-content;width:-ms-max-content;width:-o-max-content;max-width:100%;pointer-events:none}.toast-container>:not(:last-child){margin-bottom:var(--bs-toast-spacing)}.toast-header{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;padding:var(--bs-toast-padding-y) var(--bs-toast-padding-x);color:var(--bs-toast-header-color);background-color:var(--bs-toast-header-bg);background-clip:padding-box;border-bottom:var(--bs-toast-border-width) solid var(--bs-toast-header-border-color);border-top-left-radius:calc(var(--bs-toast-border-radius) - var(--bs-toast-border-width));border-top-right-radius:calc(var(--bs-toast-border-radius) - var(--bs-toast-border-width))}.toast-header .btn-close{margin-right:calc(-.5 * var(--bs-toast-padding-x));margin-left:var(--bs-toast-padding-x)}.toast-body{padding:var(--bs-toast-padding-x);word-wrap:break-word}.modal{--bs-modal-zindex: 1055;--bs-modal-width: 500px;--bs-modal-padding: 1rem;--bs-modal-margin: .5rem;--bs-modal-color: ;--bs-modal-bg: var(--bs-body-bg);--bs-modal-border-color: var(--bs-border-color-translucent);--bs-modal-border-width: var(--bs-border-width);--bs-modal-border-radius: var(--bs-border-radius-lg);--bs-modal-box-shadow: 0 0.125rem 0.25rem rgba(0,0,0,0.075);--bs-modal-inner-border-radius: calc(var(--bs-border-radius-lg) - (var(--bs-border-width)));--bs-modal-header-padding-x: 1rem;--bs-modal-header-padding-y: 1rem;--bs-modal-header-padding: 1rem 1rem;--bs-modal-header-border-color: var(--bs-border-color);--bs-modal-header-border-width: var(--bs-border-width);--bs-modal-title-line-height: 1.5;--bs-modal-footer-gap: .5rem;--bs-modal-footer-bg: ;--bs-modal-footer-border-color: var(--bs-border-color);--bs-modal-footer-border-width: var(--bs-border-width);position:fixed;top:0;left:0;z-index:var(--bs-modal-zindex);display:none;width:100%;height:100%;overflow-x:hidden;overflow-y:auto;outline:0}.modal-dialog{position:relative;width:auto;margin:var(--bs-modal-margin);pointer-events:none}.modal.fade .modal-dialog{transition:transform 0.3s ease-out;transform:translate(0, -50px)}@media (prefers-reduced-motion: reduce){.modal.fade .modal-dialog{transition:none}}.modal.show .modal-dialog{transform:none}.modal.modal-static .modal-dialog{transform:scale(1.02)}.modal-dialog-scrollable{height:calc(100% - var(--bs-modal-margin) * 2)}.modal-dialog-scrollable .modal-content{max-height:100%;overflow:hidden}.modal-dialog-scrollable .modal-body{overflow-y:auto}.modal-dialog-centered{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;min-height:calc(100% - var(--bs-modal-margin) * 2)}.modal-content{position:relative;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;width:100%;color:var(--bs-modal-color);pointer-events:auto;background-color:var(--bs-modal-bg);background-clip:padding-box;border:var(--bs-modal-border-width) solid var(--bs-modal-border-color);border-radius:var(--bs-modal-border-radius);outline:0}.modal-backdrop{--bs-backdrop-zindex: 1050;--bs-backdrop-bg: #000;--bs-backdrop-opacity: .5;position:fixed;top:0;left:0;z-index:var(--bs-backdrop-zindex);width:100vw;height:100vh;background-color:var(--bs-backdrop-bg)}.modal-backdrop.fade{opacity:0}.modal-backdrop.show{opacity:var(--bs-backdrop-opacity)}.modal-header{display:flex;display:-webkit-flex;flex-shrink:0;-webkit-flex-shrink:0;align-items:center;-webkit-align-items:center;justify-content:space-between;-webkit-justify-content:space-between;padding:var(--bs-modal-header-padding);border-bottom:var(--bs-modal-header-border-width) solid var(--bs-modal-header-border-color);border-top-left-radius:var(--bs-modal-inner-border-radius);border-top-right-radius:var(--bs-modal-inner-border-radius)}.modal-header .btn-close{padding:calc(var(--bs-modal-header-padding-y) * .5) calc(var(--bs-modal-header-padding-x) * .5);margin:calc(-.5 * var(--bs-modal-header-padding-y)) calc(-.5 * var(--bs-modal-header-padding-x)) calc(-.5 * var(--bs-modal-header-padding-y)) auto}.modal-title{margin-bottom:0;line-height:var(--bs-modal-title-line-height)}.modal-body{position:relative;flex:1 1 auto;-webkit-flex:1 1 auto;padding:var(--bs-modal-padding)}.modal-footer{display:flex;display:-webkit-flex;flex-shrink:0;-webkit-flex-shrink:0;flex-wrap:wrap;-webkit-flex-wrap:wrap;align-items:center;-webkit-align-items:center;justify-content:flex-end;-webkit-justify-content:flex-end;padding:calc(var(--bs-modal-padding) - var(--bs-modal-footer-gap) * .5);background-color:var(--bs-modal-footer-bg);border-top:var(--bs-modal-footer-border-width) solid var(--bs-modal-footer-border-color);border-bottom-right-radius:var(--bs-modal-inner-border-radius);border-bottom-left-radius:var(--bs-modal-inner-border-radius)}.modal-footer>*{margin:calc(var(--bs-modal-footer-gap) * .5)}@media (min-width: 576px){.modal{--bs-modal-margin: 1.75rem;--bs-modal-box-shadow: 0 0.5rem 1rem rgba(0,0,0,0.15)}.modal-dialog{max-width:var(--bs-modal-width);margin-right:auto;margin-left:auto}.modal-sm{--bs-modal-width: 300px}}@media (min-width: 992px){.modal-lg,.modal-xl{--bs-modal-width: 800px}}@media (min-width: 1200px){.modal-xl{--bs-modal-width: 1140px}}.modal-fullscreen{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen .modal-header,.modal-fullscreen .modal-footer{border-radius:0}.modal-fullscreen .modal-body{overflow-y:auto}@media (max-width: 575.98px){.modal-fullscreen-sm-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-sm-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-sm-down .modal-header,.modal-fullscreen-sm-down .modal-footer{border-radius:0}.modal-fullscreen-sm-down .modal-body{overflow-y:auto}}@media (max-width: 767.98px){.modal-fullscreen-md-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-md-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-md-down .modal-header,.modal-fullscreen-md-down .modal-footer{border-radius:0}.modal-fullscreen-md-down .modal-body{overflow-y:auto}}@media (max-width: 991.98px){.modal-fullscreen-lg-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-lg-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-lg-down .modal-header,.modal-fullscreen-lg-down .modal-footer{border-radius:0}.modal-fullscreen-lg-down .modal-body{overflow-y:auto}}@media (max-width: 1199.98px){.modal-fullscreen-xl-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-xl-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-xl-down .modal-header,.modal-fullscreen-xl-down .modal-footer{border-radius:0}.modal-fullscreen-xl-down .modal-body{overflow-y:auto}}@media (max-width: 1399.98px){.modal-fullscreen-xxl-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-xxl-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-xxl-down .modal-header,.modal-fullscreen-xxl-down .modal-footer{border-radius:0}.modal-fullscreen-xxl-down .modal-body{overflow-y:auto}}.tooltip{--bs-tooltip-zindex: 1080;--bs-tooltip-max-width: 200px;--bs-tooltip-padding-x: .5rem;--bs-tooltip-padding-y: .25rem;--bs-tooltip-margin: ;--bs-tooltip-font-size:.875rem;--bs-tooltip-color: var(--bs-body-bg);--bs-tooltip-bg: var(--bs-emphasis-color);--bs-tooltip-border-radius: var(--bs-border-radius);--bs-tooltip-opacity: .9;--bs-tooltip-arrow-width: .8rem;--bs-tooltip-arrow-height: .4rem;z-index:var(--bs-tooltip-zindex);display:block;margin:var(--bs-tooltip-margin);font-family:var(--bs-font-sans-serif);font-style:normal;font-weight:400;line-height:1.5;text-align:left;text-align:start;text-decoration:none;text-shadow:none;text-transform:none;letter-spacing:normal;word-break:normal;white-space:normal;word-spacing:normal;line-break:auto;font-size:var(--bs-tooltip-font-size);word-wrap:break-word;opacity:0}.tooltip.show{opacity:var(--bs-tooltip-opacity)}.tooltip .tooltip-arrow{display:block;width:var(--bs-tooltip-arrow-width);height:var(--bs-tooltip-arrow-height)}.tooltip .tooltip-arrow::before{position:absolute;content:"";border-color:transparent;border-style:solid}.bs-tooltip-top .tooltip-arrow,.bs-tooltip-auto[data-popper-placement^="top"] .tooltip-arrow{bottom:calc(-1 * var(--bs-tooltip-arrow-height))}.bs-tooltip-top .tooltip-arrow::before,.bs-tooltip-auto[data-popper-placement^="top"] .tooltip-arrow::before{top:-1px;border-width:var(--bs-tooltip-arrow-height) calc(var(--bs-tooltip-arrow-width) * .5) 0;border-top-color:var(--bs-tooltip-bg)}.bs-tooltip-end .tooltip-arrow,.bs-tooltip-auto[data-popper-placement^="right"] .tooltip-arrow{left:calc(-1 * var(--bs-tooltip-arrow-height));width:var(--bs-tooltip-arrow-height);height:var(--bs-tooltip-arrow-width)}.bs-tooltip-end .tooltip-arrow::before,.bs-tooltip-auto[data-popper-placement^="right"] .tooltip-arrow::before{right:-1px;border-width:calc(var(--bs-tooltip-arrow-width) * .5) var(--bs-tooltip-arrow-height) calc(var(--bs-tooltip-arrow-width) * .5) 0;border-right-color:var(--bs-tooltip-bg)}.bs-tooltip-bottom .tooltip-arrow,.bs-tooltip-auto[data-popper-placement^="bottom"] .tooltip-arrow{top:calc(-1 * var(--bs-tooltip-arrow-height))}.bs-tooltip-bottom .tooltip-arrow::before,.bs-tooltip-auto[data-popper-placement^="bottom"] .tooltip-arrow::before{bottom:-1px;border-width:0 calc(var(--bs-tooltip-arrow-width) * .5) var(--bs-tooltip-arrow-height);border-bottom-color:var(--bs-tooltip-bg)}.bs-tooltip-start .tooltip-arrow,.bs-tooltip-auto[data-popper-placement^="left"] .tooltip-arrow{right:calc(-1 * var(--bs-tooltip-arrow-height));width:var(--bs-tooltip-arrow-height);height:var(--bs-tooltip-arrow-width)}.bs-tooltip-start .tooltip-arrow::before,.bs-tooltip-auto[data-popper-placement^="left"] .tooltip-arrow::before{left:-1px;border-width:calc(var(--bs-tooltip-arrow-width) * .5) 0 calc(var(--bs-tooltip-arrow-width) * .5) var(--bs-tooltip-arrow-height);border-left-color:var(--bs-tooltip-bg)}.tooltip-inner{max-width:var(--bs-tooltip-max-width);padding:var(--bs-tooltip-padding-y) var(--bs-tooltip-padding-x);color:var(--bs-tooltip-color);text-align:center;background-color:var(--bs-tooltip-bg);border-radius:var(--bs-tooltip-border-radius)}.popover{--bs-popover-zindex: 1070;--bs-popover-max-width: 276px;--bs-popover-font-size:.875rem;--bs-popover-bg: var(--bs-body-bg);--bs-popover-border-width: var(--bs-border-width);--bs-popover-border-color: var(--bs-border-color-translucent);--bs-popover-border-radius: var(--bs-border-radius-lg);--bs-popover-inner-border-radius: calc(var(--bs-border-radius-lg) - var(--bs-border-width));--bs-popover-box-shadow: 0 0.5rem 1rem rgba(0,0,0,0.15);--bs-popover-header-padding-x: 1rem;--bs-popover-header-padding-y: .5rem;--bs-popover-header-font-size:1rem;--bs-popover-header-color: #2d2d2d;--bs-popover-header-bg: var(--bs-secondary-bg);--bs-popover-body-padding-x: 1rem;--bs-popover-body-padding-y: 1rem;--bs-popover-body-color: var(--bs-body-color);--bs-popover-arrow-width: 1rem;--bs-popover-arrow-height: .5rem;--bs-popover-arrow-border: var(--bs-popover-border-color);z-index:var(--bs-popover-zindex);display:block;max-width:var(--bs-popover-max-width);font-family:var(--bs-font-sans-serif);font-style:normal;font-weight:400;line-height:1.5;text-align:left;text-align:start;text-decoration:none;text-shadow:none;text-transform:none;letter-spacing:normal;word-break:normal;white-space:normal;word-spacing:normal;line-break:auto;font-size:var(--bs-popover-font-size);word-wrap:break-word;background-color:var(--bs-popover-bg);background-clip:padding-box;border:var(--bs-popover-border-width) solid var(--bs-popover-border-color);border-radius:var(--bs-popover-border-radius)}.popover .popover-arrow{display:block;width:var(--bs-popover-arrow-width);height:var(--bs-popover-arrow-height)}.popover .popover-arrow::before,.popover .popover-arrow::after{position:absolute;display:block;content:"";border-color:transparent;border-style:solid;border-width:0}.bs-popover-top>.popover-arrow,.bs-popover-auto[data-popper-placement^="top"]>.popover-arrow{bottom:calc(-1 * (var(--bs-popover-arrow-height)) - var(--bs-popover-border-width))}.bs-popover-top>.popover-arrow::before,.bs-popover-auto[data-popper-placement^="top"]>.popover-arrow::before,.bs-popover-top>.popover-arrow::after,.bs-popover-auto[data-popper-placement^="top"]>.popover-arrow::after{border-width:var(--bs-popover-arrow-height) calc(var(--bs-popover-arrow-width) * .5) 0}.bs-popover-top>.popover-arrow::before,.bs-popover-auto[data-popper-placement^="top"]>.popover-arrow::before{bottom:0;border-top-color:var(--bs-popover-arrow-border)}.bs-popover-top>.popover-arrow::after,.bs-popover-auto[data-popper-placement^="top"]>.popover-arrow::after{bottom:var(--bs-popover-border-width);border-top-color:var(--bs-popover-bg)}.bs-popover-end>.popover-arrow,.bs-popover-auto[data-popper-placement^="right"]>.popover-arrow{left:calc(-1 * (var(--bs-popover-arrow-height)) - var(--bs-popover-border-width));width:var(--bs-popover-arrow-height);height:var(--bs-popover-arrow-width)}.bs-popover-end>.popover-arrow::before,.bs-popover-auto[data-popper-placement^="right"]>.popover-arrow::before,.bs-popover-end>.popover-arrow::after,.bs-popover-auto[data-popper-placement^="right"]>.popover-arrow::after{border-width:calc(var(--bs-popover-arrow-width) * .5) var(--bs-popover-arrow-height) calc(var(--bs-popover-arrow-width) * .5) 0}.bs-popover-end>.popover-arrow::before,.bs-popover-auto[data-popper-placement^="right"]>.popover-arrow::before{left:0;border-right-color:var(--bs-popover-arrow-border)}.bs-popover-end>.popover-arrow::after,.bs-popover-auto[data-popper-placement^="right"]>.popover-arrow::after{left:var(--bs-popover-border-width);border-right-color:var(--bs-popover-bg)}.bs-popover-bottom>.popover-arrow,.bs-popover-auto[data-popper-placement^="bottom"]>.popover-arrow{top:calc(-1 * (var(--bs-popover-arrow-height)) - var(--bs-popover-border-width))}.bs-popover-bottom>.popover-arrow::before,.bs-popover-auto[data-popper-placement^="bottom"]>.popover-arrow::before,.bs-popover-bottom>.popover-arrow::after,.bs-popover-auto[data-popper-placement^="bottom"]>.popover-arrow::after{border-width:0 calc(var(--bs-popover-arrow-width) * .5) var(--bs-popover-arrow-height)}.bs-popover-bottom>.popover-arrow::before,.bs-popover-auto[data-popper-placement^="bottom"]>.popover-arrow::before{top:0;border-bottom-color:var(--bs-popover-arrow-border)}.bs-popover-bottom>.popover-arrow::after,.bs-popover-auto[data-popper-placement^="bottom"]>.popover-arrow::after{top:var(--bs-popover-border-width);border-bottom-color:var(--bs-popover-bg)}.bs-popover-bottom .popover-header::before,.bs-popover-auto[data-popper-placement^="bottom"] .popover-header::before{position:absolute;top:0;left:50%;display:block;width:var(--bs-popover-arrow-width);margin-left:calc(-.5 * var(--bs-popover-arrow-width));content:"";border-bottom:var(--bs-popover-border-width) solid var(--bs-popover-header-bg)}.bs-popover-start>.popover-arrow,.bs-popover-auto[data-popper-placement^="left"]>.popover-arrow{right:calc(-1 * (var(--bs-popover-arrow-height)) - var(--bs-popover-border-width));width:var(--bs-popover-arrow-height);height:var(--bs-popover-arrow-width)}.bs-popover-start>.popover-arrow::before,.bs-popover-auto[data-popper-placement^="left"]>.popover-arrow::before,.bs-popover-start>.popover-arrow::after,.bs-popover-auto[data-popper-placement^="left"]>.popover-arrow::after{border-width:calc(var(--bs-popover-arrow-width) * .5) 0 calc(var(--bs-popover-arrow-width) * .5) var(--bs-popover-arrow-height)}.bs-popover-start>.popover-arrow::before,.bs-popover-auto[data-popper-placement^="left"]>.popover-arrow::before{right:0;border-left-color:var(--bs-popover-arrow-border)}.bs-popover-start>.popover-arrow::after,.bs-popover-auto[data-popper-placement^="left"]>.popover-arrow::after{right:var(--bs-popover-border-width);border-left-color:var(--bs-popover-bg)}.popover-header{padding:var(--bs-popover-header-padding-y) var(--bs-popover-header-padding-x);margin-bottom:0;font-size:var(--bs-popover-header-font-size);color:var(--bs-popover-header-color);background-color:var(--bs-popover-header-bg);border-bottom:var(--bs-popover-border-width) solid var(--bs-popover-border-color);border-top-left-radius:var(--bs-popover-inner-border-radius);border-top-right-radius:var(--bs-popover-inner-border-radius)}.popover-header:empty{display:none}.popover-body{padding:var(--bs-popover-body-padding-y) var(--bs-popover-body-padding-x);color:var(--bs-popover-body-color)}.carousel{position:relative}.carousel.pointer-event{touch-action:pan-y;-webkit-touch-action:pan-y;-moz-touch-action:pan-y;-ms-touch-action:pan-y;-o-touch-action:pan-y}.carousel-inner{position:relative;width:100%;overflow:hidden}.carousel-inner::after{display:block;clear:both;content:""}.carousel-item{position:relative;display:none;float:left;width:100%;margin-right:-100%;backface-visibility:hidden;-webkit-backface-visibility:hidden;-moz-backface-visibility:hidden;-ms-backface-visibility:hidden;-o-backface-visibility:hidden;transition:transform .6s ease-in-out}@media (prefers-reduced-motion: reduce){.carousel-item{transition:none}}.carousel-item.active,.carousel-item-next,.carousel-item-prev{display:block}.carousel-item-next:not(.carousel-item-start),.active.carousel-item-end{transform:translateX(100%)}.carousel-item-prev:not(.carousel-item-end),.active.carousel-item-start{transform:translateX(-100%)}.carousel-fade .carousel-item{opacity:0;transition-property:opacity;transform:none}.carousel-fade .carousel-item.active,.carousel-fade .carousel-item-next.carousel-item-start,.carousel-fade .carousel-item-prev.carousel-item-end{z-index:1;opacity:1}.carousel-fade .active.carousel-item-start,.carousel-fade .active.carousel-item-end{z-index:0;opacity:0;transition:opacity 0s .6s}@media (prefers-reduced-motion: reduce){.carousel-fade .active.carousel-item-start,.carousel-fade .active.carousel-item-end{transition:none}}.carousel-control-prev,.carousel-control-next{position:absolute;top:0;bottom:0;z-index:1;display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;justify-content:center;-webkit-justify-content:center;width:15%;padding:0;color:#fff;text-align:center;background:none;border:0;opacity:.5;transition:opacity 0.15s ease}@media (prefers-reduced-motion: reduce){.carousel-control-prev,.carousel-control-next{transition:none}}.carousel-control-prev:hover,.carousel-control-prev:focus,.carousel-control-next:hover,.carousel-control-next:focus{color:#fff;text-decoration:none;outline:0;opacity:.9}.carousel-control-prev{left:0}.carousel-control-next{right:0}.carousel-control-prev-icon,.carousel-control-next-icon{display:inline-block;width:2rem;height:2rem;background-repeat:no-repeat;background-position:50%;background-size:100% 100%}.carousel-control-prev-icon{background-image:url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23fff'%3e%3cpath d='M11.354 1.646a.5.5 0 0 1 0 .708L5.707 8l5.647 5.646a.5.5 0 0 1-.708.708l-6-6a.5.5 0 0 1 0-.708l6-6a.5.5 0 0 1 .708 0z'/%3e%3c/svg%3e")}.carousel-control-next-icon{background-image:url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23fff'%3e%3cpath d='M4.646 1.646a.5.5 0 0 1 .708 0l6 6a.5.5 0 0 1 0 .708l-6 6a.5.5 0 0 1-.708-.708L10.293 8 4.646 2.354a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e")}.carousel-indicators{position:absolute;right:0;bottom:0;left:0;z-index:2;display:flex;display:-webkit-flex;justify-content:center;-webkit-justify-content:center;padding:0;margin-right:15%;margin-bottom:1rem;margin-left:15%}.carousel-indicators [data-bs-target]{box-sizing:content-box;flex:0 1 auto;-webkit-flex:0 1 auto;width:30px;height:3px;padding:0;margin-right:3px;margin-left:3px;text-indent:-999px;cursor:pointer;background-color:#fff;background-clip:padding-box;border:0;border-top:10px solid transparent;border-bottom:10px solid transparent;opacity:.5;transition:opacity 0.6s ease}@media (prefers-reduced-motion: reduce){.carousel-indicators [data-bs-target]{transition:none}}.carousel-indicators .active{opacity:1}.carousel-caption{position:absolute;right:15%;bottom:1.25rem;left:15%;padding-top:1.25rem;padding-bottom:1.25rem;color:#fff;text-align:center}.carousel-dark .carousel-control-prev-icon,.carousel-dark .carousel-control-next-icon{filter:invert(1) grayscale(100)}.carousel-dark .carousel-indicators [data-bs-target]{background-color:#000}.carousel-dark .carousel-caption{color:#000}[data-bs-theme="dark"] .carousel .carousel-control-prev-icon,[data-bs-theme="dark"] .carousel .carousel-control-next-icon,[data-bs-theme="dark"].carousel .carousel-control-prev-icon,[data-bs-theme="dark"].carousel .carousel-control-next-icon{filter:invert(1) grayscale(100)}[data-bs-theme="dark"] .carousel .carousel-indicators [data-bs-target],[data-bs-theme="dark"].carousel .carousel-indicators [data-bs-target]{background-color:#000}[data-bs-theme="dark"] .carousel .carousel-caption,[data-bs-theme="dark"].carousel .carousel-caption{color:#000}.spinner-grow,.spinner-border{display:inline-block;width:var(--bs-spinner-width);height:var(--bs-spinner-height);vertical-align:var(--bs-spinner-vertical-align);border-radius:50%;animation:var(--bs-spinner-animation-speed) linear infinite var(--bs-spinner-animation-name)}@keyframes spinner-border{to{transform:rotate(360deg) /* rtl:ignore */}}.spinner-border{--bs-spinner-width: 2rem;--bs-spinner-height: 2rem;--bs-spinner-vertical-align: -.125em;--bs-spinner-border-width: .25em;--bs-spinner-animation-speed: .75s;--bs-spinner-animation-name: spinner-border;border:var(--bs-spinner-border-width) solid currentcolor;border-right-color:transparent}.spinner-border-sm{--bs-spinner-width: 1rem;--bs-spinner-height: 1rem;--bs-spinner-border-width: .2em}@keyframes spinner-grow{0%{transform:scale(0)}50%{opacity:1;transform:none}}.spinner-grow{--bs-spinner-width: 2rem;--bs-spinner-height: 2rem;--bs-spinner-vertical-align: -.125em;--bs-spinner-animation-speed: .75s;--bs-spinner-animation-name: spinner-grow;background-color:currentcolor;opacity:0}.spinner-grow-sm{--bs-spinner-width: 1rem;--bs-spinner-height: 1rem}@media (prefers-reduced-motion: reduce){.spinner-border,.spinner-grow{--bs-spinner-animation-speed: 1.5s}}.offcanvas,.offcanvas-xxl,.offcanvas-xl,.offcanvas-lg,.offcanvas-md,.offcanvas-sm{--bs-offcanvas-zindex: 1045;--bs-offcanvas-width: 400px;--bs-offcanvas-height: 30vh;--bs-offcanvas-padding-x: 1rem;--bs-offcanvas-padding-y: 1rem;--bs-offcanvas-color: var(--bs-body-color);--bs-offcanvas-bg: var(--bs-body-bg);--bs-offcanvas-border-width: var(--bs-border-width);--bs-offcanvas-border-color: var(--bs-border-color-translucent);--bs-offcanvas-box-shadow: 0 0.125rem 0.25rem rgba(0,0,0,0.075);--bs-offcanvas-transition: transform .3s ease-in-out;--bs-offcanvas-title-line-height: 1.5}@media (max-width: 575.98px){.offcanvas-sm{position:fixed;bottom:0;z-index:var(--bs-offcanvas-zindex);display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;max-width:100%;color:var(--bs-offcanvas-color);visibility:hidden;background-color:var(--bs-offcanvas-bg);background-clip:padding-box;outline:0;transition:var(--bs-offcanvas-transition)}}@media (max-width: 575.98px) and (prefers-reduced-motion: reduce){.offcanvas-sm{transition:none}}@media (max-width: 575.98px){.offcanvas-sm.offcanvas-start{top:0;left:0;width:var(--bs-offcanvas-width);border-right:var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);transform:translateX(-100%)}.offcanvas-sm.offcanvas-end{top:0;right:0;width:var(--bs-offcanvas-width);border-left:var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);transform:translateX(100%)}.offcanvas-sm.offcanvas-top{top:0;right:0;left:0;height:var(--bs-offcanvas-height);max-height:100%;border-bottom:var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);transform:translateY(-100%)}.offcanvas-sm.offcanvas-bottom{right:0;left:0;height:var(--bs-offcanvas-height);max-height:100%;border-top:var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);transform:translateY(100%)}.offcanvas-sm.showing,.offcanvas-sm.show:not(.hiding){transform:none}.offcanvas-sm.showing,.offcanvas-sm.hiding,.offcanvas-sm.show{visibility:visible}}@media (min-width: 576px){.offcanvas-sm{--bs-offcanvas-height: auto;--bs-offcanvas-border-width: 0;background-color:transparent !important}.offcanvas-sm .offcanvas-header{display:none}.offcanvas-sm .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible;background-color:transparent !important}}@media (max-width: 767.98px){.offcanvas-md{position:fixed;bottom:0;z-index:var(--bs-offcanvas-zindex);display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;max-width:100%;color:var(--bs-offcanvas-color);visibility:hidden;background-color:var(--bs-offcanvas-bg);background-clip:padding-box;outline:0;transition:var(--bs-offcanvas-transition)}}@media (max-width: 767.98px) and (prefers-reduced-motion: reduce){.offcanvas-md{transition:none}}@media (max-width: 767.98px){.offcanvas-md.offcanvas-start{top:0;left:0;width:var(--bs-offcanvas-width);border-right:var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);transform:translateX(-100%)}.offcanvas-md.offcanvas-end{top:0;right:0;width:var(--bs-offcanvas-width);border-left:var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);transform:translateX(100%)}.offcanvas-md.offcanvas-top{top:0;right:0;left:0;height:var(--bs-offcanvas-height);max-height:100%;border-bottom:var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);transform:translateY(-100%)}.offcanvas-md.offcanvas-bottom{right:0;left:0;height:var(--bs-offcanvas-height);max-height:100%;border-top:var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);transform:translateY(100%)}.offcanvas-md.showing,.offcanvas-md.show:not(.hiding){transform:none}.offcanvas-md.showing,.offcanvas-md.hiding,.offcanvas-md.show{visibility:visible}}@media (min-width: 768px){.offcanvas-md{--bs-offcanvas-height: auto;--bs-offcanvas-border-width: 0;background-color:transparent !important}.offcanvas-md .offcanvas-header{display:none}.offcanvas-md .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible;background-color:transparent !important}}@media (max-width: 991.98px){.offcanvas-lg{position:fixed;bottom:0;z-index:var(--bs-offcanvas-zindex);display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;max-width:100%;color:var(--bs-offcanvas-color);visibility:hidden;background-color:var(--bs-offcanvas-bg);background-clip:padding-box;outline:0;transition:var(--bs-offcanvas-transition)}}@media (max-width: 991.98px) and (prefers-reduced-motion: reduce){.offcanvas-lg{transition:none}}@media (max-width: 991.98px){.offcanvas-lg.offcanvas-start{top:0;left:0;width:var(--bs-offcanvas-width);border-right:var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);transform:translateX(-100%)}.offcanvas-lg.offcanvas-end{top:0;right:0;width:var(--bs-offcanvas-width);border-left:var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);transform:translateX(100%)}.offcanvas-lg.offcanvas-top{top:0;right:0;left:0;height:var(--bs-offcanvas-height);max-height:100%;border-bottom:var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);transform:translateY(-100%)}.offcanvas-lg.offcanvas-bottom{right:0;left:0;height:var(--bs-offcanvas-height);max-height:100%;border-top:var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);transform:translateY(100%)}.offcanvas-lg.showing,.offcanvas-lg.show:not(.hiding){transform:none}.offcanvas-lg.showing,.offcanvas-lg.hiding,.offcanvas-lg.show{visibility:visible}}@media (min-width: 992px){.offcanvas-lg{--bs-offcanvas-height: auto;--bs-offcanvas-border-width: 0;background-color:transparent !important}.offcanvas-lg .offcanvas-header{display:none}.offcanvas-lg .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible;background-color:transparent !important}}@media (max-width: 1199.98px){.offcanvas-xl{position:fixed;bottom:0;z-index:var(--bs-offcanvas-zindex);display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;max-width:100%;color:var(--bs-offcanvas-color);visibility:hidden;background-color:var(--bs-offcanvas-bg);background-clip:padding-box;outline:0;transition:var(--bs-offcanvas-transition)}}@media (max-width: 1199.98px) and (prefers-reduced-motion: reduce){.offcanvas-xl{transition:none}}@media (max-width: 1199.98px){.offcanvas-xl.offcanvas-start{top:0;left:0;width:var(--bs-offcanvas-width);border-right:var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);transform:translateX(-100%)}.offcanvas-xl.offcanvas-end{top:0;right:0;width:var(--bs-offcanvas-width);border-left:var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);transform:translateX(100%)}.offcanvas-xl.offcanvas-top{top:0;right:0;left:0;height:var(--bs-offcanvas-height);max-height:100%;border-bottom:var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);transform:translateY(-100%)}.offcanvas-xl.offcanvas-bottom{right:0;left:0;height:var(--bs-offcanvas-height);max-height:100%;border-top:var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);transform:translateY(100%)}.offcanvas-xl.showing,.offcanvas-xl.show:not(.hiding){transform:none}.offcanvas-xl.showing,.offcanvas-xl.hiding,.offcanvas-xl.show{visibility:visible}}@media (min-width: 1200px){.offcanvas-xl{--bs-offcanvas-height: auto;--bs-offcanvas-border-width: 0;background-color:transparent !important}.offcanvas-xl .offcanvas-header{display:none}.offcanvas-xl .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible;background-color:transparent !important}}@media (max-width: 1399.98px){.offcanvas-xxl{position:fixed;bottom:0;z-index:var(--bs-offcanvas-zindex);display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;max-width:100%;color:var(--bs-offcanvas-color);visibility:hidden;background-color:var(--bs-offcanvas-bg);background-clip:padding-box;outline:0;transition:var(--bs-offcanvas-transition)}}@media (max-width: 1399.98px) and (prefers-reduced-motion: reduce){.offcanvas-xxl{transition:none}}@media (max-width: 1399.98px){.offcanvas-xxl.offcanvas-start{top:0;left:0;width:var(--bs-offcanvas-width);border-right:var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);transform:translateX(-100%)}.offcanvas-xxl.offcanvas-end{top:0;right:0;width:var(--bs-offcanvas-width);border-left:var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);transform:translateX(100%)}.offcanvas-xxl.offcanvas-top{top:0;right:0;left:0;height:var(--bs-offcanvas-height);max-height:100%;border-bottom:var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);transform:translateY(-100%)}.offcanvas-xxl.offcanvas-bottom{right:0;left:0;height:var(--bs-offcanvas-height);max-height:100%;border-top:var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);transform:translateY(100%)}.offcanvas-xxl.showing,.offcanvas-xxl.show:not(.hiding){transform:none}.offcanvas-xxl.showing,.offcanvas-xxl.hiding,.offcanvas-xxl.show{visibility:visible}}@media (min-width: 1400px){.offcanvas-xxl{--bs-offcanvas-height: auto;--bs-offcanvas-border-width: 0;background-color:transparent !important}.offcanvas-xxl .offcanvas-header{display:none}.offcanvas-xxl .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible;background-color:transparent !important}}.offcanvas{position:fixed;bottom:0;z-index:var(--bs-offcanvas-zindex);display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;max-width:100%;color:var(--bs-offcanvas-color);visibility:hidden;background-color:var(--bs-offcanvas-bg);background-clip:padding-box;outline:0;transition:var(--bs-offcanvas-transition)}@media (prefers-reduced-motion: reduce){.offcanvas{transition:none}}.offcanvas.offcanvas-start{top:0;left:0;width:var(--bs-offcanvas-width);border-right:var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);transform:translateX(-100%)}.offcanvas.offcanvas-end{top:0;right:0;width:var(--bs-offcanvas-width);border-left:var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);transform:translateX(100%)}.offcanvas.offcanvas-top{top:0;right:0;left:0;height:var(--bs-offcanvas-height);max-height:100%;border-bottom:var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);transform:translateY(-100%)}.offcanvas.offcanvas-bottom{right:0;left:0;height:var(--bs-offcanvas-height);max-height:100%;border-top:var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);transform:translateY(100%)}.offcanvas.showing,.offcanvas.show:not(.hiding){transform:none}.offcanvas.showing,.offcanvas.hiding,.offcanvas.show{visibility:visible}.offcanvas-backdrop{position:fixed;top:0;left:0;z-index:1040;width:100vw;height:100vh;background-color:#000}.offcanvas-backdrop.fade{opacity:0}.offcanvas-backdrop.show{opacity:.5}.offcanvas-header{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;justify-content:space-between;-webkit-justify-content:space-between;padding:var(--bs-offcanvas-padding-y) var(--bs-offcanvas-padding-x)}.offcanvas-header .btn-close{padding:calc(var(--bs-offcanvas-padding-y) * .5) calc(var(--bs-offcanvas-padding-x) * .5);margin-top:calc(-.5 * var(--bs-offcanvas-padding-y));margin-right:calc(-.5 * var(--bs-offcanvas-padding-x));margin-bottom:calc(-.5 * var(--bs-offcanvas-padding-y))}.offcanvas-title{margin-bottom:0;line-height:var(--bs-offcanvas-title-line-height)}.offcanvas-body{flex-grow:1;-webkit-flex-grow:1;padding:var(--bs-offcanvas-padding-y) var(--bs-offcanvas-padding-x);overflow-y:auto}.placeholder{display:inline-block;min-height:1em;vertical-align:middle;cursor:wait;background-color:currentcolor;opacity:.5}.placeholder.btn::before{display:inline-block;content:""}.placeholder-xs{min-height:.6em}.placeholder-sm{min-height:.8em}.placeholder-lg{min-height:1.2em}.placeholder-glow .placeholder{animation:placeholder-glow 2s ease-in-out infinite}@keyframes placeholder-glow{50%{opacity:.2}}.placeholder-wave{mask-image:linear-gradient(130deg, #000 55%, rgba(0,0,0,0.8) 75%, #000 95%);-webkit-mask-image:linear-gradient(130deg, #000 55%, rgba(0,0,0,0.8) 75%, #000 95%);mask-size:200% 100%;-webkit-mask-size:200% 100%;animation:placeholder-wave 2s linear infinite}@keyframes placeholder-wave{100%{mask-position:-200% 0%;-webkit-mask-position:-200% 0%}}.clearfix::after{display:block;clear:both;content:""}.text-bg-default{color:#fff !important;background-color:RGBA(var(--bs-default-rgb), var(--bs-bg-opacity, 1)) !important}.text-bg-primary{color:#fff !important;background-color:RGBA(var(--bs-primary-rgb), var(--bs-bg-opacity, 1)) !important}.text-bg-secondary{color:#fff !important;background-color:RGBA(var(--bs-secondary-rgb), var(--bs-bg-opacity, 1)) !important}.text-bg-success{color:#fff !important;background-color:RGBA(var(--bs-success-rgb), var(--bs-bg-opacity, 1)) !important}.text-bg-info{color:#fff !important;background-color:RGBA(var(--bs-info-rgb), var(--bs-bg-opacity, 1)) !important}.text-bg-warning{color:#fff !important;background-color:RGBA(var(--bs-warning-rgb), var(--bs-bg-opacity, 1)) !important}.text-bg-danger{color:#fff !important;background-color:RGBA(var(--bs-danger-rgb), var(--bs-bg-opacity, 1)) !important}.text-bg-light{color:#000 !important;background-color:RGBA(var(--bs-light-rgb), var(--bs-bg-opacity, 1)) !important}.text-bg-dark{color:#fff !important;background-color:RGBA(var(--bs-dark-rgb), var(--bs-bg-opacity, 1)) !important}.link-default{color:RGBA(var(--bs-default-rgb), var(--bs-link-opacity, 1)) !important;text-decoration-color:RGBA(var(--bs-default-rgb), var(--bs-link-underline-opacity, 1)) !important}.link-default:hover,.link-default:focus{color:RGBA(122,122,122, var(--bs-link-opacity, 1)) !important;text-decoration-color:RGBA(122,122,122, var(--bs-link-underline-opacity, 1)) !important}.link-primary{color:RGBA(var(--bs-primary-rgb), var(--bs-link-opacity, 1)) !important;text-decoration-color:RGBA(var(--bs-primary-rgb), var(--bs-link-underline-opacity, 1)) !important}.link-primary:hover,.link-primary:focus{color:RGBA(54,88,124, var(--bs-link-opacity, 1)) !important;text-decoration-color:RGBA(54,88,124, var(--bs-link-underline-opacity, 1)) !important}.link-secondary{color:RGBA(var(--bs-secondary-rgb), var(--bs-link-opacity, 1)) !important;text-decoration-color:RGBA(var(--bs-secondary-rgb), var(--bs-link-underline-opacity, 1)) !important}.link-secondary:hover,.link-secondary:focus{color:RGBA(122,122,122, var(--bs-link-opacity, 1)) !important;text-decoration-color:RGBA(122,122,122, var(--bs-link-underline-opacity, 1)) !important}.link-success{color:RGBA(var(--bs-success-rgb), var(--bs-link-opacity, 1)) !important;text-decoration-color:RGBA(var(--bs-success-rgb), var(--bs-link-underline-opacity, 1)) !important}.link-success:hover,.link-success:focus{color:RGBA(48,145,26, var(--bs-link-opacity, 1)) !important;text-decoration-color:RGBA(48,145,26, var(--bs-link-underline-opacity, 1)) !important}.link-info{color:RGBA(var(--bs-info-rgb), var(--bs-link-opacity, 1)) !important;text-decoration-color:RGBA(var(--bs-info-rgb), var(--bs-link-underline-opacity, 1)) !important}.link-info:hover,.link-info:focus{color:RGBA(41,122,194, var(--bs-link-opacity, 1)) !important;text-decoration-color:RGBA(41,122,194, var(--bs-link-underline-opacity, 1)) !important}.link-warning{color:RGBA(var(--bs-warning-rgb), var(--bs-link-opacity, 1)) !important;text-decoration-color:RGBA(var(--bs-warning-rgb), var(--bs-link-underline-opacity, 1)) !important}.link-warning:hover,.link-warning:focus{color:RGBA(170,94,0, var(--bs-link-opacity, 1)) !important;text-decoration-color:RGBA(170,94,0, var(--bs-link-underline-opacity, 1)) !important}.link-danger{color:RGBA(var(--bs-danger-rgb), var(--bs-link-opacity, 1)) !important;text-decoration-color:RGBA(var(--bs-danger-rgb), var(--bs-link-underline-opacity, 1)) !important}.link-danger:hover,.link-danger:focus{color:RGBA(164,2,0, var(--bs-link-opacity, 1)) !important;text-decoration-color:RGBA(164,2,0, var(--bs-link-underline-opacity, 1)) !important}.link-light{color:RGBA(var(--bs-light-rgb), var(--bs-link-opacity, 1)) !important;text-decoration-color:RGBA(var(--bs-light-rgb), var(--bs-link-underline-opacity, 1)) !important}.link-light:hover,.link-light:focus{color:RGBA(241,241,241, var(--bs-link-opacity, 1)) !important;text-decoration-color:RGBA(241,241,241, var(--bs-link-underline-opacity, 1)) !important}.link-dark{color:RGBA(var(--bs-dark-rgb), var(--bs-link-opacity, 1)) !important;text-decoration-color:RGBA(var(--bs-dark-rgb), var(--bs-link-underline-opacity, 1)) !important}.link-dark:hover,.link-dark:focus{color:RGBA(41,41,41, var(--bs-link-opacity, 1)) !important;text-decoration-color:RGBA(41,41,41, var(--bs-link-underline-opacity, 1)) !important}.link-body-emphasis{color:RGBA(var(--bs-emphasis-color-rgb), var(--bs-link-opacity, 1)) !important;text-decoration-color:RGBA(var(--bs-emphasis-color-rgb), var(--bs-link-underline-opacity, 1)) !important}.link-body-emphasis:hover,.link-body-emphasis:focus{color:RGBA(var(--bs-emphasis-color-rgb), var(--bs-link-opacity, 0.75)) !important;text-decoration-color:RGBA(var(--bs-emphasis-color-rgb), var(--bs-link-underline-opacity, 0.75)) !important}.focus-ring:focus{outline:0;box-shadow:var(--bs-focus-ring-x, 0) var(--bs-focus-ring-y, 0) var(--bs-focus-ring-blur, 0) var(--bs-focus-ring-width) var(--bs-focus-ring-color)}.icon-link{display:inline-flex;gap:.375rem;align-items:center;-webkit-align-items:center;text-decoration-color:rgba(var(--bs-link-color-rgb), var(--bs-link-opacity, 0.5));text-underline-offset:.25em;backface-visibility:hidden;-webkit-backface-visibility:hidden;-moz-backface-visibility:hidden;-ms-backface-visibility:hidden;-o-backface-visibility:hidden}.icon-link>.bi{flex-shrink:0;-webkit-flex-shrink:0;width:1em;height:1em;fill:currentcolor;transition:0.2s ease-in-out transform}@media (prefers-reduced-motion: reduce){.icon-link>.bi{transition:none}}.icon-link-hover:hover>.bi,.icon-link-hover:focus-visible>.bi{transform:var(--bs-icon-link-transform, translate3d(0.25em, 0, 0))}.ratio{position:relative;width:100%}.ratio::before{display:block;padding-top:var(--bs-aspect-ratio);content:""}.ratio>*{position:absolute;top:0;left:0;width:100%;height:100%}.ratio-1x1{--bs-aspect-ratio: 100%}.ratio-4x3{--bs-aspect-ratio: calc(3 / 4 * 100%)}.ratio-16x9{--bs-aspect-ratio: calc(9 / 16 * 100%)}.ratio-21x9{--bs-aspect-ratio: calc(9 / 21 * 100%)}.fixed-top{position:fixed;top:0;right:0;left:0;z-index:1030}.fixed-bottom{position:fixed;right:0;bottom:0;left:0;z-index:1030}.sticky-top{position:sticky;top:0;z-index:1020}.sticky-bottom{position:sticky;bottom:0;z-index:1020}@media (min-width: 576px){.sticky-sm-top{position:sticky;top:0;z-index:1020}.sticky-sm-bottom{position:sticky;bottom:0;z-index:1020}}@media (min-width: 768px){.sticky-md-top{position:sticky;top:0;z-index:1020}.sticky-md-bottom{position:sticky;bottom:0;z-index:1020}}@media (min-width: 992px){.sticky-lg-top{position:sticky;top:0;z-index:1020}.sticky-lg-bottom{position:sticky;bottom:0;z-index:1020}}@media (min-width: 1200px){.sticky-xl-top{position:sticky;top:0;z-index:1020}.sticky-xl-bottom{position:sticky;bottom:0;z-index:1020}}@media (min-width: 1400px){.sticky-xxl-top{position:sticky;top:0;z-index:1020}.sticky-xxl-bottom{position:sticky;bottom:0;z-index:1020}}.hstack{display:flex;display:-webkit-flex;flex-direction:row;-webkit-flex-direction:row;align-items:center;-webkit-align-items:center;align-self:stretch;-webkit-align-self:stretch}.vstack{display:flex;display:-webkit-flex;flex:1 1 auto;-webkit-flex:1 1 auto;flex-direction:column;-webkit-flex-direction:column;align-self:stretch;-webkit-align-self:stretch}.visually-hidden,.visually-hidden-focusable:not(:focus):not(:focus-within){width:1px !important;height:1px !important;padding:0 !important;margin:-1px !important;overflow:hidden !important;clip:rect(0, 0, 0, 0) !important;white-space:nowrap !important;border:0 !important}.visually-hidden:not(caption),.visually-hidden-focusable:not(:focus):not(:focus-within):not(caption){position:absolute !important}.stretched-link::after{position:absolute;top:0;right:0;bottom:0;left:0;z-index:1;content:""}.text-truncate{overflow:hidden;text-overflow:ellipsis;white-space:nowrap}.vr{display:inline-block;align-self:stretch;-webkit-align-self:stretch;width:var(--bs-border-width);min-height:1em;background-color:currentcolor;opacity:.25}.align-baseline{vertical-align:baseline !important}.align-top{vertical-align:top !important}.align-middle{vertical-align:middle !important}.align-bottom{vertical-align:bottom !important}.align-text-bottom{vertical-align:text-bottom !important}.align-text-top{vertical-align:text-top !important}.float-start{float:left !important}.float-end{float:right !important}.float-none{float:none !important}.object-fit-contain{object-fit:contain !important}.object-fit-cover{object-fit:cover !important}.object-fit-fill{object-fit:fill !important}.object-fit-scale{object-fit:scale-down !important}.object-fit-none{object-fit:none !important}.opacity-0{opacity:0 !important}.opacity-25{opacity:.25 !important}.opacity-50{opacity:.5 !important}.opacity-75{opacity:.75 !important}.opacity-100{opacity:1 !important}.overflow-auto{overflow:auto !important}.overflow-hidden{overflow:hidden !important}.overflow-visible{overflow:visible !important}.overflow-scroll{overflow:scroll !important}.overflow-x-auto{overflow-x:auto !important}.overflow-x-hidden{overflow-x:hidden !important}.overflow-x-visible{overflow-x:visible !important}.overflow-x-scroll{overflow-x:scroll !important}.overflow-y-auto{overflow-y:auto !important}.overflow-y-hidden{overflow-y:hidden !important}.overflow-y-visible{overflow-y:visible !important}.overflow-y-scroll{overflow-y:scroll !important}.d-inline{display:inline !important}.d-inline-block{display:inline-block !important}.d-block{display:block !important}.d-grid{display:grid !important}.d-inline-grid{display:inline-grid !important}.d-table{display:table !important}.d-table-row{display:table-row !important}.d-table-cell{display:table-cell !important}.d-flex{display:flex !important}.d-inline-flex{display:inline-flex !important}.d-none{display:none !important}.shadow{box-shadow:0 0.5rem 1rem rgba(0,0,0,0.15) !important}.shadow-sm{box-shadow:0 0.125rem 0.25rem rgba(0,0,0,0.075) !important}.shadow-lg{box-shadow:0 1rem 3rem rgba(0,0,0,0.175) !important}.shadow-none{box-shadow:none !important}.focus-ring-default{--bs-focus-ring-color: rgba(var(--bs-default-rgb), var(--bs-focus-ring-opacity))}.focus-ring-primary{--bs-focus-ring-color: rgba(var(--bs-primary-rgb), var(--bs-focus-ring-opacity))}.focus-ring-secondary{--bs-focus-ring-color: rgba(var(--bs-secondary-rgb), var(--bs-focus-ring-opacity))}.focus-ring-success{--bs-focus-ring-color: rgba(var(--bs-success-rgb), var(--bs-focus-ring-opacity))}.focus-ring-info{--bs-focus-ring-color: rgba(var(--bs-info-rgb), var(--bs-focus-ring-opacity))}.focus-ring-warning{--bs-focus-ring-color: rgba(var(--bs-warning-rgb), var(--bs-focus-ring-opacity))}.focus-ring-danger{--bs-focus-ring-color: rgba(var(--bs-danger-rgb), var(--bs-focus-ring-opacity))}.focus-ring-light{--bs-focus-ring-color: rgba(var(--bs-light-rgb), var(--bs-focus-ring-opacity))}.focus-ring-dark{--bs-focus-ring-color: rgba(var(--bs-dark-rgb), var(--bs-focus-ring-opacity))}.position-static{position:static !important}.position-relative{position:relative !important}.position-absolute{position:absolute !important}.position-fixed{position:fixed !important}.position-sticky{position:sticky !important}.top-0{top:0 !important}.top-50{top:50% !important}.top-100{top:100% !important}.bottom-0{bottom:0 !important}.bottom-50{bottom:50% !important}.bottom-100{bottom:100% !important}.start-0{left:0 !important}.start-50{left:50% !important}.start-100{left:100% !important}.end-0{right:0 !important}.end-50{right:50% !important}.end-100{right:100% !important}.translate-middle{transform:translate(-50%, -50%) !important}.translate-middle-x{transform:translateX(-50%) !important}.translate-middle-y{transform:translateY(-50%) !important}.border{border:var(--bs-border-width) var(--bs-border-style) var(--bs-border-color) !important}.border-0{border:0 !important}.border-top{border-top:var(--bs-border-width) var(--bs-border-style) var(--bs-border-color) !important}.border-top-0{border-top:0 !important}.border-end{border-right:var(--bs-border-width) var(--bs-border-style) var(--bs-border-color) !important}.border-end-0{border-right:0 !important}.border-bottom{border-bottom:var(--bs-border-width) var(--bs-border-style) var(--bs-border-color) !important}.border-bottom-0{border-bottom:0 !important}.border-start{border-left:var(--bs-border-width) var(--bs-border-style) var(--bs-border-color) !important}.border-start-0{border-left:0 !important}.border-default{--bs-border-opacity: 1;border-color:rgba(var(--bs-default-rgb), var(--bs-border-opacity)) !important}.border-primary{--bs-border-opacity: 1;border-color:rgba(var(--bs-primary-rgb), var(--bs-border-opacity)) !important}.border-secondary{--bs-border-opacity: 1;border-color:rgba(var(--bs-secondary-rgb), var(--bs-border-opacity)) !important}.border-success{--bs-border-opacity: 1;border-color:rgba(var(--bs-success-rgb), var(--bs-border-opacity)) !important}.border-info{--bs-border-opacity: 1;border-color:rgba(var(--bs-info-rgb), var(--bs-border-opacity)) !important}.border-warning{--bs-border-opacity: 1;border-color:rgba(var(--bs-warning-rgb), var(--bs-border-opacity)) !important}.border-danger{--bs-border-opacity: 1;border-color:rgba(var(--bs-danger-rgb), var(--bs-border-opacity)) !important}.border-light{--bs-border-opacity: 1;border-color:rgba(var(--bs-light-rgb), var(--bs-border-opacity)) !important}.border-dark{--bs-border-opacity: 1;border-color:rgba(var(--bs-dark-rgb), var(--bs-border-opacity)) !important}.border-black{--bs-border-opacity: 1;border-color:rgba(var(--bs-black-rgb), var(--bs-border-opacity)) !important}.border-white{--bs-border-opacity: 1;border-color:rgba(var(--bs-white-rgb), var(--bs-border-opacity)) !important}.border-primary-subtle{border-color:var(--bs-primary-border-subtle) !important}.border-secondary-subtle{border-color:var(--bs-secondary-border-subtle) !important}.border-success-subtle{border-color:var(--bs-success-border-subtle) !important}.border-info-subtle{border-color:var(--bs-info-border-subtle) !important}.border-warning-subtle{border-color:var(--bs-warning-border-subtle) !important}.border-danger-subtle{border-color:var(--bs-danger-border-subtle) !important}.border-light-subtle{border-color:var(--bs-light-border-subtle) !important}.border-dark-subtle{border-color:var(--bs-dark-border-subtle) !important}.border-1{border-width:1px !important}.border-2{border-width:2px !important}.border-3{border-width:3px !important}.border-4{border-width:4px !important}.border-5{border-width:5px !important}.border-opacity-10{--bs-border-opacity: .1}.border-opacity-25{--bs-border-opacity: .25}.border-opacity-50{--bs-border-opacity: .5}.border-opacity-75{--bs-border-opacity: .75}.border-opacity-100{--bs-border-opacity: 1}.w-25{width:25% !important}.w-50{width:50% !important}.w-75{width:75% !important}.w-100{width:100% !important}.w-auto{width:auto !important}.mw-100{max-width:100% !important}.vw-100{width:100vw !important}.min-vw-100{min-width:100vw !important}.h-25{height:25% !important}.h-50{height:50% !important}.h-75{height:75% !important}.h-100{height:100% !important}.h-auto{height:auto !important}.mh-100{max-height:100% !important}.vh-100{height:100vh !important}.min-vh-100{min-height:100vh !important}.flex-fill{flex:1 1 auto !important}.flex-row{flex-direction:row !important}.flex-column{flex-direction:column !important}.flex-row-reverse{flex-direction:row-reverse !important}.flex-column-reverse{flex-direction:column-reverse !important}.flex-grow-0{flex-grow:0 !important}.flex-grow-1{flex-grow:1 !important}.flex-shrink-0{flex-shrink:0 !important}.flex-shrink-1{flex-shrink:1 !important}.flex-wrap{flex-wrap:wrap !important}.flex-nowrap{flex-wrap:nowrap !important}.flex-wrap-reverse{flex-wrap:wrap-reverse !important}.justify-content-start{justify-content:flex-start !important}.justify-content-end{justify-content:flex-end !important}.justify-content-center{justify-content:center !important}.justify-content-between{justify-content:space-between !important}.justify-content-around{justify-content:space-around !important}.justify-content-evenly{justify-content:space-evenly !important}.align-items-start{align-items:flex-start !important}.align-items-end{align-items:flex-end !important}.align-items-center{align-items:center !important}.align-items-baseline{align-items:baseline !important}.align-items-stretch{align-items:stretch !important}.align-content-start{align-content:flex-start !important}.align-content-end{align-content:flex-end !important}.align-content-center{align-content:center !important}.align-content-between{align-content:space-between !important}.align-content-around{align-content:space-around !important}.align-content-stretch{align-content:stretch !important}.align-self-auto{align-self:auto !important}.align-self-start{align-self:flex-start !important}.align-self-end{align-self:flex-end !important}.align-self-center{align-self:center !important}.align-self-baseline{align-self:baseline !important}.align-self-stretch{align-self:stretch !important}.order-first{order:-1 !important}.order-0{order:0 !important}.order-1{order:1 !important}.order-2{order:2 !important}.order-3{order:3 !important}.order-4{order:4 !important}.order-5{order:5 !important}.order-last{order:6 !important}.m-0{margin:0 !important}.m-1{margin:.25rem !important}.m-2{margin:.5rem !important}.m-3{margin:1rem !important}.m-4{margin:1.5rem !important}.m-5{margin:3rem !important}.m-auto{margin:auto !important}.mx-0{margin-right:0 !important;margin-left:0 !important}.mx-1{margin-right:.25rem !important;margin-left:.25rem !important}.mx-2{margin-right:.5rem !important;margin-left:.5rem !important}.mx-3{margin-right:1rem !important;margin-left:1rem !important}.mx-4{margin-right:1.5rem !important;margin-left:1.5rem !important}.mx-5{margin-right:3rem !important;margin-left:3rem !important}.mx-auto{margin-right:auto !important;margin-left:auto !important}.my-0{margin-top:0 !important;margin-bottom:0 !important}.my-1{margin-top:.25rem !important;margin-bottom:.25rem !important}.my-2{margin-top:.5rem !important;margin-bottom:.5rem !important}.my-3{margin-top:1rem !important;margin-bottom:1rem !important}.my-4{margin-top:1.5rem !important;margin-bottom:1.5rem !important}.my-5{margin-top:3rem !important;margin-bottom:3rem !important}.my-auto{margin-top:auto !important;margin-bottom:auto !important}.mt-0{margin-top:0 !important}.mt-1{margin-top:.25rem !important}.mt-2{margin-top:.5rem !important}.mt-3{margin-top:1rem !important}.mt-4{margin-top:1.5rem !important}.mt-5{margin-top:3rem !important}.mt-auto{margin-top:auto !important}.me-0{margin-right:0 !important}.me-1{margin-right:.25rem !important}.me-2{margin-right:.5rem !important}.me-3{margin-right:1rem !important}.me-4{margin-right:1.5rem !important}.me-5{margin-right:3rem !important}.me-auto{margin-right:auto !important}.mb-0{margin-bottom:0 !important}.mb-1{margin-bottom:.25rem !important}.mb-2{margin-bottom:.5rem !important}.mb-3{margin-bottom:1rem !important}.mb-4{margin-bottom:1.5rem !important}.mb-5{margin-bottom:3rem !important}.mb-auto{margin-bottom:auto !important}.ms-0{margin-left:0 !important}.ms-1{margin-left:.25rem !important}.ms-2{margin-left:.5rem !important}.ms-3{margin-left:1rem !important}.ms-4{margin-left:1.5rem !important}.ms-5{margin-left:3rem !important}.ms-auto{margin-left:auto !important}.p-0{padding:0 !important}.p-1{padding:.25rem !important}.p-2{padding:.5rem !important}.p-3{padding:1rem !important}.p-4{padding:1.5rem !important}.p-5{padding:3rem !important}.px-0{padding-right:0 !important;padding-left:0 !important}.px-1{padding-right:.25rem !important;padding-left:.25rem !important}.px-2{padding-right:.5rem !important;padding-left:.5rem !important}.px-3{padding-right:1rem !important;padding-left:1rem !important}.px-4{padding-right:1.5rem !important;padding-left:1.5rem !important}.px-5{padding-right:3rem !important;padding-left:3rem !important}.py-0{padding-top:0 !important;padding-bottom:0 !important}.py-1{padding-top:.25rem !important;padding-bottom:.25rem !important}.py-2{padding-top:.5rem !important;padding-bottom:.5rem !important}.py-3{padding-top:1rem !important;padding-bottom:1rem !important}.py-4{padding-top:1.5rem !important;padding-bottom:1.5rem !important}.py-5{padding-top:3rem !important;padding-bottom:3rem !important}.pt-0{padding-top:0 !important}.pt-1{padding-top:.25rem !important}.pt-2{padding-top:.5rem !important}.pt-3{padding-top:1rem !important}.pt-4{padding-top:1.5rem !important}.pt-5{padding-top:3rem !important}.pe-0{padding-right:0 !important}.pe-1{padding-right:.25rem !important}.pe-2{padding-right:.5rem !important}.pe-3{padding-right:1rem !important}.pe-4{padding-right:1.5rem !important}.pe-5{padding-right:3rem !important}.pb-0{padding-bottom:0 !important}.pb-1{padding-bottom:.25rem !important}.pb-2{padding-bottom:.5rem !important}.pb-3{padding-bottom:1rem !important}.pb-4{padding-bottom:1.5rem !important}.pb-5{padding-bottom:3rem !important}.ps-0{padding-left:0 !important}.ps-1{padding-left:.25rem !important}.ps-2{padding-left:.5rem !important}.ps-3{padding-left:1rem !important}.ps-4{padding-left:1.5rem !important}.ps-5{padding-left:3rem !important}.gap-0{gap:0 !important}.gap-1{gap:.25rem !important}.gap-2{gap:.5rem !important}.gap-3{gap:1rem !important}.gap-4{gap:1.5rem !important}.gap-5{gap:3rem !important}.row-gap-0{row-gap:0 !important}.row-gap-1{row-gap:.25rem !important}.row-gap-2{row-gap:.5rem !important}.row-gap-3{row-gap:1rem !important}.row-gap-4{row-gap:1.5rem !important}.row-gap-5{row-gap:3rem !important}.column-gap-0{column-gap:0 !important}.column-gap-1{column-gap:.25rem !important}.column-gap-2{column-gap:.5rem !important}.column-gap-3{column-gap:1rem !important}.column-gap-4{column-gap:1.5rem !important}.column-gap-5{column-gap:3rem !important}.font-monospace{font-family:var(--bs-font-monospace) !important}.fs-1{font-size:calc(1.375rem + 1.5vw) !important}.fs-2{font-size:calc(1.325rem + .9vw) !important}.fs-3{font-size:calc(1.3rem + .6vw) !important}.fs-4{font-size:calc(1.275rem + .3vw) !important}.fs-5{font-size:1.25rem !important}.fs-6{font-size:1rem !important}.fst-italic{font-style:italic !important}.fst-normal{font-style:normal !important}.fw-lighter{font-weight:lighter !important}.fw-light{font-weight:300 !important}.fw-normal{font-weight:400 !important}.fw-medium{font-weight:500 !important}.fw-semibold{font-weight:600 !important}.fw-bold{font-weight:700 !important}.fw-bolder{font-weight:bolder !important}.lh-1{line-height:1 !important}.lh-sm{line-height:1.25 !important}.lh-base{line-height:1.5 !important}.lh-lg{line-height:2 !important}.text-start{text-align:left !important}.text-end{text-align:right !important}.text-center{text-align:center !important}.text-decoration-none{text-decoration:none !important}.text-decoration-underline{text-decoration:underline !important}.text-decoration-line-through{text-decoration:line-through !important}.text-lowercase{text-transform:lowercase !important}.text-uppercase{text-transform:uppercase !important}.text-capitalize{text-transform:capitalize !important}.text-wrap{white-space:normal !important}.text-nowrap{white-space:nowrap !important}.text-break{word-wrap:break-word !important;word-break:break-word !important}.text-default{--bs-text-opacity: 1;color:rgba(var(--bs-default-rgb), var(--bs-text-opacity)) !important}.text-primary{--bs-text-opacity: 1;color:rgba(var(--bs-primary-rgb), var(--bs-text-opacity)) !important}.text-secondary{--bs-text-opacity: 1;color:rgba(var(--bs-secondary-rgb), var(--bs-text-opacity)) !important}.text-success{--bs-text-opacity: 1;color:rgba(var(--bs-success-rgb), var(--bs-text-opacity)) !important}.text-info{--bs-text-opacity: 1;color:rgba(var(--bs-info-rgb), var(--bs-text-opacity)) !important}.text-warning{--bs-text-opacity: 1;color:rgba(var(--bs-warning-rgb), var(--bs-text-opacity)) !important}.text-danger{--bs-text-opacity: 1;color:rgba(var(--bs-danger-rgb), var(--bs-text-opacity)) !important}.text-light{--bs-text-opacity: 1;color:rgba(var(--bs-light-rgb), var(--bs-text-opacity)) !important}.text-dark{--bs-text-opacity: 1;color:rgba(var(--bs-dark-rgb), var(--bs-text-opacity)) !important}.text-black{--bs-text-opacity: 1;color:rgba(var(--bs-black-rgb), var(--bs-text-opacity)) !important}.text-white{--bs-text-opacity: 1;color:rgba(var(--bs-white-rgb), var(--bs-text-opacity)) !important}.text-body{--bs-text-opacity: 1;color:rgba(var(--bs-body-color-rgb), var(--bs-text-opacity)) !important}.text-muted{--bs-text-opacity: 1;color:var(--bs-secondary-color) !important}.text-black-50{--bs-text-opacity: 1;color:rgba(0,0,0,0.5) !important}.text-white-50{--bs-text-opacity: 1;color:rgba(255,255,255,0.5) !important}.text-body-secondary{--bs-text-opacity: 1;color:var(--bs-secondary-color) !important}.text-body-tertiary{--bs-text-opacity: 1;color:var(--bs-tertiary-color) !important}.text-body-emphasis{--bs-text-opacity: 1;color:var(--bs-emphasis-color) !important}.text-reset{--bs-text-opacity: 1;color:inherit !important}.text-opacity-25{--bs-text-opacity: .25}.text-opacity-50{--bs-text-opacity: .5}.text-opacity-75{--bs-text-opacity: .75}.text-opacity-100{--bs-text-opacity: 1}.text-primary-emphasis{color:var(--bs-primary-text-emphasis) !important}.text-secondary-emphasis{color:var(--bs-secondary-text-emphasis) !important}.text-success-emphasis{color:var(--bs-success-text-emphasis) !important}.text-info-emphasis{color:var(--bs-info-text-emphasis) !important}.text-warning-emphasis{color:var(--bs-warning-text-emphasis) !important}.text-danger-emphasis{color:var(--bs-danger-text-emphasis) !important}.text-light-emphasis{color:var(--bs-light-text-emphasis) !important}.text-dark-emphasis{color:var(--bs-dark-text-emphasis) !important}.link-opacity-10{--bs-link-opacity: .1}.link-opacity-10-hover:hover{--bs-link-opacity: .1}.link-opacity-25{--bs-link-opacity: .25}.link-opacity-25-hover:hover{--bs-link-opacity: .25}.link-opacity-50{--bs-link-opacity: .5}.link-opacity-50-hover:hover{--bs-link-opacity: .5}.link-opacity-75{--bs-link-opacity: .75}.link-opacity-75-hover:hover{--bs-link-opacity: .75}.link-opacity-100{--bs-link-opacity: 1}.link-opacity-100-hover:hover{--bs-link-opacity: 1}.link-offset-1{text-underline-offset:.125em !important}.link-offset-1-hover:hover{text-underline-offset:.125em !important}.link-offset-2{text-underline-offset:.25em !important}.link-offset-2-hover:hover{text-underline-offset:.25em !important}.link-offset-3{text-underline-offset:.375em !important}.link-offset-3-hover:hover{text-underline-offset:.375em !important}.link-underline-default{--bs-link-underline-opacity: 1;text-decoration-color:rgba(var(--bs-default-rgb), var(--bs-link-underline-opacity)) !important}.link-underline-primary{--bs-link-underline-opacity: 1;text-decoration-color:rgba(var(--bs-primary-rgb), var(--bs-link-underline-opacity)) !important}.link-underline-secondary{--bs-link-underline-opacity: 1;text-decoration-color:rgba(var(--bs-secondary-rgb), var(--bs-link-underline-opacity)) !important}.link-underline-success{--bs-link-underline-opacity: 1;text-decoration-color:rgba(var(--bs-success-rgb), var(--bs-link-underline-opacity)) !important}.link-underline-info{--bs-link-underline-opacity: 1;text-decoration-color:rgba(var(--bs-info-rgb), var(--bs-link-underline-opacity)) !important}.link-underline-warning{--bs-link-underline-opacity: 1;text-decoration-color:rgba(var(--bs-warning-rgb), var(--bs-link-underline-opacity)) !important}.link-underline-danger{--bs-link-underline-opacity: 1;text-decoration-color:rgba(var(--bs-danger-rgb), var(--bs-link-underline-opacity)) !important}.link-underline-light{--bs-link-underline-opacity: 1;text-decoration-color:rgba(var(--bs-light-rgb), var(--bs-link-underline-opacity)) !important}.link-underline-dark{--bs-link-underline-opacity: 1;text-decoration-color:rgba(var(--bs-dark-rgb), var(--bs-link-underline-opacity)) !important}.link-underline{--bs-link-underline-opacity: 1;text-decoration-color:rgba(var(--bs-link-color-rgb), var(--bs-link-underline-opacity, 1)) !important}.link-underline-opacity-0{--bs-link-underline-opacity: 0}.link-underline-opacity-0-hover:hover{--bs-link-underline-opacity: 0}.link-underline-opacity-10{--bs-link-underline-opacity: .1}.link-underline-opacity-10-hover:hover{--bs-link-underline-opacity: .1}.link-underline-opacity-25{--bs-link-underline-opacity: .25}.link-underline-opacity-25-hover:hover{--bs-link-underline-opacity: .25}.link-underline-opacity-50{--bs-link-underline-opacity: .5}.link-underline-opacity-50-hover:hover{--bs-link-underline-opacity: .5}.link-underline-opacity-75{--bs-link-underline-opacity: .75}.link-underline-opacity-75-hover:hover{--bs-link-underline-opacity: .75}.link-underline-opacity-100{--bs-link-underline-opacity: 1}.link-underline-opacity-100-hover:hover{--bs-link-underline-opacity: 1}.bg-default{--bs-bg-opacity: 1;background-color:rgba(var(--bs-default-rgb), var(--bs-bg-opacity)) !important}.bg-primary{--bs-bg-opacity: 1;background-color:rgba(var(--bs-primary-rgb), var(--bs-bg-opacity)) !important}.bg-secondary{--bs-bg-opacity: 1;background-color:rgba(var(--bs-secondary-rgb), var(--bs-bg-opacity)) !important}.bg-success{--bs-bg-opacity: 1;background-color:rgba(var(--bs-success-rgb), var(--bs-bg-opacity)) !important}.bg-info{--bs-bg-opacity: 1;background-color:rgba(var(--bs-info-rgb), var(--bs-bg-opacity)) !important}.bg-warning{--bs-bg-opacity: 1;background-color:rgba(var(--bs-warning-rgb), var(--bs-bg-opacity)) !important}.bg-danger{--bs-bg-opacity: 1;background-color:rgba(var(--bs-danger-rgb), var(--bs-bg-opacity)) !important}.bg-light{--bs-bg-opacity: 1;background-color:rgba(var(--bs-light-rgb), var(--bs-bg-opacity)) !important}.bg-dark{--bs-bg-opacity: 1;background-color:rgba(var(--bs-dark-rgb), var(--bs-bg-opacity)) !important}.bg-black{--bs-bg-opacity: 1;background-color:rgba(var(--bs-black-rgb), var(--bs-bg-opacity)) !important}.bg-white{--bs-bg-opacity: 1;background-color:rgba(var(--bs-white-rgb), var(--bs-bg-opacity)) !important}.bg-body{--bs-bg-opacity: 1;background-color:rgba(var(--bs-body-bg-rgb), var(--bs-bg-opacity)) !important}.bg-transparent{--bs-bg-opacity: 1;background-color:rgba(0,0,0,0) !important}.bg-body-secondary{--bs-bg-opacity: 1;background-color:rgba(var(--bs-secondary-bg-rgb), var(--bs-bg-opacity)) !important}.bg-body-tertiary{--bs-bg-opacity: 1;background-color:rgba(var(--bs-tertiary-bg-rgb), var(--bs-bg-opacity)) !important}.bg-opacity-10{--bs-bg-opacity: .1}.bg-opacity-25{--bs-bg-opacity: .25}.bg-opacity-50{--bs-bg-opacity: .5}.bg-opacity-75{--bs-bg-opacity: .75}.bg-opacity-100{--bs-bg-opacity: 1}.bg-primary-subtle{background-color:var(--bs-primary-bg-subtle) !important}.bg-secondary-subtle{background-color:var(--bs-secondary-bg-subtle) !important}.bg-success-subtle{background-color:var(--bs-success-bg-subtle) !important}.bg-info-subtle{background-color:var(--bs-info-bg-subtle) !important}.bg-warning-subtle{background-color:var(--bs-warning-bg-subtle) !important}.bg-danger-subtle{background-color:var(--bs-danger-bg-subtle) !important}.bg-light-subtle{background-color:var(--bs-light-bg-subtle) !important}.bg-dark-subtle{background-color:var(--bs-dark-bg-subtle) !important}.bg-gradient{background-image:var(--bs-gradient) !important}.user-select-all{user-select:all !important}.user-select-auto{user-select:auto !important}.user-select-none{user-select:none !important}.pe-none{pointer-events:none !important}.pe-auto{pointer-events:auto !important}.rounded{border-radius:var(--bs-border-radius) !important}.rounded-0{border-radius:0 !important}.rounded-1{border-radius:var(--bs-border-radius-sm) !important}.rounded-2{border-radius:var(--bs-border-radius) !important}.rounded-3{border-radius:var(--bs-border-radius-lg) !important}.rounded-4{border-radius:var(--bs-border-radius-xl) !important}.rounded-5{border-radius:var(--bs-border-radius-xxl) !important}.rounded-circle{border-radius:50% !important}.rounded-pill{border-radius:var(--bs-border-radius-pill) !important}.rounded-top{border-top-left-radius:var(--bs-border-radius) !important;border-top-right-radius:var(--bs-border-radius) !important}.rounded-top-0{border-top-left-radius:0 !important;border-top-right-radius:0 !important}.rounded-top-1{border-top-left-radius:var(--bs-border-radius-sm) !important;border-top-right-radius:var(--bs-border-radius-sm) !important}.rounded-top-2{border-top-left-radius:var(--bs-border-radius) !important;border-top-right-radius:var(--bs-border-radius) !important}.rounded-top-3{border-top-left-radius:var(--bs-border-radius-lg) !important;border-top-right-radius:var(--bs-border-radius-lg) !important}.rounded-top-4{border-top-left-radius:var(--bs-border-radius-xl) !important;border-top-right-radius:var(--bs-border-radius-xl) !important}.rounded-top-5{border-top-left-radius:var(--bs-border-radius-xxl) !important;border-top-right-radius:var(--bs-border-radius-xxl) !important}.rounded-top-circle{border-top-left-radius:50% !important;border-top-right-radius:50% !important}.rounded-top-pill{border-top-left-radius:var(--bs-border-radius-pill) !important;border-top-right-radius:var(--bs-border-radius-pill) !important}.rounded-end{border-top-right-radius:var(--bs-border-radius) !important;border-bottom-right-radius:var(--bs-border-radius) !important}.rounded-end-0{border-top-right-radius:0 !important;border-bottom-right-radius:0 !important}.rounded-end-1{border-top-right-radius:var(--bs-border-radius-sm) !important;border-bottom-right-radius:var(--bs-border-radius-sm) !important}.rounded-end-2{border-top-right-radius:var(--bs-border-radius) !important;border-bottom-right-radius:var(--bs-border-radius) !important}.rounded-end-3{border-top-right-radius:var(--bs-border-radius-lg) !important;border-bottom-right-radius:var(--bs-border-radius-lg) !important}.rounded-end-4{border-top-right-radius:var(--bs-border-radius-xl) !important;border-bottom-right-radius:var(--bs-border-radius-xl) !important}.rounded-end-5{border-top-right-radius:var(--bs-border-radius-xxl) !important;border-bottom-right-radius:var(--bs-border-radius-xxl) !important}.rounded-end-circle{border-top-right-radius:50% !important;border-bottom-right-radius:50% !important}.rounded-end-pill{border-top-right-radius:var(--bs-border-radius-pill) !important;border-bottom-right-radius:var(--bs-border-radius-pill) !important}.rounded-bottom{border-bottom-right-radius:var(--bs-border-radius) !important;border-bottom-left-radius:var(--bs-border-radius) !important}.rounded-bottom-0{border-bottom-right-radius:0 !important;border-bottom-left-radius:0 !important}.rounded-bottom-1{border-bottom-right-radius:var(--bs-border-radius-sm) !important;border-bottom-left-radius:var(--bs-border-radius-sm) !important}.rounded-bottom-2{border-bottom-right-radius:var(--bs-border-radius) !important;border-bottom-left-radius:var(--bs-border-radius) !important}.rounded-bottom-3{border-bottom-right-radius:var(--bs-border-radius-lg) !important;border-bottom-left-radius:var(--bs-border-radius-lg) !important}.rounded-bottom-4{border-bottom-right-radius:var(--bs-border-radius-xl) !important;border-bottom-left-radius:var(--bs-border-radius-xl) !important}.rounded-bottom-5{border-bottom-right-radius:var(--bs-border-radius-xxl) !important;border-bottom-left-radius:var(--bs-border-radius-xxl) !important}.rounded-bottom-circle{border-bottom-right-radius:50% !important;border-bottom-left-radius:50% !important}.rounded-bottom-pill{border-bottom-right-radius:var(--bs-border-radius-pill) !important;border-bottom-left-radius:var(--bs-border-radius-pill) !important}.rounded-start{border-bottom-left-radius:var(--bs-border-radius) !important;border-top-left-radius:var(--bs-border-radius) !important}.rounded-start-0{border-bottom-left-radius:0 !important;border-top-left-radius:0 !important}.rounded-start-1{border-bottom-left-radius:var(--bs-border-radius-sm) !important;border-top-left-radius:var(--bs-border-radius-sm) !important}.rounded-start-2{border-bottom-left-radius:var(--bs-border-radius) !important;border-top-left-radius:var(--bs-border-radius) !important}.rounded-start-3{border-bottom-left-radius:var(--bs-border-radius-lg) !important;border-top-left-radius:var(--bs-border-radius-lg) !important}.rounded-start-4{border-bottom-left-radius:var(--bs-border-radius-xl) !important;border-top-left-radius:var(--bs-border-radius-xl) !important}.rounded-start-5{border-bottom-left-radius:var(--bs-border-radius-xxl) !important;border-top-left-radius:var(--bs-border-radius-xxl) !important}.rounded-start-circle{border-bottom-left-radius:50% !important;border-top-left-radius:50% !important}.rounded-start-pill{border-bottom-left-radius:var(--bs-border-radius-pill) !important;border-top-left-radius:var(--bs-border-radius-pill) !important}.visible{visibility:visible !important}.invisible{visibility:hidden !important}.z-n1{z-index:-1 !important}.z-0{z-index:0 !important}.z-1{z-index:1 !important}.z-2{z-index:2 !important}.z-3{z-index:3 !important}@media (min-width: 576px){.float-sm-start{float:left !important}.float-sm-end{float:right !important}.float-sm-none{float:none !important}.object-fit-sm-contain{object-fit:contain !important}.object-fit-sm-cover{object-fit:cover !important}.object-fit-sm-fill{object-fit:fill !important}.object-fit-sm-scale{object-fit:scale-down !important}.object-fit-sm-none{object-fit:none !important}.d-sm-inline{display:inline !important}.d-sm-inline-block{display:inline-block !important}.d-sm-block{display:block !important}.d-sm-grid{display:grid !important}.d-sm-inline-grid{display:inline-grid !important}.d-sm-table{display:table !important}.d-sm-table-row{display:table-row !important}.d-sm-table-cell{display:table-cell !important}.d-sm-flex{display:flex !important}.d-sm-inline-flex{display:inline-flex !important}.d-sm-none{display:none !important}.flex-sm-fill{flex:1 1 auto !important}.flex-sm-row{flex-direction:row !important}.flex-sm-column{flex-direction:column !important}.flex-sm-row-reverse{flex-direction:row-reverse !important}.flex-sm-column-reverse{flex-direction:column-reverse !important}.flex-sm-grow-0{flex-grow:0 !important}.flex-sm-grow-1{flex-grow:1 !important}.flex-sm-shrink-0{flex-shrink:0 !important}.flex-sm-shrink-1{flex-shrink:1 !important}.flex-sm-wrap{flex-wrap:wrap !important}.flex-sm-nowrap{flex-wrap:nowrap !important}.flex-sm-wrap-reverse{flex-wrap:wrap-reverse !important}.justify-content-sm-start{justify-content:flex-start !important}.justify-content-sm-end{justify-content:flex-end !important}.justify-content-sm-center{justify-content:center !important}.justify-content-sm-between{justify-content:space-between !important}.justify-content-sm-around{justify-content:space-around !important}.justify-content-sm-evenly{justify-content:space-evenly !important}.align-items-sm-start{align-items:flex-start !important}.align-items-sm-end{align-items:flex-end !important}.align-items-sm-center{align-items:center !important}.align-items-sm-baseline{align-items:baseline !important}.align-items-sm-stretch{align-items:stretch !important}.align-content-sm-start{align-content:flex-start !important}.align-content-sm-end{align-content:flex-end !important}.align-content-sm-center{align-content:center !important}.align-content-sm-between{align-content:space-between !important}.align-content-sm-around{align-content:space-around !important}.align-content-sm-stretch{align-content:stretch !important}.align-self-sm-auto{align-self:auto !important}.align-self-sm-start{align-self:flex-start !important}.align-self-sm-end{align-self:flex-end !important}.align-self-sm-center{align-self:center !important}.align-self-sm-baseline{align-self:baseline !important}.align-self-sm-stretch{align-self:stretch !important}.order-sm-first{order:-1 !important}.order-sm-0{order:0 !important}.order-sm-1{order:1 !important}.order-sm-2{order:2 !important}.order-sm-3{order:3 !important}.order-sm-4{order:4 !important}.order-sm-5{order:5 !important}.order-sm-last{order:6 !important}.m-sm-0{margin:0 !important}.m-sm-1{margin:.25rem !important}.m-sm-2{margin:.5rem !important}.m-sm-3{margin:1rem !important}.m-sm-4{margin:1.5rem !important}.m-sm-5{margin:3rem !important}.m-sm-auto{margin:auto !important}.mx-sm-0{margin-right:0 !important;margin-left:0 !important}.mx-sm-1{margin-right:.25rem !important;margin-left:.25rem !important}.mx-sm-2{margin-right:.5rem !important;margin-left:.5rem !important}.mx-sm-3{margin-right:1rem !important;margin-left:1rem !important}.mx-sm-4{margin-right:1.5rem !important;margin-left:1.5rem !important}.mx-sm-5{margin-right:3rem !important;margin-left:3rem !important}.mx-sm-auto{margin-right:auto !important;margin-left:auto !important}.my-sm-0{margin-top:0 !important;margin-bottom:0 !important}.my-sm-1{margin-top:.25rem !important;margin-bottom:.25rem !important}.my-sm-2{margin-top:.5rem !important;margin-bottom:.5rem !important}.my-sm-3{margin-top:1rem !important;margin-bottom:1rem !important}.my-sm-4{margin-top:1.5rem !important;margin-bottom:1.5rem !important}.my-sm-5{margin-top:3rem !important;margin-bottom:3rem !important}.my-sm-auto{margin-top:auto !important;margin-bottom:auto !important}.mt-sm-0{margin-top:0 !important}.mt-sm-1{margin-top:.25rem !important}.mt-sm-2{margin-top:.5rem !important}.mt-sm-3{margin-top:1rem !important}.mt-sm-4{margin-top:1.5rem !important}.mt-sm-5{margin-top:3rem !important}.mt-sm-auto{margin-top:auto !important}.me-sm-0{margin-right:0 !important}.me-sm-1{margin-right:.25rem !important}.me-sm-2{margin-right:.5rem !important}.me-sm-3{margin-right:1rem !important}.me-sm-4{margin-right:1.5rem !important}.me-sm-5{margin-right:3rem !important}.me-sm-auto{margin-right:auto !important}.mb-sm-0{margin-bottom:0 !important}.mb-sm-1{margin-bottom:.25rem !important}.mb-sm-2{margin-bottom:.5rem !important}.mb-sm-3{margin-bottom:1rem !important}.mb-sm-4{margin-bottom:1.5rem !important}.mb-sm-5{margin-bottom:3rem !important}.mb-sm-auto{margin-bottom:auto !important}.ms-sm-0{margin-left:0 !important}.ms-sm-1{margin-left:.25rem !important}.ms-sm-2{margin-left:.5rem !important}.ms-sm-3{margin-left:1rem !important}.ms-sm-4{margin-left:1.5rem !important}.ms-sm-5{margin-left:3rem !important}.ms-sm-auto{margin-left:auto !important}.p-sm-0{padding:0 !important}.p-sm-1{padding:.25rem !important}.p-sm-2{padding:.5rem !important}.p-sm-3{padding:1rem !important}.p-sm-4{padding:1.5rem !important}.p-sm-5{padding:3rem !important}.px-sm-0{padding-right:0 !important;padding-left:0 !important}.px-sm-1{padding-right:.25rem !important;padding-left:.25rem !important}.px-sm-2{padding-right:.5rem !important;padding-left:.5rem !important}.px-sm-3{padding-right:1rem !important;padding-left:1rem !important}.px-sm-4{padding-right:1.5rem !important;padding-left:1.5rem !important}.px-sm-5{padding-right:3rem !important;padding-left:3rem !important}.py-sm-0{padding-top:0 !important;padding-bottom:0 !important}.py-sm-1{padding-top:.25rem !important;padding-bottom:.25rem !important}.py-sm-2{padding-top:.5rem !important;padding-bottom:.5rem !important}.py-sm-3{padding-top:1rem !important;padding-bottom:1rem !important}.py-sm-4{padding-top:1.5rem !important;padding-bottom:1.5rem !important}.py-sm-5{padding-top:3rem !important;padding-bottom:3rem !important}.pt-sm-0{padding-top:0 !important}.pt-sm-1{padding-top:.25rem !important}.pt-sm-2{padding-top:.5rem !important}.pt-sm-3{padding-top:1rem !important}.pt-sm-4{padding-top:1.5rem !important}.pt-sm-5{padding-top:3rem !important}.pe-sm-0{padding-right:0 !important}.pe-sm-1{padding-right:.25rem !important}.pe-sm-2{padding-right:.5rem !important}.pe-sm-3{padding-right:1rem !important}.pe-sm-4{padding-right:1.5rem !important}.pe-sm-5{padding-right:3rem !important}.pb-sm-0{padding-bottom:0 !important}.pb-sm-1{padding-bottom:.25rem !important}.pb-sm-2{padding-bottom:.5rem !important}.pb-sm-3{padding-bottom:1rem !important}.pb-sm-4{padding-bottom:1.5rem !important}.pb-sm-5{padding-bottom:3rem !important}.ps-sm-0{padding-left:0 !important}.ps-sm-1{padding-left:.25rem !important}.ps-sm-2{padding-left:.5rem !important}.ps-sm-3{padding-left:1rem !important}.ps-sm-4{padding-left:1.5rem !important}.ps-sm-5{padding-left:3rem !important}.gap-sm-0{gap:0 !important}.gap-sm-1{gap:.25rem !important}.gap-sm-2{gap:.5rem !important}.gap-sm-3{gap:1rem !important}.gap-sm-4{gap:1.5rem !important}.gap-sm-5{gap:3rem !important}.row-gap-sm-0{row-gap:0 !important}.row-gap-sm-1{row-gap:.25rem !important}.row-gap-sm-2{row-gap:.5rem !important}.row-gap-sm-3{row-gap:1rem !important}.row-gap-sm-4{row-gap:1.5rem !important}.row-gap-sm-5{row-gap:3rem !important}.column-gap-sm-0{column-gap:0 !important}.column-gap-sm-1{column-gap:.25rem !important}.column-gap-sm-2{column-gap:.5rem !important}.column-gap-sm-3{column-gap:1rem !important}.column-gap-sm-4{column-gap:1.5rem !important}.column-gap-sm-5{column-gap:3rem !important}.text-sm-start{text-align:left !important}.text-sm-end{text-align:right !important}.text-sm-center{text-align:center !important}}@media (min-width: 768px){.float-md-start{float:left !important}.float-md-end{float:right !important}.float-md-none{float:none !important}.object-fit-md-contain{object-fit:contain !important}.object-fit-md-cover{object-fit:cover !important}.object-fit-md-fill{object-fit:fill !important}.object-fit-md-scale{object-fit:scale-down !important}.object-fit-md-none{object-fit:none !important}.d-md-inline{display:inline !important}.d-md-inline-block{display:inline-block !important}.d-md-block{display:block !important}.d-md-grid{display:grid !important}.d-md-inline-grid{display:inline-grid !important}.d-md-table{display:table !important}.d-md-table-row{display:table-row !important}.d-md-table-cell{display:table-cell !important}.d-md-flex{display:flex !important}.d-md-inline-flex{display:inline-flex !important}.d-md-none{display:none !important}.flex-md-fill{flex:1 1 auto !important}.flex-md-row{flex-direction:row !important}.flex-md-column{flex-direction:column !important}.flex-md-row-reverse{flex-direction:row-reverse !important}.flex-md-column-reverse{flex-direction:column-reverse !important}.flex-md-grow-0{flex-grow:0 !important}.flex-md-grow-1{flex-grow:1 !important}.flex-md-shrink-0{flex-shrink:0 !important}.flex-md-shrink-1{flex-shrink:1 !important}.flex-md-wrap{flex-wrap:wrap !important}.flex-md-nowrap{flex-wrap:nowrap !important}.flex-md-wrap-reverse{flex-wrap:wrap-reverse !important}.justify-content-md-start{justify-content:flex-start !important}.justify-content-md-end{justify-content:flex-end !important}.justify-content-md-center{justify-content:center !important}.justify-content-md-between{justify-content:space-between !important}.justify-content-md-around{justify-content:space-around !important}.justify-content-md-evenly{justify-content:space-evenly !important}.align-items-md-start{align-items:flex-start !important}.align-items-md-end{align-items:flex-end !important}.align-items-md-center{align-items:center !important}.align-items-md-baseline{align-items:baseline !important}.align-items-md-stretch{align-items:stretch !important}.align-content-md-start{align-content:flex-start !important}.align-content-md-end{align-content:flex-end !important}.align-content-md-center{align-content:center !important}.align-content-md-between{align-content:space-between !important}.align-content-md-around{align-content:space-around !important}.align-content-md-stretch{align-content:stretch !important}.align-self-md-auto{align-self:auto !important}.align-self-md-start{align-self:flex-start !important}.align-self-md-end{align-self:flex-end !important}.align-self-md-center{align-self:center !important}.align-self-md-baseline{align-self:baseline !important}.align-self-md-stretch{align-self:stretch !important}.order-md-first{order:-1 !important}.order-md-0{order:0 !important}.order-md-1{order:1 !important}.order-md-2{order:2 !important}.order-md-3{order:3 !important}.order-md-4{order:4 !important}.order-md-5{order:5 !important}.order-md-last{order:6 !important}.m-md-0{margin:0 !important}.m-md-1{margin:.25rem !important}.m-md-2{margin:.5rem !important}.m-md-3{margin:1rem !important}.m-md-4{margin:1.5rem !important}.m-md-5{margin:3rem !important}.m-md-auto{margin:auto !important}.mx-md-0{margin-right:0 !important;margin-left:0 !important}.mx-md-1{margin-right:.25rem !important;margin-left:.25rem !important}.mx-md-2{margin-right:.5rem !important;margin-left:.5rem !important}.mx-md-3{margin-right:1rem !important;margin-left:1rem !important}.mx-md-4{margin-right:1.5rem !important;margin-left:1.5rem !important}.mx-md-5{margin-right:3rem !important;margin-left:3rem !important}.mx-md-auto{margin-right:auto !important;margin-left:auto !important}.my-md-0{margin-top:0 !important;margin-bottom:0 !important}.my-md-1{margin-top:.25rem !important;margin-bottom:.25rem !important}.my-md-2{margin-top:.5rem !important;margin-bottom:.5rem !important}.my-md-3{margin-top:1rem !important;margin-bottom:1rem !important}.my-md-4{margin-top:1.5rem !important;margin-bottom:1.5rem !important}.my-md-5{margin-top:3rem !important;margin-bottom:3rem !important}.my-md-auto{margin-top:auto !important;margin-bottom:auto !important}.mt-md-0{margin-top:0 !important}.mt-md-1{margin-top:.25rem !important}.mt-md-2{margin-top:.5rem !important}.mt-md-3{margin-top:1rem !important}.mt-md-4{margin-top:1.5rem !important}.mt-md-5{margin-top:3rem !important}.mt-md-auto{margin-top:auto !important}.me-md-0{margin-right:0 !important}.me-md-1{margin-right:.25rem !important}.me-md-2{margin-right:.5rem !important}.me-md-3{margin-right:1rem !important}.me-md-4{margin-right:1.5rem !important}.me-md-5{margin-right:3rem !important}.me-md-auto{margin-right:auto !important}.mb-md-0{margin-bottom:0 !important}.mb-md-1{margin-bottom:.25rem !important}.mb-md-2{margin-bottom:.5rem !important}.mb-md-3{margin-bottom:1rem !important}.mb-md-4{margin-bottom:1.5rem !important}.mb-md-5{margin-bottom:3rem !important}.mb-md-auto{margin-bottom:auto !important}.ms-md-0{margin-left:0 !important}.ms-md-1{margin-left:.25rem !important}.ms-md-2{margin-left:.5rem !important}.ms-md-3{margin-left:1rem !important}.ms-md-4{margin-left:1.5rem !important}.ms-md-5{margin-left:3rem !important}.ms-md-auto{margin-left:auto !important}.p-md-0{padding:0 !important}.p-md-1{padding:.25rem !important}.p-md-2{padding:.5rem !important}.p-md-3{padding:1rem !important}.p-md-4{padding:1.5rem !important}.p-md-5{padding:3rem !important}.px-md-0{padding-right:0 !important;padding-left:0 !important}.px-md-1{padding-right:.25rem !important;padding-left:.25rem !important}.px-md-2{padding-right:.5rem !important;padding-left:.5rem !important}.px-md-3{padding-right:1rem !important;padding-left:1rem !important}.px-md-4{padding-right:1.5rem !important;padding-left:1.5rem !important}.px-md-5{padding-right:3rem !important;padding-left:3rem !important}.py-md-0{padding-top:0 !important;padding-bottom:0 !important}.py-md-1{padding-top:.25rem !important;padding-bottom:.25rem !important}.py-md-2{padding-top:.5rem !important;padding-bottom:.5rem !important}.py-md-3{padding-top:1rem !important;padding-bottom:1rem !important}.py-md-4{padding-top:1.5rem !important;padding-bottom:1.5rem !important}.py-md-5{padding-top:3rem !important;padding-bottom:3rem !important}.pt-md-0{padding-top:0 !important}.pt-md-1{padding-top:.25rem !important}.pt-md-2{padding-top:.5rem !important}.pt-md-3{padding-top:1rem !important}.pt-md-4{padding-top:1.5rem !important}.pt-md-5{padding-top:3rem !important}.pe-md-0{padding-right:0 !important}.pe-md-1{padding-right:.25rem !important}.pe-md-2{padding-right:.5rem !important}.pe-md-3{padding-right:1rem !important}.pe-md-4{padding-right:1.5rem !important}.pe-md-5{padding-right:3rem !important}.pb-md-0{padding-bottom:0 !important}.pb-md-1{padding-bottom:.25rem !important}.pb-md-2{padding-bottom:.5rem !important}.pb-md-3{padding-bottom:1rem !important}.pb-md-4{padding-bottom:1.5rem !important}.pb-md-5{padding-bottom:3rem !important}.ps-md-0{padding-left:0 !important}.ps-md-1{padding-left:.25rem !important}.ps-md-2{padding-left:.5rem !important}.ps-md-3{padding-left:1rem !important}.ps-md-4{padding-left:1.5rem !important}.ps-md-5{padding-left:3rem !important}.gap-md-0{gap:0 !important}.gap-md-1{gap:.25rem !important}.gap-md-2{gap:.5rem !important}.gap-md-3{gap:1rem !important}.gap-md-4{gap:1.5rem !important}.gap-md-5{gap:3rem !important}.row-gap-md-0{row-gap:0 !important}.row-gap-md-1{row-gap:.25rem !important}.row-gap-md-2{row-gap:.5rem !important}.row-gap-md-3{row-gap:1rem !important}.row-gap-md-4{row-gap:1.5rem !important}.row-gap-md-5{row-gap:3rem !important}.column-gap-md-0{column-gap:0 !important}.column-gap-md-1{column-gap:.25rem !important}.column-gap-md-2{column-gap:.5rem !important}.column-gap-md-3{column-gap:1rem !important}.column-gap-md-4{column-gap:1.5rem !important}.column-gap-md-5{column-gap:3rem !important}.text-md-start{text-align:left !important}.text-md-end{text-align:right !important}.text-md-center{text-align:center !important}}@media (min-width: 992px){.float-lg-start{float:left !important}.float-lg-end{float:right !important}.float-lg-none{float:none !important}.object-fit-lg-contain{object-fit:contain !important}.object-fit-lg-cover{object-fit:cover !important}.object-fit-lg-fill{object-fit:fill !important}.object-fit-lg-scale{object-fit:scale-down !important}.object-fit-lg-none{object-fit:none !important}.d-lg-inline{display:inline !important}.d-lg-inline-block{display:inline-block !important}.d-lg-block{display:block !important}.d-lg-grid{display:grid !important}.d-lg-inline-grid{display:inline-grid !important}.d-lg-table{display:table !important}.d-lg-table-row{display:table-row !important}.d-lg-table-cell{display:table-cell !important}.d-lg-flex{display:flex !important}.d-lg-inline-flex{display:inline-flex !important}.d-lg-none{display:none !important}.flex-lg-fill{flex:1 1 auto !important}.flex-lg-row{flex-direction:row !important}.flex-lg-column{flex-direction:column !important}.flex-lg-row-reverse{flex-direction:row-reverse !important}.flex-lg-column-reverse{flex-direction:column-reverse !important}.flex-lg-grow-0{flex-grow:0 !important}.flex-lg-grow-1{flex-grow:1 !important}.flex-lg-shrink-0{flex-shrink:0 !important}.flex-lg-shrink-1{flex-shrink:1 !important}.flex-lg-wrap{flex-wrap:wrap !important}.flex-lg-nowrap{flex-wrap:nowrap !important}.flex-lg-wrap-reverse{flex-wrap:wrap-reverse !important}.justify-content-lg-start{justify-content:flex-start !important}.justify-content-lg-end{justify-content:flex-end !important}.justify-content-lg-center{justify-content:center !important}.justify-content-lg-between{justify-content:space-between !important}.justify-content-lg-around{justify-content:space-around !important}.justify-content-lg-evenly{justify-content:space-evenly !important}.align-items-lg-start{align-items:flex-start !important}.align-items-lg-end{align-items:flex-end !important}.align-items-lg-center{align-items:center !important}.align-items-lg-baseline{align-items:baseline !important}.align-items-lg-stretch{align-items:stretch !important}.align-content-lg-start{align-content:flex-start !important}.align-content-lg-end{align-content:flex-end !important}.align-content-lg-center{align-content:center !important}.align-content-lg-between{align-content:space-between !important}.align-content-lg-around{align-content:space-around !important}.align-content-lg-stretch{align-content:stretch !important}.align-self-lg-auto{align-self:auto !important}.align-self-lg-start{align-self:flex-start !important}.align-self-lg-end{align-self:flex-end !important}.align-self-lg-center{align-self:center !important}.align-self-lg-baseline{align-self:baseline !important}.align-self-lg-stretch{align-self:stretch !important}.order-lg-first{order:-1 !important}.order-lg-0{order:0 !important}.order-lg-1{order:1 !important}.order-lg-2{order:2 !important}.order-lg-3{order:3 !important}.order-lg-4{order:4 !important}.order-lg-5{order:5 !important}.order-lg-last{order:6 !important}.m-lg-0{margin:0 !important}.m-lg-1{margin:.25rem !important}.m-lg-2{margin:.5rem !important}.m-lg-3{margin:1rem !important}.m-lg-4{margin:1.5rem !important}.m-lg-5{margin:3rem !important}.m-lg-auto{margin:auto !important}.mx-lg-0{margin-right:0 !important;margin-left:0 !important}.mx-lg-1{margin-right:.25rem !important;margin-left:.25rem !important}.mx-lg-2{margin-right:.5rem !important;margin-left:.5rem !important}.mx-lg-3{margin-right:1rem !important;margin-left:1rem !important}.mx-lg-4{margin-right:1.5rem !important;margin-left:1.5rem !important}.mx-lg-5{margin-right:3rem !important;margin-left:3rem !important}.mx-lg-auto{margin-right:auto !important;margin-left:auto !important}.my-lg-0{margin-top:0 !important;margin-bottom:0 !important}.my-lg-1{margin-top:.25rem !important;margin-bottom:.25rem !important}.my-lg-2{margin-top:.5rem !important;margin-bottom:.5rem !important}.my-lg-3{margin-top:1rem !important;margin-bottom:1rem !important}.my-lg-4{margin-top:1.5rem !important;margin-bottom:1.5rem !important}.my-lg-5{margin-top:3rem !important;margin-bottom:3rem !important}.my-lg-auto{margin-top:auto !important;margin-bottom:auto !important}.mt-lg-0{margin-top:0 !important}.mt-lg-1{margin-top:.25rem !important}.mt-lg-2{margin-top:.5rem !important}.mt-lg-3{margin-top:1rem !important}.mt-lg-4{margin-top:1.5rem !important}.mt-lg-5{margin-top:3rem !important}.mt-lg-auto{margin-top:auto !important}.me-lg-0{margin-right:0 !important}.me-lg-1{margin-right:.25rem !important}.me-lg-2{margin-right:.5rem !important}.me-lg-3{margin-right:1rem !important}.me-lg-4{margin-right:1.5rem !important}.me-lg-5{margin-right:3rem !important}.me-lg-auto{margin-right:auto !important}.mb-lg-0{margin-bottom:0 !important}.mb-lg-1{margin-bottom:.25rem !important}.mb-lg-2{margin-bottom:.5rem !important}.mb-lg-3{margin-bottom:1rem !important}.mb-lg-4{margin-bottom:1.5rem !important}.mb-lg-5{margin-bottom:3rem !important}.mb-lg-auto{margin-bottom:auto !important}.ms-lg-0{margin-left:0 !important}.ms-lg-1{margin-left:.25rem !important}.ms-lg-2{margin-left:.5rem !important}.ms-lg-3{margin-left:1rem !important}.ms-lg-4{margin-left:1.5rem !important}.ms-lg-5{margin-left:3rem !important}.ms-lg-auto{margin-left:auto !important}.p-lg-0{padding:0 !important}.p-lg-1{padding:.25rem !important}.p-lg-2{padding:.5rem !important}.p-lg-3{padding:1rem !important}.p-lg-4{padding:1.5rem !important}.p-lg-5{padding:3rem !important}.px-lg-0{padding-right:0 !important;padding-left:0 !important}.px-lg-1{padding-right:.25rem !important;padding-left:.25rem !important}.px-lg-2{padding-right:.5rem !important;padding-left:.5rem !important}.px-lg-3{padding-right:1rem !important;padding-left:1rem !important}.px-lg-4{padding-right:1.5rem !important;padding-left:1.5rem !important}.px-lg-5{padding-right:3rem !important;padding-left:3rem !important}.py-lg-0{padding-top:0 !important;padding-bottom:0 !important}.py-lg-1{padding-top:.25rem !important;padding-bottom:.25rem !important}.py-lg-2{padding-top:.5rem !important;padding-bottom:.5rem !important}.py-lg-3{padding-top:1rem !important;padding-bottom:1rem !important}.py-lg-4{padding-top:1.5rem !important;padding-bottom:1.5rem !important}.py-lg-5{padding-top:3rem !important;padding-bottom:3rem !important}.pt-lg-0{padding-top:0 !important}.pt-lg-1{padding-top:.25rem !important}.pt-lg-2{padding-top:.5rem !important}.pt-lg-3{padding-top:1rem !important}.pt-lg-4{padding-top:1.5rem !important}.pt-lg-5{padding-top:3rem !important}.pe-lg-0{padding-right:0 !important}.pe-lg-1{padding-right:.25rem !important}.pe-lg-2{padding-right:.5rem !important}.pe-lg-3{padding-right:1rem !important}.pe-lg-4{padding-right:1.5rem !important}.pe-lg-5{padding-right:3rem !important}.pb-lg-0{padding-bottom:0 !important}.pb-lg-1{padding-bottom:.25rem !important}.pb-lg-2{padding-bottom:.5rem !important}.pb-lg-3{padding-bottom:1rem !important}.pb-lg-4{padding-bottom:1.5rem !important}.pb-lg-5{padding-bottom:3rem !important}.ps-lg-0{padding-left:0 !important}.ps-lg-1{padding-left:.25rem !important}.ps-lg-2{padding-left:.5rem !important}.ps-lg-3{padding-left:1rem !important}.ps-lg-4{padding-left:1.5rem !important}.ps-lg-5{padding-left:3rem !important}.gap-lg-0{gap:0 !important}.gap-lg-1{gap:.25rem !important}.gap-lg-2{gap:.5rem !important}.gap-lg-3{gap:1rem !important}.gap-lg-4{gap:1.5rem !important}.gap-lg-5{gap:3rem !important}.row-gap-lg-0{row-gap:0 !important}.row-gap-lg-1{row-gap:.25rem !important}.row-gap-lg-2{row-gap:.5rem !important}.row-gap-lg-3{row-gap:1rem !important}.row-gap-lg-4{row-gap:1.5rem !important}.row-gap-lg-5{row-gap:3rem !important}.column-gap-lg-0{column-gap:0 !important}.column-gap-lg-1{column-gap:.25rem !important}.column-gap-lg-2{column-gap:.5rem !important}.column-gap-lg-3{column-gap:1rem !important}.column-gap-lg-4{column-gap:1.5rem !important}.column-gap-lg-5{column-gap:3rem !important}.text-lg-start{text-align:left !important}.text-lg-end{text-align:right !important}.text-lg-center{text-align:center !important}}@media (min-width: 1200px){.float-xl-start{float:left !important}.float-xl-end{float:right !important}.float-xl-none{float:none !important}.object-fit-xl-contain{object-fit:contain !important}.object-fit-xl-cover{object-fit:cover !important}.object-fit-xl-fill{object-fit:fill !important}.object-fit-xl-scale{object-fit:scale-down !important}.object-fit-xl-none{object-fit:none !important}.d-xl-inline{display:inline !important}.d-xl-inline-block{display:inline-block !important}.d-xl-block{display:block !important}.d-xl-grid{display:grid !important}.d-xl-inline-grid{display:inline-grid !important}.d-xl-table{display:table !important}.d-xl-table-row{display:table-row !important}.d-xl-table-cell{display:table-cell !important}.d-xl-flex{display:flex !important}.d-xl-inline-flex{display:inline-flex !important}.d-xl-none{display:none !important}.flex-xl-fill{flex:1 1 auto !important}.flex-xl-row{flex-direction:row !important}.flex-xl-column{flex-direction:column !important}.flex-xl-row-reverse{flex-direction:row-reverse !important}.flex-xl-column-reverse{flex-direction:column-reverse !important}.flex-xl-grow-0{flex-grow:0 !important}.flex-xl-grow-1{flex-grow:1 !important}.flex-xl-shrink-0{flex-shrink:0 !important}.flex-xl-shrink-1{flex-shrink:1 !important}.flex-xl-wrap{flex-wrap:wrap !important}.flex-xl-nowrap{flex-wrap:nowrap !important}.flex-xl-wrap-reverse{flex-wrap:wrap-reverse !important}.justify-content-xl-start{justify-content:flex-start !important}.justify-content-xl-end{justify-content:flex-end !important}.justify-content-xl-center{justify-content:center !important}.justify-content-xl-between{justify-content:space-between !important}.justify-content-xl-around{justify-content:space-around !important}.justify-content-xl-evenly{justify-content:space-evenly !important}.align-items-xl-start{align-items:flex-start !important}.align-items-xl-end{align-items:flex-end !important}.align-items-xl-center{align-items:center !important}.align-items-xl-baseline{align-items:baseline !important}.align-items-xl-stretch{align-items:stretch !important}.align-content-xl-start{align-content:flex-start !important}.align-content-xl-end{align-content:flex-end !important}.align-content-xl-center{align-content:center !important}.align-content-xl-between{align-content:space-between !important}.align-content-xl-around{align-content:space-around !important}.align-content-xl-stretch{align-content:stretch !important}.align-self-xl-auto{align-self:auto !important}.align-self-xl-start{align-self:flex-start !important}.align-self-xl-end{align-self:flex-end !important}.align-self-xl-center{align-self:center !important}.align-self-xl-baseline{align-self:baseline !important}.align-self-xl-stretch{align-self:stretch !important}.order-xl-first{order:-1 !important}.order-xl-0{order:0 !important}.order-xl-1{order:1 !important}.order-xl-2{order:2 !important}.order-xl-3{order:3 !important}.order-xl-4{order:4 !important}.order-xl-5{order:5 !important}.order-xl-last{order:6 !important}.m-xl-0{margin:0 !important}.m-xl-1{margin:.25rem !important}.m-xl-2{margin:.5rem !important}.m-xl-3{margin:1rem !important}.m-xl-4{margin:1.5rem !important}.m-xl-5{margin:3rem !important}.m-xl-auto{margin:auto !important}.mx-xl-0{margin-right:0 !important;margin-left:0 !important}.mx-xl-1{margin-right:.25rem !important;margin-left:.25rem !important}.mx-xl-2{margin-right:.5rem !important;margin-left:.5rem !important}.mx-xl-3{margin-right:1rem !important;margin-left:1rem !important}.mx-xl-4{margin-right:1.5rem !important;margin-left:1.5rem !important}.mx-xl-5{margin-right:3rem !important;margin-left:3rem !important}.mx-xl-auto{margin-right:auto !important;margin-left:auto !important}.my-xl-0{margin-top:0 !important;margin-bottom:0 !important}.my-xl-1{margin-top:.25rem !important;margin-bottom:.25rem !important}.my-xl-2{margin-top:.5rem !important;margin-bottom:.5rem !important}.my-xl-3{margin-top:1rem !important;margin-bottom:1rem !important}.my-xl-4{margin-top:1.5rem !important;margin-bottom:1.5rem !important}.my-xl-5{margin-top:3rem !important;margin-bottom:3rem !important}.my-xl-auto{margin-top:auto !important;margin-bottom:auto !important}.mt-xl-0{margin-top:0 !important}.mt-xl-1{margin-top:.25rem !important}.mt-xl-2{margin-top:.5rem !important}.mt-xl-3{margin-top:1rem !important}.mt-xl-4{margin-top:1.5rem !important}.mt-xl-5{margin-top:3rem !important}.mt-xl-auto{margin-top:auto !important}.me-xl-0{margin-right:0 !important}.me-xl-1{margin-right:.25rem !important}.me-xl-2{margin-right:.5rem !important}.me-xl-3{margin-right:1rem !important}.me-xl-4{margin-right:1.5rem !important}.me-xl-5{margin-right:3rem !important}.me-xl-auto{margin-right:auto !important}.mb-xl-0{margin-bottom:0 !important}.mb-xl-1{margin-bottom:.25rem !important}.mb-xl-2{margin-bottom:.5rem !important}.mb-xl-3{margin-bottom:1rem !important}.mb-xl-4{margin-bottom:1.5rem !important}.mb-xl-5{margin-bottom:3rem !important}.mb-xl-auto{margin-bottom:auto !important}.ms-xl-0{margin-left:0 !important}.ms-xl-1{margin-left:.25rem !important}.ms-xl-2{margin-left:.5rem !important}.ms-xl-3{margin-left:1rem !important}.ms-xl-4{margin-left:1.5rem !important}.ms-xl-5{margin-left:3rem !important}.ms-xl-auto{margin-left:auto !important}.p-xl-0{padding:0 !important}.p-xl-1{padding:.25rem !important}.p-xl-2{padding:.5rem !important}.p-xl-3{padding:1rem !important}.p-xl-4{padding:1.5rem !important}.p-xl-5{padding:3rem !important}.px-xl-0{padding-right:0 !important;padding-left:0 !important}.px-xl-1{padding-right:.25rem !important;padding-left:.25rem !important}.px-xl-2{padding-right:.5rem !important;padding-left:.5rem !important}.px-xl-3{padding-right:1rem !important;padding-left:1rem !important}.px-xl-4{padding-right:1.5rem !important;padding-left:1.5rem !important}.px-xl-5{padding-right:3rem !important;padding-left:3rem !important}.py-xl-0{padding-top:0 !important;padding-bottom:0 !important}.py-xl-1{padding-top:.25rem !important;padding-bottom:.25rem !important}.py-xl-2{padding-top:.5rem !important;padding-bottom:.5rem !important}.py-xl-3{padding-top:1rem !important;padding-bottom:1rem !important}.py-xl-4{padding-top:1.5rem !important;padding-bottom:1.5rem !important}.py-xl-5{padding-top:3rem !important;padding-bottom:3rem !important}.pt-xl-0{padding-top:0 !important}.pt-xl-1{padding-top:.25rem !important}.pt-xl-2{padding-top:.5rem !important}.pt-xl-3{padding-top:1rem !important}.pt-xl-4{padding-top:1.5rem !important}.pt-xl-5{padding-top:3rem !important}.pe-xl-0{padding-right:0 !important}.pe-xl-1{padding-right:.25rem !important}.pe-xl-2{padding-right:.5rem !important}.pe-xl-3{padding-right:1rem !important}.pe-xl-4{padding-right:1.5rem !important}.pe-xl-5{padding-right:3rem !important}.pb-xl-0{padding-bottom:0 !important}.pb-xl-1{padding-bottom:.25rem !important}.pb-xl-2{padding-bottom:.5rem !important}.pb-xl-3{padding-bottom:1rem !important}.pb-xl-4{padding-bottom:1.5rem !important}.pb-xl-5{padding-bottom:3rem !important}.ps-xl-0{padding-left:0 !important}.ps-xl-1{padding-left:.25rem !important}.ps-xl-2{padding-left:.5rem !important}.ps-xl-3{padding-left:1rem !important}.ps-xl-4{padding-left:1.5rem !important}.ps-xl-5{padding-left:3rem !important}.gap-xl-0{gap:0 !important}.gap-xl-1{gap:.25rem !important}.gap-xl-2{gap:.5rem !important}.gap-xl-3{gap:1rem !important}.gap-xl-4{gap:1.5rem !important}.gap-xl-5{gap:3rem !important}.row-gap-xl-0{row-gap:0 !important}.row-gap-xl-1{row-gap:.25rem !important}.row-gap-xl-2{row-gap:.5rem !important}.row-gap-xl-3{row-gap:1rem !important}.row-gap-xl-4{row-gap:1.5rem !important}.row-gap-xl-5{row-gap:3rem !important}.column-gap-xl-0{column-gap:0 !important}.column-gap-xl-1{column-gap:.25rem !important}.column-gap-xl-2{column-gap:.5rem !important}.column-gap-xl-3{column-gap:1rem !important}.column-gap-xl-4{column-gap:1.5rem !important}.column-gap-xl-5{column-gap:3rem !important}.text-xl-start{text-align:left !important}.text-xl-end{text-align:right !important}.text-xl-center{text-align:center !important}}@media (min-width: 1400px){.float-xxl-start{float:left !important}.float-xxl-end{float:right !important}.float-xxl-none{float:none !important}.object-fit-xxl-contain{object-fit:contain !important}.object-fit-xxl-cover{object-fit:cover !important}.object-fit-xxl-fill{object-fit:fill !important}.object-fit-xxl-scale{object-fit:scale-down !important}.object-fit-xxl-none{object-fit:none !important}.d-xxl-inline{display:inline !important}.d-xxl-inline-block{display:inline-block !important}.d-xxl-block{display:block !important}.d-xxl-grid{display:grid !important}.d-xxl-inline-grid{display:inline-grid !important}.d-xxl-table{display:table !important}.d-xxl-table-row{display:table-row !important}.d-xxl-table-cell{display:table-cell !important}.d-xxl-flex{display:flex !important}.d-xxl-inline-flex{display:inline-flex !important}.d-xxl-none{display:none !important}.flex-xxl-fill{flex:1 1 auto !important}.flex-xxl-row{flex-direction:row !important}.flex-xxl-column{flex-direction:column !important}.flex-xxl-row-reverse{flex-direction:row-reverse !important}.flex-xxl-column-reverse{flex-direction:column-reverse !important}.flex-xxl-grow-0{flex-grow:0 !important}.flex-xxl-grow-1{flex-grow:1 !important}.flex-xxl-shrink-0{flex-shrink:0 !important}.flex-xxl-shrink-1{flex-shrink:1 !important}.flex-xxl-wrap{flex-wrap:wrap !important}.flex-xxl-nowrap{flex-wrap:nowrap !important}.flex-xxl-wrap-reverse{flex-wrap:wrap-reverse !important}.justify-content-xxl-start{justify-content:flex-start !important}.justify-content-xxl-end{justify-content:flex-end !important}.justify-content-xxl-center{justify-content:center !important}.justify-content-xxl-between{justify-content:space-between !important}.justify-content-xxl-around{justify-content:space-around !important}.justify-content-xxl-evenly{justify-content:space-evenly !important}.align-items-xxl-start{align-items:flex-start !important}.align-items-xxl-end{align-items:flex-end !important}.align-items-xxl-center{align-items:center !important}.align-items-xxl-baseline{align-items:baseline !important}.align-items-xxl-stretch{align-items:stretch !important}.align-content-xxl-start{align-content:flex-start !important}.align-content-xxl-end{align-content:flex-end !important}.align-content-xxl-center{align-content:center !important}.align-content-xxl-between{align-content:space-between !important}.align-content-xxl-around{align-content:space-around !important}.align-content-xxl-stretch{align-content:stretch !important}.align-self-xxl-auto{align-self:auto !important}.align-self-xxl-start{align-self:flex-start !important}.align-self-xxl-end{align-self:flex-end !important}.align-self-xxl-center{align-self:center !important}.align-self-xxl-baseline{align-self:baseline !important}.align-self-xxl-stretch{align-self:stretch !important}.order-xxl-first{order:-1 !important}.order-xxl-0{order:0 !important}.order-xxl-1{order:1 !important}.order-xxl-2{order:2 !important}.order-xxl-3{order:3 !important}.order-xxl-4{order:4 !important}.order-xxl-5{order:5 !important}.order-xxl-last{order:6 !important}.m-xxl-0{margin:0 !important}.m-xxl-1{margin:.25rem !important}.m-xxl-2{margin:.5rem !important}.m-xxl-3{margin:1rem !important}.m-xxl-4{margin:1.5rem !important}.m-xxl-5{margin:3rem !important}.m-xxl-auto{margin:auto !important}.mx-xxl-0{margin-right:0 !important;margin-left:0 !important}.mx-xxl-1{margin-right:.25rem !important;margin-left:.25rem !important}.mx-xxl-2{margin-right:.5rem !important;margin-left:.5rem !important}.mx-xxl-3{margin-right:1rem !important;margin-left:1rem !important}.mx-xxl-4{margin-right:1.5rem !important;margin-left:1.5rem !important}.mx-xxl-5{margin-right:3rem !important;margin-left:3rem !important}.mx-xxl-auto{margin-right:auto !important;margin-left:auto !important}.my-xxl-0{margin-top:0 !important;margin-bottom:0 !important}.my-xxl-1{margin-top:.25rem !important;margin-bottom:.25rem !important}.my-xxl-2{margin-top:.5rem !important;margin-bottom:.5rem !important}.my-xxl-3{margin-top:1rem !important;margin-bottom:1rem !important}.my-xxl-4{margin-top:1.5rem !important;margin-bottom:1.5rem !important}.my-xxl-5{margin-top:3rem !important;margin-bottom:3rem !important}.my-xxl-auto{margin-top:auto !important;margin-bottom:auto !important}.mt-xxl-0{margin-top:0 !important}.mt-xxl-1{margin-top:.25rem !important}.mt-xxl-2{margin-top:.5rem !important}.mt-xxl-3{margin-top:1rem !important}.mt-xxl-4{margin-top:1.5rem !important}.mt-xxl-5{margin-top:3rem !important}.mt-xxl-auto{margin-top:auto !important}.me-xxl-0{margin-right:0 !important}.me-xxl-1{margin-right:.25rem !important}.me-xxl-2{margin-right:.5rem !important}.me-xxl-3{margin-right:1rem !important}.me-xxl-4{margin-right:1.5rem !important}.me-xxl-5{margin-right:3rem !important}.me-xxl-auto{margin-right:auto !important}.mb-xxl-0{margin-bottom:0 !important}.mb-xxl-1{margin-bottom:.25rem !important}.mb-xxl-2{margin-bottom:.5rem !important}.mb-xxl-3{margin-bottom:1rem !important}.mb-xxl-4{margin-bottom:1.5rem !important}.mb-xxl-5{margin-bottom:3rem !important}.mb-xxl-auto{margin-bottom:auto !important}.ms-xxl-0{margin-left:0 !important}.ms-xxl-1{margin-left:.25rem !important}.ms-xxl-2{margin-left:.5rem !important}.ms-xxl-3{margin-left:1rem !important}.ms-xxl-4{margin-left:1.5rem !important}.ms-xxl-5{margin-left:3rem !important}.ms-xxl-auto{margin-left:auto !important}.p-xxl-0{padding:0 !important}.p-xxl-1{padding:.25rem !important}.p-xxl-2{padding:.5rem !important}.p-xxl-3{padding:1rem !important}.p-xxl-4{padding:1.5rem !important}.p-xxl-5{padding:3rem !important}.px-xxl-0{padding-right:0 !important;padding-left:0 !important}.px-xxl-1{padding-right:.25rem !important;padding-left:.25rem !important}.px-xxl-2{padding-right:.5rem !important;padding-left:.5rem !important}.px-xxl-3{padding-right:1rem !important;padding-left:1rem !important}.px-xxl-4{padding-right:1.5rem !important;padding-left:1.5rem !important}.px-xxl-5{padding-right:3rem !important;padding-left:3rem !important}.py-xxl-0{padding-top:0 !important;padding-bottom:0 !important}.py-xxl-1{padding-top:.25rem !important;padding-bottom:.25rem !important}.py-xxl-2{padding-top:.5rem !important;padding-bottom:.5rem !important}.py-xxl-3{padding-top:1rem !important;padding-bottom:1rem !important}.py-xxl-4{padding-top:1.5rem !important;padding-bottom:1.5rem !important}.py-xxl-5{padding-top:3rem !important;padding-bottom:3rem !important}.pt-xxl-0{padding-top:0 !important}.pt-xxl-1{padding-top:.25rem !important}.pt-xxl-2{padding-top:.5rem !important}.pt-xxl-3{padding-top:1rem !important}.pt-xxl-4{padding-top:1.5rem !important}.pt-xxl-5{padding-top:3rem !important}.pe-xxl-0{padding-right:0 !important}.pe-xxl-1{padding-right:.25rem !important}.pe-xxl-2{padding-right:.5rem !important}.pe-xxl-3{padding-right:1rem !important}.pe-xxl-4{padding-right:1.5rem !important}.pe-xxl-5{padding-right:3rem !important}.pb-xxl-0{padding-bottom:0 !important}.pb-xxl-1{padding-bottom:.25rem !important}.pb-xxl-2{padding-bottom:.5rem !important}.pb-xxl-3{padding-bottom:1rem !important}.pb-xxl-4{padding-bottom:1.5rem !important}.pb-xxl-5{padding-bottom:3rem !important}.ps-xxl-0{padding-left:0 !important}.ps-xxl-1{padding-left:.25rem !important}.ps-xxl-2{padding-left:.5rem !important}.ps-xxl-3{padding-left:1rem !important}.ps-xxl-4{padding-left:1.5rem !important}.ps-xxl-5{padding-left:3rem !important}.gap-xxl-0{gap:0 !important}.gap-xxl-1{gap:.25rem !important}.gap-xxl-2{gap:.5rem !important}.gap-xxl-3{gap:1rem !important}.gap-xxl-4{gap:1.5rem !important}.gap-xxl-5{gap:3rem !important}.row-gap-xxl-0{row-gap:0 !important}.row-gap-xxl-1{row-gap:.25rem !important}.row-gap-xxl-2{row-gap:.5rem !important}.row-gap-xxl-3{row-gap:1rem !important}.row-gap-xxl-4{row-gap:1.5rem !important}.row-gap-xxl-5{row-gap:3rem !important}.column-gap-xxl-0{column-gap:0 !important}.column-gap-xxl-1{column-gap:.25rem !important}.column-gap-xxl-2{column-gap:.5rem !important}.column-gap-xxl-3{column-gap:1rem !important}.column-gap-xxl-4{column-gap:1.5rem !important}.column-gap-xxl-5{column-gap:3rem !important}.text-xxl-start{text-align:left !important}.text-xxl-end{text-align:right !important}.text-xxl-center{text-align:center !important}}.bg-default{color:#fff}.bg-primary{color:#fff}.bg-secondary{color:#fff}.bg-success{color:#fff}.bg-info{color:#fff}.bg-warning{color:#fff}.bg-danger{color:#fff}.bg-light{color:#000}.bg-dark{color:#fff}@media (min-width: 1200px){.fs-1{font-size:2.5rem !important}.fs-2{font-size:2rem !important}.fs-3{font-size:1.75rem !important}.fs-4{font-size:1.5rem !important}}@media print{.d-print-inline{display:inline !important}.d-print-inline-block{display:inline-block !important}.d-print-block{display:block !important}.d-print-grid{display:grid !important}.d-print-inline-grid{display:inline-grid !important}.d-print-table{display:table !important}.d-print-table-row{display:table-row !important}.d-print-table-cell{display:table-cell !important}.d-print-flex{display:flex !important}.d-print-inline-flex{display:inline-flex !important}.d-print-none{display:none !important}}.table th[align=left]{text-align:left}.table th[align=right]{text-align:right}.table th[align=center]{text-align:center}:root{--bslib-spacer: 1rem;--bslib-mb-spacer: var(--bslib-spacer, 1rem)}.bslib-mb-spacing{margin-bottom:var(--bslib-mb-spacer)}.bslib-gap-spacing{gap:var(--bslib-mb-spacer)}.bslib-gap-spacing>.bslib-mb-spacing,.bslib-gap-spacing>.form-group,.bslib-gap-spacing>p,.bslib-gap-spacing>pre,.bslib-gap-spacing>.shiny-html-output>.bslib-mb-spacing,.bslib-gap-spacing>.shiny-html-output>.form-group,.bslib-gap-spacing>.shiny-html-output>p,.bslib-gap-spacing>.shiny-html-output>pre,.bslib-gap-spacing>.shiny-panel-conditional>.bslib-mb-spacing,.bslib-gap-spacing>.shiny-panel-conditional>.form-group,.bslib-gap-spacing>.shiny-panel-conditional>p,.bslib-gap-spacing>.shiny-panel-conditional>pre{margin-bottom:0}.html-fill-container>.html-fill-item.bslib-mb-spacing{margin-bottom:0}.tab-content>.tab-pane.html-fill-container{display:none}.tab-content>.active.html-fill-container{display:flex}.tab-content.html-fill-container{padding:0}.bg-blue{--bslib-color-bg: #446e9b;--bslib-color-fg: #fff;background-color:var(--bslib-color-bg);color:var(--bslib-color-fg)}.text-blue{--bslib-color-fg: #446e9b;color:var(--bslib-color-fg)}.bg-indigo{--bslib-color-bg: #6610f2;--bslib-color-fg: #fff;background-color:var(--bslib-color-bg);color:var(--bslib-color-fg)}.text-indigo{--bslib-color-fg: #6610f2;color:var(--bslib-color-fg)}.bg-purple{--bslib-color-bg: #6f42c1;--bslib-color-fg: #fff;background-color:var(--bslib-color-bg);color:var(--bslib-color-fg)}.text-purple{--bslib-color-fg: #6f42c1;color:var(--bslib-color-fg)}.bg-pink{--bslib-color-bg: #e83e8c;--bslib-color-fg: #fff;background-color:var(--bslib-color-bg);color:var(--bslib-color-fg)}.text-pink{--bslib-color-fg: #e83e8c;color:var(--bslib-color-fg)}.bg-red{--bslib-color-bg: #cd0200;--bslib-color-fg: #fff;background-color:var(--bslib-color-bg);color:var(--bslib-color-fg)}.text-red{--bslib-color-fg: #cd0200;color:var(--bslib-color-fg)}.bg-orange{--bslib-color-bg: #fd7e14;--bslib-color-fg: #000;background-color:var(--bslib-color-bg);color:var(--bslib-color-fg)}.text-orange{--bslib-color-fg: #fd7e14;color:var(--bslib-color-fg)}.bg-yellow{--bslib-color-bg: #d47500;--bslib-color-fg: #fff;background-color:var(--bslib-color-bg);color:var(--bslib-color-fg)}.text-yellow{--bslib-color-fg: #d47500;color:var(--bslib-color-fg)}.bg-green{--bslib-color-bg: #3cb521;--bslib-color-fg: #fff;background-color:var(--bslib-color-bg);color:var(--bslib-color-fg)}.text-green{--bslib-color-fg: #3cb521;color:var(--bslib-color-fg)}.bg-teal{--bslib-color-bg: #20c997;--bslib-color-fg: #000;background-color:var(--bslib-color-bg);color:var(--bslib-color-fg)}.text-teal{--bslib-color-fg: #20c997;color:var(--bslib-color-fg)}.bg-cyan{--bslib-color-bg: #3399f3;--bslib-color-fg: #fff;background-color:var(--bslib-color-bg);color:var(--bslib-color-fg)}.text-cyan{--bslib-color-fg: #3399f3;color:var(--bslib-color-fg)}.text-default{--bslib-color-fg: #999}.bg-default{--bslib-color-bg: #999;--bslib-color-fg: #fff}.text-primary{--bslib-color-fg: #446e9b}.bg-primary{--bslib-color-bg: #446e9b;--bslib-color-fg: #fff}.text-secondary{--bslib-color-fg: #999}.bg-secondary{--bslib-color-bg: #999;--bslib-color-fg: #fff}.text-success{--bslib-color-fg: #3cb521}.bg-success{--bslib-color-bg: #3cb521;--bslib-color-fg: #fff}.text-info{--bslib-color-fg: #3399f3}.bg-info{--bslib-color-bg: #3399f3;--bslib-color-fg: #fff}.text-warning{--bslib-color-fg: #d47500}.bg-warning{--bslib-color-bg: #d47500;--bslib-color-fg: #fff}.text-danger{--bslib-color-fg: #cd0200}.bg-danger{--bslib-color-bg: #cd0200;--bslib-color-fg: #fff}.text-light{--bslib-color-fg: #eee}.bg-light{--bslib-color-bg: #eee;--bslib-color-fg: #000}.text-dark{--bslib-color-fg: #333}.bg-dark{--bslib-color-bg: #333;--bslib-color-fg: #fff}.bg-gradient-blue-indigo{--bslib-color-fg: #fff;--bslib-color-bg: #5248be;background:linear-gradient(var(--bg-gradient-deg, 140deg), #446e9b var(--bg-gradient-start, 36%), #6610f2 var(--bg-gradient-end, 180%)) #5248be;color:#fff}.bg-gradient-blue-purple{--bslib-color-fg: #fff;--bslib-color-bg: #555caa;background:linear-gradient(var(--bg-gradient-deg, 140deg), #446e9b var(--bg-gradient-start, 36%), #6f42c1 var(--bg-gradient-end, 180%)) #555caa;color:#fff}.bg-gradient-blue-pink{--bslib-color-fg: #fff;--bslib-color-bg: #865b95;background:linear-gradient(var(--bg-gradient-deg, 140deg), #446e9b var(--bg-gradient-start, 36%), #e83e8c var(--bg-gradient-end, 180%)) #865b95;color:#fff}.bg-gradient-blue-red{--bslib-color-fg: #fff;--bslib-color-bg: #7b435d;background:linear-gradient(var(--bg-gradient-deg, 140deg), #446e9b var(--bg-gradient-start, 36%), #cd0200 var(--bg-gradient-end, 180%)) #7b435d;color:#fff}.bg-gradient-blue-orange{--bslib-color-fg: #fff;--bslib-color-bg: #8e7465;background:linear-gradient(var(--bg-gradient-deg, 140deg), #446e9b var(--bg-gradient-start, 36%), #fd7e14 var(--bg-gradient-end, 180%)) #8e7465;color:#fff}.bg-gradient-blue-yellow{--bslib-color-fg: #fff;--bslib-color-bg: #7e715d;background:linear-gradient(var(--bg-gradient-deg, 140deg), #446e9b var(--bg-gradient-start, 36%), #d47500 var(--bg-gradient-end, 180%)) #7e715d;color:#fff}.bg-gradient-blue-green{--bslib-color-fg: #fff;--bslib-color-bg: #418a6a;background:linear-gradient(var(--bg-gradient-deg, 140deg), #446e9b var(--bg-gradient-start, 36%), #3cb521 var(--bg-gradient-end, 180%)) #418a6a;color:#fff}.bg-gradient-blue-teal{--bslib-color-fg: #fff;--bslib-color-bg: #369299;background:linear-gradient(var(--bg-gradient-deg, 140deg), #446e9b var(--bg-gradient-start, 36%), #20c997 var(--bg-gradient-end, 180%)) #369299;color:#fff}.bg-gradient-blue-cyan{--bslib-color-fg: #fff;--bslib-color-bg: #3d7fbe;background:linear-gradient(var(--bg-gradient-deg, 140deg), #446e9b var(--bg-gradient-start, 36%), #3399f3 var(--bg-gradient-end, 180%)) #3d7fbe;color:#fff}.bg-gradient-indigo-blue{--bslib-color-fg: #fff;--bslib-color-bg: #5836cf;background:linear-gradient(var(--bg-gradient-deg, 140deg), #6610f2 var(--bg-gradient-start, 36%), #446e9b var(--bg-gradient-end, 180%)) #5836cf;color:#fff}.bg-gradient-indigo-purple{--bslib-color-fg: #fff;--bslib-color-bg: #6a24de;background:linear-gradient(var(--bg-gradient-deg, 140deg), #6610f2 var(--bg-gradient-start, 36%), #6f42c1 var(--bg-gradient-end, 180%)) #6a24de;color:#fff}.bg-gradient-indigo-pink{--bslib-color-fg: #fff;--bslib-color-bg: #9a22c9;background:linear-gradient(var(--bg-gradient-deg, 140deg), #6610f2 var(--bg-gradient-start, 36%), #e83e8c var(--bg-gradient-end, 180%)) #9a22c9;color:#fff}.bg-gradient-indigo-red{--bslib-color-fg: #fff;--bslib-color-bg: #8f0a91;background:linear-gradient(var(--bg-gradient-deg, 140deg), #6610f2 var(--bg-gradient-start, 36%), #cd0200 var(--bg-gradient-end, 180%)) #8f0a91;color:#fff}.bg-gradient-indigo-orange{--bslib-color-fg: #fff;--bslib-color-bg: #a23c99;background:linear-gradient(var(--bg-gradient-deg, 140deg), #6610f2 var(--bg-gradient-start, 36%), #fd7e14 var(--bg-gradient-end, 180%)) #a23c99;color:#fff}.bg-gradient-indigo-yellow{--bslib-color-fg: #fff;--bslib-color-bg: #923891;background:linear-gradient(var(--bg-gradient-deg, 140deg), #6610f2 var(--bg-gradient-start, 36%), #d47500 var(--bg-gradient-end, 180%)) #923891;color:#fff}.bg-gradient-indigo-green{--bslib-color-fg: #fff;--bslib-color-bg: #55529e;background:linear-gradient(var(--bg-gradient-deg, 140deg), #6610f2 var(--bg-gradient-start, 36%), #3cb521 var(--bg-gradient-end, 180%)) #55529e;color:#fff}.bg-gradient-indigo-teal{--bslib-color-fg: #fff;--bslib-color-bg: #4a5ace;background:linear-gradient(var(--bg-gradient-deg, 140deg), #6610f2 var(--bg-gradient-start, 36%), #20c997 var(--bg-gradient-end, 180%)) #4a5ace;color:#fff}.bg-gradient-indigo-cyan{--bslib-color-fg: #fff;--bslib-color-bg: #5247f2;background:linear-gradient(var(--bg-gradient-deg, 140deg), #6610f2 var(--bg-gradient-start, 36%), #3399f3 var(--bg-gradient-end, 180%)) #5247f2;color:#fff}.bg-gradient-purple-blue{--bslib-color-fg: #fff;--bslib-color-bg: #5e54b2;background:linear-gradient(var(--bg-gradient-deg, 140deg), #6f42c1 var(--bg-gradient-start, 36%), #446e9b var(--bg-gradient-end, 180%)) #5e54b2;color:#fff}.bg-gradient-purple-indigo{--bslib-color-fg: #fff;--bslib-color-bg: #6b2ed5;background:linear-gradient(var(--bg-gradient-deg, 140deg), #6f42c1 var(--bg-gradient-start, 36%), #6610f2 var(--bg-gradient-end, 180%)) #6b2ed5;color:#fff}.bg-gradient-purple-pink{--bslib-color-fg: #fff;--bslib-color-bg: #9f40ac;background:linear-gradient(var(--bg-gradient-deg, 140deg), #6f42c1 var(--bg-gradient-start, 36%), #e83e8c var(--bg-gradient-end, 180%)) #9f40ac;color:#fff}.bg-gradient-purple-red{--bslib-color-fg: #fff;--bslib-color-bg: #952874;background:linear-gradient(var(--bg-gradient-deg, 140deg), #6f42c1 var(--bg-gradient-start, 36%), #cd0200 var(--bg-gradient-end, 180%)) #952874;color:#fff}.bg-gradient-purple-orange{--bslib-color-fg: #fff;--bslib-color-bg: #a85a7c;background:linear-gradient(var(--bg-gradient-deg, 140deg), #6f42c1 var(--bg-gradient-start, 36%), #fd7e14 var(--bg-gradient-end, 180%)) #a85a7c;color:#fff}.bg-gradient-purple-yellow{--bslib-color-fg: #fff;--bslib-color-bg: #975674;background:linear-gradient(var(--bg-gradient-deg, 140deg), #6f42c1 var(--bg-gradient-start, 36%), #d47500 var(--bg-gradient-end, 180%)) #975674;color:#fff}.bg-gradient-purple-green{--bslib-color-fg: #fff;--bslib-color-bg: #5b7081;background:linear-gradient(var(--bg-gradient-deg, 140deg), #6f42c1 var(--bg-gradient-start, 36%), #3cb521 var(--bg-gradient-end, 180%)) #5b7081;color:#fff}.bg-gradient-purple-teal{--bslib-color-fg: #fff;--bslib-color-bg: #4f78b0;background:linear-gradient(var(--bg-gradient-deg, 140deg), #6f42c1 var(--bg-gradient-start, 36%), #20c997 var(--bg-gradient-end, 180%)) #4f78b0;color:#fff}.bg-gradient-purple-cyan{--bslib-color-fg: #fff;--bslib-color-bg: #5765d5;background:linear-gradient(var(--bg-gradient-deg, 140deg), #6f42c1 var(--bg-gradient-start, 36%), #3399f3 var(--bg-gradient-end, 180%)) #5765d5;color:#fff}.bg-gradient-pink-blue{--bslib-color-fg: #fff;--bslib-color-bg: #a65192;background:linear-gradient(var(--bg-gradient-deg, 140deg), #e83e8c var(--bg-gradient-start, 36%), #446e9b var(--bg-gradient-end, 180%)) #a65192;color:#fff}.bg-gradient-pink-indigo{--bslib-color-fg: #fff;--bslib-color-bg: #b42cb5;background:linear-gradient(var(--bg-gradient-deg, 140deg), #e83e8c var(--bg-gradient-start, 36%), #6610f2 var(--bg-gradient-end, 180%)) #b42cb5;color:#fff}.bg-gradient-pink-purple{--bslib-color-fg: #fff;--bslib-color-bg: #b840a1;background:linear-gradient(var(--bg-gradient-deg, 140deg), #e83e8c var(--bg-gradient-start, 36%), #6f42c1 var(--bg-gradient-end, 180%)) #b840a1;color:#fff}.bg-gradient-pink-red{--bslib-color-fg: #fff;--bslib-color-bg: #dd2654;background:linear-gradient(var(--bg-gradient-deg, 140deg), #e83e8c var(--bg-gradient-start, 36%), #cd0200 var(--bg-gradient-end, 180%)) #dd2654;color:#fff}.bg-gradient-pink-orange{--bslib-color-fg: #fff;--bslib-color-bg: #f0585c;background:linear-gradient(var(--bg-gradient-deg, 140deg), #e83e8c var(--bg-gradient-start, 36%), #fd7e14 var(--bg-gradient-end, 180%)) #f0585c;color:#fff}.bg-gradient-pink-yellow{--bslib-color-fg: #fff;--bslib-color-bg: #e05454;background:linear-gradient(var(--bg-gradient-deg, 140deg), #e83e8c var(--bg-gradient-start, 36%), #d47500 var(--bg-gradient-end, 180%)) #e05454;color:#fff}.bg-gradient-pink-green{--bslib-color-fg: #fff;--bslib-color-bg: #a36e61;background:linear-gradient(var(--bg-gradient-deg, 140deg), #e83e8c var(--bg-gradient-start, 36%), #3cb521 var(--bg-gradient-end, 180%)) #a36e61;color:#fff}.bg-gradient-pink-teal{--bslib-color-fg: #fff;--bslib-color-bg: #987690;background:linear-gradient(var(--bg-gradient-deg, 140deg), #e83e8c var(--bg-gradient-start, 36%), #20c997 var(--bg-gradient-end, 180%)) #987690;color:#fff}.bg-gradient-pink-cyan{--bslib-color-fg: #fff;--bslib-color-bg: #a062b5;background:linear-gradient(var(--bg-gradient-deg, 140deg), #e83e8c var(--bg-gradient-start, 36%), #3399f3 var(--bg-gradient-end, 180%)) #a062b5;color:#fff}.bg-gradient-red-blue{--bslib-color-fg: #fff;--bslib-color-bg: #962d3e;background:linear-gradient(var(--bg-gradient-deg, 140deg), #cd0200 var(--bg-gradient-start, 36%), #446e9b var(--bg-gradient-end, 180%)) #962d3e;color:#fff}.bg-gradient-red-indigo{--bslib-color-fg: #fff;--bslib-color-bg: #a40861;background:linear-gradient(var(--bg-gradient-deg, 140deg), #cd0200 var(--bg-gradient-start, 36%), #6610f2 var(--bg-gradient-end, 180%)) #a40861;color:#fff}.bg-gradient-red-purple{--bslib-color-fg: #fff;--bslib-color-bg: #a71c4d;background:linear-gradient(var(--bg-gradient-deg, 140deg), #cd0200 var(--bg-gradient-start, 36%), #6f42c1 var(--bg-gradient-end, 180%)) #a71c4d;color:#fff}.bg-gradient-red-pink{--bslib-color-fg: #fff;--bslib-color-bg: #d81a38;background:linear-gradient(var(--bg-gradient-deg, 140deg), #cd0200 var(--bg-gradient-start, 36%), #e83e8c var(--bg-gradient-end, 180%)) #d81a38;color:#fff}.bg-gradient-red-orange{--bslib-color-fg: #fff;--bslib-color-bg: #e03408;background:linear-gradient(var(--bg-gradient-deg, 140deg), #cd0200 var(--bg-gradient-start, 36%), #fd7e14 var(--bg-gradient-end, 180%)) #e03408;color:#fff}.bg-gradient-red-yellow{--bslib-color-fg: #fff;--bslib-color-bg: #d03000;background:linear-gradient(var(--bg-gradient-deg, 140deg), #cd0200 var(--bg-gradient-start, 36%), #d47500 var(--bg-gradient-end, 180%)) #d03000;color:#fff}.bg-gradient-red-green{--bslib-color-fg: #fff;--bslib-color-bg: #934a0d;background:linear-gradient(var(--bg-gradient-deg, 140deg), #cd0200 var(--bg-gradient-start, 36%), #3cb521 var(--bg-gradient-end, 180%)) #934a0d;color:#fff}.bg-gradient-red-teal{--bslib-color-fg: #fff;--bslib-color-bg: #88523c;background:linear-gradient(var(--bg-gradient-deg, 140deg), #cd0200 var(--bg-gradient-start, 36%), #20c997 var(--bg-gradient-end, 180%)) #88523c;color:#fff}.bg-gradient-red-cyan{--bslib-color-fg: #fff;--bslib-color-bg: #8f3e61;background:linear-gradient(var(--bg-gradient-deg, 140deg), #cd0200 var(--bg-gradient-start, 36%), #3399f3 var(--bg-gradient-end, 180%)) #8f3e61;color:#fff}.bg-gradient-orange-blue{--bslib-color-fg: #fff;--bslib-color-bg: #b3784a;background:linear-gradient(var(--bg-gradient-deg, 140deg), #fd7e14 var(--bg-gradient-start, 36%), #446e9b var(--bg-gradient-end, 180%)) #b3784a;color:#fff}.bg-gradient-orange-indigo{--bslib-color-fg: #fff;--bslib-color-bg: #c1526d;background:linear-gradient(var(--bg-gradient-deg, 140deg), #fd7e14 var(--bg-gradient-start, 36%), #6610f2 var(--bg-gradient-end, 180%)) #c1526d;color:#fff}.bg-gradient-orange-purple{--bslib-color-fg: #fff;--bslib-color-bg: #c46659;background:linear-gradient(var(--bg-gradient-deg, 140deg), #fd7e14 var(--bg-gradient-start, 36%), #6f42c1 var(--bg-gradient-end, 180%)) #c46659;color:#fff}.bg-gradient-orange-pink{--bslib-color-fg: #fff;--bslib-color-bg: #f56444;background:linear-gradient(var(--bg-gradient-deg, 140deg), #fd7e14 var(--bg-gradient-start, 36%), #e83e8c var(--bg-gradient-end, 180%)) #f56444;color:#fff}.bg-gradient-orange-red{--bslib-color-fg: #fff;--bslib-color-bg: #ea4c0c;background:linear-gradient(var(--bg-gradient-deg, 140deg), #fd7e14 var(--bg-gradient-start, 36%), #cd0200 var(--bg-gradient-end, 180%)) #ea4c0c;color:#fff}.bg-gradient-orange-yellow{--bslib-color-fg: #fff;--bslib-color-bg: #ed7a0c;background:linear-gradient(var(--bg-gradient-deg, 140deg), #fd7e14 var(--bg-gradient-start, 36%), #d47500 var(--bg-gradient-end, 180%)) #ed7a0c;color:#fff}.bg-gradient-orange-green{--bslib-color-fg: #fff;--bslib-color-bg: #b09419;background:linear-gradient(var(--bg-gradient-deg, 140deg), #fd7e14 var(--bg-gradient-start, 36%), #3cb521 var(--bg-gradient-end, 180%)) #b09419;color:#fff}.bg-gradient-orange-teal{--bslib-color-fg: #fff;--bslib-color-bg: #a59c48;background:linear-gradient(var(--bg-gradient-deg, 140deg), #fd7e14 var(--bg-gradient-start, 36%), #20c997 var(--bg-gradient-end, 180%)) #a59c48;color:#fff}.bg-gradient-orange-cyan{--bslib-color-fg: #fff;--bslib-color-bg: #ac896d;background:linear-gradient(var(--bg-gradient-deg, 140deg), #fd7e14 var(--bg-gradient-start, 36%), #3399f3 var(--bg-gradient-end, 180%)) #ac896d;color:#fff}.bg-gradient-yellow-blue{--bslib-color-fg: #fff;--bslib-color-bg: #9a723e;background:linear-gradient(var(--bg-gradient-deg, 140deg), #d47500 var(--bg-gradient-start, 36%), #446e9b var(--bg-gradient-end, 180%)) #9a723e;color:#fff}.bg-gradient-yellow-indigo{--bslib-color-fg: #fff;--bslib-color-bg: #a84d61;background:linear-gradient(var(--bg-gradient-deg, 140deg), #d47500 var(--bg-gradient-start, 36%), #6610f2 var(--bg-gradient-end, 180%)) #a84d61;color:#fff}.bg-gradient-yellow-purple{--bslib-color-fg: #fff;--bslib-color-bg: #ac614d;background:linear-gradient(var(--bg-gradient-deg, 140deg), #d47500 var(--bg-gradient-start, 36%), #6f42c1 var(--bg-gradient-end, 180%)) #ac614d;color:#fff}.bg-gradient-yellow-pink{--bslib-color-fg: #fff;--bslib-color-bg: #dc5f38;background:linear-gradient(var(--bg-gradient-deg, 140deg), #d47500 var(--bg-gradient-start, 36%), #e83e8c var(--bg-gradient-end, 180%)) #dc5f38;color:#fff}.bg-gradient-yellow-red{--bslib-color-fg: #fff;--bslib-color-bg: #d14700;background:linear-gradient(var(--bg-gradient-deg, 140deg), #d47500 var(--bg-gradient-start, 36%), #cd0200 var(--bg-gradient-end, 180%)) #d14700;color:#fff}.bg-gradient-yellow-orange{--bslib-color-fg: #fff;--bslib-color-bg: #e47908;background:linear-gradient(var(--bg-gradient-deg, 140deg), #d47500 var(--bg-gradient-start, 36%), #fd7e14 var(--bg-gradient-end, 180%)) #e47908;color:#fff}.bg-gradient-yellow-green{--bslib-color-fg: #fff;--bslib-color-bg: #978f0d;background:linear-gradient(var(--bg-gradient-deg, 140deg), #d47500 var(--bg-gradient-start, 36%), #3cb521 var(--bg-gradient-end, 180%)) #978f0d;color:#fff}.bg-gradient-yellow-teal{--bslib-color-fg: #fff;--bslib-color-bg: #8c973c;background:linear-gradient(var(--bg-gradient-deg, 140deg), #d47500 var(--bg-gradient-start, 36%), #20c997 var(--bg-gradient-end, 180%)) #8c973c;color:#fff}.bg-gradient-yellow-cyan{--bslib-color-fg: #fff;--bslib-color-bg: #948361;background:linear-gradient(var(--bg-gradient-deg, 140deg), #d47500 var(--bg-gradient-start, 36%), #3399f3 var(--bg-gradient-end, 180%)) #948361;color:#fff}.bg-gradient-green-blue{--bslib-color-fg: #fff;--bslib-color-bg: #3f9952;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3cb521 var(--bg-gradient-start, 36%), #446e9b var(--bg-gradient-end, 180%)) #3f9952;color:#fff}.bg-gradient-green-indigo{--bslib-color-fg: #fff;--bslib-color-bg: #4d7375;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3cb521 var(--bg-gradient-start, 36%), #6610f2 var(--bg-gradient-end, 180%)) #4d7375;color:#fff}.bg-gradient-green-purple{--bslib-color-fg: #fff;--bslib-color-bg: #508761;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3cb521 var(--bg-gradient-start, 36%), #6f42c1 var(--bg-gradient-end, 180%)) #508761;color:#fff}.bg-gradient-green-pink{--bslib-color-fg: #fff;--bslib-color-bg: #81854c;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3cb521 var(--bg-gradient-start, 36%), #e83e8c var(--bg-gradient-end, 180%)) #81854c;color:#fff}.bg-gradient-green-red{--bslib-color-fg: #fff;--bslib-color-bg: #766d14;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3cb521 var(--bg-gradient-start, 36%), #cd0200 var(--bg-gradient-end, 180%)) #766d14;color:#fff}.bg-gradient-green-orange{--bslib-color-fg: #fff;--bslib-color-bg: #899f1c;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3cb521 var(--bg-gradient-start, 36%), #fd7e14 var(--bg-gradient-end, 180%)) #899f1c;color:#fff}.bg-gradient-green-yellow{--bslib-color-fg: #fff;--bslib-color-bg: #799b14;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3cb521 var(--bg-gradient-start, 36%), #d47500 var(--bg-gradient-end, 180%)) #799b14;color:#fff}.bg-gradient-green-teal{--bslib-color-fg: #000;--bslib-color-bg: #31bd50;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3cb521 var(--bg-gradient-start, 36%), #20c997 var(--bg-gradient-end, 180%)) #31bd50;color:#000}.bg-gradient-green-cyan{--bslib-color-fg: #fff;--bslib-color-bg: #38aa75;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3cb521 var(--bg-gradient-start, 36%), #3399f3 var(--bg-gradient-end, 180%)) #38aa75;color:#fff}.bg-gradient-teal-blue{--bslib-color-fg: #fff;--bslib-color-bg: #2ea599;background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #446e9b var(--bg-gradient-end, 180%)) #2ea599;color:#fff}.bg-gradient-teal-indigo{--bslib-color-fg: #fff;--bslib-color-bg: #3c7fbb;background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #6610f2 var(--bg-gradient-end, 180%)) #3c7fbb;color:#fff}.bg-gradient-teal-purple{--bslib-color-fg: #fff;--bslib-color-bg: #4093a8;background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #6f42c1 var(--bg-gradient-end, 180%)) #4093a8;color:#fff}.bg-gradient-teal-pink{--bslib-color-fg: #fff;--bslib-color-bg: #709193;background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #e83e8c var(--bg-gradient-end, 180%)) #709193;color:#fff}.bg-gradient-teal-red{--bslib-color-fg: #fff;--bslib-color-bg: #65795b;background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #cd0200 var(--bg-gradient-end, 180%)) #65795b;color:#fff}.bg-gradient-teal-orange{--bslib-color-fg: #fff;--bslib-color-bg: #78ab63;background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #fd7e14 var(--bg-gradient-end, 180%)) #78ab63;color:#fff}.bg-gradient-teal-yellow{--bslib-color-fg: #fff;--bslib-color-bg: #68a75b;background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #d47500 var(--bg-gradient-end, 180%)) #68a75b;color:#fff}.bg-gradient-teal-green{--bslib-color-fg: #000;--bslib-color-bg: #2bc168;background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #3cb521 var(--bg-gradient-end, 180%)) #2bc168;color:#000}.bg-gradient-teal-cyan{--bslib-color-fg: #000;--bslib-color-bg: #28b6bc;background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #3399f3 var(--bg-gradient-end, 180%)) #28b6bc;color:#000}.bg-gradient-cyan-blue{--bslib-color-fg: #fff;--bslib-color-bg: #3a88d0;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3399f3 var(--bg-gradient-start, 36%), #446e9b var(--bg-gradient-end, 180%)) #3a88d0;color:#fff}.bg-gradient-cyan-indigo{--bslib-color-fg: #fff;--bslib-color-bg: #4762f3;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3399f3 var(--bg-gradient-start, 36%), #6610f2 var(--bg-gradient-end, 180%)) #4762f3;color:#fff}.bg-gradient-cyan-purple{--bslib-color-fg: #fff;--bslib-color-bg: #4b76df;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3399f3 var(--bg-gradient-start, 36%), #6f42c1 var(--bg-gradient-end, 180%)) #4b76df;color:#fff}.bg-gradient-cyan-pink{--bslib-color-fg: #fff;--bslib-color-bg: #7b75ca;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3399f3 var(--bg-gradient-start, 36%), #e83e8c var(--bg-gradient-end, 180%)) #7b75ca;color:#fff}.bg-gradient-cyan-red{--bslib-color-fg: #fff;--bslib-color-bg: #715d92;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3399f3 var(--bg-gradient-start, 36%), #cd0200 var(--bg-gradient-end, 180%)) #715d92;color:#fff}.bg-gradient-cyan-orange{--bslib-color-fg: #fff;--bslib-color-bg: #848e9a;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3399f3 var(--bg-gradient-start, 36%), #fd7e14 var(--bg-gradient-end, 180%)) #848e9a;color:#fff}.bg-gradient-cyan-yellow{--bslib-color-fg: #fff;--bslib-color-bg: #738b92;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3399f3 var(--bg-gradient-start, 36%), #d47500 var(--bg-gradient-end, 180%)) #738b92;color:#fff}.bg-gradient-cyan-green{--bslib-color-fg: #fff;--bslib-color-bg: #37a49f;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3399f3 var(--bg-gradient-start, 36%), #3cb521 var(--bg-gradient-end, 180%)) #37a49f;color:#fff}.bg-gradient-cyan-teal{--bslib-color-fg: #fff;--bslib-color-bg: #2bacce;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3399f3 var(--bg-gradient-start, 36%), #20c997 var(--bg-gradient-end, 180%)) #2bacce;color:#fff}.navbar .nav-link,.navbar .navbar-brand{text-shadow:-1px -1px 0 rgba(0,0,0,0.05);transition:color ease-in-out .2s}.navbar.bg-default{background-image:linear-gradient(#b1b1b1, #999 50%, #8d8d8d);filter:none;border:1px solid #7a7a7a}.navbar.bg-primary{background-image:linear-gradient(#7191b3, #446e9b 50%, #3f658f);filter:none;border:1px solid #36587c}.navbar.bg-secondary{background-image:linear-gradient(#b1b1b1, #999 50%, #8d8d8d);filter:none;border:1px solid #7a7a7a}.navbar.bg-success{background-image:linear-gradient(#6bc756, #3cb521 50%, #37a71e);filter:none;border:1px solid #30911a}.navbar.bg-info{background-image:linear-gradient(#64b1f6, #3399f3 50%, #2f8de0);filter:none;border:1px solid #297ac2}.navbar.bg-warning{background-image:linear-gradient(#de963d, #d47500 50%, #c36c00);filter:none;border:1px solid #aa5e00}.navbar.bg-danger{background-image:linear-gradient(#d93f3d, #cd0200 50%, #bd0200);filter:none;border:1px solid #a40200}.navbar.bg-light{background-image:linear-gradient(#f2f2f2, #eee 50%, #dbdbdb);filter:none;border:1px solid #bebebe}.navbar.bg-dark{background-image:linear-gradient(#646464, #333 50%, #2f2f2f);filter:none;border:1px solid #292929}.navbar.bg-light .nav-link,.navbar.bg-light .navbar-brand,.navbar.navbar-default .nav-link,.navbar.navbar-default .navbar-brand{text-shadow:1px 1px 0 rgba(255,255,255,0.1)}.navbar.bg-light .navbar-brand,.navbar.navbar-default .navbar-brand{color:rgba(0,0,0,0.65)}.navbar.bg-light .navbar-brand:hover,.navbar.navbar-default .navbar-brand:hover{color:#3399f3}.btn{text-shadow:-1px -1px 0 rgba(0,0,0,0.1)}.btn-link{text-shadow:none}.btn-default{background-image:linear-gradient(#b1b1b1, #999 50%, #8d8d8d);filter:none;border:1px solid #7a7a7a}.btn-default:not(.disabled):hover{background-image:linear-gradient(#a8a8a8, #8d8d8d 50%, #828282);filter:none;border:1px solid #717171}.btn-primary{background-image:linear-gradient(#7191b3, #446e9b 50%, #3f658f);filter:none;border:1px solid #36587c}.btn-primary:not(.disabled):hover{background-image:linear-gradient(#6d8aaa, #3f658f 50%, #3a5d84);filter:none;border:1px solid #325172}.btn-secondary,.btn-default:not(.btn-primary):not(.btn-info):not(.btn-success):not(.btn-warning):not(.btn-danger):not(.btn-dark):not(.btn-light):not([class*='btn-outline-']){background-image:linear-gradient(#b1b1b1, #999 50%, #8d8d8d);filter:none;border:1px solid #7a7a7a}.btn-secondary:not(.disabled):hover,.btn-default:not(.disabled):hover:not(.btn-primary):not(.btn-info):not(.btn-success):not(.btn-warning):not(.btn-danger):not(.btn-dark):not(.btn-light):not([class*='btn-outline-']){background-image:linear-gradient(#a8a8a8, #8d8d8d 50%, #828282);filter:none;border:1px solid #717171}.btn-success{background-image:linear-gradient(#6bc756, #3cb521 50%, #37a71e);filter:none;border:1px solid #30911a}.btn-success:not(.disabled):hover{background-image:linear-gradient(#67bc54, #37a71e 50%, #339a1c);filter:none;border:1px solid #2c8618}.btn-info{background-image:linear-gradient(#64b1f6, #3399f3 50%, #2f8de0);filter:none;border:1px solid #297ac2}.btn-info:not(.disabled):hover{background-image:linear-gradient(#61a8e7, #2f8de0 50%, #2b82ce);filter:none;border:1px solid #2671b3}.btn-warning{background-image:linear-gradient(#de963d, #d47500 50%, #c36c00);filter:none;border:1px solid #aa5e00}.btn-warning:not(.disabled):hover{background-image:linear-gradient(#d18f3d, #c36c00 50%, #b36300);filter:none;border:1px solid #9c5600}.btn-danger{background-image:linear-gradient(#d93f3d, #cd0200 50%, #bd0200);filter:none;border:1px solid #a40200}.btn-danger:not(.disabled):hover{background-image:linear-gradient(#cd3f3d, #bd0200 50%, #ae0200);filter:none;border:1px solid #970200}.btn-light{background-image:linear-gradient(#f2f2f2, #eee 50%, #dbdbdb);filter:none;border:1px solid #bebebe}.btn-light:not(.disabled):hover{background-image:linear-gradient(#e4e4e4, #dbdbdb 50%, #c9c9c9);filter:none;border:1px solid #afafaf}.btn-dark{background-image:linear-gradient(#646464, #333 50%, #2f2f2f);filter:none;border:1px solid #292929}.btn-dark:not(.disabled):hover{background-image:linear-gradient(#616161, #2f2f2f 50%, #2b2b2b);filter:none;border:1px solid #262626}[class*="btn-outline-"]{text-shadow:none}.badge.bg-light{color:#333}.card h1,.card .h1,.card h2,.card .h2,.card h3,.card .h3,.card h4,.card .h4,.card h5,.card .h5,.card h6,.card .h6,.list-group-item h1,.list-group-item .h1,.list-group-item h2,.list-group-item .h2,.list-group-item h3,.list-group-item .h3,.list-group-item h4,.list-group-item .h4,.list-group-item h5,.list-group-item .h5,.list-group-item h6,.list-group-item .h6{color:inherit}.row>main{max-width:50rem;overflow-wrap:break-word;hyphens:auto}@media (min-width: 1200px) and (max-width: 1399.98px){.container .row{justify-content:space-evenly}}@media (min-width: 1400px){body{font-size:18px}.col-md-3{margin-left:5rem}}.navbar{background:RGBA(var(--bs-body-color-rgb), 0.1);background:color-mix(in oklab, color-mix(in oklab, var(--bs-body-bg) 95%, var(--bs-primary)) 95%, var(--bs-body-color));line-height:initial}.nav-item .nav-link{border-radius:.375rem}.nav-item.active .nav-link{background:RGBA(var(--bs-body-color-rgb), 0.1)}.nav-item .nav-link:hover{background:RGBA(var(--bs-primary-rgb), 0.1)}.navbar>.container{align-items:baseline;-webkit-align-items:baseline}input[type="search"]{width:12rem}[aria-labelledby=dropdown-lightswitch] span.fa{opacity:0.5}@media (max-width: 991.98px){.algolia-autocomplete,input[type="search"],#navbar .dropdown-menu{width:100%}#navbar .dropdown-item{white-space:normal}input[type="search"]{margin:0.25rem 0}}.headroom{will-change:transform;transition:transform 400ms ease}.headroom--pinned{transform:translateY(0%)}.headroom--unpinned{transform:translateY(-100%)}.row>main,.row>aside{margin-top:56px}html,body{scroll-padding:56px}@media (min-width: 576px){#toc{position:sticky;top:56px;max-height:calc(100vh - 56px - 1rem);overflow-y:auto}}aside h2,aside .h2{margin-top:1.5rem;font-size:1.25rem}aside .roles{color:RGBA(var(--bs-body-color-rgb), 0.8)}aside .list-unstyled li{margin-bottom:0.5rem}aside .dev-status .list-unstyled li{margin-bottom:0.1rem}@media (max-width: 767.98px){.row>aside{margin:0.5rem;width:calc(100vw - 1rem);background-color:RGBA(var(--bs-body-color-rgb), 0.1);border-color:var(--bs-border-color);border-radius:.375rem}.row>aside h2:first-child,.row>aside .h2:first-child{margin-top:1rem}}body{position:relative}#toc>.nav{margin-bottom:1rem}#toc>.nav a.nav-link{color:inherit;padding:0.25rem 0.5rem;margin-bottom:2px;border-radius:.375rem}#toc>.nav a.nav-link:hover,#toc>.nav a.nav-link:focus{background-color:RGBA(var(--bs-primary-rgb), 0.1)}#toc>.nav a.nav-link.active{background-color:RGBA(var(--bs-body-color-rgb), 0.1)}#toc>.nav .nav a.nav-link{margin-left:0.5rem}#toc>.nav .nav{display:none !important}#toc>.nav a.active+.nav{display:flex !important}footer{margin:1rem 0 1rem 0;padding-top:1rem;font-size:.875em;border-top:1px solid #dee2e6;background:rgba(0,0,0,0);color:RGBA(var(--bs-body-color-rgb), 0.8);display:flex;column-gap:1rem}@media (max-width: 575.98px){footer{flex-direction:column}}@media (min-width: 576px){footer .pkgdown-footer-right{text-align:right}}footer div{flex:1 1 auto}html,body{height:100%}body>.container{min-height:100%;display:flex;flex-direction:column}body>.container .row{flex:1 0 auto}main img{max-width:100%;height:auto}main table{display:block;overflow:auto}body{font-display:fallback}.page-header{border-bottom:1px solid var(--bs-border-color);padding-bottom:0.5rem;margin-bottom:0.5rem;margin-top:1.5rem}dl{margin-bottom:0}dd{padding-left:1.5rem;margin-bottom:0.25rem}h2,.h2{font-size:1.75rem;margin-top:1.5rem}h3,.h3{font-size:1.25rem;margin-top:1rem;font-weight:bold}h4,.h4{font-size:1.1rem;font-weight:bold}h5,.h5{font-size:1rem;font-weight:bold}summary{margin-bottom:0.5rem}details{margin-bottom:1rem}.html-widget{margin-bottom:1rem}a.anchor{display:none;margin-left:2px;vertical-align:top;width:Min(0.9em, 20px);height:Min(0.9em, 20px);background-image:url(../../link.svg);background-repeat:no-repeat;background-size:Min(0.9em, 20px) Min(0.9em, 20px);background-position:center center}h2:hover .anchor,.h2:hover .anchor,h2:target .anchor,.h2:target .anchor,h3:hover .anchor,.h3:hover .anchor,h3:target .anchor,.h3:target .anchor,h4:hover .anchor,.h4:hover .anchor,h4:target .anchor,.h4:target .anchor,h5:hover .anchor,.h5:hover .anchor,h5:target .anchor,.h5:target .anchor,h6:hover .anchor,.h6:hover .anchor,h6:target .anchor,.h6:target .anchor,dt:hover .anchor,dt:target .anchor{display:inline-block}dt:target,dt:target+dd{border-left:0.25rem solid var(--bs-primary);margin-left:-0.75rem}dt:target{padding-left:0.5rem}dt:target+dd{padding-left:2rem}.orcid{color:#A6CE39;margin-right:4px}.fab{font-family:"Font Awesome 5 Brands" !important}img.logo{float:right;width:100px;margin-left:30px}.template-home img.logo{width:120px}@media (max-width: 575.98px){img.logo{width:80px}}@media (min-width: 576px){.page-header{min-height:88px}.template-home .page-header{min-height:104px}}.line-block{margin-bottom:1rem}.template-reference-index dt{font-weight:normal}.template-reference-index code{word-wrap:normal}.icon{float:right}.icon img{width:40px}a[href='#main']{position:absolute;margin:4px;padding:0.75rem;background-color:var(--bs-body-bg);text-decoration:none;z-index:2000}.lifecycle{color:var(--bs-secondary-color);background-color:var(--bs-secondary-bg);border-radius:5px}.lifecycle-stable{background-color:#108001;color:var(--bs-white)}.lifecycle-superseded{background-color:#074080;color:var(--bs-white)}.lifecycle-experimental,.lifecycle-deprecated{background-color:#fd8008;color:var(--bs-black)}a.footnote-ref{cursor:pointer}.popover{width:Min(100vw, 32rem);font-size:0.9rem;box-shadow:4px 4px 8px RGBA(var(--bs-body-color-rgb), 0.3)}.popover-body{padding:0.75rem}.popover-body p:last-child{margin-bottom:0}.tab-content{padding:1rem}.tabset-pills .tab-content{border:solid 1px #e5e5e5}.tab-content{display:flex}.tab-content>.tab-pane{display:block;visibility:hidden;margin-right:-100%;width:100%}.tab-content>.active{visibility:visible}div.csl-entry{clear:both}.hanging-indent div.csl-entry{margin-left:2em;text-indent:-2em}div.csl-left-margin{min-width:2em;float:left}div.csl-right-inline{margin-left:2em;padding-left:1em}div.csl-indent{margin-left:2em}pre,pre code{word-wrap:normal}[data-bs-theme="dark"] pre,[data-bs-theme="dark"] code{background-color:RGBA(var(--bs-body-color-rgb), 0.1)}[data-bs-theme="dark"] pre code{background:transparent}code{overflow-wrap:break-word}.hasCopyButton{position:relative}.btn-copy-ex{position:absolute;right:5px;top:5px;visibility:hidden}.hasCopyButton:hover button.btn-copy-ex{visibility:visible}pre{padding:0.75rem}pre div.gt-table{white-space:normal;margin-top:1rem}@media (max-width: 575.98px){div>div>pre{margin-left:calc(var(--bs-gutter-x) * -.5);margin-right:calc(var(--bs-gutter-x) * -.5);border-radius:0;padding-left:1rem;padding-right:1rem}.btn-copy-ex{right:calc(var(--bs-gutter-x) * -.5 + 5px)}}code a:any-link{color:inherit;text-decoration-color:RGBA(var(--bs-body-color-rgb), 0.6)}pre code{padding:0;background:transparent}pre code .error,pre code .warning{font-weight:bolder}pre .img img,pre .r-plt img{margin:5px 0;background-color:#fff}[data-bs-theme="dark"] pre img{opacity:0.66;transition:opacity 250ms ease-in-out}[data-bs-theme="dark"] pre img:hover,[data-bs-theme="dark"] pre img:focus,[data-bs-theme="dark"] pre img:active{opacity:1}@media print{code a:link:after,code a:visited:after{content:""}}a.sourceLine:hover{text-decoration:none}mark,.mark{background:linear-gradient(-100deg, RGBA(var(--bs-info-rgb), 0.2), RGBA(var(--bs-info-rgb), 0.7) 95%, RGBA(var(--bs-info-rgb), 0.1))}.algolia-autocomplete .aa-dropdown-menu{margin-top:0.5rem;padding:0.5rem 0.25rem;width:MAX(100%, 20rem);max-height:50vh;overflow-y:auto;background-color:var(--bs-body-bg);border:var(--bs-border-width) solid var(--bs-border-color);border-radius:.375rem}.algolia-autocomplete .aa-dropdown-menu .aa-suggestion{cursor:pointer;font-size:1rem;padding:0.5rem 0.25rem;line-height:1.3}.algolia-autocomplete .aa-dropdown-menu .aa-suggestion:hover{background-color:var(--bs-tertiary-bg);color:var(--bs-body-color)}.algolia-autocomplete .aa-dropdown-menu .aa-suggestion .search-details{text-decoration:underline;display:inline}span.smallcaps{font-variant:small-caps}ul.task-list{list-style:none}ul.task-list li input[type="checkbox"]{width:0.8em;margin:0 0.8em 0.2em -1em;vertical-align:middle}figure.figure{display:block}.quarto-layout-panel{margin-bottom:1em}.quarto-layout-panel>figure{width:100%}.quarto-layout-panel>figure>figcaption,.quarto-layout-panel>.panel-caption{margin-top:10pt}.quarto-layout-panel>.table-caption{margin-top:0px}.table-caption p{margin-bottom:0.5em}.quarto-layout-row{display:flex;flex-direction:row;align-items:flex-start}.quarto-layout-valign-top{align-items:flex-start}.quarto-layout-valign-bottom{align-items:flex-end}.quarto-layout-valign-center{align-items:center}.quarto-layout-cell{position:relative;margin-right:20px}.quarto-layout-cell:last-child{margin-right:0}.quarto-layout-cell figure,.quarto-layout-cell>p{margin:0.2em}.quarto-layout-cell img{max-width:100%}.quarto-layout-cell .html-widget{width:100% !important}.quarto-layout-cell div figure p{margin:0}.quarto-layout-cell figure{display:block;margin-inline-start:0;margin-inline-end:0}.quarto-layout-cell table{display:inline-table}.quarto-layout-cell-subref figcaption,figure .quarto-layout-row figure figcaption{text-align:center;font-style:italic}.quarto-figure{position:relative;margin-bottom:1em}.quarto-figure>figure{width:100%;margin-bottom:0}.quarto-figure-left>figure>p,.quarto-figure-left>figure>div{text-align:left}.quarto-figure-center>figure>p,.quarto-figure-center>figure>div{text-align:center}.quarto-figure-right>figure>p,.quarto-figure-right>figure>div{text-align:right}.quarto-figure>figure>div.cell-annotation,.quarto-figure>figure>div code{text-align:left}figure>p:empty{display:none}figure>p:first-child{margin-top:0;margin-bottom:0}figure>figcaption.quarto-float-caption-bottom{margin-bottom:0.5em}figure>figcaption.quarto-float-caption-top{margin-top:0.5em}:root{--mermaid-bg-color: transparent;--mermaid-edge-color: var(--bs-secondary);--mermaid-fg-color: var(--bs-body-color);--mermaid-fg-color--lighter: RGBA(var(--bs-body-color-rgb), 0.9);--mermaid-fg-color--lightest: RGBA(var(--bs-body-color-rgb), 0.8);--mermaid-font-family: var(--bs-body-font-family);--mermaid-label-bg-color: var(--bs-primary);--mermaid-label-fg-color: var(--bs-body-color);--mermaid-node-bg-color: RGBA(var(--bs-primary-rgb), 0.1);--mermaid-node-fg-color: var(--bs-primary)}pre{background-color:#f1f3f5}pre code{color:#003B4F}pre code span.al{color:#AD0000}pre code span.an{color:#5E5E5E}pre code span.at{color:#657422}pre code span.bn{color:#AD0000}pre code span.cf{color:#003B4F}pre code span.ch{color:#20794D}pre code span.cn{color:#8f5902}pre code span.co{color:#5E5E5E}pre code span.cv{color:#5E5E5E;font-style:italic}pre code span.do{color:#5E5E5E;font-style:italic}pre code span.dt{color:#AD0000}pre code span.dv{color:#AD0000}pre code span.er{color:#AD0000}pre code span.fl{color:#AD0000}pre code span.fu{color:#4758AB}pre code span.im{color:#00769E}pre code span.in{color:#5E5E5E}pre code span.kw{color:#003B4F}pre code span.op{color:#5E5E5E}pre code span.ot{color:#003B4F}pre code span.pp{color:#AD0000}pre code span.sc{color:#5E5E5E}pre code span.ss{color:#20794D}pre code span.st{color:#20794D}pre code span.va{color:#111111}pre code span.vs{color:#20794D}pre code span.wa{color:#5E5E5E;font-style:italic}
diff --git a/docs/deps/bootstrap-5.3.1/font.css b/docs/deps/bootstrap-5.3.1/font.css
new file mode 100644
index 00000000..d0513bf3
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/font.css
@@ -0,0 +1,400 @@
+/* cyrillic-ext */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: italic;
+  font-weight: 400;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWtE6F15M.woff2) format('woff2');
+  unicode-range: U+0460-052F, U+1C80-1C88, U+20B4, U+2DE0-2DFF, U+A640-A69F, U+FE2E-FE2F;
+}
+/* cyrillic */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: italic;
+  font-weight: 400;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWvU6F15M.woff2) format('woff2');
+  unicode-range: U+0301, U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
+}
+/* greek-ext */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: italic;
+  font-weight: 400;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWtU6F15M.woff2) format('woff2');
+  unicode-range: U+1F00-1FFF;
+}
+/* greek */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: italic;
+  font-weight: 400;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWuk6F15M.woff2) format('woff2');
+  unicode-range: U+0370-0377, U+037A-037F, U+0384-038A, U+038C, U+038E-03A1, U+03A3-03FF;
+}
+/* hebrew */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: italic;
+  font-weight: 400;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWu06F15M.woff2) format('woff2');
+  unicode-range: U+0590-05FF, U+200C-2010, U+20AA, U+25CC, U+FB1D-FB4F;
+}
+/* math */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: italic;
+  font-weight: 400;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWxU6F15M.woff2) format('woff2');
+  unicode-range: U+0302-0303, U+0305, U+0307-0308, U+0330, U+0391-03A1, U+03A3-03A9, U+03B1-03C9, U+03D1, U+03D5-03D6, U+03F0-03F1, U+03F4-03F5, U+2034-2037, U+2057, U+20D0-20DC, U+20E1, U+20E5-20EF, U+2102, U+210A-210E, U+2110-2112, U+2115, U+2119-211D, U+2124, U+2128, U+212C-212D, U+212F-2131, U+2133-2138, U+213C-2140, U+2145-2149, U+2190, U+2192, U+2194-21AE, U+21B0-21E5, U+21F1-21F2, U+21F4-2211, U+2213-2214, U+2216-22FF, U+2308-230B, U+2310, U+2319, U+231C-2321, U+2336-237A, U+237C, U+2395, U+239B-23B6, U+23D0, U+23DC-23E1, U+2474-2475, U+25AF, U+25B3, U+25B7, U+25BD, U+25C1, U+25CA, U+25CC, U+25FB, U+266D-266F, U+27C0-27FF, U+2900-2AFF, U+2B0E-2B11, U+2B30-2B4C, U+2BFE, U+FF5B, U+FF5D, U+1D400-1D7FF, U+1EE00-1EEFF;
+}
+/* symbols */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: italic;
+  font-weight: 400;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqW106F15M.woff2) format('woff2');
+  unicode-range: U+0001-000C, U+000E-001F, U+007F-009F, U+20DD-20E0, U+20E2-20E4, U+2150-218F, U+2190, U+2192, U+2194-2199, U+21AF, U+21E6-21F0, U+21F3, U+2218-2219, U+2299, U+22C4-22C6, U+2300-243F, U+2440-244A, U+2460-24FF, U+25A0-27BF, U+2800-28FF, U+2921-2922, U+2981, U+29BF, U+29EB, U+2B00-2BFF, U+4DC0-4DFF, U+FFF9-FFFB, U+10140-1018E, U+10190-1019C, U+101A0, U+101D0-101FD, U+102E0-102FB, U+10E60-10E7E, U+1D2C0-1D2D3, U+1D2E0-1D37F, U+1F000-1F0FF, U+1F100-1F1AD, U+1F1E6-1F1FF, U+1F30D-1F30F, U+1F315, U+1F31C, U+1F31E, U+1F320-1F32C, U+1F336, U+1F378, U+1F37D, U+1F382, U+1F393-1F39F, U+1F3A7-1F3A8, U+1F3AC-1F3AF, U+1F3C2, U+1F3C4-1F3C6, U+1F3CA-1F3CE, U+1F3D4-1F3E0, U+1F3ED, U+1F3F1-1F3F3, U+1F3F5-1F3F7, U+1F408, U+1F415, U+1F41F, U+1F426, U+1F43F, U+1F441-1F442, U+1F444, U+1F446-1F449, U+1F44C-1F44E, U+1F453, U+1F46A, U+1F47D, U+1F4A3, U+1F4B0, U+1F4B3, U+1F4B9, U+1F4BB, U+1F4BF, U+1F4C8-1F4CB, U+1F4D6, U+1F4DA, U+1F4DF, U+1F4E3-1F4E6, U+1F4EA-1F4ED, U+1F4F7, U+1F4F9-1F4FB, U+1F4FD-1F4FE, U+1F503, U+1F507-1F50B, U+1F50D, U+1F512-1F513, U+1F53E-1F54A, U+1F54F-1F5FA, U+1F610, U+1F650-1F67F, U+1F687, U+1F68D, U+1F691, U+1F694, U+1F698, U+1F6AD, U+1F6B2, U+1F6B9-1F6BA, U+1F6BC, U+1F6C6-1F6CF, U+1F6D3-1F6D7, U+1F6E0-1F6EA, U+1F6F0-1F6F3, U+1F6F7-1F6FC, U+1F700-1F7FF, U+1F800-1F80B, U+1F810-1F847, U+1F850-1F859, U+1F860-1F887, U+1F890-1F8AD, U+1F8B0-1F8B1, U+1F900-1F90B, U+1F93B, U+1F946, U+1F984, U+1F996, U+1F9E9, U+1FA00-1FA6F, U+1FA70-1FA7C, U+1FA80-1FA88, U+1FA90-1FABD, U+1FABF-1FAC5, U+1FACE-1FADB, U+1FAE0-1FAE8, U+1FAF0-1FAF8, U+1FB00-1FBFF;
+}
+/* vietnamese */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: italic;
+  font-weight: 400;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWtk6F15M.woff2) format('woff2');
+  unicode-range: U+0102-0103, U+0110-0111, U+0128-0129, U+0168-0169, U+01A0-01A1, U+01AF-01B0, U+0300-0301, U+0303-0304, U+0308-0309, U+0323, U+0329, U+1EA0-1EF9, U+20AB;
+}
+/* latin-ext */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: italic;
+  font-weight: 400;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWt06F15M.woff2) format('woff2');
+  unicode-range: U+0100-02AF, U+0304, U+0308, U+0329, U+1E00-1E9F, U+1EF2-1EFF, U+2020, U+20A0-20AB, U+20AD-20C0, U+2113, U+2C60-2C7F, U+A720-A7FF;
+}
+/* latin */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: italic;
+  font-weight: 400;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWuU6F.woff2) format('woff2');
+  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+0304, U+0308, U+0329, U+2000-206F, U+2074, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD;
+}
+/* cyrillic-ext */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: italic;
+  font-weight: 700;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWtE6F15M.woff2) format('woff2');
+  unicode-range: U+0460-052F, U+1C80-1C88, U+20B4, U+2DE0-2DFF, U+A640-A69F, U+FE2E-FE2F;
+}
+/* cyrillic */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: italic;
+  font-weight: 700;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWvU6F15M.woff2) format('woff2');
+  unicode-range: U+0301, U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
+}
+/* greek-ext */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: italic;
+  font-weight: 700;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWtU6F15M.woff2) format('woff2');
+  unicode-range: U+1F00-1FFF;
+}
+/* greek */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: italic;
+  font-weight: 700;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWuk6F15M.woff2) format('woff2');
+  unicode-range: U+0370-0377, U+037A-037F, U+0384-038A, U+038C, U+038E-03A1, U+03A3-03FF;
+}
+/* hebrew */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: italic;
+  font-weight: 700;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWu06F15M.woff2) format('woff2');
+  unicode-range: U+0590-05FF, U+200C-2010, U+20AA, U+25CC, U+FB1D-FB4F;
+}
+/* math */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: italic;
+  font-weight: 700;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWxU6F15M.woff2) format('woff2');
+  unicode-range: U+0302-0303, U+0305, U+0307-0308, U+0330, U+0391-03A1, U+03A3-03A9, U+03B1-03C9, U+03D1, U+03D5-03D6, U+03F0-03F1, U+03F4-03F5, U+2034-2037, U+2057, U+20D0-20DC, U+20E1, U+20E5-20EF, U+2102, U+210A-210E, U+2110-2112, U+2115, U+2119-211D, U+2124, U+2128, U+212C-212D, U+212F-2131, U+2133-2138, U+213C-2140, U+2145-2149, U+2190, U+2192, U+2194-21AE, U+21B0-21E5, U+21F1-21F2, U+21F4-2211, U+2213-2214, U+2216-22FF, U+2308-230B, U+2310, U+2319, U+231C-2321, U+2336-237A, U+237C, U+2395, U+239B-23B6, U+23D0, U+23DC-23E1, U+2474-2475, U+25AF, U+25B3, U+25B7, U+25BD, U+25C1, U+25CA, U+25CC, U+25FB, U+266D-266F, U+27C0-27FF, U+2900-2AFF, U+2B0E-2B11, U+2B30-2B4C, U+2BFE, U+FF5B, U+FF5D, U+1D400-1D7FF, U+1EE00-1EEFF;
+}
+/* symbols */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: italic;
+  font-weight: 700;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqW106F15M.woff2) format('woff2');
+  unicode-range: U+0001-000C, U+000E-001F, U+007F-009F, U+20DD-20E0, U+20E2-20E4, U+2150-218F, U+2190, U+2192, U+2194-2199, U+21AF, U+21E6-21F0, U+21F3, U+2218-2219, U+2299, U+22C4-22C6, U+2300-243F, U+2440-244A, U+2460-24FF, U+25A0-27BF, U+2800-28FF, U+2921-2922, U+2981, U+29BF, U+29EB, U+2B00-2BFF, U+4DC0-4DFF, U+FFF9-FFFB, U+10140-1018E, U+10190-1019C, U+101A0, U+101D0-101FD, U+102E0-102FB, U+10E60-10E7E, U+1D2C0-1D2D3, U+1D2E0-1D37F, U+1F000-1F0FF, U+1F100-1F1AD, U+1F1E6-1F1FF, U+1F30D-1F30F, U+1F315, U+1F31C, U+1F31E, U+1F320-1F32C, U+1F336, U+1F378, U+1F37D, U+1F382, U+1F393-1F39F, U+1F3A7-1F3A8, U+1F3AC-1F3AF, U+1F3C2, U+1F3C4-1F3C6, U+1F3CA-1F3CE, U+1F3D4-1F3E0, U+1F3ED, U+1F3F1-1F3F3, U+1F3F5-1F3F7, U+1F408, U+1F415, U+1F41F, U+1F426, U+1F43F, U+1F441-1F442, U+1F444, U+1F446-1F449, U+1F44C-1F44E, U+1F453, U+1F46A, U+1F47D, U+1F4A3, U+1F4B0, U+1F4B3, U+1F4B9, U+1F4BB, U+1F4BF, U+1F4C8-1F4CB, U+1F4D6, U+1F4DA, U+1F4DF, U+1F4E3-1F4E6, U+1F4EA-1F4ED, U+1F4F7, U+1F4F9-1F4FB, U+1F4FD-1F4FE, U+1F503, U+1F507-1F50B, U+1F50D, U+1F512-1F513, U+1F53E-1F54A, U+1F54F-1F5FA, U+1F610, U+1F650-1F67F, U+1F687, U+1F68D, U+1F691, U+1F694, U+1F698, U+1F6AD, U+1F6B2, U+1F6B9-1F6BA, U+1F6BC, U+1F6C6-1F6CF, U+1F6D3-1F6D7, U+1F6E0-1F6EA, U+1F6F0-1F6F3, U+1F6F7-1F6FC, U+1F700-1F7FF, U+1F800-1F80B, U+1F810-1F847, U+1F850-1F859, U+1F860-1F887, U+1F890-1F8AD, U+1F8B0-1F8B1, U+1F900-1F90B, U+1F93B, U+1F946, U+1F984, U+1F996, U+1F9E9, U+1FA00-1FA6F, U+1FA70-1FA7C, U+1FA80-1FA88, U+1FA90-1FABD, U+1FABF-1FAC5, U+1FACE-1FADB, U+1FAE0-1FAE8, U+1FAF0-1FAF8, U+1FB00-1FBFF;
+}
+/* vietnamese */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: italic;
+  font-weight: 700;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWtk6F15M.woff2) format('woff2');
+  unicode-range: U+0102-0103, U+0110-0111, U+0128-0129, U+0168-0169, U+01A0-01A1, U+01AF-01B0, U+0300-0301, U+0303-0304, U+0308-0309, U+0323, U+0329, U+1EA0-1EF9, U+20AB;
+}
+/* latin-ext */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: italic;
+  font-weight: 700;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWt06F15M.woff2) format('woff2');
+  unicode-range: U+0100-02AF, U+0304, U+0308, U+0329, U+1E00-1E9F, U+1EF2-1EFF, U+2020, U+20A0-20AB, U+20AD-20C0, U+2113, U+2C60-2C7F, U+A720-A7FF;
+}
+/* latin */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: italic;
+  font-weight: 700;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWuU6F.woff2) format('woff2');
+  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+0304, U+0308, U+0329, U+2000-206F, U+2074, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD;
+}
+/* cyrillic-ext */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: normal;
+  font-weight: 400;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTSKmu1aB.woff2) format('woff2');
+  unicode-range: U+0460-052F, U+1C80-1C88, U+20B4, U+2DE0-2DFF, U+A640-A69F, U+FE2E-FE2F;
+}
+/* cyrillic */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: normal;
+  font-weight: 400;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTSumu1aB.woff2) format('woff2');
+  unicode-range: U+0301, U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
+}
+/* greek-ext */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: normal;
+  font-weight: 400;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTSOmu1aB.woff2) format('woff2');
+  unicode-range: U+1F00-1FFF;
+}
+/* greek */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: normal;
+  font-weight: 400;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTSymu1aB.woff2) format('woff2');
+  unicode-range: U+0370-0377, U+037A-037F, U+0384-038A, U+038C, U+038E-03A1, U+03A3-03FF;
+}
+/* hebrew */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: normal;
+  font-weight: 400;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTS2mu1aB.woff2) format('woff2');
+  unicode-range: U+0590-05FF, U+200C-2010, U+20AA, U+25CC, U+FB1D-FB4F;
+}
+/* math */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: normal;
+  font-weight: 400;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTVOmu1aB.woff2) format('woff2');
+  unicode-range: U+0302-0303, U+0305, U+0307-0308, U+0330, U+0391-03A1, U+03A3-03A9, U+03B1-03C9, U+03D1, U+03D5-03D6, U+03F0-03F1, U+03F4-03F5, U+2034-2037, U+2057, U+20D0-20DC, U+20E1, U+20E5-20EF, U+2102, U+210A-210E, U+2110-2112, U+2115, U+2119-211D, U+2124, U+2128, U+212C-212D, U+212F-2131, U+2133-2138, U+213C-2140, U+2145-2149, U+2190, U+2192, U+2194-21AE, U+21B0-21E5, U+21F1-21F2, U+21F4-2211, U+2213-2214, U+2216-22FF, U+2308-230B, U+2310, U+2319, U+231C-2321, U+2336-237A, U+237C, U+2395, U+239B-23B6, U+23D0, U+23DC-23E1, U+2474-2475, U+25AF, U+25B3, U+25B7, U+25BD, U+25C1, U+25CA, U+25CC, U+25FB, U+266D-266F, U+27C0-27FF, U+2900-2AFF, U+2B0E-2B11, U+2B30-2B4C, U+2BFE, U+FF5B, U+FF5D, U+1D400-1D7FF, U+1EE00-1EEFF;
+}
+/* symbols */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: normal;
+  font-weight: 400;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTUGmu1aB.woff2) format('woff2');
+  unicode-range: U+0001-000C, U+000E-001F, U+007F-009F, U+20DD-20E0, U+20E2-20E4, U+2150-218F, U+2190, U+2192, U+2194-2199, U+21AF, U+21E6-21F0, U+21F3, U+2218-2219, U+2299, U+22C4-22C6, U+2300-243F, U+2440-244A, U+2460-24FF, U+25A0-27BF, U+2800-28FF, U+2921-2922, U+2981, U+29BF, U+29EB, U+2B00-2BFF, U+4DC0-4DFF, U+FFF9-FFFB, U+10140-1018E, U+10190-1019C, U+101A0, U+101D0-101FD, U+102E0-102FB, U+10E60-10E7E, U+1D2C0-1D2D3, U+1D2E0-1D37F, U+1F000-1F0FF, U+1F100-1F1AD, U+1F1E6-1F1FF, U+1F30D-1F30F, U+1F315, U+1F31C, U+1F31E, U+1F320-1F32C, U+1F336, U+1F378, U+1F37D, U+1F382, U+1F393-1F39F, U+1F3A7-1F3A8, U+1F3AC-1F3AF, U+1F3C2, U+1F3C4-1F3C6, U+1F3CA-1F3CE, U+1F3D4-1F3E0, U+1F3ED, U+1F3F1-1F3F3, U+1F3F5-1F3F7, U+1F408, U+1F415, U+1F41F, U+1F426, U+1F43F, U+1F441-1F442, U+1F444, U+1F446-1F449, U+1F44C-1F44E, U+1F453, U+1F46A, U+1F47D, U+1F4A3, U+1F4B0, U+1F4B3, U+1F4B9, U+1F4BB, U+1F4BF, U+1F4C8-1F4CB, U+1F4D6, U+1F4DA, U+1F4DF, U+1F4E3-1F4E6, U+1F4EA-1F4ED, U+1F4F7, U+1F4F9-1F4FB, U+1F4FD-1F4FE, U+1F503, U+1F507-1F50B, U+1F50D, U+1F512-1F513, U+1F53E-1F54A, U+1F54F-1F5FA, U+1F610, U+1F650-1F67F, U+1F687, U+1F68D, U+1F691, U+1F694, U+1F698, U+1F6AD, U+1F6B2, U+1F6B9-1F6BA, U+1F6BC, U+1F6C6-1F6CF, U+1F6D3-1F6D7, U+1F6E0-1F6EA, U+1F6F0-1F6F3, U+1F6F7-1F6FC, U+1F700-1F7FF, U+1F800-1F80B, U+1F810-1F847, U+1F850-1F859, U+1F860-1F887, U+1F890-1F8AD, U+1F8B0-1F8B1, U+1F900-1F90B, U+1F93B, U+1F946, U+1F984, U+1F996, U+1F9E9, U+1FA00-1FA6F, U+1FA70-1FA7C, U+1FA80-1FA88, U+1FA90-1FABD, U+1FABF-1FAC5, U+1FACE-1FADB, U+1FAE0-1FAE8, U+1FAF0-1FAF8, U+1FB00-1FBFF;
+}
+/* vietnamese */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: normal;
+  font-weight: 400;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTSCmu1aB.woff2) format('woff2');
+  unicode-range: U+0102-0103, U+0110-0111, U+0128-0129, U+0168-0169, U+01A0-01A1, U+01AF-01B0, U+0300-0301, U+0303-0304, U+0308-0309, U+0323, U+0329, U+1EA0-1EF9, U+20AB;
+}
+/* latin-ext */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: normal;
+  font-weight: 400;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTSGmu1aB.woff2) format('woff2');
+  unicode-range: U+0100-02AF, U+0304, U+0308, U+0329, U+1E00-1E9F, U+1EF2-1EFF, U+2020, U+20A0-20AB, U+20AD-20C0, U+2113, U+2C60-2C7F, U+A720-A7FF;
+}
+/* latin */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: normal;
+  font-weight: 400;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTS-muw.woff2) format('woff2');
+  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+0304, U+0308, U+0329, U+2000-206F, U+2074, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD;
+}
+/* cyrillic-ext */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: normal;
+  font-weight: 700;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTSKmu1aB.woff2) format('woff2');
+  unicode-range: U+0460-052F, U+1C80-1C88, U+20B4, U+2DE0-2DFF, U+A640-A69F, U+FE2E-FE2F;
+}
+/* cyrillic */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: normal;
+  font-weight: 700;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTSumu1aB.woff2) format('woff2');
+  unicode-range: U+0301, U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
+}
+/* greek-ext */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: normal;
+  font-weight: 700;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTSOmu1aB.woff2) format('woff2');
+  unicode-range: U+1F00-1FFF;
+}
+/* greek */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: normal;
+  font-weight: 700;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTSymu1aB.woff2) format('woff2');
+  unicode-range: U+0370-0377, U+037A-037F, U+0384-038A, U+038C, U+038E-03A1, U+03A3-03FF;
+}
+/* hebrew */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: normal;
+  font-weight: 700;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTS2mu1aB.woff2) format('woff2');
+  unicode-range: U+0590-05FF, U+200C-2010, U+20AA, U+25CC, U+FB1D-FB4F;
+}
+/* math */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: normal;
+  font-weight: 700;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTVOmu1aB.woff2) format('woff2');
+  unicode-range: U+0302-0303, U+0305, U+0307-0308, U+0330, U+0391-03A1, U+03A3-03A9, U+03B1-03C9, U+03D1, U+03D5-03D6, U+03F0-03F1, U+03F4-03F5, U+2034-2037, U+2057, U+20D0-20DC, U+20E1, U+20E5-20EF, U+2102, U+210A-210E, U+2110-2112, U+2115, U+2119-211D, U+2124, U+2128, U+212C-212D, U+212F-2131, U+2133-2138, U+213C-2140, U+2145-2149, U+2190, U+2192, U+2194-21AE, U+21B0-21E5, U+21F1-21F2, U+21F4-2211, U+2213-2214, U+2216-22FF, U+2308-230B, U+2310, U+2319, U+231C-2321, U+2336-237A, U+237C, U+2395, U+239B-23B6, U+23D0, U+23DC-23E1, U+2474-2475, U+25AF, U+25B3, U+25B7, U+25BD, U+25C1, U+25CA, U+25CC, U+25FB, U+266D-266F, U+27C0-27FF, U+2900-2AFF, U+2B0E-2B11, U+2B30-2B4C, U+2BFE, U+FF5B, U+FF5D, U+1D400-1D7FF, U+1EE00-1EEFF;
+}
+/* symbols */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: normal;
+  font-weight: 700;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTUGmu1aB.woff2) format('woff2');
+  unicode-range: U+0001-000C, U+000E-001F, U+007F-009F, U+20DD-20E0, U+20E2-20E4, U+2150-218F, U+2190, U+2192, U+2194-2199, U+21AF, U+21E6-21F0, U+21F3, U+2218-2219, U+2299, U+22C4-22C6, U+2300-243F, U+2440-244A, U+2460-24FF, U+25A0-27BF, U+2800-28FF, U+2921-2922, U+2981, U+29BF, U+29EB, U+2B00-2BFF, U+4DC0-4DFF, U+FFF9-FFFB, U+10140-1018E, U+10190-1019C, U+101A0, U+101D0-101FD, U+102E0-102FB, U+10E60-10E7E, U+1D2C0-1D2D3, U+1D2E0-1D37F, U+1F000-1F0FF, U+1F100-1F1AD, U+1F1E6-1F1FF, U+1F30D-1F30F, U+1F315, U+1F31C, U+1F31E, U+1F320-1F32C, U+1F336, U+1F378, U+1F37D, U+1F382, U+1F393-1F39F, U+1F3A7-1F3A8, U+1F3AC-1F3AF, U+1F3C2, U+1F3C4-1F3C6, U+1F3CA-1F3CE, U+1F3D4-1F3E0, U+1F3ED, U+1F3F1-1F3F3, U+1F3F5-1F3F7, U+1F408, U+1F415, U+1F41F, U+1F426, U+1F43F, U+1F441-1F442, U+1F444, U+1F446-1F449, U+1F44C-1F44E, U+1F453, U+1F46A, U+1F47D, U+1F4A3, U+1F4B0, U+1F4B3, U+1F4B9, U+1F4BB, U+1F4BF, U+1F4C8-1F4CB, U+1F4D6, U+1F4DA, U+1F4DF, U+1F4E3-1F4E6, U+1F4EA-1F4ED, U+1F4F7, U+1F4F9-1F4FB, U+1F4FD-1F4FE, U+1F503, U+1F507-1F50B, U+1F50D, U+1F512-1F513, U+1F53E-1F54A, U+1F54F-1F5FA, U+1F610, U+1F650-1F67F, U+1F687, U+1F68D, U+1F691, U+1F694, U+1F698, U+1F6AD, U+1F6B2, U+1F6B9-1F6BA, U+1F6BC, U+1F6C6-1F6CF, U+1F6D3-1F6D7, U+1F6E0-1F6EA, U+1F6F0-1F6F3, U+1F6F7-1F6FC, U+1F700-1F7FF, U+1F800-1F80B, U+1F810-1F847, U+1F850-1F859, U+1F860-1F887, U+1F890-1F8AD, U+1F8B0-1F8B1, U+1F900-1F90B, U+1F93B, U+1F946, U+1F984, U+1F996, U+1F9E9, U+1FA00-1FA6F, U+1FA70-1FA7C, U+1FA80-1FA88, U+1FA90-1FABD, U+1FABF-1FAC5, U+1FACE-1FADB, U+1FAE0-1FAE8, U+1FAF0-1FAF8, U+1FB00-1FBFF;
+}
+/* vietnamese */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: normal;
+  font-weight: 700;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTSCmu1aB.woff2) format('woff2');
+  unicode-range: U+0102-0103, U+0110-0111, U+0128-0129, U+0168-0169, U+01A0-01A1, U+01AF-01B0, U+0300-0301, U+0303-0304, U+0308-0309, U+0323, U+0329, U+1EA0-1EF9, U+20AB;
+}
+/* latin-ext */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: normal;
+  font-weight: 700;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTSGmu1aB.woff2) format('woff2');
+  unicode-range: U+0100-02AF, U+0304, U+0308, U+0329, U+1E00-1E9F, U+1EF2-1EFF, U+2020, U+20A0-20AB, U+20AD-20C0, U+2113, U+2C60-2C7F, U+A720-A7FF;
+}
+/* latin */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: normal;
+  font-weight: 700;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTS-muw.woff2) format('woff2');
+  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+0304, U+0308, U+0329, U+2000-206F, U+2074, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD;
+}
diff --git a/docs/deps/bootstrap-5.3.1/fonts/07d40e985ad7c747025dabb9f22142c4.woff2 b/docs/deps/bootstrap-5.3.1/fonts/07d40e985ad7c747025dabb9f22142c4.woff2
new file mode 100644
index 00000000..a45f4778
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/07d40e985ad7c747025dabb9f22142c4.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/1Ptug8zYS_SKggPNyC0ITw.woff2 b/docs/deps/bootstrap-5.3.1/fonts/1Ptug8zYS_SKggPNyC0ITw.woff2
new file mode 100644
index 00000000..6478b9ad
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/1Ptug8zYS_SKggPNyC0ITw.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/1Ptug8zYS_SKggPNyCAIT5lu.woff2 b/docs/deps/bootstrap-5.3.1/fonts/1Ptug8zYS_SKggPNyCAIT5lu.woff2
new file mode 100644
index 00000000..dee82d4b
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/1Ptug8zYS_SKggPNyCAIT5lu.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/1Ptug8zYS_SKggPNyCIIT5lu.woff2 b/docs/deps/bootstrap-5.3.1/fonts/1Ptug8zYS_SKggPNyCIIT5lu.woff2
new file mode 100644
index 00000000..b5664780
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/1Ptug8zYS_SKggPNyCIIT5lu.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/1Ptug8zYS_SKggPNyCMIT5lu.woff2 b/docs/deps/bootstrap-5.3.1/fonts/1Ptug8zYS_SKggPNyCMIT5lu.woff2
new file mode 100644
index 00000000..e0d65386
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/1Ptug8zYS_SKggPNyCMIT5lu.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/1Ptug8zYS_SKggPNyCkIT5lu.woff2 b/docs/deps/bootstrap-5.3.1/fonts/1Ptug8zYS_SKggPNyCkIT5lu.woff2
new file mode 100644
index 00000000..1005b7c8
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/1Ptug8zYS_SKggPNyCkIT5lu.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/1f5e011d6aae0d98fc0518e1a303e99a.woff2 b/docs/deps/bootstrap-5.3.1/fonts/1f5e011d6aae0d98fc0518e1a303e99a.woff2
new file mode 100644
index 00000000..8f9ec7cc
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/1f5e011d6aae0d98fc0518e1a303e99a.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/4iCs6KVjbNBYlgoKcQ72j00.woff2 b/docs/deps/bootstrap-5.3.1/fonts/4iCs6KVjbNBYlgoKcQ72j00.woff2
new file mode 100644
index 00000000..e5cdc99a
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/4iCs6KVjbNBYlgoKcQ72j00.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/4iCs6KVjbNBYlgoKcg72j00.woff2 b/docs/deps/bootstrap-5.3.1/fonts/4iCs6KVjbNBYlgoKcg72j00.woff2
new file mode 100644
index 00000000..3804815a
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/4iCs6KVjbNBYlgoKcg72j00.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/4iCs6KVjbNBYlgoKcw72j00.woff2 b/docs/deps/bootstrap-5.3.1/fonts/4iCs6KVjbNBYlgoKcw72j00.woff2
new file mode 100644
index 00000000..b52cff96
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/4iCs6KVjbNBYlgoKcw72j00.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/4iCs6KVjbNBYlgoKew72j00.woff2 b/docs/deps/bootstrap-5.3.1/fonts/4iCs6KVjbNBYlgoKew72j00.woff2
new file mode 100644
index 00000000..cb672f2b
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/4iCs6KVjbNBYlgoKew72j00.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/4iCs6KVjbNBYlgoKfA72j00.woff2 b/docs/deps/bootstrap-5.3.1/fonts/4iCs6KVjbNBYlgoKfA72j00.woff2
new file mode 100644
index 00000000..dc3c2953
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/4iCs6KVjbNBYlgoKfA72j00.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/4iCs6KVjbNBYlgoKfw72.woff2 b/docs/deps/bootstrap-5.3.1/fonts/4iCs6KVjbNBYlgoKfw72.woff2
new file mode 100644
index 00000000..8070e4f7
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/4iCs6KVjbNBYlgoKfw72.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/4iCv6KVjbNBYlgoCxCvjs2yNL4U.woff2 b/docs/deps/bootstrap-5.3.1/fonts/4iCv6KVjbNBYlgoCxCvjs2yNL4U.woff2
new file mode 100644
index 00000000..926a1e6f
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/4iCv6KVjbNBYlgoCxCvjs2yNL4U.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/4iCv6KVjbNBYlgoCxCvjsGyN.woff2 b/docs/deps/bootstrap-5.3.1/fonts/4iCv6KVjbNBYlgoCxCvjsGyN.woff2
new file mode 100644
index 00000000..2c08bc62
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/4iCv6KVjbNBYlgoCxCvjsGyN.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/4iCv6KVjbNBYlgoCxCvjtGyNL4U.woff2 b/docs/deps/bootstrap-5.3.1/fonts/4iCv6KVjbNBYlgoCxCvjtGyNL4U.woff2
new file mode 100644
index 00000000..6d732de1
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/4iCv6KVjbNBYlgoCxCvjtGyNL4U.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/4iCv6KVjbNBYlgoCxCvjvGyNL4U.woff2 b/docs/deps/bootstrap-5.3.1/fonts/4iCv6KVjbNBYlgoCxCvjvGyNL4U.woff2
new file mode 100644
index 00000000..d2fb863a
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/4iCv6KVjbNBYlgoCxCvjvGyNL4U.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/4iCv6KVjbNBYlgoCxCvjvWyNL4U.woff2 b/docs/deps/bootstrap-5.3.1/fonts/4iCv6KVjbNBYlgoCxCvjvWyNL4U.woff2
new file mode 100644
index 00000000..5386e806
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/4iCv6KVjbNBYlgoCxCvjvWyNL4U.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/4iCv6KVjbNBYlgoCxCvjvmyNL4U.woff2 b/docs/deps/bootstrap-5.3.1/fonts/4iCv6KVjbNBYlgoCxCvjvmyNL4U.woff2
new file mode 100644
index 00000000..0d62144c
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/4iCv6KVjbNBYlgoCxCvjvmyNL4U.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/626330658504e338ee86aec8e957426b.woff2 b/docs/deps/bootstrap-5.3.1/fonts/626330658504e338ee86aec8e957426b.woff2
new file mode 100644
index 00000000..381d41fb
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/626330658504e338ee86aec8e957426b.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/6xK1dSBYKcSV-LCoeQqfX1RYOo3qPZ7jsDJT9g.woff2 b/docs/deps/bootstrap-5.3.1/fonts/6xK1dSBYKcSV-LCoeQqfX1RYOo3qPZ7jsDJT9g.woff2
new file mode 100644
index 00000000..2bcdb320
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/6xK1dSBYKcSV-LCoeQqfX1RYOo3qPZ7jsDJT9g.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/6xK1dSBYKcSV-LCoeQqfX1RYOo3qPZ7ksDJT9g.woff2 b/docs/deps/bootstrap-5.3.1/fonts/6xK1dSBYKcSV-LCoeQqfX1RYOo3qPZ7ksDJT9g.woff2
new file mode 100644
index 00000000..6a8b92a3
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/6xK1dSBYKcSV-LCoeQqfX1RYOo3qPZ7ksDJT9g.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/6xK1dSBYKcSV-LCoeQqfX1RYOo3qPZ7nsDI.woff2 b/docs/deps/bootstrap-5.3.1/fonts/6xK1dSBYKcSV-LCoeQqfX1RYOo3qPZ7nsDI.woff2
new file mode 100644
index 00000000..5d4d718e
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/6xK1dSBYKcSV-LCoeQqfX1RYOo3qPZ7nsDI.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/6xK1dSBYKcSV-LCoeQqfX1RYOo3qPZ7osDJT9g.woff2 b/docs/deps/bootstrap-5.3.1/fonts/6xK1dSBYKcSV-LCoeQqfX1RYOo3qPZ7osDJT9g.woff2
new file mode 100644
index 00000000..c9300352
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/6xK1dSBYKcSV-LCoeQqfX1RYOo3qPZ7osDJT9g.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/6xK1dSBYKcSV-LCoeQqfX1RYOo3qPZ7psDJT9g.woff2 b/docs/deps/bootstrap-5.3.1/fonts/6xK1dSBYKcSV-LCoeQqfX1RYOo3qPZ7psDJT9g.woff2
new file mode 100644
index 00000000..190e8ac0
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/6xK1dSBYKcSV-LCoeQqfX1RYOo3qPZ7psDJT9g.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/6xK1dSBYKcSV-LCoeQqfX1RYOo3qPZ7qsDJT9g.woff2 b/docs/deps/bootstrap-5.3.1/fonts/6xK1dSBYKcSV-LCoeQqfX1RYOo3qPZ7qsDJT9g.woff2
new file mode 100644
index 00000000..7b21aa84
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/6xK1dSBYKcSV-LCoeQqfX1RYOo3qPZ7qsDJT9g.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/6xK1dSBYKcSV-LCoeQqfX1RYOo3qPZ7rsDJT9g.woff2 b/docs/deps/bootstrap-5.3.1/fonts/6xK1dSBYKcSV-LCoeQqfX1RYOo3qPZ7rsDJT9g.woff2
new file mode 100644
index 00000000..180e76d9
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/6xK1dSBYKcSV-LCoeQqfX1RYOo3qPZ7rsDJT9g.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/6xK3dSBYKcSV-LCoeQqfX1RYOo3qN67lqDY.woff2 b/docs/deps/bootstrap-5.3.1/fonts/6xK3dSBYKcSV-LCoeQqfX1RYOo3qN67lqDY.woff2
new file mode 100644
index 00000000..f9915277
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/6xK3dSBYKcSV-LCoeQqfX1RYOo3qN67lqDY.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/6xK3dSBYKcSV-LCoeQqfX1RYOo3qNK7lqDY.woff2 b/docs/deps/bootstrap-5.3.1/fonts/6xK3dSBYKcSV-LCoeQqfX1RYOo3qNK7lqDY.woff2
new file mode 100644
index 00000000..69d26d80
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/6xK3dSBYKcSV-LCoeQqfX1RYOo3qNK7lqDY.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/6xK3dSBYKcSV-LCoeQqfX1RYOo3qNa7lqDY.woff2 b/docs/deps/bootstrap-5.3.1/fonts/6xK3dSBYKcSV-LCoeQqfX1RYOo3qNa7lqDY.woff2
new file mode 100644
index 00000000..5c9f701b
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/6xK3dSBYKcSV-LCoeQqfX1RYOo3qNa7lqDY.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/6xK3dSBYKcSV-LCoeQqfX1RYOo3qNq7lqDY.woff2 b/docs/deps/bootstrap-5.3.1/fonts/6xK3dSBYKcSV-LCoeQqfX1RYOo3qNq7lqDY.woff2
new file mode 100644
index 00000000..a61ec915
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/6xK3dSBYKcSV-LCoeQqfX1RYOo3qNq7lqDY.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/6xK3dSBYKcSV-LCoeQqfX1RYOo3qO67lqDY.woff2 b/docs/deps/bootstrap-5.3.1/fonts/6xK3dSBYKcSV-LCoeQqfX1RYOo3qO67lqDY.woff2
new file mode 100644
index 00000000..33145f94
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/6xK3dSBYKcSV-LCoeQqfX1RYOo3qO67lqDY.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/6xK3dSBYKcSV-LCoeQqfX1RYOo3qOK7l.woff2 b/docs/deps/bootstrap-5.3.1/fonts/6xK3dSBYKcSV-LCoeQqfX1RYOo3qOK7l.woff2
new file mode 100644
index 00000000..b1986c23
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/6xK3dSBYKcSV-LCoeQqfX1RYOo3qOK7l.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/6xK3dSBYKcSV-LCoeQqfX1RYOo3qPK7lqDY.woff2 b/docs/deps/bootstrap-5.3.1/fonts/6xK3dSBYKcSV-LCoeQqfX1RYOo3qPK7lqDY.woff2
new file mode 100644
index 00000000..2462c511
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/6xK3dSBYKcSV-LCoeQqfX1RYOo3qPK7lqDY.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/6xKydSBYKcSV-LCoeQqfX1RYOo3i54rwkxduz8A.woff2 b/docs/deps/bootstrap-5.3.1/fonts/6xKydSBYKcSV-LCoeQqfX1RYOo3i54rwkxduz8A.woff2
new file mode 100644
index 00000000..ccf6b8e6
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/6xKydSBYKcSV-LCoeQqfX1RYOo3i54rwkxduz8A.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/6xKydSBYKcSV-LCoeQqfX1RYOo3i54rwlBduz8A.woff2 b/docs/deps/bootstrap-5.3.1/fonts/6xKydSBYKcSV-LCoeQqfX1RYOo3i54rwlBduz8A.woff2
new file mode 100644
index 00000000..8b03f91b
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/6xKydSBYKcSV-LCoeQqfX1RYOo3i54rwlBduz8A.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/6xKydSBYKcSV-LCoeQqfX1RYOo3i54rwlxdu.woff2 b/docs/deps/bootstrap-5.3.1/fonts/6xKydSBYKcSV-LCoeQqfX1RYOo3i54rwlxdu.woff2
new file mode 100644
index 00000000..3ca3e26c
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/6xKydSBYKcSV-LCoeQqfX1RYOo3i54rwlxdu.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/6xKydSBYKcSV-LCoeQqfX1RYOo3i54rwmBduz8A.woff2 b/docs/deps/bootstrap-5.3.1/fonts/6xKydSBYKcSV-LCoeQqfX1RYOo3i54rwmBduz8A.woff2
new file mode 100644
index 00000000..daa43962
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/6xKydSBYKcSV-LCoeQqfX1RYOo3i54rwmBduz8A.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/6xKydSBYKcSV-LCoeQqfX1RYOo3i54rwmRduz8A.woff2 b/docs/deps/bootstrap-5.3.1/fonts/6xKydSBYKcSV-LCoeQqfX1RYOo3i54rwmRduz8A.woff2
new file mode 100644
index 00000000..7916a329
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/6xKydSBYKcSV-LCoeQqfX1RYOo3i54rwmRduz8A.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/6xKydSBYKcSV-LCoeQqfX1RYOo3i54rwmhduz8A.woff2 b/docs/deps/bootstrap-5.3.1/fonts/6xKydSBYKcSV-LCoeQqfX1RYOo3i54rwmhduz8A.woff2
new file mode 100644
index 00000000..5cf09980
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/6xKydSBYKcSV-LCoeQqfX1RYOo3i54rwmhduz8A.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/6xKydSBYKcSV-LCoeQqfX1RYOo3i54rwmxduz8A.woff2 b/docs/deps/bootstrap-5.3.1/fonts/6xKydSBYKcSV-LCoeQqfX1RYOo3i54rwmxduz8A.woff2
new file mode 100644
index 00000000..de50d2b5
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/6xKydSBYKcSV-LCoeQqfX1RYOo3i54rwmxduz8A.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/6xKydSBYKcSV-LCoeQqfX1RYOo3ig4vwkxduz8A.woff2 b/docs/deps/bootstrap-5.3.1/fonts/6xKydSBYKcSV-LCoeQqfX1RYOo3ig4vwkxduz8A.woff2
new file mode 100644
index 00000000..cd3f1593
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/6xKydSBYKcSV-LCoeQqfX1RYOo3ig4vwkxduz8A.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/6xKydSBYKcSV-LCoeQqfX1RYOo3ig4vwlBduz8A.woff2 b/docs/deps/bootstrap-5.3.1/fonts/6xKydSBYKcSV-LCoeQqfX1RYOo3ig4vwlBduz8A.woff2
new file mode 100644
index 00000000..d0a493f5
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/6xKydSBYKcSV-LCoeQqfX1RYOo3ig4vwlBduz8A.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/6xKydSBYKcSV-LCoeQqfX1RYOo3ig4vwlxdu.woff2 b/docs/deps/bootstrap-5.3.1/fonts/6xKydSBYKcSV-LCoeQqfX1RYOo3ig4vwlxdu.woff2
new file mode 100644
index 00000000..134cce1d
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/6xKydSBYKcSV-LCoeQqfX1RYOo3ig4vwlxdu.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/6xKydSBYKcSV-LCoeQqfX1RYOo3ig4vwmBduz8A.woff2 b/docs/deps/bootstrap-5.3.1/fonts/6xKydSBYKcSV-LCoeQqfX1RYOo3ig4vwmBduz8A.woff2
new file mode 100644
index 00000000..29fb3995
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/6xKydSBYKcSV-LCoeQqfX1RYOo3ig4vwmBduz8A.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/6xKydSBYKcSV-LCoeQqfX1RYOo3ig4vwmRduz8A.woff2 b/docs/deps/bootstrap-5.3.1/fonts/6xKydSBYKcSV-LCoeQqfX1RYOo3ig4vwmRduz8A.woff2
new file mode 100644
index 00000000..889d871f
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/6xKydSBYKcSV-LCoeQqfX1RYOo3ig4vwmRduz8A.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/6xKydSBYKcSV-LCoeQqfX1RYOo3ig4vwmhduz8A.woff2 b/docs/deps/bootstrap-5.3.1/fonts/6xKydSBYKcSV-LCoeQqfX1RYOo3ig4vwmhduz8A.woff2
new file mode 100644
index 00000000..b5e72d12
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/6xKydSBYKcSV-LCoeQqfX1RYOo3ig4vwmhduz8A.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/6xKydSBYKcSV-LCoeQqfX1RYOo3ig4vwmxduz8A.woff2 b/docs/deps/bootstrap-5.3.1/fonts/6xKydSBYKcSV-LCoeQqfX1RYOo3ig4vwmxduz8A.woff2
new file mode 100644
index 00000000..d62d6fcf
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/6xKydSBYKcSV-LCoeQqfX1RYOo3ig4vwmxduz8A.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/6xKydSBYKcSV-LCoeQqfX1RYOo3ik4zwkxduz8A.woff2 b/docs/deps/bootstrap-5.3.1/fonts/6xKydSBYKcSV-LCoeQqfX1RYOo3ik4zwkxduz8A.woff2
new file mode 100644
index 00000000..9f2cdfc7
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/6xKydSBYKcSV-LCoeQqfX1RYOo3ik4zwkxduz8A.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/6xKydSBYKcSV-LCoeQqfX1RYOo3ik4zwlBduz8A.woff2 b/docs/deps/bootstrap-5.3.1/fonts/6xKydSBYKcSV-LCoeQqfX1RYOo3ik4zwlBduz8A.woff2
new file mode 100644
index 00000000..51260e3f
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/6xKydSBYKcSV-LCoeQqfX1RYOo3ik4zwlBduz8A.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/6xKydSBYKcSV-LCoeQqfX1RYOo3ik4zwlxdu.woff2 b/docs/deps/bootstrap-5.3.1/fonts/6xKydSBYKcSV-LCoeQqfX1RYOo3ik4zwlxdu.woff2
new file mode 100644
index 00000000..86e47796
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/6xKydSBYKcSV-LCoeQqfX1RYOo3ik4zwlxdu.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/6xKydSBYKcSV-LCoeQqfX1RYOo3ik4zwmBduz8A.woff2 b/docs/deps/bootstrap-5.3.1/fonts/6xKydSBYKcSV-LCoeQqfX1RYOo3ik4zwmBduz8A.woff2
new file mode 100644
index 00000000..e538c265
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/6xKydSBYKcSV-LCoeQqfX1RYOo3ik4zwmBduz8A.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/6xKydSBYKcSV-LCoeQqfX1RYOo3ik4zwmRduz8A.woff2 b/docs/deps/bootstrap-5.3.1/fonts/6xKydSBYKcSV-LCoeQqfX1RYOo3ik4zwmRduz8A.woff2
new file mode 100644
index 00000000..0a9a5205
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/6xKydSBYKcSV-LCoeQqfX1RYOo3ik4zwmRduz8A.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/6xKydSBYKcSV-LCoeQqfX1RYOo3ik4zwmhduz8A.woff2 b/docs/deps/bootstrap-5.3.1/fonts/6xKydSBYKcSV-LCoeQqfX1RYOo3ik4zwmhduz8A.woff2
new file mode 100644
index 00000000..fa70af3a
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/6xKydSBYKcSV-LCoeQqfX1RYOo3ik4zwmhduz8A.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/6xKydSBYKcSV-LCoeQqfX1RYOo3ik4zwmxduz8A.woff2 b/docs/deps/bootstrap-5.3.1/fonts/6xKydSBYKcSV-LCoeQqfX1RYOo3ik4zwmxduz8A.woff2
new file mode 100644
index 00000000..47872fbc
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/6xKydSBYKcSV-LCoeQqfX1RYOo3ik4zwmxduz8A.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/CSR54z1Qlv-GDxkbKVQ_dFsvWNReuQ.woff2 b/docs/deps/bootstrap-5.3.1/fonts/CSR54z1Qlv-GDxkbKVQ_dFsvWNReuQ.woff2
new file mode 100644
index 00000000..81e14769
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/CSR54z1Qlv-GDxkbKVQ_dFsvWNReuQ.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/CSR54z1Qlv-GDxkbKVQ_dFsvWNpeudwk.woff2 b/docs/deps/bootstrap-5.3.1/fonts/CSR54z1Qlv-GDxkbKVQ_dFsvWNpeudwk.woff2
new file mode 100644
index 00000000..3d9ec072
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/CSR54z1Qlv-GDxkbKVQ_dFsvWNpeudwk.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/CSR64z1Qlv-GDxkbKVQ_fO4KTet_.woff2 b/docs/deps/bootstrap-5.3.1/fonts/CSR64z1Qlv-GDxkbKVQ_fO4KTet_.woff2
new file mode 100644
index 00000000..12c485e0
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/CSR64z1Qlv-GDxkbKVQ_fO4KTet_.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/CSR64z1Qlv-GDxkbKVQ_fOAKTQ.woff2 b/docs/deps/bootstrap-5.3.1/fonts/CSR64z1Qlv-GDxkbKVQ_fOAKTQ.woff2
new file mode 100644
index 00000000..c58efa85
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/CSR64z1Qlv-GDxkbKVQ_fOAKTQ.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/HI_QiYsKILxRpg3hIP6sJ7fM7PqlONvQlMIXxw.woff2 b/docs/deps/bootstrap-5.3.1/fonts/HI_QiYsKILxRpg3hIP6sJ7fM7PqlONvQlMIXxw.woff2
new file mode 100644
index 00000000..5a5cd9c5
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/HI_QiYsKILxRpg3hIP6sJ7fM7PqlONvQlMIXxw.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/HI_QiYsKILxRpg3hIP6sJ7fM7PqlONvUlMI.woff2 b/docs/deps/bootstrap-5.3.1/fonts/HI_QiYsKILxRpg3hIP6sJ7fM7PqlONvUlMI.woff2
new file mode 100644
index 00000000..a455e2b9
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/HI_QiYsKILxRpg3hIP6sJ7fM7PqlONvUlMI.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/HI_QiYsKILxRpg3hIP6sJ7fM7PqlONvXlMIXxw.woff2 b/docs/deps/bootstrap-5.3.1/fonts/HI_QiYsKILxRpg3hIP6sJ7fM7PqlONvXlMIXxw.woff2
new file mode 100644
index 00000000..b455dfec
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/HI_QiYsKILxRpg3hIP6sJ7fM7PqlONvXlMIXxw.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/HI_QiYsKILxRpg3hIP6sJ7fM7PqlONvYlMIXxw.woff2 b/docs/deps/bootstrap-5.3.1/fonts/HI_QiYsKILxRpg3hIP6sJ7fM7PqlONvYlMIXxw.woff2
new file mode 100644
index 00000000..6fbd85ab
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/HI_QiYsKILxRpg3hIP6sJ7fM7PqlONvYlMIXxw.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/HI_QiYsKILxRpg3hIP6sJ7fM7PqlONvZlMIXxw.woff2 b/docs/deps/bootstrap-5.3.1/fonts/HI_QiYsKILxRpg3hIP6sJ7fM7PqlONvZlMIXxw.woff2
new file mode 100644
index 00000000..df5f1932
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/HI_QiYsKILxRpg3hIP6sJ7fM7PqlONvZlMIXxw.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/HI_QiYsKILxRpg3hIP6sJ7fM7PqlONvalMIXxw.woff2 b/docs/deps/bootstrap-5.3.1/fonts/HI_QiYsKILxRpg3hIP6sJ7fM7PqlONvalMIXxw.woff2
new file mode 100644
index 00000000..29531f47
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/HI_QiYsKILxRpg3hIP6sJ7fM7PqlONvalMIXxw.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/HI_QiYsKILxRpg3hIP6sJ7fM7PqlONvblMIXxw.woff2 b/docs/deps/bootstrap-5.3.1/fonts/HI_QiYsKILxRpg3hIP6sJ7fM7PqlONvblMIXxw.woff2
new file mode 100644
index 00000000..36428b18
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/HI_QiYsKILxRpg3hIP6sJ7fM7PqlONvblMIXxw.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/HI_SiYsKILxRpg3hIP6sJ7fM7PqlM-vWjMY.woff2 b/docs/deps/bootstrap-5.3.1/fonts/HI_SiYsKILxRpg3hIP6sJ7fM7PqlM-vWjMY.woff2
new file mode 100644
index 00000000..51cc9634
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/HI_SiYsKILxRpg3hIP6sJ7fM7PqlM-vWjMY.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/HI_SiYsKILxRpg3hIP6sJ7fM7PqlMOvWjMY.woff2 b/docs/deps/bootstrap-5.3.1/fonts/HI_SiYsKILxRpg3hIP6sJ7fM7PqlMOvWjMY.woff2
new file mode 100644
index 00000000..1059d866
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/HI_SiYsKILxRpg3hIP6sJ7fM7PqlMOvWjMY.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/HI_SiYsKILxRpg3hIP6sJ7fM7PqlMevWjMY.woff2 b/docs/deps/bootstrap-5.3.1/fonts/HI_SiYsKILxRpg3hIP6sJ7fM7PqlMevWjMY.woff2
new file mode 100644
index 00000000..82b2771f
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/HI_SiYsKILxRpg3hIP6sJ7fM7PqlMevWjMY.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/HI_SiYsKILxRpg3hIP6sJ7fM7PqlMuvWjMY.woff2 b/docs/deps/bootstrap-5.3.1/fonts/HI_SiYsKILxRpg3hIP6sJ7fM7PqlMuvWjMY.woff2
new file mode 100644
index 00000000..959a0596
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/HI_SiYsKILxRpg3hIP6sJ7fM7PqlMuvWjMY.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/HI_SiYsKILxRpg3hIP6sJ7fM7PqlOevWjMY.woff2 b/docs/deps/bootstrap-5.3.1/fonts/HI_SiYsKILxRpg3hIP6sJ7fM7PqlOevWjMY.woff2
new file mode 100644
index 00000000..b52412fc
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/HI_SiYsKILxRpg3hIP6sJ7fM7PqlOevWjMY.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/HI_SiYsKILxRpg3hIP6sJ7fM7PqlPevW.woff2 b/docs/deps/bootstrap-5.3.1/fonts/HI_SiYsKILxRpg3hIP6sJ7fM7PqlPevW.woff2
new file mode 100644
index 00000000..92ec082a
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/HI_SiYsKILxRpg3hIP6sJ7fM7PqlPevW.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/HI_SiYsKILxRpg3hIP6sJ7fM7PqlPuvWjMY.woff2 b/docs/deps/bootstrap-5.3.1/fonts/HI_SiYsKILxRpg3hIP6sJ7fM7PqlPuvWjMY.woff2
new file mode 100644
index 00000000..a15fcd88
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/HI_SiYsKILxRpg3hIP6sJ7fM7PqlPuvWjMY.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/JTUSjIg1_i6t8kCHKm459W1hyzbi.woff2 b/docs/deps/bootstrap-5.3.1/fonts/JTUSjIg1_i6t8kCHKm459W1hyzbi.woff2
new file mode 100644
index 00000000..5379c126
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/JTUSjIg1_i6t8kCHKm459W1hyzbi.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/JTUSjIg1_i6t8kCHKm459WRhyzbi.woff2 b/docs/deps/bootstrap-5.3.1/fonts/JTUSjIg1_i6t8kCHKm459WRhyzbi.woff2
new file mode 100644
index 00000000..4b7bc4a3
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/JTUSjIg1_i6t8kCHKm459WRhyzbi.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/JTUSjIg1_i6t8kCHKm459WZhyzbi.woff2 b/docs/deps/bootstrap-5.3.1/fonts/JTUSjIg1_i6t8kCHKm459WZhyzbi.woff2
new file mode 100644
index 00000000..6ec37309
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/JTUSjIg1_i6t8kCHKm459WZhyzbi.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/JTUSjIg1_i6t8kCHKm459Wdhyzbi.woff2 b/docs/deps/bootstrap-5.3.1/fonts/JTUSjIg1_i6t8kCHKm459Wdhyzbi.woff2
new file mode 100644
index 00000000..76691276
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/JTUSjIg1_i6t8kCHKm459Wdhyzbi.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/JTUSjIg1_i6t8kCHKm459Wlhyw.woff2 b/docs/deps/bootstrap-5.3.1/fonts/JTUSjIg1_i6t8kCHKm459Wlhyw.woff2
new file mode 100644
index 00000000..6122800c
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/JTUSjIg1_i6t8kCHKm459Wlhyw.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/KFOlCnqEu92Fr1MmEU9fABc4EsA.woff2 b/docs/deps/bootstrap-5.3.1/fonts/KFOlCnqEu92Fr1MmEU9fABc4EsA.woff2
new file mode 100644
index 00000000..cb5834ff
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/KFOlCnqEu92Fr1MmEU9fABc4EsA.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/KFOlCnqEu92Fr1MmEU9fBBc4.woff2 b/docs/deps/bootstrap-5.3.1/fonts/KFOlCnqEu92Fr1MmEU9fBBc4.woff2
new file mode 100644
index 00000000..29342a8d
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/KFOlCnqEu92Fr1MmEU9fBBc4.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/KFOlCnqEu92Fr1MmEU9fBxc4EsA.woff2 b/docs/deps/bootstrap-5.3.1/fonts/KFOlCnqEu92Fr1MmEU9fBxc4EsA.woff2
new file mode 100644
index 00000000..0933dfe8
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/KFOlCnqEu92Fr1MmEU9fBxc4EsA.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/KFOlCnqEu92Fr1MmEU9fCBc4EsA.woff2 b/docs/deps/bootstrap-5.3.1/fonts/KFOlCnqEu92Fr1MmEU9fCBc4EsA.woff2
new file mode 100644
index 00000000..064e94b7
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/KFOlCnqEu92Fr1MmEU9fCBc4EsA.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/KFOlCnqEu92Fr1MmEU9fCRc4EsA.woff2 b/docs/deps/bootstrap-5.3.1/fonts/KFOlCnqEu92Fr1MmEU9fCRc4EsA.woff2
new file mode 100644
index 00000000..8571683e
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/KFOlCnqEu92Fr1MmEU9fCRc4EsA.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/KFOlCnqEu92Fr1MmEU9fChc4EsA.woff2 b/docs/deps/bootstrap-5.3.1/fonts/KFOlCnqEu92Fr1MmEU9fChc4EsA.woff2
new file mode 100644
index 00000000..68f094cd
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/KFOlCnqEu92Fr1MmEU9fChc4EsA.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/KFOlCnqEu92Fr1MmEU9fCxc4EsA.woff2 b/docs/deps/bootstrap-5.3.1/fonts/KFOlCnqEu92Fr1MmEU9fCxc4EsA.woff2
new file mode 100644
index 00000000..6b0b4afe
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/KFOlCnqEu92Fr1MmEU9fCxc4EsA.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/KFOlCnqEu92Fr1MmSU5fABc4EsA.woff2 b/docs/deps/bootstrap-5.3.1/fonts/KFOlCnqEu92Fr1MmSU5fABc4EsA.woff2
new file mode 100644
index 00000000..9d7fb7f8
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/KFOlCnqEu92Fr1MmSU5fABc4EsA.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/KFOlCnqEu92Fr1MmSU5fBBc4.woff2 b/docs/deps/bootstrap-5.3.1/fonts/KFOlCnqEu92Fr1MmSU5fBBc4.woff2
new file mode 100644
index 00000000..60681387
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/KFOlCnqEu92Fr1MmSU5fBBc4.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/KFOlCnqEu92Fr1MmSU5fBxc4EsA.woff2 b/docs/deps/bootstrap-5.3.1/fonts/KFOlCnqEu92Fr1MmSU5fBxc4EsA.woff2
new file mode 100644
index 00000000..b289f002
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/KFOlCnqEu92Fr1MmSU5fBxc4EsA.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/KFOlCnqEu92Fr1MmSU5fCBc4EsA.woff2 b/docs/deps/bootstrap-5.3.1/fonts/KFOlCnqEu92Fr1MmSU5fCBc4EsA.woff2
new file mode 100644
index 00000000..87711c04
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/KFOlCnqEu92Fr1MmSU5fCBc4EsA.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/KFOlCnqEu92Fr1MmSU5fCRc4EsA.woff2 b/docs/deps/bootstrap-5.3.1/fonts/KFOlCnqEu92Fr1MmSU5fCRc4EsA.woff2
new file mode 100644
index 00000000..0f6e60b8
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/KFOlCnqEu92Fr1MmSU5fCRc4EsA.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/KFOlCnqEu92Fr1MmSU5fChc4EsA.woff2 b/docs/deps/bootstrap-5.3.1/fonts/KFOlCnqEu92Fr1MmSU5fChc4EsA.woff2
new file mode 100644
index 00000000..91231c9c
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/KFOlCnqEu92Fr1MmSU5fChc4EsA.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/KFOlCnqEu92Fr1MmSU5fCxc4EsA.woff2 b/docs/deps/bootstrap-5.3.1/fonts/KFOlCnqEu92Fr1MmSU5fCxc4EsA.woff2
new file mode 100644
index 00000000..c0099878
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/KFOlCnqEu92Fr1MmSU5fCxc4EsA.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/KFOlCnqEu92Fr1MmWUlfABc4EsA.woff2 b/docs/deps/bootstrap-5.3.1/fonts/KFOlCnqEu92Fr1MmWUlfABc4EsA.woff2
new file mode 100644
index 00000000..1bb7737c
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/KFOlCnqEu92Fr1MmWUlfABc4EsA.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/KFOlCnqEu92Fr1MmWUlfBBc4.woff2 b/docs/deps/bootstrap-5.3.1/fonts/KFOlCnqEu92Fr1MmWUlfBBc4.woff2
new file mode 100644
index 00000000..771fbecc
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/KFOlCnqEu92Fr1MmWUlfBBc4.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/KFOlCnqEu92Fr1MmWUlfBxc4EsA.woff2 b/docs/deps/bootstrap-5.3.1/fonts/KFOlCnqEu92Fr1MmWUlfBxc4EsA.woff2
new file mode 100644
index 00000000..cb9bfa71
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/KFOlCnqEu92Fr1MmWUlfBxc4EsA.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/KFOlCnqEu92Fr1MmWUlfCBc4EsA.woff2 b/docs/deps/bootstrap-5.3.1/fonts/KFOlCnqEu92Fr1MmWUlfCBc4EsA.woff2
new file mode 100644
index 00000000..a0d68e2b
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/KFOlCnqEu92Fr1MmWUlfCBc4EsA.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/KFOlCnqEu92Fr1MmWUlfCRc4EsA.woff2 b/docs/deps/bootstrap-5.3.1/fonts/KFOlCnqEu92Fr1MmWUlfCRc4EsA.woff2
new file mode 100644
index 00000000..63995528
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/KFOlCnqEu92Fr1MmWUlfCRc4EsA.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/KFOlCnqEu92Fr1MmWUlfChc4EsA.woff2 b/docs/deps/bootstrap-5.3.1/fonts/KFOlCnqEu92Fr1MmWUlfChc4EsA.woff2
new file mode 100644
index 00000000..94ab5fb0
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/KFOlCnqEu92Fr1MmWUlfChc4EsA.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/KFOlCnqEu92Fr1MmWUlfCxc4EsA.woff2 b/docs/deps/bootstrap-5.3.1/fonts/KFOlCnqEu92Fr1MmWUlfCxc4EsA.woff2
new file mode 100644
index 00000000..3c450111
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/KFOlCnqEu92Fr1MmWUlfCxc4EsA.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/KFOmCnqEu92Fr1Mu4WxKOzY.woff2 b/docs/deps/bootstrap-5.3.1/fonts/KFOmCnqEu92Fr1Mu4WxKOzY.woff2
new file mode 100644
index 00000000..fc71d944
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/KFOmCnqEu92Fr1Mu4WxKOzY.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/KFOmCnqEu92Fr1Mu4mxK.woff2 b/docs/deps/bootstrap-5.3.1/fonts/KFOmCnqEu92Fr1Mu4mxK.woff2
new file mode 100644
index 00000000..020729ef
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/KFOmCnqEu92Fr1Mu4mxK.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/KFOmCnqEu92Fr1Mu5mxKOzY.woff2 b/docs/deps/bootstrap-5.3.1/fonts/KFOmCnqEu92Fr1Mu5mxKOzY.woff2
new file mode 100644
index 00000000..47da3629
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/KFOmCnqEu92Fr1Mu5mxKOzY.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/KFOmCnqEu92Fr1Mu72xKOzY.woff2 b/docs/deps/bootstrap-5.3.1/fonts/KFOmCnqEu92Fr1Mu72xKOzY.woff2
new file mode 100644
index 00000000..22ddee9c
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/KFOmCnqEu92Fr1Mu72xKOzY.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/KFOmCnqEu92Fr1Mu7GxKOzY.woff2 b/docs/deps/bootstrap-5.3.1/fonts/KFOmCnqEu92Fr1Mu7GxKOzY.woff2
new file mode 100644
index 00000000..8a8de615
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/KFOmCnqEu92Fr1Mu7GxKOzY.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/KFOmCnqEu92Fr1Mu7WxKOzY.woff2 b/docs/deps/bootstrap-5.3.1/fonts/KFOmCnqEu92Fr1Mu7WxKOzY.woff2
new file mode 100644
index 00000000..6284d2e3
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/KFOmCnqEu92Fr1Mu7WxKOzY.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/KFOmCnqEu92Fr1Mu7mxKOzY.woff2 b/docs/deps/bootstrap-5.3.1/fonts/KFOmCnqEu92Fr1Mu7mxKOzY.woff2
new file mode 100644
index 00000000..72ce0e98
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/KFOmCnqEu92Fr1Mu7mxKOzY.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/QGYpz_kZZAGCONcK2A4bGOj8mNhN.woff2 b/docs/deps/bootstrap-5.3.1/fonts/QGYpz_kZZAGCONcK2A4bGOj8mNhN.woff2
new file mode 100644
index 00000000..5b052bb3
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/QGYpz_kZZAGCONcK2A4bGOj8mNhN.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/S6u8w4BMUTPHjxsAUi-qJCY.woff2 b/docs/deps/bootstrap-5.3.1/fonts/S6u8w4BMUTPHjxsAUi-qJCY.woff2
new file mode 100644
index 00000000..15be816a
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/S6u8w4BMUTPHjxsAUi-qJCY.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/S6u8w4BMUTPHjxsAXC-q.woff2 b/docs/deps/bootstrap-5.3.1/fonts/S6u8w4BMUTPHjxsAXC-q.woff2
new file mode 100644
index 00000000..851630ff
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/S6u8w4BMUTPHjxsAXC-q.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/S6u9w4BMUTPHh6UVSwaPGR_p.woff2 b/docs/deps/bootstrap-5.3.1/fonts/S6u9w4BMUTPHh6UVSwaPGR_p.woff2
new file mode 100644
index 00000000..2c8aaa86
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/S6u9w4BMUTPHh6UVSwaPGR_p.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/S6u9w4BMUTPHh6UVSwiPGQ.woff2 b/docs/deps/bootstrap-5.3.1/fonts/S6u9w4BMUTPHh6UVSwiPGQ.woff2
new file mode 100644
index 00000000..11de83fe
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/S6u9w4BMUTPHh6UVSwiPGQ.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/S6u9w4BMUTPHh7USSwaPGR_p.woff2 b/docs/deps/bootstrap-5.3.1/fonts/S6u9w4BMUTPHh7USSwaPGR_p.woff2
new file mode 100644
index 00000000..486d3ecf
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/S6u9w4BMUTPHh7USSwaPGR_p.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/S6u9w4BMUTPHh7USSwiPGQ.woff2 b/docs/deps/bootstrap-5.3.1/fonts/S6u9w4BMUTPHh7USSwiPGQ.woff2
new file mode 100644
index 00000000..aad98a33
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/S6u9w4BMUTPHh7USSwiPGQ.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/S6uyw4BMUTPHjx4wXg.woff2 b/docs/deps/bootstrap-5.3.1/fonts/S6uyw4BMUTPHjx4wXg.woff2
new file mode 100644
index 00000000..ff60934d
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/S6uyw4BMUTPHjx4wXg.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/S6uyw4BMUTPHjxAwXjeu.woff2 b/docs/deps/bootstrap-5.3.1/fonts/S6uyw4BMUTPHjxAwXjeu.woff2
new file mode 100644
index 00000000..edb9fa6f
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/S6uyw4BMUTPHjxAwXjeu.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/UcC73FwrK3iLTeHuS_fvQtMwCp50KnMa0ZL7SUc.woff2 b/docs/deps/bootstrap-5.3.1/fonts/UcC73FwrK3iLTeHuS_fvQtMwCp50KnMa0ZL7SUc.woff2
new file mode 100644
index 00000000..b655a438
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/UcC73FwrK3iLTeHuS_fvQtMwCp50KnMa0ZL7SUc.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/UcC73FwrK3iLTeHuS_fvQtMwCp50KnMa1ZL7.woff2 b/docs/deps/bootstrap-5.3.1/fonts/UcC73FwrK3iLTeHuS_fvQtMwCp50KnMa1ZL7.woff2
new file mode 100644
index 00000000..40255432
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/UcC73FwrK3iLTeHuS_fvQtMwCp50KnMa1ZL7.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/UcC73FwrK3iLTeHuS_fvQtMwCp50KnMa1pL7SUc.woff2 b/docs/deps/bootstrap-5.3.1/fonts/UcC73FwrK3iLTeHuS_fvQtMwCp50KnMa1pL7SUc.woff2
new file mode 100644
index 00000000..eb38b38e
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/UcC73FwrK3iLTeHuS_fvQtMwCp50KnMa1pL7SUc.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/UcC73FwrK3iLTeHuS_fvQtMwCp50KnMa25L7SUc.woff2 b/docs/deps/bootstrap-5.3.1/fonts/UcC73FwrK3iLTeHuS_fvQtMwCp50KnMa25L7SUc.woff2
new file mode 100644
index 00000000..3df865d7
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/UcC73FwrK3iLTeHuS_fvQtMwCp50KnMa25L7SUc.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/UcC73FwrK3iLTeHuS_fvQtMwCp50KnMa2JL7SUc.woff2 b/docs/deps/bootstrap-5.3.1/fonts/UcC73FwrK3iLTeHuS_fvQtMwCp50KnMa2JL7SUc.woff2
new file mode 100644
index 00000000..a61a0be5
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/UcC73FwrK3iLTeHuS_fvQtMwCp50KnMa2JL7SUc.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/UcC73FwrK3iLTeHuS_fvQtMwCp50KnMa2ZL7SUc.woff2 b/docs/deps/bootstrap-5.3.1/fonts/UcC73FwrK3iLTeHuS_fvQtMwCp50KnMa2ZL7SUc.woff2
new file mode 100644
index 00000000..9117b5b0
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/UcC73FwrK3iLTeHuS_fvQtMwCp50KnMa2ZL7SUc.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/UcC73FwrK3iLTeHuS_fvQtMwCp50KnMa2pL7SUc.woff2 b/docs/deps/bootstrap-5.3.1/fonts/UcC73FwrK3iLTeHuS_fvQtMwCp50KnMa2pL7SUc.woff2
new file mode 100644
index 00000000..ce21ca17
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/UcC73FwrK3iLTeHuS_fvQtMwCp50KnMa2pL7SUc.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/XRXV3I6Li01BKofIMeaBXso.woff2 b/docs/deps/bootstrap-5.3.1/fonts/XRXV3I6Li01BKofIMeaBXso.woff2
new file mode 100644
index 00000000..98074042
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/XRXV3I6Li01BKofIMeaBXso.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/XRXV3I6Li01BKofINeaB.woff2 b/docs/deps/bootstrap-5.3.1/fonts/XRXV3I6Li01BKofINeaB.woff2
new file mode 100644
index 00000000..042b9ab7
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/XRXV3I6Li01BKofINeaB.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/XRXV3I6Li01BKofIO-aBXso.woff2 b/docs/deps/bootstrap-5.3.1/fonts/XRXV3I6Li01BKofIO-aBXso.woff2
new file mode 100644
index 00000000..28fc6f21
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/XRXV3I6Li01BKofIO-aBXso.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/XRXV3I6Li01BKofIOOaBXso.woff2 b/docs/deps/bootstrap-5.3.1/fonts/XRXV3I6Li01BKofIOOaBXso.woff2
new file mode 100644
index 00000000..4e2a1826
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/XRXV3I6Li01BKofIOOaBXso.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/XRXV3I6Li01BKofIOuaBXso.woff2 b/docs/deps/bootstrap-5.3.1/fonts/XRXV3I6Li01BKofIOuaBXso.woff2
new file mode 100644
index 00000000..af0abf29
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/XRXV3I6Li01BKofIOuaBXso.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/c2f002b3a87d3f9bfeebb23d32cfd9f8.woff2 b/docs/deps/bootstrap-5.3.1/fonts/c2f002b3a87d3f9bfeebb23d32cfd9f8.woff2
new file mode 100644
index 00000000..ff275508
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/c2f002b3a87d3f9bfeebb23d32cfd9f8.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/ee91700cdbf7ce16c054c2bb8946c736.woff2 b/docs/deps/bootstrap-5.3.1/fonts/ee91700cdbf7ce16c054c2bb8946c736.woff2
new file mode 100644
index 00000000..b0e2c35f
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/ee91700cdbf7ce16c054c2bb8946c736.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqW106F15M.woff2 b/docs/deps/bootstrap-5.3.1/fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqW106F15M.woff2
new file mode 100644
index 00000000..c787ad8d
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqW106F15M.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWt06F15M.woff2 b/docs/deps/bootstrap-5.3.1/fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWt06F15M.woff2
new file mode 100644
index 00000000..f3b2c4d5
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWt06F15M.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWtE6F15M.woff2 b/docs/deps/bootstrap-5.3.1/fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWtE6F15M.woff2
new file mode 100644
index 00000000..87f0364b
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWtE6F15M.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWtU6F15M.woff2 b/docs/deps/bootstrap-5.3.1/fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWtU6F15M.woff2
new file mode 100644
index 00000000..3f5ef094
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWtU6F15M.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWtk6F15M.woff2 b/docs/deps/bootstrap-5.3.1/fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWtk6F15M.woff2
new file mode 100644
index 00000000..f762e912
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWtk6F15M.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWu06F15M.woff2 b/docs/deps/bootstrap-5.3.1/fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWu06F15M.woff2
new file mode 100644
index 00000000..7404f028
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWu06F15M.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWuU6F.woff2 b/docs/deps/bootstrap-5.3.1/fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWuU6F.woff2
new file mode 100644
index 00000000..8e05a7ff
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWuU6F.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWuk6F15M.woff2 b/docs/deps/bootstrap-5.3.1/fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWuk6F15M.woff2
new file mode 100644
index 00000000..7d385f37
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWuk6F15M.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWvU6F15M.woff2 b/docs/deps/bootstrap-5.3.1/fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWvU6F15M.woff2
new file mode 100644
index 00000000..387470b8
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWvU6F15M.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWxU6F15M.woff2 b/docs/deps/bootstrap-5.3.1/fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWxU6F15M.woff2
new file mode 100644
index 00000000..f53b8df9
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWxU6F15M.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTS-muw.woff2 b/docs/deps/bootstrap-5.3.1/fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTS-muw.woff2
new file mode 100644
index 00000000..0beab546
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTS-muw.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTS2mu1aB.woff2 b/docs/deps/bootstrap-5.3.1/fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTS2mu1aB.woff2
new file mode 100644
index 00000000..e1ce55f0
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTS2mu1aB.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTSCmu1aB.woff2 b/docs/deps/bootstrap-5.3.1/fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTSCmu1aB.woff2
new file mode 100644
index 00000000..9c609c81
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTSCmu1aB.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTSGmu1aB.woff2 b/docs/deps/bootstrap-5.3.1/fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTSGmu1aB.woff2
new file mode 100644
index 00000000..7cd1174c
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTSGmu1aB.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTSKmu1aB.woff2 b/docs/deps/bootstrap-5.3.1/fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTSKmu1aB.woff2
new file mode 100644
index 00000000..b8521265
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTSKmu1aB.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTSOmu1aB.woff2 b/docs/deps/bootstrap-5.3.1/fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTSOmu1aB.woff2
new file mode 100644
index 00000000..612ff5d3
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTSOmu1aB.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTSumu1aB.woff2 b/docs/deps/bootstrap-5.3.1/fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTSumu1aB.woff2
new file mode 100644
index 00000000..f482ce1d
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTSumu1aB.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTSymu1aB.woff2 b/docs/deps/bootstrap-5.3.1/fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTSymu1aB.woff2
new file mode 100644
index 00000000..b7bc8626
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTSymu1aB.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTUGmu1aB.woff2 b/docs/deps/bootstrap-5.3.1/fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTUGmu1aB.woff2
new file mode 100644
index 00000000..18862e85
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTUGmu1aB.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTVOmu1aB.woff2 b/docs/deps/bootstrap-5.3.1/fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTVOmu1aB.woff2
new file mode 100644
index 00000000..35bac5d1
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTVOmu1aB.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/q5uGsou0JOdh94bfuQltOxU.woff2 b/docs/deps/bootstrap-5.3.1/fonts/q5uGsou0JOdh94bfuQltOxU.woff2
new file mode 100644
index 00000000..28852dbb
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/q5uGsou0JOdh94bfuQltOxU.woff2
diff --git a/docs/deps/bootstrap-5.3.1/fonts/q5uGsou0JOdh94bfvQlt.woff2 b/docs/deps/bootstrap-5.3.1/fonts/q5uGsou0JOdh94bfvQlt.woff2
new file mode 100644
index 00000000..886c2d72
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/fonts/q5uGsou0JOdh94bfvQlt.woff2
diff --git a/docs/deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js b/docs/deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js
new file mode 100644
index 00000000..c628326a
--- /dev/null
+++ b/docs/deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js
@@ -0,0 +1,5 @@
+/*!
+ * Bootstrap Table of Contents v1.0.1 (http://afeld.github.io/bootstrap-toc/)
+ * Copyright 2015 Aidan Feldman
+ * Licensed under MIT (https://github.com/afeld/bootstrap-toc/blob/gh-pages/LICENSE.md) */
+!function(a){"use strict";window.Toc={helpers:{findOrFilter:function(e,t){var n=e.find(t);return e.filter(t).add(n).filter(":not([data-toc-skip])")},generateUniqueIdBase:function(e){return a(e).text().trim().replace(/\'/gi,"").replace(/[& +$,:;=?@"#{}|^~[`%!'<>\]\.\/\(\)\*\\\n\t\b\v]/g,"-").replace(/-{2,}/g,"-").substring(0,64).replace(/^-+|-+$/gm,"").toLowerCase()||e.tagName.toLowerCase()},generateUniqueId:function(e){for(var t=this.generateUniqueIdBase(e),n=0;;n++){var r=t;if(0<n&&(r+="-"+n),!document.getElementById(r))return r}},generateAnchor:function(e){if(e.id)return e.id;var t=this.generateUniqueId(e);return e.id=t},createNavList:function(){return a('<ul class="nav navbar-nav"></ul>')},createChildNavList:function(e){var t=this.createNavList();return e.append(t),t},generateNavEl:function(e,t){var n=a('<a class="nav-link"></a>');n.attr("href","#"+e),n.text(t);var r=a("<li></li>");return r.append(n),r},generateNavItem:function(e){var t=this.generateAnchor(e),n=a(e),r=n.data("toc-text")||n.text();return this.generateNavEl(t,r)},getTopLevel:function(e){for(var t=1;t<=6;t++){if(1<this.findOrFilter(e,"h"+t).length)return t}return 1},getHeadings:function(e,t){var n="h"+t,r="h"+(t+1);return this.findOrFilter(e,n+","+r)},getNavLevel:function(e){return parseInt(e.tagName.charAt(1),10)},populateNav:function(r,a,e){var i,s=r,c=this;e.each(function(e,t){var n=c.generateNavItem(t);c.getNavLevel(t)===a?s=r:i&&s===r&&(s=c.createChildNavList(i)),s.append(n),i=n})},parseOps:function(e){var t;return(t=e.jquery?{$nav:e}:e).$scope=t.$scope||a(document.body),t}},init:function(e){(e=this.helpers.parseOps(e)).$nav.attr("data-toggle","toc");var t=this.helpers.createChildNavList(e.$nav),n=this.helpers.getTopLevel(e.$scope),r=this.helpers.getHeadings(e.$scope,n);this.helpers.populateNav(t,n,r)}},a(function(){a('nav[data-toggle="toc"]').each(function(e,t){var n=a(t);Toc.init(n)})})}(jQuery);
\ No newline at end of file
diff --git a/docs/deps/clipboard.js-2.0.11/clipboard.min.js b/docs/deps/clipboard.js-2.0.11/clipboard.min.js
new file mode 100644
index 00000000..1103f811
--- /dev/null
+++ b/docs/deps/clipboard.js-2.0.11/clipboard.min.js
@@ -0,0 +1,7 @@
+/*!
+ * clipboard.js v2.0.11
+ * https://clipboardjs.com/
+ *
+ * Licensed MIT © Zeno Rocha
+ */
+!function(t,e){"object"==typeof exports&&"object"==typeof module?module.exports=e():"function"==typeof define&&define.amd?define([],e):"object"==typeof exports?exports.ClipboardJS=e():t.ClipboardJS=e()}(this,function(){return n={686:function(t,e,n){"use strict";n.d(e,{default:function(){return b}});var e=n(279),i=n.n(e),e=n(370),u=n.n(e),e=n(817),r=n.n(e);function c(t){try{return document.execCommand(t)}catch(t){return}}var a=function(t){t=r()(t);return c("cut"),t};function o(t,e){var n,o,t=(n=t,o="rtl"===document.documentElement.getAttribute("dir"),(t=document.createElement("textarea")).style.fontSize="12pt",t.style.border="0",t.style.padding="0",t.style.margin="0",t.style.position="absolute",t.style[o?"right":"left"]="-9999px",o=window.pageYOffset||document.documentElement.scrollTop,t.style.top="".concat(o,"px"),t.setAttribute("readonly",""),t.value=n,t);return e.container.appendChild(t),e=r()(t),c("copy"),t.remove(),e}var f=function(t){var e=1<arguments.length&&void 0!==arguments[1]?arguments[1]:{container:document.body},n="";return"string"==typeof t?n=o(t,e):t instanceof HTMLInputElement&&!["text","search","url","tel","password"].includes(null==t?void 0:t.type)?n=o(t.value,e):(n=r()(t),c("copy")),n};function l(t){return(l="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(t){return typeof t}:function(t){return t&&"function"==typeof Symbol&&t.constructor===Symbol&&t!==Symbol.prototype?"symbol":typeof t})(t)}var s=function(){var t=0<arguments.length&&void 0!==arguments[0]?arguments[0]:{},e=t.action,n=void 0===e?"copy":e,o=t.container,e=t.target,t=t.text;if("copy"!==n&&"cut"!==n)throw new Error('Invalid "action" value, use either "copy" or "cut"');if(void 0!==e){if(!e||"object"!==l(e)||1!==e.nodeType)throw new Error('Invalid "target" value, use a valid Element');if("copy"===n&&e.hasAttribute("disabled"))throw new Error('Invalid "target" attribute. Please use "readonly" instead of "disabled" attribute');if("cut"===n&&(e.hasAttribute("readonly")||e.hasAttribute("disabled")))throw new Error('Invalid "target" attribute. You can\'t cut text from elements with "readonly" or "disabled" attributes')}return t?f(t,{container:o}):e?"cut"===n?a(e):f(e,{container:o}):void 0};function p(t){return(p="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(t){return typeof t}:function(t){return t&&"function"==typeof Symbol&&t.constructor===Symbol&&t!==Symbol.prototype?"symbol":typeof t})(t)}function d(t,e){for(var n=0;n<e.length;n++){var o=e[n];o.enumerable=o.enumerable||!1,o.configurable=!0,"value"in o&&(o.writable=!0),Object.defineProperty(t,o.key,o)}}function y(t,e){return(y=Object.setPrototypeOf||function(t,e){return t.__proto__=e,t})(t,e)}function h(n){var o=function(){if("undefined"==typeof Reflect||!Reflect.construct)return!1;if(Reflect.construct.sham)return!1;if("function"==typeof Proxy)return!0;try{return Date.prototype.toString.call(Reflect.construct(Date,[],function(){})),!0}catch(t){return!1}}();return function(){var t,e=v(n);return t=o?(t=v(this).constructor,Reflect.construct(e,arguments,t)):e.apply(this,arguments),e=this,!(t=t)||"object"!==p(t)&&"function"!=typeof t?function(t){if(void 0!==t)return t;throw new ReferenceError("this hasn't been initialised - super() hasn't been called")}(e):t}}function v(t){return(v=Object.setPrototypeOf?Object.getPrototypeOf:function(t){return t.__proto__||Object.getPrototypeOf(t)})(t)}function m(t,e){t="data-clipboard-".concat(t);if(e.hasAttribute(t))return e.getAttribute(t)}var b=function(){!function(t,e){if("function"!=typeof e&&null!==e)throw new TypeError("Super expression must either be null or a function");t.prototype=Object.create(e&&e.prototype,{constructor:{value:t,writable:!0,configurable:!0}}),e&&y(t,e)}(r,i());var t,e,n,o=h(r);function r(t,e){var n;return function(t){if(!(t instanceof r))throw new TypeError("Cannot call a class as a function")}(this),(n=o.call(this)).resolveOptions(e),n.listenClick(t),n}return t=r,n=[{key:"copy",value:function(t){var e=1<arguments.length&&void 0!==arguments[1]?arguments[1]:{container:document.body};return f(t,e)}},{key:"cut",value:function(t){return a(t)}},{key:"isSupported",value:function(){var t=0<arguments.length&&void 0!==arguments[0]?arguments[0]:["copy","cut"],t="string"==typeof t?[t]:t,e=!!document.queryCommandSupported;return t.forEach(function(t){e=e&&!!document.queryCommandSupported(t)}),e}}],(e=[{key:"resolveOptions",value:function(){var t=0<arguments.length&&void 0!==arguments[0]?arguments[0]:{};this.action="function"==typeof t.action?t.action:this.defaultAction,this.target="function"==typeof t.target?t.target:this.defaultTarget,this.text="function"==typeof t.text?t.text:this.defaultText,this.container="object"===p(t.container)?t.container:document.body}},{key:"listenClick",value:function(t){var e=this;this.listener=u()(t,"click",function(t){return e.onClick(t)})}},{key:"onClick",value:function(t){var e=t.delegateTarget||t.currentTarget,n=this.action(e)||"copy",t=s({action:n,container:this.container,target:this.target(e),text:this.text(e)});this.emit(t?"success":"error",{action:n,text:t,trigger:e,clearSelection:function(){e&&e.focus(),window.getSelection().removeAllRanges()}})}},{key:"defaultAction",value:function(t){return m("action",t)}},{key:"defaultTarget",value:function(t){t=m("target",t);if(t)return document.querySelector(t)}},{key:"defaultText",value:function(t){return m("text",t)}},{key:"destroy",value:function(){this.listener.destroy()}}])&&d(t.prototype,e),n&&d(t,n),r}()},828:function(t){var e;"undefined"==typeof Element||Element.prototype.matches||((e=Element.prototype).matches=e.matchesSelector||e.mozMatchesSelector||e.msMatchesSelector||e.oMatchesSelector||e.webkitMatchesSelector),t.exports=function(t,e){for(;t&&9!==t.nodeType;){if("function"==typeof t.matches&&t.matches(e))return t;t=t.parentNode}}},438:function(t,e,n){var u=n(828);function i(t,e,n,o,r){var i=function(e,n,t,o){return function(t){t.delegateTarget=u(t.target,n),t.delegateTarget&&o.call(e,t)}}.apply(this,arguments);return t.addEventListener(n,i,r),{destroy:function(){t.removeEventListener(n,i,r)}}}t.exports=function(t,e,n,o,r){return"function"==typeof t.addEventListener?i.apply(null,arguments):"function"==typeof n?i.bind(null,document).apply(null,arguments):("string"==typeof t&&(t=document.querySelectorAll(t)),Array.prototype.map.call(t,function(t){return i(t,e,n,o,r)}))}},879:function(t,n){n.node=function(t){return void 0!==t&&t instanceof HTMLElement&&1===t.nodeType},n.nodeList=function(t){var e=Object.prototype.toString.call(t);return void 0!==t&&("[object NodeList]"===e||"[object HTMLCollection]"===e)&&"length"in t&&(0===t.length||n.node(t[0]))},n.string=function(t){return"string"==typeof t||t instanceof String},n.fn=function(t){return"[object Function]"===Object.prototype.toString.call(t)}},370:function(t,e,n){var f=n(879),l=n(438);t.exports=function(t,e,n){if(!t&&!e&&!n)throw new Error("Missing required arguments");if(!f.string(e))throw new TypeError("Second argument must be a String");if(!f.fn(n))throw new TypeError("Third argument must be a Function");if(f.node(t))return c=e,a=n,(u=t).addEventListener(c,a),{destroy:function(){u.removeEventListener(c,a)}};if(f.nodeList(t))return o=t,r=e,i=n,Array.prototype.forEach.call(o,function(t){t.addEventListener(r,i)}),{destroy:function(){Array.prototype.forEach.call(o,function(t){t.removeEventListener(r,i)})}};if(f.string(t))return t=t,e=e,n=n,l(document.body,t,e,n);throw new TypeError("First argument must be a String, HTMLElement, HTMLCollection, or NodeList");var o,r,i,u,c,a}},817:function(t){t.exports=function(t){var e,n="SELECT"===t.nodeName?(t.focus(),t.value):"INPUT"===t.nodeName||"TEXTAREA"===t.nodeName?((e=t.hasAttribute("readonly"))||t.setAttribute("readonly",""),t.select(),t.setSelectionRange(0,t.value.length),e||t.removeAttribute("readonly"),t.value):(t.hasAttribute("contenteditable")&&t.focus(),n=window.getSelection(),(e=document.createRange()).selectNodeContents(t),n.removeAllRanges(),n.addRange(e),n.toString());return n}},279:function(t){function e(){}e.prototype={on:function(t,e,n){var o=this.e||(this.e={});return(o[t]||(o[t]=[])).push({fn:e,ctx:n}),this},once:function(t,e,n){var o=this;function r(){o.off(t,r),e.apply(n,arguments)}return r._=e,this.on(t,r,n)},emit:function(t){for(var e=[].slice.call(arguments,1),n=((this.e||(this.e={}))[t]||[]).slice(),o=0,r=n.length;o<r;o++)n[o].fn.apply(n[o].ctx,e);return this},off:function(t,e){var n=this.e||(this.e={}),o=n[t],r=[];if(o&&e)for(var i=0,u=o.length;i<u;i++)o[i].fn!==e&&o[i].fn._!==e&&r.push(o[i]);return r.length?n[t]=r:delete n[t],this}},t.exports=e,t.exports.TinyEmitter=e}},r={},o.n=function(t){var e=t&&t.__esModule?function(){return t.default}:function(){return t};return o.d(e,{a:e}),e},o.d=function(t,e){for(var n in e)o.o(e,n)&&!o.o(t,n)&&Object.defineProperty(t,n,{enumerable:!0,get:e[n]})},o.o=function(t,e){return Object.prototype.hasOwnProperty.call(t,e)},o(686).default;function o(t){if(r[t])return r[t].exports;var e=r[t]={exports:{}};return n[t](e,e.exports,o),e.exports}var n,r});
\ No newline at end of file
diff --git a/docs/deps/data-deps.txt b/docs/deps/data-deps.txt
new file mode 100644
index 00000000..ca4dfd9c
--- /dev/null
+++ b/docs/deps/data-deps.txt
@@ -0,0 +1,13 @@
+<script src="deps/jquery-3.6.0/jquery-3.6.0.min.js"></script>
+<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no" />
+<link href="deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet" />
+<script src="deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script>
+<link href="deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet" />
+<link href="deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet" />
+<script src="deps/headroom-0.11.0/headroom.min.js"></script>
+<script src="deps/headroom-0.11.0/jQuery.headroom.min.js"></script>
+<script src="deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script>
+<script src="deps/clipboard.js-2.0.11/clipboard.min.js"></script>
+<script src="deps/search-1.0.0/autocomplete.jquery.min.js"></script>
+<script src="deps/search-1.0.0/fuse.min.js"></script>
+<script src="deps/search-1.0.0/mark.min.js"></script>
diff --git a/docs/deps/font-awesome-6.5.2/css/all.css b/docs/deps/font-awesome-6.5.2/css/all.css
new file mode 100644
index 00000000..151dd57c
--- /dev/null
+++ b/docs/deps/font-awesome-6.5.2/css/all.css
@@ -0,0 +1,8028 @@
+/*!
+ * Font Awesome Free 6.5.2 by @fontawesome - https://fontawesome.com
+ * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License)
+ * Copyright 2024 Fonticons, Inc.
+ */
+.fa {
+  font-family: var(--fa-style-family, "Font Awesome 6 Free");
+  font-weight: var(--fa-style, 900); }
+
+.fa,
+.fa-classic,
+.fa-sharp,
+.fas,
+.fa-solid,
+.far,
+.fa-regular,
+.fab,
+.fa-brands {
+  -moz-osx-font-smoothing: grayscale;
+  -webkit-font-smoothing: antialiased;
+  display: var(--fa-display, inline-block);
+  font-style: normal;
+  font-variant: normal;
+  line-height: 1;
+  text-rendering: auto; }
+
+.fas,
+.fa-classic,
+.fa-solid,
+.far,
+.fa-regular {
+  font-family: 'Font Awesome 6 Free'; }
+
+.fab,
+.fa-brands {
+  font-family: 'Font Awesome 6 Brands'; }
+
+.fa-1x {
+  font-size: 1em; }
+
+.fa-2x {
+  font-size: 2em; }
+
+.fa-3x {
+  font-size: 3em; }
+
+.fa-4x {
+  font-size: 4em; }
+
+.fa-5x {
+  font-size: 5em; }
+
+.fa-6x {
+  font-size: 6em; }
+
+.fa-7x {
+  font-size: 7em; }
+
+.fa-8x {
+  font-size: 8em; }
+
+.fa-9x {
+  font-size: 9em; }
+
+.fa-10x {
+  font-size: 10em; }
+
+.fa-2xs {
+  font-size: 0.625em;
+  line-height: 0.1em;
+  vertical-align: 0.225em; }
+
+.fa-xs {
+  font-size: 0.75em;
+  line-height: 0.08333em;
+  vertical-align: 0.125em; }
+
+.fa-sm {
+  font-size: 0.875em;
+  line-height: 0.07143em;
+  vertical-align: 0.05357em; }
+
+.fa-lg {
+  font-size: 1.25em;
+  line-height: 0.05em;
+  vertical-align: -0.075em; }
+
+.fa-xl {
+  font-size: 1.5em;
+  line-height: 0.04167em;
+  vertical-align: -0.125em; }
+
+.fa-2xl {
+  font-size: 2em;
+  line-height: 0.03125em;
+  vertical-align: -0.1875em; }
+
+.fa-fw {
+  text-align: center;
+  width: 1.25em; }
+
+.fa-ul {
+  list-style-type: none;
+  margin-left: var(--fa-li-margin, 2.5em);
+  padding-left: 0; }
+  .fa-ul > li {
+    position: relative; }
+
+.fa-li {
+  left: calc(var(--fa-li-width, 2em) * -1);
+  position: absolute;
+  text-align: center;
+  width: var(--fa-li-width, 2em);
+  line-height: inherit; }
+
+.fa-border {
+  border-color: var(--fa-border-color, #eee);
+  border-radius: var(--fa-border-radius, 0.1em);
+  border-style: var(--fa-border-style, solid);
+  border-width: var(--fa-border-width, 0.08em);
+  padding: var(--fa-border-padding, 0.2em 0.25em 0.15em); }
+
+.fa-pull-left {
+  float: left;
+  margin-right: var(--fa-pull-margin, 0.3em); }
+
+.fa-pull-right {
+  float: right;
+  margin-left: var(--fa-pull-margin, 0.3em); }
+
+.fa-beat {
+  -webkit-animation-name: fa-beat;
+          animation-name: fa-beat;
+  -webkit-animation-delay: var(--fa-animation-delay, 0s);
+          animation-delay: var(--fa-animation-delay, 0s);
+  -webkit-animation-direction: var(--fa-animation-direction, normal);
+          animation-direction: var(--fa-animation-direction, normal);
+  -webkit-animation-duration: var(--fa-animation-duration, 1s);
+          animation-duration: var(--fa-animation-duration, 1s);
+  -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+          animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+  -webkit-animation-timing-function: var(--fa-animation-timing, ease-in-out);
+          animation-timing-function: var(--fa-animation-timing, ease-in-out); }
+
+.fa-bounce {
+  -webkit-animation-name: fa-bounce;
+          animation-name: fa-bounce;
+  -webkit-animation-delay: var(--fa-animation-delay, 0s);
+          animation-delay: var(--fa-animation-delay, 0s);
+  -webkit-animation-direction: var(--fa-animation-direction, normal);
+          animation-direction: var(--fa-animation-direction, normal);
+  -webkit-animation-duration: var(--fa-animation-duration, 1s);
+          animation-duration: var(--fa-animation-duration, 1s);
+  -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+          animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+  -webkit-animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.28, 0.84, 0.42, 1));
+          animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.28, 0.84, 0.42, 1)); }
+
+.fa-fade {
+  -webkit-animation-name: fa-fade;
+          animation-name: fa-fade;
+  -webkit-animation-delay: var(--fa-animation-delay, 0s);
+          animation-delay: var(--fa-animation-delay, 0s);
+  -webkit-animation-direction: var(--fa-animation-direction, normal);
+          animation-direction: var(--fa-animation-direction, normal);
+  -webkit-animation-duration: var(--fa-animation-duration, 1s);
+          animation-duration: var(--fa-animation-duration, 1s);
+  -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+          animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+  -webkit-animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1));
+          animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1)); }
+
+.fa-beat-fade {
+  -webkit-animation-name: fa-beat-fade;
+          animation-name: fa-beat-fade;
+  -webkit-animation-delay: var(--fa-animation-delay, 0s);
+          animation-delay: var(--fa-animation-delay, 0s);
+  -webkit-animation-direction: var(--fa-animation-direction, normal);
+          animation-direction: var(--fa-animation-direction, normal);
+  -webkit-animation-duration: var(--fa-animation-duration, 1s);
+          animation-duration: var(--fa-animation-duration, 1s);
+  -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+          animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+  -webkit-animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1));
+          animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1)); }
+
+.fa-flip {
+  -webkit-animation-name: fa-flip;
+          animation-name: fa-flip;
+  -webkit-animation-delay: var(--fa-animation-delay, 0s);
+          animation-delay: var(--fa-animation-delay, 0s);
+  -webkit-animation-direction: var(--fa-animation-direction, normal);
+          animation-direction: var(--fa-animation-direction, normal);
+  -webkit-animation-duration: var(--fa-animation-duration, 1s);
+          animation-duration: var(--fa-animation-duration, 1s);
+  -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+          animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+  -webkit-animation-timing-function: var(--fa-animation-timing, ease-in-out);
+          animation-timing-function: var(--fa-animation-timing, ease-in-out); }
+
+.fa-shake {
+  -webkit-animation-name: fa-shake;
+          animation-name: fa-shake;
+  -webkit-animation-delay: var(--fa-animation-delay, 0s);
+          animation-delay: var(--fa-animation-delay, 0s);
+  -webkit-animation-direction: var(--fa-animation-direction, normal);
+          animation-direction: var(--fa-animation-direction, normal);
+  -webkit-animation-duration: var(--fa-animation-duration, 1s);
+          animation-duration: var(--fa-animation-duration, 1s);
+  -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+          animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+  -webkit-animation-timing-function: var(--fa-animation-timing, linear);
+          animation-timing-function: var(--fa-animation-timing, linear); }
+
+.fa-spin {
+  -webkit-animation-name: fa-spin;
+          animation-name: fa-spin;
+  -webkit-animation-delay: var(--fa-animation-delay, 0s);
+          animation-delay: var(--fa-animation-delay, 0s);
+  -webkit-animation-direction: var(--fa-animation-direction, normal);
+          animation-direction: var(--fa-animation-direction, normal);
+  -webkit-animation-duration: var(--fa-animation-duration, 2s);
+          animation-duration: var(--fa-animation-duration, 2s);
+  -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+          animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+  -webkit-animation-timing-function: var(--fa-animation-timing, linear);
+          animation-timing-function: var(--fa-animation-timing, linear); }
+
+.fa-spin-reverse {
+  --fa-animation-direction: reverse; }
+
+.fa-pulse,
+.fa-spin-pulse {
+  -webkit-animation-name: fa-spin;
+          animation-name: fa-spin;
+  -webkit-animation-direction: var(--fa-animation-direction, normal);
+          animation-direction: var(--fa-animation-direction, normal);
+  -webkit-animation-duration: var(--fa-animation-duration, 1s);
+          animation-duration: var(--fa-animation-duration, 1s);
+  -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+          animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+  -webkit-animation-timing-function: var(--fa-animation-timing, steps(8));
+          animation-timing-function: var(--fa-animation-timing, steps(8)); }
+
+@media (prefers-reduced-motion: reduce) {
+  .fa-beat,
+  .fa-bounce,
+  .fa-fade,
+  .fa-beat-fade,
+  .fa-flip,
+  .fa-pulse,
+  .fa-shake,
+  .fa-spin,
+  .fa-spin-pulse {
+    -webkit-animation-delay: -1ms;
+            animation-delay: -1ms;
+    -webkit-animation-duration: 1ms;
+            animation-duration: 1ms;
+    -webkit-animation-iteration-count: 1;
+            animation-iteration-count: 1;
+    -webkit-transition-delay: 0s;
+            transition-delay: 0s;
+    -webkit-transition-duration: 0s;
+            transition-duration: 0s; } }
+
+@-webkit-keyframes fa-beat {
+  0%, 90% {
+    -webkit-transform: scale(1);
+            transform: scale(1); }
+  45% {
+    -webkit-transform: scale(var(--fa-beat-scale, 1.25));
+            transform: scale(var(--fa-beat-scale, 1.25)); } }
+
+@keyframes fa-beat {
+  0%, 90% {
+    -webkit-transform: scale(1);
+            transform: scale(1); }
+  45% {
+    -webkit-transform: scale(var(--fa-beat-scale, 1.25));
+            transform: scale(var(--fa-beat-scale, 1.25)); } }
+
+@-webkit-keyframes fa-bounce {
+  0% {
+    -webkit-transform: scale(1, 1) translateY(0);
+            transform: scale(1, 1) translateY(0); }
+  10% {
+    -webkit-transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0);
+            transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); }
+  30% {
+    -webkit-transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em));
+            transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); }
+  50% {
+    -webkit-transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0);
+            transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); }
+  57% {
+    -webkit-transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em));
+            transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); }
+  64% {
+    -webkit-transform: scale(1, 1) translateY(0);
+            transform: scale(1, 1) translateY(0); }
+  100% {
+    -webkit-transform: scale(1, 1) translateY(0);
+            transform: scale(1, 1) translateY(0); } }
+
+@keyframes fa-bounce {
+  0% {
+    -webkit-transform: scale(1, 1) translateY(0);
+            transform: scale(1, 1) translateY(0); }
+  10% {
+    -webkit-transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0);
+            transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); }
+  30% {
+    -webkit-transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em));
+            transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); }
+  50% {
+    -webkit-transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0);
+            transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); }
+  57% {
+    -webkit-transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em));
+            transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); }
+  64% {
+    -webkit-transform: scale(1, 1) translateY(0);
+            transform: scale(1, 1) translateY(0); }
+  100% {
+    -webkit-transform: scale(1, 1) translateY(0);
+            transform: scale(1, 1) translateY(0); } }
+
+@-webkit-keyframes fa-fade {
+  50% {
+    opacity: var(--fa-fade-opacity, 0.4); } }
+
+@keyframes fa-fade {
+  50% {
+    opacity: var(--fa-fade-opacity, 0.4); } }
+
+@-webkit-keyframes fa-beat-fade {
+  0%, 100% {
+    opacity: var(--fa-beat-fade-opacity, 0.4);
+    -webkit-transform: scale(1);
+            transform: scale(1); }
+  50% {
+    opacity: 1;
+    -webkit-transform: scale(var(--fa-beat-fade-scale, 1.125));
+            transform: scale(var(--fa-beat-fade-scale, 1.125)); } }
+
+@keyframes fa-beat-fade {
+  0%, 100% {
+    opacity: var(--fa-beat-fade-opacity, 0.4);
+    -webkit-transform: scale(1);
+            transform: scale(1); }
+  50% {
+    opacity: 1;
+    -webkit-transform: scale(var(--fa-beat-fade-scale, 1.125));
+            transform: scale(var(--fa-beat-fade-scale, 1.125)); } }
+
+@-webkit-keyframes fa-flip {
+  50% {
+    -webkit-transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg));
+            transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); } }
+
+@keyframes fa-flip {
+  50% {
+    -webkit-transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg));
+            transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); } }
+
+@-webkit-keyframes fa-shake {
+  0% {
+    -webkit-transform: rotate(-15deg);
+            transform: rotate(-15deg); }
+  4% {
+    -webkit-transform: rotate(15deg);
+            transform: rotate(15deg); }
+  8%, 24% {
+    -webkit-transform: rotate(-18deg);
+            transform: rotate(-18deg); }
+  12%, 28% {
+    -webkit-transform: rotate(18deg);
+            transform: rotate(18deg); }
+  16% {
+    -webkit-transform: rotate(-22deg);
+            transform: rotate(-22deg); }
+  20% {
+    -webkit-transform: rotate(22deg);
+            transform: rotate(22deg); }
+  32% {
+    -webkit-transform: rotate(-12deg);
+            transform: rotate(-12deg); }
+  36% {
+    -webkit-transform: rotate(12deg);
+            transform: rotate(12deg); }
+  40%, 100% {
+    -webkit-transform: rotate(0deg);
+            transform: rotate(0deg); } }
+
+@keyframes fa-shake {
+  0% {
+    -webkit-transform: rotate(-15deg);
+            transform: rotate(-15deg); }
+  4% {
+    -webkit-transform: rotate(15deg);
+            transform: rotate(15deg); }
+  8%, 24% {
+    -webkit-transform: rotate(-18deg);
+            transform: rotate(-18deg); }
+  12%, 28% {
+    -webkit-transform: rotate(18deg);
+            transform: rotate(18deg); }
+  16% {
+    -webkit-transform: rotate(-22deg);
+            transform: rotate(-22deg); }
+  20% {
+    -webkit-transform: rotate(22deg);
+            transform: rotate(22deg); }
+  32% {
+    -webkit-transform: rotate(-12deg);
+            transform: rotate(-12deg); }
+  36% {
+    -webkit-transform: rotate(12deg);
+            transform: rotate(12deg); }
+  40%, 100% {
+    -webkit-transform: rotate(0deg);
+            transform: rotate(0deg); } }
+
+@-webkit-keyframes fa-spin {
+  0% {
+    -webkit-transform: rotate(0deg);
+            transform: rotate(0deg); }
+  100% {
+    -webkit-transform: rotate(360deg);
+            transform: rotate(360deg); } }
+
+@keyframes fa-spin {
+  0% {
+    -webkit-transform: rotate(0deg);
+            transform: rotate(0deg); }
+  100% {
+    -webkit-transform: rotate(360deg);
+            transform: rotate(360deg); } }
+
+.fa-rotate-90 {
+  -webkit-transform: rotate(90deg);
+          transform: rotate(90deg); }
+
+.fa-rotate-180 {
+  -webkit-transform: rotate(180deg);
+          transform: rotate(180deg); }
+
+.fa-rotate-270 {
+  -webkit-transform: rotate(270deg);
+          transform: rotate(270deg); }
+
+.fa-flip-horizontal {
+  -webkit-transform: scale(-1, 1);
+          transform: scale(-1, 1); }
+
+.fa-flip-vertical {
+  -webkit-transform: scale(1, -1);
+          transform: scale(1, -1); }
+
+.fa-flip-both,
+.fa-flip-horizontal.fa-flip-vertical {
+  -webkit-transform: scale(-1, -1);
+          transform: scale(-1, -1); }
+
+.fa-rotate-by {
+  -webkit-transform: rotate(var(--fa-rotate-angle, 0));
+          transform: rotate(var(--fa-rotate-angle, 0)); }
+
+.fa-stack {
+  display: inline-block;
+  height: 2em;
+  line-height: 2em;
+  position: relative;
+  vertical-align: middle;
+  width: 2.5em; }
+
+.fa-stack-1x,
+.fa-stack-2x {
+  left: 0;
+  position: absolute;
+  text-align: center;
+  width: 100%;
+  z-index: var(--fa-stack-z-index, auto); }
+
+.fa-stack-1x {
+  line-height: inherit; }
+
+.fa-stack-2x {
+  font-size: 2em; }
+
+.fa-inverse {
+  color: var(--fa-inverse, #fff); }
+
+/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen
+readers do not read off random characters that represent icons */
+
+.fa-0::before {
+  content: "\30"; }
+
+.fa-1::before {
+  content: "\31"; }
+
+.fa-2::before {
+  content: "\32"; }
+
+.fa-3::before {
+  content: "\33"; }
+
+.fa-4::before {
+  content: "\34"; }
+
+.fa-5::before {
+  content: "\35"; }
+
+.fa-6::before {
+  content: "\36"; }
+
+.fa-7::before {
+  content: "\37"; }
+
+.fa-8::before {
+  content: "\38"; }
+
+.fa-9::before {
+  content: "\39"; }
+
+.fa-fill-drip::before {
+  content: "\f576"; }
+
+.fa-arrows-to-circle::before {
+  content: "\e4bd"; }
+
+.fa-circle-chevron-right::before {
+  content: "\f138"; }
+
+.fa-chevron-circle-right::before {
+  content: "\f138"; }
+
+.fa-at::before {
+  content: "\40"; }
+
+.fa-trash-can::before {
+  content: "\f2ed"; }
+
+.fa-trash-alt::before {
+  content: "\f2ed"; }
+
+.fa-text-height::before {
+  content: "\f034"; }
+
+.fa-user-xmark::before {
+  content: "\f235"; }
+
+.fa-user-times::before {
+  content: "\f235"; }
+
+.fa-stethoscope::before {
+  content: "\f0f1"; }
+
+.fa-message::before {
+  content: "\f27a"; }
+
+.fa-comment-alt::before {
+  content: "\f27a"; }
+
+.fa-info::before {
+  content: "\f129"; }
+
+.fa-down-left-and-up-right-to-center::before {
+  content: "\f422"; }
+
+.fa-compress-alt::before {
+  content: "\f422"; }
+
+.fa-explosion::before {
+  content: "\e4e9"; }
+
+.fa-file-lines::before {
+  content: "\f15c"; }
+
+.fa-file-alt::before {
+  content: "\f15c"; }
+
+.fa-file-text::before {
+  content: "\f15c"; }
+
+.fa-wave-square::before {
+  content: "\f83e"; }
+
+.fa-ring::before {
+  content: "\f70b"; }
+
+.fa-building-un::before {
+  content: "\e4d9"; }
+
+.fa-dice-three::before {
+  content: "\f527"; }
+
+.fa-calendar-days::before {
+  content: "\f073"; }
+
+.fa-calendar-alt::before {
+  content: "\f073"; }
+
+.fa-anchor-circle-check::before {
+  content: "\e4aa"; }
+
+.fa-building-circle-arrow-right::before {
+  content: "\e4d1"; }
+
+.fa-volleyball::before {
+  content: "\f45f"; }
+
+.fa-volleyball-ball::before {
+  content: "\f45f"; }
+
+.fa-arrows-up-to-line::before {
+  content: "\e4c2"; }
+
+.fa-sort-down::before {
+  content: "\f0dd"; }
+
+.fa-sort-desc::before {
+  content: "\f0dd"; }
+
+.fa-circle-minus::before {
+  content: "\f056"; }
+
+.fa-minus-circle::before {
+  content: "\f056"; }
+
+.fa-door-open::before {
+  content: "\f52b"; }
+
+.fa-right-from-bracket::before {
+  content: "\f2f5"; }
+
+.fa-sign-out-alt::before {
+  content: "\f2f5"; }
+
+.fa-atom::before {
+  content: "\f5d2"; }
+
+.fa-soap::before {
+  content: "\e06e"; }
+
+.fa-icons::before {
+  content: "\f86d"; }
+
+.fa-heart-music-camera-bolt::before {
+  content: "\f86d"; }
+
+.fa-microphone-lines-slash::before {
+  content: "\f539"; }
+
+.fa-microphone-alt-slash::before {
+  content: "\f539"; }
+
+.fa-bridge-circle-check::before {
+  content: "\e4c9"; }
+
+.fa-pump-medical::before {
+  content: "\e06a"; }
+
+.fa-fingerprint::before {
+  content: "\f577"; }
+
+.fa-hand-point-right::before {
+  content: "\f0a4"; }
+
+.fa-magnifying-glass-location::before {
+  content: "\f689"; }
+
+.fa-search-location::before {
+  content: "\f689"; }
+
+.fa-forward-step::before {
+  content: "\f051"; }
+
+.fa-step-forward::before {
+  content: "\f051"; }
+
+.fa-face-smile-beam::before {
+  content: "\f5b8"; }
+
+.fa-smile-beam::before {
+  content: "\f5b8"; }
+
+.fa-flag-checkered::before {
+  content: "\f11e"; }
+
+.fa-football::before {
+  content: "\f44e"; }
+
+.fa-football-ball::before {
+  content: "\f44e"; }
+
+.fa-school-circle-exclamation::before {
+  content: "\e56c"; }
+
+.fa-crop::before {
+  content: "\f125"; }
+
+.fa-angles-down::before {
+  content: "\f103"; }
+
+.fa-angle-double-down::before {
+  content: "\f103"; }
+
+.fa-users-rectangle::before {
+  content: "\e594"; }
+
+.fa-people-roof::before {
+  content: "\e537"; }
+
+.fa-people-line::before {
+  content: "\e534"; }
+
+.fa-beer-mug-empty::before {
+  content: "\f0fc"; }
+
+.fa-beer::before {
+  content: "\f0fc"; }
+
+.fa-diagram-predecessor::before {
+  content: "\e477"; }
+
+.fa-arrow-up-long::before {
+  content: "\f176"; }
+
+.fa-long-arrow-up::before {
+  content: "\f176"; }
+
+.fa-fire-flame-simple::before {
+  content: "\f46a"; }
+
+.fa-burn::before {
+  content: "\f46a"; }
+
+.fa-person::before {
+  content: "\f183"; }
+
+.fa-male::before {
+  content: "\f183"; }
+
+.fa-laptop::before {
+  content: "\f109"; }
+
+.fa-file-csv::before {
+  content: "\f6dd"; }
+
+.fa-menorah::before {
+  content: "\f676"; }
+
+.fa-truck-plane::before {
+  content: "\e58f"; }
+
+.fa-record-vinyl::before {
+  content: "\f8d9"; }
+
+.fa-face-grin-stars::before {
+  content: "\f587"; }
+
+.fa-grin-stars::before {
+  content: "\f587"; }
+
+.fa-bong::before {
+  content: "\f55c"; }
+
+.fa-spaghetti-monster-flying::before {
+  content: "\f67b"; }
+
+.fa-pastafarianism::before {
+  content: "\f67b"; }
+
+.fa-arrow-down-up-across-line::before {
+  content: "\e4af"; }
+
+.fa-spoon::before {
+  content: "\f2e5"; }
+
+.fa-utensil-spoon::before {
+  content: "\f2e5"; }
+
+.fa-jar-wheat::before {
+  content: "\e517"; }
+
+.fa-envelopes-bulk::before {
+  content: "\f674"; }
+
+.fa-mail-bulk::before {
+  content: "\f674"; }
+
+.fa-file-circle-exclamation::before {
+  content: "\e4eb"; }
+
+.fa-circle-h::before {
+  content: "\f47e"; }
+
+.fa-hospital-symbol::before {
+  content: "\f47e"; }
+
+.fa-pager::before {
+  content: "\f815"; }
+
+.fa-address-book::before {
+  content: "\f2b9"; }
+
+.fa-contact-book::before {
+  content: "\f2b9"; }
+
+.fa-strikethrough::before {
+  content: "\f0cc"; }
+
+.fa-k::before {
+  content: "\4b"; }
+
+.fa-landmark-flag::before {
+  content: "\e51c"; }
+
+.fa-pencil::before {
+  content: "\f303"; }
+
+.fa-pencil-alt::before {
+  content: "\f303"; }
+
+.fa-backward::before {
+  content: "\f04a"; }
+
+.fa-caret-right::before {
+  content: "\f0da"; }
+
+.fa-comments::before {
+  content: "\f086"; }
+
+.fa-paste::before {
+  content: "\f0ea"; }
+
+.fa-file-clipboard::before {
+  content: "\f0ea"; }
+
+.fa-code-pull-request::before {
+  content: "\e13c"; }
+
+.fa-clipboard-list::before {
+  content: "\f46d"; }
+
+.fa-truck-ramp-box::before {
+  content: "\f4de"; }
+
+.fa-truck-loading::before {
+  content: "\f4de"; }
+
+.fa-user-check::before {
+  content: "\f4fc"; }
+
+.fa-vial-virus::before {
+  content: "\e597"; }
+
+.fa-sheet-plastic::before {
+  content: "\e571"; }
+
+.fa-blog::before {
+  content: "\f781"; }
+
+.fa-user-ninja::before {
+  content: "\f504"; }
+
+.fa-person-arrow-up-from-line::before {
+  content: "\e539"; }
+
+.fa-scroll-torah::before {
+  content: "\f6a0"; }
+
+.fa-torah::before {
+  content: "\f6a0"; }
+
+.fa-broom-ball::before {
+  content: "\f458"; }
+
+.fa-quidditch::before {
+  content: "\f458"; }
+
+.fa-quidditch-broom-ball::before {
+  content: "\f458"; }
+
+.fa-toggle-off::before {
+  content: "\f204"; }
+
+.fa-box-archive::before {
+  content: "\f187"; }
+
+.fa-archive::before {
+  content: "\f187"; }
+
+.fa-person-drowning::before {
+  content: "\e545"; }
+
+.fa-arrow-down-9-1::before {
+  content: "\f886"; }
+
+.fa-sort-numeric-desc::before {
+  content: "\f886"; }
+
+.fa-sort-numeric-down-alt::before {
+  content: "\f886"; }
+
+.fa-face-grin-tongue-squint::before {
+  content: "\f58a"; }
+
+.fa-grin-tongue-squint::before {
+  content: "\f58a"; }
+
+.fa-spray-can::before {
+  content: "\f5bd"; }
+
+.fa-truck-monster::before {
+  content: "\f63b"; }
+
+.fa-w::before {
+  content: "\57"; }
+
+.fa-earth-africa::before {
+  content: "\f57c"; }
+
+.fa-globe-africa::before {
+  content: "\f57c"; }
+
+.fa-rainbow::before {
+  content: "\f75b"; }
+
+.fa-circle-notch::before {
+  content: "\f1ce"; }
+
+.fa-tablet-screen-button::before {
+  content: "\f3fa"; }
+
+.fa-tablet-alt::before {
+  content: "\f3fa"; }
+
+.fa-paw::before {
+  content: "\f1b0"; }
+
+.fa-cloud::before {
+  content: "\f0c2"; }
+
+.fa-trowel-bricks::before {
+  content: "\e58a"; }
+
+.fa-face-flushed::before {
+  content: "\f579"; }
+
+.fa-flushed::before {
+  content: "\f579"; }
+
+.fa-hospital-user::before {
+  content: "\f80d"; }
+
+.fa-tent-arrow-left-right::before {
+  content: "\e57f"; }
+
+.fa-gavel::before {
+  content: "\f0e3"; }
+
+.fa-legal::before {
+  content: "\f0e3"; }
+
+.fa-binoculars::before {
+  content: "\f1e5"; }
+
+.fa-microphone-slash::before {
+  content: "\f131"; }
+
+.fa-box-tissue::before {
+  content: "\e05b"; }
+
+.fa-motorcycle::before {
+  content: "\f21c"; }
+
+.fa-bell-concierge::before {
+  content: "\f562"; }
+
+.fa-concierge-bell::before {
+  content: "\f562"; }
+
+.fa-pen-ruler::before {
+  content: "\f5ae"; }
+
+.fa-pencil-ruler::before {
+  content: "\f5ae"; }
+
+.fa-people-arrows::before {
+  content: "\e068"; }
+
+.fa-people-arrows-left-right::before {
+  content: "\e068"; }
+
+.fa-mars-and-venus-burst::before {
+  content: "\e523"; }
+
+.fa-square-caret-right::before {
+  content: "\f152"; }
+
+.fa-caret-square-right::before {
+  content: "\f152"; }
+
+.fa-scissors::before {
+  content: "\f0c4"; }
+
+.fa-cut::before {
+  content: "\f0c4"; }
+
+.fa-sun-plant-wilt::before {
+  content: "\e57a"; }
+
+.fa-toilets-portable::before {
+  content: "\e584"; }
+
+.fa-hockey-puck::before {
+  content: "\f453"; }
+
+.fa-table::before {
+  content: "\f0ce"; }
+
+.fa-magnifying-glass-arrow-right::before {
+  content: "\e521"; }
+
+.fa-tachograph-digital::before {
+  content: "\f566"; }
+
+.fa-digital-tachograph::before {
+  content: "\f566"; }
+
+.fa-users-slash::before {
+  content: "\e073"; }
+
+.fa-clover::before {
+  content: "\e139"; }
+
+.fa-reply::before {
+  content: "\f3e5"; }
+
+.fa-mail-reply::before {
+  content: "\f3e5"; }
+
+.fa-star-and-crescent::before {
+  content: "\f699"; }
+
+.fa-house-fire::before {
+  content: "\e50c"; }
+
+.fa-square-minus::before {
+  content: "\f146"; }
+
+.fa-minus-square::before {
+  content: "\f146"; }
+
+.fa-helicopter::before {
+  content: "\f533"; }
+
+.fa-compass::before {
+  content: "\f14e"; }
+
+.fa-square-caret-down::before {
+  content: "\f150"; }
+
+.fa-caret-square-down::before {
+  content: "\f150"; }
+
+.fa-file-circle-question::before {
+  content: "\e4ef"; }
+
+.fa-laptop-code::before {
+  content: "\f5fc"; }
+
+.fa-swatchbook::before {
+  content: "\f5c3"; }
+
+.fa-prescription-bottle::before {
+  content: "\f485"; }
+
+.fa-bars::before {
+  content: "\f0c9"; }
+
+.fa-navicon::before {
+  content: "\f0c9"; }
+
+.fa-people-group::before {
+  content: "\e533"; }
+
+.fa-hourglass-end::before {
+  content: "\f253"; }
+
+.fa-hourglass-3::before {
+  content: "\f253"; }
+
+.fa-heart-crack::before {
+  content: "\f7a9"; }
+
+.fa-heart-broken::before {
+  content: "\f7a9"; }
+
+.fa-square-up-right::before {
+  content: "\f360"; }
+
+.fa-external-link-square-alt::before {
+  content: "\f360"; }
+
+.fa-face-kiss-beam::before {
+  content: "\f597"; }
+
+.fa-kiss-beam::before {
+  content: "\f597"; }
+
+.fa-film::before {
+  content: "\f008"; }
+
+.fa-ruler-horizontal::before {
+  content: "\f547"; }
+
+.fa-people-robbery::before {
+  content: "\e536"; }
+
+.fa-lightbulb::before {
+  content: "\f0eb"; }
+
+.fa-caret-left::before {
+  content: "\f0d9"; }
+
+.fa-circle-exclamation::before {
+  content: "\f06a"; }
+
+.fa-exclamation-circle::before {
+  content: "\f06a"; }
+
+.fa-school-circle-xmark::before {
+  content: "\e56d"; }
+
+.fa-arrow-right-from-bracket::before {
+  content: "\f08b"; }
+
+.fa-sign-out::before {
+  content: "\f08b"; }
+
+.fa-circle-chevron-down::before {
+  content: "\f13a"; }
+
+.fa-chevron-circle-down::before {
+  content: "\f13a"; }
+
+.fa-unlock-keyhole::before {
+  content: "\f13e"; }
+
+.fa-unlock-alt::before {
+  content: "\f13e"; }
+
+.fa-cloud-showers-heavy::before {
+  content: "\f740"; }
+
+.fa-headphones-simple::before {
+  content: "\f58f"; }
+
+.fa-headphones-alt::before {
+  content: "\f58f"; }
+
+.fa-sitemap::before {
+  content: "\f0e8"; }
+
+.fa-circle-dollar-to-slot::before {
+  content: "\f4b9"; }
+
+.fa-donate::before {
+  content: "\f4b9"; }
+
+.fa-memory::before {
+  content: "\f538"; }
+
+.fa-road-spikes::before {
+  content: "\e568"; }
+
+.fa-fire-burner::before {
+  content: "\e4f1"; }
+
+.fa-flag::before {
+  content: "\f024"; }
+
+.fa-hanukiah::before {
+  content: "\f6e6"; }
+
+.fa-feather::before {
+  content: "\f52d"; }
+
+.fa-volume-low::before {
+  content: "\f027"; }
+
+.fa-volume-down::before {
+  content: "\f027"; }
+
+.fa-comment-slash::before {
+  content: "\f4b3"; }
+
+.fa-cloud-sun-rain::before {
+  content: "\f743"; }
+
+.fa-compress::before {
+  content: "\f066"; }
+
+.fa-wheat-awn::before {
+  content: "\e2cd"; }
+
+.fa-wheat-alt::before {
+  content: "\e2cd"; }
+
+.fa-ankh::before {
+  content: "\f644"; }
+
+.fa-hands-holding-child::before {
+  content: "\e4fa"; }
+
+.fa-asterisk::before {
+  content: "\2a"; }
+
+.fa-square-check::before {
+  content: "\f14a"; }
+
+.fa-check-square::before {
+  content: "\f14a"; }
+
+.fa-peseta-sign::before {
+  content: "\e221"; }
+
+.fa-heading::before {
+  content: "\f1dc"; }
+
+.fa-header::before {
+  content: "\f1dc"; }
+
+.fa-ghost::before {
+  content: "\f6e2"; }
+
+.fa-list::before {
+  content: "\f03a"; }
+
+.fa-list-squares::before {
+  content: "\f03a"; }
+
+.fa-square-phone-flip::before {
+  content: "\f87b"; }
+
+.fa-phone-square-alt::before {
+  content: "\f87b"; }
+
+.fa-cart-plus::before {
+  content: "\f217"; }
+
+.fa-gamepad::before {
+  content: "\f11b"; }
+
+.fa-circle-dot::before {
+  content: "\f192"; }
+
+.fa-dot-circle::before {
+  content: "\f192"; }
+
+.fa-face-dizzy::before {
+  content: "\f567"; }
+
+.fa-dizzy::before {
+  content: "\f567"; }
+
+.fa-egg::before {
+  content: "\f7fb"; }
+
+.fa-house-medical-circle-xmark::before {
+  content: "\e513"; }
+
+.fa-campground::before {
+  content: "\f6bb"; }
+
+.fa-folder-plus::before {
+  content: "\f65e"; }
+
+.fa-futbol::before {
+  content: "\f1e3"; }
+
+.fa-futbol-ball::before {
+  content: "\f1e3"; }
+
+.fa-soccer-ball::before {
+  content: "\f1e3"; }
+
+.fa-paintbrush::before {
+  content: "\f1fc"; }
+
+.fa-paint-brush::before {
+  content: "\f1fc"; }
+
+.fa-lock::before {
+  content: "\f023"; }
+
+.fa-gas-pump::before {
+  content: "\f52f"; }
+
+.fa-hot-tub-person::before {
+  content: "\f593"; }
+
+.fa-hot-tub::before {
+  content: "\f593"; }
+
+.fa-map-location::before {
+  content: "\f59f"; }
+
+.fa-map-marked::before {
+  content: "\f59f"; }
+
+.fa-house-flood-water::before {
+  content: "\e50e"; }
+
+.fa-tree::before {
+  content: "\f1bb"; }
+
+.fa-bridge-lock::before {
+  content: "\e4cc"; }
+
+.fa-sack-dollar::before {
+  content: "\f81d"; }
+
+.fa-pen-to-square::before {
+  content: "\f044"; }
+
+.fa-edit::before {
+  content: "\f044"; }
+
+.fa-car-side::before {
+  content: "\f5e4"; }
+
+.fa-share-nodes::before {
+  content: "\f1e0"; }
+
+.fa-share-alt::before {
+  content: "\f1e0"; }
+
+.fa-heart-circle-minus::before {
+  content: "\e4ff"; }
+
+.fa-hourglass-half::before {
+  content: "\f252"; }
+
+.fa-hourglass-2::before {
+  content: "\f252"; }
+
+.fa-microscope::before {
+  content: "\f610"; }
+
+.fa-sink::before {
+  content: "\e06d"; }
+
+.fa-bag-shopping::before {
+  content: "\f290"; }
+
+.fa-shopping-bag::before {
+  content: "\f290"; }
+
+.fa-arrow-down-z-a::before {
+  content: "\f881"; }
+
+.fa-sort-alpha-desc::before {
+  content: "\f881"; }
+
+.fa-sort-alpha-down-alt::before {
+  content: "\f881"; }
+
+.fa-mitten::before {
+  content: "\f7b5"; }
+
+.fa-person-rays::before {
+  content: "\e54d"; }
+
+.fa-users::before {
+  content: "\f0c0"; }
+
+.fa-eye-slash::before {
+  content: "\f070"; }
+
+.fa-flask-vial::before {
+  content: "\e4f3"; }
+
+.fa-hand::before {
+  content: "\f256"; }
+
+.fa-hand-paper::before {
+  content: "\f256"; }
+
+.fa-om::before {
+  content: "\f679"; }
+
+.fa-worm::before {
+  content: "\e599"; }
+
+.fa-house-circle-xmark::before {
+  content: "\e50b"; }
+
+.fa-plug::before {
+  content: "\f1e6"; }
+
+.fa-chevron-up::before {
+  content: "\f077"; }
+
+.fa-hand-spock::before {
+  content: "\f259"; }
+
+.fa-stopwatch::before {
+  content: "\f2f2"; }
+
+.fa-face-kiss::before {
+  content: "\f596"; }
+
+.fa-kiss::before {
+  content: "\f596"; }
+
+.fa-bridge-circle-xmark::before {
+  content: "\e4cb"; }
+
+.fa-face-grin-tongue::before {
+  content: "\f589"; }
+
+.fa-grin-tongue::before {
+  content: "\f589"; }
+
+.fa-chess-bishop::before {
+  content: "\f43a"; }
+
+.fa-face-grin-wink::before {
+  content: "\f58c"; }
+
+.fa-grin-wink::before {
+  content: "\f58c"; }
+
+.fa-ear-deaf::before {
+  content: "\f2a4"; }
+
+.fa-deaf::before {
+  content: "\f2a4"; }
+
+.fa-deafness::before {
+  content: "\f2a4"; }
+
+.fa-hard-of-hearing::before {
+  content: "\f2a4"; }
+
+.fa-road-circle-check::before {
+  content: "\e564"; }
+
+.fa-dice-five::before {
+  content: "\f523"; }
+
+.fa-square-rss::before {
+  content: "\f143"; }
+
+.fa-rss-square::before {
+  content: "\f143"; }
+
+.fa-land-mine-on::before {
+  content: "\e51b"; }
+
+.fa-i-cursor::before {
+  content: "\f246"; }
+
+.fa-stamp::before {
+  content: "\f5bf"; }
+
+.fa-stairs::before {
+  content: "\e289"; }
+
+.fa-i::before {
+  content: "\49"; }
+
+.fa-hryvnia-sign::before {
+  content: "\f6f2"; }
+
+.fa-hryvnia::before {
+  content: "\f6f2"; }
+
+.fa-pills::before {
+  content: "\f484"; }
+
+.fa-face-grin-wide::before {
+  content: "\f581"; }
+
+.fa-grin-alt::before {
+  content: "\f581"; }
+
+.fa-tooth::before {
+  content: "\f5c9"; }
+
+.fa-v::before {
+  content: "\56"; }
+
+.fa-bangladeshi-taka-sign::before {
+  content: "\e2e6"; }
+
+.fa-bicycle::before {
+  content: "\f206"; }
+
+.fa-staff-snake::before {
+  content: "\e579"; }
+
+.fa-rod-asclepius::before {
+  content: "\e579"; }
+
+.fa-rod-snake::before {
+  content: "\e579"; }
+
+.fa-staff-aesculapius::before {
+  content: "\e579"; }
+
+.fa-head-side-cough-slash::before {
+  content: "\e062"; }
+
+.fa-truck-medical::before {
+  content: "\f0f9"; }
+
+.fa-ambulance::before {
+  content: "\f0f9"; }
+
+.fa-wheat-awn-circle-exclamation::before {
+  content: "\e598"; }
+
+.fa-snowman::before {
+  content: "\f7d0"; }
+
+.fa-mortar-pestle::before {
+  content: "\f5a7"; }
+
+.fa-road-barrier::before {
+  content: "\e562"; }
+
+.fa-school::before {
+  content: "\f549"; }
+
+.fa-igloo::before {
+  content: "\f7ae"; }
+
+.fa-joint::before {
+  content: "\f595"; }
+
+.fa-angle-right::before {
+  content: "\f105"; }
+
+.fa-horse::before {
+  content: "\f6f0"; }
+
+.fa-q::before {
+  content: "\51"; }
+
+.fa-g::before {
+  content: "\47"; }
+
+.fa-notes-medical::before {
+  content: "\f481"; }
+
+.fa-temperature-half::before {
+  content: "\f2c9"; }
+
+.fa-temperature-2::before {
+  content: "\f2c9"; }
+
+.fa-thermometer-2::before {
+  content: "\f2c9"; }
+
+.fa-thermometer-half::before {
+  content: "\f2c9"; }
+
+.fa-dong-sign::before {
+  content: "\e169"; }
+
+.fa-capsules::before {
+  content: "\f46b"; }
+
+.fa-poo-storm::before {
+  content: "\f75a"; }
+
+.fa-poo-bolt::before {
+  content: "\f75a"; }
+
+.fa-face-frown-open::before {
+  content: "\f57a"; }
+
+.fa-frown-open::before {
+  content: "\f57a"; }
+
+.fa-hand-point-up::before {
+  content: "\f0a6"; }
+
+.fa-money-bill::before {
+  content: "\f0d6"; }
+
+.fa-bookmark::before {
+  content: "\f02e"; }
+
+.fa-align-justify::before {
+  content: "\f039"; }
+
+.fa-umbrella-beach::before {
+  content: "\f5ca"; }
+
+.fa-helmet-un::before {
+  content: "\e503"; }
+
+.fa-bullseye::before {
+  content: "\f140"; }
+
+.fa-bacon::before {
+  content: "\f7e5"; }
+
+.fa-hand-point-down::before {
+  content: "\f0a7"; }
+
+.fa-arrow-up-from-bracket::before {
+  content: "\e09a"; }
+
+.fa-folder::before {
+  content: "\f07b"; }
+
+.fa-folder-blank::before {
+  content: "\f07b"; }
+
+.fa-file-waveform::before {
+  content: "\f478"; }
+
+.fa-file-medical-alt::before {
+  content: "\f478"; }
+
+.fa-radiation::before {
+  content: "\f7b9"; }
+
+.fa-chart-simple::before {
+  content: "\e473"; }
+
+.fa-mars-stroke::before {
+  content: "\f229"; }
+
+.fa-vial::before {
+  content: "\f492"; }
+
+.fa-gauge::before {
+  content: "\f624"; }
+
+.fa-dashboard::before {
+  content: "\f624"; }
+
+.fa-gauge-med::before {
+  content: "\f624"; }
+
+.fa-tachometer-alt-average::before {
+  content: "\f624"; }
+
+.fa-wand-magic-sparkles::before {
+  content: "\e2ca"; }
+
+.fa-magic-wand-sparkles::before {
+  content: "\e2ca"; }
+
+.fa-e::before {
+  content: "\45"; }
+
+.fa-pen-clip::before {
+  content: "\f305"; }
+
+.fa-pen-alt::before {
+  content: "\f305"; }
+
+.fa-bridge-circle-exclamation::before {
+  content: "\e4ca"; }
+
+.fa-user::before {
+  content: "\f007"; }
+
+.fa-school-circle-check::before {
+  content: "\e56b"; }
+
+.fa-dumpster::before {
+  content: "\f793"; }
+
+.fa-van-shuttle::before {
+  content: "\f5b6"; }
+
+.fa-shuttle-van::before {
+  content: "\f5b6"; }
+
+.fa-building-user::before {
+  content: "\e4da"; }
+
+.fa-square-caret-left::before {
+  content: "\f191"; }
+
+.fa-caret-square-left::before {
+  content: "\f191"; }
+
+.fa-highlighter::before {
+  content: "\f591"; }
+
+.fa-key::before {
+  content: "\f084"; }
+
+.fa-bullhorn::before {
+  content: "\f0a1"; }
+
+.fa-globe::before {
+  content: "\f0ac"; }
+
+.fa-synagogue::before {
+  content: "\f69b"; }
+
+.fa-person-half-dress::before {
+  content: "\e548"; }
+
+.fa-road-bridge::before {
+  content: "\e563"; }
+
+.fa-location-arrow::before {
+  content: "\f124"; }
+
+.fa-c::before {
+  content: "\43"; }
+
+.fa-tablet-button::before {
+  content: "\f10a"; }
+
+.fa-building-lock::before {
+  content: "\e4d6"; }
+
+.fa-pizza-slice::before {
+  content: "\f818"; }
+
+.fa-money-bill-wave::before {
+  content: "\f53a"; }
+
+.fa-chart-area::before {
+  content: "\f1fe"; }
+
+.fa-area-chart::before {
+  content: "\f1fe"; }
+
+.fa-house-flag::before {
+  content: "\e50d"; }
+
+.fa-person-circle-minus::before {
+  content: "\e540"; }
+
+.fa-ban::before {
+  content: "\f05e"; }
+
+.fa-cancel::before {
+  content: "\f05e"; }
+
+.fa-camera-rotate::before {
+  content: "\e0d8"; }
+
+.fa-spray-can-sparkles::before {
+  content: "\f5d0"; }
+
+.fa-air-freshener::before {
+  content: "\f5d0"; }
+
+.fa-star::before {
+  content: "\f005"; }
+
+.fa-repeat::before {
+  content: "\f363"; }
+
+.fa-cross::before {
+  content: "\f654"; }
+
+.fa-box::before {
+  content: "\f466"; }
+
+.fa-venus-mars::before {
+  content: "\f228"; }
+
+.fa-arrow-pointer::before {
+  content: "\f245"; }
+
+.fa-mouse-pointer::before {
+  content: "\f245"; }
+
+.fa-maximize::before {
+  content: "\f31e"; }
+
+.fa-expand-arrows-alt::before {
+  content: "\f31e"; }
+
+.fa-charging-station::before {
+  content: "\f5e7"; }
+
+.fa-shapes::before {
+  content: "\f61f"; }
+
+.fa-triangle-circle-square::before {
+  content: "\f61f"; }
+
+.fa-shuffle::before {
+  content: "\f074"; }
+
+.fa-random::before {
+  content: "\f074"; }
+
+.fa-person-running::before {
+  content: "\f70c"; }
+
+.fa-running::before {
+  content: "\f70c"; }
+
+.fa-mobile-retro::before {
+  content: "\e527"; }
+
+.fa-grip-lines-vertical::before {
+  content: "\f7a5"; }
+
+.fa-spider::before {
+  content: "\f717"; }
+
+.fa-hands-bound::before {
+  content: "\e4f9"; }
+
+.fa-file-invoice-dollar::before {
+  content: "\f571"; }
+
+.fa-plane-circle-exclamation::before {
+  content: "\e556"; }
+
+.fa-x-ray::before {
+  content: "\f497"; }
+
+.fa-spell-check::before {
+  content: "\f891"; }
+
+.fa-slash::before {
+  content: "\f715"; }
+
+.fa-computer-mouse::before {
+  content: "\f8cc"; }
+
+.fa-mouse::before {
+  content: "\f8cc"; }
+
+.fa-arrow-right-to-bracket::before {
+  content: "\f090"; }
+
+.fa-sign-in::before {
+  content: "\f090"; }
+
+.fa-shop-slash::before {
+  content: "\e070"; }
+
+.fa-store-alt-slash::before {
+  content: "\e070"; }
+
+.fa-server::before {
+  content: "\f233"; }
+
+.fa-virus-covid-slash::before {
+  content: "\e4a9"; }
+
+.fa-shop-lock::before {
+  content: "\e4a5"; }
+
+.fa-hourglass-start::before {
+  content: "\f251"; }
+
+.fa-hourglass-1::before {
+  content: "\f251"; }
+
+.fa-blender-phone::before {
+  content: "\f6b6"; }
+
+.fa-building-wheat::before {
+  content: "\e4db"; }
+
+.fa-person-breastfeeding::before {
+  content: "\e53a"; }
+
+.fa-right-to-bracket::before {
+  content: "\f2f6"; }
+
+.fa-sign-in-alt::before {
+  content: "\f2f6"; }
+
+.fa-venus::before {
+  content: "\f221"; }
+
+.fa-passport::before {
+  content: "\f5ab"; }
+
+.fa-heart-pulse::before {
+  content: "\f21e"; }
+
+.fa-heartbeat::before {
+  content: "\f21e"; }
+
+.fa-people-carry-box::before {
+  content: "\f4ce"; }
+
+.fa-people-carry::before {
+  content: "\f4ce"; }
+
+.fa-temperature-high::before {
+  content: "\f769"; }
+
+.fa-microchip::before {
+  content: "\f2db"; }
+
+.fa-crown::before {
+  content: "\f521"; }
+
+.fa-weight-hanging::before {
+  content: "\f5cd"; }
+
+.fa-xmarks-lines::before {
+  content: "\e59a"; }
+
+.fa-file-prescription::before {
+  content: "\f572"; }
+
+.fa-weight-scale::before {
+  content: "\f496"; }
+
+.fa-weight::before {
+  content: "\f496"; }
+
+.fa-user-group::before {
+  content: "\f500"; }
+
+.fa-user-friends::before {
+  content: "\f500"; }
+
+.fa-arrow-up-a-z::before {
+  content: "\f15e"; }
+
+.fa-sort-alpha-up::before {
+  content: "\f15e"; }
+
+.fa-chess-knight::before {
+  content: "\f441"; }
+
+.fa-face-laugh-squint::before {
+  content: "\f59b"; }
+
+.fa-laugh-squint::before {
+  content: "\f59b"; }
+
+.fa-wheelchair::before {
+  content: "\f193"; }
+
+.fa-circle-arrow-up::before {
+  content: "\f0aa"; }
+
+.fa-arrow-circle-up::before {
+  content: "\f0aa"; }
+
+.fa-toggle-on::before {
+  content: "\f205"; }
+
+.fa-person-walking::before {
+  content: "\f554"; }
+
+.fa-walking::before {
+  content: "\f554"; }
+
+.fa-l::before {
+  content: "\4c"; }
+
+.fa-fire::before {
+  content: "\f06d"; }
+
+.fa-bed-pulse::before {
+  content: "\f487"; }
+
+.fa-procedures::before {
+  content: "\f487"; }
+
+.fa-shuttle-space::before {
+  content: "\f197"; }
+
+.fa-space-shuttle::before {
+  content: "\f197"; }
+
+.fa-face-laugh::before {
+  content: "\f599"; }
+
+.fa-laugh::before {
+  content: "\f599"; }
+
+.fa-folder-open::before {
+  content: "\f07c"; }
+
+.fa-heart-circle-plus::before {
+  content: "\e500"; }
+
+.fa-code-fork::before {
+  content: "\e13b"; }
+
+.fa-city::before {
+  content: "\f64f"; }
+
+.fa-microphone-lines::before {
+  content: "\f3c9"; }
+
+.fa-microphone-alt::before {
+  content: "\f3c9"; }
+
+.fa-pepper-hot::before {
+  content: "\f816"; }
+
+.fa-unlock::before {
+  content: "\f09c"; }
+
+.fa-colon-sign::before {
+  content: "\e140"; }
+
+.fa-headset::before {
+  content: "\f590"; }
+
+.fa-store-slash::before {
+  content: "\e071"; }
+
+.fa-road-circle-xmark::before {
+  content: "\e566"; }
+
+.fa-user-minus::before {
+  content: "\f503"; }
+
+.fa-mars-stroke-up::before {
+  content: "\f22a"; }
+
+.fa-mars-stroke-v::before {
+  content: "\f22a"; }
+
+.fa-champagne-glasses::before {
+  content: "\f79f"; }
+
+.fa-glass-cheers::before {
+  content: "\f79f"; }
+
+.fa-clipboard::before {
+  content: "\f328"; }
+
+.fa-house-circle-exclamation::before {
+  content: "\e50a"; }
+
+.fa-file-arrow-up::before {
+  content: "\f574"; }
+
+.fa-file-upload::before {
+  content: "\f574"; }
+
+.fa-wifi::before {
+  content: "\f1eb"; }
+
+.fa-wifi-3::before {
+  content: "\f1eb"; }
+
+.fa-wifi-strong::before {
+  content: "\f1eb"; }
+
+.fa-bath::before {
+  content: "\f2cd"; }
+
+.fa-bathtub::before {
+  content: "\f2cd"; }
+
+.fa-underline::before {
+  content: "\f0cd"; }
+
+.fa-user-pen::before {
+  content: "\f4ff"; }
+
+.fa-user-edit::before {
+  content: "\f4ff"; }
+
+.fa-signature::before {
+  content: "\f5b7"; }
+
+.fa-stroopwafel::before {
+  content: "\f551"; }
+
+.fa-bold::before {
+  content: "\f032"; }
+
+.fa-anchor-lock::before {
+  content: "\e4ad"; }
+
+.fa-building-ngo::before {
+  content: "\e4d7"; }
+
+.fa-manat-sign::before {
+  content: "\e1d5"; }
+
+.fa-not-equal::before {
+  content: "\f53e"; }
+
+.fa-border-top-left::before {
+  content: "\f853"; }
+
+.fa-border-style::before {
+  content: "\f853"; }
+
+.fa-map-location-dot::before {
+  content: "\f5a0"; }
+
+.fa-map-marked-alt::before {
+  content: "\f5a0"; }
+
+.fa-jedi::before {
+  content: "\f669"; }
+
+.fa-square-poll-vertical::before {
+  content: "\f681"; }
+
+.fa-poll::before {
+  content: "\f681"; }
+
+.fa-mug-hot::before {
+  content: "\f7b6"; }
+
+.fa-car-battery::before {
+  content: "\f5df"; }
+
+.fa-battery-car::before {
+  content: "\f5df"; }
+
+.fa-gift::before {
+  content: "\f06b"; }
+
+.fa-dice-two::before {
+  content: "\f528"; }
+
+.fa-chess-queen::before {
+  content: "\f445"; }
+
+.fa-glasses::before {
+  content: "\f530"; }
+
+.fa-chess-board::before {
+  content: "\f43c"; }
+
+.fa-building-circle-check::before {
+  content: "\e4d2"; }
+
+.fa-person-chalkboard::before {
+  content: "\e53d"; }
+
+.fa-mars-stroke-right::before {
+  content: "\f22b"; }
+
+.fa-mars-stroke-h::before {
+  content: "\f22b"; }
+
+.fa-hand-back-fist::before {
+  content: "\f255"; }
+
+.fa-hand-rock::before {
+  content: "\f255"; }
+
+.fa-square-caret-up::before {
+  content: "\f151"; }
+
+.fa-caret-square-up::before {
+  content: "\f151"; }
+
+.fa-cloud-showers-water::before {
+  content: "\e4e4"; }
+
+.fa-chart-bar::before {
+  content: "\f080"; }
+
+.fa-bar-chart::before {
+  content: "\f080"; }
+
+.fa-hands-bubbles::before {
+  content: "\e05e"; }
+
+.fa-hands-wash::before {
+  content: "\e05e"; }
+
+.fa-less-than-equal::before {
+  content: "\f537"; }
+
+.fa-train::before {
+  content: "\f238"; }
+
+.fa-eye-low-vision::before {
+  content: "\f2a8"; }
+
+.fa-low-vision::before {
+  content: "\f2a8"; }
+
+.fa-crow::before {
+  content: "\f520"; }
+
+.fa-sailboat::before {
+  content: "\e445"; }
+
+.fa-window-restore::before {
+  content: "\f2d2"; }
+
+.fa-square-plus::before {
+  content: "\f0fe"; }
+
+.fa-plus-square::before {
+  content: "\f0fe"; }
+
+.fa-torii-gate::before {
+  content: "\f6a1"; }
+
+.fa-frog::before {
+  content: "\f52e"; }
+
+.fa-bucket::before {
+  content: "\e4cf"; }
+
+.fa-image::before {
+  content: "\f03e"; }
+
+.fa-microphone::before {
+  content: "\f130"; }
+
+.fa-cow::before {
+  content: "\f6c8"; }
+
+.fa-caret-up::before {
+  content: "\f0d8"; }
+
+.fa-screwdriver::before {
+  content: "\f54a"; }
+
+.fa-folder-closed::before {
+  content: "\e185"; }
+
+.fa-house-tsunami::before {
+  content: "\e515"; }
+
+.fa-square-nfi::before {
+  content: "\e576"; }
+
+.fa-arrow-up-from-ground-water::before {
+  content: "\e4b5"; }
+
+.fa-martini-glass::before {
+  content: "\f57b"; }
+
+.fa-glass-martini-alt::before {
+  content: "\f57b"; }
+
+.fa-rotate-left::before {
+  content: "\f2ea"; }
+
+.fa-rotate-back::before {
+  content: "\f2ea"; }
+
+.fa-rotate-backward::before {
+  content: "\f2ea"; }
+
+.fa-undo-alt::before {
+  content: "\f2ea"; }
+
+.fa-table-columns::before {
+  content: "\f0db"; }
+
+.fa-columns::before {
+  content: "\f0db"; }
+
+.fa-lemon::before {
+  content: "\f094"; }
+
+.fa-head-side-mask::before {
+  content: "\e063"; }
+
+.fa-handshake::before {
+  content: "\f2b5"; }
+
+.fa-gem::before {
+  content: "\f3a5"; }
+
+.fa-dolly::before {
+  content: "\f472"; }
+
+.fa-dolly-box::before {
+  content: "\f472"; }
+
+.fa-smoking::before {
+  content: "\f48d"; }
+
+.fa-minimize::before {
+  content: "\f78c"; }
+
+.fa-compress-arrows-alt::before {
+  content: "\f78c"; }
+
+.fa-monument::before {
+  content: "\f5a6"; }
+
+.fa-snowplow::before {
+  content: "\f7d2"; }
+
+.fa-angles-right::before {
+  content: "\f101"; }
+
+.fa-angle-double-right::before {
+  content: "\f101"; }
+
+.fa-cannabis::before {
+  content: "\f55f"; }
+
+.fa-circle-play::before {
+  content: "\f144"; }
+
+.fa-play-circle::before {
+  content: "\f144"; }
+
+.fa-tablets::before {
+  content: "\f490"; }
+
+.fa-ethernet::before {
+  content: "\f796"; }
+
+.fa-euro-sign::before {
+  content: "\f153"; }
+
+.fa-eur::before {
+  content: "\f153"; }
+
+.fa-euro::before {
+  content: "\f153"; }
+
+.fa-chair::before {
+  content: "\f6c0"; }
+
+.fa-circle-check::before {
+  content: "\f058"; }
+
+.fa-check-circle::before {
+  content: "\f058"; }
+
+.fa-circle-stop::before {
+  content: "\f28d"; }
+
+.fa-stop-circle::before {
+  content: "\f28d"; }
+
+.fa-compass-drafting::before {
+  content: "\f568"; }
+
+.fa-drafting-compass::before {
+  content: "\f568"; }
+
+.fa-plate-wheat::before {
+  content: "\e55a"; }
+
+.fa-icicles::before {
+  content: "\f7ad"; }
+
+.fa-person-shelter::before {
+  content: "\e54f"; }
+
+.fa-neuter::before {
+  content: "\f22c"; }
+
+.fa-id-badge::before {
+  content: "\f2c1"; }
+
+.fa-marker::before {
+  content: "\f5a1"; }
+
+.fa-face-laugh-beam::before {
+  content: "\f59a"; }
+
+.fa-laugh-beam::before {
+  content: "\f59a"; }
+
+.fa-helicopter-symbol::before {
+  content: "\e502"; }
+
+.fa-universal-access::before {
+  content: "\f29a"; }
+
+.fa-circle-chevron-up::before {
+  content: "\f139"; }
+
+.fa-chevron-circle-up::before {
+  content: "\f139"; }
+
+.fa-lari-sign::before {
+  content: "\e1c8"; }
+
+.fa-volcano::before {
+  content: "\f770"; }
+
+.fa-person-walking-dashed-line-arrow-right::before {
+  content: "\e553"; }
+
+.fa-sterling-sign::before {
+  content: "\f154"; }
+
+.fa-gbp::before {
+  content: "\f154"; }
+
+.fa-pound-sign::before {
+  content: "\f154"; }
+
+.fa-viruses::before {
+  content: "\e076"; }
+
+.fa-square-person-confined::before {
+  content: "\e577"; }
+
+.fa-user-tie::before {
+  content: "\f508"; }
+
+.fa-arrow-down-long::before {
+  content: "\f175"; }
+
+.fa-long-arrow-down::before {
+  content: "\f175"; }
+
+.fa-tent-arrow-down-to-line::before {
+  content: "\e57e"; }
+
+.fa-certificate::before {
+  content: "\f0a3"; }
+
+.fa-reply-all::before {
+  content: "\f122"; }
+
+.fa-mail-reply-all::before {
+  content: "\f122"; }
+
+.fa-suitcase::before {
+  content: "\f0f2"; }
+
+.fa-person-skating::before {
+  content: "\f7c5"; }
+
+.fa-skating::before {
+  content: "\f7c5"; }
+
+.fa-filter-circle-dollar::before {
+  content: "\f662"; }
+
+.fa-funnel-dollar::before {
+  content: "\f662"; }
+
+.fa-camera-retro::before {
+  content: "\f083"; }
+
+.fa-circle-arrow-down::before {
+  content: "\f0ab"; }
+
+.fa-arrow-circle-down::before {
+  content: "\f0ab"; }
+
+.fa-file-import::before {
+  content: "\f56f"; }
+
+.fa-arrow-right-to-file::before {
+  content: "\f56f"; }
+
+.fa-square-arrow-up-right::before {
+  content: "\f14c"; }
+
+.fa-external-link-square::before {
+  content: "\f14c"; }
+
+.fa-box-open::before {
+  content: "\f49e"; }
+
+.fa-scroll::before {
+  content: "\f70e"; }
+
+.fa-spa::before {
+  content: "\f5bb"; }
+
+.fa-location-pin-lock::before {
+  content: "\e51f"; }
+
+.fa-pause::before {
+  content: "\f04c"; }
+
+.fa-hill-avalanche::before {
+  content: "\e507"; }
+
+.fa-temperature-empty::before {
+  content: "\f2cb"; }
+
+.fa-temperature-0::before {
+  content: "\f2cb"; }
+
+.fa-thermometer-0::before {
+  content: "\f2cb"; }
+
+.fa-thermometer-empty::before {
+  content: "\f2cb"; }
+
+.fa-bomb::before {
+  content: "\f1e2"; }
+
+.fa-registered::before {
+  content: "\f25d"; }
+
+.fa-address-card::before {
+  content: "\f2bb"; }
+
+.fa-contact-card::before {
+  content: "\f2bb"; }
+
+.fa-vcard::before {
+  content: "\f2bb"; }
+
+.fa-scale-unbalanced-flip::before {
+  content: "\f516"; }
+
+.fa-balance-scale-right::before {
+  content: "\f516"; }
+
+.fa-subscript::before {
+  content: "\f12c"; }
+
+.fa-diamond-turn-right::before {
+  content: "\f5eb"; }
+
+.fa-directions::before {
+  content: "\f5eb"; }
+
+.fa-burst::before {
+  content: "\e4dc"; }
+
+.fa-house-laptop::before {
+  content: "\e066"; }
+
+.fa-laptop-house::before {
+  content: "\e066"; }
+
+.fa-face-tired::before {
+  content: "\f5c8"; }
+
+.fa-tired::before {
+  content: "\f5c8"; }
+
+.fa-money-bills::before {
+  content: "\e1f3"; }
+
+.fa-smog::before {
+  content: "\f75f"; }
+
+.fa-crutch::before {
+  content: "\f7f7"; }
+
+.fa-cloud-arrow-up::before {
+  content: "\f0ee"; }
+
+.fa-cloud-upload::before {
+  content: "\f0ee"; }
+
+.fa-cloud-upload-alt::before {
+  content: "\f0ee"; }
+
+.fa-palette::before {
+  content: "\f53f"; }
+
+.fa-arrows-turn-right::before {
+  content: "\e4c0"; }
+
+.fa-vest::before {
+  content: "\e085"; }
+
+.fa-ferry::before {
+  content: "\e4ea"; }
+
+.fa-arrows-down-to-people::before {
+  content: "\e4b9"; }
+
+.fa-seedling::before {
+  content: "\f4d8"; }
+
+.fa-sprout::before {
+  content: "\f4d8"; }
+
+.fa-left-right::before {
+  content: "\f337"; }
+
+.fa-arrows-alt-h::before {
+  content: "\f337"; }
+
+.fa-boxes-packing::before {
+  content: "\e4c7"; }
+
+.fa-circle-arrow-left::before {
+  content: "\f0a8"; }
+
+.fa-arrow-circle-left::before {
+  content: "\f0a8"; }
+
+.fa-group-arrows-rotate::before {
+  content: "\e4f6"; }
+
+.fa-bowl-food::before {
+  content: "\e4c6"; }
+
+.fa-candy-cane::before {
+  content: "\f786"; }
+
+.fa-arrow-down-wide-short::before {
+  content: "\f160"; }
+
+.fa-sort-amount-asc::before {
+  content: "\f160"; }
+
+.fa-sort-amount-down::before {
+  content: "\f160"; }
+
+.fa-cloud-bolt::before {
+  content: "\f76c"; }
+
+.fa-thunderstorm::before {
+  content: "\f76c"; }
+
+.fa-text-slash::before {
+  content: "\f87d"; }
+
+.fa-remove-format::before {
+  content: "\f87d"; }
+
+.fa-face-smile-wink::before {
+  content: "\f4da"; }
+
+.fa-smile-wink::before {
+  content: "\f4da"; }
+
+.fa-file-word::before {
+  content: "\f1c2"; }
+
+.fa-file-powerpoint::before {
+  content: "\f1c4"; }
+
+.fa-arrows-left-right::before {
+  content: "\f07e"; }
+
+.fa-arrows-h::before {
+  content: "\f07e"; }
+
+.fa-house-lock::before {
+  content: "\e510"; }
+
+.fa-cloud-arrow-down::before {
+  content: "\f0ed"; }
+
+.fa-cloud-download::before {
+  content: "\f0ed"; }
+
+.fa-cloud-download-alt::before {
+  content: "\f0ed"; }
+
+.fa-children::before {
+  content: "\e4e1"; }
+
+.fa-chalkboard::before {
+  content: "\f51b"; }
+
+.fa-blackboard::before {
+  content: "\f51b"; }
+
+.fa-user-large-slash::before {
+  content: "\f4fa"; }
+
+.fa-user-alt-slash::before {
+  content: "\f4fa"; }
+
+.fa-envelope-open::before {
+  content: "\f2b6"; }
+
+.fa-handshake-simple-slash::before {
+  content: "\e05f"; }
+
+.fa-handshake-alt-slash::before {
+  content: "\e05f"; }
+
+.fa-mattress-pillow::before {
+  content: "\e525"; }
+
+.fa-guarani-sign::before {
+  content: "\e19a"; }
+
+.fa-arrows-rotate::before {
+  content: "\f021"; }
+
+.fa-refresh::before {
+  content: "\f021"; }
+
+.fa-sync::before {
+  content: "\f021"; }
+
+.fa-fire-extinguisher::before {
+  content: "\f134"; }
+
+.fa-cruzeiro-sign::before {
+  content: "\e152"; }
+
+.fa-greater-than-equal::before {
+  content: "\f532"; }
+
+.fa-shield-halved::before {
+  content: "\f3ed"; }
+
+.fa-shield-alt::before {
+  content: "\f3ed"; }
+
+.fa-book-atlas::before {
+  content: "\f558"; }
+
+.fa-atlas::before {
+  content: "\f558"; }
+
+.fa-virus::before {
+  content: "\e074"; }
+
+.fa-envelope-circle-check::before {
+  content: "\e4e8"; }
+
+.fa-layer-group::before {
+  content: "\f5fd"; }
+
+.fa-arrows-to-dot::before {
+  content: "\e4be"; }
+
+.fa-archway::before {
+  content: "\f557"; }
+
+.fa-heart-circle-check::before {
+  content: "\e4fd"; }
+
+.fa-house-chimney-crack::before {
+  content: "\f6f1"; }
+
+.fa-house-damage::before {
+  content: "\f6f1"; }
+
+.fa-file-zipper::before {
+  content: "\f1c6"; }
+
+.fa-file-archive::before {
+  content: "\f1c6"; }
+
+.fa-square::before {
+  content: "\f0c8"; }
+
+.fa-martini-glass-empty::before {
+  content: "\f000"; }
+
+.fa-glass-martini::before {
+  content: "\f000"; }
+
+.fa-couch::before {
+  content: "\f4b8"; }
+
+.fa-cedi-sign::before {
+  content: "\e0df"; }
+
+.fa-italic::before {
+  content: "\f033"; }
+
+.fa-table-cells-column-lock::before {
+  content: "\e678"; }
+
+.fa-church::before {
+  content: "\f51d"; }
+
+.fa-comments-dollar::before {
+  content: "\f653"; }
+
+.fa-democrat::before {
+  content: "\f747"; }
+
+.fa-z::before {
+  content: "\5a"; }
+
+.fa-person-skiing::before {
+  content: "\f7c9"; }
+
+.fa-skiing::before {
+  content: "\f7c9"; }
+
+.fa-road-lock::before {
+  content: "\e567"; }
+
+.fa-a::before {
+  content: "\41"; }
+
+.fa-temperature-arrow-down::before {
+  content: "\e03f"; }
+
+.fa-temperature-down::before {
+  content: "\e03f"; }
+
+.fa-feather-pointed::before {
+  content: "\f56b"; }
+
+.fa-feather-alt::before {
+  content: "\f56b"; }
+
+.fa-p::before {
+  content: "\50"; }
+
+.fa-snowflake::before {
+  content: "\f2dc"; }
+
+.fa-newspaper::before {
+  content: "\f1ea"; }
+
+.fa-rectangle-ad::before {
+  content: "\f641"; }
+
+.fa-ad::before {
+  content: "\f641"; }
+
+.fa-circle-arrow-right::before {
+  content: "\f0a9"; }
+
+.fa-arrow-circle-right::before {
+  content: "\f0a9"; }
+
+.fa-filter-circle-xmark::before {
+  content: "\e17b"; }
+
+.fa-locust::before {
+  content: "\e520"; }
+
+.fa-sort::before {
+  content: "\f0dc"; }
+
+.fa-unsorted::before {
+  content: "\f0dc"; }
+
+.fa-list-ol::before {
+  content: "\f0cb"; }
+
+.fa-list-1-2::before {
+  content: "\f0cb"; }
+
+.fa-list-numeric::before {
+  content: "\f0cb"; }
+
+.fa-person-dress-burst::before {
+  content: "\e544"; }
+
+.fa-money-check-dollar::before {
+  content: "\f53d"; }
+
+.fa-money-check-alt::before {
+  content: "\f53d"; }
+
+.fa-vector-square::before {
+  content: "\f5cb"; }
+
+.fa-bread-slice::before {
+  content: "\f7ec"; }
+
+.fa-language::before {
+  content: "\f1ab"; }
+
+.fa-face-kiss-wink-heart::before {
+  content: "\f598"; }
+
+.fa-kiss-wink-heart::before {
+  content: "\f598"; }
+
+.fa-filter::before {
+  content: "\f0b0"; }
+
+.fa-question::before {
+  content: "\3f"; }
+
+.fa-file-signature::before {
+  content: "\f573"; }
+
+.fa-up-down-left-right::before {
+  content: "\f0b2"; }
+
+.fa-arrows-alt::before {
+  content: "\f0b2"; }
+
+.fa-house-chimney-user::before {
+  content: "\e065"; }
+
+.fa-hand-holding-heart::before {
+  content: "\f4be"; }
+
+.fa-puzzle-piece::before {
+  content: "\f12e"; }
+
+.fa-money-check::before {
+  content: "\f53c"; }
+
+.fa-star-half-stroke::before {
+  content: "\f5c0"; }
+
+.fa-star-half-alt::before {
+  content: "\f5c0"; }
+
+.fa-code::before {
+  content: "\f121"; }
+
+.fa-whiskey-glass::before {
+  content: "\f7a0"; }
+
+.fa-glass-whiskey::before {
+  content: "\f7a0"; }
+
+.fa-building-circle-exclamation::before {
+  content: "\e4d3"; }
+
+.fa-magnifying-glass-chart::before {
+  content: "\e522"; }
+
+.fa-arrow-up-right-from-square::before {
+  content: "\f08e"; }
+
+.fa-external-link::before {
+  content: "\f08e"; }
+
+.fa-cubes-stacked::before {
+  content: "\e4e6"; }
+
+.fa-won-sign::before {
+  content: "\f159"; }
+
+.fa-krw::before {
+  content: "\f159"; }
+
+.fa-won::before {
+  content: "\f159"; }
+
+.fa-virus-covid::before {
+  content: "\e4a8"; }
+
+.fa-austral-sign::before {
+  content: "\e0a9"; }
+
+.fa-f::before {
+  content: "\46"; }
+
+.fa-leaf::before {
+  content: "\f06c"; }
+
+.fa-road::before {
+  content: "\f018"; }
+
+.fa-taxi::before {
+  content: "\f1ba"; }
+
+.fa-cab::before {
+  content: "\f1ba"; }
+
+.fa-person-circle-plus::before {
+  content: "\e541"; }
+
+.fa-chart-pie::before {
+  content: "\f200"; }
+
+.fa-pie-chart::before {
+  content: "\f200"; }
+
+.fa-bolt-lightning::before {
+  content: "\e0b7"; }
+
+.fa-sack-xmark::before {
+  content: "\e56a"; }
+
+.fa-file-excel::before {
+  content: "\f1c3"; }
+
+.fa-file-contract::before {
+  content: "\f56c"; }
+
+.fa-fish-fins::before {
+  content: "\e4f2"; }
+
+.fa-building-flag::before {
+  content: "\e4d5"; }
+
+.fa-face-grin-beam::before {
+  content: "\f582"; }
+
+.fa-grin-beam::before {
+  content: "\f582"; }
+
+.fa-object-ungroup::before {
+  content: "\f248"; }
+
+.fa-poop::before {
+  content: "\f619"; }
+
+.fa-location-pin::before {
+  content: "\f041"; }
+
+.fa-map-marker::before {
+  content: "\f041"; }
+
+.fa-kaaba::before {
+  content: "\f66b"; }
+
+.fa-toilet-paper::before {
+  content: "\f71e"; }
+
+.fa-helmet-safety::before {
+  content: "\f807"; }
+
+.fa-hard-hat::before {
+  content: "\f807"; }
+
+.fa-hat-hard::before {
+  content: "\f807"; }
+
+.fa-eject::before {
+  content: "\f052"; }
+
+.fa-circle-right::before {
+  content: "\f35a"; }
+
+.fa-arrow-alt-circle-right::before {
+  content: "\f35a"; }
+
+.fa-plane-circle-check::before {
+  content: "\e555"; }
+
+.fa-face-rolling-eyes::before {
+  content: "\f5a5"; }
+
+.fa-meh-rolling-eyes::before {
+  content: "\f5a5"; }
+
+.fa-object-group::before {
+  content: "\f247"; }
+
+.fa-chart-line::before {
+  content: "\f201"; }
+
+.fa-line-chart::before {
+  content: "\f201"; }
+
+.fa-mask-ventilator::before {
+  content: "\e524"; }
+
+.fa-arrow-right::before {
+  content: "\f061"; }
+
+.fa-signs-post::before {
+  content: "\f277"; }
+
+.fa-map-signs::before {
+  content: "\f277"; }
+
+.fa-cash-register::before {
+  content: "\f788"; }
+
+.fa-person-circle-question::before {
+  content: "\e542"; }
+
+.fa-h::before {
+  content: "\48"; }
+
+.fa-tarp::before {
+  content: "\e57b"; }
+
+.fa-screwdriver-wrench::before {
+  content: "\f7d9"; }
+
+.fa-tools::before {
+  content: "\f7d9"; }
+
+.fa-arrows-to-eye::before {
+  content: "\e4bf"; }
+
+.fa-plug-circle-bolt::before {
+  content: "\e55b"; }
+
+.fa-heart::before {
+  content: "\f004"; }
+
+.fa-mars-and-venus::before {
+  content: "\f224"; }
+
+.fa-house-user::before {
+  content: "\e1b0"; }
+
+.fa-home-user::before {
+  content: "\e1b0"; }
+
+.fa-dumpster-fire::before {
+  content: "\f794"; }
+
+.fa-house-crack::before {
+  content: "\e3b1"; }
+
+.fa-martini-glass-citrus::before {
+  content: "\f561"; }
+
+.fa-cocktail::before {
+  content: "\f561"; }
+
+.fa-face-surprise::before {
+  content: "\f5c2"; }
+
+.fa-surprise::before {
+  content: "\f5c2"; }
+
+.fa-bottle-water::before {
+  content: "\e4c5"; }
+
+.fa-circle-pause::before {
+  content: "\f28b"; }
+
+.fa-pause-circle::before {
+  content: "\f28b"; }
+
+.fa-toilet-paper-slash::before {
+  content: "\e072"; }
+
+.fa-apple-whole::before {
+  content: "\f5d1"; }
+
+.fa-apple-alt::before {
+  content: "\f5d1"; }
+
+.fa-kitchen-set::before {
+  content: "\e51a"; }
+
+.fa-r::before {
+  content: "\52"; }
+
+.fa-temperature-quarter::before {
+  content: "\f2ca"; }
+
+.fa-temperature-1::before {
+  content: "\f2ca"; }
+
+.fa-thermometer-1::before {
+  content: "\f2ca"; }
+
+.fa-thermometer-quarter::before {
+  content: "\f2ca"; }
+
+.fa-cube::before {
+  content: "\f1b2"; }
+
+.fa-bitcoin-sign::before {
+  content: "\e0b4"; }
+
+.fa-shield-dog::before {
+  content: "\e573"; }
+
+.fa-solar-panel::before {
+  content: "\f5ba"; }
+
+.fa-lock-open::before {
+  content: "\f3c1"; }
+
+.fa-elevator::before {
+  content: "\e16d"; }
+
+.fa-money-bill-transfer::before {
+  content: "\e528"; }
+
+.fa-money-bill-trend-up::before {
+  content: "\e529"; }
+
+.fa-house-flood-water-circle-arrow-right::before {
+  content: "\e50f"; }
+
+.fa-square-poll-horizontal::before {
+  content: "\f682"; }
+
+.fa-poll-h::before {
+  content: "\f682"; }
+
+.fa-circle::before {
+  content: "\f111"; }
+
+.fa-backward-fast::before {
+  content: "\f049"; }
+
+.fa-fast-backward::before {
+  content: "\f049"; }
+
+.fa-recycle::before {
+  content: "\f1b8"; }
+
+.fa-user-astronaut::before {
+  content: "\f4fb"; }
+
+.fa-plane-slash::before {
+  content: "\e069"; }
+
+.fa-trademark::before {
+  content: "\f25c"; }
+
+.fa-basketball::before {
+  content: "\f434"; }
+
+.fa-basketball-ball::before {
+  content: "\f434"; }
+
+.fa-satellite-dish::before {
+  content: "\f7c0"; }
+
+.fa-circle-up::before {
+  content: "\f35b"; }
+
+.fa-arrow-alt-circle-up::before {
+  content: "\f35b"; }
+
+.fa-mobile-screen-button::before {
+  content: "\f3cd"; }
+
+.fa-mobile-alt::before {
+  content: "\f3cd"; }
+
+.fa-volume-high::before {
+  content: "\f028"; }
+
+.fa-volume-up::before {
+  content: "\f028"; }
+
+.fa-users-rays::before {
+  content: "\e593"; }
+
+.fa-wallet::before {
+  content: "\f555"; }
+
+.fa-clipboard-check::before {
+  content: "\f46c"; }
+
+.fa-file-audio::before {
+  content: "\f1c7"; }
+
+.fa-burger::before {
+  content: "\f805"; }
+
+.fa-hamburger::before {
+  content: "\f805"; }
+
+.fa-wrench::before {
+  content: "\f0ad"; }
+
+.fa-bugs::before {
+  content: "\e4d0"; }
+
+.fa-rupee-sign::before {
+  content: "\f156"; }
+
+.fa-rupee::before {
+  content: "\f156"; }
+
+.fa-file-image::before {
+  content: "\f1c5"; }
+
+.fa-circle-question::before {
+  content: "\f059"; }
+
+.fa-question-circle::before {
+  content: "\f059"; }
+
+.fa-plane-departure::before {
+  content: "\f5b0"; }
+
+.fa-handshake-slash::before {
+  content: "\e060"; }
+
+.fa-book-bookmark::before {
+  content: "\e0bb"; }
+
+.fa-code-branch::before {
+  content: "\f126"; }
+
+.fa-hat-cowboy::before {
+  content: "\f8c0"; }
+
+.fa-bridge::before {
+  content: "\e4c8"; }
+
+.fa-phone-flip::before {
+  content: "\f879"; }
+
+.fa-phone-alt::before {
+  content: "\f879"; }
+
+.fa-truck-front::before {
+  content: "\e2b7"; }
+
+.fa-cat::before {
+  content: "\f6be"; }
+
+.fa-anchor-circle-exclamation::before {
+  content: "\e4ab"; }
+
+.fa-truck-field::before {
+  content: "\e58d"; }
+
+.fa-route::before {
+  content: "\f4d7"; }
+
+.fa-clipboard-question::before {
+  content: "\e4e3"; }
+
+.fa-panorama::before {
+  content: "\e209"; }
+
+.fa-comment-medical::before {
+  content: "\f7f5"; }
+
+.fa-teeth-open::before {
+  content: "\f62f"; }
+
+.fa-file-circle-minus::before {
+  content: "\e4ed"; }
+
+.fa-tags::before {
+  content: "\f02c"; }
+
+.fa-wine-glass::before {
+  content: "\f4e3"; }
+
+.fa-forward-fast::before {
+  content: "\f050"; }
+
+.fa-fast-forward::before {
+  content: "\f050"; }
+
+.fa-face-meh-blank::before {
+  content: "\f5a4"; }
+
+.fa-meh-blank::before {
+  content: "\f5a4"; }
+
+.fa-square-parking::before {
+  content: "\f540"; }
+
+.fa-parking::before {
+  content: "\f540"; }
+
+.fa-house-signal::before {
+  content: "\e012"; }
+
+.fa-bars-progress::before {
+  content: "\f828"; }
+
+.fa-tasks-alt::before {
+  content: "\f828"; }
+
+.fa-faucet-drip::before {
+  content: "\e006"; }
+
+.fa-cart-flatbed::before {
+  content: "\f474"; }
+
+.fa-dolly-flatbed::before {
+  content: "\f474"; }
+
+.fa-ban-smoking::before {
+  content: "\f54d"; }
+
+.fa-smoking-ban::before {
+  content: "\f54d"; }
+
+.fa-terminal::before {
+  content: "\f120"; }
+
+.fa-mobile-button::before {
+  content: "\f10b"; }
+
+.fa-house-medical-flag::before {
+  content: "\e514"; }
+
+.fa-basket-shopping::before {
+  content: "\f291"; }
+
+.fa-shopping-basket::before {
+  content: "\f291"; }
+
+.fa-tape::before {
+  content: "\f4db"; }
+
+.fa-bus-simple::before {
+  content: "\f55e"; }
+
+.fa-bus-alt::before {
+  content: "\f55e"; }
+
+.fa-eye::before {
+  content: "\f06e"; }
+
+.fa-face-sad-cry::before {
+  content: "\f5b3"; }
+
+.fa-sad-cry::before {
+  content: "\f5b3"; }
+
+.fa-audio-description::before {
+  content: "\f29e"; }
+
+.fa-person-military-to-person::before {
+  content: "\e54c"; }
+
+.fa-file-shield::before {
+  content: "\e4f0"; }
+
+.fa-user-slash::before {
+  content: "\f506"; }
+
+.fa-pen::before {
+  content: "\f304"; }
+
+.fa-tower-observation::before {
+  content: "\e586"; }
+
+.fa-file-code::before {
+  content: "\f1c9"; }
+
+.fa-signal::before {
+  content: "\f012"; }
+
+.fa-signal-5::before {
+  content: "\f012"; }
+
+.fa-signal-perfect::before {
+  content: "\f012"; }
+
+.fa-bus::before {
+  content: "\f207"; }
+
+.fa-heart-circle-xmark::before {
+  content: "\e501"; }
+
+.fa-house-chimney::before {
+  content: "\e3af"; }
+
+.fa-home-lg::before {
+  content: "\e3af"; }
+
+.fa-window-maximize::before {
+  content: "\f2d0"; }
+
+.fa-face-frown::before {
+  content: "\f119"; }
+
+.fa-frown::before {
+  content: "\f119"; }
+
+.fa-prescription::before {
+  content: "\f5b1"; }
+
+.fa-shop::before {
+  content: "\f54f"; }
+
+.fa-store-alt::before {
+  content: "\f54f"; }
+
+.fa-floppy-disk::before {
+  content: "\f0c7"; }
+
+.fa-save::before {
+  content: "\f0c7"; }
+
+.fa-vihara::before {
+  content: "\f6a7"; }
+
+.fa-scale-unbalanced::before {
+  content: "\f515"; }
+
+.fa-balance-scale-left::before {
+  content: "\f515"; }
+
+.fa-sort-up::before {
+  content: "\f0de"; }
+
+.fa-sort-asc::before {
+  content: "\f0de"; }
+
+.fa-comment-dots::before {
+  content: "\f4ad"; }
+
+.fa-commenting::before {
+  content: "\f4ad"; }
+
+.fa-plant-wilt::before {
+  content: "\e5aa"; }
+
+.fa-diamond::before {
+  content: "\f219"; }
+
+.fa-face-grin-squint::before {
+  content: "\f585"; }
+
+.fa-grin-squint::before {
+  content: "\f585"; }
+
+.fa-hand-holding-dollar::before {
+  content: "\f4c0"; }
+
+.fa-hand-holding-usd::before {
+  content: "\f4c0"; }
+
+.fa-bacterium::before {
+  content: "\e05a"; }
+
+.fa-hand-pointer::before {
+  content: "\f25a"; }
+
+.fa-drum-steelpan::before {
+  content: "\f56a"; }
+
+.fa-hand-scissors::before {
+  content: "\f257"; }
+
+.fa-hands-praying::before {
+  content: "\f684"; }
+
+.fa-praying-hands::before {
+  content: "\f684"; }
+
+.fa-arrow-rotate-right::before {
+  content: "\f01e"; }
+
+.fa-arrow-right-rotate::before {
+  content: "\f01e"; }
+
+.fa-arrow-rotate-forward::before {
+  content: "\f01e"; }
+
+.fa-redo::before {
+  content: "\f01e"; }
+
+.fa-biohazard::before {
+  content: "\f780"; }
+
+.fa-location-crosshairs::before {
+  content: "\f601"; }
+
+.fa-location::before {
+  content: "\f601"; }
+
+.fa-mars-double::before {
+  content: "\f227"; }
+
+.fa-child-dress::before {
+  content: "\e59c"; }
+
+.fa-users-between-lines::before {
+  content: "\e591"; }
+
+.fa-lungs-virus::before {
+  content: "\e067"; }
+
+.fa-face-grin-tears::before {
+  content: "\f588"; }
+
+.fa-grin-tears::before {
+  content: "\f588"; }
+
+.fa-phone::before {
+  content: "\f095"; }
+
+.fa-calendar-xmark::before {
+  content: "\f273"; }
+
+.fa-calendar-times::before {
+  content: "\f273"; }
+
+.fa-child-reaching::before {
+  content: "\e59d"; }
+
+.fa-head-side-virus::before {
+  content: "\e064"; }
+
+.fa-user-gear::before {
+  content: "\f4fe"; }
+
+.fa-user-cog::before {
+  content: "\f4fe"; }
+
+.fa-arrow-up-1-9::before {
+  content: "\f163"; }
+
+.fa-sort-numeric-up::before {
+  content: "\f163"; }
+
+.fa-door-closed::before {
+  content: "\f52a"; }
+
+.fa-shield-virus::before {
+  content: "\e06c"; }
+
+.fa-dice-six::before {
+  content: "\f526"; }
+
+.fa-mosquito-net::before {
+  content: "\e52c"; }
+
+.fa-bridge-water::before {
+  content: "\e4ce"; }
+
+.fa-person-booth::before {
+  content: "\f756"; }
+
+.fa-text-width::before {
+  content: "\f035"; }
+
+.fa-hat-wizard::before {
+  content: "\f6e8"; }
+
+.fa-pen-fancy::before {
+  content: "\f5ac"; }
+
+.fa-person-digging::before {
+  content: "\f85e"; }
+
+.fa-digging::before {
+  content: "\f85e"; }
+
+.fa-trash::before {
+  content: "\f1f8"; }
+
+.fa-gauge-simple::before {
+  content: "\f629"; }
+
+.fa-gauge-simple-med::before {
+  content: "\f629"; }
+
+.fa-tachometer-average::before {
+  content: "\f629"; }
+
+.fa-book-medical::before {
+  content: "\f7e6"; }
+
+.fa-poo::before {
+  content: "\f2fe"; }
+
+.fa-quote-right::before {
+  content: "\f10e"; }
+
+.fa-quote-right-alt::before {
+  content: "\f10e"; }
+
+.fa-shirt::before {
+  content: "\f553"; }
+
+.fa-t-shirt::before {
+  content: "\f553"; }
+
+.fa-tshirt::before {
+  content: "\f553"; }
+
+.fa-cubes::before {
+  content: "\f1b3"; }
+
+.fa-divide::before {
+  content: "\f529"; }
+
+.fa-tenge-sign::before {
+  content: "\f7d7"; }
+
+.fa-tenge::before {
+  content: "\f7d7"; }
+
+.fa-headphones::before {
+  content: "\f025"; }
+
+.fa-hands-holding::before {
+  content: "\f4c2"; }
+
+.fa-hands-clapping::before {
+  content: "\e1a8"; }
+
+.fa-republican::before {
+  content: "\f75e"; }
+
+.fa-arrow-left::before {
+  content: "\f060"; }
+
+.fa-person-circle-xmark::before {
+  content: "\e543"; }
+
+.fa-ruler::before {
+  content: "\f545"; }
+
+.fa-align-left::before {
+  content: "\f036"; }
+
+.fa-dice-d6::before {
+  content: "\f6d1"; }
+
+.fa-restroom::before {
+  content: "\f7bd"; }
+
+.fa-j::before {
+  content: "\4a"; }
+
+.fa-users-viewfinder::before {
+  content: "\e595"; }
+
+.fa-file-video::before {
+  content: "\f1c8"; }
+
+.fa-up-right-from-square::before {
+  content: "\f35d"; }
+
+.fa-external-link-alt::before {
+  content: "\f35d"; }
+
+.fa-table-cells::before {
+  content: "\f00a"; }
+
+.fa-th::before {
+  content: "\f00a"; }
+
+.fa-file-pdf::before {
+  content: "\f1c1"; }
+
+.fa-book-bible::before {
+  content: "\f647"; }
+
+.fa-bible::before {
+  content: "\f647"; }
+
+.fa-o::before {
+  content: "\4f"; }
+
+.fa-suitcase-medical::before {
+  content: "\f0fa"; }
+
+.fa-medkit::before {
+  content: "\f0fa"; }
+
+.fa-user-secret::before {
+  content: "\f21b"; }
+
+.fa-otter::before {
+  content: "\f700"; }
+
+.fa-person-dress::before {
+  content: "\f182"; }
+
+.fa-female::before {
+  content: "\f182"; }
+
+.fa-comment-dollar::before {
+  content: "\f651"; }
+
+.fa-business-time::before {
+  content: "\f64a"; }
+
+.fa-briefcase-clock::before {
+  content: "\f64a"; }
+
+.fa-table-cells-large::before {
+  content: "\f009"; }
+
+.fa-th-large::before {
+  content: "\f009"; }
+
+.fa-book-tanakh::before {
+  content: "\f827"; }
+
+.fa-tanakh::before {
+  content: "\f827"; }
+
+.fa-phone-volume::before {
+  content: "\f2a0"; }
+
+.fa-volume-control-phone::before {
+  content: "\f2a0"; }
+
+.fa-hat-cowboy-side::before {
+  content: "\f8c1"; }
+
+.fa-clipboard-user::before {
+  content: "\f7f3"; }
+
+.fa-child::before {
+  content: "\f1ae"; }
+
+.fa-lira-sign::before {
+  content: "\f195"; }
+
+.fa-satellite::before {
+  content: "\f7bf"; }
+
+.fa-plane-lock::before {
+  content: "\e558"; }
+
+.fa-tag::before {
+  content: "\f02b"; }
+
+.fa-comment::before {
+  content: "\f075"; }
+
+.fa-cake-candles::before {
+  content: "\f1fd"; }
+
+.fa-birthday-cake::before {
+  content: "\f1fd"; }
+
+.fa-cake::before {
+  content: "\f1fd"; }
+
+.fa-envelope::before {
+  content: "\f0e0"; }
+
+.fa-angles-up::before {
+  content: "\f102"; }
+
+.fa-angle-double-up::before {
+  content: "\f102"; }
+
+.fa-paperclip::before {
+  content: "\f0c6"; }
+
+.fa-arrow-right-to-city::before {
+  content: "\e4b3"; }
+
+.fa-ribbon::before {
+  content: "\f4d6"; }
+
+.fa-lungs::before {
+  content: "\f604"; }
+
+.fa-arrow-up-9-1::before {
+  content: "\f887"; }
+
+.fa-sort-numeric-up-alt::before {
+  content: "\f887"; }
+
+.fa-litecoin-sign::before {
+  content: "\e1d3"; }
+
+.fa-border-none::before {
+  content: "\f850"; }
+
+.fa-circle-nodes::before {
+  content: "\e4e2"; }
+
+.fa-parachute-box::before {
+  content: "\f4cd"; }
+
+.fa-indent::before {
+  content: "\f03c"; }
+
+.fa-truck-field-un::before {
+  content: "\e58e"; }
+
+.fa-hourglass::before {
+  content: "\f254"; }
+
+.fa-hourglass-empty::before {
+  content: "\f254"; }
+
+.fa-mountain::before {
+  content: "\f6fc"; }
+
+.fa-user-doctor::before {
+  content: "\f0f0"; }
+
+.fa-user-md::before {
+  content: "\f0f0"; }
+
+.fa-circle-info::before {
+  content: "\f05a"; }
+
+.fa-info-circle::before {
+  content: "\f05a"; }
+
+.fa-cloud-meatball::before {
+  content: "\f73b"; }
+
+.fa-camera::before {
+  content: "\f030"; }
+
+.fa-camera-alt::before {
+  content: "\f030"; }
+
+.fa-square-virus::before {
+  content: "\e578"; }
+
+.fa-meteor::before {
+  content: "\f753"; }
+
+.fa-car-on::before {
+  content: "\e4dd"; }
+
+.fa-sleigh::before {
+  content: "\f7cc"; }
+
+.fa-arrow-down-1-9::before {
+  content: "\f162"; }
+
+.fa-sort-numeric-asc::before {
+  content: "\f162"; }
+
+.fa-sort-numeric-down::before {
+  content: "\f162"; }
+
+.fa-hand-holding-droplet::before {
+  content: "\f4c1"; }
+
+.fa-hand-holding-water::before {
+  content: "\f4c1"; }
+
+.fa-water::before {
+  content: "\f773"; }
+
+.fa-calendar-check::before {
+  content: "\f274"; }
+
+.fa-braille::before {
+  content: "\f2a1"; }
+
+.fa-prescription-bottle-medical::before {
+  content: "\f486"; }
+
+.fa-prescription-bottle-alt::before {
+  content: "\f486"; }
+
+.fa-landmark::before {
+  content: "\f66f"; }
+
+.fa-truck::before {
+  content: "\f0d1"; }
+
+.fa-crosshairs::before {
+  content: "\f05b"; }
+
+.fa-person-cane::before {
+  content: "\e53c"; }
+
+.fa-tent::before {
+  content: "\e57d"; }
+
+.fa-vest-patches::before {
+  content: "\e086"; }
+
+.fa-check-double::before {
+  content: "\f560"; }
+
+.fa-arrow-down-a-z::before {
+  content: "\f15d"; }
+
+.fa-sort-alpha-asc::before {
+  content: "\f15d"; }
+
+.fa-sort-alpha-down::before {
+  content: "\f15d"; }
+
+.fa-money-bill-wheat::before {
+  content: "\e52a"; }
+
+.fa-cookie::before {
+  content: "\f563"; }
+
+.fa-arrow-rotate-left::before {
+  content: "\f0e2"; }
+
+.fa-arrow-left-rotate::before {
+  content: "\f0e2"; }
+
+.fa-arrow-rotate-back::before {
+  content: "\f0e2"; }
+
+.fa-arrow-rotate-backward::before {
+  content: "\f0e2"; }
+
+.fa-undo::before {
+  content: "\f0e2"; }
+
+.fa-hard-drive::before {
+  content: "\f0a0"; }
+
+.fa-hdd::before {
+  content: "\f0a0"; }
+
+.fa-face-grin-squint-tears::before {
+  content: "\f586"; }
+
+.fa-grin-squint-tears::before {
+  content: "\f586"; }
+
+.fa-dumbbell::before {
+  content: "\f44b"; }
+
+.fa-rectangle-list::before {
+  content: "\f022"; }
+
+.fa-list-alt::before {
+  content: "\f022"; }
+
+.fa-tarp-droplet::before {
+  content: "\e57c"; }
+
+.fa-house-medical-circle-check::before {
+  content: "\e511"; }
+
+.fa-person-skiing-nordic::before {
+  content: "\f7ca"; }
+
+.fa-skiing-nordic::before {
+  content: "\f7ca"; }
+
+.fa-calendar-plus::before {
+  content: "\f271"; }
+
+.fa-plane-arrival::before {
+  content: "\f5af"; }
+
+.fa-circle-left::before {
+  content: "\f359"; }
+
+.fa-arrow-alt-circle-left::before {
+  content: "\f359"; }
+
+.fa-train-subway::before {
+  content: "\f239"; }
+
+.fa-subway::before {
+  content: "\f239"; }
+
+.fa-chart-gantt::before {
+  content: "\e0e4"; }
+
+.fa-indian-rupee-sign::before {
+  content: "\e1bc"; }
+
+.fa-indian-rupee::before {
+  content: "\e1bc"; }
+
+.fa-inr::before {
+  content: "\e1bc"; }
+
+.fa-crop-simple::before {
+  content: "\f565"; }
+
+.fa-crop-alt::before {
+  content: "\f565"; }
+
+.fa-money-bill-1::before {
+  content: "\f3d1"; }
+
+.fa-money-bill-alt::before {
+  content: "\f3d1"; }
+
+.fa-left-long::before {
+  content: "\f30a"; }
+
+.fa-long-arrow-alt-left::before {
+  content: "\f30a"; }
+
+.fa-dna::before {
+  content: "\f471"; }
+
+.fa-virus-slash::before {
+  content: "\e075"; }
+
+.fa-minus::before {
+  content: "\f068"; }
+
+.fa-subtract::before {
+  content: "\f068"; }
+
+.fa-chess::before {
+  content: "\f439"; }
+
+.fa-arrow-left-long::before {
+  content: "\f177"; }
+
+.fa-long-arrow-left::before {
+  content: "\f177"; }
+
+.fa-plug-circle-check::before {
+  content: "\e55c"; }
+
+.fa-street-view::before {
+  content: "\f21d"; }
+
+.fa-franc-sign::before {
+  content: "\e18f"; }
+
+.fa-volume-off::before {
+  content: "\f026"; }
+
+.fa-hands-asl-interpreting::before {
+  content: "\f2a3"; }
+
+.fa-american-sign-language-interpreting::before {
+  content: "\f2a3"; }
+
+.fa-asl-interpreting::before {
+  content: "\f2a3"; }
+
+.fa-hands-american-sign-language-interpreting::before {
+  content: "\f2a3"; }
+
+.fa-gear::before {
+  content: "\f013"; }
+
+.fa-cog::before {
+  content: "\f013"; }
+
+.fa-droplet-slash::before {
+  content: "\f5c7"; }
+
+.fa-tint-slash::before {
+  content: "\f5c7"; }
+
+.fa-mosque::before {
+  content: "\f678"; }
+
+.fa-mosquito::before {
+  content: "\e52b"; }
+
+.fa-star-of-david::before {
+  content: "\f69a"; }
+
+.fa-person-military-rifle::before {
+  content: "\e54b"; }
+
+.fa-cart-shopping::before {
+  content: "\f07a"; }
+
+.fa-shopping-cart::before {
+  content: "\f07a"; }
+
+.fa-vials::before {
+  content: "\f493"; }
+
+.fa-plug-circle-plus::before {
+  content: "\e55f"; }
+
+.fa-place-of-worship::before {
+  content: "\f67f"; }
+
+.fa-grip-vertical::before {
+  content: "\f58e"; }
+
+.fa-arrow-turn-up::before {
+  content: "\f148"; }
+
+.fa-level-up::before {
+  content: "\f148"; }
+
+.fa-u::before {
+  content: "\55"; }
+
+.fa-square-root-variable::before {
+  content: "\f698"; }
+
+.fa-square-root-alt::before {
+  content: "\f698"; }
+
+.fa-clock::before {
+  content: "\f017"; }
+
+.fa-clock-four::before {
+  content: "\f017"; }
+
+.fa-backward-step::before {
+  content: "\f048"; }
+
+.fa-step-backward::before {
+  content: "\f048"; }
+
+.fa-pallet::before {
+  content: "\f482"; }
+
+.fa-faucet::before {
+  content: "\e005"; }
+
+.fa-baseball-bat-ball::before {
+  content: "\f432"; }
+
+.fa-s::before {
+  content: "\53"; }
+
+.fa-timeline::before {
+  content: "\e29c"; }
+
+.fa-keyboard::before {
+  content: "\f11c"; }
+
+.fa-caret-down::before {
+  content: "\f0d7"; }
+
+.fa-house-chimney-medical::before {
+  content: "\f7f2"; }
+
+.fa-clinic-medical::before {
+  content: "\f7f2"; }
+
+.fa-temperature-three-quarters::before {
+  content: "\f2c8"; }
+
+.fa-temperature-3::before {
+  content: "\f2c8"; }
+
+.fa-thermometer-3::before {
+  content: "\f2c8"; }
+
+.fa-thermometer-three-quarters::before {
+  content: "\f2c8"; }
+
+.fa-mobile-screen::before {
+  content: "\f3cf"; }
+
+.fa-mobile-android-alt::before {
+  content: "\f3cf"; }
+
+.fa-plane-up::before {
+  content: "\e22d"; }
+
+.fa-piggy-bank::before {
+  content: "\f4d3"; }
+
+.fa-battery-half::before {
+  content: "\f242"; }
+
+.fa-battery-3::before {
+  content: "\f242"; }
+
+.fa-mountain-city::before {
+  content: "\e52e"; }
+
+.fa-coins::before {
+  content: "\f51e"; }
+
+.fa-khanda::before {
+  content: "\f66d"; }
+
+.fa-sliders::before {
+  content: "\f1de"; }
+
+.fa-sliders-h::before {
+  content: "\f1de"; }
+
+.fa-folder-tree::before {
+  content: "\f802"; }
+
+.fa-network-wired::before {
+  content: "\f6ff"; }
+
+.fa-map-pin::before {
+  content: "\f276"; }
+
+.fa-hamsa::before {
+  content: "\f665"; }
+
+.fa-cent-sign::before {
+  content: "\e3f5"; }
+
+.fa-flask::before {
+  content: "\f0c3"; }
+
+.fa-person-pregnant::before {
+  content: "\e31e"; }
+
+.fa-wand-sparkles::before {
+  content: "\f72b"; }
+
+.fa-ellipsis-vertical::before {
+  content: "\f142"; }
+
+.fa-ellipsis-v::before {
+  content: "\f142"; }
+
+.fa-ticket::before {
+  content: "\f145"; }
+
+.fa-power-off::before {
+  content: "\f011"; }
+
+.fa-right-long::before {
+  content: "\f30b"; }
+
+.fa-long-arrow-alt-right::before {
+  content: "\f30b"; }
+
+.fa-flag-usa::before {
+  content: "\f74d"; }
+
+.fa-laptop-file::before {
+  content: "\e51d"; }
+
+.fa-tty::before {
+  content: "\f1e4"; }
+
+.fa-teletype::before {
+  content: "\f1e4"; }
+
+.fa-diagram-next::before {
+  content: "\e476"; }
+
+.fa-person-rifle::before {
+  content: "\e54e"; }
+
+.fa-house-medical-circle-exclamation::before {
+  content: "\e512"; }
+
+.fa-closed-captioning::before {
+  content: "\f20a"; }
+
+.fa-person-hiking::before {
+  content: "\f6ec"; }
+
+.fa-hiking::before {
+  content: "\f6ec"; }
+
+.fa-venus-double::before {
+  content: "\f226"; }
+
+.fa-images::before {
+  content: "\f302"; }
+
+.fa-calculator::before {
+  content: "\f1ec"; }
+
+.fa-people-pulling::before {
+  content: "\e535"; }
+
+.fa-n::before {
+  content: "\4e"; }
+
+.fa-cable-car::before {
+  content: "\f7da"; }
+
+.fa-tram::before {
+  content: "\f7da"; }
+
+.fa-cloud-rain::before {
+  content: "\f73d"; }
+
+.fa-building-circle-xmark::before {
+  content: "\e4d4"; }
+
+.fa-ship::before {
+  content: "\f21a"; }
+
+.fa-arrows-down-to-line::before {
+  content: "\e4b8"; }
+
+.fa-download::before {
+  content: "\f019"; }
+
+.fa-face-grin::before {
+  content: "\f580"; }
+
+.fa-grin::before {
+  content: "\f580"; }
+
+.fa-delete-left::before {
+  content: "\f55a"; }
+
+.fa-backspace::before {
+  content: "\f55a"; }
+
+.fa-eye-dropper::before {
+  content: "\f1fb"; }
+
+.fa-eye-dropper-empty::before {
+  content: "\f1fb"; }
+
+.fa-eyedropper::before {
+  content: "\f1fb"; }
+
+.fa-file-circle-check::before {
+  content: "\e5a0"; }
+
+.fa-forward::before {
+  content: "\f04e"; }
+
+.fa-mobile::before {
+  content: "\f3ce"; }
+
+.fa-mobile-android::before {
+  content: "\f3ce"; }
+
+.fa-mobile-phone::before {
+  content: "\f3ce"; }
+
+.fa-face-meh::before {
+  content: "\f11a"; }
+
+.fa-meh::before {
+  content: "\f11a"; }
+
+.fa-align-center::before {
+  content: "\f037"; }
+
+.fa-book-skull::before {
+  content: "\f6b7"; }
+
+.fa-book-dead::before {
+  content: "\f6b7"; }
+
+.fa-id-card::before {
+  content: "\f2c2"; }
+
+.fa-drivers-license::before {
+  content: "\f2c2"; }
+
+.fa-outdent::before {
+  content: "\f03b"; }
+
+.fa-dedent::before {
+  content: "\f03b"; }
+
+.fa-heart-circle-exclamation::before {
+  content: "\e4fe"; }
+
+.fa-house::before {
+  content: "\f015"; }
+
+.fa-home::before {
+  content: "\f015"; }
+
+.fa-home-alt::before {
+  content: "\f015"; }
+
+.fa-home-lg-alt::before {
+  content: "\f015"; }
+
+.fa-calendar-week::before {
+  content: "\f784"; }
+
+.fa-laptop-medical::before {
+  content: "\f812"; }
+
+.fa-b::before {
+  content: "\42"; }
+
+.fa-file-medical::before {
+  content: "\f477"; }
+
+.fa-dice-one::before {
+  content: "\f525"; }
+
+.fa-kiwi-bird::before {
+  content: "\f535"; }
+
+.fa-arrow-right-arrow-left::before {
+  content: "\f0ec"; }
+
+.fa-exchange::before {
+  content: "\f0ec"; }
+
+.fa-rotate-right::before {
+  content: "\f2f9"; }
+
+.fa-redo-alt::before {
+  content: "\f2f9"; }
+
+.fa-rotate-forward::before {
+  content: "\f2f9"; }
+
+.fa-utensils::before {
+  content: "\f2e7"; }
+
+.fa-cutlery::before {
+  content: "\f2e7"; }
+
+.fa-arrow-up-wide-short::before {
+  content: "\f161"; }
+
+.fa-sort-amount-up::before {
+  content: "\f161"; }
+
+.fa-mill-sign::before {
+  content: "\e1ed"; }
+
+.fa-bowl-rice::before {
+  content: "\e2eb"; }
+
+.fa-skull::before {
+  content: "\f54c"; }
+
+.fa-tower-broadcast::before {
+  content: "\f519"; }
+
+.fa-broadcast-tower::before {
+  content: "\f519"; }
+
+.fa-truck-pickup::before {
+  content: "\f63c"; }
+
+.fa-up-long::before {
+  content: "\f30c"; }
+
+.fa-long-arrow-alt-up::before {
+  content: "\f30c"; }
+
+.fa-stop::before {
+  content: "\f04d"; }
+
+.fa-code-merge::before {
+  content: "\f387"; }
+
+.fa-upload::before {
+  content: "\f093"; }
+
+.fa-hurricane::before {
+  content: "\f751"; }
+
+.fa-mound::before {
+  content: "\e52d"; }
+
+.fa-toilet-portable::before {
+  content: "\e583"; }
+
+.fa-compact-disc::before {
+  content: "\f51f"; }
+
+.fa-file-arrow-down::before {
+  content: "\f56d"; }
+
+.fa-file-download::before {
+  content: "\f56d"; }
+
+.fa-caravan::before {
+  content: "\f8ff"; }
+
+.fa-shield-cat::before {
+  content: "\e572"; }
+
+.fa-bolt::before {
+  content: "\f0e7"; }
+
+.fa-zap::before {
+  content: "\f0e7"; }
+
+.fa-glass-water::before {
+  content: "\e4f4"; }
+
+.fa-oil-well::before {
+  content: "\e532"; }
+
+.fa-vault::before {
+  content: "\e2c5"; }
+
+.fa-mars::before {
+  content: "\f222"; }
+
+.fa-toilet::before {
+  content: "\f7d8"; }
+
+.fa-plane-circle-xmark::before {
+  content: "\e557"; }
+
+.fa-yen-sign::before {
+  content: "\f157"; }
+
+.fa-cny::before {
+  content: "\f157"; }
+
+.fa-jpy::before {
+  content: "\f157"; }
+
+.fa-rmb::before {
+  content: "\f157"; }
+
+.fa-yen::before {
+  content: "\f157"; }
+
+.fa-ruble-sign::before {
+  content: "\f158"; }
+
+.fa-rouble::before {
+  content: "\f158"; }
+
+.fa-rub::before {
+  content: "\f158"; }
+
+.fa-ruble::before {
+  content: "\f158"; }
+
+.fa-sun::before {
+  content: "\f185"; }
+
+.fa-guitar::before {
+  content: "\f7a6"; }
+
+.fa-face-laugh-wink::before {
+  content: "\f59c"; }
+
+.fa-laugh-wink::before {
+  content: "\f59c"; }
+
+.fa-horse-head::before {
+  content: "\f7ab"; }
+
+.fa-bore-hole::before {
+  content: "\e4c3"; }
+
+.fa-industry::before {
+  content: "\f275"; }
+
+.fa-circle-down::before {
+  content: "\f358"; }
+
+.fa-arrow-alt-circle-down::before {
+  content: "\f358"; }
+
+.fa-arrows-turn-to-dots::before {
+  content: "\e4c1"; }
+
+.fa-florin-sign::before {
+  content: "\e184"; }
+
+.fa-arrow-down-short-wide::before {
+  content: "\f884"; }
+
+.fa-sort-amount-desc::before {
+  content: "\f884"; }
+
+.fa-sort-amount-down-alt::before {
+  content: "\f884"; }
+
+.fa-less-than::before {
+  content: "\3c"; }
+
+.fa-angle-down::before {
+  content: "\f107"; }
+
+.fa-car-tunnel::before {
+  content: "\e4de"; }
+
+.fa-head-side-cough::before {
+  content: "\e061"; }
+
+.fa-grip-lines::before {
+  content: "\f7a4"; }
+
+.fa-thumbs-down::before {
+  content: "\f165"; }
+
+.fa-user-lock::before {
+  content: "\f502"; }
+
+.fa-arrow-right-long::before {
+  content: "\f178"; }
+
+.fa-long-arrow-right::before {
+  content: "\f178"; }
+
+.fa-anchor-circle-xmark::before {
+  content: "\e4ac"; }
+
+.fa-ellipsis::before {
+  content: "\f141"; }
+
+.fa-ellipsis-h::before {
+  content: "\f141"; }
+
+.fa-chess-pawn::before {
+  content: "\f443"; }
+
+.fa-kit-medical::before {
+  content: "\f479"; }
+
+.fa-first-aid::before {
+  content: "\f479"; }
+
+.fa-person-through-window::before {
+  content: "\e5a9"; }
+
+.fa-toolbox::before {
+  content: "\f552"; }
+
+.fa-hands-holding-circle::before {
+  content: "\e4fb"; }
+
+.fa-bug::before {
+  content: "\f188"; }
+
+.fa-credit-card::before {
+  content: "\f09d"; }
+
+.fa-credit-card-alt::before {
+  content: "\f09d"; }
+
+.fa-car::before {
+  content: "\f1b9"; }
+
+.fa-automobile::before {
+  content: "\f1b9"; }
+
+.fa-hand-holding-hand::before {
+  content: "\e4f7"; }
+
+.fa-book-open-reader::before {
+  content: "\f5da"; }
+
+.fa-book-reader::before {
+  content: "\f5da"; }
+
+.fa-mountain-sun::before {
+  content: "\e52f"; }
+
+.fa-arrows-left-right-to-line::before {
+  content: "\e4ba"; }
+
+.fa-dice-d20::before {
+  content: "\f6cf"; }
+
+.fa-truck-droplet::before {
+  content: "\e58c"; }
+
+.fa-file-circle-xmark::before {
+  content: "\e5a1"; }
+
+.fa-temperature-arrow-up::before {
+  content: "\e040"; }
+
+.fa-temperature-up::before {
+  content: "\e040"; }
+
+.fa-medal::before {
+  content: "\f5a2"; }
+
+.fa-bed::before {
+  content: "\f236"; }
+
+.fa-square-h::before {
+  content: "\f0fd"; }
+
+.fa-h-square::before {
+  content: "\f0fd"; }
+
+.fa-podcast::before {
+  content: "\f2ce"; }
+
+.fa-temperature-full::before {
+  content: "\f2c7"; }
+
+.fa-temperature-4::before {
+  content: "\f2c7"; }
+
+.fa-thermometer-4::before {
+  content: "\f2c7"; }
+
+.fa-thermometer-full::before {
+  content: "\f2c7"; }
+
+.fa-bell::before {
+  content: "\f0f3"; }
+
+.fa-superscript::before {
+  content: "\f12b"; }
+
+.fa-plug-circle-xmark::before {
+  content: "\e560"; }
+
+.fa-star-of-life::before {
+  content: "\f621"; }
+
+.fa-phone-slash::before {
+  content: "\f3dd"; }
+
+.fa-paint-roller::before {
+  content: "\f5aa"; }
+
+.fa-handshake-angle::before {
+  content: "\f4c4"; }
+
+.fa-hands-helping::before {
+  content: "\f4c4"; }
+
+.fa-location-dot::before {
+  content: "\f3c5"; }
+
+.fa-map-marker-alt::before {
+  content: "\f3c5"; }
+
+.fa-file::before {
+  content: "\f15b"; }
+
+.fa-greater-than::before {
+  content: "\3e"; }
+
+.fa-person-swimming::before {
+  content: "\f5c4"; }
+
+.fa-swimmer::before {
+  content: "\f5c4"; }
+
+.fa-arrow-down::before {
+  content: "\f063"; }
+
+.fa-droplet::before {
+  content: "\f043"; }
+
+.fa-tint::before {
+  content: "\f043"; }
+
+.fa-eraser::before {
+  content: "\f12d"; }
+
+.fa-earth-americas::before {
+  content: "\f57d"; }
+
+.fa-earth::before {
+  content: "\f57d"; }
+
+.fa-earth-america::before {
+  content: "\f57d"; }
+
+.fa-globe-americas::before {
+  content: "\f57d"; }
+
+.fa-person-burst::before {
+  content: "\e53b"; }
+
+.fa-dove::before {
+  content: "\f4ba"; }
+
+.fa-battery-empty::before {
+  content: "\f244"; }
+
+.fa-battery-0::before {
+  content: "\f244"; }
+
+.fa-socks::before {
+  content: "\f696"; }
+
+.fa-inbox::before {
+  content: "\f01c"; }
+
+.fa-section::before {
+  content: "\e447"; }
+
+.fa-gauge-high::before {
+  content: "\f625"; }
+
+.fa-tachometer-alt::before {
+  content: "\f625"; }
+
+.fa-tachometer-alt-fast::before {
+  content: "\f625"; }
+
+.fa-envelope-open-text::before {
+  content: "\f658"; }
+
+.fa-hospital::before {
+  content: "\f0f8"; }
+
+.fa-hospital-alt::before {
+  content: "\f0f8"; }
+
+.fa-hospital-wide::before {
+  content: "\f0f8"; }
+
+.fa-wine-bottle::before {
+  content: "\f72f"; }
+
+.fa-chess-rook::before {
+  content: "\f447"; }
+
+.fa-bars-staggered::before {
+  content: "\f550"; }
+
+.fa-reorder::before {
+  content: "\f550"; }
+
+.fa-stream::before {
+  content: "\f550"; }
+
+.fa-dharmachakra::before {
+  content: "\f655"; }
+
+.fa-hotdog::before {
+  content: "\f80f"; }
+
+.fa-person-walking-with-cane::before {
+  content: "\f29d"; }
+
+.fa-blind::before {
+  content: "\f29d"; }
+
+.fa-drum::before {
+  content: "\f569"; }
+
+.fa-ice-cream::before {
+  content: "\f810"; }
+
+.fa-heart-circle-bolt::before {
+  content: "\e4fc"; }
+
+.fa-fax::before {
+  content: "\f1ac"; }
+
+.fa-paragraph::before {
+  content: "\f1dd"; }
+
+.fa-check-to-slot::before {
+  content: "\f772"; }
+
+.fa-vote-yea::before {
+  content: "\f772"; }
+
+.fa-star-half::before {
+  content: "\f089"; }
+
+.fa-boxes-stacked::before {
+  content: "\f468"; }
+
+.fa-boxes::before {
+  content: "\f468"; }
+
+.fa-boxes-alt::before {
+  content: "\f468"; }
+
+.fa-link::before {
+  content: "\f0c1"; }
+
+.fa-chain::before {
+  content: "\f0c1"; }
+
+.fa-ear-listen::before {
+  content: "\f2a2"; }
+
+.fa-assistive-listening-systems::before {
+  content: "\f2a2"; }
+
+.fa-tree-city::before {
+  content: "\e587"; }
+
+.fa-play::before {
+  content: "\f04b"; }
+
+.fa-font::before {
+  content: "\f031"; }
+
+.fa-table-cells-row-lock::before {
+  content: "\e67a"; }
+
+.fa-rupiah-sign::before {
+  content: "\e23d"; }
+
+.fa-magnifying-glass::before {
+  content: "\f002"; }
+
+.fa-search::before {
+  content: "\f002"; }
+
+.fa-table-tennis-paddle-ball::before {
+  content: "\f45d"; }
+
+.fa-ping-pong-paddle-ball::before {
+  content: "\f45d"; }
+
+.fa-table-tennis::before {
+  content: "\f45d"; }
+
+.fa-person-dots-from-line::before {
+  content: "\f470"; }
+
+.fa-diagnoses::before {
+  content: "\f470"; }
+
+.fa-trash-can-arrow-up::before {
+  content: "\f82a"; }
+
+.fa-trash-restore-alt::before {
+  content: "\f82a"; }
+
+.fa-naira-sign::before {
+  content: "\e1f6"; }
+
+.fa-cart-arrow-down::before {
+  content: "\f218"; }
+
+.fa-walkie-talkie::before {
+  content: "\f8ef"; }
+
+.fa-file-pen::before {
+  content: "\f31c"; }
+
+.fa-file-edit::before {
+  content: "\f31c"; }
+
+.fa-receipt::before {
+  content: "\f543"; }
+
+.fa-square-pen::before {
+  content: "\f14b"; }
+
+.fa-pen-square::before {
+  content: "\f14b"; }
+
+.fa-pencil-square::before {
+  content: "\f14b"; }
+
+.fa-suitcase-rolling::before {
+  content: "\f5c1"; }
+
+.fa-person-circle-exclamation::before {
+  content: "\e53f"; }
+
+.fa-chevron-down::before {
+  content: "\f078"; }
+
+.fa-battery-full::before {
+  content: "\f240"; }
+
+.fa-battery::before {
+  content: "\f240"; }
+
+.fa-battery-5::before {
+  content: "\f240"; }
+
+.fa-skull-crossbones::before {
+  content: "\f714"; }
+
+.fa-code-compare::before {
+  content: "\e13a"; }
+
+.fa-list-ul::before {
+  content: "\f0ca"; }
+
+.fa-list-dots::before {
+  content: "\f0ca"; }
+
+.fa-school-lock::before {
+  content: "\e56f"; }
+
+.fa-tower-cell::before {
+  content: "\e585"; }
+
+.fa-down-long::before {
+  content: "\f309"; }
+
+.fa-long-arrow-alt-down::before {
+  content: "\f309"; }
+
+.fa-ranking-star::before {
+  content: "\e561"; }
+
+.fa-chess-king::before {
+  content: "\f43f"; }
+
+.fa-person-harassing::before {
+  content: "\e549"; }
+
+.fa-brazilian-real-sign::before {
+  content: "\e46c"; }
+
+.fa-landmark-dome::before {
+  content: "\f752"; }
+
+.fa-landmark-alt::before {
+  content: "\f752"; }
+
+.fa-arrow-up::before {
+  content: "\f062"; }
+
+.fa-tv::before {
+  content: "\f26c"; }
+
+.fa-television::before {
+  content: "\f26c"; }
+
+.fa-tv-alt::before {
+  content: "\f26c"; }
+
+.fa-shrimp::before {
+  content: "\e448"; }
+
+.fa-list-check::before {
+  content: "\f0ae"; }
+
+.fa-tasks::before {
+  content: "\f0ae"; }
+
+.fa-jug-detergent::before {
+  content: "\e519"; }
+
+.fa-circle-user::before {
+  content: "\f2bd"; }
+
+.fa-user-circle::before {
+  content: "\f2bd"; }
+
+.fa-user-shield::before {
+  content: "\f505"; }
+
+.fa-wind::before {
+  content: "\f72e"; }
+
+.fa-car-burst::before {
+  content: "\f5e1"; }
+
+.fa-car-crash::before {
+  content: "\f5e1"; }
+
+.fa-y::before {
+  content: "\59"; }
+
+.fa-person-snowboarding::before {
+  content: "\f7ce"; }
+
+.fa-snowboarding::before {
+  content: "\f7ce"; }
+
+.fa-truck-fast::before {
+  content: "\f48b"; }
+
+.fa-shipping-fast::before {
+  content: "\f48b"; }
+
+.fa-fish::before {
+  content: "\f578"; }
+
+.fa-user-graduate::before {
+  content: "\f501"; }
+
+.fa-circle-half-stroke::before {
+  content: "\f042"; }
+
+.fa-adjust::before {
+  content: "\f042"; }
+
+.fa-clapperboard::before {
+  content: "\e131"; }
+
+.fa-circle-radiation::before {
+  content: "\f7ba"; }
+
+.fa-radiation-alt::before {
+  content: "\f7ba"; }
+
+.fa-baseball::before {
+  content: "\f433"; }
+
+.fa-baseball-ball::before {
+  content: "\f433"; }
+
+.fa-jet-fighter-up::before {
+  content: "\e518"; }
+
+.fa-diagram-project::before {
+  content: "\f542"; }
+
+.fa-project-diagram::before {
+  content: "\f542"; }
+
+.fa-copy::before {
+  content: "\f0c5"; }
+
+.fa-volume-xmark::before {
+  content: "\f6a9"; }
+
+.fa-volume-mute::before {
+  content: "\f6a9"; }
+
+.fa-volume-times::before {
+  content: "\f6a9"; }
+
+.fa-hand-sparkles::before {
+  content: "\e05d"; }
+
+.fa-grip::before {
+  content: "\f58d"; }
+
+.fa-grip-horizontal::before {
+  content: "\f58d"; }
+
+.fa-share-from-square::before {
+  content: "\f14d"; }
+
+.fa-share-square::before {
+  content: "\f14d"; }
+
+.fa-child-combatant::before {
+  content: "\e4e0"; }
+
+.fa-child-rifle::before {
+  content: "\e4e0"; }
+
+.fa-gun::before {
+  content: "\e19b"; }
+
+.fa-square-phone::before {
+  content: "\f098"; }
+
+.fa-phone-square::before {
+  content: "\f098"; }
+
+.fa-plus::before {
+  content: "\2b"; }
+
+.fa-add::before {
+  content: "\2b"; }
+
+.fa-expand::before {
+  content: "\f065"; }
+
+.fa-computer::before {
+  content: "\e4e5"; }
+
+.fa-xmark::before {
+  content: "\f00d"; }
+
+.fa-close::before {
+  content: "\f00d"; }
+
+.fa-multiply::before {
+  content: "\f00d"; }
+
+.fa-remove::before {
+  content: "\f00d"; }
+
+.fa-times::before {
+  content: "\f00d"; }
+
+.fa-arrows-up-down-left-right::before {
+  content: "\f047"; }
+
+.fa-arrows::before {
+  content: "\f047"; }
+
+.fa-chalkboard-user::before {
+  content: "\f51c"; }
+
+.fa-chalkboard-teacher::before {
+  content: "\f51c"; }
+
+.fa-peso-sign::before {
+  content: "\e222"; }
+
+.fa-building-shield::before {
+  content: "\e4d8"; }
+
+.fa-baby::before {
+  content: "\f77c"; }
+
+.fa-users-line::before {
+  content: "\e592"; }
+
+.fa-quote-left::before {
+  content: "\f10d"; }
+
+.fa-quote-left-alt::before {
+  content: "\f10d"; }
+
+.fa-tractor::before {
+  content: "\f722"; }
+
+.fa-trash-arrow-up::before {
+  content: "\f829"; }
+
+.fa-trash-restore::before {
+  content: "\f829"; }
+
+.fa-arrow-down-up-lock::before {
+  content: "\e4b0"; }
+
+.fa-lines-leaning::before {
+  content: "\e51e"; }
+
+.fa-ruler-combined::before {
+  content: "\f546"; }
+
+.fa-copyright::before {
+  content: "\f1f9"; }
+
+.fa-equals::before {
+  content: "\3d"; }
+
+.fa-blender::before {
+  content: "\f517"; }
+
+.fa-teeth::before {
+  content: "\f62e"; }
+
+.fa-shekel-sign::before {
+  content: "\f20b"; }
+
+.fa-ils::before {
+  content: "\f20b"; }
+
+.fa-shekel::before {
+  content: "\f20b"; }
+
+.fa-sheqel::before {
+  content: "\f20b"; }
+
+.fa-sheqel-sign::before {
+  content: "\f20b"; }
+
+.fa-map::before {
+  content: "\f279"; }
+
+.fa-rocket::before {
+  content: "\f135"; }
+
+.fa-photo-film::before {
+  content: "\f87c"; }
+
+.fa-photo-video::before {
+  content: "\f87c"; }
+
+.fa-folder-minus::before {
+  content: "\f65d"; }
+
+.fa-store::before {
+  content: "\f54e"; }
+
+.fa-arrow-trend-up::before {
+  content: "\e098"; }
+
+.fa-plug-circle-minus::before {
+  content: "\e55e"; }
+
+.fa-sign-hanging::before {
+  content: "\f4d9"; }
+
+.fa-sign::before {
+  content: "\f4d9"; }
+
+.fa-bezier-curve::before {
+  content: "\f55b"; }
+
+.fa-bell-slash::before {
+  content: "\f1f6"; }
+
+.fa-tablet::before {
+  content: "\f3fb"; }
+
+.fa-tablet-android::before {
+  content: "\f3fb"; }
+
+.fa-school-flag::before {
+  content: "\e56e"; }
+
+.fa-fill::before {
+  content: "\f575"; }
+
+.fa-angle-up::before {
+  content: "\f106"; }
+
+.fa-drumstick-bite::before {
+  content: "\f6d7"; }
+
+.fa-holly-berry::before {
+  content: "\f7aa"; }
+
+.fa-chevron-left::before {
+  content: "\f053"; }
+
+.fa-bacteria::before {
+  content: "\e059"; }
+
+.fa-hand-lizard::before {
+  content: "\f258"; }
+
+.fa-notdef::before {
+  content: "\e1fe"; }
+
+.fa-disease::before {
+  content: "\f7fa"; }
+
+.fa-briefcase-medical::before {
+  content: "\f469"; }
+
+.fa-genderless::before {
+  content: "\f22d"; }
+
+.fa-chevron-right::before {
+  content: "\f054"; }
+
+.fa-retweet::before {
+  content: "\f079"; }
+
+.fa-car-rear::before {
+  content: "\f5de"; }
+
+.fa-car-alt::before {
+  content: "\f5de"; }
+
+.fa-pump-soap::before {
+  content: "\e06b"; }
+
+.fa-video-slash::before {
+  content: "\f4e2"; }
+
+.fa-battery-quarter::before {
+  content: "\f243"; }
+
+.fa-battery-2::before {
+  content: "\f243"; }
+
+.fa-radio::before {
+  content: "\f8d7"; }
+
+.fa-baby-carriage::before {
+  content: "\f77d"; }
+
+.fa-carriage-baby::before {
+  content: "\f77d"; }
+
+.fa-traffic-light::before {
+  content: "\f637"; }
+
+.fa-thermometer::before {
+  content: "\f491"; }
+
+.fa-vr-cardboard::before {
+  content: "\f729"; }
+
+.fa-hand-middle-finger::before {
+  content: "\f806"; }
+
+.fa-percent::before {
+  content: "\25"; }
+
+.fa-percentage::before {
+  content: "\25"; }
+
+.fa-truck-moving::before {
+  content: "\f4df"; }
+
+.fa-glass-water-droplet::before {
+  content: "\e4f5"; }
+
+.fa-display::before {
+  content: "\e163"; }
+
+.fa-face-smile::before {
+  content: "\f118"; }
+
+.fa-smile::before {
+  content: "\f118"; }
+
+.fa-thumbtack::before {
+  content: "\f08d"; }
+
+.fa-thumb-tack::before {
+  content: "\f08d"; }
+
+.fa-trophy::before {
+  content: "\f091"; }
+
+.fa-person-praying::before {
+  content: "\f683"; }
+
+.fa-pray::before {
+  content: "\f683"; }
+
+.fa-hammer::before {
+  content: "\f6e3"; }
+
+.fa-hand-peace::before {
+  content: "\f25b"; }
+
+.fa-rotate::before {
+  content: "\f2f1"; }
+
+.fa-sync-alt::before {
+  content: "\f2f1"; }
+
+.fa-spinner::before {
+  content: "\f110"; }
+
+.fa-robot::before {
+  content: "\f544"; }
+
+.fa-peace::before {
+  content: "\f67c"; }
+
+.fa-gears::before {
+  content: "\f085"; }
+
+.fa-cogs::before {
+  content: "\f085"; }
+
+.fa-warehouse::before {
+  content: "\f494"; }
+
+.fa-arrow-up-right-dots::before {
+  content: "\e4b7"; }
+
+.fa-splotch::before {
+  content: "\f5bc"; }
+
+.fa-face-grin-hearts::before {
+  content: "\f584"; }
+
+.fa-grin-hearts::before {
+  content: "\f584"; }
+
+.fa-dice-four::before {
+  content: "\f524"; }
+
+.fa-sim-card::before {
+  content: "\f7c4"; }
+
+.fa-transgender::before {
+  content: "\f225"; }
+
+.fa-transgender-alt::before {
+  content: "\f225"; }
+
+.fa-mercury::before {
+  content: "\f223"; }
+
+.fa-arrow-turn-down::before {
+  content: "\f149"; }
+
+.fa-level-down::before {
+  content: "\f149"; }
+
+.fa-person-falling-burst::before {
+  content: "\e547"; }
+
+.fa-award::before {
+  content: "\f559"; }
+
+.fa-ticket-simple::before {
+  content: "\f3ff"; }
+
+.fa-ticket-alt::before {
+  content: "\f3ff"; }
+
+.fa-building::before {
+  content: "\f1ad"; }
+
+.fa-angles-left::before {
+  content: "\f100"; }
+
+.fa-angle-double-left::before {
+  content: "\f100"; }
+
+.fa-qrcode::before {
+  content: "\f029"; }
+
+.fa-clock-rotate-left::before {
+  content: "\f1da"; }
+
+.fa-history::before {
+  content: "\f1da"; }
+
+.fa-face-grin-beam-sweat::before {
+  content: "\f583"; }
+
+.fa-grin-beam-sweat::before {
+  content: "\f583"; }
+
+.fa-file-export::before {
+  content: "\f56e"; }
+
+.fa-arrow-right-from-file::before {
+  content: "\f56e"; }
+
+.fa-shield::before {
+  content: "\f132"; }
+
+.fa-shield-blank::before {
+  content: "\f132"; }
+
+.fa-arrow-up-short-wide::before {
+  content: "\f885"; }
+
+.fa-sort-amount-up-alt::before {
+  content: "\f885"; }
+
+.fa-house-medical::before {
+  content: "\e3b2"; }
+
+.fa-golf-ball-tee::before {
+  content: "\f450"; }
+
+.fa-golf-ball::before {
+  content: "\f450"; }
+
+.fa-circle-chevron-left::before {
+  content: "\f137"; }
+
+.fa-chevron-circle-left::before {
+  content: "\f137"; }
+
+.fa-house-chimney-window::before {
+  content: "\e00d"; }
+
+.fa-pen-nib::before {
+  content: "\f5ad"; }
+
+.fa-tent-arrow-turn-left::before {
+  content: "\e580"; }
+
+.fa-tents::before {
+  content: "\e582"; }
+
+.fa-wand-magic::before {
+  content: "\f0d0"; }
+
+.fa-magic::before {
+  content: "\f0d0"; }
+
+.fa-dog::before {
+  content: "\f6d3"; }
+
+.fa-carrot::before {
+  content: "\f787"; }
+
+.fa-moon::before {
+  content: "\f186"; }
+
+.fa-wine-glass-empty::before {
+  content: "\f5ce"; }
+
+.fa-wine-glass-alt::before {
+  content: "\f5ce"; }
+
+.fa-cheese::before {
+  content: "\f7ef"; }
+
+.fa-yin-yang::before {
+  content: "\f6ad"; }
+
+.fa-music::before {
+  content: "\f001"; }
+
+.fa-code-commit::before {
+  content: "\f386"; }
+
+.fa-temperature-low::before {
+  content: "\f76b"; }
+
+.fa-person-biking::before {
+  content: "\f84a"; }
+
+.fa-biking::before {
+  content: "\f84a"; }
+
+.fa-broom::before {
+  content: "\f51a"; }
+
+.fa-shield-heart::before {
+  content: "\e574"; }
+
+.fa-gopuram::before {
+  content: "\f664"; }
+
+.fa-earth-oceania::before {
+  content: "\e47b"; }
+
+.fa-globe-oceania::before {
+  content: "\e47b"; }
+
+.fa-square-xmark::before {
+  content: "\f2d3"; }
+
+.fa-times-square::before {
+  content: "\f2d3"; }
+
+.fa-xmark-square::before {
+  content: "\f2d3"; }
+
+.fa-hashtag::before {
+  content: "\23"; }
+
+.fa-up-right-and-down-left-from-center::before {
+  content: "\f424"; }
+
+.fa-expand-alt::before {
+  content: "\f424"; }
+
+.fa-oil-can::before {
+  content: "\f613"; }
+
+.fa-t::before {
+  content: "\54"; }
+
+.fa-hippo::before {
+  content: "\f6ed"; }
+
+.fa-chart-column::before {
+  content: "\e0e3"; }
+
+.fa-infinity::before {
+  content: "\f534"; }
+
+.fa-vial-circle-check::before {
+  content: "\e596"; }
+
+.fa-person-arrow-down-to-line::before {
+  content: "\e538"; }
+
+.fa-voicemail::before {
+  content: "\f897"; }
+
+.fa-fan::before {
+  content: "\f863"; }
+
+.fa-person-walking-luggage::before {
+  content: "\e554"; }
+
+.fa-up-down::before {
+  content: "\f338"; }
+
+.fa-arrows-alt-v::before {
+  content: "\f338"; }
+
+.fa-cloud-moon-rain::before {
+  content: "\f73c"; }
+
+.fa-calendar::before {
+  content: "\f133"; }
+
+.fa-trailer::before {
+  content: "\e041"; }
+
+.fa-bahai::before {
+  content: "\f666"; }
+
+.fa-haykal::before {
+  content: "\f666"; }
+
+.fa-sd-card::before {
+  content: "\f7c2"; }
+
+.fa-dragon::before {
+  content: "\f6d5"; }
+
+.fa-shoe-prints::before {
+  content: "\f54b"; }
+
+.fa-circle-plus::before {
+  content: "\f055"; }
+
+.fa-plus-circle::before {
+  content: "\f055"; }
+
+.fa-face-grin-tongue-wink::before {
+  content: "\f58b"; }
+
+.fa-grin-tongue-wink::before {
+  content: "\f58b"; }
+
+.fa-hand-holding::before {
+  content: "\f4bd"; }
+
+.fa-plug-circle-exclamation::before {
+  content: "\e55d"; }
+
+.fa-link-slash::before {
+  content: "\f127"; }
+
+.fa-chain-broken::before {
+  content: "\f127"; }
+
+.fa-chain-slash::before {
+  content: "\f127"; }
+
+.fa-unlink::before {
+  content: "\f127"; }
+
+.fa-clone::before {
+  content: "\f24d"; }
+
+.fa-person-walking-arrow-loop-left::before {
+  content: "\e551"; }
+
+.fa-arrow-up-z-a::before {
+  content: "\f882"; }
+
+.fa-sort-alpha-up-alt::before {
+  content: "\f882"; }
+
+.fa-fire-flame-curved::before {
+  content: "\f7e4"; }
+
+.fa-fire-alt::before {
+  content: "\f7e4"; }
+
+.fa-tornado::before {
+  content: "\f76f"; }
+
+.fa-file-circle-plus::before {
+  content: "\e494"; }
+
+.fa-book-quran::before {
+  content: "\f687"; }
+
+.fa-quran::before {
+  content: "\f687"; }
+
+.fa-anchor::before {
+  content: "\f13d"; }
+
+.fa-border-all::before {
+  content: "\f84c"; }
+
+.fa-face-angry::before {
+  content: "\f556"; }
+
+.fa-angry::before {
+  content: "\f556"; }
+
+.fa-cookie-bite::before {
+  content: "\f564"; }
+
+.fa-arrow-trend-down::before {
+  content: "\e097"; }
+
+.fa-rss::before {
+  content: "\f09e"; }
+
+.fa-feed::before {
+  content: "\f09e"; }
+
+.fa-draw-polygon::before {
+  content: "\f5ee"; }
+
+.fa-scale-balanced::before {
+  content: "\f24e"; }
+
+.fa-balance-scale::before {
+  content: "\f24e"; }
+
+.fa-gauge-simple-high::before {
+  content: "\f62a"; }
+
+.fa-tachometer::before {
+  content: "\f62a"; }
+
+.fa-tachometer-fast::before {
+  content: "\f62a"; }
+
+.fa-shower::before {
+  content: "\f2cc"; }
+
+.fa-desktop::before {
+  content: "\f390"; }
+
+.fa-desktop-alt::before {
+  content: "\f390"; }
+
+.fa-m::before {
+  content: "\4d"; }
+
+.fa-table-list::before {
+  content: "\f00b"; }
+
+.fa-th-list::before {
+  content: "\f00b"; }
+
+.fa-comment-sms::before {
+  content: "\f7cd"; }
+
+.fa-sms::before {
+  content: "\f7cd"; }
+
+.fa-book::before {
+  content: "\f02d"; }
+
+.fa-user-plus::before {
+  content: "\f234"; }
+
+.fa-check::before {
+  content: "\f00c"; }
+
+.fa-battery-three-quarters::before {
+  content: "\f241"; }
+
+.fa-battery-4::before {
+  content: "\f241"; }
+
+.fa-house-circle-check::before {
+  content: "\e509"; }
+
+.fa-angle-left::before {
+  content: "\f104"; }
+
+.fa-diagram-successor::before {
+  content: "\e47a"; }
+
+.fa-truck-arrow-right::before {
+  content: "\e58b"; }
+
+.fa-arrows-split-up-and-left::before {
+  content: "\e4bc"; }
+
+.fa-hand-fist::before {
+  content: "\f6de"; }
+
+.fa-fist-raised::before {
+  content: "\f6de"; }
+
+.fa-cloud-moon::before {
+  content: "\f6c3"; }
+
+.fa-briefcase::before {
+  content: "\f0b1"; }
+
+.fa-person-falling::before {
+  content: "\e546"; }
+
+.fa-image-portrait::before {
+  content: "\f3e0"; }
+
+.fa-portrait::before {
+  content: "\f3e0"; }
+
+.fa-user-tag::before {
+  content: "\f507"; }
+
+.fa-rug::before {
+  content: "\e569"; }
+
+.fa-earth-europe::before {
+  content: "\f7a2"; }
+
+.fa-globe-europe::before {
+  content: "\f7a2"; }
+
+.fa-cart-flatbed-suitcase::before {
+  content: "\f59d"; }
+
+.fa-luggage-cart::before {
+  content: "\f59d"; }
+
+.fa-rectangle-xmark::before {
+  content: "\f410"; }
+
+.fa-rectangle-times::before {
+  content: "\f410"; }
+
+.fa-times-rectangle::before {
+  content: "\f410"; }
+
+.fa-window-close::before {
+  content: "\f410"; }
+
+.fa-baht-sign::before {
+  content: "\e0ac"; }
+
+.fa-book-open::before {
+  content: "\f518"; }
+
+.fa-book-journal-whills::before {
+  content: "\f66a"; }
+
+.fa-journal-whills::before {
+  content: "\f66a"; }
+
+.fa-handcuffs::before {
+  content: "\e4f8"; }
+
+.fa-triangle-exclamation::before {
+  content: "\f071"; }
+
+.fa-exclamation-triangle::before {
+  content: "\f071"; }
+
+.fa-warning::before {
+  content: "\f071"; }
+
+.fa-database::before {
+  content: "\f1c0"; }
+
+.fa-share::before {
+  content: "\f064"; }
+
+.fa-mail-forward::before {
+  content: "\f064"; }
+
+.fa-bottle-droplet::before {
+  content: "\e4c4"; }
+
+.fa-mask-face::before {
+  content: "\e1d7"; }
+
+.fa-hill-rockslide::before {
+  content: "\e508"; }
+
+.fa-right-left::before {
+  content: "\f362"; }
+
+.fa-exchange-alt::before {
+  content: "\f362"; }
+
+.fa-paper-plane::before {
+  content: "\f1d8"; }
+
+.fa-road-circle-exclamation::before {
+  content: "\e565"; }
+
+.fa-dungeon::before {
+  content: "\f6d9"; }
+
+.fa-align-right::before {
+  content: "\f038"; }
+
+.fa-money-bill-1-wave::before {
+  content: "\f53b"; }
+
+.fa-money-bill-wave-alt::before {
+  content: "\f53b"; }
+
+.fa-life-ring::before {
+  content: "\f1cd"; }
+
+.fa-hands::before {
+  content: "\f2a7"; }
+
+.fa-sign-language::before {
+  content: "\f2a7"; }
+
+.fa-signing::before {
+  content: "\f2a7"; }
+
+.fa-calendar-day::before {
+  content: "\f783"; }
+
+.fa-water-ladder::before {
+  content: "\f5c5"; }
+
+.fa-ladder-water::before {
+  content: "\f5c5"; }
+
+.fa-swimming-pool::before {
+  content: "\f5c5"; }
+
+.fa-arrows-up-down::before {
+  content: "\f07d"; }
+
+.fa-arrows-v::before {
+  content: "\f07d"; }
+
+.fa-face-grimace::before {
+  content: "\f57f"; }
+
+.fa-grimace::before {
+  content: "\f57f"; }
+
+.fa-wheelchair-move::before {
+  content: "\e2ce"; }
+
+.fa-wheelchair-alt::before {
+  content: "\e2ce"; }
+
+.fa-turn-down::before {
+  content: "\f3be"; }
+
+.fa-level-down-alt::before {
+  content: "\f3be"; }
+
+.fa-person-walking-arrow-right::before {
+  content: "\e552"; }
+
+.fa-square-envelope::before {
+  content: "\f199"; }
+
+.fa-envelope-square::before {
+  content: "\f199"; }
+
+.fa-dice::before {
+  content: "\f522"; }
+
+.fa-bowling-ball::before {
+  content: "\f436"; }
+
+.fa-brain::before {
+  content: "\f5dc"; }
+
+.fa-bandage::before {
+  content: "\f462"; }
+
+.fa-band-aid::before {
+  content: "\f462"; }
+
+.fa-calendar-minus::before {
+  content: "\f272"; }
+
+.fa-circle-xmark::before {
+  content: "\f057"; }
+
+.fa-times-circle::before {
+  content: "\f057"; }
+
+.fa-xmark-circle::before {
+  content: "\f057"; }
+
+.fa-gifts::before {
+  content: "\f79c"; }
+
+.fa-hotel::before {
+  content: "\f594"; }
+
+.fa-earth-asia::before {
+  content: "\f57e"; }
+
+.fa-globe-asia::before {
+  content: "\f57e"; }
+
+.fa-id-card-clip::before {
+  content: "\f47f"; }
+
+.fa-id-card-alt::before {
+  content: "\f47f"; }
+
+.fa-magnifying-glass-plus::before {
+  content: "\f00e"; }
+
+.fa-search-plus::before {
+  content: "\f00e"; }
+
+.fa-thumbs-up::before {
+  content: "\f164"; }
+
+.fa-user-clock::before {
+  content: "\f4fd"; }
+
+.fa-hand-dots::before {
+  content: "\f461"; }
+
+.fa-allergies::before {
+  content: "\f461"; }
+
+.fa-file-invoice::before {
+  content: "\f570"; }
+
+.fa-window-minimize::before {
+  content: "\f2d1"; }
+
+.fa-mug-saucer::before {
+  content: "\f0f4"; }
+
+.fa-coffee::before {
+  content: "\f0f4"; }
+
+.fa-brush::before {
+  content: "\f55d"; }
+
+.fa-mask::before {
+  content: "\f6fa"; }
+
+.fa-magnifying-glass-minus::before {
+  content: "\f010"; }
+
+.fa-search-minus::before {
+  content: "\f010"; }
+
+.fa-ruler-vertical::before {
+  content: "\f548"; }
+
+.fa-user-large::before {
+  content: "\f406"; }
+
+.fa-user-alt::before {
+  content: "\f406"; }
+
+.fa-train-tram::before {
+  content: "\e5b4"; }
+
+.fa-user-nurse::before {
+  content: "\f82f"; }
+
+.fa-syringe::before {
+  content: "\f48e"; }
+
+.fa-cloud-sun::before {
+  content: "\f6c4"; }
+
+.fa-stopwatch-20::before {
+  content: "\e06f"; }
+
+.fa-square-full::before {
+  content: "\f45c"; }
+
+.fa-magnet::before {
+  content: "\f076"; }
+
+.fa-jar::before {
+  content: "\e516"; }
+
+.fa-note-sticky::before {
+  content: "\f249"; }
+
+.fa-sticky-note::before {
+  content: "\f249"; }
+
+.fa-bug-slash::before {
+  content: "\e490"; }
+
+.fa-arrow-up-from-water-pump::before {
+  content: "\e4b6"; }
+
+.fa-bone::before {
+  content: "\f5d7"; }
+
+.fa-user-injured::before {
+  content: "\f728"; }
+
+.fa-face-sad-tear::before {
+  content: "\f5b4"; }
+
+.fa-sad-tear::before {
+  content: "\f5b4"; }
+
+.fa-plane::before {
+  content: "\f072"; }
+
+.fa-tent-arrows-down::before {
+  content: "\e581"; }
+
+.fa-exclamation::before {
+  content: "\21"; }
+
+.fa-arrows-spin::before {
+  content: "\e4bb"; }
+
+.fa-print::before {
+  content: "\f02f"; }
+
+.fa-turkish-lira-sign::before {
+  content: "\e2bb"; }
+
+.fa-try::before {
+  content: "\e2bb"; }
+
+.fa-turkish-lira::before {
+  content: "\e2bb"; }
+
+.fa-dollar-sign::before {
+  content: "\24"; }
+
+.fa-dollar::before {
+  content: "\24"; }
+
+.fa-usd::before {
+  content: "\24"; }
+
+.fa-x::before {
+  content: "\58"; }
+
+.fa-magnifying-glass-dollar::before {
+  content: "\f688"; }
+
+.fa-search-dollar::before {
+  content: "\f688"; }
+
+.fa-users-gear::before {
+  content: "\f509"; }
+
+.fa-users-cog::before {
+  content: "\f509"; }
+
+.fa-person-military-pointing::before {
+  content: "\e54a"; }
+
+.fa-building-columns::before {
+  content: "\f19c"; }
+
+.fa-bank::before {
+  content: "\f19c"; }
+
+.fa-institution::before {
+  content: "\f19c"; }
+
+.fa-museum::before {
+  content: "\f19c"; }
+
+.fa-university::before {
+  content: "\f19c"; }
+
+.fa-umbrella::before {
+  content: "\f0e9"; }
+
+.fa-trowel::before {
+  content: "\e589"; }
+
+.fa-d::before {
+  content: "\44"; }
+
+.fa-stapler::before {
+  content: "\e5af"; }
+
+.fa-masks-theater::before {
+  content: "\f630"; }
+
+.fa-theater-masks::before {
+  content: "\f630"; }
+
+.fa-kip-sign::before {
+  content: "\e1c4"; }
+
+.fa-hand-point-left::before {
+  content: "\f0a5"; }
+
+.fa-handshake-simple::before {
+  content: "\f4c6"; }
+
+.fa-handshake-alt::before {
+  content: "\f4c6"; }
+
+.fa-jet-fighter::before {
+  content: "\f0fb"; }
+
+.fa-fighter-jet::before {
+  content: "\f0fb"; }
+
+.fa-square-share-nodes::before {
+  content: "\f1e1"; }
+
+.fa-share-alt-square::before {
+  content: "\f1e1"; }
+
+.fa-barcode::before {
+  content: "\f02a"; }
+
+.fa-plus-minus::before {
+  content: "\e43c"; }
+
+.fa-video::before {
+  content: "\f03d"; }
+
+.fa-video-camera::before {
+  content: "\f03d"; }
+
+.fa-graduation-cap::before {
+  content: "\f19d"; }
+
+.fa-mortar-board::before {
+  content: "\f19d"; }
+
+.fa-hand-holding-medical::before {
+  content: "\e05c"; }
+
+.fa-person-circle-check::before {
+  content: "\e53e"; }
+
+.fa-turn-up::before {
+  content: "\f3bf"; }
+
+.fa-level-up-alt::before {
+  content: "\f3bf"; }
+
+.sr-only,
+.fa-sr-only {
+  position: absolute;
+  width: 1px;
+  height: 1px;
+  padding: 0;
+  margin: -1px;
+  overflow: hidden;
+  clip: rect(0, 0, 0, 0);
+  white-space: nowrap;
+  border-width: 0; }
+
+.sr-only-focusable:not(:focus),
+.fa-sr-only-focusable:not(:focus) {
+  position: absolute;
+  width: 1px;
+  height: 1px;
+  padding: 0;
+  margin: -1px;
+  overflow: hidden;
+  clip: rect(0, 0, 0, 0);
+  white-space: nowrap;
+  border-width: 0; }
+:root, :host {
+  --fa-style-family-brands: 'Font Awesome 6 Brands';
+  --fa-font-brands: normal 400 1em/1 'Font Awesome 6 Brands'; }
+
+@font-face {
+  font-family: 'Font Awesome 6 Brands';
+  font-style: normal;
+  font-weight: 400;
+  font-display: block;
+  src: url("../webfonts/fa-brands-400.woff2") format("woff2"), url("../webfonts/fa-brands-400.ttf") format("truetype"); }
+
+.fab,
+.fa-brands {
+  font-weight: 400; }
+
+.fa-monero:before {
+  content: "\f3d0"; }
+
+.fa-hooli:before {
+  content: "\f427"; }
+
+.fa-yelp:before {
+  content: "\f1e9"; }
+
+.fa-cc-visa:before {
+  content: "\f1f0"; }
+
+.fa-lastfm:before {
+  content: "\f202"; }
+
+.fa-shopware:before {
+  content: "\f5b5"; }
+
+.fa-creative-commons-nc:before {
+  content: "\f4e8"; }
+
+.fa-aws:before {
+  content: "\f375"; }
+
+.fa-redhat:before {
+  content: "\f7bc"; }
+
+.fa-yoast:before {
+  content: "\f2b1"; }
+
+.fa-cloudflare:before {
+  content: "\e07d"; }
+
+.fa-ups:before {
+  content: "\f7e0"; }
+
+.fa-pixiv:before {
+  content: "\e640"; }
+
+.fa-wpexplorer:before {
+  content: "\f2de"; }
+
+.fa-dyalog:before {
+  content: "\f399"; }
+
+.fa-bity:before {
+  content: "\f37a"; }
+
+.fa-stackpath:before {
+  content: "\f842"; }
+
+.fa-buysellads:before {
+  content: "\f20d"; }
+
+.fa-first-order:before {
+  content: "\f2b0"; }
+
+.fa-modx:before {
+  content: "\f285"; }
+
+.fa-guilded:before {
+  content: "\e07e"; }
+
+.fa-vnv:before {
+  content: "\f40b"; }
+
+.fa-square-js:before {
+  content: "\f3b9"; }
+
+.fa-js-square:before {
+  content: "\f3b9"; }
+
+.fa-microsoft:before {
+  content: "\f3ca"; }
+
+.fa-qq:before {
+  content: "\f1d6"; }
+
+.fa-orcid:before {
+  content: "\f8d2"; }
+
+.fa-java:before {
+  content: "\f4e4"; }
+
+.fa-invision:before {
+  content: "\f7b0"; }
+
+.fa-creative-commons-pd-alt:before {
+  content: "\f4ed"; }
+
+.fa-centercode:before {
+  content: "\f380"; }
+
+.fa-glide-g:before {
+  content: "\f2a6"; }
+
+.fa-drupal:before {
+  content: "\f1a9"; }
+
+.fa-jxl:before {
+  content: "\e67b"; }
+
+.fa-hire-a-helper:before {
+  content: "\f3b0"; }
+
+.fa-creative-commons-by:before {
+  content: "\f4e7"; }
+
+.fa-unity:before {
+  content: "\e049"; }
+
+.fa-whmcs:before {
+  content: "\f40d"; }
+
+.fa-rocketchat:before {
+  content: "\f3e8"; }
+
+.fa-vk:before {
+  content: "\f189"; }
+
+.fa-untappd:before {
+  content: "\f405"; }
+
+.fa-mailchimp:before {
+  content: "\f59e"; }
+
+.fa-css3-alt:before {
+  content: "\f38b"; }
+
+.fa-square-reddit:before {
+  content: "\f1a2"; }
+
+.fa-reddit-square:before {
+  content: "\f1a2"; }
+
+.fa-vimeo-v:before {
+  content: "\f27d"; }
+
+.fa-contao:before {
+  content: "\f26d"; }
+
+.fa-square-font-awesome:before {
+  content: "\e5ad"; }
+
+.fa-deskpro:before {
+  content: "\f38f"; }
+
+.fa-brave:before {
+  content: "\e63c"; }
+
+.fa-sistrix:before {
+  content: "\f3ee"; }
+
+.fa-square-instagram:before {
+  content: "\e055"; }
+
+.fa-instagram-square:before {
+  content: "\e055"; }
+
+.fa-battle-net:before {
+  content: "\f835"; }
+
+.fa-the-red-yeti:before {
+  content: "\f69d"; }
+
+.fa-square-hacker-news:before {
+  content: "\f3af"; }
+
+.fa-hacker-news-square:before {
+  content: "\f3af"; }
+
+.fa-edge:before {
+  content: "\f282"; }
+
+.fa-threads:before {
+  content: "\e618"; }
+
+.fa-napster:before {
+  content: "\f3d2"; }
+
+.fa-square-snapchat:before {
+  content: "\f2ad"; }
+
+.fa-snapchat-square:before {
+  content: "\f2ad"; }
+
+.fa-google-plus-g:before {
+  content: "\f0d5"; }
+
+.fa-artstation:before {
+  content: "\f77a"; }
+
+.fa-markdown:before {
+  content: "\f60f"; }
+
+.fa-sourcetree:before {
+  content: "\f7d3"; }
+
+.fa-google-plus:before {
+  content: "\f2b3"; }
+
+.fa-diaspora:before {
+  content: "\f791"; }
+
+.fa-foursquare:before {
+  content: "\f180"; }
+
+.fa-stack-overflow:before {
+  content: "\f16c"; }
+
+.fa-github-alt:before {
+  content: "\f113"; }
+
+.fa-phoenix-squadron:before {
+  content: "\f511"; }
+
+.fa-pagelines:before {
+  content: "\f18c"; }
+
+.fa-algolia:before {
+  content: "\f36c"; }
+
+.fa-red-river:before {
+  content: "\f3e3"; }
+
+.fa-creative-commons-sa:before {
+  content: "\f4ef"; }
+
+.fa-safari:before {
+  content: "\f267"; }
+
+.fa-google:before {
+  content: "\f1a0"; }
+
+.fa-square-font-awesome-stroke:before {
+  content: "\f35c"; }
+
+.fa-font-awesome-alt:before {
+  content: "\f35c"; }
+
+.fa-atlassian:before {
+  content: "\f77b"; }
+
+.fa-linkedin-in:before {
+  content: "\f0e1"; }
+
+.fa-digital-ocean:before {
+  content: "\f391"; }
+
+.fa-nimblr:before {
+  content: "\f5a8"; }
+
+.fa-chromecast:before {
+  content: "\f838"; }
+
+.fa-evernote:before {
+  content: "\f839"; }
+
+.fa-hacker-news:before {
+  content: "\f1d4"; }
+
+.fa-creative-commons-sampling:before {
+  content: "\f4f0"; }
+
+.fa-adversal:before {
+  content: "\f36a"; }
+
+.fa-creative-commons:before {
+  content: "\f25e"; }
+
+.fa-watchman-monitoring:before {
+  content: "\e087"; }
+
+.fa-fonticons:before {
+  content: "\f280"; }
+
+.fa-weixin:before {
+  content: "\f1d7"; }
+
+.fa-shirtsinbulk:before {
+  content: "\f214"; }
+
+.fa-codepen:before {
+  content: "\f1cb"; }
+
+.fa-git-alt:before {
+  content: "\f841"; }
+
+.fa-lyft:before {
+  content: "\f3c3"; }
+
+.fa-rev:before {
+  content: "\f5b2"; }
+
+.fa-windows:before {
+  content: "\f17a"; }
+
+.fa-wizards-of-the-coast:before {
+  content: "\f730"; }
+
+.fa-square-viadeo:before {
+  content: "\f2aa"; }
+
+.fa-viadeo-square:before {
+  content: "\f2aa"; }
+
+.fa-meetup:before {
+  content: "\f2e0"; }
+
+.fa-centos:before {
+  content: "\f789"; }
+
+.fa-adn:before {
+  content: "\f170"; }
+
+.fa-cloudsmith:before {
+  content: "\f384"; }
+
+.fa-opensuse:before {
+  content: "\e62b"; }
+
+.fa-pied-piper-alt:before {
+  content: "\f1a8"; }
+
+.fa-square-dribbble:before {
+  content: "\f397"; }
+
+.fa-dribbble-square:before {
+  content: "\f397"; }
+
+.fa-codiepie:before {
+  content: "\f284"; }
+
+.fa-node:before {
+  content: "\f419"; }
+
+.fa-mix:before {
+  content: "\f3cb"; }
+
+.fa-steam:before {
+  content: "\f1b6"; }
+
+.fa-cc-apple-pay:before {
+  content: "\f416"; }
+
+.fa-scribd:before {
+  content: "\f28a"; }
+
+.fa-debian:before {
+  content: "\e60b"; }
+
+.fa-openid:before {
+  content: "\f19b"; }
+
+.fa-instalod:before {
+  content: "\e081"; }
+
+.fa-expeditedssl:before {
+  content: "\f23e"; }
+
+.fa-sellcast:before {
+  content: "\f2da"; }
+
+.fa-square-twitter:before {
+  content: "\f081"; }
+
+.fa-twitter-square:before {
+  content: "\f081"; }
+
+.fa-r-project:before {
+  content: "\f4f7"; }
+
+.fa-delicious:before {
+  content: "\f1a5"; }
+
+.fa-freebsd:before {
+  content: "\f3a4"; }
+
+.fa-vuejs:before {
+  content: "\f41f"; }
+
+.fa-accusoft:before {
+  content: "\f369"; }
+
+.fa-ioxhost:before {
+  content: "\f208"; }
+
+.fa-fonticons-fi:before {
+  content: "\f3a2"; }
+
+.fa-app-store:before {
+  content: "\f36f"; }
+
+.fa-cc-mastercard:before {
+  content: "\f1f1"; }
+
+.fa-itunes-note:before {
+  content: "\f3b5"; }
+
+.fa-golang:before {
+  content: "\e40f"; }
+
+.fa-kickstarter:before {
+  content: "\f3bb"; }
+
+.fa-square-kickstarter:before {
+  content: "\f3bb"; }
+
+.fa-grav:before {
+  content: "\f2d6"; }
+
+.fa-weibo:before {
+  content: "\f18a"; }
+
+.fa-uncharted:before {
+  content: "\e084"; }
+
+.fa-firstdraft:before {
+  content: "\f3a1"; }
+
+.fa-square-youtube:before {
+  content: "\f431"; }
+
+.fa-youtube-square:before {
+  content: "\f431"; }
+
+.fa-wikipedia-w:before {
+  content: "\f266"; }
+
+.fa-wpressr:before {
+  content: "\f3e4"; }
+
+.fa-rendact:before {
+  content: "\f3e4"; }
+
+.fa-angellist:before {
+  content: "\f209"; }
+
+.fa-galactic-republic:before {
+  content: "\f50c"; }
+
+.fa-nfc-directional:before {
+  content: "\e530"; }
+
+.fa-skype:before {
+  content: "\f17e"; }
+
+.fa-joget:before {
+  content: "\f3b7"; }
+
+.fa-fedora:before {
+  content: "\f798"; }
+
+.fa-stripe-s:before {
+  content: "\f42a"; }
+
+.fa-meta:before {
+  content: "\e49b"; }
+
+.fa-laravel:before {
+  content: "\f3bd"; }
+
+.fa-hotjar:before {
+  content: "\f3b1"; }
+
+.fa-bluetooth-b:before {
+  content: "\f294"; }
+
+.fa-square-letterboxd:before {
+  content: "\e62e"; }
+
+.fa-sticker-mule:before {
+  content: "\f3f7"; }
+
+.fa-creative-commons-zero:before {
+  content: "\f4f3"; }
+
+.fa-hips:before {
+  content: "\f452"; }
+
+.fa-behance:before {
+  content: "\f1b4"; }
+
+.fa-reddit:before {
+  content: "\f1a1"; }
+
+.fa-discord:before {
+  content: "\f392"; }
+
+.fa-chrome:before {
+  content: "\f268"; }
+
+.fa-app-store-ios:before {
+  content: "\f370"; }
+
+.fa-cc-discover:before {
+  content: "\f1f2"; }
+
+.fa-wpbeginner:before {
+  content: "\f297"; }
+
+.fa-confluence:before {
+  content: "\f78d"; }
+
+.fa-shoelace:before {
+  content: "\e60c"; }
+
+.fa-mdb:before {
+  content: "\f8ca"; }
+
+.fa-dochub:before {
+  content: "\f394"; }
+
+.fa-accessible-icon:before {
+  content: "\f368"; }
+
+.fa-ebay:before {
+  content: "\f4f4"; }
+
+.fa-amazon:before {
+  content: "\f270"; }
+
+.fa-unsplash:before {
+  content: "\e07c"; }
+
+.fa-yarn:before {
+  content: "\f7e3"; }
+
+.fa-square-steam:before {
+  content: "\f1b7"; }
+
+.fa-steam-square:before {
+  content: "\f1b7"; }
+
+.fa-500px:before {
+  content: "\f26e"; }
+
+.fa-square-vimeo:before {
+  content: "\f194"; }
+
+.fa-vimeo-square:before {
+  content: "\f194"; }
+
+.fa-asymmetrik:before {
+  content: "\f372"; }
+
+.fa-font-awesome:before {
+  content: "\f2b4"; }
+
+.fa-font-awesome-flag:before {
+  content: "\f2b4"; }
+
+.fa-font-awesome-logo-full:before {
+  content: "\f2b4"; }
+
+.fa-gratipay:before {
+  content: "\f184"; }
+
+.fa-apple:before {
+  content: "\f179"; }
+
+.fa-hive:before {
+  content: "\e07f"; }
+
+.fa-gitkraken:before {
+  content: "\f3a6"; }
+
+.fa-keybase:before {
+  content: "\f4f5"; }
+
+.fa-apple-pay:before {
+  content: "\f415"; }
+
+.fa-padlet:before {
+  content: "\e4a0"; }
+
+.fa-amazon-pay:before {
+  content: "\f42c"; }
+
+.fa-square-github:before {
+  content: "\f092"; }
+
+.fa-github-square:before {
+  content: "\f092"; }
+
+.fa-stumbleupon:before {
+  content: "\f1a4"; }
+
+.fa-fedex:before {
+  content: "\f797"; }
+
+.fa-phoenix-framework:before {
+  content: "\f3dc"; }
+
+.fa-shopify:before {
+  content: "\e057"; }
+
+.fa-neos:before {
+  content: "\f612"; }
+
+.fa-square-threads:before {
+  content: "\e619"; }
+
+.fa-hackerrank:before {
+  content: "\f5f7"; }
+
+.fa-researchgate:before {
+  content: "\f4f8"; }
+
+.fa-swift:before {
+  content: "\f8e1"; }
+
+.fa-angular:before {
+  content: "\f420"; }
+
+.fa-speakap:before {
+  content: "\f3f3"; }
+
+.fa-angrycreative:before {
+  content: "\f36e"; }
+
+.fa-y-combinator:before {
+  content: "\f23b"; }
+
+.fa-empire:before {
+  content: "\f1d1"; }
+
+.fa-envira:before {
+  content: "\f299"; }
+
+.fa-google-scholar:before {
+  content: "\e63b"; }
+
+.fa-square-gitlab:before {
+  content: "\e5ae"; }
+
+.fa-gitlab-square:before {
+  content: "\e5ae"; }
+
+.fa-studiovinari:before {
+  content: "\f3f8"; }
+
+.fa-pied-piper:before {
+  content: "\f2ae"; }
+
+.fa-wordpress:before {
+  content: "\f19a"; }
+
+.fa-product-hunt:before {
+  content: "\f288"; }
+
+.fa-firefox:before {
+  content: "\f269"; }
+
+.fa-linode:before {
+  content: "\f2b8"; }
+
+.fa-goodreads:before {
+  content: "\f3a8"; }
+
+.fa-square-odnoklassniki:before {
+  content: "\f264"; }
+
+.fa-odnoklassniki-square:before {
+  content: "\f264"; }
+
+.fa-jsfiddle:before {
+  content: "\f1cc"; }
+
+.fa-sith:before {
+  content: "\f512"; }
+
+.fa-themeisle:before {
+  content: "\f2b2"; }
+
+.fa-page4:before {
+  content: "\f3d7"; }
+
+.fa-hashnode:before {
+  content: "\e499"; }
+
+.fa-react:before {
+  content: "\f41b"; }
+
+.fa-cc-paypal:before {
+  content: "\f1f4"; }
+
+.fa-squarespace:before {
+  content: "\f5be"; }
+
+.fa-cc-stripe:before {
+  content: "\f1f5"; }
+
+.fa-creative-commons-share:before {
+  content: "\f4f2"; }
+
+.fa-bitcoin:before {
+  content: "\f379"; }
+
+.fa-keycdn:before {
+  content: "\f3ba"; }
+
+.fa-opera:before {
+  content: "\f26a"; }
+
+.fa-itch-io:before {
+  content: "\f83a"; }
+
+.fa-umbraco:before {
+  content: "\f8e8"; }
+
+.fa-galactic-senate:before {
+  content: "\f50d"; }
+
+.fa-ubuntu:before {
+  content: "\f7df"; }
+
+.fa-draft2digital:before {
+  content: "\f396"; }
+
+.fa-stripe:before {
+  content: "\f429"; }
+
+.fa-houzz:before {
+  content: "\f27c"; }
+
+.fa-gg:before {
+  content: "\f260"; }
+
+.fa-dhl:before {
+  content: "\f790"; }
+
+.fa-square-pinterest:before {
+  content: "\f0d3"; }
+
+.fa-pinterest-square:before {
+  content: "\f0d3"; }
+
+.fa-xing:before {
+  content: "\f168"; }
+
+.fa-blackberry:before {
+  content: "\f37b"; }
+
+.fa-creative-commons-pd:before {
+  content: "\f4ec"; }
+
+.fa-playstation:before {
+  content: "\f3df"; }
+
+.fa-quinscape:before {
+  content: "\f459"; }
+
+.fa-less:before {
+  content: "\f41d"; }
+
+.fa-blogger-b:before {
+  content: "\f37d"; }
+
+.fa-opencart:before {
+  content: "\f23d"; }
+
+.fa-vine:before {
+  content: "\f1ca"; }
+
+.fa-signal-messenger:before {
+  content: "\e663"; }
+
+.fa-paypal:before {
+  content: "\f1ed"; }
+
+.fa-gitlab:before {
+  content: "\f296"; }
+
+.fa-typo3:before {
+  content: "\f42b"; }
+
+.fa-reddit-alien:before {
+  content: "\f281"; }
+
+.fa-yahoo:before {
+  content: "\f19e"; }
+
+.fa-dailymotion:before {
+  content: "\e052"; }
+
+.fa-affiliatetheme:before {
+  content: "\f36b"; }
+
+.fa-pied-piper-pp:before {
+  content: "\f1a7"; }
+
+.fa-bootstrap:before {
+  content: "\f836"; }
+
+.fa-odnoklassniki:before {
+  content: "\f263"; }
+
+.fa-nfc-symbol:before {
+  content: "\e531"; }
+
+.fa-mintbit:before {
+  content: "\e62f"; }
+
+.fa-ethereum:before {
+  content: "\f42e"; }
+
+.fa-speaker-deck:before {
+  content: "\f83c"; }
+
+.fa-creative-commons-nc-eu:before {
+  content: "\f4e9"; }
+
+.fa-patreon:before {
+  content: "\f3d9"; }
+
+.fa-avianex:before {
+  content: "\f374"; }
+
+.fa-ello:before {
+  content: "\f5f1"; }
+
+.fa-gofore:before {
+  content: "\f3a7"; }
+
+.fa-bimobject:before {
+  content: "\f378"; }
+
+.fa-brave-reverse:before {
+  content: "\e63d"; }
+
+.fa-facebook-f:before {
+  content: "\f39e"; }
+
+.fa-square-google-plus:before {
+  content: "\f0d4"; }
+
+.fa-google-plus-square:before {
+  content: "\f0d4"; }
+
+.fa-web-awesome:before {
+  content: "\e682"; }
+
+.fa-mandalorian:before {
+  content: "\f50f"; }
+
+.fa-first-order-alt:before {
+  content: "\f50a"; }
+
+.fa-osi:before {
+  content: "\f41a"; }
+
+.fa-google-wallet:before {
+  content: "\f1ee"; }
+
+.fa-d-and-d-beyond:before {
+  content: "\f6ca"; }
+
+.fa-periscope:before {
+  content: "\f3da"; }
+
+.fa-fulcrum:before {
+  content: "\f50b"; }
+
+.fa-cloudscale:before {
+  content: "\f383"; }
+
+.fa-forumbee:before {
+  content: "\f211"; }
+
+.fa-mizuni:before {
+  content: "\f3cc"; }
+
+.fa-schlix:before {
+  content: "\f3ea"; }
+
+.fa-square-xing:before {
+  content: "\f169"; }
+
+.fa-xing-square:before {
+  content: "\f169"; }
+
+.fa-bandcamp:before {
+  content: "\f2d5"; }
+
+.fa-wpforms:before {
+  content: "\f298"; }
+
+.fa-cloudversify:before {
+  content: "\f385"; }
+
+.fa-usps:before {
+  content: "\f7e1"; }
+
+.fa-megaport:before {
+  content: "\f5a3"; }
+
+.fa-magento:before {
+  content: "\f3c4"; }
+
+.fa-spotify:before {
+  content: "\f1bc"; }
+
+.fa-optin-monster:before {
+  content: "\f23c"; }
+
+.fa-fly:before {
+  content: "\f417"; }
+
+.fa-aviato:before {
+  content: "\f421"; }
+
+.fa-itunes:before {
+  content: "\f3b4"; }
+
+.fa-cuttlefish:before {
+  content: "\f38c"; }
+
+.fa-blogger:before {
+  content: "\f37c"; }
+
+.fa-flickr:before {
+  content: "\f16e"; }
+
+.fa-viber:before {
+  content: "\f409"; }
+
+.fa-soundcloud:before {
+  content: "\f1be"; }
+
+.fa-digg:before {
+  content: "\f1a6"; }
+
+.fa-tencent-weibo:before {
+  content: "\f1d5"; }
+
+.fa-letterboxd:before {
+  content: "\e62d"; }
+
+.fa-symfony:before {
+  content: "\f83d"; }
+
+.fa-maxcdn:before {
+  content: "\f136"; }
+
+.fa-etsy:before {
+  content: "\f2d7"; }
+
+.fa-facebook-messenger:before {
+  content: "\f39f"; }
+
+.fa-audible:before {
+  content: "\f373"; }
+
+.fa-think-peaks:before {
+  content: "\f731"; }
+
+.fa-bilibili:before {
+  content: "\e3d9"; }
+
+.fa-erlang:before {
+  content: "\f39d"; }
+
+.fa-x-twitter:before {
+  content: "\e61b"; }
+
+.fa-cotton-bureau:before {
+  content: "\f89e"; }
+
+.fa-dashcube:before {
+  content: "\f210"; }
+
+.fa-42-group:before {
+  content: "\e080"; }
+
+.fa-innosoft:before {
+  content: "\e080"; }
+
+.fa-stack-exchange:before {
+  content: "\f18d"; }
+
+.fa-elementor:before {
+  content: "\f430"; }
+
+.fa-square-pied-piper:before {
+  content: "\e01e"; }
+
+.fa-pied-piper-square:before {
+  content: "\e01e"; }
+
+.fa-creative-commons-nd:before {
+  content: "\f4eb"; }
+
+.fa-palfed:before {
+  content: "\f3d8"; }
+
+.fa-superpowers:before {
+  content: "\f2dd"; }
+
+.fa-resolving:before {
+  content: "\f3e7"; }
+
+.fa-xbox:before {
+  content: "\f412"; }
+
+.fa-square-web-awesome-stroke:before {
+  content: "\e684"; }
+
+.fa-searchengin:before {
+  content: "\f3eb"; }
+
+.fa-tiktok:before {
+  content: "\e07b"; }
+
+.fa-square-facebook:before {
+  content: "\f082"; }
+
+.fa-facebook-square:before {
+  content: "\f082"; }
+
+.fa-renren:before {
+  content: "\f18b"; }
+
+.fa-linux:before {
+  content: "\f17c"; }
+
+.fa-glide:before {
+  content: "\f2a5"; }
+
+.fa-linkedin:before {
+  content: "\f08c"; }
+
+.fa-hubspot:before {
+  content: "\f3b2"; }
+
+.fa-deploydog:before {
+  content: "\f38e"; }
+
+.fa-twitch:before {
+  content: "\f1e8"; }
+
+.fa-ravelry:before {
+  content: "\f2d9"; }
+
+.fa-mixer:before {
+  content: "\e056"; }
+
+.fa-square-lastfm:before {
+  content: "\f203"; }
+
+.fa-lastfm-square:before {
+  content: "\f203"; }
+
+.fa-vimeo:before {
+  content: "\f40a"; }
+
+.fa-mendeley:before {
+  content: "\f7b3"; }
+
+.fa-uniregistry:before {
+  content: "\f404"; }
+
+.fa-figma:before {
+  content: "\f799"; }
+
+.fa-creative-commons-remix:before {
+  content: "\f4ee"; }
+
+.fa-cc-amazon-pay:before {
+  content: "\f42d"; }
+
+.fa-dropbox:before {
+  content: "\f16b"; }
+
+.fa-instagram:before {
+  content: "\f16d"; }
+
+.fa-cmplid:before {
+  content: "\e360"; }
+
+.fa-upwork:before {
+  content: "\e641"; }
+
+.fa-facebook:before {
+  content: "\f09a"; }
+
+.fa-gripfire:before {
+  content: "\f3ac"; }
+
+.fa-jedi-order:before {
+  content: "\f50e"; }
+
+.fa-uikit:before {
+  content: "\f403"; }
+
+.fa-fort-awesome-alt:before {
+  content: "\f3a3"; }
+
+.fa-phabricator:before {
+  content: "\f3db"; }
+
+.fa-ussunnah:before {
+  content: "\f407"; }
+
+.fa-earlybirds:before {
+  content: "\f39a"; }
+
+.fa-trade-federation:before {
+  content: "\f513"; }
+
+.fa-autoprefixer:before {
+  content: "\f41c"; }
+
+.fa-whatsapp:before {
+  content: "\f232"; }
+
+.fa-square-upwork:before {
+  content: "\e67c"; }
+
+.fa-slideshare:before {
+  content: "\f1e7"; }
+
+.fa-google-play:before {
+  content: "\f3ab"; }
+
+.fa-viadeo:before {
+  content: "\f2a9"; }
+
+.fa-line:before {
+  content: "\f3c0"; }
+
+.fa-google-drive:before {
+  content: "\f3aa"; }
+
+.fa-servicestack:before {
+  content: "\f3ec"; }
+
+.fa-simplybuilt:before {
+  content: "\f215"; }
+
+.fa-bitbucket:before {
+  content: "\f171"; }
+
+.fa-imdb:before {
+  content: "\f2d8"; }
+
+.fa-deezer:before {
+  content: "\e077"; }
+
+.fa-raspberry-pi:before {
+  content: "\f7bb"; }
+
+.fa-jira:before {
+  content: "\f7b1"; }
+
+.fa-docker:before {
+  content: "\f395"; }
+
+.fa-screenpal:before {
+  content: "\e570"; }
+
+.fa-bluetooth:before {
+  content: "\f293"; }
+
+.fa-gitter:before {
+  content: "\f426"; }
+
+.fa-d-and-d:before {
+  content: "\f38d"; }
+
+.fa-microblog:before {
+  content: "\e01a"; }
+
+.fa-cc-diners-club:before {
+  content: "\f24c"; }
+
+.fa-gg-circle:before {
+  content: "\f261"; }
+
+.fa-pied-piper-hat:before {
+  content: "\f4e5"; }
+
+.fa-kickstarter-k:before {
+  content: "\f3bc"; }
+
+.fa-yandex:before {
+  content: "\f413"; }
+
+.fa-readme:before {
+  content: "\f4d5"; }
+
+.fa-html5:before {
+  content: "\f13b"; }
+
+.fa-sellsy:before {
+  content: "\f213"; }
+
+.fa-square-web-awesome:before {
+  content: "\e683"; }
+
+.fa-sass:before {
+  content: "\f41e"; }
+
+.fa-wirsindhandwerk:before {
+  content: "\e2d0"; }
+
+.fa-wsh:before {
+  content: "\e2d0"; }
+
+.fa-buromobelexperte:before {
+  content: "\f37f"; }
+
+.fa-salesforce:before {
+  content: "\f83b"; }
+
+.fa-octopus-deploy:before {
+  content: "\e082"; }
+
+.fa-medapps:before {
+  content: "\f3c6"; }
+
+.fa-ns8:before {
+  content: "\f3d5"; }
+
+.fa-pinterest-p:before {
+  content: "\f231"; }
+
+.fa-apper:before {
+  content: "\f371"; }
+
+.fa-fort-awesome:before {
+  content: "\f286"; }
+
+.fa-waze:before {
+  content: "\f83f"; }
+
+.fa-bluesky:before {
+  content: "\e671"; }
+
+.fa-cc-jcb:before {
+  content: "\f24b"; }
+
+.fa-snapchat:before {
+  content: "\f2ab"; }
+
+.fa-snapchat-ghost:before {
+  content: "\f2ab"; }
+
+.fa-fantasy-flight-games:before {
+  content: "\f6dc"; }
+
+.fa-rust:before {
+  content: "\e07a"; }
+
+.fa-wix:before {
+  content: "\f5cf"; }
+
+.fa-square-behance:before {
+  content: "\f1b5"; }
+
+.fa-behance-square:before {
+  content: "\f1b5"; }
+
+.fa-supple:before {
+  content: "\f3f9"; }
+
+.fa-webflow:before {
+  content: "\e65c"; }
+
+.fa-rebel:before {
+  content: "\f1d0"; }
+
+.fa-css3:before {
+  content: "\f13c"; }
+
+.fa-staylinked:before {
+  content: "\f3f5"; }
+
+.fa-kaggle:before {
+  content: "\f5fa"; }
+
+.fa-space-awesome:before {
+  content: "\e5ac"; }
+
+.fa-deviantart:before {
+  content: "\f1bd"; }
+
+.fa-cpanel:before {
+  content: "\f388"; }
+
+.fa-goodreads-g:before {
+  content: "\f3a9"; }
+
+.fa-square-git:before {
+  content: "\f1d2"; }
+
+.fa-git-square:before {
+  content: "\f1d2"; }
+
+.fa-square-tumblr:before {
+  content: "\f174"; }
+
+.fa-tumblr-square:before {
+  content: "\f174"; }
+
+.fa-trello:before {
+  content: "\f181"; }
+
+.fa-creative-commons-nc-jp:before {
+  content: "\f4ea"; }
+
+.fa-get-pocket:before {
+  content: "\f265"; }
+
+.fa-perbyte:before {
+  content: "\e083"; }
+
+.fa-grunt:before {
+  content: "\f3ad"; }
+
+.fa-weebly:before {
+  content: "\f5cc"; }
+
+.fa-connectdevelop:before {
+  content: "\f20e"; }
+
+.fa-leanpub:before {
+  content: "\f212"; }
+
+.fa-black-tie:before {
+  content: "\f27e"; }
+
+.fa-themeco:before {
+  content: "\f5c6"; }
+
+.fa-python:before {
+  content: "\f3e2"; }
+
+.fa-android:before {
+  content: "\f17b"; }
+
+.fa-bots:before {
+  content: "\e340"; }
+
+.fa-free-code-camp:before {
+  content: "\f2c5"; }
+
+.fa-hornbill:before {
+  content: "\f592"; }
+
+.fa-js:before {
+  content: "\f3b8"; }
+
+.fa-ideal:before {
+  content: "\e013"; }
+
+.fa-git:before {
+  content: "\f1d3"; }
+
+.fa-dev:before {
+  content: "\f6cc"; }
+
+.fa-sketch:before {
+  content: "\f7c6"; }
+
+.fa-yandex-international:before {
+  content: "\f414"; }
+
+.fa-cc-amex:before {
+  content: "\f1f3"; }
+
+.fa-uber:before {
+  content: "\f402"; }
+
+.fa-github:before {
+  content: "\f09b"; }
+
+.fa-php:before {
+  content: "\f457"; }
+
+.fa-alipay:before {
+  content: "\f642"; }
+
+.fa-youtube:before {
+  content: "\f167"; }
+
+.fa-skyatlas:before {
+  content: "\f216"; }
+
+.fa-firefox-browser:before {
+  content: "\e007"; }
+
+.fa-replyd:before {
+  content: "\f3e6"; }
+
+.fa-suse:before {
+  content: "\f7d6"; }
+
+.fa-jenkins:before {
+  content: "\f3b6"; }
+
+.fa-twitter:before {
+  content: "\f099"; }
+
+.fa-rockrms:before {
+  content: "\f3e9"; }
+
+.fa-pinterest:before {
+  content: "\f0d2"; }
+
+.fa-buffer:before {
+  content: "\f837"; }
+
+.fa-npm:before {
+  content: "\f3d4"; }
+
+.fa-yammer:before {
+  content: "\f840"; }
+
+.fa-btc:before {
+  content: "\f15a"; }
+
+.fa-dribbble:before {
+  content: "\f17d"; }
+
+.fa-stumbleupon-circle:before {
+  content: "\f1a3"; }
+
+.fa-internet-explorer:before {
+  content: "\f26b"; }
+
+.fa-stubber:before {
+  content: "\e5c7"; }
+
+.fa-telegram:before {
+  content: "\f2c6"; }
+
+.fa-telegram-plane:before {
+  content: "\f2c6"; }
+
+.fa-old-republic:before {
+  content: "\f510"; }
+
+.fa-odysee:before {
+  content: "\e5c6"; }
+
+.fa-square-whatsapp:before {
+  content: "\f40c"; }
+
+.fa-whatsapp-square:before {
+  content: "\f40c"; }
+
+.fa-node-js:before {
+  content: "\f3d3"; }
+
+.fa-edge-legacy:before {
+  content: "\e078"; }
+
+.fa-slack:before {
+  content: "\f198"; }
+
+.fa-slack-hash:before {
+  content: "\f198"; }
+
+.fa-medrt:before {
+  content: "\f3c8"; }
+
+.fa-usb:before {
+  content: "\f287"; }
+
+.fa-tumblr:before {
+  content: "\f173"; }
+
+.fa-vaadin:before {
+  content: "\f408"; }
+
+.fa-quora:before {
+  content: "\f2c4"; }
+
+.fa-square-x-twitter:before {
+  content: "\e61a"; }
+
+.fa-reacteurope:before {
+  content: "\f75d"; }
+
+.fa-medium:before {
+  content: "\f23a"; }
+
+.fa-medium-m:before {
+  content: "\f23a"; }
+
+.fa-amilia:before {
+  content: "\f36d"; }
+
+.fa-mixcloud:before {
+  content: "\f289"; }
+
+.fa-flipboard:before {
+  content: "\f44d"; }
+
+.fa-viacoin:before {
+  content: "\f237"; }
+
+.fa-critical-role:before {
+  content: "\f6c9"; }
+
+.fa-sitrox:before {
+  content: "\e44a"; }
+
+.fa-discourse:before {
+  content: "\f393"; }
+
+.fa-joomla:before {
+  content: "\f1aa"; }
+
+.fa-mastodon:before {
+  content: "\f4f6"; }
+
+.fa-airbnb:before {
+  content: "\f834"; }
+
+.fa-wolf-pack-battalion:before {
+  content: "\f514"; }
+
+.fa-buy-n-large:before {
+  content: "\f8a6"; }
+
+.fa-gulp:before {
+  content: "\f3ae"; }
+
+.fa-creative-commons-sampling-plus:before {
+  content: "\f4f1"; }
+
+.fa-strava:before {
+  content: "\f428"; }
+
+.fa-ember:before {
+  content: "\f423"; }
+
+.fa-canadian-maple-leaf:before {
+  content: "\f785"; }
+
+.fa-teamspeak:before {
+  content: "\f4f9"; }
+
+.fa-pushed:before {
+  content: "\f3e1"; }
+
+.fa-wordpress-simple:before {
+  content: "\f411"; }
+
+.fa-nutritionix:before {
+  content: "\f3d6"; }
+
+.fa-wodu:before {
+  content: "\e088"; }
+
+.fa-google-pay:before {
+  content: "\e079"; }
+
+.fa-intercom:before {
+  content: "\f7af"; }
+
+.fa-zhihu:before {
+  content: "\f63f"; }
+
+.fa-korvue:before {
+  content: "\f42f"; }
+
+.fa-pix:before {
+  content: "\e43a"; }
+
+.fa-steam-symbol:before {
+  content: "\f3f6"; }
+:root, :host {
+  --fa-style-family-classic: 'Font Awesome 6 Free';
+  --fa-font-regular: normal 400 1em/1 'Font Awesome 6 Free'; }
+
+@font-face {
+  font-family: 'Font Awesome 6 Free';
+  font-style: normal;
+  font-weight: 400;
+  font-display: block;
+  src: url("../webfonts/fa-regular-400.woff2") format("woff2"), url("../webfonts/fa-regular-400.ttf") format("truetype"); }
+
+.far,
+.fa-regular {
+  font-weight: 400; }
+:root, :host {
+  --fa-style-family-classic: 'Font Awesome 6 Free';
+  --fa-font-solid: normal 900 1em/1 'Font Awesome 6 Free'; }
+
+@font-face {
+  font-family: 'Font Awesome 6 Free';
+  font-style: normal;
+  font-weight: 900;
+  font-display: block;
+  src: url("../webfonts/fa-solid-900.woff2") format("woff2"), url("../webfonts/fa-solid-900.ttf") format("truetype"); }
+
+.fas,
+.fa-solid {
+  font-weight: 900; }
+@font-face {
+  font-family: 'Font Awesome 5 Brands';
+  font-display: block;
+  font-weight: 400;
+  src: url("../webfonts/fa-brands-400.woff2") format("woff2"), url("../webfonts/fa-brands-400.ttf") format("truetype"); }
+
+@font-face {
+  font-family: 'Font Awesome 5 Free';
+  font-display: block;
+  font-weight: 900;
+  src: url("../webfonts/fa-solid-900.woff2") format("woff2"), url("../webfonts/fa-solid-900.ttf") format("truetype"); }
+
+@font-face {
+  font-family: 'Font Awesome 5 Free';
+  font-display: block;
+  font-weight: 400;
+  src: url("../webfonts/fa-regular-400.woff2") format("woff2"), url("../webfonts/fa-regular-400.ttf") format("truetype"); }
+@font-face {
+  font-family: 'FontAwesome';
+  font-display: block;
+  src: url("../webfonts/fa-solid-900.woff2") format("woff2"), url("../webfonts/fa-solid-900.ttf") format("truetype"); }
+
+@font-face {
+  font-family: 'FontAwesome';
+  font-display: block;
+  src: url("../webfonts/fa-brands-400.woff2") format("woff2"), url("../webfonts/fa-brands-400.ttf") format("truetype"); }
+
+@font-face {
+  font-family: 'FontAwesome';
+  font-display: block;
+  src: url("../webfonts/fa-regular-400.woff2") format("woff2"), url("../webfonts/fa-regular-400.ttf") format("truetype"); }
+
+@font-face {
+  font-family: 'FontAwesome';
+  font-display: block;
+  src: url("../webfonts/fa-v4compatibility.woff2") format("woff2"), url("../webfonts/fa-v4compatibility.ttf") format("truetype"); }
diff --git a/docs/deps/font-awesome-6.5.2/css/all.min.css b/docs/deps/font-awesome-6.5.2/css/all.min.css
new file mode 100644
index 00000000..269bceea
--- /dev/null
+++ b/docs/deps/font-awesome-6.5.2/css/all.min.css
@@ -0,0 +1,9 @@
+/*!
+ * Font Awesome Free 6.5.2 by @fontawesome - https://fontawesome.com
+ * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License)
+ * Copyright 2024 Fonticons, Inc.
+ */
+.fa{font-family:var(--fa-style-family,"Font Awesome 6 Free");font-weight:var(--fa-style,900)}.fa,.fa-brands,.fa-classic,.fa-regular,.fa-sharp,.fa-solid,.fab,.far,.fas{-moz-osx-font-smoothing:grayscale;-webkit-font-smoothing:antialiased;display:var(--fa-display,inline-block);font-style:normal;font-variant:normal;line-height:1;text-rendering:auto}.fa-classic,.fa-regular,.fa-solid,.far,.fas{font-family:"Font Awesome 6 Free"}.fa-brands,.fab{font-family:"Font Awesome 6 Brands"}.fa-1x{font-size:1em}.fa-2x{font-size:2em}.fa-3x{font-size:3em}.fa-4x{font-size:4em}.fa-5x{font-size:5em}.fa-6x{font-size:6em}.fa-7x{font-size:7em}.fa-8x{font-size:8em}.fa-9x{font-size:9em}.fa-10x{font-size:10em}.fa-2xs{font-size:.625em;line-height:.1em;vertical-align:.225em}.fa-xs{font-size:.75em;line-height:.08333em;vertical-align:.125em}.fa-sm{font-size:.875em;line-height:.07143em;vertical-align:.05357em}.fa-lg{font-size:1.25em;line-height:.05em;vertical-align:-.075em}.fa-xl{font-size:1.5em;line-height:.04167em;vertical-align:-.125em}.fa-2xl{font-size:2em;line-height:.03125em;vertical-align:-.1875em}.fa-fw{text-align:center;width:1.25em}.fa-ul{list-style-type:none;margin-left:var(--fa-li-margin,2.5em);padding-left:0}.fa-ul>li{position:relative}.fa-li{left:calc(var(--fa-li-width, 2em)*-1);position:absolute;text-align:center;width:var(--fa-li-width,2em);line-height:inherit}.fa-border{border-radius:var(--fa-border-radius,.1em);border:var(--fa-border-width,.08em) var(--fa-border-style,solid) var(--fa-border-color,#eee);padding:var(--fa-border-padding,.2em .25em .15em)}.fa-pull-left{float:left;margin-right:var(--fa-pull-margin,.3em)}.fa-pull-right{float:right;margin-left:var(--fa-pull-margin,.3em)}.fa-beat{-webkit-animation-name:fa-beat;animation-name:fa-beat;-webkit-animation-delay:var(--fa-animation-delay,0s);animation-delay:var(--fa-animation-delay,0s);-webkit-animation-direction:var(--fa-animation-direction,normal);animation-direction:var(--fa-animation-direction,normal);-webkit-animation-duration:var(--fa-animation-duration,1s);animation-duration:var(--fa-animation-duration,1s);-webkit-animation-iteration-count:var(--fa-animation-iteration-count,infinite);animation-iteration-count:var(--fa-animation-iteration-count,infinite);-webkit-animation-timing-function:var(--fa-animation-timing,ease-in-out);animation-timing-function:var(--fa-animation-timing,ease-in-out)}.fa-bounce{-webkit-animation-name:fa-bounce;animation-name:fa-bounce;-webkit-animation-delay:var(--fa-animation-delay,0s);animation-delay:var(--fa-animation-delay,0s);-webkit-animation-direction:var(--fa-animation-direction,normal);animation-direction:var(--fa-animation-direction,normal);-webkit-animation-duration:var(--fa-animation-duration,1s);animation-duration:var(--fa-animation-duration,1s);-webkit-animation-iteration-count:var(--fa-animation-iteration-count,infinite);animation-iteration-count:var(--fa-animation-iteration-count,infinite);-webkit-animation-timing-function:var(--fa-animation-timing,cubic-bezier(.28,.84,.42,1));animation-timing-function:var(--fa-animation-timing,cubic-bezier(.28,.84,.42,1))}.fa-fade{-webkit-animation-name:fa-fade;animation-name:fa-fade;-webkit-animation-iteration-count:var(--fa-animation-iteration-count,infinite);animation-iteration-count:var(--fa-animation-iteration-count,infinite);-webkit-animation-timing-function:var(--fa-animation-timing,cubic-bezier(.4,0,.6,1));animation-timing-function:var(--fa-animation-timing,cubic-bezier(.4,0,.6,1))}.fa-beat-fade,.fa-fade{-webkit-animation-delay:var(--fa-animation-delay,0s);animation-delay:var(--fa-animation-delay,0s);-webkit-animation-direction:var(--fa-animation-direction,normal);animation-direction:var(--fa-animation-direction,normal);-webkit-animation-duration:var(--fa-animation-duration,1s);animation-duration:var(--fa-animation-duration,1s)}.fa-beat-fade{-webkit-animation-name:fa-beat-fade;animation-name:fa-beat-fade;-webkit-animation-iteration-count:var(--fa-animation-iteration-count,infinite);animation-iteration-count:var(--fa-animation-iteration-count,infinite);-webkit-animation-timing-function:var(--fa-animation-timing,cubic-bezier(.4,0,.6,1));animation-timing-function:var(--fa-animation-timing,cubic-bezier(.4,0,.6,1))}.fa-flip{-webkit-animation-name:fa-flip;animation-name:fa-flip;-webkit-animation-delay:var(--fa-animation-delay,0s);animation-delay:var(--fa-animation-delay,0s);-webkit-animation-direction:var(--fa-animation-direction,normal);animation-direction:var(--fa-animation-direction,normal);-webkit-animation-duration:var(--fa-animation-duration,1s);animation-duration:var(--fa-animation-duration,1s);-webkit-animation-iteration-count:var(--fa-animation-iteration-count,infinite);animation-iteration-count:var(--fa-animation-iteration-count,infinite);-webkit-animation-timing-function:var(--fa-animation-timing,ease-in-out);animation-timing-function:var(--fa-animation-timing,ease-in-out)}.fa-shake{-webkit-animation-name:fa-shake;animation-name:fa-shake;-webkit-animation-duration:var(--fa-animation-duration,1s);animation-duration:var(--fa-animation-duration,1s);-webkit-animation-iteration-count:var(--fa-animation-iteration-count,infinite);animation-iteration-count:var(--fa-animation-iteration-count,infinite);-webkit-animation-timing-function:var(--fa-animation-timing,linear);animation-timing-function:var(--fa-animation-timing,linear)}.fa-shake,.fa-spin{-webkit-animation-delay:var(--fa-animation-delay,0s);animation-delay:var(--fa-animation-delay,0s);-webkit-animation-direction:var(--fa-animation-direction,normal);animation-direction:var(--fa-animation-direction,normal)}.fa-spin{-webkit-animation-name:fa-spin;animation-name:fa-spin;-webkit-animation-duration:var(--fa-animation-duration,2s);animation-duration:var(--fa-animation-duration,2s);-webkit-animation-iteration-count:var(--fa-animation-iteration-count,infinite);animation-iteration-count:var(--fa-animation-iteration-count,infinite);-webkit-animation-timing-function:var(--fa-animation-timing,linear);animation-timing-function:var(--fa-animation-timing,linear)}.fa-spin-reverse{--fa-animation-direction:reverse}.fa-pulse,.fa-spin-pulse{-webkit-animation-name:fa-spin;animation-name:fa-spin;-webkit-animation-direction:var(--fa-animation-direction,normal);animation-direction:var(--fa-animation-direction,normal);-webkit-animation-duration:var(--fa-animation-duration,1s);animation-duration:var(--fa-animation-duration,1s);-webkit-animation-iteration-count:var(--fa-animation-iteration-count,infinite);animation-iteration-count:var(--fa-animation-iteration-count,infinite);-webkit-animation-timing-function:var(--fa-animation-timing,steps(8));animation-timing-function:var(--fa-animation-timing,steps(8))}@media (prefers-reduced-motion:reduce){.fa-beat,.fa-beat-fade,.fa-bounce,.fa-fade,.fa-flip,.fa-pulse,.fa-shake,.fa-spin,.fa-spin-pulse{-webkit-animation-delay:-1ms;animation-delay:-1ms;-webkit-animation-duration:1ms;animation-duration:1ms;-webkit-animation-iteration-count:1;animation-iteration-count:1;-webkit-transition-delay:0s;transition-delay:0s;-webkit-transition-duration:0s;transition-duration:0s}}@-webkit-keyframes fa-beat{0%,90%{-webkit-transform:scale(1);transform:scale(1)}45%{-webkit-transform:scale(var(--fa-beat-scale,1.25));transform:scale(var(--fa-beat-scale,1.25))}}@keyframes fa-beat{0%,90%{-webkit-transform:scale(1);transform:scale(1)}45%{-webkit-transform:scale(var(--fa-beat-scale,1.25));transform:scale(var(--fa-beat-scale,1.25))}}@-webkit-keyframes fa-bounce{0%{-webkit-transform:scale(1) translateY(0);transform:scale(1) translateY(0)}10%{-webkit-transform:scale(var(--fa-bounce-start-scale-x,1.1),var(--fa-bounce-start-scale-y,.9)) translateY(0);transform:scale(var(--fa-bounce-start-scale-x,1.1),var(--fa-bounce-start-scale-y,.9)) translateY(0)}30%{-webkit-transform:scale(var(--fa-bounce-jump-scale-x,.9),var(--fa-bounce-jump-scale-y,1.1)) translateY(var(--fa-bounce-height,-.5em));transform:scale(var(--fa-bounce-jump-scale-x,.9),var(--fa-bounce-jump-scale-y,1.1)) translateY(var(--fa-bounce-height,-.5em))}50%{-webkit-transform:scale(var(--fa-bounce-land-scale-x,1.05),var(--fa-bounce-land-scale-y,.95)) translateY(0);transform:scale(var(--fa-bounce-land-scale-x,1.05),var(--fa-bounce-land-scale-y,.95)) translateY(0)}57%{-webkit-transform:scale(1) translateY(var(--fa-bounce-rebound,-.125em));transform:scale(1) translateY(var(--fa-bounce-rebound,-.125em))}64%{-webkit-transform:scale(1) translateY(0);transform:scale(1) translateY(0)}to{-webkit-transform:scale(1) translateY(0);transform:scale(1) translateY(0)}}@keyframes fa-bounce{0%{-webkit-transform:scale(1) translateY(0);transform:scale(1) translateY(0)}10%{-webkit-transform:scale(var(--fa-bounce-start-scale-x,1.1),var(--fa-bounce-start-scale-y,.9)) translateY(0);transform:scale(var(--fa-bounce-start-scale-x,1.1),var(--fa-bounce-start-scale-y,.9)) translateY(0)}30%{-webkit-transform:scale(var(--fa-bounce-jump-scale-x,.9),var(--fa-bounce-jump-scale-y,1.1)) translateY(var(--fa-bounce-height,-.5em));transform:scale(var(--fa-bounce-jump-scale-x,.9),var(--fa-bounce-jump-scale-y,1.1)) translateY(var(--fa-bounce-height,-.5em))}50%{-webkit-transform:scale(var(--fa-bounce-land-scale-x,1.05),var(--fa-bounce-land-scale-y,.95)) translateY(0);transform:scale(var(--fa-bounce-land-scale-x,1.05),var(--fa-bounce-land-scale-y,.95)) translateY(0)}57%{-webkit-transform:scale(1) translateY(var(--fa-bounce-rebound,-.125em));transform:scale(1) translateY(var(--fa-bounce-rebound,-.125em))}64%{-webkit-transform:scale(1) translateY(0);transform:scale(1) translateY(0)}to{-webkit-transform:scale(1) translateY(0);transform:scale(1) translateY(0)}}@-webkit-keyframes fa-fade{50%{opacity:var(--fa-fade-opacity,.4)}}@keyframes fa-fade{50%{opacity:var(--fa-fade-opacity,.4)}}@-webkit-keyframes fa-beat-fade{0%,to{opacity:var(--fa-beat-fade-opacity,.4);-webkit-transform:scale(1);transform:scale(1)}50%{opacity:1;-webkit-transform:scale(var(--fa-beat-fade-scale,1.125));transform:scale(var(--fa-beat-fade-scale,1.125))}}@keyframes fa-beat-fade{0%,to{opacity:var(--fa-beat-fade-opacity,.4);-webkit-transform:scale(1);transform:scale(1)}50%{opacity:1;-webkit-transform:scale(var(--fa-beat-fade-scale,1.125));transform:scale(var(--fa-beat-fade-scale,1.125))}}@-webkit-keyframes fa-flip{50%{-webkit-transform:rotate3d(var(--fa-flip-x,0),var(--fa-flip-y,1),var(--fa-flip-z,0),var(--fa-flip-angle,-180deg));transform:rotate3d(var(--fa-flip-x,0),var(--fa-flip-y,1),var(--fa-flip-z,0),var(--fa-flip-angle,-180deg))}}@keyframes fa-flip{50%{-webkit-transform:rotate3d(var(--fa-flip-x,0),var(--fa-flip-y,1),var(--fa-flip-z,0),var(--fa-flip-angle,-180deg));transform:rotate3d(var(--fa-flip-x,0),var(--fa-flip-y,1),var(--fa-flip-z,0),var(--fa-flip-angle,-180deg))}}@-webkit-keyframes fa-shake{0%{-webkit-transform:rotate(-15deg);transform:rotate(-15deg)}4%{-webkit-transform:rotate(15deg);transform:rotate(15deg)}8%,24%{-webkit-transform:rotate(-18deg);transform:rotate(-18deg)}12%,28%{-webkit-transform:rotate(18deg);transform:rotate(18deg)}16%{-webkit-transform:rotate(-22deg);transform:rotate(-22deg)}20%{-webkit-transform:rotate(22deg);transform:rotate(22deg)}32%{-webkit-transform:rotate(-12deg);transform:rotate(-12deg)}36%{-webkit-transform:rotate(12deg);transform:rotate(12deg)}40%,to{-webkit-transform:rotate(0deg);transform:rotate(0deg)}}@keyframes fa-shake{0%{-webkit-transform:rotate(-15deg);transform:rotate(-15deg)}4%{-webkit-transform:rotate(15deg);transform:rotate(15deg)}8%,24%{-webkit-transform:rotate(-18deg);transform:rotate(-18deg)}12%,28%{-webkit-transform:rotate(18deg);transform:rotate(18deg)}16%{-webkit-transform:rotate(-22deg);transform:rotate(-22deg)}20%{-webkit-transform:rotate(22deg);transform:rotate(22deg)}32%{-webkit-transform:rotate(-12deg);transform:rotate(-12deg)}36%{-webkit-transform:rotate(12deg);transform:rotate(12deg)}40%,to{-webkit-transform:rotate(0deg);transform:rotate(0deg)}}@-webkit-keyframes fa-spin{0%{-webkit-transform:rotate(0deg);transform:rotate(0deg)}to{-webkit-transform:rotate(1turn);transform:rotate(1turn)}}@keyframes fa-spin{0%{-webkit-transform:rotate(0deg);transform:rotate(0deg)}to{-webkit-transform:rotate(1turn);transform:rotate(1turn)}}.fa-rotate-90{-webkit-transform:rotate(90deg);transform:rotate(90deg)}.fa-rotate-180{-webkit-transform:rotate(180deg);transform:rotate(180deg)}.fa-rotate-270{-webkit-transform:rotate(270deg);transform:rotate(270deg)}.fa-flip-horizontal{-webkit-transform:scaleX(-1);transform:scaleX(-1)}.fa-flip-vertical{-webkit-transform:scaleY(-1);transform:scaleY(-1)}.fa-flip-both,.fa-flip-horizontal.fa-flip-vertical{-webkit-transform:scale(-1);transform:scale(-1)}.fa-rotate-by{-webkit-transform:rotate(var(--fa-rotate-angle,0));transform:rotate(var(--fa-rotate-angle,0))}.fa-stack{display:inline-block;height:2em;line-height:2em;position:relative;vertical-align:middle;width:2.5em}.fa-stack-1x,.fa-stack-2x{left:0;position:absolute;text-align:center;width:100%;z-index:var(--fa-stack-z-index,auto)}.fa-stack-1x{line-height:inherit}.fa-stack-2x{font-size:2em}.fa-inverse{color:var(--fa-inverse,#fff)}
+
+.fa-0:before{content:"\30"}.fa-1:before{content:"\31"}.fa-2:before{content:"\32"}.fa-3:before{content:"\33"}.fa-4:before{content:"\34"}.fa-5:before{content:"\35"}.fa-6:before{content:"\36"}.fa-7:before{content:"\37"}.fa-8:before{content:"\38"}.fa-9:before{content:"\39"}.fa-fill-drip:before{content:"\f576"}.fa-arrows-to-circle:before{content:"\e4bd"}.fa-chevron-circle-right:before,.fa-circle-chevron-right:before{content:"\f138"}.fa-at:before{content:"\40"}.fa-trash-alt:before,.fa-trash-can:before{content:"\f2ed"}.fa-text-height:before{content:"\f034"}.fa-user-times:before,.fa-user-xmark:before{content:"\f235"}.fa-stethoscope:before{content:"\f0f1"}.fa-comment-alt:before,.fa-message:before{content:"\f27a"}.fa-info:before{content:"\f129"}.fa-compress-alt:before,.fa-down-left-and-up-right-to-center:before{content:"\f422"}.fa-explosion:before{content:"\e4e9"}.fa-file-alt:before,.fa-file-lines:before,.fa-file-text:before{content:"\f15c"}.fa-wave-square:before{content:"\f83e"}.fa-ring:before{content:"\f70b"}.fa-building-un:before{content:"\e4d9"}.fa-dice-three:before{content:"\f527"}.fa-calendar-alt:before,.fa-calendar-days:before{content:"\f073"}.fa-anchor-circle-check:before{content:"\e4aa"}.fa-building-circle-arrow-right:before{content:"\e4d1"}.fa-volleyball-ball:before,.fa-volleyball:before{content:"\f45f"}.fa-arrows-up-to-line:before{content:"\e4c2"}.fa-sort-desc:before,.fa-sort-down:before{content:"\f0dd"}.fa-circle-minus:before,.fa-minus-circle:before{content:"\f056"}.fa-door-open:before{content:"\f52b"}.fa-right-from-bracket:before,.fa-sign-out-alt:before{content:"\f2f5"}.fa-atom:before{content:"\f5d2"}.fa-soap:before{content:"\e06e"}.fa-heart-music-camera-bolt:before,.fa-icons:before{content:"\f86d"}.fa-microphone-alt-slash:before,.fa-microphone-lines-slash:before{content:"\f539"}.fa-bridge-circle-check:before{content:"\e4c9"}.fa-pump-medical:before{content:"\e06a"}.fa-fingerprint:before{content:"\f577"}.fa-hand-point-right:before{content:"\f0a4"}.fa-magnifying-glass-location:before,.fa-search-location:before{content:"\f689"}.fa-forward-step:before,.fa-step-forward:before{content:"\f051"}.fa-face-smile-beam:before,.fa-smile-beam:before{content:"\f5b8"}.fa-flag-checkered:before{content:"\f11e"}.fa-football-ball:before,.fa-football:before{content:"\f44e"}.fa-school-circle-exclamation:before{content:"\e56c"}.fa-crop:before{content:"\f125"}.fa-angle-double-down:before,.fa-angles-down:before{content:"\f103"}.fa-users-rectangle:before{content:"\e594"}.fa-people-roof:before{content:"\e537"}.fa-people-line:before{content:"\e534"}.fa-beer-mug-empty:before,.fa-beer:before{content:"\f0fc"}.fa-diagram-predecessor:before{content:"\e477"}.fa-arrow-up-long:before,.fa-long-arrow-up:before{content:"\f176"}.fa-burn:before,.fa-fire-flame-simple:before{content:"\f46a"}.fa-male:before,.fa-person:before{content:"\f183"}.fa-laptop:before{content:"\f109"}.fa-file-csv:before{content:"\f6dd"}.fa-menorah:before{content:"\f676"}.fa-truck-plane:before{content:"\e58f"}.fa-record-vinyl:before{content:"\f8d9"}.fa-face-grin-stars:before,.fa-grin-stars:before{content:"\f587"}.fa-bong:before{content:"\f55c"}.fa-pastafarianism:before,.fa-spaghetti-monster-flying:before{content:"\f67b"}.fa-arrow-down-up-across-line:before{content:"\e4af"}.fa-spoon:before,.fa-utensil-spoon:before{content:"\f2e5"}.fa-jar-wheat:before{content:"\e517"}.fa-envelopes-bulk:before,.fa-mail-bulk:before{content:"\f674"}.fa-file-circle-exclamation:before{content:"\e4eb"}.fa-circle-h:before,.fa-hospital-symbol:before{content:"\f47e"}.fa-pager:before{content:"\f815"}.fa-address-book:before,.fa-contact-book:before{content:"\f2b9"}.fa-strikethrough:before{content:"\f0cc"}.fa-k:before{content:"\4b"}.fa-landmark-flag:before{content:"\e51c"}.fa-pencil-alt:before,.fa-pencil:before{content:"\f303"}.fa-backward:before{content:"\f04a"}.fa-caret-right:before{content:"\f0da"}.fa-comments:before{content:"\f086"}.fa-file-clipboard:before,.fa-paste:before{content:"\f0ea"}.fa-code-pull-request:before{content:"\e13c"}.fa-clipboard-list:before{content:"\f46d"}.fa-truck-loading:before,.fa-truck-ramp-box:before{content:"\f4de"}.fa-user-check:before{content:"\f4fc"}.fa-vial-virus:before{content:"\e597"}.fa-sheet-plastic:before{content:"\e571"}.fa-blog:before{content:"\f781"}.fa-user-ninja:before{content:"\f504"}.fa-person-arrow-up-from-line:before{content:"\e539"}.fa-scroll-torah:before,.fa-torah:before{content:"\f6a0"}.fa-broom-ball:before,.fa-quidditch-broom-ball:before,.fa-quidditch:before{content:"\f458"}.fa-toggle-off:before{content:"\f204"}.fa-archive:before,.fa-box-archive:before{content:"\f187"}.fa-person-drowning:before{content:"\e545"}.fa-arrow-down-9-1:before,.fa-sort-numeric-desc:before,.fa-sort-numeric-down-alt:before{content:"\f886"}.fa-face-grin-tongue-squint:before,.fa-grin-tongue-squint:before{content:"\f58a"}.fa-spray-can:before{content:"\f5bd"}.fa-truck-monster:before{content:"\f63b"}.fa-w:before{content:"\57"}.fa-earth-africa:before,.fa-globe-africa:before{content:"\f57c"}.fa-rainbow:before{content:"\f75b"}.fa-circle-notch:before{content:"\f1ce"}.fa-tablet-alt:before,.fa-tablet-screen-button:before{content:"\f3fa"}.fa-paw:before{content:"\f1b0"}.fa-cloud:before{content:"\f0c2"}.fa-trowel-bricks:before{content:"\e58a"}.fa-face-flushed:before,.fa-flushed:before{content:"\f579"}.fa-hospital-user:before{content:"\f80d"}.fa-tent-arrow-left-right:before{content:"\e57f"}.fa-gavel:before,.fa-legal:before{content:"\f0e3"}.fa-binoculars:before{content:"\f1e5"}.fa-microphone-slash:before{content:"\f131"}.fa-box-tissue:before{content:"\e05b"}.fa-motorcycle:before{content:"\f21c"}.fa-bell-concierge:before,.fa-concierge-bell:before{content:"\f562"}.fa-pen-ruler:before,.fa-pencil-ruler:before{content:"\f5ae"}.fa-people-arrows-left-right:before,.fa-people-arrows:before{content:"\e068"}.fa-mars-and-venus-burst:before{content:"\e523"}.fa-caret-square-right:before,.fa-square-caret-right:before{content:"\f152"}.fa-cut:before,.fa-scissors:before{content:"\f0c4"}.fa-sun-plant-wilt:before{content:"\e57a"}.fa-toilets-portable:before{content:"\e584"}.fa-hockey-puck:before{content:"\f453"}.fa-table:before{content:"\f0ce"}.fa-magnifying-glass-arrow-right:before{content:"\e521"}.fa-digital-tachograph:before,.fa-tachograph-digital:before{content:"\f566"}.fa-users-slash:before{content:"\e073"}.fa-clover:before{content:"\e139"}.fa-mail-reply:before,.fa-reply:before{content:"\f3e5"}.fa-star-and-crescent:before{content:"\f699"}.fa-house-fire:before{content:"\e50c"}.fa-minus-square:before,.fa-square-minus:before{content:"\f146"}.fa-helicopter:before{content:"\f533"}.fa-compass:before{content:"\f14e"}.fa-caret-square-down:before,.fa-square-caret-down:before{content:"\f150"}.fa-file-circle-question:before{content:"\e4ef"}.fa-laptop-code:before{content:"\f5fc"}.fa-swatchbook:before{content:"\f5c3"}.fa-prescription-bottle:before{content:"\f485"}.fa-bars:before,.fa-navicon:before{content:"\f0c9"}.fa-people-group:before{content:"\e533"}.fa-hourglass-3:before,.fa-hourglass-end:before{content:"\f253"}.fa-heart-broken:before,.fa-heart-crack:before{content:"\f7a9"}.fa-external-link-square-alt:before,.fa-square-up-right:before{content:"\f360"}.fa-face-kiss-beam:before,.fa-kiss-beam:before{content:"\f597"}.fa-film:before{content:"\f008"}.fa-ruler-horizontal:before{content:"\f547"}.fa-people-robbery:before{content:"\e536"}.fa-lightbulb:before{content:"\f0eb"}.fa-caret-left:before{content:"\f0d9"}.fa-circle-exclamation:before,.fa-exclamation-circle:before{content:"\f06a"}.fa-school-circle-xmark:before{content:"\e56d"}.fa-arrow-right-from-bracket:before,.fa-sign-out:before{content:"\f08b"}.fa-chevron-circle-down:before,.fa-circle-chevron-down:before{content:"\f13a"}.fa-unlock-alt:before,.fa-unlock-keyhole:before{content:"\f13e"}.fa-cloud-showers-heavy:before{content:"\f740"}.fa-headphones-alt:before,.fa-headphones-simple:before{content:"\f58f"}.fa-sitemap:before{content:"\f0e8"}.fa-circle-dollar-to-slot:before,.fa-donate:before{content:"\f4b9"}.fa-memory:before{content:"\f538"}.fa-road-spikes:before{content:"\e568"}.fa-fire-burner:before{content:"\e4f1"}.fa-flag:before{content:"\f024"}.fa-hanukiah:before{content:"\f6e6"}.fa-feather:before{content:"\f52d"}.fa-volume-down:before,.fa-volume-low:before{content:"\f027"}.fa-comment-slash:before{content:"\f4b3"}.fa-cloud-sun-rain:before{content:"\f743"}.fa-compress:before{content:"\f066"}.fa-wheat-alt:before,.fa-wheat-awn:before{content:"\e2cd"}.fa-ankh:before{content:"\f644"}.fa-hands-holding-child:before{content:"\e4fa"}.fa-asterisk:before{content:"\2a"}.fa-check-square:before,.fa-square-check:before{content:"\f14a"}.fa-peseta-sign:before{content:"\e221"}.fa-header:before,.fa-heading:before{content:"\f1dc"}.fa-ghost:before{content:"\f6e2"}.fa-list-squares:before,.fa-list:before{content:"\f03a"}.fa-phone-square-alt:before,.fa-square-phone-flip:before{content:"\f87b"}.fa-cart-plus:before{content:"\f217"}.fa-gamepad:before{content:"\f11b"}.fa-circle-dot:before,.fa-dot-circle:before{content:"\f192"}.fa-dizzy:before,.fa-face-dizzy:before{content:"\f567"}.fa-egg:before{content:"\f7fb"}.fa-house-medical-circle-xmark:before{content:"\e513"}.fa-campground:before{content:"\f6bb"}.fa-folder-plus:before{content:"\f65e"}.fa-futbol-ball:before,.fa-futbol:before,.fa-soccer-ball:before{content:"\f1e3"}.fa-paint-brush:before,.fa-paintbrush:before{content:"\f1fc"}.fa-lock:before{content:"\f023"}.fa-gas-pump:before{content:"\f52f"}.fa-hot-tub-person:before,.fa-hot-tub:before{content:"\f593"}.fa-map-location:before,.fa-map-marked:before{content:"\f59f"}.fa-house-flood-water:before{content:"\e50e"}.fa-tree:before{content:"\f1bb"}.fa-bridge-lock:before{content:"\e4cc"}.fa-sack-dollar:before{content:"\f81d"}.fa-edit:before,.fa-pen-to-square:before{content:"\f044"}.fa-car-side:before{content:"\f5e4"}.fa-share-alt:before,.fa-share-nodes:before{content:"\f1e0"}.fa-heart-circle-minus:before{content:"\e4ff"}.fa-hourglass-2:before,.fa-hourglass-half:before{content:"\f252"}.fa-microscope:before{content:"\f610"}.fa-sink:before{content:"\e06d"}.fa-bag-shopping:before,.fa-shopping-bag:before{content:"\f290"}.fa-arrow-down-z-a:before,.fa-sort-alpha-desc:before,.fa-sort-alpha-down-alt:before{content:"\f881"}.fa-mitten:before{content:"\f7b5"}.fa-person-rays:before{content:"\e54d"}.fa-users:before{content:"\f0c0"}.fa-eye-slash:before{content:"\f070"}.fa-flask-vial:before{content:"\e4f3"}.fa-hand-paper:before,.fa-hand:before{content:"\f256"}.fa-om:before{content:"\f679"}.fa-worm:before{content:"\e599"}.fa-house-circle-xmark:before{content:"\e50b"}.fa-plug:before{content:"\f1e6"}.fa-chevron-up:before{content:"\f077"}.fa-hand-spock:before{content:"\f259"}.fa-stopwatch:before{content:"\f2f2"}.fa-face-kiss:before,.fa-kiss:before{content:"\f596"}.fa-bridge-circle-xmark:before{content:"\e4cb"}.fa-face-grin-tongue:before,.fa-grin-tongue:before{content:"\f589"}.fa-chess-bishop:before{content:"\f43a"}.fa-face-grin-wink:before,.fa-grin-wink:before{content:"\f58c"}.fa-deaf:before,.fa-deafness:before,.fa-ear-deaf:before,.fa-hard-of-hearing:before{content:"\f2a4"}.fa-road-circle-check:before{content:"\e564"}.fa-dice-five:before{content:"\f523"}.fa-rss-square:before,.fa-square-rss:before{content:"\f143"}.fa-land-mine-on:before{content:"\e51b"}.fa-i-cursor:before{content:"\f246"}.fa-stamp:before{content:"\f5bf"}.fa-stairs:before{content:"\e289"}.fa-i:before{content:"\49"}.fa-hryvnia-sign:before,.fa-hryvnia:before{content:"\f6f2"}.fa-pills:before{content:"\f484"}.fa-face-grin-wide:before,.fa-grin-alt:before{content:"\f581"}.fa-tooth:before{content:"\f5c9"}.fa-v:before{content:"\56"}.fa-bangladeshi-taka-sign:before{content:"\e2e6"}.fa-bicycle:before{content:"\f206"}.fa-rod-asclepius:before,.fa-rod-snake:before,.fa-staff-aesculapius:before,.fa-staff-snake:before{content:"\e579"}.fa-head-side-cough-slash:before{content:"\e062"}.fa-ambulance:before,.fa-truck-medical:before{content:"\f0f9"}.fa-wheat-awn-circle-exclamation:before{content:"\e598"}.fa-snowman:before{content:"\f7d0"}.fa-mortar-pestle:before{content:"\f5a7"}.fa-road-barrier:before{content:"\e562"}.fa-school:before{content:"\f549"}.fa-igloo:before{content:"\f7ae"}.fa-joint:before{content:"\f595"}.fa-angle-right:before{content:"\f105"}.fa-horse:before{content:"\f6f0"}.fa-q:before{content:"\51"}.fa-g:before{content:"\47"}.fa-notes-medical:before{content:"\f481"}.fa-temperature-2:before,.fa-temperature-half:before,.fa-thermometer-2:before,.fa-thermometer-half:before{content:"\f2c9"}.fa-dong-sign:before{content:"\e169"}.fa-capsules:before{content:"\f46b"}.fa-poo-bolt:before,.fa-poo-storm:before{content:"\f75a"}.fa-face-frown-open:before,.fa-frown-open:before{content:"\f57a"}.fa-hand-point-up:before{content:"\f0a6"}.fa-money-bill:before{content:"\f0d6"}.fa-bookmark:before{content:"\f02e"}.fa-align-justify:before{content:"\f039"}.fa-umbrella-beach:before{content:"\f5ca"}.fa-helmet-un:before{content:"\e503"}.fa-bullseye:before{content:"\f140"}.fa-bacon:before{content:"\f7e5"}.fa-hand-point-down:before{content:"\f0a7"}.fa-arrow-up-from-bracket:before{content:"\e09a"}.fa-folder-blank:before,.fa-folder:before{content:"\f07b"}.fa-file-medical-alt:before,.fa-file-waveform:before{content:"\f478"}.fa-radiation:before{content:"\f7b9"}.fa-chart-simple:before{content:"\e473"}.fa-mars-stroke:before{content:"\f229"}.fa-vial:before{content:"\f492"}.fa-dashboard:before,.fa-gauge-med:before,.fa-gauge:before,.fa-tachometer-alt-average:before{content:"\f624"}.fa-magic-wand-sparkles:before,.fa-wand-magic-sparkles:before{content:"\e2ca"}.fa-e:before{content:"\45"}.fa-pen-alt:before,.fa-pen-clip:before{content:"\f305"}.fa-bridge-circle-exclamation:before{content:"\e4ca"}.fa-user:before{content:"\f007"}.fa-school-circle-check:before{content:"\e56b"}.fa-dumpster:before{content:"\f793"}.fa-shuttle-van:before,.fa-van-shuttle:before{content:"\f5b6"}.fa-building-user:before{content:"\e4da"}.fa-caret-square-left:before,.fa-square-caret-left:before{content:"\f191"}.fa-highlighter:before{content:"\f591"}.fa-key:before{content:"\f084"}.fa-bullhorn:before{content:"\f0a1"}.fa-globe:before{content:"\f0ac"}.fa-synagogue:before{content:"\f69b"}.fa-person-half-dress:before{content:"\e548"}.fa-road-bridge:before{content:"\e563"}.fa-location-arrow:before{content:"\f124"}.fa-c:before{content:"\43"}.fa-tablet-button:before{content:"\f10a"}.fa-building-lock:before{content:"\e4d6"}.fa-pizza-slice:before{content:"\f818"}.fa-money-bill-wave:before{content:"\f53a"}.fa-area-chart:before,.fa-chart-area:before{content:"\f1fe"}.fa-house-flag:before{content:"\e50d"}.fa-person-circle-minus:before{content:"\e540"}.fa-ban:before,.fa-cancel:before{content:"\f05e"}.fa-camera-rotate:before{content:"\e0d8"}.fa-air-freshener:before,.fa-spray-can-sparkles:before{content:"\f5d0"}.fa-star:before{content:"\f005"}.fa-repeat:before{content:"\f363"}.fa-cross:before{content:"\f654"}.fa-box:before{content:"\f466"}.fa-venus-mars:before{content:"\f228"}.fa-arrow-pointer:before,.fa-mouse-pointer:before{content:"\f245"}.fa-expand-arrows-alt:before,.fa-maximize:before{content:"\f31e"}.fa-charging-station:before{content:"\f5e7"}.fa-shapes:before,.fa-triangle-circle-square:before{content:"\f61f"}.fa-random:before,.fa-shuffle:before{content:"\f074"}.fa-person-running:before,.fa-running:before{content:"\f70c"}.fa-mobile-retro:before{content:"\e527"}.fa-grip-lines-vertical:before{content:"\f7a5"}.fa-spider:before{content:"\f717"}.fa-hands-bound:before{content:"\e4f9"}.fa-file-invoice-dollar:before{content:"\f571"}.fa-plane-circle-exclamation:before{content:"\e556"}.fa-x-ray:before{content:"\f497"}.fa-spell-check:before{content:"\f891"}.fa-slash:before{content:"\f715"}.fa-computer-mouse:before,.fa-mouse:before{content:"\f8cc"}.fa-arrow-right-to-bracket:before,.fa-sign-in:before{content:"\f090"}.fa-shop-slash:before,.fa-store-alt-slash:before{content:"\e070"}.fa-server:before{content:"\f233"}.fa-virus-covid-slash:before{content:"\e4a9"}.fa-shop-lock:before{content:"\e4a5"}.fa-hourglass-1:before,.fa-hourglass-start:before{content:"\f251"}.fa-blender-phone:before{content:"\f6b6"}.fa-building-wheat:before{content:"\e4db"}.fa-person-breastfeeding:before{content:"\e53a"}.fa-right-to-bracket:before,.fa-sign-in-alt:before{content:"\f2f6"}.fa-venus:before{content:"\f221"}.fa-passport:before{content:"\f5ab"}.fa-heart-pulse:before,.fa-heartbeat:before{content:"\f21e"}.fa-people-carry-box:before,.fa-people-carry:before{content:"\f4ce"}.fa-temperature-high:before{content:"\f769"}.fa-microchip:before{content:"\f2db"}.fa-crown:before{content:"\f521"}.fa-weight-hanging:before{content:"\f5cd"}.fa-xmarks-lines:before{content:"\e59a"}.fa-file-prescription:before{content:"\f572"}.fa-weight-scale:before,.fa-weight:before{content:"\f496"}.fa-user-friends:before,.fa-user-group:before{content:"\f500"}.fa-arrow-up-a-z:before,.fa-sort-alpha-up:before{content:"\f15e"}.fa-chess-knight:before{content:"\f441"}.fa-face-laugh-squint:before,.fa-laugh-squint:before{content:"\f59b"}.fa-wheelchair:before{content:"\f193"}.fa-arrow-circle-up:before,.fa-circle-arrow-up:before{content:"\f0aa"}.fa-toggle-on:before{content:"\f205"}.fa-person-walking:before,.fa-walking:before{content:"\f554"}.fa-l:before{content:"\4c"}.fa-fire:before{content:"\f06d"}.fa-bed-pulse:before,.fa-procedures:before{content:"\f487"}.fa-shuttle-space:before,.fa-space-shuttle:before{content:"\f197"}.fa-face-laugh:before,.fa-laugh:before{content:"\f599"}.fa-folder-open:before{content:"\f07c"}.fa-heart-circle-plus:before{content:"\e500"}.fa-code-fork:before{content:"\e13b"}.fa-city:before{content:"\f64f"}.fa-microphone-alt:before,.fa-microphone-lines:before{content:"\f3c9"}.fa-pepper-hot:before{content:"\f816"}.fa-unlock:before{content:"\f09c"}.fa-colon-sign:before{content:"\e140"}.fa-headset:before{content:"\f590"}.fa-store-slash:before{content:"\e071"}.fa-road-circle-xmark:before{content:"\e566"}.fa-user-minus:before{content:"\f503"}.fa-mars-stroke-up:before,.fa-mars-stroke-v:before{content:"\f22a"}.fa-champagne-glasses:before,.fa-glass-cheers:before{content:"\f79f"}.fa-clipboard:before{content:"\f328"}.fa-house-circle-exclamation:before{content:"\e50a"}.fa-file-arrow-up:before,.fa-file-upload:before{content:"\f574"}.fa-wifi-3:before,.fa-wifi-strong:before,.fa-wifi:before{content:"\f1eb"}.fa-bath:before,.fa-bathtub:before{content:"\f2cd"}.fa-underline:before{content:"\f0cd"}.fa-user-edit:before,.fa-user-pen:before{content:"\f4ff"}.fa-signature:before{content:"\f5b7"}.fa-stroopwafel:before{content:"\f551"}.fa-bold:before{content:"\f032"}.fa-anchor-lock:before{content:"\e4ad"}.fa-building-ngo:before{content:"\e4d7"}.fa-manat-sign:before{content:"\e1d5"}.fa-not-equal:before{content:"\f53e"}.fa-border-style:before,.fa-border-top-left:before{content:"\f853"}.fa-map-location-dot:before,.fa-map-marked-alt:before{content:"\f5a0"}.fa-jedi:before{content:"\f669"}.fa-poll:before,.fa-square-poll-vertical:before{content:"\f681"}.fa-mug-hot:before{content:"\f7b6"}.fa-battery-car:before,.fa-car-battery:before{content:"\f5df"}.fa-gift:before{content:"\f06b"}.fa-dice-two:before{content:"\f528"}.fa-chess-queen:before{content:"\f445"}.fa-glasses:before{content:"\f530"}.fa-chess-board:before{content:"\f43c"}.fa-building-circle-check:before{content:"\e4d2"}.fa-person-chalkboard:before{content:"\e53d"}.fa-mars-stroke-h:before,.fa-mars-stroke-right:before{content:"\f22b"}.fa-hand-back-fist:before,.fa-hand-rock:before{content:"\f255"}.fa-caret-square-up:before,.fa-square-caret-up:before{content:"\f151"}.fa-cloud-showers-water:before{content:"\e4e4"}.fa-bar-chart:before,.fa-chart-bar:before{content:"\f080"}.fa-hands-bubbles:before,.fa-hands-wash:before{content:"\e05e"}.fa-less-than-equal:before{content:"\f537"}.fa-train:before{content:"\f238"}.fa-eye-low-vision:before,.fa-low-vision:before{content:"\f2a8"}.fa-crow:before{content:"\f520"}.fa-sailboat:before{content:"\e445"}.fa-window-restore:before{content:"\f2d2"}.fa-plus-square:before,.fa-square-plus:before{content:"\f0fe"}.fa-torii-gate:before{content:"\f6a1"}.fa-frog:before{content:"\f52e"}.fa-bucket:before{content:"\e4cf"}.fa-image:before{content:"\f03e"}.fa-microphone:before{content:"\f130"}.fa-cow:before{content:"\f6c8"}.fa-caret-up:before{content:"\f0d8"}.fa-screwdriver:before{content:"\f54a"}.fa-folder-closed:before{content:"\e185"}.fa-house-tsunami:before{content:"\e515"}.fa-square-nfi:before{content:"\e576"}.fa-arrow-up-from-ground-water:before{content:"\e4b5"}.fa-glass-martini-alt:before,.fa-martini-glass:before{content:"\f57b"}.fa-rotate-back:before,.fa-rotate-backward:before,.fa-rotate-left:before,.fa-undo-alt:before{content:"\f2ea"}.fa-columns:before,.fa-table-columns:before{content:"\f0db"}.fa-lemon:before{content:"\f094"}.fa-head-side-mask:before{content:"\e063"}.fa-handshake:before{content:"\f2b5"}.fa-gem:before{content:"\f3a5"}.fa-dolly-box:before,.fa-dolly:before{content:"\f472"}.fa-smoking:before{content:"\f48d"}.fa-compress-arrows-alt:before,.fa-minimize:before{content:"\f78c"}.fa-monument:before{content:"\f5a6"}.fa-snowplow:before{content:"\f7d2"}.fa-angle-double-right:before,.fa-angles-right:before{content:"\f101"}.fa-cannabis:before{content:"\f55f"}.fa-circle-play:before,.fa-play-circle:before{content:"\f144"}.fa-tablets:before{content:"\f490"}.fa-ethernet:before{content:"\f796"}.fa-eur:before,.fa-euro-sign:before,.fa-euro:before{content:"\f153"}.fa-chair:before{content:"\f6c0"}.fa-check-circle:before,.fa-circle-check:before{content:"\f058"}.fa-circle-stop:before,.fa-stop-circle:before{content:"\f28d"}.fa-compass-drafting:before,.fa-drafting-compass:before{content:"\f568"}.fa-plate-wheat:before{content:"\e55a"}.fa-icicles:before{content:"\f7ad"}.fa-person-shelter:before{content:"\e54f"}.fa-neuter:before{content:"\f22c"}.fa-id-badge:before{content:"\f2c1"}.fa-marker:before{content:"\f5a1"}.fa-face-laugh-beam:before,.fa-laugh-beam:before{content:"\f59a"}.fa-helicopter-symbol:before{content:"\e502"}.fa-universal-access:before{content:"\f29a"}.fa-chevron-circle-up:before,.fa-circle-chevron-up:before{content:"\f139"}.fa-lari-sign:before{content:"\e1c8"}.fa-volcano:before{content:"\f770"}.fa-person-walking-dashed-line-arrow-right:before{content:"\e553"}.fa-gbp:before,.fa-pound-sign:before,.fa-sterling-sign:before{content:"\f154"}.fa-viruses:before{content:"\e076"}.fa-square-person-confined:before{content:"\e577"}.fa-user-tie:before{content:"\f508"}.fa-arrow-down-long:before,.fa-long-arrow-down:before{content:"\f175"}.fa-tent-arrow-down-to-line:before{content:"\e57e"}.fa-certificate:before{content:"\f0a3"}.fa-mail-reply-all:before,.fa-reply-all:before{content:"\f122"}.fa-suitcase:before{content:"\f0f2"}.fa-person-skating:before,.fa-skating:before{content:"\f7c5"}.fa-filter-circle-dollar:before,.fa-funnel-dollar:before{content:"\f662"}.fa-camera-retro:before{content:"\f083"}.fa-arrow-circle-down:before,.fa-circle-arrow-down:before{content:"\f0ab"}.fa-arrow-right-to-file:before,.fa-file-import:before{content:"\f56f"}.fa-external-link-square:before,.fa-square-arrow-up-right:before{content:"\f14c"}.fa-box-open:before{content:"\f49e"}.fa-scroll:before{content:"\f70e"}.fa-spa:before{content:"\f5bb"}.fa-location-pin-lock:before{content:"\e51f"}.fa-pause:before{content:"\f04c"}.fa-hill-avalanche:before{content:"\e507"}.fa-temperature-0:before,.fa-temperature-empty:before,.fa-thermometer-0:before,.fa-thermometer-empty:before{content:"\f2cb"}.fa-bomb:before{content:"\f1e2"}.fa-registered:before{content:"\f25d"}.fa-address-card:before,.fa-contact-card:before,.fa-vcard:before{content:"\f2bb"}.fa-balance-scale-right:before,.fa-scale-unbalanced-flip:before{content:"\f516"}.fa-subscript:before{content:"\f12c"}.fa-diamond-turn-right:before,.fa-directions:before{content:"\f5eb"}.fa-burst:before{content:"\e4dc"}.fa-house-laptop:before,.fa-laptop-house:before{content:"\e066"}.fa-face-tired:before,.fa-tired:before{content:"\f5c8"}.fa-money-bills:before{content:"\e1f3"}.fa-smog:before{content:"\f75f"}.fa-crutch:before{content:"\f7f7"}.fa-cloud-arrow-up:before,.fa-cloud-upload-alt:before,.fa-cloud-upload:before{content:"\f0ee"}.fa-palette:before{content:"\f53f"}.fa-arrows-turn-right:before{content:"\e4c0"}.fa-vest:before{content:"\e085"}.fa-ferry:before{content:"\e4ea"}.fa-arrows-down-to-people:before{content:"\e4b9"}.fa-seedling:before,.fa-sprout:before{content:"\f4d8"}.fa-arrows-alt-h:before,.fa-left-right:before{content:"\f337"}.fa-boxes-packing:before{content:"\e4c7"}.fa-arrow-circle-left:before,.fa-circle-arrow-left:before{content:"\f0a8"}.fa-group-arrows-rotate:before{content:"\e4f6"}.fa-bowl-food:before{content:"\e4c6"}.fa-candy-cane:before{content:"\f786"}.fa-arrow-down-wide-short:before,.fa-sort-amount-asc:before,.fa-sort-amount-down:before{content:"\f160"}.fa-cloud-bolt:before,.fa-thunderstorm:before{content:"\f76c"}.fa-remove-format:before,.fa-text-slash:before{content:"\f87d"}.fa-face-smile-wink:before,.fa-smile-wink:before{content:"\f4da"}.fa-file-word:before{content:"\f1c2"}.fa-file-powerpoint:before{content:"\f1c4"}.fa-arrows-h:before,.fa-arrows-left-right:before{content:"\f07e"}.fa-house-lock:before{content:"\e510"}.fa-cloud-arrow-down:before,.fa-cloud-download-alt:before,.fa-cloud-download:before{content:"\f0ed"}.fa-children:before{content:"\e4e1"}.fa-blackboard:before,.fa-chalkboard:before{content:"\f51b"}.fa-user-alt-slash:before,.fa-user-large-slash:before{content:"\f4fa"}.fa-envelope-open:before{content:"\f2b6"}.fa-handshake-alt-slash:before,.fa-handshake-simple-slash:before{content:"\e05f"}.fa-mattress-pillow:before{content:"\e525"}.fa-guarani-sign:before{content:"\e19a"}.fa-arrows-rotate:before,.fa-refresh:before,.fa-sync:before{content:"\f021"}.fa-fire-extinguisher:before{content:"\f134"}.fa-cruzeiro-sign:before{content:"\e152"}.fa-greater-than-equal:before{content:"\f532"}.fa-shield-alt:before,.fa-shield-halved:before{content:"\f3ed"}.fa-atlas:before,.fa-book-atlas:before{content:"\f558"}.fa-virus:before{content:"\e074"}.fa-envelope-circle-check:before{content:"\e4e8"}.fa-layer-group:before{content:"\f5fd"}.fa-arrows-to-dot:before{content:"\e4be"}.fa-archway:before{content:"\f557"}.fa-heart-circle-check:before{content:"\e4fd"}.fa-house-chimney-crack:before,.fa-house-damage:before{content:"\f6f1"}.fa-file-archive:before,.fa-file-zipper:before{content:"\f1c6"}.fa-square:before{content:"\f0c8"}.fa-glass-martini:before,.fa-martini-glass-empty:before{content:"\f000"}.fa-couch:before{content:"\f4b8"}.fa-cedi-sign:before{content:"\e0df"}.fa-italic:before{content:"\f033"}.fa-table-cells-column-lock:before{content:"\e678"}.fa-church:before{content:"\f51d"}.fa-comments-dollar:before{content:"\f653"}.fa-democrat:before{content:"\f747"}.fa-z:before{content:"\5a"}.fa-person-skiing:before,.fa-skiing:before{content:"\f7c9"}.fa-road-lock:before{content:"\e567"}.fa-a:before{content:"\41"}.fa-temperature-arrow-down:before,.fa-temperature-down:before{content:"\e03f"}.fa-feather-alt:before,.fa-feather-pointed:before{content:"\f56b"}.fa-p:before{content:"\50"}.fa-snowflake:before{content:"\f2dc"}.fa-newspaper:before{content:"\f1ea"}.fa-ad:before,.fa-rectangle-ad:before{content:"\f641"}.fa-arrow-circle-right:before,.fa-circle-arrow-right:before{content:"\f0a9"}.fa-filter-circle-xmark:before{content:"\e17b"}.fa-locust:before{content:"\e520"}.fa-sort:before,.fa-unsorted:before{content:"\f0dc"}.fa-list-1-2:before,.fa-list-numeric:before,.fa-list-ol:before{content:"\f0cb"}.fa-person-dress-burst:before{content:"\e544"}.fa-money-check-alt:before,.fa-money-check-dollar:before{content:"\f53d"}.fa-vector-square:before{content:"\f5cb"}.fa-bread-slice:before{content:"\f7ec"}.fa-language:before{content:"\f1ab"}.fa-face-kiss-wink-heart:before,.fa-kiss-wink-heart:before{content:"\f598"}.fa-filter:before{content:"\f0b0"}.fa-question:before{content:"\3f"}.fa-file-signature:before{content:"\f573"}.fa-arrows-alt:before,.fa-up-down-left-right:before{content:"\f0b2"}.fa-house-chimney-user:before{content:"\e065"}.fa-hand-holding-heart:before{content:"\f4be"}.fa-puzzle-piece:before{content:"\f12e"}.fa-money-check:before{content:"\f53c"}.fa-star-half-alt:before,.fa-star-half-stroke:before{content:"\f5c0"}.fa-code:before{content:"\f121"}.fa-glass-whiskey:before,.fa-whiskey-glass:before{content:"\f7a0"}.fa-building-circle-exclamation:before{content:"\e4d3"}.fa-magnifying-glass-chart:before{content:"\e522"}.fa-arrow-up-right-from-square:before,.fa-external-link:before{content:"\f08e"}.fa-cubes-stacked:before{content:"\e4e6"}.fa-krw:before,.fa-won-sign:before,.fa-won:before{content:"\f159"}.fa-virus-covid:before{content:"\e4a8"}.fa-austral-sign:before{content:"\e0a9"}.fa-f:before{content:"\46"}.fa-leaf:before{content:"\f06c"}.fa-road:before{content:"\f018"}.fa-cab:before,.fa-taxi:before{content:"\f1ba"}.fa-person-circle-plus:before{content:"\e541"}.fa-chart-pie:before,.fa-pie-chart:before{content:"\f200"}.fa-bolt-lightning:before{content:"\e0b7"}.fa-sack-xmark:before{content:"\e56a"}.fa-file-excel:before{content:"\f1c3"}.fa-file-contract:before{content:"\f56c"}.fa-fish-fins:before{content:"\e4f2"}.fa-building-flag:before{content:"\e4d5"}.fa-face-grin-beam:before,.fa-grin-beam:before{content:"\f582"}.fa-object-ungroup:before{content:"\f248"}.fa-poop:before{content:"\f619"}.fa-location-pin:before,.fa-map-marker:before{content:"\f041"}.fa-kaaba:before{content:"\f66b"}.fa-toilet-paper:before{content:"\f71e"}.fa-hard-hat:before,.fa-hat-hard:before,.fa-helmet-safety:before{content:"\f807"}.fa-eject:before{content:"\f052"}.fa-arrow-alt-circle-right:before,.fa-circle-right:before{content:"\f35a"}.fa-plane-circle-check:before{content:"\e555"}.fa-face-rolling-eyes:before,.fa-meh-rolling-eyes:before{content:"\f5a5"}.fa-object-group:before{content:"\f247"}.fa-chart-line:before,.fa-line-chart:before{content:"\f201"}.fa-mask-ventilator:before{content:"\e524"}.fa-arrow-right:before{content:"\f061"}.fa-map-signs:before,.fa-signs-post:before{content:"\f277"}.fa-cash-register:before{content:"\f788"}.fa-person-circle-question:before{content:"\e542"}.fa-h:before{content:"\48"}.fa-tarp:before{content:"\e57b"}.fa-screwdriver-wrench:before,.fa-tools:before{content:"\f7d9"}.fa-arrows-to-eye:before{content:"\e4bf"}.fa-plug-circle-bolt:before{content:"\e55b"}.fa-heart:before{content:"\f004"}.fa-mars-and-venus:before{content:"\f224"}.fa-home-user:before,.fa-house-user:before{content:"\e1b0"}.fa-dumpster-fire:before{content:"\f794"}.fa-house-crack:before{content:"\e3b1"}.fa-cocktail:before,.fa-martini-glass-citrus:before{content:"\f561"}.fa-face-surprise:before,.fa-surprise:before{content:"\f5c2"}.fa-bottle-water:before{content:"\e4c5"}.fa-circle-pause:before,.fa-pause-circle:before{content:"\f28b"}.fa-toilet-paper-slash:before{content:"\e072"}.fa-apple-alt:before,.fa-apple-whole:before{content:"\f5d1"}.fa-kitchen-set:before{content:"\e51a"}.fa-r:before{content:"\52"}.fa-temperature-1:before,.fa-temperature-quarter:before,.fa-thermometer-1:before,.fa-thermometer-quarter:before{content:"\f2ca"}.fa-cube:before{content:"\f1b2"}.fa-bitcoin-sign:before{content:"\e0b4"}.fa-shield-dog:before{content:"\e573"}.fa-solar-panel:before{content:"\f5ba"}.fa-lock-open:before{content:"\f3c1"}.fa-elevator:before{content:"\e16d"}.fa-money-bill-transfer:before{content:"\e528"}.fa-money-bill-trend-up:before{content:"\e529"}.fa-house-flood-water-circle-arrow-right:before{content:"\e50f"}.fa-poll-h:before,.fa-square-poll-horizontal:before{content:"\f682"}.fa-circle:before{content:"\f111"}.fa-backward-fast:before,.fa-fast-backward:before{content:"\f049"}.fa-recycle:before{content:"\f1b8"}.fa-user-astronaut:before{content:"\f4fb"}.fa-plane-slash:before{content:"\e069"}.fa-trademark:before{content:"\f25c"}.fa-basketball-ball:before,.fa-basketball:before{content:"\f434"}.fa-satellite-dish:before{content:"\f7c0"}.fa-arrow-alt-circle-up:before,.fa-circle-up:before{content:"\f35b"}.fa-mobile-alt:before,.fa-mobile-screen-button:before{content:"\f3cd"}.fa-volume-high:before,.fa-volume-up:before{content:"\f028"}.fa-users-rays:before{content:"\e593"}.fa-wallet:before{content:"\f555"}.fa-clipboard-check:before{content:"\f46c"}.fa-file-audio:before{content:"\f1c7"}.fa-burger:before,.fa-hamburger:before{content:"\f805"}.fa-wrench:before{content:"\f0ad"}.fa-bugs:before{content:"\e4d0"}.fa-rupee-sign:before,.fa-rupee:before{content:"\f156"}.fa-file-image:before{content:"\f1c5"}.fa-circle-question:before,.fa-question-circle:before{content:"\f059"}.fa-plane-departure:before{content:"\f5b0"}.fa-handshake-slash:before{content:"\e060"}.fa-book-bookmark:before{content:"\e0bb"}.fa-code-branch:before{content:"\f126"}.fa-hat-cowboy:before{content:"\f8c0"}.fa-bridge:before{content:"\e4c8"}.fa-phone-alt:before,.fa-phone-flip:before{content:"\f879"}.fa-truck-front:before{content:"\e2b7"}.fa-cat:before{content:"\f6be"}.fa-anchor-circle-exclamation:before{content:"\e4ab"}.fa-truck-field:before{content:"\e58d"}.fa-route:before{content:"\f4d7"}.fa-clipboard-question:before{content:"\e4e3"}.fa-panorama:before{content:"\e209"}.fa-comment-medical:before{content:"\f7f5"}.fa-teeth-open:before{content:"\f62f"}.fa-file-circle-minus:before{content:"\e4ed"}.fa-tags:before{content:"\f02c"}.fa-wine-glass:before{content:"\f4e3"}.fa-fast-forward:before,.fa-forward-fast:before{content:"\f050"}.fa-face-meh-blank:before,.fa-meh-blank:before{content:"\f5a4"}.fa-parking:before,.fa-square-parking:before{content:"\f540"}.fa-house-signal:before{content:"\e012"}.fa-bars-progress:before,.fa-tasks-alt:before{content:"\f828"}.fa-faucet-drip:before{content:"\e006"}.fa-cart-flatbed:before,.fa-dolly-flatbed:before{content:"\f474"}.fa-ban-smoking:before,.fa-smoking-ban:before{content:"\f54d"}.fa-terminal:before{content:"\f120"}.fa-mobile-button:before{content:"\f10b"}.fa-house-medical-flag:before{content:"\e514"}.fa-basket-shopping:before,.fa-shopping-basket:before{content:"\f291"}.fa-tape:before{content:"\f4db"}.fa-bus-alt:before,.fa-bus-simple:before{content:"\f55e"}.fa-eye:before{content:"\f06e"}.fa-face-sad-cry:before,.fa-sad-cry:before{content:"\f5b3"}.fa-audio-description:before{content:"\f29e"}.fa-person-military-to-person:before{content:"\e54c"}.fa-file-shield:before{content:"\e4f0"}.fa-user-slash:before{content:"\f506"}.fa-pen:before{content:"\f304"}.fa-tower-observation:before{content:"\e586"}.fa-file-code:before{content:"\f1c9"}.fa-signal-5:before,.fa-signal-perfect:before,.fa-signal:before{content:"\f012"}.fa-bus:before{content:"\f207"}.fa-heart-circle-xmark:before{content:"\e501"}.fa-home-lg:before,.fa-house-chimney:before{content:"\e3af"}.fa-window-maximize:before{content:"\f2d0"}.fa-face-frown:before,.fa-frown:before{content:"\f119"}.fa-prescription:before{content:"\f5b1"}.fa-shop:before,.fa-store-alt:before{content:"\f54f"}.fa-floppy-disk:before,.fa-save:before{content:"\f0c7"}.fa-vihara:before{content:"\f6a7"}.fa-balance-scale-left:before,.fa-scale-unbalanced:before{content:"\f515"}.fa-sort-asc:before,.fa-sort-up:before{content:"\f0de"}.fa-comment-dots:before,.fa-commenting:before{content:"\f4ad"}.fa-plant-wilt:before{content:"\e5aa"}.fa-diamond:before{content:"\f219"}.fa-face-grin-squint:before,.fa-grin-squint:before{content:"\f585"}.fa-hand-holding-dollar:before,.fa-hand-holding-usd:before{content:"\f4c0"}.fa-bacterium:before{content:"\e05a"}.fa-hand-pointer:before{content:"\f25a"}.fa-drum-steelpan:before{content:"\f56a"}.fa-hand-scissors:before{content:"\f257"}.fa-hands-praying:before,.fa-praying-hands:before{content:"\f684"}.fa-arrow-right-rotate:before,.fa-arrow-rotate-forward:before,.fa-arrow-rotate-right:before,.fa-redo:before{content:"\f01e"}.fa-biohazard:before{content:"\f780"}.fa-location-crosshairs:before,.fa-location:before{content:"\f601"}.fa-mars-double:before{content:"\f227"}.fa-child-dress:before{content:"\e59c"}.fa-users-between-lines:before{content:"\e591"}.fa-lungs-virus:before{content:"\e067"}.fa-face-grin-tears:before,.fa-grin-tears:before{content:"\f588"}.fa-phone:before{content:"\f095"}.fa-calendar-times:before,.fa-calendar-xmark:before{content:"\f273"}.fa-child-reaching:before{content:"\e59d"}.fa-head-side-virus:before{content:"\e064"}.fa-user-cog:before,.fa-user-gear:before{content:"\f4fe"}.fa-arrow-up-1-9:before,.fa-sort-numeric-up:before{content:"\f163"}.fa-door-closed:before{content:"\f52a"}.fa-shield-virus:before{content:"\e06c"}.fa-dice-six:before{content:"\f526"}.fa-mosquito-net:before{content:"\e52c"}.fa-bridge-water:before{content:"\e4ce"}.fa-person-booth:before{content:"\f756"}.fa-text-width:before{content:"\f035"}.fa-hat-wizard:before{content:"\f6e8"}.fa-pen-fancy:before{content:"\f5ac"}.fa-digging:before,.fa-person-digging:before{content:"\f85e"}.fa-trash:before{content:"\f1f8"}.fa-gauge-simple-med:before,.fa-gauge-simple:before,.fa-tachometer-average:before{content:"\f629"}.fa-book-medical:before{content:"\f7e6"}.fa-poo:before{content:"\f2fe"}.fa-quote-right-alt:before,.fa-quote-right:before{content:"\f10e"}.fa-shirt:before,.fa-t-shirt:before,.fa-tshirt:before{content:"\f553"}.fa-cubes:before{content:"\f1b3"}.fa-divide:before{content:"\f529"}.fa-tenge-sign:before,.fa-tenge:before{content:"\f7d7"}.fa-headphones:before{content:"\f025"}.fa-hands-holding:before{content:"\f4c2"}.fa-hands-clapping:before{content:"\e1a8"}.fa-republican:before{content:"\f75e"}.fa-arrow-left:before{content:"\f060"}.fa-person-circle-xmark:before{content:"\e543"}.fa-ruler:before{content:"\f545"}.fa-align-left:before{content:"\f036"}.fa-dice-d6:before{content:"\f6d1"}.fa-restroom:before{content:"\f7bd"}.fa-j:before{content:"\4a"}.fa-users-viewfinder:before{content:"\e595"}.fa-file-video:before{content:"\f1c8"}.fa-external-link-alt:before,.fa-up-right-from-square:before{content:"\f35d"}.fa-table-cells:before,.fa-th:before{content:"\f00a"}.fa-file-pdf:before{content:"\f1c1"}.fa-bible:before,.fa-book-bible:before{content:"\f647"}.fa-o:before{content:"\4f"}.fa-medkit:before,.fa-suitcase-medical:before{content:"\f0fa"}.fa-user-secret:before{content:"\f21b"}.fa-otter:before{content:"\f700"}.fa-female:before,.fa-person-dress:before{content:"\f182"}.fa-comment-dollar:before{content:"\f651"}.fa-briefcase-clock:before,.fa-business-time:before{content:"\f64a"}.fa-table-cells-large:before,.fa-th-large:before{content:"\f009"}.fa-book-tanakh:before,.fa-tanakh:before{content:"\f827"}.fa-phone-volume:before,.fa-volume-control-phone:before{content:"\f2a0"}.fa-hat-cowboy-side:before{content:"\f8c1"}.fa-clipboard-user:before{content:"\f7f3"}.fa-child:before{content:"\f1ae"}.fa-lira-sign:before{content:"\f195"}.fa-satellite:before{content:"\f7bf"}.fa-plane-lock:before{content:"\e558"}.fa-tag:before{content:"\f02b"}.fa-comment:before{content:"\f075"}.fa-birthday-cake:before,.fa-cake-candles:before,.fa-cake:before{content:"\f1fd"}.fa-envelope:before{content:"\f0e0"}.fa-angle-double-up:before,.fa-angles-up:before{content:"\f102"}.fa-paperclip:before{content:"\f0c6"}.fa-arrow-right-to-city:before{content:"\e4b3"}.fa-ribbon:before{content:"\f4d6"}.fa-lungs:before{content:"\f604"}.fa-arrow-up-9-1:before,.fa-sort-numeric-up-alt:before{content:"\f887"}.fa-litecoin-sign:before{content:"\e1d3"}.fa-border-none:before{content:"\f850"}.fa-circle-nodes:before{content:"\e4e2"}.fa-parachute-box:before{content:"\f4cd"}.fa-indent:before{content:"\f03c"}.fa-truck-field-un:before{content:"\e58e"}.fa-hourglass-empty:before,.fa-hourglass:before{content:"\f254"}.fa-mountain:before{content:"\f6fc"}.fa-user-doctor:before,.fa-user-md:before{content:"\f0f0"}.fa-circle-info:before,.fa-info-circle:before{content:"\f05a"}.fa-cloud-meatball:before{content:"\f73b"}.fa-camera-alt:before,.fa-camera:before{content:"\f030"}.fa-square-virus:before{content:"\e578"}.fa-meteor:before{content:"\f753"}.fa-car-on:before{content:"\e4dd"}.fa-sleigh:before{content:"\f7cc"}.fa-arrow-down-1-9:before,.fa-sort-numeric-asc:before,.fa-sort-numeric-down:before{content:"\f162"}.fa-hand-holding-droplet:before,.fa-hand-holding-water:before{content:"\f4c1"}.fa-water:before{content:"\f773"}.fa-calendar-check:before{content:"\f274"}.fa-braille:before{content:"\f2a1"}.fa-prescription-bottle-alt:before,.fa-prescription-bottle-medical:before{content:"\f486"}.fa-landmark:before{content:"\f66f"}.fa-truck:before{content:"\f0d1"}.fa-crosshairs:before{content:"\f05b"}.fa-person-cane:before{content:"\e53c"}.fa-tent:before{content:"\e57d"}.fa-vest-patches:before{content:"\e086"}.fa-check-double:before{content:"\f560"}.fa-arrow-down-a-z:before,.fa-sort-alpha-asc:before,.fa-sort-alpha-down:before{content:"\f15d"}.fa-money-bill-wheat:before{content:"\e52a"}.fa-cookie:before{content:"\f563"}.fa-arrow-left-rotate:before,.fa-arrow-rotate-back:before,.fa-arrow-rotate-backward:before,.fa-arrow-rotate-left:before,.fa-undo:before{content:"\f0e2"}.fa-hard-drive:before,.fa-hdd:before{content:"\f0a0"}.fa-face-grin-squint-tears:before,.fa-grin-squint-tears:before{content:"\f586"}.fa-dumbbell:before{content:"\f44b"}.fa-list-alt:before,.fa-rectangle-list:before{content:"\f022"}.fa-tarp-droplet:before{content:"\e57c"}.fa-house-medical-circle-check:before{content:"\e511"}.fa-person-skiing-nordic:before,.fa-skiing-nordic:before{content:"\f7ca"}.fa-calendar-plus:before{content:"\f271"}.fa-plane-arrival:before{content:"\f5af"}.fa-arrow-alt-circle-left:before,.fa-circle-left:before{content:"\f359"}.fa-subway:before,.fa-train-subway:before{content:"\f239"}.fa-chart-gantt:before{content:"\e0e4"}.fa-indian-rupee-sign:before,.fa-indian-rupee:before,.fa-inr:before{content:"\e1bc"}.fa-crop-alt:before,.fa-crop-simple:before{content:"\f565"}.fa-money-bill-1:before,.fa-money-bill-alt:before{content:"\f3d1"}.fa-left-long:before,.fa-long-arrow-alt-left:before{content:"\f30a"}.fa-dna:before{content:"\f471"}.fa-virus-slash:before{content:"\e075"}.fa-minus:before,.fa-subtract:before{content:"\f068"}.fa-chess:before{content:"\f439"}.fa-arrow-left-long:before,.fa-long-arrow-left:before{content:"\f177"}.fa-plug-circle-check:before{content:"\e55c"}.fa-street-view:before{content:"\f21d"}.fa-franc-sign:before{content:"\e18f"}.fa-volume-off:before{content:"\f026"}.fa-american-sign-language-interpreting:before,.fa-asl-interpreting:before,.fa-hands-american-sign-language-interpreting:before,.fa-hands-asl-interpreting:before{content:"\f2a3"}.fa-cog:before,.fa-gear:before{content:"\f013"}.fa-droplet-slash:before,.fa-tint-slash:before{content:"\f5c7"}.fa-mosque:before{content:"\f678"}.fa-mosquito:before{content:"\e52b"}.fa-star-of-david:before{content:"\f69a"}.fa-person-military-rifle:before{content:"\e54b"}.fa-cart-shopping:before,.fa-shopping-cart:before{content:"\f07a"}.fa-vials:before{content:"\f493"}.fa-plug-circle-plus:before{content:"\e55f"}.fa-place-of-worship:before{content:"\f67f"}.fa-grip-vertical:before{content:"\f58e"}.fa-arrow-turn-up:before,.fa-level-up:before{content:"\f148"}.fa-u:before{content:"\55"}.fa-square-root-alt:before,.fa-square-root-variable:before{content:"\f698"}.fa-clock-four:before,.fa-clock:before{content:"\f017"}.fa-backward-step:before,.fa-step-backward:before{content:"\f048"}.fa-pallet:before{content:"\f482"}.fa-faucet:before{content:"\e005"}.fa-baseball-bat-ball:before{content:"\f432"}.fa-s:before{content:"\53"}.fa-timeline:before{content:"\e29c"}.fa-keyboard:before{content:"\f11c"}.fa-caret-down:before{content:"\f0d7"}.fa-clinic-medical:before,.fa-house-chimney-medical:before{content:"\f7f2"}.fa-temperature-3:before,.fa-temperature-three-quarters:before,.fa-thermometer-3:before,.fa-thermometer-three-quarters:before{content:"\f2c8"}.fa-mobile-android-alt:before,.fa-mobile-screen:before{content:"\f3cf"}.fa-plane-up:before{content:"\e22d"}.fa-piggy-bank:before{content:"\f4d3"}.fa-battery-3:before,.fa-battery-half:before{content:"\f242"}.fa-mountain-city:before{content:"\e52e"}.fa-coins:before{content:"\f51e"}.fa-khanda:before{content:"\f66d"}.fa-sliders-h:before,.fa-sliders:before{content:"\f1de"}.fa-folder-tree:before{content:"\f802"}.fa-network-wired:before{content:"\f6ff"}.fa-map-pin:before{content:"\f276"}.fa-hamsa:before{content:"\f665"}.fa-cent-sign:before{content:"\e3f5"}.fa-flask:before{content:"\f0c3"}.fa-person-pregnant:before{content:"\e31e"}.fa-wand-sparkles:before{content:"\f72b"}.fa-ellipsis-v:before,.fa-ellipsis-vertical:before{content:"\f142"}.fa-ticket:before{content:"\f145"}.fa-power-off:before{content:"\f011"}.fa-long-arrow-alt-right:before,.fa-right-long:before{content:"\f30b"}.fa-flag-usa:before{content:"\f74d"}.fa-laptop-file:before{content:"\e51d"}.fa-teletype:before,.fa-tty:before{content:"\f1e4"}.fa-diagram-next:before{content:"\e476"}.fa-person-rifle:before{content:"\e54e"}.fa-house-medical-circle-exclamation:before{content:"\e512"}.fa-closed-captioning:before{content:"\f20a"}.fa-hiking:before,.fa-person-hiking:before{content:"\f6ec"}.fa-venus-double:before{content:"\f226"}.fa-images:before{content:"\f302"}.fa-calculator:before{content:"\f1ec"}.fa-people-pulling:before{content:"\e535"}.fa-n:before{content:"\4e"}.fa-cable-car:before,.fa-tram:before{content:"\f7da"}.fa-cloud-rain:before{content:"\f73d"}.fa-building-circle-xmark:before{content:"\e4d4"}.fa-ship:before{content:"\f21a"}.fa-arrows-down-to-line:before{content:"\e4b8"}.fa-download:before{content:"\f019"}.fa-face-grin:before,.fa-grin:before{content:"\f580"}.fa-backspace:before,.fa-delete-left:before{content:"\f55a"}.fa-eye-dropper-empty:before,.fa-eye-dropper:before,.fa-eyedropper:before{content:"\f1fb"}.fa-file-circle-check:before{content:"\e5a0"}.fa-forward:before{content:"\f04e"}.fa-mobile-android:before,.fa-mobile-phone:before,.fa-mobile:before{content:"\f3ce"}.fa-face-meh:before,.fa-meh:before{content:"\f11a"}.fa-align-center:before{content:"\f037"}.fa-book-dead:before,.fa-book-skull:before{content:"\f6b7"}.fa-drivers-license:before,.fa-id-card:before{content:"\f2c2"}.fa-dedent:before,.fa-outdent:before{content:"\f03b"}.fa-heart-circle-exclamation:before{content:"\e4fe"}.fa-home-alt:before,.fa-home-lg-alt:before,.fa-home:before,.fa-house:before{content:"\f015"}.fa-calendar-week:before{content:"\f784"}.fa-laptop-medical:before{content:"\f812"}.fa-b:before{content:"\42"}.fa-file-medical:before{content:"\f477"}.fa-dice-one:before{content:"\f525"}.fa-kiwi-bird:before{content:"\f535"}.fa-arrow-right-arrow-left:before,.fa-exchange:before{content:"\f0ec"}.fa-redo-alt:before,.fa-rotate-forward:before,.fa-rotate-right:before{content:"\f2f9"}.fa-cutlery:before,.fa-utensils:before{content:"\f2e7"}.fa-arrow-up-wide-short:before,.fa-sort-amount-up:before{content:"\f161"}.fa-mill-sign:before{content:"\e1ed"}.fa-bowl-rice:before{content:"\e2eb"}.fa-skull:before{content:"\f54c"}.fa-broadcast-tower:before,.fa-tower-broadcast:before{content:"\f519"}.fa-truck-pickup:before{content:"\f63c"}.fa-long-arrow-alt-up:before,.fa-up-long:before{content:"\f30c"}.fa-stop:before{content:"\f04d"}.fa-code-merge:before{content:"\f387"}.fa-upload:before{content:"\f093"}.fa-hurricane:before{content:"\f751"}.fa-mound:before{content:"\e52d"}.fa-toilet-portable:before{content:"\e583"}.fa-compact-disc:before{content:"\f51f"}.fa-file-arrow-down:before,.fa-file-download:before{content:"\f56d"}.fa-caravan:before{content:"\f8ff"}.fa-shield-cat:before{content:"\e572"}.fa-bolt:before,.fa-zap:before{content:"\f0e7"}.fa-glass-water:before{content:"\e4f4"}.fa-oil-well:before{content:"\e532"}.fa-vault:before{content:"\e2c5"}.fa-mars:before{content:"\f222"}.fa-toilet:before{content:"\f7d8"}.fa-plane-circle-xmark:before{content:"\e557"}.fa-cny:before,.fa-jpy:before,.fa-rmb:before,.fa-yen-sign:before,.fa-yen:before{content:"\f157"}.fa-rouble:before,.fa-rub:before,.fa-ruble-sign:before,.fa-ruble:before{content:"\f158"}.fa-sun:before{content:"\f185"}.fa-guitar:before{content:"\f7a6"}.fa-face-laugh-wink:before,.fa-laugh-wink:before{content:"\f59c"}.fa-horse-head:before{content:"\f7ab"}.fa-bore-hole:before{content:"\e4c3"}.fa-industry:before{content:"\f275"}.fa-arrow-alt-circle-down:before,.fa-circle-down:before{content:"\f358"}.fa-arrows-turn-to-dots:before{content:"\e4c1"}.fa-florin-sign:before{content:"\e184"}.fa-arrow-down-short-wide:before,.fa-sort-amount-desc:before,.fa-sort-amount-down-alt:before{content:"\f884"}.fa-less-than:before{content:"\3c"}.fa-angle-down:before{content:"\f107"}.fa-car-tunnel:before{content:"\e4de"}.fa-head-side-cough:before{content:"\e061"}.fa-grip-lines:before{content:"\f7a4"}.fa-thumbs-down:before{content:"\f165"}.fa-user-lock:before{content:"\f502"}.fa-arrow-right-long:before,.fa-long-arrow-right:before{content:"\f178"}.fa-anchor-circle-xmark:before{content:"\e4ac"}.fa-ellipsis-h:before,.fa-ellipsis:before{content:"\f141"}.fa-chess-pawn:before{content:"\f443"}.fa-first-aid:before,.fa-kit-medical:before{content:"\f479"}.fa-person-through-window:before{content:"\e5a9"}.fa-toolbox:before{content:"\f552"}.fa-hands-holding-circle:before{content:"\e4fb"}.fa-bug:before{content:"\f188"}.fa-credit-card-alt:before,.fa-credit-card:before{content:"\f09d"}.fa-automobile:before,.fa-car:before{content:"\f1b9"}.fa-hand-holding-hand:before{content:"\e4f7"}.fa-book-open-reader:before,.fa-book-reader:before{content:"\f5da"}.fa-mountain-sun:before{content:"\e52f"}.fa-arrows-left-right-to-line:before{content:"\e4ba"}.fa-dice-d20:before{content:"\f6cf"}.fa-truck-droplet:before{content:"\e58c"}.fa-file-circle-xmark:before{content:"\e5a1"}.fa-temperature-arrow-up:before,.fa-temperature-up:before{content:"\e040"}.fa-medal:before{content:"\f5a2"}.fa-bed:before{content:"\f236"}.fa-h-square:before,.fa-square-h:before{content:"\f0fd"}.fa-podcast:before{content:"\f2ce"}.fa-temperature-4:before,.fa-temperature-full:before,.fa-thermometer-4:before,.fa-thermometer-full:before{content:"\f2c7"}.fa-bell:before{content:"\f0f3"}.fa-superscript:before{content:"\f12b"}.fa-plug-circle-xmark:before{content:"\e560"}.fa-star-of-life:before{content:"\f621"}.fa-phone-slash:before{content:"\f3dd"}.fa-paint-roller:before{content:"\f5aa"}.fa-hands-helping:before,.fa-handshake-angle:before{content:"\f4c4"}.fa-location-dot:before,.fa-map-marker-alt:before{content:"\f3c5"}.fa-file:before{content:"\f15b"}.fa-greater-than:before{content:"\3e"}.fa-person-swimming:before,.fa-swimmer:before{content:"\f5c4"}.fa-arrow-down:before{content:"\f063"}.fa-droplet:before,.fa-tint:before{content:"\f043"}.fa-eraser:before{content:"\f12d"}.fa-earth-america:before,.fa-earth-americas:before,.fa-earth:before,.fa-globe-americas:before{content:"\f57d"}.fa-person-burst:before{content:"\e53b"}.fa-dove:before{content:"\f4ba"}.fa-battery-0:before,.fa-battery-empty:before{content:"\f244"}.fa-socks:before{content:"\f696"}.fa-inbox:before{content:"\f01c"}.fa-section:before{content:"\e447"}.fa-gauge-high:before,.fa-tachometer-alt-fast:before,.fa-tachometer-alt:before{content:"\f625"}.fa-envelope-open-text:before{content:"\f658"}.fa-hospital-alt:before,.fa-hospital-wide:before,.fa-hospital:before{content:"\f0f8"}.fa-wine-bottle:before{content:"\f72f"}.fa-chess-rook:before{content:"\f447"}.fa-bars-staggered:before,.fa-reorder:before,.fa-stream:before{content:"\f550"}.fa-dharmachakra:before{content:"\f655"}.fa-hotdog:before{content:"\f80f"}.fa-blind:before,.fa-person-walking-with-cane:before{content:"\f29d"}.fa-drum:before{content:"\f569"}.fa-ice-cream:before{content:"\f810"}.fa-heart-circle-bolt:before{content:"\e4fc"}.fa-fax:before{content:"\f1ac"}.fa-paragraph:before{content:"\f1dd"}.fa-check-to-slot:before,.fa-vote-yea:before{content:"\f772"}.fa-star-half:before{content:"\f089"}.fa-boxes-alt:before,.fa-boxes-stacked:before,.fa-boxes:before{content:"\f468"}.fa-chain:before,.fa-link:before{content:"\f0c1"}.fa-assistive-listening-systems:before,.fa-ear-listen:before{content:"\f2a2"}.fa-tree-city:before{content:"\e587"}.fa-play:before{content:"\f04b"}.fa-font:before{content:"\f031"}.fa-table-cells-row-lock:before{content:"\e67a"}.fa-rupiah-sign:before{content:"\e23d"}.fa-magnifying-glass:before,.fa-search:before{content:"\f002"}.fa-ping-pong-paddle-ball:before,.fa-table-tennis-paddle-ball:before,.fa-table-tennis:before{content:"\f45d"}.fa-diagnoses:before,.fa-person-dots-from-line:before{content:"\f470"}.fa-trash-can-arrow-up:before,.fa-trash-restore-alt:before{content:"\f82a"}.fa-naira-sign:before{content:"\e1f6"}.fa-cart-arrow-down:before{content:"\f218"}.fa-walkie-talkie:before{content:"\f8ef"}.fa-file-edit:before,.fa-file-pen:before{content:"\f31c"}.fa-receipt:before{content:"\f543"}.fa-pen-square:before,.fa-pencil-square:before,.fa-square-pen:before{content:"\f14b"}.fa-suitcase-rolling:before{content:"\f5c1"}.fa-person-circle-exclamation:before{content:"\e53f"}.fa-chevron-down:before{content:"\f078"}.fa-battery-5:before,.fa-battery-full:before,.fa-battery:before{content:"\f240"}.fa-skull-crossbones:before{content:"\f714"}.fa-code-compare:before{content:"\e13a"}.fa-list-dots:before,.fa-list-ul:before{content:"\f0ca"}.fa-school-lock:before{content:"\e56f"}.fa-tower-cell:before{content:"\e585"}.fa-down-long:before,.fa-long-arrow-alt-down:before{content:"\f309"}.fa-ranking-star:before{content:"\e561"}.fa-chess-king:before{content:"\f43f"}.fa-person-harassing:before{content:"\e549"}.fa-brazilian-real-sign:before{content:"\e46c"}.fa-landmark-alt:before,.fa-landmark-dome:before{content:"\f752"}.fa-arrow-up:before{content:"\f062"}.fa-television:before,.fa-tv-alt:before,.fa-tv:before{content:"\f26c"}.fa-shrimp:before{content:"\e448"}.fa-list-check:before,.fa-tasks:before{content:"\f0ae"}.fa-jug-detergent:before{content:"\e519"}.fa-circle-user:before,.fa-user-circle:before{content:"\f2bd"}.fa-user-shield:before{content:"\f505"}.fa-wind:before{content:"\f72e"}.fa-car-burst:before,.fa-car-crash:before{content:"\f5e1"}.fa-y:before{content:"\59"}.fa-person-snowboarding:before,.fa-snowboarding:before{content:"\f7ce"}.fa-shipping-fast:before,.fa-truck-fast:before{content:"\f48b"}.fa-fish:before{content:"\f578"}.fa-user-graduate:before{content:"\f501"}.fa-adjust:before,.fa-circle-half-stroke:before{content:"\f042"}.fa-clapperboard:before{content:"\e131"}.fa-circle-radiation:before,.fa-radiation-alt:before{content:"\f7ba"}.fa-baseball-ball:before,.fa-baseball:before{content:"\f433"}.fa-jet-fighter-up:before{content:"\e518"}.fa-diagram-project:before,.fa-project-diagram:before{content:"\f542"}.fa-copy:before{content:"\f0c5"}.fa-volume-mute:before,.fa-volume-times:before,.fa-volume-xmark:before{content:"\f6a9"}.fa-hand-sparkles:before{content:"\e05d"}.fa-grip-horizontal:before,.fa-grip:before{content:"\f58d"}.fa-share-from-square:before,.fa-share-square:before{content:"\f14d"}.fa-child-combatant:before,.fa-child-rifle:before{content:"\e4e0"}.fa-gun:before{content:"\e19b"}.fa-phone-square:before,.fa-square-phone:before{content:"\f098"}.fa-add:before,.fa-plus:before{content:"\2b"}.fa-expand:before{content:"\f065"}.fa-computer:before{content:"\e4e5"}.fa-close:before,.fa-multiply:before,.fa-remove:before,.fa-times:before,.fa-xmark:before{content:"\f00d"}.fa-arrows-up-down-left-right:before,.fa-arrows:before{content:"\f047"}.fa-chalkboard-teacher:before,.fa-chalkboard-user:before{content:"\f51c"}.fa-peso-sign:before{content:"\e222"}.fa-building-shield:before{content:"\e4d8"}.fa-baby:before{content:"\f77c"}.fa-users-line:before{content:"\e592"}.fa-quote-left-alt:before,.fa-quote-left:before{content:"\f10d"}.fa-tractor:before{content:"\f722"}.fa-trash-arrow-up:before,.fa-trash-restore:before{content:"\f829"}.fa-arrow-down-up-lock:before{content:"\e4b0"}.fa-lines-leaning:before{content:"\e51e"}.fa-ruler-combined:before{content:"\f546"}.fa-copyright:before{content:"\f1f9"}.fa-equals:before{content:"\3d"}.fa-blender:before{content:"\f517"}.fa-teeth:before{content:"\f62e"}.fa-ils:before,.fa-shekel-sign:before,.fa-shekel:before,.fa-sheqel-sign:before,.fa-sheqel:before{content:"\f20b"}.fa-map:before{content:"\f279"}.fa-rocket:before{content:"\f135"}.fa-photo-film:before,.fa-photo-video:before{content:"\f87c"}.fa-folder-minus:before{content:"\f65d"}.fa-store:before{content:"\f54e"}.fa-arrow-trend-up:before{content:"\e098"}.fa-plug-circle-minus:before{content:"\e55e"}.fa-sign-hanging:before,.fa-sign:before{content:"\f4d9"}.fa-bezier-curve:before{content:"\f55b"}.fa-bell-slash:before{content:"\f1f6"}.fa-tablet-android:before,.fa-tablet:before{content:"\f3fb"}.fa-school-flag:before{content:"\e56e"}.fa-fill:before{content:"\f575"}.fa-angle-up:before{content:"\f106"}.fa-drumstick-bite:before{content:"\f6d7"}.fa-holly-berry:before{content:"\f7aa"}.fa-chevron-left:before{content:"\f053"}.fa-bacteria:before{content:"\e059"}.fa-hand-lizard:before{content:"\f258"}.fa-notdef:before{content:"\e1fe"}.fa-disease:before{content:"\f7fa"}.fa-briefcase-medical:before{content:"\f469"}.fa-genderless:before{content:"\f22d"}.fa-chevron-right:before{content:"\f054"}.fa-retweet:before{content:"\f079"}.fa-car-alt:before,.fa-car-rear:before{content:"\f5de"}.fa-pump-soap:before{content:"\e06b"}.fa-video-slash:before{content:"\f4e2"}.fa-battery-2:before,.fa-battery-quarter:before{content:"\f243"}.fa-radio:before{content:"\f8d7"}.fa-baby-carriage:before,.fa-carriage-baby:before{content:"\f77d"}.fa-traffic-light:before{content:"\f637"}.fa-thermometer:before{content:"\f491"}.fa-vr-cardboard:before{content:"\f729"}.fa-hand-middle-finger:before{content:"\f806"}.fa-percent:before,.fa-percentage:before{content:"\25"}.fa-truck-moving:before{content:"\f4df"}.fa-glass-water-droplet:before{content:"\e4f5"}.fa-display:before{content:"\e163"}.fa-face-smile:before,.fa-smile:before{content:"\f118"}.fa-thumb-tack:before,.fa-thumbtack:before{content:"\f08d"}.fa-trophy:before{content:"\f091"}.fa-person-praying:before,.fa-pray:before{content:"\f683"}.fa-hammer:before{content:"\f6e3"}.fa-hand-peace:before{content:"\f25b"}.fa-rotate:before,.fa-sync-alt:before{content:"\f2f1"}.fa-spinner:before{content:"\f110"}.fa-robot:before{content:"\f544"}.fa-peace:before{content:"\f67c"}.fa-cogs:before,.fa-gears:before{content:"\f085"}.fa-warehouse:before{content:"\f494"}.fa-arrow-up-right-dots:before{content:"\e4b7"}.fa-splotch:before{content:"\f5bc"}.fa-face-grin-hearts:before,.fa-grin-hearts:before{content:"\f584"}.fa-dice-four:before{content:"\f524"}.fa-sim-card:before{content:"\f7c4"}.fa-transgender-alt:before,.fa-transgender:before{content:"\f225"}.fa-mercury:before{content:"\f223"}.fa-arrow-turn-down:before,.fa-level-down:before{content:"\f149"}.fa-person-falling-burst:before{content:"\e547"}.fa-award:before{content:"\f559"}.fa-ticket-alt:before,.fa-ticket-simple:before{content:"\f3ff"}.fa-building:before{content:"\f1ad"}.fa-angle-double-left:before,.fa-angles-left:before{content:"\f100"}.fa-qrcode:before{content:"\f029"}.fa-clock-rotate-left:before,.fa-history:before{content:"\f1da"}.fa-face-grin-beam-sweat:before,.fa-grin-beam-sweat:before{content:"\f583"}.fa-arrow-right-from-file:before,.fa-file-export:before{content:"\f56e"}.fa-shield-blank:before,.fa-shield:before{content:"\f132"}.fa-arrow-up-short-wide:before,.fa-sort-amount-up-alt:before{content:"\f885"}.fa-house-medical:before{content:"\e3b2"}.fa-golf-ball-tee:before,.fa-golf-ball:before{content:"\f450"}.fa-chevron-circle-left:before,.fa-circle-chevron-left:before{content:"\f137"}.fa-house-chimney-window:before{content:"\e00d"}.fa-pen-nib:before{content:"\f5ad"}.fa-tent-arrow-turn-left:before{content:"\e580"}.fa-tents:before{content:"\e582"}.fa-magic:before,.fa-wand-magic:before{content:"\f0d0"}.fa-dog:before{content:"\f6d3"}.fa-carrot:before{content:"\f787"}.fa-moon:before{content:"\f186"}.fa-wine-glass-alt:before,.fa-wine-glass-empty:before{content:"\f5ce"}.fa-cheese:before{content:"\f7ef"}.fa-yin-yang:before{content:"\f6ad"}.fa-music:before{content:"\f001"}.fa-code-commit:before{content:"\f386"}.fa-temperature-low:before{content:"\f76b"}.fa-biking:before,.fa-person-biking:before{content:"\f84a"}.fa-broom:before{content:"\f51a"}.fa-shield-heart:before{content:"\e574"}.fa-gopuram:before{content:"\f664"}.fa-earth-oceania:before,.fa-globe-oceania:before{content:"\e47b"}.fa-square-xmark:before,.fa-times-square:before,.fa-xmark-square:before{content:"\f2d3"}.fa-hashtag:before{content:"\23"}.fa-expand-alt:before,.fa-up-right-and-down-left-from-center:before{content:"\f424"}.fa-oil-can:before{content:"\f613"}.fa-t:before{content:"\54"}.fa-hippo:before{content:"\f6ed"}.fa-chart-column:before{content:"\e0e3"}.fa-infinity:before{content:"\f534"}.fa-vial-circle-check:before{content:"\e596"}.fa-person-arrow-down-to-line:before{content:"\e538"}.fa-voicemail:before{content:"\f897"}.fa-fan:before{content:"\f863"}.fa-person-walking-luggage:before{content:"\e554"}.fa-arrows-alt-v:before,.fa-up-down:before{content:"\f338"}.fa-cloud-moon-rain:before{content:"\f73c"}.fa-calendar:before{content:"\f133"}.fa-trailer:before{content:"\e041"}.fa-bahai:before,.fa-haykal:before{content:"\f666"}.fa-sd-card:before{content:"\f7c2"}.fa-dragon:before{content:"\f6d5"}.fa-shoe-prints:before{content:"\f54b"}.fa-circle-plus:before,.fa-plus-circle:before{content:"\f055"}.fa-face-grin-tongue-wink:before,.fa-grin-tongue-wink:before{content:"\f58b"}.fa-hand-holding:before{content:"\f4bd"}.fa-plug-circle-exclamation:before{content:"\e55d"}.fa-chain-broken:before,.fa-chain-slash:before,.fa-link-slash:before,.fa-unlink:before{content:"\f127"}.fa-clone:before{content:"\f24d"}.fa-person-walking-arrow-loop-left:before{content:"\e551"}.fa-arrow-up-z-a:before,.fa-sort-alpha-up-alt:before{content:"\f882"}.fa-fire-alt:before,.fa-fire-flame-curved:before{content:"\f7e4"}.fa-tornado:before{content:"\f76f"}.fa-file-circle-plus:before{content:"\e494"}.fa-book-quran:before,.fa-quran:before{content:"\f687"}.fa-anchor:before{content:"\f13d"}.fa-border-all:before{content:"\f84c"}.fa-angry:before,.fa-face-angry:before{content:"\f556"}.fa-cookie-bite:before{content:"\f564"}.fa-arrow-trend-down:before{content:"\e097"}.fa-feed:before,.fa-rss:before{content:"\f09e"}.fa-draw-polygon:before{content:"\f5ee"}.fa-balance-scale:before,.fa-scale-balanced:before{content:"\f24e"}.fa-gauge-simple-high:before,.fa-tachometer-fast:before,.fa-tachometer:before{content:"\f62a"}.fa-shower:before{content:"\f2cc"}.fa-desktop-alt:before,.fa-desktop:before{content:"\f390"}.fa-m:before{content:"\4d"}.fa-table-list:before,.fa-th-list:before{content:"\f00b"}.fa-comment-sms:before,.fa-sms:before{content:"\f7cd"}.fa-book:before{content:"\f02d"}.fa-user-plus:before{content:"\f234"}.fa-check:before{content:"\f00c"}.fa-battery-4:before,.fa-battery-three-quarters:before{content:"\f241"}.fa-house-circle-check:before{content:"\e509"}.fa-angle-left:before{content:"\f104"}.fa-diagram-successor:before{content:"\e47a"}.fa-truck-arrow-right:before{content:"\e58b"}.fa-arrows-split-up-and-left:before{content:"\e4bc"}.fa-fist-raised:before,.fa-hand-fist:before{content:"\f6de"}.fa-cloud-moon:before{content:"\f6c3"}.fa-briefcase:before{content:"\f0b1"}.fa-person-falling:before{content:"\e546"}.fa-image-portrait:before,.fa-portrait:before{content:"\f3e0"}.fa-user-tag:before{content:"\f507"}.fa-rug:before{content:"\e569"}.fa-earth-europe:before,.fa-globe-europe:before{content:"\f7a2"}.fa-cart-flatbed-suitcase:before,.fa-luggage-cart:before{content:"\f59d"}.fa-rectangle-times:before,.fa-rectangle-xmark:before,.fa-times-rectangle:before,.fa-window-close:before{content:"\f410"}.fa-baht-sign:before{content:"\e0ac"}.fa-book-open:before{content:"\f518"}.fa-book-journal-whills:before,.fa-journal-whills:before{content:"\f66a"}.fa-handcuffs:before{content:"\e4f8"}.fa-exclamation-triangle:before,.fa-triangle-exclamation:before,.fa-warning:before{content:"\f071"}.fa-database:before{content:"\f1c0"}.fa-mail-forward:before,.fa-share:before{content:"\f064"}.fa-bottle-droplet:before{content:"\e4c4"}.fa-mask-face:before{content:"\e1d7"}.fa-hill-rockslide:before{content:"\e508"}.fa-exchange-alt:before,.fa-right-left:before{content:"\f362"}.fa-paper-plane:before{content:"\f1d8"}.fa-road-circle-exclamation:before{content:"\e565"}.fa-dungeon:before{content:"\f6d9"}.fa-align-right:before{content:"\f038"}.fa-money-bill-1-wave:before,.fa-money-bill-wave-alt:before{content:"\f53b"}.fa-life-ring:before{content:"\f1cd"}.fa-hands:before,.fa-sign-language:before,.fa-signing:before{content:"\f2a7"}.fa-calendar-day:before{content:"\f783"}.fa-ladder-water:before,.fa-swimming-pool:before,.fa-water-ladder:before{content:"\f5c5"}.fa-arrows-up-down:before,.fa-arrows-v:before{content:"\f07d"}.fa-face-grimace:before,.fa-grimace:before{content:"\f57f"}.fa-wheelchair-alt:before,.fa-wheelchair-move:before{content:"\e2ce"}.fa-level-down-alt:before,.fa-turn-down:before{content:"\f3be"}.fa-person-walking-arrow-right:before{content:"\e552"}.fa-envelope-square:before,.fa-square-envelope:before{content:"\f199"}.fa-dice:before{content:"\f522"}.fa-bowling-ball:before{content:"\f436"}.fa-brain:before{content:"\f5dc"}.fa-band-aid:before,.fa-bandage:before{content:"\f462"}.fa-calendar-minus:before{content:"\f272"}.fa-circle-xmark:before,.fa-times-circle:before,.fa-xmark-circle:before{content:"\f057"}.fa-gifts:before{content:"\f79c"}.fa-hotel:before{content:"\f594"}.fa-earth-asia:before,.fa-globe-asia:before{content:"\f57e"}.fa-id-card-alt:before,.fa-id-card-clip:before{content:"\f47f"}.fa-magnifying-glass-plus:before,.fa-search-plus:before{content:"\f00e"}.fa-thumbs-up:before{content:"\f164"}.fa-user-clock:before{content:"\f4fd"}.fa-allergies:before,.fa-hand-dots:before{content:"\f461"}.fa-file-invoice:before{content:"\f570"}.fa-window-minimize:before{content:"\f2d1"}.fa-coffee:before,.fa-mug-saucer:before{content:"\f0f4"}.fa-brush:before{content:"\f55d"}.fa-mask:before{content:"\f6fa"}.fa-magnifying-glass-minus:before,.fa-search-minus:before{content:"\f010"}.fa-ruler-vertical:before{content:"\f548"}.fa-user-alt:before,.fa-user-large:before{content:"\f406"}.fa-train-tram:before{content:"\e5b4"}.fa-user-nurse:before{content:"\f82f"}.fa-syringe:before{content:"\f48e"}.fa-cloud-sun:before{content:"\f6c4"}.fa-stopwatch-20:before{content:"\e06f"}.fa-square-full:before{content:"\f45c"}.fa-magnet:before{content:"\f076"}.fa-jar:before{content:"\e516"}.fa-note-sticky:before,.fa-sticky-note:before{content:"\f249"}.fa-bug-slash:before{content:"\e490"}.fa-arrow-up-from-water-pump:before{content:"\e4b6"}.fa-bone:before{content:"\f5d7"}.fa-user-injured:before{content:"\f728"}.fa-face-sad-tear:before,.fa-sad-tear:before{content:"\f5b4"}.fa-plane:before{content:"\f072"}.fa-tent-arrows-down:before{content:"\e581"}.fa-exclamation:before{content:"\21"}.fa-arrows-spin:before{content:"\e4bb"}.fa-print:before{content:"\f02f"}.fa-try:before,.fa-turkish-lira-sign:before,.fa-turkish-lira:before{content:"\e2bb"}.fa-dollar-sign:before,.fa-dollar:before,.fa-usd:before{content:"\24"}.fa-x:before{content:"\58"}.fa-magnifying-glass-dollar:before,.fa-search-dollar:before{content:"\f688"}.fa-users-cog:before,.fa-users-gear:before{content:"\f509"}.fa-person-military-pointing:before{content:"\e54a"}.fa-bank:before,.fa-building-columns:before,.fa-institution:before,.fa-museum:before,.fa-university:before{content:"\f19c"}.fa-umbrella:before{content:"\f0e9"}.fa-trowel:before{content:"\e589"}.fa-d:before{content:"\44"}.fa-stapler:before{content:"\e5af"}.fa-masks-theater:before,.fa-theater-masks:before{content:"\f630"}.fa-kip-sign:before{content:"\e1c4"}.fa-hand-point-left:before{content:"\f0a5"}.fa-handshake-alt:before,.fa-handshake-simple:before{content:"\f4c6"}.fa-fighter-jet:before,.fa-jet-fighter:before{content:"\f0fb"}.fa-share-alt-square:before,.fa-square-share-nodes:before{content:"\f1e1"}.fa-barcode:before{content:"\f02a"}.fa-plus-minus:before{content:"\e43c"}.fa-video-camera:before,.fa-video:before{content:"\f03d"}.fa-graduation-cap:before,.fa-mortar-board:before{content:"\f19d"}.fa-hand-holding-medical:before{content:"\e05c"}.fa-person-circle-check:before{content:"\e53e"}.fa-level-up-alt:before,.fa-turn-up:before{content:"\f3bf"}
+.fa-sr-only,.fa-sr-only-focusable:not(:focus),.sr-only,.sr-only-focusable:not(:focus){position:absolute;width:1px;height:1px;padding:0;margin:-1px;overflow:hidden;clip:rect(0,0,0,0);white-space:nowrap;border-width:0}:host,:root{--fa-style-family-brands:"Font Awesome 6 Brands";--fa-font-brands:normal 400 1em/1 "Font Awesome 6 Brands"}@font-face{font-family:"Font Awesome 6 Brands";font-style:normal;font-weight:400;font-display:block;src: url("../webfonts/fa-brands-400.woff2") format("woff2"), url("../webfonts/fa-brands-400.ttf") format("truetype"); }.fa-brands,.fab{font-weight:400}.fa-monero:before{content:"\f3d0"}.fa-hooli:before{content:"\f427"}.fa-yelp:before{content:"\f1e9"}.fa-cc-visa:before{content:"\f1f0"}.fa-lastfm:before{content:"\f202"}.fa-shopware:before{content:"\f5b5"}.fa-creative-commons-nc:before{content:"\f4e8"}.fa-aws:before{content:"\f375"}.fa-redhat:before{content:"\f7bc"}.fa-yoast:before{content:"\f2b1"}.fa-cloudflare:before{content:"\e07d"}.fa-ups:before{content:"\f7e0"}.fa-pixiv:before{content:"\e640"}.fa-wpexplorer:before{content:"\f2de"}.fa-dyalog:before{content:"\f399"}.fa-bity:before{content:"\f37a"}.fa-stackpath:before{content:"\f842"}.fa-buysellads:before{content:"\f20d"}.fa-first-order:before{content:"\f2b0"}.fa-modx:before{content:"\f285"}.fa-guilded:before{content:"\e07e"}.fa-vnv:before{content:"\f40b"}.fa-js-square:before,.fa-square-js:before{content:"\f3b9"}.fa-microsoft:before{content:"\f3ca"}.fa-qq:before{content:"\f1d6"}.fa-orcid:before{content:"\f8d2"}.fa-java:before{content:"\f4e4"}.fa-invision:before{content:"\f7b0"}.fa-creative-commons-pd-alt:before{content:"\f4ed"}.fa-centercode:before{content:"\f380"}.fa-glide-g:before{content:"\f2a6"}.fa-drupal:before{content:"\f1a9"}.fa-jxl:before{content:"\e67b"}.fa-hire-a-helper:before{content:"\f3b0"}.fa-creative-commons-by:before{content:"\f4e7"}.fa-unity:before{content:"\e049"}.fa-whmcs:before{content:"\f40d"}.fa-rocketchat:before{content:"\f3e8"}.fa-vk:before{content:"\f189"}.fa-untappd:before{content:"\f405"}.fa-mailchimp:before{content:"\f59e"}.fa-css3-alt:before{content:"\f38b"}.fa-reddit-square:before,.fa-square-reddit:before{content:"\f1a2"}.fa-vimeo-v:before{content:"\f27d"}.fa-contao:before{content:"\f26d"}.fa-square-font-awesome:before{content:"\e5ad"}.fa-deskpro:before{content:"\f38f"}.fa-brave:before{content:"\e63c"}.fa-sistrix:before{content:"\f3ee"}.fa-instagram-square:before,.fa-square-instagram:before{content:"\e055"}.fa-battle-net:before{content:"\f835"}.fa-the-red-yeti:before{content:"\f69d"}.fa-hacker-news-square:before,.fa-square-hacker-news:before{content:"\f3af"}.fa-edge:before{content:"\f282"}.fa-threads:before{content:"\e618"}.fa-napster:before{content:"\f3d2"}.fa-snapchat-square:before,.fa-square-snapchat:before{content:"\f2ad"}.fa-google-plus-g:before{content:"\f0d5"}.fa-artstation:before{content:"\f77a"}.fa-markdown:before{content:"\f60f"}.fa-sourcetree:before{content:"\f7d3"}.fa-google-plus:before{content:"\f2b3"}.fa-diaspora:before{content:"\f791"}.fa-foursquare:before{content:"\f180"}.fa-stack-overflow:before{content:"\f16c"}.fa-github-alt:before{content:"\f113"}.fa-phoenix-squadron:before{content:"\f511"}.fa-pagelines:before{content:"\f18c"}.fa-algolia:before{content:"\f36c"}.fa-red-river:before{content:"\f3e3"}.fa-creative-commons-sa:before{content:"\f4ef"}.fa-safari:before{content:"\f267"}.fa-google:before{content:"\f1a0"}.fa-font-awesome-alt:before,.fa-square-font-awesome-stroke:before{content:"\f35c"}.fa-atlassian:before{content:"\f77b"}.fa-linkedin-in:before{content:"\f0e1"}.fa-digital-ocean:before{content:"\f391"}.fa-nimblr:before{content:"\f5a8"}.fa-chromecast:before{content:"\f838"}.fa-evernote:before{content:"\f839"}.fa-hacker-news:before{content:"\f1d4"}.fa-creative-commons-sampling:before{content:"\f4f0"}.fa-adversal:before{content:"\f36a"}.fa-creative-commons:before{content:"\f25e"}.fa-watchman-monitoring:before{content:"\e087"}.fa-fonticons:before{content:"\f280"}.fa-weixin:before{content:"\f1d7"}.fa-shirtsinbulk:before{content:"\f214"}.fa-codepen:before{content:"\f1cb"}.fa-git-alt:before{content:"\f841"}.fa-lyft:before{content:"\f3c3"}.fa-rev:before{content:"\f5b2"}.fa-windows:before{content:"\f17a"}.fa-wizards-of-the-coast:before{content:"\f730"}.fa-square-viadeo:before,.fa-viadeo-square:before{content:"\f2aa"}.fa-meetup:before{content:"\f2e0"}.fa-centos:before{content:"\f789"}.fa-adn:before{content:"\f170"}.fa-cloudsmith:before{content:"\f384"}.fa-opensuse:before{content:"\e62b"}.fa-pied-piper-alt:before{content:"\f1a8"}.fa-dribbble-square:before,.fa-square-dribbble:before{content:"\f397"}.fa-codiepie:before{content:"\f284"}.fa-node:before{content:"\f419"}.fa-mix:before{content:"\f3cb"}.fa-steam:before{content:"\f1b6"}.fa-cc-apple-pay:before{content:"\f416"}.fa-scribd:before{content:"\f28a"}.fa-debian:before{content:"\e60b"}.fa-openid:before{content:"\f19b"}.fa-instalod:before{content:"\e081"}.fa-expeditedssl:before{content:"\f23e"}.fa-sellcast:before{content:"\f2da"}.fa-square-twitter:before,.fa-twitter-square:before{content:"\f081"}.fa-r-project:before{content:"\f4f7"}.fa-delicious:before{content:"\f1a5"}.fa-freebsd:before{content:"\f3a4"}.fa-vuejs:before{content:"\f41f"}.fa-accusoft:before{content:"\f369"}.fa-ioxhost:before{content:"\f208"}.fa-fonticons-fi:before{content:"\f3a2"}.fa-app-store:before{content:"\f36f"}.fa-cc-mastercard:before{content:"\f1f1"}.fa-itunes-note:before{content:"\f3b5"}.fa-golang:before{content:"\e40f"}.fa-kickstarter:before,.fa-square-kickstarter:before{content:"\f3bb"}.fa-grav:before{content:"\f2d6"}.fa-weibo:before{content:"\f18a"}.fa-uncharted:before{content:"\e084"}.fa-firstdraft:before{content:"\f3a1"}.fa-square-youtube:before,.fa-youtube-square:before{content:"\f431"}.fa-wikipedia-w:before{content:"\f266"}.fa-rendact:before,.fa-wpressr:before{content:"\f3e4"}.fa-angellist:before{content:"\f209"}.fa-galactic-republic:before{content:"\f50c"}.fa-nfc-directional:before{content:"\e530"}.fa-skype:before{content:"\f17e"}.fa-joget:before{content:"\f3b7"}.fa-fedora:before{content:"\f798"}.fa-stripe-s:before{content:"\f42a"}.fa-meta:before{content:"\e49b"}.fa-laravel:before{content:"\f3bd"}.fa-hotjar:before{content:"\f3b1"}.fa-bluetooth-b:before{content:"\f294"}.fa-square-letterboxd:before{content:"\e62e"}.fa-sticker-mule:before{content:"\f3f7"}.fa-creative-commons-zero:before{content:"\f4f3"}.fa-hips:before{content:"\f452"}.fa-behance:before{content:"\f1b4"}.fa-reddit:before{content:"\f1a1"}.fa-discord:before{content:"\f392"}.fa-chrome:before{content:"\f268"}.fa-app-store-ios:before{content:"\f370"}.fa-cc-discover:before{content:"\f1f2"}.fa-wpbeginner:before{content:"\f297"}.fa-confluence:before{content:"\f78d"}.fa-shoelace:before{content:"\e60c"}.fa-mdb:before{content:"\f8ca"}.fa-dochub:before{content:"\f394"}.fa-accessible-icon:before{content:"\f368"}.fa-ebay:before{content:"\f4f4"}.fa-amazon:before{content:"\f270"}.fa-unsplash:before{content:"\e07c"}.fa-yarn:before{content:"\f7e3"}.fa-square-steam:before,.fa-steam-square:before{content:"\f1b7"}.fa-500px:before{content:"\f26e"}.fa-square-vimeo:before,.fa-vimeo-square:before{content:"\f194"}.fa-asymmetrik:before{content:"\f372"}.fa-font-awesome-flag:before,.fa-font-awesome-logo-full:before,.fa-font-awesome:before{content:"\f2b4"}.fa-gratipay:before{content:"\f184"}.fa-apple:before{content:"\f179"}.fa-hive:before{content:"\e07f"}.fa-gitkraken:before{content:"\f3a6"}.fa-keybase:before{content:"\f4f5"}.fa-apple-pay:before{content:"\f415"}.fa-padlet:before{content:"\e4a0"}.fa-amazon-pay:before{content:"\f42c"}.fa-github-square:before,.fa-square-github:before{content:"\f092"}.fa-stumbleupon:before{content:"\f1a4"}.fa-fedex:before{content:"\f797"}.fa-phoenix-framework:before{content:"\f3dc"}.fa-shopify:before{content:"\e057"}.fa-neos:before{content:"\f612"}.fa-square-threads:before{content:"\e619"}.fa-hackerrank:before{content:"\f5f7"}.fa-researchgate:before{content:"\f4f8"}.fa-swift:before{content:"\f8e1"}.fa-angular:before{content:"\f420"}.fa-speakap:before{content:"\f3f3"}.fa-angrycreative:before{content:"\f36e"}.fa-y-combinator:before{content:"\f23b"}.fa-empire:before{content:"\f1d1"}.fa-envira:before{content:"\f299"}.fa-google-scholar:before{content:"\e63b"}.fa-gitlab-square:before,.fa-square-gitlab:before{content:"\e5ae"}.fa-studiovinari:before{content:"\f3f8"}.fa-pied-piper:before{content:"\f2ae"}.fa-wordpress:before{content:"\f19a"}.fa-product-hunt:before{content:"\f288"}.fa-firefox:before{content:"\f269"}.fa-linode:before{content:"\f2b8"}.fa-goodreads:before{content:"\f3a8"}.fa-odnoklassniki-square:before,.fa-square-odnoklassniki:before{content:"\f264"}.fa-jsfiddle:before{content:"\f1cc"}.fa-sith:before{content:"\f512"}.fa-themeisle:before{content:"\f2b2"}.fa-page4:before{content:"\f3d7"}.fa-hashnode:before{content:"\e499"}.fa-react:before{content:"\f41b"}.fa-cc-paypal:before{content:"\f1f4"}.fa-squarespace:before{content:"\f5be"}.fa-cc-stripe:before{content:"\f1f5"}.fa-creative-commons-share:before{content:"\f4f2"}.fa-bitcoin:before{content:"\f379"}.fa-keycdn:before{content:"\f3ba"}.fa-opera:before{content:"\f26a"}.fa-itch-io:before{content:"\f83a"}.fa-umbraco:before{content:"\f8e8"}.fa-galactic-senate:before{content:"\f50d"}.fa-ubuntu:before{content:"\f7df"}.fa-draft2digital:before{content:"\f396"}.fa-stripe:before{content:"\f429"}.fa-houzz:before{content:"\f27c"}.fa-gg:before{content:"\f260"}.fa-dhl:before{content:"\f790"}.fa-pinterest-square:before,.fa-square-pinterest:before{content:"\f0d3"}.fa-xing:before{content:"\f168"}.fa-blackberry:before{content:"\f37b"}.fa-creative-commons-pd:before{content:"\f4ec"}.fa-playstation:before{content:"\f3df"}.fa-quinscape:before{content:"\f459"}.fa-less:before{content:"\f41d"}.fa-blogger-b:before{content:"\f37d"}.fa-opencart:before{content:"\f23d"}.fa-vine:before{content:"\f1ca"}.fa-signal-messenger:before{content:"\e663"}.fa-paypal:before{content:"\f1ed"}.fa-gitlab:before{content:"\f296"}.fa-typo3:before{content:"\f42b"}.fa-reddit-alien:before{content:"\f281"}.fa-yahoo:before{content:"\f19e"}.fa-dailymotion:before{content:"\e052"}.fa-affiliatetheme:before{content:"\f36b"}.fa-pied-piper-pp:before{content:"\f1a7"}.fa-bootstrap:before{content:"\f836"}.fa-odnoklassniki:before{content:"\f263"}.fa-nfc-symbol:before{content:"\e531"}.fa-mintbit:before{content:"\e62f"}.fa-ethereum:before{content:"\f42e"}.fa-speaker-deck:before{content:"\f83c"}.fa-creative-commons-nc-eu:before{content:"\f4e9"}.fa-patreon:before{content:"\f3d9"}.fa-avianex:before{content:"\f374"}.fa-ello:before{content:"\f5f1"}.fa-gofore:before{content:"\f3a7"}.fa-bimobject:before{content:"\f378"}.fa-brave-reverse:before{content:"\e63d"}.fa-facebook-f:before{content:"\f39e"}.fa-google-plus-square:before,.fa-square-google-plus:before{content:"\f0d4"}.fa-web-awesome:before{content:"\e682"}.fa-mandalorian:before{content:"\f50f"}.fa-first-order-alt:before{content:"\f50a"}.fa-osi:before{content:"\f41a"}.fa-google-wallet:before{content:"\f1ee"}.fa-d-and-d-beyond:before{content:"\f6ca"}.fa-periscope:before{content:"\f3da"}.fa-fulcrum:before{content:"\f50b"}.fa-cloudscale:before{content:"\f383"}.fa-forumbee:before{content:"\f211"}.fa-mizuni:before{content:"\f3cc"}.fa-schlix:before{content:"\f3ea"}.fa-square-xing:before,.fa-xing-square:before{content:"\f169"}.fa-bandcamp:before{content:"\f2d5"}.fa-wpforms:before{content:"\f298"}.fa-cloudversify:before{content:"\f385"}.fa-usps:before{content:"\f7e1"}.fa-megaport:before{content:"\f5a3"}.fa-magento:before{content:"\f3c4"}.fa-spotify:before{content:"\f1bc"}.fa-optin-monster:before{content:"\f23c"}.fa-fly:before{content:"\f417"}.fa-aviato:before{content:"\f421"}.fa-itunes:before{content:"\f3b4"}.fa-cuttlefish:before{content:"\f38c"}.fa-blogger:before{content:"\f37c"}.fa-flickr:before{content:"\f16e"}.fa-viber:before{content:"\f409"}.fa-soundcloud:before{content:"\f1be"}.fa-digg:before{content:"\f1a6"}.fa-tencent-weibo:before{content:"\f1d5"}.fa-letterboxd:before{content:"\e62d"}.fa-symfony:before{content:"\f83d"}.fa-maxcdn:before{content:"\f136"}.fa-etsy:before{content:"\f2d7"}.fa-facebook-messenger:before{content:"\f39f"}.fa-audible:before{content:"\f373"}.fa-think-peaks:before{content:"\f731"}.fa-bilibili:before{content:"\e3d9"}.fa-erlang:before{content:"\f39d"}.fa-x-twitter:before{content:"\e61b"}.fa-cotton-bureau:before{content:"\f89e"}.fa-dashcube:before{content:"\f210"}.fa-42-group:before,.fa-innosoft:before{content:"\e080"}.fa-stack-exchange:before{content:"\f18d"}.fa-elementor:before{content:"\f430"}.fa-pied-piper-square:before,.fa-square-pied-piper:before{content:"\e01e"}.fa-creative-commons-nd:before{content:"\f4eb"}.fa-palfed:before{content:"\f3d8"}.fa-superpowers:before{content:"\f2dd"}.fa-resolving:before{content:"\f3e7"}.fa-xbox:before{content:"\f412"}.fa-square-web-awesome-stroke:before{content:"\e684"}.fa-searchengin:before{content:"\f3eb"}.fa-tiktok:before{content:"\e07b"}.fa-facebook-square:before,.fa-square-facebook:before{content:"\f082"}.fa-renren:before{content:"\f18b"}.fa-linux:before{content:"\f17c"}.fa-glide:before{content:"\f2a5"}.fa-linkedin:before{content:"\f08c"}.fa-hubspot:before{content:"\f3b2"}.fa-deploydog:before{content:"\f38e"}.fa-twitch:before{content:"\f1e8"}.fa-ravelry:before{content:"\f2d9"}.fa-mixer:before{content:"\e056"}.fa-lastfm-square:before,.fa-square-lastfm:before{content:"\f203"}.fa-vimeo:before{content:"\f40a"}.fa-mendeley:before{content:"\f7b3"}.fa-uniregistry:before{content:"\f404"}.fa-figma:before{content:"\f799"}.fa-creative-commons-remix:before{content:"\f4ee"}.fa-cc-amazon-pay:before{content:"\f42d"}.fa-dropbox:before{content:"\f16b"}.fa-instagram:before{content:"\f16d"}.fa-cmplid:before{content:"\e360"}.fa-upwork:before{content:"\e641"}.fa-facebook:before{content:"\f09a"}.fa-gripfire:before{content:"\f3ac"}.fa-jedi-order:before{content:"\f50e"}.fa-uikit:before{content:"\f403"}.fa-fort-awesome-alt:before{content:"\f3a3"}.fa-phabricator:before{content:"\f3db"}.fa-ussunnah:before{content:"\f407"}.fa-earlybirds:before{content:"\f39a"}.fa-trade-federation:before{content:"\f513"}.fa-autoprefixer:before{content:"\f41c"}.fa-whatsapp:before{content:"\f232"}.fa-square-upwork:before{content:"\e67c"}.fa-slideshare:before{content:"\f1e7"}.fa-google-play:before{content:"\f3ab"}.fa-viadeo:before{content:"\f2a9"}.fa-line:before{content:"\f3c0"}.fa-google-drive:before{content:"\f3aa"}.fa-servicestack:before{content:"\f3ec"}.fa-simplybuilt:before{content:"\f215"}.fa-bitbucket:before{content:"\f171"}.fa-imdb:before{content:"\f2d8"}.fa-deezer:before{content:"\e077"}.fa-raspberry-pi:before{content:"\f7bb"}.fa-jira:before{content:"\f7b1"}.fa-docker:before{content:"\f395"}.fa-screenpal:before{content:"\e570"}.fa-bluetooth:before{content:"\f293"}.fa-gitter:before{content:"\f426"}.fa-d-and-d:before{content:"\f38d"}.fa-microblog:before{content:"\e01a"}.fa-cc-diners-club:before{content:"\f24c"}.fa-gg-circle:before{content:"\f261"}.fa-pied-piper-hat:before{content:"\f4e5"}.fa-kickstarter-k:before{content:"\f3bc"}.fa-yandex:before{content:"\f413"}.fa-readme:before{content:"\f4d5"}.fa-html5:before{content:"\f13b"}.fa-sellsy:before{content:"\f213"}.fa-square-web-awesome:before{content:"\e683"}.fa-sass:before{content:"\f41e"}.fa-wirsindhandwerk:before,.fa-wsh:before{content:"\e2d0"}.fa-buromobelexperte:before{content:"\f37f"}.fa-salesforce:before{content:"\f83b"}.fa-octopus-deploy:before{content:"\e082"}.fa-medapps:before{content:"\f3c6"}.fa-ns8:before{content:"\f3d5"}.fa-pinterest-p:before{content:"\f231"}.fa-apper:before{content:"\f371"}.fa-fort-awesome:before{content:"\f286"}.fa-waze:before{content:"\f83f"}.fa-bluesky:before{content:"\e671"}.fa-cc-jcb:before{content:"\f24b"}.fa-snapchat-ghost:before,.fa-snapchat:before{content:"\f2ab"}.fa-fantasy-flight-games:before{content:"\f6dc"}.fa-rust:before{content:"\e07a"}.fa-wix:before{content:"\f5cf"}.fa-behance-square:before,.fa-square-behance:before{content:"\f1b5"}.fa-supple:before{content:"\f3f9"}.fa-webflow:before{content:"\e65c"}.fa-rebel:before{content:"\f1d0"}.fa-css3:before{content:"\f13c"}.fa-staylinked:before{content:"\f3f5"}.fa-kaggle:before{content:"\f5fa"}.fa-space-awesome:before{content:"\e5ac"}.fa-deviantart:before{content:"\f1bd"}.fa-cpanel:before{content:"\f388"}.fa-goodreads-g:before{content:"\f3a9"}.fa-git-square:before,.fa-square-git:before{content:"\f1d2"}.fa-square-tumblr:before,.fa-tumblr-square:before{content:"\f174"}.fa-trello:before{content:"\f181"}.fa-creative-commons-nc-jp:before{content:"\f4ea"}.fa-get-pocket:before{content:"\f265"}.fa-perbyte:before{content:"\e083"}.fa-grunt:before{content:"\f3ad"}.fa-weebly:before{content:"\f5cc"}.fa-connectdevelop:before{content:"\f20e"}.fa-leanpub:before{content:"\f212"}.fa-black-tie:before{content:"\f27e"}.fa-themeco:before{content:"\f5c6"}.fa-python:before{content:"\f3e2"}.fa-android:before{content:"\f17b"}.fa-bots:before{content:"\e340"}.fa-free-code-camp:before{content:"\f2c5"}.fa-hornbill:before{content:"\f592"}.fa-js:before{content:"\f3b8"}.fa-ideal:before{content:"\e013"}.fa-git:before{content:"\f1d3"}.fa-dev:before{content:"\f6cc"}.fa-sketch:before{content:"\f7c6"}.fa-yandex-international:before{content:"\f414"}.fa-cc-amex:before{content:"\f1f3"}.fa-uber:before{content:"\f402"}.fa-github:before{content:"\f09b"}.fa-php:before{content:"\f457"}.fa-alipay:before{content:"\f642"}.fa-youtube:before{content:"\f167"}.fa-skyatlas:before{content:"\f216"}.fa-firefox-browser:before{content:"\e007"}.fa-replyd:before{content:"\f3e6"}.fa-suse:before{content:"\f7d6"}.fa-jenkins:before{content:"\f3b6"}.fa-twitter:before{content:"\f099"}.fa-rockrms:before{content:"\f3e9"}.fa-pinterest:before{content:"\f0d2"}.fa-buffer:before{content:"\f837"}.fa-npm:before{content:"\f3d4"}.fa-yammer:before{content:"\f840"}.fa-btc:before{content:"\f15a"}.fa-dribbble:before{content:"\f17d"}.fa-stumbleupon-circle:before{content:"\f1a3"}.fa-internet-explorer:before{content:"\f26b"}.fa-stubber:before{content:"\e5c7"}.fa-telegram-plane:before,.fa-telegram:before{content:"\f2c6"}.fa-old-republic:before{content:"\f510"}.fa-odysee:before{content:"\e5c6"}.fa-square-whatsapp:before,.fa-whatsapp-square:before{content:"\f40c"}.fa-node-js:before{content:"\f3d3"}.fa-edge-legacy:before{content:"\e078"}.fa-slack-hash:before,.fa-slack:before{content:"\f198"}.fa-medrt:before{content:"\f3c8"}.fa-usb:before{content:"\f287"}.fa-tumblr:before{content:"\f173"}.fa-vaadin:before{content:"\f408"}.fa-quora:before{content:"\f2c4"}.fa-square-x-twitter:before{content:"\e61a"}.fa-reacteurope:before{content:"\f75d"}.fa-medium-m:before,.fa-medium:before{content:"\f23a"}.fa-amilia:before{content:"\f36d"}.fa-mixcloud:before{content:"\f289"}.fa-flipboard:before{content:"\f44d"}.fa-viacoin:before{content:"\f237"}.fa-critical-role:before{content:"\f6c9"}.fa-sitrox:before{content:"\e44a"}.fa-discourse:before{content:"\f393"}.fa-joomla:before{content:"\f1aa"}.fa-mastodon:before{content:"\f4f6"}.fa-airbnb:before{content:"\f834"}.fa-wolf-pack-battalion:before{content:"\f514"}.fa-buy-n-large:before{content:"\f8a6"}.fa-gulp:before{content:"\f3ae"}.fa-creative-commons-sampling-plus:before{content:"\f4f1"}.fa-strava:before{content:"\f428"}.fa-ember:before{content:"\f423"}.fa-canadian-maple-leaf:before{content:"\f785"}.fa-teamspeak:before{content:"\f4f9"}.fa-pushed:before{content:"\f3e1"}.fa-wordpress-simple:before{content:"\f411"}.fa-nutritionix:before{content:"\f3d6"}.fa-wodu:before{content:"\e088"}.fa-google-pay:before{content:"\e079"}.fa-intercom:before{content:"\f7af"}.fa-zhihu:before{content:"\f63f"}.fa-korvue:before{content:"\f42f"}.fa-pix:before{content:"\e43a"}.fa-steam-symbol:before{content:"\f3f6"}:host,:root{--fa-font-regular:normal 400 1em/1 "Font Awesome 6 Free"}@font-face{font-family:"Font Awesome 6 Free";font-style:normal;font-weight:400;font-display:block;src: url("../webfonts/fa-regular-400.woff2") format("woff2"), url("../webfonts/fa-regular-400.ttf") format("truetype"); }.fa-regular,.far{font-weight:400}:host,:root{--fa-style-family-classic:"Font Awesome 6 Free";--fa-font-solid:normal 900 1em/1 "Font Awesome 6 Free"}@font-face{font-family:"Font Awesome 6 Free";font-style:normal;font-weight:900;font-display:block;src: url("../webfonts/fa-solid-900.woff2") format("woff2"), url("../webfonts/fa-solid-900.ttf") format("truetype"); }.fa-solid,.fas{font-weight:900}@font-face{font-family:"Font Awesome 5 Brands";font-display:block;font-weight:400;src: url("../webfonts/fa-brands-400.woff2") format("woff2"), url("../webfonts/fa-brands-400.ttf") format("truetype"); }@font-face{font-family:"Font Awesome 5 Free";font-display:block;font-weight:900;src: url("../webfonts/fa-solid-900.woff2") format("woff2"), url("../webfonts/fa-solid-900.ttf") format("truetype"); }@font-face{font-family:"Font Awesome 5 Free";font-display:block;font-weight:400;src: url("../webfonts/fa-regular-400.woff2") format("woff2"), url("../webfonts/fa-regular-400.ttf") format("truetype"); }@font-face{font-family:"FontAwesome";font-display:block;src: url("../webfonts/fa-solid-900.woff2") format("woff2"), url("../webfonts/fa-solid-900.ttf") format("truetype"); }@font-face{font-family:"FontAwesome";font-display:block;src: url("../webfonts/fa-brands-400.woff2") format("woff2"), url("../webfonts/fa-brands-400.ttf") format("truetype"); }@font-face{font-family:"FontAwesome";font-display:block;src: url("../webfonts/fa-regular-400.woff2") format("woff2"), url("../webfonts/fa-regular-400.ttf") format("truetype"); }@font-face{font-family:"FontAwesome";font-display:block;src: url("../webfonts/fa-v4compatibility.woff2") format("woff2"), url("../webfonts/fa-v4compatibility.ttf") format("truetype"); }
\ No newline at end of file
diff --git a/docs/deps/font-awesome-6.5.2/css/v4-shims.css b/docs/deps/font-awesome-6.5.2/css/v4-shims.css
new file mode 100644
index 00000000..ea60ea4d
--- /dev/null
+++ b/docs/deps/font-awesome-6.5.2/css/v4-shims.css
@@ -0,0 +1,2194 @@
+/*!
+ * Font Awesome Free 6.5.2 by @fontawesome - https://fontawesome.com
+ * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License)
+ * Copyright 2024 Fonticons, Inc.
+ */
+.fa.fa-glass:before {
+  content: "\f000"; }
+
+.fa.fa-envelope-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-envelope-o:before {
+  content: "\f0e0"; }
+
+.fa.fa-star-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-star-o:before {
+  content: "\f005"; }
+
+.fa.fa-remove:before {
+  content: "\f00d"; }
+
+.fa.fa-close:before {
+  content: "\f00d"; }
+
+.fa.fa-gear:before {
+  content: "\f013"; }
+
+.fa.fa-trash-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-trash-o:before {
+  content: "\f2ed"; }
+
+.fa.fa-home:before {
+  content: "\f015"; }
+
+.fa.fa-file-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-file-o:before {
+  content: "\f15b"; }
+
+.fa.fa-clock-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-clock-o:before {
+  content: "\f017"; }
+
+.fa.fa-arrow-circle-o-down {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-arrow-circle-o-down:before {
+  content: "\f358"; }
+
+.fa.fa-arrow-circle-o-up {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-arrow-circle-o-up:before {
+  content: "\f35b"; }
+
+.fa.fa-play-circle-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-play-circle-o:before {
+  content: "\f144"; }
+
+.fa.fa-repeat:before {
+  content: "\f01e"; }
+
+.fa.fa-rotate-right:before {
+  content: "\f01e"; }
+
+.fa.fa-refresh:before {
+  content: "\f021"; }
+
+.fa.fa-list-alt {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-list-alt:before {
+  content: "\f022"; }
+
+.fa.fa-dedent:before {
+  content: "\f03b"; }
+
+.fa.fa-video-camera:before {
+  content: "\f03d"; }
+
+.fa.fa-picture-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-picture-o:before {
+  content: "\f03e"; }
+
+.fa.fa-photo {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-photo:before {
+  content: "\f03e"; }
+
+.fa.fa-image {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-image:before {
+  content: "\f03e"; }
+
+.fa.fa-map-marker:before {
+  content: "\f3c5"; }
+
+.fa.fa-pencil-square-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-pencil-square-o:before {
+  content: "\f044"; }
+
+.fa.fa-edit {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-edit:before {
+  content: "\f044"; }
+
+.fa.fa-share-square-o:before {
+  content: "\f14d"; }
+
+.fa.fa-check-square-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-check-square-o:before {
+  content: "\f14a"; }
+
+.fa.fa-arrows:before {
+  content: "\f0b2"; }
+
+.fa.fa-times-circle-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-times-circle-o:before {
+  content: "\f057"; }
+
+.fa.fa-check-circle-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-check-circle-o:before {
+  content: "\f058"; }
+
+.fa.fa-mail-forward:before {
+  content: "\f064"; }
+
+.fa.fa-expand:before {
+  content: "\f424"; }
+
+.fa.fa-compress:before {
+  content: "\f422"; }
+
+.fa.fa-eye {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-eye-slash {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-warning:before {
+  content: "\f071"; }
+
+.fa.fa-calendar:before {
+  content: "\f073"; }
+
+.fa.fa-arrows-v:before {
+  content: "\f338"; }
+
+.fa.fa-arrows-h:before {
+  content: "\f337"; }
+
+.fa.fa-bar-chart:before {
+  content: "\e0e3"; }
+
+.fa.fa-bar-chart-o:before {
+  content: "\e0e3"; }
+
+.fa.fa-twitter-square {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-twitter-square:before {
+  content: "\f081"; }
+
+.fa.fa-facebook-square {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-facebook-square:before {
+  content: "\f082"; }
+
+.fa.fa-gears:before {
+  content: "\f085"; }
+
+.fa.fa-thumbs-o-up {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-thumbs-o-up:before {
+  content: "\f164"; }
+
+.fa.fa-thumbs-o-down {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-thumbs-o-down:before {
+  content: "\f165"; }
+
+.fa.fa-heart-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-heart-o:before {
+  content: "\f004"; }
+
+.fa.fa-sign-out:before {
+  content: "\f2f5"; }
+
+.fa.fa-linkedin-square {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-linkedin-square:before {
+  content: "\f08c"; }
+
+.fa.fa-thumb-tack:before {
+  content: "\f08d"; }
+
+.fa.fa-external-link:before {
+  content: "\f35d"; }
+
+.fa.fa-sign-in:before {
+  content: "\f2f6"; }
+
+.fa.fa-github-square {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-github-square:before {
+  content: "\f092"; }
+
+.fa.fa-lemon-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-lemon-o:before {
+  content: "\f094"; }
+
+.fa.fa-square-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-square-o:before {
+  content: "\f0c8"; }
+
+.fa.fa-bookmark-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-bookmark-o:before {
+  content: "\f02e"; }
+
+.fa.fa-twitter {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-facebook {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-facebook:before {
+  content: "\f39e"; }
+
+.fa.fa-facebook-f {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-facebook-f:before {
+  content: "\f39e"; }
+
+.fa.fa-github {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-credit-card {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-feed:before {
+  content: "\f09e"; }
+
+.fa.fa-hdd-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-hdd-o:before {
+  content: "\f0a0"; }
+
+.fa.fa-hand-o-right {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-hand-o-right:before {
+  content: "\f0a4"; }
+
+.fa.fa-hand-o-left {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-hand-o-left:before {
+  content: "\f0a5"; }
+
+.fa.fa-hand-o-up {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-hand-o-up:before {
+  content: "\f0a6"; }
+
+.fa.fa-hand-o-down {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-hand-o-down:before {
+  content: "\f0a7"; }
+
+.fa.fa-globe:before {
+  content: "\f57d"; }
+
+.fa.fa-tasks:before {
+  content: "\f828"; }
+
+.fa.fa-arrows-alt:before {
+  content: "\f31e"; }
+
+.fa.fa-group:before {
+  content: "\f0c0"; }
+
+.fa.fa-chain:before {
+  content: "\f0c1"; }
+
+.fa.fa-cut:before {
+  content: "\f0c4"; }
+
+.fa.fa-files-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-files-o:before {
+  content: "\f0c5"; }
+
+.fa.fa-floppy-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-floppy-o:before {
+  content: "\f0c7"; }
+
+.fa.fa-save {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-save:before {
+  content: "\f0c7"; }
+
+.fa.fa-navicon:before {
+  content: "\f0c9"; }
+
+.fa.fa-reorder:before {
+  content: "\f0c9"; }
+
+.fa.fa-magic:before {
+  content: "\e2ca"; }
+
+.fa.fa-pinterest {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-pinterest-square {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-pinterest-square:before {
+  content: "\f0d3"; }
+
+.fa.fa-google-plus-square {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-google-plus-square:before {
+  content: "\f0d4"; }
+
+.fa.fa-google-plus {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-google-plus:before {
+  content: "\f0d5"; }
+
+.fa.fa-money:before {
+  content: "\f3d1"; }
+
+.fa.fa-unsorted:before {
+  content: "\f0dc"; }
+
+.fa.fa-sort-desc:before {
+  content: "\f0dd"; }
+
+.fa.fa-sort-asc:before {
+  content: "\f0de"; }
+
+.fa.fa-linkedin {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-linkedin:before {
+  content: "\f0e1"; }
+
+.fa.fa-rotate-left:before {
+  content: "\f0e2"; }
+
+.fa.fa-legal:before {
+  content: "\f0e3"; }
+
+.fa.fa-tachometer:before {
+  content: "\f625"; }
+
+.fa.fa-dashboard:before {
+  content: "\f625"; }
+
+.fa.fa-comment-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-comment-o:before {
+  content: "\f075"; }
+
+.fa.fa-comments-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-comments-o:before {
+  content: "\f086"; }
+
+.fa.fa-flash:before {
+  content: "\f0e7"; }
+
+.fa.fa-clipboard:before {
+  content: "\f0ea"; }
+
+.fa.fa-lightbulb-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-lightbulb-o:before {
+  content: "\f0eb"; }
+
+.fa.fa-exchange:before {
+  content: "\f362"; }
+
+.fa.fa-cloud-download:before {
+  content: "\f0ed"; }
+
+.fa.fa-cloud-upload:before {
+  content: "\f0ee"; }
+
+.fa.fa-bell-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-bell-o:before {
+  content: "\f0f3"; }
+
+.fa.fa-cutlery:before {
+  content: "\f2e7"; }
+
+.fa.fa-file-text-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-file-text-o:before {
+  content: "\f15c"; }
+
+.fa.fa-building-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-building-o:before {
+  content: "\f1ad"; }
+
+.fa.fa-hospital-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-hospital-o:before {
+  content: "\f0f8"; }
+
+.fa.fa-tablet:before {
+  content: "\f3fa"; }
+
+.fa.fa-mobile:before {
+  content: "\f3cd"; }
+
+.fa.fa-mobile-phone:before {
+  content: "\f3cd"; }
+
+.fa.fa-circle-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-circle-o:before {
+  content: "\f111"; }
+
+.fa.fa-mail-reply:before {
+  content: "\f3e5"; }
+
+.fa.fa-github-alt {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-folder-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-folder-o:before {
+  content: "\f07b"; }
+
+.fa.fa-folder-open-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-folder-open-o:before {
+  content: "\f07c"; }
+
+.fa.fa-smile-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-smile-o:before {
+  content: "\f118"; }
+
+.fa.fa-frown-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-frown-o:before {
+  content: "\f119"; }
+
+.fa.fa-meh-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-meh-o:before {
+  content: "\f11a"; }
+
+.fa.fa-keyboard-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-keyboard-o:before {
+  content: "\f11c"; }
+
+.fa.fa-flag-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-flag-o:before {
+  content: "\f024"; }
+
+.fa.fa-mail-reply-all:before {
+  content: "\f122"; }
+
+.fa.fa-star-half-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-star-half-o:before {
+  content: "\f5c0"; }
+
+.fa.fa-star-half-empty {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-star-half-empty:before {
+  content: "\f5c0"; }
+
+.fa.fa-star-half-full {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-star-half-full:before {
+  content: "\f5c0"; }
+
+.fa.fa-code-fork:before {
+  content: "\f126"; }
+
+.fa.fa-chain-broken:before {
+  content: "\f127"; }
+
+.fa.fa-unlink:before {
+  content: "\f127"; }
+
+.fa.fa-calendar-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-calendar-o:before {
+  content: "\f133"; }
+
+.fa.fa-maxcdn {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-html5 {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-css3 {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-unlock-alt:before {
+  content: "\f09c"; }
+
+.fa.fa-minus-square-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-minus-square-o:before {
+  content: "\f146"; }
+
+.fa.fa-level-up:before {
+  content: "\f3bf"; }
+
+.fa.fa-level-down:before {
+  content: "\f3be"; }
+
+.fa.fa-pencil-square:before {
+  content: "\f14b"; }
+
+.fa.fa-external-link-square:before {
+  content: "\f360"; }
+
+.fa.fa-compass {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-caret-square-o-down {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-caret-square-o-down:before {
+  content: "\f150"; }
+
+.fa.fa-toggle-down {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-toggle-down:before {
+  content: "\f150"; }
+
+.fa.fa-caret-square-o-up {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-caret-square-o-up:before {
+  content: "\f151"; }
+
+.fa.fa-toggle-up {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-toggle-up:before {
+  content: "\f151"; }
+
+.fa.fa-caret-square-o-right {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-caret-square-o-right:before {
+  content: "\f152"; }
+
+.fa.fa-toggle-right {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-toggle-right:before {
+  content: "\f152"; }
+
+.fa.fa-eur:before {
+  content: "\f153"; }
+
+.fa.fa-euro:before {
+  content: "\f153"; }
+
+.fa.fa-gbp:before {
+  content: "\f154"; }
+
+.fa.fa-usd:before {
+  content: "\24"; }
+
+.fa.fa-dollar:before {
+  content: "\24"; }
+
+.fa.fa-inr:before {
+  content: "\e1bc"; }
+
+.fa.fa-rupee:before {
+  content: "\e1bc"; }
+
+.fa.fa-jpy:before {
+  content: "\f157"; }
+
+.fa.fa-cny:before {
+  content: "\f157"; }
+
+.fa.fa-rmb:before {
+  content: "\f157"; }
+
+.fa.fa-yen:before {
+  content: "\f157"; }
+
+.fa.fa-rub:before {
+  content: "\f158"; }
+
+.fa.fa-ruble:before {
+  content: "\f158"; }
+
+.fa.fa-rouble:before {
+  content: "\f158"; }
+
+.fa.fa-krw:before {
+  content: "\f159"; }
+
+.fa.fa-won:before {
+  content: "\f159"; }
+
+.fa.fa-btc {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-bitcoin {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-bitcoin:before {
+  content: "\f15a"; }
+
+.fa.fa-file-text:before {
+  content: "\f15c"; }
+
+.fa.fa-sort-alpha-asc:before {
+  content: "\f15d"; }
+
+.fa.fa-sort-alpha-desc:before {
+  content: "\f881"; }
+
+.fa.fa-sort-amount-asc:before {
+  content: "\f884"; }
+
+.fa.fa-sort-amount-desc:before {
+  content: "\f160"; }
+
+.fa.fa-sort-numeric-asc:before {
+  content: "\f162"; }
+
+.fa.fa-sort-numeric-desc:before {
+  content: "\f886"; }
+
+.fa.fa-youtube-square {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-youtube-square:before {
+  content: "\f431"; }
+
+.fa.fa-youtube {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-xing {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-xing-square {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-xing-square:before {
+  content: "\f169"; }
+
+.fa.fa-youtube-play {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-youtube-play:before {
+  content: "\f167"; }
+
+.fa.fa-dropbox {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-stack-overflow {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-instagram {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-flickr {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-adn {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-bitbucket {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-bitbucket-square {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-bitbucket-square:before {
+  content: "\f171"; }
+
+.fa.fa-tumblr {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-tumblr-square {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-tumblr-square:before {
+  content: "\f174"; }
+
+.fa.fa-long-arrow-down:before {
+  content: "\f309"; }
+
+.fa.fa-long-arrow-up:before {
+  content: "\f30c"; }
+
+.fa.fa-long-arrow-left:before {
+  content: "\f30a"; }
+
+.fa.fa-long-arrow-right:before {
+  content: "\f30b"; }
+
+.fa.fa-apple {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-windows {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-android {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-linux {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-dribbble {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-skype {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-foursquare {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-trello {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-gratipay {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-gittip {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-gittip:before {
+  content: "\f184"; }
+
+.fa.fa-sun-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-sun-o:before {
+  content: "\f185"; }
+
+.fa.fa-moon-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-moon-o:before {
+  content: "\f186"; }
+
+.fa.fa-vk {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-weibo {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-renren {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-pagelines {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-stack-exchange {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-arrow-circle-o-right {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-arrow-circle-o-right:before {
+  content: "\f35a"; }
+
+.fa.fa-arrow-circle-o-left {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-arrow-circle-o-left:before {
+  content: "\f359"; }
+
+.fa.fa-caret-square-o-left {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-caret-square-o-left:before {
+  content: "\f191"; }
+
+.fa.fa-toggle-left {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-toggle-left:before {
+  content: "\f191"; }
+
+.fa.fa-dot-circle-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-dot-circle-o:before {
+  content: "\f192"; }
+
+.fa.fa-vimeo-square {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-vimeo-square:before {
+  content: "\f194"; }
+
+.fa.fa-try:before {
+  content: "\e2bb"; }
+
+.fa.fa-turkish-lira:before {
+  content: "\e2bb"; }
+
+.fa.fa-plus-square-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-plus-square-o:before {
+  content: "\f0fe"; }
+
+.fa.fa-slack {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-wordpress {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-openid {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-institution:before {
+  content: "\f19c"; }
+
+.fa.fa-bank:before {
+  content: "\f19c"; }
+
+.fa.fa-mortar-board:before {
+  content: "\f19d"; }
+
+.fa.fa-yahoo {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-google {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-reddit {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-reddit-square {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-reddit-square:before {
+  content: "\f1a2"; }
+
+.fa.fa-stumbleupon-circle {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-stumbleupon {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-delicious {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-digg {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-pied-piper-pp {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-pied-piper-alt {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-drupal {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-joomla {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-behance {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-behance-square {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-behance-square:before {
+  content: "\f1b5"; }
+
+.fa.fa-steam {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-steam-square {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-steam-square:before {
+  content: "\f1b7"; }
+
+.fa.fa-automobile:before {
+  content: "\f1b9"; }
+
+.fa.fa-cab:before {
+  content: "\f1ba"; }
+
+.fa.fa-spotify {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-deviantart {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-soundcloud {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-file-pdf-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-file-pdf-o:before {
+  content: "\f1c1"; }
+
+.fa.fa-file-word-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-file-word-o:before {
+  content: "\f1c2"; }
+
+.fa.fa-file-excel-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-file-excel-o:before {
+  content: "\f1c3"; }
+
+.fa.fa-file-powerpoint-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-file-powerpoint-o:before {
+  content: "\f1c4"; }
+
+.fa.fa-file-image-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-file-image-o:before {
+  content: "\f1c5"; }
+
+.fa.fa-file-photo-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-file-photo-o:before {
+  content: "\f1c5"; }
+
+.fa.fa-file-picture-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-file-picture-o:before {
+  content: "\f1c5"; }
+
+.fa.fa-file-archive-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-file-archive-o:before {
+  content: "\f1c6"; }
+
+.fa.fa-file-zip-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-file-zip-o:before {
+  content: "\f1c6"; }
+
+.fa.fa-file-audio-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-file-audio-o:before {
+  content: "\f1c7"; }
+
+.fa.fa-file-sound-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-file-sound-o:before {
+  content: "\f1c7"; }
+
+.fa.fa-file-video-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-file-video-o:before {
+  content: "\f1c8"; }
+
+.fa.fa-file-movie-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-file-movie-o:before {
+  content: "\f1c8"; }
+
+.fa.fa-file-code-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-file-code-o:before {
+  content: "\f1c9"; }
+
+.fa.fa-vine {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-codepen {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-jsfiddle {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-life-bouy:before {
+  content: "\f1cd"; }
+
+.fa.fa-life-buoy:before {
+  content: "\f1cd"; }
+
+.fa.fa-life-saver:before {
+  content: "\f1cd"; }
+
+.fa.fa-support:before {
+  content: "\f1cd"; }
+
+.fa.fa-circle-o-notch:before {
+  content: "\f1ce"; }
+
+.fa.fa-rebel {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-ra {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-ra:before {
+  content: "\f1d0"; }
+
+.fa.fa-resistance {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-resistance:before {
+  content: "\f1d0"; }
+
+.fa.fa-empire {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-ge {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-ge:before {
+  content: "\f1d1"; }
+
+.fa.fa-git-square {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-git-square:before {
+  content: "\f1d2"; }
+
+.fa.fa-git {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-hacker-news {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-y-combinator-square {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-y-combinator-square:before {
+  content: "\f1d4"; }
+
+.fa.fa-yc-square {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-yc-square:before {
+  content: "\f1d4"; }
+
+.fa.fa-tencent-weibo {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-qq {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-weixin {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-wechat {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-wechat:before {
+  content: "\f1d7"; }
+
+.fa.fa-send:before {
+  content: "\f1d8"; }
+
+.fa.fa-paper-plane-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-paper-plane-o:before {
+  content: "\f1d8"; }
+
+.fa.fa-send-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-send-o:before {
+  content: "\f1d8"; }
+
+.fa.fa-circle-thin {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-circle-thin:before {
+  content: "\f111"; }
+
+.fa.fa-header:before {
+  content: "\f1dc"; }
+
+.fa.fa-futbol-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-futbol-o:before {
+  content: "\f1e3"; }
+
+.fa.fa-soccer-ball-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-soccer-ball-o:before {
+  content: "\f1e3"; }
+
+.fa.fa-slideshare {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-twitch {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-yelp {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-newspaper-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-newspaper-o:before {
+  content: "\f1ea"; }
+
+.fa.fa-paypal {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-google-wallet {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-cc-visa {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-cc-mastercard {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-cc-discover {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-cc-amex {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-cc-paypal {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-cc-stripe {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-bell-slash-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-bell-slash-o:before {
+  content: "\f1f6"; }
+
+.fa.fa-trash:before {
+  content: "\f2ed"; }
+
+.fa.fa-copyright {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-eyedropper:before {
+  content: "\f1fb"; }
+
+.fa.fa-area-chart:before {
+  content: "\f1fe"; }
+
+.fa.fa-pie-chart:before {
+  content: "\f200"; }
+
+.fa.fa-line-chart:before {
+  content: "\f201"; }
+
+.fa.fa-lastfm {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-lastfm-square {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-lastfm-square:before {
+  content: "\f203"; }
+
+.fa.fa-ioxhost {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-angellist {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-cc {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-cc:before {
+  content: "\f20a"; }
+
+.fa.fa-ils:before {
+  content: "\f20b"; }
+
+.fa.fa-shekel:before {
+  content: "\f20b"; }
+
+.fa.fa-sheqel:before {
+  content: "\f20b"; }
+
+.fa.fa-buysellads {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-connectdevelop {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-dashcube {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-forumbee {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-leanpub {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-sellsy {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-shirtsinbulk {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-simplybuilt {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-skyatlas {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-diamond {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-diamond:before {
+  content: "\f3a5"; }
+
+.fa.fa-transgender:before {
+  content: "\f224"; }
+
+.fa.fa-intersex:before {
+  content: "\f224"; }
+
+.fa.fa-transgender-alt:before {
+  content: "\f225"; }
+
+.fa.fa-facebook-official {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-facebook-official:before {
+  content: "\f09a"; }
+
+.fa.fa-pinterest-p {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-whatsapp {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-hotel:before {
+  content: "\f236"; }
+
+.fa.fa-viacoin {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-medium {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-y-combinator {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-yc {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-yc:before {
+  content: "\f23b"; }
+
+.fa.fa-optin-monster {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-opencart {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-expeditedssl {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-battery-4:before {
+  content: "\f240"; }
+
+.fa.fa-battery:before {
+  content: "\f240"; }
+
+.fa.fa-battery-3:before {
+  content: "\f241"; }
+
+.fa.fa-battery-2:before {
+  content: "\f242"; }
+
+.fa.fa-battery-1:before {
+  content: "\f243"; }
+
+.fa.fa-battery-0:before {
+  content: "\f244"; }
+
+.fa.fa-object-group {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-object-ungroup {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-sticky-note-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-sticky-note-o:before {
+  content: "\f249"; }
+
+.fa.fa-cc-jcb {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-cc-diners-club {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-clone {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-hourglass-o:before {
+  content: "\f254"; }
+
+.fa.fa-hourglass-1:before {
+  content: "\f251"; }
+
+.fa.fa-hourglass-2:before {
+  content: "\f252"; }
+
+.fa.fa-hourglass-3:before {
+  content: "\f253"; }
+
+.fa.fa-hand-rock-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-hand-rock-o:before {
+  content: "\f255"; }
+
+.fa.fa-hand-grab-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-hand-grab-o:before {
+  content: "\f255"; }
+
+.fa.fa-hand-paper-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-hand-paper-o:before {
+  content: "\f256"; }
+
+.fa.fa-hand-stop-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-hand-stop-o:before {
+  content: "\f256"; }
+
+.fa.fa-hand-scissors-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-hand-scissors-o:before {
+  content: "\f257"; }
+
+.fa.fa-hand-lizard-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-hand-lizard-o:before {
+  content: "\f258"; }
+
+.fa.fa-hand-spock-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-hand-spock-o:before {
+  content: "\f259"; }
+
+.fa.fa-hand-pointer-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-hand-pointer-o:before {
+  content: "\f25a"; }
+
+.fa.fa-hand-peace-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-hand-peace-o:before {
+  content: "\f25b"; }
+
+.fa.fa-registered {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-creative-commons {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-gg {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-gg-circle {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-odnoklassniki {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-odnoklassniki-square {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-odnoklassniki-square:before {
+  content: "\f264"; }
+
+.fa.fa-get-pocket {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-wikipedia-w {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-safari {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-chrome {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-firefox {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-opera {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-internet-explorer {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-television:before {
+  content: "\f26c"; }
+
+.fa.fa-contao {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-500px {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-amazon {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-calendar-plus-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-calendar-plus-o:before {
+  content: "\f271"; }
+
+.fa.fa-calendar-minus-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-calendar-minus-o:before {
+  content: "\f272"; }
+
+.fa.fa-calendar-times-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-calendar-times-o:before {
+  content: "\f273"; }
+
+.fa.fa-calendar-check-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-calendar-check-o:before {
+  content: "\f274"; }
+
+.fa.fa-map-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-map-o:before {
+  content: "\f279"; }
+
+.fa.fa-commenting:before {
+  content: "\f4ad"; }
+
+.fa.fa-commenting-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-commenting-o:before {
+  content: "\f4ad"; }
+
+.fa.fa-houzz {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-vimeo {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-vimeo:before {
+  content: "\f27d"; }
+
+.fa.fa-black-tie {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-fonticons {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-reddit-alien {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-edge {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-credit-card-alt:before {
+  content: "\f09d"; }
+
+.fa.fa-codiepie {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-modx {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-fort-awesome {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-usb {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-product-hunt {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-mixcloud {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-scribd {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-pause-circle-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-pause-circle-o:before {
+  content: "\f28b"; }
+
+.fa.fa-stop-circle-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-stop-circle-o:before {
+  content: "\f28d"; }
+
+.fa.fa-bluetooth {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-bluetooth-b {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-gitlab {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-wpbeginner {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-wpforms {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-envira {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-wheelchair-alt {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-wheelchair-alt:before {
+  content: "\f368"; }
+
+.fa.fa-question-circle-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-question-circle-o:before {
+  content: "\f059"; }
+
+.fa.fa-volume-control-phone:before {
+  content: "\f2a0"; }
+
+.fa.fa-asl-interpreting:before {
+  content: "\f2a3"; }
+
+.fa.fa-deafness:before {
+  content: "\f2a4"; }
+
+.fa.fa-hard-of-hearing:before {
+  content: "\f2a4"; }
+
+.fa.fa-glide {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-glide-g {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-signing:before {
+  content: "\f2a7"; }
+
+.fa.fa-viadeo {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-viadeo-square {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-viadeo-square:before {
+  content: "\f2aa"; }
+
+.fa.fa-snapchat {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-snapchat-ghost {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-snapchat-ghost:before {
+  content: "\f2ab"; }
+
+.fa.fa-snapchat-square {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-snapchat-square:before {
+  content: "\f2ad"; }
+
+.fa.fa-pied-piper {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-first-order {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-yoast {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-themeisle {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-google-plus-official {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-google-plus-official:before {
+  content: "\f2b3"; }
+
+.fa.fa-google-plus-circle {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-google-plus-circle:before {
+  content: "\f2b3"; }
+
+.fa.fa-font-awesome {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-fa {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-fa:before {
+  content: "\f2b4"; }
+
+.fa.fa-handshake-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-handshake-o:before {
+  content: "\f2b5"; }
+
+.fa.fa-envelope-open-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-envelope-open-o:before {
+  content: "\f2b6"; }
+
+.fa.fa-linode {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-address-book-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-address-book-o:before {
+  content: "\f2b9"; }
+
+.fa.fa-vcard:before {
+  content: "\f2bb"; }
+
+.fa.fa-address-card-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-address-card-o:before {
+  content: "\f2bb"; }
+
+.fa.fa-vcard-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-vcard-o:before {
+  content: "\f2bb"; }
+
+.fa.fa-user-circle-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-user-circle-o:before {
+  content: "\f2bd"; }
+
+.fa.fa-user-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-user-o:before {
+  content: "\f007"; }
+
+.fa.fa-id-badge {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-drivers-license:before {
+  content: "\f2c2"; }
+
+.fa.fa-id-card-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-id-card-o:before {
+  content: "\f2c2"; }
+
+.fa.fa-drivers-license-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-drivers-license-o:before {
+  content: "\f2c2"; }
+
+.fa.fa-quora {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-free-code-camp {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-telegram {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-thermometer-4:before {
+  content: "\f2c7"; }
+
+.fa.fa-thermometer:before {
+  content: "\f2c7"; }
+
+.fa.fa-thermometer-3:before {
+  content: "\f2c8"; }
+
+.fa.fa-thermometer-2:before {
+  content: "\f2c9"; }
+
+.fa.fa-thermometer-1:before {
+  content: "\f2ca"; }
+
+.fa.fa-thermometer-0:before {
+  content: "\f2cb"; }
+
+.fa.fa-bathtub:before {
+  content: "\f2cd"; }
+
+.fa.fa-s15:before {
+  content: "\f2cd"; }
+
+.fa.fa-window-maximize {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-window-restore {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-times-rectangle:before {
+  content: "\f410"; }
+
+.fa.fa-window-close-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-window-close-o:before {
+  content: "\f410"; }
+
+.fa.fa-times-rectangle-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-times-rectangle-o:before {
+  content: "\f410"; }
+
+.fa.fa-bandcamp {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-grav {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-etsy {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-imdb {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-ravelry {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-eercast {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-eercast:before {
+  content: "\f2da"; }
+
+.fa.fa-snowflake-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-snowflake-o:before {
+  content: "\f2dc"; }
+
+.fa.fa-superpowers {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-wpexplorer {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-meetup {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
diff --git a/docs/deps/font-awesome-6.5.2/css/v4-shims.min.css b/docs/deps/font-awesome-6.5.2/css/v4-shims.min.css
new file mode 100644
index 00000000..09baf5fc
--- /dev/null
+++ b/docs/deps/font-awesome-6.5.2/css/v4-shims.min.css
@@ -0,0 +1,6 @@
+/*!
+ * Font Awesome Free 6.5.2 by @fontawesome - https://fontawesome.com
+ * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License)
+ * Copyright 2024 Fonticons, Inc.
+ */
+.fa.fa-glass:before{content:"\f000"}.fa.fa-envelope-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-envelope-o:before{content:"\f0e0"}.fa.fa-star-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-star-o:before{content:"\f005"}.fa.fa-close:before,.fa.fa-remove:before{content:"\f00d"}.fa.fa-gear:before{content:"\f013"}.fa.fa-trash-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-trash-o:before{content:"\f2ed"}.fa.fa-home:before{content:"\f015"}.fa.fa-file-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-file-o:before{content:"\f15b"}.fa.fa-clock-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-clock-o:before{content:"\f017"}.fa.fa-arrow-circle-o-down{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-arrow-circle-o-down:before{content:"\f358"}.fa.fa-arrow-circle-o-up{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-arrow-circle-o-up:before{content:"\f35b"}.fa.fa-play-circle-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-play-circle-o:before{content:"\f144"}.fa.fa-repeat:before,.fa.fa-rotate-right:before{content:"\f01e"}.fa.fa-refresh:before{content:"\f021"}.fa.fa-list-alt{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-list-alt:before{content:"\f022"}.fa.fa-dedent:before{content:"\f03b"}.fa.fa-video-camera:before{content:"\f03d"}.fa.fa-picture-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-picture-o:before{content:"\f03e"}.fa.fa-photo{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-photo:before{content:"\f03e"}.fa.fa-image{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-image:before{content:"\f03e"}.fa.fa-map-marker:before{content:"\f3c5"}.fa.fa-pencil-square-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-pencil-square-o:before{content:"\f044"}.fa.fa-edit{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-edit:before{content:"\f044"}.fa.fa-share-square-o:before{content:"\f14d"}.fa.fa-check-square-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-check-square-o:before{content:"\f14a"}.fa.fa-arrows:before{content:"\f0b2"}.fa.fa-times-circle-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-times-circle-o:before{content:"\f057"}.fa.fa-check-circle-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-check-circle-o:before{content:"\f058"}.fa.fa-mail-forward:before{content:"\f064"}.fa.fa-expand:before{content:"\f424"}.fa.fa-compress:before{content:"\f422"}.fa.fa-eye,.fa.fa-eye-slash{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-warning:before{content:"\f071"}.fa.fa-calendar:before{content:"\f073"}.fa.fa-arrows-v:before{content:"\f338"}.fa.fa-arrows-h:before{content:"\f337"}.fa.fa-bar-chart-o:before,.fa.fa-bar-chart:before{content:"\e0e3"}.fa.fa-twitter-square{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-twitter-square:before{content:"\f081"}.fa.fa-facebook-square{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-facebook-square:before{content:"\f082"}.fa.fa-gears:before{content:"\f085"}.fa.fa-thumbs-o-up{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-thumbs-o-up:before{content:"\f164"}.fa.fa-thumbs-o-down{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-thumbs-o-down:before{content:"\f165"}.fa.fa-heart-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-heart-o:before{content:"\f004"}.fa.fa-sign-out:before{content:"\f2f5"}.fa.fa-linkedin-square{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-linkedin-square:before{content:"\f08c"}.fa.fa-thumb-tack:before{content:"\f08d"}.fa.fa-external-link:before{content:"\f35d"}.fa.fa-sign-in:before{content:"\f2f6"}.fa.fa-github-square{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-github-square:before{content:"\f092"}.fa.fa-lemon-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-lemon-o:before{content:"\f094"}.fa.fa-square-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-square-o:before{content:"\f0c8"}.fa.fa-bookmark-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-bookmark-o:before{content:"\f02e"}.fa.fa-facebook,.fa.fa-twitter{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-facebook:before{content:"\f39e"}.fa.fa-facebook-f{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-facebook-f:before{content:"\f39e"}.fa.fa-github{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-credit-card{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-feed:before{content:"\f09e"}.fa.fa-hdd-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-hdd-o:before{content:"\f0a0"}.fa.fa-hand-o-right{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-hand-o-right:before{content:"\f0a4"}.fa.fa-hand-o-left{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-hand-o-left:before{content:"\f0a5"}.fa.fa-hand-o-up{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-hand-o-up:before{content:"\f0a6"}.fa.fa-hand-o-down{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-hand-o-down:before{content:"\f0a7"}.fa.fa-globe:before{content:"\f57d"}.fa.fa-tasks:before{content:"\f828"}.fa.fa-arrows-alt:before{content:"\f31e"}.fa.fa-group:before{content:"\f0c0"}.fa.fa-chain:before{content:"\f0c1"}.fa.fa-cut:before{content:"\f0c4"}.fa.fa-files-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-files-o:before{content:"\f0c5"}.fa.fa-floppy-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-floppy-o:before{content:"\f0c7"}.fa.fa-save{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-save:before{content:"\f0c7"}.fa.fa-navicon:before,.fa.fa-reorder:before{content:"\f0c9"}.fa.fa-magic:before{content:"\e2ca"}.fa.fa-pinterest,.fa.fa-pinterest-square{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-pinterest-square:before{content:"\f0d3"}.fa.fa-google-plus-square{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-google-plus-square:before{content:"\f0d4"}.fa.fa-google-plus{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-google-plus:before{content:"\f0d5"}.fa.fa-money:before{content:"\f3d1"}.fa.fa-unsorted:before{content:"\f0dc"}.fa.fa-sort-desc:before{content:"\f0dd"}.fa.fa-sort-asc:before{content:"\f0de"}.fa.fa-linkedin{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-linkedin:before{content:"\f0e1"}.fa.fa-rotate-left:before{content:"\f0e2"}.fa.fa-legal:before{content:"\f0e3"}.fa.fa-dashboard:before,.fa.fa-tachometer:before{content:"\f625"}.fa.fa-comment-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-comment-o:before{content:"\f075"}.fa.fa-comments-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-comments-o:before{content:"\f086"}.fa.fa-flash:before{content:"\f0e7"}.fa.fa-clipboard:before{content:"\f0ea"}.fa.fa-lightbulb-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-lightbulb-o:before{content:"\f0eb"}.fa.fa-exchange:before{content:"\f362"}.fa.fa-cloud-download:before{content:"\f0ed"}.fa.fa-cloud-upload:before{content:"\f0ee"}.fa.fa-bell-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-bell-o:before{content:"\f0f3"}.fa.fa-cutlery:before{content:"\f2e7"}.fa.fa-file-text-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-file-text-o:before{content:"\f15c"}.fa.fa-building-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-building-o:before{content:"\f1ad"}.fa.fa-hospital-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-hospital-o:before{content:"\f0f8"}.fa.fa-tablet:before{content:"\f3fa"}.fa.fa-mobile-phone:before,.fa.fa-mobile:before{content:"\f3cd"}.fa.fa-circle-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-circle-o:before{content:"\f111"}.fa.fa-mail-reply:before{content:"\f3e5"}.fa.fa-github-alt{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-folder-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-folder-o:before{content:"\f07b"}.fa.fa-folder-open-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-folder-open-o:before{content:"\f07c"}.fa.fa-smile-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-smile-o:before{content:"\f118"}.fa.fa-frown-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-frown-o:before{content:"\f119"}.fa.fa-meh-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-meh-o:before{content:"\f11a"}.fa.fa-keyboard-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-keyboard-o:before{content:"\f11c"}.fa.fa-flag-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-flag-o:before{content:"\f024"}.fa.fa-mail-reply-all:before{content:"\f122"}.fa.fa-star-half-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-star-half-o:before{content:"\f5c0"}.fa.fa-star-half-empty{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-star-half-empty:before{content:"\f5c0"}.fa.fa-star-half-full{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-star-half-full:before{content:"\f5c0"}.fa.fa-code-fork:before{content:"\f126"}.fa.fa-chain-broken:before,.fa.fa-unlink:before{content:"\f127"}.fa.fa-calendar-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-calendar-o:before{content:"\f133"}.fa.fa-css3,.fa.fa-html5,.fa.fa-maxcdn{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-unlock-alt:before{content:"\f09c"}.fa.fa-minus-square-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-minus-square-o:before{content:"\f146"}.fa.fa-level-up:before{content:"\f3bf"}.fa.fa-level-down:before{content:"\f3be"}.fa.fa-pencil-square:before{content:"\f14b"}.fa.fa-external-link-square:before{content:"\f360"}.fa.fa-compass{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-caret-square-o-down{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-caret-square-o-down:before{content:"\f150"}.fa.fa-toggle-down{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-toggle-down:before{content:"\f150"}.fa.fa-caret-square-o-up{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-caret-square-o-up:before{content:"\f151"}.fa.fa-toggle-up{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-toggle-up:before{content:"\f151"}.fa.fa-caret-square-o-right{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-caret-square-o-right:before{content:"\f152"}.fa.fa-toggle-right{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-toggle-right:before{content:"\f152"}.fa.fa-eur:before,.fa.fa-euro:before{content:"\f153"}.fa.fa-gbp:before{content:"\f154"}.fa.fa-dollar:before,.fa.fa-usd:before{content:"\24"}.fa.fa-inr:before,.fa.fa-rupee:before{content:"\e1bc"}.fa.fa-cny:before,.fa.fa-jpy:before,.fa.fa-rmb:before,.fa.fa-yen:before{content:"\f157"}.fa.fa-rouble:before,.fa.fa-rub:before,.fa.fa-ruble:before{content:"\f158"}.fa.fa-krw:before,.fa.fa-won:before{content:"\f159"}.fa.fa-bitcoin,.fa.fa-btc{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-bitcoin:before{content:"\f15a"}.fa.fa-file-text:before{content:"\f15c"}.fa.fa-sort-alpha-asc:before{content:"\f15d"}.fa.fa-sort-alpha-desc:before{content:"\f881"}.fa.fa-sort-amount-asc:before{content:"\f884"}.fa.fa-sort-amount-desc:before{content:"\f160"}.fa.fa-sort-numeric-asc:before{content:"\f162"}.fa.fa-sort-numeric-desc:before{content:"\f886"}.fa.fa-youtube-square{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-youtube-square:before{content:"\f431"}.fa.fa-xing,.fa.fa-xing-square,.fa.fa-youtube{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-xing-square:before{content:"\f169"}.fa.fa-youtube-play{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-youtube-play:before{content:"\f167"}.fa.fa-adn,.fa.fa-bitbucket,.fa.fa-bitbucket-square,.fa.fa-dropbox,.fa.fa-flickr,.fa.fa-instagram,.fa.fa-stack-overflow{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-bitbucket-square:before{content:"\f171"}.fa.fa-tumblr,.fa.fa-tumblr-square{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-tumblr-square:before{content:"\f174"}.fa.fa-long-arrow-down:before{content:"\f309"}.fa.fa-long-arrow-up:before{content:"\f30c"}.fa.fa-long-arrow-left:before{content:"\f30a"}.fa.fa-long-arrow-right:before{content:"\f30b"}.fa.fa-android,.fa.fa-apple,.fa.fa-dribbble,.fa.fa-foursquare,.fa.fa-gittip,.fa.fa-gratipay,.fa.fa-linux,.fa.fa-skype,.fa.fa-trello,.fa.fa-windows{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-gittip:before{content:"\f184"}.fa.fa-sun-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-sun-o:before{content:"\f185"}.fa.fa-moon-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-moon-o:before{content:"\f186"}.fa.fa-pagelines,.fa.fa-renren,.fa.fa-stack-exchange,.fa.fa-vk,.fa.fa-weibo{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-arrow-circle-o-right{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-arrow-circle-o-right:before{content:"\f35a"}.fa.fa-arrow-circle-o-left{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-arrow-circle-o-left:before{content:"\f359"}.fa.fa-caret-square-o-left{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-caret-square-o-left:before{content:"\f191"}.fa.fa-toggle-left{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-toggle-left:before{content:"\f191"}.fa.fa-dot-circle-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-dot-circle-o:before{content:"\f192"}.fa.fa-vimeo-square{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-vimeo-square:before{content:"\f194"}.fa.fa-try:before,.fa.fa-turkish-lira:before{content:"\e2bb"}.fa.fa-plus-square-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-plus-square-o:before{content:"\f0fe"}.fa.fa-openid,.fa.fa-slack,.fa.fa-wordpress{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-bank:before,.fa.fa-institution:before{content:"\f19c"}.fa.fa-mortar-board:before{content:"\f19d"}.fa.fa-google,.fa.fa-reddit,.fa.fa-reddit-square,.fa.fa-yahoo{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-reddit-square:before{content:"\f1a2"}.fa.fa-behance,.fa.fa-behance-square,.fa.fa-delicious,.fa.fa-digg,.fa.fa-drupal,.fa.fa-joomla,.fa.fa-pied-piper-alt,.fa.fa-pied-piper-pp,.fa.fa-stumbleupon,.fa.fa-stumbleupon-circle{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-behance-square:before{content:"\f1b5"}.fa.fa-steam,.fa.fa-steam-square{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-steam-square:before{content:"\f1b7"}.fa.fa-automobile:before{content:"\f1b9"}.fa.fa-cab:before{content:"\f1ba"}.fa.fa-deviantart,.fa.fa-soundcloud,.fa.fa-spotify{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-file-pdf-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-file-pdf-o:before{content:"\f1c1"}.fa.fa-file-word-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-file-word-o:before{content:"\f1c2"}.fa.fa-file-excel-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-file-excel-o:before{content:"\f1c3"}.fa.fa-file-powerpoint-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-file-powerpoint-o:before{content:"\f1c4"}.fa.fa-file-image-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-file-image-o:before{content:"\f1c5"}.fa.fa-file-photo-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-file-photo-o:before{content:"\f1c5"}.fa.fa-file-picture-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-file-picture-o:before{content:"\f1c5"}.fa.fa-file-archive-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-file-archive-o:before{content:"\f1c6"}.fa.fa-file-zip-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-file-zip-o:before{content:"\f1c6"}.fa.fa-file-audio-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-file-audio-o:before{content:"\f1c7"}.fa.fa-file-sound-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-file-sound-o:before{content:"\f1c7"}.fa.fa-file-video-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-file-video-o:before{content:"\f1c8"}.fa.fa-file-movie-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-file-movie-o:before{content:"\f1c8"}.fa.fa-file-code-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-file-code-o:before{content:"\f1c9"}.fa.fa-codepen,.fa.fa-jsfiddle,.fa.fa-vine{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-life-bouy:before,.fa.fa-life-buoy:before,.fa.fa-life-saver:before,.fa.fa-support:before{content:"\f1cd"}.fa.fa-circle-o-notch:before{content:"\f1ce"}.fa.fa-ra,.fa.fa-rebel{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-ra:before{content:"\f1d0"}.fa.fa-resistance{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-resistance:before{content:"\f1d0"}.fa.fa-empire,.fa.fa-ge{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-ge:before{content:"\f1d1"}.fa.fa-git-square{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-git-square:before{content:"\f1d2"}.fa.fa-git,.fa.fa-hacker-news,.fa.fa-y-combinator-square{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-y-combinator-square:before{content:"\f1d4"}.fa.fa-yc-square{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-yc-square:before{content:"\f1d4"}.fa.fa-qq,.fa.fa-tencent-weibo,.fa.fa-wechat,.fa.fa-weixin{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-wechat:before{content:"\f1d7"}.fa.fa-send:before{content:"\f1d8"}.fa.fa-paper-plane-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-paper-plane-o:before{content:"\f1d8"}.fa.fa-send-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-send-o:before{content:"\f1d8"}.fa.fa-circle-thin{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-circle-thin:before{content:"\f111"}.fa.fa-header:before{content:"\f1dc"}.fa.fa-futbol-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-futbol-o:before{content:"\f1e3"}.fa.fa-soccer-ball-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-soccer-ball-o:before{content:"\f1e3"}.fa.fa-slideshare,.fa.fa-twitch,.fa.fa-yelp{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-newspaper-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-newspaper-o:before{content:"\f1ea"}.fa.fa-cc-amex,.fa.fa-cc-discover,.fa.fa-cc-mastercard,.fa.fa-cc-paypal,.fa.fa-cc-stripe,.fa.fa-cc-visa,.fa.fa-google-wallet,.fa.fa-paypal{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-bell-slash-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-bell-slash-o:before{content:"\f1f6"}.fa.fa-trash:before{content:"\f2ed"}.fa.fa-copyright{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-eyedropper:before{content:"\f1fb"}.fa.fa-area-chart:before{content:"\f1fe"}.fa.fa-pie-chart:before{content:"\f200"}.fa.fa-line-chart:before{content:"\f201"}.fa.fa-lastfm,.fa.fa-lastfm-square{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-lastfm-square:before{content:"\f203"}.fa.fa-angellist,.fa.fa-ioxhost{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-cc{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-cc:before{content:"\f20a"}.fa.fa-ils:before,.fa.fa-shekel:before,.fa.fa-sheqel:before{content:"\f20b"}.fa.fa-buysellads,.fa.fa-connectdevelop,.fa.fa-dashcube,.fa.fa-forumbee,.fa.fa-leanpub,.fa.fa-sellsy,.fa.fa-shirtsinbulk,.fa.fa-simplybuilt,.fa.fa-skyatlas{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-diamond{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-diamond:before{content:"\f3a5"}.fa.fa-intersex:before,.fa.fa-transgender:before{content:"\f224"}.fa.fa-transgender-alt:before{content:"\f225"}.fa.fa-facebook-official{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-facebook-official:before{content:"\f09a"}.fa.fa-pinterest-p,.fa.fa-whatsapp{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-hotel:before{content:"\f236"}.fa.fa-medium,.fa.fa-viacoin,.fa.fa-y-combinator,.fa.fa-yc{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-yc:before{content:"\f23b"}.fa.fa-expeditedssl,.fa.fa-opencart,.fa.fa-optin-monster{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-battery-4:before,.fa.fa-battery:before{content:"\f240"}.fa.fa-battery-3:before{content:"\f241"}.fa.fa-battery-2:before{content:"\f242"}.fa.fa-battery-1:before{content:"\f243"}.fa.fa-battery-0:before{content:"\f244"}.fa.fa-object-group,.fa.fa-object-ungroup,.fa.fa-sticky-note-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-sticky-note-o:before{content:"\f249"}.fa.fa-cc-diners-club,.fa.fa-cc-jcb{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-clone{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-hourglass-o:before{content:"\f254"}.fa.fa-hourglass-1:before{content:"\f251"}.fa.fa-hourglass-2:before{content:"\f252"}.fa.fa-hourglass-3:before{content:"\f253"}.fa.fa-hand-rock-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-hand-rock-o:before{content:"\f255"}.fa.fa-hand-grab-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-hand-grab-o:before{content:"\f255"}.fa.fa-hand-paper-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-hand-paper-o:before{content:"\f256"}.fa.fa-hand-stop-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-hand-stop-o:before{content:"\f256"}.fa.fa-hand-scissors-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-hand-scissors-o:before{content:"\f257"}.fa.fa-hand-lizard-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-hand-lizard-o:before{content:"\f258"}.fa.fa-hand-spock-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-hand-spock-o:before{content:"\f259"}.fa.fa-hand-pointer-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-hand-pointer-o:before{content:"\f25a"}.fa.fa-hand-peace-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-hand-peace-o:before{content:"\f25b"}.fa.fa-registered{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-creative-commons,.fa.fa-gg,.fa.fa-gg-circle,.fa.fa-odnoklassniki,.fa.fa-odnoklassniki-square{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-odnoklassniki-square:before{content:"\f264"}.fa.fa-chrome,.fa.fa-firefox,.fa.fa-get-pocket,.fa.fa-internet-explorer,.fa.fa-opera,.fa.fa-safari,.fa.fa-wikipedia-w{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-television:before{content:"\f26c"}.fa.fa-500px,.fa.fa-amazon,.fa.fa-contao{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-calendar-plus-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-calendar-plus-o:before{content:"\f271"}.fa.fa-calendar-minus-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-calendar-minus-o:before{content:"\f272"}.fa.fa-calendar-times-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-calendar-times-o:before{content:"\f273"}.fa.fa-calendar-check-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-calendar-check-o:before{content:"\f274"}.fa.fa-map-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-map-o:before{content:"\f279"}.fa.fa-commenting:before{content:"\f4ad"}.fa.fa-commenting-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-commenting-o:before{content:"\f4ad"}.fa.fa-houzz,.fa.fa-vimeo{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-vimeo:before{content:"\f27d"}.fa.fa-black-tie,.fa.fa-edge,.fa.fa-fonticons,.fa.fa-reddit-alien{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-credit-card-alt:before{content:"\f09d"}.fa.fa-codiepie,.fa.fa-fort-awesome,.fa.fa-mixcloud,.fa.fa-modx,.fa.fa-product-hunt,.fa.fa-scribd,.fa.fa-usb{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-pause-circle-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-pause-circle-o:before{content:"\f28b"}.fa.fa-stop-circle-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-stop-circle-o:before{content:"\f28d"}.fa.fa-bluetooth,.fa.fa-bluetooth-b,.fa.fa-envira,.fa.fa-gitlab,.fa.fa-wheelchair-alt,.fa.fa-wpbeginner,.fa.fa-wpforms{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-wheelchair-alt:before{content:"\f368"}.fa.fa-question-circle-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-question-circle-o:before{content:"\f059"}.fa.fa-volume-control-phone:before{content:"\f2a0"}.fa.fa-asl-interpreting:before{content:"\f2a3"}.fa.fa-deafness:before,.fa.fa-hard-of-hearing:before{content:"\f2a4"}.fa.fa-glide,.fa.fa-glide-g{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-signing:before{content:"\f2a7"}.fa.fa-viadeo,.fa.fa-viadeo-square{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-viadeo-square:before{content:"\f2aa"}.fa.fa-snapchat,.fa.fa-snapchat-ghost{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-snapchat-ghost:before{content:"\f2ab"}.fa.fa-snapchat-square{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-snapchat-square:before{content:"\f2ad"}.fa.fa-first-order,.fa.fa-google-plus-official,.fa.fa-pied-piper,.fa.fa-themeisle,.fa.fa-yoast{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-google-plus-official:before{content:"\f2b3"}.fa.fa-google-plus-circle{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-google-plus-circle:before{content:"\f2b3"}.fa.fa-fa,.fa.fa-font-awesome{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-fa:before{content:"\f2b4"}.fa.fa-handshake-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-handshake-o:before{content:"\f2b5"}.fa.fa-envelope-open-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-envelope-open-o:before{content:"\f2b6"}.fa.fa-linode{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-address-book-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-address-book-o:before{content:"\f2b9"}.fa.fa-vcard:before{content:"\f2bb"}.fa.fa-address-card-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-address-card-o:before{content:"\f2bb"}.fa.fa-vcard-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-vcard-o:before{content:"\f2bb"}.fa.fa-user-circle-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-user-circle-o:before{content:"\f2bd"}.fa.fa-user-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-user-o:before{content:"\f007"}.fa.fa-id-badge{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-drivers-license:before{content:"\f2c2"}.fa.fa-id-card-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-id-card-o:before{content:"\f2c2"}.fa.fa-drivers-license-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-drivers-license-o:before{content:"\f2c2"}.fa.fa-free-code-camp,.fa.fa-quora,.fa.fa-telegram{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-thermometer-4:before,.fa.fa-thermometer:before{content:"\f2c7"}.fa.fa-thermometer-3:before{content:"\f2c8"}.fa.fa-thermometer-2:before{content:"\f2c9"}.fa.fa-thermometer-1:before{content:"\f2ca"}.fa.fa-thermometer-0:before{content:"\f2cb"}.fa.fa-bathtub:before,.fa.fa-s15:before{content:"\f2cd"}.fa.fa-window-maximize,.fa.fa-window-restore{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-times-rectangle:before{content:"\f410"}.fa.fa-window-close-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-window-close-o:before{content:"\f410"}.fa.fa-times-rectangle-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-times-rectangle-o:before{content:"\f410"}.fa.fa-bandcamp,.fa.fa-eercast,.fa.fa-etsy,.fa.fa-grav,.fa.fa-imdb,.fa.fa-ravelry{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-eercast:before{content:"\f2da"}.fa.fa-snowflake-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-snowflake-o:before{content:"\f2dc"}.fa.fa-meetup,.fa.fa-superpowers,.fa.fa-wpexplorer{font-family:"Font Awesome 6 Brands";font-weight:400}
\ No newline at end of file
diff --git a/docs/deps/font-awesome-6.5.2/webfonts/fa-brands-400.ttf b/docs/deps/font-awesome-6.5.2/webfonts/fa-brands-400.ttf
new file mode 100644
index 00000000..1fbb1f7c
--- /dev/null
+++ b/docs/deps/font-awesome-6.5.2/webfonts/fa-brands-400.ttf
diff --git a/docs/deps/font-awesome-6.5.2/webfonts/fa-brands-400.woff2 b/docs/deps/font-awesome-6.5.2/webfonts/fa-brands-400.woff2
new file mode 100644
index 00000000..5d280216
--- /dev/null
+++ b/docs/deps/font-awesome-6.5.2/webfonts/fa-brands-400.woff2
diff --git a/docs/deps/font-awesome-6.5.2/webfonts/fa-regular-400.ttf b/docs/deps/font-awesome-6.5.2/webfonts/fa-regular-400.ttf
new file mode 100644
index 00000000..549d68dc
--- /dev/null
+++ b/docs/deps/font-awesome-6.5.2/webfonts/fa-regular-400.ttf
diff --git a/docs/deps/font-awesome-6.5.2/webfonts/fa-regular-400.woff2 b/docs/deps/font-awesome-6.5.2/webfonts/fa-regular-400.woff2
new file mode 100644
index 00000000..18400d7f
--- /dev/null
+++ b/docs/deps/font-awesome-6.5.2/webfonts/fa-regular-400.woff2
diff --git a/docs/deps/font-awesome-6.5.2/webfonts/fa-solid-900.ttf b/docs/deps/font-awesome-6.5.2/webfonts/fa-solid-900.ttf
new file mode 100644
index 00000000..bb2a8695
--- /dev/null
+++ b/docs/deps/font-awesome-6.5.2/webfonts/fa-solid-900.ttf
diff --git a/docs/deps/font-awesome-6.5.2/webfonts/fa-solid-900.woff2 b/docs/deps/font-awesome-6.5.2/webfonts/fa-solid-900.woff2
new file mode 100644
index 00000000..758dd4f6
--- /dev/null
+++ b/docs/deps/font-awesome-6.5.2/webfonts/fa-solid-900.woff2
diff --git a/docs/deps/font-awesome-6.5.2/webfonts/fa-v4compatibility.ttf b/docs/deps/font-awesome-6.5.2/webfonts/fa-v4compatibility.ttf
new file mode 100644
index 00000000..8c5864c4
--- /dev/null
+++ b/docs/deps/font-awesome-6.5.2/webfonts/fa-v4compatibility.ttf
diff --git a/docs/deps/font-awesome-6.5.2/webfonts/fa-v4compatibility.woff2 b/docs/deps/font-awesome-6.5.2/webfonts/fa-v4compatibility.woff2
new file mode 100644
index 00000000..f94bec22
--- /dev/null
+++ b/docs/deps/font-awesome-6.5.2/webfonts/fa-v4compatibility.woff2
diff --git a/docs/deps/headroom-0.11.0/headroom.min.js b/docs/deps/headroom-0.11.0/headroom.min.js
new file mode 100644
index 00000000..433069fd
--- /dev/null
+++ b/docs/deps/headroom-0.11.0/headroom.min.js
@@ -0,0 +1,7 @@
+/*!
+ * headroom.js v0.11.0 - Give your page some headroom. Hide your header until you need it
+ * Copyright (c) 2020 Nick Williams - http://wicky.nillia.ms/headroom.js
+ * License: MIT
+ */
+
+!function(t,n){"object"==typeof exports&&"undefined"!=typeof module?module.exports=n():"function"==typeof define&&define.amd?define(n):(t=t||self).Headroom=n()}(this,function(){"use strict";function t(){return"undefined"!=typeof window}function d(t){return function(t){return t&&t.document&&function(t){return 9===t.nodeType}(t.document)}(t)?function(t){var n=t.document,o=n.body,s=n.documentElement;return{scrollHeight:function(){return Math.max(o.scrollHeight,s.scrollHeight,o.offsetHeight,s.offsetHeight,o.clientHeight,s.clientHeight)},height:function(){return t.innerHeight||s.clientHeight||o.clientHeight},scrollY:function(){return void 0!==t.pageYOffset?t.pageYOffset:(s||o.parentNode||o).scrollTop}}}(t):function(t){return{scrollHeight:function(){return Math.max(t.scrollHeight,t.offsetHeight,t.clientHeight)},height:function(){return Math.max(t.offsetHeight,t.clientHeight)},scrollY:function(){return t.scrollTop}}}(t)}function n(t,s,e){var n,o=function(){var n=!1;try{var t={get passive(){n=!0}};window.addEventListener("test",t,t),window.removeEventListener("test",t,t)}catch(t){n=!1}return n}(),i=!1,r=d(t),l=r.scrollY(),a={};function c(){var t=Math.round(r.scrollY()),n=r.height(),o=r.scrollHeight();a.scrollY=t,a.lastScrollY=l,a.direction=l<t?"down":"up",a.distance=Math.abs(t-l),a.isOutOfBounds=t<0||o<t+n,a.top=t<=s.offset,a.bottom=o<=t+n,a.toleranceExceeded=a.distance>s.tolerance[a.direction],e(a),l=t,i=!1}function h(){i||(i=!0,n=requestAnimationFrame(c))}var u=!!o&&{passive:!0,capture:!1};return t.addEventListener("scroll",h,u),c(),{destroy:function(){cancelAnimationFrame(n),t.removeEventListener("scroll",h,u)}}}function o(t,n){n=n||{},Object.assign(this,o.options,n),this.classes=Object.assign({},o.options.classes,n.classes),this.elem=t,this.tolerance=function(t){return t===Object(t)?t:{down:t,up:t}}(this.tolerance),this.initialised=!1,this.frozen=!1}return o.prototype={constructor:o,init:function(){return o.cutsTheMustard&&!this.initialised&&(this.addClass("initial"),this.initialised=!0,setTimeout(function(t){t.scrollTracker=n(t.scroller,{offset:t.offset,tolerance:t.tolerance},t.update.bind(t))},100,this)),this},destroy:function(){this.initialised=!1,Object.keys(this.classes).forEach(this.removeClass,this),this.scrollTracker.destroy()},unpin:function(){!this.hasClass("pinned")&&this.hasClass("unpinned")||(this.addClass("unpinned"),this.removeClass("pinned"),this.onUnpin&&this.onUnpin.call(this))},pin:function(){this.hasClass("unpinned")&&(this.addClass("pinned"),this.removeClass("unpinned"),this.onPin&&this.onPin.call(this))},freeze:function(){this.frozen=!0,this.addClass("frozen")},unfreeze:function(){this.frozen=!1,this.removeClass("frozen")},top:function(){this.hasClass("top")||(this.addClass("top"),this.removeClass("notTop"),this.onTop&&this.onTop.call(this))},notTop:function(){this.hasClass("notTop")||(this.addClass("notTop"),this.removeClass("top"),this.onNotTop&&this.onNotTop.call(this))},bottom:function(){this.hasClass("bottom")||(this.addClass("bottom"),this.removeClass("notBottom"),this.onBottom&&this.onBottom.call(this))},notBottom:function(){this.hasClass("notBottom")||(this.addClass("notBottom"),this.removeClass("bottom"),this.onNotBottom&&this.onNotBottom.call(this))},shouldUnpin:function(t){return"down"===t.direction&&!t.top&&t.toleranceExceeded},shouldPin:function(t){return"up"===t.direction&&t.toleranceExceeded||t.top},addClass:function(t){this.elem.classList.add.apply(this.elem.classList,this.classes[t].split(" "))},removeClass:function(t){this.elem.classList.remove.apply(this.elem.classList,this.classes[t].split(" "))},hasClass:function(t){return this.classes[t].split(" ").every(function(t){return this.classList.contains(t)},this.elem)},update:function(t){t.isOutOfBounds||!0!==this.frozen&&(t.top?this.top():this.notTop(),t.bottom?this.bottom():this.notBottom(),this.shouldUnpin(t)?this.unpin():this.shouldPin(t)&&this.pin())}},o.options={tolerance:{up:0,down:0},offset:0,scroller:t()?window:null,classes:{frozen:"headroom--frozen",pinned:"headroom--pinned",unpinned:"headroom--unpinned",top:"headroom--top",notTop:"headroom--not-top",bottom:"headroom--bottom",notBottom:"headroom--not-bottom",initial:"headroom"}},o.cutsTheMustard=!!(t()&&function(){}.bind&&"classList"in document.documentElement&&Object.assign&&Object.keys&&requestAnimationFrame),o});
\ No newline at end of file
diff --git a/docs/deps/headroom-0.11.0/jQuery.headroom.min.js b/docs/deps/headroom-0.11.0/jQuery.headroom.min.js
new file mode 100644
index 00000000..17f70c9e
--- /dev/null
+++ b/docs/deps/headroom-0.11.0/jQuery.headroom.min.js
@@ -0,0 +1,7 @@
+/*!
+ * headroom.js v0.9.4 - Give your page some headroom. Hide your header until you need it
+ * Copyright (c) 2017 Nick Williams - http://wicky.nillia.ms/headroom.js
+ * License: MIT
+ */
+
+!function(a){a&&(a.fn.headroom=function(b){return this.each(function(){var c=a(this),d=c.data("headroom"),e="object"==typeof b&&b;e=a.extend(!0,{},Headroom.options,e),d||(d=new Headroom(this,e),d.init(),c.data("headroom",d)),"string"==typeof b&&(d[b](),"destroy"===b&&c.removeData("headroom"))})},a("[data-headroom]").each(function(){var b=a(this);b.headroom(b.data())}))}(window.Zepto||window.jQuery);
\ No newline at end of file
diff --git a/docs/deps/jquery-3.6.0/jquery-3.6.0.js b/docs/deps/jquery-3.6.0/jquery-3.6.0.js
new file mode 100644
index 00000000..fc6c299b
--- /dev/null
+++ b/docs/deps/jquery-3.6.0/jquery-3.6.0.js
@@ -0,0 +1,10881 @@
+/*!
+ * jQuery JavaScript Library v3.6.0
+ * https://jquery.com/
+ *
+ * Includes Sizzle.js
+ * https://sizzlejs.com/
+ *
+ * Copyright OpenJS Foundation and other contributors
+ * Released under the MIT license
+ * https://jquery.org/license
+ *
+ * Date: 2021-03-02T17:08Z
+ */
+( function( global, factory ) {
+
+	"use strict";
+
+	if ( typeof module === "object" && typeof module.exports === "object" ) {
+
+		// For CommonJS and CommonJS-like environments where a proper `window`
+		// is present, execute the factory and get jQuery.
+		// For environments that do not have a `window` with a `document`
+		// (such as Node.js), expose a factory as module.exports.
+		// This accentuates the need for the creation of a real `window`.
+		// e.g. var jQuery = require("jquery")(window);
+		// See ticket #14549 for more info.
+		module.exports = global.document ?
+			factory( global, true ) :
+			function( w ) {
+				if ( !w.document ) {
+					throw new Error( "jQuery requires a window with a document" );
+				}
+				return factory( w );
+			};
+	} else {
+		factory( global );
+	}
+
+// Pass this if window is not defined yet
+} )( typeof window !== "undefined" ? window : this, function( window, noGlobal ) {
+
+// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1
+// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode
+// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common
+// enough that all such attempts are guarded in a try block.
+"use strict";
+
+var arr = [];
+
+var getProto = Object.getPrototypeOf;
+
+var slice = arr.slice;
+
+var flat = arr.flat ? function( array ) {
+	return arr.flat.call( array );
+} : function( array ) {
+	return arr.concat.apply( [], array );
+};
+
+
+var push = arr.push;
+
+var indexOf = arr.indexOf;
+
+var class2type = {};
+
+var toString = class2type.toString;
+
+var hasOwn = class2type.hasOwnProperty;
+
+var fnToString = hasOwn.toString;
+
+var ObjectFunctionString = fnToString.call( Object );
+
+var support = {};
+
+var isFunction = function isFunction( obj ) {
+
+		// Support: Chrome <=57, Firefox <=52
+		// In some browsers, typeof returns "function" for HTML <object> elements
+		// (i.e., `typeof document.createElement( "object" ) === "function"`).
+		// We don't want to classify *any* DOM node as a function.
+		// Support: QtWeb <=3.8.5, WebKit <=534.34, wkhtmltopdf tool <=0.12.5
+		// Plus for old WebKit, typeof returns "function" for HTML collections
+		// (e.g., `typeof document.getElementsByTagName("div") === "function"`). (gh-4756)
+		return typeof obj === "function" && typeof obj.nodeType !== "number" &&
+			typeof obj.item !== "function";
+	};
+
+
+var isWindow = function isWindow( obj ) {
+		return obj != null && obj === obj.window;
+	};
+
+
+var document = window.document;
+
+
+
+	var preservedScriptAttributes = {
+		type: true,
+		src: true,
+		nonce: true,
+		noModule: true
+	};
+
+	function DOMEval( code, node, doc ) {
+		doc = doc || document;
+
+		var i, val,
+			script = doc.createElement( "script" );
+
+		script.text = code;
+		if ( node ) {
+			for ( i in preservedScriptAttributes ) {
+
+				// Support: Firefox 64+, Edge 18+
+				// Some browsers don't support the "nonce" property on scripts.
+				// On the other hand, just using `getAttribute` is not enough as
+				// the `nonce` attribute is reset to an empty string whenever it
+				// becomes browsing-context connected.
+				// See https://github.com/whatwg/html/issues/2369
+				// See https://html.spec.whatwg.org/#nonce-attributes
+				// The `node.getAttribute` check was added for the sake of
+				// `jQuery.globalEval` so that it can fake a nonce-containing node
+				// via an object.
+				val = node[ i ] || node.getAttribute && node.getAttribute( i );
+				if ( val ) {
+					script.setAttribute( i, val );
+				}
+			}
+		}
+		doc.head.appendChild( script ).parentNode.removeChild( script );
+	}
+
+
+function toType( obj ) {
+	if ( obj == null ) {
+		return obj + "";
+	}
+
+	// Support: Android <=2.3 only (functionish RegExp)
+	return typeof obj === "object" || typeof obj === "function" ?
+		class2type[ toString.call( obj ) ] || "object" :
+		typeof obj;
+}
+/* global Symbol */
+// Defining this global in .eslintrc.json would create a danger of using the global
+// unguarded in another place, it seems safer to define global only for this module
+
+
+
+var
+	version = "3.6.0",
+
+	// Define a local copy of jQuery
+	jQuery = function( selector, context ) {
+
+		// The jQuery object is actually just the init constructor 'enhanced'
+		// Need init if jQuery is called (just allow error to be thrown if not included)
+		return new jQuery.fn.init( selector, context );
+	};
+
+jQuery.fn = jQuery.prototype = {
+
+	// The current version of jQuery being used
+	jquery: version,
+
+	constructor: jQuery,
+
+	// The default length of a jQuery object is 0
+	length: 0,
+
+	toArray: function() {
+		return slice.call( this );
+	},
+
+	// Get the Nth element in the matched element set OR
+	// Get the whole matched element set as a clean array
+	get: function( num ) {
+
+		// Return all the elements in a clean array
+		if ( num == null ) {
+			return slice.call( this );
+		}
+
+		// Return just the one element from the set
+		return num < 0 ? this[ num + this.length ] : this[ num ];
+	},
+
+	// Take an array of elements and push it onto the stack
+	// (returning the new matched element set)
+	pushStack: function( elems ) {
+
+		// Build a new jQuery matched element set
+		var ret = jQuery.merge( this.constructor(), elems );
+
+		// Add the old object onto the stack (as a reference)
+		ret.prevObject = this;
+
+		// Return the newly-formed element set
+		return ret;
+	},
+
+	// Execute a callback for every element in the matched set.
+	each: function( callback ) {
+		return jQuery.each( this, callback );
+	},
+
+	map: function( callback ) {
+		return this.pushStack( jQuery.map( this, function( elem, i ) {
+			return callback.call( elem, i, elem );
+		} ) );
+	},
+
+	slice: function() {
+		return this.pushStack( slice.apply( this, arguments ) );
+	},
+
+	first: function() {
+		return this.eq( 0 );
+	},
+
+	last: function() {
+		return this.eq( -1 );
+	},
+
+	even: function() {
+		return this.pushStack( jQuery.grep( this, function( _elem, i ) {
+			return ( i + 1 ) % 2;
+		} ) );
+	},
+
+	odd: function() {
+		return this.pushStack( jQuery.grep( this, function( _elem, i ) {
+			return i % 2;
+		} ) );
+	},
+
+	eq: function( i ) {
+		var len = this.length,
+			j = +i + ( i < 0 ? len : 0 );
+		return this.pushStack( j >= 0 && j < len ? [ this[ j ] ] : [] );
+	},
+
+	end: function() {
+		return this.prevObject || this.constructor();
+	},
+
+	// For internal use only.
+	// Behaves like an Array's method, not like a jQuery method.
+	push: push,
+	sort: arr.sort,
+	splice: arr.splice
+};
+
+jQuery.extend = jQuery.fn.extend = function() {
+	var options, name, src, copy, copyIsArray, clone,
+		target = arguments[ 0 ] || {},
+		i = 1,
+		length = arguments.length,
+		deep = false;
+
+	// Handle a deep copy situation
+	if ( typeof target === "boolean" ) {
+		deep = target;
+
+		// Skip the boolean and the target
+		target = arguments[ i ] || {};
+		i++;
+	}
+
+	// Handle case when target is a string or something (possible in deep copy)
+	if ( typeof target !== "object" && !isFunction( target ) ) {
+		target = {};
+	}
+
+	// Extend jQuery itself if only one argument is passed
+	if ( i === length ) {
+		target = this;
+		i--;
+	}
+
+	for ( ; i < length; i++ ) {
+
+		// Only deal with non-null/undefined values
+		if ( ( options = arguments[ i ] ) != null ) {
+
+			// Extend the base object
+			for ( name in options ) {
+				copy = options[ name ];
+
+				// Prevent Object.prototype pollution
+				// Prevent never-ending loop
+				if ( name === "__proto__" || target === copy ) {
+					continue;
+				}
+
+				// Recurse if we're merging plain objects or arrays
+				if ( deep && copy && ( jQuery.isPlainObject( copy ) ||
+					( copyIsArray = Array.isArray( copy ) ) ) ) {
+					src = target[ name ];
+
+					// Ensure proper type for the source value
+					if ( copyIsArray && !Array.isArray( src ) ) {
+						clone = [];
+					} else if ( !copyIsArray && !jQuery.isPlainObject( src ) ) {
+						clone = {};
+					} else {
+						clone = src;
+					}
+					copyIsArray = false;
+
+					// Never move original objects, clone them
+					target[ name ] = jQuery.extend( deep, clone, copy );
+
+				// Don't bring in undefined values
+				} else if ( copy !== undefined ) {
+					target[ name ] = copy;
+				}
+			}
+		}
+	}
+
+	// Return the modified object
+	return target;
+};
+
+jQuery.extend( {
+
+	// Unique for each copy of jQuery on the page
+	expando: "jQuery" + ( version + Math.random() ).replace( /\D/g, "" ),
+
+	// Assume jQuery is ready without the ready module
+	isReady: true,
+
+	error: function( msg ) {
+		throw new Error( msg );
+	},
+
+	noop: function() {},
+
+	isPlainObject: function( obj ) {
+		var proto, Ctor;
+
+		// Detect obvious negatives
+		// Use toString instead of jQuery.type to catch host objects
+		if ( !obj || toString.call( obj ) !== "[object Object]" ) {
+			return false;
+		}
+
+		proto = getProto( obj );
+
+		// Objects with no prototype (e.g., `Object.create( null )`) are plain
+		if ( !proto ) {
+			return true;
+		}
+
+		// Objects with prototype are plain iff they were constructed by a global Object function
+		Ctor = hasOwn.call( proto, "constructor" ) && proto.constructor;
+		return typeof Ctor === "function" && fnToString.call( Ctor ) === ObjectFunctionString;
+	},
+
+	isEmptyObject: function( obj ) {
+		var name;
+
+		for ( name in obj ) {
+			return false;
+		}
+		return true;
+	},
+
+	// Evaluates a script in a provided context; falls back to the global one
+	// if not specified.
+	globalEval: function( code, options, doc ) {
+		DOMEval( code, { nonce: options && options.nonce }, doc );
+	},
+
+	each: function( obj, callback ) {
+		var length, i = 0;
+
+		if ( isArrayLike( obj ) ) {
+			length = obj.length;
+			for ( ; i < length; i++ ) {
+				if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {
+					break;
+				}
+			}
+		} else {
+			for ( i in obj ) {
+				if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {
+					break;
+				}
+			}
+		}
+
+		return obj;
+	},
+
+	// results is for internal usage only
+	makeArray: function( arr, results ) {
+		var ret = results || [];
+
+		if ( arr != null ) {
+			if ( isArrayLike( Object( arr ) ) ) {
+				jQuery.merge( ret,
+					typeof arr === "string" ?
+						[ arr ] : arr
+				);
+			} else {
+				push.call( ret, arr );
+			}
+		}
+
+		return ret;
+	},
+
+	inArray: function( elem, arr, i ) {
+		return arr == null ? -1 : indexOf.call( arr, elem, i );
+	},
+
+	// Support: Android <=4.0 only, PhantomJS 1 only
+	// push.apply(_, arraylike) throws on ancient WebKit
+	merge: function( first, second ) {
+		var len = +second.length,
+			j = 0,
+			i = first.length;
+
+		for ( ; j < len; j++ ) {
+			first[ i++ ] = second[ j ];
+		}
+
+		first.length = i;
+
+		return first;
+	},
+
+	grep: function( elems, callback, invert ) {
+		var callbackInverse,
+			matches = [],
+			i = 0,
+			length = elems.length,
+			callbackExpect = !invert;
+
+		// Go through the array, only saving the items
+		// that pass the validator function
+		for ( ; i < length; i++ ) {
+			callbackInverse = !callback( elems[ i ], i );
+			if ( callbackInverse !== callbackExpect ) {
+				matches.push( elems[ i ] );
+			}
+		}
+
+		return matches;
+	},
+
+	// arg is for internal usage only
+	map: function( elems, callback, arg ) {
+		var length, value,
+			i = 0,
+			ret = [];
+
+		// Go through the array, translating each of the items to their new values
+		if ( isArrayLike( elems ) ) {
+			length = elems.length;
+			for ( ; i < length; i++ ) {
+				value = callback( elems[ i ], i, arg );
+
+				if ( value != null ) {
+					ret.push( value );
+				}
+			}
+
+		// Go through every key on the object,
+		} else {
+			for ( i in elems ) {
+				value = callback( elems[ i ], i, arg );
+
+				if ( value != null ) {
+					ret.push( value );
+				}
+			}
+		}
+
+		// Flatten any nested arrays
+		return flat( ret );
+	},
+
+	// A global GUID counter for objects
+	guid: 1,
+
+	// jQuery.support is not used in Core but other projects attach their
+	// properties to it so it needs to exist.
+	support: support
+} );
+
+if ( typeof Symbol === "function" ) {
+	jQuery.fn[ Symbol.iterator ] = arr[ Symbol.iterator ];
+}
+
+// Populate the class2type map
+jQuery.each( "Boolean Number String Function Array Date RegExp Object Error Symbol".split( " " ),
+	function( _i, name ) {
+		class2type[ "[object " + name + "]" ] = name.toLowerCase();
+	} );
+
+function isArrayLike( obj ) {
+
+	// Support: real iOS 8.2 only (not reproducible in simulator)
+	// `in` check used to prevent JIT error (gh-2145)
+	// hasOwn isn't used here due to false negatives
+	// regarding Nodelist length in IE
+	var length = !!obj && "length" in obj && obj.length,
+		type = toType( obj );
+
+	if ( isFunction( obj ) || isWindow( obj ) ) {
+		return false;
+	}
+
+	return type === "array" || length === 0 ||
+		typeof length === "number" && length > 0 && ( length - 1 ) in obj;
+}
+var Sizzle =
+/*!
+ * Sizzle CSS Selector Engine v2.3.6
+ * https://sizzlejs.com/
+ *
+ * Copyright JS Foundation and other contributors
+ * Released under the MIT license
+ * https://js.foundation/
+ *
+ * Date: 2021-02-16
+ */
+( function( window ) {
+var i,
+	support,
+	Expr,
+	getText,
+	isXML,
+	tokenize,
+	compile,
+	select,
+	outermostContext,
+	sortInput,
+	hasDuplicate,
+
+	// Local document vars
+	setDocument,
+	document,
+	docElem,
+	documentIsHTML,
+	rbuggyQSA,
+	rbuggyMatches,
+	matches,
+	contains,
+
+	// Instance-specific data
+	expando = "sizzle" + 1 * new Date(),
+	preferredDoc = window.document,
+	dirruns = 0,
+	done = 0,
+	classCache = createCache(),
+	tokenCache = createCache(),
+	compilerCache = createCache(),
+	nonnativeSelectorCache = createCache(),
+	sortOrder = function( a, b ) {
+		if ( a === b ) {
+			hasDuplicate = true;
+		}
+		return 0;
+	},
+
+	// Instance methods
+	hasOwn = ( {} ).hasOwnProperty,
+	arr = [],
+	pop = arr.pop,
+	pushNative = arr.push,
+	push = arr.push,
+	slice = arr.slice,
+
+	// Use a stripped-down indexOf as it's faster than native
+	// https://jsperf.com/thor-indexof-vs-for/5
+	indexOf = function( list, elem ) {
+		var i = 0,
+			len = list.length;
+		for ( ; i < len; i++ ) {
+			if ( list[ i ] === elem ) {
+				return i;
+			}
+		}
+		return -1;
+	},
+
+	booleans = "checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|" +
+		"ismap|loop|multiple|open|readonly|required|scoped",
+
+	// Regular expressions
+
+	// http://www.w3.org/TR/css3-selectors/#whitespace
+	whitespace = "[\\x20\\t\\r\\n\\f]",
+
+	// https://www.w3.org/TR/css-syntax-3/#ident-token-diagram
+	identifier = "(?:\\\\[\\da-fA-F]{1,6}" + whitespace +
+		"?|\\\\[^\\r\\n\\f]|[\\w-]|[^\0-\\x7f])+",
+
+	// Attribute selectors: http://www.w3.org/TR/selectors/#attribute-selectors
+	attributes = "\\[" + whitespace + "*(" + identifier + ")(?:" + whitespace +
+
+		// Operator (capture 2)
+		"*([*^$|!~]?=)" + whitespace +
+
+		// "Attribute values must be CSS identifiers [capture 5]
+		// or strings [capture 3 or capture 4]"
+		"*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" +
+		whitespace + "*\\]",
+
+	pseudos = ":(" + identifier + ")(?:\\((" +
+
+		// To reduce the number of selectors needing tokenize in the preFilter, prefer arguments:
+		// 1. quoted (capture 3; capture 4 or capture 5)
+		"('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|" +
+
+		// 2. simple (capture 6)
+		"((?:\\\\.|[^\\\\()[\\]]|" + attributes + ")*)|" +
+
+		// 3. anything else (capture 2)
+		".*" +
+		")\\)|)",
+
+	// Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter
+	rwhitespace = new RegExp( whitespace + "+", "g" ),
+	rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" +
+		whitespace + "+$", "g" ),
+
+	rcomma = new RegExp( "^" + whitespace + "*," + whitespace + "*" ),
+	rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace +
+		"*" ),
+	rdescend = new RegExp( whitespace + "|>" ),
+
+	rpseudo = new RegExp( pseudos ),
+	ridentifier = new RegExp( "^" + identifier + "$" ),
+
+	matchExpr = {
+		"ID": new RegExp( "^#(" + identifier + ")" ),
+		"CLASS": new RegExp( "^\\.(" + identifier + ")" ),
+		"TAG": new RegExp( "^(" + identifier + "|[*])" ),
+		"ATTR": new RegExp( "^" + attributes ),
+		"PSEUDO": new RegExp( "^" + pseudos ),
+		"CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" +
+			whitespace + "*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" +
+			whitespace + "*(\\d+)|))" + whitespace + "*\\)|)", "i" ),
+		"bool": new RegExp( "^(?:" + booleans + ")$", "i" ),
+
+		// For use in libraries implementing .is()
+		// We use this for POS matching in `select`
+		"needsContext": new RegExp( "^" + whitespace +
+			"*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\(" + whitespace +
+			"*((?:-\\d)?\\d*)" + whitespace + "*\\)|)(?=[^-]|$)", "i" )
+	},
+
+	rhtml = /HTML$/i,
+	rinputs = /^(?:input|select|textarea|button)$/i,
+	rheader = /^h\d$/i,
+
+	rnative = /^[^{]+\{\s*\[native \w/,
+
+	// Easily-parseable/retrievable ID or TAG or CLASS selectors
+	rquickExpr = /^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/,
+
+	rsibling = /[+~]/,
+
+	// CSS escapes
+	// http://www.w3.org/TR/CSS21/syndata.html#escaped-characters
+	runescape = new RegExp( "\\\\[\\da-fA-F]{1,6}" + whitespace + "?|\\\\([^\\r\\n\\f])", "g" ),
+	funescape = function( escape, nonHex ) {
+		var high = "0x" + escape.slice( 1 ) - 0x10000;
+
+		return nonHex ?
+
+			// Strip the backslash prefix from a non-hex escape sequence
+			nonHex :
+
+			// Replace a hexadecimal escape sequence with the encoded Unicode code point
+			// Support: IE <=11+
+			// For values outside the Basic Multilingual Plane (BMP), manually construct a
+			// surrogate pair
+			high < 0 ?
+				String.fromCharCode( high + 0x10000 ) :
+				String.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 );
+	},
+
+	// CSS string/identifier serialization
+	// https://drafts.csswg.org/cssom/#common-serializing-idioms
+	rcssescape = /([\0-\x1f\x7f]|^-?\d)|^-$|[^\0-\x1f\x7f-\uFFFF\w-]/g,
+	fcssescape = function( ch, asCodePoint ) {
+		if ( asCodePoint ) {
+
+			// U+0000 NULL becomes U+FFFD REPLACEMENT CHARACTER
+			if ( ch === "\0" ) {
+				return "\uFFFD";
+			}
+
+			// Control characters and (dependent upon position) numbers get escaped as code points
+			return ch.slice( 0, -1 ) + "\\" +
+				ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " ";
+		}
+
+		// Other potentially-special ASCII characters get backslash-escaped
+		return "\\" + ch;
+	},
+
+	// Used for iframes
+	// See setDocument()
+	// Removing the function wrapper causes a "Permission Denied"
+	// error in IE
+	unloadHandler = function() {
+		setDocument();
+	},
+
+	inDisabledFieldset = addCombinator(
+		function( elem ) {
+			return elem.disabled === true && elem.nodeName.toLowerCase() === "fieldset";
+		},
+		{ dir: "parentNode", next: "legend" }
+	);
+
+// Optimize for push.apply( _, NodeList )
+try {
+	push.apply(
+		( arr = slice.call( preferredDoc.childNodes ) ),
+		preferredDoc.childNodes
+	);
+
+	// Support: Android<4.0
+	// Detect silently failing push.apply
+	// eslint-disable-next-line no-unused-expressions
+	arr[ preferredDoc.childNodes.length ].nodeType;
+} catch ( e ) {
+	push = { apply: arr.length ?
+
+		// Leverage slice if possible
+		function( target, els ) {
+			pushNative.apply( target, slice.call( els ) );
+		} :
+
+		// Support: IE<9
+		// Otherwise append directly
+		function( target, els ) {
+			var j = target.length,
+				i = 0;
+
+			// Can't trust NodeList.length
+			while ( ( target[ j++ ] = els[ i++ ] ) ) {}
+			target.length = j - 1;
+		}
+	};
+}
+
+function Sizzle( selector, context, results, seed ) {
+	var m, i, elem, nid, match, groups, newSelector,
+		newContext = context && context.ownerDocument,
+
+		// nodeType defaults to 9, since context defaults to document
+		nodeType = context ? context.nodeType : 9;
+
+	results = results || [];
+
+	// Return early from calls with invalid selector or context
+	if ( typeof selector !== "string" || !selector ||
+		nodeType !== 1 && nodeType !== 9 && nodeType !== 11 ) {
+
+		return results;
+	}
+
+	// Try to shortcut find operations (as opposed to filters) in HTML documents
+	if ( !seed ) {
+		setDocument( context );
+		context = context || document;
+
+		if ( documentIsHTML ) {
+
+			// If the selector is sufficiently simple, try using a "get*By*" DOM method
+			// (excepting DocumentFragment context, where the methods don't exist)
+			if ( nodeType !== 11 && ( match = rquickExpr.exec( selector ) ) ) {
+
+				// ID selector
+				if ( ( m = match[ 1 ] ) ) {
+
+					// Document context
+					if ( nodeType === 9 ) {
+						if ( ( elem = context.getElementById( m ) ) ) {
+
+							// Support: IE, Opera, Webkit
+							// TODO: identify versions
+							// getElementById can match elements by name instead of ID
+							if ( elem.id === m ) {
+								results.push( elem );
+								return results;
+							}
+						} else {
+							return results;
+						}
+
+					// Element context
+					} else {
+
+						// Support: IE, Opera, Webkit
+						// TODO: identify versions
+						// getElementById can match elements by name instead of ID
+						if ( newContext && ( elem = newContext.getElementById( m ) ) &&
+							contains( context, elem ) &&
+							elem.id === m ) {
+
+							results.push( elem );
+							return results;
+						}
+					}
+
+				// Type selector
+				} else if ( match[ 2 ] ) {
+					push.apply( results, context.getElementsByTagName( selector ) );
+					return results;
+
+				// Class selector
+				} else if ( ( m = match[ 3 ] ) && support.getElementsByClassName &&
+					context.getElementsByClassName ) {
+
+					push.apply( results, context.getElementsByClassName( m ) );
+					return results;
+				}
+			}
+
+			// Take advantage of querySelectorAll
+			if ( support.qsa &&
+				!nonnativeSelectorCache[ selector + " " ] &&
+				( !rbuggyQSA || !rbuggyQSA.test( selector ) ) &&
+
+				// Support: IE 8 only
+				// Exclude object elements
+				( nodeType !== 1 || context.nodeName.toLowerCase() !== "object" ) ) {
+
+				newSelector = selector;
+				newContext = context;
+
+				// qSA considers elements outside a scoping root when evaluating child or
+				// descendant combinators, which is not what we want.
+				// In such cases, we work around the behavior by prefixing every selector in the
+				// list with an ID selector referencing the scope context.
+				// The technique has to be used as well when a leading combinator is used
+				// as such selectors are not recognized by querySelectorAll.
+				// Thanks to Andrew Dupont for this technique.
+				if ( nodeType === 1 &&
+					( rdescend.test( selector ) || rcombinators.test( selector ) ) ) {
+
+					// Expand context for sibling selectors
+					newContext = rsibling.test( selector ) && testContext( context.parentNode ) ||
+						context;
+
+					// We can use :scope instead of the ID hack if the browser
+					// supports it & if we're not changing the context.
+					if ( newContext !== context || !support.scope ) {
+
+						// Capture the context ID, setting it first if necessary
+						if ( ( nid = context.getAttribute( "id" ) ) ) {
+							nid = nid.replace( rcssescape, fcssescape );
+						} else {
+							context.setAttribute( "id", ( nid = expando ) );
+						}
+					}
+
+					// Prefix every selector in the list
+					groups = tokenize( selector );
+					i = groups.length;
+					while ( i-- ) {
+						groups[ i ] = ( nid ? "#" + nid : ":scope" ) + " " +
+							toSelector( groups[ i ] );
+					}
+					newSelector = groups.join( "," );
+				}
+
+				try {
+					push.apply( results,
+						newContext.querySelectorAll( newSelector )
+					);
+					return results;
+				} catch ( qsaError ) {
+					nonnativeSelectorCache( selector, true );
+				} finally {
+					if ( nid === expando ) {
+						context.removeAttribute( "id" );
+					}
+				}
+			}
+		}
+	}
+
+	// All others
+	return select( selector.replace( rtrim, "$1" ), context, results, seed );
+}
+
+/**
+ * Create key-value caches of limited size
+ * @returns {function(string, object)} Returns the Object data after storing it on itself with
+ *	property name the (space-suffixed) string and (if the cache is larger than Expr.cacheLength)
+ *	deleting the oldest entry
+ */
+function createCache() {
+	var keys = [];
+
+	function cache( key, value ) {
+
+		// Use (key + " ") to avoid collision with native prototype properties (see Issue #157)
+		if ( keys.push( key + " " ) > Expr.cacheLength ) {
+
+			// Only keep the most recent entries
+			delete cache[ keys.shift() ];
+		}
+		return ( cache[ key + " " ] = value );
+	}
+	return cache;
+}
+
+/**
+ * Mark a function for special use by Sizzle
+ * @param {Function} fn The function to mark
+ */
+function markFunction( fn ) {
+	fn[ expando ] = true;
+	return fn;
+}
+
+/**
+ * Support testing using an element
+ * @param {Function} fn Passed the created element and returns a boolean result
+ */
+function assert( fn ) {
+	var el = document.createElement( "fieldset" );
+
+	try {
+		return !!fn( el );
+	} catch ( e ) {
+		return false;
+	} finally {
+
+		// Remove from its parent by default
+		if ( el.parentNode ) {
+			el.parentNode.removeChild( el );
+		}
+
+		// release memory in IE
+		el = null;
+	}
+}
+
+/**
+ * Adds the same handler for all of the specified attrs
+ * @param {String} attrs Pipe-separated list of attributes
+ * @param {Function} handler The method that will be applied
+ */
+function addHandle( attrs, handler ) {
+	var arr = attrs.split( "|" ),
+		i = arr.length;
+
+	while ( i-- ) {
+		Expr.attrHandle[ arr[ i ] ] = handler;
+	}
+}
+
+/**
+ * Checks document order of two siblings
+ * @param {Element} a
+ * @param {Element} b
+ * @returns {Number} Returns less than 0 if a precedes b, greater than 0 if a follows b
+ */
+function siblingCheck( a, b ) {
+	var cur = b && a,
+		diff = cur && a.nodeType === 1 && b.nodeType === 1 &&
+			a.sourceIndex - b.sourceIndex;
+
+	// Use IE sourceIndex if available on both nodes
+	if ( diff ) {
+		return diff;
+	}
+
+	// Check if b follows a
+	if ( cur ) {
+		while ( ( cur = cur.nextSibling ) ) {
+			if ( cur === b ) {
+				return -1;
+			}
+		}
+	}
+
+	return a ? 1 : -1;
+}
+
+/**
+ * Returns a function to use in pseudos for input types
+ * @param {String} type
+ */
+function createInputPseudo( type ) {
+	return function( elem ) {
+		var name = elem.nodeName.toLowerCase();
+		return name === "input" && elem.type === type;
+	};
+}
+
+/**
+ * Returns a function to use in pseudos for buttons
+ * @param {String} type
+ */
+function createButtonPseudo( type ) {
+	return function( elem ) {
+		var name = elem.nodeName.toLowerCase();
+		return ( name === "input" || name === "button" ) && elem.type === type;
+	};
+}
+
+/**
+ * Returns a function to use in pseudos for :enabled/:disabled
+ * @param {Boolean} disabled true for :disabled; false for :enabled
+ */
+function createDisabledPseudo( disabled ) {
+
+	// Known :disabled false positives: fieldset[disabled] > legend:nth-of-type(n+2) :can-disable
+	return function( elem ) {
+
+		// Only certain elements can match :enabled or :disabled
+		// https://html.spec.whatwg.org/multipage/scripting.html#selector-enabled
+		// https://html.spec.whatwg.org/multipage/scripting.html#selector-disabled
+		if ( "form" in elem ) {
+
+			// Check for inherited disabledness on relevant non-disabled elements:
+			// * listed form-associated elements in a disabled fieldset
+			//   https://html.spec.whatwg.org/multipage/forms.html#category-listed
+			//   https://html.spec.whatwg.org/multipage/forms.html#concept-fe-disabled
+			// * option elements in a disabled optgroup
+			//   https://html.spec.whatwg.org/multipage/forms.html#concept-option-disabled
+			// All such elements have a "form" property.
+			if ( elem.parentNode && elem.disabled === false ) {
+
+				// Option elements defer to a parent optgroup if present
+				if ( "label" in elem ) {
+					if ( "label" in elem.parentNode ) {
+						return elem.parentNode.disabled === disabled;
+					} else {
+						return elem.disabled === disabled;
+					}
+				}
+
+				// Support: IE 6 - 11
+				// Use the isDisabled shortcut property to check for disabled fieldset ancestors
+				return elem.isDisabled === disabled ||
+
+					// Where there is no isDisabled, check manually
+					/* jshint -W018 */
+					elem.isDisabled !== !disabled &&
+					inDisabledFieldset( elem ) === disabled;
+			}
+
+			return elem.disabled === disabled;
+
+		// Try to winnow out elements that can't be disabled before trusting the disabled property.
+		// Some victims get caught in our net (label, legend, menu, track), but it shouldn't
+		// even exist on them, let alone have a boolean value.
+		} else if ( "label" in elem ) {
+			return elem.disabled === disabled;
+		}
+
+		// Remaining elements are neither :enabled nor :disabled
+		return false;
+	};
+}
+
+/**
+ * Returns a function to use in pseudos for positionals
+ * @param {Function} fn
+ */
+function createPositionalPseudo( fn ) {
+	return markFunction( function( argument ) {
+		argument = +argument;
+		return markFunction( function( seed, matches ) {
+			var j,
+				matchIndexes = fn( [], seed.length, argument ),
+				i = matchIndexes.length;
+
+			// Match elements found at the specified indexes
+			while ( i-- ) {
+				if ( seed[ ( j = matchIndexes[ i ] ) ] ) {
+					seed[ j ] = !( matches[ j ] = seed[ j ] );
+				}
+			}
+		} );
+	} );
+}
+
+/**
+ * Checks a node for validity as a Sizzle context
+ * @param {Element|Object=} context
+ * @returns {Element|Object|Boolean} The input node if acceptable, otherwise a falsy value
+ */
+function testContext( context ) {
+	return context && typeof context.getElementsByTagName !== "undefined" && context;
+}
+
+// Expose support vars for convenience
+support = Sizzle.support = {};
+
+/**
+ * Detects XML nodes
+ * @param {Element|Object} elem An element or a document
+ * @returns {Boolean} True iff elem is a non-HTML XML node
+ */
+isXML = Sizzle.isXML = function( elem ) {
+	var namespace = elem && elem.namespaceURI,
+		docElem = elem && ( elem.ownerDocument || elem ).documentElement;
+
+	// Support: IE <=8
+	// Assume HTML when documentElement doesn't yet exist, such as inside loading iframes
+	// https://bugs.jquery.com/ticket/4833
+	return !rhtml.test( namespace || docElem && docElem.nodeName || "HTML" );
+};
+
+/**
+ * Sets document-related variables once based on the current document
+ * @param {Element|Object} [doc] An element or document object to use to set the document
+ * @returns {Object} Returns the current document
+ */
+setDocument = Sizzle.setDocument = function( node ) {
+	var hasCompare, subWindow,
+		doc = node ? node.ownerDocument || node : preferredDoc;
+
+	// Return early if doc is invalid or already selected
+	// Support: IE 11+, Edge 17 - 18+
+	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+	// two documents; shallow comparisons work.
+	// eslint-disable-next-line eqeqeq
+	if ( doc == document || doc.nodeType !== 9 || !doc.documentElement ) {
+		return document;
+	}
+
+	// Update global variables
+	document = doc;
+	docElem = document.documentElement;
+	documentIsHTML = !isXML( document );
+
+	// Support: IE 9 - 11+, Edge 12 - 18+
+	// Accessing iframe documents after unload throws "permission denied" errors (jQuery #13936)
+	// Support: IE 11+, Edge 17 - 18+
+	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+	// two documents; shallow comparisons work.
+	// eslint-disable-next-line eqeqeq
+	if ( preferredDoc != document &&
+		( subWindow = document.defaultView ) && subWindow.top !== subWindow ) {
+
+		// Support: IE 11, Edge
+		if ( subWindow.addEventListener ) {
+			subWindow.addEventListener( "unload", unloadHandler, false );
+
+		// Support: IE 9 - 10 only
+		} else if ( subWindow.attachEvent ) {
+			subWindow.attachEvent( "onunload", unloadHandler );
+		}
+	}
+
+	// Support: IE 8 - 11+, Edge 12 - 18+, Chrome <=16 - 25 only, Firefox <=3.6 - 31 only,
+	// Safari 4 - 5 only, Opera <=11.6 - 12.x only
+	// IE/Edge & older browsers don't support the :scope pseudo-class.
+	// Support: Safari 6.0 only
+	// Safari 6.0 supports :scope but it's an alias of :root there.
+	support.scope = assert( function( el ) {
+		docElem.appendChild( el ).appendChild( document.createElement( "div" ) );
+		return typeof el.querySelectorAll !== "undefined" &&
+			!el.querySelectorAll( ":scope fieldset div" ).length;
+	} );
+
+	/* Attributes
+	---------------------------------------------------------------------- */
+
+	// Support: IE<8
+	// Verify that getAttribute really returns attributes and not properties
+	// (excepting IE8 booleans)
+	support.attributes = assert( function( el ) {
+		el.className = "i";
+		return !el.getAttribute( "className" );
+	} );
+
+	/* getElement(s)By*
+	---------------------------------------------------------------------- */
+
+	// Check if getElementsByTagName("*") returns only elements
+	support.getElementsByTagName = assert( function( el ) {
+		el.appendChild( document.createComment( "" ) );
+		return !el.getElementsByTagName( "*" ).length;
+	} );
+
+	// Support: IE<9
+	support.getElementsByClassName = rnative.test( document.getElementsByClassName );
+
+	// Support: IE<10
+	// Check if getElementById returns elements by name
+	// The broken getElementById methods don't pick up programmatically-set names,
+	// so use a roundabout getElementsByName test
+	support.getById = assert( function( el ) {
+		docElem.appendChild( el ).id = expando;
+		return !document.getElementsByName || !document.getElementsByName( expando ).length;
+	} );
+
+	// ID filter and find
+	if ( support.getById ) {
+		Expr.filter[ "ID" ] = function( id ) {
+			var attrId = id.replace( runescape, funescape );
+			return function( elem ) {
+				return elem.getAttribute( "id" ) === attrId;
+			};
+		};
+		Expr.find[ "ID" ] = function( id, context ) {
+			if ( typeof context.getElementById !== "undefined" && documentIsHTML ) {
+				var elem = context.getElementById( id );
+				return elem ? [ elem ] : [];
+			}
+		};
+	} else {
+		Expr.filter[ "ID" ] =  function( id ) {
+			var attrId = id.replace( runescape, funescape );
+			return function( elem ) {
+				var node = typeof elem.getAttributeNode !== "undefined" &&
+					elem.getAttributeNode( "id" );
+				return node && node.value === attrId;
+			};
+		};
+
+		// Support: IE 6 - 7 only
+		// getElementById is not reliable as a find shortcut
+		Expr.find[ "ID" ] = function( id, context ) {
+			if ( typeof context.getElementById !== "undefined" && documentIsHTML ) {
+				var node, i, elems,
+					elem = context.getElementById( id );
+
+				if ( elem ) {
+
+					// Verify the id attribute
+					node = elem.getAttributeNode( "id" );
+					if ( node && node.value === id ) {
+						return [ elem ];
+					}
+
+					// Fall back on getElementsByName
+					elems = context.getElementsByName( id );
+					i = 0;
+					while ( ( elem = elems[ i++ ] ) ) {
+						node = elem.getAttributeNode( "id" );
+						if ( node && node.value === id ) {
+							return [ elem ];
+						}
+					}
+				}
+
+				return [];
+			}
+		};
+	}
+
+	// Tag
+	Expr.find[ "TAG" ] = support.getElementsByTagName ?
+		function( tag, context ) {
+			if ( typeof context.getElementsByTagName !== "undefined" ) {
+				return context.getElementsByTagName( tag );
+
+			// DocumentFragment nodes don't have gEBTN
+			} else if ( support.qsa ) {
+				return context.querySelectorAll( tag );
+			}
+		} :
+
+		function( tag, context ) {
+			var elem,
+				tmp = [],
+				i = 0,
+
+				// By happy coincidence, a (broken) gEBTN appears on DocumentFragment nodes too
+				results = context.getElementsByTagName( tag );
+
+			// Filter out possible comments
+			if ( tag === "*" ) {
+				while ( ( elem = results[ i++ ] ) ) {
+					if ( elem.nodeType === 1 ) {
+						tmp.push( elem );
+					}
+				}
+
+				return tmp;
+			}
+			return results;
+		};
+
+	// Class
+	Expr.find[ "CLASS" ] = support.getElementsByClassName && function( className, context ) {
+		if ( typeof context.getElementsByClassName !== "undefined" && documentIsHTML ) {
+			return context.getElementsByClassName( className );
+		}
+	};
+
+	/* QSA/matchesSelector
+	---------------------------------------------------------------------- */
+
+	// QSA and matchesSelector support
+
+	// matchesSelector(:active) reports false when true (IE9/Opera 11.5)
+	rbuggyMatches = [];
+
+	// qSa(:focus) reports false when true (Chrome 21)
+	// We allow this because of a bug in IE8/9 that throws an error
+	// whenever `document.activeElement` is accessed on an iframe
+	// So, we allow :focus to pass through QSA all the time to avoid the IE error
+	// See https://bugs.jquery.com/ticket/13378
+	rbuggyQSA = [];
+
+	if ( ( support.qsa = rnative.test( document.querySelectorAll ) ) ) {
+
+		// Build QSA regex
+		// Regex strategy adopted from Diego Perini
+		assert( function( el ) {
+
+			var input;
+
+			// Select is set to empty string on purpose
+			// This is to test IE's treatment of not explicitly
+			// setting a boolean content attribute,
+			// since its presence should be enough
+			// https://bugs.jquery.com/ticket/12359
+			docElem.appendChild( el ).innerHTML = "<a id='" + expando + "'></a>" +
+				"<select id='" + expando + "-\r\\' msallowcapture=''>" +
+				"<option selected=''></option></select>";
+
+			// Support: IE8, Opera 11-12.16
+			// Nothing should be selected when empty strings follow ^= or $= or *=
+			// The test attribute must be unknown in Opera but "safe" for WinRT
+			// https://msdn.microsoft.com/en-us/library/ie/hh465388.aspx#attribute_section
+			if ( el.querySelectorAll( "[msallowcapture^='']" ).length ) {
+				rbuggyQSA.push( "[*^$]=" + whitespace + "*(?:''|\"\")" );
+			}
+
+			// Support: IE8
+			// Boolean attributes and "value" are not treated correctly
+			if ( !el.querySelectorAll( "[selected]" ).length ) {
+				rbuggyQSA.push( "\\[" + whitespace + "*(?:value|" + booleans + ")" );
+			}
+
+			// Support: Chrome<29, Android<4.4, Safari<7.0+, iOS<7.0+, PhantomJS<1.9.8+
+			if ( !el.querySelectorAll( "[id~=" + expando + "-]" ).length ) {
+				rbuggyQSA.push( "~=" );
+			}
+
+			// Support: IE 11+, Edge 15 - 18+
+			// IE 11/Edge don't find elements on a `[name='']` query in some cases.
+			// Adding a temporary attribute to the document before the selection works
+			// around the issue.
+			// Interestingly, IE 10 & older don't seem to have the issue.
+			input = document.createElement( "input" );
+			input.setAttribute( "name", "" );
+			el.appendChild( input );
+			if ( !el.querySelectorAll( "[name='']" ).length ) {
+				rbuggyQSA.push( "\\[" + whitespace + "*name" + whitespace + "*=" +
+					whitespace + "*(?:''|\"\")" );
+			}
+
+			// Webkit/Opera - :checked should return selected option elements
+			// http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked
+			// IE8 throws error here and will not see later tests
+			if ( !el.querySelectorAll( ":checked" ).length ) {
+				rbuggyQSA.push( ":checked" );
+			}
+
+			// Support: Safari 8+, iOS 8+
+			// https://bugs.webkit.org/show_bug.cgi?id=136851
+			// In-page `selector#id sibling-combinator selector` fails
+			if ( !el.querySelectorAll( "a#" + expando + "+*" ).length ) {
+				rbuggyQSA.push( ".#.+[+~]" );
+			}
+
+			// Support: Firefox <=3.6 - 5 only
+			// Old Firefox doesn't throw on a badly-escaped identifier.
+			el.querySelectorAll( "\\\f" );
+			rbuggyQSA.push( "[\\r\\n\\f]" );
+		} );
+
+		assert( function( el ) {
+			el.innerHTML = "<a href='' disabled='disabled'></a>" +
+				"<select disabled='disabled'><option/></select>";
+
+			// Support: Windows 8 Native Apps
+			// The type and name attributes are restricted during .innerHTML assignment
+			var input = document.createElement( "input" );
+			input.setAttribute( "type", "hidden" );
+			el.appendChild( input ).setAttribute( "name", "D" );
+
+			// Support: IE8
+			// Enforce case-sensitivity of name attribute
+			if ( el.querySelectorAll( "[name=d]" ).length ) {
+				rbuggyQSA.push( "name" + whitespace + "*[*^$|!~]?=" );
+			}
+
+			// FF 3.5 - :enabled/:disabled and hidden elements (hidden elements are still enabled)
+			// IE8 throws error here and will not see later tests
+			if ( el.querySelectorAll( ":enabled" ).length !== 2 ) {
+				rbuggyQSA.push( ":enabled", ":disabled" );
+			}
+
+			// Support: IE9-11+
+			// IE's :disabled selector does not pick up the children of disabled fieldsets
+			docElem.appendChild( el ).disabled = true;
+			if ( el.querySelectorAll( ":disabled" ).length !== 2 ) {
+				rbuggyQSA.push( ":enabled", ":disabled" );
+			}
+
+			// Support: Opera 10 - 11 only
+			// Opera 10-11 does not throw on post-comma invalid pseudos
+			el.querySelectorAll( "*,:x" );
+			rbuggyQSA.push( ",.*:" );
+		} );
+	}
+
+	if ( ( support.matchesSelector = rnative.test( ( matches = docElem.matches ||
+		docElem.webkitMatchesSelector ||
+		docElem.mozMatchesSelector ||
+		docElem.oMatchesSelector ||
+		docElem.msMatchesSelector ) ) ) ) {
+
+		assert( function( el ) {
+
+			// Check to see if it's possible to do matchesSelector
+			// on a disconnected node (IE 9)
+			support.disconnectedMatch = matches.call( el, "*" );
+
+			// This should fail with an exception
+			// Gecko does not error, returns false instead
+			matches.call( el, "[s!='']:x" );
+			rbuggyMatches.push( "!=", pseudos );
+		} );
+	}
+
+	rbuggyQSA = rbuggyQSA.length && new RegExp( rbuggyQSA.join( "|" ) );
+	rbuggyMatches = rbuggyMatches.length && new RegExp( rbuggyMatches.join( "|" ) );
+
+	/* Contains
+	---------------------------------------------------------------------- */
+	hasCompare = rnative.test( docElem.compareDocumentPosition );
+
+	// Element contains another
+	// Purposefully self-exclusive
+	// As in, an element does not contain itself
+	contains = hasCompare || rnative.test( docElem.contains ) ?
+		function( a, b ) {
+			var adown = a.nodeType === 9 ? a.documentElement : a,
+				bup = b && b.parentNode;
+			return a === bup || !!( bup && bup.nodeType === 1 && (
+				adown.contains ?
+					adown.contains( bup ) :
+					a.compareDocumentPosition && a.compareDocumentPosition( bup ) & 16
+			) );
+		} :
+		function( a, b ) {
+			if ( b ) {
+				while ( ( b = b.parentNode ) ) {
+					if ( b === a ) {
+						return true;
+					}
+				}
+			}
+			return false;
+		};
+
+	/* Sorting
+	---------------------------------------------------------------------- */
+
+	// Document order sorting
+	sortOrder = hasCompare ?
+	function( a, b ) {
+
+		// Flag for duplicate removal
+		if ( a === b ) {
+			hasDuplicate = true;
+			return 0;
+		}
+
+		// Sort on method existence if only one input has compareDocumentPosition
+		var compare = !a.compareDocumentPosition - !b.compareDocumentPosition;
+		if ( compare ) {
+			return compare;
+		}
+
+		// Calculate position if both inputs belong to the same document
+		// Support: IE 11+, Edge 17 - 18+
+		// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+		// two documents; shallow comparisons work.
+		// eslint-disable-next-line eqeqeq
+		compare = ( a.ownerDocument || a ) == ( b.ownerDocument || b ) ?
+			a.compareDocumentPosition( b ) :
+
+			// Otherwise we know they are disconnected
+			1;
+
+		// Disconnected nodes
+		if ( compare & 1 ||
+			( !support.sortDetached && b.compareDocumentPosition( a ) === compare ) ) {
+
+			// Choose the first element that is related to our preferred document
+			// Support: IE 11+, Edge 17 - 18+
+			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+			// two documents; shallow comparisons work.
+			// eslint-disable-next-line eqeqeq
+			if ( a == document || a.ownerDocument == preferredDoc &&
+				contains( preferredDoc, a ) ) {
+				return -1;
+			}
+
+			// Support: IE 11+, Edge 17 - 18+
+			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+			// two documents; shallow comparisons work.
+			// eslint-disable-next-line eqeqeq
+			if ( b == document || b.ownerDocument == preferredDoc &&
+				contains( preferredDoc, b ) ) {
+				return 1;
+			}
+
+			// Maintain original order
+			return sortInput ?
+				( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) :
+				0;
+		}
+
+		return compare & 4 ? -1 : 1;
+	} :
+	function( a, b ) {
+
+		// Exit early if the nodes are identical
+		if ( a === b ) {
+			hasDuplicate = true;
+			return 0;
+		}
+
+		var cur,
+			i = 0,
+			aup = a.parentNode,
+			bup = b.parentNode,
+			ap = [ a ],
+			bp = [ b ];
+
+		// Parentless nodes are either documents or disconnected
+		if ( !aup || !bup ) {
+
+			// Support: IE 11+, Edge 17 - 18+
+			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+			// two documents; shallow comparisons work.
+			/* eslint-disable eqeqeq */
+			return a == document ? -1 :
+				b == document ? 1 :
+				/* eslint-enable eqeqeq */
+				aup ? -1 :
+				bup ? 1 :
+				sortInput ?
+				( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) :
+				0;
+
+		// If the nodes are siblings, we can do a quick check
+		} else if ( aup === bup ) {
+			return siblingCheck( a, b );
+		}
+
+		// Otherwise we need full lists of their ancestors for comparison
+		cur = a;
+		while ( ( cur = cur.parentNode ) ) {
+			ap.unshift( cur );
+		}
+		cur = b;
+		while ( ( cur = cur.parentNode ) ) {
+			bp.unshift( cur );
+		}
+
+		// Walk down the tree looking for a discrepancy
+		while ( ap[ i ] === bp[ i ] ) {
+			i++;
+		}
+
+		return i ?
+
+			// Do a sibling check if the nodes have a common ancestor
+			siblingCheck( ap[ i ], bp[ i ] ) :
+
+			// Otherwise nodes in our document sort first
+			// Support: IE 11+, Edge 17 - 18+
+			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+			// two documents; shallow comparisons work.
+			/* eslint-disable eqeqeq */
+			ap[ i ] == preferredDoc ? -1 :
+			bp[ i ] == preferredDoc ? 1 :
+			/* eslint-enable eqeqeq */
+			0;
+	};
+
+	return document;
+};
+
+Sizzle.matches = function( expr, elements ) {
+	return Sizzle( expr, null, null, elements );
+};
+
+Sizzle.matchesSelector = function( elem, expr ) {
+	setDocument( elem );
+
+	if ( support.matchesSelector && documentIsHTML &&
+		!nonnativeSelectorCache[ expr + " " ] &&
+		( !rbuggyMatches || !rbuggyMatches.test( expr ) ) &&
+		( !rbuggyQSA     || !rbuggyQSA.test( expr ) ) ) {
+
+		try {
+			var ret = matches.call( elem, expr );
+
+			// IE 9's matchesSelector returns false on disconnected nodes
+			if ( ret || support.disconnectedMatch ||
+
+				// As well, disconnected nodes are said to be in a document
+				// fragment in IE 9
+				elem.document && elem.document.nodeType !== 11 ) {
+				return ret;
+			}
+		} catch ( e ) {
+			nonnativeSelectorCache( expr, true );
+		}
+	}
+
+	return Sizzle( expr, document, null, [ elem ] ).length > 0;
+};
+
+Sizzle.contains = function( context, elem ) {
+
+	// Set document vars if needed
+	// Support: IE 11+, Edge 17 - 18+
+	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+	// two documents; shallow comparisons work.
+	// eslint-disable-next-line eqeqeq
+	if ( ( context.ownerDocument || context ) != document ) {
+		setDocument( context );
+	}
+	return contains( context, elem );
+};
+
+Sizzle.attr = function( elem, name ) {
+
+	// Set document vars if needed
+	// Support: IE 11+, Edge 17 - 18+
+	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+	// two documents; shallow comparisons work.
+	// eslint-disable-next-line eqeqeq
+	if ( ( elem.ownerDocument || elem ) != document ) {
+		setDocument( elem );
+	}
+
+	var fn = Expr.attrHandle[ name.toLowerCase() ],
+
+		// Don't get fooled by Object.prototype properties (jQuery #13807)
+		val = fn && hasOwn.call( Expr.attrHandle, name.toLowerCase() ) ?
+			fn( elem, name, !documentIsHTML ) :
+			undefined;
+
+	return val !== undefined ?
+		val :
+		support.attributes || !documentIsHTML ?
+			elem.getAttribute( name ) :
+			( val = elem.getAttributeNode( name ) ) && val.specified ?
+				val.value :
+				null;
+};
+
+Sizzle.escape = function( sel ) {
+	return ( sel + "" ).replace( rcssescape, fcssescape );
+};
+
+Sizzle.error = function( msg ) {
+	throw new Error( "Syntax error, unrecognized expression: " + msg );
+};
+
+/**
+ * Document sorting and removing duplicates
+ * @param {ArrayLike} results
+ */
+Sizzle.uniqueSort = function( results ) {
+	var elem,
+		duplicates = [],
+		j = 0,
+		i = 0;
+
+	// Unless we *know* we can detect duplicates, assume their presence
+	hasDuplicate = !support.detectDuplicates;
+	sortInput = !support.sortStable && results.slice( 0 );
+	results.sort( sortOrder );
+
+	if ( hasDuplicate ) {
+		while ( ( elem = results[ i++ ] ) ) {
+			if ( elem === results[ i ] ) {
+				j = duplicates.push( i );
+			}
+		}
+		while ( j-- ) {
+			results.splice( duplicates[ j ], 1 );
+		}
+	}
+
+	// Clear input after sorting to release objects
+	// See https://github.com/jquery/sizzle/pull/225
+	sortInput = null;
+
+	return results;
+};
+
+/**
+ * Utility function for retrieving the text value of an array of DOM nodes
+ * @param {Array|Element} elem
+ */
+getText = Sizzle.getText = function( elem ) {
+	var node,
+		ret = "",
+		i = 0,
+		nodeType = elem.nodeType;
+
+	if ( !nodeType ) {
+
+		// If no nodeType, this is expected to be an array
+		while ( ( node = elem[ i++ ] ) ) {
+
+			// Do not traverse comment nodes
+			ret += getText( node );
+		}
+	} else if ( nodeType === 1 || nodeType === 9 || nodeType === 11 ) {
+
+		// Use textContent for elements
+		// innerText usage removed for consistency of new lines (jQuery #11153)
+		if ( typeof elem.textContent === "string" ) {
+			return elem.textContent;
+		} else {
+
+			// Traverse its children
+			for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) {
+				ret += getText( elem );
+			}
+		}
+	} else if ( nodeType === 3 || nodeType === 4 ) {
+		return elem.nodeValue;
+	}
+
+	// Do not include comment or processing instruction nodes
+
+	return ret;
+};
+
+Expr = Sizzle.selectors = {
+
+	// Can be adjusted by the user
+	cacheLength: 50,
+
+	createPseudo: markFunction,
+
+	match: matchExpr,
+
+	attrHandle: {},
+
+	find: {},
+
+	relative: {
+		">": { dir: "parentNode", first: true },
+		" ": { dir: "parentNode" },
+		"+": { dir: "previousSibling", first: true },
+		"~": { dir: "previousSibling" }
+	},
+
+	preFilter: {
+		"ATTR": function( match ) {
+			match[ 1 ] = match[ 1 ].replace( runescape, funescape );
+
+			// Move the given value to match[3] whether quoted or unquoted
+			match[ 3 ] = ( match[ 3 ] || match[ 4 ] ||
+				match[ 5 ] || "" ).replace( runescape, funescape );
+
+			if ( match[ 2 ] === "~=" ) {
+				match[ 3 ] = " " + match[ 3 ] + " ";
+			}
+
+			return match.slice( 0, 4 );
+		},
+
+		"CHILD": function( match ) {
+
+			/* matches from matchExpr["CHILD"]
+				1 type (only|nth|...)
+				2 what (child|of-type)
+				3 argument (even|odd|\d*|\d*n([+-]\d+)?|...)
+				4 xn-component of xn+y argument ([+-]?\d*n|)
+				5 sign of xn-component
+				6 x of xn-component
+				7 sign of y-component
+				8 y of y-component
+			*/
+			match[ 1 ] = match[ 1 ].toLowerCase();
+
+			if ( match[ 1 ].slice( 0, 3 ) === "nth" ) {
+
+				// nth-* requires argument
+				if ( !match[ 3 ] ) {
+					Sizzle.error( match[ 0 ] );
+				}
+
+				// numeric x and y parameters for Expr.filter.CHILD
+				// remember that false/true cast respectively to 0/1
+				match[ 4 ] = +( match[ 4 ] ?
+					match[ 5 ] + ( match[ 6 ] || 1 ) :
+					2 * ( match[ 3 ] === "even" || match[ 3 ] === "odd" ) );
+				match[ 5 ] = +( ( match[ 7 ] + match[ 8 ] ) || match[ 3 ] === "odd" );
+
+				// other types prohibit arguments
+			} else if ( match[ 3 ] ) {
+				Sizzle.error( match[ 0 ] );
+			}
+
+			return match;
+		},
+
+		"PSEUDO": function( match ) {
+			var excess,
+				unquoted = !match[ 6 ] && match[ 2 ];
+
+			if ( matchExpr[ "CHILD" ].test( match[ 0 ] ) ) {
+				return null;
+			}
+
+			// Accept quoted arguments as-is
+			if ( match[ 3 ] ) {
+				match[ 2 ] = match[ 4 ] || match[ 5 ] || "";
+
+			// Strip excess characters from unquoted arguments
+			} else if ( unquoted && rpseudo.test( unquoted ) &&
+
+				// Get excess from tokenize (recursively)
+				( excess = tokenize( unquoted, true ) ) &&
+
+				// advance to the next closing parenthesis
+				( excess = unquoted.indexOf( ")", unquoted.length - excess ) - unquoted.length ) ) {
+
+				// excess is a negative index
+				match[ 0 ] = match[ 0 ].slice( 0, excess );
+				match[ 2 ] = unquoted.slice( 0, excess );
+			}
+
+			// Return only captures needed by the pseudo filter method (type and argument)
+			return match.slice( 0, 3 );
+		}
+	},
+
+	filter: {
+
+		"TAG": function( nodeNameSelector ) {
+			var nodeName = nodeNameSelector.replace( runescape, funescape ).toLowerCase();
+			return nodeNameSelector === "*" ?
+				function() {
+					return true;
+				} :
+				function( elem ) {
+					return elem.nodeName && elem.nodeName.toLowerCase() === nodeName;
+				};
+		},
+
+		"CLASS": function( className ) {
+			var pattern = classCache[ className + " " ];
+
+			return pattern ||
+				( pattern = new RegExp( "(^|" + whitespace +
+					")" + className + "(" + whitespace + "|$)" ) ) && classCache(
+						className, function( elem ) {
+							return pattern.test(
+								typeof elem.className === "string" && elem.className ||
+								typeof elem.getAttribute !== "undefined" &&
+									elem.getAttribute( "class" ) ||
+								""
+							);
+				} );
+		},
+
+		"ATTR": function( name, operator, check ) {
+			return function( elem ) {
+				var result = Sizzle.attr( elem, name );
+
+				if ( result == null ) {
+					return operator === "!=";
+				}
+				if ( !operator ) {
+					return true;
+				}
+
+				result += "";
+
+				/* eslint-disable max-len */
+
+				return operator === "=" ? result === check :
+					operator === "!=" ? result !== check :
+					operator === "^=" ? check && result.indexOf( check ) === 0 :
+					operator === "*=" ? check && result.indexOf( check ) > -1 :
+					operator === "$=" ? check && result.slice( -check.length ) === check :
+					operator === "~=" ? ( " " + result.replace( rwhitespace, " " ) + " " ).indexOf( check ) > -1 :
+					operator === "|=" ? result === check || result.slice( 0, check.length + 1 ) === check + "-" :
+					false;
+				/* eslint-enable max-len */
+
+			};
+		},
+
+		"CHILD": function( type, what, _argument, first, last ) {
+			var simple = type.slice( 0, 3 ) !== "nth",
+				forward = type.slice( -4 ) !== "last",
+				ofType = what === "of-type";
+
+			return first === 1 && last === 0 ?
+
+				// Shortcut for :nth-*(n)
+				function( elem ) {
+					return !!elem.parentNode;
+				} :
+
+				function( elem, _context, xml ) {
+					var cache, uniqueCache, outerCache, node, nodeIndex, start,
+						dir = simple !== forward ? "nextSibling" : "previousSibling",
+						parent = elem.parentNode,
+						name = ofType && elem.nodeName.toLowerCase(),
+						useCache = !xml && !ofType,
+						diff = false;
+
+					if ( parent ) {
+
+						// :(first|last|only)-(child|of-type)
+						if ( simple ) {
+							while ( dir ) {
+								node = elem;
+								while ( ( node = node[ dir ] ) ) {
+									if ( ofType ?
+										node.nodeName.toLowerCase() === name :
+										node.nodeType === 1 ) {
+
+										return false;
+									}
+								}
+
+								// Reverse direction for :only-* (if we haven't yet done so)
+								start = dir = type === "only" && !start && "nextSibling";
+							}
+							return true;
+						}
+
+						start = [ forward ? parent.firstChild : parent.lastChild ];
+
+						// non-xml :nth-child(...) stores cache data on `parent`
+						if ( forward && useCache ) {
+
+							// Seek `elem` from a previously-cached index
+
+							// ...in a gzip-friendly way
+							node = parent;
+							outerCache = node[ expando ] || ( node[ expando ] = {} );
+
+							// Support: IE <9 only
+							// Defend against cloned attroperties (jQuery gh-1709)
+							uniqueCache = outerCache[ node.uniqueID ] ||
+								( outerCache[ node.uniqueID ] = {} );
+
+							cache = uniqueCache[ type ] || [];
+							nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];
+							diff = nodeIndex && cache[ 2 ];
+							node = nodeIndex && parent.childNodes[ nodeIndex ];
+
+							while ( ( node = ++nodeIndex && node && node[ dir ] ||
+
+								// Fallback to seeking `elem` from the start
+								( diff = nodeIndex = 0 ) || start.pop() ) ) {
+
+								// When found, cache indexes on `parent` and break
+								if ( node.nodeType === 1 && ++diff && node === elem ) {
+									uniqueCache[ type ] = [ dirruns, nodeIndex, diff ];
+									break;
+								}
+							}
+
+						} else {
+
+							// Use previously-cached element index if available
+							if ( useCache ) {
+
+								// ...in a gzip-friendly way
+								node = elem;
+								outerCache = node[ expando ] || ( node[ expando ] = {} );
+
+								// Support: IE <9 only
+								// Defend against cloned attroperties (jQuery gh-1709)
+								uniqueCache = outerCache[ node.uniqueID ] ||
+									( outerCache[ node.uniqueID ] = {} );
+
+								cache = uniqueCache[ type ] || [];
+								nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];
+								diff = nodeIndex;
+							}
+
+							// xml :nth-child(...)
+							// or :nth-last-child(...) or :nth(-last)?-of-type(...)
+							if ( diff === false ) {
+
+								// Use the same loop as above to seek `elem` from the start
+								while ( ( node = ++nodeIndex && node && node[ dir ] ||
+									( diff = nodeIndex = 0 ) || start.pop() ) ) {
+
+									if ( ( ofType ?
+										node.nodeName.toLowerCase() === name :
+										node.nodeType === 1 ) &&
+										++diff ) {
+
+										// Cache the index of each encountered element
+										if ( useCache ) {
+											outerCache = node[ expando ] ||
+												( node[ expando ] = {} );
+
+											// Support: IE <9 only
+											// Defend against cloned attroperties (jQuery gh-1709)
+											uniqueCache = outerCache[ node.uniqueID ] ||
+												( outerCache[ node.uniqueID ] = {} );
+
+											uniqueCache[ type ] = [ dirruns, diff ];
+										}
+
+										if ( node === elem ) {
+											break;
+										}
+									}
+								}
+							}
+						}
+
+						// Incorporate the offset, then check against cycle size
+						diff -= last;
+						return diff === first || ( diff % first === 0 && diff / first >= 0 );
+					}
+				};
+		},
+
+		"PSEUDO": function( pseudo, argument ) {
+
+			// pseudo-class names are case-insensitive
+			// http://www.w3.org/TR/selectors/#pseudo-classes
+			// Prioritize by case sensitivity in case custom pseudos are added with uppercase letters
+			// Remember that setFilters inherits from pseudos
+			var args,
+				fn = Expr.pseudos[ pseudo ] || Expr.setFilters[ pseudo.toLowerCase() ] ||
+					Sizzle.error( "unsupported pseudo: " + pseudo );
+
+			// The user may use createPseudo to indicate that
+			// arguments are needed to create the filter function
+			// just as Sizzle does
+			if ( fn[ expando ] ) {
+				return fn( argument );
+			}
+
+			// But maintain support for old signatures
+			if ( fn.length > 1 ) {
+				args = [ pseudo, pseudo, "", argument ];
+				return Expr.setFilters.hasOwnProperty( pseudo.toLowerCase() ) ?
+					markFunction( function( seed, matches ) {
+						var idx,
+							matched = fn( seed, argument ),
+							i = matched.length;
+						while ( i-- ) {
+							idx = indexOf( seed, matched[ i ] );
+							seed[ idx ] = !( matches[ idx ] = matched[ i ] );
+						}
+					} ) :
+					function( elem ) {
+						return fn( elem, 0, args );
+					};
+			}
+
+			return fn;
+		}
+	},
+
+	pseudos: {
+
+		// Potentially complex pseudos
+		"not": markFunction( function( selector ) {
+
+			// Trim the selector passed to compile
+			// to avoid treating leading and trailing
+			// spaces as combinators
+			var input = [],
+				results = [],
+				matcher = compile( selector.replace( rtrim, "$1" ) );
+
+			return matcher[ expando ] ?
+				markFunction( function( seed, matches, _context, xml ) {
+					var elem,
+						unmatched = matcher( seed, null, xml, [] ),
+						i = seed.length;
+
+					// Match elements unmatched by `matcher`
+					while ( i-- ) {
+						if ( ( elem = unmatched[ i ] ) ) {
+							seed[ i ] = !( matches[ i ] = elem );
+						}
+					}
+				} ) :
+				function( elem, _context, xml ) {
+					input[ 0 ] = elem;
+					matcher( input, null, xml, results );
+
+					// Don't keep the element (issue #299)
+					input[ 0 ] = null;
+					return !results.pop();
+				};
+		} ),
+
+		"has": markFunction( function( selector ) {
+			return function( elem ) {
+				return Sizzle( selector, elem ).length > 0;
+			};
+		} ),
+
+		"contains": markFunction( function( text ) {
+			text = text.replace( runescape, funescape );
+			return function( elem ) {
+				return ( elem.textContent || getText( elem ) ).indexOf( text ) > -1;
+			};
+		} ),
+
+		// "Whether an element is represented by a :lang() selector
+		// is based solely on the element's language value
+		// being equal to the identifier C,
+		// or beginning with the identifier C immediately followed by "-".
+		// The matching of C against the element's language value is performed case-insensitively.
+		// The identifier C does not have to be a valid language name."
+		// http://www.w3.org/TR/selectors/#lang-pseudo
+		"lang": markFunction( function( lang ) {
+
+			// lang value must be a valid identifier
+			if ( !ridentifier.test( lang || "" ) ) {
+				Sizzle.error( "unsupported lang: " + lang );
+			}
+			lang = lang.replace( runescape, funescape ).toLowerCase();
+			return function( elem ) {
+				var elemLang;
+				do {
+					if ( ( elemLang = documentIsHTML ?
+						elem.lang :
+						elem.getAttribute( "xml:lang" ) || elem.getAttribute( "lang" ) ) ) {
+
+						elemLang = elemLang.toLowerCase();
+						return elemLang === lang || elemLang.indexOf( lang + "-" ) === 0;
+					}
+				} while ( ( elem = elem.parentNode ) && elem.nodeType === 1 );
+				return false;
+			};
+		} ),
+
+		// Miscellaneous
+		"target": function( elem ) {
+			var hash = window.location && window.location.hash;
+			return hash && hash.slice( 1 ) === elem.id;
+		},
+
+		"root": function( elem ) {
+			return elem === docElem;
+		},
+
+		"focus": function( elem ) {
+			return elem === document.activeElement &&
+				( !document.hasFocus || document.hasFocus() ) &&
+				!!( elem.type || elem.href || ~elem.tabIndex );
+		},
+
+		// Boolean properties
+		"enabled": createDisabledPseudo( false ),
+		"disabled": createDisabledPseudo( true ),
+
+		"checked": function( elem ) {
+
+			// In CSS3, :checked should return both checked and selected elements
+			// http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked
+			var nodeName = elem.nodeName.toLowerCase();
+			return ( nodeName === "input" && !!elem.checked ) ||
+				( nodeName === "option" && !!elem.selected );
+		},
+
+		"selected": function( elem ) {
+
+			// Accessing this property makes selected-by-default
+			// options in Safari work properly
+			if ( elem.parentNode ) {
+				// eslint-disable-next-line no-unused-expressions
+				elem.parentNode.selectedIndex;
+			}
+
+			return elem.selected === true;
+		},
+
+		// Contents
+		"empty": function( elem ) {
+
+			// http://www.w3.org/TR/selectors/#empty-pseudo
+			// :empty is negated by element (1) or content nodes (text: 3; cdata: 4; entity ref: 5),
+			//   but not by others (comment: 8; processing instruction: 7; etc.)
+			// nodeType < 6 works because attributes (2) do not appear as children
+			for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) {
+				if ( elem.nodeType < 6 ) {
+					return false;
+				}
+			}
+			return true;
+		},
+
+		"parent": function( elem ) {
+			return !Expr.pseudos[ "empty" ]( elem );
+		},
+
+		// Element/input types
+		"header": function( elem ) {
+			return rheader.test( elem.nodeName );
+		},
+
+		"input": function( elem ) {
+			return rinputs.test( elem.nodeName );
+		},
+
+		"button": function( elem ) {
+			var name = elem.nodeName.toLowerCase();
+			return name === "input" && elem.type === "button" || name === "button";
+		},
+
+		"text": function( elem ) {
+			var attr;
+			return elem.nodeName.toLowerCase() === "input" &&
+				elem.type === "text" &&
+
+				// Support: IE<8
+				// New HTML5 attribute values (e.g., "search") appear with elem.type === "text"
+				( ( attr = elem.getAttribute( "type" ) ) == null ||
+					attr.toLowerCase() === "text" );
+		},
+
+		// Position-in-collection
+		"first": createPositionalPseudo( function() {
+			return [ 0 ];
+		} ),
+
+		"last": createPositionalPseudo( function( _matchIndexes, length ) {
+			return [ length - 1 ];
+		} ),
+
+		"eq": createPositionalPseudo( function( _matchIndexes, length, argument ) {
+			return [ argument < 0 ? argument + length : argument ];
+		} ),
+
+		"even": createPositionalPseudo( function( matchIndexes, length ) {
+			var i = 0;
+			for ( ; i < length; i += 2 ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		} ),
+
+		"odd": createPositionalPseudo( function( matchIndexes, length ) {
+			var i = 1;
+			for ( ; i < length; i += 2 ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		} ),
+
+		"lt": createPositionalPseudo( function( matchIndexes, length, argument ) {
+			var i = argument < 0 ?
+				argument + length :
+				argument > length ?
+					length :
+					argument;
+			for ( ; --i >= 0; ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		} ),
+
+		"gt": createPositionalPseudo( function( matchIndexes, length, argument ) {
+			var i = argument < 0 ? argument + length : argument;
+			for ( ; ++i < length; ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		} )
+	}
+};
+
+Expr.pseudos[ "nth" ] = Expr.pseudos[ "eq" ];
+
+// Add button/input type pseudos
+for ( i in { radio: true, checkbox: true, file: true, password: true, image: true } ) {
+	Expr.pseudos[ i ] = createInputPseudo( i );
+}
+for ( i in { submit: true, reset: true } ) {
+	Expr.pseudos[ i ] = createButtonPseudo( i );
+}
+
+// Easy API for creating new setFilters
+function setFilters() {}
+setFilters.prototype = Expr.filters = Expr.pseudos;
+Expr.setFilters = new setFilters();
+
+tokenize = Sizzle.tokenize = function( selector, parseOnly ) {
+	var matched, match, tokens, type,
+		soFar, groups, preFilters,
+		cached = tokenCache[ selector + " " ];
+
+	if ( cached ) {
+		return parseOnly ? 0 : cached.slice( 0 );
+	}
+
+	soFar = selector;
+	groups = [];
+	preFilters = Expr.preFilter;
+
+	while ( soFar ) {
+
+		// Comma and first run
+		if ( !matched || ( match = rcomma.exec( soFar ) ) ) {
+			if ( match ) {
+
+				// Don't consume trailing commas as valid
+				soFar = soFar.slice( match[ 0 ].length ) || soFar;
+			}
+			groups.push( ( tokens = [] ) );
+		}
+
+		matched = false;
+
+		// Combinators
+		if ( ( match = rcombinators.exec( soFar ) ) ) {
+			matched = match.shift();
+			tokens.push( {
+				value: matched,
+
+				// Cast descendant combinators to space
+				type: match[ 0 ].replace( rtrim, " " )
+			} );
+			soFar = soFar.slice( matched.length );
+		}
+
+		// Filters
+		for ( type in Expr.filter ) {
+			if ( ( match = matchExpr[ type ].exec( soFar ) ) && ( !preFilters[ type ] ||
+				( match = preFilters[ type ]( match ) ) ) ) {
+				matched = match.shift();
+				tokens.push( {
+					value: matched,
+					type: type,
+					matches: match
+				} );
+				soFar = soFar.slice( matched.length );
+			}
+		}
+
+		if ( !matched ) {
+			break;
+		}
+	}
+
+	// Return the length of the invalid excess
+	// if we're just parsing
+	// Otherwise, throw an error or return tokens
+	return parseOnly ?
+		soFar.length :
+		soFar ?
+			Sizzle.error( selector ) :
+
+			// Cache the tokens
+			tokenCache( selector, groups ).slice( 0 );
+};
+
+function toSelector( tokens ) {
+	var i = 0,
+		len = tokens.length,
+		selector = "";
+	for ( ; i < len; i++ ) {
+		selector += tokens[ i ].value;
+	}
+	return selector;
+}
+
+function addCombinator( matcher, combinator, base ) {
+	var dir = combinator.dir,
+		skip = combinator.next,
+		key = skip || dir,
+		checkNonElements = base && key === "parentNode",
+		doneName = done++;
+
+	return combinator.first ?
+
+		// Check against closest ancestor/preceding element
+		function( elem, context, xml ) {
+			while ( ( elem = elem[ dir ] ) ) {
+				if ( elem.nodeType === 1 || checkNonElements ) {
+					return matcher( elem, context, xml );
+				}
+			}
+			return false;
+		} :
+
+		// Check against all ancestor/preceding elements
+		function( elem, context, xml ) {
+			var oldCache, uniqueCache, outerCache,
+				newCache = [ dirruns, doneName ];
+
+			// We can't set arbitrary data on XML nodes, so they don't benefit from combinator caching
+			if ( xml ) {
+				while ( ( elem = elem[ dir ] ) ) {
+					if ( elem.nodeType === 1 || checkNonElements ) {
+						if ( matcher( elem, context, xml ) ) {
+							return true;
+						}
+					}
+				}
+			} else {
+				while ( ( elem = elem[ dir ] ) ) {
+					if ( elem.nodeType === 1 || checkNonElements ) {
+						outerCache = elem[ expando ] || ( elem[ expando ] = {} );
+
+						// Support: IE <9 only
+						// Defend against cloned attroperties (jQuery gh-1709)
+						uniqueCache = outerCache[ elem.uniqueID ] ||
+							( outerCache[ elem.uniqueID ] = {} );
+
+						if ( skip && skip === elem.nodeName.toLowerCase() ) {
+							elem = elem[ dir ] || elem;
+						} else if ( ( oldCache = uniqueCache[ key ] ) &&
+							oldCache[ 0 ] === dirruns && oldCache[ 1 ] === doneName ) {
+
+							// Assign to newCache so results back-propagate to previous elements
+							return ( newCache[ 2 ] = oldCache[ 2 ] );
+						} else {
+
+							// Reuse newcache so results back-propagate to previous elements
+							uniqueCache[ key ] = newCache;
+
+							// A match means we're done; a fail means we have to keep checking
+							if ( ( newCache[ 2 ] = matcher( elem, context, xml ) ) ) {
+								return true;
+							}
+						}
+					}
+				}
+			}
+			return false;
+		};
+}
+
+function elementMatcher( matchers ) {
+	return matchers.length > 1 ?
+		function( elem, context, xml ) {
+			var i = matchers.length;
+			while ( i-- ) {
+				if ( !matchers[ i ]( elem, context, xml ) ) {
+					return false;
+				}
+			}
+			return true;
+		} :
+		matchers[ 0 ];
+}
+
+function multipleContexts( selector, contexts, results ) {
+	var i = 0,
+		len = contexts.length;
+	for ( ; i < len; i++ ) {
+		Sizzle( selector, contexts[ i ], results );
+	}
+	return results;
+}
+
+function condense( unmatched, map, filter, context, xml ) {
+	var elem,
+		newUnmatched = [],
+		i = 0,
+		len = unmatched.length,
+		mapped = map != null;
+
+	for ( ; i < len; i++ ) {
+		if ( ( elem = unmatched[ i ] ) ) {
+			if ( !filter || filter( elem, context, xml ) ) {
+				newUnmatched.push( elem );
+				if ( mapped ) {
+					map.push( i );
+				}
+			}
+		}
+	}
+
+	return newUnmatched;
+}
+
+function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postSelector ) {
+	if ( postFilter && !postFilter[ expando ] ) {
+		postFilter = setMatcher( postFilter );
+	}
+	if ( postFinder && !postFinder[ expando ] ) {
+		postFinder = setMatcher( postFinder, postSelector );
+	}
+	return markFunction( function( seed, results, context, xml ) {
+		var temp, i, elem,
+			preMap = [],
+			postMap = [],
+			preexisting = results.length,
+
+			// Get initial elements from seed or context
+			elems = seed || multipleContexts(
+				selector || "*",
+				context.nodeType ? [ context ] : context,
+				[]
+			),
+
+			// Prefilter to get matcher input, preserving a map for seed-results synchronization
+			matcherIn = preFilter && ( seed || !selector ) ?
+				condense( elems, preMap, preFilter, context, xml ) :
+				elems,
+
+			matcherOut = matcher ?
+
+				// If we have a postFinder, or filtered seed, or non-seed postFilter or preexisting results,
+				postFinder || ( seed ? preFilter : preexisting || postFilter ) ?
+
+					// ...intermediate processing is necessary
+					[] :
+
+					// ...otherwise use results directly
+					results :
+				matcherIn;
+
+		// Find primary matches
+		if ( matcher ) {
+			matcher( matcherIn, matcherOut, context, xml );
+		}
+
+		// Apply postFilter
+		if ( postFilter ) {
+			temp = condense( matcherOut, postMap );
+			postFilter( temp, [], context, xml );
+
+			// Un-match failing elements by moving them back to matcherIn
+			i = temp.length;
+			while ( i-- ) {
+				if ( ( elem = temp[ i ] ) ) {
+					matcherOut[ postMap[ i ] ] = !( matcherIn[ postMap[ i ] ] = elem );
+				}
+			}
+		}
+
+		if ( seed ) {
+			if ( postFinder || preFilter ) {
+				if ( postFinder ) {
+
+					// Get the final matcherOut by condensing this intermediate into postFinder contexts
+					temp = [];
+					i = matcherOut.length;
+					while ( i-- ) {
+						if ( ( elem = matcherOut[ i ] ) ) {
+
+							// Restore matcherIn since elem is not yet a final match
+							temp.push( ( matcherIn[ i ] = elem ) );
+						}
+					}
+					postFinder( null, ( matcherOut = [] ), temp, xml );
+				}
+
+				// Move matched elements from seed to results to keep them synchronized
+				i = matcherOut.length;
+				while ( i-- ) {
+					if ( ( elem = matcherOut[ i ] ) &&
+						( temp = postFinder ? indexOf( seed, elem ) : preMap[ i ] ) > -1 ) {
+
+						seed[ temp ] = !( results[ temp ] = elem );
+					}
+				}
+			}
+
+		// Add elements to results, through postFinder if defined
+		} else {
+			matcherOut = condense(
+				matcherOut === results ?
+					matcherOut.splice( preexisting, matcherOut.length ) :
+					matcherOut
+			);
+			if ( postFinder ) {
+				postFinder( null, results, matcherOut, xml );
+			} else {
+				push.apply( results, matcherOut );
+			}
+		}
+	} );
+}
+
+function matcherFromTokens( tokens ) {
+	var checkContext, matcher, j,
+		len = tokens.length,
+		leadingRelative = Expr.relative[ tokens[ 0 ].type ],
+		implicitRelative = leadingRelative || Expr.relative[ " " ],
+		i = leadingRelative ? 1 : 0,
+
+		// The foundational matcher ensures that elements are reachable from top-level context(s)
+		matchContext = addCombinator( function( elem ) {
+			return elem === checkContext;
+		}, implicitRelative, true ),
+		matchAnyContext = addCombinator( function( elem ) {
+			return indexOf( checkContext, elem ) > -1;
+		}, implicitRelative, true ),
+		matchers = [ function( elem, context, xml ) {
+			var ret = ( !leadingRelative && ( xml || context !== outermostContext ) ) || (
+				( checkContext = context ).nodeType ?
+					matchContext( elem, context, xml ) :
+					matchAnyContext( elem, context, xml ) );
+
+			// Avoid hanging onto element (issue #299)
+			checkContext = null;
+			return ret;
+		} ];
+
+	for ( ; i < len; i++ ) {
+		if ( ( matcher = Expr.relative[ tokens[ i ].type ] ) ) {
+			matchers = [ addCombinator( elementMatcher( matchers ), matcher ) ];
+		} else {
+			matcher = Expr.filter[ tokens[ i ].type ].apply( null, tokens[ i ].matches );
+
+			// Return special upon seeing a positional matcher
+			if ( matcher[ expando ] ) {
+
+				// Find the next relative operator (if any) for proper handling
+				j = ++i;
+				for ( ; j < len; j++ ) {
+					if ( Expr.relative[ tokens[ j ].type ] ) {
+						break;
+					}
+				}
+				return setMatcher(
+					i > 1 && elementMatcher( matchers ),
+					i > 1 && toSelector(
+
+					// If the preceding token was a descendant combinator, insert an implicit any-element `*`
+					tokens
+						.slice( 0, i - 1 )
+						.concat( { value: tokens[ i - 2 ].type === " " ? "*" : "" } )
+					).replace( rtrim, "$1" ),
+					matcher,
+					i < j && matcherFromTokens( tokens.slice( i, j ) ),
+					j < len && matcherFromTokens( ( tokens = tokens.slice( j ) ) ),
+					j < len && toSelector( tokens )
+				);
+			}
+			matchers.push( matcher );
+		}
+	}
+
+	return elementMatcher( matchers );
+}
+
+function matcherFromGroupMatchers( elementMatchers, setMatchers ) {
+	var bySet = setMatchers.length > 0,
+		byElement = elementMatchers.length > 0,
+		superMatcher = function( seed, context, xml, results, outermost ) {
+			var elem, j, matcher,
+				matchedCount = 0,
+				i = "0",
+				unmatched = seed && [],
+				setMatched = [],
+				contextBackup = outermostContext,
+
+				// We must always have either seed elements or outermost context
+				elems = seed || byElement && Expr.find[ "TAG" ]( "*", outermost ),
+
+				// Use integer dirruns iff this is the outermost matcher
+				dirrunsUnique = ( dirruns += contextBackup == null ? 1 : Math.random() || 0.1 ),
+				len = elems.length;
+
+			if ( outermost ) {
+
+				// Support: IE 11+, Edge 17 - 18+
+				// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+				// two documents; shallow comparisons work.
+				// eslint-disable-next-line eqeqeq
+				outermostContext = context == document || context || outermost;
+			}
+
+			// Add elements passing elementMatchers directly to results
+			// Support: IE<9, Safari
+			// Tolerate NodeList properties (IE: "length"; Safari: <number>) matching elements by id
+			for ( ; i !== len && ( elem = elems[ i ] ) != null; i++ ) {
+				if ( byElement && elem ) {
+					j = 0;
+
+					// Support: IE 11+, Edge 17 - 18+
+					// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+					// two documents; shallow comparisons work.
+					// eslint-disable-next-line eqeqeq
+					if ( !context && elem.ownerDocument != document ) {
+						setDocument( elem );
+						xml = !documentIsHTML;
+					}
+					while ( ( matcher = elementMatchers[ j++ ] ) ) {
+						if ( matcher( elem, context || document, xml ) ) {
+							results.push( elem );
+							break;
+						}
+					}
+					if ( outermost ) {
+						dirruns = dirrunsUnique;
+					}
+				}
+
+				// Track unmatched elements for set filters
+				if ( bySet ) {
+
+					// They will have gone through all possible matchers
+					if ( ( elem = !matcher && elem ) ) {
+						matchedCount--;
+					}
+
+					// Lengthen the array for every element, matched or not
+					if ( seed ) {
+						unmatched.push( elem );
+					}
+				}
+			}
+
+			// `i` is now the count of elements visited above, and adding it to `matchedCount`
+			// makes the latter nonnegative.
+			matchedCount += i;
+
+			// Apply set filters to unmatched elements
+			// NOTE: This can be skipped if there are no unmatched elements (i.e., `matchedCount`
+			// equals `i`), unless we didn't visit _any_ elements in the above loop because we have
+			// no element matchers and no seed.
+			// Incrementing an initially-string "0" `i` allows `i` to remain a string only in that
+			// case, which will result in a "00" `matchedCount` that differs from `i` but is also
+			// numerically zero.
+			if ( bySet && i !== matchedCount ) {
+				j = 0;
+				while ( ( matcher = setMatchers[ j++ ] ) ) {
+					matcher( unmatched, setMatched, context, xml );
+				}
+
+				if ( seed ) {
+
+					// Reintegrate element matches to eliminate the need for sorting
+					if ( matchedCount > 0 ) {
+						while ( i-- ) {
+							if ( !( unmatched[ i ] || setMatched[ i ] ) ) {
+								setMatched[ i ] = pop.call( results );
+							}
+						}
+					}
+
+					// Discard index placeholder values to get only actual matches
+					setMatched = condense( setMatched );
+				}
+
+				// Add matches to results
+				push.apply( results, setMatched );
+
+				// Seedless set matches succeeding multiple successful matchers stipulate sorting
+				if ( outermost && !seed && setMatched.length > 0 &&
+					( matchedCount + setMatchers.length ) > 1 ) {
+
+					Sizzle.uniqueSort( results );
+				}
+			}
+
+			// Override manipulation of globals by nested matchers
+			if ( outermost ) {
+				dirruns = dirrunsUnique;
+				outermostContext = contextBackup;
+			}
+
+			return unmatched;
+		};
+
+	return bySet ?
+		markFunction( superMatcher ) :
+		superMatcher;
+}
+
+compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) {
+	var i,
+		setMatchers = [],
+		elementMatchers = [],
+		cached = compilerCache[ selector + " " ];
+
+	if ( !cached ) {
+
+		// Generate a function of recursive functions that can be used to check each element
+		if ( !match ) {
+			match = tokenize( selector );
+		}
+		i = match.length;
+		while ( i-- ) {
+			cached = matcherFromTokens( match[ i ] );
+			if ( cached[ expando ] ) {
+				setMatchers.push( cached );
+			} else {
+				elementMatchers.push( cached );
+			}
+		}
+
+		// Cache the compiled function
+		cached = compilerCache(
+			selector,
+			matcherFromGroupMatchers( elementMatchers, setMatchers )
+		);
+
+		// Save selector and tokenization
+		cached.selector = selector;
+	}
+	return cached;
+};
+
+/**
+ * A low-level selection function that works with Sizzle's compiled
+ *  selector functions
+ * @param {String|Function} selector A selector or a pre-compiled
+ *  selector function built with Sizzle.compile
+ * @param {Element} context
+ * @param {Array} [results]
+ * @param {Array} [seed] A set of elements to match against
+ */
+select = Sizzle.select = function( selector, context, results, seed ) {
+	var i, tokens, token, type, find,
+		compiled = typeof selector === "function" && selector,
+		match = !seed && tokenize( ( selector = compiled.selector || selector ) );
+
+	results = results || [];
+
+	// Try to minimize operations if there is only one selector in the list and no seed
+	// (the latter of which guarantees us context)
+	if ( match.length === 1 ) {
+
+		// Reduce context if the leading compound selector is an ID
+		tokens = match[ 0 ] = match[ 0 ].slice( 0 );
+		if ( tokens.length > 2 && ( token = tokens[ 0 ] ).type === "ID" &&
+			context.nodeType === 9 && documentIsHTML && Expr.relative[ tokens[ 1 ].type ] ) {
+
+			context = ( Expr.find[ "ID" ]( token.matches[ 0 ]
+				.replace( runescape, funescape ), context ) || [] )[ 0 ];
+			if ( !context ) {
+				return results;
+
+			// Precompiled matchers will still verify ancestry, so step up a level
+			} else if ( compiled ) {
+				context = context.parentNode;
+			}
+
+			selector = selector.slice( tokens.shift().value.length );
+		}
+
+		// Fetch a seed set for right-to-left matching
+		i = matchExpr[ "needsContext" ].test( selector ) ? 0 : tokens.length;
+		while ( i-- ) {
+			token = tokens[ i ];
+
+			// Abort if we hit a combinator
+			if ( Expr.relative[ ( type = token.type ) ] ) {
+				break;
+			}
+			if ( ( find = Expr.find[ type ] ) ) {
+
+				// Search, expanding context for leading sibling combinators
+				if ( ( seed = find(
+					token.matches[ 0 ].replace( runescape, funescape ),
+					rsibling.test( tokens[ 0 ].type ) && testContext( context.parentNode ) ||
+						context
+				) ) ) {
+
+					// If seed is empty or no tokens remain, we can return early
+					tokens.splice( i, 1 );
+					selector = seed.length && toSelector( tokens );
+					if ( !selector ) {
+						push.apply( results, seed );
+						return results;
+					}
+
+					break;
+				}
+			}
+		}
+	}
+
+	// Compile and execute a filtering function if one is not provided
+	// Provide `match` to avoid retokenization if we modified the selector above
+	( compiled || compile( selector, match ) )(
+		seed,
+		context,
+		!documentIsHTML,
+		results,
+		!context || rsibling.test( selector ) && testContext( context.parentNode ) || context
+	);
+	return results;
+};
+
+// One-time assignments
+
+// Sort stability
+support.sortStable = expando.split( "" ).sort( sortOrder ).join( "" ) === expando;
+
+// Support: Chrome 14-35+
+// Always assume duplicates if they aren't passed to the comparison function
+support.detectDuplicates = !!hasDuplicate;
+
+// Initialize against the default document
+setDocument();
+
+// Support: Webkit<537.32 - Safari 6.0.3/Chrome 25 (fixed in Chrome 27)
+// Detached nodes confoundingly follow *each other*
+support.sortDetached = assert( function( el ) {
+
+	// Should return 1, but returns 4 (following)
+	return el.compareDocumentPosition( document.createElement( "fieldset" ) ) & 1;
+} );
+
+// Support: IE<8
+// Prevent attribute/property "interpolation"
+// https://msdn.microsoft.com/en-us/library/ms536429%28VS.85%29.aspx
+if ( !assert( function( el ) {
+	el.innerHTML = "<a href='#'></a>";
+	return el.firstChild.getAttribute( "href" ) === "#";
+} ) ) {
+	addHandle( "type|href|height|width", function( elem, name, isXML ) {
+		if ( !isXML ) {
+			return elem.getAttribute( name, name.toLowerCase() === "type" ? 1 : 2 );
+		}
+	} );
+}
+
+// Support: IE<9
+// Use defaultValue in place of getAttribute("value")
+if ( !support.attributes || !assert( function( el ) {
+	el.innerHTML = "<input/>";
+	el.firstChild.setAttribute( "value", "" );
+	return el.firstChild.getAttribute( "value" ) === "";
+} ) ) {
+	addHandle( "value", function( elem, _name, isXML ) {
+		if ( !isXML && elem.nodeName.toLowerCase() === "input" ) {
+			return elem.defaultValue;
+		}
+	} );
+}
+
+// Support: IE<9
+// Use getAttributeNode to fetch booleans when getAttribute lies
+if ( !assert( function( el ) {
+	return el.getAttribute( "disabled" ) == null;
+} ) ) {
+	addHandle( booleans, function( elem, name, isXML ) {
+		var val;
+		if ( !isXML ) {
+			return elem[ name ] === true ? name.toLowerCase() :
+				( val = elem.getAttributeNode( name ) ) && val.specified ?
+					val.value :
+					null;
+		}
+	} );
+}
+
+return Sizzle;
+
+} )( window );
+
+
+
+jQuery.find = Sizzle;
+jQuery.expr = Sizzle.selectors;
+
+// Deprecated
+jQuery.expr[ ":" ] = jQuery.expr.pseudos;
+jQuery.uniqueSort = jQuery.unique = Sizzle.uniqueSort;
+jQuery.text = Sizzle.getText;
+jQuery.isXMLDoc = Sizzle.isXML;
+jQuery.contains = Sizzle.contains;
+jQuery.escapeSelector = Sizzle.escape;
+
+
+
+
+var dir = function( elem, dir, until ) {
+	var matched = [],
+		truncate = until !== undefined;
+
+	while ( ( elem = elem[ dir ] ) && elem.nodeType !== 9 ) {
+		if ( elem.nodeType === 1 ) {
+			if ( truncate && jQuery( elem ).is( until ) ) {
+				break;
+			}
+			matched.push( elem );
+		}
+	}
+	return matched;
+};
+
+
+var siblings = function( n, elem ) {
+	var matched = [];
+
+	for ( ; n; n = n.nextSibling ) {
+		if ( n.nodeType === 1 && n !== elem ) {
+			matched.push( n );
+		}
+	}
+
+	return matched;
+};
+
+
+var rneedsContext = jQuery.expr.match.needsContext;
+
+
+
+function nodeName( elem, name ) {
+
+	return elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase();
+
+}
+var rsingleTag = ( /^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i );
+
+
+
+// Implement the identical functionality for filter and not
+function winnow( elements, qualifier, not ) {
+	if ( isFunction( qualifier ) ) {
+		return jQuery.grep( elements, function( elem, i ) {
+			return !!qualifier.call( elem, i, elem ) !== not;
+		} );
+	}
+
+	// Single element
+	if ( qualifier.nodeType ) {
+		return jQuery.grep( elements, function( elem ) {
+			return ( elem === qualifier ) !== not;
+		} );
+	}
+
+	// Arraylike of elements (jQuery, arguments, Array)
+	if ( typeof qualifier !== "string" ) {
+		return jQuery.grep( elements, function( elem ) {
+			return ( indexOf.call( qualifier, elem ) > -1 ) !== not;
+		} );
+	}
+
+	// Filtered directly for both simple and complex selectors
+	return jQuery.filter( qualifier, elements, not );
+}
+
+jQuery.filter = function( expr, elems, not ) {
+	var elem = elems[ 0 ];
+
+	if ( not ) {
+		expr = ":not(" + expr + ")";
+	}
+
+	if ( elems.length === 1 && elem.nodeType === 1 ) {
+		return jQuery.find.matchesSelector( elem, expr ) ? [ elem ] : [];
+	}
+
+	return jQuery.find.matches( expr, jQuery.grep( elems, function( elem ) {
+		return elem.nodeType === 1;
+	} ) );
+};
+
+jQuery.fn.extend( {
+	find: function( selector ) {
+		var i, ret,
+			len = this.length,
+			self = this;
+
+		if ( typeof selector !== "string" ) {
+			return this.pushStack( jQuery( selector ).filter( function() {
+				for ( i = 0; i < len; i++ ) {
+					if ( jQuery.contains( self[ i ], this ) ) {
+						return true;
+					}
+				}
+			} ) );
+		}
+
+		ret = this.pushStack( [] );
+
+		for ( i = 0; i < len; i++ ) {
+			jQuery.find( selector, self[ i ], ret );
+		}
+
+		return len > 1 ? jQuery.uniqueSort( ret ) : ret;
+	},
+	filter: function( selector ) {
+		return this.pushStack( winnow( this, selector || [], false ) );
+	},
+	not: function( selector ) {
+		return this.pushStack( winnow( this, selector || [], true ) );
+	},
+	is: function( selector ) {
+		return !!winnow(
+			this,
+
+			// If this is a positional/relative selector, check membership in the returned set
+			// so $("p:first").is("p:last") won't return true for a doc with two "p".
+			typeof selector === "string" && rneedsContext.test( selector ) ?
+				jQuery( selector ) :
+				selector || [],
+			false
+		).length;
+	}
+} );
+
+
+// Initialize a jQuery object
+
+
+// A central reference to the root jQuery(document)
+var rootjQuery,
+
+	// A simple way to check for HTML strings
+	// Prioritize #id over <tag> to avoid XSS via location.hash (#9521)
+	// Strict HTML recognition (#11290: must start with <)
+	// Shortcut simple #id case for speed
+	rquickExpr = /^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]+))$/,
+
+	init = jQuery.fn.init = function( selector, context, root ) {
+		var match, elem;
+
+		// HANDLE: $(""), $(null), $(undefined), $(false)
+		if ( !selector ) {
+			return this;
+		}
+
+		// Method init() accepts an alternate rootjQuery
+		// so migrate can support jQuery.sub (gh-2101)
+		root = root || rootjQuery;
+
+		// Handle HTML strings
+		if ( typeof selector === "string" ) {
+			if ( selector[ 0 ] === "<" &&
+				selector[ selector.length - 1 ] === ">" &&
+				selector.length >= 3 ) {
+
+				// Assume that strings that start and end with <> are HTML and skip the regex check
+				match = [ null, selector, null ];
+
+			} else {
+				match = rquickExpr.exec( selector );
+			}
+
+			// Match html or make sure no context is specified for #id
+			if ( match && ( match[ 1 ] || !context ) ) {
+
+				// HANDLE: $(html) -> $(array)
+				if ( match[ 1 ] ) {
+					context = context instanceof jQuery ? context[ 0 ] : context;
+
+					// Option to run scripts is true for back-compat
+					// Intentionally let the error be thrown if parseHTML is not present
+					jQuery.merge( this, jQuery.parseHTML(
+						match[ 1 ],
+						context && context.nodeType ? context.ownerDocument || context : document,
+						true
+					) );
+
+					// HANDLE: $(html, props)
+					if ( rsingleTag.test( match[ 1 ] ) && jQuery.isPlainObject( context ) ) {
+						for ( match in context ) {
+
+							// Properties of context are called as methods if possible
+							if ( isFunction( this[ match ] ) ) {
+								this[ match ]( context[ match ] );
+
+							// ...and otherwise set as attributes
+							} else {
+								this.attr( match, context[ match ] );
+							}
+						}
+					}
+
+					return this;
+
+				// HANDLE: $(#id)
+				} else {
+					elem = document.getElementById( match[ 2 ] );
+
+					if ( elem ) {
+
+						// Inject the element directly into the jQuery object
+						this[ 0 ] = elem;
+						this.length = 1;
+					}
+					return this;
+				}
+
+			// HANDLE: $(expr, $(...))
+			} else if ( !context || context.jquery ) {
+				return ( context || root ).find( selector );
+
+			// HANDLE: $(expr, context)
+			// (which is just equivalent to: $(context).find(expr)
+			} else {
+				return this.constructor( context ).find( selector );
+			}
+
+		// HANDLE: $(DOMElement)
+		} else if ( selector.nodeType ) {
+			this[ 0 ] = selector;
+			this.length = 1;
+			return this;
+
+		// HANDLE: $(function)
+		// Shortcut for document ready
+		} else if ( isFunction( selector ) ) {
+			return root.ready !== undefined ?
+				root.ready( selector ) :
+
+				// Execute immediately if ready is not present
+				selector( jQuery );
+		}
+
+		return jQuery.makeArray( selector, this );
+	};
+
+// Give the init function the jQuery prototype for later instantiation
+init.prototype = jQuery.fn;
+
+// Initialize central reference
+rootjQuery = jQuery( document );
+
+
+var rparentsprev = /^(?:parents|prev(?:Until|All))/,
+
+	// Methods guaranteed to produce a unique set when starting from a unique set
+	guaranteedUnique = {
+		children: true,
+		contents: true,
+		next: true,
+		prev: true
+	};
+
+jQuery.fn.extend( {
+	has: function( target ) {
+		var targets = jQuery( target, this ),
+			l = targets.length;
+
+		return this.filter( function() {
+			var i = 0;
+			for ( ; i < l; i++ ) {
+				if ( jQuery.contains( this, targets[ i ] ) ) {
+					return true;
+				}
+			}
+		} );
+	},
+
+	closest: function( selectors, context ) {
+		var cur,
+			i = 0,
+			l = this.length,
+			matched = [],
+			targets = typeof selectors !== "string" && jQuery( selectors );
+
+		// Positional selectors never match, since there's no _selection_ context
+		if ( !rneedsContext.test( selectors ) ) {
+			for ( ; i < l; i++ ) {
+				for ( cur = this[ i ]; cur && cur !== context; cur = cur.parentNode ) {
+
+					// Always skip document fragments
+					if ( cur.nodeType < 11 && ( targets ?
+						targets.index( cur ) > -1 :
+
+						// Don't pass non-elements to Sizzle
+						cur.nodeType === 1 &&
+							jQuery.find.matchesSelector( cur, selectors ) ) ) {
+
+						matched.push( cur );
+						break;
+					}
+				}
+			}
+		}
+
+		return this.pushStack( matched.length > 1 ? jQuery.uniqueSort( matched ) : matched );
+	},
+
+	// Determine the position of an element within the set
+	index: function( elem ) {
+
+		// No argument, return index in parent
+		if ( !elem ) {
+			return ( this[ 0 ] && this[ 0 ].parentNode ) ? this.first().prevAll().length : -1;
+		}
+
+		// Index in selector
+		if ( typeof elem === "string" ) {
+			return indexOf.call( jQuery( elem ), this[ 0 ] );
+		}
+
+		// Locate the position of the desired element
+		return indexOf.call( this,
+
+			// If it receives a jQuery object, the first element is used
+			elem.jquery ? elem[ 0 ] : elem
+		);
+	},
+
+	add: function( selector, context ) {
+		return this.pushStack(
+			jQuery.uniqueSort(
+				jQuery.merge( this.get(), jQuery( selector, context ) )
+			)
+		);
+	},
+
+	addBack: function( selector ) {
+		return this.add( selector == null ?
+			this.prevObject : this.prevObject.filter( selector )
+		);
+	}
+} );
+
+function sibling( cur, dir ) {
+	while ( ( cur = cur[ dir ] ) && cur.nodeType !== 1 ) {}
+	return cur;
+}
+
+jQuery.each( {
+	parent: function( elem ) {
+		var parent = elem.parentNode;
+		return parent && parent.nodeType !== 11 ? parent : null;
+	},
+	parents: function( elem ) {
+		return dir( elem, "parentNode" );
+	},
+	parentsUntil: function( elem, _i, until ) {
+		return dir( elem, "parentNode", until );
+	},
+	next: function( elem ) {
+		return sibling( elem, "nextSibling" );
+	},
+	prev: function( elem ) {
+		return sibling( elem, "previousSibling" );
+	},
+	nextAll: function( elem ) {
+		return dir( elem, "nextSibling" );
+	},
+	prevAll: function( elem ) {
+		return dir( elem, "previousSibling" );
+	},
+	nextUntil: function( elem, _i, until ) {
+		return dir( elem, "nextSibling", until );
+	},
+	prevUntil: function( elem, _i, until ) {
+		return dir( elem, "previousSibling", until );
+	},
+	siblings: function( elem ) {
+		return siblings( ( elem.parentNode || {} ).firstChild, elem );
+	},
+	children: function( elem ) {
+		return siblings( elem.firstChild );
+	},
+	contents: function( elem ) {
+		if ( elem.contentDocument != null &&
+
+			// Support: IE 11+
+			// <object> elements with no `data` attribute has an object
+			// `contentDocument` with a `null` prototype.
+			getProto( elem.contentDocument ) ) {
+
+			return elem.contentDocument;
+		}
+
+		// Support: IE 9 - 11 only, iOS 7 only, Android Browser <=4.3 only
+		// Treat the template element as a regular one in browsers that
+		// don't support it.
+		if ( nodeName( elem, "template" ) ) {
+			elem = elem.content || elem;
+		}
+
+		return jQuery.merge( [], elem.childNodes );
+	}
+}, function( name, fn ) {
+	jQuery.fn[ name ] = function( until, selector ) {
+		var matched = jQuery.map( this, fn, until );
+
+		if ( name.slice( -5 ) !== "Until" ) {
+			selector = until;
+		}
+
+		if ( selector && typeof selector === "string" ) {
+			matched = jQuery.filter( selector, matched );
+		}
+
+		if ( this.length > 1 ) {
+
+			// Remove duplicates
+			if ( !guaranteedUnique[ name ] ) {
+				jQuery.uniqueSort( matched );
+			}
+
+			// Reverse order for parents* and prev-derivatives
+			if ( rparentsprev.test( name ) ) {
+				matched.reverse();
+			}
+		}
+
+		return this.pushStack( matched );
+	};
+} );
+var rnothtmlwhite = ( /[^\x20\t\r\n\f]+/g );
+
+
+
+// Convert String-formatted options into Object-formatted ones
+function createOptions( options ) {
+	var object = {};
+	jQuery.each( options.match( rnothtmlwhite ) || [], function( _, flag ) {
+		object[ flag ] = true;
+	} );
+	return object;
+}
+
+/*
+ * Create a callback list using the following parameters:
+ *
+ *	options: an optional list of space-separated options that will change how
+ *			the callback list behaves or a more traditional option object
+ *
+ * By default a callback list will act like an event callback list and can be
+ * "fired" multiple times.
+ *
+ * Possible options:
+ *
+ *	once:			will ensure the callback list can only be fired once (like a Deferred)
+ *
+ *	memory:			will keep track of previous values and will call any callback added
+ *					after the list has been fired right away with the latest "memorized"
+ *					values (like a Deferred)
+ *
+ *	unique:			will ensure a callback can only be added once (no duplicate in the list)
+ *
+ *	stopOnFalse:	interrupt callings when a callback returns false
+ *
+ */
+jQuery.Callbacks = function( options ) {
+
+	// Convert options from String-formatted to Object-formatted if needed
+	// (we check in cache first)
+	options = typeof options === "string" ?
+		createOptions( options ) :
+		jQuery.extend( {}, options );
+
+	var // Flag to know if list is currently firing
+		firing,
+
+		// Last fire value for non-forgettable lists
+		memory,
+
+		// Flag to know if list was already fired
+		fired,
+
+		// Flag to prevent firing
+		locked,
+
+		// Actual callback list
+		list = [],
+
+		// Queue of execution data for repeatable lists
+		queue = [],
+
+		// Index of currently firing callback (modified by add/remove as needed)
+		firingIndex = -1,
+
+		// Fire callbacks
+		fire = function() {
+
+			// Enforce single-firing
+			locked = locked || options.once;
+
+			// Execute callbacks for all pending executions,
+			// respecting firingIndex overrides and runtime changes
+			fired = firing = true;
+			for ( ; queue.length; firingIndex = -1 ) {
+				memory = queue.shift();
+				while ( ++firingIndex < list.length ) {
+
+					// Run callback and check for early termination
+					if ( list[ firingIndex ].apply( memory[ 0 ], memory[ 1 ] ) === false &&
+						options.stopOnFalse ) {
+
+						// Jump to end and forget the data so .add doesn't re-fire
+						firingIndex = list.length;
+						memory = false;
+					}
+				}
+			}
+
+			// Forget the data if we're done with it
+			if ( !options.memory ) {
+				memory = false;
+			}
+
+			firing = false;
+
+			// Clean up if we're done firing for good
+			if ( locked ) {
+
+				// Keep an empty list if we have data for future add calls
+				if ( memory ) {
+					list = [];
+
+				// Otherwise, this object is spent
+				} else {
+					list = "";
+				}
+			}
+		},
+
+		// Actual Callbacks object
+		self = {
+
+			// Add a callback or a collection of callbacks to the list
+			add: function() {
+				if ( list ) {
+
+					// If we have memory from a past run, we should fire after adding
+					if ( memory && !firing ) {
+						firingIndex = list.length - 1;
+						queue.push( memory );
+					}
+
+					( function add( args ) {
+						jQuery.each( args, function( _, arg ) {
+							if ( isFunction( arg ) ) {
+								if ( !options.unique || !self.has( arg ) ) {
+									list.push( arg );
+								}
+							} else if ( arg && arg.length && toType( arg ) !== "string" ) {
+
+								// Inspect recursively
+								add( arg );
+							}
+						} );
+					} )( arguments );
+
+					if ( memory && !firing ) {
+						fire();
+					}
+				}
+				return this;
+			},
+
+			// Remove a callback from the list
+			remove: function() {
+				jQuery.each( arguments, function( _, arg ) {
+					var index;
+					while ( ( index = jQuery.inArray( arg, list, index ) ) > -1 ) {
+						list.splice( index, 1 );
+
+						// Handle firing indexes
+						if ( index <= firingIndex ) {
+							firingIndex--;
+						}
+					}
+				} );
+				return this;
+			},
+
+			// Check if a given callback is in the list.
+			// If no argument is given, return whether or not list has callbacks attached.
+			has: function( fn ) {
+				return fn ?
+					jQuery.inArray( fn, list ) > -1 :
+					list.length > 0;
+			},
+
+			// Remove all callbacks from the list
+			empty: function() {
+				if ( list ) {
+					list = [];
+				}
+				return this;
+			},
+
+			// Disable .fire and .add
+			// Abort any current/pending executions
+			// Clear all callbacks and values
+			disable: function() {
+				locked = queue = [];
+				list = memory = "";
+				return this;
+			},
+			disabled: function() {
+				return !list;
+			},
+
+			// Disable .fire
+			// Also disable .add unless we have memory (since it would have no effect)
+			// Abort any pending executions
+			lock: function() {
+				locked = queue = [];
+				if ( !memory && !firing ) {
+					list = memory = "";
+				}
+				return this;
+			},
+			locked: function() {
+				return !!locked;
+			},
+
+			// Call all callbacks with the given context and arguments
+			fireWith: function( context, args ) {
+				if ( !locked ) {
+					args = args || [];
+					args = [ context, args.slice ? args.slice() : args ];
+					queue.push( args );
+					if ( !firing ) {
+						fire();
+					}
+				}
+				return this;
+			},
+
+			// Call all the callbacks with the given arguments
+			fire: function() {
+				self.fireWith( this, arguments );
+				return this;
+			},
+
+			// To know if the callbacks have already been called at least once
+			fired: function() {
+				return !!fired;
+			}
+		};
+
+	return self;
+};
+
+
+function Identity( v ) {
+	return v;
+}
+function Thrower( ex ) {
+	throw ex;
+}
+
+function adoptValue( value, resolve, reject, noValue ) {
+	var method;
+
+	try {
+
+		// Check for promise aspect first to privilege synchronous behavior
+		if ( value && isFunction( ( method = value.promise ) ) ) {
+			method.call( value ).done( resolve ).fail( reject );
+
+		// Other thenables
+		} else if ( value && isFunction( ( method = value.then ) ) ) {
+			method.call( value, resolve, reject );
+
+		// Other non-thenables
+		} else {
+
+			// Control `resolve` arguments by letting Array#slice cast boolean `noValue` to integer:
+			// * false: [ value ].slice( 0 ) => resolve( value )
+			// * true: [ value ].slice( 1 ) => resolve()
+			resolve.apply( undefined, [ value ].slice( noValue ) );
+		}
+
+	// For Promises/A+, convert exceptions into rejections
+	// Since jQuery.when doesn't unwrap thenables, we can skip the extra checks appearing in
+	// Deferred#then to conditionally suppress rejection.
+	} catch ( value ) {
+
+		// Support: Android 4.0 only
+		// Strict mode functions invoked without .call/.apply get global-object context
+		reject.apply( undefined, [ value ] );
+	}
+}
+
+jQuery.extend( {
+
+	Deferred: function( func ) {
+		var tuples = [
+
+				// action, add listener, callbacks,
+				// ... .then handlers, argument index, [final state]
+				[ "notify", "progress", jQuery.Callbacks( "memory" ),
+					jQuery.Callbacks( "memory" ), 2 ],
+				[ "resolve", "done", jQuery.Callbacks( "once memory" ),
+					jQuery.Callbacks( "once memory" ), 0, "resolved" ],
+				[ "reject", "fail", jQuery.Callbacks( "once memory" ),
+					jQuery.Callbacks( "once memory" ), 1, "rejected" ]
+			],
+			state = "pending",
+			promise = {
+				state: function() {
+					return state;
+				},
+				always: function() {
+					deferred.done( arguments ).fail( arguments );
+					return this;
+				},
+				"catch": function( fn ) {
+					return promise.then( null, fn );
+				},
+
+				// Keep pipe for back-compat
+				pipe: function( /* fnDone, fnFail, fnProgress */ ) {
+					var fns = arguments;
+
+					return jQuery.Deferred( function( newDefer ) {
+						jQuery.each( tuples, function( _i, tuple ) {
+
+							// Map tuples (progress, done, fail) to arguments (done, fail, progress)
+							var fn = isFunction( fns[ tuple[ 4 ] ] ) && fns[ tuple[ 4 ] ];
+
+							// deferred.progress(function() { bind to newDefer or newDefer.notify })
+							// deferred.done(function() { bind to newDefer or newDefer.resolve })
+							// deferred.fail(function() { bind to newDefer or newDefer.reject })
+							deferred[ tuple[ 1 ] ]( function() {
+								var returned = fn && fn.apply( this, arguments );
+								if ( returned && isFunction( returned.promise ) ) {
+									returned.promise()
+										.progress( newDefer.notify )
+										.done( newDefer.resolve )
+										.fail( newDefer.reject );
+								} else {
+									newDefer[ tuple[ 0 ] + "With" ](
+										this,
+										fn ? [ returned ] : arguments
+									);
+								}
+							} );
+						} );
+						fns = null;
+					} ).promise();
+				},
+				then: function( onFulfilled, onRejected, onProgress ) {
+					var maxDepth = 0;
+					function resolve( depth, deferred, handler, special ) {
+						return function() {
+							var that = this,
+								args = arguments,
+								mightThrow = function() {
+									var returned, then;
+
+									// Support: Promises/A+ section 2.3.3.3.3
+									// https://promisesaplus.com/#point-59
+									// Ignore double-resolution attempts
+									if ( depth < maxDepth ) {
+										return;
+									}
+
+									returned = handler.apply( that, args );
+
+									// Support: Promises/A+ section 2.3.1
+									// https://promisesaplus.com/#point-48
+									if ( returned === deferred.promise() ) {
+										throw new TypeError( "Thenable self-resolution" );
+									}
+
+									// Support: Promises/A+ sections 2.3.3.1, 3.5
+									// https://promisesaplus.com/#point-54
+									// https://promisesaplus.com/#point-75
+									// Retrieve `then` only once
+									then = returned &&
+
+										// Support: Promises/A+ section 2.3.4
+										// https://promisesaplus.com/#point-64
+										// Only check objects and functions for thenability
+										( typeof returned === "object" ||
+											typeof returned === "function" ) &&
+										returned.then;
+
+									// Handle a returned thenable
+									if ( isFunction( then ) ) {
+
+										// Special processors (notify) just wait for resolution
+										if ( special ) {
+											then.call(
+												returned,
+												resolve( maxDepth, deferred, Identity, special ),
+												resolve( maxDepth, deferred, Thrower, special )
+											);
+
+										// Normal processors (resolve) also hook into progress
+										} else {
+
+											// ...and disregard older resolution values
+											maxDepth++;
+
+											then.call(
+												returned,
+												resolve( maxDepth, deferred, Identity, special ),
+												resolve( maxDepth, deferred, Thrower, special ),
+												resolve( maxDepth, deferred, Identity,
+													deferred.notifyWith )
+											);
+										}
+
+									// Handle all other returned values
+									} else {
+
+										// Only substitute handlers pass on context
+										// and multiple values (non-spec behavior)
+										if ( handler !== Identity ) {
+											that = undefined;
+											args = [ returned ];
+										}
+
+										// Process the value(s)
+										// Default process is resolve
+										( special || deferred.resolveWith )( that, args );
+									}
+								},
+
+								// Only normal processors (resolve) catch and reject exceptions
+								process = special ?
+									mightThrow :
+									function() {
+										try {
+											mightThrow();
+										} catch ( e ) {
+
+											if ( jQuery.Deferred.exceptionHook ) {
+												jQuery.Deferred.exceptionHook( e,
+													process.stackTrace );
+											}
+
+											// Support: Promises/A+ section 2.3.3.3.4.1
+											// https://promisesaplus.com/#point-61
+											// Ignore post-resolution exceptions
+											if ( depth + 1 >= maxDepth ) {
+
+												// Only substitute handlers pass on context
+												// and multiple values (non-spec behavior)
+												if ( handler !== Thrower ) {
+													that = undefined;
+													args = [ e ];
+												}
+
+												deferred.rejectWith( that, args );
+											}
+										}
+									};
+
+							// Support: Promises/A+ section 2.3.3.3.1
+							// https://promisesaplus.com/#point-57
+							// Re-resolve promises immediately to dodge false rejection from
+							// subsequent errors
+							if ( depth ) {
+								process();
+							} else {
+
+								// Call an optional hook to record the stack, in case of exception
+								// since it's otherwise lost when execution goes async
+								if ( jQuery.Deferred.getStackHook ) {
+									process.stackTrace = jQuery.Deferred.getStackHook();
+								}
+								window.setTimeout( process );
+							}
+						};
+					}
+
+					return jQuery.Deferred( function( newDefer ) {
+
+						// progress_handlers.add( ... )
+						tuples[ 0 ][ 3 ].add(
+							resolve(
+								0,
+								newDefer,
+								isFunction( onProgress ) ?
+									onProgress :
+									Identity,
+								newDefer.notifyWith
+							)
+						);
+
+						// fulfilled_handlers.add( ... )
+						tuples[ 1 ][ 3 ].add(
+							resolve(
+								0,
+								newDefer,
+								isFunction( onFulfilled ) ?
+									onFulfilled :
+									Identity
+							)
+						);
+
+						// rejected_handlers.add( ... )
+						tuples[ 2 ][ 3 ].add(
+							resolve(
+								0,
+								newDefer,
+								isFunction( onRejected ) ?
+									onRejected :
+									Thrower
+							)
+						);
+					} ).promise();
+				},
+
+				// Get a promise for this deferred
+				// If obj is provided, the promise aspect is added to the object
+				promise: function( obj ) {
+					return obj != null ? jQuery.extend( obj, promise ) : promise;
+				}
+			},
+			deferred = {};
+
+		// Add list-specific methods
+		jQuery.each( tuples, function( i, tuple ) {
+			var list = tuple[ 2 ],
+				stateString = tuple[ 5 ];
+
+			// promise.progress = list.add
+			// promise.done = list.add
+			// promise.fail = list.add
+			promise[ tuple[ 1 ] ] = list.add;
+
+			// Handle state
+			if ( stateString ) {
+				list.add(
+					function() {
+
+						// state = "resolved" (i.e., fulfilled)
+						// state = "rejected"
+						state = stateString;
+					},
+
+					// rejected_callbacks.disable
+					// fulfilled_callbacks.disable
+					tuples[ 3 - i ][ 2 ].disable,
+
+					// rejected_handlers.disable
+					// fulfilled_handlers.disable
+					tuples[ 3 - i ][ 3 ].disable,
+
+					// progress_callbacks.lock
+					tuples[ 0 ][ 2 ].lock,
+
+					// progress_handlers.lock
+					tuples[ 0 ][ 3 ].lock
+				);
+			}
+
+			// progress_handlers.fire
+			// fulfilled_handlers.fire
+			// rejected_handlers.fire
+			list.add( tuple[ 3 ].fire );
+
+			// deferred.notify = function() { deferred.notifyWith(...) }
+			// deferred.resolve = function() { deferred.resolveWith(...) }
+			// deferred.reject = function() { deferred.rejectWith(...) }
+			deferred[ tuple[ 0 ] ] = function() {
+				deferred[ tuple[ 0 ] + "With" ]( this === deferred ? undefined : this, arguments );
+				return this;
+			};
+
+			// deferred.notifyWith = list.fireWith
+			// deferred.resolveWith = list.fireWith
+			// deferred.rejectWith = list.fireWith
+			deferred[ tuple[ 0 ] + "With" ] = list.fireWith;
+		} );
+
+		// Make the deferred a promise
+		promise.promise( deferred );
+
+		// Call given func if any
+		if ( func ) {
+			func.call( deferred, deferred );
+		}
+
+		// All done!
+		return deferred;
+	},
+
+	// Deferred helper
+	when: function( singleValue ) {
+		var
+
+			// count of uncompleted subordinates
+			remaining = arguments.length,
+
+			// count of unprocessed arguments
+			i = remaining,
+
+			// subordinate fulfillment data
+			resolveContexts = Array( i ),
+			resolveValues = slice.call( arguments ),
+
+			// the primary Deferred
+			primary = jQuery.Deferred(),
+
+			// subordinate callback factory
+			updateFunc = function( i ) {
+				return function( value ) {
+					resolveContexts[ i ] = this;
+					resolveValues[ i ] = arguments.length > 1 ? slice.call( arguments ) : value;
+					if ( !( --remaining ) ) {
+						primary.resolveWith( resolveContexts, resolveValues );
+					}
+				};
+			};
+
+		// Single- and empty arguments are adopted like Promise.resolve
+		if ( remaining <= 1 ) {
+			adoptValue( singleValue, primary.done( updateFunc( i ) ).resolve, primary.reject,
+				!remaining );
+
+			// Use .then() to unwrap secondary thenables (cf. gh-3000)
+			if ( primary.state() === "pending" ||
+				isFunction( resolveValues[ i ] && resolveValues[ i ].then ) ) {
+
+				return primary.then();
+			}
+		}
+
+		// Multiple arguments are aggregated like Promise.all array elements
+		while ( i-- ) {
+			adoptValue( resolveValues[ i ], updateFunc( i ), primary.reject );
+		}
+
+		return primary.promise();
+	}
+} );
+
+
+// These usually indicate a programmer mistake during development,
+// warn about them ASAP rather than swallowing them by default.
+var rerrorNames = /^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/;
+
+jQuery.Deferred.exceptionHook = function( error, stack ) {
+
+	// Support: IE 8 - 9 only
+	// Console exists when dev tools are open, which can happen at any time
+	if ( window.console && window.console.warn && error && rerrorNames.test( error.name ) ) {
+		window.console.warn( "jQuery.Deferred exception: " + error.message, error.stack, stack );
+	}
+};
+
+
+
+
+jQuery.readyException = function( error ) {
+	window.setTimeout( function() {
+		throw error;
+	} );
+};
+
+
+
+
+// The deferred used on DOM ready
+var readyList = jQuery.Deferred();
+
+jQuery.fn.ready = function( fn ) {
+
+	readyList
+		.then( fn )
+
+		// Wrap jQuery.readyException in a function so that the lookup
+		// happens at the time of error handling instead of callback
+		// registration.
+		.catch( function( error ) {
+			jQuery.readyException( error );
+		} );
+
+	return this;
+};
+
+jQuery.extend( {
+
+	// Is the DOM ready to be used? Set to true once it occurs.
+	isReady: false,
+
+	// A counter to track how many items to wait for before
+	// the ready event fires. See #6781
+	readyWait: 1,
+
+	// Handle when the DOM is ready
+	ready: function( wait ) {
+
+		// Abort if there are pending holds or we're already ready
+		if ( wait === true ? --jQuery.readyWait : jQuery.isReady ) {
+			return;
+		}
+
+		// Remember that the DOM is ready
+		jQuery.isReady = true;
+
+		// If a normal DOM Ready event fired, decrement, and wait if need be
+		if ( wait !== true && --jQuery.readyWait > 0 ) {
+			return;
+		}
+
+		// If there are functions bound, to execute
+		readyList.resolveWith( document, [ jQuery ] );
+	}
+} );
+
+jQuery.ready.then = readyList.then;
+
+// The ready event handler and self cleanup method
+function completed() {
+	document.removeEventListener( "DOMContentLoaded", completed );
+	window.removeEventListener( "load", completed );
+	jQuery.ready();
+}
+
+// Catch cases where $(document).ready() is called
+// after the browser event has already occurred.
+// Support: IE <=9 - 10 only
+// Older IE sometimes signals "interactive" too soon
+if ( document.readyState === "complete" ||
+	( document.readyState !== "loading" && !document.documentElement.doScroll ) ) {
+
+	// Handle it asynchronously to allow scripts the opportunity to delay ready
+	window.setTimeout( jQuery.ready );
+
+} else {
+
+	// Use the handy event callback
+	document.addEventListener( "DOMContentLoaded", completed );
+
+	// A fallback to window.onload, that will always work
+	window.addEventListener( "load", completed );
+}
+
+
+
+
+// Multifunctional method to get and set values of a collection
+// The value/s can optionally be executed if it's a function
+var access = function( elems, fn, key, value, chainable, emptyGet, raw ) {
+	var i = 0,
+		len = elems.length,
+		bulk = key == null;
+
+	// Sets many values
+	if ( toType( key ) === "object" ) {
+		chainable = true;
+		for ( i in key ) {
+			access( elems, fn, i, key[ i ], true, emptyGet, raw );
+		}
+
+	// Sets one value
+	} else if ( value !== undefined ) {
+		chainable = true;
+
+		if ( !isFunction( value ) ) {
+			raw = true;
+		}
+
+		if ( bulk ) {
+
+			// Bulk operations run against the entire set
+			if ( raw ) {
+				fn.call( elems, value );
+				fn = null;
+
+			// ...except when executing function values
+			} else {
+				bulk = fn;
+				fn = function( elem, _key, value ) {
+					return bulk.call( jQuery( elem ), value );
+				};
+			}
+		}
+
+		if ( fn ) {
+			for ( ; i < len; i++ ) {
+				fn(
+					elems[ i ], key, raw ?
+						value :
+						value.call( elems[ i ], i, fn( elems[ i ], key ) )
+				);
+			}
+		}
+	}
+
+	if ( chainable ) {
+		return elems;
+	}
+
+	// Gets
+	if ( bulk ) {
+		return fn.call( elems );
+	}
+
+	return len ? fn( elems[ 0 ], key ) : emptyGet;
+};
+
+
+// Matches dashed string for camelizing
+var rmsPrefix = /^-ms-/,
+	rdashAlpha = /-([a-z])/g;
+
+// Used by camelCase as callback to replace()
+function fcamelCase( _all, letter ) {
+	return letter.toUpperCase();
+}
+
+// Convert dashed to camelCase; used by the css and data modules
+// Support: IE <=9 - 11, Edge 12 - 15
+// Microsoft forgot to hump their vendor prefix (#9572)
+function camelCase( string ) {
+	return string.replace( rmsPrefix, "ms-" ).replace( rdashAlpha, fcamelCase );
+}
+var acceptData = function( owner ) {
+
+	// Accepts only:
+	//  - Node
+	//    - Node.ELEMENT_NODE
+	//    - Node.DOCUMENT_NODE
+	//  - Object
+	//    - Any
+	return owner.nodeType === 1 || owner.nodeType === 9 || !( +owner.nodeType );
+};
+
+
+
+
+function Data() {
+	this.expando = jQuery.expando + Data.uid++;
+}
+
+Data.uid = 1;
+
+Data.prototype = {
+
+	cache: function( owner ) {
+
+		// Check if the owner object already has a cache
+		var value = owner[ this.expando ];
+
+		// If not, create one
+		if ( !value ) {
+			value = {};
+
+			// We can accept data for non-element nodes in modern browsers,
+			// but we should not, see #8335.
+			// Always return an empty object.
+			if ( acceptData( owner ) ) {
+
+				// If it is a node unlikely to be stringify-ed or looped over
+				// use plain assignment
+				if ( owner.nodeType ) {
+					owner[ this.expando ] = value;
+
+				// Otherwise secure it in a non-enumerable property
+				// configurable must be true to allow the property to be
+				// deleted when data is removed
+				} else {
+					Object.defineProperty( owner, this.expando, {
+						value: value,
+						configurable: true
+					} );
+				}
+			}
+		}
+
+		return value;
+	},
+	set: function( owner, data, value ) {
+		var prop,
+			cache = this.cache( owner );
+
+		// Handle: [ owner, key, value ] args
+		// Always use camelCase key (gh-2257)
+		if ( typeof data === "string" ) {
+			cache[ camelCase( data ) ] = value;
+
+		// Handle: [ owner, { properties } ] args
+		} else {
+
+			// Copy the properties one-by-one to the cache object
+			for ( prop in data ) {
+				cache[ camelCase( prop ) ] = data[ prop ];
+			}
+		}
+		return cache;
+	},
+	get: function( owner, key ) {
+		return key === undefined ?
+			this.cache( owner ) :
+
+			// Always use camelCase key (gh-2257)
+			owner[ this.expando ] && owner[ this.expando ][ camelCase( key ) ];
+	},
+	access: function( owner, key, value ) {
+
+		// In cases where either:
+		//
+		//   1. No key was specified
+		//   2. A string key was specified, but no value provided
+		//
+		// Take the "read" path and allow the get method to determine
+		// which value to return, respectively either:
+		//
+		//   1. The entire cache object
+		//   2. The data stored at the key
+		//
+		if ( key === undefined ||
+				( ( key && typeof key === "string" ) && value === undefined ) ) {
+
+			return this.get( owner, key );
+		}
+
+		// When the key is not a string, or both a key and value
+		// are specified, set or extend (existing objects) with either:
+		//
+		//   1. An object of properties
+		//   2. A key and value
+		//
+		this.set( owner, key, value );
+
+		// Since the "set" path can have two possible entry points
+		// return the expected data based on which path was taken[*]
+		return value !== undefined ? value : key;
+	},
+	remove: function( owner, key ) {
+		var i,
+			cache = owner[ this.expando ];
+
+		if ( cache === undefined ) {
+			return;
+		}
+
+		if ( key !== undefined ) {
+
+			// Support array or space separated string of keys
+			if ( Array.isArray( key ) ) {
+
+				// If key is an array of keys...
+				// We always set camelCase keys, so remove that.
+				key = key.map( camelCase );
+			} else {
+				key = camelCase( key );
+
+				// If a key with the spaces exists, use it.
+				// Otherwise, create an array by matching non-whitespace
+				key = key in cache ?
+					[ key ] :
+					( key.match( rnothtmlwhite ) || [] );
+			}
+
+			i = key.length;
+
+			while ( i-- ) {
+				delete cache[ key[ i ] ];
+			}
+		}
+
+		// Remove the expando if there's no more data
+		if ( key === undefined || jQuery.isEmptyObject( cache ) ) {
+
+			// Support: Chrome <=35 - 45
+			// Webkit & Blink performance suffers when deleting properties
+			// from DOM nodes, so set to undefined instead
+			// https://bugs.chromium.org/p/chromium/issues/detail?id=378607 (bug restricted)
+			if ( owner.nodeType ) {
+				owner[ this.expando ] = undefined;
+			} else {
+				delete owner[ this.expando ];
+			}
+		}
+	},
+	hasData: function( owner ) {
+		var cache = owner[ this.expando ];
+		return cache !== undefined && !jQuery.isEmptyObject( cache );
+	}
+};
+var dataPriv = new Data();
+
+var dataUser = new Data();
+
+
+
+//	Implementation Summary
+//
+//	1. Enforce API surface and semantic compatibility with 1.9.x branch
+//	2. Improve the module's maintainability by reducing the storage
+//		paths to a single mechanism.
+//	3. Use the same single mechanism to support "private" and "user" data.
+//	4. _Never_ expose "private" data to user code (TODO: Drop _data, _removeData)
+//	5. Avoid exposing implementation details on user objects (eg. expando properties)
+//	6. Provide a clear path for implementation upgrade to WeakMap in 2014
+
+var rbrace = /^(?:\{[\w\W]*\}|\[[\w\W]*\])$/,
+	rmultiDash = /[A-Z]/g;
+
+function getData( data ) {
+	if ( data === "true" ) {
+		return true;
+	}
+
+	if ( data === "false" ) {
+		return false;
+	}
+
+	if ( data === "null" ) {
+		return null;
+	}
+
+	// Only convert to a number if it doesn't change the string
+	if ( data === +data + "" ) {
+		return +data;
+	}
+
+	if ( rbrace.test( data ) ) {
+		return JSON.parse( data );
+	}
+
+	return data;
+}
+
+function dataAttr( elem, key, data ) {
+	var name;
+
+	// If nothing was found internally, try to fetch any
+	// data from the HTML5 data-* attribute
+	if ( data === undefined && elem.nodeType === 1 ) {
+		name = "data-" + key.replace( rmultiDash, "-$&" ).toLowerCase();
+		data = elem.getAttribute( name );
+
+		if ( typeof data === "string" ) {
+			try {
+				data = getData( data );
+			} catch ( e ) {}
+
+			// Make sure we set the data so it isn't changed later
+			dataUser.set( elem, key, data );
+		} else {
+			data = undefined;
+		}
+	}
+	return data;
+}
+
+jQuery.extend( {
+	hasData: function( elem ) {
+		return dataUser.hasData( elem ) || dataPriv.hasData( elem );
+	},
+
+	data: function( elem, name, data ) {
+		return dataUser.access( elem, name, data );
+	},
+
+	removeData: function( elem, name ) {
+		dataUser.remove( elem, name );
+	},
+
+	// TODO: Now that all calls to _data and _removeData have been replaced
+	// with direct calls to dataPriv methods, these can be deprecated.
+	_data: function( elem, name, data ) {
+		return dataPriv.access( elem, name, data );
+	},
+
+	_removeData: function( elem, name ) {
+		dataPriv.remove( elem, name );
+	}
+} );
+
+jQuery.fn.extend( {
+	data: function( key, value ) {
+		var i, name, data,
+			elem = this[ 0 ],
+			attrs = elem && elem.attributes;
+
+		// Gets all values
+		if ( key === undefined ) {
+			if ( this.length ) {
+				data = dataUser.get( elem );
+
+				if ( elem.nodeType === 1 && !dataPriv.get( elem, "hasDataAttrs" ) ) {
+					i = attrs.length;
+					while ( i-- ) {
+
+						// Support: IE 11 only
+						// The attrs elements can be null (#14894)
+						if ( attrs[ i ] ) {
+							name = attrs[ i ].name;
+							if ( name.indexOf( "data-" ) === 0 ) {
+								name = camelCase( name.slice( 5 ) );
+								dataAttr( elem, name, data[ name ] );
+							}
+						}
+					}
+					dataPriv.set( elem, "hasDataAttrs", true );
+				}
+			}
+
+			return data;
+		}
+
+		// Sets multiple values
+		if ( typeof key === "object" ) {
+			return this.each( function() {
+				dataUser.set( this, key );
+			} );
+		}
+
+		return access( this, function( value ) {
+			var data;
+
+			// The calling jQuery object (element matches) is not empty
+			// (and therefore has an element appears at this[ 0 ]) and the
+			// `value` parameter was not undefined. An empty jQuery object
+			// will result in `undefined` for elem = this[ 0 ] which will
+			// throw an exception if an attempt to read a data cache is made.
+			if ( elem && value === undefined ) {
+
+				// Attempt to get data from the cache
+				// The key will always be camelCased in Data
+				data = dataUser.get( elem, key );
+				if ( data !== undefined ) {
+					return data;
+				}
+
+				// Attempt to "discover" the data in
+				// HTML5 custom data-* attrs
+				data = dataAttr( elem, key );
+				if ( data !== undefined ) {
+					return data;
+				}
+
+				// We tried really hard, but the data doesn't exist.
+				return;
+			}
+
+			// Set the data...
+			this.each( function() {
+
+				// We always store the camelCased key
+				dataUser.set( this, key, value );
+			} );
+		}, null, value, arguments.length > 1, null, true );
+	},
+
+	removeData: function( key ) {
+		return this.each( function() {
+			dataUser.remove( this, key );
+		} );
+	}
+} );
+
+
+jQuery.extend( {
+	queue: function( elem, type, data ) {
+		var queue;
+
+		if ( elem ) {
+			type = ( type || "fx" ) + "queue";
+			queue = dataPriv.get( elem, type );
+
+			// Speed up dequeue by getting out quickly if this is just a lookup
+			if ( data ) {
+				if ( !queue || Array.isArray( data ) ) {
+					queue = dataPriv.access( elem, type, jQuery.makeArray( data ) );
+				} else {
+					queue.push( data );
+				}
+			}
+			return queue || [];
+		}
+	},
+
+	dequeue: function( elem, type ) {
+		type = type || "fx";
+
+		var queue = jQuery.queue( elem, type ),
+			startLength = queue.length,
+			fn = queue.shift(),
+			hooks = jQuery._queueHooks( elem, type ),
+			next = function() {
+				jQuery.dequeue( elem, type );
+			};
+
+		// If the fx queue is dequeued, always remove the progress sentinel
+		if ( fn === "inprogress" ) {
+			fn = queue.shift();
+			startLength--;
+		}
+
+		if ( fn ) {
+
+			// Add a progress sentinel to prevent the fx queue from being
+			// automatically dequeued
+			if ( type === "fx" ) {
+				queue.unshift( "inprogress" );
+			}
+
+			// Clear up the last queue stop function
+			delete hooks.stop;
+			fn.call( elem, next, hooks );
+		}
+
+		if ( !startLength && hooks ) {
+			hooks.empty.fire();
+		}
+	},
+
+	// Not public - generate a queueHooks object, or return the current one
+	_queueHooks: function( elem, type ) {
+		var key = type + "queueHooks";
+		return dataPriv.get( elem, key ) || dataPriv.access( elem, key, {
+			empty: jQuery.Callbacks( "once memory" ).add( function() {
+				dataPriv.remove( elem, [ type + "queue", key ] );
+			} )
+		} );
+	}
+} );
+
+jQuery.fn.extend( {
+	queue: function( type, data ) {
+		var setter = 2;
+
+		if ( typeof type !== "string" ) {
+			data = type;
+			type = "fx";
+			setter--;
+		}
+
+		if ( arguments.length < setter ) {
+			return jQuery.queue( this[ 0 ], type );
+		}
+
+		return data === undefined ?
+			this :
+			this.each( function() {
+				var queue = jQuery.queue( this, type, data );
+
+				// Ensure a hooks for this queue
+				jQuery._queueHooks( this, type );
+
+				if ( type === "fx" && queue[ 0 ] !== "inprogress" ) {
+					jQuery.dequeue( this, type );
+				}
+			} );
+	},
+	dequeue: function( type ) {
+		return this.each( function() {
+			jQuery.dequeue( this, type );
+		} );
+	},
+	clearQueue: function( type ) {
+		return this.queue( type || "fx", [] );
+	},
+
+	// Get a promise resolved when queues of a certain type
+	// are emptied (fx is the type by default)
+	promise: function( type, obj ) {
+		var tmp,
+			count = 1,
+			defer = jQuery.Deferred(),
+			elements = this,
+			i = this.length,
+			resolve = function() {
+				if ( !( --count ) ) {
+					defer.resolveWith( elements, [ elements ] );
+				}
+			};
+
+		if ( typeof type !== "string" ) {
+			obj = type;
+			type = undefined;
+		}
+		type = type || "fx";
+
+		while ( i-- ) {
+			tmp = dataPriv.get( elements[ i ], type + "queueHooks" );
+			if ( tmp && tmp.empty ) {
+				count++;
+				tmp.empty.add( resolve );
+			}
+		}
+		resolve();
+		return defer.promise( obj );
+	}
+} );
+var pnum = ( /[+-]?(?:\d*\.|)\d+(?:[eE][+-]?\d+|)/ ).source;
+
+var rcssNum = new RegExp( "^(?:([+-])=|)(" + pnum + ")([a-z%]*)$", "i" );
+
+
+var cssExpand = [ "Top", "Right", "Bottom", "Left" ];
+
+var documentElement = document.documentElement;
+
+
+
+	var isAttached = function( elem ) {
+			return jQuery.contains( elem.ownerDocument, elem );
+		},
+		composed = { composed: true };
+
+	// Support: IE 9 - 11+, Edge 12 - 18+, iOS 10.0 - 10.2 only
+	// Check attachment across shadow DOM boundaries when possible (gh-3504)
+	// Support: iOS 10.0-10.2 only
+	// Early iOS 10 versions support `attachShadow` but not `getRootNode`,
+	// leading to errors. We need to check for `getRootNode`.
+	if ( documentElement.getRootNode ) {
+		isAttached = function( elem ) {
+			return jQuery.contains( elem.ownerDocument, elem ) ||
+				elem.getRootNode( composed ) === elem.ownerDocument;
+		};
+	}
+var isHiddenWithinTree = function( elem, el ) {
+
+		// isHiddenWithinTree might be called from jQuery#filter function;
+		// in that case, element will be second argument
+		elem = el || elem;
+
+		// Inline style trumps all
+		return elem.style.display === "none" ||
+			elem.style.display === "" &&
+
+			// Otherwise, check computed style
+			// Support: Firefox <=43 - 45
+			// Disconnected elements can have computed display: none, so first confirm that elem is
+			// in the document.
+			isAttached( elem ) &&
+
+			jQuery.css( elem, "display" ) === "none";
+	};
+
+
+
+function adjustCSS( elem, prop, valueParts, tween ) {
+	var adjusted, scale,
+		maxIterations = 20,
+		currentValue = tween ?
+			function() {
+				return tween.cur();
+			} :
+			function() {
+				return jQuery.css( elem, prop, "" );
+			},
+		initial = currentValue(),
+		unit = valueParts && valueParts[ 3 ] || ( jQuery.cssNumber[ prop ] ? "" : "px" ),
+
+		// Starting value computation is required for potential unit mismatches
+		initialInUnit = elem.nodeType &&
+			( jQuery.cssNumber[ prop ] || unit !== "px" && +initial ) &&
+			rcssNum.exec( jQuery.css( elem, prop ) );
+
+	if ( initialInUnit && initialInUnit[ 3 ] !== unit ) {
+
+		// Support: Firefox <=54
+		// Halve the iteration target value to prevent interference from CSS upper bounds (gh-2144)
+		initial = initial / 2;
+
+		// Trust units reported by jQuery.css
+		unit = unit || initialInUnit[ 3 ];
+
+		// Iteratively approximate from a nonzero starting point
+		initialInUnit = +initial || 1;
+
+		while ( maxIterations-- ) {
+
+			// Evaluate and update our best guess (doubling guesses that zero out).
+			// Finish if the scale equals or crosses 1 (making the old*new product non-positive).
+			jQuery.style( elem, prop, initialInUnit + unit );
+			if ( ( 1 - scale ) * ( 1 - ( scale = currentValue() / initial || 0.5 ) ) <= 0 ) {
+				maxIterations = 0;
+			}
+			initialInUnit = initialInUnit / scale;
+
+		}
+
+		initialInUnit = initialInUnit * 2;
+		jQuery.style( elem, prop, initialInUnit + unit );
+
+		// Make sure we update the tween properties later on
+		valueParts = valueParts || [];
+	}
+
+	if ( valueParts ) {
+		initialInUnit = +initialInUnit || +initial || 0;
+
+		// Apply relative offset (+=/-=) if specified
+		adjusted = valueParts[ 1 ] ?
+			initialInUnit + ( valueParts[ 1 ] + 1 ) * valueParts[ 2 ] :
+			+valueParts[ 2 ];
+		if ( tween ) {
+			tween.unit = unit;
+			tween.start = initialInUnit;
+			tween.end = adjusted;
+		}
+	}
+	return adjusted;
+}
+
+
+var defaultDisplayMap = {};
+
+function getDefaultDisplay( elem ) {
+	var temp,
+		doc = elem.ownerDocument,
+		nodeName = elem.nodeName,
+		display = defaultDisplayMap[ nodeName ];
+
+	if ( display ) {
+		return display;
+	}
+
+	temp = doc.body.appendChild( doc.createElement( nodeName ) );
+	display = jQuery.css( temp, "display" );
+
+	temp.parentNode.removeChild( temp );
+
+	if ( display === "none" ) {
+		display = "block";
+	}
+	defaultDisplayMap[ nodeName ] = display;
+
+	return display;
+}
+
+function showHide( elements, show ) {
+	var display, elem,
+		values = [],
+		index = 0,
+		length = elements.length;
+
+	// Determine new display value for elements that need to change
+	for ( ; index < length; index++ ) {
+		elem = elements[ index ];
+		if ( !elem.style ) {
+			continue;
+		}
+
+		display = elem.style.display;
+		if ( show ) {
+
+			// Since we force visibility upon cascade-hidden elements, an immediate (and slow)
+			// check is required in this first loop unless we have a nonempty display value (either
+			// inline or about-to-be-restored)
+			if ( display === "none" ) {
+				values[ index ] = dataPriv.get( elem, "display" ) || null;
+				if ( !values[ index ] ) {
+					elem.style.display = "";
+				}
+			}
+			if ( elem.style.display === "" && isHiddenWithinTree( elem ) ) {
+				values[ index ] = getDefaultDisplay( elem );
+			}
+		} else {
+			if ( display !== "none" ) {
+				values[ index ] = "none";
+
+				// Remember what we're overwriting
+				dataPriv.set( elem, "display", display );
+			}
+		}
+	}
+
+	// Set the display of the elements in a second loop to avoid constant reflow
+	for ( index = 0; index < length; index++ ) {
+		if ( values[ index ] != null ) {
+			elements[ index ].style.display = values[ index ];
+		}
+	}
+
+	return elements;
+}
+
+jQuery.fn.extend( {
+	show: function() {
+		return showHide( this, true );
+	},
+	hide: function() {
+		return showHide( this );
+	},
+	toggle: function( state ) {
+		if ( typeof state === "boolean" ) {
+			return state ? this.show() : this.hide();
+		}
+
+		return this.each( function() {
+			if ( isHiddenWithinTree( this ) ) {
+				jQuery( this ).show();
+			} else {
+				jQuery( this ).hide();
+			}
+		} );
+	}
+} );
+var rcheckableType = ( /^(?:checkbox|radio)$/i );
+
+var rtagName = ( /<([a-z][^\/\0>\x20\t\r\n\f]*)/i );
+
+var rscriptType = ( /^$|^module$|\/(?:java|ecma)script/i );
+
+
+
+( function() {
+	var fragment = document.createDocumentFragment(),
+		div = fragment.appendChild( document.createElement( "div" ) ),
+		input = document.createElement( "input" );
+
+	// Support: Android 4.0 - 4.3 only
+	// Check state lost if the name is set (#11217)
+	// Support: Windows Web Apps (WWA)
+	// `name` and `type` must use .setAttribute for WWA (#14901)
+	input.setAttribute( "type", "radio" );
+	input.setAttribute( "checked", "checked" );
+	input.setAttribute( "name", "t" );
+
+	div.appendChild( input );
+
+	// Support: Android <=4.1 only
+	// Older WebKit doesn't clone checked state correctly in fragments
+	support.checkClone = div.cloneNode( true ).cloneNode( true ).lastChild.checked;
+
+	// Support: IE <=11 only
+	// Make sure textarea (and checkbox) defaultValue is properly cloned
+	div.innerHTML = "<textarea>x</textarea>";
+	support.noCloneChecked = !!div.cloneNode( true ).lastChild.defaultValue;
+
+	// Support: IE <=9 only
+	// IE <=9 replaces <option> tags with their contents when inserted outside of
+	// the select element.
+	div.innerHTML = "<option></option>";
+	support.option = !!div.lastChild;
+} )();
+
+
+// We have to close these tags to support XHTML (#13200)
+var wrapMap = {
+
+	// XHTML parsers do not magically insert elements in the
+	// same way that tag soup parsers do. So we cannot shorten
+	// this by omitting <tbody> or other required elements.
+	thead: [ 1, "<table>", "</table>" ],
+	col: [ 2, "<table><colgroup>", "</colgroup></table>" ],
+	tr: [ 2, "<table><tbody>", "</tbody></table>" ],
+	td: [ 3, "<table><tbody><tr>", "</tr></tbody></table>" ],
+
+	_default: [ 0, "", "" ]
+};
+
+wrapMap.tbody = wrapMap.tfoot = wrapMap.colgroup = wrapMap.caption = wrapMap.thead;
+wrapMap.th = wrapMap.td;
+
+// Support: IE <=9 only
+if ( !support.option ) {
+	wrapMap.optgroup = wrapMap.option = [ 1, "<select multiple='multiple'>", "</select>" ];
+}
+
+
+function getAll( context, tag ) {
+
+	// Support: IE <=9 - 11 only
+	// Use typeof to avoid zero-argument method invocation on host objects (#15151)
+	var ret;
+
+	if ( typeof context.getElementsByTagName !== "undefined" ) {
+		ret = context.getElementsByTagName( tag || "*" );
+
+	} else if ( typeof context.querySelectorAll !== "undefined" ) {
+		ret = context.querySelectorAll( tag || "*" );
+
+	} else {
+		ret = [];
+	}
+
+	if ( tag === undefined || tag && nodeName( context, tag ) ) {
+		return jQuery.merge( [ context ], ret );
+	}
+
+	return ret;
+}
+
+
+// Mark scripts as having already been evaluated
+function setGlobalEval( elems, refElements ) {
+	var i = 0,
+		l = elems.length;
+
+	for ( ; i < l; i++ ) {
+		dataPriv.set(
+			elems[ i ],
+			"globalEval",
+			!refElements || dataPriv.get( refElements[ i ], "globalEval" )
+		);
+	}
+}
+
+
+var rhtml = /<|&#?\w+;/;
+
+function buildFragment( elems, context, scripts, selection, ignored ) {
+	var elem, tmp, tag, wrap, attached, j,
+		fragment = context.createDocumentFragment(),
+		nodes = [],
+		i = 0,
+		l = elems.length;
+
+	for ( ; i < l; i++ ) {
+		elem = elems[ i ];
+
+		if ( elem || elem === 0 ) {
+
+			// Add nodes directly
+			if ( toType( elem ) === "object" ) {
+
+				// Support: Android <=4.0 only, PhantomJS 1 only
+				// push.apply(_, arraylike) throws on ancient WebKit
+				jQuery.merge( nodes, elem.nodeType ? [ elem ] : elem );
+
+			// Convert non-html into a text node
+			} else if ( !rhtml.test( elem ) ) {
+				nodes.push( context.createTextNode( elem ) );
+
+			// Convert html into DOM nodes
+			} else {
+				tmp = tmp || fragment.appendChild( context.createElement( "div" ) );
+
+				// Deserialize a standard representation
+				tag = ( rtagName.exec( elem ) || [ "", "" ] )[ 1 ].toLowerCase();
+				wrap = wrapMap[ tag ] || wrapMap._default;
+				tmp.innerHTML = wrap[ 1 ] + jQuery.htmlPrefilter( elem ) + wrap[ 2 ];
+
+				// Descend through wrappers to the right content
+				j = wrap[ 0 ];
+				while ( j-- ) {
+					tmp = tmp.lastChild;
+				}
+
+				// Support: Android <=4.0 only, PhantomJS 1 only
+				// push.apply(_, arraylike) throws on ancient WebKit
+				jQuery.merge( nodes, tmp.childNodes );
+
+				// Remember the top-level container
+				tmp = fragment.firstChild;
+
+				// Ensure the created nodes are orphaned (#12392)
+				tmp.textContent = "";
+			}
+		}
+	}
+
+	// Remove wrapper from fragment
+	fragment.textContent = "";
+
+	i = 0;
+	while ( ( elem = nodes[ i++ ] ) ) {
+
+		// Skip elements already in the context collection (trac-4087)
+		if ( selection && jQuery.inArray( elem, selection ) > -1 ) {
+			if ( ignored ) {
+				ignored.push( elem );
+			}
+			continue;
+		}
+
+		attached = isAttached( elem );
+
+		// Append to fragment
+		tmp = getAll( fragment.appendChild( elem ), "script" );
+
+		// Preserve script evaluation history
+		if ( attached ) {
+			setGlobalEval( tmp );
+		}
+
+		// Capture executables
+		if ( scripts ) {
+			j = 0;
+			while ( ( elem = tmp[ j++ ] ) ) {
+				if ( rscriptType.test( elem.type || "" ) ) {
+					scripts.push( elem );
+				}
+			}
+		}
+	}
+
+	return fragment;
+}
+
+
+var rtypenamespace = /^([^.]*)(?:\.(.+)|)/;
+
+function returnTrue() {
+	return true;
+}
+
+function returnFalse() {
+	return false;
+}
+
+// Support: IE <=9 - 11+
+// focus() and blur() are asynchronous, except when they are no-op.
+// So expect focus to be synchronous when the element is already active,
+// and blur to be synchronous when the element is not already active.
+// (focus and blur are always synchronous in other supported browsers,
+// this just defines when we can count on it).
+function expectSync( elem, type ) {
+	return ( elem === safeActiveElement() ) === ( type === "focus" );
+}
+
+// Support: IE <=9 only
+// Accessing document.activeElement can throw unexpectedly
+// https://bugs.jquery.com/ticket/13393
+function safeActiveElement() {
+	try {
+		return document.activeElement;
+	} catch ( err ) { }
+}
+
+function on( elem, types, selector, data, fn, one ) {
+	var origFn, type;
+
+	// Types can be a map of types/handlers
+	if ( typeof types === "object" ) {
+
+		// ( types-Object, selector, data )
+		if ( typeof selector !== "string" ) {
+
+			// ( types-Object, data )
+			data = data || selector;
+			selector = undefined;
+		}
+		for ( type in types ) {
+			on( elem, type, selector, data, types[ type ], one );
+		}
+		return elem;
+	}
+
+	if ( data == null && fn == null ) {
+
+		// ( types, fn )
+		fn = selector;
+		data = selector = undefined;
+	} else if ( fn == null ) {
+		if ( typeof selector === "string" ) {
+
+			// ( types, selector, fn )
+			fn = data;
+			data = undefined;
+		} else {
+
+			// ( types, data, fn )
+			fn = data;
+			data = selector;
+			selector = undefined;
+		}
+	}
+	if ( fn === false ) {
+		fn = returnFalse;
+	} else if ( !fn ) {
+		return elem;
+	}
+
+	if ( one === 1 ) {
+		origFn = fn;
+		fn = function( event ) {
+
+			// Can use an empty set, since event contains the info
+			jQuery().off( event );
+			return origFn.apply( this, arguments );
+		};
+
+		// Use same guid so caller can remove using origFn
+		fn.guid = origFn.guid || ( origFn.guid = jQuery.guid++ );
+	}
+	return elem.each( function() {
+		jQuery.event.add( this, types, fn, data, selector );
+	} );
+}
+
+/*
+ * Helper functions for managing events -- not part of the public interface.
+ * Props to Dean Edwards' addEvent library for many of the ideas.
+ */
+jQuery.event = {
+
+	global: {},
+
+	add: function( elem, types, handler, data, selector ) {
+
+		var handleObjIn, eventHandle, tmp,
+			events, t, handleObj,
+			special, handlers, type, namespaces, origType,
+			elemData = dataPriv.get( elem );
+
+		// Only attach events to objects that accept data
+		if ( !acceptData( elem ) ) {
+			return;
+		}
+
+		// Caller can pass in an object of custom data in lieu of the handler
+		if ( handler.handler ) {
+			handleObjIn = handler;
+			handler = handleObjIn.handler;
+			selector = handleObjIn.selector;
+		}
+
+		// Ensure that invalid selectors throw exceptions at attach time
+		// Evaluate against documentElement in case elem is a non-element node (e.g., document)
+		if ( selector ) {
+			jQuery.find.matchesSelector( documentElement, selector );
+		}
+
+		// Make sure that the handler has a unique ID, used to find/remove it later
+		if ( !handler.guid ) {
+			handler.guid = jQuery.guid++;
+		}
+
+		// Init the element's event structure and main handler, if this is the first
+		if ( !( events = elemData.events ) ) {
+			events = elemData.events = Object.create( null );
+		}
+		if ( !( eventHandle = elemData.handle ) ) {
+			eventHandle = elemData.handle = function( e ) {
+
+				// Discard the second event of a jQuery.event.trigger() and
+				// when an event is called after a page has unloaded
+				return typeof jQuery !== "undefined" && jQuery.event.triggered !== e.type ?
+					jQuery.event.dispatch.apply( elem, arguments ) : undefined;
+			};
+		}
+
+		// Handle multiple events separated by a space
+		types = ( types || "" ).match( rnothtmlwhite ) || [ "" ];
+		t = types.length;
+		while ( t-- ) {
+			tmp = rtypenamespace.exec( types[ t ] ) || [];
+			type = origType = tmp[ 1 ];
+			namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort();
+
+			// There *must* be a type, no attaching namespace-only handlers
+			if ( !type ) {
+				continue;
+			}
+
+			// If event changes its type, use the special event handlers for the changed type
+			special = jQuery.event.special[ type ] || {};
+
+			// If selector defined, determine special event api type, otherwise given type
+			type = ( selector ? special.delegateType : special.bindType ) || type;
+
+			// Update special based on newly reset type
+			special = jQuery.event.special[ type ] || {};
+
+			// handleObj is passed to all event handlers
+			handleObj = jQuery.extend( {
+				type: type,
+				origType: origType,
+				data: data,
+				handler: handler,
+				guid: handler.guid,
+				selector: selector,
+				needsContext: selector && jQuery.expr.match.needsContext.test( selector ),
+				namespace: namespaces.join( "." )
+			}, handleObjIn );
+
+			// Init the event handler queue if we're the first
+			if ( !( handlers = events[ type ] ) ) {
+				handlers = events[ type ] = [];
+				handlers.delegateCount = 0;
+
+				// Only use addEventListener if the special events handler returns false
+				if ( !special.setup ||
+					special.setup.call( elem, data, namespaces, eventHandle ) === false ) {
+
+					if ( elem.addEventListener ) {
+						elem.addEventListener( type, eventHandle );
+					}
+				}
+			}
+
+			if ( special.add ) {
+				special.add.call( elem, handleObj );
+
+				if ( !handleObj.handler.guid ) {
+					handleObj.handler.guid = handler.guid;
+				}
+			}
+
+			// Add to the element's handler list, delegates in front
+			if ( selector ) {
+				handlers.splice( handlers.delegateCount++, 0, handleObj );
+			} else {
+				handlers.push( handleObj );
+			}
+
+			// Keep track of which events have ever been used, for event optimization
+			jQuery.event.global[ type ] = true;
+		}
+
+	},
+
+	// Detach an event or set of events from an element
+	remove: function( elem, types, handler, selector, mappedTypes ) {
+
+		var j, origCount, tmp,
+			events, t, handleObj,
+			special, handlers, type, namespaces, origType,
+			elemData = dataPriv.hasData( elem ) && dataPriv.get( elem );
+
+		if ( !elemData || !( events = elemData.events ) ) {
+			return;
+		}
+
+		// Once for each type.namespace in types; type may be omitted
+		types = ( types || "" ).match( rnothtmlwhite ) || [ "" ];
+		t = types.length;
+		while ( t-- ) {
+			tmp = rtypenamespace.exec( types[ t ] ) || [];
+			type = origType = tmp[ 1 ];
+			namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort();
+
+			// Unbind all events (on this namespace, if provided) for the element
+			if ( !type ) {
+				for ( type in events ) {
+					jQuery.event.remove( elem, type + types[ t ], handler, selector, true );
+				}
+				continue;
+			}
+
+			special = jQuery.event.special[ type ] || {};
+			type = ( selector ? special.delegateType : special.bindType ) || type;
+			handlers = events[ type ] || [];
+			tmp = tmp[ 2 ] &&
+				new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" );
+
+			// Remove matching events
+			origCount = j = handlers.length;
+			while ( j-- ) {
+				handleObj = handlers[ j ];
+
+				if ( ( mappedTypes || origType === handleObj.origType ) &&
+					( !handler || handler.guid === handleObj.guid ) &&
+					( !tmp || tmp.test( handleObj.namespace ) ) &&
+					( !selector || selector === handleObj.selector ||
+						selector === "**" && handleObj.selector ) ) {
+					handlers.splice( j, 1 );
+
+					if ( handleObj.selector ) {
+						handlers.delegateCount--;
+					}
+					if ( special.remove ) {
+						special.remove.call( elem, handleObj );
+					}
+				}
+			}
+
+			// Remove generic event handler if we removed something and no more handlers exist
+			// (avoids potential for endless recursion during removal of special event handlers)
+			if ( origCount && !handlers.length ) {
+				if ( !special.teardown ||
+					special.teardown.call( elem, namespaces, elemData.handle ) === false ) {
+
+					jQuery.removeEvent( elem, type, elemData.handle );
+				}
+
+				delete events[ type ];
+			}
+		}
+
+		// Remove data and the expando if it's no longer used
+		if ( jQuery.isEmptyObject( events ) ) {
+			dataPriv.remove( elem, "handle events" );
+		}
+	},
+
+	dispatch: function( nativeEvent ) {
+
+		var i, j, ret, matched, handleObj, handlerQueue,
+			args = new Array( arguments.length ),
+
+			// Make a writable jQuery.Event from the native event object
+			event = jQuery.event.fix( nativeEvent ),
+
+			handlers = (
+				dataPriv.get( this, "events" ) || Object.create( null )
+			)[ event.type ] || [],
+			special = jQuery.event.special[ event.type ] || {};
+
+		// Use the fix-ed jQuery.Event rather than the (read-only) native event
+		args[ 0 ] = event;
+
+		for ( i = 1; i < arguments.length; i++ ) {
+			args[ i ] = arguments[ i ];
+		}
+
+		event.delegateTarget = this;
+
+		// Call the preDispatch hook for the mapped type, and let it bail if desired
+		if ( special.preDispatch && special.preDispatch.call( this, event ) === false ) {
+			return;
+		}
+
+		// Determine handlers
+		handlerQueue = jQuery.event.handlers.call( this, event, handlers );
+
+		// Run delegates first; they may want to stop propagation beneath us
+		i = 0;
+		while ( ( matched = handlerQueue[ i++ ] ) && !event.isPropagationStopped() ) {
+			event.currentTarget = matched.elem;
+
+			j = 0;
+			while ( ( handleObj = matched.handlers[ j++ ] ) &&
+				!event.isImmediatePropagationStopped() ) {
+
+				// If the event is namespaced, then each handler is only invoked if it is
+				// specially universal or its namespaces are a superset of the event's.
+				if ( !event.rnamespace || handleObj.namespace === false ||
+					event.rnamespace.test( handleObj.namespace ) ) {
+
+					event.handleObj = handleObj;
+					event.data = handleObj.data;
+
+					ret = ( ( jQuery.event.special[ handleObj.origType ] || {} ).handle ||
+						handleObj.handler ).apply( matched.elem, args );
+
+					if ( ret !== undefined ) {
+						if ( ( event.result = ret ) === false ) {
+							event.preventDefault();
+							event.stopPropagation();
+						}
+					}
+				}
+			}
+		}
+
+		// Call the postDispatch hook for the mapped type
+		if ( special.postDispatch ) {
+			special.postDispatch.call( this, event );
+		}
+
+		return event.result;
+	},
+
+	handlers: function( event, handlers ) {
+		var i, handleObj, sel, matchedHandlers, matchedSelectors,
+			handlerQueue = [],
+			delegateCount = handlers.delegateCount,
+			cur = event.target;
+
+		// Find delegate handlers
+		if ( delegateCount &&
+
+			// Support: IE <=9
+			// Black-hole SVG <use> instance trees (trac-13180)
+			cur.nodeType &&
+
+			// Support: Firefox <=42
+			// Suppress spec-violating clicks indicating a non-primary pointer button (trac-3861)
+			// https://www.w3.org/TR/DOM-Level-3-Events/#event-type-click
+			// Support: IE 11 only
+			// ...but not arrow key "clicks" of radio inputs, which can have `button` -1 (gh-2343)
+			!( event.type === "click" && event.button >= 1 ) ) {
+
+			for ( ; cur !== this; cur = cur.parentNode || this ) {
+
+				// Don't check non-elements (#13208)
+				// Don't process clicks on disabled elements (#6911, #8165, #11382, #11764)
+				if ( cur.nodeType === 1 && !( event.type === "click" && cur.disabled === true ) ) {
+					matchedHandlers = [];
+					matchedSelectors = {};
+					for ( i = 0; i < delegateCount; i++ ) {
+						handleObj = handlers[ i ];
+
+						// Don't conflict with Object.prototype properties (#13203)
+						sel = handleObj.selector + " ";
+
+						if ( matchedSelectors[ sel ] === undefined ) {
+							matchedSelectors[ sel ] = handleObj.needsContext ?
+								jQuery( sel, this ).index( cur ) > -1 :
+								jQuery.find( sel, this, null, [ cur ] ).length;
+						}
+						if ( matchedSelectors[ sel ] ) {
+							matchedHandlers.push( handleObj );
+						}
+					}
+					if ( matchedHandlers.length ) {
+						handlerQueue.push( { elem: cur, handlers: matchedHandlers } );
+					}
+				}
+			}
+		}
+
+		// Add the remaining (directly-bound) handlers
+		cur = this;
+		if ( delegateCount < handlers.length ) {
+			handlerQueue.push( { elem: cur, handlers: handlers.slice( delegateCount ) } );
+		}
+
+		return handlerQueue;
+	},
+
+	addProp: function( name, hook ) {
+		Object.defineProperty( jQuery.Event.prototype, name, {
+			enumerable: true,
+			configurable: true,
+
+			get: isFunction( hook ) ?
+				function() {
+					if ( this.originalEvent ) {
+						return hook( this.originalEvent );
+					}
+				} :
+				function() {
+					if ( this.originalEvent ) {
+						return this.originalEvent[ name ];
+					}
+				},
+
+			set: function( value ) {
+				Object.defineProperty( this, name, {
+					enumerable: true,
+					configurable: true,
+					writable: true,
+					value: value
+				} );
+			}
+		} );
+	},
+
+	fix: function( originalEvent ) {
+		return originalEvent[ jQuery.expando ] ?
+			originalEvent :
+			new jQuery.Event( originalEvent );
+	},
+
+	special: {
+		load: {
+
+			// Prevent triggered image.load events from bubbling to window.load
+			noBubble: true
+		},
+		click: {
+
+			// Utilize native event to ensure correct state for checkable inputs
+			setup: function( data ) {
+
+				// For mutual compressibility with _default, replace `this` access with a local var.
+				// `|| data` is dead code meant only to preserve the variable through minification.
+				var el = this || data;
+
+				// Claim the first handler
+				if ( rcheckableType.test( el.type ) &&
+					el.click && nodeName( el, "input" ) ) {
+
+					// dataPriv.set( el, "click", ... )
+					leverageNative( el, "click", returnTrue );
+				}
+
+				// Return false to allow normal processing in the caller
+				return false;
+			},
+			trigger: function( data ) {
+
+				// For mutual compressibility with _default, replace `this` access with a local var.
+				// `|| data` is dead code meant only to preserve the variable through minification.
+				var el = this || data;
+
+				// Force setup before triggering a click
+				if ( rcheckableType.test( el.type ) &&
+					el.click && nodeName( el, "input" ) ) {
+
+					leverageNative( el, "click" );
+				}
+
+				// Return non-false to allow normal event-path propagation
+				return true;
+			},
+
+			// For cross-browser consistency, suppress native .click() on links
+			// Also prevent it if we're currently inside a leveraged native-event stack
+			_default: function( event ) {
+				var target = event.target;
+				return rcheckableType.test( target.type ) &&
+					target.click && nodeName( target, "input" ) &&
+					dataPriv.get( target, "click" ) ||
+					nodeName( target, "a" );
+			}
+		},
+
+		beforeunload: {
+			postDispatch: function( event ) {
+
+				// Support: Firefox 20+
+				// Firefox doesn't alert if the returnValue field is not set.
+				if ( event.result !== undefined && event.originalEvent ) {
+					event.originalEvent.returnValue = event.result;
+				}
+			}
+		}
+	}
+};
+
+// Ensure the presence of an event listener that handles manually-triggered
+// synthetic events by interrupting progress until reinvoked in response to
+// *native* events that it fires directly, ensuring that state changes have
+// already occurred before other listeners are invoked.
+function leverageNative( el, type, expectSync ) {
+
+	// Missing expectSync indicates a trigger call, which must force setup through jQuery.event.add
+	if ( !expectSync ) {
+		if ( dataPriv.get( el, type ) === undefined ) {
+			jQuery.event.add( el, type, returnTrue );
+		}
+		return;
+	}
+
+	// Register the controller as a special universal handler for all event namespaces
+	dataPriv.set( el, type, false );
+	jQuery.event.add( el, type, {
+		namespace: false,
+		handler: function( event ) {
+			var notAsync, result,
+				saved = dataPriv.get( this, type );
+
+			if ( ( event.isTrigger & 1 ) && this[ type ] ) {
+
+				// Interrupt processing of the outer synthetic .trigger()ed event
+				// Saved data should be false in such cases, but might be a leftover capture object
+				// from an async native handler (gh-4350)
+				if ( !saved.length ) {
+
+					// Store arguments for use when handling the inner native event
+					// There will always be at least one argument (an event object), so this array
+					// will not be confused with a leftover capture object.
+					saved = slice.call( arguments );
+					dataPriv.set( this, type, saved );
+
+					// Trigger the native event and capture its result
+					// Support: IE <=9 - 11+
+					// focus() and blur() are asynchronous
+					notAsync = expectSync( this, type );
+					this[ type ]();
+					result = dataPriv.get( this, type );
+					if ( saved !== result || notAsync ) {
+						dataPriv.set( this, type, false );
+					} else {
+						result = {};
+					}
+					if ( saved !== result ) {
+
+						// Cancel the outer synthetic event
+						event.stopImmediatePropagation();
+						event.preventDefault();
+
+						// Support: Chrome 86+
+						// In Chrome, if an element having a focusout handler is blurred by
+						// clicking outside of it, it invokes the handler synchronously. If
+						// that handler calls `.remove()` on the element, the data is cleared,
+						// leaving `result` undefined. We need to guard against this.
+						return result && result.value;
+					}
+
+				// If this is an inner synthetic event for an event with a bubbling surrogate
+				// (focus or blur), assume that the surrogate already propagated from triggering the
+				// native event and prevent that from happening again here.
+				// This technically gets the ordering wrong w.r.t. to `.trigger()` (in which the
+				// bubbling surrogate propagates *after* the non-bubbling base), but that seems
+				// less bad than duplication.
+				} else if ( ( jQuery.event.special[ type ] || {} ).delegateType ) {
+					event.stopPropagation();
+				}
+
+			// If this is a native event triggered above, everything is now in order
+			// Fire an inner synthetic event with the original arguments
+			} else if ( saved.length ) {
+
+				// ...and capture the result
+				dataPriv.set( this, type, {
+					value: jQuery.event.trigger(
+
+						// Support: IE <=9 - 11+
+						// Extend with the prototype to reset the above stopImmediatePropagation()
+						jQuery.extend( saved[ 0 ], jQuery.Event.prototype ),
+						saved.slice( 1 ),
+						this
+					)
+				} );
+
+				// Abort handling of the native event
+				event.stopImmediatePropagation();
+			}
+		}
+	} );
+}
+
+jQuery.removeEvent = function( elem, type, handle ) {
+
+	// This "if" is needed for plain objects
+	if ( elem.removeEventListener ) {
+		elem.removeEventListener( type, handle );
+	}
+};
+
+jQuery.Event = function( src, props ) {
+
+	// Allow instantiation without the 'new' keyword
+	if ( !( this instanceof jQuery.Event ) ) {
+		return new jQuery.Event( src, props );
+	}
+
+	// Event object
+	if ( src && src.type ) {
+		this.originalEvent = src;
+		this.type = src.type;
+
+		// Events bubbling up the document may have been marked as prevented
+		// by a handler lower down the tree; reflect the correct value.
+		this.isDefaultPrevented = src.defaultPrevented ||
+				src.defaultPrevented === undefined &&
+
+				// Support: Android <=2.3 only
+				src.returnValue === false ?
+			returnTrue :
+			returnFalse;
+
+		// Create target properties
+		// Support: Safari <=6 - 7 only
+		// Target should not be a text node (#504, #13143)
+		this.target = ( src.target && src.target.nodeType === 3 ) ?
+			src.target.parentNode :
+			src.target;
+
+		this.currentTarget = src.currentTarget;
+		this.relatedTarget = src.relatedTarget;
+
+	// Event type
+	} else {
+		this.type = src;
+	}
+
+	// Put explicitly provided properties onto the event object
+	if ( props ) {
+		jQuery.extend( this, props );
+	}
+
+	// Create a timestamp if incoming event doesn't have one
+	this.timeStamp = src && src.timeStamp || Date.now();
+
+	// Mark it as fixed
+	this[ jQuery.expando ] = true;
+};
+
+// jQuery.Event is based on DOM3 Events as specified by the ECMAScript Language Binding
+// https://www.w3.org/TR/2003/WD-DOM-Level-3-Events-20030331/ecma-script-binding.html
+jQuery.Event.prototype = {
+	constructor: jQuery.Event,
+	isDefaultPrevented: returnFalse,
+	isPropagationStopped: returnFalse,
+	isImmediatePropagationStopped: returnFalse,
+	isSimulated: false,
+
+	preventDefault: function() {
+		var e = this.originalEvent;
+
+		this.isDefaultPrevented = returnTrue;
+
+		if ( e && !this.isSimulated ) {
+			e.preventDefault();
+		}
+	},
+	stopPropagation: function() {
+		var e = this.originalEvent;
+
+		this.isPropagationStopped = returnTrue;
+
+		if ( e && !this.isSimulated ) {
+			e.stopPropagation();
+		}
+	},
+	stopImmediatePropagation: function() {
+		var e = this.originalEvent;
+
+		this.isImmediatePropagationStopped = returnTrue;
+
+		if ( e && !this.isSimulated ) {
+			e.stopImmediatePropagation();
+		}
+
+		this.stopPropagation();
+	}
+};
+
+// Includes all common event props including KeyEvent and MouseEvent specific props
+jQuery.each( {
+	altKey: true,
+	bubbles: true,
+	cancelable: true,
+	changedTouches: true,
+	ctrlKey: true,
+	detail: true,
+	eventPhase: true,
+	metaKey: true,
+	pageX: true,
+	pageY: true,
+	shiftKey: true,
+	view: true,
+	"char": true,
+	code: true,
+	charCode: true,
+	key: true,
+	keyCode: true,
+	button: true,
+	buttons: true,
+	clientX: true,
+	clientY: true,
+	offsetX: true,
+	offsetY: true,
+	pointerId: true,
+	pointerType: true,
+	screenX: true,
+	screenY: true,
+	targetTouches: true,
+	toElement: true,
+	touches: true,
+	which: true
+}, jQuery.event.addProp );
+
+jQuery.each( { focus: "focusin", blur: "focusout" }, function( type, delegateType ) {
+	jQuery.event.special[ type ] = {
+
+		// Utilize native event if possible so blur/focus sequence is correct
+		setup: function() {
+
+			// Claim the first handler
+			// dataPriv.set( this, "focus", ... )
+			// dataPriv.set( this, "blur", ... )
+			leverageNative( this, type, expectSync );
+
+			// Return false to allow normal processing in the caller
+			return false;
+		},
+		trigger: function() {
+
+			// Force setup before trigger
+			leverageNative( this, type );
+
+			// Return non-false to allow normal event-path propagation
+			return true;
+		},
+
+		// Suppress native focus or blur as it's already being fired
+		// in leverageNative.
+		_default: function() {
+			return true;
+		},
+
+		delegateType: delegateType
+	};
+} );
+
+// Create mouseenter/leave events using mouseover/out and event-time checks
+// so that event delegation works in jQuery.
+// Do the same for pointerenter/pointerleave and pointerover/pointerout
+//
+// Support: Safari 7 only
+// Safari sends mouseenter too often; see:
+// https://bugs.chromium.org/p/chromium/issues/detail?id=470258
+// for the description of the bug (it existed in older Chrome versions as well).
+jQuery.each( {
+	mouseenter: "mouseover",
+	mouseleave: "mouseout",
+	pointerenter: "pointerover",
+	pointerleave: "pointerout"
+}, function( orig, fix ) {
+	jQuery.event.special[ orig ] = {
+		delegateType: fix,
+		bindType: fix,
+
+		handle: function( event ) {
+			var ret,
+				target = this,
+				related = event.relatedTarget,
+				handleObj = event.handleObj;
+
+			// For mouseenter/leave call the handler if related is outside the target.
+			// NB: No relatedTarget if the mouse left/entered the browser window
+			if ( !related || ( related !== target && !jQuery.contains( target, related ) ) ) {
+				event.type = handleObj.origType;
+				ret = handleObj.handler.apply( this, arguments );
+				event.type = fix;
+			}
+			return ret;
+		}
+	};
+} );
+
+jQuery.fn.extend( {
+
+	on: function( types, selector, data, fn ) {
+		return on( this, types, selector, data, fn );
+	},
+	one: function( types, selector, data, fn ) {
+		return on( this, types, selector, data, fn, 1 );
+	},
+	off: function( types, selector, fn ) {
+		var handleObj, type;
+		if ( types && types.preventDefault && types.handleObj ) {
+
+			// ( event )  dispatched jQuery.Event
+			handleObj = types.handleObj;
+			jQuery( types.delegateTarget ).off(
+				handleObj.namespace ?
+					handleObj.origType + "." + handleObj.namespace :
+					handleObj.origType,
+				handleObj.selector,
+				handleObj.handler
+			);
+			return this;
+		}
+		if ( typeof types === "object" ) {
+
+			// ( types-object [, selector] )
+			for ( type in types ) {
+				this.off( type, selector, types[ type ] );
+			}
+			return this;
+		}
+		if ( selector === false || typeof selector === "function" ) {
+
+			// ( types [, fn] )
+			fn = selector;
+			selector = undefined;
+		}
+		if ( fn === false ) {
+			fn = returnFalse;
+		}
+		return this.each( function() {
+			jQuery.event.remove( this, types, fn, selector );
+		} );
+	}
+} );
+
+
+var
+
+	// Support: IE <=10 - 11, Edge 12 - 13 only
+	// In IE/Edge using regex groups here causes severe slowdowns.
+	// See https://connect.microsoft.com/IE/feedback/details/1736512/
+	rnoInnerhtml = /<script|<style|<link/i,
+
+	// checked="checked" or checked
+	rchecked = /checked\s*(?:[^=]|=\s*.checked.)/i,
+	rcleanScript = /^\s*<!(?:\[CDATA\[|--)|(?:\]\]|--)>\s*$/g;
+
+// Prefer a tbody over its parent table for containing new rows
+function manipulationTarget( elem, content ) {
+	if ( nodeName( elem, "table" ) &&
+		nodeName( content.nodeType !== 11 ? content : content.firstChild, "tr" ) ) {
+
+		return jQuery( elem ).children( "tbody" )[ 0 ] || elem;
+	}
+
+	return elem;
+}
+
+// Replace/restore the type attribute of script elements for safe DOM manipulation
+function disableScript( elem ) {
+	elem.type = ( elem.getAttribute( "type" ) !== null ) + "/" + elem.type;
+	return elem;
+}
+function restoreScript( elem ) {
+	if ( ( elem.type || "" ).slice( 0, 5 ) === "true/" ) {
+		elem.type = elem.type.slice( 5 );
+	} else {
+		elem.removeAttribute( "type" );
+	}
+
+	return elem;
+}
+
+function cloneCopyEvent( src, dest ) {
+	var i, l, type, pdataOld, udataOld, udataCur, events;
+
+	if ( dest.nodeType !== 1 ) {
+		return;
+	}
+
+	// 1. Copy private data: events, handlers, etc.
+	if ( dataPriv.hasData( src ) ) {
+		pdataOld = dataPriv.get( src );
+		events = pdataOld.events;
+
+		if ( events ) {
+			dataPriv.remove( dest, "handle events" );
+
+			for ( type in events ) {
+				for ( i = 0, l = events[ type ].length; i < l; i++ ) {
+					jQuery.event.add( dest, type, events[ type ][ i ] );
+				}
+			}
+		}
+	}
+
+	// 2. Copy user data
+	if ( dataUser.hasData( src ) ) {
+		udataOld = dataUser.access( src );
+		udataCur = jQuery.extend( {}, udataOld );
+
+		dataUser.set( dest, udataCur );
+	}
+}
+
+// Fix IE bugs, see support tests
+function fixInput( src, dest ) {
+	var nodeName = dest.nodeName.toLowerCase();
+
+	// Fails to persist the checked state of a cloned checkbox or radio button.
+	if ( nodeName === "input" && rcheckableType.test( src.type ) ) {
+		dest.checked = src.checked;
+
+	// Fails to return the selected option to the default selected state when cloning options
+	} else if ( nodeName === "input" || nodeName === "textarea" ) {
+		dest.defaultValue = src.defaultValue;
+	}
+}
+
+function domManip( collection, args, callback, ignored ) {
+
+	// Flatten any nested arrays
+	args = flat( args );
+
+	var fragment, first, scripts, hasScripts, node, doc,
+		i = 0,
+		l = collection.length,
+		iNoClone = l - 1,
+		value = args[ 0 ],
+		valueIsFunction = isFunction( value );
+
+	// We can't cloneNode fragments that contain checked, in WebKit
+	if ( valueIsFunction ||
+			( l > 1 && typeof value === "string" &&
+				!support.checkClone && rchecked.test( value ) ) ) {
+		return collection.each( function( index ) {
+			var self = collection.eq( index );
+			if ( valueIsFunction ) {
+				args[ 0 ] = value.call( this, index, self.html() );
+			}
+			domManip( self, args, callback, ignored );
+		} );
+	}
+
+	if ( l ) {
+		fragment = buildFragment( args, collection[ 0 ].ownerDocument, false, collection, ignored );
+		first = fragment.firstChild;
+
+		if ( fragment.childNodes.length === 1 ) {
+			fragment = first;
+		}
+
+		// Require either new content or an interest in ignored elements to invoke the callback
+		if ( first || ignored ) {
+			scripts = jQuery.map( getAll( fragment, "script" ), disableScript );
+			hasScripts = scripts.length;
+
+			// Use the original fragment for the last item
+			// instead of the first because it can end up
+			// being emptied incorrectly in certain situations (#8070).
+			for ( ; i < l; i++ ) {
+				node = fragment;
+
+				if ( i !== iNoClone ) {
+					node = jQuery.clone( node, true, true );
+
+					// Keep references to cloned scripts for later restoration
+					if ( hasScripts ) {
+
+						// Support: Android <=4.0 only, PhantomJS 1 only
+						// push.apply(_, arraylike) throws on ancient WebKit
+						jQuery.merge( scripts, getAll( node, "script" ) );
+					}
+				}
+
+				callback.call( collection[ i ], node, i );
+			}
+
+			if ( hasScripts ) {
+				doc = scripts[ scripts.length - 1 ].ownerDocument;
+
+				// Reenable scripts
+				jQuery.map( scripts, restoreScript );
+
+				// Evaluate executable scripts on first document insertion
+				for ( i = 0; i < hasScripts; i++ ) {
+					node = scripts[ i ];
+					if ( rscriptType.test( node.type || "" ) &&
+						!dataPriv.access( node, "globalEval" ) &&
+						jQuery.contains( doc, node ) ) {
+
+						if ( node.src && ( node.type || "" ).toLowerCase()  !== "module" ) {
+
+							// Optional AJAX dependency, but won't run scripts if not present
+							if ( jQuery._evalUrl && !node.noModule ) {
+								jQuery._evalUrl( node.src, {
+									nonce: node.nonce || node.getAttribute( "nonce" )
+								}, doc );
+							}
+						} else {
+							DOMEval( node.textContent.replace( rcleanScript, "" ), node, doc );
+						}
+					}
+				}
+			}
+		}
+	}
+
+	return collection;
+}
+
+function remove( elem, selector, keepData ) {
+	var node,
+		nodes = selector ? jQuery.filter( selector, elem ) : elem,
+		i = 0;
+
+	for ( ; ( node = nodes[ i ] ) != null; i++ ) {
+		if ( !keepData && node.nodeType === 1 ) {
+			jQuery.cleanData( getAll( node ) );
+		}
+
+		if ( node.parentNode ) {
+			if ( keepData && isAttached( node ) ) {
+				setGlobalEval( getAll( node, "script" ) );
+			}
+			node.parentNode.removeChild( node );
+		}
+	}
+
+	return elem;
+}
+
+jQuery.extend( {
+	htmlPrefilter: function( html ) {
+		return html;
+	},
+
+	clone: function( elem, dataAndEvents, deepDataAndEvents ) {
+		var i, l, srcElements, destElements,
+			clone = elem.cloneNode( true ),
+			inPage = isAttached( elem );
+
+		// Fix IE cloning issues
+		if ( !support.noCloneChecked && ( elem.nodeType === 1 || elem.nodeType === 11 ) &&
+				!jQuery.isXMLDoc( elem ) ) {
+
+			// We eschew Sizzle here for performance reasons: https://jsperf.com/getall-vs-sizzle/2
+			destElements = getAll( clone );
+			srcElements = getAll( elem );
+
+			for ( i = 0, l = srcElements.length; i < l; i++ ) {
+				fixInput( srcElements[ i ], destElements[ i ] );
+			}
+		}
+
+		// Copy the events from the original to the clone
+		if ( dataAndEvents ) {
+			if ( deepDataAndEvents ) {
+				srcElements = srcElements || getAll( elem );
+				destElements = destElements || getAll( clone );
+
+				for ( i = 0, l = srcElements.length; i < l; i++ ) {
+					cloneCopyEvent( srcElements[ i ], destElements[ i ] );
+				}
+			} else {
+				cloneCopyEvent( elem, clone );
+			}
+		}
+
+		// Preserve script evaluation history
+		destElements = getAll( clone, "script" );
+		if ( destElements.length > 0 ) {
+			setGlobalEval( destElements, !inPage && getAll( elem, "script" ) );
+		}
+
+		// Return the cloned set
+		return clone;
+	},
+
+	cleanData: function( elems ) {
+		var data, elem, type,
+			special = jQuery.event.special,
+			i = 0;
+
+		for ( ; ( elem = elems[ i ] ) !== undefined; i++ ) {
+			if ( acceptData( elem ) ) {
+				if ( ( data = elem[ dataPriv.expando ] ) ) {
+					if ( data.events ) {
+						for ( type in data.events ) {
+							if ( special[ type ] ) {
+								jQuery.event.remove( elem, type );
+
+							// This is a shortcut to avoid jQuery.event.remove's overhead
+							} else {
+								jQuery.removeEvent( elem, type, data.handle );
+							}
+						}
+					}
+
+					// Support: Chrome <=35 - 45+
+					// Assign undefined instead of using delete, see Data#remove
+					elem[ dataPriv.expando ] = undefined;
+				}
+				if ( elem[ dataUser.expando ] ) {
+
+					// Support: Chrome <=35 - 45+
+					// Assign undefined instead of using delete, see Data#remove
+					elem[ dataUser.expando ] = undefined;
+				}
+			}
+		}
+	}
+} );
+
+jQuery.fn.extend( {
+	detach: function( selector ) {
+		return remove( this, selector, true );
+	},
+
+	remove: function( selector ) {
+		return remove( this, selector );
+	},
+
+	text: function( value ) {
+		return access( this, function( value ) {
+			return value === undefined ?
+				jQuery.text( this ) :
+				this.empty().each( function() {
+					if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
+						this.textContent = value;
+					}
+				} );
+		}, null, value, arguments.length );
+	},
+
+	append: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
+				var target = manipulationTarget( this, elem );
+				target.appendChild( elem );
+			}
+		} );
+	},
+
+	prepend: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
+				var target = manipulationTarget( this, elem );
+				target.insertBefore( elem, target.firstChild );
+			}
+		} );
+	},
+
+	before: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.parentNode ) {
+				this.parentNode.insertBefore( elem, this );
+			}
+		} );
+	},
+
+	after: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.parentNode ) {
+				this.parentNode.insertBefore( elem, this.nextSibling );
+			}
+		} );
+	},
+
+	empty: function() {
+		var elem,
+			i = 0;
+
+		for ( ; ( elem = this[ i ] ) != null; i++ ) {
+			if ( elem.nodeType === 1 ) {
+
+				// Prevent memory leaks
+				jQuery.cleanData( getAll( elem, false ) );
+
+				// Remove any remaining nodes
+				elem.textContent = "";
+			}
+		}
+
+		return this;
+	},
+
+	clone: function( dataAndEvents, deepDataAndEvents ) {
+		dataAndEvents = dataAndEvents == null ? false : dataAndEvents;
+		deepDataAndEvents = deepDataAndEvents == null ? dataAndEvents : deepDataAndEvents;
+
+		return this.map( function() {
+			return jQuery.clone( this, dataAndEvents, deepDataAndEvents );
+		} );
+	},
+
+	html: function( value ) {
+		return access( this, function( value ) {
+			var elem = this[ 0 ] || {},
+				i = 0,
+				l = this.length;
+
+			if ( value === undefined && elem.nodeType === 1 ) {
+				return elem.innerHTML;
+			}
+
+			// See if we can take a shortcut and just use innerHTML
+			if ( typeof value === "string" && !rnoInnerhtml.test( value ) &&
+				!wrapMap[ ( rtagName.exec( value ) || [ "", "" ] )[ 1 ].toLowerCase() ] ) {
+
+				value = jQuery.htmlPrefilter( value );
+
+				try {
+					for ( ; i < l; i++ ) {
+						elem = this[ i ] || {};
+
+						// Remove element nodes and prevent memory leaks
+						if ( elem.nodeType === 1 ) {
+							jQuery.cleanData( getAll( elem, false ) );
+							elem.innerHTML = value;
+						}
+					}
+
+					elem = 0;
+
+				// If using innerHTML throws an exception, use the fallback method
+				} catch ( e ) {}
+			}
+
+			if ( elem ) {
+				this.empty().append( value );
+			}
+		}, null, value, arguments.length );
+	},
+
+	replaceWith: function() {
+		var ignored = [];
+
+		// Make the changes, replacing each non-ignored context element with the new content
+		return domManip( this, arguments, function( elem ) {
+			var parent = this.parentNode;
+
+			if ( jQuery.inArray( this, ignored ) < 0 ) {
+				jQuery.cleanData( getAll( this ) );
+				if ( parent ) {
+					parent.replaceChild( elem, this );
+				}
+			}
+
+		// Force callback invocation
+		}, ignored );
+	}
+} );
+
+jQuery.each( {
+	appendTo: "append",
+	prependTo: "prepend",
+	insertBefore: "before",
+	insertAfter: "after",
+	replaceAll: "replaceWith"
+}, function( name, original ) {
+	jQuery.fn[ name ] = function( selector ) {
+		var elems,
+			ret = [],
+			insert = jQuery( selector ),
+			last = insert.length - 1,
+			i = 0;
+
+		for ( ; i <= last; i++ ) {
+			elems = i === last ? this : this.clone( true );
+			jQuery( insert[ i ] )[ original ]( elems );
+
+			// Support: Android <=4.0 only, PhantomJS 1 only
+			// .get() because push.apply(_, arraylike) throws on ancient WebKit
+			push.apply( ret, elems.get() );
+		}
+
+		return this.pushStack( ret );
+	};
+} );
+var rnumnonpx = new RegExp( "^(" + pnum + ")(?!px)[a-z%]+$", "i" );
+
+var getStyles = function( elem ) {
+
+		// Support: IE <=11 only, Firefox <=30 (#15098, #14150)
+		// IE throws on elements created in popups
+		// FF meanwhile throws on frame elements through "defaultView.getComputedStyle"
+		var view = elem.ownerDocument.defaultView;
+
+		if ( !view || !view.opener ) {
+			view = window;
+		}
+
+		return view.getComputedStyle( elem );
+	};
+
+var swap = function( elem, options, callback ) {
+	var ret, name,
+		old = {};
+
+	// Remember the old values, and insert the new ones
+	for ( name in options ) {
+		old[ name ] = elem.style[ name ];
+		elem.style[ name ] = options[ name ];
+	}
+
+	ret = callback.call( elem );
+
+	// Revert the old values
+	for ( name in options ) {
+		elem.style[ name ] = old[ name ];
+	}
+
+	return ret;
+};
+
+
+var rboxStyle = new RegExp( cssExpand.join( "|" ), "i" );
+
+
+
+( function() {
+
+	// Executing both pixelPosition & boxSizingReliable tests require only one layout
+	// so they're executed at the same time to save the second computation.
+	function computeStyleTests() {
+
+		// This is a singleton, we need to execute it only once
+		if ( !div ) {
+			return;
+		}
+
+		container.style.cssText = "position:absolute;left:-11111px;width:60px;" +
+			"margin-top:1px;padding:0;border:0";
+		div.style.cssText =
+			"position:relative;display:block;box-sizing:border-box;overflow:scroll;" +
+			"margin:auto;border:1px;padding:1px;" +
+			"width:60%;top:1%";
+		documentElement.appendChild( container ).appendChild( div );
+
+		var divStyle = window.getComputedStyle( div );
+		pixelPositionVal = divStyle.top !== "1%";
+
+		// Support: Android 4.0 - 4.3 only, Firefox <=3 - 44
+		reliableMarginLeftVal = roundPixelMeasures( divStyle.marginLeft ) === 12;
+
+		// Support: Android 4.0 - 4.3 only, Safari <=9.1 - 10.1, iOS <=7.0 - 9.3
+		// Some styles come back with percentage values, even though they shouldn't
+		div.style.right = "60%";
+		pixelBoxStylesVal = roundPixelMeasures( divStyle.right ) === 36;
+
+		// Support: IE 9 - 11 only
+		// Detect misreporting of content dimensions for box-sizing:border-box elements
+		boxSizingReliableVal = roundPixelMeasures( divStyle.width ) === 36;
+
+		// Support: IE 9 only
+		// Detect overflow:scroll screwiness (gh-3699)
+		// Support: Chrome <=64
+		// Don't get tricked when zoom affects offsetWidth (gh-4029)
+		div.style.position = "absolute";
+		scrollboxSizeVal = roundPixelMeasures( div.offsetWidth / 3 ) === 12;
+
+		documentElement.removeChild( container );
+
+		// Nullify the div so it wouldn't be stored in the memory and
+		// it will also be a sign that checks already performed
+		div = null;
+	}
+
+	function roundPixelMeasures( measure ) {
+		return Math.round( parseFloat( measure ) );
+	}
+
+	var pixelPositionVal, boxSizingReliableVal, scrollboxSizeVal, pixelBoxStylesVal,
+		reliableTrDimensionsVal, reliableMarginLeftVal,
+		container = document.createElement( "div" ),
+		div = document.createElement( "div" );
+
+	// Finish early in limited (non-browser) environments
+	if ( !div.style ) {
+		return;
+	}
+
+	// Support: IE <=9 - 11 only
+	// Style of cloned element affects source element cloned (#8908)
+	div.style.backgroundClip = "content-box";
+	div.cloneNode( true ).style.backgroundClip = "";
+	support.clearCloneStyle = div.style.backgroundClip === "content-box";
+
+	jQuery.extend( support, {
+		boxSizingReliable: function() {
+			computeStyleTests();
+			return boxSizingReliableVal;
+		},
+		pixelBoxStyles: function() {
+			computeStyleTests();
+			return pixelBoxStylesVal;
+		},
+		pixelPosition: function() {
+			computeStyleTests();
+			return pixelPositionVal;
+		},
+		reliableMarginLeft: function() {
+			computeStyleTests();
+			return reliableMarginLeftVal;
+		},
+		scrollboxSize: function() {
+			computeStyleTests();
+			return scrollboxSizeVal;
+		},
+
+		// Support: IE 9 - 11+, Edge 15 - 18+
+		// IE/Edge misreport `getComputedStyle` of table rows with width/height
+		// set in CSS while `offset*` properties report correct values.
+		// Behavior in IE 9 is more subtle than in newer versions & it passes
+		// some versions of this test; make sure not to make it pass there!
+		//
+		// Support: Firefox 70+
+		// Only Firefox includes border widths
+		// in computed dimensions. (gh-4529)
+		reliableTrDimensions: function() {
+			var table, tr, trChild, trStyle;
+			if ( reliableTrDimensionsVal == null ) {
+				table = document.createElement( "table" );
+				tr = document.createElement( "tr" );
+				trChild = document.createElement( "div" );
+
+				table.style.cssText = "position:absolute;left:-11111px;border-collapse:separate";
+				tr.style.cssText = "border:1px solid";
+
+				// Support: Chrome 86+
+				// Height set through cssText does not get applied.
+				// Computed height then comes back as 0.
+				tr.style.height = "1px";
+				trChild.style.height = "9px";
+
+				// Support: Android 8 Chrome 86+
+				// In our bodyBackground.html iframe,
+				// display for all div elements is set to "inline",
+				// which causes a problem only in Android 8 Chrome 86.
+				// Ensuring the div is display: block
+				// gets around this issue.
+				trChild.style.display = "block";
+
+				documentElement
+					.appendChild( table )
+					.appendChild( tr )
+					.appendChild( trChild );
+
+				trStyle = window.getComputedStyle( tr );
+				reliableTrDimensionsVal = ( parseInt( trStyle.height, 10 ) +
+					parseInt( trStyle.borderTopWidth, 10 ) +
+					parseInt( trStyle.borderBottomWidth, 10 ) ) === tr.offsetHeight;
+
+				documentElement.removeChild( table );
+			}
+			return reliableTrDimensionsVal;
+		}
+	} );
+} )();
+
+
+function curCSS( elem, name, computed ) {
+	var width, minWidth, maxWidth, ret,
+
+		// Support: Firefox 51+
+		// Retrieving style before computed somehow
+		// fixes an issue with getting wrong values
+		// on detached elements
+		style = elem.style;
+
+	computed = computed || getStyles( elem );
+
+	// getPropertyValue is needed for:
+	//   .css('filter') (IE 9 only, #12537)
+	//   .css('--customProperty) (#3144)
+	if ( computed ) {
+		ret = computed.getPropertyValue( name ) || computed[ name ];
+
+		if ( ret === "" && !isAttached( elem ) ) {
+			ret = jQuery.style( elem, name );
+		}
+
+		// A tribute to the "awesome hack by Dean Edwards"
+		// Android Browser returns percentage for some values,
+		// but width seems to be reliably pixels.
+		// This is against the CSSOM draft spec:
+		// https://drafts.csswg.org/cssom/#resolved-values
+		if ( !support.pixelBoxStyles() && rnumnonpx.test( ret ) && rboxStyle.test( name ) ) {
+
+			// Remember the original values
+			width = style.width;
+			minWidth = style.minWidth;
+			maxWidth = style.maxWidth;
+
+			// Put in the new values to get a computed value out
+			style.minWidth = style.maxWidth = style.width = ret;
+			ret = computed.width;
+
+			// Revert the changed values
+			style.width = width;
+			style.minWidth = minWidth;
+			style.maxWidth = maxWidth;
+		}
+	}
+
+	return ret !== undefined ?
+
+		// Support: IE <=9 - 11 only
+		// IE returns zIndex value as an integer.
+		ret + "" :
+		ret;
+}
+
+
+function addGetHookIf( conditionFn, hookFn ) {
+
+	// Define the hook, we'll check on the first run if it's really needed.
+	return {
+		get: function() {
+			if ( conditionFn() ) {
+
+				// Hook not needed (or it's not possible to use it due
+				// to missing dependency), remove it.
+				delete this.get;
+				return;
+			}
+
+			// Hook needed; redefine it so that the support test is not executed again.
+			return ( this.get = hookFn ).apply( this, arguments );
+		}
+	};
+}
+
+
+var cssPrefixes = [ "Webkit", "Moz", "ms" ],
+	emptyStyle = document.createElement( "div" ).style,
+	vendorProps = {};
+
+// Return a vendor-prefixed property or undefined
+function vendorPropName( name ) {
+
+	// Check for vendor prefixed names
+	var capName = name[ 0 ].toUpperCase() + name.slice( 1 ),
+		i = cssPrefixes.length;
+
+	while ( i-- ) {
+		name = cssPrefixes[ i ] + capName;
+		if ( name in emptyStyle ) {
+			return name;
+		}
+	}
+}
+
+// Return a potentially-mapped jQuery.cssProps or vendor prefixed property
+function finalPropName( name ) {
+	var final = jQuery.cssProps[ name ] || vendorProps[ name ];
+
+	if ( final ) {
+		return final;
+	}
+	if ( name in emptyStyle ) {
+		return name;
+	}
+	return vendorProps[ name ] = vendorPropName( name ) || name;
+}
+
+
+var
+
+	// Swappable if display is none or starts with table
+	// except "table", "table-cell", or "table-caption"
+	// See here for display values: https://developer.mozilla.org/en-US/docs/CSS/display
+	rdisplayswap = /^(none|table(?!-c[ea]).+)/,
+	rcustomProp = /^--/,
+	cssShow = { position: "absolute", visibility: "hidden", display: "block" },
+	cssNormalTransform = {
+		letterSpacing: "0",
+		fontWeight: "400"
+	};
+
+function setPositiveNumber( _elem, value, subtract ) {
+
+	// Any relative (+/-) values have already been
+	// normalized at this point
+	var matches = rcssNum.exec( value );
+	return matches ?
+
+		// Guard against undefined "subtract", e.g., when used as in cssHooks
+		Math.max( 0, matches[ 2 ] - ( subtract || 0 ) ) + ( matches[ 3 ] || "px" ) :
+		value;
+}
+
+function boxModelAdjustment( elem, dimension, box, isBorderBox, styles, computedVal ) {
+	var i = dimension === "width" ? 1 : 0,
+		extra = 0,
+		delta = 0;
+
+	// Adjustment may not be necessary
+	if ( box === ( isBorderBox ? "border" : "content" ) ) {
+		return 0;
+	}
+
+	for ( ; i < 4; i += 2 ) {
+
+		// Both box models exclude margin
+		if ( box === "margin" ) {
+			delta += jQuery.css( elem, box + cssExpand[ i ], true, styles );
+		}
+
+		// If we get here with a content-box, we're seeking "padding" or "border" or "margin"
+		if ( !isBorderBox ) {
+
+			// Add padding
+			delta += jQuery.css( elem, "padding" + cssExpand[ i ], true, styles );
+
+			// For "border" or "margin", add border
+			if ( box !== "padding" ) {
+				delta += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
+
+			// But still keep track of it otherwise
+			} else {
+				extra += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
+			}
+
+		// If we get here with a border-box (content + padding + border), we're seeking "content" or
+		// "padding" or "margin"
+		} else {
+
+			// For "content", subtract padding
+			if ( box === "content" ) {
+				delta -= jQuery.css( elem, "padding" + cssExpand[ i ], true, styles );
+			}
+
+			// For "content" or "padding", subtract border
+			if ( box !== "margin" ) {
+				delta -= jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
+			}
+		}
+	}
+
+	// Account for positive content-box scroll gutter when requested by providing computedVal
+	if ( !isBorderBox && computedVal >= 0 ) {
+
+		// offsetWidth/offsetHeight is a rounded sum of content, padding, scroll gutter, and border
+		// Assuming integer scroll gutter, subtract the rest and round down
+		delta += Math.max( 0, Math.ceil(
+			elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] -
+			computedVal -
+			delta -
+			extra -
+			0.5
+
+		// If offsetWidth/offsetHeight is unknown, then we can't determine content-box scroll gutter
+		// Use an explicit zero to avoid NaN (gh-3964)
+		) ) || 0;
+	}
+
+	return delta;
+}
+
+function getWidthOrHeight( elem, dimension, extra ) {
+
+	// Start with computed style
+	var styles = getStyles( elem ),
+
+		// To avoid forcing a reflow, only fetch boxSizing if we need it (gh-4322).
+		// Fake content-box until we know it's needed to know the true value.
+		boxSizingNeeded = !support.boxSizingReliable() || extra,
+		isBorderBox = boxSizingNeeded &&
+			jQuery.css( elem, "boxSizing", false, styles ) === "border-box",
+		valueIsBorderBox = isBorderBox,
+
+		val = curCSS( elem, dimension, styles ),
+		offsetProp = "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 );
+
+	// Support: Firefox <=54
+	// Return a confounding non-pixel value or feign ignorance, as appropriate.
+	if ( rnumnonpx.test( val ) ) {
+		if ( !extra ) {
+			return val;
+		}
+		val = "auto";
+	}
+
+
+	// Support: IE 9 - 11 only
+	// Use offsetWidth/offsetHeight for when box sizing is unreliable.
+	// In those cases, the computed value can be trusted to be border-box.
+	if ( ( !support.boxSizingReliable() && isBorderBox ||
+
+		// Support: IE 10 - 11+, Edge 15 - 18+
+		// IE/Edge misreport `getComputedStyle` of table rows with width/height
+		// set in CSS while `offset*` properties report correct values.
+		// Interestingly, in some cases IE 9 doesn't suffer from this issue.
+		!support.reliableTrDimensions() && nodeName( elem, "tr" ) ||
+
+		// Fall back to offsetWidth/offsetHeight when value is "auto"
+		// This happens for inline elements with no explicit setting (gh-3571)
+		val === "auto" ||
+
+		// Support: Android <=4.1 - 4.3 only
+		// Also use offsetWidth/offsetHeight for misreported inline dimensions (gh-3602)
+		!parseFloat( val ) && jQuery.css( elem, "display", false, styles ) === "inline" ) &&
+
+		// Make sure the element is visible & connected
+		elem.getClientRects().length ) {
+
+		isBorderBox = jQuery.css( elem, "boxSizing", false, styles ) === "border-box";
+
+		// Where available, offsetWidth/offsetHeight approximate border box dimensions.
+		// Where not available (e.g., SVG), assume unreliable box-sizing and interpret the
+		// retrieved value as a content box dimension.
+		valueIsBorderBox = offsetProp in elem;
+		if ( valueIsBorderBox ) {
+			val = elem[ offsetProp ];
+		}
+	}
+
+	// Normalize "" and auto
+	val = parseFloat( val ) || 0;
+
+	// Adjust for the element's box model
+	return ( val +
+		boxModelAdjustment(
+			elem,
+			dimension,
+			extra || ( isBorderBox ? "border" : "content" ),
+			valueIsBorderBox,
+			styles,
+
+			// Provide the current computed size to request scroll gutter calculation (gh-3589)
+			val
+		)
+	) + "px";
+}
+
+jQuery.extend( {
+
+	// Add in style property hooks for overriding the default
+	// behavior of getting and setting a style property
+	cssHooks: {
+		opacity: {
+			get: function( elem, computed ) {
+				if ( computed ) {
+
+					// We should always get a number back from opacity
+					var ret = curCSS( elem, "opacity" );
+					return ret === "" ? "1" : ret;
+				}
+			}
+		}
+	},
+
+	// Don't automatically add "px" to these possibly-unitless properties
+	cssNumber: {
+		"animationIterationCount": true,
+		"columnCount": true,
+		"fillOpacity": true,
+		"flexGrow": true,
+		"flexShrink": true,
+		"fontWeight": true,
+		"gridArea": true,
+		"gridColumn": true,
+		"gridColumnEnd": true,
+		"gridColumnStart": true,
+		"gridRow": true,
+		"gridRowEnd": true,
+		"gridRowStart": true,
+		"lineHeight": true,
+		"opacity": true,
+		"order": true,
+		"orphans": true,
+		"widows": true,
+		"zIndex": true,
+		"zoom": true
+	},
+
+	// Add in properties whose names you wish to fix before
+	// setting or getting the value
+	cssProps: {},
+
+	// Get and set the style property on a DOM Node
+	style: function( elem, name, value, extra ) {
+
+		// Don't set styles on text and comment nodes
+		if ( !elem || elem.nodeType === 3 || elem.nodeType === 8 || !elem.style ) {
+			return;
+		}
+
+		// Make sure that we're working with the right name
+		var ret, type, hooks,
+			origName = camelCase( name ),
+			isCustomProp = rcustomProp.test( name ),
+			style = elem.style;
+
+		// Make sure that we're working with the right name. We don't
+		// want to query the value if it is a CSS custom property
+		// since they are user-defined.
+		if ( !isCustomProp ) {
+			name = finalPropName( origName );
+		}
+
+		// Gets hook for the prefixed version, then unprefixed version
+		hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ];
+
+		// Check if we're setting a value
+		if ( value !== undefined ) {
+			type = typeof value;
+
+			// Convert "+=" or "-=" to relative numbers (#7345)
+			if ( type === "string" && ( ret = rcssNum.exec( value ) ) && ret[ 1 ] ) {
+				value = adjustCSS( elem, name, ret );
+
+				// Fixes bug #9237
+				type = "number";
+			}
+
+			// Make sure that null and NaN values aren't set (#7116)
+			if ( value == null || value !== value ) {
+				return;
+			}
+
+			// If a number was passed in, add the unit (except for certain CSS properties)
+			// The isCustomProp check can be removed in jQuery 4.0 when we only auto-append
+			// "px" to a few hardcoded values.
+			if ( type === "number" && !isCustomProp ) {
+				value += ret && ret[ 3 ] || ( jQuery.cssNumber[ origName ] ? "" : "px" );
+			}
+
+			// background-* props affect original clone's values
+			if ( !support.clearCloneStyle && value === "" && name.indexOf( "background" ) === 0 ) {
+				style[ name ] = "inherit";
+			}
+
+			// If a hook was provided, use that value, otherwise just set the specified value
+			if ( !hooks || !( "set" in hooks ) ||
+				( value = hooks.set( elem, value, extra ) ) !== undefined ) {
+
+				if ( isCustomProp ) {
+					style.setProperty( name, value );
+				} else {
+					style[ name ] = value;
+				}
+			}
+
+		} else {
+
+			// If a hook was provided get the non-computed value from there
+			if ( hooks && "get" in hooks &&
+				( ret = hooks.get( elem, false, extra ) ) !== undefined ) {
+
+				return ret;
+			}
+
+			// Otherwise just get the value from the style object
+			return style[ name ];
+		}
+	},
+
+	css: function( elem, name, extra, styles ) {
+		var val, num, hooks,
+			origName = camelCase( name ),
+			isCustomProp = rcustomProp.test( name );
+
+		// Make sure that we're working with the right name. We don't
+		// want to modify the value if it is a CSS custom property
+		// since they are user-defined.
+		if ( !isCustomProp ) {
+			name = finalPropName( origName );
+		}
+
+		// Try prefixed name followed by the unprefixed name
+		hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ];
+
+		// If a hook was provided get the computed value from there
+		if ( hooks && "get" in hooks ) {
+			val = hooks.get( elem, true, extra );
+		}
+
+		// Otherwise, if a way to get the computed value exists, use that
+		if ( val === undefined ) {
+			val = curCSS( elem, name, styles );
+		}
+
+		// Convert "normal" to computed value
+		if ( val === "normal" && name in cssNormalTransform ) {
+			val = cssNormalTransform[ name ];
+		}
+
+		// Make numeric if forced or a qualifier was provided and val looks numeric
+		if ( extra === "" || extra ) {
+			num = parseFloat( val );
+			return extra === true || isFinite( num ) ? num || 0 : val;
+		}
+
+		return val;
+	}
+} );
+
+jQuery.each( [ "height", "width" ], function( _i, dimension ) {
+	jQuery.cssHooks[ dimension ] = {
+		get: function( elem, computed, extra ) {
+			if ( computed ) {
+
+				// Certain elements can have dimension info if we invisibly show them
+				// but it must have a current display style that would benefit
+				return rdisplayswap.test( jQuery.css( elem, "display" ) ) &&
+
+					// Support: Safari 8+
+					// Table columns in Safari have non-zero offsetWidth & zero
+					// getBoundingClientRect().width unless display is changed.
+					// Support: IE <=11 only
+					// Running getBoundingClientRect on a disconnected node
+					// in IE throws an error.
+					( !elem.getClientRects().length || !elem.getBoundingClientRect().width ) ?
+					swap( elem, cssShow, function() {
+						return getWidthOrHeight( elem, dimension, extra );
+					} ) :
+					getWidthOrHeight( elem, dimension, extra );
+			}
+		},
+
+		set: function( elem, value, extra ) {
+			var matches,
+				styles = getStyles( elem ),
+
+				// Only read styles.position if the test has a chance to fail
+				// to avoid forcing a reflow.
+				scrollboxSizeBuggy = !support.scrollboxSize() &&
+					styles.position === "absolute",
+
+				// To avoid forcing a reflow, only fetch boxSizing if we need it (gh-3991)
+				boxSizingNeeded = scrollboxSizeBuggy || extra,
+				isBorderBox = boxSizingNeeded &&
+					jQuery.css( elem, "boxSizing", false, styles ) === "border-box",
+				subtract = extra ?
+					boxModelAdjustment(
+						elem,
+						dimension,
+						extra,
+						isBorderBox,
+						styles
+					) :
+					0;
+
+			// Account for unreliable border-box dimensions by comparing offset* to computed and
+			// faking a content-box to get border and padding (gh-3699)
+			if ( isBorderBox && scrollboxSizeBuggy ) {
+				subtract -= Math.ceil(
+					elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] -
+					parseFloat( styles[ dimension ] ) -
+					boxModelAdjustment( elem, dimension, "border", false, styles ) -
+					0.5
+				);
+			}
+
+			// Convert to pixels if value adjustment is needed
+			if ( subtract && ( matches = rcssNum.exec( value ) ) &&
+				( matches[ 3 ] || "px" ) !== "px" ) {
+
+				elem.style[ dimension ] = value;
+				value = jQuery.css( elem, dimension );
+			}
+
+			return setPositiveNumber( elem, value, subtract );
+		}
+	};
+} );
+
+jQuery.cssHooks.marginLeft = addGetHookIf( support.reliableMarginLeft,
+	function( elem, computed ) {
+		if ( computed ) {
+			return ( parseFloat( curCSS( elem, "marginLeft" ) ) ||
+				elem.getBoundingClientRect().left -
+					swap( elem, { marginLeft: 0 }, function() {
+						return elem.getBoundingClientRect().left;
+					} )
+			) + "px";
+		}
+	}
+);
+
+// These hooks are used by animate to expand properties
+jQuery.each( {
+	margin: "",
+	padding: "",
+	border: "Width"
+}, function( prefix, suffix ) {
+	jQuery.cssHooks[ prefix + suffix ] = {
+		expand: function( value ) {
+			var i = 0,
+				expanded = {},
+
+				// Assumes a single number if not a string
+				parts = typeof value === "string" ? value.split( " " ) : [ value ];
+
+			for ( ; i < 4; i++ ) {
+				expanded[ prefix + cssExpand[ i ] + suffix ] =
+					parts[ i ] || parts[ i - 2 ] || parts[ 0 ];
+			}
+
+			return expanded;
+		}
+	};
+
+	if ( prefix !== "margin" ) {
+		jQuery.cssHooks[ prefix + suffix ].set = setPositiveNumber;
+	}
+} );
+
+jQuery.fn.extend( {
+	css: function( name, value ) {
+		return access( this, function( elem, name, value ) {
+			var styles, len,
+				map = {},
+				i = 0;
+
+			if ( Array.isArray( name ) ) {
+				styles = getStyles( elem );
+				len = name.length;
+
+				for ( ; i < len; i++ ) {
+					map[ name[ i ] ] = jQuery.css( elem, name[ i ], false, styles );
+				}
+
+				return map;
+			}
+
+			return value !== undefined ?
+				jQuery.style( elem, name, value ) :
+				jQuery.css( elem, name );
+		}, name, value, arguments.length > 1 );
+	}
+} );
+
+
+function Tween( elem, options, prop, end, easing ) {
+	return new Tween.prototype.init( elem, options, prop, end, easing );
+}
+jQuery.Tween = Tween;
+
+Tween.prototype = {
+	constructor: Tween,
+	init: function( elem, options, prop, end, easing, unit ) {
+		this.elem = elem;
+		this.prop = prop;
+		this.easing = easing || jQuery.easing._default;
+		this.options = options;
+		this.start = this.now = this.cur();
+		this.end = end;
+		this.unit = unit || ( jQuery.cssNumber[ prop ] ? "" : "px" );
+	},
+	cur: function() {
+		var hooks = Tween.propHooks[ this.prop ];
+
+		return hooks && hooks.get ?
+			hooks.get( this ) :
+			Tween.propHooks._default.get( this );
+	},
+	run: function( percent ) {
+		var eased,
+			hooks = Tween.propHooks[ this.prop ];
+
+		if ( this.options.duration ) {
+			this.pos = eased = jQuery.easing[ this.easing ](
+				percent, this.options.duration * percent, 0, 1, this.options.duration
+			);
+		} else {
+			this.pos = eased = percent;
+		}
+		this.now = ( this.end - this.start ) * eased + this.start;
+
+		if ( this.options.step ) {
+			this.options.step.call( this.elem, this.now, this );
+		}
+
+		if ( hooks && hooks.set ) {
+			hooks.set( this );
+		} else {
+			Tween.propHooks._default.set( this );
+		}
+		return this;
+	}
+};
+
+Tween.prototype.init.prototype = Tween.prototype;
+
+Tween.propHooks = {
+	_default: {
+		get: function( tween ) {
+			var result;
+
+			// Use a property on the element directly when it is not a DOM element,
+			// or when there is no matching style property that exists.
+			if ( tween.elem.nodeType !== 1 ||
+				tween.elem[ tween.prop ] != null && tween.elem.style[ tween.prop ] == null ) {
+				return tween.elem[ tween.prop ];
+			}
+
+			// Passing an empty string as a 3rd parameter to .css will automatically
+			// attempt a parseFloat and fallback to a string if the parse fails.
+			// Simple values such as "10px" are parsed to Float;
+			// complex values such as "rotate(1rad)" are returned as-is.
+			result = jQuery.css( tween.elem, tween.prop, "" );
+
+			// Empty strings, null, undefined and "auto" are converted to 0.
+			return !result || result === "auto" ? 0 : result;
+		},
+		set: function( tween ) {
+
+			// Use step hook for back compat.
+			// Use cssHook if its there.
+			// Use .style if available and use plain properties where available.
+			if ( jQuery.fx.step[ tween.prop ] ) {
+				jQuery.fx.step[ tween.prop ]( tween );
+			} else if ( tween.elem.nodeType === 1 && (
+				jQuery.cssHooks[ tween.prop ] ||
+					tween.elem.style[ finalPropName( tween.prop ) ] != null ) ) {
+				jQuery.style( tween.elem, tween.prop, tween.now + tween.unit );
+			} else {
+				tween.elem[ tween.prop ] = tween.now;
+			}
+		}
+	}
+};
+
+// Support: IE <=9 only
+// Panic based approach to setting things on disconnected nodes
+Tween.propHooks.scrollTop = Tween.propHooks.scrollLeft = {
+	set: function( tween ) {
+		if ( tween.elem.nodeType && tween.elem.parentNode ) {
+			tween.elem[ tween.prop ] = tween.now;
+		}
+	}
+};
+
+jQuery.easing = {
+	linear: function( p ) {
+		return p;
+	},
+	swing: function( p ) {
+		return 0.5 - Math.cos( p * Math.PI ) / 2;
+	},
+	_default: "swing"
+};
+
+jQuery.fx = Tween.prototype.init;
+
+// Back compat <1.8 extension point
+jQuery.fx.step = {};
+
+
+
+
+var
+	fxNow, inProgress,
+	rfxtypes = /^(?:toggle|show|hide)$/,
+	rrun = /queueHooks$/;
+
+function schedule() {
+	if ( inProgress ) {
+		if ( document.hidden === false && window.requestAnimationFrame ) {
+			window.requestAnimationFrame( schedule );
+		} else {
+			window.setTimeout( schedule, jQuery.fx.interval );
+		}
+
+		jQuery.fx.tick();
+	}
+}
+
+// Animations created synchronously will run synchronously
+function createFxNow() {
+	window.setTimeout( function() {
+		fxNow = undefined;
+	} );
+	return ( fxNow = Date.now() );
+}
+
+// Generate parameters to create a standard animation
+function genFx( type, includeWidth ) {
+	var which,
+		i = 0,
+		attrs = { height: type };
+
+	// If we include width, step value is 1 to do all cssExpand values,
+	// otherwise step value is 2 to skip over Left and Right
+	includeWidth = includeWidth ? 1 : 0;
+	for ( ; i < 4; i += 2 - includeWidth ) {
+		which = cssExpand[ i ];
+		attrs[ "margin" + which ] = attrs[ "padding" + which ] = type;
+	}
+
+	if ( includeWidth ) {
+		attrs.opacity = attrs.width = type;
+	}
+
+	return attrs;
+}
+
+function createTween( value, prop, animation ) {
+	var tween,
+		collection = ( Animation.tweeners[ prop ] || [] ).concat( Animation.tweeners[ "*" ] ),
+		index = 0,
+		length = collection.length;
+	for ( ; index < length; index++ ) {
+		if ( ( tween = collection[ index ].call( animation, prop, value ) ) ) {
+
+			// We're done with this property
+			return tween;
+		}
+	}
+}
+
+function defaultPrefilter( elem, props, opts ) {
+	var prop, value, toggle, hooks, oldfire, propTween, restoreDisplay, display,
+		isBox = "width" in props || "height" in props,
+		anim = this,
+		orig = {},
+		style = elem.style,
+		hidden = elem.nodeType && isHiddenWithinTree( elem ),
+		dataShow = dataPriv.get( elem, "fxshow" );
+
+	// Queue-skipping animations hijack the fx hooks
+	if ( !opts.queue ) {
+		hooks = jQuery._queueHooks( elem, "fx" );
+		if ( hooks.unqueued == null ) {
+			hooks.unqueued = 0;
+			oldfire = hooks.empty.fire;
+			hooks.empty.fire = function() {
+				if ( !hooks.unqueued ) {
+					oldfire();
+				}
+			};
+		}
+		hooks.unqueued++;
+
+		anim.always( function() {
+
+			// Ensure the complete handler is called before this completes
+			anim.always( function() {
+				hooks.unqueued--;
+				if ( !jQuery.queue( elem, "fx" ).length ) {
+					hooks.empty.fire();
+				}
+			} );
+		} );
+	}
+
+	// Detect show/hide animations
+	for ( prop in props ) {
+		value = props[ prop ];
+		if ( rfxtypes.test( value ) ) {
+			delete props[ prop ];
+			toggle = toggle || value === "toggle";
+			if ( value === ( hidden ? "hide" : "show" ) ) {
+
+				// Pretend to be hidden if this is a "show" and
+				// there is still data from a stopped show/hide
+				if ( value === "show" && dataShow && dataShow[ prop ] !== undefined ) {
+					hidden = true;
+
+				// Ignore all other no-op show/hide data
+				} else {
+					continue;
+				}
+			}
+			orig[ prop ] = dataShow && dataShow[ prop ] || jQuery.style( elem, prop );
+		}
+	}
+
+	// Bail out if this is a no-op like .hide().hide()
+	propTween = !jQuery.isEmptyObject( props );
+	if ( !propTween && jQuery.isEmptyObject( orig ) ) {
+		return;
+	}
+
+	// Restrict "overflow" and "display" styles during box animations
+	if ( isBox && elem.nodeType === 1 ) {
+
+		// Support: IE <=9 - 11, Edge 12 - 15
+		// Record all 3 overflow attributes because IE does not infer the shorthand
+		// from identically-valued overflowX and overflowY and Edge just mirrors
+		// the overflowX value there.
+		opts.overflow = [ style.overflow, style.overflowX, style.overflowY ];
+
+		// Identify a display type, preferring old show/hide data over the CSS cascade
+		restoreDisplay = dataShow && dataShow.display;
+		if ( restoreDisplay == null ) {
+			restoreDisplay = dataPriv.get( elem, "display" );
+		}
+		display = jQuery.css( elem, "display" );
+		if ( display === "none" ) {
+			if ( restoreDisplay ) {
+				display = restoreDisplay;
+			} else {
+
+				// Get nonempty value(s) by temporarily forcing visibility
+				showHide( [ elem ], true );
+				restoreDisplay = elem.style.display || restoreDisplay;
+				display = jQuery.css( elem, "display" );
+				showHide( [ elem ] );
+			}
+		}
+
+		// Animate inline elements as inline-block
+		if ( display === "inline" || display === "inline-block" && restoreDisplay != null ) {
+			if ( jQuery.css( elem, "float" ) === "none" ) {
+
+				// Restore the original display value at the end of pure show/hide animations
+				if ( !propTween ) {
+					anim.done( function() {
+						style.display = restoreDisplay;
+					} );
+					if ( restoreDisplay == null ) {
+						display = style.display;
+						restoreDisplay = display === "none" ? "" : display;
+					}
+				}
+				style.display = "inline-block";
+			}
+		}
+	}
+
+	if ( opts.overflow ) {
+		style.overflow = "hidden";
+		anim.always( function() {
+			style.overflow = opts.overflow[ 0 ];
+			style.overflowX = opts.overflow[ 1 ];
+			style.overflowY = opts.overflow[ 2 ];
+		} );
+	}
+
+	// Implement show/hide animations
+	propTween = false;
+	for ( prop in orig ) {
+
+		// General show/hide setup for this element animation
+		if ( !propTween ) {
+			if ( dataShow ) {
+				if ( "hidden" in dataShow ) {
+					hidden = dataShow.hidden;
+				}
+			} else {
+				dataShow = dataPriv.access( elem, "fxshow", { display: restoreDisplay } );
+			}
+
+			// Store hidden/visible for toggle so `.stop().toggle()` "reverses"
+			if ( toggle ) {
+				dataShow.hidden = !hidden;
+			}
+
+			// Show elements before animating them
+			if ( hidden ) {
+				showHide( [ elem ], true );
+			}
+
+			/* eslint-disable no-loop-func */
+
+			anim.done( function() {
+
+				/* eslint-enable no-loop-func */
+
+				// The final step of a "hide" animation is actually hiding the element
+				if ( !hidden ) {
+					showHide( [ elem ] );
+				}
+				dataPriv.remove( elem, "fxshow" );
+				for ( prop in orig ) {
+					jQuery.style( elem, prop, orig[ prop ] );
+				}
+			} );
+		}
+
+		// Per-property setup
+		propTween = createTween( hidden ? dataShow[ prop ] : 0, prop, anim );
+		if ( !( prop in dataShow ) ) {
+			dataShow[ prop ] = propTween.start;
+			if ( hidden ) {
+				propTween.end = propTween.start;
+				propTween.start = 0;
+			}
+		}
+	}
+}
+
+function propFilter( props, specialEasing ) {
+	var index, name, easing, value, hooks;
+
+	// camelCase, specialEasing and expand cssHook pass
+	for ( index in props ) {
+		name = camelCase( index );
+		easing = specialEasing[ name ];
+		value = props[ index ];
+		if ( Array.isArray( value ) ) {
+			easing = value[ 1 ];
+			value = props[ index ] = value[ 0 ];
+		}
+
+		if ( index !== name ) {
+			props[ name ] = value;
+			delete props[ index ];
+		}
+
+		hooks = jQuery.cssHooks[ name ];
+		if ( hooks && "expand" in hooks ) {
+			value = hooks.expand( value );
+			delete props[ name ];
+
+			// Not quite $.extend, this won't overwrite existing keys.
+			// Reusing 'index' because we have the correct "name"
+			for ( index in value ) {
+				if ( !( index in props ) ) {
+					props[ index ] = value[ index ];
+					specialEasing[ index ] = easing;
+				}
+			}
+		} else {
+			specialEasing[ name ] = easing;
+		}
+	}
+}
+
+function Animation( elem, properties, options ) {
+	var result,
+		stopped,
+		index = 0,
+		length = Animation.prefilters.length,
+		deferred = jQuery.Deferred().always( function() {
+
+			// Don't match elem in the :animated selector
+			delete tick.elem;
+		} ),
+		tick = function() {
+			if ( stopped ) {
+				return false;
+			}
+			var currentTime = fxNow || createFxNow(),
+				remaining = Math.max( 0, animation.startTime + animation.duration - currentTime ),
+
+				// Support: Android 2.3 only
+				// Archaic crash bug won't allow us to use `1 - ( 0.5 || 0 )` (#12497)
+				temp = remaining / animation.duration || 0,
+				percent = 1 - temp,
+				index = 0,
+				length = animation.tweens.length;
+
+			for ( ; index < length; index++ ) {
+				animation.tweens[ index ].run( percent );
+			}
+
+			deferred.notifyWith( elem, [ animation, percent, remaining ] );
+
+			// If there's more to do, yield
+			if ( percent < 1 && length ) {
+				return remaining;
+			}
+
+			// If this was an empty animation, synthesize a final progress notification
+			if ( !length ) {
+				deferred.notifyWith( elem, [ animation, 1, 0 ] );
+			}
+
+			// Resolve the animation and report its conclusion
+			deferred.resolveWith( elem, [ animation ] );
+			return false;
+		},
+		animation = deferred.promise( {
+			elem: elem,
+			props: jQuery.extend( {}, properties ),
+			opts: jQuery.extend( true, {
+				specialEasing: {},
+				easing: jQuery.easing._default
+			}, options ),
+			originalProperties: properties,
+			originalOptions: options,
+			startTime: fxNow || createFxNow(),
+			duration: options.duration,
+			tweens: [],
+			createTween: function( prop, end ) {
+				var tween = jQuery.Tween( elem, animation.opts, prop, end,
+					animation.opts.specialEasing[ prop ] || animation.opts.easing );
+				animation.tweens.push( tween );
+				return tween;
+			},
+			stop: function( gotoEnd ) {
+				var index = 0,
+
+					// If we are going to the end, we want to run all the tweens
+					// otherwise we skip this part
+					length = gotoEnd ? animation.tweens.length : 0;
+				if ( stopped ) {
+					return this;
+				}
+				stopped = true;
+				for ( ; index < length; index++ ) {
+					animation.tweens[ index ].run( 1 );
+				}
+
+				// Resolve when we played the last frame; otherwise, reject
+				if ( gotoEnd ) {
+					deferred.notifyWith( elem, [ animation, 1, 0 ] );
+					deferred.resolveWith( elem, [ animation, gotoEnd ] );
+				} else {
+					deferred.rejectWith( elem, [ animation, gotoEnd ] );
+				}
+				return this;
+			}
+		} ),
+		props = animation.props;
+
+	propFilter( props, animation.opts.specialEasing );
+
+	for ( ; index < length; index++ ) {
+		result = Animation.prefilters[ index ].call( animation, elem, props, animation.opts );
+		if ( result ) {
+			if ( isFunction( result.stop ) ) {
+				jQuery._queueHooks( animation.elem, animation.opts.queue ).stop =
+					result.stop.bind( result );
+			}
+			return result;
+		}
+	}
+
+	jQuery.map( props, createTween, animation );
+
+	if ( isFunction( animation.opts.start ) ) {
+		animation.opts.start.call( elem, animation );
+	}
+
+	// Attach callbacks from options
+	animation
+		.progress( animation.opts.progress )
+		.done( animation.opts.done, animation.opts.complete )
+		.fail( animation.opts.fail )
+		.always( animation.opts.always );
+
+	jQuery.fx.timer(
+		jQuery.extend( tick, {
+			elem: elem,
+			anim: animation,
+			queue: animation.opts.queue
+		} )
+	);
+
+	return animation;
+}
+
+jQuery.Animation = jQuery.extend( Animation, {
+
+	tweeners: {
+		"*": [ function( prop, value ) {
+			var tween = this.createTween( prop, value );
+			adjustCSS( tween.elem, prop, rcssNum.exec( value ), tween );
+			return tween;
+		} ]
+	},
+
+	tweener: function( props, callback ) {
+		if ( isFunction( props ) ) {
+			callback = props;
+			props = [ "*" ];
+		} else {
+			props = props.match( rnothtmlwhite );
+		}
+
+		var prop,
+			index = 0,
+			length = props.length;
+
+		for ( ; index < length; index++ ) {
+			prop = props[ index ];
+			Animation.tweeners[ prop ] = Animation.tweeners[ prop ] || [];
+			Animation.tweeners[ prop ].unshift( callback );
+		}
+	},
+
+	prefilters: [ defaultPrefilter ],
+
+	prefilter: function( callback, prepend ) {
+		if ( prepend ) {
+			Animation.prefilters.unshift( callback );
+		} else {
+			Animation.prefilters.push( callback );
+		}
+	}
+} );
+
+jQuery.speed = function( speed, easing, fn ) {
+	var opt = speed && typeof speed === "object" ? jQuery.extend( {}, speed ) : {
+		complete: fn || !fn && easing ||
+			isFunction( speed ) && speed,
+		duration: speed,
+		easing: fn && easing || easing && !isFunction( easing ) && easing
+	};
+
+	// Go to the end state if fx are off
+	if ( jQuery.fx.off ) {
+		opt.duration = 0;
+
+	} else {
+		if ( typeof opt.duration !== "number" ) {
+			if ( opt.duration in jQuery.fx.speeds ) {
+				opt.duration = jQuery.fx.speeds[ opt.duration ];
+
+			} else {
+				opt.duration = jQuery.fx.speeds._default;
+			}
+		}
+	}
+
+	// Normalize opt.queue - true/undefined/null -> "fx"
+	if ( opt.queue == null || opt.queue === true ) {
+		opt.queue = "fx";
+	}
+
+	// Queueing
+	opt.old = opt.complete;
+
+	opt.complete = function() {
+		if ( isFunction( opt.old ) ) {
+			opt.old.call( this );
+		}
+
+		if ( opt.queue ) {
+			jQuery.dequeue( this, opt.queue );
+		}
+	};
+
+	return opt;
+};
+
+jQuery.fn.extend( {
+	fadeTo: function( speed, to, easing, callback ) {
+
+		// Show any hidden elements after setting opacity to 0
+		return this.filter( isHiddenWithinTree ).css( "opacity", 0 ).show()
+
+			// Animate to the value specified
+			.end().animate( { opacity: to }, speed, easing, callback );
+	},
+	animate: function( prop, speed, easing, callback ) {
+		var empty = jQuery.isEmptyObject( prop ),
+			optall = jQuery.speed( speed, easing, callback ),
+			doAnimation = function() {
+
+				// Operate on a copy of prop so per-property easing won't be lost
+				var anim = Animation( this, jQuery.extend( {}, prop ), optall );
+
+				// Empty animations, or finishing resolves immediately
+				if ( empty || dataPriv.get( this, "finish" ) ) {
+					anim.stop( true );
+				}
+			};
+
+		doAnimation.finish = doAnimation;
+
+		return empty || optall.queue === false ?
+			this.each( doAnimation ) :
+			this.queue( optall.queue, doAnimation );
+	},
+	stop: function( type, clearQueue, gotoEnd ) {
+		var stopQueue = function( hooks ) {
+			var stop = hooks.stop;
+			delete hooks.stop;
+			stop( gotoEnd );
+		};
+
+		if ( typeof type !== "string" ) {
+			gotoEnd = clearQueue;
+			clearQueue = type;
+			type = undefined;
+		}
+		if ( clearQueue ) {
+			this.queue( type || "fx", [] );
+		}
+
+		return this.each( function() {
+			var dequeue = true,
+				index = type != null && type + "queueHooks",
+				timers = jQuery.timers,
+				data = dataPriv.get( this );
+
+			if ( index ) {
+				if ( data[ index ] && data[ index ].stop ) {
+					stopQueue( data[ index ] );
+				}
+			} else {
+				for ( index in data ) {
+					if ( data[ index ] && data[ index ].stop && rrun.test( index ) ) {
+						stopQueue( data[ index ] );
+					}
+				}
+			}
+
+			for ( index = timers.length; index--; ) {
+				if ( timers[ index ].elem === this &&
+					( type == null || timers[ index ].queue === type ) ) {
+
+					timers[ index ].anim.stop( gotoEnd );
+					dequeue = false;
+					timers.splice( index, 1 );
+				}
+			}
+
+			// Start the next in the queue if the last step wasn't forced.
+			// Timers currently will call their complete callbacks, which
+			// will dequeue but only if they were gotoEnd.
+			if ( dequeue || !gotoEnd ) {
+				jQuery.dequeue( this, type );
+			}
+		} );
+	},
+	finish: function( type ) {
+		if ( type !== false ) {
+			type = type || "fx";
+		}
+		return this.each( function() {
+			var index,
+				data = dataPriv.get( this ),
+				queue = data[ type + "queue" ],
+				hooks = data[ type + "queueHooks" ],
+				timers = jQuery.timers,
+				length = queue ? queue.length : 0;
+
+			// Enable finishing flag on private data
+			data.finish = true;
+
+			// Empty the queue first
+			jQuery.queue( this, type, [] );
+
+			if ( hooks && hooks.stop ) {
+				hooks.stop.call( this, true );
+			}
+
+			// Look for any active animations, and finish them
+			for ( index = timers.length; index--; ) {
+				if ( timers[ index ].elem === this && timers[ index ].queue === type ) {
+					timers[ index ].anim.stop( true );
+					timers.splice( index, 1 );
+				}
+			}
+
+			// Look for any animations in the old queue and finish them
+			for ( index = 0; index < length; index++ ) {
+				if ( queue[ index ] && queue[ index ].finish ) {
+					queue[ index ].finish.call( this );
+				}
+			}
+
+			// Turn off finishing flag
+			delete data.finish;
+		} );
+	}
+} );
+
+jQuery.each( [ "toggle", "show", "hide" ], function( _i, name ) {
+	var cssFn = jQuery.fn[ name ];
+	jQuery.fn[ name ] = function( speed, easing, callback ) {
+		return speed == null || typeof speed === "boolean" ?
+			cssFn.apply( this, arguments ) :
+			this.animate( genFx( name, true ), speed, easing, callback );
+	};
+} );
+
+// Generate shortcuts for custom animations
+jQuery.each( {
+	slideDown: genFx( "show" ),
+	slideUp: genFx( "hide" ),
+	slideToggle: genFx( "toggle" ),
+	fadeIn: { opacity: "show" },
+	fadeOut: { opacity: "hide" },
+	fadeToggle: { opacity: "toggle" }
+}, function( name, props ) {
+	jQuery.fn[ name ] = function( speed, easing, callback ) {
+		return this.animate( props, speed, easing, callback );
+	};
+} );
+
+jQuery.timers = [];
+jQuery.fx.tick = function() {
+	var timer,
+		i = 0,
+		timers = jQuery.timers;
+
+	fxNow = Date.now();
+
+	for ( ; i < timers.length; i++ ) {
+		timer = timers[ i ];
+
+		// Run the timer and safely remove it when done (allowing for external removal)
+		if ( !timer() && timers[ i ] === timer ) {
+			timers.splice( i--, 1 );
+		}
+	}
+
+	if ( !timers.length ) {
+		jQuery.fx.stop();
+	}
+	fxNow = undefined;
+};
+
+jQuery.fx.timer = function( timer ) {
+	jQuery.timers.push( timer );
+	jQuery.fx.start();
+};
+
+jQuery.fx.interval = 13;
+jQuery.fx.start = function() {
+	if ( inProgress ) {
+		return;
+	}
+
+	inProgress = true;
+	schedule();
+};
+
+jQuery.fx.stop = function() {
+	inProgress = null;
+};
+
+jQuery.fx.speeds = {
+	slow: 600,
+	fast: 200,
+
+	// Default speed
+	_default: 400
+};
+
+
+// Based off of the plugin by Clint Helfers, with permission.
+// https://web.archive.org/web/20100324014747/http://blindsignals.com/index.php/2009/07/jquery-delay/
+jQuery.fn.delay = function( time, type ) {
+	time = jQuery.fx ? jQuery.fx.speeds[ time ] || time : time;
+	type = type || "fx";
+
+	return this.queue( type, function( next, hooks ) {
+		var timeout = window.setTimeout( next, time );
+		hooks.stop = function() {
+			window.clearTimeout( timeout );
+		};
+	} );
+};
+
+
+( function() {
+	var input = document.createElement( "input" ),
+		select = document.createElement( "select" ),
+		opt = select.appendChild( document.createElement( "option" ) );
+
+	input.type = "checkbox";
+
+	// Support: Android <=4.3 only
+	// Default value for a checkbox should be "on"
+	support.checkOn = input.value !== "";
+
+	// Support: IE <=11 only
+	// Must access selectedIndex to make default options select
+	support.optSelected = opt.selected;
+
+	// Support: IE <=11 only
+	// An input loses its value after becoming a radio
+	input = document.createElement( "input" );
+	input.value = "t";
+	input.type = "radio";
+	support.radioValue = input.value === "t";
+} )();
+
+
+var boolHook,
+	attrHandle = jQuery.expr.attrHandle;
+
+jQuery.fn.extend( {
+	attr: function( name, value ) {
+		return access( this, jQuery.attr, name, value, arguments.length > 1 );
+	},
+
+	removeAttr: function( name ) {
+		return this.each( function() {
+			jQuery.removeAttr( this, name );
+		} );
+	}
+} );
+
+jQuery.extend( {
+	attr: function( elem, name, value ) {
+		var ret, hooks,
+			nType = elem.nodeType;
+
+		// Don't get/set attributes on text, comment and attribute nodes
+		if ( nType === 3 || nType === 8 || nType === 2 ) {
+			return;
+		}
+
+		// Fallback to prop when attributes are not supported
+		if ( typeof elem.getAttribute === "undefined" ) {
+			return jQuery.prop( elem, name, value );
+		}
+
+		// Attribute hooks are determined by the lowercase version
+		// Grab necessary hook if one is defined
+		if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) {
+			hooks = jQuery.attrHooks[ name.toLowerCase() ] ||
+				( jQuery.expr.match.bool.test( name ) ? boolHook : undefined );
+		}
+
+		if ( value !== undefined ) {
+			if ( value === null ) {
+				jQuery.removeAttr( elem, name );
+				return;
+			}
+
+			if ( hooks && "set" in hooks &&
+				( ret = hooks.set( elem, value, name ) ) !== undefined ) {
+				return ret;
+			}
+
+			elem.setAttribute( name, value + "" );
+			return value;
+		}
+
+		if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) {
+			return ret;
+		}
+
+		ret = jQuery.find.attr( elem, name );
+
+		// Non-existent attributes return null, we normalize to undefined
+		return ret == null ? undefined : ret;
+	},
+
+	attrHooks: {
+		type: {
+			set: function( elem, value ) {
+				if ( !support.radioValue && value === "radio" &&
+					nodeName( elem, "input" ) ) {
+					var val = elem.value;
+					elem.setAttribute( "type", value );
+					if ( val ) {
+						elem.value = val;
+					}
+					return value;
+				}
+			}
+		}
+	},
+
+	removeAttr: function( elem, value ) {
+		var name,
+			i = 0,
+
+			// Attribute names can contain non-HTML whitespace characters
+			// https://html.spec.whatwg.org/multipage/syntax.html#attributes-2
+			attrNames = value && value.match( rnothtmlwhite );
+
+		if ( attrNames && elem.nodeType === 1 ) {
+			while ( ( name = attrNames[ i++ ] ) ) {
+				elem.removeAttribute( name );
+			}
+		}
+	}
+} );
+
+// Hooks for boolean attributes
+boolHook = {
+	set: function( elem, value, name ) {
+		if ( value === false ) {
+
+			// Remove boolean attributes when set to false
+			jQuery.removeAttr( elem, name );
+		} else {
+			elem.setAttribute( name, name );
+		}
+		return name;
+	}
+};
+
+jQuery.each( jQuery.expr.match.bool.source.match( /\w+/g ), function( _i, name ) {
+	var getter = attrHandle[ name ] || jQuery.find.attr;
+
+	attrHandle[ name ] = function( elem, name, isXML ) {
+		var ret, handle,
+			lowercaseName = name.toLowerCase();
+
+		if ( !isXML ) {
+
+			// Avoid an infinite loop by temporarily removing this function from the getter
+			handle = attrHandle[ lowercaseName ];
+			attrHandle[ lowercaseName ] = ret;
+			ret = getter( elem, name, isXML ) != null ?
+				lowercaseName :
+				null;
+			attrHandle[ lowercaseName ] = handle;
+		}
+		return ret;
+	};
+} );
+
+
+
+
+var rfocusable = /^(?:input|select|textarea|button)$/i,
+	rclickable = /^(?:a|area)$/i;
+
+jQuery.fn.extend( {
+	prop: function( name, value ) {
+		return access( this, jQuery.prop, name, value, arguments.length > 1 );
+	},
+
+	removeProp: function( name ) {
+		return this.each( function() {
+			delete this[ jQuery.propFix[ name ] || name ];
+		} );
+	}
+} );
+
+jQuery.extend( {
+	prop: function( elem, name, value ) {
+		var ret, hooks,
+			nType = elem.nodeType;
+
+		// Don't get/set properties on text, comment and attribute nodes
+		if ( nType === 3 || nType === 8 || nType === 2 ) {
+			return;
+		}
+
+		if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) {
+
+			// Fix name and attach hooks
+			name = jQuery.propFix[ name ] || name;
+			hooks = jQuery.propHooks[ name ];
+		}
+
+		if ( value !== undefined ) {
+			if ( hooks && "set" in hooks &&
+				( ret = hooks.set( elem, value, name ) ) !== undefined ) {
+				return ret;
+			}
+
+			return ( elem[ name ] = value );
+		}
+
+		if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) {
+			return ret;
+		}
+
+		return elem[ name ];
+	},
+
+	propHooks: {
+		tabIndex: {
+			get: function( elem ) {
+
+				// Support: IE <=9 - 11 only
+				// elem.tabIndex doesn't always return the
+				// correct value when it hasn't been explicitly set
+				// https://web.archive.org/web/20141116233347/http://fluidproject.org/blog/2008/01/09/getting-setting-and-removing-tabindex-values-with-javascript/
+				// Use proper attribute retrieval(#12072)
+				var tabindex = jQuery.find.attr( elem, "tabindex" );
+
+				if ( tabindex ) {
+					return parseInt( tabindex, 10 );
+				}
+
+				if (
+					rfocusable.test( elem.nodeName ) ||
+					rclickable.test( elem.nodeName ) &&
+					elem.href
+				) {
+					return 0;
+				}
+
+				return -1;
+			}
+		}
+	},
+
+	propFix: {
+		"for": "htmlFor",
+		"class": "className"
+	}
+} );
+
+// Support: IE <=11 only
+// Accessing the selectedIndex property
+// forces the browser to respect setting selected
+// on the option
+// The getter ensures a default option is selected
+// when in an optgroup
+// eslint rule "no-unused-expressions" is disabled for this code
+// since it considers such accessions noop
+if ( !support.optSelected ) {
+	jQuery.propHooks.selected = {
+		get: function( elem ) {
+
+			/* eslint no-unused-expressions: "off" */
+
+			var parent = elem.parentNode;
+			if ( parent && parent.parentNode ) {
+				parent.parentNode.selectedIndex;
+			}
+			return null;
+		},
+		set: function( elem ) {
+
+			/* eslint no-unused-expressions: "off" */
+
+			var parent = elem.parentNode;
+			if ( parent ) {
+				parent.selectedIndex;
+
+				if ( parent.parentNode ) {
+					parent.parentNode.selectedIndex;
+				}
+			}
+		}
+	};
+}
+
+jQuery.each( [
+	"tabIndex",
+	"readOnly",
+	"maxLength",
+	"cellSpacing",
+	"cellPadding",
+	"rowSpan",
+	"colSpan",
+	"useMap",
+	"frameBorder",
+	"contentEditable"
+], function() {
+	jQuery.propFix[ this.toLowerCase() ] = this;
+} );
+
+
+
+
+	// Strip and collapse whitespace according to HTML spec
+	// https://infra.spec.whatwg.org/#strip-and-collapse-ascii-whitespace
+	function stripAndCollapse( value ) {
+		var tokens = value.match( rnothtmlwhite ) || [];
+		return tokens.join( " " );
+	}
+
+
+function getClass( elem ) {
+	return elem.getAttribute && elem.getAttribute( "class" ) || "";
+}
+
+function classesToArray( value ) {
+	if ( Array.isArray( value ) ) {
+		return value;
+	}
+	if ( typeof value === "string" ) {
+		return value.match( rnothtmlwhite ) || [];
+	}
+	return [];
+}
+
+jQuery.fn.extend( {
+	addClass: function( value ) {
+		var classes, elem, cur, curValue, clazz, j, finalValue,
+			i = 0;
+
+		if ( isFunction( value ) ) {
+			return this.each( function( j ) {
+				jQuery( this ).addClass( value.call( this, j, getClass( this ) ) );
+			} );
+		}
+
+		classes = classesToArray( value );
+
+		if ( classes.length ) {
+			while ( ( elem = this[ i++ ] ) ) {
+				curValue = getClass( elem );
+				cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " );
+
+				if ( cur ) {
+					j = 0;
+					while ( ( clazz = classes[ j++ ] ) ) {
+						if ( cur.indexOf( " " + clazz + " " ) < 0 ) {
+							cur += clazz + " ";
+						}
+					}
+
+					// Only assign if different to avoid unneeded rendering.
+					finalValue = stripAndCollapse( cur );
+					if ( curValue !== finalValue ) {
+						elem.setAttribute( "class", finalValue );
+					}
+				}
+			}
+		}
+
+		return this;
+	},
+
+	removeClass: function( value ) {
+		var classes, elem, cur, curValue, clazz, j, finalValue,
+			i = 0;
+
+		if ( isFunction( value ) ) {
+			return this.each( function( j ) {
+				jQuery( this ).removeClass( value.call( this, j, getClass( this ) ) );
+			} );
+		}
+
+		if ( !arguments.length ) {
+			return this.attr( "class", "" );
+		}
+
+		classes = classesToArray( value );
+
+		if ( classes.length ) {
+			while ( ( elem = this[ i++ ] ) ) {
+				curValue = getClass( elem );
+
+				// This expression is here for better compressibility (see addClass)
+				cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " );
+
+				if ( cur ) {
+					j = 0;
+					while ( ( clazz = classes[ j++ ] ) ) {
+
+						// Remove *all* instances
+						while ( cur.indexOf( " " + clazz + " " ) > -1 ) {
+							cur = cur.replace( " " + clazz + " ", " " );
+						}
+					}
+
+					// Only assign if different to avoid unneeded rendering.
+					finalValue = stripAndCollapse( cur );
+					if ( curValue !== finalValue ) {
+						elem.setAttribute( "class", finalValue );
+					}
+				}
+			}
+		}
+
+		return this;
+	},
+
+	toggleClass: function( value, stateVal ) {
+		var type = typeof value,
+			isValidValue = type === "string" || Array.isArray( value );
+
+		if ( typeof stateVal === "boolean" && isValidValue ) {
+			return stateVal ? this.addClass( value ) : this.removeClass( value );
+		}
+
+		if ( isFunction( value ) ) {
+			return this.each( function( i ) {
+				jQuery( this ).toggleClass(
+					value.call( this, i, getClass( this ), stateVal ),
+					stateVal
+				);
+			} );
+		}
+
+		return this.each( function() {
+			var className, i, self, classNames;
+
+			if ( isValidValue ) {
+
+				// Toggle individual class names
+				i = 0;
+				self = jQuery( this );
+				classNames = classesToArray( value );
+
+				while ( ( className = classNames[ i++ ] ) ) {
+
+					// Check each className given, space separated list
+					if ( self.hasClass( className ) ) {
+						self.removeClass( className );
+					} else {
+						self.addClass( className );
+					}
+				}
+
+			// Toggle whole class name
+			} else if ( value === undefined || type === "boolean" ) {
+				className = getClass( this );
+				if ( className ) {
+
+					// Store className if set
+					dataPriv.set( this, "__className__", className );
+				}
+
+				// If the element has a class name or if we're passed `false`,
+				// then remove the whole classname (if there was one, the above saved it).
+				// Otherwise bring back whatever was previously saved (if anything),
+				// falling back to the empty string if nothing was stored.
+				if ( this.setAttribute ) {
+					this.setAttribute( "class",
+						className || value === false ?
+							"" :
+							dataPriv.get( this, "__className__" ) || ""
+					);
+				}
+			}
+		} );
+	},
+
+	hasClass: function( selector ) {
+		var className, elem,
+			i = 0;
+
+		className = " " + selector + " ";
+		while ( ( elem = this[ i++ ] ) ) {
+			if ( elem.nodeType === 1 &&
+				( " " + stripAndCollapse( getClass( elem ) ) + " " ).indexOf( className ) > -1 ) {
+				return true;
+			}
+		}
+
+		return false;
+	}
+} );
+
+
+
+
+var rreturn = /\r/g;
+
+jQuery.fn.extend( {
+	val: function( value ) {
+		var hooks, ret, valueIsFunction,
+			elem = this[ 0 ];
+
+		if ( !arguments.length ) {
+			if ( elem ) {
+				hooks = jQuery.valHooks[ elem.type ] ||
+					jQuery.valHooks[ elem.nodeName.toLowerCase() ];
+
+				if ( hooks &&
+					"get" in hooks &&
+					( ret = hooks.get( elem, "value" ) ) !== undefined
+				) {
+					return ret;
+				}
+
+				ret = elem.value;
+
+				// Handle most common string cases
+				if ( typeof ret === "string" ) {
+					return ret.replace( rreturn, "" );
+				}
+
+				// Handle cases where value is null/undef or number
+				return ret == null ? "" : ret;
+			}
+
+			return;
+		}
+
+		valueIsFunction = isFunction( value );
+
+		return this.each( function( i ) {
+			var val;
+
+			if ( this.nodeType !== 1 ) {
+				return;
+			}
+
+			if ( valueIsFunction ) {
+				val = value.call( this, i, jQuery( this ).val() );
+			} else {
+				val = value;
+			}
+
+			// Treat null/undefined as ""; convert numbers to string
+			if ( val == null ) {
+				val = "";
+
+			} else if ( typeof val === "number" ) {
+				val += "";
+
+			} else if ( Array.isArray( val ) ) {
+				val = jQuery.map( val, function( value ) {
+					return value == null ? "" : value + "";
+				} );
+			}
+
+			hooks = jQuery.valHooks[ this.type ] || jQuery.valHooks[ this.nodeName.toLowerCase() ];
+
+			// If set returns undefined, fall back to normal setting
+			if ( !hooks || !( "set" in hooks ) || hooks.set( this, val, "value" ) === undefined ) {
+				this.value = val;
+			}
+		} );
+	}
+} );
+
+jQuery.extend( {
+	valHooks: {
+		option: {
+			get: function( elem ) {
+
+				var val = jQuery.find.attr( elem, "value" );
+				return val != null ?
+					val :
+
+					// Support: IE <=10 - 11 only
+					// option.text throws exceptions (#14686, #14858)
+					// Strip and collapse whitespace
+					// https://html.spec.whatwg.org/#strip-and-collapse-whitespace
+					stripAndCollapse( jQuery.text( elem ) );
+			}
+		},
+		select: {
+			get: function( elem ) {
+				var value, option, i,
+					options = elem.options,
+					index = elem.selectedIndex,
+					one = elem.type === "select-one",
+					values = one ? null : [],
+					max = one ? index + 1 : options.length;
+
+				if ( index < 0 ) {
+					i = max;
+
+				} else {
+					i = one ? index : 0;
+				}
+
+				// Loop through all the selected options
+				for ( ; i < max; i++ ) {
+					option = options[ i ];
+
+					// Support: IE <=9 only
+					// IE8-9 doesn't update selected after form reset (#2551)
+					if ( ( option.selected || i === index ) &&
+
+							// Don't return options that are disabled or in a disabled optgroup
+							!option.disabled &&
+							( !option.parentNode.disabled ||
+								!nodeName( option.parentNode, "optgroup" ) ) ) {
+
+						// Get the specific value for the option
+						value = jQuery( option ).val();
+
+						// We don't need an array for one selects
+						if ( one ) {
+							return value;
+						}
+
+						// Multi-Selects return an array
+						values.push( value );
+					}
+				}
+
+				return values;
+			},
+
+			set: function( elem, value ) {
+				var optionSet, option,
+					options = elem.options,
+					values = jQuery.makeArray( value ),
+					i = options.length;
+
+				while ( i-- ) {
+					option = options[ i ];
+
+					/* eslint-disable no-cond-assign */
+
+					if ( option.selected =
+						jQuery.inArray( jQuery.valHooks.option.get( option ), values ) > -1
+					) {
+						optionSet = true;
+					}
+
+					/* eslint-enable no-cond-assign */
+				}
+
+				// Force browsers to behave consistently when non-matching value is set
+				if ( !optionSet ) {
+					elem.selectedIndex = -1;
+				}
+				return values;
+			}
+		}
+	}
+} );
+
+// Radios and checkboxes getter/setter
+jQuery.each( [ "radio", "checkbox" ], function() {
+	jQuery.valHooks[ this ] = {
+		set: function( elem, value ) {
+			if ( Array.isArray( value ) ) {
+				return ( elem.checked = jQuery.inArray( jQuery( elem ).val(), value ) > -1 );
+			}
+		}
+	};
+	if ( !support.checkOn ) {
+		jQuery.valHooks[ this ].get = function( elem ) {
+			return elem.getAttribute( "value" ) === null ? "on" : elem.value;
+		};
+	}
+} );
+
+
+
+
+// Return jQuery for attributes-only inclusion
+
+
+support.focusin = "onfocusin" in window;
+
+
+var rfocusMorph = /^(?:focusinfocus|focusoutblur)$/,
+	stopPropagationCallback = function( e ) {
+		e.stopPropagation();
+	};
+
+jQuery.extend( jQuery.event, {
+
+	trigger: function( event, data, elem, onlyHandlers ) {
+
+		var i, cur, tmp, bubbleType, ontype, handle, special, lastElement,
+			eventPath = [ elem || document ],
+			type = hasOwn.call( event, "type" ) ? event.type : event,
+			namespaces = hasOwn.call( event, "namespace" ) ? event.namespace.split( "." ) : [];
+
+		cur = lastElement = tmp = elem = elem || document;
+
+		// Don't do events on text and comment nodes
+		if ( elem.nodeType === 3 || elem.nodeType === 8 ) {
+			return;
+		}
+
+		// focus/blur morphs to focusin/out; ensure we're not firing them right now
+		if ( rfocusMorph.test( type + jQuery.event.triggered ) ) {
+			return;
+		}
+
+		if ( type.indexOf( "." ) > -1 ) {
+
+			// Namespaced trigger; create a regexp to match event type in handle()
+			namespaces = type.split( "." );
+			type = namespaces.shift();
+			namespaces.sort();
+		}
+		ontype = type.indexOf( ":" ) < 0 && "on" + type;
+
+		// Caller can pass in a jQuery.Event object, Object, or just an event type string
+		event = event[ jQuery.expando ] ?
+			event :
+			new jQuery.Event( type, typeof event === "object" && event );
+
+		// Trigger bitmask: & 1 for native handlers; & 2 for jQuery (always true)
+		event.isTrigger = onlyHandlers ? 2 : 3;
+		event.namespace = namespaces.join( "." );
+		event.rnamespace = event.namespace ?
+			new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ) :
+			null;
+
+		// Clean up the event in case it is being reused
+		event.result = undefined;
+		if ( !event.target ) {
+			event.target = elem;
+		}
+
+		// Clone any incoming data and prepend the event, creating the handler arg list
+		data = data == null ?
+			[ event ] :
+			jQuery.makeArray( data, [ event ] );
+
+		// Allow special events to draw outside the lines
+		special = jQuery.event.special[ type ] || {};
+		if ( !onlyHandlers && special.trigger && special.trigger.apply( elem, data ) === false ) {
+			return;
+		}
+
+		// Determine event propagation path in advance, per W3C events spec (#9951)
+		// Bubble up to document, then to window; watch for a global ownerDocument var (#9724)
+		if ( !onlyHandlers && !special.noBubble && !isWindow( elem ) ) {
+
+			bubbleType = special.delegateType || type;
+			if ( !rfocusMorph.test( bubbleType + type ) ) {
+				cur = cur.parentNode;
+			}
+			for ( ; cur; cur = cur.parentNode ) {
+				eventPath.push( cur );
+				tmp = cur;
+			}
+
+			// Only add window if we got to document (e.g., not plain obj or detached DOM)
+			if ( tmp === ( elem.ownerDocument || document ) ) {
+				eventPath.push( tmp.defaultView || tmp.parentWindow || window );
+			}
+		}
+
+		// Fire handlers on the event path
+		i = 0;
+		while ( ( cur = eventPath[ i++ ] ) && !event.isPropagationStopped() ) {
+			lastElement = cur;
+			event.type = i > 1 ?
+				bubbleType :
+				special.bindType || type;
+
+			// jQuery handler
+			handle = ( dataPriv.get( cur, "events" ) || Object.create( null ) )[ event.type ] &&
+				dataPriv.get( cur, "handle" );
+			if ( handle ) {
+				handle.apply( cur, data );
+			}
+
+			// Native handler
+			handle = ontype && cur[ ontype ];
+			if ( handle && handle.apply && acceptData( cur ) ) {
+				event.result = handle.apply( cur, data );
+				if ( event.result === false ) {
+					event.preventDefault();
+				}
+			}
+		}
+		event.type = type;
+
+		// If nobody prevented the default action, do it now
+		if ( !onlyHandlers && !event.isDefaultPrevented() ) {
+
+			if ( ( !special._default ||
+				special._default.apply( eventPath.pop(), data ) === false ) &&
+				acceptData( elem ) ) {
+
+				// Call a native DOM method on the target with the same name as the event.
+				// Don't do default actions on window, that's where global variables be (#6170)
+				if ( ontype && isFunction( elem[ type ] ) && !isWindow( elem ) ) {
+
+					// Don't re-trigger an onFOO event when we call its FOO() method
+					tmp = elem[ ontype ];
+
+					if ( tmp ) {
+						elem[ ontype ] = null;
+					}
+
+					// Prevent re-triggering of the same event, since we already bubbled it above
+					jQuery.event.triggered = type;
+
+					if ( event.isPropagationStopped() ) {
+						lastElement.addEventListener( type, stopPropagationCallback );
+					}
+
+					elem[ type ]();
+
+					if ( event.isPropagationStopped() ) {
+						lastElement.removeEventListener( type, stopPropagationCallback );
+					}
+
+					jQuery.event.triggered = undefined;
+
+					if ( tmp ) {
+						elem[ ontype ] = tmp;
+					}
+				}
+			}
+		}
+
+		return event.result;
+	},
+
+	// Piggyback on a donor event to simulate a different one
+	// Used only for `focus(in | out)` events
+	simulate: function( type, elem, event ) {
+		var e = jQuery.extend(
+			new jQuery.Event(),
+			event,
+			{
+				type: type,
+				isSimulated: true
+			}
+		);
+
+		jQuery.event.trigger( e, null, elem );
+	}
+
+} );
+
+jQuery.fn.extend( {
+
+	trigger: function( type, data ) {
+		return this.each( function() {
+			jQuery.event.trigger( type, data, this );
+		} );
+	},
+	triggerHandler: function( type, data ) {
+		var elem = this[ 0 ];
+		if ( elem ) {
+			return jQuery.event.trigger( type, data, elem, true );
+		}
+	}
+} );
+
+
+// Support: Firefox <=44
+// Firefox doesn't have focus(in | out) events
+// Related ticket - https://bugzilla.mozilla.org/show_bug.cgi?id=687787
+//
+// Support: Chrome <=48 - 49, Safari <=9.0 - 9.1
+// focus(in | out) events fire after focus & blur events,
+// which is spec violation - http://www.w3.org/TR/DOM-Level-3-Events/#events-focusevent-event-order
+// Related ticket - https://bugs.chromium.org/p/chromium/issues/detail?id=449857
+if ( !support.focusin ) {
+	jQuery.each( { focus: "focusin", blur: "focusout" }, function( orig, fix ) {
+
+		// Attach a single capturing handler on the document while someone wants focusin/focusout
+		var handler = function( event ) {
+			jQuery.event.simulate( fix, event.target, jQuery.event.fix( event ) );
+		};
+
+		jQuery.event.special[ fix ] = {
+			setup: function() {
+
+				// Handle: regular nodes (via `this.ownerDocument`), window
+				// (via `this.document`) & document (via `this`).
+				var doc = this.ownerDocument || this.document || this,
+					attaches = dataPriv.access( doc, fix );
+
+				if ( !attaches ) {
+					doc.addEventListener( orig, handler, true );
+				}
+				dataPriv.access( doc, fix, ( attaches || 0 ) + 1 );
+			},
+			teardown: function() {
+				var doc = this.ownerDocument || this.document || this,
+					attaches = dataPriv.access( doc, fix ) - 1;
+
+				if ( !attaches ) {
+					doc.removeEventListener( orig, handler, true );
+					dataPriv.remove( doc, fix );
+
+				} else {
+					dataPriv.access( doc, fix, attaches );
+				}
+			}
+		};
+	} );
+}
+var location = window.location;
+
+var nonce = { guid: Date.now() };
+
+var rquery = ( /\?/ );
+
+
+
+// Cross-browser xml parsing
+jQuery.parseXML = function( data ) {
+	var xml, parserErrorElem;
+	if ( !data || typeof data !== "string" ) {
+		return null;
+	}
+
+	// Support: IE 9 - 11 only
+	// IE throws on parseFromString with invalid input.
+	try {
+		xml = ( new window.DOMParser() ).parseFromString( data, "text/xml" );
+	} catch ( e ) {}
+
+	parserErrorElem = xml && xml.getElementsByTagName( "parsererror" )[ 0 ];
+	if ( !xml || parserErrorElem ) {
+		jQuery.error( "Invalid XML: " + (
+			parserErrorElem ?
+				jQuery.map( parserErrorElem.childNodes, function( el ) {
+					return el.textContent;
+				} ).join( "\n" ) :
+				data
+		) );
+	}
+	return xml;
+};
+
+
+var
+	rbracket = /\[\]$/,
+	rCRLF = /\r?\n/g,
+	rsubmitterTypes = /^(?:submit|button|image|reset|file)$/i,
+	rsubmittable = /^(?:input|select|textarea|keygen)/i;
+
+function buildParams( prefix, obj, traditional, add ) {
+	var name;
+
+	if ( Array.isArray( obj ) ) {
+
+		// Serialize array item.
+		jQuery.each( obj, function( i, v ) {
+			if ( traditional || rbracket.test( prefix ) ) {
+
+				// Treat each array item as a scalar.
+				add( prefix, v );
+
+			} else {
+
+				// Item is non-scalar (array or object), encode its numeric index.
+				buildParams(
+					prefix + "[" + ( typeof v === "object" && v != null ? i : "" ) + "]",
+					v,
+					traditional,
+					add
+				);
+			}
+		} );
+
+	} else if ( !traditional && toType( obj ) === "object" ) {
+
+		// Serialize object item.
+		for ( name in obj ) {
+			buildParams( prefix + "[" + name + "]", obj[ name ], traditional, add );
+		}
+
+	} else {
+
+		// Serialize scalar item.
+		add( prefix, obj );
+	}
+}
+
+// Serialize an array of form elements or a set of
+// key/values into a query string
+jQuery.param = function( a, traditional ) {
+	var prefix,
+		s = [],
+		add = function( key, valueOrFunction ) {
+
+			// If value is a function, invoke it and use its return value
+			var value = isFunction( valueOrFunction ) ?
+				valueOrFunction() :
+				valueOrFunction;
+
+			s[ s.length ] = encodeURIComponent( key ) + "=" +
+				encodeURIComponent( value == null ? "" : value );
+		};
+
+	if ( a == null ) {
+		return "";
+	}
+
+	// If an array was passed in, assume that it is an array of form elements.
+	if ( Array.isArray( a ) || ( a.jquery && !jQuery.isPlainObject( a ) ) ) {
+
+		// Serialize the form elements
+		jQuery.each( a, function() {
+			add( this.name, this.value );
+		} );
+
+	} else {
+
+		// If traditional, encode the "old" way (the way 1.3.2 or older
+		// did it), otherwise encode params recursively.
+		for ( prefix in a ) {
+			buildParams( prefix, a[ prefix ], traditional, add );
+		}
+	}
+
+	// Return the resulting serialization
+	return s.join( "&" );
+};
+
+jQuery.fn.extend( {
+	serialize: function() {
+		return jQuery.param( this.serializeArray() );
+	},
+	serializeArray: function() {
+		return this.map( function() {
+
+			// Can add propHook for "elements" to filter or add form elements
+			var elements = jQuery.prop( this, "elements" );
+			return elements ? jQuery.makeArray( elements ) : this;
+		} ).filter( function() {
+			var type = this.type;
+
+			// Use .is( ":disabled" ) so that fieldset[disabled] works
+			return this.name && !jQuery( this ).is( ":disabled" ) &&
+				rsubmittable.test( this.nodeName ) && !rsubmitterTypes.test( type ) &&
+				( this.checked || !rcheckableType.test( type ) );
+		} ).map( function( _i, elem ) {
+			var val = jQuery( this ).val();
+
+			if ( val == null ) {
+				return null;
+			}
+
+			if ( Array.isArray( val ) ) {
+				return jQuery.map( val, function( val ) {
+					return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) };
+				} );
+			}
+
+			return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) };
+		} ).get();
+	}
+} );
+
+
+var
+	r20 = /%20/g,
+	rhash = /#.*$/,
+	rantiCache = /([?&])_=[^&]*/,
+	rheaders = /^(.*?):[ \t]*([^\r\n]*)$/mg,
+
+	// #7653, #8125, #8152: local protocol detection
+	rlocalProtocol = /^(?:about|app|app-storage|.+-extension|file|res|widget):$/,
+	rnoContent = /^(?:GET|HEAD)$/,
+	rprotocol = /^\/\//,
+
+	/* Prefilters
+	 * 1) They are useful to introduce custom dataTypes (see ajax/jsonp.js for an example)
+	 * 2) These are called:
+	 *    - BEFORE asking for a transport
+	 *    - AFTER param serialization (s.data is a string if s.processData is true)
+	 * 3) key is the dataType
+	 * 4) the catchall symbol "*" can be used
+	 * 5) execution will start with transport dataType and THEN continue down to "*" if needed
+	 */
+	prefilters = {},
+
+	/* Transports bindings
+	 * 1) key is the dataType
+	 * 2) the catchall symbol "*" can be used
+	 * 3) selection will start with transport dataType and THEN go to "*" if needed
+	 */
+	transports = {},
+
+	// Avoid comment-prolog char sequence (#10098); must appease lint and evade compression
+	allTypes = "*/".concat( "*" ),
+
+	// Anchor tag for parsing the document origin
+	originAnchor = document.createElement( "a" );
+
+originAnchor.href = location.href;
+
+// Base "constructor" for jQuery.ajaxPrefilter and jQuery.ajaxTransport
+function addToPrefiltersOrTransports( structure ) {
+
+	// dataTypeExpression is optional and defaults to "*"
+	return function( dataTypeExpression, func ) {
+
+		if ( typeof dataTypeExpression !== "string" ) {
+			func = dataTypeExpression;
+			dataTypeExpression = "*";
+		}
+
+		var dataType,
+			i = 0,
+			dataTypes = dataTypeExpression.toLowerCase().match( rnothtmlwhite ) || [];
+
+		if ( isFunction( func ) ) {
+
+			// For each dataType in the dataTypeExpression
+			while ( ( dataType = dataTypes[ i++ ] ) ) {
+
+				// Prepend if requested
+				if ( dataType[ 0 ] === "+" ) {
+					dataType = dataType.slice( 1 ) || "*";
+					( structure[ dataType ] = structure[ dataType ] || [] ).unshift( func );
+
+				// Otherwise append
+				} else {
+					( structure[ dataType ] = structure[ dataType ] || [] ).push( func );
+				}
+			}
+		}
+	};
+}
+
+// Base inspection function for prefilters and transports
+function inspectPrefiltersOrTransports( structure, options, originalOptions, jqXHR ) {
+
+	var inspected = {},
+		seekingTransport = ( structure === transports );
+
+	function inspect( dataType ) {
+		var selected;
+		inspected[ dataType ] = true;
+		jQuery.each( structure[ dataType ] || [], function( _, prefilterOrFactory ) {
+			var dataTypeOrTransport = prefilterOrFactory( options, originalOptions, jqXHR );
+			if ( typeof dataTypeOrTransport === "string" &&
+				!seekingTransport && !inspected[ dataTypeOrTransport ] ) {
+
+				options.dataTypes.unshift( dataTypeOrTransport );
+				inspect( dataTypeOrTransport );
+				return false;
+			} else if ( seekingTransport ) {
+				return !( selected = dataTypeOrTransport );
+			}
+		} );
+		return selected;
+	}
+
+	return inspect( options.dataTypes[ 0 ] ) || !inspected[ "*" ] && inspect( "*" );
+}
+
+// A special extend for ajax options
+// that takes "flat" options (not to be deep extended)
+// Fixes #9887
+function ajaxExtend( target, src ) {
+	var key, deep,
+		flatOptions = jQuery.ajaxSettings.flatOptions || {};
+
+	for ( key in src ) {
+		if ( src[ key ] !== undefined ) {
+			( flatOptions[ key ] ? target : ( deep || ( deep = {} ) ) )[ key ] = src[ key ];
+		}
+	}
+	if ( deep ) {
+		jQuery.extend( true, target, deep );
+	}
+
+	return target;
+}
+
+/* Handles responses to an ajax request:
+ * - finds the right dataType (mediates between content-type and expected dataType)
+ * - returns the corresponding response
+ */
+function ajaxHandleResponses( s, jqXHR, responses ) {
+
+	var ct, type, finalDataType, firstDataType,
+		contents = s.contents,
+		dataTypes = s.dataTypes;
+
+	// Remove auto dataType and get content-type in the process
+	while ( dataTypes[ 0 ] === "*" ) {
+		dataTypes.shift();
+		if ( ct === undefined ) {
+			ct = s.mimeType || jqXHR.getResponseHeader( "Content-Type" );
+		}
+	}
+
+	// Check if we're dealing with a known content-type
+	if ( ct ) {
+		for ( type in contents ) {
+			if ( contents[ type ] && contents[ type ].test( ct ) ) {
+				dataTypes.unshift( type );
+				break;
+			}
+		}
+	}
+
+	// Check to see if we have a response for the expected dataType
+	if ( dataTypes[ 0 ] in responses ) {
+		finalDataType = dataTypes[ 0 ];
+	} else {
+
+		// Try convertible dataTypes
+		for ( type in responses ) {
+			if ( !dataTypes[ 0 ] || s.converters[ type + " " + dataTypes[ 0 ] ] ) {
+				finalDataType = type;
+				break;
+			}
+			if ( !firstDataType ) {
+				firstDataType = type;
+			}
+		}
+
+		// Or just use first one
+		finalDataType = finalDataType || firstDataType;
+	}
+
+	// If we found a dataType
+	// We add the dataType to the list if needed
+	// and return the corresponding response
+	if ( finalDataType ) {
+		if ( finalDataType !== dataTypes[ 0 ] ) {
+			dataTypes.unshift( finalDataType );
+		}
+		return responses[ finalDataType ];
+	}
+}
+
+/* Chain conversions given the request and the original response
+ * Also sets the responseXXX fields on the jqXHR instance
+ */
+function ajaxConvert( s, response, jqXHR, isSuccess ) {
+	var conv2, current, conv, tmp, prev,
+		converters = {},
+
+		// Work with a copy of dataTypes in case we need to modify it for conversion
+		dataTypes = s.dataTypes.slice();
+
+	// Create converters map with lowercased keys
+	if ( dataTypes[ 1 ] ) {
+		for ( conv in s.converters ) {
+			converters[ conv.toLowerCase() ] = s.converters[ conv ];
+		}
+	}
+
+	current = dataTypes.shift();
+
+	// Convert to each sequential dataType
+	while ( current ) {
+
+		if ( s.responseFields[ current ] ) {
+			jqXHR[ s.responseFields[ current ] ] = response;
+		}
+
+		// Apply the dataFilter if provided
+		if ( !prev && isSuccess && s.dataFilter ) {
+			response = s.dataFilter( response, s.dataType );
+		}
+
+		prev = current;
+		current = dataTypes.shift();
+
+		if ( current ) {
+
+			// There's only work to do if current dataType is non-auto
+			if ( current === "*" ) {
+
+				current = prev;
+
+			// Convert response if prev dataType is non-auto and differs from current
+			} else if ( prev !== "*" && prev !== current ) {
+
+				// Seek a direct converter
+				conv = converters[ prev + " " + current ] || converters[ "* " + current ];
+
+				// If none found, seek a pair
+				if ( !conv ) {
+					for ( conv2 in converters ) {
+
+						// If conv2 outputs current
+						tmp = conv2.split( " " );
+						if ( tmp[ 1 ] === current ) {
+
+							// If prev can be converted to accepted input
+							conv = converters[ prev + " " + tmp[ 0 ] ] ||
+								converters[ "* " + tmp[ 0 ] ];
+							if ( conv ) {
+
+								// Condense equivalence converters
+								if ( conv === true ) {
+									conv = converters[ conv2 ];
+
+								// Otherwise, insert the intermediate dataType
+								} else if ( converters[ conv2 ] !== true ) {
+									current = tmp[ 0 ];
+									dataTypes.unshift( tmp[ 1 ] );
+								}
+								break;
+							}
+						}
+					}
+				}
+
+				// Apply converter (if not an equivalence)
+				if ( conv !== true ) {
+
+					// Unless errors are allowed to bubble, catch and return them
+					if ( conv && s.throws ) {
+						response = conv( response );
+					} else {
+						try {
+							response = conv( response );
+						} catch ( e ) {
+							return {
+								state: "parsererror",
+								error: conv ? e : "No conversion from " + prev + " to " + current
+							};
+						}
+					}
+				}
+			}
+		}
+	}
+
+	return { state: "success", data: response };
+}
+
+jQuery.extend( {
+
+	// Counter for holding the number of active queries
+	active: 0,
+
+	// Last-Modified header cache for next request
+	lastModified: {},
+	etag: {},
+
+	ajaxSettings: {
+		url: location.href,
+		type: "GET",
+		isLocal: rlocalProtocol.test( location.protocol ),
+		global: true,
+		processData: true,
+		async: true,
+		contentType: "application/x-www-form-urlencoded; charset=UTF-8",
+
+		/*
+		timeout: 0,
+		data: null,
+		dataType: null,
+		username: null,
+		password: null,
+		cache: null,
+		throws: false,
+		traditional: false,
+		headers: {},
+		*/
+
+		accepts: {
+			"*": allTypes,
+			text: "text/plain",
+			html: "text/html",
+			xml: "application/xml, text/xml",
+			json: "application/json, text/javascript"
+		},
+
+		contents: {
+			xml: /\bxml\b/,
+			html: /\bhtml/,
+			json: /\bjson\b/
+		},
+
+		responseFields: {
+			xml: "responseXML",
+			text: "responseText",
+			json: "responseJSON"
+		},
+
+		// Data converters
+		// Keys separate source (or catchall "*") and destination types with a single space
+		converters: {
+
+			// Convert anything to text
+			"* text": String,
+
+			// Text to html (true = no transformation)
+			"text html": true,
+
+			// Evaluate text as a json expression
+			"text json": JSON.parse,
+
+			// Parse text as xml
+			"text xml": jQuery.parseXML
+		},
+
+		// For options that shouldn't be deep extended:
+		// you can add your own custom options here if
+		// and when you create one that shouldn't be
+		// deep extended (see ajaxExtend)
+		flatOptions: {
+			url: true,
+			context: true
+		}
+	},
+
+	// Creates a full fledged settings object into target
+	// with both ajaxSettings and settings fields.
+	// If target is omitted, writes into ajaxSettings.
+	ajaxSetup: function( target, settings ) {
+		return settings ?
+
+			// Building a settings object
+			ajaxExtend( ajaxExtend( target, jQuery.ajaxSettings ), settings ) :
+
+			// Extending ajaxSettings
+			ajaxExtend( jQuery.ajaxSettings, target );
+	},
+
+	ajaxPrefilter: addToPrefiltersOrTransports( prefilters ),
+	ajaxTransport: addToPrefiltersOrTransports( transports ),
+
+	// Main method
+	ajax: function( url, options ) {
+
+		// If url is an object, simulate pre-1.5 signature
+		if ( typeof url === "object" ) {
+			options = url;
+			url = undefined;
+		}
+
+		// Force options to be an object
+		options = options || {};
+
+		var transport,
+
+			// URL without anti-cache param
+			cacheURL,
+
+			// Response headers
+			responseHeadersString,
+			responseHeaders,
+
+			// timeout handle
+			timeoutTimer,
+
+			// Url cleanup var
+			urlAnchor,
+
+			// Request state (becomes false upon send and true upon completion)
+			completed,
+
+			// To know if global events are to be dispatched
+			fireGlobals,
+
+			// Loop variable
+			i,
+
+			// uncached part of the url
+			uncached,
+
+			// Create the final options object
+			s = jQuery.ajaxSetup( {}, options ),
+
+			// Callbacks context
+			callbackContext = s.context || s,
+
+			// Context for global events is callbackContext if it is a DOM node or jQuery collection
+			globalEventContext = s.context &&
+				( callbackContext.nodeType || callbackContext.jquery ) ?
+				jQuery( callbackContext ) :
+				jQuery.event,
+
+			// Deferreds
+			deferred = jQuery.Deferred(),
+			completeDeferred = jQuery.Callbacks( "once memory" ),
+
+			// Status-dependent callbacks
+			statusCode = s.statusCode || {},
+
+			// Headers (they are sent all at once)
+			requestHeaders = {},
+			requestHeadersNames = {},
+
+			// Default abort message
+			strAbort = "canceled",
+
+			// Fake xhr
+			jqXHR = {
+				readyState: 0,
+
+				// Builds headers hashtable if needed
+				getResponseHeader: function( key ) {
+					var match;
+					if ( completed ) {
+						if ( !responseHeaders ) {
+							responseHeaders = {};
+							while ( ( match = rheaders.exec( responseHeadersString ) ) ) {
+								responseHeaders[ match[ 1 ].toLowerCase() + " " ] =
+									( responseHeaders[ match[ 1 ].toLowerCase() + " " ] || [] )
+										.concat( match[ 2 ] );
+							}
+						}
+						match = responseHeaders[ key.toLowerCase() + " " ];
+					}
+					return match == null ? null : match.join( ", " );
+				},
+
+				// Raw string
+				getAllResponseHeaders: function() {
+					return completed ? responseHeadersString : null;
+				},
+
+				// Caches the header
+				setRequestHeader: function( name, value ) {
+					if ( completed == null ) {
+						name = requestHeadersNames[ name.toLowerCase() ] =
+							requestHeadersNames[ name.toLowerCase() ] || name;
+						requestHeaders[ name ] = value;
+					}
+					return this;
+				},
+
+				// Overrides response content-type header
+				overrideMimeType: function( type ) {
+					if ( completed == null ) {
+						s.mimeType = type;
+					}
+					return this;
+				},
+
+				// Status-dependent callbacks
+				statusCode: function( map ) {
+					var code;
+					if ( map ) {
+						if ( completed ) {
+
+							// Execute the appropriate callbacks
+							jqXHR.always( map[ jqXHR.status ] );
+						} else {
+
+							// Lazy-add the new callbacks in a way that preserves old ones
+							for ( code in map ) {
+								statusCode[ code ] = [ statusCode[ code ], map[ code ] ];
+							}
+						}
+					}
+					return this;
+				},
+
+				// Cancel the request
+				abort: function( statusText ) {
+					var finalText = statusText || strAbort;
+					if ( transport ) {
+						transport.abort( finalText );
+					}
+					done( 0, finalText );
+					return this;
+				}
+			};
+
+		// Attach deferreds
+		deferred.promise( jqXHR );
+
+		// Add protocol if not provided (prefilters might expect it)
+		// Handle falsy url in the settings object (#10093: consistency with old signature)
+		// We also use the url parameter if available
+		s.url = ( ( url || s.url || location.href ) + "" )
+			.replace( rprotocol, location.protocol + "//" );
+
+		// Alias method option to type as per ticket #12004
+		s.type = options.method || options.type || s.method || s.type;
+
+		// Extract dataTypes list
+		s.dataTypes = ( s.dataType || "*" ).toLowerCase().match( rnothtmlwhite ) || [ "" ];
+
+		// A cross-domain request is in order when the origin doesn't match the current origin.
+		if ( s.crossDomain == null ) {
+			urlAnchor = document.createElement( "a" );
+
+			// Support: IE <=8 - 11, Edge 12 - 15
+			// IE throws exception on accessing the href property if url is malformed,
+			// e.g. http://example.com:80x/
+			try {
+				urlAnchor.href = s.url;
+
+				// Support: IE <=8 - 11 only
+				// Anchor's host property isn't correctly set when s.url is relative
+				urlAnchor.href = urlAnchor.href;
+				s.crossDomain = originAnchor.protocol + "//" + originAnchor.host !==
+					urlAnchor.protocol + "//" + urlAnchor.host;
+			} catch ( e ) {
+
+				// If there is an error parsing the URL, assume it is crossDomain,
+				// it can be rejected by the transport if it is invalid
+				s.crossDomain = true;
+			}
+		}
+
+		// Convert data if not already a string
+		if ( s.data && s.processData && typeof s.data !== "string" ) {
+			s.data = jQuery.param( s.data, s.traditional );
+		}
+
+		// Apply prefilters
+		inspectPrefiltersOrTransports( prefilters, s, options, jqXHR );
+
+		// If request was aborted inside a prefilter, stop there
+		if ( completed ) {
+			return jqXHR;
+		}
+
+		// We can fire global events as of now if asked to
+		// Don't fire events if jQuery.event is undefined in an AMD-usage scenario (#15118)
+		fireGlobals = jQuery.event && s.global;
+
+		// Watch for a new set of requests
+		if ( fireGlobals && jQuery.active++ === 0 ) {
+			jQuery.event.trigger( "ajaxStart" );
+		}
+
+		// Uppercase the type
+		s.type = s.type.toUpperCase();
+
+		// Determine if request has content
+		s.hasContent = !rnoContent.test( s.type );
+
+		// Save the URL in case we're toying with the If-Modified-Since
+		// and/or If-None-Match header later on
+		// Remove hash to simplify url manipulation
+		cacheURL = s.url.replace( rhash, "" );
+
+		// More options handling for requests with no content
+		if ( !s.hasContent ) {
+
+			// Remember the hash so we can put it back
+			uncached = s.url.slice( cacheURL.length );
+
+			// If data is available and should be processed, append data to url
+			if ( s.data && ( s.processData || typeof s.data === "string" ) ) {
+				cacheURL += ( rquery.test( cacheURL ) ? "&" : "?" ) + s.data;
+
+				// #9682: remove data so that it's not used in an eventual retry
+				delete s.data;
+			}
+
+			// Add or update anti-cache param if needed
+			if ( s.cache === false ) {
+				cacheURL = cacheURL.replace( rantiCache, "$1" );
+				uncached = ( rquery.test( cacheURL ) ? "&" : "?" ) + "_=" + ( nonce.guid++ ) +
+					uncached;
+			}
+
+			// Put hash and anti-cache on the URL that will be requested (gh-1732)
+			s.url = cacheURL + uncached;
+
+		// Change '%20' to '+' if this is encoded form body content (gh-2658)
+		} else if ( s.data && s.processData &&
+			( s.contentType || "" ).indexOf( "application/x-www-form-urlencoded" ) === 0 ) {
+			s.data = s.data.replace( r20, "+" );
+		}
+
+		// Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode.
+		if ( s.ifModified ) {
+			if ( jQuery.lastModified[ cacheURL ] ) {
+				jqXHR.setRequestHeader( "If-Modified-Since", jQuery.lastModified[ cacheURL ] );
+			}
+			if ( jQuery.etag[ cacheURL ] ) {
+				jqXHR.setRequestHeader( "If-None-Match", jQuery.etag[ cacheURL ] );
+			}
+		}
+
+		// Set the correct header, if data is being sent
+		if ( s.data && s.hasContent && s.contentType !== false || options.contentType ) {
+			jqXHR.setRequestHeader( "Content-Type", s.contentType );
+		}
+
+		// Set the Accepts header for the server, depending on the dataType
+		jqXHR.setRequestHeader(
+			"Accept",
+			s.dataTypes[ 0 ] && s.accepts[ s.dataTypes[ 0 ] ] ?
+				s.accepts[ s.dataTypes[ 0 ] ] +
+					( s.dataTypes[ 0 ] !== "*" ? ", " + allTypes + "; q=0.01" : "" ) :
+				s.accepts[ "*" ]
+		);
+
+		// Check for headers option
+		for ( i in s.headers ) {
+			jqXHR.setRequestHeader( i, s.headers[ i ] );
+		}
+
+		// Allow custom headers/mimetypes and early abort
+		if ( s.beforeSend &&
+			( s.beforeSend.call( callbackContext, jqXHR, s ) === false || completed ) ) {
+
+			// Abort if not done already and return
+			return jqXHR.abort();
+		}
+
+		// Aborting is no longer a cancellation
+		strAbort = "abort";
+
+		// Install callbacks on deferreds
+		completeDeferred.add( s.complete );
+		jqXHR.done( s.success );
+		jqXHR.fail( s.error );
+
+		// Get transport
+		transport = inspectPrefiltersOrTransports( transports, s, options, jqXHR );
+
+		// If no transport, we auto-abort
+		if ( !transport ) {
+			done( -1, "No Transport" );
+		} else {
+			jqXHR.readyState = 1;
+
+			// Send global event
+			if ( fireGlobals ) {
+				globalEventContext.trigger( "ajaxSend", [ jqXHR, s ] );
+			}
+
+			// If request was aborted inside ajaxSend, stop there
+			if ( completed ) {
+				return jqXHR;
+			}
+
+			// Timeout
+			if ( s.async && s.timeout > 0 ) {
+				timeoutTimer = window.setTimeout( function() {
+					jqXHR.abort( "timeout" );
+				}, s.timeout );
+			}
+
+			try {
+				completed = false;
+				transport.send( requestHeaders, done );
+			} catch ( e ) {
+
+				// Rethrow post-completion exceptions
+				if ( completed ) {
+					throw e;
+				}
+
+				// Propagate others as results
+				done( -1, e );
+			}
+		}
+
+		// Callback for when everything is done
+		function done( status, nativeStatusText, responses, headers ) {
+			var isSuccess, success, error, response, modified,
+				statusText = nativeStatusText;
+
+			// Ignore repeat invocations
+			if ( completed ) {
+				return;
+			}
+
+			completed = true;
+
+			// Clear timeout if it exists
+			if ( timeoutTimer ) {
+				window.clearTimeout( timeoutTimer );
+			}
+
+			// Dereference transport for early garbage collection
+			// (no matter how long the jqXHR object will be used)
+			transport = undefined;
+
+			// Cache response headers
+			responseHeadersString = headers || "";
+
+			// Set readyState
+			jqXHR.readyState = status > 0 ? 4 : 0;
+
+			// Determine if successful
+			isSuccess = status >= 200 && status < 300 || status === 304;
+
+			// Get response data
+			if ( responses ) {
+				response = ajaxHandleResponses( s, jqXHR, responses );
+			}
+
+			// Use a noop converter for missing script but not if jsonp
+			if ( !isSuccess &&
+				jQuery.inArray( "script", s.dataTypes ) > -1 &&
+				jQuery.inArray( "json", s.dataTypes ) < 0 ) {
+				s.converters[ "text script" ] = function() {};
+			}
+
+			// Convert no matter what (that way responseXXX fields are always set)
+			response = ajaxConvert( s, response, jqXHR, isSuccess );
+
+			// If successful, handle type chaining
+			if ( isSuccess ) {
+
+				// Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode.
+				if ( s.ifModified ) {
+					modified = jqXHR.getResponseHeader( "Last-Modified" );
+					if ( modified ) {
+						jQuery.lastModified[ cacheURL ] = modified;
+					}
+					modified = jqXHR.getResponseHeader( "etag" );
+					if ( modified ) {
+						jQuery.etag[ cacheURL ] = modified;
+					}
+				}
+
+				// if no content
+				if ( status === 204 || s.type === "HEAD" ) {
+					statusText = "nocontent";
+
+				// if not modified
+				} else if ( status === 304 ) {
+					statusText = "notmodified";
+
+				// If we have data, let's convert it
+				} else {
+					statusText = response.state;
+					success = response.data;
+					error = response.error;
+					isSuccess = !error;
+				}
+			} else {
+
+				// Extract error from statusText and normalize for non-aborts
+				error = statusText;
+				if ( status || !statusText ) {
+					statusText = "error";
+					if ( status < 0 ) {
+						status = 0;
+					}
+				}
+			}
+
+			// Set data for the fake xhr object
+			jqXHR.status = status;
+			jqXHR.statusText = ( nativeStatusText || statusText ) + "";
+
+			// Success/Error
+			if ( isSuccess ) {
+				deferred.resolveWith( callbackContext, [ success, statusText, jqXHR ] );
+			} else {
+				deferred.rejectWith( callbackContext, [ jqXHR, statusText, error ] );
+			}
+
+			// Status-dependent callbacks
+			jqXHR.statusCode( statusCode );
+			statusCode = undefined;
+
+			if ( fireGlobals ) {
+				globalEventContext.trigger( isSuccess ? "ajaxSuccess" : "ajaxError",
+					[ jqXHR, s, isSuccess ? success : error ] );
+			}
+
+			// Complete
+			completeDeferred.fireWith( callbackContext, [ jqXHR, statusText ] );
+
+			if ( fireGlobals ) {
+				globalEventContext.trigger( "ajaxComplete", [ jqXHR, s ] );
+
+				// Handle the global AJAX counter
+				if ( !( --jQuery.active ) ) {
+					jQuery.event.trigger( "ajaxStop" );
+				}
+			}
+		}
+
+		return jqXHR;
+	},
+
+	getJSON: function( url, data, callback ) {
+		return jQuery.get( url, data, callback, "json" );
+	},
+
+	getScript: function( url, callback ) {
+		return jQuery.get( url, undefined, callback, "script" );
+	}
+} );
+
+jQuery.each( [ "get", "post" ], function( _i, method ) {
+	jQuery[ method ] = function( url, data, callback, type ) {
+
+		// Shift arguments if data argument was omitted
+		if ( isFunction( data ) ) {
+			type = type || callback;
+			callback = data;
+			data = undefined;
+		}
+
+		// The url can be an options object (which then must have .url)
+		return jQuery.ajax( jQuery.extend( {
+			url: url,
+			type: method,
+			dataType: type,
+			data: data,
+			success: callback
+		}, jQuery.isPlainObject( url ) && url ) );
+	};
+} );
+
+jQuery.ajaxPrefilter( function( s ) {
+	var i;
+	for ( i in s.headers ) {
+		if ( i.toLowerCase() === "content-type" ) {
+			s.contentType = s.headers[ i ] || "";
+		}
+	}
+} );
+
+
+jQuery._evalUrl = function( url, options, doc ) {
+	return jQuery.ajax( {
+		url: url,
+
+		// Make this explicit, since user can override this through ajaxSetup (#11264)
+		type: "GET",
+		dataType: "script",
+		cache: true,
+		async: false,
+		global: false,
+
+		// Only evaluate the response if it is successful (gh-4126)
+		// dataFilter is not invoked for failure responses, so using it instead
+		// of the default converter is kludgy but it works.
+		converters: {
+			"text script": function() {}
+		},
+		dataFilter: function( response ) {
+			jQuery.globalEval( response, options, doc );
+		}
+	} );
+};
+
+
+jQuery.fn.extend( {
+	wrapAll: function( html ) {
+		var wrap;
+
+		if ( this[ 0 ] ) {
+			if ( isFunction( html ) ) {
+				html = html.call( this[ 0 ] );
+			}
+
+			// The elements to wrap the target around
+			wrap = jQuery( html, this[ 0 ].ownerDocument ).eq( 0 ).clone( true );
+
+			if ( this[ 0 ].parentNode ) {
+				wrap.insertBefore( this[ 0 ] );
+			}
+
+			wrap.map( function() {
+				var elem = this;
+
+				while ( elem.firstElementChild ) {
+					elem = elem.firstElementChild;
+				}
+
+				return elem;
+			} ).append( this );
+		}
+
+		return this;
+	},
+
+	wrapInner: function( html ) {
+		if ( isFunction( html ) ) {
+			return this.each( function( i ) {
+				jQuery( this ).wrapInner( html.call( this, i ) );
+			} );
+		}
+
+		return this.each( function() {
+			var self = jQuery( this ),
+				contents = self.contents();
+
+			if ( contents.length ) {
+				contents.wrapAll( html );
+
+			} else {
+				self.append( html );
+			}
+		} );
+	},
+
+	wrap: function( html ) {
+		var htmlIsFunction = isFunction( html );
+
+		return this.each( function( i ) {
+			jQuery( this ).wrapAll( htmlIsFunction ? html.call( this, i ) : html );
+		} );
+	},
+
+	unwrap: function( selector ) {
+		this.parent( selector ).not( "body" ).each( function() {
+			jQuery( this ).replaceWith( this.childNodes );
+		} );
+		return this;
+	}
+} );
+
+
+jQuery.expr.pseudos.hidden = function( elem ) {
+	return !jQuery.expr.pseudos.visible( elem );
+};
+jQuery.expr.pseudos.visible = function( elem ) {
+	return !!( elem.offsetWidth || elem.offsetHeight || elem.getClientRects().length );
+};
+
+
+
+
+jQuery.ajaxSettings.xhr = function() {
+	try {
+		return new window.XMLHttpRequest();
+	} catch ( e ) {}
+};
+
+var xhrSuccessStatus = {
+
+		// File protocol always yields status code 0, assume 200
+		0: 200,
+
+		// Support: IE <=9 only
+		// #1450: sometimes IE returns 1223 when it should be 204
+		1223: 204
+	},
+	xhrSupported = jQuery.ajaxSettings.xhr();
+
+support.cors = !!xhrSupported && ( "withCredentials" in xhrSupported );
+support.ajax = xhrSupported = !!xhrSupported;
+
+jQuery.ajaxTransport( function( options ) {
+	var callback, errorCallback;
+
+	// Cross domain only allowed if supported through XMLHttpRequest
+	if ( support.cors || xhrSupported && !options.crossDomain ) {
+		return {
+			send: function( headers, complete ) {
+				var i,
+					xhr = options.xhr();
+
+				xhr.open(
+					options.type,
+					options.url,
+					options.async,
+					options.username,
+					options.password
+				);
+
+				// Apply custom fields if provided
+				if ( options.xhrFields ) {
+					for ( i in options.xhrFields ) {
+						xhr[ i ] = options.xhrFields[ i ];
+					}
+				}
+
+				// Override mime type if needed
+				if ( options.mimeType && xhr.overrideMimeType ) {
+					xhr.overrideMimeType( options.mimeType );
+				}
+
+				// X-Requested-With header
+				// For cross-domain requests, seeing as conditions for a preflight are
+				// akin to a jigsaw puzzle, we simply never set it to be sure.
+				// (it can always be set on a per-request basis or even using ajaxSetup)
+				// For same-domain requests, won't change header if already provided.
+				if ( !options.crossDomain && !headers[ "X-Requested-With" ] ) {
+					headers[ "X-Requested-With" ] = "XMLHttpRequest";
+				}
+
+				// Set headers
+				for ( i in headers ) {
+					xhr.setRequestHeader( i, headers[ i ] );
+				}
+
+				// Callback
+				callback = function( type ) {
+					return function() {
+						if ( callback ) {
+							callback = errorCallback = xhr.onload =
+								xhr.onerror = xhr.onabort = xhr.ontimeout =
+									xhr.onreadystatechange = null;
+
+							if ( type === "abort" ) {
+								xhr.abort();
+							} else if ( type === "error" ) {
+
+								// Support: IE <=9 only
+								// On a manual native abort, IE9 throws
+								// errors on any property access that is not readyState
+								if ( typeof xhr.status !== "number" ) {
+									complete( 0, "error" );
+								} else {
+									complete(
+
+										// File: protocol always yields status 0; see #8605, #14207
+										xhr.status,
+										xhr.statusText
+									);
+								}
+							} else {
+								complete(
+									xhrSuccessStatus[ xhr.status ] || xhr.status,
+									xhr.statusText,
+
+									// Support: IE <=9 only
+									// IE9 has no XHR2 but throws on binary (trac-11426)
+									// For XHR2 non-text, let the caller handle it (gh-2498)
+									( xhr.responseType || "text" ) !== "text"  ||
+									typeof xhr.responseText !== "string" ?
+										{ binary: xhr.response } :
+										{ text: xhr.responseText },
+									xhr.getAllResponseHeaders()
+								);
+							}
+						}
+					};
+				};
+
+				// Listen to events
+				xhr.onload = callback();
+				errorCallback = xhr.onerror = xhr.ontimeout = callback( "error" );
+
+				// Support: IE 9 only
+				// Use onreadystatechange to replace onabort
+				// to handle uncaught aborts
+				if ( xhr.onabort !== undefined ) {
+					xhr.onabort = errorCallback;
+				} else {
+					xhr.onreadystatechange = function() {
+
+						// Check readyState before timeout as it changes
+						if ( xhr.readyState === 4 ) {
+
+							// Allow onerror to be called first,
+							// but that will not handle a native abort
+							// Also, save errorCallback to a variable
+							// as xhr.onerror cannot be accessed
+							window.setTimeout( function() {
+								if ( callback ) {
+									errorCallback();
+								}
+							} );
+						}
+					};
+				}
+
+				// Create the abort callback
+				callback = callback( "abort" );
+
+				try {
+
+					// Do send the request (this may raise an exception)
+					xhr.send( options.hasContent && options.data || null );
+				} catch ( e ) {
+
+					// #14683: Only rethrow if this hasn't been notified as an error yet
+					if ( callback ) {
+						throw e;
+					}
+				}
+			},
+
+			abort: function() {
+				if ( callback ) {
+					callback();
+				}
+			}
+		};
+	}
+} );
+
+
+
+
+// Prevent auto-execution of scripts when no explicit dataType was provided (See gh-2432)
+jQuery.ajaxPrefilter( function( s ) {
+	if ( s.crossDomain ) {
+		s.contents.script = false;
+	}
+} );
+
+// Install script dataType
+jQuery.ajaxSetup( {
+	accepts: {
+		script: "text/javascript, application/javascript, " +
+			"application/ecmascript, application/x-ecmascript"
+	},
+	contents: {
+		script: /\b(?:java|ecma)script\b/
+	},
+	converters: {
+		"text script": function( text ) {
+			jQuery.globalEval( text );
+			return text;
+		}
+	}
+} );
+
+// Handle cache's special case and crossDomain
+jQuery.ajaxPrefilter( "script", function( s ) {
+	if ( s.cache === undefined ) {
+		s.cache = false;
+	}
+	if ( s.crossDomain ) {
+		s.type = "GET";
+	}
+} );
+
+// Bind script tag hack transport
+jQuery.ajaxTransport( "script", function( s ) {
+
+	// This transport only deals with cross domain or forced-by-attrs requests
+	if ( s.crossDomain || s.scriptAttrs ) {
+		var script, callback;
+		return {
+			send: function( _, complete ) {
+				script = jQuery( "<script>" )
+					.attr( s.scriptAttrs || {} )
+					.prop( { charset: s.scriptCharset, src: s.url } )
+					.on( "load error", callback = function( evt ) {
+						script.remove();
+						callback = null;
+						if ( evt ) {
+							complete( evt.type === "error" ? 404 : 200, evt.type );
+						}
+					} );
+
+				// Use native DOM manipulation to avoid our domManip AJAX trickery
+				document.head.appendChild( script[ 0 ] );
+			},
+			abort: function() {
+				if ( callback ) {
+					callback();
+				}
+			}
+		};
+	}
+} );
+
+
+
+
+var oldCallbacks = [],
+	rjsonp = /(=)\?(?=&|$)|\?\?/;
+
+// Default jsonp settings
+jQuery.ajaxSetup( {
+	jsonp: "callback",
+	jsonpCallback: function() {
+		var callback = oldCallbacks.pop() || ( jQuery.expando + "_" + ( nonce.guid++ ) );
+		this[ callback ] = true;
+		return callback;
+	}
+} );
+
+// Detect, normalize options and install callbacks for jsonp requests
+jQuery.ajaxPrefilter( "json jsonp", function( s, originalSettings, jqXHR ) {
+
+	var callbackName, overwritten, responseContainer,
+		jsonProp = s.jsonp !== false && ( rjsonp.test( s.url ) ?
+			"url" :
+			typeof s.data === "string" &&
+				( s.contentType || "" )
+					.indexOf( "application/x-www-form-urlencoded" ) === 0 &&
+				rjsonp.test( s.data ) && "data"
+		);
+
+	// Handle iff the expected data type is "jsonp" or we have a parameter to set
+	if ( jsonProp || s.dataTypes[ 0 ] === "jsonp" ) {
+
+		// Get callback name, remembering preexisting value associated with it
+		callbackName = s.jsonpCallback = isFunction( s.jsonpCallback ) ?
+			s.jsonpCallback() :
+			s.jsonpCallback;
+
+		// Insert callback into url or form data
+		if ( jsonProp ) {
+			s[ jsonProp ] = s[ jsonProp ].replace( rjsonp, "$1" + callbackName );
+		} else if ( s.jsonp !== false ) {
+			s.url += ( rquery.test( s.url ) ? "&" : "?" ) + s.jsonp + "=" + callbackName;
+		}
+
+		// Use data converter to retrieve json after script execution
+		s.converters[ "script json" ] = function() {
+			if ( !responseContainer ) {
+				jQuery.error( callbackName + " was not called" );
+			}
+			return responseContainer[ 0 ];
+		};
+
+		// Force json dataType
+		s.dataTypes[ 0 ] = "json";
+
+		// Install callback
+		overwritten = window[ callbackName ];
+		window[ callbackName ] = function() {
+			responseContainer = arguments;
+		};
+
+		// Clean-up function (fires after converters)
+		jqXHR.always( function() {
+
+			// If previous value didn't exist - remove it
+			if ( overwritten === undefined ) {
+				jQuery( window ).removeProp( callbackName );
+
+			// Otherwise restore preexisting value
+			} else {
+				window[ callbackName ] = overwritten;
+			}
+
+			// Save back as free
+			if ( s[ callbackName ] ) {
+
+				// Make sure that re-using the options doesn't screw things around
+				s.jsonpCallback = originalSettings.jsonpCallback;
+
+				// Save the callback name for future use
+				oldCallbacks.push( callbackName );
+			}
+
+			// Call if it was a function and we have a response
+			if ( responseContainer && isFunction( overwritten ) ) {
+				overwritten( responseContainer[ 0 ] );
+			}
+
+			responseContainer = overwritten = undefined;
+		} );
+
+		// Delegate to script
+		return "script";
+	}
+} );
+
+
+
+
+// Support: Safari 8 only
+// In Safari 8 documents created via document.implementation.createHTMLDocument
+// collapse sibling forms: the second one becomes a child of the first one.
+// Because of that, this security measure has to be disabled in Safari 8.
+// https://bugs.webkit.org/show_bug.cgi?id=137337
+support.createHTMLDocument = ( function() {
+	var body = document.implementation.createHTMLDocument( "" ).body;
+	body.innerHTML = "<form></form><form></form>";
+	return body.childNodes.length === 2;
+} )();
+
+
+// Argument "data" should be string of html
+// context (optional): If specified, the fragment will be created in this context,
+// defaults to document
+// keepScripts (optional): If true, will include scripts passed in the html string
+jQuery.parseHTML = function( data, context, keepScripts ) {
+	if ( typeof data !== "string" ) {
+		return [];
+	}
+	if ( typeof context === "boolean" ) {
+		keepScripts = context;
+		context = false;
+	}
+
+	var base, parsed, scripts;
+
+	if ( !context ) {
+
+		// Stop scripts or inline event handlers from being executed immediately
+		// by using document.implementation
+		if ( support.createHTMLDocument ) {
+			context = document.implementation.createHTMLDocument( "" );
+
+			// Set the base href for the created document
+			// so any parsed elements with URLs
+			// are based on the document's URL (gh-2965)
+			base = context.createElement( "base" );
+			base.href = document.location.href;
+			context.head.appendChild( base );
+		} else {
+			context = document;
+		}
+	}
+
+	parsed = rsingleTag.exec( data );
+	scripts = !keepScripts && [];
+
+	// Single tag
+	if ( parsed ) {
+		return [ context.createElement( parsed[ 1 ] ) ];
+	}
+
+	parsed = buildFragment( [ data ], context, scripts );
+
+	if ( scripts && scripts.length ) {
+		jQuery( scripts ).remove();
+	}
+
+	return jQuery.merge( [], parsed.childNodes );
+};
+
+
+/**
+ * Load a url into a page
+ */
+jQuery.fn.load = function( url, params, callback ) {
+	var selector, type, response,
+		self = this,
+		off = url.indexOf( " " );
+
+	if ( off > -1 ) {
+		selector = stripAndCollapse( url.slice( off ) );
+		url = url.slice( 0, off );
+	}
+
+	// If it's a function
+	if ( isFunction( params ) ) {
+
+		// We assume that it's the callback
+		callback = params;
+		params = undefined;
+
+	// Otherwise, build a param string
+	} else if ( params && typeof params === "object" ) {
+		type = "POST";
+	}
+
+	// If we have elements to modify, make the request
+	if ( self.length > 0 ) {
+		jQuery.ajax( {
+			url: url,
+
+			// If "type" variable is undefined, then "GET" method will be used.
+			// Make value of this field explicit since
+			// user can override it through ajaxSetup method
+			type: type || "GET",
+			dataType: "html",
+			data: params
+		} ).done( function( responseText ) {
+
+			// Save response for use in complete callback
+			response = arguments;
+
+			self.html( selector ?
+
+				// If a selector was specified, locate the right elements in a dummy div
+				// Exclude scripts to avoid IE 'Permission Denied' errors
+				jQuery( "<div>" ).append( jQuery.parseHTML( responseText ) ).find( selector ) :
+
+				// Otherwise use the full result
+				responseText );
+
+		// If the request succeeds, this function gets "data", "status", "jqXHR"
+		// but they are ignored because response was set above.
+		// If it fails, this function gets "jqXHR", "status", "error"
+		} ).always( callback && function( jqXHR, status ) {
+			self.each( function() {
+				callback.apply( this, response || [ jqXHR.responseText, status, jqXHR ] );
+			} );
+		} );
+	}
+
+	return this;
+};
+
+
+
+
+jQuery.expr.pseudos.animated = function( elem ) {
+	return jQuery.grep( jQuery.timers, function( fn ) {
+		return elem === fn.elem;
+	} ).length;
+};
+
+
+
+
+jQuery.offset = {
+	setOffset: function( elem, options, i ) {
+		var curPosition, curLeft, curCSSTop, curTop, curOffset, curCSSLeft, calculatePosition,
+			position = jQuery.css( elem, "position" ),
+			curElem = jQuery( elem ),
+			props = {};
+
+		// Set position first, in-case top/left are set even on static elem
+		if ( position === "static" ) {
+			elem.style.position = "relative";
+		}
+
+		curOffset = curElem.offset();
+		curCSSTop = jQuery.css( elem, "top" );
+		curCSSLeft = jQuery.css( elem, "left" );
+		calculatePosition = ( position === "absolute" || position === "fixed" ) &&
+			( curCSSTop + curCSSLeft ).indexOf( "auto" ) > -1;
+
+		// Need to be able to calculate position if either
+		// top or left is auto and position is either absolute or fixed
+		if ( calculatePosition ) {
+			curPosition = curElem.position();
+			curTop = curPosition.top;
+			curLeft = curPosition.left;
+
+		} else {
+			curTop = parseFloat( curCSSTop ) || 0;
+			curLeft = parseFloat( curCSSLeft ) || 0;
+		}
+
+		if ( isFunction( options ) ) {
+
+			// Use jQuery.extend here to allow modification of coordinates argument (gh-1848)
+			options = options.call( elem, i, jQuery.extend( {}, curOffset ) );
+		}
+
+		if ( options.top != null ) {
+			props.top = ( options.top - curOffset.top ) + curTop;
+		}
+		if ( options.left != null ) {
+			props.left = ( options.left - curOffset.left ) + curLeft;
+		}
+
+		if ( "using" in options ) {
+			options.using.call( elem, props );
+
+		} else {
+			curElem.css( props );
+		}
+	}
+};
+
+jQuery.fn.extend( {
+
+	// offset() relates an element's border box to the document origin
+	offset: function( options ) {
+
+		// Preserve chaining for setter
+		if ( arguments.length ) {
+			return options === undefined ?
+				this :
+				this.each( function( i ) {
+					jQuery.offset.setOffset( this, options, i );
+				} );
+		}
+
+		var rect, win,
+			elem = this[ 0 ];
+
+		if ( !elem ) {
+			return;
+		}
+
+		// Return zeros for disconnected and hidden (display: none) elements (gh-2310)
+		// Support: IE <=11 only
+		// Running getBoundingClientRect on a
+		// disconnected node in IE throws an error
+		if ( !elem.getClientRects().length ) {
+			return { top: 0, left: 0 };
+		}
+
+		// Get document-relative position by adding viewport scroll to viewport-relative gBCR
+		rect = elem.getBoundingClientRect();
+		win = elem.ownerDocument.defaultView;
+		return {
+			top: rect.top + win.pageYOffset,
+			left: rect.left + win.pageXOffset
+		};
+	},
+
+	// position() relates an element's margin box to its offset parent's padding box
+	// This corresponds to the behavior of CSS absolute positioning
+	position: function() {
+		if ( !this[ 0 ] ) {
+			return;
+		}
+
+		var offsetParent, offset, doc,
+			elem = this[ 0 ],
+			parentOffset = { top: 0, left: 0 };
+
+		// position:fixed elements are offset from the viewport, which itself always has zero offset
+		if ( jQuery.css( elem, "position" ) === "fixed" ) {
+
+			// Assume position:fixed implies availability of getBoundingClientRect
+			offset = elem.getBoundingClientRect();
+
+		} else {
+			offset = this.offset();
+
+			// Account for the *real* offset parent, which can be the document or its root element
+			// when a statically positioned element is identified
+			doc = elem.ownerDocument;
+			offsetParent = elem.offsetParent || doc.documentElement;
+			while ( offsetParent &&
+				( offsetParent === doc.body || offsetParent === doc.documentElement ) &&
+				jQuery.css( offsetParent, "position" ) === "static" ) {
+
+				offsetParent = offsetParent.parentNode;
+			}
+			if ( offsetParent && offsetParent !== elem && offsetParent.nodeType === 1 ) {
+
+				// Incorporate borders into its offset, since they are outside its content origin
+				parentOffset = jQuery( offsetParent ).offset();
+				parentOffset.top += jQuery.css( offsetParent, "borderTopWidth", true );
+				parentOffset.left += jQuery.css( offsetParent, "borderLeftWidth", true );
+			}
+		}
+
+		// Subtract parent offsets and element margins
+		return {
+			top: offset.top - parentOffset.top - jQuery.css( elem, "marginTop", true ),
+			left: offset.left - parentOffset.left - jQuery.css( elem, "marginLeft", true )
+		};
+	},
+
+	// This method will return documentElement in the following cases:
+	// 1) For the element inside the iframe without offsetParent, this method will return
+	//    documentElement of the parent window
+	// 2) For the hidden or detached element
+	// 3) For body or html element, i.e. in case of the html node - it will return itself
+	//
+	// but those exceptions were never presented as a real life use-cases
+	// and might be considered as more preferable results.
+	//
+	// This logic, however, is not guaranteed and can change at any point in the future
+	offsetParent: function() {
+		return this.map( function() {
+			var offsetParent = this.offsetParent;
+
+			while ( offsetParent && jQuery.css( offsetParent, "position" ) === "static" ) {
+				offsetParent = offsetParent.offsetParent;
+			}
+
+			return offsetParent || documentElement;
+		} );
+	}
+} );
+
+// Create scrollLeft and scrollTop methods
+jQuery.each( { scrollLeft: "pageXOffset", scrollTop: "pageYOffset" }, function( method, prop ) {
+	var top = "pageYOffset" === prop;
+
+	jQuery.fn[ method ] = function( val ) {
+		return access( this, function( elem, method, val ) {
+
+			// Coalesce documents and windows
+			var win;
+			if ( isWindow( elem ) ) {
+				win = elem;
+			} else if ( elem.nodeType === 9 ) {
+				win = elem.defaultView;
+			}
+
+			if ( val === undefined ) {
+				return win ? win[ prop ] : elem[ method ];
+			}
+
+			if ( win ) {
+				win.scrollTo(
+					!top ? val : win.pageXOffset,
+					top ? val : win.pageYOffset
+				);
+
+			} else {
+				elem[ method ] = val;
+			}
+		}, method, val, arguments.length );
+	};
+} );
+
+// Support: Safari <=7 - 9.1, Chrome <=37 - 49
+// Add the top/left cssHooks using jQuery.fn.position
+// Webkit bug: https://bugs.webkit.org/show_bug.cgi?id=29084
+// Blink bug: https://bugs.chromium.org/p/chromium/issues/detail?id=589347
+// getComputedStyle returns percent when specified for top/left/bottom/right;
+// rather than make the css module depend on the offset module, just check for it here
+jQuery.each( [ "top", "left" ], function( _i, prop ) {
+	jQuery.cssHooks[ prop ] = addGetHookIf( support.pixelPosition,
+		function( elem, computed ) {
+			if ( computed ) {
+				computed = curCSS( elem, prop );
+
+				// If curCSS returns percentage, fallback to offset
+				return rnumnonpx.test( computed ) ?
+					jQuery( elem ).position()[ prop ] + "px" :
+					computed;
+			}
+		}
+	);
+} );
+
+
+// Create innerHeight, innerWidth, height, width, outerHeight and outerWidth methods
+jQuery.each( { Height: "height", Width: "width" }, function( name, type ) {
+	jQuery.each( {
+		padding: "inner" + name,
+		content: type,
+		"": "outer" + name
+	}, function( defaultExtra, funcName ) {
+
+		// Margin is only for outerHeight, outerWidth
+		jQuery.fn[ funcName ] = function( margin, value ) {
+			var chainable = arguments.length && ( defaultExtra || typeof margin !== "boolean" ),
+				extra = defaultExtra || ( margin === true || value === true ? "margin" : "border" );
+
+			return access( this, function( elem, type, value ) {
+				var doc;
+
+				if ( isWindow( elem ) ) {
+
+					// $( window ).outerWidth/Height return w/h including scrollbars (gh-1729)
+					return funcName.indexOf( "outer" ) === 0 ?
+						elem[ "inner" + name ] :
+						elem.document.documentElement[ "client" + name ];
+				}
+
+				// Get document width or height
+				if ( elem.nodeType === 9 ) {
+					doc = elem.documentElement;
+
+					// Either scroll[Width/Height] or offset[Width/Height] or client[Width/Height],
+					// whichever is greatest
+					return Math.max(
+						elem.body[ "scroll" + name ], doc[ "scroll" + name ],
+						elem.body[ "offset" + name ], doc[ "offset" + name ],
+						doc[ "client" + name ]
+					);
+				}
+
+				return value === undefined ?
+
+					// Get width or height on the element, requesting but not forcing parseFloat
+					jQuery.css( elem, type, extra ) :
+
+					// Set width or height on the element
+					jQuery.style( elem, type, value, extra );
+			}, type, chainable ? margin : undefined, chainable );
+		};
+	} );
+} );
+
+
+jQuery.each( [
+	"ajaxStart",
+	"ajaxStop",
+	"ajaxComplete",
+	"ajaxError",
+	"ajaxSuccess",
+	"ajaxSend"
+], function( _i, type ) {
+	jQuery.fn[ type ] = function( fn ) {
+		return this.on( type, fn );
+	};
+} );
+
+
+
+
+jQuery.fn.extend( {
+
+	bind: function( types, data, fn ) {
+		return this.on( types, null, data, fn );
+	},
+	unbind: function( types, fn ) {
+		return this.off( types, null, fn );
+	},
+
+	delegate: function( selector, types, data, fn ) {
+		return this.on( types, selector, data, fn );
+	},
+	undelegate: function( selector, types, fn ) {
+
+		// ( namespace ) or ( selector, types [, fn] )
+		return arguments.length === 1 ?
+			this.off( selector, "**" ) :
+			this.off( types, selector || "**", fn );
+	},
+
+	hover: function( fnOver, fnOut ) {
+		return this.mouseenter( fnOver ).mouseleave( fnOut || fnOver );
+	}
+} );
+
+jQuery.each(
+	( "blur focus focusin focusout resize scroll click dblclick " +
+	"mousedown mouseup mousemove mouseover mouseout mouseenter mouseleave " +
+	"change select submit keydown keypress keyup contextmenu" ).split( " " ),
+	function( _i, name ) {
+
+		// Handle event binding
+		jQuery.fn[ name ] = function( data, fn ) {
+			return arguments.length > 0 ?
+				this.on( name, null, data, fn ) :
+				this.trigger( name );
+		};
+	}
+);
+
+
+
+
+// Support: Android <=4.0 only
+// Make sure we trim BOM and NBSP
+var rtrim = /^[\s\uFEFF\xA0]+|[\s\uFEFF\xA0]+$/g;
+
+// Bind a function to a context, optionally partially applying any
+// arguments.
+// jQuery.proxy is deprecated to promote standards (specifically Function#bind)
+// However, it is not slated for removal any time soon
+jQuery.proxy = function( fn, context ) {
+	var tmp, args, proxy;
+
+	if ( typeof context === "string" ) {
+		tmp = fn[ context ];
+		context = fn;
+		fn = tmp;
+	}
+
+	// Quick check to determine if target is callable, in the spec
+	// this throws a TypeError, but we will just return undefined.
+	if ( !isFunction( fn ) ) {
+		return undefined;
+	}
+
+	// Simulated bind
+	args = slice.call( arguments, 2 );
+	proxy = function() {
+		return fn.apply( context || this, args.concat( slice.call( arguments ) ) );
+	};
+
+	// Set the guid of unique handler to the same of original handler, so it can be removed
+	proxy.guid = fn.guid = fn.guid || jQuery.guid++;
+
+	return proxy;
+};
+
+jQuery.holdReady = function( hold ) {
+	if ( hold ) {
+		jQuery.readyWait++;
+	} else {
+		jQuery.ready( true );
+	}
+};
+jQuery.isArray = Array.isArray;
+jQuery.parseJSON = JSON.parse;
+jQuery.nodeName = nodeName;
+jQuery.isFunction = isFunction;
+jQuery.isWindow = isWindow;
+jQuery.camelCase = camelCase;
+jQuery.type = toType;
+
+jQuery.now = Date.now;
+
+jQuery.isNumeric = function( obj ) {
+
+	// As of jQuery 3.0, isNumeric is limited to
+	// strings and numbers (primitives or objects)
+	// that can be coerced to finite numbers (gh-2662)
+	var type = jQuery.type( obj );
+	return ( type === "number" || type === "string" ) &&
+
+		// parseFloat NaNs numeric-cast false positives ("")
+		// ...but misinterprets leading-number strings, particularly hex literals ("0x...")
+		// subtraction forces infinities to NaN
+		!isNaN( obj - parseFloat( obj ) );
+};
+
+jQuery.trim = function( text ) {
+	return text == null ?
+		"" :
+		( text + "" ).replace( rtrim, "" );
+};
+
+
+
+// Register as a named AMD module, since jQuery can be concatenated with other
+// files that may use define, but not via a proper concatenation script that
+// understands anonymous AMD modules. A named AMD is safest and most robust
+// way to register. Lowercase jquery is used because AMD module names are
+// derived from file names, and jQuery is normally delivered in a lowercase
+// file name. Do this after creating the global so that if an AMD module wants
+// to call noConflict to hide this version of jQuery, it will work.
+
+// Note that for maximum portability, libraries that are not jQuery should
+// declare themselves as anonymous modules, and avoid setting a global if an
+// AMD loader is present. jQuery is a special case. For more information, see
+// https://github.com/jrburke/requirejs/wiki/Updating-existing-libraries#wiki-anon
+
+if ( typeof define === "function" && define.amd ) {
+	define( "jquery", [], function() {
+		return jQuery;
+	} );
+}
+
+
+
+
+var
+
+	// Map over jQuery in case of overwrite
+	_jQuery = window.jQuery,
+
+	// Map over the $ in case of overwrite
+	_$ = window.$;
+
+jQuery.noConflict = function( deep ) {
+	if ( window.$ === jQuery ) {
+		window.$ = _$;
+	}
+
+	if ( deep && window.jQuery === jQuery ) {
+		window.jQuery = _jQuery;
+	}
+
+	return jQuery;
+};
+
+// Expose jQuery and $ identifiers, even in AMD
+// (#7102#comment:10, https://github.com/jquery/jquery/pull/557)
+// and CommonJS for browser emulators (#13566)
+if ( typeof noGlobal === "undefined" ) {
+	window.jQuery = window.$ = jQuery;
+}
+
+
+
+
+return jQuery;
+} );
diff --git a/docs/deps/jquery-3.6.0/jquery-3.6.0.min.js b/docs/deps/jquery-3.6.0/jquery-3.6.0.min.js
new file mode 100644
index 00000000..c4c6022f
--- /dev/null
+++ b/docs/deps/jquery-3.6.0/jquery-3.6.0.min.js
@@ -0,0 +1,2 @@
+/*! jQuery v3.6.0 | (c) OpenJS Foundation and other contributors | jquery.org/license */
+!function(e,t){"use strict";"object"==typeof module&&"object"==typeof module.exports?module.exports=e.document?t(e,!0):function(e){if(!e.document)throw new Error("jQuery requires a window with a document");return t(e)}:t(e)}("undefined"!=typeof window?window:this,function(C,e){"use strict";var t=[],r=Object.getPrototypeOf,s=t.slice,g=t.flat?function(e){return t.flat.call(e)}:function(e){return t.concat.apply([],e)},u=t.push,i=t.indexOf,n={},o=n.toString,v=n.hasOwnProperty,a=v.toString,l=a.call(Object),y={},m=function(e){return"function"==typeof e&&"number"!=typeof e.nodeType&&"function"!=typeof e.item},x=function(e){return null!=e&&e===e.window},E=C.document,c={type:!0,src:!0,nonce:!0,noModule:!0};function b(e,t,n){var r,i,o=(n=n||E).createElement("script");if(o.text=e,t)for(r in c)(i=t[r]||t.getAttribute&&t.getAttribute(r))&&o.setAttribute(r,i);n.head.appendChild(o).parentNode.removeChild(o)}function w(e){return null==e?e+"":"object"==typeof e||"function"==typeof e?n[o.call(e)]||"object":typeof e}var f="3.6.0",S=function(e,t){return new S.fn.init(e,t)};function p(e){var t=!!e&&"length"in e&&e.length,n=w(e);return!m(e)&&!x(e)&&("array"===n||0===t||"number"==typeof t&&0<t&&t-1 in e)}S.fn=S.prototype={jquery:f,constructor:S,length:0,toArray:function(){return s.call(this)},get:function(e){return null==e?s.call(this):e<0?this[e+this.length]:this[e]},pushStack:function(e){var t=S.merge(this.constructor(),e);return t.prevObject=this,t},each:function(e){return S.each(this,e)},map:function(n){return this.pushStack(S.map(this,function(e,t){return n.call(e,t,e)}))},slice:function(){return this.pushStack(s.apply(this,arguments))},first:function(){return this.eq(0)},last:function(){return this.eq(-1)},even:function(){return this.pushStack(S.grep(this,function(e,t){return(t+1)%2}))},odd:function(){return this.pushStack(S.grep(this,function(e,t){return t%2}))},eq:function(e){var t=this.length,n=+e+(e<0?t:0);return this.pushStack(0<=n&&n<t?[this[n]]:[])},end:function(){return this.prevObject||this.constructor()},push:u,sort:t.sort,splice:t.splice},S.extend=S.fn.extend=function(){var e,t,n,r,i,o,a=arguments[0]||{},s=1,u=arguments.length,l=!1;for("boolean"==typeof a&&(l=a,a=arguments[s]||{},s++),"object"==typeof a||m(a)||(a={}),s===u&&(a=this,s--);s<u;s++)if(null!=(e=arguments[s]))for(t in e)r=e[t],"__proto__"!==t&&a!==r&&(l&&r&&(S.isPlainObject(r)||(i=Array.isArray(r)))?(n=a[t],o=i&&!Array.isArray(n)?[]:i||S.isPlainObject(n)?n:{},i=!1,a[t]=S.extend(l,o,r)):void 0!==r&&(a[t]=r));return a},S.extend({expando:"jQuery"+(f+Math.random()).replace(/\D/g,""),isReady:!0,error:function(e){throw new Error(e)},noop:function(){},isPlainObject:function(e){var t,n;return!(!e||"[object Object]"!==o.call(e))&&(!(t=r(e))||"function"==typeof(n=v.call(t,"constructor")&&t.constructor)&&a.call(n)===l)},isEmptyObject:function(e){var t;for(t in e)return!1;return!0},globalEval:function(e,t,n){b(e,{nonce:t&&t.nonce},n)},each:function(e,t){var n,r=0;if(p(e)){for(n=e.length;r<n;r++)if(!1===t.call(e[r],r,e[r]))break}else for(r in e)if(!1===t.call(e[r],r,e[r]))break;return e},makeArray:function(e,t){var n=t||[];return null!=e&&(p(Object(e))?S.merge(n,"string"==typeof e?[e]:e):u.call(n,e)),n},inArray:function(e,t,n){return null==t?-1:i.call(t,e,n)},merge:function(e,t){for(var n=+t.length,r=0,i=e.length;r<n;r++)e[i++]=t[r];return e.length=i,e},grep:function(e,t,n){for(var r=[],i=0,o=e.length,a=!n;i<o;i++)!t(e[i],i)!==a&&r.push(e[i]);return r},map:function(e,t,n){var r,i,o=0,a=[];if(p(e))for(r=e.length;o<r;o++)null!=(i=t(e[o],o,n))&&a.push(i);else for(o in e)null!=(i=t(e[o],o,n))&&a.push(i);return g(a)},guid:1,support:y}),"function"==typeof Symbol&&(S.fn[Symbol.iterator]=t[Symbol.iterator]),S.each("Boolean Number String Function Array Date RegExp Object Error Symbol".split(" "),function(e,t){n["[object "+t+"]"]=t.toLowerCase()});var d=function(n){var e,d,b,o,i,h,f,g,w,u,l,T,C,a,E,v,s,c,y,S="sizzle"+1*new Date,p=n.document,k=0,r=0,m=ue(),x=ue(),A=ue(),N=ue(),j=function(e,t){return e===t&&(l=!0),0},D={}.hasOwnProperty,t=[],q=t.pop,L=t.push,H=t.push,O=t.slice,P=function(e,t){for(var n=0,r=e.length;n<r;n++)if(e[n]===t)return n;return-1},R="checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|ismap|loop|multiple|open|readonly|required|scoped",M="[\\x20\\t\\r\\n\\f]",I="(?:\\\\[\\da-fA-F]{1,6}"+M+"?|\\\\[^\\r\\n\\f]|[\\w-]|[^\0-\\x7f])+",W="\\["+M+"*("+I+")(?:"+M+"*([*^$|!~]?=)"+M+"*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|("+I+"))|)"+M+"*\\]",F=":("+I+")(?:\\((('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|((?:\\\\.|[^\\\\()[\\]]|"+W+")*)|.*)\\)|)",B=new RegExp(M+"+","g"),$=new RegExp("^"+M+"+|((?:^|[^\\\\])(?:\\\\.)*)"+M+"+$","g"),_=new RegExp("^"+M+"*,"+M+"*"),z=new RegExp("^"+M+"*([>+~]|"+M+")"+M+"*"),U=new RegExp(M+"|>"),X=new RegExp(F),V=new RegExp("^"+I+"$"),G={ID:new RegExp("^#("+I+")"),CLASS:new RegExp("^\\.("+I+")"),TAG:new RegExp("^("+I+"|[*])"),ATTR:new RegExp("^"+W),PSEUDO:new RegExp("^"+F),CHILD:new RegExp("^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\("+M+"*(even|odd|(([+-]|)(\\d*)n|)"+M+"*(?:([+-]|)"+M+"*(\\d+)|))"+M+"*\\)|)","i"),bool:new RegExp("^(?:"+R+")$","i"),needsContext:new RegExp("^"+M+"*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\("+M+"*((?:-\\d)?\\d*)"+M+"*\\)|)(?=[^-]|$)","i")},Y=/HTML$/i,Q=/^(?:input|select|textarea|button)$/i,J=/^h\d$/i,K=/^[^{]+\{\s*\[native \w/,Z=/^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/,ee=/[+~]/,te=new RegExp("\\\\[\\da-fA-F]{1,6}"+M+"?|\\\\([^\\r\\n\\f])","g"),ne=function(e,t){var n="0x"+e.slice(1)-65536;return t||(n<0?String.fromCharCode(n+65536):String.fromCharCode(n>>10|55296,1023&n|56320))},re=/([\0-\x1f\x7f]|^-?\d)|^-$|[^\0-\x1f\x7f-\uFFFF\w-]/g,ie=function(e,t){return t?"\0"===e?"\ufffd":e.slice(0,-1)+"\\"+e.charCodeAt(e.length-1).toString(16)+" ":"\\"+e},oe=function(){T()},ae=be(function(e){return!0===e.disabled&&"fieldset"===e.nodeName.toLowerCase()},{dir:"parentNode",next:"legend"});try{H.apply(t=O.call(p.childNodes),p.childNodes),t[p.childNodes.length].nodeType}catch(e){H={apply:t.length?function(e,t){L.apply(e,O.call(t))}:function(e,t){var n=e.length,r=0;while(e[n++]=t[r++]);e.length=n-1}}}function se(t,e,n,r){var i,o,a,s,u,l,c,f=e&&e.ownerDocument,p=e?e.nodeType:9;if(n=n||[],"string"!=typeof t||!t||1!==p&&9!==p&&11!==p)return n;if(!r&&(T(e),e=e||C,E)){if(11!==p&&(u=Z.exec(t)))if(i=u[1]){if(9===p){if(!(a=e.getElementById(i)))return n;if(a.id===i)return n.push(a),n}else if(f&&(a=f.getElementById(i))&&y(e,a)&&a.id===i)return n.push(a),n}else{if(u[2])return H.apply(n,e.getElementsByTagName(t)),n;if((i=u[3])&&d.getElementsByClassName&&e.getElementsByClassName)return H.apply(n,e.getElementsByClassName(i)),n}if(d.qsa&&!N[t+" "]&&(!v||!v.test(t))&&(1!==p||"object"!==e.nodeName.toLowerCase())){if(c=t,f=e,1===p&&(U.test(t)||z.test(t))){(f=ee.test(t)&&ye(e.parentNode)||e)===e&&d.scope||((s=e.getAttribute("id"))?s=s.replace(re,ie):e.setAttribute("id",s=S)),o=(l=h(t)).length;while(o--)l[o]=(s?"#"+s:":scope")+" "+xe(l[o]);c=l.join(",")}try{return H.apply(n,f.querySelectorAll(c)),n}catch(e){N(t,!0)}finally{s===S&&e.removeAttribute("id")}}}return g(t.replace($,"$1"),e,n,r)}function ue(){var r=[];return function e(t,n){return r.push(t+" ")>b.cacheLength&&delete e[r.shift()],e[t+" "]=n}}function le(e){return e[S]=!0,e}function ce(e){var t=C.createElement("fieldset");try{return!!e(t)}catch(e){return!1}finally{t.parentNode&&t.parentNode.removeChild(t),t=null}}function fe(e,t){var n=e.split("|"),r=n.length;while(r--)b.attrHandle[n[r]]=t}function pe(e,t){var n=t&&e,r=n&&1===e.nodeType&&1===t.nodeType&&e.sourceIndex-t.sourceIndex;if(r)return r;if(n)while(n=n.nextSibling)if(n===t)return-1;return e?1:-1}function de(t){return function(e){return"input"===e.nodeName.toLowerCase()&&e.type===t}}function he(n){return function(e){var t=e.nodeName.toLowerCase();return("input"===t||"button"===t)&&e.type===n}}function ge(t){return function(e){return"form"in e?e.parentNode&&!1===e.disabled?"label"in e?"label"in e.parentNode?e.parentNode.disabled===t:e.disabled===t:e.isDisabled===t||e.isDisabled!==!t&&ae(e)===t:e.disabled===t:"label"in e&&e.disabled===t}}function ve(a){return le(function(o){return o=+o,le(function(e,t){var n,r=a([],e.length,o),i=r.length;while(i--)e[n=r[i]]&&(e[n]=!(t[n]=e[n]))})})}function ye(e){return e&&"undefined"!=typeof e.getElementsByTagName&&e}for(e in d=se.support={},i=se.isXML=function(e){var t=e&&e.namespaceURI,n=e&&(e.ownerDocument||e).documentElement;return!Y.test(t||n&&n.nodeName||"HTML")},T=se.setDocument=function(e){var t,n,r=e?e.ownerDocument||e:p;return r!=C&&9===r.nodeType&&r.documentElement&&(a=(C=r).documentElement,E=!i(C),p!=C&&(n=C.defaultView)&&n.top!==n&&(n.addEventListener?n.addEventListener("unload",oe,!1):n.attachEvent&&n.attachEvent("onunload",oe)),d.scope=ce(function(e){return a.appendChild(e).appendChild(C.createElement("div")),"undefined"!=typeof e.querySelectorAll&&!e.querySelectorAll(":scope fieldset div").length}),d.attributes=ce(function(e){return e.className="i",!e.getAttribute("className")}),d.getElementsByTagName=ce(function(e){return e.appendChild(C.createComment("")),!e.getElementsByTagName("*").length}),d.getElementsByClassName=K.test(C.getElementsByClassName),d.getById=ce(function(e){return a.appendChild(e).id=S,!C.getElementsByName||!C.getElementsByName(S).length}),d.getById?(b.filter.ID=function(e){var t=e.replace(te,ne);return function(e){return e.getAttribute("id")===t}},b.find.ID=function(e,t){if("undefined"!=typeof t.getElementById&&E){var n=t.getElementById(e);return n?[n]:[]}}):(b.filter.ID=function(e){var n=e.replace(te,ne);return function(e){var t="undefined"!=typeof e.getAttributeNode&&e.getAttributeNode("id");return t&&t.value===n}},b.find.ID=function(e,t){if("undefined"!=typeof t.getElementById&&E){var n,r,i,o=t.getElementById(e);if(o){if((n=o.getAttributeNode("id"))&&n.value===e)return[o];i=t.getElementsByName(e),r=0;while(o=i[r++])if((n=o.getAttributeNode("id"))&&n.value===e)return[o]}return[]}}),b.find.TAG=d.getElementsByTagName?function(e,t){return"undefined"!=typeof t.getElementsByTagName?t.getElementsByTagName(e):d.qsa?t.querySelectorAll(e):void 0}:function(e,t){var n,r=[],i=0,o=t.getElementsByTagName(e);if("*"===e){while(n=o[i++])1===n.nodeType&&r.push(n);return r}return o},b.find.CLASS=d.getElementsByClassName&&function(e,t){if("undefined"!=typeof t.getElementsByClassName&&E)return t.getElementsByClassName(e)},s=[],v=[],(d.qsa=K.test(C.querySelectorAll))&&(ce(function(e){var t;a.appendChild(e).innerHTML="<a id='"+S+"'></a><select id='"+S+"-\r\\' msallowcapture=''><option selected=''></option></select>",e.querySelectorAll("[msallowcapture^='']").length&&v.push("[*^$]="+M+"*(?:''|\"\")"),e.querySelectorAll("[selected]").length||v.push("\\["+M+"*(?:value|"+R+")"),e.querySelectorAll("[id~="+S+"-]").length||v.push("~="),(t=C.createElement("input")).setAttribute("name",""),e.appendChild(t),e.querySelectorAll("[name='']").length||v.push("\\["+M+"*name"+M+"*="+M+"*(?:''|\"\")"),e.querySelectorAll(":checked").length||v.push(":checked"),e.querySelectorAll("a#"+S+"+*").length||v.push(".#.+[+~]"),e.querySelectorAll("\\\f"),v.push("[\\r\\n\\f]")}),ce(function(e){e.innerHTML="<a href='' disabled='disabled'></a><select disabled='disabled'><option/></select>";var t=C.createElement("input");t.setAttribute("type","hidden"),e.appendChild(t).setAttribute("name","D"),e.querySelectorAll("[name=d]").length&&v.push("name"+M+"*[*^$|!~]?="),2!==e.querySelectorAll(":enabled").length&&v.push(":enabled",":disabled"),a.appendChild(e).disabled=!0,2!==e.querySelectorAll(":disabled").length&&v.push(":enabled",":disabled"),e.querySelectorAll("*,:x"),v.push(",.*:")})),(d.matchesSelector=K.test(c=a.matches||a.webkitMatchesSelector||a.mozMatchesSelector||a.oMatchesSelector||a.msMatchesSelector))&&ce(function(e){d.disconnectedMatch=c.call(e,"*"),c.call(e,"[s!='']:x"),s.push("!=",F)}),v=v.length&&new RegExp(v.join("|")),s=s.length&&new RegExp(s.join("|")),t=K.test(a.compareDocumentPosition),y=t||K.test(a.contains)?function(e,t){var n=9===e.nodeType?e.documentElement:e,r=t&&t.parentNode;return e===r||!(!r||1!==r.nodeType||!(n.contains?n.contains(r):e.compareDocumentPosition&&16&e.compareDocumentPosition(r)))}:function(e,t){if(t)while(t=t.parentNode)if(t===e)return!0;return!1},j=t?function(e,t){if(e===t)return l=!0,0;var n=!e.compareDocumentPosition-!t.compareDocumentPosition;return n||(1&(n=(e.ownerDocument||e)==(t.ownerDocument||t)?e.compareDocumentPosition(t):1)||!d.sortDetached&&t.compareDocumentPosition(e)===n?e==C||e.ownerDocument==p&&y(p,e)?-1:t==C||t.ownerDocument==p&&y(p,t)?1:u?P(u,e)-P(u,t):0:4&n?-1:1)}:function(e,t){if(e===t)return l=!0,0;var n,r=0,i=e.parentNode,o=t.parentNode,a=[e],s=[t];if(!i||!o)return e==C?-1:t==C?1:i?-1:o?1:u?P(u,e)-P(u,t):0;if(i===o)return pe(e,t);n=e;while(n=n.parentNode)a.unshift(n);n=t;while(n=n.parentNode)s.unshift(n);while(a[r]===s[r])r++;return r?pe(a[r],s[r]):a[r]==p?-1:s[r]==p?1:0}),C},se.matches=function(e,t){return se(e,null,null,t)},se.matchesSelector=function(e,t){if(T(e),d.matchesSelector&&E&&!N[t+" "]&&(!s||!s.test(t))&&(!v||!v.test(t)))try{var n=c.call(e,t);if(n||d.disconnectedMatch||e.document&&11!==e.document.nodeType)return n}catch(e){N(t,!0)}return 0<se(t,C,null,[e]).length},se.contains=function(e,t){return(e.ownerDocument||e)!=C&&T(e),y(e,t)},se.attr=function(e,t){(e.ownerDocument||e)!=C&&T(e);var n=b.attrHandle[t.toLowerCase()],r=n&&D.call(b.attrHandle,t.toLowerCase())?n(e,t,!E):void 0;return void 0!==r?r:d.attributes||!E?e.getAttribute(t):(r=e.getAttributeNode(t))&&r.specified?r.value:null},se.escape=function(e){return(e+"").replace(re,ie)},se.error=function(e){throw new Error("Syntax error, unrecognized expression: "+e)},se.uniqueSort=function(e){var t,n=[],r=0,i=0;if(l=!d.detectDuplicates,u=!d.sortStable&&e.slice(0),e.sort(j),l){while(t=e[i++])t===e[i]&&(r=n.push(i));while(r--)e.splice(n[r],1)}return u=null,e},o=se.getText=function(e){var t,n="",r=0,i=e.nodeType;if(i){if(1===i||9===i||11===i){if("string"==typeof e.textContent)return e.textContent;for(e=e.firstChild;e;e=e.nextSibling)n+=o(e)}else if(3===i||4===i)return e.nodeValue}else while(t=e[r++])n+=o(t);return n},(b=se.selectors={cacheLength:50,createPseudo:le,match:G,attrHandle:{},find:{},relative:{">":{dir:"parentNode",first:!0}," ":{dir:"parentNode"},"+":{dir:"previousSibling",first:!0},"~":{dir:"previousSibling"}},preFilter:{ATTR:function(e){return e[1]=e[1].replace(te,ne),e[3]=(e[3]||e[4]||e[5]||"").replace(te,ne),"~="===e[2]&&(e[3]=" "+e[3]+" "),e.slice(0,4)},CHILD:function(e){return e[1]=e[1].toLowerCase(),"nth"===e[1].slice(0,3)?(e[3]||se.error(e[0]),e[4]=+(e[4]?e[5]+(e[6]||1):2*("even"===e[3]||"odd"===e[3])),e[5]=+(e[7]+e[8]||"odd"===e[3])):e[3]&&se.error(e[0]),e},PSEUDO:function(e){var t,n=!e[6]&&e[2];return G.CHILD.test(e[0])?null:(e[3]?e[2]=e[4]||e[5]||"":n&&X.test(n)&&(t=h(n,!0))&&(t=n.indexOf(")",n.length-t)-n.length)&&(e[0]=e[0].slice(0,t),e[2]=n.slice(0,t)),e.slice(0,3))}},filter:{TAG:function(e){var t=e.replace(te,ne).toLowerCase();return"*"===e?function(){return!0}:function(e){return e.nodeName&&e.nodeName.toLowerCase()===t}},CLASS:function(e){var t=m[e+" "];return t||(t=new RegExp("(^|"+M+")"+e+"("+M+"|$)"))&&m(e,function(e){return t.test("string"==typeof e.className&&e.className||"undefined"!=typeof e.getAttribute&&e.getAttribute("class")||"")})},ATTR:function(n,r,i){return function(e){var t=se.attr(e,n);return null==t?"!="===r:!r||(t+="","="===r?t===i:"!="===r?t!==i:"^="===r?i&&0===t.indexOf(i):"*="===r?i&&-1<t.indexOf(i):"$="===r?i&&t.slice(-i.length)===i:"~="===r?-1<(" "+t.replace(B," ")+" ").indexOf(i):"|="===r&&(t===i||t.slice(0,i.length+1)===i+"-"))}},CHILD:function(h,e,t,g,v){var y="nth"!==h.slice(0,3),m="last"!==h.slice(-4),x="of-type"===e;return 1===g&&0===v?function(e){return!!e.parentNode}:function(e,t,n){var r,i,o,a,s,u,l=y!==m?"nextSibling":"previousSibling",c=e.parentNode,f=x&&e.nodeName.toLowerCase(),p=!n&&!x,d=!1;if(c){if(y){while(l){a=e;while(a=a[l])if(x?a.nodeName.toLowerCase()===f:1===a.nodeType)return!1;u=l="only"===h&&!u&&"nextSibling"}return!0}if(u=[m?c.firstChild:c.lastChild],m&&p){d=(s=(r=(i=(o=(a=c)[S]||(a[S]={}))[a.uniqueID]||(o[a.uniqueID]={}))[h]||[])[0]===k&&r[1])&&r[2],a=s&&c.childNodes[s];while(a=++s&&a&&a[l]||(d=s=0)||u.pop())if(1===a.nodeType&&++d&&a===e){i[h]=[k,s,d];break}}else if(p&&(d=s=(r=(i=(o=(a=e)[S]||(a[S]={}))[a.uniqueID]||(o[a.uniqueID]={}))[h]||[])[0]===k&&r[1]),!1===d)while(a=++s&&a&&a[l]||(d=s=0)||u.pop())if((x?a.nodeName.toLowerCase()===f:1===a.nodeType)&&++d&&(p&&((i=(o=a[S]||(a[S]={}))[a.uniqueID]||(o[a.uniqueID]={}))[h]=[k,d]),a===e))break;return(d-=v)===g||d%g==0&&0<=d/g}}},PSEUDO:function(e,o){var t,a=b.pseudos[e]||b.setFilters[e.toLowerCase()]||se.error("unsupported pseudo: "+e);return a[S]?a(o):1<a.length?(t=[e,e,"",o],b.setFilters.hasOwnProperty(e.toLowerCase())?le(function(e,t){var n,r=a(e,o),i=r.length;while(i--)e[n=P(e,r[i])]=!(t[n]=r[i])}):function(e){return a(e,0,t)}):a}},pseudos:{not:le(function(e){var r=[],i=[],s=f(e.replace($,"$1"));return s[S]?le(function(e,t,n,r){var i,o=s(e,null,r,[]),a=e.length;while(a--)(i=o[a])&&(e[a]=!(t[a]=i))}):function(e,t,n){return r[0]=e,s(r,null,n,i),r[0]=null,!i.pop()}}),has:le(function(t){return function(e){return 0<se(t,e).length}}),contains:le(function(t){return t=t.replace(te,ne),function(e){return-1<(e.textContent||o(e)).indexOf(t)}}),lang:le(function(n){return V.test(n||"")||se.error("unsupported lang: "+n),n=n.replace(te,ne).toLowerCase(),function(e){var t;do{if(t=E?e.lang:e.getAttribute("xml:lang")||e.getAttribute("lang"))return(t=t.toLowerCase())===n||0===t.indexOf(n+"-")}while((e=e.parentNode)&&1===e.nodeType);return!1}}),target:function(e){var t=n.location&&n.location.hash;return t&&t.slice(1)===e.id},root:function(e){return e===a},focus:function(e){return e===C.activeElement&&(!C.hasFocus||C.hasFocus())&&!!(e.type||e.href||~e.tabIndex)},enabled:ge(!1),disabled:ge(!0),checked:function(e){var t=e.nodeName.toLowerCase();return"input"===t&&!!e.checked||"option"===t&&!!e.selected},selected:function(e){return e.parentNode&&e.parentNode.selectedIndex,!0===e.selected},empty:function(e){for(e=e.firstChild;e;e=e.nextSibling)if(e.nodeType<6)return!1;return!0},parent:function(e){return!b.pseudos.empty(e)},header:function(e){return J.test(e.nodeName)},input:function(e){return Q.test(e.nodeName)},button:function(e){var t=e.nodeName.toLowerCase();return"input"===t&&"button"===e.type||"button"===t},text:function(e){var t;return"input"===e.nodeName.toLowerCase()&&"text"===e.type&&(null==(t=e.getAttribute("type"))||"text"===t.toLowerCase())},first:ve(function(){return[0]}),last:ve(function(e,t){return[t-1]}),eq:ve(function(e,t,n){return[n<0?n+t:n]}),even:ve(function(e,t){for(var n=0;n<t;n+=2)e.push(n);return e}),odd:ve(function(e,t){for(var n=1;n<t;n+=2)e.push(n);return e}),lt:ve(function(e,t,n){for(var r=n<0?n+t:t<n?t:n;0<=--r;)e.push(r);return e}),gt:ve(function(e,t,n){for(var r=n<0?n+t:n;++r<t;)e.push(r);return e})}}).pseudos.nth=b.pseudos.eq,{radio:!0,checkbox:!0,file:!0,password:!0,image:!0})b.pseudos[e]=de(e);for(e in{submit:!0,reset:!0})b.pseudos[e]=he(e);function me(){}function xe(e){for(var t=0,n=e.length,r="";t<n;t++)r+=e[t].value;return r}function be(s,e,t){var u=e.dir,l=e.next,c=l||u,f=t&&"parentNode"===c,p=r++;return e.first?function(e,t,n){while(e=e[u])if(1===e.nodeType||f)return s(e,t,n);return!1}:function(e,t,n){var r,i,o,a=[k,p];if(n){while(e=e[u])if((1===e.nodeType||f)&&s(e,t,n))return!0}else while(e=e[u])if(1===e.nodeType||f)if(i=(o=e[S]||(e[S]={}))[e.uniqueID]||(o[e.uniqueID]={}),l&&l===e.nodeName.toLowerCase())e=e[u]||e;else{if((r=i[c])&&r[0]===k&&r[1]===p)return a[2]=r[2];if((i[c]=a)[2]=s(e,t,n))return!0}return!1}}function we(i){return 1<i.length?function(e,t,n){var r=i.length;while(r--)if(!i[r](e,t,n))return!1;return!0}:i[0]}function Te(e,t,n,r,i){for(var o,a=[],s=0,u=e.length,l=null!=t;s<u;s++)(o=e[s])&&(n&&!n(o,r,i)||(a.push(o),l&&t.push(s)));return a}function Ce(d,h,g,v,y,e){return v&&!v[S]&&(v=Ce(v)),y&&!y[S]&&(y=Ce(y,e)),le(function(e,t,n,r){var i,o,a,s=[],u=[],l=t.length,c=e||function(e,t,n){for(var r=0,i=t.length;r<i;r++)se(e,t[r],n);return n}(h||"*",n.nodeType?[n]:n,[]),f=!d||!e&&h?c:Te(c,s,d,n,r),p=g?y||(e?d:l||v)?[]:t:f;if(g&&g(f,p,n,r),v){i=Te(p,u),v(i,[],n,r),o=i.length;while(o--)(a=i[o])&&(p[u[o]]=!(f[u[o]]=a))}if(e){if(y||d){if(y){i=[],o=p.length;while(o--)(a=p[o])&&i.push(f[o]=a);y(null,p=[],i,r)}o=p.length;while(o--)(a=p[o])&&-1<(i=y?P(e,a):s[o])&&(e[i]=!(t[i]=a))}}else p=Te(p===t?p.splice(l,p.length):p),y?y(null,t,p,r):H.apply(t,p)})}function Ee(e){for(var i,t,n,r=e.length,o=b.relative[e[0].type],a=o||b.relative[" "],s=o?1:0,u=be(function(e){return e===i},a,!0),l=be(function(e){return-1<P(i,e)},a,!0),c=[function(e,t,n){var r=!o&&(n||t!==w)||((i=t).nodeType?u(e,t,n):l(e,t,n));return i=null,r}];s<r;s++)if(t=b.relative[e[s].type])c=[be(we(c),t)];else{if((t=b.filter[e[s].type].apply(null,e[s].matches))[S]){for(n=++s;n<r;n++)if(b.relative[e[n].type])break;return Ce(1<s&&we(c),1<s&&xe(e.slice(0,s-1).concat({value:" "===e[s-2].type?"*":""})).replace($,"$1"),t,s<n&&Ee(e.slice(s,n)),n<r&&Ee(e=e.slice(n)),n<r&&xe(e))}c.push(t)}return we(c)}return me.prototype=b.filters=b.pseudos,b.setFilters=new me,h=se.tokenize=function(e,t){var n,r,i,o,a,s,u,l=x[e+" "];if(l)return t?0:l.slice(0);a=e,s=[],u=b.preFilter;while(a){for(o in n&&!(r=_.exec(a))||(r&&(a=a.slice(r[0].length)||a),s.push(i=[])),n=!1,(r=z.exec(a))&&(n=r.shift(),i.push({value:n,type:r[0].replace($," ")}),a=a.slice(n.length)),b.filter)!(r=G[o].exec(a))||u[o]&&!(r=u[o](r))||(n=r.shift(),i.push({value:n,type:o,matches:r}),a=a.slice(n.length));if(!n)break}return t?a.length:a?se.error(e):x(e,s).slice(0)},f=se.compile=function(e,t){var n,v,y,m,x,r,i=[],o=[],a=A[e+" "];if(!a){t||(t=h(e)),n=t.length;while(n--)(a=Ee(t[n]))[S]?i.push(a):o.push(a);(a=A(e,(v=o,m=0<(y=i).length,x=0<v.length,r=function(e,t,n,r,i){var o,a,s,u=0,l="0",c=e&&[],f=[],p=w,d=e||x&&b.find.TAG("*",i),h=k+=null==p?1:Math.random()||.1,g=d.length;for(i&&(w=t==C||t||i);l!==g&&null!=(o=d[l]);l++){if(x&&o){a=0,t||o.ownerDocument==C||(T(o),n=!E);while(s=v[a++])if(s(o,t||C,n)){r.push(o);break}i&&(k=h)}m&&((o=!s&&o)&&u--,e&&c.push(o))}if(u+=l,m&&l!==u){a=0;while(s=y[a++])s(c,f,t,n);if(e){if(0<u)while(l--)c[l]||f[l]||(f[l]=q.call(r));f=Te(f)}H.apply(r,f),i&&!e&&0<f.length&&1<u+y.length&&se.uniqueSort(r)}return i&&(k=h,w=p),c},m?le(r):r))).selector=e}return a},g=se.select=function(e,t,n,r){var i,o,a,s,u,l="function"==typeof e&&e,c=!r&&h(e=l.selector||e);if(n=n||[],1===c.length){if(2<(o=c[0]=c[0].slice(0)).length&&"ID"===(a=o[0]).type&&9===t.nodeType&&E&&b.relative[o[1].type]){if(!(t=(b.find.ID(a.matches[0].replace(te,ne),t)||[])[0]))return n;l&&(t=t.parentNode),e=e.slice(o.shift().value.length)}i=G.needsContext.test(e)?0:o.length;while(i--){if(a=o[i],b.relative[s=a.type])break;if((u=b.find[s])&&(r=u(a.matches[0].replace(te,ne),ee.test(o[0].type)&&ye(t.parentNode)||t))){if(o.splice(i,1),!(e=r.length&&xe(o)))return H.apply(n,r),n;break}}}return(l||f(e,c))(r,t,!E,n,!t||ee.test(e)&&ye(t.parentNode)||t),n},d.sortStable=S.split("").sort(j).join("")===S,d.detectDuplicates=!!l,T(),d.sortDetached=ce(function(e){return 1&e.compareDocumentPosition(C.createElement("fieldset"))}),ce(function(e){return e.innerHTML="<a href='#'></a>","#"===e.firstChild.getAttribute("href")})||fe("type|href|height|width",function(e,t,n){if(!n)return e.getAttribute(t,"type"===t.toLowerCase()?1:2)}),d.attributes&&ce(function(e){return e.innerHTML="<input/>",e.firstChild.setAttribute("value",""),""===e.firstChild.getAttribute("value")})||fe("value",function(e,t,n){if(!n&&"input"===e.nodeName.toLowerCase())return e.defaultValue}),ce(function(e){return null==e.getAttribute("disabled")})||fe(R,function(e,t,n){var r;if(!n)return!0===e[t]?t.toLowerCase():(r=e.getAttributeNode(t))&&r.specified?r.value:null}),se}(C);S.find=d,S.expr=d.selectors,S.expr[":"]=S.expr.pseudos,S.uniqueSort=S.unique=d.uniqueSort,S.text=d.getText,S.isXMLDoc=d.isXML,S.contains=d.contains,S.escapeSelector=d.escape;var h=function(e,t,n){var r=[],i=void 0!==n;while((e=e[t])&&9!==e.nodeType)if(1===e.nodeType){if(i&&S(e).is(n))break;r.push(e)}return r},T=function(e,t){for(var n=[];e;e=e.nextSibling)1===e.nodeType&&e!==t&&n.push(e);return n},k=S.expr.match.needsContext;function A(e,t){return e.nodeName&&e.nodeName.toLowerCase()===t.toLowerCase()}var N=/^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i;function j(e,n,r){return m(n)?S.grep(e,function(e,t){return!!n.call(e,t,e)!==r}):n.nodeType?S.grep(e,function(e){return e===n!==r}):"string"!=typeof n?S.grep(e,function(e){return-1<i.call(n,e)!==r}):S.filter(n,e,r)}S.filter=function(e,t,n){var r=t[0];return n&&(e=":not("+e+")"),1===t.length&&1===r.nodeType?S.find.matchesSelector(r,e)?[r]:[]:S.find.matches(e,S.grep(t,function(e){return 1===e.nodeType}))},S.fn.extend({find:function(e){var t,n,r=this.length,i=this;if("string"!=typeof e)return this.pushStack(S(e).filter(function(){for(t=0;t<r;t++)if(S.contains(i[t],this))return!0}));for(n=this.pushStack([]),t=0;t<r;t++)S.find(e,i[t],n);return 1<r?S.uniqueSort(n):n},filter:function(e){return this.pushStack(j(this,e||[],!1))},not:function(e){return this.pushStack(j(this,e||[],!0))},is:function(e){return!!j(this,"string"==typeof e&&k.test(e)?S(e):e||[],!1).length}});var D,q=/^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]+))$/;(S.fn.init=function(e,t,n){var r,i;if(!e)return this;if(n=n||D,"string"==typeof e){if(!(r="<"===e[0]&&">"===e[e.length-1]&&3<=e.length?[null,e,null]:q.exec(e))||!r[1]&&t)return!t||t.jquery?(t||n).find(e):this.constructor(t).find(e);if(r[1]){if(t=t instanceof S?t[0]:t,S.merge(this,S.parseHTML(r[1],t&&t.nodeType?t.ownerDocument||t:E,!0)),N.test(r[1])&&S.isPlainObject(t))for(r in t)m(this[r])?this[r](t[r]):this.attr(r,t[r]);return this}return(i=E.getElementById(r[2]))&&(this[0]=i,this.length=1),this}return e.nodeType?(this[0]=e,this.length=1,this):m(e)?void 0!==n.ready?n.ready(e):e(S):S.makeArray(e,this)}).prototype=S.fn,D=S(E);var L=/^(?:parents|prev(?:Until|All))/,H={children:!0,contents:!0,next:!0,prev:!0};function O(e,t){while((e=e[t])&&1!==e.nodeType);return e}S.fn.extend({has:function(e){var t=S(e,this),n=t.length;return this.filter(function(){for(var e=0;e<n;e++)if(S.contains(this,t[e]))return!0})},closest:function(e,t){var n,r=0,i=this.length,o=[],a="string"!=typeof e&&S(e);if(!k.test(e))for(;r<i;r++)for(n=this[r];n&&n!==t;n=n.parentNode)if(n.nodeType<11&&(a?-1<a.index(n):1===n.nodeType&&S.find.matchesSelector(n,e))){o.push(n);break}return this.pushStack(1<o.length?S.uniqueSort(o):o)},index:function(e){return e?"string"==typeof e?i.call(S(e),this[0]):i.call(this,e.jquery?e[0]:e):this[0]&&this[0].parentNode?this.first().prevAll().length:-1},add:function(e,t){return this.pushStack(S.uniqueSort(S.merge(this.get(),S(e,t))))},addBack:function(e){return this.add(null==e?this.prevObject:this.prevObject.filter(e))}}),S.each({parent:function(e){var t=e.parentNode;return t&&11!==t.nodeType?t:null},parents:function(e){return h(e,"parentNode")},parentsUntil:function(e,t,n){return h(e,"parentNode",n)},next:function(e){return O(e,"nextSibling")},prev:function(e){return O(e,"previousSibling")},nextAll:function(e){return h(e,"nextSibling")},prevAll:function(e){return h(e,"previousSibling")},nextUntil:function(e,t,n){return h(e,"nextSibling",n)},prevUntil:function(e,t,n){return h(e,"previousSibling",n)},siblings:function(e){return T((e.parentNode||{}).firstChild,e)},children:function(e){return T(e.firstChild)},contents:function(e){return null!=e.contentDocument&&r(e.contentDocument)?e.contentDocument:(A(e,"template")&&(e=e.content||e),S.merge([],e.childNodes))}},function(r,i){S.fn[r]=function(e,t){var n=S.map(this,i,e);return"Until"!==r.slice(-5)&&(t=e),t&&"string"==typeof t&&(n=S.filter(t,n)),1<this.length&&(H[r]||S.uniqueSort(n),L.test(r)&&n.reverse()),this.pushStack(n)}});var P=/[^\x20\t\r\n\f]+/g;function R(e){return e}function M(e){throw e}function I(e,t,n,r){var i;try{e&&m(i=e.promise)?i.call(e).done(t).fail(n):e&&m(i=e.then)?i.call(e,t,n):t.apply(void 0,[e].slice(r))}catch(e){n.apply(void 0,[e])}}S.Callbacks=function(r){var e,n;r="string"==typeof r?(e=r,n={},S.each(e.match(P)||[],function(e,t){n[t]=!0}),n):S.extend({},r);var i,t,o,a,s=[],u=[],l=-1,c=function(){for(a=a||r.once,o=i=!0;u.length;l=-1){t=u.shift();while(++l<s.length)!1===s[l].apply(t[0],t[1])&&r.stopOnFalse&&(l=s.length,t=!1)}r.memory||(t=!1),i=!1,a&&(s=t?[]:"")},f={add:function(){return s&&(t&&!i&&(l=s.length-1,u.push(t)),function n(e){S.each(e,function(e,t){m(t)?r.unique&&f.has(t)||s.push(t):t&&t.length&&"string"!==w(t)&&n(t)})}(arguments),t&&!i&&c()),this},remove:function(){return S.each(arguments,function(e,t){var n;while(-1<(n=S.inArray(t,s,n)))s.splice(n,1),n<=l&&l--}),this},has:function(e){return e?-1<S.inArray(e,s):0<s.length},empty:function(){return s&&(s=[]),this},disable:function(){return a=u=[],s=t="",this},disabled:function(){return!s},lock:function(){return a=u=[],t||i||(s=t=""),this},locked:function(){return!!a},fireWith:function(e,t){return a||(t=[e,(t=t||[]).slice?t.slice():t],u.push(t),i||c()),this},fire:function(){return f.fireWith(this,arguments),this},fired:function(){return!!o}};return f},S.extend({Deferred:function(e){var o=[["notify","progress",S.Callbacks("memory"),S.Callbacks("memory"),2],["resolve","done",S.Callbacks("once memory"),S.Callbacks("once memory"),0,"resolved"],["reject","fail",S.Callbacks("once memory"),S.Callbacks("once memory"),1,"rejected"]],i="pending",a={state:function(){return i},always:function(){return s.done(arguments).fail(arguments),this},"catch":function(e){return a.then(null,e)},pipe:function(){var i=arguments;return S.Deferred(function(r){S.each(o,function(e,t){var n=m(i[t[4]])&&i[t[4]];s[t[1]](function(){var e=n&&n.apply(this,arguments);e&&m(e.promise)?e.promise().progress(r.notify).done(r.resolve).fail(r.reject):r[t[0]+"With"](this,n?[e]:arguments)})}),i=null}).promise()},then:function(t,n,r){var u=0;function l(i,o,a,s){return function(){var n=this,r=arguments,e=function(){var e,t;if(!(i<u)){if((e=a.apply(n,r))===o.promise())throw new TypeError("Thenable self-resolution");t=e&&("object"==typeof e||"function"==typeof e)&&e.then,m(t)?s?t.call(e,l(u,o,R,s),l(u,o,M,s)):(u++,t.call(e,l(u,o,R,s),l(u,o,M,s),l(u,o,R,o.notifyWith))):(a!==R&&(n=void 0,r=[e]),(s||o.resolveWith)(n,r))}},t=s?e:function(){try{e()}catch(e){S.Deferred.exceptionHook&&S.Deferred.exceptionHook(e,t.stackTrace),u<=i+1&&(a!==M&&(n=void 0,r=[e]),o.rejectWith(n,r))}};i?t():(S.Deferred.getStackHook&&(t.stackTrace=S.Deferred.getStackHook()),C.setTimeout(t))}}return S.Deferred(function(e){o[0][3].add(l(0,e,m(r)?r:R,e.notifyWith)),o[1][3].add(l(0,e,m(t)?t:R)),o[2][3].add(l(0,e,m(n)?n:M))}).promise()},promise:function(e){return null!=e?S.extend(e,a):a}},s={};return S.each(o,function(e,t){var n=t[2],r=t[5];a[t[1]]=n.add,r&&n.add(function(){i=r},o[3-e][2].disable,o[3-e][3].disable,o[0][2].lock,o[0][3].lock),n.add(t[3].fire),s[t[0]]=function(){return s[t[0]+"With"](this===s?void 0:this,arguments),this},s[t[0]+"With"]=n.fireWith}),a.promise(s),e&&e.call(s,s),s},when:function(e){var n=arguments.length,t=n,r=Array(t),i=s.call(arguments),o=S.Deferred(),a=function(t){return function(e){r[t]=this,i[t]=1<arguments.length?s.call(arguments):e,--n||o.resolveWith(r,i)}};if(n<=1&&(I(e,o.done(a(t)).resolve,o.reject,!n),"pending"===o.state()||m(i[t]&&i[t].then)))return o.then();while(t--)I(i[t],a(t),o.reject);return o.promise()}});var W=/^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/;S.Deferred.exceptionHook=function(e,t){C.console&&C.console.warn&&e&&W.test(e.name)&&C.console.warn("jQuery.Deferred exception: "+e.message,e.stack,t)},S.readyException=function(e){C.setTimeout(function(){throw e})};var F=S.Deferred();function B(){E.removeEventListener("DOMContentLoaded",B),C.removeEventListener("load",B),S.ready()}S.fn.ready=function(e){return F.then(e)["catch"](function(e){S.readyException(e)}),this},S.extend({isReady:!1,readyWait:1,ready:function(e){(!0===e?--S.readyWait:S.isReady)||(S.isReady=!0)!==e&&0<--S.readyWait||F.resolveWith(E,[S])}}),S.ready.then=F.then,"complete"===E.readyState||"loading"!==E.readyState&&!E.documentElement.doScroll?C.setTimeout(S.ready):(E.addEventListener("DOMContentLoaded",B),C.addEventListener("load",B));var $=function(e,t,n,r,i,o,a){var s=0,u=e.length,l=null==n;if("object"===w(n))for(s in i=!0,n)$(e,t,s,n[s],!0,o,a);else if(void 0!==r&&(i=!0,m(r)||(a=!0),l&&(a?(t.call(e,r),t=null):(l=t,t=function(e,t,n){return l.call(S(e),n)})),t))for(;s<u;s++)t(e[s],n,a?r:r.call(e[s],s,t(e[s],n)));return i?e:l?t.call(e):u?t(e[0],n):o},_=/^-ms-/,z=/-([a-z])/g;function U(e,t){return t.toUpperCase()}function X(e){return e.replace(_,"ms-").replace(z,U)}var V=function(e){return 1===e.nodeType||9===e.nodeType||!+e.nodeType};function G(){this.expando=S.expando+G.uid++}G.uid=1,G.prototype={cache:function(e){var t=e[this.expando];return t||(t={},V(e)&&(e.nodeType?e[this.expando]=t:Object.defineProperty(e,this.expando,{value:t,configurable:!0}))),t},set:function(e,t,n){var r,i=this.cache(e);if("string"==typeof t)i[X(t)]=n;else for(r in t)i[X(r)]=t[r];return i},get:function(e,t){return void 0===t?this.cache(e):e[this.expando]&&e[this.expando][X(t)]},access:function(e,t,n){return void 0===t||t&&"string"==typeof t&&void 0===n?this.get(e,t):(this.set(e,t,n),void 0!==n?n:t)},remove:function(e,t){var n,r=e[this.expando];if(void 0!==r){if(void 0!==t){n=(t=Array.isArray(t)?t.map(X):(t=X(t))in r?[t]:t.match(P)||[]).length;while(n--)delete r[t[n]]}(void 0===t||S.isEmptyObject(r))&&(e.nodeType?e[this.expando]=void 0:delete e[this.expando])}},hasData:function(e){var t=e[this.expando];return void 0!==t&&!S.isEmptyObject(t)}};var Y=new G,Q=new G,J=/^(?:\{[\w\W]*\}|\[[\w\W]*\])$/,K=/[A-Z]/g;function Z(e,t,n){var r,i;if(void 0===n&&1===e.nodeType)if(r="data-"+t.replace(K,"-$&").toLowerCase(),"string"==typeof(n=e.getAttribute(r))){try{n="true"===(i=n)||"false"!==i&&("null"===i?null:i===+i+""?+i:J.test(i)?JSON.parse(i):i)}catch(e){}Q.set(e,t,n)}else n=void 0;return n}S.extend({hasData:function(e){return Q.hasData(e)||Y.hasData(e)},data:function(e,t,n){return Q.access(e,t,n)},removeData:function(e,t){Q.remove(e,t)},_data:function(e,t,n){return Y.access(e,t,n)},_removeData:function(e,t){Y.remove(e,t)}}),S.fn.extend({data:function(n,e){var t,r,i,o=this[0],a=o&&o.attributes;if(void 0===n){if(this.length&&(i=Q.get(o),1===o.nodeType&&!Y.get(o,"hasDataAttrs"))){t=a.length;while(t--)a[t]&&0===(r=a[t].name).indexOf("data-")&&(r=X(r.slice(5)),Z(o,r,i[r]));Y.set(o,"hasDataAttrs",!0)}return i}return"object"==typeof n?this.each(function(){Q.set(this,n)}):$(this,function(e){var t;if(o&&void 0===e)return void 0!==(t=Q.get(o,n))?t:void 0!==(t=Z(o,n))?t:void 0;this.each(function(){Q.set(this,n,e)})},null,e,1<arguments.length,null,!0)},removeData:function(e){return this.each(function(){Q.remove(this,e)})}}),S.extend({queue:function(e,t,n){var r;if(e)return t=(t||"fx")+"queue",r=Y.get(e,t),n&&(!r||Array.isArray(n)?r=Y.access(e,t,S.makeArray(n)):r.push(n)),r||[]},dequeue:function(e,t){t=t||"fx";var n=S.queue(e,t),r=n.length,i=n.shift(),o=S._queueHooks(e,t);"inprogress"===i&&(i=n.shift(),r--),i&&("fx"===t&&n.unshift("inprogress"),delete o.stop,i.call(e,function(){S.dequeue(e,t)},o)),!r&&o&&o.empty.fire()},_queueHooks:function(e,t){var n=t+"queueHooks";return Y.get(e,n)||Y.access(e,n,{empty:S.Callbacks("once memory").add(function(){Y.remove(e,[t+"queue",n])})})}}),S.fn.extend({queue:function(t,n){var e=2;return"string"!=typeof t&&(n=t,t="fx",e--),arguments.length<e?S.queue(this[0],t):void 0===n?this:this.each(function(){var e=S.queue(this,t,n);S._queueHooks(this,t),"fx"===t&&"inprogress"!==e[0]&&S.dequeue(this,t)})},dequeue:function(e){return this.each(function(){S.dequeue(this,e)})},clearQueue:function(e){return this.queue(e||"fx",[])},promise:function(e,t){var n,r=1,i=S.Deferred(),o=this,a=this.length,s=function(){--r||i.resolveWith(o,[o])};"string"!=typeof e&&(t=e,e=void 0),e=e||"fx";while(a--)(n=Y.get(o[a],e+"queueHooks"))&&n.empty&&(r++,n.empty.add(s));return s(),i.promise(t)}});var ee=/[+-]?(?:\d*\.|)\d+(?:[eE][+-]?\d+|)/.source,te=new RegExp("^(?:([+-])=|)("+ee+")([a-z%]*)$","i"),ne=["Top","Right","Bottom","Left"],re=E.documentElement,ie=function(e){return S.contains(e.ownerDocument,e)},oe={composed:!0};re.getRootNode&&(ie=function(e){return S.contains(e.ownerDocument,e)||e.getRootNode(oe)===e.ownerDocument});var ae=function(e,t){return"none"===(e=t||e).style.display||""===e.style.display&&ie(e)&&"none"===S.css(e,"display")};function se(e,t,n,r){var i,o,a=20,s=r?function(){return r.cur()}:function(){return S.css(e,t,"")},u=s(),l=n&&n[3]||(S.cssNumber[t]?"":"px"),c=e.nodeType&&(S.cssNumber[t]||"px"!==l&&+u)&&te.exec(S.css(e,t));if(c&&c[3]!==l){u/=2,l=l||c[3],c=+u||1;while(a--)S.style(e,t,c+l),(1-o)*(1-(o=s()/u||.5))<=0&&(a=0),c/=o;c*=2,S.style(e,t,c+l),n=n||[]}return n&&(c=+c||+u||0,i=n[1]?c+(n[1]+1)*n[2]:+n[2],r&&(r.unit=l,r.start=c,r.end=i)),i}var ue={};function le(e,t){for(var n,r,i,o,a,s,u,l=[],c=0,f=e.length;c<f;c++)(r=e[c]).style&&(n=r.style.display,t?("none"===n&&(l[c]=Y.get(r,"display")||null,l[c]||(r.style.display="")),""===r.style.display&&ae(r)&&(l[c]=(u=a=o=void 0,a=(i=r).ownerDocument,s=i.nodeName,(u=ue[s])||(o=a.body.appendChild(a.createElement(s)),u=S.css(o,"display"),o.parentNode.removeChild(o),"none"===u&&(u="block"),ue[s]=u)))):"none"!==n&&(l[c]="none",Y.set(r,"display",n)));for(c=0;c<f;c++)null!=l[c]&&(e[c].style.display=l[c]);return e}S.fn.extend({show:function(){return le(this,!0)},hide:function(){return le(this)},toggle:function(e){return"boolean"==typeof e?e?this.show():this.hide():this.each(function(){ae(this)?S(this).show():S(this).hide()})}});var ce,fe,pe=/^(?:checkbox|radio)$/i,de=/<([a-z][^\/\0>\x20\t\r\n\f]*)/i,he=/^$|^module$|\/(?:java|ecma)script/i;ce=E.createDocumentFragment().appendChild(E.createElement("div")),(fe=E.createElement("input")).setAttribute("type","radio"),fe.setAttribute("checked","checked"),fe.setAttribute("name","t"),ce.appendChild(fe),y.checkClone=ce.cloneNode(!0).cloneNode(!0).lastChild.checked,ce.innerHTML="<textarea>x</textarea>",y.noCloneChecked=!!ce.cloneNode(!0).lastChild.defaultValue,ce.innerHTML="<option></option>",y.option=!!ce.lastChild;var ge={thead:[1,"<table>","</table>"],col:[2,"<table><colgroup>","</colgroup></table>"],tr:[2,"<table><tbody>","</tbody></table>"],td:[3,"<table><tbody><tr>","</tr></tbody></table>"],_default:[0,"",""]};function ve(e,t){var n;return n="undefined"!=typeof e.getElementsByTagName?e.getElementsByTagName(t||"*"):"undefined"!=typeof e.querySelectorAll?e.querySelectorAll(t||"*"):[],void 0===t||t&&A(e,t)?S.merge([e],n):n}function ye(e,t){for(var n=0,r=e.length;n<r;n++)Y.set(e[n],"globalEval",!t||Y.get(t[n],"globalEval"))}ge.tbody=ge.tfoot=ge.colgroup=ge.caption=ge.thead,ge.th=ge.td,y.option||(ge.optgroup=ge.option=[1,"<select multiple='multiple'>","</select>"]);var me=/<|&#?\w+;/;function xe(e,t,n,r,i){for(var o,a,s,u,l,c,f=t.createDocumentFragment(),p=[],d=0,h=e.length;d<h;d++)if((o=e[d])||0===o)if("object"===w(o))S.merge(p,o.nodeType?[o]:o);else if(me.test(o)){a=a||f.appendChild(t.createElement("div")),s=(de.exec(o)||["",""])[1].toLowerCase(),u=ge[s]||ge._default,a.innerHTML=u[1]+S.htmlPrefilter(o)+u[2],c=u[0];while(c--)a=a.lastChild;S.merge(p,a.childNodes),(a=f.firstChild).textContent=""}else p.push(t.createTextNode(o));f.textContent="",d=0;while(o=p[d++])if(r&&-1<S.inArray(o,r))i&&i.push(o);else if(l=ie(o),a=ve(f.appendChild(o),"script"),l&&ye(a),n){c=0;while(o=a[c++])he.test(o.type||"")&&n.push(o)}return f}var be=/^([^.]*)(?:\.(.+)|)/;function we(){return!0}function Te(){return!1}function Ce(e,t){return e===function(){try{return E.activeElement}catch(e){}}()==("focus"===t)}function Ee(e,t,n,r,i,o){var a,s;if("object"==typeof t){for(s in"string"!=typeof n&&(r=r||n,n=void 0),t)Ee(e,s,n,r,t[s],o);return e}if(null==r&&null==i?(i=n,r=n=void 0):null==i&&("string"==typeof n?(i=r,r=void 0):(i=r,r=n,n=void 0)),!1===i)i=Te;else if(!i)return e;return 1===o&&(a=i,(i=function(e){return S().off(e),a.apply(this,arguments)}).guid=a.guid||(a.guid=S.guid++)),e.each(function(){S.event.add(this,t,i,r,n)})}function Se(e,i,o){o?(Y.set(e,i,!1),S.event.add(e,i,{namespace:!1,handler:function(e){var t,n,r=Y.get(this,i);if(1&e.isTrigger&&this[i]){if(r.length)(S.event.special[i]||{}).delegateType&&e.stopPropagation();else if(r=s.call(arguments),Y.set(this,i,r),t=o(this,i),this[i](),r!==(n=Y.get(this,i))||t?Y.set(this,i,!1):n={},r!==n)return e.stopImmediatePropagation(),e.preventDefault(),n&&n.value}else r.length&&(Y.set(this,i,{value:S.event.trigger(S.extend(r[0],S.Event.prototype),r.slice(1),this)}),e.stopImmediatePropagation())}})):void 0===Y.get(e,i)&&S.event.add(e,i,we)}S.event={global:{},add:function(t,e,n,r,i){var o,a,s,u,l,c,f,p,d,h,g,v=Y.get(t);if(V(t)){n.handler&&(n=(o=n).handler,i=o.selector),i&&S.find.matchesSelector(re,i),n.guid||(n.guid=S.guid++),(u=v.events)||(u=v.events=Object.create(null)),(a=v.handle)||(a=v.handle=function(e){return"undefined"!=typeof S&&S.event.triggered!==e.type?S.event.dispatch.apply(t,arguments):void 0}),l=(e=(e||"").match(P)||[""]).length;while(l--)d=g=(s=be.exec(e[l])||[])[1],h=(s[2]||"").split(".").sort(),d&&(f=S.event.special[d]||{},d=(i?f.delegateType:f.bindType)||d,f=S.event.special[d]||{},c=S.extend({type:d,origType:g,data:r,handler:n,guid:n.guid,selector:i,needsContext:i&&S.expr.match.needsContext.test(i),namespace:h.join(".")},o),(p=u[d])||((p=u[d]=[]).delegateCount=0,f.setup&&!1!==f.setup.call(t,r,h,a)||t.addEventListener&&t.addEventListener(d,a)),f.add&&(f.add.call(t,c),c.handler.guid||(c.handler.guid=n.guid)),i?p.splice(p.delegateCount++,0,c):p.push(c),S.event.global[d]=!0)}},remove:function(e,t,n,r,i){var o,a,s,u,l,c,f,p,d,h,g,v=Y.hasData(e)&&Y.get(e);if(v&&(u=v.events)){l=(t=(t||"").match(P)||[""]).length;while(l--)if(d=g=(s=be.exec(t[l])||[])[1],h=(s[2]||"").split(".").sort(),d){f=S.event.special[d]||{},p=u[d=(r?f.delegateType:f.bindType)||d]||[],s=s[2]&&new RegExp("(^|\\.)"+h.join("\\.(?:.*\\.|)")+"(\\.|$)"),a=o=p.length;while(o--)c=p[o],!i&&g!==c.origType||n&&n.guid!==c.guid||s&&!s.test(c.namespace)||r&&r!==c.selector&&("**"!==r||!c.selector)||(p.splice(o,1),c.selector&&p.delegateCount--,f.remove&&f.remove.call(e,c));a&&!p.length&&(f.teardown&&!1!==f.teardown.call(e,h,v.handle)||S.removeEvent(e,d,v.handle),delete u[d])}else for(d in u)S.event.remove(e,d+t[l],n,r,!0);S.isEmptyObject(u)&&Y.remove(e,"handle events")}},dispatch:function(e){var t,n,r,i,o,a,s=new Array(arguments.length),u=S.event.fix(e),l=(Y.get(this,"events")||Object.create(null))[u.type]||[],c=S.event.special[u.type]||{};for(s[0]=u,t=1;t<arguments.length;t++)s[t]=arguments[t];if(u.delegateTarget=this,!c.preDispatch||!1!==c.preDispatch.call(this,u)){a=S.event.handlers.call(this,u,l),t=0;while((i=a[t++])&&!u.isPropagationStopped()){u.currentTarget=i.elem,n=0;while((o=i.handlers[n++])&&!u.isImmediatePropagationStopped())u.rnamespace&&!1!==o.namespace&&!u.rnamespace.test(o.namespace)||(u.handleObj=o,u.data=o.data,void 0!==(r=((S.event.special[o.origType]||{}).handle||o.handler).apply(i.elem,s))&&!1===(u.result=r)&&(u.preventDefault(),u.stopPropagation()))}return c.postDispatch&&c.postDispatch.call(this,u),u.result}},handlers:function(e,t){var n,r,i,o,a,s=[],u=t.delegateCount,l=e.target;if(u&&l.nodeType&&!("click"===e.type&&1<=e.button))for(;l!==this;l=l.parentNode||this)if(1===l.nodeType&&("click"!==e.type||!0!==l.disabled)){for(o=[],a={},n=0;n<u;n++)void 0===a[i=(r=t[n]).selector+" "]&&(a[i]=r.needsContext?-1<S(i,this).index(l):S.find(i,this,null,[l]).length),a[i]&&o.push(r);o.length&&s.push({elem:l,handlers:o})}return l=this,u<t.length&&s.push({elem:l,handlers:t.slice(u)}),s},addProp:function(t,e){Object.defineProperty(S.Event.prototype,t,{enumerable:!0,configurable:!0,get:m(e)?function(){if(this.originalEvent)return e(this.originalEvent)}:function(){if(this.originalEvent)return this.originalEvent[t]},set:function(e){Object.defineProperty(this,t,{enumerable:!0,configurable:!0,writable:!0,value:e})}})},fix:function(e){return e[S.expando]?e:new S.Event(e)},special:{load:{noBubble:!0},click:{setup:function(e){var t=this||e;return pe.test(t.type)&&t.click&&A(t,"input")&&Se(t,"click",we),!1},trigger:function(e){var t=this||e;return pe.test(t.type)&&t.click&&A(t,"input")&&Se(t,"click"),!0},_default:function(e){var t=e.target;return pe.test(t.type)&&t.click&&A(t,"input")&&Y.get(t,"click")||A(t,"a")}},beforeunload:{postDispatch:function(e){void 0!==e.result&&e.originalEvent&&(e.originalEvent.returnValue=e.result)}}}},S.removeEvent=function(e,t,n){e.removeEventListener&&e.removeEventListener(t,n)},S.Event=function(e,t){if(!(this instanceof S.Event))return new S.Event(e,t);e&&e.type?(this.originalEvent=e,this.type=e.type,this.isDefaultPrevented=e.defaultPrevented||void 0===e.defaultPrevented&&!1===e.returnValue?we:Te,this.target=e.target&&3===e.target.nodeType?e.target.parentNode:e.target,this.currentTarget=e.currentTarget,this.relatedTarget=e.relatedTarget):this.type=e,t&&S.extend(this,t),this.timeStamp=e&&e.timeStamp||Date.now(),this[S.expando]=!0},S.Event.prototype={constructor:S.Event,isDefaultPrevented:Te,isPropagationStopped:Te,isImmediatePropagationStopped:Te,isSimulated:!1,preventDefault:function(){var e=this.originalEvent;this.isDefaultPrevented=we,e&&!this.isSimulated&&e.preventDefault()},stopPropagation:function(){var e=this.originalEvent;this.isPropagationStopped=we,e&&!this.isSimulated&&e.stopPropagation()},stopImmediatePropagation:function(){var e=this.originalEvent;this.isImmediatePropagationStopped=we,e&&!this.isSimulated&&e.stopImmediatePropagation(),this.stopPropagation()}},S.each({altKey:!0,bubbles:!0,cancelable:!0,changedTouches:!0,ctrlKey:!0,detail:!0,eventPhase:!0,metaKey:!0,pageX:!0,pageY:!0,shiftKey:!0,view:!0,"char":!0,code:!0,charCode:!0,key:!0,keyCode:!0,button:!0,buttons:!0,clientX:!0,clientY:!0,offsetX:!0,offsetY:!0,pointerId:!0,pointerType:!0,screenX:!0,screenY:!0,targetTouches:!0,toElement:!0,touches:!0,which:!0},S.event.addProp),S.each({focus:"focusin",blur:"focusout"},function(e,t){S.event.special[e]={setup:function(){return Se(this,e,Ce),!1},trigger:function(){return Se(this,e),!0},_default:function(){return!0},delegateType:t}}),S.each({mouseenter:"mouseover",mouseleave:"mouseout",pointerenter:"pointerover",pointerleave:"pointerout"},function(e,i){S.event.special[e]={delegateType:i,bindType:i,handle:function(e){var t,n=e.relatedTarget,r=e.handleObj;return n&&(n===this||S.contains(this,n))||(e.type=r.origType,t=r.handler.apply(this,arguments),e.type=i),t}}}),S.fn.extend({on:function(e,t,n,r){return Ee(this,e,t,n,r)},one:function(e,t,n,r){return Ee(this,e,t,n,r,1)},off:function(e,t,n){var r,i;if(e&&e.preventDefault&&e.handleObj)return r=e.handleObj,S(e.delegateTarget).off(r.namespace?r.origType+"."+r.namespace:r.origType,r.selector,r.handler),this;if("object"==typeof e){for(i in e)this.off(i,t,e[i]);return this}return!1!==t&&"function"!=typeof t||(n=t,t=void 0),!1===n&&(n=Te),this.each(function(){S.event.remove(this,e,n,t)})}});var ke=/<script|<style|<link/i,Ae=/checked\s*(?:[^=]|=\s*.checked.)/i,Ne=/^\s*<!(?:\[CDATA\[|--)|(?:\]\]|--)>\s*$/g;function je(e,t){return A(e,"table")&&A(11!==t.nodeType?t:t.firstChild,"tr")&&S(e).children("tbody")[0]||e}function De(e){return e.type=(null!==e.getAttribute("type"))+"/"+e.type,e}function qe(e){return"true/"===(e.type||"").slice(0,5)?e.type=e.type.slice(5):e.removeAttribute("type"),e}function Le(e,t){var n,r,i,o,a,s;if(1===t.nodeType){if(Y.hasData(e)&&(s=Y.get(e).events))for(i in Y.remove(t,"handle events"),s)for(n=0,r=s[i].length;n<r;n++)S.event.add(t,i,s[i][n]);Q.hasData(e)&&(o=Q.access(e),a=S.extend({},o),Q.set(t,a))}}function He(n,r,i,o){r=g(r);var e,t,a,s,u,l,c=0,f=n.length,p=f-1,d=r[0],h=m(d);if(h||1<f&&"string"==typeof d&&!y.checkClone&&Ae.test(d))return n.each(function(e){var t=n.eq(e);h&&(r[0]=d.call(this,e,t.html())),He(t,r,i,o)});if(f&&(t=(e=xe(r,n[0].ownerDocument,!1,n,o)).firstChild,1===e.childNodes.length&&(e=t),t||o)){for(s=(a=S.map(ve(e,"script"),De)).length;c<f;c++)u=e,c!==p&&(u=S.clone(u,!0,!0),s&&S.merge(a,ve(u,"script"))),i.call(n[c],u,c);if(s)for(l=a[a.length-1].ownerDocument,S.map(a,qe),c=0;c<s;c++)u=a[c],he.test(u.type||"")&&!Y.access(u,"globalEval")&&S.contains(l,u)&&(u.src&&"module"!==(u.type||"").toLowerCase()?S._evalUrl&&!u.noModule&&S._evalUrl(u.src,{nonce:u.nonce||u.getAttribute("nonce")},l):b(u.textContent.replace(Ne,""),u,l))}return n}function Oe(e,t,n){for(var r,i=t?S.filter(t,e):e,o=0;null!=(r=i[o]);o++)n||1!==r.nodeType||S.cleanData(ve(r)),r.parentNode&&(n&&ie(r)&&ye(ve(r,"script")),r.parentNode.removeChild(r));return e}S.extend({htmlPrefilter:function(e){return e},clone:function(e,t,n){var r,i,o,a,s,u,l,c=e.cloneNode(!0),f=ie(e);if(!(y.noCloneChecked||1!==e.nodeType&&11!==e.nodeType||S.isXMLDoc(e)))for(a=ve(c),r=0,i=(o=ve(e)).length;r<i;r++)s=o[r],u=a[r],void 0,"input"===(l=u.nodeName.toLowerCase())&&pe.test(s.type)?u.checked=s.checked:"input"!==l&&"textarea"!==l||(u.defaultValue=s.defaultValue);if(t)if(n)for(o=o||ve(e),a=a||ve(c),r=0,i=o.length;r<i;r++)Le(o[r],a[r]);else Le(e,c);return 0<(a=ve(c,"script")).length&&ye(a,!f&&ve(e,"script")),c},cleanData:function(e){for(var t,n,r,i=S.event.special,o=0;void 0!==(n=e[o]);o++)if(V(n)){if(t=n[Y.expando]){if(t.events)for(r in t.events)i[r]?S.event.remove(n,r):S.removeEvent(n,r,t.handle);n[Y.expando]=void 0}n[Q.expando]&&(n[Q.expando]=void 0)}}}),S.fn.extend({detach:function(e){return Oe(this,e,!0)},remove:function(e){return Oe(this,e)},text:function(e){return $(this,function(e){return void 0===e?S.text(this):this.empty().each(function(){1!==this.nodeType&&11!==this.nodeType&&9!==this.nodeType||(this.textContent=e)})},null,e,arguments.length)},append:function(){return He(this,arguments,function(e){1!==this.nodeType&&11!==this.nodeType&&9!==this.nodeType||je(this,e).appendChild(e)})},prepend:function(){return He(this,arguments,function(e){if(1===this.nodeType||11===this.nodeType||9===this.nodeType){var t=je(this,e);t.insertBefore(e,t.firstChild)}})},before:function(){return He(this,arguments,function(e){this.parentNode&&this.parentNode.insertBefore(e,this)})},after:function(){return He(this,arguments,function(e){this.parentNode&&this.parentNode.insertBefore(e,this.nextSibling)})},empty:function(){for(var e,t=0;null!=(e=this[t]);t++)1===e.nodeType&&(S.cleanData(ve(e,!1)),e.textContent="");return this},clone:function(e,t){return e=null!=e&&e,t=null==t?e:t,this.map(function(){return S.clone(this,e,t)})},html:function(e){return $(this,function(e){var t=this[0]||{},n=0,r=this.length;if(void 0===e&&1===t.nodeType)return t.innerHTML;if("string"==typeof e&&!ke.test(e)&&!ge[(de.exec(e)||["",""])[1].toLowerCase()]){e=S.htmlPrefilter(e);try{for(;n<r;n++)1===(t=this[n]||{}).nodeType&&(S.cleanData(ve(t,!1)),t.innerHTML=e);t=0}catch(e){}}t&&this.empty().append(e)},null,e,arguments.length)},replaceWith:function(){var n=[];return He(this,arguments,function(e){var t=this.parentNode;S.inArray(this,n)<0&&(S.cleanData(ve(this)),t&&t.replaceChild(e,this))},n)}}),S.each({appendTo:"append",prependTo:"prepend",insertBefore:"before",insertAfter:"after",replaceAll:"replaceWith"},function(e,a){S.fn[e]=function(e){for(var t,n=[],r=S(e),i=r.length-1,o=0;o<=i;o++)t=o===i?this:this.clone(!0),S(r[o])[a](t),u.apply(n,t.get());return this.pushStack(n)}});var Pe=new RegExp("^("+ee+")(?!px)[a-z%]+$","i"),Re=function(e){var t=e.ownerDocument.defaultView;return t&&t.opener||(t=C),t.getComputedStyle(e)},Me=function(e,t,n){var r,i,o={};for(i in t)o[i]=e.style[i],e.style[i]=t[i];for(i in r=n.call(e),t)e.style[i]=o[i];return r},Ie=new RegExp(ne.join("|"),"i");function We(e,t,n){var r,i,o,a,s=e.style;return(n=n||Re(e))&&(""!==(a=n.getPropertyValue(t)||n[t])||ie(e)||(a=S.style(e,t)),!y.pixelBoxStyles()&&Pe.test(a)&&Ie.test(t)&&(r=s.width,i=s.minWidth,o=s.maxWidth,s.minWidth=s.maxWidth=s.width=a,a=n.width,s.width=r,s.minWidth=i,s.maxWidth=o)),void 0!==a?a+"":a}function Fe(e,t){return{get:function(){if(!e())return(this.get=t).apply(this,arguments);delete this.get}}}!function(){function e(){if(l){u.style.cssText="position:absolute;left:-11111px;width:60px;margin-top:1px;padding:0;border:0",l.style.cssText="position:relative;display:block;box-sizing:border-box;overflow:scroll;margin:auto;border:1px;padding:1px;width:60%;top:1%",re.appendChild(u).appendChild(l);var e=C.getComputedStyle(l);n="1%"!==e.top,s=12===t(e.marginLeft),l.style.right="60%",o=36===t(e.right),r=36===t(e.width),l.style.position="absolute",i=12===t(l.offsetWidth/3),re.removeChild(u),l=null}}function t(e){return Math.round(parseFloat(e))}var n,r,i,o,a,s,u=E.createElement("div"),l=E.createElement("div");l.style&&(l.style.backgroundClip="content-box",l.cloneNode(!0).style.backgroundClip="",y.clearCloneStyle="content-box"===l.style.backgroundClip,S.extend(y,{boxSizingReliable:function(){return e(),r},pixelBoxStyles:function(){return e(),o},pixelPosition:function(){return e(),n},reliableMarginLeft:function(){return e(),s},scrollboxSize:function(){return e(),i},reliableTrDimensions:function(){var e,t,n,r;return null==a&&(e=E.createElement("table"),t=E.createElement("tr"),n=E.createElement("div"),e.style.cssText="position:absolute;left:-11111px;border-collapse:separate",t.style.cssText="border:1px solid",t.style.height="1px",n.style.height="9px",n.style.display="block",re.appendChild(e).appendChild(t).appendChild(n),r=C.getComputedStyle(t),a=parseInt(r.height,10)+parseInt(r.borderTopWidth,10)+parseInt(r.borderBottomWidth,10)===t.offsetHeight,re.removeChild(e)),a}}))}();var Be=["Webkit","Moz","ms"],$e=E.createElement("div").style,_e={};function ze(e){var t=S.cssProps[e]||_e[e];return t||(e in $e?e:_e[e]=function(e){var t=e[0].toUpperCase()+e.slice(1),n=Be.length;while(n--)if((e=Be[n]+t)in $e)return e}(e)||e)}var Ue=/^(none|table(?!-c[ea]).+)/,Xe=/^--/,Ve={position:"absolute",visibility:"hidden",display:"block"},Ge={letterSpacing:"0",fontWeight:"400"};function Ye(e,t,n){var r=te.exec(t);return r?Math.max(0,r[2]-(n||0))+(r[3]||"px"):t}function Qe(e,t,n,r,i,o){var a="width"===t?1:0,s=0,u=0;if(n===(r?"border":"content"))return 0;for(;a<4;a+=2)"margin"===n&&(u+=S.css(e,n+ne[a],!0,i)),r?("content"===n&&(u-=S.css(e,"padding"+ne[a],!0,i)),"margin"!==n&&(u-=S.css(e,"border"+ne[a]+"Width",!0,i))):(u+=S.css(e,"padding"+ne[a],!0,i),"padding"!==n?u+=S.css(e,"border"+ne[a]+"Width",!0,i):s+=S.css(e,"border"+ne[a]+"Width",!0,i));return!r&&0<=o&&(u+=Math.max(0,Math.ceil(e["offset"+t[0].toUpperCase()+t.slice(1)]-o-u-s-.5))||0),u}function Je(e,t,n){var r=Re(e),i=(!y.boxSizingReliable()||n)&&"border-box"===S.css(e,"boxSizing",!1,r),o=i,a=We(e,t,r),s="offset"+t[0].toUpperCase()+t.slice(1);if(Pe.test(a)){if(!n)return a;a="auto"}return(!y.boxSizingReliable()&&i||!y.reliableTrDimensions()&&A(e,"tr")||"auto"===a||!parseFloat(a)&&"inline"===S.css(e,"display",!1,r))&&e.getClientRects().length&&(i="border-box"===S.css(e,"boxSizing",!1,r),(o=s in e)&&(a=e[s])),(a=parseFloat(a)||0)+Qe(e,t,n||(i?"border":"content"),o,r,a)+"px"}function Ke(e,t,n,r,i){return new Ke.prototype.init(e,t,n,r,i)}S.extend({cssHooks:{opacity:{get:function(e,t){if(t){var n=We(e,"opacity");return""===n?"1":n}}}},cssNumber:{animationIterationCount:!0,columnCount:!0,fillOpacity:!0,flexGrow:!0,flexShrink:!0,fontWeight:!0,gridArea:!0,gridColumn:!0,gridColumnEnd:!0,gridColumnStart:!0,gridRow:!0,gridRowEnd:!0,gridRowStart:!0,lineHeight:!0,opacity:!0,order:!0,orphans:!0,widows:!0,zIndex:!0,zoom:!0},cssProps:{},style:function(e,t,n,r){if(e&&3!==e.nodeType&&8!==e.nodeType&&e.style){var i,o,a,s=X(t),u=Xe.test(t),l=e.style;if(u||(t=ze(s)),a=S.cssHooks[t]||S.cssHooks[s],void 0===n)return a&&"get"in a&&void 0!==(i=a.get(e,!1,r))?i:l[t];"string"===(o=typeof n)&&(i=te.exec(n))&&i[1]&&(n=se(e,t,i),o="number"),null!=n&&n==n&&("number"!==o||u||(n+=i&&i[3]||(S.cssNumber[s]?"":"px")),y.clearCloneStyle||""!==n||0!==t.indexOf("background")||(l[t]="inherit"),a&&"set"in a&&void 0===(n=a.set(e,n,r))||(u?l.setProperty(t,n):l[t]=n))}},css:function(e,t,n,r){var i,o,a,s=X(t);return Xe.test(t)||(t=ze(s)),(a=S.cssHooks[t]||S.cssHooks[s])&&"get"in a&&(i=a.get(e,!0,n)),void 0===i&&(i=We(e,t,r)),"normal"===i&&t in Ge&&(i=Ge[t]),""===n||n?(o=parseFloat(i),!0===n||isFinite(o)?o||0:i):i}}),S.each(["height","width"],function(e,u){S.cssHooks[u]={get:function(e,t,n){if(t)return!Ue.test(S.css(e,"display"))||e.getClientRects().length&&e.getBoundingClientRect().width?Je(e,u,n):Me(e,Ve,function(){return Je(e,u,n)})},set:function(e,t,n){var r,i=Re(e),o=!y.scrollboxSize()&&"absolute"===i.position,a=(o||n)&&"border-box"===S.css(e,"boxSizing",!1,i),s=n?Qe(e,u,n,a,i):0;return a&&o&&(s-=Math.ceil(e["offset"+u[0].toUpperCase()+u.slice(1)]-parseFloat(i[u])-Qe(e,u,"border",!1,i)-.5)),s&&(r=te.exec(t))&&"px"!==(r[3]||"px")&&(e.style[u]=t,t=S.css(e,u)),Ye(0,t,s)}}}),S.cssHooks.marginLeft=Fe(y.reliableMarginLeft,function(e,t){if(t)return(parseFloat(We(e,"marginLeft"))||e.getBoundingClientRect().left-Me(e,{marginLeft:0},function(){return e.getBoundingClientRect().left}))+"px"}),S.each({margin:"",padding:"",border:"Width"},function(i,o){S.cssHooks[i+o]={expand:function(e){for(var t=0,n={},r="string"==typeof e?e.split(" "):[e];t<4;t++)n[i+ne[t]+o]=r[t]||r[t-2]||r[0];return n}},"margin"!==i&&(S.cssHooks[i+o].set=Ye)}),S.fn.extend({css:function(e,t){return $(this,function(e,t,n){var r,i,o={},a=0;if(Array.isArray(t)){for(r=Re(e),i=t.length;a<i;a++)o[t[a]]=S.css(e,t[a],!1,r);return o}return void 0!==n?S.style(e,t,n):S.css(e,t)},e,t,1<arguments.length)}}),((S.Tween=Ke).prototype={constructor:Ke,init:function(e,t,n,r,i,o){this.elem=e,this.prop=n,this.easing=i||S.easing._default,this.options=t,this.start=this.now=this.cur(),this.end=r,this.unit=o||(S.cssNumber[n]?"":"px")},cur:function(){var e=Ke.propHooks[this.prop];return e&&e.get?e.get(this):Ke.propHooks._default.get(this)},run:function(e){var t,n=Ke.propHooks[this.prop];return this.options.duration?this.pos=t=S.easing[this.easing](e,this.options.duration*e,0,1,this.options.duration):this.pos=t=e,this.now=(this.end-this.start)*t+this.start,this.options.step&&this.options.step.call(this.elem,this.now,this),n&&n.set?n.set(this):Ke.propHooks._default.set(this),this}}).init.prototype=Ke.prototype,(Ke.propHooks={_default:{get:function(e){var t;return 1!==e.elem.nodeType||null!=e.elem[e.prop]&&null==e.elem.style[e.prop]?e.elem[e.prop]:(t=S.css(e.elem,e.prop,""))&&"auto"!==t?t:0},set:function(e){S.fx.step[e.prop]?S.fx.step[e.prop](e):1!==e.elem.nodeType||!S.cssHooks[e.prop]&&null==e.elem.style[ze(e.prop)]?e.elem[e.prop]=e.now:S.style(e.elem,e.prop,e.now+e.unit)}}}).scrollTop=Ke.propHooks.scrollLeft={set:function(e){e.elem.nodeType&&e.elem.parentNode&&(e.elem[e.prop]=e.now)}},S.easing={linear:function(e){return e},swing:function(e){return.5-Math.cos(e*Math.PI)/2},_default:"swing"},S.fx=Ke.prototype.init,S.fx.step={};var Ze,et,tt,nt,rt=/^(?:toggle|show|hide)$/,it=/queueHooks$/;function ot(){et&&(!1===E.hidden&&C.requestAnimationFrame?C.requestAnimationFrame(ot):C.setTimeout(ot,S.fx.interval),S.fx.tick())}function at(){return C.setTimeout(function(){Ze=void 0}),Ze=Date.now()}function st(e,t){var n,r=0,i={height:e};for(t=t?1:0;r<4;r+=2-t)i["margin"+(n=ne[r])]=i["padding"+n]=e;return t&&(i.opacity=i.width=e),i}function ut(e,t,n){for(var r,i=(lt.tweeners[t]||[]).concat(lt.tweeners["*"]),o=0,a=i.length;o<a;o++)if(r=i[o].call(n,t,e))return r}function lt(o,e,t){var n,a,r=0,i=lt.prefilters.length,s=S.Deferred().always(function(){delete u.elem}),u=function(){if(a)return!1;for(var e=Ze||at(),t=Math.max(0,l.startTime+l.duration-e),n=1-(t/l.duration||0),r=0,i=l.tweens.length;r<i;r++)l.tweens[r].run(n);return s.notifyWith(o,[l,n,t]),n<1&&i?t:(i||s.notifyWith(o,[l,1,0]),s.resolveWith(o,[l]),!1)},l=s.promise({elem:o,props:S.extend({},e),opts:S.extend(!0,{specialEasing:{},easing:S.easing._default},t),originalProperties:e,originalOptions:t,startTime:Ze||at(),duration:t.duration,tweens:[],createTween:function(e,t){var n=S.Tween(o,l.opts,e,t,l.opts.specialEasing[e]||l.opts.easing);return l.tweens.push(n),n},stop:function(e){var t=0,n=e?l.tweens.length:0;if(a)return this;for(a=!0;t<n;t++)l.tweens[t].run(1);return e?(s.notifyWith(o,[l,1,0]),s.resolveWith(o,[l,e])):s.rejectWith(o,[l,e]),this}}),c=l.props;for(!function(e,t){var n,r,i,o,a;for(n in e)if(i=t[r=X(n)],o=e[n],Array.isArray(o)&&(i=o[1],o=e[n]=o[0]),n!==r&&(e[r]=o,delete e[n]),(a=S.cssHooks[r])&&"expand"in a)for(n in o=a.expand(o),delete e[r],o)n in e||(e[n]=o[n],t[n]=i);else t[r]=i}(c,l.opts.specialEasing);r<i;r++)if(n=lt.prefilters[r].call(l,o,c,l.opts))return m(n.stop)&&(S._queueHooks(l.elem,l.opts.queue).stop=n.stop.bind(n)),n;return S.map(c,ut,l),m(l.opts.start)&&l.opts.start.call(o,l),l.progress(l.opts.progress).done(l.opts.done,l.opts.complete).fail(l.opts.fail).always(l.opts.always),S.fx.timer(S.extend(u,{elem:o,anim:l,queue:l.opts.queue})),l}S.Animation=S.extend(lt,{tweeners:{"*":[function(e,t){var n=this.createTween(e,t);return se(n.elem,e,te.exec(t),n),n}]},tweener:function(e,t){m(e)?(t=e,e=["*"]):e=e.match(P);for(var n,r=0,i=e.length;r<i;r++)n=e[r],lt.tweeners[n]=lt.tweeners[n]||[],lt.tweeners[n].unshift(t)},prefilters:[function(e,t,n){var r,i,o,a,s,u,l,c,f="width"in t||"height"in t,p=this,d={},h=e.style,g=e.nodeType&&ae(e),v=Y.get(e,"fxshow");for(r in n.queue||(null==(a=S._queueHooks(e,"fx")).unqueued&&(a.unqueued=0,s=a.empty.fire,a.empty.fire=function(){a.unqueued||s()}),a.unqueued++,p.always(function(){p.always(function(){a.unqueued--,S.queue(e,"fx").length||a.empty.fire()})})),t)if(i=t[r],rt.test(i)){if(delete t[r],o=o||"toggle"===i,i===(g?"hide":"show")){if("show"!==i||!v||void 0===v[r])continue;g=!0}d[r]=v&&v[r]||S.style(e,r)}if((u=!S.isEmptyObject(t))||!S.isEmptyObject(d))for(r in f&&1===e.nodeType&&(n.overflow=[h.overflow,h.overflowX,h.overflowY],null==(l=v&&v.display)&&(l=Y.get(e,"display")),"none"===(c=S.css(e,"display"))&&(l?c=l:(le([e],!0),l=e.style.display||l,c=S.css(e,"display"),le([e]))),("inline"===c||"inline-block"===c&&null!=l)&&"none"===S.css(e,"float")&&(u||(p.done(function(){h.display=l}),null==l&&(c=h.display,l="none"===c?"":c)),h.display="inline-block")),n.overflow&&(h.overflow="hidden",p.always(function(){h.overflow=n.overflow[0],h.overflowX=n.overflow[1],h.overflowY=n.overflow[2]})),u=!1,d)u||(v?"hidden"in v&&(g=v.hidden):v=Y.access(e,"fxshow",{display:l}),o&&(v.hidden=!g),g&&le([e],!0),p.done(function(){for(r in g||le([e]),Y.remove(e,"fxshow"),d)S.style(e,r,d[r])})),u=ut(g?v[r]:0,r,p),r in v||(v[r]=u.start,g&&(u.end=u.start,u.start=0))}],prefilter:function(e,t){t?lt.prefilters.unshift(e):lt.prefilters.push(e)}}),S.speed=function(e,t,n){var r=e&&"object"==typeof e?S.extend({},e):{complete:n||!n&&t||m(e)&&e,duration:e,easing:n&&t||t&&!m(t)&&t};return S.fx.off?r.duration=0:"number"!=typeof r.duration&&(r.duration in S.fx.speeds?r.duration=S.fx.speeds[r.duration]:r.duration=S.fx.speeds._default),null!=r.queue&&!0!==r.queue||(r.queue="fx"),r.old=r.complete,r.complete=function(){m(r.old)&&r.old.call(this),r.queue&&S.dequeue(this,r.queue)},r},S.fn.extend({fadeTo:function(e,t,n,r){return this.filter(ae).css("opacity",0).show().end().animate({opacity:t},e,n,r)},animate:function(t,e,n,r){var i=S.isEmptyObject(t),o=S.speed(e,n,r),a=function(){var e=lt(this,S.extend({},t),o);(i||Y.get(this,"finish"))&&e.stop(!0)};return a.finish=a,i||!1===o.queue?this.each(a):this.queue(o.queue,a)},stop:function(i,e,o){var a=function(e){var t=e.stop;delete e.stop,t(o)};return"string"!=typeof i&&(o=e,e=i,i=void 0),e&&this.queue(i||"fx",[]),this.each(function(){var e=!0,t=null!=i&&i+"queueHooks",n=S.timers,r=Y.get(this);if(t)r[t]&&r[t].stop&&a(r[t]);else for(t in r)r[t]&&r[t].stop&&it.test(t)&&a(r[t]);for(t=n.length;t--;)n[t].elem!==this||null!=i&&n[t].queue!==i||(n[t].anim.stop(o),e=!1,n.splice(t,1));!e&&o||S.dequeue(this,i)})},finish:function(a){return!1!==a&&(a=a||"fx"),this.each(function(){var e,t=Y.get(this),n=t[a+"queue"],r=t[a+"queueHooks"],i=S.timers,o=n?n.length:0;for(t.finish=!0,S.queue(this,a,[]),r&&r.stop&&r.stop.call(this,!0),e=i.length;e--;)i[e].elem===this&&i[e].queue===a&&(i[e].anim.stop(!0),i.splice(e,1));for(e=0;e<o;e++)n[e]&&n[e].finish&&n[e].finish.call(this);delete t.finish})}}),S.each(["toggle","show","hide"],function(e,r){var i=S.fn[r];S.fn[r]=function(e,t,n){return null==e||"boolean"==typeof e?i.apply(this,arguments):this.animate(st(r,!0),e,t,n)}}),S.each({slideDown:st("show"),slideUp:st("hide"),slideToggle:st("toggle"),fadeIn:{opacity:"show"},fadeOut:{opacity:"hide"},fadeToggle:{opacity:"toggle"}},function(e,r){S.fn[e]=function(e,t,n){return this.animate(r,e,t,n)}}),S.timers=[],S.fx.tick=function(){var e,t=0,n=S.timers;for(Ze=Date.now();t<n.length;t++)(e=n[t])()||n[t]!==e||n.splice(t--,1);n.length||S.fx.stop(),Ze=void 0},S.fx.timer=function(e){S.timers.push(e),S.fx.start()},S.fx.interval=13,S.fx.start=function(){et||(et=!0,ot())},S.fx.stop=function(){et=null},S.fx.speeds={slow:600,fast:200,_default:400},S.fn.delay=function(r,e){return r=S.fx&&S.fx.speeds[r]||r,e=e||"fx",this.queue(e,function(e,t){var n=C.setTimeout(e,r);t.stop=function(){C.clearTimeout(n)}})},tt=E.createElement("input"),nt=E.createElement("select").appendChild(E.createElement("option")),tt.type="checkbox",y.checkOn=""!==tt.value,y.optSelected=nt.selected,(tt=E.createElement("input")).value="t",tt.type="radio",y.radioValue="t"===tt.value;var ct,ft=S.expr.attrHandle;S.fn.extend({attr:function(e,t){return $(this,S.attr,e,t,1<arguments.length)},removeAttr:function(e){return this.each(function(){S.removeAttr(this,e)})}}),S.extend({attr:function(e,t,n){var r,i,o=e.nodeType;if(3!==o&&8!==o&&2!==o)return"undefined"==typeof e.getAttribute?S.prop(e,t,n):(1===o&&S.isXMLDoc(e)||(i=S.attrHooks[t.toLowerCase()]||(S.expr.match.bool.test(t)?ct:void 0)),void 0!==n?null===n?void S.removeAttr(e,t):i&&"set"in i&&void 0!==(r=i.set(e,n,t))?r:(e.setAttribute(t,n+""),n):i&&"get"in i&&null!==(r=i.get(e,t))?r:null==(r=S.find.attr(e,t))?void 0:r)},attrHooks:{type:{set:function(e,t){if(!y.radioValue&&"radio"===t&&A(e,"input")){var n=e.value;return e.setAttribute("type",t),n&&(e.value=n),t}}}},removeAttr:function(e,t){var n,r=0,i=t&&t.match(P);if(i&&1===e.nodeType)while(n=i[r++])e.removeAttribute(n)}}),ct={set:function(e,t,n){return!1===t?S.removeAttr(e,n):e.setAttribute(n,n),n}},S.each(S.expr.match.bool.source.match(/\w+/g),function(e,t){var a=ft[t]||S.find.attr;ft[t]=function(e,t,n){var r,i,o=t.toLowerCase();return n||(i=ft[o],ft[o]=r,r=null!=a(e,t,n)?o:null,ft[o]=i),r}});var pt=/^(?:input|select|textarea|button)$/i,dt=/^(?:a|area)$/i;function ht(e){return(e.match(P)||[]).join(" ")}function gt(e){return e.getAttribute&&e.getAttribute("class")||""}function vt(e){return Array.isArray(e)?e:"string"==typeof e&&e.match(P)||[]}S.fn.extend({prop:function(e,t){return $(this,S.prop,e,t,1<arguments.length)},removeProp:function(e){return this.each(function(){delete this[S.propFix[e]||e]})}}),S.extend({prop:function(e,t,n){var r,i,o=e.nodeType;if(3!==o&&8!==o&&2!==o)return 1===o&&S.isXMLDoc(e)||(t=S.propFix[t]||t,i=S.propHooks[t]),void 0!==n?i&&"set"in i&&void 0!==(r=i.set(e,n,t))?r:e[t]=n:i&&"get"in i&&null!==(r=i.get(e,t))?r:e[t]},propHooks:{tabIndex:{get:function(e){var t=S.find.attr(e,"tabindex");return t?parseInt(t,10):pt.test(e.nodeName)||dt.test(e.nodeName)&&e.href?0:-1}}},propFix:{"for":"htmlFor","class":"className"}}),y.optSelected||(S.propHooks.selected={get:function(e){var t=e.parentNode;return t&&t.parentNode&&t.parentNode.selectedIndex,null},set:function(e){var t=e.parentNode;t&&(t.selectedIndex,t.parentNode&&t.parentNode.selectedIndex)}}),S.each(["tabIndex","readOnly","maxLength","cellSpacing","cellPadding","rowSpan","colSpan","useMap","frameBorder","contentEditable"],function(){S.propFix[this.toLowerCase()]=this}),S.fn.extend({addClass:function(t){var e,n,r,i,o,a,s,u=0;if(m(t))return this.each(function(e){S(this).addClass(t.call(this,e,gt(this)))});if((e=vt(t)).length)while(n=this[u++])if(i=gt(n),r=1===n.nodeType&&" "+ht(i)+" "){a=0;while(o=e[a++])r.indexOf(" "+o+" ")<0&&(r+=o+" ");i!==(s=ht(r))&&n.setAttribute("class",s)}return this},removeClass:function(t){var e,n,r,i,o,a,s,u=0;if(m(t))return this.each(function(e){S(this).removeClass(t.call(this,e,gt(this)))});if(!arguments.length)return this.attr("class","");if((e=vt(t)).length)while(n=this[u++])if(i=gt(n),r=1===n.nodeType&&" "+ht(i)+" "){a=0;while(o=e[a++])while(-1<r.indexOf(" "+o+" "))r=r.replace(" "+o+" "," ");i!==(s=ht(r))&&n.setAttribute("class",s)}return this},toggleClass:function(i,t){var o=typeof i,a="string"===o||Array.isArray(i);return"boolean"==typeof t&&a?t?this.addClass(i):this.removeClass(i):m(i)?this.each(function(e){S(this).toggleClass(i.call(this,e,gt(this),t),t)}):this.each(function(){var e,t,n,r;if(a){t=0,n=S(this),r=vt(i);while(e=r[t++])n.hasClass(e)?n.removeClass(e):n.addClass(e)}else void 0!==i&&"boolean"!==o||((e=gt(this))&&Y.set(this,"__className__",e),this.setAttribute&&this.setAttribute("class",e||!1===i?"":Y.get(this,"__className__")||""))})},hasClass:function(e){var t,n,r=0;t=" "+e+" ";while(n=this[r++])if(1===n.nodeType&&-1<(" "+ht(gt(n))+" ").indexOf(t))return!0;return!1}});var yt=/\r/g;S.fn.extend({val:function(n){var r,e,i,t=this[0];return arguments.length?(i=m(n),this.each(function(e){var t;1===this.nodeType&&(null==(t=i?n.call(this,e,S(this).val()):n)?t="":"number"==typeof t?t+="":Array.isArray(t)&&(t=S.map(t,function(e){return null==e?"":e+""})),(r=S.valHooks[this.type]||S.valHooks[this.nodeName.toLowerCase()])&&"set"in r&&void 0!==r.set(this,t,"value")||(this.value=t))})):t?(r=S.valHooks[t.type]||S.valHooks[t.nodeName.toLowerCase()])&&"get"in r&&void 0!==(e=r.get(t,"value"))?e:"string"==typeof(e=t.value)?e.replace(yt,""):null==e?"":e:void 0}}),S.extend({valHooks:{option:{get:function(e){var t=S.find.attr(e,"value");return null!=t?t:ht(S.text(e))}},select:{get:function(e){var t,n,r,i=e.options,o=e.selectedIndex,a="select-one"===e.type,s=a?null:[],u=a?o+1:i.length;for(r=o<0?u:a?o:0;r<u;r++)if(((n=i[r]).selected||r===o)&&!n.disabled&&(!n.parentNode.disabled||!A(n.parentNode,"optgroup"))){if(t=S(n).val(),a)return t;s.push(t)}return s},set:function(e,t){var n,r,i=e.options,o=S.makeArray(t),a=i.length;while(a--)((r=i[a]).selected=-1<S.inArray(S.valHooks.option.get(r),o))&&(n=!0);return n||(e.selectedIndex=-1),o}}}}),S.each(["radio","checkbox"],function(){S.valHooks[this]={set:function(e,t){if(Array.isArray(t))return e.checked=-1<S.inArray(S(e).val(),t)}},y.checkOn||(S.valHooks[this].get=function(e){return null===e.getAttribute("value")?"on":e.value})}),y.focusin="onfocusin"in C;var mt=/^(?:focusinfocus|focusoutblur)$/,xt=function(e){e.stopPropagation()};S.extend(S.event,{trigger:function(e,t,n,r){var i,o,a,s,u,l,c,f,p=[n||E],d=v.call(e,"type")?e.type:e,h=v.call(e,"namespace")?e.namespace.split("."):[];if(o=f=a=n=n||E,3!==n.nodeType&&8!==n.nodeType&&!mt.test(d+S.event.triggered)&&(-1<d.indexOf(".")&&(d=(h=d.split(".")).shift(),h.sort()),u=d.indexOf(":")<0&&"on"+d,(e=e[S.expando]?e:new S.Event(d,"object"==typeof e&&e)).isTrigger=r?2:3,e.namespace=h.join("."),e.rnamespace=e.namespace?new RegExp("(^|\\.)"+h.join("\\.(?:.*\\.|)")+"(\\.|$)"):null,e.result=void 0,e.target||(e.target=n),t=null==t?[e]:S.makeArray(t,[e]),c=S.event.special[d]||{},r||!c.trigger||!1!==c.trigger.apply(n,t))){if(!r&&!c.noBubble&&!x(n)){for(s=c.delegateType||d,mt.test(s+d)||(o=o.parentNode);o;o=o.parentNode)p.push(o),a=o;a===(n.ownerDocument||E)&&p.push(a.defaultView||a.parentWindow||C)}i=0;while((o=p[i++])&&!e.isPropagationStopped())f=o,e.type=1<i?s:c.bindType||d,(l=(Y.get(o,"events")||Object.create(null))[e.type]&&Y.get(o,"handle"))&&l.apply(o,t),(l=u&&o[u])&&l.apply&&V(o)&&(e.result=l.apply(o,t),!1===e.result&&e.preventDefault());return e.type=d,r||e.isDefaultPrevented()||c._default&&!1!==c._default.apply(p.pop(),t)||!V(n)||u&&m(n[d])&&!x(n)&&((a=n[u])&&(n[u]=null),S.event.triggered=d,e.isPropagationStopped()&&f.addEventListener(d,xt),n[d](),e.isPropagationStopped()&&f.removeEventListener(d,xt),S.event.triggered=void 0,a&&(n[u]=a)),e.result}},simulate:function(e,t,n){var r=S.extend(new S.Event,n,{type:e,isSimulated:!0});S.event.trigger(r,null,t)}}),S.fn.extend({trigger:function(e,t){return this.each(function(){S.event.trigger(e,t,this)})},triggerHandler:function(e,t){var n=this[0];if(n)return S.event.trigger(e,t,n,!0)}}),y.focusin||S.each({focus:"focusin",blur:"focusout"},function(n,r){var i=function(e){S.event.simulate(r,e.target,S.event.fix(e))};S.event.special[r]={setup:function(){var e=this.ownerDocument||this.document||this,t=Y.access(e,r);t||e.addEventListener(n,i,!0),Y.access(e,r,(t||0)+1)},teardown:function(){var e=this.ownerDocument||this.document||this,t=Y.access(e,r)-1;t?Y.access(e,r,t):(e.removeEventListener(n,i,!0),Y.remove(e,r))}}});var bt=C.location,wt={guid:Date.now()},Tt=/\?/;S.parseXML=function(e){var t,n;if(!e||"string"!=typeof e)return null;try{t=(new C.DOMParser).parseFromString(e,"text/xml")}catch(e){}return n=t&&t.getElementsByTagName("parsererror")[0],t&&!n||S.error("Invalid XML: "+(n?S.map(n.childNodes,function(e){return e.textContent}).join("\n"):e)),t};var Ct=/\[\]$/,Et=/\r?\n/g,St=/^(?:submit|button|image|reset|file)$/i,kt=/^(?:input|select|textarea|keygen)/i;function At(n,e,r,i){var t;if(Array.isArray(e))S.each(e,function(e,t){r||Ct.test(n)?i(n,t):At(n+"["+("object"==typeof t&&null!=t?e:"")+"]",t,r,i)});else if(r||"object"!==w(e))i(n,e);else for(t in e)At(n+"["+t+"]",e[t],r,i)}S.param=function(e,t){var n,r=[],i=function(e,t){var n=m(t)?t():t;r[r.length]=encodeURIComponent(e)+"="+encodeURIComponent(null==n?"":n)};if(null==e)return"";if(Array.isArray(e)||e.jquery&&!S.isPlainObject(e))S.each(e,function(){i(this.name,this.value)});else for(n in e)At(n,e[n],t,i);return r.join("&")},S.fn.extend({serialize:function(){return S.param(this.serializeArray())},serializeArray:function(){return this.map(function(){var e=S.prop(this,"elements");return e?S.makeArray(e):this}).filter(function(){var e=this.type;return this.name&&!S(this).is(":disabled")&&kt.test(this.nodeName)&&!St.test(e)&&(this.checked||!pe.test(e))}).map(function(e,t){var n=S(this).val();return null==n?null:Array.isArray(n)?S.map(n,function(e){return{name:t.name,value:e.replace(Et,"\r\n")}}):{name:t.name,value:n.replace(Et,"\r\n")}}).get()}});var Nt=/%20/g,jt=/#.*$/,Dt=/([?&])_=[^&]*/,qt=/^(.*?):[ \t]*([^\r\n]*)$/gm,Lt=/^(?:GET|HEAD)$/,Ht=/^\/\//,Ot={},Pt={},Rt="*/".concat("*"),Mt=E.createElement("a");function It(o){return function(e,t){"string"!=typeof e&&(t=e,e="*");var n,r=0,i=e.toLowerCase().match(P)||[];if(m(t))while(n=i[r++])"+"===n[0]?(n=n.slice(1)||"*",(o[n]=o[n]||[]).unshift(t)):(o[n]=o[n]||[]).push(t)}}function Wt(t,i,o,a){var s={},u=t===Pt;function l(e){var r;return s[e]=!0,S.each(t[e]||[],function(e,t){var n=t(i,o,a);return"string"!=typeof n||u||s[n]?u?!(r=n):void 0:(i.dataTypes.unshift(n),l(n),!1)}),r}return l(i.dataTypes[0])||!s["*"]&&l("*")}function Ft(e,t){var n,r,i=S.ajaxSettings.flatOptions||{};for(n in t)void 0!==t[n]&&((i[n]?e:r||(r={}))[n]=t[n]);return r&&S.extend(!0,e,r),e}Mt.href=bt.href,S.extend({active:0,lastModified:{},etag:{},ajaxSettings:{url:bt.href,type:"GET",isLocal:/^(?:about|app|app-storage|.+-extension|file|res|widget):$/.test(bt.protocol),global:!0,processData:!0,async:!0,contentType:"application/x-www-form-urlencoded; charset=UTF-8",accepts:{"*":Rt,text:"text/plain",html:"text/html",xml:"application/xml, text/xml",json:"application/json, text/javascript"},contents:{xml:/\bxml\b/,html:/\bhtml/,json:/\bjson\b/},responseFields:{xml:"responseXML",text:"responseText",json:"responseJSON"},converters:{"* text":String,"text html":!0,"text json":JSON.parse,"text xml":S.parseXML},flatOptions:{url:!0,context:!0}},ajaxSetup:function(e,t){return t?Ft(Ft(e,S.ajaxSettings),t):Ft(S.ajaxSettings,e)},ajaxPrefilter:It(Ot),ajaxTransport:It(Pt),ajax:function(e,t){"object"==typeof e&&(t=e,e=void 0),t=t||{};var c,f,p,n,d,r,h,g,i,o,v=S.ajaxSetup({},t),y=v.context||v,m=v.context&&(y.nodeType||y.jquery)?S(y):S.event,x=S.Deferred(),b=S.Callbacks("once memory"),w=v.statusCode||{},a={},s={},u="canceled",T={readyState:0,getResponseHeader:function(e){var t;if(h){if(!n){n={};while(t=qt.exec(p))n[t[1].toLowerCase()+" "]=(n[t[1].toLowerCase()+" "]||[]).concat(t[2])}t=n[e.toLowerCase()+" "]}return null==t?null:t.join(", ")},getAllResponseHeaders:function(){return h?p:null},setRequestHeader:function(e,t){return null==h&&(e=s[e.toLowerCase()]=s[e.toLowerCase()]||e,a[e]=t),this},overrideMimeType:function(e){return null==h&&(v.mimeType=e),this},statusCode:function(e){var t;if(e)if(h)T.always(e[T.status]);else for(t in e)w[t]=[w[t],e[t]];return this},abort:function(e){var t=e||u;return c&&c.abort(t),l(0,t),this}};if(x.promise(T),v.url=((e||v.url||bt.href)+"").replace(Ht,bt.protocol+"//"),v.type=t.method||t.type||v.method||v.type,v.dataTypes=(v.dataType||"*").toLowerCase().match(P)||[""],null==v.crossDomain){r=E.createElement("a");try{r.href=v.url,r.href=r.href,v.crossDomain=Mt.protocol+"//"+Mt.host!=r.protocol+"//"+r.host}catch(e){v.crossDomain=!0}}if(v.data&&v.processData&&"string"!=typeof v.data&&(v.data=S.param(v.data,v.traditional)),Wt(Ot,v,t,T),h)return T;for(i in(g=S.event&&v.global)&&0==S.active++&&S.event.trigger("ajaxStart"),v.type=v.type.toUpperCase(),v.hasContent=!Lt.test(v.type),f=v.url.replace(jt,""),v.hasContent?v.data&&v.processData&&0===(v.contentType||"").indexOf("application/x-www-form-urlencoded")&&(v.data=v.data.replace(Nt,"+")):(o=v.url.slice(f.length),v.data&&(v.processData||"string"==typeof v.data)&&(f+=(Tt.test(f)?"&":"?")+v.data,delete v.data),!1===v.cache&&(f=f.replace(Dt,"$1"),o=(Tt.test(f)?"&":"?")+"_="+wt.guid+++o),v.url=f+o),v.ifModified&&(S.lastModified[f]&&T.setRequestHeader("If-Modified-Since",S.lastModified[f]),S.etag[f]&&T.setRequestHeader("If-None-Match",S.etag[f])),(v.data&&v.hasContent&&!1!==v.contentType||t.contentType)&&T.setRequestHeader("Content-Type",v.contentType),T.setRequestHeader("Accept",v.dataTypes[0]&&v.accepts[v.dataTypes[0]]?v.accepts[v.dataTypes[0]]+("*"!==v.dataTypes[0]?", "+Rt+"; q=0.01":""):v.accepts["*"]),v.headers)T.setRequestHeader(i,v.headers[i]);if(v.beforeSend&&(!1===v.beforeSend.call(y,T,v)||h))return T.abort();if(u="abort",b.add(v.complete),T.done(v.success),T.fail(v.error),c=Wt(Pt,v,t,T)){if(T.readyState=1,g&&m.trigger("ajaxSend",[T,v]),h)return T;v.async&&0<v.timeout&&(d=C.setTimeout(function(){T.abort("timeout")},v.timeout));try{h=!1,c.send(a,l)}catch(e){if(h)throw e;l(-1,e)}}else l(-1,"No Transport");function l(e,t,n,r){var i,o,a,s,u,l=t;h||(h=!0,d&&C.clearTimeout(d),c=void 0,p=r||"",T.readyState=0<e?4:0,i=200<=e&&e<300||304===e,n&&(s=function(e,t,n){var r,i,o,a,s=e.contents,u=e.dataTypes;while("*"===u[0])u.shift(),void 0===r&&(r=e.mimeType||t.getResponseHeader("Content-Type"));if(r)for(i in s)if(s[i]&&s[i].test(r)){u.unshift(i);break}if(u[0]in n)o=u[0];else{for(i in n){if(!u[0]||e.converters[i+" "+u[0]]){o=i;break}a||(a=i)}o=o||a}if(o)return o!==u[0]&&u.unshift(o),n[o]}(v,T,n)),!i&&-1<S.inArray("script",v.dataTypes)&&S.inArray("json",v.dataTypes)<0&&(v.converters["text script"]=function(){}),s=function(e,t,n,r){var i,o,a,s,u,l={},c=e.dataTypes.slice();if(c[1])for(a in e.converters)l[a.toLowerCase()]=e.converters[a];o=c.shift();while(o)if(e.responseFields[o]&&(n[e.responseFields[o]]=t),!u&&r&&e.dataFilter&&(t=e.dataFilter(t,e.dataType)),u=o,o=c.shift())if("*"===o)o=u;else if("*"!==u&&u!==o){if(!(a=l[u+" "+o]||l["* "+o]))for(i in l)if((s=i.split(" "))[1]===o&&(a=l[u+" "+s[0]]||l["* "+s[0]])){!0===a?a=l[i]:!0!==l[i]&&(o=s[0],c.unshift(s[1]));break}if(!0!==a)if(a&&e["throws"])t=a(t);else try{t=a(t)}catch(e){return{state:"parsererror",error:a?e:"No conversion from "+u+" to "+o}}}return{state:"success",data:t}}(v,s,T,i),i?(v.ifModified&&((u=T.getResponseHeader("Last-Modified"))&&(S.lastModified[f]=u),(u=T.getResponseHeader("etag"))&&(S.etag[f]=u)),204===e||"HEAD"===v.type?l="nocontent":304===e?l="notmodified":(l=s.state,o=s.data,i=!(a=s.error))):(a=l,!e&&l||(l="error",e<0&&(e=0))),T.status=e,T.statusText=(t||l)+"",i?x.resolveWith(y,[o,l,T]):x.rejectWith(y,[T,l,a]),T.statusCode(w),w=void 0,g&&m.trigger(i?"ajaxSuccess":"ajaxError",[T,v,i?o:a]),b.fireWith(y,[T,l]),g&&(m.trigger("ajaxComplete",[T,v]),--S.active||S.event.trigger("ajaxStop")))}return T},getJSON:function(e,t,n){return S.get(e,t,n,"json")},getScript:function(e,t){return S.get(e,void 0,t,"script")}}),S.each(["get","post"],function(e,i){S[i]=function(e,t,n,r){return m(t)&&(r=r||n,n=t,t=void 0),S.ajax(S.extend({url:e,type:i,dataType:r,data:t,success:n},S.isPlainObject(e)&&e))}}),S.ajaxPrefilter(function(e){var t;for(t in e.headers)"content-type"===t.toLowerCase()&&(e.contentType=e.headers[t]||"")}),S._evalUrl=function(e,t,n){return S.ajax({url:e,type:"GET",dataType:"script",cache:!0,async:!1,global:!1,converters:{"text script":function(){}},dataFilter:function(e){S.globalEval(e,t,n)}})},S.fn.extend({wrapAll:function(e){var t;return this[0]&&(m(e)&&(e=e.call(this[0])),t=S(e,this[0].ownerDocument).eq(0).clone(!0),this[0].parentNode&&t.insertBefore(this[0]),t.map(function(){var e=this;while(e.firstElementChild)e=e.firstElementChild;return e}).append(this)),this},wrapInner:function(n){return m(n)?this.each(function(e){S(this).wrapInner(n.call(this,e))}):this.each(function(){var e=S(this),t=e.contents();t.length?t.wrapAll(n):e.append(n)})},wrap:function(t){var n=m(t);return this.each(function(e){S(this).wrapAll(n?t.call(this,e):t)})},unwrap:function(e){return this.parent(e).not("body").each(function(){S(this).replaceWith(this.childNodes)}),this}}),S.expr.pseudos.hidden=function(e){return!S.expr.pseudos.visible(e)},S.expr.pseudos.visible=function(e){return!!(e.offsetWidth||e.offsetHeight||e.getClientRects().length)},S.ajaxSettings.xhr=function(){try{return new C.XMLHttpRequest}catch(e){}};var Bt={0:200,1223:204},$t=S.ajaxSettings.xhr();y.cors=!!$t&&"withCredentials"in $t,y.ajax=$t=!!$t,S.ajaxTransport(function(i){var o,a;if(y.cors||$t&&!i.crossDomain)return{send:function(e,t){var n,r=i.xhr();if(r.open(i.type,i.url,i.async,i.username,i.password),i.xhrFields)for(n in i.xhrFields)r[n]=i.xhrFields[n];for(n in i.mimeType&&r.overrideMimeType&&r.overrideMimeType(i.mimeType),i.crossDomain||e["X-Requested-With"]||(e["X-Requested-With"]="XMLHttpRequest"),e)r.setRequestHeader(n,e[n]);o=function(e){return function(){o&&(o=a=r.onload=r.onerror=r.onabort=r.ontimeout=r.onreadystatechange=null,"abort"===e?r.abort():"error"===e?"number"!=typeof r.status?t(0,"error"):t(r.status,r.statusText):t(Bt[r.status]||r.status,r.statusText,"text"!==(r.responseType||"text")||"string"!=typeof r.responseText?{binary:r.response}:{text:r.responseText},r.getAllResponseHeaders()))}},r.onload=o(),a=r.onerror=r.ontimeout=o("error"),void 0!==r.onabort?r.onabort=a:r.onreadystatechange=function(){4===r.readyState&&C.setTimeout(function(){o&&a()})},o=o("abort");try{r.send(i.hasContent&&i.data||null)}catch(e){if(o)throw e}},abort:function(){o&&o()}}}),S.ajaxPrefilter(function(e){e.crossDomain&&(e.contents.script=!1)}),S.ajaxSetup({accepts:{script:"text/javascript, application/javascript, application/ecmascript, application/x-ecmascript"},contents:{script:/\b(?:java|ecma)script\b/},converters:{"text script":function(e){return S.globalEval(e),e}}}),S.ajaxPrefilter("script",function(e){void 0===e.cache&&(e.cache=!1),e.crossDomain&&(e.type="GET")}),S.ajaxTransport("script",function(n){var r,i;if(n.crossDomain||n.scriptAttrs)return{send:function(e,t){r=S("<script>").attr(n.scriptAttrs||{}).prop({charset:n.scriptCharset,src:n.url}).on("load error",i=function(e){r.remove(),i=null,e&&t("error"===e.type?404:200,e.type)}),E.head.appendChild(r[0])},abort:function(){i&&i()}}});var _t,zt=[],Ut=/(=)\?(?=&|$)|\?\?/;S.ajaxSetup({jsonp:"callback",jsonpCallback:function(){var e=zt.pop()||S.expando+"_"+wt.guid++;return this[e]=!0,e}}),S.ajaxPrefilter("json jsonp",function(e,t,n){var r,i,o,a=!1!==e.jsonp&&(Ut.test(e.url)?"url":"string"==typeof e.data&&0===(e.contentType||"").indexOf("application/x-www-form-urlencoded")&&Ut.test(e.data)&&"data");if(a||"jsonp"===e.dataTypes[0])return r=e.jsonpCallback=m(e.jsonpCallback)?e.jsonpCallback():e.jsonpCallback,a?e[a]=e[a].replace(Ut,"$1"+r):!1!==e.jsonp&&(e.url+=(Tt.test(e.url)?"&":"?")+e.jsonp+"="+r),e.converters["script json"]=function(){return o||S.error(r+" was not called"),o[0]},e.dataTypes[0]="json",i=C[r],C[r]=function(){o=arguments},n.always(function(){void 0===i?S(C).removeProp(r):C[r]=i,e[r]&&(e.jsonpCallback=t.jsonpCallback,zt.push(r)),o&&m(i)&&i(o[0]),o=i=void 0}),"script"}),y.createHTMLDocument=((_t=E.implementation.createHTMLDocument("").body).innerHTML="<form></form><form></form>",2===_t.childNodes.length),S.parseHTML=function(e,t,n){return"string"!=typeof e?[]:("boolean"==typeof t&&(n=t,t=!1),t||(y.createHTMLDocument?((r=(t=E.implementation.createHTMLDocument("")).createElement("base")).href=E.location.href,t.head.appendChild(r)):t=E),o=!n&&[],(i=N.exec(e))?[t.createElement(i[1])]:(i=xe([e],t,o),o&&o.length&&S(o).remove(),S.merge([],i.childNodes)));var r,i,o},S.fn.load=function(e,t,n){var r,i,o,a=this,s=e.indexOf(" ");return-1<s&&(r=ht(e.slice(s)),e=e.slice(0,s)),m(t)?(n=t,t=void 0):t&&"object"==typeof t&&(i="POST"),0<a.length&&S.ajax({url:e,type:i||"GET",dataType:"html",data:t}).done(function(e){o=arguments,a.html(r?S("<div>").append(S.parseHTML(e)).find(r):e)}).always(n&&function(e,t){a.each(function(){n.apply(this,o||[e.responseText,t,e])})}),this},S.expr.pseudos.animated=function(t){return S.grep(S.timers,function(e){return t===e.elem}).length},S.offset={setOffset:function(e,t,n){var r,i,o,a,s,u,l=S.css(e,"position"),c=S(e),f={};"static"===l&&(e.style.position="relative"),s=c.offset(),o=S.css(e,"top"),u=S.css(e,"left"),("absolute"===l||"fixed"===l)&&-1<(o+u).indexOf("auto")?(a=(r=c.position()).top,i=r.left):(a=parseFloat(o)||0,i=parseFloat(u)||0),m(t)&&(t=t.call(e,n,S.extend({},s))),null!=t.top&&(f.top=t.top-s.top+a),null!=t.left&&(f.left=t.left-s.left+i),"using"in t?t.using.call(e,f):c.css(f)}},S.fn.extend({offset:function(t){if(arguments.length)return void 0===t?this:this.each(function(e){S.offset.setOffset(this,t,e)});var e,n,r=this[0];return r?r.getClientRects().length?(e=r.getBoundingClientRect(),n=r.ownerDocument.defaultView,{top:e.top+n.pageYOffset,left:e.left+n.pageXOffset}):{top:0,left:0}:void 0},position:function(){if(this[0]){var e,t,n,r=this[0],i={top:0,left:0};if("fixed"===S.css(r,"position"))t=r.getBoundingClientRect();else{t=this.offset(),n=r.ownerDocument,e=r.offsetParent||n.documentElement;while(e&&(e===n.body||e===n.documentElement)&&"static"===S.css(e,"position"))e=e.parentNode;e&&e!==r&&1===e.nodeType&&((i=S(e).offset()).top+=S.css(e,"borderTopWidth",!0),i.left+=S.css(e,"borderLeftWidth",!0))}return{top:t.top-i.top-S.css(r,"marginTop",!0),left:t.left-i.left-S.css(r,"marginLeft",!0)}}},offsetParent:function(){return this.map(function(){var e=this.offsetParent;while(e&&"static"===S.css(e,"position"))e=e.offsetParent;return e||re})}}),S.each({scrollLeft:"pageXOffset",scrollTop:"pageYOffset"},function(t,i){var o="pageYOffset"===i;S.fn[t]=function(e){return $(this,function(e,t,n){var r;if(x(e)?r=e:9===e.nodeType&&(r=e.defaultView),void 0===n)return r?r[i]:e[t];r?r.scrollTo(o?r.pageXOffset:n,o?n:r.pageYOffset):e[t]=n},t,e,arguments.length)}}),S.each(["top","left"],function(e,n){S.cssHooks[n]=Fe(y.pixelPosition,function(e,t){if(t)return t=We(e,n),Pe.test(t)?S(e).position()[n]+"px":t})}),S.each({Height:"height",Width:"width"},function(a,s){S.each({padding:"inner"+a,content:s,"":"outer"+a},function(r,o){S.fn[o]=function(e,t){var n=arguments.length&&(r||"boolean"!=typeof e),i=r||(!0===e||!0===t?"margin":"border");return $(this,function(e,t,n){var r;return x(e)?0===o.indexOf("outer")?e["inner"+a]:e.document.documentElement["client"+a]:9===e.nodeType?(r=e.documentElement,Math.max(e.body["scroll"+a],r["scroll"+a],e.body["offset"+a],r["offset"+a],r["client"+a])):void 0===n?S.css(e,t,i):S.style(e,t,n,i)},s,n?e:void 0,n)}})}),S.each(["ajaxStart","ajaxStop","ajaxComplete","ajaxError","ajaxSuccess","ajaxSend"],function(e,t){S.fn[t]=function(e){return this.on(t,e)}}),S.fn.extend({bind:function(e,t,n){return this.on(e,null,t,n)},unbind:function(e,t){return this.off(e,null,t)},delegate:function(e,t,n,r){return this.on(t,e,n,r)},undelegate:function(e,t,n){return 1===arguments.length?this.off(e,"**"):this.off(t,e||"**",n)},hover:function(e,t){return this.mouseenter(e).mouseleave(t||e)}}),S.each("blur focus focusin focusout resize scroll click dblclick mousedown mouseup mousemove mouseover mouseout mouseenter mouseleave change select submit keydown keypress keyup contextmenu".split(" "),function(e,n){S.fn[n]=function(e,t){return 0<arguments.length?this.on(n,null,e,t):this.trigger(n)}});var Xt=/^[\s\uFEFF\xA0]+|[\s\uFEFF\xA0]+$/g;S.proxy=function(e,t){var n,r,i;if("string"==typeof t&&(n=e[t],t=e,e=n),m(e))return r=s.call(arguments,2),(i=function(){return e.apply(t||this,r.concat(s.call(arguments)))}).guid=e.guid=e.guid||S.guid++,i},S.holdReady=function(e){e?S.readyWait++:S.ready(!0)},S.isArray=Array.isArray,S.parseJSON=JSON.parse,S.nodeName=A,S.isFunction=m,S.isWindow=x,S.camelCase=X,S.type=w,S.now=Date.now,S.isNumeric=function(e){var t=S.type(e);return("number"===t||"string"===t)&&!isNaN(e-parseFloat(e))},S.trim=function(e){return null==e?"":(e+"").replace(Xt,"")},"function"==typeof define&&define.amd&&define("jquery",[],function(){return S});var Vt=C.jQuery,Gt=C.$;return S.noConflict=function(e){return C.$===S&&(C.$=Gt),e&&C.jQuery===S&&(C.jQuery=Vt),S},"undefined"==typeof e&&(C.jQuery=C.$=S),S});
diff --git a/docs/deps/jquery-3.6.0/jquery-3.6.0.min.map b/docs/deps/jquery-3.6.0/jquery-3.6.0.min.map
new file mode 100644
index 00000000..7d86eb16
--- /dev/null
+++ b/docs/deps/jquery-3.6.0/jquery-3.6.0.min.map
@@ -0,0 +1 @@
+{"version":3,"sources":["jquery-3.6.0.js"],"names":["global","factory","module","exports","document","w","Error","window","this","noGlobal","arr","getProto","Object","getPrototypeOf","slice","flat","array","call","concat","apply","push","indexOf","class2type","toString","hasOwn","hasOwnProperty","fnToString","ObjectFunctionString","support","isFunction","obj","nodeType","item","isWindow","preservedScriptAttributes","type","src","nonce","noModule","DOMEval","code","node","doc","i","val","script","createElement","text","getAttribute","setAttribute","head","appendChild","parentNode","removeChild","toType","version","jQuery","selector","context","fn","init","isArrayLike","length","prototype","jquery","constructor","toArray","get","num","pushStack","elems","ret","merge","prevObject","each","callback","map","elem","arguments","first","eq","last","even","grep","_elem","odd","len","j","end","sort","splice","extend","options","name","copy","copyIsArray","clone","target","deep","isPlainObject","Array","isArray","undefined","expando","Math","random","replace","isReady","error","msg","noop","proto","Ctor","isEmptyObject","globalEval","makeArray","results","inArray","second","invert","matches","callbackExpect","arg","value","guid","Symbol","iterator","split","_i","toLowerCase","Sizzle","Expr","getText","isXML","tokenize","compile","select","outermostContext","sortInput","hasDuplicate","setDocument","docElem","documentIsHTML","rbuggyQSA","rbuggyMatches","contains","Date","preferredDoc","dirruns","done","classCache","createCache","tokenCache","compilerCache","nonnativeSelectorCache","sortOrder","a","b","pop","pushNative","list","booleans","whitespace","identifier","attributes","pseudos","rwhitespace","RegExp","rtrim","rcomma","rcombinators","rdescend","rpseudo","ridentifier","matchExpr","ID","CLASS","TAG","ATTR","PSEUDO","CHILD","bool","needsContext","rhtml","rinputs","rheader","rnative","rquickExpr","rsibling","runescape","funescape","escape","nonHex","high","String","fromCharCode","rcssescape","fcssescape","ch","asCodePoint","charCodeAt","unloadHandler","inDisabledFieldset","addCombinator","disabled","nodeName","dir","next","childNodes","e","els","seed","m","nid","match","groups","newSelector","newContext","ownerDocument","exec","getElementById","id","getElementsByTagName","getElementsByClassName","qsa","test","testContext","scope","toSelector","join","querySelectorAll","qsaError","removeAttribute","keys","cache","key","cacheLength","shift","markFunction","assert","el","addHandle","attrs","handler","attrHandle","siblingCheck","cur","diff","sourceIndex","nextSibling","createInputPseudo","createButtonPseudo","createDisabledPseudo","isDisabled","createPositionalPseudo","argument","matchIndexes","namespace","namespaceURI","documentElement","hasCompare","subWindow","defaultView","top","addEventListener","attachEvent","className","createComment","getById","getElementsByName","filter","attrId","find","getAttributeNode","tag","tmp","input","innerHTML","matchesSelector","webkitMatchesSelector","mozMatchesSelector","oMatchesSelector","msMatchesSelector","disconnectedMatch","compareDocumentPosition","adown","bup","compare","sortDetached","aup","ap","bp","unshift","expr","elements","attr","specified","sel","uniqueSort","duplicates","detectDuplicates","sortStable","textContent","firstChild","nodeValue","selectors","createPseudo","relative",">"," ","+","~","preFilter","excess","unquoted","nodeNameSelector","pattern","operator","check","result","what","_argument","simple","forward","ofType","_context","xml","uniqueCache","outerCache","nodeIndex","start","parent","useCache","lastChild","uniqueID","pseudo","args","setFilters","idx","matched","not","matcher","unmatched","has","lang","elemLang","hash","location","root","focus","activeElement","hasFocus","href","tabIndex","enabled","checked","selected","selectedIndex","empty","header","button","_matchIndexes","lt","gt","radio","checkbox","file","password","image","submit","reset","tokens","combinator","base","skip","checkNonElements","doneName","oldCache","newCache","elementMatcher","matchers","condense","newUnmatched","mapped","setMatcher","postFilter","postFinder","postSelector","temp","preMap","postMap","preexisting","contexts","multipleContexts","matcherIn","matcherOut","matcherFromTokens","checkContext","leadingRelative","implicitRelative","matchContext","matchAnyContext","filters","parseOnly","soFar","preFilters","cached","elementMatchers","setMatchers","bySet","byElement","superMatcher","outermost","matchedCount","setMatched","contextBackup","dirrunsUnique","token","compiled","_name","defaultValue","unique","isXMLDoc","escapeSelector","until","truncate","is","siblings","n","rneedsContext","rsingleTag","winnow","qualifier","self","rootjQuery","parseHTML","ready","rparentsprev","guaranteedUnique","children","contents","prev","sibling","targets","l","closest","index","prevAll","add","addBack","parents","parentsUntil","nextAll","nextUntil","prevUntil","contentDocument","content","reverse","rnothtmlwhite","Identity","v","Thrower","ex","adoptValue","resolve","reject","noValue","method","promise","fail","then","Callbacks","object","_","flag","firing","memory","fired","locked","queue","firingIndex","fire","once","stopOnFalse","remove","disable","lock","fireWith","Deferred","func","tuples","state","always","deferred","catch","pipe","fns","newDefer","tuple","returned","progress","notify","onFulfilled","onRejected","onProgress","maxDepth","depth","special","that","mightThrow","TypeError","notifyWith","resolveWith","process","exceptionHook","stackTrace","rejectWith","getStackHook","setTimeout","stateString","when","singleValue","remaining","resolveContexts","resolveValues","primary","updateFunc","rerrorNames","stack","console","warn","message","readyException","readyList","completed","removeEventListener","readyWait","wait","readyState","doScroll","access","chainable","emptyGet","raw","bulk","_key","rmsPrefix","rdashAlpha","fcamelCase","_all","letter","toUpperCase","camelCase","string","acceptData","owner","Data","uid","defineProperty","configurable","set","data","prop","hasData","dataPriv","dataUser","rbrace","rmultiDash","dataAttr","JSON","parse","removeData","_data","_removeData","dequeue","startLength","hooks","_queueHooks","stop","setter","clearQueue","count","defer","pnum","source","rcssNum","cssExpand","isAttached","composed","getRootNode","isHiddenWithinTree","style","display","css","adjustCSS","valueParts","tween","adjusted","scale","maxIterations","currentValue","initial","unit","cssNumber","initialInUnit","defaultDisplayMap","showHide","show","values","body","hide","toggle","div","rcheckableType","rtagName","rscriptType","createDocumentFragment","checkClone","cloneNode","noCloneChecked","option","wrapMap","thead","col","tr","td","_default","getAll","setGlobalEval","refElements","tbody","tfoot","colgroup","caption","th","optgroup","buildFragment","scripts","selection","ignored","wrap","attached","fragment","nodes","htmlPrefilter","createTextNode","rtypenamespace","returnTrue","returnFalse","expectSync","err","safeActiveElement","on","types","one","origFn","event","off","leverageNative","notAsync","saved","isTrigger","delegateType","stopPropagation","stopImmediatePropagation","preventDefault","trigger","Event","handleObjIn","eventHandle","events","t","handleObj","handlers","namespaces","origType","elemData","create","handle","triggered","dispatch","bindType","delegateCount","setup","mappedTypes","origCount","teardown","removeEvent","nativeEvent","handlerQueue","fix","delegateTarget","preDispatch","isPropagationStopped","currentTarget","isImmediatePropagationStopped","rnamespace","postDispatch","matchedHandlers","matchedSelectors","addProp","hook","enumerable","originalEvent","writable","load","noBubble","click","beforeunload","returnValue","props","isDefaultPrevented","defaultPrevented","relatedTarget","timeStamp","now","isSimulated","altKey","bubbles","cancelable","changedTouches","ctrlKey","detail","eventPhase","metaKey","pageX","pageY","shiftKey","view","char","charCode","keyCode","buttons","clientX","clientY","offsetX","offsetY","pointerId","pointerType","screenX","screenY","targetTouches","toElement","touches","which","blur","mouseenter","mouseleave","pointerenter","pointerleave","orig","related","rnoInnerhtml","rchecked","rcleanScript","manipulationTarget","disableScript","restoreScript","cloneCopyEvent","dest","udataOld","udataCur","domManip","collection","hasScripts","iNoClone","valueIsFunction","html","_evalUrl","keepData","cleanData","dataAndEvents","deepDataAndEvents","srcElements","destElements","inPage","detach","append","prepend","insertBefore","before","after","replaceWith","replaceChild","appendTo","prependTo","insertAfter","replaceAll","original","insert","rnumnonpx","getStyles","opener","getComputedStyle","swap","old","rboxStyle","curCSS","computed","width","minWidth","maxWidth","getPropertyValue","pixelBoxStyles","addGetHookIf","conditionFn","hookFn","computeStyleTests","container","cssText","divStyle","pixelPositionVal","reliableMarginLeftVal","roundPixelMeasures","marginLeft","right","pixelBoxStylesVal","boxSizingReliableVal","position","scrollboxSizeVal","offsetWidth","measure","round","parseFloat","reliableTrDimensionsVal","backgroundClip","clearCloneStyle","boxSizingReliable","pixelPosition","reliableMarginLeft","scrollboxSize","reliableTrDimensions","table","trChild","trStyle","height","parseInt","borderTopWidth","borderBottomWidth","offsetHeight","cssPrefixes","emptyStyle","vendorProps","finalPropName","final","cssProps","capName","vendorPropName","rdisplayswap","rcustomProp","cssShow","visibility","cssNormalTransform","letterSpacing","fontWeight","setPositiveNumber","subtract","max","boxModelAdjustment","dimension","box","isBorderBox","styles","computedVal","extra","delta","ceil","getWidthOrHeight","valueIsBorderBox","offsetProp","getClientRects","Tween","easing","cssHooks","opacity","animationIterationCount","columnCount","fillOpacity","flexGrow","flexShrink","gridArea","gridColumn","gridColumnEnd","gridColumnStart","gridRow","gridRowEnd","gridRowStart","lineHeight","order","orphans","widows","zIndex","zoom","origName","isCustomProp","setProperty","isFinite","getBoundingClientRect","scrollboxSizeBuggy","left","margin","padding","border","prefix","suffix","expand","expanded","parts","propHooks","run","percent","eased","duration","pos","step","fx","scrollTop","scrollLeft","linear","p","swing","cos","PI","fxNow","inProgress","opt","rfxtypes","rrun","schedule","hidden","requestAnimationFrame","interval","tick","createFxNow","genFx","includeWidth","createTween","animation","Animation","tweeners","properties","stopped","prefilters","currentTime","startTime","tweens","opts","specialEasing","originalProperties","originalOptions","gotoEnd","propFilter","bind","complete","timer","anim","*","tweener","oldfire","propTween","restoreDisplay","isBox","dataShow","unqueued","overflow","overflowX","overflowY","prefilter","speed","speeds","fadeTo","to","animate","optall","doAnimation","finish","stopQueue","timers","cssFn","slideDown","slideUp","slideToggle","fadeIn","fadeOut","fadeToggle","slow","fast","delay","time","timeout","clearTimeout","checkOn","optSelected","radioValue","boolHook","removeAttr","nType","attrHooks","attrNames","getter","lowercaseName","rfocusable","rclickable","stripAndCollapse","getClass","classesToArray","removeProp","propFix","tabindex","for","class","addClass","classes","curValue","clazz","finalValue","removeClass","toggleClass","stateVal","isValidValue","classNames","hasClass","rreturn","valHooks","optionSet","focusin","rfocusMorph","stopPropagationCallback","onlyHandlers","bubbleType","ontype","lastElement","eventPath","parentWindow","simulate","triggerHandler","attaches","rquery","parseXML","parserErrorElem","DOMParser","parseFromString","rbracket","rCRLF","rsubmitterTypes","rsubmittable","buildParams","traditional","param","s","valueOrFunction","encodeURIComponent","serialize","serializeArray","r20","rhash","rantiCache","rheaders","rnoContent","rprotocol","transports","allTypes","originAnchor","addToPrefiltersOrTransports","structure","dataTypeExpression","dataType","dataTypes","inspectPrefiltersOrTransports","jqXHR","inspected","seekingTransport","inspect","prefilterOrFactory","dataTypeOrTransport","ajaxExtend","flatOptions","ajaxSettings","active","lastModified","etag","url","isLocal","protocol","processData","async","contentType","accepts","json","responseFields","converters","* text","text html","text json","text xml","ajaxSetup","settings","ajaxPrefilter","ajaxTransport","ajax","transport","cacheURL","responseHeadersString","responseHeaders","timeoutTimer","urlAnchor","fireGlobals","uncached","callbackContext","globalEventContext","completeDeferred","statusCode","requestHeaders","requestHeadersNames","strAbort","getResponseHeader","getAllResponseHeaders","setRequestHeader","overrideMimeType","mimeType","status","abort","statusText","finalText","crossDomain","host","hasContent","ifModified","headers","beforeSend","success","send","nativeStatusText","responses","isSuccess","response","modified","ct","finalDataType","firstDataType","ajaxHandleResponses","conv2","current","conv","dataFilter","throws","ajaxConvert","getJSON","getScript","text script","wrapAll","firstElementChild","wrapInner","htmlIsFunction","unwrap","visible","xhr","XMLHttpRequest","xhrSuccessStatus","0","1223","xhrSupported","cors","errorCallback","open","username","xhrFields","onload","onerror","onabort","ontimeout","onreadystatechange","responseType","responseText","binary","scriptAttrs","charset","scriptCharset","evt","oldCallbacks","rjsonp","jsonp","jsonpCallback","originalSettings","callbackName","overwritten","responseContainer","jsonProp","createHTMLDocument","implementation","keepScripts","parsed","params","animated","offset","setOffset","curPosition","curLeft","curCSSTop","curTop","curOffset","curCSSLeft","curElem","using","rect","win","pageYOffset","pageXOffset","offsetParent","parentOffset","scrollTo","Height","Width","","defaultExtra","funcName","unbind","delegate","undelegate","hover","fnOver","fnOut","proxy","holdReady","hold","parseJSON","isNumeric","isNaN","trim","define","amd","_jQuery","_$","$","noConflict"],"mappings":";CAaA,SAAYA,EAAQC,GAEnB,aAEuB,iBAAXC,QAAiD,iBAAnBA,OAAOC,QAShDD,OAAOC,QAAUH,EAAOI,SACvBH,EAASD,GAAQ,GACjB,SAAUK,GACT,IAAMA,EAAED,SACP,MAAM,IAAIE,MAAO,4CAElB,OAAOL,EAASI,IAGlBJ,EAASD,GAtBX,CA0BuB,oBAAXO,OAAyBA,OAASC,KAAM,SAAUD,EAAQE,GAMtE,aAEA,IAAIC,EAAM,GAENC,EAAWC,OAAOC,eAElBC,EAAQJ,EAAII,MAEZC,EAAOL,EAAIK,KAAO,SAAUC,GAC/B,OAAON,EAAIK,KAAKE,KAAMD,IACnB,SAAUA,GACb,OAAON,EAAIQ,OAAOC,MAAO,GAAIH,IAI1BI,EAAOV,EAAIU,KAEXC,EAAUX,EAAIW,QAEdC,EAAa,GAEbC,EAAWD,EAAWC,SAEtBC,EAASF,EAAWG,eAEpBC,EAAaF,EAAOD,SAEpBI,EAAuBD,EAAWT,KAAML,QAExCgB,EAAU,GAEVC,EAAa,SAAqBC,GASpC,MAAsB,mBAARA,GAA8C,iBAAjBA,EAAIC,UAC1B,mBAAbD,EAAIE,MAIVC,EAAW,SAAmBH,GAChC,OAAc,MAAPA,GAAeA,IAAQA,EAAIvB,QAIhCH,EAAWG,EAAOH,SAIjB8B,EAA4B,CAC/BC,MAAM,EACNC,KAAK,EACLC,OAAO,EACPC,UAAU,GAGX,SAASC,EAASC,EAAMC,EAAMC,GAG7B,IAAIC,EAAGC,EACNC,GAHDH,EAAMA,GAAOtC,GAGC0C,cAAe,UAG7B,GADAD,EAAOE,KAAOP,EACTC,EACJ,IAAME,KAAKT,GAYVU,EAAMH,EAAME,IAAOF,EAAKO,cAAgBP,EAAKO,aAAcL,KAE1DE,EAAOI,aAAcN,EAAGC,GAI3BF,EAAIQ,KAAKC,YAAaN,GAASO,WAAWC,YAAaR,GAIzD,SAASS,EAAQxB,GAChB,OAAY,MAAPA,EACGA,EAAM,GAIQ,iBAARA,GAAmC,mBAARA,EACxCR,EAAYC,EAASN,KAAMa,KAAW,gBAC/BA,EAQT,IACCyB,EAAU,QAGVC,EAAS,SAAUC,EAAUC,GAI5B,OAAO,IAAIF,EAAOG,GAAGC,KAAMH,EAAUC,IA0VvC,SAASG,EAAa/B,GAMrB,IAAIgC,IAAWhC,GAAO,WAAYA,GAAOA,EAAIgC,OAC5C3B,EAAOmB,EAAQxB,GAEhB,OAAKD,EAAYC,KAASG,EAAUH,KAIpB,UAATK,GAA+B,IAAX2B,GACR,iBAAXA,GAAgC,EAATA,GAAgBA,EAAS,KAAOhC,GArWhE0B,EAAOG,GAAKH,EAAOO,UAAY,CAG9BC,OAAQT,EAERU,YAAaT,EAGbM,OAAQ,EAERI,QAAS,WACR,OAAOpD,EAAMG,KAAMT,OAKpB2D,IAAK,SAAUC,GAGd,OAAY,MAAPA,EACGtD,EAAMG,KAAMT,MAIb4D,EAAM,EAAI5D,KAAM4D,EAAM5D,KAAKsD,QAAWtD,KAAM4D,IAKpDC,UAAW,SAAUC,GAGpB,IAAIC,EAAMf,EAAOgB,MAAOhE,KAAKyD,cAAeK,GAM5C,OAHAC,EAAIE,WAAajE,KAGV+D,GAIRG,KAAM,SAAUC,GACf,OAAOnB,EAAOkB,KAAMlE,KAAMmE,IAG3BC,IAAK,SAAUD,GACd,OAAOnE,KAAK6D,UAAWb,EAAOoB,IAAKpE,KAAM,SAAUqE,EAAMlC,GACxD,OAAOgC,EAAS1D,KAAM4D,EAAMlC,EAAGkC,OAIjC/D,MAAO,WACN,OAAON,KAAK6D,UAAWvD,EAAMK,MAAOX,KAAMsE,aAG3CC,MAAO,WACN,OAAOvE,KAAKwE,GAAI,IAGjBC,KAAM,WACL,OAAOzE,KAAKwE,IAAK,IAGlBE,KAAM,WACL,OAAO1E,KAAK6D,UAAWb,EAAO2B,KAAM3E,KAAM,SAAU4E,EAAOzC,GAC1D,OAASA,EAAI,GAAM,MAIrB0C,IAAK,WACJ,OAAO7E,KAAK6D,UAAWb,EAAO2B,KAAM3E,KAAM,SAAU4E,EAAOzC,GAC1D,OAAOA,EAAI,MAIbqC,GAAI,SAAUrC,GACb,IAAI2C,EAAM9E,KAAKsD,OACdyB,GAAK5C,GAAMA,EAAI,EAAI2C,EAAM,GAC1B,OAAO9E,KAAK6D,UAAgB,GAALkB,GAAUA,EAAID,EAAM,CAAE9E,KAAM+E,IAAQ,KAG5DC,IAAK,WACJ,OAAOhF,KAAKiE,YAAcjE,KAAKyD,eAKhC7C,KAAMA,EACNqE,KAAM/E,EAAI+E,KACVC,OAAQhF,EAAIgF,QAGblC,EAAOmC,OAASnC,EAAOG,GAAGgC,OAAS,WAClC,IAAIC,EAASC,EAAMzD,EAAK0D,EAAMC,EAAaC,EAC1CC,EAASnB,UAAW,IAAO,GAC3BnC,EAAI,EACJmB,EAASgB,UAAUhB,OACnBoC,GAAO,EAsBR,IAnBuB,kBAAXD,IACXC,EAAOD,EAGPA,EAASnB,UAAWnC,IAAO,GAC3BA,KAIsB,iBAAXsD,GAAwBpE,EAAYoE,KAC/CA,EAAS,IAILtD,IAAMmB,IACVmC,EAASzF,KACTmC,KAGOA,EAAImB,EAAQnB,IAGnB,GAAqC,OAA9BiD,EAAUd,UAAWnC,IAG3B,IAAMkD,KAAQD,EACbE,EAAOF,EAASC,GAIF,cAATA,GAAwBI,IAAWH,IAKnCI,GAAQJ,IAAUtC,EAAO2C,cAAeL,KAC1CC,EAAcK,MAAMC,QAASP,MAC/B1D,EAAM6D,EAAQJ,GAIbG,EADID,IAAgBK,MAAMC,QAASjE,GAC3B,GACI2D,GAAgBvC,EAAO2C,cAAe/D,GAG1CA,EAFA,GAIT2D,GAAc,EAGdE,EAAQJ,GAASrC,EAAOmC,OAAQO,EAAMF,EAAOF,SAGzBQ,IAATR,IACXG,EAAQJ,GAASC,IAOrB,OAAOG,GAGRzC,EAAOmC,OAAQ,CAGdY,QAAS,UAAahD,EAAUiD,KAAKC,UAAWC,QAAS,MAAO,IAGhEC,SAAS,EAETC,MAAO,SAAUC,GAChB,MAAM,IAAIvG,MAAOuG,IAGlBC,KAAM,aAENX,cAAe,SAAUrE,GACxB,IAAIiF,EAAOC,EAIX,SAAMlF,GAAgC,oBAAzBP,EAASN,KAAMa,QAI5BiF,EAAQpG,EAAUmB,KASK,mBADvBkF,EAAOxF,EAAOP,KAAM8F,EAAO,gBAAmBA,EAAM9C,cACfvC,EAAWT,KAAM+F,KAAWrF,IAGlEsF,cAAe,SAAUnF,GACxB,IAAI+D,EAEJ,IAAMA,KAAQ/D,EACb,OAAO,EAER,OAAO,GAKRoF,WAAY,SAAU1E,EAAMoD,EAASlD,GACpCH,EAASC,EAAM,CAAEH,MAAOuD,GAAWA,EAAQvD,OAASK,IAGrDgC,KAAM,SAAU5C,EAAK6C,GACpB,IAAIb,EAAQnB,EAAI,EAEhB,GAAKkB,EAAa/B,IAEjB,IADAgC,EAAShC,EAAIgC,OACLnB,EAAImB,EAAQnB,IACnB,IAAgD,IAA3CgC,EAAS1D,KAAMa,EAAKa,GAAKA,EAAGb,EAAKa,IACrC,WAIF,IAAMA,KAAKb,EACV,IAAgD,IAA3C6C,EAAS1D,KAAMa,EAAKa,GAAKA,EAAGb,EAAKa,IACrC,MAKH,OAAOb,GAIRqF,UAAW,SAAUzG,EAAK0G,GACzB,IAAI7C,EAAM6C,GAAW,GAarB,OAXY,MAAP1G,IACCmD,EAAajD,OAAQF,IACzB8C,EAAOgB,MAAOD,EACE,iBAAR7D,EACN,CAAEA,GAAQA,GAGZU,EAAKH,KAAMsD,EAAK7D,IAIX6D,GAGR8C,QAAS,SAAUxC,EAAMnE,EAAKiC,GAC7B,OAAc,MAAPjC,GAAe,EAAIW,EAAQJ,KAAMP,EAAKmE,EAAMlC,IAKpD6B,MAAO,SAAUO,EAAOuC,GAKvB,IAJA,IAAIhC,GAAOgC,EAAOxD,OACjByB,EAAI,EACJ5C,EAAIoC,EAAMjB,OAEHyB,EAAID,EAAKC,IAChBR,EAAOpC,KAAQ2E,EAAQ/B,GAKxB,OAFAR,EAAMjB,OAASnB,EAERoC,GAGRI,KAAM,SAAUb,EAAOK,EAAU4C,GAShC,IARA,IACCC,EAAU,GACV7E,EAAI,EACJmB,EAASQ,EAAMR,OACf2D,GAAkBF,EAIX5E,EAAImB,EAAQnB,KACAgC,EAAUL,EAAO3B,GAAKA,KAChB8E,GACxBD,EAAQpG,KAAMkD,EAAO3B,IAIvB,OAAO6E,GAIR5C,IAAK,SAAUN,EAAOK,EAAU+C,GAC/B,IAAI5D,EAAQ6D,EACXhF,EAAI,EACJ4B,EAAM,GAGP,GAAKV,EAAaS,GAEjB,IADAR,EAASQ,EAAMR,OACPnB,EAAImB,EAAQnB,IAGL,OAFdgF,EAAQhD,EAAUL,EAAO3B,GAAKA,EAAG+E,KAGhCnD,EAAInD,KAAMuG,QAMZ,IAAMhF,KAAK2B,EAGI,OAFdqD,EAAQhD,EAAUL,EAAO3B,GAAKA,EAAG+E,KAGhCnD,EAAInD,KAAMuG,GAMb,OAAO5G,EAAMwD,IAIdqD,KAAM,EAINhG,QAASA,IAGa,mBAAXiG,SACXrE,EAAOG,GAAIkE,OAAOC,UAAapH,EAAKmH,OAAOC,WAI5CtE,EAAOkB,KAAM,uEAAuEqD,MAAO,KAC1F,SAAUC,EAAInC,GACbvE,EAAY,WAAauE,EAAO,KAAQA,EAAKoC,gBAmB/C,IAAIC,EAWJ,SAAY3H,GACZ,IAAIoC,EACHf,EACAuG,EACAC,EACAC,EACAC,EACAC,EACAC,EACAC,EACAC,EACAC,EAGAC,EACAxI,EACAyI,EACAC,EACAC,EACAC,EACAxB,EACAyB,EAGA1C,EAAU,SAAW,EAAI,IAAI2C,KAC7BC,EAAe5I,EAAOH,SACtBgJ,EAAU,EACVC,EAAO,EACPC,EAAaC,KACbC,EAAaD,KACbE,EAAgBF,KAChBG,EAAyBH,KACzBI,EAAY,SAAUC,EAAGC,GAIxB,OAHKD,IAAMC,IACVlB,GAAe,GAET,GAIRnH,EAAS,GAAOC,eAChBf,EAAM,GACNoJ,EAAMpJ,EAAIoJ,IACVC,EAAarJ,EAAIU,KACjBA,EAAOV,EAAIU,KACXN,EAAQJ,EAAII,MAIZO,EAAU,SAAU2I,EAAMnF,GAGzB,IAFA,IAAIlC,EAAI,EACP2C,EAAM0E,EAAKlG,OACJnB,EAAI2C,EAAK3C,IAChB,GAAKqH,EAAMrH,KAAQkC,EAClB,OAAOlC,EAGT,OAAQ,GAGTsH,EAAW,6HAMXC,EAAa,sBAGbC,EAAa,0BAA4BD,EACxC,0CAGDE,EAAa,MAAQF,EAAa,KAAOC,EAAa,OAASD,EAG9D,gBAAkBA,EAIlB,2DAA6DC,EAAa,OAC1ED,EAAa,OAEdG,EAAU,KAAOF,EAAa,wFAOAC,EAAa,eAO3CE,EAAc,IAAIC,OAAQL,EAAa,IAAK,KAC5CM,EAAQ,IAAID,OAAQ,IAAML,EAAa,8BACtCA,EAAa,KAAM,KAEpBO,EAAS,IAAIF,OAAQ,IAAML,EAAa,KAAOA,EAAa,KAC5DQ,EAAe,IAAIH,OAAQ,IAAML,EAAa,WAAaA,EAAa,IAAMA,EAC7E,KACDS,EAAW,IAAIJ,OAAQL,EAAa,MAEpCU,EAAU,IAAIL,OAAQF,GACtBQ,EAAc,IAAIN,OAAQ,IAAMJ,EAAa,KAE7CW,EAAY,CACXC,GAAM,IAAIR,OAAQ,MAAQJ,EAAa,KACvCa,MAAS,IAAIT,OAAQ,QAAUJ,EAAa,KAC5Cc,IAAO,IAAIV,OAAQ,KAAOJ,EAAa,SACvCe,KAAQ,IAAIX,OAAQ,IAAMH,GAC1Be,OAAU,IAAIZ,OAAQ,IAAMF,GAC5Be,MAAS,IAAIb,OAAQ,yDACpBL,EAAa,+BAAiCA,EAAa,cAC3DA,EAAa,aAAeA,EAAa,SAAU,KACpDmB,KAAQ,IAAId,OAAQ,OAASN,EAAW,KAAM,KAI9CqB,aAAgB,IAAIf,OAAQ,IAAML,EACjC,mDAAqDA,EACrD,mBAAqBA,EAAa,mBAAoB,MAGxDqB,EAAQ,SACRC,EAAU,sCACVC,EAAU,SAEVC,EAAU,yBAGVC,EAAa,mCAEbC,GAAW,OAIXC,GAAY,IAAItB,OAAQ,uBAAyBL,EAAa,uBAAwB,KACtF4B,GAAY,SAAUC,EAAQC,GAC7B,IAAIC,EAAO,KAAOF,EAAOjL,MAAO,GAAM,MAEtC,OAAOkL,IASNC,EAAO,EACNC,OAAOC,aAAcF,EAAO,OAC5BC,OAAOC,aAAcF,GAAQ,GAAK,MAAe,KAAPA,EAAe,SAK5DG,GAAa,sDACbC,GAAa,SAAUC,EAAIC,GAC1B,OAAKA,EAGQ,OAAPD,EACG,SAIDA,EAAGxL,MAAO,GAAI,GAAM,KAC1BwL,EAAGE,WAAYF,EAAGxI,OAAS,GAAIvC,SAAU,IAAO,IAI3C,KAAO+K,GAOfG,GAAgB,WACf7D,KAGD8D,GAAqBC,GACpB,SAAU9H,GACT,OAAyB,IAAlBA,EAAK+H,UAAqD,aAAhC/H,EAAKgI,SAAS5E,eAEhD,CAAE6E,IAAK,aAAcC,KAAM,WAI7B,IACC3L,EAAKD,MACFT,EAAMI,EAAMG,KAAMkI,EAAa6D,YACjC7D,EAAa6D,YAMdtM,EAAKyI,EAAa6D,WAAWlJ,QAAS/B,SACrC,MAAQkL,GACT7L,EAAO,CAAED,MAAOT,EAAIoD,OAGnB,SAAUmC,EAAQiH,GACjBnD,EAAW5I,MAAO8E,EAAQnF,EAAMG,KAAMiM,KAKvC,SAAUjH,EAAQiH,GACjB,IAAI3H,EAAIU,EAAOnC,OACdnB,EAAI,EAGL,MAAUsD,EAAQV,KAAQ2H,EAAKvK,MAC/BsD,EAAOnC,OAASyB,EAAI,IAKvB,SAAS2C,GAAQzE,EAAUC,EAAS0D,EAAS+F,GAC5C,IAAIC,EAAGzK,EAAGkC,EAAMwI,EAAKC,EAAOC,EAAQC,EACnCC,EAAa/J,GAAWA,EAAQgK,cAGhC3L,EAAW2B,EAAUA,EAAQ3B,SAAW,EAKzC,GAHAqF,EAAUA,GAAW,GAGI,iBAAb3D,IAA0BA,GACxB,IAAb1B,GAA+B,IAAbA,GAA+B,KAAbA,EAEpC,OAAOqF,EAIR,IAAM+F,IACLvE,EAAalF,GACbA,EAAUA,GAAWtD,EAEhB0I,GAAiB,CAIrB,GAAkB,KAAb/G,IAAqBuL,EAAQ3B,EAAWgC,KAAMlK,IAGlD,GAAO2J,EAAIE,EAAO,IAGjB,GAAkB,IAAbvL,EAAiB,CACrB,KAAO8C,EAAOnB,EAAQkK,eAAgBR,IAUrC,OAAOhG,EALP,GAAKvC,EAAKgJ,KAAOT,EAEhB,OADAhG,EAAQhG,KAAMyD,GACPuC,OAYT,GAAKqG,IAAgB5I,EAAO4I,EAAWG,eAAgBR,KACtDnE,EAAUvF,EAASmB,IACnBA,EAAKgJ,KAAOT,EAGZ,OADAhG,EAAQhG,KAAMyD,GACPuC,MAKH,CAAA,GAAKkG,EAAO,GAElB,OADAlM,EAAKD,MAAOiG,EAAS1D,EAAQoK,qBAAsBrK,IAC5C2D,EAGD,IAAOgG,EAAIE,EAAO,KAAS1L,EAAQmM,wBACzCrK,EAAQqK,uBAGR,OADA3M,EAAKD,MAAOiG,EAAS1D,EAAQqK,uBAAwBX,IAC9ChG,EAKT,GAAKxF,EAAQoM,MACXtE,EAAwBjG,EAAW,QACjCsF,IAAcA,EAAUkF,KAAMxK,MAIlB,IAAb1B,GAAqD,WAAnC2B,EAAQmJ,SAAS5E,eAA+B,CAYpE,GAVAuF,EAAc/J,EACdgK,EAAa/J,EASK,IAAb3B,IACF4I,EAASsD,KAAMxK,IAAciH,EAAauD,KAAMxK,IAAe,EAGjEgK,EAAa7B,GAASqC,KAAMxK,IAAcyK,GAAaxK,EAAQN,aAC9DM,KAImBA,GAAY9B,EAAQuM,SAGhCd,EAAM3J,EAAQV,aAAc,OAClCqK,EAAMA,EAAI3G,QAAS0F,GAAYC,IAE/B3I,EAAQT,aAAc,KAAQoK,EAAM9G,IAMtC5D,GADA4K,EAASjF,EAAU7E,IACRK,OACX,MAAQnB,IACP4K,EAAQ5K,IAAQ0K,EAAM,IAAMA,EAAM,UAAa,IAC9Ce,GAAYb,EAAQ5K,IAEtB6K,EAAcD,EAAOc,KAAM,KAG5B,IAIC,OAHAjN,EAAKD,MAAOiG,EACXqG,EAAWa,iBAAkBd,IAEvBpG,EACN,MAAQmH,GACT7E,EAAwBjG,GAAU,GACjC,QACI4J,IAAQ9G,GACZ7C,EAAQ8K,gBAAiB,QAQ9B,OAAOhG,EAAQ/E,EAASiD,QAAS8D,EAAO,MAAQ9G,EAAS0D,EAAS+F,GASnE,SAAS5D,KACR,IAAIkF,EAAO,GAYX,OAVA,SAASC,EAAOC,EAAKhH,GAQpB,OALK8G,EAAKrN,KAAMuN,EAAM,KAAQxG,EAAKyG,oBAG3BF,EAAOD,EAAKI,SAEXH,EAAOC,EAAM,KAAQhH,GAShC,SAASmH,GAAcnL,GAEtB,OADAA,EAAI4C,IAAY,EACT5C,EAOR,SAASoL,GAAQpL,GAChB,IAAIqL,EAAK5O,EAAS0C,cAAe,YAEjC,IACC,QAASa,EAAIqL,GACZ,MAAQ/B,GACT,OAAO,EACN,QAGI+B,EAAG5L,YACP4L,EAAG5L,WAAWC,YAAa2L,GAI5BA,EAAK,MASP,SAASC,GAAWC,EAAOC,GAC1B,IAAIzO,EAAMwO,EAAMnH,MAAO,KACtBpF,EAAIjC,EAAIoD,OAET,MAAQnB,IACPwF,EAAKiH,WAAY1O,EAAKiC,IAAQwM,EAUhC,SAASE,GAAczF,EAAGC,GACzB,IAAIyF,EAAMzF,GAAKD,EACd2F,EAAOD,GAAsB,IAAf1F,EAAE7H,UAAiC,IAAf8H,EAAE9H,UACnC6H,EAAE4F,YAAc3F,EAAE2F,YAGpB,GAAKD,EACJ,OAAOA,EAIR,GAAKD,EACJ,MAAUA,EAAMA,EAAIG,YACnB,GAAKH,IAAQzF,EACZ,OAAQ,EAKX,OAAOD,EAAI,GAAK,EAOjB,SAAS8F,GAAmBvN,GAC3B,OAAO,SAAU0C,GAEhB,MAAgB,UADLA,EAAKgI,SAAS5E,eACEpD,EAAK1C,OAASA,GAQ3C,SAASwN,GAAoBxN,GAC5B,OAAO,SAAU0C,GAChB,IAAIgB,EAAOhB,EAAKgI,SAAS5E,cACzB,OAAkB,UAATpC,GAA6B,WAATA,IAAuBhB,EAAK1C,OAASA,GAQpE,SAASyN,GAAsBhD,GAG9B,OAAO,SAAU/H,GAKhB,MAAK,SAAUA,EASTA,EAAKzB,aAAgC,IAAlByB,EAAK+H,SAGvB,UAAW/H,EACV,UAAWA,EAAKzB,WACbyB,EAAKzB,WAAWwJ,WAAaA,EAE7B/H,EAAK+H,WAAaA,EAMpB/H,EAAKgL,aAAejD,GAI1B/H,EAAKgL,cAAgBjD,GACrBF,GAAoB7H,KAAW+H,EAG1B/H,EAAK+H,WAAaA,EAKd,UAAW/H,GACfA,EAAK+H,WAAaA,GAY5B,SAASkD,GAAwBnM,GAChC,OAAOmL,GAAc,SAAUiB,GAE9B,OADAA,GAAYA,EACLjB,GAAc,SAAU3B,EAAM3F,GACpC,IAAIjC,EACHyK,EAAerM,EAAI,GAAIwJ,EAAKrJ,OAAQiM,GACpCpN,EAAIqN,EAAalM,OAGlB,MAAQnB,IACFwK,EAAQ5H,EAAIyK,EAAcrN,MAC9BwK,EAAM5H,KAASiC,EAASjC,GAAM4H,EAAM5H,SAYzC,SAAS2I,GAAaxK,GACrB,OAAOA,GAAmD,oBAAjCA,EAAQoK,sBAAwCpK,EAkrC1E,IAAMf,KA9qCNf,EAAUsG,GAAOtG,QAAU,GAO3ByG,EAAQH,GAAOG,MAAQ,SAAUxD,GAChC,IAAIoL,EAAYpL,GAAQA,EAAKqL,aAC5BrH,EAAUhE,IAAUA,EAAK6I,eAAiB7I,GAAOsL,gBAKlD,OAAQ5E,EAAM0C,KAAMgC,GAAapH,GAAWA,EAAQgE,UAAY,SAQjEjE,EAAcV,GAAOU,YAAc,SAAUnG,GAC5C,IAAI2N,EAAYC,EACf3N,EAAMD,EAAOA,EAAKiL,eAAiBjL,EAAO0G,EAO3C,OAAKzG,GAAOtC,GAA6B,IAAjBsC,EAAIX,UAAmBW,EAAIyN,kBAMnDtH,GADAzI,EAAWsC,GACQyN,gBACnBrH,GAAkBT,EAAOjI,GAQpB+I,GAAgB/I,IAClBiQ,EAAYjQ,EAASkQ,cAAiBD,EAAUE,MAAQF,IAGrDA,EAAUG,iBACdH,EAAUG,iBAAkB,SAAU/D,IAAe,GAG1C4D,EAAUI,aACrBJ,EAAUI,YAAa,WAAYhE,KASrC7K,EAAQuM,MAAQY,GAAQ,SAAUC,GAEjC,OADAnG,EAAQ1F,YAAa6L,GAAK7L,YAAa/C,EAAS0C,cAAe,QACzB,oBAAxBkM,EAAGV,mBACfU,EAAGV,iBAAkB,uBAAwBxK,SAShDlC,EAAQwI,WAAa2E,GAAQ,SAAUC,GAEtC,OADAA,EAAG0B,UAAY,KACP1B,EAAGhM,aAAc,eAO1BpB,EAAQkM,qBAAuBiB,GAAQ,SAAUC,GAEhD,OADAA,EAAG7L,YAAa/C,EAASuQ,cAAe,MAChC3B,EAAGlB,qBAAsB,KAAMhK,SAIxClC,EAAQmM,uBAAyBrC,EAAQuC,KAAM7N,EAAS2N,wBAMxDnM,EAAQgP,QAAU7B,GAAQ,SAAUC,GAEnC,OADAnG,EAAQ1F,YAAa6L,GAAKnB,GAAKtH,GACvBnG,EAASyQ,oBAAsBzQ,EAASyQ,kBAAmBtK,GAAUzC,SAIzElC,EAAQgP,SACZzI,EAAK2I,OAAa,GAAI,SAAUjD,GAC/B,IAAIkD,EAASlD,EAAGnH,QAASmF,GAAWC,IACpC,OAAO,SAAUjH,GAChB,OAAOA,EAAK7B,aAAc,QAAW+N,IAGvC5I,EAAK6I,KAAW,GAAI,SAAUnD,EAAInK,GACjC,GAAuC,oBAA3BA,EAAQkK,gBAAkC9E,EAAiB,CACtE,IAAIjE,EAAOnB,EAAQkK,eAAgBC,GACnC,OAAOhJ,EAAO,CAAEA,GAAS,OAI3BsD,EAAK2I,OAAa,GAAK,SAAUjD,GAChC,IAAIkD,EAASlD,EAAGnH,QAASmF,GAAWC,IACpC,OAAO,SAAUjH,GAChB,IAAIpC,EAAwC,oBAA1BoC,EAAKoM,kBACtBpM,EAAKoM,iBAAkB,MACxB,OAAOxO,GAAQA,EAAKkF,QAAUoJ,IAMhC5I,EAAK6I,KAAW,GAAI,SAAUnD,EAAInK,GACjC,GAAuC,oBAA3BA,EAAQkK,gBAAkC9E,EAAiB,CACtE,IAAIrG,EAAME,EAAG2B,EACZO,EAAOnB,EAAQkK,eAAgBC,GAEhC,GAAKhJ,EAAO,CAIX,IADApC,EAAOoC,EAAKoM,iBAAkB,QACjBxO,EAAKkF,QAAUkG,EAC3B,MAAO,CAAEhJ,GAIVP,EAAQZ,EAAQmN,kBAAmBhD,GACnClL,EAAI,EACJ,MAAUkC,EAAOP,EAAO3B,KAEvB,IADAF,EAAOoC,EAAKoM,iBAAkB,QACjBxO,EAAKkF,QAAUkG,EAC3B,MAAO,CAAEhJ,GAKZ,MAAO,MAMVsD,EAAK6I,KAAY,IAAIpP,EAAQkM,qBAC5B,SAAUoD,EAAKxN,GACd,MAA6C,oBAAjCA,EAAQoK,qBACZpK,EAAQoK,qBAAsBoD,GAG1BtP,EAAQoM,IACZtK,EAAQ4K,iBAAkB4C,QAD3B,GAKR,SAAUA,EAAKxN,GACd,IAAImB,EACHsM,EAAM,GACNxO,EAAI,EAGJyE,EAAU1D,EAAQoK,qBAAsBoD,GAGzC,GAAa,MAARA,EAAc,CAClB,MAAUrM,EAAOuC,EAASzE,KACF,IAAlBkC,EAAK9C,UACToP,EAAI/P,KAAMyD,GAIZ,OAAOsM,EAER,OAAO/J,GAITe,EAAK6I,KAAc,MAAIpP,EAAQmM,wBAA0B,SAAU2C,EAAWhN,GAC7E,GAA+C,oBAAnCA,EAAQqK,wBAA0CjF,EAC7D,OAAOpF,EAAQqK,uBAAwB2C,IAUzC1H,EAAgB,GAOhBD,EAAY,IAELnH,EAAQoM,IAAMtC,EAAQuC,KAAM7N,EAASkO,qBAI3CS,GAAQ,SAAUC,GAEjB,IAAIoC,EAOJvI,EAAQ1F,YAAa6L,GAAKqC,UAAY,UAAY9K,EAAU,qBAC1CA,EAAU,kEAOvByI,EAAGV,iBAAkB,wBAAyBxK,QAClDiF,EAAU3H,KAAM,SAAW8I,EAAa,gBAKnC8E,EAAGV,iBAAkB,cAAexK,QACzCiF,EAAU3H,KAAM,MAAQ8I,EAAa,aAAeD,EAAW,KAI1D+E,EAAGV,iBAAkB,QAAU/H,EAAU,MAAOzC,QACrDiF,EAAU3H,KAAM,OAQjBgQ,EAAQhR,EAAS0C,cAAe,UAC1BG,aAAc,OAAQ,IAC5B+L,EAAG7L,YAAaiO,GACVpC,EAAGV,iBAAkB,aAAcxK,QACxCiF,EAAU3H,KAAM,MAAQ8I,EAAa,QAAUA,EAAa,KAC3DA,EAAa,gBAMT8E,EAAGV,iBAAkB,YAAaxK,QACvCiF,EAAU3H,KAAM,YAMX4N,EAAGV,iBAAkB,KAAO/H,EAAU,MAAOzC,QAClDiF,EAAU3H,KAAM,YAKjB4N,EAAGV,iBAAkB,QACrBvF,EAAU3H,KAAM,iBAGjB2N,GAAQ,SAAUC,GACjBA,EAAGqC,UAAY,oFAKf,IAAID,EAAQhR,EAAS0C,cAAe,SACpCsO,EAAMnO,aAAc,OAAQ,UAC5B+L,EAAG7L,YAAaiO,GAAQnO,aAAc,OAAQ,KAIzC+L,EAAGV,iBAAkB,YAAaxK,QACtCiF,EAAU3H,KAAM,OAAS8I,EAAa,eAKW,IAA7C8E,EAAGV,iBAAkB,YAAaxK,QACtCiF,EAAU3H,KAAM,WAAY,aAK7ByH,EAAQ1F,YAAa6L,GAAKpC,UAAW,EACc,IAA9CoC,EAAGV,iBAAkB,aAAcxK,QACvCiF,EAAU3H,KAAM,WAAY,aAK7B4N,EAAGV,iBAAkB,QACrBvF,EAAU3H,KAAM,YAIXQ,EAAQ0P,gBAAkB5F,EAAQuC,KAAQzG,EAAUqB,EAAQrB,SAClEqB,EAAQ0I,uBACR1I,EAAQ2I,oBACR3I,EAAQ4I,kBACR5I,EAAQ6I,qBAER3C,GAAQ,SAAUC,GAIjBpN,EAAQ+P,kBAAoBnK,EAAQvG,KAAM+N,EAAI,KAI9CxH,EAAQvG,KAAM+N,EAAI,aAClBhG,EAAc5H,KAAM,KAAMiJ,KAI5BtB,EAAYA,EAAUjF,QAAU,IAAIyG,OAAQxB,EAAUsF,KAAM,MAC5DrF,EAAgBA,EAAclF,QAAU,IAAIyG,OAAQvB,EAAcqF,KAAM,MAIxE+B,EAAa1E,EAAQuC,KAAMpF,EAAQ+I,yBAKnC3I,EAAWmH,GAAc1E,EAAQuC,KAAMpF,EAAQI,UAC9C,SAAUW,EAAGC,GACZ,IAAIgI,EAAuB,IAAfjI,EAAE7H,SAAiB6H,EAAEuG,gBAAkBvG,EAClDkI,EAAMjI,GAAKA,EAAEzG,WACd,OAAOwG,IAAMkI,MAAWA,GAAwB,IAAjBA,EAAI/P,YAClC8P,EAAM5I,SACL4I,EAAM5I,SAAU6I,GAChBlI,EAAEgI,yBAA8D,GAAnChI,EAAEgI,wBAAyBE,MAG3D,SAAUlI,EAAGC,GACZ,GAAKA,EACJ,MAAUA,EAAIA,EAAEzG,WACf,GAAKyG,IAAMD,EACV,OAAO,EAIV,OAAO,GAOTD,EAAYyG,EACZ,SAAUxG,EAAGC,GAGZ,GAAKD,IAAMC,EAEV,OADAlB,GAAe,EACR,EAIR,IAAIoJ,GAAWnI,EAAEgI,yBAA2B/H,EAAE+H,wBAC9C,OAAKG,IAgBU,GAPfA,GAAYnI,EAAE8D,eAAiB9D,KAASC,EAAE6D,eAAiB7D,GAC1DD,EAAEgI,wBAAyB/H,GAG3B,KAIGjI,EAAQoQ,cAAgBnI,EAAE+H,wBAAyBhI,KAAQmI,EAOzDnI,GAAKxJ,GAAYwJ,EAAE8D,eAAiBvE,GACxCF,EAAUE,EAAcS,IAChB,EAOJC,GAAKzJ,GAAYyJ,EAAE6D,eAAiBvE,GACxCF,EAAUE,EAAcU,GACjB,EAIDnB,EACJrH,EAASqH,EAAWkB,GAAMvI,EAASqH,EAAWmB,GAChD,EAGe,EAAVkI,GAAe,EAAI,IAE3B,SAAUnI,EAAGC,GAGZ,GAAKD,IAAMC,EAEV,OADAlB,GAAe,EACR,EAGR,IAAI2G,EACH3M,EAAI,EACJsP,EAAMrI,EAAExG,WACR0O,EAAMjI,EAAEzG,WACR8O,EAAK,CAAEtI,GACPuI,EAAK,CAAEtI,GAGR,IAAMoI,IAAQH,EAMb,OAAOlI,GAAKxJ,GAAY,EACvByJ,GAAKzJ,EAAW,EAEhB6R,GAAO,EACPH,EAAM,EACNpJ,EACErH,EAASqH,EAAWkB,GAAMvI,EAASqH,EAAWmB,GAChD,EAGK,GAAKoI,IAAQH,EACnB,OAAOzC,GAAczF,EAAGC,GAIzByF,EAAM1F,EACN,MAAU0F,EAAMA,EAAIlM,WACnB8O,EAAGE,QAAS9C,GAEbA,EAAMzF,EACN,MAAUyF,EAAMA,EAAIlM,WACnB+O,EAAGC,QAAS9C,GAIb,MAAQ4C,EAAIvP,KAAQwP,EAAIxP,GACvBA,IAGD,OAAOA,EAGN0M,GAAc6C,EAAIvP,GAAKwP,EAAIxP,IAO3BuP,EAAIvP,IAAOwG,GAAgB,EAC3BgJ,EAAIxP,IAAOwG,EAAe,EAE1B,IAGK/I,GAGR8H,GAAOV,QAAU,SAAU6K,EAAMC,GAChC,OAAOpK,GAAQmK,EAAM,KAAM,KAAMC,IAGlCpK,GAAOoJ,gBAAkB,SAAUzM,EAAMwN,GAGxC,GAFAzJ,EAAa/D,GAERjD,EAAQ0P,iBAAmBxI,IAC9BY,EAAwB2I,EAAO,QAC7BrJ,IAAkBA,EAAciF,KAAMoE,OACtCtJ,IAAkBA,EAAUkF,KAAMoE,IAErC,IACC,IAAI9N,EAAMiD,EAAQvG,KAAM4D,EAAMwN,GAG9B,GAAK9N,GAAO3C,EAAQ+P,mBAInB9M,EAAKzE,UAAuC,KAA3ByE,EAAKzE,SAAS2B,SAC/B,OAAOwC,EAEP,MAAQ0I,GACTvD,EAAwB2I,GAAM,GAIhC,OAAyD,EAAlDnK,GAAQmK,EAAMjS,EAAU,KAAM,CAAEyE,IAASf,QAGjDoE,GAAOe,SAAW,SAAUvF,EAASmB,GAUpC,OAHOnB,EAAQgK,eAAiBhK,IAAatD,GAC5CwI,EAAalF,GAEPuF,EAAUvF,EAASmB,IAG3BqD,GAAOqK,KAAO,SAAU1N,EAAMgB,IAOtBhB,EAAK6I,eAAiB7I,IAAUzE,GACtCwI,EAAa/D,GAGd,IAAIlB,EAAKwE,EAAKiH,WAAYvJ,EAAKoC,eAG9BrF,EAAMe,GAAMnC,EAAOP,KAAMkH,EAAKiH,WAAYvJ,EAAKoC,eAC9CtE,EAAIkB,EAAMgB,GAAOiD,QACjBxC,EAEF,YAAeA,IAAR1D,EACNA,EACAhB,EAAQwI,aAAetB,EACtBjE,EAAK7B,aAAc6C,IACjBjD,EAAMiC,EAAKoM,iBAAkBpL,KAAYjD,EAAI4P,UAC9C5P,EAAI+E,MACJ,MAGJO,GAAO6D,OAAS,SAAU0G,GACzB,OAASA,EAAM,IAAK/L,QAAS0F,GAAYC,KAG1CnE,GAAOtB,MAAQ,SAAUC,GACxB,MAAM,IAAIvG,MAAO,0CAA4CuG,IAO9DqB,GAAOwK,WAAa,SAAUtL,GAC7B,IAAIvC,EACH8N,EAAa,GACbpN,EAAI,EACJ5C,EAAI,EAOL,GAJAgG,GAAgB/G,EAAQgR,iBACxBlK,GAAa9G,EAAQiR,YAAczL,EAAQtG,MAAO,GAClDsG,EAAQ3B,KAAMkE,GAEThB,EAAe,CACnB,MAAU9D,EAAOuC,EAASzE,KACpBkC,IAASuC,EAASzE,KACtB4C,EAAIoN,EAAWvR,KAAMuB,IAGvB,MAAQ4C,IACP6B,EAAQ1B,OAAQiN,EAAYpN,GAAK,GAQnC,OAFAmD,EAAY,KAELtB,GAORgB,EAAUF,GAAOE,QAAU,SAAUvD,GACpC,IAAIpC,EACH8B,EAAM,GACN5B,EAAI,EACJZ,EAAW8C,EAAK9C,SAEjB,GAAMA,GAQC,GAAkB,IAAbA,GAA+B,IAAbA,GAA+B,KAAbA,EAAkB,CAIjE,GAAiC,iBAArB8C,EAAKiO,YAChB,OAAOjO,EAAKiO,YAIZ,IAAMjO,EAAOA,EAAKkO,WAAYlO,EAAMA,EAAOA,EAAK4K,YAC/ClL,GAAO6D,EAASvD,QAGZ,GAAkB,IAAb9C,GAA+B,IAAbA,EAC7B,OAAO8C,EAAKmO,eAnBZ,MAAUvQ,EAAOoC,EAAMlC,KAGtB4B,GAAO6D,EAAS3F,GAqBlB,OAAO8B,IAGR4D,EAAOD,GAAO+K,UAAY,CAGzBrE,YAAa,GAEbsE,aAAcpE,GAEdxB,MAAOxC,EAEPsE,WAAY,GAEZ4B,KAAM,GAENmC,SAAU,CACTC,IAAK,CAAEtG,IAAK,aAAc/H,OAAO,GACjCsO,IAAK,CAAEvG,IAAK,cACZwG,IAAK,CAAExG,IAAK,kBAAmB/H,OAAO,GACtCwO,IAAK,CAAEzG,IAAK,oBAGb0G,UAAW,CACVtI,KAAQ,SAAUoC,GAWjB,OAVAA,EAAO,GAAMA,EAAO,GAAI5G,QAASmF,GAAWC,IAG5CwB,EAAO,IAAQA,EAAO,IAAOA,EAAO,IACnCA,EAAO,IAAO,IAAK5G,QAASmF,GAAWC,IAEpB,OAAfwB,EAAO,KACXA,EAAO,GAAM,IAAMA,EAAO,GAAM,KAG1BA,EAAMxM,MAAO,EAAG,IAGxBsK,MAAS,SAAUkC,GAiClB,OArBAA,EAAO,GAAMA,EAAO,GAAIrF,cAEU,QAA7BqF,EAAO,GAAIxM,MAAO,EAAG,IAGnBwM,EAAO,IACZpF,GAAOtB,MAAO0G,EAAO,IAKtBA,EAAO,KAASA,EAAO,GACtBA,EAAO,IAAQA,EAAO,IAAO,GAC7B,GAAqB,SAAfA,EAAO,IAAiC,QAAfA,EAAO,KACvCA,EAAO,KAAWA,EAAO,GAAMA,EAAO,IAAwB,QAAfA,EAAO,KAG3CA,EAAO,IAClBpF,GAAOtB,MAAO0G,EAAO,IAGfA,GAGRnC,OAAU,SAAUmC,GACnB,IAAImG,EACHC,GAAYpG,EAAO,IAAOA,EAAO,GAElC,OAAKxC,EAAmB,MAAEmD,KAAMX,EAAO,IAC/B,MAIHA,EAAO,GACXA,EAAO,GAAMA,EAAO,IAAOA,EAAO,IAAO,GAG9BoG,GAAY9I,EAAQqD,KAAMyF,KAGnCD,EAASnL,EAAUoL,GAAU,MAG7BD,EAASC,EAASrS,QAAS,IAAKqS,EAAS5P,OAAS2P,GAAWC,EAAS5P,UAGxEwJ,EAAO,GAAMA,EAAO,GAAIxM,MAAO,EAAG2S,GAClCnG,EAAO,GAAMoG,EAAS5S,MAAO,EAAG2S,IAI1BnG,EAAMxM,MAAO,EAAG,MAIzBgQ,OAAQ,CAEP7F,IAAO,SAAU0I,GAChB,IAAI9G,EAAW8G,EAAiBjN,QAASmF,GAAWC,IAAY7D,cAChE,MAA4B,MAArB0L,EACN,WACC,OAAO,GAER,SAAU9O,GACT,OAAOA,EAAKgI,UAAYhI,EAAKgI,SAAS5E,gBAAkB4E,IAI3D7B,MAAS,SAAU0F,GAClB,IAAIkD,EAAUtK,EAAYoH,EAAY,KAEtC,OAAOkD,IACJA,EAAU,IAAIrJ,OAAQ,MAAQL,EAC/B,IAAMwG,EAAY,IAAMxG,EAAa,SAAaZ,EACjDoH,EAAW,SAAU7L,GACpB,OAAO+O,EAAQ3F,KACY,iBAAnBpJ,EAAK6L,WAA0B7L,EAAK6L,WACd,oBAAtB7L,EAAK7B,cACX6B,EAAK7B,aAAc,UACpB,OAKNkI,KAAQ,SAAUrF,EAAMgO,EAAUC,GACjC,OAAO,SAAUjP,GAChB,IAAIkP,EAAS7L,GAAOqK,KAAM1N,EAAMgB,GAEhC,OAAe,MAAVkO,EACgB,OAAbF,GAEFA,IAINE,GAAU,GAIU,MAAbF,EAAmBE,IAAWD,EACvB,OAAbD,EAAoBE,IAAWD,EAClB,OAAbD,EAAoBC,GAAqC,IAA5BC,EAAO1S,QAASyS,GAChC,OAAbD,EAAoBC,IAAoC,EAA3BC,EAAO1S,QAASyS,GAChC,OAAbD,EAAoBC,GAASC,EAAOjT,OAAQgT,EAAMhQ,UAAagQ,EAClD,OAAbD,GAA2F,GAArE,IAAME,EAAOrN,QAAS4D,EAAa,KAAQ,KAAMjJ,QAASyS,GACnE,OAAbD,IAAoBE,IAAWD,GAASC,EAAOjT,MAAO,EAAGgT,EAAMhQ,OAAS,KAAQgQ,EAAQ,QAO3F1I,MAAS,SAAUjJ,EAAM6R,EAAMC,EAAWlP,EAAOE,GAChD,IAAIiP,EAAgC,QAAvB/R,EAAKrB,MAAO,EAAG,GAC3BqT,EAA+B,SAArBhS,EAAKrB,OAAQ,GACvBsT,EAAkB,YAATJ,EAEV,OAAiB,IAAVjP,GAAwB,IAATE,EAGrB,SAAUJ,GACT,QAASA,EAAKzB,YAGf,SAAUyB,EAAMwP,EAAUC,GACzB,IAAI5F,EAAO6F,EAAaC,EAAY/R,EAAMgS,EAAWC,EACpD5H,EAAMoH,IAAWC,EAAU,cAAgB,kBAC3CQ,EAAS9P,EAAKzB,WACdyC,EAAOuO,GAAUvP,EAAKgI,SAAS5E,cAC/B2M,GAAYN,IAAQF,EACpB7E,GAAO,EAER,GAAKoF,EAAS,CAGb,GAAKT,EAAS,CACb,MAAQpH,EAAM,CACbrK,EAAOoC,EACP,MAAUpC,EAAOA,EAAMqK,GACtB,GAAKsH,EACJ3R,EAAKoK,SAAS5E,gBAAkBpC,EACd,IAAlBpD,EAAKV,SAEL,OAAO,EAKT2S,EAAQ5H,EAAe,SAAT3K,IAAoBuS,GAAS,cAE5C,OAAO,EAMR,GAHAA,EAAQ,CAAEP,EAAUQ,EAAO5B,WAAa4B,EAAOE,WAG1CV,GAAWS,EAAW,CAe1BrF,GADAkF,GADA/F,GAHA6F,GAJAC,GADA/R,EAAOkS,GACYpO,KAAe9D,EAAM8D,GAAY,KAI1B9D,EAAKqS,YAC5BN,EAAY/R,EAAKqS,UAAa,KAEZ3S,IAAU,IACZ,KAAQiH,GAAWsF,EAAO,KACzBA,EAAO,GAC3BjM,EAAOgS,GAAaE,EAAO3H,WAAYyH,GAEvC,MAAUhS,IAASgS,GAAahS,GAAQA,EAAMqK,KAG3CyC,EAAOkF,EAAY,IAAOC,EAAM5K,MAGlC,GAAuB,IAAlBrH,EAAKV,YAAoBwN,GAAQ9M,IAASoC,EAAO,CACrD0P,EAAapS,GAAS,CAAEiH,EAASqL,EAAWlF,GAC5C,YAyBF,GAlBKqF,IAaJrF,EADAkF,GADA/F,GAHA6F,GAJAC,GADA/R,EAAOoC,GACY0B,KAAe9D,EAAM8D,GAAY,KAI1B9D,EAAKqS,YAC5BN,EAAY/R,EAAKqS,UAAa,KAEZ3S,IAAU,IACZ,KAAQiH,GAAWsF,EAAO,KAMhC,IAATa,EAGJ,MAAU9M,IAASgS,GAAahS,GAAQA,EAAMqK,KAC3CyC,EAAOkF,EAAY,IAAOC,EAAM5K,MAElC,IAAOsK,EACN3R,EAAKoK,SAAS5E,gBAAkBpC,EACd,IAAlBpD,EAAKV,aACHwN,IAGGqF,KAMJL,GALAC,EAAa/R,EAAM8D,KAChB9D,EAAM8D,GAAY,KAIK9D,EAAKqS,YAC5BN,EAAY/R,EAAKqS,UAAa,KAEpB3S,GAAS,CAAEiH,EAASmG,IAG7B9M,IAASoC,GACb,MASL,OADA0K,GAAQtK,KACQF,GAAWwK,EAAOxK,GAAU,GAAqB,GAAhBwK,EAAOxK,KAK5DoG,OAAU,SAAU4J,EAAQhF,GAM3B,IAAIiF,EACHrR,EAAKwE,EAAKkC,QAAS0K,IAAY5M,EAAK8M,WAAYF,EAAO9M,gBACtDC,GAAOtB,MAAO,uBAAyBmO,GAKzC,OAAKpR,EAAI4C,GACD5C,EAAIoM,GAIK,EAAZpM,EAAGG,QACPkR,EAAO,CAAED,EAAQA,EAAQ,GAAIhF,GACtB5H,EAAK8M,WAAWxT,eAAgBsT,EAAO9M,eAC7C6G,GAAc,SAAU3B,EAAM3F,GAC7B,IAAI0N,EACHC,EAAUxR,EAAIwJ,EAAM4C,GACpBpN,EAAIwS,EAAQrR,OACb,MAAQnB,IAEPwK,EADA+H,EAAM7T,EAAS8L,EAAMgI,EAASxS,OACb6E,EAAS0N,GAAQC,EAASxS,MAG7C,SAAUkC,GACT,OAAOlB,EAAIkB,EAAM,EAAGmQ,KAIhBrR,IAIT0G,QAAS,CAGR+K,IAAOtG,GAAc,SAAUrL,GAK9B,IAAI2N,EAAQ,GACXhK,EAAU,GACViO,EAAU9M,EAAS9E,EAASiD,QAAS8D,EAAO,OAE7C,OAAO6K,EAAS9O,GACfuI,GAAc,SAAU3B,EAAM3F,EAAS6M,EAAUC,GAChD,IAAIzP,EACHyQ,EAAYD,EAASlI,EAAM,KAAMmH,EAAK,IACtC3R,EAAIwK,EAAKrJ,OAGV,MAAQnB,KACAkC,EAAOyQ,EAAW3S,MACxBwK,EAAMxK,KAAS6E,EAAS7E,GAAMkC,MAIjC,SAAUA,EAAMwP,EAAUC,GAMzB,OALAlD,EAAO,GAAMvM,EACbwQ,EAASjE,EAAO,KAAMkD,EAAKlN,GAG3BgK,EAAO,GAAM,MACLhK,EAAQ0C,SAInByL,IAAOzG,GAAc,SAAUrL,GAC9B,OAAO,SAAUoB,GAChB,OAAyC,EAAlCqD,GAAQzE,EAAUoB,GAAOf,UAIlCmF,SAAY6F,GAAc,SAAU/L,GAEnC,OADAA,EAAOA,EAAK2D,QAASmF,GAAWC,IACzB,SAAUjH,GAChB,OAAkE,GAAzDA,EAAKiO,aAAe1K,EAASvD,IAASxD,QAAS0B,MAW1DyS,KAAQ1G,GAAc,SAAU0G,GAO/B,OAJM3K,EAAYoD,KAAMuH,GAAQ,KAC/BtN,GAAOtB,MAAO,qBAAuB4O,GAEtCA,EAAOA,EAAK9O,QAASmF,GAAWC,IAAY7D,cACrC,SAAUpD,GAChB,IAAI4Q,EACJ,GACC,GAAOA,EAAW3M,EACjBjE,EAAK2Q,KACL3Q,EAAK7B,aAAc,aAAgB6B,EAAK7B,aAAc,QAGtD,OADAyS,EAAWA,EAASxN,iBACAuN,GAA2C,IAAnCC,EAASpU,QAASmU,EAAO,YAE3C3Q,EAAOA,EAAKzB,aAAkC,IAAlByB,EAAK9C,UAC7C,OAAO,KAKTkE,OAAU,SAAUpB,GACnB,IAAI6Q,EAAOnV,EAAOoV,UAAYpV,EAAOoV,SAASD,KAC9C,OAAOA,GAAQA,EAAK5U,MAAO,KAAQ+D,EAAKgJ,IAGzC+H,KAAQ,SAAU/Q,GACjB,OAAOA,IAASgE,GAGjBgN,MAAS,SAAUhR,GAClB,OAAOA,IAASzE,EAAS0V,iBACrB1V,EAAS2V,UAAY3V,EAAS2V,gBAC7BlR,EAAK1C,MAAQ0C,EAAKmR,OAASnR,EAAKoR,WAItCC,QAAWtG,IAAsB,GACjChD,SAAYgD,IAAsB,GAElCuG,QAAW,SAAUtR,GAIpB,IAAIgI,EAAWhI,EAAKgI,SAAS5E,cAC7B,MAAsB,UAAb4E,KAA0BhI,EAAKsR,SACxB,WAAbtJ,KAA2BhI,EAAKuR,UAGpCA,SAAY,SAAUvR,GASrB,OALKA,EAAKzB,YAETyB,EAAKzB,WAAWiT,eAGQ,IAAlBxR,EAAKuR,UAIbE,MAAS,SAAUzR,GAMlB,IAAMA,EAAOA,EAAKkO,WAAYlO,EAAMA,EAAOA,EAAK4K,YAC/C,GAAK5K,EAAK9C,SAAW,EACpB,OAAO,EAGT,OAAO,GAGR4S,OAAU,SAAU9P,GACnB,OAAQsD,EAAKkC,QAAiB,MAAGxF,IAIlC0R,OAAU,SAAU1R,GACnB,OAAO4G,EAAQwC,KAAMpJ,EAAKgI,WAG3BuE,MAAS,SAAUvM,GAClB,OAAO2G,EAAQyC,KAAMpJ,EAAKgI,WAG3B2J,OAAU,SAAU3R,GACnB,IAAIgB,EAAOhB,EAAKgI,SAAS5E,cACzB,MAAgB,UAATpC,GAAkC,WAAdhB,EAAK1C,MAA8B,WAAT0D,GAGtD9C,KAAQ,SAAU8B,GACjB,IAAI0N,EACJ,MAAuC,UAAhC1N,EAAKgI,SAAS5E,eACN,SAAdpD,EAAK1C,OAIuC,OAAxCoQ,EAAO1N,EAAK7B,aAAc,UACN,SAAvBuP,EAAKtK,gBAIRlD,MAAS+K,GAAwB,WAChC,MAAO,CAAE,KAGV7K,KAAQ6K,GAAwB,SAAU2G,EAAe3S,GACxD,MAAO,CAAEA,EAAS,KAGnBkB,GAAM8K,GAAwB,SAAU2G,EAAe3S,EAAQiM,GAC9D,MAAO,CAAEA,EAAW,EAAIA,EAAWjM,EAASiM,KAG7C7K,KAAQ4K,GAAwB,SAAUE,EAAclM,GAEvD,IADA,IAAInB,EAAI,EACAA,EAAImB,EAAQnB,GAAK,EACxBqN,EAAa5O,KAAMuB,GAEpB,OAAOqN,IAGR3K,IAAOyK,GAAwB,SAAUE,EAAclM,GAEtD,IADA,IAAInB,EAAI,EACAA,EAAImB,EAAQnB,GAAK,EACxBqN,EAAa5O,KAAMuB,GAEpB,OAAOqN,IAGR0G,GAAM5G,GAAwB,SAAUE,EAAclM,EAAQiM,GAM7D,IALA,IAAIpN,EAAIoN,EAAW,EAClBA,EAAWjM,EACAA,EAAXiM,EACCjM,EACAiM,EACa,KAALpN,GACTqN,EAAa5O,KAAMuB,GAEpB,OAAOqN,IAGR2G,GAAM7G,GAAwB,SAAUE,EAAclM,EAAQiM,GAE7D,IADA,IAAIpN,EAAIoN,EAAW,EAAIA,EAAWjM,EAASiM,IACjCpN,EAAImB,GACbkM,EAAa5O,KAAMuB,GAEpB,OAAOqN,OAKL3F,QAAe,IAAIlC,EAAKkC,QAAc,GAGhC,CAAEuM,OAAO,EAAMC,UAAU,EAAMC,MAAM,EAAMC,UAAU,EAAMC,OAAO,GAC5E7O,EAAKkC,QAAS1H,GAAM+M,GAAmB/M,GAExC,IAAMA,IAAK,CAAEsU,QAAQ,EAAMC,OAAO,GACjC/O,EAAKkC,QAAS1H,GAAMgN,GAAoBhN,GAIzC,SAASsS,MA0ET,SAAS7G,GAAY+I,GAIpB,IAHA,IAAIxU,EAAI,EACP2C,EAAM6R,EAAOrT,OACbL,EAAW,GACJd,EAAI2C,EAAK3C,IAChBc,GAAY0T,EAAQxU,GAAIgF,MAEzB,OAAOlE,EAGR,SAASkJ,GAAe0I,EAAS+B,EAAYC,GAC5C,IAAIvK,EAAMsK,EAAWtK,IACpBwK,EAAOF,EAAWrK,KAClB4B,EAAM2I,GAAQxK,EACdyK,EAAmBF,GAAgB,eAAR1I,EAC3B6I,EAAWnO,IAEZ,OAAO+N,EAAWrS,MAGjB,SAAUF,EAAMnB,EAAS4Q,GACxB,MAAUzP,EAAOA,EAAMiI,GACtB,GAAuB,IAAlBjI,EAAK9C,UAAkBwV,EAC3B,OAAOlC,EAASxQ,EAAMnB,EAAS4Q,GAGjC,OAAO,GAIR,SAAUzP,EAAMnB,EAAS4Q,GACxB,IAAImD,EAAUlD,EAAaC,EAC1BkD,EAAW,CAAEtO,EAASoO,GAGvB,GAAKlD,GACJ,MAAUzP,EAAOA,EAAMiI,GACtB,IAAuB,IAAlBjI,EAAK9C,UAAkBwV,IACtBlC,EAASxQ,EAAMnB,EAAS4Q,GAC5B,OAAO,OAKV,MAAUzP,EAAOA,EAAMiI,GACtB,GAAuB,IAAlBjI,EAAK9C,UAAkBwV,EAQ3B,GAHAhD,GAJAC,EAAa3P,EAAM0B,KAAe1B,EAAM0B,GAAY,KAI1B1B,EAAKiQ,YAC5BN,EAAY3P,EAAKiQ,UAAa,IAE5BwC,GAAQA,IAASzS,EAAKgI,SAAS5E,cACnCpD,EAAOA,EAAMiI,IAASjI,MAChB,CAAA,IAAO4S,EAAWlD,EAAa5F,KACrC8I,EAAU,KAAQrO,GAAWqO,EAAU,KAAQD,EAG/C,OAASE,EAAU,GAAMD,EAAU,GAOnC,IAHAlD,EAAa5F,GAAQ+I,GAGJ,GAAMrC,EAASxQ,EAAMnB,EAAS4Q,GAC9C,OAAO,EAMZ,OAAO,GAIV,SAASqD,GAAgBC,GACxB,OAAyB,EAAlBA,EAAS9T,OACf,SAAUe,EAAMnB,EAAS4Q,GACxB,IAAI3R,EAAIiV,EAAS9T,OACjB,MAAQnB,IACP,IAAMiV,EAAUjV,GAAKkC,EAAMnB,EAAS4Q,GACnC,OAAO,EAGT,OAAO,GAERsD,EAAU,GAYZ,SAASC,GAAUvC,EAAW1Q,EAAKkM,EAAQpN,EAAS4Q,GAOnD,IANA,IAAIzP,EACHiT,EAAe,GACfnV,EAAI,EACJ2C,EAAMgQ,EAAUxR,OAChBiU,EAAgB,MAAPnT,EAEFjC,EAAI2C,EAAK3C,KACTkC,EAAOyQ,EAAW3S,MAClBmO,IAAUA,EAAQjM,EAAMnB,EAAS4Q,KACtCwD,EAAa1W,KAAMyD,GACdkT,GACJnT,EAAIxD,KAAMuB,KAMd,OAAOmV,EAGR,SAASE,GAAYxE,EAAW/P,EAAU4R,EAAS4C,EAAYC,EAAYC,GAO1E,OANKF,IAAeA,EAAY1R,KAC/B0R,EAAaD,GAAYC,IAErBC,IAAeA,EAAY3R,KAC/B2R,EAAaF,GAAYE,EAAYC,IAE/BrJ,GAAc,SAAU3B,EAAM/F,EAAS1D,EAAS4Q,GACtD,IAAI8D,EAAMzV,EAAGkC,EACZwT,EAAS,GACTC,EAAU,GACVC,EAAcnR,EAAQtD,OAGtBQ,EAAQ6I,GA5CX,SAA2B1J,EAAU+U,EAAUpR,GAG9C,IAFA,IAAIzE,EAAI,EACP2C,EAAMkT,EAAS1U,OACRnB,EAAI2C,EAAK3C,IAChBuF,GAAQzE,EAAU+U,EAAU7V,GAAKyE,GAElC,OAAOA,EAsCWqR,CACfhV,GAAY,IACZC,EAAQ3B,SAAW,CAAE2B,GAAYA,EACjC,IAIDgV,GAAYlF,IAAerG,GAAS1J,EAEnCa,EADAuT,GAAUvT,EAAO+T,EAAQ7E,EAAW9P,EAAS4Q,GAG9CqE,EAAatD,EAGZ6C,IAAgB/K,EAAOqG,EAAY+E,GAAeN,GAGjD,GAGA7Q,EACDsR,EAQF,GALKrD,GACJA,EAASqD,EAAWC,EAAYjV,EAAS4Q,GAIrC2D,EAAa,CACjBG,EAAOP,GAAUc,EAAYL,GAC7BL,EAAYG,EAAM,GAAI1U,EAAS4Q,GAG/B3R,EAAIyV,EAAKtU,OACT,MAAQnB,KACAkC,EAAOuT,EAAMzV,MACnBgW,EAAYL,EAAS3V,MAAW+V,EAAWJ,EAAS3V,IAAQkC,IAK/D,GAAKsI,GACJ,GAAK+K,GAAc1E,EAAY,CAC9B,GAAK0E,EAAa,CAGjBE,EAAO,GACPzV,EAAIgW,EAAW7U,OACf,MAAQnB,KACAkC,EAAO8T,EAAYhW,KAGzByV,EAAKhX,KAAQsX,EAAW/V,GAAMkC,GAGhCqT,EAAY,KAAQS,EAAa,GAAMP,EAAM9D,GAI9C3R,EAAIgW,EAAW7U,OACf,MAAQnB,KACAkC,EAAO8T,EAAYhW,MACsC,GAA7DyV,EAAOF,EAAa7W,EAAS8L,EAAMtI,GAASwT,EAAQ1V,MAEtDwK,EAAMiL,KAAYhR,EAASgR,GAASvT,UAOvC8T,EAAad,GACZc,IAAevR,EACduR,EAAWjT,OAAQ6S,EAAaI,EAAW7U,QAC3C6U,GAEGT,EACJA,EAAY,KAAM9Q,EAASuR,EAAYrE,GAEvClT,EAAKD,MAAOiG,EAASuR,KAMzB,SAASC,GAAmBzB,GAyB3B,IAxBA,IAAI0B,EAAcxD,EAAS9P,EAC1BD,EAAM6R,EAAOrT,OACbgV,EAAkB3Q,EAAKgL,SAAUgE,EAAQ,GAAIhV,MAC7C4W,EAAmBD,GAAmB3Q,EAAKgL,SAAU,KACrDxQ,EAAImW,EAAkB,EAAI,EAG1BE,EAAerM,GAAe,SAAU9H,GACvC,OAAOA,IAASgU,GACdE,GAAkB,GACrBE,EAAkBtM,GAAe,SAAU9H,GAC1C,OAAwC,EAAjCxD,EAASwX,EAAchU,IAC5BkU,GAAkB,GACrBnB,EAAW,CAAE,SAAU/S,EAAMnB,EAAS4Q,GACrC,IAAI/P,GAASuU,IAAqBxE,GAAO5Q,IAAY+E,MAClDoQ,EAAenV,GAAU3B,SAC1BiX,EAAcnU,EAAMnB,EAAS4Q,GAC7B2E,EAAiBpU,EAAMnB,EAAS4Q,IAIlC,OADAuE,EAAe,KACRtU,IAGD5B,EAAI2C,EAAK3C,IAChB,GAAO0S,EAAUlN,EAAKgL,SAAUgE,EAAQxU,GAAIR,MAC3CyV,EAAW,CAAEjL,GAAegL,GAAgBC,GAAYvC,QAClD,CAIN,IAHAA,EAAUlN,EAAK2I,OAAQqG,EAAQxU,GAAIR,MAAOhB,MAAO,KAAMgW,EAAQxU,GAAI6E,UAGrDjB,GAAY,CAIzB,IADAhB,IAAM5C,EACE4C,EAAID,EAAKC,IAChB,GAAK4C,EAAKgL,SAAUgE,EAAQ5R,GAAIpD,MAC/B,MAGF,OAAO6V,GACF,EAAJrV,GAASgV,GAAgBC,GACrB,EAAJjV,GAASyL,GAGT+I,EACErW,MAAO,EAAG6B,EAAI,GACdzB,OAAQ,CAAEyG,MAAgC,MAAzBwP,EAAQxU,EAAI,GAAIR,KAAe,IAAM,MACtDuE,QAAS8D,EAAO,MAClB6K,EACA1S,EAAI4C,GAAKqT,GAAmBzB,EAAOrW,MAAO6B,EAAG4C,IAC7CA,EAAID,GAAOsT,GAAqBzB,EAASA,EAAOrW,MAAOyE,IACvDA,EAAID,GAAO8I,GAAY+I,IAGzBS,EAASxW,KAAMiU,GAIjB,OAAOsC,GAAgBC,GAoTxB,OAtpBA3C,GAAWlR,UAAYoE,EAAK+Q,QAAU/Q,EAAKkC,QAC3ClC,EAAK8M,WAAa,IAAIA,GAEtB3M,EAAWJ,GAAOI,SAAW,SAAU7E,EAAU0V,GAChD,IAAIhE,EAAS7H,EAAO6J,EAAQhV,EAC3BiX,EAAO7L,EAAQ8L,EACfC,EAAS9P,EAAY/F,EAAW,KAEjC,GAAK6V,EACJ,OAAOH,EAAY,EAAIG,EAAOxY,MAAO,GAGtCsY,EAAQ3V,EACR8J,EAAS,GACT8L,EAAalR,EAAKqL,UAElB,MAAQ4F,EAAQ,CA2Bf,IAAMjX,KAxBAgT,KAAa7H,EAAQ7C,EAAOkD,KAAMyL,MAClC9L,IAGJ8L,EAAQA,EAAMtY,MAAOwM,EAAO,GAAIxJ,SAAYsV,GAE7C7L,EAAOnM,KAAQ+V,EAAS,KAGzBhC,GAAU,GAGH7H,EAAQ5C,EAAaiD,KAAMyL,MACjCjE,EAAU7H,EAAMuB,QAChBsI,EAAO/V,KAAM,CACZuG,MAAOwN,EAGPhT,KAAMmL,EAAO,GAAI5G,QAAS8D,EAAO,OAElC4O,EAAQA,EAAMtY,MAAOqU,EAAQrR,SAIhBqE,EAAK2I,SACXxD,EAAQxC,EAAW3I,GAAOwL,KAAMyL,KAAgBC,EAAYlX,MAChEmL,EAAQ+L,EAAYlX,GAAQmL,MAC9B6H,EAAU7H,EAAMuB,QAChBsI,EAAO/V,KAAM,CACZuG,MAAOwN,EACPhT,KAAMA,EACNqF,QAAS8F,IAEV8L,EAAQA,EAAMtY,MAAOqU,EAAQrR,SAI/B,IAAMqR,EACL,MAOF,OAAOgE,EACNC,EAAMtV,OACNsV,EACClR,GAAOtB,MAAOnD,GAGd+F,EAAY/F,EAAU8J,GAASzM,MAAO,IA4ZzCyH,EAAUL,GAAOK,QAAU,SAAU9E,EAAU6J,GAC9C,IAAI3K,EA9H8B4W,EAAiBC,EAC/CC,EACHC,EACAC,EA4HAH,EAAc,GACdD,EAAkB,GAClBD,EAAS7P,EAAehG,EAAW,KAEpC,IAAM6V,EAAS,CAGRhM,IACLA,EAAQhF,EAAU7E,IAEnBd,EAAI2K,EAAMxJ,OACV,MAAQnB,KACP2W,EAASV,GAAmBtL,EAAO3K,KACtB4D,GACZiT,EAAYpY,KAAMkY,GAElBC,EAAgBnY,KAAMkY,IAKxBA,EAAS7P,EACRhG,GArJgC8V,EAsJNA,EArJxBE,EAA6B,GADkBD,EAsJNA,GArJrB1V,OACvB4V,EAAqC,EAAzBH,EAAgBzV,OAC5B6V,EAAe,SAAUxM,EAAMzJ,EAAS4Q,EAAKlN,EAASwS,GACrD,IAAI/U,EAAMU,EAAG8P,EACZwE,EAAe,EACflX,EAAI,IACJ2S,EAAYnI,GAAQ,GACpB2M,EAAa,GACbC,EAAgBtR,EAGhBnE,EAAQ6I,GAAQuM,GAAavR,EAAK6I,KAAY,IAAG,IAAK4I,GAGtDI,EAAkB5Q,GAA4B,MAAjB2Q,EAAwB,EAAIvT,KAAKC,UAAY,GAC1EnB,EAAMhB,EAAMR,OAcb,IAZK8V,IAMJnR,EAAmB/E,GAAWtD,GAAYsD,GAAWkW,GAM9CjX,IAAM2C,GAAgC,OAAvBT,EAAOP,EAAO3B,IAAeA,IAAM,CACzD,GAAK+W,GAAa7U,EAAO,CACxBU,EAAI,EAME7B,GAAWmB,EAAK6I,eAAiBtN,IACtCwI,EAAa/D,GACbyP,GAAOxL,GAER,MAAUuM,EAAUkE,EAAiBhU,KACpC,GAAK8P,EAASxQ,EAAMnB,GAAWtD,EAAUkU,GAAQ,CAChDlN,EAAQhG,KAAMyD,GACd,MAGG+U,IACJxQ,EAAU4Q,GAKPP,KAGG5U,GAAQwQ,GAAWxQ,IACzBgV,IAII1M,GACJmI,EAAUlU,KAAMyD,IAgBnB,GATAgV,GAAgBlX,EASX8W,GAAS9W,IAAMkX,EAAe,CAClCtU,EAAI,EACJ,MAAU8P,EAAUmE,EAAajU,KAChC8P,EAASC,EAAWwE,EAAYpW,EAAS4Q,GAG1C,GAAKnH,EAAO,CAGX,GAAoB,EAAf0M,EACJ,MAAQlX,IACC2S,EAAW3S,IAAOmX,EAAYnX,KACrCmX,EAAYnX,GAAMmH,EAAI7I,KAAMmG,IAM/B0S,EAAajC,GAAUiC,GAIxB1Y,EAAKD,MAAOiG,EAAS0S,GAGhBF,IAAczM,GAA4B,EAApB2M,EAAWhW,QACG,EAAtC+V,EAAeL,EAAY1V,QAE7BoE,GAAOwK,WAAYtL,GAUrB,OALKwS,IACJxQ,EAAU4Q,EACVvR,EAAmBsR,GAGbzE,GAGFmE,EACN3K,GAAc6K,GACdA,KAgCOlW,SAAWA,EAEnB,OAAO6V,GAYR9Q,EAASN,GAAOM,OAAS,SAAU/E,EAAUC,EAAS0D,EAAS+F,GAC9D,IAAIxK,EAAGwU,EAAQ8C,EAAO9X,EAAM6O,EAC3BkJ,EAA+B,mBAAbzW,GAA2BA,EAC7C6J,GAASH,GAAQ7E,EAAY7E,EAAWyW,EAASzW,UAAYA,GAM9D,GAJA2D,EAAUA,GAAW,GAIC,IAAjBkG,EAAMxJ,OAAe,CAIzB,GAAqB,GADrBqT,EAAS7J,EAAO,GAAMA,EAAO,GAAIxM,MAAO,IAC5BgD,QAA+C,QAA/BmW,EAAQ9C,EAAQ,IAAMhV,MAC5B,IAArBuB,EAAQ3B,UAAkB+G,GAAkBX,EAAKgL,SAAUgE,EAAQ,GAAIhV,MAAS,CAIhF,KAFAuB,GAAYyE,EAAK6I,KAAW,GAAGiJ,EAAMzS,QAAS,GAC5Cd,QAASmF,GAAWC,IAAapI,IAAa,IAAM,IAErD,OAAO0D,EAGI8S,IACXxW,EAAUA,EAAQN,YAGnBK,EAAWA,EAAS3C,MAAOqW,EAAOtI,QAAQlH,MAAM7D,QAIjDnB,EAAImI,EAA0B,aAAEmD,KAAMxK,GAAa,EAAI0T,EAAOrT,OAC9D,MAAQnB,IAAM,CAIb,GAHAsX,EAAQ9C,EAAQxU,GAGXwF,EAAKgL,SAAYhR,EAAO8X,EAAM9X,MAClC,MAED,IAAO6O,EAAO7I,EAAK6I,KAAM7O,MAGjBgL,EAAO6D,EACbiJ,EAAMzS,QAAS,GAAId,QAASmF,GAAWC,IACvCF,GAASqC,KAAMkJ,EAAQ,GAAIhV,OAAU+L,GAAaxK,EAAQN,aACzDM,IACI,CAKL,GAFAyT,EAAOzR,OAAQ/C,EAAG,KAClBc,EAAW0J,EAAKrJ,QAAUsK,GAAY+I,IAGrC,OADA/V,EAAKD,MAAOiG,EAAS+F,GACd/F,EAGR,QAeJ,OAPE8S,GAAY3R,EAAS9E,EAAU6J,IAChCH,EACAzJ,GACCoF,EACD1B,GACC1D,GAAWkI,GAASqC,KAAMxK,IAAcyK,GAAaxK,EAAQN,aAAgBM,GAExE0D,GAMRxF,EAAQiR,WAAatM,EAAQwB,MAAO,IAAKtC,KAAMkE,GAAY0E,KAAM,MAAS9H,EAI1E3E,EAAQgR,mBAAqBjK,EAG7BC,IAIAhH,EAAQoQ,aAAejD,GAAQ,SAAUC,GAGxC,OAA4E,EAArEA,EAAG4C,wBAAyBxR,EAAS0C,cAAe,eAMtDiM,GAAQ,SAAUC,GAEvB,OADAA,EAAGqC,UAAY,mBACiC,MAAzCrC,EAAG+D,WAAW/P,aAAc,WAEnCiM,GAAW,yBAA0B,SAAUpK,EAAMgB,EAAMwC,GAC1D,IAAMA,EACL,OAAOxD,EAAK7B,aAAc6C,EAA6B,SAAvBA,EAAKoC,cAA2B,EAAI,KAOjErG,EAAQwI,YAAe2E,GAAQ,SAAUC,GAG9C,OAFAA,EAAGqC,UAAY,WACfrC,EAAG+D,WAAW9P,aAAc,QAAS,IACY,KAA1C+L,EAAG+D,WAAW/P,aAAc,YAEnCiM,GAAW,QAAS,SAAUpK,EAAMsV,EAAO9R,GAC1C,IAAMA,GAAyC,UAAhCxD,EAAKgI,SAAS5E,cAC5B,OAAOpD,EAAKuV,eAOTrL,GAAQ,SAAUC,GACvB,OAAwC,MAAjCA,EAAGhM,aAAc,eAExBiM,GAAWhF,EAAU,SAAUpF,EAAMgB,EAAMwC,GAC1C,IAAIzF,EACJ,IAAMyF,EACL,OAAwB,IAAjBxD,EAAMgB,GAAkBA,EAAKoC,eACjCrF,EAAMiC,EAAKoM,iBAAkBpL,KAAYjD,EAAI4P,UAC9C5P,EAAI+E,MACJ,OAKEO,GA14EP,CA44EK3H,GAILiD,EAAOwN,KAAO9I,EACd1E,EAAO6O,KAAOnK,EAAO+K,UAGrBzP,EAAO6O,KAAM,KAAQ7O,EAAO6O,KAAKhI,QACjC7G,EAAOkP,WAAalP,EAAO6W,OAASnS,EAAOwK,WAC3ClP,EAAOT,KAAOmF,EAAOE,QACrB5E,EAAO8W,SAAWpS,EAAOG,MACzB7E,EAAOyF,SAAWf,EAAOe,SACzBzF,EAAO+W,eAAiBrS,EAAO6D,OAK/B,IAAIe,EAAM,SAAUjI,EAAMiI,EAAK0N,GAC9B,IAAIrF,EAAU,GACbsF,OAAqBnU,IAAVkU,EAEZ,OAAU3V,EAAOA,EAAMiI,KAA6B,IAAlBjI,EAAK9C,SACtC,GAAuB,IAAlB8C,EAAK9C,SAAiB,CAC1B,GAAK0Y,GAAYjX,EAAQqB,GAAO6V,GAAIF,GACnC,MAEDrF,EAAQ/T,KAAMyD,GAGhB,OAAOsQ,GAIJwF,EAAW,SAAUC,EAAG/V,GAG3B,IAFA,IAAIsQ,EAAU,GAENyF,EAAGA,EAAIA,EAAEnL,YACI,IAAfmL,EAAE7Y,UAAkB6Y,IAAM/V,GAC9BsQ,EAAQ/T,KAAMwZ,GAIhB,OAAOzF,GAIJ0F,EAAgBrX,EAAO6O,KAAK/E,MAAMhC,aAItC,SAASuB,EAAUhI,EAAMgB,GAExB,OAAOhB,EAAKgI,UAAYhI,EAAKgI,SAAS5E,gBAAkBpC,EAAKoC,cAG9D,IAAI6S,EAAa,kEAKjB,SAASC,EAAQzI,EAAU0I,EAAW5F,GACrC,OAAKvT,EAAYmZ,GACTxX,EAAO2B,KAAMmN,EAAU,SAAUzN,EAAMlC,GAC7C,QAASqY,EAAU/Z,KAAM4D,EAAMlC,EAAGkC,KAAWuQ,IAK1C4F,EAAUjZ,SACPyB,EAAO2B,KAAMmN,EAAU,SAAUzN,GACvC,OAASA,IAASmW,IAAgB5F,IAKV,iBAAd4F,EACJxX,EAAO2B,KAAMmN,EAAU,SAAUzN,GACvC,OAA4C,EAAnCxD,EAAQJ,KAAM+Z,EAAWnW,KAAkBuQ,IAK/C5R,EAAOsN,OAAQkK,EAAW1I,EAAU8C,GAG5C5R,EAAOsN,OAAS,SAAUuB,EAAM/N,EAAO8Q,GACtC,IAAIvQ,EAAOP,EAAO,GAMlB,OAJK8Q,IACJ/C,EAAO,QAAUA,EAAO,KAGH,IAAjB/N,EAAMR,QAAkC,IAAlBe,EAAK9C,SACxByB,EAAOwN,KAAKM,gBAAiBzM,EAAMwN,GAAS,CAAExN,GAAS,GAGxDrB,EAAOwN,KAAKxJ,QAAS6K,EAAM7O,EAAO2B,KAAMb,EAAO,SAAUO,GAC/D,OAAyB,IAAlBA,EAAK9C,aAIdyB,EAAOG,GAAGgC,OAAQ,CACjBqL,KAAM,SAAUvN,GACf,IAAId,EAAG4B,EACNe,EAAM9E,KAAKsD,OACXmX,EAAOza,KAER,GAAyB,iBAAbiD,EACX,OAAOjD,KAAK6D,UAAWb,EAAQC,GAAWqN,OAAQ,WACjD,IAAMnO,EAAI,EAAGA,EAAI2C,EAAK3C,IACrB,GAAKa,EAAOyF,SAAUgS,EAAMtY,GAAKnC,MAChC,OAAO,KAQX,IAFA+D,EAAM/D,KAAK6D,UAAW,IAEhB1B,EAAI,EAAGA,EAAI2C,EAAK3C,IACrBa,EAAOwN,KAAMvN,EAAUwX,EAAMtY,GAAK4B,GAGnC,OAAa,EAANe,EAAU9B,EAAOkP,WAAYnO,GAAQA,GAE7CuM,OAAQ,SAAUrN,GACjB,OAAOjD,KAAK6D,UAAW0W,EAAQva,KAAMiD,GAAY,IAAI,KAEtD2R,IAAK,SAAU3R,GACd,OAAOjD,KAAK6D,UAAW0W,EAAQva,KAAMiD,GAAY,IAAI,KAEtDiX,GAAI,SAAUjX,GACb,QAASsX,EACRva,KAIoB,iBAAbiD,GAAyBoX,EAAc5M,KAAMxK,GACnDD,EAAQC,GACRA,GAAY,IACb,GACCK,UASJ,IAAIoX,EAMHvP,EAAa,uCAENnI,EAAOG,GAAGC,KAAO,SAAUH,EAAUC,EAASkS,GACpD,IAAItI,EAAOzI,EAGX,IAAMpB,EACL,OAAOjD,KAQR,GAHAoV,EAAOA,GAAQsF,EAGU,iBAAbzX,EAAwB,CAanC,KAPC6J,EALsB,MAAlB7J,EAAU,IACsB,MAApCA,EAAUA,EAASK,OAAS,IACT,GAAnBL,EAASK,OAGD,CAAE,KAAML,EAAU,MAGlBkI,EAAWgC,KAAMlK,MAIV6J,EAAO,IAAQ5J,EA6CxB,OAAMA,GAAWA,EAAQM,QACtBN,GAAWkS,GAAO5E,KAAMvN,GAK1BjD,KAAKyD,YAAaP,GAAUsN,KAAMvN,GAhDzC,GAAK6J,EAAO,GAAM,CAYjB,GAXA5J,EAAUA,aAAmBF,EAASE,EAAS,GAAMA,EAIrDF,EAAOgB,MAAOhE,KAAMgD,EAAO2X,UAC1B7N,EAAO,GACP5J,GAAWA,EAAQ3B,SAAW2B,EAAQgK,eAAiBhK,EAAUtD,GACjE,IAII0a,EAAW7M,KAAMX,EAAO,KAAS9J,EAAO2C,cAAezC,GAC3D,IAAM4J,KAAS5J,EAGT7B,EAAYrB,KAAM8M,IACtB9M,KAAM8M,GAAS5J,EAAS4J,IAIxB9M,KAAK+R,KAAMjF,EAAO5J,EAAS4J,IAK9B,OAAO9M,KAYP,OARAqE,EAAOzE,EAASwN,eAAgBN,EAAO,OAKtC9M,KAAM,GAAMqE,EACZrE,KAAKsD,OAAS,GAERtD,KAcH,OAAKiD,EAAS1B,UACpBvB,KAAM,GAAMiD,EACZjD,KAAKsD,OAAS,EACPtD,MAIIqB,EAAY4B,QACD6C,IAAfsP,EAAKwF,MACXxF,EAAKwF,MAAO3X,GAGZA,EAAUD,GAGLA,EAAO2D,UAAW1D,EAAUjD,QAIhCuD,UAAYP,EAAOG,GAGxBuX,EAAa1X,EAAQpD,GAGrB,IAAIib,EAAe,iCAGlBC,EAAmB,CAClBC,UAAU,EACVC,UAAU,EACVzO,MAAM,EACN0O,MAAM,GAoFR,SAASC,EAASpM,EAAKxC,GACtB,OAAUwC,EAAMA,EAAKxC,KAA4B,IAAjBwC,EAAIvN,UACpC,OAAOuN,EAnFR9L,EAAOG,GAAGgC,OAAQ,CACjB4P,IAAK,SAAUtP,GACd,IAAI0V,EAAUnY,EAAQyC,EAAQzF,MAC7Bob,EAAID,EAAQ7X,OAEb,OAAOtD,KAAKsQ,OAAQ,WAEnB,IADA,IAAInO,EAAI,EACAA,EAAIiZ,EAAGjZ,IACd,GAAKa,EAAOyF,SAAUzI,KAAMmb,EAAShZ,IACpC,OAAO,KAMXkZ,QAAS,SAAU5I,EAAWvP,GAC7B,IAAI4L,EACH3M,EAAI,EACJiZ,EAAIpb,KAAKsD,OACTqR,EAAU,GACVwG,EAA+B,iBAAd1I,GAA0BzP,EAAQyP,GAGpD,IAAM4H,EAAc5M,KAAMgF,GACzB,KAAQtQ,EAAIiZ,EAAGjZ,IACd,IAAM2M,EAAM9O,KAAMmC,GAAK2M,GAAOA,IAAQ5L,EAAS4L,EAAMA,EAAIlM,WAGxD,GAAKkM,EAAIvN,SAAW,KAAQ4Z,GACH,EAAxBA,EAAQG,MAAOxM,GAGE,IAAjBA,EAAIvN,UACHyB,EAAOwN,KAAKM,gBAAiBhC,EAAK2D,IAAgB,CAEnDkC,EAAQ/T,KAAMkO,GACd,MAMJ,OAAO9O,KAAK6D,UAA4B,EAAjB8Q,EAAQrR,OAAaN,EAAOkP,WAAYyC,GAAYA,IAI5E2G,MAAO,SAAUjX,GAGhB,OAAMA,EAKe,iBAATA,EACJxD,EAAQJ,KAAMuC,EAAQqB,GAAQrE,KAAM,IAIrCa,EAAQJ,KAAMT,KAGpBqE,EAAKb,OAASa,EAAM,GAAMA,GAZjBrE,KAAM,IAAOA,KAAM,GAAI4C,WAAe5C,KAAKuE,QAAQgX,UAAUjY,QAAU,GAgBlFkY,IAAK,SAAUvY,EAAUC,GACxB,OAAOlD,KAAK6D,UACXb,EAAOkP,WACNlP,EAAOgB,MAAOhE,KAAK2D,MAAOX,EAAQC,EAAUC,OAK/CuY,QAAS,SAAUxY,GAClB,OAAOjD,KAAKwb,IAAiB,MAAZvY,EAChBjD,KAAKiE,WAAajE,KAAKiE,WAAWqM,OAAQrN,OAU7CD,EAAOkB,KAAM,CACZiQ,OAAQ,SAAU9P,GACjB,IAAI8P,EAAS9P,EAAKzB,WAClB,OAAOuR,GAA8B,KAApBA,EAAO5S,SAAkB4S,EAAS,MAEpDuH,QAAS,SAAUrX,GAClB,OAAOiI,EAAKjI,EAAM,eAEnBsX,aAAc,SAAUtX,EAAMmD,EAAIwS,GACjC,OAAO1N,EAAKjI,EAAM,aAAc2V,IAEjCzN,KAAM,SAAUlI,GACf,OAAO6W,EAAS7W,EAAM,gBAEvB4W,KAAM,SAAU5W,GACf,OAAO6W,EAAS7W,EAAM,oBAEvBuX,QAAS,SAAUvX,GAClB,OAAOiI,EAAKjI,EAAM,gBAEnBkX,QAAS,SAAUlX,GAClB,OAAOiI,EAAKjI,EAAM,oBAEnBwX,UAAW,SAAUxX,EAAMmD,EAAIwS,GAC9B,OAAO1N,EAAKjI,EAAM,cAAe2V,IAElC8B,UAAW,SAAUzX,EAAMmD,EAAIwS,GAC9B,OAAO1N,EAAKjI,EAAM,kBAAmB2V,IAEtCG,SAAU,SAAU9V,GACnB,OAAO8V,GAAY9V,EAAKzB,YAAc,IAAK2P,WAAYlO,IAExD0W,SAAU,SAAU1W,GACnB,OAAO8V,EAAU9V,EAAKkO,aAEvByI,SAAU,SAAU3W,GACnB,OAA6B,MAAxBA,EAAK0X,iBAKT5b,EAAUkE,EAAK0X,iBAER1X,EAAK0X,iBAMR1P,EAAUhI,EAAM,cACpBA,EAAOA,EAAK2X,SAAW3X,GAGjBrB,EAAOgB,MAAO,GAAIK,EAAKmI,eAE7B,SAAUnH,EAAMlC,GAClBH,EAAOG,GAAIkC,GAAS,SAAU2U,EAAO/W,GACpC,IAAI0R,EAAU3R,EAAOoB,IAAKpE,KAAMmD,EAAI6W,GAuBpC,MArB0B,UAArB3U,EAAK/E,OAAQ,KACjB2C,EAAW+W,GAGP/W,GAAgC,iBAAbA,IACvB0R,EAAU3R,EAAOsN,OAAQrN,EAAU0R,IAGjB,EAAd3U,KAAKsD,SAGHwX,EAAkBzV,IACvBrC,EAAOkP,WAAYyC,GAIfkG,EAAapN,KAAMpI,IACvBsP,EAAQsH,WAIHjc,KAAK6D,UAAW8Q,MAGzB,IAAIuH,EAAgB,oBAsOpB,SAASC,EAAUC,GAClB,OAAOA,EAER,SAASC,EAASC,GACjB,MAAMA,EAGP,SAASC,EAAYpV,EAAOqV,EAASC,EAAQC,GAC5C,IAAIC,EAEJ,IAGMxV,GAAS9F,EAAcsb,EAASxV,EAAMyV,SAC1CD,EAAOlc,KAAM0G,GAAQ0B,KAAM2T,GAAUK,KAAMJ,GAGhCtV,GAAS9F,EAAcsb,EAASxV,EAAM2V,MACjDH,EAAOlc,KAAM0G,EAAOqV,EAASC,GAQ7BD,EAAQ7b,WAAOmF,EAAW,CAAEqB,GAAQ7G,MAAOoc,IAM3C,MAAQvV,GAITsV,EAAO9b,WAAOmF,EAAW,CAAEqB,KAvO7BnE,EAAO+Z,UAAY,SAAU3X,GA9B7B,IAAwBA,EACnB4X,EAiCJ5X,EAA6B,iBAAZA,GAlCMA,EAmCPA,EAlCZ4X,EAAS,GACbha,EAAOkB,KAAMkB,EAAQ0H,MAAOoP,IAAmB,GAAI,SAAUe,EAAGC,GAC/DF,EAAQE,IAAS,IAEXF,GA+BNha,EAAOmC,OAAQ,GAAIC,GAEpB,IACC+X,EAGAC,EAGAC,EAGAC,EAGA9T,EAAO,GAGP+T,EAAQ,GAGRC,GAAe,EAGfC,EAAO,WAQN,IALAH,EAASA,GAAUlY,EAAQsY,KAI3BL,EAAQF,GAAS,EACTI,EAAMja,OAAQka,GAAe,EAAI,CACxCJ,EAASG,EAAMlP,QACf,QAAUmP,EAAchU,EAAKlG,QAGmC,IAA1DkG,EAAMgU,GAAc7c,MAAOyc,EAAQ,GAAKA,EAAQ,KACpDhY,EAAQuY,cAGRH,EAAchU,EAAKlG,OACnB8Z,GAAS,GAMNhY,EAAQgY,SACbA,GAAS,GAGVD,GAAS,EAGJG,IAIH9T,EADI4T,EACG,GAIA,KAMV3C,EAAO,CAGNe,IAAK,WA2BJ,OA1BKhS,IAGC4T,IAAWD,IACfK,EAAchU,EAAKlG,OAAS,EAC5Bia,EAAM3c,KAAMwc,IAGb,SAAW5B,EAAKhH,GACfxR,EAAOkB,KAAMsQ,EAAM,SAAUyI,EAAG/V,GAC1B7F,EAAY6F,GACV9B,EAAQyU,QAAWY,EAAK1F,IAAK7N,IAClCsC,EAAK5I,KAAMsG,GAEDA,GAAOA,EAAI5D,QAA4B,WAAlBR,EAAQoE,IAGxCsU,EAAKtU,KATR,CAYK5C,WAEA8Y,IAAWD,GACfM,KAGKzd,MAIR4d,OAAQ,WAYP,OAXA5a,EAAOkB,KAAMI,UAAW,SAAU2Y,EAAG/V,GACpC,IAAIoU,EACJ,OAA0D,GAAhDA,EAAQtY,EAAO6D,QAASK,EAAKsC,EAAM8R,IAC5C9R,EAAKtE,OAAQoW,EAAO,GAGfA,GAASkC,GACbA,MAIIxd,MAKR+U,IAAK,SAAU5R,GACd,OAAOA,GACwB,EAA9BH,EAAO6D,QAAS1D,EAAIqG,GACN,EAAdA,EAAKlG,QAIPwS,MAAO,WAIN,OAHKtM,IACJA,EAAO,IAEDxJ,MAMR6d,QAAS,WAGR,OAFAP,EAASC,EAAQ,GACjB/T,EAAO4T,EAAS,GACTpd,MAERoM,SAAU,WACT,OAAQ5C,GAMTsU,KAAM,WAKL,OAJAR,EAASC,EAAQ,GACXH,GAAWD,IAChB3T,EAAO4T,EAAS,IAEVpd,MAERsd,OAAQ,WACP,QAASA,GAIVS,SAAU,SAAU7a,EAASsR,GAS5B,OARM8I,IAEL9I,EAAO,CAAEtR,GADTsR,EAAOA,GAAQ,IACQlU,MAAQkU,EAAKlU,QAAUkU,GAC9C+I,EAAM3c,KAAM4T,GACN2I,GACLM,KAGKzd,MAIRyd,KAAM,WAEL,OADAhD,EAAKsD,SAAU/d,KAAMsE,WACdtE,MAIRqd,MAAO,WACN,QAASA,IAIZ,OAAO5C,GA4CRzX,EAAOmC,OAAQ,CAEd6Y,SAAU,SAAUC,GACnB,IAAIC,EAAS,CAIX,CAAE,SAAU,WAAYlb,EAAO+Z,UAAW,UACzC/Z,EAAO+Z,UAAW,UAAY,GAC/B,CAAE,UAAW,OAAQ/Z,EAAO+Z,UAAW,eACtC/Z,EAAO+Z,UAAW,eAAiB,EAAG,YACvC,CAAE,SAAU,OAAQ/Z,EAAO+Z,UAAW,eACrC/Z,EAAO+Z,UAAW,eAAiB,EAAG,aAExCoB,EAAQ,UACRvB,EAAU,CACTuB,MAAO,WACN,OAAOA,GAERC,OAAQ,WAEP,OADAC,EAASxV,KAAMvE,WAAYuY,KAAMvY,WAC1BtE,MAERse,QAAS,SAAUnb,GAClB,OAAOyZ,EAAQE,KAAM,KAAM3Z,IAI5Bob,KAAM,WACL,IAAIC,EAAMla,UAEV,OAAOtB,EAAOgb,SAAU,SAAUS,GACjCzb,EAAOkB,KAAMga,EAAQ,SAAU1W,EAAIkX,GAGlC,IAAIvb,EAAK9B,EAAYmd,EAAKE,EAAO,MAAWF,EAAKE,EAAO,IAKxDL,EAAUK,EAAO,IAAO,WACvB,IAAIC,EAAWxb,GAAMA,EAAGxC,MAAOX,KAAMsE,WAChCqa,GAAYtd,EAAYsd,EAAS/B,SACrC+B,EAAS/B,UACPgC,SAAUH,EAASI,QACnBhW,KAAM4V,EAASjC,SACfK,KAAM4B,EAAShC,QAEjBgC,EAAUC,EAAO,GAAM,QACtB1e,KACAmD,EAAK,CAAEwb,GAAara,eAKxBka,EAAM,OACH5B,WAELE,KAAM,SAAUgC,EAAaC,EAAYC,GACxC,IAAIC,EAAW,EACf,SAASzC,EAAS0C,EAAOb,EAAU1P,EAASwQ,GAC3C,OAAO,WACN,IAAIC,EAAOpf,KACVwU,EAAOlQ,UACP+a,EAAa,WACZ,IAAIV,EAAU7B,EAKd,KAAKoC,EAAQD,GAAb,CAQA,IAJAN,EAAWhQ,EAAQhO,MAAOye,EAAM5K,MAId6J,EAASzB,UAC1B,MAAM,IAAI0C,UAAW,4BAOtBxC,EAAO6B,IAKgB,iBAAbA,GACY,mBAAbA,IACRA,EAAS7B,KAGLzb,EAAYyb,GAGXqC,EACJrC,EAAKrc,KACJke,EACAnC,EAASyC,EAAUZ,EAAUlC,EAAUgD,GACvC3C,EAASyC,EAAUZ,EAAUhC,EAAS8C,KAOvCF,IAEAnC,EAAKrc,KACJke,EACAnC,EAASyC,EAAUZ,EAAUlC,EAAUgD,GACvC3C,EAASyC,EAAUZ,EAAUhC,EAAS8C,GACtC3C,EAASyC,EAAUZ,EAAUlC,EAC5BkC,EAASkB,eASP5Q,IAAYwN,IAChBiD,OAAOtZ,EACP0O,EAAO,CAAEmK,KAKRQ,GAAWd,EAASmB,aAAeJ,EAAM5K,MAK7CiL,EAAUN,EACTE,EACA,WACC,IACCA,IACC,MAAQ5S,GAEJzJ,EAAOgb,SAAS0B,eACpB1c,EAAOgb,SAAS0B,cAAejT,EAC9BgT,EAAQE,YAMQV,GAAbC,EAAQ,IAIPvQ,IAAY0N,IAChB+C,OAAOtZ,EACP0O,EAAO,CAAE/H,IAGV4R,EAASuB,WAAYR,EAAM5K,MAS3B0K,EACJO,KAKKzc,EAAOgb,SAAS6B,eACpBJ,EAAQE,WAAa3c,EAAOgb,SAAS6B,gBAEtC9f,EAAO+f,WAAYL,KAKtB,OAAOzc,EAAOgb,SAAU,SAAUS,GAGjCP,EAAQ,GAAK,GAAI1C,IAChBgB,EACC,EACAiC,EACApd,EAAY2d,GACXA,EACA7C,EACDsC,EAASc,aAKXrB,EAAQ,GAAK,GAAI1C,IAChBgB,EACC,EACAiC,EACApd,EAAYyd,GACXA,EACA3C,IAKH+B,EAAQ,GAAK,GAAI1C,IAChBgB,EACC,EACAiC,EACApd,EAAY0d,GACXA,EACA1C,MAGAO,WAKLA,QAAS,SAAUtb,GAClB,OAAc,MAAPA,EAAc0B,EAAOmC,OAAQ7D,EAAKsb,GAAYA,IAGvDyB,EAAW,GAkEZ,OA/DArb,EAAOkB,KAAMga,EAAQ,SAAU/b,EAAGuc,GACjC,IAAIlV,EAAOkV,EAAO,GACjBqB,EAAcrB,EAAO,GAKtB9B,EAAS8B,EAAO,IAAQlV,EAAKgS,IAGxBuE,GACJvW,EAAKgS,IACJ,WAIC2C,EAAQ4B,GAKT7B,EAAQ,EAAI/b,GAAK,GAAI0b,QAIrBK,EAAQ,EAAI/b,GAAK,GAAI0b,QAGrBK,EAAQ,GAAK,GAAIJ,KAGjBI,EAAQ,GAAK,GAAIJ,MAOnBtU,EAAKgS,IAAKkD,EAAO,GAAIjB,MAKrBY,EAAUK,EAAO,IAAQ,WAExB,OADAL,EAAUK,EAAO,GAAM,QAAU1e,OAASqe,OAAWvY,EAAY9F,KAAMsE,WAChEtE,MAMRqe,EAAUK,EAAO,GAAM,QAAWlV,EAAKuU,WAIxCnB,EAAQA,QAASyB,GAGZJ,GACJA,EAAKxd,KAAM4d,EAAUA,GAIfA,GAIR2B,KAAM,SAAUC,GACf,IAGCC,EAAY5b,UAAUhB,OAGtBnB,EAAI+d,EAGJC,EAAkBva,MAAOzD,GACzBie,EAAgB9f,EAAMG,KAAM6D,WAG5B+b,EAAUrd,EAAOgb,WAGjBsC,EAAa,SAAUne,GACtB,OAAO,SAAUgF,GAChBgZ,EAAiBhe,GAAMnC,KACvBogB,EAAeje,GAAyB,EAAnBmC,UAAUhB,OAAahD,EAAMG,KAAM6D,WAAc6C,IAC5D+Y,GACTG,EAAQb,YAAaW,EAAiBC,KAM1C,GAAKF,GAAa,IACjB3D,EAAY0D,EAAaI,EAAQxX,KAAMyX,EAAYne,IAAMqa,QAAS6D,EAAQ5D,QACxEyD,GAGuB,YAApBG,EAAQlC,SACZ9c,EAAY+e,EAAeje,IAAOie,EAAeje,GAAI2a,OAErD,OAAOuD,EAAQvD,OAKjB,MAAQ3a,IACPoa,EAAY6D,EAAeje,GAAKme,EAAYne,GAAKke,EAAQ5D,QAG1D,OAAO4D,EAAQzD,aAOjB,IAAI2D,EAAc,yDAElBvd,EAAOgb,SAAS0B,cAAgB,SAAUtZ,EAAOoa,GAI3CzgB,EAAO0gB,SAAW1gB,EAAO0gB,QAAQC,MAAQta,GAASma,EAAY9S,KAAMrH,EAAMf,OAC9EtF,EAAO0gB,QAAQC,KAAM,8BAAgCta,EAAMua,QAASva,EAAMoa,MAAOA,IAOnFxd,EAAO4d,eAAiB,SAAUxa,GACjCrG,EAAO+f,WAAY,WAClB,MAAM1Z,KAQR,IAAIya,EAAY7d,EAAOgb,WAkDvB,SAAS8C,IACRlhB,EAASmhB,oBAAqB,mBAAoBD,GAClD/gB,EAAOghB,oBAAqB,OAAQD,GACpC9d,EAAO4X,QAnDR5X,EAAOG,GAAGyX,MAAQ,SAAUzX,GAY3B,OAVA0d,EACE/D,KAAM3Z,GAKNmb,SAAO,SAAUlY,GACjBpD,EAAO4d,eAAgBxa,KAGlBpG,MAGRgD,EAAOmC,OAAQ,CAGdgB,SAAS,EAIT6a,UAAW,EAGXpG,MAAO,SAAUqG,KAGF,IAATA,IAAkBje,EAAOge,UAAYhe,EAAOmD,WAKjDnD,EAAOmD,SAAU,KAGZ8a,GAAsC,IAAnBje,EAAOge,WAK/BH,EAAUrB,YAAa5f,EAAU,CAAEoD,OAIrCA,EAAO4X,MAAMkC,KAAO+D,EAAU/D,KAaD,aAAxBld,EAASshB,YACa,YAAxBthB,EAASshB,aAA6BthB,EAAS+P,gBAAgBwR,SAGjEphB,EAAO+f,WAAY9c,EAAO4X,QAK1Bhb,EAASoQ,iBAAkB,mBAAoB8Q,GAG/C/gB,EAAOiQ,iBAAkB,OAAQ8Q,IAQlC,IAAIM,EAAS,SAAUtd,EAAOX,EAAIgL,EAAKhH,EAAOka,EAAWC,EAAUC,GAClE,IAAIpf,EAAI,EACP2C,EAAMhB,EAAMR,OACZke,EAAc,MAAPrT,EAGR,GAAuB,WAAlBrL,EAAQqL,GAEZ,IAAMhM,KADNkf,GAAY,EACDlT,EACViT,EAAQtd,EAAOX,EAAIhB,EAAGgM,EAAKhM,IAAK,EAAMmf,EAAUC,QAI3C,QAAezb,IAAVqB,IACXka,GAAY,EAENhgB,EAAY8F,KACjBoa,GAAM,GAGFC,IAGCD,GACJpe,EAAG1C,KAAMqD,EAAOqD,GAChBhE,EAAK,OAILqe,EAAOre,EACPA,EAAK,SAAUkB,EAAMod,EAAMta,GAC1B,OAAOqa,EAAK/gB,KAAMuC,EAAQqB,GAAQ8C,MAKhChE,GACJ,KAAQhB,EAAI2C,EAAK3C,IAChBgB,EACCW,EAAO3B,GAAKgM,EAAKoT,EAChBpa,EACAA,EAAM1G,KAAMqD,EAAO3B,GAAKA,EAAGgB,EAAIW,EAAO3B,GAAKgM,KAMhD,OAAKkT,EACGvd,EAIH0d,EACGre,EAAG1C,KAAMqD,GAGVgB,EAAM3B,EAAIW,EAAO,GAAKqK,GAAQmT,GAKlCI,EAAY,QACfC,EAAa,YAGd,SAASC,EAAYC,EAAMC,GAC1B,OAAOA,EAAOC,cAMf,SAASC,EAAWC,GACnB,OAAOA,EAAO/b,QAASwb,EAAW,OAAQxb,QAASyb,EAAYC,GAEhE,IAAIM,EAAa,SAAUC,GAQ1B,OAA0B,IAAnBA,EAAM5gB,UAAqC,IAAnB4gB,EAAM5gB,YAAsB4gB,EAAM5gB,UAMlE,SAAS6gB,IACRpiB,KAAK+F,QAAU/C,EAAO+C,QAAUqc,EAAKC,MAGtCD,EAAKC,IAAM,EAEXD,EAAK7e,UAAY,CAEhB2K,MAAO,SAAUiU,GAGhB,IAAIhb,EAAQgb,EAAOniB,KAAK+F,SA4BxB,OAzBMoB,IACLA,EAAQ,GAKH+a,EAAYC,KAIXA,EAAM5gB,SACV4gB,EAAOniB,KAAK+F,SAAYoB,EAMxB/G,OAAOkiB,eAAgBH,EAAOniB,KAAK+F,QAAS,CAC3CoB,MAAOA,EACPob,cAAc,MAMXpb,GAERqb,IAAK,SAAUL,EAAOM,EAAMtb,GAC3B,IAAIub,EACHxU,EAAQlO,KAAKkO,MAAOiU,GAIrB,GAAqB,iBAATM,EACXvU,EAAO8T,EAAWS,IAAWtb,OAM7B,IAAMub,KAAQD,EACbvU,EAAO8T,EAAWU,IAAWD,EAAMC,GAGrC,OAAOxU,GAERvK,IAAK,SAAUwe,EAAOhU,GACrB,YAAerI,IAARqI,EACNnO,KAAKkO,MAAOiU,GAGZA,EAAOniB,KAAK+F,UAAaoc,EAAOniB,KAAK+F,SAAWic,EAAW7T,KAE7DiT,OAAQ,SAAUe,EAAOhU,EAAKhH,GAa7B,YAAarB,IAARqI,GACCA,GAAsB,iBAARA,QAAgCrI,IAAVqB,EAElCnH,KAAK2D,IAAKwe,EAAOhU,IASzBnO,KAAKwiB,IAAKL,EAAOhU,EAAKhH,QAILrB,IAAVqB,EAAsBA,EAAQgH,IAEtCyP,OAAQ,SAAUuE,EAAOhU,GACxB,IAAIhM,EACH+L,EAAQiU,EAAOniB,KAAK+F,SAErB,QAAeD,IAAVoI,EAAL,CAIA,QAAapI,IAARqI,EAAoB,CAkBxBhM,GAXCgM,EAJIvI,MAAMC,QAASsI,GAIbA,EAAI/J,IAAK4d,IAEf7T,EAAM6T,EAAW7T,MAIJD,EACZ,CAAEC,GACAA,EAAIrB,MAAOoP,IAAmB,IAG1B5Y,OAER,MAAQnB,WACA+L,EAAOC,EAAKhM,UAKR2D,IAARqI,GAAqBnL,EAAOyD,cAAeyH,MAM1CiU,EAAM5gB,SACV4gB,EAAOniB,KAAK+F,cAAYD,SAEjBqc,EAAOniB,KAAK+F,YAItB4c,QAAS,SAAUR,GAClB,IAAIjU,EAAQiU,EAAOniB,KAAK+F,SACxB,YAAiBD,IAAVoI,IAAwBlL,EAAOyD,cAAeyH,KAGvD,IAAI0U,EAAW,IAAIR,EAEfS,EAAW,IAAIT,EAcfU,EAAS,gCACZC,EAAa,SA2Bd,SAASC,EAAU3e,EAAM8J,EAAKsU,GAC7B,IAAIpd,EA1Baod,EA8BjB,QAAc3c,IAAT2c,GAAwC,IAAlBpe,EAAK9C,SAI/B,GAHA8D,EAAO,QAAU8I,EAAIjI,QAAS6c,EAAY,OAAQtb,cAG7B,iBAFrBgb,EAAOpe,EAAK7B,aAAc6C,IAEM,CAC/B,IACCod,EAnCW,UADGA,EAoCEA,IA/BL,UAATA,IAIS,SAATA,EACG,KAIHA,KAAUA,EAAO,IACbA,EAGJK,EAAOrV,KAAMgV,GACVQ,KAAKC,MAAOT,GAGbA,GAeH,MAAQhW,IAGVoW,EAASL,IAAKne,EAAM8J,EAAKsU,QAEzBA,OAAO3c,EAGT,OAAO2c,EAGRzf,EAAOmC,OAAQ,CACdwd,QAAS,SAAUte,GAClB,OAAOwe,EAASF,QAASte,IAAUue,EAASD,QAASte,IAGtDoe,KAAM,SAAUpe,EAAMgB,EAAMod,GAC3B,OAAOI,EAASzB,OAAQ/c,EAAMgB,EAAMod,IAGrCU,WAAY,SAAU9e,EAAMgB,GAC3Bwd,EAASjF,OAAQvZ,EAAMgB,IAKxB+d,MAAO,SAAU/e,EAAMgB,EAAMod,GAC5B,OAAOG,EAASxB,OAAQ/c,EAAMgB,EAAMod,IAGrCY,YAAa,SAAUhf,EAAMgB,GAC5Bud,EAAShF,OAAQvZ,EAAMgB,MAIzBrC,EAAOG,GAAGgC,OAAQ,CACjBsd,KAAM,SAAUtU,EAAKhH,GACpB,IAAIhF,EAAGkD,EAAMod,EACZpe,EAAOrE,KAAM,GACb0O,EAAQrK,GAAQA,EAAKuF,WAGtB,QAAa9D,IAARqI,EAAoB,CACxB,GAAKnO,KAAKsD,SACTmf,EAAOI,EAASlf,IAAKU,GAEE,IAAlBA,EAAK9C,WAAmBqhB,EAASjf,IAAKU,EAAM,iBAAmB,CACnElC,EAAIuM,EAAMpL,OACV,MAAQnB,IAIFuM,EAAOvM,IAEsB,KADjCkD,EAAOqJ,EAAOvM,GAAIkD,MACRxE,QAAS,WAClBwE,EAAO2c,EAAW3c,EAAK/E,MAAO,IAC9B0iB,EAAU3e,EAAMgB,EAAMod,EAAMpd,KAI/Bud,EAASJ,IAAKne,EAAM,gBAAgB,GAItC,OAAOoe,EAIR,MAAoB,iBAARtU,EACJnO,KAAKkE,KAAM,WACjB2e,EAASL,IAAKxiB,KAAMmO,KAIfiT,EAAQphB,KAAM,SAAUmH,GAC9B,IAAIsb,EAOJ,GAAKpe,QAAkByB,IAAVqB,EAKZ,YAAcrB,KADd2c,EAAOI,EAASlf,IAAKU,EAAM8J,IAEnBsU,OAMM3c,KADd2c,EAAOO,EAAU3e,EAAM8J,IAEfsU,OAIR,EAIDziB,KAAKkE,KAAM,WAGV2e,EAASL,IAAKxiB,KAAMmO,EAAKhH,MAExB,KAAMA,EAA0B,EAAnB7C,UAAUhB,OAAY,MAAM,IAG7C6f,WAAY,SAAUhV,GACrB,OAAOnO,KAAKkE,KAAM,WACjB2e,EAASjF,OAAQ5d,KAAMmO,QAM1BnL,EAAOmC,OAAQ,CACdoY,MAAO,SAAUlZ,EAAM1C,EAAM8gB,GAC5B,IAAIlF,EAEJ,GAAKlZ,EAYJ,OAXA1C,GAASA,GAAQ,MAAS,QAC1B4b,EAAQqF,EAASjf,IAAKU,EAAM1C,GAGvB8gB,KACElF,GAAS3X,MAAMC,QAAS4c,GAC7BlF,EAAQqF,EAASxB,OAAQ/c,EAAM1C,EAAMqB,EAAO2D,UAAW8b,IAEvDlF,EAAM3c,KAAM6hB,IAGPlF,GAAS,IAIlB+F,QAAS,SAAUjf,EAAM1C,GACxBA,EAAOA,GAAQ,KAEf,IAAI4b,EAAQva,EAAOua,MAAOlZ,EAAM1C,GAC/B4hB,EAAchG,EAAMja,OACpBH,EAAKoa,EAAMlP,QACXmV,EAAQxgB,EAAOygB,YAAapf,EAAM1C,GAMvB,eAAPwB,IACJA,EAAKoa,EAAMlP,QACXkV,KAGIpgB,IAIU,OAATxB,GACJ4b,EAAM3L,QAAS,qBAIT4R,EAAME,KACbvgB,EAAG1C,KAAM4D,EApBF,WACNrB,EAAOsgB,QAASjf,EAAM1C,IAmBF6hB,KAGhBD,GAAeC,GACpBA,EAAM1N,MAAM2H,QAKdgG,YAAa,SAAUpf,EAAM1C,GAC5B,IAAIwM,EAAMxM,EAAO,aACjB,OAAOihB,EAASjf,IAAKU,EAAM8J,IAASyU,EAASxB,OAAQ/c,EAAM8J,EAAK,CAC/D2H,MAAO9S,EAAO+Z,UAAW,eAAgBvB,IAAK,WAC7CoH,EAAShF,OAAQvZ,EAAM,CAAE1C,EAAO,QAASwM,WAM7CnL,EAAOG,GAAGgC,OAAQ,CACjBoY,MAAO,SAAU5b,EAAM8gB,GACtB,IAAIkB,EAAS,EAQb,MANqB,iBAAThiB,IACX8gB,EAAO9gB,EACPA,EAAO,KACPgiB,KAGIrf,UAAUhB,OAASqgB,EAChB3gB,EAAOua,MAAOvd,KAAM,GAAK2B,QAGjBmE,IAAT2c,EACNziB,KACAA,KAAKkE,KAAM,WACV,IAAIqZ,EAAQva,EAAOua,MAAOvd,KAAM2B,EAAM8gB,GAGtCzf,EAAOygB,YAAazjB,KAAM2B,GAEZ,OAATA,GAAgC,eAAf4b,EAAO,IAC5Bva,EAAOsgB,QAAStjB,KAAM2B,MAI1B2hB,QAAS,SAAU3hB,GAClB,OAAO3B,KAAKkE,KAAM,WACjBlB,EAAOsgB,QAAStjB,KAAM2B,MAGxBiiB,WAAY,SAAUjiB,GACrB,OAAO3B,KAAKud,MAAO5b,GAAQ,KAAM,KAKlCib,QAAS,SAAUjb,EAAML,GACxB,IAAIqP,EACHkT,EAAQ,EACRC,EAAQ9gB,EAAOgb,WACflM,EAAW9R,KACXmC,EAAInC,KAAKsD,OACTkZ,EAAU,aACCqH,GACTC,EAAMtE,YAAa1N,EAAU,CAAEA,KAIb,iBAATnQ,IACXL,EAAMK,EACNA,OAAOmE,GAERnE,EAAOA,GAAQ,KAEf,MAAQQ,KACPwO,EAAMiS,EAASjf,IAAKmO,EAAU3P,GAAKR,EAAO,gBAC9BgP,EAAImF,QACf+N,IACAlT,EAAImF,MAAM0F,IAAKgB,IAIjB,OADAA,IACOsH,EAAMlH,QAAStb,MAGxB,IAAIyiB,GAAO,sCAA0CC,OAEjDC,GAAU,IAAIla,OAAQ,iBAAmBga,GAAO,cAAe,KAG/DG,GAAY,CAAE,MAAO,QAAS,SAAU,QAExCvU,GAAkB/P,EAAS+P,gBAI1BwU,GAAa,SAAU9f,GACzB,OAAOrB,EAAOyF,SAAUpE,EAAK6I,cAAe7I,IAE7C+f,GAAW,CAAEA,UAAU,GAOnBzU,GAAgB0U,cACpBF,GAAa,SAAU9f,GACtB,OAAOrB,EAAOyF,SAAUpE,EAAK6I,cAAe7I,IAC3CA,EAAKggB,YAAaD,MAAe/f,EAAK6I,gBAG1C,IAAIoX,GAAqB,SAAUjgB,EAAMmK,GAOvC,MAA8B,UAH9BnK,EAAOmK,GAAMnK,GAGDkgB,MAAMC,SACM,KAAvBngB,EAAKkgB,MAAMC,SAMXL,GAAY9f,IAEsB,SAAlCrB,EAAOyhB,IAAKpgB,EAAM,YAKrB,SAASqgB,GAAWrgB,EAAMqe,EAAMiC,EAAYC,GAC3C,IAAIC,EAAUC,EACbC,EAAgB,GAChBC,EAAeJ,EACd,WACC,OAAOA,EAAM9V,OAEd,WACC,OAAO9L,EAAOyhB,IAAKpgB,EAAMqe,EAAM,KAEjCuC,EAAUD,IACVE,EAAOP,GAAcA,EAAY,KAAS3hB,EAAOmiB,UAAWzC,GAAS,GAAK,MAG1E0C,EAAgB/gB,EAAK9C,WAClByB,EAAOmiB,UAAWzC,IAAmB,OAATwC,IAAkBD,IAChDhB,GAAQ9W,KAAMnK,EAAOyhB,IAAKpgB,EAAMqe,IAElC,GAAK0C,GAAiBA,EAAe,KAAQF,EAAO,CAInDD,GAAoB,EAGpBC,EAAOA,GAAQE,EAAe,GAG9BA,GAAiBH,GAAW,EAE5B,MAAQF,IAIP/hB,EAAOuhB,MAAOlgB,EAAMqe,EAAM0C,EAAgBF,IACnC,EAAIJ,IAAY,GAAMA,EAAQE,IAAiBC,GAAW,MAAW,IAC3EF,EAAgB,GAEjBK,GAAgCN,EAIjCM,GAAgC,EAChCpiB,EAAOuhB,MAAOlgB,EAAMqe,EAAM0C,EAAgBF,GAG1CP,EAAaA,GAAc,GAgB5B,OAbKA,IACJS,GAAiBA,IAAkBH,GAAW,EAG9CJ,EAAWF,EAAY,GACtBS,GAAkBT,EAAY,GAAM,GAAMA,EAAY,IACrDA,EAAY,GACTC,IACJA,EAAMM,KAAOA,EACbN,EAAM1Q,MAAQkR,EACdR,EAAM5f,IAAM6f,IAGPA,EAIR,IAAIQ,GAAoB,GAyBxB,SAASC,GAAUxT,EAAUyT,GAO5B,IANA,IAAIf,EAASngB,EAxBcA,EACvBuT,EACH1V,EACAmK,EACAmY,EAqBAgB,EAAS,GACTlK,EAAQ,EACRhY,EAASwO,EAASxO,OAGXgY,EAAQhY,EAAQgY,KACvBjX,EAAOyN,EAAUwJ,IACNiJ,QAIXC,EAAUngB,EAAKkgB,MAAMC,QAChBe,GAKa,SAAZf,IACJgB,EAAQlK,GAAUsH,EAASjf,IAAKU,EAAM,YAAe,KAC/CmhB,EAAQlK,KACbjX,EAAKkgB,MAAMC,QAAU,KAGK,KAAvBngB,EAAKkgB,MAAMC,SAAkBF,GAAoBjgB,KACrDmhB,EAAQlK,IA7CVkJ,EAFAtiB,EADG0V,OAAAA,EACH1V,GAF0BmC,EAiDaA,GA/C5B6I,cACXb,EAAWhI,EAAKgI,UAChBmY,EAAUa,GAAmBhZ,MAM9BuL,EAAO1V,EAAIujB,KAAK9iB,YAAaT,EAAII,cAAe+J,IAChDmY,EAAUxhB,EAAOyhB,IAAK7M,EAAM,WAE5BA,EAAKhV,WAAWC,YAAa+U,GAEZ,SAAZ4M,IACJA,EAAU,SAEXa,GAAmBhZ,GAAamY,MAkCb,SAAZA,IACJgB,EAAQlK,GAAU,OAGlBsH,EAASJ,IAAKne,EAAM,UAAWmgB,KAMlC,IAAMlJ,EAAQ,EAAGA,EAAQhY,EAAQgY,IACR,MAAnBkK,EAAQlK,KACZxJ,EAAUwJ,GAAQiJ,MAAMC,QAAUgB,EAAQlK,IAI5C,OAAOxJ,EAGR9O,EAAOG,GAAGgC,OAAQ,CACjBogB,KAAM,WACL,OAAOD,GAAUtlB,MAAM,IAExB0lB,KAAM,WACL,OAAOJ,GAAUtlB,OAElB2lB,OAAQ,SAAUxH,GACjB,MAAsB,kBAAVA,EACJA,EAAQne,KAAKulB,OAASvlB,KAAK0lB,OAG5B1lB,KAAKkE,KAAM,WACZogB,GAAoBtkB,MACxBgD,EAAQhD,MAAOulB,OAEfviB,EAAQhD,MAAO0lB,YAKnB,IAUEE,GACAhV,GAXEiV,GAAiB,wBAEjBC,GAAW,iCAEXC,GAAc,qCAMhBH,GADchmB,EAASomB,yBACRrjB,YAAa/C,EAAS0C,cAAe,SACpDsO,GAAQhR,EAAS0C,cAAe,UAM3BG,aAAc,OAAQ,SAC5BmO,GAAMnO,aAAc,UAAW,WAC/BmO,GAAMnO,aAAc,OAAQ,KAE5BmjB,GAAIjjB,YAAaiO,IAIjBxP,EAAQ6kB,WAAaL,GAAIM,WAAW,GAAOA,WAAW,GAAO7R,UAAUsB,QAIvEiQ,GAAI/U,UAAY,yBAChBzP,EAAQ+kB,iBAAmBP,GAAIM,WAAW,GAAO7R,UAAUuF,aAK3DgM,GAAI/U,UAAY,oBAChBzP,EAAQglB,SAAWR,GAAIvR,UAKxB,IAAIgS,GAAU,CAKbC,MAAO,CAAE,EAAG,UAAW,YACvBC,IAAK,CAAE,EAAG,oBAAqB,uBAC/BC,GAAI,CAAE,EAAG,iBAAkB,oBAC3BC,GAAI,CAAE,EAAG,qBAAsB,yBAE/BC,SAAU,CAAE,EAAG,GAAI,KAYpB,SAASC,GAAQzjB,EAASwN,GAIzB,IAAI3M,EAYJ,OATCA,EAD4C,oBAAjCb,EAAQoK,qBACbpK,EAAQoK,qBAAsBoD,GAAO,KAEI,oBAA7BxN,EAAQ4K,iBACpB5K,EAAQ4K,iBAAkB4C,GAAO,KAGjC,QAGM5K,IAAR4K,GAAqBA,GAAOrE,EAAUnJ,EAASwN,GAC5C1N,EAAOgB,MAAO,CAAEd,GAAWa,GAG5BA,EAKR,SAAS6iB,GAAe9iB,EAAO+iB,GAI9B,IAHA,IAAI1kB,EAAI,EACPiZ,EAAItX,EAAMR,OAEHnB,EAAIiZ,EAAGjZ,IACdygB,EAASJ,IACR1e,EAAO3B,GACP,cACC0kB,GAAejE,EAASjf,IAAKkjB,EAAa1kB,GAAK,eA1CnDkkB,GAAQS,MAAQT,GAAQU,MAAQV,GAAQW,SAAWX,GAAQY,QAAUZ,GAAQC,MAC7ED,GAAQa,GAAKb,GAAQI,GAGfrlB,EAAQglB,SACbC,GAAQc,SAAWd,GAAQD,OAAS,CAAE,EAAG,+BAAgC,cA2C1E,IAAIrb,GAAQ,YAEZ,SAASqc,GAAetjB,EAAOZ,EAASmkB,EAASC,EAAWC,GAO3D,IANA,IAAIljB,EAAMsM,EAAKD,EAAK8W,EAAMC,EAAU1iB,EACnC2iB,EAAWxkB,EAAQ8iB,yBACnB2B,EAAQ,GACRxlB,EAAI,EACJiZ,EAAItX,EAAMR,OAEHnB,EAAIiZ,EAAGjZ,IAGd,IAFAkC,EAAOP,EAAO3B,KAEQ,IAATkC,EAGZ,GAAwB,WAAnBvB,EAAQuB,GAIZrB,EAAOgB,MAAO2jB,EAAOtjB,EAAK9C,SAAW,CAAE8C,GAASA,QAG1C,GAAM0G,GAAM0C,KAAMpJ,GAIlB,CACNsM,EAAMA,GAAO+W,EAAS/kB,YAAaO,EAAQZ,cAAe,QAG1DoO,GAAQoV,GAAS3Y,KAAM9I,IAAU,CAAE,GAAI,KAAQ,GAAIoD,cACnD+f,EAAOnB,GAAS3V,IAAS2V,GAAQK,SACjC/V,EAAIE,UAAY2W,EAAM,GAAMxkB,EAAO4kB,cAAevjB,GAASmjB,EAAM,GAGjEziB,EAAIyiB,EAAM,GACV,MAAQziB,IACP4L,EAAMA,EAAI0D,UAKXrR,EAAOgB,MAAO2jB,EAAOhX,EAAInE,aAGzBmE,EAAM+W,EAASnV,YAGXD,YAAc,QAzBlBqV,EAAM/mB,KAAMsC,EAAQ2kB,eAAgBxjB,IA+BvCqjB,EAASpV,YAAc,GAEvBnQ,EAAI,EACJ,MAAUkC,EAAOsjB,EAAOxlB,KAGvB,GAAKmlB,IAAkD,EAArCtkB,EAAO6D,QAASxC,EAAMijB,GAClCC,GACJA,EAAQ3mB,KAAMyD,QAgBhB,GAXAojB,EAAWtD,GAAY9f,GAGvBsM,EAAMgW,GAAQe,EAAS/kB,YAAa0B,GAAQ,UAGvCojB,GACJb,GAAejW,GAIX0W,EAAU,CACdtiB,EAAI,EACJ,MAAUV,EAAOsM,EAAK5L,KAChBghB,GAAYtY,KAAMpJ,EAAK1C,MAAQ,KACnC0lB,EAAQzmB,KAAMyD,GAMlB,OAAOqjB,EAIR,IAAII,GAAiB,sBAErB,SAASC,KACR,OAAO,EAGR,SAASC,KACR,OAAO,EASR,SAASC,GAAY5jB,EAAM1C,GAC1B,OAAS0C,IAMV,WACC,IACC,OAAOzE,EAAS0V,cACf,MAAQ4S,KATQC,KAAqC,UAATxmB,GAY/C,SAASymB,GAAI/jB,EAAMgkB,EAAOplB,EAAUwf,EAAMtf,EAAImlB,GAC7C,IAAIC,EAAQ5mB,EAGZ,GAAsB,iBAAV0mB,EAAqB,CAShC,IAAM1mB,IANmB,iBAAbsB,IAGXwf,EAAOA,GAAQxf,EACfA,OAAW6C,GAEEuiB,EACbD,GAAI/jB,EAAM1C,EAAMsB,EAAUwf,EAAM4F,EAAO1mB,GAAQ2mB,GAEhD,OAAOjkB,EAsBR,GAnBa,MAARoe,GAAsB,MAANtf,GAGpBA,EAAKF,EACLwf,EAAOxf,OAAW6C,GACD,MAAN3C,IACc,iBAAbF,GAGXE,EAAKsf,EACLA,OAAO3c,IAIP3C,EAAKsf,EACLA,EAAOxf,EACPA,OAAW6C,KAGD,IAAP3C,EACJA,EAAK6kB,QACC,IAAM7kB,EACZ,OAAOkB,EAeR,OAZa,IAARikB,IACJC,EAASplB,GACTA,EAAK,SAAUqlB,GAId,OADAxlB,IAASylB,IAAKD,GACPD,EAAO5nB,MAAOX,KAAMsE,aAIzB8C,KAAOmhB,EAAOnhB,OAAUmhB,EAAOnhB,KAAOpE,EAAOoE,SAE1C/C,EAAKH,KAAM,WACjBlB,EAAOwlB,MAAMhN,IAAKxb,KAAMqoB,EAAOllB,EAAIsf,EAAMxf,KA+a3C,SAASylB,GAAgBla,EAAI7M,EAAMsmB,GAG5BA,GAQNrF,EAASJ,IAAKhU,EAAI7M,GAAM,GACxBqB,EAAOwlB,MAAMhN,IAAKhN,EAAI7M,EAAM,CAC3B8N,WAAW,EACXd,QAAS,SAAU6Z,GAClB,IAAIG,EAAUpV,EACbqV,EAAQhG,EAASjf,IAAK3D,KAAM2B,GAE7B,GAAyB,EAAlB6mB,EAAMK,WAAmB7oB,KAAM2B,IAKrC,GAAMinB,EAAMtlB,QAuCEN,EAAOwlB,MAAMrJ,QAASxd,IAAU,IAAKmnB,cAClDN,EAAMO,uBArBN,GAdAH,EAAQtoB,EAAMG,KAAM6D,WACpBse,EAASJ,IAAKxiB,KAAM2B,EAAMinB,GAK1BD,EAAWV,EAAYjoB,KAAM2B,GAC7B3B,KAAM2B,KAEDinB,KADLrV,EAASqP,EAASjf,IAAK3D,KAAM2B,KACJgnB,EACxB/F,EAASJ,IAAKxiB,KAAM2B,GAAM,GAE1B4R,EAAS,GAELqV,IAAUrV,EAWd,OARAiV,EAAMQ,2BACNR,EAAMS,iBAOC1V,GAAUA,EAAOpM,WAefyhB,EAAMtlB,SAGjBsf,EAASJ,IAAKxiB,KAAM2B,EAAM,CACzBwF,MAAOnE,EAAOwlB,MAAMU,QAInBlmB,EAAOmC,OAAQyjB,EAAO,GAAK5lB,EAAOmmB,MAAM5lB,WACxCqlB,EAAMtoB,MAAO,GACbN,QAKFwoB,EAAMQ,qCA/E0BljB,IAA7B8c,EAASjf,IAAK6K,EAAI7M,IACtBqB,EAAOwlB,MAAMhN,IAAKhN,EAAI7M,EAAMomB,IA5a/B/kB,EAAOwlB,MAAQ,CAEdhpB,OAAQ,GAERgc,IAAK,SAAUnX,EAAMgkB,EAAO1Z,EAAS8T,EAAMxf,GAE1C,IAAImmB,EAAaC,EAAa1Y,EAC7B2Y,EAAQC,EAAGC,EACXrK,EAASsK,EAAU9nB,EAAM+nB,EAAYC,EACrCC,EAAWhH,EAASjf,IAAKU,GAG1B,GAAM6d,EAAY7d,GAAlB,CAKKsK,EAAQA,UAEZA,GADAya,EAAcza,GACQA,QACtB1L,EAAWmmB,EAAYnmB,UAKnBA,GACJD,EAAOwN,KAAKM,gBAAiBnB,GAAiB1M,GAIzC0L,EAAQvH,OACbuH,EAAQvH,KAAOpE,EAAOoE,SAIfkiB,EAASM,EAASN,UACzBA,EAASM,EAASN,OAASlpB,OAAOypB,OAAQ,QAEnCR,EAAcO,EAASE,UAC9BT,EAAcO,EAASE,OAAS,SAAUrd,GAIzC,MAAyB,oBAAXzJ,GAA0BA,EAAOwlB,MAAMuB,YAActd,EAAE9K,KACpEqB,EAAOwlB,MAAMwB,SAASrpB,MAAO0D,EAAMC,gBAAcwB,IAMpDyjB,GADAlB,GAAUA,GAAS,IAAKvb,MAAOoP,IAAmB,CAAE,KAC1C5Y,OACV,MAAQimB,IAEP5nB,EAAOgoB,GADPhZ,EAAMmX,GAAe3a,KAAMkb,EAAOkB,KAAS,IACpB,GACvBG,GAAe/Y,EAAK,IAAO,IAAKpJ,MAAO,KAAMtC,OAGvCtD,IAKNwd,EAAUnc,EAAOwlB,MAAMrJ,QAASxd,IAAU,GAG1CA,GAASsB,EAAWkc,EAAQ2J,aAAe3J,EAAQ8K,WAActoB,EAGjEwd,EAAUnc,EAAOwlB,MAAMrJ,QAASxd,IAAU,GAG1C6nB,EAAYxmB,EAAOmC,OAAQ,CAC1BxD,KAAMA,EACNgoB,SAAUA,EACVlH,KAAMA,EACN9T,QAASA,EACTvH,KAAMuH,EAAQvH,KACdnE,SAAUA,EACV6H,aAAc7H,GAAYD,EAAO6O,KAAK/E,MAAMhC,aAAa2C,KAAMxK,GAC/DwM,UAAWia,EAAW7b,KAAM,MAC1Bub,IAGKK,EAAWH,EAAQ3nB,OAC1B8nB,EAAWH,EAAQ3nB,GAAS,IACnBuoB,cAAgB,EAGnB/K,EAAQgL,QACiD,IAA9DhL,EAAQgL,MAAM1pB,KAAM4D,EAAMoe,EAAMiH,EAAYL,IAEvChlB,EAAK2L,kBACT3L,EAAK2L,iBAAkBrO,EAAM0nB,IAK3BlK,EAAQ3D,MACZ2D,EAAQ3D,IAAI/a,KAAM4D,EAAMmlB,GAElBA,EAAU7a,QAAQvH,OACvBoiB,EAAU7a,QAAQvH,KAAOuH,EAAQvH,OAK9BnE,EACJwmB,EAASvkB,OAAQukB,EAASS,gBAAiB,EAAGV,GAE9CC,EAAS7oB,KAAM4oB,GAIhBxmB,EAAOwlB,MAAMhpB,OAAQmC,IAAS,KAMhCic,OAAQ,SAAUvZ,EAAMgkB,EAAO1Z,EAAS1L,EAAUmnB,GAEjD,IAAIrlB,EAAGslB,EAAW1Z,EACjB2Y,EAAQC,EAAGC,EACXrK,EAASsK,EAAU9nB,EAAM+nB,EAAYC,EACrCC,EAAWhH,EAASD,QAASte,IAAUue,EAASjf,IAAKU,GAEtD,GAAMulB,IAAeN,EAASM,EAASN,QAAvC,CAMAC,GADAlB,GAAUA,GAAS,IAAKvb,MAAOoP,IAAmB,CAAE,KAC1C5Y,OACV,MAAQimB,IAMP,GAJA5nB,EAAOgoB,GADPhZ,EAAMmX,GAAe3a,KAAMkb,EAAOkB,KAAS,IACpB,GACvBG,GAAe/Y,EAAK,IAAO,IAAKpJ,MAAO,KAAMtC,OAGvCtD,EAAN,CAOAwd,EAAUnc,EAAOwlB,MAAMrJ,QAASxd,IAAU,GAE1C8nB,EAAWH,EADX3nB,GAASsB,EAAWkc,EAAQ2J,aAAe3J,EAAQ8K,WAActoB,IACpC,GAC7BgP,EAAMA,EAAK,IACV,IAAI5G,OAAQ,UAAY2f,EAAW7b,KAAM,iBAAoB,WAG9Dwc,EAAYtlB,EAAI0kB,EAASnmB,OACzB,MAAQyB,IACPykB,EAAYC,EAAU1kB,IAEfqlB,GAAeT,IAAaH,EAAUG,UACzChb,GAAWA,EAAQvH,OAASoiB,EAAUpiB,MACtCuJ,IAAOA,EAAIlD,KAAM+b,EAAU/Z,YAC3BxM,GAAYA,IAAaumB,EAAUvmB,WACxB,OAAbA,IAAqBumB,EAAUvmB,YAChCwmB,EAASvkB,OAAQH,EAAG,GAEfykB,EAAUvmB,UACdwmB,EAASS,gBAEL/K,EAAQvB,QACZuB,EAAQvB,OAAOnd,KAAM4D,EAAMmlB,IAOzBa,IAAcZ,EAASnmB,SACrB6b,EAAQmL,WACkD,IAA/DnL,EAAQmL,SAAS7pB,KAAM4D,EAAMqlB,EAAYE,EAASE,SAElD9mB,EAAOunB,YAAalmB,EAAM1C,EAAMioB,EAASE,eAGnCR,EAAQ3nB,SA1Cf,IAAMA,KAAQ2nB,EACbtmB,EAAOwlB,MAAM5K,OAAQvZ,EAAM1C,EAAO0mB,EAAOkB,GAAK5a,EAAS1L,GAAU,GA8C/DD,EAAOyD,cAAe6iB,IAC1B1G,EAAShF,OAAQvZ,EAAM,mBAIzB2lB,SAAU,SAAUQ,GAEnB,IAAIroB,EAAG4C,EAAGhB,EAAK4Q,EAAS6U,EAAWiB,EAClCjW,EAAO,IAAI5O,MAAOtB,UAAUhB,QAG5BklB,EAAQxlB,EAAOwlB,MAAMkC,IAAKF,GAE1Bf,GACC7G,EAASjf,IAAK3D,KAAM,WAAcI,OAAOypB,OAAQ,OAC/CrB,EAAM7mB,OAAU,GACnBwd,EAAUnc,EAAOwlB,MAAMrJ,QAASqJ,EAAM7mB,OAAU,GAKjD,IAFA6S,EAAM,GAAMgU,EAENrmB,EAAI,EAAGA,EAAImC,UAAUhB,OAAQnB,IAClCqS,EAAMrS,GAAMmC,UAAWnC,GAMxB,GAHAqmB,EAAMmC,eAAiB3qB,MAGlBmf,EAAQyL,cAA2D,IAA5CzL,EAAQyL,YAAYnqB,KAAMT,KAAMwoB,GAA5D,CAKAiC,EAAeznB,EAAOwlB,MAAMiB,SAAShpB,KAAMT,KAAMwoB,EAAOiB,GAGxDtnB,EAAI,EACJ,OAAUwS,EAAU8V,EAActoB,QAAYqmB,EAAMqC,uBAAyB,CAC5ErC,EAAMsC,cAAgBnW,EAAQtQ,KAE9BU,EAAI,EACJ,OAAUykB,EAAY7U,EAAQ8U,SAAU1kB,QACtCyjB,EAAMuC,gCAIDvC,EAAMwC,aAAsC,IAAxBxB,EAAU/Z,YACnC+Y,EAAMwC,WAAWvd,KAAM+b,EAAU/Z,aAEjC+Y,EAAMgB,UAAYA,EAClBhB,EAAM/F,KAAO+G,EAAU/G,UAKV3c,KAHb/B,IAAUf,EAAOwlB,MAAMrJ,QAASqK,EAAUG,WAAc,IAAKG,QAC5DN,EAAU7a,SAAUhO,MAAOgU,EAAQtQ,KAAMmQ,MAGT,KAAzBgU,EAAMjV,OAASxP,KACrBykB,EAAMS,iBACNT,EAAMO,oBAYX,OAJK5J,EAAQ8L,cACZ9L,EAAQ8L,aAAaxqB,KAAMT,KAAMwoB,GAG3BA,EAAMjV,SAGdkW,SAAU,SAAUjB,EAAOiB,GAC1B,IAAItnB,EAAGqnB,EAAWvX,EAAKiZ,EAAiBC,EACvCV,EAAe,GACfP,EAAgBT,EAASS,cACzBpb,EAAM0Z,EAAM/iB,OAGb,GAAKykB,GAIJpb,EAAIvN,YAOc,UAAfinB,EAAM7mB,MAAoC,GAAhB6mB,EAAMxS,QAEnC,KAAQlH,IAAQ9O,KAAM8O,EAAMA,EAAIlM,YAAc5C,KAI7C,GAAsB,IAAjB8O,EAAIvN,WAAoC,UAAfinB,EAAM7mB,OAAqC,IAAjBmN,EAAI1C,UAAsB,CAGjF,IAFA8e,EAAkB,GAClBC,EAAmB,GACbhpB,EAAI,EAAGA,EAAI+nB,EAAe/nB,SAME2D,IAA5BqlB,EAFLlZ,GAHAuX,EAAYC,EAAUtnB,IAGNc,SAAW,OAG1BkoB,EAAkBlZ,GAAQuX,EAAU1e,cACC,EAApC9H,EAAQiP,EAAKjS,MAAOsb,MAAOxM,GAC3B9L,EAAOwN,KAAMyB,EAAKjS,KAAM,KAAM,CAAE8O,IAAQxL,QAErC6nB,EAAkBlZ,IACtBiZ,EAAgBtqB,KAAM4oB,GAGnB0B,EAAgB5nB,QACpBmnB,EAAa7pB,KAAM,CAAEyD,KAAMyK,EAAK2a,SAAUyB,IAY9C,OALApc,EAAM9O,KACDkqB,EAAgBT,EAASnmB,QAC7BmnB,EAAa7pB,KAAM,CAAEyD,KAAMyK,EAAK2a,SAAUA,EAASnpB,MAAO4pB,KAGpDO,GAGRW,QAAS,SAAU/lB,EAAMgmB,GACxBjrB,OAAOkiB,eAAgBtf,EAAOmmB,MAAM5lB,UAAW8B,EAAM,CACpDimB,YAAY,EACZ/I,cAAc,EAEd5e,IAAKtC,EAAYgqB,GAChB,WACC,GAAKrrB,KAAKurB,cACT,OAAOF,EAAMrrB,KAAKurB,gBAGpB,WACC,GAAKvrB,KAAKurB,cACT,OAAOvrB,KAAKurB,cAAelmB,IAI9Bmd,IAAK,SAAUrb,GACd/G,OAAOkiB,eAAgBtiB,KAAMqF,EAAM,CAClCimB,YAAY,EACZ/I,cAAc,EACdiJ,UAAU,EACVrkB,MAAOA,QAMXujB,IAAK,SAAUa,GACd,OAAOA,EAAevoB,EAAO+C,SAC5BwlB,EACA,IAAIvoB,EAAOmmB,MAAOoC,IAGpBpM,QAAS,CACRsM,KAAM,CAGLC,UAAU,GAEXC,MAAO,CAGNxB,MAAO,SAAU1H,GAIhB,IAAIjU,EAAKxO,MAAQyiB,EAWjB,OARKoD,GAAepY,KAAMe,EAAG7M,OAC5B6M,EAAGmd,OAAStf,EAAUmC,EAAI,UAG1Bka,GAAgBla,EAAI,QAASuZ,KAIvB,GAERmB,QAAS,SAAUzG,GAIlB,IAAIjU,EAAKxO,MAAQyiB,EAUjB,OAPKoD,GAAepY,KAAMe,EAAG7M,OAC5B6M,EAAGmd,OAAStf,EAAUmC,EAAI,UAE1Bka,GAAgBla,EAAI,UAId,GAKRkY,SAAU,SAAU8B,GACnB,IAAI/iB,EAAS+iB,EAAM/iB,OACnB,OAAOogB,GAAepY,KAAMhI,EAAO9D,OAClC8D,EAAOkmB,OAAStf,EAAU5G,EAAQ,UAClCmd,EAASjf,IAAK8B,EAAQ,UACtB4G,EAAU5G,EAAQ,OAIrBmmB,aAAc,CACbX,aAAc,SAAUzC,QAID1iB,IAAjB0iB,EAAMjV,QAAwBiV,EAAM+C,gBACxC/C,EAAM+C,cAAcM,YAAcrD,EAAMjV,YAoG7CvQ,EAAOunB,YAAc,SAAUlmB,EAAM1C,EAAMmoB,GAGrCzlB,EAAK0c,qBACT1c,EAAK0c,oBAAqBpf,EAAMmoB,IAIlC9mB,EAAOmmB,MAAQ,SAAUvnB,EAAKkqB,GAG7B,KAAQ9rB,gBAAgBgD,EAAOmmB,OAC9B,OAAO,IAAInmB,EAAOmmB,MAAOvnB,EAAKkqB,GAI1BlqB,GAAOA,EAAID,MACf3B,KAAKurB,cAAgB3pB,EACrB5B,KAAK2B,KAAOC,EAAID,KAIhB3B,KAAK+rB,mBAAqBnqB,EAAIoqB,uBACHlmB,IAAzBlE,EAAIoqB,mBAGgB,IAApBpqB,EAAIiqB,YACL9D,GACAC,GAKDhoB,KAAKyF,OAAW7D,EAAI6D,QAAkC,IAAxB7D,EAAI6D,OAAOlE,SACxCK,EAAI6D,OAAO7C,WACXhB,EAAI6D,OAELzF,KAAK8qB,cAAgBlpB,EAAIkpB,cACzB9qB,KAAKisB,cAAgBrqB,EAAIqqB,eAIzBjsB,KAAK2B,KAAOC,EAIRkqB,GACJ9oB,EAAOmC,OAAQnF,KAAM8rB,GAItB9rB,KAAKksB,UAAYtqB,GAAOA,EAAIsqB,WAAaxjB,KAAKyjB,MAG9CnsB,KAAMgD,EAAO+C,UAAY,GAK1B/C,EAAOmmB,MAAM5lB,UAAY,CACxBE,YAAaT,EAAOmmB,MACpB4C,mBAAoB/D,GACpB6C,qBAAsB7C,GACtB+C,8BAA+B/C,GAC/BoE,aAAa,EAEbnD,eAAgB,WACf,IAAIxc,EAAIzM,KAAKurB,cAEbvrB,KAAK+rB,mBAAqBhE,GAErBtb,IAAMzM,KAAKosB,aACf3f,EAAEwc,kBAGJF,gBAAiB,WAChB,IAAItc,EAAIzM,KAAKurB,cAEbvrB,KAAK6qB,qBAAuB9C,GAEvBtb,IAAMzM,KAAKosB,aACf3f,EAAEsc,mBAGJC,yBAA0B,WACzB,IAAIvc,EAAIzM,KAAKurB,cAEbvrB,KAAK+qB,8BAAgChD,GAEhCtb,IAAMzM,KAAKosB,aACf3f,EAAEuc,2BAGHhpB,KAAK+oB,oBAKP/lB,EAAOkB,KAAM,CACZmoB,QAAQ,EACRC,SAAS,EACTC,YAAY,EACZC,gBAAgB,EAChBC,SAAS,EACTC,QAAQ,EACRC,YAAY,EACZC,SAAS,EACTC,OAAO,EACPC,OAAO,EACPC,UAAU,EACVC,MAAM,EACNC,QAAQ,EACRjrB,MAAM,EACNkrB,UAAU,EACV/e,KAAK,EACLgf,SAAS,EACTnX,QAAQ,EACRoX,SAAS,EACTC,SAAS,EACTC,SAAS,EACTC,SAAS,EACTC,SAAS,EACTC,WAAW,EACXC,aAAa,EACbC,SAAS,EACTC,SAAS,EACTC,eAAe,EACfC,WAAW,EACXC,SAAS,EACTC,OAAO,GACLhrB,EAAOwlB,MAAM4C,SAEhBpoB,EAAOkB,KAAM,CAAEmR,MAAO,UAAW4Y,KAAM,YAAc,SAAUtsB,EAAMmnB,GACpE9lB,EAAOwlB,MAAMrJ,QAASxd,GAAS,CAG9BwoB,MAAO,WAQN,OAHAzB,GAAgB1oB,KAAM2B,EAAMsmB,KAGrB,GAERiB,QAAS,WAMR,OAHAR,GAAgB1oB,KAAM2B,IAGf,GAKR+kB,SAAU,WACT,OAAO,GAGRoC,aAAcA,KAYhB9lB,EAAOkB,KAAM,CACZgqB,WAAY,YACZC,WAAY,WACZC,aAAc,cACdC,aAAc,cACZ,SAAUC,EAAM5D,GAClB1nB,EAAOwlB,MAAMrJ,QAASmP,GAAS,CAC9BxF,aAAc4B,EACdT,SAAUS,EAEVZ,OAAQ,SAAUtB,GACjB,IAAIzkB,EAEHwqB,EAAU/F,EAAMyD,cAChBzC,EAAYhB,EAAMgB,UASnB,OALM+E,IAAaA,IANTvuB,MAMgCgD,EAAOyF,SANvCzI,KAMyDuuB,MAClE/F,EAAM7mB,KAAO6nB,EAAUG,SACvB5lB,EAAMylB,EAAU7a,QAAQhO,MAAOX,KAAMsE,WACrCkkB,EAAM7mB,KAAO+oB,GAEP3mB,MAKVf,EAAOG,GAAGgC,OAAQ,CAEjBijB,GAAI,SAAUC,EAAOplB,EAAUwf,EAAMtf,GACpC,OAAOilB,GAAIpoB,KAAMqoB,EAAOplB,EAAUwf,EAAMtf,IAEzCmlB,IAAK,SAAUD,EAAOplB,EAAUwf,EAAMtf,GACrC,OAAOilB,GAAIpoB,KAAMqoB,EAAOplB,EAAUwf,EAAMtf,EAAI,IAE7CslB,IAAK,SAAUJ,EAAOplB,EAAUE,GAC/B,IAAIqmB,EAAW7nB,EACf,GAAK0mB,GAASA,EAAMY,gBAAkBZ,EAAMmB,UAW3C,OARAA,EAAYnB,EAAMmB,UAClBxmB,EAAQqlB,EAAMsC,gBAAiBlC,IAC9Be,EAAU/Z,UACT+Z,EAAUG,SAAW,IAAMH,EAAU/Z,UACrC+Z,EAAUG,SACXH,EAAUvmB,SACVumB,EAAU7a,SAEJ3O,KAER,GAAsB,iBAAVqoB,EAAqB,CAGhC,IAAM1mB,KAAQ0mB,EACbroB,KAAKyoB,IAAK9mB,EAAMsB,EAAUolB,EAAO1mB,IAElC,OAAO3B,KAWR,OATkB,IAAbiD,GAA0C,mBAAbA,IAGjCE,EAAKF,EACLA,OAAW6C,IAEA,IAAP3C,IACJA,EAAK6kB,IAEChoB,KAAKkE,KAAM,WACjBlB,EAAOwlB,MAAM5K,OAAQ5d,KAAMqoB,EAAOllB,EAAIF,QAMzC,IAKCurB,GAAe,wBAGfC,GAAW,oCACXC,GAAe,2CAGhB,SAASC,GAAoBtqB,EAAM2X,GAClC,OAAK3P,EAAUhI,EAAM,UACpBgI,EAA+B,KAArB2P,EAAQza,SAAkBya,EAAUA,EAAQzJ,WAAY,OAE3DvP,EAAQqB,GAAO0W,SAAU,SAAW,IAGrC1W,EAIR,SAASuqB,GAAevqB,GAEvB,OADAA,EAAK1C,MAAyC,OAAhC0C,EAAK7B,aAAc,SAAsB,IAAM6B,EAAK1C,KAC3D0C,EAER,SAASwqB,GAAexqB,GAOvB,MAN2C,WAApCA,EAAK1C,MAAQ,IAAKrB,MAAO,EAAG,GAClC+D,EAAK1C,KAAO0C,EAAK1C,KAAKrB,MAAO,GAE7B+D,EAAK2J,gBAAiB,QAGhB3J,EAGR,SAASyqB,GAAgBltB,EAAKmtB,GAC7B,IAAI5sB,EAAGiZ,EAAGzZ,EAAgBqtB,EAAUC,EAAU3F,EAE9C,GAAuB,IAAlByF,EAAKxtB,SAAV,CAKA,GAAKqhB,EAASD,QAAS/gB,KAEtB0nB,EADW1G,EAASjf,IAAK/B,GACP0nB,QAKjB,IAAM3nB,KAFNihB,EAAShF,OAAQmR,EAAM,iBAETzF,EACb,IAAMnnB,EAAI,EAAGiZ,EAAIkO,EAAQ3nB,GAAO2B,OAAQnB,EAAIiZ,EAAGjZ,IAC9Ca,EAAOwlB,MAAMhN,IAAKuT,EAAMptB,EAAM2nB,EAAQ3nB,GAAQQ,IAO7C0gB,EAASF,QAAS/gB,KACtBotB,EAAWnM,EAASzB,OAAQxf,GAC5BqtB,EAAWjsB,EAAOmC,OAAQ,GAAI6pB,GAE9BnM,EAASL,IAAKuM,EAAME,KAkBtB,SAASC,GAAUC,EAAY3a,EAAMrQ,EAAUojB,GAG9C/S,EAAOjU,EAAMiU,GAEb,IAAIkT,EAAUnjB,EAAO8iB,EAAS+H,EAAYntB,EAAMC,EAC/CC,EAAI,EACJiZ,EAAI+T,EAAW7rB,OACf+rB,EAAWjU,EAAI,EACfjU,EAAQqN,EAAM,GACd8a,EAAkBjuB,EAAY8F,GAG/B,GAAKmoB,GACG,EAAJlU,GAA0B,iBAAVjU,IAChB/F,EAAQ6kB,YAAcwI,GAAShhB,KAAMtG,GACxC,OAAOgoB,EAAWjrB,KAAM,SAAUoX,GACjC,IAAIb,EAAO0U,EAAW3qB,GAAI8W,GACrBgU,IACJ9a,EAAM,GAAMrN,EAAM1G,KAAMT,KAAMsb,EAAOb,EAAK8U,SAE3CL,GAAUzU,EAAMjG,EAAMrQ,EAAUojB,KAIlC,GAAKnM,IAEJ7W,GADAmjB,EAAWN,GAAe5S,EAAM2a,EAAY,GAAIjiB,eAAe,EAAOiiB,EAAY5H,IACjEhV,WAEmB,IAA/BmV,EAASlb,WAAWlJ,SACxBokB,EAAWnjB,GAIPA,GAASgjB,GAAU,CAOvB,IALA6H,GADA/H,EAAUrkB,EAAOoB,IAAKuiB,GAAQe,EAAU,UAAYkH,KAC/BtrB,OAKbnB,EAAIiZ,EAAGjZ,IACdF,EAAOylB,EAEFvlB,IAAMktB,IACVptB,EAAOe,EAAOwC,MAAOvD,GAAM,GAAM,GAG5BmtB,GAIJpsB,EAAOgB,MAAOqjB,EAASV,GAAQ1kB,EAAM,YAIvCkC,EAAS1D,KAAM0uB,EAAYhtB,GAAKF,EAAME,GAGvC,GAAKitB,EAOJ,IANAltB,EAAMmlB,EAASA,EAAQ/jB,OAAS,GAAI4J,cAGpClK,EAAOoB,IAAKijB,EAASwH,IAGf1sB,EAAI,EAAGA,EAAIitB,EAAYjtB,IAC5BF,EAAOolB,EAASllB,GACX4jB,GAAYtY,KAAMxL,EAAKN,MAAQ,MAClCihB,EAASxB,OAAQnf,EAAM,eACxBe,EAAOyF,SAAUvG,EAAKD,KAEjBA,EAAKL,KAA8C,YAArCK,EAAKN,MAAQ,IAAK8F,cAG/BzE,EAAOwsB,WAAavtB,EAAKH,UAC7BkB,EAAOwsB,SAAUvtB,EAAKL,IAAK,CAC1BC,MAAOI,EAAKJ,OAASI,EAAKO,aAAc,UACtCN,GAGJH,EAASE,EAAKqQ,YAAYpM,QAASwoB,GAAc,IAAMzsB,EAAMC,IAQnE,OAAOitB,EAGR,SAASvR,GAAQvZ,EAAMpB,EAAUwsB,GAKhC,IAJA,IAAIxtB,EACH0lB,EAAQ1kB,EAAWD,EAAOsN,OAAQrN,EAAUoB,GAASA,EACrDlC,EAAI,EAE4B,OAAvBF,EAAO0lB,EAAOxlB,IAAeA,IAChCstB,GAA8B,IAAlBxtB,EAAKV,UACtByB,EAAO0sB,UAAW/I,GAAQ1kB,IAGtBA,EAAKW,aACJ6sB,GAAYtL,GAAYliB,IAC5B2kB,GAAeD,GAAQ1kB,EAAM,WAE9BA,EAAKW,WAAWC,YAAaZ,IAI/B,OAAOoC,EAGRrB,EAAOmC,OAAQ,CACdyiB,cAAe,SAAU2H,GACxB,OAAOA,GAGR/pB,MAAO,SAAUnB,EAAMsrB,EAAeC,GACrC,IAAIztB,EAAGiZ,EAAGyU,EAAaC,EApINluB,EAAKmtB,EACnB1iB,EAoIF7G,EAAQnB,EAAK6hB,WAAW,GACxB6J,EAAS5L,GAAY9f,GAGtB,KAAMjD,EAAQ+kB,gBAAsC,IAAlB9hB,EAAK9C,UAAoC,KAAlB8C,EAAK9C,UAC3DyB,EAAO8W,SAAUzV,IAMnB,IAHAyrB,EAAenJ,GAAQnhB,GAGjBrD,EAAI,EAAGiZ,GAFbyU,EAAclJ,GAAQtiB,IAEOf,OAAQnB,EAAIiZ,EAAGjZ,IAhJ5BP,EAiJLiuB,EAAa1tB,GAjJH4sB,EAiJQe,EAAc3tB,QAhJzCkK,EAGc,WAHdA,EAAW0iB,EAAK1iB,SAAS5E,gBAGAoe,GAAepY,KAAM7L,EAAID,MACrDotB,EAAKpZ,QAAU/T,EAAI+T,QAGK,UAAbtJ,GAAqC,aAAbA,IACnC0iB,EAAKnV,aAAehY,EAAIgY,cA6IxB,GAAK+V,EACJ,GAAKC,EAIJ,IAHAC,EAAcA,GAAelJ,GAAQtiB,GACrCyrB,EAAeA,GAAgBnJ,GAAQnhB,GAEjCrD,EAAI,EAAGiZ,EAAIyU,EAAYvsB,OAAQnB,EAAIiZ,EAAGjZ,IAC3C2sB,GAAgBe,EAAa1tB,GAAK2tB,EAAc3tB,SAGjD2sB,GAAgBzqB,EAAMmB,GAWxB,OAL2B,GAD3BsqB,EAAenJ,GAAQnhB,EAAO,WACZlC,QACjBsjB,GAAekJ,GAAeC,GAAUpJ,GAAQtiB,EAAM,WAIhDmB,GAGRkqB,UAAW,SAAU5rB,GAKpB,IAJA,IAAI2e,EAAMpe,EAAM1C,EACfwd,EAAUnc,EAAOwlB,MAAMrJ,QACvBhd,EAAI,OAE6B2D,KAAxBzB,EAAOP,EAAO3B,IAAqBA,IAC5C,GAAK+f,EAAY7d,GAAS,CACzB,GAAOoe,EAAOpe,EAAMue,EAAS7c,SAAc,CAC1C,GAAK0c,EAAK6G,OACT,IAAM3nB,KAAQ8gB,EAAK6G,OACbnK,EAASxd,GACbqB,EAAOwlB,MAAM5K,OAAQvZ,EAAM1C,GAI3BqB,EAAOunB,YAAalmB,EAAM1C,EAAM8gB,EAAKqH,QAOxCzlB,EAAMue,EAAS7c,cAAYD,EAEvBzB,EAAMwe,EAAS9c,WAInB1B,EAAMwe,EAAS9c,cAAYD,OAOhC9C,EAAOG,GAAGgC,OAAQ,CACjB6qB,OAAQ,SAAU/sB,GACjB,OAAO2a,GAAQ5d,KAAMiD,GAAU,IAGhC2a,OAAQ,SAAU3a,GACjB,OAAO2a,GAAQ5d,KAAMiD,IAGtBV,KAAM,SAAU4E,GACf,OAAOia,EAAQphB,KAAM,SAAUmH,GAC9B,YAAiBrB,IAAVqB,EACNnE,EAAOT,KAAMvC,MACbA,KAAK8V,QAAQ5R,KAAM,WACK,IAAlBlE,KAAKuB,UAAoC,KAAlBvB,KAAKuB,UAAqC,IAAlBvB,KAAKuB,WACxDvB,KAAKsS,YAAcnL,MAGpB,KAAMA,EAAO7C,UAAUhB,SAG3B2sB,OAAQ,WACP,OAAOf,GAAUlvB,KAAMsE,UAAW,SAAUD,GACpB,IAAlBrE,KAAKuB,UAAoC,KAAlBvB,KAAKuB,UAAqC,IAAlBvB,KAAKuB,UAC3CotB,GAAoB3uB,KAAMqE,GAChC1B,YAAa0B,MAKvB6rB,QAAS,WACR,OAAOhB,GAAUlvB,KAAMsE,UAAW,SAAUD,GAC3C,GAAuB,IAAlBrE,KAAKuB,UAAoC,KAAlBvB,KAAKuB,UAAqC,IAAlBvB,KAAKuB,SAAiB,CACzE,IAAIkE,EAASkpB,GAAoB3uB,KAAMqE,GACvCoB,EAAO0qB,aAAc9rB,EAAMoB,EAAO8M,gBAKrC6d,OAAQ,WACP,OAAOlB,GAAUlvB,KAAMsE,UAAW,SAAUD,GACtCrE,KAAK4C,YACT5C,KAAK4C,WAAWutB,aAAc9rB,EAAMrE,SAKvCqwB,MAAO,WACN,OAAOnB,GAAUlvB,KAAMsE,UAAW,SAAUD,GACtCrE,KAAK4C,YACT5C,KAAK4C,WAAWutB,aAAc9rB,EAAMrE,KAAKiP,gBAK5C6G,MAAO,WAIN,IAHA,IAAIzR,EACHlC,EAAI,EAE2B,OAAtBkC,EAAOrE,KAAMmC,IAAeA,IACd,IAAlBkC,EAAK9C,WAGTyB,EAAO0sB,UAAW/I,GAAQtiB,GAAM,IAGhCA,EAAKiO,YAAc,IAIrB,OAAOtS,MAGRwF,MAAO,SAAUmqB,EAAeC,GAI/B,OAHAD,EAAiC,MAAjBA,GAAgCA,EAChDC,EAAyC,MAArBA,EAA4BD,EAAgBC,EAEzD5vB,KAAKoE,IAAK,WAChB,OAAOpB,EAAOwC,MAAOxF,KAAM2vB,EAAeC,MAI5CL,KAAM,SAAUpoB,GACf,OAAOia,EAAQphB,KAAM,SAAUmH,GAC9B,IAAI9C,EAAOrE,KAAM,IAAO,GACvBmC,EAAI,EACJiZ,EAAIpb,KAAKsD,OAEV,QAAewC,IAAVqB,GAAyC,IAAlB9C,EAAK9C,SAChC,OAAO8C,EAAKwM,UAIb,GAAsB,iBAAV1J,IAAuBqnB,GAAa/gB,KAAMtG,KACpDkf,IAAWP,GAAS3Y,KAAMhG,IAAW,CAAE,GAAI,KAAQ,GAAIM,eAAkB,CAE1EN,EAAQnE,EAAO4kB,cAAezgB,GAE9B,IACC,KAAQhF,EAAIiZ,EAAGjZ,IAIS,KAHvBkC,EAAOrE,KAAMmC,IAAO,IAGVZ,WACTyB,EAAO0sB,UAAW/I,GAAQtiB,GAAM,IAChCA,EAAKwM,UAAY1J,GAInB9C,EAAO,EAGN,MAAQoI,KAGNpI,GACJrE,KAAK8V,QAAQma,OAAQ9oB,IAEpB,KAAMA,EAAO7C,UAAUhB,SAG3BgtB,YAAa,WACZ,IAAI/I,EAAU,GAGd,OAAO2H,GAAUlvB,KAAMsE,UAAW,SAAUD,GAC3C,IAAI8P,EAASnU,KAAK4C,WAEbI,EAAO6D,QAAS7G,KAAMunB,GAAY,IACtCvkB,EAAO0sB,UAAW/I,GAAQ3mB,OACrBmU,GACJA,EAAOoc,aAAclsB,EAAMrE,QAK3BunB,MAILvkB,EAAOkB,KAAM,CACZssB,SAAU,SACVC,UAAW,UACXN,aAAc,SACdO,YAAa,QACbC,WAAY,eACV,SAAUtrB,EAAMurB,GAClB5tB,EAAOG,GAAIkC,GAAS,SAAUpC,GAO7B,IANA,IAAIa,EACHC,EAAM,GACN8sB,EAAS7tB,EAAQC,GACjBwB,EAAOosB,EAAOvtB,OAAS,EACvBnB,EAAI,EAEGA,GAAKsC,EAAMtC,IAClB2B,EAAQ3B,IAAMsC,EAAOzE,KAAOA,KAAKwF,OAAO,GACxCxC,EAAQ6tB,EAAQ1uB,IAAOyuB,GAAY9sB,GAInClD,EAAKD,MAAOoD,EAAKD,EAAMH,OAGxB,OAAO3D,KAAK6D,UAAWE,MAGzB,IAAI+sB,GAAY,IAAI/mB,OAAQ,KAAOga,GAAO,kBAAmB,KAEzDgN,GAAY,SAAU1sB,GAKxB,IAAI2oB,EAAO3oB,EAAK6I,cAAc4C,YAM9B,OAJMkd,GAASA,EAAKgE,SACnBhE,EAAOjtB,GAGDitB,EAAKiE,iBAAkB5sB,IAG5B6sB,GAAO,SAAU7sB,EAAMe,EAASjB,GACnC,IAAIJ,EAAKsB,EACR8rB,EAAM,GAGP,IAAM9rB,KAAQD,EACb+rB,EAAK9rB,GAAShB,EAAKkgB,MAAOlf,GAC1BhB,EAAKkgB,MAAOlf,GAASD,EAASC,GAM/B,IAAMA,KAHNtB,EAAMI,EAAS1D,KAAM4D,GAGPe,EACbf,EAAKkgB,MAAOlf,GAAS8rB,EAAK9rB,GAG3B,OAAOtB,GAIJqtB,GAAY,IAAIrnB,OAAQma,GAAUrW,KAAM,KAAO,KAiJnD,SAASwjB,GAAQhtB,EAAMgB,EAAMisB,GAC5B,IAAIC,EAAOC,EAAUC,EAAU1tB,EAM9BwgB,EAAQlgB,EAAKkgB,MAqCd,OAnCA+M,EAAWA,GAAYP,GAAW1sB,MAQpB,MAFbN,EAAMutB,EAASI,iBAAkBrsB,IAAUisB,EAAUjsB,KAEjC8e,GAAY9f,KAC/BN,EAAMf,EAAOuhB,MAAOlgB,EAAMgB,KAQrBjE,EAAQuwB,kBAAoBb,GAAUrjB,KAAM1J,IAASqtB,GAAU3jB,KAAMpI,KAG1EksB,EAAQhN,EAAMgN,MACdC,EAAWjN,EAAMiN,SACjBC,EAAWlN,EAAMkN,SAGjBlN,EAAMiN,SAAWjN,EAAMkN,SAAWlN,EAAMgN,MAAQxtB,EAChDA,EAAMutB,EAASC,MAGfhN,EAAMgN,MAAQA,EACdhN,EAAMiN,SAAWA,EACjBjN,EAAMkN,SAAWA,SAIJ3rB,IAAR/B,EAINA,EAAM,GACNA,EAIF,SAAS6tB,GAAcC,EAAaC,GAGnC,MAAO,CACNnuB,IAAK,WACJ,IAAKkuB,IASL,OAAS7xB,KAAK2D,IAAMmuB,GAASnxB,MAAOX,KAAMsE,kBALlCtE,KAAK2D,OA3MhB,WAIC,SAASouB,IAGR,GAAMnM,EAAN,CAIAoM,EAAUzN,MAAM0N,QAAU,+EAE1BrM,EAAIrB,MAAM0N,QACT,4HAGDtiB,GAAgBhN,YAAaqvB,GAAYrvB,YAAaijB,GAEtD,IAAIsM,EAAWnyB,EAAOkxB,iBAAkBrL,GACxCuM,EAAoC,OAAjBD,EAASniB,IAG5BqiB,EAAsE,KAA9CC,EAAoBH,EAASI,YAIrD1M,EAAIrB,MAAMgO,MAAQ,MAClBC,EAA6D,KAAzCH,EAAoBH,EAASK,OAIjDE,EAAgE,KAAzCJ,EAAoBH,EAASX,OAMpD3L,EAAIrB,MAAMmO,SAAW,WACrBC,EAAiE,KAA9CN,EAAoBzM,EAAIgN,YAAc,GAEzDjjB,GAAgB9M,YAAamvB,GAI7BpM,EAAM,MAGP,SAASyM,EAAoBQ,GAC5B,OAAO7sB,KAAK8sB,MAAOC,WAAYF,IAGhC,IAAIV,EAAkBM,EAAsBE,EAAkBH,EAC7DQ,EAAyBZ,EACzBJ,EAAYpyB,EAAS0C,cAAe,OACpCsjB,EAAMhmB,EAAS0C,cAAe,OAGzBsjB,EAAIrB,QAMVqB,EAAIrB,MAAM0O,eAAiB,cAC3BrN,EAAIM,WAAW,GAAO3B,MAAM0O,eAAiB,GAC7C7xB,EAAQ8xB,gBAA+C,gBAA7BtN,EAAIrB,MAAM0O,eAEpCjwB,EAAOmC,OAAQ/D,EAAS,CACvB+xB,kBAAmB,WAElB,OADApB,IACOU,GAERd,eAAgB,WAEf,OADAI,IACOS,GAERY,cAAe,WAEd,OADArB,IACOI,GAERkB,mBAAoB,WAEnB,OADAtB,IACOK,GAERkB,cAAe,WAEd,OADAvB,IACOY,GAYRY,qBAAsB,WACrB,IAAIC,EAAOhN,EAAIiN,EAASC,EAmCxB,OAlCgC,MAA3BV,IACJQ,EAAQ5zB,EAAS0C,cAAe,SAChCkkB,EAAK5mB,EAAS0C,cAAe,MAC7BmxB,EAAU7zB,EAAS0C,cAAe,OAElCkxB,EAAMjP,MAAM0N,QAAU,2DACtBzL,EAAGjC,MAAM0N,QAAU,mBAKnBzL,EAAGjC,MAAMoP,OAAS,MAClBF,EAAQlP,MAAMoP,OAAS,MAQvBF,EAAQlP,MAAMC,QAAU,QAExB7U,GACEhN,YAAa6wB,GACb7wB,YAAa6jB,GACb7jB,YAAa8wB,GAEfC,EAAU3zB,EAAOkxB,iBAAkBzK,GACnCwM,EAA4BY,SAAUF,EAAQC,OAAQ,IACrDC,SAAUF,EAAQG,eAAgB,IAClCD,SAAUF,EAAQI,kBAAmB,MAAWtN,EAAGuN,aAEpDpkB,GAAgB9M,YAAa2wB,IAEvBR,MAvIV,GAsNA,IAAIgB,GAAc,CAAE,SAAU,MAAO,MACpCC,GAAar0B,EAAS0C,cAAe,OAAQiiB,MAC7C2P,GAAc,GAkBf,SAASC,GAAe9uB,GACvB,IAAI+uB,EAAQpxB,EAAOqxB,SAAUhvB,IAAU6uB,GAAa7uB,GAEpD,OAAK+uB,IAGA/uB,KAAQ4uB,GACL5uB,EAED6uB,GAAa7uB,GAxBrB,SAAyBA,GAGxB,IAAIivB,EAAUjvB,EAAM,GAAI0c,cAAgB1c,EAAK/E,MAAO,GACnD6B,EAAI6xB,GAAY1wB,OAEjB,MAAQnB,IAEP,IADAkD,EAAO2uB,GAAa7xB,GAAMmyB,KACbL,GACZ,OAAO5uB,EAeoBkvB,CAAgBlvB,IAAUA,GAIxD,IAKCmvB,GAAe,4BACfC,GAAc,MACdC,GAAU,CAAEhC,SAAU,WAAYiC,WAAY,SAAUnQ,QAAS,SACjEoQ,GAAqB,CACpBC,cAAe,IACfC,WAAY,OAGd,SAASC,GAAmBnwB,EAAOuC,EAAO6tB,GAIzC,IAAIhuB,EAAUid,GAAQ9W,KAAMhG,GAC5B,OAAOH,EAGNhB,KAAKivB,IAAK,EAAGjuB,EAAS,IAAQguB,GAAY,KAAUhuB,EAAS,IAAO,MACpEG,EAGF,SAAS+tB,GAAoB7wB,EAAM8wB,EAAWC,EAAKC,EAAaC,EAAQC,GACvE,IAAIpzB,EAAkB,UAAdgzB,EAAwB,EAAI,EACnCK,EAAQ,EACRC,EAAQ,EAGT,GAAKL,KAAUC,EAAc,SAAW,WACvC,OAAO,EAGR,KAAQlzB,EAAI,EAAGA,GAAK,EAGN,WAARizB,IACJK,GAASzyB,EAAOyhB,IAAKpgB,EAAM+wB,EAAMlR,GAAW/hB,IAAK,EAAMmzB,IAIlDD,GAmBQ,YAARD,IACJK,GAASzyB,EAAOyhB,IAAKpgB,EAAM,UAAY6f,GAAW/hB,IAAK,EAAMmzB,IAIjD,WAARF,IACJK,GAASzyB,EAAOyhB,IAAKpgB,EAAM,SAAW6f,GAAW/hB,GAAM,SAAS,EAAMmzB,MAtBvEG,GAASzyB,EAAOyhB,IAAKpgB,EAAM,UAAY6f,GAAW/hB,IAAK,EAAMmzB,GAGhD,YAARF,EACJK,GAASzyB,EAAOyhB,IAAKpgB,EAAM,SAAW6f,GAAW/hB,GAAM,SAAS,EAAMmzB,GAItEE,GAASxyB,EAAOyhB,IAAKpgB,EAAM,SAAW6f,GAAW/hB,GAAM,SAAS,EAAMmzB,IAoCzE,OAhBMD,GAA8B,GAAfE,IAIpBE,GAASzvB,KAAKivB,IAAK,EAAGjvB,KAAK0vB,KAC1BrxB,EAAM,SAAW8wB,EAAW,GAAIpT,cAAgBoT,EAAU70B,MAAO,IACjEi1B,EACAE,EACAD,EACA,MAIM,GAGDC,EAGR,SAASE,GAAkBtxB,EAAM8wB,EAAWK,GAG3C,IAAIF,EAASvE,GAAW1sB,GAKvBgxB,IADmBj0B,EAAQ+xB,qBAAuBqC,IAEE,eAAnDxyB,EAAOyhB,IAAKpgB,EAAM,aAAa,EAAOixB,GACvCM,EAAmBP,EAEnBjzB,EAAMivB,GAAQhtB,EAAM8wB,EAAWG,GAC/BO,EAAa,SAAWV,EAAW,GAAIpT,cAAgBoT,EAAU70B,MAAO,GAIzE,GAAKwwB,GAAUrjB,KAAMrL,GAAQ,CAC5B,IAAMozB,EACL,OAAOpzB,EAERA,EAAM,OAyCP,QAlCQhB,EAAQ+xB,qBAAuBkC,IAMrCj0B,EAAQmyB,wBAA0BlnB,EAAUhI,EAAM,OAI3C,SAARjC,IAIC2wB,WAAY3wB,IAA0D,WAAjDY,EAAOyhB,IAAKpgB,EAAM,WAAW,EAAOixB,KAG1DjxB,EAAKyxB,iBAAiBxyB,SAEtB+xB,EAAiE,eAAnDryB,EAAOyhB,IAAKpgB,EAAM,aAAa,EAAOixB,IAKpDM,EAAmBC,KAAcxxB,KAEhCjC,EAAMiC,EAAMwxB,MAKdzzB,EAAM2wB,WAAY3wB,IAAS,GAI1B8yB,GACC7wB,EACA8wB,EACAK,IAAWH,EAAc,SAAW,WACpCO,EACAN,EAGAlzB,GAEE,KA+SL,SAAS2zB,GAAO1xB,EAAMe,EAASsd,EAAM1d,EAAKgxB,GACzC,OAAO,IAAID,GAAMxyB,UAAUH,KAAMiB,EAAMe,EAASsd,EAAM1d,EAAKgxB,GA7S5DhzB,EAAOmC,OAAQ,CAId8wB,SAAU,CACTC,QAAS,CACRvyB,IAAK,SAAUU,EAAMitB,GACpB,GAAKA,EAAW,CAGf,IAAIvtB,EAAMstB,GAAQhtB,EAAM,WACxB,MAAe,KAARN,EAAa,IAAMA,MAO9BohB,UAAW,CACVgR,yBAA2B,EAC3BC,aAAe,EACfC,aAAe,EACfC,UAAY,EACZC,YAAc,EACdzB,YAAc,EACd0B,UAAY,EACZC,YAAc,EACdC,eAAiB,EACjBC,iBAAmB,EACnBC,SAAW,EACXC,YAAc,EACdC,cAAgB,EAChBC,YAAc,EACdb,SAAW,EACXc,OAAS,EACTC,SAAW,EACXC,QAAU,EACVC,QAAU,EACVC,MAAQ,GAKT/C,SAAU,GAGV9P,MAAO,SAAUlgB,EAAMgB,EAAM8B,EAAOquB,GAGnC,GAAMnxB,GAA0B,IAAlBA,EAAK9C,UAAoC,IAAlB8C,EAAK9C,UAAmB8C,EAAKkgB,MAAlE,CAKA,IAAIxgB,EAAKpC,EAAM6hB,EACd6T,EAAWrV,EAAW3c,GACtBiyB,EAAe7C,GAAYhnB,KAAMpI,GACjCkf,EAAQlgB,EAAKkgB,MAad,GARM+S,IACLjyB,EAAO8uB,GAAekD,IAIvB7T,EAAQxgB,EAAOizB,SAAU5wB,IAAUrC,EAAOizB,SAAUoB,QAGrCvxB,IAAVqB,EA0CJ,OAAKqc,GAAS,QAASA,QACwB1d,KAA5C/B,EAAMyf,EAAM7f,IAAKU,GAAM,EAAOmxB,IAEzBzxB,EAIDwgB,EAAOlf,GA7CA,YAHd1D,SAAcwF,KAGcpD,EAAMkgB,GAAQ9W,KAAMhG,KAAapD,EAAK,KACjEoD,EAAQud,GAAWrgB,EAAMgB,EAAMtB,GAG/BpC,EAAO,UAIM,MAATwF,GAAiBA,GAAUA,IAOlB,WAATxF,GAAsB21B,IAC1BnwB,GAASpD,GAAOA,EAAK,KAASf,EAAOmiB,UAAWkS,GAAa,GAAK,OAI7Dj2B,EAAQ8xB,iBAA6B,KAAV/rB,GAAiD,IAAjC9B,EAAKxE,QAAS,gBAC9D0jB,EAAOlf,GAAS,WAIXme,GAAY,QAASA,QACsB1d,KAA9CqB,EAAQqc,EAAMhB,IAAKne,EAAM8C,EAAOquB,MAE7B8B,EACJ/S,EAAMgT,YAAalyB,EAAM8B,GAEzBod,EAAOlf,GAAS8B,MAkBpBsd,IAAK,SAAUpgB,EAAMgB,EAAMmwB,EAAOF,GACjC,IAAIlzB,EAAKwB,EAAK4f,EACb6T,EAAWrV,EAAW3c,GA6BvB,OA5BgBovB,GAAYhnB,KAAMpI,KAMjCA,EAAO8uB,GAAekD,KAIvB7T,EAAQxgB,EAAOizB,SAAU5wB,IAAUrC,EAAOizB,SAAUoB,KAGtC,QAAS7T,IACtBphB,EAAMohB,EAAM7f,IAAKU,GAAM,EAAMmxB,SAIjB1vB,IAAR1D,IACJA,EAAMivB,GAAQhtB,EAAMgB,EAAMiwB,IAId,WAARlzB,GAAoBiD,KAAQuvB,KAChCxyB,EAAMwyB,GAAoBvvB,IAIZ,KAAVmwB,GAAgBA,GACpB5xB,EAAMmvB,WAAY3wB,IACD,IAAVozB,GAAkBgC,SAAU5zB,GAAQA,GAAO,EAAIxB,GAGhDA,KAITY,EAAOkB,KAAM,CAAE,SAAU,SAAW,SAAUsD,EAAI2tB,GACjDnyB,EAAOizB,SAAUd,GAAc,CAC9BxxB,IAAK,SAAUU,EAAMitB,EAAUkE,GAC9B,GAAKlE,EAIJ,OAAOkD,GAAa/mB,KAAMzK,EAAOyhB,IAAKpgB,EAAM,aAQxCA,EAAKyxB,iBAAiBxyB,QAAWe,EAAKozB,wBAAwBlG,MAIjEoE,GAAkBtxB,EAAM8wB,EAAWK,GAHnCtE,GAAM7sB,EAAMqwB,GAAS,WACpB,OAAOiB,GAAkBtxB,EAAM8wB,EAAWK,MAM9ChT,IAAK,SAAUne,EAAM8C,EAAOquB,GAC3B,IAAIxuB,EACHsuB,EAASvE,GAAW1sB,GAIpBqzB,GAAsBt2B,EAAQkyB,iBACT,aAApBgC,EAAO5C,SAIR2C,GADkBqC,GAAsBlC,IAEY,eAAnDxyB,EAAOyhB,IAAKpgB,EAAM,aAAa,EAAOixB,GACvCN,EAAWQ,EACVN,GACC7wB,EACA8wB,EACAK,EACAH,EACAC,GAED,EAqBF,OAjBKD,GAAeqC,IACnB1C,GAAYhvB,KAAK0vB,KAChBrxB,EAAM,SAAW8wB,EAAW,GAAIpT,cAAgBoT,EAAU70B,MAAO,IACjEyyB,WAAYuC,EAAQH,IACpBD,GAAoB7wB,EAAM8wB,EAAW,UAAU,EAAOG,GACtD,KAKGN,IAAchuB,EAAUid,GAAQ9W,KAAMhG,KACb,QAA3BH,EAAS,IAAO,QAElB3C,EAAKkgB,MAAO4Q,GAAchuB,EAC1BA,EAAQnE,EAAOyhB,IAAKpgB,EAAM8wB,IAGpBJ,GAAmB1wB,EAAM8C,EAAO6tB,OAK1ChyB,EAAOizB,SAAS3D,WAAaV,GAAcxwB,EAAQiyB,mBAClD,SAAUhvB,EAAMitB,GACf,GAAKA,EACJ,OAASyB,WAAY1B,GAAQhtB,EAAM,gBAClCA,EAAKozB,wBAAwBE,KAC5BzG,GAAM7sB,EAAM,CAAEiuB,WAAY,GAAK,WAC9B,OAAOjuB,EAAKozB,wBAAwBE,QAEnC,OAMP30B,EAAOkB,KAAM,CACZ0zB,OAAQ,GACRC,QAAS,GACTC,OAAQ,SACN,SAAUC,EAAQC,GACpBh1B,EAAOizB,SAAU8B,EAASC,GAAW,CACpCC,OAAQ,SAAU9wB,GAOjB,IANA,IAAIhF,EAAI,EACP+1B,EAAW,GAGXC,EAAyB,iBAAVhxB,EAAqBA,EAAMI,MAAO,KAAQ,CAAEJ,GAEpDhF,EAAI,EAAGA,IACd+1B,EAAUH,EAAS7T,GAAW/hB,GAAM61B,GACnCG,EAAOh2B,IAAOg2B,EAAOh2B,EAAI,IAAOg2B,EAAO,GAGzC,OAAOD,IAIO,WAAXH,IACJ/0B,EAAOizB,SAAU8B,EAASC,GAASxV,IAAMuS,MAI3C/xB,EAAOG,GAAGgC,OAAQ,CACjBsf,IAAK,SAAUpf,EAAM8B,GACpB,OAAOia,EAAQphB,KAAM,SAAUqE,EAAMgB,EAAM8B,GAC1C,IAAImuB,EAAQxwB,EACXV,EAAM,GACNjC,EAAI,EAEL,GAAKyD,MAAMC,QAASR,GAAS,CAI5B,IAHAiwB,EAASvE,GAAW1sB,GACpBS,EAAMO,EAAK/B,OAEHnB,EAAI2C,EAAK3C,IAChBiC,EAAKiB,EAAMlD,IAAQa,EAAOyhB,IAAKpgB,EAAMgB,EAAMlD,IAAK,EAAOmzB,GAGxD,OAAOlxB,EAGR,YAAiB0B,IAAVqB,EACNnE,EAAOuhB,MAAOlgB,EAAMgB,EAAM8B,GAC1BnE,EAAOyhB,IAAKpgB,EAAMgB,IACjBA,EAAM8B,EAA0B,EAAnB7C,UAAUhB,aAQ5BN,EAAO+yB,MAAQA,IAETxyB,UAAY,CACjBE,YAAasyB,GACb3yB,KAAM,SAAUiB,EAAMe,EAASsd,EAAM1d,EAAKgxB,EAAQ9Q,GACjDllB,KAAKqE,KAAOA,EACZrE,KAAK0iB,KAAOA,EACZ1iB,KAAKg2B,OAASA,GAAUhzB,EAAOgzB,OAAOtP,SACtC1mB,KAAKoF,QAAUA,EACfpF,KAAKkU,MAAQlU,KAAKmsB,IAAMnsB,KAAK8O,MAC7B9O,KAAKgF,IAAMA,EACXhF,KAAKklB,KAAOA,IAAUliB,EAAOmiB,UAAWzC,GAAS,GAAK,OAEvD5T,IAAK,WACJ,IAAI0U,EAAQuS,GAAMqC,UAAWp4B,KAAK0iB,MAElC,OAAOc,GAASA,EAAM7f,IACrB6f,EAAM7f,IAAK3D,MACX+1B,GAAMqC,UAAU1R,SAAS/iB,IAAK3D,OAEhCq4B,IAAK,SAAUC,GACd,IAAIC,EACH/U,EAAQuS,GAAMqC,UAAWp4B,KAAK0iB,MAoB/B,OAlBK1iB,KAAKoF,QAAQozB,SACjBx4B,KAAKy4B,IAAMF,EAAQv1B,EAAOgzB,OAAQh2B,KAAKg2B,QACtCsC,EAASt4B,KAAKoF,QAAQozB,SAAWF,EAAS,EAAG,EAAGt4B,KAAKoF,QAAQozB,UAG9Dx4B,KAAKy4B,IAAMF,EAAQD,EAEpBt4B,KAAKmsB,KAAQnsB,KAAKgF,IAAMhF,KAAKkU,OAAUqkB,EAAQv4B,KAAKkU,MAE/ClU,KAAKoF,QAAQszB,MACjB14B,KAAKoF,QAAQszB,KAAKj4B,KAAMT,KAAKqE,KAAMrE,KAAKmsB,IAAKnsB,MAGzCwjB,GAASA,EAAMhB,IACnBgB,EAAMhB,IAAKxiB,MAEX+1B,GAAMqC,UAAU1R,SAASlE,IAAKxiB,MAExBA,QAIOoD,KAAKG,UAAYwyB,GAAMxyB,WAEvCwyB,GAAMqC,UAAY,CACjB1R,SAAU,CACT/iB,IAAK,SAAUihB,GACd,IAAIrR,EAIJ,OAA6B,IAAxBqR,EAAMvgB,KAAK9C,UACa,MAA5BqjB,EAAMvgB,KAAMugB,EAAMlC,OAAoD,MAAlCkC,EAAMvgB,KAAKkgB,MAAOK,EAAMlC,MACrDkC,EAAMvgB,KAAMugB,EAAMlC,OAO1BnP,EAASvQ,EAAOyhB,IAAKG,EAAMvgB,KAAMugB,EAAMlC,KAAM,MAGhB,SAAXnP,EAAwBA,EAAJ,GAEvCiP,IAAK,SAAUoC,GAKT5hB,EAAO21B,GAAGD,KAAM9T,EAAMlC,MAC1B1f,EAAO21B,GAAGD,KAAM9T,EAAMlC,MAAQkC,GACK,IAAxBA,EAAMvgB,KAAK9C,WACtByB,EAAOizB,SAAUrR,EAAMlC,OAC6B,MAAnDkC,EAAMvgB,KAAKkgB,MAAO4P,GAAevP,EAAMlC,OAGxCkC,EAAMvgB,KAAMugB,EAAMlC,MAASkC,EAAMuH,IAFjCnpB,EAAOuhB,MAAOK,EAAMvgB,KAAMugB,EAAMlC,KAAMkC,EAAMuH,IAAMvH,EAAMM,UAU5C0T,UAAY7C,GAAMqC,UAAUS,WAAa,CACxDrW,IAAK,SAAUoC,GACTA,EAAMvgB,KAAK9C,UAAYqjB,EAAMvgB,KAAKzB,aACtCgiB,EAAMvgB,KAAMugB,EAAMlC,MAASkC,EAAMuH,OAKpCnpB,EAAOgzB,OAAS,CACf8C,OAAQ,SAAUC,GACjB,OAAOA,GAERC,MAAO,SAAUD,GAChB,MAAO,GAAM/yB,KAAKizB,IAAKF,EAAI/yB,KAAKkzB,IAAO,GAExCxS,SAAU,SAGX1jB,EAAO21B,GAAK5C,GAAMxyB,UAAUH,KAG5BJ,EAAO21B,GAAGD,KAAO,GAKjB,IACCS,GAAOC,GAmrBHxoB,GAEHyoB,GAprBDC,GAAW,yBACXC,GAAO,cAER,SAASC,KACHJ,MACqB,IAApBx5B,EAAS65B,QAAoB15B,EAAO25B,sBACxC35B,EAAO25B,sBAAuBF,IAE9Bz5B,EAAO+f,WAAY0Z,GAAUx2B,EAAO21B,GAAGgB,UAGxC32B,EAAO21B,GAAGiB,QAKZ,SAASC,KAIR,OAHA95B,EAAO+f,WAAY,WAClBqZ,QAAQrzB,IAEAqzB,GAAQzwB,KAAKyjB,MAIvB,SAAS2N,GAAOn4B,EAAMo4B,GACrB,IAAI/L,EACH7rB,EAAI,EACJuM,EAAQ,CAAEilB,OAAQhyB,GAKnB,IADAo4B,EAAeA,EAAe,EAAI,EAC1B53B,EAAI,EAAGA,GAAK,EAAI43B,EAEvBrrB,EAAO,UADPsf,EAAQ9J,GAAW/hB,KACSuM,EAAO,UAAYsf,GAAUrsB,EAO1D,OAJKo4B,IACJrrB,EAAMwnB,QAAUxnB,EAAM6iB,MAAQ5vB,GAGxB+M,EAGR,SAASsrB,GAAa7yB,EAAOub,EAAMuX,GAKlC,IAJA,IAAIrV,EACHuK,GAAe+K,GAAUC,SAAUzX,IAAU,IAAKhiB,OAAQw5B,GAAUC,SAAU,MAC9E7e,EAAQ,EACRhY,EAAS6rB,EAAW7rB,OACbgY,EAAQhY,EAAQgY,IACvB,GAAOsJ,EAAQuK,EAAY7T,GAAQ7a,KAAMw5B,EAAWvX,EAAMvb,GAGzD,OAAOyd,EAsNV,SAASsV,GAAW71B,EAAM+1B,EAAYh1B,GACrC,IAAImO,EACH8mB,EACA/e,EAAQ,EACRhY,EAAS42B,GAAUI,WAAWh3B,OAC9B+a,EAAWrb,EAAOgb,WAAWI,OAAQ,kBAG7Bwb,EAAKv1B,OAEbu1B,EAAO,WACN,GAAKS,EACJ,OAAO,EAYR,IAVA,IAAIE,EAAcpB,IAASU,KAC1B3Z,EAAYla,KAAKivB,IAAK,EAAGgF,EAAUO,UAAYP,EAAUzB,SAAW+B,GAKpEjC,EAAU,GADHpY,EAAY+Z,EAAUzB,UAAY,GAEzCld,EAAQ,EACRhY,EAAS22B,EAAUQ,OAAOn3B,OAEnBgY,EAAQhY,EAAQgY,IACvB2e,EAAUQ,OAAQnf,GAAQ+c,IAAKC,GAMhC,OAHAja,EAASkB,WAAYlb,EAAM,CAAE41B,EAAW3B,EAASpY,IAG5CoY,EAAU,GAAKh1B,EACZ4c,GAIF5c,GACL+a,EAASkB,WAAYlb,EAAM,CAAE41B,EAAW,EAAG,IAI5C5b,EAASmB,YAAanb,EAAM,CAAE41B,KACvB,IAERA,EAAY5b,EAASzB,QAAS,CAC7BvY,KAAMA,EACNynB,MAAO9oB,EAAOmC,OAAQ,GAAIi1B,GAC1BM,KAAM13B,EAAOmC,QAAQ,EAAM,CAC1Bw1B,cAAe,GACf3E,OAAQhzB,EAAOgzB,OAAOtP,UACpBthB,GACHw1B,mBAAoBR,EACpBS,gBAAiBz1B,EACjBo1B,UAAWrB,IAASU,KACpBrB,SAAUpzB,EAAQozB,SAClBiC,OAAQ,GACRT,YAAa,SAAUtX,EAAM1d,GAC5B,IAAI4f,EAAQ5hB,EAAO+yB,MAAO1xB,EAAM41B,EAAUS,KAAMhY,EAAM1d,EACrDi1B,EAAUS,KAAKC,cAAejY,IAAUuX,EAAUS,KAAK1E,QAExD,OADAiE,EAAUQ,OAAO75B,KAAMgkB,GAChBA,GAERlB,KAAM,SAAUoX,GACf,IAAIxf,EAAQ,EAIXhY,EAASw3B,EAAUb,EAAUQ,OAAOn3B,OAAS,EAC9C,GAAK+2B,EACJ,OAAOr6B,KAGR,IADAq6B,GAAU,EACF/e,EAAQhY,EAAQgY,IACvB2e,EAAUQ,OAAQnf,GAAQ+c,IAAK,GAUhC,OANKyC,GACJzc,EAASkB,WAAYlb,EAAM,CAAE41B,EAAW,EAAG,IAC3C5b,EAASmB,YAAanb,EAAM,CAAE41B,EAAWa,KAEzCzc,EAASuB,WAAYvb,EAAM,CAAE41B,EAAWa,IAElC96B,QAGT8rB,EAAQmO,EAAUnO,MAInB,KA/HD,SAAqBA,EAAO6O,GAC3B,IAAIrf,EAAOjW,EAAM2wB,EAAQ7uB,EAAOqc,EAGhC,IAAMlI,KAASwQ,EAed,GAbAkK,EAAS2E,EADTt1B,EAAO2c,EAAW1G,IAElBnU,EAAQ2kB,EAAOxQ,GACV1V,MAAMC,QAASsB,KACnB6uB,EAAS7uB,EAAO,GAChBA,EAAQ2kB,EAAOxQ,GAAUnU,EAAO,IAG5BmU,IAAUjW,IACdymB,EAAOzmB,GAAS8B,SACT2kB,EAAOxQ,KAGfkI,EAAQxgB,EAAOizB,SAAU5wB,KACX,WAAYme,EAMzB,IAAMlI,KALNnU,EAAQqc,EAAMyU,OAAQ9wB,UACf2kB,EAAOzmB,GAIC8B,EACNmU,KAASwQ,IAChBA,EAAOxQ,GAAUnU,EAAOmU,GACxBqf,EAAerf,GAAU0a,QAI3B2E,EAAet1B,GAAS2wB,EA6F1B+E,CAAYjP,EAAOmO,EAAUS,KAAKC,eAE1Brf,EAAQhY,EAAQgY,IAEvB,GADA/H,EAAS2mB,GAAUI,WAAYhf,GAAQ7a,KAAMw5B,EAAW51B,EAAMynB,EAAOmO,EAAUS,MAM9E,OAJKr5B,EAAYkS,EAAOmQ,QACvB1gB,EAAOygB,YAAawW,EAAU51B,KAAM41B,EAAUS,KAAKnd,OAAQmG,KAC1DnQ,EAAOmQ,KAAKsX,KAAMznB,IAEbA,EAyBT,OArBAvQ,EAAOoB,IAAK0nB,EAAOkO,GAAaC,GAE3B54B,EAAY44B,EAAUS,KAAKxmB,QAC/B+lB,EAAUS,KAAKxmB,MAAMzT,KAAM4D,EAAM41B,GAIlCA,EACErb,SAAUqb,EAAUS,KAAK9b,UACzB/V,KAAMoxB,EAAUS,KAAK7xB,KAAMoxB,EAAUS,KAAKO,UAC1Cpe,KAAMod,EAAUS,KAAK7d,MACrBuB,OAAQ6b,EAAUS,KAAKtc,QAEzBpb,EAAO21B,GAAGuC,MACTl4B,EAAOmC,OAAQy0B,EAAM,CACpBv1B,KAAMA,EACN82B,KAAMlB,EACN1c,MAAO0c,EAAUS,KAAKnd,SAIjB0c,EAGRj3B,EAAOk3B,UAAYl3B,EAAOmC,OAAQ+0B,GAAW,CAE5CC,SAAU,CACTiB,IAAK,CAAE,SAAU1Y,EAAMvb,GACtB,IAAIyd,EAAQ5kB,KAAKg6B,YAAatX,EAAMvb,GAEpC,OADAud,GAAWE,EAAMvgB,KAAMqe,EAAMuB,GAAQ9W,KAAMhG,GAASyd,GAC7CA,KAITyW,QAAS,SAAUvP,EAAO3nB,GACpB9C,EAAYyqB,IAChB3nB,EAAW2nB,EACXA,EAAQ,CAAE,MAEVA,EAAQA,EAAMhf,MAAOoP,GAOtB,IAJA,IAAIwG,EACHpH,EAAQ,EACRhY,EAASwoB,EAAMxoB,OAERgY,EAAQhY,EAAQgY,IACvBoH,EAAOoJ,EAAOxQ,GACd4e,GAAUC,SAAUzX,GAASwX,GAAUC,SAAUzX,IAAU,GAC3DwX,GAAUC,SAAUzX,GAAO9Q,QAASzN,IAItCm2B,WAAY,CA3Wb,SAA2Bj2B,EAAMynB,EAAO4O,GACvC,IAAIhY,EAAMvb,EAAOwe,EAAQnC,EAAO8X,EAASC,EAAWC,EAAgBhX,EACnEiX,EAAQ,UAAW3P,GAAS,WAAYA,EACxCqP,EAAOn7B,KACPsuB,EAAO,GACP/J,EAAQlgB,EAAKkgB,MACbkV,EAASp1B,EAAK9C,UAAY+iB,GAAoBjgB,GAC9Cq3B,EAAW9Y,EAASjf,IAAKU,EAAM,UA6BhC,IAAMqe,KA1BAgY,EAAKnd,QAEa,OADvBiG,EAAQxgB,EAAOygB,YAAapf,EAAM,OACvBs3B,WACVnY,EAAMmY,SAAW,EACjBL,EAAU9X,EAAM1N,MAAM2H,KACtB+F,EAAM1N,MAAM2H,KAAO,WACZ+F,EAAMmY,UACXL,MAIH9X,EAAMmY,WAENR,EAAK/c,OAAQ,WAGZ+c,EAAK/c,OAAQ,WACZoF,EAAMmY,WACA34B,EAAOua,MAAOlZ,EAAM,MAAOf,QAChCkgB,EAAM1N,MAAM2H,YAOFqO,EAEb,GADA3kB,EAAQ2kB,EAAOpJ,GACV4W,GAAS7rB,KAAMtG,GAAU,CAG7B,UAFO2kB,EAAOpJ,GACdiD,EAASA,GAAoB,WAAVxe,EACdA,KAAYsyB,EAAS,OAAS,QAAW,CAI7C,GAAe,SAAVtyB,IAAoBu0B,QAAiC51B,IAArB41B,EAAUhZ,GAK9C,SAJA+W,GAAS,EAOXnL,EAAM5L,GAASgZ,GAAYA,EAAUhZ,IAAU1f,EAAOuhB,MAAOlgB,EAAMqe,GAMrE,IADA6Y,GAAav4B,EAAOyD,cAAeqlB,MAChB9oB,EAAOyD,cAAe6nB,GA8DzC,IAAM5L,KAzDD+Y,GAA2B,IAAlBp3B,EAAK9C,WAMlBm5B,EAAKkB,SAAW,CAAErX,EAAMqX,SAAUrX,EAAMsX,UAAWtX,EAAMuX,WAIlC,OADvBN,EAAiBE,GAAYA,EAASlX,WAErCgX,EAAiB5Y,EAASjf,IAAKU,EAAM,YAGrB,UADjBmgB,EAAUxhB,EAAOyhB,IAAKpgB,EAAM,cAEtBm3B,EACJhX,EAAUgX,GAIVlW,GAAU,CAAEjhB,IAAQ,GACpBm3B,EAAiBn3B,EAAKkgB,MAAMC,SAAWgX,EACvChX,EAAUxhB,EAAOyhB,IAAKpgB,EAAM,WAC5BihB,GAAU,CAAEjhB,OAKG,WAAZmgB,GAAoC,iBAAZA,GAAgD,MAAlBgX,IACrB,SAAhCx4B,EAAOyhB,IAAKpgB,EAAM,WAGhBk3B,IACLJ,EAAKtyB,KAAM,WACV0b,EAAMC,QAAUgX,IAEM,MAAlBA,IACJhX,EAAUD,EAAMC,QAChBgX,EAA6B,SAAZhX,EAAqB,GAAKA,IAG7CD,EAAMC,QAAU,iBAKdkW,EAAKkB,WACTrX,EAAMqX,SAAW,SACjBT,EAAK/c,OAAQ,WACZmG,EAAMqX,SAAWlB,EAAKkB,SAAU,GAChCrX,EAAMsX,UAAYnB,EAAKkB,SAAU,GACjCrX,EAAMuX,UAAYpB,EAAKkB,SAAU,MAKnCL,GAAY,EACEjN,EAGPiN,IACAG,EACC,WAAYA,IAChBjC,EAASiC,EAASjC,QAGnBiC,EAAW9Y,EAASxB,OAAQ/c,EAAM,SAAU,CAAEmgB,QAASgX,IAInD7V,IACJ+V,EAASjC,QAAUA,GAIfA,GACJnU,GAAU,CAAEjhB,IAAQ,GAKrB82B,EAAKtyB,KAAM,WASV,IAAM6Z,KAJA+W,GACLnU,GAAU,CAAEjhB,IAEbue,EAAShF,OAAQvZ,EAAM,UACTiqB,EACbtrB,EAAOuhB,MAAOlgB,EAAMqe,EAAM4L,EAAM5L,OAMnC6Y,EAAYvB,GAAaP,EAASiC,EAAUhZ,GAAS,EAAGA,EAAMyY,GACtDzY,KAAQgZ,IACfA,EAAUhZ,GAAS6Y,EAAUrnB,MACxBulB,IACJ8B,EAAUv2B,IAAMu2B,EAAUrnB,MAC1BqnB,EAAUrnB,MAAQ,MAuMrB6nB,UAAW,SAAU53B,EAAU+rB,GACzBA,EACJgK,GAAUI,WAAW1oB,QAASzN,GAE9B+1B,GAAUI,WAAW15B,KAAMuD,MAK9BnB,EAAOg5B,MAAQ,SAAUA,EAAOhG,EAAQ7yB,GACvC,IAAIk2B,EAAM2C,GAA0B,iBAAVA,EAAqBh5B,EAAOmC,OAAQ,GAAI62B,GAAU,CAC3Ef,SAAU93B,IAAOA,GAAM6yB,GACtB30B,EAAY26B,IAAWA,EACxBxD,SAAUwD,EACVhG,OAAQ7yB,GAAM6yB,GAAUA,IAAW30B,EAAY20B,IAAYA,GAoC5D,OAhCKhzB,EAAO21B,GAAGlQ,IACd4Q,EAAIb,SAAW,EAGc,iBAAjBa,EAAIb,WACVa,EAAIb,YAAYx1B,EAAO21B,GAAGsD,OAC9B5C,EAAIb,SAAWx1B,EAAO21B,GAAGsD,OAAQ5C,EAAIb,UAGrCa,EAAIb,SAAWx1B,EAAO21B,GAAGsD,OAAOvV,UAMjB,MAAb2S,EAAI9b,QAA+B,IAAd8b,EAAI9b,QAC7B8b,EAAI9b,MAAQ,MAIb8b,EAAIlI,IAAMkI,EAAI4B,SAEd5B,EAAI4B,SAAW,WACT55B,EAAYg4B,EAAIlI,MACpBkI,EAAIlI,IAAI1wB,KAAMT,MAGVq5B,EAAI9b,OACRva,EAAOsgB,QAAStjB,KAAMq5B,EAAI9b,QAIrB8b,GAGRr2B,EAAOG,GAAGgC,OAAQ,CACjB+2B,OAAQ,SAAUF,EAAOG,EAAInG,EAAQ7xB,GAGpC,OAAOnE,KAAKsQ,OAAQgU,IAAqBG,IAAK,UAAW,GAAIc,OAG3DvgB,MAAMo3B,QAAS,CAAElG,QAASiG,GAAMH,EAAOhG,EAAQ7xB,IAElDi4B,QAAS,SAAU1Z,EAAMsZ,EAAOhG,EAAQ7xB,GACvC,IAAI2R,EAAQ9S,EAAOyD,cAAeic,GACjC2Z,EAASr5B,EAAOg5B,MAAOA,EAAOhG,EAAQ7xB,GACtCm4B,EAAc,WAGb,IAAInB,EAAOjB,GAAWl6B,KAAMgD,EAAOmC,OAAQ,GAAIud,GAAQ2Z,IAGlDvmB,GAAS8M,EAASjf,IAAK3D,KAAM,YACjCm7B,EAAKzX,MAAM,IAMd,OAFA4Y,EAAYC,OAASD,EAEdxmB,IAA0B,IAAjBumB,EAAO9e,MACtBvd,KAAKkE,KAAMo4B,GACXt8B,KAAKud,MAAO8e,EAAO9e,MAAO+e,IAE5B5Y,KAAM,SAAU/hB,EAAMiiB,EAAYkX,GACjC,IAAI0B,EAAY,SAAUhZ,GACzB,IAAIE,EAAOF,EAAME,YACVF,EAAME,KACbA,EAAMoX,IAYP,MATqB,iBAATn5B,IACXm5B,EAAUlX,EACVA,EAAajiB,EACbA,OAAOmE,GAEH8d,GACJ5jB,KAAKud,MAAO5b,GAAQ,KAAM,IAGpB3B,KAAKkE,KAAM,WACjB,IAAIof,GAAU,EACbhI,EAAgB,MAAR3Z,GAAgBA,EAAO,aAC/B86B,EAASz5B,EAAOy5B,OAChBha,EAAOG,EAASjf,IAAK3D,MAEtB,GAAKsb,EACCmH,EAAMnH,IAAWmH,EAAMnH,GAAQoI,MACnC8Y,EAAW/Z,EAAMnH,SAGlB,IAAMA,KAASmH,EACTA,EAAMnH,IAAWmH,EAAMnH,GAAQoI,MAAQ6V,GAAK9rB,KAAM6N,IACtDkhB,EAAW/Z,EAAMnH,IAKpB,IAAMA,EAAQmhB,EAAOn5B,OAAQgY,KACvBmhB,EAAQnhB,GAAQjX,OAASrE,MACnB,MAAR2B,GAAgB86B,EAAQnhB,GAAQiC,QAAU5b,IAE5C86B,EAAQnhB,GAAQ6f,KAAKzX,KAAMoX,GAC3BxX,GAAU,EACVmZ,EAAOv3B,OAAQoW,EAAO,KAOnBgI,GAAYwX,GAChB93B,EAAOsgB,QAAStjB,KAAM2B,MAIzB46B,OAAQ,SAAU56B,GAIjB,OAHc,IAATA,IACJA,EAAOA,GAAQ,MAET3B,KAAKkE,KAAM,WACjB,IAAIoX,EACHmH,EAAOG,EAASjf,IAAK3D,MACrBud,EAAQkF,EAAM9gB,EAAO,SACrB6hB,EAAQf,EAAM9gB,EAAO,cACrB86B,EAASz5B,EAAOy5B,OAChBn5B,EAASia,EAAQA,EAAMja,OAAS,EAajC,IAVAmf,EAAK8Z,QAAS,EAGdv5B,EAAOua,MAAOvd,KAAM2B,EAAM,IAErB6hB,GAASA,EAAME,MACnBF,EAAME,KAAKjjB,KAAMT,MAAM,GAIlBsb,EAAQmhB,EAAOn5B,OAAQgY,KACvBmhB,EAAQnhB,GAAQjX,OAASrE,MAAQy8B,EAAQnhB,GAAQiC,QAAU5b,IAC/D86B,EAAQnhB,GAAQ6f,KAAKzX,MAAM,GAC3B+Y,EAAOv3B,OAAQoW,EAAO,IAKxB,IAAMA,EAAQ,EAAGA,EAAQhY,EAAQgY,IAC3BiC,EAAOjC,IAAWiC,EAAOjC,GAAQihB,QACrChf,EAAOjC,GAAQihB,OAAO97B,KAAMT,aAKvByiB,EAAK8Z,YAKfv5B,EAAOkB,KAAM,CAAE,SAAU,OAAQ,QAAU,SAAUsD,EAAInC,GACxD,IAAIq3B,EAAQ15B,EAAOG,GAAIkC,GACvBrC,EAAOG,GAAIkC,GAAS,SAAU22B,EAAOhG,EAAQ7xB,GAC5C,OAAgB,MAAT63B,GAAkC,kBAAVA,EAC9BU,EAAM/7B,MAAOX,KAAMsE,WACnBtE,KAAKo8B,QAAStC,GAAOz0B,GAAM,GAAQ22B,EAAOhG,EAAQ7xB,MAKrDnB,EAAOkB,KAAM,CACZy4B,UAAW7C,GAAO,QAClB8C,QAAS9C,GAAO,QAChB+C,YAAa/C,GAAO,UACpBgD,OAAQ,CAAE5G,QAAS,QACnB6G,QAAS,CAAE7G,QAAS,QACpB8G,WAAY,CAAE9G,QAAS,WACrB,SAAU7wB,EAAMymB,GAClB9oB,EAAOG,GAAIkC,GAAS,SAAU22B,EAAOhG,EAAQ7xB,GAC5C,OAAOnE,KAAKo8B,QAAStQ,EAAOkQ,EAAOhG,EAAQ7xB,MAI7CnB,EAAOy5B,OAAS,GAChBz5B,EAAO21B,GAAGiB,KAAO,WAChB,IAAIsB,EACH/4B,EAAI,EACJs6B,EAASz5B,EAAOy5B,OAIjB,IAFAtD,GAAQzwB,KAAKyjB,MAELhqB,EAAIs6B,EAAOn5B,OAAQnB,KAC1B+4B,EAAQuB,EAAQt6B,OAGCs6B,EAAQt6B,KAAQ+4B,GAChCuB,EAAOv3B,OAAQ/C,IAAK,GAIhBs6B,EAAOn5B,QACZN,EAAO21B,GAAGjV,OAEXyV,QAAQrzB,GAGT9C,EAAO21B,GAAGuC,MAAQ,SAAUA,GAC3Bl4B,EAAOy5B,OAAO77B,KAAMs6B,GACpBl4B,EAAO21B,GAAGzkB,SAGXlR,EAAO21B,GAAGgB,SAAW,GACrB32B,EAAO21B,GAAGzkB,MAAQ,WACZklB,KAILA,IAAa,EACbI,OAGDx2B,EAAO21B,GAAGjV,KAAO,WAChB0V,GAAa,MAGdp2B,EAAO21B,GAAGsD,OAAS,CAClBgB,KAAM,IACNC,KAAM,IAGNxW,SAAU,KAMX1jB,EAAOG,GAAGg6B,MAAQ,SAAUC,EAAMz7B,GAIjC,OAHAy7B,EAAOp6B,EAAO21B,IAAK31B,EAAO21B,GAAGsD,OAAQmB,IAAiBA,EACtDz7B,EAAOA,GAAQ,KAER3B,KAAKud,MAAO5b,EAAM,SAAU4K,EAAMiX,GACxC,IAAI6Z,EAAUt9B,EAAO+f,WAAYvT,EAAM6wB,GACvC5Z,EAAME,KAAO,WACZ3jB,EAAOu9B,aAAcD,OAOnBzsB,GAAQhR,EAAS0C,cAAe,SAEnC+2B,GADSz5B,EAAS0C,cAAe,UACpBK,YAAa/C,EAAS0C,cAAe,WAEnDsO,GAAMjP,KAAO,WAIbP,EAAQm8B,QAA0B,KAAhB3sB,GAAMzJ,MAIxB/F,EAAQo8B,YAAcnE,GAAIzjB,UAI1BhF,GAAQhR,EAAS0C,cAAe,UAC1B6E,MAAQ,IACdyJ,GAAMjP,KAAO,QACbP,EAAQq8B,WAA6B,MAAhB7sB,GAAMzJ,MAI5B,IAAIu2B,GACH9uB,GAAa5L,EAAO6O,KAAKjD,WAE1B5L,EAAOG,GAAGgC,OAAQ,CACjB4M,KAAM,SAAU1M,EAAM8B,GACrB,OAAOia,EAAQphB,KAAMgD,EAAO+O,KAAM1M,EAAM8B,EAA0B,EAAnB7C,UAAUhB,SAG1Dq6B,WAAY,SAAUt4B,GACrB,OAAOrF,KAAKkE,KAAM,WACjBlB,EAAO26B,WAAY39B,KAAMqF,QAK5BrC,EAAOmC,OAAQ,CACd4M,KAAM,SAAU1N,EAAMgB,EAAM8B,GAC3B,IAAIpD,EAAKyf,EACRoa,EAAQv5B,EAAK9C,SAGd,GAAe,IAAVq8B,GAAyB,IAAVA,GAAyB,IAAVA,EAKnC,MAAkC,oBAAtBv5B,EAAK7B,aACTQ,EAAO0f,KAAMre,EAAMgB,EAAM8B,IAKlB,IAAVy2B,GAAgB56B,EAAO8W,SAAUzV,KACrCmf,EAAQxgB,EAAO66B,UAAWx4B,EAAKoC,iBAC5BzE,EAAO6O,KAAK/E,MAAMjC,KAAK4C,KAAMpI,GAASq4B,QAAW53B,SAGtCA,IAAVqB,EACW,OAAVA,OACJnE,EAAO26B,WAAYt5B,EAAMgB,GAIrBme,GAAS,QAASA,QACuB1d,KAA3C/B,EAAMyf,EAAMhB,IAAKne,EAAM8C,EAAO9B,IACzBtB,GAGRM,EAAK5B,aAAc4C,EAAM8B,EAAQ,IAC1BA,GAGHqc,GAAS,QAASA,GAA+C,QAApCzf,EAAMyf,EAAM7f,IAAKU,EAAMgB,IACjDtB,EAMM,OAHdA,EAAMf,EAAOwN,KAAKuB,KAAM1N,EAAMgB,SAGTS,EAAY/B,IAGlC85B,UAAW,CACVl8B,KAAM,CACL6gB,IAAK,SAAUne,EAAM8C,GACpB,IAAM/F,EAAQq8B,YAAwB,UAAVt2B,GAC3BkF,EAAUhI,EAAM,SAAY,CAC5B,IAAIjC,EAAMiC,EAAK8C,MAKf,OAJA9C,EAAK5B,aAAc,OAAQ0E,GACtB/E,IACJiC,EAAK8C,MAAQ/E,GAEP+E,MAMXw2B,WAAY,SAAUt5B,EAAM8C,GAC3B,IAAI9B,EACHlD,EAAI,EAIJ27B,EAAY32B,GAASA,EAAM2F,MAAOoP,GAEnC,GAAK4hB,GAA+B,IAAlBz5B,EAAK9C,SACtB,MAAU8D,EAAOy4B,EAAW37B,KAC3BkC,EAAK2J,gBAAiB3I,MAO1Bq4B,GAAW,CACVlb,IAAK,SAAUne,EAAM8C,EAAO9B,GAQ3B,OAPe,IAAV8B,EAGJnE,EAAO26B,WAAYt5B,EAAMgB,GAEzBhB,EAAK5B,aAAc4C,EAAMA,GAEnBA,IAITrC,EAAOkB,KAAMlB,EAAO6O,KAAK/E,MAAMjC,KAAKmZ,OAAOlX,MAAO,QAAU,SAAUtF,EAAInC,GACzE,IAAI04B,EAASnvB,GAAYvJ,IAAUrC,EAAOwN,KAAKuB,KAE/CnD,GAAYvJ,GAAS,SAAUhB,EAAMgB,EAAMwC,GAC1C,IAAI9D,EAAK+lB,EACRkU,EAAgB34B,EAAKoC,cAYtB,OAVMI,IAGLiiB,EAASlb,GAAYovB,GACrBpvB,GAAYovB,GAAkBj6B,EAC9BA,EAAqC,MAA/Bg6B,EAAQ15B,EAAMgB,EAAMwC,GACzBm2B,EACA,KACDpvB,GAAYovB,GAAkBlU,GAExB/lB,KAOT,IAAIk6B,GAAa,sCAChBC,GAAa,gBAyIb,SAASC,GAAkBh3B,GAE1B,OADaA,EAAM2F,MAAOoP,IAAmB,IAC/BrO,KAAM,KAItB,SAASuwB,GAAU/5B,GAClB,OAAOA,EAAK7B,cAAgB6B,EAAK7B,aAAc,UAAa,GAG7D,SAAS67B,GAAgBl3B,GACxB,OAAKvB,MAAMC,QAASsB,GACZA,EAEc,iBAAVA,GACJA,EAAM2F,MAAOoP,IAEd,GAxJRlZ,EAAOG,GAAGgC,OAAQ,CACjBud,KAAM,SAAUrd,EAAM8B,GACrB,OAAOia,EAAQphB,KAAMgD,EAAO0f,KAAMrd,EAAM8B,EAA0B,EAAnB7C,UAAUhB,SAG1Dg7B,WAAY,SAAUj5B,GACrB,OAAOrF,KAAKkE,KAAM,kBACVlE,KAAMgD,EAAOu7B,QAASl5B,IAAUA,QAK1CrC,EAAOmC,OAAQ,CACdud,KAAM,SAAUre,EAAMgB,EAAM8B,GAC3B,IAAIpD,EAAKyf,EACRoa,EAAQv5B,EAAK9C,SAGd,GAAe,IAAVq8B,GAAyB,IAAVA,GAAyB,IAAVA,EAWnC,OAPe,IAAVA,GAAgB56B,EAAO8W,SAAUzV,KAGrCgB,EAAOrC,EAAOu7B,QAASl5B,IAAUA,EACjCme,EAAQxgB,EAAOo1B,UAAW/yB,SAGZS,IAAVqB,EACCqc,GAAS,QAASA,QACuB1d,KAA3C/B,EAAMyf,EAAMhB,IAAKne,EAAM8C,EAAO9B,IACzBtB,EAGCM,EAAMgB,GAAS8B,EAGpBqc,GAAS,QAASA,GAA+C,QAApCzf,EAAMyf,EAAM7f,IAAKU,EAAMgB,IACjDtB,EAGDM,EAAMgB,IAGd+yB,UAAW,CACV3iB,SAAU,CACT9R,IAAK,SAAUU,GAOd,IAAIm6B,EAAWx7B,EAAOwN,KAAKuB,KAAM1N,EAAM,YAEvC,OAAKm6B,EACG5K,SAAU4K,EAAU,IAI3BP,GAAWxwB,KAAMpJ,EAAKgI,WACtB6xB,GAAWzwB,KAAMpJ,EAAKgI,WACtBhI,EAAKmR,KAEE,GAGA,KAKX+oB,QAAS,CACRE,MAAO,UACPC,QAAS,eAYLt9B,EAAQo8B,cACbx6B,EAAOo1B,UAAUxiB,SAAW,CAC3BjS,IAAK,SAAUU,GAId,IAAI8P,EAAS9P,EAAKzB,WAIlB,OAHKuR,GAAUA,EAAOvR,YACrBuR,EAAOvR,WAAWiT,cAEZ,MAER2M,IAAK,SAAUne,GAId,IAAI8P,EAAS9P,EAAKzB,WACbuR,IACJA,EAAO0B,cAEF1B,EAAOvR,YACXuR,EAAOvR,WAAWiT,kBAOvB7S,EAAOkB,KAAM,CACZ,WACA,WACA,YACA,cACA,cACA,UACA,UACA,SACA,cACA,mBACE,WACFlB,EAAOu7B,QAASv+B,KAAKyH,eAAkBzH,OA4BxCgD,EAAOG,GAAGgC,OAAQ,CACjBw5B,SAAU,SAAUx3B,GACnB,IAAIy3B,EAASv6B,EAAMyK,EAAK+vB,EAAUC,EAAO/5B,EAAGg6B,EAC3C58B,EAAI,EAEL,GAAKd,EAAY8F,GAChB,OAAOnH,KAAKkE,KAAM,SAAUa,GAC3B/B,EAAQhD,MAAO2+B,SAAUx3B,EAAM1G,KAAMT,KAAM+E,EAAGq5B,GAAUp+B,UAM1D,IAFA4+B,EAAUP,GAAgBl3B,IAEb7D,OACZ,MAAUe,EAAOrE,KAAMmC,KAItB,GAHA08B,EAAWT,GAAU/5B,GACrByK,EAAwB,IAAlBzK,EAAK9C,UAAoB,IAAM48B,GAAkBU,GAAa,IAEzD,CACV95B,EAAI,EACJ,MAAU+5B,EAAQF,EAAS75B,KACrB+J,EAAIjO,QAAS,IAAMi+B,EAAQ,KAAQ,IACvChwB,GAAOgwB,EAAQ,KAMZD,KADLE,EAAaZ,GAAkBrvB,KAE9BzK,EAAK5B,aAAc,QAASs8B,GAMhC,OAAO/+B,MAGRg/B,YAAa,SAAU73B,GACtB,IAAIy3B,EAASv6B,EAAMyK,EAAK+vB,EAAUC,EAAO/5B,EAAGg6B,EAC3C58B,EAAI,EAEL,GAAKd,EAAY8F,GAChB,OAAOnH,KAAKkE,KAAM,SAAUa,GAC3B/B,EAAQhD,MAAOg/B,YAAa73B,EAAM1G,KAAMT,KAAM+E,EAAGq5B,GAAUp+B,UAI7D,IAAMsE,UAAUhB,OACf,OAAOtD,KAAK+R,KAAM,QAAS,IAK5B,IAFA6sB,EAAUP,GAAgBl3B,IAEb7D,OACZ,MAAUe,EAAOrE,KAAMmC,KAMtB,GALA08B,EAAWT,GAAU/5B,GAGrByK,EAAwB,IAAlBzK,EAAK9C,UAAoB,IAAM48B,GAAkBU,GAAa,IAEzD,CACV95B,EAAI,EACJ,MAAU+5B,EAAQF,EAAS75B,KAG1B,OAA4C,EAApC+J,EAAIjO,QAAS,IAAMi+B,EAAQ,KAClChwB,EAAMA,EAAI5I,QAAS,IAAM44B,EAAQ,IAAK,KAMnCD,KADLE,EAAaZ,GAAkBrvB,KAE9BzK,EAAK5B,aAAc,QAASs8B,GAMhC,OAAO/+B,MAGRi/B,YAAa,SAAU93B,EAAO+3B,GAC7B,IAAIv9B,SAAcwF,EACjBg4B,EAAwB,WAATx9B,GAAqBiE,MAAMC,QAASsB,GAEpD,MAAyB,kBAAb+3B,GAA0BC,EAC9BD,EAAWl/B,KAAK2+B,SAAUx3B,GAAUnH,KAAKg/B,YAAa73B,GAGzD9F,EAAY8F,GACTnH,KAAKkE,KAAM,SAAU/B,GAC3Ba,EAAQhD,MAAOi/B,YACd93B,EAAM1G,KAAMT,KAAMmC,EAAGi8B,GAAUp+B,MAAQk/B,GACvCA,KAKIl/B,KAAKkE,KAAM,WACjB,IAAIgM,EAAW/N,EAAGsY,EAAM2kB,EAExB,GAAKD,EAAe,CAGnBh9B,EAAI,EACJsY,EAAOzX,EAAQhD,MACfo/B,EAAaf,GAAgBl3B,GAE7B,MAAU+I,EAAYkvB,EAAYj9B,KAG5BsY,EAAK4kB,SAAUnvB,GACnBuK,EAAKukB,YAAa9uB,GAElBuK,EAAKkkB,SAAUzuB,aAKIpK,IAAVqB,GAAgC,YAATxF,KAClCuO,EAAYkuB,GAAUp+B,QAIrB4iB,EAASJ,IAAKxiB,KAAM,gBAAiBkQ,GAOjClQ,KAAKyC,cACTzC,KAAKyC,aAAc,QAClByN,IAAuB,IAAV/I,EACZ,GACAyb,EAASjf,IAAK3D,KAAM,kBAAqB,QAO/Cq/B,SAAU,SAAUp8B,GACnB,IAAIiN,EAAW7L,EACdlC,EAAI,EAEL+N,EAAY,IAAMjN,EAAW,IAC7B,MAAUoB,EAAOrE,KAAMmC,KACtB,GAAuB,IAAlBkC,EAAK9C,WACoE,GAA3E,IAAM48B,GAAkBC,GAAU/5B,IAAW,KAAMxD,QAASqP,GAC9D,OAAO,EAIT,OAAO,KAOT,IAAIovB,GAAU,MAEdt8B,EAAOG,GAAGgC,OAAQ,CACjB/C,IAAK,SAAU+E,GACd,IAAIqc,EAAOzf,EAAKurB,EACfjrB,EAAOrE,KAAM,GAEd,OAAMsE,UAAUhB,QA0BhBgsB,EAAkBjuB,EAAY8F,GAEvBnH,KAAKkE,KAAM,SAAU/B,GAC3B,IAAIC,EAEmB,IAAlBpC,KAAKuB,WAWE,OANXa,EADIktB,EACEnoB,EAAM1G,KAAMT,KAAMmC,EAAGa,EAAQhD,MAAOoC,OAEpC+E,GAKN/E,EAAM,GAEoB,iBAARA,EAClBA,GAAO,GAEIwD,MAAMC,QAASzD,KAC1BA,EAAMY,EAAOoB,IAAKhC,EAAK,SAAU+E,GAChC,OAAgB,MAATA,EAAgB,GAAKA,EAAQ,OAItCqc,EAAQxgB,EAAOu8B,SAAUv/B,KAAK2B,OAAUqB,EAAOu8B,SAAUv/B,KAAKqM,SAAS5E,iBAGrD,QAAS+b,QAA+C1d,IAApC0d,EAAMhB,IAAKxiB,KAAMoC,EAAK,WAC3DpC,KAAKmH,MAAQ/E,OAzDTiC,GACJmf,EAAQxgB,EAAOu8B,SAAUl7B,EAAK1C,OAC7BqB,EAAOu8B,SAAUl7B,EAAKgI,SAAS5E,iBAG/B,QAAS+b,QACgC1d,KAAvC/B,EAAMyf,EAAM7f,IAAKU,EAAM,UAElBN,EAMY,iBAHpBA,EAAMM,EAAK8C,OAIHpD,EAAImC,QAASo5B,GAAS,IAIhB,MAAPv7B,EAAc,GAAKA,OAG3B,KAyCHf,EAAOmC,OAAQ,CACdo6B,SAAU,CACTnZ,OAAQ,CACPziB,IAAK,SAAUU,GAEd,IAAIjC,EAAMY,EAAOwN,KAAKuB,KAAM1N,EAAM,SAClC,OAAc,MAAPjC,EACNA,EAMA+7B,GAAkBn7B,EAAOT,KAAM8B,MAGlC2D,OAAQ,CACPrE,IAAK,SAAUU,GACd,IAAI8C,EAAOif,EAAQjkB,EAClBiD,EAAUf,EAAKe,QACfkW,EAAQjX,EAAKwR,cACbyS,EAAoB,eAAdjkB,EAAK1C,KACX6jB,EAAS8C,EAAM,KAAO,GACtB2M,EAAM3M,EAAMhN,EAAQ,EAAIlW,EAAQ9B,OAUjC,IAPCnB,EADImZ,EAAQ,EACR2Z,EAGA3M,EAAMhN,EAAQ,EAIXnZ,EAAI8yB,EAAK9yB,IAKhB,KAJAikB,EAAShhB,EAASjD,IAIJyT,UAAYzT,IAAMmZ,KAG7B8K,EAAOha,YACLga,EAAOxjB,WAAWwJ,WACnBC,EAAU+Z,EAAOxjB,WAAY,aAAiB,CAMjD,GAHAuE,EAAQnE,EAAQojB,GAAShkB,MAGpBkmB,EACJ,OAAOnhB,EAIRqe,EAAO5kB,KAAMuG,GAIf,OAAOqe,GAGRhD,IAAK,SAAUne,EAAM8C,GACpB,IAAIq4B,EAAWpZ,EACdhhB,EAAUf,EAAKe,QACfogB,EAASxiB,EAAO2D,UAAWQ,GAC3BhF,EAAIiD,EAAQ9B,OAEb,MAAQnB,MACPikB,EAAShhB,EAASjD,IAINyT,UACuD,EAAlE5S,EAAO6D,QAAS7D,EAAOu8B,SAASnZ,OAAOziB,IAAKyiB,GAAUZ,MAEtDga,GAAY,GAUd,OAHMA,IACLn7B,EAAKwR,eAAiB,GAEhB2P,OAOXxiB,EAAOkB,KAAM,CAAE,QAAS,YAAc,WACrClB,EAAOu8B,SAAUv/B,MAAS,CACzBwiB,IAAK,SAAUne,EAAM8C,GACpB,GAAKvB,MAAMC,QAASsB,GACnB,OAAS9C,EAAKsR,SAA2D,EAAjD3S,EAAO6D,QAAS7D,EAAQqB,GAAOjC,MAAO+E,KAI3D/F,EAAQm8B,UACbv6B,EAAOu8B,SAAUv/B,MAAO2D,IAAM,SAAUU,GACvC,OAAwC,OAAjCA,EAAK7B,aAAc,SAAqB,KAAO6B,EAAK8C,UAW9D/F,EAAQq+B,QAAU,cAAe1/B,EAGjC,IAAI2/B,GAAc,kCACjBC,GAA0B,SAAUlzB,GACnCA,EAAEsc,mBAGJ/lB,EAAOmC,OAAQnC,EAAOwlB,MAAO,CAE5BU,QAAS,SAAUV,EAAO/F,EAAMpe,EAAMu7B,GAErC,IAAIz9B,EAAG2M,EAAK6B,EAAKkvB,EAAYC,EAAQhW,EAAQ3K,EAAS4gB,EACrDC,EAAY,CAAE37B,GAAQzE,GACtB+B,EAAOX,EAAOP,KAAM+nB,EAAO,QAAWA,EAAM7mB,KAAO6mB,EACnDkB,EAAa1oB,EAAOP,KAAM+nB,EAAO,aAAgBA,EAAM/Y,UAAUlI,MAAO,KAAQ,GAKjF,GAHAuH,EAAMixB,EAAcpvB,EAAMtM,EAAOA,GAAQzE,EAGlB,IAAlByE,EAAK9C,UAAoC,IAAlB8C,EAAK9C,WAK5Bm+B,GAAYjyB,KAAM9L,EAAOqB,EAAOwlB,MAAMuB,cAIf,EAAvBpoB,EAAKd,QAAS,OAIlBc,GADA+nB,EAAa/nB,EAAK4F,MAAO,MACP8G,QAClBqb,EAAWzkB,QAEZ66B,EAASn+B,EAAKd,QAAS,KAAQ,GAAK,KAAOc,GAG3C6mB,EAAQA,EAAOxlB,EAAO+C,SACrByiB,EACA,IAAIxlB,EAAOmmB,MAAOxnB,EAAuB,iBAAV6mB,GAAsBA,IAGhDK,UAAY+W,EAAe,EAAI,EACrCpX,EAAM/Y,UAAYia,EAAW7b,KAAM,KACnC2a,EAAMwC,WAAaxC,EAAM/Y,UACxB,IAAI1F,OAAQ,UAAY2f,EAAW7b,KAAM,iBAAoB,WAC7D,KAGD2a,EAAMjV,YAASzN,EACT0iB,EAAM/iB,SACX+iB,EAAM/iB,OAASpB,GAIhBoe,EAAe,MAARA,EACN,CAAE+F,GACFxlB,EAAO2D,UAAW8b,EAAM,CAAE+F,IAG3BrJ,EAAUnc,EAAOwlB,MAAMrJ,QAASxd,IAAU,GACpCi+B,IAAgBzgB,EAAQ+J,UAAmD,IAAxC/J,EAAQ+J,QAAQvoB,MAAO0D,EAAMoe,IAAtE,CAMA,IAAMmd,IAAiBzgB,EAAQuM,WAAajqB,EAAU4C,GAAS,CAM9D,IAJAw7B,EAAa1gB,EAAQ2J,cAAgBnnB,EAC/B+9B,GAAYjyB,KAAMoyB,EAAal+B,KACpCmN,EAAMA,EAAIlM,YAEHkM,EAAKA,EAAMA,EAAIlM,WACtBo9B,EAAUp/B,KAAMkO,GAChB6B,EAAM7B,EAIF6B,KAAUtM,EAAK6I,eAAiBtN,IACpCogC,EAAUp/B,KAAM+P,EAAIb,aAAea,EAAIsvB,cAAgBlgC,GAKzDoC,EAAI,EACJ,OAAU2M,EAAMkxB,EAAW79B,QAAYqmB,EAAMqC,uBAC5CkV,EAAcjxB,EACd0Z,EAAM7mB,KAAW,EAAJQ,EACZ09B,EACA1gB,EAAQ8K,UAAYtoB,GAGrBmoB,GAAWlH,EAASjf,IAAKmL,EAAK,WAAc1O,OAAOypB,OAAQ,OAAUrB,EAAM7mB,OAC1EihB,EAASjf,IAAKmL,EAAK,YAEnBgb,EAAOnpB,MAAOmO,EAAK2T,IAIpBqH,EAASgW,GAAUhxB,EAAKgxB,KACThW,EAAOnpB,OAASuhB,EAAYpT,KAC1C0Z,EAAMjV,OAASuW,EAAOnpB,MAAOmO,EAAK2T,IACZ,IAAjB+F,EAAMjV,QACViV,EAAMS,kBA8CT,OA1CAT,EAAM7mB,KAAOA,EAGPi+B,GAAiBpX,EAAMuD,sBAEpB5M,EAAQuH,WACqC,IAApDvH,EAAQuH,SAAS/lB,MAAOq/B,EAAU12B,MAAOmZ,KACzCP,EAAY7d,IAIPy7B,GAAUz+B,EAAYgD,EAAM1C,MAAaF,EAAU4C,MAGvDsM,EAAMtM,EAAMy7B,MAGXz7B,EAAMy7B,GAAW,MAIlB98B,EAAOwlB,MAAMuB,UAAYpoB,EAEpB6mB,EAAMqC,wBACVkV,EAAY/vB,iBAAkBrO,EAAMg+B,IAGrCt7B,EAAM1C,KAED6mB,EAAMqC,wBACVkV,EAAYhf,oBAAqBpf,EAAMg+B,IAGxC38B,EAAOwlB,MAAMuB,eAAYjkB,EAEpB6K,IACJtM,EAAMy7B,GAAWnvB,IAMd6X,EAAMjV,SAKd2sB,SAAU,SAAUv+B,EAAM0C,EAAMmkB,GAC/B,IAAI/b,EAAIzJ,EAAOmC,OACd,IAAInC,EAAOmmB,MACXX,EACA,CACC7mB,KAAMA,EACNyqB,aAAa,IAIfppB,EAAOwlB,MAAMU,QAASzc,EAAG,KAAMpI,MAKjCrB,EAAOG,GAAGgC,OAAQ,CAEjB+jB,QAAS,SAAUvnB,EAAM8gB,GACxB,OAAOziB,KAAKkE,KAAM,WACjBlB,EAAOwlB,MAAMU,QAASvnB,EAAM8gB,EAAMziB,SAGpCmgC,eAAgB,SAAUx+B,EAAM8gB,GAC/B,IAAIpe,EAAOrE,KAAM,GACjB,GAAKqE,EACJ,OAAOrB,EAAOwlB,MAAMU,QAASvnB,EAAM8gB,EAAMpe,GAAM,MAc5CjD,EAAQq+B,SACbz8B,EAAOkB,KAAM,CAAEmR,MAAO,UAAW4Y,KAAM,YAAc,SAAUK,EAAM5D,GAGpE,IAAI/b,EAAU,SAAU6Z,GACvBxlB,EAAOwlB,MAAM0X,SAAUxV,EAAKlC,EAAM/iB,OAAQzC,EAAOwlB,MAAMkC,IAAKlC,KAG7DxlB,EAAOwlB,MAAMrJ,QAASuL,GAAQ,CAC7BP,MAAO,WAIN,IAAIjoB,EAAMlC,KAAKkN,eAAiBlN,KAAKJ,UAAYI,KAChDogC,EAAWxd,EAASxB,OAAQlf,EAAKwoB,GAE5B0V,GACLl+B,EAAI8N,iBAAkBse,EAAM3f,GAAS,GAEtCiU,EAASxB,OAAQlf,EAAKwoB,GAAO0V,GAAY,GAAM,IAEhD9V,SAAU,WACT,IAAIpoB,EAAMlC,KAAKkN,eAAiBlN,KAAKJ,UAAYI,KAChDogC,EAAWxd,EAASxB,OAAQlf,EAAKwoB,GAAQ,EAEpC0V,EAKLxd,EAASxB,OAAQlf,EAAKwoB,EAAK0V,IAJ3Bl+B,EAAI6e,oBAAqBuN,EAAM3f,GAAS,GACxCiU,EAAShF,OAAQ1b,EAAKwoB,QAS3B,IAAIvV,GAAWpV,EAAOoV,SAElBtT,GAAQ,CAAEuF,KAAMsB,KAAKyjB,OAErBkU,GAAS,KAKbr9B,EAAOs9B,SAAW,SAAU7d,GAC3B,IAAI3O,EAAKysB,EACT,IAAM9d,GAAwB,iBAATA,EACpB,OAAO,KAKR,IACC3O,GAAM,IAAM/T,EAAOygC,WAAcC,gBAAiBhe,EAAM,YACvD,MAAQhW,IAYV,OAVA8zB,EAAkBzsB,GAAOA,EAAIxG,qBAAsB,eAAiB,GAC9DwG,IAAOysB,GACZv9B,EAAOoD,MAAO,iBACbm6B,EACCv9B,EAAOoB,IAAKm8B,EAAgB/zB,WAAY,SAAUgC,GACjD,OAAOA,EAAG8D,cACPzE,KAAM,MACV4U,IAGI3O,GAIR,IACC4sB,GAAW,QACXC,GAAQ,SACRC,GAAkB,wCAClBC,GAAe,qCAEhB,SAASC,GAAa/I,EAAQz2B,EAAKy/B,EAAavlB,GAC/C,IAAInW,EAEJ,GAAKO,MAAMC,QAASvE,GAGnB0B,EAAOkB,KAAM5C,EAAK,SAAUa,EAAGia,GACzB2kB,GAAeL,GAASjzB,KAAMsqB,GAGlCvc,EAAKuc,EAAQ3b,GAKb0kB,GACC/I,EAAS,KAAqB,iBAAN3b,GAAuB,MAALA,EAAYja,EAAI,IAAO,IACjEia,EACA2kB,EACAvlB,UAKG,GAAMulB,GAAiC,WAAlBj+B,EAAQxB,GAUnCka,EAAKuc,EAAQz2B,QAPb,IAAM+D,KAAQ/D,EACbw/B,GAAa/I,EAAS,IAAM1yB,EAAO,IAAK/D,EAAK+D,GAAQ07B,EAAavlB,GAYrExY,EAAOg+B,MAAQ,SAAU53B,EAAG23B,GAC3B,IAAIhJ,EACHkJ,EAAI,GACJzlB,EAAM,SAAUrN,EAAK+yB,GAGpB,IAAI/5B,EAAQ9F,EAAY6/B,GACvBA,IACAA,EAEDD,EAAGA,EAAE39B,QAAW69B,mBAAoBhzB,GAAQ,IAC3CgzB,mBAA6B,MAATh6B,EAAgB,GAAKA,IAG5C,GAAU,MAALiC,EACJ,MAAO,GAIR,GAAKxD,MAAMC,QAASuD,IAASA,EAAE5F,SAAWR,EAAO2C,cAAeyD,GAG/DpG,EAAOkB,KAAMkF,EAAG,WACfoS,EAAKxb,KAAKqF,KAAMrF,KAAKmH,cAOtB,IAAM4wB,KAAU3uB,EACf03B,GAAa/I,EAAQ3uB,EAAG2uB,GAAUgJ,EAAavlB,GAKjD,OAAOylB,EAAEpzB,KAAM,MAGhB7K,EAAOG,GAAGgC,OAAQ,CACjBi8B,UAAW,WACV,OAAOp+B,EAAOg+B,MAAOhhC,KAAKqhC,mBAE3BA,eAAgB,WACf,OAAOrhC,KAAKoE,IAAK,WAGhB,IAAI0N,EAAW9O,EAAO0f,KAAM1iB,KAAM,YAClC,OAAO8R,EAAW9O,EAAO2D,UAAWmL,GAAa9R,OAC9CsQ,OAAQ,WACX,IAAI3O,EAAO3B,KAAK2B,KAGhB,OAAO3B,KAAKqF,OAASrC,EAAQhD,MAAOka,GAAI,cACvC2mB,GAAapzB,KAAMzN,KAAKqM,YAAeu0B,GAAgBnzB,KAAM9L,KAC3D3B,KAAK2V,UAAYkQ,GAAepY,KAAM9L,MACtCyC,IAAK,SAAUoD,EAAInD,GACtB,IAAIjC,EAAMY,EAAQhD,MAAOoC,MAEzB,OAAY,MAAPA,EACG,KAGHwD,MAAMC,QAASzD,GACZY,EAAOoB,IAAKhC,EAAK,SAAUA,GACjC,MAAO,CAAEiD,KAAMhB,EAAKgB,KAAM8B,MAAO/E,EAAI8D,QAASy6B,GAAO,WAIhD,CAAEt7B,KAAMhB,EAAKgB,KAAM8B,MAAO/E,EAAI8D,QAASy6B,GAAO,WAClDh9B,SAKN,IACC29B,GAAM,OACNC,GAAQ,OACRC,GAAa,gBACbC,GAAW,6BAIXC,GAAa,iBACbC,GAAY,QAWZrH,GAAa,GAObsH,GAAa,GAGbC,GAAW,KAAKnhC,OAAQ,KAGxBohC,GAAeliC,EAAS0C,cAAe,KAKxC,SAASy/B,GAA6BC,GAGrC,OAAO,SAAUC,EAAoBhkB,GAED,iBAAvBgkB,IACXhkB,EAAOgkB,EACPA,EAAqB,KAGtB,IAAIC,EACH//B,EAAI,EACJggC,EAAYF,EAAmBx6B,cAAcqF,MAAOoP,IAAmB,GAExE,GAAK7a,EAAY4c,GAGhB,MAAUikB,EAAWC,EAAWhgC,KAGR,MAAlB+/B,EAAU,IACdA,EAAWA,EAAS5hC,MAAO,IAAO,KAChC0hC,EAAWE,GAAaF,EAAWE,IAAc,IAAKtwB,QAASqM,KAI/D+jB,EAAWE,GAAaF,EAAWE,IAAc,IAAKthC,KAAMqd,IAQnE,SAASmkB,GAA+BJ,EAAW58B,EAASy1B,EAAiBwH,GAE5E,IAAIC,EAAY,GACfC,EAAqBP,IAAcJ,GAEpC,SAASY,EAASN,GACjB,IAAItsB,EAcJ,OAbA0sB,EAAWJ,IAAa,EACxBl/B,EAAOkB,KAAM89B,EAAWE,IAAc,GAAI,SAAUjlB,EAAGwlB,GACtD,IAAIC,EAAsBD,EAAoBr9B,EAASy1B,EAAiBwH,GACxE,MAAoC,iBAAxBK,GACVH,GAAqBD,EAAWI,GAKtBH,IACD3sB,EAAW8sB,QADf,GAHNt9B,EAAQ+8B,UAAUvwB,QAAS8wB,GAC3BF,EAASE,IACF,KAKF9sB,EAGR,OAAO4sB,EAASp9B,EAAQ+8B,UAAW,MAAUG,EAAW,MAASE,EAAS,KAM3E,SAASG,GAAYl9B,EAAQ7D,GAC5B,IAAIuM,EAAKzI,EACRk9B,EAAc5/B,EAAO6/B,aAAaD,aAAe,GAElD,IAAMz0B,KAAOvM,OACQkE,IAAflE,EAAKuM,MACPy0B,EAAaz0B,GAAQ1I,EAAWC,IAAUA,EAAO,KAAUyI,GAAQvM,EAAKuM,IAO5E,OAJKzI,GACJ1C,EAAOmC,QAAQ,EAAMM,EAAQC,GAGvBD,EA/ERq8B,GAAatsB,KAAOL,GAASK,KAgP7BxS,EAAOmC,OAAQ,CAGd29B,OAAQ,EAGRC,aAAc,GACdC,KAAM,GAENH,aAAc,CACbI,IAAK9tB,GAASK,KACd7T,KAAM,MACNuhC,QAxRgB,4DAwRQz1B,KAAM0H,GAASguB,UACvC3jC,QAAQ,EACR4jC,aAAa,EACbC,OAAO,EACPC,YAAa,mDAcbC,QAAS,CACRnI,IAAKyG,GACLt/B,KAAM,aACNgtB,KAAM,YACNzb,IAAK,4BACL0vB,KAAM,qCAGPxoB,SAAU,CACTlH,IAAK,UACLyb,KAAM,SACNiU,KAAM,YAGPC,eAAgB,CACf3vB,IAAK,cACLvR,KAAM,eACNihC,KAAM,gBAKPE,WAAY,CAGXC,SAAUj4B,OAGVk4B,aAAa,EAGbC,YAAa5gB,KAAKC,MAGlB4gB,WAAY9gC,EAAOs9B,UAOpBsC,YAAa,CACZK,KAAK,EACL//B,SAAS,IAOX6gC,UAAW,SAAUt+B,EAAQu+B,GAC5B,OAAOA,EAGNrB,GAAYA,GAAYl9B,EAAQzC,EAAO6/B,cAAgBmB,GAGvDrB,GAAY3/B,EAAO6/B,aAAcp9B,IAGnCw+B,cAAelC,GAA6BzH,IAC5C4J,cAAenC,GAA6BH,IAG5CuC,KAAM,SAAUlB,EAAK79B,GAGA,iBAAR69B,IACX79B,EAAU69B,EACVA,OAAMn9B,GAIPV,EAAUA,GAAW,GAErB,IAAIg/B,EAGHC,EAGAC,EACAC,EAGAC,EAGAC,EAGA3jB,EAGA4jB,EAGAviC,EAGAwiC,EAGA1D,EAAIj+B,EAAO+gC,UAAW,GAAI3+B,GAG1Bw/B,EAAkB3D,EAAE/9B,SAAW+9B,EAG/B4D,EAAqB5D,EAAE/9B,UACpB0hC,EAAgBrjC,UAAYqjC,EAAgBphC,QAC9CR,EAAQ4hC,GACR5hC,EAAOwlB,MAGRnK,EAAWrb,EAAOgb,WAClB8mB,EAAmB9hC,EAAO+Z,UAAW,eAGrCgoB,EAAa9D,EAAE8D,YAAc,GAG7BC,EAAiB,GACjBC,EAAsB,GAGtBC,EAAW,WAGX7C,EAAQ,CACPnhB,WAAY,EAGZikB,kBAAmB,SAAUh3B,GAC5B,IAAIrB,EACJ,GAAKgU,EAAY,CAChB,IAAMyjB,EAAkB,CACvBA,EAAkB,GAClB,MAAUz3B,EAAQ20B,GAASt0B,KAAMm3B,GAChCC,EAAiBz3B,EAAO,GAAIrF,cAAgB,MACzC88B,EAAiBz3B,EAAO,GAAIrF,cAAgB,MAAS,IACrD/G,OAAQoM,EAAO,IAGpBA,EAAQy3B,EAAiBp2B,EAAI1G,cAAgB,KAE9C,OAAgB,MAATqF,EAAgB,KAAOA,EAAMe,KAAM,OAI3Cu3B,sBAAuB,WACtB,OAAOtkB,EAAYwjB,EAAwB,MAI5Ce,iBAAkB,SAAUhgC,EAAM8B,GAMjC,OALkB,MAAb2Z,IACJzb,EAAO4/B,EAAqB5/B,EAAKoC,eAChCw9B,EAAqB5/B,EAAKoC,gBAAmBpC,EAC9C2/B,EAAgB3/B,GAAS8B,GAEnBnH,MAIRslC,iBAAkB,SAAU3jC,GAI3B,OAHkB,MAAbmf,IACJmgB,EAAEsE,SAAW5jC,GAEP3B,MAIR+kC,WAAY,SAAU3gC,GACrB,IAAIpC,EACJ,GAAKoC,EACJ,GAAK0c,EAGJuhB,EAAMjkB,OAAQha,EAAKi+B,EAAMmD,cAIzB,IAAMxjC,KAAQoC,EACb2gC,EAAY/iC,GAAS,CAAE+iC,EAAY/iC,GAAQoC,EAAKpC,IAInD,OAAOhC,MAIRylC,MAAO,SAAUC,GAChB,IAAIC,EAAYD,GAAcR,EAK9B,OAJKd,GACJA,EAAUqB,MAAOE,GAElB98B,EAAM,EAAG88B,GACF3lC,OAoBV,GAfAqe,EAASzB,QAASylB,GAKlBpB,EAAEgC,MAAUA,GAAOhC,EAAEgC,KAAO9tB,GAASK,MAAS,IAC5CtP,QAASy7B,GAAWxsB,GAASguB,SAAW,MAG1ClC,EAAEt/B,KAAOyD,EAAQuX,QAAUvX,EAAQzD,MAAQs/B,EAAEtkB,QAAUskB,EAAEt/B,KAGzDs/B,EAAEkB,WAAclB,EAAEiB,UAAY,KAAMz6B,cAAcqF,MAAOoP,IAAmB,CAAE,IAGxD,MAAjB+kB,EAAE2E,YAAsB,CAC5BnB,EAAY7kC,EAAS0C,cAAe,KAKpC,IACCmiC,EAAUjvB,KAAOyrB,EAAEgC,IAInBwB,EAAUjvB,KAAOivB,EAAUjvB,KAC3ByrB,EAAE2E,YAAc9D,GAAaqB,SAAW,KAAOrB,GAAa+D,MAC3DpB,EAAUtB,SAAW,KAAOsB,EAAUoB,KACtC,MAAQp5B,GAITw0B,EAAE2E,aAAc,GAalB,GARK3E,EAAExe,MAAQwe,EAAEmC,aAAiC,iBAAXnC,EAAExe,OACxCwe,EAAExe,KAAOzf,EAAOg+B,MAAOC,EAAExe,KAAMwe,EAAEF,cAIlCqB,GAA+B9H,GAAY2G,EAAG77B,EAASi9B,GAGlDvhB,EACJ,OAAOuhB,EA8ER,IAAMlgC,KAzENuiC,EAAc1hC,EAAOwlB,OAASyY,EAAEzhC,SAGQ,GAApBwD,EAAO8/B,UAC1B9/B,EAAOwlB,MAAMU,QAAS,aAIvB+X,EAAEt/B,KAAOs/B,EAAEt/B,KAAKogB,cAGhBkf,EAAE6E,YAAcpE,GAAWj0B,KAAMwzB,EAAEt/B,MAKnC0iC,EAAWpD,EAAEgC,IAAI/8B,QAASq7B,GAAO,IAG3BN,EAAE6E,WAwBI7E,EAAExe,MAAQwe,EAAEmC,aACoD,KAAzEnC,EAAEqC,aAAe,IAAKziC,QAAS,uCACjCogC,EAAExe,KAAOwe,EAAExe,KAAKvc,QAASo7B,GAAK,OAvB9BqD,EAAW1D,EAAEgC,IAAI3iC,MAAO+jC,EAAS/gC,QAG5B29B,EAAExe,OAAUwe,EAAEmC,aAAiC,iBAAXnC,EAAExe,QAC1C4hB,IAAchE,GAAO5yB,KAAM42B,GAAa,IAAM,KAAQpD,EAAExe,YAGjDwe,EAAExe,OAIO,IAAZwe,EAAE/yB,QACNm2B,EAAWA,EAASn+B,QAASs7B,GAAY,MACzCmD,GAAatE,GAAO5yB,KAAM42B,GAAa,IAAM,KAAQ,KAASxiC,GAAMuF,OACnEu9B,GAIF1D,EAAEgC,IAAMoB,EAAWM,GASf1D,EAAE8E,aACD/iC,EAAO+/B,aAAcsB,IACzBhC,EAAMgD,iBAAkB,oBAAqBriC,EAAO+/B,aAAcsB,IAE9DrhC,EAAOggC,KAAMqB,IACjBhC,EAAMgD,iBAAkB,gBAAiBriC,EAAOggC,KAAMqB,MAKnDpD,EAAExe,MAAQwe,EAAE6E,aAAgC,IAAlB7E,EAAEqC,aAAyBl+B,EAAQk+B,cACjEjB,EAAMgD,iBAAkB,eAAgBpE,EAAEqC,aAI3CjB,EAAMgD,iBACL,SACApE,EAAEkB,UAAW,IAAOlB,EAAEsC,QAAStC,EAAEkB,UAAW,IAC3ClB,EAAEsC,QAAStC,EAAEkB,UAAW,KACA,MAArBlB,EAAEkB,UAAW,GAAc,KAAON,GAAW,WAAa,IAC7DZ,EAAEsC,QAAS,MAIFtC,EAAE+E,QACZ3D,EAAMgD,iBAAkBljC,EAAG8+B,EAAE+E,QAAS7jC,IAIvC,GAAK8+B,EAAEgF,cAC+C,IAAnDhF,EAAEgF,WAAWxlC,KAAMmkC,EAAiBvC,EAAOpB,IAAiBngB,GAG9D,OAAOuhB,EAAMoD,QAed,GAXAP,EAAW,QAGXJ,EAAiBtpB,IAAKylB,EAAEhG,UACxBoH,EAAMx5B,KAAMo4B,EAAEiF,SACd7D,EAAMxlB,KAAMokB,EAAE76B,OAGdg+B,EAAYhC,GAA+BR,GAAYX,EAAG77B,EAASi9B,GAK5D,CASN,GARAA,EAAMnhB,WAAa,EAGdwjB,GACJG,EAAmB3b,QAAS,WAAY,CAAEmZ,EAAOpB,IAI7CngB,EACJ,OAAOuhB,EAIHpB,EAAEoC,OAAqB,EAAZpC,EAAE5D,UACjBmH,EAAezkC,EAAO+f,WAAY,WACjCuiB,EAAMoD,MAAO,YACXxE,EAAE5D,UAGN,IACCvc,GAAY,EACZsjB,EAAU+B,KAAMnB,EAAgBn8B,GAC/B,MAAQ4D,GAGT,GAAKqU,EACJ,MAAMrU,EAIP5D,GAAO,EAAG4D,SAhCX5D,GAAO,EAAG,gBAqCX,SAASA,EAAM28B,EAAQY,EAAkBC,EAAWL,GACnD,IAAIM,EAAWJ,EAAS9/B,EAAOmgC,EAAUC,EACxCd,EAAaU,EAGTtlB,IAILA,GAAY,EAGP0jB,GACJzkC,EAAOu9B,aAAckH,GAKtBJ,OAAYt+B,EAGZw+B,EAAwB0B,GAAW,GAGnC3D,EAAMnhB,WAAsB,EAATskB,EAAa,EAAI,EAGpCc,EAAsB,KAAVd,GAAiBA,EAAS,KAAkB,MAAXA,EAGxCa,IACJE,EA7lBJ,SAA8BtF,EAAGoB,EAAOgE,GAEvC,IAAII,EAAI9kC,EAAM+kC,EAAeC,EAC5B3rB,EAAWimB,EAAEjmB,SACbmnB,EAAYlB,EAAEkB,UAGf,MAA2B,MAAnBA,EAAW,GAClBA,EAAU9zB,aACEvI,IAAP2gC,IACJA,EAAKxF,EAAEsE,UAAYlD,EAAM8C,kBAAmB,iBAK9C,GAAKsB,EACJ,IAAM9kC,KAAQqZ,EACb,GAAKA,EAAUrZ,IAAUqZ,EAAUrZ,GAAO8L,KAAMg5B,GAAO,CACtDtE,EAAUvwB,QAASjQ,GACnB,MAMH,GAAKwgC,EAAW,KAAOkE,EACtBK,EAAgBvE,EAAW,OACrB,CAGN,IAAMxgC,KAAQ0kC,EAAY,CACzB,IAAMlE,EAAW,IAAOlB,EAAEyC,WAAY/hC,EAAO,IAAMwgC,EAAW,IAAQ,CACrEuE,EAAgB/kC,EAChB,MAEKglC,IACLA,EAAgBhlC,GAKlB+kC,EAAgBA,GAAiBC,EAMlC,GAAKD,EAIJ,OAHKA,IAAkBvE,EAAW,IACjCA,EAAUvwB,QAAS80B,GAEbL,EAAWK,GA0iBLE,CAAqB3F,EAAGoB,EAAOgE,KAIrCC,IACsC,EAA3CtjC,EAAO6D,QAAS,SAAUo6B,EAAEkB,YAC5Bn/B,EAAO6D,QAAS,OAAQo6B,EAAEkB,WAAc,IACxClB,EAAEyC,WAAY,eAAkB,cAIjC6C,EA9iBH,SAAsBtF,EAAGsF,EAAUlE,EAAOiE,GACzC,IAAIO,EAAOC,EAASC,EAAMp2B,EAAKsK,EAC9ByoB,EAAa,GAGbvB,EAAYlB,EAAEkB,UAAU7hC,QAGzB,GAAK6hC,EAAW,GACf,IAAM4E,KAAQ9F,EAAEyC,WACfA,EAAYqD,EAAKt/B,eAAkBw5B,EAAEyC,WAAYqD,GAInDD,EAAU3E,EAAU9zB,QAGpB,MAAQy4B,EAcP,GAZK7F,EAAEwC,eAAgBqD,KACtBzE,EAAOpB,EAAEwC,eAAgBqD,IAAcP,IAIlCtrB,GAAQqrB,GAAarF,EAAE+F,aAC5BT,EAAWtF,EAAE+F,WAAYT,EAAUtF,EAAEiB,WAGtCjnB,EAAO6rB,EACPA,EAAU3E,EAAU9zB,QAKnB,GAAiB,MAAZy4B,EAEJA,EAAU7rB,OAGJ,GAAc,MAATA,GAAgBA,IAAS6rB,EAAU,CAM9C,KAHAC,EAAOrD,EAAYzoB,EAAO,IAAM6rB,IAAapD,EAAY,KAAOoD,IAI/D,IAAMD,KAASnD,EAId,IADA/yB,EAAMk2B,EAAMt/B,MAAO,MACT,KAAQu/B,IAGjBC,EAAOrD,EAAYzoB,EAAO,IAAMtK,EAAK,KACpC+yB,EAAY,KAAO/yB,EAAK,KACb,EAGG,IAATo2B,EACJA,EAAOrD,EAAYmD,IAGgB,IAAxBnD,EAAYmD,KACvBC,EAAUn2B,EAAK,GACfwxB,EAAUvwB,QAASjB,EAAK,KAEzB,MAOJ,IAAc,IAATo2B,EAGJ,GAAKA,GAAQ9F,EAAEgG,UACdV,EAAWQ,EAAMR,QAEjB,IACCA,EAAWQ,EAAMR,GAChB,MAAQ95B,GACT,MAAO,CACN0R,MAAO,cACP/X,MAAO2gC,EAAOt6B,EAAI,sBAAwBwO,EAAO,OAAS6rB,IASjE,MAAO,CAAE3oB,MAAO,UAAWsE,KAAM8jB,GAidpBW,CAAajG,EAAGsF,EAAUlE,EAAOiE,GAGvCA,GAGCrF,EAAE8E,cACNS,EAAWnE,EAAM8C,kBAAmB,oBAEnCniC,EAAO+/B,aAAcsB,GAAamC,IAEnCA,EAAWnE,EAAM8C,kBAAmB,WAEnCniC,EAAOggC,KAAMqB,GAAamC,IAKZ,MAAXhB,GAA6B,SAAXvE,EAAEt/B,KACxB+jC,EAAa,YAGS,MAAXF,EACXE,EAAa,eAIbA,EAAaa,EAASpoB,MACtB+nB,EAAUK,EAAS9jB,KAEnB6jB,IADAlgC,EAAQmgC,EAASngC,UAMlBA,EAAQs/B,GACHF,GAAWE,IACfA,EAAa,QACRF,EAAS,IACbA,EAAS,KAMZnD,EAAMmD,OAASA,EACfnD,EAAMqD,YAAeU,GAAoBV,GAAe,GAGnDY,EACJjoB,EAASmB,YAAaolB,EAAiB,CAAEsB,EAASR,EAAYrD,IAE9DhkB,EAASuB,WAAYglB,EAAiB,CAAEvC,EAAOqD,EAAYt/B,IAI5Di8B,EAAM0C,WAAYA,GAClBA,OAAaj/B,EAER4+B,GACJG,EAAmB3b,QAASod,EAAY,cAAgB,YACvD,CAAEjE,EAAOpB,EAAGqF,EAAYJ,EAAU9/B,IAIpC0+B,EAAiB/mB,SAAU6mB,EAAiB,CAAEvC,EAAOqD,IAEhDhB,IACJG,EAAmB3b,QAAS,eAAgB,CAAEmZ,EAAOpB,MAG3Cj+B,EAAO8/B,QAChB9/B,EAAOwlB,MAAMU,QAAS,cAKzB,OAAOmZ,GAGR8E,QAAS,SAAUlE,EAAKxgB,EAAMte,GAC7B,OAAOnB,EAAOW,IAAKs/B,EAAKxgB,EAAMte,EAAU,SAGzCijC,UAAW,SAAUnE,EAAK9+B,GACzB,OAAOnB,EAAOW,IAAKs/B,OAAKn9B,EAAW3B,EAAU,aAI/CnB,EAAOkB,KAAM,CAAE,MAAO,QAAU,SAAUsD,EAAImV,GAC7C3Z,EAAQ2Z,GAAW,SAAUsmB,EAAKxgB,EAAMte,EAAUxC,GAUjD,OAPKN,EAAYohB,KAChB9gB,EAAOA,GAAQwC,EACfA,EAAWse,EACXA,OAAO3c,GAID9C,EAAOmhC,KAAMnhC,EAAOmC,OAAQ,CAClC89B,IAAKA,EACLthC,KAAMgb,EACNulB,SAAUvgC,EACV8gB,KAAMA,EACNyjB,QAAS/hC,GACPnB,EAAO2C,cAAes9B,IAASA,OAIpCjgC,EAAOihC,cAAe,SAAUhD,GAC/B,IAAI9+B,EACJ,IAAMA,KAAK8+B,EAAE+E,QACa,iBAApB7jC,EAAEsF,gBACNw5B,EAAEqC,YAAcrC,EAAE+E,QAAS7jC,IAAO,MAMrCa,EAAOwsB,SAAW,SAAUyT,EAAK79B,EAASlD,GACzC,OAAOc,EAAOmhC,KAAM,CACnBlB,IAAKA,EAGLthC,KAAM,MACNugC,SAAU,SACVh0B,OAAO,EACPm1B,OAAO,EACP7jC,QAAQ,EAKRkkC,WAAY,CACX2D,cAAe,cAEhBL,WAAY,SAAUT,GACrBvjC,EAAO0D,WAAY6/B,EAAUnhC,EAASlD,OAMzCc,EAAOG,GAAGgC,OAAQ,CACjBmiC,QAAS,SAAU/X,GAClB,IAAI/H,EAyBJ,OAvBKxnB,KAAM,KACLqB,EAAYkuB,KAChBA,EAAOA,EAAK9uB,KAAMT,KAAM,KAIzBwnB,EAAOxkB,EAAQusB,EAAMvvB,KAAM,GAAIkN,eAAgB1I,GAAI,GAAIgB,OAAO,GAEzDxF,KAAM,GAAI4C,YACd4kB,EAAK2I,aAAcnwB,KAAM,IAG1BwnB,EAAKpjB,IAAK,WACT,IAAIC,EAAOrE,KAEX,MAAQqE,EAAKkjC,kBACZljC,EAAOA,EAAKkjC,kBAGb,OAAOljC,IACJ4rB,OAAQjwB,OAGNA,MAGRwnC,UAAW,SAAUjY,GACpB,OAAKluB,EAAYkuB,GACTvvB,KAAKkE,KAAM,SAAU/B,GAC3Ba,EAAQhD,MAAOwnC,UAAWjY,EAAK9uB,KAAMT,KAAMmC,MAItCnC,KAAKkE,KAAM,WACjB,IAAIuW,EAAOzX,EAAQhD,MAClBgb,EAAWP,EAAKO,WAEZA,EAAS1X,OACb0X,EAASssB,QAAS/X,GAGlB9U,EAAKwV,OAAQV,MAKhB/H,KAAM,SAAU+H,GACf,IAAIkY,EAAiBpmC,EAAYkuB,GAEjC,OAAOvvB,KAAKkE,KAAM,SAAU/B,GAC3Ba,EAAQhD,MAAOsnC,QAASG,EAAiBlY,EAAK9uB,KAAMT,KAAMmC,GAAMotB,MAIlEmY,OAAQ,SAAUzkC,GAIjB,OAHAjD,KAAKmU,OAAQlR,GAAW2R,IAAK,QAAS1Q,KAAM,WAC3ClB,EAAQhD,MAAOswB,YAAatwB,KAAKwM,cAE3BxM,QAKTgD,EAAO6O,KAAKhI,QAAQ4vB,OAAS,SAAUp1B,GACtC,OAAQrB,EAAO6O,KAAKhI,QAAQ89B,QAAStjC,IAEtCrB,EAAO6O,KAAKhI,QAAQ89B,QAAU,SAAUtjC,GACvC,SAAWA,EAAKuuB,aAAevuB,EAAK0vB,cAAgB1vB,EAAKyxB,iBAAiBxyB,SAM3EN,EAAO6/B,aAAa+E,IAAM,WACzB,IACC,OAAO,IAAI7nC,EAAO8nC,eACjB,MAAQp7B,MAGX,IAAIq7B,GAAmB,CAGrBC,EAAG,IAIHC,KAAM,KAEPC,GAAejlC,EAAO6/B,aAAa+E,MAEpCxmC,EAAQ8mC,OAASD,IAAkB,oBAAqBA,GACxD7mC,EAAQ+iC,KAAO8D,KAAiBA,GAEhCjlC,EAAOkhC,cAAe,SAAU9+B,GAC/B,IAAIjB,EAAUgkC,EAGd,GAAK/mC,EAAQ8mC,MAAQD,KAAiB7iC,EAAQwgC,YAC7C,MAAO,CACNO,KAAM,SAAUH,EAAS/K,GACxB,IAAI94B,EACHylC,EAAMxiC,EAAQwiC,MAWf,GATAA,EAAIQ,KACHhjC,EAAQzD,KACRyD,EAAQ69B,IACR79B,EAAQi+B,MACRj+B,EAAQijC,SACRjjC,EAAQmR,UAIJnR,EAAQkjC,UACZ,IAAMnmC,KAAKiD,EAAQkjC,UAClBV,EAAKzlC,GAAMiD,EAAQkjC,UAAWnmC,GAmBhC,IAAMA,KAdDiD,EAAQmgC,UAAYqC,EAAItC,kBAC5BsC,EAAItC,iBAAkBlgC,EAAQmgC,UAQzBngC,EAAQwgC,aAAgBI,EAAS,sBACtCA,EAAS,oBAAuB,kBAItBA,EACV4B,EAAIvC,iBAAkBljC,EAAG6jC,EAAS7jC,IAInCgC,EAAW,SAAUxC,GACpB,OAAO,WACDwC,IACJA,EAAWgkC,EAAgBP,EAAIW,OAC9BX,EAAIY,QAAUZ,EAAIa,QAAUb,EAAIc,UAC/Bd,EAAIe,mBAAqB,KAEb,UAAThnC,EACJimC,EAAInC,QACgB,UAAT9jC,EAKgB,iBAAfimC,EAAIpC,OACfvK,EAAU,EAAG,SAEbA,EAGC2M,EAAIpC,OACJoC,EAAIlC,YAINzK,EACC6M,GAAkBF,EAAIpC,SAAYoC,EAAIpC,OACtCoC,EAAIlC,WAK+B,UAAjCkC,EAAIgB,cAAgB,SACM,iBAArBhB,EAAIiB,aACV,CAAEC,OAAQlB,EAAIrB,UACd,CAAEhkC,KAAMqlC,EAAIiB,cACbjB,EAAIxC,4BAQTwC,EAAIW,OAASpkC,IACbgkC,EAAgBP,EAAIY,QAAUZ,EAAIc,UAAYvkC,EAAU,cAKnC2B,IAAhB8hC,EAAIa,QACRb,EAAIa,QAAUN,EAEdP,EAAIe,mBAAqB,WAGA,IAAnBf,EAAI1mB,YAMRnhB,EAAO+f,WAAY,WACb3b,GACJgkC,OAQLhkC,EAAWA,EAAU,SAErB,IAGCyjC,EAAIzB,KAAM/gC,EAAQ0gC,YAAc1gC,EAAQqd,MAAQ,MAC/C,MAAQhW,GAGT,GAAKtI,EACJ,MAAMsI,IAKTg5B,MAAO,WACDthC,GACJA,QAWLnB,EAAOihC,cAAe,SAAUhD,GAC1BA,EAAE2E,cACN3E,EAAEjmB,SAAS3Y,QAAS,KAKtBW,EAAO+gC,UAAW,CACjBR,QAAS,CACRlhC,OAAQ,6FAGT2Y,SAAU,CACT3Y,OAAQ,2BAETqhC,WAAY,CACX2D,cAAe,SAAU9kC,GAExB,OADAS,EAAO0D,WAAYnE,GACZA,MAMVS,EAAOihC,cAAe,SAAU,SAAUhD,QACxBn7B,IAAZm7B,EAAE/yB,QACN+yB,EAAE/yB,OAAQ,GAEN+yB,EAAE2E,cACN3E,EAAEt/B,KAAO,SAKXqB,EAAOkhC,cAAe,SAAU,SAAUjD,GAIxC,IAAI5+B,EAAQ8B,EADb,GAAK88B,EAAE2E,aAAe3E,EAAE8H,YAEvB,MAAO,CACN5C,KAAM,SAAUlpB,EAAGge,GAClB54B,EAASW,EAAQ,YACf+O,KAAMkvB,EAAE8H,aAAe,IACvBrmB,KAAM,CAAEsmB,QAAS/H,EAAEgI,cAAernC,IAAKq/B,EAAEgC,MACzC7a,GAAI,aAAcjkB,EAAW,SAAU+kC,GACvC7mC,EAAOub,SACPzZ,EAAW,KACN+kC,GACJjO,EAAuB,UAAbiO,EAAIvnC,KAAmB,IAAM,IAAKunC,EAAIvnC,QAKnD/B,EAAS8C,KAAKC,YAAaN,EAAQ,KAEpCojC,MAAO,WACDthC,GACJA,QAUL,IAqGKshB,GArGD0jB,GAAe,GAClBC,GAAS,oBAGVpmC,EAAO+gC,UAAW,CACjBsF,MAAO,WACPC,cAAe,WACd,IAAInlC,EAAWglC,GAAa7/B,OAAWtG,EAAO+C,QAAU,IAAQlE,GAAMuF,OAEtE,OADApH,KAAMmE,IAAa,EACZA,KAKTnB,EAAOihC,cAAe,aAAc,SAAUhD,EAAGsI,EAAkBlH,GAElE,IAAImH,EAAcC,EAAaC,EAC9BC,GAAuB,IAAZ1I,EAAEoI,QAAqBD,GAAO37B,KAAMwzB,EAAEgC,KAChD,MACkB,iBAAXhC,EAAExe,MAE6C,KADnDwe,EAAEqC,aAAe,IACjBziC,QAAS,sCACXuoC,GAAO37B,KAAMwzB,EAAExe,OAAU,QAI5B,GAAKknB,GAAiC,UAArB1I,EAAEkB,UAAW,GA8D7B,OA3DAqH,EAAevI,EAAEqI,cAAgBjoC,EAAY4/B,EAAEqI,eAC9CrI,EAAEqI,gBACFrI,EAAEqI,cAGEK,EACJ1I,EAAG0I,GAAa1I,EAAG0I,GAAWzjC,QAASkjC,GAAQ,KAAOI,IAC/B,IAAZvI,EAAEoI,QACbpI,EAAEgC,MAAS5C,GAAO5yB,KAAMwzB,EAAEgC,KAAQ,IAAM,KAAQhC,EAAEoI,MAAQ,IAAMG,GAIjEvI,EAAEyC,WAAY,eAAkB,WAI/B,OAHMgG,GACL1mC,EAAOoD,MAAOojC,EAAe,mBAEvBE,EAAmB,IAI3BzI,EAAEkB,UAAW,GAAM,OAGnBsH,EAAc1pC,EAAQypC,GACtBzpC,EAAQypC,GAAiB,WACxBE,EAAoBplC,WAIrB+9B,EAAMjkB,OAAQ,gBAGQtY,IAAhB2jC,EACJzmC,EAAQjD,GAASu+B,WAAYkL,GAI7BzpC,EAAQypC,GAAiBC,EAIrBxI,EAAGuI,KAGPvI,EAAEqI,cAAgBC,EAAiBD,cAGnCH,GAAavoC,KAAM4oC,IAIfE,GAAqBroC,EAAYooC,IACrCA,EAAaC,EAAmB,IAGjCA,EAAoBD,OAAc3jC,IAI5B,WAYT1E,EAAQwoC,qBACHnkB,GAAO7lB,EAASiqC,eAAeD,mBAAoB,IAAKnkB,MACvD5U,UAAY,6BACiB,IAA3B4U,GAAKjZ,WAAWlJ,QAQxBN,EAAO2X,UAAY,SAAU8H,EAAMvf,EAAS4mC,GAC3C,MAAqB,iBAATrnB,EACJ,IAEgB,kBAAZvf,IACX4mC,EAAc5mC,EACdA,GAAU,GAKLA,IAIA9B,EAAQwoC,qBAMZ/yB,GALA3T,EAAUtD,EAASiqC,eAAeD,mBAAoB,KAKvCtnC,cAAe,SACzBkT,KAAO5V,EAASuV,SAASK,KAC9BtS,EAAQR,KAAKC,YAAakU,IAE1B3T,EAAUtD,GAKZynB,GAAWyiB,GAAe,IAD1BC,EAASzvB,EAAWnN,KAAMsV,IAKlB,CAAEvf,EAAQZ,cAAeynC,EAAQ,MAGzCA,EAAS3iB,GAAe,CAAE3E,GAAQvf,EAASmkB,GAEtCA,GAAWA,EAAQ/jB,QACvBN,EAAQqkB,GAAUzJ,SAGZ5a,EAAOgB,MAAO,GAAI+lC,EAAOv9B,cAlChC,IAAIqK,EAAMkzB,EAAQ1iB,GAyCnBrkB,EAAOG,GAAGsoB,KAAO,SAAUwX,EAAK+G,EAAQ7lC,GACvC,IAAIlB,EAAUtB,EAAM4kC,EACnB9rB,EAAOza,KACPyoB,EAAMwa,EAAIpiC,QAAS,KAsDpB,OApDY,EAAP4nB,IACJxlB,EAAWk7B,GAAkB8E,EAAI3iC,MAAOmoB,IACxCwa,EAAMA,EAAI3iC,MAAO,EAAGmoB,IAIhBpnB,EAAY2oC,IAGhB7lC,EAAW6lC,EACXA,OAASlkC,GAGEkkC,GAA4B,iBAAXA,IAC5BroC,EAAO,QAIW,EAAd8Y,EAAKnX,QACTN,EAAOmhC,KAAM,CACZlB,IAAKA,EAKLthC,KAAMA,GAAQ,MACdugC,SAAU,OACVzf,KAAMunB,IACHnhC,KAAM,SAAUggC,GAGnBtC,EAAWjiC,UAEXmW,EAAK8U,KAAMtsB,EAIVD,EAAQ,SAAUitB,OAAQjtB,EAAO2X,UAAWkuB,IAAiBr4B,KAAMvN,GAGnE4lC,KAKEzqB,OAAQja,GAAY,SAAUk+B,EAAOmD,GACxC/qB,EAAKvW,KAAM,WACVC,EAASxD,MAAOX,KAAMumC,GAAY,CAAElE,EAAMwG,aAAcrD,EAAQnD,QAK5DriC,MAMRgD,EAAO6O,KAAKhI,QAAQogC,SAAW,SAAU5lC,GACxC,OAAOrB,EAAO2B,KAAM3B,EAAOy5B,OAAQ,SAAUt5B,GAC5C,OAAOkB,IAASlB,EAAGkB,OAChBf,QAMLN,EAAOknC,OAAS,CACfC,UAAW,SAAU9lC,EAAMe,EAASjD,GACnC,IAAIioC,EAAaC,EAASC,EAAWC,EAAQC,EAAWC,EACvD/X,EAAW1vB,EAAOyhB,IAAKpgB,EAAM,YAC7BqmC,EAAU1nC,EAAQqB,GAClBynB,EAAQ,GAGS,WAAb4G,IACJruB,EAAKkgB,MAAMmO,SAAW,YAGvB8X,EAAYE,EAAQR,SACpBI,EAAYtnC,EAAOyhB,IAAKpgB,EAAM,OAC9BomC,EAAaznC,EAAOyhB,IAAKpgB,EAAM,SACI,aAAbquB,GAAwC,UAAbA,KACA,GAA9C4X,EAAYG,GAAa5pC,QAAS,SAMpC0pC,GADAH,EAAcM,EAAQhY,YACD3iB,IACrBs6B,EAAUD,EAAYzS,OAGtB4S,EAASxX,WAAYuX,IAAe,EACpCD,EAAUtX,WAAY0X,IAAgB,GAGlCppC,EAAY+D,KAGhBA,EAAUA,EAAQ3E,KAAM4D,EAAMlC,EAAGa,EAAOmC,OAAQ,GAAIqlC,KAGjC,MAAfplC,EAAQ2K,MACZ+b,EAAM/b,IAAQ3K,EAAQ2K,IAAMy6B,EAAUz6B,IAAQw6B,GAE1B,MAAhBnlC,EAAQuyB,OACZ7L,EAAM6L,KAASvyB,EAAQuyB,KAAO6S,EAAU7S,KAAS0S,GAG7C,UAAWjlC,EACfA,EAAQulC,MAAMlqC,KAAM4D,EAAMynB,GAG1B4e,EAAQjmB,IAAKqH,KAKhB9oB,EAAOG,GAAGgC,OAAQ,CAGjB+kC,OAAQ,SAAU9kC,GAGjB,GAAKd,UAAUhB,OACd,YAAmBwC,IAAZV,EACNpF,KACAA,KAAKkE,KAAM,SAAU/B,GACpBa,EAAOknC,OAAOC,UAAWnqC,KAAMoF,EAASjD,KAI3C,IAAIyoC,EAAMC,EACTxmC,EAAOrE,KAAM,GAEd,OAAMqE,EAQAA,EAAKyxB,iBAAiBxyB,QAK5BsnC,EAAOvmC,EAAKozB,wBACZoT,EAAMxmC,EAAK6I,cAAc4C,YAClB,CACNC,IAAK66B,EAAK76B,IAAM86B,EAAIC,YACpBnT,KAAMiT,EAAKjT,KAAOkT,EAAIE,cARf,CAAEh7B,IAAK,EAAG4nB,KAAM,QATxB,GAuBDjF,SAAU,WACT,GAAM1yB,KAAM,GAAZ,CAIA,IAAIgrC,EAAcd,EAAQhoC,EACzBmC,EAAOrE,KAAM,GACbirC,EAAe,CAAEl7B,IAAK,EAAG4nB,KAAM,GAGhC,GAAwC,UAAnC30B,EAAOyhB,IAAKpgB,EAAM,YAGtB6lC,EAAS7lC,EAAKozB,4BAER,CACNyS,EAASlqC,KAAKkqC,SAIdhoC,EAAMmC,EAAK6I,cACX89B,EAAe3mC,EAAK2mC,cAAgB9oC,EAAIyN,gBACxC,MAAQq7B,IACLA,IAAiB9oC,EAAIujB,MAAQulB,IAAiB9oC,EAAIyN,kBACT,WAA3C3M,EAAOyhB,IAAKumB,EAAc,YAE1BA,EAAeA,EAAapoC,WAExBooC,GAAgBA,IAAiB3mC,GAAkC,IAA1B2mC,EAAazpC,YAG1D0pC,EAAejoC,EAAQgoC,GAAed,UACzBn6B,KAAO/M,EAAOyhB,IAAKumB,EAAc,kBAAkB,GAChEC,EAAatT,MAAQ30B,EAAOyhB,IAAKumB,EAAc,mBAAmB,IAKpE,MAAO,CACNj7B,IAAKm6B,EAAOn6B,IAAMk7B,EAAal7B,IAAM/M,EAAOyhB,IAAKpgB,EAAM,aAAa,GACpEszB,KAAMuS,EAAOvS,KAAOsT,EAAatT,KAAO30B,EAAOyhB,IAAKpgB,EAAM,cAAc,MAc1E2mC,aAAc,WACb,OAAOhrC,KAAKoE,IAAK,WAChB,IAAI4mC,EAAehrC,KAAKgrC,aAExB,MAAQA,GAA2D,WAA3ChoC,EAAOyhB,IAAKumB,EAAc,YACjDA,EAAeA,EAAaA,aAG7B,OAAOA,GAAgBr7B,QAM1B3M,EAAOkB,KAAM,CAAE20B,WAAY,cAAeD,UAAW,eAAiB,SAAUjc,EAAQ+F,GACvF,IAAI3S,EAAM,gBAAkB2S,EAE5B1f,EAAOG,GAAIwZ,GAAW,SAAUva,GAC/B,OAAOgf,EAAQphB,KAAM,SAAUqE,EAAMsY,EAAQva,GAG5C,IAAIyoC,EAOJ,GANKppC,EAAU4C,GACdwmC,EAAMxmC,EACuB,IAAlBA,EAAK9C,WAChBspC,EAAMxmC,EAAKyL,kBAGChK,IAAR1D,EACJ,OAAOyoC,EAAMA,EAAKnoB,GAASre,EAAMsY,GAG7BkuB,EACJA,EAAIK,SACFn7B,EAAY86B,EAAIE,YAAV3oC,EACP2N,EAAM3N,EAAMyoC,EAAIC,aAIjBzmC,EAAMsY,GAAWva,GAEhBua,EAAQva,EAAKkC,UAAUhB,WAU5BN,EAAOkB,KAAM,CAAE,MAAO,QAAU,SAAUsD,EAAIkb,GAC7C1f,EAAOizB,SAAUvT,GAASkP,GAAcxwB,EAAQgyB,cAC/C,SAAU/uB,EAAMitB,GACf,GAAKA,EAIJ,OAHAA,EAAWD,GAAQhtB,EAAMqe,GAGlBoO,GAAUrjB,KAAM6jB,GACtBtuB,EAAQqB,GAAOquB,WAAYhQ,GAAS,KACpC4O,MAQLtuB,EAAOkB,KAAM,CAAEinC,OAAQ,SAAUC,MAAO,SAAW,SAAU/lC,EAAM1D,GAClEqB,EAAOkB,KAAM,CACZ2zB,QAAS,QAAUxyB,EACnB2W,QAASra,EACT0pC,GAAI,QAAUhmC,GACZ,SAAUimC,EAAcC,GAG1BvoC,EAAOG,GAAIooC,GAAa,SAAU3T,EAAQzwB,GACzC,IAAIka,EAAY/c,UAAUhB,SAAYgoC,GAAkC,kBAAX1T,GAC5DpC,EAAQ8V,KAA6B,IAAX1T,IAA6B,IAAVzwB,EAAiB,SAAW,UAE1E,OAAOia,EAAQphB,KAAM,SAAUqE,EAAM1C,EAAMwF,GAC1C,IAAIjF,EAEJ,OAAKT,EAAU4C,GAGyB,IAAhCknC,EAAS1qC,QAAS,SACxBwD,EAAM,QAAUgB,GAChBhB,EAAKzE,SAAS+P,gBAAiB,SAAWtK,GAIrB,IAAlBhB,EAAK9C,UACTW,EAAMmC,EAAKsL,gBAIJ3J,KAAKivB,IACX5wB,EAAKohB,KAAM,SAAWpgB,GAAQnD,EAAK,SAAWmD,GAC9ChB,EAAKohB,KAAM,SAAWpgB,GAAQnD,EAAK,SAAWmD,GAC9CnD,EAAK,SAAWmD,UAIDS,IAAVqB,EAGNnE,EAAOyhB,IAAKpgB,EAAM1C,EAAM6zB,GAGxBxyB,EAAOuhB,MAAOlgB,EAAM1C,EAAMwF,EAAOquB,IAChC7zB,EAAM0f,EAAYuW,OAAS9xB,EAAWub,QAM5Cre,EAAOkB,KAAM,CACZ,YACA,WACA,eACA,YACA,cACA,YACE,SAAUsD,EAAI7F,GAChBqB,EAAOG,GAAIxB,GAAS,SAAUwB,GAC7B,OAAOnD,KAAKooB,GAAIzmB,EAAMwB,MAOxBH,EAAOG,GAAGgC,OAAQ,CAEjB61B,KAAM,SAAU3S,EAAO5F,EAAMtf,GAC5B,OAAOnD,KAAKooB,GAAIC,EAAO,KAAM5F,EAAMtf,IAEpCqoC,OAAQ,SAAUnjB,EAAOllB,GACxB,OAAOnD,KAAKyoB,IAAKJ,EAAO,KAAMllB,IAG/BsoC,SAAU,SAAUxoC,EAAUolB,EAAO5F,EAAMtf,GAC1C,OAAOnD,KAAKooB,GAAIC,EAAOplB,EAAUwf,EAAMtf,IAExCuoC,WAAY,SAAUzoC,EAAUolB,EAAOllB,GAGtC,OAA4B,IAArBmB,UAAUhB,OAChBtD,KAAKyoB,IAAKxlB,EAAU,MACpBjD,KAAKyoB,IAAKJ,EAAOplB,GAAY,KAAME,IAGrCwoC,MAAO,SAAUC,EAAQC,GACxB,OAAO7rC,KAAKkuB,WAAY0d,GAASzd,WAAY0d,GAASD,MAIxD5oC,EAAOkB,KACN,wLAE4DqD,MAAO,KACnE,SAAUC,EAAInC,GAGbrC,EAAOG,GAAIkC,GAAS,SAAUod,EAAMtf,GACnC,OAA0B,EAAnBmB,UAAUhB,OAChBtD,KAAKooB,GAAI/iB,EAAM,KAAMod,EAAMtf,GAC3BnD,KAAKkpB,QAAS7jB,MAUlB,IAAI2E,GAAQ,qCAMZhH,EAAO8oC,MAAQ,SAAU3oC,EAAID,GAC5B,IAAIyN,EAAK6D,EAAMs3B,EAUf,GARwB,iBAAZ5oC,IACXyN,EAAMxN,EAAID,GACVA,EAAUC,EACVA,EAAKwN,GAKAtP,EAAY8B,GAalB,OARAqR,EAAOlU,EAAMG,KAAM6D,UAAW,IAC9BwnC,EAAQ,WACP,OAAO3oC,EAAGxC,MAAOuC,GAAWlD,KAAMwU,EAAK9T,OAAQJ,EAAMG,KAAM6D,eAItD8C,KAAOjE,EAAGiE,KAAOjE,EAAGiE,MAAQpE,EAAOoE,OAElC0kC,GAGR9oC,EAAO+oC,UAAY,SAAUC,GACvBA,EACJhpC,EAAOge,YAEPhe,EAAO4X,OAAO,IAGhB5X,EAAO6C,QAAUD,MAAMC,QACvB7C,EAAOipC,UAAYhpB,KAAKC,MACxBlgB,EAAOqJ,SAAWA,EAClBrJ,EAAO3B,WAAaA,EACpB2B,EAAOvB,SAAWA,EAClBuB,EAAOgf,UAAYA,EACnBhf,EAAOrB,KAAOmB,EAEdE,EAAOmpB,IAAMzjB,KAAKyjB,IAElBnpB,EAAOkpC,UAAY,SAAU5qC,GAK5B,IAAIK,EAAOqB,EAAOrB,KAAML,GACxB,OAAkB,WAATK,GAA8B,WAATA,KAK5BwqC,MAAO7qC,EAAMyxB,WAAYzxB,KAG5B0B,EAAOopC,KAAO,SAAU7pC,GACvB,OAAe,MAARA,EACN,IACEA,EAAO,IAAK2D,QAAS8D,GAAO,KAkBT,mBAAXqiC,QAAyBA,OAAOC,KAC3CD,OAAQ,SAAU,GAAI,WACrB,OAAOrpC,IAOT,IAGCupC,GAAUxsC,EAAOiD,OAGjBwpC,GAAKzsC,EAAO0sC,EAwBb,OAtBAzpC,EAAO0pC,WAAa,SAAUhnC,GAS7B,OARK3F,EAAO0sC,IAAMzpC,IACjBjD,EAAO0sC,EAAID,IAGP9mC,GAAQ3F,EAAOiD,SAAWA,IAC9BjD,EAAOiD,OAASupC,IAGVvpC,GAMiB,oBAAb/C,IACXF,EAAOiD,OAASjD,EAAO0sC,EAAIzpC,GAMrBA","file":"jquery-3.6.0.min.js"}
\ No newline at end of file
diff --git a/docs/deps/search-1.0.0/autocomplete.jquery.min.js b/docs/deps/search-1.0.0/autocomplete.jquery.min.js
new file mode 100644
index 00000000..fb63a6a2
--- /dev/null
+++ b/docs/deps/search-1.0.0/autocomplete.jquery.min.js
@@ -0,0 +1,7 @@
+/*!
+ * autocomplete.js 0.38.0
+ * https://github.com/algolia/autocomplete.js
+ * Copyright 2020 Algolia, Inc. and other contributors; Licensed MIT
+ */
+!function(a){function b(d){if(c[d])return c[d].exports;var e=c[d]={exports:{},id:d,loaded:!1};return a[d].call(e.exports,e,e.exports,b),e.loaded=!0,e.exports}var c={};b.m=a,b.c=c,b.p="",b(0)}([function(a,b,c){"use strict";a.exports=c(1)},function(a,b,c){"use strict";var d=c(2),e=c(3);d.element=e;var f=c(4);f.isArray=e.isArray,f.isFunction=e.isFunction,f.isObject=e.isPlainObject,f.bind=e.proxy,f.each=function(a,b){function c(a,c){return b(c,a)}e.each(a,c)},f.map=e.map,f.mixin=e.extend,f.Event=e.Event;var g,h,i,j=c(5),k=c(6);g=e.fn.autocomplete,h="aaAutocomplete",i={initialize:function(a,b){function c(){var c,d=e(this),f=new k({el:d});c=new j({input:d,eventBus:f,dropdownMenuContainer:a.dropdownMenuContainer,hint:void 0===a.hint||!!a.hint,minLength:a.minLength,autoselect:a.autoselect,autoselectOnBlur:a.autoselectOnBlur,tabAutocomplete:a.tabAutocomplete,openOnFocus:a.openOnFocus,templates:a.templates,debug:a.debug,clearOnSelected:a.clearOnSelected,cssClasses:a.cssClasses,datasets:b,keyboardShortcuts:a.keyboardShortcuts,appendTo:a.appendTo,autoWidth:a.autoWidth}),d.data(h,c)}return b=f.isArray(b)?b:[].slice.call(arguments,1),a=a||{},this.each(c)},open:function(){function a(){var a,b=e(this);(a=b.data(h))&&a.open()}return this.each(a)},close:function(){function a(){var a,b=e(this);(a=b.data(h))&&a.close()}return this.each(a)},val:function(a){function b(){var b,c=e(this);(b=c.data(h))&&b.setVal(a)}return arguments.length?this.each(b):function(a){var b,c;return(b=a.data(h))&&(c=b.getVal()),c}(this.first())},destroy:function(){function a(){var a,b=e(this);(a=b.data(h))&&(a.destroy(),b.removeData(h))}return this.each(a)}},e.fn.autocomplete=function(a){var b;return i[a]&&"initialize"!==a?(b=this.filter(function(){return!!e(this).data(h)}),i[a].apply(b,[].slice.call(arguments,1))):i.initialize.apply(this,arguments)},e.fn.autocomplete.noConflict=function(){return e.fn.autocomplete=g,this},e.fn.autocomplete.sources=j.sources,e.fn.autocomplete.escapeHighlightedString=f.escapeHighlightedString,a.exports=e.fn.autocomplete},function(a,b){"use strict";a.exports={element:null}},function(a,b){a.exports=jQuery},function(a,b,c){"use strict";function d(a){return a.replace(/[\-\[\]\/\{\}\(\)\*\+\?\.\\\^\$\|]/g,"\\$&")}var e=c(2);a.exports={isArray:null,isFunction:null,isObject:null,bind:null,each:null,map:null,mixin:null,isMsie:function(a){if(void 0===a&&(a=navigator.userAgent),/(msie|trident)/i.test(a)){var b=a.match(/(msie |rv:)(\d+(.\d+)?)/i);if(b)return b[2]}return!1},escapeRegExChars:function(a){return a.replace(/[\-\[\]\/\{\}\(\)\*\+\?\.\\\^\$\|]/g,"\\$&")},isNumber:function(a){return"number"==typeof a},toStr:function(a){return void 0===a||null===a?"":a+""},cloneDeep:function(a){var b=this.mixin({},a),c=this;return this.each(b,function(a,d){a&&(c.isArray(a)?b[d]=[].concat(a):c.isObject(a)&&(b[d]=c.cloneDeep(a)))}),b},error:function(a){throw new Error(a)},every:function(a,b){var c=!0;return a?(this.each(a,function(d,e){c&&(c=b.call(null,d,e,a)&&c)}),!!c):c},any:function(a,b){var c=!1;return a?(this.each(a,function(d,e){if(b.call(null,d,e,a))return c=!0,!1}),c):c},getUniqueId:function(){var a=0;return function(){return a++}}(),templatify:function(a){if(this.isFunction(a))return a;var b=e.element(a);return"SCRIPT"===b.prop("tagName")?function(){return b.text()}:function(){return String(a)}},defer:function(a){setTimeout(a,0)},noop:function(){},formatPrefix:function(a,b){return b?"":a+"-"},className:function(a,b,c){return(c?"":".")+a+b},escapeHighlightedString:function(a,b,c){b=b||"<em>";var e=document.createElement("div");e.appendChild(document.createTextNode(b)),c=c||"</em>";var f=document.createElement("div");f.appendChild(document.createTextNode(c));var g=document.createElement("div");return g.appendChild(document.createTextNode(a)),g.innerHTML.replace(RegExp(d(e.innerHTML),"g"),b).replace(RegExp(d(f.innerHTML),"g"),c)}}},function(a,b,c){"use strict";function d(a){var b,c;if(a=a||{},a.input||i.error("missing input"),this.isActivated=!1,this.debug=!!a.debug,this.autoselect=!!a.autoselect,this.autoselectOnBlur=!!a.autoselectOnBlur,this.openOnFocus=!!a.openOnFocus,this.minLength=i.isNumber(a.minLength)?a.minLength:1,this.autoWidth=void 0===a.autoWidth||!!a.autoWidth,this.clearOnSelected=!!a.clearOnSelected,this.tabAutocomplete=void 0===a.tabAutocomplete||!!a.tabAutocomplete,a.hint=!!a.hint,a.hint&&a.appendTo)throw new Error("[autocomplete.js] hint and appendTo options can't be used at the same time");this.css=a.css=i.mixin({},o,a.appendTo?o.appendTo:{}),this.cssClasses=a.cssClasses=i.mixin({},o.defaultClasses,a.cssClasses||{}),this.cssClasses.prefix=a.cssClasses.formattedPrefix=i.formatPrefix(this.cssClasses.prefix,this.cssClasses.noPrefix),this.listboxId=a.listboxId=[this.cssClasses.root,"listbox",i.getUniqueId()].join("-");var f=e(a);this.$node=f.wrapper;var g=this.$input=f.input;b=f.menu,c=f.hint,a.dropdownMenuContainer&&j.element(a.dropdownMenuContainer).css("position","relative").append(b.css("top","0")),g.on("blur.aa",function(a){var c=document.activeElement;i.isMsie()&&(b[0]===c||b[0].contains(c))&&(a.preventDefault(),a.stopImmediatePropagation(),i.defer(function(){g.focus()}))}),b.on("mousedown.aa",function(a){a.preventDefault()}),this.eventBus=a.eventBus||new k({el:g}),this.dropdown=new d.Dropdown({appendTo:a.appendTo,wrapper:this.$node,menu:b,datasets:a.datasets,templates:a.templates,cssClasses:a.cssClasses,minLength:this.minLength}).onSync("suggestionClicked",this._onSuggestionClicked,this).onSync("cursorMoved",this._onCursorMoved,this).onSync("cursorRemoved",this._onCursorRemoved,this).onSync("opened",this._onOpened,this).onSync("closed",this._onClosed,this).onSync("shown",this._onShown,this).onSync("empty",this._onEmpty,this).onSync("redrawn",this._onRedrawn,this).onAsync("datasetRendered",this._onDatasetRendered,this),this.input=new d.Input({input:g,hint:c}).onSync("focused",this._onFocused,this).onSync("blurred",this._onBlurred,this).onSync("enterKeyed",this._onEnterKeyed,this).onSync("tabKeyed",this._onTabKeyed,this).onSync("escKeyed",this._onEscKeyed,this).onSync("upKeyed",this._onUpKeyed,this).onSync("downKeyed",this._onDownKeyed,this).onSync("leftKeyed",this._onLeftKeyed,this).onSync("rightKeyed",this._onRightKeyed,this).onSync("queryChanged",this._onQueryChanged,this).onSync("whitespaceChanged",this._onWhitespaceChanged,this),this._bindKeyboardShortcuts(a),this._setLanguageDirection()}function e(a){var b,c,d,e;b=j.element(a.input),c=j.element(n.wrapper.replace("%ROOT%",a.cssClasses.root)).css(a.css.wrapper),a.appendTo||"block"!==b.css("display")||"table"!==b.parent().css("display")||c.css("display","table-cell");var g=n.dropdown.replace("%PREFIX%",a.cssClasses.prefix).replace("%DROPDOWN_MENU%",a.cssClasses.dropdownMenu);d=j.element(g).css(a.css.dropdown).attr({role:"listbox",id:a.listboxId}),a.templates&&a.templates.dropdownMenu&&d.html(i.templatify(a.templates.dropdownMenu)()),e=b.clone().css(a.css.hint).css(f(b)),e.val("").addClass(i.className(a.cssClasses.prefix,a.cssClasses.hint,!0)).removeAttr("id name placeholder required").prop("readonly",!0).attr({"aria-hidden":"true",autocomplete:"off",spellcheck:"false",tabindex:-1}),e.removeData&&e.removeData(),b.data(h,{"aria-autocomplete":b.attr("aria-autocomplete"),"aria-expanded":b.attr("aria-expanded"),"aria-owns":b.attr("aria-owns"),autocomplete:b.attr("autocomplete"),dir:b.attr("dir"),role:b.attr("role"),spellcheck:b.attr("spellcheck"),style:b.attr("style"),type:b.attr("type")}),b.addClass(i.className(a.cssClasses.prefix,a.cssClasses.input,!0)).attr({autocomplete:"off",spellcheck:!1,role:"combobox","aria-autocomplete":a.datasets&&a.datasets[0]&&a.datasets[0].displayKey?"both":"list","aria-expanded":"false","aria-label":a.ariaLabel,"aria-owns":a.listboxId}).css(a.hint?a.css.input:a.css.inputWithNoHint);try{b.attr("dir")||b.attr("dir","auto")}catch(a){}return c=a.appendTo?c.appendTo(j.element(a.appendTo).eq(0)).eq(0):b.wrap(c).parent(),c.prepend(a.hint?e:null).append(d),{wrapper:c,input:b,hint:e,menu:d}}function f(a){return{backgroundAttachment:a.css("background-attachment"),backgroundClip:a.css("background-clip"),backgroundColor:a.css("background-color"),backgroundImage:a.css("background-image"),backgroundOrigin:a.css("background-origin"),backgroundPosition:a.css("background-position"),backgroundRepeat:a.css("background-repeat"),backgroundSize:a.css("background-size")}}function g(a,b){var c=a.find(i.className(b.prefix,b.input));i.each(c.data(h),function(a,b){void 0===a?c.removeAttr(b):c.attr(b,a)}),c.detach().removeClass(i.className(b.prefix,b.input,!0)).insertAfter(a),c.removeData&&c.removeData(h),a.remove()}var h="aaAttrs",i=c(4),j=c(2),k=c(6),l=c(7),m=c(16),n=c(18),o=c(19);i.mixin(d.prototype,{_bindKeyboardShortcuts:function(a){if(a.keyboardShortcuts){var b=this.$input,c=[];i.each(a.keyboardShortcuts,function(a){"string"==typeof a&&(a=a.toUpperCase().charCodeAt(0)),c.push(a)}),j.element(document).keydown(function(a){var d=a.target||a.srcElement,e=d.tagName;if(!d.isContentEditable&&"INPUT"!==e&&"SELECT"!==e&&"TEXTAREA"!==e){var f=a.which||a.keyCode;c.indexOf(f)!==-1&&(b.focus(),a.stopPropagation(),a.preventDefault())}})}},_onSuggestionClicked:function(a,b){var c,d={selectionMethod:"click"};(c=this.dropdown.getDatumForSuggestion(b))&&this._select(c,d)},_onCursorMoved:function(a,b){var c=this.dropdown.getDatumForCursor(),d=this.dropdown.getCurrentCursor().attr("id");this.input.setActiveDescendant(d),c&&(b&&this.input.setInputValue(c.value,!0),this.eventBus.trigger("cursorchanged",c.raw,c.datasetName))},_onCursorRemoved:function(){this.input.resetInputValue(),this._updateHint(),this.eventBus.trigger("cursorremoved")},_onDatasetRendered:function(){this._updateHint(),this.eventBus.trigger("updated")},_onOpened:function(){this._updateHint(),this.input.expand(),this.eventBus.trigger("opened")},_onEmpty:function(){this.eventBus.trigger("empty")},_onRedrawn:function(){this.$node.css("top","0px"),this.$node.css("left","0px");var a=this.$input[0].getBoundingClientRect();this.autoWidth&&this.$node.css("width",a.width+"px");var b=this.$node[0].getBoundingClientRect(),c=a.bottom-b.top;this.$node.css("top",c+"px");var d=a.left-b.left;this.$node.css("left",d+"px"),this.eventBus.trigger("redrawn")},_onShown:function(){this.eventBus.trigger("shown"),this.autoselect&&this.dropdown.cursorTopSuggestion()},_onClosed:function(){this.input.clearHint(),this.input.removeActiveDescendant(),this.input.collapse(),this.eventBus.trigger("closed")},_onFocused:function(){if(this.isActivated=!0,this.openOnFocus){var a=this.input.getQuery();a.length>=this.minLength?this.dropdown.update(a):this.dropdown.empty(),this.dropdown.open()}},_onBlurred:function(){var a,b;a=this.dropdown.getDatumForCursor(),b=this.dropdown.getDatumForTopSuggestion();var c={selectionMethod:"blur"};this.debug||(this.autoselectOnBlur&&a?this._select(a,c):this.autoselectOnBlur&&b?this._select(b,c):(this.isActivated=!1,this.dropdown.empty(),this.dropdown.close()))},_onEnterKeyed:function(a,b){var c,d;c=this.dropdown.getDatumForCursor(),d=this.dropdown.getDatumForTopSuggestion();var e={selectionMethod:"enterKey"};c?(this._select(c,e),b.preventDefault()):this.autoselect&&d&&(this._select(d,e),b.preventDefault())},_onTabKeyed:function(a,b){if(!this.tabAutocomplete)return void this.dropdown.close();var c,d={selectionMethod:"tabKey"};(c=this.dropdown.getDatumForCursor())?(this._select(c,d),b.preventDefault()):this._autocomplete(!0)},_onEscKeyed:function(){this.dropdown.close(),this.input.resetInputValue()},_onUpKeyed:function(){var a=this.input.getQuery();this.dropdown.isEmpty&&a.length>=this.minLength?this.dropdown.update(a):this.dropdown.moveCursorUp(),this.dropdown.open()},_onDownKeyed:function(){var a=this.input.getQuery();this.dropdown.isEmpty&&a.length>=this.minLength?this.dropdown.update(a):this.dropdown.moveCursorDown(),this.dropdown.open()},_onLeftKeyed:function(){"rtl"===this.dir&&this._autocomplete()},_onRightKeyed:function(){"ltr"===this.dir&&this._autocomplete()},_onQueryChanged:function(a,b){this.input.clearHintIfInvalid(),b.length>=this.minLength?this.dropdown.update(b):this.dropdown.empty(),this.dropdown.open(),this._setLanguageDirection()},_onWhitespaceChanged:function(){this._updateHint(),this.dropdown.open()},_setLanguageDirection:function(){var a=this.input.getLanguageDirection();this.dir!==a&&(this.dir=a,this.$node.css("direction",a),this.dropdown.setLanguageDirection(a))},_updateHint:function(){var a,b,c,d,e,f;a=this.dropdown.getDatumForTopSuggestion(),a&&this.dropdown.isVisible()&&!this.input.hasOverflow()?(b=this.input.getInputValue(),c=l.normalizeQuery(b),d=i.escapeRegExChars(c),e=new RegExp("^(?:"+d+")(.+$)","i"),f=e.exec(a.value),f?this.input.setHint(b+f[1]):this.input.clearHint()):this.input.clearHint()},_autocomplete:function(a){var b,c,d,e;b=this.input.getHint(),c=this.input.getQuery(),d=a||this.input.isCursorAtEnd(),b&&c!==b&&d&&(e=this.dropdown.getDatumForTopSuggestion(),e&&this.input.setInputValue(e.value),this.eventBus.trigger("autocompleted",e.raw,e.datasetName))},_select:function(a,b){void 0!==a.value&&this.input.setQuery(a.value),this.clearOnSelected?this.setVal(""):this.input.setInputValue(a.value,!0),this._setLanguageDirection(),this.eventBus.trigger("selected",a.raw,a.datasetName,b).isDefaultPrevented()===!1&&(this.dropdown.close(),i.defer(i.bind(this.dropdown.empty,this.dropdown)))},open:function(){if(!this.isActivated){var a=this.input.getInputValue();a.length>=this.minLength?this.dropdown.update(a):this.dropdown.empty()}this.dropdown.open()},close:function(){this.dropdown.close()},setVal:function(a){a=i.toStr(a),this.isActivated?this.input.setInputValue(a):(this.input.setQuery(a),this.input.setInputValue(a,!0)),this._setLanguageDirection()},getVal:function(){return this.input.getQuery()},destroy:function(){this.input.destroy(),this.dropdown.destroy(),g(this.$node,this.cssClasses),this.$node=null},getWrapper:function(){return this.dropdown.$container[0]}}),d.Dropdown=m,d.Input=l,d.sources=c(20),a.exports=d},function(a,b,c){"use strict";function d(a){a&&a.el||e.error("EventBus initialized without el"),this.$el=f.element(a.el)}var e=c(4),f=c(2);e.mixin(d.prototype,{trigger:function(a,b,c,d){var f=e.Event("autocomplete:"+a);return this.$el.trigger(f,[b,c,d]),f}}),a.exports=d},function(a,b,c){"use strict";function d(a){var b,c,d,f,g=this;a=a||{},a.input||i.error("input is missing"),b=i.bind(this._onBlur,this),c=i.bind(this._onFocus,this),d=i.bind(this._onKeydown,this),f=i.bind(this._onInput,this),this.$hint=j.element(a.hint),this.$input=j.element(a.input).on("blur.aa",b).on("focus.aa",c).on("keydown.aa",d),0===this.$hint.length&&(this.setHint=this.getHint=this.clearHint=this.clearHintIfInvalid=i.noop),i.isMsie()?this.$input.on("keydown.aa keypress.aa cut.aa paste.aa",function(a){h[a.which||a.keyCode]||i.defer(i.bind(g._onInput,g,a))}):this.$input.on("input.aa",f),this.query=this.$input.val(),this.$overflowHelper=e(this.$input)}function e(a){return j.element('<pre aria-hidden="true"></pre>').css({position:"absolute",visibility:"hidden",whiteSpace:"pre",fontFamily:a.css("font-family"),fontSize:a.css("font-size"),fontStyle:a.css("font-style"),fontVariant:a.css("font-variant"),fontWeight:a.css("font-weight"),wordSpacing:a.css("word-spacing"),letterSpacing:a.css("letter-spacing"),textIndent:a.css("text-indent"),textRendering:a.css("text-rendering"),textTransform:a.css("text-transform")}).insertAfter(a)}function f(a,b){return d.normalizeQuery(a)===d.normalizeQuery(b)}function g(a){return a.altKey||a.ctrlKey||a.metaKey||a.shiftKey}var h;h={9:"tab",27:"esc",37:"left",39:"right",13:"enter",38:"up",40:"down"};var i=c(4),j=c(2),k=c(8);d.normalizeQuery=function(a){return(a||"").replace(/^\s*/g,"").replace(/\s{2,}/g," ")},i.mixin(d.prototype,k,{_onBlur:function(){this.resetInputValue(),this.$input.removeAttr("aria-activedescendant"),this.trigger("blurred")},_onFocus:function(){this.trigger("focused")},_onKeydown:function(a){var b=h[a.which||a.keyCode];this._managePreventDefault(b,a),b&&this._shouldTrigger(b,a)&&this.trigger(b+"Keyed",a)},_onInput:function(){this._checkInputValue()},_managePreventDefault:function(a,b){var c,d,e;switch(a){case"tab":d=this.getHint(),e=this.getInputValue(),c=d&&d!==e&&!g(b);break;case"up":case"down":c=!g(b);break;default:c=!1}c&&b.preventDefault()},_shouldTrigger:function(a,b){var c;switch(a){case"tab":c=!g(b);break;default:c=!0}return c},_checkInputValue:function(){var a,b,c;a=this.getInputValue(),b=f(a,this.query),c=!(!b||!this.query)&&this.query.length!==a.length,this.query=a,b?c&&this.trigger("whitespaceChanged",this.query):this.trigger("queryChanged",this.query)},focus:function(){this.$input.focus()},blur:function(){this.$input.blur()},getQuery:function(){return this.query},setQuery:function(a){this.query=a},getInputValue:function(){return this.$input.val()},setInputValue:function(a,b){void 0===a&&(a=this.query),this.$input.val(a),b?this.clearHint():this._checkInputValue()},expand:function(){this.$input.attr("aria-expanded","true")},collapse:function(){this.$input.attr("aria-expanded","false")},setActiveDescendant:function(a){this.$input.attr("aria-activedescendant",a)},removeActiveDescendant:function(){this.$input.removeAttr("aria-activedescendant")},resetInputValue:function(){this.setInputValue(this.query,!0)},getHint:function(){return this.$hint.val()},setHint:function(a){this.$hint.val(a)},clearHint:function(){this.setHint("")},clearHintIfInvalid:function(){var a,b,c,d;a=this.getInputValue(),b=this.getHint(),c=a!==b&&0===b.indexOf(a),(d=""!==a&&c&&!this.hasOverflow())||this.clearHint()},getLanguageDirection:function(){return(this.$input.css("direction")||"ltr").toLowerCase()},hasOverflow:function(){var a=this.$input.width()-2;return this.$overflowHelper.text(this.getInputValue()),this.$overflowHelper.width()>=a},isCursorAtEnd:function(){var a,b,c;return a=this.$input.val().length,b=this.$input[0].selectionStart,i.isNumber(b)?b===a:!document.selection||(c=document.selection.createRange(),c.moveStart("character",-a),a===c.text.length)},destroy:function(){this.$hint.off(".aa"),this.$input.off(".aa"),this.$hint=this.$input=this.$overflowHelper=null}}),a.exports=d},function(a,b,c){"use strict";function d(a,b,c,d){var e;if(!c)return this;for(b=b.split(l),c=d?j(c,d):c,this._callbacks=this._callbacks||{};e=b.shift();)this._callbacks[e]=this._callbacks[e]||{sync:[],async:[]},this._callbacks[e][a].push(c);return this}function e(a,b,c){return d.call(this,"async",a,b,c)}function f(a,b,c){return d.call(this,"sync",a,b,c)}function g(a){var b;if(!this._callbacks)return this;for(a=a.split(l);b=a.shift();)delete this._callbacks[b];return this}function h(a){var b,c,d,e,f;if(!this._callbacks)return this;for(a=a.split(l),d=[].slice.call(arguments,1);(b=a.shift())&&(c=this._callbacks[b]);)e=i(c.sync,this,[b].concat(d)),f=i(c.async,this,[b].concat(d)),e()&&k(f);return this}function i(a,b,c){function d(){for(var d,e=0,f=a.length;!d&&e<f;e+=1)d=a[e].apply(b,c)===!1;return!d}return d}function j(a,b){return a.bind?a.bind(b):function(){a.apply(b,[].slice.call(arguments,0))}}var k=c(9),l=/\s+/;a.exports={onSync:f,onAsync:e,off:g,trigger:h}},function(a,b,c){"use strict";function d(){h&&i&&(h=!1,i.length?m=i.concat(m):l=-1,m.length&&e())}function e(){if(!h){n=!1,h=!0;for(var a=m.length,b=setTimeout(d);a;){for(i=m,m=[];i&&++l<a;)i[l].run();l=-1,a=m.length}i=null,l=-1,h=!1,clearTimeout(b)}}function f(a,b){this.fun=a,this.array=b}function g(a){var b=new Array(arguments.length-1);if(arguments.length>1)for(var c=1;c<arguments.length;c++)b[c-1]=arguments[c];m.push(new f(a,b)),n||h||(n=!0,j())}for(var h,i,j,k=[c(10),c(12),c(13),c(14),c(15)],l=-1,m=[],n=!1,o=-1,p=k.length;++o<p;)if(k[o]&&k[o].test&&k[o].test()){j=k[o].install(e);break}f.prototype.run=function(){var a=this.fun,b=this.array;switch(b.length){case 0:return a();case 1:return a(b[0]);case 2:return a(b[0],b[1]);case 3:return a(b[0],b[1],b[2]);default:return a.apply(null,b)}},a.exports=g},function(a,b,c){(function(a){"use strict";b.test=function(){return void 0!==a&&!a.browser},b.install=function(b){return function(){a.nextTick(b)}}}).call(b,c(11))},function(a,b){function c(){throw new Error("setTimeout has not been defined")}function d(){throw new Error("clearTimeout has not been defined")}function e(a){if(k===setTimeout)return setTimeout(a,0);if((k===c||!k)&&setTimeout)return k=setTimeout,setTimeout(a,0);try{return k(a,0)}catch(b){try{return k.call(null,a,0)}catch(b){return k.call(this,a,0)}}}function f(a){if(l===clearTimeout)return clearTimeout(a);if((l===d||!l)&&clearTimeout)return l=clearTimeout,clearTimeout(a);try{return l(a)}catch(b){try{return l.call(null,a)}catch(b){return l.call(this,a)}}}function g(){p&&n&&(p=!1,n.length?o=n.concat(o):q=-1,o.length&&h())}function h(){if(!p){var a=e(g);p=!0;for(var b=o.length;b;){for(n=o,o=[];++q<b;)n&&n[q].run();q=-1,b=o.length}n=null,p=!1,f(a)}}function i(a,b){this.fun=a,this.array=b}function j(){}var k,l,m=a.exports={};!function(){try{k="function"==typeof setTimeout?setTimeout:c}catch(a){k=c}try{l="function"==typeof clearTimeout?clearTimeout:d}catch(a){l=d}}();var n,o=[],p=!1,q=-1;m.nextTick=function(a){var b=new Array(arguments.length-1);if(arguments.length>1)for(var c=1;c<arguments.length;c++)b[c-1]=arguments[c];o.push(new i(a,b)),1!==o.length||p||e(h)},i.prototype.run=function(){this.fun.apply(null,this.array)},m.title="browser",m.browser=!0,m.env={},m.argv=[],m.version="",m.versions={},m.on=j,m.addListener=j,m.once=j,m.off=j,m.removeListener=j,m.removeAllListeners=j,m.emit=j,m.binding=function(a){throw new Error("process.binding is not supported")},m.cwd=function(){return"/"},m.chdir=function(a){throw new Error("process.chdir is not supported")},m.umask=function(){return 0}},function(a,b){(function(a){"use strict";var c=a.MutationObserver||a.WebKitMutationObserver;b.test=function(){return c},b.install=function(b){var d=0,e=new c(b),f=a.document.createTextNode("");return e.observe(f,{characterData:!0}),function(){f.data=d=++d%2}}}).call(b,function(){return this}())},function(a,b){(function(a){"use strict";b.test=function(){return!a.setImmediate&&void 0!==a.MessageChannel},b.install=function(b){var c=new a.MessageChannel;return c.port1.onmessage=b,function(){c.port2.postMessage(0)}}}).call(b,function(){return this}())},function(a,b){(function(a){"use strict";b.test=function(){return"document"in a&&"onreadystatechange"in a.document.createElement("script")},b.install=function(b){return function(){var c=a.document.createElement("script");return c.onreadystatechange=function(){b(),c.onreadystatechange=null,c.parentNode.removeChild(c),c=null},a.document.documentElement.appendChild(c),b}}}).call(b,function(){return this}())},function(a,b){"use strict";b.test=function(){return!0},b.install=function(a){return function(){setTimeout(a,0)}}},function(a,b,c){"use strict";function d(a){var b,c,d,h=this;a=a||{},a.menu||f.error("menu is required"),f.isArray(a.datasets)||f.isObject(a.datasets)||f.error("1 or more datasets required"),a.datasets||f.error("datasets is required"),this.isOpen=!1,this.isEmpty=!0,this.minLength=a.minLength||0,this.templates={},this.appendTo=a.appendTo||!1,this.css=f.mixin({},j,a.appendTo?j.appendTo:{}),this.cssClasses=a.cssClasses=f.mixin({},j.defaultClasses,a.cssClasses||{}),this.cssClasses.prefix=a.cssClasses.formattedPrefix||f.formatPrefix(this.cssClasses.prefix,this.cssClasses.noPrefix),b=f.bind(this._onSuggestionClick,this),c=f.bind(this._onSuggestionMouseEnter,this),d=f.bind(this._onSuggestionMouseLeave,this);var i=f.className(this.cssClasses.prefix,this.cssClasses.suggestion);this.$menu=g.element(a.menu).on("mouseenter.aa",i,c).on("mouseleave.aa",i,d).on("click.aa",i,b),this.$container=a.appendTo?a.wrapper:this.$menu,a.templates&&a.templates.header&&(this.templates.header=f.templatify(a.templates.header),this.$menu.prepend(this.templates.header())),a.templates&&a.templates.empty&&(this.templates.empty=f.templatify(a.templates.empty),this.$empty=g.element('<div class="'+f.className(this.cssClasses.prefix,this.cssClasses.empty,!0)+'"></div>'),this.$menu.append(this.$empty),this.$empty.hide()),this.datasets=f.map(a.datasets,function(b){return e(h.$menu,b,a.cssClasses)}),f.each(this.datasets,function(a){var b=a.getRoot();b&&0===b.parent().length&&h.$menu.append(b),a.onSync("rendered",h._onRendered,h)}),a.templates&&a.templates.footer&&(this.templates.footer=f.templatify(a.templates.footer),this.$menu.append(this.templates.footer()));var k=this;g.element(window).resize(function(){k._redraw()})}function e(a,b,c){return new d.Dataset(f.mixin({$menu:a,cssClasses:c},b))}var f=c(4),g=c(2),h=c(8),i=c(17),j=c(19);f.mixin(d.prototype,h,{_onSuggestionClick:function(a){this.trigger("suggestionClicked",g.element(a.currentTarget))},_onSuggestionMouseEnter:function(a){var b=g.element(a.currentTarget);if(!b.hasClass(f.className(this.cssClasses.prefix,this.cssClasses.cursor,!0))){this._removeCursor();var c=this;setTimeout(function(){c._setCursor(b,!1)},0)}},_onSuggestionMouseLeave:function(a){if(a.relatedTarget){if(g.element(a.relatedTarget).closest("."+f.className(this.cssClasses.prefix,this.cssClasses.cursor,!0)).length>0)return}this._removeCursor(),this.trigger("cursorRemoved")},_onRendered:function(a,b){function c(a){return a.isEmpty()}function d(a){return a.templates&&a.templates.empty}if(this.isEmpty=f.every(this.datasets,c),this.isEmpty)if(b.length>=this.minLength&&this.trigger("empty"),this.$empty)if(b.length<this.minLength)this._hide();else{var e=this.templates.empty({query:this.datasets[0]&&this.datasets[0].query});this.$empty.html(e),this.$empty.show(),this._show()}else f.any(this.datasets,d)?b.length<this.minLength?this._hide():this._show():this._hide();else this.isOpen&&(this.$empty&&(this.$empty.empty(),this.$empty.hide()),b.length>=this.minLength?this._show():this._hide());this.trigger("datasetRendered")},_hide:function(){this.$container.hide()},_show:function(){this.$container.css("display","block"),this._redraw(),this.trigger("shown")},_redraw:function(){this.isOpen&&this.appendTo&&this.trigger("redrawn")},_getSuggestions:function(){return this.$menu.find(f.className(this.cssClasses.prefix,this.cssClasses.suggestion))},_getCursor:function(){return this.$menu.find(f.className(this.cssClasses.prefix,this.cssClasses.cursor)).first()},_setCursor:function(a,b){a.first().addClass(f.className(this.cssClasses.prefix,this.cssClasses.cursor,!0)).attr("aria-selected","true"),this.trigger("cursorMoved",b)},_removeCursor:function(){this._getCursor().removeClass(f.className(this.cssClasses.prefix,this.cssClasses.cursor,!0)).removeAttr("aria-selected")},_moveCursor:function(a){var b,c,d,e;if(this.isOpen){if(c=this._getCursor(),b=this._getSuggestions(),this._removeCursor(),d=b.index(c)+a,(d=(d+1)%(b.length+1)-1)===-1)return void this.trigger("cursorRemoved");d<-1&&(d=b.length-1),this._setCursor(e=b.eq(d),!0),this._ensureVisible(e)}},_ensureVisible:function(a){var b,c,d,e;b=a.position().top,c=b+a.height()+parseInt(a.css("margin-top"),10)+parseInt(a.css("margin-bottom"),10),d=this.$menu.scrollTop(),e=this.$menu.height()+parseInt(this.$menu.css("padding-top"),10)+parseInt(this.$menu.css("padding-bottom"),10),b<0?this.$menu.scrollTop(d+b):e<c&&this.$menu.scrollTop(d+(c-e))},close:function(){this.isOpen&&(this.isOpen=!1,this._removeCursor(),this._hide(),this.trigger("closed"))},open:function(){this.isOpen||(this.isOpen=!0,this.isEmpty||this._show(),this.trigger("opened"))},setLanguageDirection:function(a){this.$menu.css("ltr"===a?this.css.ltr:this.css.rtl)},moveCursorUp:function(){this._moveCursor(-1)},moveCursorDown:function(){this._moveCursor(1)},getDatumForSuggestion:function(a){var b=null;return a.length&&(b={raw:i.extractDatum(a),value:i.extractValue(a),datasetName:i.extractDatasetName(a)}),b},getCurrentCursor:function(){return this._getCursor().first()},getDatumForCursor:function(){return this.getDatumForSuggestion(this._getCursor().first())},getDatumForTopSuggestion:function(){return this.getDatumForSuggestion(this._getSuggestions().first())},cursorTopSuggestion:function(){this._setCursor(this._getSuggestions().first(),!1)},update:function(a){function b(b){b.update(a)}f.each(this.datasets,b)},empty:function(){function a(a){a.clear()}f.each(this.datasets,a),this.isEmpty=!0},isVisible:function(){return this.isOpen&&!this.isEmpty},destroy:function(){function a(a){a.destroy()}this.$menu.off(".aa"),this.$menu=null,f.each(this.datasets,a)}}),d.Dataset=i,a.exports=d},function(a,b,c){"use strict";function d(a){a=a||{},a.templates=a.templates||{},a.source||k.error("missing source"),a.name&&!g(a.name)&&k.error("invalid dataset name: "+a.name),this.query=null,this._isEmpty=!0,this.highlight=!!a.highlight,this.name=void 0===a.name||null===a.name?k.getUniqueId():a.name,this.source=a.source,this.displayFn=e(a.display||a.displayKey),this.debounce=a.debounce,this.cache=a.cache!==!1,this.templates=f(a.templates,this.displayFn),this.css=k.mixin({},n,a.appendTo?n.appendTo:{}),this.cssClasses=a.cssClasses=k.mixin({},n.defaultClasses,a.cssClasses||{}),this.cssClasses.prefix=a.cssClasses.formattedPrefix||k.formatPrefix(this.cssClasses.prefix,this.cssClasses.noPrefix);var b=k.className(this.cssClasses.prefix,this.cssClasses.dataset);this.$el=a.$menu&&a.$menu.find(b+"-"+this.name).length>0?l.element(a.$menu.find(b+"-"+this.name)[0]):l.element(m.dataset.replace("%CLASS%",this.name).replace("%PREFIX%",this.cssClasses.prefix).replace("%DATASET%",this.cssClasses.dataset)),this.$menu=a.$menu,this.clearCachedSuggestions()}function e(a){function b(b){return b[a]}return a=a||"value",k.isFunction(a)?a:b}function f(a,b){function c(a){return"<p>"+b(a)+"</p>"}return{empty:a.empty&&k.templatify(a.empty),header:a.header&&k.templatify(a.header),footer:a.footer&&k.templatify(a.footer),suggestion:a.suggestion||c}}function g(a){return/^[_a-zA-Z0-9-]+$/.test(a)}var h="aaDataset",i="aaValue",j="aaDatum",k=c(4),l=c(2),m=c(18),n=c(19),o=c(8);d.extractDatasetName=function(a){return l.element(a).data(h)},d.extractValue=function(a){return l.element(a).data(i)},d.extractDatum=function(a){var b=l.element(a).data(j);return"string"==typeof b&&(b=JSON.parse(b)),b},k.mixin(d.prototype,o,{_render:function(a,b){function c(){var b=[].slice.call(arguments,0);return b=[{query:a,isEmpty:!0}].concat(b),n.templates.empty.apply(this,b)}function d(){function a(a){var b,c=m.suggestion.replace("%PREFIX%",f.cssClasses.prefix).replace("%SUGGESTION%",f.cssClasses.suggestion);return b=l.element(c).attr({role:"option",id:["option",Math.floor(1e8*Math.random())].join("-")}).append(n.templates.suggestion.apply(this,[a].concat(e))),b.data(h,n.name),b.data(i,n.displayFn(a)||void 0),b.data(j,JSON.stringify(a)),b.children().each(function(){l.element(this).css(f.css.suggestionChild)}),b}var c,d,e=[].slice.call(arguments,0),f=this,g=m.suggestions.replace("%PREFIX%",this.cssClasses.prefix).replace("%SUGGESTIONS%",this.cssClasses.suggestions);return c=l.element(g).css(this.css.suggestions),d=k.map(b,a),c.append.apply(c,d),c}function e(){var b=[].slice.call(arguments,0);return b=[{query:a,isEmpty:!g}].concat(b),n.templates.header.apply(this,b)}function f(){var b=[].slice.call(arguments,0);return b=[{query:a,isEmpty:!g}].concat(b),n.templates.footer.apply(this,b)}if(this.$el){var g,n=this,o=[].slice.call(arguments,2);if(this.$el.empty(),g=b&&b.length,this._isEmpty=!g,!g&&this.templates.empty)this.$el.html(c.apply(this,o)).prepend(n.templates.header?e.apply(this,o):null).append(n.templates.footer?f.apply(this,o):null);else if(g)this.$el.html(d.apply(this,o)).prepend(n.templates.header?e.apply(this,o):null).append(n.templates.footer?f.apply(this,o):null);else if(b&&!Array.isArray(b))throw new TypeError("suggestions must be an array");this.$menu&&this.$menu.addClass(this.cssClasses.prefix+(g?"with":"without")+"-"+this.name).removeClass(this.cssClasses.prefix+(g?"without":"with")+"-"+this.name),this.trigger("rendered",a)}},getRoot:function(){return this.$el},update:function(a){function b(b){if(!this.canceled&&a===this.query){
+var c=[].slice.call(arguments,1);this.cacheSuggestions(a,b,c),this._render.apply(this,[a,b].concat(c))}}if(this.query=a,this.canceled=!1,this.shouldFetchFromCache(a))b.apply(this,[this.cachedSuggestions].concat(this.cachedRenderExtraArgs));else{var c=this,d=function(){c.canceled||c.source(a,b.bind(c))};if(this.debounce){var e=function(){c.debounceTimeout=null,d()};clearTimeout(this.debounceTimeout),this.debounceTimeout=setTimeout(e,this.debounce)}else d()}},cacheSuggestions:function(a,b,c){this.cachedQuery=a,this.cachedSuggestions=b,this.cachedRenderExtraArgs=c},shouldFetchFromCache:function(a){return this.cache&&this.cachedQuery===a&&this.cachedSuggestions&&this.cachedSuggestions.length},clearCachedSuggestions:function(){delete this.cachedQuery,delete this.cachedSuggestions,delete this.cachedRenderExtraArgs},cancel:function(){this.canceled=!0},clear:function(){this.$el&&(this.cancel(),this.$el.empty(),this.trigger("rendered",""))},isEmpty:function(){return this._isEmpty},destroy:function(){this.clearCachedSuggestions(),this.$el=null}}),a.exports=d},function(a,b){"use strict";a.exports={wrapper:'<span class="%ROOT%"></span>',dropdown:'<span class="%PREFIX%%DROPDOWN_MENU%"></span>',dataset:'<div class="%PREFIX%%DATASET%-%CLASS%"></div>',suggestions:'<span class="%PREFIX%%SUGGESTIONS%"></span>',suggestion:'<div class="%PREFIX%%SUGGESTION%"></div>'}},function(a,b,c){"use strict";var d=c(4),e={wrapper:{position:"relative",display:"inline-block"},hint:{position:"absolute",top:"0",left:"0",borderColor:"transparent",boxShadow:"none",opacity:"1"},input:{position:"relative",verticalAlign:"top",backgroundColor:"transparent"},inputWithNoHint:{position:"relative",verticalAlign:"top"},dropdown:{position:"absolute",top:"100%",left:"0",zIndex:"100",display:"none"},suggestions:{display:"block"},suggestion:{whiteSpace:"nowrap",cursor:"pointer"},suggestionChild:{whiteSpace:"normal"},ltr:{left:"0",right:"auto"},rtl:{left:"auto",right:"0"},defaultClasses:{root:"algolia-autocomplete",prefix:"aa",noPrefix:!1,dropdownMenu:"dropdown-menu",input:"input",hint:"hint",suggestions:"suggestions",suggestion:"suggestion",cursor:"cursor",dataset:"dataset",empty:"empty"},appendTo:{wrapper:{position:"absolute",zIndex:"100",display:"none"},input:{},inputWithNoHint:{},dropdown:{display:"block"}}};d.isMsie()&&d.mixin(e.input,{backgroundImage:"url(data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)"}),d.isMsie()&&d.isMsie()<=7&&d.mixin(e.input,{marginTop:"-1px"}),a.exports=e},function(a,b,c){"use strict";a.exports={hits:c(21),popularIn:c(24)}},function(a,b,c){"use strict";var d=c(4),e=c(22),f=c(23),g=function(){function a(a,d){return window.Promise.resolve().then(function(){return b.length&&(e=a.search(b),b=[]),e}).then(function(a){if(a)return c=a.results,c[d]})}var b=[],c=[],e=window.Promise.resolve();return function(c,e){return function(f,g){a(c.as,b.push({indexName:c.indexName,query:f,params:e})-1).then(function(a){a&&g(a.hits,a)}).catch(function(a){d.error(a.message)})}}}();a.exports=function(a,b){var c=f(a.as._ua);if(c&&c[0]>=3&&c[1]>20){var d="autocomplete.js "+e;a.as._ua.indexOf(d)===-1&&(a.as._ua+="; "+d)}return g(a,b)}},function(a,b){a.exports="0.38.0"},function(a,b){"use strict";a.exports=function(a){var b=a.match(/Algolia for JavaScript \((\d+\.)(\d+\.)(\d+)\)/)||a.match(/Algolia for vanilla JavaScript (\d+\.)(\d+\.)(\d+)/);if(b)return[b[1],b[2],b[3]]}},function(a,b,c){"use strict";var d=c(4),e=c(22),f=c(23);a.exports=function(a,b,c,g){function h(h,i){a.search(h,b,function(a,h){if(a)return void d.error(a.message);if(h.hits.length>0){var l=h.hits[0],m=d.mixin({hitsPerPage:0},c);delete m.source,delete m.index;var n=f(k.as._ua);return n&&n[0]>=3&&n[1]>20&&(b.additionalUA="autocomplete.js "+e),void k.search(j(l),m,function(a,b){if(a)return void d.error(a.message);var c=[];if(g.includeAll){var e=g.allTitle||"All departments";c.push(d.mixin({facet:{value:e,count:b.nbHits}},d.cloneDeep(l)))}d.each(b.facets,function(a,b){d.each(a,function(a,e){c.push(d.mixin({facet:{facet:b,value:e,count:a}},d.cloneDeep(l)))})});for(var f=1;f<h.hits.length;++f)c.push(h.hits[f]);i(c,h)})}i([])})}var i=f(a.as._ua);if(i&&i[0]>=3&&i[1]>20&&(b=b||{},b.additionalUA="autocomplete.js "+e),!c.source)return d.error("Missing 'source' key");var j=d.isFunction(c.source)?c.source:function(a){return a[c.source]};if(!c.index)return d.error("Missing 'index' key");var k=c.index;return g=g||{},h}}]);
\ No newline at end of file
diff --git a/docs/deps/search-1.0.0/fuse.min.js b/docs/deps/search-1.0.0/fuse.min.js
new file mode 100644
index 00000000..7def5985
--- /dev/null
+++ b/docs/deps/search-1.0.0/fuse.min.js
@@ -0,0 +1,9 @@
+/**
+ * Fuse.js v6.4.6 - Lightweight fuzzy-search (http://fusejs.io)
+ *
+ * Copyright (c) 2021 Kiro Risk (http://kiro.me)
+ * All Rights Reserved. Apache Software License 2.0
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ */
+var e,t;e=this,t=function(){"use strict";function e(t){return(e="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(e){return typeof e}:function(e){return e&&"function"==typeof Symbol&&e.constructor===Symbol&&e!==Symbol.prototype?"symbol":typeof e})(t)}function t(e,t){if(!(e instanceof t))throw new TypeError("Cannot call a class as a function")}function n(e,t){for(var n=0;n<t.length;n++){var r=t[n];r.enumerable=r.enumerable||!1,r.configurable=!0,"value"in r&&(r.writable=!0),Object.defineProperty(e,r.key,r)}}function r(e,t,r){return t&&n(e.prototype,t),r&&n(e,r),e}function i(e,t,n){return t in e?Object.defineProperty(e,t,{value:n,enumerable:!0,configurable:!0,writable:!0}):e[t]=n,e}function o(e,t){var n=Object.keys(e);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);t&&(r=r.filter((function(t){return Object.getOwnPropertyDescriptor(e,t).enumerable}))),n.push.apply(n,r)}return n}function c(e){for(var t=1;t<arguments.length;t++){var n=null!=arguments[t]?arguments[t]:{};t%2?o(Object(n),!0).forEach((function(t){i(e,t,n[t])})):Object.getOwnPropertyDescriptors?Object.defineProperties(e,Object.getOwnPropertyDescriptors(n)):o(Object(n)).forEach((function(t){Object.defineProperty(e,t,Object.getOwnPropertyDescriptor(n,t))}))}return e}function a(e,t){if("function"!=typeof t&&null!==t)throw new TypeError("Super expression must either be null or a function");e.prototype=Object.create(t&&t.prototype,{constructor:{value:e,writable:!0,configurable:!0}}),t&&u(e,t)}function s(e){return(s=Object.setPrototypeOf?Object.getPrototypeOf:function(e){return e.__proto__||Object.getPrototypeOf(e)})(e)}function u(e,t){return(u=Object.setPrototypeOf||function(e,t){return e.__proto__=t,e})(e,t)}function h(e,t){return!t||"object"!=typeof t&&"function"!=typeof t?function(e){if(void 0===e)throw new ReferenceError("this hasn't been initialised - super() hasn't been called");return e}(e):t}function f(e){var t=function(){if("undefined"==typeof Reflect||!Reflect.construct)return!1;if(Reflect.construct.sham)return!1;if("function"==typeof Proxy)return!0;try{return Date.prototype.toString.call(Reflect.construct(Date,[],(function(){}))),!0}catch(e){return!1}}();return function(){var n,r=s(e);if(t){var i=s(this).constructor;n=Reflect.construct(r,arguments,i)}else n=r.apply(this,arguments);return h(this,n)}}function l(e){return function(e){if(Array.isArray(e))return d(e)}(e)||function(e){if("undefined"!=typeof Symbol&&Symbol.iterator in Object(e))return Array.from(e)}(e)||function(e,t){if(e){if("string"==typeof e)return d(e,t);var n=Object.prototype.toString.call(e).slice(8,-1);return"Object"===n&&e.constructor&&(n=e.constructor.name),"Map"===n||"Set"===n?Array.from(e):"Arguments"===n||/^(?:Ui|I)nt(?:8|16|32)(?:Clamped)?Array$/.test(n)?d(e,t):void 0}}(e)||function(){throw new TypeError("Invalid attempt to spread non-iterable instance.\nIn order to be iterable, non-array objects must have a [Symbol.iterator]() method.")}()}function d(e,t){(null==t||t>e.length)&&(t=e.length);for(var n=0,r=new Array(t);n<t;n++)r[n]=e[n];return r}function v(e){return Array.isArray?Array.isArray(e):"[object Array]"===b(e)}function g(e){return"string"==typeof e}function y(e){return"number"==typeof e}function p(e){return!0===e||!1===e||function(e){return m(e)&&null!==e}(e)&&"[object Boolean]"==b(e)}function m(t){return"object"===e(t)}function k(e){return null!=e}function M(e){return!e.trim().length}function b(e){return null==e?void 0===e?"[object Undefined]":"[object Null]":Object.prototype.toString.call(e)}var x=function(e){return"Invalid value for key ".concat(e)},L=function(e){return"Pattern length exceeds max of ".concat(e,".")},S=Object.prototype.hasOwnProperty,w=function(){function e(n){var r=this;t(this,e),this._keys=[],this._keyMap={};var i=0;n.forEach((function(e){var t=_(e);i+=t.weight,r._keys.push(t),r._keyMap[t.id]=t,i+=t.weight})),this._keys.forEach((function(e){e.weight/=i}))}return r(e,[{key:"get",value:function(e){return this._keyMap[e]}},{key:"keys",value:function(){return this._keys}},{key:"toJSON",value:function(){return JSON.stringify(this._keys)}}]),e}();function _(e){var t=null,n=null,r=null,i=1;if(g(e)||v(e))r=e,t=O(e),n=j(e);else{if(!S.call(e,"name"))throw new Error(function(e){return"Missing ".concat(e," property in key")}("name"));var o=e.name;if(r=o,S.call(e,"weight")&&(i=e.weight)<=0)throw new Error(function(e){return"Property 'weight' in key '".concat(e,"' must be a positive integer")}(o));t=O(o),n=j(o)}return{path:t,id:n,weight:i,src:r}}function O(e){return v(e)?e:e.split(".")}function j(e){return v(e)?e.join("."):e}var A=c({},{isCaseSensitive:!1,includeScore:!1,keys:[],shouldSort:!0,sortFn:function(e,t){return e.score===t.score?e.idx<t.idx?-1:1:e.score<t.score?-1:1}},{},{includeMatches:!1,findAllMatches:!1,minMatchCharLength:1},{},{location:0,threshold:.6,distance:100},{},{useExtendedSearch:!1,getFn:function(e,t){var n=[],r=!1;return function e(t,i,o){if(k(t))if(i[o]){var c=t[i[o]];if(!k(c))return;if(o===i.length-1&&(g(c)||y(c)||p(c)))n.push(function(e){return null==e?"":function(e){if("string"==typeof e)return e;var t=e+"";return"0"==t&&1/e==-1/0?"-0":t}(e)}(c));else if(v(c)){r=!0;for(var a=0,s=c.length;a<s;a+=1)e(c[a],i,o+1)}else i.length&&e(c,i,o+1)}else n.push(t)}(e,g(t)?t.split("."):t,0),r?n:n[0]},ignoreLocation:!1,ignoreFieldNorm:!1}),I=/[^ ]+/g;function C(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:3,t=new Map,n=Math.pow(10,e);return{get:function(e){var r=e.match(I).length;if(t.has(r))return t.get(r);var i=1/Math.sqrt(r),o=parseFloat(Math.round(i*n)/n);return t.set(r,o),o},clear:function(){t.clear()}}}var E=function(){function e(){var n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{},r=n.getFn,i=void 0===r?A.getFn:r;t(this,e),this.norm=C(3),this.getFn=i,this.isCreated=!1,this.setIndexRecords()}return r(e,[{key:"setSources",value:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:[];this.docs=e}},{key:"setIndexRecords",value:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:[];this.records=e}},{key:"setKeys",value:function(){var e=this,t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:[];this.keys=t,this._keysMap={},t.forEach((function(t,n){e._keysMap[t.id]=n}))}},{key:"create",value:function(){var e=this;!this.isCreated&&this.docs.length&&(this.isCreated=!0,g(this.docs[0])?this.docs.forEach((function(t,n){e._addString(t,n)})):this.docs.forEach((function(t,n){e._addObject(t,n)})),this.norm.clear())}},{key:"add",value:function(e){var t=this.size();g(e)?this._addString(e,t):this._addObject(e,t)}},{key:"removeAt",value:function(e){this.records.splice(e,1);for(var t=e,n=this.size();t<n;t+=1)this.records[t].i-=1}},{key:"getValueForItemAtKeyId",value:function(e,t){return e[this._keysMap[t]]}},{key:"size",value:function(){return this.records.length}},{key:"_addString",value:function(e,t){if(k(e)&&!M(e)){var n={v:e,i:t,n:this.norm.get(e)};this.records.push(n)}}},{key:"_addObject",value:function(e,t){var n=this,r={i:t,$:{}};this.keys.forEach((function(t,i){var o=n.getFn(e,t.path);if(k(o))if(v(o))!function(){for(var e=[],t=[{nestedArrIndex:-1,value:o}];t.length;){var c=t.pop(),a=c.nestedArrIndex,s=c.value;if(k(s))if(g(s)&&!M(s)){var u={v:s,i:a,n:n.norm.get(s)};e.push(u)}else v(s)&&s.forEach((function(e,n){t.push({nestedArrIndex:n,value:e})}))}r.$[i]=e}();else if(!M(o)){var c={v:o,n:n.norm.get(o)};r.$[i]=c}})),this.records.push(r)}},{key:"toJSON",value:function(){return{keys:this.keys,records:this.records}}}]),e}();function $(e,t){var n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{},r=n.getFn,i=void 0===r?A.getFn:r,o=new E({getFn:i});return o.setKeys(e.map(_)),o.setSources(t),o.create(),o}function R(e){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{},n=t.errors,r=void 0===n?0:n,i=t.currentLocation,o=void 0===i?0:i,c=t.expectedLocation,a=void 0===c?0:c,s=t.distance,u=void 0===s?A.distance:s,h=t.ignoreLocation,f=void 0===h?A.ignoreLocation:h,l=r/e.length;if(f)return l;var d=Math.abs(a-o);return u?l+d/u:d?1:l}function F(){for(var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:[],t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:A.minMatchCharLength,n=[],r=-1,i=-1,o=0,c=e.length;o<c;o+=1){var a=e[o];a&&-1===r?r=o:a||-1===r||((i=o-1)-r+1>=t&&n.push([r,i]),r=-1)}return e[o-1]&&o-r>=t&&n.push([r,o-1]),n}function P(e){for(var t={},n=0,r=e.length;n<r;n+=1){var i=e.charAt(n);t[i]=(t[i]||0)|1<<r-n-1}return t}var N=function(){function e(n){var r=this,i=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{},o=i.location,c=void 0===o?A.location:o,a=i.threshold,s=void 0===a?A.threshold:a,u=i.distance,h=void 0===u?A.distance:u,f=i.includeMatches,l=void 0===f?A.includeMatches:f,d=i.findAllMatches,v=void 0===d?A.findAllMatches:d,g=i.minMatchCharLength,y=void 0===g?A.minMatchCharLength:g,p=i.isCaseSensitive,m=void 0===p?A.isCaseSensitive:p,k=i.ignoreLocation,M=void 0===k?A.ignoreLocation:k;if(t(this,e),this.options={location:c,threshold:s,distance:h,includeMatches:l,findAllMatches:v,minMatchCharLength:y,isCaseSensitive:m,ignoreLocation:M},this.pattern=m?n:n.toLowerCase(),this.chunks=[],this.pattern.length){var b=function(e,t){r.chunks.push({pattern:e,alphabet:P(e),startIndex:t})},x=this.pattern.length;if(x>32){for(var L=0,S=x%32,w=x-S;L<w;)b(this.pattern.substr(L,32),L),L+=32;if(S){var _=x-32;b(this.pattern.substr(_),_)}}else b(this.pattern,0)}}return r(e,[{key:"searchIn",value:function(e){var t=this.options,n=t.isCaseSensitive,r=t.includeMatches;if(n||(e=e.toLowerCase()),this.pattern===e){var i={isMatch:!0,score:0};return r&&(i.indices=[[0,e.length-1]]),i}var o=this.options,c=o.location,a=o.distance,s=o.threshold,u=o.findAllMatches,h=o.minMatchCharLength,f=o.ignoreLocation,d=[],v=0,g=!1;this.chunks.forEach((function(t){var n=t.pattern,i=t.alphabet,o=t.startIndex,y=function(e,t,n){var r=arguments.length>3&&void 0!==arguments[3]?arguments[3]:{},i=r.location,o=void 0===i?A.location:i,c=r.distance,a=void 0===c?A.distance:c,s=r.threshold,u=void 0===s?A.threshold:s,h=r.findAllMatches,f=void 0===h?A.findAllMatches:h,l=r.minMatchCharLength,d=void 0===l?A.minMatchCharLength:l,v=r.includeMatches,g=void 0===v?A.includeMatches:v,y=r.ignoreLocation,p=void 0===y?A.ignoreLocation:y;if(t.length>32)throw new Error(L(32));for(var m,k=t.length,M=e.length,b=Math.max(0,Math.min(o,M)),x=u,S=b,w=d>1||g,_=w?Array(M):[];(m=e.indexOf(t,S))>-1;){var O=R(t,{currentLocation:m,expectedLocation:b,distance:a,ignoreLocation:p});if(x=Math.min(O,x),S=m+k,w)for(var j=0;j<k;)_[m+j]=1,j+=1}S=-1;for(var I=[],C=1,E=k+M,$=1<<k-1,P=0;P<k;P+=1){for(var N=0,D=E;N<D;){var z=R(t,{errors:P,currentLocation:b+D,expectedLocation:b,distance:a,ignoreLocation:p});z<=x?N=D:E=D,D=Math.floor((E-N)/2+N)}E=D;var K=Math.max(1,b-D+1),q=f?M:Math.min(b+D,M)+k,W=Array(q+2);W[q+1]=(1<<P)-1;for(var J=q;J>=K;J-=1){var T=J-1,U=n[e.charAt(T)];if(w&&(_[T]=+!!U),W[J]=(W[J+1]<<1|1)&U,P&&(W[J]|=(I[J+1]|I[J])<<1|1|I[J+1]),W[J]&$&&(C=R(t,{errors:P,currentLocation:T,expectedLocation:b,distance:a,ignoreLocation:p}))<=x){if(x=C,(S=T)<=b)break;K=Math.max(1,2*b-S)}}var V=R(t,{errors:P+1,currentLocation:b,expectedLocation:b,distance:a,ignoreLocation:p});if(V>x)break;I=W}var B={isMatch:S>=0,score:Math.max(.001,C)};if(w){var G=F(_,d);G.length?g&&(B.indices=G):B.isMatch=!1}return B}(e,n,i,{location:c+o,distance:a,threshold:s,findAllMatches:u,minMatchCharLength:h,includeMatches:r,ignoreLocation:f}),p=y.isMatch,m=y.score,k=y.indices;p&&(g=!0),v+=m,p&&k&&(d=[].concat(l(d),l(k)))}));var y={isMatch:g,score:g?v/this.chunks.length:1};return g&&r&&(y.indices=d),y}}]),e}(),D=function(){function e(n){t(this,e),this.pattern=n}return r(e,[{key:"search",value:function(){}}],[{key:"isMultiMatch",value:function(e){return z(e,this.multiRegex)}},{key:"isSingleMatch",value:function(e){return z(e,this.singleRegex)}}]),e}();function z(e,t){var n=e.match(t);return n?n[1]:null}var K=function(e){a(i,e);var n=f(i);function i(e){return t(this,i),n.call(this,e)}return r(i,[{key:"search",value:function(e){var t=e===this.pattern;return{isMatch:t,score:t?0:1,indices:[0,this.pattern.length-1]}}}],[{key:"type",get:function(){return"exact"}},{key:"multiRegex",get:function(){return/^="(.*)"$/}},{key:"singleRegex",get:function(){return/^=(.*)$/}}]),i}(D),q=function(e){a(i,e);var n=f(i);function i(e){return t(this,i),n.call(this,e)}return r(i,[{key:"search",value:function(e){var t=-1===e.indexOf(this.pattern);return{isMatch:t,score:t?0:1,indices:[0,e.length-1]}}}],[{key:"type",get:function(){return"inverse-exact"}},{key:"multiRegex",get:function(){return/^!"(.*)"$/}},{key:"singleRegex",get:function(){return/^!(.*)$/}}]),i}(D),W=function(e){a(i,e);var n=f(i);function i(e){return t(this,i),n.call(this,e)}return r(i,[{key:"search",value:function(e){var t=e.startsWith(this.pattern);return{isMatch:t,score:t?0:1,indices:[0,this.pattern.length-1]}}}],[{key:"type",get:function(){return"prefix-exact"}},{key:"multiRegex",get:function(){return/^\^"(.*)"$/}},{key:"singleRegex",get:function(){return/^\^(.*)$/}}]),i}(D),J=function(e){a(i,e);var n=f(i);function i(e){return t(this,i),n.call(this,e)}return r(i,[{key:"search",value:function(e){var t=!e.startsWith(this.pattern);return{isMatch:t,score:t?0:1,indices:[0,e.length-1]}}}],[{key:"type",get:function(){return"inverse-prefix-exact"}},{key:"multiRegex",get:function(){return/^!\^"(.*)"$/}},{key:"singleRegex",get:function(){return/^!\^(.*)$/}}]),i}(D),T=function(e){a(i,e);var n=f(i);function i(e){return t(this,i),n.call(this,e)}return r(i,[{key:"search",value:function(e){var t=e.endsWith(this.pattern);return{isMatch:t,score:t?0:1,indices:[e.length-this.pattern.length,e.length-1]}}}],[{key:"type",get:function(){return"suffix-exact"}},{key:"multiRegex",get:function(){return/^"(.*)"\$$/}},{key:"singleRegex",get:function(){return/^(.*)\$$/}}]),i}(D),U=function(e){a(i,e);var n=f(i);function i(e){return t(this,i),n.call(this,e)}return r(i,[{key:"search",value:function(e){var t=!e.endsWith(this.pattern);return{isMatch:t,score:t?0:1,indices:[0,e.length-1]}}}],[{key:"type",get:function(){return"inverse-suffix-exact"}},{key:"multiRegex",get:function(){return/^!"(.*)"\$$/}},{key:"singleRegex",get:function(){return/^!(.*)\$$/}}]),i}(D),V=function(e){a(i,e);var n=f(i);function i(e){var r,o=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{},c=o.location,a=void 0===c?A.location:c,s=o.threshold,u=void 0===s?A.threshold:s,h=o.distance,f=void 0===h?A.distance:h,l=o.includeMatches,d=void 0===l?A.includeMatches:l,v=o.findAllMatches,g=void 0===v?A.findAllMatches:v,y=o.minMatchCharLength,p=void 0===y?A.minMatchCharLength:y,m=o.isCaseSensitive,k=void 0===m?A.isCaseSensitive:m,M=o.ignoreLocation,b=void 0===M?A.ignoreLocation:M;return t(this,i),(r=n.call(this,e))._bitapSearch=new N(e,{location:a,threshold:u,distance:f,includeMatches:d,findAllMatches:g,minMatchCharLength:p,isCaseSensitive:k,ignoreLocation:b}),r}return r(i,[{key:"search",value:function(e){return this._bitapSearch.searchIn(e)}}],[{key:"type",get:function(){return"fuzzy"}},{key:"multiRegex",get:function(){return/^"(.*)"$/}},{key:"singleRegex",get:function(){return/^(.*)$/}}]),i}(D),B=function(e){a(i,e);var n=f(i);function i(e){return t(this,i),n.call(this,e)}return r(i,[{key:"search",value:function(e){for(var t,n=0,r=[],i=this.pattern.length;(t=e.indexOf(this.pattern,n))>-1;)n=t+i,r.push([t,n-1]);var o=!!r.length;return{isMatch:o,score:o?0:1,indices:r}}}],[{key:"type",get:function(){return"include"}},{key:"multiRegex",get:function(){return/^'"(.*)"$/}},{key:"singleRegex",get:function(){return/^'(.*)$/}}]),i}(D),G=[K,B,W,J,U,T,q,V],H=G.length,Q=/ +(?=([^\"]*\"[^\"]*\")*[^\"]*$)/;function X(e){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};return e.split("|").map((function(e){for(var n=e.trim().split(Q).filter((function(e){return e&&!!e.trim()})),r=[],i=0,o=n.length;i<o;i+=1){for(var c=n[i],a=!1,s=-1;!a&&++s<H;){var u=G[s],h=u.isMultiMatch(c);h&&(r.push(new u(h,t)),a=!0)}if(!a)for(s=-1;++s<H;){var f=G[s],l=f.isSingleMatch(c);if(l){r.push(new f(l,t));break}}}return r}))}var Y=new Set([V.type,B.type]),Z=function(){function e(n){var r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{},i=r.isCaseSensitive,o=void 0===i?A.isCaseSensitive:i,c=r.includeMatches,a=void 0===c?A.includeMatches:c,s=r.minMatchCharLength,u=void 0===s?A.minMatchCharLength:s,h=r.ignoreLocation,f=void 0===h?A.ignoreLocation:h,l=r.findAllMatches,d=void 0===l?A.findAllMatches:l,v=r.location,g=void 0===v?A.location:v,y=r.threshold,p=void 0===y?A.threshold:y,m=r.distance,k=void 0===m?A.distance:m;t(this,e),this.query=null,this.options={isCaseSensitive:o,includeMatches:a,minMatchCharLength:u,findAllMatches:d,ignoreLocation:f,location:g,threshold:p,distance:k},this.pattern=o?n:n.toLowerCase(),this.query=X(this.pattern,this.options)}return r(e,[{key:"searchIn",value:function(e){var t=this.query;if(!t)return{isMatch:!1,score:1};var n=this.options,r=n.includeMatches;e=n.isCaseSensitive?e:e.toLowerCase();for(var i=0,o=[],c=0,a=0,s=t.length;a<s;a+=1){var u=t[a];o.length=0,i=0;for(var h=0,f=u.length;h<f;h+=1){var d=u[h],v=d.search(e),g=v.isMatch,y=v.indices,p=v.score;if(!g){c=0,i=0,o.length=0;break}if(i+=1,c+=p,r){var m=d.constructor.type;Y.has(m)?o=[].concat(l(o),l(y)):o.push(y)}}if(i){var k={isMatch:!0,score:c/i};return r&&(k.indices=o),k}}return{isMatch:!1,score:1}}}],[{key:"condition",value:function(e,t){return t.useExtendedSearch}}]),e}(),ee=[];function te(e,t){for(var n=0,r=ee.length;n<r;n+=1){var i=ee[n];if(i.condition(e,t))return new i(e,t)}return new N(e,t)}var ne="$and",re="$or",ie="$path",oe="$val",ce=function(e){return!(!e[ne]&&!e[re])},ae=function(e){return!!e[ie]},se=function(e){return!v(e)&&m(e)&&!ce(e)},ue=function(e){return i({},ne,Object.keys(e).map((function(t){return i({},t,e[t])})))};function he(e,t){var n=t.ignoreFieldNorm,r=void 0===n?A.ignoreFieldNorm:n;e.forEach((function(e){var t=1;e.matches.forEach((function(e){var n=e.key,i=e.norm,o=e.score,c=n?n.weight:null;t*=Math.pow(0===o&&c?Number.EPSILON:o,(c||1)*(r?1:i))})),e.score=t}))}function fe(e,t){var n=e.matches;t.matches=[],k(n)&&n.forEach((function(e){if(k(e.indices)&&e.indices.length){var n={indices:e.indices,value:e.value};e.key&&(n.key=e.key.src),e.idx>-1&&(n.refIndex=e.idx),t.matches.push(n)}}))}function le(e,t){t.score=e.score}function de(e,t){var n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{},r=n.includeMatches,i=void 0===r?A.includeMatches:r,o=n.includeScore,c=void 0===o?A.includeScore:o,a=[];return i&&a.push(fe),c&&a.push(le),e.map((function(e){var n=e.idx,r={item:t[n],refIndex:n};return a.length&&a.forEach((function(t){t(e,r)})),r}))}var ve=function(){function e(n){var r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{},i=arguments.length>2?arguments[2]:void 0;t(this,e),this.options=c({},A,{},r),this.options.useExtendedSearch,this._keyStore=new w(this.options.keys),this.setCollection(n,i)}return r(e,[{key:"setCollection",value:function(e,t){if(this._docs=e,t&&!(t instanceof E))throw new Error("Incorrect 'index' type");this._myIndex=t||$(this.options.keys,this._docs,{getFn:this.options.getFn})}},{key:"add",value:function(e){k(e)&&(this._docs.push(e),this._myIndex.add(e))}},{key:"remove",value:function(){for(var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:function(){return!1},t=[],n=0,r=this._docs.length;n<r;n+=1){var i=this._docs[n];e(i,n)&&(this.removeAt(n),n-=1,r-=1,t.push(i))}return t}},{key:"removeAt",value:function(e){this._docs.splice(e,1),this._myIndex.removeAt(e)}},{key:"getIndex",value:function(){return this._myIndex}},{key:"search",value:function(e){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{},n=t.limit,r=void 0===n?-1:n,i=this.options,o=i.includeMatches,c=i.includeScore,a=i.shouldSort,s=i.sortFn,u=i.ignoreFieldNorm,h=g(e)?g(this._docs[0])?this._searchStringList(e):this._searchObjectList(e):this._searchLogical(e);return he(h,{ignoreFieldNorm:u}),a&&h.sort(s),y(r)&&r>-1&&(h=h.slice(0,r)),de(h,this._docs,{includeMatches:o,includeScore:c})}},{key:"_searchStringList",value:function(e){var t=te(e,this.options),n=this._myIndex.records,r=[];return n.forEach((function(e){var n=e.v,i=e.i,o=e.n;if(k(n)){var c=t.searchIn(n),a=c.isMatch,s=c.score,u=c.indices;a&&r.push({item:n,idx:i,matches:[{score:s,value:n,norm:o,indices:u}]})}})),r}},{key:"_searchLogical",value:function(e){var t=this,n=function(e,t){var n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{},r=n.auto,i=void 0===r||r,o=function e(n){var r=Object.keys(n),o=ae(n);if(!o&&r.length>1&&!ce(n))return e(ue(n));if(se(n)){var c=o?n[ie]:r[0],a=o?n[oe]:n[c];if(!g(a))throw new Error(x(c));var s={keyId:j(c),pattern:a};return i&&(s.searcher=te(a,t)),s}var u={children:[],operator:r[0]};return r.forEach((function(t){var r=n[t];v(r)&&r.forEach((function(t){u.children.push(e(t))}))})),u};return ce(e)||(e=ue(e)),o(e)}(e,this.options),r=this._myIndex.records,i={},o=[];return r.forEach((function(e){var r=e.$,c=e.i;if(k(r)){var a=function e(n,r,i){if(!n.children){var o=n.keyId,c=n.searcher,a=t._findMatches({key:t._keyStore.get(o),value:t._myIndex.getValueForItemAtKeyId(r,o),searcher:c});return a&&a.length?[{idx:i,item:r,matches:a}]:[]}switch(n.operator){case ne:for(var s=[],u=0,h=n.children.length;u<h;u+=1){var f=e(n.children[u],r,i);if(!f.length)return[];s.push.apply(s,l(f))}return s;case re:for(var d=[],v=0,g=n.children.length;v<g;v+=1){var y=e(n.children[v],r,i);if(y.length){d.push.apply(d,l(y));break}}return d}}(n,r,c);a.length&&(i[c]||(i[c]={idx:c,item:r,matches:[]},o.push(i[c])),a.forEach((function(e){var t,n=e.matches;(t=i[c].matches).push.apply(t,l(n))})))}})),o}},{key:"_searchObjectList",value:function(e){var t=this,n=te(e,this.options),r=this._myIndex,i=r.keys,o=r.records,c=[];return o.forEach((function(e){var r=e.$,o=e.i;if(k(r)){var a=[];i.forEach((function(e,i){a.push.apply(a,l(t._findMatches({key:e,value:r[i],searcher:n})))})),a.length&&c.push({idx:o,item:r,matches:a})}})),c}},{key:"_findMatches",value:function(e){var t=e.key,n=e.value,r=e.searcher;if(!k(n))return[];var i=[];if(v(n))n.forEach((function(e){var n=e.v,o=e.i,c=e.n;if(k(n)){var a=r.searchIn(n),s=a.isMatch,u=a.score,h=a.indices;s&&i.push({score:u,key:t,value:n,idx:o,norm:c,indices:h})}}));else{var o=n.v,c=n.n,a=r.searchIn(o),s=a.isMatch,u=a.score,h=a.indices;s&&i.push({score:u,key:t,value:o,norm:c,indices:h})}return i}}]),e}();return ve.version="6.4.6",ve.createIndex=$,ve.parseIndex=function(e){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{},n=t.getFn,r=void 0===n?A.getFn:n,i=e.keys,o=e.records,c=new E({getFn:r});return c.setKeys(i),c.setIndexRecords(o),c},ve.config=A,function(){ee.push.apply(ee,arguments)}(Z),ve},"object"==typeof exports&&"undefined"!=typeof module?module.exports=t():"function"==typeof define&&define.amd?define(t):(e=e||self).Fuse=t();
\ No newline at end of file
diff --git a/docs/deps/search-1.0.0/mark.min.js b/docs/deps/search-1.0.0/mark.min.js
new file mode 100644
index 00000000..1eea0533
--- /dev/null
+++ b/docs/deps/search-1.0.0/mark.min.js
@@ -0,0 +1,7 @@
+/*!***************************************************
+* mark.js v8.11.1
+* https://markjs.io/
+* Copyright (c) 2014–2018, Julian Kühnel
+* Released under the MIT license https://git.io/vwTVl
+*****************************************************/
+!function(e,t){"object"==typeof exports&&"undefined"!=typeof module?module.exports=t():"function"==typeof define&&define.amd?define(t):e.Mark=t()}(this,function(){"use strict";var e="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(e){return typeof e}:function(e){return e&&"function"==typeof Symbol&&e.constructor===Symbol&&e!==Symbol.prototype?"symbol":typeof e},t=function(e,t){if(!(e instanceof t))throw new TypeError("Cannot call a class as a function")},n=function(){function e(e,t){for(var n=0;n<t.length;n++){var r=t[n];r.enumerable=r.enumerable||!1,r.configurable=!0,"value"in r&&(r.writable=!0),Object.defineProperty(e,r.key,r)}}return function(t,n,r){return n&&e(t.prototype,n),r&&e(t,r),t}}(),r=Object.assign||function(e){for(var t=1;t<arguments.length;t++){var n=arguments[t];for(var r in n)Object.prototype.hasOwnProperty.call(n,r)&&(e[r]=n[r])}return e},i=function(){function e(n){var r=!(arguments.length>1&&void 0!==arguments[1])||arguments[1],i=arguments.length>2&&void 0!==arguments[2]?arguments[2]:[],o=arguments.length>3&&void 0!==arguments[3]?arguments[3]:5e3;t(this,e),this.ctx=n,this.iframes=r,this.exclude=i,this.iframesTimeout=o}return n(e,[{key:"getContexts",value:function(){var e=[];return(void 0!==this.ctx&&this.ctx?NodeList.prototype.isPrototypeOf(this.ctx)?Array.prototype.slice.call(this.ctx):Array.isArray(this.ctx)?this.ctx:"string"==typeof this.ctx?Array.prototype.slice.call(document.querySelectorAll(this.ctx)):[this.ctx]:[]).forEach(function(t){var n=e.filter(function(e){return e.contains(t)}).length>0;-1!==e.indexOf(t)||n||e.push(t)}),e}},{key:"getIframeContents",value:function(e,t){var n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:function(){},r=void 0;try{var i=e.contentWindow;if(r=i.document,!i||!r)throw new Error("iframe inaccessible")}catch(e){n()}r&&t(r)}},{key:"isIframeBlank",value:function(e){var t=e.getAttribute("src").trim();return"about:blank"===e.contentWindow.location.href&&"about:blank"!==t&&t}},{key:"observeIframeLoad",value:function(e,t,n){var r=this,i=!1,o=null,a=function a(){if(!i){i=!0,clearTimeout(o);try{r.isIframeBlank(e)||(e.removeEventListener("load",a),r.getIframeContents(e,t,n))}catch(e){n()}}};e.addEventListener("load",a),o=setTimeout(a,this.iframesTimeout)}},{key:"onIframeReady",value:function(e,t,n){try{"complete"===e.contentWindow.document.readyState?this.isIframeBlank(e)?this.observeIframeLoad(e,t,n):this.getIframeContents(e,t,n):this.observeIframeLoad(e,t,n)}catch(e){n()}}},{key:"waitForIframes",value:function(e,t){var n=this,r=0;this.forEachIframe(e,function(){return!0},function(e){r++,n.waitForIframes(e.querySelector("html"),function(){--r||t()})},function(e){e||t()})}},{key:"forEachIframe",value:function(t,n,r){var i=this,o=arguments.length>3&&void 0!==arguments[3]?arguments[3]:function(){},a=t.querySelectorAll("iframe"),s=a.length,c=0;a=Array.prototype.slice.call(a);var u=function(){--s<=0&&o(c)};s||u(),a.forEach(function(t){e.matches(t,i.exclude)?u():i.onIframeReady(t,function(e){n(t)&&(c++,r(e)),u()},u)})}},{key:"createIterator",value:function(e,t,n){return document.createNodeIterator(e,t,n,!1)}},{key:"createInstanceOnIframe",value:function(t){return new e(t.querySelector("html"),this.iframes)}},{key:"compareNodeIframe",value:function(e,t,n){if(e.compareDocumentPosition(n)&Node.DOCUMENT_POSITION_PRECEDING){if(null===t)return!0;if(t.compareDocumentPosition(n)&Node.DOCUMENT_POSITION_FOLLOWING)return!0}return!1}},{key:"getIteratorNode",value:function(e){var t=e.previousNode();return{prevNode:t,node:null===t?e.nextNode():e.nextNode()&&e.nextNode()}}},{key:"checkIframeFilter",value:function(e,t,n,r){var i=!1,o=!1;return r.forEach(function(e,t){e.val===n&&(i=t,o=e.handled)}),this.compareNodeIframe(e,t,n)?(!1!==i||o?!1===i||o||(r[i].handled=!0):r.push({val:n,handled:!0}),!0):(!1===i&&r.push({val:n,handled:!1}),!1)}},{key:"handleOpenIframes",value:function(e,t,n,r){var i=this;e.forEach(function(e){e.handled||i.getIframeContents(e.val,function(e){i.createInstanceOnIframe(e).forEachNode(t,n,r)})})}},{key:"iterateThroughNodes",value:function(e,t,n,r,i){for(var o,a=this,s=this.createIterator(t,e,r),c=[],u=[],l=void 0,h=void 0;void 0,o=a.getIteratorNode(s),h=o.prevNode,l=o.node;)this.iframes&&this.forEachIframe(t,function(e){return a.checkIframeFilter(l,h,e,c)},function(t){a.createInstanceOnIframe(t).forEachNode(e,function(e){return u.push(e)},r)}),u.push(l);u.forEach(function(e){n(e)}),this.iframes&&this.handleOpenIframes(c,e,n,r),i()}},{key:"forEachNode",value:function(e,t,n){var r=this,i=arguments.length>3&&void 0!==arguments[3]?arguments[3]:function(){},o=this.getContexts(),a=o.length;a||i(),o.forEach(function(o){var s=function(){r.iterateThroughNodes(e,o,t,n,function(){--a<=0&&i()})};r.iframes?r.waitForIframes(o,s):s()})}}],[{key:"matches",value:function(e,t){var n="string"==typeof t?[t]:t,r=e.matches||e.matchesSelector||e.msMatchesSelector||e.mozMatchesSelector||e.oMatchesSelector||e.webkitMatchesSelector;if(r){var i=!1;return n.every(function(t){return!r.call(e,t)||(i=!0,!1)}),i}return!1}}]),e}(),o=function(){function o(e){t(this,o),this.ctx=e,this.ie=!1;var n=window.navigator.userAgent;(n.indexOf("MSIE")>-1||n.indexOf("Trident")>-1)&&(this.ie=!0)}return n(o,[{key:"log",value:function(t){var n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:"debug",r=this.opt.log;this.opt.debug&&"object"===(void 0===r?"undefined":e(r))&&"function"==typeof r[n]&&r[n]("mark.js: "+t)}},{key:"escapeStr",value:function(e){return e.replace(/[\-\[\]\/\{\}\(\)\*\+\?\.\\\^\$\|]/g,"\\$&")}},{key:"createRegExp",value:function(e){return"disabled"!==this.opt.wildcards&&(e=this.setupWildcardsRegExp(e)),e=this.escapeStr(e),Object.keys(this.opt.synonyms).length&&(e=this.createSynonymsRegExp(e)),(this.opt.ignoreJoiners||this.opt.ignorePunctuation.length)&&(e=this.setupIgnoreJoinersRegExp(e)),this.opt.diacritics&&(e=this.createDiacriticsRegExp(e)),e=this.createMergedBlanksRegExp(e),(this.opt.ignoreJoiners||this.opt.ignorePunctuation.length)&&(e=this.createJoinersRegExp(e)),"disabled"!==this.opt.wildcards&&(e=this.createWildcardsRegExp(e)),e=this.createAccuracyRegExp(e)}},{key:"createSynonymsRegExp",value:function(e){var t=this.opt.synonyms,n=this.opt.caseSensitive?"":"i",r=this.opt.ignoreJoiners||this.opt.ignorePunctuation.length?"\0":"";for(var i in t)if(t.hasOwnProperty(i)){var o=t[i],a="disabled"!==this.opt.wildcards?this.setupWildcardsRegExp(i):this.escapeStr(i),s="disabled"!==this.opt.wildcards?this.setupWildcardsRegExp(o):this.escapeStr(o);""!==a&&""!==s&&(e=e.replace(new RegExp("("+this.escapeStr(a)+"|"+this.escapeStr(s)+")","gm"+n),r+"("+this.processSynomyms(a)+"|"+this.processSynomyms(s)+")"+r))}return e}},{key:"processSynomyms",value:function(e){return(this.opt.ignoreJoiners||this.opt.ignorePunctuation.length)&&(e=this.setupIgnoreJoinersRegExp(e)),e}},{key:"setupWildcardsRegExp",value:function(e){return(e=e.replace(/(?:\\)*\?/g,function(e){return"\\"===e.charAt(0)?"?":"
"})).replace(/(?:\\)*\*/g,function(e){return"\\"===e.charAt(0)?"*":""})}},{key:"createWildcardsRegExp",value:function(e){var t="withSpaces"===this.opt.wildcards;return e.replace(/\u0001/g,t?"[\\S\\s]?":"\\S?").replace(/\u0002/g,t?"[\\S\\s]*?":"\\S*")}},{key:"setupIgnoreJoinersRegExp",value:function(e){return e.replace(/[^(|)\\]/g,function(e,t,n){var r=n.charAt(t+1);return/[(|)\\]/.test(r)||""===r?e:e+"\0"})}},{key:"createJoinersRegExp",value:function(e){var t=[],n=this.opt.ignorePunctuation;return Array.isArray(n)&&n.length&&t.push(this.escapeStr(n.join(""))),this.opt.ignoreJoiners&&t.push("\\u00ad\\u200b\\u200c\\u200d"),t.length?e.split(/\u0000+/).join("["+t.join("")+"]*"):e}},{key:"createDiacriticsRegExp",value:function(e){var t=this.opt.caseSensitive?"":"i",n=this.opt.caseSensitive?["aàáảãạăằắẳẵặâầấẩẫậäåāą","AÀÁẢÃẠĂẰẮẲẴẶÂẦẤẨẪẬÄÅĀĄ","cçćč","CÇĆČ","dđď","DĐĎ","eèéẻẽẹêềếểễệëěēę","EÈÉẺẼẸÊỀẾỂỄỆËĚĒĘ","iìíỉĩịîïī","IÌÍỈĨỊÎÏĪ","lł","LŁ","nñňń","NÑŇŃ","oòóỏõọôồốổỗộơởỡớờợöøō","OÒÓỎÕỌÔỒỐỔỖỘƠỞỠỚỜỢÖØŌ","rř","RŘ","sšśșş","SŠŚȘŞ","tťțţ","TŤȚŢ","uùúủũụưừứửữựûüůū","UÙÚỦŨỤƯỪỨỬỮỰÛÜŮŪ","yýỳỷỹỵÿ","YÝỲỶỸỴŸ","zžżź","ZŽŻŹ"]:["aàáảãạăằắẳẵặâầấẩẫậäåāąAÀÁẢÃẠĂẰẮẲẴẶÂẦẤẨẪẬÄÅĀĄ","cçćčCÇĆČ","dđďDĐĎ","eèéẻẽẹêềếểễệëěēęEÈÉẺẼẸÊỀẾỂỄỆËĚĒĘ","iìíỉĩịîïīIÌÍỈĨỊÎÏĪ","lłLŁ","nñňńNÑŇŃ","oòóỏõọôồốổỗộơởỡớờợöøōOÒÓỎÕỌÔỒỐỔỖỘƠỞỠỚỜỢÖØŌ","rřRŘ","sšśșşSŠŚȘŞ","tťțţTŤȚŢ","uùúủũụưừứửữựûüůūUÙÚỦŨỤƯỪỨỬỮỰÛÜŮŪ","yýỳỷỹỵÿYÝỲỶỸỴŸ","zžżźZŽŻŹ"],r=[];return e.split("").forEach(function(i){n.every(function(n){if(-1!==n.indexOf(i)){if(r.indexOf(n)>-1)return!1;e=e.replace(new RegExp("["+n+"]","gm"+t),"["+n+"]"),r.push(n)}return!0})}),e}},{key:"createMergedBlanksRegExp",value:function(e){return e.replace(/[\s]+/gim,"[\\s]+")}},{key:"createAccuracyRegExp",value:function(e){var t=this,n=this.opt.accuracy,r="string"==typeof n?n:n.value,i="";switch(("string"==typeof n?[]:n.limiters).forEach(function(e){i+="|"+t.escapeStr(e)}),r){case"partially":default:return"()("+e+")";case"complementary":return"()([^"+(i="\\s"+(i||this.escapeStr("!\"#$%&'()*+,-./:;<=>?@[\\]^_`{|}~¡¿")))+"]*"+e+"[^"+i+"]*)";case"exactly":return"(^|\\s"+i+")("+e+")(?=$|\\s"+i+")"}}},{key:"getSeparatedKeywords",value:function(e){var t=this,n=[];return e.forEach(function(e){t.opt.separateWordSearch?e.split(" ").forEach(function(e){e.trim()&&-1===n.indexOf(e)&&n.push(e)}):e.trim()&&-1===n.indexOf(e)&&n.push(e)}),{keywords:n.sort(function(e,t){return t.length-e.length}),length:n.length}}},{key:"isNumeric",value:function(e){return Number(parseFloat(e))==e}},{key:"checkRanges",value:function(e){var t=this;if(!Array.isArray(e)||"[object Object]"!==Object.prototype.toString.call(e[0]))return this.log("markRanges() will only accept an array of objects"),this.opt.noMatch(e),[];var n=[],r=0;return e.sort(function(e,t){return e.start-t.start}).forEach(function(e){var i=t.callNoMatchOnInvalidRanges(e,r),o=i.start,a=i.end;i.valid&&(e.start=o,e.length=a-o,n.push(e),r=a)}),n}},{key:"callNoMatchOnInvalidRanges",value:function(e,t){var n=void 0,r=void 0,i=!1;return e&&void 0!==e.start?(r=(n=parseInt(e.start,10))+parseInt(e.length,10),this.isNumeric(e.start)&&this.isNumeric(e.length)&&r-t>0&&r-n>0?i=!0:(this.log("Ignoring invalid or overlapping range: "+JSON.stringify(e)),this.opt.noMatch(e))):(this.log("Ignoring invalid range: "+JSON.stringify(e)),this.opt.noMatch(e)),{start:n,end:r,valid:i}}},{key:"checkWhitespaceRanges",value:function(e,t,n){var r=void 0,i=!0,o=n.length,a=t-o,s=parseInt(e.start,10)-a;return(r=(s=s>o?o:s)+parseInt(e.length,10))>o&&(r=o,this.log("End range automatically set to the max value of "+o)),s<0||r-s<0||s>o||r>o?(i=!1,this.log("Invalid range: "+JSON.stringify(e)),this.opt.noMatch(e)):""===n.substring(s,r).replace(/\s+/g,"")&&(i=!1,this.log("Skipping whitespace only range: "+JSON.stringify(e)),this.opt.noMatch(e)),{start:s,end:r,valid:i}}},{key:"getTextNodes",value:function(e){var t=this,n="",r=[];this.iterator.forEachNode(NodeFilter.SHOW_TEXT,function(e){r.push({start:n.length,end:(n+=e.textContent).length,node:e})},function(e){return t.matchesExclude(e.parentNode)?NodeFilter.FILTER_REJECT:NodeFilter.FILTER_ACCEPT},function(){e({value:n,nodes:r})})}},{key:"matchesExclude",value:function(e){return i.matches(e,this.opt.exclude.concat(["script","style","title","head","html"]))}},{key:"wrapRangeInTextNode",value:function(e,t,n){var r=this.opt.element?this.opt.element:"mark",i=e.splitText(t),o=i.splitText(n-t),a=document.createElement(r);return a.setAttribute("data-markjs","true"),this.opt.className&&a.setAttribute("class",this.opt.className),a.textContent=i.textContent,i.parentNode.replaceChild(a,i),o}},{key:"wrapRangeInMappedTextNode",value:function(e,t,n,r,i){var o=this;e.nodes.every(function(a,s){var c=e.nodes[s+1];if(void 0===c||c.start>t){if(!r(a.node))return!1;var u=t-a.start,l=(n>a.end?a.end:n)-a.start,h=e.value.substr(0,a.start),f=e.value.substr(l+a.start);if(a.node=o.wrapRangeInTextNode(a.node,u,l),e.value=h+f,e.nodes.forEach(function(t,n){n>=s&&(e.nodes[n].start>0&&n!==s&&(e.nodes[n].start-=l),e.nodes[n].end-=l)}),n-=l,i(a.node.previousSibling,a.start),!(n>a.end))return!1;t=a.end}return!0})}},{key:"wrapMatches",value:function(e,t,n,r,i){var o=this,a=0===t?0:t+1;this.getTextNodes(function(t){t.nodes.forEach(function(t){t=t.node;for(var i=void 0;null!==(i=e.exec(t.textContent))&&""!==i[a];)if(n(i[a],t)){var s=i.index;if(0!==a)for(var c=1;c<a;c++)s+=i[c].length;t=o.wrapRangeInTextNode(t,s,s+i[a].length),r(t.previousSibling),e.lastIndex=0}}),i()})}},{key:"wrapMatchesAcrossElements",value:function(e,t,n,r,i){var o=this,a=0===t?0:t+1;this.getTextNodes(function(t){for(var s=void 0;null!==(s=e.exec(t.value))&&""!==s[a];){var c=s.index;if(0!==a)for(var u=1;u<a;u++)c+=s[u].length;var l=c+s[a].length;o.wrapRangeInMappedTextNode(t,c,l,function(e){return n(s[a],e)},function(t,n){e.lastIndex=n,r(t)})}i()})}},{key:"wrapRangeFromIndex",value:function(e,t,n,r){var i=this;this.getTextNodes(function(o){var a=o.value.length;e.forEach(function(e,r){var s=i.checkWhitespaceRanges(e,a,o.value),c=s.start,u=s.end;s.valid&&i.wrapRangeInMappedTextNode(o,c,u,function(n){return t(n,e,o.value.substring(c,u),r)},function(t){n(t,e)})}),r()})}},{key:"unwrapMatches",value:function(e){for(var t=e.parentNode,n=document.createDocumentFragment();e.firstChild;)n.appendChild(e.removeChild(e.firstChild));t.replaceChild(n,e),this.ie?this.normalizeTextNode(t):t.normalize()}},{key:"normalizeTextNode",value:function(e){if(e){if(3===e.nodeType)for(;e.nextSibling&&3===e.nextSibling.nodeType;)e.nodeValue+=e.nextSibling.nodeValue,e.parentNode.removeChild(e.nextSibling);else this.normalizeTextNode(e.firstChild);this.normalizeTextNode(e.nextSibling)}}},{key:"markRegExp",value:function(e,t){var n=this;this.opt=t,this.log('Searching with expression "'+e+'"');var r=0,i="wrapMatches";this.opt.acrossElements&&(i="wrapMatchesAcrossElements"),this[i](e,this.opt.ignoreGroups,function(e,t){return n.opt.filter(t,e,r)},function(e){r++,n.opt.each(e)},function(){0===r&&n.opt.noMatch(e),n.opt.done(r)})}},{key:"mark",value:function(e,t){var n=this;this.opt=t;var r=0,i="wrapMatches",o=this.getSeparatedKeywords("string"==typeof e?[e]:e),a=o.keywords,s=o.length,c=this.opt.caseSensitive?"":"i";this.opt.acrossElements&&(i="wrapMatchesAcrossElements"),0===s?this.opt.done(r):function e(t){var o=new RegExp(n.createRegExp(t),"gm"+c),u=0;n.log('Searching with expression "'+o+'"'),n[i](o,1,function(e,i){return n.opt.filter(i,t,r,u)},function(e){u++,r++,n.opt.each(e)},function(){0===u&&n.opt.noMatch(t),a[s-1]===t?n.opt.done(r):e(a[a.indexOf(t)+1])})}(a[0])}},{key:"markRanges",value:function(e,t){var n=this;this.opt=t;var r=0,i=this.checkRanges(e);i&&i.length?(this.log("Starting to mark with the following ranges: "+JSON.stringify(i)),this.wrapRangeFromIndex(i,function(e,t,r,i){return n.opt.filter(e,t,r,i)},function(e,t){r++,n.opt.each(e,t)},function(){n.opt.done(r)})):this.opt.done(r)}},{key:"unmark",value:function(e){var t=this;this.opt=e;var n=this.opt.element?this.opt.element:"*";n+="[data-markjs]",this.opt.className&&(n+="."+this.opt.className),this.log('Removal selector "'+n+'"'),this.iterator.forEachNode(NodeFilter.SHOW_ELEMENT,function(e){t.unwrapMatches(e)},function(e){var r=i.matches(e,n),o=t.matchesExclude(e);return!r||o?NodeFilter.FILTER_REJECT:NodeFilter.FILTER_ACCEPT},this.opt.done)}},{key:"opt",set:function(e){this._opt=r({},{element:"",className:"",exclude:[],iframes:!1,iframesTimeout:5e3,separateWordSearch:!0,diacritics:!0,synonyms:{},accuracy:"partially",acrossElements:!1,caseSensitive:!1,ignoreJoiners:!1,ignoreGroups:0,ignorePunctuation:[],wildcards:"disabled",each:function(){},noMatch:function(){},filter:function(){return!0},done:function(){},debug:!1,log:window.console},e)},get:function(){return this._opt}},{key:"iterator",get:function(){return new i(this.ctx,this.opt.iframes,this.opt.exclude,this.opt.iframesTimeout)}}]),o}();return function(e){var t=this,n=new o(e);return this.mark=function(e,r){return n.mark(e,r),t},this.markRegExp=function(e,r){return n.markRegExp(e,r),t},this.markRanges=function(e,r){return n.markRanges(e,r),t},this.unmark=function(e){return n.unmark(e),t},this}});
diff --git a/docs/dev/404.html b/docs/dev/404.html
deleted file mode 100644
index 38898979..00000000
--- a/docs/dev/404.html
+++ /dev/null
@@ -1,175 +0,0 @@
-<!-- Generated by pkgdown: do not edit by hand -->
-<!DOCTYPE html>
-<html lang="en">
-  <head>
-  <meta charset="utf-8">
-<meta http-equiv="X-UA-Compatible" content="IE=edge">
-<meta name="viewport" content="width=device-width, initial-scale=1.0">
-
-<title>Page not found (404) • mkin</title>
-
-
-<!-- jquery -->
-<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script>
-<!-- Bootstrap -->
-
-<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous" />
-
-<script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script>
-
-<!-- bootstrap-toc -->
-<link rel="stylesheet" href="https://pkgdown.jrwb.de/mkin/bootstrap-toc.css">
-<script src="https://pkgdown.jrwb.de/mkin/bootstrap-toc.js"></script>
-
-<!-- Font Awesome icons -->
-<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous" />
-<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous" />
-
-<!-- clipboard.js -->
-<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script>
-
-<!-- headroom.js -->
-<script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script>
-<script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script>
-
-<!-- pkgdown -->
-<link href="https://pkgdown.jrwb.de/mkin/pkgdown.css" rel="stylesheet">
-<script src="https://pkgdown.jrwb.de/mkin/pkgdown.js"></script>
-
-
-
-
-<meta property="og:title" content="Page not found (404)" />
-
-
-<meta name="robots" content="noindex">
-
-<!-- mathjax -->
-<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script>
-<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script>
-
-<!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]-->
-
-
-
-  </head>
-
-  <body data-spy="scroll" data-target="#toc">
-    <div class="container template-title-body">
-      <header>
-      <div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="https://pkgdown.jrwb.de/mkin/index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.1.0</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav">
-        <li>
-  <a href="reference/index.html">Functions and data</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu">
-    <li>
-      <a href="articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li>
-      <a href="articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li>
-      <a href="articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-    <li>
-      <a href="articles/web_only/benchmarks.html">Some benchmark timings</a>
-    </li>
-  </ul>
-</li>
-<li>
-  <a href="news/index.html">News</a>
-</li>
-      </ul>
-      <ul class="nav navbar-nav navbar-right">
-        <li>
-  <a href="https://github.com/jranke/mkin/">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul>
-      
-    </div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header>
-
-<div class="row">
-  <div class="contents col-md-9">
-    <div class="page-header">
-      <h1>Page not found (404)</h1>
-    </div>
-
-Content not found. Please use links in the navbar.
-
-  </div>
-
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-
-</div>
-
-
-
-      <footer>
-      <div class="copyright">
-  <p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p>
-</div>
-
-      </footer>
-   </div>
-
-  
-
-
-  </body>
-</html>
-
-
diff --git a/docs/dev/articles/2022_wp_1.1_dmta_parent.html b/docs/dev/articles/2022_wp_1.1_dmta_parent.html
deleted file mode 100644
index 61bb81d3..00000000
--- a/docs/dev/articles/2022_wp_1.1_dmta_parent.html
+++ /dev/null
@@ -1,2177 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en">
-<head>
-<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
-<meta charset="utf-8">
-<meta http-equiv="X-UA-Compatible" content="IE=edge">
-<meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>Work package 1.1: Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P • mkin</title>
-<!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous">
-<script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css">
-<script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous">
-<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous">
-<!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet">
-<script src="../pkgdown.js"></script><meta property="og:title" content="Work package 1.1: Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P">
-<meta property="og:description" content="mkin">
-<meta name="robots" content="noindex">
-<!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]-->
-</head>
-<body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-article">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav">
-<li>
-  <a href="../reference/index.html">Functions and data</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu">
-<li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-  </ul>
-</li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul>
-<ul class="nav navbar-nav navbar-right">
-<li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul>
-</div>
-<!--/.nav-collapse -->
-  </div>
-<!--/.container -->
-</div>
-<!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header toc-ignore">
-      <h1 data-toc-skip>Work package 1.1: Testing hierarchical parent
-degradation kinetics with residue data on dimethenamid and
-dimethenamid-P</h1>
-                        <h4 data-toc-skip class="author">Johannes
-Ranke</h4>
-            
-            <h4 data-toc-skip class="date">Last change on 5 January
-2022, last compiled on 5 Januar 2023</h4>
-      
-      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/2022_wp_1.1_dmta_parent.rmd" class="external-link"><code>vignettes/2022_wp_1.1_dmta_parent.rmd</code></a></small>
-      <div class="hidden name"><code>2022_wp_1.1_dmta_parent.rmd</code></div>
-
-    </div>
-
-    
-    
-<div class="section level2">
-<h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a>
-</h2>
-<p>The purpose of this document is to demonstrate how nonlinear
-hierarchical models (NLHM) based on the parent degradation models SFO,
-FOMC, DFOP and HS can be fitted with the mkin package.</p>
-<p>The mkin package is used in version 1.2.2. It contains the test data
-and the functions used in the evaluations. The <code>saemix</code>
-package is used as a backend for fitting the NLHM, but is also loaded to
-make the convergence plot function available.</p>
-<p>This document is processed with the <code>knitr</code> package, which
-also provides the <code>kable</code> function that is used to improve
-the display of tabular data in R markdown documents. For parallel
-processing, the <code>parallel</code> package is used.</p>
-<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://pkgdown.jrwb.de/mkin/">mkin</a></span><span class="op">)</span></span>
-<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://yihui.org/knitr/" class="external-link">knitr</a></span><span class="op">)</span></span>
-<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va">saemix</span><span class="op">)</span></span>
-<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va">parallel</span><span class="op">)</span></span>
-<span><span class="va">n_cores</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/parallel/detectCores.html" class="external-link">detectCores</a></span><span class="op">(</span><span class="op">)</span></span>
-<span><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/Sys.info.html" class="external-link">Sys.info</a></span><span class="op">(</span><span class="op">)</span><span class="op">[</span><span class="st">"sysname"</span><span class="op">]</span> <span class="op">==</span> <span class="st">"Windows"</span><span class="op">)</span> <span class="op">{</span></span>
-<span>  <span class="va">cl</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/parallel/makeCluster.html" class="external-link">makePSOCKcluster</a></span><span class="op">(</span><span class="va">n_cores</span><span class="op">)</span></span>
-<span><span class="op">}</span> <span class="kw">else</span> <span class="op">{</span></span>
-<span>  <span class="va">cl</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/parallel/makeCluster.html" class="external-link">makeForkCluster</a></span><span class="op">(</span><span class="va">n_cores</span><span class="op">)</span></span>
-<span><span class="op">}</span></span></code></pre></div>
-<div class="section level3">
-<h3 id="preprocessing-of-test-data">Preprocessing of test data<a class="anchor" aria-label="anchor" href="#preprocessing-of-test-data"></a>
-</h3>
-<p>The test data are available in the mkin package as an object of class
-<code>mkindsg</code> (mkin dataset group) under the identifier
-<code>dimethenamid_2018</code>. The following preprocessing steps are
-still necessary:</p>
-<ul>
-<li>The data available for the enantiomer dimethenamid-P (DMTAP) are
-renamed to have the same substance name as the data for the racemic
-mixture dimethenamid (DMTA). The reason for this is that no difference
-between their degradation behaviour was identified in the EU risk
-assessment.</li>
-<li>The data for transformation products and unnecessary columns are
-discarded</li>
-<li>The observation times of each dataset are multiplied with the
-corresponding normalisation factor also available in the dataset, in
-order to make it possible to describe all datasets with a single set of
-parameters that are independent of temperature</li>
-<li>Finally, datasets observed in the same soil (<code>Elliot 1</code>
-and <code>Elliot 2</code>) are combined, resulting in dimethenamid
-(DMTA) data from six soils.</li>
-</ul>
-<p>The following commented R code performs this preprocessing.</p>
-<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="co"># Apply a function to each of the seven datasets in the mkindsg object to create a list</span></span>
-<span><span class="va">dmta_ds</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fl">7</span>, <span class="kw">function</span><span class="op">(</span><span class="va">i</span><span class="op">)</span> <span class="op">{</span></span>
-<span>  <span class="va">ds_i</span> <span class="op">&lt;-</span> <span class="va">dimethenamid_2018</span><span class="op">$</span><span class="va">ds</span><span class="op">[[</span><span class="va">i</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">data</span>                     <span class="co"># Get a dataset</span></span>
-<span>  <span class="va">ds_i</span><span class="op">[</span><span class="va">ds_i</span><span class="op">$</span><span class="va">name</span> <span class="op">==</span> <span class="st">"DMTAP"</span>, <span class="st">"name"</span><span class="op">]</span> <span class="op">&lt;-</span>  <span class="st">"DMTA"</span>              <span class="co"># Rename DMTAP to DMTA</span></span>
-<span>  <span class="va">ds_i</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/subset.html" class="external-link">subset</a></span><span class="op">(</span><span class="va">ds_i</span>, <span class="va">name</span> <span class="op">==</span> <span class="st">"DMTA"</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"name"</span>, <span class="st">"time"</span>, <span class="st">"value"</span><span class="op">)</span><span class="op">)</span> <span class="co"># Select data</span></span>
-<span>  <span class="va">ds_i</span><span class="op">$</span><span class="va">time</span> <span class="op">&lt;-</span> <span class="va">ds_i</span><span class="op">$</span><span class="va">time</span> <span class="op">*</span> <span class="va">dimethenamid_2018</span><span class="op">$</span><span class="va">f_time_norm</span><span class="op">[</span><span class="va">i</span><span class="op">]</span>  <span class="co"># Normalise time</span></span>
-<span>  <span class="va">ds_i</span>                                                       <span class="co"># Return the dataset</span></span>
-<span><span class="op">}</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># Use dataset titles as names for the list elements</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">dmta_ds</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">sapply</a></span><span class="op">(</span><span class="va">dimethenamid_2018</span><span class="op">$</span><span class="va">ds</span>, <span class="kw">function</span><span class="op">(</span><span class="va">ds</span><span class="op">)</span> <span class="va">ds</span><span class="op">$</span><span class="va">title</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># Combine data for Elliot soil to obtain a named list with six elements</span></span>
-<span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot"</span><span class="op">]</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/cbind.html" class="external-link">rbind</a></span><span class="op">(</span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 1"</span><span class="op">]</span><span class="op">]</span>, <span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 2"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span> <span class="co">#</span></span>
-<span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 1"</span><span class="op">]</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="cn">NULL</span></span>
-<span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 2"</span><span class="op">]</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="cn">NULL</span></span></code></pre></div>
-<p>The following tables show the 6 datasets.</p>
-<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="kw">for</span> <span class="op">(</span><span class="va">ds_name</span> <span class="kw">in</span> <span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">dmta_ds</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
-<span>    <span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="fu"><a href="../reference/mkin_long_to_wide.html">mkin_long_to_wide</a></span><span class="op">(</span><span class="va">dmta_ds</span><span class="op">[[</span><span class="va">ds_name</span><span class="op">]</span><span class="op">]</span><span class="op">)</span>,</span>
-<span>      caption <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste</a></span><span class="op">(</span><span class="st">"Dataset"</span>, <span class="va">ds_name</span><span class="op">)</span>,</span>
-<span>      label <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span><span class="st">"tab:"</span>, <span class="va">ds_name</span><span class="op">)</span>, booktabs <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span></span>
-<span>    <span class="fu"><a href="https://rdrr.io/r/base/cat.html" class="external-link">cat</a></span><span class="op">(</span><span class="st">"\n\\clearpage\n"</span><span class="op">)</span></span>
-<span><span class="op">}</span></span></code></pre></div>
-<table class="table">
-<caption>Dataset Calke</caption>
-<thead><tr class="header">
-<th align="right">time</th>
-<th align="right">DMTA</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="right">0</td>
-<td align="right">95.8</td>
-</tr>
-<tr class="even">
-<td align="right">0</td>
-<td align="right">98.7</td>
-</tr>
-<tr class="odd">
-<td align="right">14</td>
-<td align="right">60.5</td>
-</tr>
-<tr class="even">
-<td align="right">30</td>
-<td align="right">39.1</td>
-</tr>
-<tr class="odd">
-<td align="right">59</td>
-<td align="right">15.2</td>
-</tr>
-<tr class="even">
-<td align="right">120</td>
-<td align="right">4.8</td>
-</tr>
-<tr class="odd">
-<td align="right">120</td>
-<td align="right">4.6</td>
-</tr>
-</tbody>
-</table>
-<table class="table">
-<caption>Dataset Borstel</caption>
-<thead><tr class="header">
-<th align="right">time</th>
-<th align="right">DMTA</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="right">0.000000</td>
-<td align="right">100.5</td>
-</tr>
-<tr class="even">
-<td align="right">0.000000</td>
-<td align="right">99.6</td>
-</tr>
-<tr class="odd">
-<td align="right">1.941295</td>
-<td align="right">91.9</td>
-</tr>
-<tr class="even">
-<td align="right">1.941295</td>
-<td align="right">91.3</td>
-</tr>
-<tr class="odd">
-<td align="right">6.794534</td>
-<td align="right">81.8</td>
-</tr>
-<tr class="even">
-<td align="right">6.794534</td>
-<td align="right">82.1</td>
-</tr>
-<tr class="odd">
-<td align="right">13.589067</td>
-<td align="right">69.1</td>
-</tr>
-<tr class="even">
-<td align="right">13.589067</td>
-<td align="right">68.0</td>
-</tr>
-<tr class="odd">
-<td align="right">27.178135</td>
-<td align="right">51.4</td>
-</tr>
-<tr class="even">
-<td align="right">27.178135</td>
-<td align="right">51.4</td>
-</tr>
-<tr class="odd">
-<td align="right">56.297565</td>
-<td align="right">27.6</td>
-</tr>
-<tr class="even">
-<td align="right">56.297565</td>
-<td align="right">26.8</td>
-</tr>
-<tr class="odd">
-<td align="right">86.387643</td>
-<td align="right">15.7</td>
-</tr>
-<tr class="even">
-<td align="right">86.387643</td>
-<td align="right">15.3</td>
-</tr>
-<tr class="odd">
-<td align="right">115.507073</td>
-<td align="right">7.9</td>
-</tr>
-<tr class="even">
-<td align="right">115.507073</td>
-<td align="right">8.1</td>
-</tr>
-</tbody>
-</table>
-<table class="table">
-<caption>Dataset Flaach</caption>
-<thead><tr class="header">
-<th align="right">time</th>
-<th align="right">DMTA</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="right">0.0000000</td>
-<td align="right">96.5</td>
-</tr>
-<tr class="even">
-<td align="right">0.0000000</td>
-<td align="right">96.8</td>
-</tr>
-<tr class="odd">
-<td align="right">0.0000000</td>
-<td align="right">97.0</td>
-</tr>
-<tr class="even">
-<td align="right">0.6233856</td>
-<td align="right">82.9</td>
-</tr>
-<tr class="odd">
-<td align="right">0.6233856</td>
-<td align="right">86.7</td>
-</tr>
-<tr class="even">
-<td align="right">0.6233856</td>
-<td align="right">87.4</td>
-</tr>
-<tr class="odd">
-<td align="right">1.8701567</td>
-<td align="right">72.8</td>
-</tr>
-<tr class="even">
-<td align="right">1.8701567</td>
-<td align="right">69.9</td>
-</tr>
-<tr class="odd">
-<td align="right">1.8701567</td>
-<td align="right">71.9</td>
-</tr>
-<tr class="even">
-<td align="right">4.3636989</td>
-<td align="right">51.4</td>
-</tr>
-<tr class="odd">
-<td align="right">4.3636989</td>
-<td align="right">52.9</td>
-</tr>
-<tr class="even">
-<td align="right">4.3636989</td>
-<td align="right">48.6</td>
-</tr>
-<tr class="odd">
-<td align="right">8.7273979</td>
-<td align="right">28.5</td>
-</tr>
-<tr class="even">
-<td align="right">8.7273979</td>
-<td align="right">27.3</td>
-</tr>
-<tr class="odd">
-<td align="right">8.7273979</td>
-<td align="right">27.5</td>
-</tr>
-<tr class="even">
-<td align="right">13.0910968</td>
-<td align="right">14.8</td>
-</tr>
-<tr class="odd">
-<td align="right">13.0910968</td>
-<td align="right">13.4</td>
-</tr>
-<tr class="even">
-<td align="right">13.0910968</td>
-<td align="right">14.4</td>
-</tr>
-<tr class="odd">
-<td align="right">17.4547957</td>
-<td align="right">7.7</td>
-</tr>
-<tr class="even">
-<td align="right">17.4547957</td>
-<td align="right">7.3</td>
-</tr>
-<tr class="odd">
-<td align="right">17.4547957</td>
-<td align="right">8.1</td>
-</tr>
-<tr class="even">
-<td align="right">26.1821936</td>
-<td align="right">2.0</td>
-</tr>
-<tr class="odd">
-<td align="right">26.1821936</td>
-<td align="right">1.5</td>
-</tr>
-<tr class="even">
-<td align="right">26.1821936</td>
-<td align="right">1.9</td>
-</tr>
-<tr class="odd">
-<td align="right">34.9095915</td>
-<td align="right">1.3</td>
-</tr>
-<tr class="even">
-<td align="right">34.9095915</td>
-<td align="right">1.0</td>
-</tr>
-<tr class="odd">
-<td align="right">34.9095915</td>
-<td align="right">1.1</td>
-</tr>
-<tr class="even">
-<td align="right">43.6369893</td>
-<td align="right">0.9</td>
-</tr>
-<tr class="odd">
-<td align="right">43.6369893</td>
-<td align="right">0.7</td>
-</tr>
-<tr class="even">
-<td align="right">43.6369893</td>
-<td align="right">0.7</td>
-</tr>
-<tr class="odd">
-<td align="right">52.3643872</td>
-<td align="right">0.6</td>
-</tr>
-<tr class="even">
-<td align="right">52.3643872</td>
-<td align="right">0.4</td>
-</tr>
-<tr class="odd">
-<td align="right">52.3643872</td>
-<td align="right">0.5</td>
-</tr>
-<tr class="even">
-<td align="right">74.8062674</td>
-<td align="right">0.4</td>
-</tr>
-<tr class="odd">
-<td align="right">74.8062674</td>
-<td align="right">0.3</td>
-</tr>
-<tr class="even">
-<td align="right">74.8062674</td>
-<td align="right">0.3</td>
-</tr>
-</tbody>
-</table>
-<table class="table">
-<caption>Dataset BBA 2.2</caption>
-<thead><tr class="header">
-<th align="right">time</th>
-<th align="right">DMTA</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="right">0.0000000</td>
-<td align="right">98.09</td>
-</tr>
-<tr class="even">
-<td align="right">0.0000000</td>
-<td align="right">98.77</td>
-</tr>
-<tr class="odd">
-<td align="right">0.7678922</td>
-<td align="right">93.52</td>
-</tr>
-<tr class="even">
-<td align="right">0.7678922</td>
-<td align="right">92.03</td>
-</tr>
-<tr class="odd">
-<td align="right">2.3036765</td>
-<td align="right">88.39</td>
-</tr>
-<tr class="even">
-<td align="right">2.3036765</td>
-<td align="right">87.18</td>
-</tr>
-<tr class="odd">
-<td align="right">5.3752452</td>
-<td align="right">69.38</td>
-</tr>
-<tr class="even">
-<td align="right">5.3752452</td>
-<td align="right">71.06</td>
-</tr>
-<tr class="odd">
-<td align="right">10.7504904</td>
-<td align="right">45.21</td>
-</tr>
-<tr class="even">
-<td align="right">10.7504904</td>
-<td align="right">46.81</td>
-</tr>
-<tr class="odd">
-<td align="right">16.1257355</td>
-<td align="right">30.54</td>
-</tr>
-<tr class="even">
-<td align="right">16.1257355</td>
-<td align="right">30.07</td>
-</tr>
-<tr class="odd">
-<td align="right">21.5009807</td>
-<td align="right">21.60</td>
-</tr>
-<tr class="even">
-<td align="right">21.5009807</td>
-<td align="right">20.41</td>
-</tr>
-<tr class="odd">
-<td align="right">32.2514711</td>
-<td align="right">9.10</td>
-</tr>
-<tr class="even">
-<td align="right">32.2514711</td>
-<td align="right">9.70</td>
-</tr>
-<tr class="odd">
-<td align="right">43.0019614</td>
-<td align="right">6.58</td>
-</tr>
-<tr class="even">
-<td align="right">43.0019614</td>
-<td align="right">6.31</td>
-</tr>
-<tr class="odd">
-<td align="right">53.7524518</td>
-<td align="right">3.47</td>
-</tr>
-<tr class="even">
-<td align="right">53.7524518</td>
-<td align="right">3.52</td>
-</tr>
-<tr class="odd">
-<td align="right">64.5029421</td>
-<td align="right">3.40</td>
-</tr>
-<tr class="even">
-<td align="right">64.5029421</td>
-<td align="right">3.67</td>
-</tr>
-<tr class="odd">
-<td align="right">91.3791680</td>
-<td align="right">1.62</td>
-</tr>
-<tr class="even">
-<td align="right">91.3791680</td>
-<td align="right">1.62</td>
-</tr>
-</tbody>
-</table>
-<table class="table">
-<caption>Dataset BBA 2.3</caption>
-<thead><tr class="header">
-<th align="right">time</th>
-<th align="right">DMTA</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="right">0.0000000</td>
-<td align="right">99.33</td>
-</tr>
-<tr class="even">
-<td align="right">0.0000000</td>
-<td align="right">97.44</td>
-</tr>
-<tr class="odd">
-<td align="right">0.6733938</td>
-<td align="right">93.73</td>
-</tr>
-<tr class="even">
-<td align="right">0.6733938</td>
-<td align="right">93.77</td>
-</tr>
-<tr class="odd">
-<td align="right">2.0201814</td>
-<td align="right">87.84</td>
-</tr>
-<tr class="even">
-<td align="right">2.0201814</td>
-<td align="right">89.82</td>
-</tr>
-<tr class="odd">
-<td align="right">4.7137565</td>
-<td align="right">71.61</td>
-</tr>
-<tr class="even">
-<td align="right">4.7137565</td>
-<td align="right">71.42</td>
-</tr>
-<tr class="odd">
-<td align="right">9.4275131</td>
-<td align="right">45.60</td>
-</tr>
-<tr class="even">
-<td align="right">9.4275131</td>
-<td align="right">45.42</td>
-</tr>
-<tr class="odd">
-<td align="right">14.1412696</td>
-<td align="right">31.12</td>
-</tr>
-<tr class="even">
-<td align="right">14.1412696</td>
-<td align="right">31.68</td>
-</tr>
-<tr class="odd">
-<td align="right">18.8550262</td>
-<td align="right">23.20</td>
-</tr>
-<tr class="even">
-<td align="right">18.8550262</td>
-<td align="right">24.13</td>
-</tr>
-<tr class="odd">
-<td align="right">28.2825393</td>
-<td align="right">9.43</td>
-</tr>
-<tr class="even">
-<td align="right">28.2825393</td>
-<td align="right">9.82</td>
-</tr>
-<tr class="odd">
-<td align="right">37.7100523</td>
-<td align="right">7.08</td>
-</tr>
-<tr class="even">
-<td align="right">37.7100523</td>
-<td align="right">8.64</td>
-</tr>
-<tr class="odd">
-<td align="right">47.1375654</td>
-<td align="right">4.41</td>
-</tr>
-<tr class="even">
-<td align="right">47.1375654</td>
-<td align="right">4.78</td>
-</tr>
-<tr class="odd">
-<td align="right">56.5650785</td>
-<td align="right">4.92</td>
-</tr>
-<tr class="even">
-<td align="right">56.5650785</td>
-<td align="right">5.08</td>
-</tr>
-<tr class="odd">
-<td align="right">80.1338612</td>
-<td align="right">2.13</td>
-</tr>
-<tr class="even">
-<td align="right">80.1338612</td>
-<td align="right">2.23</td>
-</tr>
-</tbody>
-</table>
-<table class="table">
-<caption>Dataset Elliot</caption>
-<thead><tr class="header">
-<th align="right">time</th>
-<th align="right">DMTA</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="right">0.000000</td>
-<td align="right">97.5</td>
-</tr>
-<tr class="even">
-<td align="right">0.000000</td>
-<td align="right">100.7</td>
-</tr>
-<tr class="odd">
-<td align="right">1.228478</td>
-<td align="right">86.4</td>
-</tr>
-<tr class="even">
-<td align="right">1.228478</td>
-<td align="right">88.5</td>
-</tr>
-<tr class="odd">
-<td align="right">3.685435</td>
-<td align="right">69.8</td>
-</tr>
-<tr class="even">
-<td align="right">3.685435</td>
-<td align="right">77.1</td>
-</tr>
-<tr class="odd">
-<td align="right">8.599349</td>
-<td align="right">59.0</td>
-</tr>
-<tr class="even">
-<td align="right">8.599349</td>
-<td align="right">54.2</td>
-</tr>
-<tr class="odd">
-<td align="right">17.198697</td>
-<td align="right">31.3</td>
-</tr>
-<tr class="even">
-<td align="right">17.198697</td>
-<td align="right">33.5</td>
-</tr>
-<tr class="odd">
-<td align="right">25.798046</td>
-<td align="right">19.6</td>
-</tr>
-<tr class="even">
-<td align="right">25.798046</td>
-<td align="right">20.9</td>
-</tr>
-<tr class="odd">
-<td align="right">34.397395</td>
-<td align="right">13.3</td>
-</tr>
-<tr class="even">
-<td align="right">34.397395</td>
-<td align="right">15.8</td>
-</tr>
-<tr class="odd">
-<td align="right">51.596092</td>
-<td align="right">6.7</td>
-</tr>
-<tr class="even">
-<td align="right">51.596092</td>
-<td align="right">8.7</td>
-</tr>
-<tr class="odd">
-<td align="right">68.794789</td>
-<td align="right">8.8</td>
-</tr>
-<tr class="even">
-<td align="right">68.794789</td>
-<td align="right">8.7</td>
-</tr>
-<tr class="odd">
-<td align="right">103.192184</td>
-<td align="right">6.0</td>
-</tr>
-<tr class="even">
-<td align="right">103.192184</td>
-<td align="right">4.4</td>
-</tr>
-<tr class="odd">
-<td align="right">146.188928</td>
-<td align="right">3.3</td>
-</tr>
-<tr class="even">
-<td align="right">146.188928</td>
-<td align="right">2.8</td>
-</tr>
-<tr class="odd">
-<td align="right">223.583066</td>
-<td align="right">1.4</td>
-</tr>
-<tr class="even">
-<td align="right">223.583066</td>
-<td align="right">1.8</td>
-</tr>
-<tr class="odd">
-<td align="right">0.000000</td>
-<td align="right">93.4</td>
-</tr>
-<tr class="even">
-<td align="right">0.000000</td>
-<td align="right">103.2</td>
-</tr>
-<tr class="odd">
-<td align="right">1.228478</td>
-<td align="right">89.2</td>
-</tr>
-<tr class="even">
-<td align="right">1.228478</td>
-<td align="right">86.6</td>
-</tr>
-<tr class="odd">
-<td align="right">3.685435</td>
-<td align="right">78.2</td>
-</tr>
-<tr class="even">
-<td align="right">3.685435</td>
-<td align="right">78.1</td>
-</tr>
-<tr class="odd">
-<td align="right">8.599349</td>
-<td align="right">55.6</td>
-</tr>
-<tr class="even">
-<td align="right">8.599349</td>
-<td align="right">53.0</td>
-</tr>
-<tr class="odd">
-<td align="right">17.198697</td>
-<td align="right">33.7</td>
-</tr>
-<tr class="even">
-<td align="right">17.198697</td>
-<td align="right">33.2</td>
-</tr>
-<tr class="odd">
-<td align="right">25.798046</td>
-<td align="right">20.9</td>
-</tr>
-<tr class="even">
-<td align="right">25.798046</td>
-<td align="right">19.9</td>
-</tr>
-<tr class="odd">
-<td align="right">34.397395</td>
-<td align="right">18.2</td>
-</tr>
-<tr class="even">
-<td align="right">34.397395</td>
-<td align="right">12.7</td>
-</tr>
-<tr class="odd">
-<td align="right">51.596092</td>
-<td align="right">7.8</td>
-</tr>
-<tr class="even">
-<td align="right">51.596092</td>
-<td align="right">9.0</td>
-</tr>
-<tr class="odd">
-<td align="right">68.794789</td>
-<td align="right">11.4</td>
-</tr>
-<tr class="even">
-<td align="right">68.794789</td>
-<td align="right">9.0</td>
-</tr>
-<tr class="odd">
-<td align="right">103.192184</td>
-<td align="right">3.9</td>
-</tr>
-<tr class="even">
-<td align="right">103.192184</td>
-<td align="right">4.4</td>
-</tr>
-<tr class="odd">
-<td align="right">146.188928</td>
-<td align="right">2.6</td>
-</tr>
-<tr class="even">
-<td align="right">146.188928</td>
-<td align="right">3.4</td>
-</tr>
-<tr class="odd">
-<td align="right">223.583066</td>
-<td align="right">2.0</td>
-</tr>
-<tr class="even">
-<td align="right">223.583066</td>
-<td align="right">1.7</td>
-</tr>
-</tbody>
-</table>
-</div>
-</div>
-<div class="section level2">
-<h2 id="separate-evaluations">Separate evaluations<a class="anchor" aria-label="anchor" href="#separate-evaluations"></a>
-</h2>
-<p>In order to obtain suitable starting parameters for the NLHM fits,
-separate fits of the four models to the data for each soil are generated
-using the <code>mmkin</code> function from the <code>mkin</code>
-package. In a first step, constant variance is assumed. Convergence is
-checked with the <code>status</code> function.</p>
-<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">deg_mods</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"FOMC"</span>, <span class="st">"DFOP"</span>, <span class="st">"HS"</span><span class="op">)</span></span>
-<span><span class="va">f_sep_const</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/mmkin.html">mmkin</a></span><span class="op">(</span></span>
-<span>  <span class="va">deg_mods</span>,</span>
-<span>  <span class="va">dmta_ds</span>,</span>
-<span>  error_model <span class="op">=</span> <span class="st">"const"</span>,</span>
-<span>  quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu"><a href="../reference/status.html">status</a></span><span class="op">(</span><span class="va">f_sep_const</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
-<thead><tr class="header">
-<th align="left"></th>
-<th align="left">Calke</th>
-<th align="left">Borstel</th>
-<th align="left">Flaach</th>
-<th align="left">BBA 2.2</th>
-<th align="left">BBA 2.3</th>
-<th align="left">Elliot</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="left">SFO</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-</tr>
-<tr class="even">
-<td align="left">FOMC</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-</tr>
-<tr class="odd">
-<td align="left">DFOP</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-</tr>
-<tr class="even">
-<td align="left">HS</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">C</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-</tr>
-</tbody>
-</table>
-<p>In the table above, OK indicates convergence, and C indicates failure
-to converge. All separate fits with constant variance converged, with
-the sole exception of the HS fit to the BBA 2.2 data. To prepare for
-fitting NLHM using the two-component error model, the separate fits are
-updated assuming two-component error.</p>
-<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">f_sep_tc</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_sep_const</span>, error_model <span class="op">=</span> <span class="st">"tc"</span><span class="op">)</span></span>
-<span><span class="fu"><a href="../reference/status.html">status</a></span><span class="op">(</span><span class="va">f_sep_tc</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
-<thead><tr class="header">
-<th align="left"></th>
-<th align="left">Calke</th>
-<th align="left">Borstel</th>
-<th align="left">Flaach</th>
-<th align="left">BBA 2.2</th>
-<th align="left">BBA 2.3</th>
-<th align="left">Elliot</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="left">SFO</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-</tr>
-<tr class="even">
-<td align="left">FOMC</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">C</td>
-<td align="left">OK</td>
-</tr>
-<tr class="odd">
-<td align="left">DFOP</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">C</td>
-<td align="left">OK</td>
-<td align="left">C</td>
-<td align="left">OK</td>
-</tr>
-<tr class="even">
-<td align="left">HS</td>
-<td align="left">OK</td>
-<td align="left">C</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-</tr>
-</tbody>
-</table>
-<p>Using the two-component error model, the one fit that did not
-converge with constant variance did converge, but other non-SFO fits
-failed to converge.</p>
-</div>
-<div class="section level2">
-<h2 id="hierarchichal-model-fits">Hierarchichal model fits<a class="anchor" aria-label="anchor" href="#hierarchichal-model-fits"></a>
-</h2>
-<p>The following code fits eight versions of hierarchical models to the
-data, using SFO, FOMC, DFOP and HS for the parent compound, and using
-either constant variance or two-component error for the error model. The
-default parameter distribution model in mkin allows for variation of all
-degradation parameters across the assumed population of soils. In other
-words, each degradation parameter is associated with a random effect as
-a first step. The <code>mhmkin</code> function makes it possible to fit
-all eight versions in parallel (given a sufficient number of computing
-cores being available) to save execution time.</p>
-<p>Convergence plots and summaries for these fits are shown in the
-appendix.</p>
-<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">f_saem</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/mhmkin.html">mhmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">f_sep_const</span>, <span class="va">f_sep_tc</span><span class="op">)</span>, transformations <span class="op">=</span> <span class="st">"saemix"</span><span class="op">)</span></span></code></pre></div>
-<p>The output of the <code>status</code> function shows that all fits
-terminated successfully.</p>
-<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="../reference/status.html">status</a></span><span class="op">(</span><span class="va">f_saem</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
-<thead><tr class="header">
-<th align="left"></th>
-<th align="left">const</th>
-<th align="left">tc</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="left">SFO</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-</tr>
-<tr class="even">
-<td align="left">FOMC</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-</tr>
-<tr class="odd">
-<td align="left">DFOP</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-</tr>
-<tr class="even">
-<td align="left">HS</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-</tr>
-</tbody>
-</table>
-<p>The AIC and BIC values show that the biphasic models DFOP and HS give
-the best fits.</p>
-<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_saem</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span>digits <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
-<thead><tr class="header">
-<th align="left"></th>
-<th align="right">npar</th>
-<th align="right">AIC</th>
-<th align="right">BIC</th>
-<th align="right">Lik</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="left">SFO const</td>
-<td align="right">5</td>
-<td align="right">796.3</td>
-<td align="right">795.3</td>
-<td align="right">-393.2</td>
-</tr>
-<tr class="even">
-<td align="left">SFO tc</td>
-<td align="right">6</td>
-<td align="right">798.3</td>
-<td align="right">797.1</td>
-<td align="right">-393.2</td>
-</tr>
-<tr class="odd">
-<td align="left">FOMC const</td>
-<td align="right">7</td>
-<td align="right">734.2</td>
-<td align="right">732.7</td>
-<td align="right">-360.1</td>
-</tr>
-<tr class="even">
-<td align="left">FOMC tc</td>
-<td align="right">8</td>
-<td align="right">720.4</td>
-<td align="right">718.8</td>
-<td align="right">-352.2</td>
-</tr>
-<tr class="odd">
-<td align="left">DFOP const</td>
-<td align="right">9</td>
-<td align="right">711.8</td>
-<td align="right">710.0</td>
-<td align="right">-346.9</td>
-</tr>
-<tr class="even">
-<td align="left">HS const</td>
-<td align="right">9</td>
-<td align="right">714.0</td>
-<td align="right">712.1</td>
-<td align="right">-348.0</td>
-</tr>
-<tr class="odd">
-<td align="left">DFOP tc</td>
-<td align="right">10</td>
-<td align="right">665.5</td>
-<td align="right">663.4</td>
-<td align="right">-322.8</td>
-</tr>
-<tr class="even">
-<td align="left">HS tc</td>
-<td align="right">10</td>
-<td align="right">667.1</td>
-<td align="right">665.0</td>
-<td align="right">-323.6</td>
-</tr>
-</tbody>
-</table>
-<p>The DFOP model is preferred here, as it has a better mechanistic
-basis for batch experiments with constant incubation conditions. Also,
-it shows the lowest AIC and BIC values in the first set of fits when
-combined with the two-component error model. Therefore, the DFOP model
-was selected for further refinements of the fits with the aim to make
-the model fully identifiable.</p>
-<div class="section level3">
-<h3 id="parameter-identifiability-based-on-the-fisher-information-matrix">Parameter identifiability based on the Fisher Information
-Matrix<a class="anchor" aria-label="anchor" href="#parameter-identifiability-based-on-the-fisher-information-matrix"></a>
-</h3>
-<p>Using the <code>illparms</code> function, ill-defined statistical
-model parameters such as standard deviations of the degradation
-parameters in the population and error model parameters can be
-found.</p>
-<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="../reference/illparms.html">illparms</a></span><span class="op">(</span><span class="va">f_saem</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
-<thead><tr class="header">
-<th align="left"></th>
-<th align="left">const</th>
-<th align="left">tc</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="left">SFO</td>
-<td align="left"></td>
-<td align="left">b.1</td>
-</tr>
-<tr class="even">
-<td align="left">FOMC</td>
-<td align="left"></td>
-<td align="left">sd(DMTA_0)</td>
-</tr>
-<tr class="odd">
-<td align="left">DFOP</td>
-<td align="left">sd(k2)</td>
-<td align="left">sd(k2)</td>
-</tr>
-<tr class="even">
-<td align="left">HS</td>
-<td align="left"></td>
-<td align="left">sd(tb)</td>
-</tr>
-</tbody>
-</table>
-<p>According to the <code>illparms</code> function, the fitted standard
-deviation of the second kinetic rate constant <code>k2</code> is
-ill-defined in both DFOP fits. This suggests that different values would
-be obtained for this standard deviation when using different starting
-values.</p>
-<p>The thus identified overparameterisation is addressed by removing the
-random effect for <code>k2</code> from the parameter model.</p>
-<div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">f_saem_dfop_tc_no_ranef_k2</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_saem</span><span class="op">[[</span><span class="st">"DFOP"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span>,</span>
-<span>  no_random_effect <span class="op">=</span> <span class="st">"k2"</span><span class="op">)</span></span></code></pre></div>
-<p>For the resulting fit, it is checked whether there are still
-ill-defined parameters,</p>
-<div class="sourceCode" id="cb11"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="../reference/illparms.html">illparms</a></span><span class="op">(</span><span class="va">f_saem_dfop_tc_no_ranef_k2</span><span class="op">)</span></span></code></pre></div>
-<p>which is not the case. Below, the refined model is compared with the
-previous best model. The model without random effect for <code>k2</code>
-is a reduced version of the previous model. Therefore, the models are
-nested and can be compared using the likelihood ratio test. This is
-achieved with the argument <code>test = TRUE</code> to the
-<code>anova</code> function.</p>
-<div class="sourceCode" id="cb12"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_saem</span><span class="op">[[</span><span class="st">"DFOP"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span>, <span class="va">f_saem_dfop_tc_no_ranef_k2</span>, test <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span> <span class="op">|&gt;</span></span>
-<span>  <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span>format.args <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>digits <span class="op">=</span> <span class="fl">4</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
-<colgroup>
-<col width="37%">
-<col width="6%">
-<col width="8%">
-<col width="8%">
-<col width="9%">
-<col width="9%">
-<col width="4%">
-<col width="15%">
-</colgroup>
-<thead><tr class="header">
-<th align="left"></th>
-<th align="right">npar</th>
-<th align="right">AIC</th>
-<th align="right">BIC</th>
-<th align="right">Lik</th>
-<th align="right">Chisq</th>
-<th align="right">Df</th>
-<th align="right">Pr(&gt;Chisq)</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="left">f_saem_dfop_tc_no_ranef_k2</td>
-<td align="right">9</td>
-<td align="right">663.8</td>
-<td align="right">661.9</td>
-<td align="right">-322.9</td>
-<td align="right">NA</td>
-<td align="right">NA</td>
-<td align="right">NA</td>
-</tr>
-<tr class="even">
-<td align="left">f_saem[[“DFOP”, “tc”]]</td>
-<td align="right">10</td>
-<td align="right">665.5</td>
-<td align="right">663.4</td>
-<td align="right">-322.8</td>
-<td align="right">0.2809</td>
-<td align="right">1</td>
-<td align="right">0.5961</td>
-</tr>
-</tbody>
-</table>
-<p>The AIC and BIC criteria are lower after removal of the ill-defined
-random effect for <code>k2</code>. The p value of the likelihood ratio
-test is much greater than 0.05, indicating that the model with the
-higher likelihood (here the model with random effects for all
-degradation parameters <code>f_saem[["DFOP", "tc"]]</code>) does not fit
-significantly better than the model with the lower likelihood (the
-reduced model <code>f_saem_dfop_tc_no_ranef_k2</code>).</p>
-<p>Therefore, AIC, BIC and likelihood ratio test suggest the use of the
-reduced model.</p>
-<p>The convergence of the fit is checked visually.</p>
-<div class="figure" style="text-align: center">
-<img src="2022_wp_1.1_dmta_parent_files/figure-html/convergence-saem-dfop-tc-no-ranef-k2-1.png" alt="Convergence plot for the NLHM DFOP fit with two-component error and without a random effect on 'k2'" width="864"><p class="caption">
-Convergence plot for the NLHM DFOP fit with two-component error and
-without a random effect on ‘k2’
-</p>
-</div>
-<p>All parameters appear to have converged to a satisfactory degree. The
-final fit is plotted using the plot method from the mkin package.</p>
-<div class="sourceCode" id="cb13"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_saem_dfop_tc_no_ranef_k2</span><span class="op">)</span></span></code></pre></div>
-<div class="figure" style="text-align: center">
-<img src="2022_wp_1.1_dmta_parent_files/figure-html/plot-saem-dfop-tc-no-ranef-k2-1.png" alt="Plot of the final NLHM DFOP fit" width="864"><p class="caption">
-Plot of the final NLHM DFOP fit
-</p>
-</div>
-<p>Finally, a summary report of the fit is produced.</p>
-<div class="sourceCode" id="cb14"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">f_saem_dfop_tc_no_ranef_k2</span><span class="op">)</span></span></code></pre></div>
-<pre><code>saemix version used for fitting:      3.2 
-mkin version used for pre-fitting:  1.2.2 
-R version used for fitting:         4.2.2 
-Date of fit:     Thu Jan  5 08:19:13 2023 
-Date of summary: Thu Jan  5 08:19:13 2023 
-
-Equations:
-d_DMTA/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
-           time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))
-           * DMTA
-
-Data:
-155 observations of 1 variable(s) grouped in 6 datasets
-
-Model predictions using solution type analytical 
-
-Fitted in 4.075 s
-Using 300, 100 iterations and 9 chains
-
-Variance model: Two-component variance function 
-
-Starting values for degradation parameters:
-   DMTA_0        k1        k2         g 
-98.759266  0.087034  0.009933  0.930827 
-
-Fixed degradation parameter values:
-None
-
-Starting values for random effects (square root of initial entries in omega):
-       DMTA_0 k1 k2 g
-DMTA_0  98.76  0  0 0
-k1       0.00  1  0 0
-k2       0.00  0  1 0
-g        0.00  0  0 1
-
-Starting values for error model parameters:
-a.1 b.1 
-  1   1 
-
-Results:
-
-Likelihood computed by importance sampling
-    AIC   BIC logLik
-  663.8 661.9 -322.9
-
-Optimised parameters:
-               est.     lower     upper
-DMTA_0    98.228939 96.285869 100.17201
-k1         0.064063  0.033477   0.09465
-k2         0.008297  0.005824   0.01077
-g          0.953821  0.914328   0.99331
-a.1        1.068479  0.869538   1.26742
-b.1        0.029424  0.022406   0.03644
-SD.DMTA_0  2.030437  0.404824   3.65605
-SD.k1      0.594692  0.256660   0.93272
-SD.g       1.006754  0.361327   1.65218
-
-Correlation: 
-   DMTA_0  k1      k2     
-k1  0.0218                
-k2  0.0556  0.0355        
-g  -0.0516 -0.0284 -0.2800
-
-Random effects:
-            est.  lower  upper
-SD.DMTA_0 2.0304 0.4048 3.6560
-SD.k1     0.5947 0.2567 0.9327
-SD.g      1.0068 0.3613 1.6522
-
-Variance model:
-       est.   lower   upper
-a.1 1.06848 0.86954 1.26742
-b.1 0.02942 0.02241 0.03644
-
-Estimated disappearance times:
-      DT50 DT90 DT50back DT50_k1 DT50_k2
-DMTA 11.45 41.4    12.46   10.82   83.54</code></pre>
-</div>
-<div class="section level3">
-<h3 id="alternative-check-of-parameter-identifiability">Alternative check of parameter identifiability<a class="anchor" aria-label="anchor" href="#alternative-check-of-parameter-identifiability"></a>
-</h3>
-<p>The parameter check used in the <code>illparms</code> function is
-based on a quadratic approximation of the likelihood surface near its
-optimum, which is calculated using the Fisher Information Matrix (FIM).
-An alternative way to check parameter identifiability based on a
-multistart approach has recently been implemented in mkin.</p>
-<p>The graph below shows boxplots of the parameters obtained in 50 runs
-of the saem algorithm with different parameter combinations, sampled
-from the range of the parameters obtained for the individual datasets
-fitted separately using nonlinear regression.</p>
-<div class="sourceCode" id="cb16"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">f_saem_dfop_tc_multi</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/multistart.html">multistart</a></span><span class="op">(</span><span class="va">f_saem</span><span class="op">[[</span><span class="st">"DFOP"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span>, n <span class="op">=</span> <span class="fl">50</span>, cores <span class="op">=</span> <span class="fl">15</span><span class="op">)</span></span></code></pre></div>
-<div class="sourceCode" id="cb17"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/par.html" class="external-link">par</a></span><span class="op">(</span>mar <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">6.1</span>, <span class="fl">4.1</span>, <span class="fl">2.1</span>, <span class="fl">2.1</span><span class="op">)</span><span class="op">)</span></span>
-<span><span class="fu"><a href="../reference/parplot.html">parplot</a></span><span class="op">(</span><span class="va">f_saem_dfop_tc_multi</span>, lpos <span class="op">=</span> <span class="st">"bottomright"</span>, ylim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.3</span>, <span class="fl">10</span><span class="op">)</span>, las <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span></code></pre></div>
-<div class="figure" style="text-align: center">
-<img src="2022_wp_1.1_dmta_parent_files/figure-html/multistart-full-par-1.png" alt="Scaled parameters from the multistart runs, full model" width="960"><p class="caption">
-Scaled parameters from the multistart runs, full model
-</p>
-</div>
-<p>The graph clearly confirms the lack of identifiability of the
-variance of <code>k2</code> in the full model. The overparameterisation
-of the model also indicates a lack of identifiability of the variance of
-parameter <code>g</code>.</p>
-<p>The parameter boxplots of the multistart runs with the reduced model
-shown below indicate that all runs give similar results, regardless of
-the starting parameters.</p>
-<div class="sourceCode" id="cb18"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">f_saem_dfop_tc_no_ranef_k2_multi</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/multistart.html">multistart</a></span><span class="op">(</span><span class="va">f_saem_dfop_tc_no_ranef_k2</span>,</span>
-<span>  n <span class="op">=</span> <span class="fl">50</span>, cores <span class="op">=</span> <span class="fl">15</span><span class="op">)</span></span></code></pre></div>
-<div class="sourceCode" id="cb19"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/par.html" class="external-link">par</a></span><span class="op">(</span>mar <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">6.1</span>, <span class="fl">4.1</span>, <span class="fl">2.1</span>, <span class="fl">2.1</span><span class="op">)</span><span class="op">)</span></span>
-<span><span class="fu"><a href="../reference/parplot.html">parplot</a></span><span class="op">(</span><span class="va">f_saem_dfop_tc_no_ranef_k2_multi</span>, ylim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.5</span>, <span class="fl">2</span><span class="op">)</span>, las <span class="op">=</span> <span class="fl">2</span>,</span>
-<span>  lpos <span class="op">=</span> <span class="st">"bottomright"</span><span class="op">)</span></span></code></pre></div>
-<div class="figure" style="text-align: center">
-<img src="2022_wp_1.1_dmta_parent_files/figure-html/multistart-reduced-par-1.png" alt="Scaled parameters from the multistart runs, reduced model" width="960"><p class="caption">
-Scaled parameters from the multistart runs, reduced model
-</p>
-</div>
-<p>When only the parameters of the top 25% of the fits are shown (based
-on a feature introduced in mkin 1.2.2 currently under development), the
-scatter is even less as shown below.</p>
-<div class="sourceCode" id="cb20"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/par.html" class="external-link">par</a></span><span class="op">(</span>mar <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">6.1</span>, <span class="fl">4.1</span>, <span class="fl">2.1</span>, <span class="fl">2.1</span><span class="op">)</span><span class="op">)</span></span>
-<span><span class="fu"><a href="../reference/parplot.html">parplot</a></span><span class="op">(</span><span class="va">f_saem_dfop_tc_no_ranef_k2_multi</span>, ylim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.5</span>, <span class="fl">2</span><span class="op">)</span>, las <span class="op">=</span> <span class="fl">2</span>, llquant <span class="op">=</span> <span class="fl">0.25</span>,</span>
-<span>  lpos <span class="op">=</span> <span class="st">"bottomright"</span><span class="op">)</span></span></code></pre></div>
-<div class="figure" style="text-align: center">
-<img src="2022_wp_1.1_dmta_parent_files/figure-html/multistart-reduced-par-llquant-1.png" alt="Scaled parameters from the multistart runs, reduced model, fits with the top 25\% likelihood values" width="960"><p class="caption">
-Scaled parameters from the multistart runs, reduced model, fits with the
-top 25% likelihood values
-</p>
-</div>
-</div>
-</div>
-<div class="section level2">
-<h2 id="conclusions">Conclusions<a class="anchor" aria-label="anchor" href="#conclusions"></a>
-</h2>
-<p>Fitting the four parent degradation models SFO, FOMC, DFOP and HS as
-part of hierarchical model fits with two different error models and
-normal distributions of the transformed degradation parameters works
-without technical problems. The biphasic models DFOP and HS gave the
-best fit to the data, but the default parameter distribution model was
-not fully identifiable. Removing the random effect for the second
-kinetic rate constant of the DFOP model resulted in a reduced model that
-was fully identifiable and showed the lowest values for the model
-selection criteria AIC and BIC. The reliability of the identification of
-all model parameters was confirmed using multiple starting values.</p>
-</div>
-<div class="section level2">
-<h2 id="appendix">Appendix<a class="anchor" aria-label="anchor" href="#appendix"></a>
-</h2>
-<div class="section level3">
-<h3 id="hierarchical-model-fit-listings">Hierarchical model fit listings<a class="anchor" aria-label="anchor" href="#hierarchical-model-fit-listings"></a>
-</h3>
-<caption>
-Hierarchical mkin fit of the SFO model with error model const
-</caption>
-<pre><code>
-saemix version used for fitting:      3.2 
-mkin version used for pre-fitting:  1.2.2 
-R version used for fitting:         4.2.2 
-Date of fit:     Thu Jan  5 08:19:06 2023 
-Date of summary: Thu Jan  5 08:20:11 2023 
-
-Equations:
-d_DMTA/dt = - k_DMTA * DMTA
-
-Data:
-155 observations of 1 variable(s) grouped in 6 datasets
-
-Model predictions using solution type analytical 
-
-Fitted in 1.09 s
-Using 300, 100 iterations and 9 chains
-
-Variance model: Constant variance 
-
-Starting values for degradation parameters:
- DMTA_0  k_DMTA 
-97.2953  0.0566 
-
-Fixed degradation parameter values:
-None
-
-Starting values for random effects (square root of initial entries in omega):
-       DMTA_0 k_DMTA
-DMTA_0   97.3      0
-k_DMTA    0.0      1
-
-Starting values for error model parameters:
-a.1 
-  1 
-
-Results:
-
-Likelihood computed by importance sampling
-    AIC   BIC logLik
-  796.3 795.3 -393.2
-
-Optimised parameters:
-              est.    lower   upper
-DMTA_0    97.28130 95.71113 98.8515
-k_DMTA     0.05665  0.02909  0.0842
-a.1        2.66442  2.35579  2.9731
-SD.DMTA_0  1.54776  0.15447  2.9411
-SD.k_DMTA  0.60690  0.26248  0.9513
-
-Correlation: 
-       DMTA_0
-k_DMTA 0.0168
-
-Random effects:
-            est.  lower  upper
-SD.DMTA_0 1.5478 0.1545 2.9411
-SD.k_DMTA 0.6069 0.2625 0.9513
-
-Variance model:
-     est. lower upper
-a.1 2.664 2.356 2.973
-
-Estimated disappearance times:
-      DT50  DT90
-DMTA 12.24 40.65
-
-</code></pre>
-<p></p>
-<caption>
-Hierarchical mkin fit of the SFO model with error model tc
-</caption>
-<pre><code>
-saemix version used for fitting:      3.2 
-mkin version used for pre-fitting:  1.2.2 
-R version used for fitting:         4.2.2 
-Date of fit:     Thu Jan  5 08:19:07 2023 
-Date of summary: Thu Jan  5 08:20:11 2023 
-
-Equations:
-d_DMTA/dt = - k_DMTA * DMTA
-
-Data:
-155 observations of 1 variable(s) grouped in 6 datasets
-
-Model predictions using solution type analytical 
-
-Fitted in 2.441 s
-Using 300, 100 iterations and 9 chains
-
-Variance model: Two-component variance function 
-
-Starting values for degradation parameters:
-  DMTA_0   k_DMTA 
-96.99175  0.05603 
-
-Fixed degradation parameter values:
-None
-
-Starting values for random effects (square root of initial entries in omega):
-       DMTA_0 k_DMTA
-DMTA_0  96.99      0
-k_DMTA   0.00      1
-
-Starting values for error model parameters:
-a.1 b.1 
-  1   1 
-
-Results:
-
-Likelihood computed by importance sampling
-    AIC   BIC logLik
-  798.3 797.1 -393.2
-
-Optimised parameters:
-               est.     lower    upper
-DMTA_0    97.271822 95.703157 98.84049
-k_DMTA     0.056638  0.029110  0.08417
-a.1        2.660081  2.230398  3.08976
-b.1        0.001665 -0.006911  0.01024
-SD.DMTA_0  1.545520  0.145035  2.94601
-SD.k_DMTA  0.606422  0.262274  0.95057
-
-Correlation: 
-       DMTA_0
-k_DMTA 0.0169
-
-Random effects:
-            est.  lower  upper
-SD.DMTA_0 1.5455 0.1450 2.9460
-SD.k_DMTA 0.6064 0.2623 0.9506
-
-Variance model:
-        est.     lower   upper
-a.1 2.660081  2.230398 3.08976
-b.1 0.001665 -0.006911 0.01024
-
-Estimated disappearance times:
-      DT50  DT90
-DMTA 12.24 40.65
-
-</code></pre>
-<p></p>
-<caption>
-Hierarchical mkin fit of the FOMC model with error model const
-</caption>
-<pre><code>
-saemix version used for fitting:      3.2 
-mkin version used for pre-fitting:  1.2.2 
-R version used for fitting:         4.2.2 
-Date of fit:     Thu Jan  5 08:19:06 2023 
-Date of summary: Thu Jan  5 08:20:11 2023 
-
-Equations:
-d_DMTA/dt = - (alpha/beta) * 1/((time/beta) + 1) * DMTA
-
-Data:
-155 observations of 1 variable(s) grouped in 6 datasets
-
-Model predictions using solution type analytical 
-
-Fitted in 1.156 s
-Using 300, 100 iterations and 9 chains
-
-Variance model: Constant variance 
-
-Starting values for degradation parameters:
- DMTA_0   alpha    beta 
- 98.292   9.909 156.341 
-
-Fixed degradation parameter values:
-None
-
-Starting values for random effects (square root of initial entries in omega):
-       DMTA_0 alpha beta
-DMTA_0  98.29     0    0
-alpha    0.00     1    0
-beta     0.00     0    1
-
-Starting values for error model parameters:
-a.1 
-  1 
-
-Results:
-
-Likelihood computed by importance sampling
-    AIC   BIC logLik
-  734.2 732.7 -360.1
-
-Optimised parameters:
-              est.   lower   upper
-DMTA_0     98.3435 96.9033  99.784
-alpha       7.2007  2.5889  11.812
-beta      112.8746 34.8816 190.868
-a.1         2.0459  1.8054   2.286
-SD.DMTA_0   1.4795  0.2717   2.687
-SD.alpha    0.6396  0.1509   1.128
-SD.beta     0.6874  0.1587   1.216
-
-Correlation: 
-      DMTA_0  alpha  
-alpha -0.1125        
-beta  -0.1227  0.3632
-
-Random effects:
-            est.  lower upper
-SD.DMTA_0 1.4795 0.2717 2.687
-SD.alpha  0.6396 0.1509 1.128
-SD.beta   0.6874 0.1587 1.216
-
-Variance model:
-     est. lower upper
-a.1 2.046 1.805 2.286
-
-Estimated disappearance times:
-      DT50  DT90 DT50back
-DMTA 11.41 42.53     12.8
-
-</code></pre>
-<p></p>
-<caption>
-Hierarchical mkin fit of the FOMC model with error model tc
-</caption>
-<pre><code>
-saemix version used for fitting:      3.2 
-mkin version used for pre-fitting:  1.2.2 
-R version used for fitting:         4.2.2 
-Date of fit:     Thu Jan  5 08:19:07 2023 
-Date of summary: Thu Jan  5 08:20:11 2023 
-
-Equations:
-d_DMTA/dt = - (alpha/beta) * 1/((time/beta) + 1) * DMTA
-
-Data:
-155 observations of 1 variable(s) grouped in 6 datasets
-
-Model predictions using solution type analytical 
-
-Fitted in 2.729 s
-Using 300, 100 iterations and 9 chains
-
-Variance model: Two-component variance function 
-
-Starting values for degradation parameters:
-DMTA_0  alpha   beta 
-98.772  4.663 92.597 
-
-Fixed degradation parameter values:
-None
-
-Starting values for random effects (square root of initial entries in omega):
-       DMTA_0 alpha beta
-DMTA_0  98.77     0    0
-alpha    0.00     1    0
-beta     0.00     0    1
-
-Starting values for error model parameters:
-a.1 b.1 
-  1   1 
-
-Results:
-
-Likelihood computed by importance sampling
-    AIC   BIC logLik
-  720.4 718.8 -352.2
-
-Optimised parameters:
-              est.    lower     upper
-DMTA_0    98.99136 97.26011 100.72261
-alpha      5.86312  2.57485   9.15138
-beta      88.55571 29.20889 147.90254
-a.1        1.51063  1.24384   1.77741
-b.1        0.02824  0.02040   0.03609
-SD.DMTA_0  1.57436 -0.04867   3.19739
-SD.alpha   0.59871  0.17132   1.02611
-SD.beta    0.72994  0.22849   1.23139
-
-Correlation: 
-      DMTA_0  alpha  
-alpha -0.1363        
-beta  -0.1414  0.2542
-
-Random effects:
-            est.    lower upper
-SD.DMTA_0 1.5744 -0.04867 3.197
-SD.alpha  0.5987  0.17132 1.026
-SD.beta   0.7299  0.22849 1.231
-
-Variance model:
-       est.  lower   upper
-a.1 1.51063 1.2438 1.77741
-b.1 0.02824 0.0204 0.03609
-
-Estimated disappearance times:
-      DT50 DT90 DT50back
-DMTA 11.11 42.6    12.82
-
-</code></pre>
-<p></p>
-<caption>
-Hierarchical mkin fit of the DFOP model with error model const
-</caption>
-<pre><code>
-saemix version used for fitting:      3.2 
-mkin version used for pre-fitting:  1.2.2 
-R version used for fitting:         4.2.2 
-Date of fit:     Thu Jan  5 08:19:07 2023 
-Date of summary: Thu Jan  5 08:20:11 2023 
-
-Equations:
-d_DMTA/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
-           time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))
-           * DMTA
-
-Data:
-155 observations of 1 variable(s) grouped in 6 datasets
-
-Model predictions using solution type analytical 
-
-Fitted in 2.007 s
-Using 300, 100 iterations and 9 chains
-
-Variance model: Constant variance 
-
-Starting values for degradation parameters:
-  DMTA_0       k1       k2        g 
-98.64383  0.09211  0.02999  0.76814 
-
-Fixed degradation parameter values:
-None
-
-Starting values for random effects (square root of initial entries in omega):
-       DMTA_0 k1 k2 g
-DMTA_0  98.64  0  0 0
-k1       0.00  1  0 0
-k2       0.00  0  1 0
-g        0.00  0  0 1
-
-Starting values for error model parameters:
-a.1 
-  1 
-
-Results:
-
-Likelihood computed by importance sampling
-    AIC BIC logLik
-  711.8 710 -346.9
-
-Optimised parameters:
-               est.     lower    upper
-DMTA_0    98.092481 96.573898 99.61106
-k1         0.062499  0.030336  0.09466
-k2         0.009065 -0.005133  0.02326
-g          0.948967  0.862079  1.03586
-a.1        1.821671  1.604774  2.03857
-SD.DMTA_0  1.677785  0.472066  2.88350
-SD.k1      0.634962  0.270788  0.99914
-SD.k2      1.033498 -0.205994  2.27299
-SD.g       1.710046  0.428642  2.99145
-
-Correlation: 
-   DMTA_0  k1      k2     
-k1  0.0246                
-k2  0.0491  0.0953        
-g  -0.0552 -0.0889 -0.4795
-
-Random effects:
-           est.   lower  upper
-SD.DMTA_0 1.678  0.4721 2.8835
-SD.k1     0.635  0.2708 0.9991
-SD.k2     1.033 -0.2060 2.2730
-SD.g      1.710  0.4286 2.9914
-
-Variance model:
-     est. lower upper
-a.1 1.822 1.605 2.039
-
-Estimated disappearance times:
-      DT50 DT90 DT50back DT50_k1 DT50_k2
-DMTA 11.79 42.8    12.88   11.09   76.46
-
-</code></pre>
-<p></p>
-<caption>
-Hierarchical mkin fit of the DFOP model with error model tc
-</caption>
-<pre><code>
-saemix version used for fitting:      3.2 
-mkin version used for pre-fitting:  1.2.2 
-R version used for fitting:         4.2.2 
-Date of fit:     Thu Jan  5 08:19:08 2023 
-Date of summary: Thu Jan  5 08:20:11 2023 
-
-Equations:
-d_DMTA/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
-           time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))
-           * DMTA
-
-Data:
-155 observations of 1 variable(s) grouped in 6 datasets
-
-Model predictions using solution type analytical 
-
-Fitted in 3.033 s
-Using 300, 100 iterations and 9 chains
-
-Variance model: Two-component variance function 
-
-Starting values for degradation parameters:
-   DMTA_0        k1        k2         g 
-98.759266  0.087034  0.009933  0.930827 
-
-Fixed degradation parameter values:
-None
-
-Starting values for random effects (square root of initial entries in omega):
-       DMTA_0 k1 k2 g
-DMTA_0  98.76  0  0 0
-k1       0.00  1  0 0
-k2       0.00  0  1 0
-g        0.00  0  0 1
-
-Starting values for error model parameters:
-a.1 b.1 
-  1   1 
-
-Results:
-
-Likelihood computed by importance sampling
-    AIC   BIC logLik
-  665.5 663.4 -322.8
-
-Optimised parameters:
-               est.     lower     upper
-DMTA_0    98.377019 96.447952 100.30609
-k1         0.064843  0.034607   0.09508
-k2         0.008895  0.006368   0.01142
-g          0.949696  0.903815   0.99558
-a.1        1.065241  0.865754   1.26473
-b.1        0.029340  0.022336   0.03634
-SD.DMTA_0  2.007754  0.387982   3.62753
-SD.k1      0.580473  0.250286   0.91066
-SD.k2      0.006105 -4.920337   4.93255
-SD.g       1.097149  0.412779   1.78152
-
-Correlation: 
-   DMTA_0  k1      k2     
-k1  0.0235                
-k2  0.0595  0.0424        
-g  -0.0470 -0.0278 -0.2731
-
-Random effects:
-              est.   lower  upper
-SD.DMTA_0 2.007754  0.3880 3.6275
-SD.k1     0.580473  0.2503 0.9107
-SD.k2     0.006105 -4.9203 4.9325
-SD.g      1.097149  0.4128 1.7815
-
-Variance model:
-       est.   lower   upper
-a.1 1.06524 0.86575 1.26473
-b.1 0.02934 0.02234 0.03634
-
-Estimated disappearance times:
-      DT50  DT90 DT50back DT50_k1 DT50_k2
-DMTA 11.36 41.32    12.44   10.69   77.92
-
-</code></pre>
-<p></p>
-<caption>
-Hierarchical mkin fit of the HS model with error model const
-</caption>
-<pre><code>
-saemix version used for fitting:      3.2 
-mkin version used for pre-fitting:  1.2.2 
-R version used for fitting:         4.2.2 
-Date of fit:     Thu Jan  5 08:19:07 2023 
-Date of summary: Thu Jan  5 08:20:11 2023 
-
-Equations:
-d_DMTA/dt = - ifelse(time &lt;= tb, k1, k2) * DMTA
-
-Data:
-155 observations of 1 variable(s) grouped in 6 datasets
-
-Model predictions using solution type analytical 
-
-Fitted in 2.004 s
-Using 300, 100 iterations and 9 chains
-
-Variance model: Constant variance 
-
-Starting values for degradation parameters:
-  DMTA_0       k1       k2       tb 
-97.82176  0.06931  0.02997 11.13945 
-
-Fixed degradation parameter values:
-None
-
-Starting values for random effects (square root of initial entries in omega):
-       DMTA_0 k1 k2 tb
-DMTA_0  97.82  0  0  0
-k1       0.00  1  0  0
-k2       0.00  0  1  0
-tb       0.00  0  0  1
-
-Starting values for error model parameters:
-a.1 
-  1 
-
-Results:
-
-Likelihood computed by importance sampling
-  AIC   BIC logLik
-  714 712.1   -348
-
-Optimised parameters:
-              est.    lower    upper
-DMTA_0    98.16102 96.47747 99.84456
-k1         0.07876  0.05261  0.10491
-k2         0.02227  0.01706  0.02747
-tb        13.99089 -7.40049 35.38228
-a.1        1.82305  1.60700  2.03910
-SD.DMTA_0  1.88413  0.56204  3.20622
-SD.k1      0.34292  0.10482  0.58102
-SD.k2      0.19851  0.01718  0.37985
-SD.tb      1.68168  0.58064  2.78272
-
-Correlation: 
-   DMTA_0  k1      k2     
-k1  0.0142                
-k2  0.0001 -0.0025        
-tb  0.0165 -0.1256 -0.0301
-
-Random effects:
-            est.   lower  upper
-SD.DMTA_0 1.8841 0.56204 3.2062
-SD.k1     0.3429 0.10482 0.5810
-SD.k2     0.1985 0.01718 0.3798
-SD.tb     1.6817 0.58064 2.7827
-
-Variance model:
-     est. lower upper
-a.1 1.823 1.607 2.039
-
-Estimated disappearance times:
-      DT50  DT90 DT50back DT50_k1 DT50_k2
-DMTA 8.801 67.91    20.44   8.801   31.13
-
-</code></pre>
-<p></p>
-<caption>
-Hierarchical mkin fit of the HS model with error model tc
-</caption>
-<pre><code>
-saemix version used for fitting:      3.2 
-mkin version used for pre-fitting:  1.2.2 
-R version used for fitting:         4.2.2 
-Date of fit:     Thu Jan  5 08:19:08 2023 
-Date of summary: Thu Jan  5 08:20:11 2023 
-
-Equations:
-d_DMTA/dt = - ifelse(time &lt;= tb, k1, k2) * DMTA
-
-Data:
-155 observations of 1 variable(s) grouped in 6 datasets
-
-Model predictions using solution type analytical 
-
-Fitted in 3.287 s
-Using 300, 100 iterations and 9 chains
-
-Variance model: Two-component variance function 
-
-Starting values for degradation parameters:
-  DMTA_0       k1       k2       tb 
-98.45190  0.07525  0.02576 19.19375 
-
-Fixed degradation parameter values:
-None
-
-Starting values for random effects (square root of initial entries in omega):
-       DMTA_0 k1 k2 tb
-DMTA_0  98.45  0  0  0
-k1       0.00  1  0  0
-k2       0.00  0  1  0
-tb       0.00  0  0  1
-
-Starting values for error model parameters:
-a.1 b.1 
-  1   1 
-
-Results:
-
-Likelihood computed by importance sampling
-    AIC BIC logLik
-  667.1 665 -323.6
-
-Optimised parameters:
-              est.    lower    upper
-DMTA_0    97.76570 95.81350 99.71791
-k1         0.05855  0.03080  0.08630
-k2         0.02337  0.01664  0.03010
-tb        31.09638 29.38289 32.80987
-a.1        1.08835  0.88590  1.29080
-b.1        0.02964  0.02257  0.03671
-SD.DMTA_0  2.04877  0.42607  3.67147
-SD.k1      0.59166  0.25621  0.92711
-SD.k2      0.30698  0.09561  0.51835
-SD.tb      0.01274 -0.10914  0.13462
-
-Correlation: 
-   DMTA_0  k1      k2     
-k1  0.0160                
-k2 -0.0070 -0.0024        
-tb -0.0668 -0.0103 -0.2013
-
-Random effects:
-             est.    lower  upper
-SD.DMTA_0 2.04877  0.42607 3.6715
-SD.k1     0.59166  0.25621 0.9271
-SD.k2     0.30698  0.09561 0.5183
-SD.tb     0.01274 -0.10914 0.1346
-
-Variance model:
-       est.   lower   upper
-a.1 1.08835 0.88590 1.29080
-b.1 0.02964 0.02257 0.03671
-
-Estimated disappearance times:
-      DT50  DT90 DT50back DT50_k1 DT50_k2
-DMTA 11.84 51.71    15.57   11.84   29.66
-
-</code></pre>
-<p></p>
-</div>
-<div class="section level3">
-<h3 id="hierarchical-model-convergence-plots">Hierarchical model convergence plots<a class="anchor" aria-label="anchor" href="#hierarchical-model-convergence-plots"></a>
-</h3>
-<div class="figure" style="text-align: center">
-<img src="2022_wp_1.1_dmta_parent_files/figure-html/convergence-saem-sfo-const-1.png" alt="Convergence plot for the NLHM SFO fit with constant variance" width="864"><p class="caption">
-Convergence plot for the NLHM SFO fit with constant variance
-</p>
-</div>
-<div class="figure" style="text-align: center">
-<img src="2022_wp_1.1_dmta_parent_files/figure-html/convergence-saem-sfo-tc-1.png" alt="Convergence plot for the NLHM SFO fit with two-component error" width="864"><p class="caption">
-Convergence plot for the NLHM SFO fit with two-component error
-</p>
-</div>
-<div class="figure" style="text-align: center">
-<img src="2022_wp_1.1_dmta_parent_files/figure-html/convergence-saem-fomc-const-1.png" alt="Convergence plot for the NLHM FOMC fit with constant variance" width="864"><p class="caption">
-Convergence plot for the NLHM FOMC fit with constant variance
-</p>
-</div>
-<div class="figure" style="text-align: center">
-<img src="2022_wp_1.1_dmta_parent_files/figure-html/convergence-saem-fomc-tc-1.png" alt="Convergence plot for the NLHM FOMC fit with two-component error" width="864"><p class="caption">
-Convergence plot for the NLHM FOMC fit with two-component error
-</p>
-</div>
-<div class="figure" style="text-align: center">
-<img src="2022_wp_1.1_dmta_parent_files/figure-html/convergence-saem-dfop-const-1.png" alt="Convergence plot for the NLHM DFOP fit with constant variance" width="864"><p class="caption">
-Convergence plot for the NLHM DFOP fit with constant variance
-</p>
-</div>
-<div class="figure" style="text-align: center">
-<img src="2022_wp_1.1_dmta_parent_files/figure-html/convergence-saem-dfop-tc-1.png" alt="Convergence plot for the NLHM DFOP fit with two-component error" width="864"><p class="caption">
-Convergence plot for the NLHM DFOP fit with two-component error
-</p>
-</div>
-<div class="figure" style="text-align: center">
-<img src="2022_wp_1.1_dmta_parent_files/figure-html/convergence-saem-hs-const-1.png" alt="Convergence plot for the NLHM HS fit with constant variance" width="864"><p class="caption">
-Convergence plot for the NLHM HS fit with constant variance
-</p>
-</div>
-<div class="figure" style="text-align: center">
-<img src="2022_wp_1.1_dmta_parent_files/figure-html/convergence-saem-hs-tc-1.png" alt="Convergence plot for the NLHM HS fit with two-component error" width="864"><p class="caption">
-Convergence plot for the NLHM HS fit with two-component error
-</p>
-</div>
-</div>
-<div class="section level3">
-<h3 id="session-info">Session info<a class="anchor" aria-label="anchor" href="#session-info"></a>
-</h3>
-<pre><code>R version 4.2.2 Patched (2022-11-10 r83330)
-Platform: x86_64-pc-linux-gnu (64-bit)
-Running under: Debian GNU/Linux bookworm/sid
-
-Matrix products: default
-BLAS:   /usr/lib/x86_64-linux-gnu/openblas-serial/libblas.so.3
-LAPACK: /usr/lib/x86_64-linux-gnu/openblas-serial/libopenblas-r0.3.21.so
-
-locale:
- [1] LC_CTYPE=de_DE.UTF-8       LC_NUMERIC=C              
- [3] LC_TIME=de_DE.UTF-8        LC_COLLATE=de_DE.UTF-8    
- [5] LC_MONETARY=de_DE.UTF-8    LC_MESSAGES=de_DE.UTF-8   
- [7] LC_PAPER=de_DE.UTF-8       LC_NAME=C                 
- [9] LC_ADDRESS=C               LC_TELEPHONE=C            
-[11] LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=C       
-
-attached base packages:
-[1] parallel  stats     graphics  grDevices utils     datasets  methods  
-[8] base     
-
-other attached packages:
-[1] saemix_3.2 npde_3.3   knitr_1.41 mkin_1.2.2
-
-loaded via a namespace (and not attached):
- [1] deSolve_1.34      zoo_1.8-11        tidyselect_1.2.0  xfun_0.35        
- [5] bslib_0.4.2       purrr_1.0.0       lattice_0.20-45   colorspace_2.0-3 
- [9] vctrs_0.5.1       generics_0.1.3    htmltools_0.5.4   yaml_2.3.6       
-[13] utf8_1.2.2        rlang_1.0.6       pkgdown_2.0.7     jquerylib_0.1.4  
-[17] pillar_1.8.1      glue_1.6.2        DBI_1.1.3         lifecycle_1.0.3  
-[21] stringr_1.5.0     munsell_0.5.0     gtable_0.3.1      ragg_1.2.4       
-[25] codetools_0.2-18  memoise_2.0.1     evaluate_0.19     fastmap_1.1.0    
-[29] lmtest_0.9-40     fansi_1.0.3       highr_0.9         scales_1.2.1     
-[33] cachem_1.0.6      desc_1.4.2        jsonlite_1.8.4    systemfonts_1.0.4
-[37] fs_1.5.2          textshaping_0.3.6 gridExtra_2.3     ggplot2_3.4.0    
-[41] digest_0.6.31     stringi_1.7.8     dplyr_1.0.10      grid_4.2.2       
-[45] rprojroot_2.0.3   cli_3.5.0         tools_4.2.2       magrittr_2.0.3   
-[49] sass_0.4.4        tibble_3.1.8      pkgconfig_2.0.3   assertthat_0.2.1 
-[53] rmarkdown_2.19    R6_2.5.1          mclust_6.0.0      nlme_3.1-161     
-[57] compiler_4.2.2   </code></pre>
-</div>
-</div>
-  </div>
-
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-
-        <nav id="toc" data-toggle="toc"><h2 data-toc-skip>Contents</h2>
-    </nav>
-</div>
-
-</div>
-
-
-
-      <footer><div class="copyright">
-  <p></p>
-<p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p>
-<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer>
-</div>
-
-  
-
-
-  
-
-  </body>
-</html>
diff --git a/docs/dev/articles/2022_wp_1.1_dmta_parent_files/figure-html/convergence-saem-dfop-const-1.png b/docs/dev/articles/2022_wp_1.1_dmta_parent_files/figure-html/convergence-saem-dfop-const-1.png
deleted file mode 100644
index 4f87b956..00000000
--- a/docs/dev/articles/2022_wp_1.1_dmta_parent_files/figure-html/convergence-saem-dfop-const-1.png
+++ /dev/null
diff --git a/docs/dev/articles/2022_wp_1.1_dmta_parent_files/figure-html/convergence-saem-dfop-tc-1.png b/docs/dev/articles/2022_wp_1.1_dmta_parent_files/figure-html/convergence-saem-dfop-tc-1.png
deleted file mode 100644
index 58825300..00000000
--- a/docs/dev/articles/2022_wp_1.1_dmta_parent_files/figure-html/convergence-saem-dfop-tc-1.png
+++ /dev/null
diff --git a/docs/dev/articles/2022_wp_1.1_dmta_parent_files/figure-html/convergence-saem-dfop-tc-no-ranef-k2-1.png b/docs/dev/articles/2022_wp_1.1_dmta_parent_files/figure-html/convergence-saem-dfop-tc-no-ranef-k2-1.png
deleted file mode 100644
index 17defde1..00000000
--- a/docs/dev/articles/2022_wp_1.1_dmta_parent_files/figure-html/convergence-saem-dfop-tc-no-ranef-k2-1.png
+++ /dev/null
diff --git a/docs/dev/articles/2022_wp_1.1_dmta_parent_files/figure-html/convergence-saem-fomc-const-1.png b/docs/dev/articles/2022_wp_1.1_dmta_parent_files/figure-html/convergence-saem-fomc-const-1.png
deleted file mode 100644
index b802acc6..00000000
--- a/docs/dev/articles/2022_wp_1.1_dmta_parent_files/figure-html/convergence-saem-fomc-const-1.png
+++ /dev/null
diff --git a/docs/dev/articles/2022_wp_1.1_dmta_parent_files/figure-html/convergence-saem-fomc-tc-1.png b/docs/dev/articles/2022_wp_1.1_dmta_parent_files/figure-html/convergence-saem-fomc-tc-1.png
deleted file mode 100644
index 2d6427d5..00000000
--- a/docs/dev/articles/2022_wp_1.1_dmta_parent_files/figure-html/convergence-saem-fomc-tc-1.png
+++ /dev/null
diff --git a/docs/dev/articles/2022_wp_1.1_dmta_parent_files/figure-html/convergence-saem-hs-const-1.png b/docs/dev/articles/2022_wp_1.1_dmta_parent_files/figure-html/convergence-saem-hs-const-1.png
deleted file mode 100644
index f15137d0..00000000
--- a/docs/dev/articles/2022_wp_1.1_dmta_parent_files/figure-html/convergence-saem-hs-const-1.png
+++ /dev/null
diff --git a/docs/dev/articles/2022_wp_1.1_dmta_parent_files/figure-html/convergence-saem-hs-tc-1.png b/docs/dev/articles/2022_wp_1.1_dmta_parent_files/figure-html/convergence-saem-hs-tc-1.png
deleted file mode 100644
index 322668f0..00000000
--- a/docs/dev/articles/2022_wp_1.1_dmta_parent_files/figure-html/convergence-saem-hs-tc-1.png
+++ /dev/null
diff --git a/docs/dev/articles/2022_wp_1.1_dmta_parent_files/figure-html/convergence-saem-sfo-const-1.png b/docs/dev/articles/2022_wp_1.1_dmta_parent_files/figure-html/convergence-saem-sfo-const-1.png
deleted file mode 100644
index 4ceb281f..00000000
--- a/docs/dev/articles/2022_wp_1.1_dmta_parent_files/figure-html/convergence-saem-sfo-const-1.png
+++ /dev/null
diff --git a/docs/dev/articles/2022_wp_1.1_dmta_parent_files/figure-html/convergence-saem-sfo-tc-1.png b/docs/dev/articles/2022_wp_1.1_dmta_parent_files/figure-html/convergence-saem-sfo-tc-1.png
deleted file mode 100644
index 07383871..00000000
--- a/docs/dev/articles/2022_wp_1.1_dmta_parent_files/figure-html/convergence-saem-sfo-tc-1.png
+++ /dev/null
diff --git a/docs/dev/articles/2022_wp_1.1_dmta_parent_files/figure-html/multistart-full-par-1.png b/docs/dev/articles/2022_wp_1.1_dmta_parent_files/figure-html/multistart-full-par-1.png
deleted file mode 100644
index cf4b058e..00000000
--- a/docs/dev/articles/2022_wp_1.1_dmta_parent_files/figure-html/multistart-full-par-1.png
+++ /dev/null
diff --git a/docs/dev/articles/2022_wp_1.1_dmta_parent_files/figure-html/multistart-reduced-par-1.png b/docs/dev/articles/2022_wp_1.1_dmta_parent_files/figure-html/multistart-reduced-par-1.png
deleted file mode 100644
index d9ed8685..00000000
--- a/docs/dev/articles/2022_wp_1.1_dmta_parent_files/figure-html/multistart-reduced-par-1.png
+++ /dev/null
diff --git a/docs/dev/articles/2022_wp_1.1_dmta_parent_files/figure-html/multistart-reduced-par-llquant-1.png b/docs/dev/articles/2022_wp_1.1_dmta_parent_files/figure-html/multistart-reduced-par-llquant-1.png
deleted file mode 100644
index 45dd7eb4..00000000
--- a/docs/dev/articles/2022_wp_1.1_dmta_parent_files/figure-html/multistart-reduced-par-llquant-1.png
+++ /dev/null
diff --git a/docs/dev/articles/2022_wp_1.1_dmta_parent_files/figure-html/plot-saem-dfop-tc-no-ranef-k2-1.png b/docs/dev/articles/2022_wp_1.1_dmta_parent_files/figure-html/plot-saem-dfop-tc-no-ranef-k2-1.png
deleted file mode 100644
index 5a3bd434..00000000
--- a/docs/dev/articles/2022_wp_1.1_dmta_parent_files/figure-html/plot-saem-dfop-tc-no-ranef-k2-1.png
+++ /dev/null
diff --git a/docs/dev/articles/FOCUS_D.html b/docs/dev/articles/FOCUS_D.html
deleted file mode 100644
index 4a9406de..00000000
--- a/docs/dev/articles/FOCUS_D.html
+++ /dev/null
@@ -1,397 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en">
-<head>
-<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
-<meta charset="utf-8">
-<meta http-equiv="X-UA-Compatible" content="IE=edge">
-<meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>Example evaluation of FOCUS Example Dataset D • mkin</title>
-<!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous">
-<script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css">
-<script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous">
-<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous">
-<!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet">
-<script src="../pkgdown.js"></script><meta property="og:title" content="Example evaluation of FOCUS Example Dataset D">
-<meta property="og:description" content="mkin">
-<meta name="robots" content="noindex">
-<!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]-->
-</head>
-<body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-article">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav">
-<li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu">
-<li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul>
-</li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul>
-<ul class="nav navbar-nav navbar-right">
-<li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul>
-</div>
-<!--/.nav-collapse -->
-  </div>
-<!--/.container -->
-</div>
-<!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header toc-ignore">
-      <h1 data-toc-skip>Example evaluation of FOCUS Example Dataset
-D</h1>
-                        <h4 data-toc-skip class="author">Johannes
-Ranke</h4>
-            
-            <h4 data-toc-skip class="date">Last change 31 January 2019
-(rebuilt 2023-04-16)</h4>
-      
-      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/FOCUS_D.rmd" class="external-link"><code>vignettes/FOCUS_D.rmd</code></a></small>
-      <div class="hidden name"><code>FOCUS_D.rmd</code></div>
-
-    </div>
-
-    
-    
-<p>This is just a very simple vignette showing how to fit a degradation
-model for a parent compound with one transformation product using
-<code>mkin</code>. After loading the library we look at the data. We
-have observed concentrations in the column named <code>value</code> at
-the times specified in column <code>time</code> for the two observed
-variables named <code>parent</code> and <code>m1</code>.</p>
-<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://pkgdown.jrwb.de/mkin/">mkin</a></span>, quietly <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">FOCUS_2006_D</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">##      name time  value</span></span>
-<span><span class="co">## 1  parent    0  99.46</span></span>
-<span><span class="co">## 2  parent    0 102.04</span></span>
-<span><span class="co">## 3  parent    1  93.50</span></span>
-<span><span class="co">## 4  parent    1  92.50</span></span>
-<span><span class="co">## 5  parent    3  63.23</span></span>
-<span><span class="co">## 6  parent    3  68.99</span></span>
-<span><span class="co">## 7  parent    7  52.32</span></span>
-<span><span class="co">## 8  parent    7  55.13</span></span>
-<span><span class="co">## 9  parent   14  27.27</span></span>
-<span><span class="co">## 10 parent   14  26.64</span></span>
-<span><span class="co">## 11 parent   21  11.50</span></span>
-<span><span class="co">## 12 parent   21  11.64</span></span>
-<span><span class="co">## 13 parent   35   2.85</span></span>
-<span><span class="co">## 14 parent   35   2.91</span></span>
-<span><span class="co">## 15 parent   50   0.69</span></span>
-<span><span class="co">## 16 parent   50   0.63</span></span>
-<span><span class="co">## 17 parent   75   0.05</span></span>
-<span><span class="co">## 18 parent   75   0.06</span></span>
-<span><span class="co">## 19 parent  100     NA</span></span>
-<span><span class="co">## 20 parent  100     NA</span></span>
-<span><span class="co">## 21 parent  120     NA</span></span>
-<span><span class="co">## 22 parent  120     NA</span></span>
-<span><span class="co">## 23     m1    0   0.00</span></span>
-<span><span class="co">## 24     m1    0   0.00</span></span>
-<span><span class="co">## 25     m1    1   4.84</span></span>
-<span><span class="co">## 26     m1    1   5.64</span></span>
-<span><span class="co">## 27     m1    3  12.91</span></span>
-<span><span class="co">## 28     m1    3  12.96</span></span>
-<span><span class="co">## 29     m1    7  22.97</span></span>
-<span><span class="co">## 30     m1    7  24.47</span></span>
-<span><span class="co">## 31     m1   14  41.69</span></span>
-<span><span class="co">## 32     m1   14  33.21</span></span>
-<span><span class="co">## 33     m1   21  44.37</span></span>
-<span><span class="co">## 34     m1   21  46.44</span></span>
-<span><span class="co">## 35     m1   35  41.22</span></span>
-<span><span class="co">## 36     m1   35  37.95</span></span>
-<span><span class="co">## 37     m1   50  41.19</span></span>
-<span><span class="co">## 38     m1   50  40.01</span></span>
-<span><span class="co">## 39     m1   75  40.09</span></span>
-<span><span class="co">## 40     m1   75  33.85</span></span>
-<span><span class="co">## 41     m1  100  31.04</span></span>
-<span><span class="co">## 42     m1  100  33.13</span></span>
-<span><span class="co">## 43     m1  120  25.15</span></span>
-<span><span class="co">## 44     m1  120  33.31</span></span></code></pre>
-<p>Next we specify the degradation model: The parent compound degrades
-with simple first-order kinetics (SFO) to one metabolite named m1, which
-also degrades with SFO kinetics.</p>
-<p>The call to mkinmod returns a degradation model. The differential
-equations represented in R code can be found in the character vector
-<code>$diffs</code> of the <code>mkinmod</code> object. If a C compiler
-(gcc) is installed and functional, the differential equation model will
-be compiled from auto-generated C code.</p>
-<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">SFO_SFO</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"m1"</span><span class="op">)</span>, m1 <span class="op">=</span> <span class="fu"><a href="../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## Temporary DLL for differentials generated and loaded</span></span></code></pre>
-<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">SFO_SFO</span><span class="op">$</span><span class="va">diffs</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">##                                                    parent </span></span>
-<span><span class="co">##                          "d_parent = - k_parent * parent" </span></span>
-<span><span class="co">##                                                        m1 </span></span>
-<span><span class="co">## "d_m1 = + f_parent_to_m1 * k_parent * parent - k_m1 * m1"</span></span></code></pre>
-<p>We do the fitting without progress report
-(<code>quiet = TRUE</code>).</p>
-<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">fit</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="va">FOCUS_2006_D</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## Warning in mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE): Observations with</span></span>
-<span><span class="co">## value of zero were removed from the data</span></span></code></pre>
-<p>A plot of the fit including a residual plot for both observed
-variables is obtained using the <code>plot_sep</code> method for
-<code>mkinfit</code> objects, which shows separate graphs for all
-compounds and their residuals.</p>
-<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="../reference/plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">fit</span>, lpos <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"topright"</span>, <span class="st">"bottomright"</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
-<p><img src="FOCUS_D_files/figure-html/plot-1.png" width="768"></p>
-<p>Confidence intervals for the parameter estimates are obtained using
-the <code>mkinparplot</code> function.</p>
-<div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="../reference/mkinparplot.html">mkinparplot</a></span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></span></code></pre></div>
-<p><img src="FOCUS_D_files/figure-html/plot_2-1.png" width="768"></p>
-<p>A comprehensive report of the results is obtained using the
-<code>summary</code> method for <code>mkinfit</code> objects.</p>
-<div class="sourceCode" id="cb11"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## mkin version used for fitting:    1.2.3 </span></span>
-<span><span class="co">## R version used for fitting:       4.2.3 </span></span>
-<span><span class="co">## Date of fit:     Sun Apr 16 08:35:12 2023 </span></span>
-<span><span class="co">## Date of summary: Sun Apr 16 08:35:12 2023 </span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Equations:</span></span>
-<span><span class="co">## d_parent/dt = - k_parent * parent</span></span>
-<span><span class="co">## d_m1/dt = + f_parent_to_m1 * k_parent * parent - k_m1 * m1</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Model predictions using solution type analytical </span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Fitted using 401 model solutions performed in 0.05 s</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Error model: Constant variance </span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Error model algorithm: OLS </span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Starting values for parameters to be optimised:</span></span>
-<span><span class="co">##                   value   type</span></span>
-<span><span class="co">## parent_0       100.7500  state</span></span>
-<span><span class="co">## k_parent         0.1000 deparm</span></span>
-<span><span class="co">## k_m1             0.1001 deparm</span></span>
-<span><span class="co">## f_parent_to_m1   0.5000 deparm</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Starting values for the transformed parameters actually optimised:</span></span>
-<span><span class="co">##                      value lower upper</span></span>
-<span><span class="co">## parent_0        100.750000  -Inf   Inf</span></span>
-<span><span class="co">## log_k_parent     -2.302585  -Inf   Inf</span></span>
-<span><span class="co">## log_k_m1         -2.301586  -Inf   Inf</span></span>
-<span><span class="co">## f_parent_qlogis   0.000000  -Inf   Inf</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Fixed parameter values:</span></span>
-<span><span class="co">##      value  type</span></span>
-<span><span class="co">## m1_0     0 state</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Warning(s): </span></span>
-<span><span class="co">## Observations with value of zero were removed from the data</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Results:</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">##        AIC      BIC    logLik</span></span>
-<span><span class="co">##   204.4486 212.6365 -97.22429</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Optimised, transformed parameters with symmetric confidence intervals:</span></span>
-<span><span class="co">##                 Estimate Std. Error   Lower    Upper</span></span>
-<span><span class="co">## parent_0        99.60000    1.57000 96.4000 102.8000</span></span>
-<span><span class="co">## log_k_parent    -2.31600    0.04087 -2.3990  -2.2330</span></span>
-<span><span class="co">## log_k_m1        -5.24700    0.13320 -5.5180  -4.9770</span></span>
-<span><span class="co">## f_parent_qlogis  0.05792    0.08926 -0.1237   0.2395</span></span>
-<span><span class="co">## sigma            3.12600    0.35850  2.3960   3.8550</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Parameter correlation:</span></span>
-<span><span class="co">##                   parent_0 log_k_parent   log_k_m1 f_parent_qlogis      sigma</span></span>
-<span><span class="co">## parent_0         1.000e+00    5.174e-01 -1.688e-01      -5.471e-01 -1.172e-06</span></span>
-<span><span class="co">## log_k_parent     5.174e-01    1.000e+00 -3.263e-01      -5.426e-01 -8.483e-07</span></span>
-<span><span class="co">## log_k_m1        -1.688e-01   -3.263e-01  1.000e+00       7.478e-01  8.205e-07</span></span>
-<span><span class="co">## f_parent_qlogis -5.471e-01   -5.426e-01  7.478e-01       1.000e+00  1.305e-06</span></span>
-<span><span class="co">## sigma           -1.172e-06   -8.483e-07  8.205e-07       1.305e-06  1.000e+00</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Backtransformed parameters:</span></span>
-<span><span class="co">## Confidence intervals for internally transformed parameters are asymmetric.</span></span>
-<span><span class="co">## t-test (unrealistically) based on the assumption of normal distribution</span></span>
-<span><span class="co">## for estimators of untransformed parameters.</span></span>
-<span><span class="co">##                 Estimate t value    Pr(&gt;t)     Lower     Upper</span></span>
-<span><span class="co">## parent_0       99.600000  63.430 2.298e-36 96.400000 1.028e+02</span></span>
-<span><span class="co">## k_parent        0.098700  24.470 4.955e-23  0.090820 1.073e-01</span></span>
-<span><span class="co">## k_m1            0.005261   7.510 6.165e-09  0.004012 6.898e-03</span></span>
-<span><span class="co">## f_parent_to_m1  0.514500  23.070 3.104e-22  0.469100 5.596e-01</span></span>
-<span><span class="co">## sigma           3.126000   8.718 2.235e-10  2.396000 3.855e+00</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## FOCUS Chi2 error levels in percent:</span></span>
-<span><span class="co">##          err.min n.optim df</span></span>
-<span><span class="co">## All data   6.398       4 15</span></span>
-<span><span class="co">## parent     6.459       2  7</span></span>
-<span><span class="co">## m1         4.690       2  8</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Resulting formation fractions:</span></span>
-<span><span class="co">##                 ff</span></span>
-<span><span class="co">## parent_m1   0.5145</span></span>
-<span><span class="co">## parent_sink 0.4855</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Estimated disappearance times:</span></span>
-<span><span class="co">##           DT50   DT90</span></span>
-<span><span class="co">## parent   7.023  23.33</span></span>
-<span><span class="co">## m1     131.761 437.70</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Data:</span></span>
-<span><span class="co">##  time variable observed predicted   residual</span></span>
-<span><span class="co">##     0   parent    99.46  99.59848 -1.385e-01</span></span>
-<span><span class="co">##     0   parent   102.04  99.59848  2.442e+00</span></span>
-<span><span class="co">##     1   parent    93.50  90.23787  3.262e+00</span></span>
-<span><span class="co">##     1   parent    92.50  90.23787  2.262e+00</span></span>
-<span><span class="co">##     3   parent    63.23  74.07319 -1.084e+01</span></span>
-<span><span class="co">##     3   parent    68.99  74.07319 -5.083e+00</span></span>
-<span><span class="co">##     7   parent    52.32  49.91207  2.408e+00</span></span>
-<span><span class="co">##     7   parent    55.13  49.91207  5.218e+00</span></span>
-<span><span class="co">##    14   parent    27.27  25.01258  2.257e+00</span></span>
-<span><span class="co">##    14   parent    26.64  25.01258  1.627e+00</span></span>
-<span><span class="co">##    21   parent    11.50  12.53462 -1.035e+00</span></span>
-<span><span class="co">##    21   parent    11.64  12.53462 -8.946e-01</span></span>
-<span><span class="co">##    35   parent     2.85   3.14787 -2.979e-01</span></span>
-<span><span class="co">##    35   parent     2.91   3.14787 -2.379e-01</span></span>
-<span><span class="co">##    50   parent     0.69   0.71624 -2.624e-02</span></span>
-<span><span class="co">##    50   parent     0.63   0.71624 -8.624e-02</span></span>
-<span><span class="co">##    75   parent     0.05   0.06074 -1.074e-02</span></span>
-<span><span class="co">##    75   parent     0.06   0.06074 -7.382e-04</span></span>
-<span><span class="co">##     1       m1     4.84   4.80296  3.704e-02</span></span>
-<span><span class="co">##     1       m1     5.64   4.80296  8.370e-01</span></span>
-<span><span class="co">##     3       m1    12.91  13.02400 -1.140e-01</span></span>
-<span><span class="co">##     3       m1    12.96  13.02400 -6.400e-02</span></span>
-<span><span class="co">##     7       m1    22.97  25.04476 -2.075e+00</span></span>
-<span><span class="co">##     7       m1    24.47  25.04476 -5.748e-01</span></span>
-<span><span class="co">##    14       m1    41.69  36.69003  5.000e+00</span></span>
-<span><span class="co">##    14       m1    33.21  36.69003 -3.480e+00</span></span>
-<span><span class="co">##    21       m1    44.37  41.65310  2.717e+00</span></span>
-<span><span class="co">##    21       m1    46.44  41.65310  4.787e+00</span></span>
-<span><span class="co">##    35       m1    41.22  43.31313 -2.093e+00</span></span>
-<span><span class="co">##    35       m1    37.95  43.31313 -5.363e+00</span></span>
-<span><span class="co">##    50       m1    41.19  41.21832 -2.832e-02</span></span>
-<span><span class="co">##    50       m1    40.01  41.21832 -1.208e+00</span></span>
-<span><span class="co">##    75       m1    40.09  36.44704  3.643e+00</span></span>
-<span><span class="co">##    75       m1    33.85  36.44704 -2.597e+00</span></span>
-<span><span class="co">##   100       m1    31.04  31.98162 -9.416e-01</span></span>
-<span><span class="co">##   100       m1    33.13  31.98162  1.148e+00</span></span>
-<span><span class="co">##   120       m1    25.15  28.78984 -3.640e+00</span></span>
-<span><span class="co">##   120       m1    33.31  28.78984  4.520e+00</span></span></code></pre>
-  </div>
-
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-
-      </div>
-
-</div>
-
-
-
-      <footer><div class="copyright">
-  <p></p>
-<p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p>
-<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer>
-</div>
-
-  
-
-
-  
-
-  </body>
-</html>
diff --git a/docs/dev/articles/FOCUS_D_files/accessible-code-block-0.0.1/empty-anchor.js b/docs/dev/articles/FOCUS_D_files/accessible-code-block-0.0.1/empty-anchor.js
deleted file mode 100644
index ca349fd6..00000000
--- a/docs/dev/articles/FOCUS_D_files/accessible-code-block-0.0.1/empty-anchor.js
+++ /dev/null
@@ -1,15 +0,0 @@
-// Hide empty <a> tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) -->
-// v0.0.1
-// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020.
-
-document.addEventListener('DOMContentLoaded', function() {
-  const codeList = document.getElementsByClassName("sourceCode");
-  for (var i = 0; i < codeList.length; i++) {
-    var linkList = codeList[i].getElementsByTagName('a');
-    for (var j = 0; j < linkList.length; j++) {
-      if (linkList[j].innerHTML === "") {
-        linkList[j].setAttribute('aria-hidden', 'true');
-      }
-    }
-  }
-});
diff --git a/docs/dev/articles/FOCUS_D_files/anchor-sections-1.0/anchor-sections.css b/docs/dev/articles/FOCUS_D_files/anchor-sections-1.0/anchor-sections.css
deleted file mode 100644
index 07aee5fc..00000000
--- a/docs/dev/articles/FOCUS_D_files/anchor-sections-1.0/anchor-sections.css
+++ /dev/null
@@ -1,4 +0,0 @@
-/* Styles for section anchors */
-a.anchor-section {margin-left: 10px; visibility: hidden; color: inherit;}
-a.anchor-section::before {content: '#';}
-.hasAnchor:hover a.anchor-section {visibility: visible;}
diff --git a/docs/dev/articles/FOCUS_D_files/anchor-sections-1.0/anchor-sections.js b/docs/dev/articles/FOCUS_D_files/anchor-sections-1.0/anchor-sections.js
deleted file mode 100644
index 570f99a0..00000000
--- a/docs/dev/articles/FOCUS_D_files/anchor-sections-1.0/anchor-sections.js
+++ /dev/null
@@ -1,33 +0,0 @@
-// Anchor sections v1.0 written by Atsushi Yasumoto on Oct 3rd, 2020.
-document.addEventListener('DOMContentLoaded', function() {
-  // Do nothing if AnchorJS is used
-  if (typeof window.anchors === 'object' && anchors.hasOwnProperty('hasAnchorJSLink')) {
-    return;
-  }
-
-  const h = document.querySelectorAll('h1, h2, h3, h4, h5, h6');
-
-  // Do nothing if sections are already anchored
-  if (Array.from(h).some(x => x.classList.contains('hasAnchor'))) {
-    return null;
-  }
-
-  // Use section id when pandoc runs with --section-divs
-  const section_id = function(x) {
-    return ((x.classList.contains('section') || (x.tagName === 'SECTION'))
-            ? x.id : '');
-  };
-
-  // Add anchors
-  h.forEach(function(x) {
-    const id = x.id || section_id(x.parentElement);
-    if (id === '') {
-      return null;
-    }
-    let anchor = document.createElement('a');
-    anchor.href = '#' + id;
-    anchor.classList = ['anchor-section'];
-    x.classList.add('hasAnchor');
-    x.appendChild(anchor);
-  });
-});
diff --git a/docs/dev/articles/FOCUS_D_files/figure-html/plot-1.png b/docs/dev/articles/FOCUS_D_files/figure-html/plot-1.png
deleted file mode 100644
index c0832a1a..00000000
--- a/docs/dev/articles/FOCUS_D_files/figure-html/plot-1.png
+++ /dev/null
diff --git a/docs/dev/articles/FOCUS_D_files/figure-html/plot_2-1.png b/docs/dev/articles/FOCUS_D_files/figure-html/plot_2-1.png
deleted file mode 100644
index 02cfcfb4..00000000
--- a/docs/dev/articles/FOCUS_D_files/figure-html/plot_2-1.png
+++ /dev/null
diff --git a/docs/dev/articles/FOCUS_D_files/header-attrs-2.6/header-attrs.js b/docs/dev/articles/FOCUS_D_files/header-attrs-2.6/header-attrs.js
deleted file mode 100644
index dd57d92e..00000000
--- a/docs/dev/articles/FOCUS_D_files/header-attrs-2.6/header-attrs.js
+++ /dev/null
@@ -1,12 +0,0 @@
-// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
-// be compatible with the behavior of Pandoc < 2.8).
-document.addEventListener('DOMContentLoaded', function(e) {
-  var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
-  var i, h, a;
-  for (i = 0; i < hs.length; i++) {
-    h = hs[i];
-    if (!/^h[1-6]$/i.test(h.tagName)) continue;  // it should be a header h1-h6
-    a = h.attributes;
-    while (a.length > 0) h.removeAttribute(a[0].name);
-  }
-});
diff --git a/docs/dev/articles/FOCUS_L.html b/docs/dev/articles/FOCUS_L.html
deleted file mode 100644
index 853194fb..00000000
--- a/docs/dev/articles/FOCUS_L.html
+++ /dev/null
@@ -1,935 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en">
-<head>
-<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
-<meta charset="utf-8">
-<meta http-equiv="X-UA-Compatible" content="IE=edge">
-<meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>Example evaluation of FOCUS Laboratory Data L1 to L3 • mkin</title>
-<!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous">
-<script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css">
-<script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous">
-<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous">
-<!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet">
-<script src="../pkgdown.js"></script><meta property="og:title" content="Example evaluation of FOCUS Laboratory Data L1 to L3">
-<meta property="og:description" content="mkin">
-<meta name="robots" content="noindex">
-<!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]-->
-</head>
-<body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-article">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav">
-<li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu">
-<li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul>
-</li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul>
-<ul class="nav navbar-nav navbar-right">
-<li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul>
-</div>
-<!--/.nav-collapse -->
-  </div>
-<!--/.container -->
-</div>
-<!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header toc-ignore">
-      <h1 data-toc-skip>Example evaluation of FOCUS Laboratory Data L1
-to L3</h1>
-                        <h4 data-toc-skip class="author">Johannes
-Ranke</h4>
-            
-            <h4 data-toc-skip class="date">Last change 18 May 2022
-(rebuilt 2023-04-16)</h4>
-      
-      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/FOCUS_L.rmd" class="external-link"><code>vignettes/FOCUS_L.rmd</code></a></small>
-      <div class="hidden name"><code>FOCUS_L.rmd</code></div>
-
-    </div>
-
-    
-    
-<div class="section level2">
-<h2 id="laboratory-data-l1">Laboratory Data L1<a class="anchor" aria-label="anchor" href="#laboratory-data-l1"></a>
-</h2>
-<p>The following code defines example dataset L1 from the FOCUS kinetics
-report, p. 284:</p>
-<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="st"><a href="https://pkgdown.jrwb.de/mkin/">"mkin"</a></span>, quietly <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
-<span><span class="va">FOCUS_2006_L1</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span></span>
-<span>  t <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/rep.html" class="external-link">rep</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span>, <span class="fl">2</span>, <span class="fl">3</span>, <span class="fl">5</span>, <span class="fl">7</span>, <span class="fl">14</span>, <span class="fl">21</span>, <span class="fl">30</span><span class="op">)</span>, each <span class="op">=</span> <span class="fl">2</span><span class="op">)</span>,</span>
-<span>  parent <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">88.3</span>, <span class="fl">91.4</span>, <span class="fl">85.6</span>, <span class="fl">84.5</span>, <span class="fl">78.9</span>, <span class="fl">77.6</span>,</span>
-<span>             <span class="fl">72.0</span>, <span class="fl">71.9</span>, <span class="fl">50.3</span>, <span class="fl">59.4</span>, <span class="fl">47.0</span>, <span class="fl">45.1</span>,</span>
-<span>             <span class="fl">27.7</span>, <span class="fl">27.3</span>, <span class="fl">10.0</span>, <span class="fl">10.4</span>, <span class="fl">2.9</span>, <span class="fl">4.0</span><span class="op">)</span><span class="op">)</span></span>
-<span><span class="va">FOCUS_2006_L1_mkin</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/mkin_wide_to_long.html">mkin_wide_to_long</a></span><span class="op">(</span><span class="va">FOCUS_2006_L1</span><span class="op">)</span></span></code></pre></div>
-<p>Here we use the assumptions of simple first order (SFO), the case of
-declining rate constant over time (FOMC) and the case of two different
-phases of the kinetics (DFOP). For a more detailed discussion of the
-models, please see the FOCUS kinetics report.</p>
-<p>Since mkin version 0.9-32 (July 2014), we can use shorthand notation
-like <code>"SFO"</code> for parent only degradation models. The
-following two lines fit the model and produce the summary report of the
-model fit. This covers the numerical analysis given in the FOCUS
-report.</p>
-<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">m.L1.SFO</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="va">FOCUS_2006_L1_mkin</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
-<span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">m.L1.SFO</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## mkin version used for fitting:    1.2.3 </span></span>
-<span><span class="co">## R version used for fitting:       4.2.3 </span></span>
-<span><span class="co">## Date of fit:     Sun Apr 16 08:35:14 2023 </span></span>
-<span><span class="co">## Date of summary: Sun Apr 16 08:35:14 2023 </span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Equations:</span></span>
-<span><span class="co">## d_parent/dt = - k_parent * parent</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Model predictions using solution type analytical </span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Fitted using 133 model solutions performed in 0.011 s</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Error model: Constant variance </span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Error model algorithm: OLS </span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Starting values for parameters to be optimised:</span></span>
-<span><span class="co">##          value   type</span></span>
-<span><span class="co">## parent_0 89.85  state</span></span>
-<span><span class="co">## k_parent  0.10 deparm</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Starting values for the transformed parameters actually optimised:</span></span>
-<span><span class="co">##                  value lower upper</span></span>
-<span><span class="co">## parent_0     89.850000  -Inf   Inf</span></span>
-<span><span class="co">## log_k_parent -2.302585  -Inf   Inf</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Fixed parameter values:</span></span>
-<span><span class="co">## None</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Results:</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">##        AIC     BIC    logLik</span></span>
-<span><span class="co">##   93.88778 96.5589 -43.94389</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Optimised, transformed parameters with symmetric confidence intervals:</span></span>
-<span><span class="co">##              Estimate Std. Error  Lower  Upper</span></span>
-<span><span class="co">## parent_0       92.470    1.28200 89.740 95.200</span></span>
-<span><span class="co">## log_k_parent   -2.347    0.03763 -2.428 -2.267</span></span>
-<span><span class="co">## sigma           2.780    0.46330  1.792  3.767</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Parameter correlation:</span></span>
-<span><span class="co">##                parent_0 log_k_parent      sigma</span></span>
-<span><span class="co">## parent_0      1.000e+00    6.186e-01 -1.516e-09</span></span>
-<span><span class="co">## log_k_parent  6.186e-01    1.000e+00 -3.124e-09</span></span>
-<span><span class="co">## sigma        -1.516e-09   -3.124e-09  1.000e+00</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Backtransformed parameters:</span></span>
-<span><span class="co">## Confidence intervals for internally transformed parameters are asymmetric.</span></span>
-<span><span class="co">## t-test (unrealistically) based on the assumption of normal distribution</span></span>
-<span><span class="co">## for estimators of untransformed parameters.</span></span>
-<span><span class="co">##          Estimate t value    Pr(&gt;t)    Lower   Upper</span></span>
-<span><span class="co">## parent_0 92.47000   72.13 8.824e-21 89.74000 95.2000</span></span>
-<span><span class="co">## k_parent  0.09561   26.57 2.487e-14  0.08824  0.1036</span></span>
-<span><span class="co">## sigma     2.78000    6.00 1.216e-05  1.79200  3.7670</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## FOCUS Chi2 error levels in percent:</span></span>
-<span><span class="co">##          err.min n.optim df</span></span>
-<span><span class="co">## All data   3.424       2  7</span></span>
-<span><span class="co">## parent     3.424       2  7</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Estimated disappearance times:</span></span>
-<span><span class="co">##         DT50  DT90</span></span>
-<span><span class="co">## parent 7.249 24.08</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Data:</span></span>
-<span><span class="co">##  time variable observed predicted residual</span></span>
-<span><span class="co">##     0   parent     88.3    92.471  -4.1710</span></span>
-<span><span class="co">##     0   parent     91.4    92.471  -1.0710</span></span>
-<span><span class="co">##     1   parent     85.6    84.039   1.5610</span></span>
-<span><span class="co">##     1   parent     84.5    84.039   0.4610</span></span>
-<span><span class="co">##     2   parent     78.9    76.376   2.5241</span></span>
-<span><span class="co">##     2   parent     77.6    76.376   1.2241</span></span>
-<span><span class="co">##     3   parent     72.0    69.412   2.5884</span></span>
-<span><span class="co">##     3   parent     71.9    69.412   2.4884</span></span>
-<span><span class="co">##     5   parent     50.3    57.330  -7.0301</span></span>
-<span><span class="co">##     5   parent     59.4    57.330   2.0699</span></span>
-<span><span class="co">##     7   parent     47.0    47.352  -0.3515</span></span>
-<span><span class="co">##     7   parent     45.1    47.352  -2.2515</span></span>
-<span><span class="co">##    14   parent     27.7    24.247   3.4528</span></span>
-<span><span class="co">##    14   parent     27.3    24.247   3.0528</span></span>
-<span><span class="co">##    21   parent     10.0    12.416  -2.4163</span></span>
-<span><span class="co">##    21   parent     10.4    12.416  -2.0163</span></span>
-<span><span class="co">##    30   parent      2.9     5.251  -2.3513</span></span>
-<span><span class="co">##    30   parent      4.0     5.251  -1.2513</span></span></code></pre>
-<p>A plot of the fit is obtained with the plot function for mkinfit
-objects.</p>
-<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">m.L1.SFO</span>, show_errmin <span class="op">=</span> <span class="cn">TRUE</span>, main <span class="op">=</span> <span class="st">"FOCUS L1 - SFO"</span><span class="op">)</span></span></code></pre></div>
-<p><img src="FOCUS_L_files/figure-html/unnamed-chunk-4-1.png" width="576"></p>
-<p>The residual plot can be easily obtained by</p>
-<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="../reference/mkinresplot.html">mkinresplot</a></span><span class="op">(</span><span class="va">m.L1.SFO</span>, ylab <span class="op">=</span> <span class="st">"Observed"</span>, xlab <span class="op">=</span> <span class="st">"Time"</span><span class="op">)</span></span></code></pre></div>
-<p><img src="FOCUS_L_files/figure-html/unnamed-chunk-5-1.png" width="576"></p>
-<p>For comparison, the FOMC model is fitted as well, and the <span class="math inline">\(\chi^2\)</span> error level is checked.</p>
-<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">m.L1.FOMC</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"FOMC"</span>, <span class="va">FOCUS_2006_L1_mkin</span>, quiet<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## Warning in mkinfit("FOMC", FOCUS_2006_L1_mkin, quiet = TRUE): Optimisation did not converge:</span></span>
-<span><span class="co">## false convergence (8)</span></span></code></pre>
-<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">m.L1.FOMC</span>, show_errmin <span class="op">=</span> <span class="cn">TRUE</span>, main <span class="op">=</span> <span class="st">"FOCUS L1 - FOMC"</span><span class="op">)</span></span></code></pre></div>
-<p><img src="FOCUS_L_files/figure-html/unnamed-chunk-6-1.png" width="576"></p>
-<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">m.L1.FOMC</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## Warning in sqrt(diag(covar)): NaNs produced</span></span></code></pre>
-<pre><code><span><span class="co">## Warning in sqrt(1/diag(V)): NaNs produced</span></span></code></pre>
-<pre><code><span><span class="co">## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result</span></span>
-<span><span class="co">## is doubtful</span></span></code></pre>
-<pre><code><span><span class="co">## mkin version used for fitting:    1.2.3 </span></span>
-<span><span class="co">## R version used for fitting:       4.2.3 </span></span>
-<span><span class="co">## Date of fit:     Sun Apr 16 08:35:14 2023 </span></span>
-<span><span class="co">## Date of summary: Sun Apr 16 08:35:14 2023 </span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Equations:</span></span>
-<span><span class="co">## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Model predictions using solution type analytical </span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Fitted using 369 model solutions performed in 0.025 s</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Error model: Constant variance </span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Error model algorithm: OLS </span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Starting values for parameters to be optimised:</span></span>
-<span><span class="co">##          value   type</span></span>
-<span><span class="co">## parent_0 89.85  state</span></span>
-<span><span class="co">## alpha     1.00 deparm</span></span>
-<span><span class="co">## beta     10.00 deparm</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Starting values for the transformed parameters actually optimised:</span></span>
-<span><span class="co">##               value lower upper</span></span>
-<span><span class="co">## parent_0  89.850000  -Inf   Inf</span></span>
-<span><span class="co">## log_alpha  0.000000  -Inf   Inf</span></span>
-<span><span class="co">## log_beta   2.302585  -Inf   Inf</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Fixed parameter values:</span></span>
-<span><span class="co">## None</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Warning(s): </span></span>
-<span><span class="co">## Optimisation did not converge:</span></span>
-<span><span class="co">## false convergence (8)</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Results:</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">##        AIC      BIC   logLik</span></span>
-<span><span class="co">##   95.88781 99.44929 -43.9439</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Optimised, transformed parameters with symmetric confidence intervals:</span></span>
-<span><span class="co">##           Estimate Std. Error  Lower  Upper</span></span>
-<span><span class="co">## parent_0     92.47     1.2820 89.720 95.220</span></span>
-<span><span class="co">## log_alpha    13.78        NaN    NaN    NaN</span></span>
-<span><span class="co">## log_beta     16.13        NaN    NaN    NaN</span></span>
-<span><span class="co">## sigma         2.78     0.4598  1.794  3.766</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Parameter correlation:</span></span>
-<span><span class="co">##            parent_0 log_alpha log_beta     sigma</span></span>
-<span><span class="co">## parent_0  1.0000000       NaN      NaN 0.0001671</span></span>
-<span><span class="co">## log_alpha       NaN         1      NaN       NaN</span></span>
-<span><span class="co">## log_beta        NaN       NaN        1       NaN</span></span>
-<span><span class="co">## sigma     0.0001671       NaN      NaN 1.0000000</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Backtransformed parameters:</span></span>
-<span><span class="co">## Confidence intervals for internally transformed parameters are asymmetric.</span></span>
-<span><span class="co">## t-test (unrealistically) based on the assumption of normal distribution</span></span>
-<span><span class="co">## for estimators of untransformed parameters.</span></span>
-<span><span class="co">##           Estimate t value Pr(&gt;t)  Lower  Upper</span></span>
-<span><span class="co">## parent_0 9.247e+01      NA     NA 89.720 95.220</span></span>
-<span><span class="co">## alpha    9.658e+05      NA     NA     NA     NA</span></span>
-<span><span class="co">## beta     1.010e+07      NA     NA     NA     NA</span></span>
-<span><span class="co">## sigma    2.780e+00      NA     NA  1.794  3.766</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## FOCUS Chi2 error levels in percent:</span></span>
-<span><span class="co">##          err.min n.optim df</span></span>
-<span><span class="co">## All data   3.619       3  6</span></span>
-<span><span class="co">## parent     3.619       3  6</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Estimated disappearance times:</span></span>
-<span><span class="co">##        DT50  DT90 DT50back</span></span>
-<span><span class="co">## parent 7.25 24.08     7.25</span></span></code></pre>
-<p>We get a warning that the default optimisation algorithm
-<code>Port</code> did not converge, which is an indication that the
-model is overparameterised, <em>i.e.</em> contains too many parameters
-that are ill-defined as a consequence.</p>
-<p>And in fact, due to the higher number of parameters, and the lower
-number of degrees of freedom of the fit, the <span class="math inline">\(\chi^2\)</span> error level is actually higher for
-the FOMC model (3.6%) than for the SFO model (3.4%). Additionally, the
-parameters <code>log_alpha</code> and <code>log_beta</code> internally
-fitted in the model have excessive confidence intervals, that span more
-than 25 orders of magnitude (!) when backtransformed to the scale of
-<code>alpha</code> and <code>beta</code>. Also, the t-test for
-significant difference from zero does not indicate such a significant
-difference, with p-values greater than 0.1, and finally, the parameter
-correlation of <code>log_alpha</code> and <code>log_beta</code> is
-1.000, clearly indicating that the model is overparameterised.</p>
-<p>The <span class="math inline">\(\chi^2\)</span> error levels reported
-in Appendix 3 and Appendix 7 to the FOCUS kinetics report are rounded to
-integer percentages and partly deviate by one percentage point from the
-results calculated by mkin. The reason for this is not known. However,
-mkin gives the same <span class="math inline">\(\chi^2\)</span> error
-levels as the kinfit package and the calculation routines of the kinfit
-package have been extensively compared to the results obtained by the
-KinGUI software, as documented in the kinfit package vignette. KinGUI
-was the first widely used standard package in this field. Also, the
-calculation of <span class="math inline">\(\chi^2\)</span> error levels
-was compared with KinGUII, CAKE and DegKin manager in a project
-sponsored by the German Umweltbundesamt <span class="citation">(Ranke
-2014)</span>.</p>
-</div>
-<div class="section level2">
-<h2 id="laboratory-data-l2">Laboratory Data L2<a class="anchor" aria-label="anchor" href="#laboratory-data-l2"></a>
-</h2>
-<p>The following code defines example dataset L2 from the FOCUS kinetics
-report, p. 287:</p>
-<div class="sourceCode" id="cb14"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">FOCUS_2006_L2</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span></span>
-<span>  t <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/rep.html" class="external-link">rep</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span>, <span class="fl">3</span>, <span class="fl">7</span>, <span class="fl">14</span>, <span class="fl">28</span><span class="op">)</span>, each <span class="op">=</span> <span class="fl">2</span><span class="op">)</span>,</span>
-<span>  parent <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">96.1</span>, <span class="fl">91.8</span>, <span class="fl">41.4</span>, <span class="fl">38.7</span>,</span>
-<span>             <span class="fl">19.3</span>, <span class="fl">22.3</span>, <span class="fl">4.6</span>, <span class="fl">4.6</span>,</span>
-<span>             <span class="fl">2.6</span>, <span class="fl">1.2</span>, <span class="fl">0.3</span>, <span class="fl">0.6</span><span class="op">)</span><span class="op">)</span></span>
-<span><span class="va">FOCUS_2006_L2_mkin</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/mkin_wide_to_long.html">mkin_wide_to_long</a></span><span class="op">(</span><span class="va">FOCUS_2006_L2</span><span class="op">)</span></span></code></pre></div>
-<div class="section level3">
-<h3 id="sfo-fit-for-l2">SFO fit for L2<a class="anchor" aria-label="anchor" href="#sfo-fit-for-l2"></a>
-</h3>
-<p>Again, the SFO model is fitted and the result is plotted. The
-residual plot can be obtained simply by adding the argument
-<code>show_residuals</code> to the plot command.</p>
-<div class="sourceCode" id="cb15"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">m.L2.SFO</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="va">FOCUS_2006_L2_mkin</span>, quiet<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">m.L2.SFO</span>, show_residuals <span class="op">=</span> <span class="cn">TRUE</span>, show_errmin <span class="op">=</span> <span class="cn">TRUE</span>,</span>
-<span>     main <span class="op">=</span> <span class="st">"FOCUS L2 - SFO"</span><span class="op">)</span></span></code></pre></div>
-<p><img src="FOCUS_L_files/figure-html/unnamed-chunk-8-1.png" width="672"></p>
-<p>The <span class="math inline">\(\chi^2\)</span> error level of 14%
-suggests that the model does not fit very well. This is also obvious
-from the plots of the fit, in which we have included the residual
-plot.</p>
-<p>In the FOCUS kinetics report, it is stated that there is no apparent
-systematic error observed from the residual plot up to the measured DT90
-(approximately at day 5), and there is an underestimation beyond that
-point.</p>
-<p>We may add that it is difficult to judge the random nature of the
-residuals just from the three samplings at days 0, 1 and 3. Also, it is
-not clear <em>a priori</em> why a consistent underestimation after the
-approximate DT90 should be irrelevant. However, this can be rationalised
-by the fact that the FOCUS fate models generally only implement SFO
-kinetics.</p>
-</div>
-<div class="section level3">
-<h3 id="fomc-fit-for-l2">FOMC fit for L2<a class="anchor" aria-label="anchor" href="#fomc-fit-for-l2"></a>
-</h3>
-<p>For comparison, the FOMC model is fitted as well, and the <span class="math inline">\(\chi^2\)</span> error level is checked.</p>
-<div class="sourceCode" id="cb16"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">m.L2.FOMC</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"FOMC"</span>, <span class="va">FOCUS_2006_L2_mkin</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">m.L2.FOMC</span>, show_residuals <span class="op">=</span> <span class="cn">TRUE</span>,</span>
-<span>     main <span class="op">=</span> <span class="st">"FOCUS L2 - FOMC"</span><span class="op">)</span></span></code></pre></div>
-<p><img src="FOCUS_L_files/figure-html/unnamed-chunk-9-1.png" width="672"></p>
-<div class="sourceCode" id="cb17"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">m.L2.FOMC</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## mkin version used for fitting:    1.2.3 </span></span>
-<span><span class="co">## R version used for fitting:       4.2.3 </span></span>
-<span><span class="co">## Date of fit:     Sun Apr 16 08:35:14 2023 </span></span>
-<span><span class="co">## Date of summary: Sun Apr 16 08:35:14 2023 </span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Equations:</span></span>
-<span><span class="co">## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Model predictions using solution type analytical </span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Fitted using 239 model solutions performed in 0.015 s</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Error model: Constant variance </span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Error model algorithm: OLS </span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Starting values for parameters to be optimised:</span></span>
-<span><span class="co">##          value   type</span></span>
-<span><span class="co">## parent_0 93.95  state</span></span>
-<span><span class="co">## alpha     1.00 deparm</span></span>
-<span><span class="co">## beta     10.00 deparm</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Starting values for the transformed parameters actually optimised:</span></span>
-<span><span class="co">##               value lower upper</span></span>
-<span><span class="co">## parent_0  93.950000  -Inf   Inf</span></span>
-<span><span class="co">## log_alpha  0.000000  -Inf   Inf</span></span>
-<span><span class="co">## log_beta   2.302585  -Inf   Inf</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Fixed parameter values:</span></span>
-<span><span class="co">## None</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Results:</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">##        AIC      BIC    logLik</span></span>
-<span><span class="co">##   61.78966 63.72928 -26.89483</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Optimised, transformed parameters with symmetric confidence intervals:</span></span>
-<span><span class="co">##           Estimate Std. Error    Lower   Upper</span></span>
-<span><span class="co">## parent_0   93.7700     1.6130 90.05000 97.4900</span></span>
-<span><span class="co">## log_alpha   0.3180     0.1559 -0.04149  0.6776</span></span>
-<span><span class="co">## log_beta    0.2102     0.2493 -0.36460  0.7850</span></span>
-<span><span class="co">## sigma       2.2760     0.4645  1.20500  3.3470</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Parameter correlation:</span></span>
-<span><span class="co">##             parent_0  log_alpha   log_beta      sigma</span></span>
-<span><span class="co">## parent_0   1.000e+00 -1.151e-01 -2.085e-01 -7.828e-09</span></span>
-<span><span class="co">## log_alpha -1.151e-01  1.000e+00  9.741e-01 -1.602e-07</span></span>
-<span><span class="co">## log_beta  -2.085e-01  9.741e-01  1.000e+00 -1.372e-07</span></span>
-<span><span class="co">## sigma     -7.828e-09 -1.602e-07 -1.372e-07  1.000e+00</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Backtransformed parameters:</span></span>
-<span><span class="co">## Confidence intervals for internally transformed parameters are asymmetric.</span></span>
-<span><span class="co">## t-test (unrealistically) based on the assumption of normal distribution</span></span>
-<span><span class="co">## for estimators of untransformed parameters.</span></span>
-<span><span class="co">##          Estimate t value    Pr(&gt;t)   Lower  Upper</span></span>
-<span><span class="co">## parent_0   93.770  58.120 4.267e-12 90.0500 97.490</span></span>
-<span><span class="co">## alpha       1.374   6.414 1.030e-04  0.9594  1.969</span></span>
-<span><span class="co">## beta        1.234   4.012 1.942e-03  0.6945  2.192</span></span>
-<span><span class="co">## sigma       2.276   4.899 5.977e-04  1.2050  3.347</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## FOCUS Chi2 error levels in percent:</span></span>
-<span><span class="co">##          err.min n.optim df</span></span>
-<span><span class="co">## All data   6.205       3  3</span></span>
-<span><span class="co">## parent     6.205       3  3</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Estimated disappearance times:</span></span>
-<span><span class="co">##          DT50  DT90 DT50back</span></span>
-<span><span class="co">## parent 0.8092 5.356    1.612</span></span></code></pre>
-<p>The error level at which the <span class="math inline">\(\chi^2\)</span> test passes is much lower in this
-case. Therefore, the FOMC model provides a better description of the
-data, as less experimental error has to be assumed in order to explain
-the data.</p>
-</div>
-<div class="section level3">
-<h3 id="dfop-fit-for-l2">DFOP fit for L2<a class="anchor" aria-label="anchor" href="#dfop-fit-for-l2"></a>
-</h3>
-<p>Fitting the four parameter DFOP model further reduces the <span class="math inline">\(\chi^2\)</span> error level.</p>
-<div class="sourceCode" id="cb19"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">m.L2.DFOP</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="va">FOCUS_2006_L2_mkin</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">m.L2.DFOP</span>, show_residuals <span class="op">=</span> <span class="cn">TRUE</span>, show_errmin <span class="op">=</span> <span class="cn">TRUE</span>,</span>
-<span>     main <span class="op">=</span> <span class="st">"FOCUS L2 - DFOP"</span><span class="op">)</span></span></code></pre></div>
-<p><img src="FOCUS_L_files/figure-html/unnamed-chunk-10-1.png" width="672"></p>
-<div class="sourceCode" id="cb20"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">m.L2.DFOP</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## mkin version used for fitting:    1.2.3 </span></span>
-<span><span class="co">## R version used for fitting:       4.2.3 </span></span>
-<span><span class="co">## Date of fit:     Sun Apr 16 08:35:15 2023 </span></span>
-<span><span class="co">## Date of summary: Sun Apr 16 08:35:15 2023 </span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Equations:</span></span>
-<span><span class="co">## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *</span></span>
-<span><span class="co">##            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))</span></span>
-<span><span class="co">##            * parent</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Model predictions using solution type analytical </span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Fitted using 581 model solutions performed in 0.04 s</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Error model: Constant variance </span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Error model algorithm: OLS </span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Starting values for parameters to be optimised:</span></span>
-<span><span class="co">##          value   type</span></span>
-<span><span class="co">## parent_0 93.95  state</span></span>
-<span><span class="co">## k1        0.10 deparm</span></span>
-<span><span class="co">## k2        0.01 deparm</span></span>
-<span><span class="co">## g         0.50 deparm</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Starting values for the transformed parameters actually optimised:</span></span>
-<span><span class="co">##              value lower upper</span></span>
-<span><span class="co">## parent_0 93.950000  -Inf   Inf</span></span>
-<span><span class="co">## log_k1   -2.302585  -Inf   Inf</span></span>
-<span><span class="co">## log_k2   -4.605170  -Inf   Inf</span></span>
-<span><span class="co">## g_qlogis  0.000000  -Inf   Inf</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Fixed parameter values:</span></span>
-<span><span class="co">## None</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Results:</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">##        AIC      BIC    logLik</span></span>
-<span><span class="co">##   52.36695 54.79148 -21.18347</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Optimised, transformed parameters with symmetric confidence intervals:</span></span>
-<span><span class="co">##          Estimate Std. Error      Lower     Upper</span></span>
-<span><span class="co">## parent_0   93.950  9.998e-01    91.5900   96.3100</span></span>
-<span><span class="co">## log_k1      3.112  1.842e+03 -4353.0000 4359.0000</span></span>
-<span><span class="co">## log_k2     -1.088  6.285e-02    -1.2370   -0.9394</span></span>
-<span><span class="co">## g_qlogis   -0.399  9.946e-02    -0.6342   -0.1638</span></span>
-<span><span class="co">## sigma       1.414  2.886e-01     0.7314    2.0960</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Parameter correlation:</span></span>
-<span><span class="co">##            parent_0     log_k1     log_k2   g_qlogis      sigma</span></span>
-<span><span class="co">## parent_0  1.000e+00  6.783e-07 -3.390e-10  2.665e-01 -2.967e-10</span></span>
-<span><span class="co">## log_k1    6.783e-07  1.000e+00  1.116e-04 -2.196e-04 -1.031e-05</span></span>
-<span><span class="co">## log_k2   -3.390e-10  1.116e-04  1.000e+00 -7.903e-01  2.917e-09</span></span>
-<span><span class="co">## g_qlogis  2.665e-01 -2.196e-04 -7.903e-01  1.000e+00 -4.408e-09</span></span>
-<span><span class="co">## sigma    -2.967e-10 -1.031e-05  2.917e-09 -4.408e-09  1.000e+00</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Backtransformed parameters:</span></span>
-<span><span class="co">## Confidence intervals for internally transformed parameters are asymmetric.</span></span>
-<span><span class="co">## t-test (unrealistically) based on the assumption of normal distribution</span></span>
-<span><span class="co">## for estimators of untransformed parameters.</span></span>
-<span><span class="co">##          Estimate   t value    Pr(&gt;t)   Lower   Upper</span></span>
-<span><span class="co">## parent_0  93.9500 9.397e+01 2.036e-12 91.5900 96.3100</span></span>
-<span><span class="co">## k1        22.4800 5.553e-04 4.998e-01  0.0000     Inf</span></span>
-<span><span class="co">## k2         0.3369 1.591e+01 4.697e-07  0.2904  0.3909</span></span>
-<span><span class="co">## g          0.4016 1.680e+01 3.238e-07  0.3466  0.4591</span></span>
-<span><span class="co">## sigma      1.4140 4.899e+00 8.776e-04  0.7314  2.0960</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## FOCUS Chi2 error levels in percent:</span></span>
-<span><span class="co">##          err.min n.optim df</span></span>
-<span><span class="co">## All data    2.53       4  2</span></span>
-<span><span class="co">## parent      2.53       4  2</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Estimated disappearance times:</span></span>
-<span><span class="co">##          DT50  DT90 DT50back DT50_k1 DT50_k2</span></span>
-<span><span class="co">## parent 0.5335 5.311    1.599 0.03084   2.058</span></span></code></pre>
-<p>Here, the DFOP model is clearly the best-fit model for dataset L2
-based on the chi^2 error level criterion.</p>
-</div>
-</div>
-<div class="section level2">
-<h2 id="laboratory-data-l3">Laboratory Data L3<a class="anchor" aria-label="anchor" href="#laboratory-data-l3"></a>
-</h2>
-<p>The following code defines example dataset L3 from the FOCUS kinetics
-report, p. 290.</p>
-<div class="sourceCode" id="cb22"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">FOCUS_2006_L3</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span></span>
-<span>  t <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">3</span>, <span class="fl">7</span>, <span class="fl">14</span>, <span class="fl">30</span>, <span class="fl">60</span>, <span class="fl">91</span>, <span class="fl">120</span><span class="op">)</span>,</span>
-<span>  parent <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">97.8</span>, <span class="fl">60</span>, <span class="fl">51</span>, <span class="fl">43</span>, <span class="fl">35</span>, <span class="fl">22</span>, <span class="fl">15</span>, <span class="fl">12</span><span class="op">)</span><span class="op">)</span></span>
-<span><span class="va">FOCUS_2006_L3_mkin</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/mkin_wide_to_long.html">mkin_wide_to_long</a></span><span class="op">(</span><span class="va">FOCUS_2006_L3</span><span class="op">)</span></span></code></pre></div>
-<div class="section level3">
-<h3 id="fit-multiple-models">Fit multiple models<a class="anchor" aria-label="anchor" href="#fit-multiple-models"></a>
-</h3>
-<p>As of mkin version 0.9-39 (June 2015), we can fit several models to
-one or more datasets in one call to the function <code>mmkin</code>. The
-datasets have to be passed in a list, in this case a named list holding
-only the L3 dataset prepared above.</p>
-<div class="sourceCode" id="cb23"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="co"># Only use one core here, not to offend the CRAN checks</span></span>
-<span><span class="va">mm.L3</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"FOMC"</span>, <span class="st">"DFOP"</span><span class="op">)</span>, cores <span class="op">=</span> <span class="fl">1</span>,</span>
-<span>               <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="st">"FOCUS L3"</span> <span class="op">=</span> <span class="va">FOCUS_2006_L3_mkin</span><span class="op">)</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">mm.L3</span><span class="op">)</span></span></code></pre></div>
-<p><img src="FOCUS_L_files/figure-html/unnamed-chunk-12-1.png" width="700"></p>
-<p>The <span class="math inline">\(\chi^2\)</span> error level of 21% as
-well as the plot suggest that the SFO model does not fit very well. The
-FOMC model performs better, with an error level at which the <span class="math inline">\(\chi^2\)</span> test passes of 7%. Fitting the
-four parameter DFOP model further reduces the <span class="math inline">\(\chi^2\)</span> error level considerably.</p>
-</div>
-<div class="section level3">
-<h3 id="accessing-mmkin-objects">Accessing mmkin objects<a class="anchor" aria-label="anchor" href="#accessing-mmkin-objects"></a>
-</h3>
-<p>The objects returned by mmkin are arranged like a matrix, with models
-as a row index and datasets as a column index.</p>
-<p>We can extract the summary and plot for <em>e.g.</em> the DFOP fit,
-using square brackets for indexing which will result in the use of the
-summary and plot functions working on mkinfit objects.</p>
-<div class="sourceCode" id="cb24"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">mm.L3</span><span class="op">[[</span><span class="st">"DFOP"</span>, <span class="fl">1</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## mkin version used for fitting:    1.2.3 </span></span>
-<span><span class="co">## R version used for fitting:       4.2.3 </span></span>
-<span><span class="co">## Date of fit:     Sun Apr 16 08:35:15 2023 </span></span>
-<span><span class="co">## Date of summary: Sun Apr 16 08:35:15 2023 </span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Equations:</span></span>
-<span><span class="co">## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *</span></span>
-<span><span class="co">##            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))</span></span>
-<span><span class="co">##            * parent</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Model predictions using solution type analytical </span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Fitted using 376 model solutions performed in 0.024 s</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Error model: Constant variance </span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Error model algorithm: OLS </span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Starting values for parameters to be optimised:</span></span>
-<span><span class="co">##          value   type</span></span>
-<span><span class="co">## parent_0 97.80  state</span></span>
-<span><span class="co">## k1        0.10 deparm</span></span>
-<span><span class="co">## k2        0.01 deparm</span></span>
-<span><span class="co">## g         0.50 deparm</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Starting values for the transformed parameters actually optimised:</span></span>
-<span><span class="co">##              value lower upper</span></span>
-<span><span class="co">## parent_0 97.800000  -Inf   Inf</span></span>
-<span><span class="co">## log_k1   -2.302585  -Inf   Inf</span></span>
-<span><span class="co">## log_k2   -4.605170  -Inf   Inf</span></span>
-<span><span class="co">## g_qlogis  0.000000  -Inf   Inf</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Fixed parameter values:</span></span>
-<span><span class="co">## None</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Results:</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">##        AIC      BIC    logLik</span></span>
-<span><span class="co">##   32.97732 33.37453 -11.48866</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Optimised, transformed parameters with symmetric confidence intervals:</span></span>
-<span><span class="co">##          Estimate Std. Error   Lower      Upper</span></span>
-<span><span class="co">## parent_0  97.7500    1.01900 94.5000 101.000000</span></span>
-<span><span class="co">## log_k1    -0.6612    0.10050 -0.9812  -0.341300</span></span>
-<span><span class="co">## log_k2    -4.2860    0.04322 -4.4230  -4.148000</span></span>
-<span><span class="co">## g_qlogis  -0.1739    0.05270 -0.3416  -0.006142</span></span>
-<span><span class="co">## sigma      1.0170    0.25430  0.2079   1.827000</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Parameter correlation:</span></span>
-<span><span class="co">##            parent_0     log_k1     log_k2   g_qlogis      sigma</span></span>
-<span><span class="co">## parent_0  1.000e+00  1.732e-01  2.282e-02  4.009e-01 -9.664e-08</span></span>
-<span><span class="co">## log_k1    1.732e-01  1.000e+00  4.945e-01 -5.809e-01  7.147e-07</span></span>
-<span><span class="co">## log_k2    2.282e-02  4.945e-01  1.000e+00 -6.812e-01  1.022e-06</span></span>
-<span><span class="co">## g_qlogis  4.009e-01 -5.809e-01 -6.812e-01  1.000e+00 -7.926e-07</span></span>
-<span><span class="co">## sigma    -9.664e-08  7.147e-07  1.022e-06 -7.926e-07  1.000e+00</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Backtransformed parameters:</span></span>
-<span><span class="co">## Confidence intervals for internally transformed parameters are asymmetric.</span></span>
-<span><span class="co">## t-test (unrealistically) based on the assumption of normal distribution</span></span>
-<span><span class="co">## for estimators of untransformed parameters.</span></span>
-<span><span class="co">##          Estimate t value    Pr(&gt;t)    Lower     Upper</span></span>
-<span><span class="co">## parent_0 97.75000  95.960 1.248e-06 94.50000 101.00000</span></span>
-<span><span class="co">## k1        0.51620   9.947 1.081e-03  0.37490   0.71090</span></span>
-<span><span class="co">## k2        0.01376  23.140 8.840e-05  0.01199   0.01579</span></span>
-<span><span class="co">## g         0.45660  34.920 2.581e-05  0.41540   0.49850</span></span>
-<span><span class="co">## sigma     1.01700   4.000 1.400e-02  0.20790   1.82700</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## FOCUS Chi2 error levels in percent:</span></span>
-<span><span class="co">##          err.min n.optim df</span></span>
-<span><span class="co">## All data   2.225       4  4</span></span>
-<span><span class="co">## parent     2.225       4  4</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Estimated disappearance times:</span></span>
-<span><span class="co">##         DT50 DT90 DT50back DT50_k1 DT50_k2</span></span>
-<span><span class="co">## parent 7.464  123    37.03   1.343   50.37</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Data:</span></span>
-<span><span class="co">##  time variable observed predicted residual</span></span>
-<span><span class="co">##     0   parent     97.8     97.75  0.05396</span></span>
-<span><span class="co">##     3   parent     60.0     60.45 -0.44933</span></span>
-<span><span class="co">##     7   parent     51.0     49.44  1.56338</span></span>
-<span><span class="co">##    14   parent     43.0     43.84 -0.83632</span></span>
-<span><span class="co">##    30   parent     35.0     35.15 -0.14707</span></span>
-<span><span class="co">##    60   parent     22.0     23.26 -1.25919</span></span>
-<span><span class="co">##    91   parent     15.0     15.18 -0.18181</span></span>
-<span><span class="co">##   120   parent     12.0     10.19  1.81395</span></span></code></pre>
-<div class="sourceCode" id="cb26"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">mm.L3</span><span class="op">[[</span><span class="st">"DFOP"</span>, <span class="fl">1</span><span class="op">]</span><span class="op">]</span>, show_errmin <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
-<p><img src="FOCUS_L_files/figure-html/unnamed-chunk-13-1.png" width="700"></p>
-<p>Here, a look to the model plot, the confidence intervals of the
-parameters and the correlation matrix suggest that the parameter
-estimates are reliable, and the DFOP model can be used as the best-fit
-model based on the <span class="math inline">\(\chi^2\)</span> error
-level criterion for laboratory data L3.</p>
-<p>This is also an example where the standard t-test for the parameter
-<code>g_ilr</code> is misleading, as it tests for a significant
-difference from zero. In this case, zero appears to be the correct value
-for this parameter, and the confidence interval for the backtransformed
-parameter <code>g</code> is quite narrow.</p>
-</div>
-</div>
-<div class="section level2">
-<h2 id="laboratory-data-l4">Laboratory Data L4<a class="anchor" aria-label="anchor" href="#laboratory-data-l4"></a>
-</h2>
-<p>The following code defines example dataset L4 from the FOCUS kinetics
-report, p. 293:</p>
-<div class="sourceCode" id="cb27"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">FOCUS_2006_L4</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span></span>
-<span>  t <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">3</span>, <span class="fl">7</span>, <span class="fl">14</span>, <span class="fl">30</span>, <span class="fl">60</span>, <span class="fl">91</span>, <span class="fl">120</span><span class="op">)</span>,</span>
-<span>  parent <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">96.6</span>, <span class="fl">96.3</span>, <span class="fl">94.3</span>, <span class="fl">88.8</span>, <span class="fl">74.9</span>, <span class="fl">59.9</span>, <span class="fl">53.5</span>, <span class="fl">49.0</span><span class="op">)</span><span class="op">)</span></span>
-<span><span class="va">FOCUS_2006_L4_mkin</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/mkin_wide_to_long.html">mkin_wide_to_long</a></span><span class="op">(</span><span class="va">FOCUS_2006_L4</span><span class="op">)</span></span></code></pre></div>
-<p>Fits of the SFO and FOMC models, plots and summaries are produced
-below:</p>
-<div class="sourceCode" id="cb28"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="co"># Only use one core here, not to offend the CRAN checks</span></span>
-<span><span class="va">mm.L4</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"FOMC"</span><span class="op">)</span>, cores <span class="op">=</span> <span class="fl">1</span>,</span>
-<span>               <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="st">"FOCUS L4"</span> <span class="op">=</span> <span class="va">FOCUS_2006_L4_mkin</span><span class="op">)</span>,</span>
-<span>               quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">mm.L4</span><span class="op">)</span></span></code></pre></div>
-<p><img src="FOCUS_L_files/figure-html/unnamed-chunk-15-1.png" width="700"></p>
-<p>The <span class="math inline">\(\chi^2\)</span> error level of 3.3%
-as well as the plot suggest that the SFO model fits very well. The error
-level at which the <span class="math inline">\(\chi^2\)</span> test
-passes is slightly lower for the FOMC model. However, the difference
-appears negligible.</p>
-<div class="sourceCode" id="cb29"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">mm.L4</span><span class="op">[[</span><span class="st">"SFO"</span>, <span class="fl">1</span><span class="op">]</span><span class="op">]</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## mkin version used for fitting:    1.2.3 </span></span>
-<span><span class="co">## R version used for fitting:       4.2.3 </span></span>
-<span><span class="co">## Date of fit:     Sun Apr 16 08:35:15 2023 </span></span>
-<span><span class="co">## Date of summary: Sun Apr 16 08:35:16 2023 </span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Equations:</span></span>
-<span><span class="co">## d_parent/dt = - k_parent * parent</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Model predictions using solution type analytical </span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Fitted using 142 model solutions performed in 0.009 s</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Error model: Constant variance </span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Error model algorithm: OLS </span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Starting values for parameters to be optimised:</span></span>
-<span><span class="co">##          value   type</span></span>
-<span><span class="co">## parent_0  96.6  state</span></span>
-<span><span class="co">## k_parent   0.1 deparm</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Starting values for the transformed parameters actually optimised:</span></span>
-<span><span class="co">##                  value lower upper</span></span>
-<span><span class="co">## parent_0     96.600000  -Inf   Inf</span></span>
-<span><span class="co">## log_k_parent -2.302585  -Inf   Inf</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Fixed parameter values:</span></span>
-<span><span class="co">## None</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Results:</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">##        AIC      BIC    logLik</span></span>
-<span><span class="co">##   47.12133 47.35966 -20.56067</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Optimised, transformed parameters with symmetric confidence intervals:</span></span>
-<span><span class="co">##              Estimate Std. Error  Lower   Upper</span></span>
-<span><span class="co">## parent_0       96.440    1.69900 92.070 100.800</span></span>
-<span><span class="co">## log_k_parent   -5.030    0.07059 -5.211  -4.848</span></span>
-<span><span class="co">## sigma           3.162    0.79050  1.130   5.194</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Parameter correlation:</span></span>
-<span><span class="co">##               parent_0 log_k_parent     sigma</span></span>
-<span><span class="co">## parent_0     1.000e+00    5.938e-01 3.387e-07</span></span>
-<span><span class="co">## log_k_parent 5.938e-01    1.000e+00 5.830e-07</span></span>
-<span><span class="co">## sigma        3.387e-07    5.830e-07 1.000e+00</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Backtransformed parameters:</span></span>
-<span><span class="co">## Confidence intervals for internally transformed parameters are asymmetric.</span></span>
-<span><span class="co">## t-test (unrealistically) based on the assumption of normal distribution</span></span>
-<span><span class="co">## for estimators of untransformed parameters.</span></span>
-<span><span class="co">##           Estimate t value    Pr(&gt;t)     Lower     Upper</span></span>
-<span><span class="co">## parent_0 96.440000   56.77 1.604e-08 92.070000 1.008e+02</span></span>
-<span><span class="co">## k_parent  0.006541   14.17 1.578e-05  0.005455 7.842e-03</span></span>
-<span><span class="co">## sigma     3.162000    4.00 5.162e-03  1.130000 5.194e+00</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## FOCUS Chi2 error levels in percent:</span></span>
-<span><span class="co">##          err.min n.optim df</span></span>
-<span><span class="co">## All data   3.287       2  6</span></span>
-<span><span class="co">## parent     3.287       2  6</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Estimated disappearance times:</span></span>
-<span><span class="co">##        DT50 DT90</span></span>
-<span><span class="co">## parent  106  352</span></span></code></pre>
-<div class="sourceCode" id="cb31"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">mm.L4</span><span class="op">[[</span><span class="st">"FOMC"</span>, <span class="fl">1</span><span class="op">]</span><span class="op">]</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## mkin version used for fitting:    1.2.3 </span></span>
-<span><span class="co">## R version used for fitting:       4.2.3 </span></span>
-<span><span class="co">## Date of fit:     Sun Apr 16 08:35:15 2023 </span></span>
-<span><span class="co">## Date of summary: Sun Apr 16 08:35:16 2023 </span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Equations:</span></span>
-<span><span class="co">## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Model predictions using solution type analytical </span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Fitted using 224 model solutions performed in 0.014 s</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Error model: Constant variance </span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Error model algorithm: OLS </span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Starting values for parameters to be optimised:</span></span>
-<span><span class="co">##          value   type</span></span>
-<span><span class="co">## parent_0  96.6  state</span></span>
-<span><span class="co">## alpha      1.0 deparm</span></span>
-<span><span class="co">## beta      10.0 deparm</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Starting values for the transformed parameters actually optimised:</span></span>
-<span><span class="co">##               value lower upper</span></span>
-<span><span class="co">## parent_0  96.600000  -Inf   Inf</span></span>
-<span><span class="co">## log_alpha  0.000000  -Inf   Inf</span></span>
-<span><span class="co">## log_beta   2.302585  -Inf   Inf</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Fixed parameter values:</span></span>
-<span><span class="co">## None</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Results:</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">##        AIC      BIC    logLik</span></span>
-<span><span class="co">##   40.37255 40.69032 -16.18628</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Optimised, transformed parameters with symmetric confidence intervals:</span></span>
-<span><span class="co">##           Estimate Std. Error   Lower    Upper</span></span>
-<span><span class="co">## parent_0   99.1400     1.2670 95.6300 102.7000</span></span>
-<span><span class="co">## log_alpha  -0.3506     0.2616 -1.0770   0.3756</span></span>
-<span><span class="co">## log_beta    4.1740     0.3938  3.0810   5.2670</span></span>
-<span><span class="co">## sigma       1.8300     0.4575  0.5598   3.1000</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Parameter correlation:</span></span>
-<span><span class="co">##             parent_0  log_alpha   log_beta      sigma</span></span>
-<span><span class="co">## parent_0   1.000e+00 -4.696e-01 -5.543e-01 -2.468e-07</span></span>
-<span><span class="co">## log_alpha -4.696e-01  1.000e+00  9.889e-01  2.478e-08</span></span>
-<span><span class="co">## log_beta  -5.543e-01  9.889e-01  1.000e+00  5.211e-08</span></span>
-<span><span class="co">## sigma     -2.468e-07  2.478e-08  5.211e-08  1.000e+00</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Backtransformed parameters:</span></span>
-<span><span class="co">## Confidence intervals for internally transformed parameters are asymmetric.</span></span>
-<span><span class="co">## t-test (unrealistically) based on the assumption of normal distribution</span></span>
-<span><span class="co">## for estimators of untransformed parameters.</span></span>
-<span><span class="co">##          Estimate t value    Pr(&gt;t)   Lower   Upper</span></span>
-<span><span class="co">## parent_0  99.1400  78.250 7.993e-08 95.6300 102.700</span></span>
-<span><span class="co">## alpha      0.7042   3.823 9.365e-03  0.3407   1.456</span></span>
-<span><span class="co">## beta      64.9800   2.540 3.201e-02 21.7800 193.900</span></span>
-<span><span class="co">## sigma      1.8300   4.000 8.065e-03  0.5598   3.100</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## FOCUS Chi2 error levels in percent:</span></span>
-<span><span class="co">##          err.min n.optim df</span></span>
-<span><span class="co">## All data   2.029       3  5</span></span>
-<span><span class="co">## parent     2.029       3  5</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Estimated disappearance times:</span></span>
-<span><span class="co">##         DT50 DT90 DT50back</span></span>
-<span><span class="co">## parent 108.9 1644    494.9</span></span></code></pre>
-</div>
-<div class="section level2">
-<h2 class="unnumbered" id="references">References<a class="anchor" aria-label="anchor" href="#references"></a>
-</h2>
-<div id="refs" class="references csl-bib-body hanging-indent">
-<div id="ref-ranke2014" class="csl-entry">
-Ranke, Johannes. 2014. <span>“<span class="nocase">Prüfung und
-Validierung von Modellierungssoftware als Alternative zu ModelMaker
-4.0</span>.”</span> Umweltbundesamt Projektnummer 27452.
-</div>
-</div>
-</div>
-  </div>
-
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-
-        <nav id="toc" data-toggle="toc"><h2 data-toc-skip>Contents</h2>
-    </nav>
-</div>
-
-</div>
-
-
-
-      <footer><div class="copyright">
-  <p></p>
-<p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p>
-<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer>
-</div>
-
-  
-
-
-  
-
-  </body>
-</html>
diff --git a/docs/dev/articles/FOCUS_L_files/accessible-code-block-0.0.1/empty-anchor.js b/docs/dev/articles/FOCUS_L_files/accessible-code-block-0.0.1/empty-anchor.js
deleted file mode 100644
index ca349fd6..00000000
--- a/docs/dev/articles/FOCUS_L_files/accessible-code-block-0.0.1/empty-anchor.js
+++ /dev/null
@@ -1,15 +0,0 @@
-// Hide empty <a> tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) -->
-// v0.0.1
-// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020.
-
-document.addEventListener('DOMContentLoaded', function() {
-  const codeList = document.getElementsByClassName("sourceCode");
-  for (var i = 0; i < codeList.length; i++) {
-    var linkList = codeList[i].getElementsByTagName('a');
-    for (var j = 0; j < linkList.length; j++) {
-      if (linkList[j].innerHTML === "") {
-        linkList[j].setAttribute('aria-hidden', 'true');
-      }
-    }
-  }
-});
diff --git a/docs/dev/articles/FOCUS_L_files/anchor-sections-1.0/anchor-sections.css b/docs/dev/articles/FOCUS_L_files/anchor-sections-1.0/anchor-sections.css
deleted file mode 100644
index 07aee5fc..00000000
--- a/docs/dev/articles/FOCUS_L_files/anchor-sections-1.0/anchor-sections.css
+++ /dev/null
@@ -1,4 +0,0 @@
-/* Styles for section anchors */
-a.anchor-section {margin-left: 10px; visibility: hidden; color: inherit;}
-a.anchor-section::before {content: '#';}
-.hasAnchor:hover a.anchor-section {visibility: visible;}
diff --git a/docs/dev/articles/FOCUS_L_files/anchor-sections-1.0/anchor-sections.js b/docs/dev/articles/FOCUS_L_files/anchor-sections-1.0/anchor-sections.js
deleted file mode 100644
index 570f99a0..00000000
--- a/docs/dev/articles/FOCUS_L_files/anchor-sections-1.0/anchor-sections.js
+++ /dev/null
@@ -1,33 +0,0 @@
-// Anchor sections v1.0 written by Atsushi Yasumoto on Oct 3rd, 2020.
-document.addEventListener('DOMContentLoaded', function() {
-  // Do nothing if AnchorJS is used
-  if (typeof window.anchors === 'object' && anchors.hasOwnProperty('hasAnchorJSLink')) {
-    return;
-  }
-
-  const h = document.querySelectorAll('h1, h2, h3, h4, h5, h6');
-
-  // Do nothing if sections are already anchored
-  if (Array.from(h).some(x => x.classList.contains('hasAnchor'))) {
-    return null;
-  }
-
-  // Use section id when pandoc runs with --section-divs
-  const section_id = function(x) {
-    return ((x.classList.contains('section') || (x.tagName === 'SECTION'))
-            ? x.id : '');
-  };
-
-  // Add anchors
-  h.forEach(function(x) {
-    const id = x.id || section_id(x.parentElement);
-    if (id === '') {
-      return null;
-    }
-    let anchor = document.createElement('a');
-    anchor.href = '#' + id;
-    anchor.classList = ['anchor-section'];
-    x.classList.add('hasAnchor');
-    x.appendChild(anchor);
-  });
-});
diff --git a/docs/dev/articles/FOCUS_L_files/figure-html/unnamed-chunk-10-1.png b/docs/dev/articles/FOCUS_L_files/figure-html/unnamed-chunk-10-1.png
deleted file mode 100644
index 11706305..00000000
--- a/docs/dev/articles/FOCUS_L_files/figure-html/unnamed-chunk-10-1.png
+++ /dev/null
diff --git a/docs/dev/articles/FOCUS_L_files/figure-html/unnamed-chunk-12-1.png b/docs/dev/articles/FOCUS_L_files/figure-html/unnamed-chunk-12-1.png
deleted file mode 100644
index daa488a3..00000000
--- a/docs/dev/articles/FOCUS_L_files/figure-html/unnamed-chunk-12-1.png
+++ /dev/null
diff --git a/docs/dev/articles/FOCUS_L_files/figure-html/unnamed-chunk-13-1.png b/docs/dev/articles/FOCUS_L_files/figure-html/unnamed-chunk-13-1.png
deleted file mode 100644
index 5caea744..00000000
--- a/docs/dev/articles/FOCUS_L_files/figure-html/unnamed-chunk-13-1.png
+++ /dev/null
diff --git a/docs/dev/articles/FOCUS_L_files/figure-html/unnamed-chunk-15-1.png b/docs/dev/articles/FOCUS_L_files/figure-html/unnamed-chunk-15-1.png
deleted file mode 100644
index 0dc9d57d..00000000
--- a/docs/dev/articles/FOCUS_L_files/figure-html/unnamed-chunk-15-1.png
+++ /dev/null
diff --git a/docs/dev/articles/FOCUS_L_files/figure-html/unnamed-chunk-4-1.png b/docs/dev/articles/FOCUS_L_files/figure-html/unnamed-chunk-4-1.png
deleted file mode 100644
index 13344b25..00000000
--- a/docs/dev/articles/FOCUS_L_files/figure-html/unnamed-chunk-4-1.png
+++ /dev/null
diff --git a/docs/dev/articles/FOCUS_L_files/figure-html/unnamed-chunk-5-1.png b/docs/dev/articles/FOCUS_L_files/figure-html/unnamed-chunk-5-1.png
deleted file mode 100644
index ec234b6e..00000000
--- a/docs/dev/articles/FOCUS_L_files/figure-html/unnamed-chunk-5-1.png
+++ /dev/null
diff --git a/docs/dev/articles/FOCUS_L_files/figure-html/unnamed-chunk-6-1.png b/docs/dev/articles/FOCUS_L_files/figure-html/unnamed-chunk-6-1.png
deleted file mode 100644
index c3f55dd6..00000000
--- a/docs/dev/articles/FOCUS_L_files/figure-html/unnamed-chunk-6-1.png
+++ /dev/null
diff --git a/docs/dev/articles/FOCUS_L_files/figure-html/unnamed-chunk-8-1.png b/docs/dev/articles/FOCUS_L_files/figure-html/unnamed-chunk-8-1.png
deleted file mode 100644
index d3551b47..00000000
--- a/docs/dev/articles/FOCUS_L_files/figure-html/unnamed-chunk-8-1.png
+++ /dev/null
diff --git a/docs/dev/articles/FOCUS_L_files/figure-html/unnamed-chunk-9-1.png b/docs/dev/articles/FOCUS_L_files/figure-html/unnamed-chunk-9-1.png
deleted file mode 100644
index 5f8afc00..00000000
--- a/docs/dev/articles/FOCUS_L_files/figure-html/unnamed-chunk-9-1.png
+++ /dev/null
diff --git a/docs/dev/articles/FOCUS_L_files/header-attrs-2.6/header-attrs.js b/docs/dev/articles/FOCUS_L_files/header-attrs-2.6/header-attrs.js
deleted file mode 100644
index dd57d92e..00000000
--- a/docs/dev/articles/FOCUS_L_files/header-attrs-2.6/header-attrs.js
+++ /dev/null
@@ -1,12 +0,0 @@
-// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
-// be compatible with the behavior of Pandoc < 2.8).
-document.addEventListener('DOMContentLoaded', function(e) {
-  var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
-  var i, h, a;
-  for (i = 0; i < hs.length; i++) {
-    h = hs[i];
-    if (!/^h[1-6]$/i.test(h.tagName)) continue;  // it should be a header h1-h6
-    a = h.attributes;
-    while (a.length > 0) h.removeAttribute(a[0].name);
-  }
-});
diff --git a/docs/dev/articles/index.html b/docs/dev/articles/index.html
deleted file mode 100644
index c9116643..00000000
--- a/docs/dev/articles/index.html
+++ /dev/null
@@ -1,159 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Articles • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Articles"><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-article-index">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.4</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-      <h1>Articles</h1>
-    </div>
-
-    <div class="section ">
-      <h3>All vignettes</h3>
-      <p class="section-desc"></p>
-
-      <dl><dt><a href="FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></dt>
-        <dd>
-        </dd><dt><a href="FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></dt>
-        <dd>
-        </dd><dt><a href="mkin.html">Introduction to mkin</a></dt>
-        <dd>
-        </dd><dt><a href="prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></dt>
-        <dd>
-        </dd><dt><a href="prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></dt>
-        <dd>
-        </dd><dt><a href="prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></dt>
-        <dd>
-        </dd><dt><a href="twa.html">Calculation of time weighted average concentrations with mkin</a></dt>
-        <dd>
-        </dd><dt><a href="web_only/FOCUS_Z.html">Example evaluation of FOCUS dataset Z</a></dt>
-        <dd>
-        </dd><dt><a href="web_only/NAFTA_examples.html">Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance</a></dt>
-        <dd>
-        </dd><dt><a href="web_only/benchmarks.html">Benchmark timings for mkin</a></dt>
-        <dd>
-        </dd><dt><a href="web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></dt>
-        <dd>
-        </dd><dt><a href="web_only/dimethenamid_2018.html">Example evaluations of the dimethenamid data from 2018</a></dt>
-        <dd>
-        </dd><dt><a href="web_only/multistart.html">Short demo of the multistart method</a></dt>
-        <dd>
-        </dd><dt><a href="web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></dt>
-        <dd>
-      </dd></dl></div>
-  </div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/articles/mkin.html b/docs/dev/articles/mkin.html
deleted file mode 100644
index fa3ac11c..00000000
--- a/docs/dev/articles/mkin.html
+++ /dev/null
@@ -1,472 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en">
-<head>
-<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
-<meta charset="utf-8">
-<meta http-equiv="X-UA-Compatible" content="IE=edge">
-<meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>Introduction to mkin • mkin</title>
-<!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous">
-<script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css">
-<script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous">
-<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous">
-<!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet">
-<script src="../pkgdown.js"></script><meta property="og:title" content="Introduction to mkin">
-<meta property="og:description" content="mkin">
-<meta name="robots" content="noindex">
-<!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]-->
-</head>
-<body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-article">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav">
-<li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu">
-<li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul>
-</li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul>
-<ul class="nav navbar-nav navbar-right">
-<li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul>
-</div>
-<!--/.nav-collapse -->
-  </div>
-<!--/.container -->
-</div>
-<!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header toc-ignore">
-      <h1 data-toc-skip>Introduction to mkin</h1>
-                        <h4 data-toc-skip class="author">Johannes
-Ranke</h4>
-            
-            <h4 data-toc-skip class="date">Last change 15 February 2021
-(rebuilt 2023-04-16)</h4>
-      
-      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/mkin.rmd" class="external-link"><code>vignettes/mkin.rmd</code></a></small>
-      <div class="hidden name"><code>mkin.rmd</code></div>
-
-    </div>
-
-    
-    
-<p><a href="https://www.jrwb.de" class="external-link">Wissenschaftlicher Berater, Kronacher
-Str. 12, 79639 Grenzach-Wyhlen, Germany</a><br> Privatdozent at the
-University of Freiburg</p>
-<div class="section level2">
-<h2 id="abstract">Abstract<a class="anchor" aria-label="anchor" href="#abstract"></a>
-</h2>
-<p>In the regulatory evaluation of chemical substances like plant
-protection products (pesticides), biocides and other chemicals,
-degradation data play an important role. For the evaluation of pesticide
-degradation experiments, detailed guidance has been developed, based on
-nonlinear optimisation. The <code>R</code> add-on package
-<code>mkin</code> implements fitting some of the models recommended in
-this guidance from within R and calculates some statistical measures for
-data series within one or more compartments, for parent and
-metabolites.</p>
-<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="st"><a href="https://pkgdown.jrwb.de/mkin/">"mkin"</a></span>, quietly <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
-<span><span class="co"># Define the kinetic model</span></span>
-<span><span class="va">m_SFO_SFO_SFO</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"M1"</span><span class="op">)</span>,</span>
-<span>                         M1 <span class="op">=</span> <span class="fu"><a href="../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"M2"</span><span class="op">)</span>,</span>
-<span>                         M2 <span class="op">=</span> <span class="fu"><a href="../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span>
-<span>                         use_of_ff <span class="op">=</span> <span class="st">"max"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
-<span></span>
-<span></span>
-<span><span class="co"># Produce model predictions using some arbitrary parameters</span></span>
-<span><span class="va">sampling_times</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span>, <span class="fl">3</span>, <span class="fl">7</span>, <span class="fl">14</span>, <span class="fl">28</span>, <span class="fl">60</span>, <span class="fl">90</span>, <span class="fl">120</span><span class="op">)</span></span>
-<span><span class="va">d_SFO_SFO_SFO</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/mkinpredict.html">mkinpredict</a></span><span class="op">(</span><span class="va">m_SFO_SFO_SFO</span>,</span>
-<span>  <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_parent <span class="op">=</span> <span class="fl">0.03</span>,</span>
-<span>    f_parent_to_M1 <span class="op">=</span> <span class="fl">0.5</span>, k_M1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Log.html" class="external-link">log</a></span><span class="op">(</span><span class="fl">2</span><span class="op">)</span><span class="op">/</span><span class="fl">100</span>,</span>
-<span>    f_M1_to_M2 <span class="op">=</span> <span class="fl">0.9</span>, k_M2 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Log.html" class="external-link">log</a></span><span class="op">(</span><span class="fl">2</span><span class="op">)</span><span class="op">/</span><span class="fl">50</span><span class="op">)</span>,</span>
-<span>  <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fl">100</span>, M1 <span class="op">=</span> <span class="fl">0</span>, M2 <span class="op">=</span> <span class="fl">0</span><span class="op">)</span>,</span>
-<span>  <span class="va">sampling_times</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># Generate a dataset by adding normally distributed errors with</span></span>
-<span><span class="co"># standard deviation 3, for two replicates at each sampling time</span></span>
-<span><span class="va">d_SFO_SFO_SFO_err</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/add_err.html">add_err</a></span><span class="op">(</span><span class="va">d_SFO_SFO_SFO</span>, reps <span class="op">=</span> <span class="fl">2</span>,</span>
-<span>                             sdfunc <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fl">3</span>,</span>
-<span>                             n <span class="op">=</span> <span class="fl">1</span>, seed <span class="op">=</span> <span class="fl">123456789</span> <span class="op">)</span></span>
-<span></span>
-<span><span class="co"># Fit the model to the dataset</span></span>
-<span><span class="va">f_SFO_SFO_SFO</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">m_SFO_SFO_SFO</span>, <span class="va">d_SFO_SFO_SFO_err</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># Plot the results separately for parent and metabolites</span></span>
-<span><span class="fu"><a href="../reference/plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">f_SFO_SFO_SFO</span>, lpos <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"topright"</span>, <span class="st">"bottomright"</span>, <span class="st">"bottomright"</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
-<p><img src="mkin_files/figure-html/unnamed-chunk-2-1.png" width="768"></p>
-</div>
-<div class="section level2">
-<h2 id="background">Background<a class="anchor" aria-label="anchor" href="#background"></a>
-</h2>
-<p>The <code>mkin</code> package <span class="citation">(J. Ranke
-2021)</span> implements the approach to degradation kinetics recommended
-in the kinetics report provided by the FOrum for Co-ordination of
-pesticide fate models and their USe <span class="citation">(FOCUS Work
-Group on Degradation Kinetics 2006, 2014)</span>. It covers data series
-describing the decline of one compound, data series with transformation
-products (commonly termed metabolites) and data series for more than one
-compartment. It is possible to include back reactions. Therefore,
-equilibrium reactions and equilibrium partitioning can be specified,
-although this often leads to an overparameterisation of the model.</p>
-<p>When the first <code>mkin</code> code was published in 2010, the most
-commonly used tools for fitting more complex kinetic degradation models
-to experimental data were KinGUI <span class="citation">(Schäfer et al.
-2007)</span>, a MATLAB based tool with a graphical user interface that
-was specifically tailored to the task and included some output as
-proposed by the FOCUS Kinetics Workgroup, and ModelMaker, a general
-purpose compartment based tool providing infrastructure for fitting
-dynamic simulation models based on differential equations to data.</p>
-<p>The ‘mkin’ code was first uploaded to the BerliOS development
-platform. When this was taken down, the version control history was
-imported into the R-Forge site (see <em>e.g.</em> <a href="https://cgit.jrwb.de/mkin/commit/?id=30cbb4092f6d2d3beff5800603374a0d009ad770" class="external-link">the
-initial commit on 11 May 2010</a>), where the code is still being
-updated.</p>
-<p>At that time, the R package <code>FME</code> (Flexible Modelling
-Environment) <span class="citation">(Soetaert and Petzoldt 2010)</span>
-was already available, and provided a good basis for developing a
-package specifically tailored to the task. The remaining challenge was
-to make it as easy as possible for the users (including the author of
-this vignette) to specify the system of differential equations and to
-include the output requested by the FOCUS guidance, such as the <span class="math inline">\(\chi^2\)</span> error level as defined in this
-guidance.</p>
-<p>Also, <code>mkin</code> introduced using analytical solutions for
-parent only kinetics for improved optimization speed. Later, Eigenvalue
-based solutions were introduced to <code>mkin</code> for the case of
-linear differential equations (<em>i.e.</em> where the FOMC or DFOP
-models were not used for the parent compound), greatly improving the
-optimization speed for these cases. This, has become somehow obsolete,
-as the use of compiled code described below gives even faster execution
-times.</p>
-<p>The possibility to specify back-reactions and a biphasic model
-(SFORB) for metabolites were present in <code>mkin</code> from the very
-beginning.</p>
-<div class="section level3">
-<h3 id="derived-software-tools">Derived software tools<a class="anchor" aria-label="anchor" href="#derived-software-tools"></a>
-</h3>
-<p>Soon after the publication of <code>mkin</code>, two derived tools
-were published, namely KinGUII (developed at Bayer Crop Science) and
-CAKE (commissioned to Tessella by Syngenta), which added a graphical
-user interface (GUI), and added fitting by iteratively reweighted least
-squares (IRLS) and characterisation of likely parameter distributions by
-Markov Chain Monte Carlo (MCMC) sampling.</p>
-<p>CAKE focuses on a smooth use experience, sacrificing some flexibility
-in the model definition, originally allowing only two primary
-metabolites in parallel. The current version 3.4 of CAKE released in May
-2020 uses a scheme for up to six metabolites in a flexible arrangement
-and supports biphasic modelling of metabolites, but does not support
-back-reactions (non-instantaneous equilibria).</p>
-<p>KinGUI offers an even more flexible widget for specifying complex
-kinetic models. Back-reactions (non-instantaneous equilibria) were
-supported early on, but until 2014, only simple first-order models could
-be specified for transformation products. Starting with KinGUII version
-2.1, biphasic modelling of metabolites was also available in
-KinGUII.</p>
-<p>A further graphical user interface (GUI) that has recently been
-brought to a decent degree of maturity is the browser based GUI named
-<code>gmkin</code>. Please see its <a href="https://pkgdown.jrwb.de/gmkin/" class="external-link">documentation page</a> and <a href="https://pkgdown.jrwb.de/gmkin/articles/gmkin_manual.html" class="external-link">manual</a>
-for further information.</p>
-<p>A comparison of scope, usability and numerical results obtained with
-these tools has been recently been published by <span class="citation">Johannes Ranke, Wöltjen, and Meinecke
-(2018)</span>.</p>
-</div>
-</div>
-<div class="section level2">
-<h2 id="unique-features">Unique features<a class="anchor" aria-label="anchor" href="#unique-features"></a>
-</h2>
-<p>Currently, the main unique features available in <code>mkin</code>
-are</p>
-<ul>
-<li>the <a href="https://pkgdown.jrwb.de/mkin/articles/web_only/compiled_models.html">speed
-increase</a> by using compiled code when a compiler is present,</li>
-<li>parallel model fitting on multicore machines using the <a href="https://pkgdown.jrwb.de/mkin/reference/mmkin.html"><code>mmkin</code>
-function</a>,</li>
-<li>the estimation of parameter confidence intervals based on
-transformed parameters (see below) and</li>
-<li>the possibility to use the <a href="https://pkgdown.jrwb.de/mkin/reference/sigma_twocomp.html">two-component
-error model</a>
-</li>
-</ul>
-<p>The iteratively reweighted least squares fitting of different
-variances for each variable as introduced by <span class="citation">Gao
-et al. (2011)</span> has been available in mkin since <a href="https://pkgdown.jrwb.de/mkin/news/index.html#mkin-0-9-22-2013-10-26">version
-0.9-22</a>. With <a href="https://pkgdown.jrwb.de/mkin/news/index.html#mkin-0-9-49-5-2019-07-04">release
-0.9.49.5</a>, the IRLS algorithm has been complemented by direct or
-step-wise maximisation of the likelihood function, which makes it
-possible not only to fit the variance by variable error model but also a
-<a href="https://pkgdown.jrwb.de/mkin/reference/sigma_twocomp.html">two-component
-error model</a> inspired by error models developed in analytical
-chemistry <span class="citation">(Johannes Ranke and Meinecke
-2019)</span>.</p>
-</div>
-<div class="section level2">
-<h2 id="internal-parameter-transformations">Internal parameter transformations<a class="anchor" aria-label="anchor" href="#internal-parameter-transformations"></a>
-</h2>
-<p>For rate constants, the log transformation is used, as proposed by
-Bates and Watts <span class="citation">(1988, 77, 149)</span>.
-Approximate intervals are constructed for the transformed rate constants
-<span class="citation">(compare Bates and Watts 1988, 135)</span>,
-<em>i.e.</em> for their logarithms. Confidence intervals for the rate
-constants are then obtained using the appropriate backtransformation
-using the exponential function.</p>
-<p>In the first version of <code>mkin</code> allowing for specifying
-models using formation fractions, a home-made reparameterisation was
-used in order to ensure that the sum of formation fractions would not
-exceed unity.</p>
-<p>This method is still used in the current version of KinGUII (v2.1
-from April 2014), with a modification that allows for fixing the pathway
-to sink to zero. CAKE uses penalties in the objective function in order
-to enforce this constraint.</p>
-<p>In 2012, an alternative reparameterisation of the formation fractions
-was proposed together with René Lehmann <span class="citation">(J. Ranke
-and Lehmann 2012)</span>, based on isometric logratio transformation
-(ILR). The aim was to improve the validity of the linear approximation
-of the objective function during the parameter estimation procedure as
-well as in the subsequent calculation of parameter confidence intervals.
-In the current version of mkin, a logit transformation is used for
-parameters that are bound between 0 and 1, such as the g parameter of
-the DFOP model.</p>
-<div class="section level3">
-<h3 id="confidence-intervals-based-on-transformed-parameters">Confidence intervals based on transformed parameters<a class="anchor" aria-label="anchor" href="#confidence-intervals-based-on-transformed-parameters"></a>
-</h3>
-<p>In the first attempt at providing improved parameter confidence
-intervals introduced to <code>mkin</code> in 2013, confidence intervals
-obtained from FME on the transformed parameters were simply all
-backtransformed one by one to yield asymmetric confidence intervals for
-the backtransformed parameters.</p>
-<p>However, while there is a 1:1 relation between the rate constants in
-the model and the transformed parameters fitted in the model, the
-parameters obtained by the isometric logratio transformation are
-calculated from the set of formation fractions that quantify the paths
-to each of the compounds formed from a specific parent compound, and no
-such 1:1 relation exists.</p>
-<p>Therefore, parameter confidence intervals for formation fractions
-obtained with this method only appear valid for the case of a single
-transformation product, where currently the logit transformation is used
-for the formation fraction.</p>
-<p>The confidence intervals obtained by backtransformation for the cases
-where a 1:1 relation between transformed and original parameter exist
-are considered by the author of this vignette to be more accurate than
-those obtained using a re-estimation of the Hessian matrix after
-backtransformation, as implemented in the FME package.</p>
-</div>
-<div class="section level3">
-<h3 id="parameter-t-test-based-on-untransformed-parameters">Parameter t-test based on untransformed parameters<a class="anchor" aria-label="anchor" href="#parameter-t-test-based-on-untransformed-parameters"></a>
-</h3>
-<p>The standard output of many nonlinear regression software packages
-includes the results from a test for significant difference from zero
-for all parameters. Such a test is also recommended to check the
-validity of rate constants in the FOCUS guidance <span class="citation">(FOCUS Work Group on Degradation Kinetics 2014,
-96ff)</span>.</p>
-<p>It has been argued that the precondition for this test, <em>i.e.</em>
-normal distribution of the estimator for the parameters, is not
-fulfilled in the case of nonlinear regression <span class="citation">(J.
-Ranke and Lehmann 2015)</span>. However, this test is commonly used by
-industry, consultants and national authorities in order to decide on the
-reliability of parameter estimates, based on the FOCUS guidance
-mentioned above. Therefore, the results of this one-sided t-test are
-included in the summary output from <code>mkin</code>.</p>
-<p>As it is not reasonable to test for significant difference of the
-transformed parameters (<em>e.g.</em> <span class="math inline">\(log(k)\)</span>) from zero, the t-test is
-calculated based on the model definition before parameter
-transformation, <em>i.e.</em> in a similar way as in packages that do
-not apply such an internal parameter transformation. A note is included
-in the <code>mkin</code> output, pointing to the fact that the t-test is
-based on the unjustified assumption of normal distribution of the
-parameter estimators.</p>
-</div>
-</div>
-<div class="section level2">
-<h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a>
-</h2>
-<!-- vim: set foldmethod=syntax: -->
-<div id="refs" class="references csl-bib-body hanging-indent">
-<div id="ref-bates1988" class="csl-entry">
-Bates, D., and D. Watts. 1988. <em>Nonlinear Regression and Its
-Applications</em>. Wiley-Interscience.
-</div>
-<div id="ref-FOCUS2006" class="csl-entry">
-FOCUS Work Group on Degradation Kinetics. 2006. <em>Guidance Document on
-Estimating Persistence and Degradation Kinetics from Environmental Fate
-Studies on Pesticides in EU Registration. Report of the FOCUS Work Group
-on Degradation Kinetics</em>. <a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics" class="external-link">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a>.
-</div>
-<div id="ref-FOCUSkinetics2014" class="csl-entry">
-———. 2014. <em>Generic Guidance for Estimating Persistence and
-Degradation Kinetics from Environmental Fate Studies on Pesticides in EU
-Registration</em>. 1.1 ed. <a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics" class="external-link">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a>.
-</div>
-<div id="ref-gao11" class="csl-entry">
-Gao, Z., J. W. Green, J. Vanderborght, and W. Schmitt. 2011.
-<span>“Improving Uncertainty Analysis in Kinetic Evaluations Using
-Iteratively Reweighted Least Squares.”</span> Journal. <em>Environmental
-Science and Technology</em> 45: 4429–37.
-</div>
-<div id="ref-pkg:mkin" class="csl-entry">
-Ranke, J. 2021. <em>‘<span class="nocase">mkin</span>‘:
-<span>K</span>inetic Evaluation of Chemical Degradation Data</em>. <a href="https://CRAN.R-project.org/package=mkin" class="external-link">https://CRAN.R-project.org/package=mkin</a>.
-</div>
-<div id="ref-ranke2012" class="csl-entry">
-Ranke, J., and R. Lehmann. 2012. <span>“Parameter Reliability in Kinetic
-Evaluation of Environmental Metabolism Data - Assessment and the
-Influence of Model Specification.”</span> In <em>SETAC World 20-24
-May</em>. Berlin. <a href="https://jrwb.de/posters/Poster_SETAC_2012_Kinetic_parameter_uncertainty_model_parameterization_Lehmann_Ranke.pdf" class="external-link">https://jrwb.de/posters/Poster_SETAC_2012_Kinetic_parameter_uncertainty_model_parameterization_Lehmann_Ranke.pdf</a>.
-</div>
-<div id="ref-ranke2015" class="csl-entry">
-———. 2015. <span>“To t-Test or Not to t-Test, That Is the
-Question.”</span> In <em>XV Symposium on Pesticide Chemistry 2-4
-September 2015</em>. Piacenza. <a href="https://jrwb.de/posters/piacenza_2015.pdf" class="external-link">https://jrwb.de/posters/piacenza_2015.pdf</a>.
-</div>
-<div id="ref-ranke2019" class="csl-entry">
-Ranke, Johannes, and Stefan Meinecke. 2019. <span>“Error Models for the
-Kinetic Evaluation of Chemical Degradation Data.”</span>
-<em>Environments</em> 6 (12). <a href="https://doi.org/10.3390/environments6120124" class="external-link">https://doi.org/10.3390/environments6120124</a>.
-</div>
-<div id="ref-ranke2018" class="csl-entry">
-Ranke, Johannes, Janina Wöltjen, and Stefan Meinecke. 2018.
-<span>“Comparison of Software Tools for Kinetic Evaluation of Chemical
-Degradation Data.”</span> <em>Environmental Sciences Europe</em> 30 (1):
-17. <a href="https://doi.org/10.1186/s12302-018-0145-1" class="external-link">https://doi.org/10.1186/s12302-018-0145-1</a>.
-</div>
-<div id="ref-schaefer2007" class="csl-entry">
-Schäfer, D., B. Mikolasch, P. Rainbird, and B. Harvey. 2007.
-<span>“<span>KinGUI</span>: A New Kinetic Software Tool for Evaluations
-According to <span>FOCUS</span> Degradation Kinetics.”</span> In
-<em>Proceedings of the XIII Symposium Pesticide Chemistry</em>, edited
-by Del Re A. A. M., Capri E., Fragoulis G., and Trevisan M., 916–23.
-Piacenza.
-</div>
-<div id="ref-soetaert2010" class="csl-entry">
-Soetaert, Karline, and Thomas Petzoldt. 2010. <span>“Inverse Modelling,
-Sensitivity and Monte Carlo Analysis in <span>R</span> Using Package
-<span>FME</span>.”</span> <em>Journal of Statistical Software</em> 33
-(3): 1–28. <a href="https://doi.org/10.18637/jss.v033.i03" class="external-link">https://doi.org/10.18637/jss.v033.i03</a>.
-</div>
-</div>
-</div>
-  </div>
-
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-
-        <nav id="toc" data-toggle="toc"><h2 data-toc-skip>Contents</h2>
-    </nav>
-</div>
-
-</div>
-
-
-
-      <footer><div class="copyright">
-  <p></p>
-<p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p>
-<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer>
-</div>
-
-  
-
-
-  
-
-  </body>
-</html>
diff --git a/docs/dev/articles/mkin_files/accessible-code-block-0.0.1/empty-anchor.js b/docs/dev/articles/mkin_files/accessible-code-block-0.0.1/empty-anchor.js
deleted file mode 100644
index ca349fd6..00000000
--- a/docs/dev/articles/mkin_files/accessible-code-block-0.0.1/empty-anchor.js
+++ /dev/null
@@ -1,15 +0,0 @@
-// Hide empty <a> tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) -->
-// v0.0.1
-// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020.
-
-document.addEventListener('DOMContentLoaded', function() {
-  const codeList = document.getElementsByClassName("sourceCode");
-  for (var i = 0; i < codeList.length; i++) {
-    var linkList = codeList[i].getElementsByTagName('a');
-    for (var j = 0; j < linkList.length; j++) {
-      if (linkList[j].innerHTML === "") {
-        linkList[j].setAttribute('aria-hidden', 'true');
-      }
-    }
-  }
-});
diff --git a/docs/dev/articles/mkin_files/anchor-sections-1.0/anchor-sections.css b/docs/dev/articles/mkin_files/anchor-sections-1.0/anchor-sections.css
deleted file mode 100644
index 07aee5fc..00000000
--- a/docs/dev/articles/mkin_files/anchor-sections-1.0/anchor-sections.css
+++ /dev/null
@@ -1,4 +0,0 @@
-/* Styles for section anchors */
-a.anchor-section {margin-left: 10px; visibility: hidden; color: inherit;}
-a.anchor-section::before {content: '#';}
-.hasAnchor:hover a.anchor-section {visibility: visible;}
diff --git a/docs/dev/articles/mkin_files/anchor-sections-1.0/anchor-sections.js b/docs/dev/articles/mkin_files/anchor-sections-1.0/anchor-sections.js
deleted file mode 100644
index 570f99a0..00000000
--- a/docs/dev/articles/mkin_files/anchor-sections-1.0/anchor-sections.js
+++ /dev/null
@@ -1,33 +0,0 @@
-// Anchor sections v1.0 written by Atsushi Yasumoto on Oct 3rd, 2020.
-document.addEventListener('DOMContentLoaded', function() {
-  // Do nothing if AnchorJS is used
-  if (typeof window.anchors === 'object' && anchors.hasOwnProperty('hasAnchorJSLink')) {
-    return;
-  }
-
-  const h = document.querySelectorAll('h1, h2, h3, h4, h5, h6');
-
-  // Do nothing if sections are already anchored
-  if (Array.from(h).some(x => x.classList.contains('hasAnchor'))) {
-    return null;
-  }
-
-  // Use section id when pandoc runs with --section-divs
-  const section_id = function(x) {
-    return ((x.classList.contains('section') || (x.tagName === 'SECTION'))
-            ? x.id : '');
-  };
-
-  // Add anchors
-  h.forEach(function(x) {
-    const id = x.id || section_id(x.parentElement);
-    if (id === '') {
-      return null;
-    }
-    let anchor = document.createElement('a');
-    anchor.href = '#' + id;
-    anchor.classList = ['anchor-section'];
-    x.classList.add('hasAnchor');
-    x.appendChild(anchor);
-  });
-});
diff --git a/docs/dev/articles/mkin_files/figure-html/unnamed-chunk-2-1.png b/docs/dev/articles/mkin_files/figure-html/unnamed-chunk-2-1.png
deleted file mode 100644
index 7ba861ea..00000000
--- a/docs/dev/articles/mkin_files/figure-html/unnamed-chunk-2-1.png
+++ /dev/null
diff --git a/docs/dev/articles/mkin_files/header-attrs-2.6/header-attrs.js b/docs/dev/articles/mkin_files/header-attrs-2.6/header-attrs.js
deleted file mode 100644
index dd57d92e..00000000
--- a/docs/dev/articles/mkin_files/header-attrs-2.6/header-attrs.js
+++ /dev/null
@@ -1,12 +0,0 @@
-// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
-// be compatible with the behavior of Pandoc < 2.8).
-document.addEventListener('DOMContentLoaded', function(e) {
-  var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
-  var i, h, a;
-  for (i = 0; i < hs.length; i++) {
-    h = hs[i];
-    if (!/^h[1-6]$/i.test(h.tagName)) continue;  // it should be a header h1-h6
-    a = h.attributes;
-    while (a.length > 0) h.removeAttribute(a[0].name);
-  }
-});
diff --git a/docs/dev/articles/prebuilt/2022_cyan_pathway.html b/docs/dev/articles/prebuilt/2022_cyan_pathway.html
deleted file mode 100644
index 7bb0fa5b..00000000
--- a/docs/dev/articles/prebuilt/2022_cyan_pathway.html
+++ /dev/null
@@ -1,5661 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en">
-<head>
-<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
-<meta charset="utf-8">
-<meta http-equiv="X-UA-Compatible" content="IE=edge">
-<meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>Testing hierarchical pathway kinetics with residue data on cyantraniliprole • mkin</title>
-<!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous">
-<script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../../bootstrap-toc.css">
-<script src="../../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous">
-<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous">
-<!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../../pkgdown.css" rel="stylesheet">
-<script src="../../pkgdown.js"></script><meta property="og:title" content="Testing hierarchical pathway kinetics with residue data on cyantraniliprole">
-<meta property="og:description" content="mkin">
-<meta name="robots" content="noindex">
-<!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]-->
-</head>
-<body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-article">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.4</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav">
-<li>
-  <a href="../../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu">
-<li>
-      <a href="../../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul>
-</li>
-<li>
-  <a href="../../news/index.html">News</a>
-</li>
-      </ul>
-<ul class="nav navbar-nav navbar-right">
-<li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul>
-</div>
-<!--/.nav-collapse -->
-  </div>
-<!--/.container -->
-</div>
-<!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header toc-ignore">
-      <h1 data-toc-skip>Testing hierarchical pathway kinetics with
-residue data on cyantraniliprole</h1>
-                        <h4 data-toc-skip class="author">Johannes
-Ranke</h4>
-            
-            <h4 data-toc-skip class="date">Last change on 20 April 2023,
-last compiled on 20 April 2023</h4>
-      
-      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/prebuilt/2022_cyan_pathway.rmd" class="external-link"><code>vignettes/prebuilt/2022_cyan_pathway.rmd</code></a></small>
-      <div class="hidden name"><code>2022_cyan_pathway.rmd</code></div>
-
-    </div>
-
-    
-    
-<div class="section level2">
-<h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a>
-</h2>
-<p>The purpose of this document is to test demonstrate how nonlinear
-hierarchical models (NLHM) based on the parent degradation models SFO,
-FOMC, DFOP and HS, with serial formation of two or more metabolites can
-be fitted with the mkin package.</p>
-<p>It was assembled in the course of work package 1.2 of Project Number
-173340 (Application of nonlinear hierarchical models to the kinetic
-evaluation of chemical degradation data) of the German Environment
-Agency carried out in 2022 and 2023.</p>
-<p>The mkin package is used in version 1.2.4 which is currently under
-development. The newly introduced functionality that is used here is a
-simplification of excluding random effects for a set of fits based on a
-related set of fits with a reduced model, and the documentation of the
-starting parameters of the fit, so that all starting parameters of
-<code>saem</code> fits are now listed in the summary. The
-<code>saemix</code> package is used as a backend for fitting the NLHM,
-but is also loaded to make the convergence plot function available.</p>
-<p>This document is processed with the <code>knitr</code> package, which
-also provides the <code>kable</code> function that is used to improve
-the display of tabular data in R markdown documents. For parallel
-processing, the <code>parallel</code> package is used.</p>
-<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://pkgdown.jrwb.de/mkin/">mkin</a></span><span class="op">)</span></span>
-<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://yihui.org/knitr/" class="external-link">knitr</a></span><span class="op">)</span></span>
-<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va">saemix</span><span class="op">)</span></span>
-<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va">parallel</span><span class="op">)</span></span>
-<span><span class="va">n_cores</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/parallel/detectCores.html" class="external-link">detectCores</a></span><span class="op">(</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># We need to start a new cluster after defining a compiled model that is</span></span>
-<span><span class="co"># saved as a DLL to the user directory, therefore we define a function</span></span>
-<span><span class="co"># This is used again after defining the pathway model</span></span>
-<span><span class="va">start_cluster</span> <span class="op">&lt;-</span> <span class="kw">function</span><span class="op">(</span><span class="va">n_cores</span><span class="op">)</span> <span class="op">{</span></span>
-<span>  <span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/Sys.info.html" class="external-link">Sys.info</a></span><span class="op">(</span><span class="op">)</span><span class="op">[</span><span class="st">"sysname"</span><span class="op">]</span> <span class="op">==</span> <span class="st">"Windows"</span><span class="op">)</span> <span class="op">{</span></span>
-<span>    <span class="va">ret</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/parallel/makeCluster.html" class="external-link">makePSOCKcluster</a></span><span class="op">(</span><span class="va">n_cores</span><span class="op">)</span></span>
-<span>  <span class="op">}</span> <span class="kw">else</span> <span class="op">{</span></span>
-<span>    <span class="va">ret</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/parallel/makeCluster.html" class="external-link">makeForkCluster</a></span><span class="op">(</span><span class="va">n_cores</span><span class="op">)</span></span>
-<span>  <span class="op">}</span></span>
-<span>  <span class="kw"><a href="https://rdrr.io/r/base/function.html" class="external-link">return</a></span><span class="op">(</span><span class="va">ret</span><span class="op">)</span></span>
-<span><span class="op">}</span></span>
-<span><span class="va">cl</span> <span class="op">&lt;-</span> <span class="fu">start_cluster</span><span class="op">(</span><span class="va">n_cores</span><span class="op">)</span></span></code></pre></div>
-<div class="section level3">
-<h3 id="test-data">Test data<a class="anchor" aria-label="anchor" href="#test-data"></a>
-</h3>
-<p>The example data are taken from the final addendum to the DAR from
-2014 and are distributed with the mkin package. Residue data and time
-step normalisation factors are read in using the function
-<code>read_spreadsheet</code> from the mkin package. This function also
-performs the time step normalisation.</p>
-<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">data_file</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/system.file.html" class="external-link">system.file</a></span><span class="op">(</span></span>
-<span>  <span class="st">"testdata"</span>, <span class="st">"cyantraniliprole_soil_efsa_2014.xlsx"</span>,</span>
-<span>  package <span class="op">=</span> <span class="st">"mkin"</span><span class="op">)</span></span>
-<span><span class="va">cyan_ds</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/read_spreadsheet.html">read_spreadsheet</a></span><span class="op">(</span><span class="va">data_file</span>, parent_only <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div>
-<p>The following tables show the covariate data and the 5 datasets that
-were read in from the spreadsheet file.</p>
-<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">pH</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/attr.html" class="external-link">attr</a></span><span class="op">(</span><span class="va">cyan_ds</span>, <span class="st">"covariates"</span><span class="op">)</span></span>
-<span><span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="va">pH</span>, caption <span class="op">=</span> <span class="st">"Covariate data"</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
-<caption>Covariate data</caption>
-<thead><tr class="header">
-<th align="left"></th>
-<th align="right">pH</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="left">Nambsheim</td>
-<td align="right">7.90</td>
-</tr>
-<tr class="even">
-<td align="left">Tama</td>
-<td align="right">6.20</td>
-</tr>
-<tr class="odd">
-<td align="left">Gross-Umstadt</td>
-<td align="right">7.04</td>
-</tr>
-<tr class="even">
-<td align="left">Sassafras</td>
-<td align="right">4.62</td>
-</tr>
-<tr class="odd">
-<td align="left">Lleida</td>
-<td align="right">8.05</td>
-</tr>
-</tbody>
-</table>
-<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="kw">for</span> <span class="op">(</span><span class="va">ds_name</span> <span class="kw">in</span> <span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">cyan_ds</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
-<span>  <span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span></span>
-<span>    <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="fu"><a href="../../reference/mkin_long_to_wide.html">mkin_long_to_wide</a></span><span class="op">(</span><span class="va">cyan_ds</span><span class="op">[[</span><span class="va">ds_name</span><span class="op">]</span><span class="op">]</span><span class="op">)</span>,</span>
-<span>      caption <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste</a></span><span class="op">(</span><span class="st">"Dataset"</span>, <span class="va">ds_name</span><span class="op">)</span>,</span>
-<span>      booktabs <span class="op">=</span> <span class="cn">TRUE</span>, row.names <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span><span class="op">)</span></span>
-<span>    <span class="fu"><a href="https://rdrr.io/r/base/cat.html" class="external-link">cat</a></span><span class="op">(</span><span class="st">"\n\\clearpage\n"</span><span class="op">)</span></span>
-<span><span class="op">}</span></span></code></pre></div>
-<table class="table">
-<caption>Dataset Nambsheim</caption>
-<thead><tr class="header">
-<th align="right">time</th>
-<th align="right">cyan</th>
-<th align="right">JCZ38</th>
-<th align="right">J9C38</th>
-<th align="right">JSE76</th>
-<th align="right">J9Z38</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="right">0.000000</td>
-<td align="right">105.79</td>
-<td align="right">NA</td>
-<td align="right">NA</td>
-<td align="right">NA</td>
-<td align="right">NA</td>
-</tr>
-<tr class="even">
-<td align="right">3.210424</td>
-<td align="right">77.26</td>
-<td align="right">7.92</td>
-<td align="right">11.94</td>
-<td align="right">5.58</td>
-<td align="right">9.12</td>
-</tr>
-<tr class="odd">
-<td align="right">7.490988</td>
-<td align="right">57.13</td>
-<td align="right">15.46</td>
-<td align="right">16.58</td>
-<td align="right">12.59</td>
-<td align="right">11.74</td>
-</tr>
-<tr class="even">
-<td align="right">17.122259</td>
-<td align="right">37.74</td>
-<td align="right">15.98</td>
-<td align="right">13.36</td>
-<td align="right">26.05</td>
-<td align="right">10.77</td>
-</tr>
-<tr class="odd">
-<td align="right">23.543105</td>
-<td align="right">31.47</td>
-<td align="right">6.05</td>
-<td align="right">14.49</td>
-<td align="right">34.71</td>
-<td align="right">4.96</td>
-</tr>
-<tr class="even">
-<td align="right">43.875788</td>
-<td align="right">16.74</td>
-<td align="right">6.07</td>
-<td align="right">7.57</td>
-<td align="right">40.38</td>
-<td align="right">6.52</td>
-</tr>
-<tr class="odd">
-<td align="right">67.418893</td>
-<td align="right">8.85</td>
-<td align="right">10.34</td>
-<td align="right">6.39</td>
-<td align="right">30.71</td>
-<td align="right">8.90</td>
-</tr>
-<tr class="even">
-<td align="right">107.014116</td>
-<td align="right">5.19</td>
-<td align="right">9.61</td>
-<td align="right">1.95</td>
-<td align="right">20.41</td>
-<td align="right">12.93</td>
-</tr>
-<tr class="odd">
-<td align="right">129.487080</td>
-<td align="right">3.45</td>
-<td align="right">6.18</td>
-<td align="right">1.36</td>
-<td align="right">21.78</td>
-<td align="right">6.99</td>
-</tr>
-<tr class="even">
-<td align="right">195.835832</td>
-<td align="right">2.15</td>
-<td align="right">9.13</td>
-<td align="right">0.95</td>
-<td align="right">16.29</td>
-<td align="right">7.69</td>
-</tr>
-<tr class="odd">
-<td align="right">254.693596</td>
-<td align="right">1.92</td>
-<td align="right">6.92</td>
-<td align="right">0.20</td>
-<td align="right">13.57</td>
-<td align="right">7.16</td>
-</tr>
-<tr class="even">
-<td align="right">321.042348</td>
-<td align="right">2.26</td>
-<td align="right">7.02</td>
-<td align="right">NA</td>
-<td align="right">11.12</td>
-<td align="right">8.66</td>
-</tr>
-<tr class="odd">
-<td align="right">383.110535</td>
-<td align="right">NA</td>
-<td align="right">5.05</td>
-<td align="right">NA</td>
-<td align="right">10.64</td>
-<td align="right">5.56</td>
-</tr>
-<tr class="even">
-<td align="right">0.000000</td>
-<td align="right">105.57</td>
-<td align="right">NA</td>
-<td align="right">NA</td>
-<td align="right">NA</td>
-<td align="right">NA</td>
-</tr>
-<tr class="odd">
-<td align="right">3.210424</td>
-<td align="right">78.88</td>
-<td align="right">12.77</td>
-<td align="right">11.94</td>
-<td align="right">5.47</td>
-<td align="right">9.12</td>
-</tr>
-<tr class="even">
-<td align="right">7.490988</td>
-<td align="right">59.94</td>
-<td align="right">15.27</td>
-<td align="right">16.58</td>
-<td align="right">13.60</td>
-<td align="right">11.74</td>
-</tr>
-<tr class="odd">
-<td align="right">17.122259</td>
-<td align="right">39.67</td>
-<td align="right">14.26</td>
-<td align="right">13.36</td>
-<td align="right">29.44</td>
-<td align="right">10.77</td>
-</tr>
-<tr class="even">
-<td align="right">23.543105</td>
-<td align="right">30.21</td>
-<td align="right">16.07</td>
-<td align="right">14.49</td>
-<td align="right">35.90</td>
-<td align="right">4.96</td>
-</tr>
-<tr class="odd">
-<td align="right">43.875788</td>
-<td align="right">18.06</td>
-<td align="right">9.44</td>
-<td align="right">7.57</td>
-<td align="right">42.30</td>
-<td align="right">6.52</td>
-</tr>
-<tr class="even">
-<td align="right">67.418893</td>
-<td align="right">8.54</td>
-<td align="right">5.78</td>
-<td align="right">6.39</td>
-<td align="right">34.70</td>
-<td align="right">8.90</td>
-</tr>
-<tr class="odd">
-<td align="right">107.014116</td>
-<td align="right">7.26</td>
-<td align="right">4.54</td>
-<td align="right">1.95</td>
-<td align="right">23.33</td>
-<td align="right">12.93</td>
-</tr>
-<tr class="even">
-<td align="right">129.487080</td>
-<td align="right">3.60</td>
-<td align="right">4.22</td>
-<td align="right">1.36</td>
-<td align="right">23.56</td>
-<td align="right">6.99</td>
-</tr>
-<tr class="odd">
-<td align="right">195.835832</td>
-<td align="right">2.84</td>
-<td align="right">3.05</td>
-<td align="right">0.95</td>
-<td align="right">16.21</td>
-<td align="right">7.69</td>
-</tr>
-<tr class="even">
-<td align="right">254.693596</td>
-<td align="right">2.00</td>
-<td align="right">2.90</td>
-<td align="right">0.20</td>
-<td align="right">15.53</td>
-<td align="right">7.16</td>
-</tr>
-<tr class="odd">
-<td align="right">321.042348</td>
-<td align="right">1.79</td>
-<td align="right">0.94</td>
-<td align="right">NA</td>
-<td align="right">9.80</td>
-<td align="right">8.66</td>
-</tr>
-<tr class="even">
-<td align="right">383.110535</td>
-<td align="right">NA</td>
-<td align="right">1.82</td>
-<td align="right">NA</td>
-<td align="right">9.49</td>
-<td align="right">5.56</td>
-</tr>
-</tbody>
-</table>
-<table class="table">
-<caption>Dataset Tama</caption>
-<thead><tr class="header">
-<th align="right">time</th>
-<th align="right">cyan</th>
-<th align="right">JCZ38</th>
-<th align="right">J9Z38</th>
-<th align="right">JSE76</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="right">0.000000</td>
-<td align="right">106.14</td>
-<td align="right">NA</td>
-<td align="right">NA</td>
-<td align="right">NA</td>
-</tr>
-<tr class="even">
-<td align="right">2.400833</td>
-<td align="right">93.47</td>
-<td align="right">6.46</td>
-<td align="right">2.85</td>
-<td align="right">NA</td>
-</tr>
-<tr class="odd">
-<td align="right">5.601943</td>
-<td align="right">88.39</td>
-<td align="right">10.86</td>
-<td align="right">4.65</td>
-<td align="right">3.85</td>
-</tr>
-<tr class="even">
-<td align="right">12.804442</td>
-<td align="right">72.29</td>
-<td align="right">11.97</td>
-<td align="right">4.91</td>
-<td align="right">11.24</td>
-</tr>
-<tr class="odd">
-<td align="right">17.606108</td>
-<td align="right">65.79</td>
-<td align="right">13.11</td>
-<td align="right">6.63</td>
-<td align="right">13.79</td>
-</tr>
-<tr class="even">
-<td align="right">32.811382</td>
-<td align="right">53.16</td>
-<td align="right">11.24</td>
-<td align="right">8.90</td>
-<td align="right">23.40</td>
-</tr>
-<tr class="odd">
-<td align="right">50.417490</td>
-<td align="right">44.01</td>
-<td align="right">11.34</td>
-<td align="right">9.98</td>
-<td align="right">29.56</td>
-</tr>
-<tr class="even">
-<td align="right">80.027761</td>
-<td align="right">33.23</td>
-<td align="right">8.82</td>
-<td align="right">11.31</td>
-<td align="right">35.63</td>
-</tr>
-<tr class="odd">
-<td align="right">96.833591</td>
-<td align="right">40.68</td>
-<td align="right">5.94</td>
-<td align="right">8.32</td>
-<td align="right">29.09</td>
-</tr>
-<tr class="even">
-<td align="right">146.450803</td>
-<td align="right">20.65</td>
-<td align="right">4.49</td>
-<td align="right">8.72</td>
-<td align="right">36.88</td>
-</tr>
-<tr class="odd">
-<td align="right">190.466072</td>
-<td align="right">17.71</td>
-<td align="right">4.66</td>
-<td align="right">11.10</td>
-<td align="right">40.97</td>
-</tr>
-<tr class="even">
-<td align="right">240.083284</td>
-<td align="right">14.86</td>
-<td align="right">2.27</td>
-<td align="right">11.62</td>
-<td align="right">40.11</td>
-</tr>
-<tr class="odd">
-<td align="right">286.499386</td>
-<td align="right">12.02</td>
-<td align="right">NA</td>
-<td align="right">10.73</td>
-<td align="right">42.58</td>
-</tr>
-<tr class="even">
-<td align="right">0.000000</td>
-<td align="right">109.11</td>
-<td align="right">NA</td>
-<td align="right">NA</td>
-<td align="right">NA</td>
-</tr>
-<tr class="odd">
-<td align="right">2.400833</td>
-<td align="right">96.84</td>
-<td align="right">5.52</td>
-<td align="right">2.04</td>
-<td align="right">2.02</td>
-</tr>
-<tr class="even">
-<td align="right">5.601943</td>
-<td align="right">85.29</td>
-<td align="right">9.65</td>
-<td align="right">2.99</td>
-<td align="right">4.39</td>
-</tr>
-<tr class="odd">
-<td align="right">12.804442</td>
-<td align="right">73.68</td>
-<td align="right">12.48</td>
-<td align="right">5.05</td>
-<td align="right">11.47</td>
-</tr>
-<tr class="even">
-<td align="right">17.606108</td>
-<td align="right">64.89</td>
-<td align="right">12.44</td>
-<td align="right">6.29</td>
-<td align="right">15.00</td>
-</tr>
-<tr class="odd">
-<td align="right">32.811382</td>
-<td align="right">52.27</td>
-<td align="right">10.86</td>
-<td align="right">7.65</td>
-<td align="right">23.30</td>
-</tr>
-<tr class="even">
-<td align="right">50.417490</td>
-<td align="right">42.61</td>
-<td align="right">10.54</td>
-<td align="right">9.37</td>
-<td align="right">31.06</td>
-</tr>
-<tr class="odd">
-<td align="right">80.027761</td>
-<td align="right">34.29</td>
-<td align="right">10.02</td>
-<td align="right">9.04</td>
-<td align="right">37.87</td>
-</tr>
-<tr class="even">
-<td align="right">96.833591</td>
-<td align="right">30.50</td>
-<td align="right">6.34</td>
-<td align="right">8.14</td>
-<td align="right">33.97</td>
-</tr>
-<tr class="odd">
-<td align="right">146.450803</td>
-<td align="right">19.21</td>
-<td align="right">6.29</td>
-<td align="right">8.52</td>
-<td align="right">26.15</td>
-</tr>
-<tr class="even">
-<td align="right">190.466072</td>
-<td align="right">17.55</td>
-<td align="right">5.81</td>
-<td align="right">9.89</td>
-<td align="right">32.08</td>
-</tr>
-<tr class="odd">
-<td align="right">240.083284</td>
-<td align="right">13.22</td>
-<td align="right">5.99</td>
-<td align="right">10.79</td>
-<td align="right">40.66</td>
-</tr>
-<tr class="even">
-<td align="right">286.499386</td>
-<td align="right">11.09</td>
-<td align="right">6.05</td>
-<td align="right">8.82</td>
-<td align="right">42.90</td>
-</tr>
-</tbody>
-</table>
-<table class="table">
-<caption>Dataset Gross-Umstadt</caption>
-<thead><tr class="header">
-<th align="right">time</th>
-<th align="right">cyan</th>
-<th align="right">JCZ38</th>
-<th align="right">J9Z38</th>
-<th align="right">JSE76</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="right">0.0000000</td>
-<td align="right">103.03</td>
-<td align="right">NA</td>
-<td align="right">NA</td>
-<td align="right">NA</td>
-</tr>
-<tr class="even">
-<td align="right">2.1014681</td>
-<td align="right">87.85</td>
-<td align="right">4.79</td>
-<td align="right">3.26</td>
-<td align="right">0.62</td>
-</tr>
-<tr class="odd">
-<td align="right">4.9034255</td>
-<td align="right">77.35</td>
-<td align="right">8.05</td>
-<td align="right">9.89</td>
-<td align="right">1.32</td>
-</tr>
-<tr class="even">
-<td align="right">10.5073404</td>
-<td align="right">69.33</td>
-<td align="right">9.74</td>
-<td align="right">12.32</td>
-<td align="right">4.74</td>
-</tr>
-<tr class="odd">
-<td align="right">21.0146807</td>
-<td align="right">55.65</td>
-<td align="right">14.57</td>
-<td align="right">13.59</td>
-<td align="right">9.84</td>
-</tr>
-<tr class="even">
-<td align="right">31.5220211</td>
-<td align="right">49.03</td>
-<td align="right">14.66</td>
-<td align="right">16.71</td>
-<td align="right">12.32</td>
-</tr>
-<tr class="odd">
-<td align="right">42.0293615</td>
-<td align="right">41.86</td>
-<td align="right">15.97</td>
-<td align="right">13.64</td>
-<td align="right">15.53</td>
-</tr>
-<tr class="even">
-<td align="right">63.0440422</td>
-<td align="right">34.88</td>
-<td align="right">18.20</td>
-<td align="right">14.12</td>
-<td align="right">22.02</td>
-</tr>
-<tr class="odd">
-<td align="right">84.0587230</td>
-<td align="right">28.26</td>
-<td align="right">15.64</td>
-<td align="right">14.06</td>
-<td align="right">25.60</td>
-</tr>
-<tr class="even">
-<td align="right">0.0000000</td>
-<td align="right">104.05</td>
-<td align="right">NA</td>
-<td align="right">NA</td>
-<td align="right">NA</td>
-</tr>
-<tr class="odd">
-<td align="right">2.1014681</td>
-<td align="right">85.25</td>
-<td align="right">2.68</td>
-<td align="right">7.32</td>
-<td align="right">0.69</td>
-</tr>
-<tr class="even">
-<td align="right">4.9034255</td>
-<td align="right">77.22</td>
-<td align="right">7.28</td>
-<td align="right">8.37</td>
-<td align="right">1.45</td>
-</tr>
-<tr class="odd">
-<td align="right">10.5073404</td>
-<td align="right">65.23</td>
-<td align="right">10.73</td>
-<td align="right">10.93</td>
-<td align="right">4.74</td>
-</tr>
-<tr class="even">
-<td align="right">21.0146807</td>
-<td align="right">57.78</td>
-<td align="right">12.29</td>
-<td align="right">14.80</td>
-<td align="right">9.05</td>
-</tr>
-<tr class="odd">
-<td align="right">31.5220211</td>
-<td align="right">54.83</td>
-<td align="right">14.05</td>
-<td align="right">12.01</td>
-<td align="right">11.05</td>
-</tr>
-<tr class="even">
-<td align="right">42.0293615</td>
-<td align="right">45.17</td>
-<td align="right">12.12</td>
-<td align="right">17.89</td>
-<td align="right">15.71</td>
-</tr>
-<tr class="odd">
-<td align="right">63.0440422</td>
-<td align="right">34.83</td>
-<td align="right">12.90</td>
-<td align="right">15.86</td>
-<td align="right">22.52</td>
-</tr>
-<tr class="even">
-<td align="right">84.0587230</td>
-<td align="right">26.59</td>
-<td align="right">14.28</td>
-<td align="right">14.91</td>
-<td align="right">28.48</td>
-</tr>
-<tr class="odd">
-<td align="right">0.0000000</td>
-<td align="right">104.62</td>
-<td align="right">NA</td>
-<td align="right">NA</td>
-<td align="right">NA</td>
-</tr>
-<tr class="even">
-<td align="right">0.8145225</td>
-<td align="right">97.21</td>
-<td align="right">NA</td>
-<td align="right">4.00</td>
-<td align="right">NA</td>
-</tr>
-<tr class="odd">
-<td align="right">1.9005525</td>
-<td align="right">89.64</td>
-<td align="right">3.59</td>
-<td align="right">5.24</td>
-<td align="right">NA</td>
-</tr>
-<tr class="even">
-<td align="right">4.0726125</td>
-<td align="right">87.90</td>
-<td align="right">4.10</td>
-<td align="right">9.58</td>
-<td align="right">NA</td>
-</tr>
-<tr class="odd">
-<td align="right">8.1452251</td>
-<td align="right">86.90</td>
-<td align="right">5.96</td>
-<td align="right">9.45</td>
-<td align="right">NA</td>
-</tr>
-<tr class="even">
-<td align="right">12.2178376</td>
-<td align="right">74.74</td>
-<td align="right">7.83</td>
-<td align="right">15.03</td>
-<td align="right">5.33</td>
-</tr>
-<tr class="odd">
-<td align="right">16.2904502</td>
-<td align="right">74.13</td>
-<td align="right">8.84</td>
-<td align="right">14.41</td>
-<td align="right">5.10</td>
-</tr>
-<tr class="even">
-<td align="right">24.4356753</td>
-<td align="right">65.26</td>
-<td align="right">11.84</td>
-<td align="right">18.33</td>
-<td align="right">6.71</td>
-</tr>
-<tr class="odd">
-<td align="right">32.5809004</td>
-<td align="right">57.70</td>
-<td align="right">12.74</td>
-<td align="right">19.93</td>
-<td align="right">9.74</td>
-</tr>
-<tr class="even">
-<td align="right">0.0000000</td>
-<td align="right">101.94</td>
-<td align="right">NA</td>
-<td align="right">NA</td>
-<td align="right">NA</td>
-</tr>
-<tr class="odd">
-<td align="right">0.8145225</td>
-<td align="right">99.94</td>
-<td align="right">NA</td>
-<td align="right">NA</td>
-<td align="right">NA</td>
-</tr>
-<tr class="even">
-<td align="right">1.9005525</td>
-<td align="right">94.87</td>
-<td align="right">NA</td>
-<td align="right">4.56</td>
-<td align="right">NA</td>
-</tr>
-<tr class="odd">
-<td align="right">4.0726125</td>
-<td align="right">86.96</td>
-<td align="right">6.75</td>
-<td align="right">6.90</td>
-<td align="right">NA</td>
-</tr>
-<tr class="even">
-<td align="right">8.1452251</td>
-<td align="right">80.51</td>
-<td align="right">10.68</td>
-<td align="right">7.43</td>
-<td align="right">2.58</td>
-</tr>
-<tr class="odd">
-<td align="right">12.2178376</td>
-<td align="right">78.38</td>
-<td align="right">10.35</td>
-<td align="right">9.46</td>
-<td align="right">3.69</td>
-</tr>
-<tr class="even">
-<td align="right">16.2904502</td>
-<td align="right">70.05</td>
-<td align="right">13.73</td>
-<td align="right">9.27</td>
-<td align="right">7.18</td>
-</tr>
-<tr class="odd">
-<td align="right">24.4356753</td>
-<td align="right">61.28</td>
-<td align="right">12.57</td>
-<td align="right">13.28</td>
-<td align="right">13.19</td>
-</tr>
-<tr class="even">
-<td align="right">32.5809004</td>
-<td align="right">52.85</td>
-<td align="right">12.67</td>
-<td align="right">12.95</td>
-<td align="right">13.69</td>
-</tr>
-</tbody>
-</table>
-<table class="table">
-<caption>Dataset Sassafras</caption>
-<thead><tr class="header">
-<th align="right">time</th>
-<th align="right">cyan</th>
-<th align="right">JCZ38</th>
-<th align="right">J9Z38</th>
-<th align="right">JSE76</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="right">0.000000</td>
-<td align="right">102.17</td>
-<td align="right">NA</td>
-<td align="right">NA</td>
-<td align="right">NA</td>
-</tr>
-<tr class="even">
-<td align="right">2.216719</td>
-<td align="right">95.49</td>
-<td align="right">1.11</td>
-<td align="right">0.10</td>
-<td align="right">0.83</td>
-</tr>
-<tr class="odd">
-<td align="right">5.172343</td>
-<td align="right">83.35</td>
-<td align="right">6.43</td>
-<td align="right">2.89</td>
-<td align="right">3.30</td>
-</tr>
-<tr class="even">
-<td align="right">11.083593</td>
-<td align="right">78.18</td>
-<td align="right">10.00</td>
-<td align="right">5.59</td>
-<td align="right">0.81</td>
-</tr>
-<tr class="odd">
-<td align="right">22.167186</td>
-<td align="right">70.44</td>
-<td align="right">17.21</td>
-<td align="right">4.23</td>
-<td align="right">1.09</td>
-</tr>
-<tr class="even">
-<td align="right">33.250779</td>
-<td align="right">68.00</td>
-<td align="right">20.45</td>
-<td align="right">5.86</td>
-<td align="right">1.17</td>
-</tr>
-<tr class="odd">
-<td align="right">44.334371</td>
-<td align="right">59.64</td>
-<td align="right">24.64</td>
-<td align="right">3.17</td>
-<td align="right">2.72</td>
-</tr>
-<tr class="even">
-<td align="right">66.501557</td>
-<td align="right">50.73</td>
-<td align="right">27.50</td>
-<td align="right">6.19</td>
-<td align="right">1.27</td>
-</tr>
-<tr class="odd">
-<td align="right">88.668742</td>
-<td align="right">45.65</td>
-<td align="right">32.77</td>
-<td align="right">5.69</td>
-<td align="right">4.54</td>
-</tr>
-<tr class="even">
-<td align="right">0.000000</td>
-<td align="right">100.43</td>
-<td align="right">NA</td>
-<td align="right">NA</td>
-<td align="right">NA</td>
-</tr>
-<tr class="odd">
-<td align="right">2.216719</td>
-<td align="right">95.34</td>
-<td align="right">3.21</td>
-<td align="right">0.14</td>
-<td align="right">0.46</td>
-</tr>
-<tr class="even">
-<td align="right">5.172343</td>
-<td align="right">84.38</td>
-<td align="right">5.73</td>
-<td align="right">4.75</td>
-<td align="right">0.62</td>
-</tr>
-<tr class="odd">
-<td align="right">11.083593</td>
-<td align="right">78.50</td>
-<td align="right">11.89</td>
-<td align="right">3.99</td>
-<td align="right">0.73</td>
-</tr>
-<tr class="even">
-<td align="right">22.167186</td>
-<td align="right">71.17</td>
-<td align="right">17.28</td>
-<td align="right">4.39</td>
-<td align="right">0.66</td>
-</tr>
-<tr class="odd">
-<td align="right">33.250779</td>
-<td align="right">59.41</td>
-<td align="right">18.73</td>
-<td align="right">11.85</td>
-<td align="right">2.65</td>
-</tr>
-<tr class="even">
-<td align="right">44.334371</td>
-<td align="right">64.57</td>
-<td align="right">22.93</td>
-<td align="right">5.13</td>
-<td align="right">2.01</td>
-</tr>
-<tr class="odd">
-<td align="right">66.501557</td>
-<td align="right">49.08</td>
-<td align="right">33.39</td>
-<td align="right">5.67</td>
-<td align="right">3.63</td>
-</tr>
-<tr class="even">
-<td align="right">88.668742</td>
-<td align="right">40.41</td>
-<td align="right">39.60</td>
-<td align="right">5.93</td>
-<td align="right">6.17</td>
-</tr>
-</tbody>
-</table>
-<table class="table">
-<caption>Dataset Lleida</caption>
-<thead><tr class="header">
-<th align="right">time</th>
-<th align="right">cyan</th>
-<th align="right">JCZ38</th>
-<th align="right">J9Z38</th>
-<th align="right">JSE76</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="right">0.000000</td>
-<td align="right">102.71</td>
-<td align="right">NA</td>
-<td align="right">NA</td>
-<td align="right">NA</td>
-</tr>
-<tr class="even">
-<td align="right">2.821051</td>
-<td align="right">79.11</td>
-<td align="right">5.70</td>
-<td align="right">8.07</td>
-<td align="right">0.97</td>
-</tr>
-<tr class="odd">
-<td align="right">6.582451</td>
-<td align="right">70.03</td>
-<td align="right">7.17</td>
-<td align="right">11.31</td>
-<td align="right">4.72</td>
-</tr>
-<tr class="even">
-<td align="right">14.105253</td>
-<td align="right">50.93</td>
-<td align="right">10.25</td>
-<td align="right">14.84</td>
-<td align="right">9.95</td>
-</tr>
-<tr class="odd">
-<td align="right">28.210505</td>
-<td align="right">33.43</td>
-<td align="right">10.40</td>
-<td align="right">14.82</td>
-<td align="right">24.06</td>
-</tr>
-<tr class="even">
-<td align="right">42.315758</td>
-<td align="right">24.69</td>
-<td align="right">9.75</td>
-<td align="right">16.38</td>
-<td align="right">29.38</td>
-</tr>
-<tr class="odd">
-<td align="right">56.421010</td>
-<td align="right">22.99</td>
-<td align="right">10.06</td>
-<td align="right">15.51</td>
-<td align="right">29.25</td>
-</tr>
-<tr class="even">
-<td align="right">84.631516</td>
-<td align="right">14.63</td>
-<td align="right">5.63</td>
-<td align="right">14.74</td>
-<td align="right">31.04</td>
-</tr>
-<tr class="odd">
-<td align="right">112.842021</td>
-<td align="right">12.43</td>
-<td align="right">4.17</td>
-<td align="right">13.53</td>
-<td align="right">33.28</td>
-</tr>
-<tr class="even">
-<td align="right">0.000000</td>
-<td align="right">99.31</td>
-<td align="right">NA</td>
-<td align="right">NA</td>
-<td align="right">NA</td>
-</tr>
-<tr class="odd">
-<td align="right">2.821051</td>
-<td align="right">82.07</td>
-<td align="right">6.55</td>
-<td align="right">5.60</td>
-<td align="right">1.12</td>
-</tr>
-<tr class="even">
-<td align="right">6.582451</td>
-<td align="right">70.65</td>
-<td align="right">7.61</td>
-<td align="right">8.01</td>
-<td align="right">3.21</td>
-</tr>
-<tr class="odd">
-<td align="right">14.105253</td>
-<td align="right">53.52</td>
-<td align="right">11.48</td>
-<td align="right">10.82</td>
-<td align="right">12.24</td>
-</tr>
-<tr class="even">
-<td align="right">28.210505</td>
-<td align="right">35.60</td>
-<td align="right">11.19</td>
-<td align="right">15.43</td>
-<td align="right">23.53</td>
-</tr>
-<tr class="odd">
-<td align="right">42.315758</td>
-<td align="right">34.26</td>
-<td align="right">11.09</td>
-<td align="right">13.26</td>
-<td align="right">27.42</td>
-</tr>
-<tr class="even">
-<td align="right">56.421010</td>
-<td align="right">21.79</td>
-<td align="right">4.80</td>
-<td align="right">18.30</td>
-<td align="right">30.20</td>
-</tr>
-<tr class="odd">
-<td align="right">84.631516</td>
-<td align="right">14.06</td>
-<td align="right">6.30</td>
-<td align="right">16.35</td>
-<td align="right">32.32</td>
-</tr>
-<tr class="even">
-<td align="right">112.842021</td>
-<td align="right">11.51</td>
-<td align="right">5.57</td>
-<td align="right">12.64</td>
-<td align="right">32.51</td>
-</tr>
-</tbody>
-</table>
-</div>
-</div>
-<div class="section level2">
-<h2 id="parent-only-evaluations">Parent only evaluations<a class="anchor" aria-label="anchor" href="#parent-only-evaluations"></a>
-</h2>
-<p>As the pathway fits have very long run times, evaluations of the
-parent data are performed first, in order to determine for each
-hierarchical parent degradation model which random effects on the
-degradation model parameters are ill-defined.</p>
-<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">cyan_sep_const</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"FOMC"</span>, <span class="st">"DFOP"</span>, <span class="st">"SFORB"</span>, <span class="st">"HS"</span><span class="op">)</span>,</span>
-<span>  <span class="va">cyan_ds</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, cores <span class="op">=</span> <span class="va">n_cores</span><span class="op">)</span></span>
-<span><span class="va">cyan_sep_tc</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">cyan_sep_const</span>, error_model <span class="op">=</span> <span class="st">"tc"</span><span class="op">)</span></span>
-<span><span class="va">cyan_saem_full</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/mhmkin.html">mhmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">cyan_sep_const</span>, <span class="va">cyan_sep_tc</span><span class="op">)</span><span class="op">)</span></span>
-<span><span class="fu"><a href="../../reference/status.html">status</a></span><span class="op">(</span><span class="va">cyan_saem_full</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
-<thead><tr class="header">
-<th align="left"></th>
-<th align="left">const</th>
-<th align="left">tc</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="left">SFO</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-</tr>
-<tr class="even">
-<td align="left">FOMC</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-</tr>
-<tr class="odd">
-<td align="left">DFOP</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-</tr>
-<tr class="even">
-<td align="left">SFORB</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-</tr>
-<tr class="odd">
-<td align="left">HS</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-</tr>
-</tbody>
-</table>
-<p>All fits converged successfully.</p>
-<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="../../reference/illparms.html">illparms</a></span><span class="op">(</span><span class="va">cyan_saem_full</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
-<thead><tr class="header">
-<th align="left"></th>
-<th align="left">const</th>
-<th align="left">tc</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="left">SFO</td>
-<td align="left">sd(cyan_0)</td>
-<td align="left">sd(cyan_0)</td>
-</tr>
-<tr class="even">
-<td align="left">FOMC</td>
-<td align="left">sd(log_beta)</td>
-<td align="left">sd(cyan_0)</td>
-</tr>
-<tr class="odd">
-<td align="left">DFOP</td>
-<td align="left">sd(cyan_0)</td>
-<td align="left">sd(cyan_0), sd(log_k1)</td>
-</tr>
-<tr class="even">
-<td align="left">SFORB</td>
-<td align="left">sd(cyan_free_0)</td>
-<td align="left">sd(cyan_free_0), sd(log_k_cyan_free_bound)</td>
-</tr>
-<tr class="odd">
-<td align="left">HS</td>
-<td align="left">sd(cyan_0)</td>
-<td align="left">sd(cyan_0)</td>
-</tr>
-</tbody>
-</table>
-<p>In almost all models, the random effect for the initial concentration
-of the parent compound is ill-defined. For the biexponential models DFOP
-and SFORB, the random effect of one additional parameter is ill-defined
-when the two-component error model is used.</p>
-<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">cyan_saem_full</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span>digits <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
-<thead><tr class="header">
-<th align="left"></th>
-<th align="right">npar</th>
-<th align="right">AIC</th>
-<th align="right">BIC</th>
-<th align="right">Lik</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="left">SFO const</td>
-<td align="right">5</td>
-<td align="right">833.9</td>
-<td align="right">832.0</td>
-<td align="right">-412.0</td>
-</tr>
-<tr class="even">
-<td align="left">SFO tc</td>
-<td align="right">6</td>
-<td align="right">831.6</td>
-<td align="right">829.3</td>
-<td align="right">-409.8</td>
-</tr>
-<tr class="odd">
-<td align="left">FOMC const</td>
-<td align="right">7</td>
-<td align="right">709.1</td>
-<td align="right">706.4</td>
-<td align="right">-347.6</td>
-</tr>
-<tr class="even">
-<td align="left">FOMC tc</td>
-<td align="right">8</td>
-<td align="right">689.2</td>
-<td align="right">686.1</td>
-<td align="right">-336.6</td>
-</tr>
-<tr class="odd">
-<td align="left">DFOP const</td>
-<td align="right">9</td>
-<td align="right">703.0</td>
-<td align="right">699.5</td>
-<td align="right">-342.5</td>
-</tr>
-<tr class="even">
-<td align="left">SFORB const</td>
-<td align="right">9</td>
-<td align="right">701.3</td>
-<td align="right">697.8</td>
-<td align="right">-341.7</td>
-</tr>
-<tr class="odd">
-<td align="left">HS const</td>
-<td align="right">9</td>
-<td align="right">718.6</td>
-<td align="right">715.1</td>
-<td align="right">-350.3</td>
-</tr>
-<tr class="even">
-<td align="left">DFOP tc</td>
-<td align="right">10</td>
-<td align="right">703.1</td>
-<td align="right">699.2</td>
-<td align="right">-341.6</td>
-</tr>
-<tr class="odd">
-<td align="left">SFORB tc</td>
-<td align="right">10</td>
-<td align="right">700.1</td>
-<td align="right">696.2</td>
-<td align="right">-340.1</td>
-</tr>
-<tr class="even">
-<td align="left">HS tc</td>
-<td align="right">10</td>
-<td align="right">716.7</td>
-<td align="right">712.8</td>
-<td align="right">-348.3</td>
-</tr>
-</tbody>
-</table>
-<p>Model comparison based on AIC and BIC indicates that the
-two-component error model is preferable for all parent models with the
-exception of DFOP. The lowest AIC and BIC values are are obtained with
-the FOMC model, followed by SFORB and DFOP.</p>
-<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/parallel/makeCluster.html" class="external-link">stopCluster</a></span><span class="op">(</span><span class="va">cl</span><span class="op">)</span></span></code></pre></div>
-</div>
-<div class="section level2">
-<h2 id="pathway-fits">Pathway fits<a class="anchor" aria-label="anchor" href="#pathway-fits"></a>
-</h2>
-<div class="section level3">
-<h3 id="evaluations-with-pathway-established-previously">Evaluations with pathway established previously<a class="anchor" aria-label="anchor" href="#evaluations-with-pathway-established-previously"></a>
-</h3>
-<p>To test the technical feasibility of coupling the relevant parent
-degradation models with different transformation pathway models, a list
-of <code>mkinmod</code> models is set up below. As in the EU evaluation,
-parallel formation of metabolites JCZ38 and J9Z38 and secondary
-formation of metabolite JSE76 from JCZ38 is used.</p>
-<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="kw">if</span> <span class="op">(</span><span class="op">!</span><span class="fu"><a href="https://rdrr.io/r/base/files2.html" class="external-link">dir.exists</a></span><span class="op">(</span><span class="st">"cyan_dlls"</span><span class="op">)</span><span class="op">)</span> <span class="fu"><a href="https://rdrr.io/r/base/files2.html" class="external-link">dir.create</a></span><span class="op">(</span><span class="st">"cyan_dlls"</span><span class="op">)</span></span>
-<span><span class="va">cyan_path_1</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span></span>
-<span>  sfo_path_1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span></span>
-<span>    cyan <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"JCZ38"</span>, <span class="st">"J9Z38"</span><span class="op">)</span><span class="op">)</span>,</span>
-<span>    JCZ38 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"JSE76"</span><span class="op">)</span>,</span>
-<span>    J9Z38 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span>
-<span>    JSE76 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>,</span>
-<span>    name <span class="op">=</span> <span class="st">"sfo_path_1"</span>, dll_dir <span class="op">=</span> <span class="st">"cyan_dlls"</span>, overwrite <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>,</span>
-<span>  fomc_path_1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span></span>
-<span>    cyan <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"FOMC"</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"JCZ38"</span>, <span class="st">"J9Z38"</span><span class="op">)</span><span class="op">)</span>,</span>
-<span>    JCZ38 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"JSE76"</span><span class="op">)</span>,</span>
-<span>    J9Z38 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span>
-<span>    JSE76 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>,</span>
-<span>    name <span class="op">=</span> <span class="st">"fomc_path_1"</span>, dll_dir <span class="op">=</span> <span class="st">"cyan_dlls"</span>, overwrite <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>,</span>
-<span>  dfop_path_1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span></span>
-<span>    cyan <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"JCZ38"</span>, <span class="st">"J9Z38"</span><span class="op">)</span><span class="op">)</span>,</span>
-<span>    JCZ38 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"JSE76"</span><span class="op">)</span>,</span>
-<span>    J9Z38 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span>
-<span>    JSE76 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>,</span>
-<span>    name <span class="op">=</span> <span class="st">"dfop_path_1"</span>, dll_dir <span class="op">=</span> <span class="st">"cyan_dlls"</span>, overwrite <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>,</span>
-<span>  sforb_path_1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span></span>
-<span>    cyan <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFORB"</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"JCZ38"</span>, <span class="st">"J9Z38"</span><span class="op">)</span><span class="op">)</span>,</span>
-<span>    JCZ38 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"JSE76"</span><span class="op">)</span>,</span>
-<span>    J9Z38 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span>
-<span>    JSE76 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>,</span>
-<span>    name <span class="op">=</span> <span class="st">"sforb_path_1"</span>, dll_dir <span class="op">=</span> <span class="st">"cyan_dlls"</span>, overwrite <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>,</span>
-<span>  hs_path_1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span></span>
-<span>    cyan <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"HS"</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"JCZ38"</span>, <span class="st">"J9Z38"</span><span class="op">)</span><span class="op">)</span>,</span>
-<span>    JCZ38 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"JSE76"</span><span class="op">)</span>,</span>
-<span>    J9Z38 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span>
-<span>    JSE76 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>,</span>
-<span>    name <span class="op">=</span> <span class="st">"hs_path_1"</span>, dll_dir <span class="op">=</span> <span class="st">"cyan_dlls"</span>, overwrite <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
-<span><span class="op">)</span></span>
-<span><span class="va">cl_path_1</span> <span class="op">&lt;-</span> <span class="fu">start_cluster</span><span class="op">(</span><span class="va">n_cores</span><span class="op">)</span></span></code></pre></div>
-<p>To obtain suitable starting values for the NLHM fits, separate
-pathway fits are performed for all datasets.</p>
-<div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">f_sep_1_const</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/mmkin.html">mmkin</a></span><span class="op">(</span></span>
-<span>  <span class="va">cyan_path_1</span>,</span>
-<span>  <span class="va">cyan_ds</span>,</span>
-<span>  error_model <span class="op">=</span> <span class="st">"const"</span>,</span>
-<span>  cluster <span class="op">=</span> <span class="va">cl_path_1</span>,</span>
-<span>  quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
-<span><span class="fu"><a href="../../reference/status.html">status</a></span><span class="op">(</span><span class="va">f_sep_1_const</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
-<thead><tr class="header">
-<th align="left"></th>
-<th align="left">Nambsheim</th>
-<th align="left">Tama</th>
-<th align="left">Gross-Umstadt</th>
-<th align="left">Sassafras</th>
-<th align="left">Lleida</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="left">sfo_path_1</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-</tr>
-<tr class="even">
-<td align="left">fomc_path_1</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-</tr>
-<tr class="odd">
-<td align="left">dfop_path_1</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-</tr>
-<tr class="even">
-<td align="left">sforb_path_1</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-</tr>
-<tr class="odd">
-<td align="left">hs_path_1</td>
-<td align="left">C</td>
-<td align="left">C</td>
-<td align="left">C</td>
-<td align="left">C</td>
-<td align="left">C</td>
-</tr>
-</tbody>
-</table>
-<div class="sourceCode" id="cb11"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">f_sep_1_tc</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_sep_1_const</span>, error_model <span class="op">=</span> <span class="st">"tc"</span><span class="op">)</span></span>
-<span><span class="fu"><a href="../../reference/status.html">status</a></span><span class="op">(</span><span class="va">f_sep_1_tc</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
-<thead><tr class="header">
-<th align="left"></th>
-<th align="left">Nambsheim</th>
-<th align="left">Tama</th>
-<th align="left">Gross-Umstadt</th>
-<th align="left">Sassafras</th>
-<th align="left">Lleida</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="left">sfo_path_1</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-</tr>
-<tr class="even">
-<td align="left">fomc_path_1</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">C</td>
-</tr>
-<tr class="odd">
-<td align="left">dfop_path_1</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-</tr>
-<tr class="even">
-<td align="left">sforb_path_1</td>
-<td align="left">OK</td>
-<td align="left">C</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-</tr>
-<tr class="odd">
-<td align="left">hs_path_1</td>
-<td align="left">C</td>
-<td align="left">OK</td>
-<td align="left">C</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-</tr>
-</tbody>
-</table>
-<p>Most separate fits converged successfully. The biggest convergence
-problems are seen when using the HS model with constant variance.</p>
-<p>For the hierarchical pathway fits, those random effects that could
-not be quantified in the corresponding parent data analyses are
-excluded.</p>
-<p>In the code below, the output of the <code>illparms</code> function
-for the parent only fits is used as an argument
-<code>no_random_effect</code> to the <code>mhmkin</code> function. The
-possibility to do so was introduced in mkin version <code>1.2.2</code>
-which is currently under development.</p>
-<div class="sourceCode" id="cb12"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">f_saem_1</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/mhmkin.html">mhmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">f_sep_1_const</span>, <span class="va">f_sep_1_tc</span><span class="op">)</span>,</span>
-<span>  no_random_effect <span class="op">=</span> <span class="fu"><a href="../../reference/illparms.html">illparms</a></span><span class="op">(</span><span class="va">cyan_saem_full</span><span class="op">)</span>,</span>
-<span>  cluster <span class="op">=</span> <span class="va">cl_path_1</span><span class="op">)</span></span></code></pre></div>
-<div class="sourceCode" id="cb13"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="../../reference/status.html">status</a></span><span class="op">(</span><span class="va">f_saem_1</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
-<thead><tr class="header">
-<th align="left"></th>
-<th align="left">const</th>
-<th align="left">tc</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="left">sfo_path_1</td>
-<td align="left">Fth, FO</td>
-<td align="left">Fth, FO</td>
-</tr>
-<tr class="even">
-<td align="left">fomc_path_1</td>
-<td align="left">OK</td>
-<td align="left">Fth, FO</td>
-</tr>
-<tr class="odd">
-<td align="left">dfop_path_1</td>
-<td align="left">Fth, FO</td>
-<td align="left">Fth, FO</td>
-</tr>
-<tr class="even">
-<td align="left">sforb_path_1</td>
-<td align="left">Fth, FO</td>
-<td align="left">Fth, FO</td>
-</tr>
-<tr class="odd">
-<td align="left">hs_path_1</td>
-<td align="left">Fth, FO</td>
-<td align="left">Fth, FO</td>
-</tr>
-</tbody>
-</table>
-<p>The status information from the individual fits shows that all fits
-completed successfully. The matrix entries Fth and FO indicate that the
-Fisher Information Matrix could not be inverted for the fixed effects
-(theta) and the random effects (Omega), respectively. For the affected
-fits, ill-defined parameters cannot be determined using the
-<code>illparms</code> function, because it relies on the Fisher
-Information Matrix.</p>
-<div class="sourceCode" id="cb14"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="../../reference/illparms.html">illparms</a></span><span class="op">(</span><span class="va">f_saem_1</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
-<colgroup>
-<col width="18%">
-<col width="77%">
-<col width="4%">
-</colgroup>
-<thead><tr class="header">
-<th align="left"></th>
-<th align="left">const</th>
-<th align="left">tc</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="left">sfo_path_1</td>
-<td align="left">NA</td>
-<td align="left">NA</td>
-</tr>
-<tr class="even">
-<td align="left">fomc_path_1</td>
-<td align="left">sd(log_k_J9Z38), sd(f_cyan_ilr_2),
-sd(f_JCZ38_qlogis)</td>
-<td align="left">NA</td>
-</tr>
-<tr class="odd">
-<td align="left">dfop_path_1</td>
-<td align="left">NA</td>
-<td align="left">NA</td>
-</tr>
-<tr class="even">
-<td align="left">sforb_path_1</td>
-<td align="left">NA</td>
-<td align="left">NA</td>
-</tr>
-<tr class="odd">
-<td align="left">hs_path_1</td>
-<td align="left">NA</td>
-<td align="left">NA</td>
-</tr>
-</tbody>
-</table>
-<p>The model comparison below suggests that the pathway fits using DFOP
-or SFORB for the parent compound provide the best fit.</p>
-<div class="sourceCode" id="cb15"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_saem_1</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span>digits <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
-<thead><tr class="header">
-<th align="left"></th>
-<th align="right">npar</th>
-<th align="right">AIC</th>
-<th align="right">BIC</th>
-<th align="right">Lik</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="left">sfo_path_1 const</td>
-<td align="right">16</td>
-<td align="right">2692.8</td>
-<td align="right">2686.6</td>
-<td align="right">-1330.4</td>
-</tr>
-<tr class="even">
-<td align="left">sfo_path_1 tc</td>
-<td align="right">17</td>
-<td align="right">2657.7</td>
-<td align="right">2651.1</td>
-<td align="right">-1311.9</td>
-</tr>
-<tr class="odd">
-<td align="left">fomc_path_1 const</td>
-<td align="right">18</td>
-<td align="right">2427.8</td>
-<td align="right">2420.8</td>
-<td align="right">-1195.9</td>
-</tr>
-<tr class="even">
-<td align="left">fomc_path_1 tc</td>
-<td align="right">19</td>
-<td align="right">2423.4</td>
-<td align="right">2416.0</td>
-<td align="right">-1192.7</td>
-</tr>
-<tr class="odd">
-<td align="left">dfop_path_1 const</td>
-<td align="right">20</td>
-<td align="right">2403.2</td>
-<td align="right">2395.4</td>
-<td align="right">-1181.6</td>
-</tr>
-<tr class="even">
-<td align="left">sforb_path_1 const</td>
-<td align="right">20</td>
-<td align="right">2401.4</td>
-<td align="right">2393.6</td>
-<td align="right">-1180.7</td>
-</tr>
-<tr class="odd">
-<td align="left">hs_path_1 const</td>
-<td align="right">20</td>
-<td align="right">2427.3</td>
-<td align="right">2419.5</td>
-<td align="right">-1193.7</td>
-</tr>
-<tr class="even">
-<td align="left">dfop_path_1 tc</td>
-<td align="right">20</td>
-<td align="right">2398.0</td>
-<td align="right">2390.2</td>
-<td align="right">-1179.0</td>
-</tr>
-<tr class="odd">
-<td align="left">sforb_path_1 tc</td>
-<td align="right">20</td>
-<td align="right">2399.8</td>
-<td align="right">2392.0</td>
-<td align="right">-1179.9</td>
-</tr>
-<tr class="even">
-<td align="left">hs_path_1 tc</td>
-<td align="right">21</td>
-<td align="right">2422.3</td>
-<td align="right">2414.1</td>
-<td align="right">-1190.2</td>
-</tr>
-</tbody>
-</table>
-<p>For these two parent model, successful fits are shown below. Plots of
-the fits with the other parent models are shown in the Appendix.</p>
-<div class="sourceCode" id="cb16"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_saem_1</span><span class="op">[[</span><span class="st">"dfop_path_1"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div>
-<div class="figure" style="text-align: center">
-<img src="2022_cyan_pathway_files/figure-html/unnamed-chunk-7-1.png" alt="DFOP pathway fit with two-component error" width="700"><p class="caption">
-DFOP pathway fit with two-component error
-</p>
-</div>
-<div class="sourceCode" id="cb17"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_saem_1</span><span class="op">[[</span><span class="st">"sforb_path_1"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div>
-<div class="figure" style="text-align: center">
-<img src="2022_cyan_pathway_files/figure-html/unnamed-chunk-8-1.png" alt="SFORB pathway fit with two-component error" width="700"><p class="caption">
-SFORB pathway fit with two-component error
-</p>
-</div>
-<p>A closer graphical analysis of these Figures shows that the residues
-of transformation product JCZ38 in the soils Tama and Nambsheim observed
-at later time points are strongly and systematically underestimated.</p>
-<div class="sourceCode" id="cb18"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/parallel/makeCluster.html" class="external-link">stopCluster</a></span><span class="op">(</span><span class="va">cl_path_1</span><span class="op">)</span></span></code></pre></div>
-</div>
-<div class="section level3">
-<h3 id="alternative-pathway-fits">Alternative pathway fits<a class="anchor" aria-label="anchor" href="#alternative-pathway-fits"></a>
-</h3>
-<p>To improve the fit for JCZ38, a back-reaction from JSE76 to JCZ38 was
-introduced in an alternative version of the transformation pathway, in
-analogy to the back-reaction from K5A78 to K5A77. Both pairs of
-transformation products are pairs of an organic acid with its
-corresponding amide (Addendum 2014, p. 109). As FOMC provided the best
-fit for the parent, and the biexponential models DFOP and SFORB provided
-the best initial pathway fits, these three parent models are used in the
-alternative pathway fits.</p>
-<div class="sourceCode" id="cb19"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">cyan_path_2</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span></span>
-<span>  fomc_path_2 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span></span>
-<span>    cyan <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"FOMC"</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"JCZ38"</span>, <span class="st">"J9Z38"</span><span class="op">)</span><span class="op">)</span>,</span>
-<span>    JCZ38 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"JSE76"</span><span class="op">)</span>,</span>
-<span>    J9Z38 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span>
-<span>    JSE76 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"JCZ38"</span><span class="op">)</span>,</span>
-<span>    name <span class="op">=</span> <span class="st">"fomc_path_2"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>,</span>
-<span>    dll_dir <span class="op">=</span> <span class="st">"cyan_dlls"</span>,</span>
-<span>    overwrite <span class="op">=</span> <span class="cn">TRUE</span></span>
-<span>  <span class="op">)</span>,</span>
-<span>  dfop_path_2 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span></span>
-<span>    cyan <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"JCZ38"</span>, <span class="st">"J9Z38"</span><span class="op">)</span><span class="op">)</span>,</span>
-<span>    JCZ38 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"JSE76"</span><span class="op">)</span>,</span>
-<span>    J9Z38 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span>
-<span>    JSE76 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"JCZ38"</span><span class="op">)</span>,</span>
-<span>    name <span class="op">=</span> <span class="st">"dfop_path_2"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>,</span>
-<span>    dll_dir <span class="op">=</span> <span class="st">"cyan_dlls"</span>,</span>
-<span>    overwrite <span class="op">=</span> <span class="cn">TRUE</span></span>
-<span>  <span class="op">)</span>,</span>
-<span>  sforb_path_2 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span></span>
-<span>    cyan <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFORB"</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"JCZ38"</span>, <span class="st">"J9Z38"</span><span class="op">)</span><span class="op">)</span>,</span>
-<span>    JCZ38 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"JSE76"</span><span class="op">)</span>,</span>
-<span>    J9Z38 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span>
-<span>    JSE76 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"JCZ38"</span><span class="op">)</span>,</span>
-<span>    name <span class="op">=</span> <span class="st">"sforb_path_2"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>,</span>
-<span>    dll_dir <span class="op">=</span> <span class="st">"cyan_dlls"</span>,</span>
-<span>    overwrite <span class="op">=</span> <span class="cn">TRUE</span></span>
-<span>  <span class="op">)</span></span>
-<span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">cl_path_2</span> <span class="op">&lt;-</span> <span class="fu">start_cluster</span><span class="op">(</span><span class="va">n_cores</span><span class="op">)</span></span>
-<span><span class="va">f_sep_2_const</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/mmkin.html">mmkin</a></span><span class="op">(</span></span>
-<span>  <span class="va">cyan_path_2</span>,</span>
-<span>  <span class="va">cyan_ds</span>,</span>
-<span>  error_model <span class="op">=</span> <span class="st">"const"</span>,</span>
-<span>  cluster <span class="op">=</span> <span class="va">cl_path_2</span>,</span>
-<span>  quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu"><a href="../../reference/status.html">status</a></span><span class="op">(</span><span class="va">f_sep_2_const</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
-<thead><tr class="header">
-<th align="left"></th>
-<th align="left">Nambsheim</th>
-<th align="left">Tama</th>
-<th align="left">Gross-Umstadt</th>
-<th align="left">Sassafras</th>
-<th align="left">Lleida</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="left">fomc_path_2</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">C</td>
-<td align="left">OK</td>
-</tr>
-<tr class="even">
-<td align="left">dfop_path_2</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">C</td>
-<td align="left">OK</td>
-</tr>
-<tr class="odd">
-<td align="left">sforb_path_2</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">C</td>
-<td align="left">OK</td>
-</tr>
-</tbody>
-</table>
-<p>Using constant variance, separate fits converge with the exception of
-the fits to the Sassafras soil data.</p>
-<div class="sourceCode" id="cb20"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">f_sep_2_tc</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_sep_2_const</span>, error_model <span class="op">=</span> <span class="st">"tc"</span><span class="op">)</span></span>
-<span><span class="fu"><a href="../../reference/status.html">status</a></span><span class="op">(</span><span class="va">f_sep_2_tc</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
-<thead><tr class="header">
-<th align="left"></th>
-<th align="left">Nambsheim</th>
-<th align="left">Tama</th>
-<th align="left">Gross-Umstadt</th>
-<th align="left">Sassafras</th>
-<th align="left">Lleida</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="left">fomc_path_2</td>
-<td align="left">OK</td>
-<td align="left">C</td>
-<td align="left">OK</td>
-<td align="left">C</td>
-<td align="left">OK</td>
-</tr>
-<tr class="even">
-<td align="left">dfop_path_2</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">C</td>
-<td align="left">OK</td>
-</tr>
-<tr class="odd">
-<td align="left">sforb_path_2</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-</tr>
-</tbody>
-</table>
-<p>Using the two-component error model, all separate fits converge with
-the exception of the alternative pathway fit with DFOP used for the
-parent and the Sassafras dataset.</p>
-<div class="sourceCode" id="cb21"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">f_saem_2</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/mhmkin.html">mhmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">f_sep_2_const</span>, <span class="va">f_sep_2_tc</span><span class="op">)</span>,</span>
-<span>  no_random_effect <span class="op">=</span> <span class="fu"><a href="../../reference/illparms.html">illparms</a></span><span class="op">(</span><span class="va">cyan_saem_full</span><span class="op">[</span><span class="fl">2</span><span class="op">:</span><span class="fl">4</span>, <span class="op">]</span><span class="op">)</span>,</span>
-<span>  cluster <span class="op">=</span> <span class="va">cl_path_2</span><span class="op">)</span></span></code></pre></div>
-<div class="sourceCode" id="cb22"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="../../reference/status.html">status</a></span><span class="op">(</span><span class="va">f_saem_2</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
-<thead><tr class="header">
-<th align="left"></th>
-<th align="left">const</th>
-<th align="left">tc</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="left">fomc_path_2</td>
-<td align="left">OK</td>
-<td align="left">FO</td>
-</tr>
-<tr class="even">
-<td align="left">dfop_path_2</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-</tr>
-<tr class="odd">
-<td align="left">sforb_path_2</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-</tr>
-</tbody>
-</table>
-<p>The hierarchical fits for the alternative pathway completed
-successfully.</p>
-<div class="sourceCode" id="cb23"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="../../reference/illparms.html">illparms</a></span><span class="op">(</span><span class="va">f_saem_2</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
-<colgroup>
-<col width="14%">
-<col width="42%">
-<col width="42%">
-</colgroup>
-<thead><tr class="header">
-<th align="left"></th>
-<th align="left">const</th>
-<th align="left">tc</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="left">fomc_path_2</td>
-<td align="left">sd(f_JCZ38_qlogis), sd(f_JSE76_qlogis)</td>
-<td align="left">NA</td>
-</tr>
-<tr class="even">
-<td align="left">dfop_path_2</td>
-<td align="left">sd(f_JCZ38_qlogis), sd(f_JSE76_qlogis)</td>
-<td align="left">sd(f_JCZ38_qlogis), sd(f_JSE76_qlogis)</td>
-</tr>
-<tr class="odd">
-<td align="left">sforb_path_2</td>
-<td align="left">sd(f_JCZ38_qlogis), sd(f_JSE76_qlogis)</td>
-<td align="left">sd(f_JCZ38_qlogis), sd(f_JSE76_qlogis)</td>
-</tr>
-</tbody>
-</table>
-<p>In both fits, the random effects for the formation fractions for the
-pathways from JCZ38 to JSE76, and for the reverse pathway from JSE76 to
-JCZ38 are ill-defined.</p>
-<div class="sourceCode" id="cb24"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_saem_2</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span>digits <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
-<thead><tr class="header">
-<th align="left"></th>
-<th align="right">npar</th>
-<th align="right">AIC</th>
-<th align="right">BIC</th>
-<th align="right">Lik</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="left">fomc_path_2 const</td>
-<td align="right">20</td>
-<td align="right">2308.3</td>
-<td align="right">2300.5</td>
-<td align="right">-1134.2</td>
-</tr>
-<tr class="even">
-<td align="left">fomc_path_2 tc</td>
-<td align="right">21</td>
-<td align="right">2248.3</td>
-<td align="right">2240.1</td>
-<td align="right">-1103.2</td>
-</tr>
-<tr class="odd">
-<td align="left">dfop_path_2 const</td>
-<td align="right">22</td>
-<td align="right">2289.6</td>
-<td align="right">2281.0</td>
-<td align="right">-1122.8</td>
-</tr>
-<tr class="even">
-<td align="left">sforb_path_2 const</td>
-<td align="right">22</td>
-<td align="right">2284.1</td>
-<td align="right">2275.5</td>
-<td align="right">-1120.0</td>
-</tr>
-<tr class="odd">
-<td align="left">dfop_path_2 tc</td>
-<td align="right">22</td>
-<td align="right">2234.4</td>
-<td align="right">2225.8</td>
-<td align="right">-1095.2</td>
-</tr>
-<tr class="even">
-<td align="left">sforb_path_2 tc</td>
-<td align="right">22</td>
-<td align="right">2240.4</td>
-<td align="right">2231.8</td>
-<td align="right">-1098.2</td>
-</tr>
-</tbody>
-</table>
-<p>The variants using the biexponential models DFOP and SFORB for the
-parent compound and the two-component error model give the lowest AIC
-and BIC values and are plotted below. Compared with the original
-pathway, the AIC and BIC values indicate a large improvement. This is
-confirmed by the plots, which show that the metabolite JCZ38 is fitted
-much better with this model.</p>
-<div class="sourceCode" id="cb25"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_saem_2</span><span class="op">[[</span><span class="st">"fomc_path_2"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div>
-<div class="figure" style="text-align: center">
-<img src="2022_cyan_pathway_files/figure-html/unnamed-chunk-13-1.png" alt="FOMC pathway fit with two-component error, alternative pathway" width="700"><p class="caption">
-FOMC pathway fit with two-component error, alternative pathway
-</p>
-</div>
-<div class="sourceCode" id="cb26"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_saem_2</span><span class="op">[[</span><span class="st">"dfop_path_2"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div>
-<div class="figure" style="text-align: center">
-<img src="2022_cyan_pathway_files/figure-html/unnamed-chunk-14-1.png" alt="DFOP pathway fit with two-component error, alternative pathway" width="700"><p class="caption">
-DFOP pathway fit with two-component error, alternative pathway
-</p>
-</div>
-<div class="sourceCode" id="cb27"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_saem_2</span><span class="op">[[</span><span class="st">"sforb_path_2"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div>
-<div class="figure" style="text-align: center">
-<img src="2022_cyan_pathway_files/figure-html/unnamed-chunk-15-1.png" alt="SFORB pathway fit with two-component error, alternative pathway" width="700"><p class="caption">
-SFORB pathway fit with two-component error, alternative pathway
-</p>
-</div>
-</div>
-<div class="section level3">
-<h3 id="refinement-of-alternative-pathway-fits">Refinement of alternative pathway fits<a class="anchor" aria-label="anchor" href="#refinement-of-alternative-pathway-fits"></a>
-</h3>
-<p>All ill-defined random effects that were identified in the parent
-only fits and in the above pathway fits, are excluded for the final
-evaluations below. For this purpose, a list of character vectors is
-created below that can be indexed by row and column indices, and which
-contains the degradation parameter names for which random effects should
-be excluded for each of the hierarchical fits contained in
-<code>f_saem_2</code>.</p>
-<div class="sourceCode" id="cb28"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">no_ranef</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/matrix.html" class="external-link">matrix</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="op">)</span>, nrow <span class="op">=</span> <span class="fl">3</span>, ncol <span class="op">=</span> <span class="fl">2</span>, dimnames <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/dimnames.html" class="external-link">dimnames</a></span><span class="op">(</span><span class="va">f_saem_2</span><span class="op">)</span><span class="op">)</span></span>
-<span><span class="va">no_ranef</span><span class="op">[[</span><span class="st">"fomc_path_2"</span>, <span class="st">"const"</span><span class="op">]</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"log_beta"</span>, <span class="st">"f_JCZ38_qlogis"</span>, <span class="st">"f_JSE76_qlogis"</span><span class="op">)</span></span>
-<span><span class="va">no_ranef</span><span class="op">[[</span><span class="st">"fomc_path_2"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"cyan_0"</span>, <span class="st">"f_JCZ38_qlogis"</span>, <span class="st">"f_JSE76_qlogis"</span><span class="op">)</span></span>
-<span><span class="va">no_ranef</span><span class="op">[[</span><span class="st">"dfop_path_2"</span>, <span class="st">"const"</span><span class="op">]</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"cyan_0"</span>, <span class="st">"f_JCZ38_qlogis"</span>, <span class="st">"f_JSE76_qlogis"</span><span class="op">)</span></span>
-<span><span class="va">no_ranef</span><span class="op">[[</span><span class="st">"dfop_path_2"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"cyan_0"</span>, <span class="st">"log_k1"</span>, <span class="st">"f_JCZ38_qlogis"</span>, <span class="st">"f_JSE76_qlogis"</span><span class="op">)</span></span>
-<span><span class="va">no_ranef</span><span class="op">[[</span><span class="st">"sforb_path_2"</span>, <span class="st">"const"</span><span class="op">]</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"cyan_free_0"</span>,</span>
-<span>  <span class="st">"f_JCZ38_qlogis"</span>, <span class="st">"f_JSE76_qlogis"</span><span class="op">)</span></span>
-<span><span class="va">no_ranef</span><span class="op">[[</span><span class="st">"sforb_path_2"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"cyan_free_0"</span>, <span class="st">"log_k_cyan_free_bound"</span>,</span>
-<span>  <span class="st">"f_JCZ38_qlogis"</span>, <span class="st">"f_JSE76_qlogis"</span><span class="op">)</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/parallel/clusterApply.html" class="external-link">clusterExport</a></span><span class="op">(</span><span class="va">cl_path_2</span>, <span class="st">"no_ranef"</span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">f_saem_3</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_saem_2</span>,</span>
-<span>  no_random_effect <span class="op">=</span> <span class="va">no_ranef</span>,</span>
-<span>  cluster <span class="op">=</span> <span class="va">cl_path_2</span><span class="op">)</span></span></code></pre></div>
-<div class="sourceCode" id="cb29"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="../../reference/status.html">status</a></span><span class="op">(</span><span class="va">f_saem_3</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
-<thead><tr class="header">
-<th align="left"></th>
-<th align="left">const</th>
-<th align="left">tc</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="left">fomc_path_2</td>
-<td align="left">E</td>
-<td align="left">Fth</td>
-</tr>
-<tr class="even">
-<td align="left">dfop_path_2</td>
-<td align="left">Fth</td>
-<td align="left">Fth</td>
-</tr>
-<tr class="odd">
-<td align="left">sforb_path_2</td>
-<td align="left">Fth</td>
-<td align="left">Fth</td>
-</tr>
-</tbody>
-</table>
-<p>With the exception of the FOMC pathway fit with constant variance,
-all updated fits completed successfully. However, the Fisher Information
-Matrix for the fixed effects (Fth) could not be inverted, so no
-confidence intervals for the optimised parameters are available.</p>
-<div class="sourceCode" id="cb30"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="../../reference/illparms.html">illparms</a></span><span class="op">(</span><span class="va">f_saem_3</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
-<thead><tr class="header">
-<th align="left"></th>
-<th align="left">const</th>
-<th align="left">tc</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="left">fomc_path_2</td>
-<td align="left">E</td>
-<td align="left"></td>
-</tr>
-<tr class="even">
-<td align="left">dfop_path_2</td>
-<td align="left"></td>
-<td align="left"></td>
-</tr>
-<tr class="odd">
-<td align="left">sforb_path_2</td>
-<td align="left"></td>
-<td align="left"></td>
-</tr>
-</tbody>
-</table>
-<div class="sourceCode" id="cb31"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_saem_3</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span>digits <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
-<thead><tr class="header">
-<th align="left"></th>
-<th align="right">npar</th>
-<th align="right">AIC</th>
-<th align="right">BIC</th>
-<th align="right">Lik</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="left">fomc_path_2 tc</td>
-<td align="right">19</td>
-<td align="right">2250.9</td>
-<td align="right">2243.5</td>
-<td align="right">-1106.5</td>
-</tr>
-<tr class="even">
-<td align="left">dfop_path_2 const</td>
-<td align="right">20</td>
-<td align="right">2281.7</td>
-<td align="right">2273.9</td>
-<td align="right">-1120.8</td>
-</tr>
-<tr class="odd">
-<td align="left">sforb_path_2 const</td>
-<td align="right">20</td>
-<td align="right">2279.5</td>
-<td align="right">2271.7</td>
-<td align="right">-1119.7</td>
-</tr>
-<tr class="even">
-<td align="left">dfop_path_2 tc</td>
-<td align="right">20</td>
-<td align="right">2231.5</td>
-<td align="right">2223.7</td>
-<td align="right">-1095.8</td>
-</tr>
-<tr class="odd">
-<td align="left">sforb_path_2 tc</td>
-<td align="right">20</td>
-<td align="right">2235.7</td>
-<td align="right">2227.9</td>
-<td align="right">-1097.9</td>
-</tr>
-</tbody>
-</table>
-<p>While the AIC and BIC values of the best fit (DFOP pathway fit with
-two-component error) are lower than in the previous fits with the
-alternative pathway, the practical value of these refined evaluations is
-limited as no confidence intervals are obtained.</p>
-<div class="sourceCode" id="cb32"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/parallel/makeCluster.html" class="external-link">stopCluster</a></span><span class="op">(</span><span class="va">cl_path_2</span><span class="op">)</span></span></code></pre></div>
-</div>
-</div>
-<div class="section level2">
-<h2 id="conclusion">Conclusion<a class="anchor" aria-label="anchor" href="#conclusion"></a>
-</h2>
-<p>It was demonstrated that a relatively complex transformation pathway
-with parallel formation of two primary metabolites and one secondary
-metabolite can be fitted even if the data in the individual datasets are
-quite different and partly only cover the formation phase.</p>
-<p>The run times of the pathway fits were several hours, limiting the
-practical feasibility of iterative refinements based on ill-defined
-parameters and of alternative checks of parameter identifiability based
-on multistart runs.</p>
-</div>
-<div class="section level2">
-<h2 id="acknowledgements">Acknowledgements<a class="anchor" aria-label="anchor" href="#acknowledgements"></a>
-</h2>
-<p>The helpful comments by Janina Wöltjen of the German Environment
-Agency are gratefully acknowledged.</p>
-</div>
-<div class="section level2">
-<h2 id="appendix">Appendix<a class="anchor" aria-label="anchor" href="#appendix"></a>
-</h2>
-<div class="section level3">
-<h3 id="plots-of-fits-that-were-not-refined-further">Plots of fits that were not refined further<a class="anchor" aria-label="anchor" href="#plots-of-fits-that-were-not-refined-further"></a>
-</h3>
-<div class="sourceCode" id="cb33"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_saem_1</span><span class="op">[[</span><span class="st">"sfo_path_1"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div>
-<div class="figure" style="text-align: center">
-<img src="2022_cyan_pathway_files/figure-html/unnamed-chunk-20-1.png" alt="SFO pathway fit with two-component error" width="700"><p class="caption">
-SFO pathway fit with two-component error
-</p>
-</div>
-<div class="sourceCode" id="cb34"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_saem_1</span><span class="op">[[</span><span class="st">"fomc_path_1"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div>
-<div class="figure" style="text-align: center">
-<img src="2022_cyan_pathway_files/figure-html/unnamed-chunk-21-1.png" alt="FOMC pathway fit with two-component error" width="700"><p class="caption">
-FOMC pathway fit with two-component error
-</p>
-</div>
-<div class="sourceCode" id="cb35"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_saem_1</span><span class="op">[[</span><span class="st">"sforb_path_1"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div>
-<div class="figure" style="text-align: center">
-<img src="2022_cyan_pathway_files/figure-html/unnamed-chunk-22-1.png" alt="HS pathway fit with two-component error" width="700"><p class="caption">
-HS pathway fit with two-component error
-</p>
-</div>
-</div>
-<div class="section level3">
-<h3 id="hierarchical-fit-listings">Hierarchical fit listings<a class="anchor" aria-label="anchor" href="#hierarchical-fit-listings"></a>
-</h3>
-<div class="section level4">
-<h4 id="pathway-1">Pathway 1<a class="anchor" aria-label="anchor" href="#pathway-1"></a>
-</h4>
-<caption>
-Hierarchical SFO path 1 fit with constant variance
-</caption>
-<pre><code>
-saemix version used for fitting:      3.2 
-mkin version used for pre-fitting:  1.2.4 
-R version used for fitting:         4.2.3 
-Date of fit:     Thu Apr 20 20:33:05 2023 
-Date of summary: Thu Apr 20 21:04:34 2023 
-
-Equations:
-d_cyan/dt = - k_cyan * cyan
-d_JCZ38/dt = + f_cyan_to_JCZ38 * k_cyan * cyan - k_JCZ38 * JCZ38
-d_J9Z38/dt = + f_cyan_to_J9Z38 * k_cyan * cyan - k_J9Z38 * J9Z38
-d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76
-
-Data:
-433 observations of 4 variable(s) grouped in 5 datasets
-
-Model predictions using solution type deSolve 
-
-Fitted in 438.011 s
-Using 300, 100 iterations and 10 chains
-
-Variance model: Constant variance 
-
-Starting values for degradation parameters:
-        cyan_0     log_k_cyan    log_k_JCZ38    log_k_J9Z38    log_k_JSE76 
-       95.3304        -3.8459        -3.1305        -5.0678        -5.3196 
-  f_cyan_ilr_1   f_cyan_ilr_2 f_JCZ38_qlogis 
-        0.8158        22.5404        10.4289 
-
-Fixed degradation parameter values:
-None
-
-Starting values for random effects (square root of initial entries in omega):
-               cyan_0 log_k_cyan log_k_JCZ38 log_k_J9Z38 log_k_JSE76
-cyan_0          4.797     0.0000       0.000       0.000      0.0000
-log_k_cyan      0.000     0.9619       0.000       0.000      0.0000
-log_k_JCZ38     0.000     0.0000       2.139       0.000      0.0000
-log_k_J9Z38     0.000     0.0000       0.000       1.639      0.0000
-log_k_JSE76     0.000     0.0000       0.000       0.000      0.7894
-f_cyan_ilr_1    0.000     0.0000       0.000       0.000      0.0000
-f_cyan_ilr_2    0.000     0.0000       0.000       0.000      0.0000
-f_JCZ38_qlogis  0.000     0.0000       0.000       0.000      0.0000
-               f_cyan_ilr_1 f_cyan_ilr_2 f_JCZ38_qlogis
-cyan_0               0.0000        0.000           0.00
-log_k_cyan           0.0000        0.000           0.00
-log_k_JCZ38          0.0000        0.000           0.00
-log_k_J9Z38          0.0000        0.000           0.00
-log_k_JSE76          0.0000        0.000           0.00
-f_cyan_ilr_1         0.7714        0.000           0.00
-f_cyan_ilr_2         0.0000        8.684           0.00
-f_JCZ38_qlogis       0.0000        0.000          13.48
-
-Starting values for error model parameters:
-a.1 
-  1 
-
-Results:
-
-Likelihood computed by importance sampling
-   AIC  BIC logLik
-  2693 2687  -1330
-
-Optimised parameters:
-                     est. lower upper
-cyan_0            95.0946    NA    NA
-log_k_cyan        -3.8544    NA    NA
-log_k_JCZ38       -3.0402    NA    NA
-log_k_J9Z38       -5.0109    NA    NA
-log_k_JSE76       -5.2857    NA    NA
-f_cyan_ilr_1       0.8069    NA    NA
-f_cyan_ilr_2      16.6623    NA    NA
-f_JCZ38_qlogis     1.3602    NA    NA
-a.1                4.8326    NA    NA
-SD.log_k_cyan      0.5842    NA    NA
-SD.log_k_JCZ38     1.2680    NA    NA
-SD.log_k_J9Z38     0.3626    NA    NA
-SD.log_k_JSE76     0.5244    NA    NA
-SD.f_cyan_ilr_1    0.2752    NA    NA
-SD.f_cyan_ilr_2    2.3556    NA    NA
-SD.f_JCZ38_qlogis  0.2400    NA    NA
-
-Correlation is not available
-
-Random effects:
-                    est. lower upper
-SD.log_k_cyan     0.5842    NA    NA
-SD.log_k_JCZ38    1.2680    NA    NA
-SD.log_k_J9Z38    0.3626    NA    NA
-SD.log_k_JSE76    0.5244    NA    NA
-SD.f_cyan_ilr_1   0.2752    NA    NA
-SD.f_cyan_ilr_2   2.3556    NA    NA
-SD.f_JCZ38_qlogis 0.2400    NA    NA
-
-Variance model:
-     est. lower upper
-a.1 4.833    NA    NA
-
-Backtransformed parameters:
-                      est. lower upper
-cyan_0           95.094581    NA    NA
-k_cyan            0.021186    NA    NA
-k_JCZ38           0.047825    NA    NA
-k_J9Z38           0.006665    NA    NA
-k_JSE76           0.005063    NA    NA
-f_cyan_to_JCZ38   0.757885    NA    NA
-f_cyan_to_J9Z38   0.242115    NA    NA
-f_JCZ38_to_JSE76  0.795792    NA    NA
-
-Resulting formation fractions:
-                   ff
-cyan_JCZ38  7.579e-01
-cyan_J9Z38  2.421e-01
-cyan_sink   5.877e-10
-JCZ38_JSE76 7.958e-01
-JCZ38_sink  2.042e-01
-
-Estimated disappearance times:
-        DT50   DT90
-cyan   32.72 108.68
-JCZ38  14.49  48.15
-J9Z38 103.99 345.46
-JSE76 136.90 454.76
-
-</code></pre>
-<p></p>
-<caption>
-Hierarchical SFO path 1 fit with two-component error
-</caption>
-<pre><code>
-saemix version used for fitting:      3.2 
-mkin version used for pre-fitting:  1.2.4 
-R version used for fitting:         4.2.3 
-Date of fit:     Thu Apr 20 20:32:55 2023 
-Date of summary: Thu Apr 20 21:04:34 2023 
-
-Equations:
-d_cyan/dt = - k_cyan * cyan
-d_JCZ38/dt = + f_cyan_to_JCZ38 * k_cyan * cyan - k_JCZ38 * JCZ38
-d_J9Z38/dt = + f_cyan_to_J9Z38 * k_cyan * cyan - k_J9Z38 * J9Z38
-d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76
-
-Data:
-433 observations of 4 variable(s) grouped in 5 datasets
-
-Model predictions using solution type deSolve 
-
-Fitted in 427.249 s
-Using 300, 100 iterations and 10 chains
-
-Variance model: Two-component variance function 
-
-Starting values for degradation parameters:
-        cyan_0     log_k_cyan    log_k_JCZ38    log_k_J9Z38    log_k_JSE76 
-       96.0039        -3.8907        -3.1276        -5.0069        -4.9367 
-  f_cyan_ilr_1   f_cyan_ilr_2 f_JCZ38_qlogis 
-        0.7937        20.0030        15.1336 
-
-Fixed degradation parameter values:
-None
-
-Starting values for random effects (square root of initial entries in omega):
-               cyan_0 log_k_cyan log_k_JCZ38 log_k_J9Z38 log_k_JSE76
-cyan_0          4.859      0.000        0.00        0.00      0.0000
-log_k_cyan      0.000      0.962        0.00        0.00      0.0000
-log_k_JCZ38     0.000      0.000        2.04        0.00      0.0000
-log_k_J9Z38     0.000      0.000        0.00        1.72      0.0000
-log_k_JSE76     0.000      0.000        0.00        0.00      0.9076
-f_cyan_ilr_1    0.000      0.000        0.00        0.00      0.0000
-f_cyan_ilr_2    0.000      0.000        0.00        0.00      0.0000
-f_JCZ38_qlogis  0.000      0.000        0.00        0.00      0.0000
-               f_cyan_ilr_1 f_cyan_ilr_2 f_JCZ38_qlogis
-cyan_0               0.0000        0.000           0.00
-log_k_cyan           0.0000        0.000           0.00
-log_k_JCZ38          0.0000        0.000           0.00
-log_k_J9Z38          0.0000        0.000           0.00
-log_k_JSE76          0.0000        0.000           0.00
-f_cyan_ilr_1         0.7598        0.000           0.00
-f_cyan_ilr_2         0.0000        7.334           0.00
-f_JCZ38_qlogis       0.0000        0.000          11.78
-
-Starting values for error model parameters:
-a.1 b.1 
-  1   1 
-
-Results:
-
-Likelihood computed by importance sampling
-   AIC  BIC logLik
-  2658 2651  -1312
-
-Optimised parameters:
-                      est. lower upper
-cyan_0            94.72923    NA    NA
-log_k_cyan        -3.91670    NA    NA
-log_k_JCZ38       -3.12917    NA    NA
-log_k_J9Z38       -5.06070    NA    NA
-log_k_JSE76       -5.09254    NA    NA
-f_cyan_ilr_1       0.81116    NA    NA
-f_cyan_ilr_2      39.97850    NA    NA
-f_JCZ38_qlogis     3.09728    NA    NA
-a.1                3.95044    NA    NA
-b.1                0.07998    NA    NA
-SD.log_k_cyan      0.58855    NA    NA
-SD.log_k_JCZ38     1.29753    NA    NA
-SD.log_k_J9Z38     0.62851    NA    NA
-SD.log_k_JSE76     0.37235    NA    NA
-SD.f_cyan_ilr_1    0.37346    NA    NA
-SD.f_cyan_ilr_2    1.41667    NA    NA
-SD.f_JCZ38_qlogis  1.81467    NA    NA
-
-Correlation is not available
-
-Random effects:
-                    est. lower upper
-SD.log_k_cyan     0.5886    NA    NA
-SD.log_k_JCZ38    1.2975    NA    NA
-SD.log_k_J9Z38    0.6285    NA    NA
-SD.log_k_JSE76    0.3724    NA    NA
-SD.f_cyan_ilr_1   0.3735    NA    NA
-SD.f_cyan_ilr_2   1.4167    NA    NA
-SD.f_JCZ38_qlogis 1.8147    NA    NA
-
-Variance model:
-       est. lower upper
-a.1 3.95044    NA    NA
-b.1 0.07998    NA    NA
-
-Backtransformed parameters:
-                      est. lower upper
-cyan_0           94.729229    NA    NA
-k_cyan            0.019907    NA    NA
-k_JCZ38           0.043754    NA    NA
-k_J9Z38           0.006341    NA    NA
-k_JSE76           0.006142    NA    NA
-f_cyan_to_JCZ38   0.758991    NA    NA
-f_cyan_to_J9Z38   0.241009    NA    NA
-f_JCZ38_to_JSE76  0.956781    NA    NA
-
-Resulting formation fractions:
-                 ff
-cyan_JCZ38  0.75899
-cyan_J9Z38  0.24101
-cyan_sink   0.00000
-JCZ38_JSE76 0.95678
-JCZ38_sink  0.04322
-
-Estimated disappearance times:
-        DT50   DT90
-cyan   34.82 115.67
-JCZ38  15.84  52.63
-J9Z38 109.31 363.12
-JSE76 112.85 374.87
-
-</code></pre>
-<p></p>
-<caption>
-Hierarchical FOMC path 1 fit with constant variance
-</caption>
-<pre><code>
-saemix version used for fitting:      3.2 
-mkin version used for pre-fitting:  1.2.4 
-R version used for fitting:         4.2.3 
-Date of fit:     Thu Apr 20 20:33:49 2023 
-Date of summary: Thu Apr 20 21:04:34 2023 
-
-Equations:
-d_cyan/dt = - (alpha/beta) * 1/((time/beta) + 1) * cyan
-d_JCZ38/dt = + f_cyan_to_JCZ38 * (alpha/beta) * 1/((time/beta) + 1) *
-           cyan - k_JCZ38 * JCZ38
-d_J9Z38/dt = + f_cyan_to_J9Z38 * (alpha/beta) * 1/((time/beta) + 1) *
-           cyan - k_J9Z38 * J9Z38
-d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76
-
-Data:
-433 observations of 4 variable(s) grouped in 5 datasets
-
-Model predictions using solution type deSolve 
-
-Fitted in 481.497 s
-Using 300, 100 iterations and 10 chains
-
-Variance model: Constant variance 
-
-Starting values for degradation parameters:
-        cyan_0    log_k_JCZ38    log_k_J9Z38    log_k_JSE76   f_cyan_ilr_1 
-      101.2314        -3.3680        -5.1108        -5.9416         0.7144 
-  f_cyan_ilr_2 f_JCZ38_qlogis      log_alpha       log_beta 
-        7.3870        15.7604        -0.1791         2.9811 
-
-Fixed degradation parameter values:
-None
-
-Starting values for random effects (square root of initial entries in omega):
-               cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1
-cyan_0          5.416       0.000         0.0       0.000       0.0000
-log_k_JCZ38     0.000       2.439         0.0       0.000       0.0000
-log_k_J9Z38     0.000       0.000         1.7       0.000       0.0000
-log_k_JSE76     0.000       0.000         0.0       1.856       0.0000
-f_cyan_ilr_1    0.000       0.000         0.0       0.000       0.7164
-f_cyan_ilr_2    0.000       0.000         0.0       0.000       0.0000
-f_JCZ38_qlogis  0.000       0.000         0.0       0.000       0.0000
-log_alpha       0.000       0.000         0.0       0.000       0.0000
-log_beta        0.000       0.000         0.0       0.000       0.0000
-               f_cyan_ilr_2 f_JCZ38_qlogis log_alpha log_beta
-cyan_0                 0.00           0.00    0.0000   0.0000
-log_k_JCZ38            0.00           0.00    0.0000   0.0000
-log_k_J9Z38            0.00           0.00    0.0000   0.0000
-log_k_JSE76            0.00           0.00    0.0000   0.0000
-f_cyan_ilr_1           0.00           0.00    0.0000   0.0000
-f_cyan_ilr_2          12.33           0.00    0.0000   0.0000
-f_JCZ38_qlogis         0.00          20.42    0.0000   0.0000
-log_alpha              0.00           0.00    0.4144   0.0000
-log_beta               0.00           0.00    0.0000   0.5077
-
-Starting values for error model parameters:
-a.1 
-  1 
-
-Results:
-
-Likelihood computed by importance sampling
-   AIC  BIC logLik
-  2428 2421  -1196
-
-Optimised parameters:
-                      est.     lower    upper
-cyan_0            101.0225 98.306270 103.7387
-log_k_JCZ38        -3.3786 -4.770657  -1.9866
-log_k_J9Z38        -5.2603 -5.902085  -4.6186
-log_k_JSE76        -6.1427 -7.318336  -4.9671
-f_cyan_ilr_1        0.7437  0.421215   1.0663
-f_cyan_ilr_2        0.9108  0.267977   1.5537
-f_JCZ38_qlogis      2.0487  0.524897   3.5724
-log_alpha          -0.2268 -0.618049   0.1644
-log_beta            2.8986  2.700701   3.0964
-a.1                 3.4058  3.169913   3.6416
-SD.cyan_0           2.5279  0.454190   4.6016
-SD.log_k_JCZ38      1.5636  0.572824   2.5543
-SD.log_k_J9Z38      0.5316 -0.004405   1.0677
-SD.log_k_JSE76      0.9903  0.106325   1.8742
-SD.f_cyan_ilr_1     0.3464  0.112066   0.5807
-SD.f_cyan_ilr_2     0.2804 -0.393900   0.9546
-SD.f_JCZ38_qlogis   0.9416 -0.152986   2.0362
-SD.log_alpha        0.4273  0.161044   0.6936
-
-Correlation: 
-               cyan_0  l__JCZ3 l__J9Z3 l__JSE7 f_cy__1 f_cy__2 f_JCZ38 log_lph
-log_k_JCZ38    -0.0156                                                        
-log_k_J9Z38    -0.0493  0.0073                                                
-log_k_JSE76    -0.0329  0.0018  0.0069                                        
-f_cyan_ilr_1   -0.0086  0.0180 -0.1406  0.0012                                
-f_cyan_ilr_2   -0.2629  0.0779  0.2826  0.0274  0.0099                        
-f_JCZ38_qlogis  0.0713 -0.0747 -0.0505  0.1169 -0.1022 -0.4893                
-log_alpha      -0.0556  0.0120  0.0336  0.0193  0.0036  0.0840 -0.0489        
-log_beta       -0.2898  0.0460  0.1305  0.0768  0.0190  0.4071 -0.1981  0.2772
-
-Random effects:
-                    est.     lower  upper
-SD.cyan_0         2.5279  0.454190 4.6016
-SD.log_k_JCZ38    1.5636  0.572824 2.5543
-SD.log_k_J9Z38    0.5316 -0.004405 1.0677
-SD.log_k_JSE76    0.9903  0.106325 1.8742
-SD.f_cyan_ilr_1   0.3464  0.112066 0.5807
-SD.f_cyan_ilr_2   0.2804 -0.393900 0.9546
-SD.f_JCZ38_qlogis 0.9416 -0.152986 2.0362
-SD.log_alpha      0.4273  0.161044 0.6936
-
-Variance model:
-     est. lower upper
-a.1 3.406  3.17 3.642
-
-Backtransformed parameters:
-                      est.     lower     upper
-cyan_0           1.010e+02 9.831e+01 1.037e+02
-k_JCZ38          3.409e-02 8.475e-03 1.372e-01
-k_J9Z38          5.194e-03 2.734e-03 9.867e-03
-k_JSE76          2.149e-03 6.633e-04 6.963e-03
-f_cyan_to_JCZ38  6.481e-01        NA        NA
-f_cyan_to_J9Z38  2.264e-01        NA        NA
-f_JCZ38_to_JSE76 8.858e-01 6.283e-01 9.727e-01
-alpha            7.971e-01 5.390e-01 1.179e+00
-beta             1.815e+01 1.489e+01 2.212e+01
-
-Resulting formation fractions:
-                ff
-cyan_JCZ38  0.6481
-cyan_J9Z38  0.2264
-cyan_sink   0.1255
-JCZ38_JSE76 0.8858
-JCZ38_sink  0.1142
-
-Estimated disappearance times:
-        DT50    DT90 DT50back
-cyan   25.15  308.01    92.72
-JCZ38  20.33   67.54       NA
-J9Z38 133.46  443.35       NA
-JSE76 322.53 1071.42       NA
-
-</code></pre>
-<p></p>
-<caption>
-Hierarchical FOMC path 1 fit with two-component error
-</caption>
-<pre><code>
-saemix version used for fitting:      3.2 
-mkin version used for pre-fitting:  1.2.4 
-R version used for fitting:         4.2.3 
-Date of fit:     Thu Apr 20 20:33:59 2023 
-Date of summary: Thu Apr 20 21:04:34 2023 
-
-Equations:
-d_cyan/dt = - (alpha/beta) * 1/((time/beta) + 1) * cyan
-d_JCZ38/dt = + f_cyan_to_JCZ38 * (alpha/beta) * 1/((time/beta) + 1) *
-           cyan - k_JCZ38 * JCZ38
-d_J9Z38/dt = + f_cyan_to_J9Z38 * (alpha/beta) * 1/((time/beta) + 1) *
-           cyan - k_J9Z38 * J9Z38
-d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76
-
-Data:
-433 observations of 4 variable(s) grouped in 5 datasets
-
-Model predictions using solution type deSolve 
-
-Fitted in 491.071 s
-Using 300, 100 iterations and 10 chains
-
-Variance model: Two-component variance function 
-
-Starting values for degradation parameters:
-        cyan_0    log_k_JCZ38    log_k_J9Z38    log_k_JSE76   f_cyan_ilr_1 
-     101.13827       -3.32493       -5.08921       -5.93478        0.71330 
-  f_cyan_ilr_2 f_JCZ38_qlogis      log_alpha       log_beta 
-      10.05989       12.79248       -0.09621        3.10646 
-
-Fixed degradation parameter values:
-None
-
-Starting values for random effects (square root of initial entries in omega):
-               cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1
-cyan_0          5.643       0.000       0.000        0.00       0.0000
-log_k_JCZ38     0.000       2.319       0.000        0.00       0.0000
-log_k_J9Z38     0.000       0.000       1.731        0.00       0.0000
-log_k_JSE76     0.000       0.000       0.000        1.86       0.0000
-f_cyan_ilr_1    0.000       0.000       0.000        0.00       0.7186
-f_cyan_ilr_2    0.000       0.000       0.000        0.00       0.0000
-f_JCZ38_qlogis  0.000       0.000       0.000        0.00       0.0000
-log_alpha       0.000       0.000       0.000        0.00       0.0000
-log_beta        0.000       0.000       0.000        0.00       0.0000
-               f_cyan_ilr_2 f_JCZ38_qlogis log_alpha log_beta
-cyan_0                 0.00           0.00    0.0000   0.0000
-log_k_JCZ38            0.00           0.00    0.0000   0.0000
-log_k_J9Z38            0.00           0.00    0.0000   0.0000
-log_k_JSE76            0.00           0.00    0.0000   0.0000
-f_cyan_ilr_1           0.00           0.00    0.0000   0.0000
-f_cyan_ilr_2          12.49           0.00    0.0000   0.0000
-f_JCZ38_qlogis         0.00          20.19    0.0000   0.0000
-log_alpha              0.00           0.00    0.3142   0.0000
-log_beta               0.00           0.00    0.0000   0.7331
-
-Starting values for error model parameters:
-a.1 b.1 
-  1   1 
-
-Results:
-
-Likelihood computed by importance sampling
-   AIC  BIC logLik
-  2423 2416  -1193
-
-Optimised parameters:
-                       est. lower upper
-cyan_0            100.57649    NA    NA
-log_k_JCZ38        -3.46250    NA    NA
-log_k_J9Z38        -5.24442    NA    NA
-log_k_JSE76        -5.75229    NA    NA
-f_cyan_ilr_1        0.68480    NA    NA
-f_cyan_ilr_2        0.61670    NA    NA
-f_JCZ38_qlogis     87.97407    NA    NA
-log_alpha          -0.15699    NA    NA
-log_beta            3.01540    NA    NA
-a.1                 3.11518    NA    NA
-b.1                 0.04445    NA    NA
-SD.log_k_JCZ38      1.40732    NA    NA
-SD.log_k_J9Z38      0.56510    NA    NA
-SD.log_k_JSE76      0.72067    NA    NA
-SD.f_cyan_ilr_1     0.31199    NA    NA
-SD.f_cyan_ilr_2     0.36894    NA    NA
-SD.f_JCZ38_qlogis   6.92892    NA    NA
-SD.log_alpha        0.25662    NA    NA
-SD.log_beta         0.35845    NA    NA
-
-Correlation is not available
-
-Random effects:
-                    est. lower upper
-SD.log_k_JCZ38    1.4073    NA    NA
-SD.log_k_J9Z38    0.5651    NA    NA
-SD.log_k_JSE76    0.7207    NA    NA
-SD.f_cyan_ilr_1   0.3120    NA    NA
-SD.f_cyan_ilr_2   0.3689    NA    NA
-SD.f_JCZ38_qlogis 6.9289    NA    NA
-SD.log_alpha      0.2566    NA    NA
-SD.log_beta       0.3585    NA    NA
-
-Variance model:
-       est. lower upper
-a.1 3.11518    NA    NA
-b.1 0.04445    NA    NA
-
-Backtransformed parameters:
-                      est. lower upper
-cyan_0           1.006e+02    NA    NA
-k_JCZ38          3.135e-02    NA    NA
-k_J9Z38          5.277e-03    NA    NA
-k_JSE76          3.175e-03    NA    NA
-f_cyan_to_JCZ38  5.991e-01    NA    NA
-f_cyan_to_J9Z38  2.275e-01    NA    NA
-f_JCZ38_to_JSE76 1.000e+00    NA    NA
-alpha            8.547e-01    NA    NA
-beta             2.040e+01    NA    NA
-
-Resulting formation fractions:
-                ff
-cyan_JCZ38  0.5991
-cyan_J9Z38  0.2275
-cyan_sink   0.1734
-JCZ38_JSE76 1.0000
-JCZ38_sink  0.0000
-
-Estimated disappearance times:
-        DT50   DT90 DT50back
-cyan   25.50 281.29    84.68
-JCZ38  22.11  73.44       NA
-J9Z38 131.36 436.35       NA
-JSE76 218.28 725.11       NA
-
-</code></pre>
-<p></p>
-<caption>
-Hierarchical DFOP path 1 fit with constant variance
-</caption>
-<pre><code>
-saemix version used for fitting:      3.2 
-mkin version used for pre-fitting:  1.2.4 
-R version used for fitting:         4.2.3 
-Date of fit:     Thu Apr 20 20:34:33 2023 
-Date of summary: Thu Apr 20 21:04:34 2023 
-
-Equations:
-d_cyan/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
-           time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))
-           * cyan
-d_JCZ38/dt = + f_cyan_to_JCZ38 * ((k1 * g * exp(-k1 * time) + k2 * (1 -
-           g) * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *
-           exp(-k2 * time))) * cyan - k_JCZ38 * JCZ38
-d_J9Z38/dt = + f_cyan_to_J9Z38 * ((k1 * g * exp(-k1 * time) + k2 * (1 -
-           g) * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *
-           exp(-k2 * time))) * cyan - k_J9Z38 * J9Z38
-d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76
-
-Data:
-433 observations of 4 variable(s) grouped in 5 datasets
-
-Model predictions using solution type deSolve 
-
-Fitted in 525.551 s
-Using 300, 100 iterations and 10 chains
-
-Variance model: Constant variance 
-
-Starting values for degradation parameters:
-        cyan_0    log_k_JCZ38    log_k_J9Z38    log_k_JSE76   f_cyan_ilr_1 
-      102.0644        -3.4008        -5.0024        -5.8613         0.6855 
-  f_cyan_ilr_2 f_JCZ38_qlogis         log_k1         log_k2       g_qlogis 
-        1.2365        13.7245        -1.8641        -4.5063        -0.6468 
-
-Fixed degradation parameter values:
-None
-
-Starting values for random effects (square root of initial entries in omega):
-               cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1
-cyan_0          4.466       0.000       0.000       0.000       0.0000
-log_k_JCZ38     0.000       2.382       0.000       0.000       0.0000
-log_k_J9Z38     0.000       0.000       1.595       0.000       0.0000
-log_k_JSE76     0.000       0.000       0.000       1.245       0.0000
-f_cyan_ilr_1    0.000       0.000       0.000       0.000       0.6852
-f_cyan_ilr_2    0.000       0.000       0.000       0.000       0.0000
-f_JCZ38_qlogis  0.000       0.000       0.000       0.000       0.0000
-log_k1          0.000       0.000       0.000       0.000       0.0000
-log_k2          0.000       0.000       0.000       0.000       0.0000
-g_qlogis        0.000       0.000       0.000       0.000       0.0000
-               f_cyan_ilr_2 f_JCZ38_qlogis log_k1 log_k2 g_qlogis
-cyan_0                 0.00           0.00 0.0000 0.0000    0.000
-log_k_JCZ38            0.00           0.00 0.0000 0.0000    0.000
-log_k_J9Z38            0.00           0.00 0.0000 0.0000    0.000
-log_k_JSE76            0.00           0.00 0.0000 0.0000    0.000
-f_cyan_ilr_1           0.00           0.00 0.0000 0.0000    0.000
-f_cyan_ilr_2           1.28           0.00 0.0000 0.0000    0.000
-f_JCZ38_qlogis         0.00          16.11 0.0000 0.0000    0.000
-log_k1                 0.00           0.00 0.9866 0.0000    0.000
-log_k2                 0.00           0.00 0.0000 0.5953    0.000
-g_qlogis               0.00           0.00 0.0000 0.0000    1.583
-
-Starting values for error model parameters:
-a.1 
-  1 
-
-Results:
-
-Likelihood computed by importance sampling
-   AIC  BIC logLik
-  2403 2395  -1182
-
-Optimised parameters:
-                      est. lower upper
-cyan_0            102.6079    NA    NA
-log_k_JCZ38        -3.4855    NA    NA
-log_k_J9Z38        -5.1686    NA    NA
-log_k_JSE76        -5.6697    NA    NA
-f_cyan_ilr_1        0.6714    NA    NA
-f_cyan_ilr_2        0.4986    NA    NA
-f_JCZ38_qlogis     55.4760    NA    NA
-log_k1             -1.8409    NA    NA
-log_k2             -4.4915    NA    NA
-g_qlogis           -0.6403    NA    NA
-a.1                 3.2387    NA    NA
-SD.log_k_JCZ38      1.4524    NA    NA
-SD.log_k_J9Z38      0.5151    NA    NA
-SD.log_k_JSE76      0.6514    NA    NA
-SD.f_cyan_ilr_1     0.3023    NA    NA
-SD.f_cyan_ilr_2     0.2959    NA    NA
-SD.f_JCZ38_qlogis   1.9984    NA    NA
-SD.log_k1           0.5188    NA    NA
-SD.log_k2           0.3894    NA    NA
-SD.g_qlogis         0.8579    NA    NA
-
-Correlation is not available
-
-Random effects:
-                    est. lower upper
-SD.log_k_JCZ38    1.4524    NA    NA
-SD.log_k_J9Z38    0.5151    NA    NA
-SD.log_k_JSE76    0.6514    NA    NA
-SD.f_cyan_ilr_1   0.3023    NA    NA
-SD.f_cyan_ilr_2   0.2959    NA    NA
-SD.f_JCZ38_qlogis 1.9984    NA    NA
-SD.log_k1         0.5188    NA    NA
-SD.log_k2         0.3894    NA    NA
-SD.g_qlogis       0.8579    NA    NA
-
-Variance model:
-     est. lower upper
-a.1 3.239    NA    NA
-
-Backtransformed parameters:
-                      est. lower upper
-cyan_0           1.026e+02    NA    NA
-k_JCZ38          3.064e-02    NA    NA
-k_J9Z38          5.692e-03    NA    NA
-k_JSE76          3.449e-03    NA    NA
-f_cyan_to_JCZ38  5.798e-01    NA    NA
-f_cyan_to_J9Z38  2.243e-01    NA    NA
-f_JCZ38_to_JSE76 1.000e+00    NA    NA
-k1               1.587e-01    NA    NA
-k2               1.120e-02    NA    NA
-g                3.452e-01    NA    NA
-
-Resulting formation fractions:
-                ff
-cyan_JCZ38  0.5798
-cyan_J9Z38  0.2243
-cyan_sink   0.1958
-JCZ38_JSE76 1.0000
-JCZ38_sink  0.0000
-
-Estimated disappearance times:
-        DT50   DT90 DT50back DT50_k1 DT50_k2
-cyan   25.21 167.73    50.49   4.368   61.87
-JCZ38  22.62  75.15       NA      NA      NA
-J9Z38 121.77 404.50       NA      NA      NA
-JSE76 200.98 667.64       NA      NA      NA
-
-</code></pre>
-<p></p>
-<caption>
-Hierarchical DFOP path 1 fit with two-component error
-</caption>
-<pre><code>
-saemix version used for fitting:      3.2 
-mkin version used for pre-fitting:  1.2.4 
-R version used for fitting:         4.2.3 
-Date of fit:     Thu Apr 20 20:37:03 2023 
-Date of summary: Thu Apr 20 21:04:34 2023 
-
-Equations:
-d_cyan/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
-           time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))
-           * cyan
-d_JCZ38/dt = + f_cyan_to_JCZ38 * ((k1 * g * exp(-k1 * time) + k2 * (1 -
-           g) * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *
-           exp(-k2 * time))) * cyan - k_JCZ38 * JCZ38
-d_J9Z38/dt = + f_cyan_to_J9Z38 * ((k1 * g * exp(-k1 * time) + k2 * (1 -
-           g) * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *
-           exp(-k2 * time))) * cyan - k_J9Z38 * J9Z38
-d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76
-
-Data:
-433 observations of 4 variable(s) grouped in 5 datasets
-
-Model predictions using solution type deSolve 
-
-Fitted in 675.804 s
-Using 300, 100 iterations and 10 chains
-
-Variance model: Two-component variance function 
-
-Starting values for degradation parameters:
-        cyan_0    log_k_JCZ38    log_k_J9Z38    log_k_JSE76   f_cyan_ilr_1 
-      101.3964        -3.3626        -4.9792        -5.8727         0.6814 
-  f_cyan_ilr_2 f_JCZ38_qlogis         log_k1         log_k2       g_qlogis 
-        6.7799        13.7245        -1.9222        -4.5035        -0.7172 
-
-Fixed degradation parameter values:
-None
-
-Starting values for random effects (square root of initial entries in omega):
-               cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1
-cyan_0          5.317       0.000       0.000       0.000       0.0000
-log_k_JCZ38     0.000       2.272       0.000       0.000       0.0000
-log_k_J9Z38     0.000       0.000       1.633       0.000       0.0000
-log_k_JSE76     0.000       0.000       0.000       1.271       0.0000
-f_cyan_ilr_1    0.000       0.000       0.000       0.000       0.6838
-f_cyan_ilr_2    0.000       0.000       0.000       0.000       0.0000
-f_JCZ38_qlogis  0.000       0.000       0.000       0.000       0.0000
-log_k1          0.000       0.000       0.000       0.000       0.0000
-log_k2          0.000       0.000       0.000       0.000       0.0000
-g_qlogis        0.000       0.000       0.000       0.000       0.0000
-               f_cyan_ilr_2 f_JCZ38_qlogis log_k1 log_k2 g_qlogis
-cyan_0                 0.00           0.00 0.0000 0.0000    0.000
-log_k_JCZ38            0.00           0.00 0.0000 0.0000    0.000
-log_k_J9Z38            0.00           0.00 0.0000 0.0000    0.000
-log_k_JSE76            0.00           0.00 0.0000 0.0000    0.000
-f_cyan_ilr_1           0.00           0.00 0.0000 0.0000    0.000
-f_cyan_ilr_2          11.77           0.00 0.0000 0.0000    0.000
-f_JCZ38_qlogis         0.00          16.11 0.0000 0.0000    0.000
-log_k1                 0.00           0.00 0.9496 0.0000    0.000
-log_k2                 0.00           0.00 0.0000 0.5846    0.000
-g_qlogis               0.00           0.00 0.0000 0.0000    1.719
-
-Starting values for error model parameters:
-a.1 b.1 
-  1   1 
-
-Results:
-
-Likelihood computed by importance sampling
-   AIC  BIC logLik
-  2398 2390  -1179
-
-Optimised parameters:
-                      est. lower upper
-cyan_0            100.8076    NA    NA
-log_k_JCZ38        -3.4684    NA    NA
-log_k_J9Z38        -5.0844    NA    NA
-log_k_JSE76        -5.5743    NA    NA
-f_cyan_ilr_1        0.6669    NA    NA
-f_cyan_ilr_2        0.7912    NA    NA
-f_JCZ38_qlogis     84.1825    NA    NA
-log_k1             -2.1671    NA    NA
-log_k2             -4.5447    NA    NA
-g_qlogis           -0.5631    NA    NA
-a.1                 2.9627    NA    NA
-b.1                 0.0444    NA    NA
-SD.log_k_JCZ38      1.4044    NA    NA
-SD.log_k_J9Z38      0.6410    NA    NA
-SD.log_k_JSE76      0.5391    NA    NA
-SD.f_cyan_ilr_1     0.3203    NA    NA
-SD.f_cyan_ilr_2     0.5038    NA    NA
-SD.f_JCZ38_qlogis   3.5865    NA    NA
-SD.log_k2           0.3119    NA    NA
-SD.g_qlogis         0.8276    NA    NA
-
-Correlation is not available
-
-Random effects:
-                    est. lower upper
-SD.log_k_JCZ38    1.4044    NA    NA
-SD.log_k_J9Z38    0.6410    NA    NA
-SD.log_k_JSE76    0.5391    NA    NA
-SD.f_cyan_ilr_1   0.3203    NA    NA
-SD.f_cyan_ilr_2   0.5038    NA    NA
-SD.f_JCZ38_qlogis 3.5865    NA    NA
-SD.log_k2         0.3119    NA    NA
-SD.g_qlogis       0.8276    NA    NA
-
-Variance model:
-      est. lower upper
-a.1 2.9627    NA    NA
-b.1 0.0444    NA    NA
-
-Backtransformed parameters:
-                      est. lower upper
-cyan_0           1.008e+02    NA    NA
-k_JCZ38          3.117e-02    NA    NA
-k_J9Z38          6.193e-03    NA    NA
-k_JSE76          3.794e-03    NA    NA
-f_cyan_to_JCZ38  6.149e-01    NA    NA
-f_cyan_to_J9Z38  2.395e-01    NA    NA
-f_JCZ38_to_JSE76 1.000e+00    NA    NA
-k1               1.145e-01    NA    NA
-k2               1.062e-02    NA    NA
-g                3.628e-01    NA    NA
-
-Resulting formation fractions:
-                ff
-cyan_JCZ38  0.6149
-cyan_J9Z38  0.2395
-cyan_sink   0.1456
-JCZ38_JSE76 1.0000
-JCZ38_sink  0.0000
-
-Estimated disappearance times:
-        DT50   DT90 DT50back DT50_k1 DT50_k2
-cyan   26.26 174.32    52.47   6.053   65.25
-JCZ38  22.24  73.88       NA      NA      NA
-J9Z38 111.93 371.82       NA      NA      NA
-JSE76 182.69 606.88       NA      NA      NA
-
-</code></pre>
-<p></p>
-<caption>
-Hierarchical SFORB path 1 fit with constant variance
-</caption>
-<pre><code>
-saemix version used for fitting:      3.2 
-mkin version used for pre-fitting:  1.2.4 
-R version used for fitting:         4.2.3 
-Date of fit:     Thu Apr 20 20:34:43 2023 
-Date of summary: Thu Apr 20 21:04:34 2023 
-
-Equations:
-d_cyan_free/dt = - k_cyan_free * cyan_free - k_cyan_free_bound *
-           cyan_free + k_cyan_bound_free * cyan_bound
-d_cyan_bound/dt = + k_cyan_free_bound * cyan_free - k_cyan_bound_free *
-           cyan_bound
-d_JCZ38/dt = + f_cyan_free_to_JCZ38 * k_cyan_free * cyan_free - k_JCZ38
-           * JCZ38
-d_J9Z38/dt = + f_cyan_free_to_J9Z38 * k_cyan_free * cyan_free - k_J9Z38
-           * J9Z38
-d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76
-
-Data:
-433 observations of 4 variable(s) grouped in 5 datasets
-
-Model predictions using solution type deSolve 
-
-Fitted in 535.818 s
-Using 300, 100 iterations and 10 chains
-
-Variance model: Constant variance 
-
-Starting values for degradation parameters:
-          cyan_free_0       log_k_cyan_free log_k_cyan_free_bound 
-             102.0643               -2.8987               -2.7077 
-log_k_cyan_bound_free           log_k_JCZ38           log_k_J9Z38 
-              -3.4717               -3.4008               -5.0024 
-          log_k_JSE76          f_cyan_ilr_1          f_cyan_ilr_2 
-              -5.8613                0.6855                1.2366 
-       f_JCZ38_qlogis 
-              13.7418 
-
-Fixed degradation parameter values:
-None
-
-Starting values for random effects (square root of initial entries in omega):
-                      cyan_free_0 log_k_cyan_free log_k_cyan_free_bound
-cyan_free_0                 4.466          0.0000                 0.000
-log_k_cyan_free             0.000          0.6158                 0.000
-log_k_cyan_free_bound       0.000          0.0000                 1.463
-log_k_cyan_bound_free       0.000          0.0000                 0.000
-log_k_JCZ38                 0.000          0.0000                 0.000
-log_k_J9Z38                 0.000          0.0000                 0.000
-log_k_JSE76                 0.000          0.0000                 0.000
-f_cyan_ilr_1                0.000          0.0000                 0.000
-f_cyan_ilr_2                0.000          0.0000                 0.000
-f_JCZ38_qlogis              0.000          0.0000                 0.000
-                      log_k_cyan_bound_free log_k_JCZ38 log_k_J9Z38 log_k_JSE76
-cyan_free_0                           0.000       0.000       0.000       0.000
-log_k_cyan_free                       0.000       0.000       0.000       0.000
-log_k_cyan_free_bound                 0.000       0.000       0.000       0.000
-log_k_cyan_bound_free                 1.058       0.000       0.000       0.000
-log_k_JCZ38                           0.000       2.382       0.000       0.000
-log_k_J9Z38                           0.000       0.000       1.595       0.000
-log_k_JSE76                           0.000       0.000       0.000       1.245
-f_cyan_ilr_1                          0.000       0.000       0.000       0.000
-f_cyan_ilr_2                          0.000       0.000       0.000       0.000
-f_JCZ38_qlogis                        0.000       0.000       0.000       0.000
-                      f_cyan_ilr_1 f_cyan_ilr_2 f_JCZ38_qlogis
-cyan_free_0                 0.0000         0.00           0.00
-log_k_cyan_free             0.0000         0.00           0.00
-log_k_cyan_free_bound       0.0000         0.00           0.00
-log_k_cyan_bound_free       0.0000         0.00           0.00
-log_k_JCZ38                 0.0000         0.00           0.00
-log_k_J9Z38                 0.0000         0.00           0.00
-log_k_JSE76                 0.0000         0.00           0.00
-f_cyan_ilr_1                0.6852         0.00           0.00
-f_cyan_ilr_2                0.0000         1.28           0.00
-f_JCZ38_qlogis              0.0000         0.00          16.14
-
-Starting values for error model parameters:
-a.1 
-  1 
-
-Results:
-
-Likelihood computed by importance sampling
-   AIC  BIC logLik
-  2401 2394  -1181
-
-Optimised parameters:
-                             est. lower upper
-cyan_free_0              102.7803    NA    NA
-log_k_cyan_free           -2.8068    NA    NA
-log_k_cyan_free_bound     -2.5714    NA    NA
-log_k_cyan_bound_free     -3.4426    NA    NA
-log_k_JCZ38               -3.4994    NA    NA
-log_k_J9Z38               -5.1148    NA    NA
-log_k_JSE76               -5.6335    NA    NA
-f_cyan_ilr_1               0.6597    NA    NA
-f_cyan_ilr_2               0.5132    NA    NA
-f_JCZ38_qlogis            37.2090    NA    NA
-a.1                        3.2367    NA    NA
-SD.log_k_cyan_free         0.3161    NA    NA
-SD.log_k_cyan_free_bound   0.8103    NA    NA
-SD.log_k_cyan_bound_free   0.5554    NA    NA
-SD.log_k_JCZ38             1.4858    NA    NA
-SD.log_k_J9Z38             0.5859    NA    NA
-SD.log_k_JSE76             0.6195    NA    NA
-SD.f_cyan_ilr_1            0.3118    NA    NA
-SD.f_cyan_ilr_2            0.3344    NA    NA
-SD.f_JCZ38_qlogis          0.5518    NA    NA
-
-Correlation is not available
-
-Random effects:
-                           est. lower upper
-SD.log_k_cyan_free       0.3161    NA    NA
-SD.log_k_cyan_free_bound 0.8103    NA    NA
-SD.log_k_cyan_bound_free 0.5554    NA    NA
-SD.log_k_JCZ38           1.4858    NA    NA
-SD.log_k_J9Z38           0.5859    NA    NA
-SD.log_k_JSE76           0.6195    NA    NA
-SD.f_cyan_ilr_1          0.3118    NA    NA
-SD.f_cyan_ilr_2          0.3344    NA    NA
-SD.f_JCZ38_qlogis        0.5518    NA    NA
-
-Variance model:
-     est. lower upper
-a.1 3.237    NA    NA
-
-Backtransformed parameters:
-                          est. lower upper
-cyan_free_0          1.028e+02    NA    NA
-k_cyan_free          6.040e-02    NA    NA
-k_cyan_free_bound    7.643e-02    NA    NA
-k_cyan_bound_free    3.198e-02    NA    NA
-k_JCZ38              3.022e-02    NA    NA
-k_J9Z38              6.007e-03    NA    NA
-k_JSE76              3.576e-03    NA    NA
-f_cyan_free_to_JCZ38 5.787e-01    NA    NA
-f_cyan_free_to_J9Z38 2.277e-01    NA    NA
-f_JCZ38_to_JSE76     1.000e+00    NA    NA
-
-Estimated Eigenvalues of SFORB model(s):
-cyan_b1 cyan_b2  cyan_g 
-0.15646 0.01235 0.33341 
-
-Resulting formation fractions:
-                    ff
-cyan_free_JCZ38 0.5787
-cyan_free_J9Z38 0.2277
-cyan_free_sink  0.1936
-cyan_free       1.0000
-JCZ38_JSE76     1.0000
-JCZ38_sink      0.0000
-
-Estimated disappearance times:
-        DT50  DT90 DT50back DT50_cyan_b1 DT50_cyan_b2
-cyan   24.48 153.7    46.26         4.43        56.15
-JCZ38  22.94  76.2       NA           NA           NA
-J9Z38 115.39 383.3       NA           NA           NA
-JSE76 193.84 643.9       NA           NA           NA
-
-</code></pre>
-<p></p>
-<caption>
-Hierarchical SFORB path 1 fit with two-component error
-</caption>
-<pre><code>
-saemix version used for fitting:      3.2 
-mkin version used for pre-fitting:  1.2.4 
-R version used for fitting:         4.2.3 
-Date of fit:     Thu Apr 20 20:37:02 2023 
-Date of summary: Thu Apr 20 21:04:34 2023 
-
-Equations:
-d_cyan_free/dt = - k_cyan_free * cyan_free - k_cyan_free_bound *
-           cyan_free + k_cyan_bound_free * cyan_bound
-d_cyan_bound/dt = + k_cyan_free_bound * cyan_free - k_cyan_bound_free *
-           cyan_bound
-d_JCZ38/dt = + f_cyan_free_to_JCZ38 * k_cyan_free * cyan_free - k_JCZ38
-           * JCZ38
-d_J9Z38/dt = + f_cyan_free_to_J9Z38 * k_cyan_free * cyan_free - k_J9Z38
-           * J9Z38
-d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76
-
-Data:
-433 observations of 4 variable(s) grouped in 5 datasets
-
-Model predictions using solution type deSolve 
-
-Fitted in 674.859 s
-Using 300, 100 iterations and 10 chains
-
-Variance model: Two-component variance function 
-
-Starting values for degradation parameters:
-          cyan_free_0       log_k_cyan_free log_k_cyan_free_bound 
-             101.3964               -2.9881               -2.7949 
-log_k_cyan_bound_free           log_k_JCZ38           log_k_J9Z38 
-              -3.4376               -3.3626               -4.9792 
-          log_k_JSE76          f_cyan_ilr_1          f_cyan_ilr_2 
-              -5.8727                0.6814                6.8139 
-       f_JCZ38_qlogis 
-              13.7419 
-
-Fixed degradation parameter values:
-None
-
-Starting values for random effects (square root of initial entries in omega):
-                      cyan_free_0 log_k_cyan_free log_k_cyan_free_bound
-cyan_free_0                 5.317          0.0000                 0.000
-log_k_cyan_free             0.000          0.7301                 0.000
-log_k_cyan_free_bound       0.000          0.0000                 1.384
-log_k_cyan_bound_free       0.000          0.0000                 0.000
-log_k_JCZ38                 0.000          0.0000                 0.000
-log_k_J9Z38                 0.000          0.0000                 0.000
-log_k_JSE76                 0.000          0.0000                 0.000
-f_cyan_ilr_1                0.000          0.0000                 0.000
-f_cyan_ilr_2                0.000          0.0000                 0.000
-f_JCZ38_qlogis              0.000          0.0000                 0.000
-                      log_k_cyan_bound_free log_k_JCZ38 log_k_J9Z38 log_k_JSE76
-cyan_free_0                           0.000       0.000       0.000       0.000
-log_k_cyan_free                       0.000       0.000       0.000       0.000
-log_k_cyan_free_bound                 0.000       0.000       0.000       0.000
-log_k_cyan_bound_free                 1.109       0.000       0.000       0.000
-log_k_JCZ38                           0.000       2.272       0.000       0.000
-log_k_J9Z38                           0.000       0.000       1.633       0.000
-log_k_JSE76                           0.000       0.000       0.000       1.271
-f_cyan_ilr_1                          0.000       0.000       0.000       0.000
-f_cyan_ilr_2                          0.000       0.000       0.000       0.000
-f_JCZ38_qlogis                        0.000       0.000       0.000       0.000
-                      f_cyan_ilr_1 f_cyan_ilr_2 f_JCZ38_qlogis
-cyan_free_0                 0.0000         0.00           0.00
-log_k_cyan_free             0.0000         0.00           0.00
-log_k_cyan_free_bound       0.0000         0.00           0.00
-log_k_cyan_bound_free       0.0000         0.00           0.00
-log_k_JCZ38                 0.0000         0.00           0.00
-log_k_J9Z38                 0.0000         0.00           0.00
-log_k_JSE76                 0.0000         0.00           0.00
-f_cyan_ilr_1                0.6838         0.00           0.00
-f_cyan_ilr_2                0.0000        11.84           0.00
-f_JCZ38_qlogis              0.0000         0.00          16.14
-
-Starting values for error model parameters:
-a.1 b.1 
-  1   1 
-
-Results:
-
-Likelihood computed by importance sampling
-   AIC  BIC logLik
-  2400 2392  -1180
-
-Optimised parameters:
-                              est. lower upper
-cyan_free_0              100.69983    NA    NA
-log_k_cyan_free           -3.11584    NA    NA
-log_k_cyan_free_bound     -3.15216    NA    NA
-log_k_cyan_bound_free     -3.65986    NA    NA
-log_k_JCZ38               -3.47811    NA    NA
-log_k_J9Z38               -5.08835    NA    NA
-log_k_JSE76               -5.55514    NA    NA
-f_cyan_ilr_1               0.66764    NA    NA
-f_cyan_ilr_2               0.78329    NA    NA
-f_JCZ38_qlogis            25.35245    NA    NA
-a.1                        2.99088    NA    NA
-b.1                        0.04346    NA    NA
-SD.log_k_cyan_free         0.48797    NA    NA
-SD.log_k_cyan_bound_free   0.27243    NA    NA
-SD.log_k_JCZ38             1.42450    NA    NA
-SD.log_k_J9Z38             0.63496    NA    NA
-SD.log_k_JSE76             0.55951    NA    NA
-SD.f_cyan_ilr_1            0.32687    NA    NA
-SD.f_cyan_ilr_2            0.48056    NA    NA
-SD.f_JCZ38_qlogis          0.43818    NA    NA
-
-Correlation is not available
-
-Random effects:
-                           est. lower upper
-SD.log_k_cyan_free       0.4880    NA    NA
-SD.log_k_cyan_bound_free 0.2724    NA    NA
-SD.log_k_JCZ38           1.4245    NA    NA
-SD.log_k_J9Z38           0.6350    NA    NA
-SD.log_k_JSE76           0.5595    NA    NA
-SD.f_cyan_ilr_1          0.3269    NA    NA
-SD.f_cyan_ilr_2          0.4806    NA    NA
-SD.f_JCZ38_qlogis        0.4382    NA    NA
-
-Variance model:
-       est. lower upper
-a.1 2.99088    NA    NA
-b.1 0.04346    NA    NA
-
-Backtransformed parameters:
-                          est. lower upper
-cyan_free_0          1.007e+02    NA    NA
-k_cyan_free          4.434e-02    NA    NA
-k_cyan_free_bound    4.276e-02    NA    NA
-k_cyan_bound_free    2.574e-02    NA    NA
-k_JCZ38              3.087e-02    NA    NA
-k_J9Z38              6.168e-03    NA    NA
-k_JSE76              3.868e-03    NA    NA
-f_cyan_free_to_JCZ38 6.143e-01    NA    NA
-f_cyan_free_to_J9Z38 2.389e-01    NA    NA
-f_JCZ38_to_JSE76     1.000e+00    NA    NA
-
-Estimated Eigenvalues of SFORB model(s):
-cyan_b1 cyan_b2  cyan_g 
-0.10161 0.01123 0.36636 
-
-Resulting formation fractions:
-                       ff
-cyan_free_JCZ38 6.143e-01
-cyan_free_J9Z38 2.389e-01
-cyan_free_sink  1.468e-01
-cyan_free       1.000e+00
-JCZ38_JSE76     1.000e+00
-JCZ38_sink      9.763e-12
-
-Estimated disappearance times:
-        DT50  DT90 DT50back DT50_cyan_b1 DT50_cyan_b2
-cyan   25.91 164.4    49.49        6.822        61.72
-JCZ38  22.46  74.6       NA           NA           NA
-J9Z38 112.37 373.3       NA           NA           NA
-JSE76 179.22 595.4       NA           NA           NA
-
-</code></pre>
-<p></p>
-<caption>
-Hierarchical HS path 1 fit with constant variance
-</caption>
-<pre><code>
-saemix version used for fitting:      3.2 
-mkin version used for pre-fitting:  1.2.4 
-R version used for fitting:         4.2.3 
-Date of fit:     Thu Apr 20 20:34:41 2023 
-Date of summary: Thu Apr 20 21:04:34 2023 
-
-Equations:
-d_cyan/dt = - ifelse(time &lt;= tb, k1, k2) * cyan
-d_JCZ38/dt = + f_cyan_to_JCZ38 * ifelse(time &lt;= tb, k1, k2) * cyan -
-           k_JCZ38 * JCZ38
-d_J9Z38/dt = + f_cyan_to_J9Z38 * ifelse(time &lt;= tb, k1, k2) * cyan -
-           k_J9Z38 * J9Z38
-d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76
-
-Data:
-433 observations of 4 variable(s) grouped in 5 datasets
-
-Model predictions using solution type deSolve 
-
-Fitted in 533.787 s
-Using 300, 100 iterations and 10 chains
-
-Variance model: Constant variance 
-
-Starting values for degradation parameters:
-        cyan_0    log_k_JCZ38    log_k_J9Z38    log_k_JSE76   f_cyan_ilr_1 
-      102.8738        -3.4490        -4.9348        -5.5989         0.6469 
-  f_cyan_ilr_2 f_JCZ38_qlogis         log_k1         log_k2         log_tb 
-        1.2854         9.7193        -2.9084        -4.1810         1.7813 
-
-Fixed degradation parameter values:
-None
-
-Starting values for random effects (square root of initial entries in omega):
-               cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1
-cyan_0          5.409        0.00        0.00       0.000       0.0000
-log_k_JCZ38     0.000        2.33        0.00       0.000       0.0000
-log_k_J9Z38     0.000        0.00        1.59       0.000       0.0000
-log_k_JSE76     0.000        0.00        0.00       1.006       0.0000
-f_cyan_ilr_1    0.000        0.00        0.00       0.000       0.6371
-f_cyan_ilr_2    0.000        0.00        0.00       0.000       0.0000
-f_JCZ38_qlogis  0.000        0.00        0.00       0.000       0.0000
-log_k1          0.000        0.00        0.00       0.000       0.0000
-log_k2          0.000        0.00        0.00       0.000       0.0000
-log_tb          0.000        0.00        0.00       0.000       0.0000
-               f_cyan_ilr_2 f_JCZ38_qlogis log_k1 log_k2 log_tb
-cyan_0                0.000           0.00 0.0000 0.0000 0.0000
-log_k_JCZ38           0.000           0.00 0.0000 0.0000 0.0000
-log_k_J9Z38           0.000           0.00 0.0000 0.0000 0.0000
-log_k_JSE76           0.000           0.00 0.0000 0.0000 0.0000
-f_cyan_ilr_1          0.000           0.00 0.0000 0.0000 0.0000
-f_cyan_ilr_2          2.167           0.00 0.0000 0.0000 0.0000
-f_JCZ38_qlogis        0.000          10.22 0.0000 0.0000 0.0000
-log_k1                0.000           0.00 0.7003 0.0000 0.0000
-log_k2                0.000           0.00 0.0000 0.8928 0.0000
-log_tb                0.000           0.00 0.0000 0.0000 0.6774
-
-Starting values for error model parameters:
-a.1 
-  1 
-
-Results:
-
-Likelihood computed by importance sampling
-   AIC  BIC logLik
-  2427 2420  -1194
-
-Optimised parameters:
-                       est. lower upper
-cyan_0            101.84849    NA    NA
-log_k_JCZ38        -3.47365    NA    NA
-log_k_J9Z38        -5.10562    NA    NA
-log_k_JSE76        -5.60318    NA    NA
-f_cyan_ilr_1        0.66127    NA    NA
-f_cyan_ilr_2        0.60283    NA    NA
-f_JCZ38_qlogis     45.06408    NA    NA
-log_k1             -3.10124    NA    NA
-log_k2             -4.39028    NA    NA
-log_tb              2.32256    NA    NA
-a.1                 3.32683    NA    NA
-SD.log_k_JCZ38      1.41427    NA    NA
-SD.log_k_J9Z38      0.54767    NA    NA
-SD.log_k_JSE76      0.62147    NA    NA
-SD.f_cyan_ilr_1     0.30189    NA    NA
-SD.f_cyan_ilr_2     0.34960    NA    NA
-SD.f_JCZ38_qlogis   0.04644    NA    NA
-SD.log_k1           0.39534    NA    NA
-SD.log_k2           0.43468    NA    NA
-SD.log_tb           0.60781    NA    NA
-
-Correlation is not available
-
-Random effects:
-                     est. lower upper
-SD.log_k_JCZ38    1.41427    NA    NA
-SD.log_k_J9Z38    0.54767    NA    NA
-SD.log_k_JSE76    0.62147    NA    NA
-SD.f_cyan_ilr_1   0.30189    NA    NA
-SD.f_cyan_ilr_2   0.34960    NA    NA
-SD.f_JCZ38_qlogis 0.04644    NA    NA
-SD.log_k1         0.39534    NA    NA
-SD.log_k2         0.43468    NA    NA
-SD.log_tb         0.60781    NA    NA
-
-Variance model:
-     est. lower upper
-a.1 3.327    NA    NA
-
-Backtransformed parameters:
-                      est. lower upper
-cyan_0           1.018e+02    NA    NA
-k_JCZ38          3.100e-02    NA    NA
-k_J9Z38          6.063e-03    NA    NA
-k_JSE76          3.686e-03    NA    NA
-f_cyan_to_JCZ38  5.910e-01    NA    NA
-f_cyan_to_J9Z38  2.320e-01    NA    NA
-f_JCZ38_to_JSE76 1.000e+00    NA    NA
-k1               4.499e-02    NA    NA
-k2               1.240e-02    NA    NA
-tb               1.020e+01    NA    NA
-
-Resulting formation fractions:
-               ff
-cyan_JCZ38  0.591
-cyan_J9Z38  0.232
-cyan_sink   0.177
-JCZ38_JSE76 1.000
-JCZ38_sink  0.000
-
-Estimated disappearance times:
-        DT50   DT90 DT50back DT50_k1 DT50_k2
-cyan   29.09 158.91    47.84   15.41   55.91
-JCZ38  22.36  74.27       NA      NA      NA
-J9Z38 114.33 379.80       NA      NA      NA
-JSE76 188.04 624.66       NA      NA      NA
-
-</code></pre>
-<p></p>
-<caption>
-Hierarchical HS path 1 fit with two-component error
-</caption>
-<pre><code>
-saemix version used for fitting:      3.2 
-mkin version used for pre-fitting:  1.2.4 
-R version used for fitting:         4.2.3 
-Date of fit:     Thu Apr 20 20:34:39 2023 
-Date of summary: Thu Apr 20 21:04:34 2023 
-
-Equations:
-d_cyan/dt = - ifelse(time &lt;= tb, k1, k2) * cyan
-d_JCZ38/dt = + f_cyan_to_JCZ38 * ifelse(time &lt;= tb, k1, k2) * cyan -
-           k_JCZ38 * JCZ38
-d_J9Z38/dt = + f_cyan_to_J9Z38 * ifelse(time &lt;= tb, k1, k2) * cyan -
-           k_J9Z38 * J9Z38
-d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76
-
-Data:
-433 observations of 4 variable(s) grouped in 5 datasets
-
-Model predictions using solution type deSolve 
-
-Fitted in 531.084 s
-Using 300, 100 iterations and 10 chains
-
-Variance model: Two-component variance function 
-
-Starting values for degradation parameters:
-        cyan_0    log_k_JCZ38    log_k_J9Z38    log_k_JSE76   f_cyan_ilr_1 
-       101.168         -3.358         -4.941         -5.794          0.676 
-  f_cyan_ilr_2 f_JCZ38_qlogis         log_k1         log_k2         log_tb 
-         5.740         13.863         -3.147         -4.262          2.173 
-
-Fixed degradation parameter values:
-None
-
-Starting values for random effects (square root of initial entries in omega):
-               cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1
-cyan_0           5.79       0.000       0.000       0.000       0.0000
-log_k_JCZ38      0.00       2.271       0.000       0.000       0.0000
-log_k_J9Z38      0.00       0.000       1.614       0.000       0.0000
-log_k_JSE76      0.00       0.000       0.000       1.264       0.0000
-f_cyan_ilr_1     0.00       0.000       0.000       0.000       0.6761
-f_cyan_ilr_2     0.00       0.000       0.000       0.000       0.0000
-f_JCZ38_qlogis   0.00       0.000       0.000       0.000       0.0000
-log_k1           0.00       0.000       0.000       0.000       0.0000
-log_k2           0.00       0.000       0.000       0.000       0.0000
-log_tb           0.00       0.000       0.000       0.000       0.0000
-               f_cyan_ilr_2 f_JCZ38_qlogis log_k1 log_k2 log_tb
-cyan_0                0.000           0.00 0.0000 0.0000  0.000
-log_k_JCZ38           0.000           0.00 0.0000 0.0000  0.000
-log_k_J9Z38           0.000           0.00 0.0000 0.0000  0.000
-log_k_JSE76           0.000           0.00 0.0000 0.0000  0.000
-f_cyan_ilr_1          0.000           0.00 0.0000 0.0000  0.000
-f_cyan_ilr_2          9.572           0.00 0.0000 0.0000  0.000
-f_JCZ38_qlogis        0.000          19.19 0.0000 0.0000  0.000
-log_k1                0.000           0.00 0.8705 0.0000  0.000
-log_k2                0.000           0.00 0.0000 0.9288  0.000
-log_tb                0.000           0.00 0.0000 0.0000  1.065
-
-Starting values for error model parameters:
-a.1 b.1 
-  1   1 
-
-Results:
-
-Likelihood computed by importance sampling
-   AIC  BIC logLik
-  2422 2414  -1190
-
-Optimised parameters:
-                      est. lower upper
-cyan_0            100.9521    NA    NA
-log_k_JCZ38        -3.4629    NA    NA
-log_k_J9Z38        -5.0346    NA    NA
-log_k_JSE76        -5.5722    NA    NA
-f_cyan_ilr_1        0.6560    NA    NA
-f_cyan_ilr_2        0.7983    NA    NA
-f_JCZ38_qlogis     42.7949    NA    NA
-log_k1             -3.1721    NA    NA
-log_k2             -4.4039    NA    NA
-log_tb              2.3994    NA    NA
-a.1                 3.0586    NA    NA
-b.1                 0.0380    NA    NA
-SD.log_k_JCZ38      1.3754    NA    NA
-SD.log_k_J9Z38      0.6703    NA    NA
-SD.log_k_JSE76      0.5876    NA    NA
-SD.f_cyan_ilr_1     0.3272    NA    NA
-SD.f_cyan_ilr_2     0.5300    NA    NA
-SD.f_JCZ38_qlogis   6.4465    NA    NA
-SD.log_k1           0.4135    NA    NA
-SD.log_k2           0.4182    NA    NA
-SD.log_tb           0.6035    NA    NA
-
-Correlation is not available
-
-Random effects:
-                    est. lower upper
-SD.log_k_JCZ38    1.3754    NA    NA
-SD.log_k_J9Z38    0.6703    NA    NA
-SD.log_k_JSE76    0.5876    NA    NA
-SD.f_cyan_ilr_1   0.3272    NA    NA
-SD.f_cyan_ilr_2   0.5300    NA    NA
-SD.f_JCZ38_qlogis 6.4465    NA    NA
-SD.log_k1         0.4135    NA    NA
-SD.log_k2         0.4182    NA    NA
-SD.log_tb         0.6035    NA    NA
-
-Variance model:
-     est. lower upper
-a.1 3.059    NA    NA
-b.1 0.038    NA    NA
-
-Backtransformed parameters:
-                      est. lower upper
-cyan_0           1.010e+02    NA    NA
-k_JCZ38          3.134e-02    NA    NA
-k_J9Z38          6.509e-03    NA    NA
-k_JSE76          3.802e-03    NA    NA
-f_cyan_to_JCZ38  6.127e-01    NA    NA
-f_cyan_to_J9Z38  2.423e-01    NA    NA
-f_JCZ38_to_JSE76 1.000e+00    NA    NA
-k1               4.191e-02    NA    NA
-k2               1.223e-02    NA    NA
-tb               1.102e+01    NA    NA
-
-Resulting formation fractions:
-                ff
-cyan_JCZ38  0.6127
-cyan_J9Z38  0.2423
-cyan_sink   0.1449
-JCZ38_JSE76 1.0000
-JCZ38_sink  0.0000
-
-Estimated disappearance times:
-        DT50   DT90 DT50back DT50_k1 DT50_k2
-cyan   29.94 161.54    48.63   16.54   56.68
-JCZ38  22.12  73.47       NA      NA      NA
-J9Z38 106.50 353.77       NA      NA      NA
-JSE76 182.30 605.60       NA      NA      NA
-
-</code></pre>
-<p></p>
-</div>
-<div class="section level4">
-<h4 id="pathway-2">Pathway 2<a class="anchor" aria-label="anchor" href="#pathway-2"></a>
-</h4>
-<caption>
-Hierarchical FOMC path 2 fit with constant variance
-</caption>
-<pre><code>
-saemix version used for fitting:      3.2 
-mkin version used for pre-fitting:  1.2.4 
-R version used for fitting:         4.2.3 
-Date of fit:     Thu Apr 20 20:45:51 2023 
-Date of summary: Thu Apr 20 21:04:34 2023 
-
-Equations:
-d_cyan/dt = - (alpha/beta) * 1/((time/beta) + 1) * cyan
-d_JCZ38/dt = + f_cyan_to_JCZ38 * (alpha/beta) * 1/((time/beta) + 1) *
-           cyan - k_JCZ38 * JCZ38 + f_JSE76_to_JCZ38 * k_JSE76 * JSE76
-d_J9Z38/dt = + f_cyan_to_J9Z38 * (alpha/beta) * 1/((time/beta) + 1) *
-           cyan - k_J9Z38 * J9Z38
-d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76
-
-Data:
-433 observations of 4 variable(s) grouped in 5 datasets
-
-Model predictions using solution type deSolve 
-
-Fitted in 517.002 s
-Using 300, 100 iterations and 10 chains
-
-Variance model: Constant variance 
-
-Starting values for degradation parameters:
-        cyan_0    log_k_JCZ38    log_k_J9Z38    log_k_JSE76   f_cyan_ilr_1 
-      101.8173        -1.8998        -5.1449        -2.5415         0.6705 
-  f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis      log_alpha       log_beta 
-        4.4669        16.1281        13.3327        -0.2314         2.8738 
-
-Fixed degradation parameter values:
-None
-
-Starting values for random effects (square root of initial entries in omega):
-               cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1
-cyan_0          5.742       0.000       0.000        0.00       0.0000
-log_k_JCZ38     0.000       1.402       0.000        0.00       0.0000
-log_k_J9Z38     0.000       0.000       1.718        0.00       0.0000
-log_k_JSE76     0.000       0.000       0.000        3.57       0.0000
-f_cyan_ilr_1    0.000       0.000       0.000        0.00       0.5926
-f_cyan_ilr_2    0.000       0.000       0.000        0.00       0.0000
-f_JCZ38_qlogis  0.000       0.000       0.000        0.00       0.0000
-f_JSE76_qlogis  0.000       0.000       0.000        0.00       0.0000
-log_alpha       0.000       0.000       0.000        0.00       0.0000
-log_beta        0.000       0.000       0.000        0.00       0.0000
-               f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis log_alpha log_beta
-cyan_0                 0.00           0.00           0.00    0.0000   0.0000
-log_k_JCZ38            0.00           0.00           0.00    0.0000   0.0000
-log_k_J9Z38            0.00           0.00           0.00    0.0000   0.0000
-log_k_JSE76            0.00           0.00           0.00    0.0000   0.0000
-f_cyan_ilr_1           0.00           0.00           0.00    0.0000   0.0000
-f_cyan_ilr_2          10.56           0.00           0.00    0.0000   0.0000
-f_JCZ38_qlogis         0.00          12.04           0.00    0.0000   0.0000
-f_JSE76_qlogis         0.00           0.00          15.26    0.0000   0.0000
-log_alpha              0.00           0.00           0.00    0.4708   0.0000
-log_beta               0.00           0.00           0.00    0.0000   0.4432
-
-Starting values for error model parameters:
-a.1 
-  1 
-
-Results:
-
-Likelihood computed by importance sampling
-   AIC  BIC logLik
-  2308 2301  -1134
-
-Optimised parameters:
-                      est.    lower     upper
-cyan_0            101.9586 99.22024 104.69700
-log_k_JCZ38        -2.4861 -3.17661  -1.79560
-log_k_J9Z38        -5.3926 -6.08842  -4.69684
-log_k_JSE76        -3.1193 -4.12904  -2.10962
-f_cyan_ilr_1        0.7368  0.42085   1.05276
-f_cyan_ilr_2        0.6196  0.06052   1.17861
-f_JCZ38_qlogis      4.8970 -4.68003  14.47398
-f_JSE76_qlogis      4.4066 -1.02087   9.83398
-log_alpha          -0.3021 -0.68264   0.07838
-log_beta            2.7438  2.57970   2.90786
-a.1                 2.9008  2.69920   3.10245
-SD.cyan_0           2.7081  0.64216   4.77401
-SD.log_k_JCZ38      0.7043  0.19951   1.20907
-SD.log_k_J9Z38      0.6248  0.05790   1.19180
-SD.log_k_JSE76      1.0750  0.33157   1.81839
-SD.f_cyan_ilr_1     0.3429  0.11688   0.56892
-SD.f_cyan_ilr_2     0.4774  0.09381   0.86097
-SD.f_JCZ38_qlogis   1.5565 -7.83970  10.95279
-SD.f_JSE76_qlogis   1.6871 -1.25577   4.63000
-SD.log_alpha        0.4216  0.15913   0.68405
-
-Correlation: 
-               cyan_0  l__JCZ3 l__J9Z3 l__JSE7 f_cy__1 f_cy__2 f_JCZ38 f_JSE76
-log_k_JCZ38    -0.0167                                                        
-log_k_J9Z38    -0.0307  0.0057                                                
-log_k_JSE76    -0.0032  0.1358  0.0009                                        
-f_cyan_ilr_1   -0.0087  0.0206 -0.1158 -0.0009                                
-f_cyan_ilr_2   -0.1598  0.0690  0.1770  0.0002 -0.0007                        
-f_JCZ38_qlogis  0.0966 -0.1132 -0.0440  0.0182 -0.1385 -0.4583                
-f_JSE76_qlogis -0.0647  0.1157  0.0333 -0.0026  0.1110  0.3620 -0.8586        
-log_alpha      -0.0389  0.0113  0.0209  0.0021  0.0041  0.0451 -0.0605  0.0412
-log_beta       -0.2508  0.0533  0.0977  0.0098  0.0220  0.2741 -0.2934  0.1999
-               log_lph
-log_k_JCZ38           
-log_k_J9Z38           
-log_k_JSE76           
-f_cyan_ilr_1          
-f_cyan_ilr_2          
-f_JCZ38_qlogis        
-f_JSE76_qlogis        
-log_alpha             
-log_beta        0.2281
-
-Random effects:
-                    est.    lower   upper
-SD.cyan_0         2.7081  0.64216  4.7740
-SD.log_k_JCZ38    0.7043  0.19951  1.2091
-SD.log_k_J9Z38    0.6248  0.05790  1.1918
-SD.log_k_JSE76    1.0750  0.33157  1.8184
-SD.f_cyan_ilr_1   0.3429  0.11688  0.5689
-SD.f_cyan_ilr_2   0.4774  0.09381  0.8610
-SD.f_JCZ38_qlogis 1.5565 -7.83970 10.9528
-SD.f_JSE76_qlogis 1.6871 -1.25577  4.6300
-SD.log_alpha      0.4216  0.15913  0.6840
-
-Variance model:
-     est. lower upper
-a.1 2.901 2.699 3.102
-
-Backtransformed parameters:
-                      est.     lower     upper
-cyan_0           101.95862 99.220240 1.047e+02
-k_JCZ38            0.08323  0.041727 1.660e-01
-k_J9Z38            0.00455  0.002269 9.124e-03
-k_JSE76            0.04419  0.016098 1.213e-01
-f_cyan_to_JCZ38    0.61318        NA        NA
-f_cyan_to_J9Z38    0.21630        NA        NA
-f_JCZ38_to_JSE76   0.99259  0.009193 1.000e+00
-f_JSE76_to_JCZ38   0.98795  0.264857 9.999e-01
-alpha              0.73924  0.505281 1.082e+00
-beta              15.54568 13.193194 1.832e+01
-
-Resulting formation fractions:
-                  ff
-cyan_JCZ38  0.613182
-cyan_J9Z38  0.216298
-cyan_sink   0.170519
-JCZ38_JSE76 0.992586
-JCZ38_sink  0.007414
-JSE76_JCZ38 0.987950
-JSE76_sink  0.012050
-
-Estimated disappearance times:
-         DT50   DT90 DT50back
-cyan   24.157 334.68    100.7
-JCZ38   8.328  27.66       NA
-J9Z38 152.341 506.06       NA
-JSE76  15.687  52.11       NA
-
-</code></pre>
-<p></p>
-<caption>
-Hierarchical FOMC path 2 fit with two-component error
-</caption>
-<pre><code>
-saemix version used for fitting:      3.2 
-mkin version used for pre-fitting:  1.2.4 
-R version used for fitting:         4.2.3 
-Date of fit:     Thu Apr 20 20:45:39 2023 
-Date of summary: Thu Apr 20 21:04:34 2023 
-
-Equations:
-d_cyan/dt = - (alpha/beta) * 1/((time/beta) + 1) * cyan
-d_JCZ38/dt = + f_cyan_to_JCZ38 * (alpha/beta) * 1/((time/beta) + 1) *
-           cyan - k_JCZ38 * JCZ38 + f_JSE76_to_JCZ38 * k_JSE76 * JSE76
-d_J9Z38/dt = + f_cyan_to_J9Z38 * (alpha/beta) * 1/((time/beta) + 1) *
-           cyan - k_J9Z38 * J9Z38
-d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76
-
-Data:
-433 observations of 4 variable(s) grouped in 5 datasets
-
-Model predictions using solution type deSolve 
-
-Fitted in 505.619 s
-Using 300, 100 iterations and 10 chains
-
-Variance model: Two-component variance function 
-
-Starting values for degradation parameters:
-        cyan_0    log_k_JCZ38    log_k_J9Z38    log_k_JSE76   f_cyan_ilr_1 
-      101.9028        -1.9055        -5.0249        -2.5646         0.6807 
-  f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis      log_alpha       log_beta 
-        4.8883        16.0676         9.3923        -0.1346         3.0364 
-
-Fixed degradation parameter values:
-None
-
-Starting values for random effects (square root of initial entries in omega):
-               cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1
-cyan_0          6.321       0.000       0.000       0.000       0.0000
-log_k_JCZ38     0.000       1.392       0.000       0.000       0.0000
-log_k_J9Z38     0.000       0.000       1.561       0.000       0.0000
-log_k_JSE76     0.000       0.000       0.000       3.614       0.0000
-f_cyan_ilr_1    0.000       0.000       0.000       0.000       0.6339
-f_cyan_ilr_2    0.000       0.000       0.000       0.000       0.0000
-f_JCZ38_qlogis  0.000       0.000       0.000       0.000       0.0000
-f_JSE76_qlogis  0.000       0.000       0.000       0.000       0.0000
-log_alpha       0.000       0.000       0.000       0.000       0.0000
-log_beta        0.000       0.000       0.000       0.000       0.0000
-               f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis log_alpha log_beta
-cyan_0                 0.00           0.00           0.00    0.0000   0.0000
-log_k_JCZ38            0.00           0.00           0.00    0.0000   0.0000
-log_k_J9Z38            0.00           0.00           0.00    0.0000   0.0000
-log_k_JSE76            0.00           0.00           0.00    0.0000   0.0000
-f_cyan_ilr_1           0.00           0.00           0.00    0.0000   0.0000
-f_cyan_ilr_2          10.41           0.00           0.00    0.0000   0.0000
-f_JCZ38_qlogis         0.00          12.24           0.00    0.0000   0.0000
-f_JSE76_qlogis         0.00           0.00          15.13    0.0000   0.0000
-log_alpha              0.00           0.00           0.00    0.3701   0.0000
-log_beta               0.00           0.00           0.00    0.0000   0.5662
-
-Starting values for error model parameters:
-a.1 b.1 
-  1   1 
-
-Results:
-
-Likelihood computed by importance sampling
-   AIC  BIC logLik
-  2248 2240  -1103
-
-Optimised parameters:
-                       est.      lower      upper
-cyan_0            101.55545  9.920e+01  1.039e+02
-log_k_JCZ38        -2.37354 -2.928e+00 -1.819e+00
-log_k_J9Z38        -5.14736 -5.960e+00 -4.335e+00
-log_k_JSE76        -3.07802 -4.243e+00 -1.913e+00
-f_cyan_ilr_1        0.71263  3.655e-01  1.060e+00
-f_cyan_ilr_2        0.95202  2.701e-01  1.634e+00
-f_JCZ38_qlogis      3.58473  1.251e+00  5.919e+00
-f_JSE76_qlogis     19.03623 -1.037e+07  1.037e+07
-log_alpha          -0.15297 -4.490e-01  1.431e-01
-log_beta            2.99230  2.706e+00  3.278e+00
-a.1                 2.04816         NA         NA
-b.1                 0.06886         NA         NA
-SD.log_k_JCZ38      0.56174         NA         NA
-SD.log_k_J9Z38      0.86509         NA         NA
-SD.log_k_JSE76      1.28450         NA         NA
-SD.f_cyan_ilr_1     0.38705         NA         NA
-SD.f_cyan_ilr_2     0.54153         NA         NA
-SD.f_JCZ38_qlogis   1.65311         NA         NA
-SD.f_JSE76_qlogis   7.51468         NA         NA
-SD.log_alpha        0.31586         NA         NA
-SD.log_beta         0.24696         NA         NA
-
-Correlation is not available
-
-Random effects:
-                    est. lower upper
-SD.log_k_JCZ38    0.5617    NA    NA
-SD.log_k_J9Z38    0.8651    NA    NA
-SD.log_k_JSE76    1.2845    NA    NA
-SD.f_cyan_ilr_1   0.3870    NA    NA
-SD.f_cyan_ilr_2   0.5415    NA    NA
-SD.f_JCZ38_qlogis 1.6531    NA    NA
-SD.f_JSE76_qlogis 7.5147    NA    NA
-SD.log_alpha      0.3159    NA    NA
-SD.log_beta       0.2470    NA    NA
-
-Variance model:
-       est. lower upper
-a.1 2.04816    NA    NA
-b.1 0.06886    NA    NA
-
-Backtransformed parameters:
-                      est.    lower    upper
-cyan_0           1.016e+02 99.20301 103.9079
-k_JCZ38          9.315e-02  0.05349   0.1622
-k_J9Z38          5.815e-03  0.00258   0.0131
-k_JSE76          4.605e-02  0.01436   0.1477
-f_cyan_to_JCZ38  6.438e-01       NA       NA
-f_cyan_to_J9Z38  2.350e-01       NA       NA
-f_JCZ38_to_JSE76 9.730e-01  0.77745   0.9973
-f_JSE76_to_JCZ38 1.000e+00  0.00000   1.0000
-alpha            8.582e-01  0.63824   1.1538
-beta             1.993e+01 14.97621  26.5262
-
-Resulting formation fractions:
-                   ff
-cyan_JCZ38  6.438e-01
-cyan_J9Z38  2.350e-01
-cyan_sink   1.212e-01
-JCZ38_JSE76 9.730e-01
-JCZ38_sink  2.700e-02
-JSE76_JCZ38 1.000e+00
-JSE76_sink  5.403e-09
-
-Estimated disappearance times:
-         DT50   DT90 DT50back
-cyan   24.771 271.70    81.79
-JCZ38   7.441  24.72       NA
-J9Z38 119.205 395.99       NA
-JSE76  15.052  50.00       NA
-
-</code></pre>
-<p></p>
-<caption>
-Hierarchical DFOP path 2 fit with constant variance
-</caption>
-<pre><code>
-saemix version used for fitting:      3.2 
-mkin version used for pre-fitting:  1.2.4 
-R version used for fitting:         4.2.3 
-Date of fit:     Thu Apr 20 20:46:46 2023 
-Date of summary: Thu Apr 20 21:04:34 2023 
-
-Equations:
-d_cyan/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
-           time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))
-           * cyan
-d_JCZ38/dt = + f_cyan_to_JCZ38 * ((k1 * g * exp(-k1 * time) + k2 * (1 -
-           g) * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *
-           exp(-k2 * time))) * cyan - k_JCZ38 * JCZ38 +
-           f_JSE76_to_JCZ38 * k_JSE76 * JSE76
-d_J9Z38/dt = + f_cyan_to_J9Z38 * ((k1 * g * exp(-k1 * time) + k2 * (1 -
-           g) * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *
-           exp(-k2 * time))) * cyan - k_J9Z38 * J9Z38
-d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76
-
-Data:
-433 observations of 4 variable(s) grouped in 5 datasets
-
-Model predictions using solution type deSolve 
-
-Fitted in 572.382 s
-Using 300, 100 iterations and 10 chains
-
-Variance model: Constant variance 
-
-Starting values for degradation parameters:
-        cyan_0    log_k_JCZ38    log_k_J9Z38    log_k_JSE76   f_cyan_ilr_1 
-      102.4358        -2.3107        -5.3123        -3.7120         0.6753 
-  f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis         log_k1         log_k2 
-        1.1462        12.4095        12.3630        -1.9317        -4.4557 
-      g_qlogis 
-       -0.5648 
-
-Fixed degradation parameter values:
-None
-
-Starting values for random effects (square root of initial entries in omega):
-               cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1
-cyan_0          4.594      0.0000       0.000         0.0       0.0000
-log_k_JCZ38     0.000      0.7966       0.000         0.0       0.0000
-log_k_J9Z38     0.000      0.0000       1.561         0.0       0.0000
-log_k_JSE76     0.000      0.0000       0.000         0.8       0.0000
-f_cyan_ilr_1    0.000      0.0000       0.000         0.0       0.6349
-f_cyan_ilr_2    0.000      0.0000       0.000         0.0       0.0000
-f_JCZ38_qlogis  0.000      0.0000       0.000         0.0       0.0000
-f_JSE76_qlogis  0.000      0.0000       0.000         0.0       0.0000
-log_k1          0.000      0.0000       0.000         0.0       0.0000
-log_k2          0.000      0.0000       0.000         0.0       0.0000
-g_qlogis        0.000      0.0000       0.000         0.0       0.0000
-               f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis log_k1 log_k2
-cyan_0                0.000           0.00            0.0  0.000 0.0000
-log_k_JCZ38           0.000           0.00            0.0  0.000 0.0000
-log_k_J9Z38           0.000           0.00            0.0  0.000 0.0000
-log_k_JSE76           0.000           0.00            0.0  0.000 0.0000
-f_cyan_ilr_1          0.000           0.00            0.0  0.000 0.0000
-f_cyan_ilr_2          1.797           0.00            0.0  0.000 0.0000
-f_JCZ38_qlogis        0.000          13.85            0.0  0.000 0.0000
-f_JSE76_qlogis        0.000           0.00           14.1  0.000 0.0000
-log_k1                0.000           0.00            0.0  1.106 0.0000
-log_k2                0.000           0.00            0.0  0.000 0.6141
-g_qlogis              0.000           0.00            0.0  0.000 0.0000
-               g_qlogis
-cyan_0            0.000
-log_k_JCZ38       0.000
-log_k_J9Z38       0.000
-log_k_JSE76       0.000
-f_cyan_ilr_1      0.000
-f_cyan_ilr_2      0.000
-f_JCZ38_qlogis    0.000
-f_JSE76_qlogis    0.000
-log_k1            0.000
-log_k2            0.000
-g_qlogis          1.595
-
-Starting values for error model parameters:
-a.1 
-  1 
-
-Results:
-
-Likelihood computed by importance sampling
-   AIC  BIC logLik
-  2290 2281  -1123
-
-Optimised parameters:
-                      est.     lower    upper
-cyan_0            102.6903 101.44420 103.9365
-log_k_JCZ38        -2.4018  -2.98058  -1.8230
-log_k_J9Z38        -5.1865  -5.92931  -4.4437
-log_k_JSE76        -3.0784  -4.25226  -1.9045
-f_cyan_ilr_1        0.7157   0.37625   1.0551
-f_cyan_ilr_2        0.7073   0.20136   1.2132
-f_JCZ38_qlogis      4.6797   0.43240   8.9269
-f_JSE76_qlogis      5.0080  -1.01380  11.0299
-log_k1             -1.9620  -2.62909  -1.2949
-log_k2             -4.4894  -4.94958  -4.0292
-g_qlogis           -0.4658  -1.34443   0.4129
-a.1                 2.7158   2.52576   2.9059
-SD.log_k_JCZ38      0.5818   0.15679   1.0067
-SD.log_k_J9Z38      0.7421   0.16751   1.3167
-SD.log_k_JSE76      1.2841   0.43247   2.1356
-SD.f_cyan_ilr_1     0.3748   0.13040   0.6192
-SD.f_cyan_ilr_2     0.4550   0.08396   0.8261
-SD.f_JCZ38_qlogis   2.0862  -0.73390   4.9062
-SD.f_JSE76_qlogis   1.9585  -3.14773   7.0647
-SD.log_k1           0.7389   0.25761   1.2201
-SD.log_k2           0.5132   0.18143   0.8450
-SD.g_qlogis         0.9870   0.35773   1.6164
-
-Correlation: 
-               cyan_0  l__JCZ3 l__J9Z3 l__JSE7 f_cy__1 f_cy__2 f_JCZ38 f_JSE76
-log_k_JCZ38    -0.0170                                                        
-log_k_J9Z38    -0.0457  0.0016                                                
-log_k_JSE76    -0.0046  0.1183  0.0005                                        
-f_cyan_ilr_1    0.0079  0.0072 -0.0909  0.0003                                
-f_cyan_ilr_2   -0.3114  0.0343  0.1542  0.0023 -0.0519                        
-f_JCZ38_qlogis  0.0777 -0.0601 -0.0152  0.0080 -0.0520 -0.2524                
-f_JSE76_qlogis -0.0356  0.0817  0.0073  0.0051  0.0388  0.1959 -0.6236        
-log_k1          0.0848 -0.0028  0.0010 -0.0010 -0.0014 -0.0245  0.0121 -0.0177
-log_k2          0.0274 -0.0001  0.0075  0.0000 -0.0023 -0.0060  0.0000 -0.0130
-g_qlogis        0.0159  0.0002 -0.0095  0.0002  0.0029 -0.0140 -0.0001  0.0149
-               log_k1  log_k2 
-log_k_JCZ38                   
-log_k_J9Z38                   
-log_k_JSE76                   
-f_cyan_ilr_1                  
-f_cyan_ilr_2                  
-f_JCZ38_qlogis                
-f_JSE76_qlogis                
-log_k1                        
-log_k2          0.0280        
-g_qlogis       -0.0278 -0.0310
-
-Random effects:
-                    est.    lower  upper
-SD.log_k_JCZ38    0.5818  0.15679 1.0067
-SD.log_k_J9Z38    0.7421  0.16751 1.3167
-SD.log_k_JSE76    1.2841  0.43247 2.1356
-SD.f_cyan_ilr_1   0.3748  0.13040 0.6192
-SD.f_cyan_ilr_2   0.4550  0.08396 0.8261
-SD.f_JCZ38_qlogis 2.0862 -0.73390 4.9062
-SD.f_JSE76_qlogis 1.9585 -3.14773 7.0647
-SD.log_k1         0.7389  0.25761 1.2201
-SD.log_k2         0.5132  0.18143 0.8450
-SD.g_qlogis       0.9870  0.35773 1.6164
-
-Variance model:
-     est. lower upper
-a.1 2.716 2.526 2.906
-
-Backtransformed parameters:
-                      est.     lower     upper
-cyan_0           1.027e+02 1.014e+02 103.93649
-k_JCZ38          9.056e-02 5.076e-02   0.16154
-k_J9Z38          5.591e-03 2.660e-03   0.01175
-k_JSE76          4.603e-02 1.423e-02   0.14890
-f_cyan_to_JCZ38  6.184e-01        NA        NA
-f_cyan_to_J9Z38  2.248e-01        NA        NA
-f_JCZ38_to_JSE76 9.908e-01 6.064e-01   0.99987
-f_JSE76_to_JCZ38 9.934e-01 2.662e-01   0.99998
-k1               1.406e-01 7.214e-02   0.27393
-k2               1.123e-02 7.086e-03   0.01779
-g                3.856e-01 2.068e-01   0.60177
-
-Resulting formation fractions:
-                  ff
-cyan_JCZ38  0.618443
-cyan_J9Z38  0.224770
-cyan_sink   0.156787
-JCZ38_JSE76 0.990803
-JCZ38_sink  0.009197
-JSE76_JCZ38 0.993360
-JSE76_sink  0.006640
-
-Estimated disappearance times:
-         DT50   DT90 DT50back DT50_k1 DT50_k2
-cyan   21.674 161.70    48.68   4.931   61.74
-JCZ38   7.654  25.43       NA      NA      NA
-J9Z38 123.966 411.81       NA      NA      NA
-JSE76  15.057  50.02       NA      NA      NA
-
-</code></pre>
-<p></p>
-<caption>
-Hierarchical DFOP path 2 fit with two-component error
-</caption>
-<pre><code>
-saemix version used for fitting:      3.2 
-mkin version used for pre-fitting:  1.2.4 
-R version used for fitting:         4.2.3 
-Date of fit:     Thu Apr 20 20:49:18 2023 
-Date of summary: Thu Apr 20 21:04:34 2023 
-
-Equations:
-d_cyan/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
-           time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))
-           * cyan
-d_JCZ38/dt = + f_cyan_to_JCZ38 * ((k1 * g * exp(-k1 * time) + k2 * (1 -
-           g) * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *
-           exp(-k2 * time))) * cyan - k_JCZ38 * JCZ38 +
-           f_JSE76_to_JCZ38 * k_JSE76 * JSE76
-d_J9Z38/dt = + f_cyan_to_J9Z38 * ((k1 * g * exp(-k1 * time) + k2 * (1 -
-           g) * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *
-           exp(-k2 * time))) * cyan - k_J9Z38 * J9Z38
-d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76
-
-Data:
-433 observations of 4 variable(s) grouped in 5 datasets
-
-Model predictions using solution type deSolve 
-
-Fitted in 724.515 s
-Using 300, 100 iterations and 10 chains
-
-Variance model: Two-component variance function 
-
-Starting values for degradation parameters:
-        cyan_0    log_k_JCZ38    log_k_J9Z38    log_k_JSE76   f_cyan_ilr_1 
-      101.7523        -1.5948        -5.0119        -2.2723         0.6719 
-  f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis         log_k1         log_k2 
-        5.1681        12.8238        12.4130        -2.0057        -4.5526 
-      g_qlogis 
-       -0.5805 
-
-Fixed degradation parameter values:
-None
-
-Starting values for random effects (square root of initial entries in omega):
-               cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1
-cyan_0          5.627       0.000       0.000       0.000       0.0000
-log_k_JCZ38     0.000       2.327       0.000       0.000       0.0000
-log_k_J9Z38     0.000       0.000       1.664       0.000       0.0000
-log_k_JSE76     0.000       0.000       0.000       4.566       0.0000
-f_cyan_ilr_1    0.000       0.000       0.000       0.000       0.6519
-f_cyan_ilr_2    0.000       0.000       0.000       0.000       0.0000
-f_JCZ38_qlogis  0.000       0.000       0.000       0.000       0.0000
-f_JSE76_qlogis  0.000       0.000       0.000       0.000       0.0000
-log_k1          0.000       0.000       0.000       0.000       0.0000
-log_k2          0.000       0.000       0.000       0.000       0.0000
-g_qlogis        0.000       0.000       0.000       0.000       0.0000
-               f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis log_k1 log_k2
-cyan_0                  0.0           0.00           0.00 0.0000 0.0000
-log_k_JCZ38             0.0           0.00           0.00 0.0000 0.0000
-log_k_J9Z38             0.0           0.00           0.00 0.0000 0.0000
-log_k_JSE76             0.0           0.00           0.00 0.0000 0.0000
-f_cyan_ilr_1            0.0           0.00           0.00 0.0000 0.0000
-f_cyan_ilr_2           10.1           0.00           0.00 0.0000 0.0000
-f_JCZ38_qlogis          0.0          13.99           0.00 0.0000 0.0000
-f_JSE76_qlogis          0.0           0.00          14.15 0.0000 0.0000
-log_k1                  0.0           0.00           0.00 0.8452 0.0000
-log_k2                  0.0           0.00           0.00 0.0000 0.5968
-g_qlogis                0.0           0.00           0.00 0.0000 0.0000
-               g_qlogis
-cyan_0            0.000
-log_k_JCZ38       0.000
-log_k_J9Z38       0.000
-log_k_JSE76       0.000
-f_cyan_ilr_1      0.000
-f_cyan_ilr_2      0.000
-f_JCZ38_qlogis    0.000
-f_JSE76_qlogis    0.000
-log_k1            0.000
-log_k2            0.000
-g_qlogis          1.691
-
-Starting values for error model parameters:
-a.1 b.1 
-  1   1 
-
-Results:
-
-Likelihood computed by importance sampling
-   AIC  BIC logLik
-  2234 2226  -1095
-
-Optimised parameters:
-                       est.      lower      upper
-cyan_0            101.10667  9.903e+01  103.18265
-log_k_JCZ38        -2.49437 -3.297e+00   -1.69221
-log_k_J9Z38        -5.08171 -5.875e+00   -4.28846
-log_k_JSE76        -3.20072 -4.180e+00   -2.22163
-f_cyan_ilr_1        0.71059  3.639e-01    1.05727
-f_cyan_ilr_2        1.15398  2.981e-01    2.00984
-f_JCZ38_qlogis      3.18027  1.056e+00    5.30452
-f_JSE76_qlogis      5.61578 -2.505e+01   36.28077
-log_k1             -2.38875 -2.517e+00   -2.26045
-log_k2             -4.67246 -4.928e+00   -4.41715
-g_qlogis           -0.28231 -1.135e+00    0.57058
-a.1                 2.08190  1.856e+00    2.30785
-b.1                 0.06114  5.015e-02    0.07214
-SD.log_k_JCZ38      0.84622  2.637e-01    1.42873
-SD.log_k_J9Z38      0.84564  2.566e-01    1.43464
-SD.log_k_JSE76      1.04385  3.242e-01    1.76351
-SD.f_cyan_ilr_1     0.38568  1.362e-01    0.63514
-SD.f_cyan_ilr_2     0.68046  7.166e-02    1.28925
-SD.f_JCZ38_qlogis   1.25244 -4.213e-02    2.54700
-SD.f_JSE76_qlogis   0.28202 -1.515e+03 1515.87968
-SD.log_k2           0.25749  7.655e-02    0.43843
-SD.g_qlogis         0.94535  3.490e-01    1.54174
-
-Correlation: 
-               cyan_0  l__JCZ3 l__J9Z3 l__JSE7 f_cy__1 f_cy__2 f_JCZ38 f_JSE76
-log_k_JCZ38    -0.0086                                                        
-log_k_J9Z38    -0.0363 -0.0007                                                
-log_k_JSE76     0.0015  0.1210 -0.0017                                        
-f_cyan_ilr_1   -0.0048  0.0095 -0.0572  0.0030                                
-f_cyan_ilr_2   -0.4788  0.0328  0.1143  0.0027 -0.0316                        
-f_JCZ38_qlogis  0.0736 -0.0664 -0.0137  0.0145 -0.0444 -0.2175                
-f_JSE76_qlogis -0.0137  0.0971  0.0035  0.0009  0.0293  0.1333 -0.6767        
-log_k1          0.2345 -0.0350 -0.0099 -0.0113 -0.0126 -0.1652  0.1756 -0.2161
-log_k2          0.0440 -0.0133  0.0199 -0.0040 -0.0097 -0.0119  0.0604 -0.1306
-g_qlogis        0.0438  0.0078 -0.0123  0.0029  0.0046 -0.0363 -0.0318  0.0736
-               log_k1  log_k2 
-log_k_JCZ38                   
-log_k_J9Z38                   
-log_k_JSE76                   
-f_cyan_ilr_1                  
-f_cyan_ilr_2                  
-f_JCZ38_qlogis                
-f_JSE76_qlogis                
-log_k1                        
-log_k2          0.3198        
-g_qlogis       -0.1666 -0.0954
-
-Random effects:
-                    est.      lower     upper
-SD.log_k_JCZ38    0.8462  2.637e-01    1.4287
-SD.log_k_J9Z38    0.8456  2.566e-01    1.4346
-SD.log_k_JSE76    1.0439  3.242e-01    1.7635
-SD.f_cyan_ilr_1   0.3857  1.362e-01    0.6351
-SD.f_cyan_ilr_2   0.6805  7.166e-02    1.2893
-SD.f_JCZ38_qlogis 1.2524 -4.213e-02    2.5470
-SD.f_JSE76_qlogis 0.2820 -1.515e+03 1515.8797
-SD.log_k2         0.2575  7.655e-02    0.4384
-SD.g_qlogis       0.9453  3.490e-01    1.5417
-
-Variance model:
-       est.   lower   upper
-a.1 2.08190 1.85595 2.30785
-b.1 0.06114 0.05015 0.07214
-
-Backtransformed parameters:
-                      est.     lower     upper
-cyan_0           1.011e+02 9.903e+01 103.18265
-k_JCZ38          8.255e-02 3.701e-02   0.18411
-k_J9Z38          6.209e-03 2.809e-03   0.01373
-k_JSE76          4.073e-02 1.530e-02   0.10843
-f_cyan_to_JCZ38  6.608e-01        NA        NA
-f_cyan_to_J9Z38  2.419e-01        NA        NA
-f_JCZ38_to_JSE76 9.601e-01 7.419e-01   0.99506
-f_JSE76_to_JCZ38 9.964e-01 1.322e-11   1.00000
-k1               9.174e-02 8.070e-02   0.10430
-k2               9.349e-03 7.243e-03   0.01207
-g                4.299e-01 2.432e-01   0.63890
-
-Resulting formation fractions:
-                  ff
-cyan_JCZ38  0.660808
-cyan_J9Z38  0.241904
-cyan_sink   0.097288
-JCZ38_JSE76 0.960085
-JCZ38_sink  0.039915
-JSE76_JCZ38 0.996373
-JSE76_sink  0.003627
-
-Estimated disappearance times:
-         DT50   DT90 DT50back DT50_k1 DT50_k2
-cyan   24.359 186.18    56.05   7.555   74.14
-JCZ38   8.397  27.89       NA      NA      NA
-J9Z38 111.631 370.83       NA      NA      NA
-JSE76  17.017  56.53       NA      NA      NA
-
-</code></pre>
-<p></p>
-<caption>
-Hierarchical SFORB path 2 fit with constant variance
-</caption>
-<pre><code>
-saemix version used for fitting:      3.2 
-mkin version used for pre-fitting:  1.2.4 
-R version used for fitting:         4.2.3 
-Date of fit:     Thu Apr 20 20:46:33 2023 
-Date of summary: Thu Apr 20 21:04:34 2023 
-
-Equations:
-d_cyan_free/dt = - k_cyan_free * cyan_free - k_cyan_free_bound *
-           cyan_free + k_cyan_bound_free * cyan_bound
-d_cyan_bound/dt = + k_cyan_free_bound * cyan_free - k_cyan_bound_free *
-           cyan_bound
-d_JCZ38/dt = + f_cyan_free_to_JCZ38 * k_cyan_free * cyan_free - k_JCZ38
-           * JCZ38 + f_JSE76_to_JCZ38 * k_JSE76 * JSE76
-d_J9Z38/dt = + f_cyan_free_to_J9Z38 * k_cyan_free * cyan_free - k_J9Z38
-           * J9Z38
-d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76
-
-Data:
-433 observations of 4 variable(s) grouped in 5 datasets
-
-Model predictions using solution type deSolve 
-
-Fitted in 559.097 s
-Using 300, 100 iterations and 10 chains
-
-Variance model: Constant variance 
-
-Starting values for degradation parameters:
-          cyan_free_0       log_k_cyan_free log_k_cyan_free_bound 
-             102.4394               -2.7673               -2.8942 
-log_k_cyan_bound_free           log_k_JCZ38           log_k_J9Z38 
-              -3.6201               -2.3107               -5.3123 
-          log_k_JSE76          f_cyan_ilr_1          f_cyan_ilr_2 
-              -3.7120                0.6754                1.1448 
-       f_JCZ38_qlogis        f_JSE76_qlogis 
-              13.2672               13.3538 
-
-Fixed degradation parameter values:
-None
-
-Starting values for random effects (square root of initial entries in omega):
-                      cyan_free_0 log_k_cyan_free log_k_cyan_free_bound
-cyan_free_0                 4.589          0.0000                  0.00
-log_k_cyan_free             0.000          0.4849                  0.00
-log_k_cyan_free_bound       0.000          0.0000                  1.62
-log_k_cyan_bound_free       0.000          0.0000                  0.00
-log_k_JCZ38                 0.000          0.0000                  0.00
-log_k_J9Z38                 0.000          0.0000                  0.00
-log_k_JSE76                 0.000          0.0000                  0.00
-f_cyan_ilr_1                0.000          0.0000                  0.00
-f_cyan_ilr_2                0.000          0.0000                  0.00
-f_JCZ38_qlogis              0.000          0.0000                  0.00
-f_JSE76_qlogis              0.000          0.0000                  0.00
-                      log_k_cyan_bound_free log_k_JCZ38 log_k_J9Z38 log_k_JSE76
-cyan_free_0                           0.000      0.0000       0.000         0.0
-log_k_cyan_free                       0.000      0.0000       0.000         0.0
-log_k_cyan_free_bound                 0.000      0.0000       0.000         0.0
-log_k_cyan_bound_free                 1.197      0.0000       0.000         0.0
-log_k_JCZ38                           0.000      0.7966       0.000         0.0
-log_k_J9Z38                           0.000      0.0000       1.561         0.0
-log_k_JSE76                           0.000      0.0000       0.000         0.8
-f_cyan_ilr_1                          0.000      0.0000       0.000         0.0
-f_cyan_ilr_2                          0.000      0.0000       0.000         0.0
-f_JCZ38_qlogis                        0.000      0.0000       0.000         0.0
-f_JSE76_qlogis                        0.000      0.0000       0.000         0.0
-                      f_cyan_ilr_1 f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis
-cyan_free_0                 0.0000        0.000           0.00           0.00
-log_k_cyan_free             0.0000        0.000           0.00           0.00
-log_k_cyan_free_bound       0.0000        0.000           0.00           0.00
-log_k_cyan_bound_free       0.0000        0.000           0.00           0.00
-log_k_JCZ38                 0.0000        0.000           0.00           0.00
-log_k_J9Z38                 0.0000        0.000           0.00           0.00
-log_k_JSE76                 0.0000        0.000           0.00           0.00
-f_cyan_ilr_1                0.6349        0.000           0.00           0.00
-f_cyan_ilr_2                0.0000        1.797           0.00           0.00
-f_JCZ38_qlogis              0.0000        0.000          13.84           0.00
-f_JSE76_qlogis              0.0000        0.000           0.00          14.66
-
-Starting values for error model parameters:
-a.1 
-  1 
-
-Results:
-
-Likelihood computed by importance sampling
-   AIC  BIC logLik
-  2284 2275  -1120
-
-Optimised parameters:
-                             est.      lower      upper
-cyan_free_0              102.7730  1.015e+02  1.041e+02
-log_k_cyan_free           -2.8530 -3.167e+00 -2.539e+00
-log_k_cyan_free_bound     -2.7326 -3.543e+00 -1.922e+00
-log_k_cyan_bound_free     -3.5582 -4.126e+00 -2.990e+00
-log_k_JCZ38               -2.3810 -2.921e+00 -1.841e+00
-log_k_J9Z38               -5.2301 -5.963e+00 -4.497e+00
-log_k_JSE76               -3.0286 -4.286e+00 -1.771e+00
-f_cyan_ilr_1               0.7081  3.733e-01  1.043e+00
-f_cyan_ilr_2               0.5847  7.846e-03  1.162e+00
-f_JCZ38_qlogis             9.5676 -1.323e+03  1.342e+03
-f_JSE76_qlogis             3.7042  7.254e-02  7.336e+00
-a.1                        2.7222  2.532e+00  2.913e+00
-SD.log_k_cyan_free         0.3338  1.086e-01  5.589e-01
-SD.log_k_cyan_free_bound   0.8888  3.023e-01  1.475e+00
-SD.log_k_cyan_bound_free   0.6220  2.063e-01  1.038e+00
-SD.log_k_JCZ38             0.5221  1.334e-01  9.108e-01
-SD.log_k_J9Z38             0.7104  1.371e-01  1.284e+00
-SD.log_k_JSE76             1.3837  4.753e-01  2.292e+00
-SD.f_cyan_ilr_1            0.3620  1.248e-01  5.992e-01
-SD.f_cyan_ilr_2            0.4259  8.145e-02  7.704e-01
-SD.f_JCZ38_qlogis          3.5332 -1.037e+05  1.037e+05
-SD.f_JSE76_qlogis          1.6990 -2.771e-01  3.675e+00
-
-Correlation: 
-                      cyn_f_0 lg_k_c_ lg_k_cyn_f_ lg_k_cyn_b_ l__JCZ3 l__J9Z3
-log_k_cyan_free        0.2126                                                
-log_k_cyan_free_bound  0.0894  0.0871                                        
-log_k_cyan_bound_free  0.0033  0.0410  0.0583                                
-log_k_JCZ38           -0.0708 -0.0280 -0.0147      0.0019                    
-log_k_J9Z38           -0.0535 -0.0138  0.0012      0.0148      0.0085        
-log_k_JSE76           -0.0066 -0.0030 -0.0021     -0.0005      0.1090  0.0010
-f_cyan_ilr_1          -0.0364 -0.0157 -0.0095     -0.0015      0.0458 -0.0960
-f_cyan_ilr_2          -0.3814 -0.1104 -0.0423      0.0146      0.1540  0.1526
-f_JCZ38_qlogis         0.2507  0.0969  0.0482     -0.0097     -0.2282 -0.0363
-f_JSE76_qlogis        -0.1648 -0.0710 -0.0443     -0.0087      0.2002  0.0226
-                      l__JSE7 f_cy__1 f_cy__2 f_JCZ38
-log_k_cyan_free                                      
-log_k_cyan_free_bound                                
-log_k_cyan_bound_free                                
-log_k_JCZ38                                          
-log_k_J9Z38                                          
-log_k_JSE76                                          
-f_cyan_ilr_1           0.0001                        
-f_cyan_ilr_2           0.0031  0.0586                
-f_JCZ38_qlogis         0.0023 -0.1867 -0.6255        
-f_JSE76_qlogis         0.0082  0.1356  0.4519 -0.7951
-
-Random effects:
-                           est.      lower     upper
-SD.log_k_cyan_free       0.3338  1.086e-01 5.589e-01
-SD.log_k_cyan_free_bound 0.8888  3.023e-01 1.475e+00
-SD.log_k_cyan_bound_free 0.6220  2.063e-01 1.038e+00
-SD.log_k_JCZ38           0.5221  1.334e-01 9.108e-01
-SD.log_k_J9Z38           0.7104  1.371e-01 1.284e+00
-SD.log_k_JSE76           1.3837  4.753e-01 2.292e+00
-SD.f_cyan_ilr_1          0.3620  1.248e-01 5.992e-01
-SD.f_cyan_ilr_2          0.4259  8.145e-02 7.704e-01
-SD.f_JCZ38_qlogis        3.5332 -1.037e+05 1.037e+05
-SD.f_JSE76_qlogis        1.6990 -2.771e-01 3.675e+00
-
-Variance model:
-     est. lower upper
-a.1 2.722 2.532 2.913
-
-Backtransformed parameters:
-                          est.     lower     upper
-cyan_free_0          1.028e+02 1.015e+02 104.06475
-k_cyan_free          5.767e-02 4.213e-02   0.07894
-k_cyan_free_bound    6.505e-02 2.892e-02   0.14633
-k_cyan_bound_free    2.849e-02 1.614e-02   0.05028
-k_JCZ38              9.246e-02 5.390e-02   0.15859
-k_J9Z38              5.353e-03 2.572e-03   0.01114
-k_JSE76              4.838e-02 1.376e-02   0.17009
-f_cyan_free_to_JCZ38 6.011e-01 5.028e-01   0.83792
-f_cyan_free_to_J9Z38 2.208e-01 5.028e-01   0.83792
-f_JCZ38_to_JSE76     9.999e-01 0.000e+00   1.00000
-f_JSE76_to_JCZ38     9.760e-01 5.181e-01   0.99935
-
-Estimated Eigenvalues of SFORB model(s):
-cyan_b1 cyan_b2  cyan_g 
-0.13942 0.01178 0.35948 
-
-Resulting formation fractions:
-                       ff
-cyan_free_JCZ38 6.011e-01
-cyan_free_J9Z38 2.208e-01
-cyan_free_sink  1.780e-01
-cyan_free       1.000e+00
-JCZ38_JSE76     9.999e-01
-JCZ38_sink      6.996e-05
-JSE76_JCZ38     9.760e-01
-JSE76_sink      2.403e-02
-
-Estimated disappearance times:
-         DT50   DT90 DT50back DT50_cyan_b1 DT50_cyan_b2
-cyan   23.390 157.60    47.44        4.971        58.82
-JCZ38   7.497  24.90       NA           NA           NA
-J9Z38 129.482 430.13       NA           NA           NA
-JSE76  14.326  47.59       NA           NA           NA
-
-</code></pre>
-<p></p>
-<caption>
-Hierarchical SFORB path 2 fit with two-component error
-</caption>
-<pre><code>
-saemix version used for fitting:      3.2 
-mkin version used for pre-fitting:  1.2.4 
-R version used for fitting:         4.2.3 
-Date of fit:     Thu Apr 20 20:49:20 2023 
-Date of summary: Thu Apr 20 21:04:34 2023 
-
-Equations:
-d_cyan_free/dt = - k_cyan_free * cyan_free - k_cyan_free_bound *
-           cyan_free + k_cyan_bound_free * cyan_bound
-d_cyan_bound/dt = + k_cyan_free_bound * cyan_free - k_cyan_bound_free *
-           cyan_bound
-d_JCZ38/dt = + f_cyan_free_to_JCZ38 * k_cyan_free * cyan_free - k_JCZ38
-           * JCZ38 + f_JSE76_to_JCZ38 * k_JSE76 * JSE76
-d_J9Z38/dt = + f_cyan_free_to_J9Z38 * k_cyan_free * cyan_free - k_J9Z38
-           * J9Z38
-d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76
-
-Data:
-433 observations of 4 variable(s) grouped in 5 datasets
-
-Model predictions using solution type deSolve 
-
-Fitted in 726.293 s
-Using 300, 100 iterations and 10 chains
-
-Variance model: Two-component variance function 
-
-Starting values for degradation parameters:
-          cyan_free_0       log_k_cyan_free log_k_cyan_free_bound 
-              101.751                -2.837                -3.016 
-log_k_cyan_bound_free           log_k_JCZ38           log_k_J9Z38 
-               -3.660                -2.299                -5.313 
-          log_k_JSE76          f_cyan_ilr_1          f_cyan_ilr_2 
-               -3.699                 0.672                 5.873 
-       f_JCZ38_qlogis        f_JSE76_qlogis 
-               13.216                13.338 
-
-Fixed degradation parameter values:
-None
-
-Starting values for random effects (square root of initial entries in omega):
-                      cyan_free_0 log_k_cyan_free log_k_cyan_free_bound
-cyan_free_0                 5.629           0.000                 0.000
-log_k_cyan_free             0.000           0.446                 0.000
-log_k_cyan_free_bound       0.000           0.000                 1.449
-log_k_cyan_bound_free       0.000           0.000                 0.000
-log_k_JCZ38                 0.000           0.000                 0.000
-log_k_J9Z38                 0.000           0.000                 0.000
-log_k_JSE76                 0.000           0.000                 0.000
-f_cyan_ilr_1                0.000           0.000                 0.000
-f_cyan_ilr_2                0.000           0.000                 0.000
-f_JCZ38_qlogis              0.000           0.000                 0.000
-f_JSE76_qlogis              0.000           0.000                 0.000
-                      log_k_cyan_bound_free log_k_JCZ38 log_k_J9Z38 log_k_JSE76
-cyan_free_0                           0.000      0.0000       0.000      0.0000
-log_k_cyan_free                       0.000      0.0000       0.000      0.0000
-log_k_cyan_free_bound                 0.000      0.0000       0.000      0.0000
-log_k_cyan_bound_free                 1.213      0.0000       0.000      0.0000
-log_k_JCZ38                           0.000      0.7801       0.000      0.0000
-log_k_J9Z38                           0.000      0.0000       1.575      0.0000
-log_k_JSE76                           0.000      0.0000       0.000      0.8078
-f_cyan_ilr_1                          0.000      0.0000       0.000      0.0000
-f_cyan_ilr_2                          0.000      0.0000       0.000      0.0000
-f_JCZ38_qlogis                        0.000      0.0000       0.000      0.0000
-f_JSE76_qlogis                        0.000      0.0000       0.000      0.0000
-                      f_cyan_ilr_1 f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis
-cyan_free_0                 0.0000         0.00           0.00           0.00
-log_k_cyan_free             0.0000         0.00           0.00           0.00
-log_k_cyan_free_bound       0.0000         0.00           0.00           0.00
-log_k_cyan_bound_free       0.0000         0.00           0.00           0.00
-log_k_JCZ38                 0.0000         0.00           0.00           0.00
-log_k_J9Z38                 0.0000         0.00           0.00           0.00
-log_k_JSE76                 0.0000         0.00           0.00           0.00
-f_cyan_ilr_1                0.6519         0.00           0.00           0.00
-f_cyan_ilr_2                0.0000        10.78           0.00           0.00
-f_JCZ38_qlogis              0.0000         0.00          13.96           0.00
-f_JSE76_qlogis              0.0000         0.00           0.00          14.69
-
-Starting values for error model parameters:
-a.1 b.1 
-  1   1 
-
-Results:
-
-Likelihood computed by importance sampling
-   AIC  BIC logLik
-  2240 2232  -1098
-
-Optimised parameters:
-                              est.     lower    upper
-cyan_free_0              101.10205  98.99221 103.2119
-log_k_cyan_free           -3.16929  -3.61395  -2.7246
-log_k_cyan_free_bound     -3.38259  -3.63022  -3.1350
-log_k_cyan_bound_free     -3.81075  -4.13888  -3.4826
-log_k_JCZ38               -2.42057  -3.00756  -1.8336
-log_k_J9Z38               -5.07501  -5.85138  -4.2986
-log_k_JSE76               -3.12442  -4.21277  -2.0361
-f_cyan_ilr_1               0.70577   0.35788   1.0537
-f_cyan_ilr_2               1.14824   0.15810   2.1384
-f_JCZ38_qlogis             3.52245   0.43257   6.6123
-f_JSE76_qlogis             5.65140 -21.22295  32.5257
-a.1                        2.07062   1.84329   2.2980
-b.1                        0.06227   0.05124   0.0733
-SD.log_k_cyan_free         0.49468   0.18566   0.8037
-SD.log_k_cyan_bound_free   0.28972   0.07188   0.5076
-SD.log_k_JCZ38             0.58852   0.16800   1.0090
-SD.log_k_J9Z38             0.82500   0.24730   1.4027
-SD.log_k_JSE76             1.19201   0.40313   1.9809
-SD.f_cyan_ilr_1            0.38534   0.13640   0.6343
-SD.f_cyan_ilr_2            0.72463   0.10076   1.3485
-SD.f_JCZ38_qlogis          1.38223  -0.20997   2.9744
-SD.f_JSE76_qlogis          2.07989 -72.53027  76.6901
-
-Correlation: 
-                      cyn_f_0 lg_k_c_ lg_k_cyn_f_ lg_k_cyn_b_ l__JCZ3 l__J9Z3
-log_k_cyan_free        0.1117                                                
-log_k_cyan_free_bound  0.1763  0.1828                                        
-log_k_cyan_bound_free  0.0120  0.0593  0.5030                                
-log_k_JCZ38           -0.0459 -0.0230 -0.0931     -0.0337                    
-log_k_J9Z38           -0.0381 -0.0123 -0.0139      0.0237      0.0063        
-log_k_JSE76           -0.0044 -0.0038 -0.0175     -0.0072      0.1120  0.0003
-f_cyan_ilr_1          -0.0199 -0.0087 -0.0407     -0.0233      0.0268 -0.0552
-f_cyan_ilr_2          -0.4806 -0.1015 -0.2291     -0.0269      0.1156  0.1113
-f_JCZ38_qlogis         0.1805  0.0825  0.3085      0.0963     -0.1674 -0.0314
-f_JSE76_qlogis        -0.1586 -0.0810 -0.3560     -0.1563      0.2025  0.0278
-                      l__JSE7 f_cy__1 f_cy__2 f_JCZ38
-log_k_cyan_free                                      
-log_k_cyan_free_bound                                
-log_k_cyan_bound_free                                
-log_k_JCZ38                                          
-log_k_J9Z38                                          
-log_k_JSE76                                          
-f_cyan_ilr_1           0.0024                        
-f_cyan_ilr_2           0.0087  0.0172                
-f_JCZ38_qlogis        -0.0016 -0.1047 -0.4656        
-f_JSE76_qlogis         0.0119  0.1034  0.4584 -0.8137
-
-Random effects:
-                           est.     lower   upper
-SD.log_k_cyan_free       0.4947   0.18566  0.8037
-SD.log_k_cyan_bound_free 0.2897   0.07188  0.5076
-SD.log_k_JCZ38           0.5885   0.16800  1.0090
-SD.log_k_J9Z38           0.8250   0.24730  1.4027
-SD.log_k_JSE76           1.1920   0.40313  1.9809
-SD.f_cyan_ilr_1          0.3853   0.13640  0.6343
-SD.f_cyan_ilr_2          0.7246   0.10076  1.3485
-SD.f_JCZ38_qlogis        1.3822  -0.20997  2.9744
-SD.f_JSE76_qlogis        2.0799 -72.53027 76.6901
-
-Variance model:
-       est.   lower  upper
-a.1 2.07062 1.84329 2.2980
-b.1 0.06227 0.05124 0.0733
-
-Backtransformed parameters:
-                          est.     lower     upper
-cyan_free_0          1.011e+02 9.899e+01 103.21190
-k_cyan_free          4.203e-02 2.695e-02   0.06557
-k_cyan_free_bound    3.396e-02 2.651e-02   0.04350
-k_cyan_bound_free    2.213e-02 1.594e-02   0.03073
-k_JCZ38              8.887e-02 4.941e-02   0.15984
-k_J9Z38              6.251e-03 2.876e-03   0.01359
-k_JSE76              4.396e-02 1.481e-02   0.13054
-f_cyan_free_to_JCZ38 6.590e-01 5.557e-01   0.95365
-f_cyan_free_to_J9Z38 2.429e-01 5.557e-01   0.95365
-f_JCZ38_to_JSE76     9.713e-01 6.065e-01   0.99866
-f_JSE76_to_JCZ38     9.965e-01 6.067e-10   1.00000
-
-Estimated Eigenvalues of SFORB model(s):
-cyan_b1 cyan_b2  cyan_g 
-0.08749 0.01063 0.40855 
-
-Resulting formation fractions:
-                     ff
-cyan_free_JCZ38 0.65905
-cyan_free_J9Z38 0.24291
-cyan_free_sink  0.09805
-cyan_free       1.00000
-JCZ38_JSE76     0.97132
-JCZ38_sink      0.02868
-JSE76_JCZ38     0.99650
-JSE76_sink      0.00350
-
-Estimated disappearance times:
-        DT50   DT90 DT50back DT50_cyan_b1 DT50_cyan_b2
-cyan   24.91 167.16    50.32        7.922        65.19
-JCZ38   7.80  25.91       NA           NA           NA
-J9Z38 110.89 368.36       NA           NA           NA
-JSE76  15.77  52.38       NA           NA           NA
-
-</code></pre>
-<p></p>
-</div>
-<div class="section level4">
-<h4 id="pathway-2-refined-fits">Pathway 2, refined fits<a class="anchor" aria-label="anchor" href="#pathway-2-refined-fits"></a>
-</h4>
-<caption>
-Hierarchical FOMC path 2 fit with reduced random effects, two-component
-error
-</caption>
-<pre><code>
-saemix version used for fitting:      3.2 
-mkin version used for pre-fitting:  1.2.4 
-R version used for fitting:         4.2.3 
-Date of fit:     Thu Apr 20 21:02:39 2023 
-Date of summary: Thu Apr 20 21:04:34 2023 
-
-Equations:
-d_cyan/dt = - (alpha/beta) * 1/((time/beta) + 1) * cyan
-d_JCZ38/dt = + f_cyan_to_JCZ38 * (alpha/beta) * 1/((time/beta) + 1) *
-           cyan - k_JCZ38 * JCZ38 + f_JSE76_to_JCZ38 * k_JSE76 * JSE76
-d_J9Z38/dt = + f_cyan_to_J9Z38 * (alpha/beta) * 1/((time/beta) + 1) *
-           cyan - k_J9Z38 * J9Z38
-d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76
-
-Data:
-433 observations of 4 variable(s) grouped in 5 datasets
-
-Model predictions using solution type deSolve 
-
-Fitted in 796.615 s
-Using 300, 100 iterations and 10 chains
-
-Variance model: Two-component variance function 
-
-Starting values for degradation parameters:
-        cyan_0    log_k_JCZ38    log_k_J9Z38    log_k_JSE76   f_cyan_ilr_1 
-      101.9028        -1.9055        -5.0249        -2.5646         0.6807 
-  f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis      log_alpha       log_beta 
-        4.8883        16.0676         9.3923        -0.1346         3.0364 
-
-Fixed degradation parameter values:
-None
-
-Starting values for random effects (square root of initial entries in omega):
-               cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1
-cyan_0          6.321       0.000       0.000       0.000       0.0000
-log_k_JCZ38     0.000       1.392       0.000       0.000       0.0000
-log_k_J9Z38     0.000       0.000       1.561       0.000       0.0000
-log_k_JSE76     0.000       0.000       0.000       3.614       0.0000
-f_cyan_ilr_1    0.000       0.000       0.000       0.000       0.6339
-f_cyan_ilr_2    0.000       0.000       0.000       0.000       0.0000
-f_JCZ38_qlogis  0.000       0.000       0.000       0.000       0.0000
-f_JSE76_qlogis  0.000       0.000       0.000       0.000       0.0000
-log_alpha       0.000       0.000       0.000       0.000       0.0000
-log_beta        0.000       0.000       0.000       0.000       0.0000
-               f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis log_alpha log_beta
-cyan_0                 0.00           0.00           0.00    0.0000   0.0000
-log_k_JCZ38            0.00           0.00           0.00    0.0000   0.0000
-log_k_J9Z38            0.00           0.00           0.00    0.0000   0.0000
-log_k_JSE76            0.00           0.00           0.00    0.0000   0.0000
-f_cyan_ilr_1           0.00           0.00           0.00    0.0000   0.0000
-f_cyan_ilr_2          10.41           0.00           0.00    0.0000   0.0000
-f_JCZ38_qlogis         0.00          12.24           0.00    0.0000   0.0000
-f_JSE76_qlogis         0.00           0.00          15.13    0.0000   0.0000
-log_alpha              0.00           0.00           0.00    0.3701   0.0000
-log_beta               0.00           0.00           0.00    0.0000   0.5662
-
-Starting values for error model parameters:
-a.1 b.1 
-  1   1 
-
-Results:
-
-Likelihood computed by importance sampling
-   AIC  BIC logLik
-  2251 2244  -1106
-
-Optimised parameters:
-                      est.   lower   upper
-cyan_0           101.05768      NA      NA
-log_k_JCZ38       -2.73252      NA      NA
-log_k_J9Z38       -5.07399      NA      NA
-log_k_JSE76       -3.52863      NA      NA
-f_cyan_ilr_1       0.72176      NA      NA
-f_cyan_ilr_2       1.34610      NA      NA
-f_JCZ38_qlogis     2.08337      NA      NA
-f_JSE76_qlogis  1590.31880      NA      NA
-log_alpha         -0.09336      NA      NA
-log_beta           3.10191      NA      NA
-a.1                2.08557 1.85439 2.31675
-b.1                0.06998 0.05800 0.08197
-SD.log_k_JCZ38     1.20053 0.43329 1.96777
-SD.log_k_J9Z38     0.85854 0.26708 1.45000
-SD.log_k_JSE76     0.62528 0.16061 1.08995
-SD.f_cyan_ilr_1    0.35190 0.12340 0.58039
-SD.f_cyan_ilr_2    0.85385 0.15391 1.55378
-SD.log_alpha       0.28971 0.08718 0.49225
-SD.log_beta        0.31614 0.05938 0.57290
-
-Correlation is not available
-
-Random effects:
-                  est.   lower  upper
-SD.log_k_JCZ38  1.2005 0.43329 1.9678
-SD.log_k_J9Z38  0.8585 0.26708 1.4500
-SD.log_k_JSE76  0.6253 0.16061 1.0900
-SD.f_cyan_ilr_1 0.3519 0.12340 0.5804
-SD.f_cyan_ilr_2 0.8538 0.15391 1.5538
-SD.log_alpha    0.2897 0.08718 0.4923
-SD.log_beta     0.3161 0.05938 0.5729
-
-Variance model:
-       est. lower   upper
-a.1 2.08557 1.854 2.31675
-b.1 0.06998 0.058 0.08197
-
-Backtransformed parameters:
-                      est. lower upper
-cyan_0           1.011e+02    NA    NA
-k_JCZ38          6.506e-02    NA    NA
-k_J9Z38          6.257e-03    NA    NA
-k_JSE76          2.935e-02    NA    NA
-f_cyan_to_JCZ38  6.776e-01    NA    NA
-f_cyan_to_J9Z38  2.442e-01    NA    NA
-f_JCZ38_to_JSE76 8.893e-01    NA    NA
-f_JSE76_to_JCZ38 1.000e+00    NA    NA
-alpha            9.109e-01    NA    NA
-beta             2.224e+01    NA    NA
-
-Resulting formation fractions:
-                 ff
-cyan_JCZ38  0.67761
-cyan_J9Z38  0.24417
-cyan_sink   0.07822
-JCZ38_JSE76 0.88928
-JCZ38_sink  0.11072
-JSE76_JCZ38 1.00000
-JSE76_sink  0.00000
-
-Estimated disappearance times:
-        DT50   DT90 DT50back
-cyan   25.36 256.37    77.18
-JCZ38  10.65  35.39       NA
-J9Z38 110.77 367.98       NA
-JSE76  23.62  78.47       NA
-
-</code></pre>
-<p></p>
-<caption>
-Hierarchical DFOP path 2 fit with reduced random effects, constant
-variance
-</caption>
-<pre><code>
-saemix version used for fitting:      3.2 
-mkin version used for pre-fitting:  1.2.4 
-R version used for fitting:         4.2.3 
-Date of fit:     Thu Apr 20 21:04:15 2023 
-Date of summary: Thu Apr 20 21:04:34 2023 
-
-Equations:
-d_cyan/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
-           time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))
-           * cyan
-d_JCZ38/dt = + f_cyan_to_JCZ38 * ((k1 * g * exp(-k1 * time) + k2 * (1 -
-           g) * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *
-           exp(-k2 * time))) * cyan - k_JCZ38 * JCZ38 +
-           f_JSE76_to_JCZ38 * k_JSE76 * JSE76
-d_J9Z38/dt = + f_cyan_to_J9Z38 * ((k1 * g * exp(-k1 * time) + k2 * (1 -
-           g) * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *
-           exp(-k2 * time))) * cyan - k_J9Z38 * J9Z38
-d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76
-
-Data:
-433 observations of 4 variable(s) grouped in 5 datasets
-
-Model predictions using solution type deSolve 
-
-Fitted in 893.328 s
-Using 300, 100 iterations and 10 chains
-
-Variance model: Constant variance 
-
-Starting values for degradation parameters:
-        cyan_0    log_k_JCZ38    log_k_J9Z38    log_k_JSE76   f_cyan_ilr_1 
-      102.4358        -2.3107        -5.3123        -3.7120         0.6753 
-  f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis         log_k1         log_k2 
-        1.1462        12.4095        12.3630        -1.9317        -4.4557 
-      g_qlogis 
-       -0.5648 
-
-Fixed degradation parameter values:
-None
-
-Starting values for random effects (square root of initial entries in omega):
-               cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1
-cyan_0          4.594      0.0000       0.000         0.0       0.0000
-log_k_JCZ38     0.000      0.7966       0.000         0.0       0.0000
-log_k_J9Z38     0.000      0.0000       1.561         0.0       0.0000
-log_k_JSE76     0.000      0.0000       0.000         0.8       0.0000
-f_cyan_ilr_1    0.000      0.0000       0.000         0.0       0.6349
-f_cyan_ilr_2    0.000      0.0000       0.000         0.0       0.0000
-f_JCZ38_qlogis  0.000      0.0000       0.000         0.0       0.0000
-f_JSE76_qlogis  0.000      0.0000       0.000         0.0       0.0000
-log_k1          0.000      0.0000       0.000         0.0       0.0000
-log_k2          0.000      0.0000       0.000         0.0       0.0000
-g_qlogis        0.000      0.0000       0.000         0.0       0.0000
-               f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis log_k1 log_k2
-cyan_0                0.000           0.00            0.0  0.000 0.0000
-log_k_JCZ38           0.000           0.00            0.0  0.000 0.0000
-log_k_J9Z38           0.000           0.00            0.0  0.000 0.0000
-log_k_JSE76           0.000           0.00            0.0  0.000 0.0000
-f_cyan_ilr_1          0.000           0.00            0.0  0.000 0.0000
-f_cyan_ilr_2          1.797           0.00            0.0  0.000 0.0000
-f_JCZ38_qlogis        0.000          13.85            0.0  0.000 0.0000
-f_JSE76_qlogis        0.000           0.00           14.1  0.000 0.0000
-log_k1                0.000           0.00            0.0  1.106 0.0000
-log_k2                0.000           0.00            0.0  0.000 0.6141
-g_qlogis              0.000           0.00            0.0  0.000 0.0000
-               g_qlogis
-cyan_0            0.000
-log_k_JCZ38       0.000
-log_k_J9Z38       0.000
-log_k_JSE76       0.000
-f_cyan_ilr_1      0.000
-f_cyan_ilr_2      0.000
-f_JCZ38_qlogis    0.000
-f_JSE76_qlogis    0.000
-log_k1            0.000
-log_k2            0.000
-g_qlogis          1.595
-
-Starting values for error model parameters:
-a.1 
-  1 
-
-Results:
-
-Likelihood computed by importance sampling
-   AIC  BIC logLik
-  2282 2274  -1121
-
-Optimised parameters:
-                     est.   lower  upper
-cyan_0           102.5254      NA     NA
-log_k_JCZ38       -2.9358      NA     NA
-log_k_J9Z38       -5.1424      NA     NA
-log_k_JSE76       -3.6458      NA     NA
-f_cyan_ilr_1       0.6957      NA     NA
-f_cyan_ilr_2       0.6635      NA     NA
-f_JCZ38_qlogis  4984.8163      NA     NA
-f_JSE76_qlogis     1.9415      NA     NA
-log_k1            -1.9456      NA     NA
-log_k2            -4.4705      NA     NA
-g_qlogis          -0.5117      NA     NA
-a.1                2.7455 2.55392 2.9370
-SD.log_k_JCZ38     1.3163 0.47635 2.1563
-SD.log_k_J9Z38     0.7162 0.16133 1.2711
-SD.log_k_JSE76     0.6457 0.15249 1.1390
-SD.f_cyan_ilr_1    0.3424 0.11714 0.5677
-SD.f_cyan_ilr_2    0.4524 0.09709 0.8077
-SD.log_k1          0.7353 0.25445 1.2161
-SD.log_k2          0.5137 0.18206 0.8453
-SD.g_qlogis        0.9857 0.35651 1.6148
-
-Correlation is not available
-
-Random effects:
-                  est.   lower  upper
-SD.log_k_JCZ38  1.3163 0.47635 2.1563
-SD.log_k_J9Z38  0.7162 0.16133 1.2711
-SD.log_k_JSE76  0.6457 0.15249 1.1390
-SD.f_cyan_ilr_1 0.3424 0.11714 0.5677
-SD.f_cyan_ilr_2 0.4524 0.09709 0.8077
-SD.log_k1       0.7353 0.25445 1.2161
-SD.log_k2       0.5137 0.18206 0.8453
-SD.g_qlogis     0.9857 0.35651 1.6148
-
-Variance model:
-     est. lower upper
-a.1 2.745 2.554 2.937
-
-Backtransformed parameters:
-                      est. lower upper
-cyan_0           1.025e+02    NA    NA
-k_JCZ38          5.309e-02    NA    NA
-k_J9Z38          5.844e-03    NA    NA
-k_JSE76          2.610e-02    NA    NA
-f_cyan_to_JCZ38  6.079e-01    NA    NA
-f_cyan_to_J9Z38  2.272e-01    NA    NA
-f_JCZ38_to_JSE76 1.000e+00    NA    NA
-f_JSE76_to_JCZ38 8.745e-01    NA    NA
-k1               1.429e-01    NA    NA
-k2               1.144e-02    NA    NA
-g                3.748e-01    NA    NA
-
-Resulting formation fractions:
-                ff
-cyan_JCZ38  0.6079
-cyan_J9Z38  0.2272
-cyan_sink   0.1649
-JCZ38_JSE76 1.0000
-JCZ38_sink  0.0000
-JSE76_JCZ38 0.8745
-JSE76_sink  0.1255
-
-Estimated disappearance times:
-        DT50   DT90 DT50back DT50_k1 DT50_k2
-cyan   22.29 160.20    48.22    4.85   60.58
-JCZ38  13.06  43.37       NA      NA      NA
-J9Z38 118.61 394.02       NA      NA      NA
-JSE76  26.56  88.22       NA      NA      NA
-
-</code></pre>
-<p></p>
-<caption>
-Hierarchical DFOP path 2 fit with reduced random effects, two-component
-error
-</caption>
-<pre><code>
-saemix version used for fitting:      3.2 
-mkin version used for pre-fitting:  1.2.4 
-R version used for fitting:         4.2.3 
-Date of fit:     Thu Apr 20 21:04:33 2023 
-Date of summary: Thu Apr 20 21:04:34 2023 
-
-Equations:
-d_cyan/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
-           time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))
-           * cyan
-d_JCZ38/dt = + f_cyan_to_JCZ38 * ((k1 * g * exp(-k1 * time) + k2 * (1 -
-           g) * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *
-           exp(-k2 * time))) * cyan - k_JCZ38 * JCZ38 +
-           f_JSE76_to_JCZ38 * k_JSE76 * JSE76
-d_J9Z38/dt = + f_cyan_to_J9Z38 * ((k1 * g * exp(-k1 * time) + k2 * (1 -
-           g) * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *
-           exp(-k2 * time))) * cyan - k_J9Z38 * J9Z38
-d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76
-
-Data:
-433 observations of 4 variable(s) grouped in 5 datasets
-
-Model predictions using solution type deSolve 
-
-Fitted in 910.788 s
-Using 300, 100 iterations and 10 chains
-
-Variance model: Two-component variance function 
-
-Starting values for degradation parameters:
-        cyan_0    log_k_JCZ38    log_k_J9Z38    log_k_JSE76   f_cyan_ilr_1 
-      101.7523        -1.5948        -5.0119        -2.2723         0.6719 
-  f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis         log_k1         log_k2 
-        5.1681        12.8238        12.4130        -2.0057        -4.5526 
-      g_qlogis 
-       -0.5805 
-
-Fixed degradation parameter values:
-None
-
-Starting values for random effects (square root of initial entries in omega):
-               cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1
-cyan_0          5.627       0.000       0.000       0.000       0.0000
-log_k_JCZ38     0.000       2.327       0.000       0.000       0.0000
-log_k_J9Z38     0.000       0.000       1.664       0.000       0.0000
-log_k_JSE76     0.000       0.000       0.000       4.566       0.0000
-f_cyan_ilr_1    0.000       0.000       0.000       0.000       0.6519
-f_cyan_ilr_2    0.000       0.000       0.000       0.000       0.0000
-f_JCZ38_qlogis  0.000       0.000       0.000       0.000       0.0000
-f_JSE76_qlogis  0.000       0.000       0.000       0.000       0.0000
-log_k1          0.000       0.000       0.000       0.000       0.0000
-log_k2          0.000       0.000       0.000       0.000       0.0000
-g_qlogis        0.000       0.000       0.000       0.000       0.0000
-               f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis log_k1 log_k2
-cyan_0                  0.0           0.00           0.00 0.0000 0.0000
-log_k_JCZ38             0.0           0.00           0.00 0.0000 0.0000
-log_k_J9Z38             0.0           0.00           0.00 0.0000 0.0000
-log_k_JSE76             0.0           0.00           0.00 0.0000 0.0000
-f_cyan_ilr_1            0.0           0.00           0.00 0.0000 0.0000
-f_cyan_ilr_2           10.1           0.00           0.00 0.0000 0.0000
-f_JCZ38_qlogis          0.0          13.99           0.00 0.0000 0.0000
-f_JSE76_qlogis          0.0           0.00          14.15 0.0000 0.0000
-log_k1                  0.0           0.00           0.00 0.8452 0.0000
-log_k2                  0.0           0.00           0.00 0.0000 0.5968
-g_qlogis                0.0           0.00           0.00 0.0000 0.0000
-               g_qlogis
-cyan_0            0.000
-log_k_JCZ38       0.000
-log_k_J9Z38       0.000
-log_k_JSE76       0.000
-f_cyan_ilr_1      0.000
-f_cyan_ilr_2      0.000
-f_JCZ38_qlogis    0.000
-f_JSE76_qlogis    0.000
-log_k1            0.000
-log_k2            0.000
-g_qlogis          1.691
-
-Starting values for error model parameters:
-a.1 b.1 
-  1   1 
-
-Results:
-
-Likelihood computed by importance sampling
-   AIC  BIC logLik
-  2232 2224  -1096
-
-Optimised parameters:
-                     est.   lower   upper
-cyan_0          101.20051      NA      NA
-log_k_JCZ38      -2.93542      NA      NA
-log_k_J9Z38      -5.03151      NA      NA
-log_k_JSE76      -3.67679      NA      NA
-f_cyan_ilr_1      0.67290      NA      NA
-f_cyan_ilr_2      0.99787      NA      NA
-f_JCZ38_qlogis  348.32484      NA      NA
-f_JSE76_qlogis    1.87846      NA      NA
-log_k1           -2.32738      NA      NA
-log_k2           -4.61295      NA      NA
-g_qlogis         -0.38342      NA      NA
-a.1               2.06184 1.83746 2.28622
-b.1               0.06329 0.05211 0.07447
-SD.log_k_JCZ38    1.29042 0.47468 2.10617
-SD.log_k_J9Z38    0.84235 0.25903 1.42566
-SD.log_k_JSE76    0.56930 0.13934 0.99926
-SD.f_cyan_ilr_1   0.35183 0.12298 0.58068
-SD.f_cyan_ilr_2   0.77269 0.17908 1.36631
-SD.log_k2         0.28549 0.09210 0.47888
-SD.g_qlogis       0.93830 0.34568 1.53093
-
-Correlation is not available
-
-Random effects:
-                  est.  lower  upper
-SD.log_k_JCZ38  1.2904 0.4747 2.1062
-SD.log_k_J9Z38  0.8423 0.2590 1.4257
-SD.log_k_JSE76  0.5693 0.1393 0.9993
-SD.f_cyan_ilr_1 0.3518 0.1230 0.5807
-SD.f_cyan_ilr_2 0.7727 0.1791 1.3663
-SD.log_k2       0.2855 0.0921 0.4789
-SD.g_qlogis     0.9383 0.3457 1.5309
-
-Variance model:
-       est.   lower   upper
-a.1 2.06184 1.83746 2.28622
-b.1 0.06329 0.05211 0.07447
-
-Backtransformed parameters:
-                      est. lower upper
-cyan_0           1.012e+02    NA    NA
-k_JCZ38          5.311e-02    NA    NA
-k_J9Z38          6.529e-03    NA    NA
-k_JSE76          2.530e-02    NA    NA
-f_cyan_to_JCZ38  6.373e-01    NA    NA
-f_cyan_to_J9Z38  2.461e-01    NA    NA
-f_JCZ38_to_JSE76 1.000e+00    NA    NA
-f_JSE76_to_JCZ38 8.674e-01    NA    NA
-k1               9.755e-02    NA    NA
-k2               9.922e-03    NA    NA
-g                4.053e-01    NA    NA
-
-Resulting formation fractions:
-                ff
-cyan_JCZ38  0.6373
-cyan_J9Z38  0.2461
-cyan_sink   0.1167
-JCZ38_JSE76 1.0000
-JCZ38_sink  0.0000
-JSE76_JCZ38 0.8674
-JSE76_sink  0.1326
-
-Estimated disappearance times:
-        DT50   DT90 DT50back DT50_k1 DT50_k2
-cyan   24.93 179.68    54.09   7.105   69.86
-JCZ38  13.05  43.36       NA      NA      NA
-J9Z38 106.16 352.67       NA      NA      NA
-JSE76  27.39  91.00       NA      NA      NA
-
-</code></pre>
-<p></p>
-<caption>
-Hierarchical SFORB path 2 fit with reduced random effects, constant
-variance
-</caption>
-<pre><code>
-saemix version used for fitting:      3.2 
-mkin version used for pre-fitting:  1.2.4 
-R version used for fitting:         4.2.3 
-Date of fit:     Thu Apr 20 21:04:09 2023 
-Date of summary: Thu Apr 20 21:04:34 2023 
-
-Equations:
-d_cyan_free/dt = - k_cyan_free * cyan_free - k_cyan_free_bound *
-           cyan_free + k_cyan_bound_free * cyan_bound
-d_cyan_bound/dt = + k_cyan_free_bound * cyan_free - k_cyan_bound_free *
-           cyan_bound
-d_JCZ38/dt = + f_cyan_free_to_JCZ38 * k_cyan_free * cyan_free - k_JCZ38
-           * JCZ38 + f_JSE76_to_JCZ38 * k_JSE76 * JSE76
-d_J9Z38/dt = + f_cyan_free_to_J9Z38 * k_cyan_free * cyan_free - k_J9Z38
-           * J9Z38
-d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76
-
-Data:
-433 observations of 4 variable(s) grouped in 5 datasets
-
-Model predictions using solution type deSolve 
-
-Fitted in 887.369 s
-Using 300, 100 iterations and 10 chains
-
-Variance model: Constant variance 
-
-Starting values for degradation parameters:
-          cyan_free_0       log_k_cyan_free log_k_cyan_free_bound 
-             102.4394               -2.7673               -2.8942 
-log_k_cyan_bound_free           log_k_JCZ38           log_k_J9Z38 
-              -3.6201               -2.3107               -5.3123 
-          log_k_JSE76          f_cyan_ilr_1          f_cyan_ilr_2 
-              -3.7120                0.6754                1.1448 
-       f_JCZ38_qlogis        f_JSE76_qlogis 
-              13.2672               13.3538 
-
-Fixed degradation parameter values:
-None
-
-Starting values for random effects (square root of initial entries in omega):
-                      cyan_free_0 log_k_cyan_free log_k_cyan_free_bound
-cyan_free_0                 4.589          0.0000                  0.00
-log_k_cyan_free             0.000          0.4849                  0.00
-log_k_cyan_free_bound       0.000          0.0000                  1.62
-log_k_cyan_bound_free       0.000          0.0000                  0.00
-log_k_JCZ38                 0.000          0.0000                  0.00
-log_k_J9Z38                 0.000          0.0000                  0.00
-log_k_JSE76                 0.000          0.0000                  0.00
-f_cyan_ilr_1                0.000          0.0000                  0.00
-f_cyan_ilr_2                0.000          0.0000                  0.00
-f_JCZ38_qlogis              0.000          0.0000                  0.00
-f_JSE76_qlogis              0.000          0.0000                  0.00
-                      log_k_cyan_bound_free log_k_JCZ38 log_k_J9Z38 log_k_JSE76
-cyan_free_0                           0.000      0.0000       0.000         0.0
-log_k_cyan_free                       0.000      0.0000       0.000         0.0
-log_k_cyan_free_bound                 0.000      0.0000       0.000         0.0
-log_k_cyan_bound_free                 1.197      0.0000       0.000         0.0
-log_k_JCZ38                           0.000      0.7966       0.000         0.0
-log_k_J9Z38                           0.000      0.0000       1.561         0.0
-log_k_JSE76                           0.000      0.0000       0.000         0.8
-f_cyan_ilr_1                          0.000      0.0000       0.000         0.0
-f_cyan_ilr_2                          0.000      0.0000       0.000         0.0
-f_JCZ38_qlogis                        0.000      0.0000       0.000         0.0
-f_JSE76_qlogis                        0.000      0.0000       0.000         0.0
-                      f_cyan_ilr_1 f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis
-cyan_free_0                 0.0000        0.000           0.00           0.00
-log_k_cyan_free             0.0000        0.000           0.00           0.00
-log_k_cyan_free_bound       0.0000        0.000           0.00           0.00
-log_k_cyan_bound_free       0.0000        0.000           0.00           0.00
-log_k_JCZ38                 0.0000        0.000           0.00           0.00
-log_k_J9Z38                 0.0000        0.000           0.00           0.00
-log_k_JSE76                 0.0000        0.000           0.00           0.00
-f_cyan_ilr_1                0.6349        0.000           0.00           0.00
-f_cyan_ilr_2                0.0000        1.797           0.00           0.00
-f_JCZ38_qlogis              0.0000        0.000          13.84           0.00
-f_JSE76_qlogis              0.0000        0.000           0.00          14.66
-
-Starting values for error model parameters:
-a.1 
-  1 
-
-Results:
-
-Likelihood computed by importance sampling
-   AIC  BIC logLik
-  2279 2272  -1120
-
-Optimised parameters:
-                              est.   lower  upper
-cyan_free_0               102.5621      NA     NA
-log_k_cyan_free            -2.8531      NA     NA
-log_k_cyan_free_bound      -2.6916      NA     NA
-log_k_cyan_bound_free      -3.5032      NA     NA
-log_k_JCZ38                -2.9436      NA     NA
-log_k_J9Z38                -5.1140      NA     NA
-log_k_JSE76                -3.6472      NA     NA
-f_cyan_ilr_1                0.6887      NA     NA
-f_cyan_ilr_2                0.6874      NA     NA
-f_JCZ38_qlogis           4063.6389      NA     NA
-f_JSE76_qlogis              1.9556      NA     NA
-a.1                         2.7460 2.55451 2.9376
-SD.log_k_cyan_free          0.3131 0.09841 0.5277
-SD.log_k_cyan_free_bound    0.8850 0.29909 1.4710
-SD.log_k_cyan_bound_free    0.6167 0.20391 1.0295
-SD.log_k_JCZ38              1.3555 0.49101 2.2200
-SD.log_k_J9Z38              0.7200 0.16166 1.2783
-SD.log_k_JSE76              0.6252 0.14619 1.1042
-SD.f_cyan_ilr_1             0.3386 0.11447 0.5627
-SD.f_cyan_ilr_2             0.4699 0.09810 0.8417
-
-Correlation is not available
-
-Random effects:
-                           est.   lower  upper
-SD.log_k_cyan_free       0.3131 0.09841 0.5277
-SD.log_k_cyan_free_bound 0.8850 0.29909 1.4710
-SD.log_k_cyan_bound_free 0.6167 0.20391 1.0295
-SD.log_k_JCZ38           1.3555 0.49101 2.2200
-SD.log_k_J9Z38           0.7200 0.16166 1.2783
-SD.log_k_JSE76           0.6252 0.14619 1.1042
-SD.f_cyan_ilr_1          0.3386 0.11447 0.5627
-SD.f_cyan_ilr_2          0.4699 0.09810 0.8417
-
-Variance model:
-     est. lower upper
-a.1 2.746 2.555 2.938
-
-Backtransformed parameters:
-                          est. lower upper
-cyan_free_0          1.026e+02    NA    NA
-k_cyan_free          5.767e-02    NA    NA
-k_cyan_free_bound    6.777e-02    NA    NA
-k_cyan_bound_free    3.010e-02    NA    NA
-k_JCZ38              5.267e-02    NA    NA
-k_J9Z38              6.012e-03    NA    NA
-k_JSE76              2.606e-02    NA    NA
-f_cyan_free_to_JCZ38 6.089e-01    NA    NA
-f_cyan_free_to_J9Z38 2.299e-01    NA    NA
-f_JCZ38_to_JSE76     1.000e+00    NA    NA
-f_JSE76_to_JCZ38     8.761e-01    NA    NA
-
-Estimated Eigenvalues of SFORB model(s):
-cyan_b1 cyan_b2  cyan_g 
- 0.1434  0.0121  0.3469 
-
-Resulting formation fractions:
-                    ff
-cyan_free_JCZ38 0.6089
-cyan_free_J9Z38 0.2299
-cyan_free_sink  0.1612
-cyan_free       1.0000
-JCZ38_JSE76     1.0000
-JCZ38_sink      0.0000
-JSE76_JCZ38     0.8761
-JSE76_sink      0.1239
-
-Estimated disappearance times:
-        DT50   DT90 DT50back DT50_cyan_b1 DT50_cyan_b2
-cyan   23.94 155.06    46.68        4.832        57.28
-JCZ38  13.16  43.71       NA           NA           NA
-J9Z38 115.30 383.02       NA           NA           NA
-JSE76  26.59  88.35       NA           NA           NA
-
-</code></pre>
-<p></p>
-<caption>
-Hierarchical SFORB path 2 fit with reduced random effects, two-component
-error
-</caption>
-<pre><code>
-saemix version used for fitting:      3.2 
-mkin version used for pre-fitting:  1.2.4 
-R version used for fitting:         4.2.3 
-Date of fit:     Thu Apr 20 21:04:32 2023 
-Date of summary: Thu Apr 20 21:04:34 2023 
-
-Equations:
-d_cyan_free/dt = - k_cyan_free * cyan_free - k_cyan_free_bound *
-           cyan_free + k_cyan_bound_free * cyan_bound
-d_cyan_bound/dt = + k_cyan_free_bound * cyan_free - k_cyan_bound_free *
-           cyan_bound
-d_JCZ38/dt = + f_cyan_free_to_JCZ38 * k_cyan_free * cyan_free - k_JCZ38
-           * JCZ38 + f_JSE76_to_JCZ38 * k_JSE76 * JSE76
-d_J9Z38/dt = + f_cyan_free_to_J9Z38 * k_cyan_free * cyan_free - k_J9Z38
-           * J9Z38
-d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76
-
-Data:
-433 observations of 4 variable(s) grouped in 5 datasets
-
-Model predictions using solution type deSolve 
-
-Fitted in 910.017 s
-Using 300, 100 iterations and 10 chains
-
-Variance model: Two-component variance function 
-
-Starting values for degradation parameters:
-          cyan_free_0       log_k_cyan_free log_k_cyan_free_bound 
-              101.751                -2.837                -3.016 
-log_k_cyan_bound_free           log_k_JCZ38           log_k_J9Z38 
-               -3.660                -2.299                -5.313 
-          log_k_JSE76          f_cyan_ilr_1          f_cyan_ilr_2 
-               -3.699                 0.672                 5.873 
-       f_JCZ38_qlogis        f_JSE76_qlogis 
-               13.216                13.338 
-
-Fixed degradation parameter values:
-None
-
-Starting values for random effects (square root of initial entries in omega):
-                      cyan_free_0 log_k_cyan_free log_k_cyan_free_bound
-cyan_free_0                 5.629           0.000                 0.000
-log_k_cyan_free             0.000           0.446                 0.000
-log_k_cyan_free_bound       0.000           0.000                 1.449
-log_k_cyan_bound_free       0.000           0.000                 0.000
-log_k_JCZ38                 0.000           0.000                 0.000
-log_k_J9Z38                 0.000           0.000                 0.000
-log_k_JSE76                 0.000           0.000                 0.000
-f_cyan_ilr_1                0.000           0.000                 0.000
-f_cyan_ilr_2                0.000           0.000                 0.000
-f_JCZ38_qlogis              0.000           0.000                 0.000
-f_JSE76_qlogis              0.000           0.000                 0.000
-                      log_k_cyan_bound_free log_k_JCZ38 log_k_J9Z38 log_k_JSE76
-cyan_free_0                           0.000      0.0000       0.000      0.0000
-log_k_cyan_free                       0.000      0.0000       0.000      0.0000
-log_k_cyan_free_bound                 0.000      0.0000       0.000      0.0000
-log_k_cyan_bound_free                 1.213      0.0000       0.000      0.0000
-log_k_JCZ38                           0.000      0.7801       0.000      0.0000
-log_k_J9Z38                           0.000      0.0000       1.575      0.0000
-log_k_JSE76                           0.000      0.0000       0.000      0.8078
-f_cyan_ilr_1                          0.000      0.0000       0.000      0.0000
-f_cyan_ilr_2                          0.000      0.0000       0.000      0.0000
-f_JCZ38_qlogis                        0.000      0.0000       0.000      0.0000
-f_JSE76_qlogis                        0.000      0.0000       0.000      0.0000
-                      f_cyan_ilr_1 f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis
-cyan_free_0                 0.0000         0.00           0.00           0.00
-log_k_cyan_free             0.0000         0.00           0.00           0.00
-log_k_cyan_free_bound       0.0000         0.00           0.00           0.00
-log_k_cyan_bound_free       0.0000         0.00           0.00           0.00
-log_k_JCZ38                 0.0000         0.00           0.00           0.00
-log_k_J9Z38                 0.0000         0.00           0.00           0.00
-log_k_JSE76                 0.0000         0.00           0.00           0.00
-f_cyan_ilr_1                0.6519         0.00           0.00           0.00
-f_cyan_ilr_2                0.0000        10.78           0.00           0.00
-f_JCZ38_qlogis              0.0000         0.00          13.96           0.00
-f_JSE76_qlogis              0.0000         0.00           0.00          14.69
-
-Starting values for error model parameters:
-a.1 b.1 
-  1   1 
-
-Results:
-
-Likelihood computed by importance sampling
-   AIC  BIC logLik
-  2236 2228  -1098
-
-Optimised parameters:
-                              est.   lower   upper
-cyan_free_0              100.72760      NA      NA
-log_k_cyan_free           -3.18281      NA      NA
-log_k_cyan_free_bound     -3.37924      NA      NA
-log_k_cyan_bound_free     -3.77107      NA      NA
-log_k_JCZ38               -2.92811      NA      NA
-log_k_J9Z38               -5.02759      NA      NA
-log_k_JSE76               -3.65835      NA      NA
-f_cyan_ilr_1               0.67390      NA      NA
-f_cyan_ilr_2               1.15106      NA      NA
-f_JCZ38_qlogis           827.82299      NA      NA
-f_JSE76_qlogis             1.83064      NA      NA
-a.1                        2.06921 1.84443 2.29399
-b.1                        0.06391 0.05267 0.07515
-SD.log_k_cyan_free         0.50518 0.18962 0.82075
-SD.log_k_cyan_bound_free   0.30991 0.08170 0.53813
-SD.log_k_JCZ38             1.26661 0.46578 2.06744
-SD.log_k_J9Z38             0.88272 0.27813 1.48730
-SD.log_k_JSE76             0.53050 0.12561 0.93538
-SD.f_cyan_ilr_1            0.35547 0.12461 0.58633
-SD.f_cyan_ilr_2            0.91446 0.20131 1.62761
-
-Correlation is not available
-
-Random effects:
-                           est.  lower  upper
-SD.log_k_cyan_free       0.5052 0.1896 0.8207
-SD.log_k_cyan_bound_free 0.3099 0.0817 0.5381
-SD.log_k_JCZ38           1.2666 0.4658 2.0674
-SD.log_k_J9Z38           0.8827 0.2781 1.4873
-SD.log_k_JSE76           0.5305 0.1256 0.9354
-SD.f_cyan_ilr_1          0.3555 0.1246 0.5863
-SD.f_cyan_ilr_2          0.9145 0.2013 1.6276
-
-Variance model:
-       est.   lower   upper
-a.1 2.06921 1.84443 2.29399
-b.1 0.06391 0.05267 0.07515
-
-Backtransformed parameters:
-                          est. lower upper
-cyan_free_0          1.007e+02    NA    NA
-k_cyan_free          4.147e-02    NA    NA
-k_cyan_free_bound    3.407e-02    NA    NA
-k_cyan_bound_free    2.303e-02    NA    NA
-k_JCZ38              5.350e-02    NA    NA
-k_J9Z38              6.555e-03    NA    NA
-k_JSE76              2.578e-02    NA    NA
-f_cyan_free_to_JCZ38 6.505e-01    NA    NA
-f_cyan_free_to_J9Z38 2.508e-01    NA    NA
-f_JCZ38_to_JSE76     1.000e+00    NA    NA
-f_JSE76_to_JCZ38     8.618e-01    NA    NA
-
-Estimated Eigenvalues of SFORB model(s):
-cyan_b1 cyan_b2  cyan_g 
-0.08768 0.01089 0.39821 
-
-Resulting formation fractions:
-                     ff
-cyan_free_JCZ38 0.65053
-cyan_free_J9Z38 0.25082
-cyan_free_sink  0.09864
-cyan_free       1.00000
-JCZ38_JSE76     1.00000
-JCZ38_sink      0.00000
-JSE76_JCZ38     0.86184
-JSE76_sink      0.13816
-
-Estimated disappearance times:
-        DT50   DT90 DT50back DT50_cyan_b1 DT50_cyan_b2
-cyan   25.32 164.79    49.61        7.906        63.64
-JCZ38  12.96  43.04       NA           NA           NA
-J9Z38 105.75 351.29       NA           NA           NA
-JSE76  26.89  89.33       NA           NA           NA
-
-</code></pre>
-<p></p>
-</div>
-</div>
-<div class="section level3">
-<h3 id="session-info">Session info<a class="anchor" aria-label="anchor" href="#session-info"></a>
-</h3>
-<pre><code>R version 4.2.3 (2023-03-15)
-Platform: x86_64-pc-linux-gnu (64-bit)
-Running under: Debian GNU/Linux 12 (bookworm)
-
-Matrix products: default
-BLAS:   /usr/lib/x86_64-linux-gnu/openblas-serial/libblas.so.3
-LAPACK: /usr/lib/x86_64-linux-gnu/openblas-serial/libopenblas-r0.3.21.so
-
-locale:
- [1] LC_CTYPE=de_DE.UTF-8       LC_NUMERIC=C              
- [3] LC_TIME=de_DE.UTF-8        LC_COLLATE=de_DE.UTF-8    
- [5] LC_MONETARY=de_DE.UTF-8    LC_MESSAGES=de_DE.UTF-8   
- [7] LC_PAPER=de_DE.UTF-8       LC_NAME=C                 
- [9] LC_ADDRESS=C               LC_TELEPHONE=C            
-[11] LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=C       
-
-attached base packages:
-[1] parallel  stats     graphics  grDevices utils     datasets  methods  
-[8] base     
-
-other attached packages:
-[1] saemix_3.2 npde_3.3   knitr_1.42 mkin_1.2.4
-
-loaded via a namespace (and not attached):
- [1] deSolve_1.35      zoo_1.8-12        tidyselect_1.2.0  xfun_0.38        
- [5] bslib_0.4.2       purrr_1.0.1       lattice_0.21-8    colorspace_2.1-0 
- [9] vctrs_0.6.1       generics_0.1.3    htmltools_0.5.5   yaml_2.3.7       
-[13] utf8_1.2.3        rlang_1.1.0       pkgbuild_1.4.0    pkgdown_2.0.7    
-[17] jquerylib_0.1.4   pillar_1.9.0      glue_1.6.2        DBI_1.1.3        
-[21] readxl_1.4.2      lifecycle_1.0.3   stringr_1.5.0     munsell_0.5.0    
-[25] gtable_0.3.3      cellranger_1.1.0  ragg_1.2.5        codetools_0.2-19 
-[29] memoise_2.0.1     evaluate_0.20     inline_0.3.19     callr_3.7.3      
-[33] fastmap_1.1.1     ps_1.7.4          lmtest_0.9-40     fansi_1.0.4      
-[37] highr_0.10        scales_1.2.1      cachem_1.0.7      desc_1.4.2       
-[41] jsonlite_1.8.4    systemfonts_1.0.4 fs_1.6.1          textshaping_0.3.6
-[45] gridExtra_2.3     ggplot2_3.4.2     digest_0.6.31     stringi_1.7.12   
-[49] processx_3.8.0    dplyr_1.1.1       grid_4.2.3        rprojroot_2.0.3  
-[53] cli_3.6.1         tools_4.2.3       magrittr_2.0.3    sass_0.4.5       
-[57] tibble_3.2.1      crayon_1.5.2      pkgconfig_2.0.3   prettyunits_1.1.1
-[61] rmarkdown_2.21    R6_2.5.1          mclust_6.0.0      nlme_3.1-162     
-[65] compiler_4.2.3   </code></pre>
-</div>
-<div class="section level3">
-<h3 id="hardware-info">Hardware info<a class="anchor" aria-label="anchor" href="#hardware-info"></a>
-</h3>
-<pre><code>CPU model: AMD Ryzen 9 7950X 16-Core Processor</code></pre>
-<pre><code>MemTotal:       64936316 kB</code></pre>
-</div>
-</div>
-  </div>
-
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-
-        <nav id="toc" data-toggle="toc"><h2 data-toc-skip>Contents</h2>
-    </nav>
-</div>
-
-</div>
-
-
-
-      <footer><div class="copyright">
-  <p></p>
-<p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p>
-<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer>
-</div>
-
-  
-
-
-  
-
-  </body>
-</html>
diff --git a/docs/dev/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-11-1.png b/docs/dev/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-11-1.png
deleted file mode 100644
index b969f2ff..00000000
--- a/docs/dev/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-11-1.png
+++ /dev/null
diff --git a/docs/dev/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-12-1.png b/docs/dev/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-12-1.png
deleted file mode 100644
index 60393da3..00000000
--- a/docs/dev/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-12-1.png
+++ /dev/null
diff --git a/docs/dev/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-13-1.png b/docs/dev/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-13-1.png
deleted file mode 100644
index b969f2ff..00000000
--- a/docs/dev/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-13-1.png
+++ /dev/null
diff --git a/docs/dev/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-14-1.png b/docs/dev/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-14-1.png
deleted file mode 100644
index 60393da3..00000000
--- a/docs/dev/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-14-1.png
+++ /dev/null
diff --git a/docs/dev/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-15-1.png b/docs/dev/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-15-1.png
deleted file mode 100644
index b9a410f7..00000000
--- a/docs/dev/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-15-1.png
+++ /dev/null
diff --git a/docs/dev/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-17-1.png b/docs/dev/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-17-1.png
deleted file mode 100644
index cf921dab..00000000
--- a/docs/dev/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-17-1.png
+++ /dev/null
diff --git a/docs/dev/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-18-1.png b/docs/dev/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-18-1.png
deleted file mode 100644
index ff732730..00000000
--- a/docs/dev/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-18-1.png
+++ /dev/null
diff --git a/docs/dev/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-19-1.png b/docs/dev/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-19-1.png
deleted file mode 100644
index e30011bc..00000000
--- a/docs/dev/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-19-1.png
+++ /dev/null
diff --git a/docs/dev/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-20-1.png b/docs/dev/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-20-1.png
deleted file mode 100644
index cf921dab..00000000
--- a/docs/dev/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-20-1.png
+++ /dev/null
diff --git a/docs/dev/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-21-1.png b/docs/dev/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-21-1.png
deleted file mode 100644
index ff732730..00000000
--- a/docs/dev/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-21-1.png
+++ /dev/null
diff --git a/docs/dev/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-22-1.png b/docs/dev/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-22-1.png
deleted file mode 100644
index e30011bc..00000000
--- a/docs/dev/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-22-1.png
+++ /dev/null
diff --git a/docs/dev/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-6-1.png b/docs/dev/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-6-1.png
deleted file mode 100644
index 4aad76df..00000000
--- a/docs/dev/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-6-1.png
+++ /dev/null
diff --git a/docs/dev/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-7-1.png b/docs/dev/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-7-1.png
deleted file mode 100644
index 4aad76df..00000000
--- a/docs/dev/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-7-1.png
+++ /dev/null
diff --git a/docs/dev/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-8-1.png b/docs/dev/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-8-1.png
deleted file mode 100644
index e30011bc..00000000
--- a/docs/dev/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-8-1.png
+++ /dev/null
diff --git a/docs/dev/articles/prebuilt/2022_dmta_parent.html b/docs/dev/articles/prebuilt/2022_dmta_parent.html
deleted file mode 100644
index 92259add..00000000
--- a/docs/dev/articles/prebuilt/2022_dmta_parent.html
+++ /dev/null
@@ -1,2225 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en">
-<head>
-<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
-<meta charset="utf-8">
-<meta http-equiv="X-UA-Compatible" content="IE=edge">
-<meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P • mkin</title>
-<!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous">
-<script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../../bootstrap-toc.css">
-<script src="../../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous">
-<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous">
-<!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../../pkgdown.css" rel="stylesheet">
-<script src="../../pkgdown.js"></script><meta property="og:title" content="Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P">
-<meta property="og:description" content="mkin">
-<meta name="robots" content="noindex">
-<!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]-->
-</head>
-<body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-article">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav">
-<li>
-  <a href="../../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu">
-<li>
-      <a href="../../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul>
-</li>
-<li>
-  <a href="../../news/index.html">News</a>
-</li>
-      </ul>
-<ul class="nav navbar-nav navbar-right">
-<li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul>
-</div>
-<!--/.nav-collapse -->
-  </div>
-<!--/.container -->
-</div>
-<!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header toc-ignore">
-      <h1 data-toc-skip>Testing hierarchical parent degradation kinetics
-with residue data on dimethenamid and dimethenamid-P</h1>
-                        <h4 data-toc-skip class="author">Johannes
-Ranke</h4>
-            
-            <h4 data-toc-skip class="date">Last change on 5 January
-2023, last compiled on 16 April 2023</h4>
-      
-      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/prebuilt/2022_dmta_parent.rmd" class="external-link"><code>vignettes/prebuilt/2022_dmta_parent.rmd</code></a></small>
-      <div class="hidden name"><code>2022_dmta_parent.rmd</code></div>
-
-    </div>
-
-    
-    
-<div class="section level2">
-<h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a>
-</h2>
-<p>The purpose of this document is to demonstrate how nonlinear
-hierarchical models (NLHM) based on the parent degradation models SFO,
-FOMC, DFOP and HS can be fitted with the mkin package.</p>
-<p>It was assembled in the course of work package 1.1 of Project Number
-173340 (Application of nonlinear hierarchical models to the kinetic
-evaluation of chemical degradation data) of the German Environment
-Agency carried out in 2022 and 2023.</p>
-<p>The mkin package is used in version 1.2.3. It contains the test data
-and the functions used in the evaluations. The <code>saemix</code>
-package is used as a backend for fitting the NLHM, but is also loaded to
-make the convergence plot function available.</p>
-<p>This document is processed with the <code>knitr</code> package, which
-also provides the <code>kable</code> function that is used to improve
-the display of tabular data in R markdown documents. For parallel
-processing, the <code>parallel</code> package is used.</p>
-<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://pkgdown.jrwb.de/mkin/">mkin</a></span><span class="op">)</span></span>
-<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://yihui.org/knitr/" class="external-link">knitr</a></span><span class="op">)</span></span>
-<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va">saemix</span><span class="op">)</span></span>
-<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va">parallel</span><span class="op">)</span></span>
-<span><span class="va">n_cores</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/parallel/detectCores.html" class="external-link">detectCores</a></span><span class="op">(</span><span class="op">)</span></span>
-<span><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/Sys.info.html" class="external-link">Sys.info</a></span><span class="op">(</span><span class="op">)</span><span class="op">[</span><span class="st">"sysname"</span><span class="op">]</span> <span class="op">==</span> <span class="st">"Windows"</span><span class="op">)</span> <span class="op">{</span></span>
-<span>  <span class="va">cl</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/parallel/makeCluster.html" class="external-link">makePSOCKcluster</a></span><span class="op">(</span><span class="va">n_cores</span><span class="op">)</span></span>
-<span><span class="op">}</span> <span class="kw">else</span> <span class="op">{</span></span>
-<span>  <span class="va">cl</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/parallel/makeCluster.html" class="external-link">makeForkCluster</a></span><span class="op">(</span><span class="va">n_cores</span><span class="op">)</span></span>
-<span><span class="op">}</span></span></code></pre></div>
-</div>
-<div class="section level2">
-<h2 id="data">Data<a class="anchor" aria-label="anchor" href="#data"></a>
-</h2>
-<p>The test data are available in the mkin package as an object of class
-<code>mkindsg</code> (mkin dataset group) under the identifier
-<code>dimethenamid_2018</code>. The following preprocessing steps are
-still necessary:</p>
-<ul>
-<li>The data available for the enantiomer dimethenamid-P (DMTAP) are
-renamed to have the same substance name as the data for the racemic
-mixture dimethenamid (DMTA). The reason for this is that no difference
-between their degradation behaviour was identified in the EU risk
-assessment.</li>
-<li>The data for transformation products and unnecessary columns are
-discarded</li>
-<li>The observation times of each dataset are multiplied with the
-corresponding normalisation factor also available in the dataset, in
-order to make it possible to describe all datasets with a single set of
-parameters that are independent of temperature</li>
-<li>Finally, datasets observed in the same soil (<code>Elliot 1</code>
-and <code>Elliot 2</code>) are combined, resulting in dimethenamid
-(DMTA) data from six soils.</li>
-</ul>
-<p>The following commented R code performs this preprocessing.</p>
-<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="co"># Apply a function to each of the seven datasets in the mkindsg object to create a list</span></span>
-<span><span class="va">dmta_ds</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fl">7</span>, <span class="kw">function</span><span class="op">(</span><span class="va">i</span><span class="op">)</span> <span class="op">{</span></span>
-<span>  <span class="va">ds_i</span> <span class="op">&lt;-</span> <span class="va">dimethenamid_2018</span><span class="op">$</span><span class="va">ds</span><span class="op">[[</span><span class="va">i</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">data</span>                     <span class="co"># Get a dataset</span></span>
-<span>  <span class="va">ds_i</span><span class="op">[</span><span class="va">ds_i</span><span class="op">$</span><span class="va">name</span> <span class="op">==</span> <span class="st">"DMTAP"</span>, <span class="st">"name"</span><span class="op">]</span> <span class="op">&lt;-</span>  <span class="st">"DMTA"</span>              <span class="co"># Rename DMTAP to DMTA</span></span>
-<span>  <span class="va">ds_i</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/subset.html" class="external-link">subset</a></span><span class="op">(</span><span class="va">ds_i</span>, <span class="va">name</span> <span class="op">==</span> <span class="st">"DMTA"</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"name"</span>, <span class="st">"time"</span>, <span class="st">"value"</span><span class="op">)</span><span class="op">)</span> <span class="co"># Select data</span></span>
-<span>  <span class="va">ds_i</span><span class="op">$</span><span class="va">time</span> <span class="op">&lt;-</span> <span class="va">ds_i</span><span class="op">$</span><span class="va">time</span> <span class="op">*</span> <span class="va">dimethenamid_2018</span><span class="op">$</span><span class="va">f_time_norm</span><span class="op">[</span><span class="va">i</span><span class="op">]</span>  <span class="co"># Normalise time</span></span>
-<span>  <span class="va">ds_i</span>                                                       <span class="co"># Return the dataset</span></span>
-<span><span class="op">}</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># Use dataset titles as names for the list elements</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">dmta_ds</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">sapply</a></span><span class="op">(</span><span class="va">dimethenamid_2018</span><span class="op">$</span><span class="va">ds</span>, <span class="kw">function</span><span class="op">(</span><span class="va">ds</span><span class="op">)</span> <span class="va">ds</span><span class="op">$</span><span class="va">title</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># Combine data for Elliot soil to obtain a named list with six elements</span></span>
-<span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot"</span><span class="op">]</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/cbind.html" class="external-link">rbind</a></span><span class="op">(</span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 1"</span><span class="op">]</span><span class="op">]</span>, <span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 2"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span> <span class="co">#</span></span>
-<span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 1"</span><span class="op">]</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="cn">NULL</span></span>
-<span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 2"</span><span class="op">]</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="cn">NULL</span></span></code></pre></div>
-<p>The following tables show the 6 datasets.</p>
-<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="kw">for</span> <span class="op">(</span><span class="va">ds_name</span> <span class="kw">in</span> <span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">dmta_ds</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
-<span>    <span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="fu"><a href="../../reference/mkin_long_to_wide.html">mkin_long_to_wide</a></span><span class="op">(</span><span class="va">dmta_ds</span><span class="op">[[</span><span class="va">ds_name</span><span class="op">]</span><span class="op">]</span><span class="op">)</span>,</span>
-<span>      caption <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste</a></span><span class="op">(</span><span class="st">"Dataset"</span>, <span class="va">ds_name</span><span class="op">)</span>,</span>
-<span>      label <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span><span class="st">"tab:"</span>, <span class="va">ds_name</span><span class="op">)</span>, booktabs <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span></span>
-<span>    <span class="fu"><a href="https://rdrr.io/r/base/cat.html" class="external-link">cat</a></span><span class="op">(</span><span class="st">"\n\\clearpage\n"</span><span class="op">)</span></span>
-<span><span class="op">}</span></span></code></pre></div>
-<table class="table">
-<caption>Dataset Calke</caption>
-<thead><tr class="header">
-<th align="right">time</th>
-<th align="right">DMTA</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="right">0</td>
-<td align="right">95.8</td>
-</tr>
-<tr class="even">
-<td align="right">0</td>
-<td align="right">98.7</td>
-</tr>
-<tr class="odd">
-<td align="right">14</td>
-<td align="right">60.5</td>
-</tr>
-<tr class="even">
-<td align="right">30</td>
-<td align="right">39.1</td>
-</tr>
-<tr class="odd">
-<td align="right">59</td>
-<td align="right">15.2</td>
-</tr>
-<tr class="even">
-<td align="right">120</td>
-<td align="right">4.8</td>
-</tr>
-<tr class="odd">
-<td align="right">120</td>
-<td align="right">4.6</td>
-</tr>
-</tbody>
-</table>
-<table class="table">
-<caption>Dataset Borstel</caption>
-<thead><tr class="header">
-<th align="right">time</th>
-<th align="right">DMTA</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="right">0.000000</td>
-<td align="right">100.5</td>
-</tr>
-<tr class="even">
-<td align="right">0.000000</td>
-<td align="right">99.6</td>
-</tr>
-<tr class="odd">
-<td align="right">1.941295</td>
-<td align="right">91.9</td>
-</tr>
-<tr class="even">
-<td align="right">1.941295</td>
-<td align="right">91.3</td>
-</tr>
-<tr class="odd">
-<td align="right">6.794534</td>
-<td align="right">81.8</td>
-</tr>
-<tr class="even">
-<td align="right">6.794534</td>
-<td align="right">82.1</td>
-</tr>
-<tr class="odd">
-<td align="right">13.589067</td>
-<td align="right">69.1</td>
-</tr>
-<tr class="even">
-<td align="right">13.589067</td>
-<td align="right">68.0</td>
-</tr>
-<tr class="odd">
-<td align="right">27.178135</td>
-<td align="right">51.4</td>
-</tr>
-<tr class="even">
-<td align="right">27.178135</td>
-<td align="right">51.4</td>
-</tr>
-<tr class="odd">
-<td align="right">56.297565</td>
-<td align="right">27.6</td>
-</tr>
-<tr class="even">
-<td align="right">56.297565</td>
-<td align="right">26.8</td>
-</tr>
-<tr class="odd">
-<td align="right">86.387643</td>
-<td align="right">15.7</td>
-</tr>
-<tr class="even">
-<td align="right">86.387643</td>
-<td align="right">15.3</td>
-</tr>
-<tr class="odd">
-<td align="right">115.507073</td>
-<td align="right">7.9</td>
-</tr>
-<tr class="even">
-<td align="right">115.507073</td>
-<td align="right">8.1</td>
-</tr>
-</tbody>
-</table>
-<table class="table">
-<caption>Dataset Flaach</caption>
-<thead><tr class="header">
-<th align="right">time</th>
-<th align="right">DMTA</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="right">0.0000000</td>
-<td align="right">96.5</td>
-</tr>
-<tr class="even">
-<td align="right">0.0000000</td>
-<td align="right">96.8</td>
-</tr>
-<tr class="odd">
-<td align="right">0.0000000</td>
-<td align="right">97.0</td>
-</tr>
-<tr class="even">
-<td align="right">0.6233856</td>
-<td align="right">82.9</td>
-</tr>
-<tr class="odd">
-<td align="right">0.6233856</td>
-<td align="right">86.7</td>
-</tr>
-<tr class="even">
-<td align="right">0.6233856</td>
-<td align="right">87.4</td>
-</tr>
-<tr class="odd">
-<td align="right">1.8701567</td>
-<td align="right">72.8</td>
-</tr>
-<tr class="even">
-<td align="right">1.8701567</td>
-<td align="right">69.9</td>
-</tr>
-<tr class="odd">
-<td align="right">1.8701567</td>
-<td align="right">71.9</td>
-</tr>
-<tr class="even">
-<td align="right">4.3636989</td>
-<td align="right">51.4</td>
-</tr>
-<tr class="odd">
-<td align="right">4.3636989</td>
-<td align="right">52.9</td>
-</tr>
-<tr class="even">
-<td align="right">4.3636989</td>
-<td align="right">48.6</td>
-</tr>
-<tr class="odd">
-<td align="right">8.7273979</td>
-<td align="right">28.5</td>
-</tr>
-<tr class="even">
-<td align="right">8.7273979</td>
-<td align="right">27.3</td>
-</tr>
-<tr class="odd">
-<td align="right">8.7273979</td>
-<td align="right">27.5</td>
-</tr>
-<tr class="even">
-<td align="right">13.0910968</td>
-<td align="right">14.8</td>
-</tr>
-<tr class="odd">
-<td align="right">13.0910968</td>
-<td align="right">13.4</td>
-</tr>
-<tr class="even">
-<td align="right">13.0910968</td>
-<td align="right">14.4</td>
-</tr>
-<tr class="odd">
-<td align="right">17.4547957</td>
-<td align="right">7.7</td>
-</tr>
-<tr class="even">
-<td align="right">17.4547957</td>
-<td align="right">7.3</td>
-</tr>
-<tr class="odd">
-<td align="right">17.4547957</td>
-<td align="right">8.1</td>
-</tr>
-<tr class="even">
-<td align="right">26.1821936</td>
-<td align="right">2.0</td>
-</tr>
-<tr class="odd">
-<td align="right">26.1821936</td>
-<td align="right">1.5</td>
-</tr>
-<tr class="even">
-<td align="right">26.1821936</td>
-<td align="right">1.9</td>
-</tr>
-<tr class="odd">
-<td align="right">34.9095915</td>
-<td align="right">1.3</td>
-</tr>
-<tr class="even">
-<td align="right">34.9095915</td>
-<td align="right">1.0</td>
-</tr>
-<tr class="odd">
-<td align="right">34.9095915</td>
-<td align="right">1.1</td>
-</tr>
-<tr class="even">
-<td align="right">43.6369893</td>
-<td align="right">0.9</td>
-</tr>
-<tr class="odd">
-<td align="right">43.6369893</td>
-<td align="right">0.7</td>
-</tr>
-<tr class="even">
-<td align="right">43.6369893</td>
-<td align="right">0.7</td>
-</tr>
-<tr class="odd">
-<td align="right">52.3643872</td>
-<td align="right">0.6</td>
-</tr>
-<tr class="even">
-<td align="right">52.3643872</td>
-<td align="right">0.4</td>
-</tr>
-<tr class="odd">
-<td align="right">52.3643872</td>
-<td align="right">0.5</td>
-</tr>
-<tr class="even">
-<td align="right">74.8062674</td>
-<td align="right">0.4</td>
-</tr>
-<tr class="odd">
-<td align="right">74.8062674</td>
-<td align="right">0.3</td>
-</tr>
-<tr class="even">
-<td align="right">74.8062674</td>
-<td align="right">0.3</td>
-</tr>
-</tbody>
-</table>
-<table class="table">
-<caption>Dataset BBA 2.2</caption>
-<thead><tr class="header">
-<th align="right">time</th>
-<th align="right">DMTA</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="right">0.0000000</td>
-<td align="right">98.09</td>
-</tr>
-<tr class="even">
-<td align="right">0.0000000</td>
-<td align="right">98.77</td>
-</tr>
-<tr class="odd">
-<td align="right">0.7678922</td>
-<td align="right">93.52</td>
-</tr>
-<tr class="even">
-<td align="right">0.7678922</td>
-<td align="right">92.03</td>
-</tr>
-<tr class="odd">
-<td align="right">2.3036765</td>
-<td align="right">88.39</td>
-</tr>
-<tr class="even">
-<td align="right">2.3036765</td>
-<td align="right">87.18</td>
-</tr>
-<tr class="odd">
-<td align="right">5.3752452</td>
-<td align="right">69.38</td>
-</tr>
-<tr class="even">
-<td align="right">5.3752452</td>
-<td align="right">71.06</td>
-</tr>
-<tr class="odd">
-<td align="right">10.7504904</td>
-<td align="right">45.21</td>
-</tr>
-<tr class="even">
-<td align="right">10.7504904</td>
-<td align="right">46.81</td>
-</tr>
-<tr class="odd">
-<td align="right">16.1257355</td>
-<td align="right">30.54</td>
-</tr>
-<tr class="even">
-<td align="right">16.1257355</td>
-<td align="right">30.07</td>
-</tr>
-<tr class="odd">
-<td align="right">21.5009807</td>
-<td align="right">21.60</td>
-</tr>
-<tr class="even">
-<td align="right">21.5009807</td>
-<td align="right">20.41</td>
-</tr>
-<tr class="odd">
-<td align="right">32.2514711</td>
-<td align="right">9.10</td>
-</tr>
-<tr class="even">
-<td align="right">32.2514711</td>
-<td align="right">9.70</td>
-</tr>
-<tr class="odd">
-<td align="right">43.0019614</td>
-<td align="right">6.58</td>
-</tr>
-<tr class="even">
-<td align="right">43.0019614</td>
-<td align="right">6.31</td>
-</tr>
-<tr class="odd">
-<td align="right">53.7524518</td>
-<td align="right">3.47</td>
-</tr>
-<tr class="even">
-<td align="right">53.7524518</td>
-<td align="right">3.52</td>
-</tr>
-<tr class="odd">
-<td align="right">64.5029421</td>
-<td align="right">3.40</td>
-</tr>
-<tr class="even">
-<td align="right">64.5029421</td>
-<td align="right">3.67</td>
-</tr>
-<tr class="odd">
-<td align="right">91.3791680</td>
-<td align="right">1.62</td>
-</tr>
-<tr class="even">
-<td align="right">91.3791680</td>
-<td align="right">1.62</td>
-</tr>
-</tbody>
-</table>
-<table class="table">
-<caption>Dataset BBA 2.3</caption>
-<thead><tr class="header">
-<th align="right">time</th>
-<th align="right">DMTA</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="right">0.0000000</td>
-<td align="right">99.33</td>
-</tr>
-<tr class="even">
-<td align="right">0.0000000</td>
-<td align="right">97.44</td>
-</tr>
-<tr class="odd">
-<td align="right">0.6733938</td>
-<td align="right">93.73</td>
-</tr>
-<tr class="even">
-<td align="right">0.6733938</td>
-<td align="right">93.77</td>
-</tr>
-<tr class="odd">
-<td align="right">2.0201814</td>
-<td align="right">87.84</td>
-</tr>
-<tr class="even">
-<td align="right">2.0201814</td>
-<td align="right">89.82</td>
-</tr>
-<tr class="odd">
-<td align="right">4.7137565</td>
-<td align="right">71.61</td>
-</tr>
-<tr class="even">
-<td align="right">4.7137565</td>
-<td align="right">71.42</td>
-</tr>
-<tr class="odd">
-<td align="right">9.4275131</td>
-<td align="right">45.60</td>
-</tr>
-<tr class="even">
-<td align="right">9.4275131</td>
-<td align="right">45.42</td>
-</tr>
-<tr class="odd">
-<td align="right">14.1412696</td>
-<td align="right">31.12</td>
-</tr>
-<tr class="even">
-<td align="right">14.1412696</td>
-<td align="right">31.68</td>
-</tr>
-<tr class="odd">
-<td align="right">18.8550262</td>
-<td align="right">23.20</td>
-</tr>
-<tr class="even">
-<td align="right">18.8550262</td>
-<td align="right">24.13</td>
-</tr>
-<tr class="odd">
-<td align="right">28.2825393</td>
-<td align="right">9.43</td>
-</tr>
-<tr class="even">
-<td align="right">28.2825393</td>
-<td align="right">9.82</td>
-</tr>
-<tr class="odd">
-<td align="right">37.7100523</td>
-<td align="right">7.08</td>
-</tr>
-<tr class="even">
-<td align="right">37.7100523</td>
-<td align="right">8.64</td>
-</tr>
-<tr class="odd">
-<td align="right">47.1375654</td>
-<td align="right">4.41</td>
-</tr>
-<tr class="even">
-<td align="right">47.1375654</td>
-<td align="right">4.78</td>
-</tr>
-<tr class="odd">
-<td align="right">56.5650785</td>
-<td align="right">4.92</td>
-</tr>
-<tr class="even">
-<td align="right">56.5650785</td>
-<td align="right">5.08</td>
-</tr>
-<tr class="odd">
-<td align="right">80.1338612</td>
-<td align="right">2.13</td>
-</tr>
-<tr class="even">
-<td align="right">80.1338612</td>
-<td align="right">2.23</td>
-</tr>
-</tbody>
-</table>
-<table class="table">
-<caption>Dataset Elliot</caption>
-<thead><tr class="header">
-<th align="right">time</th>
-<th align="right">DMTA</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="right">0.000000</td>
-<td align="right">97.5</td>
-</tr>
-<tr class="even">
-<td align="right">0.000000</td>
-<td align="right">100.7</td>
-</tr>
-<tr class="odd">
-<td align="right">1.228478</td>
-<td align="right">86.4</td>
-</tr>
-<tr class="even">
-<td align="right">1.228478</td>
-<td align="right">88.5</td>
-</tr>
-<tr class="odd">
-<td align="right">3.685435</td>
-<td align="right">69.8</td>
-</tr>
-<tr class="even">
-<td align="right">3.685435</td>
-<td align="right">77.1</td>
-</tr>
-<tr class="odd">
-<td align="right">8.599349</td>
-<td align="right">59.0</td>
-</tr>
-<tr class="even">
-<td align="right">8.599349</td>
-<td align="right">54.2</td>
-</tr>
-<tr class="odd">
-<td align="right">17.198697</td>
-<td align="right">31.3</td>
-</tr>
-<tr class="even">
-<td align="right">17.198697</td>
-<td align="right">33.5</td>
-</tr>
-<tr class="odd">
-<td align="right">25.798046</td>
-<td align="right">19.6</td>
-</tr>
-<tr class="even">
-<td align="right">25.798046</td>
-<td align="right">20.9</td>
-</tr>
-<tr class="odd">
-<td align="right">34.397395</td>
-<td align="right">13.3</td>
-</tr>
-<tr class="even">
-<td align="right">34.397395</td>
-<td align="right">15.8</td>
-</tr>
-<tr class="odd">
-<td align="right">51.596092</td>
-<td align="right">6.7</td>
-</tr>
-<tr class="even">
-<td align="right">51.596092</td>
-<td align="right">8.7</td>
-</tr>
-<tr class="odd">
-<td align="right">68.794789</td>
-<td align="right">8.8</td>
-</tr>
-<tr class="even">
-<td align="right">68.794789</td>
-<td align="right">8.7</td>
-</tr>
-<tr class="odd">
-<td align="right">103.192184</td>
-<td align="right">6.0</td>
-</tr>
-<tr class="even">
-<td align="right">103.192184</td>
-<td align="right">4.4</td>
-</tr>
-<tr class="odd">
-<td align="right">146.188928</td>
-<td align="right">3.3</td>
-</tr>
-<tr class="even">
-<td align="right">146.188928</td>
-<td align="right">2.8</td>
-</tr>
-<tr class="odd">
-<td align="right">223.583066</td>
-<td align="right">1.4</td>
-</tr>
-<tr class="even">
-<td align="right">223.583066</td>
-<td align="right">1.8</td>
-</tr>
-<tr class="odd">
-<td align="right">0.000000</td>
-<td align="right">93.4</td>
-</tr>
-<tr class="even">
-<td align="right">0.000000</td>
-<td align="right">103.2</td>
-</tr>
-<tr class="odd">
-<td align="right">1.228478</td>
-<td align="right">89.2</td>
-</tr>
-<tr class="even">
-<td align="right">1.228478</td>
-<td align="right">86.6</td>
-</tr>
-<tr class="odd">
-<td align="right">3.685435</td>
-<td align="right">78.2</td>
-</tr>
-<tr class="even">
-<td align="right">3.685435</td>
-<td align="right">78.1</td>
-</tr>
-<tr class="odd">
-<td align="right">8.599349</td>
-<td align="right">55.6</td>
-</tr>
-<tr class="even">
-<td align="right">8.599349</td>
-<td align="right">53.0</td>
-</tr>
-<tr class="odd">
-<td align="right">17.198697</td>
-<td align="right">33.7</td>
-</tr>
-<tr class="even">
-<td align="right">17.198697</td>
-<td align="right">33.2</td>
-</tr>
-<tr class="odd">
-<td align="right">25.798046</td>
-<td align="right">20.9</td>
-</tr>
-<tr class="even">
-<td align="right">25.798046</td>
-<td align="right">19.9</td>
-</tr>
-<tr class="odd">
-<td align="right">34.397395</td>
-<td align="right">18.2</td>
-</tr>
-<tr class="even">
-<td align="right">34.397395</td>
-<td align="right">12.7</td>
-</tr>
-<tr class="odd">
-<td align="right">51.596092</td>
-<td align="right">7.8</td>
-</tr>
-<tr class="even">
-<td align="right">51.596092</td>
-<td align="right">9.0</td>
-</tr>
-<tr class="odd">
-<td align="right">68.794789</td>
-<td align="right">11.4</td>
-</tr>
-<tr class="even">
-<td align="right">68.794789</td>
-<td align="right">9.0</td>
-</tr>
-<tr class="odd">
-<td align="right">103.192184</td>
-<td align="right">3.9</td>
-</tr>
-<tr class="even">
-<td align="right">103.192184</td>
-<td align="right">4.4</td>
-</tr>
-<tr class="odd">
-<td align="right">146.188928</td>
-<td align="right">2.6</td>
-</tr>
-<tr class="even">
-<td align="right">146.188928</td>
-<td align="right">3.4</td>
-</tr>
-<tr class="odd">
-<td align="right">223.583066</td>
-<td align="right">2.0</td>
-</tr>
-<tr class="even">
-<td align="right">223.583066</td>
-<td align="right">1.7</td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="section level2">
-<h2 id="separate-evaluations">Separate evaluations<a class="anchor" aria-label="anchor" href="#separate-evaluations"></a>
-</h2>
-<p>In order to obtain suitable starting parameters for the NLHM fits,
-separate fits of the four models to the data for each soil are generated
-using the <code>mmkin</code> function from the <code>mkin</code>
-package. In a first step, constant variance is assumed. Convergence is
-checked with the <code>status</code> function.</p>
-<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">deg_mods</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"FOMC"</span>, <span class="st">"DFOP"</span>, <span class="st">"HS"</span><span class="op">)</span></span>
-<span><span class="va">f_sep_const</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/mmkin.html">mmkin</a></span><span class="op">(</span></span>
-<span>  <span class="va">deg_mods</span>,</span>
-<span>  <span class="va">dmta_ds</span>,</span>
-<span>  error_model <span class="op">=</span> <span class="st">"const"</span>,</span>
-<span>  quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu"><a href="../../reference/status.html">status</a></span><span class="op">(</span><span class="va">f_sep_const</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
-<thead><tr class="header">
-<th align="left"></th>
-<th align="left">Calke</th>
-<th align="left">Borstel</th>
-<th align="left">Flaach</th>
-<th align="left">BBA 2.2</th>
-<th align="left">BBA 2.3</th>
-<th align="left">Elliot</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="left">SFO</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-</tr>
-<tr class="even">
-<td align="left">FOMC</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-</tr>
-<tr class="odd">
-<td align="left">DFOP</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-</tr>
-<tr class="even">
-<td align="left">HS</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">C</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-</tr>
-</tbody>
-</table>
-<p>In the table above, OK indicates convergence, and C indicates failure
-to converge. All separate fits with constant variance converged, with
-the sole exception of the HS fit to the BBA 2.2 data. To prepare for
-fitting NLHM using the two-component error model, the separate fits are
-updated assuming two-component error.</p>
-<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">f_sep_tc</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_sep_const</span>, error_model <span class="op">=</span> <span class="st">"tc"</span><span class="op">)</span></span>
-<span><span class="fu"><a href="../../reference/status.html">status</a></span><span class="op">(</span><span class="va">f_sep_tc</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
-<thead><tr class="header">
-<th align="left"></th>
-<th align="left">Calke</th>
-<th align="left">Borstel</th>
-<th align="left">Flaach</th>
-<th align="left">BBA 2.2</th>
-<th align="left">BBA 2.3</th>
-<th align="left">Elliot</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="left">SFO</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-</tr>
-<tr class="even">
-<td align="left">FOMC</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">C</td>
-<td align="left">OK</td>
-</tr>
-<tr class="odd">
-<td align="left">DFOP</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">C</td>
-<td align="left">OK</td>
-<td align="left">C</td>
-<td align="left">OK</td>
-</tr>
-<tr class="even">
-<td align="left">HS</td>
-<td align="left">OK</td>
-<td align="left">C</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-</tr>
-</tbody>
-</table>
-<p>Using the two-component error model, the one fit that did not
-converge with constant variance did converge, but other non-SFO fits
-failed to converge.</p>
-</div>
-<div class="section level2">
-<h2 id="hierarchichal-model-fits">Hierarchichal model fits<a class="anchor" aria-label="anchor" href="#hierarchichal-model-fits"></a>
-</h2>
-<p>The following code fits eight versions of hierarchical models to the
-data, using SFO, FOMC, DFOP and HS for the parent compound, and using
-either constant variance or two-component error for the error model. The
-default parameter distribution model in mkin allows for variation of all
-degradation parameters across the assumed population of soils. In other
-words, each degradation parameter is associated with a random effect as
-a first step. The <code>mhmkin</code> function makes it possible to fit
-all eight versions in parallel (given a sufficient number of computing
-cores being available) to save execution time.</p>
-<p>Convergence plots and summaries for these fits are shown in the
-appendix.</p>
-<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">f_saem</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/mhmkin.html">mhmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">f_sep_const</span>, <span class="va">f_sep_tc</span><span class="op">)</span>, transformations <span class="op">=</span> <span class="st">"saemix"</span><span class="op">)</span></span></code></pre></div>
-<p>The output of the <code>status</code> function shows that all fits
-terminated successfully.</p>
-<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="../../reference/status.html">status</a></span><span class="op">(</span><span class="va">f_saem</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
-<thead><tr class="header">
-<th align="left"></th>
-<th align="left">const</th>
-<th align="left">tc</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="left">SFO</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-</tr>
-<tr class="even">
-<td align="left">FOMC</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-</tr>
-<tr class="odd">
-<td align="left">DFOP</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-</tr>
-<tr class="even">
-<td align="left">HS</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-</tr>
-</tbody>
-</table>
-<p>The AIC and BIC values show that the biphasic models DFOP and HS give
-the best fits.</p>
-<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_saem</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span>digits <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
-<thead><tr class="header">
-<th align="left"></th>
-<th align="right">npar</th>
-<th align="right">AIC</th>
-<th align="right">BIC</th>
-<th align="right">Lik</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="left">SFO const</td>
-<td align="right">5</td>
-<td align="right">796.3</td>
-<td align="right">795.3</td>
-<td align="right">-393.2</td>
-</tr>
-<tr class="even">
-<td align="left">SFO tc</td>
-<td align="right">6</td>
-<td align="right">798.3</td>
-<td align="right">797.1</td>
-<td align="right">-393.2</td>
-</tr>
-<tr class="odd">
-<td align="left">FOMC const</td>
-<td align="right">7</td>
-<td align="right">734.2</td>
-<td align="right">732.7</td>
-<td align="right">-360.1</td>
-</tr>
-<tr class="even">
-<td align="left">FOMC tc</td>
-<td align="right">8</td>
-<td align="right">720.4</td>
-<td align="right">718.8</td>
-<td align="right">-352.2</td>
-</tr>
-<tr class="odd">
-<td align="left">DFOP const</td>
-<td align="right">9</td>
-<td align="right">711.8</td>
-<td align="right">710.0</td>
-<td align="right">-346.9</td>
-</tr>
-<tr class="even">
-<td align="left">HS const</td>
-<td align="right">9</td>
-<td align="right">714.0</td>
-<td align="right">712.1</td>
-<td align="right">-348.0</td>
-</tr>
-<tr class="odd">
-<td align="left">DFOP tc</td>
-<td align="right">10</td>
-<td align="right">665.5</td>
-<td align="right">663.4</td>
-<td align="right">-322.8</td>
-</tr>
-<tr class="even">
-<td align="left">HS tc</td>
-<td align="right">10</td>
-<td align="right">667.1</td>
-<td align="right">665.0</td>
-<td align="right">-323.6</td>
-</tr>
-</tbody>
-</table>
-<p>The DFOP model is preferred here, as it has a better mechanistic
-basis for batch experiments with constant incubation conditions. Also,
-it shows the lowest AIC and BIC values in the first set of fits when
-combined with the two-component error model. Therefore, the DFOP model
-was selected for further refinements of the fits with the aim to make
-the model fully identifiable.</p>
-<div class="section level3">
-<h3 id="parameter-identifiability-based-on-the-fisher-information-matrix">Parameter identifiability based on the Fisher Information
-Matrix<a class="anchor" aria-label="anchor" href="#parameter-identifiability-based-on-the-fisher-information-matrix"></a>
-</h3>
-<p>Using the <code>illparms</code> function, ill-defined statistical
-model parameters such as standard deviations of the degradation
-parameters in the population and error model parameters can be
-found.</p>
-<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="../../reference/illparms.html">illparms</a></span><span class="op">(</span><span class="va">f_saem</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
-<thead><tr class="header">
-<th align="left"></th>
-<th align="left">const</th>
-<th align="left">tc</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="left">SFO</td>
-<td align="left"></td>
-<td align="left">b.1</td>
-</tr>
-<tr class="even">
-<td align="left">FOMC</td>
-<td align="left"></td>
-<td align="left">sd(DMTA_0)</td>
-</tr>
-<tr class="odd">
-<td align="left">DFOP</td>
-<td align="left">sd(k2)</td>
-<td align="left">sd(k2)</td>
-</tr>
-<tr class="even">
-<td align="left">HS</td>
-<td align="left"></td>
-<td align="left">sd(tb)</td>
-</tr>
-</tbody>
-</table>
-<p>According to the <code>illparms</code> function, the fitted standard
-deviation of the second kinetic rate constant <code>k2</code> is
-ill-defined in both DFOP fits. This suggests that different values would
-be obtained for this standard deviation when using different starting
-values.</p>
-<p>The thus identified overparameterisation is addressed by removing the
-random effect for <code>k2</code> from the parameter model.</p>
-<div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">f_saem_dfop_tc_no_ranef_k2</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_saem</span><span class="op">[[</span><span class="st">"DFOP"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span>,</span>
-<span>  no_random_effect <span class="op">=</span> <span class="st">"k2"</span><span class="op">)</span></span></code></pre></div>
-<p>For the resulting fit, it is checked whether there are still
-ill-defined parameters,</p>
-<div class="sourceCode" id="cb11"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="../../reference/illparms.html">illparms</a></span><span class="op">(</span><span class="va">f_saem_dfop_tc_no_ranef_k2</span><span class="op">)</span></span></code></pre></div>
-<p>which is not the case. Below, the refined model is compared with the
-previous best model. The model without random effect for <code>k2</code>
-is a reduced version of the previous model. Therefore, the models are
-nested and can be compared using the likelihood ratio test. This is
-achieved with the argument <code>test = TRUE</code> to the
-<code>anova</code> function.</p>
-<div class="sourceCode" id="cb12"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_saem</span><span class="op">[[</span><span class="st">"DFOP"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span>, <span class="va">f_saem_dfop_tc_no_ranef_k2</span>, test <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span> <span class="op">|&gt;</span></span>
-<span>  <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span>format.args <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>digits <span class="op">=</span> <span class="fl">4</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
-<colgroup>
-<col width="37%">
-<col width="6%">
-<col width="8%">
-<col width="8%">
-<col width="9%">
-<col width="9%">
-<col width="4%">
-<col width="15%">
-</colgroup>
-<thead><tr class="header">
-<th align="left"></th>
-<th align="right">npar</th>
-<th align="right">AIC</th>
-<th align="right">BIC</th>
-<th align="right">Lik</th>
-<th align="right">Chisq</th>
-<th align="right">Df</th>
-<th align="right">Pr(&gt;Chisq)</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="left">f_saem_dfop_tc_no_ranef_k2</td>
-<td align="right">9</td>
-<td align="right">663.8</td>
-<td align="right">661.9</td>
-<td align="right">-322.9</td>
-<td align="right">NA</td>
-<td align="right">NA</td>
-<td align="right">NA</td>
-</tr>
-<tr class="even">
-<td align="left">f_saem[[“DFOP”, “tc”]]</td>
-<td align="right">10</td>
-<td align="right">665.5</td>
-<td align="right">663.4</td>
-<td align="right">-322.8</td>
-<td align="right">0.2809</td>
-<td align="right">1</td>
-<td align="right">0.5961</td>
-</tr>
-</tbody>
-</table>
-<p>The AIC and BIC criteria are lower after removal of the ill-defined
-random effect for <code>k2</code>. The p value of the likelihood ratio
-test is much greater than 0.05, indicating that the model with the
-higher likelihood (here the model with random effects for all
-degradation parameters <code>f_saem[["DFOP", "tc"]]</code>) does not fit
-significantly better than the model with the lower likelihood (the
-reduced model <code>f_saem_dfop_tc_no_ranef_k2</code>).</p>
-<p>Therefore, AIC, BIC and likelihood ratio test suggest the use of the
-reduced model.</p>
-<p>The convergence of the fit is checked visually.</p>
-<div class="figure" style="text-align: center">
-<img src="2022_dmta_parent_files/figure-html/convergence-saem-dfop-tc-no-ranef-k2-1.png" alt="Convergence plot for the NLHM DFOP fit with two-component error and without a random effect on 'k2'" width="864"><p class="caption">
-Convergence plot for the NLHM DFOP fit with two-component error and
-without a random effect on ‘k2’
-</p>
-</div>
-<p>All parameters appear to have converged to a satisfactory degree. The
-final fit is plotted using the plot method from the mkin package.</p>
-<div class="sourceCode" id="cb13"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_saem_dfop_tc_no_ranef_k2</span><span class="op">)</span></span></code></pre></div>
-<div class="figure" style="text-align: center">
-<img src="2022_dmta_parent_files/figure-html/plot-saem-dfop-tc-no-ranef-k2-1.png" alt="Plot of the final NLHM DFOP fit" width="864"><p class="caption">
-Plot of the final NLHM DFOP fit
-</p>
-</div>
-<p>Finally, a summary report of the fit is produced.</p>
-<div class="sourceCode" id="cb14"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">f_saem_dfop_tc_no_ranef_k2</span><span class="op">)</span></span></code></pre></div>
-<pre><code>saemix version used for fitting:      3.2 
-mkin version used for pre-fitting:  1.2.2 
-R version used for fitting:         4.2.2 
-Date of fit:     Sat Jan 28 11:22:51 2023 
-Date of summary: Sat Jan 28 11:22:52 2023 
-
-Equations:
-d_DMTA/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
-           time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))
-           * DMTA
-
-Data:
-155 observations of 1 variable(s) grouped in 6 datasets
-
-Model predictions using solution type analytical 
-
-Fitted in 3.74 s
-Using 300, 100 iterations and 9 chains
-
-Variance model: Two-component variance function 
-
-Starting values for degradation parameters:
-   DMTA_0        k1        k2         g 
-98.759266  0.087034  0.009933  0.930827 
-
-Fixed degradation parameter values:
-None
-
-Starting values for random effects (square root of initial entries in omega):
-       DMTA_0 k1 k2 g
-DMTA_0  98.76  0  0 0
-k1       0.00  1  0 0
-k2       0.00  0  1 0
-g        0.00  0  0 1
-
-Starting values for error model parameters:
-a.1 b.1 
-  1   1 
-
-Results:
-
-Likelihood computed by importance sampling
-    AIC   BIC logLik
-  663.8 661.9 -322.9
-
-Optimised parameters:
-               est.     lower     upper
-DMTA_0    98.228939 96.285869 100.17201
-k1         0.064063  0.033477   0.09465
-k2         0.008297  0.005824   0.01077
-g          0.953821  0.914328   0.99331
-a.1        1.068479  0.869538   1.26742
-b.1        0.029424  0.022406   0.03644
-SD.DMTA_0  2.030437  0.404824   3.65605
-SD.k1      0.594692  0.256660   0.93272
-SD.g       1.006754  0.361327   1.65218
-
-Correlation: 
-   DMTA_0  k1      k2     
-k1  0.0218                
-k2  0.0556  0.0355        
-g  -0.0516 -0.0284 -0.2800
-
-Random effects:
-            est.  lower  upper
-SD.DMTA_0 2.0304 0.4048 3.6560
-SD.k1     0.5947 0.2567 0.9327
-SD.g      1.0068 0.3613 1.6522
-
-Variance model:
-       est.   lower   upper
-a.1 1.06848 0.86954 1.26742
-b.1 0.02942 0.02241 0.03644
-
-Estimated disappearance times:
-      DT50 DT90 DT50back DT50_k1 DT50_k2
-DMTA 11.45 41.4    12.46   10.82   83.54</code></pre>
-</div>
-<div class="section level3">
-<h3 id="alternative-check-of-parameter-identifiability">Alternative check of parameter identifiability<a class="anchor" aria-label="anchor" href="#alternative-check-of-parameter-identifiability"></a>
-</h3>
-<p>The parameter check used in the <code>illparms</code> function is
-based on a quadratic approximation of the likelihood surface near its
-optimum, which is calculated using the Fisher Information Matrix (FIM).
-An alternative way to check parameter identifiability <span class="citation">(Duchesne et al. 2021)</span> based on a multistart
-approach has recently been implemented in mkin.</p>
-<p>The graph below shows boxplots of the parameters obtained in 50 runs
-of the saem algorithm with different parameter combinations, sampled
-from the range of the parameters obtained for the individual datasets
-fitted separately using nonlinear regression.</p>
-<div class="sourceCode" id="cb16"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">f_saem_dfop_tc_multi</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/multistart.html">multistart</a></span><span class="op">(</span><span class="va">f_saem</span><span class="op">[[</span><span class="st">"DFOP"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span>, n <span class="op">=</span> <span class="fl">50</span>, cores <span class="op">=</span> <span class="fl">15</span><span class="op">)</span></span></code></pre></div>
-<div class="sourceCode" id="cb17"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/par.html" class="external-link">par</a></span><span class="op">(</span>mar <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">6.1</span>, <span class="fl">4.1</span>, <span class="fl">2.1</span>, <span class="fl">2.1</span><span class="op">)</span><span class="op">)</span></span>
-<span><span class="fu"><a href="../../reference/parplot.html">parplot</a></span><span class="op">(</span><span class="va">f_saem_dfop_tc_multi</span>, lpos <span class="op">=</span> <span class="st">"bottomright"</span>, ylim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.3</span>, <span class="fl">10</span><span class="op">)</span>, las <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span></code></pre></div>
-<div class="figure" style="text-align: center">
-<img src="2022_dmta_parent_files/figure-html/multistart-full-par-1.png" alt="Scaled parameters from the multistart runs, full model" width="960"><p class="caption">
-Scaled parameters from the multistart runs, full model
-</p>
-</div>
-<p>The graph clearly confirms the lack of identifiability of the
-variance of <code>k2</code> in the full model. The overparameterisation
-of the model also indicates a lack of identifiability of the variance of
-parameter <code>g</code>.</p>
-<p>The parameter boxplots of the multistart runs with the reduced model
-shown below indicate that all runs give similar results, regardless of
-the starting parameters.</p>
-<div class="sourceCode" id="cb18"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">f_saem_dfop_tc_no_ranef_k2_multi</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/multistart.html">multistart</a></span><span class="op">(</span><span class="va">f_saem_dfop_tc_no_ranef_k2</span>,</span>
-<span>  n <span class="op">=</span> <span class="fl">50</span>, cores <span class="op">=</span> <span class="fl">15</span><span class="op">)</span></span></code></pre></div>
-<div class="sourceCode" id="cb19"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/par.html" class="external-link">par</a></span><span class="op">(</span>mar <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">6.1</span>, <span class="fl">4.1</span>, <span class="fl">2.1</span>, <span class="fl">2.1</span><span class="op">)</span><span class="op">)</span></span>
-<span><span class="fu"><a href="../../reference/parplot.html">parplot</a></span><span class="op">(</span><span class="va">f_saem_dfop_tc_no_ranef_k2_multi</span>, ylim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.5</span>, <span class="fl">2</span><span class="op">)</span>, las <span class="op">=</span> <span class="fl">2</span>,</span>
-<span>  lpos <span class="op">=</span> <span class="st">"bottomright"</span><span class="op">)</span></span></code></pre></div>
-<div class="figure" style="text-align: center">
-<img src="2022_dmta_parent_files/figure-html/multistart-reduced-par-1.png" alt="Scaled parameters from the multistart runs, reduced model" width="960"><p class="caption">
-Scaled parameters from the multistart runs, reduced model
-</p>
-</div>
-<p>When only the parameters of the top 25% of the fits are shown (based
-on a feature introduced in mkin 1.2.2 currently under development), the
-scatter is even less as shown below.</p>
-<div class="sourceCode" id="cb20"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/par.html" class="external-link">par</a></span><span class="op">(</span>mar <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">6.1</span>, <span class="fl">4.1</span>, <span class="fl">2.1</span>, <span class="fl">2.1</span><span class="op">)</span><span class="op">)</span></span>
-<span><span class="fu"><a href="../../reference/parplot.html">parplot</a></span><span class="op">(</span><span class="va">f_saem_dfop_tc_no_ranef_k2_multi</span>, ylim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.5</span>, <span class="fl">2</span><span class="op">)</span>, las <span class="op">=</span> <span class="fl">2</span>, llquant <span class="op">=</span> <span class="fl">0.25</span>,</span>
-<span>  lpos <span class="op">=</span> <span class="st">"bottomright"</span><span class="op">)</span></span></code></pre></div>
-<div class="figure" style="text-align: center">
-<img src="2022_dmta_parent_files/figure-html/multistart-reduced-par-llquant-1.png" alt="Scaled parameters from the multistart runs, reduced model, fits with the top 25\% likelihood values" width="960"><p class="caption">
-Scaled parameters from the multistart runs, reduced model, fits with the
-top 25% likelihood values
-</p>
-</div>
-</div>
-</div>
-<div class="section level2">
-<h2 id="conclusions">Conclusions<a class="anchor" aria-label="anchor" href="#conclusions"></a>
-</h2>
-<p>Fitting the four parent degradation models SFO, FOMC, DFOP and HS as
-part of hierarchical model fits with two different error models and
-normal distributions of the transformed degradation parameters works
-without technical problems. The biphasic models DFOP and HS gave the
-best fit to the data, but the default parameter distribution model was
-not fully identifiable. Removing the random effect for the second
-kinetic rate constant of the DFOP model resulted in a reduced model that
-was fully identifiable and showed the lowest values for the model
-selection criteria AIC and BIC. The reliability of the identification of
-all model parameters was confirmed using multiple starting values.</p>
-</div>
-<div class="section level2">
-<h2 id="acknowledgements">Acknowledgements<a class="anchor" aria-label="anchor" href="#acknowledgements"></a>
-</h2>
-<p>The helpful comments by Janina Wöltjen of the German Environment
-Agency are gratefully acknowledged.</p>
-</div>
-<div class="section level2">
-<h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a>
-</h2>
-<div id="refs" class="references csl-bib-body hanging-indent">
-<div id="ref-duchesne_2021" class="csl-entry">
-Duchesne, Ronan, Anissa Guillemin, Olivier Gandrillon, and Fabien
-Crauste. 2021. <span>“Practical Identifiability in the Frame of
-Nonlinear Mixed Effects Models: The Example of the in Vitro
-Erythropoiesis.”</span> <em>BMC Bioinformatics</em> 22 (478). <a href="https://doi.org/10.1186/s12859-021-04373-4" class="external-link">https://doi.org/10.1186/s12859-021-04373-4</a>.
-</div>
-</div>
-</div>
-<div class="section level2">
-<h2 id="appendix">Appendix<a class="anchor" aria-label="anchor" href="#appendix"></a>
-</h2>
-<div class="section level3">
-<h3 id="hierarchical-model-fit-listings">Hierarchical model fit listings<a class="anchor" aria-label="anchor" href="#hierarchical-model-fit-listings"></a>
-</h3>
-<caption>
-Hierarchical mkin fit of the SFO model with error model const
-</caption>
-<pre><code>
-saemix version used for fitting:      3.2 
-mkin version used for pre-fitting:  1.2.2 
-R version used for fitting:         4.2.2 
-Date of fit:     Sat Jan 28 11:22:44 2023 
-Date of summary: Sat Jan 28 11:23:57 2023 
-
-Equations:
-d_DMTA/dt = - k_DMTA * DMTA
-
-Data:
-155 observations of 1 variable(s) grouped in 6 datasets
-
-Model predictions using solution type analytical 
-
-Fitted in 0.982 s
-Using 300, 100 iterations and 9 chains
-
-Variance model: Constant variance 
-
-Starting values for degradation parameters:
- DMTA_0  k_DMTA 
-97.2953  0.0566 
-
-Fixed degradation parameter values:
-None
-
-Starting values for random effects (square root of initial entries in omega):
-       DMTA_0 k_DMTA
-DMTA_0   97.3      0
-k_DMTA    0.0      1
-
-Starting values for error model parameters:
-a.1 
-  1 
-
-Results:
-
-Likelihood computed by importance sampling
-    AIC   BIC logLik
-  796.3 795.3 -393.2
-
-Optimised parameters:
-              est.    lower   upper
-DMTA_0    97.28130 95.71113 98.8515
-k_DMTA     0.05665  0.02909  0.0842
-a.1        2.66442  2.35579  2.9731
-SD.DMTA_0  1.54776  0.15447  2.9411
-SD.k_DMTA  0.60690  0.26248  0.9513
-
-Correlation: 
-       DMTA_0
-k_DMTA 0.0168
-
-Random effects:
-            est.  lower  upper
-SD.DMTA_0 1.5478 0.1545 2.9411
-SD.k_DMTA 0.6069 0.2625 0.9513
-
-Variance model:
-     est. lower upper
-a.1 2.664 2.356 2.973
-
-Estimated disappearance times:
-      DT50  DT90
-DMTA 12.24 40.65
-
-</code></pre>
-<p></p>
-<caption>
-Hierarchical mkin fit of the SFO model with error model tc
-</caption>
-<pre><code>
-saemix version used for fitting:      3.2 
-mkin version used for pre-fitting:  1.2.2 
-R version used for fitting:         4.2.2 
-Date of fit:     Sat Jan 28 11:22:46 2023 
-Date of summary: Sat Jan 28 11:23:57 2023 
-
-Equations:
-d_DMTA/dt = - k_DMTA * DMTA
-
-Data:
-155 observations of 1 variable(s) grouped in 6 datasets
-
-Model predictions using solution type analytical 
-
-Fitted in 2.39 s
-Using 300, 100 iterations and 9 chains
-
-Variance model: Two-component variance function 
-
-Starting values for degradation parameters:
-  DMTA_0   k_DMTA 
-96.99175  0.05603 
-
-Fixed degradation parameter values:
-None
-
-Starting values for random effects (square root of initial entries in omega):
-       DMTA_0 k_DMTA
-DMTA_0  96.99      0
-k_DMTA   0.00      1
-
-Starting values for error model parameters:
-a.1 b.1 
-  1   1 
-
-Results:
-
-Likelihood computed by importance sampling
-    AIC   BIC logLik
-  798.3 797.1 -393.2
-
-Optimised parameters:
-               est.     lower    upper
-DMTA_0    97.271822 95.703157 98.84049
-k_DMTA     0.056638  0.029110  0.08417
-a.1        2.660081  2.230398  3.08976
-b.1        0.001665 -0.006911  0.01024
-SD.DMTA_0  1.545520  0.145035  2.94601
-SD.k_DMTA  0.606422  0.262274  0.95057
-
-Correlation: 
-       DMTA_0
-k_DMTA 0.0169
-
-Random effects:
-            est.  lower  upper
-SD.DMTA_0 1.5455 0.1450 2.9460
-SD.k_DMTA 0.6064 0.2623 0.9506
-
-Variance model:
-        est.     lower   upper
-a.1 2.660081  2.230398 3.08976
-b.1 0.001665 -0.006911 0.01024
-
-Estimated disappearance times:
-      DT50  DT90
-DMTA 12.24 40.65
-
-</code></pre>
-<p></p>
-<caption>
-Hierarchical mkin fit of the FOMC model with error model const
-</caption>
-<pre><code>
-saemix version used for fitting:      3.2 
-mkin version used for pre-fitting:  1.2.2 
-R version used for fitting:         4.2.2 
-Date of fit:     Sat Jan 28 11:22:45 2023 
-Date of summary: Sat Jan 28 11:23:57 2023 
-
-Equations:
-d_DMTA/dt = - (alpha/beta) * 1/((time/beta) + 1) * DMTA
-
-Data:
-155 observations of 1 variable(s) grouped in 6 datasets
-
-Model predictions using solution type analytical 
-
-Fitted in 1.552 s
-Using 300, 100 iterations and 9 chains
-
-Variance model: Constant variance 
-
-Starting values for degradation parameters:
- DMTA_0   alpha    beta 
- 98.292   9.909 156.341 
-
-Fixed degradation parameter values:
-None
-
-Starting values for random effects (square root of initial entries in omega):
-       DMTA_0 alpha beta
-DMTA_0  98.29     0    0
-alpha    0.00     1    0
-beta     0.00     0    1
-
-Starting values for error model parameters:
-a.1 
-  1 
-
-Results:
-
-Likelihood computed by importance sampling
-    AIC   BIC logLik
-  734.2 732.7 -360.1
-
-Optimised parameters:
-              est.   lower   upper
-DMTA_0     98.3435 96.9033  99.784
-alpha       7.2007  2.5889  11.812
-beta      112.8746 34.8816 190.868
-a.1         2.0459  1.8054   2.286
-SD.DMTA_0   1.4795  0.2717   2.687
-SD.alpha    0.6396  0.1509   1.128
-SD.beta     0.6874  0.1587   1.216
-
-Correlation: 
-      DMTA_0  alpha  
-alpha -0.1125        
-beta  -0.1227  0.3632
-
-Random effects:
-            est.  lower upper
-SD.DMTA_0 1.4795 0.2717 2.687
-SD.alpha  0.6396 0.1509 1.128
-SD.beta   0.6874 0.1587 1.216
-
-Variance model:
-     est. lower upper
-a.1 2.046 1.805 2.286
-
-Estimated disappearance times:
-      DT50  DT90 DT50back
-DMTA 11.41 42.53     12.8
-
-</code></pre>
-<p></p>
-<caption>
-Hierarchical mkin fit of the FOMC model with error model tc
-</caption>
-<pre><code>
-saemix version used for fitting:      3.2 
-mkin version used for pre-fitting:  1.2.2 
-R version used for fitting:         4.2.2 
-Date of fit:     Sat Jan 28 11:22:46 2023 
-Date of summary: Sat Jan 28 11:23:57 2023 
-
-Equations:
-d_DMTA/dt = - (alpha/beta) * 1/((time/beta) + 1) * DMTA
-
-Data:
-155 observations of 1 variable(s) grouped in 6 datasets
-
-Model predictions using solution type analytical 
-
-Fitted in 2.764 s
-Using 300, 100 iterations and 9 chains
-
-Variance model: Two-component variance function 
-
-Starting values for degradation parameters:
-DMTA_0  alpha   beta 
-98.772  4.663 92.597 
-
-Fixed degradation parameter values:
-None
-
-Starting values for random effects (square root of initial entries in omega):
-       DMTA_0 alpha beta
-DMTA_0  98.77     0    0
-alpha    0.00     1    0
-beta     0.00     0    1
-
-Starting values for error model parameters:
-a.1 b.1 
-  1   1 
-
-Results:
-
-Likelihood computed by importance sampling
-    AIC   BIC logLik
-  720.4 718.8 -352.2
-
-Optimised parameters:
-              est.    lower     upper
-DMTA_0    98.99136 97.26011 100.72261
-alpha      5.86312  2.57485   9.15138
-beta      88.55571 29.20889 147.90254
-a.1        1.51063  1.24384   1.77741
-b.1        0.02824  0.02040   0.03609
-SD.DMTA_0  1.57436 -0.04867   3.19739
-SD.alpha   0.59871  0.17132   1.02611
-SD.beta    0.72994  0.22849   1.23139
-
-Correlation: 
-      DMTA_0  alpha  
-alpha -0.1363        
-beta  -0.1414  0.2542
-
-Random effects:
-            est.    lower upper
-SD.DMTA_0 1.5744 -0.04867 3.197
-SD.alpha  0.5987  0.17132 1.026
-SD.beta   0.7299  0.22849 1.231
-
-Variance model:
-       est.  lower   upper
-a.1 1.51063 1.2438 1.77741
-b.1 0.02824 0.0204 0.03609
-
-Estimated disappearance times:
-      DT50 DT90 DT50back
-DMTA 11.11 42.6    12.82
-
-</code></pre>
-<p></p>
-<caption>
-Hierarchical mkin fit of the DFOP model with error model const
-</caption>
-<pre><code>
-saemix version used for fitting:      3.2 
-mkin version used for pre-fitting:  1.2.2 
-R version used for fitting:         4.2.2 
-Date of fit:     Sat Jan 28 11:22:45 2023 
-Date of summary: Sat Jan 28 11:23:57 2023 
-
-Equations:
-d_DMTA/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
-           time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))
-           * DMTA
-
-Data:
-155 observations of 1 variable(s) grouped in 6 datasets
-
-Model predictions using solution type analytical 
-
-Fitted in 1.649 s
-Using 300, 100 iterations and 9 chains
-
-Variance model: Constant variance 
-
-Starting values for degradation parameters:
-  DMTA_0       k1       k2        g 
-98.64383  0.09211  0.02999  0.76814 
-
-Fixed degradation parameter values:
-None
-
-Starting values for random effects (square root of initial entries in omega):
-       DMTA_0 k1 k2 g
-DMTA_0  98.64  0  0 0
-k1       0.00  1  0 0
-k2       0.00  0  1 0
-g        0.00  0  0 1
-
-Starting values for error model parameters:
-a.1 
-  1 
-
-Results:
-
-Likelihood computed by importance sampling
-    AIC BIC logLik
-  711.8 710 -346.9
-
-Optimised parameters:
-               est.     lower    upper
-DMTA_0    98.092481 96.573898 99.61106
-k1         0.062499  0.030336  0.09466
-k2         0.009065 -0.005133  0.02326
-g          0.948967  0.862079  1.03586
-a.1        1.821671  1.604774  2.03857
-SD.DMTA_0  1.677785  0.472066  2.88350
-SD.k1      0.634962  0.270788  0.99914
-SD.k2      1.033498 -0.205994  2.27299
-SD.g       1.710046  0.428642  2.99145
-
-Correlation: 
-   DMTA_0  k1      k2     
-k1  0.0246                
-k2  0.0491  0.0953        
-g  -0.0552 -0.0889 -0.4795
-
-Random effects:
-           est.   lower  upper
-SD.DMTA_0 1.678  0.4721 2.8835
-SD.k1     0.635  0.2708 0.9991
-SD.k2     1.033 -0.2060 2.2730
-SD.g      1.710  0.4286 2.9914
-
-Variance model:
-     est. lower upper
-a.1 1.822 1.605 2.039
-
-Estimated disappearance times:
-      DT50 DT90 DT50back DT50_k1 DT50_k2
-DMTA 11.79 42.8    12.88   11.09   76.46
-
-</code></pre>
-<p></p>
-<caption>
-Hierarchical mkin fit of the DFOP model with error model tc
-</caption>
-<pre><code>
-saemix version used for fitting:      3.2 
-mkin version used for pre-fitting:  1.2.2 
-R version used for fitting:         4.2.2 
-Date of fit:     Sat Jan 28 11:22:46 2023 
-Date of summary: Sat Jan 28 11:23:57 2023 
-
-Equations:
-d_DMTA/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
-           time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))
-           * DMTA
-
-Data:
-155 observations of 1 variable(s) grouped in 6 datasets
-
-Model predictions using solution type analytical 
-
-Fitted in 3.288 s
-Using 300, 100 iterations and 9 chains
-
-Variance model: Two-component variance function 
-
-Starting values for degradation parameters:
-   DMTA_0        k1        k2         g 
-98.759266  0.087034  0.009933  0.930827 
-
-Fixed degradation parameter values:
-None
-
-Starting values for random effects (square root of initial entries in omega):
-       DMTA_0 k1 k2 g
-DMTA_0  98.76  0  0 0
-k1       0.00  1  0 0
-k2       0.00  0  1 0
-g        0.00  0  0 1
-
-Starting values for error model parameters:
-a.1 b.1 
-  1   1 
-
-Results:
-
-Likelihood computed by importance sampling
-    AIC   BIC logLik
-  665.5 663.4 -322.8
-
-Optimised parameters:
-               est.     lower     upper
-DMTA_0    98.377019 96.447952 100.30609
-k1         0.064843  0.034607   0.09508
-k2         0.008895  0.006368   0.01142
-g          0.949696  0.903815   0.99558
-a.1        1.065241  0.865754   1.26473
-b.1        0.029340  0.022336   0.03634
-SD.DMTA_0  2.007754  0.387982   3.62753
-SD.k1      0.580473  0.250286   0.91066
-SD.k2      0.006105 -4.920337   4.93255
-SD.g       1.097149  0.412779   1.78152
-
-Correlation: 
-   DMTA_0  k1      k2     
-k1  0.0235                
-k2  0.0595  0.0424        
-g  -0.0470 -0.0278 -0.2731
-
-Random effects:
-              est.   lower  upper
-SD.DMTA_0 2.007754  0.3880 3.6275
-SD.k1     0.580473  0.2503 0.9107
-SD.k2     0.006105 -4.9203 4.9325
-SD.g      1.097149  0.4128 1.7815
-
-Variance model:
-       est.   lower   upper
-a.1 1.06524 0.86575 1.26473
-b.1 0.02934 0.02234 0.03634
-
-Estimated disappearance times:
-      DT50  DT90 DT50back DT50_k1 DT50_k2
-DMTA 11.36 41.32    12.44   10.69   77.92
-
-</code></pre>
-<p></p>
-<caption>
-Hierarchical mkin fit of the HS model with error model const
-</caption>
-<pre><code>
-saemix version used for fitting:      3.2 
-mkin version used for pre-fitting:  1.2.2 
-R version used for fitting:         4.2.2 
-Date of fit:     Sat Jan 28 11:22:45 2023 
-Date of summary: Sat Jan 28 11:23:57 2023 
-
-Equations:
-d_DMTA/dt = - ifelse(time &lt;= tb, k1, k2) * DMTA
-
-Data:
-155 observations of 1 variable(s) grouped in 6 datasets
-
-Model predictions using solution type analytical 
-
-Fitted in 2.006 s
-Using 300, 100 iterations and 9 chains
-
-Variance model: Constant variance 
-
-Starting values for degradation parameters:
-  DMTA_0       k1       k2       tb 
-97.82176  0.06931  0.02997 11.13945 
-
-Fixed degradation parameter values:
-None
-
-Starting values for random effects (square root of initial entries in omega):
-       DMTA_0 k1 k2 tb
-DMTA_0  97.82  0  0  0
-k1       0.00  1  0  0
-k2       0.00  0  1  0
-tb       0.00  0  0  1
-
-Starting values for error model parameters:
-a.1 
-  1 
-
-Results:
-
-Likelihood computed by importance sampling
-  AIC   BIC logLik
-  714 712.1   -348
-
-Optimised parameters:
-              est.    lower    upper
-DMTA_0    98.16102 96.47747 99.84456
-k1         0.07876  0.05261  0.10491
-k2         0.02227  0.01706  0.02747
-tb        13.99089 -7.40049 35.38228
-a.1        1.82305  1.60700  2.03910
-SD.DMTA_0  1.88413  0.56204  3.20622
-SD.k1      0.34292  0.10482  0.58102
-SD.k2      0.19851  0.01718  0.37985
-SD.tb      1.68168  0.58064  2.78272
-
-Correlation: 
-   DMTA_0  k1      k2     
-k1  0.0142                
-k2  0.0001 -0.0025        
-tb  0.0165 -0.1256 -0.0301
-
-Random effects:
-            est.   lower  upper
-SD.DMTA_0 1.8841 0.56204 3.2062
-SD.k1     0.3429 0.10482 0.5810
-SD.k2     0.1985 0.01718 0.3798
-SD.tb     1.6817 0.58064 2.7827
-
-Variance model:
-     est. lower upper
-a.1 1.823 1.607 2.039
-
-Estimated disappearance times:
-      DT50  DT90 DT50back DT50_k1 DT50_k2
-DMTA 8.801 67.91    20.44   8.801   31.13
-
-</code></pre>
-<p></p>
-<caption>
-Hierarchical mkin fit of the HS model with error model tc
-</caption>
-<pre><code>
-saemix version used for fitting:      3.2 
-mkin version used for pre-fitting:  1.2.2 
-R version used for fitting:         4.2.2 
-Date of fit:     Sat Jan 28 11:22:46 2023 
-Date of summary: Sat Jan 28 11:23:57 2023 
-
-Equations:
-d_DMTA/dt = - ifelse(time &lt;= tb, k1, k2) * DMTA
-
-Data:
-155 observations of 1 variable(s) grouped in 6 datasets
-
-Model predictions using solution type analytical 
-
-Fitted in 3.267 s
-Using 300, 100 iterations and 9 chains
-
-Variance model: Two-component variance function 
-
-Starting values for degradation parameters:
-  DMTA_0       k1       k2       tb 
-98.45190  0.07525  0.02576 19.19375 
-
-Fixed degradation parameter values:
-None
-
-Starting values for random effects (square root of initial entries in omega):
-       DMTA_0 k1 k2 tb
-DMTA_0  98.45  0  0  0
-k1       0.00  1  0  0
-k2       0.00  0  1  0
-tb       0.00  0  0  1
-
-Starting values for error model parameters:
-a.1 b.1 
-  1   1 
-
-Results:
-
-Likelihood computed by importance sampling
-    AIC BIC logLik
-  667.1 665 -323.6
-
-Optimised parameters:
-              est.    lower    upper
-DMTA_0    97.76570 95.81350 99.71791
-k1         0.05855  0.03080  0.08630
-k2         0.02337  0.01664  0.03010
-tb        31.09638 29.38289 32.80987
-a.1        1.08835  0.88590  1.29080
-b.1        0.02964  0.02257  0.03671
-SD.DMTA_0  2.04877  0.42607  3.67147
-SD.k1      0.59166  0.25621  0.92711
-SD.k2      0.30698  0.09561  0.51835
-SD.tb      0.01274 -0.10914  0.13462
-
-Correlation: 
-   DMTA_0  k1      k2     
-k1  0.0160                
-k2 -0.0070 -0.0024        
-tb -0.0668 -0.0103 -0.2013
-
-Random effects:
-             est.    lower  upper
-SD.DMTA_0 2.04877  0.42607 3.6715
-SD.k1     0.59166  0.25621 0.9271
-SD.k2     0.30698  0.09561 0.5183
-SD.tb     0.01274 -0.10914 0.1346
-
-Variance model:
-       est.   lower   upper
-a.1 1.08835 0.88590 1.29080
-b.1 0.02964 0.02257 0.03671
-
-Estimated disappearance times:
-      DT50  DT90 DT50back DT50_k1 DT50_k2
-DMTA 11.84 51.71    15.57   11.84   29.66
-
-</code></pre>
-<p></p>
-</div>
-<div class="section level3">
-<h3 id="hierarchical-model-convergence-plots">Hierarchical model convergence plots<a class="anchor" aria-label="anchor" href="#hierarchical-model-convergence-plots"></a>
-</h3>
-<div class="figure" style="text-align: center">
-<img src="2022_dmta_parent_files/figure-html/convergence-saem-sfo-const-1.png" alt="Convergence plot for the NLHM SFO fit with constant variance" width="864"><p class="caption">
-Convergence plot for the NLHM SFO fit with constant variance
-</p>
-</div>
-<div class="figure" style="text-align: center">
-<img src="2022_dmta_parent_files/figure-html/convergence-saem-sfo-tc-1.png" alt="Convergence plot for the NLHM SFO fit with two-component error" width="864"><p class="caption">
-Convergence plot for the NLHM SFO fit with two-component error
-</p>
-</div>
-<div class="figure" style="text-align: center">
-<img src="2022_dmta_parent_files/figure-html/convergence-saem-fomc-const-1.png" alt="Convergence plot for the NLHM FOMC fit with constant variance" width="864"><p class="caption">
-Convergence plot for the NLHM FOMC fit with constant variance
-</p>
-</div>
-<div class="figure" style="text-align: center">
-<img src="2022_dmta_parent_files/figure-html/convergence-saem-fomc-tc-1.png" alt="Convergence plot for the NLHM FOMC fit with two-component error" width="864"><p class="caption">
-Convergence plot for the NLHM FOMC fit with two-component error
-</p>
-</div>
-<div class="figure" style="text-align: center">
-<img src="2022_dmta_parent_files/figure-html/convergence-saem-dfop-const-1.png" alt="Convergence plot for the NLHM DFOP fit with constant variance" width="864"><p class="caption">
-Convergence plot for the NLHM DFOP fit with constant variance
-</p>
-</div>
-<div class="figure" style="text-align: center">
-<img src="2022_dmta_parent_files/figure-html/convergence-saem-dfop-tc-1.png" alt="Convergence plot for the NLHM DFOP fit with two-component error" width="864"><p class="caption">
-Convergence plot for the NLHM DFOP fit with two-component error
-</p>
-</div>
-<div class="figure" style="text-align: center">
-<img src="2022_dmta_parent_files/figure-html/convergence-saem-hs-const-1.png" alt="Convergence plot for the NLHM HS fit with constant variance" width="864"><p class="caption">
-Convergence plot for the NLHM HS fit with constant variance
-</p>
-</div>
-<div class="figure" style="text-align: center">
-<img src="2022_dmta_parent_files/figure-html/convergence-saem-hs-tc-1.png" alt="Convergence plot for the NLHM HS fit with two-component error" width="864"><p class="caption">
-Convergence plot for the NLHM HS fit with two-component error
-</p>
-</div>
-</div>
-<div class="section level3">
-<h3 id="session-info">Session info<a class="anchor" aria-label="anchor" href="#session-info"></a>
-</h3>
-<pre><code>R version 4.2.2 Patched (2022-11-10 r83330)
-Platform: x86_64-pc-linux-gnu (64-bit)
-Running under: Debian GNU/Linux bookworm/sid
-
-Matrix products: default
-BLAS:   /usr/lib/x86_64-linux-gnu/openblas-serial/libblas.so.3
-LAPACK: /usr/lib/x86_64-linux-gnu/openblas-serial/libopenblas-r0.3.21.so
-
-locale:
- [1] LC_CTYPE=de_DE.UTF-8       LC_NUMERIC=C              
- [3] LC_TIME=de_DE.UTF-8        LC_COLLATE=de_DE.UTF-8    
- [5] LC_MONETARY=de_DE.UTF-8    LC_MESSAGES=de_DE.UTF-8   
- [7] LC_PAPER=de_DE.UTF-8       LC_NAME=C                 
- [9] LC_ADDRESS=C               LC_TELEPHONE=C            
-[11] LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=C       
-
-attached base packages:
-[1] parallel  stats     graphics  grDevices utils     datasets  methods  
-[8] base     
-
-other attached packages:
-[1] saemix_3.2 npde_3.3   knitr_1.41 mkin_1.2.2
-
-loaded via a namespace (and not attached):
- [1] deSolve_1.34      zoo_1.8-11        tidyselect_1.2.0  xfun_0.35        
- [5] bslib_0.4.2       purrr_1.0.0       lattice_0.20-45   colorspace_2.0-3 
- [9] vctrs_0.5.1       generics_0.1.3    htmltools_0.5.4   yaml_2.3.6       
-[13] utf8_1.2.2        rlang_1.0.6       pkgdown_2.0.7     jquerylib_0.1.4  
-[17] pillar_1.8.1      glue_1.6.2        DBI_1.1.3         lifecycle_1.0.3  
-[21] stringr_1.5.0     munsell_0.5.0     gtable_0.3.1      ragg_1.2.4       
-[25] codetools_0.2-18  memoise_2.0.1     evaluate_0.19     fastmap_1.1.0    
-[29] lmtest_0.9-40     fansi_1.0.3       highr_0.9         scales_1.2.1     
-[33] cachem_1.0.6      desc_1.4.2        jsonlite_1.8.4    systemfonts_1.0.4
-[37] fs_1.5.2          textshaping_0.3.6 gridExtra_2.3     ggplot2_3.4.0    
-[41] digest_0.6.31     stringi_1.7.8     dplyr_1.0.10      grid_4.2.2       
-[45] rprojroot_2.0.3   cli_3.5.0         tools_4.2.2       magrittr_2.0.3   
-[49] sass_0.4.4        tibble_3.1.8      pkgconfig_2.0.3   assertthat_0.2.1 
-[53] rmarkdown_2.19    R6_2.5.1          mclust_6.0.0      nlme_3.1-161     
-[57] compiler_4.2.2   </code></pre>
-</div>
-<div class="section level3">
-<h3 id="hardware-info">Hardware info<a class="anchor" aria-label="anchor" href="#hardware-info"></a>
-</h3>
-<pre><code>CPU model: AMD Ryzen 9 7950X 16-Core Processor</code></pre>
-<pre><code>MemTotal:       64940452 kB</code></pre>
-</div>
-</div>
-  </div>
-
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-
-        <nav id="toc" data-toggle="toc"><h2 data-toc-skip>Contents</h2>
-    </nav>
-</div>
-
-</div>
-
-
-
-      <footer><div class="copyright">
-  <p></p>
-<p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p>
-<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer>
-</div>
-
-  
-
-
-  
-
-  </body>
-</html>
diff --git a/docs/dev/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-dfop-const-1.png b/docs/dev/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-dfop-const-1.png
deleted file mode 100644
index 3f145074..00000000
--- a/docs/dev/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-dfop-const-1.png
+++ /dev/null
diff --git a/docs/dev/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-dfop-tc-1.png b/docs/dev/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-dfop-tc-1.png
deleted file mode 100644
index e5457fc9..00000000
--- a/docs/dev/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-dfop-tc-1.png
+++ /dev/null
diff --git a/docs/dev/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-dfop-tc-no-ranef-k2-1.png b/docs/dev/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-dfop-tc-no-ranef-k2-1.png
deleted file mode 100644
index 14707641..00000000
--- a/docs/dev/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-dfop-tc-no-ranef-k2-1.png
+++ /dev/null
diff --git a/docs/dev/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-fomc-const-1.png b/docs/dev/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-fomc-const-1.png
deleted file mode 100644
index c7ed69a3..00000000
--- a/docs/dev/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-fomc-const-1.png
+++ /dev/null
diff --git a/docs/dev/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-fomc-tc-1.png b/docs/dev/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-fomc-tc-1.png
deleted file mode 100644
index 1a48524c..00000000
--- a/docs/dev/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-fomc-tc-1.png
+++ /dev/null
diff --git a/docs/dev/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-hs-const-1.png b/docs/dev/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-hs-const-1.png
deleted file mode 100644
index 0f3b1184..00000000
--- a/docs/dev/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-hs-const-1.png
+++ /dev/null
diff --git a/docs/dev/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-hs-tc-1.png b/docs/dev/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-hs-tc-1.png
deleted file mode 100644
index 901a1579..00000000
--- a/docs/dev/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-hs-tc-1.png
+++ /dev/null
diff --git a/docs/dev/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-sfo-const-1.png b/docs/dev/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-sfo-const-1.png
deleted file mode 100644
index a3e3a51f..00000000
--- a/docs/dev/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-sfo-const-1.png
+++ /dev/null
diff --git a/docs/dev/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-sfo-tc-1.png b/docs/dev/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-sfo-tc-1.png
deleted file mode 100644
index b85691eb..00000000
--- a/docs/dev/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-sfo-tc-1.png
+++ /dev/null
diff --git a/docs/dev/articles/prebuilt/2022_dmta_parent_files/figure-html/multistart-full-par-1.png b/docs/dev/articles/prebuilt/2022_dmta_parent_files/figure-html/multistart-full-par-1.png
deleted file mode 100644
index a42950f0..00000000
--- a/docs/dev/articles/prebuilt/2022_dmta_parent_files/figure-html/multistart-full-par-1.png
+++ /dev/null
diff --git a/docs/dev/articles/prebuilt/2022_dmta_parent_files/figure-html/multistart-reduced-par-1.png b/docs/dev/articles/prebuilt/2022_dmta_parent_files/figure-html/multistart-reduced-par-1.png
deleted file mode 100644
index caebc768..00000000
--- a/docs/dev/articles/prebuilt/2022_dmta_parent_files/figure-html/multistart-reduced-par-1.png
+++ /dev/null
diff --git a/docs/dev/articles/prebuilt/2022_dmta_parent_files/figure-html/multistart-reduced-par-llquant-1.png b/docs/dev/articles/prebuilt/2022_dmta_parent_files/figure-html/multistart-reduced-par-llquant-1.png
deleted file mode 100644
index 45ae57f1..00000000
--- a/docs/dev/articles/prebuilt/2022_dmta_parent_files/figure-html/multistart-reduced-par-llquant-1.png
+++ /dev/null
diff --git a/docs/dev/articles/prebuilt/2022_dmta_parent_files/figure-html/plot-saem-dfop-tc-no-ranef-k2-1.png b/docs/dev/articles/prebuilt/2022_dmta_parent_files/figure-html/plot-saem-dfop-tc-no-ranef-k2-1.png
deleted file mode 100644
index 1f8eb9f0..00000000
--- a/docs/dev/articles/prebuilt/2022_dmta_parent_files/figure-html/plot-saem-dfop-tc-no-ranef-k2-1.png
+++ /dev/null
diff --git a/docs/dev/articles/prebuilt/2022_dmta_pathway.html b/docs/dev/articles/prebuilt/2022_dmta_pathway.html
deleted file mode 100644
index 959f3429..00000000
--- a/docs/dev/articles/prebuilt/2022_dmta_pathway.html
+++ /dev/null
@@ -1,2054 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en">
-<head>
-<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
-<meta charset="utf-8">
-<meta http-equiv="X-UA-Compatible" content="IE=edge">
-<meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P • mkin</title>
-<!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous">
-<script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../../bootstrap-toc.css">
-<script src="../../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous">
-<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous">
-<!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../../pkgdown.css" rel="stylesheet">
-<script src="../../pkgdown.js"></script><meta property="og:title" content="Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P">
-<meta property="og:description" content="mkin">
-<meta name="robots" content="noindex">
-<!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]-->
-</head>
-<body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-article">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.4</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav">
-<li>
-  <a href="../../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu">
-<li>
-      <a href="../../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul>
-</li>
-<li>
-  <a href="../../news/index.html">News</a>
-</li>
-      </ul>
-<ul class="nav navbar-nav navbar-right">
-<li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul>
-</div>
-<!--/.nav-collapse -->
-  </div>
-<!--/.container -->
-</div>
-<!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header toc-ignore">
-      <h1 data-toc-skip>Testing hierarchical pathway kinetics with
-residue data on dimethenamid and dimethenamid-P</h1>
-                        <h4 data-toc-skip class="author">Johannes
-Ranke</h4>
-            
-            <h4 data-toc-skip class="date">Last change on 20 April 2023,
-last compiled on 20 April 2023</h4>
-      
-      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/prebuilt/2022_dmta_pathway.rmd" class="external-link"><code>vignettes/prebuilt/2022_dmta_pathway.rmd</code></a></small>
-      <div class="hidden name"><code>2022_dmta_pathway.rmd</code></div>
-
-    </div>
-
-    
-    
-<div class="section level2">
-<h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a>
-</h2>
-<p>The purpose of this document is to test demonstrate how nonlinear
-hierarchical models (NLHM) based on the parent degradation models SFO,
-FOMC, DFOP and HS, with parallel formation of two or more metabolites
-can be fitted with the mkin package.</p>
-<p>It was assembled in the course of work package 1.2 of Project Number
-173340 (Application of nonlinear hierarchical models to the kinetic
-evaluation of chemical degradation data) of the German Environment
-Agency carried out in 2022 and 2023.</p>
-<p>The mkin package is used in version 1.2.4, which is currently under
-development. It contains the test data, and the functions used in the
-evaluations. The <code>saemix</code> package is used as a backend for
-fitting the NLHM, but is also loaded to make the convergence plot
-function available.</p>
-<p>This document is processed with the <code>knitr</code> package, which
-also provides the <code>kable</code> function that is used to improve
-the display of tabular data in R markdown documents. For parallel
-processing, the <code>parallel</code> package is used.</p>
-<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://pkgdown.jrwb.de/mkin/">mkin</a></span><span class="op">)</span></span>
-<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://yihui.org/knitr/" class="external-link">knitr</a></span><span class="op">)</span></span>
-<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va">saemix</span><span class="op">)</span></span>
-<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va">parallel</span><span class="op">)</span></span>
-<span><span class="va">n_cores</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/parallel/detectCores.html" class="external-link">detectCores</a></span><span class="op">(</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># We need to start a new cluster after defining a compiled model that is</span></span>
-<span><span class="co"># saved as a DLL to the user directory, therefore we define a function</span></span>
-<span><span class="co"># This is used again after defining the pathway model</span></span>
-<span><span class="va">start_cluster</span> <span class="op">&lt;-</span> <span class="kw">function</span><span class="op">(</span><span class="va">n_cores</span><span class="op">)</span> <span class="op">{</span></span>
-<span>  <span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/Sys.info.html" class="external-link">Sys.info</a></span><span class="op">(</span><span class="op">)</span><span class="op">[</span><span class="st">"sysname"</span><span class="op">]</span> <span class="op">==</span> <span class="st">"Windows"</span><span class="op">)</span> <span class="op">{</span></span>
-<span>    <span class="va">ret</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/parallel/makeCluster.html" class="external-link">makePSOCKcluster</a></span><span class="op">(</span><span class="va">n_cores</span><span class="op">)</span></span>
-<span>  <span class="op">}</span> <span class="kw">else</span> <span class="op">{</span></span>
-<span>    <span class="va">ret</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/parallel/makeCluster.html" class="external-link">makeForkCluster</a></span><span class="op">(</span><span class="va">n_cores</span><span class="op">)</span></span>
-<span>  <span class="op">}</span></span>
-<span>  <span class="kw"><a href="https://rdrr.io/r/base/function.html" class="external-link">return</a></span><span class="op">(</span><span class="va">ret</span><span class="op">)</span></span>
-<span><span class="op">}</span></span></code></pre></div>
-</div>
-<div class="section level2">
-<h2 id="data">Data<a class="anchor" aria-label="anchor" href="#data"></a>
-</h2>
-<p>The test data are available in the mkin package as an object of class
-<code>mkindsg</code> (mkin dataset group) under the identifier
-<code>dimethenamid_2018</code>. The following preprocessing steps are
-done in this document.</p>
-<ul>
-<li>The data available for the enantiomer dimethenamid-P (DMTAP) are
-renamed to have the same substance name as the data for the racemic
-mixture dimethenamid (DMTA). The reason for this is that no difference
-between their degradation behaviour was identified in the EU risk
-assessment.</li>
-<li>Unnecessary columns are discarded</li>
-<li>The observation times of each dataset are multiplied with the
-corresponding normalisation factor also available in the dataset, in
-order to make it possible to describe all datasets with a single set of
-parameters that are independent of temperature</li>
-<li>Finally, datasets observed in the same soil (<code>Elliot 1</code>
-and <code>Elliot 2</code>) are combined, resulting in dimethenamid
-(DMTA) data from six soils.</li>
-</ul>
-<p>The following commented R code performs this preprocessing.</p>
-<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="co"># Apply a function to each of the seven datasets in the mkindsg object to create a list</span></span>
-<span><span class="va">dmta_ds</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fl">7</span>, <span class="kw">function</span><span class="op">(</span><span class="va">i</span><span class="op">)</span> <span class="op">{</span></span>
-<span>  <span class="va">ds_i</span> <span class="op">&lt;-</span> <span class="va">dimethenamid_2018</span><span class="op">$</span><span class="va">ds</span><span class="op">[[</span><span class="va">i</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">data</span>                     <span class="co"># Get a dataset</span></span>
-<span>  <span class="va">ds_i</span><span class="op">[</span><span class="va">ds_i</span><span class="op">$</span><span class="va">name</span> <span class="op">==</span> <span class="st">"DMTAP"</span>, <span class="st">"name"</span><span class="op">]</span> <span class="op">&lt;-</span>  <span class="st">"DMTA"</span>              <span class="co"># Rename DMTAP to DMTA</span></span>
-<span>  <span class="va">ds_i</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/subset.html" class="external-link">subset</a></span><span class="op">(</span><span class="va">ds_i</span>, select <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"name"</span>, <span class="st">"time"</span>, <span class="st">"value"</span><span class="op">)</span><span class="op">)</span> <span class="co"># Select data</span></span>
-<span>  <span class="va">ds_i</span><span class="op">$</span><span class="va">time</span> <span class="op">&lt;-</span> <span class="va">ds_i</span><span class="op">$</span><span class="va">time</span> <span class="op">*</span> <span class="va">dimethenamid_2018</span><span class="op">$</span><span class="va">f_time_norm</span><span class="op">[</span><span class="va">i</span><span class="op">]</span>  <span class="co"># Normalise time</span></span>
-<span>  <span class="va">ds_i</span>                                                       <span class="co"># Return the dataset</span></span>
-<span><span class="op">}</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># Use dataset titles as names for the list elements</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">dmta_ds</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">sapply</a></span><span class="op">(</span><span class="va">dimethenamid_2018</span><span class="op">$</span><span class="va">ds</span>, <span class="kw">function</span><span class="op">(</span><span class="va">ds</span><span class="op">)</span> <span class="va">ds</span><span class="op">$</span><span class="va">title</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># Combine data for Elliot soil to obtain a named list with six elements</span></span>
-<span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot"</span><span class="op">]</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/cbind.html" class="external-link">rbind</a></span><span class="op">(</span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 1"</span><span class="op">]</span><span class="op">]</span>, <span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 2"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span> <span class="co">#</span></span>
-<span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 1"</span><span class="op">]</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="cn">NULL</span></span>
-<span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 2"</span><span class="op">]</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="cn">NULL</span></span></code></pre></div>
-<p>The following tables show the 6 datasets.</p>
-<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="kw">for</span> <span class="op">(</span><span class="va">ds_name</span> <span class="kw">in</span> <span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">dmta_ds</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
-<span>  <span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span></span>
-<span>    <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="fu"><a href="../../reference/mkin_long_to_wide.html">mkin_long_to_wide</a></span><span class="op">(</span><span class="va">dmta_ds</span><span class="op">[[</span><span class="va">ds_name</span><span class="op">]</span><span class="op">]</span><span class="op">)</span>,</span>
-<span>      caption <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste</a></span><span class="op">(</span><span class="st">"Dataset"</span>, <span class="va">ds_name</span><span class="op">)</span>,</span>
-<span>      booktabs <span class="op">=</span> <span class="cn">TRUE</span>, row.names <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span><span class="op">)</span></span>
-<span>    <span class="fu"><a href="https://rdrr.io/r/base/cat.html" class="external-link">cat</a></span><span class="op">(</span><span class="st">"\n\\clearpage\n"</span><span class="op">)</span></span>
-<span><span class="op">}</span></span></code></pre></div>
-<table class="table">
-<caption>Dataset Calke</caption>
-<thead><tr class="header">
-<th align="right">time</th>
-<th align="right">DMTA</th>
-<th align="right">M23</th>
-<th align="right">M27</th>
-<th align="right">M31</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="right">0</td>
-<td align="right">95.8</td>
-<td align="right">NA</td>
-<td align="right">NA</td>
-<td align="right">NA</td>
-</tr>
-<tr class="even">
-<td align="right">0</td>
-<td align="right">98.7</td>
-<td align="right">NA</td>
-<td align="right">NA</td>
-<td align="right">NA</td>
-</tr>
-<tr class="odd">
-<td align="right">14</td>
-<td align="right">60.5</td>
-<td align="right">4.1</td>
-<td align="right">1.5</td>
-<td align="right">2.0</td>
-</tr>
-<tr class="even">
-<td align="right">30</td>
-<td align="right">39.1</td>
-<td align="right">5.3</td>
-<td align="right">2.4</td>
-<td align="right">2.1</td>
-</tr>
-<tr class="odd">
-<td align="right">59</td>
-<td align="right">15.2</td>
-<td align="right">6.0</td>
-<td align="right">3.2</td>
-<td align="right">2.2</td>
-</tr>
-<tr class="even">
-<td align="right">120</td>
-<td align="right">4.8</td>
-<td align="right">4.3</td>
-<td align="right">3.8</td>
-<td align="right">1.8</td>
-</tr>
-<tr class="odd">
-<td align="right">120</td>
-<td align="right">4.6</td>
-<td align="right">4.1</td>
-<td align="right">3.7</td>
-<td align="right">2.1</td>
-</tr>
-</tbody>
-</table>
-<table class="table">
-<caption>Dataset Borstel</caption>
-<thead><tr class="header">
-<th align="right">time</th>
-<th align="right">DMTA</th>
-<th align="right">M23</th>
-<th align="right">M27</th>
-<th align="right">M31</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="right">0.000000</td>
-<td align="right">100.5</td>
-<td align="right">NA</td>
-<td align="right">NA</td>
-<td align="right">NA</td>
-</tr>
-<tr class="even">
-<td align="right">0.000000</td>
-<td align="right">99.6</td>
-<td align="right">NA</td>
-<td align="right">NA</td>
-<td align="right">NA</td>
-</tr>
-<tr class="odd">
-<td align="right">1.941295</td>
-<td align="right">91.9</td>
-<td align="right">0.4</td>
-<td align="right">NA</td>
-<td align="right">NA</td>
-</tr>
-<tr class="even">
-<td align="right">1.941295</td>
-<td align="right">91.3</td>
-<td align="right">0.5</td>
-<td align="right">0.3</td>
-<td align="right">0.1</td>
-</tr>
-<tr class="odd">
-<td align="right">6.794534</td>
-<td align="right">81.8</td>
-<td align="right">1.2</td>
-<td align="right">0.8</td>
-<td align="right">1.0</td>
-</tr>
-<tr class="even">
-<td align="right">6.794534</td>
-<td align="right">82.1</td>
-<td align="right">1.3</td>
-<td align="right">0.9</td>
-<td align="right">0.9</td>
-</tr>
-<tr class="odd">
-<td align="right">13.589067</td>
-<td align="right">69.1</td>
-<td align="right">2.8</td>
-<td align="right">1.4</td>
-<td align="right">2.0</td>
-</tr>
-<tr class="even">
-<td align="right">13.589067</td>
-<td align="right">68.0</td>
-<td align="right">2.0</td>
-<td align="right">1.4</td>
-<td align="right">2.5</td>
-</tr>
-<tr class="odd">
-<td align="right">27.178135</td>
-<td align="right">51.4</td>
-<td align="right">2.9</td>
-<td align="right">2.7</td>
-<td align="right">4.3</td>
-</tr>
-<tr class="even">
-<td align="right">27.178135</td>
-<td align="right">51.4</td>
-<td align="right">4.9</td>
-<td align="right">2.6</td>
-<td align="right">3.2</td>
-</tr>
-<tr class="odd">
-<td align="right">56.297565</td>
-<td align="right">27.6</td>
-<td align="right">12.2</td>
-<td align="right">4.4</td>
-<td align="right">4.3</td>
-</tr>
-<tr class="even">
-<td align="right">56.297565</td>
-<td align="right">26.8</td>
-<td align="right">12.2</td>
-<td align="right">4.7</td>
-<td align="right">4.8</td>
-</tr>
-<tr class="odd">
-<td align="right">86.387643</td>
-<td align="right">15.7</td>
-<td align="right">12.2</td>
-<td align="right">5.4</td>
-<td align="right">5.0</td>
-</tr>
-<tr class="even">
-<td align="right">86.387643</td>
-<td align="right">15.3</td>
-<td align="right">12.0</td>
-<td align="right">5.2</td>
-<td align="right">5.1</td>
-</tr>
-<tr class="odd">
-<td align="right">115.507073</td>
-<td align="right">7.9</td>
-<td align="right">10.4</td>
-<td align="right">5.4</td>
-<td align="right">4.3</td>
-</tr>
-<tr class="even">
-<td align="right">115.507073</td>
-<td align="right">8.1</td>
-<td align="right">11.6</td>
-<td align="right">5.4</td>
-<td align="right">4.4</td>
-</tr>
-</tbody>
-</table>
-<table class="table">
-<caption>Dataset Flaach</caption>
-<thead><tr class="header">
-<th align="right">time</th>
-<th align="right">DMTA</th>
-<th align="right">M23</th>
-<th align="right">M27</th>
-<th align="right">M31</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="right">0.0000000</td>
-<td align="right">96.5</td>
-<td align="right">NA</td>
-<td align="right">NA</td>
-<td align="right">NA</td>
-</tr>
-<tr class="even">
-<td align="right">0.0000000</td>
-<td align="right">96.8</td>
-<td align="right">NA</td>
-<td align="right">NA</td>
-<td align="right">NA</td>
-</tr>
-<tr class="odd">
-<td align="right">0.0000000</td>
-<td align="right">97.0</td>
-<td align="right">NA</td>
-<td align="right">NA</td>
-<td align="right">NA</td>
-</tr>
-<tr class="even">
-<td align="right">0.6233856</td>
-<td align="right">82.9</td>
-<td align="right">0.7</td>
-<td align="right">1.1</td>
-<td align="right">0.3</td>
-</tr>
-<tr class="odd">
-<td align="right">0.6233856</td>
-<td align="right">86.7</td>
-<td align="right">0.7</td>
-<td align="right">1.1</td>
-<td align="right">0.3</td>
-</tr>
-<tr class="even">
-<td align="right">0.6233856</td>
-<td align="right">87.4</td>
-<td align="right">0.2</td>
-<td align="right">0.3</td>
-<td align="right">0.1</td>
-</tr>
-<tr class="odd">
-<td align="right">1.8701567</td>
-<td align="right">72.8</td>
-<td align="right">2.2</td>
-<td align="right">2.6</td>
-<td align="right">0.7</td>
-</tr>
-<tr class="even">
-<td align="right">1.8701567</td>
-<td align="right">69.9</td>
-<td align="right">1.8</td>
-<td align="right">2.4</td>
-<td align="right">0.6</td>
-</tr>
-<tr class="odd">
-<td align="right">1.8701567</td>
-<td align="right">71.9</td>
-<td align="right">1.6</td>
-<td align="right">2.3</td>
-<td align="right">0.7</td>
-</tr>
-<tr class="even">
-<td align="right">4.3636989</td>
-<td align="right">51.4</td>
-<td align="right">4.1</td>
-<td align="right">5.0</td>
-<td align="right">1.3</td>
-</tr>
-<tr class="odd">
-<td align="right">4.3636989</td>
-<td align="right">52.9</td>
-<td align="right">4.2</td>
-<td align="right">5.9</td>
-<td align="right">1.2</td>
-</tr>
-<tr class="even">
-<td align="right">4.3636989</td>
-<td align="right">48.6</td>
-<td align="right">4.2</td>
-<td align="right">4.8</td>
-<td align="right">1.4</td>
-</tr>
-<tr class="odd">
-<td align="right">8.7273979</td>
-<td align="right">28.5</td>
-<td align="right">7.5</td>
-<td align="right">8.5</td>
-<td align="right">2.4</td>
-</tr>
-<tr class="even">
-<td align="right">8.7273979</td>
-<td align="right">27.3</td>
-<td align="right">7.1</td>
-<td align="right">8.5</td>
-<td align="right">2.1</td>
-</tr>
-<tr class="odd">
-<td align="right">8.7273979</td>
-<td align="right">27.5</td>
-<td align="right">7.5</td>
-<td align="right">8.3</td>
-<td align="right">2.3</td>
-</tr>
-<tr class="even">
-<td align="right">13.0910968</td>
-<td align="right">14.8</td>
-<td align="right">8.4</td>
-<td align="right">9.3</td>
-<td align="right">3.3</td>
-</tr>
-<tr class="odd">
-<td align="right">13.0910968</td>
-<td align="right">13.4</td>
-<td align="right">6.8</td>
-<td align="right">8.7</td>
-<td align="right">2.4</td>
-</tr>
-<tr class="even">
-<td align="right">13.0910968</td>
-<td align="right">14.4</td>
-<td align="right">8.0</td>
-<td align="right">9.1</td>
-<td align="right">2.6</td>
-</tr>
-<tr class="odd">
-<td align="right">17.4547957</td>
-<td align="right">7.7</td>
-<td align="right">7.2</td>
-<td align="right">8.6</td>
-<td align="right">4.0</td>
-</tr>
-<tr class="even">
-<td align="right">17.4547957</td>
-<td align="right">7.3</td>
-<td align="right">7.2</td>
-<td align="right">8.5</td>
-<td align="right">3.6</td>
-</tr>
-<tr class="odd">
-<td align="right">17.4547957</td>
-<td align="right">8.1</td>
-<td align="right">6.9</td>
-<td align="right">8.9</td>
-<td align="right">3.3</td>
-</tr>
-<tr class="even">
-<td align="right">26.1821936</td>
-<td align="right">2.0</td>
-<td align="right">4.9</td>
-<td align="right">8.1</td>
-<td align="right">2.1</td>
-</tr>
-<tr class="odd">
-<td align="right">26.1821936</td>
-<td align="right">1.5</td>
-<td align="right">4.3</td>
-<td align="right">7.7</td>
-<td align="right">1.7</td>
-</tr>
-<tr class="even">
-<td align="right">26.1821936</td>
-<td align="right">1.9</td>
-<td align="right">4.5</td>
-<td align="right">7.4</td>
-<td align="right">1.8</td>
-</tr>
-<tr class="odd">
-<td align="right">34.9095915</td>
-<td align="right">1.3</td>
-<td align="right">3.8</td>
-<td align="right">5.9</td>
-<td align="right">1.6</td>
-</tr>
-<tr class="even">
-<td align="right">34.9095915</td>
-<td align="right">1.0</td>
-<td align="right">3.1</td>
-<td align="right">6.0</td>
-<td align="right">1.6</td>
-</tr>
-<tr class="odd">
-<td align="right">34.9095915</td>
-<td align="right">1.1</td>
-<td align="right">3.1</td>
-<td align="right">5.9</td>
-<td align="right">1.4</td>
-</tr>
-<tr class="even">
-<td align="right">43.6369893</td>
-<td align="right">0.9</td>
-<td align="right">2.7</td>
-<td align="right">5.6</td>
-<td align="right">1.8</td>
-</tr>
-<tr class="odd">
-<td align="right">43.6369893</td>
-<td align="right">0.7</td>
-<td align="right">2.3</td>
-<td align="right">5.2</td>
-<td align="right">1.5</td>
-</tr>
-<tr class="even">
-<td align="right">43.6369893</td>
-<td align="right">0.7</td>
-<td align="right">2.1</td>
-<td align="right">5.6</td>
-<td align="right">1.3</td>
-</tr>
-<tr class="odd">
-<td align="right">52.3643872</td>
-<td align="right">0.6</td>
-<td align="right">1.6</td>
-<td align="right">4.3</td>
-<td align="right">1.2</td>
-</tr>
-<tr class="even">
-<td align="right">52.3643872</td>
-<td align="right">0.4</td>
-<td align="right">1.1</td>
-<td align="right">3.7</td>
-<td align="right">0.9</td>
-</tr>
-<tr class="odd">
-<td align="right">52.3643872</td>
-<td align="right">0.5</td>
-<td align="right">1.3</td>
-<td align="right">3.9</td>
-<td align="right">1.1</td>
-</tr>
-<tr class="even">
-<td align="right">74.8062674</td>
-<td align="right">0.4</td>
-<td align="right">0.4</td>
-<td align="right">2.5</td>
-<td align="right">0.5</td>
-</tr>
-<tr class="odd">
-<td align="right">74.8062674</td>
-<td align="right">0.3</td>
-<td align="right">0.4</td>
-<td align="right">2.4</td>
-<td align="right">0.5</td>
-</tr>
-<tr class="even">
-<td align="right">74.8062674</td>
-<td align="right">0.3</td>
-<td align="right">0.3</td>
-<td align="right">2.2</td>
-<td align="right">0.3</td>
-</tr>
-</tbody>
-</table>
-<table class="table">
-<caption>Dataset BBA 2.2</caption>
-<thead><tr class="header">
-<th align="right">time</th>
-<th align="right">DMTA</th>
-<th align="right">M23</th>
-<th align="right">M27</th>
-<th align="right">M31</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="right">0.0000000</td>
-<td align="right">98.09</td>
-<td align="right">NA</td>
-<td align="right">NA</td>
-<td align="right">NA</td>
-</tr>
-<tr class="even">
-<td align="right">0.0000000</td>
-<td align="right">98.77</td>
-<td align="right">NA</td>
-<td align="right">NA</td>
-<td align="right">NA</td>
-</tr>
-<tr class="odd">
-<td align="right">0.7678922</td>
-<td align="right">93.52</td>
-<td align="right">0.36</td>
-<td align="right">0.42</td>
-<td align="right">0.36</td>
-</tr>
-<tr class="even">
-<td align="right">0.7678922</td>
-<td align="right">92.03</td>
-<td align="right">0.40</td>
-<td align="right">0.47</td>
-<td align="right">0.33</td>
-</tr>
-<tr class="odd">
-<td align="right">2.3036765</td>
-<td align="right">88.39</td>
-<td align="right">1.03</td>
-<td align="right">0.71</td>
-<td align="right">0.55</td>
-</tr>
-<tr class="even">
-<td align="right">2.3036765</td>
-<td align="right">87.18</td>
-<td align="right">1.07</td>
-<td align="right">0.82</td>
-<td align="right">0.64</td>
-</tr>
-<tr class="odd">
-<td align="right">5.3752452</td>
-<td align="right">69.38</td>
-<td align="right">3.60</td>
-<td align="right">2.19</td>
-<td align="right">1.94</td>
-</tr>
-<tr class="even">
-<td align="right">5.3752452</td>
-<td align="right">71.06</td>
-<td align="right">3.66</td>
-<td align="right">2.28</td>
-<td align="right">1.62</td>
-</tr>
-<tr class="odd">
-<td align="right">10.7504904</td>
-<td align="right">45.21</td>
-<td align="right">6.97</td>
-<td align="right">5.45</td>
-<td align="right">4.22</td>
-</tr>
-<tr class="even">
-<td align="right">10.7504904</td>
-<td align="right">46.81</td>
-<td align="right">7.22</td>
-<td align="right">5.19</td>
-<td align="right">4.37</td>
-</tr>
-<tr class="odd">
-<td align="right">16.1257355</td>
-<td align="right">30.54</td>
-<td align="right">8.65</td>
-<td align="right">8.81</td>
-<td align="right">6.31</td>
-</tr>
-<tr class="even">
-<td align="right">16.1257355</td>
-<td align="right">30.07</td>
-<td align="right">8.38</td>
-<td align="right">7.93</td>
-<td align="right">6.85</td>
-</tr>
-<tr class="odd">
-<td align="right">21.5009807</td>
-<td align="right">21.60</td>
-<td align="right">9.10</td>
-<td align="right">10.25</td>
-<td align="right">7.05</td>
-</tr>
-<tr class="even">
-<td align="right">21.5009807</td>
-<td align="right">20.41</td>
-<td align="right">8.63</td>
-<td align="right">10.77</td>
-<td align="right">6.84</td>
-</tr>
-<tr class="odd">
-<td align="right">32.2514711</td>
-<td align="right">9.10</td>
-<td align="right">7.63</td>
-<td align="right">10.89</td>
-<td align="right">6.53</td>
-</tr>
-<tr class="even">
-<td align="right">32.2514711</td>
-<td align="right">9.70</td>
-<td align="right">8.01</td>
-<td align="right">10.85</td>
-<td align="right">7.11</td>
-</tr>
-<tr class="odd">
-<td align="right">43.0019614</td>
-<td align="right">6.58</td>
-<td align="right">6.40</td>
-<td align="right">10.41</td>
-<td align="right">6.06</td>
-</tr>
-<tr class="even">
-<td align="right">43.0019614</td>
-<td align="right">6.31</td>
-<td align="right">6.35</td>
-<td align="right">10.35</td>
-<td align="right">6.05</td>
-</tr>
-<tr class="odd">
-<td align="right">53.7524518</td>
-<td align="right">3.47</td>
-<td align="right">5.35</td>
-<td align="right">9.92</td>
-<td align="right">5.50</td>
-</tr>
-<tr class="even">
-<td align="right">53.7524518</td>
-<td align="right">3.52</td>
-<td align="right">5.06</td>
-<td align="right">9.42</td>
-<td align="right">5.07</td>
-</tr>
-<tr class="odd">
-<td align="right">64.5029421</td>
-<td align="right">3.40</td>
-<td align="right">5.14</td>
-<td align="right">9.15</td>
-<td align="right">4.94</td>
-</tr>
-<tr class="even">
-<td align="right">64.5029421</td>
-<td align="right">3.67</td>
-<td align="right">5.91</td>
-<td align="right">9.25</td>
-<td align="right">4.39</td>
-</tr>
-<tr class="odd">
-<td align="right">91.3791680</td>
-<td align="right">1.62</td>
-<td align="right">3.35</td>
-<td align="right">7.14</td>
-<td align="right">3.64</td>
-</tr>
-<tr class="even">
-<td align="right">91.3791680</td>
-<td align="right">1.62</td>
-<td align="right">2.87</td>
-<td align="right">7.13</td>
-<td align="right">3.55</td>
-</tr>
-</tbody>
-</table>
-<table class="table">
-<caption>Dataset BBA 2.3</caption>
-<thead><tr class="header">
-<th align="right">time</th>
-<th align="right">DMTA</th>
-<th align="right">M23</th>
-<th align="right">M27</th>
-<th align="right">M31</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="right">0.0000000</td>
-<td align="right">99.33</td>
-<td align="right">NA</td>
-<td align="right">NA</td>
-<td align="right">NA</td>
-</tr>
-<tr class="even">
-<td align="right">0.0000000</td>
-<td align="right">97.44</td>
-<td align="right">NA</td>
-<td align="right">NA</td>
-<td align="right">NA</td>
-</tr>
-<tr class="odd">
-<td align="right">0.6733938</td>
-<td align="right">93.73</td>
-<td align="right">0.18</td>
-<td align="right">0.50</td>
-<td align="right">0.47</td>
-</tr>
-<tr class="even">
-<td align="right">0.6733938</td>
-<td align="right">93.77</td>
-<td align="right">0.18</td>
-<td align="right">0.83</td>
-<td align="right">0.34</td>
-</tr>
-<tr class="odd">
-<td align="right">2.0201814</td>
-<td align="right">87.84</td>
-<td align="right">0.52</td>
-<td align="right">1.25</td>
-<td align="right">1.00</td>
-</tr>
-<tr class="even">
-<td align="right">2.0201814</td>
-<td align="right">89.82</td>
-<td align="right">0.43</td>
-<td align="right">1.09</td>
-<td align="right">0.89</td>
-</tr>
-<tr class="odd">
-<td align="right">4.7137565</td>
-<td align="right">71.61</td>
-<td align="right">1.19</td>
-<td align="right">3.28</td>
-<td align="right">3.58</td>
-</tr>
-<tr class="even">
-<td align="right">4.7137565</td>
-<td align="right">71.42</td>
-<td align="right">1.11</td>
-<td align="right">3.24</td>
-<td align="right">3.41</td>
-</tr>
-<tr class="odd">
-<td align="right">9.4275131</td>
-<td align="right">45.60</td>
-<td align="right">2.26</td>
-<td align="right">7.17</td>
-<td align="right">8.74</td>
-</tr>
-<tr class="even">
-<td align="right">9.4275131</td>
-<td align="right">45.42</td>
-<td align="right">1.99</td>
-<td align="right">7.91</td>
-<td align="right">8.28</td>
-</tr>
-<tr class="odd">
-<td align="right">14.1412696</td>
-<td align="right">31.12</td>
-<td align="right">2.81</td>
-<td align="right">10.15</td>
-<td align="right">9.67</td>
-</tr>
-<tr class="even">
-<td align="right">14.1412696</td>
-<td align="right">31.68</td>
-<td align="right">2.83</td>
-<td align="right">9.55</td>
-<td align="right">8.95</td>
-</tr>
-<tr class="odd">
-<td align="right">18.8550262</td>
-<td align="right">23.20</td>
-<td align="right">3.39</td>
-<td align="right">12.09</td>
-<td align="right">10.34</td>
-</tr>
-<tr class="even">
-<td align="right">18.8550262</td>
-<td align="right">24.13</td>
-<td align="right">3.56</td>
-<td align="right">11.89</td>
-<td align="right">10.00</td>
-</tr>
-<tr class="odd">
-<td align="right">28.2825393</td>
-<td align="right">9.43</td>
-<td align="right">3.49</td>
-<td align="right">13.32</td>
-<td align="right">7.89</td>
-</tr>
-<tr class="even">
-<td align="right">28.2825393</td>
-<td align="right">9.82</td>
-<td align="right">3.28</td>
-<td align="right">12.05</td>
-<td align="right">8.13</td>
-</tr>
-<tr class="odd">
-<td align="right">37.7100523</td>
-<td align="right">7.08</td>
-<td align="right">2.80</td>
-<td align="right">10.04</td>
-<td align="right">5.06</td>
-</tr>
-<tr class="even">
-<td align="right">37.7100523</td>
-<td align="right">8.64</td>
-<td align="right">2.97</td>
-<td align="right">10.78</td>
-<td align="right">5.54</td>
-</tr>
-<tr class="odd">
-<td align="right">47.1375654</td>
-<td align="right">4.41</td>
-<td align="right">2.42</td>
-<td align="right">9.32</td>
-<td align="right">3.79</td>
-</tr>
-<tr class="even">
-<td align="right">47.1375654</td>
-<td align="right">4.78</td>
-<td align="right">2.51</td>
-<td align="right">9.62</td>
-<td align="right">4.11</td>
-</tr>
-<tr class="odd">
-<td align="right">56.5650785</td>
-<td align="right">4.92</td>
-<td align="right">2.22</td>
-<td align="right">8.00</td>
-<td align="right">3.11</td>
-</tr>
-<tr class="even">
-<td align="right">56.5650785</td>
-<td align="right">5.08</td>
-<td align="right">1.95</td>
-<td align="right">8.45</td>
-<td align="right">2.98</td>
-</tr>
-<tr class="odd">
-<td align="right">80.1338612</td>
-<td align="right">2.13</td>
-<td align="right">1.28</td>
-<td align="right">5.71</td>
-<td align="right">1.78</td>
-</tr>
-<tr class="even">
-<td align="right">80.1338612</td>
-<td align="right">2.23</td>
-<td align="right">0.99</td>
-<td align="right">3.33</td>
-<td align="right">1.55</td>
-</tr>
-</tbody>
-</table>
-<table class="table">
-<caption>Dataset Elliot</caption>
-<thead><tr class="header">
-<th align="right">time</th>
-<th align="right">DMTA</th>
-<th align="right">M23</th>
-<th align="right">M27</th>
-<th align="right">M31</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="right">0.000000</td>
-<td align="right">97.5</td>
-<td align="right">NA</td>
-<td align="right">NA</td>
-<td align="right">NA</td>
-</tr>
-<tr class="even">
-<td align="right">0.000000</td>
-<td align="right">100.7</td>
-<td align="right">NA</td>
-<td align="right">NA</td>
-<td align="right">NA</td>
-</tr>
-<tr class="odd">
-<td align="right">1.228478</td>
-<td align="right">86.4</td>
-<td align="right">NA</td>
-<td align="right">NA</td>
-<td align="right">NA</td>
-</tr>
-<tr class="even">
-<td align="right">1.228478</td>
-<td align="right">88.5</td>
-<td align="right">NA</td>
-<td align="right">NA</td>
-<td align="right">1.5</td>
-</tr>
-<tr class="odd">
-<td align="right">3.685435</td>
-<td align="right">69.8</td>
-<td align="right">2.8</td>
-<td align="right">2.3</td>
-<td align="right">5.0</td>
-</tr>
-<tr class="even">
-<td align="right">3.685435</td>
-<td align="right">77.1</td>
-<td align="right">1.7</td>
-<td align="right">2.1</td>
-<td align="right">2.4</td>
-</tr>
-<tr class="odd">
-<td align="right">8.599349</td>
-<td align="right">59.0</td>
-<td align="right">4.3</td>
-<td align="right">4.0</td>
-<td align="right">4.3</td>
-</tr>
-<tr class="even">
-<td align="right">8.599349</td>
-<td align="right">54.2</td>
-<td align="right">5.8</td>
-<td align="right">3.4</td>
-<td align="right">5.0</td>
-</tr>
-<tr class="odd">
-<td align="right">17.198697</td>
-<td align="right">31.3</td>
-<td align="right">8.2</td>
-<td align="right">6.6</td>
-<td align="right">8.0</td>
-</tr>
-<tr class="even">
-<td align="right">17.198697</td>
-<td align="right">33.5</td>
-<td align="right">5.2</td>
-<td align="right">6.9</td>
-<td align="right">7.7</td>
-</tr>
-<tr class="odd">
-<td align="right">25.798046</td>
-<td align="right">19.6</td>
-<td align="right">5.1</td>
-<td align="right">8.2</td>
-<td align="right">7.8</td>
-</tr>
-<tr class="even">
-<td align="right">25.798046</td>
-<td align="right">20.9</td>
-<td align="right">6.1</td>
-<td align="right">8.8</td>
-<td align="right">6.5</td>
-</tr>
-<tr class="odd">
-<td align="right">34.397395</td>
-<td align="right">13.3</td>
-<td align="right">6.0</td>
-<td align="right">9.7</td>
-<td align="right">8.0</td>
-</tr>
-<tr class="even">
-<td align="right">34.397395</td>
-<td align="right">15.8</td>
-<td align="right">6.0</td>
-<td align="right">8.8</td>
-<td align="right">7.4</td>
-</tr>
-<tr class="odd">
-<td align="right">51.596092</td>
-<td align="right">6.7</td>
-<td align="right">5.0</td>
-<td align="right">8.3</td>
-<td align="right">6.9</td>
-</tr>
-<tr class="even">
-<td align="right">51.596092</td>
-<td align="right">8.7</td>
-<td align="right">4.2</td>
-<td align="right">9.2</td>
-<td align="right">9.0</td>
-</tr>
-<tr class="odd">
-<td align="right">68.794789</td>
-<td align="right">8.8</td>
-<td align="right">3.9</td>
-<td align="right">9.3</td>
-<td align="right">5.5</td>
-</tr>
-<tr class="even">
-<td align="right">68.794789</td>
-<td align="right">8.7</td>
-<td align="right">2.9</td>
-<td align="right">8.5</td>
-<td align="right">6.1</td>
-</tr>
-<tr class="odd">
-<td align="right">103.192184</td>
-<td align="right">6.0</td>
-<td align="right">1.9</td>
-<td align="right">8.6</td>
-<td align="right">6.1</td>
-</tr>
-<tr class="even">
-<td align="right">103.192184</td>
-<td align="right">4.4</td>
-<td align="right">1.5</td>
-<td align="right">6.0</td>
-<td align="right">4.0</td>
-</tr>
-<tr class="odd">
-<td align="right">146.188928</td>
-<td align="right">3.3</td>
-<td align="right">2.0</td>
-<td align="right">5.6</td>
-<td align="right">3.1</td>
-</tr>
-<tr class="even">
-<td align="right">146.188928</td>
-<td align="right">2.8</td>
-<td align="right">2.3</td>
-<td align="right">4.5</td>
-<td align="right">2.9</td>
-</tr>
-<tr class="odd">
-<td align="right">223.583066</td>
-<td align="right">1.4</td>
-<td align="right">1.2</td>
-<td align="right">4.1</td>
-<td align="right">1.8</td>
-</tr>
-<tr class="even">
-<td align="right">223.583066</td>
-<td align="right">1.8</td>
-<td align="right">1.9</td>
-<td align="right">3.9</td>
-<td align="right">2.6</td>
-</tr>
-<tr class="odd">
-<td align="right">0.000000</td>
-<td align="right">93.4</td>
-<td align="right">NA</td>
-<td align="right">NA</td>
-<td align="right">NA</td>
-</tr>
-<tr class="even">
-<td align="right">0.000000</td>
-<td align="right">103.2</td>
-<td align="right">NA</td>
-<td align="right">NA</td>
-<td align="right">NA</td>
-</tr>
-<tr class="odd">
-<td align="right">1.228478</td>
-<td align="right">89.2</td>
-<td align="right">NA</td>
-<td align="right">NA</td>
-<td align="right">1.3</td>
-</tr>
-<tr class="even">
-<td align="right">1.228478</td>
-<td align="right">86.6</td>
-<td align="right">NA</td>
-<td align="right">NA</td>
-<td align="right">NA</td>
-</tr>
-<tr class="odd">
-<td align="right">3.685435</td>
-<td align="right">78.2</td>
-<td align="right">2.6</td>
-<td align="right">1.0</td>
-<td align="right">3.1</td>
-</tr>
-<tr class="even">
-<td align="right">3.685435</td>
-<td align="right">78.1</td>
-<td align="right">2.4</td>
-<td align="right">2.6</td>
-<td align="right">2.3</td>
-</tr>
-<tr class="odd">
-<td align="right">8.599349</td>
-<td align="right">55.6</td>
-<td align="right">5.5</td>
-<td align="right">4.5</td>
-<td align="right">3.4</td>
-</tr>
-<tr class="even">
-<td align="right">8.599349</td>
-<td align="right">53.0</td>
-<td align="right">5.6</td>
-<td align="right">4.6</td>
-<td align="right">4.3</td>
-</tr>
-<tr class="odd">
-<td align="right">17.198697</td>
-<td align="right">33.7</td>
-<td align="right">7.3</td>
-<td align="right">7.6</td>
-<td align="right">7.8</td>
-</tr>
-<tr class="even">
-<td align="right">17.198697</td>
-<td align="right">33.2</td>
-<td align="right">6.5</td>
-<td align="right">6.7</td>
-<td align="right">8.7</td>
-</tr>
-<tr class="odd">
-<td align="right">25.798046</td>
-<td align="right">20.9</td>
-<td align="right">5.8</td>
-<td align="right">8.7</td>
-<td align="right">7.7</td>
-</tr>
-<tr class="even">
-<td align="right">25.798046</td>
-<td align="right">19.9</td>
-<td align="right">7.7</td>
-<td align="right">7.6</td>
-<td align="right">6.5</td>
-</tr>
-<tr class="odd">
-<td align="right">34.397395</td>
-<td align="right">18.2</td>
-<td align="right">7.8</td>
-<td align="right">8.0</td>
-<td align="right">6.3</td>
-</tr>
-<tr class="even">
-<td align="right">34.397395</td>
-<td align="right">12.7</td>
-<td align="right">7.3</td>
-<td align="right">8.6</td>
-<td align="right">8.7</td>
-</tr>
-<tr class="odd">
-<td align="right">51.596092</td>
-<td align="right">7.8</td>
-<td align="right">7.0</td>
-<td align="right">7.4</td>
-<td align="right">5.7</td>
-</tr>
-<tr class="even">
-<td align="right">51.596092</td>
-<td align="right">9.0</td>
-<td align="right">6.3</td>
-<td align="right">7.2</td>
-<td align="right">4.2</td>
-</tr>
-<tr class="odd">
-<td align="right">68.794789</td>
-<td align="right">11.4</td>
-<td align="right">4.3</td>
-<td align="right">10.3</td>
-<td align="right">3.2</td>
-</tr>
-<tr class="even">
-<td align="right">68.794789</td>
-<td align="right">9.0</td>
-<td align="right">3.8</td>
-<td align="right">9.4</td>
-<td align="right">4.2</td>
-</tr>
-<tr class="odd">
-<td align="right">103.192184</td>
-<td align="right">3.9</td>
-<td align="right">2.6</td>
-<td align="right">6.5</td>
-<td align="right">3.8</td>
-</tr>
-<tr class="even">
-<td align="right">103.192184</td>
-<td align="right">4.4</td>
-<td align="right">2.8</td>
-<td align="right">6.9</td>
-<td align="right">4.0</td>
-</tr>
-<tr class="odd">
-<td align="right">146.188928</td>
-<td align="right">2.6</td>
-<td align="right">1.6</td>
-<td align="right">4.6</td>
-<td align="right">4.5</td>
-</tr>
-<tr class="even">
-<td align="right">146.188928</td>
-<td align="right">3.4</td>
-<td align="right">1.1</td>
-<td align="right">4.5</td>
-<td align="right">4.5</td>
-</tr>
-<tr class="odd">
-<td align="right">223.583066</td>
-<td align="right">2.0</td>
-<td align="right">1.4</td>
-<td align="right">4.3</td>
-<td align="right">3.8</td>
-</tr>
-<tr class="even">
-<td align="right">223.583066</td>
-<td align="right">1.7</td>
-<td align="right">1.3</td>
-<td align="right">4.2</td>
-<td align="right">2.3</td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="section level2">
-<h2 id="separate-evaluations">Separate evaluations<a class="anchor" aria-label="anchor" href="#separate-evaluations"></a>
-</h2>
-<p>As a first step to obtain suitable starting parameters for the NLHM
-fits, we do separate fits of several variants of the pathway model used
-previously <span class="citation">(Ranke et al. 2021)</span>, varying
-the kinetic model for the parent compound. Because the SFORB model often
-provides faster convergence than the DFOP model, and can sometimes be
-fitted where the DFOP model results in errors, it is included in the set
-of parent models tested here.</p>
-<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="kw">if</span> <span class="op">(</span><span class="op">!</span><span class="fu"><a href="https://rdrr.io/r/base/files2.html" class="external-link">dir.exists</a></span><span class="op">(</span><span class="st">"dmta_dlls"</span><span class="op">)</span><span class="op">)</span> <span class="fu"><a href="https://rdrr.io/r/base/files2.html" class="external-link">dir.create</a></span><span class="op">(</span><span class="st">"dmta_dlls"</span><span class="op">)</span></span>
-<span><span class="va">m_sfo_path_1</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span></span>
-<span>  DMTA <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"M23"</span>, <span class="st">"M27"</span>, <span class="st">"M31"</span><span class="op">)</span><span class="op">)</span>,</span>
-<span>  M23 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span>
-<span>  M27 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span>
-<span>  M31 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"M27"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span>
-<span>  name <span class="op">=</span> <span class="st">"m_sfo_path"</span>, dll_dir <span class="op">=</span> <span class="st">"dmta_dlls"</span>,</span>
-<span>  unload <span class="op">=</span> <span class="cn">TRUE</span>, overwrite <span class="op">=</span> <span class="cn">TRUE</span>,</span>
-<span>  quiet <span class="op">=</span> <span class="cn">TRUE</span></span>
-<span><span class="op">)</span></span>
-<span><span class="va">m_fomc_path_1</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span></span>
-<span>  DMTA <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"FOMC"</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"M23"</span>, <span class="st">"M27"</span>, <span class="st">"M31"</span><span class="op">)</span><span class="op">)</span>,</span>
-<span>  M23 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span>
-<span>  M27 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span>
-<span>  M31 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"M27"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span>
-<span>  name <span class="op">=</span> <span class="st">"m_fomc_path"</span>, dll_dir <span class="op">=</span> <span class="st">"dmta_dlls"</span>,</span>
-<span>  unload <span class="op">=</span> <span class="cn">TRUE</span>, overwrite <span class="op">=</span> <span class="cn">TRUE</span>,</span>
-<span>  quiet <span class="op">=</span> <span class="cn">TRUE</span></span>
-<span><span class="op">)</span></span>
-<span><span class="va">m_dfop_path_1</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span></span>
-<span>  DMTA <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"M23"</span>, <span class="st">"M27"</span>, <span class="st">"M31"</span><span class="op">)</span><span class="op">)</span>,</span>
-<span>  M23 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span>
-<span>  M27 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span>
-<span>  M31 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"M27"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span>
-<span>  name <span class="op">=</span> <span class="st">"m_dfop_path"</span>, dll_dir <span class="op">=</span> <span class="st">"dmta_dlls"</span>,</span>
-<span>  unload <span class="op">=</span> <span class="cn">TRUE</span>, overwrite <span class="op">=</span> <span class="cn">TRUE</span>,</span>
-<span>  quiet <span class="op">=</span> <span class="cn">TRUE</span></span>
-<span><span class="op">)</span></span>
-<span><span class="va">m_sforb_path_1</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span></span>
-<span>  DMTA <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFORB"</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"M23"</span>, <span class="st">"M27"</span>, <span class="st">"M31"</span><span class="op">)</span><span class="op">)</span>,</span>
-<span>  M23 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span>
-<span>  M27 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span>
-<span>  M31 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"M27"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span>
-<span>  name <span class="op">=</span> <span class="st">"m_sforb_path"</span>, dll_dir <span class="op">=</span> <span class="st">"dmta_dlls"</span>,</span>
-<span>  unload <span class="op">=</span> <span class="cn">TRUE</span>, overwrite <span class="op">=</span> <span class="cn">TRUE</span>,</span>
-<span>  quiet <span class="op">=</span> <span class="cn">TRUE</span></span>
-<span><span class="op">)</span></span>
-<span><span class="va">m_hs_path_1</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span></span>
-<span>  DMTA <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"HS"</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"M23"</span>, <span class="st">"M27"</span>, <span class="st">"M31"</span><span class="op">)</span><span class="op">)</span>,</span>
-<span>  M23 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span>
-<span>  M27 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span>
-<span>  M31 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"M27"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span>
-<span>  name <span class="op">=</span> <span class="st">"m_hs_path"</span>, dll_dir <span class="op">=</span> <span class="st">"dmta_dlls"</span>,</span>
-<span>  unload <span class="op">=</span> <span class="cn">TRUE</span>, overwrite <span class="op">=</span> <span class="cn">TRUE</span>,</span>
-<span>  quiet <span class="op">=</span> <span class="cn">TRUE</span></span>
-<span><span class="op">)</span></span>
-<span><span class="va">cl</span> <span class="op">&lt;-</span> <span class="fu">start_cluster</span><span class="op">(</span><span class="va">n_cores</span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">deg_mods_1</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span></span>
-<span>  sfo_path_1 <span class="op">=</span> <span class="va">m_sfo_path_1</span>,</span>
-<span>  fomc_path_1 <span class="op">=</span> <span class="va">m_fomc_path_1</span>,</span>
-<span>  dfop_path_1 <span class="op">=</span> <span class="va">m_dfop_path_1</span>,</span>
-<span>  sforb_path_1 <span class="op">=</span> <span class="va">m_sforb_path_1</span>,</span>
-<span>  hs_path_1 <span class="op">=</span> <span class="va">m_hs_path_1</span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">sep_1_const</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/mmkin.html">mmkin</a></span><span class="op">(</span></span>
-<span>  <span class="va">deg_mods_1</span>,</span>
-<span>  <span class="va">dmta_ds</span>,</span>
-<span>  error_model <span class="op">=</span> <span class="st">"const"</span>,</span>
-<span>  quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu"><a href="../../reference/status.html">status</a></span><span class="op">(</span><span class="va">sep_1_const</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
-<thead><tr class="header">
-<th align="left"></th>
-<th align="left">Calke</th>
-<th align="left">Borstel</th>
-<th align="left">Flaach</th>
-<th align="left">BBA 2.2</th>
-<th align="left">BBA 2.3</th>
-<th align="left">Elliot</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="left">sfo_path_1</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-</tr>
-<tr class="even">
-<td align="left">fomc_path_1</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-</tr>
-<tr class="odd">
-<td align="left">dfop_path_1</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">C</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-</tr>
-<tr class="even">
-<td align="left">sforb_path_1</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">C</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-</tr>
-<tr class="odd">
-<td align="left">hs_path_1</td>
-<td align="left">C</td>
-<td align="left">C</td>
-<td align="left">C</td>
-<td align="left">C</td>
-<td align="left">C</td>
-<td align="left">C</td>
-</tr>
-</tbody>
-</table>
-<p>All separate pathway fits with SFO or FOMC for the parent and
-constant variance converged (status OK). Most fits with DFOP or SFORB
-for the parent converged as well. The fits with HS for the parent did
-not converge with default settings.</p>
-<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">sep_1_tc</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">sep_1_const</span>, error_model <span class="op">=</span> <span class="st">"tc"</span><span class="op">)</span></span>
-<span><span class="fu"><a href="../../reference/status.html">status</a></span><span class="op">(</span><span class="va">sep_1_tc</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
-<thead><tr class="header">
-<th align="left"></th>
-<th align="left">Calke</th>
-<th align="left">Borstel</th>
-<th align="left">Flaach</th>
-<th align="left">BBA 2.2</th>
-<th align="left">BBA 2.3</th>
-<th align="left">Elliot</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="left">sfo_path_1</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-</tr>
-<tr class="even">
-<td align="left">fomc_path_1</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">C</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">C</td>
-</tr>
-<tr class="odd">
-<td align="left">dfop_path_1</td>
-<td align="left">OK</td>
-<td align="left">C</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-</tr>
-<tr class="even">
-<td align="left">sforb_path_1</td>
-<td align="left">OK</td>
-<td align="left">C</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-</tr>
-<tr class="odd">
-<td align="left">hs_path_1</td>
-<td align="left">C</td>
-<td align="left">C</td>
-<td align="left">C</td>
-<td align="left">C</td>
-<td align="left">C</td>
-<td align="left">OK</td>
-</tr>
-</tbody>
-</table>
-<p>With the two-component error model, the set of fits with convergence
-problems is slightly different, with convergence problems appearing for
-different data sets when applying the DFOP and SFORB model and some
-additional convergence problems when using the FOMC model for the
-parent.</p>
-</div>
-<div class="section level2">
-<h2 id="hierarchichal-model-fits">Hierarchichal model fits<a class="anchor" aria-label="anchor" href="#hierarchichal-model-fits"></a>
-</h2>
-<p>The following code fits two sets of the corresponding hierarchical
-models to the data, one assuming constant variance, and one assuming
-two-component error.</p>
-<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">saem_1</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/mhmkin.html">mhmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">sep_1_const</span>, <span class="va">sep_1_tc</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
-<p>The run time for these fits was around two hours on five year old
-hardware. After a recent hardware upgrade these fits complete in less
-than twenty minutes.</p>
-<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="../../reference/status.html">status</a></span><span class="op">(</span><span class="va">saem_1</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
-<thead><tr class="header">
-<th align="left"></th>
-<th align="left">const</th>
-<th align="left">tc</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="left">sfo_path_1</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-</tr>
-<tr class="even">
-<td align="left">fomc_path_1</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-</tr>
-<tr class="odd">
-<td align="left">dfop_path_1</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-</tr>
-<tr class="even">
-<td align="left">sforb_path_1</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-</tr>
-<tr class="odd">
-<td align="left">hs_path_1</td>
-<td align="left">OK</td>
-<td align="left">OK</td>
-</tr>
-</tbody>
-</table>
-<p>According to the <code>status</code> function, all fits terminated
-successfully.</p>
-<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">saem_1</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span>digits <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></code></pre></div>
-<pre><code>Warning in FUN(X[[i]], ...): Could not obtain log likelihood with 'is' method
-for sforb_path_1 const</code></pre>
-<table class="table">
-<thead><tr class="header">
-<th align="left"></th>
-<th align="right">npar</th>
-<th align="right">AIC</th>
-<th align="right">BIC</th>
-<th align="right">Lik</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="left">sfo_path_1 const</td>
-<td align="right">17</td>
-<td align="right">2291.8</td>
-<td align="right">2288.3</td>
-<td align="right">-1128.9</td>
-</tr>
-<tr class="even">
-<td align="left">sfo_path_1 tc</td>
-<td align="right">18</td>
-<td align="right">2276.3</td>
-<td align="right">2272.5</td>
-<td align="right">-1120.1</td>
-</tr>
-<tr class="odd">
-<td align="left">fomc_path_1 const</td>
-<td align="right">19</td>
-<td align="right">2099.0</td>
-<td align="right">2095.0</td>
-<td align="right">-1030.5</td>
-</tr>
-<tr class="even">
-<td align="left">fomc_path_1 tc</td>
-<td align="right">20</td>
-<td align="right">1939.6</td>
-<td align="right">1935.5</td>
-<td align="right">-949.8</td>
-</tr>
-<tr class="odd">
-<td align="left">dfop_path_1 const</td>
-<td align="right">21</td>
-<td align="right">2038.8</td>
-<td align="right">2034.4</td>
-<td align="right">-998.4</td>
-</tr>
-<tr class="even">
-<td align="left">hs_path_1 const</td>
-<td align="right">21</td>
-<td align="right">2024.2</td>
-<td align="right">2019.8</td>
-<td align="right">-991.1</td>
-</tr>
-<tr class="odd">
-<td align="left">dfop_path_1 tc</td>
-<td align="right">22</td>
-<td align="right">1879.8</td>
-<td align="right">1875.2</td>
-<td align="right">-917.9</td>
-</tr>
-<tr class="even">
-<td align="left">sforb_path_1 tc</td>
-<td align="right">22</td>
-<td align="right">1832.9</td>
-<td align="right">1828.3</td>
-<td align="right">-894.4</td>
-</tr>
-<tr class="odd">
-<td align="left">hs_path_1 tc</td>
-<td align="right">22</td>
-<td align="right">1831.4</td>
-<td align="right">1826.8</td>
-<td align="right">-893.7</td>
-</tr>
-</tbody>
-</table>
-<p>When the goodness-of-fit of the models is compared, a warning is
-obtained, indicating that the likelihood of the pathway fit with SFORB
-for the parent compound and constant variance could not be calculated
-with importance sampling (method ‘is’). As this is the default method on
-which all AIC and BIC comparisons are based, this variant is not
-included in the model comparison table. Comparing the goodness-of-fit of
-the remaining models, HS model model with two-component error provides
-the best fit. However, for batch experiments performed with constant
-conditions such as the experiments evaluated here, there is no reason to
-assume a discontinuity, so the SFORB model is preferable from a
-mechanistic viewpoint. In addition, the information criteria AIC and BIC
-are very similar for HS and SFORB. Therefore, the SFORB model is
-selected here for further refinements.</p>
-<div class="section level3">
-<h3 id="parameter-identifiability-based-on-the-fisher-information-matrix">Parameter identifiability based on the Fisher Information
-Matrix<a class="anchor" aria-label="anchor" href="#parameter-identifiability-based-on-the-fisher-information-matrix"></a>
-</h3>
-<p>Using the <code>illparms</code> function, ill-defined statistical
-model parameters such as standard deviations of the degradation
-parameters in the population and error model parameters can be
-found.</p>
-<div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="../../reference/illparms.html">illparms</a></span><span class="op">(</span><span class="va">saem_1</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
-<thead><tr class="header">
-<th align="left"></th>
-<th align="left">const</th>
-<th align="left">tc</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="left">sfo_path_1</td>
-<td align="left"></td>
-<td align="left">sd(DMTA_0)</td>
-</tr>
-<tr class="even">
-<td align="left">fomc_path_1</td>
-<td align="left"></td>
-<td align="left">sd(DMTA_0)</td>
-</tr>
-<tr class="odd">
-<td align="left">dfop_path_1</td>
-<td align="left"></td>
-<td align="left"></td>
-</tr>
-<tr class="even">
-<td align="left">sforb_path_1</td>
-<td align="left"></td>
-<td align="left">sd(log_k_DMTA_bound_free)</td>
-</tr>
-<tr class="odd">
-<td align="left">hs_path_1</td>
-<td align="left"></td>
-<td align="left">sd(log_tb)</td>
-</tr>
-</tbody>
-</table>
-<p>When using constant variance, no ill-defined variance parameters are
-identified with the <code>illparms</code> function in any of the
-degradation models. When using the two-component error model, there is
-one ill-defined variance parameter in all variants except for the
-variant using DFOP for the parent compound.</p>
-<p>For the selected combination of the SFORB pathway model with
-two-component error, the random effect for the rate constant from
-reversibly bound DMTA to the free DMTA (<code>k_DMTA_bound_free</code>)
-is not well-defined. Therefore, the fit is updated without assuming a
-random effect for this parameter.</p>
-<div class="sourceCode" id="cb11"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">saem_sforb_path_1_tc_reduced</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">saem_1</span><span class="op">[[</span><span class="st">"sforb_path_1"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span>,</span>
-<span>  no_random_effect <span class="op">=</span> <span class="st">"log_k_DMTA_bound_free"</span><span class="op">)</span></span>
-<span><span class="fu"><a href="../../reference/illparms.html">illparms</a></span><span class="op">(</span><span class="va">saem_sforb_path_1_tc_reduced</span><span class="op">)</span></span></code></pre></div>
-<p>As expected, no ill-defined parameters remain. The model comparison
-below shows that the reduced model is preferable.</p>
-<div class="sourceCode" id="cb12"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">saem_1</span><span class="op">[[</span><span class="st">"sforb_path_1"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span>, <span class="va">saem_sforb_path_1_tc_reduced</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span>digits <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
-<thead><tr class="header">
-<th align="left"></th>
-<th align="right">npar</th>
-<th align="right">AIC</th>
-<th align="right">BIC</th>
-<th align="right">Lik</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="left">saem_sforb_path_1_tc_reduced</td>
-<td align="right">21</td>
-<td align="right">1830.3</td>
-<td align="right">1825.9</td>
-<td align="right">-894.2</td>
-</tr>
-<tr class="even">
-<td align="left">saem_1[[“sforb_path_1”, “tc”]]</td>
-<td align="right">22</td>
-<td align="right">1832.9</td>
-<td align="right">1828.3</td>
-<td align="right">-894.4</td>
-</tr>
-</tbody>
-</table>
-<p>The convergence plot of the refined fit is shown below.</p>
-<div class="sourceCode" id="cb13"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">saem_sforb_path_1_tc_reduced</span><span class="op">$</span><span class="va">so</span>, plot.type <span class="op">=</span> <span class="st">"convergence"</span><span class="op">)</span></span></code></pre></div>
-<p><img src="2022_dmta_pathway_files/figure-html/saem-sforb-path-1-tc-reduced-convergence-1.png" width="700" style="display: block; margin: auto;"></p>
-<p>For some parameters, for example for <code>f_DMTA_ilr_1</code> and
-<code>f_DMTA_ilr_2</code>, i.e. for two of the parameters determining
-the formation fractions of the parallel formation of the three
-metabolites, some movement of the parameters is still visible in the
-second phase of the algorithm. However, the amplitude of this movement
-is in the range of the amplitude towards the end of the first phase.
-Therefore, it is likely that an increase in iterations would not improve
-the parameter estimates very much, and it is proposed that the fit is
-acceptable. No numeric convergence criterion is implemented in
-saemix.</p>
-</div>
-<div class="section level3">
-<h3 id="alternative-check-of-parameter-identifiability">Alternative check of parameter identifiability<a class="anchor" aria-label="anchor" href="#alternative-check-of-parameter-identifiability"></a>
-</h3>
-<p>As an alternative check of parameter identifiability <span class="citation">(Duchesne et al. 2021)</span>, multistart runs were
-performed on the basis of the refined fit shown above.</p>
-<div class="sourceCode" id="cb14"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">saem_sforb_path_1_tc_reduced_multi</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/multistart.html">multistart</a></span><span class="op">(</span><span class="va">saem_sforb_path_1_tc_reduced</span>,</span>
-<span>  n <span class="op">=</span> <span class="fl">32</span>, cores <span class="op">=</span> <span class="fl">10</span><span class="op">)</span></span></code></pre></div>
-<div class="sourceCode" id="cb15"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">saem_sforb_path_1_tc_reduced_multi</span><span class="op">)</span></span></code></pre></div>
-<pre><code>&lt;multistart&gt; object with 32 fits:
- E OK 
-15 17 
-OK: Fit terminated successfully
-E: Error</code></pre>
-<p>Out of the 32 fits that were initiated, only 17 terminated without an
-error. The reason for this is that the wide variation of starting
-parameters in combination with the parameter variation that is used in
-the SAEM algorithm leads to parameter combinations for the degradation
-model that the numerical integration routine cannot cope with. Because
-of this variation of initial parameters, some of the model fits take up
-to two times more time than the original fit.</p>
-<div class="sourceCode" id="cb17"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/par.html" class="external-link">par</a></span><span class="op">(</span>mar <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">12.1</span>, <span class="fl">4.1</span>, <span class="fl">2.1</span>, <span class="fl">2.1</span><span class="op">)</span><span class="op">)</span></span>
-<span><span class="fu"><a href="../../reference/parplot.html">parplot</a></span><span class="op">(</span><span class="va">saem_sforb_path_1_tc_reduced_multi</span>, ylim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.5</span>, <span class="fl">2</span><span class="op">)</span>, las <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span></code></pre></div>
-<div class="figure" style="text-align: center">
-<img src="2022_dmta_pathway_files/figure-html/unnamed-chunk-2-1.png" alt="Parameter boxplots for the multistart runs that succeeded" width="960"><p class="caption">
-Parameter boxplots for the multistart runs that succeeded
-</p>
-</div>
-<p>However, visual analysis of the boxplot of the parameters obtained in
-the successful fits confirms that the results are sufficiently
-independent of the starting parameters, and there are no remaining
-ill-defined parameters.</p>
-</div>
-</div>
-<div class="section level2">
-<h2 id="plots-of-selected-fits">Plots of selected fits<a class="anchor" aria-label="anchor" href="#plots-of-selected-fits"></a>
-</h2>
-<p>The SFORB pathway fits with full and reduced parameter distribution
-model are shown below.</p>
-<div class="sourceCode" id="cb18"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">saem_1</span><span class="op">[[</span><span class="st">"sforb_path_1"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div>
-<div class="figure" style="text-align: center">
-<img src="2022_dmta_pathway_files/figure-html/unnamed-chunk-3-1.png" alt="SFORB pathway fit with two-component error" width="700"><p class="caption">
-SFORB pathway fit with two-component error
-</p>
-</div>
-<div class="sourceCode" id="cb19"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">saem_sforb_path_1_tc_reduced</span><span class="op">)</span></span></code></pre></div>
-<div class="figure" style="text-align: center">
-<img src="2022_dmta_pathway_files/figure-html/unnamed-chunk-4-1.png" alt="SFORB pathway fit with two-component error, reduced parameter model" width="700"><p class="caption">
-SFORB pathway fit with two-component error, reduced parameter model
-</p>
-</div>
-<p>Plots of the remaining fits and listings for all successful fits are
-shown in the Appendix.</p>
-<div class="sourceCode" id="cb20"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/parallel/makeCluster.html" class="external-link">stopCluster</a></span><span class="op">(</span><span class="va">cl</span><span class="op">)</span></span></code></pre></div>
-</div>
-<div class="section level2">
-<h2 id="conclusions">Conclusions<a class="anchor" aria-label="anchor" href="#conclusions"></a>
-</h2>
-<p>Pathway fits with SFO, FOMC, DFOP, SFORB and HS models for the parent
-compound could be successfully performed.</p>
-</div>
-<div class="section level2">
-<h2 id="acknowledgements">Acknowledgements<a class="anchor" aria-label="anchor" href="#acknowledgements"></a>
-</h2>
-<p>The helpful comments by Janina Wöltjen of the German Environment
-Agency on earlier versions of this document are gratefully
-acknowledged.</p>
-</div>
-<div class="section level2">
-<h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a>
-</h2>
-<div id="refs" class="references csl-bib-body hanging-indent">
-<div id="ref-duchesne_2021" class="csl-entry">
-Duchesne, Ronan, Anissa Guillemin, Olivier Gandrillon, and Fabien
-Crauste. 2021. <span>“Practical Identifiability in the Frame of
-Nonlinear Mixed Effects Models: The Example of the in Vitro
-Erythropoiesis.”</span> <em>BMC Bioinformatics</em> 22 (478). <a href="https://doi.org/10.1186/s12859-021-04373-4" class="external-link">https://doi.org/10.1186/s12859-021-04373-4</a>.
-</div>
-<div id="ref-ranke2021" class="csl-entry">
-Ranke, Johannes, Janina Wöltjen, Jana Schmidt, and Emmanuelle Comets.
-2021. <span>“Taking Kinetic Evaluations of Degradation Data to the Next
-Level with Nonlinear Mixed-Effects Models.”</span> <em>Environments</em>
-8 (8). <a href="https://doi.org/10.3390/environments8080071" class="external-link">https://doi.org/10.3390/environments8080071</a>.
-</div>
-</div>
-</div>
-<div class="section level2">
-<h2 id="appendix">Appendix<a class="anchor" aria-label="anchor" href="#appendix"></a>
-</h2>
-<div class="section level3">
-<h3 id="plots-of-hierarchical-fits-not-selected-for-refinement">Plots of hierarchical fits not selected for refinement<a class="anchor" aria-label="anchor" href="#plots-of-hierarchical-fits-not-selected-for-refinement"></a>
-</h3>
-<div class="sourceCode" id="cb21"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">saem_1</span><span class="op">[[</span><span class="st">"sfo_path_1"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div>
-<div class="figure" style="text-align: center">
-<img src="2022_dmta_pathway_files/figure-html/unnamed-chunk-6-1.png" alt="SFO pathway fit with two-component error" width="700"><p class="caption">
-SFO pathway fit with two-component error
-</p>
-</div>
-<div class="sourceCode" id="cb22"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">saem_1</span><span class="op">[[</span><span class="st">"fomc_path_1"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div>
-<div class="figure" style="text-align: center">
-<img src="2022_dmta_pathway_files/figure-html/unnamed-chunk-7-1.png" alt="FOMC pathway fit with two-component error" width="700"><p class="caption">
-FOMC pathway fit with two-component error
-</p>
-</div>
-<div class="sourceCode" id="cb23"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">saem_1</span><span class="op">[[</span><span class="st">"sforb_path_1"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div>
-<div class="figure" style="text-align: center">
-<img src="2022_dmta_pathway_files/figure-html/unnamed-chunk-8-1.png" alt="HS pathway fit with two-component error" width="700"><p class="caption">
-HS pathway fit with two-component error
-</p>
-</div>
-</div>
-<div class="section level3">
-<h3 id="hierarchical-model-fit-listings">Hierarchical model fit listings<a class="anchor" aria-label="anchor" href="#hierarchical-model-fit-listings"></a>
-</h3>
-<div class="section level4">
-<h4 id="fits-with-random-effects-for-all-degradation-parameters">Fits with random effects for all degradation parameters<a class="anchor" aria-label="anchor" href="#fits-with-random-effects-for-all-degradation-parameters"></a>
-</h4>
-
-</div>
-<div class="section level4">
-<h4 id="improved-fit-of-the-sforb-pathway-model-with-two-component-error">Improved fit of the SFORB pathway model with two-component
-error<a class="anchor" aria-label="anchor" href="#improved-fit-of-the-sforb-pathway-model-with-two-component-error"></a>
-</h4>
-
-</div>
-</div>
-<div class="section level3">
-<h3 id="session-info">Session info<a class="anchor" aria-label="anchor" href="#session-info"></a>
-</h3>
-<pre><code>R version 4.2.3 (2023-03-15)
-Platform: x86_64-pc-linux-gnu (64-bit)
-Running under: Debian GNU/Linux 12 (bookworm)
-
-Matrix products: default
-BLAS:   /usr/lib/x86_64-linux-gnu/openblas-serial/libblas.so.3
-LAPACK: /usr/lib/x86_64-linux-gnu/openblas-serial/libopenblas-r0.3.21.so
-
-locale:
- [1] LC_CTYPE=de_DE.UTF-8       LC_NUMERIC=C              
- [3] LC_TIME=de_DE.UTF-8        LC_COLLATE=de_DE.UTF-8    
- [5] LC_MONETARY=de_DE.UTF-8    LC_MESSAGES=de_DE.UTF-8   
- [7] LC_PAPER=de_DE.UTF-8       LC_NAME=C                 
- [9] LC_ADDRESS=C               LC_TELEPHONE=C            
-[11] LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=C       
-
-attached base packages:
-[1] parallel  stats     graphics  grDevices utils     datasets  methods  
-[8] base     
-
-other attached packages:
-[1] saemix_3.2 npde_3.3   knitr_1.42 mkin_1.2.4
-
-loaded via a namespace (and not attached):
- [1] deSolve_1.35      zoo_1.8-12        tidyselect_1.2.0  xfun_0.38        
- [5] bslib_0.4.2       purrr_1.0.1       lattice_0.21-8    colorspace_2.1-0 
- [9] vctrs_0.6.1       generics_0.1.3    htmltools_0.5.5   yaml_2.3.7       
-[13] utf8_1.2.3        rlang_1.1.0       pkgbuild_1.4.0    pkgdown_2.0.7    
-[17] jquerylib_0.1.4   pillar_1.9.0      glue_1.6.2        DBI_1.1.3        
-[21] lifecycle_1.0.3   stringr_1.5.0     munsell_0.5.0     gtable_0.3.3     
-[25] ragg_1.2.5        codetools_0.2-19  memoise_2.0.1     evaluate_0.20    
-[29] inline_0.3.19     callr_3.7.3       fastmap_1.1.1     ps_1.7.4         
-[33] lmtest_0.9-40     fansi_1.0.4       highr_0.10        scales_1.2.1     
-[37] cachem_1.0.7      desc_1.4.2        jsonlite_1.8.4    systemfonts_1.0.4
-[41] fs_1.6.1          textshaping_0.3.6 gridExtra_2.3     ggplot2_3.4.2    
-[45] digest_0.6.31     stringi_1.7.12    processx_3.8.0    dplyr_1.1.1      
-[49] grid_4.2.3        rprojroot_2.0.3   cli_3.6.1         tools_4.2.3      
-[53] magrittr_2.0.3    sass_0.4.5        tibble_3.2.1      crayon_1.5.2     
-[57] pkgconfig_2.0.3   prettyunits_1.1.1 rmarkdown_2.21    R6_2.5.1         
-[61] mclust_6.0.0      nlme_3.1-162      compiler_4.2.3   </code></pre>
-</div>
-<div class="section level3">
-<h3 id="hardware-info">Hardware info<a class="anchor" aria-label="anchor" href="#hardware-info"></a>
-</h3>
-<pre><code>CPU model: AMD Ryzen 9 7950X 16-Core Processor</code></pre>
-<pre><code>MemTotal:       64936316 kB</code></pre>
-</div>
-</div>
-  </div>
-
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-
-        <nav id="toc" data-toggle="toc"><h2 data-toc-skip>Contents</h2>
-    </nav>
-</div>
-
-</div>
-
-
-
-      <footer><div class="copyright">
-  <p></p>
-<p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p>
-<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer>
-</div>
-
-  
-
-
-  
-
-  </body>
-</html>
diff --git a/docs/dev/articles/prebuilt/2022_dmta_pathway_files/figure-html/saem-sforb-path-1-tc-reduced-convergence-1.png b/docs/dev/articles/prebuilt/2022_dmta_pathway_files/figure-html/saem-sforb-path-1-tc-reduced-convergence-1.png
deleted file mode 100644
index 206c424d..00000000
--- a/docs/dev/articles/prebuilt/2022_dmta_pathway_files/figure-html/saem-sforb-path-1-tc-reduced-convergence-1.png
+++ /dev/null
diff --git a/docs/dev/articles/prebuilt/2022_dmta_pathway_files/figure-html/unnamed-chunk-2-1.png b/docs/dev/articles/prebuilt/2022_dmta_pathway_files/figure-html/unnamed-chunk-2-1.png
deleted file mode 100644
index 0fe084d3..00000000
--- a/docs/dev/articles/prebuilt/2022_dmta_pathway_files/figure-html/unnamed-chunk-2-1.png
+++ /dev/null
diff --git a/docs/dev/articles/prebuilt/2022_dmta_pathway_files/figure-html/unnamed-chunk-3-1.png b/docs/dev/articles/prebuilt/2022_dmta_pathway_files/figure-html/unnamed-chunk-3-1.png
deleted file mode 100644
index 1c81601e..00000000
--- a/docs/dev/articles/prebuilt/2022_dmta_pathway_files/figure-html/unnamed-chunk-3-1.png
+++ /dev/null
diff --git a/docs/dev/articles/prebuilt/2022_dmta_pathway_files/figure-html/unnamed-chunk-4-1.png b/docs/dev/articles/prebuilt/2022_dmta_pathway_files/figure-html/unnamed-chunk-4-1.png
deleted file mode 100644
index e0961dce..00000000
--- a/docs/dev/articles/prebuilt/2022_dmta_pathway_files/figure-html/unnamed-chunk-4-1.png
+++ /dev/null
diff --git a/docs/dev/articles/prebuilt/2022_dmta_pathway_files/figure-html/unnamed-chunk-5-1.png b/docs/dev/articles/prebuilt/2022_dmta_pathway_files/figure-html/unnamed-chunk-5-1.png
deleted file mode 100644
index 00db0c76..00000000
--- a/docs/dev/articles/prebuilt/2022_dmta_pathway_files/figure-html/unnamed-chunk-5-1.png
+++ /dev/null
diff --git a/docs/dev/articles/prebuilt/2022_dmta_pathway_files/figure-html/unnamed-chunk-6-1.png b/docs/dev/articles/prebuilt/2022_dmta_pathway_files/figure-html/unnamed-chunk-6-1.png
deleted file mode 100644
index 00db0c76..00000000
--- a/docs/dev/articles/prebuilt/2022_dmta_pathway_files/figure-html/unnamed-chunk-6-1.png
+++ /dev/null
diff --git a/docs/dev/articles/prebuilt/2022_dmta_pathway_files/figure-html/unnamed-chunk-7-1.png b/docs/dev/articles/prebuilt/2022_dmta_pathway_files/figure-html/unnamed-chunk-7-1.png
deleted file mode 100644
index ac5271ec..00000000
--- a/docs/dev/articles/prebuilt/2022_dmta_pathway_files/figure-html/unnamed-chunk-7-1.png
+++ /dev/null
diff --git a/docs/dev/articles/prebuilt/2022_dmta_pathway_files/figure-html/unnamed-chunk-8-1.png b/docs/dev/articles/prebuilt/2022_dmta_pathway_files/figure-html/unnamed-chunk-8-1.png
deleted file mode 100644
index 1c81601e..00000000
--- a/docs/dev/articles/prebuilt/2022_dmta_pathway_files/figure-html/unnamed-chunk-8-1.png
+++ /dev/null
diff --git a/docs/dev/articles/twa.html b/docs/dev/articles/twa.html
deleted file mode 100644
index 4aa8f5b1..00000000
--- a/docs/dev/articles/twa.html
+++ /dev/null
@@ -1,244 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en">
-<head>
-<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
-<meta charset="utf-8">
-<meta http-equiv="X-UA-Compatible" content="IE=edge">
-<meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>Calculation of time weighted average concentrations with mkin • mkin</title>
-<!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous">
-<script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css">
-<script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous">
-<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous">
-<!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet">
-<script src="../pkgdown.js"></script><meta property="og:title" content="Calculation of time weighted average concentrations with mkin">
-<meta property="og:description" content="mkin">
-<meta name="robots" content="noindex">
-<!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]-->
-</head>
-<body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-article">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav">
-<li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu">
-<li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul>
-</li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul>
-<ul class="nav navbar-nav navbar-right">
-<li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul>
-</div>
-<!--/.nav-collapse -->
-  </div>
-<!--/.container -->
-</div>
-<!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header toc-ignore">
-      <h1 data-toc-skip>Calculation of time weighted average
-concentrations with mkin</h1>
-                        <h4 data-toc-skip class="author">Johannes
-Ranke</h4>
-            
-            <h4 data-toc-skip class="date">Last change 18 September 2019
-(rebuilt 2023-04-16)</h4>
-      
-      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/twa.rmd" class="external-link"><code>vignettes/twa.rmd</code></a></small>
-      <div class="hidden name"><code>twa.rmd</code></div>
-
-    </div>
-
-    
-    
-<p>Since version 0.9.45.1 of the ‘mkin’ package, a function for
-calculating time weighted average concentrations for decline kinetics
-(<em>i.e.</em> only for the compound applied in the experiment) is
-included. Strictly speaking, they are maximum moving window time
-weighted average concentrations, <em>i.e.</em> the maximum time weighted
-average concentration that can be found when moving a time window of a
-specified width over the decline curve.</p>
-<p>Time weighted average concentrations for the SFO, FOMC and the DFOP
-model are calculated using the formulas given in the FOCUS kinetics
-guidance <span class="citation">(FOCUS Work Group on Degradation
-Kinetics 2014, 251)</span>:</p>
-<p>SFO:</p>
-<p><span class="math display">\[c_\textrm{twa} = c_0 \frac{\left( 1 -
-e^{- k t} \right)}{ k t} \]</span></p>
-<p>FOMC:</p>
-<p><span class="math display">\[c_\textrm{twa} = c_0 \frac{\beta}{t (1 -
-\alpha)}
-                   \left( \left(\frac{t}{\beta} + 1 \right)^{1 - \alpha}
-- 1 \right) \]</span></p>
-<p>DFOP:</p>
-<p><span class="math display">\[c_\textrm{twa} = \frac{c_0}{t} \left(
-  \frac{g}{k_1} \left( 1 - e^{- k_1 t} \right) +
-  \frac{1-g}{k_2} \left( 1 - e^{- k_2 t} \right)  \right) \]</span></p>
-<p>HS for <span class="math inline">\(t &gt; t_b\)</span>:</p>
-<p><span class="math display">\[c_\textrm{twa} = \frac{c_0}{t} \left(
-  \frac{1}{k_1} \left( 1 - e^{- k_1 t_b} \right) +
-  \frac{e^{- k_1 t_b}}{k_2} \left( 1 - e^{- k_2 (t - t_b)}
-\right)  \right) \]</span></p>
-<p>Often, the ratio between the time weighted average concentration
-<span class="math inline">\(c_\textrm{twa}\)</span> and the initial
-concentration <span class="math inline">\(c_0\)</span></p>
-<p><span class="math display">\[f_\textrm{twa} =
-\frac{c_\textrm{twa}}{c_0}\]</span></p>
-<p>is needed. This can be calculated from the fitted initial
-concentration <span class="math inline">\(c_0\)</span> and the time
-weighted average concentration <span class="math inline">\(c_\textrm{twa}\)</span>, or directly from the
-model parameters using the following formulas:</p>
-<p>SFO:</p>
-<p><span class="math display">\[f_\textrm{twa} = \frac{\left( 1 - e^{- k
-t} \right)}{k t} \]</span></p>
-<p>FOMC:</p>
-<p><span class="math display">\[f_\textrm{twa} = \frac{\beta}{t (1 -
-\alpha)}
-                   \left( \left(\frac{t}{\beta} + 1 \right)^{1 - \alpha}
-- 1 \right) \]</span></p>
-<p>DFOP:</p>
-<p><span class="math display">\[f_\textrm{twa} = \frac{1}{t} \left(
-  \frac{g}{k_1} \left( 1 - e^{- k_1 t} \right) +
-  \frac{1-g}{k_2} \left( 1 - e^{- k_2 t} \right)  \right) \]</span></p>
-<p>HS for <span class="math inline">\(t &gt; t_b\)</span>:</p>
-<p><span class="math display">\[f_\textrm{twa} = \frac{1}{t} \left(
-  \frac{1}{k_1} \left( 1 - e^{- k_1 t_b} \right) +
-  \frac{e^{- k_1 t_b}}{k_2} \left( 1 - e^{- k_2 (t - t_b)}
-\right)  \right) \]</span></p>
-<p>Note that a method for calculating maximum moving window time
-weighted average concentrations for a model fitted by ‘mkinfit’ or from
-parent decline model parameters is included in the
-<code><a href="../reference/max_twa_parent.html">max_twa_parent()</a></code> function. If the same is needed for
-metabolites, the function <code><a href="https://pkgdown.jrwb.de/pfm/reference/max_twa.html" class="external-link">pfm::max_twa()</a></code> from the ‘pfm’
-package can be used.</p>
-<div id="refs" class="references csl-bib-body hanging-indent">
-<div id="ref-FOCUSkinetics2014" class="csl-entry">
-FOCUS Work Group on Degradation Kinetics. 2014. <em>Generic Guidance for
-Estimating Persistence and Degradation Kinetics from Environmental Fate
-Studies on Pesticides in EU Registration</em>. 1.1 ed. <a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics" class="external-link">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a>.
-</div>
-</div>
-  </div>
-
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-
-      </div>
-
-</div>
-
-
-
-      <footer><div class="copyright">
-  <p></p>
-<p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p>
-<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer>
-</div>
-
-  
-
-
-  
-
-  </body>
-</html>
diff --git a/docs/dev/articles/twa_files/accessible-code-block-0.0.1/empty-anchor.js b/docs/dev/articles/twa_files/accessible-code-block-0.0.1/empty-anchor.js
deleted file mode 100644
index ca349fd6..00000000
--- a/docs/dev/articles/twa_files/accessible-code-block-0.0.1/empty-anchor.js
+++ /dev/null
@@ -1,15 +0,0 @@
-// Hide empty <a> tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) -->
-// v0.0.1
-// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020.
-
-document.addEventListener('DOMContentLoaded', function() {
-  const codeList = document.getElementsByClassName("sourceCode");
-  for (var i = 0; i < codeList.length; i++) {
-    var linkList = codeList[i].getElementsByTagName('a');
-    for (var j = 0; j < linkList.length; j++) {
-      if (linkList[j].innerHTML === "") {
-        linkList[j].setAttribute('aria-hidden', 'true');
-      }
-    }
-  }
-});
diff --git a/docs/dev/articles/twa_files/anchor-sections-1.0/anchor-sections.css b/docs/dev/articles/twa_files/anchor-sections-1.0/anchor-sections.css
deleted file mode 100644
index 07aee5fc..00000000
--- a/docs/dev/articles/twa_files/anchor-sections-1.0/anchor-sections.css
+++ /dev/null
@@ -1,4 +0,0 @@
-/* Styles for section anchors */
-a.anchor-section {margin-left: 10px; visibility: hidden; color: inherit;}
-a.anchor-section::before {content: '#';}
-.hasAnchor:hover a.anchor-section {visibility: visible;}
diff --git a/docs/dev/articles/twa_files/anchor-sections-1.0/anchor-sections.js b/docs/dev/articles/twa_files/anchor-sections-1.0/anchor-sections.js
deleted file mode 100644
index 570f99a0..00000000
--- a/docs/dev/articles/twa_files/anchor-sections-1.0/anchor-sections.js
+++ /dev/null
@@ -1,33 +0,0 @@
-// Anchor sections v1.0 written by Atsushi Yasumoto on Oct 3rd, 2020.
-document.addEventListener('DOMContentLoaded', function() {
-  // Do nothing if AnchorJS is used
-  if (typeof window.anchors === 'object' && anchors.hasOwnProperty('hasAnchorJSLink')) {
-    return;
-  }
-
-  const h = document.querySelectorAll('h1, h2, h3, h4, h5, h6');
-
-  // Do nothing if sections are already anchored
-  if (Array.from(h).some(x => x.classList.contains('hasAnchor'))) {
-    return null;
-  }
-
-  // Use section id when pandoc runs with --section-divs
-  const section_id = function(x) {
-    return ((x.classList.contains('section') || (x.tagName === 'SECTION'))
-            ? x.id : '');
-  };
-
-  // Add anchors
-  h.forEach(function(x) {
-    const id = x.id || section_id(x.parentElement);
-    if (id === '') {
-      return null;
-    }
-    let anchor = document.createElement('a');
-    anchor.href = '#' + id;
-    anchor.classList = ['anchor-section'];
-    x.classList.add('hasAnchor');
-    x.appendChild(anchor);
-  });
-});
diff --git a/docs/dev/articles/twa_files/header-attrs-2.6/header-attrs.js b/docs/dev/articles/twa_files/header-attrs-2.6/header-attrs.js
deleted file mode 100644
index dd57d92e..00000000
--- a/docs/dev/articles/twa_files/header-attrs-2.6/header-attrs.js
+++ /dev/null
@@ -1,12 +0,0 @@
-// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
-// be compatible with the behavior of Pandoc < 2.8).
-document.addEventListener('DOMContentLoaded', function(e) {
-  var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
-  var i, h, a;
-  for (i = 0; i < hs.length; i++) {
-    h = hs[i];
-    if (!/^h[1-6]$/i.test(h.tagName)) continue;  // it should be a header h1-h6
-    a = h.attributes;
-    while (a.length > 0) h.removeAttribute(a[0].name);
-  }
-});
diff --git a/docs/dev/articles/web_only/FOCUS_Z.html b/docs/dev/articles/web_only/FOCUS_Z.html
deleted file mode 100644
index 979cf2c6..00000000
--- a/docs/dev/articles/web_only/FOCUS_Z.html
+++ /dev/null
@@ -1,504 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en">
-<head>
-<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
-<meta charset="utf-8">
-<meta http-equiv="X-UA-Compatible" content="IE=edge">
-<meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>Example evaluation of FOCUS dataset Z • mkin</title>
-<!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous">
-<script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../../bootstrap-toc.css">
-<script src="../../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous">
-<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous">
-<!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../../pkgdown.css" rel="stylesheet">
-<script src="../../pkgdown.js"></script><meta property="og:title" content="Example evaluation of FOCUS dataset Z">
-<meta property="og:description" content="mkin">
-<meta name="robots" content="noindex">
-<!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]-->
-</head>
-<body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-article">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav">
-<li>
-  <a href="../../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu">
-<li>
-      <a href="../../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul>
-</li>
-<li>
-  <a href="../../news/index.html">News</a>
-</li>
-      </ul>
-<ul class="nav navbar-nav navbar-right">
-<li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul>
-</div>
-<!--/.nav-collapse -->
-  </div>
-<!--/.container -->
-</div>
-<!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header toc-ignore">
-      <h1 data-toc-skip>Example evaluation of FOCUS dataset Z</h1>
-                        <h4 data-toc-skip class="author">Johannes
-Ranke</h4>
-            
-            <h4 data-toc-skip class="date">Last change 16 January 2018
-(rebuilt 2023-04-16)</h4>
-      
-      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/web_only/FOCUS_Z.rmd" class="external-link"><code>vignettes/web_only/FOCUS_Z.rmd</code></a></small>
-      <div class="hidden name"><code>FOCUS_Z.rmd</code></div>
-
-    </div>
-
-    
-    
-<p><a href="http://www.jrwb.de" class="external-link">Wissenschaftlicher Berater, Kronacher
-Str. 12, 79639 Grenzach-Wyhlen, Germany</a><br><a href="http://chem.uft.uni-bremen.de/ranke" class="external-link">Privatdozent at the
-University of Bremen</a></p>
-<div class="section level2">
-<h2 id="the-data">The data<a class="anchor" aria-label="anchor" href="#the-data"></a>
-</h2>
-<p>The following code defines the example dataset from Appendix 7 to the
-FOCUS kinetics report <span class="citation">(FOCUS Work Group on
-Degradation Kinetics 2014, 354)</span>.</p>
-<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://pkgdown.jrwb.de/mkin/">mkin</a></span>, quietly <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
-<span><span class="va">LOD</span> <span class="op">=</span> <span class="fl">0.5</span></span>
-<span><span class="va">FOCUS_2006_Z</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span></span>
-<span>  t <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">0.04</span>, <span class="fl">0.125</span>, <span class="fl">0.29</span>, <span class="fl">0.54</span>, <span class="fl">1</span>, <span class="fl">2</span>, <span class="fl">3</span>, <span class="fl">4</span>, <span class="fl">7</span>, <span class="fl">10</span>, <span class="fl">14</span>, <span class="fl">21</span>,</span>
-<span>        <span class="fl">42</span>, <span class="fl">61</span>, <span class="fl">96</span>, <span class="fl">124</span><span class="op">)</span>,</span>
-<span>  Z0 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">100</span>, <span class="fl">81.7</span>, <span class="fl">70.4</span>, <span class="fl">51.1</span>, <span class="fl">41.2</span>, <span class="fl">6.6</span>, <span class="fl">4.6</span>, <span class="fl">3.9</span>, <span class="fl">4.6</span>, <span class="fl">4.3</span>, <span class="fl">6.8</span>,</span>
-<span>         <span class="fl">2.9</span>, <span class="fl">3.5</span>, <span class="fl">5.3</span>, <span class="fl">4.4</span>, <span class="fl">1.2</span>, <span class="fl">0.7</span><span class="op">)</span>,</span>
-<span>  Z1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">18.3</span>, <span class="fl">29.6</span>, <span class="fl">46.3</span>, <span class="fl">55.1</span>, <span class="fl">65.7</span>, <span class="fl">39.1</span>, <span class="fl">36</span>, <span class="fl">15.3</span>, <span class="fl">5.6</span>, <span class="fl">1.1</span>,</span>
-<span>         <span class="fl">1.6</span>, <span class="fl">0.6</span>, <span class="fl">0.5</span> <span class="op">*</span> <span class="va">LOD</span>, <span class="cn">NA</span>, <span class="cn">NA</span>, <span class="cn">NA</span><span class="op">)</span>,</span>
-<span>  Z2 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="cn">NA</span>, <span class="fl">0.5</span> <span class="op">*</span> <span class="va">LOD</span>, <span class="fl">2.6</span>, <span class="fl">3.8</span>, <span class="fl">15.3</span>, <span class="fl">37.2</span>, <span class="fl">31.7</span>, <span class="fl">35.6</span>, <span class="fl">14.5</span>,</span>
-<span>         <span class="fl">0.8</span>, <span class="fl">2.1</span>, <span class="fl">1.9</span>, <span class="fl">0.5</span> <span class="op">*</span> <span class="va">LOD</span>, <span class="cn">NA</span>, <span class="cn">NA</span>, <span class="cn">NA</span><span class="op">)</span>,</span>
-<span>  Z3 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="cn">NA</span>, <span class="cn">NA</span>, <span class="cn">NA</span>, <span class="cn">NA</span>, <span class="fl">0.5</span> <span class="op">*</span> <span class="va">LOD</span>, <span class="fl">9.2</span>, <span class="fl">13.1</span>, <span class="fl">22.3</span>, <span class="fl">28.4</span>, <span class="fl">32.5</span>,</span>
-<span>         <span class="fl">25.2</span>, <span class="fl">17.2</span>, <span class="fl">4.8</span>, <span class="fl">4.5</span>, <span class="fl">2.8</span>, <span class="fl">4.4</span><span class="op">)</span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">FOCUS_2006_Z_mkin</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/mkin_wide_to_long.html">mkin_wide_to_long</a></span><span class="op">(</span><span class="va">FOCUS_2006_Z</span><span class="op">)</span></span></code></pre></div>
-</div>
-<div class="section level2">
-<h2 id="parent-and-one-metabolite">Parent and one metabolite<a class="anchor" aria-label="anchor" href="#parent-and-one-metabolite"></a>
-</h2>
-<p>The next step is to set up the models used for the kinetic analysis.
-As the simultaneous fit of parent and the first metabolite is usually
-straightforward, Step 1 (SFO for parent only) is skipped here. We start
-with the model 2a, with formation and decline of metabolite Z1 and the
-pathway from parent directly to sink included (default in mkin).</p>
-<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">Z.2a</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span>Z0 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z1"</span><span class="op">)</span>,</span>
-<span>                Z1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## Temporary DLL for differentials generated and loaded</span></span></code></pre>
-<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">m.Z.2a</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">Z.2a</span>, <span class="va">FOCUS_2006_Z_mkin</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## Warning in mkinfit(Z.2a, FOCUS_2006_Z_mkin, quiet = TRUE): Observations with</span></span>
-<span><span class="co">## value of zero were removed from the data</span></span></code></pre>
-<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="../../reference/plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">m.Z.2a</span><span class="op">)</span></span></code></pre></div>
-<p><img src="FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_1-1.png" width="700"></p>
-<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">m.Z.2a</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span><span class="op">$</span><span class="va">bpar</span></span></code></pre></div>
-<pre><code><span><span class="co">##            Estimate se_notrans t value     Pr(&gt;t)    Lower    Upper</span></span>
-<span><span class="co">## Z0_0       97.01488   3.301084 29.3888 3.2971e-21 91.66556 102.3642</span></span>
-<span><span class="co">## k_Z0        2.23601   0.207078 10.7979 3.3309e-11  1.95303   2.5600</span></span>
-<span><span class="co">## k_Z1        0.48212   0.063265  7.6207 2.8154e-08  0.40341   0.5762</span></span>
-<span><span class="co">## f_Z0_to_Z1  1.00000   0.094764 10.5525 5.3560e-11  0.00000   1.0000</span></span>
-<span><span class="co">## sigma       4.80411   0.635638  7.5579 3.2592e-08  3.52677   6.0815</span></span></code></pre>
-<p>As obvious from the parameter summary (the component of the summary),
-the kinetic rate constant from parent compound Z to sink is very small
-and the t-test for this parameter suggests that it is not significantly
-different from zero. This suggests, in agreement with the analysis in
-the FOCUS kinetics report, to simplify the model by removing the pathway
-to sink.</p>
-<p>A similar result can be obtained when formation fractions are used in
-the model formulation:</p>
-<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">Z.2a.ff</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span>Z0 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z1"</span><span class="op">)</span>,</span>
-<span>                   Z1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span>
-<span>                   use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## Temporary DLL for differentials generated and loaded</span></span></code></pre>
-<div class="sourceCode" id="cb11"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">m.Z.2a.ff</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">Z.2a.ff</span>, <span class="va">FOCUS_2006_Z_mkin</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## Warning in mkinfit(Z.2a.ff, FOCUS_2006_Z_mkin, quiet = TRUE): Observations with</span></span>
-<span><span class="co">## value of zero were removed from the data</span></span></code></pre>
-<div class="sourceCode" id="cb13"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="../../reference/plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">m.Z.2a.ff</span><span class="op">)</span></span></code></pre></div>
-<p><img src="FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_2-1.png" width="700"></p>
-<div class="sourceCode" id="cb14"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">m.Z.2a.ff</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span><span class="op">$</span><span class="va">bpar</span></span></code></pre></div>
-<pre><code><span><span class="co">##            Estimate se_notrans t value     Pr(&gt;t)    Lower    Upper</span></span>
-<span><span class="co">## Z0_0       97.01488   3.301084 29.3888 3.2971e-21 91.66556 102.3642</span></span>
-<span><span class="co">## k_Z0        2.23601   0.207078 10.7979 3.3309e-11  1.95303   2.5600</span></span>
-<span><span class="co">## k_Z1        0.48212   0.063265  7.6207 2.8154e-08  0.40341   0.5762</span></span>
-<span><span class="co">## f_Z0_to_Z1  1.00000   0.094764 10.5525 5.3560e-11  0.00000   1.0000</span></span>
-<span><span class="co">## sigma       4.80411   0.635638  7.5579 3.2592e-08  3.52677   6.0815</span></span></code></pre>
-<p>Here, the ilr transformed formation fraction fitted in the model
-takes a very large value, and the backtransformed formation fraction
-from parent Z to Z1 is practically unity. Here, the covariance matrix
-used for the calculation of confidence intervals is not returned as the
-model is overparameterised.</p>
-<p>A simplified model is obtained by removing the pathway to the sink.
-</p>
-<p>In the following, we use the parameterisation with formation
-fractions in order to be able to compare with the results in the FOCUS
-guidance, and as it makes it easier to use parameters obtained in a
-previous fit when adding a further metabolite.</p>
-<div class="sourceCode" id="cb16"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">Z.3</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span>Z0 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z1"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span>
-<span>               Z1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## Temporary DLL for differentials generated and loaded</span></span></code></pre>
-<div class="sourceCode" id="cb18"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">m.Z.3</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">Z.3</span>, <span class="va">FOCUS_2006_Z_mkin</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## Warning in mkinfit(Z.3, FOCUS_2006_Z_mkin, quiet = TRUE): Observations with</span></span>
-<span><span class="co">## value of zero were removed from the data</span></span></code></pre>
-<div class="sourceCode" id="cb20"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="../../reference/plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">m.Z.3</span><span class="op">)</span></span></code></pre></div>
-<p><img src="FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_3-1.png" width="700"></p>
-<div class="sourceCode" id="cb21"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">m.Z.3</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span><span class="op">$</span><span class="va">bpar</span></span></code></pre></div>
-<pre><code><span><span class="co">##       Estimate se_notrans t value     Pr(&gt;t)    Lower    Upper</span></span>
-<span><span class="co">## Z0_0  97.01488   2.597342  37.352 2.0106e-24 91.67597 102.3538</span></span>
-<span><span class="co">## k_Z0   2.23601   0.146904  15.221 9.1477e-15  1.95354   2.5593</span></span>
-<span><span class="co">## k_Z1   0.48212   0.041727  11.554 4.8268e-12  0.40355   0.5760</span></span>
-<span><span class="co">## sigma  4.80411   0.620208   7.746 1.6110e-08  3.52925   6.0790</span></span></code></pre>
-<p>As there is only one transformation product for Z0 and no pathway to
-sink, the formation fraction is internally fixed to unity.</p>
-</div>
-<div class="section level2">
-<h2 id="metabolites-z2-and-z3">Metabolites Z2 and Z3<a class="anchor" aria-label="anchor" href="#metabolites-z2-and-z3"></a>
-</h2>
-<p>As suggested in the FOCUS report, the pathway to sink was removed for
-metabolite Z1 as well in the next step. While this step appears
-questionable on the basis of the above results, it is followed here for
-the purpose of comparison. Also, in the FOCUS report, it is assumed that
-there is additional empirical evidence that Z1 quickly and exclusively
-hydrolyses to Z2.</p>
-<div class="sourceCode" id="cb23"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">Z.5</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span>Z0 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z1"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span>
-<span>               Z1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z2"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span>
-<span>               Z2 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## Temporary DLL for differentials generated and loaded</span></span></code></pre>
-<div class="sourceCode" id="cb25"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">m.Z.5</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">Z.5</span>, <span class="va">FOCUS_2006_Z_mkin</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## Warning in mkinfit(Z.5, FOCUS_2006_Z_mkin, quiet = TRUE): Observations with</span></span>
-<span><span class="co">## value of zero were removed from the data</span></span></code></pre>
-<div class="sourceCode" id="cb27"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="../../reference/plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">m.Z.5</span><span class="op">)</span></span></code></pre></div>
-<p><img src="FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_5-1.png" width="700"></p>
-<p>Finally, metabolite Z3 is added to the model. We use the optimised
-differential equation parameter values from the previous fit in order to
-accelerate the optimization.</p>
-<div class="sourceCode" id="cb28"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">Z.FOCUS</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span>Z0 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z1"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span>
-<span>                   Z1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z2"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span>
-<span>                   Z2 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z3"</span><span class="op">)</span>,</span>
-<span>                   Z3 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span>
-<span>                   use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## Temporary DLL for differentials generated and loaded</span></span></code></pre>
-<div class="sourceCode" id="cb30"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">m.Z.FOCUS</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">Z.FOCUS</span>, <span class="va">FOCUS_2006_Z_mkin</span>,</span>
-<span>                     parms.ini <span class="op">=</span> <span class="va">m.Z.5</span><span class="op">$</span><span class="va">bparms.ode</span>,</span>
-<span>                     quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## Warning in mkinfit(Z.FOCUS, FOCUS_2006_Z_mkin, parms.ini = m.Z.5$bparms.ode, :</span></span>
-<span><span class="co">## Observations with value of zero were removed from the data</span></span></code></pre>
-<pre><code><span><span class="co">## Warning in mkinfit(Z.FOCUS, FOCUS_2006_Z_mkin, parms.ini = m.Z.5$bparms.ode, : Optimisation did not converge:</span></span>
-<span><span class="co">## false convergence (8)</span></span></code></pre>
-<div class="sourceCode" id="cb33"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="../../reference/plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">m.Z.FOCUS</span><span class="op">)</span></span></code></pre></div>
-<p><img src="FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_6-1.png" width="700"></p>
-<div class="sourceCode" id="cb34"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">m.Z.FOCUS</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span><span class="op">$</span><span class="va">bpar</span></span></code></pre></div>
-<pre><code><span><span class="co">##             Estimate se_notrans t value     Pr(&gt;t)     Lower      Upper</span></span>
-<span><span class="co">## Z0_0       96.838822   1.994274 48.5584 4.0280e-42 92.826981 100.850664</span></span>
-<span><span class="co">## k_Z0        2.215393   0.118458 18.7019 1.0413e-23  1.989456   2.466989</span></span>
-<span><span class="co">## k_Z1        0.478305   0.028258 16.9266 6.2418e-22  0.424708   0.538666</span></span>
-<span><span class="co">## k_Z2        0.451627   0.042139 10.7176 1.6314e-14  0.374339   0.544872</span></span>
-<span><span class="co">## k_Z3        0.058692   0.015245  3.8499 1.7803e-04  0.034808   0.098965</span></span>
-<span><span class="co">## f_Z2_to_Z3  0.471502   0.058351  8.0805 9.6608e-11  0.357769   0.588274</span></span>
-<span><span class="co">## sigma       3.984431   0.383402 10.3923 4.5575e-14  3.213126   4.755736</span></span></code></pre>
-<div class="sourceCode" id="cb36"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="../../reference/endpoints.html">endpoints</a></span><span class="op">(</span><span class="va">m.Z.FOCUS</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## $ff</span></span>
-<span><span class="co">##   Z2_Z3 Z2_sink </span></span>
-<span><span class="co">##  0.4715  0.5285 </span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## $distimes</span></span>
-<span><span class="co">##        DT50    DT90</span></span>
-<span><span class="co">## Z0  0.31288  1.0394</span></span>
-<span><span class="co">## Z1  1.44917  4.8141</span></span>
-<span><span class="co">## Z2  1.53478  5.0984</span></span>
-<span><span class="co">## Z3 11.80986 39.2315</span></span></code></pre>
-<p>This fit corresponds to the final result chosen in Appendix 7 of the
-FOCUS report. Confidence intervals returned by mkin are based on
-internally transformed parameters, however.</p>
-</div>
-<div class="section level2">
-<h2 id="using-the-sforb-model">Using the SFORB model<a class="anchor" aria-label="anchor" href="#using-the-sforb-model"></a>
-</h2>
-<p>As the FOCUS report states, there is a certain tailing of the time
-course of metabolite Z3. Also, the time course of the parent compound is
-not fitted very well using the SFO model, as residues at a certain low
-level remain.</p>
-<p>Therefore, an additional model is offered here, using the single
-first-order reversible binding (SFORB) model for metabolite Z3. As
-expected, the <span class="math inline">\(\chi^2\)</span> error level is
-lower for metabolite Z3 using this model and the graphical fit for Z3 is
-improved. However, the covariance matrix is not returned.</p>
-<div class="sourceCode" id="cb38"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">Z.mkin.1</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span>Z0 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z1"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span>
-<span>                    Z1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z2"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span>
-<span>                    Z2 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z3"</span><span class="op">)</span>,</span>
-<span>                    Z3 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFORB"</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## Temporary DLL for differentials generated and loaded</span></span></code></pre>
-<div class="sourceCode" id="cb40"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">m.Z.mkin.1</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">Z.mkin.1</span>, <span class="va">FOCUS_2006_Z_mkin</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## Warning in mkinfit(Z.mkin.1, FOCUS_2006_Z_mkin, quiet = TRUE): Observations</span></span>
-<span><span class="co">## with value of zero were removed from the data</span></span></code></pre>
-<div class="sourceCode" id="cb42"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="../../reference/plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">m.Z.mkin.1</span><span class="op">)</span></span></code></pre></div>
-<p><img src="FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_7-1.png" width="700"></p>
-<div class="sourceCode" id="cb43"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">m.Z.mkin.1</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span><span class="op">$</span><span class="va">cov.unscaled</span></span></code></pre></div>
-<pre><code><span><span class="co">## NULL</span></span></code></pre>
-<p>Therefore, a further stepwise model building is performed starting
-from the stage of parent and two metabolites, starting from the
-assumption that the model fit for the parent compound can be improved by
-using the SFORB model.</p>
-<div class="sourceCode" id="cb45"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">Z.mkin.3</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span>Z0 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFORB"</span>, <span class="st">"Z1"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span>
-<span>                    Z1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z2"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span>
-<span>                    Z2 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## Temporary DLL for differentials generated and loaded</span></span></code></pre>
-<div class="sourceCode" id="cb47"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">m.Z.mkin.3</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">Z.mkin.3</span>, <span class="va">FOCUS_2006_Z_mkin</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## Warning in mkinfit(Z.mkin.3, FOCUS_2006_Z_mkin, quiet = TRUE): Observations</span></span>
-<span><span class="co">## with value of zero were removed from the data</span></span></code></pre>
-<div class="sourceCode" id="cb49"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="../../reference/plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">m.Z.mkin.3</span><span class="op">)</span></span></code></pre></div>
-<p><img src="FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_9-1.png" width="700"></p>
-<p>This results in a much better representation of the behaviour of the
-parent compound Z0.</p>
-<p>Finally, Z3 is added as well. These models appear overparameterised
-(no covariance matrix returned) if the sink for Z1 is left in the
-models.</p>
-<div class="sourceCode" id="cb50"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">Z.mkin.4</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span>Z0 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFORB"</span>, <span class="st">"Z1"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span>
-<span>                    Z1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z2"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span>
-<span>                    Z2 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z3"</span><span class="op">)</span>,</span>
-<span>                    Z3 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## Temporary DLL for differentials generated and loaded</span></span></code></pre>
-<div class="sourceCode" id="cb52"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">m.Z.mkin.4</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">Z.mkin.4</span>, <span class="va">FOCUS_2006_Z_mkin</span>,</span>
-<span>                      parms.ini <span class="op">=</span> <span class="va">m.Z.mkin.3</span><span class="op">$</span><span class="va">bparms.ode</span>,</span>
-<span>                      quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## Warning in mkinfit(Z.mkin.4, FOCUS_2006_Z_mkin, parms.ini =</span></span>
-<span><span class="co">## m.Z.mkin.3$bparms.ode, : Observations with value of zero were removed from the</span></span>
-<span><span class="co">## data</span></span></code></pre>
-<div class="sourceCode" id="cb54"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="../../reference/plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">m.Z.mkin.4</span><span class="op">)</span></span></code></pre></div>
-<p><img src="FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_10-1.png" width="700"></p>
-<p>The error level of the fit, but especially of metabolite Z3, can be
-improved if the SFORB model is chosen for this metabolite, as this model
-is capable of representing the tailing of the metabolite decline
-phase.</p>
-<div class="sourceCode" id="cb55"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">Z.mkin.5</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span>Z0 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFORB"</span>, <span class="st">"Z1"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span>
-<span>                    Z1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z2"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span>
-<span>                    Z2 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z3"</span><span class="op">)</span>,</span>
-<span>                    Z3 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFORB"</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## Temporary DLL for differentials generated and loaded</span></span></code></pre>
-<div class="sourceCode" id="cb57"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">m.Z.mkin.5</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">Z.mkin.5</span>, <span class="va">FOCUS_2006_Z_mkin</span>,</span>
-<span>                      parms.ini <span class="op">=</span> <span class="va">m.Z.mkin.4</span><span class="op">$</span><span class="va">bparms.ode</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">4</span><span class="op">]</span>,</span>
-<span>                      quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## Warning in mkinfit(Z.mkin.5, FOCUS_2006_Z_mkin, parms.ini =</span></span>
-<span><span class="co">## m.Z.mkin.4$bparms.ode[1:4], : Observations with value of zero were removed from</span></span>
-<span><span class="co">## the data</span></span></code></pre>
-<div class="sourceCode" id="cb59"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="../../reference/plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">m.Z.mkin.5</span><span class="op">)</span></span></code></pre></div>
-<p><img src="FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11-1.png" width="700"></p>
-<p>The summary view of the backtransformed parameters shows that we get
-no confidence intervals due to overparameterisation. As the optimized is
-excessively small, it seems reasonable to fix it to zero.</p>
-<div class="sourceCode" id="cb60"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">m.Z.mkin.5a</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">Z.mkin.5</span>, <span class="va">FOCUS_2006_Z_mkin</span>,</span>
-<span>                       parms.ini <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="va">m.Z.mkin.5</span><span class="op">$</span><span class="va">bparms.ode</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">7</span><span class="op">]</span>,</span>
-<span>                                     k_Z3_bound_free <span class="op">=</span> <span class="fl">0</span><span class="op">)</span>,</span>
-<span>                       fixed_parms <span class="op">=</span> <span class="st">"k_Z3_bound_free"</span>,</span>
-<span>                       quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## Warning in mkinfit(Z.mkin.5, FOCUS_2006_Z_mkin, parms.ini =</span></span>
-<span><span class="co">## c(m.Z.mkin.5$bparms.ode[1:7], : Observations with value of zero were removed</span></span>
-<span><span class="co">## from the data</span></span></code></pre>
-<div class="sourceCode" id="cb62"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="../../reference/plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">m.Z.mkin.5a</span><span class="op">)</span></span></code></pre></div>
-<p><img src="FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11a-1.png" width="700"></p>
-<p>As expected, the residual plots for Z0 and Z3 are more random than in
-the case of the all SFO model for which they were shown above. In
-conclusion, the model is proposed as the best-fit model for the dataset
-from Appendix 7 of the FOCUS report.</p>
-<p>A graphical representation of the confidence intervals can finally be
-obtained.</p>
-<div class="sourceCode" id="cb63"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="../../reference/mkinparplot.html">mkinparplot</a></span><span class="op">(</span><span class="va">m.Z.mkin.5a</span><span class="op">)</span></span></code></pre></div>
-<p><img src="FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11b-1.png" width="700"></p>
-<p>The endpoints obtained with this model are</p>
-<div class="sourceCode" id="cb64"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="../../reference/endpoints.html">endpoints</a></span><span class="op">(</span><span class="va">m.Z.mkin.5a</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## $ff</span></span>
-<span><span class="co">## Z0_free   Z2_Z3 Z2_sink Z3_free </span></span>
-<span><span class="co">## 1.00000 0.53656 0.46344 1.00000 </span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## $SFORB</span></span>
-<span><span class="co">##     Z0_b1     Z0_b2      Z0_g     Z3_b1     Z3_b2      Z3_g </span></span>
-<span><span class="co">## 2.4471322 0.0075125 0.9519862 0.0800069 0.0000000 0.9347820 </span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## $distimes</span></span>
-<span><span class="co">##      DT50   DT90 DT50back DT50_Z0_b1 DT50_Z0_b2 DT50_Z3_b1 DT50_Z3_b2</span></span>
-<span><span class="co">## Z0 0.3043 1.1848  0.35666    0.28325     92.266         NA         NA</span></span>
-<span><span class="co">## Z1 1.5148 5.0320       NA         NA         NA         NA         NA</span></span>
-<span><span class="co">## Z2 1.6414 5.4526       NA         NA         NA         NA         NA</span></span>
-<span><span class="co">## Z3     NA     NA       NA         NA         NA     8.6636        Inf</span></span></code></pre>
-<p>It is clear the degradation rate of Z3 towards the end of the
-experiment is very low as DT50_Z3_b2 (the second Eigenvalue of the
-system of two differential equations representing the SFORB system for
-Z3, corresponding to the slower rate constant of the DFOP model) is
-reported to be infinity. However, this appears to be a feature of the
-data.</p>
-</div>
-<div class="section level2">
-<h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a>
-</h2>
-<!-- vim: set foldmethod=syntax: -->
-<div id="refs" class="references csl-bib-body hanging-indent">
-<div id="ref-FOCUSkinetics2014" class="csl-entry">
-FOCUS Work Group on Degradation Kinetics. 2014. <em>Generic Guidance for
-Estimating Persistence and Degradation Kinetics from Environmental Fate
-Studies on Pesticides in EU Registration</em>. 1.1 ed. <a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics" class="external-link">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a>.
-</div>
-</div>
-</div>
-  </div>
-
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-
-        <nav id="toc" data-toggle="toc"><h2 data-toc-skip>Contents</h2>
-    </nav>
-</div>
-
-</div>
-
-
-
-      <footer><div class="copyright">
-  <p></p>
-<p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p>
-<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer>
-</div>
-
-  
-
-
-  
-
-  </body>
-</html>
diff --git a/docs/dev/articles/web_only/FOCUS_Z_files/accessible-code-block-0.0.1/empty-anchor.js b/docs/dev/articles/web_only/FOCUS_Z_files/accessible-code-block-0.0.1/empty-anchor.js
deleted file mode 100644
index ca349fd6..00000000
--- a/docs/dev/articles/web_only/FOCUS_Z_files/accessible-code-block-0.0.1/empty-anchor.js
+++ /dev/null
@@ -1,15 +0,0 @@
-// Hide empty <a> tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) -->
-// v0.0.1
-// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020.
-
-document.addEventListener('DOMContentLoaded', function() {
-  const codeList = document.getElementsByClassName("sourceCode");
-  for (var i = 0; i < codeList.length; i++) {
-    var linkList = codeList[i].getElementsByTagName('a');
-    for (var j = 0; j < linkList.length; j++) {
-      if (linkList[j].innerHTML === "") {
-        linkList[j].setAttribute('aria-hidden', 'true');
-      }
-    }
-  }
-});
diff --git a/docs/dev/articles/web_only/FOCUS_Z_files/anchor-sections-1.0/anchor-sections.css b/docs/dev/articles/web_only/FOCUS_Z_files/anchor-sections-1.0/anchor-sections.css
deleted file mode 100644
index 07aee5fc..00000000
--- a/docs/dev/articles/web_only/FOCUS_Z_files/anchor-sections-1.0/anchor-sections.css
+++ /dev/null
@@ -1,4 +0,0 @@
-/* Styles for section anchors */
-a.anchor-section {margin-left: 10px; visibility: hidden; color: inherit;}
-a.anchor-section::before {content: '#';}
-.hasAnchor:hover a.anchor-section {visibility: visible;}
diff --git a/docs/dev/articles/web_only/FOCUS_Z_files/anchor-sections-1.0/anchor-sections.js b/docs/dev/articles/web_only/FOCUS_Z_files/anchor-sections-1.0/anchor-sections.js
deleted file mode 100644
index 570f99a0..00000000
--- a/docs/dev/articles/web_only/FOCUS_Z_files/anchor-sections-1.0/anchor-sections.js
+++ /dev/null
@@ -1,33 +0,0 @@
-// Anchor sections v1.0 written by Atsushi Yasumoto on Oct 3rd, 2020.
-document.addEventListener('DOMContentLoaded', function() {
-  // Do nothing if AnchorJS is used
-  if (typeof window.anchors === 'object' && anchors.hasOwnProperty('hasAnchorJSLink')) {
-    return;
-  }
-
-  const h = document.querySelectorAll('h1, h2, h3, h4, h5, h6');
-
-  // Do nothing if sections are already anchored
-  if (Array.from(h).some(x => x.classList.contains('hasAnchor'))) {
-    return null;
-  }
-
-  // Use section id when pandoc runs with --section-divs
-  const section_id = function(x) {
-    return ((x.classList.contains('section') || (x.tagName === 'SECTION'))
-            ? x.id : '');
-  };
-
-  // Add anchors
-  h.forEach(function(x) {
-    const id = x.id || section_id(x.parentElement);
-    if (id === '') {
-      return null;
-    }
-    let anchor = document.createElement('a');
-    anchor.href = '#' + id;
-    anchor.classList = ['anchor-section'];
-    x.classList.add('hasAnchor');
-    x.appendChild(anchor);
-  });
-});
diff --git a/docs/dev/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_1-1.png b/docs/dev/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_1-1.png
deleted file mode 100644
index 98bc135b..00000000
--- a/docs/dev/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_1-1.png
+++ /dev/null
diff --git a/docs/dev/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_10-1.png b/docs/dev/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_10-1.png
deleted file mode 100644
index 33269a34..00000000
--- a/docs/dev/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_10-1.png
+++ /dev/null
diff --git a/docs/dev/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11-1.png b/docs/dev/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11-1.png
deleted file mode 100644
index 6e1877f4..00000000
--- a/docs/dev/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11-1.png
+++ /dev/null
diff --git a/docs/dev/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11a-1.png b/docs/dev/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11a-1.png
deleted file mode 100644
index 113c1b0b..00000000
--- a/docs/dev/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11a-1.png
+++ /dev/null
diff --git a/docs/dev/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11b-1.png b/docs/dev/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11b-1.png
deleted file mode 100644
index 6b0dbc34..00000000
--- a/docs/dev/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11b-1.png
+++ /dev/null
diff --git a/docs/dev/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_2-1.png b/docs/dev/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_2-1.png
deleted file mode 100644
index 98bc135b..00000000
--- a/docs/dev/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_2-1.png
+++ /dev/null
diff --git a/docs/dev/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_3-1.png b/docs/dev/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_3-1.png
deleted file mode 100644
index 0380ba43..00000000
--- a/docs/dev/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_3-1.png
+++ /dev/null
diff --git a/docs/dev/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_5-1.png b/docs/dev/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_5-1.png
deleted file mode 100644
index d080a57a..00000000
--- a/docs/dev/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_5-1.png
+++ /dev/null
diff --git a/docs/dev/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_6-1.png b/docs/dev/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_6-1.png
deleted file mode 100644
index 3119be2d..00000000
--- a/docs/dev/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_6-1.png
+++ /dev/null
diff --git a/docs/dev/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_7-1.png b/docs/dev/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_7-1.png
deleted file mode 100644
index 87af8874..00000000
--- a/docs/dev/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_7-1.png
+++ /dev/null
diff --git a/docs/dev/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_9-1.png b/docs/dev/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_9-1.png
deleted file mode 100644
index 1938b499..00000000
--- a/docs/dev/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_9-1.png
+++ /dev/null
diff --git a/docs/dev/articles/web_only/FOCUS_Z_files/header-attrs-2.6/header-attrs.js b/docs/dev/articles/web_only/FOCUS_Z_files/header-attrs-2.6/header-attrs.js
deleted file mode 100644
index dd57d92e..00000000
--- a/docs/dev/articles/web_only/FOCUS_Z_files/header-attrs-2.6/header-attrs.js
+++ /dev/null
@@ -1,12 +0,0 @@
-// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
-// be compatible with the behavior of Pandoc < 2.8).
-document.addEventListener('DOMContentLoaded', function(e) {
-  var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
-  var i, h, a;
-  for (i = 0; i < hs.length; i++) {
-    h = hs[i];
-    if (!/^h[1-6]$/i.test(h.tagName)) continue;  // it should be a header h1-h6
-    a = h.attributes;
-    while (a.length > 0) h.removeAttribute(a[0].name);
-  }
-});
diff --git a/docs/dev/articles/web_only/NAFTA_examples.html b/docs/dev/articles/web_only/NAFTA_examples.html
deleted file mode 100644
index 9feecfce..00000000
--- a/docs/dev/articles/web_only/NAFTA_examples.html
+++ /dev/null
@@ -1,1118 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en">
-<head>
-<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
-<meta charset="utf-8">
-<meta http-equiv="X-UA-Compatible" content="IE=edge">
-<meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance • mkin</title>
-<!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous">
-<script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../../bootstrap-toc.css">
-<script src="../../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous">
-<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous">
-<!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../../pkgdown.css" rel="stylesheet">
-<script src="../../pkgdown.js"></script><meta property="og:title" content="Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance">
-<meta property="og:description" content="mkin">
-<meta name="robots" content="noindex">
-<!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]-->
-</head>
-<body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-article">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav">
-<li>
-  <a href="../../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu">
-<li>
-      <a href="../../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul>
-</li>
-<li>
-  <a href="../../news/index.html">News</a>
-</li>
-      </ul>
-<ul class="nav navbar-nav navbar-right">
-<li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul>
-</div>
-<!--/.nav-collapse -->
-  </div>
-<!--/.container -->
-</div>
-<!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header toc-ignore">
-      <h1 data-toc-skip>Evaluation of example datasets from Attachment 1
-to the US EPA SOP for the NAFTA guidance</h1>
-                        <h4 data-toc-skip class="author">Johannes
-Ranke</h4>
-            
-            <h4 data-toc-skip class="date">26 February 2019 (rebuilt
-2023-04-16)</h4>
-      
-      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/web_only/NAFTA_examples.rmd" class="external-link"><code>vignettes/web_only/NAFTA_examples.rmd</code></a></small>
-      <div class="hidden name"><code>NAFTA_examples.rmd</code></div>
-
-    </div>
-
-    
-    
-<div class="section level2">
-<h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a>
-</h2>
-<p>In this document, the example evaluations provided in Attachment 1 to
-the SOP of US EPA for using the NAFTA guidance <span class="citation">(US EPA 2015)</span> are repeated using mkin. The
-original evaluations reported in the attachment were performed using
-PestDF in version 0.8.4. Note that PestDF 0.8.13 is the version
-distributed at the US EPA website today (2019-02-26).</p>
-<p>The datasets are now distributed with the mkin package.</p>
-</div>
-<div class="section level2">
-<h2 id="examples-where-dfop-did-not-converge-with-pestdf-0-8-4">Examples where DFOP did not converge with PestDF 0.8.4<a class="anchor" aria-label="anchor" href="#examples-where-dfop-did-not-converge-with-pestdf-0-8-4"></a>
-</h2>
-<p>In attachment 1, it is reported that the DFOP model does not converge
-for these datasets when PestDF 0.8.4 was used. For all four datasets,
-the DFOP model can be fitted with mkin (see below). The negative
-half-life given by PestDF 0.8.4 for these fits appears to be the result
-of a bug. The results for the other two models (SFO and IORE) are the
-same.</p>
-<div class="section level3">
-<h3 id="example-on-page-5-upper-panel">Example on page 5, upper panel<a class="anchor" aria-label="anchor" href="#example-on-page-5-upper-panel"></a>
-</h3>
-<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">p5a</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p5a"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre>
-<pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre>
-<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p5a</span><span class="op">)</span></span></code></pre></div>
-<p><img src="NAFTA_examples_files/figure-html/p5a-1.png" width="700"></p>
-<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p5a</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## Sums of squares:</span></span>
-<span><span class="co">##       SFO      IORE      DFOP </span></span>
-<span><span class="co">## 465.21753  56.27506  32.06401 </span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Critical sum of squares for checking the SFO model:</span></span>
-<span><span class="co">## [1] 64.4304</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Parameters:</span></span>
-<span><span class="co">## $SFO</span></span>
-<span><span class="co">##          Estimate   Pr(&gt;t)  Lower   Upper</span></span>
-<span><span class="co">## parent_0  95.8401 4.67e-21 92.245 99.4357</span></span>
-<span><span class="co">## k_parent   0.0102 3.92e-12  0.009  0.0117</span></span>
-<span><span class="co">## sigma      4.8230 3.81e-06  3.214  6.4318</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## $IORE</span></span>
-<span><span class="co">##                Estimate Pr(&gt;t)    Lower    Upper</span></span>
-<span><span class="co">## parent_0       1.01e+02     NA 9.91e+01 1.02e+02</span></span>
-<span><span class="co">## k__iore_parent 1.54e-05     NA 4.08e-06 5.84e-05</span></span>
-<span><span class="co">## N_parent       2.57e+00     NA 2.25e+00 2.89e+00</span></span>
-<span><span class="co">## sigma          1.68e+00     NA 1.12e+00 2.24e+00</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## $DFOP</span></span>
-<span><span class="co">##          Estimate   Pr(&gt;t)   Lower    Upper</span></span>
-<span><span class="co">## parent_0 9.99e+01 1.41e-26 98.8116 101.0810</span></span>
-<span><span class="co">## k1       2.67e-02 5.05e-06  0.0243   0.0295</span></span>
-<span><span class="co">## k2       2.26e-12 5.00e-01  0.0000      Inf</span></span>
-<span><span class="co">## g        6.47e-01 3.67e-06  0.6248   0.6677</span></span>
-<span><span class="co">## sigma    1.27e+00 8.91e-06  0.8395   1.6929</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## DTx values:</span></span>
-<span><span class="co">##      DT50     DT90 DT50_rep</span></span>
-<span><span class="co">## SFO  67.7 2.25e+02 6.77e+01</span></span>
-<span><span class="co">## IORE 58.2 1.07e+03 3.22e+02</span></span>
-<span><span class="co">## DFOP 55.5 5.59e+11 3.07e+11</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Representative half-life:</span></span>
-<span><span class="co">## [1] 321.51</span></span></code></pre>
-</div>
-<div class="section level3">
-<h3 id="example-on-page-5-lower-panel">Example on page 5, lower panel<a class="anchor" aria-label="anchor" href="#example-on-page-5-lower-panel"></a>
-</h3>
-<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">p5b</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p5b"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre>
-<pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre>
-<div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p5b</span><span class="op">)</span></span></code></pre></div>
-<p><img src="NAFTA_examples_files/figure-html/p5b-1.png" width="700"></p>
-<div class="sourceCode" id="cb11"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p5b</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## Sums of squares:</span></span>
-<span><span class="co">##      SFO     IORE     DFOP </span></span>
-<span><span class="co">## 94.81123 10.10936  7.55871 </span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Critical sum of squares for checking the SFO model:</span></span>
-<span><span class="co">## [1] 11.77879</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Parameters:</span></span>
-<span><span class="co">## $SFO</span></span>
-<span><span class="co">##          Estimate   Pr(&gt;t)    Lower    Upper</span></span>
-<span><span class="co">## parent_0   96.497 2.32e-24 94.85271 98.14155</span></span>
-<span><span class="co">## k_parent    0.008 3.42e-14  0.00737  0.00869</span></span>
-<span><span class="co">## sigma       2.295 1.22e-05  1.47976  3.11036</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## $IORE</span></span>
-<span><span class="co">##                Estimate   Pr(&gt;t)    Lower    Upper</span></span>
-<span><span class="co">## parent_0       9.85e+01 1.17e-28 9.79e+01 9.92e+01</span></span>
-<span><span class="co">## k__iore_parent 1.53e-04 6.50e-03 7.21e-05 3.26e-04</span></span>
-<span><span class="co">## N_parent       1.94e+00 5.88e-13 1.76e+00 2.12e+00</span></span>
-<span><span class="co">## sigma          7.49e-01 1.63e-05 4.82e-01 1.02e+00</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## $DFOP</span></span>
-<span><span class="co">##          Estimate   Pr(&gt;t)   Lower   Upper</span></span>
-<span><span class="co">## parent_0 9.84e+01 1.24e-27 97.8078 98.9187</span></span>
-<span><span class="co">## k1       1.55e-02 4.10e-04  0.0143  0.0167</span></span>
-<span><span class="co">## k2       8.63e-12 5.00e-01  0.0000     Inf</span></span>
-<span><span class="co">## g        6.89e-01 2.92e-03  0.6626  0.7142</span></span>
-<span><span class="co">## sigma    6.48e-01 2.38e-05  0.4147  0.8813</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## DTx values:</span></span>
-<span><span class="co">##      DT50     DT90 DT50_rep</span></span>
-<span><span class="co">## SFO  86.6 2.88e+02 8.66e+01</span></span>
-<span><span class="co">## IORE 85.5 7.17e+02 2.16e+02</span></span>
-<span><span class="co">## DFOP 83.6 1.32e+11 8.04e+10</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Representative half-life:</span></span>
-<span><span class="co">## [1] 215.87</span></span></code></pre>
-</div>
-<div class="section level3">
-<h3 id="example-on-page-6">Example on page 6<a class="anchor" aria-label="anchor" href="#example-on-page-6"></a>
-</h3>
-<div class="sourceCode" id="cb13"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">p6</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p6"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre>
-<pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre>
-<div class="sourceCode" id="cb16"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p6</span><span class="op">)</span></span></code></pre></div>
-<p><img src="NAFTA_examples_files/figure-html/p6-1.png" width="700"></p>
-<div class="sourceCode" id="cb17"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p6</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## Sums of squares:</span></span>
-<span><span class="co">##       SFO      IORE      DFOP </span></span>
-<span><span class="co">## 188.45361  51.00699  42.46931 </span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Critical sum of squares for checking the SFO model:</span></span>
-<span><span class="co">## [1] 58.39888</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Parameters:</span></span>
-<span><span class="co">## $SFO</span></span>
-<span><span class="co">##          Estimate   Pr(&gt;t)   Lower   Upper</span></span>
-<span><span class="co">## parent_0  94.7759 7.29e-24 92.3478 97.2039</span></span>
-<span><span class="co">## k_parent   0.0179 8.02e-16  0.0166  0.0194</span></span>
-<span><span class="co">## sigma      3.0696 3.81e-06  2.0456  4.0936</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## $IORE</span></span>
-<span><span class="co">##                Estimate   Pr(&gt;t)    Lower    Upper</span></span>
-<span><span class="co">## parent_0       97.12446 2.63e-26 95.62461 98.62431</span></span>
-<span><span class="co">## k__iore_parent  0.00252 1.95e-03  0.00134  0.00472</span></span>
-<span><span class="co">## N_parent        1.49587 4.07e-13  1.33896  1.65279</span></span>
-<span><span class="co">## sigma           1.59698 5.05e-06  1.06169  2.13227</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## $DFOP</span></span>
-<span><span class="co">##          Estimate   Pr(&gt;t)   Lower   Upper</span></span>
-<span><span class="co">## parent_0 9.66e+01 1.57e-25 95.3476 97.8979</span></span>
-<span><span class="co">## k1       2.55e-02 7.33e-06  0.0233  0.0278</span></span>
-<span><span class="co">## k2       3.22e-11 5.00e-01  0.0000     Inf</span></span>
-<span><span class="co">## g        8.61e-01 7.55e-06  0.8314  0.8867</span></span>
-<span><span class="co">## sigma    1.46e+00 6.93e-06  0.9661  1.9483</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## DTx values:</span></span>
-<span><span class="co">##      DT50     DT90 DT50_rep</span></span>
-<span><span class="co">## SFO  38.6 1.28e+02 3.86e+01</span></span>
-<span><span class="co">## IORE 34.0 1.77e+02 5.32e+01</span></span>
-<span><span class="co">## DFOP 34.1 1.01e+10 2.15e+10</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Representative half-life:</span></span>
-<span><span class="co">## [1] 53.17</span></span></code></pre>
-</div>
-<div class="section level3">
-<h3 id="example-on-page-7">Example on page 7<a class="anchor" aria-label="anchor" href="#example-on-page-7"></a>
-</h3>
-<div class="sourceCode" id="cb19"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">p7</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p7"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre>
-<pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre>
-<div class="sourceCode" id="cb22"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p7</span><span class="op">)</span></span></code></pre></div>
-<p><img src="NAFTA_examples_files/figure-html/p7-1.png" width="700"></p>
-<div class="sourceCode" id="cb23"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p7</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## Sums of squares:</span></span>
-<span><span class="co">##      SFO     IORE     DFOP </span></span>
-<span><span class="co">## 3661.661 3195.030 3174.145 </span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Critical sum of squares for checking the SFO model:</span></span>
-<span><span class="co">## [1] 3334.194</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Parameters:</span></span>
-<span><span class="co">## $SFO</span></span>
-<span><span class="co">##          Estimate   Pr(&gt;t)    Lower    Upper</span></span>
-<span><span class="co">## parent_0 96.41796 4.80e-53 93.32245 99.51347</span></span>
-<span><span class="co">## k_parent  0.00735 7.64e-21  0.00641  0.00843</span></span>
-<span><span class="co">## sigma     7.94557 1.83e-15  6.46713  9.42401</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## $IORE</span></span>
-<span><span class="co">##                Estimate Pr(&gt;t)    Lower    Upper</span></span>
-<span><span class="co">## parent_0       9.92e+01     NA 9.55e+01 1.03e+02</span></span>
-<span><span class="co">## k__iore_parent 1.60e-05     NA 1.45e-07 1.77e-03</span></span>
-<span><span class="co">## N_parent       2.45e+00     NA 1.35e+00 3.54e+00</span></span>
-<span><span class="co">## sigma          7.42e+00     NA 6.04e+00 8.80e+00</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## $DFOP</span></span>
-<span><span class="co">##          Estimate   Pr(&gt;t)   Lower    Upper</span></span>
-<span><span class="co">## parent_0 9.89e+01 9.44e-49 95.4640 102.2573</span></span>
-<span><span class="co">## k1       1.81e-02 1.75e-01  0.0116   0.0281</span></span>
-<span><span class="co">## k2       3.63e-10 5.00e-01  0.0000      Inf</span></span>
-<span><span class="co">## g        6.06e-01 2.19e-01  0.4826   0.7178</span></span>
-<span><span class="co">## sigma    7.40e+00 2.97e-15  6.0201   8.7754</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## DTx values:</span></span>
-<span><span class="co">##      DT50     DT90 DT50_rep</span></span>
-<span><span class="co">## SFO  94.3 3.13e+02 9.43e+01</span></span>
-<span><span class="co">## IORE 96.7 1.51e+03 4.55e+02</span></span>
-<span><span class="co">## DFOP 96.4 3.77e+09 1.91e+09</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Representative half-life:</span></span>
-<span><span class="co">## [1] 454.55</span></span></code></pre>
-</div>
-</div>
-<div class="section level2">
-<h2 id="examples-where-the-representative-half-life-deviates-from-the-observed-dt50">Examples where the representative half-life deviates from the
-observed DT50<a class="anchor" aria-label="anchor" href="#examples-where-the-representative-half-life-deviates-from-the-observed-dt50"></a>
-</h2>
-<div class="section level3">
-<h3 id="example-on-page-8">Example on page 8<a class="anchor" aria-label="anchor" href="#example-on-page-8"></a>
-</h3>
-<p>For this dataset, the IORE fit does not converge when the default
-starting values used by mkin for the IORE model are used. Therefore, a
-lower value for the rate constant is used here.</p>
-<div class="sourceCode" id="cb25"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">p8</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p8"</span><span class="op">]</span><span class="op">]</span>, parms.ini <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k__iore_parent <span class="op">=</span> <span class="fl">1e-3</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre>
-<pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre>
-<div class="sourceCode" id="cb28"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p8</span><span class="op">)</span></span></code></pre></div>
-<p><img src="NAFTA_examples_files/figure-html/p8-1.png" width="700"></p>
-<div class="sourceCode" id="cb29"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p8</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## Sums of squares:</span></span>
-<span><span class="co">##       SFO      IORE      DFOP </span></span>
-<span><span class="co">## 1996.9408  444.9237  547.5616 </span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Critical sum of squares for checking the SFO model:</span></span>
-<span><span class="co">## [1] 477.4924</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Parameters:</span></span>
-<span><span class="co">## $SFO</span></span>
-<span><span class="co">##          Estimate   Pr(&gt;t)    Lower    Upper</span></span>
-<span><span class="co">## parent_0 88.16549 6.53e-29 83.37344 92.95754</span></span>
-<span><span class="co">## k_parent  0.00803 1.67e-13  0.00674  0.00957</span></span>
-<span><span class="co">## sigma     7.44786 4.17e-10  5.66209  9.23363</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## $IORE</span></span>
-<span><span class="co">##                Estimate   Pr(&gt;t)    Lower    Upper</span></span>
-<span><span class="co">## parent_0       9.77e+01 7.03e-35 9.44e+01 1.01e+02</span></span>
-<span><span class="co">## k__iore_parent 6.14e-05 3.20e-02 2.12e-05 1.78e-04</span></span>
-<span><span class="co">## N_parent       2.27e+00 4.23e-18 2.00e+00 2.54e+00</span></span>
-<span><span class="co">## sigma          3.52e+00 5.36e-10 2.67e+00 4.36e+00</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## $DFOP</span></span>
-<span><span class="co">##          Estimate   Pr(&gt;t)    Lower    Upper</span></span>
-<span><span class="co">## parent_0 95.70619 8.99e-32 91.87941 99.53298</span></span>
-<span><span class="co">## k1        0.02500 5.25e-04  0.01422  0.04394</span></span>
-<span><span class="co">## k2        0.00273 6.84e-03  0.00125  0.00597</span></span>
-<span><span class="co">## g         0.58835 2.84e-06  0.36595  0.77970</span></span>
-<span><span class="co">## sigma     3.90001 6.94e-10  2.96260  4.83741</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## DTx values:</span></span>
-<span><span class="co">##      DT50 DT90 DT50_rep</span></span>
-<span><span class="co">## SFO  86.3  287     86.3</span></span>
-<span><span class="co">## IORE 53.4  668    201.0</span></span>
-<span><span class="co">## DFOP 55.6  517    253.0</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Representative half-life:</span></span>
-<span><span class="co">## [1] 201.03</span></span></code></pre>
-</div>
-</div>
-<div class="section level2">
-<h2 id="examples-where-sfo-was-not-selected-for-an-abiotic-study">Examples where SFO was not selected for an abiotic study<a class="anchor" aria-label="anchor" href="#examples-where-sfo-was-not-selected-for-an-abiotic-study"></a>
-</h2>
-<div class="section level3">
-<h3 id="example-on-page-9-upper-panel">Example on page 9, upper panel<a class="anchor" aria-label="anchor" href="#example-on-page-9-upper-panel"></a>
-</h3>
-<div class="sourceCode" id="cb31"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">p9a</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p9a"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre>
-<pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre>
-<div class="sourceCode" id="cb34"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p9a</span><span class="op">)</span></span></code></pre></div>
-<p><img src="NAFTA_examples_files/figure-html/p9a-1.png" width="700"></p>
-<div class="sourceCode" id="cb35"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p9a</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## Sums of squares:</span></span>
-<span><span class="co">##       SFO      IORE      DFOP </span></span>
-<span><span class="co">## 839.35238  88.57064   9.93363 </span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Critical sum of squares for checking the SFO model:</span></span>
-<span><span class="co">## [1] 105.5678</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Parameters:</span></span>
-<span><span class="co">## $SFO</span></span>
-<span><span class="co">##          Estimate   Pr(&gt;t)   Lower   Upper</span></span>
-<span><span class="co">## parent_0  88.1933 3.06e-12 79.9447 96.4419</span></span>
-<span><span class="co">## k_parent   0.0409 2.07e-07  0.0324  0.0516</span></span>
-<span><span class="co">## sigma      7.2429 3.92e-05  4.4768 10.0090</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## $IORE</span></span>
-<span><span class="co">##                Estimate   Pr(&gt;t)    Lower    Upper</span></span>
-<span><span class="co">## parent_0       9.89e+01 1.12e-16 9.54e+01 1.02e+02</span></span>
-<span><span class="co">## k__iore_parent 1.93e-05 1.13e-01 3.49e-06 1.06e-04</span></span>
-<span><span class="co">## N_parent       2.91e+00 1.45e-09 2.50e+00 3.32e+00</span></span>
-<span><span class="co">## sigma          2.35e+00 5.31e-05 1.45e+00 3.26e+00</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## $DFOP</span></span>
-<span><span class="co">##          Estimate   Pr(&gt;t)  Lower  Upper</span></span>
-<span><span class="co">## parent_0 9.85e+01 2.54e-20 97.390 99.672</span></span>
-<span><span class="co">## k1       1.38e-01 3.52e-05  0.131  0.146</span></span>
-<span><span class="co">## k2       9.02e-13 5.00e-01  0.000    Inf</span></span>
-<span><span class="co">## g        6.52e-01 8.13e-06  0.642  0.661</span></span>
-<span><span class="co">## sigma    7.88e-01 6.13e-02  0.481  1.095</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## DTx values:</span></span>
-<span><span class="co">##      DT50     DT90 DT50_rep</span></span>
-<span><span class="co">## SFO  16.9 5.63e+01 1.69e+01</span></span>
-<span><span class="co">## IORE 11.6 3.37e+02 1.01e+02</span></span>
-<span><span class="co">## DFOP 10.5 1.38e+12 7.69e+11</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Representative half-life:</span></span>
-<span><span class="co">## [1] 101.43</span></span></code></pre>
-<p>In this example, the residuals of the SFO indicate a lack of fit of
-this model, so even if it was an abiotic experiment, the data do not
-suggest a simple exponential decline.</p>
-</div>
-<div class="section level3">
-<h3 id="example-on-page-9-lower-panel">Example on page 9, lower panel<a class="anchor" aria-label="anchor" href="#example-on-page-9-lower-panel"></a>
-</h3>
-<div class="sourceCode" id="cb37"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">p9b</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p9b"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## Warning in sqrt(diag(covar)): NaNs produced</span></span></code></pre>
-<pre><code><span><span class="co">## Warning in sqrt(diag(covar_notrans)): NaNs produced</span></span></code></pre>
-<pre><code><span><span class="co">## Warning in sqrt(1/diag(V)): NaNs produced</span></span></code></pre>
-<pre><code><span><span class="co">## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result</span></span>
-<span><span class="co">## is doubtful</span></span></code></pre>
-<pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre>
-<pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre>
-<div class="sourceCode" id="cb44"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p9b</span><span class="op">)</span></span></code></pre></div>
-<p><img src="NAFTA_examples_files/figure-html/p9b-1.png" width="700"></p>
-<div class="sourceCode" id="cb45"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p9b</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## Sums of squares:</span></span>
-<span><span class="co">##      SFO     IORE     DFOP </span></span>
-<span><span class="co">## 35.64867 23.22334 35.64867 </span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Critical sum of squares for checking the SFO model:</span></span>
-<span><span class="co">## [1] 28.54188</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Parameters:</span></span>
-<span><span class="co">## $SFO</span></span>
-<span><span class="co">##          Estimate   Pr(&gt;t)  Lower   Upper</span></span>
-<span><span class="co">## parent_0  94.7123 2.15e-19 93.178 96.2464</span></span>
-<span><span class="co">## k_parent   0.0389 4.47e-14  0.037  0.0408</span></span>
-<span><span class="co">## sigma      1.5957 1.28e-04  0.932  2.2595</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## $IORE</span></span>
-<span><span class="co">##                Estimate   Pr(&gt;t)   Lower  Upper</span></span>
-<span><span class="co">## parent_0         93.863 2.32e-18 92.4565 95.269</span></span>
-<span><span class="co">## k__iore_parent    0.127 1.85e-02  0.0504  0.321</span></span>
-<span><span class="co">## N_parent          0.711 1.88e-05  0.4843  0.937</span></span>
-<span><span class="co">## sigma             1.288 1.76e-04  0.7456  1.830</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## $DFOP</span></span>
-<span><span class="co">##          Estimate   Pr(&gt;t)   Lower   Upper</span></span>
-<span><span class="co">## parent_0  94.7123 1.61e-16 93.1355 96.2891</span></span>
-<span><span class="co">## k1         0.0389 1.08e-04  0.0266  0.0569</span></span>
-<span><span class="co">## k2         0.0389 2.23e-04  0.0255  0.0592</span></span>
-<span><span class="co">## g          0.5256      NaN      NA      NA</span></span>
-<span><span class="co">## sigma      1.5957 2.50e-04  0.9135  2.2779</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## DTx values:</span></span>
-<span><span class="co">##      DT50 DT90 DT50_rep</span></span>
-<span><span class="co">## SFO  17.8 59.2     17.8</span></span>
-<span><span class="co">## IORE 18.4 49.2     14.8</span></span>
-<span><span class="co">## DFOP 17.8 59.2     17.8</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Representative half-life:</span></span>
-<span><span class="co">## [1] 14.8</span></span></code></pre>
-<p>Here, mkin gives a longer slow DT50 for the DFOP model (17.8 days)
-than PestDF (13.5 days). Presumably, this is related to the fact that
-PestDF gives a negative value for the proportion of the fast degradation
-which should be between 0 and 1, inclusive. This parameter is called f
-in PestDF and g in mkin. In mkin, it is restricted to the interval from
-0 to 1.</p>
-</div>
-<div class="section level3">
-<h3 id="example-on-page-10">Example on page 10<a class="anchor" aria-label="anchor" href="#example-on-page-10"></a>
-</h3>
-<div class="sourceCode" id="cb47"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">p10</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p10"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## Warning in sqrt(diag(covar)): NaNs produced</span></span></code></pre>
-<pre><code><span><span class="co">## Warning in sqrt(1/diag(V)): NaNs produced</span></span></code></pre>
-<pre><code><span><span class="co">## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result</span></span>
-<span><span class="co">## is doubtful</span></span></code></pre>
-<pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre>
-<pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre>
-<div class="sourceCode" id="cb53"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p10</span><span class="op">)</span></span></code></pre></div>
-<p><img src="NAFTA_examples_files/figure-html/p10-1.png" width="700"></p>
-<div class="sourceCode" id="cb54"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p10</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## Sums of squares:</span></span>
-<span><span class="co">##      SFO     IORE     DFOP </span></span>
-<span><span class="co">## 899.4089 336.4348 899.4089 </span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Critical sum of squares for checking the SFO model:</span></span>
-<span><span class="co">## [1] 413.4841</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Parameters:</span></span>
-<span><span class="co">## $SFO</span></span>
-<span><span class="co">##          Estimate   Pr(&gt;t)   Lower    Upper</span></span>
-<span><span class="co">## parent_0 101.7315 6.42e-11 91.9259 111.5371</span></span>
-<span><span class="co">## k_parent   0.0495 1.70e-07  0.0404   0.0607</span></span>
-<span><span class="co">## sigma      8.0152 1.28e-04  4.6813  11.3491</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## $IORE</span></span>
-<span><span class="co">##                Estimate   Pr(&gt;t)  Lower   Upper</span></span>
-<span><span class="co">## parent_0          96.86 3.32e-12 90.848 102.863</span></span>
-<span><span class="co">## k__iore_parent     2.96 7.91e-02  0.687  12.761</span></span>
-<span><span class="co">## N_parent           0.00 5.00e-01 -0.372   0.372</span></span>
-<span><span class="co">## sigma              4.90 1.77e-04  2.837   6.968</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## $DFOP</span></span>
-<span><span class="co">##          Estimate   Pr(&gt;t)   Lower    Upper</span></span>
-<span><span class="co">## parent_0 101.7315 1.41e-09 91.6534 111.8097</span></span>
-<span><span class="co">## k1         0.0495 6.58e-03  0.0303   0.0809</span></span>
-<span><span class="co">## k2         0.0495 2.60e-03  0.0410   0.0598</span></span>
-<span><span class="co">## g          0.4487 5.00e-01      NA       NA</span></span>
-<span><span class="co">## sigma      8.0152 2.50e-04  4.5886  11.4418</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## DTx values:</span></span>
-<span><span class="co">##      DT50 DT90 DT50_rep</span></span>
-<span><span class="co">## SFO  14.0 46.5    14.00</span></span>
-<span><span class="co">## IORE 16.4 29.4     8.86</span></span>
-<span><span class="co">## DFOP 14.0 46.5    14.00</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Representative half-life:</span></span>
-<span><span class="co">## [1] 8.86</span></span></code></pre>
-<p>Here, a value below N is given for the IORE model, because the data
-suggests a faster decline towards the end of the experiment, which
-appears physically rather unlikely in the case of a photolysis study. It
-seems PestDF does not constrain N to values above zero, thus the slight
-difference in IORE model parameters between PestDF and mkin.</p>
-</div>
-</div>
-<div class="section level2">
-<h2 id="the-dt50-was-not-observed-during-the-study">The DT50 was not observed during the study<a class="anchor" aria-label="anchor" href="#the-dt50-was-not-observed-during-the-study"></a>
-</h2>
-<div class="section level3">
-<h3 id="example-on-page-11">Example on page 11<a class="anchor" aria-label="anchor" href="#example-on-page-11"></a>
-</h3>
-<div class="sourceCode" id="cb56"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">p11</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p11"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre>
-<pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre>
-<div class="sourceCode" id="cb59"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p11</span><span class="op">)</span></span></code></pre></div>
-<p><img src="NAFTA_examples_files/figure-html/p11-1.png" width="700"></p>
-<div class="sourceCode" id="cb60"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p11</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## Sums of squares:</span></span>
-<span><span class="co">##      SFO     IORE     DFOP </span></span>
-<span><span class="co">## 579.6805 204.7932 144.7783 </span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Critical sum of squares for checking the SFO model:</span></span>
-<span><span class="co">## [1] 251.6944</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Parameters:</span></span>
-<span><span class="co">## $SFO</span></span>
-<span><span class="co">##          Estimate   Pr(&gt;t)    Lower    Upper</span></span>
-<span><span class="co">## parent_0 96.15820 4.83e-13 90.24934 1.02e+02</span></span>
-<span><span class="co">## k_parent  0.00321 4.71e-05  0.00222 4.64e-03</span></span>
-<span><span class="co">## sigma     6.43473 1.28e-04  3.75822 9.11e+00</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## $IORE</span></span>
-<span><span class="co">##                Estimate Pr(&gt;t)    Lower    Upper</span></span>
-<span><span class="co">## parent_0       1.05e+02     NA 9.90e+01 1.10e+02</span></span>
-<span><span class="co">## k__iore_parent 3.11e-17     NA 1.35e-20 7.18e-14</span></span>
-<span><span class="co">## N_parent       8.36e+00     NA 6.62e+00 1.01e+01</span></span>
-<span><span class="co">## sigma          3.82e+00     NA 2.21e+00 5.44e+00</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## $DFOP</span></span>
-<span><span class="co">##          Estimate   Pr(&gt;t)    Lower    Upper</span></span>
-<span><span class="co">## parent_0 1.05e+02 9.47e-13  99.9990 109.1224</span></span>
-<span><span class="co">## k1       4.41e-02 5.95e-03   0.0296   0.0658</span></span>
-<span><span class="co">## k2       9.94e-13 5.00e-01   0.0000      Inf</span></span>
-<span><span class="co">## g        3.22e-01 1.45e-03   0.2814   0.3650</span></span>
-<span><span class="co">## sigma    3.22e+00 3.52e-04   1.8410   4.5906</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## DTx values:</span></span>
-<span><span class="co">##          DT50     DT90 DT50_rep</span></span>
-<span><span class="co">## SFO  2.16e+02 7.18e+02 2.16e+02</span></span>
-<span><span class="co">## IORE 9.73e+02 1.37e+08 4.11e+07</span></span>
-<span><span class="co">## DFOP 3.07e+11 1.93e+12 6.98e+11</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Representative half-life:</span></span>
-<span><span class="co">## [1] 41148170</span></span></code></pre>
-<p>In this case, the DFOP fit reported for PestDF resulted in a negative
-value for the slower rate constant, which is not possible in mkin. The
-other results are in agreement.</p>
-</div>
-</div>
-<div class="section level2">
-<h2 id="n-is-less-than-1-and-the-dfop-rate-constants-are-like-the-sfo-rate-constant">N is less than 1 and the DFOP rate constants are like the SFO rate
-constant<a class="anchor" aria-label="anchor" href="#n-is-less-than-1-and-the-dfop-rate-constants-are-like-the-sfo-rate-constant"></a>
-</h2>
-<p>In the following three examples, the same results are obtained with
-mkin as reported for PestDF. As in the case on page 10, the N values
-below 1 are deemed unrealistic and appear to be the result of an
-overparameterisation.</p>
-<div class="section level3">
-<h3 id="example-on-page-12-upper-panel">Example on page 12, upper panel<a class="anchor" aria-label="anchor" href="#example-on-page-12-upper-panel"></a>
-</h3>
-<div class="sourceCode" id="cb62"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">p12a</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p12a"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## Warning in summary.mkinfit(x): Could not calculate correlation; no covariance</span></span>
-<span><span class="co">## matrix</span></span></code></pre>
-<pre><code><span><span class="co">## Warning in sqrt(diag(covar)): NaNs produced</span></span></code></pre>
-<pre><code><span><span class="co">## Warning in sqrt(diag(covar_notrans)): NaNs produced</span></span></code></pre>
-<pre><code><span><span class="co">## Warning in sqrt(1/diag(V)): NaNs produced</span></span></code></pre>
-<pre><code><span><span class="co">## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result</span></span>
-<span><span class="co">## is doubtful</span></span></code></pre>
-<pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre>
-<pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre>
-<div class="sourceCode" id="cb70"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p12a</span><span class="op">)</span></span></code></pre></div>
-<p><img src="NAFTA_examples_files/figure-html/p12a-1.png" width="700"></p>
-<div class="sourceCode" id="cb71"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p12a</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## Sums of squares:</span></span>
-<span><span class="co">##      SFO     IORE     DFOP </span></span>
-<span><span class="co">## 695.4440 220.0685 695.4440 </span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Critical sum of squares for checking the SFO model:</span></span>
-<span><span class="co">## [1] 270.4679</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Parameters:</span></span>
-<span><span class="co">## $SFO</span></span>
-<span><span class="co">##          Estimate   Pr(&gt;t)  Lower   Upper</span></span>
-<span><span class="co">## parent_0  100.521 8.75e-12 92.461 108.581</span></span>
-<span><span class="co">## k_parent    0.124 3.61e-08  0.104   0.148</span></span>
-<span><span class="co">## sigma       7.048 1.28e-04  4.116   9.980</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## $IORE</span></span>
-<span><span class="co">##                Estimate Pr(&gt;t) Lower Upper</span></span>
-<span><span class="co">## parent_0         96.823     NA    NA    NA</span></span>
-<span><span class="co">## k__iore_parent    2.436     NA    NA    NA</span></span>
-<span><span class="co">## N_parent          0.263     NA    NA    NA</span></span>
-<span><span class="co">## sigma             3.965     NA    NA    NA</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## $DFOP</span></span>
-<span><span class="co">##          Estimate   Pr(&gt;t)   Lower   Upper</span></span>
-<span><span class="co">## parent_0  100.521 2.74e-10 92.2366 108.805</span></span>
-<span><span class="co">## k1          0.124 2.53e-05  0.0908   0.170</span></span>
-<span><span class="co">## k2          0.124 2.52e-02  0.0456   0.339</span></span>
-<span><span class="co">## g           0.793      NaN      NA      NA</span></span>
-<span><span class="co">## sigma       7.048 2.50e-04  4.0349  10.061</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## DTx values:</span></span>
-<span><span class="co">##      DT50 DT90 DT50_rep</span></span>
-<span><span class="co">## SFO  5.58 18.5     5.58</span></span>
-<span><span class="co">## IORE 6.49 13.2     3.99</span></span>
-<span><span class="co">## DFOP 5.58 18.5     5.58</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Representative half-life:</span></span>
-<span><span class="co">## [1] 3.99</span></span></code></pre>
-</div>
-<div class="section level3">
-<h3 id="example-on-page-12-lower-panel">Example on page 12, lower panel<a class="anchor" aria-label="anchor" href="#example-on-page-12-lower-panel"></a>
-</h3>
-<div class="sourceCode" id="cb73"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">p12b</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p12b"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## Warning in qt(alpha/2, rdf): NaNs produced</span></span></code></pre>
-<pre><code><span><span class="co">## Warning in qt(1 - alpha/2, rdf): NaNs produced</span></span></code></pre>
-<pre><code><span><span class="co">## Warning in sqrt(diag(covar_notrans)): NaNs produced</span></span></code></pre>
-<pre><code><span><span class="co">## Warning in pt(abs(tval), rdf, lower.tail = FALSE): NaNs produced</span></span></code></pre>
-<pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre>
-<pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre>
-<div class="sourceCode" id="cb80"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p12b</span><span class="op">)</span></span></code></pre></div>
-<p><img src="NAFTA_examples_files/figure-html/p12b-1.png" width="700"></p>
-<div class="sourceCode" id="cb81"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p12b</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## Sums of squares:</span></span>
-<span><span class="co">##      SFO     IORE     DFOP </span></span>
-<span><span class="co">## 58.90242 19.06353 58.90242 </span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Critical sum of squares for checking the SFO model:</span></span>
-<span><span class="co">## [1] 51.51756</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Parameters:</span></span>
-<span><span class="co">## $SFO</span></span>
-<span><span class="co">##          Estimate  Pr(&gt;t)   Lower    Upper</span></span>
-<span><span class="co">## parent_0  97.6840 0.00039 85.9388 109.4292</span></span>
-<span><span class="co">## k_parent   0.0589 0.00261  0.0431   0.0805</span></span>
-<span><span class="co">## sigma      3.4323 0.04356 -1.2377   8.1023</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## $IORE</span></span>
-<span><span class="co">##                Estimate Pr(&gt;t)     Lower  Upper</span></span>
-<span><span class="co">## parent_0         95.523 0.0055 74.539157 116.51</span></span>
-<span><span class="co">## k__iore_parent    0.333 0.1433  0.000717 154.57</span></span>
-<span><span class="co">## N_parent          0.568 0.0677 -0.989464   2.13</span></span>
-<span><span class="co">## sigma             1.953 0.0975 -5.893100   9.80</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## $DFOP</span></span>
-<span><span class="co">##          Estimate Pr(&gt;t) Lower Upper</span></span>
-<span><span class="co">## parent_0  97.6840    NaN   NaN   NaN</span></span>
-<span><span class="co">## k1         0.0589    NaN    NA    NA</span></span>
-<span><span class="co">## k2         0.0589    NaN    NA    NA</span></span>
-<span><span class="co">## g          0.6473    NaN    NA    NA</span></span>
-<span><span class="co">## sigma      3.4323    NaN   NaN   NaN</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## DTx values:</span></span>
-<span><span class="co">##      DT50 DT90 DT50_rep</span></span>
-<span><span class="co">## SFO  11.8 39.1    11.80</span></span>
-<span><span class="co">## IORE 12.9 31.4     9.46</span></span>
-<span><span class="co">## DFOP 11.8 39.1    11.80</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Representative half-life:</span></span>
-<span><span class="co">## [1] 9.46</span></span></code></pre>
-</div>
-<div class="section level3">
-<h3 id="example-on-page-13">Example on page 13<a class="anchor" aria-label="anchor" href="#example-on-page-13"></a>
-</h3>
-<div class="sourceCode" id="cb83"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">p13</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p13"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre>
-<pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre>
-<div class="sourceCode" id="cb86"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p13</span><span class="op">)</span></span></code></pre></div>
-<p><img src="NAFTA_examples_files/figure-html/p13-1.png" width="700"></p>
-<div class="sourceCode" id="cb87"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p13</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## Sums of squares:</span></span>
-<span><span class="co">##      SFO     IORE     DFOP </span></span>
-<span><span class="co">## 174.5971 142.3951 174.5971 </span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Critical sum of squares for checking the SFO model:</span></span>
-<span><span class="co">## [1] 172.131</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Parameters:</span></span>
-<span><span class="co">## $SFO</span></span>
-<span><span class="co">##          Estimate   Pr(&gt;t)    Lower    Upper</span></span>
-<span><span class="co">## parent_0 92.73500 5.99e-17 89.61936 95.85065</span></span>
-<span><span class="co">## k_parent  0.00258 2.42e-09  0.00223  0.00299</span></span>
-<span><span class="co">## sigma     3.41172 7.07e-05  2.05455  4.76888</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## $IORE</span></span>
-<span><span class="co">##                Estimate   Pr(&gt;t)    Lower  Upper</span></span>
-<span><span class="co">## parent_0        91.6016 6.34e-16 88.53086 94.672</span></span>
-<span><span class="co">## k__iore_parent   0.0396 2.36e-01  0.00207  0.759</span></span>
-<span><span class="co">## N_parent         0.3541 1.46e-01 -0.35153  1.060</span></span>
-<span><span class="co">## sigma            3.0811 9.64e-05  1.84296  4.319</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## $DFOP</span></span>
-<span><span class="co">##          Estimate Pr(&gt;t)    Lower    Upper</span></span>
-<span><span class="co">## parent_0 92.73500     NA 8.95e+01 95.92118</span></span>
-<span><span class="co">## k1        0.00258     NA 4.14e-04  0.01611</span></span>
-<span><span class="co">## k2        0.00258     NA 1.74e-03  0.00383</span></span>
-<span><span class="co">## g         0.16452     NA 0.00e+00  1.00000</span></span>
-<span><span class="co">## sigma     3.41172     NA 2.02e+00  4.79960</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## DTx values:</span></span>
-<span><span class="co">##      DT50 DT90 DT50_rep</span></span>
-<span><span class="co">## SFO   269  892      269</span></span>
-<span><span class="co">## IORE  261  560      169</span></span>
-<span><span class="co">## DFOP  269  892      269</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Representative half-life:</span></span>
-<span><span class="co">## [1] 168.51</span></span></code></pre>
-</div>
-</div>
-<div class="section level2">
-<h2 id="dt50-not-observed-in-the-study-and-dfop-problems-in-pestdf">DT50 not observed in the study and DFOP problems in PestDF<a class="anchor" aria-label="anchor" href="#dt50-not-observed-in-the-study-and-dfop-problems-in-pestdf"></a>
-</h2>
-<div class="sourceCode" id="cb89"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">p14</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p14"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## Warning in sqrt(diag(covar)): NaNs produced</span></span></code></pre>
-<pre><code><span><span class="co">## Warning in sqrt(1/diag(V)): NaNs produced</span></span></code></pre>
-<pre><code><span><span class="co">## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result</span></span>
-<span><span class="co">## is doubtful</span></span></code></pre>
-<pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre>
-<pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre>
-<div class="sourceCode" id="cb95"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p14</span><span class="op">)</span></span></code></pre></div>
-<p><img src="NAFTA_examples_files/figure-html/p14-1.png" width="700"></p>
-<div class="sourceCode" id="cb96"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p14</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## Sums of squares:</span></span>
-<span><span class="co">##      SFO     IORE     DFOP </span></span>
-<span><span class="co">## 48.43249 28.67746 27.26248 </span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Critical sum of squares for checking the SFO model:</span></span>
-<span><span class="co">## [1] 32.83337</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Parameters:</span></span>
-<span><span class="co">## $SFO</span></span>
-<span><span class="co">##          Estimate   Pr(&gt;t)    Lower    Upper</span></span>
-<span><span class="co">## parent_0 99.47124 2.06e-30 98.42254 1.01e+02</span></span>
-<span><span class="co">## k_parent  0.00279 3.75e-15  0.00256 3.04e-03</span></span>
-<span><span class="co">## sigma     1.55616 3.81e-06  1.03704 2.08e+00</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## $IORE</span></span>
-<span><span class="co">##                Estimate Pr(&gt;t) Lower Upper</span></span>
-<span><span class="co">## parent_0       1.00e+02     NA   NaN   NaN</span></span>
-<span><span class="co">## k__iore_parent 9.44e-08     NA   NaN   NaN</span></span>
-<span><span class="co">## N_parent       3.31e+00     NA   NaN   NaN</span></span>
-<span><span class="co">## sigma          1.20e+00     NA 0.796   1.6</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## $DFOP</span></span>
-<span><span class="co">##          Estimate   Pr(&gt;t)    Lower    Upper</span></span>
-<span><span class="co">## parent_0 1.00e+02 2.96e-28 99.40280 101.2768</span></span>
-<span><span class="co">## k1       9.53e-03 1.20e-01  0.00638   0.0143</span></span>
-<span><span class="co">## k2       6.08e-12 5.00e-01  0.00000      Inf</span></span>
-<span><span class="co">## g        3.98e-01 2.19e-01  0.30481   0.4998</span></span>
-<span><span class="co">## sigma    1.17e+00 7.68e-06  0.77406   1.5610</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## DTx values:</span></span>
-<span><span class="co">##          DT50     DT90 DT50_rep</span></span>
-<span><span class="co">## SFO  2.48e+02 8.25e+02 2.48e+02</span></span>
-<span><span class="co">## IORE 4.34e+02 2.22e+04 6.70e+03</span></span>
-<span><span class="co">## DFOP 3.05e+10 2.95e+11 1.14e+11</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Representative half-life:</span></span>
-<span><span class="co">## [1] 6697.44</span></span></code></pre>
-<p>The slower rate constant reported by PestDF is negative, which is not
-physically realistic, and not possible in mkin. The other fits give the
-same results in mkin and PestDF.</p>
-</div>
-<div class="section level2">
-<h2 id="n-is-less-than-1-and-dfop-fraction-parameter-is-below-zero">N is less than 1 and DFOP fraction parameter is below zero<a class="anchor" aria-label="anchor" href="#n-is-less-than-1-and-dfop-fraction-parameter-is-below-zero"></a>
-</h2>
-<div class="sourceCode" id="cb98"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">p15a</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p15a"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre>
-<pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre>
-<div class="sourceCode" id="cb101"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p15a</span><span class="op">)</span></span></code></pre></div>
-<p><img src="NAFTA_examples_files/figure-html/p15a-1.png" width="700"></p>
-<div class="sourceCode" id="cb102"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p15a</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## Sums of squares:</span></span>
-<span><span class="co">##      SFO     IORE     DFOP </span></span>
-<span><span class="co">## 245.5248 135.0132 245.5248 </span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Critical sum of squares for checking the SFO model:</span></span>
-<span><span class="co">## [1] 165.9335</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Parameters:</span></span>
-<span><span class="co">## $SFO</span></span>
-<span><span class="co">##          Estimate   Pr(&gt;t)    Lower   Upper</span></span>
-<span><span class="co">## parent_0 97.96751 2.00e-15 94.32049 101.615</span></span>
-<span><span class="co">## k_parent  0.00952 4.93e-09  0.00824   0.011</span></span>
-<span><span class="co">## sigma     4.18778 1.28e-04  2.44588   5.930</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## $IORE</span></span>
-<span><span class="co">##                Estimate   Pr(&gt;t)  Lower  Upper</span></span>
-<span><span class="co">## parent_0         95.874 2.94e-15 92.937 98.811</span></span>
-<span><span class="co">## k__iore_parent    0.629 2.11e-01  0.044  8.982</span></span>
-<span><span class="co">## N_parent          0.000 5.00e-01 -0.642  0.642</span></span>
-<span><span class="co">## sigma             3.105 1.78e-04  1.795  4.416</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## $DFOP</span></span>
-<span><span class="co">##          Estimate   Pr(&gt;t)    Lower    Upper</span></span>
-<span><span class="co">## parent_0 97.96751 2.85e-13 94.21913 101.7159</span></span>
-<span><span class="co">## k1        0.00952 6.28e-02  0.00250   0.0363</span></span>
-<span><span class="co">## k2        0.00952 1.27e-04  0.00646   0.0140</span></span>
-<span><span class="co">## g         0.21241 5.00e-01  0.00000   1.0000</span></span>
-<span><span class="co">## sigma     4.18778 2.50e-04  2.39747   5.9781</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## DTx values:</span></span>
-<span><span class="co">##      DT50 DT90 DT50_rep</span></span>
-<span><span class="co">## SFO  72.8  242     72.8</span></span>
-<span><span class="co">## IORE 76.3  137     41.3</span></span>
-<span><span class="co">## DFOP 72.8  242     72.8</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Representative half-life:</span></span>
-<span><span class="co">## [1] 41.33</span></span></code></pre>
-<div class="sourceCode" id="cb104"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">p15b</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p15b"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## Warning in sqrt(diag(covar)): NaNs produced</span></span></code></pre>
-<pre><code><span><span class="co">## Warning in sqrt(1/diag(V)): NaNs produced</span></span></code></pre>
-<pre><code><span><span class="co">## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result</span></span>
-<span><span class="co">## is doubtful</span></span></code></pre>
-<pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre>
-<pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre>
-<div class="sourceCode" id="cb110"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p15b</span><span class="op">)</span></span></code></pre></div>
-<p><img src="NAFTA_examples_files/figure-html/p15b-1.png" width="700"></p>
-<div class="sourceCode" id="cb111"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p15b</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## Sums of squares:</span></span>
-<span><span class="co">##       SFO      IORE      DFOP </span></span>
-<span><span class="co">## 106.91629  68.55574 106.91629 </span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Critical sum of squares for checking the SFO model:</span></span>
-<span><span class="co">## [1] 84.25618</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Parameters:</span></span>
-<span><span class="co">## $SFO</span></span>
-<span><span class="co">##          Estimate   Pr(&gt;t)    Lower    Upper</span></span>
-<span><span class="co">## parent_0 1.01e+02 3.06e-17 98.31594 1.03e+02</span></span>
-<span><span class="co">## k_parent 4.86e-03 2.48e-10  0.00435 5.42e-03</span></span>
-<span><span class="co">## sigma    2.76e+00 1.28e-04  1.61402 3.91e+00</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## $IORE</span></span>
-<span><span class="co">##                Estimate   Pr(&gt;t)    Lower  Upper</span></span>
-<span><span class="co">## parent_0          99.83 1.81e-16 97.51349 102.14</span></span>
-<span><span class="co">## k__iore_parent     0.38 3.22e-01  0.00352  41.05</span></span>
-<span><span class="co">## N_parent           0.00 5.00e-01 -1.07696   1.08</span></span>
-<span><span class="co">## sigma              2.21 2.57e-04  1.23245   3.19</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## $DFOP</span></span>
-<span><span class="co">##          Estimate Pr(&gt;t)    Lower    Upper</span></span>
-<span><span class="co">## parent_0 1.01e+02     NA 9.82e+01 1.04e+02</span></span>
-<span><span class="co">## k1       4.86e-03     NA 8.63e-04 2.73e-02</span></span>
-<span><span class="co">## k2       4.86e-03     NA 3.21e-03 7.35e-03</span></span>
-<span><span class="co">## g        1.88e-01     NA       NA       NA</span></span>
-<span><span class="co">## sigma    2.76e+00     NA 1.58e+00 3.94e+00</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## DTx values:</span></span>
-<span><span class="co">##      DT50 DT90 DT50_rep</span></span>
-<span><span class="co">## SFO   143  474    143.0</span></span>
-<span><span class="co">## IORE  131  236     71.2</span></span>
-<span><span class="co">## DFOP  143  474    143.0</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Representative half-life:</span></span>
-<span><span class="co">## [1] 71.18</span></span></code></pre>
-<p>In mkin, only the IORE fit is affected (deemed unrealistic), as the
-fraction parameter of the DFOP model is restricted to the interval
-between 0 and 1 in mkin. The SFO fits give the same results for both
-mkin and PestDF.</p>
-</div>
-<div class="section level2">
-<h2 id="the-dfop-fraction-parameter-is-greater-than-1">The DFOP fraction parameter is greater than 1<a class="anchor" aria-label="anchor" href="#the-dfop-fraction-parameter-is-greater-than-1"></a>
-</h2>
-<div class="sourceCode" id="cb113"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">p16</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p16"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre>
-<pre><code><span><span class="co">## The representative half-life of the IORE model is longer than the one corresponding</span></span></code></pre>
-<pre><code><span><span class="co">## to the terminal degradation rate found with the DFOP model.</span></span></code></pre>
-<pre><code><span><span class="co">## The representative half-life obtained from the DFOP model may be used</span></span></code></pre>
-<div class="sourceCode" id="cb118"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p16</span><span class="op">)</span></span></code></pre></div>
-<p><img src="NAFTA_examples_files/figure-html/p16-1.png" width="700"></p>
-<div class="sourceCode" id="cb119"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p16</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## Sums of squares:</span></span>
-<span><span class="co">##      SFO     IORE     DFOP </span></span>
-<span><span class="co">## 3831.804 2062.008 1550.980 </span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Critical sum of squares for checking the SFO model:</span></span>
-<span><span class="co">## [1] 2247.348</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Parameters:</span></span>
-<span><span class="co">## $SFO</span></span>
-<span><span class="co">##          Estimate   Pr(&gt;t)  Lower Upper</span></span>
-<span><span class="co">## parent_0   71.953 2.33e-13 60.509 83.40</span></span>
-<span><span class="co">## k_parent    0.159 4.86e-05  0.102  0.25</span></span>
-<span><span class="co">## sigma      11.302 1.25e-08  8.308 14.30</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## $IORE</span></span>
-<span><span class="co">##                Estimate   Pr(&gt;t)    Lower    Upper</span></span>
-<span><span class="co">## parent_0       8.74e+01 2.48e-16 7.72e+01 97.52972</span></span>
-<span><span class="co">## k__iore_parent 4.55e-04 2.16e-01 3.48e-05  0.00595</span></span>
-<span><span class="co">## N_parent       2.70e+00 1.21e-08 1.99e+00  3.40046</span></span>
-<span><span class="co">## sigma          8.29e+00 1.61e-08 6.09e+00 10.49062</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## $DFOP</span></span>
-<span><span class="co">##          Estimate   Pr(&gt;t)   Lower  Upper</span></span>
-<span><span class="co">## parent_0  88.5333 7.40e-18 79.9836 97.083</span></span>
-<span><span class="co">## k1        18.8461 5.00e-01  0.0000    Inf</span></span>
-<span><span class="co">## k2         0.0776 1.41e-05  0.0518  0.116</span></span>
-<span><span class="co">## g          0.4733 1.41e-09  0.3674  0.582</span></span>
-<span><span class="co">## sigma      7.1902 2.11e-08  5.2785  9.102</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## DTx values:</span></span>
-<span><span class="co">##      DT50 DT90 DT50_rep</span></span>
-<span><span class="co">## SFO  4.35 14.4     4.35</span></span>
-<span><span class="co">## IORE 1.48 32.1     9.67</span></span>
-<span><span class="co">## DFOP 0.67 21.4     8.93</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">## Representative half-life:</span></span>
-<span><span class="co">## [1] 8.93</span></span></code></pre>
-<p>In PestDF, the DFOP fit seems to have stuck in a local minimum, as
-mkin finds a solution with a much lower <span class="math inline">\(\chi^2\)</span> error level. As the half-life from
-the slower rate constant of the DFOP model is larger than the IORE
-derived half-life, the NAFTA recommendation obtained with mkin is to use
-the DFOP representative half-life of 8.9 days.</p>
-</div>
-<div class="section level2">
-<h2 id="conclusions">Conclusions<a class="anchor" aria-label="anchor" href="#conclusions"></a>
-</h2>
-<p>The results obtained with mkin deviate from the results obtained with
-PestDF either in cases where one of the interpretive rules would apply,
-i.e. the IORE parameter N is less than one or the DFOP k values obtained
-with PestDF are equal to the SFO k values, or in cases where the DFOP
-model did not converge, which often lead to negative rate constants
-returned by PestDF.</p>
-<p>Therefore, mkin appears to suitable for kinetic evaluations according
-to the NAFTA guidance.</p>
-</div>
-<div class="section level2">
-<h2 class="unnumbered" id="references">References<a class="anchor" aria-label="anchor" href="#references"></a>
-</h2>
-<div id="refs" class="references csl-bib-body hanging-indent">
-<div id="ref-usepa2015" class="csl-entry">
-US EPA. 2015. <span>“Standard Operating Procedure for Using the NAFTA
-Guidance to Calculate Representative Half-Life Values and Characterizing
-Pesticide Degradation.”</span> <a href="https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/standard-operating-procedure-using-nafta-guidance" class="external-link">https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/standard-operating-procedure-using-nafta-guidance</a>.
-</div>
-</div>
-</div>
-  </div>
-
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-
-        <nav id="toc" data-toggle="toc"><h2 data-toc-skip>Contents</h2>
-    </nav>
-</div>
-
-</div>
-
-
-
-      <footer><div class="copyright">
-  <p></p>
-<p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p>
-<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer>
-</div>
-
-  
-
-
-  
-
-  </body>
-</html>
diff --git a/docs/dev/articles/web_only/NAFTA_examples_files/accessible-code-block-0.0.1/empty-anchor.js b/docs/dev/articles/web_only/NAFTA_examples_files/accessible-code-block-0.0.1/empty-anchor.js
deleted file mode 100644
index ca349fd6..00000000
--- a/docs/dev/articles/web_only/NAFTA_examples_files/accessible-code-block-0.0.1/empty-anchor.js
+++ /dev/null
@@ -1,15 +0,0 @@
-// Hide empty <a> tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) -->
-// v0.0.1
-// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020.
-
-document.addEventListener('DOMContentLoaded', function() {
-  const codeList = document.getElementsByClassName("sourceCode");
-  for (var i = 0; i < codeList.length; i++) {
-    var linkList = codeList[i].getElementsByTagName('a');
-    for (var j = 0; j < linkList.length; j++) {
-      if (linkList[j].innerHTML === "") {
-        linkList[j].setAttribute('aria-hidden', 'true');
-      }
-    }
-  }
-});
diff --git a/docs/dev/articles/web_only/NAFTA_examples_files/anchor-sections-1.0/anchor-sections.css b/docs/dev/articles/web_only/NAFTA_examples_files/anchor-sections-1.0/anchor-sections.css
deleted file mode 100644
index 07aee5fc..00000000
--- a/docs/dev/articles/web_only/NAFTA_examples_files/anchor-sections-1.0/anchor-sections.css
+++ /dev/null
@@ -1,4 +0,0 @@
-/* Styles for section anchors */
-a.anchor-section {margin-left: 10px; visibility: hidden; color: inherit;}
-a.anchor-section::before {content: '#';}
-.hasAnchor:hover a.anchor-section {visibility: visible;}
diff --git a/docs/dev/articles/web_only/NAFTA_examples_files/anchor-sections-1.0/anchor-sections.js b/docs/dev/articles/web_only/NAFTA_examples_files/anchor-sections-1.0/anchor-sections.js
deleted file mode 100644
index 570f99a0..00000000
--- a/docs/dev/articles/web_only/NAFTA_examples_files/anchor-sections-1.0/anchor-sections.js
+++ /dev/null
@@ -1,33 +0,0 @@
-// Anchor sections v1.0 written by Atsushi Yasumoto on Oct 3rd, 2020.
-document.addEventListener('DOMContentLoaded', function() {
-  // Do nothing if AnchorJS is used
-  if (typeof window.anchors === 'object' && anchors.hasOwnProperty('hasAnchorJSLink')) {
-    return;
-  }
-
-  const h = document.querySelectorAll('h1, h2, h3, h4, h5, h6');
-
-  // Do nothing if sections are already anchored
-  if (Array.from(h).some(x => x.classList.contains('hasAnchor'))) {
-    return null;
-  }
-
-  // Use section id when pandoc runs with --section-divs
-  const section_id = function(x) {
-    return ((x.classList.contains('section') || (x.tagName === 'SECTION'))
-            ? x.id : '');
-  };
-
-  // Add anchors
-  h.forEach(function(x) {
-    const id = x.id || section_id(x.parentElement);
-    if (id === '') {
-      return null;
-    }
-    let anchor = document.createElement('a');
-    anchor.href = '#' + id;
-    anchor.classList = ['anchor-section'];
-    x.classList.add('hasAnchor');
-    x.appendChild(anchor);
-  });
-});
diff --git a/docs/dev/articles/web_only/NAFTA_examples_files/figure-html/p10-1.png b/docs/dev/articles/web_only/NAFTA_examples_files/figure-html/p10-1.png
deleted file mode 100644
index 566625ea..00000000
--- a/docs/dev/articles/web_only/NAFTA_examples_files/figure-html/p10-1.png
+++ /dev/null
diff --git a/docs/dev/articles/web_only/NAFTA_examples_files/figure-html/p11-1.png b/docs/dev/articles/web_only/NAFTA_examples_files/figure-html/p11-1.png
deleted file mode 100644
index 71fc4699..00000000
--- a/docs/dev/articles/web_only/NAFTA_examples_files/figure-html/p11-1.png
+++ /dev/null
diff --git a/docs/dev/articles/web_only/NAFTA_examples_files/figure-html/p12a-1.png b/docs/dev/articles/web_only/NAFTA_examples_files/figure-html/p12a-1.png
deleted file mode 100644
index a1d3a084..00000000
--- a/docs/dev/articles/web_only/NAFTA_examples_files/figure-html/p12a-1.png
+++ /dev/null
diff --git a/docs/dev/articles/web_only/NAFTA_examples_files/figure-html/p12b-1.png b/docs/dev/articles/web_only/NAFTA_examples_files/figure-html/p12b-1.png
deleted file mode 100644
index 1a6fdd03..00000000
--- a/docs/dev/articles/web_only/NAFTA_examples_files/figure-html/p12b-1.png
+++ /dev/null
diff --git a/docs/dev/articles/web_only/NAFTA_examples_files/figure-html/p13-1.png b/docs/dev/articles/web_only/NAFTA_examples_files/figure-html/p13-1.png
deleted file mode 100644
index f9b9f637..00000000
--- a/docs/dev/articles/web_only/NAFTA_examples_files/figure-html/p13-1.png
+++ /dev/null
diff --git a/docs/dev/articles/web_only/NAFTA_examples_files/figure-html/p14-1.png b/docs/dev/articles/web_only/NAFTA_examples_files/figure-html/p14-1.png
deleted file mode 100644
index 9f7b0cc5..00000000
--- a/docs/dev/articles/web_only/NAFTA_examples_files/figure-html/p14-1.png
+++ /dev/null
diff --git a/docs/dev/articles/web_only/NAFTA_examples_files/figure-html/p15a-1.png b/docs/dev/articles/web_only/NAFTA_examples_files/figure-html/p15a-1.png
deleted file mode 100644
index b5fd7d91..00000000
--- a/docs/dev/articles/web_only/NAFTA_examples_files/figure-html/p15a-1.png
+++ /dev/null
diff --git a/docs/dev/articles/web_only/NAFTA_examples_files/figure-html/p15b-1.png b/docs/dev/articles/web_only/NAFTA_examples_files/figure-html/p15b-1.png
deleted file mode 100644
index dfbc996f..00000000
--- a/docs/dev/articles/web_only/NAFTA_examples_files/figure-html/p15b-1.png
+++ /dev/null
diff --git a/docs/dev/articles/web_only/NAFTA_examples_files/figure-html/p16-1.png b/docs/dev/articles/web_only/NAFTA_examples_files/figure-html/p16-1.png
deleted file mode 100644
index 75ac7e5b..00000000
--- a/docs/dev/articles/web_only/NAFTA_examples_files/figure-html/p16-1.png
+++ /dev/null
diff --git a/docs/dev/articles/web_only/NAFTA_examples_files/figure-html/p5a-1.png b/docs/dev/articles/web_only/NAFTA_examples_files/figure-html/p5a-1.png
deleted file mode 100644
index 12a62954..00000000
--- a/docs/dev/articles/web_only/NAFTA_examples_files/figure-html/p5a-1.png
+++ /dev/null
diff --git a/docs/dev/articles/web_only/NAFTA_examples_files/figure-html/p5b-1.png b/docs/dev/articles/web_only/NAFTA_examples_files/figure-html/p5b-1.png
deleted file mode 100644
index 6fd175cb..00000000
--- a/docs/dev/articles/web_only/NAFTA_examples_files/figure-html/p5b-1.png
+++ /dev/null
diff --git a/docs/dev/articles/web_only/NAFTA_examples_files/figure-html/p6-1.png b/docs/dev/articles/web_only/NAFTA_examples_files/figure-html/p6-1.png
deleted file mode 100644
index 856c6778..00000000
--- a/docs/dev/articles/web_only/NAFTA_examples_files/figure-html/p6-1.png
+++ /dev/null
diff --git a/docs/dev/articles/web_only/NAFTA_examples_files/figure-html/p7-1.png b/docs/dev/articles/web_only/NAFTA_examples_files/figure-html/p7-1.png
deleted file mode 100644
index b078fb88..00000000
--- a/docs/dev/articles/web_only/NAFTA_examples_files/figure-html/p7-1.png
+++ /dev/null
diff --git a/docs/dev/articles/web_only/NAFTA_examples_files/figure-html/p8-1.png b/docs/dev/articles/web_only/NAFTA_examples_files/figure-html/p8-1.png
deleted file mode 100644
index a1e3bf25..00000000
--- a/docs/dev/articles/web_only/NAFTA_examples_files/figure-html/p8-1.png
+++ /dev/null
diff --git a/docs/dev/articles/web_only/NAFTA_examples_files/figure-html/p9a-1.png b/docs/dev/articles/web_only/NAFTA_examples_files/figure-html/p9a-1.png
deleted file mode 100644
index c247fd4e..00000000
--- a/docs/dev/articles/web_only/NAFTA_examples_files/figure-html/p9a-1.png
+++ /dev/null
diff --git a/docs/dev/articles/web_only/NAFTA_examples_files/figure-html/p9b-1.png b/docs/dev/articles/web_only/NAFTA_examples_files/figure-html/p9b-1.png
deleted file mode 100644
index 99d593fc..00000000
--- a/docs/dev/articles/web_only/NAFTA_examples_files/figure-html/p9b-1.png
+++ /dev/null
diff --git a/docs/dev/articles/web_only/NAFTA_examples_files/header-attrs-2.6/header-attrs.js b/docs/dev/articles/web_only/NAFTA_examples_files/header-attrs-2.6/header-attrs.js
deleted file mode 100644
index dd57d92e..00000000
--- a/docs/dev/articles/web_only/NAFTA_examples_files/header-attrs-2.6/header-attrs.js
+++ /dev/null
@@ -1,12 +0,0 @@
-// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
-// be compatible with the behavior of Pandoc < 2.8).
-document.addEventListener('DOMContentLoaded', function(e) {
-  var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
-  var i, h, a;
-  for (i = 0; i < hs.length; i++) {
-    h = hs[i];
-    if (!/^h[1-6]$/i.test(h.tagName)) continue;  // it should be a header h1-h6
-    a = h.attributes;
-    while (a.length > 0) h.removeAttribute(a[0].name);
-  }
-});
diff --git a/docs/dev/articles/web_only/benchmarks.html b/docs/dev/articles/web_only/benchmarks.html
deleted file mode 100644
index 87bdd55f..00000000
--- a/docs/dev/articles/web_only/benchmarks.html
+++ /dev/null
@@ -1,1002 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en">
-<head>
-<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
-<meta charset="utf-8">
-<meta http-equiv="X-UA-Compatible" content="IE=edge">
-<meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>Benchmark timings for mkin • mkin</title>
-<!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous">
-<script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../../bootstrap-toc.css">
-<script src="../../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous">
-<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous">
-<!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../../pkgdown.css" rel="stylesheet">
-<script src="../../pkgdown.js"></script><meta property="og:title" content="Benchmark timings for mkin">
-<meta property="og:description" content="mkin">
-<meta name="robots" content="noindex">
-<!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]-->
-</head>
-<body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-article">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav">
-<li>
-  <a href="../../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu">
-<li>
-      <a href="../../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul>
-</li>
-<li>
-  <a href="../../news/index.html">News</a>
-</li>
-      </ul>
-<ul class="nav navbar-nav navbar-right">
-<li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul>
-</div>
-<!--/.nav-collapse -->
-  </div>
-<!--/.container -->
-</div>
-<!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header toc-ignore">
-      <h1 data-toc-skip>Benchmark timings for mkin</h1>
-                        <h4 data-toc-skip class="author">Johannes
-Ranke</h4>
-            
-            <h4 data-toc-skip class="date">Last change 17 February 2023
-(rebuilt 2023-04-16)</h4>
-      
-      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/web_only/benchmarks.rmd" class="external-link"><code>vignettes/web_only/benchmarks.rmd</code></a></small>
-      <div class="hidden name"><code>benchmarks.rmd</code></div>
-
-    </div>
-
-    
-    
-<p>Each system is characterized by the operating system type, the CPU
-type, the mkin version, and, as in June 2022 the current R version lead
-to worse performance, the R version. A compiler was available, so if no
-analytical solution was available, compiled ODE models are used.</p>
-<p>Every fit is only performed once, so the accuracy of the benchmarks
-is limited.</p>
-<p>The following wrapper function for <code>mmkin</code> is used because
-the way the error model is specified was changed in mkin version
-0.9.49.1.</p>
-<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/utils/packageDescription.html" class="external-link">packageVersion</a></span><span class="op">(</span><span class="st">"mkin"</span><span class="op">)</span> <span class="op">&gt;</span> <span class="st">"0.9.48.1"</span><span class="op">)</span> <span class="op">{</span></span>
-<span>  <span class="va">mmkin_bench</span> <span class="op">&lt;-</span> <span class="kw">function</span><span class="op">(</span><span class="va">models</span>, <span class="va">datasets</span>, <span class="va">error_model</span> <span class="op">=</span> <span class="st">"const"</span><span class="op">)</span> <span class="op">{</span></span>
-<span>    <span class="fu"><a href="../../reference/mmkin.html">mmkin</a></span><span class="op">(</span><span class="va">models</span>, <span class="va">datasets</span>, error_model <span class="op">=</span> <span class="va">error_model</span>, cores <span class="op">=</span> <span class="fl">1</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
-<span>  <span class="op">}</span></span>
-<span><span class="op">}</span> <span class="kw">else</span> <span class="op">{</span></span>
-<span>  <span class="va">mmkin_bench</span> <span class="op">&lt;-</span> <span class="kw">function</span><span class="op">(</span><span class="va">models</span>, <span class="va">datasets</span>, <span class="va">error_model</span> <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span> <span class="op">{</span></span>
-<span>    <span class="fu"><a href="../../reference/mmkin.html">mmkin</a></span><span class="op">(</span><span class="va">models</span>, <span class="va">datasets</span>, reweight.method <span class="op">=</span> <span class="va">error_model</span>, cores <span class="op">=</span> <span class="fl">1</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
-<span>  <span class="op">}</span></span>
-<span><span class="op">}</span></span></code></pre></div>
-<div class="section level2">
-<h2 id="test-cases">Test cases<a class="anchor" aria-label="anchor" href="#test-cases"></a>
-</h2>
-<p>Parent only:</p>
-<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">FOCUS_C</span> <span class="op">&lt;-</span> <span class="va">FOCUS_2006_C</span></span>
-<span><span class="va">FOCUS_D</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/subset.html" class="external-link">subset</a></span><span class="op">(</span><span class="va">FOCUS_2006_D</span>, <span class="va">value</span> <span class="op">!=</span> <span class="fl">0</span><span class="op">)</span></span>
-<span><span class="va">parent_datasets</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">FOCUS_C</span>, <span class="va">FOCUS_D</span><span class="op">)</span></span>
-<span></span>
-<span></span>
-<span><span class="va">t1</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/system.time.html" class="external-link">system.time</a></span><span class="op">(</span><span class="fu">mmkin_bench</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"FOMC"</span>, <span class="st">"DFOP"</span>, <span class="st">"HS"</span><span class="op">)</span>, <span class="va">parent_datasets</span><span class="op">)</span><span class="op">)</span><span class="op">[[</span><span class="st">"elapsed"</span><span class="op">]</span><span class="op">]</span></span>
-<span><span class="va">t2</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/system.time.html" class="external-link">system.time</a></span><span class="op">(</span><span class="fu">mmkin_bench</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"FOMC"</span>, <span class="st">"DFOP"</span>, <span class="st">"HS"</span><span class="op">)</span>, <span class="va">parent_datasets</span>,</span>
-<span>    error_model <span class="op">=</span> <span class="st">"tc"</span><span class="op">)</span><span class="op">)</span><span class="op">[[</span><span class="st">"elapsed"</span><span class="op">]</span><span class="op">]</span></span></code></pre></div>
-<p>One metabolite:</p>
-<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">SFO_SFO</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span></span>
-<span>  parent <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"m1"</span><span class="op">)</span>,</span>
-<span>  m1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span>
-<span><span class="va">FOMC_SFO</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span></span>
-<span>  parent <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"FOMC"</span>, <span class="st">"m1"</span><span class="op">)</span>,</span>
-<span>  m1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span>
-<span><span class="va">DFOP_SFO</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span></span>
-<span>  parent <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"FOMC"</span>, <span class="st">"m1"</span><span class="op">)</span>, <span class="co"># erroneously used FOMC twice, not fixed for consistency</span></span>
-<span>  m1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span>
-<span><span class="va">t3</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/system.time.html" class="external-link">system.time</a></span><span class="op">(</span><span class="fu">mmkin_bench</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="va">FOMC_SFO</span>, <span class="va">DFOP_SFO</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">FOCUS_D</span><span class="op">)</span><span class="op">)</span><span class="op">)</span><span class="op">[[</span><span class="st">"elapsed"</span><span class="op">]</span><span class="op">]</span></span>
-<span><span class="va">t4</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/system.time.html" class="external-link">system.time</a></span><span class="op">(</span><span class="fu">mmkin_bench</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="va">FOMC_SFO</span>, <span class="va">DFOP_SFO</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">FOCUS_D</span><span class="op">)</span>,</span>
-<span>    error_model <span class="op">=</span> <span class="st">"tc"</span><span class="op">)</span><span class="op">)</span><span class="op">[[</span><span class="st">"elapsed"</span><span class="op">]</span><span class="op">]</span></span>
-<span><span class="va">t5</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/system.time.html" class="external-link">system.time</a></span><span class="op">(</span><span class="fu">mmkin_bench</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="va">FOMC_SFO</span>, <span class="va">DFOP_SFO</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">FOCUS_D</span><span class="op">)</span>,</span>
-<span>    error_model <span class="op">=</span> <span class="st">"obs"</span><span class="op">)</span><span class="op">)</span><span class="op">[[</span><span class="st">"elapsed"</span><span class="op">]</span><span class="op">]</span></span></code></pre></div>
-<p>Two metabolites, synthetic data:</p>
-<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">m_synth_SFO_lin</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"M1"</span><span class="op">)</span>,</span>
-<span>                           M1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"M2"</span><span class="op">)</span>,</span>
-<span>                           M2 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span>
-<span>                           use_of_ff <span class="op">=</span> <span class="st">"max"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">m_synth_DFOP_par</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"M1"</span>, <span class="st">"M2"</span><span class="op">)</span><span class="op">)</span>,</span>
-<span>                           M1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span>
-<span>                           M2 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span>
-<span>                           use_of_ff <span class="op">=</span> <span class="st">"max"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">SFO_lin_a</span> <span class="op">&lt;-</span> <span class="va">synthetic_data_for_UBA_2014</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">data</span></span>
-<span></span>
-<span><span class="va">DFOP_par_c</span> <span class="op">&lt;-</span> <span class="va">synthetic_data_for_UBA_2014</span><span class="op">[[</span><span class="fl">12</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">data</span></span>
-<span></span>
-<span><span class="va">t6</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/system.time.html" class="external-link">system.time</a></span><span class="op">(</span><span class="fu">mmkin_bench</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">m_synth_SFO_lin</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">SFO_lin_a</span><span class="op">)</span><span class="op">)</span><span class="op">)</span><span class="op">[[</span><span class="st">"elapsed"</span><span class="op">]</span><span class="op">]</span></span>
-<span><span class="va">t7</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/system.time.html" class="external-link">system.time</a></span><span class="op">(</span><span class="fu">mmkin_bench</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">m_synth_DFOP_par</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">DFOP_par_c</span><span class="op">)</span><span class="op">)</span><span class="op">)</span><span class="op">[[</span><span class="st">"elapsed"</span><span class="op">]</span><span class="op">]</span></span>
-<span></span>
-<span><span class="va">t8</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/system.time.html" class="external-link">system.time</a></span><span class="op">(</span><span class="fu">mmkin_bench</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">m_synth_SFO_lin</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">SFO_lin_a</span><span class="op">)</span>,</span>
-<span>    error_model <span class="op">=</span> <span class="st">"tc"</span><span class="op">)</span><span class="op">)</span><span class="op">[[</span><span class="st">"elapsed"</span><span class="op">]</span><span class="op">]</span></span>
-<span><span class="va">t9</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/system.time.html" class="external-link">system.time</a></span><span class="op">(</span><span class="fu">mmkin_bench</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">m_synth_DFOP_par</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">DFOP_par_c</span><span class="op">)</span>,</span>
-<span>    error_model <span class="op">=</span> <span class="st">"tc"</span><span class="op">)</span><span class="op">)</span><span class="op">[[</span><span class="st">"elapsed"</span><span class="op">]</span><span class="op">]</span></span>
-<span></span>
-<span><span class="va">t10</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/system.time.html" class="external-link">system.time</a></span><span class="op">(</span><span class="fu">mmkin_bench</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">m_synth_SFO_lin</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">SFO_lin_a</span><span class="op">)</span>,</span>
-<span>    error_model <span class="op">=</span> <span class="st">"obs"</span><span class="op">)</span><span class="op">)</span><span class="op">[[</span><span class="st">"elapsed"</span><span class="op">]</span><span class="op">]</span></span>
-<span><span class="va">t11</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/system.time.html" class="external-link">system.time</a></span><span class="op">(</span><span class="fu">mmkin_bench</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">m_synth_DFOP_par</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">DFOP_par_c</span><span class="op">)</span>,</span>
-<span>    error_model <span class="op">=</span> <span class="st">"obs"</span><span class="op">)</span><span class="op">)</span><span class="op">[[</span><span class="st">"elapsed"</span><span class="op">]</span><span class="op">]</span></span></code></pre></div>
-</div>
-<div class="section level2">
-<h2 id="results">Results<a class="anchor" aria-label="anchor" href="#results"></a>
-</h2>
-<p>Benchmarks for all available error models are shown. They are
-intended for improving mkin, not for comparing CPUs or operating
-systems. All trademarks belong to their respective owners.</p>
-<div class="section level3">
-<h3 id="parent-only">Parent only<a class="anchor" aria-label="anchor" href="#parent-only"></a>
-</h3>
-<p>Constant variance (t1) and two-component error model (t2) for four
-models fitted to two datasets, i.e. eight fits for each test.</p>
-<table class="table">
-<thead><tr class="header">
-<th align="left">OS</th>
-<th align="left">CPU</th>
-<th align="left">R</th>
-<th align="left">mkin</th>
-<th align="right">t1</th>
-<th align="right">t2</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="left">Linux</td>
-<td align="left">Ryzen 7 1700</td>
-<td align="left">NA</td>
-<td align="left">0.9.48.1</td>
-<td align="right">3.610</td>
-<td align="right">11.019</td>
-</tr>
-<tr class="even">
-<td align="left">Linux</td>
-<td align="left">Ryzen 7 1700</td>
-<td align="left">NA</td>
-<td align="left">0.9.49.1</td>
-<td align="right">8.184</td>
-<td align="right">22.889</td>
-</tr>
-<tr class="odd">
-<td align="left">Linux</td>
-<td align="left">Ryzen 7 1700</td>
-<td align="left">NA</td>
-<td align="left">0.9.49.2</td>
-<td align="right">7.064</td>
-<td align="right">12.558</td>
-</tr>
-<tr class="even">
-<td align="left">Linux</td>
-<td align="left">Ryzen 7 1700</td>
-<td align="left">NA</td>
-<td align="left">0.9.49.3</td>
-<td align="right">7.296</td>
-<td align="right">21.239</td>
-</tr>
-<tr class="odd">
-<td align="left">Linux</td>
-<td align="left">Ryzen 7 1700</td>
-<td align="left">NA</td>
-<td align="left">0.9.49.4</td>
-<td align="right">5.936</td>
-<td align="right">20.545</td>
-</tr>
-<tr class="even">
-<td align="left">Linux</td>
-<td align="left">Ryzen 7 1700</td>
-<td align="left">NA</td>
-<td align="left">0.9.50.2</td>
-<td align="right">1.714</td>
-<td align="right">3.971</td>
-</tr>
-<tr class="odd">
-<td align="left">Linux</td>
-<td align="left">Ryzen 7 1700</td>
-<td align="left">NA</td>
-<td align="left">0.9.50.3</td>
-<td align="right">1.752</td>
-<td align="right">4.156</td>
-</tr>
-<tr class="even">
-<td align="left">Linux</td>
-<td align="left">Ryzen 7 1700</td>
-<td align="left">NA</td>
-<td align="left">0.9.50.4</td>
-<td align="right">1.786</td>
-<td align="right">3.729</td>
-</tr>
-<tr class="odd">
-<td align="left">Linux</td>
-<td align="left">Ryzen 7 1700</td>
-<td align="left">NA</td>
-<td align="left">1.0.3</td>
-<td align="right">1.881</td>
-<td align="right">3.504</td>
-</tr>
-<tr class="even">
-<td align="left">Linux</td>
-<td align="left">Ryzen 7 1700</td>
-<td align="left">NA</td>
-<td align="left">1.0.4</td>
-<td align="right">1.867</td>
-<td align="right">3.450</td>
-</tr>
-<tr class="odd">
-<td align="left">Linux</td>
-<td align="left">Ryzen 7 1700</td>
-<td align="left">4.1.3</td>
-<td align="left">1.1.0</td>
-<td align="right">1.791</td>
-<td align="right">3.289</td>
-</tr>
-<tr class="even">
-<td align="left">Linux</td>
-<td align="left">Ryzen 7 1700</td>
-<td align="left">4.2.1</td>
-<td align="left">1.1.0</td>
-<td align="right">1.842</td>
-<td align="right">3.453</td>
-</tr>
-<tr class="odd">
-<td align="left">Linux</td>
-<td align="left">i7-4710MQ</td>
-<td align="left">4.2.1</td>
-<td align="left">1.1.0</td>
-<td align="right">1.959</td>
-<td align="right">4.116</td>
-</tr>
-<tr class="even">
-<td align="left">Linux</td>
-<td align="left">i7-4710MQ</td>
-<td align="left">4.1.3</td>
-<td align="left">1.1.0</td>
-<td align="right">1.877</td>
-<td align="right">3.906</td>
-</tr>
-<tr class="odd">
-<td align="left">Linux</td>
-<td align="left">i7-4710MQ</td>
-<td align="left">4.2.1</td>
-<td align="left">1.1.1</td>
-<td align="right">1.644</td>
-<td align="right">3.172</td>
-</tr>
-<tr class="even">
-<td align="left">Linux</td>
-<td align="left">Ryzen 7 1700</td>
-<td align="left">4.2.1</td>
-<td align="left">1.1.1</td>
-<td align="right">1.770</td>
-<td align="right">3.377</td>
-</tr>
-<tr class="odd">
-<td align="left">Linux</td>
-<td align="left">Ryzen 7 1700</td>
-<td align="left">4.2.1</td>
-<td align="left">1.1.2</td>
-<td align="right">1.957</td>
-<td align="right">3.633</td>
-</tr>
-<tr class="even">
-<td align="left">Linux</td>
-<td align="left">Ryzen 7 1700</td>
-<td align="left">4.2.2</td>
-<td align="left">1.2.0</td>
-<td align="right">2.140</td>
-<td align="right">3.774</td>
-</tr>
-<tr class="odd">
-<td align="left">Linux</td>
-<td align="left">Ryzen 7 1700</td>
-<td align="left">4.2.2</td>
-<td align="left">1.2.2</td>
-<td align="right">2.187</td>
-<td align="right">3.851</td>
-</tr>
-<tr class="even">
-<td align="left">Linux</td>
-<td align="left">Ryzen 9 7950X</td>
-<td align="left">4.2.2</td>
-<td align="left">1.2.0</td>
-<td align="right">1.288</td>
-<td align="right">1.794</td>
-</tr>
-<tr class="odd">
-<td align="left">Linux</td>
-<td align="left">Ryzen 9 7950X</td>
-<td align="left">4.2.2</td>
-<td align="left">1.2.2</td>
-<td align="right">1.276</td>
-<td align="right">1.804</td>
-</tr>
-<tr class="even">
-<td align="left">Linux</td>
-<td align="left">Ryzen 9 7950X</td>
-<td align="left">4.2.2</td>
-<td align="left">1.2.3</td>
-<td align="right">1.370</td>
-<td align="right">1.883</td>
-</tr>
-<tr class="odd">
-<td align="left">Linux</td>
-<td align="left">Ryzen 9 7950X</td>
-<td align="left">4.2.3</td>
-<td align="left">1.2.3</td>
-<td align="right">1.404</td>
-<td align="right">1.933</td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="section level3">
-<h3 id="one-metabolite">One metabolite<a class="anchor" aria-label="anchor" href="#one-metabolite"></a>
-</h3>
-<p>Constant variance (t3), two-component error model (t4), and variance
-by variable (t5) for three models fitted to one dataset, i.e. three fits
-for each test.</p>
-<table class="table">
-<thead><tr class="header">
-<th align="left">OS</th>
-<th align="left">CPU</th>
-<th align="left">R</th>
-<th align="left">mkin</th>
-<th align="right">t3</th>
-<th align="right">t4</th>
-<th align="right">t5</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="left">Linux</td>
-<td align="left">Ryzen 7 1700</td>
-<td align="left">NA</td>
-<td align="left">0.9.48.1</td>
-<td align="right">3.764</td>
-<td align="right">14.347</td>
-<td align="right">9.495</td>
-</tr>
-<tr class="even">
-<td align="left">Linux</td>
-<td align="left">Ryzen 7 1700</td>
-<td align="left">NA</td>
-<td align="left">0.9.49.1</td>
-<td align="right">4.649</td>
-<td align="right">13.789</td>
-<td align="right">6.395</td>
-</tr>
-<tr class="odd">
-<td align="left">Linux</td>
-<td align="left">Ryzen 7 1700</td>
-<td align="left">NA</td>
-<td align="left">0.9.49.2</td>
-<td align="right">4.786</td>
-<td align="right">8.461</td>
-<td align="right">5.675</td>
-</tr>
-<tr class="even">
-<td align="left">Linux</td>
-<td align="left">Ryzen 7 1700</td>
-<td align="left">NA</td>
-<td align="left">0.9.49.3</td>
-<td align="right">4.510</td>
-<td align="right">13.805</td>
-<td align="right">7.386</td>
-</tr>
-<tr class="odd">
-<td align="left">Linux</td>
-<td align="left">Ryzen 7 1700</td>
-<td align="left">NA</td>
-<td align="left">0.9.49.4</td>
-<td align="right">4.446</td>
-<td align="right">15.335</td>
-<td align="right">6.002</td>
-</tr>
-<tr class="even">
-<td align="left">Linux</td>
-<td align="left">Ryzen 7 1700</td>
-<td align="left">NA</td>
-<td align="left">0.9.50.2</td>
-<td align="right">1.402</td>
-<td align="right">6.174</td>
-<td align="right">2.764</td>
-</tr>
-<tr class="odd">
-<td align="left">Linux</td>
-<td align="left">Ryzen 7 1700</td>
-<td align="left">NA</td>
-<td align="left">0.9.50.3</td>
-<td align="right">1.430</td>
-<td align="right">6.615</td>
-<td align="right">2.878</td>
-</tr>
-<tr class="even">
-<td align="left">Linux</td>
-<td align="left">Ryzen 7 1700</td>
-<td align="left">NA</td>
-<td align="left">0.9.50.4</td>
-<td align="right">1.397</td>
-<td align="right">7.251</td>
-<td align="right">2.810</td>
-</tr>
-<tr class="odd">
-<td align="left">Linux</td>
-<td align="left">Ryzen 7 1700</td>
-<td align="left">NA</td>
-<td align="left">1.0.3</td>
-<td align="right">1.430</td>
-<td align="right">6.344</td>
-<td align="right">2.798</td>
-</tr>
-<tr class="even">
-<td align="left">Linux</td>
-<td align="left">Ryzen 7 1700</td>
-<td align="left">NA</td>
-<td align="left">1.0.4</td>
-<td align="right">1.415</td>
-<td align="right">6.364</td>
-<td align="right">2.820</td>
-</tr>
-<tr class="odd">
-<td align="left">Linux</td>
-<td align="left">Ryzen 7 1700</td>
-<td align="left">4.1.3</td>
-<td align="left">1.1.0</td>
-<td align="right">1.310</td>
-<td align="right">6.279</td>
-<td align="right">2.681</td>
-</tr>
-<tr class="even">
-<td align="left">Linux</td>
-<td align="left">Ryzen 7 1700</td>
-<td align="left">4.2.1</td>
-<td align="left">1.1.0</td>
-<td align="right">3.802</td>
-<td align="right">21.247</td>
-<td align="right">8.461</td>
-</tr>
-<tr class="odd">
-<td align="left">Linux</td>
-<td align="left">i7-4710MQ</td>
-<td align="left">4.2.1</td>
-<td align="left">1.1.0</td>
-<td align="right">3.334</td>
-<td align="right">19.521</td>
-<td align="right">7.565</td>
-</tr>
-<tr class="even">
-<td align="left">Linux</td>
-<td align="left">i7-4710MQ</td>
-<td align="left">4.1.3</td>
-<td align="left">1.1.0</td>
-<td align="right">1.578</td>
-<td align="right">8.058</td>
-<td align="right">3.339</td>
-</tr>
-<tr class="odd">
-<td align="left">Linux</td>
-<td align="left">i7-4710MQ</td>
-<td align="left">4.2.1</td>
-<td align="left">1.1.1</td>
-<td align="right">1.230</td>
-<td align="right">5.839</td>
-<td align="right">2.444</td>
-</tr>
-<tr class="even">
-<td align="left">Linux</td>
-<td align="left">Ryzen 7 1700</td>
-<td align="left">4.2.1</td>
-<td align="left">1.1.1</td>
-<td align="right">1.308</td>
-<td align="right">5.758</td>
-<td align="right">2.558</td>
-</tr>
-<tr class="odd">
-<td align="left">Linux</td>
-<td align="left">Ryzen 7 1700</td>
-<td align="left">4.2.1</td>
-<td align="left">1.1.2</td>
-<td align="right">1.503</td>
-<td align="right">6.147</td>
-<td align="right">2.803</td>
-</tr>
-<tr class="even">
-<td align="left">Linux</td>
-<td align="left">Ryzen 7 1700</td>
-<td align="left">4.2.2</td>
-<td align="left">1.2.0</td>
-<td align="right">1.554</td>
-<td align="right">6.193</td>
-<td align="right">2.843</td>
-</tr>
-<tr class="odd">
-<td align="left">Linux</td>
-<td align="left">Ryzen 7 1700</td>
-<td align="left">4.2.2</td>
-<td align="left">1.2.2</td>
-<td align="right">1.585</td>
-<td align="right">6.335</td>
-<td align="right">3.003</td>
-</tr>
-<tr class="even">
-<td align="left">Linux</td>
-<td align="left">Ryzen 9 7950X</td>
-<td align="left">4.2.2</td>
-<td align="left">1.2.0</td>
-<td align="right">0.792</td>
-<td align="right">2.378</td>
-<td align="right">1.245</td>
-</tr>
-<tr class="odd">
-<td align="left">Linux</td>
-<td align="left">Ryzen 9 7950X</td>
-<td align="left">4.2.2</td>
-<td align="left">1.2.2</td>
-<td align="right">0.784</td>
-<td align="right">2.355</td>
-<td align="right">1.233</td>
-</tr>
-<tr class="even">
-<td align="left">Linux</td>
-<td align="left">Ryzen 9 7950X</td>
-<td align="left">4.2.2</td>
-<td align="left">1.2.3</td>
-<td align="right">0.770</td>
-<td align="right">2.011</td>
-<td align="right">1.123</td>
-</tr>
-<tr class="odd">
-<td align="left">Linux</td>
-<td align="left">Ryzen 9 7950X</td>
-<td align="left">4.2.3</td>
-<td align="left">1.2.3</td>
-<td align="right">0.797</td>
-<td align="right">2.124</td>
-<td align="right">1.182</td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="section level3">
-<h3 id="two-metabolites">Two metabolites<a class="anchor" aria-label="anchor" href="#two-metabolites"></a>
-</h3>
-<p>Constant variance (t6 and t7), two-component error model (t8 and t9),
-and variance by variable (t10 and t11) for one model fitted to one
-dataset, i.e. one fit for each test.</p>
-<table class="table">
-<colgroup>
-<col width="8%">
-<col width="19%">
-<col width="8%">
-<col width="12%">
-<col width="8%">
-<col width="8%">
-<col width="8%">
-<col width="9%">
-<col width="8%">
-<col width="9%">
-</colgroup>
-<thead><tr class="header">
-<th align="left">OS</th>
-<th align="left">CPU</th>
-<th align="left">R</th>
-<th align="left">mkin</th>
-<th align="right">t6</th>
-<th align="right">t7</th>
-<th align="right">t8</th>
-<th align="right">t9</th>
-<th align="right">t10</th>
-<th align="right">t11</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="left">Linux</td>
-<td align="left">Ryzen 7 1700</td>
-<td align="left">NA</td>
-<td align="left">0.9.48.1</td>
-<td align="right">2.623</td>
-<td align="right">4.587</td>
-<td align="right">7.525</td>
-<td align="right">16.621</td>
-<td align="right">8.576</td>
-<td align="right">31.267</td>
-</tr>
-<tr class="even">
-<td align="left">Linux</td>
-<td align="left">Ryzen 7 1700</td>
-<td align="left">NA</td>
-<td align="left">0.9.49.1</td>
-<td align="right">2.542</td>
-<td align="right">4.128</td>
-<td align="right">4.632</td>
-<td align="right">8.171</td>
-<td align="right">3.676</td>
-<td align="right">5.636</td>
-</tr>
-<tr class="odd">
-<td align="left">Linux</td>
-<td align="left">Ryzen 7 1700</td>
-<td align="left">NA</td>
-<td align="left">0.9.49.2</td>
-<td align="right">2.723</td>
-<td align="right">4.478</td>
-<td align="right">4.862</td>
-<td align="right">7.618</td>
-<td align="right">3.579</td>
-<td align="right">5.574</td>
-</tr>
-<tr class="even">
-<td align="left">Linux</td>
-<td align="left">Ryzen 7 1700</td>
-<td align="left">NA</td>
-<td align="left">0.9.49.3</td>
-<td align="right">2.643</td>
-<td align="right">4.374</td>
-<td align="right">7.020</td>
-<td align="right">11.124</td>
-<td align="right">5.388</td>
-<td align="right">7.365</td>
-</tr>
-<tr class="odd">
-<td align="left">Linux</td>
-<td align="left">Ryzen 7 1700</td>
-<td align="left">NA</td>
-<td align="left">0.9.49.4</td>
-<td align="right">2.635</td>
-<td align="right">4.259</td>
-<td align="right">4.737</td>
-<td align="right">7.763</td>
-<td align="right">3.427</td>
-<td align="right">5.626</td>
-</tr>
-<tr class="even">
-<td align="left">Linux</td>
-<td align="left">Ryzen 7 1700</td>
-<td align="left">NA</td>
-<td align="left">0.9.50.2</td>
-<td align="right">0.777</td>
-<td align="right">1.236</td>
-<td align="right">1.332</td>
-<td align="right">2.872</td>
-<td align="right">2.069</td>
-<td align="right">2.987</td>
-</tr>
-<tr class="odd">
-<td align="left">Linux</td>
-<td align="left">Ryzen 7 1700</td>
-<td align="left">NA</td>
-<td align="left">0.9.50.3</td>
-<td align="right">0.858</td>
-<td align="right">1.264</td>
-<td align="right">1.333</td>
-<td align="right">2.984</td>
-<td align="right">2.113</td>
-<td align="right">3.073</td>
-</tr>
-<tr class="even">
-<td align="left">Linux</td>
-<td align="left">Ryzen 7 1700</td>
-<td align="left">NA</td>
-<td align="left">0.9.50.4</td>
-<td align="right">0.783</td>
-<td align="right">1.282</td>
-<td align="right">1.486</td>
-<td align="right">3.815</td>
-<td align="right">1.958</td>
-<td align="right">3.105</td>
-</tr>
-<tr class="odd">
-<td align="left">Linux</td>
-<td align="left">Ryzen 7 1700</td>
-<td align="left">NA</td>
-<td align="left">1.0.3</td>
-<td align="right">0.763</td>
-<td align="right">1.244</td>
-<td align="right">1.457</td>
-<td align="right">3.054</td>
-<td align="right">1.923</td>
-<td align="right">2.839</td>
-</tr>
-<tr class="even">
-<td align="left">Linux</td>
-<td align="left">Ryzen 7 1700</td>
-<td align="left">NA</td>
-<td align="left">1.0.4</td>
-<td align="right">0.785</td>
-<td align="right">1.252</td>
-<td align="right">1.466</td>
-<td align="right">3.091</td>
-<td align="right">1.936</td>
-<td align="right">2.826</td>
-</tr>
-<tr class="odd">
-<td align="left">Linux</td>
-<td align="left">Ryzen 7 1700</td>
-<td align="left">4.1.3</td>
-<td align="left">1.1.0</td>
-<td align="right">0.744</td>
-<td align="right">1.227</td>
-<td align="right">1.288</td>
-<td align="right">3.553</td>
-<td align="right">1.895</td>
-<td align="right">2.738</td>
-</tr>
-<tr class="even">
-<td align="left">Linux</td>
-<td align="left">Ryzen 7 1700</td>
-<td align="left">4.2.1</td>
-<td align="left">1.1.0</td>
-<td align="right">3.018</td>
-<td align="right">4.165</td>
-<td align="right">5.036</td>
-<td align="right">10.844</td>
-<td align="right">6.623</td>
-<td align="right">9.722</td>
-</tr>
-<tr class="odd">
-<td align="left">Linux</td>
-<td align="left">i7-4710MQ</td>
-<td align="left">4.2.1</td>
-<td align="left">1.1.0</td>
-<td align="right">2.522</td>
-<td align="right">3.792</td>
-<td align="right">4.143</td>
-<td align="right">11.268</td>
-<td align="right">5.935</td>
-<td align="right">8.728</td>
-</tr>
-<tr class="even">
-<td align="left">Linux</td>
-<td align="left">i7-4710MQ</td>
-<td align="left">4.1.3</td>
-<td align="left">1.1.0</td>
-<td align="right">0.907</td>
-<td align="right">1.535</td>
-<td align="right">1.589</td>
-<td align="right">4.544</td>
-<td align="right">2.302</td>
-<td align="right">3.463</td>
-</tr>
-<tr class="odd">
-<td align="left">Linux</td>
-<td align="left">i7-4710MQ</td>
-<td align="left">4.2.1</td>
-<td align="left">1.1.1</td>
-<td align="right">0.678</td>
-<td align="right">1.095</td>
-<td align="right">1.149</td>
-<td align="right">3.247</td>
-<td align="right">1.658</td>
-<td align="right">2.472</td>
-</tr>
-<tr class="even">
-<td align="left">Linux</td>
-<td align="left">Ryzen 7 1700</td>
-<td align="left">4.2.1</td>
-<td align="left">1.1.1</td>
-<td align="right">0.696</td>
-<td align="right">1.124</td>
-<td align="right">1.321</td>
-<td align="right">2.786</td>
-<td align="right">1.744</td>
-<td align="right">2.566</td>
-</tr>
-<tr class="odd">
-<td align="left">Linux</td>
-<td align="left">Ryzen 7 1700</td>
-<td align="left">4.2.1</td>
-<td align="left">1.1.2</td>
-<td align="right">0.861</td>
-<td align="right">1.295</td>
-<td align="right">1.507</td>
-<td align="right">3.102</td>
-<td align="right">1.961</td>
-<td align="right">2.852</td>
-</tr>
-<tr class="even">
-<td align="left">Linux</td>
-<td align="left">Ryzen 7 1700</td>
-<td align="left">4.2.2</td>
-<td align="left">1.2.0</td>
-<td align="right">0.913</td>
-<td align="right">1.345</td>
-<td align="right">1.539</td>
-<td align="right">3.011</td>
-<td align="right">1.987</td>
-<td align="right">2.802</td>
-</tr>
-<tr class="odd">
-<td align="left">Linux</td>
-<td align="left">Ryzen 7 1700</td>
-<td align="left">4.2.2</td>
-<td align="left">1.2.2</td>
-<td align="right">0.935</td>
-<td align="right">1.381</td>
-<td align="right">1.551</td>
-<td align="right">3.209</td>
-<td align="right">1.976</td>
-<td align="right">3.013</td>
-</tr>
-<tr class="even">
-<td align="left">Linux</td>
-<td align="left">Ryzen 9 7950X</td>
-<td align="left">4.2.2</td>
-<td align="left">1.2.0</td>
-<td align="right">0.445</td>
-<td align="right">0.591</td>
-<td align="right">0.660</td>
-<td align="right">1.190</td>
-<td align="right">0.814</td>
-<td align="right">1.100</td>
-</tr>
-<tr class="odd">
-<td align="left">Linux</td>
-<td align="left">Ryzen 9 7950X</td>
-<td align="left">4.2.2</td>
-<td align="left">1.2.2</td>
-<td align="right">0.443</td>
-<td align="right">0.586</td>
-<td align="right">0.661</td>
-<td align="right">1.176</td>
-<td align="right">0.803</td>
-<td align="right">1.097</td>
-</tr>
-<tr class="even">
-<td align="left">Linux</td>
-<td align="left">Ryzen 9 7950X</td>
-<td align="left">4.2.2</td>
-<td align="left">1.2.3</td>
-<td align="right">0.418</td>
-<td align="right">0.530</td>
-<td align="right">0.591</td>
-<td align="right">1.006</td>
-<td align="right">0.716</td>
-<td align="right">0.949</td>
-</tr>
-<tr class="odd">
-<td align="left">Linux</td>
-<td align="left">Ryzen 9 7950X</td>
-<td align="left">4.2.3</td>
-<td align="left">1.2.3</td>
-<td align="right">0.432</td>
-<td align="right">0.551</td>
-<td align="right">0.616</td>
-<td align="right">1.039</td>
-<td align="right">0.734</td>
-<td align="right">0.981</td>
-</tr>
-</tbody>
-</table>
-</div>
-</div>
-  </div>
-
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-
-        <nav id="toc" data-toggle="toc"><h2 data-toc-skip>Contents</h2>
-    </nav>
-</div>
-
-</div>
-
-
-
-      <footer><div class="copyright">
-  <p></p>
-<p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p>
-<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer>
-</div>
-
-  
-
-
-  
-
-  </body>
-</html>
diff --git a/docs/dev/articles/web_only/benchmarks_files/accessible-code-block-0.0.1/empty-anchor.js b/docs/dev/articles/web_only/benchmarks_files/accessible-code-block-0.0.1/empty-anchor.js
deleted file mode 100644
index ca349fd6..00000000
--- a/docs/dev/articles/web_only/benchmarks_files/accessible-code-block-0.0.1/empty-anchor.js
+++ /dev/null
@@ -1,15 +0,0 @@
-// Hide empty <a> tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) -->
-// v0.0.1
-// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020.
-
-document.addEventListener('DOMContentLoaded', function() {
-  const codeList = document.getElementsByClassName("sourceCode");
-  for (var i = 0; i < codeList.length; i++) {
-    var linkList = codeList[i].getElementsByTagName('a');
-    for (var j = 0; j < linkList.length; j++) {
-      if (linkList[j].innerHTML === "") {
-        linkList[j].setAttribute('aria-hidden', 'true');
-      }
-    }
-  }
-});
diff --git a/docs/dev/articles/web_only/benchmarks_files/anchor-sections-1.0/anchor-sections.css b/docs/dev/articles/web_only/benchmarks_files/anchor-sections-1.0/anchor-sections.css
deleted file mode 100644
index 07aee5fc..00000000
--- a/docs/dev/articles/web_only/benchmarks_files/anchor-sections-1.0/anchor-sections.css
+++ /dev/null
@@ -1,4 +0,0 @@
-/* Styles for section anchors */
-a.anchor-section {margin-left: 10px; visibility: hidden; color: inherit;}
-a.anchor-section::before {content: '#';}
-.hasAnchor:hover a.anchor-section {visibility: visible;}
diff --git a/docs/dev/articles/web_only/benchmarks_files/anchor-sections-1.0/anchor-sections.js b/docs/dev/articles/web_only/benchmarks_files/anchor-sections-1.0/anchor-sections.js
deleted file mode 100644
index 570f99a0..00000000
--- a/docs/dev/articles/web_only/benchmarks_files/anchor-sections-1.0/anchor-sections.js
+++ /dev/null
@@ -1,33 +0,0 @@
-// Anchor sections v1.0 written by Atsushi Yasumoto on Oct 3rd, 2020.
-document.addEventListener('DOMContentLoaded', function() {
-  // Do nothing if AnchorJS is used
-  if (typeof window.anchors === 'object' && anchors.hasOwnProperty('hasAnchorJSLink')) {
-    return;
-  }
-
-  const h = document.querySelectorAll('h1, h2, h3, h4, h5, h6');
-
-  // Do nothing if sections are already anchored
-  if (Array.from(h).some(x => x.classList.contains('hasAnchor'))) {
-    return null;
-  }
-
-  // Use section id when pandoc runs with --section-divs
-  const section_id = function(x) {
-    return ((x.classList.contains('section') || (x.tagName === 'SECTION'))
-            ? x.id : '');
-  };
-
-  // Add anchors
-  h.forEach(function(x) {
-    const id = x.id || section_id(x.parentElement);
-    if (id === '') {
-      return null;
-    }
-    let anchor = document.createElement('a');
-    anchor.href = '#' + id;
-    anchor.classList = ['anchor-section'];
-    x.classList.add('hasAnchor');
-    x.appendChild(anchor);
-  });
-});
diff --git a/docs/dev/articles/web_only/benchmarks_files/header-attrs-2.6/header-attrs.js b/docs/dev/articles/web_only/benchmarks_files/header-attrs-2.6/header-attrs.js
deleted file mode 100644
index dd57d92e..00000000
--- a/docs/dev/articles/web_only/benchmarks_files/header-attrs-2.6/header-attrs.js
+++ /dev/null
@@ -1,12 +0,0 @@
-// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
-// be compatible with the behavior of Pandoc < 2.8).
-document.addEventListener('DOMContentLoaded', function(e) {
-  var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
-  var i, h, a;
-  for (i = 0; i < hs.length; i++) {
-    h = hs[i];
-    if (!/^h[1-6]$/i.test(h.tagName)) continue;  // it should be a header h1-h6
-    a = h.attributes;
-    while (a.length > 0) h.removeAttribute(a[0].name);
-  }
-});
diff --git a/docs/dev/articles/web_only/compiled_models.html b/docs/dev/articles/web_only/compiled_models.html
deleted file mode 100644
index e7905860..00000000
--- a/docs/dev/articles/web_only/compiled_models.html
+++ /dev/null
@@ -1,292 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en">
-<head>
-<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
-<meta charset="utf-8">
-<meta http-equiv="X-UA-Compatible" content="IE=edge">
-<meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>Performance benefit by using compiled model definitions in mkin • mkin</title>
-<!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous">
-<script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../../bootstrap-toc.css">
-<script src="../../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous">
-<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous">
-<!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../../pkgdown.css" rel="stylesheet">
-<script src="../../pkgdown.js"></script><meta property="og:title" content="Performance benefit by using compiled model definitions in mkin">
-<meta property="og:description" content="mkin">
-<meta name="robots" content="noindex">
-<!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]-->
-</head>
-<body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-article">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav">
-<li>
-  <a href="../../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu">
-<li>
-      <a href="../../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul>
-</li>
-<li>
-  <a href="../../news/index.html">News</a>
-</li>
-      </ul>
-<ul class="nav navbar-nav navbar-right">
-<li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul>
-</div>
-<!--/.nav-collapse -->
-  </div>
-<!--/.container -->
-</div>
-<!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header toc-ignore">
-      <h1 data-toc-skip>Performance benefit by using compiled model
-definitions in mkin</h1>
-                        <h4 data-toc-skip class="author">Johannes
-Ranke</h4>
-            
-            <h4 data-toc-skip class="date">2023-04-16</h4>
-      
-      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/web_only/compiled_models.rmd" class="external-link"><code>vignettes/web_only/compiled_models.rmd</code></a></small>
-      <div class="hidden name"><code>compiled_models.rmd</code></div>
-
-    </div>
-
-    
-    
-<div class="section level2">
-<h2 id="how-to-benefit-from-compiled-models">How to benefit from compiled models<a class="anchor" aria-label="anchor" href="#how-to-benefit-from-compiled-models"></a>
-</h2>
-<p>When using an mkin version equal to or greater than 0.9-36 and a C
-compiler is available, you will see a message that the model is being
-compiled from autogenerated C code when defining a model using mkinmod.
-Starting from version 0.9.49.9, the <code><a href="../../reference/mkinmod.html">mkinmod()</a></code> function
-checks for presence of a compiler using</p>
-<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu">pkgbuild</span><span class="fu">::</span><span class="fu"><a href="https://r-lib.github.io/pkgbuild/reference/has_compiler.html" class="external-link">has_compiler</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
-<p>In previous versions, it used <code>Sys.which("gcc")</code> for this
-check.</p>
-<p>On Linux, you need to have the essential build tools like make and
-gcc or clang installed. On Debian based linux distributions, these will
-be pulled in by installing the build-essential package.</p>
-<p>On MacOS, which I do not use personally, I have had reports that a
-compiler is available by default.</p>
-<p>On Windows, you need to install Rtools and have the path to its bin
-directory in your PATH variable. You do not need to modify the PATH
-variable when installing Rtools. Instead, I would recommend to put the
-line</p>
-<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/Sys.setenv.html" class="external-link">Sys.setenv</a></span><span class="op">(</span>PATH <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste</a></span><span class="op">(</span><span class="st">"C:/Rtools/bin"</span>, <span class="fu"><a href="https://rdrr.io/r/base/Sys.getenv.html" class="external-link">Sys.getenv</a></span><span class="op">(</span><span class="st">"PATH"</span><span class="op">)</span>, sep<span class="op">=</span><span class="st">";"</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
-<p>into your .Rprofile startup file. This is just a text file with some
-R code that is executed when your R session starts. It has to be named
-.Rprofile and has to be located in your home directory, which will
-generally be your Documents folder. You can check the location of the
-home directory used by R by issuing</p>
-<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/Sys.getenv.html" class="external-link">Sys.getenv</a></span><span class="op">(</span><span class="st">"HOME"</span><span class="op">)</span></span></code></pre></div>
-</div>
-<div class="section level2">
-<h2 id="comparison-with-other-solution-methods">Comparison with other solution methods<a class="anchor" aria-label="anchor" href="#comparison-with-other-solution-methods"></a>
-</h2>
-<p>First, we build a simple degradation model for a parent compound with
-one metabolite, and we remove zero values from the dataset.</p>
-<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="st"><a href="https://pkgdown.jrwb.de/mkin/">"mkin"</a></span>, quietly <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
-<span><span class="va">SFO_SFO</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span></span>
-<span>  parent <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"m1"</span><span class="op">)</span>,</span>
-<span>  m1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## Temporary DLL for differentials generated and loaded</span></span></code></pre>
-<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">FOCUS_D</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/subset.html" class="external-link">subset</a></span><span class="op">(</span><span class="va">FOCUS_2006_D</span>, <span class="va">value</span> <span class="op">!=</span> <span class="fl">0</span><span class="op">)</span></span></code></pre></div>
-<p>We can compare the performance of the Eigenvalue based solution
-against the compiled version and the R implementation of the
-differential equations using the benchmark package. In the output of
-below code, the warnings about zero being removed from the FOCUS D
-dataset are suppressed. Since mkin version 0.9.49.11, an analytical
-solution is also implemented, which is included in the tests below.</p>
-<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="kw">if</span> <span class="op">(</span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">require</a></span><span class="op">(</span><span class="va"><a href="http://rbenchmark.googlecode.com" class="external-link">rbenchmark</a></span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
-<span>  <span class="va">b.1</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/rbenchmark/man/benchmark.html" class="external-link">benchmark</a></span><span class="op">(</span></span>
-<span>    <span class="st">"deSolve, not compiled"</span> <span class="op">=</span> <span class="fu"><a href="../../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="va">FOCUS_D</span>,</span>
-<span>       solution_type <span class="op">=</span> <span class="st">"deSolve"</span>,</span>
-<span>       use_compiled <span class="op">=</span> <span class="cn">FALSE</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>,</span>
-<span>    <span class="st">"Eigenvalue based"</span> <span class="op">=</span> <span class="fu"><a href="../../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="va">FOCUS_D</span>,</span>
-<span>       solution_type <span class="op">=</span> <span class="st">"eigen"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>,</span>
-<span>    <span class="st">"deSolve, compiled"</span> <span class="op">=</span> <span class="fu"><a href="../../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="va">FOCUS_D</span>,</span>
-<span>       solution_type <span class="op">=</span> <span class="st">"deSolve"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>,</span>
-<span>    <span class="st">"analytical"</span> <span class="op">=</span> <span class="fu"><a href="../../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="va">FOCUS_D</span>,</span>
-<span>       solution_type <span class="op">=</span> <span class="st">"analytical"</span>,</span>
-<span>       use_compiled <span class="op">=</span> <span class="cn">FALSE</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>,</span>
-<span>    replications <span class="op">=</span> <span class="fl">1</span>, order <span class="op">=</span> <span class="st">"relative"</span>,</span>
-<span>    columns <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"test"</span>, <span class="st">"replications"</span>, <span class="st">"relative"</span>, <span class="st">"elapsed"</span><span class="op">)</span><span class="op">)</span></span>
-<span>  <span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">b.1</span><span class="op">)</span></span>
-<span><span class="op">}</span> <span class="kw">else</span> <span class="op">{</span></span>
-<span>  <span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="st">"R package rbenchmark is not available"</span><span class="op">)</span></span>
-<span><span class="op">}</span></span></code></pre></div>
-<pre><code><span><span class="co">##                    test replications relative elapsed</span></span>
-<span><span class="co">## 4            analytical            1    1.000   0.105</span></span>
-<span><span class="co">## 3     deSolve, compiled            1    1.276   0.134</span></span>
-<span><span class="co">## 2      Eigenvalue based            1    1.762   0.185</span></span>
-<span><span class="co">## 1 deSolve, not compiled            1   21.914   2.301</span></span></code></pre>
-<p>We see that using the compiled model is by more than a factor of 10
-faster than using deSolve without compiled code.</p>
-</div>
-<div class="section level2">
-<h2 id="model-without-analytical-solution">Model without analytical solution<a class="anchor" aria-label="anchor" href="#model-without-analytical-solution"></a>
-</h2>
-<p>This evaluation is also taken from the example section of mkinfit. No
-analytical solution is available for this system, and now Eigenvalue
-based solution is possible, so only deSolve using with or without
-compiled code is available.</p>
-<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="kw">if</span> <span class="op">(</span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">require</a></span><span class="op">(</span><span class="va"><a href="http://rbenchmark.googlecode.com" class="external-link">rbenchmark</a></span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
-<span>  <span class="va">FOMC_SFO</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span></span>
-<span>    parent <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"FOMC"</span>, <span class="st">"m1"</span><span class="op">)</span>,</span>
-<span>    m1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span> <span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span>
-<span></span>
-<span>  <span class="va">b.2</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/rbenchmark/man/benchmark.html" class="external-link">benchmark</a></span><span class="op">(</span></span>
-<span>    <span class="st">"deSolve, not compiled"</span> <span class="op">=</span> <span class="fu"><a href="../../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">FOMC_SFO</span>, <span class="va">FOCUS_D</span>,</span>
-<span>                                      use_compiled <span class="op">=</span> <span class="cn">FALSE</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>,</span>
-<span>    <span class="st">"deSolve, compiled"</span> <span class="op">=</span> <span class="fu"><a href="../../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">FOMC_SFO</span>, <span class="va">FOCUS_D</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>,</span>
-<span>    replications <span class="op">=</span> <span class="fl">1</span>, order <span class="op">=</span> <span class="st">"relative"</span>,</span>
-<span>    columns <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"test"</span>, <span class="st">"replications"</span>, <span class="st">"relative"</span>, <span class="st">"elapsed"</span><span class="op">)</span><span class="op">)</span></span>
-<span>  <span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">b.2</span><span class="op">)</span></span>
-<span>  <span class="va">factor_FOMC_SFO</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/Round.html" class="external-link">round</a></span><span class="op">(</span><span class="va">b.2</span><span class="op">[</span><span class="st">"1"</span>, <span class="st">"relative"</span><span class="op">]</span><span class="op">)</span></span>
-<span><span class="op">}</span> <span class="kw">else</span> <span class="op">{</span></span>
-<span>  <span class="va">factor_FOMC_SFO</span> <span class="op">&lt;-</span> <span class="cn">NA</span></span>
-<span>  <span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="st">"R package benchmark is not available"</span><span class="op">)</span></span>
-<span><span class="op">}</span></span></code></pre></div>
-<pre><code><span><span class="co">## Temporary DLL for differentials generated and loaded</span></span></code></pre>
-<pre><code><span><span class="co">##                    test replications relative elapsed</span></span>
-<span><span class="co">## 2     deSolve, compiled            1    1.000   0.176</span></span>
-<span><span class="co">## 1 deSolve, not compiled            1   23.938   4.213</span></span></code></pre>
-<p>Here we get a performance benefit of a factor of 24 using the version
-of the differential equation model compiled from C code!</p>
-<p>This vignette was built with mkin 1.2.3 on</p>
-<pre><code><span><span class="co">## R version 4.2.3 (2023-03-15)</span></span>
-<span><span class="co">## Platform: x86_64-pc-linux-gnu (64-bit)</span></span>
-<span><span class="co">## Running under: Debian GNU/Linux 12 (bookworm)</span></span></code></pre>
-<pre><code><span><span class="co">## CPU model: AMD Ryzen 9 7950X 16-Core Processor</span></span></code></pre>
-</div>
-  </div>
-
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-
-        <nav id="toc" data-toggle="toc"><h2 data-toc-skip>Contents</h2>
-    </nav>
-</div>
-
-</div>
-
-
-
-      <footer><div class="copyright">
-  <p></p>
-<p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p>
-<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer>
-</div>
-
-  
-
-
-  
-
-  </body>
-</html>
diff --git a/docs/dev/articles/web_only/compiled_models_files/accessible-code-block-0.0.1/empty-anchor.js b/docs/dev/articles/web_only/compiled_models_files/accessible-code-block-0.0.1/empty-anchor.js
deleted file mode 100644
index ca349fd6..00000000
--- a/docs/dev/articles/web_only/compiled_models_files/accessible-code-block-0.0.1/empty-anchor.js
+++ /dev/null
@@ -1,15 +0,0 @@
-// Hide empty <a> tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) -->
-// v0.0.1
-// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020.
-
-document.addEventListener('DOMContentLoaded', function() {
-  const codeList = document.getElementsByClassName("sourceCode");
-  for (var i = 0; i < codeList.length; i++) {
-    var linkList = codeList[i].getElementsByTagName('a');
-    for (var j = 0; j < linkList.length; j++) {
-      if (linkList[j].innerHTML === "") {
-        linkList[j].setAttribute('aria-hidden', 'true');
-      }
-    }
-  }
-});
diff --git a/docs/dev/articles/web_only/compiled_models_files/anchor-sections-1.0/anchor-sections.css b/docs/dev/articles/web_only/compiled_models_files/anchor-sections-1.0/anchor-sections.css
deleted file mode 100644
index 07aee5fc..00000000
--- a/docs/dev/articles/web_only/compiled_models_files/anchor-sections-1.0/anchor-sections.css
+++ /dev/null
@@ -1,4 +0,0 @@
-/* Styles for section anchors */
-a.anchor-section {margin-left: 10px; visibility: hidden; color: inherit;}
-a.anchor-section::before {content: '#';}
-.hasAnchor:hover a.anchor-section {visibility: visible;}
diff --git a/docs/dev/articles/web_only/compiled_models_files/anchor-sections-1.0/anchor-sections.js b/docs/dev/articles/web_only/compiled_models_files/anchor-sections-1.0/anchor-sections.js
deleted file mode 100644
index 570f99a0..00000000
--- a/docs/dev/articles/web_only/compiled_models_files/anchor-sections-1.0/anchor-sections.js
+++ /dev/null
@@ -1,33 +0,0 @@
-// Anchor sections v1.0 written by Atsushi Yasumoto on Oct 3rd, 2020.
-document.addEventListener('DOMContentLoaded', function() {
-  // Do nothing if AnchorJS is used
-  if (typeof window.anchors === 'object' && anchors.hasOwnProperty('hasAnchorJSLink')) {
-    return;
-  }
-
-  const h = document.querySelectorAll('h1, h2, h3, h4, h5, h6');
-
-  // Do nothing if sections are already anchored
-  if (Array.from(h).some(x => x.classList.contains('hasAnchor'))) {
-    return null;
-  }
-
-  // Use section id when pandoc runs with --section-divs
-  const section_id = function(x) {
-    return ((x.classList.contains('section') || (x.tagName === 'SECTION'))
-            ? x.id : '');
-  };
-
-  // Add anchors
-  h.forEach(function(x) {
-    const id = x.id || section_id(x.parentElement);
-    if (id === '') {
-      return null;
-    }
-    let anchor = document.createElement('a');
-    anchor.href = '#' + id;
-    anchor.classList = ['anchor-section'];
-    x.classList.add('hasAnchor');
-    x.appendChild(anchor);
-  });
-});
diff --git a/docs/dev/articles/web_only/compiled_models_files/header-attrs-2.6/header-attrs.js b/docs/dev/articles/web_only/compiled_models_files/header-attrs-2.6/header-attrs.js
deleted file mode 100644
index dd57d92e..00000000
--- a/docs/dev/articles/web_only/compiled_models_files/header-attrs-2.6/header-attrs.js
+++ /dev/null
@@ -1,12 +0,0 @@
-// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
-// be compatible with the behavior of Pandoc < 2.8).
-document.addEventListener('DOMContentLoaded', function(e) {
-  var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
-  var i, h, a;
-  for (i = 0; i < hs.length; i++) {
-    h = hs[i];
-    if (!/^h[1-6]$/i.test(h.tagName)) continue;  // it should be a header h1-h6
-    a = h.attributes;
-    while (a.length > 0) h.removeAttribute(a[0].name);
-  }
-});
diff --git a/docs/dev/articles/web_only/dimethenamid_2018.html b/docs/dev/articles/web_only/dimethenamid_2018.html
deleted file mode 100644
index eb4a11a5..00000000
--- a/docs/dev/articles/web_only/dimethenamid_2018.html
+++ /dev/null
@@ -1,710 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en">
-<head>
-<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
-<meta charset="utf-8">
-<meta http-equiv="X-UA-Compatible" content="IE=edge">
-<meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>Example evaluations of the dimethenamid data from 2018 • mkin</title>
-<!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous">
-<script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../../bootstrap-toc.css">
-<script src="../../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous">
-<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous">
-<!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../../pkgdown.css" rel="stylesheet">
-<script src="../../pkgdown.js"></script><meta property="og:title" content="Example evaluations of the dimethenamid data from 2018">
-<meta property="og:description" content="mkin">
-<meta name="robots" content="noindex">
-<!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]-->
-</head>
-<body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-article">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav">
-<li>
-  <a href="../../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu">
-<li>
-      <a href="../../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul>
-</li>
-<li>
-  <a href="../../news/index.html">News</a>
-</li>
-      </ul>
-<ul class="nav navbar-nav navbar-right">
-<li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul>
-</div>
-<!--/.nav-collapse -->
-  </div>
-<!--/.container -->
-</div>
-<!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header toc-ignore">
-      <h1 data-toc-skip>Example evaluations of the dimethenamid data
-from 2018</h1>
-                        <h4 data-toc-skip class="author">Johannes
-Ranke</h4>
-            
-            <h4 data-toc-skip class="date">Last change 1 July 2022,
-built on 16 Apr 2023</h4>
-      
-      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/web_only/dimethenamid_2018.rmd" class="external-link"><code>vignettes/web_only/dimethenamid_2018.rmd</code></a></small>
-      <div class="hidden name"><code>dimethenamid_2018.rmd</code></div>
-
-    </div>
-
-    
-    
-<p><a href="http://www.jrwb.de" class="external-link">Wissenschaftlicher Berater, Kronacher
-Str. 12, 79639 Grenzach-Wyhlen, Germany</a></p>
-<div class="section level2">
-<h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a>
-</h2>
-<p>A first analysis of the data analysed here was presented in a recent
-journal article on nonlinear mixed-effects models in degradation
-kinetics <span class="citation">(Ranke et al. 2021)</span>. That
-analysis was based on the <code>nlme</code> package and a development
-version of the <code>saemix</code> package that was unpublished at the
-time. Meanwhile, version 3.0 of the <code>saemix</code> package is
-available from the CRAN repository. Also, it turned out that there was
-an error in the handling of the Borstel data in the mkin package at the
-time, leading to the duplication of a few data points from that soil.
-The dataset in the mkin package has been corrected, and the interface to
-<code>saemix</code> in the mkin package has been updated to use the
-released version.</p>
-<p>This vignette is intended to present an up to date analysis of the
-data, using the corrected dataset and released versions of
-<code>mkin</code> and <code>saemix</code>.</p>
-</div>
-<div class="section level2">
-<h2 id="data">Data<a class="anchor" aria-label="anchor" href="#data"></a>
-</h2>
-<p>Residue data forming the basis for the endpoints derived in the
-conclusion on the peer review of the pesticide risk assessment of
-dimethenamid-P published by the European Food Safety Authority (EFSA) in
-2018 <span class="citation">(EFSA 2018)</span> were transcribed from the
-risk assessment report <span class="citation">(Rapporteur Member State
-Germany, Co-Rapporteur Member State Bulgaria 2018)</span> which can be
-downloaded from the Open EFSA repository <a href="https://open.efsa.europa.eu" class="external-link">https://open.efsa.europa.eu/study-inventory/EFSA-Q-2014-00716</a>.</p>
-<p>The data are <a href="https://pkgdown.jrwb.de/mkin/reference/dimethenamid_2018.html">available
-in the mkin package</a>. The following code (hidden by default, please
-use the button to the right to show it) treats the data available for
-the racemic mixture dimethenamid (DMTA) and its enantiomer
-dimethenamid-P (DMTAP) in the same way, as no difference between their
-degradation behaviour was identified in the EU risk assessment. The
-observation times of each dataset are multiplied with the corresponding
-normalisation factor also available in the dataset, in order to make it
-possible to describe all datasets with a single set of parameters.</p>
-<p>Also, datasets observed in the same soil are merged, resulting in
-dimethenamid (DMTA) data from six soils.</p>
-<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://pkgdown.jrwb.de/mkin/">mkin</a></span>, quietly <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
-<span><span class="va">dmta_ds</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fl">7</span>, <span class="kw">function</span><span class="op">(</span><span class="va">i</span><span class="op">)</span> <span class="op">{</span></span>
-<span>  <span class="va">ds_i</span> <span class="op">&lt;-</span> <span class="va">dimethenamid_2018</span><span class="op">$</span><span class="va">ds</span><span class="op">[[</span><span class="va">i</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">data</span></span>
-<span>  <span class="va">ds_i</span><span class="op">[</span><span class="va">ds_i</span><span class="op">$</span><span class="va">name</span> <span class="op">==</span> <span class="st">"DMTAP"</span>, <span class="st">"name"</span><span class="op">]</span> <span class="op">&lt;-</span>  <span class="st">"DMTA"</span></span>
-<span>  <span class="va">ds_i</span><span class="op">$</span><span class="va">time</span> <span class="op">&lt;-</span> <span class="va">ds_i</span><span class="op">$</span><span class="va">time</span> <span class="op">*</span> <span class="va">dimethenamid_2018</span><span class="op">$</span><span class="va">f_time_norm</span><span class="op">[</span><span class="va">i</span><span class="op">]</span></span>
-<span>  <span class="va">ds_i</span></span>
-<span><span class="op">}</span><span class="op">)</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">dmta_ds</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">sapply</a></span><span class="op">(</span><span class="va">dimethenamid_2018</span><span class="op">$</span><span class="va">ds</span>, <span class="kw">function</span><span class="op">(</span><span class="va">ds</span><span class="op">)</span> <span class="va">ds</span><span class="op">$</span><span class="va">title</span><span class="op">)</span></span>
-<span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot"</span><span class="op">]</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/cbind.html" class="external-link">rbind</a></span><span class="op">(</span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 1"</span><span class="op">]</span><span class="op">]</span>, <span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 2"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span>
-<span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 1"</span><span class="op">]</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="cn">NULL</span></span>
-<span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 2"</span><span class="op">]</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="cn">NULL</span></span></code></pre></div>
-</div>
-<div class="section level2">
-<h2 id="parent-degradation">Parent degradation<a class="anchor" aria-label="anchor" href="#parent-degradation"></a>
-</h2>
-<p>We evaluate the observed degradation of the parent compound using
-simple exponential decline (SFO) and biexponential decline (DFOP), using
-constant variance (const) and a two-component variance (tc) as error
-models.</p>
-<div class="section level3">
-<h3 id="separate-evaluations">Separate evaluations<a class="anchor" aria-label="anchor" href="#separate-evaluations"></a>
-</h3>
-<p>As a first step, to get a visual impression of the fit of the
-different models, we do separate evaluations for each soil using the
-mmkin function from the mkin package:</p>
-<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">f_parent_mkin_const</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"DFOP"</span><span class="op">)</span>, <span class="va">dmta_ds</span>,</span>
-<span>  error_model <span class="op">=</span> <span class="st">"const"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
-<span><span class="va">f_parent_mkin_tc</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"DFOP"</span><span class="op">)</span>, <span class="va">dmta_ds</span>,</span>
-<span>  error_model <span class="op">=</span> <span class="st">"tc"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
-<p>The plot of the individual SFO fits shown below suggests that at
-least in some datasets the degradation slows down towards later time
-points, and that the scatter of the residuals error is smaller for
-smaller values (panel to the right):</p>
-<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="fu"><a href="../../reference/mixed.html">mixed</a></span><span class="op">(</span><span class="va">f_parent_mkin_const</span><span class="op">[</span><span class="st">"SFO"</span>, <span class="op">]</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
-<p><img src="dimethenamid_2018_files/figure-html/f_parent_mkin_sfo_const-1.png" width="700"></p>
-<p>Using biexponential decline (DFOP) results in a slightly more random
-scatter of the residuals:</p>
-<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="fu"><a href="../../reference/mixed.html">mixed</a></span><span class="op">(</span><span class="va">f_parent_mkin_const</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
-<p><img src="dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_const-1.png" width="700"></p>
-<p>The population curve (bold line) in the above plot results from
-taking the mean of the individual transformed parameters, i.e. of log k1
-and log k2, as well as of the logit of the g parameter of the DFOP
-model). Here, this procedure does not result in parameters that
-represent the degradation well, because in some datasets the fitted
-value for k2 is extremely close to zero, leading to a log k2 value that
-dominates the average. This is alleviated if only rate constants that
-pass the t-test for significant difference from zero (on the
-untransformed scale) are considered in the averaging:</p>
-<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="fu"><a href="../../reference/mixed.html">mixed</a></span><span class="op">(</span><span class="va">f_parent_mkin_const</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span><span class="op">)</span>, test_log_parms <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
-<p><img src="dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_const_test-1.png" width="700"></p>
-<p>While this is visually much more satisfactory, such an average
-procedure could introduce a bias, as not all results from the individual
-fits enter the population curve with the same weight. This is where
-nonlinear mixed-effects models can help out by treating all datasets
-with equally by fitting a parameter distribution model together with the
-degradation model and the error model (see below).</p>
-<p>The remaining trend of the residuals to be higher for higher
-predicted residues is reduced by using the two-component error
-model:</p>
-<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="fu"><a href="../../reference/mixed.html">mixed</a></span><span class="op">(</span><span class="va">f_parent_mkin_tc</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span><span class="op">)</span>, test_log_parms <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
-<p><img src="dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_tc_test-1.png" width="700"></p>
-<p>However, note that in the case of using this error model, the fits to
-the Flaach and BBA 2.3 datasets appear to be ill-defined, indicated by
-the fact that they did not converge:</p>
-<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">f_parent_mkin_tc</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span><span class="op">)</span></span></code></pre></div>
-<pre><code>&lt;mmkin&gt; object
-Status of individual fits:
-
-      dataset
-model  Calke Borstel Flaach BBA 2.2 BBA 2.3 Elliot
-  DFOP OK    OK      C      OK      C       OK    
-
-C: Optimisation did not converge:
-iteration limit reached without convergence (10)
-OK: No warnings</code></pre>
-</div>
-<div class="section level3">
-<h3 id="nonlinear-mixed-effects-models">Nonlinear mixed-effects models<a class="anchor" aria-label="anchor" href="#nonlinear-mixed-effects-models"></a>
-</h3>
-<p>Instead of taking a model selection decision for each of the
-individual fits, we fit nonlinear mixed-effects models (using different
-fitting algorithms as implemented in different packages) and do model
-selection using all available data at the same time. In order to make
-sure that these decisions are not unduly influenced by the type of
-algorithm used, by implementation details or by the use of wrong control
-parameters, we compare the model selection results obtained with
-different R packages, with different algorithms and checking control
-parameters.</p>
-<div class="section level4">
-<h4 id="nlme">nlme<a class="anchor" aria-label="anchor" href="#nlme"></a>
-</h4>
-<p>The nlme package was the first R extension providing facilities to
-fit nonlinear mixed-effects models. We would like to do model selection
-from all four combinations of degradation models and error models based
-on the AIC. However, fitting the DFOP model with constant variance and
-using default control parameters results in an error, signalling that
-the maximum number of 50 iterations was reached, potentially indicating
-overparameterisation. Nevertheless, the algorithm converges when the
-two-component error model is used in combination with the DFOP model.
-This can be explained by the fact that the smaller residues observed at
-later sampling times get more weight when using the two-component error
-model which will counteract the tendency of the algorithm to try
-parameter combinations unsuitable for fitting these data.</p>
-<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://svn.r-project.org/R-packages/trunk/nlme/" class="external-link">nlme</a></span><span class="op">)</span></span>
-<span><span class="va">f_parent_nlme_sfo_const</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a></span><span class="op">(</span><span class="va">f_parent_mkin_const</span><span class="op">[</span><span class="st">"SFO"</span>, <span class="op">]</span><span class="op">)</span></span>
-<span><span class="co"># f_parent_nlme_dfop_const &lt;- nlme(f_parent_mkin_const["DFOP", ])</span></span>
-<span><span class="va">f_parent_nlme_sfo_tc</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a></span><span class="op">(</span><span class="va">f_parent_mkin_tc</span><span class="op">[</span><span class="st">"SFO"</span>, <span class="op">]</span><span class="op">)</span></span>
-<span><span class="va">f_parent_nlme_dfop_tc</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a></span><span class="op">(</span><span class="va">f_parent_mkin_tc</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span><span class="op">)</span></span></code></pre></div>
-<p>Note that a certain degree of overparameterisation is also indicated
-by a warning obtained when fitting DFOP with the two-component error
-model (‘false convergence’ in the ‘LME step’ in iteration 3). However,
-as this warning does not occur in later iterations, and specifically not
-in the last of the 5 iterations, we can ignore this warning.</p>
-<p>The model comparison function of the nlme package can directly be
-applied to these fits showing a much lower AIC for the DFOP model fitted
-with the two-component error model. Also, the likelihood ratio test
-indicates that this difference is significant as the p-value is below
-0.0001.</p>
-<div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span></span>
-<span>  <span class="va">f_parent_nlme_sfo_const</span>, <span class="va">f_parent_nlme_sfo_tc</span>, <span class="va">f_parent_nlme_dfop_tc</span></span>
-<span><span class="op">)</span></span></code></pre></div>
-<pre><code>                        Model df    AIC    BIC  logLik   Test L.Ratio p-value
-f_parent_nlme_sfo_const     1  5 796.60 811.82 -393.30                       
-f_parent_nlme_sfo_tc        2  6 798.60 816.86 -393.30 1 vs 2    0.00   0.998
-f_parent_nlme_dfop_tc       3 10 671.91 702.34 -325.96 2 vs 3  134.69  &lt;.0001</code></pre>
-<p>In addition to these fits, attempts were also made to include
-correlations between random effects by using the log Cholesky
-parameterisation of the matrix specifying them. The code used for these
-attempts can be made visible below.</p>
-<div class="sourceCode" id="cb12"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">f_parent_nlme_sfo_const_logchol</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a></span><span class="op">(</span><span class="va">f_parent_mkin_const</span><span class="op">[</span><span class="st">"SFO"</span>, <span class="op">]</span>,</span>
-<span>  random <span class="op">=</span> <span class="fu">nlme</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/pkg/nlme/man/pdLogChol.html" class="external-link">pdLogChol</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">DMTA_0</span> <span class="op">~</span> <span class="fl">1</span>, <span class="va">log_k_DMTA</span> <span class="op">~</span> <span class="fl">1</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_parent_nlme_sfo_const</span>, <span class="va">f_parent_nlme_sfo_const_logchol</span><span class="op">)</span></span>
-<span><span class="va">f_parent_nlme_sfo_tc_logchol</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a></span><span class="op">(</span><span class="va">f_parent_mkin_tc</span><span class="op">[</span><span class="st">"SFO"</span>, <span class="op">]</span>,</span>
-<span>  random <span class="op">=</span> <span class="fu">nlme</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/pkg/nlme/man/pdLogChol.html" class="external-link">pdLogChol</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">DMTA_0</span> <span class="op">~</span> <span class="fl">1</span>, <span class="va">log_k_DMTA</span> <span class="op">~</span> <span class="fl">1</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_parent_nlme_sfo_tc</span>, <span class="va">f_parent_nlme_sfo_tc_logchol</span><span class="op">)</span></span>
-<span><span class="va">f_parent_nlme_dfop_tc_logchol</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a></span><span class="op">(</span><span class="va">f_parent_mkin_const</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span>,</span>
-<span>  random <span class="op">=</span> <span class="fu">nlme</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/pkg/nlme/man/pdLogChol.html" class="external-link">pdLogChol</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">DMTA_0</span> <span class="op">~</span> <span class="fl">1</span>, <span class="va">log_k1</span> <span class="op">~</span> <span class="fl">1</span>, <span class="va">log_k2</span> <span class="op">~</span> <span class="fl">1</span>, <span class="va">g_qlogis</span> <span class="op">~</span> <span class="fl">1</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_parent_nlme_dfop_tc</span>, <span class="va">f_parent_nlme_dfop_tc_logchol</span><span class="op">)</span></span></code></pre></div>
-<p>While the SFO variants converge fast, the additional parameters
-introduced by this lead to convergence warnings for the DFOP model. The
-model comparison clearly show that adding correlations between random
-effects does not improve the fits.</p>
-<p>The selected model (DFOP with two-component error) fitted to the data
-assuming no correlations between random effects is shown below.</p>
-<div class="sourceCode" id="cb13"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_parent_nlme_dfop_tc</span><span class="op">)</span></span></code></pre></div>
-<p><img src="dimethenamid_2018_files/figure-html/plot_parent_nlme-1.png" width="700"></p>
-</div>
-<div class="section level4">
-<h4 id="saemix">saemix<a class="anchor" aria-label="anchor" href="#saemix"></a>
-</h4>
-<p>The saemix package provided the first Open Source implementation of
-the Stochastic Approximation to the Expectation Maximisation (SAEM)
-algorithm. SAEM fits of degradation models can be conveniently performed
-using an interface to the saemix package available in current
-development versions of the mkin package.</p>
-<p>The corresponding SAEM fits of the four combinations of degradation
-and error models are fitted below. As there is no convergence criterion
-implemented in the saemix package, the convergence plots need to be
-manually checked for every fit. We define control settings that work
-well for all the parent data fits shown in this vignette.</p>
-<div class="sourceCode" id="cb14"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va">saemix</span><span class="op">)</span></span>
-<span><span class="va">saemix_control</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/saemix/man/saemixControl.html" class="external-link">saemixControl</a></span><span class="op">(</span>nbiter.saemix <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">800</span>, <span class="fl">300</span><span class="op">)</span>, nb.chains <span class="op">=</span> <span class="fl">15</span>,</span>
-<span>    print <span class="op">=</span> <span class="cn">FALSE</span>, save <span class="op">=</span> <span class="cn">FALSE</span>, save.graphs <span class="op">=</span> <span class="cn">FALSE</span>, displayProgress <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span>
-<span><span class="va">saemix_control_moreiter</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/saemix/man/saemixControl.html" class="external-link">saemixControl</a></span><span class="op">(</span>nbiter.saemix <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1600</span>, <span class="fl">300</span><span class="op">)</span>, nb.chains <span class="op">=</span> <span class="fl">15</span>,</span>
-<span>    print <span class="op">=</span> <span class="cn">FALSE</span>, save <span class="op">=</span> <span class="cn">FALSE</span>, save.graphs <span class="op">=</span> <span class="cn">FALSE</span>, displayProgress <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span>
-<span><span class="va">saemix_control_10k</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/saemix/man/saemixControl.html" class="external-link">saemixControl</a></span><span class="op">(</span>nbiter.saemix <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">10000</span>, <span class="fl">300</span><span class="op">)</span>, nb.chains <span class="op">=</span> <span class="fl">15</span>,</span>
-<span>    print <span class="op">=</span> <span class="cn">FALSE</span>, save <span class="op">=</span> <span class="cn">FALSE</span>, save.graphs <span class="op">=</span> <span class="cn">FALSE</span>, displayProgress <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div>
-<p>The convergence plot for the SFO model using constant variance is
-shown below.</p>
-<div class="sourceCode" id="cb15"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">f_parent_saemix_sfo_const</span> <span class="op">&lt;-</span> <span class="fu">mkin</span><span class="fu">::</span><span class="fu"><a href="../../reference/saem.html">saem</a></span><span class="op">(</span><span class="va">f_parent_mkin_const</span><span class="op">[</span><span class="st">"SFO"</span>, <span class="op">]</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>,</span>
-<span>  control <span class="op">=</span> <span class="va">saemix_control</span>, transformations <span class="op">=</span> <span class="st">"saemix"</span><span class="op">)</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_parent_saemix_sfo_const</span><span class="op">$</span><span class="va">so</span>, plot.type <span class="op">=</span> <span class="st">"convergence"</span><span class="op">)</span></span></code></pre></div>
-<p><img src="dimethenamid_2018_files/figure-html/f_parent_saemix_sfo_const-1.png" width="700"></p>
-<p>Obviously the selected number of iterations is sufficient to reach
-convergence. This can also be said for the SFO fit using the
-two-component error model.</p>
-<div class="sourceCode" id="cb16"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">f_parent_saemix_sfo_tc</span> <span class="op">&lt;-</span> <span class="fu">mkin</span><span class="fu">::</span><span class="fu"><a href="../../reference/saem.html">saem</a></span><span class="op">(</span><span class="va">f_parent_mkin_tc</span><span class="op">[</span><span class="st">"SFO"</span>, <span class="op">]</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>,</span>
-<span>  control <span class="op">=</span> <span class="va">saemix_control</span>, transformations <span class="op">=</span> <span class="st">"saemix"</span><span class="op">)</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_parent_saemix_sfo_tc</span><span class="op">$</span><span class="va">so</span>, plot.type <span class="op">=</span> <span class="st">"convergence"</span><span class="op">)</span></span></code></pre></div>
-<p><img src="dimethenamid_2018_files/figure-html/f_parent_saemix_sfo_tc-1.png" width="700"></p>
-<p>When fitting the DFOP model with constant variance (see below),
-parameter convergence is not as unambiguous.</p>
-<div class="sourceCode" id="cb17"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">f_parent_saemix_dfop_const</span> <span class="op">&lt;-</span> <span class="fu">mkin</span><span class="fu">::</span><span class="fu"><a href="../../reference/saem.html">saem</a></span><span class="op">(</span><span class="va">f_parent_mkin_const</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>,</span>
-<span>  control <span class="op">=</span> <span class="va">saemix_control</span>, transformations <span class="op">=</span> <span class="st">"saemix"</span><span class="op">)</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_parent_saemix_dfop_const</span><span class="op">$</span><span class="va">so</span>, plot.type <span class="op">=</span> <span class="st">"convergence"</span><span class="op">)</span></span></code></pre></div>
-<p><img src="dimethenamid_2018_files/figure-html/f_parent_saemix_dfop_const-1.png" width="700"></p>
-<div class="sourceCode" id="cb18"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">f_parent_saemix_dfop_const</span><span class="op">)</span></span></code></pre></div>
-<pre><code>Kinetic nonlinear mixed-effects model fit by SAEM
-Structural model:
-d_DMTA/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
-           time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))
-           * DMTA
-
-Data:
-155 observations of 1 variable(s) grouped in 6 datasets
-
-Likelihood computed by importance sampling
-  AIC BIC logLik
-  706 704   -344
-
-Fitted parameters:
-          estimate    lower   upper
-DMTA_0    97.99583 96.50079 99.4909
-k1         0.06377  0.03432  0.0932
-k2         0.00848  0.00444  0.0125
-g          0.95701  0.91313  1.0009
-a.1        1.82141  1.65122  1.9916
-SD.DMTA_0  1.64787  0.45772  2.8380
-SD.k1      0.57439  0.24731  0.9015
-SD.k2      0.03296 -2.50195  2.5679
-SD.g       1.10266  0.32369  1.8816</code></pre>
-<p>While the other parameters converge to credible values, the variance
-of k2 (<code>omega2.k2</code>) converges to a very small value. The
-printout of the <code>saem.mmkin</code> model shows that the estimated
-standard deviation of k2 across the population of soils
-(<code>SD.k2</code>) is ill-defined, indicating overparameterisation of
-this model.</p>
-<p>When the DFOP model is fitted with the two-component error model, we
-also observe that the estimated variance of k2 becomes very small, while
-being ill-defined, as illustrated by the excessive confidence interval
-of <code>SD.k2</code>.</p>
-<div class="sourceCode" id="cb20"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">f_parent_saemix_dfop_tc</span> <span class="op">&lt;-</span> <span class="fu">mkin</span><span class="fu">::</span><span class="fu"><a href="../../reference/saem.html">saem</a></span><span class="op">(</span><span class="va">f_parent_mkin_tc</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>,</span>
-<span>  control <span class="op">=</span> <span class="va">saemix_control</span>, transformations <span class="op">=</span> <span class="st">"saemix"</span><span class="op">)</span></span>
-<span><span class="va">f_parent_saemix_dfop_tc_moreiter</span> <span class="op">&lt;-</span> <span class="fu">mkin</span><span class="fu">::</span><span class="fu"><a href="../../reference/saem.html">saem</a></span><span class="op">(</span><span class="va">f_parent_mkin_tc</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>,</span>
-<span>  control <span class="op">=</span> <span class="va">saemix_control_moreiter</span>, transformations <span class="op">=</span> <span class="st">"saemix"</span><span class="op">)</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_parent_saemix_dfop_tc</span><span class="op">$</span><span class="va">so</span>, plot.type <span class="op">=</span> <span class="st">"convergence"</span><span class="op">)</span></span></code></pre></div>
-<p><img src="dimethenamid_2018_files/figure-html/f_parent_saemix_dfop_tc-1.png" width="700"></p>
-<div class="sourceCode" id="cb21"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">f_parent_saemix_dfop_tc</span><span class="op">)</span></span></code></pre></div>
-<pre><code>Kinetic nonlinear mixed-effects model fit by SAEM
-Structural model:
-d_DMTA/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
-           time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))
-           * DMTA
-
-Data:
-155 observations of 1 variable(s) grouped in 6 datasets
-
-Likelihood computed by importance sampling
-  AIC BIC logLik
-  666 664   -323
-
-Fitted parameters:
-          estimate    lower    upper
-DMTA_0    98.27617  96.3088 100.2436
-k1         0.06437   0.0337   0.0950
-k2         0.00880   0.0063   0.0113
-g          0.95249   0.9100   0.9949
-a.1        1.06161   0.8625   1.2607
-b.1        0.02967   0.0226   0.0367
-SD.DMTA_0  2.06075   0.4187   3.7028
-SD.k1      0.59357   0.2561   0.9310
-SD.k2      0.00292 -10.2960  10.3019
-SD.g       1.05725   0.3808   1.7337</code></pre>
-<p>Doubling the number of iterations in the first phase of the algorithm
-leads to a slightly lower likelihood, and therefore to slightly higher
-AIC and BIC values. With even more iterations, the algorithm stops with
-an error message. This is related to the variance of k2 approximating
-zero and has been submitted as a <a href="https://github.com/saemixdevelopment/saemixextension/issues/29" class="external-link">bug
-to the saemix package</a>, as the algorithm does not converge in this
-case.</p>
-<p>An alternative way to fit DFOP in combination with the two-component
-error model is to use the model formulation with transformed parameters
-as used per default in mkin. When using this option, convergence is
-slower, but eventually the algorithm stops as well with the same error
-message.</p>
-<p>The four combinations (SFO/const, SFO/tc, DFOP/const and DFOP/tc) and
-the version with increased iterations can be compared using the model
-comparison function of the saemix package:</p>
-<div class="sourceCode" id="cb23"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">AIC_parent_saemix</span> <span class="op">&lt;-</span> <span class="fu">saemix</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/compare.saemix.html" class="external-link">compare.saemix</a></span><span class="op">(</span></span>
-<span>  <span class="va">f_parent_saemix_sfo_const</span><span class="op">$</span><span class="va">so</span>,</span>
-<span>  <span class="va">f_parent_saemix_sfo_tc</span><span class="op">$</span><span class="va">so</span>,</span>
-<span>  <span class="va">f_parent_saemix_dfop_const</span><span class="op">$</span><span class="va">so</span>,</span>
-<span>  <span class="va">f_parent_saemix_dfop_tc</span><span class="op">$</span><span class="va">so</span>,</span>
-<span>  <span class="va">f_parent_saemix_dfop_tc_moreiter</span><span class="op">$</span><span class="va">so</span><span class="op">)</span></span></code></pre></div>
-<pre><code>Likelihoods calculated by importance sampling</code></pre>
-<div class="sourceCode" id="cb25"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/colnames.html" class="external-link">rownames</a></span><span class="op">(</span><span class="va">AIC_parent_saemix</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span></span>
-<span>  <span class="st">"SFO const"</span>, <span class="st">"SFO tc"</span>, <span class="st">"DFOP const"</span>, <span class="st">"DFOP tc"</span>, <span class="st">"DFOP tc more iterations"</span><span class="op">)</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">AIC_parent_saemix</span><span class="op">)</span></span></code></pre></div>
-<pre><code>                           AIC    BIC
-SFO const               796.38 795.34
-SFO tc                  798.38 797.13
-DFOP const              705.75 703.88
-DFOP tc                 665.65 663.57
-DFOP tc more iterations 665.88 663.80</code></pre>
-<p>In order to check the influence of the likelihood calculation
-algorithms implemented in saemix, the likelihood from Gaussian
-quadrature is added to the best fit, and the AIC values obtained from
-the three methods are compared.</p>
-<div class="sourceCode" id="cb27"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">f_parent_saemix_dfop_tc</span><span class="op">$</span><span class="va">so</span> <span class="op">&lt;-</span></span>
-<span>  <span class="fu">saemix</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/llgq.saemix.html" class="external-link">llgq.saemix</a></span><span class="op">(</span><span class="va">f_parent_saemix_dfop_tc</span><span class="op">$</span><span class="va">so</span><span class="op">)</span></span>
-<span><span class="va">AIC_parent_saemix_methods</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span></span>
-<span>  is <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">AIC</a></span><span class="op">(</span><span class="va">f_parent_saemix_dfop_tc</span><span class="op">$</span><span class="va">so</span>, method <span class="op">=</span> <span class="st">"is"</span><span class="op">)</span>,</span>
-<span>  gq <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">AIC</a></span><span class="op">(</span><span class="va">f_parent_saemix_dfop_tc</span><span class="op">$</span><span class="va">so</span>, method <span class="op">=</span> <span class="st">"gq"</span><span class="op">)</span>,</span>
-<span>  lin <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">AIC</a></span><span class="op">(</span><span class="va">f_parent_saemix_dfop_tc</span><span class="op">$</span><span class="va">so</span>, method <span class="op">=</span> <span class="st">"lin"</span><span class="op">)</span></span>
-<span><span class="op">)</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">AIC_parent_saemix_methods</span><span class="op">)</span></span></code></pre></div>
-<pre><code>    is     gq    lin 
-665.65 665.68 665.11 </code></pre>
-<p>The AIC values based on importance sampling and Gaussian quadrature
-are very similar. Using linearisation is known to be less accurate, but
-still gives a similar value.</p>
-<p>In order to illustrate that the comparison of the three method
-depends on the degree of convergence obtained in the fit, the same
-comparison is shown below for the fit using the defaults for the number
-of iterations and the number of MCMC chains.</p>
-<p>When using OpenBlas for linear algebra, there is a large difference
-in the values obtained with Gaussian quadrature, so the larger number of
-iterations makes a lot of difference. When using the LAPACK version
-coming with Debian Bullseye, the AIC based on Gaussian quadrature is
-almost the same as the one obtained with the other methods, also when
-using defaults for the fit.</p>
-<div class="sourceCode" id="cb29"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">f_parent_saemix_dfop_tc_defaults</span> <span class="op">&lt;-</span> <span class="fu">mkin</span><span class="fu">::</span><span class="fu"><a href="../../reference/saem.html">saem</a></span><span class="op">(</span><span class="va">f_parent_mkin_tc</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span><span class="op">)</span></span>
-<span><span class="va">f_parent_saemix_dfop_tc_defaults</span><span class="op">$</span><span class="va">so</span> <span class="op">&lt;-</span></span>
-<span>  <span class="fu">saemix</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/llgq.saemix.html" class="external-link">llgq.saemix</a></span><span class="op">(</span><span class="va">f_parent_saemix_dfop_tc_defaults</span><span class="op">$</span><span class="va">so</span><span class="op">)</span></span>
-<span><span class="va">AIC_parent_saemix_methods_defaults</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span></span>
-<span>  is <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">AIC</a></span><span class="op">(</span><span class="va">f_parent_saemix_dfop_tc_defaults</span><span class="op">$</span><span class="va">so</span>, method <span class="op">=</span> <span class="st">"is"</span><span class="op">)</span>,</span>
-<span>  gq <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">AIC</a></span><span class="op">(</span><span class="va">f_parent_saemix_dfop_tc_defaults</span><span class="op">$</span><span class="va">so</span>, method <span class="op">=</span> <span class="st">"gq"</span><span class="op">)</span>,</span>
-<span>  lin <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">AIC</a></span><span class="op">(</span><span class="va">f_parent_saemix_dfop_tc_defaults</span><span class="op">$</span><span class="va">so</span>, method <span class="op">=</span> <span class="st">"lin"</span><span class="op">)</span></span>
-<span><span class="op">)</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">AIC_parent_saemix_methods_defaults</span><span class="op">)</span></span></code></pre></div>
-<pre><code>    is     gq    lin 
-669.77 669.36 670.95 </code></pre>
-</div>
-</div>
-<div class="section level3">
-<h3 id="comparison">Comparison<a class="anchor" aria-label="anchor" href="#comparison"></a>
-</h3>
-<p>The following table gives the AIC values obtained with both backend
-packages using the same control parameters (800 iterations burn-in, 300
-iterations second phase, 15 chains).</p>
-<div class="sourceCode" id="cb31"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">AIC_all</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span></span>
-<span>  check.names <span class="op">=</span> <span class="cn">FALSE</span>,</span>
-<span>  <span class="st">"Degradation model"</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"SFO"</span>, <span class="st">"DFOP"</span>, <span class="st">"DFOP"</span><span class="op">)</span>,</span>
-<span>  <span class="st">"Error model"</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"const"</span>, <span class="st">"tc"</span>, <span class="st">"const"</span>, <span class="st">"tc"</span><span class="op">)</span>,</span>
-<span>  nlme <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">AIC</a></span><span class="op">(</span><span class="va">f_parent_nlme_sfo_const</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">AIC</a></span><span class="op">(</span><span class="va">f_parent_nlme_sfo_tc</span><span class="op">)</span>, <span class="cn">NA</span>, <span class="fu"><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">AIC</a></span><span class="op">(</span><span class="va">f_parent_nlme_dfop_tc</span><span class="op">)</span><span class="op">)</span>,</span>
-<span>  saemix_lin <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">sapply</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">f_parent_saemix_sfo_const</span><span class="op">$</span><span class="va">so</span>, <span class="va">f_parent_saemix_sfo_tc</span><span class="op">$</span><span class="va">so</span>,</span>
-<span>    <span class="va">f_parent_saemix_dfop_const</span><span class="op">$</span><span class="va">so</span>, <span class="va">f_parent_saemix_dfop_tc</span><span class="op">$</span><span class="va">so</span><span class="op">)</span>, <span class="va">AIC</span>, method <span class="op">=</span> <span class="st">"lin"</span><span class="op">)</span>,</span>
-<span>  saemix_is <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">sapply</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">f_parent_saemix_sfo_const</span><span class="op">$</span><span class="va">so</span>, <span class="va">f_parent_saemix_sfo_tc</span><span class="op">$</span><span class="va">so</span>,</span>
-<span>    <span class="va">f_parent_saemix_dfop_const</span><span class="op">$</span><span class="va">so</span>, <span class="va">f_parent_saemix_dfop_tc</span><span class="op">$</span><span class="va">so</span><span class="op">)</span>, <span class="va">AIC</span>, method <span class="op">=</span> <span class="st">"is"</span><span class="op">)</span></span>
-<span><span class="op">)</span></span>
-<span><span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="va">AIC_all</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
-<thead><tr class="header">
-<th align="left">Degradation model</th>
-<th align="left">Error model</th>
-<th align="right">nlme</th>
-<th align="right">saemix_lin</th>
-<th align="right">saemix_is</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="left">SFO</td>
-<td align="left">const</td>
-<td align="right">796.60</td>
-<td align="right">796.60</td>
-<td align="right">796.38</td>
-</tr>
-<tr class="even">
-<td align="left">SFO</td>
-<td align="left">tc</td>
-<td align="right">798.60</td>
-<td align="right">798.60</td>
-<td align="right">798.38</td>
-</tr>
-<tr class="odd">
-<td align="left">DFOP</td>
-<td align="left">const</td>
-<td align="right">NA</td>
-<td align="right">709.26</td>
-<td align="right">705.75</td>
-</tr>
-<tr class="even">
-<td align="left">DFOP</td>
-<td align="left">tc</td>
-<td align="right">671.91</td>
-<td align="right">665.11</td>
-<td align="right">665.65</td>
-</tr>
-</tbody>
-</table>
-</div>
-</div>
-<div class="section level2">
-<h2 id="conclusion">Conclusion<a class="anchor" aria-label="anchor" href="#conclusion"></a>
-</h2>
-<p>A more detailed analysis of the dimethenamid dataset confirmed that
-the DFOP model provides the most appropriate description of the decline
-of the parent compound in these data. On the other hand, closer
-inspection of the results revealed that the variability of the k2
-parameter across the population of soils is ill-defined. This coincides
-with the observation that this parameter cannot robustly be quantified
-for some of the soils.</p>
-<p>Regarding the regulatory use of these data, it is claimed that an
-improved characterisation of the mean parameter values across the
-population is obtained using the nonlinear mixed-effects models
-presented here. However, attempts to quantify the variability of the
-slower rate constant of the biphasic decline of dimethenamid indicate
-that the data are not sufficient to characterise this variability to a
-satisfactory precision.</p>
-</div>
-<div class="section level2">
-<h2 id="session-info">Session Info<a class="anchor" aria-label="anchor" href="#session-info"></a>
-</h2>
-<div class="sourceCode" id="cb32"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/utils/sessionInfo.html" class="external-link">sessionInfo</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
-<pre><code>R version 4.2.3 (2023-03-15)
-Platform: x86_64-pc-linux-gnu (64-bit)
-Running under: Debian GNU/Linux 12 (bookworm)
-
-Matrix products: default
-BLAS:   /usr/lib/x86_64-linux-gnu/openblas-serial/libblas.so.3
-LAPACK: /usr/lib/x86_64-linux-gnu/openblas-serial/libopenblas-r0.3.21.so
-
-locale:
- [1] LC_CTYPE=de_DE.UTF-8       LC_NUMERIC=C              
- [3] LC_TIME=C                  LC_COLLATE=de_DE.UTF-8    
- [5] LC_MONETARY=de_DE.UTF-8    LC_MESSAGES=de_DE.UTF-8   
- [7] LC_PAPER=de_DE.UTF-8       LC_NAME=C                 
- [9] LC_ADDRESS=C               LC_TELEPHONE=C            
-[11] LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=C       
-
-attached base packages:
-[1] stats     graphics  grDevices utils     datasets  methods   base     
-
-other attached packages:
-[1] saemix_3.2   npde_3.3     nlme_3.1-162 mkin_1.2.3   knitr_1.42  
-
-loaded via a namespace (and not attached):
- [1] highr_0.10        pillar_1.9.0      bslib_0.4.2       compiler_4.2.3   
- [5] jquerylib_0.1.4   tools_4.2.3       mclust_6.0.0      digest_0.6.31    
- [9] tibble_3.2.1      jsonlite_1.8.4    evaluate_0.20     memoise_2.0.1    
-[13] lifecycle_1.0.3   gtable_0.3.3      lattice_0.21-8    pkgconfig_2.0.3  
-[17] rlang_1.1.0       DBI_1.1.3         cli_3.6.1         yaml_2.3.7       
-[21] parallel_4.2.3    pkgdown_2.0.7     xfun_0.38         fastmap_1.1.1    
-[25] gridExtra_2.3     dplyr_1.1.1       stringr_1.5.0     generics_0.1.3   
-[29] desc_1.4.2        fs_1.6.1          vctrs_0.6.1       sass_0.4.5       
-[33] systemfonts_1.0.4 tidyselect_1.2.0  rprojroot_2.0.3   lmtest_0.9-40    
-[37] grid_4.2.3        glue_1.6.2        R6_2.5.1          textshaping_0.3.6
-[41] fansi_1.0.4       rmarkdown_2.21    purrr_1.0.1       ggplot2_3.4.2    
-[45] magrittr_2.0.3    codetools_0.2-19  scales_1.2.1      htmltools_0.5.5  
-[49] colorspace_2.1-0  ragg_1.2.5        utf8_1.2.3        stringi_1.7.12   
-[53] munsell_0.5.0     cachem_1.0.7      zoo_1.8-12       </code></pre>
-</div>
-<div class="section level2">
-<h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a>
-</h2>
-<!-- vim: set foldmethod=syntax: -->
-<div id="refs" class="references csl-bib-body hanging-indent">
-<div id="ref-efsa_2018_dimethenamid" class="csl-entry">
-EFSA. 2018. <span>“Peer Review of the Pesticide Risk Assessment of the
-Active Substance Dimethenamid-p.”</span> <em>EFSA Journal</em> 16: 5211.
-</div>
-<div id="ref-ranke2021" class="csl-entry">
-Ranke, Johannes, Janina Wöltjen, Jana Schmidt, and Emmanuelle Comets.
-2021. <span>“Taking Kinetic Evaluations of Degradation Data to the Next
-Level with Nonlinear Mixed-Effects Models.”</span> <em>Environments</em>
-8 (8). <a href="https://doi.org/10.3390/environments8080071" class="external-link">https://doi.org/10.3390/environments8080071</a>.
-</div>
-<div id="ref-dimethenamid_rar_2018_b8" class="csl-entry">
-Rapporteur Member State Germany, Co-Rapporteur Member State Bulgaria.
-2018. <span>“<span class="nocase">Renewal Assessment Report
-Dimethenamid-P Volume 3 - B.8 Environmental fate and behaviour, Rev. 2 -
-November 2017</span>.”</span> <a href="https://open.efsa.europa.eu/study-inventory/EFSA-Q-2014-00716" class="external-link">https://open.efsa.europa.eu/study-inventory/EFSA-Q-2014-00716</a>.
-</div>
-</div>
-</div>
-  </div>
-
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-
-        <nav id="toc" data-toggle="toc"><h2 data-toc-skip>Contents</h2>
-    </nav>
-</div>
-
-</div>
-
-
-
-      <footer><div class="copyright">
-  <p></p>
-<p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p>
-<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer>
-</div>
-
-  
-
-
-  
-
-  </body>
-</html>
diff --git a/docs/dev/articles/web_only/dimethenamid_2018_files/accessible-code-block-0.0.1/empty-anchor.js b/docs/dev/articles/web_only/dimethenamid_2018_files/accessible-code-block-0.0.1/empty-anchor.js
deleted file mode 100644
index ca349fd6..00000000
--- a/docs/dev/articles/web_only/dimethenamid_2018_files/accessible-code-block-0.0.1/empty-anchor.js
+++ /dev/null
@@ -1,15 +0,0 @@
-// Hide empty <a> tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) -->
-// v0.0.1
-// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020.
-
-document.addEventListener('DOMContentLoaded', function() {
-  const codeList = document.getElementsByClassName("sourceCode");
-  for (var i = 0; i < codeList.length; i++) {
-    var linkList = codeList[i].getElementsByTagName('a');
-    for (var j = 0; j < linkList.length; j++) {
-      if (linkList[j].innerHTML === "") {
-        linkList[j].setAttribute('aria-hidden', 'true');
-      }
-    }
-  }
-});
diff --git a/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_const-1.png b/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_const-1.png
deleted file mode 100644
index 505072ce..00000000
--- a/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_const-1.png
+++ /dev/null
diff --git a/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_const_test-1.png b/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_const_test-1.png
deleted file mode 100644
index 505072ce..00000000
--- a/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_const_test-1.png
+++ /dev/null
diff --git a/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_tc_test-1.png b/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_tc_test-1.png
deleted file mode 100644
index 0dd4da39..00000000
--- a/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_tc_test-1.png
+++ /dev/null
diff --git a/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_mkin_sfo_const-1.png b/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_mkin_sfo_const-1.png
deleted file mode 100644
index 0ed7448d..00000000
--- a/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_mkin_sfo_const-1.png
+++ /dev/null
diff --git a/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_nlmixr_saem_dfop_const-1.png b/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_nlmixr_saem_dfop_const-1.png
deleted file mode 100644
index 88089aaf..00000000
--- a/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_nlmixr_saem_dfop_const-1.png
+++ /dev/null
diff --git a/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_nlmixr_saem_dfop_tc-1.png b/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_nlmixr_saem_dfop_tc-1.png
deleted file mode 100644
index efc37a5f..00000000
--- a/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_nlmixr_saem_dfop_tc-1.png
+++ /dev/null
diff --git a/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_nlmixr_saem_dfop_tc_10k-1.png b/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_nlmixr_saem_dfop_tc_10k-1.png
deleted file mode 100644
index ab2b1b2d..00000000
--- a/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_nlmixr_saem_dfop_tc_10k-1.png
+++ /dev/null
diff --git a/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_nlmixr_saem_dfop_tc_1k-1.png b/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_nlmixr_saem_dfop_tc_1k-1.png
deleted file mode 100644
index 70987378..00000000
--- a/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_nlmixr_saem_dfop_tc_1k-1.png
+++ /dev/null
diff --git a/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_nlmixr_saem_sfo_const-1.png b/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_nlmixr_saem_sfo_const-1.png
deleted file mode 100644
index de0a0ded..00000000
--- a/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_nlmixr_saem_sfo_const-1.png
+++ /dev/null
diff --git a/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_nlmixr_saem_sfo_tc-1.png b/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_nlmixr_saem_sfo_tc-1.png
deleted file mode 100644
index 0b7f5090..00000000
--- a/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_nlmixr_saem_sfo_tc-1.png
+++ /dev/null
diff --git a/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_saemix_dfop_const-1.png b/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_saemix_dfop_const-1.png
deleted file mode 100644
index 84a6fc92..00000000
--- a/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_saemix_dfop_const-1.png
+++ /dev/null
diff --git a/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_saemix_dfop_tc-1.png b/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_saemix_dfop_tc-1.png
deleted file mode 100644
index d154dc9b..00000000
--- a/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_saemix_dfop_tc-1.png
+++ /dev/null
diff --git a/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_saemix_dfop_tc_10k-1.png b/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_saemix_dfop_tc_10k-1.png
deleted file mode 100644
index 0975126f..00000000
--- a/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_saemix_dfop_tc_10k-1.png
+++ /dev/null
diff --git a/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_saemix_dfop_tc_mkin-1.png b/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_saemix_dfop_tc_mkin-1.png
deleted file mode 100644
index 9a6547a2..00000000
--- a/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_saemix_dfop_tc_mkin-1.png
+++ /dev/null
diff --git a/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_saemix_dfop_tc_mkin_10k-1.png b/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_saemix_dfop_tc_mkin_10k-1.png
deleted file mode 100644
index ae8c1555..00000000
--- a/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_saemix_dfop_tc_mkin_10k-1.png
+++ /dev/null
diff --git a/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_saemix_dfop_tc_mkin_moreiter-1.png b/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_saemix_dfop_tc_mkin_moreiter-1.png
deleted file mode 100644
index 30ee4ea0..00000000
--- a/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_saemix_dfop_tc_mkin_moreiter-1.png
+++ /dev/null
diff --git a/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_saemix_dfop_tc_moreiter-1.png b/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_saemix_dfop_tc_moreiter-1.png
deleted file mode 100644
index 1c8fc837..00000000
--- a/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_saemix_dfop_tc_moreiter-1.png
+++ /dev/null
diff --git a/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_saemix_sfo_const-1.png b/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_saemix_sfo_const-1.png
deleted file mode 100644
index 7862fc65..00000000
--- a/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_saemix_sfo_const-1.png
+++ /dev/null
diff --git a/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_saemix_sfo_tc-1.png b/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_saemix_sfo_tc-1.png
deleted file mode 100644
index d941f3e6..00000000
--- a/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_saemix_sfo_tc-1.png
+++ /dev/null
diff --git a/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/plot_parent_nlme-1.png b/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/plot_parent_nlme-1.png
deleted file mode 100644
index a799b14c..00000000
--- a/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/plot_parent_nlme-1.png
+++ /dev/null
diff --git a/docs/dev/articles/web_only/dimethenamid_2018_files/header-attrs-2.11/header-attrs.js b/docs/dev/articles/web_only/dimethenamid_2018_files/header-attrs-2.11/header-attrs.js
deleted file mode 100644
index dd57d92e..00000000
--- a/docs/dev/articles/web_only/dimethenamid_2018_files/header-attrs-2.11/header-attrs.js
+++ /dev/null
@@ -1,12 +0,0 @@
-// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
-// be compatible with the behavior of Pandoc < 2.8).
-document.addEventListener('DOMContentLoaded', function(e) {
-  var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
-  var i, h, a;
-  for (i = 0; i < hs.length; i++) {
-    h = hs[i];
-    if (!/^h[1-6]$/i.test(h.tagName)) continue;  // it should be a header h1-h6
-    a = h.attributes;
-    while (a.length > 0) h.removeAttribute(a[0].name);
-  }
-});
diff --git a/docs/dev/articles/web_only/dimethenamid_2018_files/header-attrs-2.9/header-attrs.js b/docs/dev/articles/web_only/dimethenamid_2018_files/header-attrs-2.9/header-attrs.js
deleted file mode 100644
index dd57d92e..00000000
--- a/docs/dev/articles/web_only/dimethenamid_2018_files/header-attrs-2.9/header-attrs.js
+++ /dev/null
@@ -1,12 +0,0 @@
-// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
-// be compatible with the behavior of Pandoc < 2.8).
-document.addEventListener('DOMContentLoaded', function(e) {
-  var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
-  var i, h, a;
-  for (i = 0; i < hs.length; i++) {
-    h = hs[i];
-    if (!/^h[1-6]$/i.test(h.tagName)) continue;  // it should be a header h1-h6
-    a = h.attributes;
-    while (a.length > 0) h.removeAttribute(a[0].name);
-  }
-});
diff --git a/docs/dev/articles/web_only/multistart.html b/docs/dev/articles/web_only/multistart.html
deleted file mode 100644
index 2de5059c..00000000
--- a/docs/dev/articles/web_only/multistart.html
+++ /dev/null
@@ -1,237 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en">
-<head>
-<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
-<meta charset="utf-8">
-<meta http-equiv="X-UA-Compatible" content="IE=edge">
-<meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>Short demo of the multistart method • mkin</title>
-<!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous">
-<script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../../bootstrap-toc.css">
-<script src="../../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous">
-<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous">
-<!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../../pkgdown.css" rel="stylesheet">
-<script src="../../pkgdown.js"></script><meta property="og:title" content="Short demo of the multistart method">
-<meta property="og:description" content="mkin">
-<meta name="robots" content="noindex">
-<!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]-->
-</head>
-<body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-article">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.4</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav">
-<li>
-  <a href="../../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu">
-<li>
-      <a href="../../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul>
-</li>
-<li>
-  <a href="../../news/index.html">News</a>
-</li>
-      </ul>
-<ul class="nav navbar-nav navbar-right">
-<li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul>
-</div>
-<!--/.nav-collapse -->
-  </div>
-<!--/.container -->
-</div>
-<!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header toc-ignore">
-      <h1 data-toc-skip>Short demo of the multistart method</h1>
-                        <h4 data-toc-skip class="author">Johannes
-Ranke</h4>
-            
-            <h4 data-toc-skip class="date">Last change 20 April 2023
-(rebuilt 2023-04-20)</h4>
-      
-      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/web_only/multistart.rmd" class="external-link"><code>vignettes/web_only/multistart.rmd</code></a></small>
-      <div class="hidden name"><code>multistart.rmd</code></div>
-
-    </div>
-
-    
-    
-<p>The dimethenamid data from 2018 from seven soils is used as example
-data in this vignette.</p>
-<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://pkgdown.jrwb.de/mkin/">mkin</a></span><span class="op">)</span></span>
-<span><span class="va">dmta_ds</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fl">7</span>, <span class="kw">function</span><span class="op">(</span><span class="va">i</span><span class="op">)</span> <span class="op">{</span></span>
-<span>  <span class="va">ds_i</span> <span class="op">&lt;-</span> <span class="va">dimethenamid_2018</span><span class="op">$</span><span class="va">ds</span><span class="op">[[</span><span class="va">i</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">data</span></span>
-<span>  <span class="va">ds_i</span><span class="op">[</span><span class="va">ds_i</span><span class="op">$</span><span class="va">name</span> <span class="op">==</span> <span class="st">"DMTAP"</span>, <span class="st">"name"</span><span class="op">]</span> <span class="op">&lt;-</span>  <span class="st">"DMTA"</span></span>
-<span>  <span class="va">ds_i</span><span class="op">$</span><span class="va">time</span> <span class="op">&lt;-</span> <span class="va">ds_i</span><span class="op">$</span><span class="va">time</span> <span class="op">*</span> <span class="va">dimethenamid_2018</span><span class="op">$</span><span class="va">f_time_norm</span><span class="op">[</span><span class="va">i</span><span class="op">]</span></span>
-<span>  <span class="va">ds_i</span></span>
-<span><span class="op">}</span><span class="op">)</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">dmta_ds</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">sapply</a></span><span class="op">(</span><span class="va">dimethenamid_2018</span><span class="op">$</span><span class="va">ds</span>, <span class="kw">function</span><span class="op">(</span><span class="va">ds</span><span class="op">)</span> <span class="va">ds</span><span class="op">$</span><span class="va">title</span><span class="op">)</span></span>
-<span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot"</span><span class="op">]</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/cbind.html" class="external-link">rbind</a></span><span class="op">(</span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 1"</span><span class="op">]</span><span class="op">]</span>, <span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 2"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span>
-<span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 1"</span><span class="op">]</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 2"</span><span class="op">]</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="cn">NULL</span></span></code></pre></div>
-<p>First, we check the DFOP model with the two-component error model and
-random effects for all degradation parameters.</p>
-<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">f_mmkin</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/mmkin.html">mmkin</a></span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="va">dmta_ds</span>, error_model <span class="op">=</span> <span class="st">"tc"</span>, cores <span class="op">=</span> <span class="fl">7</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
-<span><span class="va">f_saem_full</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/saem.html">saem</a></span><span class="op">(</span><span class="va">f_mmkin</span><span class="op">)</span></span>
-<span><span class="fu"><a href="../../reference/illparms.html">illparms</a></span><span class="op">(</span><span class="va">f_saem_full</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## [1] "sd(log_k2)"</span></span></code></pre>
-<p>We see that not all variability parameters are identifiable. The
-<code>illparms</code> function tells us that the confidence interval for
-the standard deviation of ‘log_k2’ includes zero. We check this
-assessment using multiple runs with different starting values.</p>
-<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">f_saem_full_multi</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/multistart.html">multistart</a></span><span class="op">(</span><span class="va">f_saem_full</span>, n <span class="op">=</span> <span class="fl">16</span>, cores <span class="op">=</span> <span class="fl">16</span><span class="op">)</span></span>
-<span><span class="fu"><a href="../../reference/parplot.html">parplot</a></span><span class="op">(</span><span class="va">f_saem_full_multi</span>, lpos <span class="op">=</span> <span class="st">"topleft"</span><span class="op">)</span></span></code></pre></div>
-<p><img src="multistart_files/figure-html/unnamed-chunk-3-1.png" width="700"></p>
-<p>This confirms that the variance of k2 is the most problematic
-parameter, so we reduce the parameter distribution model by removing the
-intersoil variability for k2.</p>
-<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">f_saem_reduced</span> <span class="op">&lt;-</span> <span class="fu">stats</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_saem_full</span>, no_random_effect <span class="op">=</span> <span class="st">"log_k2"</span><span class="op">)</span></span>
-<span><span class="fu"><a href="../../reference/illparms.html">illparms</a></span><span class="op">(</span><span class="va">f_saem_reduced</span><span class="op">)</span></span>
-<span><span class="va">f_saem_reduced_multi</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/multistart.html">multistart</a></span><span class="op">(</span><span class="va">f_saem_reduced</span>, n <span class="op">=</span> <span class="fl">16</span>, cores <span class="op">=</span> <span class="fl">16</span><span class="op">)</span></span>
-<span><span class="fu"><a href="../../reference/parplot.html">parplot</a></span><span class="op">(</span><span class="va">f_saem_reduced_multi</span>, lpos <span class="op">=</span> <span class="st">"topright"</span>, ylim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.5</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
-<p><img src="multistart_files/figure-html/unnamed-chunk-4-1.png" width="700"></p>
-<p>The results confirm that all remaining parameters can be determined
-with sufficient certainty.</p>
-<p>We can also analyse the log-likelihoods obtained in the multiple
-runs:</p>
-<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="../../reference/llhist.html">llhist</a></span><span class="op">(</span><span class="va">f_saem_reduced_multi</span><span class="op">)</span></span></code></pre></div>
-<p><img src="multistart_files/figure-html/unnamed-chunk-5-1.png" width="700"></p>
-<p>We can use the <code>anova</code> method to compare the models.</p>
-<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_saem_full</span>, <span class="fu"><a href="../../reference/multistart.html">best</a></span><span class="op">(</span><span class="va">f_saem_full_multi</span><span class="op">)</span>,</span>
-<span>  <span class="va">f_saem_reduced</span>, <span class="fu"><a href="../../reference/multistart.html">best</a></span><span class="op">(</span><span class="va">f_saem_reduced_multi</span><span class="op">)</span>, test <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## Data: 155 observations of 1 variable(s) grouped in 6 datasets</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">##                            npar    AIC    BIC     Lik  Chisq Df Pr(&gt;Chisq)</span></span>
-<span><span class="co">## f_saem_reduced                9 663.73 661.86 -322.86                     </span></span>
-<span><span class="co">## best(f_saem_reduced_multi)    9 663.69 661.82 -322.85 0.0361  0           </span></span>
-<span><span class="co">## f_saem_full                  10 669.77 667.69 -324.89 0.0000  1          1</span></span>
-<span><span class="co">## best(f_saem_full_multi)      10 665.56 663.48 -322.78 4.2060  0</span></span></code></pre>
-<p>The reduced model results in lower AIC and BIC values, so it is
-clearly preferable. Using multiple starting values gives a large
-improvement in case of the full model, because it is less well-defined,
-which impedes convergence. For the reduced model, using multiple
-starting values only results in a small improvement of the model
-fit.</p>
-  </div>
-
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-
-      </div>
-
-</div>
-
-
-
-      <footer><div class="copyright">
-  <p></p>
-<p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p>
-<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer>
-</div>
-
-  
-
-
-  
-
-  </body>
-</html>
diff --git a/docs/dev/articles/web_only/multistart_files/accessible-code-block-0.0.1/empty-anchor.js b/docs/dev/articles/web_only/multistart_files/accessible-code-block-0.0.1/empty-anchor.js
deleted file mode 100644
index ca349fd6..00000000
--- a/docs/dev/articles/web_only/multistart_files/accessible-code-block-0.0.1/empty-anchor.js
+++ /dev/null
@@ -1,15 +0,0 @@
-// Hide empty <a> tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) -->
-// v0.0.1
-// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020.
-
-document.addEventListener('DOMContentLoaded', function() {
-  const codeList = document.getElementsByClassName("sourceCode");
-  for (var i = 0; i < codeList.length; i++) {
-    var linkList = codeList[i].getElementsByTagName('a');
-    for (var j = 0; j < linkList.length; j++) {
-      if (linkList[j].innerHTML === "") {
-        linkList[j].setAttribute('aria-hidden', 'true');
-      }
-    }
-  }
-});
diff --git a/docs/dev/articles/web_only/multistart_files/figure-html/unnamed-chunk-2-1.png b/docs/dev/articles/web_only/multistart_files/figure-html/unnamed-chunk-2-1.png
deleted file mode 100644
index e3baa59b..00000000
--- a/docs/dev/articles/web_only/multistart_files/figure-html/unnamed-chunk-2-1.png
+++ /dev/null
diff --git a/docs/dev/articles/web_only/multistart_files/figure-html/unnamed-chunk-3-1.png b/docs/dev/articles/web_only/multistart_files/figure-html/unnamed-chunk-3-1.png
deleted file mode 100644
index 1ef2ba24..00000000
--- a/docs/dev/articles/web_only/multistart_files/figure-html/unnamed-chunk-3-1.png
+++ /dev/null
diff --git a/docs/dev/articles/web_only/multistart_files/figure-html/unnamed-chunk-4-1.png b/docs/dev/articles/web_only/multistart_files/figure-html/unnamed-chunk-4-1.png
deleted file mode 100644
index b1582557..00000000
--- a/docs/dev/articles/web_only/multistart_files/figure-html/unnamed-chunk-4-1.png
+++ /dev/null
diff --git a/docs/dev/articles/web_only/multistart_files/figure-html/unnamed-chunk-5-1.png b/docs/dev/articles/web_only/multistart_files/figure-html/unnamed-chunk-5-1.png
deleted file mode 100644
index f0270537..00000000
--- a/docs/dev/articles/web_only/multistart_files/figure-html/unnamed-chunk-5-1.png
+++ /dev/null
diff --git a/docs/dev/articles/web_only/multistart_files/figure-html/unnamed-chunk-6-1.png b/docs/dev/articles/web_only/multistart_files/figure-html/unnamed-chunk-6-1.png
deleted file mode 100644
index b1582557..00000000
--- a/docs/dev/articles/web_only/multistart_files/figure-html/unnamed-chunk-6-1.png
+++ /dev/null
diff --git a/docs/dev/articles/web_only/saem_benchmarks.html b/docs/dev/articles/web_only/saem_benchmarks.html
deleted file mode 100644
index 66740a11..00000000
--- a/docs/dev/articles/web_only/saem_benchmarks.html
+++ /dev/null
@@ -1,639 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en">
-<head>
-<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
-<meta charset="utf-8">
-<meta http-equiv="X-UA-Compatible" content="IE=edge">
-<meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>Benchmark timings for saem.mmkin • mkin</title>
-<!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous">
-<script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../../bootstrap-toc.css">
-<script src="../../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous">
-<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous">
-<!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../../pkgdown.css" rel="stylesheet">
-<script src="../../pkgdown.js"></script><meta property="og:title" content="Benchmark timings for saem.mmkin">
-<meta property="og:description" content="mkin">
-<meta name="robots" content="noindex">
-<!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]-->
-</head>
-<body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-article">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav">
-<li>
-  <a href="../../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu">
-<li>
-      <a href="../../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul>
-</li>
-<li>
-  <a href="../../news/index.html">News</a>
-</li>
-      </ul>
-<ul class="nav navbar-nav navbar-right">
-<li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul>
-</div>
-<!--/.nav-collapse -->
-  </div>
-<!--/.container -->
-</div>
-<!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header toc-ignore">
-      <h1 data-toc-skip>Benchmark timings for saem.mmkin</h1>
-                        <h4 data-toc-skip class="author">Johannes
-Ranke</h4>
-            
-            <h4 data-toc-skip class="date">Last change 17 February 2023
-(rebuilt 2023-04-16)</h4>
-      
-      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/web_only/saem_benchmarks.rmd" class="external-link"><code>vignettes/web_only/saem_benchmarks.rmd</code></a></small>
-      <div class="hidden name"><code>saem_benchmarks.rmd</code></div>
-
-    </div>
-
-    
-    
-<p>Each system is characterized by operating system type, CPU type, mkin
-version, saemix version and R version. A compiler was available, so if
-no analytical solution was available, compiled ODE models are used.</p>
-<p>Every fit is only performed once, so the accuracy of the benchmarks
-is limited.</p>
-<p>For the initial mmkin fits, we use all available cores.</p>
-<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">n_cores</span> <span class="op">&lt;-</span> <span class="fu">parallel</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/r/parallel/detectCores.html" class="external-link">detectCores</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
-<div class="section level2">
-<h2 id="test-data">Test data<a class="anchor" aria-label="anchor" href="#test-data"></a>
-</h2>
-<p>Please refer to the vignette <code>dimethenamid_2018</code> for an
-explanation of the following preprocessing.</p>
-<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">dmta_ds</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fl">7</span>, <span class="kw">function</span><span class="op">(</span><span class="va">i</span><span class="op">)</span> <span class="op">{</span></span>
-<span>  <span class="va">ds_i</span> <span class="op">&lt;-</span> <span class="va">dimethenamid_2018</span><span class="op">$</span><span class="va">ds</span><span class="op">[[</span><span class="va">i</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">data</span></span>
-<span>  <span class="va">ds_i</span><span class="op">[</span><span class="va">ds_i</span><span class="op">$</span><span class="va">name</span> <span class="op">==</span> <span class="st">"DMTAP"</span>, <span class="st">"name"</span><span class="op">]</span> <span class="op">&lt;-</span>  <span class="st">"DMTA"</span></span>
-<span>  <span class="va">ds_i</span><span class="op">$</span><span class="va">time</span> <span class="op">&lt;-</span> <span class="va">ds_i</span><span class="op">$</span><span class="va">time</span> <span class="op">*</span> <span class="va">dimethenamid_2018</span><span class="op">$</span><span class="va">f_time_norm</span><span class="op">[</span><span class="va">i</span><span class="op">]</span></span>
-<span>  <span class="va">ds_i</span></span>
-<span><span class="op">}</span><span class="op">)</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">dmta_ds</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">sapply</a></span><span class="op">(</span><span class="va">dimethenamid_2018</span><span class="op">$</span><span class="va">ds</span>, <span class="kw">function</span><span class="op">(</span><span class="va">ds</span><span class="op">)</span> <span class="va">ds</span><span class="op">$</span><span class="va">title</span><span class="op">)</span></span>
-<span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot"</span><span class="op">]</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/cbind.html" class="external-link">rbind</a></span><span class="op">(</span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 1"</span><span class="op">]</span><span class="op">]</span>, <span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 2"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span>
-<span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 1"</span><span class="op">]</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="cn">NULL</span></span>
-<span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 2"</span><span class="op">]</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="cn">NULL</span></span></code></pre></div>
-</div>
-<div class="section level2">
-<h2 id="test-cases">Test cases<a class="anchor" aria-label="anchor" href="#test-cases"></a>
-</h2>
-<div class="section level3">
-<h3 id="parent-only">Parent only<a class="anchor" aria-label="anchor" href="#parent-only"></a>
-</h3>
-<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">parent_mods</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"DFOP"</span>, <span class="st">"SFORB"</span>, <span class="st">"HS"</span><span class="op">)</span></span>
-<span><span class="va">parent_sep_const</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/mmkin.html">mmkin</a></span><span class="op">(</span><span class="va">parent_mods</span>, <span class="va">dmta_ds</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, cores <span class="op">=</span> <span class="va">n_cores</span><span class="op">)</span></span>
-<span><span class="va">parent_sep_tc</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">parent_sep_const</span>, error_model <span class="op">=</span> <span class="st">"tc"</span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">t1</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/system.time.html" class="external-link">system.time</a></span><span class="op">(</span><span class="va">sfo_const</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/saem.html">saem</a></span><span class="op">(</span><span class="va">parent_sep_const</span><span class="op">[</span><span class="st">"SFO"</span>, <span class="op">]</span><span class="op">)</span><span class="op">)</span><span class="op">[[</span><span class="st">"elapsed"</span><span class="op">]</span><span class="op">]</span></span>
-<span><span class="va">t2</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/system.time.html" class="external-link">system.time</a></span><span class="op">(</span><span class="va">dfop_const</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/saem.html">saem</a></span><span class="op">(</span><span class="va">parent_sep_const</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span><span class="op">)</span><span class="op">)</span><span class="op">[[</span><span class="st">"elapsed"</span><span class="op">]</span><span class="op">]</span></span>
-<span><span class="va">t3</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/system.time.html" class="external-link">system.time</a></span><span class="op">(</span><span class="va">sforb_const</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/saem.html">saem</a></span><span class="op">(</span><span class="va">parent_sep_const</span><span class="op">[</span><span class="st">"SFORB"</span>, <span class="op">]</span><span class="op">)</span><span class="op">)</span><span class="op">[[</span><span class="st">"elapsed"</span><span class="op">]</span><span class="op">]</span></span>
-<span><span class="va">t4</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/system.time.html" class="external-link">system.time</a></span><span class="op">(</span><span class="va">hs_const</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/saem.html">saem</a></span><span class="op">(</span><span class="va">parent_sep_const</span><span class="op">[</span><span class="st">"HS"</span>, <span class="op">]</span><span class="op">)</span><span class="op">)</span><span class="op">[[</span><span class="st">"elapsed"</span><span class="op">]</span><span class="op">]</span></span>
-<span><span class="va">t5</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/system.time.html" class="external-link">system.time</a></span><span class="op">(</span><span class="va">sfo_tc</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/saem.html">saem</a></span><span class="op">(</span><span class="va">parent_sep_tc</span><span class="op">[</span><span class="st">"SFO"</span>, <span class="op">]</span><span class="op">)</span><span class="op">)</span><span class="op">[[</span><span class="st">"elapsed"</span><span class="op">]</span><span class="op">]</span></span>
-<span><span class="va">t6</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/system.time.html" class="external-link">system.time</a></span><span class="op">(</span><span class="va">dfop_tc</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/saem.html">saem</a></span><span class="op">(</span><span class="va">parent_sep_tc</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span><span class="op">)</span><span class="op">)</span><span class="op">[[</span><span class="st">"elapsed"</span><span class="op">]</span><span class="op">]</span></span>
-<span><span class="va">t7</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/system.time.html" class="external-link">system.time</a></span><span class="op">(</span><span class="va">sforb_tc</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/saem.html">saem</a></span><span class="op">(</span><span class="va">parent_sep_tc</span><span class="op">[</span><span class="st">"SFORB"</span>, <span class="op">]</span><span class="op">)</span><span class="op">)</span><span class="op">[[</span><span class="st">"elapsed"</span><span class="op">]</span><span class="op">]</span></span>
-<span><span class="va">t8</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/system.time.html" class="external-link">system.time</a></span><span class="op">(</span><span class="va">hs_tc</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/saem.html">saem</a></span><span class="op">(</span><span class="va">parent_sep_tc</span><span class="op">[</span><span class="st">"HS"</span>, <span class="op">]</span><span class="op">)</span><span class="op">)</span><span class="op">[[</span><span class="st">"elapsed"</span><span class="op">]</span><span class="op">]</span></span></code></pre></div>
-<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span></span>
-<span>  <span class="va">sfo_const</span>, <span class="va">dfop_const</span>, <span class="va">sforb_const</span>, <span class="va">hs_const</span>,</span>
-<span>  <span class="va">sfo_tc</span>, <span class="va">dfop_tc</span>, <span class="va">sforb_tc</span>, <span class="va">hs_tc</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span>, digits <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
-<thead><tr class="header">
-<th align="left"></th>
-<th align="right">npar</th>
-<th align="right">AIC</th>
-<th align="right">BIC</th>
-<th align="right">Lik</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="left">sfo_const</td>
-<td align="right">5</td>
-<td align="right">796.3</td>
-<td align="right">795.3</td>
-<td align="right">-393.2</td>
-</tr>
-<tr class="even">
-<td align="left">sfo_tc</td>
-<td align="right">6</td>
-<td align="right">798.3</td>
-<td align="right">797.1</td>
-<td align="right">-393.2</td>
-</tr>
-<tr class="odd">
-<td align="left">dfop_const</td>
-<td align="right">9</td>
-<td align="right">709.4</td>
-<td align="right">707.5</td>
-<td align="right">-345.7</td>
-</tr>
-<tr class="even">
-<td align="left">sforb_const</td>
-<td align="right">9</td>
-<td align="right">710.0</td>
-<td align="right">708.1</td>
-<td align="right">-346.0</td>
-</tr>
-<tr class="odd">
-<td align="left">hs_const</td>
-<td align="right">9</td>
-<td align="right">713.7</td>
-<td align="right">711.8</td>
-<td align="right">-347.8</td>
-</tr>
-<tr class="even">
-<td align="left">dfop_tc</td>
-<td align="right">10</td>
-<td align="right">669.8</td>
-<td align="right">667.7</td>
-<td align="right">-324.9</td>
-</tr>
-<tr class="odd">
-<td align="left">sforb_tc</td>
-<td align="right">10</td>
-<td align="right">662.8</td>
-<td align="right">660.7</td>
-<td align="right">-321.4</td>
-</tr>
-<tr class="even">
-<td align="left">hs_tc</td>
-<td align="right">10</td>
-<td align="right">667.3</td>
-<td align="right">665.2</td>
-<td align="right">-323.6</td>
-</tr>
-</tbody>
-</table>
-<p>The above model comparison suggests to use the SFORB model with
-two-component error. For comparison, we keep the DFOP model with
-two-component error, as it competes with SFORB for biphasic curves.</p>
-<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="../../reference/illparms.html">illparms</a></span><span class="op">(</span><span class="va">dfop_tc</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## [1] "sd(log_k2)"</span></span></code></pre>
-<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="../../reference/illparms.html">illparms</a></span><span class="op">(</span><span class="va">sforb_tc</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## [1] "sd(log_k_DMTA_bound_free)"</span></span></code></pre>
-<p>For these two models, random effects for the transformed parameters
-<code>k2</code> and <code>k_DMTA_bound_free</code> could not be
-quantified.</p>
-</div>
-<div class="section level3">
-<h3 id="one-metabolite">One metabolite<a class="anchor" aria-label="anchor" href="#one-metabolite"></a>
-</h3>
-<p>We remove parameters that were found to be ill-defined in the parent
-only fits.</p>
-<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">one_met_mods</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span></span>
-<span>  DFOP_SFO <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span></span>
-<span>    DMTA <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="st">"M23"</span><span class="op">)</span>,</span>
-<span>    M23 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span>,</span>
-<span>  SFORB_SFO <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span></span>
-<span>    DMTA <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFORB"</span>, <span class="st">"M23"</span><span class="op">)</span>,</span>
-<span>    M23 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">one_met_sep_const</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/mmkin.html">mmkin</a></span><span class="op">(</span><span class="va">one_met_mods</span>, <span class="va">dmta_ds</span>, error_model <span class="op">=</span> <span class="st">"const"</span>,</span>
-<span>  cores <span class="op">=</span> <span class="va">n_cores</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
-<span><span class="va">one_met_sep_tc</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/mmkin.html">mmkin</a></span><span class="op">(</span><span class="va">one_met_mods</span>, <span class="va">dmta_ds</span>, error_model <span class="op">=</span> <span class="st">"tc"</span>,</span>
-<span>  cores <span class="op">=</span> <span class="va">n_cores</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">t9</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/system.time.html" class="external-link">system.time</a></span><span class="op">(</span><span class="va">dfop_sfo_tc</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/saem.html">saem</a></span><span class="op">(</span><span class="va">one_met_sep_tc</span><span class="op">[</span><span class="st">"DFOP_SFO"</span>, <span class="op">]</span>,</span>
-<span>    no_random_effect <span class="op">=</span> <span class="st">"log_k2"</span><span class="op">)</span><span class="op">)</span><span class="op">[[</span><span class="st">"elapsed"</span><span class="op">]</span><span class="op">]</span></span>
-<span><span class="va">t10</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/system.time.html" class="external-link">system.time</a></span><span class="op">(</span><span class="va">sforb_sfo_tc</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/saem.html">saem</a></span><span class="op">(</span><span class="va">one_met_sep_tc</span><span class="op">[</span><span class="st">"SFORB_SFO"</span>, <span class="op">]</span>,</span>
-<span>    no_random_effect <span class="op">=</span> <span class="st">"log_k_DMTA_bound_free"</span><span class="op">)</span><span class="op">)</span><span class="op">[[</span><span class="st">"elapsed"</span><span class="op">]</span><span class="op">]</span></span></code></pre></div>
-</div>
-<div class="section level3">
-<h3 id="three-metabolites">Three metabolites<a class="anchor" aria-label="anchor" href="#three-metabolites"></a>
-</h3>
-<p>For the case of three metabolites, we only keep the SFORB model in
-order to limit the time for compiling this vignette, and as fitting in
-parallel may disturb the benchmark. Again, we do not include random
-effects that were ill-defined in previous fits of subsets of the
-degradation model.</p>
-<div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="../../reference/illparms.html">illparms</a></span><span class="op">(</span><span class="va">sforb_sfo_tc</span><span class="op">)</span></span></code></pre></div>
-<div class="sourceCode" id="cb11"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">three_met_mods</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span></span>
-<span>  SFORB_SFO3_plus <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span></span>
-<span>    DMTA <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFORB"</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"M23"</span>, <span class="st">"M27"</span>, <span class="st">"M31"</span><span class="op">)</span><span class="op">)</span>,</span>
-<span>    M23 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span>
-<span>    M27 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span>
-<span>    M31 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"M27"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">three_met_sep_tc</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/mmkin.html">mmkin</a></span><span class="op">(</span><span class="va">three_met_mods</span>, <span class="va">dmta_ds</span>, error_model <span class="op">=</span> <span class="st">"tc"</span>,</span>
-<span>  cores <span class="op">=</span> <span class="va">n_cores</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">t11</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/system.time.html" class="external-link">system.time</a></span><span class="op">(</span><span class="va">sforb_sfo3_plus_const</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/saem.html">saem</a></span><span class="op">(</span><span class="va">three_met_sep_tc</span><span class="op">[</span><span class="st">"SFORB_SFO3_plus"</span>, <span class="op">]</span>,</span>
-<span>    no_random_effect <span class="op">=</span> <span class="st">"log_k_DMTA_bound_free"</span><span class="op">)</span><span class="op">)</span><span class="op">[[</span><span class="st">"elapsed"</span><span class="op">]</span><span class="op">]</span></span></code></pre></div>
-</div>
-</div>
-<div class="section level2">
-<h2 id="results">Results<a class="anchor" aria-label="anchor" href="#results"></a>
-</h2>
-<p>Benchmarks for all available error models are shown. They are
-intended for improving mkin, not for comparing CPUs or operating
-systems. All trademarks belong to their respective owners.</p>
-<div class="section level3">
-<h3 id="parent-only-1">Parent only<a class="anchor" aria-label="anchor" href="#parent-only-1"></a>
-</h3>
-<p>Constant variance for SFO, DFOP, SFORB and HS.</p>
-<table class="table">
-<thead><tr class="header">
-<th align="left">CPU</th>
-<th align="left">OS</th>
-<th align="left">mkin</th>
-<th align="left">saemix</th>
-<th align="right">t1</th>
-<th align="right">t2</th>
-<th align="right">t3</th>
-<th align="right">t4</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="left">Ryzen 7 1700</td>
-<td align="left">Linux</td>
-<td align="left">1.2.0</td>
-<td align="left">3.2</td>
-<td align="right">2.140</td>
-<td align="right">4.626</td>
-<td align="right">4.328</td>
-<td align="right">4.998</td>
-</tr>
-<tr class="even">
-<td align="left">Ryzen 7 1700</td>
-<td align="left">Linux</td>
-<td align="left">1.2.2</td>
-<td align="left">3.2</td>
-<td align="right">2.427</td>
-<td align="right">4.550</td>
-<td align="right">4.217</td>
-<td align="right">4.851</td>
-</tr>
-<tr class="odd">
-<td align="left">Ryzen 9 7950X</td>
-<td align="left">Linux</td>
-<td align="left">1.2.1</td>
-<td align="left">3.2</td>
-<td align="right">1.352</td>
-<td align="right">2.813</td>
-<td align="right">2.401</td>
-<td align="right">2.074</td>
-</tr>
-<tr class="even">
-<td align="left">Ryzen 9 7950X</td>
-<td align="left">Linux</td>
-<td align="left">1.2.2</td>
-<td align="left">3.2</td>
-<td align="right">1.328</td>
-<td align="right">2.738</td>
-<td align="right">2.336</td>
-<td align="right">2.023</td>
-</tr>
-<tr class="odd">
-<td align="left">Ryzen 9 7950X</td>
-<td align="left">Linux</td>
-<td align="left">1.2.3</td>
-<td align="left">3.2</td>
-<td align="right">1.118</td>
-<td align="right">2.036</td>
-<td align="right">2.010</td>
-<td align="right">2.088</td>
-</tr>
-<tr class="even">
-<td align="left">Ryzen 9 7950X</td>
-<td align="left">Linux</td>
-<td align="left">1.2.3</td>
-<td align="left">3.2</td>
-<td align="right">1.389</td>
-<td align="right">2.300</td>
-<td align="right">1.880</td>
-<td align="right">2.355</td>
-</tr>
-</tbody>
-</table>
-<p>Two-component error fits for SFO, DFOP, SFORB and HS.</p>
-<table class="table">
-<thead><tr class="header">
-<th align="left">CPU</th>
-<th align="left">OS</th>
-<th align="left">mkin</th>
-<th align="left">saemix</th>
-<th align="right">t5</th>
-<th align="right">t6</th>
-<th align="right">t7</th>
-<th align="right">t8</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="left">Ryzen 7 1700</td>
-<td align="left">Linux</td>
-<td align="left">1.2.0</td>
-<td align="left">3.2</td>
-<td align="right">5.678</td>
-<td align="right">7.441</td>
-<td align="right">8.000</td>
-<td align="right">7.980</td>
-</tr>
-<tr class="even">
-<td align="left">Ryzen 7 1700</td>
-<td align="left">Linux</td>
-<td align="left">1.2.2</td>
-<td align="left">3.2</td>
-<td align="right">5.352</td>
-<td align="right">7.201</td>
-<td align="right">8.174</td>
-<td align="right">8.401</td>
-</tr>
-<tr class="odd">
-<td align="left">Ryzen 9 7950X</td>
-<td align="left">Linux</td>
-<td align="left">1.2.1</td>
-<td align="left">3.2</td>
-<td align="right">2.388</td>
-<td align="right">3.033</td>
-<td align="right">3.532</td>
-<td align="right">3.310</td>
-</tr>
-<tr class="even">
-<td align="left">Ryzen 9 7950X</td>
-<td align="left">Linux</td>
-<td align="left">1.2.2</td>
-<td align="left">3.2</td>
-<td align="right">2.341</td>
-<td align="right">2.968</td>
-<td align="right">3.465</td>
-<td align="right">3.341</td>
-</tr>
-<tr class="odd">
-<td align="left">Ryzen 9 7950X</td>
-<td align="left">Linux</td>
-<td align="left">1.2.3</td>
-<td align="left">3.2</td>
-<td align="right">2.159</td>
-<td align="right">3.584</td>
-<td align="right">3.307</td>
-<td align="right">3.460</td>
-</tr>
-<tr class="even">
-<td align="left">Ryzen 9 7950X</td>
-<td align="left">Linux</td>
-<td align="left">1.2.3</td>
-<td align="left">3.2</td>
-<td align="right">2.300</td>
-<td align="right">3.075</td>
-<td align="right">3.206</td>
-<td align="right">3.471</td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="section level3">
-<h3 id="one-metabolite-1">One metabolite<a class="anchor" aria-label="anchor" href="#one-metabolite-1"></a>
-</h3>
-<p>Two-component error for DFOP-SFO and SFORB-SFO.</p>
-<table class="table">
-<thead><tr class="header">
-<th align="left">CPU</th>
-<th align="left">OS</th>
-<th align="left">mkin</th>
-<th align="left">saemix</th>
-<th align="right">t9</th>
-<th align="right">t10</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="left">Ryzen 7 1700</td>
-<td align="left">Linux</td>
-<td align="left">1.2.0</td>
-<td align="left">3.2</td>
-<td align="right">24.465</td>
-<td align="right">800.266</td>
-</tr>
-<tr class="even">
-<td align="left">Ryzen 7 1700</td>
-<td align="left">Linux</td>
-<td align="left">1.2.2</td>
-<td align="left">3.2</td>
-<td align="right">25.193</td>
-<td align="right">798.580</td>
-</tr>
-<tr class="odd">
-<td align="left">Ryzen 9 7950X</td>
-<td align="left">Linux</td>
-<td align="left">1.2.1</td>
-<td align="left">3.2</td>
-<td align="right">11.247</td>
-<td align="right">285.216</td>
-</tr>
-<tr class="even">
-<td align="left">Ryzen 9 7950X</td>
-<td align="left">Linux</td>
-<td align="left">1.2.2</td>
-<td align="left">3.2</td>
-<td align="right">11.242</td>
-<td align="right">284.258</td>
-</tr>
-<tr class="odd">
-<td align="left">Ryzen 9 7950X</td>
-<td align="left">Linux</td>
-<td align="left">1.2.3</td>
-<td align="left">3.2</td>
-<td align="right">11.796</td>
-<td align="right">216.012</td>
-</tr>
-<tr class="even">
-<td align="left">Ryzen 9 7950X</td>
-<td align="left">Linux</td>
-<td align="left">1.2.3</td>
-<td align="left">3.2</td>
-<td align="right">12.951</td>
-<td align="right">294.082</td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="section level3">
-<h3 id="three-metabolites-1">Three metabolites<a class="anchor" aria-label="anchor" href="#three-metabolites-1"></a>
-</h3>
-<p>Two-component error for SFORB-SFO3-plus</p>
-<table class="table">
-<thead><tr class="header">
-<th align="left">CPU</th>
-<th align="left">OS</th>
-<th align="left">mkin</th>
-<th align="left">saemix</th>
-<th align="right">t11</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="left">Ryzen 7 1700</td>
-<td align="left">Linux</td>
-<td align="left">1.2.0</td>
-<td align="left">3.2</td>
-<td align="right">1289.198</td>
-</tr>
-<tr class="even">
-<td align="left">Ryzen 7 1700</td>
-<td align="left">Linux</td>
-<td align="left">1.2.2</td>
-<td align="left">3.2</td>
-<td align="right">1312.445</td>
-</tr>
-<tr class="odd">
-<td align="left">Ryzen 9 7950X</td>
-<td align="left">Linux</td>
-<td align="left">1.2.1</td>
-<td align="left">3.2</td>
-<td align="right">489.939</td>
-</tr>
-<tr class="even">
-<td align="left">Ryzen 9 7950X</td>
-<td align="left">Linux</td>
-<td align="left">1.2.2</td>
-<td align="left">3.2</td>
-<td align="right">482.970</td>
-</tr>
-<tr class="odd">
-<td align="left">Ryzen 9 7950X</td>
-<td align="left">Linux</td>
-<td align="left">1.2.3</td>
-<td align="left">3.2</td>
-<td align="right">392.364</td>
-</tr>
-<tr class="even">
-<td align="left">Ryzen 9 7950X</td>
-<td align="left">Linux</td>
-<td align="left">1.2.3</td>
-<td align="left">3.2</td>
-<td align="right">477.297</td>
-</tr>
-</tbody>
-</table>
-</div>
-</div>
-  </div>
-
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-
-        <nav id="toc" data-toggle="toc"><h2 data-toc-skip>Contents</h2>
-    </nav>
-</div>
-
-</div>
-
-
-
-      <footer><div class="copyright">
-  <p></p>
-<p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p>
-<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer>
-</div>
-
-  
-
-
-  
-
-  </body>
-</html>
diff --git a/docs/dev/articles/web_only/saem_benchmarks_files/accessible-code-block-0.0.1/empty-anchor.js b/docs/dev/articles/web_only/saem_benchmarks_files/accessible-code-block-0.0.1/empty-anchor.js
deleted file mode 100644
index ca349fd6..00000000
--- a/docs/dev/articles/web_only/saem_benchmarks_files/accessible-code-block-0.0.1/empty-anchor.js
+++ /dev/null
@@ -1,15 +0,0 @@
-// Hide empty <a> tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) -->
-// v0.0.1
-// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020.
-
-document.addEventListener('DOMContentLoaded', function() {
-  const codeList = document.getElementsByClassName("sourceCode");
-  for (var i = 0; i < codeList.length; i++) {
-    var linkList = codeList[i].getElementsByTagName('a');
-    for (var j = 0; j < linkList.length; j++) {
-      if (linkList[j].innerHTML === "") {
-        linkList[j].setAttribute('aria-hidden', 'true');
-      }
-    }
-  }
-});
diff --git a/docs/dev/authors.html b/docs/dev/authors.html
deleted file mode 100644
index 1e2fc48f..00000000
--- a/docs/dev/authors.html
+++ /dev/null
@@ -1,167 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Authors and Citation • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="bootstrap-toc.css"><script src="bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="pkgdown.css" rel="stylesheet"><script src="pkgdown.js"></script><meta property="og:title" content="Authors and Citation"><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-citation-authors">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.4</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="contents col-md-9">
-    <div class="section level2 authors-section">
-      <div class="page-header">
-        <h1>Authors</h1>
-      </div>
-
-      
-      <ul class="list-unstyled"><li>
-          <p><strong>Johannes Ranke</strong>. Author, maintainer, copyright holder. <a href="https://orcid.org/0000-0003-4371-6538" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
-          </p>
-        </li>
-        <li>
-          <p><strong>Katrin Lindenberger</strong>. Contributor. 
-          <br><small>contributed to mkinresplot()</small></p>
-        </li>
-        <li>
-          <p><strong>René Lehmann</strong>. Contributor. 
-          <br><small>ilr() and invilr()</small></p>
-        </li>
-        <li>
-          <p><strong>Eurofins Regulatory AG</strong>. Copyright holder. 
-          <br><small>copyright for some of the contributions of JR 2012-2014</small></p>
-        </li>
-      </ul></div>
-    <div class="section level2 citation-section">
-    <div>
-      <h1 id="citation">Citation</h1>
-      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/DESCRIPTION" class="external-link"><code>DESCRIPTION</code></a></small>
-    </div>
-    </div>
-
-
-    <p>Ranke J (2023).
-<em>mkin: Kinetic Evaluation of Chemical Degradation Data</em>.
-R package version 1.2.4, <a href="https://pkgdown.jrwb.de/mkin/">https://pkgdown.jrwb.de/mkin/</a>. 
-</p>
-    <pre>@Manual{,
-  title = {mkin: Kinetic Evaluation of Chemical Degradation Data},
-  author = {Johannes Ranke},
-  year = {2023},
-  note = {R package version 1.2.4},
-  url = {https://pkgdown.jrwb.de/mkin/},
-}</pre>
-
-  </div>
-
-</div>
-
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/bootstrap-toc.css b/docs/dev/bootstrap-toc.css
deleted file mode 100644
index 5a859415..00000000
--- a/docs/dev/bootstrap-toc.css
+++ /dev/null
@@ -1,60 +0,0 @@
-/*!
- * Bootstrap Table of Contents v0.4.1 (http://afeld.github.io/bootstrap-toc/)
- * Copyright 2015 Aidan Feldman
- * Licensed under MIT (https://github.com/afeld/bootstrap-toc/blob/gh-pages/LICENSE.md) */
-
-/* modified from https://github.com/twbs/bootstrap/blob/94b4076dd2efba9af71f0b18d4ee4b163aa9e0dd/docs/assets/css/src/docs.css#L548-L601 */
-
-/* All levels of nav */
-nav[data-toggle='toc'] .nav > li > a {
-  display: block;
-  padding: 4px 20px;
-  font-size: 13px;
-  font-weight: 500;
-  color: #767676;
-}
-nav[data-toggle='toc'] .nav > li > a:hover,
-nav[data-toggle='toc'] .nav > li > a:focus {
-  padding-left: 19px;
-  color: #563d7c;
-  text-decoration: none;
-  background-color: transparent;
-  border-left: 1px solid #563d7c;
-}
-nav[data-toggle='toc'] .nav > .active > a,
-nav[data-toggle='toc'] .nav > .active:hover > a,
-nav[data-toggle='toc'] .nav > .active:focus > a {
-  padding-left: 18px;
-  font-weight: bold;
-  color: #563d7c;
-  background-color: transparent;
-  border-left: 2px solid #563d7c;
-}
-
-/* Nav: second level (shown on .active) */
-nav[data-toggle='toc'] .nav .nav {
-  display: none; /* Hide by default, but at >768px, show it */
-  padding-bottom: 10px;
-}
-nav[data-toggle='toc'] .nav .nav > li > a {
-  padding-top: 1px;
-  padding-bottom: 1px;
-  padding-left: 30px;
-  font-size: 12px;
-  font-weight: normal;
-}
-nav[data-toggle='toc'] .nav .nav > li > a:hover,
-nav[data-toggle='toc'] .nav .nav > li > a:focus {
-  padding-left: 29px;
-}
-nav[data-toggle='toc'] .nav .nav > .active > a,
-nav[data-toggle='toc'] .nav .nav > .active:hover > a,
-nav[data-toggle='toc'] .nav .nav > .active:focus > a {
-  padding-left: 28px;
-  font-weight: 500;
-}
-
-/* from https://github.com/twbs/bootstrap/blob/e38f066d8c203c3e032da0ff23cd2d6098ee2dd6/docs/assets/css/src/docs.css#L631-L634 */
-nav[data-toggle='toc'] .nav > .active > ul {
-  display: block;
-}
diff --git a/docs/dev/bootstrap-toc.js b/docs/dev/bootstrap-toc.js
deleted file mode 100644
index 1cdd573b..00000000
--- a/docs/dev/bootstrap-toc.js
+++ /dev/null
@@ -1,159 +0,0 @@
-/*!
- * Bootstrap Table of Contents v0.4.1 (http://afeld.github.io/bootstrap-toc/)
- * Copyright 2015 Aidan Feldman
- * Licensed under MIT (https://github.com/afeld/bootstrap-toc/blob/gh-pages/LICENSE.md) */
-(function() {
-  'use strict';
-
-  window.Toc = {
-    helpers: {
-      // return all matching elements in the set, or their descendants
-      findOrFilter: function($el, selector) {
-        // http://danielnouri.org/notes/2011/03/14/a-jquery-find-that-also-finds-the-root-element/
-        // http://stackoverflow.com/a/12731439/358804
-        var $descendants = $el.find(selector);
-        return $el.filter(selector).add($descendants).filter(':not([data-toc-skip])');
-      },
-
-      generateUniqueIdBase: function(el) {
-        var text = $(el).text();
-        var anchor = text.trim().toLowerCase().replace(/[^A-Za-z0-9]+/g, '-');
-        return anchor || el.tagName.toLowerCase();
-      },
-
-      generateUniqueId: function(el) {
-        var anchorBase = this.generateUniqueIdBase(el);
-        for (var i = 0; ; i++) {
-          var anchor = anchorBase;
-          if (i > 0) {
-            // add suffix
-            anchor += '-' + i;
-          }
-          // check if ID already exists
-          if (!document.getElementById(anchor)) {
-            return anchor;
-          }
-        }
-      },
-
-      generateAnchor: function(el) {
-        if (el.id) {
-          return el.id;
-        } else {
-          var anchor = this.generateUniqueId(el);
-          el.id = anchor;
-          return anchor;
-        }
-      },
-
-      createNavList: function() {
-        return $('<ul class="nav"></ul>');
-      },
-
-      createChildNavList: function($parent) {
-        var $childList = this.createNavList();
-        $parent.append($childList);
-        return $childList;
-      },
-
-      generateNavEl: function(anchor, text) {
-        var $a = $('<a></a>');
-        $a.attr('href', '#' + anchor);
-        $a.text(text);
-        var $li = $('<li></li>');
-        $li.append($a);
-        return $li;
-      },
-
-      generateNavItem: function(headingEl) {
-        var anchor = this.generateAnchor(headingEl);
-        var $heading = $(headingEl);
-        var text = $heading.data('toc-text') || $heading.text();
-        return this.generateNavEl(anchor, text);
-      },
-
-      // Find the first heading level (`<h1>`, then `<h2>`, etc.) that has more than one element. Defaults to 1 (for `<h1>`).
-      getTopLevel: function($scope) {
-        for (var i = 1; i <= 6; i++) {
-          var $headings = this.findOrFilter($scope, 'h' + i);
-          if ($headings.length > 1) {
-            return i;
-          }
-        }
-
-        return 1;
-      },
-
-      // returns the elements for the top level, and the next below it
-      getHeadings: function($scope, topLevel) {
-        var topSelector = 'h' + topLevel;
-
-        var secondaryLevel = topLevel + 1;
-        var secondarySelector = 'h' + secondaryLevel;
-
-        return this.findOrFilter($scope, topSelector + ',' + secondarySelector);
-      },
-
-      getNavLevel: function(el) {
-        return parseInt(el.tagName.charAt(1), 10);
-      },
-
-      populateNav: function($topContext, topLevel, $headings) {
-        var $context = $topContext;
-        var $prevNav;
-
-        var helpers = this;
-        $headings.each(function(i, el) {
-          var $newNav = helpers.generateNavItem(el);
-          var navLevel = helpers.getNavLevel(el);
-
-          // determine the proper $context
-          if (navLevel === topLevel) {
-            // use top level
-            $context = $topContext;
-          } else if ($prevNav && $context === $topContext) {
-            // create a new level of the tree and switch to it
-            $context = helpers.createChildNavList($prevNav);
-          } // else use the current $context
-
-          $context.append($newNav);
-
-          $prevNav = $newNav;
-        });
-      },
-
-      parseOps: function(arg) {
-        var opts;
-        if (arg.jquery) {
-          opts = {
-            $nav: arg
-          };
-        } else {
-          opts = arg;
-        }
-        opts.$scope = opts.$scope || $(document.body);
-        return opts;
-      }
-    },
-
-    // accepts a jQuery object, or an options object
-    init: function(opts) {
-      opts = this.helpers.parseOps(opts);
-
-      // ensure that the data attribute is in place for styling
-      opts.$nav.attr('data-toggle', 'toc');
-
-      var $topContext = this.helpers.createChildNavList(opts.$nav);
-      var topLevel = this.helpers.getTopLevel(opts.$scope);
-      var $headings = this.helpers.getHeadings(opts.$scope, topLevel);
-      this.helpers.populateNav($topContext, topLevel, $headings);
-    }
-  };
-
-  $(function() {
-    $('nav[data-toggle="toc"]').each(function(i, el) {
-      var $nav = $(el);
-      Toc.init($nav);
-    });
-  });
-})();
diff --git a/docs/dev/docsearch.css b/docs/dev/docsearch.css
deleted file mode 100644
index e5f1fe1d..00000000
--- a/docs/dev/docsearch.css
+++ /dev/null
@@ -1,148 +0,0 @@
-/* Docsearch -------------------------------------------------------------- */
-/*
-  Source: https://github.com/algolia/docsearch/
-  License: MIT
-*/
-
-.algolia-autocomplete {
-  display: block;
-  -webkit-box-flex: 1;
-  -ms-flex: 1;
-  flex: 1
-}
-
-.algolia-autocomplete .ds-dropdown-menu {
-  width: 100%;
-  min-width: none;
-  max-width: none;
-  padding: .75rem 0;
-  background-color: #fff;
-  background-clip: padding-box;
-  border: 1px solid rgba(0, 0, 0, .1);
-  box-shadow: 0 .5rem 1rem rgba(0, 0, 0, .175);
-}
-
-@media (min-width:768px) {
-  .algolia-autocomplete .ds-dropdown-menu {
-      width: 175%
-  }
-}
-
-.algolia-autocomplete .ds-dropdown-menu::before {
-  display: none
-}
-
-.algolia-autocomplete .ds-dropdown-menu [class^=ds-dataset-] {
-  padding: 0;
-  background-color: rgb(255,255,255);
-  border: 0;
-  max-height: 80vh;
-}
-
-.algolia-autocomplete .ds-dropdown-menu .ds-suggestions {
-  margin-top: 0
-}
-
-.algolia-autocomplete .algolia-docsearch-suggestion {
-  padding: 0;
-  overflow: visible
-}
-
-.algolia-autocomplete .algolia-docsearch-suggestion--category-header {
-  padding: .125rem 1rem;
-  margin-top: 0;
-  font-size: 1.3em;
-  font-weight: 500;
-  color: #00008B;
-  border-bottom: 0
-}
-
-.algolia-autocomplete .algolia-docsearch-suggestion--wrapper {
-    float: none;
-    padding-top: 0
-}
-
-.algolia-autocomplete .algolia-docsearch-suggestion--subcategory-column {
-  float: none;
-  width: auto;
-  padding: 0;
-  text-align: left
-}
-
-.algolia-autocomplete .algolia-docsearch-suggestion--content {
-  float: none;
-  width: auto;
-  padding: 0
-}
-
-.algolia-autocomplete .algolia-docsearch-suggestion--content::before {
-  display: none
-}
-
-.algolia-autocomplete .ds-suggestion:not(:first-child) .algolia-docsearch-suggestion--category-header {
-  padding-top: .75rem;
-  margin-top: .75rem;
-  border-top: 1px solid rgba(0, 0, 0, .1)
-}
-
-.algolia-autocomplete .ds-suggestion .algolia-docsearch-suggestion--subcategory-column {
-  display: block;
-  padding: .1rem 1rem;
-  margin-bottom: 0.1;
-  font-size: 1.0em;
-  font-weight: 400
-  /* display: none */
-}
-
-.algolia-autocomplete .algolia-docsearch-suggestion--title {
-  display: block;
-  padding: .25rem 1rem;
-  margin-bottom: 0;
-  font-size: 0.9em;
-  font-weight: 400
-}
-
-.algolia-autocomplete .algolia-docsearch-suggestion--text {
-  padding: 0 1rem .5rem;
-  margin-top: -.25rem;
-  font-size: 0.8em;
-  font-weight: 400;
-  line-height: 1.25
-}
-
-.algolia-autocomplete .algolia-docsearch-footer {
-  width: 110px;
-  height: 20px;
-  z-index: 3;
-  margin-top: 10.66667px;
-  float: right;
-  font-size: 0;
-  line-height: 0;
-}
-
-.algolia-autocomplete .algolia-docsearch-footer--logo {
-  background-image: url("data:image/svg+xml;utf8,<svg viewBox='0 0 130 18' xmlns='http://www.w3.org/2000/svg'><defs><linearGradient x1='-36.868%' y1='134.936%' x2='129.432%' y2='-27.7%' id='a'><stop stop-color='%2300AEFF' offset='0%'/><stop stop-color='%233369E7' offset='100%'/></linearGradient></defs><g fill='none' fill-rule='evenodd'><path d='M59.399.022h13.299a2.372 2.372 0 0 1 2.377 2.364V15.62a2.372 2.372 0 0 1-2.377 2.364H59.399a2.372 2.372 0 0 1-2.377-2.364V2.381A2.368 2.368 0 0 1 59.399.022z' fill='url(%23a)'/><path d='M66.257 4.56c-2.815 0-5.1 2.272-5.1 5.078 0 2.806 2.284 5.072 5.1 5.072 2.815 0 5.1-2.272 5.1-5.078 0-2.806-2.279-5.072-5.1-5.072zm0 8.652c-1.983 0-3.593-1.602-3.593-3.574 0-1.972 1.61-3.574 3.593-3.574 1.983 0 3.593 1.602 3.593 3.574a3.582 3.582 0 0 1-3.593 3.574zm0-6.418v2.664c0 .076.082.131.153.093l2.377-1.226c.055-.027.071-.093.044-.147a2.96 2.96 0 0 0-2.465-1.487c-.055 0-.11.044-.11.104l.001-.001zm-3.33-1.956l-.312-.311a.783.783 0 0 0-1.106 0l-.372.37a.773.773 0 0 0 0 1.101l.307.305c.049.049.121.038.164-.011.181-.245.378-.479.597-.697.225-.223.455-.42.707-.599.055-.033.06-.109.016-.158h-.001zm5.001-.806v-.616a.781.781 0 0 0-.783-.779h-1.824a.78.78 0 0 0-.783.779v.632c0 .071.066.12.137.104a5.736 5.736 0 0 1 1.588-.223c.52 0 1.035.071 1.534.207a.106.106 0 0 0 .131-.104z' fill='%23FFF'/><path d='M102.162 13.762c0 1.455-.372 2.517-1.123 3.193-.75.676-1.895 1.013-3.44 1.013-.564 0-1.736-.109-2.673-.316l.345-1.689c.783.163 1.819.207 2.361.207.86 0 1.473-.174 1.84-.523.367-.349.548-.866.548-1.553v-.349a6.374 6.374 0 0 1-.838.316 4.151 4.151 0 0 1-1.194.158 4.515 4.515 0 0 1-1.616-.278 3.385 3.385 0 0 1-1.254-.817 3.744 3.744 0 0 1-.811-1.351c-.192-.539-.29-1.504-.29-2.212 0-.665.104-1.498.307-2.054a3.925 3.925 0 0 1 .904-1.433 4.124 4.124 0 0 1 1.441-.926 5.31 5.31 0 0 1 1.945-.365c.696 0 1.337.087 1.961.191a15.86 15.86 0 0 1 1.588.332v8.456h-.001zm-5.954-4.206c0 .893.197 1.885.592 2.299.394.414.904.621 1.528.621.34 0 .663-.049.964-.142a2.75 2.75 0 0 0 .734-.332v-5.29a8.531 8.531 0 0 0-1.413-.18c-.778-.022-1.369.294-1.786.801-.411.507-.619 1.395-.619 2.223zm16.12 0c0 .719-.104 1.264-.318 1.858a4.389 4.389 0 0 1-.904 1.52c-.389.42-.854.746-1.402.975-.548.229-1.391.36-1.813.36-.422-.005-1.26-.125-1.802-.36a4.088 4.088 0 0 1-1.397-.975 4.486 4.486 0 0 1-.909-1.52 5.037 5.037 0 0 1-.329-1.858c0-.719.099-1.411.318-1.999.219-.588.526-1.09.92-1.509.394-.42.865-.741 1.402-.97a4.547 4.547 0 0 1 1.786-.338 4.69 4.69 0 0 1 1.791.338c.548.229 1.019.55 1.402.97.389.42.69.921.909 1.509.23.588.345 1.28.345 1.999h.001zm-2.191.005c0-.921-.203-1.689-.597-2.223-.394-.539-.948-.806-1.654-.806-.707 0-1.26.267-1.654.806-.394.539-.586 1.302-.586 2.223 0 .932.197 1.558.592 2.098.394.545.948.812 1.654.812.707 0 1.26-.272 1.654-.812.394-.545.592-1.166.592-2.098h-.001zm6.962 4.707c-3.511.016-3.511-2.822-3.511-3.274L113.583.926l2.142-.338v10.003c0 .256 0 1.88 1.375 1.885v1.792h-.001zm3.774 0h-2.153V5.072l2.153-.338v9.534zm-1.079-10.542c.718 0 1.304-.578 1.304-1.291 0-.714-.581-1.291-1.304-1.291-.723 0-1.304.578-1.304 1.291 0 .714.586 1.291 1.304 1.291zm6.431 1.013c.707 0 1.304.087 1.786.262.482.174.871.42 1.156.73.285.311.488.735.608 1.182.126.447.186.937.186 1.476v5.481a25.24 25.24 0 0 1-1.495.251c-.668.098-1.419.147-2.251.147a6.829 6.829 0 0 1-1.517-.158 3.213 3.213 0 0 1-1.178-.507 2.455 2.455 0 0 1-.761-.904c-.181-.37-.274-.893-.274-1.438 0-.523.104-.855.307-1.215.208-.36.487-.654.838-.883a3.609 3.609 0 0 1 1.227-.49 7.073 7.073 0 0 1 2.202-.103c.263.027.537.076.833.147v-.349c0-.245-.027-.479-.088-.697a1.486 1.486 0 0 0-.307-.583c-.148-.169-.34-.3-.581-.392a2.536 2.536 0 0 0-.915-.163c-.493 0-.942.06-1.353.131-.411.071-.75.153-1.008.245l-.257-1.749c.268-.093.668-.185 1.183-.278a9.335 9.335 0 0 1 1.66-.142l-.001-.001zm.181 7.731c.657 0 1.145-.038 1.484-.104v-2.168a5.097 5.097 0 0 0-1.978-.104c-.241.033-.46.098-.652.191a1.167 1.167 0 0 0-.466.392c-.121.169-.175.267-.175.523 0 .501.175.79.493.981.323.196.75.289 1.293.289h.001zM84.109 4.794c.707 0 1.304.087 1.786.262.482.174.871.42 1.156.73.29.316.487.735.608 1.182.126.447.186.937.186 1.476v5.481a25.24 25.24 0 0 1-1.495.251c-.668.098-1.419.147-2.251.147a6.829 6.829 0 0 1-1.517-.158 3.213 3.213 0 0 1-1.178-.507 2.455 2.455 0 0 1-.761-.904c-.181-.37-.274-.893-.274-1.438 0-.523.104-.855.307-1.215.208-.36.487-.654.838-.883a3.609 3.609 0 0 1 1.227-.49 7.073 7.073 0 0 1 2.202-.103c.257.027.537.076.833.147v-.349c0-.245-.027-.479-.088-.697a1.486 1.486 0 0 0-.307-.583c-.148-.169-.34-.3-.581-.392a2.536 2.536 0 0 0-.915-.163c-.493 0-.942.06-1.353.131-.411.071-.75.153-1.008.245l-.257-1.749c.268-.093.668-.185 1.183-.278a8.89 8.89 0 0 1 1.66-.142l-.001-.001zm.186 7.736c.657 0 1.145-.038 1.484-.104v-2.168a5.097 5.097 0 0 0-1.978-.104c-.241.033-.46.098-.652.191a1.167 1.167 0 0 0-.466.392c-.121.169-.175.267-.175.523 0 .501.175.79.493.981.318.191.75.289 1.293.289h.001zm8.682 1.738c-3.511.016-3.511-2.822-3.511-3.274L89.461.926l2.142-.338v10.003c0 .256 0 1.88 1.375 1.885v1.792h-.001z' fill='%23182359'/><path d='M5.027 11.025c0 .698-.252 1.246-.757 1.644-.505.397-1.201.596-2.089.596-.888 0-1.615-.138-2.181-.414v-1.214c.358.168.739.301 1.141.397.403.097.778.145 1.125.145.508 0 .884-.097 1.125-.29a.945.945 0 0 0 .363-.779.978.978 0 0 0-.333-.747c-.222-.204-.68-.446-1.375-.725-.716-.29-1.221-.621-1.515-.994-.294-.372-.44-.82-.44-1.343 0-.655.233-1.171.698-1.547.466-.376 1.09-.564 1.875-.564.752 0 1.5.165 2.245.494l-.408 1.047c-.698-.294-1.321-.44-1.869-.44-.415 0-.73.09-.945.271a.89.89 0 0 0-.322.717c0 .204.043.379.129.524.086.145.227.282.424.411.197.129.551.299 1.063.51.577.24.999.464 1.268.671.269.208.466.442.591.704.125.261.188.569.188.924l-.001.002zm3.98 2.24c-.924 0-1.646-.269-2.167-.808-.521-.539-.782-1.281-.782-2.226 0-.97.242-1.733.725-2.288.483-.555 1.148-.833 1.993-.833.784 0 1.404.238 1.858.714.455.476.682 1.132.682 1.966v.682H7.357c.018.577.174 1.02.467 1.329.294.31.707.465 1.241.465.351 0 .678-.033.98-.099a5.1 5.1 0 0 0 .975-.33v1.026a3.865 3.865 0 0 1-.935.312 5.723 5.723 0 0 1-1.08.091l.002-.001zm-.231-5.199c-.401 0-.722.127-.964.381s-.386.625-.432 1.112h2.696c-.007-.491-.125-.862-.354-1.115-.229-.252-.544-.379-.945-.379l-.001.001zm7.692 5.092l-.252-.827h-.043c-.286.362-.575.608-.865.739-.29.131-.662.196-1.117.196-.584 0-1.039-.158-1.367-.473-.328-.315-.491-.761-.491-1.337 0-.612.227-1.074.682-1.386.455-.312 1.148-.482 2.079-.51l1.026-.032v-.317c0-.38-.089-.663-.266-.851-.177-.188-.452-.282-.824-.282-.304 0-.596.045-.876.134a6.68 6.68 0 0 0-.806.317l-.408-.902a4.414 4.414 0 0 1 1.058-.384 4.856 4.856 0 0 1 1.085-.132c.756 0 1.326.165 1.711.494.385.329.577.847.577 1.552v4.002h-.902l-.001-.001zm-1.88-.859c.458 0 .826-.128 1.104-.384.278-.256.416-.615.416-1.077v-.516l-.763.032c-.594.021-1.027.121-1.297.298s-.406.448-.406.814c0 .265.079.47.236.615.158.145.394.218.709.218h.001zm7.557-5.189c.254 0 .464.018.628.054l-.124 1.176a2.383 2.383 0 0 0-.559-.064c-.505 0-.914.165-1.227.494-.313.329-.47.757-.47 1.284v3.105h-1.262V7.218h.988l.167 1.047h.064c.197-.354.454-.636.771-.843a1.83 1.83 0 0 1 1.023-.312h.001zm4.125 6.155c-.899 0-1.582-.262-2.049-.787-.467-.525-.701-1.277-.701-2.259 0-.999.244-1.767.733-2.304.489-.537 1.195-.806 2.119-.806.627 0 1.191.116 1.692.349l-.381 1.015c-.534-.208-.974-.312-1.321-.312-1.028 0-1.542.682-1.542 2.046 0 .666.128 1.166.384 1.501.256.335.631.502 1.125.502a3.23 3.23 0 0 0 1.595-.419v1.101a2.53 2.53 0 0 1-.722.285 4.356 4.356 0 0 1-.932.086v.002zm8.277-.107h-1.268V9.506c0-.458-.092-.8-.277-1.026-.184-.226-.477-.338-.878-.338-.53 0-.919.158-1.168.475-.249.317-.373.848-.373 1.593v2.949h-1.262V4.801h1.262v2.122c0 .34-.021.704-.064 1.09h.081a1.76 1.76 0 0 1 .717-.666c.306-.158.663-.236 1.072-.236 1.439 0 2.159.725 2.159 2.175v3.873l-.001-.001zm7.649-6.048c.741 0 1.319.269 1.732.806.414.537.62 1.291.62 2.261 0 .974-.209 1.732-.628 2.275-.419.542-1.001.814-1.746.814-.752 0-1.336-.27-1.751-.811h-.086l-.231.704h-.945V4.801h1.262v1.987l-.021.655-.032.553h.054c.401-.591.992-.886 1.772-.886zm-.328 1.031c-.508 0-.875.149-1.098.448-.224.299-.339.799-.346 1.501v.086c0 .723.115 1.247.344 1.571.229.324.603.486 1.123.486.448 0 .787-.177 1.018-.532.231-.354.346-.867.346-1.536 0-1.35-.462-2.025-1.386-2.025l-.001.001zm3.244-.924h1.375l1.209 3.368c.183.48.304.931.365 1.354h.043c.032-.197.091-.436.177-.717.086-.281.541-1.616 1.364-4.004h1.364l-2.541 6.73c-.462 1.235-1.232 1.853-2.31 1.853-.279 0-.551-.03-.816-.091v-.999c.19.043.406.064.65.064.609 0 1.037-.353 1.284-1.058l.22-.559-2.385-5.941h.001z' fill='%231D3657'/></g></svg>");
-  background-repeat: no-repeat;
-  background-position: 50%;
-  background-size: 100%;
-  overflow: hidden;
-  text-indent: -9000px;
-  width: 100%;
-  height: 100%;
-  display: block;
-  transform: translate(-8px);
-}
-
-.algolia-autocomplete .algolia-docsearch-suggestion--highlight {
-  color: #FF8C00;
-  background: rgba(232, 189, 54, 0.1)
-}
-
-
-.algolia-autocomplete .algolia-docsearch-suggestion--text .algolia-docsearch-suggestion--highlight {
-  box-shadow: inset 0 -2px 0 0 rgba(105, 105, 105, .5)
-}
-
-.algolia-autocomplete .ds-suggestion.ds-cursor .algolia-docsearch-suggestion--content {
-  background-color: rgba(192, 192, 192, .15)
-}
diff --git a/docs/dev/docsearch.js b/docs/dev/docsearch.js
deleted file mode 100644
index b35504cd..00000000
--- a/docs/dev/docsearch.js
+++ /dev/null
@@ -1,85 +0,0 @@
-$(function() {
-
-  // register a handler to move the focus to the search bar
-  // upon pressing shift + "/" (i.e. "?")
-  $(document).on('keydown', function(e) {
-    if (e.shiftKey && e.keyCode == 191) {
-      e.preventDefault();
-      $("#search-input").focus();
-    }
-  });
-
-  $(document).ready(function() {
-    // do keyword highlighting
-    /* modified from https://jsfiddle.net/julmot/bL6bb5oo/ */
-    var mark = function() {
-
-      var referrer = document.URL ;
-      var paramKey = "q" ;
-
-      if (referrer.indexOf("?") !== -1) {
-        var qs = referrer.substr(referrer.indexOf('?') + 1);
-        var qs_noanchor = qs.split('#')[0];
-        var qsa = qs_noanchor.split('&');
-        var keyword = "";
-
-        for (var i = 0; i < qsa.length; i++) {
-          var currentParam = qsa[i].split('=');
-
-          if (currentParam.length !== 2) {
-            continue;
-          }
-
-          if (currentParam[0] == paramKey) {
-            keyword = decodeURIComponent(currentParam[1].replace(/\+/g, "%20"));
-          }
-        }
-
-        if (keyword !== "") {
-          $(".contents").unmark({
-            done: function() {
-              $(".contents").mark(keyword);
-            }
-          });
-        }
-      }
-    };
-
-    mark();
-  });
-});
-
-/* Search term highlighting ------------------------------*/
-
-function matchedWords(hit) {
-  var words = [];
-
-  var hierarchy = hit._highlightResult.hierarchy;
-  // loop to fetch from lvl0, lvl1, etc.
-  for (var idx in hierarchy) {
-    words = words.concat(hierarchy[idx].matchedWords);
-  }
-
-  var content = hit._highlightResult.content;
-  if (content) {
-    words = words.concat(content.matchedWords);
-  }
-
-  // return unique words
-  var words_uniq = [...new Set(words)];
-  return words_uniq;
-}
-
-function updateHitURL(hit) {
-
-  var words = matchedWords(hit);
-  var url = "";
-
-  if (hit.anchor) {
-    url = hit.url_without_anchor + '?q=' + escape(words.join(" ")) + '#' + hit.anchor;
-  } else {
-    url = hit.url + '?q=' + escape(words.join(" "));
-  }
-
-  return url;
-}
diff --git a/docs/dev/index.html b/docs/dev/index.html
deleted file mode 100644
index 59b78ff0..00000000
--- a/docs/dev/index.html
+++ /dev/null
@@ -1,331 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en">
-<head>
-<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
-<meta charset="utf-8">
-<meta http-equiv="X-UA-Compatible" content="IE=edge">
-<meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>Kinetic Evaluation of Chemical Degradation Data • mkin</title>
-<!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous">
-<script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="bootstrap-toc.css">
-<script src="bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous">
-<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous">
-<!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="pkgdown.css" rel="stylesheet">
-<script src="pkgdown.js"></script><meta property="og:title" content="Kinetic Evaluation of Chemical Degradation Data">
-<meta property="og:description" content="Calculation routines based on the FOCUS Kinetics Report (2006,
-  2014).  Includes a function for conveniently defining differential equation
-  models, model solution based on eigenvalues if possible or using numerical
-  solvers.  If a C compiler (on windows: Rtools) is installed, differential
-  equation models are solved using automatically generated C functions.
-  Heteroscedasticity can be taken into account using variance by variable or
-  two-component error models as described by Ranke and Meinecke (2018)
-  &lt;doi:10.3390/environments6120124&gt;.  Hierarchical degradation models can
-  be fitted using nonlinear mixed-effects model packages as a back end as
-  described by Ranke et al. (2021) &lt;doi:10.3390/environments8080071&gt;.  Please
-  note that no warranty is implied for correctness of results or fitness for a
-  particular purpose.">
-<meta name="robots" content="noindex">
-<!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]-->
-</head>
-<body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-home">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.4</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav">
-<li>
-  <a href="reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu">
-<li>
-      <a href="articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Performance</li>
-    <li>
-      <a href="articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul>
-</li>
-<li>
-  <a href="news/index.html">News</a>
-</li>
-      </ul>
-<ul class="nav navbar-nav navbar-right">
-<li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul>
-</div>
-<!--/.nav-collapse -->
-  </div>
-<!--/.container -->
-</div>
-<!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="contents col-md-9">
-<div class="section level1">
-<div class="page-header"><h1 id="mkin">mkin<a class="anchor" aria-label="anchor" href="#mkin"></a>
-</h1></div>
-<p><a href="https://cran.r-project.org/package=mkin" class="external-link"><img src="https://www.r-pkg.org/badges/version/mkin"></a> <a href="https://jranke.r-universe.dev/ui/#package:mkin" class="external-link"><img src="https://jranke.r-universe.dev/badges/mkin" alt="mkin status badge"></a> <a href="https://app.travis-ci.com/github/jranke/mkin" class="external-link"><img src="https://travis-ci.com/jranke/mkin.svg?branch=main" alt="Build Status"></a> <a href="https://app.codecov.io/gh/jranke/mkin" class="external-link"><img src="https://codecov.io/github/jranke/mkin/branch/main/graphs/badge.svg" alt="codecov"></a></p>
-<p>The <a href="https://www.r-project.org" class="external-link">R</a> package <strong>mkin</strong> provides calculation routines for the analysis of chemical degradation data, including <b>m</b>ulticompartment <b>kin</b>etics as needed for modelling the formation and decline of transformation products, or if several degradation compartments are involved. It provides stable functionality for kinetic evaluations according to the FOCUS guidance (see below for details). In addition, it provides functionality to do hierarchical kinetics based on nonlinear mixed-effects models.</p>
-<div class="section level2">
-<h2 id="installation">Installation<a class="anchor" aria-label="anchor" href="#installation"></a>
-</h2>
-<p>You can install the latest released version from <a href="https://cran.r-project.org/package=mkin" class="external-link">CRAN</a> from within R:</p>
-<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/utils/install.packages.html" class="external-link">install.packages</a></span><span class="op">(</span><span class="st">"mkin"</span><span class="op">)</span></span></code></pre></div>
-</div>
-<div class="section level2">
-<h2 id="background">Background<a class="anchor" aria-label="anchor" href="#background"></a>
-</h2>
-<p>In the regulatory evaluation of chemical substances like plant protection products (pesticides), biocides and other chemicals, degradation data play an important role. For the evaluation of pesticide degradation experiments, detailed guidance and various helpful tools have been developed as detailed in ‘Credits and historical remarks’ below. This package aims to provide a one stop solution for degradation kinetics, addressing modellers that are willing to, or even prefer to work with R.</p>
-</div>
-<div class="section level2">
-<h2 id="basic-usage">Basic usage<a class="anchor" aria-label="anchor" href="#basic-usage"></a>
-</h2>
-<p>For a start, have a look at the code examples provided for <a href="https://pkgdown.jrwb.de/mkin/reference/plot.mkinfit.html"><code>plot.mkinfit</code></a> and <a href="https://pkgdown.jrwb.de/mkin/reference/plot.mmkin.html"><code>plot.mmkin</code></a>, and at the package vignettes <a href="https://pkgdown.jrwb.de/mkin/articles/FOCUS_L.html"><code>FOCUS L</code></a> and <a href="https://pkgdown.jrwb.de/mkin/articles/FOCUS_D.html"><code>FOCUS D</code></a>.</p>
-</div>
-<div class="section level2">
-<h2 id="documentation">Documentation<a class="anchor" aria-label="anchor" href="#documentation"></a>
-</h2>
-<p>The HTML documentation of the latest version released to CRAN is available at <a href="https://pkgdown.jrwb.de/mkin/">jrwb.de</a> and <a href="https://jranke.github.io/mkin/" class="external-link">github</a>.</p>
-<p>Documentation of the development version is found in the <a href="https://pkgdown.jrwb.de/mkin/dev/">‘dev’ subdirectory</a>. In the articles section of this documentation, you can also find demonstrations of the application of nonlinear hierarchical models, also known as nonlinear mixed-effects models, to more complex data, including transformation products and covariates.</p>
-</div>
-<div class="section level2">
-<h2 id="features">Features<a class="anchor" aria-label="anchor" href="#features"></a>
-</h2>
-<div class="section level3">
-<h3 id="general">General<a class="anchor" aria-label="anchor" href="#general"></a>
-</h3>
-<ul>
-<li>Highly flexible model specification using <a href="https://pkgdown.jrwb.de/mkin/reference/mkinmod.html"><code>mkinmod</code></a>, including equilibrium reactions and using the single first-order reversible binding (SFORB) model, which will automatically create two state variables for the observed variable.</li>
-<li>Model solution (forward modelling) in the function <a href="https://pkgdown.jrwb.de/mkin/reference/mkinpredict.html"><code>mkinpredict</code></a> is performed either using the analytical solution for the case of parent only degradation or some simple models involving a single transformation product, , an eigenvalue based solution if only simple first-order (SFO) or SFORB kinetics are used in the model, or using a numeric solver from the <code>deSolve</code> package (default is <code>lsoda</code>).</li>
-<li>The usual one-sided t-test for significant difference from zero is shown based on estimators for the untransformed parameters.</li>
-<li>Summary and plotting functions. The <code>summary</code> of an <code>mkinfit</code> object is in fact a full report that should give enough information to be able to approximately reproduce the fit with other tools.</li>
-<li>The chi-squared error level as defined in the FOCUS kinetics guidance (see below) is calculated for each observed variable.</li>
-<li>The ‘variance by variable’ error model which is often fitted using Iteratively Reweighted Least Squares (IRLS) can be specified as <code>error_model = "obs"</code>.</li>
-</ul>
-</div>
-<div class="section level3">
-<h3 id="unique-in-mkin">Unique in mkin<a class="anchor" aria-label="anchor" href="#unique-in-mkin"></a>
-</h3>
-<ul>
-<li>Three different error models can be selected using the argument <code>error_model</code> to the <a href="https://pkgdown.jrwb.de/mkin/reference/mkinfit.html"><code>mkinfit</code></a> function. A two-component error model similar to the one proposed by <a href="https://pkgdown.jrwb.de/mkin/reference/sigma_twocomp.html">Rocke and Lorenzato</a> can be selected using the argument <code>error_model = "tc"</code>.</li>
-<li>Model comparisons using the Akaike Information Criterion (AIC) are supported which can also be used for non-constant variance. In such cases the FOCUS chi-squared error level is not meaningful.</li>
-<li>By default, kinetic rate constants and kinetic formation fractions are transformed internally using <a href="https://pkgdown.jrwb.de/mkin/reference/transform_odeparms.html"><code>transform_odeparms</code></a> so their estimators can more reasonably be expected to follow a normal distribution.</li>
-<li>When parameter estimates are backtransformed to match the model definition, confidence intervals calculated from standard errors are also backtransformed to the correct scale, and will not include meaningless values like negative rate constants or formation fractions adding up to more than 1, which cannot occur in a single experiment with a single defined radiolabel position.</li>
-<li>When a metabolite decline phase is not described well by SFO kinetics, SFORB kinetics can be used for the metabolite. Mathematically, the SFORB model is equivalent to the DFOP model. However, the SFORB model has the advantage that there is a mechanistic interpretation of the model parameters.</li>
-<li>Nonlinear mixed-effects models (hierarchical models) can be created from fits of the same degradation model to different datasets for the same compound by using the <a href="https://pkgdown.jrwb.de/mkin/reference/nlme.mmkin.html">nlme.mmkin</a> and <a href="https://pkgdown.jrwb.de/mkin/reference/saem.html">saem.mmkin</a> methods. Note that the convergence of the nlme fits depends on the quality of the data. Convergence is better for simple models and data for many groups (e.g. soils). The saem method uses the <code>saemix</code> package as a backend. Analytical solutions suitable for use with this package have been implemented for parent only models and the most important models including one metabolite (SFO-SFO and DFOP-SFO). Fitting other models with <code>saem.mmkin</code>, while it makes use of the compiled ODE models that mkin provides, has longer run times (from a couple of minutes to more than an hour).</li>
-</ul>
-</div>
-<div class="section level3">
-<h3 id="performance">Performance<a class="anchor" aria-label="anchor" href="#performance"></a>
-</h3>
-<ul>
-<li>Parallel fitting of several models to several datasets is supported, see for example <a href="https://pkgdown.jrwb.de/mkin/reference/plot.mmkin.html"><code>plot.mmkin</code></a>.</li>
-<li>If a C compiler is installed, the kinetic models are compiled from automatically generated C code, see <a href="https://pkgdown.jrwb.de/mkin/articles/web_only/compiled_models.html">vignette <code>compiled_models</code></a>. The autogeneration of C code was inspired by the <a href="https://github.com/karlines/ccSolve" class="external-link"><code>ccSolve</code></a> package. Thanks to Karline Soetaert for her work on that.</li>
-<li>Even if no compiler is installed, many degradation models still give <a href="https://pkgdown.jrwb.de/mkin/articles/web_only/benchmarks.html">very good performance</a>, as current versions of mkin also have <a href="https://jrwb.de/performance-improvements-mkin/" class="external-link">analytical solutions for some models with one metabolite</a>, and if SFO or SFORB are used for the parent compound, Eigenvalue based solutions of the degradation model are available.</li>
-</ul>
-</div>
-</div>
-<div class="section level2">
-<h2 id="gui">GUI<a class="anchor" aria-label="anchor" href="#gui"></a>
-</h2>
-<p>There is a graphical user interface that may be useful. Please refer to its <a href="https://pkgdown.jrwb.de/gmkin/" class="external-link">documentation page</a> for installation instructions and a manual. It only supports evaluations using (generalised) nonlinear regression, but not simultaneous fits using nonlinear mixed-effects models.</p>
-</div>
-<div class="section level2">
-<h2 id="news">News<a class="anchor" aria-label="anchor" href="#news"></a>
-</h2>
-<p>There is a list of changes for the latest <a href="https://cran.r-project.org/package=mkin/news/news.html" class="external-link">CRAN release</a> and one for each github branch, e.g. <a href="https://github.com/jranke/mkin/blob/main/NEWS.md" class="external-link">the main branch</a>.</p>
-</div>
-<div class="section level2">
-<h2 id="credits-and-historical-remarks">Credits and historical remarks<a class="anchor" aria-label="anchor" href="#credits-and-historical-remarks"></a>
-</h2>
-<p><code>mkin</code> would not be possible without the underlying software stack consisting of, among others, R and the package <a href="https://cran.r-project.org/package=deSolve" class="external-link">deSolve</a>. In previous version, <code>mkin</code> was also using the functionality of the <a href="https://cran.r-project.org/package=FME" class="external-link">FME</a> package. Please refer to the <a href="https://cran.r-project.org/package=mkin" class="external-link">package page on CRAN</a> for the full list of imported and suggested R packages. Also, <a href="https://debian.org" class="external-link">Debian Linux</a>, the vim editor and the <a href="https://github.com/jalvesaq/Nvim-R" class="external-link">Nvim-R</a> plugin have been invaluable in its development.</p>
-<p><code>mkin</code> could not have been written without me being introduced to regulatory fate modelling of pesticides by Adrian Gurney during my time at Harlan Laboratories Ltd (formerly RCC Ltd). <code>mkin</code> greatly profits from and largely follows the work done by the <a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics" class="external-link">FOCUS Degradation Kinetics Workgroup</a>, as detailed in their guidance document from 2006, slightly updated in 2011 and in 2014.</p>
-<p>Also, it was inspired by the first version of KinGUI developed by BayerCropScience, which is based on the MatLab runtime environment.</p>
-<p>The companion package <a href="http://kinfit.r-forge.r-project.org/kinfit_static/index.html" class="external-link">kinfit</a> (now deprecated) was <a href="https://r-forge.r-project.org/scm/viewvc.php?view=rev&amp;root=kinfit&amp;revision=2" class="external-link">started in 2008</a> and <a href="https://cran.r-project.org/src/contrib/Archive/kinfit/" class="external-link">first published</a> on CRAN on 01 May 2010.</p>
-<p>The first <code>mkin</code> code was <a href="https://r-forge.r-project.org/scm/viewvc.php?view=rev&amp;root=kinfit&amp;revision=8" class="external-link">published on 11 May 2010</a> and the <a href="https://cran.r-project.org/src/contrib/Archive/mkin/" class="external-link">first CRAN version</a> on 18 May 2010.</p>
-<p>In 2011, Bayer Crop Science started to distribute an R based successor to KinGUI named KinGUII whose R code is based on <code>mkin</code>, but which added, among other refinements, a closed source graphical user interface (GUI), iteratively reweighted least squares (IRLS) optimisation of the variance for each of the observed variables, and Markov Chain Monte Carlo (MCMC) simulation functionality, similar to what is available e.g. in the <code>FME</code> package.</p>
-<p>Somewhat in parallel, Syngenta has sponsored the development of an <code>mkin</code> and KinGUII based GUI application called CAKE, which also adds IRLS and MCMC, is more limited in the model formulation, but puts more weight on usability. CAKE is available for download from the <a href="https://cake-kinetics.org" class="external-link">CAKE website</a>, where you can also find a zip archive of the R scripts derived from <code>mkin</code>, published under the GPL license.</p>
-<p>Finally, there is <a href="https://github.com/zhenglei-gao/KineticEval" class="external-link">KineticEval</a>, which contains some further development of the scripts used for KinGUII.</p>
-<p>Thanks to René Lehmann, formerly working at the Umweltbundesamt, for the nice cooperation on parameter transformations, especially the isometric log-ratio transformation that is now used for formation fractions in case there are more than two transformation targets.</p>
-<p>Many inspirations for improvements of mkin resulted from doing kinetic evaluations of degradation data for my clients while working at Harlan Laboratories and at Eurofins Regulatory AG, and now as an independent consultant.</p>
-<p>Funding was received from the Umweltbundesamt in the course of the projects</p>
-<ul>
-<li>Project Number 27452 (Testing and validation of modelling software as an alternative to ModelMaker 4.0, 2014-2015)</li>
-<li>Project Number 56703 (Optimization of gmkin for routine use in the Umweltbundesamt, 2015)</li>
-<li>Project Number 92570 (Update of Project Number 27452, 2017-2018)</li>
-<li>Project Number 112407 (Testing the feasibility of using an error model according to Rocke and Lorenzato for more realistic parameter estimates in the kinetic evaluation of degradation data, 2018-2019)</li>
-<li>Project Number 120667 (Development of objective criteria for the evaluation of the visual fit in the kinetic evaluation of degradation data, 2019-2020)</li>
-<li>Project Number 146839 (Checking the feasibility of using mixed-effects models for the derivation of kinetic modelling parameters from degradation studies, 2020-2021)</li>
-<li>Project Number 173340 (Application of nonlinear hierarchical models to the kinetic evaluation of chemical degradation data)</li>
-</ul>
-<p>Thanks to everyone involved for collaboration and support!</p>
-<p>Thanks are due also to Emmanuelle Comets, maintainer of the saemix package, for her interest and support for using the SAEM algorithm and its implementation in saemix for the evaluation of chemical degradation data.</p>
-</div>
-<div class="section level2">
-<h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a>
-</h2>
-<table class="table">
-<tr>
-<td>
-Ranke J, Wöltjen J, Schmidt J, and Comets E (2021) Taking kinetic evaluations of degradation data to the next level with nonlinear mixed-effects models. <i>Environments</i> <b>8</b> (8) 71 <a href="https://doi.org/10.3390/environments8080071" class="external-link">doi:10.3390/environments8080071</a>
-</td>
-</tr>
-<tr>
-<td>
-Ranke J, Meinecke S (2019) Error Models for the Kinetic Evaluation of Chemical Degradation Data <i>Environments</i> <b>6</b> (12) 124 <a href="https://doi.org/10.3390/environments6120124" class="external-link">doi:10.3390/environments6120124</a>
-</td>
-</tr>
-<tr>
-<td>
-Ranke J, Wöltjen J, Meinecke S (2018) Comparison of software tools for kinetic evaluation of chemical degradation data <i>Environmental Sciences Europe</i> <b>30</b> 17 <a href="https://doi.org/10.1186/s12302-018-0145-1" class="external-link">doi:10.1186/s12302-018-0145-1</a>
-</td>
-</tr>
-</table>
-</div>
-<div class="section level2">
-<h2 id="development">Development<a class="anchor" aria-label="anchor" href="#development"></a>
-</h2>
-<p>Contributions are welcome!</p>
-</div>
-</div>
-  </div>
-
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <div class="links">
-<h2 data-toc-skip>Links</h2>
-<ul class="list-unstyled">
-<li><a href="https://cloud.r-project.org/package=mkin" class="external-link">View on CRAN</a></li>
-<li><a href="https://github.com/jranke/mkin/" class="external-link">Browse source code</a></li>
-<li><a href="https://github.com/jranke/mkin/issues/" class="external-link">Report a bug</a></li>
-</ul>
-</div>
-
-<div class="license">
-<h2 data-toc-skip>License</h2>
-<ul class="list-unstyled">
-<li>GPL</li>
-</ul>
-</div>
-
-
-<div class="citation">
-<h2 data-toc-skip>Citation</h2>
-<ul class="list-unstyled">
-<li><a href="authors.html#citation">Citing mkin</a></li>
-</ul>
-</div>
-
-<div class="developers">
-<h2 data-toc-skip>Developers</h2>
-<ul class="list-unstyled">
-<li>Johannes Ranke <br><small class="roles"> Author, maintainer, copyright holder </small> <a href="https://orcid.org/0000-0003-4371-6538" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a> </li>
-<li><a href="authors.html">More about authors...</a></li>
-</ul>
-</div>
-
-
-
-  </div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p>
-<p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p>
-<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer>
-</div>
-
-  
-
-
-  
-
-  </body>
-</html>
diff --git a/docs/dev/link.svg b/docs/dev/link.svg
deleted file mode 100644
index 88ad8276..00000000
--- a/docs/dev/link.svg
+++ /dev/null
@@ -1,12 +0,0 @@
-<?xml version="1.0" encoding="utf-8"?>
-<!-- Generator: Adobe Illustrator 19.2.1, SVG Export Plug-In . SVG Version: 6.00 Build 0)  -->
-<svg version="1.1" id="Layer_1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" x="0px" y="0px"
-	 viewBox="0 0 20 20" style="enable-background:new 0 0 20 20;" xml:space="preserve">
-<style type="text/css">
-	.st0{fill:#75AADB;}
-</style>
-<path class="st0" d="M4,11.3h1.3v1.3H4c-2,0-4-2.3-4-4.7s2.1-4.7,4-4.7h5.3c1.9,0,4,2.3,4,4.7c0,1.9-1.2,3.6-2.7,4.3v-1.5
-	C11.4,10.2,12,9.1,12,8c0-1.7-1.4-3.3-2.7-3.3H4C2.7,4.7,1.3,6.3,1.3,8S2.7,11.3,4,11.3z M16,7.3h-1.3v1.3H16c1.3,0,2.7,1.6,2.7,3.3
-	s-1.4,3.3-2.7,3.3h-5.3C9.4,15.3,8,13.7,8,12c0-1.1,0.6-2.2,1.3-2.8V7.7C7.9,8.4,6.7,10.1,6.7,12c0,2.4,2.1,4.7,4,4.7H16
-	c1.9,0,4-2.3,4-4.7S18,7.3,16,7.3z"/>
-</svg>
diff --git a/docs/dev/news/index.html b/docs/dev/news/index.html
deleted file mode 100644
index 93207241..00000000
--- a/docs/dev/news/index.html
+++ /dev/null
@@ -1,697 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Changelog • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Changelog"><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-news">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.4</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-      <h1 data-toc-skip>Changelog <small></small></h1>
-      <small>Source: <a href="https://github.com/jranke/mkin/blob/HEAD/NEWS.md" class="external-link"><code>NEWS.md</code></a></small>
-    </div>
-
-    <div class="section level2">
-<h2 class="page-header" data-toc-text="1.2.4" id="mkin-124">mkin 1.2.4<a class="anchor" aria-label="anchor" href="#mkin-124"></a></h2>
-</div>
-    <div class="section level2">
-<h2 class="page-header" data-toc-text="1.2.3.1" id="mkin-1231-unreleased">mkin 1.2.3.1 (unreleased)<a class="anchor" aria-label="anchor" href="#mkin-1231-unreleased"></a></h2>
-<ul><li>Small fixes to get the online docs right (example code in R/hierarchical_kinetics, cluster setup in cyantraniliprole and dmta pathway vignettes, graphics and model comparison in multistart vignette), rebuild online docs</li>
-</ul></div>
-    <div class="section level2">
-<h2 class="page-header" data-toc-text="1.2.3" id="mkin-123">mkin 1.2.3<a class="anchor" aria-label="anchor" href="#mkin-123"></a></h2>
-<ul><li><p>‘R/{endpoints,parms,plot.mixed.mmkin,summary.saem.mmkin}.R’: Calculate parameters and endpoints and plot population curves for specific covariate values, or specific percentiles of covariate values used in saem fits.</p></li>
-<li><p>Depend on current deSolve version with the possibility to avoid resolving symbols in a shared library (compiled models) over and over, thanks to Thomas Petzoldt.</p></li>
-<li><p>‘inst/rmarkdown/templates/hierarchical_kinetics/skeleton/skeleton.Rmd’: Start a new cluster after creating a model stored in the user specified location, because otherwise symbols are not found by the worker processes.</p></li>
-<li><p>‘tests/testthat/test_compiled_symbols.R’: Some new tests to control problems that may have been introduced by the possibility to use pre-resolved symbols.</p></li>
-<li><p>‘R/mkinerrmin.R’: Fix typo in subset (use of = instead of ==), thanks to Sebastian Meyer for spotting this during his work on R 4.3.0.</p></li>
-</ul></div>
-    <div class="section level2">
-<h2 class="page-header" data-toc-text="1.2.2" id="mkin-122-unreleased">mkin 1.2.2 (unreleased)<a class="anchor" aria-label="anchor" href="#mkin-122-unreleased"></a></h2>
-<ul><li><p>‘inst/rmarkdown/templates/hierarchical_kinetics’: R markdown template to facilitate the application of hierarchical kinetic models.</p></li>
-<li><p>‘inst/testdata/{cyantraniliprole_soil_efsa_2014,lambda-cyhalothrin_soil_efsa_2014}.xlsx’: Example spreadsheets for use with ‘read_spreadsheet()’.</p></li>
-<li><p>‘R/mhmkin.R’: Allow an ‘illparms.mhmkin’ object or a list with suitable dimensions as value of the argument ‘no_random_effects’, making it possible to exclude random effects that were ill-defined in simpler variants of the set of degradation models. Remove the possibility to exclude random effects based on separate fits, as it did not work well.</p></li>
-<li><p>‘R/summary.saem.mmkin.R’: List all initial parameter values in the summary, including random effects and error model parameters. Avoid redundant warnings that occurred in the calculation of correlations of the fixed effects in the case that the Fisher information matrix could not be inverted. List correlations of random effects if specified by the user in the covariance model.</p></li>
-<li><p>‘R/parplot.R’: Possibility to select the top ‘llquant’ fraction of the fits for the parameter plots, and improved legend text.</p></li>
-<li><p>‘R/illparms.R’: Also check if confidence intervals for slope parameters in covariate models include zero. Only implemented for fits obtained with the saemix backend.</p></li>
-<li><p>‘R/parplot.R’: Make the function work also in the case that some of the multistart runs failed.</p></li>
-<li><p>‘R/intervals.R’: Include correlations of random effects in the model in case there are any.</p></li>
-</ul></div>
-    <div class="section level2">
-<h2 class="page-header" data-toc-text="1.2.1" id="mkin-121-2022-11-19">mkin 1.2.1 (2022-11-19)<a class="anchor" aria-label="anchor" href="#mkin-121-2022-11-19"></a></h2>
-<ul><li><p>‘{data,R}/ds_mixed.rda’: Include the test data in the package instead of generating it in ‘tests/testthat/setup_script.R’. Refactor the generating code to make it consistent and update tests.</p></li>
-<li><p>‘tests/testthat/setup_script.R’: Excluded another ill-defined random effect for the DFOP fit with ‘saem’, in an attempt to avoid a platform dependence that surfaced on Fedora systems on the CRAN check farm</p></li>
-<li><p>‘tests/testthat/test_mixed.R’: Round parameters found by saemix to two significant digits before printing, to also help to avoid platform dependence of tests</p></li>
-<li><p>‘R/saem.R’: Fix a bug that prevented that ‘error.ini’ is passed to ‘saemix_model’, and set default to c(1, 1) to avoid changing test results</p></li>
-<li><p>‘R/parplot.R’: Show initial values for error model parameters</p></li>
-<li><p>‘R/loglik.mkinfit.R’: Add ‘nobs’ attribute to the resulting ‘logLik’ object, in order to make test_AIC.R succeed on current R-devel</p></li>
-</ul></div>
-    <div class="section level2">
-<h2 class="page-header" data-toc-text="1.2.0" id="mkin-120-2022-11-17">mkin 1.2.0 (2022-11-17)<a class="anchor" aria-label="anchor" href="#mkin-120-2022-11-17"></a></h2>
-<ul><li><p>‘R/saem.R’: ‘logLik’, ‘update’ and ‘anova’ methods for ‘saem.mmkin’ objects.</p></li>
-<li><p>‘R/saem.R’: Automatic estimation of start parameters for random effects for the case of mkin transformations, nicely improving convergence and reducing problems with iterative ODE solutions.</p></li>
-<li><p>‘R/status.R’: New generic to show status information for fit array objects with methods for ‘mmkin’, ‘mhmkin’ and ‘multistart’ objects.</p></li>
-<li><p>‘R/mhmkin.R’: New method for performing multiple hierarchical mkin fits in one function call, optionally in parallel.</p></li>
-<li><p>‘R/mhmkin.R’: ‘anova.mhmkin’ for conveniently comparing the resulting fits.</p></li>
-<li><p>‘R/illparms.R’: New generic to show ill-defined parameters with methods for ‘mkinfit’, ‘mmkin’, ‘saem.mmkin’ and ‘mhmkin’ objects.</p></li>
-<li><p>‘R/multistart.R’: New method for testing multiple start parameters for hierarchical model fits, with function ‘llhist’ and new generic ‘parplot’ for diagnostics, and new generics ‘which.best’ and ‘best’ for extracting the fit with the highest likelihood</p></li>
-<li><p>‘R/summary.mmkin.R’: Summary method for mmkin objects.</p></li>
-<li><p>‘R/saem.R’: Implement and test saemix transformations for FOMC and HS. Also, error out if saemix transformations are requested but not supported.</p></li>
-<li><p>‘R/read_spreadsheet.R’: Conveniently read in data from a spreadsheet file.</p></li>
-<li><p>‘R/tex_listings.R’: Conveniently include summaries of fit objects in R markdown documents that are compiled to LaTeX.</p></li>
-</ul></div>
-    <div class="section level2">
-<h2 class="page-header" data-toc-text="1.1.1" id="mkin-111-2022-07-12">mkin 1.1.1 (2022-07-12)<a class="anchor" aria-label="anchor" href="#mkin-111-2022-07-12"></a></h2>
-<ul><li><p>’R/{mkinmod,mkinpredict}.R: Store DLL information in mkinmod objects and use that information in mkinpredict to avoid a performance regression brought by a bugfix in R 4.2.x. Thanks to Tomas Kalibera for his analysis of the problem on the r-package-devel list and his suggestion on how to fix it.</p></li>
-<li><p>‘vignettes/FOCUS_L.rmd’: Remove an outdated note referring to a failure to calculate the covariance matrix for DFOP with the L2 dataset. Since 0.9.45.5 the covariance matrix is available</p></li>
-<li><p>‘vignettes/web_only/benchmarks.rmd’: Add the first benchmark data using my laptop system, therefore add the CPU when showing the benchmark results.</p></li>
-<li><p>‘dimethenamid_2018’: Update example code to use saemix</p></li>
-<li><p>‘CAKE_export’: Check for validity of the map argument, updates</p></li>
-<li><p>‘saem()’: Slightly improve speed in the case that no analytical solution for saemix is implemented, activate a test of the respective code</p></li>
-<li><p>‘mean_degparms’: New argument ‘default_log_parms’ that makes it possible to supply a surrogate value (default) for log parameters that fail the t-test</p></li>
-<li><p>‘plot.mixed.mmkin’: Pass the frame argument also to residual plots, take the ‘default_log_parms’ argument for ‘mean_degparms’ used for constructing approximate population curves, plot population curve last to avoid that it is covered by data</p></li>
-<li><p>‘plot.mkinfit’: Respect argument ‘maxabs’ for residual plots, and make it possible to give ylim as a list, for row layouts</p></li>
-</ul></div>
-    <div class="section level2">
-<h2 class="page-header" data-toc-text="1.1.0" id="mkin-110-2022-03-14">mkin 1.1.0 (2022-03-14)<a class="anchor" aria-label="anchor" href="#mkin-110-2022-03-14"></a></h2>
-<div class="section level3">
-<h3 id="mixed-effects-models-1-1-0">Mixed-effects models<a class="anchor" aria-label="anchor" href="#mixed-effects-models-1-1-0"></a></h3>
-<ul><li><p>Reintroduce the interface to saemix version 3.0 (now on CRAN), in particular the generic function ‘saem’ with a generator ‘saem.mmkin’, currently using ‘saemix_model’ and ‘saemix_data’, summary and plot methods</p></li>
-<li><p>‘mean_degparms’: New argument ‘test_log_parms’ that makes the function only consider log-transformed parameters where the untransformed parameters pass the t-test for a certain confidence level. This can be used to obtain more plausible starting parameters for the different mixed-effects model backends</p></li>
-<li><p>‘plot.mixed.mmkin’: Gains arguments ‘test_log_parms’ and ‘conf.level’</p></li>
-<li><p>‘vignettes/web_only/dimethenamid_2018.rmd’: Example evaluations of the dimethenamid data.</p></li>
-<li><p>‘intervals’: Provide a method of this nlme function for ‘saem.mmkin’ objects.</p></li>
-</ul></div>
-</div>
-    <div class="section level2">
-<h2 class="page-header" data-toc-text="1.0.5" id="mkin-105-2021-09-15">mkin 1.0.5 (2021-09-15)<a class="anchor" aria-label="anchor" href="#mkin-105-2021-09-15"></a></h2>
-<ul><li>‘dimethenamid_2018’: Correct the data for the Borstel soil. The five observations from Staudenmaier (2013) that were previously stored as “Borstel 2” are actually just a subset of the 16 observations in “Borstel 1” which is now simply “Borstel”</li>
-</ul></div>
-    <div class="section level2">
-<h2 class="page-header" data-toc-text="1.0.4" id="mkin-104-2021-04-20">mkin 1.0.4 (2021-04-20)<a class="anchor" aria-label="anchor" href="#mkin-104-2021-04-20"></a></h2>
-<ul><li><p>All plotting functions setting graphical parameters: Use on.exit() for resetting graphical parameters</p></li>
-<li><p>‘plot.mkinfit’: Use xlab and xlim for the residual plot if show_residuals is TRUE</p></li>
-<li><p>‘mmkin’: Use cores = 1 per default on Windows to make it easier for first time users</p></li>
-</ul></div>
-    <div class="section level2">
-<h2 class="page-header" data-toc-text="1.0.3" id="mkin-103-2021-02-15">mkin 1.0.3 (2021-02-15)<a class="anchor" aria-label="anchor" href="#mkin-103-2021-02-15"></a></h2>
-<ul><li>Review and update README, the ‘Introduction to mkin’ vignette and some of the help pages</li>
-</ul></div>
-    <div class="section level2">
-<h2 class="page-header" data-toc-text="1.0.2" id="mkin-102-unreleased">mkin 1.0.2 (Unreleased)<a class="anchor" aria-label="anchor" href="#mkin-102-unreleased"></a></h2>
-<ul><li>‘mkinfit’: Keep model names stored in ‘mkinmod’ objects, avoiding their loss in ‘gmkin’</li>
-</ul></div>
-    <div class="section level2">
-<h2 class="page-header" data-toc-text="1.0.1" id="mkin-101-2021-02-10">mkin 1.0.1 (2021-02-10)<a class="anchor" aria-label="anchor" href="#mkin-101-2021-02-10"></a></h2>
-<ul><li><p>‘confint.mmkin’, ‘nlme.mmkin’, ‘transform_odeparms’: Fix example code in dontrun sections that failed with current defaults</p></li>
-<li><p>‘logLik.mkinfit’: Improve example code to avoid warnings and show convenient syntax</p></li>
-<li><p>‘mkinresplot’: Re-add Katrin Lindenberger as coauthor who was accidentally removed long ago</p></li>
-<li><p>Remove tests relying on non-convergence of the FOMC fit to the FOCUS A dataset as this is platform dependent (revealed by the new additional tests on CRAN, thanks!)</p></li>
-<li><p>Increase test tolerance for some parameter comparisons that also proved to be platform dependent</p></li>
-</ul></div>
-    <div class="section level2">
-<h2 class="page-header" data-toc-text="1.0.0" id="mkin-100-2021-02-03">mkin 1.0.0 (2021-02-03)<a class="anchor" aria-label="anchor" href="#mkin-100-2021-02-03"></a></h2>
-<div class="section level3">
-<h3 id="general-1-0-0">General<a class="anchor" aria-label="anchor" href="#general-1-0-0"></a></h3>
-<ul><li><p>‘mkinmod’ models gain arguments ‘name’ and ‘dll_dir’ which, in conjunction with a current version of the ‘inline’ package, make it possible to still use the DLL used for fast ODE solutions with ‘deSolve’ after saving and restoring the ‘mkinmod’ object.</p></li>
-<li><p>‘mkindsg’ R6 class for groups of ‘mkinds’ datasets with metadata</p></li>
-<li><p>‘f_norm_temp_focus’ generic function to normalise time intervals using the FOCUS method, with methods for numeric vectors and ‘mkindsg’ objects</p></li>
-<li><p>‘D24_2014’ and ‘dimethenamid_2018’ datasets</p></li>
-<li><p>‘focus_soil_moisture’ FOCUS default soil moisture data</p></li>
-<li><p>‘update’ method for ‘mmkin’ objects</p></li>
-<li><p>‘transform_odeparms’, ‘backtransform_odeparms’: Use logit transformation for solitary fractions like the g parameter of the DFOP model, or formation fractions for a pathway to only one target variable</p></li>
-<li><p>‘plot.mmkin’: Add a ylab argument, making it possible to customize the y axis label of the panels on the left without affecting the residual plots. Reduce legend size and vertical distance between panels</p></li>
-<li><p>‘plot.mkinfit’: Change default ylab from “Observed” to “Residue”. Pass xlab to residual plot if show_residuals is TRUE.</p></li>
-</ul></div>
-<div class="section level3">
-<h3 id="mixed-effects-models-1-0-0">Mixed-effects models<a class="anchor" aria-label="anchor" href="#mixed-effects-models-1-0-0"></a></h3>
-<ul><li><p>‘mixed.mmkin’ New container for mmkin objects for plotting with the ‘plot.mixed.mmkin’ method</p></li>
-<li><p>‘plot.mixed.mmkin’ method used for ‘nlme.mmkin’ inheriting from ‘mixed.mmkin’ (currently virtual)</p></li>
-<li><p>‘plot’, ‘summary’ and ‘print’ methods for ‘nlme.mmkin’ objects</p></li>
-</ul></div>
-</div>
-    <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9.50.3" id="mkin-09503-2020-10-08">mkin 0.9.50.3 (2020-10-08)<a class="anchor" aria-label="anchor" href="#mkin-09503-2020-10-08"></a></h2>
-<ul><li><p>‘parms’: Add a method for mmkin objects</p></li>
-<li><p>‘mmkin’ and ‘confint(method = ’profile’): Use all cores detected by parallel::detectCores() per default</p></li>
-<li><p>‘confint(method = ’profile’): Choose accuracy based on ‘rel_tol’ argument, relative to the bounds obtained by the quadratic approximation</p></li>
-<li><p>‘mkinfit’: Make ‘use_of_ff’ = “max” also the default for models specified using short names like “SFO” or “FOMC”</p></li>
-<li><p>‘mkinfit’: Run ‘stats::shapiro.test()’ on standardized residuals and warn if p &lt; 0.05</p></li>
-<li><p>‘mkinfit’: ‘error_model_algorithm’ = ‘d_3’ does not fail if direct fitting fails, but reports that the results for the threestep algorithm are returned</p></li>
-<li><p>‘mmkin’: Do not fail any more if one of the fits fails, but assign the try-error to the respective position in the mmkin object</p></li>
-<li><p>‘mkinfit’: Ignore components of state.ini that do not correspond to state variables in the model</p></li>
-<li><p>‘endpoints’: Back-calculate DT50 value from DT90 also for the biphasic models DFOP, HS and SFORB</p></li>
-</ul></div>
-    <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9.50.2" id="mkin-09502-2020-05-12">mkin 0.9.50.2 (2020-05-12)<a class="anchor" aria-label="anchor" href="#mkin-09502-2020-05-12"></a></h2>
-<ul><li><p>Increase tolerance for a platform specific test results on the Solaris test machine on CRAN</p></li>
-<li><p>Updates and corrections (using the spelling package) to the documentation</p></li>
-</ul></div>
-    <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9.50.1" id="mkin-09501-2020-05-11">mkin 0.9.50.1 (2020-05-11)<a class="anchor" aria-label="anchor" href="#mkin-09501-2020-05-11"></a></h2>
-<ul><li><p>Support SFORB with formation fractions</p></li>
-<li><p>‘mkinmod’: Make ‘use_of_ff’ = “max” the default</p></li>
-<li><p>Improve performance by a) avoiding expensive calls in the cost function like merge() and data.frame(), and b) by implementing analytical solutions for SFO-SFO and DFOP-SFO</p></li>
-</ul></div>
-    <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9.49.11" id="mkin-094911-2020-04-20">mkin 0.9.49.11 (2020-04-20)<a class="anchor" aria-label="anchor" href="#mkin-094911-2020-04-20"></a></h2>
-<ul><li>Increase a test tolerance to make it pass on all CRAN check machines</li>
-</ul></div>
-    <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9.49.10" id="mkin-094910-2020-04-18">mkin 0.9.49.10 (2020-04-18)<a class="anchor" aria-label="anchor" href="#mkin-094910-2020-04-18"></a></h2>
-<ul><li><p>‘nlme.mmkin’: An nlme method for mmkin row objects and an associated S3 class with print, plot, anova and endpoint methods</p></li>
-<li><p>‘mean_degparms, nlme_data, nlme_function’: Three new functions to facilitate building nlme models from mmkin row objects</p></li>
-<li><p>‘endpoints’: Don’t return the SFORB list component if it’s empty. This reduces distraction and complies with the documentation</p></li>
-<li><p>Article in compiled models: Add some platform specific code and suppress warnings about zero values being removed from the FOCUS D dataset</p></li>
-<li><p>‘plot.mmkin’: Add the argument ‘standardized’ to avoid warnings that occurred when it was passed as part of the additional arguments captured by the dots (…)</p></li>
-<li><p>‘summary.mkinfit’: Add AIC, BIC and log likelihood to the summary</p></li>
-</ul></div>
-    <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9.49.9" id="mkin-09499-2020-03-31">mkin 0.9.49.9 (2020-03-31)<a class="anchor" aria-label="anchor" href="#mkin-09499-2020-03-31"></a></h2>
-<ul><li><p>‘mkinmod’: Use pkgbuild::has_compiler instead of Sys.which(‘gcc’), as the latter will often fail even if Rtools are installed</p></li>
-<li><p>‘mkinds’: Use roxygen for documenting fields and methods of this R6 class</p></li>
-</ul></div>
-    <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9.49.8" id="mkin-09498-2020-01-09">mkin 0.9.49.8 (2020-01-09)<a class="anchor" aria-label="anchor" href="#mkin-09498-2020-01-09"></a></h2>
-<ul><li><p>‘aw’: Generic function for calculating Akaike weights, methods for mkinfit objects and mmkin columns</p></li>
-<li><p>‘loftest’: Add a lack-of-fit test</p></li>
-<li><p>‘plot_res’, ‘plot_sep’ and ‘mkinerrplot’: Add the possibility to show standardized residuals and make it the default for fits with error models other than ‘const’</p></li>
-<li><p>‘lrtest.mkinfit’: Improve naming of the compared fits in the case of fixed parameters</p></li>
-<li><p>‘confint.mkinfit’: Make the quadratic approximation the default, as the likelihood profiling takes a lot of time, especially if the fit has more than three parameters</p></li>
-</ul></div>
-    <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9.49.7" id="mkin-09497-2019-11-01">mkin 0.9.49.7 (2019-11-01)<a class="anchor" aria-label="anchor" href="#mkin-09497-2019-11-01"></a></h2>
-<ul><li><p>Fix a bug introduced in 0.9.49.6 that occurred if the direct optimisation yielded a higher likelihood than the three-step optimisation in the d_3 algorithm, which caused the fitted parameters of the three-step optimisation to be returned instead of the parameters of the direct optimisation</p></li>
-<li><p>Add a ‘nobs’ method for mkinfit objects, enabling the default ‘BIC’ method from the stats package. Also, add a ‘BIC’ method for mmkin column objects.</p></li>
-</ul></div>
-    <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9.49.6" id="mkin-09496-2019-10-31">mkin 0.9.49.6 (2019-10-31)<a class="anchor" aria-label="anchor" href="#mkin-09496-2019-10-31"></a></h2>
-<ul><li><p>Implement a likelihood ratio test as a method for ‘lrtest’ from the lmtest package</p></li>
-<li><p>Add an ‘update’ method for mkinfit objects which remembers fitted parameters if appropriate</p></li>
-<li><p>Add a ‘residuals’ method for mkinfit objects that supports scaling based on the error model</p></li>
-<li><p>Fix a bug in ‘mkinfit’ that prevented summaries of objects fitted with fixed parameters to be generated</p></li>
-<li><p>Add ‘parms’ and ‘confint’ methods for mkinfit objects. Confidence intervals based on the quadratic approximation as in the summary, and based on the profile likelihood</p></li>
-<li><p>Move long-running tests to tests/testthat/slow with a separate test log. They currently take around 7 minutes on my system</p></li>
-<li><p>‘mkinfit’: Clean the code and return functions to calculate the log-likelihood and the sum of squared residuals</p></li>
-<li><p>Vignette ‘twa.html’: Add the maximum time weighted average formulas for the hockey stick model</p></li>
-<li><p>Support frameless plots (‘frame = FALSE’)</p></li>
-<li><p>Support to suppress the chi2 error level (‘show_errmin = FALSE’) in ‘plot.mmkin’</p></li>
-<li><p>Update README and the introductory vignette</p></li>
-<li><p>Report ‘OLS’ as error_model_algorithm in the summary in the case that the default error_model (‘const’) is used</p></li>
-<li><p>Support summarizing ‘mkinfit’ objects generated with versions &lt; 0.9.49.5</p></li>
-</ul></div>
-    <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9.49.5" id="mkin-09495-2019-07-04">mkin 0.9.49.5 (2019-07-04)<a class="anchor" aria-label="anchor" href="#mkin-09495-2019-07-04"></a></h2>
-<ul><li><p>Several algorithms for minimization of the negative log-likelihood for non-constant error models (two-component and variance by variable). In the case the error model is constant variance, least squares is used as this is more stable. The default algorithm ‘d_3’ tries direct minimization and a three-step procedure, and returns the model with the highest likelihood.</p></li>
-<li><p>The argument ‘reweight.method’ to mkinfit and mmkin is now obsolete, use ‘error_model’ and ‘error_model_algorithm’ instead</p></li>
-<li><p>Add a test that checks if we get the best known AIC for parent only fits to 12 test datasets. Add these test datasets for this purpose.</p></li>
-<li><p>New function ‘mkinerrplot’. This function is also used for residual plots in ‘plot.mmkin’ if the argument ‘resplot = “errmod”’ is given, and in ‘plot.mkinfit’ if ‘show_errplot’ is set to TRUE.</p></li>
-<li><p>Remove dependency on FME, only use nlminb for optimisation (‘Port’ algorithm). I cannot remember cases where one of the other optimisation algorithms was preferable, except that I sometime used Levenberg-Marquardt for speed in cases where I did not expect to get trapped in a local minimum.</p></li>
-<li><p>Use the numDeriv package to calculate hessians. This results in slightly different confidence intervals, takes a bit longer, but is apparently more robust</p></li>
-<li><p>Add a simple benchmark vignette to document the impact on performance.</p></li>
-<li><p>The code for manual weighting was removed. This functionality might get added again at a later time. For the time being, please use an earlier version, e.g. 0.9.48.1 if you want to do manual weighting.</p></li>
-<li><p>The fitting time reported in the summary now includes the time used for calculation of the hessians</p></li>
-<li><p>Adapt tests</p></li>
-<li><p>Fix an error in the FOCUS chi2 error level calculations that occurred if parameters were specified in parms.ini that were not in the model. A warning was already issued, but when fitting in parallel via mmkin this could go unnoticed.</p></li>
-<li><p>Add example datasets obtained from risk assessment reports published by the European Food Safety Agency.</p></li>
-</ul></div>
-    <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9.48.1" id="mkin-09481-2019-03-04">mkin 0.9.48.1 (2019-03-04)<a class="anchor" aria-label="anchor" href="#mkin-09481-2019-03-04"></a></h2>
-<ul><li><p>Add the function ‘logLik.mkinfit’ which makes it possible to calculate an AIC for mkinfit objects</p></li>
-<li><p>Add the function ‘AIC.mmkin’ to make it easy to compare columns of mmkin objects</p></li>
-<li><p>‘add_err’: Respect the argument giving the number of replicates in the synthetic dataset</p></li>
-<li><p>‘max_twa_parent’: Support maximum time weighted average concentration calculations for the hockey stick (HS) model</p></li>
-<li><p>‘mkinpredict’: Make the function generic and create a method for mkinfit objects</p></li>
-<li><p>‘mkinfit’: Improve the correctness of the fitted two component error model by fitting the mean absolute deviance at each observation against the observed values, weighting with the current two-component error model</p></li>
-<li><p>‘tests/testthat/test_irls.R’: Test if the components of the error model used to generate the data can be reproduced with moderate accuracy</p></li>
-<li><p>Add the function ‘CAKE_export’ to facilitate cross-checking of results</p></li>
-<li><p>Implement the logistic model (only tested for parent fits)</p></li>
-<li><p>‘nafta’: Add evaluations according to the NAFTA guidance</p></li>
-</ul></div>
-    <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9.47.5" id="mkin-09475-2018-09-14">mkin 0.9.47.5 (2018-09-14)<a class="anchor" aria-label="anchor" href="#mkin-09475-2018-09-14"></a></h2>
-<ul><li><p>Make the two-component error model stop in cases where it is inadequate to avoid nls crashes on windows</p></li>
-<li><p>Move two vignettes to a location where they will not be built on CRAN (to avoid more NOTES from long execution times)</p></li>
-<li><p>Exclude more example code from testing on CRAN to avoid NOTES from long execution times</p></li>
-</ul></div>
-    <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9.47.3" id="mkin-09473">mkin 0.9.47.3<a class="anchor" aria-label="anchor" href="#mkin-09473"></a></h2>
-<ul><li><p>‘mkinfit’: Improve fitting the error model for reweight.method = ‘tc’. Add ‘manual’ to possible arguments for ‘weight’</p></li>
-<li><p>Test that FOCUS_2006_C can be evaluated with DFOP and reweight.method = ‘tc’</p></li>
-</ul></div>
-    <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9.47.2" id="mkin-09472-2018-07-19">mkin 0.9.47.2 (2018-07-19)<a class="anchor" aria-label="anchor" href="#mkin-09472-2018-07-19"></a></h2>
-<ul><li><p>‘sigma_twocomp’: Rename ‘sigma_rl’ to ‘sigma_twocomp’ as the Rocke and Lorenzato model assumes lognormal distribution for large y. Correct references to the Rocke and Lorenzato model accordingly.</p></li>
-<li><p>‘mkinfit’: Use 1.1 as starting value for N parameter of IORE models to obtain convergence in more difficult cases. Show parameter names when ‘trace_parms’ is ‘TRUE’.</p></li>
-</ul></div>
-    <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9.47.1" id="mkin-09471-2018-02-06">mkin 0.9.47.1 (2018-02-06)<a class="anchor" aria-label="anchor" href="#mkin-09471-2018-02-06"></a></h2>
-<ul><li><p>Skip some tests on CRAN and winbuilder to avoid timeouts</p></li>
-<li><p>‘test_data_from_UBA_2014’: Added this list of datasets containing experimental data used in the expertise from 2014</p></li>
-<li><p>‘mkinfit’: Added the iterative reweighting method ‘tc’ using the two-component error model from Rocke and Lorenzato. NA values in the data are not returned any more.</p></li>
-<li><p>‘mkinfit’: Work around a bug in the current FME version that prevented the convergence message to be returned in the case of non-convergence.</p></li>
-<li><p>‘summary.mkinfit’: Improved output regarding weighting method. No predictions are returned for NA values in the model (see above).</p></li>
-<li><p>‘summary.mkinfit’: Show versions of mkin and R used for fitting (not the ones used for the summary) if the fit was generated with mkin &gt;= 0.9.47.1</p></li>
-</ul></div>
-    <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9.46.3" id="mkin-09463-2017-11-16">mkin 0.9.46.3 (2017-11-16)<a class="anchor" aria-label="anchor" href="#mkin-09463-2017-11-16"></a></h2>
-<ul><li><p><code>README.md</code>, <code>vignettes/mkin.Rmd</code>: URLs were updated</p></li>
-<li><p><code>synthetic_data_for_UBA</code>: Add the code used to generate the data in the interest of reproducibility</p></li>
-</ul></div>
-    <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9.46.2" id="mkin-09462-2017-10-10">mkin 0.9.46.2 (2017-10-10)<a class="anchor" aria-label="anchor" href="#mkin-09462-2017-10-10"></a></h2>
-<ul><li><p>Converted the vignette FOCUS_Z from tex/pdf to markdown/html</p></li>
-<li><p><code>DESCRIPTION</code>: Add ORCID</p></li>
-</ul></div>
-    <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9.46.1" id="mkin-09461-2017-09-14">mkin 0.9.46.1 (2017-09-14)<a class="anchor" aria-label="anchor" href="#mkin-09461-2017-09-14"></a></h2>
-<ul><li><p><code>plot.mkinfit</code>: Fix scaling of residual plots for the case of separate plots for each observed variable</p></li>
-<li><p><code>plot.mkinfit</code>: Use all data points of the fitted curve for y axis scaling for the case of separate plots for each observed variable</p></li>
-<li><p>Documentation updates</p></li>
-</ul></div>
-    <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9.46" id="mkin-0946-2017-07-24">mkin 0.9.46 (2017-07-24)<a class="anchor" aria-label="anchor" href="#mkin-0946-2017-07-24"></a></h2>
-<ul><li>Remove <code>test_FOMC_ill-defined.R</code> as it is too platform dependent</li>
-</ul></div>
-    <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9.45.2" id="mkin-09452-2017-07-24">mkin 0.9.45.2 (2017-07-24)<a class="anchor" aria-label="anchor" href="#mkin-09452-2017-07-24"></a></h2>
-<ul><li><p>Rename <code>twa</code> to <code>max_twa_parent</code> to avoid conflict with <code>twa</code> from my <code>pfm</code> package</p></li>
-<li><p>Update URLs in documentation</p></li>
-<li><p>Limit test code to one core to pass on windows</p></li>
-<li><p>Switch from <code>microbenchmark</code> to <code>rbenchmark</code> as the former is not supported on all platforms</p></li>
-</ul></div>
-    <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9.45.1" id="mkin-09451-2016-12-20">mkin 0.9.45.1 (2016-12-20)<a class="anchor" aria-label="anchor" href="#mkin-09451-2016-12-20"></a></h2>
-<div class="section level3">
-<h3 id="new-features-0-9-45-1">New features<a class="anchor" aria-label="anchor" href="#new-features-0-9-45-1"></a></h3>
-<ul><li>A <code>twa</code> function, calculating maximum time weighted average concentrations for the parent (SFO, FOMC and DFOP).</li>
-</ul></div>
-</div>
-    <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9.45" id="mkin-0945-2016-12-08">mkin 0.9.45 (2016-12-08)<a class="anchor" aria-label="anchor" href="#mkin-0945-2016-12-08"></a></h2>
-<div class="section level3">
-<h3 id="minor-changes-0-9-45">Minor changes<a class="anchor" aria-label="anchor" href="#minor-changes-0-9-45"></a></h3>
-<ul><li><p><code>plot.mkinfit</code> and <code>plot.mmkin</code>: If the plotting device is <code>tikz</code>, LaTeX markup is being used for the chi2 error in the graphs.</p></li>
-<li><p>Use <code>pkgdown</code>, the successor of <code>staticdocs</code> for generating static HTML documentation. Include example output and graphs also for <code>dontrun</code> sections.</p></li>
-<li><p><code>plot.mkinfit</code>: Plotting does not fail any more when the compiled model is not available, e.g. because it was removed from the temporary directory. In this case, the uncompiled model is now used for plotting</p></li>
-</ul></div>
-</div>
-    <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9.44" id="mkin-0944-2016-06-29">mkin 0.9.44 (2016-06-29)<a class="anchor" aria-label="anchor" href="#mkin-0944-2016-06-29"></a></h2>
-<div class="section level3">
-<h3 id="bug-fixes-0-9-44">Bug fixes<a class="anchor" aria-label="anchor" href="#bug-fixes-0-9-44"></a></h3>
-<ul><li>The test <code>test_FOMC_ill-defined</code> failed on several architectures, so the test is now skipped</li>
-</ul></div>
-</div>
-    <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9.43" id="mkin-0943-2016-06-28">mkin 0.9.43 (2016-06-28)<a class="anchor" aria-label="anchor" href="#mkin-0943-2016-06-28"></a></h2>
-<div class="section level3">
-<h3 id="major-changes-0-9-43">Major changes<a class="anchor" aria-label="anchor" href="#major-changes-0-9-43"></a></h3>
-<ul><li><p>The title was changed to <code>Kinetic evaluations of chemical degradation data</code></p></li>
-<li><p><code>plot.mkinfit</code>: Add the possibility to show fits (and residual plots if requested) separately for the observed variables</p></li>
-<li><p><code>plot.mkinfit</code>: Add the possibility to show the chi2 error levels in the plot, similar to the way they are shown in <code>plot.mmkin</code></p></li>
-<li><p><code>plot_sep</code>: Add this function as a convenience wrapper for plotting observed variables of mkinfit objects separately, with chi2 error values and residual plots.</p></li>
-<li><p>Vignettes: The main vignette <code>mkin</code> was converted to R markdown and updated. The other vignettes were also updated to show current improved functionality.</p></li>
-<li><p>The function <code>add_err</code> was added to the package, making it easy to generate simulated data using an error model based on the normal distribution</p></li>
-</ul></div>
-<div class="section level3">
-<h3 id="minor-changes-0-9-43">Minor changes<a class="anchor" aria-label="anchor" href="#minor-changes-0-9-43"></a></h3>
-<ul><li><p>Remove an outdated reference to the inline package in the <code>compiled_models</code> vignette</p></li>
-<li><p><code>mkinfit</code>: Do not error out in cases where the fit converges, but the Jacobian for the untransformed model cost can not be estimated. Give a warning instead and return NA for the t-test results.</p></li>
-<li><p><code>summary.mkinfit</code>: Give a warning message when the covariance matrix can not be obtained.</p></li>
-<li><p>A test has been added to containing a corresponding edge case to check that the warnings are correctly issued and the fit does not terminate.</p></li>
-<li><p><code>plot.mmkin</code>: Round the chi2 error value to three significant digits, instead of two decimal digits.</p></li>
-<li><p><code>mkinfit</code>: Return the <code>err</code> values used on weighted fits as a column named <code>err</code>. Also include these inverse weights when the column <code>value</code> in the observed data is used, which is returned as <code>observed</code> in the data component of the mkinfit object.</p></li>
-</ul></div>
-<div class="section level3">
-<h3 id="bug-fixes-0-9-43">Bug fixes<a class="anchor" aria-label="anchor" href="#bug-fixes-0-9-43"></a></h3>
-<ul><li><p><code>endpoints</code>: When the name of a substance degrading to a metabolite (e.g. a parent compound) used in the model formulation ended in the letter <code>f</code>, some rate parameters could be listed as formation fractions with mixed up names. These would also appear in the summary.</p></li>
-<li><p><code>mkinfit</code>: Check for all observed variables when checking if the user tried to fix formation fractions when fitting them using ilr transformation.</p></li>
-<li><p><code>plot.mmkin</code>: Set the plot margins correctly, also in the case of a single fit to be plotted, so the main title is placed in a reasonable way.</p></li>
-<li><p><code>plot.mkinfit</code>: Correct default values for <code>col_obs</code>, <code>pch_obs</code> and <code>lty_obs</code> for the case that <code>obs_vars</code> is specified.</p></li>
-</ul></div>
-</div>
-    <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9.42" id="mkin-0942-2016-03-25">mkin 0.9.42 (2016-03-25)<a class="anchor" aria-label="anchor" href="#mkin-0942-2016-03-25"></a></h2>
-<div class="section level3">
-<h3 id="major-changes-0-9-42">Major changes<a class="anchor" aria-label="anchor" href="#major-changes-0-9-42"></a></h3>
-<ul><li>Add the argument <code>from_max_mean</code> to <code>mkinfit</code>, for fitting only the decline from the maximum observed value for models with a single observed variable</li>
-</ul></div>
-<div class="section level3">
-<h3 id="minor-changes-0-9-42">Minor changes<a class="anchor" aria-label="anchor" href="#minor-changes-0-9-42"></a></h3>
-<ul><li><p>Add plots to <code>compiled_models</code> vignette</p></li>
-<li><p>Give an explanatory error message when mkinmod fails due to a missing definition of a target variable</p></li>
-<li><p><code><a href="../reference/mkinmod.html">print.mkinmod()</a></code>: Improve formatting when printing mkinmod model definitions</p></li>
-</ul></div>
-</div>
-    <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9-41" id="mkin-09-41-2015-11-09">mkin 0.9-41 (2015-11-09)<a class="anchor" aria-label="anchor" href="#mkin-09-41-2015-11-09"></a></h2>
-<div class="section level3">
-<h3 id="minor-changes-0-9-41">Minor changes<a class="anchor" aria-label="anchor" href="#minor-changes-0-9-41"></a></h3>
-<ul><li><p>Add an R6 class <code>mkinds</code> representing datasets with a printing method</p></li>
-<li><p>Add a printing method for mkinmod objects</p></li>
-<li><p>Make it possible to specify arbitrary strings as names for the compounds in <code>mkinmod</code>, and show them in the plot</p></li>
-<li><p>Use an index.r file to group help topics in static documentation</p></li>
-</ul></div>
-<div class="section level3">
-<h3 id="bug-fixes-0-9-41">Bug fixes<a class="anchor" aria-label="anchor" href="#bug-fixes-0-9-41"></a></h3>
-<ul><li>
-<code><a href="../reference/summary.mkinfit.html">print.summary.mkinfit()</a></code>: Avoid an error that occurred when printing summaries generated with mkin versions before 0.9-36</li>
-</ul></div>
-</div>
-    <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9-40" id="mkin-09-40-2015-07-21">mkin 0.9-40 (2015-07-21)<a class="anchor" aria-label="anchor" href="#mkin-09-40-2015-07-21"></a></h2>
-<div class="section level3">
-<h3 id="bug-fixes-0-9-40">Bug fixes<a class="anchor" aria-label="anchor" href="#bug-fixes-0-9-40"></a></h3>
-<ul><li>
-<code><a href="../reference/endpoints.html">endpoints()</a></code>: For DFOP and SFORB models, where <code><a href="https://rdrr.io/r/stats/optimize.html" class="external-link">optimize()</a></code> is used, make use of the fact that the DT50 must be between DT50_k1 and DT50_k2 (DFOP) or DT50_b1 and DT50_b2 (SFORB), as <code><a href="https://rdrr.io/r/stats/optimize.html" class="external-link">optimize()</a></code> sometimes did not find the minimum. Likewise for finding DT90 values. Also fit on the log scale to make the function more efficient.</li>
-</ul></div>
-<div class="section level3">
-<h3 id="internal-changes-0-9-40">Internal changes<a class="anchor" aria-label="anchor" href="#internal-changes-0-9-40"></a></h3>
-<ul><li>
-<code>DESCRIPTION</code>, <code>NAMESPACE</code>, <code>R/*.R</code>: Import (from) stats, graphics and methods packages, and qualify some function calls for non-base packages installed with R to avoid NOTES made by R CMD check –as-cran with upcoming R versions.</li>
-</ul></div>
-</div>
-    <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9-39" id="mkin-09-39-2015-06-26">mkin 0.9-39 (2015-06-26)<a class="anchor" aria-label="anchor" href="#mkin-09-39-2015-06-26"></a></h2>
-<div class="section level3">
-<h3 id="major-changes-0-9-39">Major changes<a class="anchor" aria-label="anchor" href="#major-changes-0-9-39"></a></h3>
-<ul><li><p>New function <code><a href="../reference/mmkin.html">mmkin()</a></code>: This function takes a character vector of model shorthand names, or alternatively a list of mkinmod models, as well as a list of dataset as main arguments. It returns a matrix of mkinfit objects, with a row for each model and a column for each dataset. A subsetting method with single brackets is available. Fitting the models in parallel using the <code>parallel</code> package is supported.</p></li>
-<li><p>New function <code><a href="../reference/plot.mmkin.html">plot.mmkin()</a></code>: Plots single-row or single-column <code>mmkin</code> objects including residual plots.</p></li>
-</ul></div>
-<div class="section level3">
-<h3 id="bug-fixes-0-9-39">Bug fixes<a class="anchor" aria-label="anchor" href="#bug-fixes-0-9-39"></a></h3>
-<ul><li>
-<code><a href="../reference/mkinparplot.html">mkinparplot()</a></code>: Fix the x axis scaling for rate constants and formation fractions that got confused by the introduction of the t-values of transformed parameters.</li>
-</ul></div>
-</div>
-    <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9-38" id="mkin-09-38-2015-06-24">mkin 0.9-38 (2015-06-24)<a class="anchor" aria-label="anchor" href="#mkin-09-38-2015-06-24"></a></h2>
-<div class="section level3">
-<h3 id="minor-changes-0-9-38">Minor changes<a class="anchor" aria-label="anchor" href="#minor-changes-0-9-38"></a></h3>
-<ul><li><p><code>vignettes/compiled_models.html</code>: Show the performance improvement factor actually obtained when building the vignette, as well as mkin version, some system info and the CPU model used for building the vignette.</p></li>
-<li><p><code>GNUMakefile</code>,<code>vignettes/*</code>: Clean up vignette generation and include table of contents in HTML vignettes.</p></li>
-</ul></div>
-<div class="section level3">
-<h3 id="bug-fixes-0-9-38">Bug fixes<a class="anchor" aria-label="anchor" href="#bug-fixes-0-9-38"></a></h3>
-<ul><li>
-<code><a href="../reference/mkinmod.html">mkinmod()</a></code>: When generating the C code for the derivatives, only declare the time variable when it is needed and remove the ‘-W-no-unused-variable’ compiler flag as the C compiler used in the CRAN checks on Solaris does not know it.</li>
-</ul></div>
-</div>
-    <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9-36" id="mkin-09-36-2015-06-21">mkin 0.9-36 (2015-06-21)<a class="anchor" aria-label="anchor" href="#mkin-09-36-2015-06-21"></a></h2>
-<div class="section level3">
-<h3 id="major-changes-0-9-36">Major changes<a class="anchor" aria-label="anchor" href="#major-changes-0-9-36"></a></h3>
-<ul><li><p><code><a href="../reference/summary.mkinfit.html">summary.mkinfit()</a></code>: A one-sided t-test for significant difference of untransformed parameters from zero is now always shown, based on the assumption of normal distribution for estimators of all untransformed parameters. Use with caution, as this assumption is unrealistic e.g. for rate constants in these nonlinear kinetic models.</p></li>
-<li><p>If a compiler (gcc) is installed, use a version of the differential equation model compiled from C code, which is a huge performance boost for models where only the deSolve method works.</p></li>
-<li><p><code><a href="../reference/mkinmod.html">mkinmod()</a></code>: Create a list component $cf (of class CFuncList) in the list returned by mkinmod, if a version can be compiled from autogenerated C code (see above).</p></li>
-<li><p><code><a href="../reference/mkinfit.html">mkinfit()</a></code>: Set the default <code>solution_type</code> to <code>deSolve</code> when a compiled version of the model is present, except when an analytical solution is possible.</p></li>
-</ul></div>
-<div class="section level3">
-<h3 id="minor-changes-0-9-36">Minor changes<a class="anchor" aria-label="anchor" href="#minor-changes-0-9-36"></a></h3>
-<ul><li>Added a simple showcase vignette with an evaluation of FOCUS example dataset D</li>
-</ul></div>
-</div>
-    <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9-35" id="mkin-09-35-2015-05-15">mkin 0.9-35 (2015-05-15)<a class="anchor" aria-label="anchor" href="#mkin-09-35-2015-05-15"></a></h2>
-<div class="section level3">
-<h3 id="major-changes-0-9-35">Major changes<a class="anchor" aria-label="anchor" href="#major-changes-0-9-35"></a></h3>
-<ul><li>Switch from RUnit to testthat for testing</li>
-</ul></div>
-<div class="section level3">
-<h3 id="bug-fixes-0-9-35">Bug fixes<a class="anchor" aria-label="anchor" href="#bug-fixes-0-9-35"></a></h3>
-<ul><li><p><code><a href="../reference/mkinparplot.html">mkinparplot()</a></code>: Avoid warnings that occurred when not all confidence intervals were available in the summary of the fit</p></li>
-<li><p><code><a href="../reference/summary.mkinfit.html">print.summary.mkinfit()</a></code>: Fix printing the summary for the case that the number of iterations is not available</p></li>
-<li><p>NAMESPACE: export S3 methods plot.mkinfit, summary.mkinfit and print.summary.mkinfit to satisfy R CMD check on R-devel</p></li>
-<li><p><code><a href="../reference/mkinparplot.html">mkinparplot()</a></code>: Avoid warning in R CMD check about undeclared global variable <code>Lower</code></p></li>
-</ul></div>
-<div class="section level3">
-<h3 id="new-features-0-9-35">New features<a class="anchor" aria-label="anchor" href="#new-features-0-9-35"></a></h3>
-<ul><li><p><code><a href="../reference/mkinfit.html">mkinfit()</a></code>: Report successful termination when quiet = FALSE. This is helpful for more difficult problems fitted with reweight.method = obs, as no progress is often indicated during the reweighting.</p></li>
-<li><p>A first test using results established in the expertise written for the German Federal Environmental Agency (UBA) was added.</p></li>
-<li><p>Add synthetic datasets generated for expertise written for the German Federal Environmental Agency UBA</p></li>
-<li><p>Add tests based on these datasets</p></li>
-</ul></div>
-</div>
-    <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9-34" id="mkin-09-34-2014-11-22">mkin 0.9-34 (2014-11-22)<a class="anchor" aria-label="anchor" href="#mkin-09-34-2014-11-22"></a></h2>
-<div class="section level3">
-<h3 id="new-features-0-9-34">New features<a class="anchor" aria-label="anchor" href="#new-features-0-9-34"></a></h3>
-<ul><li><p>Add the convenience function <code><a href="../reference/mkinmod.html">mkinsub()</a></code> for creating the lists used in <code><a href="../reference/mkinmod.html">mkinmod()</a></code></p></li>
-<li><p>Add the possibility to fit indeterminate order rate equation (IORE) models using an analytical solution (parent only) or a numeric solution. Paths from IORE compounds to metabolites are supported when using formation fractions (use_of_ff = ‘max’). Note that the numerical solution (method.ode = ‘deSolve’) of the IORE differential equations sometimes fails due to numerical problems.</p></li>
-<li><p>Switch to using the Port algorithm (using a model/trust region approach) per default. While needing more iterations than the Levenberg-Marquardt algorithm previously used per default, it is less sensitive to starting parameters.</p></li>
-</ul></div>
-<div class="section level3">
-<h3 id="minor-changes-0-9-34">Minor changes<a class="anchor" aria-label="anchor" href="#minor-changes-0-9-34"></a></h3>
-<ul><li><p>The formatting of differential equations in the summary was further improved</p></li>
-<li><p>Always include 0 on y axis when plotting during the fit</p></li>
-</ul></div>
-</div>
-    <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9-33" id="mkin-09-33-2014-10-22">mkin 0.9-33 (2014-10-22)<a class="anchor" aria-label="anchor" href="#mkin-09-33-2014-10-22"></a></h2>
-<div class="section level3">
-<h3 id="new-features-0-9-33">New features<a class="anchor" aria-label="anchor" href="#new-features-0-9-33"></a></h3>
-<ul><li><p>The initial value (state.ini) for the observed variable with the highest observed residue is set to 100 in case it has no time zero observation and <code>state.ini = "auto"</code></p></li>
-<li><p>A basic unit test for <code><a href="../reference/mkinerrmin.html">mkinerrmin()</a></code> was written</p></li>
-</ul></div>
-<div class="section level3">
-<h3 id="bug-fixes-0-9-33">Bug fixes<a class="anchor" aria-label="anchor" href="#bug-fixes-0-9-33"></a></h3>
-<ul><li><p><code><a href="../reference/mkinfit.html">mkinfit()</a></code>: The internally fitted parameter for <code>g</code> was named <code>g_ilr</code> even when <code>transform_fractions=FALSE</code></p></li>
-<li><p><code><a href="../reference/mkinfit.html">mkinfit()</a></code>: The initial value (state.ini) for the parent compound was not set when the parent was not the (only) variable with the highest value in the observed data.</p></li>
-<li><p><code><a href="../reference/mkinerrmin.html">mkinerrmin()</a></code>: When checking for degrees of freedom for metabolites, check if their time zero value is fixed instead of checking if the observed value is zero. This ensures correct calculation of degrees of freedom also in cases where the metabolite residue at time zero is greater zero.</p></li>
-<li><p><code><a href="../reference/plot.mkinfit.html">plot.mkinfit()</a></code>: Avoid a warning message about only using the first component of ylim that occurred when ylim was specified explicitly</p></li>
-</ul></div>
-<div class="section level3">
-<h3 id="minor-changes-0-9-33">Minor changes<a class="anchor" aria-label="anchor" href="#minor-changes-0-9-33"></a></h3>
-<ul><li><p>The formatting of differential equations in the summary was improved by wrapping overly long lines</p></li>
-<li><p>The FOCUS_Z vignette was rebuilt with the above improvement and using a width of 70 to avoid output outside of the grey area</p></li>
-<li><p><code><a href="../reference/summary.mkinfit.html">print.summary.mkinfit()</a></code>: Avoid a warning that occurred when gmkin showed summaries of initial fits without iterations</p></li>
-<li><p><code><a href="../reference/mkinfit.html">mkinfit()</a></code>: Avoid a warning that occurred when summarising a fit that was performed with maxitmodFit = 0 as done in gmkin for configuring new fits.</p></li>
-</ul></div>
-</div>
-    <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9-32" id="mkin-09-32-2014-07-24">mkin 0.9-32 (2014-07-24)<a class="anchor" aria-label="anchor" href="#mkin-09-32-2014-07-24"></a></h2>
-<div class="section level3">
-<h3 id="new-features-0-9-32">New features<a class="anchor" aria-label="anchor" href="#new-features-0-9-32"></a></h3>
-<ul><li><p>The number of degrees of freedom is difficult to define in the case of ilr transformation of formation fractions. Now for each source compartment the number of ilr parameters (=number of optimised parameters) is divided by the number of pathways to metabolites (=number of affected data series) which leads to fractional degrees of freedom in some cases.</p></li>
-<li><p>The default for the initial value for the first state value is now taken from the mean of the observations at time zero, if available.</p></li>
-<li><p>The kinetic model can alternatively be specified with a shorthand name for parent only degradation models, e.g. <code>SFO</code>, or <code>DFOP</code>.</p></li>
-<li><p>Optimisation method, number of model evaluations and time elapsed during optimisation are given in the summary of mkinfit objects.</p></li>
-<li><p>The maximum number of iterations in the optimisation algorithm can be specified using the argument <code>maxit.modFit</code> to the mkinfit function.</p></li>
-<li><p>mkinfit gives a warning when the fit does not converge (does not apply to SANN method). This warning is included in the summary.</p></li>
-</ul></div>
-<div class="section level3">
-<h3 id="bug-fixes-0-9-32">Bug fixes<a class="anchor" aria-label="anchor" href="#bug-fixes-0-9-32"></a></h3>
-<ul><li><p>Avoid plotting an artificial 0 residual at time zero in <code>mkinresplot</code></p></li>
-<li><p>In the determination of the degrees of freedom in <code>mkinerrmin</code>, formation fractions were accounted for multiple times in the case of parallel formation of metabolites. See the new feature described above for the solution.</p></li>
-<li><p><code>transform_rates=FALSE</code> in <code>mkinfit</code> now also works for FOMC and HS models.</p></li>
-<li><p>Initial values for formation fractions were not set in all cases.</p></li>
-<li><p>No warning was given when the fit did not converge when a method other than the default Levenberg-Marquardt method <code>Marq</code> was used.</p></li>
-</ul></div>
-<div class="section level3">
-<h3 id="minor-changes-0-9-32">Minor changes<a class="anchor" aria-label="anchor" href="#minor-changes-0-9-32"></a></h3>
-<ul><li><p>Vignettes were rebuilt to reflect the changes in the summary method.</p></li>
-<li><p>Algorithm <code>Pseudo</code> was excluded because it needs user-defined parameter limits which are not supported.</p></li>
-<li><p>Algorithm <code>Newton</code> was excluded because of its different way to specify the maximum number of iterations and because it does not appear to provide additional benefits.</p></li>
-</ul></div>
-</div>
-    <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9-31" id="mkin-09-31-2014-07-14">mkin 0.9-31 (2014-07-14)<a class="anchor" aria-label="anchor" href="#mkin-09-31-2014-07-14"></a></h2>
-<div class="section level3">
-<h3 id="bug-fixes-0-9-31">Bug fixes<a class="anchor" aria-label="anchor" href="#bug-fixes-0-9-31"></a></h3>
-<ul><li>The internal renaming of optimised parameters in Version 0.9-30 led to errors in the determination of the degrees of freedom for the chi2 error level calulations in <code><a href="../reference/mkinerrmin.html">mkinerrmin()</a></code> used by the summary function.</li>
-</ul></div>
-</div>
-    <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9-30" id="mkin-09-30-2014-07-11">mkin 0.9-30 (2014-07-11)<a class="anchor" aria-label="anchor" href="#mkin-09-30-2014-07-11"></a></h2>
-<div class="section level3">
-<h3 id="new-features-0-9-30">New features<a class="anchor" aria-label="anchor" href="#new-features-0-9-30"></a></h3>
-<ul><li>It is now possible to use formation fractions in combination with turning off the sink in <code><a href="../reference/mkinmod.html">mkinmod()</a></code>.</li>
-</ul></div>
-<div class="section level3">
-<h3 id="major-changes-0-9-30">Major changes<a class="anchor" aria-label="anchor" href="#major-changes-0-9-30"></a></h3>
-<ul><li><p>The original and the transformed parameters now have different names (e.g. <code>k_parent</code> and <code>log_k_parent</code>. They also differ in how many they are when we have formation fractions but no pathway to sink.</p></li>
-<li><p>The order of some of the information blocks in <code>print.summary.mkinfit.R()</code> has been ordered in a more logical way.</p></li>
-</ul></div>
-<div class="section level3">
-<h3 id="minor-changes-0-9-30">Minor changes<a class="anchor" aria-label="anchor" href="#minor-changes-0-9-30"></a></h3>
-<ul><li><p>The vignette FOCUS_Z has been simplified to use formation fractions with turning off the sink, and slightly amended to use the new versions of DT50 values calculated since mkin 0.9-29.</p></li>
-<li><p>All vignettes have been rebuilt so they reflect all changes.</p></li>
-<li><p>The ChangeLog was renamed to NEWS.md and the entries were converted to markdown syntax compatible with the <code>tools::news()</code> function built into R.</p></li>
-<li><p>The test suite was overhauled. Tests of the DFOP and SFORB models with dataset FOCUS_2006_A were removed, as they were too much dependent on the optimisation algorithm and/or starting parameters, because the dataset is SFO (compare kinfit vignette).</p></li>
-<li><p>Also, the Schaefer complex case can now be fitted using formation fractions, and with the ‘Port’ optimisation method we also fit A2 in the same way as published in the Piacenza paper.</p></li>
-<li><p>Some more checks were introduced to <code><a href="../reference/mkinfit.html">mkinfit()</a></code>, leading to warnings or stopping execution if unsupported combinations of methods and parameters are requested.</p></li>
-</ul></div>
-</div>
-    <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9-29" id="mkin-09-29-2014-06-27">mkin 0.9-29 (2014-06-27)<a class="anchor" aria-label="anchor" href="#mkin-09-29-2014-06-27"></a></h2>
-<ul><li><p>R/mkinresplot.R: Make it possible to specify <code>xlim</code></p></li>
-<li><p>R/geometric_mean.R, man/geometric_mean.Rd: Add geometric mean function</p></li>
-<li><p>R/endpoints.R, man/endpoints.Rd: Calculate additional (pseudo)-DT50 values for FOMC, DFOP, HS and SFORB. Avoid calculation of formation fractions from rate constants when they are directly fitted</p></li>
-</ul></div>
-    <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9-28" id="mkin-09-28-2014-05-20">mkin 0.9-28 (2014-05-20)<a class="anchor" aria-label="anchor" href="#mkin-09-28-2014-05-20"></a></h2>
-<ul><li><p>Do not backtransform confidence intervals for formation fractions if more than one compound is formed, as such parameters only define the pathways as a set</p></li>
-<li><p>Add historical remarks and some background to the main package vignette</p></li>
-<li><p>Correct ‘isotropic’ into ‘isometric’ for the ilr transformation</p></li>
-</ul></div>
-    <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9-27" id="mkin-09-27-2014-05-10">mkin 0.9-27 (2014-05-10)<a class="anchor" aria-label="anchor" href="#mkin-09-27-2014-05-10"></a></h2>
-<ul><li><p>Fork the GUI into a separate package <a href="https://github.com/jranke/gmkin" class="external-link">gmkin</a></p></li>
-<li><p>DESCRIPTION, NAMESPACE, TODO: Adapt and add copyright information</p></li>
-<li><p>Remove files belonging to the GUI</p></li>
-<li><p>Possibility to fit without parameter transformations, using bounds as implemented in FME</p></li>
-<li><p>Add McCall 2,4,5-T dataset</p></li>
-<li><p>Enable selection of observed variables in plotting</p></li>
-<li><p>Add possibility to show residual plot in <code>plot.mkinfit</code></p></li>
-<li><p>R/mkinparplot.R, man/mkinparplot.Rd: plot parameters with confidence intervals</p></li>
-<li><p>Change vignette format from Sweave to knitr</p></li>
-<li><p>Split examples vignette to FOCUS_L and FOCUS_Z</p></li>
-<li><p>Remove warning about constant formation fractions in mkinmod as it was based on a misconception</p></li>
-<li><p>Restrict the unit test with the Schaefer data to parent and primary metabolites as formation fraction and DT50 for A2 are highly correlated and passing the test is platform dependent. For example, the test fails in 1 out of 14 platforms on CRAN as of today.</p></li>
-<li><p>Add Eurofins Regulatory AG copyright notices</p></li>
-<li><p>Import FME and deSolve instead of depending on them to have clean startup</p></li>
-<li><p>Add a starter function for the GUI: <code>gmkin()</code></p></li>
-<li><p>Change the format of the workspace files of gmkin so they can be distributed and documented in the package</p></li>
-<li><p>Add gmkin workspace datasets FOCUS_2006_gmkin and FOCUS_2006_Z_gmkin</p></li>
-</ul></div>
-    <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9-24" id="mkin-09-24-2013-11-06">mkin 0.9-24 (2013-11-06)<a class="anchor" aria-label="anchor" href="#mkin-09-24-2013-11-06"></a></h2>
-<ul><li><p>Bugfix re-enabling the fixing of any combination of initial values for state variables</p></li>
-<li><p>Default values for kinetic rate constants are not all 0.1 any more but are “salted” with a small increment to avoid numeric artefacts with the eigenvalue based solutions</p></li>
-<li><p>Backtransform fixed ODE parameters for the summary</p></li>
-</ul></div>
-    <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9-22" id="mkin-09-22-2013-10-26">mkin 0.9-22 (2013-10-26)<a class="anchor" aria-label="anchor" href="#mkin-09-22-2013-10-26"></a></h2>
-<ul><li><p>Get rid of the optimisation step in <code>mkinerrmin</code> - this was unnecessary. Thanks to KinGUII for the inspiration - actually this is equation 6-2 in FOCUS kinetics p. 91 that I had overlooked originally</p></li>
-<li><p>Fix <code>plot.mkinfit</code> as it passed graphical arguments like main to the solver</p></li>
-<li><p>Do not use <code>plot=TRUE</code> in <code><a href="../reference/mkinfit.html">mkinfit()</a></code> example</p></li>
-<li><p>The first successful fits in the not so simple GUI</p></li>
-<li><p>Fix iteratively reweighted least squares for the case of many metabolites</p></li>
-<li><p>Unify naming of initial values of state variables</p></li>
-<li><p>Unify naming in dataframes of optimised and fixed parameters in the summary</p></li>
-<li><p>Show the weighting method for residuals in the summary</p></li>
-<li><p>Correct the output of the data in the case of manual weighting</p></li>
-<li><p>Implement IRLS assuming different variances for observed variables</p></li>
-<li><p>Do not use 0 values at time zero for chi2 error level calculations. This is the way it is done in KinGUII and it makes sense. It does impact the chi2 error levels in the output. Generally they seem to be lower for metabolites now, presumably because the mean of the observed values is higher</p></li>
-</ul><p>For a detailed list of changes to the mkin source please consult the commit history on <a href="http://github.com/jranke/mkin" class="external-link uri">http://github.com/jranke/mkin</a></p>
-</div>
-  </div>
-
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/pkgdown.css b/docs/dev/pkgdown.css
deleted file mode 100644
index 80ea5b83..00000000
--- a/docs/dev/pkgdown.css
+++ /dev/null
@@ -1,384 +0,0 @@
-/* Sticky footer */
-
-/**
- * Basic idea: https://philipwalton.github.io/solved-by-flexbox/demos/sticky-footer/
- * Details: https://github.com/philipwalton/solved-by-flexbox/blob/master/assets/css/components/site.css
- *
- * .Site -> body > .container
- * .Site-content -> body > .container .row
- * .footer -> footer
- *
- * Key idea seems to be to ensure that .container and __all its parents__
- * have height set to 100%
- *
- */
-
-html, body {
-  height: 100%;
-}
-
-body {
-  position: relative;
-}
-
-body > .container {
-  display: flex;
-  height: 100%;
-  flex-direction: column;
-}
-
-body > .container .row {
-  flex: 1 0 auto;
-}
-
-footer {
-  margin-top: 45px;
-  padding: 35px 0 36px;
-  border-top: 1px solid #e5e5e5;
-  color: #666;
-  display: flex;
-  flex-shrink: 0;
-}
-footer p {
-  margin-bottom: 0;
-}
-footer div {
-  flex: 1;
-}
-footer .pkgdown {
-  text-align: right;
-}
-footer p {
-  margin-bottom: 0;
-}
-
-img.icon {
-  float: right;
-}
-
-/* Ensure in-page images don't run outside their container */
-.contents img {
-  max-width: 100%;
-  height: auto;
-}
-
-/* Fix bug in bootstrap (only seen in firefox) */
-summary {
-  display: list-item;
-}
-
-/* Typographic tweaking ---------------------------------*/
-
-.contents .page-header {
-  margin-top: calc(-60px + 1em);
-}
-
-dd {
-  margin-left: 3em;
-}
-
-/* Section anchors ---------------------------------*/
-
-a.anchor {
-  display: none;
-  margin-left: 5px;
-  width: 20px;
-  height: 20px;
-
-  background-image: url(./link.svg);
-  background-repeat: no-repeat;
-  background-size: 20px 20px;
-  background-position: center center;
-}
-
-h1:hover .anchor,
-h2:hover .anchor,
-h3:hover .anchor,
-h4:hover .anchor,
-h5:hover .anchor,
-h6:hover .anchor {
-  display: inline-block;
-}
-
-/* Fixes for fixed navbar --------------------------*/
-
-.contents h1, .contents h2, .contents h3, .contents h4 {
-  padding-top: 60px;
-  margin-top: -40px;
-}
-
-/* Navbar submenu --------------------------*/
-
-.dropdown-submenu {
-  position: relative;
-}
-
-.dropdown-submenu>.dropdown-menu {
-  top: 0;
-  left: 100%;
-  margin-top: -6px;
-  margin-left: -1px;
-  border-radius: 0 6px 6px 6px;
-}
-
-.dropdown-submenu:hover>.dropdown-menu {
-  display: block;
-}
-
-.dropdown-submenu>a:after {
-  display: block;
-  content: " ";
-  float: right;
-  width: 0;
-  height: 0;
-  border-color: transparent;
-  border-style: solid;
-  border-width: 5px 0 5px 5px;
-  border-left-color: #cccccc;
-  margin-top: 5px;
-  margin-right: -10px;
-}
-
-.dropdown-submenu:hover>a:after {
-  border-left-color: #ffffff;
-}
-
-.dropdown-submenu.pull-left {
-  float: none;
-}
-
-.dropdown-submenu.pull-left>.dropdown-menu {
-  left: -100%;
-  margin-left: 10px;
-  border-radius: 6px 0 6px 6px;
-}
-
-/* Sidebar --------------------------*/
-
-#pkgdown-sidebar {
-  margin-top: 30px;
-  position: -webkit-sticky;
-  position: sticky;
-  top: 70px;
-}
-
-#pkgdown-sidebar h2 {
-  font-size: 1.5em;
-  margin-top: 1em;
-}
-
-#pkgdown-sidebar h2:first-child {
-  margin-top: 0;
-}
-
-#pkgdown-sidebar .list-unstyled li {
-  margin-bottom: 0.5em;
-}
-
-/* bootstrap-toc tweaks ------------------------------------------------------*/
-
-/* All levels of nav */
-
-nav[data-toggle='toc'] .nav > li > a {
-  padding: 4px 20px 4px 6px;
-  font-size: 1.5rem;
-  font-weight: 400;
-  color: inherit;
-}
-
-nav[data-toggle='toc'] .nav > li > a:hover,
-nav[data-toggle='toc'] .nav > li > a:focus {
-  padding-left: 5px;
-  color: inherit;
-  border-left: 1px solid #878787;
-}
-
-nav[data-toggle='toc'] .nav > .active > a,
-nav[data-toggle='toc'] .nav > .active:hover > a,
-nav[data-toggle='toc'] .nav > .active:focus > a {
-  padding-left: 5px;
-  font-size: 1.5rem;
-  font-weight: 400;
-  color: inherit;
-  border-left: 2px solid #878787;
-}
-
-/* Nav: second level (shown on .active) */
-
-nav[data-toggle='toc'] .nav .nav {
-  display: none; /* Hide by default, but at >768px, show it */
-  padding-bottom: 10px;
-}
-
-nav[data-toggle='toc'] .nav .nav > li > a {
-  padding-left: 16px;
-  font-size: 1.35rem;
-}
-
-nav[data-toggle='toc'] .nav .nav > li > a:hover,
-nav[data-toggle='toc'] .nav .nav > li > a:focus {
-  padding-left: 15px;
-}
-
-nav[data-toggle='toc'] .nav .nav > .active > a,
-nav[data-toggle='toc'] .nav .nav > .active:hover > a,
-nav[data-toggle='toc'] .nav .nav > .active:focus > a {
-  padding-left: 15px;
-  font-weight: 500;
-  font-size: 1.35rem;
-}
-
-/* orcid ------------------------------------------------------------------- */
-
-.orcid {
-  font-size: 16px;
-  color: #A6CE39;
-  /* margins are required by official ORCID trademark and display guidelines */
-  margin-left:4px;
-  margin-right:4px;
-  vertical-align: middle;
-}
-
-/* Reference index & topics ----------------------------------------------- */
-
-.ref-index th {font-weight: normal;}
-
-.ref-index td {vertical-align: top; min-width: 100px}
-.ref-index .icon {width: 40px;}
-.ref-index .alias {width: 40%;}
-.ref-index-icons .alias {width: calc(40% - 40px);}
-.ref-index .title {width: 60%;}
-
-.ref-arguments th {text-align: right; padding-right: 10px;}
-.ref-arguments th, .ref-arguments td {vertical-align: top; min-width: 100px}
-.ref-arguments .name {width: 20%;}
-.ref-arguments .desc {width: 80%;}
-
-/* Nice scrolling for wide elements --------------------------------------- */
-
-table {
-  display: block;
-  overflow: auto;
-}
-
-/* Syntax highlighting ---------------------------------------------------- */
-
-pre, code, pre code {
-  background-color: #f8f8f8;
-  color: #333;
-}
-pre, pre code {
-  white-space: pre-wrap;
-  word-break: break-all;
-  overflow-wrap: break-word;
-}
-
-pre {
-  border: 1px solid #eee;
-}
-
-pre .img, pre .r-plt {
-  margin: 5px 0;
-}
-
-pre .img img, pre .r-plt img {
-  background-color: #fff;
-}
-
-code a, pre a {
-  color: #375f84;
-}
-
-a.sourceLine:hover {
-  text-decoration: none;
-}
-
-.fl      {color: #1514b5;}
-.fu      {color: #000000;} /* function */
-.ch,.st  {color: #036a07;} /* string */
-.kw      {color: #264D66;} /* keyword */
-.co      {color: #888888;} /* comment */
-
-.error   {font-weight: bolder;}
-.warning {font-weight: bolder;}
-
-/* Clipboard --------------------------*/
-
-.hasCopyButton {
-  position: relative;
-}
-
-.btn-copy-ex {
-  position: absolute;
-  right: 0;
-  top: 0;
-  visibility: hidden;
-}
-
-.hasCopyButton:hover button.btn-copy-ex {
-  visibility: visible;
-}
-
-/* headroom.js ------------------------ */
-
-.headroom {
-  will-change: transform;
-  transition: transform 200ms linear;
-}
-.headroom--pinned {
-  transform: translateY(0%);
-}
-.headroom--unpinned {
-  transform: translateY(-100%);
-}
-
-/* mark.js ----------------------------*/
-
-mark {
-  background-color: rgba(255, 255, 51, 0.5);
-  border-bottom: 2px solid rgba(255, 153, 51, 0.3);
-  padding: 1px;
-}
-
-/* vertical spacing after htmlwidgets */
-.html-widget {
-  margin-bottom: 10px;
-}
-
-/* fontawesome ------------------------ */
-
-.fab {
-    font-family: "Font Awesome 5 Brands" !important;
-}
-
-/* don't display links in code chunks when printing */
-/* source: https://stackoverflow.com/a/10781533 */
-@media print {
-  code a:link:after, code a:visited:after {
-    content: "";
-  }
-}
-
-/* Section anchors ---------------------------------
-   Added in pandoc 2.11: https://github.com/jgm/pandoc-templates/commit/9904bf71
-*/
-
-div.csl-bib-body { }
-div.csl-entry {
-  clear: both;
-}
-.hanging-indent div.csl-entry {
-  margin-left:2em;
-  text-indent:-2em;
-}
-div.csl-left-margin {
-  min-width:2em;
-  float:left;
-}
-div.csl-right-inline {
-  margin-left:2em;
-  padding-left:1em;
-}
-div.csl-indent {
-  margin-left: 2em;
-}
diff --git a/docs/dev/pkgdown.js b/docs/dev/pkgdown.js
deleted file mode 100644
index 6f0eee40..00000000
--- a/docs/dev/pkgdown.js
+++ /dev/null
@@ -1,108 +0,0 @@
-/* http://gregfranko.com/blog/jquery-best-practices/ */
-(function($) {
-  $(function() {
-
-    $('.navbar-fixed-top').headroom();
-
-    $('body').css('padding-top', $('.navbar').height() + 10);
-    $(window).resize(function(){
-      $('body').css('padding-top', $('.navbar').height() + 10);
-    });
-
-    $('[data-toggle="tooltip"]').tooltip();
-
-    var cur_path = paths(location.pathname);
-    var links = $("#navbar ul li a");
-    var max_length = -1;
-    var pos = -1;
-    for (var i = 0; i < links.length; i++) {
-      if (links[i].getAttribute("href") === "#")
-        continue;
-      // Ignore external links
-      if (links[i].host !== location.host)
-        continue;
-
-      var nav_path = paths(links[i].pathname);
-
-      var length = prefix_length(nav_path, cur_path);
-      if (length > max_length) {
-        max_length = length;
-        pos = i;
-      }
-    }
-
-    // Add class to parent <li>, and enclosing <li> if in dropdown
-    if (pos >= 0) {
-      var menu_anchor = $(links[pos]);
-      menu_anchor.parent().addClass("active");
-      menu_anchor.closest("li.dropdown").addClass("active");
-    }
-  });
-
-  function paths(pathname) {
-    var pieces = pathname.split("/");
-    pieces.shift(); // always starts with /
-
-    var end = pieces[pieces.length - 1];
-    if (end === "index.html" || end === "")
-      pieces.pop();
-    return(pieces);
-  }
-
-  // Returns -1 if not found
-  function prefix_length(needle, haystack) {
-    if (needle.length > haystack.length)
-      return(-1);
-
-    // Special case for length-0 haystack, since for loop won't run
-    if (haystack.length === 0) {
-      return(needle.length === 0 ? 0 : -1);
-    }
-
-    for (var i = 0; i < haystack.length; i++) {
-      if (needle[i] != haystack[i])
-        return(i);
-    }
-
-    return(haystack.length);
-  }
-
-  /* Clipboard --------------------------*/
-
-  function changeTooltipMessage(element, msg) {
-    var tooltipOriginalTitle=element.getAttribute('data-original-title');
-    element.setAttribute('data-original-title', msg);
-    $(element).tooltip('show');
-    element.setAttribute('data-original-title', tooltipOriginalTitle);
-  }
-
-  if(ClipboardJS.isSupported()) {
-    $(document).ready(function() {
-      var copyButton = "<button type='button' class='btn btn-primary btn-copy-ex' type = 'submit' title='Copy to clipboard' aria-label='Copy to clipboard' data-toggle='tooltip' data-placement='left auto' data-trigger='hover' data-clipboard-copy><i class='fa fa-copy'></i></button>";
-
-      $("div.sourceCode").addClass("hasCopyButton");
-
-      // Insert copy buttons:
-      $(copyButton).prependTo(".hasCopyButton");
-
-      // Initialize tooltips:
-      $('.btn-copy-ex').tooltip({container: 'body'});
-
-      // Initialize clipboard:
-      var clipboardBtnCopies = new ClipboardJS('[data-clipboard-copy]', {
-        text: function(trigger) {
-          return trigger.parentNode.textContent.replace(/\n#>[^\n]*/g, "");
-        }
-      });
-
-      clipboardBtnCopies.on('success', function(e) {
-        changeTooltipMessage(e.trigger, 'Copied!');
-        e.clearSelection();
-      });
-
-      clipboardBtnCopies.on('error', function() {
-        changeTooltipMessage(e.trigger,'Press Ctrl+C or Command+C to copy');
-      });
-    });
-  }
-})(window.jQuery || window.$)
diff --git a/docs/dev/pkgdown.yml b/docs/dev/pkgdown.yml
deleted file mode 100644
index c3829f60..00000000
--- a/docs/dev/pkgdown.yml
+++ /dev/null
@@ -1,23 +0,0 @@
-pandoc: 2.17.1.1
-pkgdown: 2.0.7
-pkgdown_sha: ~
-articles:
-  FOCUS_D: FOCUS_D.html
-  FOCUS_L: FOCUS_L.html
-  mkin: mkin.html
-  2022_cyan_pathway: prebuilt/2022_cyan_pathway.html
-  2022_dmta_parent: prebuilt/2022_dmta_parent.html
-  2022_dmta_pathway: prebuilt/2022_dmta_pathway.html
-  twa: twa.html
-  FOCUS_Z: web_only/FOCUS_Z.html
-  NAFTA_examples: web_only/NAFTA_examples.html
-  benchmarks: web_only/benchmarks.html
-  compiled_models: web_only/compiled_models.html
-  dimethenamid_2018: web_only/dimethenamid_2018.html
-  multistart: web_only/multistart.html
-  saem_benchmarks: web_only/saem_benchmarks.html
-last_built: 2023-04-20T18:24Z
-urls:
-  reference: https://pkgdown.jrwb.de/mkin/reference
-  article: https://pkgdown.jrwb.de/mkin/articles
-
diff --git a/docs/dev/reference/AIC.mmkin.html b/docs/dev/reference/AIC.mmkin.html
deleted file mode 100644
index ebfe052d..00000000
--- a/docs/dev/reference/AIC.mmkin.html
+++ /dev/null
@@ -1,220 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Calculate the AIC for a column of an mmkin object — AIC.mmkin • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Calculate the AIC for a column of an mmkin object — AIC.mmkin"><meta property="og:description" content="Provides a convenient way to compare different kinetic models fitted to the
-same dataset."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Calculate the AIC for a column of an mmkin object</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/AIC.mmkin.R" class="external-link"><code>R/AIC.mmkin.R</code></a></small>
-    <div class="hidden name"><code>AIC.mmkin.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Provides a convenient way to compare different kinetic models fitted to the
-same dataset.</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for mmkin</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">AIC</a></span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span>, k <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># S3 method for mmkin</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">BIC</a></span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
-    </div>
-
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>object</dt>
-<dd><p>An object of class <code><a href="mmkin.html">mmkin</a></code>, containing only one
-column.</p></dd>
-
-
-<dt>...</dt>
-<dd><p>For compatibility with the generic method</p></dd>
-
-
-<dt>k</dt>
-<dd><p>As in the generic method</p></dd>
-
-</dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>As in the generic method (a numeric value for single fits, or a
-dataframe if there are several fits in the column).</p>
-    </div>
-    <div id="author">
-    <h2>Author</h2>
-    <p>Johannes Ranke</p>
-    </div>
-
-    <div id="ref-examples">
-    <h2>Examples</h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span>
-<span class="r-in"><span>   <span class="co"># skip, as it takes &gt; 10 s on winbuilder</span></span></span>
-<span class="r-in"><span>  <span class="va">f</span> <span class="op">&lt;-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"FOMC"</span>, <span class="st">"DFOP"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>    <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="st">"FOCUS A"</span> <span class="op">=</span> <span class="va">FOCUS_2006_A</span>,</span></span>
-<span class="r-in"><span>         <span class="st">"FOCUS C"</span> <span class="op">=</span> <span class="va">FOCUS_2006_C</span><span class="op">)</span>, cores <span class="op">=</span> <span class="fl">1</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-in"><span>  <span class="co"># We get a warning because the FOMC model does not converge for the</span></span></span>
-<span class="r-in"><span>  <span class="co"># FOCUS A dataset, as it is well described by SFO</span></span></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">AIC</a></span><span class="op">(</span><span class="va">f</span><span class="op">[</span><span class="st">"SFO"</span>, <span class="st">"FOCUS A"</span><span class="op">]</span><span class="op">)</span> <span class="co"># We get a single number for a single fit</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 55.28197</span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">AIC</a></span><span class="op">(</span><span class="va">f</span><span class="op">[[</span><span class="st">"SFO"</span>, <span class="st">"FOCUS A"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span> <span class="co"># or when extracting an mkinfit object</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 55.28197</span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span>  <span class="co"># For FOCUS A, the models fit almost equally well, so the higher the number</span></span></span>
-<span class="r-in"><span>  <span class="co"># of parameters, the higher (worse) the AIC</span></span></span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">AIC</a></span><span class="op">(</span><span class="va">f</span><span class="op">[</span>, <span class="st">"FOCUS A"</span><span class="op">]</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>      df      AIC</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> SFO   3 55.28197</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> FOMC  4 57.28222</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> DFOP  5 59.28197</span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">AIC</a></span><span class="op">(</span><span class="va">f</span><span class="op">[</span>, <span class="st">"FOCUS A"</span><span class="op">]</span>, k <span class="op">=</span> <span class="fl">0</span><span class="op">)</span> <span class="co"># If we do not penalize additional parameters, we get nearly the same</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>      df      AIC</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> SFO   3 49.28197</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> FOMC  4 49.28222</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> DFOP  5 49.28197</span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">BIC</a></span><span class="op">(</span><span class="va">f</span><span class="op">[</span>, <span class="st">"FOCUS A"</span><span class="op">]</span><span class="op">)</span>        <span class="co"># Comparing the BIC gives a very similar picture</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>      df      BIC</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> SFO   3 55.52030</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> FOMC  4 57.59999</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> DFOP  5 59.67918</span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span>  <span class="co"># For FOCUS C, the more complex models fit better</span></span></span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">AIC</a></span><span class="op">(</span><span class="va">f</span><span class="op">[</span>, <span class="st">"FOCUS C"</span><span class="op">]</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>      df      AIC</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> SFO   3 59.29336</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> FOMC  4 44.68652</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> DFOP  5 29.02372</span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">BIC</a></span><span class="op">(</span><span class="va">f</span><span class="op">[</span>, <span class="st">"FOCUS C"</span><span class="op">]</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>      df      BIC</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> SFO   3 59.88504</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> FOMC  4 45.47542</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> DFOP  5 30.00984</span>
-<span class="r-in"><span>  </span></span>
-<span class="r-in"><span></span></span>
-</code></pre></div>
-    </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/CAKE_export.html b/docs/dev/reference/CAKE_export.html
deleted file mode 100644
index 7b52a8c5..00000000
--- a/docs/dev/reference/CAKE_export.html
+++ /dev/null
@@ -1,225 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Export a list of datasets format to a CAKE study file — CAKE_export • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Export a list of datasets format to a CAKE study file — CAKE_export"><meta property="og:description" content="In addition to the datasets, the pathways in the degradation model can be
-specified as well."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Export a list of datasets format to a CAKE study file</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/CAKE_export.R" class="external-link"><code>R/CAKE_export.R</code></a></small>
-    <div class="hidden name"><code>CAKE_export.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>In addition to the datasets, the pathways in the degradation model can be
-specified as well.</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">CAKE_export</span><span class="op">(</span></span>
-<span>  <span class="va">ds</span>,</span>
-<span>  map <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="st">"Parent"</span><span class="op">)</span>,</span>
-<span>  links <span class="op">=</span> <span class="cn">NA</span>,</span>
-<span>  filename <span class="op">=</span> <span class="st">"CAKE_export.csf"</span>,</span>
-<span>  path <span class="op">=</span> <span class="st">"."</span>,</span>
-<span>  overwrite <span class="op">=</span> <span class="cn">FALSE</span>,</span>
-<span>  study <span class="op">=</span> <span class="st">"Degradinol aerobic soil degradation"</span>,</span>
-<span>  description <span class="op">=</span> <span class="st">""</span>,</span>
-<span>  time_unit <span class="op">=</span> <span class="st">"days"</span>,</span>
-<span>  res_unit <span class="op">=</span> <span class="st">"% AR"</span>,</span>
-<span>  comment <span class="op">=</span> <span class="st">""</span>,</span>
-<span>  date <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Sys.time.html" class="external-link">Sys.Date</a></span><span class="op">(</span><span class="op">)</span>,</span>
-<span>  optimiser <span class="op">=</span> <span class="st">"IRLS"</span></span>
-<span><span class="op">)</span></span></code></pre></div>
-    </div>
-
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>ds</dt>
-<dd><p>A named list of datasets in long format as compatible with
-<code><a href="mkinfit.html">mkinfit</a></code>.</p></dd>
-
-
-<dt>map</dt>
-<dd><p>A character vector with CAKE compartment names (Parent, A1, ...),
-named with the names used in the list of datasets.</p></dd>
-
-
-<dt>links</dt>
-<dd><p>An optional character vector of target compartments, named with
-the names of the source compartments. In order to make this easier, the
-names are used as in the datasets supplied.</p></dd>
-
-
-<dt>filename</dt>
-<dd><p>Where to write the result. Should end in .csf in order to be
-compatible with CAKE.</p></dd>
-
-
-<dt>path</dt>
-<dd><p>An optional path to the output file.</p></dd>
-
-
-<dt>overwrite</dt>
-<dd><p>If TRUE, existing files are overwritten.</p></dd>
-
-
-<dt>study</dt>
-<dd><p>The name of the study.</p></dd>
-
-
-<dt>description</dt>
-<dd><p>An optional description.</p></dd>
-
-
-<dt>time_unit</dt>
-<dd><p>The time unit for the residue data.</p></dd>
-
-
-<dt>res_unit</dt>
-<dd><p>The unit used for the residues.</p></dd>
-
-
-<dt>comment</dt>
-<dd><p>An optional comment.</p></dd>
-
-
-<dt>date</dt>
-<dd><p>The date of file creation.</p></dd>
-
-
-<dt>optimiser</dt>
-<dd><p>Can be OLS or IRLS.</p></dd>
-
-</dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>The function is called for its side effect.</p>
-    </div>
-    <div id="author">
-    <h2>Author</h2>
-    <p>Johannes Ranke</p>
-    </div>
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/D24_2014.html b/docs/dev/reference/D24_2014.html
deleted file mode 100644
index 8af0a8e2..00000000
--- a/docs/dev/reference/D24_2014.html
+++ /dev/null
@@ -1,261 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Aerobic soil degradation data on 2,4-D from the EU assessment in 2014 — D24_2014 • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Aerobic soil degradation data on 2,4-D from the EU assessment in 2014 — D24_2014"><meta property="og:description" content="The five datasets were extracted from the active substance evaluation dossier
-published by EFSA. Kinetic evaluations shown for these datasets are intended
-to illustrate and advance kinetic modelling. The fact that these data and
-some results are shown here does not imply a license to use them in the
-context of pesticide  registrations, as the use of the data may be
-constrained by data protection regulations."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Aerobic soil degradation data on 2,4-D from the EU assessment in 2014</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/D24_2014.R" class="external-link"><code>R/D24_2014.R</code></a></small>
-    <div class="hidden name"><code>D24_2014.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>The five datasets were extracted from the active substance evaluation dossier
-published by EFSA. Kinetic evaluations shown for these datasets are intended
-to illustrate and advance kinetic modelling. The fact that these data and
-some results are shown here does not imply a license to use them in the
-context of pesticide  registrations, as the use of the data may be
-constrained by data protection regulations.</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="va">D24_2014</span></span></code></pre></div>
-    </div>
-
-    <div id="format">
-    <h2>Format</h2>
-    <p>An <a href="mkindsg.html">mkindsg</a> object grouping five datasets</p>
-    </div>
-    <div id="source">
-    <h2>Source</h2>
-    <p>Hellenic Ministry of Rural Development and Agriculture (2014)
-Final addendum to the Renewal Assessment Report - public version - 2,4-D
-Volume 3 Annex B.8 Fate and behaviour in the environment
-<a href="https://open.efsa.europa.eu/study-inventory/EFSA-Q-2013-00811" class="external-link">https://open.efsa.europa.eu/study-inventory/EFSA-Q-2013-00811</a></p>
-    </div>
-    <div id="details">
-    <h2>Details</h2>
-    <p>Data for the first dataset are from p. 685. Data for the other four
-datasets were used in the preprocessed versions given in the kinetics
-section (p. 761ff.), with the exception of residues smaller than 1 for DCP
-in the soil from Site I2, where the values given on p. 694 were used.</p>
-<p>The R code used to create this data object is installed with this package
-in the 'dataset_generation' directory. In the code, page numbers are given for
-specific pieces of information in the comments.</p>
-    </div>
-
-    <div id="ref-examples">
-    <h2>Examples</h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">D24_2014</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> &lt;mkindsg&gt; holding 5 mkinds objects</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Title $title:  Aerobic soil degradation data on 2,4-D from the EU assessment in 2014 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Occurrence of observed compounds $observed_n:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> D24 DCP DCA </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   5   4   4 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Time normalisation factors $f_time_norm:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 1.6062378 0.7118732 0.7156063 0.7156063 0.8977124</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Meta information $meta:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                                 study usda_soil_type study_moisture_ref_type</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Mississippi                Cohen 1991      Silt loam                    &lt;NA&gt;</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Fayette     Liu and Adelfinskaya 2011      Silt loam                     pF1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> RefSol 03-G Liu and Adelfinskaya 2011           Loam                     pF1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Site E1     Liu and Adelfinskaya 2011           Loam                     pF1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Site I2     Liu and Adelfinskaya 2011     Loamy sand                     pF1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>             rel_moisture temperature</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Mississippi           NA          25</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Fayette              0.5          20</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> RefSol 03-G          0.5          20</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Site E1              0.5          20</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Site I2              0.5          20</span>
-<span class="r-in"><span><span class="co"># \dontrun{</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">D24_2014</span><span class="op">$</span><span class="va">ds</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span>, data <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> &lt;mkinds&gt; with $title:  Mississippi </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Observed compounds $observed:  D24 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Sampling times $sampling_times:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 0, 2, 4, 7, 15, 24, 35, 56, 71, 114, 183, 273, 365 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> With a maximum of  1  replicates</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    time  D24</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1     0 96.8</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2     2 81.0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 3     4 81.7</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 4     7 88.2</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5    15 66.3</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 6    24 72.9</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 7    35 62.6</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 8    56 54.6</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 9    71 35.2</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 10  114 18.0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 11  183 11.3</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 12  273  9.9</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 13  365  6.3</span>
-<span class="r-in"><span><span class="va">m_D24</span> <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>D24 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"DCP"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  DCP <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"DCA"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  DCA <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Temporary DLL for differentials generated and loaded</span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">m_D24</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> &lt;mkinmod&gt; model generated with</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Use of formation fractions $use_of_ff: max </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Specification $spec:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> $D24</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> $type: SFO; $to: DCP; $sink: TRUE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> $DCP</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> $type: SFO; $to: DCA; $sink: TRUE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> $DCA</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> $type: SFO; $sink: TRUE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Coefficient matrix $coefmat available</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Compiled model $cf available</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Differential equations:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> d_D24/dt = - k_D24 * D24</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> d_DCP/dt = + f_D24_to_DCP * k_D24 * D24 - k_DCP * DCP</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> d_DCA/dt = + f_DCP_to_DCA * k_DCP * DCP - k_DCA * DCA</span>
-<span class="r-in"><span><span class="va">m_D24_2</span> <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>D24 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"DFOP"</span>, to <span class="op">=</span> <span class="st">"DCP"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  DCP <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"DCA"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  DCA <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Temporary DLL for differentials generated and loaded</span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">m_D24_2</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> &lt;mkinmod&gt; model generated with</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Use of formation fractions $use_of_ff: max </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Specification $spec:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> $D24</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> $type: DFOP; $to: DCP; $sink: TRUE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> $DCP</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> $type: SFO; $to: DCA; $sink: TRUE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> $DCA</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> $type: SFO; $sink: TRUE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Compiled model $cf available</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Differential equations:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> d_D24/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>            * D24</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> d_DCP/dt = + f_D24_to_DCP * ((k1 * g * exp(-k1 * time) + k2 * (1 - g) *</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>            exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>            * time))) * D24 - k_DCP * DCP</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> d_DCA/dt = + f_DCP_to_DCA * k_DCP * DCP - k_DCA * DCA</span>
-<span class="r-in"><span><span class="co"># }</span></span></span>
-</code></pre></div>
-    </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/DFOP.solution-1.png b/docs/dev/reference/DFOP.solution-1.png
deleted file mode 100644
index 6b78836f..00000000
--- a/docs/dev/reference/DFOP.solution-1.png
+++ /dev/null
diff --git a/docs/dev/reference/DFOP.solution.html b/docs/dev/reference/DFOP.solution.html
deleted file mode 100644
index d462133c..00000000
--- a/docs/dev/reference/DFOP.solution.html
+++ /dev/null
@@ -1,202 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Double First-Order in Parallel kinetics — DFOP.solution • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Double First-Order in Parallel kinetics — DFOP.solution"><meta property="og:description" content="Function describing decline from a defined starting value using the sum of
-two exponential decline functions."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Double First-Order in Parallel kinetics</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/parent_solutions.R" class="external-link"><code>R/parent_solutions.R</code></a></small>
-    <div class="hidden name"><code>DFOP.solution.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Function describing decline from a defined starting value using the sum of
-two exponential decline functions.</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">DFOP.solution</span><span class="op">(</span><span class="va">t</span>, <span class="va">parent_0</span>, <span class="va">k1</span>, <span class="va">k2</span>, <span class="va">g</span><span class="op">)</span></span></code></pre></div>
-    </div>
-
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>t</dt>
-<dd><p>Time.</p></dd>
-
-
-<dt>parent_0</dt>
-<dd><p>Starting value for the response variable at time zero.</p></dd>
-
-
-<dt>k1</dt>
-<dd><p>First kinetic constant.</p></dd>
-
-
-<dt>k2</dt>
-<dd><p>Second kinetic constant.</p></dd>
-
-
-<dt>g</dt>
-<dd><p>Fraction of the starting value declining according to the first
-kinetic constant.</p></dd>
-
-</dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>The value of the response variable at time <code>t</code>.</p>
-    </div>
-    <div id="references">
-    <h2>References</h2>
-    <p>FOCUS (2006) “Guidance Document on Estimating Persistence
-and Degradation Kinetics from Environmental Fate Studies on Pesticides in
-EU Registration” Report of the FOCUS Work Group on Degradation Kinetics,
-EC Document Reference Sanco/10058/2005 version 2.0, 434 pp,
-<a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics" class="external-link">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a>
-FOCUS (2014) “Generic guidance for Estimating Persistence
-and Degradation Kinetics from Environmental Fate Studies on Pesticides in
-EU Registration” Report of the FOCUS Work Group on Degradation Kinetics,
-Version 1.1, 18 December 2014
-<a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics" class="external-link">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p>
-    </div>
-    <div id="see-also">
-    <h2>See also</h2>
-    <div class="dont-index"><p>Other parent solutions: 
-<code><a href="FOMC.solution.html">FOMC.solution</a>()</code>,
-<code><a href="HS.solution.html">HS.solution</a>()</code>,
-<code><a href="IORE.solution.html">IORE.solution</a>()</code>,
-<code><a href="SFO.solution.html">SFO.solution</a>()</code>,
-<code><a href="SFORB.solution.html">SFORB.solution</a>()</code>,
-<code><a href="logistic.solution.html">logistic.solution</a>()</code></p></div>
-    </div>
-
-    <div id="ref-examples">
-    <h2>Examples</h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fu">DFOP.solution</span><span class="op">(</span><span class="va">x</span>, <span class="fl">100</span>, <span class="fl">5</span>, <span class="fl">0.5</span>, <span class="fl">0.3</span><span class="op">)</span>, <span class="fl">0</span>, <span class="fl">4</span>, ylim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">100</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-plt img"><img src="DFOP.solution-1.png" alt="" width="700" height="433"></span>
-<span class="r-in"><span></span></span>
-</code></pre></div>
-    </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/Extract.mmkin.html b/docs/dev/reference/Extract.mmkin.html
deleted file mode 100644
index 3a22d14b..00000000
--- a/docs/dev/reference/Extract.mmkin.html
+++ /dev/null
@@ -1,235 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Subsetting method for mmkin objects — [.mmkin • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Subsetting method for mmkin objects — [.mmkin"><meta property="og:description" content="Subsetting method for mmkin objects"><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Subsetting method for mmkin objects</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/mmkin.R" class="external-link"><code>R/mmkin.R</code></a></small>
-    <div class="hidden name"><code>Extract.mmkin.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Subsetting method for mmkin objects</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre><code># S3 method for mmkin
-[(x, i, j, ..., drop = FALSE)</code></pre></div>
-    </div>
-
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>x</dt>
-<dd><p>An <code><a href="mmkin.html">mmkin</a> object</code></p></dd>
-
-
-<dt>i</dt>
-<dd><p>Row index selecting the fits for specific models</p></dd>
-
-
-<dt>j</dt>
-<dd><p>Column index selecting the fits to specific datasets</p></dd>
-
-
-<dt>...</dt>
-<dd><p>Not used, only there to satisfy the generic method definition</p></dd>
-
-
-<dt>drop</dt>
-<dd><p>If FALSE, the method always returns an mmkin object, otherwise
-either a list of mkinfit objects or a single mkinfit object.</p></dd>
-
-</dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>An object of class <code><a href="mmkin.html">mmkin</a></code>.</p>
-    </div>
-    <div id="author">
-    <h2>Author</h2>
-    <p>Johannes Ranke</p>
-    </div>
-
-    <div id="ref-examples">
-    <h2>Examples</h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span>
-<span class="r-in"><span>  <span class="co"># Only use one core, to pass R CMD check --as-cran</span></span></span>
-<span class="r-in"><span>  <span class="va">fits</span> <span class="op">&lt;-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"FOMC"</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>B <span class="op">=</span> <span class="va">FOCUS_2006_B</span>, C <span class="op">=</span> <span class="va">FOCUS_2006_C</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>                cores <span class="op">=</span> <span class="fl">1</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-in"><span>  <span class="va">fits</span><span class="op">[</span><span class="st">"FOMC"</span>, <span class="op">]</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> &lt;mmkin&gt; object</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Status of individual fits:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>       dataset</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> model  B  C </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   FOMC OK OK</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> OK: No warnings</span>
-<span class="r-in"><span>  <span class="va">fits</span><span class="op">[</span>, <span class="st">"B"</span><span class="op">]</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> &lt;mmkin&gt; object</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Status of individual fits:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>       dataset</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> model  B </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   SFO  OK</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   FOMC OK</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> OK: No warnings</span>
-<span class="r-in"><span>  <span class="va">fits</span><span class="op">[</span><span class="st">"SFO"</span>, <span class="st">"B"</span><span class="op">]</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> &lt;mmkin&gt; object</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Status of individual fits:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>      dataset</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> model B </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   SFO OK</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> OK: No warnings</span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span></span></span>
-<span class="r-in"><span>    <span class="co"># This extracts an mkinfit object with lots of components</span></span></span>
-<span class="r-in"><span>    <span class="va">fits</span><span class="op">[[</span><span class="st">"FOMC"</span>, <span class="st">"B"</span><span class="op">]</span><span class="op">]</span></span></span>
-<span class="r-in"><span>  <span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> $par</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  parent_0 log_alpha  log_beta     sigma </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 99.666192  2.549850  5.050586  1.890202 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> $objective</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 28.58291</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> $convergence</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> $iterations</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 21</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> $evaluations</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> function gradient </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>       25       78 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> $message</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "both X-convergence and relative convergence (5)"</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-</code></pre></div>
-    </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/FOCUS_2006_DFOP_ref_A_to_B.html b/docs/dev/reference/FOCUS_2006_DFOP_ref_A_to_B.html
deleted file mode 100644
index a8eb25cf..00000000
--- a/docs/dev/reference/FOCUS_2006_DFOP_ref_A_to_B.html
+++ /dev/null
@@ -1,189 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Results of fitting the DFOP model to Datasets A to B of FOCUS (2006) — FOCUS_2006_DFOP_ref_A_to_B • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Results of fitting the DFOP model to Datasets A to B of FOCUS (2006) — FOCUS_2006_DFOP_ref_A_to_B"><meta property="og:description" content="A table with the fitted parameters and the resulting DT50 and DT90 values
-generated with different software packages. Taken directly from FOCUS (2006).
-The results from fitting the data with the Topfit software was removed, as
-the initial concentration of the parent compound was fixed to a value of 100
-in this fit."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Results of fitting the DFOP model to Datasets A to B of FOCUS (2006)</h1>
-    
-    <div class="hidden name"><code>FOCUS_2006_DFOP_ref_A_to_B.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>A table with the fitted parameters and the resulting DT50 and DT90 values
-generated with different software packages. Taken directly from FOCUS (2006).
-The results from fitting the data with the Topfit software was removed, as
-the initial concentration of the parent compound was fixed to a value of 100
-in this fit.</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="va">FOCUS_2006_DFOP_ref_A_to_B</span></span></code></pre></div>
-    </div>
-
-    <div id="format">
-    <h2>Format</h2>
-    <p>A data frame containing the following variables.</p><dl><dt><code>package</code></dt>
-<dd><p>a factor giving the name of the software package</p></dd>
-
-    <dt><code>M0</code></dt>
-<dd><p>The fitted initial concentration of the parent compound</p></dd>
-
-    <dt><code>f</code></dt>
-<dd><p>The fitted f parameter</p></dd>
-
-    <dt><code>k1</code></dt>
-<dd><p>The fitted k1 parameter</p></dd>
-
-    <dt><code>k2</code></dt>
-<dd><p>The fitted k2 parameter</p></dd>
-
-    <dt><code>DT50</code></dt>
-<dd><p>The resulting half-life of the parent compound</p></dd>
-
-    <dt><code>DT90</code></dt>
-<dd><p>The resulting DT90 of the parent compound</p></dd>
-
-    <dt><code>dataset</code></dt>
-<dd><p>The FOCUS dataset that was used</p></dd>
-
-  
-</dl></div>
-    <div id="source">
-    <h2>Source</h2>
-    <p>FOCUS (2006) “Guidance Document on Estimating Persistence and
-  Degradation Kinetics from Environmental Fate Studies on Pesticides in EU
-  Registration” Report of the FOCUS Work Group on Degradation Kinetics,
-  EC Document Reference Sanco/10058/2005 version 2.0, 434 pp,
-  <a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics" class="external-link">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p>
-    </div>
-
-    <div id="ref-examples">
-    <h2>Examples</h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/utils/data.html" class="external-link">data</a></span><span class="op">(</span><span class="va">FOCUS_2006_DFOP_ref_A_to_B</span><span class="op">)</span></span></span>
-</code></pre></div>
-    </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/FOCUS_2006_FOMC_ref_A_to_F.html b/docs/dev/reference/FOCUS_2006_FOMC_ref_A_to_F.html
deleted file mode 100644
index 24297c78..00000000
--- a/docs/dev/reference/FOCUS_2006_FOMC_ref_A_to_F.html
+++ /dev/null
@@ -1,186 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Results of fitting the FOMC model to Datasets A to F of FOCUS (2006) — FOCUS_2006_FOMC_ref_A_to_F • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Results of fitting the FOMC model to Datasets A to F of FOCUS (2006) — FOCUS_2006_FOMC_ref_A_to_F"><meta property="og:description" content="A table with the fitted parameters and the resulting DT50 and DT90 values
-generated with different software packages. Taken directly from FOCUS (2006).
-The results from fitting the data with the Topfit software was removed, as
-the initial concentration of the parent compound was fixed to a value of 100
-in this fit."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Results of fitting the FOMC model to Datasets A to F of FOCUS (2006)</h1>
-    
-    <div class="hidden name"><code>FOCUS_2006_FOMC_ref_A_to_F.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>A table with the fitted parameters and the resulting DT50 and DT90 values
-generated with different software packages. Taken directly from FOCUS (2006).
-The results from fitting the data with the Topfit software was removed, as
-the initial concentration of the parent compound was fixed to a value of 100
-in this fit.</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="va">FOCUS_2006_FOMC_ref_A_to_F</span></span></code></pre></div>
-    </div>
-
-    <div id="format">
-    <h2>Format</h2>
-    <p>A data frame containing the following variables.</p><dl><dt><code>package</code></dt>
-<dd><p>a factor giving the name of the software package</p></dd>
-
-    <dt><code>M0</code></dt>
-<dd><p>The fitted initial concentration of the parent compound</p></dd>
-
-    <dt><code>alpha</code></dt>
-<dd><p>The fitted alpha parameter</p></dd>
-
-    <dt><code>beta</code></dt>
-<dd><p>The fitted beta parameter</p></dd>
-
-    <dt><code>DT50</code></dt>
-<dd><p>The resulting half-life of the parent compound</p></dd>
-
-    <dt><code>DT90</code></dt>
-<dd><p>The resulting DT90 of the parent compound</p></dd>
-
-    <dt><code>dataset</code></dt>
-<dd><p>The FOCUS dataset that was used</p></dd>
-
-  
-</dl></div>
-    <div id="source">
-    <h2>Source</h2>
-    <p>FOCUS (2006) “Guidance Document on Estimating Persistence and
-  Degradation Kinetics from Environmental Fate Studies on Pesticides in EU
-  Registration” Report of the FOCUS Work Group on Degradation Kinetics,
-  EC Document Reference Sanco/10058/2005 version 2.0, 434 pp,
-  <a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics" class="external-link">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p>
-    </div>
-
-    <div id="ref-examples">
-    <h2>Examples</h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/utils/data.html" class="external-link">data</a></span><span class="op">(</span><span class="va">FOCUS_2006_FOMC_ref_A_to_F</span><span class="op">)</span></span></span>
-</code></pre></div>
-    </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/FOCUS_2006_HS_ref_A_to_F.html b/docs/dev/reference/FOCUS_2006_HS_ref_A_to_F.html
deleted file mode 100644
index caf258ba..00000000
--- a/docs/dev/reference/FOCUS_2006_HS_ref_A_to_F.html
+++ /dev/null
@@ -1,189 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Results of fitting the HS model to Datasets A to F of FOCUS (2006) — FOCUS_2006_HS_ref_A_to_F • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Results of fitting the HS model to Datasets A to F of FOCUS (2006) — FOCUS_2006_HS_ref_A_to_F"><meta property="og:description" content="A table with the fitted parameters and the resulting DT50 and DT90 values
-generated with different software packages. Taken directly from FOCUS (2006).
-The results from fitting the data with the Topfit software was removed, as
-the initial concentration of the parent compound was fixed to a value of 100
-in this fit."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Results of fitting the HS model to Datasets A to F of FOCUS (2006)</h1>
-    
-    <div class="hidden name"><code>FOCUS_2006_HS_ref_A_to_F.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>A table with the fitted parameters and the resulting DT50 and DT90 values
-generated with different software packages. Taken directly from FOCUS (2006).
-The results from fitting the data with the Topfit software was removed, as
-the initial concentration of the parent compound was fixed to a value of 100
-in this fit.</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="va">FOCUS_2006_HS_ref_A_to_F</span></span></code></pre></div>
-    </div>
-
-    <div id="format">
-    <h2>Format</h2>
-    <p>A data frame containing the following variables.</p><dl><dt><code>package</code></dt>
-<dd><p>a factor giving the name of the software package</p></dd>
-
-    <dt><code>M0</code></dt>
-<dd><p>The fitted initial concentration of the parent compound</p></dd>
-
-    <dt><code>tb</code></dt>
-<dd><p>The fitted tb parameter</p></dd>
-
-    <dt><code>k1</code></dt>
-<dd><p>The fitted k1 parameter</p></dd>
-
-    <dt><code>k2</code></dt>
-<dd><p>The fitted k2 parameter</p></dd>
-
-    <dt><code>DT50</code></dt>
-<dd><p>The resulting half-life of the parent compound</p></dd>
-
-    <dt><code>DT90</code></dt>
-<dd><p>The resulting DT90 of the parent compound</p></dd>
-
-    <dt><code>dataset</code></dt>
-<dd><p>The FOCUS dataset that was used</p></dd>
-
-  
-</dl></div>
-    <div id="source">
-    <h2>Source</h2>
-    <p>FOCUS (2006) “Guidance Document on Estimating Persistence and
-  Degradation Kinetics from Environmental Fate Studies on Pesticides in EU
-  Registration” Report of the FOCUS Work Group on Degradation Kinetics,
-  EC Document Reference Sanco/10058/2005 version 2.0, 434 pp,
-  <a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics" class="external-link">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p>
-    </div>
-
-    <div id="ref-examples">
-    <h2>Examples</h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/utils/data.html" class="external-link">data</a></span><span class="op">(</span><span class="va">FOCUS_2006_HS_ref_A_to_F</span><span class="op">)</span></span></span>
-</code></pre></div>
-    </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/FOCUS_2006_SFO_ref_A_to_F.html b/docs/dev/reference/FOCUS_2006_SFO_ref_A_to_F.html
deleted file mode 100644
index bc3d1c08..00000000
--- a/docs/dev/reference/FOCUS_2006_SFO_ref_A_to_F.html
+++ /dev/null
@@ -1,183 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Results of fitting the SFO model to Datasets A to F of FOCUS (2006) — FOCUS_2006_SFO_ref_A_to_F • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Results of fitting the SFO model to Datasets A to F of FOCUS (2006) — FOCUS_2006_SFO_ref_A_to_F"><meta property="og:description" content="A table with the fitted parameters and the resulting DT50 and DT90 values
-generated with different software packages. Taken directly from FOCUS (2006).
-The results from fitting the data with the Topfit software was removed, as
-the initial concentration of the parent compound was fixed to a value of 100
-in this fit."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Results of fitting the SFO model to Datasets A to F of FOCUS (2006)</h1>
-    
-    <div class="hidden name"><code>FOCUS_2006_SFO_ref_A_to_F.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>A table with the fitted parameters and the resulting DT50 and DT90 values
-generated with different software packages. Taken directly from FOCUS (2006).
-The results from fitting the data with the Topfit software was removed, as
-the initial concentration of the parent compound was fixed to a value of 100
-in this fit.</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="va">FOCUS_2006_SFO_ref_A_to_F</span></span></code></pre></div>
-    </div>
-
-    <div id="format">
-    <h2>Format</h2>
-    <p>A data frame containing the following variables.</p><dl><dt><code>package</code></dt>
-<dd><p>a factor giving the name of the software package</p></dd>
-
-    <dt><code>M0</code></dt>
-<dd><p>The fitted initial concentration of the parent compound</p></dd>
-
-    <dt><code>k</code></dt>
-<dd><p>The fitted first-order degradation rate constant</p></dd>
-
-    <dt><code>DT50</code></dt>
-<dd><p>The resulting half-life of the parent compound</p></dd>
-
-    <dt><code>DT90</code></dt>
-<dd><p>The resulting DT90 of the parent compound</p></dd>
-
-    <dt><code>dataset</code></dt>
-<dd><p>The FOCUS dataset that was used</p></dd>
-
-  
-</dl></div>
-    <div id="source">
-    <h2>Source</h2>
-    <p>FOCUS (2006) “Guidance Document on Estimating Persistence and
-  Degradation Kinetics from Environmental Fate Studies on Pesticides in EU
-  Registration” Report of the FOCUS Work Group on Degradation Kinetics,
-  EC Document Reference Sanco/10058/2005 version 2.0, 434 pp,
-  <a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics" class="external-link">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p>
-    </div>
-
-    <div id="ref-examples">
-    <h2>Examples</h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/utils/data.html" class="external-link">data</a></span><span class="op">(</span><span class="va">FOCUS_2006_SFO_ref_A_to_F</span><span class="op">)</span></span></span>
-</code></pre></div>
-    </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/FOCUS_2006_datasets.html b/docs/dev/reference/FOCUS_2006_datasets.html
deleted file mode 100644
index 78f8e81b..00000000
--- a/docs/dev/reference/FOCUS_2006_datasets.html
+++ /dev/null
@@ -1,181 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Datasets A to F from the FOCUS Kinetics report from 2006 — FOCUS_2006_datasets • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Datasets A to F from the FOCUS Kinetics report from 2006 — FOCUS_2006_datasets"><meta property="og:description" content="Data taken from FOCUS (2006), p. 258."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Datasets A to F from the FOCUS Kinetics report from 2006</h1>
-    
-    <div class="hidden name"><code>FOCUS_2006_datasets.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Data taken from FOCUS (2006), p. 258.</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="va">FOCUS_2006_A</span></span>
-<span>  <span class="va">FOCUS_2006_B</span></span>
-<span>  <span class="va">FOCUS_2006_C</span></span>
-<span>  <span class="va">FOCUS_2006_D</span></span>
-<span>  <span class="va">FOCUS_2006_E</span></span>
-<span>  <span class="va">FOCUS_2006_F</span></span></code></pre></div>
-    </div>
-
-    <div id="format">
-    <h2>Format</h2>
-    <p>6 datasets with observations on the following variables.</p><dl><dt><code>name</code></dt>
-<dd><p>a factor containing the name of the observed variable</p></dd>
-
-    <dt><code>time</code></dt>
-<dd><p>a numeric vector containing time points</p></dd>
-
-    <dt><code>value</code></dt>
-<dd><p>a numeric vector containing concentrations in percent of applied radioactivity</p></dd>
-
-  
-</dl></div>
-    <div id="source">
-    <h2>Source</h2>
-    <p>FOCUS (2006) “Guidance Document on Estimating Persistence and
-  Degradation Kinetics from Environmental Fate Studies on Pesticides in EU
-  Registration” Report of the FOCUS Work Group on Degradation Kinetics,
-  EC Document Reference Sanco/10058/2005 version 2.0, 434 pp,
-  <a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics" class="external-link">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p>
-    </div>
-
-    <div id="ref-examples">
-    <h2>Examples</h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">FOCUS_2006_C</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     name time value</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1 parent    0  85.1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2 parent    1  57.9</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 3 parent    3  29.9</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 4 parent    7  14.6</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5 parent   14   9.7</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 6 parent   28   6.6</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 7 parent   63   4.0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 8 parent   91   3.9</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 9 parent  119   0.6</span>
-</code></pre></div>
-    </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/FOMC.solution-1.png b/docs/dev/reference/FOMC.solution-1.png
deleted file mode 100644
index 18a4b586..00000000
--- a/docs/dev/reference/FOMC.solution-1.png
+++ /dev/null
diff --git a/docs/dev/reference/FOMC.solution.html b/docs/dev/reference/FOMC.solution.html
deleted file mode 100644
index e1813261..00000000
--- a/docs/dev/reference/FOMC.solution.html
+++ /dev/null
@@ -1,213 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>First-Order Multi-Compartment kinetics — FOMC.solution • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="First-Order Multi-Compartment kinetics — FOMC.solution"><meta property="og:description" content="Function describing exponential decline from a defined starting value, with
-a decreasing rate constant."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>First-Order Multi-Compartment kinetics</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/parent_solutions.R" class="external-link"><code>R/parent_solutions.R</code></a></small>
-    <div class="hidden name"><code>FOMC.solution.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Function describing exponential decline from a defined starting value, with
-a decreasing rate constant.</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">FOMC.solution</span><span class="op">(</span><span class="va">t</span>, <span class="va">parent_0</span>, <span class="va">alpha</span>, <span class="va">beta</span><span class="op">)</span></span></code></pre></div>
-    </div>
-
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>t</dt>
-<dd><p>Time.</p></dd>
-
-
-<dt>parent_0</dt>
-<dd><p>Starting value for the response variable at time zero.</p></dd>
-
-
-<dt>alpha</dt>
-<dd><p>Shape parameter determined by coefficient of variation of rate
-constant values.</p></dd>
-
-
-<dt>beta</dt>
-<dd><p>Location parameter.</p></dd>
-
-</dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>The value of the response variable at time <code>t</code>.</p>
-    </div>
-    <div id="details">
-    <h2>Details</h2>
-    <p>The form given here differs slightly from the original reference by
-Gustafson and Holden (1990). The parameter <code>beta</code> corresponds to 1/beta
-in the original equation.</p>
-    </div>
-    <div id="note">
-    <h2>Note</h2>
-    <p>The solution of the FOMC kinetic model reduces to the
-<code><a href="SFO.solution.html">SFO.solution</a></code> for large values of <code>alpha</code> and <code>beta</code>
-with \(k = \frac{\beta}{\alpha}\).</p>
-    </div>
-    <div id="references">
-    <h2>References</h2>
-    <p>FOCUS (2006) “Guidance Document on Estimating Persistence
-and Degradation Kinetics from Environmental Fate Studies on Pesticides in
-EU Registration” Report of the FOCUS Work Group on Degradation Kinetics,
-EC Document Reference Sanco/10058/2005 version 2.0, 434 pp,
-<a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics" class="external-link">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p>
-<p>FOCUS (2014) “Generic guidance for Estimating Persistence
-and Degradation Kinetics from Environmental Fate Studies on Pesticides in
-EU Registration” Report of the FOCUS Work Group on Degradation Kinetics,
-Version 1.1, 18 December 2014
-<a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics" class="external-link">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p>
-<p>Gustafson DI and Holden LR (1990) Nonlinear pesticide dissipation in soil:
-A new model based on spatial variability. <em>Environmental Science and
-Technology</em> <b>24</b>, 1032-1038</p>
-    </div>
-    <div id="see-also">
-    <h2>See also</h2>
-    <div class="dont-index"><p>Other parent solutions: 
-<code><a href="DFOP.solution.html">DFOP.solution</a>()</code>,
-<code><a href="HS.solution.html">HS.solution</a>()</code>,
-<code><a href="IORE.solution.html">IORE.solution</a>()</code>,
-<code><a href="SFO.solution.html">SFO.solution</a>()</code>,
-<code><a href="SFORB.solution.html">SFORB.solution</a>()</code>,
-<code><a href="logistic.solution.html">logistic.solution</a>()</code></p></div>
-    </div>
-
-    <div id="ref-examples">
-    <h2>Examples</h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fu">FOMC.solution</span><span class="op">(</span><span class="va">x</span>, <span class="fl">100</span>, <span class="fl">10</span>, <span class="fl">2</span><span class="op">)</span>, <span class="fl">0</span>, <span class="fl">2</span>, ylim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">100</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-plt img"><img src="FOMC.solution-1.png" alt="" width="700" height="433"></span>
-<span class="r-in"><span></span></span>
-</code></pre></div>
-    </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/HS.solution-1.png b/docs/dev/reference/HS.solution-1.png
deleted file mode 100644
index 61d89dbc..00000000
--- a/docs/dev/reference/HS.solution-1.png
+++ /dev/null
diff --git a/docs/dev/reference/HS.solution.html b/docs/dev/reference/HS.solution.html
deleted file mode 100644
index d42eaef6..00000000
--- a/docs/dev/reference/HS.solution.html
+++ /dev/null
@@ -1,203 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Hockey-Stick kinetics — HS.solution • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Hockey-Stick kinetics — HS.solution"><meta property="og:description" content="Function describing two exponential decline functions with a break point
-between them."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Hockey-Stick kinetics</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/parent_solutions.R" class="external-link"><code>R/parent_solutions.R</code></a></small>
-    <div class="hidden name"><code>HS.solution.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Function describing two exponential decline functions with a break point
-between them.</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">HS.solution</span><span class="op">(</span><span class="va">t</span>, <span class="va">parent_0</span>, <span class="va">k1</span>, <span class="va">k2</span>, <span class="va">tb</span><span class="op">)</span></span></code></pre></div>
-    </div>
-
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>t</dt>
-<dd><p>Time.</p></dd>
-
-
-<dt>parent_0</dt>
-<dd><p>Starting value for the response variable at time zero.</p></dd>
-
-
-<dt>k1</dt>
-<dd><p>First kinetic constant.</p></dd>
-
-
-<dt>k2</dt>
-<dd><p>Second kinetic constant.</p></dd>
-
-
-<dt>tb</dt>
-<dd><p>Break point. Before this time, exponential decline according to
-<code>k1</code> is calculated, after this time, exponential decline proceeds
-according to <code>k2</code>.</p></dd>
-
-</dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>The value of the response variable at time <code>t</code>.</p>
-    </div>
-    <div id="references">
-    <h2>References</h2>
-    <p>FOCUS (2006) “Guidance Document on Estimating Persistence
-and Degradation Kinetics from Environmental Fate Studies on Pesticides in
-EU Registration” Report of the FOCUS Work Group on Degradation Kinetics,
-EC Document Reference Sanco/10058/2005 version 2.0, 434 pp,
-<a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics" class="external-link">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a>
-FOCUS (2014) “Generic guidance for Estimating Persistence
-and Degradation Kinetics from Environmental Fate Studies on Pesticides in
-EU Registration” Report of the FOCUS Work Group on Degradation Kinetics,
-Version 1.1, 18 December 2014
-<a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics" class="external-link">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p>
-    </div>
-    <div id="see-also">
-    <h2>See also</h2>
-    <div class="dont-index"><p>Other parent solutions: 
-<code><a href="DFOP.solution.html">DFOP.solution</a>()</code>,
-<code><a href="FOMC.solution.html">FOMC.solution</a>()</code>,
-<code><a href="IORE.solution.html">IORE.solution</a>()</code>,
-<code><a href="SFO.solution.html">SFO.solution</a>()</code>,
-<code><a href="SFORB.solution.html">SFORB.solution</a>()</code>,
-<code><a href="logistic.solution.html">logistic.solution</a>()</code></p></div>
-    </div>
-
-    <div id="ref-examples">
-    <h2>Examples</h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fu">HS.solution</span><span class="op">(</span><span class="va">x</span>, <span class="fl">100</span>, <span class="fl">2</span>, <span class="fl">0.3</span>, <span class="fl">0.5</span><span class="op">)</span>, <span class="fl">0</span>, <span class="fl">2</span>, ylim<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">100</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-plt img"><img src="HS.solution-1.png" alt="" width="700" height="433"></span>
-<span class="r-in"><span></span></span>
-</code></pre></div>
-    </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/IORE.solution-1.png b/docs/dev/reference/IORE.solution-1.png
deleted file mode 100644
index 54c9dcae..00000000
--- a/docs/dev/reference/IORE.solution-1.png
+++ /dev/null
diff --git a/docs/dev/reference/IORE.solution.html b/docs/dev/reference/IORE.solution.html
deleted file mode 100644
index b9d4734f..00000000
--- a/docs/dev/reference/IORE.solution.html
+++ /dev/null
@@ -1,218 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Indeterminate order rate equation kinetics — IORE.solution • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Indeterminate order rate equation kinetics — IORE.solution"><meta property="og:description" content="Function describing exponential decline from a defined starting value, with
-a concentration dependent rate constant."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Indeterminate order rate equation kinetics</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/parent_solutions.R" class="external-link"><code>R/parent_solutions.R</code></a></small>
-    <div class="hidden name"><code>IORE.solution.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Function describing exponential decline from a defined starting value, with
-a concentration dependent rate constant.</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">IORE.solution</span><span class="op">(</span><span class="va">t</span>, <span class="va">parent_0</span>, <span class="va">k__iore</span>, <span class="va">N</span><span class="op">)</span></span></code></pre></div>
-    </div>
-
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>t</dt>
-<dd><p>Time.</p></dd>
-
-
-<dt>parent_0</dt>
-<dd><p>Starting value for the response variable at time zero.</p></dd>
-
-
-<dt>k__iore</dt>
-<dd><p>Rate constant. Note that this depends on the concentration
-units used.</p></dd>
-
-
-<dt>N</dt>
-<dd><p>Exponent describing the nonlinearity of the rate equation</p></dd>
-
-</dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>The value of the response variable at time <code>t</code>.</p>
-    </div>
-    <div id="note">
-    <h2>Note</h2>
-    <p>The solution of the IORE kinetic model reduces to the
-<code><a href="SFO.solution.html">SFO.solution</a></code> if N = 1.  The parameters of the IORE model can
-be transformed to equivalent parameters of the FOMC mode - see the NAFTA
-guidance for details.</p>
-    </div>
-    <div id="references">
-    <h2>References</h2>
-    <p>NAFTA Technical Working Group on Pesticides (not dated) Guidance
-for Evaluating and Calculating Degradation Kinetics in Environmental Media</p>
-    </div>
-    <div id="see-also">
-    <h2>See also</h2>
-    <div class="dont-index"><p>Other parent solutions: 
-<code><a href="DFOP.solution.html">DFOP.solution</a>()</code>,
-<code><a href="FOMC.solution.html">FOMC.solution</a>()</code>,
-<code><a href="HS.solution.html">HS.solution</a>()</code>,
-<code><a href="SFO.solution.html">SFO.solution</a>()</code>,
-<code><a href="SFORB.solution.html">SFORB.solution</a>()</code>,
-<code><a href="logistic.solution.html">logistic.solution</a>()</code></p></div>
-    </div>
-
-    <div id="ref-examples">
-    <h2>Examples</h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fu">IORE.solution</span><span class="op">(</span><span class="va">x</span>, <span class="fl">100</span>, <span class="fl">0.2</span>, <span class="fl">1.3</span><span class="op">)</span>, <span class="fl">0</span>, <span class="fl">2</span>, ylim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">100</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-plt img"><img src="IORE.solution-1.png" alt="" width="700" height="433"></span>
-<span class="r-in"><span>  <span class="co"># \dontrun{</span></span></span>
-<span class="r-in"><span>    <span class="va">fit.fomc</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"FOMC"</span>, <span class="va">FOCUS_2006_C</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-in"><span>    <span class="va">fit.iore</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"IORE"</span>, <span class="va">FOCUS_2006_C</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-in"><span>    <span class="va">fit.iore.deS</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"IORE"</span>, <span class="va">FOCUS_2006_C</span>, solution_type <span class="op">=</span> <span class="st">"deSolve"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Error in is.loaded(initfunc, PACKAGE = dllname, type = "") : </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   invalid 'PACKAGE' argument</span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span>    <span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span><span class="va">fit.fomc</span><span class="op">$</span><span class="va">par</span>, <span class="va">fit.iore</span><span class="op">$</span><span class="va">par</span>, <span class="va">fit.iore.deS</span><span class="op">$</span><span class="va">par</span>,</span></span>
-<span class="r-in"><span>                     row.names <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste</a></span><span class="op">(</span><span class="st">"model par"</span>, <span class="fl">1</span><span class="op">:</span><span class="fl">4</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>             fit.fomc.par fit.iore.par fit.iore.deS.par</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> model par 1  85.87489063    85.874890        85.874890</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> model par 2   0.05192238    -4.826631        -4.826631</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> model par 3   0.65096665     1.949403         1.949403</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> model par 4   1.85744396     1.857444         1.857444</span>
-<span class="r-in"><span>    <span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/cbind.html" class="external-link">rbind</a></span><span class="op">(</span>fomc <span class="op">=</span> <span class="fu"><a href="endpoints.html">endpoints</a></span><span class="op">(</span><span class="va">fit.fomc</span><span class="op">)</span><span class="op">$</span><span class="va">distimes</span>, iore <span class="op">=</span> <span class="fu"><a href="endpoints.html">endpoints</a></span><span class="op">(</span><span class="va">fit.iore</span><span class="op">)</span><span class="op">$</span><span class="va">distimes</span>,</span></span>
-<span class="r-in"><span>                iore.deS <span class="op">=</span> <span class="fu"><a href="endpoints.html">endpoints</a></span><span class="op">(</span><span class="va">fit.iore</span><span class="op">)</span><span class="op">$</span><span class="va">distimes</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>              DT50    DT90 DT50back</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> fomc     1.785233 15.1479 4.559973</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> iore     1.785233 15.1479 4.559973</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> iore.deS 1.785233 15.1479 4.559973</span>
-<span class="r-in"><span>  <span class="co"># }</span></span></span>
-<span class="r-in"><span></span></span>
-</code></pre></div>
-    </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/NAFTA_SOP_2015-1.png b/docs/dev/reference/NAFTA_SOP_2015-1.png
deleted file mode 100644
index 98d4246c..00000000
--- a/docs/dev/reference/NAFTA_SOP_2015-1.png
+++ /dev/null
diff --git a/docs/dev/reference/NAFTA_SOP_2015.html b/docs/dev/reference/NAFTA_SOP_2015.html
deleted file mode 100644
index 299863d7..00000000
--- a/docs/dev/reference/NAFTA_SOP_2015.html
+++ /dev/null
@@ -1,215 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Example datasets from the NAFTA SOP published 2015 — NAFTA_SOP_2015 • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Example datasets from the NAFTA SOP published 2015 — NAFTA_SOP_2015"><meta property="og:description" content="Data taken from US EPA (2015), p. 19 and 23."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Example datasets from the NAFTA SOP published 2015</h1>
-    
-    <div class="hidden name"><code>NAFTA_SOP_2015.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Data taken from US EPA (2015), p. 19 and 23.</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="va">NAFTA_SOP_Appendix_B</span></span>
-<span>  <span class="va">NAFTA_SOP_Appendix_D</span></span></code></pre></div>
-    </div>
-
-    <div id="format">
-    <h2>Format</h2>
-    <p>2 datasets with observations on the following variables.</p><dl><dt><code>name</code></dt>
-<dd><p>a factor containing the name of the observed variable</p></dd>
-
-    <dt><code>time</code></dt>
-<dd><p>a numeric vector containing time points</p></dd>
-
-    <dt><code>value</code></dt>
-<dd><p>a numeric vector containing concentrations</p></dd>
-
-  
-</dl></div>
-    <div id="source">
-    <h2>Source</h2>
-    <p>NAFTA (2011) Guidance for evaluating and calculating degradation kinetics
-  in environmental media. NAFTA Technical Working Group on Pesticides
-  <a href="https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/guidance-evaluating-and-calculating-degradation" class="external-link">https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/guidance-evaluating-and-calculating-degradation</a>
-  accessed 2019-02-22</p>
-<p>US EPA (2015) Standard Operating Procedure for Using the NAFTA Guidance to
-  Calculate Representative Half-life Values and Characterizing Pesticide
-  Degradation
-  <a href="https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/standard-operating-procedure-using-nafta-guidance" class="external-link">https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/standard-operating-procedure-using-nafta-guidance</a></p>
-    </div>
-
-    <div id="ref-examples">
-    <h2>Examples</h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span>  <span class="va">nafta_evaluation</span> <span class="op">&lt;-</span> <span class="fu"><a href="nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Appendix_D</span>, cores <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> The representative half-life of the IORE model is longer than the one corresponding</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> to the terminal degradation rate found with the DFOP model.</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> The representative half-life obtained from the DFOP model may be used</span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">nafta_evaluation</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Sums of squares:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>       SFO      IORE      DFOP </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1378.6832  615.7730  517.8836 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Critical sum of squares for checking the SFO model:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 717.4598</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Parameters:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> $SFO</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>          Estimate   Pr(&gt;t)    Lower   Upper</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0  83.7558 1.80e-14 77.18268 90.3288</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_parent   0.0017 7.43e-05  0.00112  0.0026</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma      8.7518 1.22e-05  5.64278 11.8608</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> $IORE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                Estimate Pr(&gt;t)    Lower    Upper</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0       9.69e+01     NA 8.88e+01 1.05e+02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k__iore_parent 8.40e-14     NA 1.79e-18 3.94e-09</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> N_parent       6.68e+00     NA 4.19e+00 9.17e+00</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma          5.85e+00     NA 3.76e+00 7.94e+00</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> $DFOP</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>          Estimate   Pr(&gt;t)    Lower    Upper</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0 9.76e+01 1.94e-13 9.02e+01 1.05e+02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k1       4.24e-02 5.92e-03 2.03e-02 8.88e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k2       8.24e-04 6.48e-03 3.89e-04 1.75e-03</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> g        2.88e-01 2.47e-05 1.95e-01 4.03e-01</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma    5.36e+00 2.22e-05 3.43e+00 7.30e+00</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> DTx values:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>      DT50    DT90 DT50_rep</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> SFO   407    1350      407</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> IORE  541 5190000  1560000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> DFOP  429    2380      841</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Representative half-life:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 841.41</span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">nafta_evaluation</span><span class="op">)</span></span></span>
-<span class="r-plt img"><img src="NAFTA_SOP_2015-1.png" alt="" width="700" height="433"></span>
-</code></pre></div>
-    </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/NAFTA_SOP_Attachment-1.png b/docs/dev/reference/NAFTA_SOP_Attachment-1.png
deleted file mode 100644
index a6066441..00000000
--- a/docs/dev/reference/NAFTA_SOP_Attachment-1.png
+++ /dev/null
diff --git a/docs/dev/reference/NAFTA_SOP_Attachment.html b/docs/dev/reference/NAFTA_SOP_Attachment.html
deleted file mode 100644
index 6bc86b46..00000000
--- a/docs/dev/reference/NAFTA_SOP_Attachment.html
+++ /dev/null
@@ -1,204 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Example datasets from Attachment 1 to the NAFTA SOP published 2015 — NAFTA_SOP_Attachment • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Example datasets from Attachment 1 to the NAFTA SOP published 2015 — NAFTA_SOP_Attachment"><meta property="og:description" content="Data taken from from Attachment 1 of the SOP."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Example datasets from Attachment 1 to the NAFTA SOP published 2015</h1>
-    
-    <div class="hidden name"><code>NAFTA_SOP_Attachment.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Data taken from from Attachment 1 of the SOP.</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="va">NAFTA_SOP_Attachment</span></span></code></pre></div>
-    </div>
-
-    <div id="format">
-    <h2>Format</h2>
-    <p>A list (NAFTA_SOP_Attachment) containing 16 datasets suitable
-  for the evaluation with <code><a href="nafta.html">nafta</a></code></p>
-    </div>
-    <div id="source">
-    <h2>Source</h2>
-    <p>NAFTA (2011) Guidance for evaluating and calculating degradation kinetics
-  in environmental media. NAFTA Technical Working Group on Pesticides
-  <a href="https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/guidance-evaluating-and-calculating-degradation" class="external-link">https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/guidance-evaluating-and-calculating-degradation</a>
-  accessed 2019-02-22</p>
-<p>US EPA (2015) Standard Operating Procedure for Using the NAFTA Guidance to
-  Calculate Representative Half-life Values and Characterizing Pesticide
-  Degradation
-  <a href="https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/standard-operating-procedure-using-nafta-guidance" class="external-link">https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/standard-operating-procedure-using-nafta-guidance</a></p>
-    </div>
-
-    <div id="ref-examples">
-    <h2>Examples</h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span>  <span class="va">nafta_att_p5a</span> <span class="op">&lt;-</span> <span class="fu"><a href="nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p5a"</span><span class="op">]</span><span class="op">]</span>, cores <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> The half-life obtained from the IORE model may be used</span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">nafta_att_p5a</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Sums of squares:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>       SFO      IORE      DFOP </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 465.21753  56.27506  32.06401 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Critical sum of squares for checking the SFO model:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 64.4304</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Parameters:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> $SFO</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>          Estimate   Pr(&gt;t)  Lower   Upper</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0  95.8401 4.67e-21 92.245 99.4357</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_parent   0.0102 3.92e-12  0.009  0.0117</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma      4.8230 3.81e-06  3.214  6.4318</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> $IORE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                Estimate Pr(&gt;t)    Lower    Upper</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0       1.01e+02     NA 9.91e+01 1.02e+02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k__iore_parent 1.54e-05     NA 4.08e-06 5.84e-05</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> N_parent       2.57e+00     NA 2.25e+00 2.89e+00</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma          1.68e+00     NA 1.12e+00 2.24e+00</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> $DFOP</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>          Estimate   Pr(&gt;t)   Lower    Upper</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0 9.99e+01 1.41e-26 98.8116 101.0810</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k1       2.67e-02 5.05e-06  0.0243   0.0295</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k2       2.26e-12 5.00e-01  0.0000      Inf</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> g        6.47e-01 3.67e-06  0.6248   0.6677</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma    1.27e+00 8.91e-06  0.8395   1.6929</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> DTx values:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>      DT50     DT90 DT50_rep</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> SFO  67.7 2.25e+02 6.77e+01</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> IORE 58.2 1.07e+03 3.22e+02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> DFOP 55.5 5.59e+11 3.07e+11</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Representative half-life:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 321.51</span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">nafta_att_p5a</span><span class="op">)</span></span></span>
-<span class="r-plt img"><img src="NAFTA_SOP_Attachment-1.png" alt="" width="700" height="433"></span>
-</code></pre></div>
-    </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/Rplot001.png b/docs/dev/reference/Rplot001.png
deleted file mode 100644
index 70d084a3..00000000
--- a/docs/dev/reference/Rplot001.png
+++ /dev/null
diff --git a/docs/dev/reference/Rplot002.png b/docs/dev/reference/Rplot002.png
deleted file mode 100644
index 0f7d4405..00000000
--- a/docs/dev/reference/Rplot002.png
+++ /dev/null
diff --git a/docs/dev/reference/Rplot003.png b/docs/dev/reference/Rplot003.png
deleted file mode 100644
index 30cf38f3..00000000
--- a/docs/dev/reference/Rplot003.png
+++ /dev/null
diff --git a/docs/dev/reference/Rplot004.png b/docs/dev/reference/Rplot004.png
deleted file mode 100644
index 377229db..00000000
--- a/docs/dev/reference/Rplot004.png
+++ /dev/null
diff --git a/docs/dev/reference/Rplot005.png b/docs/dev/reference/Rplot005.png
deleted file mode 100644
index c1324477..00000000
--- a/docs/dev/reference/Rplot005.png
+++ /dev/null
diff --git a/docs/dev/reference/Rplot006.png b/docs/dev/reference/Rplot006.png
deleted file mode 100644
index f646fa66..00000000
--- a/docs/dev/reference/Rplot006.png
+++ /dev/null
diff --git a/docs/dev/reference/Rplot007.png b/docs/dev/reference/Rplot007.png
deleted file mode 100644
index d3b6ddd4..00000000
--- a/docs/dev/reference/Rplot007.png
+++ /dev/null
diff --git a/docs/dev/reference/SFO.solution-1.png b/docs/dev/reference/SFO.solution-1.png
deleted file mode 100644
index 34fdd460..00000000
--- a/docs/dev/reference/SFO.solution-1.png
+++ /dev/null
diff --git a/docs/dev/reference/SFO.solution.html b/docs/dev/reference/SFO.solution.html
deleted file mode 100644
index 08e031e7..00000000
--- a/docs/dev/reference/SFO.solution.html
+++ /dev/null
@@ -1,191 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Single First-Order kinetics — SFO.solution • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Single First-Order kinetics — SFO.solution"><meta property="og:description" content="Function describing exponential decline from a defined starting value."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Single First-Order kinetics</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/parent_solutions.R" class="external-link"><code>R/parent_solutions.R</code></a></small>
-    <div class="hidden name"><code>SFO.solution.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Function describing exponential decline from a defined starting value.</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">SFO.solution</span><span class="op">(</span><span class="va">t</span>, <span class="va">parent_0</span>, <span class="va">k</span><span class="op">)</span></span></code></pre></div>
-    </div>
-
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>t</dt>
-<dd><p>Time.</p></dd>
-
-
-<dt>parent_0</dt>
-<dd><p>Starting value for the response variable at time zero.</p></dd>
-
-
-<dt>k</dt>
-<dd><p>Kinetic rate constant.</p></dd>
-
-</dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>The value of the response variable at time <code>t</code>.</p>
-    </div>
-    <div id="references">
-    <h2>References</h2>
-    <p>FOCUS (2006) “Guidance Document on Estimating Persistence
-and Degradation Kinetics from Environmental Fate Studies on Pesticides in
-EU Registration” Report of the FOCUS Work Group on Degradation Kinetics,
-EC Document Reference Sanco/10058/2005 version 2.0, 434 pp,
-<a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics" class="external-link">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a>
-FOCUS (2014) “Generic guidance for Estimating Persistence
-and Degradation Kinetics from Environmental Fate Studies on Pesticides in
-EU Registration” Report of the FOCUS Work Group on Degradation Kinetics,
-Version 1.1, 18 December 2014
-<a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics" class="external-link">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p>
-    </div>
-    <div id="see-also">
-    <h2>See also</h2>
-    <div class="dont-index"><p>Other parent solutions: 
-<code><a href="DFOP.solution.html">DFOP.solution</a>()</code>,
-<code><a href="FOMC.solution.html">FOMC.solution</a>()</code>,
-<code><a href="HS.solution.html">HS.solution</a>()</code>,
-<code><a href="IORE.solution.html">IORE.solution</a>()</code>,
-<code><a href="SFORB.solution.html">SFORB.solution</a>()</code>,
-<code><a href="logistic.solution.html">logistic.solution</a>()</code></p></div>
-    </div>
-
-    <div id="ref-examples">
-    <h2>Examples</h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fu">SFO.solution</span><span class="op">(</span><span class="va">x</span>, <span class="fl">100</span>, <span class="fl">3</span><span class="op">)</span>, <span class="fl">0</span>, <span class="fl">2</span><span class="op">)</span></span></span>
-<span class="r-plt img"><img src="SFO.solution-1.png" alt="" width="700" height="433"></span>
-<span class="r-in"><span></span></span>
-</code></pre></div>
-    </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/SFORB.solution-1.png b/docs/dev/reference/SFORB.solution-1.png
deleted file mode 100644
index 08d25616..00000000
--- a/docs/dev/reference/SFORB.solution-1.png
+++ /dev/null
diff --git a/docs/dev/reference/SFORB.solution.html b/docs/dev/reference/SFORB.solution.html
deleted file mode 100644
index 7e584ace..00000000
--- a/docs/dev/reference/SFORB.solution.html
+++ /dev/null
@@ -1,209 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Single First-Order Reversible Binding kinetics — SFORB.solution • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Single First-Order Reversible Binding kinetics — SFORB.solution"><meta property="og:description" content="Function describing the solution of the differential equations describing
-the kinetic model with first-order terms for a two-way transfer from a free
-to a bound fraction, and a first-order degradation term for the free
-fraction.  The initial condition is a defined amount in the free fraction
-and no substance in the bound fraction."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Single First-Order Reversible Binding kinetics</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/parent_solutions.R" class="external-link"><code>R/parent_solutions.R</code></a></small>
-    <div class="hidden name"><code>SFORB.solution.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Function describing the solution of the differential equations describing
-the kinetic model with first-order terms for a two-way transfer from a free
-to a bound fraction, and a first-order degradation term for the free
-fraction.  The initial condition is a defined amount in the free fraction
-and no substance in the bound fraction.</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">SFORB.solution</span><span class="op">(</span><span class="va">t</span>, <span class="va">parent_0</span>, <span class="va">k_12</span>, <span class="va">k_21</span>, <span class="va">k_1output</span><span class="op">)</span></span></code></pre></div>
-    </div>
-
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>t</dt>
-<dd><p>Time.</p></dd>
-
-
-<dt>parent_0</dt>
-<dd><p>Starting value for the response variable at time zero.</p></dd>
-
-
-<dt>k_12</dt>
-<dd><p>Kinetic constant describing transfer from free to bound.</p></dd>
-
-
-<dt>k_21</dt>
-<dd><p>Kinetic constant describing transfer from bound to free.</p></dd>
-
-
-<dt>k_1output</dt>
-<dd><p>Kinetic constant describing degradation of the free
-fraction.</p></dd>
-
-</dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>The value of the response variable, which is the sum of free and
-bound fractions at time <code>t</code>.</p>
-    </div>
-    <div id="references">
-    <h2>References</h2>
-    <p>FOCUS (2006) “Guidance Document on Estimating Persistence
-and Degradation Kinetics from Environmental Fate Studies on Pesticides in
-EU Registration” Report of the FOCUS Work Group on Degradation Kinetics,
-EC Document Reference Sanco/10058/2005 version 2.0, 434 pp,
-<a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics" class="external-link">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a>
-FOCUS (2014) “Generic guidance for Estimating Persistence
-and Degradation Kinetics from Environmental Fate Studies on Pesticides in
-EU Registration” Report of the FOCUS Work Group on Degradation Kinetics,
-Version 1.1, 18 December 2014
-<a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics" class="external-link">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p>
-    </div>
-    <div id="see-also">
-    <h2>See also</h2>
-    <div class="dont-index"><p>Other parent solutions: 
-<code><a href="DFOP.solution.html">DFOP.solution</a>()</code>,
-<code><a href="FOMC.solution.html">FOMC.solution</a>()</code>,
-<code><a href="HS.solution.html">HS.solution</a>()</code>,
-<code><a href="IORE.solution.html">IORE.solution</a>()</code>,
-<code><a href="SFO.solution.html">SFO.solution</a>()</code>,
-<code><a href="logistic.solution.html">logistic.solution</a>()</code></p></div>
-    </div>
-
-    <div id="ref-examples">
-    <h2>Examples</h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fu">SFORB.solution</span><span class="op">(</span><span class="va">x</span>, <span class="fl">100</span>, <span class="fl">0.5</span>, <span class="fl">2</span>, <span class="fl">3</span><span class="op">)</span>, <span class="fl">0</span>, <span class="fl">2</span><span class="op">)</span></span></span>
-<span class="r-plt img"><img src="SFORB.solution-1.png" alt="" width="700" height="433"></span>
-<span class="r-in"><span></span></span>
-</code></pre></div>
-    </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/add_err-1.png b/docs/dev/reference/add_err-1.png
deleted file mode 100644
index 68cfb344..00000000
--- a/docs/dev/reference/add_err-1.png
+++ /dev/null
diff --git a/docs/dev/reference/add_err-2.png b/docs/dev/reference/add_err-2.png
deleted file mode 100644
index d2f0cf08..00000000
--- a/docs/dev/reference/add_err-2.png
+++ /dev/null
diff --git a/docs/dev/reference/add_err-3.png b/docs/dev/reference/add_err-3.png
deleted file mode 100644
index 17b5416a..00000000
--- a/docs/dev/reference/add_err-3.png
+++ /dev/null
diff --git a/docs/dev/reference/add_err.html b/docs/dev/reference/add_err.html
deleted file mode 100644
index 57db40d1..00000000
--- a/docs/dev/reference/add_err.html
+++ /dev/null
@@ -1,262 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Add normally distributed errors to simulated kinetic degradation data — add_err • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Add normally distributed errors to simulated kinetic degradation data — add_err"><meta property="og:description" content="Normally distributed errors are added to data predicted for a specific
-degradation model using mkinpredict. The variance of the error
-may depend on the predicted value and is specified as a standard deviation."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Add normally distributed errors to simulated kinetic degradation data</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/add_err.R" class="external-link"><code>R/add_err.R</code></a></small>
-    <div class="hidden name"><code>add_err.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Normally distributed errors are added to data predicted for a specific
-degradation model using <code><a href="mkinpredict.html">mkinpredict</a></code>. The variance of the error
-may depend on the predicted value and is specified as a standard deviation.</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">add_err</span><span class="op">(</span></span>
-<span>  <span class="va">prediction</span>,</span>
-<span>  <span class="va">sdfunc</span>,</span>
-<span>  secondary <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"M1"</span>, <span class="st">"M2"</span><span class="op">)</span>,</span>
-<span>  n <span class="op">=</span> <span class="fl">10</span>,</span>
-<span>  LOD <span class="op">=</span> <span class="fl">0.1</span>,</span>
-<span>  reps <span class="op">=</span> <span class="fl">2</span>,</span>
-<span>  digits <span class="op">=</span> <span class="fl">1</span>,</span>
-<span>  seed <span class="op">=</span> <span class="cn">NA</span></span>
-<span><span class="op">)</span></span></code></pre></div>
-    </div>
-
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>prediction</dt>
-<dd><p>A prediction from a kinetic model as produced by
-<code><a href="mkinpredict.html">mkinpredict</a></code>.</p></dd>
-
-
-<dt>sdfunc</dt>
-<dd><p>A function taking the predicted value as its only argument and
-returning a standard deviation that should be used for generating the
-random error terms for this value.</p></dd>
-
-
-<dt>secondary</dt>
-<dd><p>The names of state variables that should have an initial
-value of zero</p></dd>
-
-
-<dt>n</dt>
-<dd><p>The number of datasets to be generated.</p></dd>
-
-
-<dt>LOD</dt>
-<dd><p>The limit of detection (LOD). Values that are below the LOD after
-adding the random error will be set to NA.</p></dd>
-
-
-<dt>reps</dt>
-<dd><p>The number of replicates to be generated within the datasets.</p></dd>
-
-
-<dt>digits</dt>
-<dd><p>The number of digits to which the values will be rounded.</p></dd>
-
-
-<dt>seed</dt>
-<dd><p>The seed used for the generation of random numbers. If NA, the
-seed is not set.</p></dd>
-
-</dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>A list of datasets compatible with <code><a href="mmkin.html">mmkin</a></code>, i.e. the
-components of the list are datasets compatible with <code><a href="mkinfit.html">mkinfit</a></code>.</p>
-    </div>
-    <div id="references">
-    <h2>References</h2>
-    <p>Ranke J and Lehmann R (2015) To t-test or not to t-test, that is
-the question. XV Symposium on Pesticide Chemistry 2-4 September 2015,
-Piacenza, Italy
-https://jrwb.de/posters/piacenza_2015.pdf</p>
-    </div>
-    <div id="author">
-    <h2>Author</h2>
-    <p>Johannes Ranke</p>
-    </div>
-
-    <div id="ref-examples">
-    <h2>Examples</h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co"># The kinetic model</span></span></span>
-<span class="r-in"><span><span class="va">m_SFO_SFO</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"M1"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>                     M1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Temporary DLL for differentials generated and loaded</span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co"># Generate a prediction for a specific set of parameters</span></span></span>
-<span class="r-in"><span><span class="va">sampling_times</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span>, <span class="fl">3</span>, <span class="fl">7</span>, <span class="fl">14</span>, <span class="fl">28</span>, <span class="fl">60</span>, <span class="fl">90</span>, <span class="fl">120</span><span class="op">)</span></span></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co"># This is the prediction used for the "Type 2 datasets" on the Piacenza poster</span></span></span>
-<span class="r-in"><span><span class="co"># from 2015</span></span></span>
-<span class="r-in"><span><span class="va">d_SFO_SFO</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinpredict.html">mkinpredict</a></span><span class="op">(</span><span class="va">m_SFO_SFO</span>,</span></span>
-<span class="r-in"><span>                         <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_parent <span class="op">=</span> <span class="fl">0.1</span>, f_parent_to_M1 <span class="op">=</span> <span class="fl">0.5</span>,</span></span>
-<span class="r-in"><span>                           k_M1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Log.html" class="external-link">log</a></span><span class="op">(</span><span class="fl">2</span><span class="op">)</span><span class="op">/</span><span class="fl">1000</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>                         <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fl">100</span>, M1 <span class="op">=</span> <span class="fl">0</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>                         <span class="va">sampling_times</span><span class="op">)</span></span></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co"># Add an error term with a constant (independent of the value) standard deviation</span></span></span>
-<span class="r-in"><span><span class="co"># of 10, and generate three datasets</span></span></span>
-<span class="r-in"><span><span class="va">d_SFO_SFO_err</span> <span class="op">&lt;-</span> <span class="fu">add_err</span><span class="op">(</span><span class="va">d_SFO_SFO</span>, <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fl">10</span>, n <span class="op">=</span> <span class="fl">3</span>, seed <span class="op">=</span> <span class="fl">123456789</span> <span class="op">)</span></span></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co"># Name the datasets for nicer plotting</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">d_SFO_SFO_err</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste</a></span><span class="op">(</span><span class="st">"Dataset"</span>, <span class="fl">1</span><span class="op">:</span><span class="fl">3</span><span class="op">)</span></span></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co"># Name the model in the list of models (with only one member in this case) for</span></span></span>
-<span class="r-in"><span><span class="co"># nicer plotting later on.  Be quiet and use only one core not to offend CRAN</span></span></span>
-<span class="r-in"><span><span class="co"># checks</span></span></span>
-<span class="r-in"><span><span class="co"># \dontrun{</span></span></span>
-<span class="r-in"><span><span class="va">f_SFO_SFO</span> <span class="op">&lt;-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="st">"SFO-SFO"</span> <span class="op">=</span> <span class="va">m_SFO_SFO</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>                   <span class="va">d_SFO_SFO_err</span>, cores <span class="op">=</span> <span class="fl">1</span>,</span></span>
-<span class="r-in"><span>                   quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_SFO_SFO</span><span class="op">)</span></span></span>
-<span class="r-plt img"><img src="add_err-1.png" alt="" width="700" height="433"></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co"># We would like to inspect the fit for dataset 3 more closely</span></span></span>
-<span class="r-in"><span><span class="co"># Using double brackets makes the returned object an mkinfit object</span></span></span>
-<span class="r-in"><span><span class="co"># instead of a list of mkinfit objects, so plot.mkinfit is used</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_SFO_SFO</span><span class="op">[[</span><span class="fl">3</span><span class="op">]</span><span class="op">]</span>, show_residuals <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-plt img"><img src="add_err-2.png" alt="" width="700" height="433"></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co"># If we use single brackets, we should give two indices (model and dataset),</span></span></span>
-<span class="r-in"><span><span class="co"># and plot.mmkin is used</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_SFO_SFO</span><span class="op">[</span><span class="fl">1</span>, <span class="fl">3</span><span class="op">]</span><span class="op">)</span></span></span>
-<span class="r-plt img"><img src="add_err-3.png" alt="" width="700" height="433"></span>
-<span class="r-in"><span><span class="co"># }</span></span></span>
-<span class="r-in"><span></span></span>
-</code></pre></div>
-    </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/anova.saem.mmkin.html b/docs/dev/reference/anova.saem.mmkin.html
deleted file mode 100644
index 39a30dff..00000000
--- a/docs/dev/reference/anova.saem.mmkin.html
+++ /dev/null
@@ -1,185 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Anova method for saem.mmkin objects — anova.saem.mmkin • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Anova method for saem.mmkin objects — anova.saem.mmkin"><meta property="og:description" content="Generate an anova object. The method to calculate the BIC is that from the
-saemix package. As in other prominent anova methods, models are sorted by
-number of parameters, and the tests (if requested) are always relative to
-the model on the previous line."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Anova method for saem.mmkin objects</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/anova.saem.mmkin.R" class="external-link"><code>R/anova.saem.mmkin.R</code></a></small>
-    <div class="hidden name"><code>anova.saem.mmkin.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Generate an anova object. The method to calculate the BIC is that from the
-saemix package. As in other prominent anova methods, models are sorted by
-number of parameters, and the tests (if requested) are always relative to
-the model on the previous line.</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for saem.mmkin</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span></span>
-<span>  <span class="va">object</span>,</span>
-<span>  <span class="va">...</span>,</span>
-<span>  method <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"is"</span>, <span class="st">"lin"</span>, <span class="st">"gq"</span><span class="op">)</span>,</span>
-<span>  test <span class="op">=</span> <span class="cn">FALSE</span>,</span>
-<span>  model.names <span class="op">=</span> <span class="cn">NULL</span></span>
-<span><span class="op">)</span></span></code></pre></div>
-    </div>
-
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>object</dt>
-<dd><p>An <a href="saem.html">saem.mmkin</a> object</p></dd>
-
-
-<dt>...</dt>
-<dd><p>further such objects</p></dd>
-
-
-<dt>method</dt>
-<dd><p>Method for likelihood calculation: "is" (importance sampling),
-"lin" (linear approximation), or "gq" (Gaussian quadrature). Passed
-to <a href="https://rdrr.io/pkg/saemix/man/logLik.html" class="external-link">saemix::logLik.SaemixObject</a></p></dd>
-
-
-<dt>test</dt>
-<dd><p>Should a likelihood ratio test be performed? If TRUE,
-the alternative models are tested against the first model. Should
-only be done for nested models.</p></dd>
-
-
-<dt>model.names</dt>
-<dd><p>Optional character vector of model names</p></dd>
-
-</dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>an "anova" data frame; the traditional (S3) result of anova()</p>
-    </div>
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/aw.html b/docs/dev/reference/aw.html
deleted file mode 100644
index fc5ff777..00000000
--- a/docs/dev/reference/aw.html
+++ /dev/null
@@ -1,197 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Calculate Akaike weights for model averaging — aw • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Calculate Akaike weights for model averaging — aw"><meta property="og:description" content="Akaike weights are calculated based on the relative
-expected Kullback-Leibler information as specified
-by Burnham and Anderson (2004)."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Calculate Akaike weights for model averaging</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/aw.R" class="external-link"><code>R/aw.R</code></a></small>
-    <div class="hidden name"><code>aw.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Akaike weights are calculated based on the relative
-expected Kullback-Leibler information as specified
-by Burnham and Anderson (2004).</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">aw</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># S3 method for mkinfit</span></span>
-<span><span class="fu">aw</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># S3 method for mmkin</span></span>
-<span><span class="fu">aw</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># S3 method for mixed.mmkin</span></span>
-<span><span class="fu">aw</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># S3 method for multistart</span></span>
-<span><span class="fu">aw</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
-    </div>
-
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>object</dt>
-<dd><p>An <a href="mmkin.html">mmkin</a> column object, containing two or more
-<a href="mkinfit.html">mkinfit</a> models that have been fitted to the same data,
-or an mkinfit object. In the latter case, further mkinfit
-objects fitted to the same data should be specified
-as dots arguments.</p></dd>
-
-
-<dt>...</dt>
-<dd><p>Not used in the method for <a href="mmkin.html">mmkin</a> column objects,
-further <a href="mkinfit.html">mkinfit</a> objects in the method for mkinfit objects.</p></dd>
-
-</dl></div>
-    <div id="references">
-    <h2>References</h2>
-    <p>Burnham KP and Anderson DR (2004) Multimodel
-Inference: Understanding AIC and BIC in Model Selection.
-<em>Sociological Methods &amp; Research</em> <strong>33</strong>(2) 261-304</p>
-    </div>
-
-    <div id="ref-examples">
-    <h2>Examples</h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># \dontrun{</span></span></span>
-<span class="r-in"><span><span class="va">f_sfo</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="va">FOCUS_2006_D</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">f_dfop</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="va">FOCUS_2006_D</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">aw_sfo_dfop</span> <span class="op">&lt;-</span> <span class="fu">aw</span><span class="op">(</span><span class="va">f_sfo</span>, <span class="va">f_dfop</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/sum.html" class="external-link">sum</a></span><span class="op">(</span><span class="va">aw_sfo_dfop</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 1</span>
-<span class="r-in"><span><span class="va">aw_sfo_dfop</span> <span class="co"># SFO gets more weight as it has less parameters and a similar fit</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 0.5970258 0.4029742</span>
-<span class="r-in"><span><span class="va">f</span> <span class="op">&lt;-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"FOMC"</span>, <span class="st">"DFOP"</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="st">"FOCUS D"</span> <span class="op">=</span> <span class="va">FOCUS_2006_D</span><span class="op">)</span>, cores <span class="op">=</span> <span class="fl">1</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu">aw</span><span class="op">(</span><span class="va">f</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 0.4808722 0.1945539 0.3245740</span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/sum.html" class="external-link">sum</a></span><span class="op">(</span><span class="fu">aw</span><span class="op">(</span><span class="va">f</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 1</span>
-<span class="r-in"><span><span class="fu">aw</span><span class="op">(</span><span class="va">f</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"DFOP"</span><span class="op">)</span><span class="op">]</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 0.5970258 0.4029742</span>
-<span class="r-in"><span><span class="co"># }</span></span></span>
-</code></pre></div>
-    </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/confint.mkinfit.html b/docs/dev/reference/confint.mkinfit.html
deleted file mode 100644
index a3571fa5..00000000
--- a/docs/dev/reference/confint.mkinfit.html
+++ /dev/null
@@ -1,423 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Confidence intervals for parameters of mkinfit objects — confint.mkinfit • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Confidence intervals for parameters of mkinfit objects — confint.mkinfit"><meta property="og:description" content="The default method 'quadratic' is based on the quadratic approximation of
-the curvature of the likelihood function at the maximum likelihood parameter
-estimates.
-The alternative method 'profile' is based on the profile likelihood for each
-parameter. The 'profile' method uses two nested optimisations and can take a
-very long time, even if parallelized by specifying 'cores' on unixoid
-platforms. The speed of the method could likely be improved by using the
-method of Venzon and Moolgavkar (1988)."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Confidence intervals for parameters of mkinfit objects</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/confint.mkinfit.R" class="external-link"><code>R/confint.mkinfit.R</code></a></small>
-    <div class="hidden name"><code>confint.mkinfit.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>The default method 'quadratic' is based on the quadratic approximation of
-the curvature of the likelihood function at the maximum likelihood parameter
-estimates.
-The alternative method 'profile' is based on the profile likelihood for each
-parameter. The 'profile' method uses two nested optimisations and can take a
-very long time, even if parallelized by specifying 'cores' on unixoid
-platforms. The speed of the method could likely be improved by using the
-method of Venzon and Moolgavkar (1988).</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for mkinfit</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/stats/confint.html" class="external-link">confint</a></span><span class="op">(</span></span>
-<span>  <span class="va">object</span>,</span>
-<span>  <span class="va">parm</span>,</span>
-<span>  level <span class="op">=</span> <span class="fl">0.95</span>,</span>
-<span>  alpha <span class="op">=</span> <span class="fl">1</span> <span class="op">-</span> <span class="va">level</span>,</span>
-<span>  <span class="va">cutoff</span>,</span>
-<span>  method <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"quadratic"</span>, <span class="st">"profile"</span><span class="op">)</span>,</span>
-<span>  transformed <span class="op">=</span> <span class="cn">TRUE</span>,</span>
-<span>  backtransform <span class="op">=</span> <span class="cn">TRUE</span>,</span>
-<span>  cores <span class="op">=</span> <span class="fu">parallel</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/r/parallel/detectCores.html" class="external-link">detectCores</a></span><span class="op">(</span><span class="op">)</span>,</span>
-<span>  rel_tol <span class="op">=</span> <span class="fl">0.01</span>,</span>
-<span>  quiet <span class="op">=</span> <span class="cn">FALSE</span>,</span>
-<span>  <span class="va">...</span></span>
-<span><span class="op">)</span></span></code></pre></div>
-    </div>
-
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>object</dt>
-<dd><p>An <code><a href="mkinfit.html">mkinfit</a></code> object</p></dd>
-
-
-<dt>parm</dt>
-<dd><p>A vector of names of the parameters which are to be given
-confidence intervals. If missing, all parameters are considered.</p></dd>
-
-
-<dt>level</dt>
-<dd><p>The confidence level required</p></dd>
-
-
-<dt>alpha</dt>
-<dd><p>The allowed error probability, overrides 'level' if specified.</p></dd>
-
-
-<dt>cutoff</dt>
-<dd><p>Possibility to specify an alternative cutoff for the difference
-in the log-likelihoods at the confidence boundary. Specifying an explicit
-cutoff value overrides arguments 'level' and 'alpha'</p></dd>
-
-
-<dt>method</dt>
-<dd><p>The 'quadratic' method approximates the likelihood function at
-the optimised parameters using the second term of the Taylor expansion,
-using a second derivative (hessian) contained in the object.
-The 'profile' method searches the parameter space for the
-cutoff of the confidence intervals by means of a likelihood ratio test.</p></dd>
-
-
-<dt>transformed</dt>
-<dd><p>If the quadratic approximation is used, should it be
-applied to the likelihood based on the transformed parameters?</p></dd>
-
-
-<dt>backtransform</dt>
-<dd><p>If we approximate the likelihood in terms of the
-transformed parameters, should we backtransform the parameters with
-their confidence intervals?</p></dd>
-
-
-<dt>cores</dt>
-<dd><p>The number of cores to be used for multicore processing.
-On Windows machines, cores &gt; 1 is currently not supported.</p></dd>
-
-
-<dt>rel_tol</dt>
-<dd><p>If the method is 'profile', what should be the accuracy
-of the lower and upper bounds, relative to the estimate obtained from
-the quadratic method?</p></dd>
-
-
-<dt>quiet</dt>
-<dd><p>Should we suppress the message "Profiling the likelihood"</p></dd>
-
-
-<dt>...</dt>
-<dd><p>Not used</p></dd>
-
-</dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>A matrix with columns giving lower and upper confidence limits for
-each parameter.</p>
-    </div>
-    <div id="references">
-    <h2>References</h2>
-    <p>Bates DM and Watts GW (1988) Nonlinear regression analysis &amp; its applications</p>
-<p>Pawitan Y (2013) In all likelihood - Statistical modelling and
-inference using likelihood. Clarendon Press, Oxford.</p>
-<p>Venzon DJ and Moolgavkar SH (1988) A Method for Computing
-Profile-Likelihood Based Confidence Intervals, Applied Statistics, 37,
-87–94.</p>
-    </div>
-
-    <div id="ref-examples">
-    <h2>Examples</h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">f</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="va">FOCUS_2006_C</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/confint.html" class="external-link">confint</a></span><span class="op">(</span><span class="va">f</span>, method <span class="op">=</span> <span class="st">"quadratic"</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                2.5%      97.5%</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0 71.8242430 93.1600766</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_parent  0.2109541  0.4440528</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma     1.9778868  7.3681380</span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co"># \dontrun{</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/confint.html" class="external-link">confint</a></span><span class="op">(</span><span class="va">f</span>, method <span class="op">=</span> <span class="st">"profile"</span><span class="op">)</span></span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Profiling the likelihood</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                2.5%      97.5%</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0 73.0641834 92.1392181</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_parent  0.2170293  0.4235348</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma     3.1307772  8.0628314</span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co"># Set the number of cores for the profiling method for further examples</span></span></span>
-<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/identical.html" class="external-link">identical</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/Sys.getenv.html" class="external-link">Sys.getenv</a></span><span class="op">(</span><span class="st">"NOT_CRAN"</span><span class="op">)</span>, <span class="st">"true"</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span></span>
-<span class="r-in"><span>  <span class="va">n_cores</span> <span class="op">&lt;-</span> <span class="fu">parallel</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/r/parallel/detectCores.html" class="external-link">detectCores</a></span><span class="op">(</span><span class="op">)</span> <span class="op">-</span> <span class="fl">1</span></span></span>
-<span class="r-in"><span><span class="op">}</span> <span class="kw">else</span> <span class="op">{</span></span></span>
-<span class="r-in"><span>  <span class="va">n_cores</span> <span class="op">&lt;-</span> <span class="fl">1</span></span></span>
-<span class="r-in"><span><span class="op">}</span></span></span>
-<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/Sys.getenv.html" class="external-link">Sys.getenv</a></span><span class="op">(</span><span class="st">"TRAVIS"</span><span class="op">)</span> <span class="op">!=</span> <span class="st">""</span><span class="op">)</span> <span class="va">n_cores</span> <span class="op">=</span> <span class="fl">1</span></span></span>
-<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/Sys.info.html" class="external-link">Sys.info</a></span><span class="op">(</span><span class="op">)</span><span class="op">[</span><span class="st">"sysname"</span><span class="op">]</span> <span class="op">==</span> <span class="st">"Windows"</span><span class="op">)</span> <span class="va">n_cores</span> <span class="op">=</span> <span class="fl">1</span></span></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="va">SFO_SFO</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"m1"</span><span class="op">)</span>, m1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  use_of_ff <span class="op">=</span> <span class="st">"min"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">SFO_SFO.ff</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"m1"</span><span class="op">)</span>, m1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  use_of_ff <span class="op">=</span> <span class="st">"max"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">f_d_1</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="fu"><a href="https://rdrr.io/r/base/subset.html" class="external-link">subset</a></span><span class="op">(</span><span class="va">FOCUS_2006_D</span>, <span class="va">value</span> <span class="op">!=</span> <span class="fl">0</span><span class="op">)</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/system.time.html" class="external-link">system.time</a></span><span class="op">(</span><span class="va">ci_profile</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/confint.html" class="external-link">confint</a></span><span class="op">(</span><span class="va">f_d_1</span>, method <span class="op">=</span> <span class="st">"profile"</span>, cores <span class="op">=</span> <span class="fl">1</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    user  system elapsed </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   1.183   0.000   1.182 </span>
-<span class="r-in"><span><span class="co"># Using more cores does not save much time here, as parent_0 takes up most of the time</span></span></span>
-<span class="r-in"><span><span class="co"># If we additionally exclude parent_0 (the confidence of which is often of</span></span></span>
-<span class="r-in"><span><span class="co"># minor interest), we get a nice performance improvement if we use at least 4 cores</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/system.time.html" class="external-link">system.time</a></span><span class="op">(</span><span class="va">ci_profile_no_parent_0</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/confint.html" class="external-link">confint</a></span><span class="op">(</span><span class="va">f_d_1</span>, method <span class="op">=</span> <span class="st">"profile"</span>,</span></span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"k_parent_sink"</span>, <span class="st">"k_parent_m1"</span>, <span class="st">"k_m1_sink"</span>, <span class="st">"sigma"</span><span class="op">)</span>, cores <span class="op">=</span> <span class="va">n_cores</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Profiling the likelihood</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    user  system elapsed </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   0.418   0.116   0.294 </span>
-<span class="r-in"><span><span class="va">ci_profile</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                       2.5%        97.5%</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0      96.456003640 1.027703e+02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_parent_sink  0.040762501 5.549764e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_parent_m1    0.046786482 5.500879e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_m1_sink      0.003892605 6.702778e-03</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma          2.535612399 3.985263e+00</span>
-<span class="r-in"><span><span class="va">ci_quadratic_transformed</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/confint.html" class="external-link">confint</a></span><span class="op">(</span><span class="va">f_d_1</span>, method <span class="op">=</span> <span class="st">"quadratic"</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">ci_quadratic_transformed</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                       2.5%        97.5%</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0      96.403841640 1.027931e+02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_parent_sink  0.041033378 5.596269e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_parent_m1    0.046777902 5.511931e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_m1_sink      0.004012217 6.897547e-03</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma          2.396089689 3.854918e+00</span>
-<span class="r-in"><span><span class="va">ci_quadratic_untransformed</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/confint.html" class="external-link">confint</a></span><span class="op">(</span><span class="va">f_d_1</span>, method <span class="op">=</span> <span class="st">"quadratic"</span>, transformed <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">ci_quadratic_untransformed</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                       2.5%        97.5%</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0      96.403841645 102.79312449</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_parent_sink  0.040485331   0.05535491</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_parent_m1    0.046611582   0.05494364</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_m1_sink      0.003835483   0.00668582</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma          2.396089689   3.85491806</span>
-<span class="r-in"><span><span class="co"># Against the expectation based on Bates and Watts (1988), the confidence</span></span></span>
-<span class="r-in"><span><span class="co"># intervals based on the internal parameter transformation are less</span></span></span>
-<span class="r-in"><span><span class="co"># congruent with the likelihood based intervals. Note the superiority of the</span></span></span>
-<span class="r-in"><span><span class="co"># interval based on the untransformed fit for k_m1_sink</span></span></span>
-<span class="r-in"><span><span class="va">rel_diffs_transformed</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/MathFun.html" class="external-link">abs</a></span><span class="op">(</span><span class="op">(</span><span class="va">ci_quadratic_transformed</span> <span class="op">-</span> <span class="va">ci_profile</span><span class="op">)</span><span class="op">/</span><span class="va">ci_profile</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">rel_diffs_untransformed</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/MathFun.html" class="external-link">abs</a></span><span class="op">(</span><span class="op">(</span><span class="va">ci_quadratic_untransformed</span> <span class="op">-</span> <span class="va">ci_profile</span><span class="op">)</span><span class="op">/</span><span class="va">ci_profile</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">rel_diffs_transformed</span> <span class="op">&lt;</span> <span class="va">rel_diffs_untransformed</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                2.5% 97.5%</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0      FALSE FALSE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_parent_sink  TRUE FALSE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_parent_m1    TRUE FALSE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_m1_sink     FALSE FALSE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma         FALSE FALSE</span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/Round.html" class="external-link">signif</a></span><span class="op">(</span><span class="va">rel_diffs_transformed</span>, <span class="fl">3</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                   2.5%    97.5%</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0      0.000541 0.000222</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_parent_sink 0.006650 0.008380</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_parent_m1   0.000183 0.002010</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_m1_sink     0.030700 0.029100</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma         0.055000 0.032700</span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/Round.html" class="external-link">signif</a></span><span class="op">(</span><span class="va">rel_diffs_untransformed</span>, <span class="fl">3</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                   2.5%    97.5%</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0      0.000541 0.000222</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_parent_sink 0.006800 0.002570</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_parent_m1   0.003740 0.001180</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_m1_sink     0.014700 0.002530</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma         0.055000 0.032700</span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co"># Investigate a case with formation fractions</span></span></span>
-<span class="r-in"><span><span class="va">f_d_2</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO.ff</span>, <span class="fu"><a href="https://rdrr.io/r/base/subset.html" class="external-link">subset</a></span><span class="op">(</span><span class="va">FOCUS_2006_D</span>, <span class="va">value</span> <span class="op">!=</span> <span class="fl">0</span><span class="op">)</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">ci_profile_ff</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/confint.html" class="external-link">confint</a></span><span class="op">(</span><span class="va">f_d_2</span>, method <span class="op">=</span> <span class="st">"profile"</span>, cores <span class="op">=</span> <span class="va">n_cores</span><span class="op">)</span></span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Profiling the likelihood</span>
-<span class="r-in"><span><span class="va">ci_profile_ff</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                        2.5%        97.5%</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0       96.456003640 1.027703e+02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_parent        0.090911032 1.071578e-01</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_m1            0.003892606 6.702775e-03</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_parent_to_m1  0.471328495 5.611550e-01</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma           2.535612399 3.985263e+00</span>
-<span class="r-in"><span><span class="va">ci_quadratic_transformed_ff</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/confint.html" class="external-link">confint</a></span><span class="op">(</span><span class="va">f_d_2</span>, method <span class="op">=</span> <span class="st">"quadratic"</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">ci_quadratic_transformed_ff</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                        2.5%        97.5%</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0       96.403833578 102.79311649</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_parent        0.090823771   0.10725430</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_m1            0.004012219   0.00689755</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_parent_to_m1  0.469118824   0.55959615</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma           2.396089689   3.85491806</span>
-<span class="r-in"><span><span class="va">ci_quadratic_untransformed_ff</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/confint.html" class="external-link">confint</a></span><span class="op">(</span><span class="va">f_d_2</span>, method <span class="op">=</span> <span class="st">"quadratic"</span>, transformed <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">ci_quadratic_untransformed_ff</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                        2.5%        97.5%</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0       96.403833583 1.027931e+02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_parent        0.090491913 1.069035e-01</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_m1            0.003835485 6.685823e-03</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_parent_to_m1  0.469113477 5.598387e-01</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma           2.396089689 3.854918e+00</span>
-<span class="r-in"><span><span class="va">rel_diffs_transformed_ff</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/MathFun.html" class="external-link">abs</a></span><span class="op">(</span><span class="op">(</span><span class="va">ci_quadratic_transformed_ff</span> <span class="op">-</span> <span class="va">ci_profile_ff</span><span class="op">)</span><span class="op">/</span><span class="va">ci_profile_ff</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">rel_diffs_untransformed_ff</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/MathFun.html" class="external-link">abs</a></span><span class="op">(</span><span class="op">(</span><span class="va">ci_quadratic_untransformed_ff</span> <span class="op">-</span> <span class="va">ci_profile_ff</span><span class="op">)</span><span class="op">/</span><span class="va">ci_profile_ff</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="co"># While the confidence interval for the parent rate constant is closer to</span></span></span>
-<span class="r-in"><span><span class="co"># the profile based interval when using the internal parameter</span></span></span>
-<span class="r-in"><span><span class="co"># transformation, the interval for the metabolite rate constant is 'better</span></span></span>
-<span class="r-in"><span><span class="co"># without internal parameter transformation.</span></span></span>
-<span class="r-in"><span><span class="va">rel_diffs_transformed_ff</span> <span class="op">&lt;</span> <span class="va">rel_diffs_untransformed_ff</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                 2.5% 97.5%</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0       FALSE FALSE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_parent        TRUE  TRUE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_m1           FALSE FALSE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_parent_to_m1  TRUE FALSE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma           TRUE FALSE</span>
-<span class="r-in"><span><span class="va">rel_diffs_transformed_ff</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                        2.5%        97.5%</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0       0.0005408690 0.0002217233</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_parent       0.0009598532 0.0009001864</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_m1           0.0307283041 0.0290588361</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_parent_to_m1 0.0046881769 0.0027780063</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma          0.0550252516 0.0327066836</span>
-<span class="r-in"><span><span class="va">rel_diffs_untransformed_ff</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                        2.5%        97.5%</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0       0.0005408689 0.0002217232</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_parent       0.0046102156 0.0023732281</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_m1           0.0146740690 0.0025291820</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_parent_to_m1 0.0046995211 0.0023457712</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma          0.0550252516 0.0327066836</span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co"># The profiling for the following fit does not finish in a reasonable time,</span></span></span>
-<span class="r-in"><span><span class="co"># therefore we use the quadratic approximation</span></span></span>
-<span class="r-in"><span><span class="va">m_synth_DFOP_par</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"M1"</span>, <span class="st">"M2"</span><span class="op">)</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  M1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  M2 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  use_of_ff <span class="op">=</span> <span class="st">"max"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">DFOP_par_c</span> <span class="op">&lt;-</span> <span class="va">synthetic_data_for_UBA_2014</span><span class="op">[[</span><span class="fl">12</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">data</span></span></span>
-<span class="r-in"><span><span class="va">f_tc_2</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">m_synth_DFOP_par</span>, <span class="va">DFOP_par_c</span>, error_model <span class="op">=</span> <span class="st">"tc"</span>,</span></span>
-<span class="r-in"><span>  error_model_algorithm <span class="op">=</span> <span class="st">"direct"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/confint.html" class="external-link">confint</a></span><span class="op">(</span><span class="va">f_tc_2</span>, method <span class="op">=</span> <span class="st">"quadratic"</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                        2.5%        97.5%</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0       94.596039609 106.19954892</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_M1            0.037605368   0.04490762</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_M2            0.008568731   0.01087676</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_parent_to_M1  0.021462489   0.62023882</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_parent_to_M2  0.015165617   0.37975348</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k1              0.273897348   0.33388101</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k2              0.018614554   0.02250378</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> g               0.671943411   0.73583305</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma_low       0.251283495   0.83992077</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> rsd_high        0.040411024   0.07662008</span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/confint.html" class="external-link">confint</a></span><span class="op">(</span><span class="va">f_tc_2</span>, <span class="st">"parent_0"</span>, method <span class="op">=</span> <span class="st">"quadratic"</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>              2.5%    97.5%</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0 94.59604 106.1995</span>
-<span class="r-in"><span><span class="co"># }</span></span></span>
-</code></pre></div>
-    </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/convergence.html b/docs/dev/reference/convergence.html
deleted file mode 100644
index e9fac3bb..00000000
--- a/docs/dev/reference/convergence.html
+++ /dev/null
@@ -1,163 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Method to get convergence information — convergence • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Method to get convergence information — convergence"><meta property="og:description" content="Method to get convergence information"><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.1.2</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Functions and data</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Method to get convergence information</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/convergence.R" class="external-link"><code>R/convergence.R</code></a></small>
-    <div class="hidden name"><code>convergence.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Method to get convergence information</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">convergence</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># S3 method for mmkin</span></span>
-<span><span class="fu">convergence</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># S3 method for convergence.mmkin</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
-    </div>
-
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>object</dt>
-<dd><p>The object to investigate</p></dd>
-
-
-<dt>...</dt>
-<dd><p>For potential future extensions</p></dd>
-
-
-<dt>x</dt>
-<dd><p>The object to be printed</p></dd>
-
-</dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>For <a href="mkinfit.html">mkinfit</a> objects, a character vector containing
-For <a href="mmkin.html">mmkin</a> objects, an object of class 'convergence.mmkin' with a
-suitable printing method.</p>
-    </div>
-
-    <div id="ref-examples">
-    <h2>Examples</h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># \dontrun{</span></span></span>
-<span class="r-in"><span><span class="va">fits</span> <span class="op">&lt;-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span></span></span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"FOMC"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="st">"FOCUS A"</span> <span class="op">=</span> <span class="va">FOCUS_2006_A</span>,</span></span>
-<span class="r-in"><span>       <span class="st">"FOCUS B"</span> <span class="op">=</span> <span class="va">FOCUS_2006_C</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu">convergence</span><span class="op">(</span><span class="va">fits</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>       dataset</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> model  FOCUS A FOCUS B</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   SFO  OK      OK     </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   FOMC OK      OK     </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> OK: No warnings</span>
-<span class="r-in"><span><span class="co"># }</span></span></span>
-</code></pre></div>
-    </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/create_deg_func.html b/docs/dev/reference/create_deg_func.html
deleted file mode 100644
index fd8325c0..00000000
--- a/docs/dev/reference/create_deg_func.html
+++ /dev/null
@@ -1,196 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Create degradation functions for known analytical solutions — create_deg_func • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Create degradation functions for known analytical solutions — create_deg_func"><meta property="og:description" content="Create degradation functions for known analytical solutions"><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Create degradation functions for known analytical solutions</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/create_deg_func.R" class="external-link"><code>R/create_deg_func.R</code></a></small>
-    <div class="hidden name"><code>create_deg_func.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Create degradation functions for known analytical solutions</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">create_deg_func</span><span class="op">(</span><span class="va">spec</span>, use_of_ff <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"min"</span>, <span class="st">"max"</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
-    </div>
-
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>spec</dt>
-<dd><p>List of model specifications as contained in mkinmod objects</p></dd>
-
-
-<dt>use_of_ff</dt>
-<dd><p>Minimum or maximum use of formation fractions</p></dd>
-
-</dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>Degradation function to be attached to mkinmod objects</p>
-    </div>
-
-    <div id="ref-examples">
-    <h2>Examples</h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="va">SFO_SFO</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span></span></span>
-<span class="r-in"><span>  parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"m1"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  m1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Temporary DLL for differentials generated and loaded</span>
-<span class="r-in"><span><span class="va">FOCUS_D</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/subset.html" class="external-link">subset</a></span><span class="op">(</span><span class="va">FOCUS_2006_D</span>, <span class="va">value</span> <span class="op">!=</span> <span class="fl">0</span><span class="op">)</span> <span class="co"># to avoid warnings</span></span></span>
-<span class="r-in"><span><span class="va">fit_1</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="va">FOCUS_D</span>, solution_type <span class="op">=</span> <span class="st">"analytical"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="co"># \dontrun{</span></span></span>
-<span class="r-in"><span><span class="va">fit_2</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="va">FOCUS_D</span>, solution_type <span class="op">=</span> <span class="st">"deSolve"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">require</a></span><span class="op">(</span><span class="va"><a href="http://rbenchmark.googlecode.com" class="external-link">rbenchmark</a></span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/pkg/rbenchmark/man/benchmark.html" class="external-link">benchmark</a></span><span class="op">(</span></span></span>
-<span class="r-in"><span>    analytical <span class="op">=</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="va">FOCUS_D</span>, solution_type <span class="op">=</span> <span class="st">"analytical"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>    deSolve <span class="op">=</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="va">FOCUS_D</span>, solution_type <span class="op">=</span> <span class="st">"deSolve"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>    replications <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Loading required package: rbenchmark</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         test replications elapsed relative user.self sys.self user.child</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1 analytical            2   0.235    1.000     0.235        0          0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2    deSolve            2   0.294    1.251     0.294        0          0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   sys.child</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1         0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2         0</span>
-<span class="r-in"><span>  <span class="va">DFOP_SFO</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span></span></span>
-<span class="r-in"><span>    parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="st">"m1"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>    m1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Temporary DLL for differentials generated and loaded</span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/pkg/rbenchmark/man/benchmark.html" class="external-link">benchmark</a></span><span class="op">(</span></span></span>
-<span class="r-in"><span>    analytical <span class="op">=</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">DFOP_SFO</span>, <span class="va">FOCUS_D</span>, solution_type <span class="op">=</span> <span class="st">"analytical"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>    deSolve <span class="op">=</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">DFOP_SFO</span>, <span class="va">FOCUS_D</span>, solution_type <span class="op">=</span> <span class="st">"deSolve"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>    replications <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         test replications elapsed relative user.self sys.self user.child</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1 analytical            2   0.374    1.000     0.374        0          0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2    deSolve            2   0.527    1.409     0.526        0          0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   sys.child</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1         0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2         0</span>
-<span class="r-in"><span><span class="co"># }</span></span></span>
-</code></pre></div>
-    </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/dimethenamid_2018-1.png b/docs/dev/reference/dimethenamid_2018-1.png
deleted file mode 100644
index 27ed5329..00000000
--- a/docs/dev/reference/dimethenamid_2018-1.png
+++ /dev/null
diff --git a/docs/dev/reference/dimethenamid_2018-2.png b/docs/dev/reference/dimethenamid_2018-2.png
deleted file mode 100644
index 36db063c..00000000
--- a/docs/dev/reference/dimethenamid_2018-2.png
+++ /dev/null
diff --git a/docs/dev/reference/dimethenamid_2018-3.png b/docs/dev/reference/dimethenamid_2018-3.png
deleted file mode 100644
index 7c876208..00000000
--- a/docs/dev/reference/dimethenamid_2018-3.png
+++ /dev/null
diff --git a/docs/dev/reference/dimethenamid_2018.html b/docs/dev/reference/dimethenamid_2018.html
deleted file mode 100644
index 0fcd0c61..00000000
--- a/docs/dev/reference/dimethenamid_2018.html
+++ /dev/null
@@ -1,390 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Aerobic soil degradation data on dimethenamid and dimethenamid-P from the EU assessment in 2018 — dimethenamid_2018 • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Aerobic soil degradation data on dimethenamid and dimethenamid-P from the EU assessment in 2018 — dimethenamid_2018"><meta property="og:description" content="The datasets were extracted from the active substance evaluation dossier
-published by EFSA. Kinetic evaluations shown for these datasets are intended
-to illustrate and advance kinetic modelling. The fact that these data and
-some results are shown here does not imply a license to use them in the
-context of pesticide  registrations, as the use of the data may be
-constrained by data protection regulations."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Aerobic soil degradation data on dimethenamid and dimethenamid-P from the EU assessment in 2018</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/dimethenamid_2018.R" class="external-link"><code>R/dimethenamid_2018.R</code></a></small>
-    <div class="hidden name"><code>dimethenamid_2018.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>The datasets were extracted from the active substance evaluation dossier
-published by EFSA. Kinetic evaluations shown for these datasets are intended
-to illustrate and advance kinetic modelling. The fact that these data and
-some results are shown here does not imply a license to use them in the
-context of pesticide  registrations, as the use of the data may be
-constrained by data protection regulations.</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="va">dimethenamid_2018</span></span></code></pre></div>
-    </div>
-
-    <div id="format">
-    <h2>Format</h2>
-    <p>An <a href="mkindsg.html">mkindsg</a> object grouping seven datasets with some meta information</p>
-    </div>
-    <div id="source">
-    <h2>Source</h2>
-    <p>Rapporteur Member State Germany, Co-Rapporteur Member State Bulgaria (2018)
-Renewal Assessment Report Dimethenamid-P Volume 3 - B.8 Environmental fate and behaviour
-Rev. 2 - November 2017
-<a href="https://open.efsa.europa.eu/study-inventory/EFSA-Q-2014-00716" class="external-link">https://open.efsa.europa.eu/study-inventory/EFSA-Q-2014-00716</a></p>
-    </div>
-    <div id="details">
-    <h2>Details</h2>
-    <p>The R code used to create this data object is installed with this package
-in the 'dataset_generation' directory. In the code, page numbers are given for
-specific pieces of information in the comments.</p>
-    </div>
-
-    <div id="ref-examples">
-    <h2>Examples</h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">dimethenamid_2018</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> &lt;mkindsg&gt; holding 7 mkinds objects</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Title $title:  Aerobic soil degradation data on dimethenamid-P from the EU assessment in 2018 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Occurrence of observed compounds $observed_n:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> DMTAP   M23   M27   M31  DMTA </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     3     7     7     7     4 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Time normalisation factors $f_time_norm:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 1.0000000 0.9706477 1.2284784 1.2284784 0.6233856 0.7678922 0.6733938</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Meta information $meta:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                      study  usda_soil_type study_moisture_ref_type rel_moisture</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Calke        Unsworth 2014      Sandy loam                     pF2         1.00</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Borstel  Staudenmaier 2009            Sand                     pF1         0.50</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Elliot 1        Wendt 1997       Clay loam                   pF2.5         0.75</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Elliot 2        Wendt 1997       Clay loam                   pF2.5         0.75</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Flaach          König 1996 Sandy clay loam                     pF1         0.40</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> BBA 2.2         König 1995      Loamy sand                     pF1         0.40</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> BBA 2.3         König 1995      Sandy loam                     pF1         0.40</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>          study_ref_moisture temperature</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Calke                    NA          20</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Borstel               23.00          20</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Elliot 1              33.37          23</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Elliot 2              33.37          23</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Flaach                   NA          20</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> BBA 2.2                  NA          20</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> BBA 2.3                  NA          20</span>
-<span class="r-in"><span><span class="va">dmta_ds</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fl">7</span>, <span class="kw">function</span><span class="op">(</span><span class="va">i</span><span class="op">)</span> <span class="op">{</span></span></span>
-<span class="r-in"><span>  <span class="va">ds_i</span> <span class="op">&lt;-</span> <span class="va">dimethenamid_2018</span><span class="op">$</span><span class="va">ds</span><span class="op">[[</span><span class="va">i</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">data</span></span></span>
-<span class="r-in"><span>  <span class="va">ds_i</span><span class="op">[</span><span class="va">ds_i</span><span class="op">$</span><span class="va">name</span> <span class="op">==</span> <span class="st">"DMTAP"</span>, <span class="st">"name"</span><span class="op">]</span> <span class="op">&lt;-</span>  <span class="st">"DMTA"</span></span></span>
-<span class="r-in"><span>  <span class="va">ds_i</span><span class="op">$</span><span class="va">time</span> <span class="op">&lt;-</span> <span class="va">ds_i</span><span class="op">$</span><span class="va">time</span> <span class="op">*</span> <span class="va">dimethenamid_2018</span><span class="op">$</span><span class="va">f_time_norm</span><span class="op">[</span><span class="va">i</span><span class="op">]</span></span></span>
-<span class="r-in"><span>  <span class="va">ds_i</span></span></span>
-<span class="r-in"><span><span class="op">}</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">dmta_ds</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">sapply</a></span><span class="op">(</span><span class="va">dimethenamid_2018</span><span class="op">$</span><span class="va">ds</span>, <span class="kw">function</span><span class="op">(</span><span class="va">ds</span><span class="op">)</span> <span class="va">ds</span><span class="op">$</span><span class="va">title</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot"</span><span class="op">]</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/cbind.html" class="external-link">rbind</a></span><span class="op">(</span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 1"</span><span class="op">]</span><span class="op">]</span>, <span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 2"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 1"</span><span class="op">]</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="cn">NULL</span></span></span>
-<span class="r-in"><span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 2"</span><span class="op">]</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="cn">NULL</span></span></span>
-<span class="r-in"><span><span class="co"># \dontrun{</span></span></span>
-<span class="r-in"><span><span class="co"># We don't use DFOP for the parent compound, as this gives numerical</span></span></span>
-<span class="r-in"><span><span class="co"># instabilities in the fits</span></span></span>
-<span class="r-in"><span><span class="va">sfo_sfo3p</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span></span></span>
-<span class="r-in"><span> DMTA <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"M23"</span>, <span class="st">"M27"</span>, <span class="st">"M31"</span><span class="op">)</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span> M23 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span> M27 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span> M31 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"M27"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span> quiet <span class="op">=</span> <span class="cn">TRUE</span></span></span>
-<span class="r-in"><span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">dmta_sfo_sfo3p_tc</span> <span class="op">&lt;-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="st">"SFO-SFO3+"</span> <span class="op">=</span> <span class="va">sfo_sfo3p</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  <span class="va">dmta_ds</span>, error_model <span class="op">=</span> <span class="st">"tc"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">dmta_sfo_sfo3p_tc</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> &lt;mmkin&gt; object</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Status of individual fits:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>            dataset</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> model       Calke Borstel Flaach BBA 2.2 BBA 2.3 Elliot</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   SFO-SFO3+ OK    OK      OK     OK      OK      OK    </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> OK: No warnings</span>
-<span class="r-in"><span><span class="co"># The default (test_log_parms = FALSE) gives an undue</span></span></span>
-<span class="r-in"><span><span class="co"># influence of ill-defined rate constants that have</span></span></span>
-<span class="r-in"><span><span class="co"># extremely small values:</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="fu"><a href="mixed.html">mixed</a></span><span class="op">(</span><span class="va">dmta_sfo_sfo3p_tc</span><span class="op">)</span>, test_log_parms <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="co"># If we disregards ill-defined rate constants, the results</span></span></span>
-<span class="r-in"><span><span class="co"># look more plausible, but the truth is likely to be in</span></span></span>
-<span class="r-in"><span><span class="co"># between these variants</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="fu"><a href="mixed.html">mixed</a></span><span class="op">(</span><span class="va">dmta_sfo_sfo3p_tc</span><span class="op">)</span>, test_log_parms <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-plt img"><img src="dimethenamid_2018-1.png" alt="" width="700" height="433"></span>
-<span class="r-in"><span><span class="co"># We can also specify a default value for the failing</span></span></span>
-<span class="r-in"><span><span class="co"># log parameters, to mimic FOCUS guidance</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="fu"><a href="mixed.html">mixed</a></span><span class="op">(</span><span class="va">dmta_sfo_sfo3p_tc</span><span class="op">)</span>, test_log_parms <span class="op">=</span> <span class="cn">TRUE</span>,</span></span>
-<span class="r-in"><span>  default_log_parms <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Log.html" class="external-link">log</a></span><span class="op">(</span><span class="fl">2</span><span class="op">)</span><span class="op">/</span><span class="fl">1000</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="co"># As these attempts are not satisfying, we use nonlinear mixed-effects models</span></span></span>
-<span class="r-in"><span><span class="co"># f_dmta_nlme_tc &lt;- nlme(dmta_sfo_sfo3p_tc)</span></span></span>
-<span class="r-in"><span><span class="co"># nlme reaches maxIter = 50 without convergence</span></span></span>
-<span class="r-in"><span><span class="va">f_dmta_saem_tc</span> <span class="op">&lt;-</span> <span class="fu"><a href="saem.html">saem</a></span><span class="op">(</span><span class="va">dmta_sfo_sfo3p_tc</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="co"># I am commenting out the convergence plot as rendering them</span></span></span>
-<span class="r-in"><span><span class="co"># with pkgdown fails (at least without further tweaks to the</span></span></span>
-<span class="r-in"><span><span class="co"># graphics device used)</span></span></span>
-<span class="r-in"><span><span class="co">#saemix::plot(f_dmta_saem_tc$so, plot.type = "convergence")</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">f_dmta_saem_tc</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> saemix version used for fitting:      3.2 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> mkin version used for pre-fitting:  1.2.3 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> R version used for fitting:         4.2.3 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Date of fit:     Sun Apr 16 08:30:03 2023 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Date of summary: Sun Apr 16 08:30:03 2023 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Equations:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> d_DMTA/dt = - k_DMTA * DMTA</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> d_M23/dt = + f_DMTA_to_M23 * k_DMTA * DMTA - k_M23 * M23</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> d_M27/dt = + f_DMTA_to_M27 * k_DMTA * DMTA - k_M27 * M27 + k_M31 * M31</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> d_M31/dt = + f_DMTA_to_M31 * k_DMTA * DMTA - k_M31 * M31</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Data:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 563 observations of 4 variable(s) grouped in 6 datasets</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Model predictions using solution type deSolve </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Fitted in 304.528 s</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Using 300, 100 iterations and 9 chains</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Variance model: Two-component variance function </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Starting values for degradation parameters:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>       DMTA_0   log_k_DMTA    log_k_M23    log_k_M27    log_k_M31 f_DMTA_ilr_1 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>      95.5662      -2.9048      -3.8130      -4.1600      -4.1486       0.1341 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_DMTA_ilr_2 f_DMTA_ilr_3 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>       0.1385      -1.6700 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Fixed degradation parameter values:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> None</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Starting values for random effects (square root of initial entries in omega):</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>              DMTA_0 log_k_DMTA log_k_M23 log_k_M27 log_k_M31 f_DMTA_ilr_1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> DMTA_0        4.802     0.0000    0.0000     0.000    0.0000       0.0000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k_DMTA    0.000     0.9834    0.0000     0.000    0.0000       0.0000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k_M23     0.000     0.0000    0.6983     0.000    0.0000       0.0000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k_M27     0.000     0.0000    0.0000     1.028    0.0000       0.0000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k_M31     0.000     0.0000    0.0000     0.000    0.9841       0.0000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_DMTA_ilr_1  0.000     0.0000    0.0000     0.000    0.0000       0.7185</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_DMTA_ilr_2  0.000     0.0000    0.0000     0.000    0.0000       0.0000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_DMTA_ilr_3  0.000     0.0000    0.0000     0.000    0.0000       0.0000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>              f_DMTA_ilr_2 f_DMTA_ilr_3</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> DMTA_0             0.0000       0.0000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k_DMTA         0.0000       0.0000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k_M23          0.0000       0.0000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k_M27          0.0000       0.0000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k_M31          0.0000       0.0000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_DMTA_ilr_1       0.0000       0.0000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_DMTA_ilr_2       0.7378       0.0000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_DMTA_ilr_3       0.0000       0.4451</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Starting values for error model parameters:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> a.1 b.1 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   1   1 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Results:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Likelihood computed by importance sampling</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    AIC  BIC logLik</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   2276 2273  -1120</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Optimised parameters:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                    est.   lower   upper</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> DMTA_0          88.3192 83.8656 92.7729</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k_DMTA      -3.0530 -3.5686 -2.5373</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k_M23       -4.0620 -4.9202 -3.2038</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k_M27       -3.8633 -4.2668 -3.4598</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k_M31       -3.9731 -4.4763 -3.4699</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_DMTA_ilr_1     0.1346 -0.2150  0.4841</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_DMTA_ilr_2     0.1449 -0.2593  0.5491</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_DMTA_ilr_3    -1.3882 -1.7011 -1.0753</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> a.1              0.9156  0.8217  1.0095</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> b.1              0.1383  0.1216  0.1550</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> SD.DMTA_0        3.7280 -0.6949  8.1508</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> SD.log_k_DMTA    0.6431  0.2781  1.0080</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> SD.log_k_M23     1.0096  0.3782  1.6409</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> SD.log_k_M27     0.4583  0.1541  0.7625</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> SD.log_k_M31     0.5738  0.1942  0.9533</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> SD.f_DMTA_ilr_1  0.4119  0.1528  0.6709</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> SD.f_DMTA_ilr_2  0.4780  0.1806  0.7754</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> SD.f_DMTA_ilr_3  0.3657  0.1383  0.5931</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Correlation: </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>              DMTA_0  l__DMTA lg__M23 lg__M27 lg__M31 f_DMTA__1 f_DMTA__2</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k_DMTA    0.0303                                                    </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k_M23    -0.0229 -0.0032                                            </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k_M27    -0.0372 -0.0049  0.0041                                    </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k_M31    -0.0245 -0.0032  0.0022  0.0815                            </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_DMTA_ilr_1 -0.0046 -0.0006  0.0415 -0.0433  0.0324                    </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_DMTA_ilr_2 -0.0008 -0.0002  0.0214 -0.0267 -0.0893 -0.0361            </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_DMTA_ilr_3 -0.1755 -0.0135  0.0423  0.0775  0.0377 -0.0066    0.0060  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Random effects:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                   est.   lower  upper</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> SD.DMTA_0       3.7280 -0.6949 8.1508</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> SD.log_k_DMTA   0.6431  0.2781 1.0080</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> SD.log_k_M23    1.0096  0.3782 1.6409</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> SD.log_k_M27    0.4583  0.1541 0.7625</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> SD.log_k_M31    0.5738  0.1942 0.9533</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> SD.f_DMTA_ilr_1 0.4119  0.1528 0.6709</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> SD.f_DMTA_ilr_2 0.4780  0.1806 0.7754</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> SD.f_DMTA_ilr_3 0.3657  0.1383 0.5931</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Variance model:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>       est.  lower upper</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> a.1 0.9156 0.8217 1.009</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> b.1 0.1383 0.1216 0.155</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Backtransformed parameters:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                   est.     lower    upper</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> DMTA_0        88.31924 83.865625 92.77286</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_DMTA         0.04722  0.028196  0.07908</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_M23          0.01721  0.007298  0.04061</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_M27          0.02100  0.014027  0.03144</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_M31          0.01882  0.011375  0.03112</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_DMTA_to_M23  0.14608        NA       NA</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_DMTA_to_M27  0.12077        NA       NA</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_DMTA_to_M31  0.11123        NA       NA</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Resulting formation fractions:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>               ff</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> DMTA_M23  0.1461</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> DMTA_M27  0.1208</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> DMTA_M31  0.1112</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> DMTA_sink 0.6219</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Estimated disappearance times:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>       DT50   DT90</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> DMTA 14.68  48.76</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> M23  40.27 133.76</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> M27  33.01 109.65</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> M31  36.84 122.38</span>
-<span class="r-in"><span><span class="co"># As the confidence interval for the random effects of DMTA_0</span></span></span>
-<span class="r-in"><span><span class="co"># includes zero, we could try an alternative model without</span></span></span>
-<span class="r-in"><span><span class="co"># such random effects</span></span></span>
-<span class="r-in"><span><span class="co"># f_dmta_saem_tc_2 &lt;- saem(dmta_sfo_sfo3p_tc,</span></span></span>
-<span class="r-in"><span><span class="co">#   covariance.model = diag(c(0, rep(1, 7))))</span></span></span>
-<span class="r-in"><span><span class="co"># saemix::plot(f_dmta_saem_tc_2$so, plot.type = "convergence")</span></span></span>
-<span class="r-in"><span><span class="co"># This does not perform better judged by AIC and BIC</span></span></span>
-<span class="r-in"><span><span class="co"># saemix::compare.saemix(f_dmta_saem_tc$so, f_dmta_saem_tc_2$so)</span></span></span>
-<span class="r-in"><span><span class="co"># }</span></span></span>
-</code></pre></div>
-    </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/ds_mixed-1.png b/docs/dev/reference/ds_mixed-1.png
deleted file mode 100644
index d8505ffd..00000000
--- a/docs/dev/reference/ds_mixed-1.png
+++ /dev/null
diff --git a/docs/dev/reference/ds_mixed.html b/docs/dev/reference/ds_mixed.html
deleted file mode 100644
index 2d0274ff..00000000
--- a/docs/dev/reference/ds_mixed.html
+++ /dev/null
@@ -1,257 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Synthetic data for hierarchical kinetic degradation models — ds_mixed • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Synthetic data for hierarchical kinetic degradation models — ds_mixed"><meta property="og:description" content="The R code used to create this data object is installed with this package in
-the 'dataset_generation' directory."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Synthetic data for hierarchical kinetic degradation models</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/ds_mixed.R" class="external-link"><code>R/ds_mixed.R</code></a></small>
-    <div class="hidden name"><code>ds_mixed.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>The R code used to create this data object is installed with this package in
-the 'dataset_generation' directory.</p>
-    </div>
-
-
-
-    <div id="ref-examples">
-    <h2>Examples</h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># \dontrun{</span></span></span>
-<span class="r-in"><span>  <span class="va">sfo_mmkin</span> <span class="op">&lt;-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="va">ds_sfo</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, error_model <span class="op">=</span> <span class="st">"tc"</span>, cores <span class="op">=</span> <span class="fl">15</span><span class="op">)</span></span></span>
-<span class="r-in"><span>  <span class="va">sfo_saem</span> <span class="op">&lt;-</span> <span class="fu"><a href="saem.html">saem</a></span><span class="op">(</span><span class="va">sfo_mmkin</span>, no_random_effect <span class="op">=</span> <span class="st">"parent_0"</span><span class="op">)</span></span></span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">sfo_saem</span><span class="op">)</span></span></span>
-<span class="r-plt img"><img src="ds_mixed-1.png" alt="" width="700" height="433"></span>
-<span class="r-in"><span><span class="co"># }</span></span></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co"># This is the code used to generate the datasets</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/cat.html" class="external-link">cat</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/readLines.html" class="external-link">readLines</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/system.file.html" class="external-link">system.file</a></span><span class="op">(</span><span class="st">"dataset_generation/ds_mixed.R"</span>, package <span class="op">=</span> <span class="st">"mkin"</span><span class="op">)</span><span class="op">)</span>, sep <span class="op">=</span> <span class="st">"\n"</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> # Synthetic data for hierarchical kinetic models</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> # Refactored version of the code previously in tests/testthat/setup_script.R</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> # The number of datasets was 3 for FOMC, and 10 for HS in that script, now it</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> # is always 15 for consistency</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> library(mkin)  # We use mkinmod and mkinpredict</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> n &lt;- 15</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_sd &lt;- 0.3</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> err_1 = list(const = 1, prop = 0.05)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> tc &lt;- function(value) sigma_twocomp(value, err_1$const, err_1$prop)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> const &lt;- function(value) 2</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> set.seed(123456)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> SFO &lt;- mkinmod(parent = mkinsub("SFO"))</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sfo_pop &lt;- list(parent_0 = 100, k_parent = 0.03)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sfo_parms &lt;- as.matrix(data.frame(</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     k_parent = rlnorm(n, log(sfo_pop$k_parent), log_sd)))</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> set.seed(123456)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> ds_sfo &lt;- lapply(1:n, function(i) {</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   ds_mean &lt;- mkinpredict(SFO, sfo_parms[i, ],</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     c(parent = sfo_pop$parent_0), sampling_times)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   add_err(ds_mean, tc, n = 1)[[1]]</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> })</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> attr(ds_sfo, "pop") &lt;- sfo_pop</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> attr(ds_sfo, "parms") &lt;- sfo_parms</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> set.seed(123456)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> FOMC &lt;- mkinmod(parent = mkinsub("FOMC"))</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> fomc_pop &lt;- list(parent_0 = 100, alpha = 2, beta = 8)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> fomc_parms &lt;- as.matrix(data.frame(</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     alpha = rlnorm(n, log(fomc_pop$alpha), 0.4),</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     beta = rlnorm(n, log(fomc_pop$beta), 0.2)))</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> set.seed(123456)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> ds_fomc &lt;- lapply(1:n, function(i) {</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   ds_mean &lt;- mkinpredict(FOMC, fomc_parms[i, ],</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     c(parent = fomc_pop$parent_0), sampling_times)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   add_err(ds_mean, tc, n = 1)[[1]]</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> })</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> attr(ds_fomc, "pop") &lt;- fomc_pop</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> attr(ds_fomc, "parms") &lt;- fomc_parms</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> set.seed(123456)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> DFOP &lt;- mkinmod(parent = mkinsub("DFOP"))</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> dfop_pop &lt;- list(parent_0 = 100, k1 = 0.06, k2 = 0.015, g = 0.4)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> dfop_parms &lt;- as.matrix(data.frame(</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   k1 = rlnorm(n, log(dfop_pop$k1), log_sd),</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   k2 = rlnorm(n, log(dfop_pop$k2), log_sd),</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   g = plogis(rnorm(n, qlogis(dfop_pop$g), log_sd))))</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> set.seed(123456)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> ds_dfop &lt;- lapply(1:n, function(i) {</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   ds_mean &lt;- mkinpredict(DFOP, dfop_parms[i, ],</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     c(parent = dfop_pop$parent_0), sampling_times)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   add_err(ds_mean, tc, n = 1)[[1]]</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> })</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> attr(ds_dfop, "pop") &lt;- dfop_pop</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> attr(ds_dfop, "parms") &lt;- dfop_parms</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> set.seed(123456)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> HS &lt;- mkinmod(parent = mkinsub("HS"))</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> hs_pop &lt;- list(parent_0 = 100, k1 = 0.08, k2 = 0.01, tb = 15)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> hs_parms &lt;- as.matrix(data.frame(</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   k1 = rlnorm(n, log(hs_pop$k1), log_sd),</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   k2 = rlnorm(n, log(hs_pop$k2), log_sd),</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   tb = rlnorm(n, log(hs_pop$tb), 0.1)))</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> set.seed(123456)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> ds_hs &lt;- lapply(1:n, function(i) {</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   ds_mean &lt;- mkinpredict(HS, hs_parms[i, ],</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     c(parent = hs_pop$parent_0), sampling_times)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   add_err(ds_mean, const, n = 1)[[1]]</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> })</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> attr(ds_hs, "pop") &lt;- hs_pop</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> attr(ds_hs, "parms") &lt;- hs_parms</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> set.seed(123456)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> DFOP_SFO &lt;- mkinmod(</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   parent = mkinsub("DFOP", "m1"),</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   m1 = mkinsub("SFO"),</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   quiet = TRUE)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> dfop_sfo_pop &lt;- list(parent_0 = 100,</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   k_m1 = 0.007, f_parent_to_m1 = 0.5,</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   k1 = 0.1, k2 = 0.02, g = 0.5)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> dfop_sfo_parms &lt;- as.matrix(data.frame(</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   k1 = rlnorm(n, log(dfop_sfo_pop$k1), log_sd),</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   k2 = rlnorm(n, log(dfop_sfo_pop$k2), log_sd),</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   g = plogis(rnorm(n, qlogis(dfop_sfo_pop$g), log_sd)),</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   f_parent_to_m1 = plogis(rnorm(n,</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>       qlogis(dfop_sfo_pop$f_parent_to_m1), log_sd)),</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   k_m1 = rlnorm(n, log(dfop_sfo_pop$k_m1), log_sd)))</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> ds_dfop_sfo_mean &lt;- lapply(1:n,</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   function(i) {</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     mkinpredict(DFOP_SFO, dfop_sfo_parms[i, ],</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>       c(parent = dfop_sfo_pop$parent_0, m1 = 0), sampling_times)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   }</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> )</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> set.seed(123456)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> ds_dfop_sfo &lt;- lapply(ds_dfop_sfo_mean, function(ds) {</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   add_err(ds,</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     sdfunc = function(value) sqrt(err_1$const^2 + value^2 * err_1$prop^2),</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     n = 1, secondary = "m1")[[1]]</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> })</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> attr(ds_dfop_sfo, "pop") &lt;- dfop_sfo_pop</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> attr(ds_dfop_sfo, "parms") &lt;- dfop_sfo_parms</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> #save(ds_sfo, ds_fomc, ds_dfop, ds_hs, ds_dfop_sfo, file = "data/ds_mixed.rda", version = 2)</span>
-</code></pre></div>
-    </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/endpoints.html b/docs/dev/reference/endpoints.html
deleted file mode 100644
index 3cda0f3c..00000000
--- a/docs/dev/reference/endpoints.html
+++ /dev/null
@@ -1,228 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Function to calculate endpoints for further use from kinetic models fitted
-with mkinfit — endpoints • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Function to calculate endpoints for further use from kinetic models fitted
-with mkinfit — endpoints"><meta property="og:description" content="This function calculates DT50 and DT90 values as well as formation fractions
-from kinetic models fitted with mkinfit. If the SFORB model was specified
-for one of the parents or metabolites, the Eigenvalues are returned. These
-are equivalent to the rate constants of the DFOP model, but with the
-advantage that the SFORB model can also be used for metabolites."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Function to calculate endpoints for further use from kinetic models fitted
-with mkinfit</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/endpoints.R" class="external-link"><code>R/endpoints.R</code></a></small>
-    <div class="hidden name"><code>endpoints.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>This function calculates DT50 and DT90 values as well as formation fractions
-from kinetic models fitted with mkinfit. If the SFORB model was specified
-for one of the parents or metabolites, the Eigenvalues are returned. These
-are equivalent to the rate constants of the DFOP model, but with the
-advantage that the SFORB model can also be used for metabolites.</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">endpoints</span><span class="op">(</span><span class="va">fit</span>, covariates <span class="op">=</span> <span class="cn">NULL</span>, covariate_quantile <span class="op">=</span> <span class="fl">0.5</span><span class="op">)</span></span></code></pre></div>
-    </div>
-
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>fit</dt>
-<dd><p>An object of class <a href="mkinfit.html">mkinfit</a>, <a href="nlme.mmkin.html">nlme.mmkin</a> or <a href="saem.html">saem.mmkin</a>, or
-another object that has list components mkinmod containing an <a href="mkinmod.html">mkinmod</a>
-degradation model, and two numeric vectors, bparms.optim and bparms.fixed,
-that contain parameter values for that model.</p></dd>
-
-
-<dt>covariates</dt>
-<dd><p>Numeric vector with covariate values for all variables in
-any covariate models in the object. If given, it overrides 'covariate_quantile'.</p></dd>
-
-
-<dt>covariate_quantile</dt>
-<dd><p>This argument only has an effect if the fitted
-object has covariate models. If so, the default is to show endpoints
-for the median of the covariate values (50th percentile).</p></dd>
-
-</dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>A list with a matrix of dissipation times named distimes, and, if
-applicable, a vector of formation fractions named ff and, if the SFORB model
-was in use, a vector of eigenvalues of these SFORB models, equivalent to
-DFOP rate constants</p>
-    </div>
-    <div id="details">
-    <h2>Details</h2>
-    <p>Additional DT50 values are calculated from the FOMC DT90 and k1 and k2 from
-HS and DFOP, as well as from Eigenvalues b1 and b2 of any SFORB models</p>
-    </div>
-    <div id="note">
-    <h2>Note</h2>
-    <p>The function is used internally by <a href="summary.mkinfit.html">summary.mkinfit</a>,
-<a href="summary.nlme.mmkin.html">summary.nlme.mmkin</a> and <a href="summary.saem.mmkin.html">summary.saem.mmkin</a>.</p>
-    </div>
-    <div id="author">
-    <h2>Author</h2>
-    <p>Johannes Ranke</p>
-    </div>
-
-    <div id="ref-examples">
-    <h2>Examples</h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span>
-<span class="r-in"><span>  <span class="va">fit</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"FOMC"</span>, <span class="va">FOCUS_2006_C</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-in"><span>  <span class="fu">endpoints</span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> $distimes</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>            DT50    DT90 DT50back</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent 1.785233 15.1479 4.559973</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-in"><span>  <span class="co"># \dontrun{</span></span></span>
-<span class="r-in"><span>    <span class="va">fit_2</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="va">FOCUS_2006_C</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-in"><span>    <span class="fu">endpoints</span><span class="op">(</span><span class="va">fit_2</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> $distimes</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>            DT50     DT90 DT50back  DT50_k1  DT50_k2</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent 1.886925 21.25106 6.397207 1.508293 38.83438</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-in"><span>    <span class="va">fit_3</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"SFORB"</span>, <span class="va">FOCUS_2006_C</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-in"><span>    <span class="fu">endpoints</span><span class="op">(</span><span class="va">fit_3</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> $ff</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_free </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>           1 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> $SFORB</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_b1 parent_b2  parent_g </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 0.4595574 0.0178488 0.8539454 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> $distimes</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>            DT50     DT90 DT50back DT50_parent_b1 DT50_parent_b2</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent 1.886925 21.25106 6.397208       1.508293       38.83438</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-in"><span>  <span class="co"># }</span></span></span>
-<span class="r-in"><span></span></span>
-</code></pre></div>
-    </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/example_analysis/dlls/sforb_sfo2.so b/docs/dev/reference/example_analysis/dlls/sforb_sfo2.so
deleted file mode 100755
index a692256d..00000000
--- a/docs/dev/reference/example_analysis/dlls/sforb_sfo2.so
+++ /dev/null
diff --git a/docs/dev/reference/example_analysis/example_analysis.Rmd b/docs/dev/reference/example_analysis/example_analysis.Rmd
deleted file mode 100644
index 38a6bd20..00000000
--- a/docs/dev/reference/example_analysis/example_analysis.Rmd
+++ /dev/null
@@ -1,314 +0,0 @@
----
-title: "Hierarchical kinetic modelling of degradation data"
-author:
-date:
-output: mkin::hierarchical_kinetics
-geometry: margin=2cm
----
-
-\clearpage
-
-# Setup
-
-```{r packages, cache = FALSE, message = FALSE}
-library(mkin)
-library(knitr)
-library(saemix)
-library(parallel)
-library(readxl)
-```
-
-```{r n_cores, cache = FALSE}
-n_cores <- detectCores()
-
-if (Sys.info()["sysname"] == "Windows") {
-  cl <- makePSOCKcluster(n_cores)
-} else {
-  cl <- makeForkCluster(n_cores)
-}
-```
-
-\clearpage
-
-# Introduction
-
-This report shows hierarchical kinetic modelling for ...
-The data were obtained from ...
-
-```{r ds}
-data_path <- system.file(
-  "testdata", "lambda-cyhalothrin_soil_efsa_2014.xlsx",
-  package = "mkin")
-ds <- read_spreadsheet(data_path, valid_datasets = c(1:4, 7:13))
-covariates <- attr(ds, "covariates")
-```
-
-The covariate data are shown below.
-
-```{r results = "asis", dependson = "ds", echo = FALSE}
-kable(covariates, caption = "Covariate data for all datasets")
-```
-
-\clearpage
-
-The datasets with the residue time series are shown in the tables below. Please
-refer to the spreadsheet for details like data sources, treatment of values
-below reporting limits and time step normalisation factors.
-
-```{r results = "asis", dependson = "ds", echo = FALSE}
-for (ds_name in names(ds)) {
-  print(
-    kable(mkin_long_to_wide(ds[[ds_name]]),
-      caption = paste("Dataset", ds_name),
-      booktabs = TRUE, row.names = FALSE))
-  cat("\n\\clearpage\n")
-}
-```
-
-# Parent only evaluations
-
-The following code performs separate fits of the candidate degradation models
-to all datasets using constant variance and the two-component error model.
-
-```{r parent-sep, dependson = "ds"}
-parent_deg_mods <- c("SFO", "FOMC", "DFOP", "SFORB")
-errmods <- c(const = "constant variance", tc = "two-component error")
-parent_sep_const <- mmkin(
-  parent_deg_mods, ds,
-  error_model = "const",
-  cluster = cl, quiet = TRUE)
-parent_sep_tc <- update(parent_sep_const, error_model = "tc")
-```
-
-To select the parent model, the corresponding hierarchical fits are performed below.
-
-```{r parent-mhmkin, dependson = "parent-sep"}
-parent_mhmkin <- mhmkin(list(parent_sep_const, parent_sep_tc), cluster = cl)
-status(parent_mhmkin) |> kable()
-```
-
-All fits terminate without errors (status OK). The check for ill-defined
-parameters shows that not all random effect parameters can be robustly
-quantified.
-
-```{r dependson = "parent_mhmkin"}
-illparms(parent_mhmkin) |> kable()
-```
-
-Therefore, the fits are updated, excluding random effects that were
-ill-defined according to the `illparms` function. The status of the fits
-is checked.
-
-```{r parent-mhmkin-refined}
-parent_mhmkin_refined <- update(parent_mhmkin,
-  no_random_effect = illparms(parent_mhmkin))
-status(parent_mhmkin_refined) |> kable()
-```
-
-Also, it is checked if the AIC values of the refined fits are actually smaller
-than the AIC values of the original fits.
-
-```{r dependson = "parent-mhmkin-refined"}
-(AIC(parent_mhmkin_refined) < AIC(parent_mhmkin)) |> kable()
-```
-
-From the refined fits, the most suitable model is selected using the AIC.
-
-```{r parent-best, dependson = "parent-mhmkin"}
-aic_parent <- AIC(parent_mhmkin_refined)
-min_aic <- which(aic_parent == min(aic_parent), arr.ind = TRUE)
-best_degmod_parent <- rownames(aic_parent)[min_aic[1]]
-best_errmod_parent <- colnames(aic_parent)[min_aic[2]]
-anova(parent_mhmkin_refined) |> kable(digits = 1)
-parent_best <- parent_mhmkin_refined[[best_degmod_parent, best_errmod_parent]]
-```
-
-Based on the AIC, the combination of the `r best_degmod_parent` degradation
-model with the error model `r errmods[best_errmod_parent]` is identified to
-be most suitable for the degradation of the parent. The check below
-confirms that no ill-defined parameters remain for this combined model.
-
-```{r dependson = "parent-best"}
-illparms(parent_best)
-```
-
-The corresponding fit is plotted below.
-
-```{r dependson = "parent-best"}
-plot(parent_best)
-```
-The fitted parameters, together with approximate confidence
-intervals are listed below.
-
-```{r dependson = "parent-best"}
-parms(parent_best, ci = TRUE) |> kable(digits = 3)
-```
-
-To investigate a potential covariate influence on degradation parameters, a
-covariate model is added to the hierarchical model for each of the degradation
-parameters with well-defined random effects. Also, a version with covariate
-models for both of them is fitted.
-
-```{r parent-best-pH}
-parent_best_pH_1 <- update(parent_best, covariates = covariates,
-  covariate_models = list(log_k_lambda_free ~ pH))
-parent_best_pH_2 <- update(parent_best, covariates = covariates,
-  covariate_models = list(log_k_lambda_bound_free ~ pH))
-parent_best_pH_3 <- update(parent_best, covariates = covariates,
-  covariate_models = list(log_k_lambda_free ~ pH, log_k_lambda_bound_free ~ pH))
-```
-
-The resulting models are compared.
-
-```{r dependson = "parent-best-pH"}
-anova(parent_best, parent_best_pH_1, parent_best_pH_2, parent_best_pH_3) |>
-  kable(digits = 1)
-```
-
-The model fit with the lowest AIC is the one with a pH correlation of the
-desorption rate constant `k_lambda_bound_free`. Plot and parameter listing
-of this fit are shown below. Also, it is confirmed that no ill-defined
-variance parameters are found.
-
-```{r dependson = "parent-best-pH"}
-plot(parent_best_pH_2)
-```
-
-```{r dependson = "parent-best-pH"}
-illparms(parent_best_pH_2)
-parms(parent_best_pH_2, ci = TRUE) |> kable(digits = 3)
-```
-
-\clearpage
-
-# Pathway fits
-
-As an example of a pathway fit, a model with SFORB for the parent compound and
-parallel formation of two metabolites is set up.
-
-```{r path-1-degmod}
-if (!dir.exists("dlls")) dir.create("dlls")
-
-m_sforb_sfo2 = mkinmod(
-  lambda = mkinsub("SFORB", to = c("c_V", "c_XV")),
-  c_V = mkinsub("SFO"),
-  c_XV = mkinsub("SFO"),
-  name = "sforb_sfo2",
-  dll_dir = "dlls",
-  overwrite = TRUE, quiet = TRUE
-)
-```
-
-Separate evaluations of all datasets are performed with constant variance
-and using two-component error.
-
-```{r path-1-sep, dependson = c("path-1-degmod", "ds")}
-sforb_sep_const <- mmkin(list(sforb_path = m_sforb_sfo2), ds,
-  cluster = cl, quiet = TRUE)
-sforb_sep_tc <- update(sforb_sep_const, error_model = "tc")
-```
-
-The separate fits with constant variance are plotted.
-
-```{r dependson = "path-1-sep", fig.height = 9}
-plot(mixed(sforb_sep_const))
-```
-
-The two corresponding hierarchical fits, with the random effects for the parent
-degradation parameters excluded as discussed above, and including the covariate
-model that was identified for the parent degradation, are attempted below.
-
-```{r path-1, dependson = "path-1-sep"}
-path_1 <- mhmkin(list(sforb_sep_const, sforb_sep_tc),
-  no_random_effect = c("lambda_free_0", "log_k_lambda_free_bound"),
-  covariates = covariates, covariate_models = list(log_k_lambda_bound_free ~ pH),
-  cluster = cl)
-```
-
-```{r dependson = "path-1"}
-status(path_1) |> kable()
-```
-
-The status information shows that both fits were successfully completed.
-
-```{r dependson = "path-1"}
-anova(path_1) |> kable(digits = 1)
-```
-Model comparison shows that the two-component error model provides a much
-better fit.
-
-```{r dependson = "path-1"}
-illparms(path_1[["sforb_path", "tc"]])
-```
-
-Two ill-defined variance components are found. Therefore, the fit is
-repeated with the corresponding random effects removed.
-
-```{r path-1-refined, dependson = "path-1"}
-path_1_refined <- update(path_1[["sforb_path", "tc"]],
-  no_random_effect = c("lambda_free_0", "log_k_lambda_free_bound",
-    "log_k_c_XV", "f_lambda_ilr_2"))
-```
-
-The empty output of the illparms function indicates that there are no
-ill-defined parameters remaining in the refined fit.
-
-```{r dependson = "path-1-refined"}
-illparms(path_1_refined)
-```
-
-Below, the refined fit is plotted and the fitted parameters are shown together
-with their 95% confidence intervals.
-
-```{r dependson = "path-1-refined", fig.height = 9}
-plot(path_1_refined)
-```
-
-```{r dependson = "path-1-refined", fig.height = 9}
-parms(path_1_refined, ci = TRUE) |> kable(digits = 3)
-```
-
-\clearpage
-
-# Appendix
-
-## Listings of initial parent fits
-
-```{r listings-parent, results = "asis", echo = FALSE, dependson = "parent_mhmkin"}
-for (deg_mod in parent_deg_mods) {
-  for (err_mod in c("const", "tc")) {
-    caption <- paste("Hierarchical", deg_mod, "fit with", errmods[err_mod])
-    tex_listing(parent_mhmkin[[deg_mod, err_mod]], caption)
-  }
-}
-```
-
-## Listings of refined parent fits
-
-```{r listings-parent-refined, results = "asis", echo = FALSE, dependson = "parent_mhmkin_refined"}
-for (deg_mod in parent_deg_mods) {
-  for (err_mod in c("const", "tc")) {
-    caption <- paste("Refined hierarchical", deg_mod, "fit with", errmods[err_mod])
-    tex_listing(parent_mhmkin_refined[[deg_mod, err_mod]], caption)
-  }
-}
-```
-
-## Listings of pathway fits
-
-```{r listings-path-1, results = "asis", echo = FALSE, dependson = "path-1-refined"}
-tex_listing(path_1[["sforb_path", "const"]],
-  caption = "Hierarchical fit of SFORB-SFO2 with constant variance")
-tex_listing(path_1[["sforb_path", "tc"]],
-  caption = "Hierarchical fit of SFORB-SFO2 with two-component error")
-tex_listing(path_1_refined,
-  caption = "Refined hierarchical fit of SFORB-SFO2 with two-component error")
-```
-
-## Session info
-
-```{r echo = FALSE, cache = FALSE}
-parallel::stopCluster(cl)
-sessionInfo()
-```
-
diff --git a/docs/dev/reference/example_analysis/header.tex b/docs/dev/reference/example_analysis/header.tex
deleted file mode 100644
index a2b7ce83..00000000
--- a/docs/dev/reference/example_analysis/header.tex
+++ /dev/null
@@ -1 +0,0 @@
-\definecolor{shadecolor}{RGB}{248,248,248}
diff --git a/docs/dev/reference/example_analysis/skeleton.pdf b/docs/dev/reference/example_analysis/skeleton.pdf
deleted file mode 100644
index 53c5fb31..00000000
--- a/docs/dev/reference/example_analysis/skeleton.pdf
+++ /dev/null
diff --git a/docs/dev/reference/example_analysis/skeleton_files/figure-latex/unnamed-chunk-11-1.pdf b/docs/dev/reference/example_analysis/skeleton_files/figure-latex/unnamed-chunk-11-1.pdf
deleted file mode 100644
index ab685d92..00000000
--- a/docs/dev/reference/example_analysis/skeleton_files/figure-latex/unnamed-chunk-11-1.pdf
+++ /dev/null
diff --git a/docs/dev/reference/example_analysis/skeleton_files/figure-latex/unnamed-chunk-16-1.pdf b/docs/dev/reference/example_analysis/skeleton_files/figure-latex/unnamed-chunk-16-1.pdf
deleted file mode 100644
index 5d88063b..00000000
--- a/docs/dev/reference/example_analysis/skeleton_files/figure-latex/unnamed-chunk-16-1.pdf
+++ /dev/null
diff --git a/docs/dev/reference/example_analysis/skeleton_files/figure-latex/unnamed-chunk-6-1.pdf b/docs/dev/reference/example_analysis/skeleton_files/figure-latex/unnamed-chunk-6-1.pdf
deleted file mode 100644
index 5e0d7b6f..00000000
--- a/docs/dev/reference/example_analysis/skeleton_files/figure-latex/unnamed-chunk-6-1.pdf
+++ /dev/null
diff --git a/docs/dev/reference/example_analysis/skeleton_files/figure-latex/unnamed-chunk-9-1.pdf b/docs/dev/reference/example_analysis/skeleton_files/figure-latex/unnamed-chunk-9-1.pdf
deleted file mode 100644
index eecd06a8..00000000
--- a/docs/dev/reference/example_analysis/skeleton_files/figure-latex/unnamed-chunk-9-1.pdf
+++ /dev/null
diff --git a/docs/dev/reference/experimental_data_for_UBA-1.png b/docs/dev/reference/experimental_data_for_UBA-1.png
deleted file mode 100644
index 49e1c6c9..00000000
--- a/docs/dev/reference/experimental_data_for_UBA-1.png
+++ /dev/null
diff --git a/docs/dev/reference/experimental_data_for_UBA.html b/docs/dev/reference/experimental_data_for_UBA.html
deleted file mode 100644
index 77ff6ce4..00000000
--- a/docs/dev/reference/experimental_data_for_UBA.html
+++ /dev/null
@@ -1,275 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Experimental datasets used for development and testing of error models — experimental_data_for_UBA_2019 • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Experimental datasets used for development and testing of error models — experimental_data_for_UBA_2019"><meta property="og:description" content="The 12 datasets were extracted from active substance evaluation dossiers published
- by EFSA. Kinetic evaluations shown for these datasets are intended to illustrate
- and advance error model specifications. The fact that these data and some
- results are shown here do not imply a license to use them in the context of
- pesticide registrations, as the use of the data may be constrained by
- data protection regulations.
-Preprocessing of data was performed based on the recommendations of the FOCUS
- kinetics workgroup (FOCUS, 2014) as described below.
-Datasets 1 and 2 are from the Renewal Assessment Report (RAR) for imazamox
- (France, 2015, p. 15). For setting values reported as zero, an LOQ of 0.1
- was assumed. Metabolite residues reported for day zero were added to the
- parent compound residues.
-Datasets 3 and 4 are from the Renewal Assessment Report (RAR) for isofetamid
- (Belgium, 2014, p. 8) and show the data for two different radiolabels. For
- dataset 4, the value given for the metabolite in the day zero sampling
- in replicate B was added to the parent compound, following the respective
- FOCUS recommendation.
-Dataset 5 is from the Renewal Assessment Report (RAR) for ethofumesate
- (Austria, 2015, p. 16).
-Datasets 6 to 10 are from the Renewal Assessment Report (RAR) for glyphosate
- (Germany, 2013, pages 8, 28, 50, 51). For the initial sampling,
- the residues given for the metabolite were added to the parent
- value, following the recommendation of the FOCUS kinetics workgroup.
-Dataset 11 is from the Renewal Assessment Report (RAR) for 2,4-D
- (Hellas, 2013, p. 644). Values reported as zero were set to NA, with
- the exception of the day three sampling of metabolite A2, which was set
- to one half of the LOD reported to be 1% AR.
-Dataset 12 is from the Renewal Assessment Report (RAR) for thifensulfuron-methyl
- (United Kingdom, 2014, p. 81)."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Experimental datasets used for development and testing of error models</h1>
-    
-    <div class="hidden name"><code>experimental_data_for_UBA.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>The 12 datasets were extracted from active substance evaluation dossiers published
- by EFSA. Kinetic evaluations shown for these datasets are intended to illustrate
- and advance error model specifications. The fact that these data and some
- results are shown here do not imply a license to use them in the context of
- pesticide registrations, as the use of the data may be constrained by
- data protection regulations.</p>
-<p>Preprocessing of data was performed based on the recommendations of the FOCUS
- kinetics workgroup (FOCUS, 2014) as described below.</p>
-<p>Datasets 1 and 2 are from the Renewal Assessment Report (RAR) for imazamox
- (France, 2015, p. 15). For setting values reported as zero, an LOQ of 0.1
- was assumed. Metabolite residues reported for day zero were added to the
- parent compound residues.</p>
-<p>Datasets 3 and 4 are from the Renewal Assessment Report (RAR) for isofetamid
- (Belgium, 2014, p. 8) and show the data for two different radiolabels. For
- dataset 4, the value given for the metabolite in the day zero sampling
- in replicate B was added to the parent compound, following the respective
- FOCUS recommendation.</p>
-<p>Dataset 5 is from the Renewal Assessment Report (RAR) for ethofumesate
- (Austria, 2015, p. 16).</p>
-<p>Datasets 6 to 10 are from the Renewal Assessment Report (RAR) for glyphosate
- (Germany, 2013, pages 8, 28, 50, 51). For the initial sampling,
- the residues given for the metabolite were added to the parent
- value, following the recommendation of the FOCUS kinetics workgroup.</p>
-<p>Dataset 11 is from the Renewal Assessment Report (RAR) for 2,4-D
- (Hellas, 2013, p. 644). Values reported as zero were set to NA, with
- the exception of the day three sampling of metabolite A2, which was set
- to one half of the LOD reported to be 1% AR.</p>
-<p>Dataset 12 is from the Renewal Assessment Report (RAR) for thifensulfuron-methyl
- (United Kingdom, 2014, p. 81).</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="va">experimental_data_for_UBA_2019</span></span></code></pre></div>
-    </div>
-
-    <div id="format">
-    <h2>Format</h2>
-    <p>A list containing twelve datasets as an R6 class defined by <code><a href="mkinds.html">mkinds</a></code>,
-  each containing, among others, the following components</p><dl><dt><code>title</code></dt>
-<dd><p>The name of the dataset, e.g. <code>Soil 1</code></p></dd>
-
-    <dt><code>data</code></dt>
-<dd><p>A data frame with the data in the form expected by <code><a href="mkinfit.html">mkinfit</a></code></p></dd>
-
-  
-</dl></div>
-    <div id="source">
-    <h2>Source</h2>
-    
-
-<p>Austria (2015). Ethofumesate Renewal Assessment Report Volume 3 Annex B.8 (AS)</p>
-<p>Belgium (2014). Isofetamid (IKF-5411) Draft Assessment Report Volume 3 Annex B.8 (AS)</p>
-<p>France (2015). Imazamox Draft Renewal Assessment Report Volume 3 Annex B.8 (AS)</p>
-<p>FOCUS (2014) “Generic guidance for Estimating Persistence and
-  Degradation Kinetics from Environmental Fate Studies on Pesticides in EU
-  Registration” Report of the FOCUS Work Group on Degradation Kinetics,
-  Version 1.1, 18 December 2014
-  <a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics" class="external-link">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p>
-<p>Germany (2013). Renewal Assessment Report Glyphosate Volume 3 Annex B.8: Environmental Fate
-  and Behaviour</p>
-<p>Hellas (2013). Renewal Assessment Report 2,4-D Volume 3 Annex B.8: Fate and behaviour in the
-  environment</p>
-<p>Ranke (2019) Documentation of results obtained for the error model expertise
-  written for the German Umweltbundesamt.</p>
-<p>United Kingdom (2014). Thifensulfuron-methyl - Annex B.8 (Volume 3) to the Report and Proposed
-  Decision of the United Kingdom made to the European Commission under Regulation (EC) No.
-  1141/2010 for renewal of an active substance</p>
-    </div>
-
-    <div id="ref-examples">
-    <h2>Examples</h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># \dontrun{</span></span></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co"># Model definitions</span></span></span>
-<span class="r-in"><span><span class="va">sfo_sfo</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span></span></span>
-<span class="r-in"><span>  parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"A1"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  A1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  use_of_ff <span class="op">=</span> <span class="st">"max"</span></span></span>
-<span class="r-in"><span><span class="op">)</span></span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Temporary DLL for differentials generated and loaded</span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="va">dfop_sfo</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span></span></span>
-<span class="r-in"><span>  parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"DFOP"</span>, to <span class="op">=</span> <span class="st">"A1"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  A1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  use_of_ff <span class="op">=</span> <span class="st">"max"</span></span></span>
-<span class="r-in"><span><span class="op">)</span></span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Temporary DLL for differentials generated and loaded</span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="va">sfo_sfo_sfo</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span></span></span>
-<span class="r-in"><span>  parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"A1"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  A1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"A2"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  A2 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  use_of_ff <span class="op">=</span> <span class="st">"max"</span></span></span>
-<span class="r-in"><span><span class="op">)</span></span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Temporary DLL for differentials generated and loaded</span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="va">dfop_sfo_sfo</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span></span></span>
-<span class="r-in"><span>  parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"DFOP"</span>, to <span class="op">=</span> <span class="st">"A1"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  A1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"A2"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  A2 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  use_of_ff <span class="op">=</span> <span class="st">"max"</span></span></span>
-<span class="r-in"><span><span class="op">)</span></span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Temporary DLL for differentials generated and loaded</span>
-<span class="r-in"><span><span class="va">d_1_2</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="va">experimental_data_for_UBA_2019</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">2</span><span class="op">]</span>, <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="va">x</span><span class="op">$</span><span class="va">data</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">d_1_2</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste</a></span><span class="op">(</span><span class="st">"Soil"</span>, <span class="fl">1</span><span class="op">:</span><span class="fl">2</span><span class="op">)</span></span></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="va">f_1_2_tc</span> <span class="op">&lt;-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="st">"DFOP-SFO-SFO"</span> <span class="op">=</span> <span class="va">dfop_sfo_sfo</span><span class="op">)</span>, <span class="va">d_1_2</span>, error_model <span class="op">=</span> <span class="st">"tc"</span><span class="op">)</span></span></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_1_2_tc</span>, resplot <span class="op">=</span> <span class="st">"errmod"</span><span class="op">)</span></span></span>
-<span class="r-plt img"><img src="experimental_data_for_UBA-1.png" alt="" width="700" height="433"></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co"># }</span></span></span>
-</code></pre></div>
-    </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/f_time_norm_focus.html b/docs/dev/reference/f_time_norm_focus.html
deleted file mode 100644
index b0722bc6..00000000
--- a/docs/dev/reference/f_time_norm_focus.html
+++ /dev/null
@@ -1,248 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Normalisation factors for aerobic soil degradation according to FOCUS guidance — f_time_norm_focus • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Normalisation factors for aerobic soil degradation according to FOCUS guidance — f_time_norm_focus"><meta property="og:description" content="Time step normalisation factors for aerobic soil degradation as described
-in Appendix 8 to the FOCUS kinetics guidance (FOCUS 2014, p. 369)."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Normalisation factors for aerobic soil degradation according to FOCUS guidance</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/f_time_norm_focus.R" class="external-link"><code>R/f_time_norm_focus.R</code></a></small>
-    <div class="hidden name"><code>f_time_norm_focus.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Time step normalisation factors for aerobic soil degradation as described
-in Appendix 8 to the FOCUS kinetics guidance (FOCUS 2014, p. 369).</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">f_time_norm_focus</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># S3 method for numeric</span></span>
-<span><span class="fu">f_time_norm_focus</span><span class="op">(</span></span>
-<span>  <span class="va">object</span>,</span>
-<span>  moisture <span class="op">=</span> <span class="cn">NA</span>,</span>
-<span>  field_moisture <span class="op">=</span> <span class="cn">NA</span>,</span>
-<span>  temperature <span class="op">=</span> <span class="va">object</span>,</span>
-<span>  Q10 <span class="op">=</span> <span class="fl">2.58</span>,</span>
-<span>  walker <span class="op">=</span> <span class="fl">0.7</span>,</span>
-<span>  f_na <span class="op">=</span> <span class="cn">NA</span>,</span>
-<span>  <span class="va">...</span></span>
-<span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># S3 method for mkindsg</span></span>
-<span><span class="fu">f_time_norm_focus</span><span class="op">(</span></span>
-<span>  <span class="va">object</span>,</span>
-<span>  study_moisture_ref_source <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"auto"</span>, <span class="st">"meta"</span>, <span class="st">"focus"</span><span class="op">)</span>,</span>
-<span>  Q10 <span class="op">=</span> <span class="fl">2.58</span>,</span>
-<span>  walker <span class="op">=</span> <span class="fl">0.7</span>,</span>
-<span>  f_na <span class="op">=</span> <span class="cn">NA</span>,</span>
-<span>  <span class="va">...</span></span>
-<span><span class="op">)</span></span></code></pre></div>
-    </div>
-
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>object</dt>
-<dd><p>An object containing information used for the calculations</p></dd>
-
-
-<dt>...</dt>
-<dd><p>Currently not used</p></dd>
-
-
-<dt>moisture</dt>
-<dd><p>Numeric vector of moisture contents in \% w/w</p></dd>
-
-
-<dt>field_moisture</dt>
-<dd><p>Numeric vector of moisture contents at field capacity
-(pF2) in \% w/w</p></dd>
-
-
-<dt>temperature</dt>
-<dd><p>Numeric vector of temperatures in °C</p></dd>
-
-
-<dt>Q10</dt>
-<dd><p>The Q10 value used for temperature normalisation</p></dd>
-
-
-<dt>walker</dt>
-<dd><p>The Walker exponent used for moisture normalisation</p></dd>
-
-
-<dt>f_na</dt>
-<dd><p>The factor to use for NA values. If set to NA, only factors
-for complete cases will be returned.</p></dd>
-
-
-<dt>study_moisture_ref_source</dt>
-<dd><p>Source for the reference value
-used to calculate the study moisture. If 'auto', preference is given
-to a reference moisture given in the meta information, otherwise
-the focus soil moisture for the soil class is used</p></dd>
-
-</dl></div>
-    <div id="references">
-    <h2>References</h2>
-    <p>FOCUS (2006) “Guidance Document on Estimating Persistence
-and Degradation Kinetics from Environmental Fate Studies on Pesticides in
-EU Registration” Report of the FOCUS Work Group on Degradation Kinetics,
-EC Document Reference Sanco/10058/2005 version 2.0, 434 pp,
-<a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics" class="external-link">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a>
-FOCUS (2014) “Generic guidance for Estimating Persistence
-and Degradation Kinetics from Environmental Fate Studies on Pesticides in
-EU Registration” Report of the FOCUS Work Group on Degradation Kinetics,
-Version 1.1, 18 December 2014
-<a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics" class="external-link">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p>
-    </div>
-    <div id="see-also">
-    <h2>See also</h2>
-    <div class="dont-index"><p><a href="focus_soil_moisture.html">focus_soil_moisture</a></p></div>
-    </div>
-
-    <div id="ref-examples">
-    <h2>Examples</h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="fu">f_time_norm_focus</span><span class="op">(</span><span class="fl">25</span>, <span class="fl">20</span>, <span class="fl">25</span><span class="op">)</span> <span class="co"># 1.37, compare FOCUS 2014 p. 184</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 1.373956</span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="va">D24_2014</span><span class="op">$</span><span class="va">meta</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                                 study usda_soil_type study_moisture_ref_type</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Mississippi                Cohen 1991      Silt loam                    &lt;NA&gt;</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Fayette     Liu and Adelfinskaya 2011      Silt loam                     pF1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> RefSol 03-G Liu and Adelfinskaya 2011           Loam                     pF1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Site E1     Liu and Adelfinskaya 2011           Loam                     pF1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Site I2     Liu and Adelfinskaya 2011     Loamy sand                     pF1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>             rel_moisture temperature</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Mississippi           NA          25</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Fayette              0.5          20</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> RefSol 03-G          0.5          20</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Site E1              0.5          20</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Site I2              0.5          20</span>
-<span class="r-in"><span><span class="co"># No moisture normalisation in the first dataset, so we use f_na = 1 to get</span></span></span>
-<span class="r-in"><span><span class="co"># temperature only normalisation as in the EU evaluation</span></span></span>
-<span class="r-in"><span><span class="fu">f_time_norm_focus</span><span class="op">(</span><span class="va">D24_2014</span>, study_moisture_ref_source <span class="op">=</span> <span class="st">"focus"</span>, f_na <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> $f_time_norm was (re)set to normalised values</span>
-</code></pre></div>
-    </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/focus_soil_moisture.html b/docs/dev/reference/focus_soil_moisture.html
deleted file mode 100644
index cb4a035a..00000000
--- a/docs/dev/reference/focus_soil_moisture.html
+++ /dev/null
@@ -1,170 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>FOCUS default values for soil moisture contents at field capacity, MWHC and 1/3 bar — focus_soil_moisture • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="FOCUS default values for soil moisture contents at field capacity, MWHC and 1/3 bar — focus_soil_moisture"><meta property="og:description" content="The value were transcribed from p. 36. The table assumes field capacity
-corresponds to pF2, MWHC to pF 1 and 1/3 bar to pF 2.5."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>FOCUS default values for soil moisture contents at field capacity, MWHC and 1/3 bar</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/focus_soil_moisture.R" class="external-link"><code>R/focus_soil_moisture.R</code></a></small>
-    <div class="hidden name"><code>focus_soil_moisture.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>The value were transcribed from p. 36. The table assumes field capacity
-corresponds to pF2, MWHC to pF 1 and 1/3 bar to pF 2.5.</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="va">focus_soil_moisture</span></span></code></pre></div>
-    </div>
-
-    <div id="format">
-    <h2>Format</h2>
-    <p>A matrix with upper case USDA soil classes as row names, and water tension
-('pF1', 'pF2', 'pF 2.5') as column names</p>
-    </div>
-    <div id="source">
-    <h2>Source</h2>
-    <p>Anonymous (2014) Generic Guidance for Tier 1 FOCUS Ground Water Assessment
-Version 2.2, May 2014 <a href="https://esdac.jrc.ec.europa.eu/projects/ground-water" class="external-link">https://esdac.jrc.ec.europa.eu/projects/ground-water</a></p>
-    </div>
-
-    <div id="ref-examples">
-    <h2>Examples</h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">focus_soil_moisture</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                 pF1 pF2 pF2.5</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Sand             24  12     7</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Loamy sand       24  14     9</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Sandy loam       27  19    15</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Sandy clay loam  28  22    18</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Clay loam        32  28    25</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Loam             31  25    21</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Silt loam        32  26    21</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Silty clay loam  34  30    27</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Silt             31  27    21</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Sandy clay       41  35    31</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Silty clay       44  40    36</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Clay             53  48    43</span>
-</code></pre></div>
-    </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/get_deg_func.html b/docs/dev/reference/get_deg_func.html
deleted file mode 100644
index cf81a591..00000000
--- a/docs/dev/reference/get_deg_func.html
+++ /dev/null
@@ -1,147 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Retrieve a degradation function from the mmkin namespace — get_deg_func • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Retrieve a degradation function from the mmkin namespace — get_deg_func"><meta property="og:description" content="Retrieve a degradation function from the mmkin namespace"><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Retrieve a degradation function from the mmkin namespace</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/nlme.mmkin.R" class="external-link"><code>R/nlme.mmkin.R</code></a></small>
-    <div class="hidden name"><code>get_deg_func.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Retrieve a degradation function from the mmkin namespace</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">get_deg_func</span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
-    </div>
-
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>A function that was likely previously assigned from within
-nlme.mmkin</p>
-    </div>
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/hierarchical_kinetics.html b/docs/dev/reference/hierarchical_kinetics.html
deleted file mode 100644
index 7ece90cc..00000000
--- a/docs/dev/reference/hierarchical_kinetics.html
+++ /dev/null
@@ -1,197 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Hierarchical kinetics template — hierarchical_kinetics • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Hierarchical kinetics template — hierarchical_kinetics"><meta property="og:description" content='R markdown format for setting up hierarchical kinetics based on a template
-provided with the mkin package. This format is based on rmarkdown::pdf_document.
-Chunk options are adapted. Echoing R code from code chunks and caching are
-turned on per default. character for prepending output from code chunks is
-set to the empty string, code tidying is off, figure alignment defaults to
-centering, and positioning of figures is set to "H", which means that
-figures will not move around in the document, but stay where the user
-includes them.'><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.4</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Hierarchical kinetics template</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/hierarchical_kinetics.R" class="external-link"><code>R/hierarchical_kinetics.R</code></a></small>
-    <div class="hidden name"><code>hierarchical_kinetics.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>R markdown format for setting up hierarchical kinetics based on a template
-provided with the mkin package. This format is based on <a href="https://pkgs.rstudio.com/rmarkdown/reference/pdf_document.html" class="external-link">rmarkdown::pdf_document</a>.
-Chunk options are adapted. Echoing R code from code chunks and caching are
-turned on per default. character for prepending output from code chunks is
-set to the empty string, code tidying is off, figure alignment defaults to
-centering, and positioning of figures is set to "H", which means that
-figures will not move around in the document, but stay where the user
-includes them.</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">hierarchical_kinetics</span><span class="op">(</span><span class="va">...</span>, keep_tex <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div>
-    </div>
-
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>...</dt>
-<dd><p>Arguments to <code><a href="https://pkgs.rstudio.com/rmarkdown/reference/pdf_document.html" class="external-link">rmarkdown::pdf_document</a></code></p></dd>
-
-
-<dt>keep_tex</dt>
-<dd><p>Keep the intermediate tex file used in the conversion to PDF</p></dd>
-
-</dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>R Markdown output format to pass to
-<code><a href="https://pkgs.rstudio.com/rmarkdown/reference/render.html" class="external-link">render</a></code></p>
-
-
-    </div>
-    <div id="details">
-    <h2>Details</h2>
-    <p>The latter feature (positioning the figures with "H") depends on the LaTeX
-package 'float'. In addition, the LaTeX package 'listing' is used in the
-template for showing model fit summaries in the Appendix. This means that
-the LaTeX packages 'float' and 'listing' need to be installed in the TeX
-distribution used.</p>
-<p>On Windows, the easiest way to achieve this (if no TeX distribution
-is present before) is to install the 'tinytex' R package, to run
-'tinytex::install_tinytex()' to get the basic tiny Tex distribution,
-and then to run 'tinytex::tlmgr_install(c("float", "listing"))'.</p>
-    </div>
-
-    <div id="ref-examples">
-    <h2>Examples</h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co"># \dontrun{</span></span></span>
-<span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/rstudio/rmarkdown" class="external-link">rmarkdown</a></span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="co"># The following is now commented out after the relase of v1.2.3 for the generation</span></span></span>
-<span class="r-in"><span><span class="co"># of online docs, as the command creates a directory and opens an editor</span></span></span>
-<span class="r-in"><span><span class="co">#draft("example_analysis.rmd", template = "hierarchical_kinetics", package = "mkin")</span></span></span>
-<span class="r-in"><span><span class="co"># }</span></span></span>
-<span class="r-in"><span></span></span>
-</code></pre></div>
-    </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/illparms.html b/docs/dev/reference/illparms.html
deleted file mode 100644
index 7bf6c1fe..00000000
--- a/docs/dev/reference/illparms.html
+++ /dev/null
@@ -1,253 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Method to get the names of ill-defined parameters — illparms • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Method to get the names of ill-defined parameters — illparms"><meta property="og:description" content="The method for generalised nonlinear regression fits as obtained
-with mkinfit and mmkin checks if the degradation parameters
-pass the Wald test (in degradation kinetics often simply called t-test) for
-significant difference from zero. For this test, the parameterisation
-without parameter transformations is used."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Method to get the names of ill-defined parameters</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/illparms.R" class="external-link"><code>R/illparms.R</code></a></small>
-    <div class="hidden name"><code>illparms.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>The method for generalised nonlinear regression fits as obtained
-with <a href="mkinfit.html">mkinfit</a> and <a href="mmkin.html">mmkin</a> checks if the degradation parameters
-pass the Wald test (in degradation kinetics often simply called t-test) for
-significant difference from zero. For this test, the parameterisation
-without parameter transformations is used.</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">illparms</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># S3 method for mkinfit</span></span>
-<span><span class="fu">illparms</span><span class="op">(</span><span class="va">object</span>, conf.level <span class="op">=</span> <span class="fl">0.95</span>, <span class="va">...</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># S3 method for illparms.mkinfit</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># S3 method for mmkin</span></span>
-<span><span class="fu">illparms</span><span class="op">(</span><span class="va">object</span>, conf.level <span class="op">=</span> <span class="fl">0.95</span>, <span class="va">...</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># S3 method for illparms.mmkin</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># S3 method for saem.mmkin</span></span>
-<span><span class="fu">illparms</span><span class="op">(</span></span>
-<span>  <span class="va">object</span>,</span>
-<span>  conf.level <span class="op">=</span> <span class="fl">0.95</span>,</span>
-<span>  random <span class="op">=</span> <span class="cn">TRUE</span>,</span>
-<span>  errmod <span class="op">=</span> <span class="cn">TRUE</span>,</span>
-<span>  slopes <span class="op">=</span> <span class="cn">TRUE</span>,</span>
-<span>  <span class="va">...</span></span>
-<span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># S3 method for illparms.saem.mmkin</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># S3 method for mhmkin</span></span>
-<span><span class="fu">illparms</span><span class="op">(</span><span class="va">object</span>, conf.level <span class="op">=</span> <span class="fl">0.95</span>, random <span class="op">=</span> <span class="cn">TRUE</span>, errmod <span class="op">=</span> <span class="cn">TRUE</span>, <span class="va">...</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># S3 method for illparms.mhmkin</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
-    </div>
-
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>object</dt>
-<dd><p>The object to investigate</p></dd>
-
-
-<dt>...</dt>
-<dd><p>For potential future extensions</p></dd>
-
-
-<dt>conf.level</dt>
-<dd><p>The confidence level for checking p values</p></dd>
-
-
-<dt>x</dt>
-<dd><p>The object to be printed</p></dd>
-
-
-<dt>random</dt>
-<dd><p>For hierarchical fits, should random effects be tested?</p></dd>
-
-
-<dt>errmod</dt>
-<dd><p>For hierarchical fits, should error model parameters be
-tested?</p></dd>
-
-
-<dt>slopes</dt>
-<dd><p>For hierarchical <a href="saem.html">saem</a> fits using saemix as backend,
-should slope parameters in the covariate model(starting with 'beta_') be
-tested?</p></dd>
-
-</dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>For <a href="mkinfit.html">mkinfit</a> or <a href="saem.html">saem</a> objects, a character vector of parameter
-names. For <a href="mmkin.html">mmkin</a> or <a href="mhmkin.html">mhmkin</a> objects, a matrix like object of class
-'illparms.mmkin' or 'illparms.mhmkin'.</p>
-    </div>
-    <div id="details">
-    <h2>Details</h2>
-    <p>The method for hierarchical model fits, also known as nonlinear
-mixed-effects model fits as obtained with <a href="saem.html">saem</a> and <a href="mhmkin.html">mhmkin</a>
-checks if any of the confidence intervals for the random
-effects expressed as standard deviations include zero, and if
-the confidence intervals for the error model parameters include
-zero.</p>
-    </div>
-    <div id="note">
-    <h2>Note</h2>
-    <p>All return objects have printing methods. For the single fits, printing
-does not output anything in the case no ill-defined parameters are found.</p>
-    </div>
-
-    <div id="ref-examples">
-    <h2>Examples</h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">fit</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"FOMC"</span>, <span class="va">FOCUS_2006_A</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu">illparms</span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "parent_0" "alpha"    "beta"     "sigma"   </span>
-<span class="r-in"><span><span class="co"># \dontrun{</span></span></span>
-<span class="r-in"><span><span class="va">fits</span> <span class="op">&lt;-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span></span></span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"FOMC"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="st">"FOCUS A"</span> <span class="op">=</span> <span class="va">FOCUS_2006_A</span>,</span></span>
-<span class="r-in"><span>       <span class="st">"FOCUS C"</span> <span class="op">=</span> <span class="va">FOCUS_2006_C</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu">illparms</span><span class="op">(</span><span class="va">fits</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>       dataset</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> model  FOCUS A                      FOCUS C</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   SFO                                      </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   FOMC parent_0, alpha, beta, sigma        </span>
-<span class="r-in"><span><span class="co"># }</span></span></span>
-</code></pre></div>
-    </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/ilr.html b/docs/dev/reference/ilr.html
deleted file mode 100644
index 94b23a76..00000000
--- a/docs/dev/reference/ilr.html
+++ /dev/null
@@ -1,210 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Function to perform isometric log-ratio transformation — ilr • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Function to perform isometric log-ratio transformation — ilr"><meta property="og:description" content="This implementation is a special case of the class of isometric log-ratio
-transformations."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Function to perform isometric log-ratio transformation</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/ilr.R" class="external-link"><code>R/ilr.R</code></a></small>
-    <div class="hidden name"><code>ilr.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>This implementation is a special case of the class of isometric log-ratio
-transformations.</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">ilr</span><span class="op">(</span><span class="va">x</span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">invilr</span><span class="op">(</span><span class="va">x</span><span class="op">)</span></span></code></pre></div>
-    </div>
-
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>x</dt>
-<dd><p>A numeric vector. Naturally, the forward transformation is only
-sensible for vectors with all elements being greater than zero.</p></dd>
-
-</dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>The result of the forward or backward transformation. The returned
-components always sum to 1 for the case of the inverse log-ratio
-transformation.</p>
-    </div>
-    <div id="references">
-    <h2>References</h2>
-    <p>Peter Filzmoser, Karel Hron (2008) Outlier Detection for
-Compositional Data Using Robust Methods. Math Geosci 40 233-248</p>
-    </div>
-    <div id="see-also">
-    <h2>See also</h2>
-    <div class="dont-index"><p>Another implementation can be found in R package
-<code>robCompositions</code>.</p></div>
-    </div>
-    <div id="author">
-    <h2>Author</h2>
-    <p>René Lehmann and Johannes Ranke</p>
-    </div>
-
-    <div id="ref-examples">
-    <h2>Examples</h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co"># Order matters</span></span></span>
-<span class="r-in"><span><span class="fu">ilr</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.1</span>, <span class="fl">1</span>, <span class="fl">10</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] -1.628174 -2.820079</span>
-<span class="r-in"><span><span class="fu">ilr</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">10</span>, <span class="fl">1</span>, <span class="fl">0.1</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 1.628174 2.820079</span>
-<span class="r-in"><span><span class="co"># Equal entries give ilr transformations with zeros as elements</span></span></span>
-<span class="r-in"><span><span class="fu">ilr</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">3</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 0 0</span>
-<span class="r-in"><span><span class="co"># Almost equal entries give small numbers</span></span></span>
-<span class="r-in"><span><span class="fu">ilr</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.3</span>, <span class="fl">0.4</span>, <span class="fl">0.3</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] -0.2034219  0.1174457</span>
-<span class="r-in"><span><span class="co"># Only the ratio between the numbers counts, not their sum</span></span></span>
-<span class="r-in"><span><span class="fu">invilr</span><span class="op">(</span><span class="fu">ilr</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.7</span>, <span class="fl">0.29</span>, <span class="fl">0.01</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 0.70 0.29 0.01</span>
-<span class="r-in"><span><span class="fu">invilr</span><span class="op">(</span><span class="fu">ilr</span><span class="op">(</span><span class="fl">2.1</span> <span class="op">*</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.7</span>, <span class="fl">0.29</span>, <span class="fl">0.01</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 0.70 0.29 0.01</span>
-<span class="r-in"><span><span class="co"># Inverse transformation of larger numbers gives unequal elements</span></span></span>
-<span class="r-in"><span><span class="fu">invilr</span><span class="op">(</span><span class="op">-</span><span class="fl">10</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 7.213536e-07 9.999993e-01</span>
-<span class="r-in"><span><span class="fu">invilr</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="op">-</span><span class="fl">10</span>, <span class="fl">0</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 7.207415e-07 9.991507e-01 8.486044e-04</span>
-<span class="r-in"><span><span class="co"># The sum of the elements of the inverse ilr is 1</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/sum.html" class="external-link">sum</a></span><span class="op">(</span><span class="fu">invilr</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="op">-</span><span class="fl">10</span>, <span class="fl">0</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 1</span>
-<span class="r-in"><span><span class="co"># This is why we do not need all elements of the inverse transformation to go back:</span></span></span>
-<span class="r-in"><span><span class="va">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.1</span>, <span class="fl">0.3</span>, <span class="fl">0.5</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">b</span> <span class="op">&lt;-</span> <span class="fu">invilr</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/length.html" class="external-link">length</a></span><span class="op">(</span><span class="va">b</span><span class="op">)</span> <span class="co"># Four elements</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 4</span>
-<span class="r-in"><span><span class="fu">ilr</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="va">b</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">3</span><span class="op">]</span>, <span class="fl">1</span> <span class="op">-</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html" class="external-link">sum</a></span><span class="op">(</span><span class="va">b</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">3</span><span class="op">]</span><span class="op">)</span><span class="op">)</span><span class="op">)</span> <span class="co"># Gives c(0.1, 0.3, 0.5)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 0.1 0.3 0.5</span>
-<span class="r-in"><span></span></span>
-</code></pre></div>
-    </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/index.html b/docs/dev/reference/index.html
deleted file mode 100644
index d1b789c3..00000000
--- a/docs/dev/reference/index.html
+++ /dev/null
@@ -1,528 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Function reference • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Function reference"><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-index">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.4</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="contents col-md-9">
-    <div class="page-header">
-      <h1>Reference</h1>
-    </div>
-
-    <table class="ref-index"><colgroup><col class="alias"><col class="title"></colgroup><tbody><tr><th colspan="2">
-          <h2 id="main-functions">Main functions <a href="#main-functions" class="anchor" aria-hidden="true"></a></h2>
-          <p class="section-desc"></p><p>Essential functionality</p>
-        </th>
-      </tr></tbody><tbody><tr><td>
-          <p><code><a href="mkinmod.html">mkinmod()</a></code> <code><a href="mkinmod.html">print(<i>&lt;mkinmod&gt;</i>)</a></code> <code><a href="mkinmod.html">mkinsub()</a></code> </p>
-        </td>
-        <td><p>Function to set up a kinetic model with one or more state variables</p></td>
-      </tr><tr><td>
-          <p><code><a href="mkinfit.html">mkinfit()</a></code> </p>
-        </td>
-        <td><p>Fit a kinetic model to data with one or more state variables</p></td>
-      </tr><tr><td>
-          <p><code><a href="mmkin.html">mmkin()</a></code> <code><a href="mmkin.html">print(<i>&lt;mmkin&gt;</i>)</a></code> </p>
-        </td>
-        <td><p>Fit one or more kinetic models with one or more state variables to one or
-more datasets</p></td>
-      </tr><tr><td>
-          <p><code><a href="mhmkin.html">mhmkin()</a></code> <code><a href="mhmkin.html">`[`(<i>&lt;mhmkin&gt;</i>)</a></code> <code><a href="mhmkin.html">print(<i>&lt;mhmkin&gt;</i>)</a></code> </p>
-        </td>
-        <td><p>Fit nonlinear mixed-effects models built from one or more kinetic
-degradation models and one or more error models</p></td>
-      </tr></tbody><tbody><tr><th colspan="2">
-          <h2 id="generics">Generics <a href="#generics" class="anchor" aria-hidden="true"></a></h2>
-          <p class="section-desc"></p><p>Generic functions introduced by the package</p>
-        </th>
-      </tr></tbody><tbody><tr><td>
-          <p><code><a href="parms.html">parms()</a></code> </p>
-        </td>
-        <td><p>Extract model parameters</p></td>
-      </tr><tr><td>
-          <p><code><a href="status.html">status()</a></code> <code><a href="status.html">print(<i>&lt;status.mmkin&gt;</i>)</a></code> <code><a href="status.html">print(<i>&lt;status.mhmkin&gt;</i>)</a></code> </p>
-        </td>
-        <td><p>Method to get status information for fit array objects</p></td>
-      </tr><tr><td>
-          <p><code><a href="illparms.html">illparms()</a></code> <code><a href="illparms.html">print(<i>&lt;illparms.mkinfit&gt;</i>)</a></code> <code><a href="illparms.html">print(<i>&lt;illparms.mmkin&gt;</i>)</a></code> <code><a href="illparms.html">print(<i>&lt;illparms.saem.mmkin&gt;</i>)</a></code> <code><a href="illparms.html">print(<i>&lt;illparms.mhmkin&gt;</i>)</a></code> </p>
-        </td>
-        <td><p>Method to get the names of ill-defined parameters</p></td>
-      </tr><tr><td>
-          <p><code><a href="endpoints.html">endpoints()</a></code> </p>
-        </td>
-        <td><p>Function to calculate endpoints for further use from kinetic models fitted
-with mkinfit</p></td>
-      </tr><tr><td>
-          <p><code><a href="aw.html">aw()</a></code> </p>
-        </td>
-        <td><p>Calculate Akaike weights for model averaging</p></td>
-      </tr></tbody><tbody><tr><th colspan="2">
-          <h2 id="show-results">Show results <a href="#show-results" class="anchor" aria-hidden="true"></a></h2>
-          <p class="section-desc"></p><p>Functions working with mkinfit objects</p>
-        </th>
-      </tr></tbody><tbody><tr><td>
-          <p><code><a href="plot.mkinfit.html">plot(<i>&lt;mkinfit&gt;</i>)</a></code> <code><a href="plot.mkinfit.html">plot_sep()</a></code> <code><a href="plot.mkinfit.html">plot_res()</a></code> <code><a href="plot.mkinfit.html">plot_err()</a></code> </p>
-        </td>
-        <td><p>Plot the observed data and the fitted model of an mkinfit object</p></td>
-      </tr><tr><td>
-          <p><code><a href="summary.mkinfit.html">summary(<i>&lt;mkinfit&gt;</i>)</a></code> <code><a href="summary.mkinfit.html">print(<i>&lt;summary.mkinfit&gt;</i>)</a></code> </p>
-        </td>
-        <td><p>Summary method for class "mkinfit"</p></td>
-      </tr><tr><td>
-          <p><code><a href="confint.mkinfit.html">confint(<i>&lt;mkinfit&gt;</i>)</a></code> </p>
-        </td>
-        <td><p>Confidence intervals for parameters of mkinfit objects</p></td>
-      </tr><tr><td>
-          <p><code><a href="update.mkinfit.html">update(<i>&lt;mkinfit&gt;</i>)</a></code> </p>
-        </td>
-        <td><p>Update an mkinfit model with different arguments</p></td>
-      </tr><tr><td>
-          <p><code><a href="lrtest.mkinfit.html">lrtest(<i>&lt;mkinfit&gt;</i>)</a></code> <code><a href="lrtest.mkinfit.html">lrtest(<i>&lt;mmkin&gt;</i>)</a></code> </p>
-        </td>
-        <td><p>Likelihood ratio test for mkinfit models</p></td>
-      </tr><tr><td>
-          <p><code><a href="loftest.html">loftest()</a></code> </p>
-        </td>
-        <td><p>Lack-of-fit test for models fitted to data with replicates</p></td>
-      </tr><tr><td>
-          <p><code><a href="mkinerrmin.html">mkinerrmin()</a></code> </p>
-        </td>
-        <td><p>Calculate the minimum error to assume in order to pass the variance test</p></td>
-      </tr><tr><td>
-          <p><code><a href="CAKE_export.html">CAKE_export()</a></code> </p>
-        </td>
-        <td><p>Export a list of datasets format to a CAKE study file</p></td>
-      </tr></tbody><tbody><tr><th colspan="2">
-          <h2 id="work-with-mmkin-objects">Work with mmkin objects <a href="#work-with-mmkin-objects" class="anchor" aria-hidden="true"></a></h2>
-          <p class="section-desc"></p><p>Functions working with aggregated results</p>
-        </th>
-      </tr></tbody><tbody><tr><td>
-          <p><code><a href="Extract.mmkin.html">`[`(<i>&lt;mmkin&gt;</i>)</a></code> </p>
-        </td>
-        <td><p>Subsetting method for mmkin objects</p></td>
-      </tr><tr><td>
-          <p><code><a href="plot.mmkin.html">plot(<i>&lt;mmkin&gt;</i>)</a></code> </p>
-        </td>
-        <td><p>Plot model fits (observed and fitted) and the residuals for a row or column
-of an mmkin object</p></td>
-      </tr><tr><td>
-          <p><code><a href="AIC.mmkin.html">AIC(<i>&lt;mmkin&gt;</i>)</a></code> <code><a href="AIC.mmkin.html">BIC(<i>&lt;mmkin&gt;</i>)</a></code> </p>
-        </td>
-        <td><p>Calculate the AIC for a column of an mmkin object</p></td>
-      </tr><tr><td>
-          <p><code><a href="summary.mmkin.html">summary(<i>&lt;mmkin&gt;</i>)</a></code> <code><a href="summary.mmkin.html">print(<i>&lt;summary.mmkin&gt;</i>)</a></code> </p>
-        </td>
-        <td><p>Summary method for class "mmkin"</p></td>
-      </tr></tbody><tbody><tr><th colspan="2">
-          <h2 id="mixed-models">Mixed models <a href="#mixed-models" class="anchor" aria-hidden="true"></a></h2>
-          <p class="section-desc"></p><p>Create and work with nonlinear hierarchical models</p>
-        </th>
-      </tr></tbody><tbody><tr><td>
-          <p><code><a href="hierarchical_kinetics.html">hierarchical_kinetics()</a></code> </p>
-        </td>
-        <td><p>Hierarchical kinetics template</p></td>
-      </tr><tr><td>
-          <p><code><a href="read_spreadsheet.html">read_spreadsheet()</a></code> </p>
-        </td>
-        <td><p>Read datasets and relevant meta information from a spreadsheet file</p></td>
-      </tr><tr><td>
-          <p><code><a href="nlme.mmkin.html">nlme(<i>&lt;mmkin&gt;</i>)</a></code> <code><a href="nlme.mmkin.html">print(<i>&lt;nlme.mmkin&gt;</i>)</a></code> <code><a href="nlme.mmkin.html">update(<i>&lt;nlme.mmkin&gt;</i>)</a></code> </p>
-        </td>
-        <td><p>Create an nlme model for an mmkin row object</p></td>
-      </tr><tr><td>
-          <p><code><a href="saem.html">saem()</a></code> <code><a href="saem.html">print(<i>&lt;saem.mmkin&gt;</i>)</a></code> <code><a href="saem.html">saemix_model()</a></code> <code><a href="saem.html">saemix_data()</a></code> </p>
-        </td>
-        <td><p>Fit nonlinear mixed models with SAEM</p></td>
-      </tr><tr><td>
-          <p><code><a href="mhmkin.html">mhmkin()</a></code> <code><a href="mhmkin.html">`[`(<i>&lt;mhmkin&gt;</i>)</a></code> <code><a href="mhmkin.html">print(<i>&lt;mhmkin&gt;</i>)</a></code> </p>
-        </td>
-        <td><p>Fit nonlinear mixed-effects models built from one or more kinetic
-degradation models and one or more error models</p></td>
-      </tr><tr><td>
-          <p><code><a href="plot.mixed.mmkin.html">plot(<i>&lt;mixed.mmkin&gt;</i>)</a></code> </p>
-        </td>
-        <td><p>Plot predictions from a fitted nonlinear mixed model obtained via an mmkin row object</p></td>
-      </tr><tr><td>
-          <p><code><a href="summary.nlme.mmkin.html">summary(<i>&lt;nlme.mmkin&gt;</i>)</a></code> <code><a href="summary.nlme.mmkin.html">print(<i>&lt;summary.nlme.mmkin&gt;</i>)</a></code> </p>
-        </td>
-        <td><p>Summary method for class "nlme.mmkin"</p></td>
-      </tr><tr><td>
-          <p><code><a href="summary.saem.mmkin.html">summary(<i>&lt;saem.mmkin&gt;</i>)</a></code> <code><a href="summary.saem.mmkin.html">print(<i>&lt;summary.saem.mmkin&gt;</i>)</a></code> </p>
-        </td>
-        <td><p>Summary method for class "saem.mmkin"</p></td>
-      </tr><tr><td>
-          <p><code><a href="anova.saem.mmkin.html">anova(<i>&lt;saem.mmkin&gt;</i>)</a></code> </p>
-        </td>
-        <td><p>Anova method for saem.mmkin objects</p></td>
-      </tr><tr><td>
-          <p><code><a href="logLik.saem.mmkin.html">logLik(<i>&lt;saem.mmkin&gt;</i>)</a></code> </p>
-        </td>
-        <td><p>logLik method for saem.mmkin objects</p></td>
-      </tr><tr><td>
-          <p><code><a href="nlme.html">nlme_function()</a></code> <code><a href="nlme.html">nlme_data()</a></code> </p>
-        </td>
-        <td><p>Helper functions to create nlme models from mmkin row objects</p></td>
-      </tr><tr><td>
-          <p><code><a href="get_deg_func.html">get_deg_func()</a></code> </p>
-        </td>
-        <td><p>Retrieve a degradation function from the mmkin namespace</p></td>
-      </tr><tr><td>
-          <p><code><a href="mixed.html">mixed()</a></code> <code><a href="mixed.html">print(<i>&lt;mixed.mmkin&gt;</i>)</a></code> </p>
-        </td>
-        <td><p>Create a mixed effects model from an mmkin row object</p></td>
-      </tr><tr><td>
-          <p><code><a href="reexports.html">reexports</a></code> <code><a href="reexports.html">intervals</a></code> <code><a href="reexports.html">lrtest</a></code> <code><a href="reexports.html">nlme</a></code> </p>
-        </td>
-        <td><p>Objects exported from other packages</p></td>
-      </tr><tr><td>
-          <p><code><a href="intervals.saem.mmkin.html">intervals(<i>&lt;saem.mmkin&gt;</i>)</a></code> </p>
-        </td>
-        <td><p>Confidence intervals for parameters in saem.mmkin objects</p></td>
-      </tr><tr><td>
-          <p><code><a href="multistart.html">multistart()</a></code> <code><a href="multistart.html">print(<i>&lt;multistart&gt;</i>)</a></code> <code><a href="multistart.html">best()</a></code> <code><a href="multistart.html">which.best()</a></code> </p>
-        </td>
-        <td><p>Perform a hierarchical model fit with multiple starting values</p></td>
-      </tr><tr><td>
-          <p><code><a href="llhist.html">llhist()</a></code> </p>
-        </td>
-        <td><p>Plot the distribution of log likelihoods from multistart objects</p></td>
-      </tr><tr><td>
-          <p><code><a href="parplot.html">parplot()</a></code> </p>
-        </td>
-        <td><p>Plot parameter variability of multistart objects</p></td>
-      </tr></tbody><tbody><tr><th colspan="2">
-          <h2 id="datasets-and-known-results">Datasets and known results <a href="#datasets-and-known-results" class="anchor" aria-hidden="true"></a></h2>
-          <p class="section-desc"></p>
-        </th>
-      </tr></tbody><tbody><tr><td>
-          <p><code><a href="ds_mixed.html">ds_mixed</a></code> <code><a href="ds_mixed.html">ds_sfo</a></code> <code><a href="ds_mixed.html">ds_fomc</a></code> <code><a href="ds_mixed.html">ds_dfop</a></code> <code><a href="ds_mixed.html">ds_hs</a></code> <code><a href="ds_mixed.html">ds_dfop_sfo</a></code> </p>
-        </td>
-        <td><p>Synthetic data for hierarchical kinetic degradation models</p></td>
-      </tr><tr><td>
-          <p><code><a href="D24_2014.html">D24_2014</a></code> </p>
-        </td>
-        <td><p>Aerobic soil degradation data on 2,4-D from the EU assessment in 2014</p></td>
-      </tr><tr><td>
-          <p><code><a href="dimethenamid_2018.html">dimethenamid_2018</a></code> </p>
-        </td>
-        <td><p>Aerobic soil degradation data on dimethenamid and dimethenamid-P from the EU assessment in 2018</p></td>
-      </tr><tr><td>
-          <p><code><a href="FOCUS_2006_datasets.html">FOCUS_2006_A</a></code> <code><a href="FOCUS_2006_datasets.html">FOCUS_2006_B</a></code> <code><a href="FOCUS_2006_datasets.html">FOCUS_2006_C</a></code> <code><a href="FOCUS_2006_datasets.html">FOCUS_2006_D</a></code> <code><a href="FOCUS_2006_datasets.html">FOCUS_2006_E</a></code> <code><a href="FOCUS_2006_datasets.html">FOCUS_2006_F</a></code> </p>
-        </td>
-        <td><p>Datasets A to F from the FOCUS Kinetics report from 2006</p></td>
-      </tr><tr><td>
-          <p><code><a href="FOCUS_2006_SFO_ref_A_to_F.html">FOCUS_2006_SFO_ref_A_to_F</a></code> </p>
-        </td>
-        <td><p>Results of fitting the SFO model to Datasets A to F of FOCUS (2006)</p></td>
-      </tr><tr><td>
-          <p><code><a href="FOCUS_2006_FOMC_ref_A_to_F.html">FOCUS_2006_FOMC_ref_A_to_F</a></code> </p>
-        </td>
-        <td><p>Results of fitting the FOMC model to Datasets A to F of FOCUS (2006)</p></td>
-      </tr><tr><td>
-          <p><code><a href="FOCUS_2006_HS_ref_A_to_F.html">FOCUS_2006_HS_ref_A_to_F</a></code> </p>
-        </td>
-        <td><p>Results of fitting the HS model to Datasets A to F of FOCUS (2006)</p></td>
-      </tr><tr><td>
-          <p><code><a href="FOCUS_2006_DFOP_ref_A_to_B.html">FOCUS_2006_DFOP_ref_A_to_B</a></code> </p>
-        </td>
-        <td><p>Results of fitting the DFOP model to Datasets A to B of FOCUS (2006)</p></td>
-      </tr><tr><td>
-          <p><code><a href="NAFTA_SOP_2015.html">NAFTA_SOP_Appendix_B</a></code> <code><a href="NAFTA_SOP_2015.html">NAFTA_SOP_Appendix_D</a></code> </p>
-        </td>
-        <td><p>Example datasets from the NAFTA SOP published 2015</p></td>
-      </tr><tr><td>
-          <p><code><a href="NAFTA_SOP_Attachment.html">NAFTA_SOP_Attachment</a></code> </p>
-        </td>
-        <td><p>Example datasets from Attachment 1 to the NAFTA SOP published 2015</p></td>
-      </tr><tr><td>
-          <p><code><a href="mccall81_245T.html">mccall81_245T</a></code> </p>
-        </td>
-        <td><p>Datasets on aerobic soil metabolism of 2,4,5-T in six soils</p></td>
-      </tr><tr><td>
-          <p><code><a href="schaefer07_complex_case.html">schaefer07_complex_case</a></code> </p>
-        </td>
-        <td><p>Metabolism data set used for checking the software quality of KinGUI</p></td>
-      </tr><tr><td>
-          <p><code><a href="synthetic_data_for_UBA_2014.html">synthetic_data_for_UBA_2014</a></code> </p>
-        </td>
-        <td><p>Synthetic datasets for one parent compound with two metabolites</p></td>
-      </tr><tr><td>
-          <p><code><a href="experimental_data_for_UBA.html">experimental_data_for_UBA_2019</a></code> </p>
-        </td>
-        <td><p>Experimental datasets used for development and testing of error models</p></td>
-      </tr><tr><td>
-          <p><code><a href="test_data_from_UBA_2014.html">test_data_from_UBA_2014</a></code> </p>
-        </td>
-        <td><p>Three experimental datasets from two water sediment systems and one soil</p></td>
-      </tr><tr><td>
-          <p><code><a href="focus_soil_moisture.html">focus_soil_moisture</a></code> </p>
-        </td>
-        <td><p>FOCUS default values for soil moisture contents at field capacity, MWHC and 1/3 bar</p></td>
-      </tr><tr><td>
-          <p><code><a href="mkinds.html">print(<i>&lt;mkinds&gt;</i>)</a></code> </p>
-        </td>
-        <td><p>A dataset class for mkin</p></td>
-      </tr><tr><td>
-          <p><code><a href="mkindsg.html">print(<i>&lt;mkindsg&gt;</i>)</a></code> </p>
-        </td>
-        <td><p>A class for dataset groups for mkin</p></td>
-      </tr></tbody><tbody><tr><th colspan="2">
-          <h2 id="nafta-guidance">NAFTA guidance <a href="#nafta-guidance" class="anchor" aria-hidden="true"></a></h2>
-          <p class="section-desc"></p>
-        </th>
-      </tr></tbody><tbody><tr><td>
-          <p><code><a href="nafta.html">nafta()</a></code> <code><a href="nafta.html">print(<i>&lt;nafta&gt;</i>)</a></code> </p>
-        </td>
-        <td><p>Evaluate parent kinetics using the NAFTA guidance</p></td>
-      </tr><tr><td>
-          <p><code><a href="plot.nafta.html">plot(<i>&lt;nafta&gt;</i>)</a></code> </p>
-        </td>
-        <td><p>Plot the results of the three models used in the NAFTA scheme.</p></td>
-      </tr></tbody><tbody><tr><th colspan="2">
-          <h2 id="utility-functions">Utility functions <a href="#utility-functions" class="anchor" aria-hidden="true"></a></h2>
-          <p class="section-desc"></p>
-        </th>
-      </tr></tbody><tbody><tr><td>
-          <p><code><a href="summary_listing.html">summary_listing()</a></code> <code><a href="summary_listing.html">tex_listing()</a></code> <code><a href="summary_listing.html">html_listing()</a></code> </p>
-        </td>
-        <td><p>Display the output of a summary function according to the output format</p></td>
-      </tr><tr><td>
-          <p><code><a href="f_time_norm_focus.html">f_time_norm_focus()</a></code> </p>
-        </td>
-        <td><p>Normalisation factors for aerobic soil degradation according to FOCUS guidance</p></td>
-      </tr><tr><td>
-          <p><code><a href="set_nd_nq.html">set_nd_nq()</a></code> <code><a href="set_nd_nq.html">set_nd_nq_focus()</a></code> </p>
-        </td>
-        <td><p>Set non-detects and unquantified values in residue series without replicates</p></td>
-      </tr><tr><td>
-          <p><code><a href="max_twa_parent.html">max_twa_parent()</a></code> <code><a href="max_twa_parent.html">max_twa_sfo()</a></code> <code><a href="max_twa_parent.html">max_twa_fomc()</a></code> <code><a href="max_twa_parent.html">max_twa_dfop()</a></code> <code><a href="max_twa_parent.html">max_twa_hs()</a></code> </p>
-        </td>
-        <td><p>Function to calculate maximum time weighted average concentrations from
-kinetic models fitted with mkinfit</p></td>
-      </tr><tr><td>
-          <p><code><a href="mkin_wide_to_long.html">mkin_wide_to_long()</a></code> </p>
-        </td>
-        <td><p>Convert a dataframe with observations over time into long format</p></td>
-      </tr><tr><td>
-          <p><code><a href="mkin_long_to_wide.html">mkin_long_to_wide()</a></code> </p>
-        </td>
-        <td><p>Convert a dataframe from long to wide format</p></td>
-      </tr></tbody><tbody><tr><th colspan="2">
-          <h2 id="helper-functions-mainly-used-internally">Helper functions mainly used internally <a href="#helper-functions-mainly-used-internally" class="anchor" aria-hidden="true"></a></h2>
-          <p class="section-desc"></p>
-        </th>
-      </tr></tbody><tbody><tr><td>
-          <p><code><a href="mkinpredict.html">mkinpredict()</a></code> </p>
-        </td>
-        <td><p>Produce predictions from a kinetic model using specific parameters</p></td>
-      </tr><tr><td>
-          <p><code><a href="transform_odeparms.html">transform_odeparms()</a></code> <code><a href="transform_odeparms.html">backtransform_odeparms()</a></code> </p>
-        </td>
-        <td><p>Functions to transform and backtransform kinetic parameters for fitting</p></td>
-      </tr><tr><td>
-          <p><code><a href="ilr.html">ilr()</a></code> <code><a href="ilr.html">invilr()</a></code> </p>
-        </td>
-        <td><p>Function to perform isometric log-ratio transformation</p></td>
-      </tr><tr><td>
-          <p><code><a href="logLik.mkinfit.html">logLik(<i>&lt;mkinfit&gt;</i>)</a></code> </p>
-        </td>
-        <td><p>Calculated the log-likelihood of a fitted mkinfit object</p></td>
-      </tr><tr><td>
-          <p><code><a href="residuals.mkinfit.html">residuals(<i>&lt;mkinfit&gt;</i>)</a></code> </p>
-        </td>
-        <td><p>Extract residuals from an mkinfit model</p></td>
-      </tr><tr><td>
-          <p><code><a href="nobs.mkinfit.html">nobs(<i>&lt;mkinfit&gt;</i>)</a></code> </p>
-        </td>
-        <td><p>Number of observations on which an mkinfit object was fitted</p></td>
-      </tr><tr><td>
-          <p><code><a href="mkinresplot.html">mkinresplot()</a></code> </p>
-        </td>
-        <td><p>Function to plot residuals stored in an mkin object</p></td>
-      </tr><tr><td>
-          <p><code><a href="mkinparplot.html">mkinparplot()</a></code> </p>
-        </td>
-        <td><p>Function to plot the confidence intervals obtained using mkinfit</p></td>
-      </tr><tr><td>
-          <p><code><a href="mkinerrplot.html">mkinerrplot()</a></code> </p>
-        </td>
-        <td><p>Function to plot squared residuals and the error model for an mkin object</p></td>
-      </tr><tr><td>
-          <p><code><a href="mean_degparms.html">mean_degparms()</a></code> </p>
-        </td>
-        <td><p>Calculate mean degradation parameters for an mmkin row object</p></td>
-      </tr><tr><td>
-          <p><code><a href="create_deg_func.html">create_deg_func()</a></code> </p>
-        </td>
-        <td><p>Create degradation functions for known analytical solutions</p></td>
-      </tr></tbody><tbody><tr><th colspan="2">
-          <h2 id="analytical-solutions">Analytical solutions <a href="#analytical-solutions" class="anchor" aria-hidden="true"></a></h2>
-          <p class="section-desc"></p><p>Parent only model solutions</p>
-        </th>
-      </tr></tbody><tbody><tr><td>
-          <p><code><a href="SFO.solution.html">SFO.solution()</a></code> </p>
-        </td>
-        <td><p>Single First-Order kinetics</p></td>
-      </tr><tr><td>
-          <p><code><a href="FOMC.solution.html">FOMC.solution()</a></code> </p>
-        </td>
-        <td><p>First-Order Multi-Compartment kinetics</p></td>
-      </tr><tr><td>
-          <p><code><a href="DFOP.solution.html">DFOP.solution()</a></code> </p>
-        </td>
-        <td><p>Double First-Order in Parallel kinetics</p></td>
-      </tr><tr><td>
-          <p><code><a href="SFORB.solution.html">SFORB.solution()</a></code> </p>
-        </td>
-        <td><p>Single First-Order Reversible Binding kinetics</p></td>
-      </tr><tr><td>
-          <p><code><a href="HS.solution.html">HS.solution()</a></code> </p>
-        </td>
-        <td><p>Hockey-Stick kinetics</p></td>
-      </tr><tr><td>
-          <p><code><a href="IORE.solution.html">IORE.solution()</a></code> </p>
-        </td>
-        <td><p>Indeterminate order rate equation kinetics</p></td>
-      </tr><tr><td>
-          <p><code><a href="logistic.solution.html">logistic.solution()</a></code> </p>
-        </td>
-        <td><p>Logistic kinetics</p></td>
-      </tr></tbody><tbody><tr><th colspan="2">
-          <h2 id="generate-synthetic-datasets">Generate synthetic datasets <a href="#generate-synthetic-datasets" class="anchor" aria-hidden="true"></a></h2>
-          <p class="section-desc"></p>
-        </th>
-      </tr></tbody><tbody><tr><td>
-          <p><code><a href="add_err.html">add_err()</a></code> </p>
-        </td>
-        <td><p>Add normally distributed errors to simulated kinetic degradation data</p></td>
-      </tr><tr><td>
-          <p><code><a href="sigma_twocomp.html">sigma_twocomp()</a></code> </p>
-        </td>
-        <td><p>Two-component error model</p></td>
-      </tr></tbody><tbody><tr><th colspan="2">
-          <h2 id="deprecated-functions">Deprecated functions <a href="#deprecated-functions" class="anchor" aria-hidden="true"></a></h2>
-          <p class="section-desc"></p><p>Functions that have been superseded</p>
-        </th>
-      </tr></tbody><tbody><tr><td>
-          <p><code><a href="mkinplot.html">mkinplot()</a></code> </p>
-        </td>
-        <td><p>Plot the observed data and the fitted model of an mkinfit object</p></td>
-      </tr></tbody></table></div>
-
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/intervals.nlmixr.mmkin.html b/docs/dev/reference/intervals.nlmixr.mmkin.html
deleted file mode 100644
index 14f11c0c..00000000
--- a/docs/dev/reference/intervals.nlmixr.mmkin.html
+++ /dev/null
@@ -1,132 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Confidence intervals for parameters in nlmixr.mmkin objects — intervals.nlmixr.mmkin • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Confidence intervals for parameters in nlmixr.mmkin objects — intervals.nlmixr.mmkin"><meta property="og:description" content="Confidence intervals for parameters in nlmixr.mmkin objects"><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.1.0</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Functions and data</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Confidence intervals for parameters in nlmixr.mmkin objects</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/intervals.R" class="external-link"><code>R/intervals.R</code></a></small>
-    <div class="hidden name"><code>intervals.nlmixr.mmkin.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Confidence intervals for parameters in nlmixr.mmkin objects</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="co"># S3 method for nlmixr.mmkin</span>
-<span class="fu"><a href="https://rdrr.io/pkg/nlme/man/intervals.html" class="external-link">intervals</a></span><span class="op">(</span><span class="va">object</span>, level <span class="op">=</span> <span class="fl">0.95</span>, backtransform <span class="op">=</span> <span class="cn">TRUE</span>, <span class="va">...</span><span class="op">)</span></code></pre></div>
-    </div>
-
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>object</dt>
-<dd><p>The fitted saem.mmkin object</p></dd>
-<dt>level</dt>
-<dd><p>The confidence level.</p></dd>
-<dt>backtransform</dt>
-<dd><p>Should we backtransform the parameters where a one to
-one correlation between transformed and backtransformed parameters exists?</p></dd>
-<dt>...</dt>
-<dd><p>For compatibility with the generic method</p></dd>
-</dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    <p>An object with 'intervals.saem.mmkin' and 'intervals.lme' in the
-class attribute</p>
-    </div>
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.2.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/intervals.saem.mmkin.html b/docs/dev/reference/intervals.saem.mmkin.html
deleted file mode 100644
index a566ee8d..00000000
--- a/docs/dev/reference/intervals.saem.mmkin.html
+++ /dev/null
@@ -1,169 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Confidence intervals for parameters in saem.mmkin objects — intervals.saem.mmkin • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Confidence intervals for parameters in saem.mmkin objects — intervals.saem.mmkin"><meta property="og:description" content="Confidence intervals for parameters in saem.mmkin objects"><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Confidence intervals for parameters in saem.mmkin objects</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/intervals.R" class="external-link"><code>R/intervals.R</code></a></small>
-    <div class="hidden name"><code>intervals.saem.mmkin.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Confidence intervals for parameters in saem.mmkin objects</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for saem.mmkin</span></span>
-<span><span class="fu"><a href="https://rdrr.io/pkg/nlme/man/intervals.html" class="external-link">intervals</a></span><span class="op">(</span><span class="va">object</span>, level <span class="op">=</span> <span class="fl">0.95</span>, backtransform <span class="op">=</span> <span class="cn">TRUE</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
-    </div>
-
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>object</dt>
-<dd><p>The fitted saem.mmkin object</p></dd>
-
-
-<dt>level</dt>
-<dd><p>The confidence level. Must be the default of 0.95 as this is what
-is available in the saemix object</p></dd>
-
-
-<dt>backtransform</dt>
-<dd><p>In case the model was fitted with mkin transformations,
-should we backtransform the parameters where a one to one correlation
-between transformed and backtransformed parameters exists?</p></dd>
-
-
-<dt>...</dt>
-<dd><p>For compatibility with the generic method</p></dd>
-
-</dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>An object with 'intervals.saem.mmkin' and 'intervals.lme' in the
-class attribute</p>
-    </div>
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/llhist.html b/docs/dev/reference/llhist.html
deleted file mode 100644
index 2b5afa25..00000000
--- a/docs/dev/reference/llhist.html
+++ /dev/null
@@ -1,168 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Plot the distribution of log likelihoods from multistart objects — llhist • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Plot the distribution of log likelihoods from multistart objects — llhist"><meta property="og:description" content="Produces a histogram of log-likelihoods. In addition, the likelihood of the
-original fit is shown as a red vertical line."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Plot the distribution of log likelihoods from multistart objects</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/llhist.R" class="external-link"><code>R/llhist.R</code></a></small>
-    <div class="hidden name"><code>llhist.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Produces a histogram of log-likelihoods. In addition, the likelihood of the
-original fit is shown as a red vertical line.</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">llhist</span><span class="op">(</span><span class="va">object</span>, breaks <span class="op">=</span> <span class="st">"Sturges"</span>, lpos <span class="op">=</span> <span class="st">"topleft"</span>, main <span class="op">=</span> <span class="st">""</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
-    </div>
-
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>object</dt>
-<dd><p>The <a href="multistart.html">multistart</a> object</p></dd>
-
-
-<dt>breaks</dt>
-<dd><p>Passed to <a href="https://rdrr.io/r/graphics/hist.html" class="external-link">hist</a></p></dd>
-
-
-<dt>lpos</dt>
-<dd><p>Positioning of the legend.</p></dd>
-
-
-<dt>main</dt>
-<dd><p>Title of the plot</p></dd>
-
-
-<dt>...</dt>
-<dd><p>Passed to <a href="https://rdrr.io/r/graphics/hist.html" class="external-link">hist</a></p></dd>
-
-</dl></div>
-    <div id="see-also">
-    <h2>See also</h2>
-    <div class="dont-index"><p><a href="multistart.html">multistart</a></p></div>
-    </div>
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/loftest-1.png b/docs/dev/reference/loftest-1.png
deleted file mode 100644
index 750a2acf..00000000
--- a/docs/dev/reference/loftest-1.png
+++ /dev/null
diff --git a/docs/dev/reference/loftest-2.png b/docs/dev/reference/loftest-2.png
deleted file mode 100644
index d12c26e7..00000000
--- a/docs/dev/reference/loftest-2.png
+++ /dev/null
diff --git a/docs/dev/reference/loftest-3.png b/docs/dev/reference/loftest-3.png
deleted file mode 100644
index 9f45c74d..00000000
--- a/docs/dev/reference/loftest-3.png
+++ /dev/null
diff --git a/docs/dev/reference/loftest-4.png b/docs/dev/reference/loftest-4.png
deleted file mode 100644
index 3beb9d1a..00000000
--- a/docs/dev/reference/loftest-4.png
+++ /dev/null
diff --git a/docs/dev/reference/loftest-5.png b/docs/dev/reference/loftest-5.png
deleted file mode 100644
index 1a3aaeea..00000000
--- a/docs/dev/reference/loftest-5.png
+++ /dev/null
diff --git a/docs/dev/reference/loftest.html b/docs/dev/reference/loftest.html
deleted file mode 100644
index 871dfc88..00000000
--- a/docs/dev/reference/loftest.html
+++ /dev/null
@@ -1,342 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Lack-of-fit test for models fitted to data with replicates — loftest • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Lack-of-fit test for models fitted to data with replicates — loftest"><meta property="og:description" content="This is a generic function with a method currently only defined for mkinfit
-objects. It fits an anova model to the data contained in the object and
-compares the likelihoods using the likelihood ratio test
-lrtest.default from the lmtest package."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Lack-of-fit test for models fitted to data with replicates</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/loftest.R" class="external-link"><code>R/loftest.R</code></a></small>
-    <div class="hidden name"><code>loftest.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>This is a generic function with a method currently only defined for mkinfit
-objects. It fits an anova model to the data contained in the object and
-compares the likelihoods using the likelihood ratio test
-<code><a href="https://rdrr.io/pkg/lmtest/man/lrtest.html" class="external-link">lrtest.default</a></code> from the lmtest package.</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">loftest</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># S3 method for mkinfit</span></span>
-<span><span class="fu">loftest</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
-    </div>
-
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>object</dt>
-<dd><p>A model object with a defined loftest method</p></dd>
-
-
-<dt>...</dt>
-<dd><p>Not used</p></dd>
-
-</dl></div>
-    <div id="details">
-    <h2>Details</h2>
-    <p>The anova model is interpreted as the simplest form of an mkinfit model,
-assuming only a constant variance about the means, but not enforcing any
-structure of the means, so we have one model parameter for every mean
-of replicate samples.</p>
-    </div>
-    <div id="see-also">
-    <h2>See also</h2>
-    <div class="dont-index"><p>lrtest</p></div>
-    </div>
-
-    <div id="ref-examples">
-    <h2>Examples</h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># \dontrun{</span></span></span>
-<span class="r-in"><span><span class="va">test_data</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/subset.html" class="external-link">subset</a></span><span class="op">(</span><span class="va">synthetic_data_for_UBA_2014</span><span class="op">[[</span><span class="fl">12</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">data</span>, <span class="va">name</span> <span class="op">==</span> <span class="st">"parent"</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">sfo_fit</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="va">test_data</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="plot.mkinfit.html">plot_res</a></span><span class="op">(</span><span class="va">sfo_fit</span><span class="op">)</span> <span class="co"># We see a clear pattern in the residuals</span></span></span>
-<span class="r-plt img"><img src="loftest-1.png" alt="" width="700" height="433"></span>
-<span class="r-in"><span><span class="fu">loftest</span><span class="op">(</span><span class="va">sfo_fit</span><span class="op">)</span>  <span class="co"># We have a clear lack of fit</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Likelihood ratio test</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Model 1: ANOVA with error model const</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Model 2: SFO with error model const</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   #Df  LogLik Df  Chisq Pr(&gt;Chisq)    </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1  10 -40.710                         </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2   3 -63.954 -7 46.487  7.027e-08 ***</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> ---</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1</span>
-<span class="r-in"><span><span class="co">#</span></span></span>
-<span class="r-in"><span><span class="co"># We try a different model (the one that was used to generate the data)</span></span></span>
-<span class="r-in"><span><span class="va">dfop_fit</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="va">test_data</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="plot.mkinfit.html">plot_res</a></span><span class="op">(</span><span class="va">dfop_fit</span><span class="op">)</span> <span class="co"># We don't see systematic deviations, but heteroscedastic residuals</span></span></span>
-<span class="r-plt img"><img src="loftest-2.png" alt="" width="700" height="433"></span>
-<span class="r-in"><span><span class="co"># therefore we should consider adapting the error model, although we have</span></span></span>
-<span class="r-in"><span><span class="fu">loftest</span><span class="op">(</span><span class="va">dfop_fit</span><span class="op">)</span> <span class="co"># no lack of fit</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Likelihood ratio test</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Model 1: ANOVA with error model const</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Model 2: DFOP with error model const</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   #Df  LogLik Df Chisq Pr(&gt;Chisq)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1  10 -40.710                    </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2   5 -42.453 -5 3.485     0.6257</span>
-<span class="r-in"><span><span class="co">#</span></span></span>
-<span class="r-in"><span><span class="co"># This is the anova model used internally for the comparison</span></span></span>
-<span class="r-in"><span><span class="va">test_data_anova</span> <span class="op">&lt;-</span> <span class="va">test_data</span></span></span>
-<span class="r-in"><span><span class="va">test_data_anova</span><span class="op">$</span><span class="va">time</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/factor.html" class="external-link">as.factor</a></span><span class="op">(</span><span class="va">test_data_anova</span><span class="op">$</span><span class="va">time</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">anova_fit</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/lm.html" class="external-link">lm</a></span><span class="op">(</span><span class="va">value</span> <span class="op">~</span> <span class="va">time</span>, data <span class="op">=</span> <span class="va">test_data_anova</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">anova_fit</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Call:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> lm(formula = value ~ time, data = test_data_anova)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Residuals:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     Min      1Q  Median      3Q     Max </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -6.1000 -0.5625  0.0000  0.5625  6.1000 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Coefficients:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>             Estimate Std. Error t value Pr(&gt;|t|)    </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> (Intercept)  103.150      2.323  44.409 7.44e-12 ***</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> time1        -19.950      3.285  -6.073 0.000185 ***</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> time3        -50.800      3.285 -15.465 8.65e-08 ***</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> time7        -68.500      3.285 -20.854 6.28e-09 ***</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> time14       -79.750      3.285 -24.278 1.63e-09 ***</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> time28       -86.000      3.285 -26.181 8.35e-10 ***</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> time60       -94.900      3.285 -28.891 3.48e-10 ***</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> time90       -98.500      3.285 -29.986 2.49e-10 ***</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> time120     -100.450      3.285 -30.580 2.09e-10 ***</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> ---</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Residual standard error: 3.285 on 9 degrees of freedom</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Multiple R-squared:  0.9953,	Adjusted R-squared:  0.9912 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> F-statistic: 240.5 on 8 and 9 DF,  p-value: 1.417e-09</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/logLik.html" class="external-link">logLik</a></span><span class="op">(</span><span class="va">anova_fit</span><span class="op">)</span> <span class="co"># We get the same likelihood and degrees of freedom</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 'log Lik.' -40.71015 (df=10)</span>
-<span class="r-in"><span><span class="co">#</span></span></span>
-<span class="r-in"><span><span class="va">test_data_2</span> <span class="op">&lt;-</span> <span class="va">synthetic_data_for_UBA_2014</span><span class="op">[[</span><span class="fl">12</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">data</span></span></span>
-<span class="r-in"><span><span class="va">m_synth_SFO_lin</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"M1"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  M1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"M2"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  M2 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span>, use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Temporary DLL for differentials generated and loaded</span>
-<span class="r-in"><span><span class="va">sfo_lin_fit</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">m_synth_SFO_lin</span>, <span class="va">test_data_2</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="plot.mkinfit.html">plot_res</a></span><span class="op">(</span><span class="va">sfo_lin_fit</span><span class="op">)</span> <span class="co"># not a good model, we try parallel formation</span></span></span>
-<span class="r-plt img"><img src="loftest-3.png" alt="" width="700" height="433"></span>
-<span class="r-in"><span><span class="fu">loftest</span><span class="op">(</span><span class="va">sfo_lin_fit</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Likelihood ratio test</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Model 1: ANOVA with error model const</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Model 2: m_synth_SFO_lin with error model const and fixed parameter(s) M1_0, M2_0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   #Df   LogLik  Df  Chisq Pr(&gt;Chisq)    </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1  28  -93.606                          </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2   7 -171.927 -21 156.64  &lt; 2.2e-16 ***</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> ---</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1</span>
-<span class="r-in"><span><span class="co">#</span></span></span>
-<span class="r-in"><span><span class="va">m_synth_SFO_par</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span>, to <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"M1"</span>, <span class="st">"M2"</span><span class="op">)</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  M1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  M2 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span>, use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Temporary DLL for differentials generated and loaded</span>
-<span class="r-in"><span><span class="va">sfo_par_fit</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">m_synth_SFO_par</span>, <span class="va">test_data_2</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="plot.mkinfit.html">plot_res</a></span><span class="op">(</span><span class="va">sfo_par_fit</span><span class="op">)</span> <span class="co"># much better for metabolites</span></span></span>
-<span class="r-plt img"><img src="loftest-4.png" alt="" width="700" height="433"></span>
-<span class="r-in"><span><span class="fu">loftest</span><span class="op">(</span><span class="va">sfo_par_fit</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Likelihood ratio test</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Model 1: ANOVA with error model const</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Model 2: m_synth_SFO_par with error model const and fixed parameter(s) M1_0, M2_0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   #Df   LogLik  Df  Chisq Pr(&gt;Chisq)    </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1  28  -93.606                          </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2   7 -156.331 -21 125.45  &lt; 2.2e-16 ***</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> ---</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1</span>
-<span class="r-in"><span><span class="co">#</span></span></span>
-<span class="r-in"><span><span class="va">m_synth_DFOP_par</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"DFOP"</span>, to <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"M1"</span>, <span class="st">"M2"</span><span class="op">)</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  M1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  M2 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span>, use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Temporary DLL for differentials generated and loaded</span>
-<span class="r-in"><span><span class="va">dfop_par_fit</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">m_synth_DFOP_par</span>, <span class="va">test_data_2</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="plot.mkinfit.html">plot_res</a></span><span class="op">(</span><span class="va">dfop_par_fit</span><span class="op">)</span> <span class="co"># No visual lack of fit</span></span></span>
-<span class="r-plt img"><img src="loftest-5.png" alt="" width="700" height="433"></span>
-<span class="r-in"><span><span class="fu">loftest</span><span class="op">(</span><span class="va">dfop_par_fit</span><span class="op">)</span>  <span class="co"># no lack of fit found by the test</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Likelihood ratio test</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Model 1: ANOVA with error model const</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Model 2: m_synth_DFOP_par with error model const and fixed parameter(s) M1_0, M2_0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   #Df   LogLik  Df  Chisq Pr(&gt;Chisq)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1  28  -93.606                      </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2   9 -102.763 -19 18.313     0.5016</span>
-<span class="r-in"><span><span class="co">#</span></span></span>
-<span class="r-in"><span><span class="co"># The anova model used for comparison in the case of transformation products</span></span></span>
-<span class="r-in"><span><span class="va">test_data_anova_2</span> <span class="op">&lt;-</span> <span class="va">dfop_par_fit</span><span class="op">$</span><span class="va">data</span></span></span>
-<span class="r-in"><span><span class="va">test_data_anova_2</span><span class="op">$</span><span class="va">variable</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/factor.html" class="external-link">as.factor</a></span><span class="op">(</span><span class="va">test_data_anova_2</span><span class="op">$</span><span class="va">variable</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">test_data_anova_2</span><span class="op">$</span><span class="va">time</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/factor.html" class="external-link">as.factor</a></span><span class="op">(</span><span class="va">test_data_anova_2</span><span class="op">$</span><span class="va">time</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">anova_fit_2</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/lm.html" class="external-link">lm</a></span><span class="op">(</span><span class="va">observed</span> <span class="op">~</span> <span class="va">time</span><span class="op">:</span><span class="va">variable</span> <span class="op">-</span> <span class="fl">1</span>, data <span class="op">=</span> <span class="va">test_data_anova_2</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">anova_fit_2</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Call:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> lm(formula = observed ~ time:variable - 1, data = test_data_anova_2)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Residuals:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     Min      1Q  Median      3Q     Max </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -6.1000 -0.5875  0.0000  0.5875  6.1000 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Coefficients: (2 not defined because of singularities)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                        Estimate Std. Error t value Pr(&gt;|t|)    </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> time0:variableparent    103.150      1.573  65.562  &lt; 2e-16 ***</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> time1:variableparent     83.200      1.573  52.882  &lt; 2e-16 ***</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> time3:variableparent     52.350      1.573  33.274  &lt; 2e-16 ***</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> time7:variableparent     34.650      1.573  22.024  &lt; 2e-16 ***</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> time14:variableparent    23.400      1.573  14.873 6.35e-14 ***</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> time28:variableparent    17.150      1.573  10.901 5.47e-11 ***</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> time60:variableparent     8.250      1.573   5.244 1.99e-05 ***</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> time90:variableparent     4.650      1.573   2.956 0.006717 ** </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> time120:variableparent    2.700      1.573   1.716 0.098507 .  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> time0:variableM1             NA         NA      NA       NA    </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> time1:variableM1         11.850      1.573   7.532 6.93e-08 ***</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> time3:variableM1         22.700      1.573  14.428 1.26e-13 ***</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> time7:variableM1         33.050      1.573  21.007  &lt; 2e-16 ***</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> time14:variableM1        31.250      1.573  19.863  &lt; 2e-16 ***</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> time28:variableM1        18.900      1.573  12.013 7.02e-12 ***</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> time60:variableM1         7.550      1.573   4.799 6.28e-05 ***</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> time90:variableM1         3.850      1.573   2.447 0.021772 *  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> time120:variableM1        2.050      1.573   1.303 0.204454    </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> time0:variableM2             NA         NA      NA       NA    </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> time1:variableM2          6.700      1.573   4.259 0.000254 ***</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> time3:variableM2         16.750      1.573  10.646 8.93e-11 ***</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> time7:variableM2         25.800      1.573  16.399 6.89e-15 ***</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> time14:variableM2        28.600      1.573  18.178 6.35e-16 ***</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> time28:variableM2        25.400      1.573  16.144 9.85e-15 ***</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> time60:variableM2        21.600      1.573  13.729 3.81e-13 ***</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> time90:variableM2        17.800      1.573  11.314 2.51e-11 ***</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> time120:variableM2       14.100      1.573   8.962 2.79e-09 ***</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> ---</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Residual standard error: 2.225 on 25 degrees of freedom</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Multiple R-squared:  0.9979,	Adjusted R-squared:  0.9957 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> F-statistic: 469.2 on 25 and 25 DF,  p-value: &lt; 2.2e-16</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-in"><span><span class="co"># }</span></span></span>
-</code></pre></div>
-    </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/logLik.mkinfit.html b/docs/dev/reference/logLik.mkinfit.html
deleted file mode 100644
index e51a52eb..00000000
--- a/docs/dev/reference/logLik.mkinfit.html
+++ /dev/null
@@ -1,204 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Calculated the log-likelihood of a fitted mkinfit object — logLik.mkinfit • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Calculated the log-likelihood of a fitted mkinfit object — logLik.mkinfit"><meta property="og:description" content="This function returns the product of the likelihood densities of each
-observed value, as calculated as part of the fitting procedure using
-dnorm, i.e. assuming normal distribution, and with the means
-predicted by the degradation model, and the standard deviations predicted by
-the error model."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Calculated the log-likelihood of a fitted mkinfit object</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/logLik.mkinfit.R" class="external-link"><code>R/logLik.mkinfit.R</code></a></small>
-    <div class="hidden name"><code>logLik.mkinfit.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>This function returns the product of the likelihood densities of each
-observed value, as calculated as part of the fitting procedure using
-<code><a href="https://rdrr.io/r/stats/Normal.html" class="external-link">dnorm</a></code>, i.e. assuming normal distribution, and with the means
-predicted by the degradation model, and the standard deviations predicted by
-the error model.</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for mkinfit</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/stats/logLik.html" class="external-link">logLik</a></span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
-    </div>
-
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>object</dt>
-<dd><p>An object of class <code><a href="mkinfit.html">mkinfit</a></code>.</p></dd>
-
-
-<dt>...</dt>
-<dd><p>For compatibility with the generic method</p></dd>
-
-</dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>An object of class <code><a href="https://rdrr.io/r/stats/logLik.html" class="external-link">logLik</a></code> with the number of estimated
-parameters (degradation model parameters plus variance model parameters)
-as attribute.</p>
-    </div>
-    <div id="details">
-    <h2>Details</h2>
-    <p>The total number of estimated parameters returned with the value of the
-likelihood is calculated as the sum of fitted degradation model parameters
-and the fitted error model parameters.</p>
-    </div>
-    <div id="see-also">
-    <h2>See also</h2>
-    <div class="dont-index"><p>Compare the AIC of columns of <code><a href="mmkin.html">mmkin</a></code> objects using
-<code><a href="AIC.mmkin.html">AIC.mmkin</a></code>.</p></div>
-    </div>
-    <div id="author">
-    <h2>Author</h2>
-    <p>Johannes Ranke</p>
-    </div>
-
-    <div id="ref-examples">
-    <h2>Examples</h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span>
-<span class="r-in"><span>  <span class="co"># \dontrun{</span></span></span>
-<span class="r-in"><span>  <span class="va">sfo_sfo</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span></span></span>
-<span class="r-in"><span>    parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"m1"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>    m1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span></span></span>
-<span class="r-in"><span>  <span class="op">)</span></span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Temporary DLL for differentials generated and loaded</span>
-<span class="r-in"><span>  <span class="va">d_t</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/subset.html" class="external-link">subset</a></span><span class="op">(</span><span class="va">FOCUS_2006_D</span>, <span class="va">value</span> <span class="op">!=</span> <span class="fl">0</span><span class="op">)</span></span></span>
-<span class="r-in"><span>  <span class="va">f_nw</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">sfo_sfo</span>, <span class="va">d_t</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span> <span class="co"># no weighting (weights are unity)</span></span></span>
-<span class="r-in"><span>  <span class="va">f_obs</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_nw</span>, error_model <span class="op">=</span> <span class="st">"obs"</span><span class="op">)</span></span></span>
-<span class="r-in"><span>  <span class="va">f_tc</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_nw</span>, error_model <span class="op">=</span> <span class="st">"tc"</span><span class="op">)</span></span></span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">AIC</a></span><span class="op">(</span><span class="va">f_nw</span>, <span class="va">f_obs</span>, <span class="va">f_tc</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>       df      AIC</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_nw   5 204.4486</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_obs  6 205.8727</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_tc   6 141.9656</span>
-<span class="r-in"><span>  <span class="co"># }</span></span></span>
-<span class="r-in"><span></span></span>
-</code></pre></div>
-    </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/logLik.saem.mmkin.html b/docs/dev/reference/logLik.saem.mmkin.html
deleted file mode 100644
index 6d0102c1..00000000
--- a/docs/dev/reference/logLik.saem.mmkin.html
+++ /dev/null
@@ -1,155 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>logLik method for saem.mmkin objects — logLik.saem.mmkin • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="logLik method for saem.mmkin objects — logLik.saem.mmkin"><meta property="og:description" content="logLik method for saem.mmkin objects"><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>logLik method for saem.mmkin objects</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/saem.R" class="external-link"><code>R/saem.R</code></a></small>
-    <div class="hidden name"><code>logLik.saem.mmkin.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>logLik method for saem.mmkin objects</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for saem.mmkin</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/stats/logLik.html" class="external-link">logLik</a></span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span>, method <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"is"</span>, <span class="st">"lin"</span>, <span class="st">"gq"</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
-    </div>
-
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>object</dt>
-<dd><p>The fitted <a href="saem.html">saem.mmkin</a> object</p></dd>
-
-
-<dt>...</dt>
-<dd><p>Passed to <a href="https://rdrr.io/pkg/saemix/man/logLik.html" class="external-link">saemix::logLik.SaemixObject</a></p></dd>
-
-
-<dt>method</dt>
-<dd><p>Passed to <a href="https://rdrr.io/pkg/saemix/man/logLik.html" class="external-link">saemix::logLik.SaemixObject</a></p></dd>
-
-</dl></div>
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/logistic.solution-1.png b/docs/dev/reference/logistic.solution-1.png
deleted file mode 100644
index 23c031de..00000000
--- a/docs/dev/reference/logistic.solution-1.png
+++ /dev/null
diff --git a/docs/dev/reference/logistic.solution-2.png b/docs/dev/reference/logistic.solution-2.png
deleted file mode 100644
index b56db0cb..00000000
--- a/docs/dev/reference/logistic.solution-2.png
+++ /dev/null
diff --git a/docs/dev/reference/logistic.solution.html b/docs/dev/reference/logistic.solution.html
deleted file mode 100644
index 9cfebf03..00000000
--- a/docs/dev/reference/logistic.solution.html
+++ /dev/null
@@ -1,253 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Logistic kinetics — logistic.solution • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Logistic kinetics — logistic.solution"><meta property="og:description" content="Function describing exponential decline from a defined starting value, with
-an increasing rate constant, supposedly caused by microbial growth"><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Logistic kinetics</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/parent_solutions.R" class="external-link"><code>R/parent_solutions.R</code></a></small>
-    <div class="hidden name"><code>logistic.solution.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Function describing exponential decline from a defined starting value, with
-an increasing rate constant, supposedly caused by microbial growth</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">logistic.solution</span><span class="op">(</span><span class="va">t</span>, <span class="va">parent_0</span>, <span class="va">kmax</span>, <span class="va">k0</span>, <span class="va">r</span><span class="op">)</span></span></code></pre></div>
-    </div>
-
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>t</dt>
-<dd><p>Time.</p></dd>
-
-
-<dt>parent_0</dt>
-<dd><p>Starting value for the response variable at time zero.</p></dd>
-
-
-<dt>kmax</dt>
-<dd><p>Maximum rate constant.</p></dd>
-
-
-<dt>k0</dt>
-<dd><p>Minimum rate constant effective at time zero.</p></dd>
-
-
-<dt>r</dt>
-<dd><p>Growth rate of the increase in the rate constant.</p></dd>
-
-</dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>The value of the response variable at time <code>t</code>.</p>
-    </div>
-    <div id="note">
-    <h2>Note</h2>
-    <p>The solution of the logistic model reduces to the
-<code><a href="SFO.solution.html">SFO.solution</a></code> if <code>k0</code> is equal to <code>kmax</code>.</p>
-    </div>
-    <div id="references">
-    <h2>References</h2>
-    <p>FOCUS (2006) “Guidance Document on Estimating Persistence
-and Degradation Kinetics from Environmental Fate Studies on Pesticides in
-EU Registration” Report of the FOCUS Work Group on Degradation Kinetics,
-EC Document Reference Sanco/10058/2005 version 2.0, 434 pp,
-<a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics" class="external-link">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a>
-FOCUS (2014) “Generic guidance for Estimating Persistence
-and Degradation Kinetics from Environmental Fate Studies on Pesticides in
-EU Registration” Report of the FOCUS Work Group on Degradation Kinetics,
-Version 1.1, 18 December 2014
-<a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics" class="external-link">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p>
-    </div>
-    <div id="see-also">
-    <h2>See also</h2>
-    <div class="dont-index"><p>Other parent solutions: 
-<code><a href="DFOP.solution.html">DFOP.solution</a>()</code>,
-<code><a href="FOMC.solution.html">FOMC.solution</a>()</code>,
-<code><a href="HS.solution.html">HS.solution</a>()</code>,
-<code><a href="IORE.solution.html">IORE.solution</a>()</code>,
-<code><a href="SFO.solution.html">SFO.solution</a>()</code>,
-<code><a href="SFORB.solution.html">SFORB.solution</a>()</code></p></div>
-    </div>
-
-    <div id="ref-examples">
-    <h2>Examples</h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span>
-<span class="r-in"><span>  <span class="co"># Reproduce the plot on page 57 of FOCUS (2014)</span></span></span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fu">logistic.solution</span><span class="op">(</span><span class="va">x</span>, <span class="fl">100</span>, <span class="fl">0.08</span>, <span class="fl">0.0001</span>, <span class="fl">0.2</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>       from <span class="op">=</span> <span class="fl">0</span>, to <span class="op">=</span> <span class="fl">100</span>, ylim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">100</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>       xlab <span class="op">=</span> <span class="st">"Time"</span>, ylab <span class="op">=</span> <span class="st">"Residue"</span><span class="op">)</span></span></span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fu">logistic.solution</span><span class="op">(</span><span class="va">x</span>, <span class="fl">100</span>, <span class="fl">0.08</span>, <span class="fl">0.0001</span>, <span class="fl">0.4</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>       from <span class="op">=</span> <span class="fl">0</span>, to <span class="op">=</span> <span class="fl">100</span>, add <span class="op">=</span> <span class="cn">TRUE</span>, lty <span class="op">=</span> <span class="fl">2</span>, col <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span></span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fu">logistic.solution</span><span class="op">(</span><span class="va">x</span>, <span class="fl">100</span>, <span class="fl">0.08</span>, <span class="fl">0.0001</span>, <span class="fl">0.8</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>       from <span class="op">=</span> <span class="fl">0</span>, to <span class="op">=</span> <span class="fl">100</span>, add <span class="op">=</span> <span class="cn">TRUE</span>, lty <span class="op">=</span> <span class="fl">3</span>, col <span class="op">=</span> <span class="fl">3</span><span class="op">)</span></span></span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fu">logistic.solution</span><span class="op">(</span><span class="va">x</span>, <span class="fl">100</span>, <span class="fl">0.08</span>, <span class="fl">0.001</span>, <span class="fl">0.2</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>       from <span class="op">=</span> <span class="fl">0</span>, to <span class="op">=</span> <span class="fl">100</span>, add <span class="op">=</span> <span class="cn">TRUE</span>, lty <span class="op">=</span> <span class="fl">4</span>, col <span class="op">=</span> <span class="fl">4</span><span class="op">)</span></span></span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fu">logistic.solution</span><span class="op">(</span><span class="va">x</span>, <span class="fl">100</span>, <span class="fl">0.08</span>, <span class="fl">0.08</span>, <span class="fl">0.2</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>       from <span class="op">=</span> <span class="fl">0</span>, to <span class="op">=</span> <span class="fl">100</span>, add <span class="op">=</span> <span class="cn">TRUE</span>, lty <span class="op">=</span> <span class="fl">5</span>, col <span class="op">=</span> <span class="fl">5</span><span class="op">)</span></span></span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/graphics/legend.html" class="external-link">legend</a></span><span class="op">(</span><span class="st">"topright"</span>, inset <span class="op">=</span> <span class="fl">0.05</span>,</span></span>
-<span class="r-in"><span>         legend <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span><span class="st">"k0 = "</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.0001</span>, <span class="fl">0.0001</span>, <span class="fl">0.0001</span>, <span class="fl">0.001</span>, <span class="fl">0.08</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>                         <span class="st">", r = "</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.2</span>, <span class="fl">0.4</span>, <span class="fl">0.8</span>, <span class="fl">0.2</span>, <span class="fl">0.2</span><span class="op">)</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>         lty <span class="op">=</span> <span class="fl">1</span><span class="op">:</span><span class="fl">5</span>, col <span class="op">=</span> <span class="fl">1</span><span class="op">:</span><span class="fl">5</span><span class="op">)</span></span></span>
-<span class="r-plt img"><img src="logistic.solution-1.png" alt="" width="700" height="433"></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span>  <span class="co"># Fit with synthetic data</span></span></span>
-<span class="r-in"><span>  <span class="va">logistic</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"logistic"</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span>  <span class="va">sampling_times</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span>, <span class="fl">3</span>, <span class="fl">7</span>, <span class="fl">14</span>, <span class="fl">28</span>, <span class="fl">60</span>, <span class="fl">90</span>, <span class="fl">120</span><span class="op">)</span></span></span>
-<span class="r-in"><span>  <span class="va">parms_logistic</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>kmax <span class="op">=</span> <span class="fl">0.08</span>, k0 <span class="op">=</span> <span class="fl">0.0001</span>, r <span class="op">=</span> <span class="fl">0.2</span><span class="op">)</span></span></span>
-<span class="r-in"><span>  <span class="va">d_logistic</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinpredict.html">mkinpredict</a></span><span class="op">(</span><span class="va">logistic</span>,</span></span>
-<span class="r-in"><span>    <span class="va">parms_logistic</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fl">100</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>    <span class="va">sampling_times</span><span class="op">)</span></span></span>
-<span class="r-in"><span>  <span class="va">d_2_1</span> <span class="op">&lt;-</span> <span class="fu"><a href="add_err.html">add_err</a></span><span class="op">(</span><span class="va">d_logistic</span>,</span></span>
-<span class="r-in"><span>    sdfunc <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fu"><a href="sigma_twocomp.html">sigma_twocomp</a></span><span class="op">(</span><span class="va">x</span>, <span class="fl">0.5</span>, <span class="fl">0.07</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>    n <span class="op">=</span> <span class="fl">1</span>, reps <span class="op">=</span> <span class="fl">2</span>, digits <span class="op">=</span> <span class="fl">5</span>, LOD <span class="op">=</span> <span class="fl">0.1</span>, seed <span class="op">=</span> <span class="fl">123456</span><span class="op">)</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span></span></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span>  <span class="va">m</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"logistic"</span>, <span class="va">d_2_1</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-in"><span>  <span class="fu"><a href="plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">m</span><span class="op">)</span></span></span>
-<span class="r-plt img"><img src="logistic.solution-2.png" alt="" width="700" height="433"></span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">m</span><span class="op">)</span><span class="op">$</span><span class="va">bpar</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>              Estimate   se_notrans   t value       Pr(&gt;t)        Lower</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0 1.057896e+02 1.9023449590 55.610120 3.768360e-16 1.016451e+02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> kmax     6.398190e-02 0.0143201029  4.467978 3.841828e-04 3.929235e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k0       1.612775e-04 0.0005866813  0.274898 3.940351e-01 5.846688e-08</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> r        2.263946e-01 0.1718110662  1.317695 1.061043e-01 4.335843e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma    5.332935e+00 0.9145907310  5.830952 4.036926e-05 3.340213e+00</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                Upper</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0 109.9341588</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> kmax       0.1041853</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k0         0.4448749</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> r          1.1821120</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma      7.3256566</span>
-<span class="r-in"><span>  <span class="fu"><a href="endpoints.html">endpoints</a></span><span class="op">(</span><span class="va">m</span><span class="op">)</span><span class="op">$</span><span class="va">distimes</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>            DT50     DT90  DT50_k0 DT50_kmax</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent 36.86533 62.41511 4297.853  10.83349</span>
-<span class="r-in"><span></span></span>
-</code></pre></div>
-    </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/lrtest.mkinfit.html b/docs/dev/reference/lrtest.mkinfit.html
deleted file mode 100644
index d59590c0..00000000
--- a/docs/dev/reference/lrtest.mkinfit.html
+++ /dev/null
@@ -1,233 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Likelihood ratio test for mkinfit models — lrtest.mkinfit • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Likelihood ratio test for mkinfit models — lrtest.mkinfit"><meta property="og:description" content="Compare two mkinfit models based on their likelihood. If two fitted
-mkinfit objects are given as arguments, it is checked if they have been
-fitted to the same data. It is the responsibility of the user to make sure
-that the models are nested, i.e. one of them has less degrees of freedom
-and can be expressed by fixing the parameters of the other."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Likelihood ratio test for mkinfit models</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/lrtest.mkinfit.R" class="external-link"><code>R/lrtest.mkinfit.R</code></a></small>
-    <div class="hidden name"><code>lrtest.mkinfit.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Compare two mkinfit models based on their likelihood. If two fitted
-mkinfit objects are given as arguments, it is checked if they have been
-fitted to the same data. It is the responsibility of the user to make sure
-that the models are nested, i.e. one of them has less degrees of freedom
-and can be expressed by fixing the parameters of the other.</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for mkinfit</span></span>
-<span><span class="fu"><a href="https://rdrr.io/pkg/lmtest/man/lrtest.html" class="external-link">lrtest</a></span><span class="op">(</span><span class="va">object</span>, object_2 <span class="op">=</span> <span class="cn">NULL</span>, <span class="va">...</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># S3 method for mmkin</span></span>
-<span><span class="fu"><a href="https://rdrr.io/pkg/lmtest/man/lrtest.html" class="external-link">lrtest</a></span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
-    </div>
-
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>object</dt>
-<dd><p>An <code><a href="mkinfit.html">mkinfit</a></code> object, or an <code><a href="mmkin.html">mmkin</a></code> column
-object containing two fits to the same data.</p></dd>
-
-
-<dt>object_2</dt>
-<dd><p>Optionally, another mkinfit object fitted to the same data.</p></dd>
-
-
-<dt>...</dt>
-<dd><p>Argument to <code><a href="mkinfit.html">mkinfit</a></code>, passed to
-<code><a href="update.mkinfit.html">update.mkinfit</a></code> for creating the alternative fitted object.</p></dd>
-
-</dl></div>
-    <div id="details">
-    <h2>Details</h2>
-    <p>Alternatively, an argument to mkinfit can be given which is then passed
-to <code><a href="update.mkinfit.html">update.mkinfit</a></code> to obtain the alternative model.</p>
-<p>The comparison is then made by the <code><a href="https://rdrr.io/pkg/lmtest/man/lrtest.html" class="external-link">lrtest.default</a></code>
-method from the lmtest package. The model with the higher number of fitted
-parameters (alternative hypothesis) is listed first, then the model with the
-lower number of fitted parameters (null hypothesis).</p>
-    </div>
-
-    <div id="ref-examples">
-    <h2>Examples</h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># \dontrun{</span></span></span>
-<span class="r-in"><span><span class="va">test_data</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/subset.html" class="external-link">subset</a></span><span class="op">(</span><span class="va">synthetic_data_for_UBA_2014</span><span class="op">[[</span><span class="fl">12</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">data</span>, <span class="va">name</span> <span class="op">==</span> <span class="st">"parent"</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">sfo_fit</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="va">test_data</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">dfop_fit</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="va">test_data</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/pkg/lmtest/man/lrtest.html" class="external-link">lrtest</a></span><span class="op">(</span><span class="va">dfop_fit</span>, <span class="va">sfo_fit</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Likelihood ratio test</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Model 1: DFOP with error model const</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Model 2: SFO with error model const</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   #Df  LogLik Df  Chisq Pr(&gt;Chisq)    </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1   5 -42.453                         </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2   3 -63.954 -2 43.002  4.594e-10 ***</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> ---</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1</span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/pkg/lmtest/man/lrtest.html" class="external-link">lrtest</a></span><span class="op">(</span><span class="va">sfo_fit</span>, <span class="va">dfop_fit</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Likelihood ratio test</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Model 1: DFOP with error model const</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Model 2: SFO with error model const</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   #Df  LogLik Df  Chisq Pr(&gt;Chisq)    </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1   5 -42.453                         </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2   3 -63.954 -2 43.002  4.594e-10 ***</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> ---</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1</span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co"># The following two examples are commented out as they fail during</span></span></span>
-<span class="r-in"><span><span class="co"># generation of the static help pages by pkgdown</span></span></span>
-<span class="r-in"><span><span class="co">#lrtest(dfop_fit, error_model = "tc")</span></span></span>
-<span class="r-in"><span><span class="co">#lrtest(dfop_fit, fixed_parms = c(k2 = 0))</span></span></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co"># However, this equivalent syntax also works for static help pages</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/pkg/lmtest/man/lrtest.html" class="external-link">lrtest</a></span><span class="op">(</span><span class="va">dfop_fit</span>, <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">dfop_fit</span>, error_model <span class="op">=</span> <span class="st">"tc"</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Likelihood ratio test</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Model 1: DFOP with error model tc</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Model 2: DFOP with error model const</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   #Df  LogLik Df  Chisq Pr(&gt;Chisq)    </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1   6 -34.587                         </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2   5 -42.453 -1 15.731  7.302e-05 ***</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> ---</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1</span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/pkg/lmtest/man/lrtest.html" class="external-link">lrtest</a></span><span class="op">(</span><span class="va">dfop_fit</span>, <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">dfop_fit</span>, fixed_parms <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k2 <span class="op">=</span> <span class="fl">0</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Likelihood ratio test</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Model 1: DFOP with error model const</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Model 2: DFOP with error model const and fixed parameter(s) k2</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   #Df  LogLik Df  Chisq Pr(&gt;Chisq)    </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1   5 -42.453                         </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2   4 -57.340 -1 29.776  4.851e-08 ***</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> ---</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1</span>
-<span class="r-in"><span><span class="co"># }</span></span></span>
-</code></pre></div>
-    </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/max_twa_parent.html b/docs/dev/reference/max_twa_parent.html
deleted file mode 100644
index 507a0758..00000000
--- a/docs/dev/reference/max_twa_parent.html
+++ /dev/null
@@ -1,239 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Function to calculate maximum time weighted average concentrations from
-kinetic models fitted with mkinfit — max_twa_parent • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Function to calculate maximum time weighted average concentrations from
-kinetic models fitted with mkinfit — max_twa_parent"><meta property="og:description" content="This function calculates maximum moving window time weighted average
-concentrations (TWAs) for kinetic models fitted with mkinfit.
-Currently, only calculations for the parent are implemented for the SFO,
-FOMC, DFOP and HS models, using the analytical formulas given in the PEC
-soil section of the FOCUS guidance."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Function to calculate maximum time weighted average concentrations from
-kinetic models fitted with mkinfit</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/max_twa_parent.R" class="external-link"><code>R/max_twa_parent.R</code></a></small>
-    <div class="hidden name"><code>max_twa_parent.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>This function calculates maximum moving window time weighted average
-concentrations (TWAs) for kinetic models fitted with <code><a href="mkinfit.html">mkinfit</a></code>.
-Currently, only calculations for the parent are implemented for the SFO,
-FOMC, DFOP and HS models, using the analytical formulas given in the PEC
-soil section of the FOCUS guidance.</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">max_twa_parent</span><span class="op">(</span><span class="va">fit</span>, <span class="va">windows</span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">max_twa_sfo</span><span class="op">(</span>M0 <span class="op">=</span> <span class="fl">1</span>, <span class="va">k</span>, <span class="va">t</span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">max_twa_fomc</span><span class="op">(</span>M0 <span class="op">=</span> <span class="fl">1</span>, <span class="va">alpha</span>, <span class="va">beta</span>, <span class="va">t</span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">max_twa_dfop</span><span class="op">(</span>M0 <span class="op">=</span> <span class="fl">1</span>, <span class="va">k1</span>, <span class="va">k2</span>, <span class="va">g</span>, <span class="va">t</span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">max_twa_hs</span><span class="op">(</span>M0 <span class="op">=</span> <span class="fl">1</span>, <span class="va">k1</span>, <span class="va">k2</span>, <span class="va">tb</span>, <span class="va">t</span><span class="op">)</span></span></code></pre></div>
-    </div>
-
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>fit</dt>
-<dd><p>An object of class <code><a href="mkinfit.html">mkinfit</a></code>.</p></dd>
-
-
-<dt>windows</dt>
-<dd><p>The width of the time windows for which the TWAs should be
-calculated.</p></dd>
-
-
-<dt>M0</dt>
-<dd><p>The initial concentration for which the maximum time weighted
-average over the decline curve should be calculated. The default is to use
-a value of 1, which means that a relative maximum time weighted average
-factor (f_twa) is calculated.</p></dd>
-
-
-<dt>k</dt>
-<dd><p>The rate constant in the case of SFO kinetics.</p></dd>
-
-
-<dt>t</dt>
-<dd><p>The width of the time window.</p></dd>
-
-
-<dt>alpha</dt>
-<dd><p>Parameter of the FOMC model.</p></dd>
-
-
-<dt>beta</dt>
-<dd><p>Parameter of the FOMC model.</p></dd>
-
-
-<dt>k1</dt>
-<dd><p>The first rate constant of the DFOP or the HS kinetics.</p></dd>
-
-
-<dt>k2</dt>
-<dd><p>The second rate constant of the DFOP or the HS kinetics.</p></dd>
-
-
-<dt>g</dt>
-<dd><p>Parameter of the DFOP model.</p></dd>
-
-
-<dt>tb</dt>
-<dd><p>Parameter of the HS model.</p></dd>
-
-</dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>For <code>max_twa_parent</code>, a numeric vector, named using the
-<code>windows</code> argument.  For the other functions, a numeric vector of
-length one (also known as 'a number').</p>
-    </div>
-    <div id="references">
-    <h2>References</h2>
-    <p>FOCUS (2006) “Guidance Document on Estimating Persistence
-and Degradation Kinetics from Environmental Fate Studies on Pesticides in
-EU Registration” Report of the FOCUS Work Group on Degradation Kinetics,
-EC Document Reference Sanco/10058/2005 version 2.0, 434 pp,
-<a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics" class="external-link">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p>
-    </div>
-    <div id="author">
-    <h2>Author</h2>
-    <p>Johannes Ranke</p>
-    </div>
-
-    <div id="ref-examples">
-    <h2>Examples</h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span>
-<span class="r-in"><span>  <span class="va">fit</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"FOMC"</span>, <span class="va">FOCUS_2006_C</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-in"><span>  <span class="fu">max_twa_parent</span><span class="op">(</span><span class="va">fit</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">7</span>, <span class="fl">21</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>        7       21 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 34.71343 18.22124 </span>
-<span class="r-in"><span></span></span>
-</code></pre></div>
-    </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/mccall81_245T-1.png b/docs/dev/reference/mccall81_245T-1.png
deleted file mode 100644
index 5e4ea6ef..00000000
--- a/docs/dev/reference/mccall81_245T-1.png
+++ /dev/null
diff --git a/docs/dev/reference/mccall81_245T.html b/docs/dev/reference/mccall81_245T.html
deleted file mode 100644
index c0e9cb37..00000000
--- a/docs/dev/reference/mccall81_245T.html
+++ /dev/null
@@ -1,247 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Datasets on aerobic soil metabolism of 2,4,5-T in six soils — mccall81_245T • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Datasets on aerobic soil metabolism of 2,4,5-T in six soils — mccall81_245T"><meta property="og:description" content="Time course of 2,4,5-trichlorophenoxyacetic acid, and the corresponding
- 2,4,5-trichlorophenol and 2,4,5-trichloroanisole as recovered in diethylether
- extracts."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Datasets on aerobic soil metabolism of 2,4,5-T in six soils</h1>
-    
-    <div class="hidden name"><code>mccall81_245T.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Time course of 2,4,5-trichlorophenoxyacetic acid, and the corresponding
- 2,4,5-trichlorophenol and 2,4,5-trichloroanisole as recovered in diethylether
- extracts.</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="va">mccall81_245T</span></span></code></pre></div>
-    </div>
-
-    <div id="format">
-    <h2>Format</h2>
-    <p>A dataframe containing the following variables.</p><dl><dt><code>name</code></dt>
-<dd><p>the name of the compound observed. Note that T245 is used as
-                       an acronym for 2,4,5-T. T245 is a legitimate object name
-                       in R, which is necessary for specifying models using
-                       <code><a href="mkinmod.html">mkinmod</a></code>.</p></dd>
-
-    <dt><code>time</code></dt>
-<dd><p>a numeric vector containing sampling times in days after
-                       treatment</p></dd>
-
-    <dt><code>value</code></dt>
-<dd><p>a numeric vector containing concentrations in percent of applied radioactivity</p></dd>
-
-    <dt><code>soil</code></dt>
-<dd><p>a factor containing the name of the soil</p></dd>
-
-  
-</dl></div>
-    <div id="source">
-    <h2>Source</h2>
-    <p>McCall P, Vrona SA, Kelley SS (1981) Fate of uniformly carbon-14 ring labelled 2,4,5-Trichlorophenoxyacetic acid and 2,4-dichlorophenoxyacetic acid. J Agric Chem 29, 100-107
-  <a href="https://doi.org/10.1021/jf00103a026" class="external-link">doi:10.1021/jf00103a026</a></p>
-    </div>
-
-    <div id="ref-examples">
-    <h2>Examples</h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span>  <span class="va">SFO_SFO_SFO</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>T245 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"phenol"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>    phenol <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"anisole"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>    anisole <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Temporary DLL for differentials generated and loaded</span>
-<span class="r-in"><span>  <span class="co"># \dontrun{</span></span></span>
-<span class="r-in"><span>    <span class="va">fit.1</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO_SFO</span>, <span class="fu"><a href="https://rdrr.io/r/base/subset.html" class="external-link">subset</a></span><span class="op">(</span><span class="va">mccall81_245T</span>, <span class="va">soil</span> <span class="op">==</span> <span class="st">"Commerce"</span><span class="op">)</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>Observations with value of zero were removed from the data</span>
-<span class="r-in"><span>    <span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">fit.1</span><span class="op">)</span><span class="op">$</span><span class="va">bpar</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                         Estimate   se_notrans   t value       Pr(&gt;t)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> T245_0              1.038550e+02 2.1847074945 47.537272 4.472189e-18</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_T245              4.337042e-02 0.0018983965 22.845818 2.276911e-13</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_phenol            4.050581e-01 0.2986993563  1.356073 9.756989e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_anisole           6.678742e-03 0.0008021439  8.326114 2.623177e-07</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_T245_to_phenol    6.227599e-01 0.3985340558  1.562627 6.949413e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_phenol_to_anisole 1.000000e+00 0.6718439825  1.488441 7.867789e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma               2.514628e+00 0.4907558883  5.123989 6.233157e-05</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                            Lower        Upper</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> T245_0              99.246061385 1.084640e+02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_T245               0.039631621 4.746194e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_phenol             0.218013879 7.525762e-01</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_anisole            0.005370739 8.305299e-03</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_T245_to_phenol     0.547559081 6.924813e-01</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_phenol_to_anisole  0.000000000 1.000000e+00</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma                1.706607296 3.322649e+00</span>
-<span class="r-in"><span>    <span class="fu"><a href="endpoints.html">endpoints</a></span><span class="op">(</span><span class="va">fit.1</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> $ff</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    T245_phenol      T245_sink phenol_anisole    phenol_sink </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   6.227599e-01   3.772401e-01   1.000000e+00   3.773626e-10 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> $distimes</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>               DT50      DT90</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> T245     15.982025  53.09114</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> phenol    1.711229   5.68458</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> anisole 103.784093 344.76329</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-in"><span>    <span class="co"># formation fraction from phenol to anisol is practically 1. As we cannot</span></span></span>
-<span class="r-in"><span>    <span class="co"># fix formation fractions when using the ilr transformation, we can turn of</span></span></span>
-<span class="r-in"><span>    <span class="co"># the sink in the model generation</span></span></span>
-<span class="r-in"><span>    <span class="va">SFO_SFO_SFO_2</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>T245 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"phenol"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>      phenol <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"anisole"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>      anisole <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Temporary DLL for differentials generated and loaded</span>
-<span class="r-in"><span>    <span class="va">fit.2</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO_SFO_2</span>, <span class="fu"><a href="https://rdrr.io/r/base/subset.html" class="external-link">subset</a></span><span class="op">(</span><span class="va">mccall81_245T</span>, <span class="va">soil</span> <span class="op">==</span> <span class="st">"Commerce"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>      quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>Observations with value of zero were removed from the data</span>
-<span class="r-in"><span>    <span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">fit.2</span><span class="op">)</span><span class="op">$</span><span class="va">bpar</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                      Estimate   se_notrans   t value       Pr(&gt;t)        Lower</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> T245_0           1.038550e+02 2.1623653066 48.028439 4.993108e-19 99.271020284</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_T245           4.337042e-02 0.0018343666 23.643268 3.573556e-14  0.039650976</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_phenol         4.050582e-01 0.1177237473  3.440752 1.679254e-03  0.218746587</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_anisole        6.678742e-03 0.0006829745  9.778903 1.872894e-08  0.005377083</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_T245_to_phenol 6.227599e-01 0.0342197875 18.198824 2.039411e-12  0.547975637</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma            2.514628e+00 0.3790944250  6.633250 2.875782e-06  1.710983655</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                         Upper</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> T245_0           108.43904074</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_T245             0.04743877</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_phenol           0.75005585</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_anisole          0.00829550</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_T245_to_phenol   0.69212308</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma              3.31827222</span>
-<span class="r-in"><span>    <span class="fu"><a href="endpoints.html">endpoints</a></span><span class="op">(</span><span class="va">fit.1</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> $ff</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    T245_phenol      T245_sink phenol_anisole    phenol_sink </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   6.227599e-01   3.772401e-01   1.000000e+00   3.773626e-10 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> $distimes</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>               DT50      DT90</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> T245     15.982025  53.09114</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> phenol    1.711229   5.68458</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> anisole 103.784093 344.76329</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-in"><span>    <span class="fu"><a href="plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">fit.2</span><span class="op">)</span></span></span>
-<span class="r-plt img"><img src="mccall81_245T-1.png" alt="" width="700" height="433"></span>
-<span class="r-in"><span>  <span class="co"># }</span></span></span>
-</code></pre></div>
-    </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/mean_degparms.html b/docs/dev/reference/mean_degparms.html
deleted file mode 100644
index a374d532..00000000
--- a/docs/dev/reference/mean_degparms.html
+++ /dev/null
@@ -1,182 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Calculate mean degradation parameters for an mmkin row object — mean_degparms • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Calculate mean degradation parameters for an mmkin row object — mean_degparms"><meta property="og:description" content="Calculate mean degradation parameters for an mmkin row object"><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Calculate mean degradation parameters for an mmkin row object</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/mean_degparms.R" class="external-link"><code>R/mean_degparms.R</code></a></small>
-    <div class="hidden name"><code>mean_degparms.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Calculate mean degradation parameters for an mmkin row object</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">mean_degparms</span><span class="op">(</span></span>
-<span>  <span class="va">object</span>,</span>
-<span>  random <span class="op">=</span> <span class="cn">FALSE</span>,</span>
-<span>  test_log_parms <span class="op">=</span> <span class="cn">FALSE</span>,</span>
-<span>  conf.level <span class="op">=</span> <span class="fl">0.6</span>,</span>
-<span>  default_log_parms <span class="op">=</span> <span class="cn">NA</span></span>
-<span><span class="op">)</span></span></code></pre></div>
-    </div>
-
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>object</dt>
-<dd><p>An mmkin row object containing several fits of the same model to different datasets</p></dd>
-
-
-<dt>random</dt>
-<dd><p>Should a list with fixed and random effects be returned?</p></dd>
-
-
-<dt>test_log_parms</dt>
-<dd><p>If TRUE, log parameters are only considered in
-the mean calculations if their untransformed counterparts (most likely
-rate constants) pass the t-test for significant difference from zero.</p></dd>
-
-
-<dt>conf.level</dt>
-<dd><p>Possibility to adjust the required confidence level
-for parameter that are tested if requested by 'test_log_parms'.</p></dd>
-
-
-<dt>default_log_parms</dt>
-<dd><p>If set to a numeric value, this is used
-as a default value for the tested log parameters that failed the
-t-test.</p></dd>
-
-</dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>If random is FALSE (default), a named vector containing mean values
-of the fitted degradation model parameters. If random is TRUE, a list with
-fixed and random effects, in the format required by the start argument of
-nlme for the case of a single grouping variable ds.</p>
-    </div>
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/mhmkin-1.png b/docs/dev/reference/mhmkin-1.png
deleted file mode 100644
index 1c99aead..00000000
--- a/docs/dev/reference/mhmkin-1.png
+++ /dev/null
diff --git a/docs/dev/reference/mhmkin-2.png b/docs/dev/reference/mhmkin-2.png
deleted file mode 100644
index ea04ebfd..00000000
--- a/docs/dev/reference/mhmkin-2.png
+++ /dev/null
diff --git a/docs/dev/reference/mhmkin.html b/docs/dev/reference/mhmkin.html
deleted file mode 100644
index b41c11df..00000000
--- a/docs/dev/reference/mhmkin.html
+++ /dev/null
@@ -1,336 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Fit nonlinear mixed-effects models built from one or more kinetic
-degradation models and one or more error models — mhmkin • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Fit nonlinear mixed-effects models built from one or more kinetic
-degradation models and one or more error models — mhmkin"><meta property="og:description" content="The name of the methods expresses that (multiple) hierarchichal
-(also known as multilevel) multicompartment kinetic models are
-fitted. Our kinetic models are nonlinear, so we can use various nonlinear
-mixed-effects model fitting functions."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Fit nonlinear mixed-effects models built from one or more kinetic
-degradation models and one or more error models</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/mhmkin.R" class="external-link"><code>R/mhmkin.R</code></a></small>
-    <div class="hidden name"><code>mhmkin.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>The name of the methods expresses that (<strong>m</strong>ultiple) <strong>h</strong>ierarchichal
-(also known as multilevel) <strong>m</strong>ulticompartment <strong>kin</strong>etic models are
-fitted. Our kinetic models are nonlinear, so we can use various nonlinear
-mixed-effects model fitting functions.</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre><code>mhmkin(objects, ...)
-
-# S3 method for mmkin
-mhmkin(objects, ...)
-
-# S3 method for list
-mhmkin(
-  objects,
-  backend = "saemix",
-  algorithm = "saem",
-  no_random_effect = NULL,
-  ...,
-  cores = if (Sys.info()["sysname"] == "Windows") 1 else parallel::detectCores(),
-  cluster = NULL
-)
-
-# S3 method for mhmkin
-[(x, i, j, ..., drop = FALSE)
-
-# S3 method for mhmkin
-print(x, ...)</code></pre></div>
-    </div>
-
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>objects</dt>
-<dd><p>A list of <a href="mmkin.html">mmkin</a> objects containing fits of the same
-degradation models to the same data, but using different error models.
-Alternatively, a single <a href="mmkin.html">mmkin</a> object containing fits of several
-degradation models to the same data</p></dd>
-
-
-<dt>...</dt>
-<dd><p>Further arguments that will be passed to the nonlinear mixed-effects
-model fitting function.</p></dd>
-
-
-<dt>backend</dt>
-<dd><p>The backend to be used for fitting. Currently, only saemix is
-supported</p></dd>
-
-
-<dt>algorithm</dt>
-<dd><p>The algorithm to be used for fitting (currently not used)</p></dd>
-
-
-<dt>no_random_effect</dt>
-<dd><p>Default is NULL and will be passed to <a href="saem.html">saem</a>. If a
-character vector is supplied, it will be passed to all calls to <a href="saem.html">saem</a>,
-which will exclude random effects for all matching parameters. Alternatively,
-a list of character vectors or an object of class <a href="illparms.html">illparms.mhmkin</a> can be
-specified. They have to have the same dimensions that the return object of
-the current call will have, i.e. the number of rows must match the number
-of degradation models in the mmkin object(s), and the number of columns must
-match the number of error models used in the mmkin object(s).</p></dd>
-
-
-<dt>cores</dt>
-<dd><p>The number of cores to be used for multicore processing. This
-is only used when the <code>cluster</code> argument is <code>NULL</code>. On Windows
-machines, cores &gt; 1 is not supported, you need to use the <code>cluster</code>
-argument to use multiple logical processors. Per default, all cores detected
-by <code><a href="https://rdrr.io/r/parallel/detectCores.html" class="external-link">parallel::detectCores()</a></code> are used, except on Windows where the default
-is 1.</p></dd>
-
-
-<dt>cluster</dt>
-<dd><p>A cluster as returned by makeCluster to be used for
-parallel execution.</p></dd>
-
-
-<dt>x</dt>
-<dd><p>An mhmkin object.</p></dd>
-
-
-<dt>i</dt>
-<dd><p>Row index selecting the fits for specific models</p></dd>
-
-
-<dt>j</dt>
-<dd><p>Column index selecting the fits to specific datasets</p></dd>
-
-
-<dt>drop</dt>
-<dd><p>If FALSE, the method always returns an mhmkin object, otherwise
-either a list of fit objects or a single fit object.</p></dd>
-
-</dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>A two-dimensional <a href="https://rdrr.io/r/base/array.html" class="external-link">array</a> of fit objects and/or try-errors that can
-be indexed using the degradation model names for the first index (row index)
-and the error model names for the second index (column index), with class
-attribute 'mhmkin'.</p>
-
-
-<p>An object inheriting from <code>mhmkin</code>.</p>
-    </div>
-    <div id="see-also">
-    <h2>See also</h2>
-    <div class="dont-index"><p><code>[.mhmkin</code> for subsetting mhmkin objects</p></div>
-    </div>
-    <div id="author">
-    <h2>Author</h2>
-    <p>Johannes Ranke</p>
-    </div>
-
-    <div id="ref-examples">
-    <h2>Examples</h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># \dontrun{</span></span></span>
-<span class="r-in"><span><span class="co"># We start with separate evaluations of all the first six datasets with two</span></span></span>
-<span class="r-in"><span><span class="co"># degradation models and two error models</span></span></span>
-<span class="r-in"><span><span class="va">f_sep_const</span> <span class="op">&lt;-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"FOMC"</span><span class="op">)</span>, <span class="va">ds_fomc</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">6</span><span class="op">]</span>, cores <span class="op">=</span> <span class="fl">2</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">f_sep_tc</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_sep_const</span>, error_model <span class="op">=</span> <span class="st">"tc"</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="co"># The mhmkin function sets up hierarchical degradation models aka</span></span></span>
-<span class="r-in"><span><span class="co"># nonlinear mixed-effects models for all four combinations, specifying</span></span></span>
-<span class="r-in"><span><span class="co"># uncorrelated random effects for all degradation parameters</span></span></span>
-<span class="r-in"><span><span class="va">f_saem_1</span> <span class="op">&lt;-</span> <span class="fu">mhmkin</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">f_sep_const</span>, <span class="va">f_sep_tc</span><span class="op">)</span>, cores <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="status.html">status</a></span><span class="op">(</span><span class="va">f_saem_1</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>            error</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> degradation const tc</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>        SFO  OK    OK</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>        FOMC OK    OK</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> OK: Fit terminated successfully</span>
-<span class="r-in"><span><span class="co"># The 'illparms' function shows that in all hierarchical fits, at least</span></span></span>
-<span class="r-in"><span><span class="co"># one random effect is ill-defined (the confidence interval for the</span></span></span>
-<span class="r-in"><span><span class="co"># random effect expressed as standard deviation includes zero)</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="illparms.html">illparms</a></span><span class="op">(</span><span class="va">f_saem_1</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>            error</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> degradation const        tc                        </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>        SFO  sd(parent_0) sd(parent_0)              </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>        FOMC sd(log_beta) sd(parent_0), sd(log_beta)</span>
-<span class="r-in"><span><span class="co"># Therefore we repeat the fits, excluding the ill-defined random effects</span></span></span>
-<span class="r-in"><span><span class="va">f_saem_2</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_saem_1</span>, no_random_effect <span class="op">=</span> <span class="fu"><a href="illparms.html">illparms</a></span><span class="op">(</span><span class="va">f_saem_1</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="status.html">status</a></span><span class="op">(</span><span class="va">f_saem_2</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>            error</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> degradation const tc</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>        SFO  OK    OK</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>        FOMC OK    OK</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> OK: Fit terminated successfully</span>
-<span class="r-in"><span><span class="fu"><a href="illparms.html">illparms</a></span><span class="op">(</span><span class="va">f_saem_2</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>            error</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> degradation const tc</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>        SFO          </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>        FOMC         </span>
-<span class="r-in"><span><span class="co"># Model comparisons show that FOMC with two-component error is preferable,</span></span></span>
-<span class="r-in"><span><span class="co"># and confirms our reduction of the default parameter model</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_saem_1</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Data: 95 observations of 1 variable(s) grouped in 6 datasets</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>            npar    AIC    BIC     Lik</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> SFO const     5 574.40 573.35 -282.20</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> SFO tc        6 543.72 542.47 -265.86</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> FOMC const    7 489.67 488.22 -237.84</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> FOMC tc       8 406.11 404.44 -195.05</span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_saem_2</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Data: 95 observations of 1 variable(s) grouped in 6 datasets</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>            npar    AIC    BIC     Lik</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> SFO const     4 572.22 571.39 -282.11</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> SFO tc        5 541.63 540.59 -265.81</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> FOMC const    6 487.38 486.13 -237.69</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> FOMC tc       6 402.12 400.88 -195.06</span>
-<span class="r-in"><span><span class="co"># The convergence plot for the selected model looks fine</span></span></span>
-<span class="r-in"><span><span class="fu">saemix</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_saem_2</span><span class="op">[[</span><span class="st">"FOMC"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">so</span>, plot.type <span class="op">=</span> <span class="st">"convergence"</span><span class="op">)</span></span></span>
-<span class="r-plt img"><img src="mhmkin-1.png" alt="" width="700" height="433"></span>
-<span class="r-in"><span><span class="co"># The plot of predictions versus data shows that we have a pretty data-rich</span></span></span>
-<span class="r-in"><span><span class="co"># situation with homogeneous distribution of residuals, because we used the</span></span></span>
-<span class="r-in"><span><span class="co"># same degradation model, error model and parameter distribution model that</span></span></span>
-<span class="r-in"><span><span class="co"># was used in the data generation.</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_saem_2</span><span class="op">[[</span><span class="st">"FOMC"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></span>
-<span class="r-plt img"><img src="mhmkin-2.png" alt="" width="700" height="433"></span>
-<span class="r-in"><span><span class="co"># We can specify the same parameter model reductions manually</span></span></span>
-<span class="r-in"><span><span class="va">no_ranef</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="st">"parent_0"</span>, <span class="st">"log_beta"</span>, <span class="st">"parent_0"</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"parent_0"</span>, <span class="st">"log_beta"</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/dim.html" class="external-link">dim</a></span><span class="op">(</span><span class="va">no_ranef</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">2</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">f_saem_2m</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_saem_1</span>, no_random_effect <span class="op">=</span> <span class="va">no_ranef</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_saem_2m</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Data: 95 observations of 1 variable(s) grouped in 6 datasets</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>            npar    AIC    BIC     Lik</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> SFO const     4 572.22 571.39 -282.11</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> SFO tc        5 541.63 540.59 -265.81</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> FOMC const    6 487.38 486.13 -237.69</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> FOMC tc       6 402.12 400.88 -195.06</span>
-<span class="r-in"><span><span class="co"># }</span></span></span>
-</code></pre></div>
-    </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/mixed-1.png b/docs/dev/reference/mixed-1.png
deleted file mode 100644
index b053d9c9..00000000
--- a/docs/dev/reference/mixed-1.png
+++ /dev/null
diff --git a/docs/dev/reference/mixed.html b/docs/dev/reference/mixed.html
deleted file mode 100644
index 1709944a..00000000
--- a/docs/dev/reference/mixed.html
+++ /dev/null
@@ -1,245 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Create a mixed effects model from an mmkin row object — mixed • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Create a mixed effects model from an mmkin row object — mixed"><meta property="og:description" content="Create a mixed effects model from an mmkin row object"><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Create a mixed effects model from an mmkin row object</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/mixed.mmkin.R" class="external-link"><code>R/mixed.mmkin.R</code></a></small>
-    <div class="hidden name"><code>mixed.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Create a mixed effects model from an mmkin row object</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">mixed</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># S3 method for mmkin</span></span>
-<span><span class="fu">mixed</span><span class="op">(</span><span class="va">object</span>, method <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"none"</span><span class="op">)</span>, <span class="va">...</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># S3 method for mixed.mmkin</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, digits <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html" class="external-link">max</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fu"><a href="https://rdrr.io/r/base/options.html" class="external-link">getOption</a></span><span class="op">(</span><span class="st">"digits"</span><span class="op">)</span> <span class="op">-</span> <span class="fl">3</span><span class="op">)</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
-    </div>
-
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>object</dt>
-<dd><p>An <a href="mmkin.html">mmkin</a> row object</p></dd>
-
-
-<dt>...</dt>
-<dd><p>Currently not used</p></dd>
-
-
-<dt>method</dt>
-<dd><p>The method to be used</p></dd>
-
-
-<dt>x</dt>
-<dd><p>A mixed.mmkin object to print</p></dd>
-
-
-<dt>digits</dt>
-<dd><p>Number of digits to use for printing.</p></dd>
-
-</dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>An object of class 'mixed.mmkin' which has the observed data in a
-single dataframe which is convenient for plotting</p>
-    </div>
-
-    <div id="ref-examples">
-    <h2>Examples</h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">sampling_times</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span>, <span class="fl">3</span>, <span class="fl">7</span>, <span class="fl">14</span>, <span class="fl">28</span>, <span class="fl">60</span>, <span class="fl">90</span>, <span class="fl">120</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">n_biphasic</span> <span class="op">&lt;-</span> <span class="fl">8</span></span></span>
-<span class="r-in"><span><span class="va">err_1</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>const <span class="op">=</span> <span class="fl">1</span>, prop <span class="op">=</span> <span class="fl">0.07</span><span class="op">)</span></span></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="va">DFOP_SFO</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span></span></span>
-<span class="r-in"><span>  parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="st">"m1"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  m1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/Random.html" class="external-link">set.seed</a></span><span class="op">(</span><span class="fl">123456</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">log_sd</span> <span class="op">&lt;-</span> <span class="fl">0.3</span></span></span>
-<span class="r-in"><span><span class="va">syn_biphasic_parms</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/matrix.html" class="external-link">as.matrix</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span></span></span>
-<span class="r-in"><span>  k1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/stats/Lognormal.html" class="external-link">rlnorm</a></span><span class="op">(</span><span class="va">n_biphasic</span>, <span class="fu"><a href="https://rdrr.io/r/base/Log.html" class="external-link">log</a></span><span class="op">(</span><span class="fl">0.05</span><span class="op">)</span>, <span class="va">log_sd</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  k2 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/stats/Lognormal.html" class="external-link">rlnorm</a></span><span class="op">(</span><span class="va">n_biphasic</span>, <span class="fu"><a href="https://rdrr.io/r/base/Log.html" class="external-link">log</a></span><span class="op">(</span><span class="fl">0.01</span><span class="op">)</span>, <span class="va">log_sd</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  g <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/stats/Logistic.html" class="external-link">plogis</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/stats/Normal.html" class="external-link">rnorm</a></span><span class="op">(</span><span class="va">n_biphasic</span>, <span class="fl">0</span>, <span class="va">log_sd</span><span class="op">)</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  f_parent_to_m1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/stats/Logistic.html" class="external-link">plogis</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/stats/Normal.html" class="external-link">rnorm</a></span><span class="op">(</span><span class="va">n_biphasic</span>, <span class="fl">0</span>, <span class="va">log_sd</span><span class="op">)</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  k_m1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/stats/Lognormal.html" class="external-link">rlnorm</a></span><span class="op">(</span><span class="va">n_biphasic</span>, <span class="fu"><a href="https://rdrr.io/r/base/Log.html" class="external-link">log</a></span><span class="op">(</span><span class="fl">0.002</span><span class="op">)</span>, <span class="va">log_sd</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="va">ds_biphasic_mean</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="va">n_biphasic</span>,</span></span>
-<span class="r-in"><span>  <span class="kw">function</span><span class="op">(</span><span class="va">i</span><span class="op">)</span> <span class="op">{</span></span></span>
-<span class="r-in"><span>    <span class="fu"><a href="mkinpredict.html">mkinpredict</a></span><span class="op">(</span><span class="va">DFOP_SFO</span>, <span class="va">syn_biphasic_parms</span><span class="op">[</span><span class="va">i</span>, <span class="op">]</span>,</span></span>
-<span class="r-in"><span>      <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fl">100</span>, m1 <span class="op">=</span> <span class="fl">0</span><span class="op">)</span>, <span class="va">sampling_times</span><span class="op">)</span></span></span>
-<span class="r-in"><span>  <span class="op">}</span></span></span>
-<span class="r-in"><span><span class="op">)</span></span></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/Random.html" class="external-link">set.seed</a></span><span class="op">(</span><span class="fl">123456L</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">ds_biphasic</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="va">ds_biphasic_mean</span>, <span class="kw">function</span><span class="op">(</span><span class="va">ds</span><span class="op">)</span> <span class="op">{</span></span></span>
-<span class="r-in"><span>  <span class="fu"><a href="add_err.html">add_err</a></span><span class="op">(</span><span class="va">ds</span>,</span></span>
-<span class="r-in"><span>    sdfunc <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">value</span><span class="op">)</span> <span class="fu"><a href="https://rdrr.io/r/base/MathFun.html" class="external-link">sqrt</a></span><span class="op">(</span><span class="va">err_1</span><span class="op">$</span><span class="va">const</span><span class="op">^</span><span class="fl">2</span> <span class="op">+</span> <span class="va">value</span><span class="op">^</span><span class="fl">2</span> <span class="op">*</span> <span class="va">err_1</span><span class="op">$</span><span class="va">prop</span><span class="op">^</span><span class="fl">2</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>    n <span class="op">=</span> <span class="fl">1</span>, secondary <span class="op">=</span> <span class="st">"m1"</span><span class="op">)</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span></span></span>
-<span class="r-in"><span><span class="op">}</span><span class="op">)</span></span></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co"># \dontrun{</span></span></span>
-<span class="r-in"><span><span class="va">f_mmkin</span> <span class="op">&lt;-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="st">"DFOP-SFO"</span> <span class="op">=</span> <span class="va">DFOP_SFO</span><span class="op">)</span>, <span class="va">ds_biphasic</span>, error_model <span class="op">=</span> <span class="st">"tc"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="va">f_mixed</span> <span class="op">&lt;-</span> <span class="fu">mixed</span><span class="op">(</span><span class="va">f_mmkin</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">f_mixed</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Kinetic model fitted by nonlinear regression to each dataset</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Structural model:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>            * parent</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> d_m1/dt = + f_parent_to_m1 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>            * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>            exp(-k2 * time))) * parent - k_m1 * m1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Data:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 271 observations of 2 variable(s) grouped in 8 datasets</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> &lt;mmkin&gt; object</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Status of individual fits:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>           dataset</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> model      1  2  3  4  5  6  7  8 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   DFOP-SFO OK OK OK OK OK OK OK OK</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> OK: No warnings</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Mean fitted parameters:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>        parent_0        log_k_m1 f_parent_qlogis          log_k1          log_k2 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>      100.674757       -8.761916       -0.004347       -3.348812       -3.986853 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>        g_qlogis </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>       -0.087392 </span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_mixed</span><span class="op">)</span></span></span>
-<span class="r-plt img"><img src="mixed-1.png" alt="" width="700" height="433"></span>
-<span class="r-in"><span><span class="co"># }</span></span></span>
-</code></pre></div>
-    </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/mkin_long_to_wide.html b/docs/dev/reference/mkin_long_to_wide.html
deleted file mode 100644
index 990506b5..00000000
--- a/docs/dev/reference/mkin_long_to_wide.html
+++ /dev/null
@@ -1,201 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Convert a dataframe from long to wide format — mkin_long_to_wide • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Convert a dataframe from long to wide format — mkin_long_to_wide"><meta property="og:description" content="This function takes a dataframe in the long form, i.e. with a row for each
-observed value, and converts it into a dataframe with one independent
-variable and several dependent variables as columns."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Convert a dataframe from long to wide format</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/mkin_long_to_wide.R" class="external-link"><code>R/mkin_long_to_wide.R</code></a></small>
-    <div class="hidden name"><code>mkin_long_to_wide.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>This function takes a dataframe in the long form, i.e. with a row for each
-observed value, and converts it into a dataframe with one independent
-variable and several dependent variables as columns.</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">mkin_long_to_wide</span><span class="op">(</span><span class="va">long_data</span>, time <span class="op">=</span> <span class="st">"time"</span>, outtime <span class="op">=</span> <span class="st">"time"</span><span class="op">)</span></span></code></pre></div>
-    </div>
-
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>long_data</dt>
-<dd><p>The dataframe must contain one variable called "time" with
-the time values specified by the <code>time</code> argument, one column called
-"name" with the grouping of the observed values, and finally one column of
-observed values called "value".</p></dd>
-
-
-<dt>time</dt>
-<dd><p>The name of the time variable in the long input data.</p></dd>
-
-
-<dt>outtime</dt>
-<dd><p>The name of the time variable in the wide output data.</p></dd>
-
-</dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>Dataframe in wide format.</p>
-    </div>
-    <div id="author">
-    <h2>Author</h2>
-    <p>Johannes Ranke</p>
-    </div>
-
-    <div id="ref-examples">
-    <h2>Examples</h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="fu">mkin_long_to_wide</span><span class="op">(</span><span class="va">FOCUS_2006_D</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    time parent    m1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1     0  99.46  0.00</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2     0 102.04  0.00</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 3     1  93.50  4.84</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 4     1  92.50  5.64</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5     3  63.23 12.91</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 6     3  68.99 12.96</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 7     7  52.32 22.97</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 8     7  55.13 24.47</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 9    14  27.27 41.69</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 10   14  26.64 33.21</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 11   21  11.50 44.37</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 12   21  11.64 46.44</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 13   35   2.85 41.22</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 14   35   2.91 37.95</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 15   50   0.69 41.19</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 16   50   0.63 40.01</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 17   75   0.05 40.09</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 18   75   0.06 33.85</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 19  100     NA 31.04</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 20  100     NA 33.13</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 21  120     NA 25.15</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 22  120     NA 33.31</span>
-<span class="r-in"><span></span></span>
-</code></pre></div>
-    </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/mkin_wide_to_long.html b/docs/dev/reference/mkin_wide_to_long.html
deleted file mode 100644
index a324d3b8..00000000
--- a/docs/dev/reference/mkin_wide_to_long.html
+++ /dev/null
@@ -1,181 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Convert a dataframe with observations over time into long format — mkin_wide_to_long • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Convert a dataframe with observations over time into long format — mkin_wide_to_long"><meta property="og:description" content="This function simply takes a dataframe with one independent variable and
-several dependent variable and converts it into the long form as required by
-mkinfit."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Convert a dataframe with observations over time into long format</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/mkin_wide_to_long.R" class="external-link"><code>R/mkin_wide_to_long.R</code></a></small>
-    <div class="hidden name"><code>mkin_wide_to_long.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>This function simply takes a dataframe with one independent variable and
-several dependent variable and converts it into the long form as required by
-<code><a href="mkinfit.html">mkinfit</a></code>.</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">mkin_wide_to_long</span><span class="op">(</span><span class="va">wide_data</span>, time <span class="op">=</span> <span class="st">"t"</span><span class="op">)</span></span></code></pre></div>
-    </div>
-
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>wide_data</dt>
-<dd><p>The dataframe must contain one variable with the time
-values specified by the <code>time</code> argument and usually more than one
-column of observed values.</p></dd>
-
-
-<dt>time</dt>
-<dd><p>The name of the time variable.</p></dd>
-
-</dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>Dataframe in long format as needed for <code><a href="mkinfit.html">mkinfit</a></code>.</p>
-    </div>
-    <div id="author">
-    <h2>Author</h2>
-    <p>Johannes Ranke</p>
-    </div>
-
-    <div id="ref-examples">
-    <h2>Examples</h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="va">wide</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span>t <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>,<span class="fl">2</span>,<span class="fl">3</span><span class="op">)</span>, x <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>,<span class="fl">4</span>,<span class="fl">7</span><span class="op">)</span>, y <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>,<span class="fl">4</span>,<span class="fl">5</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu">mkin_wide_to_long</span><span class="op">(</span><span class="va">wide</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   name time value</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1    x    1     1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2    x    2     4</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 3    x    3     7</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 4    y    1     3</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5    y    2     4</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 6    y    3     5</span>
-<span class="r-in"><span></span></span>
-</code></pre></div>
-    </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/mkinds.html b/docs/dev/reference/mkinds.html
deleted file mode 100644
index 6219766e..00000000
--- a/docs/dev/reference/mkinds.html
+++ /dev/null
@@ -1,262 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>A dataset class for mkin — mkinds • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="A dataset class for mkin — mkinds"><meta property="og:description" content="At the moment this dataset class is hardly used in mkin. For example,
-mkinfit does not take mkinds datasets as argument, but works with dataframes
-such as the on contained in the data field of mkinds objects. Some datasets
-provided by this package come as mkinds objects nevertheless."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>A dataset class for mkin</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/mkinds.R" class="external-link"><code>R/mkinds.R</code></a></small>
-    <div class="hidden name"><code>mkinds.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>At the moment this dataset class is hardly used in mkin. For example,
-mkinfit does not take mkinds datasets as argument, but works with dataframes
-such as the on contained in the data field of mkinds objects. Some datasets
-provided by this package come as mkinds objects nevertheless.</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for mkinds</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, data <span class="op">=</span> <span class="cn">FALSE</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
-    </div>
-
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>x</dt>
-<dd><p>An mkinds object.</p></dd>
-
-
-<dt>data</dt>
-<dd><p>Should the data be printed?</p></dd>
-
-
-<dt>...</dt>
-<dd><p>Not used.</p></dd>
-
-</dl></div>
-    <div id="public-fields">
-    <h2>Public fields</h2>
-    <p></p><div class="r6-fields"><dl><dt><code>title</code></dt>
-<dd><p>A full title for the dataset</p></dd>
-
-
-<dt><code>sampling_times</code></dt>
-<dd><p>The sampling times</p></dd>
-
-
-<dt><code>time_unit</code></dt>
-<dd><p>The time unit</p></dd>
-
-
-<dt><code>observed</code></dt>
-<dd><p>Names of the observed variables</p></dd>
-
-
-<dt><code>unit</code></dt>
-<dd><p>The unit of the observations</p></dd>
-
-
-<dt><code>replicates</code></dt>
-<dd><p>The maximum number of replicates per sampling time</p></dd>
-
-
-<dt><code>data</code></dt>
-<dd><p>A data frame with at least the columns name, time
-and value in order to be compatible with mkinfit</p></dd>
-
-
-</dl><p></p></div>
-    </div>
-    <div id="methods">
-    <h2>Methods</h2>
-    
-<div class="section">
-<h3 id="public-methods">Public methods<a class="anchor" aria-label="anchor" href="#public-methods"></a></h3>
-
-<ul><li><p><a href="#method-mkinds-new"><code>mkinds$new()</code></a></p></li>
-<li><p><a href="#method-mkinds-clone"><code>mkinds$clone()</code></a></p></li>
-</ul></div><p></p><hr><a id="method-mkinds-new"></a><div class="section">
-<h3 id="method-new-">Method <code>new()</code><a class="anchor" aria-label="anchor" href="#method-new-"></a></h3>
-<p>Create a new mkinds object</p><div class="section">
-<h4 id="usage">Usage<a class="anchor" aria-label="anchor" href="#usage"></a></h4>
-<p></p><div class="r"><div class="sourceCode"><pre><code><span><span class="va"><a href="../reference/mkinds.html">mkinds</a></span><span class="op">$</span><span class="fu">new</span><span class="op">(</span>title <span class="op">=</span> <span class="st">""</span>, <span class="va">data</span>, time_unit <span class="op">=</span> <span class="cn">NA</span>, unit <span class="op">=</span> <span class="cn">NA</span><span class="op">)</span></span></code></pre></div><p></p></div>
-</div>
-
-<div class="section">
-<h4 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h4>
-<p></p><div class="arguments"><dl><dt><code>title</code></dt>
-<dd><p>The dataset title</p></dd>
-
-
-<dt><code>data</code></dt>
-<dd><p>The data</p></dd>
-
-
-<dt><code>time_unit</code></dt>
-<dd><p>The time unit</p></dd>
-
-
-<dt><code>unit</code></dt>
-<dd><p>The unit of the observations</p></dd>
-
-
-</dl><p></p></div>
-</div>
-
-</div><p></p><hr><a id="method-mkinds-clone"></a><div class="section">
-<h3 id="method-clone-">Method <code>clone()</code><a class="anchor" aria-label="anchor" href="#method-clone-"></a></h3>
-<p>The objects of this class are cloneable with this method.</p><div class="section">
-<h4 id="usage-1">Usage<a class="anchor" aria-label="anchor" href="#usage-1"></a></h4>
-<p></p><div class="r"><div class="sourceCode"><pre><code><span><span class="va">mkinds</span><span class="op">$</span><span class="fu">clone</span><span class="op">(</span>deep <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div><p></p></div>
-</div>
-
-<div class="section">
-<h4 id="arguments-1">Arguments<a class="anchor" aria-label="anchor" href="#arguments-1"></a></h4>
-<p></p><div class="arguments"><dl><dt><code>deep</code></dt>
-<dd><p>Whether to make a deep clone.</p></dd>
-
-
-</dl><p></p></div>
-</div>
-
-</div>
-
-    </div>
-
-    <div id="ref-examples">
-    <h2>Examples</h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="va">mds</span> <span class="op">&lt;-</span> <span class="va">mkinds</span><span class="op">$</span><span class="fu">new</span><span class="op">(</span><span class="st">"FOCUS A"</span>, <span class="va">FOCUS_2006_A</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">mds</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> &lt;mkinds&gt; with $title:  FOCUS A </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Observed compounds $observed:  parent </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Sampling times $sampling_times:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 0, 3, 7, 14, 30, 62, 90, 118 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> With a maximum of  1  replicates</span>
-<span class="r-in"><span></span></span>
-</code></pre></div>
-    </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/mkindsg.html b/docs/dev/reference/mkindsg.html
deleted file mode 100644
index 6e221a23..00000000
--- a/docs/dev/reference/mkindsg.html
+++ /dev/null
@@ -1,450 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>A class for dataset groups for mkin — mkindsg • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="A class for dataset groups for mkin — mkindsg"><meta property="og:description" content="A container for working with datasets that share at least one compound,
-so that combined evaluations are desirable.
-Time normalisation factors are initialised with a value of 1 for each
-dataset if no data are supplied."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>A class for dataset groups for mkin</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/mkinds.R" class="external-link"><code>R/mkinds.R</code></a></small>
-    <div class="hidden name"><code>mkindsg.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>A container for working with datasets that share at least one compound,
-so that combined evaluations are desirable.</p>
-<p>Time normalisation factors are initialised with a value of 1 for each
-dataset if no data are supplied.</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for mkindsg</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, data <span class="op">=</span> <span class="cn">FALSE</span>, verbose <span class="op">=</span> <span class="va">data</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
-    </div>
-
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>x</dt>
-<dd><p>An mkindsg object.</p></dd>
-
-
-<dt>data</dt>
-<dd><p>Should the mkinds objects be printed with their data?</p></dd>
-
-
-<dt>verbose</dt>
-<dd><p>Should the mkinds objects be printed?</p></dd>
-
-
-<dt>...</dt>
-<dd><p>Not used.</p></dd>
-
-</dl></div>
-    <div id="public-fields">
-    <h2>Public fields</h2>
-    <p></p><div class="r6-fields"><dl><dt><code>title</code></dt>
-<dd><p>A title for the dataset group</p></dd>
-
-
-<dt><code>ds</code></dt>
-<dd><p>A list of mkinds objects</p></dd>
-
-
-<dt><code>observed_n</code></dt>
-<dd><p>Occurrence counts of compounds in datasets</p></dd>
-
-
-<dt><code>f_time_norm</code></dt>
-<dd><p>Time normalisation factors</p></dd>
-
-
-<dt><code>meta</code></dt>
-<dd><p>A data frame with a row for each dataset,
-containing additional information in the form
-of categorical data (factors) or numerical data
-(e.g. temperature, moisture,
-or covariates like soil pH).</p></dd>
-
-
-</dl><p></p></div>
-    </div>
-    <div id="methods">
-    <h2>Methods</h2>
-    
-<div class="section">
-<h3 id="public-methods">Public methods<a class="anchor" aria-label="anchor" href="#public-methods"></a></h3>
-
-<ul><li><p><a href="#method-mkindsg-new"><code>mkindsg$new()</code></a></p></li>
-<li><p><a href="#method-mkindsg-clone"><code>mkindsg$clone()</code></a></p></li>
-</ul></div><p></p><hr><a id="method-mkindsg-new"></a><div class="section">
-<h3 id="method-new-">Method <code>new()</code><a class="anchor" aria-label="anchor" href="#method-new-"></a></h3>
-<p>Create a new mkindsg object</p><div class="section">
-<h4 id="usage">Usage<a class="anchor" aria-label="anchor" href="#usage"></a></h4>
-<p></p><div class="r"><div class="sourceCode"><pre><code><span><span class="va"><a href="../reference/mkindsg.html">mkindsg</a></span><span class="op">$</span><span class="fu">new</span><span class="op">(</span>title <span class="op">=</span> <span class="st">""</span>, <span class="va">ds</span>, f_time_norm <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/rep.html" class="external-link">rep</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fu"><a href="https://rdrr.io/r/base/length.html" class="external-link">length</a></span><span class="op">(</span><span class="va">ds</span><span class="op">)</span><span class="op">)</span>, <span class="va">meta</span><span class="op">)</span></span></code></pre></div><p></p></div>
-</div>
-
-<div class="section">
-<h4 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h4>
-<p></p><div class="arguments"><dl><dt><code>title</code></dt>
-<dd><p>The title</p></dd>
-
-
-<dt><code>ds</code></dt>
-<dd><p>A list of mkinds objects</p></dd>
-
-
-<dt><code>f_time_norm</code></dt>
-<dd><p>Time normalisation factors</p></dd>
-
-
-<dt><code>meta</code></dt>
-<dd><p>The meta data</p></dd>
-
-
-</dl><p></p></div>
-</div>
-
-</div><p></p><hr><a id="method-mkindsg-clone"></a><div class="section">
-<h3 id="method-clone-">Method <code>clone()</code><a class="anchor" aria-label="anchor" href="#method-clone-"></a></h3>
-<p>The objects of this class are cloneable with this method.</p><div class="section">
-<h4 id="usage-1">Usage<a class="anchor" aria-label="anchor" href="#usage-1"></a></h4>
-<p></p><div class="r"><div class="sourceCode"><pre><code><span><span class="va">mkindsg</span><span class="op">$</span><span class="fu">clone</span><span class="op">(</span>deep <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div><p></p></div>
-</div>
-
-<div class="section">
-<h4 id="arguments-1">Arguments<a class="anchor" aria-label="anchor" href="#arguments-1"></a></h4>
-<p></p><div class="arguments"><dl><dt><code>deep</code></dt>
-<dd><p>Whether to make a deep clone.</p></dd>
-
-
-</dl><p></p></div>
-</div>
-
-</div>
-
-    </div>
-
-    <div id="ref-examples">
-    <h2>Examples</h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="va">mdsg</span> <span class="op">&lt;-</span> <span class="va">mkindsg</span><span class="op">$</span><span class="fu">new</span><span class="op">(</span><span class="st">"Experimental X"</span>, <span class="va">experimental_data_for_UBA_2019</span><span class="op">[</span><span class="fl">6</span><span class="op">:</span><span class="fl">10</span><span class="op">]</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">mdsg</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> &lt;mkindsg&gt; holding 5 mkinds objects</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Title $title:  Experimental X </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Occurrence of observed compounds $observed_n:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent     A1 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>      5      5 </span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">mdsg</span>, verbose <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> &lt;mkindsg&gt; holding 5 mkinds objects</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Title $title:  Experimental X </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Occurrence of observed compounds $observed_n:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent     A1 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>      5      5 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Datasets $ds:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> &lt;mkinds&gt; with $title:  Soil 6 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Observed compounds $observed:  parent, A1 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Sampling times $sampling_times:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 0, 3, 6, 10, 20, 34, 55, 90, 112, 132 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> With a maximum of  2  replicates</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Time unit:  days </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Observation unit:  \%AR </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> &lt;mkinds&gt; with $title:  Soil 7 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Observed compounds $observed:  parent, A1 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Sampling times $sampling_times:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 0, 3, 7, 14, 30, 60, 90, 120, 180 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> With a maximum of  2  replicates</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Time unit:  days </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Observation unit:  \%AR </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> &lt;mkinds&gt; with $title:  Soil 8 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Observed compounds $observed:  parent, A1 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Sampling times $sampling_times:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 0, 1, 3, 8, 14, 27, 48, 70 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> With a maximum of  2  replicates</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Time unit:  days </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Observation unit:  \%AR </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> &lt;mkinds&gt; with $title:  Soil 9 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Observed compounds $observed:  parent, A1 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Sampling times $sampling_times:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 0, 1, 3, 8, 14, 27, 48, 70, 91, 120 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> With a maximum of  2  replicates</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Time unit:  days </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Observation unit:  \%AR </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> &lt;mkinds&gt; with $title:  Soil 10 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Observed compounds $observed:  parent, A1 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Sampling times $sampling_times:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 0, 8, 14, 21, 41, 63, 91, 120 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> With a maximum of  2  replicates</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Time unit:  days </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Observation unit:  \%AR </span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">mdsg</span>, verbose <span class="op">=</span> <span class="cn">TRUE</span>, data <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> &lt;mkindsg&gt; holding 5 mkinds objects</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Title $title:  Experimental X </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Occurrence of observed compounds $observed_n:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent     A1 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>      5      5 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Datasets $ds:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> &lt;mkinds&gt; with $title:  Soil 6 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Observed compounds $observed:  parent, A1 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Sampling times $sampling_times:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 0, 3, 6, 10, 20, 34, 55, 90, 112, 132 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> With a maximum of  2  replicates</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Time unit:  days </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Observation unit:  \%AR </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    time parent   A1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1     0   97.2   NA</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2     0   96.4   NA</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 3     3   71.1  4.3</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 4     3   69.2  4.6</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5     6   58.1  7.0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 6     6   56.6  7.2</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 7    10   44.4  8.2</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 8    10   43.4  8.0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 9    20   33.3 11.0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 10   20   29.2 13.7</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 11   34   17.6 11.5</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 12   34   18.0 12.7</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 13   55   10.5 14.9</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 14   55    9.3 14.5</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 15   90    4.5 12.1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 16   90    4.7 12.3</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 17  112    3.0  9.9</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 18  112    3.4 10.2</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 19  132    2.3  8.8</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 20  132    2.7  7.8</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> &lt;mkinds&gt; with $title:  Soil 7 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Observed compounds $observed:  parent, A1 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Sampling times $sampling_times:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 0, 3, 7, 14, 30, 60, 90, 120, 180 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> With a maximum of  2  replicates</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Time unit:  days </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Observation unit:  \%AR </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    time parent   A1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1     0   93.6   NA</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2     0   92.3   NA</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 3     3   87.0  3.9</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 4     3   82.2  3.1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5     7   74.0  6.9</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 6     7   73.9  6.6</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 7    14   64.2 10.4</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 8    14   69.5  8.3</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 9    30   54.0 14.4</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 10   30   54.6 13.7</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 11   60   41.1 22.1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 12   60   38.4 22.3</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 13   90   32.5 27.5</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 14   90   35.5 25.4</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 15  120   28.1 28.0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 16  120   29.0 26.6</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 17  180   26.5 25.8</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 18  180   27.6 25.3</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> &lt;mkinds&gt; with $title:  Soil 8 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Observed compounds $observed:  parent, A1 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Sampling times $sampling_times:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 0, 1, 3, 8, 14, 27, 48, 70 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> With a maximum of  2  replicates</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Time unit:  days </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Observation unit:  \%AR </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    time parent   A1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1     0   91.9   NA</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2     0   90.8   NA</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 3     1   64.9  9.6</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 4     1   66.2  7.7</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5     3   43.5 15.0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 6     3   44.1 15.1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 7     8   18.3 21.2</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 8     8   18.1 21.1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 9    14   10.2 19.7</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 10   14   10.8 18.9</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 11   27    4.9 17.5</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 12   27    3.3 15.9</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 13   48    1.6  9.5</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 14   48    1.5  9.8</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 15   70    1.1  6.2</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 16   70    0.9  6.1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> &lt;mkinds&gt; with $title:  Soil 9 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Observed compounds $observed:  parent, A1 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Sampling times $sampling_times:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 0, 1, 3, 8, 14, 27, 48, 70, 91, 120 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> With a maximum of  2  replicates</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Time unit:  days </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Observation unit:  \%AR </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    time parent   A1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1     0   99.8   NA</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2     0   98.3   NA</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 3     1   77.1  4.2</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 4     1   77.2  3.9</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5     3   59.0  7.4</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 6     3   58.1  7.9</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 7     8   27.4 14.5</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 8     8   29.2 13.7</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 9    14   19.1 14.2</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 10   14   29.6 12.2</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 11   27   10.1 13.7</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 12   27   18.2 13.2</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 13   48    4.5 13.6</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 14   48    9.1 15.4</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 15   70    2.3 10.4</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 16   70    2.9 11.6</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 17   91    2.0 10.0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 18   91    1.8  9.5</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 19  120    2.0  9.1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 20  120    2.2  9.0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> &lt;mkinds&gt; with $title:  Soil 10 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Observed compounds $observed:  parent, A1 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Sampling times $sampling_times:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 0, 8, 14, 21, 41, 63, 91, 120 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> With a maximum of  2  replicates</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Time unit:  days </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Observation unit:  \%AR </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    time parent   A1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1     0   96.1   NA</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2     0   94.3   NA</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 3     8   73.9  3.3</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 4     8   73.9  3.4</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5    14   69.4  3.9</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 6    14   73.1  2.9</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 7    21   65.6  6.4</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 8    21   65.3  7.2</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 9    41   55.9  9.1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 10   41   54.4  8.5</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 11   63   47.0 11.7</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 12   63   49.3 12.0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 13   91   44.7 13.3</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 14   91   46.7 13.2</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 15  120   42.1 14.3</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 16  120   41.3 12.1</span>
-<span class="r-in"><span></span></span>
-</code></pre></div>
-    </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/mkinerrmin.html b/docs/dev/reference/mkinerrmin.html
deleted file mode 100644
index 3a8b9610..00000000
--- a/docs/dev/reference/mkinerrmin.html
+++ /dev/null
@@ -1,210 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Calculate the minimum error to assume in order to pass the variance test — mkinerrmin • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Calculate the minimum error to assume in order to pass the variance test — mkinerrmin"><meta property="og:description" content="This function finds the smallest relative error still resulting in passing
-the chi-squared test as defined in the FOCUS kinetics report from 2006."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Calculate the minimum error to assume in order to pass the variance test</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/mkinerrmin.R" class="external-link"><code>R/mkinerrmin.R</code></a></small>
-    <div class="hidden name"><code>mkinerrmin.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>This function finds the smallest relative error still resulting in passing
-the chi-squared test as defined in the FOCUS kinetics report from 2006.</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">mkinerrmin</span><span class="op">(</span><span class="va">fit</span>, alpha <span class="op">=</span> <span class="fl">0.05</span><span class="op">)</span></span></code></pre></div>
-    </div>
-
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>fit</dt>
-<dd><p>an object of class <code><a href="mkinfit.html">mkinfit</a></code>.</p></dd>
-
-
-<dt>alpha</dt>
-<dd><p>The confidence level chosen for the chi-squared test.</p></dd>
-
-</dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>A dataframe with the following components:</p>
-<dl><dt>err.min</dt>
-<dd><p>The
-relative error, expressed as a fraction.</p></dd>
- <dt>n.optim</dt>
-<dd><p>The number of
-optimised parameters attributed to the data series.</p></dd>
- <dt>df</dt>
-<dd><p>The number of
-remaining degrees of freedom for the chi2 error level calculations.  Note
-that mean values are used for the chi2 statistic and therefore every time
-point with observed values in the series only counts one time.</p></dd>
-</dl><p>The
-dataframe has one row for the total dataset and one further row for each
-observed state variable in the model.</p>
-    </div>
-    <div id="details">
-    <h2>Details</h2>
-    <p>This function is used internally by <code><a href="summary.mkinfit.html">summary.mkinfit</a></code>.</p>
-    </div>
-    <div id="references">
-    <h2>References</h2>
-    <p>FOCUS (2006) “Guidance Document on Estimating Persistence
-and Degradation Kinetics from Environmental Fate Studies on Pesticides in EU
-Registration” Report of the FOCUS Work Group on Degradation Kinetics, EC
-Document Reference Sanco/10058/2005 version 2.0, 434 pp,
-<a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics" class="external-link">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p>
-    </div>
-
-    <div id="ref-examples">
-    <h2>Examples</h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="va">SFO_SFO</span> <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"m1"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>                  m1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>                  use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Temporary DLL for differentials generated and loaded</span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="va">fit_FOCUS_D</span> <span class="op">=</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="va">FOCUS_2006_D</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>Observations with value of zero were removed from the data</span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/Round.html" class="external-link">round</a></span><span class="op">(</span><span class="fu">mkinerrmin</span><span class="op">(</span><span class="va">fit_FOCUS_D</span><span class="op">)</span>, <span class="fl">4</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>          err.min n.optim df</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> All data  0.0640       4 15</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent    0.0646       2  7</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> m1        0.0469       2  8</span>
-<span class="r-in"><span><span class="co"># \dontrun{</span></span></span>
-<span class="r-in"><span>  <span class="va">fit_FOCUS_E</span> <span class="op">=</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="va">FOCUS_2006_E</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/base/Round.html" class="external-link">round</a></span><span class="op">(</span><span class="fu">mkinerrmin</span><span class="op">(</span><span class="va">fit_FOCUS_E</span><span class="op">)</span>, <span class="fl">4</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>          err.min n.optim df</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> All data  0.1544       4 13</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent    0.1659       2  7</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> m1        0.1095       2  6</span>
-<span class="r-in"><span><span class="co"># }</span></span></span>
-<span class="r-in"><span></span></span>
-</code></pre></div>
-    </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/mkinerrplot-1.png b/docs/dev/reference/mkinerrplot-1.png
deleted file mode 100644
index 078614fc..00000000
--- a/docs/dev/reference/mkinerrplot-1.png
+++ /dev/null
diff --git a/docs/dev/reference/mkinerrplot.html b/docs/dev/reference/mkinerrplot.html
deleted file mode 100644
index bc9b828a..00000000
--- a/docs/dev/reference/mkinerrplot.html
+++ /dev/null
@@ -1,245 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Function to plot squared residuals and the error model for an mkin object — mkinerrplot • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Function to plot squared residuals and the error model for an mkin object — mkinerrplot"><meta property="og:description" content="This function plots the squared residuals for the specified subset of the
-observed variables from an mkinfit object. In addition, one or more dashed
-line(s) show the fitted error model.  A combined plot of the fitted model
-and this error model plot can be obtained with plot.mkinfit
-using the argument show_errplot = TRUE."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Function to plot squared residuals and the error model for an mkin object</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/mkinerrplot.R" class="external-link"><code>R/mkinerrplot.R</code></a></small>
-    <div class="hidden name"><code>mkinerrplot.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>This function plots the squared residuals for the specified subset of the
-observed variables from an mkinfit object. In addition, one or more dashed
-line(s) show the fitted error model.  A combined plot of the fitted model
-and this error model plot can be obtained with <code><a href="plot.mkinfit.html">plot.mkinfit</a></code>
-using the argument <code>show_errplot = TRUE</code>.</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">mkinerrplot</span><span class="op">(</span></span>
-<span>  <span class="va">object</span>,</span>
-<span>  obs_vars <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">object</span><span class="op">$</span><span class="va">mkinmod</span><span class="op">$</span><span class="va">map</span><span class="op">)</span>,</span>
-<span>  xlim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1.1</span> <span class="op">*</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html" class="external-link">max</a></span><span class="op">(</span><span class="va">object</span><span class="op">$</span><span class="va">data</span><span class="op">$</span><span class="va">predicted</span><span class="op">)</span><span class="op">)</span>,</span>
-<span>  xlab <span class="op">=</span> <span class="st">"Predicted"</span>,</span>
-<span>  ylab <span class="op">=</span> <span class="st">"Squared residual"</span>,</span>
-<span>  maxy <span class="op">=</span> <span class="st">"auto"</span>,</span>
-<span>  legend <span class="op">=</span> <span class="cn">TRUE</span>,</span>
-<span>  lpos <span class="op">=</span> <span class="st">"topright"</span>,</span>
-<span>  col_obs <span class="op">=</span> <span class="st">"auto"</span>,</span>
-<span>  pch_obs <span class="op">=</span> <span class="st">"auto"</span>,</span>
-<span>  frame <span class="op">=</span> <span class="cn">TRUE</span>,</span>
-<span>  <span class="va">...</span></span>
-<span><span class="op">)</span></span></code></pre></div>
-    </div>
-
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>object</dt>
-<dd><p>A fit represented in an <code><a href="mkinfit.html">mkinfit</a></code> object.</p></dd>
-
-
-<dt>obs_vars</dt>
-<dd><p>A character vector of names of the observed variables for
-which residuals should be plotted. Defaults to all observed variables in
-the model</p></dd>
-
-
-<dt>xlim</dt>
-<dd><p>plot range in x direction.</p></dd>
-
-
-<dt>xlab</dt>
-<dd><p>Label for the x axis.</p></dd>
-
-
-<dt>ylab</dt>
-<dd><p>Label for the y axis.</p></dd>
-
-
-<dt>maxy</dt>
-<dd><p>Maximum value of the residuals. This is used for the scaling of
-the y axis and defaults to "auto".</p></dd>
-
-
-<dt>legend</dt>
-<dd><p>Should a legend be plotted?</p></dd>
-
-
-<dt>lpos</dt>
-<dd><p>Where should the legend be placed? Default is "topright". Will
-be passed on to <code><a href="https://rdrr.io/r/graphics/legend.html" class="external-link">legend</a></code>.</p></dd>
-
-
-<dt>col_obs</dt>
-<dd><p>Colors for the observed variables.</p></dd>
-
-
-<dt>pch_obs</dt>
-<dd><p>Symbols to be used for the observed variables.</p></dd>
-
-
-<dt>frame</dt>
-<dd><p>Should a frame be drawn around the plots?</p></dd>
-
-
-<dt>...</dt>
-<dd><p>further arguments passed to <code><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></code>.</p></dd>
-
-</dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>Nothing is returned by this function, as it is called for its side
-effect, namely to produce a plot.</p>
-    </div>
-    <div id="see-also">
-    <h2>See also</h2>
-    <div class="dont-index"><p><code><a href="mkinplot.html">mkinplot</a></code>, for a way to plot the data and the fitted
-lines of the mkinfit object.</p></div>
-    </div>
-    <div id="author">
-    <h2>Author</h2>
-    <p>Johannes Ranke</p>
-    </div>
-
-    <div id="ref-examples">
-    <h2>Examples</h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co"># \dontrun{</span></span></span>
-<span class="r-in"><span><span class="va">model</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"m1"</span><span class="op">)</span>, m1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Temporary DLL for differentials generated and loaded</span>
-<span class="r-in"><span><span class="va">fit</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">model</span>, <span class="va">FOCUS_2006_D</span>, error_model <span class="op">=</span> <span class="st">"tc"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>Observations with value of zero were removed from the data</span>
-<span class="r-in"><span><span class="fu">mkinerrplot</span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></span></span>
-<span class="r-plt img"><img src="mkinerrplot-1.png" alt="" width="700" height="433"></span>
-<span class="r-in"><span><span class="co"># }</span></span></span>
-<span class="r-in"><span></span></span>
-</code></pre></div>
-    </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/mkinfit-1.png b/docs/dev/reference/mkinfit-1.png
deleted file mode 100644
index 578f64a5..00000000
--- a/docs/dev/reference/mkinfit-1.png
+++ /dev/null
diff --git a/docs/dev/reference/mkinfit.html b/docs/dev/reference/mkinfit.html
deleted file mode 100644
index 237d903e..00000000
--- a/docs/dev/reference/mkinfit.html
+++ /dev/null
@@ -1,719 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Fit a kinetic model to data with one or more state variables — mkinfit • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Fit a kinetic model to data with one or more state variables — mkinfit"><meta property="og:description" content="This function maximises the likelihood of the observed data using the Port
-algorithm stats::nlminb(), and the specified initial or fixed
-parameters and starting values.  In each step of the optimisation, the
-kinetic model is solved using the function mkinpredict(), except
-if an analytical solution is implemented, in which case the model is solved
-using the degradation function in the mkinmod object. The
-parameters of the selected error model are fitted simultaneously with the
-degradation model parameters, as both of them are arguments of the
-likelihood function."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Fit a kinetic model to data with one or more state variables</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/mkinfit.R" class="external-link"><code>R/mkinfit.R</code></a></small>
-    <div class="hidden name"><code>mkinfit.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>This function maximises the likelihood of the observed data using the Port
-algorithm <code><a href="https://rdrr.io/r/stats/nlminb.html" class="external-link">stats::nlminb()</a></code>, and the specified initial or fixed
-parameters and starting values.  In each step of the optimisation, the
-kinetic model is solved using the function <code><a href="mkinpredict.html">mkinpredict()</a></code>, except
-if an analytical solution is implemented, in which case the model is solved
-using the degradation function in the <a href="mkinmod.html">mkinmod</a> object. The
-parameters of the selected error model are fitted simultaneously with the
-degradation model parameters, as both of them are arguments of the
-likelihood function.</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">mkinfit</span><span class="op">(</span></span>
-<span>  <span class="va">mkinmod</span>,</span>
-<span>  <span class="va">observed</span>,</span>
-<span>  parms.ini <span class="op">=</span> <span class="st">"auto"</span>,</span>
-<span>  state.ini <span class="op">=</span> <span class="st">"auto"</span>,</span>
-<span>  err.ini <span class="op">=</span> <span class="st">"auto"</span>,</span>
-<span>  fixed_parms <span class="op">=</span> <span class="cn">NULL</span>,</span>
-<span>  fixed_initials <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">mkinmod</span><span class="op">$</span><span class="va">diffs</span><span class="op">)</span><span class="op">[</span><span class="op">-</span><span class="fl">1</span><span class="op">]</span>,</span>
-<span>  from_max_mean <span class="op">=</span> <span class="cn">FALSE</span>,</span>
-<span>  solution_type <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"auto"</span>, <span class="st">"analytical"</span>, <span class="st">"eigen"</span>, <span class="st">"deSolve"</span><span class="op">)</span>,</span>
-<span>  method.ode <span class="op">=</span> <span class="st">"lsoda"</span>,</span>
-<span>  use_compiled <span class="op">=</span> <span class="st">"auto"</span>,</span>
-<span>  control <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>eval.max <span class="op">=</span> <span class="fl">300</span>, iter.max <span class="op">=</span> <span class="fl">200</span><span class="op">)</span>,</span>
-<span>  transform_rates <span class="op">=</span> <span class="cn">TRUE</span>,</span>
-<span>  transform_fractions <span class="op">=</span> <span class="cn">TRUE</span>,</span>
-<span>  quiet <span class="op">=</span> <span class="cn">FALSE</span>,</span>
-<span>  atol <span class="op">=</span> <span class="fl">1e-08</span>,</span>
-<span>  rtol <span class="op">=</span> <span class="fl">1e-10</span>,</span>
-<span>  error_model <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"const"</span>, <span class="st">"obs"</span>, <span class="st">"tc"</span><span class="op">)</span>,</span>
-<span>  error_model_algorithm <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"auto"</span>, <span class="st">"d_3"</span>, <span class="st">"direct"</span>, <span class="st">"twostep"</span>, <span class="st">"threestep"</span>, <span class="st">"fourstep"</span>,</span>
-<span>    <span class="st">"IRLS"</span>, <span class="st">"OLS"</span><span class="op">)</span>,</span>
-<span>  reweight.tol <span class="op">=</span> <span class="fl">1e-08</span>,</span>
-<span>  reweight.max.iter <span class="op">=</span> <span class="fl">10</span>,</span>
-<span>  trace_parms <span class="op">=</span> <span class="cn">FALSE</span>,</span>
-<span>  test_residuals <span class="op">=</span> <span class="cn">FALSE</span>,</span>
-<span>  <span class="va">...</span></span>
-<span><span class="op">)</span></span></code></pre></div>
-    </div>
-
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>mkinmod</dt>
-<dd><p>A list of class <a href="mkinmod.html">mkinmod</a>, containing the kinetic
-model to be fitted to the data, or one of the shorthand names ("SFO",
-"FOMC", "DFOP", "HS", "SFORB", "IORE"). If a shorthand name is given, a
-parent only degradation model is generated for the variable with the
-highest value in <code>observed</code>.</p></dd>
-
-
-<dt>observed</dt>
-<dd><p>A dataframe with the observed data.  The first column called
-"name" must contain the name of the observed variable for each data point.
-The second column must contain the times of observation, named "time".
-The third column must be named "value" and contain the observed values.
-Zero values in the "value" column will be removed, with a warning, in
-order to avoid problems with fitting the two-component error model. This
-is not expected to be a problem, because in general, values of zero are
-not observed in degradation data, because there is a lower limit of
-detection.</p></dd>
-
-
-<dt>parms.ini</dt>
-<dd><p>A named vector of initial values for the parameters,
-including parameters to be optimised and potentially also fixed parameters
-as indicated by <code>fixed_parms</code>.  If set to "auto", initial values for
-rate constants are set to default values.  Using parameter names that are
-not in the model gives an error.</p>
-<p>It is possible to only specify a subset of the parameters that the model
-needs. You can use the parameter lists "bparms.ode" from a previously
-fitted model, which contains the differential equation parameters from
-this model.  This works nicely if the models are nested. An example is
-given below.</p></dd>
-
-
-<dt>state.ini</dt>
-<dd><p>A named vector of initial values for the state variables of
-the model. In case the observed variables are represented by more than one
-model variable, the names will differ from the names of the observed
-variables (see <code>map</code> component of <a href="mkinmod.html">mkinmod</a>). The default
-is to set the initial value of the first model variable to the mean of the
-time zero values for the variable with the maximum observed value, and all
-others to 0.  If this variable has no time zero observations, its initial
-value is set to 100.</p></dd>
-
-
-<dt>err.ini</dt>
-<dd><p>A named vector of initial values for the error model
-parameters to be optimised.  If set to "auto", initial values are set to
-default values.  Otherwise, inital values for all error model parameters
-must be given.</p></dd>
-
-
-<dt>fixed_parms</dt>
-<dd><p>The names of parameters that should not be optimised but
-rather kept at the values specified in <code>parms.ini</code>. Alternatively,
-a named numeric vector of parameters to be fixed, regardless of the values
-in parms.ini.</p></dd>
-
-
-<dt>fixed_initials</dt>
-<dd><p>The names of model variables for which the initial
-state at time 0 should be excluded from the optimisation. Defaults to all
-state variables except for the first one.</p></dd>
-
-
-<dt>from_max_mean</dt>
-<dd><p>If this is set to TRUE, and the model has only one
-observed variable, then data before the time of the maximum observed value
-(after averaging for each sampling time) are discarded, and this time is
-subtracted from all remaining time values, so the time of the maximum
-observed mean value is the new time zero.</p></dd>
-
-
-<dt>solution_type</dt>
-<dd><p>If set to "eigen", the solution of the system of
-differential equations is based on the spectral decomposition of the
-coefficient matrix in cases that this is possible. If set to "deSolve", a
-numerical <a href="https://rdrr.io/pkg/deSolve/man/ode.html" class="external-link">ode solver from package deSolve</a> is used. If
-set to "analytical", an analytical solution of the model is used. This is
-only implemented for relatively simple degradation models.  The default is
-"auto", which uses "analytical" if possible, otherwise "deSolve" if a
-compiler is present, and "eigen" if no compiler is present and the model
-can be expressed using eigenvalues and eigenvectors.</p></dd>
-
-
-<dt>method.ode</dt>
-<dd><p>The solution method passed via <code><a href="mkinpredict.html">mkinpredict()</a></code>
-to <code><a href="https://rdrr.io/pkg/deSolve/man/ode.html" class="external-link">deSolve::ode()</a></code> in case the solution type is "deSolve". The default
-"lsoda" is performant, but sometimes fails to converge.</p></dd>
-
-
-<dt>use_compiled</dt>
-<dd><p>If set to <code>FALSE</code>, no compiled version of the
-<a href="mkinmod.html">mkinmod</a> model is used in the calls to <code><a href="mkinpredict.html">mkinpredict()</a></code> even if a compiled
-version is present.</p></dd>
-
-
-<dt>control</dt>
-<dd><p>A list of control arguments passed to <code><a href="https://rdrr.io/r/stats/nlminb.html" class="external-link">stats::nlminb()</a></code>.</p></dd>
-
-
-<dt>transform_rates</dt>
-<dd><p>Boolean specifying if kinetic rate constants should
-be transformed in the model specification used in the fitting for better
-compliance with the assumption of normal distribution of the estimator. If
-TRUE, also alpha and beta parameters of the FOMC model are
-log-transformed, as well as k1 and k2 rate constants for the DFOP and HS
-models and the break point tb of the HS model.  If FALSE, zero is used as
-a lower bound for the rates in the optimisation.</p></dd>
-
-
-<dt>transform_fractions</dt>
-<dd><p>Boolean specifying if formation fractions
-should be transformed in the model specification used in the fitting for
-better compliance with the assumption of normal distribution of the
-estimator. The default (TRUE) is to do transformations. If TRUE,
-the g parameter of the DFOP model is also transformed. Transformations
-are described in <a href="transform_odeparms.html">transform_odeparms</a>.</p></dd>
-
-
-<dt>quiet</dt>
-<dd><p>Suppress printing out the current value of the negative
-log-likelihood after each improvement?</p></dd>
-
-
-<dt>atol</dt>
-<dd><p>Absolute error tolerance, passed to <code><a href="https://rdrr.io/pkg/deSolve/man/ode.html" class="external-link">deSolve::ode()</a></code>. Default
-is 1e-8, which is lower than the default in the <code><a href="https://rdrr.io/pkg/deSolve/man/lsoda.html" class="external-link">deSolve::lsoda()</a></code>
-function which is used per default.</p></dd>
-
-
-<dt>rtol</dt>
-<dd><p>Absolute error tolerance, passed to <code><a href="https://rdrr.io/pkg/deSolve/man/ode.html" class="external-link">deSolve::ode()</a></code>. Default
-is 1e-10, much lower than in <code><a href="https://rdrr.io/pkg/deSolve/man/lsoda.html" class="external-link">deSolve::lsoda()</a></code>.</p></dd>
-
-
-<dt>error_model</dt>
-<dd><p>If the error model is "const", a constant standard
-deviation is assumed.</p>
-<p>If the error model is "obs", each observed variable is assumed to have its
-own variance.</p>
-<p>If the error model is "tc" (two-component error model), a two component
-error model similar to the one described by Rocke and Lorenzato (1995) is
-used for setting up the likelihood function.  Note that this model
-deviates from the model by Rocke and Lorenzato, as their model implies
-that the errors follow a lognormal distribution for large values, not a
-normal distribution as assumed by this method.</p></dd>
-
-
-<dt>error_model_algorithm</dt>
-<dd><p>If "auto", the selected algorithm depends on
-the error model.  If the error model is "const", unweighted nonlinear
-least squares fitting ("OLS") is selected. If the error model is "obs", or
-"tc", the "d_3" algorithm is selected.</p>
-<p>The algorithm "d_3" will directly minimize the negative log-likelihood
-and independently also use the three step algorithm described below.
-The fit with the higher likelihood is returned.</p>
-<p>The algorithm "direct" will directly minimize the negative log-likelihood.</p>
-<p>The algorithm "twostep" will minimize the negative log-likelihood after an
-initial unweighted least squares optimisation step.</p>
-<p>The algorithm "threestep" starts with unweighted least squares, then
-optimizes only the error model using the degradation model parameters
-found, and then minimizes the negative log-likelihood with free
-degradation and error model parameters.</p>
-<p>The algorithm "fourstep" starts with unweighted least squares, then
-optimizes only the error model using the degradation model parameters
-found, then optimizes the degradation model again with fixed error model
-parameters, and finally minimizes the negative log-likelihood with free
-degradation and error model parameters.</p>
-<p>The algorithm "IRLS" (Iteratively Reweighted Least Squares) starts with
-unweighted least squares, and then iterates optimization of the error
-model parameters and subsequent optimization of the degradation model
-using those error model parameters, until the error model parameters
-converge.</p></dd>
-
-
-<dt>reweight.tol</dt>
-<dd><p>Tolerance for the convergence criterion calculated from
-the error model parameters in IRLS fits.</p></dd>
-
-
-<dt>reweight.max.iter</dt>
-<dd><p>Maximum number of iterations in IRLS fits.</p></dd>
-
-
-<dt>trace_parms</dt>
-<dd><p>Should a trace of the parameter values be listed?</p></dd>
-
-
-<dt>test_residuals</dt>
-<dd><p>Should the residuals be tested for normal distribution?</p></dd>
-
-
-<dt>...</dt>
-<dd><p>Further arguments that will be passed on to
-<code><a href="https://rdrr.io/pkg/deSolve/man/ode.html" class="external-link">deSolve::ode()</a></code>.</p></dd>
-
-</dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>A list with "mkinfit" in the class attribute.</p>
-    </div>
-    <div id="details">
-    <h2>Details</h2>
-    <p>Per default, parameters in the kinetic models are internally transformed in
-order to better satisfy the assumption of a normal distribution of their
-estimators.</p>
-    </div>
-    <div id="note">
-    <h2>Note</h2>
-    <p>When using the "IORE" submodel for metabolites, fitting with
-"transform_rates = TRUE" (the default) often leads to failures of the
-numerical ODE solver. In this situation it may help to switch off the
-internal rate transformation.</p>
-    </div>
-    <div id="references">
-    <h2>References</h2>
-    <p>Rocke DM and Lorenzato S (1995) A two-component model
-for measurement error in analytical chemistry. <em>Technometrics</em> 37(2), 176-184.</p>
-<p>Ranke J and Meinecke S (2019) Error Models for the Kinetic Evaluation of Chemical
-Degradation Data. <em>Environments</em> 6(12) 124
-<a href="https://doi.org/10.3390/environments6120124" class="external-link">doi:10.3390/environments6120124</a>
-.</p>
-    </div>
-    <div id="see-also">
-    <h2>See also</h2>
-    <div class="dont-index"><p><a href="summary.mkinfit.html">summary.mkinfit</a>, <a href="plot.mkinfit.html">plot.mkinfit</a>, <a href="parms.html">parms</a> and <a href="https://rdrr.io/pkg/lmtest/man/lrtest.html" class="external-link">lrtest</a>.</p>
-<p>Comparisons of models fitted to the same data can be made using
-<code><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">AIC</a></code> by virtue of the method <code><a href="logLik.mkinfit.html">logLik.mkinfit</a></code>.</p>
-<p>Fitting of several models to several datasets in a single call to
-<code><a href="mmkin.html">mmkin</a></code>.</p></div>
-    </div>
-    <div id="author">
-    <h2>Author</h2>
-    <p>Johannes Ranke</p>
-    </div>
-
-    <div id="ref-examples">
-    <h2>Examples</h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co"># Use shorthand notation for parent only degradation</span></span></span>
-<span class="r-in"><span><span class="va">fit</span> <span class="op">&lt;-</span> <span class="fu">mkinfit</span><span class="op">(</span><span class="st">"FOMC"</span>, <span class="va">FOCUS_2006_C</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> mkin version used for fitting:    1.2.3 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> R version used for fitting:       4.2.3 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Date of fit:     Sun Apr 16 08:30:40 2023 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Date of summary: Sun Apr 16 08:30:40 2023 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Equations:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Model predictions using solution type analytical </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Fitted using 222 model solutions performed in 0.014 s</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Error model: Constant variance </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Error model algorithm: OLS </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Starting values for parameters to be optimised:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>          value   type</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0  85.1  state</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> alpha      1.0 deparm</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> beta      10.0 deparm</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Starting values for the transformed parameters actually optimised:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>               value lower upper</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0  85.100000  -Inf   Inf</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_alpha  0.000000  -Inf   Inf</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_beta   2.302585  -Inf   Inf</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Fixed parameter values:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> None</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Results:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>        AIC      BIC    logLik</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   44.68652 45.47542 -18.34326</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Optimised, transformed parameters with symmetric confidence intervals:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>           Estimate Std. Error    Lower   Upper</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0  85.87000     1.8070 81.23000 90.5200</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_alpha  0.05192     0.1353 -0.29580  0.3996</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_beta   0.65100     0.2287  0.06315  1.2390</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma      1.85700     0.4378  0.73200  2.9830</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Parameter correlation:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>             parent_0  log_alpha   log_beta     sigma</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0   1.000e+00 -1.565e-01 -3.142e-01 4.772e-08</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_alpha -1.565e-01  1.000e+00  9.564e-01 1.005e-07</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_beta  -3.142e-01  9.564e-01  1.000e+00 8.541e-08</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma      4.772e-08  1.005e-07  8.541e-08 1.000e+00</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Backtransformed parameters:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Confidence intervals for internally transformed parameters are asymmetric.</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> t-test (unrealistically) based on the assumption of normal distribution</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> for estimators of untransformed parameters.</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>          Estimate t value    Pr(&gt;t)   Lower  Upper</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0   85.870  47.530 3.893e-08 81.2300 90.520</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> alpha       1.053   7.393 3.562e-04  0.7439  1.491</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> beta        1.917   4.373 3.601e-03  1.0650  3.451</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma       1.857   4.243 4.074e-03  0.7320  2.983</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> FOCUS Chi2 error levels in percent:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>          err.min n.optim df</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> All data   6.657       3  6</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent     6.657       3  6</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Estimated disappearance times:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         DT50  DT90 DT50back</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent 1.785 15.15     4.56</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Data:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  time variable observed predicted residual</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     0   parent     85.1    85.875  -0.7749</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     1   parent     57.9    55.191   2.7091</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     3   parent     29.9    31.845  -1.9452</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     7   parent     14.6    17.012  -2.4124</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    14   parent      9.7     9.241   0.4590</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    28   parent      6.6     4.754   1.8460</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    63   parent      4.0     2.102   1.8977</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    91   parent      3.9     1.441   2.4590</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   119   parent      0.6     1.092  -0.4919</span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co"># One parent compound, one metabolite, both single first order.</span></span></span>
-<span class="r-in"><span><span class="co"># We remove zero values from FOCUS dataset D in order to avoid warnings</span></span></span>
-<span class="r-in"><span><span class="va">FOCUS_D</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/subset.html" class="external-link">subset</a></span><span class="op">(</span><span class="va">FOCUS_2006_D</span>, <span class="va">value</span> <span class="op">!=</span> <span class="fl">0</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="co"># Use mkinsub for convenience in model formulation. Pathway to sink included per default.</span></span></span>
-<span class="r-in"><span><span class="va">SFO_SFO</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span></span></span>
-<span class="r-in"><span>  parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"m1"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  m1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Temporary DLL for differentials generated and loaded</span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co"># Fit the model quietly to the FOCUS example dataset D using defaults</span></span></span>
-<span class="r-in"><span><span class="va">fit</span> <span class="op">&lt;-</span> <span class="fu">mkinfit</span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="va">FOCUS_D</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></span></span>
-<span class="r-plt img"><img src="mkinfit-1.png" alt="" width="700" height="433"></span>
-<span class="r-in"><span><span class="co"># As lower parent values appear to have lower variance, we try an alternative error model</span></span></span>
-<span class="r-in"><span><span class="va">fit.tc</span> <span class="op">&lt;-</span> <span class="fu">mkinfit</span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="va">FOCUS_D</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, error_model <span class="op">=</span> <span class="st">"tc"</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="co"># This avoids the warning, and the likelihood ratio test confirms it is preferable</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/pkg/lmtest/man/lrtest.html" class="external-link">lrtest</a></span><span class="op">(</span><span class="va">fit.tc</span>, <span class="va">fit</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Likelihood ratio test</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Model 1: SFO_SFO with error model tc and fixed parameter(s) m1_0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Model 2: SFO_SFO with error model const and fixed parameter(s) m1_0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   #Df  LogLik Df  Chisq Pr(&gt;Chisq)    </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1   6 -64.983                         </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2   5 -97.224 -1 64.483  9.737e-16 ***</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> ---</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1</span>
-<span class="r-in"><span><span class="co"># We can also allow for different variances of parent and metabolite as error model</span></span></span>
-<span class="r-in"><span><span class="va">fit.obs</span> <span class="op">&lt;-</span> <span class="fu">mkinfit</span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="va">FOCUS_D</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, error_model <span class="op">=</span> <span class="st">"obs"</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="co"># The two-component error model has significantly higher likelihood</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/pkg/lmtest/man/lrtest.html" class="external-link">lrtest</a></span><span class="op">(</span><span class="va">fit.obs</span>, <span class="va">fit.tc</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Likelihood ratio test</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Model 1: SFO_SFO with error model tc and fixed parameter(s) m1_0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Model 2: SFO_SFO with error model obs and fixed parameter(s) m1_0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   #Df  LogLik Df  Chisq Pr(&gt;Chisq)    </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1   6 -64.983                         </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2   6 -96.936  0 63.907  &lt; 2.2e-16 ***</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> ---</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1</span>
-<span class="r-in"><span><span class="fu"><a href="parms.html">parms</a></span><span class="op">(</span><span class="va">fit.tc</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>       parent_0       k_parent           k_m1 f_parent_to_m1      sigma_low </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   1.007343e+02   1.005562e-01   5.166712e-03   5.083933e-01   3.049883e-03 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>       rsd_high </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   7.928118e-02 </span>
-<span class="r-in"><span><span class="fu"><a href="endpoints.html">endpoints</a></span><span class="op">(</span><span class="va">fit.tc</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> $ff</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   parent_m1 parent_sink </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   0.5083933   0.4916067 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> $distimes</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>             DT50      DT90</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent   6.89313  22.89848</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> m1     134.15634 445.65772</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co"># We can show a quick (only one replication) benchmark for this case, as we</span></span></span>
-<span class="r-in"><span><span class="co"># have several alternative solution methods for the model. We skip</span></span></span>
-<span class="r-in"><span><span class="co"># uncompiled deSolve, as it is so slow. More benchmarks are found in the</span></span></span>
-<span class="r-in"><span><span class="co"># benchmark vignette</span></span></span>
-<span class="r-in"><span><span class="co"># \dontrun{</span></span></span>
-<span class="r-in"><span><span class="kw">if</span><span class="op">(</span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">require</a></span><span class="op">(</span><span class="va"><a href="http://rbenchmark.googlecode.com" class="external-link">rbenchmark</a></span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span></span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/pkg/rbenchmark/man/benchmark.html" class="external-link">benchmark</a></span><span class="op">(</span>replications <span class="op">=</span> <span class="fl">1</span>, order <span class="op">=</span> <span class="st">"relative"</span>, columns <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"test"</span>, <span class="st">"relative"</span>, <span class="st">"elapsed"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>    deSolve_compiled <span class="op">=</span> <span class="fu">mkinfit</span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="va">FOCUS_D</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, error_model <span class="op">=</span> <span class="st">"tc"</span>,</span></span>
-<span class="r-in"><span>      solution_type <span class="op">=</span> <span class="st">"deSolve"</span>, use_compiled <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>    eigen <span class="op">=</span> <span class="fu">mkinfit</span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="va">FOCUS_D</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, error_model <span class="op">=</span> <span class="st">"tc"</span>,</span></span>
-<span class="r-in"><span>      solution_type <span class="op">=</span> <span class="st">"eigen"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>    analytical <span class="op">=</span> <span class="fu">mkinfit</span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="va">FOCUS_D</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, error_model <span class="op">=</span> <span class="st">"tc"</span>,</span></span>
-<span class="r-in"><span>      solution_type <span class="op">=</span> <span class="st">"analytical"</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="op">}</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>               test relative elapsed</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 3       analytical    1.000   0.236</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1 deSolve_compiled    1.263   0.298</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2            eigen    2.373   0.560</span>
-<span class="r-in"><span><span class="co"># }</span></span></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co"># Use stepwise fitting, using optimised parameters from parent only fit, FOMC-SFO</span></span></span>
-<span class="r-in"><span><span class="co"># \dontrun{</span></span></span>
-<span class="r-in"><span><span class="va">FOMC_SFO</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span></span></span>
-<span class="r-in"><span>  parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"FOMC"</span>, <span class="st">"m1"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  m1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Temporary DLL for differentials generated and loaded</span>
-<span class="r-in"><span><span class="va">fit.FOMC_SFO</span> <span class="op">&lt;-</span> <span class="fu">mkinfit</span><span class="op">(</span><span class="va">FOMC_SFO</span>, <span class="va">FOCUS_D</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="co"># Again, we get a warning and try a more sophisticated error model</span></span></span>
-<span class="r-in"><span><span class="va">fit.FOMC_SFO.tc</span> <span class="op">&lt;-</span> <span class="fu">mkinfit</span><span class="op">(</span><span class="va">FOMC_SFO</span>, <span class="va">FOCUS_D</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, error_model <span class="op">=</span> <span class="st">"tc"</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="co"># This model has a higher likelihood, but not significantly so</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/pkg/lmtest/man/lrtest.html" class="external-link">lrtest</a></span><span class="op">(</span><span class="va">fit.tc</span>, <span class="va">fit.FOMC_SFO.tc</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Likelihood ratio test</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Model 1: FOMC_SFO with error model tc and fixed parameter(s) m1_0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Model 2: SFO_SFO with error model tc and fixed parameter(s) m1_0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   #Df  LogLik Df  Chisq Pr(&gt;Chisq)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1   7 -64.829                     </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2   6 -64.983 -1 0.3075     0.5792</span>
-<span class="r-in"><span><span class="co"># Also, the missing standard error for log_beta and the t-tests for alpha</span></span></span>
-<span class="r-in"><span><span class="co"># and beta indicate overparameterisation</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">fit.FOMC_SFO.tc</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>NaNs produced</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>NaNs produced</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>diag(.) had 0 or NA entries; non-finite result is doubtful</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> mkin version used for fitting:    1.2.3 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> R version used for fitting:       4.2.3 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Date of fit:     Sun Apr 16 08:30:44 2023 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Date of summary: Sun Apr 16 08:30:44 2023 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Equations:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> d_m1/dt = + f_parent_to_m1 * (alpha/beta) * 1/((time/beta) + 1) *</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>            parent - k_m1 * m1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Model predictions using solution type deSolve </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Fitted using 3729 model solutions performed in 0.688 s</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Error model: Two-component variance function </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Error model algorithm: d_3 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Direct fitting and three-step fitting yield approximately the same likelihood </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Starting values for parameters to be optimised:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                 value   type</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0       100.75  state</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> alpha            1.00 deparm</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> beta            10.00 deparm</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_m1             0.10 deparm</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_parent_to_m1   0.50 deparm</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma_low        0.10  error</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> rsd_high         0.10  error</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Starting values for the transformed parameters actually optimised:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                      value lower upper</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0        100.750000  -Inf   Inf</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k_m1         -2.302585  -Inf   Inf</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_parent_qlogis   0.000000  -Inf   Inf</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_alpha         0.000000  -Inf   Inf</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_beta          2.302585  -Inf   Inf</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma_low         0.100000     0   Inf</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> rsd_high          0.100000     0   Inf</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Fixed parameter values:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>      value  type</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> m1_0     0 state</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Results:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>       AIC      BIC    logLik</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   143.658 155.1211 -64.82902</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Optimised, transformed parameters with symmetric confidence intervals:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                   Estimate Std. Error     Lower      Upper</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0        101.600000  2.6400000 96.240000 107.000000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k_m1         -5.284000  0.0929100 -5.474000  -5.095000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_parent_qlogis   0.001426  0.0767000 -0.155000   0.157800</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_alpha         5.522000  0.0077320  5.506000   5.538000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_beta          7.806000        NaN       NaN        NaN</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma_low         0.002488  0.0002431  0.001992   0.002984</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> rsd_high          0.079210  0.0093280  0.060180   0.098230</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Parameter correlation:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                  parent_0  log_k_m1 f_parent_qlogis log_alpha log_beta</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0         1.000000 -0.095226        -0.76678   0.70544      NaN</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k_m1        -0.095226  1.000000         0.51432  -0.14387      NaN</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_parent_qlogis -0.766780  0.514321         1.00000  -0.61396      NaN</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_alpha        0.705444 -0.143872        -0.61396   1.00000      NaN</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_beta              NaN       NaN             NaN       NaN        1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma_low        0.016073  0.001586         0.01548   5.87007      NaN</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> rsd_high         0.006626 -0.011700        -0.05357   0.04849      NaN</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                 sigma_low  rsd_high</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0         0.016073  0.006626</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k_m1         0.001586 -0.011700</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_parent_qlogis  0.015476 -0.053566</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_alpha        5.870075  0.048487</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_beta              NaN       NaN</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma_low        1.000000 -0.652558</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> rsd_high        -0.652558  1.000000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Backtransformed parameters:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Confidence intervals for internally transformed parameters are asymmetric.</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> t-test (unrealistically) based on the assumption of normal distribution</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> for estimators of untransformed parameters.</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                 Estimate t value    Pr(&gt;t)     Lower     Upper</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0       1.016e+02 32.7800 6.311e-26 9.624e+01 1.070e+02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_m1           5.072e-03 10.1200 1.216e-11 4.196e-03 6.130e-03</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_parent_to_m1 5.004e-01 20.8300 4.317e-20 4.613e-01 5.394e-01</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> alpha          2.502e+02  0.5624 2.889e-01 2.463e+02 2.542e+02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> beta           2.455e+03  0.5549 2.915e-01        NA        NA</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma_low      2.488e-03  0.4843 3.158e-01 1.992e-03 2.984e-03</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> rsd_high       7.921e-02  8.4300 8.001e-10 6.018e-02 9.823e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> FOCUS Chi2 error levels in percent:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>          err.min n.optim df</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> All data   6.781       5 14</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent     7.141       3  6</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> m1         4.640       2  8</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Resulting formation fractions:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                 ff</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_m1   0.5004</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_sink 0.4996</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Estimated disappearance times:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>           DT50  DT90 DT50back</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent   6.812  22.7    6.834</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> m1     136.661 454.0       NA</span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co"># We can easily use starting parameters from the parent only fit (only for illustration)</span></span></span>
-<span class="r-in"><span><span class="va">fit.FOMC</span> <span class="op">=</span> <span class="fu">mkinfit</span><span class="op">(</span><span class="st">"FOMC"</span>, <span class="va">FOCUS_2006_D</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, error_model <span class="op">=</span> <span class="st">"tc"</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">fit.FOMC_SFO</span> <span class="op">&lt;-</span> <span class="fu">mkinfit</span><span class="op">(</span><span class="va">FOMC_SFO</span>, <span class="va">FOCUS_D</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>,</span></span>
-<span class="r-in"><span>  parms.ini <span class="op">=</span> <span class="va">fit.FOMC</span><span class="op">$</span><span class="va">bparms.ode</span>, error_model <span class="op">=</span> <span class="st">"tc"</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="co"># }</span></span></span>
-</code></pre></div>
-    </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/mkinmod.html b/docs/dev/reference/mkinmod.html
deleted file mode 100644
index 50ec64fa..00000000
--- a/docs/dev/reference/mkinmod.html
+++ /dev/null
@@ -1,426 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Function to set up a kinetic model with one or more state variables — mkinmod • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Function to set up a kinetic model with one or more state variables — mkinmod"><meta property="og:description" content="This function is usually called using a call to mkinsub() for each observed
-variable, specifying the corresponding submodel as well as outgoing pathways
-(see examples).
-Print mkinmod objects in a way that the user finds his way to get to its
-components."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Function to set up a kinetic model with one or more state variables</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/mkinmod.R" class="external-link"><code>R/mkinmod.R</code></a>, <a href="https://github.com/jranke/mkin/blob/HEAD/R/mkinsub.R" class="external-link"><code>R/mkinsub.R</code></a></small>
-    <div class="hidden name"><code>mkinmod.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>This function is usually called using a call to <code>mkinsub()</code> for each observed
-variable, specifying the corresponding submodel as well as outgoing pathways
-(see examples).</p>
-<p>Print mkinmod objects in a way that the user finds his way to get to its
-components.</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">mkinmod</span><span class="op">(</span></span>
-<span>  <span class="va">...</span>,</span>
-<span>  use_of_ff <span class="op">=</span> <span class="st">"max"</span>,</span>
-<span>  name <span class="op">=</span> <span class="cn">NULL</span>,</span>
-<span>  speclist <span class="op">=</span> <span class="cn">NULL</span>,</span>
-<span>  quiet <span class="op">=</span> <span class="cn">FALSE</span>,</span>
-<span>  verbose <span class="op">=</span> <span class="cn">FALSE</span>,</span>
-<span>  dll_dir <span class="op">=</span> <span class="cn">NULL</span>,</span>
-<span>  unload <span class="op">=</span> <span class="cn">FALSE</span>,</span>
-<span>  overwrite <span class="op">=</span> <span class="cn">FALSE</span></span>
-<span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># S3 method for mkinmod</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">mkinsub</span><span class="op">(</span><span class="va">submodel</span>, to <span class="op">=</span> <span class="cn">NULL</span>, sink <span class="op">=</span> <span class="cn">TRUE</span>, full_name <span class="op">=</span> <span class="cn">NA</span><span class="op">)</span></span></code></pre></div>
-    </div>
-
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>...</dt>
-<dd><p>For each observed variable, a list as obtained by <code>mkinsub()</code>
-has to be specified as an argument (see examples).  Currently, single
-first order kinetics "SFO", indeterminate order rate equation kinetics
-"IORE", or single first order with reversible binding "SFORB" are
-implemented for all variables, while "FOMC", "DFOP", "HS" and "logistic"
-can additionally be chosen for the first variable which is assumed to be
-the source compartment.
-Additionally, <code>mkinsub()</code> has an argument <code>to</code>, specifying names of
-variables to which a transfer is to be assumed in the model.
-If the argument <code>use_of_ff</code> is set to "min"
-and the model for the compartment is "SFO" or "SFORB", an
-additional <code>mkinsub()</code> argument can be <code>sink = FALSE</code>, effectively
-fixing the flux to sink to zero.
-In print.mkinmod, this argument is currently not used.</p></dd>
-
-
-<dt>use_of_ff</dt>
-<dd><p>Specification of the use of formation fractions in the
-model equations and, if applicable, the coefficient matrix.  If "max",
-formation fractions are always used (default).  If "min", a minimum use of
-formation fractions is made, i.e. each first-order pathway to a metabolite
-has its own rate constant.</p></dd>
-
-
-<dt>name</dt>
-<dd><p>A name for the model. Should be a valid R object name.</p></dd>
-
-
-<dt>speclist</dt>
-<dd><p>The specification of the observed variables and their
-submodel types and pathways can be given as a single list using this
-argument. Default is NULL.</p></dd>
-
-
-<dt>quiet</dt>
-<dd><p>Should messages be suppressed?</p></dd>
-
-
-<dt>verbose</dt>
-<dd><p>If <code>TRUE</code>, passed to <code><a href="https://rdrr.io/pkg/inline/man/cfunction.html" class="external-link">inline::cfunction()</a></code> if
-applicable to give detailed information about the C function being built.</p></dd>
-
-
-<dt>dll_dir</dt>
-<dd><p>Directory where an DLL object, if generated internally by
-<code><a href="https://rdrr.io/pkg/inline/man/cfunction.html" class="external-link">inline::cfunction()</a></code>, should be saved.  The DLL will only be stored in a
-permanent location for use in future sessions, if 'dll_dir' and 'name'
-are specified. This is helpful if fit objects are cached e.g. by knitr,
-as the cache remains functional across sessions if the DLL is stored in
-a user defined location.</p></dd>
-
-
-<dt>unload</dt>
-<dd><p>If a DLL from the target location in 'dll_dir' is already
-loaded, should that be unloaded first?</p></dd>
-
-
-<dt>overwrite</dt>
-<dd><p>If a file exists at the target DLL location in 'dll_dir',
-should this be overwritten?</p></dd>
-
-
-<dt>x</dt>
-<dd><p>An <code>mkinmod</code> object.</p></dd>
-
-
-<dt>submodel</dt>
-<dd><p>Character vector of length one to specify the submodel type.
-See <code>mkinmod</code> for the list of allowed submodel names.</p></dd>
-
-
-<dt>to</dt>
-<dd><p>Vector of the names of the state variable to which a
-transformation shall be included in the model.</p></dd>
-
-
-<dt>sink</dt>
-<dd><p>Should a pathway to sink be included in the model in addition to
-the pathways to other state variables?</p></dd>
-
-
-<dt>full_name</dt>
-<dd><p>An optional name to be used e.g. for plotting fits
-performed with the model.  You can use non-ASCII characters here, but then
-your R code will not be portable, <em>i.e.</em> may produce unintended plot
-results on other operating systems or system configurations.</p></dd>
-
-</dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>A list of class <code>mkinmod</code> for use with <code><a href="mkinfit.html">mkinfit()</a></code>,
-containing, among others,</p>
-<dl><dt>diffs</dt>
-<dd><p>A vector of string representations of differential equations, one for
-each modelling variable.</p></dd>
-
-<dt>map</dt>
-<dd><p>A list containing named character vectors for each observed variable,
-specifying the modelling variables by which it is represented.</p></dd>
-
-<dt>use_of_ff</dt>
-<dd><p>The content of <code>use_of_ff</code> is passed on in this list component.</p></dd>
-
-<dt>deg_func</dt>
-<dd><p>If generated, a function containing the solution of the degradation
-model.</p></dd>
-
-<dt>coefmat</dt>
-<dd><p>The coefficient matrix, if the system of differential equations can be
-represented by one.</p></dd>
-
-<dt>cf</dt>
-<dd><p>If generated, a compiled function calculating the derivatives as
-returned by cfunction.</p></dd>
-
-
-</dl><p>A list for use with <code>mkinmod</code>.</p>
-    </div>
-    <div id="details">
-    <h2>Details</h2>
-    <p>For the definition of model types and their parameters, the equations given
-in the FOCUS and NAFTA guidance documents are used.</p>
-<p>For kinetic models with more than one observed variable, a symbolic solution
-of the system of differential equations is included in the resulting
-mkinmod object in some cases, speeding up the solution.</p>
-<p>If a C compiler is found by <code><a href="https://r-lib.github.io/pkgbuild/reference/has_compiler.html" class="external-link">pkgbuild::has_compiler()</a></code> and there
-is more than one observed variable in the specification, C code is generated
-for evaluating the differential equations, compiled using
-<code><a href="https://rdrr.io/pkg/inline/man/cfunction.html" class="external-link">inline::cfunction()</a></code> and added to the resulting mkinmod object.</p>
-    </div>
-    <div id="note">
-    <h2>Note</h2>
-    <p>The IORE submodel is not well tested for metabolites. When using this
-model for metabolites, you may want to read the note in the help
-page to <a href="mkinfit.html">mkinfit</a>.</p>
-    </div>
-    <div id="references">
-    <h2>References</h2>
-    <p>FOCUS (2006) “Guidance Document on Estimating Persistence
-and Degradation Kinetics from Environmental Fate Studies on Pesticides in
-EU Registration” Report of the FOCUS Work Group on Degradation Kinetics,
-EC Document Reference Sanco/10058/2005 version 2.0, 434 pp,
-<a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics" class="external-link">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p>
-<p>NAFTA Technical Working Group on Pesticides (not dated) Guidance for
-Evaluating and Calculating Degradation Kinetics in Environmental Media</p>
-    </div>
-    <div id="author">
-    <h2>Author</h2>
-    <p>Johannes Ranke</p>
-    </div>
-
-    <div id="ref-examples">
-    <h2>Examples</h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co"># Specify the SFO model (this is not needed any more, as we can now mkinfit("SFO", ...)</span></span></span>
-<span class="r-in"><span><span class="va">SFO</span> <span class="op">&lt;-</span> <span class="fu">mkinmod</span><span class="op">(</span>parent <span class="op">=</span> <span class="fu">mkinsub</span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co"># One parent compound, one metabolite, both single first order</span></span></span>
-<span class="r-in"><span><span class="va">SFO_SFO</span> <span class="op">&lt;-</span> <span class="fu">mkinmod</span><span class="op">(</span></span></span>
-<span class="r-in"><span>  parent <span class="op">=</span> <span class="fu">mkinsub</span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"m1"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  m1 <span class="op">=</span> <span class="fu">mkinsub</span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Temporary DLL for differentials generated and loaded</span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">SFO_SFO</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> &lt;mkinmod&gt; model generated with</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Use of formation fractions $use_of_ff: max </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Specification $spec:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> $parent</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> $type: SFO; $to: m1; $sink: TRUE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> $m1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> $type: SFO; $sink: TRUE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Coefficient matrix $coefmat available</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Compiled model $cf available</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Differential equations:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> d_parent/dt = - k_parent * parent</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> d_m1/dt = + f_parent_to_m1 * k_parent * parent - k_m1 * m1</span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co"># \dontrun{</span></span></span>
-<span class="r-in"><span> <span class="va">fit_sfo_sfo</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="va">FOCUS_2006_D</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, solution_type <span class="op">=</span> <span class="st">"deSolve"</span><span class="op">)</span></span></span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>Observations with value of zero were removed from the data</span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span> <span class="co"># Now supplying compound names used for plotting, and write to user defined location</span></span></span>
-<span class="r-in"><span> <span class="co"># We need to choose a path outside the session tempdir because this gets removed</span></span></span>
-<span class="r-in"><span> <span class="va">DLL_dir</span> <span class="op">&lt;-</span> <span class="st">"~/.local/share/mkin"</span></span></span>
-<span class="r-in"><span> <span class="kw">if</span> <span class="op">(</span><span class="op">!</span><span class="fu"><a href="https://rdrr.io/r/base/files2.html" class="external-link">dir.exists</a></span><span class="op">(</span><span class="va">DLL_dir</span><span class="op">)</span><span class="op">)</span> <span class="fu"><a href="https://rdrr.io/r/base/files2.html" class="external-link">dir.create</a></span><span class="op">(</span><span class="va">DLL_dir</span><span class="op">)</span></span></span>
-<span class="r-in"><span> <span class="va">SFO_SFO.2</span> <span class="op">&lt;-</span> <span class="fu">mkinmod</span><span class="op">(</span></span></span>
-<span class="r-in"><span>   parent <span class="op">=</span> <span class="fu">mkinsub</span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"m1"</span>, full_name <span class="op">=</span> <span class="st">"Test compound"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>   m1 <span class="op">=</span> <span class="fu">mkinsub</span><span class="op">(</span><span class="st">"SFO"</span>, full_name <span class="op">=</span> <span class="st">"Metabolite M1"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>   name <span class="op">=</span> <span class="st">"SFO_SFO"</span>, dll_dir <span class="op">=</span> <span class="va">DLL_dir</span>, unload <span class="op">=</span> <span class="cn">TRUE</span>, overwrite <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Temporary DLL for differentials generated and loaded</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Copied DLL from /tmp/RtmpiGRJ6t/file5c7f7365e02a0.so to /home/jranke/.local/share/mkin/SFO_SFO.so</span>
-<span class="r-in"><span><span class="co"># Now we can save the model and restore it in a new session</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/readRDS.html" class="external-link">saveRDS</a></span><span class="op">(</span><span class="va">SFO_SFO.2</span>, file <span class="op">=</span> <span class="st">"~/SFO_SFO.rds"</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="co"># Terminate the R session here if you would like to check, and then do</span></span></span>
-<span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://pkgdown.jrwb.de/mkin/">mkin</a></span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">SFO_SFO.3</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/readRDS.html" class="external-link">readRDS</a></span><span class="op">(</span><span class="st">"~/SFO_SFO.rds"</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">fit_sfo_sfo</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO.3</span>, <span class="va">FOCUS_2006_D</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, solution_type <span class="op">=</span> <span class="st">"deSolve"</span><span class="op">)</span></span></span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>Observations with value of zero were removed from the data</span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co"># Show details of creating the C function</span></span></span>
-<span class="r-in"><span><span class="va">SFO_SFO</span> <span class="op">&lt;-</span> <span class="fu">mkinmod</span><span class="op">(</span></span></span>
-<span class="r-in"><span>  parent <span class="op">=</span> <span class="fu">mkinsub</span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"m1"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  m1 <span class="op">=</span> <span class="fu">mkinsub</span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, verbose <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Program source:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   1: #include &lt;R.h&gt;</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   2: </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   3: </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   4: static double parms [3];</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   5: #define k_parent parms[0]</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   6: #define f_parent_to_m1 parms[1]</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   7: #define k_m1 parms[2]</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   8: </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   9: void initpar(void (* odeparms)(int *, double *)) {</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  10:     int N = 3;</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  11:     odeparms(&amp;N, parms);</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  12: }</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  13: </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  14: </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  15: void diffs ( int * n, double * t, double * y, double * f, double * rpar, int * ipar ) {</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  16: </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  17: f[0] = - k_parent * y[0];</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  18: f[1] = + f_parent_to_m1 * k_parent * y[0] - k_m1 * y[1];</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  19: }</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Temporary DLL for differentials generated and loaded</span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co"># The symbolic solution which is available in this case is not</span></span></span>
-<span class="r-in"><span><span class="co"># made for human reading but for speed of computation</span></span></span>
-<span class="r-in"><span><span class="va">SFO_SFO</span><span class="op">$</span><span class="va">deg_func</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> function (observed, odeini, odeparms) </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> {</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     predicted &lt;- numeric(0)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     with(as.list(odeparms), {</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         t &lt;- observed[observed$name == "parent", "time"]</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         predicted &lt;&lt;- c(predicted, SFO.solution(t, odeini["parent"], </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>             k_parent))</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         t &lt;- observed[observed$name == "m1", "time"]</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         predicted &lt;&lt;- c(predicted, (((k_m1 - k_parent) * odeini["m1"] - </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>             f_parent_to_m1 * k_parent * odeini["parent"]) * exp(-k_m1 * </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>             t) + f_parent_to_m1 * k_parent * odeini["parent"] * </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>             exp(-k_parent * t))/(k_m1 - k_parent))</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     })</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     return(predicted)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> }</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> &lt;environment: 0x555558bd6708&gt;</span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co"># If we have several parallel metabolites</span></span></span>
-<span class="r-in"><span><span class="co"># (compare tests/testthat/test_synthetic_data_for_UBA_2014.R)</span></span></span>
-<span class="r-in"><span><span class="va">m_synth_DFOP_par</span> <span class="op">&lt;-</span> <span class="fu">mkinmod</span><span class="op">(</span></span></span>
-<span class="r-in"><span> parent <span class="op">=</span> <span class="fu">mkinsub</span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"M1"</span>, <span class="st">"M2"</span><span class="op">)</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span> M1 <span class="op">=</span> <span class="fu">mkinsub</span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span> M2 <span class="op">=</span> <span class="fu">mkinsub</span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span> quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="va">fit_DFOP_par_c</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">m_synth_DFOP_par</span>,</span></span>
-<span class="r-in"><span>  <span class="va">synthetic_data_for_UBA_2014</span><span class="op">[[</span><span class="fl">12</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">data</span>,</span></span>
-<span class="r-in"><span>  quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="co"># }</span></span></span>
-<span class="r-in"><span></span></span>
-</code></pre></div>
-    </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/mkinparplot-1.png b/docs/dev/reference/mkinparplot-1.png
deleted file mode 100644
index 6e7bf34f..00000000
--- a/docs/dev/reference/mkinparplot-1.png
+++ /dev/null
diff --git a/docs/dev/reference/mkinparplot.html b/docs/dev/reference/mkinparplot.html
deleted file mode 100644
index f7d404ef..00000000
--- a/docs/dev/reference/mkinparplot.html
+++ /dev/null
@@ -1,177 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Function to plot the confidence intervals obtained using mkinfit — mkinparplot • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Function to plot the confidence intervals obtained using mkinfit — mkinparplot"><meta property="og:description" content="This function plots the confidence intervals for the parameters fitted using
-mkinfit."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Function to plot the confidence intervals obtained using mkinfit</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/mkinparplot.R" class="external-link"><code>R/mkinparplot.R</code></a></small>
-    <div class="hidden name"><code>mkinparplot.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>This function plots the confidence intervals for the parameters fitted using
-<code><a href="mkinfit.html">mkinfit</a></code>.</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">mkinparplot</span><span class="op">(</span><span class="va">object</span><span class="op">)</span></span></code></pre></div>
-    </div>
-
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>object</dt>
-<dd><p>A fit represented in an <code><a href="mkinfit.html">mkinfit</a></code> object.</p></dd>
-
-</dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>Nothing is returned by this function, as it is called for its side
-effect, namely to produce a plot.</p>
-    </div>
-    <div id="author">
-    <h2>Author</h2>
-    <p>Johannes Ranke</p>
-    </div>
-
-    <div id="ref-examples">
-    <h2>Examples</h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co"># \dontrun{</span></span></span>
-<span class="r-in"><span><span class="va">model</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span></span></span>
-<span class="r-in"><span>  T245 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, to <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"phenol"</span><span class="op">)</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  phenol <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, to <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"anisole"</span><span class="op">)</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  anisole <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Temporary DLL for differentials generated and loaded</span>
-<span class="r-in"><span><span class="va">fit</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">model</span>, <span class="fu"><a href="https://rdrr.io/r/base/subset.html" class="external-link">subset</a></span><span class="op">(</span><span class="va">mccall81_245T</span>, <span class="va">soil</span> <span class="op">==</span> <span class="st">"Commerce"</span><span class="op">)</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>Observations with value of zero were removed from the data</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>Optimisation did not converge:</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> false convergence (8)</span>
-<span class="r-in"><span><span class="fu">mkinparplot</span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></span></span>
-<span class="r-plt img"><img src="mkinparplot-1.png" alt="" width="700" height="433"></span>
-<span class="r-in"><span><span class="co"># }</span></span></span>
-</code></pre></div>
-    </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/mkinplot.html b/docs/dev/reference/mkinplot.html
deleted file mode 100644
index d83ea3f4..00000000
--- a/docs/dev/reference/mkinplot.html
+++ /dev/null
@@ -1,162 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Plot the observed data and the fitted model of an mkinfit object — mkinplot • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Plot the observed data and the fitted model of an mkinfit object — mkinplot"><meta property="og:description" content="Deprecated function. It now only calls the plot method
-plot.mkinfit."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Plot the observed data and the fitted model of an mkinfit object</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/plot.mkinfit.R" class="external-link"><code>R/plot.mkinfit.R</code></a></small>
-    <div class="hidden name"><code>mkinplot.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Deprecated function. It now only calls the plot method
-<code><a href="plot.mkinfit.html">plot.mkinfit</a></code>.</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">mkinplot</span><span class="op">(</span><span class="va">fit</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
-    </div>
-
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>fit</dt>
-<dd><p>an object of class <code><a href="mkinfit.html">mkinfit</a></code>.</p></dd>
-
-
-<dt>...</dt>
-<dd><p>further arguments passed to <code><a href="plot.mkinfit.html">plot.mkinfit</a></code>.</p></dd>
-
-</dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>The function is called for its side effect.</p>
-    </div>
-    <div id="author">
-    <h2>Author</h2>
-    <p>Johannes Ranke</p>
-    </div>
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/mkinpredict.html b/docs/dev/reference/mkinpredict.html
deleted file mode 100644
index 7d8e7c26..00000000
--- a/docs/dev/reference/mkinpredict.html
+++ /dev/null
@@ -1,437 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Produce predictions from a kinetic model using specific parameters — mkinpredict • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Produce predictions from a kinetic model using specific parameters — mkinpredict"><meta property="og:description" content="This function produces a time series for all the observed variables in a
-kinetic model as specified by mkinmod, using a specific set of
-kinetic parameters and initial values for the state variables."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Produce predictions from a kinetic model using specific parameters</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/mkinpredict.R" class="external-link"><code>R/mkinpredict.R</code></a></small>
-    <div class="hidden name"><code>mkinpredict.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>This function produces a time series for all the observed variables in a
-kinetic model as specified by <a href="mkinmod.html">mkinmod</a>, using a specific set of
-kinetic parameters and initial values for the state variables.</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">mkinpredict</span><span class="op">(</span><span class="va">x</span>, <span class="va">odeparms</span>, <span class="va">odeini</span>, <span class="va">outtimes</span>, <span class="va">...</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># S3 method for mkinmod</span></span>
-<span><span class="fu">mkinpredict</span><span class="op">(</span></span>
-<span>  <span class="va">x</span>,</span>
-<span>  odeparms <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_parent_sink <span class="op">=</span> <span class="fl">0.1</span><span class="op">)</span>,</span>
-<span>  odeini <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fl">100</span><span class="op">)</span>,</span>
-<span>  outtimes <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html" class="external-link">seq</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">120</span>, by <span class="op">=</span> <span class="fl">0.1</span><span class="op">)</span>,</span>
-<span>  solution_type <span class="op">=</span> <span class="st">"deSolve"</span>,</span>
-<span>  use_compiled <span class="op">=</span> <span class="st">"auto"</span>,</span>
-<span>  use_symbols <span class="op">=</span> <span class="cn">FALSE</span>,</span>
-<span>  method.ode <span class="op">=</span> <span class="st">"lsoda"</span>,</span>
-<span>  atol <span class="op">=</span> <span class="fl">1e-08</span>,</span>
-<span>  rtol <span class="op">=</span> <span class="fl">1e-10</span>,</span>
-<span>  maxsteps <span class="op">=</span> <span class="fl">20000L</span>,</span>
-<span>  map_output <span class="op">=</span> <span class="cn">TRUE</span>,</span>
-<span>  na_stop <span class="op">=</span> <span class="cn">TRUE</span>,</span>
-<span>  <span class="va">...</span></span>
-<span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># S3 method for mkinfit</span></span>
-<span><span class="fu">mkinpredict</span><span class="op">(</span></span>
-<span>  <span class="va">x</span>,</span>
-<span>  odeparms <span class="op">=</span> <span class="va">x</span><span class="op">$</span><span class="va">bparms.ode</span>,</span>
-<span>  odeini <span class="op">=</span> <span class="va">x</span><span class="op">$</span><span class="va">bparms.state</span>,</span>
-<span>  outtimes <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html" class="external-link">seq</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">120</span>, by <span class="op">=</span> <span class="fl">0.1</span><span class="op">)</span>,</span>
-<span>  solution_type <span class="op">=</span> <span class="st">"deSolve"</span>,</span>
-<span>  use_compiled <span class="op">=</span> <span class="st">"auto"</span>,</span>
-<span>  method.ode <span class="op">=</span> <span class="st">"lsoda"</span>,</span>
-<span>  atol <span class="op">=</span> <span class="fl">1e-08</span>,</span>
-<span>  rtol <span class="op">=</span> <span class="fl">1e-10</span>,</span>
-<span>  map_output <span class="op">=</span> <span class="cn">TRUE</span>,</span>
-<span>  <span class="va">...</span></span>
-<span><span class="op">)</span></span></code></pre></div>
-    </div>
-
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>x</dt>
-<dd><p>A kinetic model as produced by <a href="mkinmod.html">mkinmod</a>, or a kinetic fit as
-fitted by <a href="mkinfit.html">mkinfit</a>. In the latter case, the fitted parameters are used for
-the prediction.</p></dd>
-
-
-<dt>odeparms</dt>
-<dd><p>A numeric vector specifying the parameters used in the
-kinetic model, which is generally defined as a set of ordinary differential
-equations.</p></dd>
-
-
-<dt>odeini</dt>
-<dd><p>A numeric vector containing the initial values of the state
-variables of the model. Note that the state variables can differ from the
-observed variables, for example in the case of the SFORB model.</p></dd>
-
-
-<dt>outtimes</dt>
-<dd><p>A numeric vector specifying the time points for which model
-predictions should be generated.</p></dd>
-
-
-<dt>...</dt>
-<dd><p>Further arguments passed to the ode solver in case such a
-solver is used.</p></dd>
-
-
-<dt>solution_type</dt>
-<dd><p>The method that should be used for producing the
-predictions. This should generally be "analytical" if there is only one
-observed variable, and usually "deSolve" in the case of several observed
-variables. The third possibility "eigen" is fast in comparison to uncompiled
-ODE models, but not applicable to some models, e.g. using FOMC for the
-parent compound.</p></dd>
-
-
-<dt>use_compiled</dt>
-<dd><p>If set to <code>FALSE</code>, no compiled version of the
-<a href="mkinmod.html">mkinmod</a> model is used, even if is present.</p></dd>
-
-
-<dt>use_symbols</dt>
-<dd><p>If set to <code>TRUE</code> (default), symbol info present in
-the <a href="mkinmod.html">mkinmod</a> object is used if available for accessing compiled code</p></dd>
-
-
-<dt>method.ode</dt>
-<dd><p>The solution method passed via mkinpredict to ode] in
-case the solution type is "deSolve" and we are not using compiled code.
-When using compiled code, only lsoda is supported.</p></dd>
-
-
-<dt>atol</dt>
-<dd><p>Absolute error tolerance, passed to the ode solver.</p></dd>
-
-
-<dt>rtol</dt>
-<dd><p>Absolute error tolerance, passed to the ode solver.</p></dd>
-
-
-<dt>maxsteps</dt>
-<dd><p>Maximum number of steps, passed to the ode solver.</p></dd>
-
-
-<dt>map_output</dt>
-<dd><p>Boolean to specify if the output should list values for
-the observed variables (default) or for all state variables (if set to
-FALSE). Setting this to FALSE has no effect for analytical solutions,
-as these always return mapped output.</p></dd>
-
-
-<dt>na_stop</dt>
-<dd><p>Should it be an error if ode returns NaN values</p></dd>
-
-</dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>A matrix with the numeric solution in wide format</p>
-    </div>
-    <div id="author">
-    <h2>Author</h2>
-    <p>Johannes Ranke</p>
-    </div>
-
-    <div id="ref-examples">
-    <h2>Examples</h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="va">SFO</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>degradinol <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="co"># Compare solution types</span></span></span>
-<span class="r-in"><span><span class="fu">mkinpredict</span><span class="op">(</span><span class="va">SFO</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_degradinol <span class="op">=</span> <span class="fl">0.3</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>degradinol <span class="op">=</span> <span class="fl">100</span><span class="op">)</span>, <span class="fl">0</span><span class="op">:</span><span class="fl">20</span>,</span></span>
-<span class="r-in"><span>      solution_type <span class="op">=</span> <span class="st">"analytical"</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    time  degradinol</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 0     0 100.0000000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1     1  74.0818221</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2     2  54.8811636</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 3     3  40.6569660</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 4     4  30.1194212</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5     5  22.3130160</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 6     6  16.5298888</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 7     7  12.2456428</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 8     8   9.0717953</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 9     9   6.7205513</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 10   10   4.9787068</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 11   11   3.6883167</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 12   12   2.7323722</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 13   13   2.0241911</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 14   14   1.4995577</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 15   15   1.1108997</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 16   16   0.8229747</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 17   17   0.6096747</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 18   18   0.4516581</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 19   19   0.3345965</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 20   20   0.2478752</span>
-<span class="r-in"><span><span class="fu">mkinpredict</span><span class="op">(</span><span class="va">SFO</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_degradinol <span class="op">=</span> <span class="fl">0.3</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>degradinol <span class="op">=</span> <span class="fl">100</span><span class="op">)</span>, <span class="fl">0</span><span class="op">:</span><span class="fl">20</span>,</span></span>
-<span class="r-in"><span>      solution_type <span class="op">=</span> <span class="st">"deSolve"</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    time  degradinol</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 0     0 100.0000000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1     1  74.0818221</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2     2  54.8811636</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 3     3  40.6569660</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 4     4  30.1194212</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5     5  22.3130160</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 6     6  16.5298888</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 7     7  12.2456428</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 8     8   9.0717953</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 9     9   6.7205513</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 10   10   4.9787068</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 11   11   3.6883167</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 12   12   2.7323722</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 13   13   2.0241911</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 14   14   1.4995577</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 15   15   1.1108996</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 16   16   0.8229747</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 17   17   0.6096747</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 18   18   0.4516581</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 19   19   0.3345965</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 20   20   0.2478752</span>
-<span class="r-in"><span><span class="fu">mkinpredict</span><span class="op">(</span><span class="va">SFO</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_degradinol <span class="op">=</span> <span class="fl">0.3</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>degradinol <span class="op">=</span> <span class="fl">100</span><span class="op">)</span>, <span class="fl">0</span><span class="op">:</span><span class="fl">20</span>,</span></span>
-<span class="r-in"><span>      solution_type <span class="op">=</span> <span class="st">"deSolve"</span>, use_compiled <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    time  degradinol</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 0     0 100.0000000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1     1  74.0818221</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2     2  54.8811636</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 3     3  40.6569660</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 4     4  30.1194212</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5     5  22.3130160</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 6     6  16.5298888</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 7     7  12.2456428</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 8     8   9.0717953</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 9     9   6.7205513</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 10   10   4.9787068</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 11   11   3.6883167</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 12   12   2.7323722</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 13   13   2.0241911</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 14   14   1.4995577</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 15   15   1.1108996</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 16   16   0.8229747</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 17   17   0.6096747</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 18   18   0.4516581</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 19   19   0.3345965</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 20   20   0.2478752</span>
-<span class="r-in"><span><span class="fu">mkinpredict</span><span class="op">(</span><span class="va">SFO</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_degradinol <span class="op">=</span> <span class="fl">0.3</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>degradinol <span class="op">=</span> <span class="fl">100</span><span class="op">)</span>, <span class="fl">0</span><span class="op">:</span><span class="fl">20</span>,</span></span>
-<span class="r-in"><span>      solution_type <span class="op">=</span> <span class="st">"eigen"</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    time  degradinol</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 0     0 100.0000000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1     1  74.0818221</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2     2  54.8811636</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 3     3  40.6569660</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 4     4  30.1194212</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5     5  22.3130160</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 6     6  16.5298888</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 7     7  12.2456428</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 8     8   9.0717953</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 9     9   6.7205513</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 10   10   4.9787068</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 11   11   3.6883167</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 12   12   2.7323722</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 13   13   2.0241911</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 14   14   1.4995577</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 15   15   1.1108997</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 16   16   0.8229747</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 17   17   0.6096747</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 18   18   0.4516581</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 19   19   0.3345965</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 20   20   0.2478752</span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co"># Compare integration methods to analytical solution</span></span></span>
-<span class="r-in"><span><span class="fu">mkinpredict</span><span class="op">(</span><span class="va">SFO</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_degradinol <span class="op">=</span> <span class="fl">0.3</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>degradinol <span class="op">=</span> <span class="fl">100</span><span class="op">)</span>, <span class="fl">0</span><span class="op">:</span><span class="fl">20</span>,</span></span>
-<span class="r-in"><span>      solution_type <span class="op">=</span> <span class="st">"analytical"</span><span class="op">)</span><span class="op">[</span><span class="fl">21</span>,<span class="op">]</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>       time degradinol </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 20.0000000  0.2478752 </span>
-<span class="r-in"><span><span class="fu">mkinpredict</span><span class="op">(</span><span class="va">SFO</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_degradinol <span class="op">=</span> <span class="fl">0.3</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>degradinol <span class="op">=</span> <span class="fl">100</span><span class="op">)</span>, <span class="fl">0</span><span class="op">:</span><span class="fl">20</span>,</span></span>
-<span class="r-in"><span>      method <span class="op">=</span> <span class="st">"lsoda"</span>, use_compiled <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span><span class="op">[</span><span class="fl">21</span>,<span class="op">]</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>       time degradinol </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 20.0000000  0.2478752 </span>
-<span class="r-in"><span><span class="fu">mkinpredict</span><span class="op">(</span><span class="va">SFO</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_degradinol <span class="op">=</span> <span class="fl">0.3</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>degradinol <span class="op">=</span> <span class="fl">100</span><span class="op">)</span>, <span class="fl">0</span><span class="op">:</span><span class="fl">20</span>,</span></span>
-<span class="r-in"><span>      method <span class="op">=</span> <span class="st">"ode45"</span>, use_compiled <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span><span class="op">[</span><span class="fl">21</span>,<span class="op">]</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>       time degradinol </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 20.0000000  0.2478752 </span>
-<span class="r-in"><span><span class="fu">mkinpredict</span><span class="op">(</span><span class="va">SFO</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_degradinol <span class="op">=</span> <span class="fl">0.3</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>degradinol <span class="op">=</span> <span class="fl">100</span><span class="op">)</span>, <span class="fl">0</span><span class="op">:</span><span class="fl">20</span>,</span></span>
-<span class="r-in"><span>      method <span class="op">=</span> <span class="st">"rk4"</span>, use_compiled <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span><span class="op">[</span><span class="fl">21</span>,<span class="op">]</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>       time degradinol </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 20.0000000  0.2480043 </span>
-<span class="r-in"><span><span class="co"># rk4 is not as precise here</span></span></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co"># The number of output times used to make a lot of difference until the</span></span></span>
-<span class="r-in"><span><span class="co"># default for atol was adjusted</span></span></span>
-<span class="r-in"><span><span class="fu">mkinpredict</span><span class="op">(</span><span class="va">SFO</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_degradinol <span class="op">=</span> <span class="fl">0.3</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>degradinol <span class="op">=</span> <span class="fl">100</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>      <span class="fu"><a href="https://rdrr.io/r/base/seq.html" class="external-link">seq</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">20</span>, by <span class="op">=</span> <span class="fl">0.1</span><span class="op">)</span><span class="op">)</span><span class="op">[</span><span class="fl">201</span>,<span class="op">]</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>       time degradinol </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 20.0000000  0.2478752 </span>
-<span class="r-in"><span><span class="fu">mkinpredict</span><span class="op">(</span><span class="va">SFO</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_degradinol <span class="op">=</span> <span class="fl">0.3</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>degradinol <span class="op">=</span> <span class="fl">100</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>      <span class="fu"><a href="https://rdrr.io/r/base/seq.html" class="external-link">seq</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">20</span>, by <span class="op">=</span> <span class="fl">0.01</span><span class="op">)</span><span class="op">)</span><span class="op">[</span><span class="fl">2001</span>,<span class="op">]</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>       time degradinol </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 20.0000000  0.2478752 </span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co"># Comparison of the performance of solution types</span></span></span>
-<span class="r-in"><span><span class="va">SFO_SFO</span> <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"m1"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>                  m1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span>, use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Temporary DLL for differentials generated and loaded</span>
-<span class="r-in"><span><span class="kw">if</span><span class="op">(</span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">require</a></span><span class="op">(</span><span class="va"><a href="http://rbenchmark.googlecode.com" class="external-link">rbenchmark</a></span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span></span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/pkg/rbenchmark/man/benchmark.html" class="external-link">benchmark</a></span><span class="op">(</span>replications <span class="op">=</span> <span class="fl">10</span>, order <span class="op">=</span> <span class="st">"relative"</span>, columns <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"test"</span>, <span class="st">"relative"</span>, <span class="st">"elapsed"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>    eigen <span class="op">=</span> <span class="fu">mkinpredict</span><span class="op">(</span><span class="va">SFO_SFO</span>,</span></span>
-<span class="r-in"><span>      <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_parent <span class="op">=</span> <span class="fl">0.15</span>, f_parent_to_m1 <span class="op">=</span> <span class="fl">0.5</span>, k_m1 <span class="op">=</span> <span class="fl">0.01</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>      <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fl">100</span>, m1 <span class="op">=</span> <span class="fl">0</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/seq.html" class="external-link">seq</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">20</span>, by <span class="op">=</span> <span class="fl">0.1</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>      solution_type <span class="op">=</span> <span class="st">"eigen"</span><span class="op">)</span><span class="op">[</span><span class="fl">201</span>,<span class="op">]</span>,</span></span>
-<span class="r-in"><span>    deSolve_compiled <span class="op">=</span> <span class="fu">mkinpredict</span><span class="op">(</span><span class="va">SFO_SFO</span>,</span></span>
-<span class="r-in"><span>      <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_parent <span class="op">=</span> <span class="fl">0.15</span>, f_parent_to_m1 <span class="op">=</span> <span class="fl">0.5</span>, k_m1 <span class="op">=</span> <span class="fl">0.01</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>      <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fl">100</span>, m1 <span class="op">=</span> <span class="fl">0</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/seq.html" class="external-link">seq</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">20</span>, by <span class="op">=</span> <span class="fl">0.1</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>      solution_type <span class="op">=</span> <span class="st">"deSolve"</span><span class="op">)</span><span class="op">[</span><span class="fl">201</span>,<span class="op">]</span>,</span></span>
-<span class="r-in"><span>    deSolve <span class="op">=</span> <span class="fu">mkinpredict</span><span class="op">(</span><span class="va">SFO_SFO</span>,</span></span>
-<span class="r-in"><span>      <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_parent <span class="op">=</span> <span class="fl">0.15</span>, f_parent_to_m1 <span class="op">=</span> <span class="fl">0.5</span>, k_m1 <span class="op">=</span> <span class="fl">0.01</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>      <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fl">100</span>, m1 <span class="op">=</span> <span class="fl">0</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/seq.html" class="external-link">seq</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">20</span>, by <span class="op">=</span> <span class="fl">0.1</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>      solution_type <span class="op">=</span> <span class="st">"deSolve"</span>, use_compiled <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span><span class="op">[</span><span class="fl">201</span>,<span class="op">]</span>,</span></span>
-<span class="r-in"><span>    analytical <span class="op">=</span> <span class="fu">mkinpredict</span><span class="op">(</span><span class="va">SFO_SFO</span>,</span></span>
-<span class="r-in"><span>      <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_parent <span class="op">=</span> <span class="fl">0.15</span>, f_parent_to_m1 <span class="op">=</span> <span class="fl">0.5</span>, k_m1 <span class="op">=</span> <span class="fl">0.01</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>      <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fl">100</span>, m1 <span class="op">=</span> <span class="fl">0</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/seq.html" class="external-link">seq</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">20</span>, by <span class="op">=</span> <span class="fl">0.1</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>      solution_type <span class="op">=</span> <span class="st">"analytical"</span>, use_compiled <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span><span class="op">[</span><span class="fl">201</span>,<span class="op">]</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="op">}</span></span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Loading required package: rbenchmark</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>               test relative elapsed</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2 deSolve_compiled     1.00   0.004</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1            eigen     4.00   0.016</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 4       analytical     4.25   0.017</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 3          deSolve    40.75   0.163</span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co"># \dontrun{</span></span></span>
-<span class="r-in"><span>  <span class="co"># Predict from a fitted model</span></span></span>
-<span class="r-in"><span>  <span class="va">f</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="va">FOCUS_2006_C</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-in"><span>  <span class="va">f</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="va">FOCUS_2006_C</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, solution_type <span class="op">=</span> <span class="st">"deSolve"</span><span class="op">)</span></span></span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="fu">mkinpredict</span><span class="op">(</span><span class="va">f</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-err co"><span class="r-pr">#&gt;</span> <span class="error">Error in !is.null(x$symbols) &amp; use_symbols:</span> operations are possible only for numeric, logical or complex types</span>
-<span class="r-in"><span><span class="co"># }</span></span></span>
-<span class="r-in"><span></span></span>
-</code></pre></div>
-    </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/mkinresplot-1.png b/docs/dev/reference/mkinresplot-1.png
deleted file mode 100644
index 7c64d0f0..00000000
--- a/docs/dev/reference/mkinresplot-1.png
+++ /dev/null
diff --git a/docs/dev/reference/mkinresplot.html b/docs/dev/reference/mkinresplot.html
deleted file mode 100644
index e73ac93c..00000000
--- a/docs/dev/reference/mkinresplot.html
+++ /dev/null
@@ -1,248 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Function to plot residuals stored in an mkin object — mkinresplot • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Function to plot residuals stored in an mkin object — mkinresplot"><meta property="og:description" content="This function plots the residuals for the specified subset of the observed
-variables from an mkinfit object. A combined plot of the fitted model and
-the residuals can be obtained using plot.mkinfit using the
-argument show_residuals = TRUE."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Function to plot residuals stored in an mkin object</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/mkinresplot.R" class="external-link"><code>R/mkinresplot.R</code></a></small>
-    <div class="hidden name"><code>mkinresplot.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>This function plots the residuals for the specified subset of the observed
-variables from an mkinfit object. A combined plot of the fitted model and
-the residuals can be obtained using <code><a href="plot.mkinfit.html">plot.mkinfit</a></code> using the
-argument <code>show_residuals = TRUE</code>.</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">mkinresplot</span><span class="op">(</span></span>
-<span>  <span class="va">object</span>,</span>
-<span>  obs_vars <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">object</span><span class="op">$</span><span class="va">mkinmod</span><span class="op">$</span><span class="va">map</span><span class="op">)</span>,</span>
-<span>  xlim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1.1</span> <span class="op">*</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html" class="external-link">max</a></span><span class="op">(</span><span class="va">object</span><span class="op">$</span><span class="va">data</span><span class="op">$</span><span class="va">time</span><span class="op">)</span><span class="op">)</span>,</span>
-<span>  standardized <span class="op">=</span> <span class="cn">FALSE</span>,</span>
-<span>  xlab <span class="op">=</span> <span class="st">"Time"</span>,</span>
-<span>  ylab <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html" class="external-link">ifelse</a></span><span class="op">(</span><span class="va">standardized</span>, <span class="st">"Standardized residual"</span>, <span class="st">"Residual"</span><span class="op">)</span>,</span>
-<span>  maxabs <span class="op">=</span> <span class="st">"auto"</span>,</span>
-<span>  legend <span class="op">=</span> <span class="cn">TRUE</span>,</span>
-<span>  lpos <span class="op">=</span> <span class="st">"topright"</span>,</span>
-<span>  col_obs <span class="op">=</span> <span class="st">"auto"</span>,</span>
-<span>  pch_obs <span class="op">=</span> <span class="st">"auto"</span>,</span>
-<span>  frame <span class="op">=</span> <span class="cn">TRUE</span>,</span>
-<span>  <span class="va">...</span></span>
-<span><span class="op">)</span></span></code></pre></div>
-    </div>
-
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>object</dt>
-<dd><p>A fit represented in an <code><a href="mkinfit.html">mkinfit</a></code> object.</p></dd>
-
-
-<dt>obs_vars</dt>
-<dd><p>A character vector of names of the observed variables for
-which residuals should be plotted. Defaults to all observed variables in
-the model</p></dd>
-
-
-<dt>xlim</dt>
-<dd><p>plot range in x direction.</p></dd>
-
-
-<dt>standardized</dt>
-<dd><p>Should the residuals be standardized by dividing by the
-standard deviation given by the error model of the fit?</p></dd>
-
-
-<dt>xlab</dt>
-<dd><p>Label for the x axis.</p></dd>
-
-
-<dt>ylab</dt>
-<dd><p>Label for the y axis.</p></dd>
-
-
-<dt>maxabs</dt>
-<dd><p>Maximum absolute value of the residuals. This is used for the
-scaling of the y axis and defaults to "auto".</p></dd>
-
-
-<dt>legend</dt>
-<dd><p>Should a legend be plotted?</p></dd>
-
-
-<dt>lpos</dt>
-<dd><p>Where should the legend be placed? Default is "topright". Will
-be passed on to <code><a href="https://rdrr.io/r/graphics/legend.html" class="external-link">legend</a></code>.</p></dd>
-
-
-<dt>col_obs</dt>
-<dd><p>Colors for the observed variables.</p></dd>
-
-
-<dt>pch_obs</dt>
-<dd><p>Symbols to be used for the observed variables.</p></dd>
-
-
-<dt>frame</dt>
-<dd><p>Should a frame be drawn around the plots?</p></dd>
-
-
-<dt>...</dt>
-<dd><p>further arguments passed to <code><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></code>.</p></dd>
-
-</dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>Nothing is returned by this function, as it is called for its side
-effect, namely to produce a plot.</p>
-    </div>
-    <div id="see-also">
-    <h2>See also</h2>
-    <div class="dont-index"><p><code><a href="mkinplot.html">mkinplot</a></code>, for a way to plot the data and the fitted
-lines of the mkinfit object, and <code><a href="plot.mkinfit.html">plot_res</a></code> for a function
-combining the plot of the fit and the residual plot.</p></div>
-    </div>
-    <div id="author">
-    <h2>Author</h2>
-    <p>Johannes Ranke and Katrin Lindenberger</p>
-    </div>
-
-    <div id="ref-examples">
-    <h2>Examples</h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="va">model</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"m1"</span><span class="op">)</span>, m1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Temporary DLL for differentials generated and loaded</span>
-<span class="r-in"><span><span class="va">fit</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">model</span>, <span class="va">FOCUS_2006_D</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>Observations with value of zero were removed from the data</span>
-<span class="r-in"><span><span class="fu">mkinresplot</span><span class="op">(</span><span class="va">fit</span>, <span class="st">"m1"</span><span class="op">)</span></span></span>
-<span class="r-plt img"><img src="mkinresplot-1.png" alt="" width="700" height="433"></span>
-<span class="r-in"><span></span></span>
-</code></pre></div>
-    </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/mmkin-1.png b/docs/dev/reference/mmkin-1.png
deleted file mode 100644
index dae64316..00000000
--- a/docs/dev/reference/mmkin-1.png
+++ /dev/null
diff --git a/docs/dev/reference/mmkin-2.png b/docs/dev/reference/mmkin-2.png
deleted file mode 100644
index b281cd7e..00000000
--- a/docs/dev/reference/mmkin-2.png
+++ /dev/null
diff --git a/docs/dev/reference/mmkin-3.png b/docs/dev/reference/mmkin-3.png
deleted file mode 100644
index 23b0725c..00000000
--- a/docs/dev/reference/mmkin-3.png
+++ /dev/null
diff --git a/docs/dev/reference/mmkin-4.png b/docs/dev/reference/mmkin-4.png
deleted file mode 100644
index 11eae1f9..00000000
--- a/docs/dev/reference/mmkin-4.png
+++ /dev/null
diff --git a/docs/dev/reference/mmkin-5.png b/docs/dev/reference/mmkin-5.png
deleted file mode 100644
index e88bd59f..00000000
--- a/docs/dev/reference/mmkin-5.png
+++ /dev/null
diff --git a/docs/dev/reference/mmkin.html b/docs/dev/reference/mmkin.html
deleted file mode 100644
index 64623f20..00000000
--- a/docs/dev/reference/mmkin.html
+++ /dev/null
@@ -1,289 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Fit one or more kinetic models with one or more state variables to one or
-more datasets — mmkin • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Fit one or more kinetic models with one or more state variables to one or
-more datasets — mmkin"><meta property="og:description" content="This function calls mkinfit on all combinations of models and
-datasets specified in its first two arguments."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Fit one or more kinetic models with one or more state variables to one or
-more datasets</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/mmkin.R" class="external-link"><code>R/mmkin.R</code></a></small>
-    <div class="hidden name"><code>mmkin.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>This function calls <code><a href="mkinfit.html">mkinfit</a></code> on all combinations of models and
-datasets specified in its first two arguments.</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">mmkin</span><span class="op">(</span></span>
-<span>  models <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"FOMC"</span>, <span class="st">"DFOP"</span><span class="op">)</span>,</span>
-<span>  <span class="va">datasets</span>,</span>
-<span>  cores <span class="op">=</span> <span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/Sys.info.html" class="external-link">Sys.info</a></span><span class="op">(</span><span class="op">)</span><span class="op">[</span><span class="st">"sysname"</span><span class="op">]</span> <span class="op">==</span> <span class="st">"Windows"</span><span class="op">)</span> <span class="fl">1</span> <span class="kw">else</span> <span class="fu">parallel</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/r/parallel/detectCores.html" class="external-link">detectCores</a></span><span class="op">(</span><span class="op">)</span>,</span>
-<span>  cluster <span class="op">=</span> <span class="cn">NULL</span>,</span>
-<span>  <span class="va">...</span></span>
-<span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># S3 method for mmkin</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
-    </div>
-
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>models</dt>
-<dd><p>Either a character vector of shorthand names like
-<code>c("SFO", "FOMC", "DFOP", "HS", "SFORB")</code>, or an optionally named
-list of <code><a href="mkinmod.html">mkinmod</a></code> objects.</p></dd>
-
-
-<dt>datasets</dt>
-<dd><p>An optionally named list of datasets suitable as observed
-data for <code><a href="mkinfit.html">mkinfit</a></code>.</p></dd>
-
-
-<dt>cores</dt>
-<dd><p>The number of cores to be used for multicore processing. This
-is only used when the <code>cluster</code> argument is <code>NULL</code>. On Windows
-machines, cores &gt; 1 is not supported, you need to use the <code>cluster</code>
-argument to use multiple logical processors. Per default, all cores
-detected by <code><a href="https://rdrr.io/r/parallel/detectCores.html" class="external-link">parallel::detectCores()</a></code> are used, except on Windows where
-the default is 1.</p></dd>
-
-
-<dt>cluster</dt>
-<dd><p>A cluster as returned by <code>makeCluster</code> to be used
-for parallel execution.</p></dd>
-
-
-<dt>...</dt>
-<dd><p>Not used.</p></dd>
-
-
-<dt>x</dt>
-<dd><p>An mmkin object.</p></dd>
-
-</dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>A two-dimensional <code><a href="https://rdrr.io/r/base/array.html" class="external-link">array</a></code> of <code><a href="mkinfit.html">mkinfit</a></code></p>
-
-
-<p>objects and/or try-errors that can be indexed using the model names for the
-first index (row index) and the dataset names for the second index (column
-index).</p>
-    </div>
-    <div id="see-also">
-    <h2>See also</h2>
-    <div class="dont-index"><p><code><a href="Extract.mmkin.html">[.mmkin</a></code> for subsetting, <code><a href="plot.mmkin.html">plot.mmkin</a></code> for
-plotting.</p></div>
-    </div>
-    <div id="author">
-    <h2>Author</h2>
-    <p>Johannes Ranke</p>
-    </div>
-
-    <div id="ref-examples">
-    <h2>Examples</h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co"># \dontrun{</span></span></span>
-<span class="r-in"><span><span class="va">m_synth_SFO_lin</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"M1"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>                           M1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"M2"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>                           M2 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Temporary DLL for differentials generated and loaded</span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="va">m_synth_FOMC_lin</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"FOMC"</span>, <span class="st">"M1"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>                            M1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"M2"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>                            M2 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Temporary DLL for differentials generated and loaded</span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="va">models</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>SFO_lin <span class="op">=</span> <span class="va">m_synth_SFO_lin</span>, FOMC_lin <span class="op">=</span> <span class="va">m_synth_FOMC_lin</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">datasets</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="va">synthetic_data_for_UBA_2014</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">3</span><span class="op">]</span>, <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="va">x</span><span class="op">$</span><span class="va">data</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">datasets</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste</a></span><span class="op">(</span><span class="st">"Dataset"</span>, <span class="fl">1</span><span class="op">:</span><span class="fl">3</span><span class="op">)</span></span></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="va">time_default</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/system.time.html" class="external-link">system.time</a></span><span class="op">(</span><span class="va">fits.0</span> <span class="op">&lt;-</span> <span class="fu">mmkin</span><span class="op">(</span><span class="va">models</span>, <span class="va">datasets</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">time_1</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/system.time.html" class="external-link">system.time</a></span><span class="op">(</span><span class="va">fits.4</span> <span class="op">&lt;-</span> <span class="fu">mmkin</span><span class="op">(</span><span class="va">models</span>, <span class="va">datasets</span>, cores <span class="op">=</span> <span class="fl">1</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="va">time_default</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    user  system elapsed </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   1.560   0.668   0.742 </span>
-<span class="r-in"><span><span class="va">time_1</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    user  system elapsed </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   2.033   0.004   2.037 </span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="fu"><a href="endpoints.html">endpoints</a></span><span class="op">(</span><span class="va">fits.0</span><span class="op">[[</span><span class="st">"SFO_lin"</span>, <span class="fl">2</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> $ff</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   parent_M1 parent_sink       M1_M2     M1_sink </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   0.7340481   0.2659519   0.7505683   0.2494317 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> $distimes</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>             DT50       DT90</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent  0.877769   2.915885</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> M1      2.325744   7.725956</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> M2     33.720100 112.015749</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co"># plot.mkinfit handles rows or columns of mmkin result objects</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">fits.0</span><span class="op">[</span><span class="fl">1</span>, <span class="op">]</span><span class="op">)</span></span></span>
-<span class="r-plt img"><img src="mmkin-1.png" alt="" width="700" height="433"></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">fits.0</span><span class="op">[</span><span class="fl">1</span>, <span class="op">]</span>, obs_var <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"M1"</span>, <span class="st">"M2"</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-plt img"><img src="mmkin-2.png" alt="" width="700" height="433"></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">fits.0</span><span class="op">[</span>, <span class="fl">1</span><span class="op">]</span><span class="op">)</span></span></span>
-<span class="r-plt img"><img src="mmkin-3.png" alt="" width="700" height="433"></span>
-<span class="r-in"><span><span class="co"># Use double brackets to extract a single mkinfit object, which will be plotted</span></span></span>
-<span class="r-in"><span><span class="co"># by plot.mkinfit and can be plotted using plot_sep</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">fits.0</span><span class="op">[[</span><span class="fl">1</span>, <span class="fl">1</span><span class="op">]</span><span class="op">]</span>, sep_obs <span class="op">=</span> <span class="cn">TRUE</span>, show_residuals <span class="op">=</span> <span class="cn">TRUE</span>, show_errmin <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-plt img"><img src="mmkin-4.png" alt="" width="700" height="433"></span>
-<span class="r-in"><span><span class="fu"><a href="plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">fits.0</span><span class="op">[[</span><span class="fl">1</span>, <span class="fl">1</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="co"># Plotting with mmkin (single brackets, extracting an mmkin object) does not</span></span></span>
-<span class="r-in"><span><span class="co"># allow to plot the observed variables separately</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">fits.0</span><span class="op">[</span><span class="fl">1</span>, <span class="fl">1</span><span class="op">]</span><span class="op">)</span></span></span>
-<span class="r-plt img"><img src="mmkin-5.png" alt="" width="700" height="433"></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co"># On Windows, we can use multiple cores by making a cluster first</span></span></span>
-<span class="r-in"><span><span class="va">cl</span> <span class="op">&lt;-</span> <span class="fu">parallel</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/r/parallel/makeCluster.html" class="external-link">makePSOCKcluster</a></span><span class="op">(</span><span class="fl">12</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">f</span> <span class="op">&lt;-</span> <span class="fu">mmkin</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"FOMC"</span>, <span class="st">"DFOP"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>A <span class="op">=</span> <span class="va">FOCUS_2006_A</span>, B <span class="op">=</span> <span class="va">FOCUS_2006_B</span>, C <span class="op">=</span> <span class="va">FOCUS_2006_C</span>, D <span class="op">=</span> <span class="va">FOCUS_2006_D</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  cluster <span class="op">=</span> <span class="va">cl</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">f</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> &lt;mmkin&gt; object</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Status of individual fits:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>       dataset</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> model  A  B  C  D </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   SFO  OK OK OK OK</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   FOMC OK OK OK OK</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   DFOP OK OK OK OK</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> OK: No warnings</span>
-<span class="r-in"><span><span class="co"># We get false convergence for the FOMC fit to FOCUS_2006_A because this</span></span></span>
-<span class="r-in"><span><span class="co"># dataset is really SFO, and the FOMC fit is overparameterised</span></span></span>
-<span class="r-in"><span><span class="fu">parallel</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/r/parallel/makeCluster.html" class="external-link">stopCluster</a></span><span class="op">(</span><span class="va">cl</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="co"># }</span></span></span>
-<span class="r-in"><span></span></span>
-</code></pre></div>
-    </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/multistart-1.png b/docs/dev/reference/multistart-1.png
deleted file mode 100644
index c7937d67..00000000
--- a/docs/dev/reference/multistart-1.png
+++ /dev/null
diff --git a/docs/dev/reference/multistart-2.png b/docs/dev/reference/multistart-2.png
deleted file mode 100644
index b6e29ba5..00000000
--- a/docs/dev/reference/multistart-2.png
+++ /dev/null
diff --git a/docs/dev/reference/multistart.html b/docs/dev/reference/multistart.html
deleted file mode 100644
index 6fc88621..00000000
--- a/docs/dev/reference/multistart.html
+++ /dev/null
@@ -1,259 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Perform a hierarchical model fit with multiple starting values — multistart • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Perform a hierarchical model fit with multiple starting values — multistart"><meta property="og:description" content="The purpose of this method is to check if a certain algorithm for fitting
-nonlinear hierarchical models (also known as nonlinear mixed-effects models)
-will reliably yield results that are sufficiently similar to each other, if
-started with a certain range of reasonable starting parameters. It is
-inspired by the article on practical identifiabiliy in the frame of nonlinear
-mixed-effects models by Duchesne et al (2021)."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.4</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Perform a hierarchical model fit with multiple starting values</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/multistart.R" class="external-link"><code>R/multistart.R</code></a></small>
-    <div class="hidden name"><code>multistart.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>The purpose of this method is to check if a certain algorithm for fitting
-nonlinear hierarchical models (also known as nonlinear mixed-effects models)
-will reliably yield results that are sufficiently similar to each other, if
-started with a certain range of reasonable starting parameters. It is
-inspired by the article on practical identifiabiliy in the frame of nonlinear
-mixed-effects models by Duchesne et al (2021).</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">multistart</span><span class="op">(</span></span>
-<span>  <span class="va">object</span>,</span>
-<span>  n <span class="op">=</span> <span class="fl">50</span>,</span>
-<span>  cores <span class="op">=</span> <span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/Sys.info.html" class="external-link">Sys.info</a></span><span class="op">(</span><span class="op">)</span><span class="op">[</span><span class="st">"sysname"</span><span class="op">]</span> <span class="op">==</span> <span class="st">"Windows"</span><span class="op">)</span> <span class="fl">1</span> <span class="kw">else</span> <span class="fu">parallel</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/r/parallel/detectCores.html" class="external-link">detectCores</a></span><span class="op">(</span><span class="op">)</span>,</span>
-<span>  cluster <span class="op">=</span> <span class="cn">NULL</span>,</span>
-<span>  <span class="va">...</span></span>
-<span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># S3 method for saem.mmkin</span></span>
-<span><span class="fu">multistart</span><span class="op">(</span><span class="va">object</span>, n <span class="op">=</span> <span class="fl">50</span>, cores <span class="op">=</span> <span class="fl">1</span>, cluster <span class="op">=</span> <span class="cn">NULL</span>, <span class="va">...</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># S3 method for multistart</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">best</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># S3 method for default</span></span>
-<span><span class="fu">best</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">which.best</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># S3 method for default</span></span>
-<span><span class="fu">which.best</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
-    </div>
-
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>object</dt>
-<dd><p>The fit object to work with</p></dd>
-
-
-<dt>n</dt>
-<dd><p>How many different combinations of starting parameters should be
-used?</p></dd>
-
-
-<dt>cores</dt>
-<dd><p>How many fits should be run in parallel (only on posix platforms)?</p></dd>
-
-
-<dt>cluster</dt>
-<dd><p>A cluster as returned by <a href="https://rdrr.io/r/parallel/makeCluster.html" class="external-link">parallel::makeCluster</a> to be used
-for parallel execution.</p></dd>
-
-
-<dt>...</dt>
-<dd><p>Passed to the update function.</p></dd>
-
-
-<dt>x</dt>
-<dd><p>The multistart object to print</p></dd>
-
-</dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>A list of <a href="saem.html">saem.mmkin</a> objects, with class attributes
-'multistart.saem.mmkin' and 'multistart'.</p>
-
-
-<p>The object with the highest likelihood</p>
-
-
-<p>The index of the object with the highest likelihood</p>
-    </div>
-    <div id="references">
-    <h2>References</h2>
-    <p>Duchesne R, Guillemin A, Gandrillon O, Crauste F. Practical
-identifiability in the frame of nonlinear mixed effects models: the example
-of the in vitro erythropoiesis. BMC Bioinformatics. 2021 Oct 4;22(1):478.
-doi: 10.1186/s12859-021-04373-4.</p>
-    </div>
-    <div id="see-also">
-    <h2>See also</h2>
-    <div class="dont-index"><p><a href="parplot.html">parplot</a>, <a href="llhist.html">llhist</a></p></div>
-    </div>
-
-    <div id="ref-examples">
-    <h2>Examples</h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># \dontrun{</span></span></span>
-<span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://pkgdown.jrwb.de/mkin/">mkin</a></span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">dmta_ds</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fl">7</span>, <span class="kw">function</span><span class="op">(</span><span class="va">i</span><span class="op">)</span> <span class="op">{</span></span></span>
-<span class="r-in"><span>  <span class="va">ds_i</span> <span class="op">&lt;-</span> <span class="va">dimethenamid_2018</span><span class="op">$</span><span class="va">ds</span><span class="op">[[</span><span class="va">i</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">data</span></span></span>
-<span class="r-in"><span>  <span class="va">ds_i</span><span class="op">[</span><span class="va">ds_i</span><span class="op">$</span><span class="va">name</span> <span class="op">==</span> <span class="st">"DMTAP"</span>, <span class="st">"name"</span><span class="op">]</span> <span class="op">&lt;-</span>  <span class="st">"DMTA"</span></span></span>
-<span class="r-in"><span>  <span class="va">ds_i</span><span class="op">$</span><span class="va">time</span> <span class="op">&lt;-</span> <span class="va">ds_i</span><span class="op">$</span><span class="va">time</span> <span class="op">*</span> <span class="va">dimethenamid_2018</span><span class="op">$</span><span class="va">f_time_norm</span><span class="op">[</span><span class="va">i</span><span class="op">]</span></span></span>
-<span class="r-in"><span>  <span class="va">ds_i</span></span></span>
-<span class="r-in"><span><span class="op">}</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">dmta_ds</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">sapply</a></span><span class="op">(</span><span class="va">dimethenamid_2018</span><span class="op">$</span><span class="va">ds</span>, <span class="kw">function</span><span class="op">(</span><span class="va">ds</span><span class="op">)</span> <span class="va">ds</span><span class="op">$</span><span class="va">title</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot"</span><span class="op">]</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/cbind.html" class="external-link">rbind</a></span><span class="op">(</span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 1"</span><span class="op">]</span><span class="op">]</span>, <span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 2"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 1"</span><span class="op">]</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 2"</span><span class="op">]</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="cn">NULL</span></span></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="va">f_mmkin</span> <span class="op">&lt;-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="va">dmta_ds</span>, error_model <span class="op">=</span> <span class="st">"tc"</span>, cores <span class="op">=</span> <span class="fl">7</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">f_saem_full</span> <span class="op">&lt;-</span> <span class="fu"><a href="saem.html">saem</a></span><span class="op">(</span><span class="va">f_mmkin</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">f_saem_full_multi</span> <span class="op">&lt;-</span> <span class="fu">multistart</span><span class="op">(</span><span class="va">f_saem_full</span>, n <span class="op">=</span> <span class="fl">16</span>, cores <span class="op">=</span> <span class="fl">16</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="parplot.html">parplot</a></span><span class="op">(</span><span class="va">f_saem_full_multi</span>, lpos <span class="op">=</span> <span class="st">"topleft"</span><span class="op">)</span></span></span>
-<span class="r-plt img"><img src="multistart-1.png" alt="" width="700" height="433"></span>
-<span class="r-in"><span><span class="fu"><a href="illparms.html">illparms</a></span><span class="op">(</span><span class="va">f_saem_full</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "sd(log_k2)"</span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="va">f_saem_reduced</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_saem_full</span>, no_random_effect <span class="op">=</span> <span class="st">"log_k2"</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="illparms.html">illparms</a></span><span class="op">(</span><span class="va">f_saem_reduced</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="co"># On Windows, we need to create a PSOCK cluster first and refer to it</span></span></span>
-<span class="r-in"><span><span class="co"># in the call to multistart()</span></span></span>
-<span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va">parallel</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">cl</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/parallel/makeCluster.html" class="external-link">makePSOCKcluster</a></span><span class="op">(</span><span class="fl">12</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">f_saem_reduced_multi</span> <span class="op">&lt;-</span> <span class="fu">multistart</span><span class="op">(</span><span class="va">f_saem_reduced</span>, n <span class="op">=</span> <span class="fl">16</span>, cluster <span class="op">=</span> <span class="va">cl</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="parplot.html">parplot</a></span><span class="op">(</span><span class="va">f_saem_reduced_multi</span>, lpos <span class="op">=</span> <span class="st">"topright"</span>, ylim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.5</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-plt img"><img src="multistart-2.png" alt="" width="700" height="433"></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/parallel/makeCluster.html" class="external-link">stopCluster</a></span><span class="op">(</span><span class="va">cl</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="co"># }</span></span></span>
-</code></pre></div>
-    </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/nafta-1.png b/docs/dev/reference/nafta-1.png
deleted file mode 100644
index 98d4246c..00000000
--- a/docs/dev/reference/nafta-1.png
+++ /dev/null
diff --git a/docs/dev/reference/nafta.html b/docs/dev/reference/nafta.html
deleted file mode 100644
index 6d568718..00000000
--- a/docs/dev/reference/nafta.html
+++ /dev/null
@@ -1,259 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Evaluate parent kinetics using the NAFTA guidance — nafta • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Evaluate parent kinetics using the NAFTA guidance — nafta"><meta property="og:description" content="The function fits the SFO, IORE and DFOP models using mmkin
-and returns an object of class nafta that has methods for printing
-and plotting.
-Print nafta objects. The results for the three models are printed in the
-order of increasing model complexity, i.e. SFO, then IORE, and finally DFOP."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Evaluate parent kinetics using the NAFTA guidance</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/nafta.R" class="external-link"><code>R/nafta.R</code></a></small>
-    <div class="hidden name"><code>nafta.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>The function fits the SFO, IORE and DFOP models using <code><a href="mmkin.html">mmkin</a></code>
-and returns an object of class <code>nafta</code> that has methods for printing
-and plotting.</p>
-<p>Print nafta objects. The results for the three models are printed in the
-order of increasing model complexity, i.e. SFO, then IORE, and finally DFOP.</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">nafta</span><span class="op">(</span><span class="va">ds</span>, title <span class="op">=</span> <span class="cn">NA</span>, quiet <span class="op">=</span> <span class="cn">FALSE</span>, <span class="va">...</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># S3 method for nafta</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, digits <span class="op">=</span> <span class="fl">3</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
-    </div>
-
-    <div id="source">
-    <h2>Source</h2>
-    <p>NAFTA (2011) Guidance for evaluating and calculating degradation
-kinetics in environmental media. NAFTA Technical Working Group on
-Pesticides
-<a href="https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/guidance-evaluating-and-calculating-degradation" class="external-link">https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/guidance-evaluating-and-calculating-degradation</a>
-accessed 2019-02-22</p>
-<p>US EPA (2015) Standard Operating Procedure for Using the NAFTA Guidance to
-Calculate Representative Half-life Values and Characterizing Pesticide
-Degradation
-<a href="https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/standard-operating-procedure-using-nafta-guidance" class="external-link">https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/standard-operating-procedure-using-nafta-guidance</a></p>
-    </div>
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>ds</dt>
-<dd><p>A dataframe that must contain one variable called "time" with the
-time values specified by the <code>time</code> argument, one column called
-"name" with the grouping of the observed values, and finally one column of
-observed values called "value".</p></dd>
-
-
-<dt>title</dt>
-<dd><p>Optional title of the dataset</p></dd>
-
-
-<dt>quiet</dt>
-<dd><p>Should the evaluation text be shown?</p></dd>
-
-
-<dt>...</dt>
-<dd><p>Further arguments passed to <code><a href="mmkin.html">mmkin</a></code> (not for the
-printing method).</p></dd>
-
-
-<dt>x</dt>
-<dd><p>An <code>nafta</code> object.</p></dd>
-
-
-<dt>digits</dt>
-<dd><p>Number of digits to be used for printing parameters and
-dissipation times.</p></dd>
-
-</dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>An list of class <code>nafta</code>. The list element named "mmkin" is the
-<code><a href="mmkin.html">mmkin</a></code> object containing the fits of the three models. The
-list element named "title" contains the title of the dataset used. The
-list element "data" contains the dataset used in the fits.</p>
-    </div>
-    <div id="author">
-    <h2>Author</h2>
-    <p>Johannes Ranke</p>
-    </div>
-
-    <div id="ref-examples">
-    <h2>Examples</h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span>
-<span class="r-in"><span>  <span class="va">nafta_evaluation</span> <span class="op">&lt;-</span> <span class="fu">nafta</span><span class="op">(</span><span class="va">NAFTA_SOP_Appendix_D</span>, cores <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> The representative half-life of the IORE model is longer than the one corresponding</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> to the terminal degradation rate found with the DFOP model.</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> The representative half-life obtained from the DFOP model may be used</span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">nafta_evaluation</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Sums of squares:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>       SFO      IORE      DFOP </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1378.6832  615.7730  517.8836 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Critical sum of squares for checking the SFO model:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 717.4598</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Parameters:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> $SFO</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>          Estimate   Pr(&gt;t)    Lower   Upper</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0  83.7558 1.80e-14 77.18268 90.3288</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_parent   0.0017 7.43e-05  0.00112  0.0026</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma      8.7518 1.22e-05  5.64278 11.8608</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> $IORE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                Estimate Pr(&gt;t)    Lower    Upper</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0       9.69e+01     NA 8.88e+01 1.05e+02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k__iore_parent 8.40e-14     NA 1.79e-18 3.94e-09</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> N_parent       6.68e+00     NA 4.19e+00 9.17e+00</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma          5.85e+00     NA 3.76e+00 7.94e+00</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> $DFOP</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>          Estimate   Pr(&gt;t)    Lower    Upper</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0 9.76e+01 1.94e-13 9.02e+01 1.05e+02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k1       4.24e-02 5.92e-03 2.03e-02 8.88e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k2       8.24e-04 6.48e-03 3.89e-04 1.75e-03</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> g        2.88e-01 2.47e-05 1.95e-01 4.03e-01</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma    5.36e+00 2.22e-05 3.43e+00 7.30e+00</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> DTx values:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>      DT50    DT90 DT50_rep</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> SFO   407    1350      407</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> IORE  541 5190000  1560000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> DFOP  429    2380      841</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Representative half-life:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 841.41</span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">nafta_evaluation</span><span class="op">)</span></span></span>
-<span class="r-plt img"><img src="nafta-1.png" alt="" width="700" height="433"></span>
-<span class="r-in"><span></span></span>
-</code></pre></div>
-    </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/nlme-1.png b/docs/dev/reference/nlme-1.png
deleted file mode 100644
index 9583da2a..00000000
--- a/docs/dev/reference/nlme-1.png
+++ /dev/null
diff --git a/docs/dev/reference/nlme-2.png b/docs/dev/reference/nlme-2.png
deleted file mode 100644
index e941687c..00000000
--- a/docs/dev/reference/nlme-2.png
+++ /dev/null
diff --git a/docs/dev/reference/nlme.html b/docs/dev/reference/nlme.html
deleted file mode 100644
index 81c45ab9..00000000
--- a/docs/dev/reference/nlme.html
+++ /dev/null
@@ -1,244 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Helper functions to create nlme models from mmkin row objects — nlme_function • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Helper functions to create nlme models from mmkin row objects — nlme_function"><meta property="og:description" content="These functions facilitate setting up a nonlinear mixed effects model for
-an mmkin row object. An mmkin row object is essentially a list of mkinfit
-objects that have been obtained by fitting the same model to a list of
-datasets. They are used internally by the nlme.mmkin() method."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Helper functions to create nlme models from mmkin row objects</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/nlme.R" class="external-link"><code>R/nlme.R</code></a></small>
-    <div class="hidden name"><code>nlme.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>These functions facilitate setting up a nonlinear mixed effects model for
-an mmkin row object. An mmkin row object is essentially a list of mkinfit
-objects that have been obtained by fitting the same model to a list of
-datasets. They are used internally by the <code><a href="nlme.mmkin.html">nlme.mmkin()</a></code> method.</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">nlme_function</span><span class="op">(</span><span class="va">object</span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">nlme_data</span><span class="op">(</span><span class="va">object</span><span class="op">)</span></span></code></pre></div>
-    </div>
-
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>object</dt>
-<dd><p>An mmkin row object containing several fits of the same model to different datasets</p></dd>
-
-</dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>A function that can be used with nlme</p>
-
-
-<p>A <code><a href="https://rdrr.io/pkg/nlme/man/groupedData.html" class="external-link">groupedData</a></code> object</p>
-    </div>
-    <div id="see-also">
-    <h2>See also</h2>
-    <div class="dont-index"><p><code><a href="nlme.mmkin.html">nlme.mmkin</a></code></p></div>
-    </div>
-
-    <div id="ref-examples">
-    <h2>Examples</h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">sampling_times</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span>, <span class="fl">3</span>, <span class="fl">7</span>, <span class="fl">14</span>, <span class="fl">28</span>, <span class="fl">60</span>, <span class="fl">90</span>, <span class="fl">120</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">m_SFO</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">d_SFO_1</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinpredict.html">mkinpredict</a></span><span class="op">(</span><span class="va">m_SFO</span>,</span></span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_parent <span class="op">=</span> <span class="fl">0.1</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fl">98</span><span class="op">)</span>, <span class="va">sampling_times</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">d_SFO_1_long</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkin_wide_to_long.html">mkin_wide_to_long</a></span><span class="op">(</span><span class="va">d_SFO_1</span>, time <span class="op">=</span> <span class="st">"time"</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">d_SFO_2</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinpredict.html">mkinpredict</a></span><span class="op">(</span><span class="va">m_SFO</span>,</span></span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_parent <span class="op">=</span> <span class="fl">0.05</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fl">102</span><span class="op">)</span>, <span class="va">sampling_times</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">d_SFO_2_long</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkin_wide_to_long.html">mkin_wide_to_long</a></span><span class="op">(</span><span class="va">d_SFO_2</span>, time <span class="op">=</span> <span class="st">"time"</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">d_SFO_3</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinpredict.html">mkinpredict</a></span><span class="op">(</span><span class="va">m_SFO</span>,</span></span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_parent <span class="op">=</span> <span class="fl">0.02</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fl">103</span><span class="op">)</span>, <span class="va">sampling_times</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">d_SFO_3_long</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkin_wide_to_long.html">mkin_wide_to_long</a></span><span class="op">(</span><span class="va">d_SFO_3</span>, time <span class="op">=</span> <span class="st">"time"</span><span class="op">)</span></span></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="va">d1</span> <span class="op">&lt;-</span> <span class="fu"><a href="add_err.html">add_err</a></span><span class="op">(</span><span class="va">d_SFO_1</span>, <span class="kw">function</span><span class="op">(</span><span class="va">value</span><span class="op">)</span> <span class="fl">3</span>, n <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">d2</span> <span class="op">&lt;-</span> <span class="fu"><a href="add_err.html">add_err</a></span><span class="op">(</span><span class="va">d_SFO_2</span>, <span class="kw">function</span><span class="op">(</span><span class="va">value</span><span class="op">)</span> <span class="fl">2</span>, n <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">d3</span> <span class="op">&lt;-</span> <span class="fu"><a href="add_err.html">add_err</a></span><span class="op">(</span><span class="va">d_SFO_3</span>, <span class="kw">function</span><span class="op">(</span><span class="va">value</span><span class="op">)</span> <span class="fl">4</span>, n <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">ds</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>d1 <span class="op">=</span> <span class="va">d1</span>, d2 <span class="op">=</span> <span class="va">d2</span>, d3 <span class="op">=</span> <span class="va">d3</span><span class="op">)</span></span></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="va">f</span> <span class="op">&lt;-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="va">ds</span>, cores <span class="op">=</span> <span class="fl">1</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">mean_dp</span> <span class="op">&lt;-</span> <span class="fu"><a href="mean_degparms.html">mean_degparms</a></span><span class="op">(</span><span class="va">f</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">grouped_data</span> <span class="op">&lt;-</span> <span class="fu">nlme_data</span><span class="op">(</span><span class="va">f</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">nlme_f</span> <span class="op">&lt;-</span> <span class="fu">nlme_function</span><span class="op">(</span><span class="va">f</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="co"># These assignments are necessary for these objects to be</span></span></span>
-<span class="r-in"><span><span class="co"># visible to nlme and augPred when evaluation is done by</span></span></span>
-<span class="r-in"><span><span class="co"># pkgdown to generate the html docs.</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/assign.html" class="external-link">assign</a></span><span class="op">(</span><span class="st">"nlme_f"</span>, <span class="va">nlme_f</span>, <span class="fu"><a href="https://rdrr.io/r/base/environment.html" class="external-link">globalenv</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/assign.html" class="external-link">assign</a></span><span class="op">(</span><span class="st">"grouped_data"</span>, <span class="va">grouped_data</span>, <span class="fu"><a href="https://rdrr.io/r/base/environment.html" class="external-link">globalenv</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://svn.r-project.org/R-packages/trunk/nlme/" class="external-link">nlme</a></span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">m_nlme</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a></span><span class="op">(</span><span class="va">value</span> <span class="op">~</span> <span class="fu">nlme_f</span><span class="op">(</span><span class="va">name</span>, <span class="va">time</span>, <span class="va">parent_0</span>, <span class="va">log_k_parent_sink</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  data <span class="op">=</span> <span class="va">grouped_data</span>,</span></span>
-<span class="r-in"><span>  fixed <span class="op">=</span> <span class="va">parent_0</span> <span class="op">+</span> <span class="va">log_k_parent_sink</span> <span class="op">~</span> <span class="fl">1</span>,</span></span>
-<span class="r-in"><span>  random <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/pdDiag.html" class="external-link">pdDiag</a></span><span class="op">(</span><span class="va">parent_0</span> <span class="op">+</span> <span class="va">log_k_parent_sink</span> <span class="op">~</span> <span class="fl">1</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  start <span class="op">=</span> <span class="va">mean_dp</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">m_nlme</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Nonlinear mixed-effects model fit by maximum likelihood</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Model: value ~ nlme_f(name, time, parent_0, log_k_parent_sink) </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Data: grouped_data </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>        AIC      BIC    logLik</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   266.6428 275.8935 -128.3214</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Random effects:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Formula: list(parent_0 ~ 1, log_k_parent_sink ~ 1)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Level: ds</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Structure: Diagonal</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>             parent_0 log_k_parent_sink Residual</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> StdDev: 0.0004253489         0.7058039 3.065183</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Fixed effects:  parent_0 + log_k_parent_sink ~ 1 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                       Value Std.Error DF   t-value p-value</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0          101.18323 0.7900461 43 128.07256       0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k_parent_sink  -3.08708 0.4171755 43  -7.39995       0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Correlation: </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                   prnt_0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k_parent_sink 0.031 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Standardized Within-Group Residuals:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         Min          Q1         Med          Q3         Max </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -2.38427071 -0.52059848  0.03593021  0.39987268  2.73188969 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Number of Observations: 47</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Number of Groups: 3 </span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/pkg/nlme/man/augPred.html" class="external-link">augPred</a></span><span class="op">(</span><span class="va">m_nlme</span>, level <span class="op">=</span> <span class="fl">0</span><span class="op">:</span><span class="fl">1</span><span class="op">)</span>, layout <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">1</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-plt img"><img src="nlme-1.png" alt="" width="700" height="433"></span>
-<span class="r-in"><span><span class="co"># augPred does not work on fits with more than one state</span></span></span>
-<span class="r-in"><span><span class="co"># variable</span></span></span>
-<span class="r-in"><span><span class="co">#</span></span></span>
-<span class="r-in"><span><span class="co"># The procedure is greatly simplified by the nlme.mmkin function</span></span></span>
-<span class="r-in"><span><span class="va">f_nlme</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a></span><span class="op">(</span><span class="va">f</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_nlme</span><span class="op">)</span></span></span>
-<span class="r-plt img"><img src="nlme-2.png" alt="" width="700" height="433"></span>
-</code></pre></div>
-    </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/nlme.mmkin-1.png b/docs/dev/reference/nlme.mmkin-1.png
deleted file mode 100644
index 818c23a2..00000000
--- a/docs/dev/reference/nlme.mmkin-1.png
+++ /dev/null
diff --git a/docs/dev/reference/nlme.mmkin-2.png b/docs/dev/reference/nlme.mmkin-2.png
deleted file mode 100644
index 779adbdb..00000000
--- a/docs/dev/reference/nlme.mmkin-2.png
+++ /dev/null
diff --git a/docs/dev/reference/nlme.mmkin-3.png b/docs/dev/reference/nlme.mmkin-3.png
deleted file mode 100644
index b3785a78..00000000
--- a/docs/dev/reference/nlme.mmkin-3.png
+++ /dev/null
diff --git a/docs/dev/reference/nlme.mmkin.html b/docs/dev/reference/nlme.mmkin.html
deleted file mode 100644
index 76d667b9..00000000
--- a/docs/dev/reference/nlme.mmkin.html
+++ /dev/null
@@ -1,464 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Create an nlme model for an mmkin row object — nlme.mmkin • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Create an nlme model for an mmkin row object — nlme.mmkin"><meta property="og:description" content="This functions sets up a nonlinear mixed effects model for an mmkin row
-object. An mmkin row object is essentially a list of mkinfit objects that
-have been obtained by fitting the same model to a list of datasets."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Create an nlme model for an mmkin row object</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/nlme.mmkin.R" class="external-link"><code>R/nlme.mmkin.R</code></a></small>
-    <div class="hidden name"><code>nlme.mmkin.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>This functions sets up a nonlinear mixed effects model for an mmkin row
-object. An mmkin row object is essentially a list of mkinfit objects that
-have been obtained by fitting the same model to a list of datasets.</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for mmkin</span></span>
-<span><span class="fu"><a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a></span><span class="op">(</span></span>
-<span>  <span class="va">model</span>,</span>
-<span>  data <span class="op">=</span> <span class="st">"auto"</span>,</span>
-<span>  fixed <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">as.list</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="fu"><a href="mean_degparms.html">mean_degparms</a></span><span class="op">(</span><span class="va">model</span><span class="op">)</span><span class="op">)</span><span class="op">)</span>, <span class="kw">function</span><span class="op">(</span><span class="va">el</span><span class="op">)</span> <span class="fu"><a href="https://rdrr.io/r/base/eval.html" class="external-link">eval</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/parse.html" class="external-link">parse</a></span><span class="op">(</span>text <span class="op">=</span></span>
-<span>    <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste</a></span><span class="op">(</span><span class="va">el</span>, <span class="fl">1</span>, sep <span class="op">=</span> <span class="st">"~"</span><span class="op">)</span><span class="op">)</span><span class="op">)</span><span class="op">)</span>,</span>
-<span>  random <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/pdDiag.html" class="external-link">pdDiag</a></span><span class="op">(</span><span class="va">fixed</span><span class="op">)</span>,</span>
-<span>  <span class="va">groups</span>,</span>
-<span>  start <span class="op">=</span> <span class="fu"><a href="mean_degparms.html">mean_degparms</a></span><span class="op">(</span><span class="va">model</span>, random <span class="op">=</span> <span class="cn">TRUE</span>, test_log_parms <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>,</span>
-<span>  correlation <span class="op">=</span> <span class="cn">NULL</span>,</span>
-<span>  weights <span class="op">=</span> <span class="cn">NULL</span>,</span>
-<span>  <span class="va">subset</span>,</span>
-<span>  method <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"ML"</span>, <span class="st">"REML"</span><span class="op">)</span>,</span>
-<span>  na.action <span class="op">=</span> <span class="va">na.fail</span>,</span>
-<span>  <span class="va">naPattern</span>,</span>
-<span>  control <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="op">)</span>,</span>
-<span>  verbose <span class="op">=</span> <span class="cn">FALSE</span></span>
-<span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># S3 method for nlme.mmkin</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, digits <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html" class="external-link">max</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fu"><a href="https://rdrr.io/r/base/options.html" class="external-link">getOption</a></span><span class="op">(</span><span class="st">"digits"</span><span class="op">)</span> <span class="op">-</span> <span class="fl">3</span><span class="op">)</span>, <span class="va">...</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># S3 method for nlme.mmkin</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
-    </div>
-
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>model</dt>
-<dd><p>An <a href="mmkin.html">mmkin</a> row object.</p></dd>
-
-
-<dt>data</dt>
-<dd><p>Ignored, data are taken from the mmkin model</p></dd>
-
-
-<dt>fixed</dt>
-<dd><p>Ignored, all degradation parameters fitted in the
-mmkin model are used as fixed parameters</p></dd>
-
-
-<dt>random</dt>
-<dd><p>If not specified, no correlations between random effects are
-set up for the optimised degradation model parameters. This is
-achieved by using the <a href="https://rdrr.io/pkg/nlme/man/pdDiag.html" class="external-link">nlme::pdDiag</a> method.</p></dd>
-
-
-<dt>groups</dt>
-<dd><p>See the documentation of nlme</p></dd>
-
-
-<dt>start</dt>
-<dd><p>If not specified, mean values of the fitted degradation
-parameters taken from the mmkin object are used</p></dd>
-
-
-<dt>correlation</dt>
-<dd><p>See the documentation of nlme</p></dd>
-
-
-<dt>weights</dt>
-<dd><p>passed to nlme</p></dd>
-
-
-<dt>subset</dt>
-<dd><p>passed to nlme</p></dd>
-
-
-<dt>method</dt>
-<dd><p>passed to nlme</p></dd>
-
-
-<dt>na.action</dt>
-<dd><p>passed to nlme</p></dd>
-
-
-<dt>naPattern</dt>
-<dd><p>passed to nlme</p></dd>
-
-
-<dt>control</dt>
-<dd><p>passed to nlme</p></dd>
-
-
-<dt>verbose</dt>
-<dd><p>passed to nlme</p></dd>
-
-
-<dt>x</dt>
-<dd><p>An nlme.mmkin object to print</p></dd>
-
-
-<dt>digits</dt>
-<dd><p>Number of digits to use for printing</p></dd>
-
-
-<dt>...</dt>
-<dd><p>Update specifications passed to update.nlme</p></dd>
-
-
-<dt>object</dt>
-<dd><p>An nlme.mmkin object to update</p></dd>
-
-</dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>Upon success, a fitted 'nlme.mmkin' object, which is an nlme object
-with additional elements. It also inherits from 'mixed.mmkin'.</p>
-    </div>
-    <div id="details">
-    <h2>Details</h2>
-    <p>Note that the convergence of the nlme algorithms depends on the quality
-of the data. In degradation kinetics, we often only have few datasets
-(e.g. data for few soils) and complicated degradation models, which may
-make it impossible to obtain convergence with nlme.</p>
-    </div>
-    <div id="note">
-    <h2>Note</h2>
-    <p>As the object inherits from <a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme::nlme</a>, there is a wealth of
-methods that will automatically work on 'nlme.mmkin' objects, such as
-<code><a href="https://rdrr.io/pkg/nlme/man/intervals.html" class="external-link">nlme::intervals()</a></code>, <code><a href="https://rdrr.io/pkg/nlme/man/anova.lme.html" class="external-link">nlme::anova.lme()</a></code> and <code><a href="https://rdrr.io/pkg/nlme/man/coef.lme.html" class="external-link">nlme::coef.lme()</a></code>.</p>
-    </div>
-    <div id="see-also">
-    <h2>See also</h2>
-    <div class="dont-index"><p><code><a href="nlme.html">nlme_function()</a></code>, <a href="plot.mixed.mmkin.html">plot.mixed.mmkin</a>, <a href="summary.nlme.mmkin.html">summary.nlme.mmkin</a></p></div>
-    </div>
-
-    <div id="ref-examples">
-    <h2>Examples</h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">ds</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="va">experimental_data_for_UBA_2019</span><span class="op">[</span><span class="fl">6</span><span class="op">:</span><span class="fl">10</span><span class="op">]</span>,</span></span>
-<span class="r-in"><span> <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fu"><a href="https://rdrr.io/r/base/subset.html" class="external-link">subset</a></span><span class="op">(</span><span class="va">x</span><span class="op">$</span><span class="va">data</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"name"</span>, <span class="st">"time"</span>, <span class="st">"value"</span><span class="op">)</span><span class="op">]</span>, <span class="va">name</span> <span class="op">==</span> <span class="st">"parent"</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co"># \dontrun{</span></span></span>
-<span class="r-in"><span>  <span class="va">f</span> <span class="op">&lt;-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"DFOP"</span><span class="op">)</span>, <span class="va">ds</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, cores <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></span>
-<span class="r-in"><span>  <span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://svn.r-project.org/R-packages/trunk/nlme/" class="external-link">nlme</a></span><span class="op">)</span></span></span>
-<span class="r-in"><span>  <span class="va">f_nlme_sfo</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a></span><span class="op">(</span><span class="va">f</span><span class="op">[</span><span class="st">"SFO"</span>, <span class="op">]</span><span class="op">)</span></span></span>
-<span class="r-in"><span>  <span class="va">f_nlme_dfop</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a></span><span class="op">(</span><span class="va">f</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span><span class="op">)</span></span></span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_nlme_sfo</span>, <span class="va">f_nlme_dfop</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>             Model df      AIC      BIC    logLik   Test  L.Ratio p-value</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_nlme_sfo      1  5 625.0539 637.5529 -307.5269                        </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_nlme_dfop     2  9 495.1270 517.6253 -238.5635 1 vs 2 137.9268  &lt;.0001</span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">f_nlme_dfop</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Kinetic nonlinear mixed-effects model fit by maximum likelihood</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Structural model:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>            * parent</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Data:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 90 observations of 1 variable(s) grouped in 5 datasets</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Log-likelihood: -238.6</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Fixed effects:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  list(parent_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1) </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0   log_k1   log_k2 g_qlogis </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  94.1702  -1.8002  -4.1474   0.0324 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Random effects:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Formula: list(parent_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Level: ds</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Structure: Diagonal</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         parent_0 log_k1 log_k2 g_qlogis Residual</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> StdDev:    2.488 0.8447   1.33   0.4652    2.321</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_nlme_dfop</span><span class="op">)</span></span></span>
-<span class="r-plt img"><img src="nlme.mmkin-1.png" alt="" width="700" height="433"></span>
-<span class="r-in"><span>  <span class="fu"><a href="endpoints.html">endpoints</a></span><span class="op">(</span><span class="va">f_nlme_dfop</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> $distimes</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>            DT50     DT90 DT50back  DT50_k1  DT50_k2</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent 10.79857 100.7937 30.34192 4.193936 43.85441</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span>  <span class="va">ds_2</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="va">experimental_data_for_UBA_2019</span><span class="op">[</span><span class="fl">6</span><span class="op">:</span><span class="fl">10</span><span class="op">]</span>,</span></span>
-<span class="r-in"><span>   <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="va">x</span><span class="op">$</span><span class="va">data</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"name"</span>, <span class="st">"time"</span>, <span class="st">"value"</span><span class="op">)</span><span class="op">]</span><span class="op">)</span></span></span>
-<span class="r-in"><span>  <span class="va">m_sfo_sfo</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"A1"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>    A1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, use_of_ff <span class="op">=</span> <span class="st">"min"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-in"><span>  <span class="va">m_sfo_sfo_ff</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"A1"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>    A1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, use_of_ff <span class="op">=</span> <span class="st">"max"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-in"><span>  <span class="va">m_dfop_sfo</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="st">"A1"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>    A1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span>  <span class="va">f_2</span> <span class="op">&lt;-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="st">"SFO-SFO"</span> <span class="op">=</span> <span class="va">m_sfo_sfo</span>,</span></span>
-<span class="r-in"><span>   <span class="st">"SFO-SFO-ff"</span> <span class="op">=</span> <span class="va">m_sfo_sfo_ff</span>,</span></span>
-<span class="r-in"><span>   <span class="st">"DFOP-SFO"</span> <span class="op">=</span> <span class="va">m_dfop_sfo</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>    <span class="va">ds_2</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span>  <span class="va">f_nlme_sfo_sfo</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a></span><span class="op">(</span><span class="va">f_2</span><span class="op">[</span><span class="st">"SFO-SFO"</span>, <span class="op">]</span><span class="op">)</span></span></span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_nlme_sfo_sfo</span><span class="op">)</span></span></span>
-<span class="r-plt img"><img src="nlme.mmkin-2.png" alt="" width="700" height="433"></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span>  <span class="co"># With formation fractions this does not coverge with defaults</span></span></span>
-<span class="r-in"><span>  <span class="co"># f_nlme_sfo_sfo_ff &lt;- nlme(f_2["SFO-SFO-ff", ])</span></span></span>
-<span class="r-in"><span>  <span class="co">#plot(f_nlme_sfo_sfo_ff)</span></span></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span>  <span class="co"># For the following, we need to increase pnlsMaxIter and the tolerance</span></span></span>
-<span class="r-in"><span>  <span class="co"># to get convergence</span></span></span>
-<span class="r-in"><span>  <span class="va">f_nlme_dfop_sfo</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a></span><span class="op">(</span><span class="va">f_2</span><span class="op">[</span><span class="st">"DFOP-SFO"</span>, <span class="op">]</span>,</span></span>
-<span class="r-in"><span>    control <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>pnlsMaxIter <span class="op">=</span> <span class="fl">120</span>, tolerance <span class="op">=</span> <span class="fl">5e-4</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_nlme_dfop_sfo</span><span class="op">)</span></span></span>
-<span class="r-plt img"><img src="nlme.mmkin-3.png" alt="" width="700" height="433"></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_nlme_dfop_sfo</span>, <span class="va">f_nlme_sfo_sfo</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                 Model df       AIC       BIC    logLik   Test  L.Ratio p-value</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_nlme_dfop_sfo     1 13  843.8547  884.6201 -408.9274                        </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_nlme_sfo_sfo      2  9 1085.1821 1113.4043 -533.5910 1 vs 2 249.3274  &lt;.0001</span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span>  <span class="fu"><a href="endpoints.html">endpoints</a></span><span class="op">(</span><span class="va">f_nlme_sfo_sfo</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> $ff</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_sink   parent_A1     A1_sink </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   0.5912432   0.4087568   1.0000000 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> $distimes</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>            DT50     DT90</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent 19.13518  63.5657</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> A1     66.02155 219.3189</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-in"><span>  <span class="fu"><a href="endpoints.html">endpoints</a></span><span class="op">(</span><span class="va">f_nlme_dfop_sfo</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> $ff</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   parent_A1 parent_sink </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   0.2768575   0.7231425 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> $distimes</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>             DT50     DT90 DT50back  DT50_k1  DT50_k2</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent  11.07091 104.6320 31.49737 4.462384 46.20825</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> A1     162.30507 539.1658       NA       NA       NA</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span>  <span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/length.html" class="external-link">length</a></span><span class="op">(</span><span class="fu">findFunction</span><span class="op">(</span><span class="st">"varConstProp"</span><span class="op">)</span><span class="op">)</span> <span class="op">&gt;</span> <span class="fl">0</span><span class="op">)</span> <span class="op">{</span> <span class="co"># tc error model for nlme available</span></span></span>
-<span class="r-in"><span>    <span class="co"># Attempts to fit metabolite kinetics with the tc error model are possible,</span></span></span>
-<span class="r-in"><span>    <span class="co"># but need tweeking of control values and sometimes do not converge</span></span></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span>    <span class="va">f_tc</span> <span class="op">&lt;-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"DFOP"</span><span class="op">)</span>, <span class="va">ds</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, error_model <span class="op">=</span> <span class="st">"tc"</span><span class="op">)</span></span></span>
-<span class="r-in"><span>    <span class="va">f_nlme_sfo_tc</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a></span><span class="op">(</span><span class="va">f_tc</span><span class="op">[</span><span class="st">"SFO"</span>, <span class="op">]</span><span class="op">)</span></span></span>
-<span class="r-in"><span>    <span class="va">f_nlme_dfop_tc</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a></span><span class="op">(</span><span class="va">f_tc</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span><span class="op">)</span></span></span>
-<span class="r-in"><span>    <span class="fu"><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">AIC</a></span><span class="op">(</span><span class="va">f_nlme_sfo</span>, <span class="va">f_nlme_sfo_tc</span>, <span class="va">f_nlme_dfop</span>, <span class="va">f_nlme_dfop_tc</span><span class="op">)</span></span></span>
-<span class="r-in"><span>    <span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">f_nlme_dfop_tc</span><span class="op">)</span></span></span>
-<span class="r-in"><span>  <span class="op">}</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Kinetic nonlinear mixed-effects model fit by maximum likelihood</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Structural model:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>            * parent</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Data:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 90 observations of 1 variable(s) grouped in 5 datasets</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Log-likelihood: -238.4</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Fixed effects:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  list(parent_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1) </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0   log_k1   log_k2 g_qlogis </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 94.04773 -1.82340 -4.16716  0.05686 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Random effects:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Formula: list(parent_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Level: ds</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Structure: Diagonal</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         parent_0 log_k1 log_k2 g_qlogis Residual</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> StdDev:    2.474   0.85  1.337   0.4659        1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Variance function:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Structure: Constant plus proportion of variance covariate</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Formula: ~fitted(.) </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Parameter estimates:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>      const       prop </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2.23223593 0.01262367 </span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span>  <span class="va">f_2_obs</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_2</span>, error_model <span class="op">=</span> <span class="st">"obs"</span><span class="op">)</span></span></span>
-<span class="r-in"><span>  <span class="va">f_nlme_sfo_sfo_obs</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a></span><span class="op">(</span><span class="va">f_2_obs</span><span class="op">[</span><span class="st">"SFO-SFO"</span>, <span class="op">]</span><span class="op">)</span></span></span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">f_nlme_sfo_sfo_obs</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Kinetic nonlinear mixed-effects model fit by maximum likelihood</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Structural model:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> d_parent/dt = - k_parent_sink * parent - k_parent_A1 * parent</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> d_A1/dt = + k_parent_A1 * parent - k_A1_sink * A1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Data:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 170 observations of 2 variable(s) grouped in 5 datasets</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Log-likelihood: -473</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Fixed effects:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  list(parent_0 ~ 1, log_k_parent_sink ~ 1, log_k_parent_A1 ~ 1,      log_k_A1_sink ~ 1) </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>          parent_0 log_k_parent_sink   log_k_parent_A1     log_k_A1_sink </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>            87.976            -3.670            -4.164            -4.645 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Random effects:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Formula: list(parent_0 ~ 1, log_k_parent_sink ~ 1, log_k_parent_A1 ~ 1,      log_k_A1_sink ~ 1)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Level: ds</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Structure: Diagonal</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         parent_0 log_k_parent_sink log_k_parent_A1 log_k_A1_sink Residual</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> StdDev:    3.992             1.777           1.055        0.4821    6.483</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Variance function:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Structure: Different standard deviations per stratum</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Formula: ~1 | name </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Parameter estimates:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    parent        A1 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1.0000000 0.2049994 </span>
-<span class="r-in"><span>  <span class="va">f_nlme_dfop_sfo_obs</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a></span><span class="op">(</span><span class="va">f_2_obs</span><span class="op">[</span><span class="st">"DFOP-SFO"</span>, <span class="op">]</span>,</span></span>
-<span class="r-in"><span>    control <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>pnlsMaxIter <span class="op">=</span> <span class="fl">120</span>, tolerance <span class="op">=</span> <span class="fl">5e-4</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span>  <span class="va">f_2_tc</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_2</span>, error_model <span class="op">=</span> <span class="st">"tc"</span><span class="op">)</span></span></span>
-<span class="r-in"><span>  <span class="co"># f_nlme_sfo_sfo_tc &lt;- nlme(f_2_tc["SFO-SFO", ]) # No convergence with 50 iterations</span></span></span>
-<span class="r-in"><span>  <span class="co"># f_nlme_dfop_sfo_tc &lt;- nlme(f_2_tc["DFOP-SFO", ],</span></span></span>
-<span class="r-in"><span>  <span class="co">#  control = list(pnlsMaxIter = 120, tolerance = 5e-4)) # Error in X[, fmap[[nm]]] &lt;- gradnm</span></span></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_nlme_dfop_sfo</span>, <span class="va">f_nlme_dfop_sfo_obs</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                     Model df      AIC      BIC    logLik   Test  L.Ratio</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_nlme_dfop_sfo         1 13 843.8547 884.6201 -408.9274                </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_nlme_dfop_sfo_obs     2 14 817.5338 861.4350 -394.7669 1 vs 2 28.32093</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                     p-value</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_nlme_dfop_sfo            </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_nlme_dfop_sfo_obs  &lt;.0001</span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co"># }</span></span></span>
-</code></pre></div>
-    </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/nlmixr.mmkin-1.png b/docs/dev/reference/nlmixr.mmkin-1.png
deleted file mode 100644
index 42a266e5..00000000
--- a/docs/dev/reference/nlmixr.mmkin-1.png
+++ /dev/null
diff --git a/docs/dev/reference/nlmixr.mmkin-2.png b/docs/dev/reference/nlmixr.mmkin-2.png
deleted file mode 100644
index 0c440fa8..00000000
--- a/docs/dev/reference/nlmixr.mmkin-2.png
+++ /dev/null
diff --git a/docs/dev/reference/nlmixr.mmkin.html b/docs/dev/reference/nlmixr.mmkin.html
deleted file mode 100644
index 82cef25d..00000000
--- a/docs/dev/reference/nlmixr.mmkin.html
+++ /dev/null
@@ -1,13271 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Fit nonlinear mixed models using nlmixr — nlmixr.mmkin • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Fit nonlinear mixed models using nlmixr — nlmixr.mmkin"><meta property="og:description" content="This function uses nlmixr::nlmixr() as a backend for fitting nonlinear mixed
-effects models created from mmkin row objects using the Stochastic Approximation
-Expectation Maximisation algorithm (SAEM) or First Order Conditional
-Estimation with Interaction (FOCEI)."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.1.0</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Functions and data</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Fit nonlinear mixed models using nlmixr</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/nlmixr.R" class="external-link"><code>R/nlmixr.R</code></a></small>
-    <div class="hidden name"><code>nlmixr.mmkin.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>This function uses <code><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr::nlmixr()</a></code> as a backend for fitting nonlinear mixed
-effects models created from <a href="mmkin.html">mmkin</a> row objects using the Stochastic Approximation
-Expectation Maximisation algorithm (SAEM) or First Order Conditional
-Estimation with Interaction (FOCEI).</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="co"># S3 method for mmkin</span>
-<span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr</a></span><span class="op">(</span>
-  <span class="va">object</span>,
-  data <span class="op">=</span> <span class="cn">NULL</span>,
-  est <span class="op">=</span> <span class="cn">NULL</span>,
-  control <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="op">)</span>,
-  table <span class="op">=</span> <span class="fu">tableControl</span><span class="op">(</span><span class="op">)</span>,
-  error_model <span class="op">=</span> <span class="va">object</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">err_mod</span>,
-  test_log_parms <span class="op">=</span> <span class="cn">TRUE</span>,
-  conf.level <span class="op">=</span> <span class="fl">0.6</span>,
-  degparms_start <span class="op">=</span> <span class="st">"auto"</span>,
-  eta_start <span class="op">=</span> <span class="st">"auto"</span>,
-  <span class="va">...</span>,
-  save <span class="op">=</span> <span class="cn">NULL</span>,
-  envir <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sys.parent.html" class="external-link">parent.frame</a></span><span class="op">(</span><span class="op">)</span>
-<span class="op">)</span>
-
-<span class="co"># S3 method for nlmixr.mmkin</span>
-<span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, digits <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html" class="external-link">max</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fu"><a href="https://rdrr.io/r/base/options.html" class="external-link">getOption</a></span><span class="op">(</span><span class="st">"digits"</span><span class="op">)</span> <span class="op">-</span> <span class="fl">3</span><span class="op">)</span>, <span class="va">...</span><span class="op">)</span>
-
-<span class="fu">nlmixr_model</span><span class="op">(</span>
-  <span class="va">object</span>,
-  est <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"saem"</span>, <span class="st">"focei"</span><span class="op">)</span>,
-  degparms_start <span class="op">=</span> <span class="st">"auto"</span>,
-  eta_start <span class="op">=</span> <span class="st">"auto"</span>,
-  test_log_parms <span class="op">=</span> <span class="cn">TRUE</span>,
-  conf.level <span class="op">=</span> <span class="fl">0.6</span>,
-  error_model <span class="op">=</span> <span class="va">object</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">err_mod</span>,
-  add_attributes <span class="op">=</span> <span class="cn">FALSE</span>
-<span class="op">)</span>
-
-<span class="fu">nlmixr_data</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></code></pre></div>
-    </div>
-
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>object</dt>
-<dd><p>An <a href="mmkin.html">mmkin</a> row object containing several fits of the same
-<a href="mkinmod.html">mkinmod</a> model to different datasets</p></dd>
-<dt>data</dt>
-<dd><p>Not used, as the data are extracted from the mmkin row object</p></dd>
-<dt>est</dt>
-<dd><p>Estimation method passed to <a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr::nlmixr</a></p></dd>
-<dt>control</dt>
-<dd><p>Passed to <a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr::nlmixr</a></p></dd>
-<dt>table</dt>
-<dd><p>Passed to <a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr::nlmixr</a></p></dd>
-<dt>error_model</dt>
-<dd><p>Optional argument to override the error model which is
-being set based on the error model used in the mmkin row object.</p></dd>
-<dt>test_log_parms</dt>
-<dd><p>If TRUE, an attempt is made to use more robust starting
-values for population parameters fitted as log parameters in mkin (like
-rate constants) by only considering rate constants that pass the t-test
-when calculating mean degradation parameters using <a href="mean_degparms.html">mean_degparms</a>.</p></dd>
-<dt>conf.level</dt>
-<dd><p>Possibility to adjust the required confidence level
-for parameter that are tested if requested by 'test_log_parms'.</p></dd>
-<dt>degparms_start</dt>
-<dd><p>Parameter values given as a named numeric vector will
-be used to override the starting values obtained from the 'mmkin' object.</p></dd>
-<dt>eta_start</dt>
-<dd><p>Standard deviations on the transformed scale given as a
-named numeric vector will be used to override the starting values obtained
-from the 'mmkin' object.</p></dd>
-<dt>...</dt>
-<dd><p>Passed to nlmixr_model</p></dd>
-<dt>save</dt>
-<dd><p>Passed to <a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr::nlmixr</a></p></dd>
-<dt>envir</dt>
-<dd><p>Passed to <a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr::nlmixr</a></p></dd>
-<dt>x</dt>
-<dd><p>An nlmixr.mmkin object to print</p></dd>
-<dt>digits</dt>
-<dd><p>Number of digits to use for printing</p></dd>
-<dt>add_attributes</dt>
-<dd><p>Should the starting values used for degradation model
-parameters and their distribution and for the error model parameters
-be returned as attributes?</p></dd>
-</dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    <p>An S3 object of class 'nlmixr.mmkin', containing the fitted
-<a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr::nlmixr</a> object as a list component named 'nm'. The
-object also inherits from 'mixed.mmkin'.
-An function defining a model suitable for fitting with <a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr::nlmixr</a>.
-An dataframe suitable for use with <a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr::nlmixr</a></p>
-    </div>
-    <div id="details">
-    <h2>Details</h2>
-    <p>An mmkin row object is essentially a list of mkinfit objects that have been
-obtained by fitting the same model to a list of datasets using <a href="mkinfit.html">mkinfit</a>.</p>
-    </div>
-    <div id="see-also">
-    <h2>See also</h2>
-    <div class="dont-index"><p><a href="summary.nlmixr.mmkin.html">summary.nlmixr.mmkin</a> <a href="plot.mixed.mmkin.html">plot.mixed.mmkin</a></p></div>
-    </div>
-
-    <div id="ref-examples">
-    <h2>Examples</h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="co"># \dontrun{</span></span>
-<span class="r-in"><span class="va">ds</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="va">experimental_data_for_UBA_2019</span><span class="op">[</span><span class="fl">6</span><span class="op">:</span><span class="fl">10</span><span class="op">]</span>,</span>
-<span class="r-in"> <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fu"><a href="https://rdrr.io/r/base/subset.html" class="external-link">subset</a></span><span class="op">(</span><span class="va">x</span><span class="op">$</span><span class="va">data</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"name"</span>, <span class="st">"time"</span>, <span class="st">"value"</span><span class="op">)</span><span class="op">]</span><span class="op">)</span><span class="op">)</span></span>
-<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">ds</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste</a></span><span class="op">(</span><span class="st">"Dataset"</span>, <span class="fl">6</span><span class="op">:</span><span class="fl">10</span><span class="op">)</span></span>
-<span class="r-in"></span>
-<span class="r-in"><span class="va">f_mmkin_parent</span> <span class="op">&lt;-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"FOMC"</span>, <span class="st">"DFOP"</span>, <span class="st">"HS"</span><span class="op">)</span>, <span class="va">ds</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, cores <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span>
-<span class="r-in"><span class="va">f_mmkin_parent_tc</span> <span class="op">&lt;-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"FOMC"</span>, <span class="st">"DFOP"</span><span class="op">)</span>, <span class="va">ds</span>, error_model <span class="op">=</span> <span class="st">"tc"</span>,</span>
-<span class="r-in">  cores <span class="op">=</span> <span class="fl">1</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
-<span class="r-in"></span>
-<span class="r-in"><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/nlmixrdevelopment/nlmixr" class="external-link">nlmixr</a></span><span class="op">)</span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Attaching package: ‘nlmixr’</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> The following object is masked from ‘package:mkin’:</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>     saem</span>
-<span class="r-in"><span class="va">f_nlmixr_sfo_saem</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr</a></span><span class="op">(</span><span class="va">f_mmkin_parent</span><span class="op">[</span><span class="st">"SFO"</span>, <span class="op">]</span>, est <span class="op">=</span> <span class="st">"saem"</span>,</span>
-<span class="r-in">  control <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/saemControl.html" class="external-link">saemControl</a></span><span class="op">(</span>print <span class="op">=</span> <span class="fl">0</span><span class="op">)</span><span class="op">)</span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → generate SAEM model</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> RxODE 1.1.4 using 8 threads (see ?getRxThreads)</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>   no cache: create with `rxCreateCache()`</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating covariance matrix</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling EBE model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating residuals/tables</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-in"><span class="va">f_nlmixr_sfo_focei</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr</a></span><span class="op">(</span><span class="va">f_mmkin_parent</span><span class="op">[</span><span class="st">"SFO"</span>, <span class="op">]</span>, est <span class="op">=</span> <span class="st">"focei"</span>,</span>
-<span class="r-in">  control <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/foceiControl.html" class="external-link">foceiControl</a></span><span class="op">(</span>print <span class="op">=</span> <span class="fl">0</span><span class="op">)</span><span class="op">)</span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate jacobian</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate sensitivities</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate ∂(f)/∂(η)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate ∂(R²)/∂(η)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in inner model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in inner model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in EBE model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in EBE model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling inner model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in FD model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling EBE model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling events FD model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> calculating covariance matrix</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating residuals/tables</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-in"></span>
-<span class="r-in"><span class="va">f_nlmixr_fomc_saem</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr</a></span><span class="op">(</span><span class="va">f_mmkin_parent</span><span class="op">[</span><span class="st">"FOMC"</span>, <span class="op">]</span>, est <span class="op">=</span> <span class="st">"saem"</span>,</span>
-<span class="r-in">  control <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/saemControl.html" class="external-link">saemControl</a></span><span class="op">(</span>print <span class="op">=</span> <span class="fl">0</span><span class="op">)</span><span class="op">)</span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → generate SAEM model</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating covariance matrix</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling EBE model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating residuals/tables</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-in"><span class="va">f_nlmixr_fomc_focei</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr</a></span><span class="op">(</span><span class="va">f_mmkin_parent</span><span class="op">[</span><span class="st">"FOMC"</span>, <span class="op">]</span>, est <span class="op">=</span> <span class="st">"focei"</span>,</span>
-<span class="r-in">  control <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/foceiControl.html" class="external-link">foceiControl</a></span><span class="op">(</span>print <span class="op">=</span> <span class="fl">0</span><span class="op">)</span><span class="op">)</span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate jacobian</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate sensitivities</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate ∂(f)/∂(η)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate ∂(R²)/∂(η)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in inner model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in inner model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in EBE model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in EBE model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling inner model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in FD model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in FD model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling EBE model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling events FD model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> calculating covariance matrix</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating residuals/tables</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-in"></span>
-<span class="r-in"><span class="va">f_nlmixr_dfop_saem</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr</a></span><span class="op">(</span><span class="va">f_mmkin_parent</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span>, est <span class="op">=</span> <span class="st">"saem"</span>,</span>
-<span class="r-in">  control <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/saemControl.html" class="external-link">saemControl</a></span><span class="op">(</span>print <span class="op">=</span> <span class="fl">0</span><span class="op">)</span><span class="op">)</span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → generate SAEM model</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating covariance matrix</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling EBE model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating residuals/tables</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-in"><span class="va">f_nlmixr_dfop_focei</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr</a></span><span class="op">(</span><span class="va">f_mmkin_parent</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span>, est <span class="op">=</span> <span class="st">"focei"</span>,</span>
-<span class="r-in">  control <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/foceiControl.html" class="external-link">foceiControl</a></span><span class="op">(</span>print <span class="op">=</span> <span class="fl">0</span><span class="op">)</span><span class="op">)</span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate jacobian</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate sensitivities</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate ∂(f)/∂(η)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate ∂(R²)/∂(η)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in inner model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in inner model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in EBE model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in EBE model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling inner model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in FD model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in FD model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling EBE model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling events FD model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> calculating covariance matrix</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating residuals/tables</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-in"></span>
-<span class="r-in"><span class="va">f_nlmixr_hs_saem</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr</a></span><span class="op">(</span><span class="va">f_mmkin_parent</span><span class="op">[</span><span class="st">"HS"</span>, <span class="op">]</span>, est <span class="op">=</span> <span class="st">"saem"</span>,</span>
-<span class="r-in">  control <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/saemControl.html" class="external-link">saemControl</a></span><span class="op">(</span>print <span class="op">=</span> <span class="fl">0</span><span class="op">)</span><span class="op">)</span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → generate SAEM model</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating covariance matrix</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling EBE model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating residuals/tables</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-in"><span class="va">f_nlmixr_hs_focei</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr</a></span><span class="op">(</span><span class="va">f_mmkin_parent</span><span class="op">[</span><span class="st">"HS"</span>, <span class="op">]</span>, est <span class="op">=</span> <span class="st">"focei"</span>,</span>
-<span class="r-in">  control <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/foceiControl.html" class="external-link">foceiControl</a></span><span class="op">(</span>print <span class="op">=</span> <span class="fl">0</span><span class="op">)</span><span class="op">)</span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate jacobian</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate sensitivities</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate ∂(f)/∂(η)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate ∂(R²)/∂(η)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in inner model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in inner model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in EBE model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in EBE model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling inner model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in FD model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in FD model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling EBE model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling events FD model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> calculating covariance matrix</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating residuals/tables</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-in"></span>
-<span class="r-in"><span class="va">f_nlmixr_fomc_saem_tc</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr</a></span><span class="op">(</span><span class="va">f_mmkin_parent_tc</span><span class="op">[</span><span class="st">"FOMC"</span>, <span class="op">]</span>, est <span class="op">=</span> <span class="st">"saem"</span>,</span>
-<span class="r-in">  control <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/saemControl.html" class="external-link">saemControl</a></span><span class="op">(</span>print <span class="op">=</span> <span class="fl">0</span><span class="op">)</span><span class="op">)</span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating covariance matrix</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling EBE model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating residuals/tables</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-in"><span class="va">f_nlmixr_fomc_focei_tc</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr</a></span><span class="op">(</span><span class="va">f_mmkin_parent_tc</span><span class="op">[</span><span class="st">"FOMC"</span>, <span class="op">]</span>, est <span class="op">=</span> <span class="st">"focei"</span>,</span>
-<span class="r-in">  control <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/foceiControl.html" class="external-link">foceiControl</a></span><span class="op">(</span>print <span class="op">=</span> <span class="fl">0</span><span class="op">)</span><span class="op">)</span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate jacobian</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate sensitivities</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate ∂(f)/∂(η)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate ∂(R²)/∂(η)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in inner model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in inner model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in EBE model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in EBE model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling inner model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in FD model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in FD model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling EBE model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling events FD model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating residuals/tables</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-in"></span>
-<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">AIC</a></span><span class="op">(</span></span>
-<span class="r-in">  <span class="va">f_nlmixr_sfo_saem</span><span class="op">$</span><span class="va">nm</span>, <span class="va">f_nlmixr_sfo_focei</span><span class="op">$</span><span class="va">nm</span>,</span>
-<span class="r-in">  <span class="va">f_nlmixr_fomc_saem</span><span class="op">$</span><span class="va">nm</span>, <span class="va">f_nlmixr_fomc_focei</span><span class="op">$</span><span class="va">nm</span>,</span>
-<span class="r-in">  <span class="va">f_nlmixr_dfop_saem</span><span class="op">$</span><span class="va">nm</span>, <span class="va">f_nlmixr_dfop_focei</span><span class="op">$</span><span class="va">nm</span>,</span>
-<span class="r-in">  <span class="va">f_nlmixr_hs_saem</span><span class="op">$</span><span class="va">nm</span>, <span class="va">f_nlmixr_hs_focei</span><span class="op">$</span><span class="va">nm</span>,</span>
-<span class="r-in">  <span class="va">f_nlmixr_fomc_saem_tc</span><span class="op">$</span><span class="va">nm</span>, <span class="va">f_nlmixr_fomc_focei_tc</span><span class="op">$</span><span class="va">nm</span><span class="op">)</span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating -2LL by Gaussian quadrature (nnodes=3,nsd=1.6)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating -2LL by Gaussian quadrature (nnodes=3,nsd=1.6)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating -2LL by Gaussian quadrature (nnodes=3,nsd=1.6)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating -2LL by Gaussian quadrature (nnodes=3,nsd=1.6)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating -2LL by Gaussian quadrature (nnodes=3,nsd=1.6)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                           df      AIC</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_nlmixr_sfo_saem$nm       5 624.9492</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_nlmixr_sfo_focei$nm      5 625.0695</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_nlmixr_fomc_saem$nm      7 463.7577</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_nlmixr_fomc_focei$nm     7 468.0861</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_nlmixr_dfop_saem$nm      9 495.1980</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_nlmixr_dfop_focei$nm     9 495.1072</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_nlmixr_hs_saem$nm        9 531.0689</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_nlmixr_hs_focei$nm       9 545.6728</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_nlmixr_fomc_saem_tc$nm   8 462.1411</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_nlmixr_fomc_focei_tc$nm  8 470.0745</span>
-<span class="r-in"></span>
-<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">AIC</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a></span><span class="op">(</span><span class="va">f_mmkin_parent</span><span class="op">[</span><span class="st">"FOMC"</span>, <span class="op">]</span><span class="op">)</span><span class="op">)</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 468.0781</span>
-<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">AIC</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a></span><span class="op">(</span><span class="va">f_mmkin_parent</span><span class="op">[</span><span class="st">"HS"</span>, <span class="op">]</span><span class="op">)</span><span class="op">)</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 535.609</span>
-<span class="r-in"></span>
-<span class="r-in"><span class="co"># The FOCEI fit of FOMC with constant variance or the tc error model is best</span></span>
-<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_nlmixr_fomc_saem_tc</span><span class="op">)</span></span>
-<span class="r-plt img"><img src="nlmixr.mmkin-1.png" alt="" width="700" height="433"></span>
-<span class="r-in"></span>
-<span class="r-in"><span class="va">sfo_sfo</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"A1"</span><span class="op">)</span>,</span>
-<span class="r-in">  A1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
-<span class="r-in"><span class="va">fomc_sfo</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"FOMC"</span>, <span class="st">"A1"</span><span class="op">)</span>,</span>
-<span class="r-in">  A1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
-<span class="r-in"><span class="va">dfop_sfo</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="st">"A1"</span><span class="op">)</span>,</span>
-<span class="r-in">  A1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
-<span class="r-in"></span>
-<span class="r-in"><span class="va">f_mmkin_const</span> <span class="op">&lt;-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span></span>
-<span class="r-in">    <span class="st">"SFO-SFO"</span> <span class="op">=</span> <span class="va">sfo_sfo</span>, <span class="st">"FOMC-SFO"</span> <span class="op">=</span> <span class="va">fomc_sfo</span>, <span class="st">"DFOP-SFO"</span> <span class="op">=</span> <span class="va">dfop_sfo</span><span class="op">)</span>,</span>
-<span class="r-in">  <span class="va">ds</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, error_model <span class="op">=</span> <span class="st">"const"</span><span class="op">)</span></span>
-<span class="r-in"><span class="va">f_mmkin_obs</span> <span class="op">&lt;-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span></span>
-<span class="r-in">    <span class="st">"SFO-SFO"</span> <span class="op">=</span> <span class="va">sfo_sfo</span>, <span class="st">"FOMC-SFO"</span> <span class="op">=</span> <span class="va">fomc_sfo</span>, <span class="st">"DFOP-SFO"</span> <span class="op">=</span> <span class="va">dfop_sfo</span><span class="op">)</span>,</span>
-<span class="r-in">  <span class="va">ds</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, error_model <span class="op">=</span> <span class="st">"obs"</span><span class="op">)</span></span>
-<span class="r-in"><span class="va">f_mmkin_tc</span> <span class="op">&lt;-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span></span>
-<span class="r-in">    <span class="st">"SFO-SFO"</span> <span class="op">=</span> <span class="va">sfo_sfo</span>, <span class="st">"FOMC-SFO"</span> <span class="op">=</span> <span class="va">fomc_sfo</span>, <span class="st">"DFOP-SFO"</span> <span class="op">=</span> <span class="va">dfop_sfo</span><span class="op">)</span>,</span>
-<span class="r-in">  <span class="va">ds</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, error_model <span class="op">=</span> <span class="st">"tc"</span><span class="op">)</span></span>
-<span class="r-in"></span>
-<span class="r-in"><span class="fu">nlmixr_model</span><span class="op">(</span><span class="va">f_mmkin_const</span><span class="op">[</span><span class="st">"SFO-SFO"</span>, <span class="op">]</span><span class="op">)</span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Constant variance for more than one variable is not supported for est = 'saem'</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Changing the error model to 'obs' (variance by observed variable)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> function () </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> {</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     ini({</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         parent_0 = 87</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         eta.parent_0 ~ 4.2</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         log_k_parent = -3.2</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         eta.log_k_parent ~ 1.5</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         log_k_A1 = -4.6</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         eta.log_k_A1 ~ 0.56</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         f_parent_qlogis = -0.33</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         eta.f_parent_qlogis ~ 1.1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         sigma_parent = 4.3</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         sigma_A1 = 4.3</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     })</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     model({</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         parent_0_model = parent_0 + eta.parent_0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         parent(0) = parent_0_model</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         k_parent = exp(log_k_parent + eta.log_k_parent)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         k_A1 = exp(log_k_A1 + eta.log_k_A1)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         f_parent_to_A1 = expit(f_parent_qlogis + eta.f_parent_qlogis)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         d/dt(parent) = -k_parent * parent</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         d/dt(A1) = +f_parent_to_A1 * k_parent * parent - k_A1 * </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>             A1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         parent ~ add(sigma_parent)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         A1 ~ add(sigma_A1)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     })</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> }</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> &lt;environment: 0x55556c775068&gt;</span>
-<span class="r-in"></span>
-<span class="r-in"><span class="co"># A single constant variance is currently only possible with est = 'focei' in nlmixr</span></span>
-<span class="r-in"><span class="va">f_nlmixr_sfo_sfo_focei_const</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr</a></span><span class="op">(</span><span class="va">f_mmkin_const</span><span class="op">[</span><span class="st">"SFO-SFO"</span>, <span class="op">]</span>, est <span class="op">=</span> <span class="st">"focei"</span><span class="op">)</span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate jacobian</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate sensitivities</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate ∂(f)/∂(η)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate ∂(R²)/∂(η)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in inner model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in inner model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in EBE model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in EBE model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling inner model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in FD model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in FD model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling EBE model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling events FD model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Needed Covariates:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "CMT"</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="font-weight: bold;">Key:</span> U: Unscaled Parameters; X: Back-transformed parameters; G: Gill difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> F: Forward difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> C: Central difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> M: Mixed forward and central difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Unscaled parameters for Omegas=chol(solve(omega));</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Diagonals are transformed, as specified by foceiControl(diagXform=)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |-----+---------------+-----------+-----------+-----------+-----------|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    #| Objective Fun |  parent_0 |log_k_parent |  log_k_A1 |f_parent_qlogis |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     sigma |        o1 |        o2 |        o3 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|        o4 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    1</span>|     756.61847 |     1.000 |   -0.9694 |    -1.000 |   -0.9068 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8057 |   -0.8843 |   -0.8798 |   -0.8743 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8782 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     756.61847 |     87.00 |    -3.200 |    -4.600 |   -0.3300 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.300 |    0.6985 |    0.9036 |     1.156 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.9765 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     756.61847</span> |     87.00 |   0.04076 |   0.01005 |    0.4182 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.300 |    0.6985 |    0.9036 |     1.156 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.9765 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    G|    Gill Diff. |     104.1 |   0.02915 |    0.3320 |    0.4427 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -29.46 |     6.499 |     3.260 |    -8.158 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -5.501 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    2</span>|     4014.8405 |   0.04387 |   -0.9697 |    -1.003 |   -0.9108 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5352 |   -0.9440 |   -0.9098 |   -0.7994 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8277 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     4014.8405 |     3.817 |    -3.200 |    -4.603 |   -0.3313 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.882 |    0.6569 |    0.8766 |     1.243 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.026 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     4014.8405</span> |     3.817 |   0.04075 |   0.01002 |    0.4179 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.882 |    0.6569 |    0.8766 |     1.243 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.026 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    3</span>|     784.09766 |    0.9044 |   -0.9695 |    -1.000 |   -0.9072 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7786 |   -0.8903 |   -0.8828 |   -0.8668 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8732 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     784.09766 |     78.68 |    -3.200 |    -4.600 |   -0.3301 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.358 |    0.6944 |    0.9009 |     1.165 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.9814 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     784.09766</span> |     78.68 |   0.04076 |   0.01005 |    0.4182 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.358 |    0.6944 |    0.9009 |     1.165 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.9814 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    4</span>|     755.85897 |    0.9864 |   -0.9694 |    -1.000 |   -0.9068 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8018 |   -0.8852 |   -0.8803 |   -0.8733 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8775 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     755.85897 |     85.82 |    -3.200 |    -4.600 |   -0.3300 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.308 |    0.6979 |    0.9032 |     1.157 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.9772 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     755.85897</span> |     85.82 |   0.04076 |   0.01005 |    0.4182 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.308 |    0.6979 |    0.9032 |     1.157 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.9772 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -10.67 |    0.1182 |    0.2197 |    0.3686 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -28.82 |     3.860 |     3.200 |    -8.294 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -5.254 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    5</span>|     755.50135 |    0.9911 |   -0.9695 |    -1.000 |   -0.9070 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7893 |   -0.8868 |   -0.8816 |   -0.8697 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8752 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     755.50135 |     86.22 |    -3.200 |    -4.600 |   -0.3301 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.335 |    0.6968 |    0.9020 |     1.161 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.9794 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     755.50135</span> |     86.22 |   0.04076 |   0.01005 |    0.4182 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.335 |    0.6968 |    0.9020 |     1.161 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.9794 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    6</span>|     755.34910 |    0.9966 |   -0.9695 |    -1.000 |   -0.9072 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7744 |   -0.8888 |   -0.8833 |   -0.8654 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8725 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      755.3491 |     86.70 |    -3.200 |    -4.600 |   -0.3301 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.367 |    0.6954 |    0.9005 |     1.166 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.9820 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      755.3491</span> |     86.70 |   0.04076 |   0.01005 |    0.4182 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.367 |    0.6954 |    0.9005 |     1.166 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.9820 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     73.92 |   0.04965 |    0.3063 |    0.4373 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -23.46 |     5.143 |     2.934 |    -7.746 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -5.165 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    7</span>|     754.44379 |    0.9831 |   -0.9697 |    -1.001 |   -0.9076 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7489 |   -0.8930 |   -0.8865 |   -0.8566 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8669 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     754.44379 |     85.53 |    -3.200 |    -4.601 |   -0.3303 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.422 |    0.6925 |    0.8975 |     1.176 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.9875 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     754.44379</span> |     85.53 |   0.04075 |   0.01005 |    0.4182 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.422 |    0.6925 |    0.8975 |     1.176 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.9875 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -36.39 |    0.1354 |    0.1953 |    0.3724 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -19.20 |     3.020 |     2.621 |    -7.506 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -4.671 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    8</span>|     753.82350 |    0.9908 |   -0.9699 |    -1.001 |   -0.9085 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7249 |   -0.8992 |   -0.8910 |   -0.8427 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8580 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      753.8235 |     86.20 |    -3.200 |    -4.601 |   -0.3306 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.474 |    0.6881 |    0.8935 |     1.193 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.9962 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      753.8235</span> |     86.20 |   0.04074 |   0.01004 |    0.4181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.474 |    0.6881 |    0.8935 |     1.193 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.9962 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     25.33 |   0.08213 |    0.2542 |    0.4339 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -14.89 |     3.322 |     2.230 |    -6.934 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -4.324 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    9</span>|     753.42397 |    0.9836 |   -0.9702 |    -1.002 |   -0.9101 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7094 |   -0.9058 |   -0.8962 |   -0.8215 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8457 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     753.42397 |     85.57 |    -3.201 |    -4.602 |   -0.3311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.507 |    0.6835 |    0.8888 |     1.217 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.008 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     753.42397</span> |     85.57 |   0.04073 |   0.01003 |    0.4180 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.507 |    0.6835 |    0.8888 |     1.217 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.008 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -31.38 |    0.1220 |    0.1752 |    0.4111 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -12.58 |     2.402 |     1.769 |    -6.044 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -3.541 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   10</span>|     753.10125 |    0.9902 |   -0.9707 |    -1.003 |   -0.9128 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7033 |   -0.9147 |   -0.8999 |   -0.7966 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8328 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     753.10125 |     86.15 |    -3.201 |    -4.603 |   -0.3320 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.520 |    0.6773 |    0.8855 |     1.246 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.021 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     753.10125</span> |     86.15 |   0.04071 |   0.01002 |    0.4178 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.520 |    0.6773 |    0.8855 |     1.246 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.021 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     18.19 |   0.07644 |    0.2005 |    0.4693 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -11.34 |     2.660 |     1.470 |    -4.866 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -2.908 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   11</span>|     752.83558 |    0.9856 |   -0.9716 |    -1.004 |   -0.9194 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6931 |   -0.9294 |   -0.8999 |   -0.7740 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8246 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.83558 |     85.75 |    -3.202 |    -4.604 |   -0.3342 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.542 |    0.6671 |    0.8855 |     1.272 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.029 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.83558</span> |     85.75 |   0.04067 |   0.01001 |    0.4172 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.542 |    0.6671 |    0.8855 |     1.272 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.029 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -17.42 |   0.09700 |    0.1175 |    0.4453 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -9.793 |     1.829 |     1.489 |    -3.997 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -2.337 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   12</span>|     752.64046 |    0.9898 |   -0.9731 |    -1.005 |   -0.9331 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6718 |   -0.9413 |   -0.9101 |   -0.7759 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8318 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.64046 |     86.12 |    -3.204 |    -4.605 |   -0.3387 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.588 |    0.6587 |    0.8763 |     1.270 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.022 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.64046</span> |     86.12 |   0.04061 |   0.01000 |    0.4161 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.588 |    0.6587 |    0.8763 |     1.270 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.022 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     12.96 |   0.06018 |    0.1556 |    0.4415 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -6.445 |     1.822 |    0.6282 |    -4.092 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -2.761 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   13</span>|     752.52461 |    0.9874 |   -0.9741 |    -1.006 |   -0.9430 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6755 |   -0.9483 |   -0.9069 |   -0.7553 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8127 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.52461 |     85.90 |    -3.205 |    -4.606 |   -0.3420 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.580 |    0.6538 |    0.8792 |     1.294 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.040 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.52461</span> |     85.90 |   0.04057 |  0.009996 |    0.4153 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.580 |    0.6538 |    0.8792 |     1.294 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.040 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -5.319 |   0.06758 |    0.1323 |    0.4528 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -7.018 |     1.348 |    0.9128 |    -3.312 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.706 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   14</span>|     752.47650 |    0.9920 |   -0.9750 |    -1.008 |   -0.9578 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6735 |   -0.9481 |   -0.9014 |   -0.7293 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8100 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      752.4765 |     86.30 |    -3.206 |    -4.608 |   -0.3468 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.584 |    0.6539 |    0.8841 |     1.324 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.043 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      752.4765</span> |     86.30 |   0.04054 |  0.009974 |    0.4142 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.584 |    0.6539 |    0.8841 |     1.324 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.043 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     26.38 |   0.03719 |   0.08701 |    0.4621 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -6.642 |     1.808 |     1.401 |    -2.463 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.595 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   15</span>|     752.37074 |    0.9880 |   -0.9759 |    -1.009 |   -0.9778 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6665 |   -0.9465 |   -0.9156 |   -0.7192 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8239 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.37074 |     85.96 |    -3.206 |    -4.609 |   -0.3534 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.599 |    0.6551 |    0.8713 |     1.335 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.030 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.37074</span> |     85.96 |   0.04050 |  0.009963 |    0.4125 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.599 |    0.6551 |    0.8713 |     1.335 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.030 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |   -0.8866 |   0.05090 |  -0.01541 |    0.3812 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -5.712 |     1.436 |    0.1646 |    -2.191 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -2.247 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   16</span>|     752.26949 |    0.9921 |   -0.9761 |    -1.009 |   -0.9796 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6402 |   -0.9531 |   -0.9164 |   -0.7091 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8135 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.26949 |     86.31 |    -3.207 |    -4.609 |   -0.3540 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.656 |    0.6505 |    0.8706 |     1.347 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.040 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.26949</span> |     86.31 |   0.04049 |  0.009963 |    0.4124 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.656 |    0.6505 |    0.8706 |     1.347 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.040 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     26.82 |   0.02710 |   0.03892 |    0.4267 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.005 |     1.500 |    0.1022 |    -1.862 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.773 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   17</span>|     752.20271 |    0.9873 |   -0.9768 |    -1.006 |   -0.9927 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6361 |   -0.9677 |   -0.9007 |   -0.7162 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7982 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.20271 |     85.89 |    -3.207 |    -4.606 |   -0.3584 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.664 |    0.6403 |    0.8848 |     1.339 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.055 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.20271</span> |     85.89 |   0.04046 |  0.009992 |    0.4114 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.664 |    0.6403 |    0.8848 |     1.339 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.055 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -7.259 |   0.05081 |    0.1320 |    0.4166 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.497 |    0.4663 |     1.471 |    -1.984 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.9590 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   18</span>|     752.16826 |    0.9883 |   -0.9778 |    -1.006 |    -1.005 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6437 |   -0.9795 |   -0.9210 |   -0.7076 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7873 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.16826 |     85.98 |    -3.208 |    -4.606 |   -0.3626 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.648 |    0.6320 |    0.8664 |     1.349 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.065 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.16826</span> |     85.98 |   0.04042 |  0.009989 |    0.4103 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.648 |    0.6320 |    0.8664 |     1.349 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.065 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.732 |   0.03428 |    0.1666 |    0.4265 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.413 |    0.2526 |   -0.2557 |    -1.689 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.3553 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   19</span>|     752.25223 |    0.9952 |   -0.9788 |    -1.010 |    -1.023 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6370 |   -0.9826 |   -0.9156 |   -0.6853 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7912 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.25223 |     86.58 |    -3.209 |    -4.610 |   -0.3684 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.663 |    0.6299 |    0.8713 |     1.374 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.061 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.25223</span> |     86.58 |   0.04038 |  0.009949 |    0.4089 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.663 |    0.6299 |    0.8713 |     1.374 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.061 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   20</span>|     752.18605 |    0.9920 |   -0.9778 |    -1.007 |    -1.006 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6387 |   -0.9801 |   -0.9204 |   -0.7040 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7866 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.18605 |     86.30 |    -3.208 |    -4.607 |   -0.3629 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.659 |    0.6316 |    0.8669 |     1.353 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.066 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.18605</span> |     86.30 |   0.04042 |  0.009985 |    0.4103 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.659 |    0.6316 |    0.8669 |     1.353 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.066 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   21</span>|     752.16428 |    0.9894 |   -0.9778 |    -1.006 |    -1.006 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6422 |   -0.9797 |   -0.9208 |   -0.7065 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7871 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.16428 |     86.08 |    -3.208 |    -4.606 |   -0.3627 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.651 |    0.6319 |    0.8666 |     1.350 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.065 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.16428</span> |     86.08 |   0.04042 |  0.009988 |    0.4103 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.651 |    0.6319 |    0.8666 |     1.350 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.065 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     5.144 |   0.02886 |    0.1726 |    0.4333 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.192 |    0.3285 |   -0.2407 |    -1.661 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.3568 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   22</span>|     752.15919 |    0.9884 |   -0.9779 |    -1.007 |    -1.007 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6420 |   -0.9798 |   -0.9206 |   -0.7051 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7874 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.15919 |     85.99 |    -3.208 |    -4.607 |   -0.3630 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.652 |    0.6319 |    0.8668 |     1.352 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.065 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.15919</span> |     85.99 |   0.04042 |  0.009985 |    0.4102 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.652 |    0.6319 |    0.8668 |     1.352 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.065 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.486 |   0.03361 |    0.1549 |    0.4244 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.177 |    0.2364 |   -0.2213 |    -1.616 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.3570 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   23</span>|     752.15545 |    0.9894 |   -0.9779 |    -1.007 |    -1.007 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6405 |   -0.9799 |   -0.9204 |   -0.7040 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7872 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.15545 |     86.07 |    -3.208 |    -4.607 |   -0.3631 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.655 |    0.6318 |    0.8669 |     1.353 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.065 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.15545</span> |     86.07 |   0.04042 |  0.009984 |    0.4102 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.655 |    0.6318 |    0.8669 |     1.353 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.065 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     4.936 |   0.02854 |    0.1600 |    0.4306 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.962 |    0.3065 |   -0.2063 |    -1.586 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.3570 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   24</span>|     752.15077 |    0.9884 |   -0.9779 |    -1.007 |    -1.008 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6403 |   -0.9800 |   -0.9202 |   -0.7026 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7875 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.15077 |     85.99 |    -3.209 |    -4.607 |   -0.3635 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.656 |    0.6317 |    0.8672 |     1.355 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.065 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.15077</span> |     85.99 |   0.04042 |  0.009981 |    0.4101 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.656 |    0.6317 |    0.8672 |     1.355 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.065 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.303 |   0.03292 |    0.1430 |    0.4220 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.955 |    0.2207 |   -0.1874 |    -1.542 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.3581 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   25</span>|     752.14731 |    0.9893 |   -0.9780 |    -1.007 |    -1.009 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6389 |   -0.9801 |   -0.9200 |   -0.7014 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7873 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.14731 |     86.07 |    -3.209 |    -4.607 |   -0.3637 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.659 |    0.6316 |    0.8673 |     1.356 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.065 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.14731</span> |     86.07 |   0.04042 |  0.009980 |    0.4101 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.659 |    0.6316 |    0.8673 |     1.356 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.065 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     4.764 |   0.02806 |    0.1473 |    0.4277 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.749 |    0.2865 |   -0.1727 |    -1.510 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.3572 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   26</span>|     752.14299 |    0.9884 |   -0.9780 |    -1.007 |    -1.010 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6388 |   -0.9801 |   -0.9198 |   -0.7000 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7876 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.14299 |     85.99 |    -3.209 |    -4.607 |   -0.3641 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.659 |    0.6316 |    0.8675 |     1.358 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.065 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.14299</span> |     85.99 |   0.04041 |  0.009978 |    0.4100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.659 |    0.6316 |    0.8675 |     1.358 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.065 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.130 |   0.03192 |    0.1311 |    0.4194 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.750 |    0.2064 |   -0.1542 |    -1.466 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.3587 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   27</span>|     752.13982 |    0.9893 |   -0.9781 |    -1.008 |    -1.010 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6374 |   -0.9803 |   -0.9196 |   -0.6987 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7873 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.13982 |     86.07 |    -3.209 |    -4.608 |   -0.3642 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.662 |    0.6315 |    0.8676 |     1.359 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.065 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.13982</span> |     86.07 |   0.04041 |  0.009977 |    0.4099 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.662 |    0.6315 |    0.8676 |     1.359 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.065 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     4.635 |   0.02760 |    0.1350 |    0.4248 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.550 |    0.2683 |   -0.1407 |    -1.433 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.3557 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   28</span>|     752.13580 |    0.9884 |   -0.9782 |    -1.008 |    -1.012 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6373 |   -0.9803 |   -0.9194 |   -0.6973 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7876 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      752.1358 |     85.99 |    -3.209 |    -4.608 |   -0.3647 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.662 |    0.6315 |    0.8678 |     1.361 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.065 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      752.1358</span> |     85.99 |   0.04041 |  0.009975 |    0.4098 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.662 |    0.6315 |    0.8678 |     1.361 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.065 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |   -0.9657 |   0.03147 |    0.1207 |    0.4166 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.557 |    0.1931 |   -0.1227 |    -1.391 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.3574 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   29</span>|     752.13295 |    0.9893 |   -0.9782 |    -1.008 |    -1.012 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6359 |   -0.9805 |   -0.9193 |   -0.6961 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7873 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.13295 |     86.07 |    -3.209 |    -4.608 |   -0.3648 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.665 |    0.6314 |    0.8679 |     1.362 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.065 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.13295</span> |     86.07 |   0.04041 |  0.009973 |    0.4098 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.665 |    0.6314 |    0.8679 |     1.362 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.065 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     4.554 |   0.02702 |    0.1245 |    0.4220 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.357 |    0.2511 |   -0.1114 |    -1.356 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.3512 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   30</span>|     752.12919 |    0.9885 |   -0.9783 |    -1.008 |    -1.014 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6359 |   -0.9804 |   -0.9191 |   -0.6947 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7876 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.12919 |     86.00 |    -3.209 |    -4.608 |   -0.3653 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.665 |    0.6314 |    0.8681 |     1.364 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.065 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.12919</span> |     86.00 |   0.04040 |  0.009972 |    0.4097 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.665 |    0.6314 |    0.8681 |     1.364 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.065 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |   -0.8077 |   0.03076 |    0.1104 |    0.4140 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.370 |    0.1799 |  -0.09417 |    -1.307 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.3529 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   31</span>|     752.12656 |    0.9893 |   -0.9783 |    -1.008 |    -1.014 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6345 |   -0.9806 |   -0.9190 |   -0.6934 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7872 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.12656 |     86.07 |    -3.209 |    -4.608 |   -0.3654 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.668 |    0.6313 |    0.8682 |     1.365 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.065 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.12656</span> |     86.07 |   0.04040 |  0.009970 |    0.4096 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.668 |    0.6313 |    0.8682 |     1.365 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.065 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     4.375 |   0.02651 |    0.1140 |    0.4191 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.173 |    0.2330 |  -0.08481 |    -1.281 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.3435 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   32</span>|     752.12310 |    0.9885 |   -0.9784 |    -1.008 |    -1.016 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6345 |   -0.9806 |   -0.9188 |   -0.6922 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7875 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      752.1231 |     86.00 |    -3.209 |    -4.608 |   -0.3659 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.668 |    0.6313 |    0.8684 |     1.367 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.065 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      752.1231</span> |     86.00 |   0.04040 |  0.009969 |    0.4095 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.668 |    0.6313 |    0.8684 |     1.367 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.065 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |   -0.6515 |   0.02994 |    0.1012 |    0.4113 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.192 |    0.1669 |  -0.06829 |    -1.233 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.3455 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   33</span>|     752.12055 |    0.9892 |   -0.9784 |    -1.008 |    -1.016 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6332 |   -0.9808 |   -0.9187 |   -0.6908 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7871 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.12055 |     86.06 |    -3.209 |    -4.608 |   -0.3661 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.671 |    0.6312 |    0.8685 |     1.368 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.065 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.12055</span> |     86.06 |   0.04040 |  0.009968 |    0.4095 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.671 |    0.6312 |    0.8685 |     1.368 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.065 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     3.998 |   0.02610 |    0.1041 |    0.4159 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.002 |    0.2127 |  -0.06061 |    -1.206 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.3333 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   34</span>|     752.11747 |    0.9885 |   -0.9785 |    -1.009 |    -1.018 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6332 |   -0.9807 |   -0.9185 |   -0.6896 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7874 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.11747 |     86.00 |    -3.209 |    -4.609 |   -0.3666 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.671 |    0.6312 |    0.8687 |     1.370 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.065 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.11747</span> |     86.00 |   0.04039 |  0.009966 |    0.4094 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.671 |    0.6312 |    0.8687 |     1.370 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.065 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |   -0.5772 |   0.02918 |   0.09258 |    0.4085 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.024 |    0.1535 |  -0.04467 |    -1.159 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.3360 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   35</span>|     752.11522 |    0.9892 |   -0.9785 |    -1.009 |    -1.018 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6319 |   -0.9809 |   -0.9185 |   -0.6881 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7870 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.11522 |     86.06 |    -3.209 |    -4.609 |   -0.3668 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.674 |    0.6311 |    0.8687 |     1.371 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.066 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.11522</span> |     86.06 |   0.04039 |  0.009965 |    0.4093 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.674 |    0.6311 |    0.8687 |     1.371 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.066 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     3.959 |   0.02535 |   0.09612 |    0.4130 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8451 |    0.1980 |  -0.03883 |    -1.121 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.3219 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   36</span>|     752.11234 |    0.9885 |   -0.9786 |    -1.009 |    -1.020 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6320 |   -0.9808 |   -0.9183 |   -0.6870 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7872 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.11234 |     86.00 |    -3.209 |    -4.609 |   -0.3673 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.673 |    0.6311 |    0.8689 |     1.373 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.065 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.11234</span> |     86.00 |   0.04039 |  0.009964 |    0.4092 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.673 |    0.6311 |    0.8689 |     1.373 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.065 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |   -0.5042 |   0.02791 |   0.08363 |    0.4056 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8741 |    0.1402 |  -0.02542 |    -1.088 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.3257 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   37</span>|     752.11033 |    0.9892 |   -0.9787 |    -1.009 |    -1.020 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6308 |   -0.9810 |   -0.9182 |   -0.6855 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7868 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.11033 |     86.06 |    -3.209 |    -4.609 |   -0.3675 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.676 |    0.6310 |    0.8689 |     1.374 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.066 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.11033</span> |     86.06 |   0.04039 |  0.009963 |    0.4091 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.676 |    0.6310 |    0.8689 |     1.374 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.066 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     3.874 |   0.02465 |   0.08719 |    0.4100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7032 |    0.1835 |  -0.01978 |    -1.047 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.3084 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   38</span>|     752.10764 |    0.9885 |   -0.9787 |    -1.009 |    -1.022 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6309 |   -0.9810 |   -0.9181 |   -0.6844 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7870 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.10764 |     86.00 |    -3.209 |    -4.609 |   -0.3681 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.676 |    0.6310 |    0.8691 |     1.376 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.066 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.10764</span> |     86.00 |   0.04038 |  0.009962 |    0.4090 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.676 |    0.6310 |    0.8691 |     1.376 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.066 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |   -0.4131 |   0.02699 |   0.07721 |    0.4026 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7354 |    0.1282 | -0.007503 |    -1.011 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.3125 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   39</span>|     752.10572 |    0.9892 |   -0.9788 |    -1.009 |    -1.023 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6298 |   -0.9812 |   -0.9181 |   -0.6829 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7865 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.10572 |     86.06 |    -3.209 |    -4.609 |   -0.3683 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.678 |    0.6309 |    0.8691 |     1.377 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.066 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.10572</span> |     86.06 |   0.04038 |  0.009960 |    0.4090 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.678 |    0.6309 |    0.8691 |     1.377 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.066 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     3.580 |   0.02411 |   0.07834 |    0.4067 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5755 |    0.1666 | -0.003596 |   -0.9604 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2924 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   40</span>|     752.10333 |    0.9885 |   -0.9789 |    -1.009 |    -1.024 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6300 |   -0.9811 |   -0.9179 |   -0.6819 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7867 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.10333 |     86.00 |    -3.209 |    -4.609 |   -0.3688 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.678 |    0.6309 |    0.8692 |     1.378 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.066 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.10333</span> |     86.00 |   0.04038 |  0.009959 |    0.4088 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.678 |    0.6309 |    0.8692 |     1.378 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.066 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |   -0.3772 |   0.02648 |   0.06875 |    0.3997 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6082 |    0.1162 |  0.008208 |   -0.9328 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2962 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   41</span>|     752.10169 |    0.9892 |   -0.9789 |    -1.009 |    -1.025 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6289 |   -0.9813 |   -0.9179 |   -0.6803 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7862 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.10169 |     86.06 |    -3.209 |    -4.609 |   -0.3691 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.680 |    0.6308 |    0.8692 |     1.380 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.066 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.10169</span> |     86.06 |   0.04038 |  0.009958 |    0.4088 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.680 |    0.6308 |    0.8692 |     1.380 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.066 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     3.644 |   0.02338 |   0.07127 |    0.4037 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4618 |    0.1554 |  0.009391 |   -0.8908 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2755 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   42</span>|     752.09938 |    0.9886 |   -0.9790 |    -1.009 |    -1.027 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6291 |   -0.9812 |   -0.9178 |   -0.6794 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7864 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.09938 |     86.00 |    -3.210 |    -4.609 |   -0.3697 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.680 |    0.6308 |    0.8693 |     1.381 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.066 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.09938</span> |     86.00 |   0.04037 |  0.009958 |    0.4086 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.680 |    0.6308 |    0.8693 |     1.381 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.066 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |   -0.2627 |   0.02548 |   0.06256 |    0.3967 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4955 |    0.1055 |   0.02027 |   -0.8653 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2789 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   43</span>|     752.09757 |    0.9890 |   -0.9791 |    -1.010 |    -1.028 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6281 |   -0.9814 |   -0.9178 |   -0.6778 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7859 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.09757 |     86.05 |    -3.210 |    -4.610 |   -0.3699 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.682 |    0.6307 |    0.8693 |     1.383 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.067 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.09757</span> |     86.05 |   0.04037 |  0.009956 |    0.4086 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.682 |    0.6307 |    0.8693 |     1.383 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.067 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     2.867 |   0.02296 |   0.06369 |    0.3997 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3645 |    0.1327 |   0.01894 |   -0.8216 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2549 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   44</span>|     752.09570 |    0.9886 |   -0.9791 |    -1.010 |    -1.030 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6283 |   -0.9814 |   -0.9177 |   -0.6770 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7861 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      752.0957 |     86.00 |    -3.210 |    -4.610 |   -0.3705 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.681 |    0.6307 |    0.8694 |     1.384 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.066 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      752.0957</span> |     86.00 |   0.04037 |  0.009956 |    0.4084 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.681 |    0.6307 |    0.8694 |     1.384 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.066 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |   -0.2846 |   0.02474 |   0.05675 |    0.3935 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3960 |   0.09353 |   0.02976 |   -0.7961 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2595 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   45</span>|     752.09434 |    0.9891 |   -0.9792 |    -1.010 |    -1.030 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6275 |   -0.9816 |   -0.9177 |   -0.6754 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7856 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.09434 |     86.05 |    -3.210 |    -4.610 |   -0.3708 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.683 |    0.6306 |    0.8694 |     1.386 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.067 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.09434</span> |     86.05 |   0.04037 |  0.009955 |    0.4083 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.683 |    0.6306 |    0.8694 |     1.386 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.067 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     3.348 |   0.02147 |   0.05816 |    0.3967 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2833 |    0.1286 |   0.02562 |   -0.7529 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2380 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   46</span>|     752.09236 |    0.9886 |   -0.9793 |    -1.010 |    -1.032 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6277 |   -0.9815 |   -0.9176 |   -0.6746 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7858 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.09236 |     86.01 |    -3.210 |    -4.610 |   -0.3715 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.683 |    0.6306 |    0.8695 |     1.387 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.067 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.09236</span> |     86.01 |   0.04036 |  0.009954 |    0.4082 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.683 |    0.6306 |    0.8695 |     1.387 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.067 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |   -0.1856 |   0.02378 |   0.05144 |    0.3902 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3147 |   0.08386 |   0.03545 |   -0.7433 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2408 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   47</span>|     752.09077 |    0.9890 |   -0.9793 |    -1.010 |    -1.033 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6269 |   -0.9817 |   -0.9177 |   -0.6729 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7852 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.09077 |     86.04 |    -3.210 |    -4.610 |   -0.3717 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.684 |    0.6305 |    0.8694 |     1.389 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.067 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.09077</span> |     86.04 |   0.04036 |  0.009953 |    0.4081 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.684 |    0.6305 |    0.8694 |     1.389 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.067 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   48</span>|     752.08937 |    0.9892 |   -0.9795 |    -1.010 |    -1.036 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6267 |   -0.9818 |   -0.9176 |   -0.6714 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7852 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.08937 |     86.06 |    -3.210 |    -4.610 |   -0.3725 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.685 |    0.6304 |    0.8695 |     1.391 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.067 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.08937</span> |     86.06 |   0.04036 |  0.009952 |    0.4079 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.685 |    0.6304 |    0.8695 |     1.391 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.067 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     3.945 |   0.01952 |   0.05064 |    0.3906 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1866 |    0.1233 |   0.03740 |   -0.6554 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2138 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   49</span>|     752.08593 |    0.9886 |   -0.9796 |    -1.010 |    -1.040 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6271 |   -0.9817 |   -0.9174 |   -0.6701 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7856 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.08593 |     86.01 |    -3.210 |    -4.610 |   -0.3740 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.684 |    0.6305 |    0.8697 |     1.392 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.067 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.08593</span> |     86.01 |   0.04035 |  0.009951 |    0.4076 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.684 |    0.6305 |    0.8697 |     1.392 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.067 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |  -0.04707 |   0.02143 |   0.04311 |    0.3795 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2514 |   0.07631 |   0.05739 |   -0.6184 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2313 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   50</span>|     752.08243 |    0.9889 |   -0.9798 |    -1.010 |    -1.042 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6256 |   -0.9821 |   -0.9177 |   -0.6665 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7842 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.08243 |     86.03 |    -3.210 |    -4.610 |   -0.3748 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.687 |    0.6302 |    0.8694 |     1.396 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.068 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.08243</span> |     86.03 |   0.04034 |  0.009949 |    0.4074 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.687 |    0.6302 |    0.8694 |     1.396 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.068 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   51</span>|     752.07616 |    0.9895 |   -0.9803 |    -1.011 |    -1.054 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6231 |   -0.9827 |   -0.9181 |   -0.6582 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7817 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.07616 |     86.08 |    -3.211 |    -4.611 |   -0.3786 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.693 |    0.6298 |    0.8690 |     1.406 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.071 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.07616</span> |     86.08 |   0.04032 |  0.009942 |    0.4065 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.693 |    0.6298 |    0.8690 |     1.406 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.071 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     5.128 |   0.01286 |   0.03023 |    0.3726 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2708 |   0.07184 |  -0.02487 |   -0.2959 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|  -0.03882 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   52</span>|     752.06706 |    0.9888 |   -0.9810 |    -1.011 |    -1.073 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6241 |   -0.9834 |   -0.9174 |   -0.6562 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7830 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.06706 |     86.03 |    -3.212 |    -4.611 |   -0.3848 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.690 |    0.6293 |    0.8696 |     1.408 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.069 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.06706</span> |     86.03 |   0.04030 |  0.009938 |    0.4050 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.690 |    0.6293 |    0.8696 |     1.408 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.069 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   53</span>|     752.05726 |    0.9892 |   -0.9821 |    -1.012 |    -1.106 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6257 |   -0.9847 |   -0.9162 |   -0.6528 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7853 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.05726 |     86.06 |    -3.213 |    -4.612 |   -0.3958 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.687 |    0.6284 |    0.8707 |     1.412 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.067 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.05726</span> |     86.06 |   0.04025 |  0.009929 |    0.4023 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.687 |    0.6284 |    0.8707 |     1.412 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.067 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   54</span>|     752.04392 |    0.9898 |   -0.9854 |    -1.015 |    -1.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6305 |   -0.9883 |   -0.9128 |   -0.6430 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7918 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.04392 |     86.11 |    -3.216 |    -4.615 |   -0.4269 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.677 |    0.6259 |    0.8738 |     1.423 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.061 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.04392</span> |     86.11 |   0.04012 |  0.009906 |    0.3949 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.677 |    0.6259 |    0.8738 |     1.423 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.061 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     5.566 |  -0.02004 |   0.02472 |    0.1678 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6767 | -0.003606 |    0.4773 |   0.08205 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6933 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   55</span>|     752.05860 |    0.9880 |   -0.9900 |    -1.027 |    -1.486 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6186 |   -0.9338 |   -0.9168 |   -0.6653 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7443 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      752.0586 |     85.96 |    -3.221 |    -4.627 |   -0.5212 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.702 |    0.6640 |    0.8702 |     1.398 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.107 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      752.0586</span> |     85.96 |   0.03993 |  0.009780 |    0.3726 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.702 |    0.6640 |    0.8702 |     1.398 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.107 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   56</span>|     752.02137 |    0.9885 |   -0.9874 |    -1.020 |    -1.325 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6252 |   -0.9645 |   -0.9146 |   -0.6528 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7710 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.02137 |     86.00 |    -3.218 |    -4.620 |   -0.4681 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.688 |    0.6425 |    0.8722 |     1.412 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.081 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.02137</span> |     86.00 |   0.04004 |  0.009850 |    0.3851 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.688 |    0.6425 |    0.8722 |     1.412 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.081 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.728 |  -0.02729 |   0.08518 |   0.04185 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2367 |    0.4063 |    0.2432 |   0.01436 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|  -0.07563 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   57</span>|     752.06656 |    0.9886 |   -0.9792 |    -1.053 |    -1.433 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6165 |    -1.020 |   -0.9133 |   -0.6801 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7607 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.06656 |     86.01 |    -3.210 |    -4.653 |   -0.5037 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.707 |    0.6037 |    0.8734 |     1.380 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.091 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.06656</span> |     86.01 |   0.04036 |  0.009533 |    0.3767 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.707 |    0.6037 |    0.8734 |     1.380 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.091 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   58</span>|     752.02137 |    0.9885 |   -0.9874 |    -1.020 |    -1.325 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6252 |   -0.9645 |   -0.9146 |   -0.6528 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7710 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.02137 |     86.00 |    -3.218 |    -4.620 |   -0.4681 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.688 |    0.6425 |    0.8722 |     1.412 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.081 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.02137</span> |     86.00 |   0.04004 |  0.009850 |    0.3851 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.688 |    0.6425 |    0.8722 |     1.412 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.081 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> calculating covariance matrix</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating residuals/tables</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-in"><span class="va">f_nlmixr_fomc_sfo_focei_const</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr</a></span><span class="op">(</span><span class="va">f_mmkin_const</span><span class="op">[</span><span class="st">"FOMC-SFO"</span>, <span class="op">]</span>, est <span class="op">=</span> <span class="st">"focei"</span><span class="op">)</span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate jacobian</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate sensitivities</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate ∂(f)/∂(η)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate ∂(R²)/∂(η)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in inner model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in inner model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in EBE model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in EBE model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling inner model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in FD model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in FD model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling EBE model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling events FD model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Needed Covariates:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "CMT"</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="font-weight: bold;">Key:</span> U: Unscaled Parameters; X: Back-transformed parameters; G: Gill difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> F: Forward difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> C: Central difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> M: Mixed forward and central difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Unscaled parameters for Omegas=chol(solve(omega));</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Diagonals are transformed, as specified by foceiControl(diagXform=)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |-----+---------------+-----------+-----------+-----------+-----------|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    #| Objective Fun |  parent_0 |  log_k_A1 |f_parent_qlogis | log_alpha |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  log_beta |     sigma |        o1 |        o2 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|        o3 |        o4 |        o5 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    1</span>|     494.78160 |     1.000 |    -1.000 |   -0.9115 |   -0.8954 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8491 |   -0.8592 |   -0.8761 |   -0.8740 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8674 |   -0.8694 |   -0.8683 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      494.7816 |     94.00 |    -5.400 |    -1.000 |   -0.2000 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.100 |     1.600 |    0.7598 |    0.8633 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.189 |     1.089 |     1.146 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      494.7816</span> |     94.00 |  0.004517 |    0.2689 |    0.8187 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.166 |     1.600 |    0.7598 |    0.8633 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.189 |     1.089 |     1.146 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    G|    Gill Diff. |    -28.01 |     1.933 |   -0.2086 |   0.01492 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1687 |    -59.79 |     10.74 |     9.966 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -11.82 |    -8.862 |    -10.52 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    2</span>|     1353.4477 |     1.400 |    -1.028 |   -0.9085 |   -0.8956 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8467 | -0.005584 |    -1.029 |    -1.016 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6987 |   -0.7429 |   -0.7181 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     1353.4477 |     131.6 |    -5.428 |   -0.9970 |   -0.2002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.102 |     2.283 |    0.6433 |    0.7405 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.390 |     1.227 |     1.318 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     1353.4477</span> |     131.6 |  0.004394 |    0.2695 |    0.8186 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.186 |     2.283 |    0.6433 |    0.7405 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.390 |     1.227 |     1.318 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    3</span>|     504.82409 |     1.040 |    -1.003 |   -0.9112 |   -0.8954 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8489 |   -0.7738 |   -0.8914 |   -0.8882 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8506 |   -0.8568 |   -0.8533 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     504.82409 |     97.76 |    -5.403 |   -0.9997 |   -0.2000 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.100 |     1.668 |    0.7482 |    0.8511 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.209 |     1.103 |     1.163 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     504.82409</span> |     97.76 |  0.004504 |    0.2690 |    0.8187 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.168 |     1.668 |    0.7482 |    0.8511 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.209 |     1.103 |     1.163 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    4</span>|     494.10898 |     1.008 |    -1.001 |   -0.9114 |   -0.8954 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8490 |   -0.8416 |   -0.8792 |   -0.8769 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8640 |   -0.8668 |   -0.8652 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     494.10898 |     94.77 |    -5.401 |   -0.9999 |   -0.2000 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.100 |     1.614 |    0.7574 |    0.8608 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.193 |     1.092 |     1.149 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     494.10898</span> |     94.77 |  0.004514 |    0.2690 |    0.8187 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.167 |     1.614 |    0.7574 |    0.8608 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.193 |     1.092 |     1.149 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     147.0 |     1.955 |  -0.08761 |   0.06834 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.05948 |    -55.61 |     11.89 |     8.304 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -12.01 |    -8.510 |    -10.24 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    5</span>|     492.58255 |    0.9992 |    -1.001 |   -0.9114 |   -0.8954 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8490 |   -0.8245 |   -0.8825 |   -0.8797 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8605 |   -0.8643 |   -0.8621 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     492.58255 |     93.93 |    -5.401 |   -0.9999 |   -0.2000 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.100 |     1.628 |    0.7550 |    0.8584 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.197 |     1.095 |     1.153 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     492.58255</span> |     93.93 |  0.004511 |    0.2690 |    0.8187 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.167 |     1.628 |    0.7550 |    0.8584 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.197 |     1.095 |     1.153 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -43.22 |     1.882 |   -0.2206 | -0.004719 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1970 |    -51.78 |     10.22 |     6.620 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -11.81 |    -8.346 |    -10.04 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    6</span>|     491.66702 |     1.006 |    -1.002 |   -0.9113 |   -0.8954 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8490 |   -0.8063 |   -0.8860 |   -0.8822 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8565 |   -0.8614 |   -0.8588 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     491.66702 |     94.52 |    -5.402 |   -0.9998 |   -0.2000 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.100 |     1.642 |    0.7523 |    0.8562 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.202 |     1.098 |     1.157 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     491.66702</span> |     94.52 |  0.004509 |    0.2690 |    0.8187 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.167 |     1.642 |    0.7523 |    0.8562 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.202 |     1.098 |     1.157 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     87.79 |     1.893 |   -0.1269 |   0.04418 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.01699 |    -47.85 |     10.34 |     7.758 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -11.67 |    -8.213 |    -9.916 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    7</span>|     490.57489 |    0.9987 |    -1.002 |   -0.9112 |   -0.8954 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8489 |   -0.7885 |   -0.8895 |   -0.8851 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8525 |   -0.8586 |   -0.8553 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     490.57489 |     93.88 |    -5.402 |   -0.9998 |   -0.2000 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.100 |     1.657 |    0.7496 |    0.8537 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.207 |     1.101 |     1.161 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     490.57489</span> |     93.88 |  0.004506 |    0.2690 |    0.8187 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.168 |     1.657 |    0.7496 |    0.8537 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.207 |     1.101 |     1.161 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -52.56 |     1.834 |   -0.2285 |  -0.01046 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2159 |    -44.44 |     9.379 |     7.248 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -11.47 |    -8.044 |    -9.710 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    8</span>|     489.67804 |     1.004 |    -1.003 |   -0.9112 |   -0.8954 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8489 |   -0.7706 |   -0.8932 |   -0.8884 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8482 |   -0.8554 |   -0.8516 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     489.67804 |     94.42 |    -5.403 |   -0.9997 |   -0.2000 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.100 |     1.671 |    0.7468 |    0.8509 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.212 |     1.105 |     1.165 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     489.67804</span> |     94.42 |  0.004503 |    0.2690 |    0.8187 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.168 |     1.671 |    0.7468 |    0.8509 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.212 |     1.105 |     1.165 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     63.10 |     1.841 |   -0.1396 |   0.03713 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.05177 |    -40.65 |     9.345 |     6.018 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -11.29 |    -7.879 |    -9.567 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    9</span>|     488.80457 |    0.9984 |    -1.004 |   -0.9111 |   -0.8954 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8488 |   -0.7529 |   -0.8970 |   -0.8913 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8435 |   -0.8521 |   -0.8475 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     488.80457 |     93.85 |    -5.404 |   -0.9996 |   -0.2000 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.100 |     1.685 |    0.7439 |    0.8484 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.218 |     1.108 |     1.170 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     488.80457</span> |     93.85 |  0.004499 |    0.2690 |    0.8187 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.168 |     1.685 |    0.7439 |    0.8484 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.218 |     1.108 |     1.170 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -56.52 |     1.788 |   -0.2313 |  -0.01570 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2287 |    -37.43 |     8.740 |     5.512 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -11.08 |    -7.680 |    -9.353 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   10</span>|     487.98244 |     1.004 |    -1.005 |   -0.9110 |   -0.8954 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8487 |   -0.7356 |   -0.9012 |   -0.8939 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8380 |   -0.8482 |   -0.8428 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     487.98244 |     94.36 |    -5.405 |   -0.9995 |   -0.2000 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.100 |     1.699 |    0.7407 |    0.8461 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.224 |     1.112 |     1.175 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     487.98244</span> |     94.36 |  0.004495 |    0.2690 |    0.8187 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.169 |     1.699 |    0.7407 |    0.8461 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.224 |     1.112 |     1.175 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     49.57 |     1.794 |   -0.1466 |   0.03517 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.07178 |    -34.12 |     8.494 |     6.684 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -10.82 |    -7.482 |    -9.140 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   11</span>|     487.23587 |    0.9987 |    -1.006 |   -0.9109 |   -0.8954 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8487 |   -0.7192 |   -0.9058 |   -0.8980 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8316 |   -0.8438 |   -0.8374 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     487.23587 |     93.88 |    -5.406 |   -0.9994 |   -0.2001 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.100 |     1.712 |    0.7372 |    0.8426 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.232 |     1.117 |     1.181 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     487.23587</span> |     93.88 |  0.004490 |    0.2691 |    0.8187 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.170 |     1.712 |    0.7372 |    0.8426 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.232 |     1.117 |     1.181 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -49.22 |     1.745 |   -0.2274 | -0.009194 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2301 |    -31.48 |     7.992 |     6.132 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -10.64 |    -7.269 |    -8.903 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   12</span>|     486.53337 |     1.004 |    -1.007 |   -0.9107 |   -0.8954 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8485 |   -0.7047 |   -0.9109 |   -0.9037 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8240 |   -0.8386 |   -0.8310 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     486.53337 |     94.34 |    -5.407 |   -0.9993 |   -0.2001 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.101 |     1.724 |    0.7334 |    0.8376 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.241 |     1.123 |     1.189 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     486.53337</span> |     94.34 |  0.004484 |    0.2691 |    0.8187 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.171 |     1.724 |    0.7334 |    0.8376 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.241 |     1.123 |     1.189 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     43.47 |     1.742 |   -0.1424 |   0.02918 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.07089 |    -28.76 |     7.629 |     4.806 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -10.24 |    -6.953 |    -8.584 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   13</span>|     485.91669 |    0.9989 |    -1.009 |   -0.9105 |   -0.8955 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8484 |   -0.6920 |   -0.9165 |   -0.9072 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8145 |   -0.8325 |   -0.8231 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     485.91669 |     93.90 |    -5.409 |   -0.9991 |   -0.2001 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.101 |     1.734 |    0.7291 |    0.8346 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.252 |     1.130 |     1.198 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     485.91669</span> |     93.90 |  0.004476 |    0.2691 |    0.8187 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.172 |     1.734 |    0.7291 |    0.8346 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.252 |     1.130 |     1.198 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -44.30 |     1.699 |   -0.2182 |  0.001936 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2284 |    -26.86 |     7.286 |     5.487 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -9.898 |    -6.659 |    -8.234 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   14</span>|     485.33976 |     1.003 |    -1.011 |   -0.9103 |   -0.8955 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8481 |   -0.6819 |   -0.9228 |   -0.9110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8035 |   -0.8257 |   -0.8143 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     485.33976 |     94.29 |    -5.411 |   -0.9988 |   -0.2001 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.101 |     1.742 |    0.7243 |    0.8314 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.265 |     1.137 |     1.208 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     485.33976</span> |     94.29 |  0.004467 |    0.2692 |    0.8186 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.174 |     1.742 |    0.7243 |    0.8314 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.265 |     1.137 |     1.208 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   15</span>|     484.77317 |     1.003 |    -1.014 |   -0.9100 |   -0.8956 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8479 |   -0.6718 |   -0.9302 |   -0.9153 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7902 |   -0.8175 |   -0.8035 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     484.77317 |     94.32 |    -5.414 |   -0.9986 |   -0.2002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.101 |     1.750 |    0.7187 |    0.8276 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.281 |     1.146 |     1.220 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     484.77317</span> |     94.32 |  0.004455 |    0.2692 |    0.8186 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.176 |     1.750 |    0.7187 |    0.8276 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.281 |     1.146 |     1.220 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   16</span>|     483.17588 |     1.004 |    -1.023 |   -0.9091 |   -0.8958 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8469 |   -0.6384 |   -0.9549 |   -0.9296 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7463 |   -0.7903 |   -0.7681 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     483.17588 |     94.41 |    -5.423 |   -0.9976 |   -0.2004 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.102 |     1.777 |    0.6999 |    0.8153 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.333 |     1.176 |     1.261 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     483.17588</span> |     94.41 |  0.004415 |    0.2694 |    0.8184 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.184 |     1.777 |    0.6999 |    0.8153 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.333 |     1.176 |     1.261 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   17</span>|     481.21481 |     1.006 |    -1.040 |   -0.9072 |   -0.8961 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8451 |   -0.5721 |    -1.004 |   -0.9579 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6592 |   -0.7365 |   -0.6977 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     481.21481 |     94.58 |    -5.440 |   -0.9958 |   -0.2008 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.104 |     1.830 |    0.6628 |    0.7909 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.437 |     1.234 |     1.341 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     481.21481</span> |     94.58 |  0.004338 |    0.2698 |    0.8181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.199 |     1.830 |    0.6628 |    0.7909 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.437 |     1.234 |     1.341 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     62.86 |     1.476 |   0.06730 |   0.05792 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1123 |    -8.977 |     1.055 |     2.914 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -3.044 |    -1.094 |    -2.299 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   18</span>|     481.29476 |     1.000 |    -1.145 |   -0.9129 |   -0.8997 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8485 |   -0.5378 |   -0.8985 |    -1.168 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6228 |   -0.8149 |   -0.6969 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     481.29476 |     94.03 |    -5.545 |    -1.001 |   -0.2043 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.101 |     1.857 |    0.7428 |    0.6099 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.480 |     1.149 |     1.342 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     481.29476</span> |     94.03 |  0.003907 |    0.2687 |    0.8152 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.171 |     1.857 |    0.7428 |    0.6099 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.480 |     1.149 |     1.342 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   19</span>|     480.77138 |     1.000 |    -1.090 |   -0.9099 |   -0.8978 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8467 |   -0.5555 |   -0.9542 |    -1.057 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6419 |   -0.7733 |   -0.6972 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.77138 |     94.01 |    -5.490 |   -0.9984 |   -0.2025 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.102 |     1.843 |    0.7004 |    0.7055 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.457 |     1.194 |     1.342 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.77138</span> |     94.01 |  0.004129 |    0.2693 |    0.8167 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.186 |     1.843 |    0.7004 |    0.7055 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.457 |     1.194 |     1.342 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -25.72 |     1.151 |   0.08772 | -0.007897 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.07232 |    -6.930 |     3.330 |    -2.491 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -2.626 |    -3.111 |    -2.500 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   20</span>|     480.61717 |     1.001 |    -1.203 |   -0.9227 |   -0.9002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8503 |   -0.5418 |   -0.9405 |    -1.001 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6281 |   -0.7705 |   -0.6908 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.61717 |     94.09 |    -5.603 |    -1.011 |   -0.2049 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.099 |     1.854 |    0.7108 |    0.7537 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.474 |     1.197 |     1.349 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.61717</span> |     94.09 |  0.003688 |    0.2667 |    0.8147 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.156 |     1.854 |    0.7108 |    0.7537 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.474 |     1.197 |     1.349 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -10.61 |    0.9224 |   -0.5881 |  -0.06621 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1838 |    -5.929 |     3.775 |     2.798 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.936 |    -2.993 |    -2.208 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   21</span>|     480.72130 |     1.009 |    -1.314 |   -0.9169 |   -0.9003 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8494 |   -0.5516 |   -0.9873 |    -1.003 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6340 |   -0.7292 |   -0.6819 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      480.7213 |     94.81 |    -5.714 |    -1.005 |   -0.2049 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.100 |     1.846 |    0.6753 |    0.7523 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.467 |     1.242 |     1.359 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      480.7213</span> |     94.81 |  0.003299 |    0.2679 |    0.8147 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.163 |     1.846 |    0.6753 |    0.7523 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.467 |     1.242 |     1.359 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   22</span>|     480.86254 |     1.009 |    -1.247 |   -0.9202 |   -0.9002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8499 |   -0.5429 |   -0.9605 |    -1.003 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6295 |   -0.7530 |   -0.6863 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.86254 |     94.84 |    -5.647 |    -1.009 |   -0.2048 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.099 |     1.853 |    0.6957 |    0.7520 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.472 |     1.216 |     1.354 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.86254</span> |     94.84 |  0.003530 |    0.2672 |    0.8148 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.160 |     1.853 |    0.6957 |    0.7520 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.472 |     1.216 |     1.354 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   23</span>|     480.98412 |     1.009 |    -1.209 |   -0.9220 |   -0.9002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8501 |   -0.5381 |   -0.9458 |    -1.003 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6270 |   -0.7661 |   -0.6887 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.98412 |     94.85 |    -5.609 |    -1.010 |   -0.2048 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.099 |     1.857 |    0.7069 |    0.7519 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.475 |     1.202 |     1.352 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.98412</span> |     94.85 |  0.003664 |    0.2669 |    0.8148 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.158 |     1.857 |    0.7069 |    0.7519 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.475 |     1.202 |     1.352 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   24</span>|     480.60990 |     1.003 |    -1.203 |   -0.9226 |   -0.9002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8503 |   -0.5410 |   -0.9411 |    -1.001 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6278 |   -0.7701 |   -0.6905 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      480.6099 |     94.24 |    -5.603 |    -1.011 |   -0.2049 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.099 |     1.855 |    0.7104 |    0.7534 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.474 |     1.198 |     1.350 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      480.6099</span> |     94.24 |  0.003688 |    0.2668 |    0.8148 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.156 |     1.855 |    0.7104 |    0.7534 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.474 |     1.198 |     1.350 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     15.29 |    0.9278 |   -0.5289 |  -0.04762 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1043 |    -5.468 |     3.876 |    0.6273 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.897 |    -2.941 |    -2.183 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   25</span>|     480.59557 |     1.001 |    -1.204 |   -0.9225 |   -0.9002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8503 |   -0.5407 |   -0.9419 |    -1.001 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6277 |   -0.7694 |   -0.6902 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.59557 |     94.13 |    -5.604 |    -1.011 |   -0.2049 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.099 |     1.855 |    0.7098 |    0.7533 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.474 |     1.198 |     1.350 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.59557</span> |     94.13 |  0.003683 |    0.2668 |    0.8148 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.157 |     1.855 |    0.7098 |    0.7533 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.474 |     1.198 |     1.350 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.733 |    0.9212 |   -0.5619 |  -0.05855 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1664 |    -5.388 |     3.833 |    0.5854 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.924 |    -2.927 |    -2.102 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   26</span>|     480.58581 |     1.002 |    -1.204 |   -0.9224 |   -0.9002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8502 |   -0.5395 |   -0.9428 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6273 |   -0.7688 |   -0.6897 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.58581 |     94.23 |    -5.604 |    -1.011 |   -0.2048 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.099 |     1.856 |    0.7092 |    0.7532 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.475 |     1.199 |     1.351 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.58581</span> |     94.23 |  0.003683 |    0.2668 |    0.8148 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.157 |     1.856 |    0.7092 |    0.7532 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.475 |     1.199 |     1.351 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     12.31 |    0.9226 |   -0.5246 |  -0.05019 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1134 |    -5.293 |     3.771 |    0.5822 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.938 |    -2.904 |    -2.175 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   27</span>|     480.57360 |     1.001 |    -1.205 |   -0.9222 |   -0.9002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8502 |   -0.5392 |   -0.9437 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6271 |   -0.7681 |   -0.6894 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      480.5736 |     94.13 |    -5.605 |    -1.011 |   -0.2048 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.099 |     1.856 |    0.7085 |    0.7532 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.475 |     1.200 |     1.351 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      480.5736</span> |     94.13 |  0.003678 |    0.2668 |    0.8148 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.157 |     1.856 |    0.7085 |    0.7532 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.475 |     1.200 |     1.351 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.864 |    0.9151 |   -0.5515 |  -0.06112 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1679 |    -5.184 |     3.617 |    0.5746 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.832 |    -2.849 |    -2.141 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   28</span>|     480.56429 |     1.002 |    -1.206 |   -0.9220 |   -0.9002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8502 |   -0.5382 |   -0.9446 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6268 |   -0.7674 |   -0.6889 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.56429 |     94.23 |    -5.606 |    -1.011 |   -0.2048 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.099 |     1.857 |    0.7078 |    0.7531 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.475 |     1.201 |     1.351 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.56429</span> |     94.23 |  0.003676 |    0.2669 |    0.8148 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.158 |     1.857 |    0.7078 |    0.7531 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.475 |     1.201 |     1.351 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     12.18 |    0.9169 |   -0.5105 |  -0.05061 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1125 |    -5.131 |     3.532 |    0.5638 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.931 |    -2.821 |    -2.132 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   29</span>|     480.55246 |     1.001 |    -1.207 |   -0.9218 |   -0.9002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8501 |   -0.5380 |   -0.9454 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6266 |   -0.7667 |   -0.6886 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.55246 |     94.13 |    -5.607 |    -1.010 |   -0.2048 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.099 |     1.857 |    0.7071 |    0.7532 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.476 |     1.201 |     1.352 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.55246</span> |     94.13 |  0.003672 |    0.2669 |    0.8148 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.158 |     1.857 |    0.7071 |    0.7532 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.476 |     1.201 |     1.352 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.480 |    0.9098 |   -0.5353 |  -0.06055 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1647 |    -5.053 |     3.625 |    0.5654 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.860 |    -2.782 |    -2.112 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   30</span>|     480.54335 |     1.002 |    -1.207 |   -0.9217 |   -0.9002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8501 |   -0.5368 |   -0.9463 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6262 |   -0.7660 |   -0.6881 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.54335 |     94.23 |    -5.607 |    -1.010 |   -0.2048 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.099 |     1.858 |    0.7065 |    0.7530 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.476 |     1.202 |     1.352 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.54335</span> |     94.23 |  0.003671 |    0.2669 |    0.8148 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.158 |     1.858 |    0.7065 |    0.7530 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.476 |     1.202 |     1.352 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     12.09 |    0.9120 |   -0.4955 |  -0.05014 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1107 |    -4.912 |     3.583 |    0.5835 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.814 |    -2.702 |    -2.057 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   31</span>|     480.53185 |     1.001 |    -1.209 |   -0.9215 |   -0.9002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8500 |   -0.5366 |   -0.9472 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6260 |   -0.7654 |   -0.6878 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.53185 |     94.13 |    -5.609 |    -1.010 |   -0.2048 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.099 |     1.858 |    0.7058 |    0.7531 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.476 |     1.203 |     1.353 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.53185</span> |     94.13 |  0.003666 |    0.2670 |    0.8148 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.158 |     1.858 |    0.7058 |    0.7531 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.476 |     1.203 |     1.353 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.509 |    0.9048 |   -0.5214 |  -0.06027 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1665 |    -4.870 |     3.525 |    0.5641 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.813 |    -2.699 |    -2.069 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   32</span>|     480.52276 |     1.002 |    -1.209 |   -0.9214 |   -0.9001 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8500 |   -0.5356 |   -0.9481 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6256 |   -0.7647 |   -0.6873 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.52276 |     94.22 |    -5.609 |    -1.010 |   -0.2048 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.099 |     1.859 |    0.7051 |    0.7530 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.477 |     1.203 |     1.353 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.52276</span> |     94.22 |  0.003664 |    0.2670 |    0.8148 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.159 |     1.859 |    0.7051 |    0.7530 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.477 |     1.203 |     1.353 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     11.38 |    0.9060 |   -0.4821 |  -0.05014 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1110 |    -4.747 |     3.479 |    0.5798 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.777 |    -2.636 |    -2.020 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   33</span>|     480.51194 |     1.001 |    -1.211 |   -0.9212 |   -0.9001 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8500 |   -0.5355 |   -0.9490 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6255 |   -0.7641 |   -0.6869 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.51194 |     94.13 |    -5.611 |    -1.010 |   -0.2048 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.099 |     1.859 |    0.7044 |    0.7530 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.477 |     1.204 |     1.354 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.51194</span> |     94.13 |  0.003659 |    0.2670 |    0.8148 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.159 |     1.859 |    0.7044 |    0.7530 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.477 |     1.204 |     1.354 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.485 |    0.8991 |   -0.5064 |  -0.06148 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1643 |    -4.758 |     3.403 |    0.5432 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.772 |    -2.633 |    -2.022 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   34</span>|     480.50302 |     1.002 |    -1.211 |   -0.9210 |   -0.9001 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8499 |   -0.5345 |   -0.9500 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6251 |   -0.7633 |   -0.6864 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.50302 |     94.22 |    -5.611 |    -1.010 |   -0.2047 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.099 |     1.860 |    0.7037 |    0.7529 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.477 |     1.205 |     1.354 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.50302</span> |     94.22 |  0.003656 |    0.2671 |    0.8149 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.159 |     1.860 |    0.7037 |    0.7529 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.477 |     1.205 |     1.354 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     10.83 |    0.8997 |   -0.4680 |  -0.05021 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1106 |    -4.703 |     3.332 |    0.5674 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.765 |    -2.559 |    -1.986 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   35</span>|     480.49281 |     1.001 |    -1.213 |   -0.9209 |   -0.9001 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8499 |   -0.5344 |   -0.9509 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6250 |   -0.7627 |   -0.6860 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.49281 |     94.13 |    -5.613 |    -1.009 |   -0.2047 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.099 |     1.860 |    0.7030 |    0.7529 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.478 |     1.206 |     1.355 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.49281</span> |     94.13 |  0.003652 |    0.2671 |    0.8149 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.160 |     1.860 |    0.7030 |    0.7529 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.478 |     1.206 |     1.355 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.447 |    0.8934 |   -0.4900 |  -0.06030 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1577 |    -4.708 |     3.266 |    0.5080 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.782 |    -2.544 |    -1.983 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   36</span>|     480.48415 |     1.002 |    -1.213 |   -0.9207 |   -0.9001 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8498 |   -0.5335 |   -0.9518 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6246 |   -0.7620 |   -0.6855 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.48415 |     94.22 |    -5.613 |    -1.009 |   -0.2047 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.099 |     1.861 |    0.7023 |    0.7528 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.478 |     1.206 |     1.355 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.48415</span> |     94.22 |  0.003649 |    0.2671 |    0.8149 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.160 |     1.861 |    0.7023 |    0.7528 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.478 |     1.206 |     1.355 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     10.47 |    0.8932 |   -0.4526 |  -0.04997 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1093 |    -4.502 |     3.250 |    0.5653 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.769 |    -2.486 |    -1.944 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   37</span>|     480.47437 |     1.001 |    -1.215 |   -0.9205 |   -0.9001 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8498 |   -0.5334 |   -0.9527 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6244 |   -0.7613 |   -0.6852 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.47437 |     94.13 |    -5.615 |    -1.009 |   -0.2047 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.099 |     1.861 |    0.7016 |    0.7528 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.478 |     1.207 |     1.356 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.47437</span> |     94.13 |  0.003644 |    0.2672 |    0.8149 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.161 |     1.861 |    0.7016 |    0.7528 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.478 |     1.207 |     1.356 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.431 |    0.8869 |   -0.4735 |  -0.05983 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1554 |    -4.492 |     3.189 |   -0.6996 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.700 |    -2.467 |    -1.937 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   38</span>|     480.46651 |     1.002 |    -1.216 |   -0.9203 |   -0.9000 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8497 |   -0.5326 |   -0.9537 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6240 |   -0.7606 |   -0.6847 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.46651 |     94.21 |    -5.616 |    -1.009 |   -0.2047 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.099 |     1.861 |    0.7009 |    0.7530 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.479 |     1.208 |     1.356 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.46651</span> |     94.21 |  0.003640 |    0.2672 |    0.8149 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.161 |     1.861 |    0.7009 |    0.7530 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.479 |     1.208 |     1.356 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     9.502 |    0.8860 |   -0.4381 |  -0.05035 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1103 |    -4.453 |     3.114 |    0.5384 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.770 |    -2.407 |    -1.897 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   39</span>|     480.45755 |     1.001 |    -1.217 |   -0.9200 |   -0.9000 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8497 |   -0.5325 |   -0.9546 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6238 |   -0.7600 |   -0.6843 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.45755 |     94.13 |    -5.617 |    -1.009 |   -0.2047 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.099 |     1.861 |    0.7002 |    0.7530 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.479 |     1.209 |     1.357 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.45755</span> |     94.13 |  0.003635 |    0.2673 |    0.8149 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.162 |     1.861 |    0.7002 |    0.7530 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.479 |     1.209 |     1.357 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.779 |    0.8798 |   -0.4544 |  -0.05816 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1541 |    -4.432 |     3.060 |    0.5236 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.719 |    -2.365 |    -1.874 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   40</span>|     480.44943 |     1.002 |    -1.218 |   -0.9199 |   -0.9000 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8496 |   -0.5319 |   -0.9555 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6235 |   -0.7592 |   -0.6838 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.44943 |     94.21 |    -5.618 |    -1.008 |   -0.2046 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.099 |     1.862 |    0.6995 |    0.7529 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.479 |     1.209 |     1.357 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.44943</span> |     94.21 |  0.003631 |    0.2673 |    0.8149 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.162 |     1.862 |    0.6995 |    0.7529 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.479 |     1.209 |     1.357 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     8.806 |    0.8785 |   -0.4202 |  -0.05024 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1124 |    -4.289 |     3.039 |    0.5974 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.651 |    -2.322 |    -1.843 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   41</span>|     480.44100 |     1.001 |    -1.220 |   -0.9197 |   -0.9000 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8496 |   -0.5318 |   -0.9564 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6233 |   -0.7586 |   -0.6835 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|       480.441 |     94.13 |    -5.620 |    -1.008 |   -0.2046 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.100 |     1.862 |    0.6988 |    0.7529 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.480 |     1.210 |     1.358 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">       480.441</span> |     94.13 |  0.003626 |    0.2673 |    0.8150 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.162 |     1.862 |    0.6988 |    0.7529 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.480 |     1.210 |     1.358 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.662 |    0.8724 |   -0.4382 |  -0.05735 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1541 |    -4.192 |     3.008 |    0.5718 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.633 |    -2.283 |    -1.828 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   42</span>|     480.43316 |     1.002 |    -1.221 |   -0.9195 |   -0.9000 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8495 |   -0.5313 |   -0.9573 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6230 |   -0.7579 |   -0.6830 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.43316 |     94.21 |    -5.621 |    -1.008 |   -0.2046 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.100 |     1.862 |    0.6981 |    0.7529 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.480 |     1.211 |     1.358 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.43316</span> |     94.21 |  0.003622 |    0.2674 |    0.8150 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.163 |     1.862 |    0.6981 |    0.7529 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.480 |     1.211 |     1.358 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     8.374 |    0.8709 |   -0.4057 |  -0.04981 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1077 |    -4.274 |     2.898 |    0.5670 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.709 |    -2.249 |    -1.806 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   43</span>|     480.42514 |     1.001 |    -1.222 |   -0.9194 |   -0.8999 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8495 |   -0.5312 |   -0.9582 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6227 |   -0.7573 |   -0.6826 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.42514 |     94.13 |    -5.622 |    -1.008 |   -0.2046 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.100 |     1.862 |    0.6974 |    0.7529 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.480 |     1.212 |     1.359 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.42514</span> |     94.13 |  0.003617 |    0.2674 |    0.8150 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.163 |     1.862 |    0.6974 |    0.7529 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.480 |     1.212 |     1.359 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.560 |    0.8648 |   -0.4238 |  -0.05673 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1505 |    -4.130 |     2.894 |    0.5588 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.637 |    -2.210 |    -1.785 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   44</span>|     480.41757 |     1.002 |    -1.223 |   -0.9192 |   -0.8999 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8494 |   -0.5307 |   -0.9591 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6223 |   -0.7566 |   -0.6821 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.41757 |     94.21 |    -5.623 |    -1.008 |   -0.2046 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.100 |     1.863 |    0.6967 |    0.7528 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.481 |     1.212 |     1.359 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.41757</span> |     94.21 |  0.003613 |    0.2674 |    0.8150 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.164 |     1.863 |    0.6967 |    0.7528 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.481 |     1.212 |     1.359 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     8.260 |    0.8632 |   -0.3905 |  -0.04898 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1050 |    -4.163 |     2.793 |    0.5626 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.680 |    -2.170 |    -1.767 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   45</span>|     480.40986 |     1.001 |    -1.225 |   -0.9190 |   -0.8999 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8494 |   -0.5306 |   -0.9600 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6221 |   -0.7560 |   -0.6818 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.40986 |     94.13 |    -5.625 |    -1.008 |   -0.2045 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.100 |     1.863 |    0.6961 |    0.7528 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.481 |     1.213 |     1.360 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.40986</span> |     94.13 |  0.003607 |    0.2675 |    0.8150 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.164 |     1.863 |    0.6961 |    0.7528 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.481 |     1.213 |     1.360 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.433 |    0.8570 |   -0.4082 |  -0.05598 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1439 |    -4.083 |     2.776 |   -0.7191 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.623 |    -2.134 |    -1.744 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   46</span>|     480.40309 |     1.002 |    -1.226 |   -0.9188 |   -0.8999 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8493 |   -0.5301 |   -0.9609 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6217 |   -0.7553 |   -0.6813 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.40309 |     94.20 |    -5.626 |    -1.007 |   -0.2045 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.100 |     1.863 |    0.6954 |    0.7529 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.481 |     1.214 |     1.360 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.40309</span> |     94.20 |  0.003603 |    0.2675 |    0.8151 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.165 |     1.863 |    0.6954 |    0.7529 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.481 |     1.214 |     1.360 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     7.640 |    0.8551 |   -0.3719 |  -0.04853 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1037 |    -4.111 |     2.689 |    0.5674 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.653 |    -2.095 |    -1.726 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   47</span>|     480.39597 |     1.001 |    -1.227 |   -0.9185 |   -0.8998 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8492 |   -0.5300 |   -0.9618 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6215 |   -0.7547 |   -0.6809 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.39597 |     94.13 |    -5.627 |    -1.007 |   -0.2045 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.100 |     1.863 |    0.6947 |    0.7530 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.482 |     1.214 |     1.361 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.39597</span> |     94.13 |  0.003598 |    0.2676 |    0.8151 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.165 |     1.863 |    0.6947 |    0.7530 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.482 |     1.214 |     1.361 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.827 |    0.8487 |   -0.3865 |  -0.05558 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1453 |    -3.997 |     2.662 |    0.5449 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.580 |    -2.061 |    -1.701 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   48</span>|     480.38900 |     1.002 |    -1.229 |   -0.9184 |   -0.8998 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8491 |   -0.5297 |   -0.9626 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6211 |   -0.7540 |   -0.6805 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|       480.389 |     94.20 |    -5.629 |    -1.007 |   -0.2044 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.100 |     1.864 |    0.6940 |    0.7529 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.482 |     1.215 |     1.361 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">       480.389</span> |     94.20 |  0.003593 |    0.2676 |    0.8151 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.166 |     1.864 |    0.6940 |    0.7529 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.482 |     1.215 |     1.361 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     7.052 |    0.8465 |   -0.3544 |  -0.04836 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1022 |    -3.917 |     2.636 |    0.5926 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.553 |    -2.027 |    -1.689 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   49</span>|     480.38219 |     1.001 |    -1.230 |   -0.9183 |   -0.8998 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8491 |   -0.5296 |   -0.9634 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6209 |   -0.7534 |   -0.6801 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.38219 |     94.13 |    -5.630 |    -1.007 |   -0.2044 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.100 |     1.864 |    0.6934 |    0.7529 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.482 |     1.216 |     1.362 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.38219</span> |     94.13 |  0.003587 |    0.2676 |    0.8151 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.166 |     1.864 |    0.6934 |    0.7529 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.482 |     1.216 |     1.362 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.619 |    0.8406 |   -0.3725 |  -0.05439 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1411 |    -3.915 |     2.594 |   -0.6808 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.519 |    -1.986 |    -1.659 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   50</span>|     480.37598 |     1.002 |    -1.232 |   -0.9180 |   -0.8998 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8490 |   -0.5292 |   -0.9644 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6206 |   -0.7528 |   -0.6796 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.37598 |     94.20 |    -5.632 |    -1.007 |   -0.2044 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.100 |     1.864 |    0.6927 |    0.7530 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.483 |     1.216 |     1.362 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.37598</span> |     94.20 |  0.003582 |    0.2677 |    0.8152 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.167 |     1.864 |    0.6927 |    0.7530 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.483 |     1.216 |     1.362 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     6.566 |    0.8383 |   -0.3380 |  -0.04700 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.09943 |    -3.949 |     2.377 |    0.5751 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.561 |    -1.951 |    -1.641 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   51</span>|     480.36982 |     1.001 |    -1.233 |   -0.9178 |   -0.8997 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8489 |   -0.5291 |   -0.9651 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6204 |   -0.7522 |   -0.6792 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.36982 |     94.12 |    -5.633 |    -1.006 |   -0.2043 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.100 |     1.864 |    0.6922 |    0.7530 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.483 |     1.217 |     1.363 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.36982</span> |     94.12 |  0.003577 |    0.2677 |    0.8152 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.167 |     1.864 |    0.6922 |    0.7530 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.483 |     1.217 |     1.363 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -5.243 |    0.8317 |   -0.3515 |  -0.05392 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1358 |    -3.830 |     2.493 |    0.5823 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.510 |    -1.918 |    -1.620 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   52</span>|     480.36331 |     1.002 |    -1.235 |   -0.9176 |   -0.8997 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8489 |   -0.5289 |   -0.9659 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6201 |   -0.7516 |   -0.6788 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.36331 |     94.19 |    -5.635 |    -1.006 |   -0.2043 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.100 |     1.864 |    0.6916 |    0.7530 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.483 |     1.218 |     1.363 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.36331</span> |     94.19 |  0.003572 |    0.2677 |    0.8152 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.168 |     1.864 |    0.6916 |    0.7530 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.483 |     1.218 |     1.363 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     5.492 |    0.8288 |   -0.3226 |  -0.04732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.09956 |    -3.829 |     2.435 |    0.5676 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.517 |    -1.882 |    -1.604 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   53</span>|     480.35731 |     1.001 |    -1.236 |   -0.9175 |   -0.8996 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8488 |   -0.5288 |   -0.9666 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6198 |   -0.7510 |   -0.6783 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.35731 |     94.12 |    -5.636 |    -1.006 |   -0.2043 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.100 |     1.864 |    0.6910 |    0.7529 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.484 |     1.218 |     1.364 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.35731</span> |     94.12 |  0.003566 |    0.2678 |    0.8152 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.168 |     1.864 |    0.6910 |    0.7529 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.484 |     1.218 |     1.364 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -5.442 |    0.8230 |   -0.3401 |  -0.05270 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1329 |    -3.818 |     2.394 |   -0.6865 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.462 |    -1.826 |    -1.568 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   54</span>|     480.35158 |     1.002 |    -1.238 |   -0.9173 |   -0.8996 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8487 |   -0.5286 |   -0.9675 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6197 |   -0.7504 |   -0.6779 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.35158 |     94.19 |    -5.638 |    -1.006 |   -0.2042 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.100 |     1.864 |    0.6904 |    0.7530 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.484 |     1.219 |     1.364 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.35158</span> |     94.19 |  0.003561 |    0.2678 |    0.8153 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.169 |     1.864 |    0.6904 |    0.7530 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.484 |     1.219 |     1.364 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     5.119 |    0.8198 |   -0.3066 |  -0.04724 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1005 |    -3.787 |     2.338 |    0.5896 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.465 |    -1.812 |    -1.559 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   55</span>|     480.34603 |     1.001 |    -1.239 |   -0.9170 |   -0.8996 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8486 |   -0.5283 |   -0.9683 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6195 |   -0.7499 |   -0.6775 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.34603 |     94.12 |    -5.639 |    -1.006 |   -0.2042 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.100 |     1.865 |    0.6897 |    0.7530 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.484 |     1.220 |     1.364 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.34603</span> |     94.12 |  0.003555 |    0.2679 |    0.8153 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.170 |     1.865 |    0.6897 |    0.7530 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.484 |     1.220 |     1.364 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -5.669 |    0.8137 |   -0.3182 |  -0.05158 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1292 |    -3.845 |     2.251 |    0.5349 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.532 |    -1.781 |    -1.537 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   56</span>|     480.33997 |     1.002 |    -1.241 |   -0.9169 |   -0.8995 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8486 |   -0.5282 |   -0.9690 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6192 |   -0.7493 |   -0.6772 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.33997 |     94.19 |    -5.641 |    -1.005 |   -0.2041 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.101 |     1.865 |    0.6892 |    0.7529 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.484 |     1.220 |     1.365 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.33997</span> |     94.19 |  0.003550 |    0.2679 |    0.8153 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.170 |     1.865 |    0.6892 |    0.7529 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.484 |     1.220 |     1.365 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     4.944 |    0.8109 |   -0.2900 |  -0.04418 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.09220 |    -3.817 |     2.207 |    0.5423 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.530 |    -1.750 |    -1.524 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   57</span>|     480.33440 |     1.001 |    -1.242 |   -0.9168 |   -0.8995 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8485 |   -0.5280 |   -0.9697 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6188 |   -0.7487 |   -0.6768 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      480.3344 |     94.12 |    -5.642 |    -1.005 |   -0.2041 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.101 |     1.865 |    0.6887 |    0.7529 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.485 |     1.221 |     1.365 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      480.3344</span> |     94.12 |  0.003544 |    0.2679 |    0.8154 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.171 |     1.865 |    0.6887 |    0.7529 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.485 |     1.221 |     1.365 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -5.630 |    0.8048 |   -0.3070 |  -0.04965 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1246 |    -3.770 |     2.179 |    0.5400 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.465 |    -1.710 |    -1.500 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   58</span>|     480.32852 |     1.002 |    -1.244 |   -0.9167 |   -0.8994 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8485 |   -0.5278 |   -0.9703 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6184 |   -0.7482 |   -0.6764 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.32852 |     94.19 |    -5.644 |    -1.005 |   -0.2041 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.101 |     1.865 |    0.6882 |    0.7528 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.485 |     1.221 |     1.366 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.32852</span> |     94.19 |  0.003538 |    0.2679 |    0.8154 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.171 |     1.865 |    0.6882 |    0.7528 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.485 |     1.221 |     1.366 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     4.819 |    0.8018 |   -0.2789 |  -0.04260 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.08823 |    -3.653 |     2.177 |   -0.6593 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.384 |    -1.678 |    -1.478 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   59</span>|     480.32382 |     1.001 |    -1.246 |   -0.9164 |   -0.8994 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8484 |   -0.5276 |   -0.9711 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6183 |   -0.7477 |   -0.6761 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.32382 |     94.12 |    -5.646 |    -1.005 |   -0.2040 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.101 |     1.865 |    0.6876 |    0.7529 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.486 |     1.222 |     1.366 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.32382</span> |     94.12 |  0.003533 |    0.2680 |    0.8155 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.172 |     1.865 |    0.6876 |    0.7529 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.486 |     1.222 |     1.366 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -5.225 |    0.7951 |   -0.2896 |  -0.04843 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1194 |    -3.720 |     2.091 |   -0.7072 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.430 |    -1.641 |    -1.454 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   60</span>|     480.31902 |     1.002 |    -1.247 |   -0.9160 |   -0.8993 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8482 |   -0.5273 |   -0.9719 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6182 |   -0.7473 |   -0.6757 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.31902 |     94.19 |    -5.647 |    -1.005 |   -0.2039 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.101 |     1.865 |    0.6870 |    0.7531 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.486 |     1.222 |     1.367 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.31902</span> |     94.19 |  0.003527 |    0.2681 |    0.8155 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.173 |     1.865 |    0.6870 |    0.7531 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.486 |     1.222 |     1.367 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     4.904 |    0.7922 |   -0.2469 |  -0.04034 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.08539 |    -3.545 |     2.094 |   -0.6237 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.331 |    -1.619 |    -1.454 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   61</span>|     480.31471 |     1.001 |    -1.249 |   -0.9156 |   -0.8993 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8481 |   -0.5271 |   -0.9727 |    -1.001 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6181 |   -0.7468 |   -0.6753 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.31471 |     94.12 |    -5.649 |    -1.004 |   -0.2039 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.101 |     1.866 |    0.6864 |    0.7533 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.486 |     1.223 |     1.367 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.31471</span> |     94.12 |  0.003522 |    0.2681 |    0.8156 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.174 |     1.866 |    0.6864 |    0.7533 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.486 |     1.223 |     1.367 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -5.295 |    0.7853 |   -0.2507 |  -0.04627 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1126 |    -3.760 |     1.942 |   -0.7288 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.482 |    -1.617 |    -1.436 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   62</span>|     480.31013 |     1.002 |    -1.250 |   -0.9152 |   -0.8992 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8480 |   -0.5269 |   -0.9734 |    -1.001 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6180 |   -0.7464 |   -0.6750 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.31013 |     94.19 |    -5.650 |    -1.004 |   -0.2038 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.101 |     1.866 |    0.6859 |    0.7535 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.486 |     1.223 |     1.367 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.31013</span> |     94.19 |  0.003516 |    0.2682 |    0.8156 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.175 |     1.866 |    0.6859 |    0.7535 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.486 |     1.223 |     1.367 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     4.435 |    0.7821 |   -0.2118 |  -0.03855 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.07590 |    -3.495 |     2.000 |   -0.6027 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.382 |    -1.576 |    -1.413 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   63</span>|     480.30581 |     1.001 |    -1.252 |   -0.9149 |   -0.8991 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8479 |   -0.5266 |   -0.9741 |    -1.001 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6178 |   -0.7459 |   -0.6746 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.30581 |     94.12 |    -5.652 |    -1.003 |   -0.2037 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.101 |     1.866 |    0.6854 |    0.7536 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.486 |     1.224 |     1.368 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.30581</span> |     94.12 |  0.003510 |    0.2683 |    0.8157 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.176 |     1.866 |    0.6854 |    0.7536 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.486 |     1.224 |     1.368 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -5.315 |    0.7789 |   -0.2090 |  -0.03720 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.09944 |    -3.577 |     1.911 |   -0.6518 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.378 |    -1.528 |    -1.376 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   64</span>|     480.30125 |     1.002 |    -1.254 |   -0.9146 |   -0.8990 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8478 |   -0.5264 |   -0.9748 |    -1.001 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6176 |   -0.7454 |   -0.6743 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.30125 |     94.19 |    -5.654 |    -1.003 |   -0.2037 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.101 |     1.866 |    0.6848 |    0.7538 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.486 |     1.224 |     1.368 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.30125</span> |     94.19 |  0.003505 |    0.2683 |    0.8157 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.177 |     1.866 |    0.6848 |    0.7538 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.486 |     1.224 |     1.368 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     4.353 |    0.7723 |   -0.1834 |  -0.03622 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.07047 |    -3.672 |     1.822 |   -0.6713 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.445 |    -1.532 |    -1.389 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   65</span>|     480.29714 |     1.001 |    -1.255 |   -0.9143 |   -0.8990 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8476 |   -0.5261 |   -0.9754 |    -1.001 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6174 |   -0.7450 |   -0.6740 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.29714 |     94.12 |    -5.655 |    -1.003 |   -0.2036 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.101 |     1.866 |    0.6843 |    0.7539 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.487 |     1.225 |     1.369 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.29714</span> |     94.12 |  0.003499 |    0.2684 |    0.8158 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.178 |     1.866 |    0.6843 |    0.7539 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.487 |     1.225 |     1.369 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -5.435 |    0.7663 |   -0.1878 |  -0.03986 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1019 |    -3.343 |     1.893 |    0.6711 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.269 |    -1.487 |    -1.354 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   66</span>|     480.29215 |     1.002 |    -1.257 |   -0.9143 |   -0.8989 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8476 |   -0.5260 |   -0.9760 |    -1.001 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6172 |   -0.7444 |   -0.6736 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.29215 |     94.18 |    -5.657 |    -1.003 |   -0.2036 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.102 |     1.867 |    0.6839 |    0.7538 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.487 |     1.226 |     1.369 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.29215</span> |     94.18 |  0.003493 |    0.2684 |    0.8158 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.179 |     1.867 |    0.6839 |    0.7538 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.487 |     1.226 |     1.369 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     4.161 |    0.7621 |   -0.1686 |  -0.03452 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.06632 |    -3.552 |     1.775 |    0.6057 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.379 |    -1.473 |    -1.364 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   67</span>|     480.28721 |     1.001 |    -1.259 |   -0.9145 |   -0.8989 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8476 |   -0.5257 |   -0.9765 |    -1.001 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6170 |   -0.7439 |   -0.6732 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.28721 |     94.12 |    -5.659 |    -1.003 |   -0.2035 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.102 |     1.867 |    0.6836 |    0.7537 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.487 |     1.226 |     1.369 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.28721</span> |     94.12 |  0.003487 |    0.2684 |    0.8159 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.179 |     1.867 |    0.6836 |    0.7537 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.487 |     1.226 |     1.369 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.925 |    0.7584 |   -0.1913 |  -0.03513 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.09132 |    -3.418 |     1.781 |   -0.6203 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.349 |    -1.417 |    -1.313 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   68</span>|     480.28278 |     1.002 |    -1.260 |   -0.9144 |   -0.8988 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8475 |   -0.5255 |   -0.9770 |    -1.001 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6167 |   -0.7434 |   -0.6728 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.28278 |     94.19 |    -5.660 |    -1.003 |   -0.2035 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.102 |     1.867 |    0.6832 |    0.7537 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.487 |     1.227 |     1.370 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.28278</span> |     94.19 |  0.003481 |    0.2684 |    0.8159 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.179 |     1.867 |    0.6832 |    0.7537 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.487 |     1.227 |     1.370 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     4.442 |    0.7524 |   -0.1734 |  -0.03276 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.06267 |    -3.400 |     1.746 |   -0.5960 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.307 |    -1.411 |    -1.323 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   69</span>|     480.27897 |     1.001 |    -1.262 |   -0.9141 |   -0.8988 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8474 |   -0.5252 |   -0.9776 |    -1.001 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6165 |   -0.7431 |   -0.6724 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.27897 |     94.13 |    -5.662 |    -1.003 |   -0.2034 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.102 |     1.867 |    0.6827 |    0.7539 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.488 |     1.227 |     1.370 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.27897</span> |     94.13 |  0.003475 |    0.2684 |    0.8159 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.180 |     1.867 |    0.6827 |    0.7539 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.488 |     1.227 |     1.370 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.732 |    0.7463 |   -0.1764 |  -0.03625 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.08933 |    -3.375 |     1.716 |    0.6486 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.273 |    -1.374 |    -1.283 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   70</span>|     480.27440 |     1.002 |    -1.264 |   -0.9140 |   -0.8987 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8473 |   -0.5250 |   -0.9782 |    -1.001 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6164 |   -0.7427 |   -0.6721 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      480.2744 |     94.19 |    -5.664 |    -1.003 |   -0.2034 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.102 |     1.867 |    0.6823 |    0.7538 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.488 |     1.227 |     1.371 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      480.2744</span> |     94.19 |  0.003469 |    0.2684 |    0.8160 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.181 |     1.867 |    0.6823 |    0.7538 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.488 |     1.227 |     1.371 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     4.305 |    0.7431 |   -0.1535 |  -0.02884 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.05624 |    -3.424 |     1.641 |   -0.6166 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.366 |    -1.368 |    -1.291 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   71</span>|     480.27043 |     1.001 |    -1.266 |   -0.9140 |   -0.8987 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8473 |   -0.5247 |   -0.9786 |    -1.001 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6161 |   -0.7423 |   -0.6718 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.27043 |     94.13 |    -5.666 |    -1.002 |   -0.2033 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.102 |     1.868 |    0.6819 |    0.7539 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.488 |     1.228 |     1.371 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.27043</span> |     94.13 |  0.003463 |    0.2685 |    0.8160 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.181 |     1.868 |    0.6819 |    0.7539 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.488 |     1.228 |     1.371 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.306 |    0.7372 |   -0.1665 |  -0.03186 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.08221 |    -3.333 |     1.631 |   -0.6040 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.265 |    -1.322 |    -1.255 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   72</span>|     480.26665 |     1.002 |    -1.267 |   -0.9136 |   -0.8986 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8471 |   -0.5244 |   -0.9791 |    -1.001 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6160 |   -0.7419 |   -0.6714 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.26665 |     94.19 |    -5.667 |    -1.002 |   -0.2032 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.102 |     1.868 |    0.6815 |    0.7540 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.488 |     1.228 |     1.371 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.26665</span> |     94.19 |  0.003457 |    0.2685 |    0.8161 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.182 |     1.868 |    0.6815 |    0.7540 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.488 |     1.228 |     1.371 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     4.654 |    0.7327 |   -0.1326 |  -0.02617 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.04981 |    -3.394 |     1.559 |   -0.6139 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.358 |    -1.341 |    -1.268 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   73</span>|     480.26300 |     1.001 |    -1.269 |   -0.9132 |   -0.8985 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8470 |   -0.5241 |   -0.9796 |    -1.000 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6158 |   -0.7416 |   -0.6711 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|       480.263 |     94.13 |    -5.669 |    -1.002 |   -0.2032 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.102 |     1.868 |    0.6812 |    0.7542 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.488 |     1.229 |     1.372 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">       480.263</span> |     94.13 |  0.003451 |    0.2686 |    0.8162 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.183 |     1.868 |    0.6812 |    0.7542 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.488 |     1.229 |     1.372 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.009 |    0.7258 |   -0.1343 |  -0.03035 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.07619 |    -3.164 |     1.609 |   -0.5397 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.251 |    -1.282 |    -1.219 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   74</span>|     480.25930 |     1.002 |    -1.271 |   -0.9130 |   -0.8985 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8469 |   -0.5239 |   -0.9802 |    -1.000 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6155 |   -0.7412 |   -0.6708 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      480.2593 |     94.19 |    -5.671 |    -1.002 |   -0.2031 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.102 |     1.868 |    0.6807 |    0.7543 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.489 |     1.229 |     1.372 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      480.2593</span> |     94.19 |  0.003445 |    0.2686 |    0.8162 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.184 |     1.868 |    0.6807 |    0.7543 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.489 |     1.229 |     1.372 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     4.851 |    0.7223 |   -0.1045 |  -0.02290 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.04316 |    -3.376 |     1.478 |    0.6193 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.343 |    -1.302 |    -1.232 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   75</span>|     480.25486 |     1.001 |    -1.273 |   -0.9131 |   -0.8984 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8469 |   -0.5237 |   -0.9805 |    -1.000 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6152 |   -0.7408 |   -0.6705 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.25486 |     94.14 |    -5.673 |    -1.002 |   -0.2030 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.102 |     1.868 |    0.6805 |    0.7542 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.489 |     1.230 |     1.373 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.25486</span> |     94.14 |  0.003439 |    0.2686 |    0.8162 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.184 |     1.868 |    0.6805 |    0.7542 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.489 |     1.230 |     1.373 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -3.282 |    0.7167 |   -0.1236 |  -0.02586 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.06793 |    -3.294 |     1.470 |    0.6693 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.247 |    -1.242 |    -1.194 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   76</span>|     480.25000 |     1.002 |    -1.274 |   -0.9134 |   -0.8984 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8469 |   -0.5231 |   -0.9806 |    -1.001 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6147 |   -0.7402 |   -0.6701 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|        480.25 |     94.20 |    -5.674 |    -1.002 |   -0.2030 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.102 |     1.869 |    0.6804 |    0.7539 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.490 |     1.230 |     1.373 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">        480.25</span> |     94.20 |  0.003434 |    0.2686 |    0.8163 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.184 |     1.869 |    0.6804 |    0.7539 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.490 |     1.230 |     1.373 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     5.823 |    0.7134 |   -0.1173 |  -0.02040 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.04062 |    -3.151 |     1.491 |   -0.5685 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.243 |    -1.237 |    -1.210 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   77</span>|     480.24598 |     1.001 |    -1.276 |   -0.9134 |   -0.8984 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8469 |   -0.5229 |   -0.9809 |    -1.001 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6145 |   -0.7399 |   -0.6698 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.24598 |     94.13 |    -5.676 |    -1.002 |   -0.2030 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.102 |     1.869 |    0.6802 |    0.7539 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.490 |     1.231 |     1.373 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.24598</span> |     94.13 |  0.003427 |    0.2686 |    0.8163 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.184 |     1.869 |    0.6802 |    0.7539 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.490 |     1.231 |     1.373 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -3.294 |    0.7076 |   -0.1362 |  -0.02383 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.06566 |    -3.147 |     1.461 |    0.6663 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.244 |    -1.185 |    -1.155 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   78</span>|     480.24152 |     1.002 |    -1.278 |   -0.9134 |   -0.8983 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8469 |   -0.5225 |   -0.9813 |    -1.001 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6142 |   -0.7395 |   -0.6694 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.24152 |     94.19 |    -5.678 |    -1.002 |   -0.2030 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.102 |     1.869 |    0.6799 |    0.7538 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.490 |     1.231 |     1.374 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.24152</span> |     94.19 |  0.003421 |    0.2686 |    0.8163 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.184 |     1.869 |    0.6799 |    0.7538 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.490 |     1.231 |     1.374 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     4.573 |    0.7031 |   -0.1214 |  -0.01999 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.04022 |    -3.129 |     1.425 |    0.6287 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.274 |    -1.197 |    -1.174 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   79</span>|     480.23703 |     1.001 |    -1.280 |   -0.9136 |   -0.8983 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8469 |   -0.5223 |   -0.9815 |    -1.001 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6136 |   -0.7392 |   -0.6691 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.23703 |     94.13 |    -5.680 |    -1.002 |   -0.2030 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.102 |     1.869 |    0.6797 |    0.7536 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.491 |     1.231 |     1.374 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.23703</span> |     94.13 |  0.003415 |    0.2685 |    0.8163 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.184 |     1.869 |    0.6797 |    0.7536 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.491 |     1.231 |     1.374 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -3.496 |    0.6977 |   -0.1464 |  -0.02500 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.06997 |    -3.031 |     1.316 |    0.6455 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.198 |    -1.144 |    -1.124 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   80</span>|     480.23246 |     1.002 |    -1.281 |   -0.9138 |   -0.8983 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8469 |   -0.5219 |   -0.9816 |    -1.001 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6131 |   -0.7387 |   -0.6687 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.23246 |     94.19 |    -5.681 |    -1.002 |   -0.2030 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.102 |     1.870 |    0.6796 |    0.7533 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.492 |     1.232 |     1.375 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.23246</span> |     94.19 |  0.003409 |    0.2685 |    0.8163 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.184 |     1.870 |    0.6796 |    0.7533 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.492 |     1.232 |     1.375 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     4.825 |    0.6940 |   -0.1347 |  -0.01919 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.03939 |    -3.118 |     1.378 |    0.5922 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.219 |    -1.136 |    -1.127 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   81</span>|     480.22809 |     1.001 |    -1.283 |   -0.9138 |   -0.8983 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8469 |   -0.5217 |   -0.9816 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6127 |   -0.7384 |   -0.6684 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.22809 |     94.14 |    -5.683 |    -1.002 |   -0.2029 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.102 |     1.870 |    0.6796 |    0.7531 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.492 |     1.232 |     1.375 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.22809</span> |     94.14 |  0.003403 |    0.2685 |    0.8163 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.184 |     1.870 |    0.6796 |    0.7531 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.492 |     1.232 |     1.375 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -3.001 |    0.6885 |   -0.1518 |  -0.02256 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.06414 |    -2.892 |     1.443 |    0.6415 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.085 |    -1.098 |    -1.101 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   82</span>|     480.22387 |     1.002 |    -1.285 |   -0.9139 |   -0.8983 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8469 |   -0.5212 |   -0.9821 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6124 |   -0.7381 |   -0.6680 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.22387 |     94.19 |    -5.685 |    -1.002 |   -0.2029 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.102 |     1.870 |    0.6793 |    0.7529 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.492 |     1.232 |     1.375 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.22387</span> |     94.19 |  0.003397 |    0.2685 |    0.8163 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.184 |     1.870 |    0.6793 |    0.7529 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.492 |     1.232 |     1.375 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     5.360 |    0.6846 |   -0.1392 |  -0.01938 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.04006 |    -2.853 |     1.421 |   -0.5715 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.064 |    -1.117 |    -1.118 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   83</span>|     480.22054 |     1.001 |    -1.287 |   -0.9139 |   -0.8983 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8469 |   -0.5210 |   -0.9826 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6125 |   -0.7380 |   -0.6676 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.22054 |     94.14 |    -5.687 |    -1.002 |   -0.2029 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.102 |     1.871 |    0.6789 |    0.7530 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.492 |     1.233 |     1.376 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.22054</span> |     94.14 |  0.003391 |    0.2685 |    0.8163 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.184 |     1.871 |    0.6789 |    0.7530 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.492 |     1.233 |     1.376 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -2.774 |    0.6781 |   -0.1521 |  -0.02419 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.06697 |    -2.800 |     1.392 |    0.6467 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.111 |    -1.074 |    -1.064 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   84</span>|     480.21670 |     1.002 |    -1.288 |   -0.9138 |   -0.8983 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8469 |   -0.5206 |   -0.9832 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6123 |   -0.7378 |   -0.6672 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      480.2167 |     94.19 |    -5.688 |    -1.002 |   -0.2029 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.102 |     1.871 |    0.6784 |    0.7529 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.493 |     1.233 |     1.376 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      480.2167</span> |     94.19 |  0.003385 |    0.2685 |    0.8163 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.184 |     1.871 |    0.6784 |    0.7529 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.493 |     1.233 |     1.376 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     4.519 |    0.6730 |   -0.1351 |  -0.02088 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.04118 |    -2.856 |     1.311 |    0.6094 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.165 |    -1.062 |    -1.048 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   85</span>|     480.21269 |     1.001 |    -1.290 |   -0.9139 |   -0.8983 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8469 |   -0.5205 |   -0.9835 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6119 |   -0.7376 |   -0.6670 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.21269 |     94.14 |    -5.690 |    -1.002 |   -0.2029 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.102 |     1.871 |    0.6782 |    0.7527 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.493 |     1.233 |     1.377 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.21269</span> |     94.14 |  0.003379 |    0.2685 |    0.8163 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.184 |     1.871 |    0.6782 |    0.7527 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.493 |     1.233 |     1.377 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -2.933 |    0.6686 |   -0.1517 |  -0.01805 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.06304 |    -2.806 |     1.298 |    0.6045 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.073 |    -1.045 |    -1.026 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   86</span>|     480.20865 |     1.002 |    -1.292 |   -0.9139 |   -0.8983 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8469 |   -0.5201 |   -0.9839 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6115 |   -0.7373 |   -0.6667 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.20865 |     94.19 |    -5.692 |    -1.002 |   -0.2029 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.102 |     1.871 |    0.6779 |    0.7525 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.494 |     1.233 |     1.377 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.20865</span> |     94.19 |  0.003373 |    0.2685 |    0.8163 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.184 |     1.871 |    0.6779 |    0.7525 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.494 |     1.233 |     1.377 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     4.802 |    0.6647 |   -0.1367 |  -0.01906 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.03994 |    -2.699 |     1.311 |   -0.5807 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.078 |    -1.030 |    -1.030 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   87</span>|     480.20558 |     1.001 |    -1.294 |   -0.9137 |   -0.8983 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8468 |   -0.5199 |   -0.9842 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6113 |   -0.7372 |   -0.6665 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.20558 |     94.14 |    -5.694 |    -1.002 |   -0.2029 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.102 |     1.871 |    0.6776 |    0.7525 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.494 |     1.233 |     1.377 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.20558</span> |     94.14 |  0.003367 |    0.2685 |    0.8163 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.185 |     1.871 |    0.6776 |    0.7525 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.494 |     1.233 |     1.377 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -2.911 |    0.6576 |   -0.1438 |  -0.02051 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.06613 |    -2.751 |     1.246 |   -0.6218 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.053 |    -1.017 |    -1.010 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   88</span>|     480.20291 |     1.002 |    -1.296 |   -0.9132 |   -0.8982 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8467 |   -0.5195 |   -0.9848 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6112 |   -0.7370 |   -0.6662 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.20291 |     94.19 |    -5.696 |    -1.002 |   -0.2029 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.102 |     1.872 |    0.6772 |    0.7527 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.494 |     1.234 |     1.377 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.20291</span> |     94.19 |  0.003361 |    0.2686 |    0.8164 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.186 |     1.872 |    0.6772 |    0.7527 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.494 |     1.234 |     1.377 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     4.296 |    0.6533 |   -0.1055 |  -0.01383 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.03435 |    -2.750 |     1.192 |   -0.6155 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.121 |    -1.037 |    -1.054 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   89</span>|     480.20010 |     1.001 |    -1.297 |   -0.9127 |   -0.8982 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8466 |   -0.5193 |   -0.9850 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6110 |   -0.7368 |   -0.6658 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      480.2001 |     94.14 |    -5.697 |    -1.001 |   -0.2028 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.103 |     1.872 |    0.6771 |    0.7528 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.494 |     1.234 |     1.378 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      480.2001</span> |     94.14 |  0.003355 |    0.2687 |    0.8164 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.187 |     1.872 |    0.6771 |    0.7528 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.494 |     1.234 |     1.378 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -2.841 |    0.6461 |  -0.09617 |  -0.01892 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.05600 |    -2.503 |     1.255 |    0.6797 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.9587 |    -1.006 |   -0.9794 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   90</span>|     480.19652 |     1.002 |    -1.299 |   -0.9127 |   -0.8982 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8466 |   -0.5191 |   -0.9853 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6108 |   -0.7364 |   -0.6653 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.19652 |     94.19 |    -5.699 |    -1.001 |   -0.2028 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.103 |     1.872 |    0.6768 |    0.7526 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.494 |     1.234 |     1.378 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.19652</span> |     94.19 |  0.003349 |    0.2687 |    0.8164 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.187 |     1.872 |    0.6768 |    0.7526 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.494 |     1.234 |     1.378 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   91</span>|     480.19461 |     1.002 |    -1.301 |   -0.9128 |   -0.8982 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8466 |   -0.5193 |   -0.9855 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6107 |   -0.7361 |   -0.6650 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.19461 |     94.18 |    -5.701 |    -1.001 |   -0.2028 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.103 |     1.872 |    0.6767 |    0.7526 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.494 |     1.235 |     1.379 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.19461</span> |     94.18 |  0.003342 |    0.2687 |    0.8164 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.187 |     1.872 |    0.6767 |    0.7526 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.494 |     1.235 |     1.379 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   92</span>|     480.18441 |     1.002 |    -1.313 |   -0.9134 |   -0.8983 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8466 |   -0.5209 |   -0.9862 |    -1.003 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6104 |   -0.7344 |   -0.6628 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.18441 |     94.17 |    -5.713 |    -1.002 |   -0.2029 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.103 |     1.871 |    0.6762 |    0.7522 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.495 |     1.237 |     1.381 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.18441</span> |     94.17 |  0.003303 |    0.2686 |    0.8164 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.187 |     1.871 |    0.6762 |    0.7522 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.495 |     1.237 |     1.381 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   93</span>|     480.15712 |     1.001 |    -1.360 |   -0.9157 |   -0.8984 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8466 |   -0.5271 |   -0.9890 |    -1.004 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6089 |   -0.7277 |   -0.6540 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.15712 |     94.12 |    -5.760 |    -1.004 |   -0.2031 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.103 |     1.866 |    0.6740 |    0.7509 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.497 |     1.244 |     1.391 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.15712</span> |     94.12 |  0.003151 |    0.2681 |    0.8162 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.187 |     1.866 |    0.6740 |    0.7509 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.497 |     1.244 |     1.391 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   94</span>|     480.23418 |    0.9997 |    -1.543 |   -0.9246 |   -0.8991 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8465 |   -0.5509 |    -1.000 |    -1.011 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6032 |   -0.7017 |   -0.6198 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.23418 |     93.97 |    -5.943 |    -1.013 |   -0.2037 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.103 |     1.847 |    0.6655 |    0.7454 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.503 |     1.272 |     1.431 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.23418</span> |     93.97 |  0.002624 |    0.2664 |    0.8157 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.187 |     1.847 |    0.6655 |    0.7454 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.503 |     1.272 |     1.431 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -6.048 |    0.4781 |   -0.2230 |  -0.02217 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.05480 |    -3.577 |    0.9301 |    0.6494 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.028 |   -0.4525 |   -0.4232 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   95</span>|     480.10748 |     1.002 |    -1.612 |   -0.8919 |   -0.8955 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8419 |   -0.5057 |    -1.005 |    -1.011 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6089 |   -0.7402 |   -0.6757 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.10748 |     94.20 |    -6.012 |   -0.9804 |   -0.2001 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.107 |     1.883 |    0.6618 |    0.7449 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.497 |     1.230 |     1.367 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.10748</span> |     94.20 |  0.002450 |    0.2728 |    0.8186 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.225 |     1.883 |    0.6618 |    0.7449 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.497 |     1.230 |     1.367 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     5.588 |   -0.2032 |     1.050 |   0.05863 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1556 |    -1.013 |   -0.1797 |    0.1430 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.088 |    -1.192 |    -1.504 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   96</span>|     480.44664 |     1.003 |    -1.804 |   -0.9880 |   -0.8940 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8524 |   -0.4601 |   -0.9127 |    -1.040 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5485 |   -0.7099 |   -0.6226 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.44664 |     94.31 |    -6.204 |    -1.077 |   -0.1987 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.097 |     1.919 |    0.7320 |    0.7196 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.568 |     1.263 |     1.427 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.44664</span> |     94.31 |  0.002022 |    0.2542 |    0.8198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.139 |     1.919 |    0.7320 |    0.7196 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.568 |     1.263 |     1.427 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   97</span>|     480.05051 |     1.002 |    -1.657 |   -0.9147 |   -0.8952 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8444 |   -0.4949 |   -0.9832 |    -1.018 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5946 |   -0.7329 |   -0.6630 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.05051 |     94.19 |    -6.057 |    -1.003 |   -0.1998 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.105 |     1.891 |    0.6784 |    0.7389 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.514 |     1.238 |     1.381 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.05051</span> |     94.19 |  0.002341 |    0.2683 |    0.8189 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.205 |     1.891 |    0.6784 |    0.7389 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.514 |     1.238 |     1.381 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     3.048 |   -0.2764 |  -0.02525 |   0.07726 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1002 |    0.1555 |     1.072 |    0.1458 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.5013 |   -0.7465 |   -0.9442 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   98</span>|     480.05873 |     1.002 |    -1.641 |   -0.9145 |   -0.9051 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8597 |   -0.5017 |   -0.9961 |    -1.019 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5552 |   -0.7600 |   -0.6392 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.05873 |     94.21 |    -6.041 |    -1.003 |   -0.2097 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.089 |     1.886 |    0.6686 |    0.7381 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.561 |     1.209 |     1.408 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.05873</span> |     94.21 |  0.002380 |    0.2684 |    0.8108 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.080 |     1.886 |    0.6686 |    0.7381 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.561 |     1.209 |     1.408 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   99</span>|     480.03299 |     1.002 |    -1.650 |   -0.9146 |   -0.8993 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8508 |   -0.4977 |   -0.9887 |    -1.018 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5780 |   -0.7442 |   -0.6529 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.03299 |     94.17 |    -6.050 |    -1.003 |   -0.2040 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.098 |     1.889 |    0.6742 |    0.7386 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.533 |     1.226 |     1.393 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.03299</span> |     94.17 |  0.002357 |    0.2683 |    0.8155 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.152 |     1.889 |    0.6742 |    0.7386 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.533 |     1.226 |     1.393 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |  -0.07884 |   -0.2508 |  -0.03233 |  -0.02314 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1302 |   0.06288 |    0.7629 |    0.2261 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.3850 |    -1.277 |   -0.6610 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  100</span>|     480.00970 |     1.003 |    -1.641 |   -0.9125 |   -0.9005 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8500 |   -0.4980 |   -0.9983 |    -1.021 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5841 |   -0.7275 |   -0.6414 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      480.0097 |     94.26 |    -6.041 |    -1.001 |   -0.2051 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.099 |     1.889 |    0.6670 |    0.7367 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.526 |     1.244 |     1.406 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      480.0097</span> |     94.26 |  0.002380 |    0.2687 |    0.8145 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.159 |     1.889 |    0.6670 |    0.7367 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.526 |     1.244 |     1.406 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     12.51 |   -0.2151 |    0.1066 |  -0.04414 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.06212 |   -0.2557 |   0.09002 |  -0.09728 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   0.06582 |   -0.3883 |   0.07016 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  101</span>|     480.02569 |     1.000 |    -1.627 |   -0.9257 |   -0.9015 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8496 |   -0.4974 |    -1.010 |    -1.023 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5876 |   -0.7200 |   -0.6493 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.02569 |     94.03 |    -6.027 |    -1.014 |   -0.2061 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.099 |     1.889 |    0.6581 |    0.7348 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.522 |     1.252 |     1.397 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.02569</span> |     94.03 |  0.002413 |    0.2662 |    0.8138 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.162 |     1.889 |    0.6581 |    0.7348 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.522 |     1.252 |     1.397 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  102</span>|     480.01783 |     1.000 |    -1.636 |   -0.9171 |   -0.9008 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8499 |   -0.4978 |    -1.002 |    -1.021 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5853 |   -0.7249 |   -0.6441 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.01783 |     94.03 |    -6.036 |    -1.006 |   -0.2055 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.099 |     1.889 |    0.6639 |    0.7361 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.525 |     1.247 |     1.403 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.01783</span> |     94.03 |  0.002392 |    0.2678 |    0.8143 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.160 |     1.889 |    0.6639 |    0.7361 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.525 |     1.247 |     1.403 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  103</span>|     480.01762 |     1.000 |    -1.639 |   -0.9140 |   -0.9006 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8500 |   -0.4979 |   -0.9995 |    -1.021 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5844 |   -0.7266 |   -0.6422 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.01762 |     94.04 |    -6.039 |    -1.002 |   -0.2052 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.099 |     1.889 |    0.6660 |    0.7365 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.526 |     1.245 |     1.405 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.01762</span> |     94.04 |  0.002384 |    0.2685 |    0.8145 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.159 |     1.889 |    0.6660 |    0.7365 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.526 |     1.245 |     1.405 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  104</span>|     480.00603 |     1.001 |    -1.641 |   -0.9125 |   -0.9005 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8500 |   -0.4980 |   -0.9983 |    -1.021 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5841 |   -0.7275 |   -0.6414 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.00603 |     94.12 |    -6.041 |    -1.001 |   -0.2051 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.099 |     1.889 |    0.6670 |    0.7367 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.526 |     1.244 |     1.406 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.00603</span> |     94.12 |  0.002380 |    0.2687 |    0.8145 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.159 |     1.889 |    0.6670 |    0.7367 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.526 |     1.244 |     1.406 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -6.885 |   -0.2187 |   0.06031 |  -0.05864 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1316 |   -0.6942 |  -0.07940 |    0.7419 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2628 |   -0.6303 |   -0.1748 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  105</span>|     480.00355 |     1.002 |    -1.640 |   -0.9125 |   -0.9004 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8497 |   -0.4981 |   -0.9983 |    -1.021 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5835 |   -0.7267 |   -0.6420 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.00355 |     94.17 |    -6.040 |    -1.001 |   -0.2050 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.099 |     1.889 |    0.6669 |    0.7366 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.527 |     1.245 |     1.405 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.00355</span> |     94.17 |  0.002382 |    0.2687 |    0.8147 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.162 |     1.889 |    0.6669 |    0.7366 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.527 |     1.245 |     1.405 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |   -0.1332 |   -0.2152 |   0.07761 |  -0.04946 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.09731 |  -0.07704 |    0.1258 |    0.9492 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   0.08309 |   -0.3432 |   0.03979 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  106</span>|     480.00003 |     1.002 |    -1.640 |   -0.9126 |   -0.9003 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8495 |   -0.4980 |   -0.9985 |    -1.022 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5836 |   -0.7262 |   -0.6420 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.00003 |     94.19 |    -6.040 |    -1.001 |   -0.2049 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.100 |     1.889 |    0.6668 |    0.7355 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.527 |     1.245 |     1.405 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.00003</span> |     94.19 |  0.002382 |    0.2687 |    0.8147 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.163 |     1.889 |    0.6668 |    0.7355 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.527 |     1.245 |     1.405 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     2.294 |   -0.2073 |   0.08146 |  -0.04541 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.07807 |   -0.2293 |   0.05730 |    0.8075 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................| -0.008164 |   -0.3171 |   0.03808 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  107</span>|     479.99578 |     1.002 |    -1.638 |   -0.9109 |   -0.8997 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8483 |   -0.4984 |   -0.9975 |    -1.023 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5809 |   -0.7230 |   -0.6440 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.99578 |     94.17 |    -6.038 |   -0.9994 |   -0.2043 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.101 |     1.889 |    0.6675 |    0.7343 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.530 |     1.249 |     1.403 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.99578</span> |     94.17 |  0.002387 |    0.2691 |    0.8152 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.172 |     1.889 |    0.6675 |    0.7343 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.530 |     1.249 |     1.403 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  108</span>|     479.99301 |     1.002 |    -1.632 |   -0.9055 |   -0.8980 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8447 |   -0.4999 |   -0.9947 |    -1.027 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5728 |   -0.7134 |   -0.6498 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.99301 |     94.16 |    -6.032 |   -0.9941 |   -0.2026 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.104 |     1.887 |    0.6697 |    0.7310 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.540 |     1.259 |     1.396 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.99301</span> |     94.16 |  0.002402 |    0.2701 |    0.8166 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.202 |     1.887 |    0.6697 |    0.7310 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.540 |     1.259 |     1.396 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |   -0.5972 |   -0.1625 |    0.4650 |  0.009686 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.08636 |    -1.694 |   -0.1652 |    -1.042 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1517 |    0.4204 |   -0.3659 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  109</span>|     479.98697 |     1.002 |    -1.611 |   -0.9101 |   -0.8945 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8385 |   -0.4966 |   -0.9980 |    -1.027 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5750 |   -0.7140 |   -0.6459 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.98697 |     94.17 |    -6.011 |   -0.9986 |   -0.1991 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.111 |     1.890 |    0.6672 |    0.7309 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.537 |     1.259 |     1.401 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.98697</span> |     94.17 |  0.002451 |    0.2692 |    0.8195 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.253 |     1.890 |    0.6672 |    0.7309 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.537 |     1.259 |     1.401 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. | -0.005684 |   -0.1115 |    0.2082 |   0.08938 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3008 |   0.01411 |    0.1052 |   -0.7061 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.4111 |    0.4010 |   -0.1199 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  110</span>|     479.98697 |     1.002 |    -1.611 |   -0.9101 |   -0.8945 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8385 |   -0.4966 |   -0.9980 |    -1.027 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5750 |   -0.7140 |   -0.6459 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.98697 |     94.17 |    -6.011 |   -0.9986 |   -0.1991 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.111 |     1.890 |    0.6672 |    0.7309 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.537 |     1.259 |     1.401 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.98697</span> |     94.17 |  0.002451 |    0.2692 |    0.8195 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.253 |     1.890 |    0.6672 |    0.7309 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.537 |     1.259 |     1.401 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> calculating covariance matrix</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating residuals/tables</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-in"><span class="va">f_nlmixr_dfop_sfo_focei_const</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr</a></span><span class="op">(</span><span class="va">f_mmkin_const</span><span class="op">[</span><span class="st">"DFOP-SFO"</span>, <span class="op">]</span>, est <span class="op">=</span> <span class="st">"focei"</span><span class="op">)</span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate jacobian</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate sensitivities</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate ∂(f)/∂(η)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate ∂(R²)/∂(η)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in inner model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in inner model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in EBE model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in EBE model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling inner model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in FD model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in FD model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling EBE model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling events FD model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Needed Covariates:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "CMT"</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="font-weight: bold;">Key:</span> U: Unscaled Parameters; X: Back-transformed parameters; G: Gill difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> F: Forward difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> C: Central difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> M: Mixed forward and central difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Unscaled parameters for Omegas=chol(solve(omega));</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Diagonals are transformed, as specified by foceiControl(diagXform=)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |-----+---------------+-----------+-----------+-----------+-----------|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    #| Objective Fun |  parent_0 |  log_k_A1 |f_parent_qlogis |    log_k1 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    log_k2 |  g_qlogis |     sigma |        o1 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|        o2 |        o3 |        o4 |        o5 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|        o6 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    1</span>|     517.20934 |     1.000 |    -1.000 |   -0.9109 |   -0.9296 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9779 |   -0.8885 |   -0.8592 |   -0.8766 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8740 |   -0.8675 |   -0.8712 |   -0.8735 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8680 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     517.20934 |     94.00 |    -5.400 |   -0.9700 |    -1.900 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.300 |    0.1400 |     1.600 |    0.7311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8633 |     1.183 |     1.000 |    0.8891 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.161 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     517.20934</span> |     94.00 |  0.004517 |    0.2749 |    0.1496 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01357 |    0.5349 |     1.600 |    0.7311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8633 |     1.183 |     1.000 |    0.8891 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.161 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    G|    Gill Diff. |     64.43 |     1.648 |  -0.07882 |   -0.2050 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4304 |   0.05992 |    -56.51 |     17.73 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.983 |    -11.00 |    -3.771 |     3.593 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -10.58 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    2</span>|     2737.3115 |    0.2806 |    -1.018 |   -0.9100 |   -0.9273 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9731 |   -0.8892 |   -0.2282 |    -1.075 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9854 |   -0.7447 |   -0.8291 |   -0.9136 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7499 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     2737.3115 |     26.38 |    -5.418 |   -0.9691 |    -1.898 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.295 |    0.1399 |     2.105 |    0.5864 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7671 |     1.329 |     1.042 |    0.8535 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.298 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     2737.3115</span> |     26.38 |  0.004434 |    0.2751 |    0.1499 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01363 |    0.5349 |     2.105 |    0.5864 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7671 |     1.329 |     1.042 |    0.8535 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.298 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    3</span>|     569.57476 |    0.9281 |    -1.002 |   -0.9108 |   -0.9293 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9774 |   -0.8886 |   -0.7961 |   -0.8964 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8851 |   -0.8553 |   -0.8670 |   -0.8775 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8562 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     569.57476 |     87.24 |    -5.402 |   -0.9699 |    -1.900 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.300 |    0.1400 |     1.650 |    0.7166 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8537 |     1.198 |     1.004 |    0.8856 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.175 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     569.57476</span> |     87.24 |  0.004508 |    0.2749 |    0.1496 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01358 |    0.5349 |     1.650 |    0.7166 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8537 |     1.198 |     1.004 |    0.8856 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.175 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    4</span>|     531.41065 |    0.9928 |    -1.000 |   -0.9109 |   -0.9296 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9778 |   -0.8885 |   -0.8528 |   -0.8786 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8751 |   -0.8663 |   -0.8708 |   -0.8739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8668 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     531.41065 |     93.32 |    -5.400 |   -0.9700 |    -1.900 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.300 |    0.1400 |     1.605 |    0.7297 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8624 |     1.185 |     1.000 |    0.8888 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.163 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     531.41065</span> |     93.32 |  0.004516 |    0.2749 |    0.1496 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01357 |    0.5349 |     1.605 |    0.7297 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8624 |     1.185 |     1.000 |    0.8888 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.163 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    5</span>|     531.74000 |    0.9993 |    -1.000 |   -0.9109 |   -0.9296 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9779 |   -0.8885 |   -0.8585 |   -0.8768 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8741 |   -0.8674 |   -0.8712 |   -0.8735 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8679 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|        531.74 |     93.93 |    -5.400 |   -0.9700 |    -1.900 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.300 |    0.1400 |     1.601 |    0.7310 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8632 |     1.183 |     1.000 |    0.8891 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.161 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">        531.74</span> |     93.93 |  0.004516 |    0.2749 |    0.1496 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01357 |    0.5349 |     1.601 |    0.7310 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8632 |     1.183 |     1.000 |    0.8891 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.161 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    6</span>|     531.81753 |    0.9999 |    -1.000 |   -0.9109 |   -0.9296 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9779 |   -0.8885 |   -0.8591 |   -0.8767 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8740 |   -0.8675 |   -0.8712 |   -0.8735 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8680 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     531.81753 |     93.99 |    -5.400 |   -0.9700 |    -1.900 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.300 |    0.1400 |     1.600 |    0.7311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8633 |     1.183 |     1.000 |    0.8891 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.161 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     531.81753</span> |     93.99 |  0.004517 |    0.2749 |    0.1496 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01357 |    0.5349 |     1.600 |    0.7311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8633 |     1.183 |     1.000 |    0.8891 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.161 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    7</span>|     531.82573 |     1.000 |    -1.000 |   -0.9109 |   -0.9296 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9779 |   -0.8885 |   -0.8591 |   -0.8766 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8740 |   -0.8675 |   -0.8712 |   -0.8735 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8680 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     531.82573 |     94.00 |    -5.400 |   -0.9700 |    -1.900 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.300 |    0.1400 |     1.600 |    0.7311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8633 |     1.183 |     1.000 |    0.8891 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.161 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     531.82573</span> |     94.00 |  0.004517 |    0.2749 |    0.1496 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01357 |    0.5349 |     1.600 |    0.7311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8633 |     1.183 |     1.000 |    0.8891 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.161 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    8</span>|     531.82668 |     1.000 |    -1.000 |   -0.9109 |   -0.9296 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9779 |   -0.8885 |   -0.8592 |   -0.8766 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8740 |   -0.8675 |   -0.8712 |   -0.8735 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8680 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     531.82668 |     94.00 |    -5.400 |   -0.9700 |    -1.900 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.300 |    0.1400 |     1.600 |    0.7311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8633 |     1.183 |     1.000 |    0.8891 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.161 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     531.82668</span> |     94.00 |  0.004517 |    0.2749 |    0.1496 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01357 |    0.5349 |     1.600 |    0.7311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8633 |     1.183 |     1.000 |    0.8891 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.161 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    9</span>|     531.82678 |     1.000 |    -1.000 |   -0.9109 |   -0.9296 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9779 |   -0.8885 |   -0.8592 |   -0.8766 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8740 |   -0.8675 |   -0.8712 |   -0.8735 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8680 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     531.82678 |     94.00 |    -5.400 |   -0.9700 |    -1.900 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.300 |    0.1400 |     1.600 |    0.7311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8633 |     1.183 |     1.000 |    0.8891 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.161 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     531.82678</span> |     94.00 |  0.004517 |    0.2749 |    0.1496 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01357 |    0.5349 |     1.600 |    0.7311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8633 |     1.183 |     1.000 |    0.8891 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.161 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   10</span>|     531.82679 |     1.000 |    -1.000 |   -0.9109 |   -0.9296 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9779 |   -0.8885 |   -0.8592 |   -0.8766 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8740 |   -0.8675 |   -0.8712 |   -0.8735 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8680 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     531.82679 |     94.00 |    -5.400 |   -0.9700 |    -1.900 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.300 |    0.1400 |     1.600 |    0.7311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8633 |     1.183 |     1.000 |    0.8891 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.161 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     531.82679</span> |     94.00 |  0.004517 |    0.2749 |    0.1496 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01357 |    0.5349 |     1.600 |    0.7311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8633 |     1.183 |     1.000 |    0.8891 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.161 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   11</span>|     531.82679 |     1.000 |    -1.000 |   -0.9109 |   -0.9296 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9779 |   -0.8885 |   -0.8592 |   -0.8766 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8740 |   -0.8675 |   -0.8712 |   -0.8735 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8680 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     531.82679 |     94.00 |    -5.400 |   -0.9700 |    -1.900 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.300 |    0.1400 |     1.600 |    0.7311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8633 |     1.183 |     1.000 |    0.8891 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.161 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     531.82679</span> |     94.00 |  0.004517 |    0.2749 |    0.1496 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01357 |    0.5349 |     1.600 |    0.7311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8633 |     1.183 |     1.000 |    0.8891 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.161 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   12</span>|     531.82678 |     1.000 |    -1.000 |   -0.9109 |   -0.9296 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9779 |   -0.8885 |   -0.8592 |   -0.8766 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8740 |   -0.8675 |   -0.8712 |   -0.8735 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8680 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     531.82678 |     94.00 |    -5.400 |   -0.9700 |    -1.900 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.300 |    0.1400 |     1.600 |    0.7311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8633 |     1.183 |     1.000 |    0.8891 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.161 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     531.82678</span> |     94.00 |  0.004517 |    0.2749 |    0.1496 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01357 |    0.5349 |     1.600 |    0.7311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8633 |     1.183 |     1.000 |    0.8891 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.161 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   13</span>|     531.82679 |     1.000 |    -1.000 |   -0.9109 |   -0.9296 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9779 |   -0.8885 |   -0.8592 |   -0.8766 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8740 |   -0.8675 |   -0.8712 |   -0.8735 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8680 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     531.82679 |     94.00 |    -5.400 |   -0.9700 |    -1.900 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.300 |    0.1400 |     1.600 |    0.7311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8633 |     1.183 |     1.000 |    0.8891 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.161 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     531.82679</span> |     94.00 |  0.004517 |    0.2749 |    0.1496 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01357 |    0.5349 |     1.600 |    0.7311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8633 |     1.183 |     1.000 |    0.8891 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.161 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   14</span>|     531.82679 |     1.000 |    -1.000 |   -0.9109 |   -0.9296 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9779 |   -0.8885 |   -0.8592 |   -0.8766 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8740 |   -0.8675 |   -0.8712 |   -0.8735 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8680 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     531.82679 |     94.00 |    -5.400 |   -0.9700 |    -1.900 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.300 |    0.1400 |     1.600 |    0.7311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8633 |     1.183 |     1.000 |    0.8891 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.161 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     531.82679</span> |     94.00 |  0.004517 |    0.2749 |    0.1496 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01357 |    0.5349 |     1.600 |    0.7311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8633 |     1.183 |     1.000 |    0.8891 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.161 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   15</span>|     531.82679 |     1.000 |    -1.000 |   -0.9109 |   -0.9296 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9779 |   -0.8885 |   -0.8592 |   -0.8766 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8740 |   -0.8675 |   -0.8712 |   -0.8735 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8680 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     531.82679 |     94.00 |    -5.400 |   -0.9700 |    -1.900 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.300 |    0.1400 |     1.600 |    0.7311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8633 |     1.183 |     1.000 |    0.8891 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.161 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     531.82679</span> |     94.00 |  0.004517 |    0.2749 |    0.1496 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01357 |    0.5349 |     1.600 |    0.7311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8633 |     1.183 |     1.000 |    0.8891 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.161 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   16</span>|     531.82679 |     1.000 |    -1.000 |   -0.9109 |   -0.9296 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9779 |   -0.8885 |   -0.8592 |   -0.8766 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8740 |   -0.8675 |   -0.8712 |   -0.8735 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8680 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     531.82679 |     94.00 |    -5.400 |   -0.9700 |    -1.900 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.300 |    0.1400 |     1.600 |    0.7311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8633 |     1.183 |     1.000 |    0.8891 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.161 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     531.82679</span> |     94.00 |  0.004517 |    0.2749 |    0.1496 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01357 |    0.5349 |     1.600 |    0.7311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8633 |     1.183 |     1.000 |    0.8891 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.161 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   17</span>|     531.82679 |     1.000 |    -1.000 |   -0.9109 |   -0.9296 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9779 |   -0.8885 |   -0.8592 |   -0.8766 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8740 |   -0.8675 |   -0.8712 |   -0.8735 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8680 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     531.82679 |     94.00 |    -5.400 |   -0.9700 |    -1.900 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.300 |    0.1400 |     1.600 |    0.7311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8633 |     1.183 |     1.000 |    0.8891 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.161 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     531.82679</span> |     94.00 |  0.004517 |    0.2749 |    0.1496 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01357 |    0.5349 |     1.600 |    0.7311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8633 |     1.183 |     1.000 |    0.8891 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.161 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> calculating covariance matrix</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating residuals/tables</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-in"></span>
-<span class="r-in"><span class="co"># Variance by variable is supported by 'saem' and 'focei'</span></span>
-<span class="r-in"><span class="va">f_nlmixr_fomc_sfo_saem_obs</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr</a></span><span class="op">(</span><span class="va">f_mmkin_obs</span><span class="op">[</span><span class="st">"FOMC-SFO"</span>, <span class="op">]</span>, est <span class="op">=</span> <span class="st">"saem"</span><span class="op">)</span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → generate SAEM model</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1:    93.4067   -5.7935   -0.0604    2.2993   -1.1624    2.9450    1.7342    0.6650    0.5890    0.4750   14.5215    9.1023</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2:    93.8811   -5.7873   -0.0289    2.3640   -1.0762    2.7977    2.0710    0.6317    0.5595    0.4512   11.1033    4.6425</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 3:    94.0397   -5.9934    0.0119    2.4035   -1.0703    3.0693    2.4524    0.6124    0.5316    0.4287   10.0698    3.4243</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 4:    93.8834   -6.0401    0.0041    2.3944   -1.0097    2.9159    3.1645    0.6120    0.5050    0.4073    9.2013    3.2162</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5:    94.0163   -5.8381   -0.0267    2.3580   -1.0239    2.7701    3.0063    0.5814    0.4797    0.3869    9.0330    3.0330</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 6:    93.9753   -5.8371   -0.0315    2.3598   -1.0052    2.6316    2.8559    0.5708    0.4558    0.3675    8.6051    2.6518</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 7:    93.6109   -5.8741   -0.0401    2.3570   -1.0025    2.5000    2.7131    0.5691    0.4330    0.3492    8.4407    2.4701</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 8:    93.2480   -6.0361   -0.0523    2.3504   -1.0028    2.3750    3.1584    0.5407    0.4113    0.3317    8.6121    2.2437</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 9:    93.2245   -6.0431   -0.0503    2.3552   -0.9828    2.2562    3.9790    0.5395    0.3908    0.3151    8.6609    2.1129</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 10:    93.3040   -6.1080   -0.0503    2.3613   -0.9784    2.1434    4.8606    0.5401    0.3712    0.2994    8.6497    2.0865</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 11:    93.4509   -5.9532   -0.0503    2.3444   -0.9823    3.0948    4.6176    0.5679    0.3527    0.2844    8.1651    2.0310</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 12:    93.5099   -6.1699   -0.0503    2.3408   -0.9746    3.2814    4.3867    0.5679    0.3350    0.2702    8.1716    1.9862</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 13:    93.8052   -6.1984   -0.0474    2.3233   -0.9922    3.1173    4.6967    0.5644    0.3183    0.2567    8.2982    2.0040</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 14:    93.6510   -6.0090   -0.0429    2.3601   -0.9885    2.9615    4.4618    0.5526    0.3024    0.2438    8.3254    2.0605</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 15:    93.8952   -6.3354   -0.0394    2.3580   -0.9792    2.8134    5.2117    0.5657    0.2872    0.2316    8.1329    2.0520</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 16:    93.4703   -6.0722   -0.0434    2.3386   -1.0026    2.6727    4.9512    0.5781    0.2729    0.2201    8.0866    2.0994</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 17:    93.4238   -6.2132   -0.0755    2.3120   -1.0119    2.5391    4.7036    0.5495    0.2592    0.2091    7.5958    2.2864</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 18:    93.5288   -6.2747   -0.0616    2.3223   -1.0105    2.4121    4.4684    0.5467    0.2463    0.1986    7.1910    1.9828</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 19:    93.2607   -6.3635   -0.0631    2.3206   -1.0045    3.6271    4.8142    0.5405    0.2340    0.1928    7.3672    1.8187</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 20:    93.3918   -6.3241   -0.0742    2.2941   -1.0140    3.4457    4.9242    0.5596    0.2223    0.1950    7.1427    1.8754</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 21:    93.6794   -6.1336   -0.0758    2.3000   -1.0048    3.2734    4.6780    0.5565    0.2111    0.1852    7.0989    1.9232</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 22:    94.0006   -6.1882   -0.0800    2.3099   -1.0252    3.1098    4.4441    0.5354    0.2006    0.1870    7.0038    1.9920</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 23:    93.6433   -6.2626   -0.0841    2.2791   -1.0183    4.8893    4.4476    0.5276    0.1906    0.1798    6.3698    1.8787</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 24:    93.9545   -6.3772   -0.0816    2.2887   -1.0019    4.6448    5.1698    0.5293    0.1810    0.1779    6.5903    1.9474</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 25:    94.2280   -6.3235   -0.0839    2.2600   -0.9932    5.2801    4.9113    0.5262    0.1720    0.1806    6.5267    1.9807</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 26:    94.2022   -6.2830   -0.0883    2.2643   -1.0012    5.9595    4.6658    0.5225    0.1634    0.1795    6.3678    1.9659</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 27:    94.4398   -6.1769   -0.0894    2.2564   -1.0177    9.0771    4.4325    0.5207    0.1552    0.1880    6.4522    1.8590</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 28:    94.2586   -6.1652   -0.0882    2.2574   -1.0226    8.6233    4.2108    0.5158    0.1475    0.1881    6.3701    1.7882</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 29:    94.3490   -6.1505   -0.0854    2.2615   -1.0081    9.6333    4.0003    0.5109    0.1423    0.1833    6.3601    1.8485</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 30:    94.6929   -6.0285   -0.0909    2.2610   -1.0082    9.1517    3.8003    0.5117    0.1401    0.2097    6.2461    1.8606</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 31:    94.2553   -6.0390   -0.0896    2.2625   -1.0078    8.6941    3.6103    0.5072    0.1378    0.2088    6.3337    1.8220</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 32:    94.3096   -5.7252   -0.0886    2.2677   -0.9967    9.0244    3.4298    0.5051    0.1325    0.2015    6.4601    1.8880</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 33:    94.8327   -5.8775   -0.0865    2.2684   -0.9976    8.6305    3.2583    0.4920    0.1333    0.1993    6.4804    1.8203</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 34:    94.3527   -5.9488   -0.0826    2.2879   -0.9989    8.1989    3.1538    0.4969    0.1388    0.1984    6.4201    1.7696</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 35:    94.4411   -6.1171   -0.0826    2.2913   -0.9964    8.4377    3.7937    0.4969    0.1387    0.1972    6.3878    1.7612</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 36:    94.2058   -6.1151   -0.0844    2.2920   -1.0069    8.0158    3.7963    0.4962    0.1395    0.1938    6.2469    1.6680</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 37:    93.7449   -6.1251   -0.0925    2.2736   -1.0019    7.6150    3.8477    0.5101    0.1325    0.1841    6.1375    1.7472</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 38:    93.6861   -6.0575   -0.0934    2.2803   -1.0097    7.2343    3.6553    0.5119    0.1259    0.1862    6.0729    1.7608</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 39:    93.9767   -6.0314   -0.0999    2.2548   -1.0231    6.8726    3.4725    0.5195    0.1234    0.1951    6.1947    1.8276</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 40:    94.0297   -6.1559   -0.0989    2.2440   -1.0374    6.5290    3.8559    0.5199    0.1279    0.1891    6.0195    1.8906</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 41:    94.2069   -6.3055   -0.0820    2.2710   -1.0275    6.2025    4.7866    0.5304    0.1215    0.1858    6.1777    1.8541</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 42:    94.2400   -6.3179   -0.0790    2.2783   -1.0379    5.8924    4.6915    0.5253    0.1154    0.1794    6.0530    1.8960</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 43:    93.9851   -6.4096   -0.0784    2.2832   -1.0341    5.5978    5.0608    0.5163    0.1097    0.1773    6.0057    1.8136</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 44:    94.1440   -6.2214   -0.0746    2.2969   -1.0262    5.3179    4.8077    0.5130    0.1079    0.1851    6.1182    1.8390</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 45:    93.8847   -6.3883   -0.0745    2.3059   -1.0132    6.8202    5.1378    0.5130    0.1174    0.1769    6.1000    1.8391</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 46:    93.7228   -6.3305   -0.0794    2.3033   -1.0107    6.4792    4.8809    0.5095    0.1196    0.1783    6.2794    1.7304</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 47:    93.7031   -6.4232   -0.0796    2.3006   -1.0028    6.1552    5.5756    0.5092    0.1243    0.1887    6.1716    1.7279</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 48:    93.5210   -6.2770   -0.0794    2.2948   -1.0122    5.8475    5.2968    0.5098    0.1247    0.1863    5.9847    1.7994</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 49:    93.3676   -6.4486   -0.0754    2.3079   -1.0061    5.5551    5.4356    0.5018    0.1198    0.1858    6.1108    1.7598</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 50:    93.8573   -6.3944   -0.0755    2.3057   -1.0038    5.2773    5.2487    0.5063    0.1193    0.1843    6.0935    1.7725</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 51:    93.7004   -6.2783   -0.0854    2.2836   -1.0074    5.0135    4.9863    0.4932    0.1269    0.1858    6.1630    1.8063</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 52:    93.4843   -6.3763   -0.0962    2.2731   -1.0073    4.7628    5.0969    0.4815    0.1345    0.1924    6.0823    1.8013</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 53:    93.6971   -6.5002   -0.0887    2.2774   -1.0111    4.5246    5.7428    0.4726    0.1346    0.1982    6.1744    1.7695</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 54:    93.6176   -6.4928   -0.0917    2.2648   -1.0220    4.2984    5.4557    0.4765    0.1419    0.2017    6.3732    1.8195</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 55:    93.7072   -6.5760   -0.0833    2.2865   -1.0282    4.0835    5.9775    0.4732    0.1434    0.1963    6.3653    1.7028</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 56:    94.1360   -6.6854   -0.0901    2.2941   -1.0244    3.8793    7.2562    0.4637    0.1393    0.1935    6.3001    1.7878</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 57:    93.4627   -6.6255   -0.1049    2.2502   -1.0233    3.6854    6.8934    0.4847    0.1323    0.1878    6.2357    1.8480</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 58:    93.8066   -6.6603   -0.1049    2.2362   -1.0280    3.5011    6.9334    0.4847    0.1397    0.1872    6.3582    1.7787</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 59:    93.8599   -6.7837   -0.1046    2.2450   -1.0274    3.3260    8.4672    0.4853    0.1332    0.1833    6.1248    1.8066</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 60:    93.6190   -6.5618   -0.1055    2.2381   -1.0244    3.1597    8.0438    0.4742    0.1390    0.1858    6.2589    1.7881</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 61:    93.7045   -6.6482   -0.1159    2.2413   -1.0272    3.2942    7.8485    0.4655    0.1406    0.1827    5.8425    1.7744</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 62:    93.5232   -6.5027   -0.1168    2.2418   -1.0187    3.3810    7.4560    0.4766    0.1414    0.1790    5.9349    1.7717</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 63:    93.4884   -6.4221   -0.1164    2.2505   -1.0062    3.2119    7.0832    0.4768    0.1422    0.1752    6.0193    1.7434</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 64:    93.2305   -6.4456   -0.1153    2.2573   -1.0062    4.1468    6.7291    0.4776    0.1395    0.1753    5.8355    1.7529</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 65:    93.3743   -6.3237   -0.1227    2.2484   -1.0102    3.9395    6.3926    0.4864    0.1374    0.1800    5.6731    1.7808</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 66:    93.7132   -6.3178   -0.1186    2.2389   -0.9894    4.0557    6.0730    0.4909    0.1426    0.1807    5.7099    1.7283</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 67:    93.7490   -6.3514   -0.1155    2.2445   -0.9946    3.8529    5.7693    0.4938    0.1379    0.1830    5.7366    1.7847</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 68:    93.7617   -6.1181   -0.1251    2.2487   -0.9841    3.7650    5.4809    0.4816    0.1549    0.1769    5.6569    1.7415</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 69:    93.4342   -6.3588   -0.1301    2.2350   -0.9813    4.5688    5.2068    0.4745    0.1624    0.1736    5.5771    1.7091</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 70:    93.5303   -6.3266   -0.1330    2.2384   -0.9753    4.3404    4.9465    0.4734    0.1563    0.1699    5.5332    1.7256</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 71:    93.4733   -6.2859   -0.1364    2.2170   -0.9781    4.1234    4.6992    0.4701    0.1604    0.1649    5.6661    1.7335</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 72:    93.3055   -6.2502   -0.1462    2.2156   -0.9724    3.9172    4.7693    0.4519    0.1607    0.1582    5.4776    1.7679</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 73:    93.3010   -6.6844   -0.1490    2.2262   -0.9777    3.7213    6.9975    0.4426    0.1848    0.1640    5.7066    1.7588</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 74:    93.1104   -6.6720   -0.1484    2.2079   -0.9982    3.5353    6.7811    0.4436    0.1807    0.1732    5.7700    1.7343</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 75:    93.4534   -6.9644   -0.1480    2.2078   -0.9930    3.3585    8.7027    0.4497    0.1717    0.1708    5.5371    1.7098</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 76:    93.5886   -6.3503   -0.1491    2.1958   -0.9884    3.7136    8.2676    0.4509    0.1731    0.1706    5.3943    1.7340</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 77:    93.4345   -6.4976   -0.1531    2.1831   -0.9928    4.5945    7.8542    0.4592    0.1741    0.1703    5.5564    1.7164</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 78:    93.6007   -6.4885   -0.1538    2.1850   -0.9919    4.3647    7.4615    0.4607    0.1803    0.1692    5.4698    1.7354</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 79:    93.2897   -7.0329   -0.1518    2.1935   -0.9863    4.6922   10.9870    0.4569    0.1790    0.1608    5.3799    1.7484</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 80:    93.4130   -6.6634   -0.1531    2.1865   -0.9839    5.4720   10.4377    0.4585    0.1852    0.1543    5.4298    1.7237</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 81:    93.5828   -6.7204   -0.1563    2.1914   -0.9882    5.1984    9.9158    0.4548    0.1973    0.1600    5.4425    1.7741</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 82:    93.4450   -6.7357   -0.1537    2.1964   -0.9958    4.9384   10.1331    0.4577    0.1874    0.1632    5.6874    1.7789</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 83:    93.6109   -6.9249   -0.1493    2.2061   -0.9945    4.6915   11.1537    0.4474    0.1781    0.1686    5.4249    1.7317</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 84:    93.7133   -6.8029   -0.1493    2.2016   -0.9886    4.4569   10.9568    0.4474    0.1715    0.1689    5.5426    1.7227</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 85:    93.8040   -6.6434   -0.1483    2.2032   -0.9861    5.4991   10.4090    0.4330    0.1749    0.1726    5.4570    1.7332</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 86:    93.9029   -6.7750   -0.1472    2.2066   -0.9892    5.2241   11.7325    0.4352    0.1819    0.1644    5.5652    1.6802</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 87:    93.8127   -6.7015   -0.1499    2.2019   -0.9891    4.9629   11.1459    0.4292    0.1977    0.1661    5.7122    1.6713</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 88:    93.6777   -6.7044   -0.1440    2.2074   -1.0050    4.7148   10.5886    0.4379    0.1878    0.1750    5.6084    1.7096</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 89:    94.0481   -6.2990   -0.1443    2.2085   -0.9869    4.4790   10.0591    0.4355    0.1951    0.1688    5.4280    1.8093</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 90:    93.6399   -6.3965   -0.1429    2.2138   -0.9737    5.1306    9.5562    0.4367    0.1917    0.1604    5.5652    1.7458</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 91:    93.8670   -6.3075   -0.1426    2.2128   -0.9856    5.1368    9.0784    0.4427    0.1993    0.1546    5.3927    1.8246</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 92:    93.7332   -6.4793   -0.1426    2.2091   -0.9835    5.0102    8.6245    0.4427    0.1986    0.1585    5.4463    1.7343</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 93:    93.8211   -6.3270   -0.1416    2.2123   -0.9941    4.7597    8.1932    0.4431    0.1908    0.1689    5.5213    1.7093</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 94:    93.7499   -6.0880   -0.1390    2.2158   -0.9960    5.1992    7.7836    0.4444    0.1958    0.1733    5.5329    1.7880</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 95:    93.6253   -6.2196   -0.1436    2.2126   -1.0053    4.9392    7.3944    0.4383    0.2011    0.1717    5.6042    1.7460</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 96:    93.8862   -6.1475   -0.1408    2.2211   -0.9922    4.6923    7.0247    0.4347    0.2077    0.1669    5.6807    1.6943</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 97:    93.7610   -6.2409   -0.1368    2.2281   -0.9864    4.4576    6.6734    0.4313    0.2084    0.1723    5.5387    1.7075</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 98:    93.5362   -6.3378   -0.1368    2.2294   -0.9813    4.2348    6.3398    0.4313    0.2127    0.1877    5.5850    1.6627</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 99:    93.5044   -6.2557   -0.1311    2.2282   -0.9993    4.0230    6.0228    0.4461    0.2167    0.1879    5.6437    1.7076</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 100:    93.3102   -6.3602   -0.1311    2.2368   -1.0040    3.8219    5.7216    0.4461    0.2139    0.1875    5.8029    1.7592</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 101:    93.4687   -6.0385   -0.1241    2.2347   -1.0031    3.6308    5.4356    0.4483    0.2035    0.1816    6.0097    1.7002</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 102:    93.6536   -6.2867   -0.1299    2.2421   -1.0002    3.4492    5.1743    0.4434    0.1993    0.1872    5.8540    1.7162</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 103:    93.9532   -6.2261   -0.1277    2.2361   -0.9931    3.2768    5.0546    0.4450    0.2069    0.1884    5.6688    1.7324</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 104:    93.9839   -6.1980   -0.1287    2.2286   -1.0081    3.1129    5.0671    0.4475    0.1997    0.1985    5.7690    1.7636</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 105:    94.1682   -6.1671   -0.1283    2.2217   -1.0154    2.9573    4.8137    0.4481    0.1976    0.1965    5.9277    1.7386</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 106:    94.2778   -6.1839   -0.1243    2.2323   -1.0022    3.5381    4.5730    0.4707    0.1980    0.1932    5.7059    1.7184</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 107:    94.3667   -5.9941   -0.1182    2.2283   -1.0191    3.3612    4.3444    0.4753    0.1984    0.1986    5.7813    1.7446</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 108:    94.2722   -6.1869   -0.1171    2.2293   -1.0027    3.6659    4.6329    0.4742    0.1986    0.2011    5.7827    1.7074</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 109:    94.1997   -6.2385   -0.1172    2.2241   -1.0083    3.4826    4.7954    0.4721    0.2027    0.2020    5.8339    1.7650</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 110:    94.3017   -6.3774   -0.1291    2.2229   -0.9857    3.7634    5.8516    0.4810    0.2126    0.1984    5.7961    1.6706</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 111:    93.9803   -6.0240   -0.1258    2.2273   -0.9879    3.5752    5.5590    0.4749    0.2060    0.1976    5.6243    1.7082</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 112:    94.1307   -6.0036   -0.1253    2.2365   -0.9886    4.0368    5.2810    0.4760    0.2060    0.1975    5.5732    1.7063</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 113:    93.8676   -6.2496   -0.1118    2.2600   -1.0080    3.8350    5.0170    0.4855    0.2109    0.2006    5.6406    1.7357</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 114:    93.5949   -6.3200   -0.1044    2.2449   -1.0172    3.6472    4.7661    0.4868    0.2095    0.2131    5.7690    1.7428</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 115:    93.6997   -6.3282   -0.1046    2.2567   -1.0135    3.4648    4.8622    0.4876    0.2264    0.2134    5.8853    1.7823</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 116:    93.8191   -6.0802   -0.1087    2.2535   -1.0011    3.4347    4.6191    0.4786    0.2176    0.2108    5.6553    1.7802</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 117:    93.8575   -6.0930   -0.1022    2.2498   -0.9898    3.3071    4.3881    0.4822    0.2163    0.2075    5.7806    1.8150</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 118:    93.9164   -5.9787   -0.1133    2.2535   -0.9861    4.2578    4.1687    0.4687    0.2198    0.2088    5.4441    1.8411</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 119:    93.8748   -6.0108   -0.1165    2.2488   -0.9775    4.0449    3.9603    0.4653    0.2271    0.2032    5.6119    1.7501</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 120:    93.6001   -6.0447   -0.1144    2.2477   -0.9821    3.8426    3.7623    0.4641    0.2223    0.2055    5.6454    1.7244</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 121:    93.6712   -5.9851   -0.1195    2.2484   -0.9917    3.6505    3.5742    0.4600    0.2143    0.2010    5.4083    1.7965</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 122:    93.6859   -6.0390   -0.1145    2.2497   -0.9888    3.4680    3.5595    0.4618    0.2136    0.1986    5.4111    1.7519</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 123:    93.6014   -5.8383   -0.1145    2.2584   -0.9893    3.6047    3.3815    0.4618    0.2155    0.2045    5.3624    1.7023</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 124:    93.6333   -5.7861   -0.1131    2.2556   -0.9872    3.4245    3.2125    0.4621    0.2153    0.2025    5.3930    1.7036</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 125:    93.4504   -5.9483   -0.1154    2.2531   -0.9924    3.2533    3.0518    0.4640    0.2175    0.2030    5.5097    1.6830</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 126:    93.5693   -5.8818   -0.1120    2.2506   -0.9960    3.0906    2.8992    0.4606    0.2267    0.2016    5.4583    1.6650</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 127:    93.7074   -5.8191   -0.1178    2.2412   -0.9891    2.9361    2.7543    0.4688    0.2234    0.2039    5.4861    1.8125</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 128:    93.5959   -5.8842   -0.1179    2.2544   -0.9985    3.0224    2.6166    0.4700    0.2237    0.2019    5.6417    1.8454</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 129:    93.5600   -5.8683   -0.1161    2.2365   -0.9987    3.4186    2.4857    0.4721    0.2182    0.1918    5.4391    1.8145</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 130:    93.4104   -5.8226   -0.1126    2.2355   -0.9892    3.3648    2.4231    0.4763    0.2212    0.1873    5.3999    1.7457</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 131:    93.5045   -5.7255   -0.1118    2.2486   -0.9918    4.1951    2.3020    0.4776    0.2121    0.1927    5.4342    1.7744</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 132:    93.2626   -5.8379   -0.1097    2.2510   -0.9933    3.9853    2.5572    0.4763    0.2114    0.1966    5.2979    1.7239</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 133:    93.4370   -5.9097   -0.1049    2.2578   -0.9939    4.2190    2.8246    0.4738    0.2036    0.2020    5.2853    1.6765</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 134:    93.8665   -5.9439   -0.1035    2.2654   -0.9832    4.0081    3.1228    0.4756    0.2102    0.1961    5.3467    1.7177</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 135:    93.6301   -5.8062   -0.1031    2.2702   -0.9737    3.9024    2.9667    0.4748    0.2101    0.2003    5.3053    1.6977</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 136:    93.7744   -5.9328   -0.1055    2.2685   -0.9764    3.7072    2.9952    0.4721    0.2096    0.1949    5.4319    1.6864</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 137:    93.6734   -5.9886   -0.1107    2.2517   -0.9732    3.8811    3.4962    0.4642    0.2204    0.1924    5.4294    1.6684</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 138:    93.7128   -5.9927   -0.1119    2.2517   -0.9775    3.6870    3.4543    0.4667    0.2204    0.1953    5.3912    1.7060</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 139:    93.6530   -6.1296   -0.1210    2.2456   -0.9929    3.5027    4.6472    0.4527    0.2296    0.1855    5.4953    1.7242</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 140:    93.9344   -6.2519   -0.1384    2.2304   -0.9970    3.6814    4.4707    0.4337    0.2182    0.1762    5.5643    1.7596</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 141:    93.7356   -6.2804   -0.1296    2.2549   -0.9921    3.9811    4.4876    0.4218    0.2361    0.1674    5.3531    1.7175</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 142:    93.7399   -6.0023   -0.1098    2.2611   -0.9815    3.7821    4.2632    0.4395    0.2421    0.1807    5.3139    1.7191</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 143:    93.4732   -6.0376   -0.1108    2.2689   -0.9734    3.5930    4.0500    0.4396    0.2541    0.1718    5.2930    1.6229</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 144:    93.4900   -5.9517   -0.1093    2.2876   -0.9675    3.4133    3.8475    0.4337    0.2542    0.1711    5.4258    1.5660</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 145:    93.4090   -5.9596   -0.1000    2.2942   -0.9756    3.2426    3.6551    0.4274    0.2422    0.1671    5.3539    1.6971</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 146:    93.4142   -5.9549   -0.0982    2.2846   -0.9704    3.0805    3.6092    0.4226    0.2339    0.1745    5.5092    1.6696</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 147:    93.4409   -6.0720   -0.0971    2.2942   -0.9891    2.9265    3.9922    0.4224    0.2373    0.1781    5.5599    1.6080</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 148:    93.4504   -6.2201   -0.0980    2.2855   -0.9832    2.7802    4.6985    0.4104    0.2464    0.1856    5.5016    1.5877</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 149:    93.4240   -6.2122   -0.1005    2.2728   -0.9881    3.7659    4.7755    0.4082    0.2642    0.1885    5.4942    1.5534</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 150:    93.5094   -6.1295   -0.1087    2.2717   -0.9941    3.5776    4.5367    0.4109    0.2611    0.1928    5.3468    1.5585</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 151:    93.4038   -6.2751   -0.1130    2.2643   -0.9892    3.3987    4.9866    0.4172    0.2638    0.1905    5.4955    1.6256</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 152:    93.5072   -6.3361   -0.1147    2.2627   -0.9988    2.3580    5.3824    0.4175    0.2656    0.1819    5.7685    1.6126</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 153:    93.3582   -6.2019   -0.1227    2.2526   -0.9929    2.5874    4.7052    0.4348    0.2621    0.1810    5.5149    1.6181</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 154:    93.1890   -6.3537   -0.1263    2.2446   -0.9871    2.4073    5.7070    0.4351    0.2586    0.1829    5.3136    1.6272</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 155:    93.1706   -6.4117   -0.1260    2.2484   -0.9845    2.3035    6.0004    0.4463    0.2586    0.1781    5.4260    1.6494</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 156:    93.3240   -6.3931   -0.1259    2.2469   -0.9824    2.5659    5.7375    0.4466    0.2781    0.1754    5.6202    1.6591</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 157:    93.3239   -6.1812   -0.1242    2.2541   -0.9743    1.6054    4.5906    0.4478    0.2657    0.1758    5.6806    1.6367</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 158:    93.3756   -6.2562   -0.1264    2.2706   -0.9888    1.5329    4.6790    0.4458    0.2532    0.1728    5.7756    1.6248</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 159:    93.3034   -6.3291   -0.1217    2.2524   -0.9954    1.7774    5.7204    0.4532    0.2642    0.1715    5.8189    1.6830</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 160:    93.4387   -6.5115   -0.1196    2.2488   -0.9846    2.2219    6.4960    0.4555    0.2728    0.1689    5.5273    1.6332</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 161:    93.7646   -6.3820   -0.1231    2.2520   -0.9837    2.8322    5.7269    0.4498    0.2712    0.1730    5.3659    1.5787</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 162:    93.6252   -6.4563   -0.1243    2.2472   -0.9867    2.8322    5.9119    0.4502    0.2671    0.1726    5.4519    1.5819</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 163:    93.6787   -6.6444   -0.1292    2.2366   -0.9899    2.0520    7.4835    0.4556    0.2705    0.1689    5.4095    1.5883</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 164:    93.7458   -6.9330   -0.1257    2.2430   -0.9872    1.7825    9.4340    0.4495    0.2701    0.1593    5.4517    1.6116</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 165:    93.7370   -6.9118   -0.1250    2.2421   -0.9832    1.9949   10.6549    0.4499    0.2685    0.1622    5.6272    1.6075</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 166:    94.0889   -6.8704   -0.1276    2.2409   -0.9872    1.5618   10.4435    0.4482    0.2608    0.1648    5.5891    1.6208</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 167:    94.1319   -7.2779   -0.1206    2.2505   -0.9894    1.5063   13.4312    0.4393    0.2549    0.1661    5.6013    1.6043</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 168:    93.8341   -6.9310   -0.1140    2.2495   -0.9844    1.7329   11.3224    0.4463    0.2540    0.1629    5.8366    1.6765</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 169:    93.8923   -6.6547   -0.1173    2.2620   -0.9872    1.4531    8.4608    0.4419    0.2414    0.1666    5.8784    1.6268</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 170:    94.0072   -6.3970   -0.1173    2.2606   -0.9861    1.4164    6.9237    0.4419    0.2468    0.1656    5.8793    1.6387</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 171:    93.8690   -6.5151   -0.1079    2.2482   -0.9880    1.9225    7.7681    0.4547    0.2404    0.1523    6.0158    1.6281</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 172:    93.6847   -6.3416   -0.1095    2.2438   -0.9849    2.0143    5.5739    0.4498    0.2450    0.1534    6.1355    1.6422</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 173:    93.4817   -6.3165   -0.1115    2.2573   -0.9885    1.6855    5.5626    0.4440    0.2471    0.1542    6.1343    1.6337</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 174:    93.6781   -6.2722   -0.1113    2.2521   -0.9958    1.9186    5.3383    0.4471    0.2427    0.1565    6.1081    1.6358</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 175:    93.7764   -6.1664   -0.1113    2.2468   -0.9864    1.6286    4.6233    0.4471    0.2426    0.1596    5.8892    1.6375</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 176:    93.9246   -6.2164   -0.1160    2.2529   -0.9853    1.0357    4.8013    0.4422    0.2471    0.1756    5.7340    1.6016</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 177:    93.9711   -6.1274   -0.1112    2.2540   -0.9883    1.2079    4.1536    0.4415    0.2492    0.1783    5.8399    1.6291</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 178:    93.9212   -6.0532   -0.1116    2.2593   -0.9742    1.2409    3.6443    0.4489    0.2458    0.1683    5.8422    1.6290</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 179:    94.0137   -6.0739   -0.1095    2.2664   -0.9778    1.5060    3.9878    0.4506    0.2370    0.1746    6.0349    1.6326</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 180:    93.9247   -6.0681   -0.1130    2.2660   -0.9934    1.9619    3.9582    0.4474    0.2328    0.1773    5.8082    1.6740</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 181:    93.7150   -6.0191   -0.1153    2.2558   -0.9915    2.6849    3.7075    0.4491    0.2283    0.1759    5.7187    1.6842</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 182:    93.5908   -6.1098   -0.1111    2.2769   -0.9942    3.0096    3.9325    0.4643    0.2275    0.1736    5.9243    1.6466</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 183:    93.3386   -6.0987   -0.1131    2.2630   -0.9962    3.5457    4.1285    0.4693    0.2373    0.1751    5.6948    1.7222</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 184:    93.4889   -6.3097   -0.1134    2.2660   -0.9720    3.0855    5.2642    0.4648    0.2255    0.1585    5.6827    1.7444</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 185:    93.6387   -6.1883   -0.1188    2.2622   -0.9603    3.3568    4.8291    0.4554    0.2223    0.1681    5.7089    1.8164</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 186:    93.3420   -6.2909   -0.1195    2.2656   -0.9835    3.2124    4.8317    0.4541    0.2286    0.1531    5.7574    1.7708</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 187:    93.4395   -6.0358   -0.1165    2.2528   -0.9917    3.8299    3.3301    0.4518    0.2370    0.1593    5.8508    1.6988</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 188:    93.5358   -6.0105   -0.1161    2.2540   -0.9813    5.1249    3.3448    0.4522    0.2361    0.1660    5.8700    1.6525</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 189:    93.4932   -6.1199   -0.1129    2.2636   -0.9812    4.5430    4.3213    0.4428    0.2359    0.1907    5.6970    1.7268</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 190:    93.4754   -5.9088   -0.1171    2.2564   -0.9614    4.6253    3.2590    0.4410    0.2399    0.1864    5.7116    1.8140</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 191:    93.4709   -5.9676   -0.1171    2.2568   -0.9748    4.8326    3.6704    0.4410    0.2428    0.1812    5.5925    1.7267</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 192:    93.3895   -5.9940   -0.1191    2.2523   -0.9691    4.3019    3.5174    0.4360    0.2484    0.1591    5.4631    1.7057</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 193:    93.4904   -6.0400   -0.1173    2.2519   -0.9697    4.6476    3.6255    0.4389    0.2388    0.1694    5.5362    1.7174</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 194:    93.4591   -5.9642   -0.1245    2.2626   -0.9559    5.3125    3.8133    0.4297    0.2550    0.1660    5.7591    1.7344</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 195:    93.6610   -6.2211   -0.1226    2.2580   -0.9669    5.2051    4.9271    0.4318    0.2414    0.1811    5.7010    1.7710</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 196:    93.4249   -5.9570   -0.1068    2.2735   -0.9727    5.1049    3.4872    0.4442    0.2429    0.1815    5.7753    1.7379</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 197:    93.4082   -6.0568   -0.1054    2.2754   -0.9865    5.2827    3.9305    0.4554    0.2408    0.1925    5.7514    1.7163</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 198:    93.3856   -5.8616   -0.1087    2.2708   -0.9686    4.1369    3.0197    0.4530    0.2473    0.1878    5.6920    1.7043</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 199:    93.5488   -6.0494   -0.1167    2.2682   -0.9719    3.8010    3.8897    0.4600    0.2445    0.1860    5.7126    1.6605</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 200:    93.3779   -5.9779   -0.1110    2.2761   -0.9780    3.5955    3.4721    0.4595    0.2468    0.1941    5.7539    1.6736</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 201:    93.4946   -6.0259   -0.1102    2.2676   -0.9755    3.0583    3.7305    0.4592    0.2554    0.1921    5.8877    1.6730</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 202:    93.4698   -6.0522   -0.1110    2.2685   -0.9703    2.9421    3.8718    0.4579    0.2595    0.1906    5.8527    1.6701</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 203:    93.4625   -6.0744   -0.1132    2.2642   -0.9696    3.1854    4.0983    0.4589    0.2596    0.1886    5.7532    1.6655</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 204:    93.4984   -6.0853   -0.1138    2.2589   -0.9718    3.2826    4.1667    0.4581    0.2588    0.1851    5.7274    1.6694</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 205:    93.5279   -6.1054   -0.1151    2.2562   -0.9742    3.3257    4.2680    0.4569    0.2584    0.1832    5.6976    1.6777</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 206:    93.6025   -6.1087   -0.1174    2.2518   -0.9767    3.2399    4.2443    0.4582    0.2589    0.1822    5.6809    1.6775</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 207:    93.6382   -6.0990   -0.1204    2.2481   -0.9801    3.2768    4.1473    0.4579    0.2591    0.1823    5.6460    1.6819</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 208:    93.6250   -6.0878   -0.1224    2.2438   -0.9812    3.1815    4.0872    0.4579    0.2577    0.1818    5.6256    1.6887</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 209:    93.6102   -6.0740   -0.1255    2.2394   -0.9803    3.1716    3.9968    0.4561    0.2573    0.1812    5.6063    1.6886</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 210:    93.6005   -6.0571   -0.1277    2.2348   -0.9799    3.2408    3.8923    0.4548    0.2572    0.1798    5.5849    1.6912</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 211:    93.6270   -6.0425   -0.1306    2.2292   -0.9807    3.3500    3.8267    0.4538    0.2586    0.1792    5.5578    1.6992</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 212:    93.6641   -6.0403   -0.1331    2.2253   -0.9806    3.4487    3.8366    0.4529    0.2596    0.1786    5.5422    1.7022</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 213:    93.6743   -6.0344   -0.1354    2.2214   -0.9800    3.5484    3.8260    0.4518    0.2606    0.1781    5.5250    1.7069</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 214:    93.6719   -6.0405   -0.1377    2.2179   -0.9804    3.5538    3.8785    0.4506    0.2612    0.1769    5.5148    1.7087</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 215:    93.6743   -6.0403   -0.1396    2.2146   -0.9801    3.5578    3.9180    0.4496    0.2615    0.1761    5.5118    1.7094</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 216:    93.6666   -6.0436   -0.1413    2.2115   -0.9796    3.5848    3.9484    0.4488    0.2624    0.1755    5.5015    1.7116</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 217:    93.6715   -6.0438   -0.1430    2.2086   -0.9794    3.6188    3.9603    0.4478    0.2631    0.1748    5.4884    1.7132</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 218:    93.6765   -6.0488   -0.1441    2.2060   -0.9796    3.6126    3.9885    0.4471    0.2632    0.1746    5.4714    1.7156</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 219:    93.6714   -6.0557   -0.1453    2.2038   -0.9798    3.6603    4.0118    0.4463    0.2632    0.1735    5.4593    1.7235</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 220:    93.6728   -6.0711   -0.1462    2.2027   -0.9794    3.7244    4.0910    0.4457    0.2639    0.1730    5.4531    1.7241</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 221:    93.6723   -6.0822   -0.1470    2.2015   -0.9788    3.7754    4.1554    0.4450    0.2647    0.1724    5.4511    1.7247</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 222:    93.6789   -6.0745   -0.1480    2.1998   -0.9780    3.8735    4.1186    0.4442    0.2662    0.1718    5.4419    1.7258</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 223:    93.6891   -6.0700   -0.1488    2.1984   -0.9778    3.9353    4.0998    0.4434    0.2681    0.1715    5.4375    1.7251</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 224:    93.7125   -6.0705   -0.1496    2.1976   -0.9774    4.0220    4.0861    0.4427    0.2697    0.1711    5.4378    1.7230</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 225:    93.7332   -6.0695   -0.1502    2.1966   -0.9775    4.0553    4.0669    0.4422    0.2712    0.1711    5.4355    1.7205</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 226:    93.7631   -6.0712   -0.1508    2.1951   -0.9779    4.0755    4.0572    0.4417    0.2728    0.1712    5.4347    1.7190</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 227:    93.7912   -6.0687   -0.1512    2.1938   -0.9785    4.0621    4.0439    0.4409    0.2742    0.1716    5.4325    1.7189</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 228:    93.8077   -6.0644   -0.1517    2.1927   -0.9791    4.0246    4.0190    0.4400    0.2755    0.1722    5.4269    1.7193</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 229:    93.8255   -6.0661   -0.1521    2.1909   -0.9796    3.9958    4.0166    0.4392    0.2769    0.1725    5.4214    1.7214</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 230:    93.8403   -6.0766   -0.1530    2.1895   -0.9802    4.0152    4.0548    0.4380    0.2788    0.1730    5.4214    1.7232</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 231:    93.8549   -6.0768   -0.1541    2.1877   -0.9810    4.0690    4.0542    0.4368    0.2803    0.1734    5.4157    1.7236</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 232:    93.8666   -6.0728   -0.1550    2.1858   -0.9816    4.0852    4.0337    0.4356    0.2818    0.1736    5.4136    1.7224</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 233:    93.8728   -6.0672   -0.1557    2.1844   -0.9820    4.1001    4.0000    0.4346    0.2828    0.1738    5.4028    1.7243</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 234:    93.8862   -6.0646   -0.1563    2.1830   -0.9825    4.1303    3.9850    0.4337    0.2838    0.1737    5.3924    1.7222</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 235:    93.8862   -6.0632   -0.1570    2.1819   -0.9827    4.1149    3.9735    0.4329    0.2847    0.1737    5.3846    1.7225</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 236:    93.8827   -6.0639   -0.1577    2.1814   -0.9834    4.1004    3.9711    0.4322    0.2852    0.1739    5.3838    1.7220</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 237:    93.8729   -6.0680   -0.1582    2.1808   -0.9840    4.0606    3.9866    0.4316    0.2857    0.1741    5.3806    1.7213</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 238:    93.8739   -6.0733   -0.1587    2.1806   -0.9845    4.0331    4.0011    0.4311    0.2859    0.1742    5.3775    1.7199</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 239:    93.8732   -6.0729   -0.1591    2.1802   -0.9853    3.9850    3.9892    0.4307    0.2859    0.1744    5.3782    1.7216</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 240:    93.8760   -6.0754   -0.1595    2.1796   -0.9854    3.9349    3.9867    0.4303    0.2858    0.1747    5.3781    1.7232</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 241:    93.8779   -6.0749   -0.1599    2.1791   -0.9853    3.9241    3.9801    0.4299    0.2858    0.1748    5.3757    1.7228</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 242:    93.8842   -6.0716   -0.1602    2.1786   -0.9852    3.9394    3.9651    0.4297    0.2859    0.1749    5.3726    1.7224</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 243:    93.8884   -6.0719   -0.1606    2.1778   -0.9851    3.9310    3.9741    0.4295    0.2857    0.1750    5.3705    1.7224</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 244:    93.8910   -6.0712   -0.1610    2.1771   -0.9850    3.9173    3.9819    0.4294    0.2856    0.1749    5.3700    1.7222</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 245:    93.9054   -6.0736   -0.1614    2.1764   -0.9854    3.9106    3.9984    0.4293    0.2856    0.1748    5.3711    1.7217</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 246:    93.9209   -6.0753   -0.1617    2.1757   -0.9859    3.9080    4.0071    0.4291    0.2852    0.1746    5.3711    1.7215</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 247:    93.9273   -6.0846   -0.1621    2.1752   -0.9861    3.8790    4.0580    0.4286    0.2851    0.1745    5.3755    1.7206</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 248:    93.9265   -6.0884   -0.1625    2.1749   -0.9865    3.8613    4.0784    0.4286    0.2848    0.1744    5.3760    1.7198</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 249:    93.9286   -6.0926   -0.1627    2.1746   -0.9872    3.8663    4.1008    0.4287    0.2845    0.1745    5.3755    1.7195</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 250:    93.9287   -6.0968   -0.1629    2.1744   -0.9878    3.8822    4.1269    0.4289    0.2844    0.1743    5.3755    1.7201</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 251:    93.9314   -6.1017   -0.1630    2.1739   -0.9882    3.8878    4.1495    0.4291    0.2843    0.1744    5.3729    1.7200</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 252:    93.9351   -6.1036   -0.1632    2.1734   -0.9885    3.8908    4.1545    0.4293    0.2843    0.1746    5.3684    1.7191</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 253:    93.9415   -6.1053   -0.1634    2.1729   -0.9889    3.8727    4.1602    0.4294    0.2842    0.1747    5.3650    1.7196</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 254:    93.9473   -6.1088   -0.1636    2.1723   -0.9891    3.8657    4.1769    0.4296    0.2843    0.1749    5.3666    1.7190</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 255:    93.9505   -6.1087   -0.1639    2.1717   -0.9888    3.8457    4.1720    0.4298    0.2841    0.1749    5.3617    1.7191</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 256:    93.9497   -6.1054   -0.1642    2.1715   -0.9885    3.8395    4.1559    0.4299    0.2839    0.1749    5.3598    1.7192</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 257:    93.9477   -6.1008   -0.1643    2.1713   -0.9882    3.8383    4.1360    0.4299    0.2836    0.1749    5.3567    1.7196</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 258:    93.9402   -6.0998   -0.1645    2.1713   -0.9880    3.8516    4.1288    0.4300    0.2832    0.1749    5.3565    1.7190</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 259:    93.9318   -6.0979   -0.1646    2.1711   -0.9879    3.8396    4.1182    0.4301    0.2829    0.1750    5.3583    1.7188</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 260:    93.9277   -6.1002   -0.1646    2.1713   -0.9879    3.8173    4.1273    0.4304    0.2826    0.1751    5.3611    1.7186</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 261:    93.9251   -6.0973   -0.1646    2.1714   -0.9879    3.8140    4.1105    0.4306    0.2822    0.1750    5.3588    1.7192</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 262:    93.9186   -6.1009   -0.1647    2.1715   -0.9880    3.8177    4.1303    0.4308    0.2822    0.1750    5.3604    1.7194</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 263:    93.9130   -6.1033   -0.1646    2.1715   -0.9880    3.8029    4.1529    0.4309    0.2822    0.1748    5.3647    1.7188</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 264:    93.9040   -6.1077   -0.1645    2.1717   -0.9879    3.7950    4.1820    0.4310    0.2822    0.1747    5.3699    1.7184</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 265:    93.9012   -6.1071   -0.1645    2.1714   -0.9879    3.7834    4.1895    0.4310    0.2822    0.1746    5.3755    1.7191</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 266:    93.8988   -6.1051   -0.1644    2.1714   -0.9879    3.7788    4.1822    0.4311    0.2822    0.1745    5.3765    1.7203</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 267:    93.8964   -6.1059   -0.1643    2.1714   -0.9877    3.7774    4.1896    0.4311    0.2822    0.1744    5.3792    1.7197</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 268:    93.8901   -6.1092   -0.1643    2.1715   -0.9876    3.7790    4.2198    0.4310    0.2822    0.1742    5.3811    1.7200</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 269:    93.8842   -6.1105   -0.1643    2.1717   -0.9875    3.7620    4.2336    0.4310    0.2823    0.1742    5.3838    1.7193</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 270:    93.8760   -6.1125   -0.1643    2.1721   -0.9874    3.7668    4.2483    0.4308    0.2823    0.1741    5.3852    1.7181</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 271:    93.8705   -6.1149   -0.1643    2.1722   -0.9873    3.7785    4.2625    0.4306    0.2823    0.1742    5.3853    1.7172</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 272:    93.8674   -6.1149   -0.1643    2.1723   -0.9871    3.7829    4.2581    0.4304    0.2823    0.1743    5.3836    1.7162</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 273:    93.8644   -6.1159   -0.1642    2.1725   -0.9870    3.7910    4.2631    0.4303    0.2825    0.1743    5.3818    1.7154</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 274:    93.8585   -6.1158   -0.1640    2.1728   -0.9869    3.7926    4.2612    0.4302    0.2825    0.1743    5.3816    1.7147</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 275:    93.8564   -6.1151   -0.1639    2.1732   -0.9867    3.8053    4.2581    0.4301    0.2826    0.1743    5.3804    1.7143</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 276:    93.8564   -6.1132   -0.1638    2.1736   -0.9866    3.7958    4.2486    0.4300    0.2827    0.1745    5.3810    1.7144</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 277:    93.8564   -6.1120   -0.1637    2.1741   -0.9867    3.7952    4.2426    0.4298    0.2829    0.1747    5.3808    1.7148</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 278:    93.8528   -6.1113   -0.1636    2.1743   -0.9867    3.7922    4.2352    0.4297    0.2832    0.1750    5.3819    1.7144</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 279:    93.8503   -6.1124   -0.1636    2.1744   -0.9867    3.7930    4.2390    0.4298    0.2834    0.1753    5.3826    1.7144</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 280:    93.8466   -6.1146   -0.1636    2.1743   -0.9867    3.7946    4.2470    0.4299    0.2838    0.1755    5.3832    1.7142</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 281:    93.8435   -6.1165   -0.1638    2.1743   -0.9867    3.7994    4.2552    0.4298    0.2840    0.1756    5.3828    1.7140</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 282:    93.8421   -6.1162   -0.1639    2.1741   -0.9868    3.7967    4.2496    0.4296    0.2843    0.1758    5.3816    1.7137</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 283:    93.8382   -6.1146   -0.1641    2.1740   -0.9866    3.7957    4.2387    0.4294    0.2845    0.1760    5.3796    1.7135</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 284:    93.8356   -6.1131   -0.1641    2.1740   -0.9865    3.7904    4.2263    0.4292    0.2848    0.1762    5.3776    1.7127</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 285:    93.8349   -6.1113   -0.1642    2.1740   -0.9865    3.7841    4.2129    0.4291    0.2850    0.1765    5.3763    1.7130</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 286:    93.8372   -6.1095   -0.1643    2.1741   -0.9864    3.7801    4.2035    0.4289    0.2853    0.1769    5.3779    1.7130</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 287:    93.8393   -6.1077   -0.1643    2.1741   -0.9864    3.7804    4.1908    0.4287    0.2857    0.1771    5.3785    1.7132</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 288:    93.8395   -6.1071   -0.1644    2.1740   -0.9866    3.7714    4.1834    0.4284    0.2859    0.1772    5.3798    1.7120</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 289:    93.8398   -6.1065   -0.1645    2.1738   -0.9865    3.7635    4.1765    0.4282    0.2861    0.1774    5.3821    1.7111</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 290:    93.8376   -6.1089   -0.1647    2.1737   -0.9865    3.7495    4.1853    0.4281    0.2863    0.1776    5.3852    1.7106</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 291:    93.8341   -6.1091   -0.1647    2.1738   -0.9863    3.7340    4.1854    0.4278    0.2865    0.1776    5.3868    1.7098</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 292:    93.8329   -6.1080   -0.1647    2.1741   -0.9863    3.7189    4.1760    0.4275    0.2868    0.1777    5.3885    1.7091</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 293:    93.8312   -6.1065   -0.1647    2.1743   -0.9862    3.7091    4.1651    0.4272    0.2871    0.1778    5.3896    1.7086</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 294:    93.8310   -6.1042   -0.1647    2.1745   -0.9861    3.6946    4.1521    0.4269    0.2874    0.1780    5.3908    1.7077</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 295:    93.8299   -6.1033   -0.1648    2.1747   -0.9861    3.6968    4.1433    0.4265    0.2880    0.1781    5.3904    1.7066</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 296:    93.8276   -6.1027   -0.1648    2.1749   -0.9862    3.7072    4.1361    0.4261    0.2885    0.1782    5.3910    1.7056</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 297:    93.8212   -6.1015   -0.1647    2.1750   -0.9859    3.7253    4.1278    0.4256    0.2891    0.1783    5.3927    1.7051</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 298:    93.8173   -6.0980   -0.1647    2.1751   -0.9858    3.7552    4.1115    0.4251    0.2897    0.1784    5.3932    1.7045</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 299:    93.8168   -6.0984   -0.1646    2.1754   -0.9856    3.7687    4.1143    0.4246    0.2902    0.1786    5.3949    1.7037</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 300:    93.8154   -6.0967   -0.1646    2.1756   -0.9855    3.7888    4.1072    0.4241    0.2906    0.1788    5.3962    1.7030</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 301:    93.8129   -6.0946   -0.1646    2.1757   -0.9852    3.8097    4.1006    0.4236    0.2911    0.1791    5.3971    1.7026</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 302:    93.8120   -6.0932   -0.1646    2.1759   -0.9849    3.8403    4.0955    0.4231    0.2917    0.1792    5.3971    1.7021</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 303:    93.8115   -6.0942   -0.1646    2.1762   -0.9850    3.8602    4.1004    0.4227    0.2922    0.1795    5.3976    1.7020</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 304:    93.8106   -6.0979   -0.1645    2.1765   -0.9850    3.8898    4.1217    0.4222    0.2926    0.1798    5.3975    1.7024</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 305:    93.8091   -6.1009   -0.1644    2.1767   -0.9851    3.9165    4.1374    0.4218    0.2931    0.1801    5.3989    1.7024</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 306:    93.8090   -6.1043   -0.1644    2.1770   -0.9850    3.9485    4.1617    0.4214    0.2936    0.1803    5.3998    1.7021</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 307:    93.8082   -6.1063   -0.1644    2.1772   -0.9850    3.9730    4.1797    0.4210    0.2940    0.1803    5.3998    1.7017</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 308:    93.8093   -6.1090   -0.1644    2.1775   -0.9850    3.9874    4.2026    0.4205    0.2945    0.1804    5.3996    1.7006</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 309:    93.8092   -6.1122   -0.1643    2.1777   -0.9849    3.9948    4.2297    0.4201    0.2948    0.1804    5.4001    1.6998</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 310:    93.8080   -6.1142   -0.1642    2.1780   -0.9850    3.9976    4.2436    0.4197    0.2951    0.1803    5.4016    1.6989</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 311:    93.8094   -6.1164   -0.1641    2.1784   -0.9851    4.0015    4.2552    0.4194    0.2952    0.1803    5.4033    1.6978</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 312:    93.8107   -6.1184   -0.1640    2.1788   -0.9851    4.0006    4.2628    0.4190    0.2954    0.1802    5.4042    1.6972</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 313:    93.8118   -6.1190   -0.1640    2.1789   -0.9851    3.9991    4.2638    0.4186    0.2955    0.1802    5.4053    1.6967</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 314:    93.8139   -6.1188   -0.1639    2.1791   -0.9851    4.0019    4.2619    0.4183    0.2956    0.1802    5.4049    1.6966</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 315:    93.8152   -6.1173   -0.1639    2.1792   -0.9851    4.0111    4.2553    0.4179    0.2957    0.1802    5.4052    1.6966</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 316:    93.8178   -6.1158   -0.1639    2.1792   -0.9851    4.0073    4.2498    0.4175    0.2957    0.1802    5.4050    1.6966</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 317:    93.8205   -6.1155   -0.1639    2.1792   -0.9851    3.9999    4.2491    0.4172    0.2957    0.1802    5.4048    1.6963</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 318:    93.8216   -6.1145   -0.1639    2.1792   -0.9850    3.9961    4.2438    0.4168    0.2957    0.1802    5.4031    1.6961</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 319:    93.8241   -6.1143   -0.1639    2.1792   -0.9849    4.0009    4.2412    0.4164    0.2958    0.1801    5.4004    1.6956</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 320:    93.8257   -6.1142   -0.1639    2.1792   -0.9849    4.0018    4.2380    0.4160    0.2958    0.1801    5.3986    1.6952</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 321:    93.8280   -6.1134   -0.1639    2.1792   -0.9849    4.0055    4.2339    0.4156    0.2959    0.1802    5.3966    1.6950</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 322:    93.8299   -6.1122   -0.1639    2.1793   -0.9848    4.0075    4.2274    0.4152    0.2959    0.1802    5.3969    1.6948</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 323:    93.8318   -6.1123   -0.1639    2.1794   -0.9848    4.0087    4.2257    0.4149    0.2960    0.1802    5.3980    1.6941</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 324:    93.8352   -6.1098   -0.1639    2.1795   -0.9848    4.0123    4.2136    0.4145    0.2960    0.1802    5.3988    1.6936</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 325:    93.8374   -6.1072   -0.1638    2.1796   -0.9848    4.0230    4.2005    0.4142    0.2961    0.1802    5.3991    1.6933</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 326:    93.8410   -6.1050   -0.1637    2.1796   -0.9849    4.0327    4.1891    0.4139    0.2963    0.1802    5.4004    1.6927</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 327:    93.8457   -6.1023   -0.1637    2.1796   -0.9849    4.0327    4.1767    0.4135    0.2964    0.1802    5.4013    1.6922</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 328:    93.8493   -6.1017   -0.1637    2.1797   -0.9850    4.0440    4.1730    0.4131    0.2964    0.1802    5.4019    1.6915</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 329:    93.8515   -6.1001   -0.1637    2.1799   -0.9851    4.0556    4.1648    0.4128    0.2963    0.1803    5.4017    1.6912</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 330:    93.8541   -6.1001   -0.1636    2.1800   -0.9852    4.0606    4.1631    0.4124    0.2962    0.1804    5.4025    1.6912</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 331:    93.8539   -6.0994   -0.1635    2.1801   -0.9854    4.0654    4.1584    0.4120    0.2961    0.1805    5.4025    1.6907</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 332:    93.8536   -6.0999   -0.1634    2.1803   -0.9854    4.0696    4.1601    0.4116    0.2960    0.1808    5.4027    1.6904</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 333:    93.8531   -6.1002   -0.1633    2.1806   -0.9853    4.0682    4.1646    0.4112    0.2960    0.1810    5.4038    1.6893</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 334:    93.8543   -6.1005   -0.1632    2.1809   -0.9852    4.0771    4.1690    0.4108    0.2960    0.1812    5.4040    1.6883</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 335:    93.8552   -6.1010   -0.1631    2.1813   -0.9851    4.0888    4.1775    0.4104    0.2960    0.1813    5.4044    1.6878</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 336:    93.8555   -6.1016   -0.1630    2.1817   -0.9850    4.0969    4.1858    0.4099    0.2961    0.1814    5.4046    1.6873</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 337:    93.8553   -6.1025   -0.1628    2.1820   -0.9849    4.1152    4.1921    0.4094    0.2962    0.1815    5.4070    1.6863</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 338:    93.8553   -6.1018   -0.1626    2.1824   -0.9848    4.1314    4.1904    0.4090    0.2963    0.1816    5.4080    1.6852</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 339:    93.8567   -6.1004   -0.1625    2.1828   -0.9847    4.1449    4.1855    0.4087    0.2964    0.1817    5.4087    1.6841</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 340:    93.8582   -6.0987   -0.1623    2.1832   -0.9846    4.1603    4.1799    0.4083    0.2965    0.1819    5.4086    1.6832</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 341:    93.8589   -6.0962   -0.1620    2.1836   -0.9844    4.1692    4.1694    0.4079    0.2965    0.1820    5.4102    1.6828</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 342:    93.8583   -6.0932   -0.1618    2.1841   -0.9844    4.1729    4.1563    0.4075    0.2966    0.1821    5.4117    1.6821</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 343:    93.8590   -6.0906   -0.1615    2.1845   -0.9844    4.1840    4.1447    0.4071    0.2966    0.1822    5.4125    1.6819</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 344:    93.8582   -6.0890   -0.1613    2.1851   -0.9845    4.1860    4.1347    0.4068    0.2968    0.1826    5.4135    1.6814</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 345:    93.8576   -6.0876   -0.1610    2.1857   -0.9846    4.1889    4.1252    0.4064    0.2969    0.1829    5.4143    1.6810</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 346:    93.8549   -6.0853   -0.1608    2.1862   -0.9845    4.1925    4.1121    0.4061    0.2969    0.1832    5.4157    1.6803</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 347:    93.8540   -6.0829   -0.1605    2.1867   -0.9845    4.2031    4.0990    0.4058    0.2970    0.1834    5.4158    1.6803</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 348:    93.8516   -6.0814   -0.1602    2.1873   -0.9845    4.2169    4.0885    0.4055    0.2971    0.1836    5.4174    1.6801</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 349:    93.8505   -6.0797   -0.1600    2.1879   -0.9845    4.2253    4.0779    0.4052    0.2971    0.1837    5.4190    1.6799</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 350:    93.8512   -6.0773   -0.1597    2.1885   -0.9845    4.2268    4.0657    0.4049    0.2972    0.1839    5.4209    1.6797</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 351:    93.8507   -6.0754   -0.1595    2.1890   -0.9845    4.2245    4.0559    0.4046    0.2972    0.1840    5.4230    1.6794</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 352:    93.8490   -6.0736   -0.1592    2.1896   -0.9845    4.2296    4.0474    0.4044    0.2973    0.1841    5.4252    1.6790</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 353:    93.8460   -6.0718   -0.1589    2.1901   -0.9845    4.2356    4.0374    0.4042    0.2973    0.1843    5.4272    1.6790</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 354:    93.8437   -6.0695   -0.1586    2.1906   -0.9845    4.2408    4.0255    0.4041    0.2973    0.1844    5.4303    1.6787</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 355:    93.8420   -6.0679   -0.1584    2.1912   -0.9844    4.2428    4.0166    0.4040    0.2973    0.1845    5.4342    1.6785</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 356:    93.8413   -6.0666   -0.1581    2.1916   -0.9843    4.2418    4.0072    0.4040    0.2973    0.1845    5.4348    1.6792</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 357:    93.8406   -6.0650   -0.1578    2.1921   -0.9844    4.2531    3.9973    0.4040    0.2973    0.1846    5.4350    1.6798</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 358:    93.8404   -6.0639   -0.1575    2.1926   -0.9844    4.2596    3.9901    0.4040    0.2973    0.1846    5.4357    1.6805</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 359:    93.8373   -6.0630   -0.1572    2.1931   -0.9845    4.2724    3.9820    0.4039    0.2973    0.1848    5.4368    1.6816</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 360:    93.8347   -6.0622   -0.1569    2.1936   -0.9845    4.2788    3.9747    0.4039    0.2973    0.1849    5.4377    1.6824</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 361:    93.8327   -6.0626   -0.1566    2.1942   -0.9846    4.2919    3.9749    0.4038    0.2973    0.1850    5.4382    1.6831</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 362:    93.8326   -6.0629   -0.1562    2.1947   -0.9846    4.2989    3.9737    0.4038    0.2973    0.1850    5.4408    1.6838</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 363:    93.8316   -6.0629   -0.1559    2.1953   -0.9846    4.3007    3.9725    0.4037    0.2972    0.1850    5.4420    1.6842</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 364:    93.8317   -6.0629   -0.1556    2.1957   -0.9846    4.2910    3.9739    0.4038    0.2971    0.1850    5.4430    1.6840</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 365:    93.8317   -6.0629   -0.1553    2.1962   -0.9846    4.2878    3.9759    0.4040    0.2967    0.1849    5.4441    1.6839</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 366:    93.8319   -6.0633   -0.1549    2.1966   -0.9845    4.2870    3.9823    0.4042    0.2963    0.1849    5.4461    1.6839</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 367:    93.8320   -6.0636   -0.1546    2.1971   -0.9845    4.2828    3.9850    0.4042    0.2959    0.1849    5.4479    1.6843</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 368:    93.8312   -6.0635   -0.1544    2.1975   -0.9844    4.2781    3.9859    0.4043    0.2955    0.1849    5.4475    1.6841</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 369:    93.8301   -6.0640   -0.1541    2.1979   -0.9844    4.2760    3.9891    0.4043    0.2953    0.1850    5.4472    1.6835</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 370:    93.8306   -6.0651   -0.1539    2.1983   -0.9844    4.2690    3.9967    0.4042    0.2950    0.1850    5.4484    1.6829</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 371:    93.8312   -6.0669   -0.1536    2.1988   -0.9845    4.2613    4.0111    0.4042    0.2947    0.1851    5.4494    1.6822</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 372:    93.8312   -6.0681   -0.1533    2.1992   -0.9845    4.2585    4.0252    0.4042    0.2945    0.1852    5.4513    1.6817</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 373:    93.8307   -6.0681   -0.1531    2.1996   -0.9846    4.2605    4.0284    0.4042    0.2943    0.1853    5.4523    1.6815</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 374:    93.8305   -6.0678   -0.1529    2.1999   -0.9846    4.2676    4.0279    0.4040    0.2942    0.1853    5.4533    1.6816</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 375:    93.8313   -6.0673   -0.1528    2.2002   -0.9846    4.2708    4.0232    0.4038    0.2940    0.1854    5.4543    1.6813</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 376:    93.8310   -6.0672   -0.1527    2.2004   -0.9846    4.2775    4.0214    0.4037    0.2938    0.1855    5.4538    1.6809</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 377:    93.8298   -6.0666   -0.1526    2.2007   -0.9846    4.2787    4.0166    0.4035    0.2937    0.1856    5.4532    1.6806</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 378:    93.8276   -6.0664   -0.1525    2.2009   -0.9846    4.2781    4.0135    0.4033    0.2935    0.1857    5.4538    1.6801</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 379:    93.8262   -6.0671   -0.1524    2.2012   -0.9846    4.2800    4.0157    0.4031    0.2932    0.1857    5.4530    1.6803</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 380:    93.8243   -6.0675   -0.1523    2.2015   -0.9846    4.2735    4.0168    0.4029    0.2929    0.1857    5.4523    1.6803</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 381:    93.8233   -6.0677   -0.1522    2.2017   -0.9845    4.2670    4.0189    0.4027    0.2926    0.1858    5.4517    1.6798</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 382:    93.8229   -6.0674   -0.1521    2.2019   -0.9845    4.2655    4.0211    0.4025    0.2924    0.1858    5.4510    1.6794</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 383:    93.8196   -6.0681   -0.1521    2.2021   -0.9843    4.2696    4.0291    0.4023    0.2922    0.1859    5.4504    1.6788</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 384:    93.8174   -6.0688   -0.1520    2.2023   -0.9842    4.2851    4.0333    0.4021    0.2919    0.1860    5.4500    1.6783</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 385:    93.8156   -6.0682   -0.1518    2.2028   -0.9840    4.3056    4.0305    0.4019    0.2920    0.1862    5.4503    1.6774</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 386:    93.8145   -6.0684   -0.1516    2.2032   -0.9838    4.3195    4.0302    0.4016    0.2920    0.1863    5.4499    1.6765</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 387:    93.8121   -6.0679   -0.1514    2.2036   -0.9837    4.3290    4.0272    0.4014    0.2920    0.1864    5.4501    1.6756</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 388:    93.8105   -6.0676   -0.1513    2.2040   -0.9835    4.3393    4.0267    0.4011    0.2920    0.1865    5.4509    1.6751</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 389:    93.8089   -6.0664   -0.1510    2.2045   -0.9833    4.3458    4.0224    0.4009    0.2920    0.1865    5.4512    1.6746</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 390:    93.8080   -6.0658   -0.1508    2.2049   -0.9832    4.3422    4.0199    0.4007    0.2920    0.1866    5.4514    1.6744</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 391:    93.8077   -6.0669   -0.1507    2.2053   -0.9831    4.3500    4.0277    0.4007    0.2920    0.1867    5.4511    1.6740</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 392:    93.8069   -6.0676   -0.1505    2.2056   -0.9831    4.3525    4.0326    0.4006    0.2920    0.1868    5.4500    1.6733</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 393:    93.8072   -6.0678   -0.1504    2.2059   -0.9830    4.3562    4.0344    0.4005    0.2920    0.1868    5.4491    1.6725</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 394:    93.8083   -6.0684   -0.1503    2.2061   -0.9829    4.3577    4.0386    0.4004    0.2920    0.1869    5.4493    1.6716</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 395:    93.8086   -6.0685   -0.1501    2.2064   -0.9828    4.3574    4.0394    0.4003    0.2920    0.1869    5.4493    1.6709</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 396:    93.8078   -6.0677   -0.1500    2.2067   -0.9827    4.3591    4.0355    0.4002    0.2921    0.1870    5.4493    1.6707</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 397:    93.8064   -6.0668   -0.1499    2.2071   -0.9825    4.3621    4.0317    0.4000    0.2922    0.1871    5.4495    1.6704</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 398:    93.8058   -6.0661   -0.1499    2.2073   -0.9823    4.3701    4.0285    0.3999    0.2923    0.1872    5.4491    1.6699</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 399:    93.8057   -6.0651   -0.1498    2.2075   -0.9822    4.3803    4.0243    0.3997    0.2924    0.1872    5.4485    1.6699</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 400:    93.8057   -6.0647   -0.1498    2.2076   -0.9820    4.3854    4.0225    0.3996    0.2924    0.1872    5.4488    1.6699</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 401:    93.8059   -6.0635   -0.1498    2.2078   -0.9819    4.3939    4.0178    0.3995    0.2925    0.1873    5.4491    1.6702</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 402:    93.8063   -6.0621   -0.1498    2.2079   -0.9818    4.4033    4.0120    0.3993    0.2926    0.1875    5.4492    1.6704</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 403:    93.8055   -6.0609   -0.1498    2.2080   -0.9816    4.4098    4.0069    0.3992    0.2926    0.1876    5.4494    1.6703</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 404:    93.8040   -6.0608   -0.1499    2.2081   -0.9815    4.4153    4.0050    0.3991    0.2927    0.1877    5.4494    1.6701</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 405:    93.8030   -6.0604   -0.1499    2.2082   -0.9814    4.4181    4.0047    0.3990    0.2928    0.1879    5.4491    1.6700</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 406:    93.8002   -6.0597   -0.1499    2.2083   -0.9812    4.4257    4.0025    0.3989    0.2928    0.1880    5.4498    1.6702</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 407:    93.7969   -6.0593   -0.1500    2.2085   -0.9810    4.4343    3.9994    0.3988    0.2928    0.1881    5.4514    1.6703</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 408:    93.7947   -6.0582   -0.1499    2.2087   -0.9809    4.4509    3.9936    0.3987    0.2927    0.1882    5.4526    1.6704</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 409:    93.7929   -6.0579   -0.1499    2.2088   -0.9807    4.4584    3.9918    0.3987    0.2926    0.1882    5.4537    1.6707</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 410:    93.7896   -6.0593   -0.1499    2.2089   -0.9807    4.4664    4.0002    0.3987    0.2925    0.1883    5.4560    1.6705</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 411:    93.7875   -6.0601   -0.1499    2.2090   -0.9807    4.4626    4.0057    0.3987    0.2924    0.1883    5.4567    1.6705</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 412:    93.7862   -6.0602   -0.1499    2.2091   -0.9808    4.4574    4.0095    0.3986    0.2923    0.1884    5.4565    1.6706</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 413:    93.7858   -6.0606   -0.1498    2.2092   -0.9808    4.4527    4.0146    0.3986    0.2922    0.1885    5.4561    1.6708</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 414:    93.7859   -6.0615   -0.1498    2.2093   -0.9808    4.4451    4.0211    0.3987    0.2921    0.1885    5.4574    1.6708</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 415:    93.7864   -6.0629   -0.1498    2.2092   -0.9808    4.4443    4.0298    0.3986    0.2921    0.1885    5.4576    1.6707</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 416:    93.7851   -6.0639   -0.1498    2.2092   -0.9808    4.4462    4.0362    0.3985    0.2920    0.1884    5.4575    1.6706</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 417:    93.7820   -6.0644   -0.1499    2.2092   -0.9808    4.4425    4.0387    0.3985    0.2920    0.1884    5.4577    1.6703</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 418:    93.7801   -6.0652   -0.1499    2.2093   -0.9808    4.4365    4.0420    0.3985    0.2920    0.1883    5.4589    1.6702</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 419:    93.7799   -6.0648   -0.1499    2.2094   -0.9807    4.4303    4.0418    0.3984    0.2921    0.1883    5.4596    1.6703</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 420:    93.7797   -6.0641   -0.1498    2.2095   -0.9807    4.4216    4.0383    0.3983    0.2921    0.1884    5.4607    1.6702</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 421:    93.7803   -6.0641   -0.1498    2.2096   -0.9808    4.4120    4.0396    0.3983    0.2922    0.1885    5.4621    1.6702</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 422:    93.7806   -6.0638   -0.1498    2.2097   -0.9809    4.4017    4.0373    0.3981    0.2923    0.1885    5.4635    1.6702</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 423:    93.7800   -6.0636   -0.1498    2.2098   -0.9810    4.3948    4.0339    0.3981    0.2923    0.1885    5.4640    1.6701</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 424:    93.7789   -6.0638   -0.1498    2.2099   -0.9810    4.3884    4.0336    0.3980    0.2924    0.1885    5.4651    1.6700</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 425:    93.7774   -6.0628   -0.1498    2.2101   -0.9810    4.3814    4.0271    0.3978    0.2925    0.1884    5.4666    1.6699</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 426:    93.7755   -6.0619   -0.1498    2.2102   -0.9811    4.3841    4.0221    0.3977    0.2925    0.1884    5.4693    1.6697</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 427:    93.7753   -6.0612   -0.1498    2.2103   -0.9810    4.3785    4.0167    0.3975    0.2926    0.1883    5.4712    1.6696</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 428:    93.7741   -6.0613   -0.1498    2.2104   -0.9810    4.3767    4.0161    0.3973    0.2926    0.1882    5.4729    1.6696</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 429:    93.7717   -6.0613   -0.1498    2.2105   -0.9810    4.3744    4.0158    0.3971    0.2926    0.1880    5.4743    1.6694</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 430:    93.7690   -6.0619   -0.1498    2.2106   -0.9809    4.3744    4.0189    0.3969    0.2926    0.1879    5.4753    1.6692</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 431:    93.7668   -6.0615   -0.1498    2.2107   -0.9809    4.3709    4.0174    0.3967    0.2925    0.1878    5.4762    1.6691</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 432:    93.7656   -6.0619   -0.1498    2.2108   -0.9809    4.3710    4.0173    0.3966    0.2926    0.1878    5.4770    1.6688</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 433:    93.7632   -6.0625   -0.1498    2.2108   -0.9809    4.3689    4.0204    0.3964    0.2926    0.1877    5.4774    1.6687</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 434:    93.7624   -6.0621   -0.1498    2.2109   -0.9808    4.3687    4.0178    0.3962    0.2926    0.1876    5.4774    1.6683</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 435:    93.7608   -6.0622   -0.1497    2.2111   -0.9808    4.3714    4.0182    0.3961    0.2927    0.1875    5.4777    1.6679</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 436:    93.7586   -6.0622   -0.1498    2.2112   -0.9808    4.3695    4.0178    0.3959    0.2928    0.1874    5.4784    1.6679</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 437:    93.7558   -6.0622   -0.1498    2.2114   -0.9809    4.3658    4.0167    0.3957    0.2928    0.1873    5.4795    1.6678</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 438:    93.7519   -6.0627   -0.1499    2.2115   -0.9808    4.3601    4.0183    0.3955    0.2928    0.1872    5.4807    1.6674</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 439:    93.7478   -6.0618   -0.1499    2.2115   -0.9807    4.3538    4.0133    0.3952    0.2928    0.1871    5.4814    1.6672</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 440:    93.7449   -6.0612   -0.1499    2.2117   -0.9805    4.3504    4.0103    0.3951    0.2928    0.1870    5.4820    1.6669</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 441:    93.7433   -6.0608   -0.1499    2.2117   -0.9805    4.3438    4.0077    0.3949    0.2928    0.1869    5.4818    1.6666</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 442:    93.7417   -6.0604   -0.1500    2.2118   -0.9805    4.3354    4.0058    0.3947    0.2927    0.1868    5.4824    1.6666</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 443:    93.7407   -6.0604   -0.1500    2.2119   -0.9805    4.3281    4.0046    0.3946    0.2927    0.1867    5.4832    1.6667</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 444:    93.7397   -6.0608   -0.1500    2.2118   -0.9805    4.3180    4.0069    0.3944    0.2928    0.1866    5.4857    1.6664</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 445:    93.7387   -6.0613   -0.1501    2.2118   -0.9805    4.3092    4.0085    0.3942    0.2929    0.1866    5.4866    1.6663</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 446:    93.7376   -6.0612   -0.1502    2.2117   -0.9805    4.3022    4.0075    0.3939    0.2930    0.1865    5.4866    1.6660</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 447:    93.7375   -6.0613   -0.1503    2.2116   -0.9806    4.2981    4.0076    0.3937    0.2931    0.1865    5.4863    1.6658</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 448:    93.7388   -6.0617   -0.1504    2.2115   -0.9806    4.2970    4.0087    0.3935    0.2932    0.1864    5.4870    1.6658</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 449:    93.7400   -6.0617   -0.1504    2.2115   -0.9807    4.2904    4.0069    0.3933    0.2933    0.1864    5.4880    1.6655</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 450:    93.7414   -6.0623   -0.1505    2.2113   -0.9808    4.2829    4.0095    0.3930    0.2935    0.1863    5.4880    1.6653</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 451:    93.7426   -6.0634   -0.1506    2.2112   -0.9808    4.2797    4.0146    0.3928    0.2936    0.1863    5.4877    1.6655</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 452:    93.7447   -6.0642   -0.1507    2.2110   -0.9809    4.2770    4.0171    0.3926    0.2938    0.1862    5.4876    1.6657</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 453:    93.7465   -6.0644   -0.1508    2.2108   -0.9810    4.2698    4.0184    0.3924    0.2940    0.1862    5.4877    1.6652</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 454:    93.7478   -6.0645   -0.1509    2.2106   -0.9810    4.2657    4.0182    0.3922    0.2941    0.1861    5.4874    1.6648</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 455:    93.7486   -6.0650   -0.1511    2.2104   -0.9811    4.2656    4.0203    0.3921    0.2943    0.1861    5.4871    1.6644</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 456:    93.7485   -6.0659   -0.1512    2.2102   -0.9812    4.2646    4.0240    0.3920    0.2945    0.1860    5.4869    1.6641</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 457:    93.7487   -6.0668   -0.1514    2.2099   -0.9813    4.2613    4.0277    0.3919    0.2946    0.1860    5.4866    1.6640</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 458:    93.7484   -6.0667   -0.1515    2.2097   -0.9812    4.2586    4.0263    0.3918    0.2947    0.1859    5.4854    1.6639</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 459:    93.7468   -6.0661   -0.1517    2.2095   -0.9812    4.2565    4.0225    0.3917    0.2948    0.1858    5.4853    1.6639</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 460:    93.7458   -6.0652   -0.1518    2.2093   -0.9812    4.2548    4.0171    0.3916    0.2950    0.1858    5.4843    1.6638</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 461:    93.7435   -6.0645   -0.1519    2.2091   -0.9811    4.2554    4.0121    0.3914    0.2951    0.1858    5.4843    1.6637</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 462:    93.7421   -6.0637   -0.1521    2.2089   -0.9811    4.2509    4.0079    0.3913    0.2953    0.1857    5.4840    1.6638</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 463:    93.7406   -6.0630   -0.1522    2.2088   -0.9810    4.2534    4.0036    0.3912    0.2955    0.1857    5.4834    1.6640</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 464:    93.7406   -6.0622   -0.1524    2.2086   -0.9810    4.2548    3.9986    0.3911    0.2956    0.1857    5.4828    1.6639</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 465:    93.7404   -6.0617   -0.1525    2.2084   -0.9810    4.2511    3.9955    0.3910    0.2958    0.1857    5.4828    1.6638</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 466:    93.7414   -6.0610   -0.1527    2.2080   -0.9809    4.2468    3.9918    0.3910    0.2959    0.1857    5.4812    1.6640</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 467:    93.7419   -6.0606   -0.1529    2.2077   -0.9810    4.2446    3.9885    0.3909    0.2960    0.1858    5.4799    1.6642</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 468:    93.7427   -6.0604   -0.1531    2.2074   -0.9809    4.2473    3.9872    0.3909    0.2961    0.1858    5.4790    1.6643</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 469:    93.7435   -6.0608   -0.1532    2.2073   -0.9810    4.2457    3.9891    0.3907    0.2963    0.1858    5.4789    1.6641</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 470:    93.7442   -6.0611   -0.1533    2.2071   -0.9811    4.2443    3.9895    0.3905    0.2965    0.1858    5.4785    1.6640</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 471:    93.7443   -6.0618   -0.1534    2.2070   -0.9812    4.2415    3.9932    0.3903    0.2967    0.1858    5.4773    1.6639</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 472:    93.7433   -6.0624   -0.1535    2.2069   -0.9812    4.2414    3.9958    0.3901    0.2969    0.1858    5.4765    1.6637</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 473:    93.7421   -6.0621   -0.1536    2.2067   -0.9811    4.2330    3.9962    0.3899    0.2970    0.1858    5.4762    1.6637</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 474:    93.7415   -6.0621   -0.1537    2.2066   -0.9812    4.2263    3.9968    0.3897    0.2972    0.1858    5.4755    1.6638</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 475:    93.7396   -6.0621   -0.1538    2.2066   -0.9812    4.2261    3.9956    0.3895    0.2973    0.1859    5.4751    1.6637</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 476:    93.7373   -6.0626   -0.1539    2.2065   -0.9811    4.2265    3.9976    0.3893    0.2975    0.1859    5.4743    1.6634</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 477:    93.7357   -6.0623   -0.1540    2.2063   -0.9811    4.2222    3.9953    0.3891    0.2976    0.1859    5.4741    1.6635</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 478:    93.7345   -6.0620   -0.1541    2.2062   -0.9811    4.2165    3.9939    0.3889    0.2978    0.1860    5.4745    1.6638</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 479:    93.7341   -6.0615   -0.1542    2.2062   -0.9812    4.2122    3.9916    0.3887    0.2979    0.1860    5.4751    1.6637</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 480:    93.7338   -6.0619   -0.1543    2.2061   -0.9812    4.2084    3.9932    0.3885    0.2980    0.1860    5.4753    1.6635</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 481:    93.7336   -6.0618   -0.1544    2.2060   -0.9812    4.2066    3.9945    0.3883    0.2981    0.1860    5.4748    1.6637</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 482:    93.7340   -6.0622   -0.1545    2.2058   -0.9812    4.2069    3.9969    0.3882    0.2983    0.1860    5.4741    1.6636</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 483:    93.7328   -6.0627   -0.1547    2.2057   -0.9813    4.2062    3.9992    0.3879    0.2985    0.1861    5.4739    1.6636</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 484:    93.7323   -6.0629   -0.1547    2.2056   -0.9813    4.2075    3.9995    0.3877    0.2987    0.1861    5.4730    1.6638</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 485:    93.7326   -6.0634   -0.1548    2.2055   -0.9814    4.2045    4.0013    0.3875    0.2989    0.1862    5.4727    1.6637</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 486:    93.7320   -6.0632   -0.1549    2.2055   -0.9814    4.2084    3.9989    0.3872    0.2991    0.1863    5.4718    1.6635</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 487:    93.7321   -6.0625   -0.1549    2.2054   -0.9814    4.2105    3.9949    0.3870    0.2993    0.1864    5.4712    1.6634</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 488:    93.7326   -6.0623   -0.1550    2.2054   -0.9814    4.2104    3.9935    0.3868    0.2996    0.1865    5.4721    1.6635</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 489:    93.7342   -6.0615   -0.1551    2.2054   -0.9815    4.2103    3.9893    0.3866    0.2998    0.1866    5.4725    1.6635</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 490:    93.7360   -6.0610   -0.1551    2.2053   -0.9815    4.2116    3.9857    0.3864    0.3001    0.1867    5.4730    1.6636</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 491:    93.7374   -6.0601   -0.1551    2.2052   -0.9815    4.2128    3.9804    0.3862    0.3003    0.1868    5.4722    1.6638</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 492:    93.7386   -6.0597   -0.1552    2.2050   -0.9816    4.2171    3.9774    0.3861    0.3006    0.1869    5.4714    1.6642</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 493:    93.7399   -6.0597   -0.1553    2.2049   -0.9816    4.2262    3.9766    0.3859    0.3008    0.1869    5.4712    1.6643</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 494:    93.7404   -6.0596   -0.1553    2.2047   -0.9816    4.2347    3.9746    0.3857    0.3011    0.1870    5.4711    1.6646</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 495:    93.7406   -6.0590   -0.1554    2.2046   -0.9817    4.2395    3.9711    0.3855    0.3014    0.1871    5.4705    1.6650</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 496:    93.7416   -6.0582   -0.1554    2.2045   -0.9817    4.2456    3.9660    0.3853    0.3017    0.1871    5.4707    1.6656</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 497:    93.7433   -6.0575   -0.1555    2.2044   -0.9818    4.2511    3.9631    0.3851    0.3020    0.1871    5.4714    1.6658</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 498:    93.7444   -6.0574   -0.1556    2.2042   -0.9817    4.2516    3.9632    0.3848    0.3023    0.1871    5.4721    1.6660</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 499:    93.7457   -6.0576   -0.1557    2.2041   -0.9817    4.2518    3.9660    0.3846    0.3025    0.1871    5.4728    1.6660</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 500:    93.7469   -6.0586   -0.1557    2.2040   -0.9817    4.2481    3.9757    0.3844    0.3028    0.1871    5.4738    1.6661</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating covariance matrix</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling EBE model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Needed Covariates:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "CMT"</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating residuals/tables</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-in"><span class="va">f_nlmixr_fomc_sfo_focei_obs</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr</a></span><span class="op">(</span><span class="va">f_mmkin_obs</span><span class="op">[</span><span class="st">"FOMC-SFO"</span>, <span class="op">]</span>, est <span class="op">=</span> <span class="st">"focei"</span><span class="op">)</span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate jacobian</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate sensitivities</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate ∂(f)/∂(η)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate ∂(R²)/∂(η)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in inner model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in inner model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in EBE model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in EBE model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling inner model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in FD model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in FD model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling EBE model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling events FD model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Needed Covariates:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "CMT"</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="font-weight: bold;">Key:</span> U: Unscaled Parameters; X: Back-transformed parameters; G: Gill difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> F: Forward difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> C: Central difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> M: Mixed forward and central difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Unscaled parameters for Omegas=chol(solve(omega));</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Diagonals are transformed, as specified by foceiControl(diagXform=)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |-----+---------------+-----------+-----------+-----------+-----------|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    #| Objective Fun |  parent_0 |  log_k_A1 |f_parent_qlogis | log_alpha |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  log_beta |sigma_parent |  sigma_A1 |        o1 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|        o2 |        o3 |        o4 |        o5 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    1</span>|     468.02617 |     1.000 |    -1.000 |   -0.9113 |   -0.8954 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8491 |   -0.8511 |   -0.8672 |   -0.8762 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8737 |   -0.8674 |   -0.8694 |   -0.8687 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.02617 |     94.00 |    -5.400 |   -0.9900 |   -0.2000 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.100 |     2.000 |     1.200 |    0.7536 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8758 |     1.189 |     1.093 |     1.127 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.02617</span> |     94.00 |  0.004517 |    0.2709 |    0.8187 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.166 |     2.000 |     1.200 |    0.7536 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8758 |     1.189 |     1.093 |     1.127 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    G|    Gill Diff. |     49.30 |     2.016 |   -0.2473 |   -0.3737 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.227 |    -27.89 |    -10.29 |     8.753 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     11.17 |    -12.52 |    -9.819 |    -8.910 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    2</span>|     4021.4865 |    0.2059 |    -1.032 |   -0.9073 |   -0.8894 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8293 |   -0.4019 |   -0.7014 |    -1.017 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.054 |   -0.6658 |   -0.7112 |   -0.7251 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     4021.4865 |     19.35 |    -5.432 |   -0.9861 |   -0.1940 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.120 |     2.449 |     1.299 |    0.6474 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7182 |     1.429 |     1.266 |     1.289 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     4021.4865</span> |     19.35 |  0.004372 |    0.2717 |    0.8237 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.329 |     2.449 |     1.299 |    0.6474 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7182 |     1.429 |     1.266 |     1.289 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    3</span>|     518.20369 |    0.9206 |    -1.003 |   -0.9109 |   -0.8948 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8471 |   -0.8062 |   -0.8506 |   -0.8903 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8917 |   -0.8473 |   -0.8535 |   -0.8543 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     518.20369 |     86.53 |    -5.403 |   -0.9896 |   -0.1994 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.102 |     2.045 |     1.210 |    0.7430 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8600 |     1.213 |     1.111 |     1.143 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     518.20369</span> |     86.53 |  0.004502 |    0.2710 |    0.8192 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.182 |     2.045 |     1.210 |    0.7430 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8600 |     1.213 |     1.111 |     1.143 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    4</span>|     467.99742 |    0.9921 |    -1.000 |   -0.9112 |   -0.8953 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8489 |   -0.8466 |   -0.8655 |   -0.8776 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8755 |   -0.8654 |   -0.8678 |   -0.8672 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     467.99742 |     93.25 |    -5.400 |   -0.9900 |   -0.1999 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.100 |     2.004 |     1.201 |    0.7526 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8742 |     1.192 |     1.095 |     1.129 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     467.99742</span> |     93.25 |  0.004515 |    0.2709 |    0.8188 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.168 |     2.004 |     1.201 |    0.7526 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8742 |     1.192 |     1.095 |     1.129 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -98.28 |     1.929 |   -0.4044 |   -0.4503 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.484 |    -29.27 |    -9.987 |     8.922 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.417 |    -11.79 |    -9.521 |    -8.343 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    5</span>|     467.67344 |    0.9967 |    -1.000 |   -0.9112 |   -0.8953 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8488 |   -0.8452 |   -0.8651 |   -0.8780 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8760 |   -0.8648 |   -0.8673 |   -0.8668 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     467.67344 |     93.69 |    -5.400 |   -0.9899 |   -0.1999 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.100 |     2.006 |     1.201 |    0.7523 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8738 |     1.192 |     1.095 |     1.129 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     467.67344</span> |     93.69 |  0.004515 |    0.2709 |    0.8188 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.168 |     2.006 |     1.201 |    0.7523 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8738 |     1.192 |     1.095 |     1.129 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -11.92 |     1.963 |   -0.3242 |   -0.4184 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.350 |    -28.16 |    -10.02 |     8.541 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.305 |    -11.80 |    -9.512 |    -8.408 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    6</span>|     467.50396 |    0.9983 |    -1.001 |   -0.9112 |   -0.8952 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8487 |   -0.8416 |   -0.8638 |   -0.8791 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8771 |   -0.8633 |   -0.8661 |   -0.8658 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     467.50396 |     93.84 |    -5.401 |   -0.9899 |   -0.1999 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.100 |     2.010 |     1.202 |    0.7514 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8729 |     1.194 |     1.097 |     1.130 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     467.50396</span> |     93.84 |  0.004514 |    0.2709 |    0.8188 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.170 |     2.010 |     1.202 |    0.7514 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8729 |     1.194 |     1.097 |     1.130 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    7</span>|     467.27231 |     1.003 |    -1.001 |   -0.9110 |   -0.8951 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8481 |   -0.8306 |   -0.8599 |   -0.8825 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8803 |   -0.8587 |   -0.8624 |   -0.8625 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     467.27231 |     94.27 |    -5.401 |   -0.9898 |   -0.1997 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.101 |     2.021 |     1.204 |    0.7489 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8700 |     1.200 |     1.101 |     1.134 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     467.27231</span> |     94.27 |  0.004510 |    0.2710 |    0.8190 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.174 |     2.021 |     1.204 |    0.7489 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8700 |     1.200 |     1.101 |     1.134 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     101.6 |     1.991 |   -0.1997 |   -0.3688 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.177 |    -25.72 |    -9.853 |     9.249 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.335 |    -11.58 |    -9.288 |    -8.277 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    8</span>|     466.36087 |    0.9961 |    -1.002 |   -0.9109 |   -0.8949 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8474 |   -0.8165 |   -0.8547 |   -0.8873 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8853 |   -0.8526 |   -0.8575 |   -0.8582 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     466.36087 |     93.63 |    -5.402 |   -0.9896 |   -0.1995 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.102 |     2.035 |     1.207 |    0.7453 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8657 |     1.207 |     1.106 |     1.139 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     466.36087</span> |     93.63 |  0.004506 |    0.2710 |    0.8191 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.180 |     2.035 |     1.207 |    0.7453 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8657 |     1.207 |     1.106 |     1.139 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -21.78 |     1.909 |   -0.3215 |   -0.4291 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.401 |    -25.38 |    -9.315 |     7.655 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     10.17 |    -11.26 |    -9.035 |    -7.894 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    9</span>|     465.79764 |     1.000 |    -1.004 |   -0.9107 |   -0.8946 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8467 |   -0.8024 |   -0.8495 |   -0.8920 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8917 |   -0.8462 |   -0.8524 |   -0.8537 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     465.79764 |     94.04 |    -5.404 |   -0.9895 |   -0.1993 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.102 |     2.049 |     1.211 |    0.7417 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8600 |     1.214 |     1.112 |     1.144 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     465.79764</span> |     94.04 |  0.004501 |    0.2710 |    0.8193 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.186 |     2.049 |     1.211 |    0.7417 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8600 |     1.214 |     1.112 |     1.144 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     54.81 |     1.910 |   -0.2489 |   -0.4009 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.285 |    -22.17 |    -8.389 |     8.020 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     7.340 |    -11.02 |    -8.786 |    -7.720 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   10</span>|     465.18897 |    0.9945 |    -1.005 |   -0.9105 |   -0.8943 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8457 |   -0.7893 |   -0.8445 |   -0.8971 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8975 |   -0.8390 |   -0.8467 |   -0.8487 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     465.18897 |     93.48 |    -5.405 |   -0.9893 |   -0.1990 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.103 |     2.062 |     1.214 |    0.7379 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8549 |     1.223 |     1.118 |     1.149 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     465.18897</span> |     93.48 |  0.004495 |    0.2711 |    0.8196 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.194 |     2.062 |     1.214 |    0.7379 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8549 |     1.223 |     1.118 |     1.149 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -47.52 |     1.834 |   -0.3684 |   -0.4503 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.489 |    -22.39 |    -7.996 |     7.294 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.249 |    -10.90 |    -8.741 |    -7.575 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   11</span>|     464.56229 |    0.9983 |    -1.006 |   -0.9102 |   -0.8940 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8445 |   -0.7772 |   -0.8401 |   -0.9030 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9029 |   -0.8305 |   -0.8400 |   -0.8430 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     464.56229 |     93.84 |    -5.406 |   -0.9890 |   -0.1986 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.105 |     2.074 |     1.216 |    0.7334 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8503 |     1.233 |     1.125 |     1.156 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     464.56229</span> |     93.84 |  0.004488 |    0.2711 |    0.8199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.204 |     2.074 |     1.216 |    0.7334 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8503 |     1.233 |     1.125 |     1.156 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   12</span>|     463.71730 |    0.9982 |    -1.009 |   -0.9098 |   -0.8933 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8424 |   -0.7583 |   -0.8332 |   -0.9128 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9112 |   -0.8167 |   -0.8291 |   -0.8337 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      463.7173 |     93.83 |    -5.409 |   -0.9885 |   -0.1980 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.107 |     2.093 |     1.220 |    0.7260 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8430 |     1.249 |     1.137 |     1.166 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      463.7173</span> |     93.83 |  0.004478 |    0.2712 |    0.8204 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.221 |     2.093 |     1.220 |    0.7260 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8430 |     1.249 |     1.137 |     1.166 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   13</span>|     461.03699 |    0.9978 |    -1.018 |   -0.9080 |   -0.8909 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8343 |   -0.6839 |   -0.8059 |   -0.9516 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9441 |   -0.7622 |   -0.7862 |   -0.7969 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     461.03699 |     93.79 |    -5.418 |   -0.9868 |   -0.1955 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.115 |     2.167 |     1.237 |    0.6968 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8141 |     1.314 |     1.184 |     1.208 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     461.03699</span> |     93.79 |  0.004435 |    0.2716 |    0.8224 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.288 |     2.167 |     1.237 |    0.6968 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8141 |     1.314 |     1.184 |     1.208 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   14</span>|     458.83693 |    0.9972 |    -1.033 |   -0.9052 |   -0.8871 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8218 |   -0.5692 |   -0.7639 |    -1.011 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9948 |   -0.6782 |   -0.7201 |   -0.7403 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.83693 |     93.74 |    -5.433 |   -0.9840 |   -0.1917 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.127 |     2.282 |     1.262 |    0.6519 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7697 |     1.414 |     1.256 |     1.272 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.83693</span> |     93.74 |  0.004371 |    0.2721 |    0.8255 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.393 |     2.282 |     1.262 |    0.6519 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7697 |     1.414 |     1.256 |     1.272 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |   0.05416 |     1.397 |   -0.2200 |   -0.5344 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.585 |    -3.387 |    -1.306 |   -0.2250 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.392 |    -3.452 |    -2.065 |    -1.405 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   15</span>|     459.30045 |    0.9957 |    -1.166 |   -0.8845 |   -0.8313 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6584 |   -0.5528 |   -0.7505 |   -0.8569 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.017 |   -0.4560 |   -0.6245 |   -0.7036 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     459.30045 |     93.60 |    -5.566 |   -0.9635 |   -0.1360 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.291 |     2.298 |     1.270 |    0.7682 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7505 |     1.678 |     1.361 |     1.313 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     459.30045</span> |     93.60 |  0.003827 |    0.2762 |    0.8729 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.881 |     2.298 |     1.270 |    0.7682 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7505 |     1.678 |     1.361 |     1.313 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   16</span>|     458.36319 |    0.9960 |    -1.071 |   -0.8992 |   -0.8719 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7770 |   -0.5045 |   -0.7383 |   -0.9927 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.023 |   -0.5931 |   -0.6727 |   -0.7107 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.36319 |     93.63 |    -5.471 |   -0.9780 |   -0.1766 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.172 |     2.347 |     1.277 |    0.6658 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7452 |     1.515 |     1.308 |     1.305 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.36319</span> |     93.63 |  0.004206 |    0.2733 |    0.8381 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.777 |     2.347 |     1.277 |    0.6658 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7452 |     1.515 |     1.308 |     1.305 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -12.30 |     1.214 |   0.06343 |   -0.2616 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6069 |   0.08029 |    0.4273 |   0.07297 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3937 |    0.3470 |    0.5201 |   0.09241 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   17</span>|     458.37724 |    0.9977 |    -1.183 |   -0.9046 |   -0.8476 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7196 |   -0.4765 |   -0.7567 |    -1.014 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9850 |   -0.5955 |   -0.6985 |   -0.7030 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.37724 |     93.79 |    -5.583 |   -0.9834 |   -0.1522 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.229 |     2.375 |     1.266 |    0.6497 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7783 |     1.513 |     1.280 |     1.314 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.37724</span> |     93.79 |  0.003762 |    0.2722 |    0.8588 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.295 |     2.375 |     1.266 |    0.6497 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7783 |     1.513 |     1.280 |     1.314 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   18</span>|     458.32800 |    0.9976 |    -1.124 |   -0.9017 |   -0.8605 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7499 |   -0.4913 |   -0.7470 |    -1.003 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.005 |   -0.5943 |   -0.6849 |   -0.7071 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|       458.328 |     93.78 |    -5.524 |   -0.9806 |   -0.1651 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.199 |     2.360 |     1.272 |    0.6583 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7608 |     1.514 |     1.295 |     1.309 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">       458.328</span> |     93.78 |  0.003990 |    0.2728 |    0.8478 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.017 |     2.360 |     1.272 |    0.6583 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7608 |     1.514 |     1.295 |     1.309 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     9.757 |     1.093 |  -0.06310 |   0.02237 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.09811 |    0.3115 |   -0.3381 |   -0.2098 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8196 |    0.7052 |   0.04425 |    0.2610 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   19</span>|     458.25889 |    0.9968 |    -1.183 |   -0.9011 |   -0.8543 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7363 |   -0.4871 |   -0.7422 |    -1.003 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.023 |   -0.6115 |   -0.6911 |   -0.7106 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.25889 |     93.70 |    -5.583 |   -0.9799 |   -0.1589 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.213 |     2.364 |     1.275 |    0.6583 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7450 |     1.494 |     1.288 |     1.305 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.25889</span> |     93.70 |  0.003760 |    0.2729 |    0.8531 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.141 |     2.364 |     1.275 |    0.6583 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7450 |     1.494 |     1.288 |     1.305 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |   -0.6125 |    0.8824 |  -0.01905 |    0.1697 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4368 |    0.4743 |   0.05191 |   -0.6440 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4868 |   -0.9136 |   -0.3225 |  -0.06220 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   20</span>|     458.18570 |    0.9978 |    -1.246 |   -0.9006 |   -0.8585 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7495 |   -0.4978 |   -0.7385 |   -0.9895 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.024 |   -0.6046 |   -0.6874 |   -0.7124 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      458.1857 |     93.79 |    -5.646 |   -0.9794 |   -0.1632 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.200 |     2.353 |     1.277 |    0.6683 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7440 |     1.502 |     1.292 |     1.303 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      458.1857</span> |     93.79 |  0.003532 |    0.2730 |    0.8495 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.022 |     2.353 |     1.277 |    0.6683 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7440 |     1.502 |     1.292 |     1.303 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   21</span>|     458.13464 |    0.9963 |    -1.435 |   -0.8992 |   -0.8705 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7875 |   -0.5278 |   -0.7264 |   -0.9531 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.031 |   -0.5901 |   -0.6784 |   -0.7184 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.13464 |     93.65 |    -5.835 |   -0.9781 |   -0.1751 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.162 |     2.323 |     1.284 |    0.6957 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7378 |     1.519 |     1.302 |     1.296 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.13464</span> |     93.65 |  0.002922 |    0.2733 |    0.8394 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.685 |     2.323 |     1.284 |    0.6957 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7378 |     1.519 |     1.302 |     1.296 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -15.15 |    0.2290 |    0.2476 |   -0.1455 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7111 |    -1.138 |    0.8895 |     1.880 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.837 |    0.2029 |    0.3485 |   -0.6531 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   22</span>|     458.67841 |    0.9992 |    -1.651 |   -0.9027 |   -0.9245 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8733 |   -0.5209 |   -0.7232 |    -1.036 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.001 |   -0.6164 |   -0.6125 |   -0.6771 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.67841 |     93.92 |    -6.051 |   -0.9815 |   -0.2291 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.076 |     2.330 |     1.286 |    0.6333 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7647 |     1.488 |     1.374 |     1.343 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.67841</span> |     93.92 |  0.002355 |    0.2726 |    0.7952 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     7.971 |     2.330 |     1.286 |    0.6333 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7647 |     1.488 |     1.374 |     1.343 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   23</span>|     458.25487 |     1.002 |    -1.469 |   -0.8998 |   -0.8789 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8006 |   -0.5263 |   -0.7262 |   -0.9666 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5942 |   -0.6683 |   -0.7117 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.25487 |     94.17 |    -5.869 |   -0.9787 |   -0.1835 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.148 |     2.325 |     1.285 |    0.6855 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7425 |     1.514 |     1.313 |     1.304 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.25487</span> |     94.17 |  0.002825 |    0.2732 |    0.8324 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.572 |     2.325 |     1.285 |    0.6855 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7425 |     1.514 |     1.313 |     1.304 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   24</span>|     458.28425 |     1.002 |    -1.442 |   -0.8994 |   -0.8721 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7899 |   -0.5271 |   -0.7267 |   -0.9564 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.030 |   -0.5910 |   -0.6765 |   -0.7168 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.28425 |     94.21 |    -5.842 |   -0.9783 |   -0.1768 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.159 |     2.324 |     1.284 |    0.6932 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7392 |     1.518 |     1.304 |     1.298 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.28425</span> |     94.21 |  0.002902 |    0.2732 |    0.8380 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.664 |     2.324 |     1.284 |    0.6932 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7392 |     1.518 |     1.304 |     1.298 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   25</span>|     458.13208 |    0.9983 |    -1.435 |   -0.8993 |   -0.8705 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7874 |   -0.5276 |   -0.7266 |   -0.9533 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.031 |   -0.5901 |   -0.6785 |   -0.7183 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.13208 |     93.84 |    -5.835 |   -0.9781 |   -0.1751 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.162 |     2.323 |     1.284 |    0.6955 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7380 |     1.519 |     1.302 |     1.296 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.13208</span> |     93.84 |  0.002922 |    0.2733 |    0.8394 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.686 |     2.323 |     1.284 |    0.6955 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7380 |     1.519 |     1.302 |     1.296 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     13.75 |    0.2391 |    0.3431 |   -0.1129 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6079 |    -1.684 |    0.2301 |     1.587 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9924 |   -0.4675 |    0.3927 |   -0.7567 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   26</span>|     458.12484 |    0.9973 |    -1.435 |   -0.8993 |   -0.8705 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7874 |   -0.5275 |   -0.7266 |   -0.9535 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.031 |   -0.5900 |   -0.6785 |   -0.7182 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.12484 |     93.75 |    -5.835 |   -0.9782 |   -0.1751 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.162 |     2.324 |     1.284 |    0.6954 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7379 |     1.519 |     1.302 |     1.297 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.12484</span> |     93.75 |  0.002922 |    0.2733 |    0.8394 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.686 |     2.324 |     1.284 |    0.6954 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7379 |     1.519 |     1.302 |     1.297 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |   -0.4576 |    0.2336 |    0.2904 |   -0.1274 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6585 |    -1.014 |    0.9040 |     1.932 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.695 |    0.2950 |    0.3980 |   -0.7211 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   27</span>|     458.12349 |    0.9975 |    -1.436 |   -0.8994 |   -0.8704 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7872 |   -0.5272 |   -0.7269 |   -0.9542 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.031 |   -0.5901 |   -0.6787 |   -0.7180 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.12349 |     93.76 |    -5.836 |   -0.9783 |   -0.1751 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.162 |     2.324 |     1.284 |    0.6949 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7384 |     1.519 |     1.302 |     1.297 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.12349</span> |     93.76 |  0.002922 |    0.2732 |    0.8394 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.688 |     2.324 |     1.284 |    0.6949 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7384 |     1.519 |     1.302 |     1.297 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     1.734 |    0.2328 |    0.2907 |   -0.1244 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6480 |   -0.5259 |     1.203 |     2.086 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.687 |    0.2714 |    0.3976 |   -0.7167 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   28</span>|     458.12069 |    0.9972 |    -1.436 |   -0.8995 |   -0.8704 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7868 |   -0.5270 |   -0.7278 |   -0.9557 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.030 |   -0.5902 |   -0.6785 |   -0.7174 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.12069 |     93.74 |    -5.836 |   -0.9784 |   -0.1750 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.162 |     2.324 |     1.284 |    0.6937 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7392 |     1.519 |     1.302 |     1.297 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.12069</span> |     93.74 |  0.002921 |    0.2732 |    0.8394 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.691 |     2.324 |     1.284 |    0.6937 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7392 |     1.519 |     1.302 |     1.297 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.995 |    0.2265 |    0.2648 |   -0.1319 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6577 |    -1.056 |    0.5532 |     1.629 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4851 |    0.2582 |    0.3806 |   -0.6784 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   29</span>|     458.12793 |    0.9986 |    -1.436 |   -0.8997 |   -0.8703 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7863 |   -0.5263 |   -0.7282 |   -0.9568 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.029 |   -0.5904 |   -0.6788 |   -0.7169 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.12793 |     93.87 |    -5.836 |   -0.9785 |   -0.1749 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.163 |     2.325 |     1.283 |    0.6929 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7395 |     1.519 |     1.302 |     1.298 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.12793</span> |     93.87 |  0.002920 |    0.2732 |    0.8395 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.695 |     2.325 |     1.283 |    0.6929 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7395 |     1.519 |     1.302 |     1.298 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   30</span>|     458.12002 |    0.9975 |    -1.436 |   -0.8996 |   -0.8704 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7866 |   -0.5269 |   -0.7279 |   -0.9559 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.030 |   -0.5903 |   -0.6786 |   -0.7173 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.12002 |     93.77 |    -5.836 |   -0.9784 |   -0.1750 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.162 |     2.324 |     1.284 |    0.6935 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7392 |     1.519 |     1.302 |     1.298 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.12002</span> |     93.77 |  0.002921 |    0.2732 |    0.8395 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.692 |     2.324 |     1.284 |    0.6935 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7392 |     1.519 |     1.302 |     1.298 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     2.647 |    0.2293 |    0.2815 |   -0.1267 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6372 |   -0.9129 |    0.8290 |     1.823 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.624 |    0.2659 |    0.4184 |   -0.6478 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   31</span>|     458.11922 |    0.9973 |    -1.436 |   -0.8996 |   -0.8704 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7866 |   -0.5268 |   -0.7280 |   -0.9563 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.029 |   -0.5903 |   -0.6786 |   -0.7171 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.11922 |     93.74 |    -5.836 |   -0.9784 |   -0.1750 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.163 |     2.324 |     1.284 |    0.6933 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7394 |     1.519 |     1.302 |     1.298 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.11922</span> |     93.74 |  0.002920 |    0.2732 |    0.8395 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.693 |     2.324 |     1.284 |    0.6933 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7394 |     1.519 |     1.302 |     1.298 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |   -0.8024 |    0.2274 |    0.2665 |   -0.1307 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6475 |    -1.019 |    0.5594 |     1.618 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.681 |    0.3232 |    0.3907 |   -0.6658 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   32</span>|     458.11869 |    0.9974 |    -1.436 |   -0.8996 |   -0.8703 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7864 |   -0.5266 |   -0.7281 |   -0.9565 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.029 |   -0.5904 |   -0.6787 |   -0.7170 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.11869 |     93.76 |    -5.836 |   -0.9785 |   -0.1750 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.163 |     2.324 |     1.283 |    0.6931 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7397 |     1.519 |     1.302 |     1.298 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.11869</span> |     93.76 |  0.002920 |    0.2732 |    0.8395 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.694 |     2.324 |     1.283 |    0.6931 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7397 |     1.519 |     1.302 |     1.298 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     1.112 |    0.2271 |    0.2694 |   -0.1286 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6401 |    -1.529 |    0.3516 |     1.832 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3653 |    0.2635 |    0.3774 |   -0.6589 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   33</span>|     458.11797 |    0.9972 |    -1.436 |   -0.8997 |   -0.8703 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7863 |   -0.5263 |   -0.7282 |   -0.9569 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.029 |   -0.5904 |   -0.6788 |   -0.7169 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.11797 |     93.74 |    -5.836 |   -0.9785 |   -0.1749 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.163 |     2.325 |     1.283 |    0.6928 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7397 |     1.519 |     1.302 |     1.298 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.11797</span> |     93.74 |  0.002920 |    0.2732 |    0.8395 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.695 |     2.325 |     1.283 |    0.6928 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7397 |     1.519 |     1.302 |     1.298 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.528 |    0.2260 |    0.2581 |   -0.1311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6478 |    -1.074 |    0.7096 |     1.705 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.609 |    0.2690 |    0.3809 |   -0.6419 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   34</span>|     458.11744 |    0.9975 |    -1.436 |   -0.8997 |   -0.8703 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7862 |   -0.5262 |   -0.7283 |   -0.9571 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.029 |   -0.5904 |   -0.6788 |   -0.7168 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.11744 |     93.76 |    -5.836 |   -0.9786 |   -0.1749 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.163 |     2.325 |     1.283 |    0.6926 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7399 |     1.519 |     1.302 |     1.298 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.11744</span> |     93.76 |  0.002920 |    0.2732 |    0.8395 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.696 |     2.325 |     1.283 |    0.6926 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7399 |     1.519 |     1.302 |     1.298 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     1.737 |    0.2262 |    0.2659 |   -0.1276 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6346 |   -0.9458 |    0.5100 |     1.567 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.640 |    0.3111 |    0.3526 |   -0.6225 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   35</span>|     458.11714 |    0.9972 |    -1.436 |   -0.8998 |   -0.8703 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7861 |   -0.5260 |   -0.7284 |   -0.9574 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.029 |   -0.5905 |   -0.6789 |   -0.7167 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.11714 |     93.74 |    -5.836 |   -0.9786 |   -0.1749 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.163 |     2.325 |     1.283 |    0.6924 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7402 |     1.519 |     1.302 |     1.298 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.11714</span> |     93.74 |  0.002920 |    0.2732 |    0.8395 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.697 |     2.325 |     1.283 |    0.6924 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7402 |     1.519 |     1.302 |     1.298 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.976 |    0.2241 |    0.2491 |   -0.1309 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6467 |   -0.8649 |    0.8521 |     1.757 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.585 |    0.2641 |    0.3618 |   -0.6092 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   36</span>|     458.11663 |    0.9975 |    -1.436 |   -0.8998 |   -0.8703 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7860 |   -0.5259 |   -0.7285 |   -0.9576 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.028 |   -0.5905 |   -0.6789 |   -0.7166 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.11663 |     93.76 |    -5.836 |   -0.9787 |   -0.1749 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.163 |     2.325 |     1.283 |    0.6923 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7404 |     1.518 |     1.301 |     1.298 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.11663</span> |     93.76 |  0.002920 |    0.2732 |    0.8396 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.698 |     2.325 |     1.283 |    0.6923 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7404 |     1.518 |     1.301 |     1.298 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     2.001 |    0.2249 |    0.2609 |   -0.1271 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6310 |   -0.8317 |    0.8110 |     1.745 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3162 |    0.2564 |    0.3586 |   -0.6097 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   37</span>|     458.11610 |    0.9971 |    -1.436 |   -0.8999 |   -0.8702 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7859 |   -0.5258 |   -0.7286 |   -0.9579 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.028 |   -0.5906 |   -0.6790 |   -0.7165 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      458.1161 |     93.73 |    -5.836 |   -0.9787 |   -0.1749 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.163 |     2.325 |     1.283 |    0.6920 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7404 |     1.518 |     1.301 |     1.298 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      458.1161</span> |     93.73 |  0.002920 |    0.2732 |    0.8396 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.699 |     2.325 |     1.283 |    0.6920 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7404 |     1.518 |     1.301 |     1.298 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -2.471 |    0.2228 |    0.2417 |   -0.1310 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6447 |    -1.605 |  0.009434 |     1.531 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.615 |    0.2909 |    0.3664 |   -0.6093 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   38</span>|     458.11531 |    0.9974 |    -1.436 |   -0.8999 |   -0.8702 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7859 |   -0.5256 |   -0.7287 |   -0.9582 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.028 |   -0.5905 |   -0.6791 |   -0.7164 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.11531 |     93.76 |    -5.836 |   -0.9788 |   -0.1749 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.163 |     2.326 |     1.283 |    0.6918 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7405 |     1.518 |     1.301 |     1.299 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.11531</span> |     93.76 |  0.002919 |    0.2731 |    0.8396 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.699 |     2.326 |     1.283 |    0.6918 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7405 |     1.518 |     1.301 |     1.299 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     1.540 |    0.2236 |    0.2522 |   -0.1268 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6308 |   -0.9638 |    0.7239 |     1.714 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.540 |    0.2567 |    0.3599 |   -0.5942 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   39</span>|     458.11496 |    0.9972 |    -1.436 |   -0.8999 |   -0.8702 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7858 |   -0.5254 |   -0.7288 |   -0.9585 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.028 |   -0.5906 |   -0.6791 |   -0.7163 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.11496 |     93.74 |    -5.836 |   -0.9788 |   -0.1748 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.163 |     2.326 |     1.283 |    0.6916 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7408 |     1.518 |     1.301 |     1.299 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.11496</span> |     93.74 |  0.002919 |    0.2731 |    0.8396 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.700 |     2.326 |     1.283 |    0.6916 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7408 |     1.518 |     1.301 |     1.299 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.772 |    0.2217 |    0.2376 |   -0.1305 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6412 |    -1.736 |    0.1210 |     1.307 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.540 |    0.2780 |    0.3483 |   -0.5916 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   40</span>|     458.11458 |    0.9975 |    -1.436 |   -0.9000 |   -0.8702 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7857 |   -0.5252 |   -0.7289 |   -0.9587 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.028 |   -0.5906 |   -0.6792 |   -0.7163 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.11458 |     93.76 |    -5.836 |   -0.9788 |   -0.1748 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.163 |     2.326 |     1.283 |    0.6915 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7410 |     1.518 |     1.301 |     1.299 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.11458</span> |     93.76 |  0.002919 |    0.2731 |    0.8396 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.701 |     2.326 |     1.283 |    0.6915 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7410 |     1.518 |     1.301 |     1.299 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     1.896 |    0.2223 |    0.2476 |   -0.1262 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6268 |   -0.4531 |    0.7600 |     1.698 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.483 |    0.2984 |    0.3374 |   -0.6006 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   41</span>|     458.11430 |    0.9972 |    -1.436 |   -0.9000 |   -0.8702 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7856 |   -0.5251 |   -0.7290 |   -0.9589 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.027 |   -0.5907 |   -0.6792 |   -0.7162 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      458.1143 |     93.73 |    -5.836 |   -0.9789 |   -0.1748 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.164 |     2.326 |     1.283 |    0.6913 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7412 |     1.518 |     1.301 |     1.299 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      458.1143</span> |     93.73 |  0.002919 |    0.2731 |    0.8396 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.702 |     2.326 |     1.283 |    0.6913 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7412 |     1.518 |     1.301 |     1.299 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -2.121 |    0.2201 |    0.2306 |   -0.1307 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6392 |   -0.8820 |    0.4853 |     1.475 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.568 |    0.2856 |    0.3442 |   -0.5942 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   42</span>|     458.11392 |    0.9975 |    -1.437 |   -0.9001 |   -0.8702 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7855 |   -0.5250 |   -0.7290 |   -0.9592 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.027 |   -0.5907 |   -0.6793 |   -0.7161 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.11392 |     93.76 |    -5.837 |   -0.9789 |   -0.1748 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.164 |     2.326 |     1.283 |    0.6911 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7414 |     1.518 |     1.301 |     1.299 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.11392</span> |     93.76 |  0.002919 |    0.2731 |    0.8396 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.702 |     2.326 |     1.283 |    0.6911 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7414 |     1.518 |     1.301 |     1.299 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     2.218 |    0.2203 |    0.2415 |   -0.1269 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6242 |    -1.700 |   -0.1298 |     1.090 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.097 |   -0.4639 |    0.3406 |   -0.5940 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   43</span>|     458.11292 |    0.9972 |    -1.437 |   -0.9001 |   -0.8702 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7854 |   -0.5247 |   -0.7291 |   -0.9594 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.027 |   -0.5906 |   -0.6794 |   -0.7160 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.11292 |     93.74 |    -5.837 |   -0.9790 |   -0.1748 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.164 |     2.326 |     1.283 |    0.6909 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7412 |     1.518 |     1.301 |     1.299 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.11292</span> |     93.74 |  0.002919 |    0.2731 |    0.8396 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.703 |     2.326 |     1.283 |    0.6909 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7412 |     1.518 |     1.301 |     1.299 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.114 |    0.2202 |    0.2288 |   -0.1307 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6328 |   -0.7727 |    0.7964 |     1.664 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2569 |    0.2827 |    0.3132 |   -0.5835 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   44</span>|     458.11219 |    0.9975 |    -1.437 |   -0.9002 |   -0.8701 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7853 |   -0.5246 |   -0.7293 |   -0.9597 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.027 |   -0.5907 |   -0.6795 |   -0.7158 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.11219 |     93.76 |    -5.837 |   -0.9790 |   -0.1748 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.164 |     2.327 |     1.283 |    0.6907 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7413 |     1.518 |     1.301 |     1.299 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.11219</span> |     93.76 |  0.002919 |    0.2731 |    0.8397 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.704 |     2.327 |     1.283 |    0.6907 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7413 |     1.518 |     1.301 |     1.299 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     1.924 |    0.2207 |    0.2357 |   -0.1254 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6202 |   -0.4451 |    0.8288 |     1.643 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.561 |    0.2786 |    0.3167 |   -0.5797 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   45</span>|     458.11161 |    0.9972 |    -1.437 |   -0.9002 |   -0.8701 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7852 |   -0.5246 |   -0.7295 |   -0.9601 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.027 |   -0.5907 |   -0.6795 |   -0.7157 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.11161 |     93.74 |    -5.837 |   -0.9791 |   -0.1747 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.164 |     2.327 |     1.283 |    0.6904 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7414 |     1.518 |     1.301 |     1.299 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.11161</span> |     93.74 |  0.002918 |    0.2731 |    0.8397 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.705 |     2.327 |     1.283 |    0.6904 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7414 |     1.518 |     1.301 |     1.299 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.221 |    0.2188 |    0.2217 |   -0.1291 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6301 |    -1.629 |    0.1332 |     1.223 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.491 |    0.2969 |    0.3215 |   -0.5516 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   46</span>|     458.11130 |    0.9974 |    -1.437 |   -0.9003 |   -0.8701 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7851 |   -0.5243 |   -0.7295 |   -0.9603 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.027 |   -0.5907 |   -0.6796 |   -0.7156 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      458.1113 |     93.76 |    -5.837 |   -0.9791 |   -0.1747 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.164 |     2.327 |     1.283 |    0.6902 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7416 |     1.518 |     1.301 |     1.299 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      458.1113</span> |     93.76 |  0.002918 |    0.2731 |    0.8397 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.706 |     2.327 |     1.283 |    0.6902 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7416 |     1.518 |     1.301 |     1.299 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     1.605 |    0.2188 |    0.2279 |   -0.1262 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6191 |    -1.596 |    0.1216 |     1.254 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.158 |   -0.4084 |    0.3005 |   -0.5648 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   47</span>|     458.11059 |    0.9972 |    -1.437 |   -0.9003 |   -0.8701 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7850 |   -0.5240 |   -0.7295 |   -0.9605 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.027 |   -0.5906 |   -0.6796 |   -0.7155 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.11059 |     93.73 |    -5.837 |   -0.9791 |   -0.1747 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.164 |     2.327 |     1.283 |    0.6901 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7415 |     1.518 |     1.301 |     1.300 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.11059</span> |     93.73 |  0.002918 |    0.2731 |    0.8397 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.707 |     2.327 |     1.283 |    0.6901 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7415 |     1.518 |     1.301 |     1.300 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -2.022 |    0.2179 |    0.2140 |   -0.1317 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6294 |   -0.4112 |    0.8588 |     1.629 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.524 |    0.3410 |    0.3226 |   -0.5400 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   48</span>|     458.10995 |    0.9975 |    -1.437 |   -0.9003 |   -0.8701 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7849 |   -0.5239 |   -0.7297 |   -0.9608 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.027 |   -0.5906 |   -0.6797 |   -0.7154 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.10995 |     93.76 |    -5.837 |   -0.9792 |   -0.1747 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.164 |     2.327 |     1.283 |    0.6899 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7416 |     1.518 |     1.301 |     1.300 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.10995</span> |     93.76 |  0.002918 |    0.2731 |    0.8397 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.708 |     2.327 |     1.283 |    0.6899 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7416 |     1.518 |     1.301 |     1.300 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     1.872 |    0.2188 |    0.2245 |   -0.1278 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6141 |    -1.055 |    0.5121 |     1.433 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.504 |    0.3641 |    0.3235 |   -0.5302 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   49</span>|     458.10978 |    0.9972 |    -1.437 |   -0.9004 |   -0.8700 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7848 |   -0.5238 |   -0.7297 |   -0.9610 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.027 |   -0.5907 |   -0.6797 |   -0.7153 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.10978 |     93.73 |    -5.837 |   -0.9792 |   -0.1747 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.164 |     2.327 |     1.282 |    0.6897 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7418 |     1.518 |     1.301 |     1.300 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.10978</span> |     93.73 |  0.002918 |    0.2730 |    0.8397 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.709 |     2.327 |     1.282 |    0.6897 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7418 |     1.518 |     1.301 |     1.300 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    C| Central Diff. |    -2.410 |    0.2142 |    0.1743 |   -0.1457 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6441 |   -0.7806 |    0.7426 |     1.188 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.06181 |   -0.3659 |    0.5158 |   -0.5913 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   50</span>|     458.10914 |    0.9975 |    -1.437 |   -0.9004 |   -0.8700 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7847 |   -0.5236 |   -0.7299 |   -0.9612 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.027 |   -0.5906 |   -0.6798 |   -0.7152 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.10914 |     93.77 |    -5.837 |   -0.9792 |   -0.1747 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.164 |     2.327 |     1.282 |    0.6895 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7418 |     1.518 |     1.300 |     1.300 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.10914</span> |     93.77 |  0.002918 |    0.2730 |    0.8397 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.709 |     2.327 |     1.282 |    0.6895 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7418 |     1.518 |     1.300 |     1.300 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     2.690 |    0.2171 |    0.2203 |   -0.1291 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6099 |   -0.7940 |    0.4528 |     1.377 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.452 |    0.2831 |    0.2911 |   -0.5300 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   51</span>|     458.10832 |    0.9972 |    -1.437 |   -0.9004 |   -0.8700 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7846 |   -0.5235 |   -0.7300 |   -0.9615 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.027 |   -0.5905 |   -0.6799 |   -0.7151 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.10832 |     93.74 |    -5.837 |   -0.9793 |   -0.1747 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.164 |     2.328 |     1.282 |    0.6893 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7418 |     1.518 |     1.300 |     1.300 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.10832</span> |     93.74 |  0.002918 |    0.2730 |    0.8397 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.710 |     2.328 |     1.282 |    0.6893 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7418 |     1.518 |     1.300 |     1.300 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.386 |    0.2164 |    0.2066 |   -0.1317 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6210 |   -0.6390 |    0.8493 |     1.598 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.491 |    0.2964 |    0.2278 |   -0.4988 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   52</span>|     458.10801 |    0.9974 |    -1.437 |   -0.9005 |   -0.8700 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7845 |   -0.5234 |   -0.7302 |   -0.9618 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5906 |   -0.6800 |   -0.7150 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.10801 |     93.76 |    -5.837 |   -0.9793 |   -0.1746 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.165 |     2.328 |     1.282 |    0.6891 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7421 |     1.518 |     1.300 |     1.300 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.10801</span> |     93.76 |  0.002918 |    0.2730 |    0.8398 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.711 |     2.328 |     1.282 |    0.6891 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7421 |     1.518 |     1.300 |     1.300 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     1.854 |    0.2167 |    0.2150 |   -0.1282 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6083 |   -0.7461 |    0.7029 |     1.539 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1981 |    0.2733 |    0.2717 |   -0.5257 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   53</span>|     458.10765 |    0.9971 |    -1.437 |   -0.9005 |   -0.8700 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7844 |   -0.5232 |   -0.7303 |   -0.9621 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5906 |   -0.6800 |   -0.7149 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.10765 |     93.73 |    -5.837 |   -0.9794 |   -0.1746 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.165 |     2.328 |     1.282 |    0.6889 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7421 |     1.518 |     1.300 |     1.300 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.10765</span> |     93.73 |  0.002917 |    0.2730 |    0.8398 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.712 |     2.328 |     1.282 |    0.6889 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7421 |     1.518 |     1.300 |     1.300 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -2.678 |    0.2148 |    0.1964 |   -0.1329 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6222 |   -0.7140 |    0.5004 |     1.353 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2647 |    0.3470 |    0.3026 |   -0.5041 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   54</span>|     458.10677 |    0.9974 |    -1.437 |   -0.9006 |   -0.8700 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7843 |   -0.5231 |   -0.7305 |   -0.9624 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5907 |   -0.6801 |   -0.7148 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.10677 |     93.76 |    -5.837 |   -0.9794 |   -0.1746 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.165 |     2.328 |     1.282 |    0.6887 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7421 |     1.518 |     1.300 |     1.300 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.10677</span> |     93.76 |  0.002917 |    0.2730 |    0.8398 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.713 |     2.328 |     1.282 |    0.6887 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7421 |     1.518 |     1.300 |     1.300 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     1.911 |    0.2164 |    0.2105 |   -0.1281 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6034 |   -0.7920 |    0.6471 |     1.488 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2445 |    0.2110 |    0.2380 |   -0.4469 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   55</span>|     458.10609 |    0.9972 |    -1.437 |   -0.9006 |   -0.8700 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7842 |   -0.5230 |   -0.7306 |   -0.9627 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5907 |   -0.6801 |   -0.7147 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.10609 |     93.74 |    -5.837 |   -0.9794 |   -0.1746 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.165 |     2.328 |     1.282 |    0.6884 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7421 |     1.518 |     1.300 |     1.300 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.10609</span> |     93.74 |  0.002917 |    0.2730 |    0.8398 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.714 |     2.328 |     1.282 |    0.6884 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7421 |     1.518 |     1.300 |     1.300 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.589 |    0.2148 |    0.1951 |   -0.1322 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6145 |   -0.8575 |    0.5942 |     1.427 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.492 |    0.2699 |    0.1872 |   -0.4796 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   56</span>|     458.10567 |    0.9974 |    -1.437 |   -0.9006 |   -0.8699 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7841 |   -0.5228 |   -0.7308 |   -0.9630 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5907 |   -0.6801 |   -0.7146 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.10567 |     93.76 |    -5.837 |   -0.9795 |   -0.1746 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.165 |     2.328 |     1.282 |    0.6882 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7422 |     1.518 |     1.300 |     1.301 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.10567</span> |     93.76 |  0.002917 |    0.2730 |    0.8398 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.715 |     2.328 |     1.282 |    0.6882 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7422 |     1.518 |     1.300 |     1.301 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     1.760 |    0.2153 |    0.2043 |   -0.1283 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6005 |   -0.7529 |    0.6292 |     1.454 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2107 |    0.2534 |    0.2280 |   -0.4318 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   57</span>|     458.10540 |    0.9971 |    -1.437 |   -0.9007 |   -0.8699 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7840 |   -0.5227 |   -0.7309 |   -0.9633 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5907 |   -0.6801 |   -0.7146 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      458.1054 |     93.73 |    -5.837 |   -0.9795 |   -0.1745 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.165 |     2.328 |     1.282 |    0.6880 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.518 |     1.300 |     1.301 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      458.1054</span> |     93.73 |  0.002917 |    0.2730 |    0.8398 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.716 |     2.328 |     1.282 |    0.6880 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.518 |     1.300 |     1.301 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -2.789 |    0.2130 |    0.1850 |   -0.1339 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6160 |   -0.6903 |    0.7130 |     1.481 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1975 |    0.2824 |    0.2646 |   -0.4868 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   58</span>|     458.10444 |    0.9974 |    -1.437 |   -0.9007 |   -0.8699 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7839 |   -0.5226 |   -0.7311 |   -0.9636 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5907 |   -0.6801 |   -0.7145 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.10444 |     93.76 |    -5.837 |   -0.9796 |   -0.1745 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.165 |     2.329 |     1.282 |    0.6878 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.518 |     1.300 |     1.301 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.10444</span> |     93.76 |  0.002916 |    0.2730 |    0.8398 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.717 |     2.329 |     1.282 |    0.6878 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.518 |     1.300 |     1.301 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     1.285 |    0.2143 |    0.1972 |   -0.1290 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5987 |   -0.7355 |    0.6064 |     1.421 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.460 |    0.2526 |    0.2188 |   -0.4206 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   59</span>|     458.10435 |    0.9972 |    -1.437 |   -0.9008 |   -0.8699 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7838 |   -0.5224 |   -0.7312 |   -0.9638 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5908 |   -0.6802 |   -0.7144 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.10435 |     93.73 |    -5.837 |   -0.9796 |   -0.1745 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.165 |     2.329 |     1.282 |    0.6876 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7425 |     1.518 |     1.300 |     1.301 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.10435</span> |     93.73 |  0.002916 |    0.2730 |    0.8399 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.718 |     2.329 |     1.282 |    0.6876 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7425 |     1.518 |     1.300 |     1.301 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    C| Central Diff. |    -2.184 |    0.2097 |    0.1486 |   -0.1446 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6255 |   -0.7028 |    0.6444 |     1.495 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.416 |    0.3010 |    0.4942 |   -0.5303 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   60</span>|     458.10393 |    0.9975 |    -1.438 |   -0.9008 |   -0.8699 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7837 |   -0.5223 |   -0.7313 |   -0.9641 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5908 |   -0.6802 |   -0.7143 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.10393 |     93.76 |    -5.838 |   -0.9796 |   -0.1745 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.165 |     2.329 |     1.282 |    0.6873 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7426 |     1.518 |     1.300 |     1.301 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.10393</span> |     93.76 |  0.002916 |    0.2730 |    0.8399 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.718 |     2.329 |     1.282 |    0.6873 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7426 |     1.518 |     1.300 |     1.301 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     2.045 |    0.2119 |    0.1940 |   -0.1305 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5955 |   -0.7252 |    0.6285 |     1.413 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2070 |    0.2194 |    0.2208 |   -0.3980 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   61</span>|     458.10364 |    0.9971 |    -1.438 |   -0.9008 |   -0.8699 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7836 |   -0.5222 |   -0.7314 |   -0.9644 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5908 |   -0.6803 |   -0.7142 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.10364 |     93.73 |    -5.838 |   -0.9796 |   -0.1745 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.166 |     2.329 |     1.281 |    0.6871 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7427 |     1.518 |     1.300 |     1.301 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.10364</span> |     93.73 |  0.002916 |    0.2730 |    0.8399 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.719 |     2.329 |     1.281 |    0.6871 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7427 |     1.518 |     1.300 |     1.301 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -2.727 |    0.2108 |    0.1771 |   -0.1339 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6094 |   -0.7304 |    0.6317 |     1.376 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.443 |    0.2644 |    0.2580 |   -0.4629 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   62</span>|     458.10286 |    0.9974 |    -1.438 |   -0.9008 |   -0.8699 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7835 |   -0.5221 |   -0.7316 |   -0.9647 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5908 |   -0.6803 |   -0.7141 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.10286 |     93.75 |    -5.838 |   -0.9796 |   -0.1745 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.166 |     2.329 |     1.281 |    0.6869 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7427 |     1.518 |     1.300 |     1.301 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.10286</span> |     93.75 |  0.002915 |    0.2730 |    0.8399 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.720 |     2.329 |     1.281 |    0.6869 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7427 |     1.518 |     1.300 |     1.301 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     1.225 |    0.2118 |    0.1898 |   -0.1294 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5932 |   -0.6493 |    0.6517 |     1.411 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1402 |    0.3233 |    0.2428 |   -0.3732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   63</span>|     458.10253 |    0.9971 |    -1.438 |   -0.9009 |   -0.8698 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7834 |   -0.5219 |   -0.7318 |   -0.9650 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5909 |   -0.6803 |   -0.7141 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.10253 |     93.73 |    -5.838 |   -0.9797 |   -0.1745 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.166 |     2.329 |     1.281 |    0.6867 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7428 |     1.518 |     1.300 |     1.301 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.10253</span> |     93.73 |  0.002915 |    0.2730 |    0.8399 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.721 |     2.329 |     1.281 |    0.6867 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7428 |     1.518 |     1.300 |     1.301 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -2.449 |    0.2099 |    0.1736 |   -0.1338 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6049 |   -0.7015 |    0.6330 |     1.384 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1863 |    0.2615 |    0.2563 |   -0.4532 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   64</span>|     458.10167 |    0.9974 |    -1.438 |   -0.9009 |   -0.8698 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7832 |   -0.5218 |   -0.7320 |   -0.9653 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5909 |   -0.6804 |   -0.7140 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.10167 |     93.76 |    -5.838 |   -0.9797 |   -0.1744 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.166 |     2.329 |     1.281 |    0.6864 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7427 |     1.518 |     1.300 |     1.301 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.10167</span> |     93.76 |  0.002915 |    0.2729 |    0.8399 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.722 |     2.329 |     1.281 |    0.6864 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7427 |     1.518 |     1.300 |     1.301 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     1.422 |    0.2110 |    0.1849 |   -0.1293 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5889 |   -0.5964 |    0.6731 |     1.405 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1535 |    0.3114 |    0.2496 |   -0.4521 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   65</span>|     458.10133 |    0.9971 |    -1.438 |   -0.9009 |   -0.8698 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7831 |   -0.5217 |   -0.7321 |   -0.9657 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5909 |   -0.6804 |   -0.7139 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.10133 |     93.73 |    -5.838 |   -0.9798 |   -0.1744 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.166 |     2.329 |     1.281 |    0.6862 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7428 |     1.518 |     1.300 |     1.301 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.10133</span> |     93.73 |  0.002915 |    0.2729 |    0.8400 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.723 |     2.329 |     1.281 |    0.6862 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7428 |     1.518 |     1.300 |     1.301 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -2.461 |    0.2094 |    0.1688 |   -0.1332 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6003 |   -0.7109 |    0.5914 |     1.349 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.404 |    0.2951 |    0.2552 |   -0.4331 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   66</span>|     458.10059 |    0.9974 |    -1.438 |   -0.9009 |   -0.8698 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7830 |   -0.5215 |   -0.7323 |   -0.9660 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5909 |   -0.6804 |   -0.7138 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.10059 |     93.75 |    -5.838 |   -0.9798 |   -0.1744 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.166 |     2.330 |     1.281 |    0.6860 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7428 |     1.518 |     1.300 |     1.302 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.10059</span> |     93.75 |  0.002915 |    0.2729 |    0.8400 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.724 |     2.330 |     1.281 |    0.6860 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7428 |     1.518 |     1.300 |     1.302 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     1.228 |    0.2101 |    0.1802 |   -0.1294 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5857 |   -0.2287 |    0.9339 |     1.558 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.376 |    0.3488 |    0.2665 |   -0.4324 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   67</span>|     458.10050 |    0.9972 |    -1.438 |   -0.9010 |   -0.8697 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7829 |   -0.5215 |   -0.7325 |   -0.9663 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.025 |   -0.5910 |   -0.6804 |   -0.7137 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      458.1005 |     93.73 |    -5.838 |   -0.9798 |   -0.1744 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.166 |     2.330 |     1.281 |    0.6857 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7430 |     1.518 |     1.300 |     1.302 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      458.1005</span> |     93.73 |  0.002915 |    0.2729 |    0.8400 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.725 |     2.330 |     1.281 |    0.6857 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7430 |     1.518 |     1.300 |     1.302 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    C| Central Diff. |    -2.045 |    0.2058 |    0.1327 |   -0.1461 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6105 |   -0.6040 |    0.6130 |    0.9518 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01902 |    0.3702 |    0.4676 |   -0.4886 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   68</span>|     458.10013 |    0.9974 |    -1.438 |   -0.9010 |   -0.8697 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7829 |   -0.5214 |   -0.7325 |   -0.9664 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.025 |   -0.5910 |   -0.6805 |   -0.7136 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.10013 |     93.75 |    -5.838 |   -0.9798 |   -0.1744 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.166 |     2.330 |     1.281 |    0.6857 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7430 |     1.518 |     1.300 |     1.302 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.10013</span> |     93.75 |  0.002914 |    0.2729 |    0.8400 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.725 |     2.330 |     1.281 |    0.6857 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7430 |     1.518 |     1.300 |     1.302 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    0.8557 |    0.2088 |    0.1747 |   -0.1300 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5847 |   -0.2393 |    0.9114 |     1.536 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.367 |    0.3016 |    0.2557 |   -0.4123 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   69</span>|     458.10002 |    0.9973 |    -1.438 |   -0.9010 |   -0.8697 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7828 |   -0.5214 |   -0.7326 |   -0.9665 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.025 |   -0.5911 |   -0.6805 |   -0.7136 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.10002 |     93.74 |    -5.838 |   -0.9798 |   -0.1744 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.166 |     2.330 |     1.281 |    0.6856 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7432 |     1.518 |     1.300 |     1.302 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.10002</span> |     93.74 |  0.002914 |    0.2729 |    0.8400 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.726 |     2.330 |     1.281 |    0.6856 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7432 |     1.518 |     1.300 |     1.302 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    C| Central Diff. |   -0.5131 |    0.2058 |    0.1358 |   -0.1441 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6029 |   -0.6262 |    0.5998 |     1.158 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.373 |    0.3470 |    0.4566 |   -0.4872 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   70</span>|     458.09992 |    0.9973 |    -1.438 |   -0.9010 |   -0.8697 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7827 |   -0.5214 |   -0.7327 |   -0.9666 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.025 |   -0.5911 |   -0.6806 |   -0.7135 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.09992 |     93.75 |    -5.838 |   -0.9799 |   -0.1743 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.166 |     2.330 |     1.281 |    0.6855 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7433 |     1.518 |     1.300 |     1.302 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.09992</span> |     93.75 |  0.002914 |    0.2729 |    0.8400 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.726 |     2.330 |     1.281 |    0.6855 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7433 |     1.518 |     1.300 |     1.302 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    C| Central Diff. |    0.2584 |    0.2056 |    0.1371 |   -0.1438 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5991 |   -0.6198 |    0.6082 |    0.9676 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.295 |    0.3221 |    0.4533 |   -0.4750 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   71</span>|     458.09944 |    0.9973 |    -1.438 |   -0.9010 |   -0.8697 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7827 |   -0.5213 |   -0.7328 |   -0.9668 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.025 |   -0.5911 |   -0.6806 |   -0.7135 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.09944 |     93.75 |    -5.838 |   -0.9799 |   -0.1743 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.166 |     2.330 |     1.281 |    0.6854 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7431 |     1.518 |     1.300 |     1.302 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.09944</span> |     93.75 |  0.002914 |    0.2729 |    0.8400 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.727 |     2.330 |     1.281 |    0.6854 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7431 |     1.518 |     1.300 |     1.302 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   72</span>|     458.09825 |    0.9974 |    -1.438 |   -0.9011 |   -0.8697 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7824 |   -0.5210 |   -0.7332 |   -0.9672 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.025 |   -0.5911 |   -0.6808 |   -0.7131 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.09825 |     93.76 |    -5.838 |   -0.9799 |   -0.1743 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.167 |     2.330 |     1.280 |    0.6851 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7430 |     1.518 |     1.299 |     1.302 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.09825</span> |     93.76 |  0.002914 |    0.2729 |    0.8401 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.729 |     2.330 |     1.280 |    0.6851 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7430 |     1.518 |     1.299 |     1.302 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   73</span>|     458.09793 |    0.9981 |    -1.438 |   -0.9013 |   -0.8695 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7815 |   -0.5202 |   -0.7349 |   -0.9686 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5909 |   -0.6812 |   -0.7117 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.09793 |     93.82 |    -5.838 |   -0.9801 |   -0.1741 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.168 |     2.331 |     1.279 |    0.6840 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7428 |     1.518 |     1.299 |     1.304 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.09793</span> |     93.82 |  0.002913 |    0.2729 |    0.8402 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.737 |     2.331 |     1.279 |    0.6840 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7428 |     1.518 |     1.299 |     1.304 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     10.76 |    0.2116 |    0.1923 |   -0.1162 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5255 |   -0.5796 |    0.3777 |     1.210 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.409 |    0.2855 |    0.1875 |   -0.3085 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   74</span>|     458.08987 |    0.9971 |    -1.438 |   -0.9013 |   -0.8695 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7808 |   -0.5187 |   -0.7369 |   -0.9692 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5907 |   -0.6832 |   -0.7095 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.08987 |     93.72 |    -5.838 |   -0.9802 |   -0.1741 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.168 |     2.332 |     1.278 |    0.6836 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7427 |     1.518 |     1.297 |     1.306 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.08987</span> |     93.72 |  0.002914 |    0.2729 |    0.8402 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.743 |     2.332 |     1.278 |    0.6836 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7427 |     1.518 |     1.297 |     1.306 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -2.512 |    0.2078 |    0.1434 |   -0.1372 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5667 |   -0.8061 |    0.1467 |     1.033 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.202 |   -0.4065 |   0.09417 |   -0.1935 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   75</span>|     458.08564 |    0.9973 |    -1.438 |   -0.9016 |   -0.8695 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7801 |   -0.5170 |   -0.7384 |   -0.9704 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5898 |   -0.6859 |   -0.7082 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.08564 |     93.74 |    -5.838 |   -0.9804 |   -0.1741 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.169 |     2.334 |     1.277 |    0.6827 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7424 |     1.519 |     1.294 |     1.308 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.08564</span> |     93.74 |  0.002914 |    0.2728 |    0.8402 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.749 |     2.334 |     1.277 |    0.6827 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7424 |     1.519 |     1.294 |     1.308 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   76</span>|     458.08078 |    0.9972 |    -1.438 |   -0.9021 |   -0.8695 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7788 |   -0.5136 |   -0.7416 |   -0.9727 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.027 |   -0.5879 |   -0.6916 |   -0.7053 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.08078 |     93.73 |    -5.838 |   -0.9809 |   -0.1742 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.170 |     2.338 |     1.275 |    0.6809 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7420 |     1.522 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.08078</span> |     93.73 |  0.002915 |    0.2727 |    0.8402 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.761 |     2.338 |     1.275 |    0.6809 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7420 |     1.522 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |   -0.8456 |    0.2109 |    0.1052 |   -0.1453 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5381 |   -0.2225 |   -0.1274 |    0.8336 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2653 |    0.4698 |   -0.4321 |   0.07917 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   77</span>|     458.08109 |    0.9983 |    -1.445 |   -0.9066 |   -0.8702 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7727 |   -0.5143 |   -0.7362 |   -0.9770 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.027 |   -0.5885 |   -0.6915 |   -0.7058 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.08109 |     93.84 |    -5.845 |   -0.9853 |   -0.1748 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.176 |     2.337 |     1.279 |    0.6777 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7418 |     1.521 |     1.288 |     1.310 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.08109</span> |     93.84 |  0.002894 |    0.2718 |    0.8396 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.815 |     2.337 |     1.279 |    0.6777 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7418 |     1.521 |     1.288 |     1.310 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   78</span>|     458.08775 |    0.9985 |    -1.441 |   -0.9040 |   -0.8696 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7757 |   -0.5136 |   -0.7393 |   -0.9753 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5887 |   -0.6911 |   -0.7056 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.08775 |     93.86 |    -5.841 |   -0.9828 |   -0.1743 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.173 |     2.338 |     1.277 |    0.6789 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7421 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.08775</span> |     93.86 |  0.002906 |    0.2723 |    0.8401 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.788 |     2.338 |     1.277 |    0.6789 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7421 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   79</span>|     458.08407 |    0.9980 |    -1.438 |   -0.9022 |   -0.8694 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7783 |   -0.5133 |   -0.7415 |   -0.9736 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5884 |   -0.6912 |   -0.7054 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.08407 |     93.81 |    -5.838 |   -0.9810 |   -0.1740 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.171 |     2.338 |     1.275 |    0.6802 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7422 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.08407</span> |     93.81 |  0.002915 |    0.2727 |    0.8403 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.766 |     2.338 |     1.275 |    0.6802 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7422 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   80</span>|     458.08069 |    0.9973 |    -1.438 |   -0.9021 |   -0.8695 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7787 |   -0.5135 |   -0.7416 |   -0.9729 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5880 |   -0.6915 |   -0.7053 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.08069 |     93.75 |    -5.838 |   -0.9809 |   -0.1741 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.170 |     2.338 |     1.275 |    0.6807 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7420 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.08069</span> |     93.75 |  0.002915 |    0.2727 |    0.8402 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.762 |     2.338 |     1.275 |    0.6807 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7420 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     1.424 |    0.2112 |    0.1114 |   -0.1426 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5267 |   -0.3290 |  -0.02249 |    0.9218 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.516 |    0.4273 |   -0.4325 |   0.09748 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   81</span>|     458.08076 |    0.9972 |    -1.438 |   -0.9021 |   -0.8695 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7786 |   -0.5134 |   -0.7416 |   -0.9730 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5880 |   -0.6914 |   -0.7055 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.08076 |     93.74 |    -5.838 |   -0.9810 |   -0.1741 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.170 |     2.338 |     1.275 |    0.6807 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7421 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.08076</span> |     93.74 |  0.002915 |    0.2727 |    0.8402 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.763 |     2.338 |     1.275 |    0.6807 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7421 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   82</span>|     458.08078 |    0.9973 |    -1.438 |   -0.9021 |   -0.8695 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7787 |   -0.5135 |   -0.7416 |   -0.9730 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5880 |   -0.6915 |   -0.7053 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.08078 |     93.74 |    -5.838 |   -0.9809 |   -0.1741 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.170 |     2.338 |     1.275 |    0.6807 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7421 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.08078</span> |     93.74 |  0.002915 |    0.2727 |    0.8402 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.762 |     2.338 |     1.275 |    0.6807 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7421 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   83</span>|     458.08068 |    0.9973 |    -1.438 |   -0.9021 |   -0.8695 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7787 |   -0.5135 |   -0.7416 |   -0.9729 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5880 |   -0.6915 |   -0.7053 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.08068 |     93.75 |    -5.838 |   -0.9809 |   -0.1741 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.170 |     2.338 |     1.275 |    0.6807 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7420 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.08068</span> |     93.75 |  0.002915 |    0.2727 |    0.8402 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.762 |     2.338 |     1.275 |    0.6807 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7420 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    C| Central Diff. |    0.7458 |    0.2086 |   0.07146 |   -0.1568 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5453 |   -0.1723 |    0.1066 |    0.6006 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.05957 |    0.1717 |   -0.3314 |   0.06442 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   84</span>|     458.08065 |    0.9973 |    -1.438 |   -0.9021 |   -0.8695 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7787 |   -0.5135 |   -0.7416 |   -0.9729 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5880 |   -0.6915 |   -0.7053 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.08065 |     93.74 |    -5.838 |   -0.9809 |   -0.1741 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.170 |     2.338 |     1.275 |    0.6807 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7420 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.08065</span> |     93.74 |  0.002915 |    0.2727 |    0.8402 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.762 |     2.338 |     1.275 |    0.6807 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7420 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    C| Central Diff. |    0.5271 |    0.2085 |   0.07055 |   -0.1570 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5454 |   -0.3138 |   0.09492 |    0.7765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.192 |    0.4934 |   -0.3382 |    0.1071 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   85</span>|     458.08062 |    0.9973 |    -1.438 |   -0.9021 |   -0.8695 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7786 |   -0.5135 |   -0.7416 |   -0.9730 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5880 |   -0.6915 |   -0.7054 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.08062 |     93.74 |    -5.838 |   -0.9809 |   -0.1741 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.170 |     2.338 |     1.275 |    0.6807 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7420 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.08062</span> |     93.74 |  0.002915 |    0.2727 |    0.8402 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.762 |     2.338 |     1.275 |    0.6807 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7420 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    C| Central Diff. |    0.1077 |    0.2083 |   0.06880 |   -0.1572 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5469 |   -0.1727 |    0.1219 |    0.9565 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.457 |    0.5128 |   -0.3399 |    0.1022 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   86</span>|     458.08061 |    0.9972 |    -1.438 |   -0.9021 |   -0.8695 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7786 |   -0.5135 |   -0.7416 |   -0.9730 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5881 |   -0.6914 |   -0.7054 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.08061 |     93.74 |    -5.838 |   -0.9809 |   -0.1741 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.170 |     2.338 |     1.275 |    0.6807 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7421 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.08061</span> |     93.74 |  0.002915 |    0.2727 |    0.8402 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.763 |     2.338 |     1.275 |    0.6807 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7421 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    C| Central Diff. |   -0.6930 |    0.2078 |   0.06712 |   -0.1577 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5484 |   -0.1767 |    0.1148 |    0.9863 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2038 |    0.5855 |   -0.3296 |    0.1002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   87</span>|     458.08058 |    0.9972 |    -1.438 |   -0.9021 |   -0.8695 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7786 |   -0.5135 |   -0.7416 |   -0.9731 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5881 |   -0.6914 |   -0.7054 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.08058 |     93.74 |    -5.838 |   -0.9809 |   -0.1741 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.171 |     2.338 |     1.275 |    0.6806 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7421 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.08058</span> |     93.74 |  0.002915 |    0.2727 |    0.8402 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.763 |     2.338 |     1.275 |    0.6806 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7421 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    C| Central Diff. |   -0.3701 |    0.2079 |   0.06808 |   -0.1573 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5470 |   -0.1729 |  0.005725 |    0.9526 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.465 |    0.5101 |   -0.1466 |  -0.02770 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   88</span>|     458.08052 |    0.9973 |    -1.438 |   -0.9021 |   -0.8695 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7785 |   -0.5135 |   -0.7416 |   -0.9731 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5881 |   -0.6914 |   -0.7053 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.08052 |     93.74 |    -5.838 |   -0.9809 |   -0.1741 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.171 |     2.338 |     1.275 |    0.6806 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7421 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.08052</span> |     93.74 |  0.002915 |    0.2727 |    0.8402 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.763 |     2.338 |     1.275 |    0.6806 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7421 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    C| Central Diff. |    0.2062 |    0.2080 |   0.06837 |   -0.1566 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5439 |   -0.1704 |   0.09858 |    0.5928 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.05984 |    0.5131 |   -0.3447 |   0.06046 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   89</span>|     458.08047 |    0.9972 |    -1.438 |   -0.9021 |   -0.8694 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7785 |   -0.5134 |   -0.7416 |   -0.9732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5882 |   -0.6914 |   -0.7054 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.08047 |     93.74 |    -5.838 |   -0.9809 |   -0.1741 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.171 |     2.338 |     1.275 |    0.6805 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7421 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.08047</span> |     93.74 |  0.002915 |    0.2727 |    0.8402 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.764 |     2.338 |     1.275 |    0.6805 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7421 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    C| Central Diff. |  -0.06148 |    0.2077 |   0.06828 |   -0.1566 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5434 |   -0.1748 |    0.1045 |    0.7316 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7607 |    0.4924 |   -0.3350 |    0.1046 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   90</span>|     458.08041 |    0.9972 |    -1.438 |   -0.9021 |   -0.8694 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7784 |   -0.5134 |   -0.7417 |   -0.9733 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5883 |   -0.6913 |   -0.7054 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.08041 |     93.74 |    -5.838 |   -0.9810 |   -0.1740 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.171 |     2.338 |     1.275 |    0.6804 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7421 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.08041</span> |     93.74 |  0.002915 |    0.2727 |    0.8403 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.765 |     2.338 |     1.275 |    0.6804 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7421 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    C| Central Diff. |   -0.5076 |    0.2073 |   0.06528 |   -0.1564 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5454 |   -0.1802 |    0.1061 |    0.9740 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1992 |    0.5430 |   -0.2714 |    0.1031 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   91</span>|     458.08026 |    0.9972 |    -1.438 |   -0.9022 |   -0.8694 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7782 |   -0.5133 |   -0.7417 |   -0.9736 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5884 |   -0.6912 |   -0.7055 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.08026 |     93.74 |    -5.838 |   -0.9810 |   -0.1740 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.171 |     2.338 |     1.275 |    0.6802 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7422 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.08026</span> |     93.74 |  0.002914 |    0.2727 |    0.8403 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.766 |     2.338 |     1.275 |    0.6802 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7422 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    C| Central Diff. |   -0.3779 |    0.2092 |   0.07455 |   -0.1547 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5349 |    0.4172 |   -0.5362 |    0.7903 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.451 |    0.5182 |   -0.2163 |   -0.4361 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   92</span>|     458.08039 |    0.9975 |    -1.438 |   -0.9022 |   -0.8693 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7778 |   -0.5133 |   -0.7416 |   -0.9742 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5887 |   -0.6910 |   -0.7056 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.08039 |     93.76 |    -5.838 |   -0.9811 |   -0.1739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.171 |     2.338 |     1.275 |    0.6798 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7424 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.08039</span> |     93.76 |  0.002914 |    0.2727 |    0.8404 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.769 |     2.338 |     1.275 |    0.6798 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7424 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   93</span>|     458.08025 |    0.9973 |    -1.438 |   -0.9022 |   -0.8693 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7780 |   -0.5133 |   -0.7416 |   -0.9739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5886 |   -0.6911 |   -0.7055 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.08025 |     93.75 |    -5.838 |   -0.9810 |   -0.1739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.171 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.08025</span> |     93.75 |  0.002914 |    0.2727 |    0.8403 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.768 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    C| Central Diff. |     1.019 |    0.2066 |   0.06601 |   -0.1530 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5270 |   -0.1637 |    0.1163 |    0.9184 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.439 |    0.4715 |   -0.2217 |   0.03756 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   94</span>|     458.08029 |    0.9972 |    -1.438 |   -0.9022 |   -0.8693 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7780 |   -0.5133 |   -0.7416 |   -0.9739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5886 |   -0.6911 |   -0.7055 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.08029 |     93.74 |    -5.838 |   -0.9810 |   -0.1739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.171 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7424 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.08029</span> |     93.74 |  0.002914 |    0.2727 |    0.8404 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.768 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7424 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   95</span>|     458.08025 |    0.9973 |    -1.438 |   -0.9022 |   -0.8693 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7780 |   -0.5133 |   -0.7416 |   -0.9739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5886 |   -0.6911 |   -0.7055 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.08025 |     93.74 |    -5.838 |   -0.9810 |   -0.1739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.171 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7424 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.08025</span> |     93.74 |  0.002914 |    0.2727 |    0.8404 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.768 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7424 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   96</span>|     458.08029 |    0.9973 |    -1.438 |   -0.9022 |   -0.8693 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7780 |   -0.5133 |   -0.7416 |   -0.9739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5886 |   -0.6911 |   -0.7055 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.08029 |     93.75 |    -5.838 |   -0.9810 |   -0.1739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.171 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.08029</span> |     93.75 |  0.002914 |    0.2727 |    0.8403 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.768 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   97</span>|     458.08024 |    0.9973 |    -1.438 |   -0.9022 |   -0.8693 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7780 |   -0.5133 |   -0.7416 |   -0.9739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5886 |   -0.6911 |   -0.7055 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.08024 |     93.75 |    -5.838 |   -0.9810 |   -0.1739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.171 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.08024</span> |     93.75 |  0.002914 |    0.2727 |    0.8403 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.768 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    C| Central Diff. |    0.9275 |    0.2065 |   0.06562 |   -0.1530 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5278 |   -0.1682 |    0.1168 |    0.9523 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1838 |    0.4888 |   -0.3154 |   0.09539 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   98</span>|     458.08024 |    0.9973 |    -1.438 |   -0.9022 |   -0.8693 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7780 |   -0.5133 |   -0.7416 |   -0.9739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5886 |   -0.6911 |   -0.7055 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.08024 |     93.75 |    -5.838 |   -0.9810 |   -0.1739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.171 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.08024</span> |     93.75 |  0.002914 |    0.2727 |    0.8403 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.768 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    C| Central Diff. |    0.8715 |    0.2065 |   0.06540 |   -0.1531 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5308 |  -0.07756 |    0.1164 |    0.5595 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.04465 |   -0.2577 |   -0.3240 |    0.1066 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   99</span>|     458.08023 |    0.9973 |    -1.438 |   -0.9022 |   -0.8693 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7780 |   -0.5133 |   -0.7416 |   -0.9739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5886 |   -0.6911 |   -0.7055 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.08023 |     93.75 |    -5.838 |   -0.9810 |   -0.1739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.171 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.08023</span> |     93.75 |  0.002914 |    0.2727 |    0.8403 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.768 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  100</span>|     458.08013 |    0.9973 |    -1.438 |   -0.9022 |   -0.8693 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7780 |   -0.5133 |   -0.7416 |   -0.9739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5886 |   -0.6911 |   -0.7055 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.08013 |     93.75 |    -5.838 |   -0.9810 |   -0.1739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.171 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.08013</span> |     93.75 |  0.002914 |    0.2727 |    0.8403 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.768 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    C| Central Diff. |    0.7255 |    0.2064 |   0.06482 |   -0.1531 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5283 |   -0.1617 |    0.1081 |    0.9192 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.441 |    0.4906 |   -0.2900 |    0.1161 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  101</span>|     458.08017 |    0.9973 |    -1.438 |   -0.9022 |   -0.8693 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7780 |   -0.5133 |   -0.7416 |   -0.9739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5885 |   -0.6911 |   -0.7055 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.08017 |     93.74 |    -5.838 |   -0.9810 |   -0.1739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.171 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.08017</span> |     93.74 |  0.002914 |    0.2727 |    0.8404 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.768 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  102</span>|     458.08021 |    0.9973 |    -1.438 |   -0.9022 |   -0.8693 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7780 |   -0.5133 |   -0.7416 |   -0.9739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5886 |   -0.6911 |   -0.7055 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.08021 |     93.74 |    -5.838 |   -0.9810 |   -0.1739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.171 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.08021</span> |     93.74 |  0.002914 |    0.2727 |    0.8404 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.768 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  103</span>|     458.08029 |    0.9973 |    -1.438 |   -0.9022 |   -0.8693 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7780 |   -0.5133 |   -0.7416 |   -0.9739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5886 |   -0.6911 |   -0.7055 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.08029 |     93.75 |    -5.838 |   -0.9810 |   -0.1739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.171 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.08029</span> |     93.75 |  0.002914 |    0.2727 |    0.8404 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.768 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  104</span>|     458.08032 |    0.9973 |    -1.438 |   -0.9022 |   -0.8693 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7780 |   -0.5133 |   -0.7416 |   -0.9739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5886 |   -0.6911 |   -0.7055 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.08032 |     93.75 |    -5.838 |   -0.9810 |   -0.1739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.171 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.08032</span> |     93.75 |  0.002914 |    0.2727 |    0.8403 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.768 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  105</span>|     458.08033 |    0.9973 |    -1.438 |   -0.9022 |   -0.8693 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7780 |   -0.5133 |   -0.7416 |   -0.9739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5886 |   -0.6911 |   -0.7055 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.08033 |     93.75 |    -5.838 |   -0.9810 |   -0.1739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.171 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.08033</span> |     93.75 |  0.002914 |    0.2727 |    0.8403 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.768 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  106</span>|     458.08032 |    0.9973 |    -1.438 |   -0.9022 |   -0.8693 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7780 |   -0.5133 |   -0.7416 |   -0.9739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5886 |   -0.6911 |   -0.7055 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.08032 |     93.75 |    -5.838 |   -0.9810 |   -0.1739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.171 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.08032</span> |     93.75 |  0.002914 |    0.2727 |    0.8403 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.768 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  107</span>|     458.08032 |    0.9973 |    -1.438 |   -0.9022 |   -0.8693 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7780 |   -0.5133 |   -0.7416 |   -0.9739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5886 |   -0.6911 |   -0.7055 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.08032 |     93.75 |    -5.838 |   -0.9810 |   -0.1739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.171 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.08032</span> |     93.75 |  0.002914 |    0.2727 |    0.8403 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.768 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  108</span>|     458.08032 |    0.9973 |    -1.438 |   -0.9022 |   -0.8693 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7780 |   -0.5133 |   -0.7416 |   -0.9739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5886 |   -0.6911 |   -0.7055 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.08032 |     93.75 |    -5.838 |   -0.9810 |   -0.1739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.171 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.08032</span> |     93.75 |  0.002914 |    0.2727 |    0.8403 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.768 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  109</span>|     458.08032 |    0.9973 |    -1.438 |   -0.9022 |   -0.8693 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7780 |   -0.5133 |   -0.7416 |   -0.9739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5886 |   -0.6911 |   -0.7055 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.08032 |     93.75 |    -5.838 |   -0.9810 |   -0.1739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.171 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.08032</span> |     93.75 |  0.002914 |    0.2727 |    0.8403 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.768 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  110</span>|     458.08032 |    0.9973 |    -1.438 |   -0.9022 |   -0.8693 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7780 |   -0.5133 |   -0.7416 |   -0.9739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5886 |   -0.6911 |   -0.7055 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.08032 |     93.75 |    -5.838 |   -0.9810 |   -0.1739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.171 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.08032</span> |     93.75 |  0.002914 |    0.2727 |    0.8403 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.768 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  111</span>|     458.08032 |    0.9973 |    -1.438 |   -0.9022 |   -0.8693 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7780 |   -0.5133 |   -0.7416 |   -0.9739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5886 |   -0.6911 |   -0.7055 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.08032 |     93.75 |    -5.838 |   -0.9810 |   -0.1739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.171 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.08032</span> |     93.75 |  0.002914 |    0.2727 |    0.8403 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.768 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  112</span>|     458.08032 |    0.9973 |    -1.438 |   -0.9022 |   -0.8693 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7780 |   -0.5133 |   -0.7416 |   -0.9739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5886 |   -0.6911 |   -0.7055 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.08032 |     93.75 |    -5.838 |   -0.9810 |   -0.1739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.171 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.08032</span> |     93.75 |  0.002914 |    0.2727 |    0.8403 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.768 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> calculating covariance matrix</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating residuals/tables</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-in"><span class="va">f_nlmixr_dfop_sfo_saem_obs</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr</a></span><span class="op">(</span><span class="va">f_mmkin_obs</span><span class="op">[</span><span class="st">"DFOP-SFO"</span>, <span class="op">]</span>, est <span class="op">=</span> <span class="st">"saem"</span><span class="op">)</span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → generate SAEM model</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1:    93.0952   -5.6337   -1.8988   -4.1294   -1.2035    0.1038    5.2152    1.6150    1.0450    2.6377    0.5035    0.5225   20.0768   11.4566</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2:    93.1464   -5.6948   -1.8729   -4.2029   -1.1302    0.1295    5.5478    1.5342    0.9927    2.5059    0.4783    0.5147   11.2089    8.1577</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 3:    93.1719   -5.7029   -1.8810   -4.2024   -1.0686    0.1063    5.2764    1.5217    0.9431    2.3806    0.4544    0.5587    9.3753    5.7166</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 4:    92.8647   -5.8139   -1.8979   -4.1992   -1.0168    0.1122    5.4892    1.7893    0.8960    2.2615    0.4317    0.5307    9.4134    4.7166</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5:    92.7759   -5.8627   -1.8873   -4.1402   -1.0347    0.1243    6.2028    2.1819    0.8795    2.1485    0.4101    0.5042    9.5964    4.7340</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 6:    92.8080   -5.8954   -1.9186   -4.1772   -0.9727    0.1311    5.8926    2.0728    0.8633    2.0410    0.3896    0.4790    8.7334    4.1957</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 7:    92.9042   -5.9034   -1.9603   -4.1854   -0.9717    0.1630    5.5980    2.2068    0.8672    1.9390    0.3701    0.4550    8.7213    3.3409</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 8:    92.9302   -5.8545   -1.9240   -4.2343   -0.9748    0.1686    5.3181    2.3934    0.8239    1.9533    0.3516    0.4323    7.6950    2.9174</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 9:    92.9518   -5.5561   -1.9734   -4.1856   -0.9322    0.2129    5.0522    2.2738    0.7830    2.0138    0.3340    0.4689    7.9230    2.1930</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 10:    93.0672   -5.5564   -1.9633   -4.1456   -0.9343    0.2049    4.7996    2.1601    0.7438    2.0517    0.3173    0.4771    8.0644    2.0682</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 11:    92.6849   -5.6187   -1.9773   -4.1793   -0.9342    0.2290    4.5596    2.0521    0.7123    2.0774    0.3015    0.5052    8.2355    1.9349</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 12:    92.6425   -5.6989   -2.0144   -4.1395   -0.9342    0.2669    4.3316    2.2634    0.7142    2.3008    0.2864    0.5728    7.5188    1.8028</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 13:    92.2996   -5.6700   -1.9885   -4.0443   -0.9202    0.2750    4.5787    2.1608    0.7019    2.7515    0.2721    0.6641    7.0744    1.8958</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 14:    92.2457   -5.5825   -2.0314   -3.9788   -0.9142    0.2908    6.3744    2.0527    0.7018    3.2703    0.2585    0.6387    6.9499    1.8421</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 15:    92.1741   -5.5333   -2.0318   -3.9664   -0.9059    0.2894    6.0557    1.9501    0.7179    3.2869    0.2455    0.6068    7.0951    1.6824</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 16:    92.5772   -5.4982   -2.0352   -3.9471   -0.9063    0.2930    5.7529    1.8526    0.7244    3.6994    0.2333    0.5764    7.2138    1.7042</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 17:    92.7024   -5.4902   -2.0438   -4.0347   -0.9079    0.2959    5.4652    1.7600    0.7393    3.5144    0.2216    0.5765    7.0258    1.6793</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 18:    92.0240   -5.5539   -2.0520   -3.9601   -0.9079    0.2884    6.5142    1.9232    0.7320    3.9052    0.2157    0.5521    7.2568    1.6151</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 19:    92.6532   -5.6325   -2.0456   -3.9091   -0.9179    0.3047    6.5743    2.3363    0.7405    4.5825    0.2049    0.5425    7.6201    1.6565</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 20:    92.3488   -5.6617   -2.0554   -3.9501   -0.9216    0.3056    6.7080    2.7562    0.7218    4.7569    0.2083    0.5432    7.3481    1.8059</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 21:    92.5679   -5.7469   -2.0614   -4.0829   -0.8890    0.3158    6.6510    3.1993    0.7136    4.5191    0.1978    0.5281    7.3723    1.7461</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 22:    92.4785   -5.9527   -2.0657   -4.0580   -0.8971    0.3084    6.3184    4.1791    0.7144    4.2931    0.1929    0.5157    7.1922    1.6984</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 23:    92.5594   -5.8590   -2.0723   -4.0580   -0.9020    0.2952    6.0025    3.9702    0.7335    4.0784    0.1833    0.5129    7.7560    1.6302</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 24:    92.6152   -5.9488   -2.0658   -4.1187   -0.9050    0.2997    5.7024    4.5381    0.7215    3.8745    0.1875    0.5129    7.6182    1.6362</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 25:    92.4658   -5.8715   -2.0791   -4.0949   -0.8922    0.3074    6.1428    4.3112    0.7353    3.6808    0.1896    0.5157    7.2703    1.5633</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 26:    92.2507   -5.9797   -2.0707   -4.0834   -0.8868    0.3266    6.4124    4.7125    0.7334    3.5368    0.1851    0.5217    7.3085    1.5828</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 27:    92.7055   -5.9792   -2.0779   -4.2066   -0.8887    0.2993    6.0918    4.8624    0.7659    3.7456    0.1841    0.4956    7.3534    1.5240</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 28:    92.3971   -6.0253   -2.0648   -4.1406   -0.9042    0.2897    5.7872    4.7690    0.7449    3.5583    0.1823    0.4709    7.2479    1.5008</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 29:    92.4045   -5.9045   -2.0730   -4.1407   -0.8969    0.3103    5.6683    4.5306    0.7349    3.3816    0.1849    0.5054    7.1719    1.6004</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 30:    92.1714   -5.8598   -2.0645   -4.0913   -0.8964    0.2743    5.3849    4.3040    0.7538    3.3509    0.1757    0.4801    7.3739    1.5736</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 31:    91.9134   -5.7867   -2.0367   -4.0563   -0.8957    0.2280    5.1156    4.0888    0.7299    3.3452    0.1749    0.4673    6.5991    1.5909</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 32:    92.3743   -5.9967   -2.0255   -4.0668   -0.9025    0.2247    4.8599    4.6666    0.7256    3.3271    0.1714    0.5008    6.4693    1.5458</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 33:    92.5239   -5.9301   -2.0387   -4.0365   -0.9033    0.2558    4.6169    4.4333    0.7492    3.6109    0.1688    0.5424    6.7730    1.5771</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 34:    92.6892   -6.1135   -2.0587   -4.0280   -0.8993    0.2430    4.3860    6.0227    0.7798    3.5725    0.1672    0.5153    6.8110    1.5417</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 35:    92.7276   -6.0815   -2.0629   -4.0833   -0.8943    0.2375    4.6811    5.7216    0.8010    3.3939    0.1715    0.4895    6.6812    1.5478</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 36:    92.7316   -6.0691   -2.0882   -4.0626   -0.9030    0.2405    4.4614    5.4355    0.8355    3.2242    0.1789    0.4650    6.8443    1.4806</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 37:    92.6685   -5.7905   -2.0666   -4.0663   -0.8998    0.2784    5.1916    5.1637    0.7937    3.0641    0.1823    0.4418    6.5421    1.6284</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 38:    93.0325   -5.6829   -2.0674   -4.0772   -0.9167    0.2499    4.9320    4.9055    0.7768    3.1213    0.1887    0.4290    6.8220    1.5224</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 39:    93.0378   -5.5554   -2.0743   -4.0772   -0.9189    0.2361    4.6854    4.6603    0.7822    3.1213    0.1813    0.4242    7.1137    1.5021</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 40:    93.3297   -5.6270   -2.0591   -4.1200   -0.9167    0.2035    4.4511    4.4272    0.8128    2.9653    0.1829    0.4030    7.3894    1.5064</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 41:    93.3408   -5.5437   -2.0344   -4.1042   -0.9078    0.1744    4.2286    4.2059    0.8228    2.8170    0.1881    0.3829    7.2734    1.5519</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 42:    93.1691   -5.4436   -2.0551   -4.1048   -0.8984    0.1732    4.0172    3.9956    0.7816    2.6762    0.1853    0.3637    6.9712    1.5332</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 43:    93.2443   -5.5247   -2.0722   -4.0980   -0.8992    0.1756    3.8163    3.7958    0.7781    2.7200    0.1956    0.3455    6.7012    1.5344</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 44:    92.9509   -5.5020   -2.0495   -4.0961   -0.8964    0.1777    3.6255    3.6060    0.7555    2.8172    0.1994    0.3283    6.2180    1.6137</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 45:    92.8898   -5.4913   -2.0462   -4.1079   -0.9003    0.1701    4.1177    3.4257    0.7491    2.8491    0.1989    0.3372    6.2876    1.6205</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 46:    92.6044   -5.6429   -2.0499   -4.1079   -0.9069    0.1999    4.2103    3.2544    0.7657    2.8491    0.1921    0.3532    6.2261    1.6435</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 47:    92.7740   -5.6128   -2.0804   -4.1186   -0.8976    0.1864    5.2188    3.0917    0.7381    2.9036    0.2030    0.3391    6.6803    1.6177</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 48:    92.4691   -5.6645   -2.0600   -4.1288   -0.8796    0.1913    6.2045    2.9371    0.7774    2.9223    0.1966    0.3396    6.7169    1.6215</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 49:    92.4128   -5.7079   -2.0802   -4.1166   -0.8798    0.1741    5.8943    3.1446    0.7854    2.8715    0.1935    0.3369    6.9151    1.4834</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 50:    91.8883   -5.7713   -2.0932   -4.0899   -0.8886    0.1758    5.5996    3.5478    0.8033    2.8192    0.2014    0.3201    7.0775    1.4635</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 51:    92.1187   -5.7306   -2.0903   -4.0878   -0.8923    0.2024    5.3196    3.6534    0.7823    2.7891    0.1969    0.3140    6.9879    1.5430</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 52:    92.3209   -5.7897   -2.0903   -4.1622   -0.9072    0.2261    5.3781    3.4707    0.8001    3.0801    0.2063    0.3088    6.7047    1.4499</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 53:    92.4318   -5.6954   -2.0950   -4.1866   -0.9082    0.2131    7.1200    3.2972    0.7711    3.3398    0.2034    0.3186    6.6152    1.5123</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 54:    92.5380   -5.6975   -2.0782   -4.2394   -0.8976    0.2118    6.7640    3.1323    0.7744    3.5385    0.2175    0.3296    6.4402    1.5403</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 55:    92.7213   -5.6663   -2.0427   -4.3288   -0.9028    0.2032    8.1796    2.9757    0.7748    4.4495    0.2142    0.3316    6.4111    1.5369</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 56:    92.7219   -5.6865   -2.0557   -4.3434   -0.9084    0.2021    7.7706    2.8269    0.7989    4.6691    0.2174    0.3433    6.4256    1.4639</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 57:    92.8932   -5.5736   -2.0497   -4.3955   -0.9216    0.1694    7.3821    2.6856    0.8205    4.9791    0.2142    0.3500    6.5378    1.5406</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 58:    92.9219   -5.5789   -2.0547   -4.3094   -0.9183    0.1838    7.0130    2.5513    0.7958    4.7302    0.2193    0.3495    6.2662    1.4940</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 59:    93.0432   -5.6201   -2.0395   -4.2527   -0.9199    0.2302    6.6623    2.4237    0.8241    4.4937    0.2241    0.3321    5.8693    1.6176</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 60:    92.9724   -5.5971   -2.0537   -4.3509   -0.9219    0.2118    7.5905    2.3026    0.8418    4.4546    0.2177    0.3155    5.5960    1.5040</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 61:    93.1235   -5.6332   -2.0671   -4.3572   -0.9219    0.1917    7.2521    2.3037    0.8527    4.5707    0.2230    0.2997    5.8136    1.4516</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 62:    93.5431   -5.6373   -2.0371   -4.3384   -0.9276    0.1997    8.5187    2.3895    0.8575    4.4378    0.2258    0.3041    5.5746    1.4572</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 63:    93.5009   -5.5634   -2.0224   -4.3560   -0.9339    0.2167    8.0927    2.2700    0.8462    4.4449    0.2266    0.3162    5.4922    1.5233</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 64:    93.7369   -5.6032   -2.0362   -4.2323   -0.9355    0.2159    9.6684    2.1949    0.8514    4.2227    0.2300    0.3318    5.7597    1.4866</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 65:    93.5021   -5.5769   -2.0311   -4.3204   -0.9375    0.1927    9.5537    2.0852    0.8559    4.6151    0.2208    0.3550    5.6975    1.5031</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 66:    93.4211   -5.7184   -2.0384   -4.3041   -0.9228    0.2121    9.0760    2.6390    0.8430    4.3843    0.2098    0.3755    5.8990    1.5133</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 67:    93.3435   -5.6579   -2.0456   -4.2222   -0.9183    0.1825    8.6222    2.5071    0.8719    4.1651    0.2152    0.3567    5.9004    1.4874</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 68:    93.3914   -5.6554   -2.0420   -4.1618   -0.9303    0.1714    8.1911    2.3817    0.8856    3.9569    0.2272    0.3388    6.1793    1.4917</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 69:    93.5112   -5.6834   -2.0349   -4.1591   -0.9331    0.1759    8.9900    2.5191    0.9184    3.7590    0.2236    0.3219    6.1618    1.4697</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 70:    93.4289   -5.6576   -2.0273   -4.1763   -0.9399    0.1738    8.5405    2.4302    0.8725    3.5711    0.2149    0.3288    6.5324    1.5410</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 71:    93.2936   -5.6699   -2.0256   -4.1405   -0.9370    0.1922    8.1134    2.3831    0.8289    3.3925    0.2080    0.3548    5.9794    1.5904</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 72:    92.9152   -5.6742   -2.0382   -4.1523   -0.9411    0.1991    7.7078    2.4322    0.7875    3.2916    0.2007    0.3456    6.0999    1.6022</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 73:    92.8129   -5.8099   -2.0372   -4.1735   -0.9377    0.1605    8.2752    2.8931    0.7737    3.4015    0.2057    0.3283    6.0140    1.5472</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 74:    92.7806   -5.7269   -2.0315   -4.1877   -0.9399    0.1675    8.4688    2.7484    0.7773    3.4620    0.2174    0.3249    5.8495    1.5779</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 75:    92.9128   -5.8680   -2.0304   -4.1459   -0.9387    0.1567    8.0454    3.2365    0.7681    3.2935    0.2198    0.3187    5.8539    1.5815</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 76:    92.8931   -5.8483   -2.0253   -4.1815   -0.9387    0.1540    9.6800    3.0747    0.8007    3.5838    0.2198    0.3291    5.9005    1.5053</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 77:    92.7474   -5.7995   -2.0202   -4.1904   -0.9333    0.1724   10.0488    2.9210    0.7841    3.7255    0.2148    0.3465    5.9418    1.5569</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 78:    92.8150   -5.9608   -2.0025   -4.1583   -0.9511    0.1566    9.5464    3.5249    0.7858    3.6988    0.2246    0.3292    5.7737    1.5904</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 79:    92.6663   -6.0485   -2.0245   -4.1437   -0.9446    0.1786    9.0690    4.1252    0.7803    3.6526    0.2155    0.3335    5.9109    1.5521</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 80:    92.5932   -6.1001   -2.0304   -4.1969   -0.9409    0.1424    8.6156    4.6478    0.8007    4.0379    0.2114    0.3168    6.1732    1.5544</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 81:    92.6789   -6.3510   -2.0062   -4.2272   -0.9541    0.1360    8.3363    5.9861    0.7719    3.8800    0.2202    0.3010    5.6841    1.6317</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 82:    92.9996   -6.5680   -2.0048   -4.1609   -0.9578    0.1367   10.6901    7.4391    0.7962    3.6860    0.2092    0.2859    5.7335    1.6391</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 83:    93.1087   -6.5298   -1.9757   -4.1694   -0.9596    0.1323   10.5524    7.4660    0.8237    3.8009    0.2134    0.2716    5.9664    1.6316</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 84:    93.1929   -6.5844   -2.0191   -4.2021   -0.9616    0.1384   10.0248    7.7313    0.8230    3.6109    0.2110    0.2580    5.7854    1.5609</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 85:    92.9161   -6.4934   -2.0281   -4.2008   -0.9600    0.1107    9.5236    7.4269    0.8448    3.5630    0.2110    0.2451    5.6111    1.5333</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 86:    92.9816   -6.5397   -2.0234   -4.1830   -0.9694    0.1111    9.1945    7.4382    0.8481    3.3849    0.2166    0.2329    5.8375    1.5263</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 87:    93.0930   -6.5749   -2.0168   -4.1986   -0.9816    0.0926    8.7348    8.4431    0.8646    3.3032    0.2155    0.2212    5.7542    1.5736</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 88:    93.0765   -6.4914   -2.0274   -4.2274   -0.9870    0.1148    8.4666    8.0209    0.8666    3.5292    0.2048    0.2102    5.8988    1.5329</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 89:    92.8699   -6.6431   -2.0199   -4.2175   -0.9463    0.1011    8.4648    7.7973    0.8908    3.3980    0.1945    0.2102    5.6876    1.5177</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 90:    92.8225   -6.6932   -2.0211   -4.2175   -0.9505    0.0887   10.1862    8.7787    0.8730    3.3980    0.1877    0.1997    5.9135    1.4821</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 91:    92.7874   -6.7007   -2.0420   -4.2936   -0.9468    0.0927   11.2275    8.3397    0.8600    3.9490    0.1885    0.2147    6.1108    1.4315</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 92:    93.2320   -6.8088   -2.0341   -4.2817   -0.9582    0.1270   12.4029    9.6204    0.8503    3.7516    0.1950    0.2070    5.9434    1.5251</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 93:    93.8996   -6.5471   -2.0440   -4.2123   -0.9543    0.1140   11.7828    9.1394    0.8493    3.5640    0.1988    0.2017    6.1302    1.5568</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 94:    93.4416   -6.4623   -2.0448   -4.2188   -0.9595    0.1174   12.1277    8.6824    0.8568    3.3858    0.1975    0.1938    5.9173    1.5204</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 95:    93.2953   -6.1521   -2.0477   -4.2216   -0.9551    0.1152   11.5213    8.2483    0.8356    3.2165    0.1979    0.1855    5.8298    1.5357</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 96:    92.9577   -6.0477   -2.0579   -4.2534   -0.9465    0.1284   10.9452    7.8359    0.8202    3.3357    0.1937    0.1917    5.8590    1.5738</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 97:    92.8703   -6.1037   -2.0745   -4.1960   -0.9405    0.1417   10.4858    7.4441    0.8488    3.1690    0.1969    0.2016    5.7948    1.4759</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 98:    92.9728   -6.3534   -2.0868   -4.2155   -0.9444    0.1069    9.9615    7.1788    0.8736    3.1913    0.1998    0.2114    5.6533    1.4322</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 99:    93.4116   -6.1712   -2.0837   -4.2280   -0.9473    0.1197    9.4634    6.8198    0.8959    3.1806    0.1899    0.2167    5.9149    1.3768</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 100:    93.2598   -6.1345   -2.0645   -4.2237   -0.9575    0.1099    8.9903    6.4788    0.9204    3.2429    0.1903    0.2058    5.7085    1.4133</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 101:    93.3082   -6.1574   -2.0474   -4.2315   -0.9619    0.1161    9.0819    6.1549    0.8744    3.2049    0.1891    0.2278    5.6493    1.4894</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 102:    93.5741   -6.2923   -2.0484   -4.2853   -0.9665    0.1325   10.4108    5.8522    0.8804    3.5370    0.2024    0.2324    5.6995    1.4594</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 103:    92.9199   -6.1797   -2.0522   -4.2940   -0.9568    0.1289    9.8903    5.5596    0.8722    3.6682    0.1975    0.2398    5.5536    1.4510</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 104:    93.1139   -6.1630   -2.0546   -4.2912   -0.9613    0.1115    9.3958    5.2816    0.8648    3.6673    0.2010    0.2278    5.5768    1.4812</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 105:    93.4085   -6.0359   -2.0450   -4.2889   -0.9591    0.1258    8.9260    5.0175    0.8412    3.7286    0.1917    0.2171    5.6780    1.5203</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 106:    93.3103   -6.1029   -2.0425   -4.2948   -0.9579    0.0934    8.4797    4.7667    0.8539    3.6947    0.1947    0.2234    5.7210    1.4760</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 107:    93.6389   -5.9750   -2.0309   -4.2630   -0.9660    0.1110    8.0557    4.5283    0.8470    3.5100    0.2044    0.2299    5.6280    1.5525</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 108:    93.8641   -5.8551   -2.0360   -4.2311   -0.9629    0.0900    7.6529    4.3019    0.8567    3.3345    0.2014    0.2302    5.7841    1.5978</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 109:    93.6274   -6.0268   -2.0445   -4.3047   -0.9488    0.0979    7.2703    4.7807    0.8565    3.7665    0.1929    0.2271    5.7941    1.5580</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 110:    93.6320   -5.8321   -2.0413   -4.2855   -0.9477    0.1143    6.9068    4.5417    0.8412    3.6165    0.1901    0.2199    5.8169    1.5477</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 111:    93.6245   -5.8256   -2.0157   -4.2797   -0.9612    0.0940    6.5774    4.3146    0.8460    3.5782    0.1837    0.2182    5.6157    1.6424</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 112:    93.8512   -5.9045   -2.0116   -4.2409   -0.9708    0.0883    6.2486    4.0989    0.8658    3.5059    0.1761    0.2073    5.8852    1.6073</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 113:    93.7080   -5.9935   -2.0306   -4.1884   -0.9690    0.0740    5.9361    4.0088    0.9072    3.3400    0.1957    0.2169    6.3792    1.4770</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 114:    93.8574   -5.9185   -2.0233   -4.2030   -0.9588    0.1137    5.6393    3.8084    0.9322    3.2878    0.1939    0.2098    5.8891    1.5325</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 115:    93.7414   -5.8789   -2.0183   -4.2256   -0.9701    0.1105    5.6148    3.6180    0.9222    3.5507    0.1921    0.1993    5.6441    1.5458</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 116:    93.4104   -5.9704   -2.0428   -4.2091   -0.9807    0.1099    5.3341    4.1157    0.9363    3.4004    0.1968    0.2134    5.7764    1.4617</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 117:    93.5239   -5.9057   -2.0518   -4.2494   -0.9812    0.1127    6.8839    3.9099    0.9151    3.6604    0.1921    0.2095    5.4753    1.4249</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 118:    93.7599   -5.9418   -2.0482   -4.2272   -0.9822    0.1094    6.8133    3.7144    0.9198    3.5160    0.1971    0.2039    5.6467    1.4116</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 119:    93.6617   -6.0020   -2.0483   -4.2146   -0.9816    0.1003    6.4727    3.8251    0.9103    3.4716    0.1950    0.2109    5.8513    1.4268</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 120:    93.5436   -5.9804   -2.0458   -4.1906   -0.9819    0.1065    6.1490    3.6756    0.9088    3.2980    0.1989    0.2123    5.8268    1.4689</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 121:    93.4880   -5.9047   -2.0452   -4.1957   -0.9640    0.1349    5.8416    3.4918    0.8824    3.2437    0.1889    0.2017    5.7152    1.4382</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 122:    93.7406   -5.9844   -2.0596   -4.2328   -0.9558    0.1563    5.5495    3.7606    0.8489    3.3043    0.1795    0.2015    5.5095    1.5112</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 123:    93.6728   -6.0394   -2.0372   -4.2812   -0.9592    0.1507    5.2720    4.2004    0.8341    3.5274    0.1817    0.2008    5.6936    1.6011</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 124:    93.9591   -6.0483   -2.0280   -4.2613   -0.9594    0.1463    5.3846    4.1913    0.8351    3.4341    0.1870    0.2193    5.5694    1.5684</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 125:    94.0201   -5.9102   -2.0507   -4.2686   -0.9697    0.1455    5.1154    3.9818    0.8512    3.3475    0.1859    0.2165    5.6224    1.5643</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 126:    93.8825   -5.8970   -2.0543   -4.2569   -0.9663    0.1493    4.9144    3.7827    0.8907    3.3030    0.1876    0.2180    5.7351    1.4722</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 127:    93.9893   -5.8955   -2.0624   -4.2430   -0.9802    0.1476    7.2413    3.5935    0.8915    3.2740    0.1851    0.2110    5.7614    1.4305</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 128:    94.1849   -5.9123   -2.0624   -4.2385   -0.9786    0.1523    7.9575    3.4139    0.9094    3.2656    0.1807    0.2198    5.7366    1.4264</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 129:    94.0812   -6.0044   -2.0696   -4.3056   -0.9770    0.1693    8.7809    3.7526    0.9111    3.6172    0.1859    0.2255    5.8064    1.4718</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 130:    93.6046   -6.1387   -2.0718   -4.3056   -0.9821    0.1477    8.3419    4.3029    0.9177    3.6172    0.1867    0.2143    5.8893    1.4447</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 131:    93.5216   -6.1347   -2.0740   -4.3114   -0.9766    0.1288    8.2937    4.3407    0.9250    3.5298    0.1881    0.2293    5.8054    1.4219</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 132:    93.6142   -6.2789   -2.0786   -4.3297   -0.9716    0.1288    8.3731    5.1225    0.9236    3.6683    0.1929    0.2295    5.8064    1.4194</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 133:    93.4410   -5.9177   -2.0916   -4.3557   -0.9798    0.1066    7.9544    4.8663    0.9537    3.7076    0.1937    0.2279    5.9844    1.4297</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 134:    93.4716   -5.9152   -2.0838   -4.3611   -0.9818    0.1332    7.5567    4.6230    0.9161    3.7833    0.2017    0.2308    6.0611    1.5717</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 135:    93.3787   -6.0381   -2.0728   -4.2627   -0.9719    0.1051    7.2396    4.3919    0.8970    3.5941    0.1916    0.2193    5.8837    1.6057</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 136:    93.4339   -5.9876   -2.0801   -4.3002   -0.9690    0.1214    6.8776    4.1723    0.8888    3.4144    0.2002    0.2227    6.0141    1.5231</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 137:    93.7639   -6.0411   -2.0803   -4.2799   -0.9646    0.1484    6.5337    3.9765    0.8969    3.2437    0.1995    0.2115    5.9404    1.6402</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 138:    93.6414   -6.0122   -2.0714   -4.2666   -0.9755    0.1506    7.4057    3.7962    0.9242    3.1524    0.1934    0.2010    6.0666    1.5001</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 139:    93.7743   -5.7966   -2.0613   -4.2289   -0.9722    0.1383    7.9358    3.6064    0.9015    2.9948    0.1946    0.2029    5.9655    1.5250</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 140:    93.1082   -5.7994   -2.0388   -4.2289   -0.9659    0.1382    8.0282    3.4261    0.9053    2.9079    0.1909    0.2138    5.9183    1.5191</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 141:    93.2122   -6.0181   -2.0396   -4.2398   -0.9587    0.1016    8.9769    3.9426    0.9028    2.9136    0.1941    0.2195    6.1560    1.4902</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 142:    93.4684   -6.1438   -2.0273   -4.2541   -0.9508    0.0848    8.5281    4.8727    0.9056    2.9875    0.1901    0.2233    6.2546    1.4695</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 143:    93.0059   -6.0964   -2.0145   -4.2760   -0.9563    0.0826    8.1017    4.6291    0.9312    3.1063    0.1850    0.2138    6.5768    1.4391</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 144:    93.0612   -6.1127   -1.9951   -4.2589   -0.9539    0.0904    7.6966    4.6160    0.9623    3.1681    0.1824    0.2032    6.1506    1.4497</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 145:    93.5170   -6.1066   -1.9951   -4.3574   -0.9478    0.1040    7.3118    4.6263    0.9639    3.6914    0.1986    0.2292    5.9389    1.4867</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 146:    93.4915   -6.3235   -2.0006   -4.3866   -0.9579    0.1202    6.9462    6.0529    0.9514    3.9899    0.1887    0.2335    5.9265    1.4978</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 147:    93.8963   -6.1119   -2.0055   -4.3446   -0.9682    0.1242    7.1711    5.7503    0.9315    3.7904    0.1923    0.2332    5.9346    1.5021</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 148:    93.6758   -5.9705   -2.0137   -4.2906   -0.9614    0.1125    6.8125    5.4628    0.9506    3.6009    0.1913    0.2371    6.2579    1.4384</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 149:    93.6499   -6.0049   -2.0246   -4.2730   -0.9776    0.0996    6.4719    5.1896    0.9736    3.4209    0.1817    0.2252    6.3224    1.3878</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 150:    94.0242   -5.9760   -2.0176   -4.2182   -0.9773    0.1032    6.1483    4.9301    0.9944    3.2498    0.1807    0.2290    6.5662    1.3618</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 151:    93.9234   -5.8772   -2.0132   -4.2362   -0.9651    0.1058    7.2453    4.6836    0.9824    3.4860    0.1806    0.2175    6.2575    1.4370</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 152:    94.2513   -5.9391   -2.0814   -4.2278   -0.9753    0.1221    4.9753    3.4013    0.9808    3.3265    0.1836    0.1897    6.6966    1.3457</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 153:    93.9434   -6.1294   -2.0570   -4.2447   -0.9761    0.1527    5.0454    4.3115    0.9494    3.3663    0.1684    0.1680    5.9106    1.4777</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 154:    93.8141   -6.2749   -2.0366   -4.2383   -0.9835    0.1440    5.5466    5.3351    0.9320    3.3337    0.1731    0.1913    5.8842    1.4325</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 155:    94.1987   -6.1029   -2.0252   -4.2520   -0.9780    0.1132    7.1508    4.2713    0.9457    3.3428    0.1727    0.1691    6.1632    1.4658</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 156:    94.0626   -6.2640   -2.0263   -4.2461   -0.9854    0.1346    5.6296    5.1645    0.9423    3.3987    0.1697    0.1778    5.9631    1.4483</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 157:    93.9319   -6.1392   -2.0388   -4.2294   -0.9921    0.1265    5.6768    4.7366    0.9210    3.3673    0.1733    0.1789    5.9114    1.4976</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 158:    93.9123   -6.1970   -2.0165   -4.2241   -0.9924    0.1540    7.2552    4.7537    0.9369    3.2727    0.1805    0.1923    5.9324    1.5351</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 159:    93.9704   -6.4018   -2.0455   -4.2127   -0.9919    0.1413    7.7561    6.0732    0.9802    3.3376    0.1806    0.2260    6.4511    1.4227</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 160:    94.1967   -6.2985   -2.0412   -4.2264   -0.9795    0.1206    8.3836    6.1319    0.9689    3.4685    0.1792    0.2150    6.5116    1.4706</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 161:    94.3500   -6.1427   -2.0189   -4.2242   -1.0022    0.0837    8.0282    4.5505    0.9524    3.3269    0.1859    0.1816    6.0642    1.4746</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 162:    94.2711   -5.9578   -2.0215   -4.2078   -1.0110    0.0946    7.8634    3.3072    0.9559    3.2374    0.1876    0.1791    6.0798    1.4903</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 163:    93.9824   -5.8794   -2.0409   -4.2367   -0.9970    0.1150    9.3872    3.0498    0.9830    3.3179    0.1880    0.1828    5.8091    1.4852</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 164:    94.2013   -5.8651   -2.0122   -4.2257   -0.9906    0.1267    7.1953    3.0510    0.9697    3.2713    0.1871    0.1832    5.8741    1.5313</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 165:    94.1804   -5.7868   -2.0200   -4.2053   -0.9812    0.1219    6.7375    2.4769    0.9688    3.2706    0.1910    0.1859    5.7890    1.5188</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 166:    93.9790   -5.8156   -2.0311   -4.2438   -0.9784    0.1247    5.7617    2.6907    0.9533    3.5342    0.1953    0.1872    5.8816    1.5243</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 167:    93.2524   -5.8603   -2.0497   -4.2594   -0.9787    0.1265    4.7086    2.9121    0.9117    3.4696    0.1943    0.1832    5.9672    1.4567</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 168:    93.2924   -6.0371   -2.0528   -4.2607   -0.9727    0.1201    5.5273    4.0286    0.9177    3.4501    0.1918    0.1908    5.7790    1.4701</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 169:    93.4838   -6.1497   -2.0389   -4.2716   -0.9639    0.1127    5.4524    4.2700    0.9544    3.4329    0.1940    0.1871    5.7795    1.4575</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 170:    93.3951   -6.2298   -2.0438   -4.4133   -0.9939    0.1088    6.1460    4.6552    0.9645    4.5240    0.1978    0.2091    5.7549    1.5233</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 171:    93.4113   -6.1187   -2.0536   -4.4019   -0.9771    0.0899    6.8123    4.0073    0.9531    4.4290    0.1878    0.1970    5.9067    1.4857</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 172:    93.1140   -5.9515   -2.0530   -4.3250   -0.9723    0.1094    5.1247    3.4502    0.9572    3.8504    0.1954    0.1944    5.8583    1.4867</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 173:    92.9782   -6.0415   -2.0633   -4.2887   -0.9608    0.1081    4.1020    3.8967    0.9478    3.7222    0.1890    0.1812    5.9473    1.4583</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 174:    92.9661   -5.9295   -2.0457   -4.2907   -0.9626    0.0991    5.7954    3.3581    0.9785    3.7311    0.1867    0.2026    5.8087    1.4797</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 175:    93.2577   -5.8895   -2.0281   -4.2845   -0.9560    0.0829    7.3434    3.1501    0.9975    3.6334    0.1920    0.2151    5.4717    1.4832</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 176:    93.1210   -5.9567   -2.0370   -4.2848   -0.9488    0.0787    6.8946    3.4999    0.9983    3.6159    0.1922    0.2233    5.8426    1.4096</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 177:    92.5456   -6.1797   -2.0319   -4.2684   -0.9401    0.0873    6.9744    5.2939    0.9928    3.4880    0.1989    0.2213    5.9613    1.4367</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 178:    92.6854   -6.1483   -2.0278   -4.2705   -0.9504    0.0630    5.0582    5.0622    0.9953    3.4915    0.1930    0.2238    5.9775    1.4263</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 179:    93.1323   -6.1739   -2.0353   -4.2590   -0.9455    0.0584    4.9914    4.7898    0.9817    3.4163    0.1899    0.2124    5.9579    1.4242</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 180:    93.0611   -6.2228   -2.0441   -4.2977   -0.9387    0.0320    4.0323    5.5685    0.9890    3.7202    0.1940    0.2335    6.2224    1.4194</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 181:    92.7741   -6.1462   -2.0477   -4.3335   -0.9454    0.1011    3.7007    5.1590    0.9807    3.8469    0.1939    0.2463    5.9703    1.4343</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 182:    93.0775   -6.0640   -2.0496   -4.3171   -0.9444    0.0897    5.0266    4.5597    0.9792    3.7741    0.1931    0.2186    5.6727    1.4858</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 183:    93.2566   -6.1757   -2.0368   -4.2888   -0.9560    0.0809    5.8284    5.2504    0.9636    3.7078    0.1939    0.2262    5.5170    1.4560</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 184:    93.0357   -6.1158   -2.0217   -4.3111   -0.9453    0.0901    6.7209    5.4048    0.9503    3.8949    0.1967    0.2209    5.3578    1.4704</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 185:    93.0173   -6.1998   -2.0371   -4.3713   -0.9451    0.0752    6.1040    5.8272    0.9333    4.4038    0.1951    0.2238    5.5896    1.4202</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 186:    93.2835   -6.1217   -2.0383   -4.3308   -0.9574    0.1110    6.0519    4.7669    0.9400    4.0265    0.1972    0.2274    5.4560    1.4602</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 187:    93.5312   -6.3356   -2.0253   -4.3418   -0.9583    0.1166    6.7561    5.8784    0.9346    4.0264    0.2038    0.2281    5.5024    1.4994</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 188:    93.6460   -5.8426   -2.0237   -4.4519   -0.9594    0.1283    6.3492    3.4189    0.9091    4.9358    0.2086    0.2348    5.4301    1.5893</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 189:    93.8538   -6.0183   -2.0178   -4.3911   -0.9797    0.1262    8.7939    3.7358    0.9008    4.4894    0.2125    0.2199    5.6613    1.5073</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 190:    93.1543   -6.1364   -2.0451   -4.4389   -0.9708    0.1584    9.9803    4.2747    0.8922    4.8507    0.2084    0.2607    5.9136    1.4572</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 191:    93.4334   -6.1466   -2.0389   -4.4661   -0.9678    0.1565    7.8390    4.4393    0.9022    4.7857    0.2105    0.2634    5.7161    1.5325</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 192:    93.3623   -6.0940   -2.0240   -4.4569   -0.9673    0.1420    8.0856    4.3185    0.8895    4.4721    0.2113    0.2350    5.5282    1.5221</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 193:    93.1990   -5.9864   -2.0301   -4.4538   -0.9563    0.1515    8.4425    3.7598    0.8814    4.4376    0.2013    0.2257    5.4205    1.4820</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 194:    93.3165   -6.0045   -2.0353   -4.4314   -0.9525    0.1486    8.2370    3.6742    0.8947    4.4594    0.1960    0.2248    5.4579    1.4767</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 195:    93.1288   -6.1006   -2.0551   -4.5184   -0.9503    0.1583    9.6259    4.2294    0.9040    5.1981    0.1950    0.1962    5.5602    1.4254</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 196:    93.1943   -5.9871   -2.0607   -4.4728   -0.9446    0.1482    9.3401    3.5579    0.8925    4.7901    0.1892    0.1879    5.7296    1.4172</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 197:    93.5803   -5.9131   -2.0522   -4.3675   -0.9476    0.1571    7.2599    3.4241    0.8857    3.8551    0.1886    0.1793    5.4832    1.6006</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 198:    93.5703   -5.9980   -2.0550   -4.3578   -0.9519    0.1491    7.0416    3.8805    0.8563    3.7930    0.1882    0.1896    5.4355    1.5402</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 199:    93.2909   -5.8288   -2.0532   -4.3605   -0.9518    0.1692    8.3926    3.0173    0.8566    3.8610    0.1902    0.2033    5.5735    1.5647</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 200:    93.4049   -5.7474   -2.0447   -4.3548   -0.9517    0.1812    7.4977    2.8256    0.8520    3.8236    0.1897    0.2060    5.5092    1.5699</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 201:    93.3386   -5.8209   -2.0398   -4.4174   -0.9597    0.1738    6.3555    3.0871    0.8431    4.3211    0.1853    0.2062    5.5460    1.5925</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 202:    93.3397   -5.8313   -2.0387   -4.4364   -0.9580    0.1669    6.0946    3.1018    0.8460    4.5093    0.1821    0.2068    5.6622    1.5740</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 203:    93.3071   -5.8276   -2.0384   -4.4758   -0.9571    0.1639    5.7815    3.0575    0.8608    4.9441    0.1826    0.2052    5.6855    1.5570</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 204:    93.3138   -5.8477   -2.0368   -4.4714   -0.9541    0.1658    5.7526    3.1322    0.8704    4.9129    0.1816    0.2057    5.6642    1.5606</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 205:    93.3066   -5.8537   -2.0395   -4.4842   -0.9521    0.1642    5.6045    3.1633    0.8748    5.0053    0.1805    0.2036    5.6633    1.5550</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 206:    93.3042   -5.8790   -2.0453   -4.4977   -0.9501    0.1633    5.7219    3.3320    0.8807    5.1121    0.1793    0.1999    5.6888    1.5413</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 207:    93.3281   -5.9005   -2.0504   -4.5109   -0.9480    0.1629    5.8004    3.4696    0.8865    5.2248    0.1789    0.1961    5.7206    1.5357</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 208:    93.3437   -5.8972   -2.0569   -4.5200   -0.9452    0.1641    5.7523    3.4604    0.8875    5.2848    0.1787    0.1933    5.7450    1.5288</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 209:    93.3265   -5.8864   -2.0628   -4.5092   -0.9440    0.1639    5.5355    3.4138    0.8882    5.1586    0.1791    0.1916    5.7744    1.5283</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 210:    93.3087   -5.8812   -2.0671   -4.5004   -0.9426    0.1677    5.3488    3.3975    0.8895    5.0589    0.1798    0.1924    5.7781    1.5307</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 211:    93.2807   -5.8760   -2.0703   -4.5009   -0.9413    0.1709    5.2654    3.3770    0.8894    5.0377    0.1805    0.1927    5.7808    1.5282</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 212:    93.2815   -5.8637   -2.0711   -4.4955   -0.9409    0.1708    5.3028    3.3250    0.8914    4.9702    0.1819    0.1941    5.7827    1.5274</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 213:    93.2828   -5.8481   -2.0709   -4.4895   -0.9396    0.1702    5.3840    3.2614    0.8913    4.9108    0.1826    0.1953    5.7744    1.5301</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 214:    93.2645   -5.8422   -2.0704   -4.4882   -0.9384    0.1710    5.3939    3.2358    0.8931    4.8944    0.1828    0.1955    5.7797    1.5351</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 215:    93.2591   -5.8519   -2.0709   -4.4858   -0.9380    0.1713    5.5142    3.2959    0.8953    4.8587    0.1822    0.1960    5.7853    1.5369</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 216:    93.2595   -5.8523   -2.0715   -4.4827   -0.9376    0.1723    5.5563    3.3077    0.8964    4.8306    0.1817    0.1973    5.7975    1.5396</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 217:    93.2503   -5.8512   -2.0732   -4.4737   -0.9373    0.1734    5.5036    3.3182    0.8959    4.7582    0.1817    0.1992    5.7940    1.5365</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 218:    93.2345   -5.8463   -2.0749   -4.4639   -0.9372    0.1747    5.5046    3.2959    0.8959    4.6771    0.1817    0.2008    5.7889    1.5340</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 219:    93.2264   -5.8439   -2.0757   -4.4549   -0.9364    0.1759    5.4970    3.2802    0.8966    4.6015    0.1819    0.2027    5.7804    1.5321</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 220:    93.2259   -5.8464   -2.0758   -4.4493   -0.9363    0.1771    5.4793    3.2944    0.8950    4.5498    0.1823    0.2049    5.7745    1.5344</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 221:    93.2243   -5.8505   -2.0768   -4.4443   -0.9367    0.1785    5.5360    3.3132    0.8924    4.5028    0.1829    0.2068    5.7556    1.5329</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 222:    93.2385   -5.8603   -2.0776   -4.4371   -0.9376    0.1800    5.5401    3.3878    0.8903    4.4416    0.1834    0.2096    5.7522    1.5317</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 223:    93.2339   -5.8634   -2.0780   -4.4309   -0.9377    0.1803    5.5522    3.4132    0.8882    4.3844    0.1838    0.2118    5.7457    1.5323</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 224:    93.2379   -5.8686   -2.0778   -4.4262   -0.9374    0.1803    5.5469    3.4421    0.8848    4.3375    0.1842    0.2137    5.7429    1.5323</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 225:    93.2329   -5.8654   -2.0784   -4.4222   -0.9373    0.1811    5.5553    3.4255    0.8843    4.2952    0.1848    0.2160    5.7452    1.5295</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 226:    93.2330   -5.8621   -2.0789   -4.4182   -0.9366    0.1816    5.5838    3.4123    0.8838    4.2565    0.1858    0.2176    5.7453    1.5284</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 227:    93.2309   -5.8549   -2.0794   -4.4153   -0.9365    0.1823    5.6720    3.3787    0.8827    4.2227    0.1866    0.2181    5.7339    1.5287</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 228:    93.2248   -5.8556   -2.0794   -4.4116   -0.9372    0.1832    5.7344    3.3780    0.8830    4.1863    0.1873    0.2200    5.7232    1.5308</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 229:    93.2215   -5.8615   -2.0798   -4.4081   -0.9373    0.1844    5.8478    3.4087    0.8821    4.1558    0.1878    0.2217    5.7174    1.5283</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 230:    93.2122   -5.8646   -2.0807   -4.4053   -0.9372    0.1858    5.8987    3.4233    0.8800    4.1288    0.1881    0.2233    5.7073    1.5272</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 231:    93.2080   -5.8665   -2.0816   -4.4025   -0.9372    0.1872    5.9544    3.4381    0.8782    4.1008    0.1883    0.2250    5.7006    1.5283</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 232:    93.1921   -5.8677   -2.0829   -4.3997   -0.9370    0.1887    5.9768    3.4440    0.8770    4.0748    0.1883    0.2268    5.7012    1.5261</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 233:    93.1794   -5.8674   -2.0840   -4.3972   -0.9363    0.1892    6.0074    3.4397    0.8757    4.0495    0.1884    0.2281    5.6997    1.5235</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 234:    93.1623   -5.8677   -2.0853   -4.3959   -0.9358    0.1898    5.9759    3.4442    0.8750    4.0330    0.1887    0.2295    5.7000    1.5223</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 235:    93.1499   -5.8709   -2.0862   -4.3918   -0.9356    0.1900    5.9951    3.4709    0.8747    4.0020    0.1891    0.2309    5.7002    1.5219</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 236:    93.1408   -5.8764   -2.0875   -4.3879   -0.9349    0.1898    6.0359    3.5027    0.8752    3.9720    0.1895    0.2321    5.7098    1.5196</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 237:    93.1307   -5.8766   -2.0887   -4.3843   -0.9344    0.1896    6.0589    3.5108    0.8755    3.9437    0.1900    0.2330    5.7176    1.5174</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 238:    93.1233   -5.8767   -2.0889   -4.3806   -0.9341    0.1891    6.0959    3.5158    0.8745    3.9173    0.1907    0.2339    5.7198    1.5169</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 239:    93.1245   -5.8810   -2.0889   -4.3775   -0.9337    0.1885    6.1196    3.5614    0.8746    3.8935    0.1915    0.2349    5.7177    1.5172</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 240:    93.1180   -5.8836   -2.0893   -4.3745   -0.9332    0.1883    6.1647    3.6004    0.8741    3.8709    0.1921    0.2360    5.7150    1.5192</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 241:    93.1096   -5.8838   -2.0898   -4.3714   -0.9327    0.1883    6.2283    3.6196    0.8743    3.8487    0.1927    0.2366    5.7177    1.5202</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 242:    93.1058   -5.8804   -2.0912   -4.3684   -0.9320    0.1888    6.2553    3.6018    0.8723    3.8274    0.1933    0.2378    5.7265    1.5198</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 243:    93.0953   -5.8762   -2.0924   -4.3668   -0.9315    0.1892    6.2692    3.5785    0.8705    3.8130    0.1940    0.2391    5.7353    1.5205</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 244:    93.0840   -5.8758   -2.0928   -4.3658   -0.9310    0.1889    6.2702    3.5746    0.8695    3.8007    0.1947    0.2401    5.7382    1.5205</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 245:    93.0720   -5.8795   -2.0933   -4.3647   -0.9306    0.1895    6.3022    3.5971    0.8685    3.7886    0.1953    0.2407    5.7367    1.5192</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 246:    93.0626   -5.8798   -2.0933   -4.3637   -0.9301    0.1898    6.2987    3.5992    0.8680    3.7781    0.1957    0.2410    5.7331    1.5184</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 247:    93.0526   -5.8805   -2.0934   -4.3610   -0.9298    0.1903    6.3067    3.6060    0.8682    3.7618    0.1963    0.2414    5.7329    1.5189</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 248:    93.0481   -5.8780   -2.0933   -4.3583   -0.9296    0.1911    6.3135    3.6007    0.8683    3.7460    0.1967    0.2420    5.7344    1.5191</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 249:    93.0483   -5.8762   -2.0933   -4.3558   -0.9294    0.1913    6.3095    3.5961    0.8685    3.7298    0.1970    0.2422    5.7414    1.5179</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 250:    93.0520   -5.8768   -2.0931   -4.3538   -0.9292    0.1912    6.2900    3.6003    0.8692    3.7176    0.1973    0.2424    5.7547    1.5148</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 251:    93.0430   -5.8769   -2.0930   -4.3516   -0.9291    0.1905    6.2815    3.6041    0.8704    3.7045    0.1975    0.2427    5.7653    1.5123</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 252:    93.0300   -5.8743   -2.0928   -4.3490   -0.9291    0.1901    6.2896    3.5885    0.8716    3.6919    0.1978    0.2428    5.7797    1.5106</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 253:    93.0217   -5.8740   -2.0926   -4.3468   -0.9289    0.1898    6.3238    3.5875    0.8731    3.6817    0.1981    0.2429    5.7885    1.5102</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 254:    93.0147   -5.8729   -2.0924   -4.3439   -0.9289    0.1892    6.3418    3.5857    0.8732    3.6683    0.1980    0.2426    5.7912    1.5102</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 255:    93.0144   -5.8743   -2.0922   -4.3407   -0.9290    0.1885    6.3755    3.5933    0.8735    3.6580    0.1979    0.2420    5.7932    1.5086</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 256:    93.0136   -5.8778   -2.0919   -4.3376   -0.9290    0.1876    6.3932    3.6240    0.8741    3.6481    0.1980    0.2418    5.7969    1.5066</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 257:    93.0116   -5.8792   -2.0917   -4.3345   -0.9291    0.1862    6.4096    3.6459    0.8744    3.6385    0.1980    0.2414    5.7990    1.5065</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 258:    93.0084   -5.8812   -2.0913   -4.3319   -0.9290    0.1842    6.4231    3.6686    0.8753    3.6281    0.1980    0.2414    5.8024    1.5050</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 259:    93.0090   -5.8866   -2.0909   -4.3293   -0.9287    0.1825    6.4361    3.7063    0.8762    3.6181    0.1981    0.2413    5.8106    1.5030</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 260:    93.0067   -5.8911   -2.0902   -4.3265   -0.9283    0.1811    6.4128    3.7384    0.8765    3.6076    0.1981    0.2412    5.8102    1.5026</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 261:    93.0060   -5.8933   -2.0894   -4.3237   -0.9284    0.1799    6.4253    3.7604    0.8765    3.5968    0.1981    0.2410    5.8080    1.5026</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 262:    93.0051   -5.8934   -2.0884   -4.3208   -0.9285    0.1789    6.4008    3.7597    0.8762    3.5855    0.1981    0.2412    5.8046    1.5020</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 263:    93.0019   -5.8945   -2.0875   -4.3182   -0.9287    0.1781    6.3788    3.7644    0.8758    3.5756    0.1982    0.2411    5.8048    1.5023</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 264:    93.0021   -5.8959   -2.0870   -4.3158   -0.9293    0.1773    6.3614    3.7682    0.8749    3.5667    0.1983    0.2410    5.8017    1.5021</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 265:    93.0053   -5.8989   -2.0866   -4.3130   -0.9296    0.1766    6.3506    3.7814    0.8739    3.5567    0.1982    0.2409    5.7995    1.5018</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 266:    93.0061   -5.8992   -2.0864   -4.3104   -0.9300    0.1757    6.3307    3.7730    0.8733    3.5471    0.1982    0.2408    5.7994    1.5012</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 267:    93.0098   -5.9009   -2.0861   -4.3077   -0.9302    0.1749    6.3326    3.7738    0.8730    3.5388    0.1983    0.2407    5.7964    1.5004</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 268:    93.0144   -5.9000   -2.0853   -4.3054   -0.9304    0.1740    6.3487    3.7623    0.8729    3.5290    0.1985    0.2405    5.7897    1.5014</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 269:    93.0146   -5.8984   -2.0847   -4.3032   -0.9306    0.1731    6.3716    3.7485    0.8735    3.5197    0.1985    0.2402    5.7867    1.5016</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 270:    93.0159   -5.8950   -2.0842   -4.3013   -0.9307    0.1722    6.3630    3.7266    0.8743    3.5098    0.1986    0.2400    5.7837    1.5016</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 271:    93.0161   -5.8925   -2.0837   -4.2995   -0.9309    0.1715    6.3539    3.7068    0.8744    3.5001    0.1986    0.2400    5.7843    1.5020</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 272:    93.0195   -5.8919   -2.0837   -4.2977   -0.9314    0.1710    6.3430    3.6964    0.8744    3.4922    0.1985    0.2400    5.7859    1.5031</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 273:    93.0184   -5.8923   -2.0837   -4.2961   -0.9318    0.1705    6.3531    3.6914    0.8743    3.4854    0.1984    0.2402    5.7880    1.5030</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 274:    93.0178   -5.8927   -2.0834   -4.2951   -0.9320    0.1701    6.3818    3.6865    0.8749    3.4824    0.1984    0.2403    5.7928    1.5020</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 275:    93.0204   -5.8937   -2.0833   -4.2942   -0.9324    0.1700    6.3961    3.6842    0.8754    3.4790    0.1984    0.2402    5.7949    1.5011</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 276:    93.0228   -5.8958   -2.0833   -4.2932   -0.9327    0.1699    6.3965    3.6870    0.8760    3.4752    0.1984    0.2402    5.7934    1.5002</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 277:    93.0251   -5.9000   -2.0833   -4.2921   -0.9329    0.1695    6.4076    3.7032    0.8766    3.4712    0.1985    0.2400    5.7979    1.4989</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 278:    93.0297   -5.9021   -2.0833   -4.2912   -0.9331    0.1690    6.4279    3.7073    0.8771    3.4671    0.1986    0.2399    5.7978    1.4981</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 279:    93.0273   -5.9021   -2.0832   -4.2902   -0.9332    0.1684    6.4349    3.7003    0.8779    3.4622    0.1988    0.2400    5.7993    1.4972</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 280:    93.0236   -5.9030   -2.0831   -4.2892   -0.9334    0.1674    6.4776    3.6986    0.8787    3.4576    0.1990    0.2401    5.8026    1.4963</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 281:    93.0172   -5.9037   -2.0829   -4.2881   -0.9337    0.1665    6.4942    3.6984    0.8797    3.4536    0.1992    0.2402    5.8056    1.4955</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 282:    93.0147   -5.9055   -2.0827   -4.2872   -0.9339    0.1656    6.4861    3.7031    0.8810    3.4492    0.1992    0.2403    5.8107    1.4954</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 283:    93.0138   -5.9053   -2.0824   -4.2865   -0.9341    0.1646    6.4962    3.6967    0.8822    3.4451    0.1993    0.2406    5.8142    1.4959</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 284:    93.0132   -5.9067   -2.0821   -4.2864   -0.9343    0.1636    6.5021    3.7013    0.8838    3.4437    0.1993    0.2406    5.8146    1.4955</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 285:    93.0138   -5.9081   -2.0819   -4.2859   -0.9343    0.1628    6.5037    3.7043    0.8851    3.4406    0.1994    0.2406    5.8146    1.4945</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 286:    93.0119   -5.9086   -2.0815   -4.2858   -0.9342    0.1620    6.5068    3.7037    0.8864    3.4395    0.1994    0.2404    5.8133    1.4936</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 287:    93.0122   -5.9089   -2.0813   -4.2860   -0.9342    0.1614    6.5202    3.7044    0.8872    3.4399    0.1997    0.2401    5.8096    1.4929</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 288:    93.0104   -5.9083   -2.0812   -4.2858   -0.9342    0.1609    6.5237    3.6997    0.8876    3.4376    0.1999    0.2398    5.8041    1.4924</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 289:    93.0076   -5.9066   -2.0812   -4.2854   -0.9342    0.1604    6.5121    3.6893    0.8881    3.4342    0.1999    0.2394    5.8021    1.4922</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 290:    93.0064   -5.9052   -2.0813   -4.2851   -0.9343    0.1602    6.5106    3.6772    0.8886    3.4309    0.2000    0.2389    5.7988    1.4915</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 291:    93.0071   -5.9031   -2.0813   -4.2849   -0.9345    0.1601    6.5031    3.6628    0.8891    3.4284    0.2000    0.2384    5.7959    1.4908</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 292:    93.0114   -5.9023   -2.0809   -4.2841   -0.9346    0.1594    6.4930    3.6538    0.8894    3.4237    0.2001    0.2383    5.7923    1.4902</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 293:    93.0148   -5.9032   -2.0807   -4.2834   -0.9348    0.1589    6.4836    3.6542    0.8898    3.4206    0.2002    0.2380    5.7893    1.4893</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 294:    93.0161   -5.9026   -2.0806   -4.2826   -0.9349    0.1582    6.4719    3.6469    0.8901    3.4176    0.2005    0.2375    5.7898    1.4886</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 295:    93.0212   -5.9008   -2.0806   -4.2817   -0.9350    0.1576    6.4649    3.6372    0.8904    3.4145    0.2007    0.2370    5.7885    1.4890</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 296:    93.0270   -5.8989   -2.0805   -4.2809   -0.9351    0.1569    6.4835    3.6279    0.8911    3.4124    0.2010    0.2366    5.7886    1.4884</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 297:    93.0291   -5.8969   -2.0803   -4.2800   -0.9352    0.1560    6.5014    3.6177    0.8915    3.4094    0.2012    0.2364    5.7873    1.4880</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 298:    93.0332   -5.8963   -2.0801   -4.2790   -0.9352    0.1551    6.5168    3.6111    0.8919    3.4065    0.2014    0.2361    5.7855    1.4875</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 299:    93.0339   -5.8961   -2.0799   -4.2782   -0.9352    0.1542    6.5288    3.6081    0.8927    3.4038    0.2015    0.2358    5.7844    1.4869</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 300:    93.0335   -5.8971   -2.0797   -4.2772   -0.9351    0.1533    6.5409    3.6097    0.8937    3.4009    0.2017    0.2356    5.7844    1.4859</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 301:    93.0320   -5.8979   -2.0796   -4.2762   -0.9350    0.1522    6.5553    3.6126    0.8945    3.3977    0.2020    0.2354    5.7898    1.4847</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 302:    93.0321   -5.8997   -2.0795   -4.2751   -0.9351    0.1513    6.5737    3.6195    0.8956    3.3943    0.2023    0.2351    5.7917    1.4835</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 303:    93.0328   -5.8984   -2.0792   -4.2739   -0.9351    0.1504    6.5888    3.6111    0.8964    3.3912    0.2025    0.2350    5.7915    1.4825</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 304:    93.0357   -5.8969   -2.0790   -4.2728   -0.9350    0.1494    6.5907    3.6018    0.8975    3.3882    0.2026    0.2348    5.7929    1.4813</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 305:    93.0351   -5.8953   -2.0786   -4.2718   -0.9349    0.1484    6.5764    3.5916    0.8986    3.3858    0.2027    0.2345    5.7937    1.4816</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 306:    93.0352   -5.8950   -2.0784   -4.2707   -0.9350    0.1475    6.5727    3.5890    0.8999    3.3835    0.2028    0.2341    5.7981    1.4810</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 307:    93.0354   -5.8947   -2.0784   -4.2699   -0.9351    0.1466    6.5759    3.5853    0.9010    3.3820    0.2030    0.2339    5.8028    1.4799</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 308:    93.0336   -5.8938   -2.0783   -4.2690   -0.9351    0.1459    6.5855    3.5776    0.9022    3.3809    0.2031    0.2333    5.8014    1.4788</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 309:    93.0311   -5.8931   -2.0780   -4.2683   -0.9351    0.1452    6.5799    3.5717    0.9038    3.3805    0.2033    0.2328    5.8048    1.4779</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 310:    93.0303   -5.8915   -2.0778   -4.2675   -0.9352    0.1447    6.5716    3.5609    0.9046    3.3797    0.2033    0.2323    5.8060    1.4774</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 311:    93.0275   -5.8915   -2.0776   -4.2668   -0.9352    0.1441    6.5679    3.5581    0.9052    3.3788    0.2034    0.2319    5.8056    1.4770</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 312:    93.0258   -5.8920   -2.0775   -4.2659   -0.9352    0.1435    6.5573    3.5571    0.9058    3.3775    0.2033    0.2314    5.8053    1.4766</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 313:    93.0234   -5.8928   -2.0773   -4.2649   -0.9353    0.1431    6.5510    3.5573    0.9065    3.3761    0.2033    0.2309    5.8046    1.4757</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 314:    93.0237   -5.8937   -2.0771   -4.2639   -0.9355    0.1425    6.5488    3.5578    0.9074    3.3747    0.2033    0.2303    5.8029    1.4751</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 315:    93.0237   -5.8940   -2.0769   -4.2629   -0.9357    0.1420    6.5480    3.5565    0.9081    3.3730    0.2034    0.2299    5.8010    1.4746</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 316:    93.0218   -5.8936   -2.0767   -4.2617   -0.9358    0.1413    6.5424    3.5517    0.9087    3.3705    0.2034    0.2296    5.7992    1.4741</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 317:    93.0218   -5.8932   -2.0766   -4.2606   -0.9359    0.1406    6.5576    3.5474    0.9091    3.3679    0.2034    0.2292    5.7965    1.4736</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 318:    93.0215   -5.8927   -2.0765   -4.2597   -0.9361    0.1402    6.5884    3.5420    0.9096    3.3661    0.2034    0.2288    5.7960    1.4727</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 319:    93.0201   -5.8938   -2.0764   -4.2588   -0.9361    0.1397    6.6095    3.5439    0.9101    3.3642    0.2034    0.2283    5.7943    1.4719</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 320:    93.0188   -5.8926   -2.0763   -4.2579   -0.9361    0.1392    6.6170    3.5368    0.9103    3.3622    0.2034    0.2282    5.7930    1.4711</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 321:    93.0155   -5.8908   -2.0762   -4.2569   -0.9361    0.1385    6.6328    3.5268    0.9105    3.3598    0.2033    0.2282    5.7921    1.4702</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 322:    93.0133   -5.8894   -2.0760   -4.2561   -0.9360    0.1378    6.6415    3.5192    0.9110    3.3580    0.2032    0.2282    5.7903    1.4698</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 323:    93.0089   -5.8888   -2.0759   -4.2555   -0.9360    0.1372    6.6480    3.5131    0.9107    3.3563    0.2031    0.2285    5.7915    1.4691</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 324:    93.0038   -5.8881   -2.0758   -4.2547   -0.9359    0.1364    6.6639    3.5076    0.9106    3.3547    0.2029    0.2287    5.7912    1.4687</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 325:    93.0011   -5.8871   -2.0756   -4.2540   -0.9359    0.1361    6.6587    3.5005    0.9102    3.3531    0.2028    0.2289    5.7896    1.4686</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 326:    93.0033   -5.8874   -2.0755   -4.2535   -0.9360    0.1360    6.6621    3.4995    0.9098    3.3510    0.2026    0.2294    5.7883    1.4686</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 327:    93.0039   -5.8885   -2.0754   -4.2532   -0.9361    0.1359    6.6589    3.5051    0.9093    3.3497    0.2025    0.2297    5.7869    1.4687</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 328:    93.0061   -5.8896   -2.0753   -4.2529   -0.9362    0.1355    6.6671    3.5132    0.9088    3.3484    0.2024    0.2299    5.7854    1.4687</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 329:    93.0077   -5.8915   -2.0755   -4.2525   -0.9363    0.1352    6.6719    3.5255    0.9085    3.3464    0.2024    0.2301    5.7850    1.4682</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 330:    93.0061   -5.8942   -2.0757   -4.2518   -0.9365    0.1351    6.6696    3.5399    0.9083    3.3438    0.2023    0.2302    5.7840    1.4680</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 331:    93.0032   -5.8953   -2.0759   -4.2513   -0.9366    0.1348    6.6554    3.5433    0.9080    3.3411    0.2022    0.2302    5.7839    1.4677</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 332:    93.0013   -5.8956   -2.0761   -4.2507   -0.9367    0.1347    6.6298    3.5423    0.9079    3.3385    0.2021    0.2302    5.7851    1.4672</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 333:    93.0026   -5.8950   -2.0764   -4.2502   -0.9368    0.1346    6.6207    3.5365    0.9078    3.3357    0.2021    0.2303    5.7849    1.4667</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 334:    93.0019   -5.8933   -2.0767   -4.2497   -0.9369    0.1348    6.6145    3.5259    0.9077    3.3330    0.2021    0.2302    5.7856    1.4662</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 335:    93.0038   -5.8930   -2.0768   -4.2492   -0.9370    0.1348    6.6200    3.5227    0.9078    3.3307    0.2020    0.2303    5.7845    1.4654</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 336:    93.0038   -5.8923   -2.0765   -4.2488   -0.9371    0.1348    6.6316    3.5180    0.9081    3.3291    0.2019    0.2304    5.7837    1.4654</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 337:    93.0074   -5.8927   -2.0764   -4.2485   -0.9373    0.1349    6.6509    3.5187    0.9083    3.3275    0.2018    0.2304    5.7808    1.4655</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 338:    93.0117   -5.8950   -2.0761   -4.2483   -0.9377    0.1349    6.6559    3.5291    0.9087    3.3263    0.2018    0.2303    5.7770    1.4657</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 339:    93.0172   -5.8960   -2.0759   -4.2482   -0.9380    0.1349    6.6610    3.5304    0.9090    3.3260    0.2017    0.2302    5.7744    1.4657</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 340:    93.0179   -5.8977   -2.0757   -4.2481   -0.9383    0.1349    6.6583    3.5340    0.9093    3.3263    0.2017    0.2301    5.7749    1.4650</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 341:    93.0201   -5.8986   -2.0755   -4.2484   -0.9386    0.1348    6.6603    3.5337    0.9092    3.3283    0.2018    0.2300    5.7738    1.4650</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 342:    93.0245   -5.8990   -2.0752   -4.2484   -0.9389    0.1348    6.6680    3.5324    0.9093    3.3297    0.2018    0.2300    5.7727    1.4649</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 343:    93.0302   -5.9006   -2.0751   -4.2484   -0.9391    0.1347    6.6715    3.5367    0.9093    3.3313    0.2017    0.2300    5.7729    1.4645</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 344:    93.0340   -5.9026   -2.0749   -4.2484   -0.9394    0.1346    6.6793    3.5438    0.9093    3.3326    0.2017    0.2301    5.7709    1.4646</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 345:    93.0372   -5.9049   -2.0746   -4.2484   -0.9397    0.1347    6.6874    3.5515    0.9090    3.3340    0.2017    0.2301    5.7688    1.4648</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 346:    93.0372   -5.9063   -2.0743   -4.2483   -0.9399    0.1348    6.6963    3.5592    0.9090    3.3348    0.2018    0.2299    5.7680    1.4656</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 347:    93.0383   -5.9075   -2.0742   -4.2481   -0.9402    0.1350    6.7101    3.5658    0.9093    3.3353    0.2018    0.2299    5.7672    1.4649</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 348:    93.0412   -5.9084   -2.0742   -4.2479   -0.9405    0.1351    6.7183    3.5707    0.9095    3.3356    0.2019    0.2297    5.7657    1.4645</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 349:    93.0436   -5.9097   -2.0742   -4.2477   -0.9407    0.1351    6.7143    3.5783    0.9098    3.3359    0.2019    0.2295    5.7646    1.4643</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 350:    93.0476   -5.9105   -2.0742   -4.2474   -0.9409    0.1351    6.7239    3.5808    0.9100    3.3354    0.2019    0.2294    5.7628    1.4639</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 351:    93.0506   -5.9113   -2.0741   -4.2473   -0.9411    0.1352    6.7270    3.5825    0.9103    3.3356    0.2019    0.2292    5.7604    1.4637</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 352:    93.0529   -5.9127   -2.0740   -4.2471   -0.9413    0.1353    6.7312    3.5886    0.9107    3.3358    0.2019    0.2290    5.7594    1.4634</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 353:    93.0580   -5.9139   -2.0739   -4.2470   -0.9415    0.1354    6.7315    3.5922    0.9111    3.3357    0.2019    0.2288    5.7571    1.4636</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 354:    93.0639   -5.9129   -2.0738   -4.2468   -0.9417    0.1354    6.7390    3.5876    0.9112    3.3356    0.2018    0.2286    5.7541    1.4642</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 355:    93.0671   -5.9131   -2.0737   -4.2467   -0.9417    0.1353    6.7348    3.5906    0.9113    3.3354    0.2017    0.2284    5.7520    1.4648</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 356:    93.0682   -5.9134   -2.0737   -4.2465   -0.9418    0.1352    6.7329    3.5962    0.9113    3.3353    0.2017    0.2283    5.7505    1.4649</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 357:    93.0698   -5.9128   -2.0738   -4.2471   -0.9418    0.1354    6.7397    3.5933    0.9115    3.3388    0.2016    0.2280    5.7512    1.4651</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 358:    93.0709   -5.9129   -2.0740   -4.2475   -0.9419    0.1355    6.7379    3.5915    0.9119    3.3416    0.2016    0.2278    5.7526    1.4644</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 359:    93.0718   -5.9128   -2.0742   -4.2478   -0.9419    0.1358    6.7428    3.5886    0.9123    3.3432    0.2015    0.2275    5.7516    1.4641</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 360:    93.0693   -5.9136   -2.0743   -4.2481   -0.9419    0.1360    6.7385    3.5930    0.9125    3.3443    0.2015    0.2272    5.7511    1.4636</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 361:    93.0674   -5.9148   -2.0744   -4.2484   -0.9419    0.1361    6.7230    3.6002    0.9127    3.3458    0.2015    0.2270    5.7514    1.4634</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 362:    93.0660   -5.9168   -2.0745   -4.2486   -0.9420    0.1361    6.7313    3.6117    0.9130    3.3473    0.2014    0.2270    5.7506    1.4636</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 363:    93.0635   -5.9196   -2.0746   -4.2490   -0.9421    0.1360    6.7388    3.6275    0.9132    3.3500    0.2014    0.2269    5.7493    1.4636</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 364:    93.0631   -5.9210   -2.0747   -4.2497   -0.9421    0.1361    6.7383    3.6323    0.9135    3.3548    0.2015    0.2268    5.7483    1.4634</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 365:    93.0635   -5.9219   -2.0747   -4.2504   -0.9421    0.1361    6.7402    3.6341    0.9137    3.3590    0.2015    0.2268    5.7461    1.4635</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 366:    93.0640   -5.9232   -2.0746   -4.2511   -0.9422    0.1362    6.7477    3.6409    0.9142    3.3624    0.2015    0.2267    5.7451    1.4641</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 367:    93.0616   -5.9247   -2.0746   -4.2518   -0.9422    0.1364    6.7557    3.6473    0.9148    3.3653    0.2015    0.2269    5.7472    1.4640</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 368:    93.0601   -5.9247   -2.0746   -4.2522   -0.9422    0.1366    6.7632    3.6452    0.9150    3.3678    0.2015    0.2270    5.7482    1.4639</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 369:    93.0583   -5.9240   -2.0748   -4.2527   -0.9423    0.1369    6.7737    3.6395    0.9148    3.3695    0.2015    0.2273    5.7499    1.4636</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 370:    93.0591   -5.9236   -2.0752   -4.2532   -0.9423    0.1373    6.7721    3.6352    0.9145    3.3718    0.2015    0.2276    5.7513    1.4636</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 371:    93.0607   -5.9235   -2.0755   -4.2540   -0.9424    0.1378    6.7682    3.6330    0.9143    3.3754    0.2015    0.2280    5.7535    1.4635</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 372:    93.0615   -5.9229   -2.0759   -4.2549   -0.9424    0.1382    6.7640    3.6288    0.9142    3.3795    0.2014    0.2284    5.7553    1.4633</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 373:    93.0612   -5.9237   -2.0763   -4.2557   -0.9424    0.1385    6.7641    3.6327    0.9140    3.3832    0.2012    0.2288    5.7570    1.4629</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 374:    93.0611   -5.9240   -2.0766   -4.2565   -0.9424    0.1389    6.7701    3.6341    0.9137    3.3872    0.2011    0.2293    5.7599    1.4625</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 375:    93.0615   -5.9247   -2.0770   -4.2573   -0.9424    0.1393    6.7729    3.6362    0.9134    3.3912    0.2009    0.2296    5.7629    1.4620</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 376:    93.0621   -5.9248   -2.0772   -4.2578   -0.9425    0.1397    6.7732    3.6371    0.9132    3.3931    0.2008    0.2298    5.7654    1.4613</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 377:    93.0622   -5.9255   -2.0774   -4.2582   -0.9426    0.1401    6.7681    3.6389    0.9130    3.3953    0.2007    0.2300    5.7678    1.4607</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 378:    93.0609   -5.9256   -2.0775   -4.2585   -0.9426    0.1402    6.7705    3.6381    0.9128    3.3972    0.2006    0.2301    5.7673    1.4602</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 379:    93.0590   -5.9262   -2.0776   -4.2589   -0.9426    0.1404    6.7777    3.6382    0.9127    3.3991    0.2005    0.2303    5.7668    1.4599</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 380:    93.0607   -5.9258   -2.0777   -4.2595   -0.9427    0.1407    6.7836    3.6350    0.9127    3.4031    0.2004    0.2305    5.7665    1.4598</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 381:    93.0617   -5.9251   -2.0777   -4.2596   -0.9427    0.1410    6.7880    3.6294    0.9125    3.4030    0.2003    0.2307    5.7651    1.4601</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 382:    93.0631   -5.9252   -2.0777   -4.2599   -0.9428    0.1413    6.7912    3.6278    0.9123    3.4038    0.2002    0.2310    5.7649    1.4606</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 383:    93.0621   -5.9253   -2.0778   -4.2602   -0.9429    0.1415    6.7834    3.6279    0.9119    3.4053    0.2000    0.2312    5.7657    1.4607</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 384:    93.0614   -5.9254   -2.0779   -4.2604   -0.9430    0.1416    6.7853    3.6280    0.9115    3.4066    0.1999    0.2313    5.7662    1.4608</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 385:    93.0613   -5.9259   -2.0780   -4.2605   -0.9430    0.1418    6.7757    3.6301    0.9112    3.4066    0.1997    0.2315    5.7678    1.4609</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 386:    93.0614   -5.9264   -2.0780   -4.2607   -0.9431    0.1418    6.7610    3.6331    0.9110    3.4073    0.1995    0.2317    5.7696    1.4612</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 387:    93.0631   -5.9276   -2.0780   -4.2610   -0.9432    0.1420    6.7595    3.6397    0.9108    3.4085    0.1993    0.2318    5.7716    1.4612</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 388:    93.0650   -5.9282   -2.0779   -4.2613   -0.9433    0.1421    6.7552    3.6445    0.9106    3.4092    0.1992    0.2318    5.7731    1.4612</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 389:    93.0644   -5.9286   -2.0779   -4.2616   -0.9434    0.1422    6.7488    3.6471    0.9104    3.4098    0.1991    0.2317    5.7724    1.4614</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 390:    93.0653   -5.9297   -2.0778   -4.2619   -0.9435    0.1423    6.7412    3.6524    0.9101    3.4103    0.1990    0.2317    5.7722    1.4615</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 391:    93.0647   -5.9297   -2.0778   -4.2623   -0.9435    0.1425    6.7508    3.6524    0.9101    3.4115    0.1989    0.2317    5.7729    1.4621</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 392:    93.0637   -5.9293   -2.0778   -4.2624   -0.9436    0.1427    6.7572    3.6498    0.9102    3.4109    0.1988    0.2317    5.7727    1.4623</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 393:    93.0657   -5.9294   -2.0778   -4.2632   -0.9436    0.1429    6.7607    3.6496    0.9104    3.4148    0.1987    0.2318    5.7719    1.4621</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 394:    93.0689   -5.9293   -2.0779   -4.2635   -0.9438    0.1431    6.7635    3.6489    0.9108    3.4141    0.1987    0.2318    5.7724    1.4623</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 395:    93.0705   -5.9295   -2.0780   -4.2640   -0.9438    0.1433    6.7753    3.6500    0.9110    3.4145    0.1986    0.2319    5.7724    1.4622</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 396:    93.0704   -5.9296   -2.0780   -4.2648   -0.9438    0.1435    6.7793    3.6511    0.9112    3.4175    0.1985    0.2319    5.7720    1.4618</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 397:    93.0715   -5.9302   -2.0781   -4.2656   -0.9438    0.1437    6.7782    3.6530    0.9114    3.4206    0.1985    0.2320    5.7706    1.4617</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 398:    93.0719   -5.9297   -2.0781   -4.2666   -0.9438    0.1439    6.7774    3.6510    0.9120    3.4258    0.1984    0.2319    5.7709    1.4616</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 399:    93.0720   -5.9296   -2.0781   -4.2678   -0.9438    0.1441    6.7819    3.6526    0.9126    3.4317    0.1984    0.2319    5.7717    1.4615</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 400:    93.0730   -5.9296   -2.0783   -4.2691   -0.9438    0.1443    6.7786    3.6538    0.9129    3.4389    0.1983    0.2319    5.7716    1.4614</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 401:    93.0728   -5.9292   -2.0783   -4.2706   -0.9437    0.1445    6.7731    3.6521    0.9133    3.4478    0.1982    0.2319    5.7701    1.4615</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 402:    93.0732   -5.9289   -2.0784   -4.2718   -0.9438    0.1447    6.7698    3.6517    0.9137    3.4542    0.1981    0.2319    5.7689    1.4618</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 403:    93.0732   -5.9301   -2.0785   -4.2730   -0.9438    0.1450    6.7640    3.6576    0.9142    3.4593    0.1980    0.2320    5.7693    1.4615</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 404:    93.0710   -5.9316   -2.0787   -4.2740   -0.9439    0.1453    6.7544    3.6647    0.9147    3.4644    0.1979    0.2320    5.7708    1.4611</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 405:    93.0687   -5.9322   -2.0788   -4.2750   -0.9440    0.1454    6.7547    3.6663    0.9153    3.4693    0.1978    0.2322    5.7714    1.4608</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 406:    93.0673   -5.9337   -2.0789   -4.2760   -0.9440    0.1456    6.7563    3.6726    0.9163    3.4742    0.1977    0.2324    5.7718    1.4603</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 407:    93.0653   -5.9345   -2.0791   -4.2769   -0.9440    0.1456    6.7601    3.6756    0.9169    3.4786    0.1976    0.2327    5.7732    1.4598</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 408:    93.0640   -5.9357   -2.0793   -4.2779   -0.9440    0.1455    6.7547    3.6821    0.9177    3.4838    0.1974    0.2329    5.7751    1.4592</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 409:    93.0646   -5.9371   -2.0795   -4.2784   -0.9440    0.1456    6.7486    3.6915    0.9184    3.4863    0.1973    0.2330    5.7766    1.4588</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 410:    93.0639   -5.9391   -2.0797   -4.2791   -0.9441    0.1457    6.7523    3.7048    0.9192    3.4889    0.1972    0.2329    5.7770    1.4582</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 411:    93.0628   -5.9401   -2.0799   -4.2799   -0.9442    0.1457    6.7529    3.7113    0.9199    3.4929    0.1972    0.2329    5.7761    1.4580</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 412:    93.0620   -5.9402   -2.0801   -4.2808   -0.9443    0.1458    6.7502    3.7099    0.9207    3.4972    0.1971    0.2328    5.7767    1.4577</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 413:    93.0628   -5.9403   -2.0804   -4.2816   -0.9443    0.1461    6.7466    3.7091    0.9214    3.5007    0.1970    0.2326    5.7771    1.4572</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 414:    93.0627   -5.9405   -2.0807   -4.2825   -0.9443    0.1463    6.7480    3.7089    0.9221    3.5048    0.1969    0.2325    5.7774    1.4567</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 415:    93.0614   -5.9402   -2.0810   -4.2836   -0.9443    0.1465    6.7474    3.7066    0.9226    3.5098    0.1969    0.2323    5.7780    1.4564</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 416:    93.0610   -5.9408   -2.0813   -4.2848   -0.9443    0.1469    6.7544    3.7116    0.9229    3.5157    0.1968    0.2321    5.7786    1.4559</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 417:    93.0601   -5.9413   -2.0815   -4.2860   -0.9443    0.1471    6.7592    3.7158    0.9234    3.5206    0.1967    0.2319    5.7793    1.4556</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 418:    93.0606   -5.9424   -2.0817   -4.2868   -0.9444    0.1473    6.7589    3.7214    0.9238    3.5237    0.1966    0.2318    5.7787    1.4553</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 419:    93.0605   -5.9433   -2.0818   -4.2877   -0.9444    0.1476    6.7641    3.7253    0.9242    3.5274    0.1965    0.2316    5.7780    1.4552</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 420:    93.0623   -5.9427   -2.0819   -4.2886   -0.9445    0.1478    6.7593    3.7216    0.9246    3.5310    0.1965    0.2314    5.7772    1.4551</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 421:    93.0630   -5.9418   -2.0819   -4.2895   -0.9445    0.1480    6.7489    3.7166    0.9250    3.5337    0.1964    0.2312    5.7764    1.4552</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 422:    93.0626   -5.9410   -2.0820   -4.2900   -0.9445    0.1482    6.7452    3.7124    0.9253    3.5350    0.1963    0.2311    5.7758    1.4549</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 423:    93.0629   -5.9407   -2.0822   -4.2906   -0.9446    0.1484    6.7409    3.7095    0.9256    3.5360    0.1963    0.2309    5.7753    1.4546</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 424:    93.0637   -5.9404   -2.0821   -4.2912   -0.9446    0.1485    6.7387    3.7073    0.9258    3.5370    0.1962    0.2308    5.7733    1.4546</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 425:    93.0625   -5.9402   -2.0821   -4.2917   -0.9447    0.1486    6.7330    3.7061    0.9258    3.5381    0.1961    0.2306    5.7722    1.4548</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 426:    93.0624   -5.9399   -2.0820   -4.2921   -0.9447    0.1487    6.7256    3.7034    0.9259    3.5387    0.1961    0.2304    5.7719    1.4545</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 427:    93.0606   -5.9397   -2.0821   -4.2925   -0.9447    0.1488    6.7139    3.7010    0.9257    3.5394    0.1961    0.2301    5.7730    1.4545</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 428:    93.0601   -5.9394   -2.0822   -4.2929   -0.9447    0.1489    6.7062    3.6984    0.9256    3.5405    0.1961    0.2298    5.7723    1.4543</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 429:    93.0615   -5.9391   -2.0821   -4.2933   -0.9447    0.1490    6.7040    3.6956    0.9254    3.5412    0.1960    0.2297    5.7714    1.4544</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 430:    93.0654   -5.9389   -2.0820   -4.2934   -0.9448    0.1491    6.7016    3.6934    0.9253    3.5412    0.1960    0.2295    5.7710    1.4546</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 431:    93.0674   -5.9392   -2.0819   -4.2936   -0.9449    0.1491    6.6946    3.6935    0.9252    3.5414    0.1960    0.2294    5.7699    1.4547</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 432:    93.0683   -5.9401   -2.0818   -4.2938   -0.9450    0.1491    6.6895    3.6960    0.9250    3.5417    0.1961    0.2292    5.7687    1.4549</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 433:    93.0693   -5.9411   -2.0815   -4.2942   -0.9451    0.1491    6.6826    3.7003    0.9254    3.5433    0.1961    0.2291    5.7679    1.4549</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 434:    93.0720   -5.9410   -2.0813   -4.2945   -0.9452    0.1491    6.6842    3.6998    0.9258    3.5440    0.1960    0.2290    5.7662    1.4548</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 435:    93.0735   -5.9417   -2.0811   -4.2947   -0.9453    0.1490    6.6907    3.7029    0.9261    3.5446    0.1960    0.2290    5.7651    1.4548</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 436:    93.0752   -5.9430   -2.0809   -4.2949   -0.9454    0.1489    6.6939    3.7083    0.9266    3.5459    0.1959    0.2290    5.7638    1.4547</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 437:    93.0774   -5.9442   -2.0807   -4.2952   -0.9455    0.1487    6.7016    3.7147    0.9270    3.5474    0.1960    0.2290    5.7624    1.4550</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 438:    93.0795   -5.9453   -2.0805   -4.2954   -0.9456    0.1485    6.7089    3.7212    0.9275    3.5491    0.1959    0.2291    5.7614    1.4551</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 439:    93.0816   -5.9467   -2.0802   -4.2956   -0.9457    0.1484    6.7230    3.7291    0.9281    3.5503    0.1959    0.2293    5.7616    1.4551</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 440:    93.0834   -5.9475   -2.0800   -4.2957   -0.9458    0.1480    6.7306    3.7340    0.9287    3.5512    0.1960    0.2295    5.7631    1.4551</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 441:    93.0855   -5.9480   -2.0797   -4.2961   -0.9459    0.1478    6.7436    3.7373    0.9292    3.5534    0.1960    0.2299    5.7642    1.4553</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 442:    93.0885   -5.9488   -2.0793   -4.2963   -0.9460    0.1474    6.7533    3.7419    0.9297    3.5546    0.1960    0.2304    5.7630    1.4554</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 443:    93.0907   -5.9497   -2.0789   -4.2967   -0.9461    0.1471    6.7641    3.7469    0.9304    3.5570    0.1960    0.2308    5.7616    1.4554</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 444:    93.0905   -5.9504   -2.0785   -4.2972   -0.9462    0.1467    6.7570    3.7486    0.9312    3.5601    0.1960    0.2311    5.7604    1.4553</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 445:    93.0903   -5.9515   -2.0782   -4.2977   -0.9462    0.1462    6.7547    3.7543    0.9319    3.5635    0.1960    0.2314    5.7595    1.4550</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 446:    93.0902   -5.9530   -2.0778   -4.2982   -0.9462    0.1459    6.7562    3.7615    0.9325    3.5664    0.1960    0.2316    5.7580    1.4548</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 447:    93.0905   -5.9541   -2.0775   -4.2990   -0.9463    0.1455    6.7639    3.7668    0.9333    3.5719    0.1960    0.2318    5.7574    1.4543</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 448:    93.0912   -5.9555   -2.0772   -4.2999   -0.9463    0.1452    6.7671    3.7736    0.9340    3.5783    0.1960    0.2322    5.7572    1.4540</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 449:    93.0918   -5.9570   -2.0769   -4.3004   -0.9464    0.1448    6.7824    3.7802    0.9345    3.5809    0.1960    0.2326    5.7561    1.4537</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 450:    93.0901   -5.9579   -2.0766   -4.3011   -0.9464    0.1444    6.7866    3.7829    0.9351    3.5853    0.1959    0.2329    5.7551    1.4536</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 451:    93.0899   -5.9594   -2.0763   -4.3016   -0.9465    0.1439    6.7875    3.7896    0.9356    3.5888    0.1959    0.2332    5.7550    1.4537</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 452:    93.0902   -5.9603   -2.0759   -4.3023   -0.9465    0.1435    6.7953    3.7926    0.9363    3.5927    0.1958    0.2335    5.7537    1.4538</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 453:    93.0906   -5.9615   -2.0755   -4.3026   -0.9465    0.1430    6.7996    3.7982    0.9372    3.5950    0.1958    0.2338    5.7531    1.4539</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 454:    93.0909   -5.9614   -2.0751   -4.3029   -0.9465    0.1425    6.8016    3.7969    0.9381    3.5976    0.1958    0.2340    5.7532    1.4540</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 455:    93.0909   -5.9610   -2.0749   -4.3035   -0.9464    0.1420    6.8022    3.7940    0.9391    3.6022    0.1957    0.2342    5.7532    1.4539</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 456:    93.0916   -5.9600   -2.0746   -4.3045   -0.9463    0.1416    6.7942    3.7893    0.9398    3.6104    0.1957    0.2344    5.7540    1.4537</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 457:    93.0905   -5.9595   -2.0744   -4.3058   -0.9463    0.1411    6.7931    3.7866    0.9407    3.6210    0.1956    0.2345    5.7551    1.4534</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 458:    93.0905   -5.9597   -2.0742   -4.3071   -0.9462    0.1407    6.7877    3.7882    0.9416    3.6327    0.1955    0.2347    5.7566    1.4532</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 459:    93.0895   -5.9598   -2.0741   -4.3078   -0.9461    0.1402    6.7884    3.7889    0.9425    3.6383    0.1955    0.2349    5.7601    1.4527</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 460:    93.0871   -5.9602   -2.0741   -4.3086   -0.9460    0.1398    6.7889    3.7912    0.9434    3.6439    0.1954    0.2350    5.7619    1.4522</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 461:    93.0854   -5.9613   -2.0739   -4.3091   -0.9459    0.1393    6.7813    3.7973    0.9440    3.6481    0.1953    0.2351    5.7620    1.4520</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 462:    93.0838   -5.9623   -2.0737   -4.3093   -0.9458    0.1388    6.7791    3.8037    0.9445    3.6498    0.1953    0.2353    5.7616    1.4518</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 463:    93.0816   -5.9629   -2.0734   -4.3095   -0.9457    0.1384    6.7733    3.8070    0.9451    3.6508    0.1952    0.2355    5.7626    1.4519</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 464:    93.0792   -5.9631   -2.0731   -4.3095   -0.9457    0.1379    6.7697    3.8081    0.9460    3.6507    0.1951    0.2358    5.7638    1.4518</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 465:    93.0775   -5.9633   -2.0728   -4.3095   -0.9456    0.1374    6.7717    3.8095    0.9466    3.6506    0.1950    0.2361    5.7647    1.4519</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 466:    93.0774   -5.9640   -2.0725   -4.3097   -0.9455    0.1368    6.7686    3.8141    0.9473    3.6516    0.1949    0.2363    5.7662    1.4516</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 467:    93.0773   -5.9646   -2.0722   -4.3100   -0.9454    0.1364    6.7668    3.8172    0.9480    3.6535    0.1948    0.2366    5.7671    1.4516</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 468:    93.0777   -5.9653   -2.0719   -4.3106   -0.9453    0.1358    6.7648    3.8226    0.9487    3.6566    0.1947    0.2371    5.7681    1.4514</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 469:    93.0778   -5.9657   -2.0717   -4.3112   -0.9453    0.1353    6.7617    3.8253    0.9495    3.6588    0.1947    0.2375    5.7686    1.4510</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 470:    93.0769   -5.9663   -2.0715   -4.3117   -0.9452    0.1347    6.7650    3.8278    0.9503    3.6613    0.1947    0.2379    5.7688    1.4506</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 471:    93.0749   -5.9664   -2.0714   -4.3123   -0.9451    0.1342    6.7708    3.8280    0.9510    3.6643    0.1947    0.2383    5.7699    1.4505</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 472:    93.0721   -5.9668   -2.0713   -4.3127   -0.9450    0.1337    6.7756    3.8284    0.9517    3.6665    0.1947    0.2386    5.7710    1.4501</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 473:    93.0696   -5.9670   -2.0713   -4.3133   -0.9449    0.1333    6.7784    3.8280    0.9522    3.6698    0.1947    0.2388    5.7716    1.4498</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 474:    93.0674   -5.9673   -2.0714   -4.3138   -0.9448    0.1329    6.7761    3.8281    0.9527    3.6731    0.1947    0.2390    5.7729    1.4496</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 475:    93.0655   -5.9679   -2.0714   -4.3143   -0.9447    0.1324    6.7779    3.8302    0.9531    3.6762    0.1947    0.2391    5.7743    1.4493</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 476:    93.0643   -5.9682   -2.0713   -4.3144   -0.9447    0.1321    6.7776    3.8314    0.9536    3.6768    0.1946    0.2391    5.7762    1.4492</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 477:    93.0631   -5.9684   -2.0714   -4.3146   -0.9446    0.1317    6.7725    3.8326    0.9540    3.6787    0.1945    0.2392    5.7768    1.4492</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 478:    93.0630   -5.9693   -2.0715   -4.3148   -0.9447    0.1314    6.7626    3.8365    0.9543    3.6788    0.1944    0.2395    5.7785    1.4492</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 479:    93.0634   -5.9710   -2.0714   -4.3148   -0.9447    0.1310    6.7499    3.8462    0.9547    3.6778    0.1943    0.2399    5.7798    1.4490</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 480:    93.0651   -5.9732   -2.0715   -4.3147   -0.9447    0.1307    6.7463    3.8616    0.9548    3.6770    0.1943    0.2403    5.7810    1.4488</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 481:    93.0664   -5.9747   -2.0715   -4.3149   -0.9447    0.1304    6.7442    3.8722    0.9550    3.6780    0.1941    0.2409    5.7811    1.4487</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 482:    93.0647   -5.9762   -2.0714   -4.3151   -0.9447    0.1300    6.7389    3.8834    0.9554    3.6784    0.1941    0.2414    5.7816    1.4484</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 483:    93.0628   -5.9778   -2.0714   -4.3153   -0.9447    0.1295    6.7352    3.8943    0.9557    3.6783    0.1940    0.2419    5.7820    1.4483</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 484:    93.0614   -5.9787   -2.0715   -4.3153   -0.9447    0.1291    6.7314    3.8999    0.9559    3.6774    0.1939    0.2424    5.7819    1.4481</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 485:    93.0593   -5.9789   -2.0715   -4.3153   -0.9447    0.1288    6.7293    3.9007    0.9561    3.6765    0.1938    0.2430    5.7815    1.4480</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 486:    93.0575   -5.9793   -2.0716   -4.3152   -0.9447    0.1284    6.7331    3.9021    0.9563    3.6753    0.1938    0.2434    5.7806    1.4480</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 487:    93.0555   -5.9796   -2.0716   -4.3152   -0.9447    0.1281    6.7342    3.9036    0.9566    3.6741    0.1937    0.2439    5.7805    1.4479</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 488:    93.0545   -5.9795   -2.0716   -4.3152   -0.9447    0.1277    6.7365    3.9024    0.9569    3.6729    0.1937    0.2444    5.7795    1.4480</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 489:    93.0550   -5.9800   -2.0716   -4.3152   -0.9447    0.1274    6.7336    3.9046    0.9571    3.6719    0.1937    0.2448    5.7778    1.4481</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 490:    93.0571   -5.9793   -2.0715   -4.3152   -0.9447    0.1272    6.7331    3.9009    0.9573    3.6704    0.1936    0.2450    5.7767    1.4484</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 491:    93.0584   -5.9787   -2.0714   -4.3151   -0.9447    0.1270    6.7280    3.8980    0.9575    3.6688    0.1936    0.2452    5.7764    1.4487</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 492:    93.0589   -5.9786   -2.0714   -4.3150   -0.9447    0.1267    6.7264    3.8971    0.9578    3.6675    0.1936    0.2455    5.7759    1.4489</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 493:    93.0593   -5.9790   -2.0711   -4.3149   -0.9447    0.1265    6.7273    3.8982    0.9584    3.6660    0.1935    0.2456    5.7759    1.4490</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 494:    93.0594   -5.9805   -2.0710   -4.3149   -0.9448    0.1263    6.7291    3.9066    0.9590    3.6650    0.1935    0.2457    5.7758    1.4489</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 495:    93.0596   -5.9816   -2.0709   -4.3148   -0.9448    0.1261    6.7281    3.9123    0.9593    3.6639    0.1936    0.2459    5.7745    1.4490</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 496:    93.0590   -5.9829   -2.0707   -4.3148   -0.9448    0.1259    6.7319    3.9227    0.9597    3.6628    0.1936    0.2461    5.7740    1.4491</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 497:    93.0580   -5.9842   -2.0706   -4.3148   -0.9448    0.1257    6.7388    3.9326    0.9600    3.6623    0.1936    0.2463    5.7732    1.4492</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 498:    93.0578   -5.9848   -2.0705   -4.3148   -0.9448    0.1255    6.7447    3.9368    0.9605    3.6618    0.1936    0.2464    5.7726    1.4494</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 499:    93.0568   -5.9843   -2.0704   -4.3147   -0.9447    0.1253    6.7475    3.9341    0.9609    3.6612    0.1937    0.2467    5.7718    1.4495</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 500:    93.0563   -5.9831   -2.0703   -4.3147   -0.9447    0.1251    6.7500    3.9292    0.9614    3.6607    0.1937    0.2469    5.7712    1.4496</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating covariance matrix</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling EBE model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Needed Covariates:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "CMT"</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating residuals/tables</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-in"><span class="va">f_nlmixr_dfop_sfo_focei_obs</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr</a></span><span class="op">(</span><span class="va">f_mmkin_obs</span><span class="op">[</span><span class="st">"DFOP-SFO"</span>, <span class="op">]</span>, est <span class="op">=</span> <span class="st">"focei"</span><span class="op">)</span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate jacobian</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate sensitivities</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate ∂(f)/∂(η)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate ∂(R²)/∂(η)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in inner model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in inner model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in EBE model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in EBE model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling inner model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in FD model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in FD model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling EBE model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling events FD model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Needed Covariates:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "CMT"</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="font-weight: bold;">Key:</span> U: Unscaled Parameters; X: Back-transformed parameters; G: Gill difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> F: Forward difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> C: Central difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> M: Mixed forward and central difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Unscaled parameters for Omegas=chol(solve(omega));</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Diagonals are transformed, as specified by foceiControl(diagXform=)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |-----+---------------+-----------+-----------+-----------+-----------|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    #| Objective Fun |  parent_0 |  log_k_A1 |f_parent_qlogis |    log_k1 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    log_k2 |  g_qlogis |sigma_parent |  sigma_A1 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|        o1 |        o2 |        o3 |        o4 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|        o5 |        o6 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    1</span>|     488.98943 |     1.000 |    -1.000 |   -0.9115 |   -0.9329 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9817 |   -0.8883 |   -0.8515 |   -0.8698 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8775 |   -0.8743 |   -0.8683 |   -0.8723 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8746 |   -0.8685 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     488.98943 |     93.00 |    -5.300 |   -0.9500 |    -2.000 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.400 |    0.1900 |     2.000 |     1.100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7210 |    0.8758 |     1.172 |    0.9765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.8633 |     1.161 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     488.98943</span> |     93.00 |  0.004992 |    0.2789 |    0.1353 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01228 |    0.5474 |     2.000 |     1.100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7210 |    0.8758 |     1.172 |    0.9765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.8633 |     1.161 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    G|    Gill Diff. |    -27.68 |     2.403 |   -0.1248 |   -0.3242 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3705 |   0.07384 |    -31.92 |    -15.13 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     14.74 |     13.03 |    -12.01 |    -2.072 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     5.553 |    -10.09 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    2</span>|     1205.4696 |     1.534 |    -1.046 |   -0.9091 |   -0.9266 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9745 |   -0.8897 |   -0.2358 |   -0.5780 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.162 |    -1.126 |   -0.6367 |   -0.8323 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.9817 |   -0.6738 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     1205.4696 |     142.6 |    -5.346 |   -0.9477 |    -1.994 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.393 |    0.1897 |     2.616 |     1.260 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.5160 |    0.6557 |     1.444 |     1.015 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.7709 |     1.387 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     1205.4696</span> |     142.6 |  0.004766 |    0.2793 |    0.1362 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01237 |    0.5473 |     2.616 |     1.260 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.5160 |    0.6557 |     1.444 |     1.015 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.7709 |     1.387 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    3</span>|     510.70816 |     1.053 |    -1.005 |   -0.9113 |   -0.9322 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9810 |   -0.8884 |   -0.7899 |   -0.8406 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9059 |   -0.8995 |   -0.8452 |   -0.8683 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8853 |   -0.8491 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     510.70816 |     97.96 |    -5.305 |   -0.9498 |    -1.999 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.399 |    0.1900 |     2.062 |     1.116 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7005 |    0.8538 |     1.199 |    0.9804 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.8541 |     1.184 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     510.70816</span> |     97.96 |  0.004969 |    0.2789 |    0.1354 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01229 |    0.5474 |     2.062 |     1.116 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7005 |    0.8538 |     1.199 |    0.9804 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.8541 |     1.184 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    4</span>|     494.11470 |     1.005 |    -1.000 |   -0.9115 |   -0.9328 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9816 |   -0.8883 |   -0.8453 |   -0.8669 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8803 |   -0.8769 |   -0.8660 |   -0.8719 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8757 |   -0.8666 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      494.1147 |     93.50 |    -5.300 |   -0.9500 |    -2.000 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.400 |    0.1900 |     2.006 |     1.102 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7190 |    0.8736 |     1.175 |    0.9768 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.8624 |     1.163 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      494.1147</span> |     93.50 |  0.004989 |    0.2789 |    0.1353 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01228 |    0.5474 |     2.006 |     1.102 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7190 |    0.8736 |     1.175 |    0.9768 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.8624 |     1.163 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    5</span>|     494.35784 |     1.001 |    -1.000 |   -0.9115 |   -0.9329 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9817 |   -0.8883 |   -0.8509 |   -0.8695 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8778 |   -0.8746 |   -0.8681 |   -0.8723 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8747 |   -0.8683 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     494.35784 |     93.05 |    -5.300 |   -0.9500 |    -2.000 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.400 |    0.1900 |     2.001 |     1.100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7208 |    0.8755 |     1.172 |    0.9765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.8632 |     1.161 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     494.35784</span> |     93.05 |  0.004991 |    0.2789 |    0.1353 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01228 |    0.5474 |     2.001 |     1.100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7208 |    0.8755 |     1.172 |    0.9765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.8632 |     1.161 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    6</span>|     494.40319 |     1.000 |    -1.000 |   -0.9115 |   -0.9329 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9817 |   -0.8883 |   -0.8514 |   -0.8698 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8775 |   -0.8744 |   -0.8683 |   -0.8723 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8746 |   -0.8685 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     494.40319 |     93.00 |    -5.300 |   -0.9500 |    -2.000 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.400 |    0.1900 |     2.000 |     1.100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7210 |    0.8757 |     1.172 |    0.9765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.8633 |     1.161 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     494.40319</span> |     93.00 |  0.004992 |    0.2789 |    0.1353 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01228 |    0.5474 |     2.000 |     1.100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7210 |    0.8757 |     1.172 |    0.9765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.8633 |     1.161 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    7</span>|     494.40793 |     1.000 |    -1.000 |   -0.9115 |   -0.9329 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9817 |   -0.8883 |   -0.8515 |   -0.8698 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8775 |   -0.8744 |   -0.8683 |   -0.8723 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8746 |   -0.8685 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     494.40793 |     93.00 |    -5.300 |   -0.9500 |    -2.000 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.400 |    0.1900 |     2.000 |     1.100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7210 |    0.8758 |     1.172 |    0.9765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.8633 |     1.161 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     494.40793</span> |     93.00 |  0.004992 |    0.2789 |    0.1353 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01228 |    0.5474 |     2.000 |     1.100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7210 |    0.8758 |     1.172 |    0.9765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.8633 |     1.161 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    8</span>|     494.40830 |     1.000 |    -1.000 |   -0.9115 |   -0.9329 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9817 |   -0.8883 |   -0.8515 |   -0.8698 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8775 |   -0.8743 |   -0.8683 |   -0.8723 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8746 |   -0.8685 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      494.4083 |     93.00 |    -5.300 |   -0.9500 |    -2.000 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.400 |    0.1900 |     2.000 |     1.100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7210 |    0.8758 |     1.172 |    0.9765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.8633 |     1.161 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      494.4083</span> |     93.00 |  0.004992 |    0.2789 |    0.1353 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01228 |    0.5474 |     2.000 |     1.100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7210 |    0.8758 |     1.172 |    0.9765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.8633 |     1.161 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    9</span>|     494.40832 |     1.000 |    -1.000 |   -0.9115 |   -0.9329 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9817 |   -0.8883 |   -0.8515 |   -0.8698 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8775 |   -0.8743 |   -0.8683 |   -0.8723 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8746 |   -0.8685 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     494.40832 |     93.00 |    -5.300 |   -0.9500 |    -2.000 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.400 |    0.1900 |     2.000 |     1.100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7210 |    0.8758 |     1.172 |    0.9765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.8633 |     1.161 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     494.40832</span> |     93.00 |  0.004992 |    0.2789 |    0.1353 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01228 |    0.5474 |     2.000 |     1.100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7210 |    0.8758 |     1.172 |    0.9765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.8633 |     1.161 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   10</span>|     494.40832 |     1.000 |    -1.000 |   -0.9115 |   -0.9329 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9817 |   -0.8883 |   -0.8515 |   -0.8698 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8775 |   -0.8743 |   -0.8683 |   -0.8723 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8746 |   -0.8685 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     494.40832 |     93.00 |    -5.300 |   -0.9500 |    -2.000 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.400 |    0.1900 |     2.000 |     1.100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7210 |    0.8758 |     1.172 |    0.9765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.8633 |     1.161 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     494.40832</span> |     93.00 |  0.004992 |    0.2789 |    0.1353 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01228 |    0.5474 |     2.000 |     1.100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7210 |    0.8758 |     1.172 |    0.9765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.8633 |     1.161 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   11</span>|     494.40832 |     1.000 |    -1.000 |   -0.9115 |   -0.9329 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9817 |   -0.8883 |   -0.8515 |   -0.8698 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8775 |   -0.8743 |   -0.8683 |   -0.8723 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8746 |   -0.8685 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     494.40832 |     93.00 |    -5.300 |   -0.9500 |    -2.000 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.400 |    0.1900 |     2.000 |     1.100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7210 |    0.8758 |     1.172 |    0.9765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.8633 |     1.161 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     494.40832</span> |     93.00 |  0.004992 |    0.2789 |    0.1353 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01228 |    0.5474 |     2.000 |     1.100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7210 |    0.8758 |     1.172 |    0.9765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.8633 |     1.161 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   12</span>|     494.40832 |     1.000 |    -1.000 |   -0.9115 |   -0.9329 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9817 |   -0.8883 |   -0.8515 |   -0.8698 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8775 |   -0.8743 |   -0.8683 |   -0.8723 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8746 |   -0.8685 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     494.40832 |     93.00 |    -5.300 |   -0.9500 |    -2.000 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.400 |    0.1900 |     2.000 |     1.100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7210 |    0.8758 |     1.172 |    0.9765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.8633 |     1.161 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     494.40832</span> |     93.00 |  0.004992 |    0.2789 |    0.1353 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01228 |    0.5474 |     2.000 |     1.100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7210 |    0.8758 |     1.172 |    0.9765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.8633 |     1.161 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   13</span>|     494.40832 |     1.000 |    -1.000 |   -0.9115 |   -0.9329 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9817 |   -0.8883 |   -0.8515 |   -0.8698 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8775 |   -0.8743 |   -0.8683 |   -0.8723 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8746 |   -0.8685 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     494.40832 |     93.00 |    -5.300 |   -0.9500 |    -2.000 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.400 |    0.1900 |     2.000 |     1.100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7210 |    0.8758 |     1.172 |    0.9765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.8633 |     1.161 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     494.40832</span> |     93.00 |  0.004992 |    0.2789 |    0.1353 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01228 |    0.5474 |     2.000 |     1.100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7210 |    0.8758 |     1.172 |    0.9765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.8633 |     1.161 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   14</span>|     494.40832 |     1.000 |    -1.000 |   -0.9115 |   -0.9329 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9817 |   -0.8883 |   -0.8515 |   -0.8698 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8775 |   -0.8743 |   -0.8683 |   -0.8723 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8746 |   -0.8685 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     494.40832 |     93.00 |    -5.300 |   -0.9500 |    -2.000 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.400 |    0.1900 |     2.000 |     1.100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7210 |    0.8758 |     1.172 |    0.9765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.8633 |     1.161 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     494.40832</span> |     93.00 |  0.004992 |    0.2789 |    0.1353 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01228 |    0.5474 |     2.000 |     1.100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7210 |    0.8758 |     1.172 |    0.9765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.8633 |     1.161 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   15</span>|     494.40832 |     1.000 |    -1.000 |   -0.9115 |   -0.9329 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9817 |   -0.8883 |   -0.8515 |   -0.8698 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8775 |   -0.8743 |   -0.8683 |   -0.8723 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8746 |   -0.8685 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     494.40832 |     93.00 |    -5.300 |   -0.9500 |    -2.000 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.400 |    0.1900 |     2.000 |     1.100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7210 |    0.8758 |     1.172 |    0.9765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.8633 |     1.161 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     494.40832</span> |     93.00 |  0.004992 |    0.2789 |    0.1353 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01228 |    0.5474 |     2.000 |     1.100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7210 |    0.8758 |     1.172 |    0.9765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.8633 |     1.161 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   16</span>|     494.40832 |     1.000 |    -1.000 |   -0.9115 |   -0.9329 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9817 |   -0.8883 |   -0.8515 |   -0.8698 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8775 |   -0.8743 |   -0.8683 |   -0.8723 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8746 |   -0.8685 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     494.40832 |     93.00 |    -5.300 |   -0.9500 |    -2.000 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.400 |    0.1900 |     2.000 |     1.100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7210 |    0.8758 |     1.172 |    0.9765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.8633 |     1.161 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     494.40832</span> |     93.00 |  0.004992 |    0.2789 |    0.1353 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01228 |    0.5474 |     2.000 |     1.100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7210 |    0.8758 |     1.172 |    0.9765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.8633 |     1.161 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   17</span>|     494.40832 |     1.000 |    -1.000 |   -0.9115 |   -0.9329 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9817 |   -0.8883 |   -0.8515 |   -0.8698 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8775 |   -0.8743 |   -0.8683 |   -0.8723 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8746 |   -0.8685 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     494.40832 |     93.00 |    -5.300 |   -0.9500 |    -2.000 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.400 |    0.1900 |     2.000 |     1.100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7210 |    0.8758 |     1.172 |    0.9765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.8633 |     1.161 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     494.40832</span> |     93.00 |  0.004992 |    0.2789 |    0.1353 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01228 |    0.5474 |     2.000 |     1.100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7210 |    0.8758 |     1.172 |    0.9765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.8633 |     1.161 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> calculating covariance matrix</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating residuals/tables</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-in"></span>
-<span class="r-in"><span class="co"># Identical two-component error for all variables is only possible with</span></span>
-<span class="r-in"><span class="co"># est = 'focei' in nlmixr</span></span>
-<span class="r-in"><span class="va">f_nlmixr_fomc_sfo_focei_tc</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr</a></span><span class="op">(</span><span class="va">f_mmkin_tc</span><span class="op">[</span><span class="st">"FOMC-SFO"</span>, <span class="op">]</span>, est <span class="op">=</span> <span class="st">"focei"</span><span class="op">)</span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate jacobian</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate sensitivities</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate ∂(f)/∂(η)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate ∂(R²)/∂(η)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in inner model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in inner model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in EBE model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in EBE model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling inner model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in FD model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in FD model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling EBE model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling events FD model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Needed Covariates:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "CMT"</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="font-weight: bold;">Key:</span> U: Unscaled Parameters; X: Back-transformed parameters; G: Gill difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> F: Forward difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> C: Central difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> M: Mixed forward and central difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Unscaled parameters for Omegas=chol(solve(omega));</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Diagonals are transformed, as specified by foceiControl(diagXform=)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |-----+---------------+-----------+-----------+-----------+-----------|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    #| Objective Fun |  parent_0 |  log_k_A1 |f_parent_qlogis | log_alpha |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  log_beta | sigma_low |  rsd_high |        o1 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|        o2 |        o3 |        o4 |        o5 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    1</span>|     500.20030 |     1.000 |    -1.000 |   -0.9113 |   -0.8944 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8454 |   -0.8678 |   -0.8916 |   -0.8767 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8743 |   -0.8675 |   -0.8704 |   -0.8704 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      500.2003 |     93.00 |    -5.300 |   -0.9400 |   -0.1100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.200 |   0.03000 |    0.7598 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8758 |     1.214 |     1.068 |     1.071 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      500.2003</span> |     93.00 |  0.004992 |    0.2809 |    0.8958 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.974 |     1.200 |   0.03000 |    0.7598 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8758 |     1.214 |     1.068 |     1.071 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    G|    Gill Diff. |     48.88 |     2.383 |    0.1231 |    0.1986 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1571 |    -64.31 |    -21.89 |    0.6250 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     11.41 |    -12.48 |    -9.903 |    -10.91 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    2</span>|     2909.4393 |    0.4361 |    -1.027 |   -0.9127 |   -0.8967 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8472 |   -0.1258 |   -0.6390 |   -0.8839 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.006 |   -0.7235 |   -0.7562 |   -0.7445 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     2909.4393 |     40.56 |    -5.327 |   -0.9413 |   -0.1123 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.298 |     1.645 |   0.03379 |    0.7544 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7605 |     1.389 |     1.189 |     1.206 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     2909.4393</span> |     40.56 |  0.004856 |    0.2806 |    0.8938 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.956 |     1.645 |   0.03379 |    0.7544 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7605 |     1.389 |     1.189 |     1.206 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    3</span>|     515.24373 |    0.9436 |    -1.003 |   -0.9114 |   -0.8946 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8456 |   -0.7936 |   -0.8663 |   -0.8774 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8875 |   -0.8531 |   -0.8590 |   -0.8578 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     515.24373 |     87.76 |    -5.303 |   -0.9401 |   -0.1102 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.245 |   0.03038 |    0.7593 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8642 |     1.232 |     1.080 |     1.084 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     515.24373</span> |     87.76 |  0.004978 |    0.2809 |    0.8956 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.972 |     1.245 |   0.03038 |    0.7593 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8642 |     1.232 |     1.080 |     1.084 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    4</span>|     499.40695 |    0.9897 |    -1.001 |   -0.9113 |   -0.8944 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8454 |   -0.8542 |   -0.8869 |   -0.8768 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8768 |   -0.8648 |   -0.8684 |   -0.8681 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     499.40695 |     92.04 |    -5.301 |   -0.9400 |   -0.1100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.208 |   0.03007 |    0.7597 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8737 |     1.217 |     1.070 |     1.073 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     499.40695</span> |     92.04 |  0.004989 |    0.2809 |    0.8958 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.974 |     1.208 |   0.03007 |    0.7597 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8737 |     1.217 |     1.070 |     1.073 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -99.71 |     2.245 |   -0.1707 |    0.1202 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1546 |    -61.69 |    -22.54 |     1.475 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     7.677 |    -11.72 |    -9.584 |    -10.53 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    5</span>|     498.17135 |     1.001 |    -1.001 |   -0.9113 |   -0.8945 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8454 |   -0.8410 |   -0.8822 |   -0.8771 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8786 |   -0.8623 |   -0.8663 |   -0.8658 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     498.17135 |     93.06 |    -5.301 |   -0.9400 |   -0.1101 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.216 |   0.03014 |    0.7595 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8720 |     1.221 |     1.072 |     1.076 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     498.17135</span> |     93.06 |  0.004987 |    0.2809 |    0.8958 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.974 |     1.216 |   0.03014 |    0.7595 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8720 |     1.221 |     1.072 |     1.076 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     55.25 |     2.349 |    0.1359 |    0.2180 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1993 |    -60.48 |    -20.28 |     1.106 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.662 |    -11.74 |    -9.501 |    -10.55 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    6</span>|     497.33758 |    0.9906 |    -1.002 |   -0.9113 |   -0.8945 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8454 |   -0.8273 |   -0.8776 |   -0.8773 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8808 |   -0.8596 |   -0.8642 |   -0.8634 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     497.33758 |     92.12 |    -5.302 |   -0.9400 |   -0.1101 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.224 |   0.03021 |    0.7594 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8701 |     1.224 |     1.074 |     1.078 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     497.33758</span> |     92.12 |  0.004984 |    0.2809 |    0.8957 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.974 |     1.224 |   0.03021 |    0.7594 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8701 |     1.224 |     1.074 |     1.078 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -88.13 |     2.220 |   -0.1371 |    0.1382 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1101 |    -57.44 |    -20.90 |     1.111 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     7.358 |    -11.52 |    -9.371 |    -10.34 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    7</span>|     496.20963 |     1.001 |    -1.002 |   -0.9113 |   -0.8946 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8454 |   -0.8137 |   -0.8728 |   -0.8776 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8827 |   -0.8569 |   -0.8620 |   -0.8610 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     496.20963 |     93.07 |    -5.302 |   -0.9400 |   -0.1102 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.232 |   0.03028 |    0.7592 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8684 |     1.227 |     1.077 |     1.081 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     496.20963</span> |     93.07 |  0.004981 |    0.2809 |    0.8957 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.974 |     1.232 |   0.03028 |    0.7592 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8684 |     1.227 |     1.077 |     1.081 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     55.31 |     2.316 |    0.1573 |    0.2363 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2327 |    -56.27 |    -18.79 |    0.9054 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.277 |    -11.53 |    -9.283 |    -10.35 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    8</span>|     495.35926 |    0.9914 |    -1.003 |   -0.9113 |   -0.8946 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8455 |   -0.7996 |   -0.8680 |   -0.8778 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8850 |   -0.8541 |   -0.8597 |   -0.8584 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     495.35926 |     92.20 |    -5.303 |   -0.9401 |   -0.1102 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.241 |   0.03035 |    0.7590 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8664 |     1.231 |     1.079 |     1.084 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     495.35926</span> |     92.20 |  0.004979 |    0.2809 |    0.8956 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.973 |     1.241 |   0.03035 |    0.7590 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8664 |     1.231 |     1.079 |     1.084 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -77.78 |     2.194 |   -0.1077 |    0.1552 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.06714 |    -52.97 |    -19.27 |    0.8916 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     7.037 |    -11.29 |    -9.130 |    -10.15 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    9</span>|     494.33654 |     1.001 |    -1.003 |   -0.9113 |   -0.8947 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8455 |   -0.7857 |   -0.8631 |   -0.8780 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8870 |   -0.8511 |   -0.8573 |   -0.8558 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     494.33654 |     93.09 |    -5.303 |   -0.9400 |   -0.1103 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.249 |   0.03043 |    0.7588 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8647 |     1.234 |     1.082 |     1.087 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     494.33654</span> |     93.09 |  0.004976 |    0.2809 |    0.8956 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.973 |     1.249 |   0.03043 |    0.7588 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8647 |     1.234 |     1.082 |     1.087 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     55.95 |     2.282 |    0.1850 |    0.2507 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2656 |    -51.81 |    -17.28 |    0.8212 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     7.543 |    -11.27 |    -9.029 |    -10.13 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   10</span>|     493.47922 |    0.9922 |    -1.004 |   -0.9114 |   -0.8947 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8455 |   -0.7714 |   -0.8582 |   -0.8782 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8891 |   -0.8480 |   -0.8548 |   -0.8530 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     493.47922 |     92.28 |    -5.304 |   -0.9401 |   -0.1103 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.258 |   0.03050 |    0.7587 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8629 |     1.238 |     1.084 |     1.090 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     493.47922</span> |     92.28 |  0.004973 |    0.2809 |    0.8955 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.973 |     1.258 |   0.03050 |    0.7587 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8629 |     1.238 |     1.084 |     1.090 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -67.18 |     2.173 |  -0.07166 |    0.1818 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.01313 |    -49.19 |    -17.69 |    0.7047 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.114 |    -11.03 |    -8.882 |    -9.923 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   11</span>|     492.53845 |     1.001 |    -1.004 |   -0.9114 |   -0.8948 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8456 |   -0.7572 |   -0.8532 |   -0.8784 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8914 |   -0.8448 |   -0.8522 |   -0.8501 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     492.53845 |     93.09 |    -5.304 |   -0.9401 |   -0.1104 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.266 |   0.03057 |    0.7585 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8608 |     1.242 |     1.087 |     1.093 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     492.53845</span> |     93.09 |  0.004969 |    0.2809 |    0.8955 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.972 |     1.266 |   0.03057 |    0.7585 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8608 |     1.242 |     1.087 |     1.093 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     53.38 |     2.243 |    0.1941 |    0.2566 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2879 |    -47.96 |    -15.83 |    0.7595 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.537 |    -10.98 |    -8.771 |    -9.890 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   12</span>|     491.72645 |    0.9926 |    -1.005 |   -0.9114 |   -0.8949 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8456 |   -0.7429 |   -0.8484 |   -0.8786 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8941 |   -0.8415 |   -0.8496 |   -0.8471 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     491.72645 |     92.31 |    -5.305 |   -0.9401 |   -0.1105 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.275 |   0.03065 |    0.7584 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8584 |     1.246 |     1.090 |     1.096 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     491.72645</span> |     92.31 |  0.004966 |    0.2809 |    0.8954 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.972 |     1.275 |   0.03065 |    0.7584 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8584 |     1.246 |     1.090 |     1.096 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -63.56 |     2.131 |  -0.05387 |    0.1833 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................| 0.0009134 |    -45.55 |    -16.30 |    0.3872 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.447 |    -10.76 |    -8.612 |    -9.684 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   13</span>|     490.83850 |     1.001 |    -1.006 |   -0.9114 |   -0.8949 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8457 |   -0.7286 |   -0.8435 |   -0.8787 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8974 |   -0.8380 |   -0.8468 |   -0.8440 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      490.8385 |     93.07 |    -5.306 |   -0.9401 |   -0.1105 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.283 |   0.03072 |    0.7583 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8556 |     1.250 |     1.093 |     1.099 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      490.8385</span> |     93.07 |  0.004963 |    0.2809 |    0.8954 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.971 |     1.283 |   0.03072 |    0.7583 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8556 |     1.250 |     1.093 |     1.099 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     49.01 |     2.198 |    0.2057 |    0.2666 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3102 |    -44.06 |    -14.52 |    0.5614 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.039 |    -10.72 |    -8.482 |    -9.628 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   14</span>|     490.09324 |    0.9928 |    -1.007 |   -0.9115 |   -0.8950 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8458 |   -0.7143 |   -0.8387 |   -0.8788 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9004 |   -0.8343 |   -0.8439 |   -0.8407 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     490.09324 |     92.33 |    -5.307 |   -0.9402 |   -0.1106 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.292 |   0.03079 |    0.7582 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8529 |     1.254 |     1.096 |     1.103 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     490.09324</span> |     92.33 |  0.004959 |    0.2809 |    0.8953 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.970 |     1.292 |   0.03079 |    0.7582 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8529 |     1.254 |     1.096 |     1.103 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -62.13 |     2.095 |  -0.03472 |    0.1999 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03562 |    -41.55 |    -14.84 |    0.5236 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     7.264 |    -10.46 |    -8.310 |    -9.412 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   15</span>|     489.25271 |     1.001 |    -1.007 |   -0.9115 |   -0.8951 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8458 |   -0.7001 |   -0.8338 |   -0.8789 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9032 |   -0.8304 |   -0.8408 |   -0.8372 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     489.25271 |     93.06 |    -5.307 |   -0.9402 |   -0.1107 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.301 |   0.03087 |    0.7581 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8505 |     1.259 |     1.099 |     1.107 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     489.25271</span> |     93.06 |  0.004955 |    0.2809 |    0.8952 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.970 |     1.301 |   0.03087 |    0.7581 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8505 |     1.259 |     1.099 |     1.107 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     44.98 |     2.155 |    0.2191 |    0.2769 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3339 |    -40.42 |    -13.24 |    0.4473 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     7.595 |    -10.35 |    -8.165 |    -9.335 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   16</span>|     488.54089 |    0.9934 |    -1.008 |   -0.9116 |   -0.8952 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8459 |   -0.6857 |   -0.8290 |   -0.8790 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9061 |   -0.8262 |   -0.8376 |   -0.8335 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     488.54089 |     92.38 |    -5.308 |   -0.9403 |   -0.1108 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.299 |     1.309 |   0.03094 |    0.7581 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8479 |     1.264 |     1.103 |     1.111 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     488.54089</span> |     92.38 |  0.004951 |    0.2808 |    0.8951 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.968 |     1.309 |   0.03094 |    0.7581 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8479 |     1.264 |     1.103 |     1.111 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -55.81 |     2.061 |  -0.02096 |    0.2050 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.07367 |    -38.08 |    -13.50 |    0.4111 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     6.904 |    -10.08 |    -7.981 |    -9.106 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   17</span>|     487.77387 |     1.001 |    -1.009 |   -0.9116 |   -0.8953 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8461 |   -0.6716 |   -0.8243 |   -0.8791 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9092 |   -0.8218 |   -0.8341 |   -0.8294 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     487.77387 |     93.07 |    -5.309 |   -0.9403 |   -0.1109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.299 |     1.318 |   0.03101 |    0.7580 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8453 |     1.270 |     1.106 |     1.115 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     487.77387</span> |     93.07 |  0.004946 |    0.2808 |    0.8950 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.967 |     1.318 |   0.03101 |    0.7580 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8453 |     1.270 |     1.106 |     1.115 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     43.45 |     2.114 |    0.2402 |    0.2940 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3672 |    -36.87 |    -12.03 |    0.3081 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     7.143 |    -10.02 |    -7.799 |    -9.000 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   18</span>|     487.09815 |    0.9939 |    -1.010 |   -0.9118 |   -0.8955 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8463 |   -0.6575 |   -0.8197 |   -0.8791 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9124 |   -0.8169 |   -0.8304 |   -0.8251 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     487.09815 |     92.43 |    -5.310 |   -0.9404 |   -0.1111 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.299 |     1.326 |   0.03108 |    0.7580 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8425 |     1.276 |     1.110 |     1.120 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     487.09815</span> |     92.43 |  0.004941 |    0.2808 |    0.8948 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.965 |     1.326 |   0.03108 |    0.7580 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8425 |     1.276 |     1.110 |     1.120 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -50.03 |     2.024 |  0.005993 |    0.2193 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1119 |    -34.69 |    -12.26 |    0.2072 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     6.453 |    -9.722 |    -7.597 |    -8.758 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   19</span>|     486.39882 |     1.001 |    -1.011 |   -0.9119 |   -0.8957 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8465 |   -0.6438 |   -0.8151 |   -0.8789 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9156 |   -0.8116 |   -0.8264 |   -0.8203 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     486.39882 |     93.07 |    -5.311 |   -0.9405 |   -0.1113 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.299 |     1.334 |   0.03115 |    0.7582 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8396 |     1.282 |     1.114 |     1.125 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     486.39882</span> |     93.07 |  0.004935 |    0.2808 |    0.8947 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.963 |     1.334 |   0.03115 |    0.7582 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8396 |     1.282 |     1.114 |     1.125 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     41.24 |     2.069 |    0.2581 |    0.3038 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3965 |    -33.71 |    -10.93 |    0.1523 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     5.279 |    -9.597 |    -7.397 |    -8.617 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   20</span>|     485.77269 |    0.9943 |    -1.013 |   -0.9120 |   -0.8959 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8467 |   -0.6301 |   -0.8107 |   -0.8786 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9175 |   -0.8058 |   -0.8222 |   -0.8151 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     485.77269 |     92.47 |    -5.313 |   -0.9407 |   -0.1115 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.299 |     1.343 |   0.03121 |    0.7584 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8380 |     1.289 |     1.119 |     1.130 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     485.77269</span> |     92.47 |  0.004928 |    0.2808 |    0.8945 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.960 |     1.343 |   0.03121 |    0.7584 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8380 |     1.289 |     1.119 |     1.130 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -45.38 |     1.992 |   0.03513 |    0.2356 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1513 |    -31.62 |    -11.12 |   0.08802 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     6.113 |    -9.278 |    -7.167 |    -8.332 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   21</span>|     485.13787 |     1.001 |    -1.014 |   -0.9122 |   -0.8962 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8471 |   -0.6169 |   -0.8065 |   -0.8780 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9196 |   -0.7993 |   -0.8176 |   -0.8094 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     485.13787 |     93.06 |    -5.314 |   -0.9409 |   -0.1118 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.298 |     1.351 |   0.03128 |    0.7588 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8361 |     1.297 |     1.124 |     1.136 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     485.13787</span> |     93.06 |  0.004921 |    0.2807 |    0.8942 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.957 |     1.351 |   0.03128 |    0.7588 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8361 |     1.297 |     1.124 |     1.136 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     37.95 |     2.033 |    0.2726 |    0.3147 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4223 |    -30.68 |    -9.906 |   0.05100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.975 |    -9.039 |    -6.928 |    -8.144 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   22</span>|     484.56781 |    0.9947 |    -1.016 |   -0.9125 |   -0.8965 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8476 |   -0.6040 |   -0.8026 |   -0.8774 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9219 |   -0.7925 |   -0.8127 |   -0.8032 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     484.56781 |     92.51 |    -5.316 |   -0.9411 |   -0.1121 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.298 |     1.358 |   0.03133 |    0.7593 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8341 |     1.305 |     1.129 |     1.143 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     484.56781</span> |     92.51 |  0.004912 |    0.2807 |    0.8939 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.952 |     1.358 |   0.03133 |    0.7593 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8341 |     1.305 |     1.129 |     1.143 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -42.28 |     1.959 |   0.05438 |    0.2483 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1829 |    -28.95 |    -10.12 |  -0.04344 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.379 |    -8.677 |    -6.653 |    -7.817 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   23</span>|     484.00832 |     1.000 |    -1.018 |   -0.9128 |   -0.8970 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8481 |   -0.5915 |   -0.7988 |   -0.8764 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9214 |   -0.7852 |   -0.8076 |   -0.7966 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     484.00832 |     93.05 |    -5.318 |   -0.9414 |   -0.1125 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.297 |     1.366 |   0.03139 |    0.7601 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8346 |     1.314 |     1.135 |     1.150 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     484.00832</span> |     93.05 |  0.004901 |    0.2806 |    0.8936 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.947 |     1.366 |   0.03139 |    0.7601 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8346 |     1.314 |     1.135 |     1.150 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     34.54 |     2.001 |    0.2786 |    0.3182 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4381 |    -28.02 |    -9.026 |  -0.09975 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     6.146 |    -8.496 |    -6.417 |    -7.602 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   24</span>|     483.48726 |    0.9952 |    -1.021 |   -0.9132 |   -0.8975 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8489 |   -0.5798 |   -0.7955 |   -0.8750 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9256 |   -0.7775 |   -0.8025 |   -0.7898 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     483.48726 |     92.55 |    -5.321 |   -0.9418 |   -0.1131 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.296 |     1.373 |   0.03144 |    0.7611 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8309 |     1.323 |     1.140 |     1.157 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     483.48726</span> |     92.55 |  0.004889 |    0.2805 |    0.8931 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.939 |     1.373 |   0.03144 |    0.7611 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8309 |     1.323 |     1.140 |     1.157 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -37.52 |     1.926 |   0.07054 |    0.2526 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2102 |    -26.43 |    -9.184 |   -0.1123 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     5.591 |    -8.057 |    -6.119 |    -7.239 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   25</span>|     482.99669 |     1.001 |    -1.023 |   -0.9136 |   -0.8980 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8497 |   -0.5700 |   -0.7928 |   -0.8735 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9342 |   -0.7702 |   -0.7978 |   -0.7834 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     482.99669 |     93.05 |    -5.323 |   -0.9422 |   -0.1136 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.296 |     1.379 |   0.03148 |    0.7623 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8234 |     1.332 |     1.145 |     1.164 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     482.99669</span> |     93.05 |  0.004876 |    0.2805 |    0.8926 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.932 |     1.379 |   0.03148 |    0.7623 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8234 |     1.332 |     1.145 |     1.164 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     33.56 |     1.934 |    0.3091 |    0.3219 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4700 |    -25.85 |    -8.255 |  -0.09267 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     5.467 |    -7.833 |    -5.883 |    -7.031 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   26</span>|     482.53338 |    0.9957 |    -1.027 |   -0.9143 |   -0.8987 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8507 |   -0.5600 |   -0.7904 |   -0.8719 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9423 |   -0.7626 |   -0.7931 |   -0.7767 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     482.53338 |     92.60 |    -5.327 |   -0.9428 |   -0.1143 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.295 |     1.385 |   0.03152 |    0.7635 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8163 |     1.342 |     1.150 |     1.171 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     482.53338</span> |     92.60 |  0.004861 |    0.2803 |    0.8920 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.921 |     1.385 |   0.03152 |    0.7635 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8163 |     1.342 |     1.150 |     1.171 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -33.71 |     1.852 |    0.1405 |    0.2615 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2476 |    -24.53 |    -8.462 |   -0.2274 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.657 |    -7.455 |    -5.599 |    -6.689 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   27</span>|     482.07760 |     1.001 |    -1.030 |   -0.9154 |   -0.8996 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8522 |   -0.5495 |   -0.7881 |   -0.8694 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9481 |   -0.7546 |   -0.7885 |   -0.7696 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      482.0776 |     93.06 |    -5.330 |   -0.9439 |   -0.1152 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.293 |     1.391 |   0.03155 |    0.7654 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8112 |     1.351 |     1.155 |     1.179 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      482.0776</span> |     93.06 |  0.004842 |    0.2801 |    0.8912 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.906 |     1.391 |   0.03155 |    0.7654 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8112 |     1.351 |     1.155 |     1.179 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     31.51 |     1.862 |    0.3171 |    0.3253 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4890 |    -23.74 |    -7.570 |   -0.1627 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.673 |    -7.176 |    -5.365 |    -6.441 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   28</span>|     481.65018 |    0.9960 |    -1.035 |   -0.9168 |   -0.9007 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8541 |   -0.5386 |   -0.7859 |   -0.8662 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9515 |   -0.7465 |   -0.7840 |   -0.7624 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     481.65018 |     92.63 |    -5.335 |   -0.9452 |   -0.1163 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.291 |     1.398 |   0.03158 |    0.7678 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8082 |     1.361 |     1.160 |     1.187 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     481.65018</span> |     92.63 |  0.004820 |    0.2799 |    0.8902 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.887 |     1.398 |   0.03158 |    0.7678 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8082 |     1.361 |     1.160 |     1.187 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -31.18 |     1.794 |    0.1244 |    0.2518 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2479 |    -22.54 |    -7.736 |   -0.2537 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.155 |    -6.794 |    -5.094 |    -6.088 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   29</span>|     481.23911 |     1.000 |    -1.041 |   -0.9182 |   -0.9020 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8564 |   -0.5274 |   -0.7840 |   -0.8624 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9539 |   -0.7386 |   -0.7800 |   -0.7556 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     481.23911 |     93.04 |    -5.341 |   -0.9465 |   -0.1176 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.289 |     1.404 |   0.03161 |    0.7707 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8061 |     1.371 |     1.164 |     1.194 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     481.23911</span> |     93.04 |  0.004792 |    0.2796 |    0.8890 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.865 |     1.404 |   0.03161 |    0.7707 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8061 |     1.371 |     1.164 |     1.194 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   30</span>|     480.84332 |     1.001 |    -1.048 |   -0.9201 |   -0.9037 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8593 |   -0.5164 |   -0.7829 |   -0.8573 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9562 |   -0.7293 |   -0.7754 |   -0.7475 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.84332 |     93.06 |    -5.348 |   -0.9483 |   -0.1193 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.286 |     1.411 |   0.03163 |    0.7746 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8041 |     1.382 |     1.169 |     1.203 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.84332</span> |     93.06 |  0.004757 |    0.2792 |    0.8875 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.836 |     1.411 |   0.03163 |    0.7746 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8041 |     1.382 |     1.169 |     1.203 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   31</span>|     479.47395 |     1.001 |    -1.077 |   -0.9274 |   -0.9105 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8708 |   -0.4737 |   -0.7785 |   -0.8375 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9655 |   -0.6927 |   -0.7575 |   -0.7158 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.47395 |     93.12 |    -5.377 |   -0.9551 |   -0.1261 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.275 |     1.436 |   0.03170 |    0.7896 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7959 |     1.426 |     1.188 |     1.237 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.47395</span> |     93.12 |  0.004620 |    0.2779 |    0.8816 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.723 |     1.436 |   0.03170 |    0.7896 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7959 |     1.426 |     1.188 |     1.237 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   32</span>|     477.01144 |     1.003 |    -1.162 |   -0.9485 |   -0.9300 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9044 |   -0.3493 |   -0.7656 |   -0.7799 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9925 |   -0.5865 |   -0.7057 |   -0.6237 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     477.01144 |     93.31 |    -5.462 |   -0.9750 |   -0.1456 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.241 |     1.511 |   0.03189 |    0.8334 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7723 |     1.555 |     1.243 |     1.335 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     477.01144</span> |     93.31 |  0.004245 |    0.2739 |    0.8645 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.403 |     1.511 |   0.03189 |    0.8334 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7723 |     1.555 |     1.243 |     1.335 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     48.38 |     1.353 |   -0.3553 |  -0.05530 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................| -0.006768 |    -8.661 |    -2.325 |   -0.1540 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.503 |   -0.2811 |   -0.6438 |   -0.5229 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   33</span>|     477.99463 |     1.002 |    -1.284 |   -0.8933 |   -0.9162 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8846 |   -0.1951 |   -0.7385 |   -0.7367 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.093 |   -0.7697 |   -0.8046 |   -0.7596 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     477.99463 |     93.20 |    -5.584 |   -0.9231 |   -0.1318 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.261 |     1.604 |   0.03230 |    0.8662 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6840 |     1.333 |     1.138 |     1.190 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     477.99463</span> |     93.20 |  0.003757 |    0.2843 |    0.8766 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.591 |     1.604 |   0.03230 |    0.8662 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6840 |     1.333 |     1.138 |     1.190 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   34</span>|     476.67952 |     1.000 |    -1.201 |   -0.9310 |   -0.9256 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8981 |   -0.3000 |   -0.7569 |   -0.7662 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.025 |   -0.6445 |   -0.7370 |   -0.6668 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     476.67952 |     93.04 |    -5.501 |   -0.9585 |   -0.1412 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.247 |     1.541 |   0.03202 |    0.8438 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7442 |     1.485 |     1.210 |     1.289 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     476.67952</span> |     93.04 |  0.004084 |    0.2772 |    0.8683 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.462 |     1.541 |   0.03202 |    0.8438 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7442 |     1.485 |     1.210 |     1.289 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     3.308 |     1.138 |    0.3206 |  0.007327 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................| -0.005220 |    -6.420 |    -1.952 |   -0.8085 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4295 |    -3.531 |    -2.349 |    -2.485 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   35</span>|     477.00015 |    0.9939 |    -1.268 |   -0.9234 |   -0.9152 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8823 |   -0.2647 |   -0.7585 |   -0.7168 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9725 |   -0.6365 |   -0.7310 |   -0.6853 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     477.00015 |     92.43 |    -5.568 |   -0.9514 |   -0.1308 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.263 |     1.562 |   0.03200 |    0.8813 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7898 |     1.495 |     1.216 |     1.269 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     477.00015</span> |     92.43 |  0.003818 |    0.2786 |    0.8774 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.613 |     1.562 |   0.03200 |    0.8813 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7898 |     1.495 |     1.216 |     1.269 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   36</span>|     476.87328 |    0.9941 |    -1.216 |   -0.9300 |   -0.9236 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8950 |   -0.2832 |   -0.7542 |   -0.7554 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.014 |   -0.6375 |   -0.7322 |   -0.6666 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     476.87328 |     92.45 |    -5.516 |   -0.9576 |   -0.1392 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.250 |     1.551 |   0.03206 |    0.8520 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7536 |     1.493 |     1.215 |     1.289 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     476.87328</span> |     92.45 |  0.004024 |    0.2774 |    0.8700 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.491 |     1.551 |   0.03206 |    0.8520 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7536 |     1.493 |     1.215 |     1.289 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   37</span>|     476.68202 |    0.9977 |    -1.202 |   -0.9313 |   -0.9256 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8981 |   -0.2947 |   -0.7553 |   -0.7655 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.024 |   -0.6416 |   -0.7351 |   -0.6648 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     476.68202 |     92.79 |    -5.502 |   -0.9588 |   -0.1412 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.247 |     1.544 |   0.03204 |    0.8443 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7445 |     1.488 |     1.212 |     1.291 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     476.68202</span> |     92.79 |  0.004080 |    0.2771 |    0.8683 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.462 |     1.544 |   0.03204 |    0.8443 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7445 |     1.488 |     1.212 |     1.291 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   38</span>|     476.66620 |    0.9991 |    -1.201 |   -0.9311 |   -0.9256 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8981 |   -0.2974 |   -0.7561 |   -0.7659 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.024 |   -0.6431 |   -0.7361 |   -0.6658 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      476.6662 |     92.92 |    -5.501 |   -0.9587 |   -0.1412 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.247 |     1.542 |   0.03203 |    0.8441 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7443 |     1.487 |     1.211 |     1.290 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      476.6662</span> |     92.92 |  0.004082 |    0.2771 |    0.8683 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.462 |     1.542 |   0.03203 |    0.8441 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7443 |     1.487 |     1.211 |     1.290 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -16.67 |     1.127 |    0.2138 |  -0.01630 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1134 |    -4.869 |    -1.703 |  -0.03057 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.03000 |    -2.848 |    -2.302 |    -2.432 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   39</span>|     476.65034 |     1.001 |    -1.204 |   -0.9308 |   -0.9253 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8974 |   -0.2967 |   -0.7561 |   -0.7660 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.024 |   -0.6432 |   -0.7350 |   -0.6655 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     476.65034 |     93.06 |    -5.504 |   -0.9584 |   -0.1409 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.248 |     1.543 |   0.03203 |    0.8440 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7448 |     1.487 |     1.212 |     1.290 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     476.65034</span> |     93.06 |  0.004070 |    0.2772 |    0.8686 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.468 |     1.543 |   0.03203 |    0.8440 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7448 |     1.487 |     1.212 |     1.290 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     7.111 |     1.131 |    0.3498 |   0.02199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03596 |    -6.336 |    -1.893 |   -0.8646 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4089 |    -3.511 |    -2.253 |    -2.437 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   40</span>|     476.63921 |    0.9998 |    -1.207 |   -0.9306 |   -0.9249 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8968 |   -0.2957 |   -0.7561 |   -0.7661 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.024 |   -0.6430 |   -0.7338 |   -0.6650 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     476.63921 |     92.98 |    -5.507 |   -0.9581 |   -0.1405 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.249 |     1.543 |   0.03203 |    0.8439 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7451 |     1.487 |     1.213 |     1.291 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     476.63921</span> |     92.98 |  0.004058 |    0.2773 |    0.8689 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.474 |     1.543 |   0.03203 |    0.8439 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7451 |     1.487 |     1.213 |     1.291 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -6.837 |     1.116 |    0.2875 |   0.01149 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.02542 |    -4.741 |    -1.572 |   0.01643 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.427 |    -2.805 |    -2.162 |    -2.393 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   41</span>|     476.63321 |     1.001 |    -1.209 |   -0.9304 |   -0.9247 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8965 |   -0.2943 |   -0.7557 |   -0.7669 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.022 |   -0.6432 |   -0.7330 |   -0.6644 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     476.63321 |     93.07 |    -5.509 |   -0.9580 |   -0.1403 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.249 |     1.544 |   0.03204 |    0.8433 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7468 |     1.487 |     1.214 |     1.292 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     476.63321</span> |     93.07 |  0.004050 |    0.2773 |    0.8691 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.477 |     1.544 |   0.03204 |    0.8433 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7468 |     1.487 |     1.214 |     1.292 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     8.780 |     1.119 |    0.3715 |   0.03544 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.06786 |    -4.773 |    -1.470 |  -0.02124 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.265 |    -2.850 |    -2.132 |    -2.390 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   42</span>|     476.62737 |    0.9998 |    -1.211 |   -0.9303 |   -0.9246 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8963 |   -0.2932 |   -0.7554 |   -0.7683 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.020 |   -0.6436 |   -0.7322 |   -0.6637 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     476.62737 |     92.98 |    -5.511 |   -0.9578 |   -0.1402 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.249 |     1.545 |   0.03204 |    0.8422 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7485 |     1.486 |     1.215 |     1.292 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     476.62737</span> |     92.98 |  0.004043 |    0.2773 |    0.8692 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.479 |     1.545 |   0.03204 |    0.8422 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7485 |     1.486 |     1.215 |     1.292 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -5.468 |     1.108 |    0.2964 |   0.01787 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.01029 |    -4.584 |    -1.512 |  -0.04750 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.233 |    -2.839 |    -2.097 |    -2.338 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   43</span>|     476.62183 |     1.001 |    -1.213 |   -0.9301 |   -0.9245 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8960 |   -0.2918 |   -0.7549 |   -0.7696 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.018 |   -0.6438 |   -0.7313 |   -0.6628 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     476.62183 |     93.07 |    -5.513 |   -0.9577 |   -0.1401 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.249 |     1.546 |   0.03205 |    0.8412 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7501 |     1.486 |     1.216 |     1.293 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     476.62183</span> |     93.07 |  0.004035 |    0.2773 |    0.8693 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.481 |     1.546 |   0.03205 |    0.8412 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7501 |     1.486 |     1.216 |     1.293 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     8.726 |     1.111 |    0.3721 |   0.03969 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.07339 |    -4.588 |    -1.391 |  -0.04583 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.059 |    -2.851 |    -2.052 |    -2.306 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   44</span>|     476.61645 |    0.9998 |    -1.215 |   -0.9300 |   -0.9243 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8958 |   -0.2908 |   -0.7546 |   -0.7711 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.016 |   -0.6442 |   -0.7306 |   -0.6621 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     476.61645 |     92.98 |    -5.515 |   -0.9576 |   -0.1399 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.250 |     1.546 |   0.03205 |    0.8401 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7517 |     1.485 |     1.217 |     1.294 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     476.61645</span> |     92.98 |  0.004027 |    0.2774 |    0.8694 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.484 |     1.546 |   0.03205 |    0.8401 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7517 |     1.485 |     1.217 |     1.294 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -5.224 |     1.099 |    0.2980 |   0.02349 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................| -0.002638 |    -4.438 |    -1.447 |  -0.09166 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4490 |    -2.896 |    -2.021 |    -2.267 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   45</span>|     476.60491 |     1.001 |    -1.217 |   -0.9300 |   -0.9242 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8956 |   -0.2899 |   -0.7543 |   -0.7729 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.016 |   -0.6437 |   -0.7294 |   -0.6608 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     476.60491 |     93.08 |    -5.517 |   -0.9576 |   -0.1398 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.250 |     1.547 |   0.03206 |    0.8387 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7514 |     1.486 |     1.218 |     1.295 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     476.60491</span> |     93.08 |  0.004018 |    0.2774 |    0.8695 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.485 |     1.547 |   0.03206 |    0.8387 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7514 |     1.486 |     1.218 |     1.295 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     9.891 |     1.101 |    0.3805 |   0.04781 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.09121 |    -4.602 |    -1.355 |   -0.1211 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9908 |    -2.875 |    -1.954 |    -2.212 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   46</span>|     476.59275 |    0.9999 |    -1.219 |   -0.9301 |   -0.9241 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8954 |   -0.2896 |   -0.7542 |   -0.7748 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.017 |   -0.6434 |   -0.7284 |   -0.6597 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     476.59275 |     92.99 |    -5.519 |   -0.9576 |   -0.1397 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.250 |     1.547 |   0.03206 |    0.8373 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7512 |     1.486 |     1.219 |     1.297 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     476.59275</span> |     92.99 |  0.004009 |    0.2774 |    0.8696 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.487 |     1.547 |   0.03206 |    0.8373 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7512 |     1.486 |     1.219 |     1.297 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.741 |     1.086 |    0.3018 |   0.02978 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01334 |    -4.300 |    -1.393 |  -0.08082 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4335 |    -2.821 |    -1.884 |    -2.156 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   47</span>|     476.58049 |     1.001 |    -1.222 |   -0.9302 |   -0.9241 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8953 |   -0.2889 |   -0.7541 |   -0.7767 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.017 |   -0.6427 |   -0.7275 |   -0.6585 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     476.58049 |     93.06 |    -5.522 |   -0.9577 |   -0.1397 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.250 |     1.547 |   0.03206 |    0.8359 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7507 |     1.487 |     1.220 |     1.298 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     476.58049</span> |     93.06 |  0.003999 |    0.2773 |    0.8697 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.488 |     1.547 |   0.03206 |    0.8359 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7507 |     1.487 |     1.220 |     1.298 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   48</span>|     476.57085 |     1.001 |    -1.225 |   -0.9302 |   -0.9239 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8951 |   -0.2891 |   -0.7542 |   -0.7796 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.018 |   -0.6424 |   -0.7265 |   -0.6573 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     476.57085 |     93.06 |    -5.525 |   -0.9578 |   -0.1395 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.250 |     1.547 |   0.03206 |    0.8336 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7502 |     1.488 |     1.221 |     1.299 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     476.57085</span> |     93.06 |  0.003985 |    0.2773 |    0.8698 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.490 |     1.547 |   0.03206 |    0.8336 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7502 |     1.488 |     1.221 |     1.299 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   49</span>|     476.52700 |     1.000 |    -1.243 |   -0.9306 |   -0.9233 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8940 |   -0.2898 |   -0.7549 |   -0.7948 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.021 |   -0.6409 |   -0.7216 |   -0.6509 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|       476.527 |     93.02 |    -5.543 |   -0.9582 |   -0.1389 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.251 |     1.547 |   0.03205 |    0.8221 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7475 |     1.489 |     1.226 |     1.306 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">       476.527</span> |     93.02 |  0.003915 |    0.2772 |    0.8703 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.501 |     1.547 |   0.03205 |    0.8221 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7475 |     1.489 |     1.226 |     1.306 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   50</span>|     476.45166 |    0.9988 |    -1.314 |   -0.9321 |   -0.9209 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8895 |   -0.2927 |   -0.7576 |   -0.8554 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.033 |   -0.6351 |   -0.7022 |   -0.6254 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     476.45166 |     92.89 |    -5.614 |   -0.9596 |   -0.1365 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.256 |     1.545 |   0.03201 |    0.7760 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7365 |     1.496 |     1.247 |     1.333 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     476.45166</span> |     92.89 |  0.003648 |    0.2770 |    0.8724 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.543 |     1.545 |   0.03201 |    0.7760 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7365 |     1.496 |     1.247 |     1.333 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -21.31 |    0.8191 |    0.2022 |    0.1018 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1327 |    -4.505 |    -1.303 |    -1.202 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.080 |    -2.304 |   -0.3706 |   -0.4948 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   51</span>|     476.56836 |     1.004 |    -1.424 |   -0.9336 |   -0.9183 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8856 |   -0.2810 |   -0.7637 |   -0.9206 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.004 |   -0.6303 |   -0.6962 |   -0.6148 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     476.56836 |     93.36 |    -5.724 |   -0.9609 |   -0.1338 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.260 |     1.552 |   0.03192 |    0.7265 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7619 |     1.502 |     1.254 |     1.345 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     476.56836</span> |     93.36 |  0.003266 |    0.2767 |    0.8747 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.581 |     1.552 |   0.03192 |    0.7265 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7619 |     1.502 |     1.254 |     1.345 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   52</span>|     476.44457 |     1.002 |    -1.351 |   -0.9326 |   -0.9200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8882 |   -0.2885 |   -0.7595 |   -0.8773 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.024 |   -0.6333 |   -0.7001 |   -0.6218 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     476.44457 |     93.15 |    -5.651 |   -0.9600 |   -0.1356 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.257 |     1.548 |   0.03198 |    0.7594 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7451 |     1.499 |     1.249 |     1.337 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     476.44457</span> |     93.15 |  0.003514 |    0.2769 |    0.8732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.556 |     1.548 |   0.03198 |    0.7594 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7451 |     1.499 |     1.249 |     1.337 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     15.82 |    0.7276 |    0.3571 |    0.1746 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4004 |    -4.436 |   -0.9222 |    -1.572 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.287 |    -2.164 |   -0.2873 |   -0.3883 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   53</span>|     476.40417 |     1.000 |    -1.371 |   -0.9349 |   -0.9209 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8904 |   -0.2869 |   -0.7634 |   -0.8782 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.024 |   -0.6262 |   -0.7046 |   -0.6250 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     476.40417 |     93.02 |    -5.671 |   -0.9622 |   -0.1365 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.255 |     1.549 |   0.03192 |    0.7587 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7444 |     1.507 |     1.245 |     1.334 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     476.40417</span> |     93.02 |  0.003446 |    0.2764 |    0.8724 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.535 |     1.549 |   0.03192 |    0.7587 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7444 |     1.507 |     1.245 |     1.334 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   54</span>|     476.37918 |     1.000 |    -1.391 |   -0.9372 |   -0.9218 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8927 |   -0.2856 |   -0.7675 |   -0.8792 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.025 |   -0.6191 |   -0.7092 |   -0.6283 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     476.37918 |     93.03 |    -5.691 |   -0.9644 |   -0.1374 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.253 |     1.549 |   0.03186 |    0.7580 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7434 |     1.516 |     1.240 |     1.330 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     476.37918</span> |     93.03 |  0.003376 |    0.2760 |    0.8716 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.513 |     1.549 |   0.03186 |    0.7580 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7434 |     1.516 |     1.240 |     1.330 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   55</span>|     476.33357 |     1.001 |    -1.461 |   -0.9453 |   -0.9249 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9004 |   -0.2814 |   -0.7818 |   -0.8827 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.029 |   -0.5944 |   -0.7253 |   -0.6399 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     476.33357 |     93.07 |    -5.761 |   -0.9719 |   -0.1405 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.245 |     1.552 |   0.03165 |    0.7553 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7403 |     1.546 |     1.222 |     1.318 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     476.33357</span> |     93.07 |  0.003146 |    0.2745 |    0.8689 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.440 |     1.552 |   0.03165 |    0.7553 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7403 |     1.546 |     1.222 |     1.318 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.553 |    0.4475 |   -0.2195 |   0.07220 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.05655 |    -4.350 |    -1.061 |    -1.750 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1436 |   -0.4527 |    -1.673 |    -1.245 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   56</span>|     476.20746 |     1.002 |    -1.571 |   -0.9452 |   -0.9313 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9123 |   -0.2680 |   -0.8139 |   -0.8382 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.032 |   -0.6009 |   -0.7081 |   -0.6381 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     476.20746 |     93.20 |    -5.871 |   -0.9719 |   -0.1469 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.233 |     1.560 |   0.03117 |    0.7891 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7373 |     1.538 |     1.241 |     1.320 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     476.20746</span> |     93.20 |  0.002821 |    0.2745 |    0.8634 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.328 |     1.560 |   0.03117 |    0.7891 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7373 |     1.538 |     1.241 |     1.320 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     12.15 |    0.1791 |  -0.01894 | -0.004675 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.06593 |    -5.850 |    -1.512 |    -1.794 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.396 |    -1.647 |   -0.7085 |    -1.309 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   57</span>|     476.15444 |     1.000 |    -1.669 |   -0.9317 |   -0.9229 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8889 |   -0.2480 |   -0.8424 |   -0.7961 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.030 |   -0.6117 |   -0.7275 |   -0.5959 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     476.15444 |     93.03 |    -5.969 |   -0.9591 |   -0.1385 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.256 |     1.572 |   0.03074 |    0.8211 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7395 |     1.525 |     1.220 |     1.365 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     476.15444</span> |     93.03 |  0.002558 |    0.2771 |    0.8707 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.549 |     1.572 |   0.03074 |    0.8211 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7395 |     1.525 |     1.220 |     1.365 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -13.97 |  -0.09883 |    0.5245 |    0.1410 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.5845 |    -3.407 |    -1.272 |   -0.2800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9652 |    -1.487 |    -1.684 |    0.3572 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   58</span>|     476.18235 |     1.000 |    -1.690 |   -0.9781 |   -0.8961 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8818 |   -0.1915 |   -0.8548 |   -0.7635 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.041 |   -0.6068 |   -0.6702 |   -0.6625 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     476.18235 |     93.04 |    -5.990 |    -1.003 |   -0.1117 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.264 |     1.606 |   0.03055 |    0.8458 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7299 |     1.531 |     1.281 |     1.294 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     476.18235</span> |     93.04 |  0.002505 |    0.2684 |    0.8943 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.617 |     1.606 |   0.03055 |    0.8458 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7299 |     1.531 |     1.281 |     1.294 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   59</span>|     476.08231 |     1.003 |    -1.678 |   -0.9530 |   -0.9107 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8858 |   -0.2215 |   -0.8479 |   -0.7811 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.035 |   -0.6092 |   -0.7010 |   -0.6265 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     476.08231 |     93.25 |    -5.978 |   -0.9792 |   -0.1263 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.260 |     1.588 |   0.03066 |    0.8325 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7350 |     1.528 |     1.248 |     1.332 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     476.08231</span> |     93.25 |  0.002533 |    0.2730 |    0.8814 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.579 |     1.588 |   0.03066 |    0.8325 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7350 |     1.528 |     1.248 |     1.332 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     18.25 |  -0.08933 |   -0.2508 |    0.5157 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8830 |    -3.835 |    -1.133 |    -1.026 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.010 |    -2.153 |   -0.2439 |    -1.007 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   60</span>|     476.03808 |     1.001 |    -1.672 |   -0.9320 |   -0.9310 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9291 |   -0.2079 |   -0.8419 |   -0.7733 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.039 |   -0.6026 |   -0.6913 |   -0.6380 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     476.03808 |     93.13 |    -5.972 |   -0.9594 |   -0.1466 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.216 |     1.596 |   0.03074 |    0.8384 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7314 |     1.536 |     1.259 |     1.320 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     476.03808</span> |     93.13 |  0.002548 |    0.2770 |    0.8636 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.173 |     1.596 |   0.03074 |    0.8384 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7314 |     1.536 |     1.259 |     1.320 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -5.407 |  -0.06545 |    0.6727 |   0.05373 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4541 |    -1.330 |   -0.5709 |   -0.1224 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8702 |    -1.228 |    0.2640 |    -1.506 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   61</span>|     476.03300 |     1.003 |    -1.664 |   -0.9621 |   -0.9598 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9520 |   -0.2020 |   -0.8337 |   -0.7650 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.042 |   -0.6106 |   -0.7076 |   -0.6154 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|       476.033 |     93.29 |    -5.964 |   -0.9878 |   -0.1754 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.193 |     1.599 |   0.03087 |    0.8447 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7289 |     1.526 |     1.241 |     1.344 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">       476.033</span> |     93.29 |  0.002569 |    0.2714 |    0.8391 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.966 |     1.599 |   0.03087 |    0.8447 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7289 |     1.526 |     1.241 |     1.344 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     15.25 | -0.008049 |   -0.5780 |   -0.5632 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9601 |   -0.9870 |   -0.1614 |  -0.08306 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3446 |    -1.564 |   -0.5735 |   -0.5777 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   62</span>|     475.98457 |     1.001 |    -1.657 |   -0.9620 |   -0.9650 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9284 |   -0.2016 |   -0.8312 |   -0.7567 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.045 |   -0.6004 |   -0.7091 |   -0.6133 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     475.98457 |     93.13 |    -5.957 |   -0.9877 |   -0.1806 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.217 |     1.600 |   0.03091 |    0.8511 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7262 |     1.538 |     1.240 |     1.346 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     475.98457</span> |     93.13 |  0.002588 |    0.2714 |    0.8347 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.180 |     1.600 |   0.03091 |    0.8511 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7262 |     1.538 |     1.240 |     1.346 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   63</span>|     475.96536 |     1.003 |    -1.649 |   -0.9620 |   -0.9705 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9043 |   -0.2014 |   -0.8286 |   -0.7480 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.048 |   -0.5903 |   -0.7109 |   -0.6113 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     475.96536 |     93.24 |    -5.949 |   -0.9876 |   -0.1861 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.241 |     1.600 |   0.03094 |    0.8576 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7235 |     1.551 |     1.238 |     1.349 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     475.96536</span> |     93.24 |  0.002608 |    0.2714 |    0.8302 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.403 |     1.600 |   0.03094 |    0.8576 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7235 |     1.551 |     1.238 |     1.349 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   64</span>|     476.05821 |     1.003 |    -1.627 |   -0.9618 |   -0.9866 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8332 |   -0.2007 |   -0.8211 |   -0.7226 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.057 |   -0.5602 |   -0.7159 |   -0.6052 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     476.05821 |     93.28 |    -5.927 |   -0.9874 |   -0.2022 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.312 |     1.600 |   0.03106 |    0.8769 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7156 |     1.587 |     1.233 |     1.355 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     476.05821</span> |     93.28 |  0.002666 |    0.2714 |    0.8169 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     10.10 |     1.600 |   0.03106 |    0.8769 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7156 |     1.587 |     1.233 |     1.355 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     11.90 |   0.05098 |   -0.6601 |   -0.8657 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4347 |   -0.9523 |   -0.3178 |   -0.1283 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1024 |   -0.7800 |   -0.7284 |   -0.4981 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   65</span>|     476.00714 |     1.001 |    -1.643 |   -0.9512 |   -0.8390 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8932 |   -0.2051 |   -0.7956 |   -0.7365 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.045 |   -0.5599 |   -0.7127 |   -0.5744 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     476.00714 |     93.11 |    -5.943 |   -0.9775 |  -0.05460 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.252 |     1.598 |   0.03144 |    0.8663 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7264 |     1.588 |     1.236 |     1.388 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     476.00714</span> |     93.11 |  0.002624 |    0.2734 |    0.9469 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.508 |     1.598 |   0.03144 |    0.8663 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7264 |     1.588 |     1.236 |     1.388 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   66</span>|     475.93814 |     1.000 |    -1.647 |   -0.9575 |   -0.9171 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8999 |   -0.2027 |   -0.8152 |   -0.7434 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.047 |   -0.5779 |   -0.7115 |   -0.5963 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     475.93814 |     93.01 |    -5.947 |   -0.9834 |   -0.1327 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.245 |     1.599 |   0.03115 |    0.8612 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7247 |     1.566 |     1.237 |     1.365 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     475.93814</span> |     93.01 |  0.002614 |    0.2722 |    0.8757 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.445 |     1.599 |   0.03115 |    0.8612 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7247 |     1.566 |     1.237 |     1.365 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -23.67 |   0.03897 |   -0.7058 |    0.2979 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2507 |   -0.5051 |   -0.4675 |  -0.05050 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.329 |   -0.1594 |   -0.7628 |    0.1885 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   67</span>|     475.98668 |     1.003 |    -1.653 |   -0.9119 |   -0.8938 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8940 |   -0.2050 |   -0.7980 |   -0.7479 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.040 |   -0.5617 |   -0.7056 |   -0.5853 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     475.98668 |     93.26 |    -5.953 |   -0.9406 |   -0.1094 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.251 |     1.598 |   0.03140 |    0.8577 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7310 |     1.586 |     1.243 |     1.376 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     475.98668</span> |     93.26 |  0.002598 |    0.2808 |    0.8964 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.501 |     1.598 |   0.03140 |    0.8577 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7310 |     1.586 |     1.243 |     1.376 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   68</span>|     475.93401 |     1.003 |    -1.649 |   -0.9419 |   -0.9092 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8979 |   -0.2035 |   -0.8093 |   -0.7449 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.044 |   -0.5723 |   -0.7094 |   -0.5925 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     475.93401 |     93.26 |    -5.949 |   -0.9687 |   -0.1248 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.247 |     1.599 |   0.03123 |    0.8600 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7270 |     1.573 |     1.239 |     1.369 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     475.93401</span> |     93.26 |  0.002609 |    0.2751 |    0.8827 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.464 |     1.599 |   0.03123 |    0.8600 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7270 |     1.573 |     1.239 |     1.369 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     21.62 |   0.03755 |    0.3553 |    0.5344 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.5780 |   -0.8226 |   -0.2181 |   -0.2167 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.049 |  0.002537 |   -0.7279 |    0.2347 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   69</span>|     475.92580 |     1.001 |    -1.653 |   -0.9417 |   -0.9124 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8966 |   -0.2037 |   -0.8058 |   -0.7501 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.041 |   -0.5739 |   -0.7048 |   -0.5926 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      475.9258 |     93.12 |    -5.953 |   -0.9686 |   -0.1280 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.249 |     1.598 |   0.03129 |    0.8560 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7297 |     1.571 |     1.244 |     1.369 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      475.9258</span> |     93.12 |  0.002598 |    0.2752 |    0.8798 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.476 |     1.598 |   0.03129 |    0.8560 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7297 |     1.571 |     1.244 |     1.369 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.238 |  0.003180 |    0.2025 |    0.4289 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4493 |   -0.2935 |   -0.2270 |   -0.1209 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3327 |  -0.04065 |   -0.4864 |    0.2705 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   70</span>|     475.92421 |     1.002 |    -1.655 |   -0.9475 |   -0.9180 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8971 |   -0.2051 |   -0.8017 |   -0.7499 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.041 |   -0.5760 |   -0.7024 |   -0.5951 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     475.92421 |     93.14 |    -5.955 |   -0.9740 |   -0.1336 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.248 |     1.598 |   0.03135 |    0.8562 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7299 |     1.568 |     1.247 |     1.366 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     475.92421</span> |     93.14 |  0.002593 |    0.2741 |    0.8749 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.472 |     1.598 |   0.03135 |    0.8562 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7299 |     1.568 |     1.247 |     1.366 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     1.572 | 0.0005078 |  -0.07939 |    0.3039 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4520 |   -0.5123 |   -0.2298 |   -0.1955 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3506 |   -0.1759 |   -0.3471 |    0.1717 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   71</span>|     475.91356 |     1.001 |    -1.654 |   -0.9476 |   -0.9223 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8992 |   -0.2071 |   -0.7965 |   -0.7440 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.043 |   -0.5749 |   -0.7035 |   -0.5974 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     475.91356 |     93.12 |    -5.954 |   -0.9741 |   -0.1379 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.246 |     1.596 |   0.03143 |    0.8607 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7278 |     1.569 |     1.246 |     1.363 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     475.91356</span> |     93.12 |  0.002596 |    0.2741 |    0.8712 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.452 |     1.596 |   0.03143 |    0.8607 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7278 |     1.569 |     1.246 |     1.363 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   72</span>|     475.89054 |     1.001 |    -1.650 |   -0.9479 |   -0.9349 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9054 |   -0.2136 |   -0.7808 |   -0.7261 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.051 |   -0.5716 |   -0.7071 |   -0.6043 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     475.89054 |     93.11 |    -5.950 |   -0.9744 |   -0.1505 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.240 |     1.592 |   0.03166 |    0.8742 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7214 |     1.574 |     1.242 |     1.356 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     475.89054</span> |     93.11 |  0.002606 |    0.2740 |    0.8602 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.393 |     1.592 |   0.03166 |    0.8742 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7214 |     1.574 |     1.242 |     1.356 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -3.467 |   0.05330 |   -0.1133 |   -0.1045 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1362 |   -0.3304 |   -0.2172 |   0.04726 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.416 |   0.08323 |   -0.4566 |   -0.2851 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   73</span>|     476.06529 |     1.002 |    -1.688 |   -0.8959 |   -0.9784 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9076 |   -0.2354 |   -0.6823 |   -0.7302 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.035 |   -0.5603 |   -0.6700 |   -0.6079 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     476.06529 |     93.20 |    -5.988 |   -0.9255 |   -0.1940 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.238 |     1.579 |   0.03314 |    0.8712 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7347 |     1.587 |     1.281 |     1.352 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     476.06529</span> |     93.20 |  0.002510 |    0.2838 |    0.8236 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.372 |     1.579 |   0.03314 |    0.8712 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7347 |     1.587 |     1.281 |     1.352 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   74</span>|     475.89054 |     1.001 |    -1.650 |   -0.9479 |   -0.9349 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9054 |   -0.2136 |   -0.7808 |   -0.7261 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.051 |   -0.5716 |   -0.7071 |   -0.6043 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     475.89054 |     93.11 |    -5.950 |   -0.9744 |   -0.1505 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.240 |     1.592 |   0.03166 |    0.8742 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7214 |     1.574 |     1.242 |     1.356 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     475.89054</span> |     93.11 |  0.002606 |    0.2740 |    0.8602 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.393 |     1.592 |   0.03166 |    0.8742 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7214 |     1.574 |     1.242 |     1.356 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> calculating covariance matrix</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating residuals/tables</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-in"><span class="va">f_nlmixr_dfop_sfo_focei_tc</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr</a></span><span class="op">(</span><span class="va">f_mmkin_tc</span><span class="op">[</span><span class="st">"DFOP-SFO"</span>, <span class="op">]</span>, est <span class="op">=</span> <span class="st">"focei"</span><span class="op">)</span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate jacobian</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate sensitivities</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate ∂(f)/∂(η)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate ∂(R²)/∂(η)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in inner model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in inner model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in EBE model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in EBE model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling inner model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in FD model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in FD model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling EBE model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling events FD model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Needed Covariates:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "CMT"</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="font-weight: bold;">Key:</span> U: Unscaled Parameters; X: Back-transformed parameters; G: Gill difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> F: Forward difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> C: Central difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> M: Mixed forward and central difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Unscaled parameters for Omegas=chol(solve(omega));</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Diagonals are transformed, as specified by foceiControl(diagXform=)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |-----+---------------+-----------+-----------+-----------+-----------|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    #| Objective Fun |  parent_0 |  log_k_A1 |f_parent_qlogis |    log_k1 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    log_k2 |  g_qlogis | sigma_low |  rsd_high |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|        o1 |        o2 |        o3 |        o4 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|        o5 |        o6 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    1</span>|     495.48573 |     1.000 |    -1.000 |   -0.9104 |   -0.9376 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9875 |   -0.8823 |   -0.8746 |   -0.8907 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8767 |   -0.8731 |   -0.8673 |   -0.8720 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8739 |   -0.8666 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     495.48573 |     91.00 |    -5.200 |   -0.8900 |    -2.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.600 |    0.4600 |    0.8300 |   0.05800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7311 |    0.9036 |     1.183 |    0.9554 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.8633 |     1.214 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     495.48573</span> |     91.00 |  0.005517 |    0.2911 |    0.1108 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01005 |    0.6130 |    0.8300 |   0.05800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7311 |    0.9036 |     1.183 |    0.9554 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.8633 |     1.214 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    G|    Gill Diff. |   -0.9650 |     2.223 |   -0.3153 |  -0.01817 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3350 |    0.6789 |    -29.17 |    -19.58 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9642 |     9.851 |    -11.94 |    -1.319 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     8.578 |    -12.45 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    2</span>|     487.27153 |     1.023 |    -1.054 |   -0.9028 |   -0.9372 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9794 |   -0.8987 |   -0.1695 |   -0.4175 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9000 |    -1.111 |   -0.5788 |   -0.8401 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.081 |   -0.5657 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     487.27153 |     93.12 |    -5.254 |   -0.8832 |    -2.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.592 |    0.4525 |     1.123 |   0.07172 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7141 |    0.6884 |     1.525 |    0.9859 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6843 |     1.580 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     487.27153</span> |     93.12 |  0.005228 |    0.2925 |    0.1109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01013 |    0.6112 |     1.123 |   0.07172 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7141 |    0.6884 |     1.525 |    0.9859 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6843 |     1.580 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     131.2 |     1.375 |     2.844 |   -0.2311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2724 |    0.4206 |     9.234 |     14.82 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3432 |    -1.547 |    -2.137 |     2.699 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -4.845 |    -6.389 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    3</span>|     3806.3530 |    0.1967 |    -1.082 |   -0.9181 |   -0.9356 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9782 |   -0.9073 |   0.02635 |   -0.3409 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9062 |    -1.204 |   -0.4610 |   -0.8458 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.125 |   -0.4164 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      3806.353 |     17.90 |    -5.282 |   -0.8969 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.591 |    0.4485 |     1.204 |   0.07394 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7095 |    0.6043 |     1.664 |    0.9805 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6463 |     1.761 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      3806.353</span> |     17.90 |  0.005083 |    0.2897 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01015 |    0.6103 |     1.204 |   0.07394 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7095 |    0.6043 |     1.664 |    0.9805 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6463 |     1.761 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    4</span>|     498.29847 |    0.9363 |    -1.055 |   -0.9047 |   -0.9370 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9796 |   -0.8990 |   -0.1756 |   -0.4273 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8998 |    -1.110 |   -0.5773 |   -0.8419 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.078 |   -0.5615 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     498.29847 |     85.20 |    -5.255 |   -0.8849 |    -2.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.592 |    0.4523 |     1.120 |   0.07144 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7142 |    0.6894 |     1.526 |    0.9842 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6871 |     1.585 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     498.29847</span> |     85.20 |  0.005223 |    0.2922 |    0.1109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01013 |    0.6112 |     1.120 |   0.07144 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7142 |    0.6894 |     1.526 |    0.9842 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6871 |     1.585 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    5</span>|     485.66266 |     1.001 |    -1.054 |   -0.9033 |   -0.9372 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9795 |   -0.8988 |   -0.1711 |   -0.4200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8999 |    -1.111 |   -0.5784 |   -0.8406 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.080 |   -0.5646 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     485.66266 |     91.08 |    -5.254 |   -0.8836 |    -2.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.592 |    0.4524 |     1.122 |   0.07165 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7141 |    0.6887 |     1.525 |    0.9855 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6850 |     1.581 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     485.66266</span> |     91.08 |  0.005227 |    0.2924 |    0.1109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01013 |    0.6112 |     1.122 |   0.07165 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7141 |    0.6887 |     1.525 |    0.9855 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6850 |     1.581 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     5.221 |     1.276 |    0.8286 |   0.07146 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3378 |    0.6177 |     8.950 |     14.42 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.232 |    -2.934 |    -2.090 |     2.822 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -4.031 |    -6.085 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    6</span>|     485.32609 |    0.9950 |    -1.055 |   -0.9042 |   -0.9372 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9799 |   -0.8995 |   -0.1813 |   -0.4364 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8974 |    -1.108 |   -0.5760 |   -0.8438 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.076 |   -0.5577 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     485.32609 |     90.54 |    -5.255 |   -0.8845 |    -2.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.592 |    0.4521 |     1.118 |   0.07117 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7160 |    0.6917 |     1.528 |    0.9824 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6890 |     1.589 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     485.32609</span> |     90.54 |  0.005219 |    0.2922 |    0.1108 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01013 |    0.6111 |     1.118 |   0.07117 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7160 |    0.6917 |     1.528 |    0.9824 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6890 |     1.589 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -30.72 |     1.256 |    0.2407 |    0.1468 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3576 |    0.6982 |     8.550 |     13.79 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.152 |    -3.092 |    -1.869 |     2.672 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -3.399 |    -5.827 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    7</span>|     484.94767 |     1.003 |    -1.055 |   -0.9045 |   -0.9373 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9801 |   -0.8996 |   -0.1956 |   -0.4497 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8963 |    -1.103 |   -0.5795 |   -0.8455 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.071 |   -0.5595 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     484.94767 |     91.28 |    -5.255 |   -0.8848 |    -2.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.593 |    0.4521 |     1.112 |   0.07079 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7168 |    0.6961 |     1.524 |    0.9808 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6930 |     1.587 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     484.94767</span> |     91.28 |  0.005220 |    0.2922 |    0.1108 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01013 |    0.6111 |     1.112 |   0.07079 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7168 |    0.6961 |     1.524 |    0.9808 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6930 |     1.587 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     16.95 |     1.308 |    0.9181 |   0.03760 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3308 |    0.6549 |     8.124 |     13.74 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.975 |    -2.369 |    -2.107 |     2.437 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -3.334 |    -5.928 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    8</span>|     484.63747 |    0.9965 |    -1.055 |   -0.9051 |   -0.9373 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9805 |   -0.8998 |   -0.2100 |   -0.4642 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8952 |    -1.098 |   -0.5823 |   -0.8472 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.067 |   -0.5602 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     484.63747 |     90.68 |    -5.255 |   -0.8853 |    -2.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.593 |    0.4520 |     1.106 |   0.07037 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7176 |    0.7002 |     1.521 |    0.9792 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6968 |     1.586 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     484.63747</span> |     90.68 |  0.005220 |    0.2921 |    0.1108 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01012 |    0.6111 |     1.106 |   0.07037 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7176 |    0.7002 |     1.521 |    0.9792 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6968 |     1.586 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -22.67 |     1.288 |    0.2773 |    0.1211 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3459 |    0.7203 |     8.028 |     13.24 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.967 |    -2.555 |    -2.117 |     2.333 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -2.789 |    -5.812 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    9</span>|     484.31288 |     1.003 |    -1.055 |   -0.9054 |   -0.9374 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9808 |   -0.9000 |   -0.2248 |   -0.4783 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8942 |    -1.094 |   -0.5856 |   -0.8486 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.063 |   -0.5617 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     484.31288 |     91.27 |    -5.255 |   -0.8855 |    -2.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.593 |    0.4519 |     1.100 |   0.06996 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7183 |    0.7043 |     1.517 |    0.9778 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.7003 |     1.585 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     484.31288</span> |     91.27 |  0.005220 |    0.2920 |    0.1108 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01012 |    0.6111 |     1.100 |   0.06996 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7183 |    0.7043 |     1.517 |    0.9778 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.7003 |     1.585 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     16.32 |     1.333 |    0.8110 |   0.03416 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3225 |    0.6774 |     7.502 |     12.95 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.990 |    -1.849 |    -2.190 |     2.193 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -2.714 |    -5.891 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   10</span>|     484.03445 |    0.9966 |    -1.055 |   -0.9058 |   -0.9374 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9811 |   -0.9003 |   -0.2393 |   -0.4932 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8929 |    -1.090 |   -0.5881 |   -0.8501 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.059 |   -0.5620 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     484.03445 |     90.69 |    -5.255 |   -0.8859 |    -2.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.594 |    0.4517 |     1.094 |   0.06953 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7192 |    0.7079 |     1.514 |    0.9764 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.7034 |     1.584 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     484.03445</span> |     90.69 |  0.005219 |    0.2919 |    0.1108 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01012 |    0.6110 |     1.094 |   0.06953 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7192 |    0.7079 |     1.514 |    0.9764 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.7034 |     1.584 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -23.34 |     1.311 |    0.1997 |    0.1136 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3397 |    0.7422 |     7.043 |     12.17 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.843 |    -2.075 |    -2.300 |     2.141 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -2.235 |    -5.778 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   11</span>|     483.72794 |     1.003 |    -1.056 |   -0.9061 |   -0.9374 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9815 |   -0.9008 |   -0.2540 |   -0.5081 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8918 |    -1.086 |   -0.5906 |   -0.8514 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.056 |   -0.5623 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     483.72794 |     91.28 |    -5.256 |   -0.8861 |    -2.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.594 |    0.4515 |     1.088 |   0.06909 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7200 |    0.7113 |     1.511 |    0.9751 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.7061 |     1.584 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     483.72794</span> |     91.28 |  0.005217 |    0.2919 |    0.1108 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01011 |    0.6110 |     1.088 |   0.06909 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7200 |    0.7113 |     1.511 |    0.9751 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.7061 |     1.584 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     16.56 |     1.355 |    0.7367 |   0.03002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3166 |    0.7021 |     6.528 |     11.89 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.880 |    -1.512 |    -2.404 |     1.917 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -2.215 |    -5.846 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   12</span>|     483.44272 |    0.9974 |    -1.057 |   -0.9064 |   -0.9375 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9820 |   -0.9016 |   -0.2678 |   -0.5243 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8904 |    -1.083 |   -0.5914 |   -0.8528 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.054 |   -0.5601 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     483.44272 |     90.76 |    -5.257 |   -0.8865 |    -2.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.594 |    0.4511 |     1.082 |   0.06862 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7211 |    0.7141 |     1.510 |    0.9738 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.7081 |     1.587 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     483.44272</span> |     90.76 |  0.005212 |    0.2918 |    0.1108 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01011 |    0.6109 |     1.082 |   0.06862 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7211 |    0.7141 |     1.510 |    0.9738 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.7081 |     1.587 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -19.50 |     1.332 |    0.2061 |    0.1022 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3336 |    0.7519 |     5.944 |     11.09 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.878 |    -1.675 |    -2.404 |     1.054 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.833 |    -5.715 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   13</span>|     483.13758 |     1.003 |    -1.059 |   -0.9067 |   -0.9375 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9826 |   -0.9029 |   -0.2791 |   -0.5415 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8881 |    -1.081 |   -0.5896 |   -0.8508 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.053 |   -0.5541 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     483.13758 |     91.30 |    -5.259 |   -0.8867 |    -2.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.595 |    0.4505 |     1.077 |   0.06813 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7227 |    0.7157 |     1.512 |    0.9758 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.7089 |     1.594 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     483.13758</span> |     91.30 |  0.005202 |    0.2918 |    0.1108 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01010 |    0.6108 |     1.077 |   0.06813 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7227 |    0.7157 |     1.512 |    0.9758 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.7089 |     1.594 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     16.83 |     1.368 |    0.7006 |   0.02276 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3159 |    0.7144 |     5.487 |     10.74 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.773 |    -1.125 |    -2.251 |     1.956 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.882 |    -5.671 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   14</span>|     482.85180 |    0.9981 |    -1.062 |   -0.9072 |   -0.9376 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9834 |   -0.9050 |   -0.2850 |   -0.5582 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8853 |    -1.082 |   -0.5852 |   -0.8503 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.053 |   -0.5428 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      482.8518 |     90.83 |    -5.262 |   -0.8872 |    -2.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.596 |    0.4496 |     1.075 |   0.06764 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7248 |    0.7152 |     1.517 |    0.9762 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.7084 |     1.607 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      482.8518</span> |     90.83 |  0.005184 |    0.2917 |    0.1108 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01009 |    0.6105 |     1.075 |   0.06764 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7248 |    0.7152 |     1.517 |    0.9762 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.7084 |     1.607 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -16.64 |     1.337 |    0.2421 |   0.09047 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3389 |    0.7643 |     5.051 |     10.06 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.787 |    -1.445 |    -2.047 |     2.073 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.627 |    -5.410 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   15</span>|     482.60290 |     1.003 |    -1.066 |   -0.9079 |   -0.9377 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9844 |   -0.9075 |   -0.2858 |   -0.5723 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8822 |    -1.083 |   -0.5806 |   -0.8571 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.055 |   -0.5294 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      482.6029 |     91.28 |    -5.266 |   -0.8878 |    -2.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.597 |    0.4484 |     1.074 |   0.06723 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7271 |    0.7136 |     1.523 |    0.9697 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.7072 |     1.624 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      482.6029</span> |     91.28 |  0.005162 |    0.2916 |    0.1108 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01008 |    0.6103 |     1.074 |   0.06723 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7271 |    0.7136 |     1.523 |    0.9697 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.7072 |     1.624 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     14.50 |     1.352 |    0.6742 |   0.02192 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3317 |    0.7297 |     4.803 |     9.894 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.694 |    -1.220 |    -2.031 |     1.506 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.722 |    -5.269 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   16</span>|     482.37953 |    0.9987 |    -1.071 |   -0.9090 |   -0.9377 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9857 |   -0.9106 |   -0.2840 |   -0.5862 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8787 |    -1.084 |   -0.5758 |   -0.8611 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.056 |   -0.5150 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     482.37953 |     90.88 |    -5.271 |   -0.8888 |    -2.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.598 |    0.4470 |     1.075 |   0.06683 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7296 |    0.7132 |     1.528 |    0.9659 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.7058 |     1.641 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     482.37953</span> |     90.88 |  0.005137 |    0.2914 |    0.1108 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01007 |    0.6099 |     1.075 |   0.06683 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7296 |    0.7132 |     1.528 |    0.9659 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.7058 |     1.641 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   17</span>|     482.17662 |    0.9986 |    -1.077 |   -0.9102 |   -0.9378 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9871 |   -0.9141 |   -0.2800 |   -0.5994 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8750 |    -1.085 |   -0.5707 |   -0.8654 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.059 |   -0.4996 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     482.17662 |     90.88 |    -5.277 |   -0.8898 |    -2.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.600 |    0.4454 |     1.077 |   0.06645 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7323 |    0.7123 |     1.534 |    0.9618 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.7037 |     1.660 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     482.17662</span> |     90.88 |  0.005108 |    0.2911 |    0.1108 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01006 |    0.6095 |     1.077 |   0.06645 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7323 |    0.7123 |     1.534 |    0.9618 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.7037 |     1.660 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   18</span>|     481.54428 |    0.9984 |    -1.097 |   -0.9142 |   -0.9379 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9921 |   -0.9262 |   -0.2660 |   -0.6456 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8623 |    -1.088 |   -0.5530 |   -0.8805 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.068 |   -0.4457 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     481.54428 |     90.86 |    -5.297 |   -0.8934 |    -2.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.605 |    0.4398 |     1.083 |   0.06511 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7417 |    0.7091 |     1.555 |    0.9473 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6961 |     1.725 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     481.54428</span> |     90.86 |  0.005009 |    0.2904 |    0.1108 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01001 |    0.6082 |     1.083 |   0.06511 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7417 |    0.7091 |     1.555 |    0.9473 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6961 |     1.725 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   19</span>|     480.42060 |    0.9980 |    -1.149 |   -0.9249 |   -0.9384 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.006 |   -0.9588 |   -0.2285 |   -0.7695 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8280 |    -1.098 |   -0.5055 |   -0.9211 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.091 |   -0.3011 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      480.4206 |     90.81 |    -5.349 |   -0.9029 |    -2.201 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.618 |    0.4248 |     1.098 |   0.06151 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7667 |    0.7005 |     1.611 |    0.9085 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6759 |     1.901 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      480.4206</span> |     90.81 |  0.004753 |    0.2885 |    0.1107 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009872 |    0.6046 |     1.098 |   0.06151 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7667 |    0.7005 |     1.611 |    0.9085 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6759 |     1.901 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -36.54 |     1.160 |   -0.4282 |   0.02550 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4248 |    0.7330 |     2.572 |     4.769 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.047 |    -1.982 |     1.244 |    -3.305 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.792 |    -2.494 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   20</span>|     480.52888 |     1.003 |    -1.232 |   -0.9300 |   -0.9345 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.037 |    -1.013 |   -0.2660 |   -0.9542 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7770 |    -1.021 |   -0.6177 |   -0.7306 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.035 |   -0.1927 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.52888 |     91.28 |    -5.432 |   -0.9075 |    -2.197 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.649 |    0.3997 |     1.083 |   0.05616 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8040 |    0.7699 |     1.479 |     1.091 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.7247 |     2.033 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.52888</span> |     91.28 |  0.004374 |    0.2875 |    0.1111 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009571 |    0.5986 |     1.083 |   0.05616 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8040 |    0.7699 |     1.479 |     1.091 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.7247 |     2.033 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   21</span>|     479.69850 |     1.004 |    -1.189 |   -0.9273 |   -0.9365 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.020 |   -0.9850 |   -0.2467 |   -0.8583 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8035 |    -1.061 |   -0.5593 |   -0.8297 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.064 |   -0.2490 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      479.6985 |     91.34 |    -5.389 |   -0.9050 |    -2.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.633 |    0.4128 |     1.091 |   0.05894 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7846 |    0.7338 |     1.548 |    0.9959 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6994 |     1.964 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      479.6985</span> |     91.34 |  0.004567 |    0.2880 |    0.1109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009727 |    0.6018 |     1.091 |   0.05894 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7846 |    0.7338 |     1.548 |    0.9959 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6994 |     1.964 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     5.264 |     1.163 |  -0.05494 |  -0.06753 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3641 |    0.6248 |    0.1998 |     1.877 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5696 |    0.3809 |    0.8989 |     3.758 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2123 |    -1.578 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   22</span>|     479.87893 |     1.001 |    -1.256 |   -0.9150 |   -0.9312 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.045 |    -1.024 |   -0.2268 |   -0.8946 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7799 |    -1.052 |   -0.6825 |   -0.8629 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.056 |   -0.2060 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.87893 |     91.06 |    -5.456 |   -0.8941 |    -2.194 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.658 |    0.3949 |     1.099 |   0.05789 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8019 |    0.7416 |     1.402 |    0.9642 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.7063 |     2.016 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.87893</span> |     91.06 |  0.004269 |    0.2903 |    0.1115 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009490 |    0.5975 |     1.099 |   0.05789 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8019 |    0.7416 |     1.402 |    0.9642 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.7063 |     2.016 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   23</span>|     479.70356 |    0.9996 |    -1.214 |   -0.9229 |   -0.9346 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.029 |   -0.9992 |   -0.2397 |   -0.8723 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7947 |    -1.058 |   -0.6038 |   -0.8437 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.061 |   -0.2328 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.70356 |     90.96 |    -5.414 |   -0.9011 |    -2.197 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.642 |    0.4062 |     1.093 |   0.05853 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7910 |    0.7364 |     1.495 |    0.9825 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.7017 |     1.984 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.70356</span> |     90.96 |  0.004455 |    0.2888 |    0.1111 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009639 |    0.6002 |     1.093 |   0.05853 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7910 |    0.7364 |     1.495 |    0.9825 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.7017 |     1.984 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   24</span>|     479.71523 |    0.9993 |    -1.201 |   -0.9253 |   -0.9356 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.025 |   -0.9918 |   -0.2436 |   -0.8656 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7992 |    -1.060 |   -0.5801 |   -0.8379 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.063 |   -0.2408 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.71523 |     90.93 |    -5.401 |   -0.9032 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.637 |    0.4096 |     1.092 |   0.05873 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7877 |    0.7349 |     1.523 |    0.9880 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.7004 |     1.974 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.71523</span> |     90.93 |  0.004513 |    0.2884 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009685 |    0.6010 |     1.092 |   0.05873 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7877 |    0.7349 |     1.523 |    0.9880 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.7004 |     1.974 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   25</span>|     479.73471 |    0.9991 |    -1.194 |   -0.9266 |   -0.9362 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.022 |   -0.9877 |   -0.2457 |   -0.8619 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8017 |    -1.061 |   -0.5670 |   -0.8347 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.064 |   -0.2453 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.73471 |     90.92 |    -5.394 |   -0.9044 |    -2.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.635 |    0.4115 |     1.091 |   0.05884 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7859 |    0.7340 |     1.539 |    0.9911 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6996 |     1.969 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.73471</span> |     90.92 |  0.004545 |    0.2881 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009710 |    0.6015 |     1.091 |   0.05884 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7859 |    0.7340 |     1.539 |    0.9911 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6996 |     1.969 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   26</span>|     479.71271 |     1.001 |    -1.190 |   -0.9273 |   -0.9365 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.021 |   -0.9854 |   -0.2468 |   -0.8594 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8032 |    -1.061 |   -0.5599 |   -0.8320 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.064 |   -0.2481 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.71271 |     91.05 |    -5.390 |   -0.9050 |    -2.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.633 |    0.4126 |     1.091 |   0.05891 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7849 |    0.7336 |     1.547 |    0.9937 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6993 |     1.965 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.71271</span> |     91.05 |  0.004564 |    0.2880 |    0.1109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009725 |    0.6017 |     1.091 |   0.05891 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7849 |    0.7336 |     1.547 |    0.9937 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6993 |     1.965 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   27</span>|     479.69386 |     1.003 |    -1.189 |   -0.9273 |   -0.9365 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.020 |   -0.9851 |   -0.2467 |   -0.8587 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8034 |    -1.061 |   -0.5595 |   -0.8305 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.064 |   -0.2487 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.69386 |     91.24 |    -5.389 |   -0.9050 |    -2.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.633 |    0.4127 |     1.091 |   0.05893 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7847 |    0.7338 |     1.548 |    0.9951 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6994 |     1.965 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.69386</span> |     91.24 |  0.004566 |    0.2880 |    0.1109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009726 |    0.6017 |     1.091 |   0.05893 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7847 |    0.7338 |     1.548 |    0.9951 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6994 |     1.965 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -3.722 |     1.155 |   -0.1645 |  -0.05121 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3669 |    0.6376 |   0.06785 |     1.772 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5854 |    0.2936 |    0.9229 |     3.709 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2520 |    -1.579 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   28</span>|     479.68901 |     1.004 |    -1.189 |   -0.9273 |   -0.9365 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.021 |   -0.9853 |   -0.2467 |   -0.8591 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8033 |    -1.061 |   -0.5597 |   -0.8314 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.064 |   -0.2483 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.68901 |     91.32 |    -5.389 |   -0.9050 |    -2.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.633 |    0.4126 |     1.091 |   0.05892 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7848 |    0.7337 |     1.547 |    0.9942 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6993 |     1.965 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.68901</span> |     91.32 |  0.004565 |    0.2880 |    0.1109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009725 |    0.6017 |     1.091 |   0.05892 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7848 |    0.7337 |     1.547 |    0.9942 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6993 |     1.965 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     3.772 |     1.160 |  -0.07003 |  -0.06455 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3646 |    0.6238 |   0.08418 |     1.806 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5672 |    0.3711 |    0.9250 |     3.651 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2161 |    -1.579 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   29</span>|     479.68474 |     1.003 |    -1.190 |   -0.9272 |   -0.9365 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.021 |   -0.9854 |   -0.2468 |   -0.8595 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8031 |    -1.061 |   -0.5600 |   -0.8323 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.064 |   -0.2479 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.68474 |     91.24 |    -5.390 |   -0.9050 |    -2.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.633 |    0.4126 |     1.091 |   0.05890 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7849 |    0.7336 |     1.547 |    0.9934 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6993 |     1.966 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.68474</span> |     91.24 |  0.004563 |    0.2880 |    0.1109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009724 |    0.6017 |     1.091 |   0.05890 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7849 |    0.7336 |     1.547 |    0.9934 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6993 |     1.966 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -3.834 |     1.153 |   -0.1599 |  -0.05010 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3669 |    0.6355 |   0.09191 |     1.764 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5796 |    0.2889 |     1.006 |     3.561 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2610 |    -1.565 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   30</span>|     479.68016 |     1.004 |    -1.190 |   -0.9272 |   -0.9365 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.021 |   -0.9856 |   -0.2468 |   -0.8600 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8030 |    -1.061 |   -0.5602 |   -0.8332 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.064 |   -0.2475 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.68016 |     91.32 |    -5.390 |   -0.9050 |    -2.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.633 |    0.4125 |     1.091 |   0.05889 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7850 |    0.7336 |     1.547 |    0.9926 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6992 |     1.966 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.68016</span> |     91.32 |  0.004562 |    0.2880 |    0.1109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009723 |    0.6017 |     1.091 |   0.05889 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7850 |    0.7336 |     1.547 |    0.9926 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6992 |     1.966 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     3.891 |     1.158 |  -0.06530 |  -0.06447 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3645 |    0.6207 |    0.1821 |     1.812 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5486 |    0.3656 |     1.002 |     3.562 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2233 |    -1.574 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   31</span>|     479.67614 |     1.003 |    -1.190 |   -0.9272 |   -0.9365 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.021 |   -0.9857 |   -0.2468 |   -0.8604 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8028 |    -1.061 |   -0.5604 |   -0.8341 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.064 |   -0.2472 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.67614 |     91.24 |    -5.390 |   -0.9049 |    -2.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.633 |    0.4124 |     1.091 |   0.05888 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7851 |    0.7335 |     1.546 |    0.9917 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6992 |     1.966 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.67614</span> |     91.24 |  0.004561 |    0.2880 |    0.1109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009722 |    0.6017 |     1.091 |   0.05888 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7851 |    0.7335 |     1.546 |    0.9917 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6992 |     1.966 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -3.915 |     1.151 |   -0.1574 |  -0.04977 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3672 |    0.6321 |   0.06058 |     1.705 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5691 |    0.2601 |    0.8724 |     3.420 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2638 |    -1.562 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   32</span>|     479.67180 |     1.004 |    -1.191 |   -0.9271 |   -0.9364 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.021 |   -0.9859 |   -0.2469 |   -0.8608 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8027 |    -1.061 |   -0.5607 |   -0.8349 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.064 |   -0.2468 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      479.6718 |     91.33 |    -5.391 |   -0.9049 |    -2.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.633 |    0.4124 |     1.091 |   0.05887 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7852 |    0.7334 |     1.546 |    0.9909 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6991 |     1.967 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      479.6718</span> |     91.33 |  0.004560 |    0.2880 |    0.1109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009722 |    0.6017 |     1.091 |   0.05887 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7852 |    0.7334 |     1.546 |    0.9909 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6991 |     1.967 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     4.074 |     1.157 |  -0.05990 |  -0.06452 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3645 |    0.6170 |    0.1286 |     1.744 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5576 |    0.3161 |    0.9007 |     3.345 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2216 |    -1.572 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   33</span>|     479.66809 |     1.003 |    -1.191 |   -0.9271 |   -0.9364 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.021 |   -0.9860 |   -0.2469 |   -0.8613 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8026 |    -1.062 |   -0.5609 |   -0.8357 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.064 |   -0.2464 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.66809 |     91.23 |    -5.391 |   -0.9049 |    -2.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.634 |    0.4123 |     1.091 |   0.05885 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7853 |    0.7334 |     1.546 |    0.9901 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6991 |     1.967 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.66809</span> |     91.23 |  0.004558 |    0.2880 |    0.1109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009721 |    0.6016 |     1.091 |   0.05885 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7853 |    0.7334 |     1.546 |    0.9901 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6991 |     1.967 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.161 |     1.149 |   -0.1570 |  -0.04911 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3674 |    0.6293 |   0.04215 |     1.668 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5621 |    0.2625 |    0.8940 |     3.313 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2611 |    -1.561 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   34</span>|     479.66397 |     1.004 |    -1.191 |   -0.9271 |   -0.9364 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.021 |   -0.9862 |   -0.2469 |   -0.8617 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8024 |    -1.062 |   -0.5611 |   -0.8366 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.064 |   -0.2460 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.66397 |     91.33 |    -5.391 |   -0.9049 |    -2.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.634 |    0.4122 |     1.091 |   0.05884 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7854 |    0.7333 |     1.546 |    0.9893 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6990 |     1.968 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.66397</span> |     91.33 |  0.004557 |    0.2881 |    0.1109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009720 |    0.6016 |     1.091 |   0.05884 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7854 |    0.7333 |     1.546 |    0.9893 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6990 |     1.968 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     4.197 |     1.155 |  -0.05534 |  -0.06448 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3644 |    0.6140 |    0.1526 |     1.765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5340 |    0.3555 |    0.9900 |     3.325 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2199 |    -1.571 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   35</span>|     479.66043 |     1.003 |    -1.191 |   -0.9271 |   -0.9364 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.021 |   -0.9863 |   -0.2469 |   -0.8621 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8023 |    -1.062 |   -0.5613 |   -0.8374 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.064 |   -0.2456 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.66043 |     91.23 |    -5.391 |   -0.9048 |    -2.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.634 |    0.4122 |     1.090 |   0.05883 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7855 |    0.7332 |     1.545 |    0.9886 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6989 |     1.968 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.66043</span> |     91.23 |  0.004556 |    0.2881 |    0.1109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009719 |    0.6016 |     1.090 |   0.05883 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7855 |    0.7332 |     1.545 |    0.9886 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6989 |     1.968 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.161 |     1.147 |   -0.1538 |  -0.04891 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3674 |    0.6262 |   0.06581 |     1.677 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5453 |    0.2828 |     1.029 |     3.251 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2683 |    -1.555 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   36</span>|     479.65652 |     1.004 |    -1.192 |   -0.9270 |   -0.9364 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.021 |   -0.9865 |   -0.2469 |   -0.8625 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8022 |    -1.062 |   -0.5616 |   -0.8382 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.064 |   -0.2452 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.65652 |     91.33 |    -5.392 |   -0.9048 |    -2.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.634 |    0.4121 |     1.090 |   0.05882 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7856 |    0.7332 |     1.545 |    0.9878 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6989 |     1.969 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.65652</span> |     91.33 |  0.004554 |    0.2881 |    0.1109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009718 |    0.6016 |     1.090 |   0.05882 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7856 |    0.7332 |     1.545 |    0.9878 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6989 |     1.969 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     4.221 |     1.153 |  -0.05203 |  -0.06424 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3643 |    0.6104 |   0.04854 |     1.680 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5365 |    0.2999 |    0.7877 |     3.080 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2284 |    -1.566 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   37</span>|     479.65328 |     1.003 |    -1.192 |   -0.9270 |   -0.9364 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.021 |   -0.9866 |   -0.2470 |   -0.8629 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8020 |    -1.062 |   -0.5618 |   -0.8389 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.064 |   -0.2448 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.65328 |     91.23 |    -5.392 |   -0.9048 |    -2.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.634 |    0.4120 |     1.090 |   0.05880 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7857 |    0.7331 |     1.545 |    0.9871 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6988 |     1.969 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.65328</span> |     91.23 |  0.004553 |    0.2881 |    0.1109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009717 |    0.6016 |     1.090 |   0.05880 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7857 |    0.7331 |     1.545 |    0.9871 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6988 |     1.969 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.438 |     1.145 |   -0.1538 |  -0.04822 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3675 |    0.6235 | 0.0004265 |     1.625 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5487 |    0.2329 |    0.8499 |     3.058 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2658 |    -1.554 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   38</span>|     479.64923 |     1.004 |    -1.192 |   -0.9270 |   -0.9364 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.022 |   -0.9868 |   -0.2469 |   -0.8633 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8019 |    -1.062 |   -0.5621 |   -0.8396 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.065 |   -0.2444 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.64923 |     91.32 |    -5.392 |   -0.9048 |    -2.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.634 |    0.4119 |     1.091 |   0.05879 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7858 |    0.7330 |     1.544 |    0.9864 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6988 |     1.970 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.64923</span> |     91.32 |  0.004551 |    0.2881 |    0.1109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009716 |    0.6015 |     1.091 |   0.05879 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7858 |    0.7330 |     1.544 |    0.9864 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6988 |     1.970 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     3.878 |     1.150 |  -0.05277 |  -0.06333 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3643 |    0.6074 |   0.07271 |     1.651 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5369 |    0.2992 |    0.8896 |     2.970 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2314 |    -1.562 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   39</span>|     479.64621 |     1.003 |    -1.193 |   -0.9270 |   -0.9363 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.022 |   -0.9870 |   -0.2469 |   -0.8638 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8017 |    -1.062 |   -0.5623 |   -0.8404 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.065 |   -0.2440 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.64621 |     91.23 |    -5.393 |   -0.9047 |    -2.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.634 |    0.4119 |     1.091 |   0.05878 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7859 |    0.7330 |     1.544 |    0.9856 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6987 |     1.970 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.64621</span> |     91.23 |  0.004550 |    0.2881 |    0.1109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009715 |    0.6015 |     1.091 |   0.05878 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7859 |    0.7330 |     1.544 |    0.9856 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6987 |     1.970 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.471 |     1.143 |   -0.1506 |  -0.04802 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3674 |    0.6203 | -0.003354 |     1.603 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5429 |    0.2243 |    0.9025 |     2.920 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2732 |    -1.548 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   40</span>|     479.64228 |     1.004 |    -1.193 |   -0.9269 |   -0.9363 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.022 |   -0.9872 |   -0.2468 |   -0.8642 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8016 |    -1.062 |   -0.5627 |   -0.8411 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.065 |   -0.2435 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.64228 |     91.32 |    -5.393 |   -0.9047 |    -2.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.634 |    0.4118 |     1.091 |   0.05877 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7860 |    0.7329 |     1.544 |    0.9850 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6987 |     1.971 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.64228</span> |     91.32 |  0.004548 |    0.2881 |    0.1109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009714 |    0.6015 |     1.091 |   0.05877 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7860 |    0.7329 |     1.544 |    0.9850 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6987 |     1.971 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     3.744 |     1.148 |  -0.05088 |  -0.06289 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3642 |    0.6037 |   0.03047 |     1.629 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5287 |    0.2733 |    0.8334 |     2.850 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2290 |    -1.558 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   41</span>|     479.63935 |     1.003 |    -1.193 |   -0.9269 |   -0.9363 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.022 |   -0.9874 |   -0.2468 |   -0.8646 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8014 |    -1.062 |   -0.5630 |   -0.8419 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.065 |   -0.2431 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.63935 |     91.23 |    -5.393 |   -0.9047 |    -2.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.634 |    0.4117 |     1.091 |   0.05876 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7861 |    0.7329 |     1.543 |    0.9843 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6986 |     1.971 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.63935</span> |     91.23 |  0.004547 |    0.2881 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009713 |    0.6015 |     1.091 |   0.05876 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7861 |    0.7329 |     1.543 |    0.9843 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6986 |     1.971 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.432 |     1.140 |   -0.1464 |  -0.04798 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3672 |    0.6157 |  -0.06295 |     1.547 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5341 |    0.1919 |    0.7582 |     2.801 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2698 |    -1.544 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   42</span>|     479.63543 |     1.004 |    -1.194 |   -0.9268 |   -0.9363 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.022 |   -0.9876 |   -0.2467 |   -0.8650 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8013 |    -1.062 |   -0.5633 |   -0.8425 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.065 |   -0.2426 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.63543 |     91.32 |    -5.394 |   -0.9046 |    -2.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.634 |    0.4116 |     1.091 |   0.05874 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7863 |    0.7328 |     1.543 |    0.9837 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6985 |     1.972 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.63543</span> |     91.32 |  0.004545 |    0.2881 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009712 |    0.6015 |     1.091 |   0.05874 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7863 |    0.7328 |     1.543 |    0.9837 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6985 |     1.972 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     3.612 |     1.146 |  -0.04876 |  -0.06242 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3640 |    0.6002 |   0.04743 |     1.619 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5199 |    0.2573 |    0.7734 |     2.739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2298 |    -1.553 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   43</span>|     479.63248 |     1.003 |    -1.194 |   -0.9268 |   -0.9363 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.022 |   -0.9878 |   -0.2466 |   -0.8655 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8011 |    -1.062 |   -0.5636 |   -0.8432 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.065 |   -0.2421 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.63248 |     91.23 |    -5.394 |   -0.9046 |    -2.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.635 |    0.4115 |     1.091 |   0.05873 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7864 |    0.7328 |     1.543 |    0.9830 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6985 |     1.973 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.63248</span> |     91.23 |  0.004543 |    0.2881 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009710 |    0.6014 |     1.091 |   0.05873 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7864 |    0.7328 |     1.543 |    0.9830 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6985 |     1.973 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.237 |     1.138 |   -0.1400 |  -0.04830 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3668 |    0.6108 |  -0.07665 |     1.521 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5353 |    0.1893 |    0.7456 |     2.704 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2668 |    -1.543 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   44</span>|     479.62869 |     1.004 |    -1.195 |   -0.9268 |   -0.9362 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.022 |   -0.9880 |   -0.2464 |   -0.8659 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8009 |    -1.062 |   -0.5639 |   -0.8438 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.065 |   -0.2415 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.62869 |     91.32 |    -5.395 |   -0.9046 |    -2.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.635 |    0.4114 |     1.091 |   0.05872 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7865 |    0.7327 |     1.542 |    0.9824 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6984 |     1.973 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.62869</span> |     91.32 |  0.004541 |    0.2881 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009709 |    0.6014 |     1.091 |   0.05872 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7865 |    0.7327 |     1.542 |    0.9824 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6984 |     1.973 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     3.552 |     1.143 |  -0.04554 |  -0.06221 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3637 |    0.5952 |  -0.01323 |     1.563 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3448 |    0.2470 |    0.7924 |     2.643 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2272 |    -1.550 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   45</span>|     479.62584 |     1.003 |    -1.195 |   -0.9268 |   -0.9362 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.022 |   -0.9883 |   -0.2463 |   -0.8663 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8008 |    -1.062 |   -0.5642 |   -0.8446 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.065 |   -0.2410 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.62584 |     91.24 |    -5.395 |   -0.9046 |    -2.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.635 |    0.4113 |     1.091 |   0.05871 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7866 |    0.7326 |     1.542 |    0.9817 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6983 |     1.974 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.62584</span> |     91.24 |  0.004539 |    0.2881 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009708 |    0.6014 |     1.091 |   0.05871 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7866 |    0.7326 |     1.542 |    0.9817 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6983 |     1.974 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.148 |     1.135 |   -0.1348 |  -0.04846 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3664 |    0.6063 | -0.009834 |     1.510 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5271 |    0.1803 |    0.7932 |     2.587 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2684 |    -1.536 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   46</span>|     479.62216 |     1.004 |    -1.195 |   -0.9267 |   -0.9362 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.023 |   -0.9885 |   -0.2462 |   -0.8667 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8007 |    -1.062 |   -0.5646 |   -0.8451 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.065 |   -0.2404 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.62216 |     91.32 |    -5.395 |   -0.9045 |    -2.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.635 |    0.4112 |     1.091 |   0.05870 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7867 |    0.7326 |     1.541 |    0.9811 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6983 |     1.975 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.62216</span> |     91.32 |  0.004537 |    0.2881 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009706 |    0.6014 |     1.091 |   0.05870 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7867 |    0.7326 |     1.541 |    0.9811 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6983 |     1.975 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     3.404 |     1.140 |  -0.04564 |  -0.06187 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3633 |    0.5903 |  -0.01970 |     1.541 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5054 |    0.2303 |    0.6483 |     2.528 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2288 |    -1.542 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   47</span>|     479.61938 |     1.003 |    -1.196 |   -0.9267 |   -0.9362 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.023 |   -0.9887 |   -0.2461 |   -0.8672 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8005 |    -1.062 |   -0.5649 |   -0.8458 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.065 |   -0.2398 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.61938 |     91.24 |    -5.396 |   -0.9045 |    -2.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.635 |    0.4111 |     1.091 |   0.05868 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7868 |    0.7325 |     1.541 |    0.9805 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6982 |     1.975 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.61938</span> |     91.24 |  0.004535 |    0.2881 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009705 |    0.6013 |     1.091 |   0.05868 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7868 |    0.7325 |     1.541 |    0.9805 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6982 |     1.975 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.197 |     1.132 |   -0.1312 |  -0.04839 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3660 |    0.6012 |   0.05916 |     1.560 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4869 |    0.2015 |    0.8305 |     2.573 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2666 |    -1.531 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   48</span>|     479.61563 |     1.003 |    -1.196 |   -0.9266 |   -0.9361 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.023 |   -0.9890 |   -0.2459 |   -0.8676 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8003 |    -1.062 |   -0.5653 |   -0.8464 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.065 |   -0.2392 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.61563 |     91.32 |    -5.396 |   -0.9044 |    -2.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.635 |    0.4109 |     1.091 |   0.05867 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7870 |    0.7325 |     1.541 |    0.9799 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6981 |     1.976 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.61563</span> |     91.32 |  0.004533 |    0.2881 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009703 |    0.6013 |     1.091 |   0.05867 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7870 |    0.7325 |     1.541 |    0.9799 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6981 |     1.976 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     3.024 |     1.137 |  -0.04591 |  -0.06118 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3629 |    0.5856 |  0.004035 |     1.536 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4978 |    0.2329 |    0.6617 |     2.436 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2318 |    -1.535 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   49</span>|     479.61337 |     1.003 |    -1.197 |   -0.9266 |   -0.9361 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.023 |   -0.9892 |   -0.2459 |   -0.8680 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8001 |    -1.063 |   -0.5655 |   -0.8471 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.065 |   -0.2388 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.61337 |     91.23 |    -5.397 |   -0.9044 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.635 |    0.4109 |     1.091 |   0.05866 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7871 |    0.7324 |     1.540 |    0.9792 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6980 |     1.977 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.61337</span> |     91.23 |  0.004531 |    0.2881 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009702 |    0.6013 |     1.091 |   0.05866 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7871 |    0.7324 |     1.540 |    0.9792 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6980 |     1.977 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.815 |     1.129 |   -0.1342 |  -0.04706 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3660 |    0.5971 |  -0.07697 |     1.438 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5059 |    0.1462 |    0.6633 |     2.381 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2676 |    -1.522 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   50</span>|     479.60932 |     1.003 |    -1.197 |   -0.9266 |   -0.9361 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.023 |   -0.9894 |   -0.2456 |   -0.8684 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8000 |    -1.063 |   -0.5659 |   -0.8476 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.066 |   -0.2381 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.60932 |     91.31 |    -5.397 |   -0.9044 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.636 |    0.4107 |     1.091 |   0.05865 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7872 |    0.7324 |     1.540 |    0.9788 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6979 |     1.977 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.60932</span> |     91.31 |  0.004529 |    0.2882 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009700 |    0.6013 |     1.091 |   0.05865 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7872 |    0.7324 |     1.540 |    0.9788 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6979 |     1.977 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     2.699 |     1.134 |  -0.04549 |  -0.06017 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3630 |    0.5814 |  -0.04512 |     1.484 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4915 |    0.2556 |    0.7248 |     2.322 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2317 |    -1.529 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   51</span>|     479.60706 |     1.003 |    -1.198 |   -0.9265 |   -0.9361 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.023 |   -0.9896 |   -0.2455 |   -0.8689 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7998 |    -1.063 |   -0.5661 |   -0.8484 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.066 |   -0.2376 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.60706 |     91.23 |    -5.398 |   -0.9044 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.636 |    0.4106 |     1.091 |   0.05863 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7873 |    0.7323 |     1.540 |    0.9781 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6979 |     1.978 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.60706</span> |     91.23 |  0.004527 |    0.2882 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009699 |    0.6012 |     1.091 |   0.05863 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7873 |    0.7323 |     1.540 |    0.9781 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6979 |     1.978 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.618 |     1.127 |   -0.1276 |  -0.04760 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3656 |    0.5915 |  -0.08622 |     1.415 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5531 |    0.1512 |    0.6569 |     2.298 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2620 |    -1.540 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   52</span>|     479.60314 |     1.003 |    -1.198 |   -0.9265 |   -0.9360 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.023 |   -0.9899 |   -0.2452 |   -0.8692 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7996 |    -1.063 |   -0.5665 |   -0.8488 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.066 |   -0.2369 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.60314 |     91.31 |    -5.398 |   -0.9043 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.636 |    0.4105 |     1.091 |   0.05862 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7875 |    0.7322 |     1.539 |    0.9776 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6978 |     1.979 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.60314</span> |     91.31 |  0.004525 |    0.2882 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009698 |    0.6012 |     1.091 |   0.05862 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7875 |    0.7322 |     1.539 |    0.9776 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6978 |     1.979 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     2.641 |     1.131 |  -0.04213 |  -0.06002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3627 |    0.5770 |  -0.05114 |     1.464 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4913 |    0.1735 |    0.5980 |     2.238 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2116 |    -1.521 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   53</span>|     479.60093 |     1.003 |    -1.199 |   -0.9265 |   -0.9360 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.024 |   -0.9901 |   -0.2452 |   -0.8697 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7994 |    -1.063 |   -0.5667 |   -0.8495 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.066 |   -0.2363 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.60093 |     91.23 |    -5.399 |   -0.9043 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.636 |    0.4104 |     1.091 |   0.05861 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7876 |    0.7322 |     1.539 |    0.9769 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6977 |     1.980 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.60093</span> |     91.23 |  0.004523 |    0.2882 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009696 |    0.6012 |     1.091 |   0.05861 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7876 |    0.7322 |     1.539 |    0.9769 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6977 |     1.980 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.657 |     1.124 |   -0.1240 |  -0.04721 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3655 |    0.5872 |  -0.09119 |     1.382 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4948 |    0.1646 |    0.6965 |     2.186 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2747 |    -1.512 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   54</span>|     479.59701 |     1.003 |    -1.199 |   -0.9264 |   -0.9360 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.024 |   -0.9904 |   -0.2448 |   -0.8700 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7992 |    -1.063 |   -0.5670 |   -0.8499 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.066 |   -0.2356 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.59701 |     91.31 |    -5.399 |   -0.9043 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.636 |    0.4103 |     1.091 |   0.05860 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7877 |    0.7321 |     1.539 |    0.9766 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6976 |     1.980 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.59701</span> |     91.31 |  0.004520 |    0.2882 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009695 |    0.6012 |     1.091 |   0.05860 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7877 |    0.7321 |     1.539 |    0.9766 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6976 |     1.980 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     2.447 |     1.128 |  -0.03826 |  -0.05934 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3628 |    0.5721 |  -0.04376 |     1.440 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4844 |    0.2233 |   -0.3355 |     1.410 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.3677 |    -1.539 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   55</span>|     479.59436 |     1.003 |    -1.200 |   -0.9264 |   -0.9360 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.024 |   -0.9907 |   -0.2446 |   -0.8705 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7990 |    -1.063 |   -0.5669 |   -0.8504 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.066 |   -0.2349 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.59436 |     91.23 |    -5.400 |   -0.9042 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.636 |    0.4102 |     1.091 |   0.05858 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7879 |    0.7320 |     1.539 |    0.9761 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6977 |     1.981 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.59436</span> |     91.23 |  0.004518 |    0.2882 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009693 |    0.6011 |     1.091 |   0.05858 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7879 |    0.7320 |     1.539 |    0.9761 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6977 |     1.981 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.478 |     1.121 |   -0.1165 |  -0.04761 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3651 |    0.5835 |  -0.09604 |     1.373 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4943 |    0.1086 |   -0.4551 |     1.395 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.3088 |    -1.502 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   56</span>|     479.58979 |     1.003 |    -1.200 |   -0.9263 |   -0.9359 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.024 |   -0.9910 |   -0.2442 |   -0.8708 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7988 |    -1.063 |   -0.5665 |   -0.8506 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.066 |   -0.2341 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.58979 |     91.30 |    -5.400 |   -0.9042 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.637 |    0.4100 |     1.092 |   0.05858 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7880 |    0.7319 |     1.539 |    0.9759 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6978 |     1.982 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.58979</span> |     91.30 |  0.004516 |    0.2882 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009691 |    0.6011 |     1.092 |   0.05858 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7880 |    0.7319 |     1.539 |    0.9759 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6978 |     1.982 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     1.813 |     1.125 |  -0.03904 |  -0.05862 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3624 |    0.5728 | -0.008587 |     1.448 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2657 |    0.1639 |    0.6610 |     2.108 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2622 |    -1.501 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   57</span>|     479.58727 |     1.003 |    -1.201 |   -0.9263 |   -0.9359 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.024 |   -0.9912 |   -0.2441 |   -0.8713 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7987 |    -1.063 |   -0.5668 |   -0.8514 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.066 |   -0.2335 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.58727 |     91.24 |    -5.401 |   -0.9042 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.637 |    0.4099 |     1.092 |   0.05856 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7881 |    0.7318 |     1.539 |    0.9751 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6978 |     1.983 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.58727</span> |     91.24 |  0.004513 |    0.2882 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009690 |    0.6011 |     1.092 |   0.05856 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7881 |    0.7318 |     1.539 |    0.9751 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6978 |     1.983 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -3.987 |     1.119 |   -0.1055 |  -0.04878 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3644 |    0.5797 |  -0.03160 |     1.359 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4809 |   0.09403 |   -0.4184 |     1.337 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2770 |    -1.489 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   58</span>|     479.58366 |     1.004 |    -1.201 |   -0.9263 |   -0.9359 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.024 |   -0.9915 |   -0.2438 |   -0.8717 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7986 |    -1.063 |   -0.5667 |   -0.8517 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.066 |   -0.2327 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.58366 |     91.32 |    -5.401 |   -0.9041 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.637 |    0.4098 |     1.092 |   0.05855 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7882 |    0.7317 |     1.539 |    0.9749 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6978 |     1.984 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.58366</span> |     91.32 |  0.004511 |    0.2882 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009688 |    0.6010 |     1.092 |   0.05855 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7882 |    0.7317 |     1.539 |    0.9749 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6978 |     1.984 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     3.434 |     1.122 |  -0.01954 |  -0.06326 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3597 |    0.5645 |  -0.03484 |     1.404 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4782 |    0.1697 |  -0.05991 |     1.573 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2504 |    -1.489 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   59</span>|     479.57994 |     1.003 |    -1.202 |   -0.9262 |   -0.9358 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.025 |   -0.9918 |   -0.2433 |   -0.8720 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7984 |    -1.063 |   -0.5664 |   -0.8520 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.066 |   -0.2319 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.57994 |     91.26 |    -5.402 |   -0.9041 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.637 |    0.4096 |     1.092 |   0.05854 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7884 |    0.7316 |     1.539 |    0.9746 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6977 |     1.985 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.57994</span> |     91.26 |  0.004508 |    0.2882 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009686 |    0.6010 |     1.092 |   0.05854 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7884 |    0.7316 |     1.539 |    0.9746 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6977 |     1.985 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -2.328 |     1.117 |  -0.07952 |  -0.05137 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3636 |    0.5738 |   0.02815 |     1.418 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4669 |    0.1345 |    0.7258 |     2.078 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2949 |    -1.476 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   60</span>|     479.57683 |     1.004 |    -1.202 |   -0.9262 |   -0.9358 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.025 |   -0.9921 |   -0.2431 |   -0.8724 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7982 |    -1.064 |   -0.5667 |   -0.8526 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.066 |   -0.2311 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.57683 |     91.32 |    -5.402 |   -0.9041 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.637 |    0.4095 |     1.092 |   0.05853 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7885 |    0.7315 |     1.539 |    0.9740 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6976 |     1.986 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.57683</span> |     91.32 |  0.004505 |    0.2882 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009684 |    0.6010 |     1.092 |   0.05853 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7885 |    0.7315 |     1.539 |    0.9740 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6976 |     1.986 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     3.369 |     1.121 |  -0.01236 |  -0.06033 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3618 |    0.5608 | -0.009614 |     1.424 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4677 |    0.1416 |    0.6194 |     1.932 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2634 |    -1.483 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   61</span>|     479.57433 |     1.003 |    -1.203 |   -0.9262 |   -0.9358 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.025 |   -0.9924 |   -0.2429 |   -0.8728 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7980 |    -1.064 |   -0.5671 |   -0.8530 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.066 |   -0.2304 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.57433 |     91.25 |    -5.403 |   -0.9041 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.637 |    0.4094 |     1.092 |   0.05852 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7886 |    0.7315 |     1.539 |    0.9736 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6975 |     1.987 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.57433</span> |     91.25 |  0.004503 |    0.2882 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009683 |    0.6009 |     1.092 |   0.05852 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7886 |    0.7315 |     1.539 |    0.9736 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6975 |     1.987 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -3.507 |     1.111 |  -0.09183 |  -0.05192 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3614 |    0.5665 |  -0.03887 |     1.349 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4753 |   0.07826 |    0.5528 |     1.905 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2844 |    -1.468 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   62</span>|     479.57116 |     1.003 |    -1.204 |   -0.9262 |   -0.9357 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.025 |   -0.9927 |   -0.2425 |   -0.8732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7978 |    -1.064 |   -0.5674 |   -0.8534 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.066 |   -0.2297 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.57116 |     91.32 |    -5.404 |   -0.9040 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.638 |    0.4092 |     1.092 |   0.05851 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7888 |    0.7315 |     1.538 |    0.9732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6973 |     1.988 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.57116</span> |     91.32 |  0.004500 |    0.2882 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009681 |    0.6009 |     1.092 |   0.05851 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7888 |    0.7315 |     1.538 |    0.9732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6973 |     1.988 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     2.953 |     1.117 |  -0.01332 |  -0.05913 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3618 |    0.5550 |  -0.01942 |     1.377 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4588 |    0.1396 |    0.5378 |     1.901 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2564 |    -1.475 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   63</span>|     479.56857 |     1.003 |    -1.204 |   -0.9261 |   -0.9357 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.025 |   -0.9930 |   -0.2422 |   -0.8735 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7976 |    -1.064 |   -0.5677 |   -0.8539 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.066 |   -0.2289 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.56857 |     91.25 |    -5.404 |   -0.9040 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.638 |    0.4091 |     1.092 |   0.05850 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7889 |    0.7314 |     1.538 |    0.9728 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6972 |     1.989 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.56857</span> |     91.25 |  0.004497 |    0.2882 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009679 |    0.6009 |     1.092 |   0.05850 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7889 |    0.7314 |     1.538 |    0.9728 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6972 |     1.989 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -3.279 |     1.109 |  -0.08378 |  -0.05051 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3625 |    0.5609 |  -0.06234 |     1.320 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5093 |   0.06989 |    -1.266 |     1.852 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2753 |    -1.463 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   64</span>|     479.56430 |     1.003 |    -1.205 |   -0.9261 |   -0.9357 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.9933 |   -0.2419 |   -0.8738 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7974 |    -1.064 |   -0.5672 |   -0.8543 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.067 |   -0.2281 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      479.5643 |     91.31 |    -5.405 |   -0.9040 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.638 |    0.4090 |     1.093 |   0.05849 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7891 |    0.7314 |     1.538 |    0.9724 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6971 |     1.990 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      479.5643</span> |     91.31 |  0.004495 |    0.2882 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009677 |    0.6008 |     1.093 |   0.05849 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7891 |    0.7314 |     1.538 |    0.9724 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6971 |     1.990 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     2.181 |     1.113 |  -0.01711 |  -0.05862 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3610 |    0.5490 |  -0.06295 |     1.399 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4495 |    0.1474 |    0.6781 |     1.913 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2455 |    -1.468 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   65</span>|     479.56239 |     1.003 |    -1.205 |   -0.9261 |   -0.9356 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.9935 |   -0.2417 |   -0.8744 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7972 |    -1.064 |   -0.5674 |   -0.8549 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.067 |   -0.2275 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.56239 |     91.24 |    -5.405 |   -0.9040 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.638 |    0.4089 |     1.093 |   0.05847 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7892 |    0.7313 |     1.538 |    0.9718 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6970 |     1.990 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.56239</span> |     91.24 |  0.004493 |    0.2882 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009675 |    0.6008 |     1.093 |   0.05847 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7892 |    0.7313 |     1.538 |    0.9718 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6970 |     1.990 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.516 |     1.106 |  -0.09308 |  -0.04714 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3636 |    0.5580 |  -0.04406 |     1.488 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4662 |   0.05187 |    0.6104 |     1.787 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2769 |    -1.453 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   66</span>|     479.55877 |     1.003 |    -1.206 |   -0.9261 |   -0.9356 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.9938 |   -0.2414 |   -0.8747 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7970 |    -1.064 |   -0.5678 |   -0.8553 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.067 |   -0.2268 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.55877 |     91.31 |    -5.406 |   -0.9039 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.638 |    0.4087 |     1.093 |   0.05846 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7894 |    0.7313 |     1.538 |    0.9714 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6969 |     1.991 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.55877</span> |     91.31 |  0.004490 |    0.2882 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009674 |    0.6008 |     1.093 |   0.05846 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7894 |    0.7313 |     1.538 |    0.9714 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6969 |     1.991 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     2.254 |     1.111 |  -0.01255 |  -0.05783 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3613 |    0.5437 |  -0.01079 |     1.366 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4954 |    0.1048 |    0.5565 |     1.745 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2417 |    -1.458 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   67</span>|     479.55696 |     1.003 |    -1.206 |   -0.9261 |   -0.9356 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.9940 |   -0.2413 |   -0.8753 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7968 |    -1.064 |   -0.5681 |   -0.8559 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.067 |   -0.2262 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.55696 |     91.24 |    -5.406 |   -0.9039 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.639 |    0.4086 |     1.093 |   0.05845 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7895 |    0.7312 |     1.537 |    0.9708 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6968 |     1.992 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.55696</span> |     91.24 |  0.004488 |    0.2882 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009672 |    0.6008 |     1.093 |   0.05845 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7895 |    0.7312 |     1.537 |    0.9708 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6968 |     1.992 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.420 |     1.104 |  -0.08938 |  -0.04652 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3638 |    0.5524 |  -0.03600 |     1.474 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4537 |   0.08714 |    0.5943 |     1.686 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2777 |    -1.443 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   68</span>|     479.55331 |     1.003 |    -1.207 |   -0.9260 |   -0.9356 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.9943 |   -0.2409 |   -0.8756 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7966 |    -1.064 |   -0.5684 |   -0.8562 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.067 |   -0.2254 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.55331 |     91.31 |    -5.407 |   -0.9039 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.639 |    0.4085 |     1.093 |   0.05844 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7897 |    0.7312 |     1.537 |    0.9706 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6966 |     1.993 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.55331</span> |     91.31 |  0.004485 |    0.2882 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009670 |    0.6007 |     1.093 |   0.05844 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7897 |    0.7312 |     1.537 |    0.9706 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6966 |     1.993 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     1.940 |     1.107 |  -0.01326 |  -0.05734 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3610 |    0.5380 |  -0.03050 |     1.334 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4419 |   0.09953 |    0.4758 |     1.660 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2363 |    -1.448 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   69</span>|     479.55178 |     1.003 |    -1.208 |   -0.9260 |   -0.9355 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.9945 |   -0.2409 |   -0.8762 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7964 |    -1.064 |   -0.5686 |   -0.8569 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.067 |   -0.2248 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.55178 |     91.23 |    -5.408 |   -0.9039 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.639 |    0.4084 |     1.093 |   0.05842 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7898 |    0.7311 |     1.537 |    0.9699 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6965 |     1.994 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.55178</span> |     91.23 |  0.004483 |    0.2882 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009668 |    0.6007 |     1.093 |   0.05842 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7898 |    0.7311 |     1.537 |    0.9699 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6965 |     1.994 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.846 |     1.100 |  -0.09130 |  -0.04553 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3638 |    0.5474 |  -0.02936 |     1.430 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4494 |   0.05105 |    0.6245 |     1.611 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2724 |    -1.435 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   70</span>|     479.54790 |     1.003 |    -1.208 |   -0.9260 |   -0.9355 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.027 |   -0.9948 |   -0.2405 |   -0.8765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7962 |    -1.064 |   -0.5690 |   -0.8571 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.067 |   -0.2240 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      479.5479 |     91.30 |    -5.408 |   -0.9039 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.639 |    0.4083 |     1.093 |   0.05841 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7899 |    0.7311 |     1.536 |    0.9697 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6964 |     1.995 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      479.5479</span> |     91.30 |  0.004480 |    0.2883 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009667 |    0.6007 |     1.093 |   0.05841 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7899 |    0.7311 |     1.536 |    0.9697 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6964 |     1.995 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     1.570 |     1.104 |  -0.01594 |  -0.05674 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3607 |    0.5327 |  -0.02577 |     1.253 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4401 |   0.07544 |   -0.5163 |    0.8741 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.3456 |    -1.445 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   71</span>|     479.54587 |     1.003 |    -1.209 |   -0.9260 |   -0.9355 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.027 |   -0.9951 |   -0.2405 |   -0.8771 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7960 |    -1.064 |   -0.5687 |   -0.8576 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.067 |   -0.2233 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.54587 |     91.23 |    -5.409 |   -0.9039 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.639 |    0.4081 |     1.093 |   0.05839 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7901 |    0.7310 |     1.537 |    0.9693 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6965 |     1.995 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.54587</span> |     91.23 |  0.004478 |    0.2883 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009665 |    0.6006 |     1.093 |   0.05839 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7901 |    0.7310 |     1.537 |    0.9693 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6965 |     1.995 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.902 |     1.097 |  -0.08880 |  -0.04568 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3633 |    0.5436 |  -0.03820 |     1.421 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4438 |   0.04451 |    0.5507 |     1.558 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2862 |    -1.423 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   72</span>|     479.54157 |     1.003 |    -1.209 |   -0.9260 |   -0.9354 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.027 |   -0.9954 |   -0.2399 |   -0.8773 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7958 |    -1.064 |   -0.5687 |   -0.8577 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.067 |   -0.2225 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.54157 |     91.30 |    -5.409 |   -0.9038 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.639 |    0.4080 |     1.093 |   0.05839 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7902 |    0.7309 |     1.537 |    0.9691 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6965 |     1.996 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.54157</span> |     91.30 |  0.004475 |    0.2883 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009663 |    0.6006 |     1.093 |   0.05839 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7902 |    0.7309 |     1.537 |    0.9691 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6965 |     1.996 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     1.063 |     1.100 |  -0.01768 |  -0.05631 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3603 |    0.5311 |  -0.05904 |     1.468 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4400 |   0.06384 |    0.5216 |     1.564 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2548 |    -1.433 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   73</span>|     479.53906 |     1.003 |    -1.210 |   -0.9259 |   -0.9354 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.027 |   -0.9956 |   -0.2399 |   -0.8780 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7956 |    -1.064 |   -0.5689 |   -0.8584 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.067 |   -0.2218 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.53906 |     91.25 |    -5.410 |   -0.9038 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.640 |    0.4079 |     1.093 |   0.05837 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7904 |    0.7309 |     1.536 |    0.9684 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6964 |     1.997 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.53906</span> |     91.25 |  0.004472 |    0.2883 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009661 |    0.6006 |     1.093 |   0.05837 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7904 |    0.7309 |     1.536 |    0.9684 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6964 |     1.997 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -3.131 |     1.095 |  -0.06506 |  -0.04908 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3620 |    0.5356 | -0.007514 |     1.441 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4274 |   0.08578 |   -0.3543 |    0.8441 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2944 |    -1.418 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   74</span>|     479.53616 |     1.004 |    -1.210 |   -0.9259 |   -0.9354 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.027 |   -0.9959 |   -0.2396 |   -0.8785 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7954 |    -1.064 |   -0.5688 |   -0.8586 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.067 |   -0.2210 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.53616 |     91.33 |    -5.410 |   -0.9038 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.640 |    0.4077 |     1.094 |   0.05835 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7905 |    0.7308 |     1.537 |    0.9682 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6965 |     1.998 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.53616</span> |     91.33 |  0.004470 |    0.2883 |    0.1111 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009659 |    0.6005 |     1.094 |   0.05835 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7905 |    0.7308 |     1.537 |    0.9682 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6965 |     1.998 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     3.979 |     1.099 |   0.01619 |  -0.06233 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3580 |    0.5217 |  -0.09749 |     1.245 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4289 |    0.1115 |   -0.5282 |    0.7580 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.3266 |    -1.417 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   75</span>|     479.53242 |     1.003 |    -1.211 |   -0.9259 |   -0.9353 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.028 |   -0.9962 |   -0.2391 |   -0.8787 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7953 |    -1.065 |   -0.5685 |   -0.8588 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.067 |   -0.2202 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.53242 |     91.27 |    -5.411 |   -0.9038 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.640 |    0.4076 |     1.094 |   0.05835 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7906 |    0.7307 |     1.537 |    0.9681 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6966 |     1.999 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.53242</span> |     91.27 |  0.004467 |    0.2883 |    0.1111 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009657 |    0.6005 |     1.094 |   0.05835 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7906 |    0.7307 |     1.537 |    0.9681 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6966 |     1.999 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.679 |     1.093 |  -0.04308 |  -0.05224 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3605 |    0.5318 | -0.002555 |     1.446 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4215 |   0.05786 |    0.6079 |     1.538 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2997 |    -1.401 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   76</span>|     479.52958 |     1.004 |    -1.212 |   -0.9259 |   -0.9353 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.028 |   -0.9965 |   -0.2388 |   -0.8793 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7951 |    -1.065 |   -0.5686 |   -0.8593 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.067 |   -0.2194 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.52958 |     91.33 |    -5.412 |   -0.9038 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.640 |    0.4075 |     1.094 |   0.05833 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7908 |    0.7306 |     1.537 |    0.9676 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6965 |     2.000 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.52958</span> |     91.33 |  0.004464 |    0.2883 |    0.1111 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009655 |    0.6005 |     1.094 |   0.05833 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7908 |    0.7306 |     1.537 |    0.9676 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6965 |     2.000 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     3.139 |     1.094 |   0.01046 |  -0.06226 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3570 |    0.5194 |  -0.09592 |     1.421 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4323 |   0.06052 |   -0.5454 |    0.7071 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2962 |    -1.410 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   77</span>|     479.52646 |     1.003 |    -1.212 |   -0.9259 |   -0.9353 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.028 |   -0.9968 |   -0.2384 |   -0.8797 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7949 |    -1.065 |   -0.5684 |   -0.8594 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.067 |   -0.2186 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.52646 |     91.26 |    -5.412 |   -0.9038 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.640 |    0.4073 |     1.094 |   0.05832 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7909 |    0.7304 |     1.537 |    0.9675 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6966 |     2.001 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.52646</span> |     91.26 |  0.004461 |    0.2883 |    0.1111 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009653 |    0.6005 |     1.094 |   0.05832 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7909 |    0.7304 |     1.537 |    0.9675 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6966 |     2.001 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -2.758 |     1.088 |  -0.05217 |  -0.05158 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3593 |    0.5291 |  -0.05980 |     1.185 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4227 |    -2.218 |   -0.3659 |    0.8223 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2423 |    -1.392 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   78</span>|     479.52294 |     1.003 |    -1.213 |   -0.9258 |   -0.9352 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.028 |   -0.9971 |   -0.2379 |   -0.8800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7947 |    -1.064 |   -0.5681 |   -0.8596 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.067 |   -0.2178 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.52294 |     91.30 |    -5.413 |   -0.9037 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.641 |    0.4072 |     1.094 |   0.05831 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7910 |    0.7311 |     1.537 |    0.9673 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6967 |     2.002 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.52294</span> |     91.30 |  0.004458 |    0.2883 |    0.1111 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009652 |    0.6004 |     1.094 |   0.05831 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7910 |    0.7311 |     1.537 |    0.9673 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6967 |     2.002 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    0.7571 |     1.091 |  -0.01328 |  -0.05733 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3573 |    0.5211 |  0.006105 |     1.423 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4119 |    0.1268 |    0.6771 |     1.465 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.3146 |    -1.386 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   79</span>|     479.52041 |     1.003 |    -1.213 |   -0.9258 |   -0.9352 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.028 |   -0.9973 |   -0.2380 |   -0.8807 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7945 |    -1.064 |   -0.5684 |   -0.8604 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.067 |   -0.2172 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.52041 |     91.27 |    -5.413 |   -0.9037 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.641 |    0.4071 |     1.094 |   0.05829 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7912 |    0.7311 |     1.537 |    0.9666 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6965 |     2.003 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.52041</span> |     91.27 |  0.004456 |    0.2883 |    0.1111 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009650 |    0.6004 |     1.094 |   0.05829 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7912 |    0.7311 |     1.537 |    0.9666 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6965 |     2.003 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -2.400 |     1.087 |  -0.04852 |  -0.05189 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3585 |    0.5236 |  -0.05100 |     1.183 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4319 |   0.01564 |    0.3566 |     1.324 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.3221 |    -1.379 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   80</span>|     479.51911 |     1.004 |    -1.214 |   -0.9258 |   -0.9352 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.029 |   -0.9976 |   -0.2379 |   -0.8812 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7944 |    -1.064 |   -0.5686 |   -0.8609 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.067 |   -0.2166 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.51911 |     91.35 |    -5.414 |   -0.9037 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.641 |    0.4070 |     1.094 |   0.05828 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7913 |    0.7310 |     1.537 |    0.9661 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6964 |     2.004 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.51911</span> |     91.35 |  0.004454 |    0.2883 |    0.1111 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009648 |    0.6004 |     1.094 |   0.05828 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7913 |    0.7310 |     1.537 |    0.9661 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6964 |     2.004 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     5.458 |     1.092 |   0.04306 |  -0.06507 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3552 |    0.5068 |  -0.06807 |     1.418 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4090 |    0.1358 |   -0.5109 |    0.5676 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2804 |    -1.390 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   81</span>|     479.51487 |     1.003 |    -1.215 |   -0.9258 |   -0.9352 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.029 |   -0.9978 |   -0.2377 |   -0.8817 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7942 |    -1.064 |   -0.5685 |   -0.8612 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.067 |   -0.2158 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.51487 |     91.27 |    -5.415 |   -0.9037 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.641 |    0.4069 |     1.094 |   0.05826 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7914 |    0.7307 |     1.537 |    0.9658 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6963 |     2.005 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.51487</span> |     91.27 |  0.004451 |    0.2883 |    0.1111 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009647 |    0.6003 |     1.094 |   0.05826 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7914 |    0.7307 |     1.537 |    0.9658 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6963 |     2.005 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.582 |     1.084 |  -0.03533 |  -0.05340 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3581 |    0.5175 |  -0.06480 |     1.157 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4224 |   0.03489 |   -0.5536 |    0.6041 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2386 |    -1.371 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   82</span>|     479.51208 |     1.004 |    -1.215 |   -0.9258 |   -0.9351 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.029 |   -0.9981 |   -0.2375 |   -0.8823 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7939 |    -1.065 |   -0.5682 |   -0.8615 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.067 |   -0.2150 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.51208 |     91.34 |    -5.415 |   -0.9037 |    -2.197 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.641 |    0.4067 |     1.094 |   0.05824 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7916 |    0.7306 |     1.537 |    0.9655 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6964 |     2.006 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.51208</span> |     91.34 |  0.004449 |    0.2883 |    0.1111 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009645 |    0.6003 |     1.094 |   0.05824 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7916 |    0.7306 |     1.537 |    0.9655 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6964 |     2.006 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     3.943 |     1.087 |   0.02817 |  -0.06279 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3555 |    0.5065 |  -0.06060 |     1.305 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4115 |   0.06912 |    0.4865 |     1.240 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2990 |    -1.371 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   83</span>|     479.50842 |     1.003 |    -1.216 |   -0.9258 |   -0.9351 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.029 |   -0.9984 |   -0.2372 |   -0.8827 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7937 |    -1.065 |   -0.5683 |   -0.8618 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.067 |   -0.2141 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.50842 |     91.29 |    -5.416 |   -0.9037 |    -2.197 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.642 |    0.4066 |     1.095 |   0.05823 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7918 |    0.7302 |     1.537 |    0.9652 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6962 |     2.007 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.50842</span> |     91.29 |  0.004446 |    0.2883 |    0.1111 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009643 |    0.6003 |     1.095 |   0.05823 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7918 |    0.7302 |     1.537 |    0.9652 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6962 |     2.007 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |   -0.7463 |     1.081 |  -0.02105 |  -0.05532 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3572 |    0.5118 |  -0.07839 |     1.134 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4206 |   0.01709 |   -0.5616 |    0.5218 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2429 |    -1.361 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   84</span>|     479.50515 |     1.004 |    -1.216 |   -0.9258 |   -0.9351 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.029 |   -0.9987 |   -0.2372 |   -0.8834 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7935 |    -1.065 |   -0.5680 |   -0.8621 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.067 |   -0.2133 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.50515 |     91.33 |    -5.416 |   -0.9037 |    -2.197 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.642 |    0.4065 |     1.095 |   0.05821 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7919 |    0.7302 |     1.537 |    0.9649 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6964 |     2.008 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.50515</span> |     91.33 |  0.004443 |    0.2883 |    0.1111 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009641 |    0.6002 |     1.095 |   0.05821 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7919 |    0.7302 |     1.537 |    0.9649 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6964 |     2.008 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   85</span>|     479.49887 |     1.004 |    -1.219 |   -0.9257 |   -0.9350 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.030 |   -0.9997 |   -0.2363 |   -0.8851 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7928 |    -1.066 |   -0.5675 |   -0.8630 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.067 |   -0.2104 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.49887 |     91.39 |    -5.419 |   -0.9037 |    -2.197 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.642 |    0.4060 |     1.095 |   0.05816 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7925 |    0.7293 |     1.538 |    0.9641 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6963 |     2.011 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.49887</span> |     91.39 |  0.004434 |    0.2883 |    0.1111 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009634 |    0.6001 |     1.095 |   0.05816 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7925 |    0.7293 |     1.538 |    0.9641 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6963 |     2.011 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     8.922 |     1.081 |    0.1004 |  -0.07252 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3520 |    0.4871 |    0.2543 |     1.578 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3854 |   0.03465 |   -0.5794 |    0.4083 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2550 |    -1.344 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   86</span>|     479.48147 |     1.003 |    -1.221 |   -0.9257 |   -0.9348 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.031 |    -1.001 |   -0.2344 |   -0.8861 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7921 |    -1.067 |   -0.5658 |   -0.8636 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.067 |   -0.2068 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.48147 |     91.29 |    -5.421 |   -0.9036 |    -2.197 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.643 |    0.4054 |     1.096 |   0.05813 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7930 |    0.7283 |     1.540 |    0.9635 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6963 |     2.016 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.48147</span> |     91.29 |  0.004421 |    0.2883 |    0.1111 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009626 |    0.6000 |     1.096 |   0.05813 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7930 |    0.7283 |     1.540 |    0.9635 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6963 |     2.016 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   87</span>|     479.46291 |     1.003 |    -1.226 |   -0.9256 |   -0.9345 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.032 |    -1.003 |   -0.2312 |   -0.8874 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7910 |    -1.069 |   -0.5631 |   -0.8644 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.067 |   -0.2012 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.46291 |     91.29 |    -5.426 |   -0.9035 |    -2.197 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.645 |    0.4045 |     1.097 |   0.05809 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7937 |    0.7267 |     1.543 |    0.9627 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6964 |     2.022 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.46291</span> |     91.29 |  0.004402 |    0.2883 |    0.1111 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009612 |    0.5998 |     1.097 |   0.05809 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7937 |    0.7267 |     1.543 |    0.9627 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6964 |     2.022 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   88</span>|     479.38653 |     1.003 |    -1.248 |   -0.9250 |   -0.9332 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.039 |    -1.013 |   -0.2154 |   -0.8940 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7859 |    -1.078 |   -0.5497 |   -0.8687 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.067 |   -0.1734 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.38653 |     91.26 |    -5.448 |   -0.9030 |    -2.196 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.652 |    0.4000 |     1.104 |   0.05790 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7975 |    0.7188 |     1.559 |    0.9587 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6966 |     2.056 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.38653</span> |     91.26 |  0.004306 |    0.2884 |    0.1113 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009547 |    0.5987 |     1.104 |   0.05790 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7975 |    0.7188 |     1.559 |    0.9587 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6966 |     2.056 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   89</span>|     479.33405 |     1.002 |    -1.333 |   -0.9226 |   -0.9281 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.066 |    -1.051 |   -0.1533 |   -0.9198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7659 |    -1.112 |   -0.4971 |   -0.8853 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.066 |  -0.06439 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.33405 |     91.15 |    -5.533 |   -0.9008 |    -2.190 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.678 |    0.3823 |     1.129 |   0.05715 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8121 |    0.6876 |     1.621 |    0.9427 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6975 |     2.188 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.33405</span> |     91.15 |  0.003953 |    0.2889 |    0.1119 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009294 |    0.5944 |     1.129 |   0.05715 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8121 |    0.6876 |     1.621 |    0.9427 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6975 |     2.188 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -22.35 |    0.8049 |    0.3265 |  -0.06456 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3038 |    0.2091 |     1.763 |     1.473 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5535 |    -2.463 |     1.244 |   -0.6274 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.702 |   -0.2760 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   90</span>|     479.70817 |     1.005 |    -1.492 |   -0.9504 |   -0.9122 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.121 |    -1.106 |   -0.1774 |   -0.9510 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7305 |    -1.145 |   -0.4385 |   -0.8800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.177 |  -0.01012 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.70817 |     91.47 |    -5.692 |   -0.9256 |    -2.175 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.734 |    0.3572 |     1.119 |   0.05625 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8380 |    0.6579 |     1.691 |    0.9478 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6020 |     2.254 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.70817</span> |     91.47 |  0.003371 |    0.2838 |    0.1137 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008794 |    0.5884 |     1.119 |   0.05625 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8380 |    0.6579 |     1.691 |    0.9478 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6020 |     2.254 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   91</span>|     479.27063 |     1.005 |    -1.375 |   -0.9298 |   -0.9240 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.080 |    -1.065 |   -0.1597 |   -0.9281 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7567 |    -1.120 |   -0.4820 |   -0.8839 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.095 |  -0.05030 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.27063 |     91.42 |    -5.575 |   -0.9073 |    -2.186 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.693 |    0.3758 |     1.127 |   0.05692 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8189 |    0.6801 |     1.639 |    0.9441 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6726 |     2.206 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.27063</span> |     91.42 |  0.003793 |    0.2876 |    0.1123 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009162 |    0.5929 |     1.127 |   0.05692 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8189 |    0.6801 |     1.639 |    0.9441 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6726 |     2.206 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     2.862 |    0.7516 |    0.3576 |  -0.07893 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2806 |   -0.4298 |     1.369 |     1.213 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5227 |    -2.562 |     2.801 |    -1.010 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.234 |   -0.5388 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   92</span>|     479.18239 |     1.005 |    -1.423 |   -0.9401 |   -0.9189 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.098 |    -1.064 |   -0.1729 |   -0.9195 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7414 |    -1.117 |   -0.4967 |   -0.8806 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.083 |  -0.04375 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.18239 |     91.43 |    -5.623 |   -0.9165 |    -2.181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.710 |    0.3764 |     1.121 |   0.05716 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8300 |    0.6834 |     1.622 |    0.9472 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6830 |     2.213 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.18239</span> |     91.43 |  0.003613 |    0.2857 |    0.1129 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009001 |    0.5930 |     1.121 |   0.05716 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8300 |    0.6834 |     1.622 |    0.9472 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6830 |     2.213 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     2.797 |    0.6564 |   -0.1913 |  -0.04289 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2359 |   -0.3012 |     1.209 |     1.124 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4349 |    -2.322 |     2.160 |   -0.7215 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1578 |   -0.4171 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   93</span>|     479.53483 |    0.9938 |    -1.508 |   -0.8927 |   -0.9141 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.127 |    -1.054 |   -0.1802 |   -0.9016 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7110 |    -1.089 |   -0.5260 |   -0.8567 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.093 |  -0.01567 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.53483 |     90.44 |    -5.708 |   -0.8742 |    -2.177 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.740 |    0.3812 |     1.118 |   0.05768 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8523 |    0.7081 |     1.587 |    0.9700 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6741 |     2.248 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.53483</span> |     90.44 |  0.003320 |    0.2944 |    0.1134 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008741 |    0.5942 |     1.118 |   0.05768 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8523 |    0.7081 |     1.587 |    0.9700 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6741 |     2.248 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   94</span>|     479.57437 |    0.9943 |    -1.436 |   -0.9336 |   -0.9181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.102 |    -1.062 |   -0.1777 |   -0.9209 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7362 |    -1.106 |   -0.5073 |   -0.8753 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.084 |  -0.03890 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.57437 |     90.49 |    -5.636 |   -0.9106 |    -2.181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.715 |    0.3775 |     1.119 |   0.05712 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8338 |    0.6933 |     1.609 |    0.9523 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6824 |     2.219 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.57437</span> |     90.49 |  0.003567 |    0.2869 |    0.1130 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008961 |    0.5933 |     1.119 |   0.05712 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8338 |    0.6933 |     1.609 |    0.9523 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6824 |     2.219 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   95</span>|     479.18328 |     1.003 |    -1.424 |   -0.9400 |   -0.9189 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.098 |    -1.064 |   -0.1736 |   -0.9201 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7412 |    -1.115 |   -0.4980 |   -0.8802 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.083 |  -0.04351 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.18328 |     91.28 |    -5.624 |   -0.9164 |    -2.181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.711 |    0.3765 |     1.121 |   0.05715 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8302 |    0.6847 |     1.620 |    0.9476 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6831 |     2.214 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.18328</span> |     91.28 |  0.003612 |    0.2857 |    0.1129 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009000 |    0.5930 |     1.121 |   0.05715 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8302 |    0.6847 |     1.620 |    0.9476 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6831 |     2.214 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   96</span>|     479.17990 |     1.004 |    -1.423 |   -0.9401 |   -0.9189 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.098 |    -1.064 |   -0.1732 |   -0.9198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7413 |    -1.116 |   -0.4973 |   -0.8805 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.083 |  -0.04364 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      479.1799 |     91.36 |    -5.623 |   -0.9164 |    -2.181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.710 |    0.3764 |     1.121 |   0.05716 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8301 |    0.6840 |     1.621 |    0.9474 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6831 |     2.214 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      479.1799</span> |     91.36 |  0.003612 |    0.2857 |    0.1129 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009001 |    0.5930 |     1.121 |   0.05716 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8301 |    0.6840 |     1.621 |    0.9474 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6831 |     2.214 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -3.933 |    0.6514 |   -0.2757 |  -0.03244 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2382 |   -0.2823 |     1.183 |     1.090 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4397 |    -2.345 |     2.145 |   -0.7620 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1240 |   -0.3947 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   97</span>|     479.17667 |     1.005 |    -1.424 |   -0.9399 |   -0.9189 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.098 |    -1.064 |   -0.1735 |   -0.9200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7411 |    -1.116 |   -0.4978 |   -0.8802 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.083 |  -0.04350 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.17667 |     91.44 |    -5.624 |   -0.9162 |    -2.181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.711 |    0.3765 |     1.121 |   0.05715 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8302 |    0.6845 |     1.621 |    0.9476 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6831 |     2.214 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.17667</span> |     91.44 |  0.003611 |    0.2857 |    0.1129 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009000 |    0.5930 |     1.121 |   0.05715 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8302 |    0.6845 |     1.621 |    0.9476 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6831 |     2.214 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     3.521 |    0.6547 |   -0.1751 |  -0.04313 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2353 |   -0.2995 |     1.118 |     1.121 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4506 |    -2.323 |     2.084 |   -0.7259 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1653 |   -0.4503 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   98</span>|     479.17418 |     1.004 |    -1.424 |   -0.9398 |   -0.9188 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.098 |    -1.064 |   -0.1738 |   -0.9202 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7410 |    -1.115 |   -0.4983 |   -0.8800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.083 |  -0.04335 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.17418 |     91.36 |    -5.624 |   -0.9161 |    -2.181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.711 |    0.3765 |     1.121 |   0.05714 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8303 |    0.6850 |     1.620 |    0.9478 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6831 |     2.214 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.17418</span> |     91.36 |  0.003610 |    0.2857 |    0.1129 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008999 |    0.5930 |     1.121 |   0.05714 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8303 |    0.6850 |     1.620 |    0.9478 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6831 |     2.214 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -3.859 |    0.6491 |   -0.2708 |  -0.03145 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2380 |   -0.2786 |     1.113 |     1.074 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4387 |    -2.285 |     2.045 |   -0.7498 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1354 |   -0.4011 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   99</span>|     479.17107 |     1.005 |    -1.424 |   -0.9396 |   -0.9188 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.098 |    -1.064 |   -0.1740 |   -0.9204 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7408 |    -1.114 |   -0.4988 |   -0.8798 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.083 |  -0.04320 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.17107 |     91.44 |    -5.624 |   -0.9160 |    -2.181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.711 |    0.3766 |     1.121 |   0.05714 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8305 |    0.6855 |     1.619 |    0.9480 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6831 |     2.214 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.17107</span> |     91.44 |  0.003609 |    0.2858 |    0.1129 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008997 |    0.5930 |     1.121 |   0.05714 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8305 |    0.6855 |     1.619 |    0.9480 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6831 |     2.214 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     3.569 |    0.6522 |   -0.1642 |  -0.04243 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2349 |   -0.2958 |     1.101 |     1.106 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4222 |    -2.201 |     1.058 |   -0.2096 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2358 |   -0.4015 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  100</span>|     479.16873 |     1.004 |    -1.425 |   -0.9393 |   -0.9188 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.099 |    -1.064 |   -0.1743 |   -0.9206 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7406 |    -1.114 |   -0.4991 |   -0.8797 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.083 |  -0.04303 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.16873 |     91.36 |    -5.625 |   -0.9158 |    -2.181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.711 |    0.3766 |     1.121 |   0.05713 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8306 |    0.6860 |     1.619 |    0.9481 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6830 |     2.214 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.16873</span> |     91.36 |  0.003607 |    0.2858 |    0.1129 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008996 |    0.5931 |     1.121 |   0.05713 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8306 |    0.6860 |     1.619 |    0.9481 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6830 |     2.214 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -3.833 |    0.6464 |   -0.2551 |  -0.03020 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2388 |   -0.2745 |     1.092 |     1.036 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4378 |    -2.238 |     1.100 |   -0.2036 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2823 |   -0.3907 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  101</span>|     479.16547 |     1.005 |    -1.426 |   -0.9390 |   -0.9187 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.099 |    -1.063 |   -0.1745 |   -0.9206 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7403 |    -1.113 |   -0.4993 |   -0.8795 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.083 |  -0.04283 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.16547 |     91.42 |    -5.626 |   -0.9155 |    -2.181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.711 |    0.3767 |     1.121 |   0.05713 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8308 |    0.6865 |     1.619 |    0.9483 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6830 |     2.215 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.16547</span> |     91.42 |  0.003605 |    0.2859 |    0.1129 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008994 |    0.5931 |     1.121 |   0.05713 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8308 |    0.6865 |     1.619 |    0.9483 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6830 |     2.215 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     2.367 |    0.6482 |   -0.1602 |  -0.03907 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2347 |   -0.2874 |     1.147 |     1.057 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4142 |    -2.141 |     1.081 |   -0.1748 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2414 |   -0.4049 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  102</span>|     479.16316 |     1.004 |    -1.426 |   -0.9389 |   -0.9187 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.099 |    -1.063 |   -0.1748 |   -0.9208 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7401 |    -1.113 |   -0.4996 |   -0.8794 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.083 |  -0.04264 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.16316 |     91.36 |    -5.626 |   -0.9154 |    -2.181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.711 |    0.3768 |     1.120 |   0.05713 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8310 |    0.6872 |     1.618 |    0.9484 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6829 |     2.215 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.16316</span> |     91.36 |  0.003603 |    0.2859 |    0.1129 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008992 |    0.5931 |     1.120 |   0.05713 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8310 |    0.6872 |     1.618 |    0.9484 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6829 |     2.215 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -3.810 |    0.6431 |   -0.2376 |  -0.02872 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2384 |   -0.2689 |     1.073 |     1.021 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4195 |    -2.179 |     2.025 |   -0.6985 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1403 |   -0.3873 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  103</span>|     479.15976 |     1.005 |    -1.427 |   -0.9385 |   -0.9187 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.099 |    -1.063 |   -0.1751 |   -0.9208 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7399 |    -1.112 |   -0.5000 |   -0.8792 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.083 |  -0.04242 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.15976 |     91.41 |    -5.627 |   -0.9150 |    -2.181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.712 |    0.3768 |     1.120 |   0.05713 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8311 |    0.6876 |     1.618 |    0.9486 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6829 |     2.215 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.15976</span> |     91.41 |  0.003601 |    0.2860 |    0.1129 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008990 |    0.5931 |     1.120 |   0.05713 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8311 |    0.6876 |     1.618 |    0.9486 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6829 |     2.215 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     1.385 |    0.6444 |   -0.1513 |  -0.03575 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2363 |   -0.2788 |     1.057 |     1.037 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4304 |    -2.135 |     1.940 |   -0.6607 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1552 |   -0.4034 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  104</span>|     479.15697 |     1.004 |    -1.427 |   -0.9385 |   -0.9187 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.099 |    -1.063 |   -0.1754 |   -0.9212 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7397 |    -1.111 |   -0.5007 |   -0.8790 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.083 |  -0.04228 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.15697 |     91.37 |    -5.627 |   -0.9150 |    -2.181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.712 |    0.3769 |     1.120 |   0.05712 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8312 |    0.6883 |     1.617 |    0.9488 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6829 |     2.215 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.15697</span> |     91.37 |  0.003600 |    0.2860 |    0.1129 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008990 |    0.5931 |     1.120 |   0.05712 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8312 |    0.6883 |     1.617 |    0.9488 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6829 |     2.215 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -2.828 |    0.6406 |   -0.2090 |  -0.02895 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2376 |   -0.2659 |     1.059 |    0.9695 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4112 |    -2.078 |     1.041 |   -0.1304 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2608 |   -0.3859 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  105</span>|     479.15524 |     1.005 |    -1.427 |   -0.9384 |   -0.9187 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.099 |    -1.063 |   -0.1758 |   -0.9215 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7396 |    -1.111 |   -0.5010 |   -0.8789 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.083 |  -0.04216 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.15524 |     91.45 |    -5.627 |   -0.9149 |    -2.181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.712 |    0.3769 |     1.120 |   0.05711 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8313 |    0.6888 |     1.617 |    0.9489 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6828 |     2.215 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.15524</span> |     91.45 |  0.003599 |    0.2860 |    0.1129 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008989 |    0.5931 |     1.120 |   0.05711 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8313 |    0.6888 |     1.617 |    0.9489 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6828 |     2.215 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     5.191 |    0.6447 |   -0.1017 |  -0.04048 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2348 |   -0.2858 |     1.095 |     1.009 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3975 |    -1.983 |     1.956 |   -0.5702 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1591 |   -0.3996 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  106</span>|     479.15192 |     1.004 |    -1.428 |   -0.9381 |   -0.9186 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.100 |    -1.063 |   -0.1760 |   -0.9214 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7393 |    -1.110 |   -0.5013 |   -0.8787 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.083 |  -0.04194 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.15192 |     91.38 |    -5.628 |   -0.9146 |    -2.181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.712 |    0.3770 |     1.120 |   0.05711 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8315 |    0.6892 |     1.616 |    0.9490 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6828 |     2.216 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.15192</span> |     91.38 |  0.003597 |    0.2861 |    0.1129 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008987 |    0.5932 |     1.120 |   0.05711 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8315 |    0.6892 |     1.616 |    0.9490 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6828 |     2.216 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.012 |    0.6390 |   -0.1711 |  -0.03027 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2366 |   -0.2653 |     1.098 |    0.9640 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4101 |    -2.024 |    0.9749 |   -0.1142 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2645 |   -0.3871 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  107</span>|     479.14897 |     1.005 |    -1.428 |   -0.9380 |   -0.9186 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.100 |    -1.063 |   -0.1764 |   -0.9218 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7392 |    -1.110 |   -0.5017 |   -0.8787 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.083 |  -0.04178 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.14897 |     91.42 |    -5.628 |   -0.9146 |    -2.181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.712 |    0.3771 |     1.120 |   0.05710 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8317 |    0.6900 |     1.616 |    0.9491 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6827 |     2.216 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.14897</span> |     91.42 |  0.003596 |    0.2861 |    0.1129 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008986 |    0.5932 |     1.120 |   0.05710 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8317 |    0.6900 |     1.616 |    0.9491 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6827 |     2.216 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  108</span>|     479.14773 |     1.005 |    -1.428 |   -0.9379 |   -0.9186 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.100 |    -1.062 |   -0.1770 |   -0.9223 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7390 |    -1.109 |   -0.5022 |   -0.8786 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.083 |  -0.04159 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.14773 |     91.47 |    -5.628 |   -0.9145 |    -2.181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.712 |    0.3771 |     1.120 |   0.05708 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8318 |    0.6909 |     1.615 |    0.9491 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6826 |     2.216 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.14773</span> |     91.47 |  0.003595 |    0.2861 |    0.1129 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008985 |    0.5932 |     1.120 |   0.05708 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8318 |    0.6909 |     1.615 |    0.9491 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6826 |     2.216 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     7.333 |    0.6414 |  -0.06030 |  -0.04156 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2338 |   -0.2836 |     1.014 |     1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3897 |    -1.922 |    0.8816 |   -0.1288 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.1940 |   -0.4011 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  109</span>|     479.14119 |     1.004 |    -1.430 |   -0.9374 |   -0.9185 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.100 |    -1.062 |   -0.1775 |   -0.9218 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7383 |    -1.108 |   -0.5019 |   -0.8783 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.083 |  -0.04112 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.14119 |     91.39 |    -5.630 |   -0.9140 |    -2.181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.713 |    0.3774 |     1.119 |   0.05710 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8323 |    0.6917 |     1.616 |    0.9495 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6826 |     2.217 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.14119</span> |     91.39 |  0.003588 |    0.2862 |    0.1129 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008979 |    0.5933 |     1.119 |   0.05710 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8323 |    0.6917 |     1.616 |    0.9495 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6826 |     2.217 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |   -0.4299 |    0.6321 |   -0.1435 |  -0.02760 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2360 |   -0.2479 |     1.019 |    0.9518 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3967 |    -1.860 |     1.900 |   -0.5922 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1599 |   -0.3806 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  110</span>|     479.13501 |     1.005 |    -1.431 |   -0.9373 |   -0.9185 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.101 |    -1.062 |   -0.1783 |   -0.9226 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7379 |    -1.106 |   -0.5035 |   -0.8778 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.083 |  -0.04080 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.13501 |     91.42 |    -5.631 |   -0.9139 |    -2.181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.713 |    0.3775 |     1.119 |   0.05707 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8326 |    0.6932 |     1.614 |    0.9500 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6827 |     2.217 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.13501</span> |     91.42 |  0.003586 |    0.2862 |    0.1129 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008977 |    0.5933 |     1.119 |   0.05707 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8326 |    0.6932 |     1.614 |    0.9500 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6827 |     2.217 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     2.960 |    0.6324 |   -0.1010 |  -0.03176 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2344 |   -0.2516 |    0.9454 |    0.8792 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3822 |    -1.742 |    0.9146 |  -0.04263 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2425 |   -0.3771 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  111</span>|     479.13244 |     1.004 |    -1.432 |   -0.9368 |   -0.9184 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.101 |    -1.061 |   -0.1790 |   -0.9224 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7372 |    -1.105 |   -0.5036 |   -0.8775 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.083 |  -0.04025 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.13244 |     91.33 |    -5.632 |   -0.9135 |    -2.181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.714 |    0.3778 |     1.119 |   0.05708 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8331 |    0.6941 |     1.614 |    0.9502 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6825 |     2.218 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.13244</span> |     91.33 |  0.003580 |    0.2863 |    0.1130 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008971 |    0.5933 |     1.119 |   0.05708 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8331 |    0.6941 |     1.614 |    0.9502 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6825 |     2.218 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -5.808 |    0.6219 |   -0.1958 |  -0.01683 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2369 |   -0.2179 |    0.9347 |    0.8364 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3987 |    -1.797 |    0.8150 |  -0.05765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2618 |   -0.3704 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  112</span>|     479.12666 |     1.004 |    -1.434 |   -0.9365 |   -0.9183 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.102 |    -1.060 |   -0.1796 |   -0.9220 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7365 |    -1.104 |   -0.5033 |   -0.8772 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.083 |  -0.03975 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.12666 |     91.40 |    -5.634 |   -0.9132 |    -2.181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.714 |    0.3782 |     1.118 |   0.05709 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8336 |    0.6951 |     1.614 |    0.9505 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6824 |     2.218 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.12666</span> |     91.40 |  0.003574 |    0.2863 |    0.1130 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008965 |    0.5934 |     1.118 |   0.05709 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8336 |    0.6951 |     1.614 |    0.9505 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6824 |     2.218 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    0.9955 |    0.6218 |  -0.09617 |  -0.02448 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2342 |   -0.2176 |    0.9924 |    0.8560 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3772 |    -1.645 |     1.883 |   -0.4422 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1320 |   -0.3750 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  113</span>|     479.12255 |     1.004 |    -1.436 |   -0.9361 |   -0.9182 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.103 |    -1.060 |   -0.1805 |   -0.9221 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7358 |    -1.103 |   -0.5043 |   -0.8768 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.083 |  -0.03920 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.12255 |     91.34 |    -5.636 |   -0.9129 |    -2.181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.715 |    0.3784 |     1.118 |   0.05709 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8341 |    0.6962 |     1.613 |    0.9509 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6824 |     2.219 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.12255</span> |     91.34 |  0.003569 |    0.2864 |    0.1130 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008960 |    0.5935 |     1.118 |   0.05709 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8341 |    0.6962 |     1.613 |    0.9509 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6824 |     2.219 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.522 |    0.6133 |   -0.1571 |  -0.01492 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2353 |   -0.1923 |    0.9045 |    0.7846 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3572 |    -1.629 |    0.8508 |   0.03564 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2811 |   -0.3561 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  114</span>|     479.11855 |     1.005 |    -1.437 |   -0.9356 |   -0.9182 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.103 |    -1.059 |   -0.1812 |   -0.9217 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7351 |    -1.102 |   -0.5043 |   -0.8766 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.084 |  -0.03859 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.11855 |     91.41 |    -5.637 |   -0.9124 |    -2.181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.716 |    0.3787 |     1.118 |   0.05710 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8346 |    0.6968 |     1.613 |    0.9510 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6823 |     2.220 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.11855</span> |     91.41 |  0.003562 |    0.2865 |    0.1130 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008953 |    0.5936 |     1.118 |   0.05710 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8346 |    0.6968 |     1.613 |    0.9510 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6823 |     2.220 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     2.813 |    0.6131 |  -0.03713 |  -0.02450 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2319 |   -0.1976 |    0.8863 |    0.8390 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3589 |    -1.516 |    0.8806 |   -0.4426 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1647 |   -0.3784 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  115</span>|     479.11487 |     1.004 |    -1.439 |   -0.9352 |   -0.9181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.104 |    -1.058 |   -0.1819 |   -0.9213 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7344 |    -1.101 |   -0.5040 |   -0.8763 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.084 |  -0.03810 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.11487 |     91.37 |    -5.639 |   -0.9121 |    -2.180 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.716 |    0.3790 |     1.117 |   0.05711 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8352 |    0.6977 |     1.613 |    0.9514 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6823 |     2.220 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.11487</span> |     91.37 |  0.003555 |    0.2866 |    0.1130 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008947 |    0.5936 |     1.117 |   0.05711 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8352 |    0.6977 |     1.613 |    0.9514 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6823 |     2.220 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.684 |    0.6054 |  -0.08346 |  -0.01559 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2328 |   -0.1708 |    0.9518 |    0.8099 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3621 |    -1.485 |    0.9267 |   -0.3776 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1041 |   -0.3537 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  116</span>|     479.11139 |     1.005 |    -1.441 |   -0.9347 |   -0.9181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.105 |    -1.058 |   -0.1829 |   -0.9211 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7337 |    -1.100 |   -0.5039 |   -0.8760 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.083 |  -0.03771 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.11139 |     91.41 |    -5.641 |   -0.9117 |    -2.180 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.717 |    0.3792 |     1.117 |   0.05712 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8357 |    0.6988 |     1.613 |    0.9517 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6824 |     2.221 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.11139</span> |     91.41 |  0.003549 |    0.2867 |    0.1130 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008941 |    0.5937 |     1.117 |   0.05712 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8357 |    0.6988 |     1.613 |    0.9517 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6824 |     2.221 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     3.241 |    0.6036 | 0.0009951 |  -0.02154 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2300 |   -0.1727 |    0.8524 |    0.8311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3642 |    -1.386 |     1.866 |   -0.3472 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1024 |   -0.3634 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  117</span>|     479.10851 |     1.004 |    -1.443 |   -0.9342 |   -0.9180 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.105 |    -1.058 |   -0.1837 |   -0.9207 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7329 |    -1.100 |   -0.5043 |   -0.8759 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.084 |  -0.03710 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.10851 |     91.37 |    -5.643 |   -0.9112 |    -2.180 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.718 |    0.3794 |     1.117 |   0.05713 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8363 |    0.6988 |     1.613 |    0.9517 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6823 |     2.222 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.10851</span> |     91.37 |  0.003542 |    0.2868 |    0.1130 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008933 |    0.5937 |     1.117 |   0.05713 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8363 |    0.6988 |     1.613 |    0.9517 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6823 |     2.222 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.500 |    0.5956 |  -0.03344 |  -0.01385 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2306 |   -0.1498 |    0.8910 |    0.8240 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3570 |    -1.405 |    0.8461 |   -0.3572 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|  -0.08808 |   -0.3487 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  118</span>|     479.10606 |     1.005 |    -1.445 |   -0.9338 |   -0.9180 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.106 |    -1.057 |   -0.1846 |   -0.9207 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7320 |    -1.099 |   -0.5047 |   -0.8757 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.084 |  -0.03650 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.10606 |     91.43 |    -5.645 |   -0.9108 |    -2.180 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.719 |    0.3796 |     1.116 |   0.05713 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8369 |    0.6994 |     1.612 |    0.9519 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6822 |     2.222 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.10606</span> |     91.43 |  0.003535 |    0.2868 |    0.1130 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008927 |    0.5938 |     1.116 |   0.05713 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8369 |    0.6994 |     1.612 |    0.9519 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6822 |     2.222 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     5.260 |    0.5953 |   0.07226 |  -0.02302 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2275 |   -0.1573 |    0.8150 |    0.8159 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3410 |    -1.340 |    0.8208 |    0.1422 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2750 |   -0.3500 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  119</span>|     479.10199 |     1.004 |    -1.447 |   -0.9337 |   -0.9181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.107 |    -1.057 |   -0.1854 |   -0.9204 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7311 |    -1.099 |   -0.5045 |   -0.8757 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.084 |  -0.03604 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.10199 |     91.37 |    -5.647 |   -0.9107 |    -2.180 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.720 |    0.3799 |     1.116 |   0.05714 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8375 |    0.6999 |     1.613 |    0.9520 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6821 |     2.223 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.10199</span> |     91.37 |  0.003528 |    0.2869 |    0.1130 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008919 |    0.5938 |     1.116 |   0.05714 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8375 |    0.6999 |     1.613 |    0.9520 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6821 |     2.223 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |   -0.7698 |    0.5858 | -0.004343 |  -0.01418 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2278 |   -0.1298 |    0.8244 |    0.7686 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3575 |    -1.372 |    0.8320 |   -0.3359 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2977 |   -0.3384 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  120</span>|     479.09844 |     1.005 |    -1.448 |   -0.9337 |   -0.9180 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.108 |    -1.056 |   -0.1863 |   -0.9208 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7304 |    -1.097 |   -0.5051 |   -0.8752 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.084 |  -0.03534 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.09844 |     91.43 |    -5.648 |   -0.9108 |    -2.180 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.720 |    0.3801 |     1.116 |   0.05713 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8381 |    0.7011 |     1.612 |    0.9524 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6817 |     2.224 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.09844</span> |     91.43 |  0.003523 |    0.2868 |    0.1130 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008914 |    0.5939 |     1.116 |   0.05713 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8381 |    0.7011 |     1.612 |    0.9524 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6817 |     2.224 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     4.894 |    0.5851 |   0.05981 |  -0.02077 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2261 |   -0.1389 |    0.7622 |    0.7617 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3411 |    -1.236 |    0.7863 |    0.1583 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2091 |   -0.3951 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  121</span>|     479.09485 |     1.004 |    -1.450 |   -0.9338 |   -0.9180 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.108 |    -1.056 |   -0.1867 |   -0.9202 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7295 |    -1.097 |   -0.5051 |   -0.8750 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.085 |  -0.03445 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.09485 |     91.37 |    -5.650 |   -0.9108 |    -2.180 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.721 |    0.3803 |     1.116 |   0.05714 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8388 |    0.7016 |     1.612 |    0.9526 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6813 |     2.225 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.09485</span> |     91.37 |  0.003517 |    0.2868 |    0.1130 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008907 |    0.5939 |     1.116 |   0.05714 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8388 |    0.7016 |     1.612 |    0.9526 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6813 |     2.225 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.077 |    0.5762 |  -0.02502 |  -0.01122 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2275 |   -0.1137 |    0.7953 |    0.7347 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3473 |    -1.249 |    0.8683 |    0.1895 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2333 |   -0.3625 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  122</span>|     479.09211 |     1.005 |    -1.452 |   -0.9337 |   -0.9180 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.109 |    -1.055 |   -0.1878 |   -0.9202 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7286 |    -1.096 |   -0.5055 |   -0.8752 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.085 |  -0.03372 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.09211 |     91.41 |    -5.652 |   -0.9107 |    -2.180 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.722 |    0.3805 |     1.115 |   0.05714 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8394 |    0.7021 |     1.612 |    0.9524 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6810 |     2.226 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.09211</span> |     91.41 |  0.003510 |    0.2868 |    0.1130 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008900 |    0.5940 |     1.115 |   0.05714 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8394 |    0.7021 |     1.612 |    0.9524 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6810 |     2.226 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     3.651 |    0.5754 |   0.04071 |  -0.01634 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2261 |   -0.1150 |    0.8084 |    0.7089 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3268 |    -1.156 |    0.8610 |    0.1793 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2137 |   -0.3471 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  123</span>|     479.08947 |     1.004 |    -1.454 |   -0.9335 |   -0.9181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.110 |    -1.055 |   -0.1890 |   -0.9199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7277 |    -1.096 |   -0.5056 |   -0.8754 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.085 |  -0.03307 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.08947 |     91.37 |    -5.654 |   -0.9105 |    -2.180 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.722 |    0.3808 |     1.115 |   0.05715 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8401 |    0.7021 |     1.611 |    0.9522 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6809 |     2.226 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.08947</span> |     91.37 |  0.003504 |    0.2869 |    0.1130 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008893 |    0.5941 |     1.115 |   0.05715 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8401 |    0.7021 |     1.611 |    0.9522 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6809 |     2.226 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |   -0.8529 |    0.5679 | -0.006947 | -0.009666 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2263 |  -0.09399 |    0.7582 |    0.6848 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3382 |    -1.232 |    0.7736 |   -0.3452 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1950 |   -0.3796 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  124</span>|     479.08673 |     1.005 |    -1.456 |   -0.9331 |   -0.9181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.111 |    -1.054 |   -0.1901 |   -0.9198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7269 |    -1.095 |   -0.5060 |   -0.8751 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.085 |  -0.03242 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.08673 |     91.42 |    -5.656 |   -0.9102 |    -2.180 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.723 |    0.3808 |     1.114 |   0.05716 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8406 |    0.7030 |     1.611 |    0.9525 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6810 |     2.227 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.08673</span> |     91.42 |  0.003498 |    0.2870 |    0.1130 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008887 |    0.5941 |     1.114 |   0.05716 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8406 |    0.7030 |     1.611 |    0.9525 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6810 |     2.227 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     4.055 |    0.5667 |   0.07240 |  -0.01542 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2241 |   -0.1033 |    0.7160 |    0.6904 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3260 |    -1.123 |    0.7675 |    0.1856 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.1986 |   -0.4973 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  125</span>|     479.08385 |     1.004 |    -1.457 |   -0.9328 |   -0.9181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.112 |    -1.054 |   -0.1908 |   -0.9191 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7261 |    -1.095 |   -0.5059 |   -0.8749 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.085 |  -0.03140 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.08385 |     91.37 |    -5.657 |   -0.9100 |    -2.180 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.724 |    0.3808 |     1.114 |   0.05718 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8412 |    0.7034 |     1.611 |    0.9527 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6808 |     2.228 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.08385</span> |     91.37 |  0.003491 |    0.2870 |    0.1130 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008879 |    0.5941 |     1.114 |   0.05718 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8412 |    0.7034 |     1.611 |    0.9527 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6808 |     2.228 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |   -0.7735 |    0.5583 |   0.01997 | -0.008335 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2243 |  -0.08958 |    0.6952 |    0.6337 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3199 |    -1.102 |    0.8222 |    0.2014 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2310 |   -0.4538 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  126</span>|     479.08143 |     1.005 |    -1.459 |   -0.9328 |   -0.9181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.112 |    -1.054 |   -0.1918 |   -0.9191 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7254 |    -1.094 |   -0.5066 |   -0.8749 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.086 |  -0.03018 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.08143 |     91.42 |    -5.659 |   -0.9100 |    -2.180 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.725 |    0.3810 |     1.113 |   0.05718 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8417 |    0.7042 |     1.610 |    0.9527 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6804 |     2.230 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.08143</span> |     91.42 |  0.003486 |    0.2870 |    0.1130 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008874 |    0.5941 |     1.113 |   0.05718 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8417 |    0.7042 |     1.610 |    0.9527 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6804 |     2.230 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     4.589 |    0.5568 |   0.08446 |  -0.01637 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2217 |  -0.09943 |    0.6530 |    0.6713 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3160 |    -1.044 |    0.6971 |   -0.3035 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2323 |   -0.3746 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  127</span>|     479.07858 |     1.004 |    -1.461 |   -0.9326 |   -0.9181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.113 |    -1.054 |   -0.1923 |   -0.9184 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7246 |    -1.093 |   -0.5066 |   -0.8747 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.086 |  -0.02916 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.07858 |     91.36 |    -5.661 |   -0.9098 |    -2.181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.725 |    0.3811 |     1.113 |   0.05720 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8423 |    0.7045 |     1.610 |    0.9529 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6802 |     2.231 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.07858</span> |     91.36 |  0.003479 |    0.2870 |    0.1130 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008866 |    0.5941 |     1.113 |   0.05720 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8423 |    0.7045 |     1.610 |    0.9529 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6802 |     2.231 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.147 |    0.5485 |   0.01974 | -0.006990 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2231 |  -0.08394 |    0.6583 |    0.6424 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3232 |    -1.114 |    0.8219 |    0.1886 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.1911 |   -0.3375 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  128</span>|     479.07618 |     1.004 |    -1.463 |   -0.9323 |   -0.9182 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.114 |    -1.054 |   -0.1929 |   -0.9181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7237 |    -1.093 |   -0.5068 |   -0.8746 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.086 |  -0.02850 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.07618 |     91.40 |    -5.663 |   -0.9095 |    -2.181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.726 |    0.3811 |     1.113 |   0.05721 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8430 |    0.7050 |     1.610 |    0.9530 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6800 |     2.232 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.07618</span> |     91.40 |  0.003472 |    0.2871 |    0.1130 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008858 |    0.5941 |     1.113 |   0.05721 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8430 |    0.7050 |     1.610 |    0.9530 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6800 |     2.232 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     2.586 |    0.5458 |   0.07904 |  -0.01251 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2203 |  -0.08960 |    0.6458 |    0.6586 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3031 |   -0.9906 |    0.7687 |    0.2646 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.1721 |   -0.3284 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  129</span>|     479.07405 |     1.004 |    -1.465 |   -0.9322 |   -0.9183 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.115 |    -1.053 |   -0.1937 |   -0.9179 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7228 |    -1.093 |   -0.5070 |   -0.8747 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.087 |  -0.02798 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.07405 |     91.36 |    -5.665 |   -0.9094 |    -2.181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.727 |    0.3813 |     1.113 |   0.05721 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8437 |    0.7050 |     1.610 |    0.9529 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6797 |     2.233 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.07405</span> |     91.36 |  0.003465 |    0.2871 |    0.1130 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008850 |    0.5942 |     1.113 |   0.05721 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8437 |    0.7050 |     1.610 |    0.9529 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6797 |     2.233 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.296 |    0.5389 |   0.03360 | -0.006173 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2208 |  -0.07255 |    0.6853 |    0.5995 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2906 |    -1.015 |    0.7685 |    0.2327 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.1765 |   -0.3245 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  130</span>|     479.07209 |     1.004 |    -1.467 |   -0.9321 |   -0.9183 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.116 |    -1.053 |   -0.1949 |   -0.9178 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7220 |    -1.093 |   -0.5073 |   -0.8750 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.087 |  -0.02746 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.07209 |     91.39 |    -5.667 |   -0.9093 |    -2.181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.728 |    0.3814 |     1.112 |   0.05721 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8442 |    0.7050 |     1.609 |    0.9526 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6796 |     2.233 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.07209</span> |     91.39 |  0.003458 |    0.2871 |    0.1130 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008841 |    0.5942 |     1.112 |   0.05721 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8442 |    0.7050 |     1.609 |    0.9526 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6796 |     2.233 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     2.160 |    0.5372 |   0.08826 |  -0.01153 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2184 |  -0.07756 |    0.6386 |    0.6404 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2977 |   -0.9812 |    0.7463 |    0.2047 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.1647 |   -0.3238 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  131</span>|     479.07029 |     1.004 |    -1.469 |   -0.9320 |   -0.9184 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.117 |    -1.053 |   -0.1960 |   -0.9175 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7213 |    -1.093 |   -0.5075 |   -0.8752 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.087 |  -0.02689 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.07029 |     91.35 |    -5.669 |   -0.9092 |    -2.181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.729 |    0.3815 |     1.112 |   0.05722 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8447 |    0.7049 |     1.609 |    0.9524 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6795 |     2.234 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.07029</span> |     91.35 |  0.003451 |    0.2872 |    0.1130 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008833 |    0.5942 |     1.112 |   0.05722 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8447 |    0.7049 |     1.609 |    0.9524 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6795 |     2.234 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.487 |    0.5301 |   0.04502 | -0.006786 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2180 |  -0.06576 |    0.6433 |    0.5523 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2822 |    -1.039 |     1.637 |   -0.2973 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2152 |   -0.3175 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  132</span>|     479.06833 |     1.004 |    -1.471 |   -0.9316 |   -0.9185 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.118 |    -1.053 |   -0.1965 |   -0.9170 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7207 |    -1.093 |   -0.5082 |   -0.8751 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.087 |  -0.02616 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.06833 |     91.38 |    -5.671 |   -0.9088 |    -2.181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.730 |    0.3814 |     1.111 |   0.05724 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8452 |    0.7048 |     1.608 |    0.9525 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6792 |     2.235 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.06833</span> |     91.38 |  0.003444 |    0.2872 |    0.1129 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008825 |    0.5942 |     1.111 |   0.05724 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8452 |    0.7048 |     1.608 |    0.9525 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6792 |     2.235 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    0.6466 |    0.5255 |   0.09166 |  -0.01288 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2142 |  -0.07904 |    0.5622 |    0.5865 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2779 |    -1.008 |    0.7011 |    0.1998 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.1404 |   -0.3181 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  133</span>|     479.06868 |     1.003 |    -1.472 |   -0.9317 |   -0.9185 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.118 |    -1.053 |   -0.1973 |   -0.9180 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7202 |    -1.092 |   -0.5093 |   -0.8754 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.087 |  -0.02567 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.06868 |     91.29 |    -5.672 |   -0.9090 |    -2.181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.731 |    0.3814 |     1.111 |   0.05721 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8455 |    0.7062 |     1.607 |    0.9522 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6790 |     2.235 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.06868</span> |     91.29 |  0.003441 |    0.2872 |    0.1129 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008822 |    0.5942 |     1.111 |   0.05721 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8455 |    0.7062 |     1.607 |    0.9522 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6790 |     2.235 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  134</span>|     479.06735 |     1.004 |    -1.472 |   -0.9316 |   -0.9185 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.118 |    -1.053 |   -0.1969 |   -0.9175 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7205 |    -1.092 |   -0.5087 |   -0.8753 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.087 |  -0.02593 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.06735 |     91.33 |    -5.672 |   -0.9089 |    -2.181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.730 |    0.3814 |     1.111 |   0.05722 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8453 |    0.7054 |     1.608 |    0.9523 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6791 |     2.235 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.06735</span> |     91.33 |  0.003443 |    0.2872 |    0.1129 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008823 |    0.5942 |     1.111 |   0.05722 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8453 |    0.7054 |     1.608 |    0.9523 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6791 |     2.235 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -3.476 |    0.5223 |   0.03269 | -0.003763 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2173 |  -0.06616 |    0.5281 |    0.5229 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3041 |    -1.023 |     1.607 |   -0.2773 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2325 |   -0.3192 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  135</span>|     479.06543 |     1.004 |    -1.472 |   -0.9319 |   -0.9185 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.118 |    -1.053 |   -0.1968 |   -0.9175 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7201 |    -1.092 |   -0.5088 |   -0.8751 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.088 |  -0.02567 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.06543 |     91.36 |    -5.672 |   -0.9091 |    -2.181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.731 |    0.3815 |     1.111 |   0.05722 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8456 |    0.7056 |     1.608 |    0.9525 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6790 |     2.235 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.06543</span> |     91.36 |  0.003439 |    0.2872 |    0.1129 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008820 |    0.5942 |     1.111 |   0.05722 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8456 |    0.7056 |     1.608 |    0.9525 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6790 |     2.235 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  136</span>|     479.06390 |     1.004 |    -1.474 |   -0.9324 |   -0.9185 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.119 |    -1.053 |   -0.1965 |   -0.9174 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7194 |    -1.092 |   -0.5089 |   -0.8750 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.088 |  -0.02528 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      479.0639 |     91.36 |    -5.674 |   -0.9095 |    -2.181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.732 |    0.3816 |     1.111 |   0.05723 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8461 |    0.7059 |     1.607 |    0.9526 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6786 |     2.236 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      479.0639</span> |     91.36 |  0.003434 |    0.2871 |    0.1129 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008813 |    0.5943 |     1.111 |   0.05723 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8461 |    0.7059 |     1.607 |    0.9526 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6786 |     2.236 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  137</span>|     479.05708 |     1.004 |    -1.481 |   -0.9346 |   -0.9186 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.123 |    -1.051 |   -0.1952 |   -0.9171 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7164 |    -1.090 |   -0.5089 |   -0.8741 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.090 |  -0.02338 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.05708 |     91.37 |    -5.681 |   -0.9115 |    -2.181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.735 |    0.3824 |     1.112 |   0.05724 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8483 |    0.7072 |     1.607 |    0.9535 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6769 |     2.238 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.05708</span> |     91.37 |  0.003409 |    0.2867 |    0.1129 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008783 |    0.5944 |     1.112 |   0.05724 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8483 |    0.7072 |     1.607 |    0.9535 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6769 |     2.238 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  138</span>|     479.03987 |     1.004 |    -1.510 |   -0.9434 |   -0.9188 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.136 |    -1.045 |   -0.1899 |   -0.9157 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7043 |    -1.085 |   -0.5092 |   -0.8705 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.098 |  -0.01577 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.03987 |     91.37 |    -5.710 |   -0.9193 |    -2.181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.749 |    0.3852 |     1.114 |   0.05728 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8571 |    0.7123 |     1.607 |    0.9569 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6702 |     2.247 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.03987</span> |     91.37 |  0.003311 |    0.2851 |    0.1129 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008662 |    0.5951 |     1.114 |   0.05728 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8571 |    0.7123 |     1.607 |    0.9569 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6702 |     2.247 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |   -0.3651 |    0.4285 |   -0.5283 | -0.001988 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1991 |   0.06799 |    0.8278 |    0.7155 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2362 |   -0.3851 |    0.6274 |    0.1204 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.9270 |   -0.3308 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  139</span>|     479.01407 |     1.005 |    -1.553 |   -0.9371 |   -0.9187 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.158 |    -1.041 |   -0.1978 |   -0.9162 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6881 |    -1.086 |   -0.5088 |   -0.8721 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.097 |  -0.01436 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.01407 |     91.44 |    -5.753 |   -0.9138 |    -2.181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.770 |    0.3869 |     1.111 |   0.05726 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8690 |    0.7117 |     1.608 |    0.9554 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6704 |     2.249 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.01407</span> |     91.44 |  0.003172 |    0.2862 |    0.1129 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008479 |    0.5955 |     1.111 |   0.05726 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8690 |    0.7117 |     1.608 |    0.9554 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6704 |     2.249 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     7.322 |    0.3385 |  -0.09736 | -0.005783 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1607 |    0.1322 |    0.5556 |    0.5176 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1562 |   -0.2690 |     1.549 |  -0.03161 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8578 |   -0.3240 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  140</span>|     478.99946 |     1.003 |    -1.595 |   -0.9294 |   -0.9194 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.179 |    -1.045 |   -0.1992 |   -0.9136 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6715 |    -1.093 |   -0.5132 |   -0.8724 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.100 | -0.009543 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     478.99946 |     91.31 |    -5.795 |   -0.9069 |    -2.182 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.791 |    0.3851 |     1.110 |   0.05734 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8811 |    0.7049 |     1.602 |    0.9551 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6678 |     2.255 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     478.99946</span> |     91.31 |  0.003042 |    0.2876 |    0.1128 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008303 |    0.5951 |     1.110 |   0.05734 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8811 |    0.7049 |     1.602 |    0.9551 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6678 |     2.255 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -5.440 |    0.2525 |    0.1677 |  0.001517 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1272 |   0.05864 |    0.6253 |    0.4352 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1457 |   -0.7321 |     1.353 | 0.0009973 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.018 |   -0.3498 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  141</span>|     479.00064 |     1.004 |    -1.626 |   -0.9429 |   -0.9240 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.201 |    -1.064 |   -0.2165 |   -0.9169 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6612 |    -1.100 |   -0.5189 |   -0.8809 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.100 | -0.001744 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.00064 |     91.38 |    -5.826 |   -0.9189 |    -2.186 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.814 |    0.3763 |     1.103 |   0.05724 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8887 |    0.6986 |     1.596 |    0.9470 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6683 |     2.264 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.00064</span> |     91.38 |  0.002950 |    0.2852 |    0.1123 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008117 |    0.5930 |     1.103 |   0.05724 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8887 |    0.6986 |     1.596 |    0.9470 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6683 |     2.264 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  142</span>|     478.99385 |     1.004 |    -1.610 |   -0.9359 |   -0.9216 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.190 |    -1.054 |   -0.2077 |   -0.9152 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6665 |    -1.096 |   -0.5161 |   -0.8765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.100 | -0.005738 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     478.99385 |     91.39 |    -5.810 |   -0.9127 |    -2.184 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.802 |    0.3808 |     1.107 |   0.05729 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8848 |    0.7019 |     1.599 |    0.9512 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6681 |     2.260 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     478.99385</span> |     91.39 |  0.002997 |    0.2864 |    0.1126 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008212 |    0.5941 |     1.107 |   0.05729 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8848 |    0.7019 |     1.599 |    0.9512 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6681 |     2.260 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     2.499 |    0.2382 |  -0.03703 |  -0.03958 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.09629 |   -0.2111 |    0.3210 |    0.2393 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1233 |   -0.7892 |     1.126 |   -0.4047 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.051 |   -0.3984 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  143</span>|     478.98455 |     1.004 |    -1.625 |   -0.9347 |   -0.9200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.200 |    -1.054 |   -0.2131 |   -0.9088 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6653 |    -1.094 |   -0.5167 |   -0.8757 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.099 |  0.008183 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     478.98455 |     91.36 |    -5.825 |   -0.9116 |    -2.182 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.813 |    0.3812 |     1.105 |   0.05748 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8857 |    0.7042 |     1.598 |    0.9520 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6689 |     2.277 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     478.98455</span> |     91.36 |  0.002951 |    0.2867 |    0.1128 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008125 |    0.5942 |     1.105 |   0.05748 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8857 |    0.7042 |     1.598 |    0.9520 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6689 |     2.277 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |   -0.2799 |    0.1926 |  -0.02074 |  -0.02333 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.08052 |   -0.1468 |    0.2817 |    0.3348 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1470 |   -0.6425 |     1.062 |   -0.3208 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8181 |   -0.3186 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  144</span>|     478.97736 |     1.005 |    -1.639 |   -0.9325 |   -0.9174 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.210 |    -1.043 |   -0.2100 |   -0.9166 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6557 |    -1.093 |   -0.5206 |   -0.8754 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.097 |   0.01446 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     478.97736 |     91.42 |    -5.839 |   -0.9097 |    -2.180 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.822 |    0.3861 |     1.106 |   0.05725 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8927 |    0.7053 |     1.594 |    0.9522 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6711 |     2.284 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     478.97736</span> |     91.42 |  0.002912 |    0.2871 |    0.1131 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008047 |    0.5953 |     1.106 |   0.05725 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8927 |    0.7053 |     1.594 |    0.9522 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6711 |     2.284 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     5.719 |    0.1644 |    0.1764 | 0.0002018 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.06754 |    0.1935 |  -0.08189 |   -0.1310 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.08678 |    -2.826 |    0.8338 |   -0.2552 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.4561 |   -0.1956 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  145</span>|     478.97194 |     1.004 |    -1.652 |   -0.9304 |   -0.9153 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.221 |    -1.039 |   -0.2044 |   -0.9254 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6443 |    -1.087 |   -0.5238 |   -0.8740 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.092 |   0.01749 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     478.97194 |     91.40 |    -5.852 |   -0.9078 |    -2.178 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.833 |    0.3880 |     1.108 |   0.05699 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9010 |    0.7106 |     1.590 |    0.9536 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6749 |     2.288 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     478.97194</span> |     91.40 |  0.002874 |    0.2875 |    0.1133 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.007961 |    0.5958 |     1.108 |   0.05699 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9010 |    0.7106 |     1.590 |    0.9536 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6749 |     2.288 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     2.925 |    0.1117 |    0.2312 |   0.02461 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.04286 |    0.3818 |    0.1548 |   -0.1592 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.04325 |   -0.1382 |    0.7430 |   -0.2262 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|  -0.01070 |   -0.1316 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  146</span>|     479.08778 |    0.9990 |    -1.662 |   -0.9298 |   -0.9158 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.224 |    -1.050 |   -0.2095 |   -0.9175 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6325 |    -1.087 |   -0.5258 |   -0.8715 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.094 |   0.02816 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.08778 |     90.91 |    -5.862 |   -0.9073 |    -2.178 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.836 |    0.3828 |     1.106 |   0.05722 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9096 |    0.7103 |     1.587 |    0.9559 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6733 |     2.301 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.08778</span> |     90.91 |  0.002845 |    0.2876 |    0.1132 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.007936 |    0.5946 |     1.106 |   0.05722 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9096 |    0.7103 |     1.587 |    0.9559 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6733 |     2.301 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  147</span>|     478.99303 |     1.002 |    -1.652 |   -0.9306 |   -0.9153 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.221 |    -1.039 |   -0.2045 |   -0.9253 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6442 |    -1.087 |   -0.5244 |   -0.8738 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.092 |   0.01760 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     478.99303 |     91.18 |    -5.852 |   -0.9080 |    -2.178 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.833 |    0.3879 |     1.108 |   0.05700 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9011 |    0.7107 |     1.589 |    0.9537 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6749 |     2.288 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     478.99303</span> |     91.18 |  0.002874 |    0.2874 |    0.1133 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.007961 |    0.5958 |     1.108 |   0.05700 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9011 |    0.7107 |     1.589 |    0.9537 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6749 |     2.288 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  148</span>|     478.97158 |     1.004 |    -1.652 |   -0.9304 |   -0.9153 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.221 |    -1.039 |   -0.2044 |   -0.9254 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6443 |    -1.087 |   -0.5239 |   -0.8740 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.092 |   0.01751 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     478.97158 |     91.37 |    -5.852 |   -0.9078 |    -2.178 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.833 |    0.3880 |     1.108 |   0.05699 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9010 |    0.7106 |     1.590 |    0.9536 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6749 |     2.288 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     478.97158</span> |     91.37 |  0.002874 |    0.2874 |    0.1133 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.007961 |    0.5958 |     1.108 |   0.05699 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9010 |    0.7106 |     1.590 |    0.9536 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6749 |     2.288 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |   -0.1518 |    0.1094 |    0.1916 |   0.02921 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.04385 |    0.3896 |   -0.2077 |   -0.4379 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.03471 |    -2.421 |    0.7262 |   -0.1589 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   0.01110 |   -0.1305 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  149</span>|     478.97152 |     1.004 |    -1.652 |   -0.9304 |   -0.9153 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.221 |    -1.039 |   -0.2044 |   -0.9254 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6443 |    -1.086 |   -0.5240 |   -0.8739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.092 |   0.01753 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     478.97152 |     91.37 |    -5.852 |   -0.9078 |    -2.178 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.833 |    0.3880 |     1.108 |   0.05699 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9010 |    0.7109 |     1.590 |    0.9536 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6749 |     2.288 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     478.97152</span> |     91.37 |  0.002874 |    0.2874 |    0.1133 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.007961 |    0.5958 |     1.108 |   0.05699 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9010 |    0.7109 |     1.590 |    0.9536 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6749 |     2.288 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    C| Central Diff. |   -0.2246 |    0.1050 |    0.1574 |   0.02036 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.04103 |    0.3470 |   -0.3387 |   -0.1919 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1172 |   -0.1870 |    0.6243 |   -0.2341 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|  -0.06769 |   -0.1265 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  150</span>|     478.97142 |     1.004 |    -1.652 |   -0.9305 |   -0.9153 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.221 |    -1.039 |   -0.2042 |   -0.9253 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6442 |    -1.086 |   -0.5242 |   -0.8739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.092 |   0.01757 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     478.97142 |     91.38 |    -5.852 |   -0.9079 |    -2.178 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.833 |    0.3879 |     1.108 |   0.05700 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9011 |    0.7110 |     1.589 |    0.9537 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6749 |     2.288 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     478.97142</span> |     91.38 |  0.002874 |    0.2874 |    0.1133 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.007961 |    0.5958 |     1.108 |   0.05700 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9011 |    0.7110 |     1.589 |    0.9537 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6749 |     2.288 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    C| Central Diff. |    0.5593 |    0.1052 |    0.1640 |   0.01900 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.04052 |    0.3419 |   -0.3227 |   -0.1646 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1158 |   -0.1768 |    0.1839 |   -0.2151 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|  -0.04116 |   -0.1006 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  151</span>|     478.97143 |     1.004 |    -1.652 |   -0.9306 |   -0.9153 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.221 |    -1.039 |   -0.2041 |   -0.9252 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6442 |    -1.086 |   -0.5243 |   -0.8738 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.092 |   0.01761 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     478.97143 |     91.36 |    -5.852 |   -0.9079 |    -2.178 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.833 |    0.3879 |     1.108 |   0.05700 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9011 |    0.7110 |     1.589 |    0.9538 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6749 |     2.288 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     478.97143</span> |     91.36 |  0.002874 |    0.2874 |    0.1133 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.007961 |    0.5958 |     1.108 |   0.05700 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9011 |    0.7110 |     1.589 |    0.9538 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6749 |     2.288 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  152</span>|     478.97137 |     1.004 |    -1.652 |   -0.9305 |   -0.9153 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.221 |    -1.039 |   -0.2042 |   -0.9253 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6442 |    -1.086 |   -0.5242 |   -0.8738 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.092 |   0.01759 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     478.97137 |     91.37 |    -5.852 |   -0.9079 |    -2.178 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.833 |    0.3879 |     1.108 |   0.05700 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9011 |    0.7110 |     1.589 |    0.9537 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6749 |     2.288 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     478.97137</span> |     91.37 |  0.002874 |    0.2874 |    0.1133 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.007961 |    0.5958 |     1.108 |   0.05700 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9011 |    0.7110 |     1.589 |    0.9537 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6749 |     2.288 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    C| Central Diff. |   -0.4660 |    0.1043 |    0.1497 |   0.02048 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.04080 |    0.3434 |   -0.3050 |   -0.1804 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2015 |    -1.954 |    0.1460 |   -0.2263 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|  -0.03239 |   -0.1147 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  153</span>|     478.97135 |     1.004 |    -1.652 |   -0.9305 |   -0.9153 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.221 |    -1.039 |   -0.2041 |   -0.9253 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6441 |    -1.086 |   -0.5242 |   -0.8738 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.092 |   0.01760 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     478.97135 |     91.36 |    -5.852 |   -0.9079 |    -2.178 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.833 |    0.3879 |     1.108 |   0.05700 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9012 |    0.7110 |     1.589 |    0.9538 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6749 |     2.288 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     478.97135</span> |     91.36 |  0.002874 |    0.2874 |    0.1133 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.007961 |    0.5958 |     1.108 |   0.05700 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9012 |    0.7110 |     1.589 |    0.9538 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6749 |     2.288 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    C| Central Diff. |   -0.8144 |    0.1038 |    0.1446 |   0.02101 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.04083 |    0.3431 |   -0.3133 |   -0.1844 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.05440 |    -1.096 |    0.1463 |   -0.2004 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2780 |   -0.1269 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  154</span>|     478.97125 |     1.004 |    -1.652 |   -0.9305 |   -0.9153 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.221 |    -1.039 |   -0.2041 |   -0.9252 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6441 |    -1.086 |   -0.5243 |   -0.8738 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.092 |   0.01762 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     478.97125 |     91.37 |    -5.852 |   -0.9079 |    -2.178 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.833 |    0.3879 |     1.108 |   0.05700 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9012 |    0.7111 |     1.589 |    0.9538 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6749 |     2.288 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     478.97125</span> |     91.37 |  0.002874 |    0.2874 |    0.1133 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.007961 |    0.5958 |     1.108 |   0.05700 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9012 |    0.7111 |     1.589 |    0.9538 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6749 |     2.288 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    C| Central Diff. |   0.03985 |    0.1039 |    0.1540 |   0.01970 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.04056 |    0.3392 |   -0.3415 |   -0.1666 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1386 |   -0.1574 |   0.08901 |   -0.2003 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|  -0.04000 |   -0.1187 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  155</span>|     478.97118 |     1.004 |    -1.652 |   -0.9306 |   -0.9153 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.221 |    -1.039 |   -0.2040 |   -0.9252 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6440 |    -1.086 |   -0.5243 |   -0.8737 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.092 |   0.01765 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     478.97118 |     91.37 |    -5.852 |   -0.9080 |    -2.178 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.833 |    0.3878 |     1.108 |   0.05700 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9012 |    0.7112 |     1.589 |    0.9538 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6749 |     2.288 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     478.97118</span> |     91.37 |  0.002874 |    0.2874 |    0.1133 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.007961 |    0.5958 |     1.108 |   0.05700 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9012 |    0.7112 |     1.589 |    0.9538 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6749 |     2.288 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    C| Central Diff. |  -0.05720 |    0.1036 |    0.1507 |   0.01972 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.04048 |    0.3375 |   -0.2940 |   -0.1684 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1979 |    -1.920 |    0.1339 |   -0.2027 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|  -0.04399 |   -0.1216 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  156</span>|     478.97118 |     1.004 |    -1.652 |   -0.9306 |   -0.9153 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.221 |    -1.039 |   -0.2040 |   -0.9252 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6440 |    -1.086 |   -0.5243 |   -0.8737 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.092 |   0.01765 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     478.97118 |     91.37 |    -5.852 |   -0.9080 |    -2.178 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.833 |    0.3878 |     1.108 |   0.05700 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9012 |    0.7112 |     1.589 |    0.9538 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6749 |     2.288 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     478.97118</span> |     91.37 |  0.002874 |    0.2874 |    0.1133 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.007961 |    0.5958 |     1.108 |   0.05700 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9012 |    0.7112 |     1.589 |    0.9538 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6749 |     2.288 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> calculating covariance matrix</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating residuals/tables</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-in"></span>
-<span class="r-in"><span class="co"># Two-component error by variable is possible with both estimation methods</span></span>
-<span class="r-in"><span class="co"># Variance by variable is supported by 'saem' and 'focei'</span></span>
-<span class="r-in"><span class="va">f_nlmixr_fomc_sfo_saem_obs_tc</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr</a></span><span class="op">(</span><span class="va">f_mmkin_tc</span><span class="op">[</span><span class="st">"FOMC-SFO"</span>, <span class="op">]</span>, est <span class="op">=</span> <span class="st">"saem"</span>,</span>
-<span class="r-in">  error_model <span class="op">=</span> <span class="st">"obs_tc"</span><span class="op">)</span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1:    92.0624   -5.2854    0.1952    1.9494   -1.9431    2.8500    1.6150    0.7315    0.7220    0.4370    6.8425    0.4265    7.3797    0.5659</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2:    92.7371   -5.3819    0.0345    2.0839   -2.0310    2.7075    1.5342    0.6949    0.8358    0.4151    7.2043    0.0003    8.1096    0.0003</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 3:    9.2941e+01  -5.7880e+00   1.0336e-01   2.5772e+00  -1.5152e+00   2.5721e+00   1.4575e+00   6.6018e-01   7.9403e-01   3.9439e-01   4.5749e+00   1.5986e-05   5.1354e+00   2.8796e-05</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 4:    92.6277   -5.8599    0.0858    2.5068   -1.3253    2.4435    1.3847    0.6272    0.7543    0.3747    3.4165    0.0001    3.9071    0.0016</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5:    93.0289   -6.0258    0.0528    2.4932   -1.1961    2.3213    1.3154    0.5958    0.7166    0.3559    3.2552    0.0069    3.3744    0.0170</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 6:    93.2107   -6.2881    0.0143    2.4052   -1.1930    2.2053    2.2853    0.5660    0.6808    0.3381    2.8020    0.0086    3.1436    0.0256</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 7:    93.0563   -6.3624   -0.0104    2.3989   -1.1521    2.0950    2.4414    0.5377    0.6467    0.3212    2.6528    0.0209    2.8462    0.0289</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 8:    93.0567   -6.2699   -0.0303    2.3700   -1.0941    1.9903    2.5004    0.5204    0.6144    0.3052    2.3448    0.0314    2.5026    0.0349</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 9:    92.8366   -6.2013   -0.0507    2.3798   -1.0963    1.8907    2.6552    0.4943    0.5837    0.2899    2.2219    0.0368    2.2604    0.0448</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 10:    92.9069   -6.2499   -0.1116    2.3100   -1.0557    1.9625    3.9231    0.4696    0.5545    0.2754    2.0980    0.0359    2.1413    0.0325</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 11:    92.9942   -6.2134   -0.1037    2.3109   -1.0441    1.8643    3.7270    0.4755    0.5268    0.2616    1.9711    0.0372    1.9901    0.0355</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 12:    92.9262   -6.2511   -0.1133    2.2707   -1.0465    1.8430    4.3304    0.4697    0.5004    0.2486    1.8083    0.0346    1.9184    0.0356</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 13:    93.0761   -6.3559   -0.1106    2.2534   -1.0162    2.4826    5.2857    0.4685    0.4754    0.2361    1.7896    0.0331    1.9865    0.0342</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 14:    93.0929   -6.1852   -0.1058    2.2972   -1.0175    2.9964    5.0214    0.4451    0.4516    0.2243    1.8316    0.0309    1.9112    0.0348</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 15:    93.3343   -6.3408   -0.1025    2.2905   -0.9962    3.1132    4.9630    0.4594    0.4291    0.2131    1.8198    0.0338    1.9154    0.0372</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 16:    92.9959   -6.2227   -0.1016    2.2869   -1.0103    3.3338    4.7149    0.4598    0.4076    0.2025    1.8726    0.0327    1.9617    0.0361</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 17:    93.0394   -6.2586   -0.1027    2.2932   -0.9991    3.1671    4.8730    0.4591    0.3872    0.1923    1.8405    0.0336    2.0192    0.0242</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 18:    93.2374   -6.3410   -0.1110    2.2965   -0.9689    3.0087    5.4325    0.4632    0.3679    0.1827    1.9051    0.0351    1.9247    0.0277</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 19:    93.0557   -6.4503   -0.1011    2.2941   -0.9884    2.8583    6.2872    0.4742    0.3495    0.1736    1.8054    0.0345    1.9173    0.0260</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 20:    92.9280   -6.4945   -0.1018    2.2902   -0.9864    3.5934    6.8737    0.4751    0.3320    0.1649    1.7097    0.0372    1.9483    0.0225</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 21:    93.2023   -6.3418   -0.1033    2.2846   -0.9863    3.4138    6.5300    0.4710    0.3154    0.1567    1.7507    0.0318    1.9030    0.0249</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 22:    93.5494   -6.1985   -0.1078    2.2991   -1.0171    3.2431    6.2035    0.4475    0.2996    0.1488    1.7370    0.0342    1.8124    0.0282</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 23:    93.3588   -6.2577   -0.1084    2.2654   -0.9956    3.0809    5.8933    0.4393    0.2869    0.1481    1.6928    0.0380    1.8288    0.0254</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 24:    93.5705   -6.4876   -0.1083    2.2606   -1.0062    3.7095    6.7801    0.4459    0.2726    0.1684    1.7001    0.0377    1.9012    0.0249</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 25:    93.9726   -6.5638   -0.1239    2.2374   -1.0071    5.1357    6.6571    0.4608    0.2755    0.1600    1.6615    0.0382    1.8846    0.0213</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 26:    93.8894   -6.5073   -0.1120    2.2645   -1.0327    4.8789    6.3243    0.4473    0.3014    0.1520    1.7152    0.0340    1.8990    0.0254</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 27:    94.5348   -6.3868   -0.0891    2.3139   -1.0361    5.3856    6.0081    0.4250    0.3080    0.1453    1.7028    0.0385    1.7853    0.0304</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 28:    93.9221   -6.1747   -0.1039    2.3013   -1.0087    5.1163    5.7077    0.4214    0.3136    0.1634    1.6823    0.0340    1.7487    0.0353</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 29:    93.4946   -6.0264   -0.0940    2.3124   -1.0166    5.3744    5.4223    0.4404    0.3114    0.1813    1.6310    0.0370    1.7646    0.0367</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 30:    93.9287   -5.9114   -0.0967    2.3078   -1.0090    5.2017    5.1512    0.4381    0.3048    0.2091    1.5825    0.0393    1.7431    0.0312</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 31:    93.7065   -6.0174   -0.0928    2.2917   -0.9880    5.1935    4.8936    0.4352    0.3109    0.1986    1.5876    0.0392    1.8465    0.0280</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 32:    93.7675   -5.6444   -0.0918    2.3145   -0.9664    4.9338    4.6489    0.4332    0.3090    0.1918    1.6874    0.0332    1.7501    0.0331</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 33:    94.2589   -5.7637   -0.0865    2.3130   -0.9733    4.6871    4.4165    0.4235    0.2935    0.1865    1.7173    0.0355    1.7365    0.0334</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 34:    94.0788   -5.7432   -0.1022    2.3049   -0.9681    4.4528    4.1957    0.4319    0.2962    0.1772    1.6357    0.0361    1.4755    0.0574</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 35:    94.0798   -5.8549   -0.0929    2.2856   -0.9849    4.2301    3.9859    0.4391    0.2937    0.1734    1.6460    0.0275    1.7739    0.0379</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 36:    93.9997   -5.8708   -0.0890    2.2870   -1.0134    4.0186    3.7866    0.4268    0.2819    0.1840    1.5830    0.0258    1.8922    0.0320</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 37:    93.8186   -5.8979   -0.0973    2.2807   -1.0197    3.8177    3.5973    0.4280    0.2778    0.1799    1.5783    0.0282    1.7697    0.0404</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 38:    94.0728   -5.9601   -0.0936    2.3011   -1.0381    3.6268    3.4174    0.4186    0.2944    0.1864    1.5780    0.0294    1.5210    0.0558</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 39:    94.0504   -5.8302   -0.0995    2.2847   -1.0418    3.4455    3.2465    0.4240    0.2796    0.1771    1.6888    0.0242    1.7744    0.0366</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 40:    93.8918   -5.9418   -0.0988    2.2925   -1.0613    3.2732    3.0842    0.4144    0.2672    0.1837    1.7275    0.0232    1.7840    0.0357</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 41:    93.7961   -6.0741   -0.0896    2.2932   -1.0359    3.1095    3.2890    0.4236    0.2606    0.1782    1.7591    0.0212    1.8920    0.0273</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 42:    93.9458   -6.0319   -0.0909    2.3114   -1.0281    3.0544    3.1246    0.4196    0.2598    0.1839    1.6854    0.0310    1.7586    0.0308</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 43:    93.7623   -6.0165   -0.1056    2.2837   -1.0378    3.9092    3.2100    0.4298    0.2468    0.1747    1.7064    0.0286    1.6676    0.0397</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 44:    93.4300   -5.9019   -0.1114    2.2716   -1.0167    4.4100    3.0495    0.4476    0.2345    0.1687    1.7989    0.0253    1.7741    0.0299</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 45:    93.3475   -5.9962   -0.1231    2.2546   -1.0007    6.4879    3.3965    0.4528    0.2452    0.1602    1.7283    0.0276    1.7219    0.0379</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 46:    93.3100   -6.0073   -0.1255    2.2483   -0.9962    6.6009    3.5942    0.4520    0.2533    0.1568    1.6991    0.0258    1.1783    0.0706</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 47:    93.4422   -5.8442   -0.1286    2.2578   -0.9819    6.2709    3.4145    0.4492    0.2637    0.1694    1.6754    0.0317    1.3442    0.0599</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 48:    93.0996   -5.5881   -0.1307    2.2541   -0.9882    5.9573    3.2438    0.4480    0.2665    0.1695    1.7040    0.0285    1.6825    0.0426</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 49:    93.3649   -5.8011   -0.1260    2.2595   -0.9704    5.7230    3.0816    0.4399    0.2646    0.1715    1.6544    0.0297    1.6414    0.0418</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 50:    93.9331   -5.8731   -0.1195    2.2615   -0.9837    5.4368    2.9275    0.4453    0.2735    0.1940    1.6316    0.0289    1.6885    0.0392</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 51:    93.6092   -5.8721   -0.1237    2.2599   -1.0015    5.1650    2.7811    0.4413    0.2865    0.1939    1.6492    0.0272    1.7686    0.0358</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 52:    93.3008   -5.9775   -0.1277    2.2628   -0.9962    4.9067    3.1139    0.4438    0.2946    0.1963    1.5642    0.0337    1.7391    0.0335</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 53:    93.6347   -6.0805   -0.1189    2.2663   -1.0177    4.6614    3.5169    0.4335    0.2962    0.2043    1.5275    0.0343    1.7417    0.0366</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 54:    93.5781   -6.0412   -0.1166    2.2573   -1.0015    4.4283    3.3411    0.4395    0.2912    0.2080    1.5464    0.0346    1.7584    0.0359</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 55:    93.9675   -6.0867   -0.0952    2.2970   -0.9987    4.2069    3.6762    0.4550    0.2780    0.1988    1.5138    0.0405    1.6251    0.0472</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 56:    94.4069   -6.1375   -0.0943    2.2975   -1.0190    3.9966    4.2280    0.4550    0.2806    0.1945    1.5294    0.0425    1.6443    0.0468</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 57:    93.8076   -6.1618   -0.0936    2.2687   -1.0237    3.7967    4.3085    0.4524    0.2666    0.1897    1.5227    0.0414    1.6955    0.0438</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 58:    93.9188   -6.2272   -0.0947    2.2767   -1.0408    3.6069    4.6707    0.4536    0.2676    0.1873    1.5201    0.0425    1.5336    0.0550</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 59:    93.9572   -6.3460   -0.1074    2.2746   -1.0069    3.4266    5.8058    0.4614    0.2718    0.1919    1.5533    0.0417    1.6227    0.0460</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 60:    93.5430   -6.1622   -0.1048    2.2861   -0.9959    3.2552    5.5155    0.4535    0.2848    0.1823    1.4745    0.0431    1.5242    0.0522</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 61:    93.8016   -6.4079   -0.1065    2.2897   -1.0325    3.0925    5.9252    0.4583    0.2901    0.1732    1.5364    0.0365    1.4623    0.0550</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 62:    93.4249   -6.2284   -0.0967    2.3126   -1.0208    2.9378    5.6289    0.4429    0.2756    0.1645    1.6111    0.0325    1.3996    0.0618</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 63:    93.2335   -6.0996   -0.1051    2.3047   -0.9976    2.7909    5.3475    0.4292    0.2719    0.1633    1.6010    0.0326    1.5115    0.0494</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 64:    92.7091   -6.1109   -0.1041    2.3032   -0.9746    3.2610    5.0801    0.4301    0.2793    0.1751    1.5979    0.0302    1.3867    0.0575</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 65:    92.6059   -6.0049   -0.1077    2.3062   -0.9853    3.0979    4.8261    0.4316    0.2826    0.1714    1.6357    0.0272    1.3931    0.0543</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 66:    92.7778   -6.0856   -0.1040    2.3057   -0.9828    2.9430    4.5848    0.4362    0.2888    0.1628    1.6126    0.0316    1.4340    0.0505</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 67:    93.0325   -6.1775   -0.1085    2.3122   -0.9714    2.7959    4.3790    0.4464    0.2843    0.1547    1.6090    0.0354    1.4966    0.0448</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 68:    92.8987   -5.8741   -0.1089    2.3184   -0.9567    2.6561    4.1601    0.4389    0.2866    0.1470    1.5888    0.0347    1.4492    0.0507</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 69:    92.7300   -6.0473   -0.1070    2.3082   -0.9739    2.5233    4.0183    0.4371    0.3158    0.1527    1.5904    0.0298    1.5387    0.0477</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 70:    92.5285   -6.0962   -0.1115    2.3179   -0.9785    2.4856    4.0612    0.4380    0.3059    0.1504    1.5237    0.0402    1.4541    0.0463</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 71:    92.3975   -6.2090   -0.1269    2.2819   -0.9713    2.3613    4.3811    0.4173    0.2906    0.1520    1.5388    0.0316    1.3713    0.0584</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 72:    92.1593   -6.2214   -0.1217    2.2950   -0.9481    2.2433    5.1205    0.4227    0.2760    0.1605    1.5431    0.0312    1.5797    0.0459</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 73:    92.1195   -6.6667   -0.1244    2.3076   -0.9642    2.1311    7.6538    0.4124    0.2622    0.1525    1.4505    0.0375    1.2810    0.0595</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 74:    92.2191   -6.6665   -0.1278    2.2989   -0.9703    2.0246    7.2711    0.4098    0.2491    0.1666    1.4309    0.0384    1.2717    0.0638</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 75:    92.4656   -6.8881   -0.1274    2.3011   -0.9595    1.9233    8.3835    0.4147    0.2367    0.1656    1.4181    0.0424    1.2498    0.0620</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 76:    92.4593   -6.2323   -0.1203    2.2912   -0.9799    1.8272    7.9643    0.3967    0.2301    0.1596    1.5448    0.0336    1.6758    0.0359</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 77:    92.4501   -6.1123   -0.1219    2.2889   -0.9426    1.7358    7.5661    0.4049    0.2299    0.1594    1.5380    0.0327    1.6947    0.0364</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 78:    92.5059   -6.0339   -0.1198    2.2985   -0.9544    1.6490    7.1878    0.4007    0.2422    0.1568    1.5722    0.0302    1.5665    0.0415</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 79:    92.4302   -6.6428   -0.1141    2.3119   -0.9421    1.5666    9.0889    0.3986    0.2340    0.1489    1.5029    0.0306    1.1894    0.0638</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 80:    92.5305   -6.3036   -0.1127    2.3099   -0.9406    1.9062    8.6344    0.4091    0.2327    0.1648    1.5558    0.0308    1.0136    0.0749</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 81:    92.6652   -6.0832   -0.1167    2.2994   -0.9552    2.3641    8.2027    0.4142    0.2348    0.1566    1.6232    0.0300    1.1320    0.0712</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 82:    92.5109   -6.1524   -0.1100    2.3037   -0.9686    2.2459    7.7926    0.4145    0.2381    0.1695    1.6128    0.0306    1.6145    0.0413</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 83:    92.6455   -6.3979   -0.1059    2.3245   -0.9573    2.1336    8.1077    0.4060    0.2348    0.1667    1.6039    0.0304    1.6251    0.0419</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 84:    92.5768   -6.3469   -0.0979    2.3177   -0.9346    2.0269    8.2125    0.4141    0.2439    0.1720    1.6334    0.0302    1.5803    0.0450</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 85:    92.7920   -6.3536   -0.0969    2.3363   -0.9389    1.9256    8.1820    0.4035    0.2512    0.1713    1.5815    0.0338    1.1193    0.0719</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 86:    92.8571   -6.2567   -0.0911    2.3426   -0.9633    1.8293    8.1211    0.4008    0.2659    0.1627    1.5840    0.0356    1.2425    0.0639</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 87:    92.9101   -6.1557   -0.0937    2.3326   -0.9624    1.7378    7.7151    0.3950    0.2839    0.1546    1.6017    0.0354    1.0600    0.0766</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 88:    92.7649   -6.2151   -0.0881    2.3275   -0.9967    1.6509    7.3293    0.4046    0.2697    0.1469    1.6092    0.0302    1.1839    0.0693</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 89:    92.9350   -5.9222   -0.0903    2.3372   -0.9612    1.5684    6.9628    0.4058    0.2818    0.1395    1.5957    0.0335    1.5450    0.0444</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 90:    92.7895   -5.9348   -0.0890    2.3457   -0.9601    1.4900    6.6147    0.4070    0.2705    0.1430    1.5716    0.0378    1.4918    0.0439</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 91:    93.0113   -5.9302   -0.0997    2.3316   -0.9489    1.4155    6.2840    0.4424    0.2929    0.1410    1.5582    0.0377    1.2459    0.0582</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 92:    92.8603   -6.3366   -0.0918    2.3356   -0.9938    1.3447    5.9698    0.4287    0.2929    0.1531    1.4906    0.0398    1.2421    0.0605</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 93:    92.8094   -6.1097   -0.0953    2.3382   -0.9996    1.2775    5.6713    0.4309    0.2783    0.1597    1.4317    0.0468    1.2694    0.0584</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 94:    92.7100   -6.1043   -0.0909    2.3287   -0.9928    1.3828    5.3877    0.4337    0.2698    0.1517    1.4173    0.0489    1.4335    0.0476</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 95:    92.2055   -6.1792   -0.0889    2.3553   -0.9983    1.3137    5.1183    0.4209    0.3273    0.1566    1.4865    0.0417    1.2485    0.0681</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 96:    92.3225   -6.2710   -0.0811    2.3734   -1.0043    1.3428    4.8624    0.4121    0.3110    0.1532    1.5817    0.0374    1.5209    0.0532</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 97:    92.2983   -6.3023   -0.0776    2.3615   -0.9801    1.2757    4.6193    0.4221    0.3018    0.1455    1.5971    0.0337    1.2137    0.0719</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 98:    92.1433   -6.3114   -0.0935    2.3269   -0.9866    1.2227    4.8121    0.4327    0.2907    0.1694    1.5524    0.0303    1.1779    0.0727</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 99:    92.1631   -6.3090   -0.0908    2.3197   -0.9891    1.1615    4.9271    0.4169    0.2859    0.1736    1.5303    0.0334    1.6227    0.0511</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 100:    92.1185   -6.3603   -0.0950    2.3280   -1.0209    1.1035    5.0957    0.4213    0.2924    0.1817    1.5991    0.0330    1.7197    0.0445</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 101:    92.3964   -6.0447   -0.0898    2.3347   -1.0164    1.0483    4.8409    0.4251    0.2778    0.1727    1.5708    0.0422    1.6197    0.0461</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 102:    92.6597   -6.2545   -0.0954    2.3264   -1.0206    0.9959    4.5989    0.4218    0.2736    0.1640    1.5853    0.0408    1.5912    0.0455</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 103:    92.7866   -6.3292   -0.0933    2.3148   -1.0013    0.9461    4.8935    0.4234    0.2599    0.1688    1.6299    0.0399    1.5921    0.0445</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 104:    92.8098   -6.2608   -0.1009    2.3014   -0.9947    0.8988    4.7750    0.4306    0.2548    0.1813    1.6363    0.0378    1.6713    0.0427</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 105:    92.8300   -5.9827   -0.1028    2.3155   -0.9896    0.8538    4.5362    0.4339    0.2664    0.1722    1.6079    0.0397    1.3691    0.0573</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 106:    92.8218   -6.1138   -0.1024    2.3462   -0.9750    0.8299    4.3094    0.4494    0.2530    0.1636    1.5761    0.0389    1.2857    0.0595</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 107:    92.9304   -6.0063   -0.1002    2.3269   -0.9778    0.7884    4.0939    0.4600    0.2404    0.1620    1.6499    0.0373    1.2101    0.0675</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 108:    92.9072   -6.0928   -0.0991    2.3183   -0.9732    0.7489    4.4394    0.4584    0.2518    0.1539    1.6509    0.0332    1.2346    0.0670</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 109:    92.7504   -6.1545   -0.0862    2.3423   -0.9618    0.7115    4.7501    0.4371    0.2766    0.1465    1.5755    0.0349    1.1552    0.0701</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 110:    92.9277   -6.3786   -0.0904    2.3468   -0.9402    0.6759    5.8620    0.4328    0.2821    0.1703    1.4661    0.0439    1.3292    0.0609</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 111:    92.8023   -5.8686   -0.0886    2.3653   -0.9417    0.7635    5.5689    0.4288    0.2680    0.1618    1.4278    0.0502    1.4715    0.0468</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 112:    92.9411   -5.8095   -0.0883    2.3625   -0.9322    0.8592    5.2905    0.4562    0.2586    0.1537    1.3920    0.0513    1.1766    0.0648</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 113:    92.9845   -6.0499   -0.0761    2.3626   -0.9533    0.8163    5.0259    0.4403    0.2595    0.1532    1.4306    0.0450    1.1416    0.0645</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 114:    92.7735   -6.0605   -0.0694    2.3497   -0.9646    0.7754    4.7746    0.4394    0.2682    0.1576    1.4866    0.0344    1.1127    0.0745</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 115:    92.7048   -6.1327   -0.0702    2.3427   -0.9910    0.7367    4.5359    0.4257    0.2736    0.1497    1.5045    0.0413    1.2783    0.0677</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 116:    92.7131   -6.0115   -0.0751    2.3370   -0.9899    0.6998    4.3091    0.4187    0.2638    0.1422    1.6036    0.0325    1.1109    0.0748</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 117:    92.7720   -5.9163   -0.0709    2.3503   -0.9566    0.6648    4.0937    0.4185    0.2555    0.1417    1.6016    0.0297    1.0778    0.0774</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 118:    92.9182   -5.9312   -0.0812    2.3436   -0.9731    0.6316    3.8890    0.4084    0.2667    0.1400    1.5698    0.0320    1.2364    0.0665</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 119:    92.9546   -5.9622   -0.0870    2.3330   -0.9934    0.6000    3.6945    0.4035    0.2867    0.1486    1.5364    0.0343    1.5892    0.0489</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 120:    92.9168   -6.0966   -0.0905    2.3301   -1.0045    0.5700    3.6677    0.3988    0.2871    0.1567    1.5378    0.0349    1.0886    0.0796</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 121:    92.7767   -6.1038   -0.0802    2.3396   -0.9815    0.5981    4.3032    0.3803    0.2884    0.1684    1.4788    0.0335    1.0886    0.0796</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 122:    92.8439   -6.2425   -0.0799    2.3323   -0.9987    0.6537    4.5745    0.3884    0.2834    0.1600    1.4786    0.0346    1.2146    0.0682</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 123:    92.8219   -5.7869   -0.0872    2.3478   -0.9619    0.7886    4.3458    0.3951    0.2887    0.1658    1.4827    0.0341    1.3911    0.0559</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 124:    92.7930   -5.7551   -0.0819    2.3559   -0.9809    0.8922    4.1285    0.3907    0.2972    0.1575    1.5329    0.0330    1.3824    0.0594</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 125:    92.7488   -5.9406   -0.0819    2.3550   -0.9797    0.8476    3.9221    0.3907    0.2996    0.1733    1.5096    0.0333    1.3148    0.0667</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 126:    92.7926   -5.7106   -0.0824    2.3560   -0.9653    0.8052    3.7260    0.3918    0.3018    0.1854    1.4237    0.0381    1.2607    0.0639</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 127:    92.9153   -5.6682   -0.0717    2.3654   -0.9645    0.7649    3.5397    0.3722    0.2948    0.1784    1.4679    0.0415    1.2310    0.0695</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 128:    92.8827   -5.7984   -0.0783    2.3813   -1.0047    1.1012    3.3627    0.3802    0.2997    0.1694    1.5187    0.0442    1.2884    0.0649</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 129:    92.8727   -5.7191   -0.0785    2.3426   -0.9857    1.0461    3.1946    0.3784    0.2984    0.1610    1.4757    0.0398    1.1695    0.0775</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 130:    92.8689   -5.8036   -0.0935    2.3123   -0.9783    0.9938    3.0348    0.4184    0.2980    0.1723    1.5143    0.0319    1.2298    0.0738</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 131:    92.8733   -5.6707   -0.0988    2.3045   -0.9936    1.3313    2.8831    0.4277    0.2831    0.1816    1.5919    0.0329    1.5260    0.0533</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 132:    92.5761   -5.7773   -0.0925    2.3211   -0.9661    1.2647    2.7389    0.4216    0.2815    0.1995    1.5306    0.0321    1.5863    0.0520</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 133:    92.4512   -5.7800   -0.0920    2.3269   -0.9625    1.2015    2.6020    0.4242    0.2674    0.1955    1.5669    0.0288    1.4087    0.0606</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 134:    92.5625   -5.7968   -0.0965    2.3271   -0.9728    1.1414    2.4719    0.4201    0.2753    0.1945    1.5935    0.0311    1.5348    0.0534</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 135:    92.3448   -5.6624   -0.0961    2.3303   -0.9600    1.0843    2.3483    0.4193    0.2728    0.1983    1.6245    0.0305    1.6281    0.0506</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 136:    92.3407   -5.7523   -0.0932    2.3208   -0.9897    1.0301    2.3267    0.4107    0.2668    0.1884    1.5933    0.0358    1.2970    0.0657</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 137:    92.3940   -6.0148   -0.1046    2.2981   -0.9866    0.9786    3.1382    0.4166    0.3012    0.1790    1.5077    0.0385    1.1944    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 138:    92.3556   -5.9912   -0.1039    2.2969   -0.9920    0.9297    3.2144    0.4208    0.3039    0.1703    1.5042    0.0378    1.2588    0.0717</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 139:    92.5548   -5.9253   -0.1177    2.2723   -1.0079    0.8832    3.2648    0.4049    0.3263    0.1618    1.5558    0.0382    1.2673    0.0698</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 140:    92.6546   -6.0596   -0.1368    2.2659   -0.9922    0.8390    3.3893    0.3846    0.3100    0.1537    1.5475    0.0355    1.3190    0.0629</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 141:    92.6661   -5.9744   -0.1379    2.2793   -0.9754    0.8287    3.2725    0.3800    0.3178    0.1528    1.5076    0.0361    1.4087    0.0540</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 142:    92.5681   -5.7650   -0.1033    2.3249   -0.9613    0.7872    3.1089    0.3610    0.3564    0.1701    1.4733    0.0332    1.2603    0.0720</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 143:    92.4184   -5.6070   -0.1033    2.3520   -0.9764    0.7479    2.9534    0.3591    0.3758    0.1674    1.5420    0.0390    1.3280    0.0614</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 144:    92.5354   -5.6144   -0.0978    2.3801   -0.9425    0.7105    2.8058    0.3538    0.3606    0.1722    1.4884    0.0400    1.3455    0.0620</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 145:    92.4207   -5.6541   -0.0746    2.3873   -0.9633    0.6750    2.6655    0.3361    0.3426    0.1718    1.4981    0.0416    1.2440    0.0685</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 146:    92.3058   -5.6608   -0.0731    2.3755   -0.9732    0.6412    2.5322    0.3193    0.3304    0.1719    1.6391    0.0348    1.1788    0.0743</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 147:    92.4067   -5.7615   -0.0746    2.3775   -0.9903    0.6091    2.4056    0.3148    0.3311    0.1709    1.6695    0.0314    1.2931    0.0676</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 148:    92.3739   -5.8812   -0.0820    2.3644   -0.9823    0.5787    2.7260    0.3332    0.3296    0.1794    1.6168    0.0303    1.3206    0.0670</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 149:    92.4456   -5.8277   -0.0921    2.3508   -0.9924    0.5498    2.8750    0.3368    0.3305    0.1917    1.5602    0.0302    1.3622    0.0608</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 150:    92.5049   -5.7964   -0.1003    2.3353   -0.9809    0.5223    2.7312    0.3291    0.3634    0.1874    1.5035    0.0301    1.4002    0.0615</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 151:    92.3292   -6.0377   -0.1039    2.3301   -0.9866    0.4962    3.3342    0.3348    0.3626    0.1780    1.4819    0.0298    1.3217    0.0672</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 152:    92.3747   -6.0460   -0.1023    2.3154   -1.0242    0.5079    3.4530    0.3371    0.3518    0.1674    1.6034    0.0296    1.2304    0.0749</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 153:    92.3909   -5.8707   -0.1008    2.3284   -0.9897    0.5623    2.8180    0.3480    0.3792    0.1749    1.4953    0.0291    1.3442    0.0677</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 154:    92.2532   -5.9313   -0.0991    2.3232   -0.9839    0.5340    3.3946    0.3502    0.3723    0.1729    1.4837    0.0280    1.1452    0.0776</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 155:    92.1782   -6.1012   -0.0988    2.3339   -0.9600    0.3900    3.9676    0.3593    0.3704    0.1531    1.4927    0.0286    1.1848    0.0730</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 156:    92.2225   -5.8387   -0.0991    2.3393   -0.9459    0.3294    3.0685    0.3598    0.3808    0.1568    1.5540    0.0318    1.3419    0.0671</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 157:    92.2411   -5.7045   -0.1038    2.3578   -0.9252    0.2797    2.5238    0.3716    0.3579    0.1653    1.4704    0.0373    1.1959    0.0686</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 158:    92.2865   -5.6692   -0.1009    2.3771   -0.9532    0.2840    2.4144    0.3592    0.3610    0.1641    1.5065    0.0389    1.1780    0.0674</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 159:    92.2771   -5.7526   -0.0782    2.3632   -0.9771    0.2996    2.7295    0.3357    0.3779    0.1606    1.5818    0.0366    1.1512    0.0781</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 160:    92.3400   -5.8039   -0.0811    2.3615   -0.9453    0.2707    2.7305    0.3373    0.3834    0.1528    1.4765    0.0370    1.1427    0.0756</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 161:    92.4180   -5.7448   -0.0961    2.3653   -0.9508    0.2965    2.6051    0.3666    0.3833    0.1647    1.4581    0.0364    0.9817    0.0827</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 162:    92.4487   -5.8952   -0.0889    2.3460   -0.9646    0.2370    3.1413    0.3460    0.3801    0.1571    1.4528    0.0346    1.0006    0.0798</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 163:    92.5251   -5.8567   -0.0987    2.3342   -0.9615    0.1453    2.9917    0.3535    0.3735    0.1452    1.4521    0.0330    1.0498    0.0766</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 164:    92.5949   -6.1580   -0.1008    2.3332   -0.9749    0.1095    4.1797    0.3586    0.3767    0.1355    1.4659    0.0310    1.0218    0.0797</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 165:    92.5794   -6.0542   -0.1001    2.3321   -0.9795    0.1332    4.2924    0.3588    0.3752    0.1386    1.4858    0.0313    0.9836    0.0812</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 166:    92.6686   -6.0065   -0.0960    2.3623   -0.9532    0.1465    4.0810    0.3439    0.3683    0.1647    1.3929    0.0372    0.9354    0.0837</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 167:    92.7086   -6.2096   -0.0895    2.3656   -0.9552    0.1938    4.6857    0.3325    0.3519    0.1681    1.4281    0.0362    1.0329    0.0784</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 168:    92.6096   -6.0709   -0.0880    2.3595   -0.9474    0.2141    4.1277    0.3379    0.3527    0.1620    1.4264    0.0347    0.9627    0.0836</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 169:    92.5365   -5.9418   -0.0919    2.3546   -0.9575    0.3620    3.7767    0.3489    0.3589    0.1609    1.4761    0.0360    1.1348    0.0745</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 170:    92.6270   -5.8404   -0.0935    2.3562   -0.9517    0.3312    3.2506    0.3552    0.3607    0.1560    1.4506    0.0383    1.0731    0.0722</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 171:    92.5606   -5.9213   -0.0899    2.3513   -0.9554    0.4399    3.5587    0.3599    0.3646    0.1385    1.4779    0.0349    0.9844    0.0775</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 172:    92.3630   -5.7066   -0.0765    2.3798   -0.9314    0.5208    2.5578    0.3458    0.3657    0.1547    1.4900    0.0348    1.0485    0.0765</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 173:    92.2527   -5.5811   -0.0713    2.4173   -0.9221    0.8674    2.2100    0.3448    0.3370    0.1513    1.5403    0.0445    1.4403    0.0518</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 174:    92.3852   -5.5348   -0.0712    2.4066   -0.9033    0.7543    2.1568    0.3457    0.3324    0.1482    1.5322    0.0407    1.2744    0.0623</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 175:    92.4798   -5.5494   -0.0712    2.4004   -0.9100    0.5675    2.0147    0.3457    0.3333    0.1629    1.5229    0.0383    1.2917    0.0655</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 176:    92.5881   -5.5613   -0.0675    2.4180   -0.9188    0.4011    2.0736    0.3413    0.3487    0.1781    1.5149    0.0389    1.1499    0.0669</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 177:    92.6015   -5.4951   -0.0494    2.4324   -0.9525    0.4992    1.9009    0.3629    0.3353    0.1919    1.4881    0.0393    1.2289    0.0650</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 178:    92.6317   -5.4943   -0.0505    2.4373   -0.9298    0.5087    1.6116    0.3631    0.3324    0.1906    1.4475    0.0454    1.0764    0.0701</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 179:    92.7043   -5.5326   -0.0419    2.4386   -0.9376    0.4350    1.8241    0.3577    0.3757    0.1991    1.4445    0.0424    0.9626    0.0833</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 180:    92.7457   -5.5591   -0.0371    2.4684   -0.9450    0.3973    1.7797    0.3507    0.3707    0.1944    1.4392    0.0415    1.0784    0.0764</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 181:    92.6287   -5.5741   -0.0368    2.4489   -0.9459    0.4744    1.8016    0.3502    0.3544    0.1970    1.4317    0.0392    0.9225    0.0848</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 182:    92.6121   -5.5593   -0.0316    2.4610   -0.9345    0.6054    1.9206    0.3492    0.3538    0.1855    1.4410    0.0367    0.8977    0.0878</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 183:    92.4258   -5.4737   -0.0393    2.4592   -0.9319    0.8062    1.7845    0.3528    0.3544    0.1649    1.4545    0.0409    1.0723    0.0751</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 184:    92.3939   -5.5961   -0.0479    2.4268   -0.9435    1.0246    2.2534    0.3497    0.3289    0.1363    1.5022    0.0370    1.1058    0.0729</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 185:    92.4673   -5.5415   -0.0525    2.4157   -0.9058    0.8296    2.2848    0.3406    0.3314    0.1706    1.4935    0.0367    1.1362    0.0722</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 186:    92.4122   -5.6594   -0.0574    2.4466   -0.9430    0.9133    2.3350    0.3327    0.3596    0.1506    1.4450    0.0404    1.2273    0.0629</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 187:    92.5416   -5.5472   -0.0521    2.4393   -0.9261    0.9731    1.9228    0.3208    0.3673    0.1421    1.4890    0.0409    1.2553    0.0626</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 188:    92.5502   -5.6425   -0.0591    2.4345   -0.9246    0.9315    2.2041    0.3184    0.3639    0.1319    1.4896    0.0392    1.0986    0.0684</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 189:    92.4180   -5.6737   -0.0504    2.4448   -0.9191    0.9006    2.4647    0.3102    0.3743    0.1648    1.4649    0.0397    1.1320    0.0742</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 190:    92.5821   -5.5823   -0.0363    2.4588   -0.9205    0.9862    2.2692    0.2820    0.4087    0.1459    1.3938    0.0412    0.9562    0.0815</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 191:    92.3708   -5.5825   -0.0418    2.4621   -0.9231    0.9867    2.4743    0.2890    0.4196    0.1445    1.4619    0.0403    0.9630    0.0811</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 192:    92.2628   -5.5337   -0.0366    2.4392   -0.9194    0.6944    2.2771    0.2909    0.4169    0.1279    1.4745    0.0363    0.8601    0.0892</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 193:    92.4854   -5.6303   -0.0352    2.4484   -0.9316    0.4403    2.3253    0.2928    0.4109    0.1282    1.4768    0.0399    0.8886    0.0871</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 194:    92.4900   -5.5824   -0.0372    2.4648   -0.9451    0.4891    2.6428    0.2899    0.4160    0.1331    1.5124    0.0412    0.9340    0.0853</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 195:    92.4685   -5.7658   -0.0357    2.4659   -0.9217    0.4549    3.3767    0.2921    0.4171    0.1552    1.5062    0.0396    1.0336    0.0817</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 196:    92.4125   -5.7544   -0.0255    2.4834   -0.9230    0.4146    3.1963    0.3000    0.4369    0.1501    1.4560    0.0430    0.9942    0.0813</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 197:    92.3284   -5.9347   -0.0305    2.4860   -0.9448    0.4115    3.5642    0.3129    0.4314    0.1740    1.3441    0.0473    1.0954    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 198:    92.3428   -5.9255   -0.0266    2.4822   -0.9463    0.3258    3.4818    0.3185    0.4163    0.1716    1.3868    0.0447    0.9452    0.0788</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 199:    92.1692   -6.0683   -0.0227    2.4860   -0.9495    0.2685    4.4044    0.3149    0.4064    0.1824    1.3558    0.0478    1.0294    0.0749</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 200:    92.0965   -6.1541   -0.0261    2.4793   -0.9288    0.2210    4.4026    0.3265    0.4200    0.1772    1.3348    0.0441    1.0018    0.0761</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 201:    92.1362   -6.1170   -0.0244    2.4776   -0.9237    0.1801    4.4160    0.3276    0.4303    0.1732    1.3479    0.0418    0.9419    0.0809</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 202:    92.1114   -6.0898   -0.0233    2.4862   -0.9253    0.1506    4.4494    0.3286    0.4305    0.1656    1.3473    0.0440    0.9394    0.0802</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 203:    92.0893   -6.1307   -0.0233    2.4894   -0.9258    0.1510    4.7958    0.3310    0.4233    0.1673    1.3396    0.0457    0.9617    0.0788</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 204:    92.0870   -6.0914   -0.0230    2.4867   -0.9234    0.1589    4.6265    0.3316    0.4237    0.1643    1.3478    0.0456    0.9509    0.0793</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 205:    92.0800   -6.0960   -0.0228    2.4867   -0.9256    0.1613    4.6319    0.3321    0.4226    0.1623    1.3399    0.0463    0.9547    0.0789</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 206:    92.1037   -6.0869   -0.0238    2.4833   -0.9264    0.1648    4.5547    0.3341    0.4186    0.1606    1.3351    0.0466    0.9453    0.0797</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 207:    92.1212   -6.0561   -0.0247    2.4821   -0.9299    0.1677    4.3736    0.3363    0.4150    0.1602    1.3394    0.0467    0.9552    0.0790</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 208:    92.1203   -6.0351   -0.0260    2.4804   -0.9309    0.1616    4.2520    0.3369    0.4120    0.1613    1.3366    0.0466    0.9667    0.0785</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 209:    92.1169   -6.0133   -0.0277    2.4792   -0.9300    0.1596    4.1237    0.3352    0.4111    0.1616    1.3344    0.0464    0.9741    0.0778</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 210:    92.1195   -5.9866   -0.0289    2.4752   -0.9287    0.1586    3.9853    0.3352    0.4091    0.1602    1.3410    0.0461    0.9708    0.0778</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 211:    92.1243   -5.9458   -0.0316    2.4687   -0.9287    0.1606    3.8383    0.3366    0.4094    0.1607    1.3525    0.0455    0.9759    0.0778</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 212:    92.1384   -5.9338   -0.0345    2.4640   -0.9280    0.1635    3.7875    0.3375    0.4105    0.1594    1.3589    0.0454    0.9800    0.0776</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 213:    92.1481   -5.9191   -0.0379    2.4587   -0.9269    0.1629    3.7150    0.3375    0.4100    0.1586    1.3673    0.0449    0.9839    0.0775</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 214:    92.1523   -5.9189   -0.0408    2.4542   -0.9270    0.1593    3.7061    0.3375    0.4092    0.1579    1.3731    0.0446    0.9847    0.0773</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 215:    92.1548   -5.9183   -0.0429    2.4499   -0.9269    0.1604    3.7075    0.3369    0.4095    0.1574    1.3765    0.0442    0.9821    0.0775</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 216:    92.1547   -5.9171   -0.0450    2.4455   -0.9270    0.1600    3.6967    0.3368    0.4100    0.1571    1.3811    0.0438    0.9830    0.0776</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 217:    92.1556   -5.9141   -0.0468    2.4422   -0.9281    0.1580    3.6732    0.3361    0.4102    0.1578    1.3864    0.0435    0.9995    0.0770</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 218:    92.1587   -5.9173   -0.0477    2.4395   -0.9287    0.1545    3.6822    0.3351    0.4112    0.1580    1.3882    0.0432    1.0001    0.0772</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 219:    92.1590   -5.9188   -0.0483    2.4374   -0.9303    0.1540    3.6595    0.3341    0.4132    0.1578    1.3889    0.0430    0.9983    0.0775</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 220:    92.1614   -5.9303   -0.0493    2.4353   -0.9321    0.1559    3.6914    0.3336    0.4141    0.1581    1.3914    0.0427    1.0003    0.0775</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 221:    92.1657   -5.9455   -0.0504    2.4328   -0.9344    0.1585    3.7569    0.3329    0.4140    0.1591    1.3938    0.0424    1.0064    0.0773</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 222:    92.1697   -5.9320   -0.0515    2.4306   -0.9358    0.1617    3.6783    0.3321    0.4145    0.1600    1.3992    0.0421    1.0107    0.0772</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 223:    92.1754   -5.9205   -0.0525    2.4280   -0.9375    0.1630    3.6233    0.3313    0.4158    0.1604    1.4072    0.0416    1.0216    0.0770</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 224:    92.1809   -5.9149   -0.0534    2.4264   -0.9386    0.1623    3.5812    0.3306    0.4167    0.1607    1.4130    0.0413    1.0349    0.0764</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 225:    92.1872   -5.9099   -0.0543    2.4243   -0.9390    0.1619    3.5446    0.3299    0.4174    0.1607    1.4152    0.0411    1.0348    0.0766</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 226:    92.1935   -5.9046   -0.0554    2.4224   -0.9396    0.1631    3.5046    0.3299    0.4183    0.1608    1.4157    0.0409    1.0375    0.0766</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 227:    92.2026   -5.8964   -0.0567    2.4198   -0.9400    0.1637    3.4591    0.3303    0.4183    0.1614    1.4144    0.0408    1.0444    0.0763</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 228:    92.2069   -5.8852   -0.0578    2.4175   -0.9407    0.1633    3.4091    0.3308    0.4185    0.1621    1.4144    0.0407    1.0485    0.0763</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 229:    92.2122   -5.8844   -0.0591    2.4145   -0.9418    0.1631    3.3899    0.3314    0.4194    0.1622    1.4173    0.0404    1.0514    0.0763</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 230:    92.2183   -5.8907   -0.0604    2.4116   -0.9416    0.1640    3.4053    0.3319    0.4202    0.1616    1.4200    0.0401    1.0527    0.0764</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 231:    92.2246   -5.8895   -0.0619    2.4087   -0.9422    0.1651    3.3985    0.3323    0.4208    0.1609    1.4234    0.0400    1.0560    0.0762</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 232:    92.2282   -5.8840   -0.0632    2.4058   -0.9423    0.1641    3.3771    0.3327    0.4215    0.1609    1.4266    0.0398    1.0585    0.0762</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 233:    92.2294   -5.8775   -0.0645    2.4032   -0.9424    0.1620    3.3481    0.3335    0.4216    0.1607    1.4311    0.0395    1.0600    0.0763</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 234:    92.2311   -5.8709   -0.0658    2.4006   -0.9421    0.1626    3.3242    0.3343    0.4220    0.1602    1.4340    0.0393    1.0604    0.0764</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 235:    92.2312   -5.8656   -0.0668    2.3985   -0.9420    0.1608    3.3023    0.3350    0.4225    0.1596    1.4381    0.0391    1.0600    0.0765</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 236:    92.2289   -5.8633   -0.0675    2.3974   -0.9423    0.1599    3.2811    0.3352    0.4227    0.1589    1.4392    0.0390    1.0594    0.0765</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 237:    92.2251   -5.8641   -0.0683    2.3960   -0.9435    0.1586    3.2734    0.3351    0.4226    0.1588    1.4403    0.0389    1.0623    0.0765</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 238:    92.2242   -5.8633   -0.0690    2.3949   -0.9443    0.1579    3.2547    0.3349    0.4230    0.1590    1.4409    0.0389    1.0659    0.0765</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 239:    92.2250   -5.8612   -0.0693    2.3951   -0.9449    0.1558    3.2309    0.3346    0.4234    0.1599    1.4396    0.0389    1.0740    0.0762</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 240:    92.2264   -5.8597   -0.0696    2.3949   -0.9446    0.1550    3.2124    0.3342    0.4234    0.1603    1.4401    0.0388    1.0791    0.0760</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 241:    92.2281   -5.8562   -0.0699    2.3948   -0.9449    0.1544    3.1933    0.3339    0.4236    0.1604    1.4411    0.0388    1.0825    0.0760</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 242:    92.2289   -5.8512   -0.0703    2.3942   -0.9450    0.1534    3.1709    0.3338    0.4235    0.1606    1.4421    0.0388    1.0870    0.0760</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 243:    92.2296   -5.8489   -0.0708    2.3934   -0.9446    0.1531    3.1689    0.3342    0.4230    0.1605    1.4423    0.0387    1.0876    0.0760</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 244:    92.2311   -5.8446   -0.0715    2.3929   -0.9443    0.1528    3.1630    0.3349    0.4228    0.1604    1.4429    0.0387    1.0916    0.0759</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 245:    92.2338   -5.8421   -0.0718    2.3924   -0.9443    0.1528    3.1616    0.3352    0.4224    0.1610    1.4438    0.0387    1.0980    0.0755</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 246:    92.2361   -5.8389   -0.0721    2.3918   -0.9439    0.1525    3.1525    0.3354    0.4216    0.1620    1.4438    0.0388    1.1018    0.0753</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 247:    92.2374   -5.8388   -0.0724    2.3917   -0.9434    0.1510    3.1605    0.3356    0.4212    0.1629    1.4438    0.0389    1.1050    0.0751</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 248:    92.2360   -5.8367   -0.0728    2.3918   -0.9432    0.1505    3.1559    0.3360    0.4207    0.1638    1.4437    0.0389    1.1090    0.0748</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 249:    92.2361   -5.8351   -0.0732    2.3914   -0.9435    0.1499    3.1521    0.3363    0.4204    0.1646    1.4433    0.0389    1.1117    0.0748</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 250:    92.2353   -5.8349   -0.0733    2.3906   -0.9436    0.1502    3.1607    0.3365    0.4202    0.1646    1.4457    0.0388    1.1107    0.0749</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 251:    92.2343   -5.8318   -0.0736    2.3903   -0.9430    0.1494    3.1513    0.3367    0.4201    0.1648    1.4453    0.0387    1.1093    0.0750</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 252:    92.2356   -5.8244   -0.0739    2.3895   -0.9424    0.1477    3.1240    0.3369    0.4200    0.1651    1.4460    0.0386    1.1083    0.0750</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 253:    92.2367   -5.8188   -0.0742    2.3890   -0.9423    0.1465    3.1025    0.3369    0.4200    0.1649    1.4477    0.0385    1.1092    0.0750</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 254:    92.2392   -5.8154   -0.0747    2.3880   -0.9421    0.1458    3.0888    0.3372    0.4195    0.1644    1.4494    0.0384    1.1080    0.0750</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 255:    92.2404   -5.8131   -0.0751    2.3870   -0.9417    0.1451    3.0778    0.3375    0.4191    0.1639    1.4501    0.0383    1.1070    0.0751</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 256:    92.2408   -5.8109   -0.0753    2.3867   -0.9413    0.1445    3.0740    0.3376    0.4192    0.1633    1.4510    0.0382    1.1064    0.0751</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 257:    92.2413   -5.8066   -0.0754    2.3866   -0.9409    0.1446    3.0636    0.3376    0.4191    0.1628    1.4522    0.0382    1.1080    0.0750</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 258:    92.2412   -5.8035   -0.0754    2.3869   -0.9404    0.1436    3.0502    0.3372    0.4191    0.1623    1.4534    0.0381    1.1057    0.0751</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 259:    92.2399   -5.7996   -0.0753    2.3871   -0.9399    0.1427    3.0357    0.3370    0.4195    0.1621    1.4540    0.0380    1.1037    0.0752</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 260:    92.2389   -5.7955   -0.0755    2.3872   -0.9391    0.1420    3.0251    0.3366    0.4199    0.1619    1.4555    0.0380    1.1041    0.0752</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 261:    92.2378   -5.7941   -0.0755    2.3875   -0.9385    0.1412    3.0240    0.3362    0.4201    0.1613    1.4575    0.0380    1.1048    0.0751</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 262:    92.2361   -5.7920   -0.0756    2.3879   -0.9379    0.1407    3.0182    0.3358    0.4205    0.1607    1.4581    0.0381    1.1047    0.0750</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 263:    92.2338   -5.7891   -0.0754    2.3883   -0.9375    0.1407    3.0072    0.3356    0.4211    0.1603    1.4585    0.0381    1.1041    0.0750</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 264:    92.2313   -5.7882   -0.0752    2.3886   -0.9371    0.1407    3.0009    0.3355    0.4217    0.1601    1.4574    0.0381    1.1036    0.0750</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 265:    92.2312   -5.7843   -0.0752    2.3883   -0.9372    0.1402    2.9872    0.3358    0.4220    0.1599    1.4577    0.0381    1.1026    0.0750</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 266:    92.2312   -5.7800   -0.0753    2.3880   -0.9370    0.1396    2.9708    0.3363    0.4223    0.1597    1.4584    0.0381    1.1019    0.0750</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 267:    92.2306   -5.7785   -0.0755    2.3878   -0.9372    0.1397    2.9619    0.3368    0.4227    0.1595    1.4578    0.0381    1.1021    0.0750</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 268:    92.2287   -5.7803   -0.0758    2.3870   -0.9372    0.1394    2.9777    0.3375    0.4226    0.1594    1.4578    0.0380    1.1032    0.0749</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 269:    92.2265   -5.7804   -0.0761    2.3865   -0.9371    0.1399    2.9816    0.3382    0.4226    0.1592    1.4589    0.0380    1.1058    0.0747</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 270:    92.2236   -5.7811   -0.0765    2.3860   -0.9369    0.1411    2.9893    0.3386    0.4227    0.1591    1.4597    0.0380    1.1081    0.0745</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 271:    92.2211   -5.7793   -0.0769    2.3852   -0.9365    0.1421    2.9888    0.3390    0.4228    0.1591    1.4606    0.0379    1.1083    0.0745</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 272:    92.2187   -5.7773   -0.0773    2.3845   -0.9361    0.1423    2.9810    0.3396    0.4232    0.1589    1.4617    0.0378    1.1076    0.0746</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 273:    92.2164   -5.7769   -0.0776    2.3838   -0.9356    0.1427    2.9809    0.3402    0.4240    0.1589    1.4615    0.0378    1.1073    0.0746</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 274:    92.2145   -5.7763   -0.0778    2.3836   -0.9355    0.1434    2.9795    0.3407    0.4248    0.1589    1.4615    0.0378    1.1069    0.0746</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 275:    92.2133   -5.7762   -0.0779    2.3836   -0.9352    0.1436    2.9837    0.3410    0.4253    0.1589    1.4621    0.0378    1.1064    0.0746</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 276:    92.2123   -5.7764   -0.0781    2.3835   -0.9348    0.1431    2.9889    0.3414    0.4255    0.1589    1.4632    0.0378    1.1073    0.0746</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 277:    92.2113   -5.7771   -0.0781    2.3839   -0.9347    0.1423    2.9925    0.3423    0.4264    0.1589    1.4630    0.0378    1.1104    0.0744</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 278:    92.2099   -5.7774   -0.0780    2.3841   -0.9349    0.1418    2.9927    0.3429    0.4270    0.1590    1.4634    0.0378    1.1127    0.0742</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 279:    92.2087   -5.7798   -0.0779    2.3844   -0.9352    0.1413    2.9997    0.3437    0.4276    0.1590    1.4630    0.0378    1.1133    0.0742</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 280:    92.2077   -5.7802   -0.0778    2.3841   -0.9358    0.1407    2.9971    0.3445    0.4284    0.1586    1.4634    0.0378    1.1136    0.0742</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 281:    92.2061   -5.7814   -0.0777    2.3837   -0.9362    0.1401    3.0004    0.3452    0.4291    0.1582    1.4629    0.0378    1.1133    0.0743</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 282:    92.2058   -5.7802   -0.0777    2.3834   -0.9363    0.1386    2.9994    0.3459    0.4297    0.1579    1.4634    0.0378    1.1126    0.0743</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 283:    92.2057   -5.7780   -0.0778    2.3828   -0.9361    0.1376    2.9917    0.3469    0.4298    0.1576    1.4645    0.0378    1.1147    0.0742</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 284:    92.2051   -5.7788   -0.0780    2.3824   -0.9360    0.1367    2.9968    0.3479    0.4300    0.1573    1.4647    0.0378    1.1149    0.0741</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 285:    92.2041   -5.7793   -0.0782    2.3821   -0.9362    0.1359    2.9941    0.3487    0.4302    0.1573    1.4653    0.0378    1.1164    0.0740</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 286:    92.2040   -5.7803   -0.0783    2.3819   -0.9364    0.1352    2.9957    0.3495    0.4304    0.1573    1.4655    0.0378    1.1180    0.0738</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 287:    92.2044   -5.7809   -0.0784    2.3817   -0.9365    0.1348    2.9961    0.3502    0.4304    0.1572    1.4657    0.0379    1.1178    0.0738</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 288:    92.2043   -5.7835   -0.0785    2.3814   -0.9366    0.1350    3.0073    0.3509    0.4304    0.1572    1.4658    0.0379    1.1184    0.0738</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 289:    92.2041   -5.7845   -0.0787    2.3809   -0.9364    0.1347    3.0126    0.3516    0.4304    0.1571    1.4653    0.0379    1.1190    0.0737</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 290:    92.2032   -5.7852   -0.0788    2.3807   -0.9363    0.1349    3.0161    0.3521    0.4304    0.1568    1.4656    0.0379    1.1191    0.0737</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 291:    92.2019   -5.7848   -0.0790    2.3802   -0.9363    0.1356    3.0155    0.3528    0.4300    0.1565    1.4664    0.0380    1.1210    0.0736</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 292:    92.2012   -5.7846   -0.0792    2.3799   -0.9363    0.1361    3.0167    0.3535    0.4296    0.1563    1.4670    0.0380    1.1223    0.0735</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 293:    92.2007   -5.7845   -0.0793    2.3795   -0.9363    0.1365    3.0175    0.3542    0.4291    0.1561    1.4681    0.0380    1.1236    0.0734</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 294:    92.2009   -5.7850   -0.0795    2.3791   -0.9360    0.1365    3.0189    0.3550    0.4287    0.1560    1.4682    0.0380    1.1243    0.0733</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 295:    92.2009   -5.7865   -0.0797    2.3786   -0.9360    0.1360    3.0279    0.3556    0.4286    0.1558    1.4689    0.0380    1.1243    0.0734</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 296:    92.2007   -5.7878   -0.0798    2.3781   -0.9362    0.1358    3.0345    0.3562    0.4283    0.1555    1.4694    0.0379    1.1247    0.0734</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 297:    92.1992   -5.7891   -0.0801    2.3774   -0.9364    0.1358    3.0403    0.3568    0.4278    0.1554    1.4699    0.0379    1.1267    0.0734</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 298:    92.1978   -5.7892   -0.0803    2.3768   -0.9366    0.1357    3.0383    0.3573    0.4274    0.1553    1.4706    0.0378    1.1276    0.0734</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 299:    92.1968   -5.7906   -0.0805    2.3763   -0.9369    0.1353    3.0408    0.3579    0.4269    0.1553    1.4712    0.0378    1.1282    0.0733</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 300:    92.1954   -5.7929   -0.0807    2.3760   -0.9369    0.1352    3.0477    0.3583    0.4265    0.1551    1.4716    0.0379    1.1286    0.0733</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 301:    92.1941   -5.7934   -0.0809    2.3757   -0.9369    0.1352    3.0483    0.3588    0.4261    0.1548    1.4727    0.0378    1.1288    0.0732</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 302:    92.1929   -5.7949   -0.0811    2.3754   -0.9370    0.1354    3.0560    0.3592    0.4256    0.1548    1.4728    0.0379    1.1296    0.0731</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 303:    92.1919   -5.7972   -0.0813    2.3751   -0.9368    0.1352    3.0671    0.3597    0.4250    0.1550    1.4730    0.0379    1.1302    0.0731</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 304:    92.1906   -5.8018   -0.0814    2.3750   -0.9368    0.1349    3.0935    0.3602    0.4245    0.1552    1.4731    0.0379    1.1314    0.0730</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 305:    92.1897   -5.8063   -0.0817    2.3744   -0.9370    0.1350    3.1211    0.3606    0.4238    0.1554    1.4727    0.0379    1.1323    0.0730</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 306:    92.1896   -5.8116   -0.0820    2.3740   -0.9373    0.1347    3.1571    0.3610    0.4233    0.1555    1.4727    0.0379    1.1351    0.0728</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 307:    92.1895   -5.8156   -0.0822    2.3735   -0.9373    0.1341    3.1826    0.3613    0.4226    0.1552    1.4741    0.0379    1.1374    0.0726</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 308:    92.1899   -5.8202   -0.0824    2.3732   -0.9376    0.1338    3.2124    0.3617    0.4220    0.1554    1.4745    0.0379    1.1408    0.0724</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 309:    92.1903   -5.8232   -0.0827    2.3728   -0.9377    0.1337    3.2330    0.3620    0.4213    0.1554    1.4749    0.0379    1.1419    0.0724</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 310:    92.1902   -5.8249   -0.0828    2.3727   -0.9378    0.1335    3.2463    0.3621    0.4207    0.1554    1.4752    0.0379    1.1435    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 311:    92.1910   -5.8267   -0.0828    2.3726   -0.9378    0.1335    3.2581    0.3623    0.4200    0.1554    1.4754    0.0379    1.1441    0.0722</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 312:    92.1918   -5.8271   -0.0829    2.3726   -0.9378    0.1333    3.2567    0.3624    0.4195    0.1552    1.4751    0.0380    1.1432    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 313:    92.1926   -5.8260   -0.0829    2.3725   -0.9380    0.1334    3.2497    0.3626    0.4190    0.1552    1.4751    0.0380    1.1434    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 314:    92.1934   -5.8256   -0.0829    2.3724   -0.9381    0.1330    3.2426    0.3628    0.4185    0.1553    1.4746    0.0380    1.1441    0.0722</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 315:    92.1938   -5.8237   -0.0829    2.3724   -0.9384    0.1326    3.2327    0.3630    0.4179    0.1555    1.4746    0.0380    1.1468    0.0721</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 316:    92.1950   -5.8235   -0.0829    2.3722   -0.9385    0.1324    3.2283    0.3631    0.4172    0.1557    1.4745    0.0380    1.1476    0.0721</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 317:    92.1963   -5.8239   -0.0829    2.3722   -0.9385    0.1322    3.2260    0.3633    0.4167    0.1560    1.4741    0.0380    1.1481    0.0721</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 318:    92.1975   -5.8242   -0.0829    2.3722   -0.9387    0.1321    3.2240    0.3634    0.4163    0.1559    1.4740    0.0380    1.1477    0.0722</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 319:    92.1990   -5.8250   -0.0829    2.3721   -0.9387    0.1320    3.2215    0.3634    0.4159    0.1560    1.4736    0.0381    1.1496    0.0720</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 320:    92.2003   -5.8256   -0.0829    2.3722   -0.9386    0.1322    3.2199    0.3635    0.4155    0.1561    1.4730    0.0381    1.1508    0.0719</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 321:    92.2018   -5.8249   -0.0829    2.3723   -0.9386    0.1326    3.2150    0.3635    0.4151    0.1562    1.4727    0.0381    1.1514    0.0718</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 322:    92.2031   -5.8239   -0.0829    2.3724   -0.9386    0.1333    3.2081    0.3635    0.4147    0.1561    1.4729    0.0381    1.1527    0.0717</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 323:    92.2045   -5.8244   -0.0829    2.3725   -0.9384    0.1336    3.2065    0.3635    0.4143    0.1562    1.4729    0.0381    1.1541    0.0716</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 324:    92.2060   -5.8228   -0.0828    2.3726   -0.9383    0.1339    3.1996    0.3635    0.4139    0.1562    1.4729    0.0382    1.1562    0.0714</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 325:    92.2071   -5.8212   -0.0828    2.3727   -0.9383    0.1339    3.1921    0.3636    0.4135    0.1563    1.4733    0.0382    1.1580    0.0713</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 326:    92.2083   -5.8201   -0.0826    2.3728   -0.9382    0.1340    3.1888    0.3639    0.4134    0.1562    1.4734    0.0382    1.1587    0.0713</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 327:    92.2095   -5.8185   -0.0825    2.3729   -0.9379    0.1340    3.1831    0.3641    0.4134    0.1562    1.4737    0.0382    1.1592    0.0713</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 328:    92.2105   -5.8188   -0.0823    2.3731   -0.9379    0.1338    3.1836    0.3643    0.4132    0.1561    1.4736    0.0383    1.1589    0.0713</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 329:    92.2115   -5.8182   -0.0821    2.3732   -0.9381    0.1339    3.1807    0.3646    0.4129    0.1561    1.4737    0.0383    1.1585    0.0713</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 330:    92.2127   -5.8181   -0.0819    2.3734   -0.9381    0.1338    3.1827    0.3648    0.4127    0.1563    1.4735    0.0383    1.1578    0.0714</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 331:    92.2134   -5.8180   -0.0815    2.3739   -0.9381    0.1340    3.1837    0.3647    0.4128    0.1566    1.4735    0.0383    1.1575    0.0714</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 332:    92.2136   -5.8184   -0.0811    2.3746   -0.9379    0.1340    3.1847    0.3647    0.4130    0.1570    1.4728    0.0384    1.1577    0.0714</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 333:    92.2133   -5.8180   -0.0807    2.3753   -0.9376    0.1340    3.1853    0.3646    0.4132    0.1572    1.4725    0.0384    1.1573    0.0714</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 334:    92.2136   -5.8173   -0.0803    2.3760   -0.9374    0.1342    3.1838    0.3645    0.4133    0.1576    1.4720    0.0385    1.1570    0.0714</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 335:    92.2139   -5.8187   -0.0800    2.3769   -0.9372    0.1344    3.1947    0.3643    0.4135    0.1576    1.4714    0.0386    1.1567    0.0714</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 336:    92.2134   -5.8198   -0.0796    2.3777   -0.9370    0.1348    3.2008    0.3641    0.4135    0.1577    1.4706    0.0387    1.1557    0.0714</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 337:    92.2130   -5.8201   -0.0792    2.3784   -0.9370    0.1357    3.2050    0.3640    0.4137    0.1579    1.4703    0.0388    1.1558    0.0714</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 338:    92.2130   -5.8190   -0.0787    2.3791   -0.9368    0.1362    3.2036    0.3638    0.4139    0.1580    1.4708    0.0388    1.1558    0.0714</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 339:    92.2132   -5.8177   -0.0783    2.3798   -0.9368    0.1369    3.2006    0.3637    0.4142    0.1581    1.4712    0.0388    1.1551    0.0714</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 340:    92.2137   -5.8167   -0.0778    2.3806   -0.9366    0.1376    3.1984    0.3636    0.4143    0.1581    1.4712    0.0388    1.1540    0.0715</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 341:    92.2141   -5.8145   -0.0773    2.3814   -0.9364    0.1378    3.1916    0.3635    0.4142    0.1581    1.4712    0.0389    1.1529    0.0715</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 342:    92.2142   -5.8123   -0.0769    2.3821   -0.9364    0.1383    3.1840    0.3634    0.4142    0.1581    1.4718    0.0389    1.1513    0.0716</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 343:    92.2139   -5.8109   -0.0764    2.3830   -0.9364    0.1389    3.1806    0.3632    0.4142    0.1581    1.4721    0.0389    1.1501    0.0716</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 344:    92.2137   -5.8117   -0.0759    2.3839   -0.9366    0.1390    3.1830    0.3631    0.4144    0.1582    1.4711    0.0390    1.1492    0.0717</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 345:    92.2133   -5.8118   -0.0754    2.3849   -0.9368    0.1391    3.1827    0.3630    0.4146    0.1582    1.4703    0.0391    1.1488    0.0717</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 346:    92.2127   -5.8113   -0.0748    2.3858   -0.9369    0.1389    3.1793    0.3629    0.4147    0.1581    1.4700    0.0391    1.1475    0.0717</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 347:    92.2121   -5.8107   -0.0743    2.3867   -0.9371    0.1386    3.1748    0.3628    0.4149    0.1579    1.4701    0.0392    1.1463    0.0718</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 348:    92.2106   -5.8109   -0.0738    2.3876   -0.9374    0.1385    3.1726    0.3626    0.4151    0.1577    1.4704    0.0392    1.1453    0.0719</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 349:    92.2096   -5.8111   -0.0732    2.3883   -0.9377    0.1382    3.1703    0.3626    0.4151    0.1575    1.4705    0.0392    1.1448    0.0719</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 350:    92.2088   -5.8108   -0.0727    2.3890   -0.9378    0.1380    3.1674    0.3625    0.4152    0.1574    1.4704    0.0392    1.1439    0.0720</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 351:    92.2077   -5.8103   -0.0722    2.3899   -0.9379    0.1379    3.1634    0.3623    0.4154    0.1572    1.4701    0.0393    1.1432    0.0720</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 352:    92.2069   -5.8103   -0.0718    2.3906   -0.9381    0.1380    3.1626    0.3623    0.4154    0.1570    1.4701    0.0393    1.1425    0.0721</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 353:    92.2058   -5.8107   -0.0714    2.3913   -0.9381    0.1382    3.1629    0.3624    0.4154    0.1570    1.4695    0.0394    1.1426    0.0720</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 354:    92.2046   -5.8102   -0.0710    2.3921   -0.9381    0.1384    3.1576    0.3624    0.4154    0.1571    1.4691    0.0394    1.1424    0.0720</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 355:    92.2034   -5.8084   -0.0707    2.3928   -0.9381    0.1388    3.1501    0.3624    0.4154    0.1570    1.4686    0.0395    1.1414    0.0721</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 356:    92.2027   -5.8079   -0.0703    2.3935   -0.9382    0.1392    3.1463    0.3626    0.4155    0.1569    1.4682    0.0396    1.1405    0.0721</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 357:    92.2019   -5.8084   -0.0698    2.3943   -0.9382    0.1390    3.1444    0.3628    0.4156    0.1569    1.4665    0.0397    1.1403    0.0721</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 358:    92.2014   -5.8085   -0.0694    2.3950   -0.9381    0.1389    3.1413    0.3630    0.4158    0.1569    1.4662    0.0398    1.1398    0.0722</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 359:    92.2005   -5.8091   -0.0689    2.3956   -0.9383    0.1387    3.1393    0.3633    0.4159    0.1570    1.4664    0.0398    1.1401    0.0722</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 360:    92.1994   -5.8091   -0.0685    2.3962   -0.9385    0.1385    3.1362    0.3635    0.4159    0.1572    1.4664    0.0398    1.1413    0.0722</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 361:    92.1983   -5.8094   -0.0682    2.3966   -0.9386    0.1384    3.1340    0.3638    0.4159    0.1573    1.4669    0.0398    1.1420    0.0722</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 362:    92.1976   -5.8086   -0.0678    2.3971   -0.9389    0.1380    3.1277    0.3639    0.4160    0.1573    1.4671    0.0398    1.1414    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 363:    92.1967   -5.8081   -0.0675    2.3976   -0.9389    0.1377    3.1239    0.3641    0.4161    0.1573    1.4669    0.0399    1.1412    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 364:    92.1960   -5.8073   -0.0671    2.3982   -0.9390    0.1373    3.1208    0.3643    0.4161    0.1572    1.4668    0.0399    1.1411    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 365:    92.1958   -5.8066   -0.0667    2.3988   -0.9389    0.1369    3.1164    0.3645    0.4160    0.1572    1.4672    0.0399    1.1416    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 366:    92.1956   -5.8063   -0.0664    2.3992   -0.9390    0.1364    3.1127    0.3650    0.4156    0.1573    1.4674    0.0399    1.1425    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 367:    92.1952   -5.8056   -0.0661    2.3996   -0.9389    0.1361    3.1082    0.3652    0.4155    0.1574    1.4675    0.0399    1.1416    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 368:    92.1948   -5.8059   -0.0658    2.4001   -0.9389    0.1359    3.1068    0.3655    0.4154    0.1575    1.4671    0.0399    1.1406    0.0724</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 369:    92.1948   -5.8064   -0.0655    2.4005   -0.9388    0.1360    3.1055    0.3658    0.4152    0.1576    1.4669    0.0399    1.1408    0.0724</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 370:    92.1951   -5.8072   -0.0652    2.4010   -0.9389    0.1361    3.1060    0.3660    0.4151    0.1576    1.4669    0.0399    1.1406    0.0724</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 371:    92.1956   -5.8080   -0.0649    2.4015   -0.9390    0.1362    3.1095    0.3662    0.4150    0.1576    1.4669    0.0400    1.1411    0.0724</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 372:    92.1962   -5.8096   -0.0645    2.4020   -0.9390    0.1363    3.1168    0.3665    0.4149    0.1576    1.4667    0.0400    1.1411    0.0724</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 373:    92.1968   -5.8098   -0.0642    2.4025   -0.9392    0.1363    3.1154    0.3666    0.4147    0.1576    1.4664    0.0400    1.1418    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 374:    92.1972   -5.8098   -0.0640    2.4029   -0.9395    0.1363    3.1117    0.3667    0.4146    0.1576    1.4661    0.0401    1.1421    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 375:    92.1979   -5.8101   -0.0637    2.4032   -0.9397    0.1364    3.1090    0.3668    0.4143    0.1577    1.4654    0.0401    1.1425    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 376:    92.1981   -5.8106   -0.0635    2.4035   -0.9399    0.1363    3.1084    0.3669    0.4142    0.1576    1.4650    0.0401    1.1423    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 377:    92.1986   -5.8104   -0.0633    2.4039   -0.9399    0.1360    3.1053    0.3670    0.4142    0.1576    1.4645    0.0402    1.1424    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 378:    92.1989   -5.8104   -0.0631    2.4043   -0.9399    0.1358    3.1026    0.3671    0.4142    0.1577    1.4638    0.0402    1.1421    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 379:    92.1990   -5.8114   -0.0628    2.4047   -0.9399    0.1355    3.1068    0.3671    0.4141    0.1577    1.4636    0.0402    1.1413    0.0724</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 380:    92.1989   -5.8119   -0.0627    2.4048   -0.9400    0.1351    3.1095    0.3673    0.4136    0.1577    1.4634    0.0402    1.1413    0.0724</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 381:    92.1989   -5.8127   -0.0627    2.4048   -0.9400    0.1349    3.1152    0.3676    0.4131    0.1577    1.4631    0.0403    1.1408    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 382:    92.1994   -5.8126   -0.0627    2.4049   -0.9399    0.1348    3.1188    0.3678    0.4126    0.1576    1.4635    0.0403    1.1403    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 383:    92.2000   -5.8129   -0.0626    2.4049   -0.9397    0.1346    3.1256    0.3681    0.4120    0.1576    1.4637    0.0403    1.1396    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 384:    92.2006   -5.8133   -0.0626    2.4050   -0.9396    0.1347    3.1290    0.3683    0.4115    0.1575    1.4641    0.0402    1.1389    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 385:    92.2015   -5.8131   -0.0626    2.4050   -0.9394    0.1350    3.1287    0.3686    0.4110    0.1575    1.4642    0.0402    1.1382    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 386:    92.2023   -5.8140   -0.0625    2.4049   -0.9391    0.1349    3.1324    0.3690    0.4104    0.1574    1.4647    0.0402    1.1374    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 387:    92.2030   -5.8147   -0.0625    2.4050   -0.9390    0.1349    3.1345    0.3693    0.4099    0.1572    1.4652    0.0402    1.1366    0.0724</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 388:    92.2040   -5.8157   -0.0625    2.4050   -0.9389    0.1349    3.1381    0.3696    0.4094    0.1570    1.4652    0.0402    1.1362    0.0724</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 389:    92.2051   -5.8156   -0.0625    2.4051   -0.9387    0.1349    3.1373    0.3699    0.4090    0.1570    1.4653    0.0402    1.1354    0.0724</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 390:    92.2063   -5.8152   -0.0625    2.4051   -0.9386    0.1349    3.1350    0.3702    0.4087    0.1570    1.4655    0.0402    1.1345    0.0725</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 391:    92.2076   -5.8160   -0.0625    2.4051   -0.9384    0.1350    3.1380    0.3705    0.4083    0.1571    1.4656    0.0402    1.1341    0.0725</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 392:    92.2086   -5.8166   -0.0626    2.4049   -0.9384    0.1349    3.1397    0.3707    0.4081    0.1572    1.4658    0.0402    1.1345    0.0725</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 393:    92.2095   -5.8168   -0.0626    2.4047   -0.9383    0.1348    3.1405    0.3708    0.4080    0.1573    1.4659    0.0402    1.1344    0.0725</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 394:    92.2107   -5.8174   -0.0627    2.4045   -0.9382    0.1347    3.1433    0.3710    0.4079    0.1575    1.4660    0.0401    1.1342    0.0725</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 395:    92.2118   -5.8174   -0.0628    2.4043   -0.9380    0.1346    3.1437    0.3711    0.4078    0.1575    1.4661    0.0401    1.1343    0.0725</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 396:    92.2127   -5.8170   -0.0629    2.4041   -0.9379    0.1344    3.1424    0.3712    0.4077    0.1575    1.4664    0.0401    1.1342    0.0725</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 397:    92.2137   -5.8161   -0.0630    2.4040   -0.9376    0.1342    3.1386    0.3713    0.4076    0.1577    1.4667    0.0401    1.1341    0.0725</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 398:    92.2146   -5.8152   -0.0633    2.4036   -0.9374    0.1341    3.1344    0.3713    0.4075    0.1578    1.4671    0.0401    1.1334    0.0725</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 399:    92.2157   -5.8138   -0.0635    2.4032   -0.9373    0.1340    3.1296    0.3714    0.4074    0.1578    1.4678    0.0401    1.1336    0.0726</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 400:    92.2165   -5.8131   -0.0638    2.4027   -0.9372    0.1340    3.1262    0.3715    0.4072    0.1579    1.4681    0.0400    1.1332    0.0726</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 401:    92.2173   -5.8118   -0.0641    2.4022   -0.9372    0.1341    3.1220    0.3716    0.4069    0.1579    1.4686    0.0400    1.1339    0.0726</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 402:    92.2181   -5.8107   -0.0643    2.4018   -0.9370    0.1344    3.1192    0.3718    0.4065    0.1580    1.4694    0.0400    1.1338    0.0726</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 403:    92.2190   -5.8099   -0.0646    2.4013   -0.9371    0.1348    3.1166    0.3720    0.4061    0.1581    1.4700    0.0400    1.1344    0.0725</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 404:    92.2198   -5.8104   -0.0649    2.4008   -0.9372    0.1348    3.1161    0.3723    0.4058    0.1582    1.4704    0.0399    1.1357    0.0725</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 405:    92.2203   -5.8107   -0.0652    2.4004   -0.9372    0.1348    3.1177    0.3725    0.4055    0.1582    1.4705    0.0400    1.1358    0.0724</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 406:    92.2204   -5.8103   -0.0653    2.4002   -0.9371    0.1348    3.1157    0.3724    0.4051    0.1582    1.4709    0.0400    1.1361    0.0724</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 407:    92.2201   -5.8094   -0.0655    2.4001   -0.9369    0.1348    3.1121    0.3724    0.4048    0.1583    1.4713    0.0400    1.1360    0.0724</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 408:    92.2201   -5.8087   -0.0657    2.3999   -0.9368    0.1350    3.1085    0.3724    0.4044    0.1582    1.4714    0.0400    1.1360    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 409:    92.2201   -5.8083   -0.0658    2.3998   -0.9365    0.1355    3.1073    0.3724    0.4043    0.1582    1.4713    0.0401    1.1358    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 410:    92.2196   -5.8094   -0.0659    2.3996   -0.9365    0.1361    3.1123    0.3724    0.4041    0.1582    1.4712    0.0401    1.1357    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 411:    92.2192   -5.8099   -0.0661    2.3994   -0.9366    0.1363    3.1168    0.3724    0.4038    0.1582    1.4715    0.0401    1.1354    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 412:    92.2187   -5.8105   -0.0662    2.3993   -0.9365    0.1365    3.1215    0.3725    0.4035    0.1582    1.4716    0.0401    1.1359    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 413:    92.2185   -5.8109   -0.0663    2.3992   -0.9365    0.1367    3.1268    0.3725    0.4031    0.1583    1.4719    0.0401    1.1357    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 414:    92.2183   -5.8111   -0.0665    2.3987   -0.9365    0.1370    3.1286    0.3727    0.4026    0.1582    1.4724    0.0400    1.1349    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 415:    92.2181   -5.8122   -0.0667    2.3983   -0.9366    0.1370    3.1351    0.3729    0.4021    0.1581    1.4726    0.0400    1.1341    0.0724</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 416:    92.2180   -5.8133   -0.0669    2.3979   -0.9367    0.1372    3.1409    0.3731    0.4015    0.1578    1.4734    0.0400    1.1333    0.0724</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 417:    92.2176   -5.8138   -0.0671    2.3976   -0.9368    0.1374    3.1426    0.3733    0.4009    0.1576    1.4739    0.0400    1.1325    0.0724</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 418:    92.2173   -5.8150   -0.0673    2.3973   -0.9369    0.1376    3.1479    0.3735    0.4003    0.1575    1.4740    0.0400    1.1316    0.0725</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 419:    92.2174   -5.8154   -0.0674    2.3970   -0.9369    0.1375    3.1497    0.3736    0.3998    0.1574    1.4741    0.0400    1.1314    0.0724</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 420:    92.2175   -5.8154   -0.0676    2.3968   -0.9369    0.1375    3.1489    0.3737    0.3993    0.1574    1.4742    0.0400    1.1315    0.0724</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 421:    92.2176   -5.8154   -0.0678    2.3964   -0.9369    0.1374    3.1485    0.3739    0.3989    0.1573    1.4743    0.0400    1.1312    0.0724</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 422:    92.2174   -5.8155   -0.0680    2.3961   -0.9370    0.1375    3.1483    0.3741    0.3985    0.1572    1.4744    0.0400    1.1309    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 423:    92.2170   -5.8156   -0.0681    2.3958   -0.9371    0.1375    3.1481    0.3742    0.3980    0.1571    1.4741    0.0400    1.1308    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 424:    92.2169   -5.8168   -0.0683    2.3956   -0.9372    0.1374    3.1524    0.3744    0.3976    0.1571    1.4740    0.0400    1.1316    0.0722</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 425:    92.2167   -5.8171   -0.0685    2.3954   -0.9372    0.1373    3.1520    0.3744    0.3972    0.1571    1.4736    0.0401    1.1317    0.0722</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 426:    92.2164   -5.8170   -0.0687    2.3951   -0.9372    0.1373    3.1502    0.3745    0.3968    0.1570    1.4734    0.0401    1.1320    0.0722</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 427:    92.2166   -5.8169   -0.0688    2.3949   -0.9372    0.1372    3.1480    0.3745    0.3964    0.1570    1.4734    0.0401    1.1317    0.0721</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 428:    92.2166   -5.8170   -0.0689    2.3948   -0.9374    0.1371    3.1460    0.3745    0.3959    0.1570    1.4735    0.0401    1.1315    0.0721</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 429:    92.2162   -5.8171   -0.0691    2.3946   -0.9374    0.1369    3.1446    0.3745    0.3954    0.1570    1.4736    0.0401    1.1316    0.0721</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 430:    92.2158   -5.8178   -0.0692    2.3944   -0.9375    0.1370    3.1464    0.3745    0.3950    0.1570    1.4736    0.0401    1.1314    0.0721</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 431:    92.2153   -5.8180   -0.0693    2.3942   -0.9375    0.1371    3.1470    0.3745    0.3946    0.1570    1.4735    0.0401    1.1314    0.0721</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 432:    92.2150   -5.8182   -0.0695    2.3940   -0.9375    0.1372    3.1477    0.3746    0.3942    0.1570    1.4735    0.0401    1.1315    0.0721</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 433:    92.2145   -5.8189   -0.0696    2.3938   -0.9375    0.1374    3.1512    0.3746    0.3938    0.1571    1.4736    0.0400    1.1323    0.0720</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 434:    92.2141   -5.8186   -0.0697    2.3936   -0.9373    0.1376    3.1530    0.3746    0.3933    0.1571    1.4736    0.0400    1.1330    0.0719</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 435:    92.2133   -5.8187   -0.0699    2.3934   -0.9373    0.1381    3.1571    0.3747    0.3929    0.1570    1.4738    0.0400    1.1325    0.0720</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 436:    92.2129   -5.8180   -0.0700    2.3932   -0.9374    0.1380    3.1544    0.3746    0.3925    0.1570    1.4740    0.0400    1.1332    0.0719</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 437:    92.2122   -5.8186   -0.0702    2.3930   -0.9375    0.1380    3.1574    0.3746    0.3921    0.1570    1.4739    0.0400    1.1342    0.0718</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 438:    92.2114   -5.8187   -0.0703    2.3928   -0.9376    0.1380    3.1583    0.3745    0.3918    0.1569    1.4740    0.0400    1.1346    0.0718</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 439:    92.2104   -5.8181   -0.0705    2.3925   -0.9377    0.1381    3.1568    0.3744    0.3914    0.1568    1.4743    0.0400    1.1352    0.0718</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 440:    92.2095   -5.8178   -0.0706    2.3923   -0.9377    0.1381    3.1555    0.3743    0.3910    0.1566    1.4745    0.0400    1.1349    0.0718</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 441:    92.2088   -5.8176   -0.0707    2.3923   -0.9378    0.1381    3.1559    0.3742    0.3907    0.1565    1.4748    0.0400    1.1349    0.0718</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 442:    92.2081   -5.8172   -0.0708    2.3921   -0.9379    0.1383    3.1539    0.3741    0.3903    0.1564    1.4754    0.0400    1.1350    0.0717</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 443:    92.2074   -5.8171   -0.0709    2.3920   -0.9380    0.1387    3.1526    0.3740    0.3901    0.1563    1.4756    0.0400    1.1349    0.0717</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 444:    92.2068   -5.8175   -0.0711    2.3918   -0.9380    0.1390    3.1533    0.3739    0.3898    0.1562    1.4758    0.0400    1.1353    0.0717</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 445:    92.2061   -5.8180   -0.0712    2.3915   -0.9382    0.1394    3.1529    0.3737    0.3896    0.1562    1.4758    0.0400    1.1355    0.0717</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 446:    92.2054   -5.8177   -0.0714    2.3912   -0.9384    0.1398    3.1496    0.3735    0.3894    0.1562    1.4760    0.0399    1.1367    0.0716</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 447:    92.2051   -5.8180   -0.0716    2.3910   -0.9385    0.1400    3.1484    0.3734    0.3891    0.1563    1.4762    0.0399    1.1370    0.0715</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 448:    92.2053   -5.8189   -0.0717    2.3909   -0.9387    0.1405    3.1499    0.3732    0.3889    0.1563    1.4764    0.0399    1.1377    0.0715</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 449:    92.2054   -5.8195   -0.0718    2.3908   -0.9388    0.1411    3.1497    0.3730    0.3887    0.1562    1.4768    0.0399    1.1382    0.0715</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 450:    92.2054   -5.8205   -0.0719    2.3906   -0.9390    0.1417    3.1528    0.3728    0.3885    0.1562    1.4769    0.0399    1.1378    0.0715</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 451:    92.2053   -5.8213   -0.0720    2.3905   -0.9391    0.1423    3.1544    0.3725    0.3883    0.1561    1.4774    0.0399    1.1379    0.0715</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 452:    92.2054   -5.8218   -0.0720    2.3905   -0.9393    0.1426    3.1541    0.3722    0.3882    0.1560    1.4779    0.0398    1.1378    0.0715</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 453:    92.2054   -5.8217   -0.0721    2.3903   -0.9395    0.1428    3.1530    0.3721    0.3880    0.1559    1.4785    0.0398    1.1378    0.0715</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 454:    92.2053   -5.8214   -0.0722    2.3901   -0.9395    0.1431    3.1508    0.3720    0.3878    0.1559    1.4786    0.0398    1.1375    0.0715</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 455:    92.2052   -5.8220   -0.0724    2.3898   -0.9396    0.1433    3.1519    0.3720    0.3875    0.1559    1.4791    0.0398    1.1379    0.0715</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 456:    92.2053   -5.8230   -0.0727    2.3894   -0.9397    0.1434    3.1544    0.3720    0.3873    0.1560    1.4793    0.0398    1.1384    0.0714</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 457:    92.2053   -5.8240   -0.0729    2.3889   -0.9398    0.1437    3.1568    0.3720    0.3869    0.1559    1.4793    0.0398    1.1393    0.0714</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 458:    92.2051   -5.8245   -0.0731    2.3885   -0.9399    0.1440    3.1571    0.3721    0.3865    0.1558    1.4797    0.0397    1.1404    0.0713</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 459:    92.2045   -5.8246   -0.0732    2.3882   -0.9402    0.1442    3.1562    0.3721    0.3862    0.1558    1.4801    0.0397    1.1407    0.0713</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 460:    92.2040   -5.8244   -0.0734    2.3878   -0.9403    0.1442    3.1536    0.3722    0.3859    0.1558    1.4806    0.0397    1.1406    0.0713</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 461:    92.2030   -5.8245   -0.0736    2.3874   -0.9404    0.1444    3.1517    0.3722    0.3856    0.1557    1.4811    0.0397    1.1412    0.0713</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 462:    92.2022   -5.8253   -0.0738    2.3870   -0.9405    0.1445    3.1531    0.3723    0.3853    0.1556    1.4817    0.0396    1.1425    0.0712</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 463:    92.2014   -5.8260   -0.0740    2.3866   -0.9405    0.1449    3.1545    0.3724    0.3849    0.1556    1.4823    0.0396    1.1441    0.0711</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 464:    92.2008   -5.8257   -0.0742    2.3862   -0.9405    0.1453    3.1522    0.3726    0.3845    0.1555    1.4828    0.0396    1.1451    0.0711</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 465:    92.2002   -5.8256   -0.0744    2.3859   -0.9404    0.1458    3.1511    0.3727    0.3842    0.1555    1.4830    0.0396    1.1459    0.0710</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 466:    92.1997   -5.8256   -0.0747    2.3855   -0.9403    0.1463    3.1516    0.3728    0.3839    0.1555    1.4834    0.0396    1.1476    0.0709</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 467:    92.1993   -5.8258   -0.0749    2.3850   -0.9404    0.1468    3.1521    0.3730    0.3836    0.1555    1.4835    0.0395    1.1490    0.0708</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 468:    92.1990   -5.8259   -0.0752    2.3846   -0.9404    0.1473    3.1546    0.3731    0.3834    0.1555    1.4837    0.0395    1.1500    0.0708</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 469:    92.1987   -5.8263   -0.0753    2.3844   -0.9403    0.1479    3.1598    0.3731    0.3831    0.1555    1.4839    0.0395    1.1504    0.0707</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 470:    92.1987   -5.8267   -0.0755    2.3841   -0.9403    0.1482    3.1611    0.3730    0.3829    0.1555    1.4839    0.0395    1.1496    0.0708</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 471:    92.1987   -5.8269   -0.0756    2.3839   -0.9404    0.1482    3.1627    0.3730    0.3826    0.1555    1.4839    0.0395    1.1492    0.0708</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 472:    92.1983   -5.8269   -0.0758    2.3838   -0.9403    0.1480    3.1618    0.3729    0.3823    0.1555    1.4839    0.0395    1.1493    0.0708</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 473:    92.1980   -5.8264   -0.0760    2.3836   -0.9402    0.1478    3.1602    0.3728    0.3820    0.1554    1.4839    0.0395    1.1489    0.0708</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 474:    92.1976   -5.8256   -0.0762    2.3834   -0.9402    0.1475    3.1565    0.3727    0.3818    0.1554    1.4842    0.0395    1.1487    0.0708</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 475:    92.1971   -5.8252   -0.0763    2.3832   -0.9402    0.1473    3.1543    0.3726    0.3816    0.1553    1.4845    0.0395    1.1487    0.0707</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 476:    92.1965   -5.8250   -0.0765    2.3830   -0.9402    0.1469    3.1523    0.3725    0.3814    0.1552    1.4846    0.0395    1.1484    0.0707</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 477:    92.1960   -5.8242   -0.0766    2.3827   -0.9402    0.1465    3.1483    0.3724    0.3811    0.1552    1.4849    0.0395    1.1483    0.0708</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 478:    92.1955   -5.8236   -0.0767    2.3826   -0.9402    0.1463    3.1447    0.3722    0.3808    0.1553    1.4854    0.0395    1.1481    0.0708</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 479:    92.1952   -5.8232   -0.0769    2.3824   -0.9401    0.1462    3.1421    0.3722    0.3805    0.1554    1.4857    0.0395    1.1478    0.0708</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 480:    92.1948   -5.8235   -0.0770    2.3822   -0.9400    0.1461    3.1426    0.3721    0.3803    0.1554    1.4862    0.0395    1.1478    0.0708</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 481:    92.1947   -5.8240   -0.0772    2.3820   -0.9399    0.1459    3.1455    0.3721    0.3801    0.1554    1.4868    0.0395    1.1483    0.0708</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 482:    92.1948   -5.8244   -0.0774    2.3817   -0.9399    0.1456    3.1476    0.3720    0.3799    0.1553    1.4873    0.0395    1.1488    0.0708</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 483:    92.1944   -5.8247   -0.0776    2.3815   -0.9397    0.1455    3.1487    0.3719    0.3797    0.1553    1.4876    0.0394    1.1494    0.0708</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 484:    92.1941   -5.8249   -0.0778    2.3811   -0.9396    0.1454    3.1493    0.3719    0.3795    0.1554    1.4879    0.0394    1.1501    0.0707</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 485:    92.1940   -5.8252   -0.0780    2.3809   -0.9396    0.1453    3.1501    0.3718    0.3793    0.1554    1.4881    0.0394    1.1503    0.0707</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 486:    92.1936   -5.8249   -0.0781    2.3807   -0.9395    0.1453    3.1486    0.3717    0.3792    0.1554    1.4884    0.0394    1.1508    0.0707</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 487:    92.1935   -5.8248   -0.0783    2.3804   -0.9394    0.1453    3.1485    0.3716    0.3791    0.1553    1.4887    0.0393    1.1507    0.0707</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 488:    92.1932   -5.8246   -0.0785    2.3800   -0.9394    0.1454    3.1478    0.3715    0.3788    0.1552    1.4892    0.0393    1.1510    0.0707</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 489:    92.1931   -5.8241   -0.0787    2.3796   -0.9394    0.1455    3.1468    0.3715    0.3787    0.1551    1.4895    0.0393    1.1514    0.0706</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 490:    92.1934   -5.8242   -0.0790    2.3793   -0.9393    0.1456    3.1478    0.3715    0.3786    0.1551    1.4898    0.0393    1.1528    0.0706</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 491:    92.1936   -5.8241   -0.0792    2.3788   -0.9392    0.1455    3.1472    0.3715    0.3785    0.1551    1.4900    0.0393    1.1533    0.0706</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 492:    92.1940   -5.8234   -0.0794    2.3783   -0.9391    0.1455    3.1449    0.3714    0.3783    0.1551    1.4903    0.0392    1.1538    0.0705</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 493:    92.1943   -5.8230   -0.0797    2.3779   -0.9390    0.1455    3.1426    0.3714    0.3783    0.1552    1.4907    0.0392    1.1541    0.0705</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 494:    92.1946   -5.8226   -0.0799    2.3774   -0.9390    0.1458    3.1405    0.3713    0.3782    0.1551    1.4911    0.0392    1.1541    0.0706</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 495:    92.1948   -5.8219   -0.0802    2.3770   -0.9391    0.1459    3.1366    0.3712    0.3782    0.1551    1.4916    0.0392    1.1543    0.0706</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 496:    92.1948   -5.8213   -0.0804    2.3766   -0.9392    0.1460    3.1331    0.3711    0.3781    0.1552    1.4920    0.0392    1.1556    0.0705</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 497:    92.1949   -5.8213   -0.0806    2.3762   -0.9393    0.1462    3.1326    0.3711    0.3780    0.1553    1.4923    0.0391    1.1564    0.0705</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 498:    92.1950   -5.8215   -0.0808    2.3758   -0.9394    0.1462    3.1331    0.3710    0.3779    0.1553    1.4926    0.0391    1.1568    0.0705</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 499:    92.1953   -5.8219   -0.0810    2.3754   -0.9395    0.1461    3.1343    0.3709    0.3778    0.1554    1.4929    0.0391    1.1567    0.0705</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 500:    92.1957   -5.8232   -0.0812    2.3751   -0.9395    0.1459    3.1411    0.3709    0.3776    0.1554    1.4931    0.0391    1.1575    0.0705</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating covariance matrix</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling EBE model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Needed Covariates:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "CMT"</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating residuals/tables</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-in"><span class="va">f_nlmixr_fomc_sfo_focei_obs_tc</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr</a></span><span class="op">(</span><span class="va">f_mmkin_tc</span><span class="op">[</span><span class="st">"FOMC-SFO"</span>, <span class="op">]</span>, est <span class="op">=</span> <span class="st">"focei"</span>,</span>
-<span class="r-in">  error_model <span class="op">=</span> <span class="st">"obs_tc"</span><span class="op">)</span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate jacobian</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate sensitivities</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate ∂(f)/∂(η)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate ∂(R²)/∂(η)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in inner model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in inner model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in EBE model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in EBE model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling inner model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in FD model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in FD model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling EBE model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling events FD model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Needed Covariates:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "CMT"</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="font-weight: bold;">Key:</span> U: Unscaled Parameters; X: Back-transformed parameters; G: Gill difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> F: Forward difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> C: Central difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> M: Mixed forward and central difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Unscaled parameters for Omegas=chol(solve(omega));</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Diagonals are transformed, as specified by foceiControl(diagXform=)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |-----+---------------+-----------+-----------+-----------+-----------|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    #| Objective Fun |  parent_0 |  log_k_A1 |f_parent_qlogis | log_alpha |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  log_beta |sigma_low_parent |rsd_high_parent |sigma_low_A1 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|rsd_high_A1 |        o1 |        o2 |        o3 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|        o4 |        o5 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    1</span>|     500.20030 |     1.000 |    -1.000 |   -0.9113 |   -0.8944 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8454 |   -0.8678 |   -0.8916 |   -0.8678 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8916 |   -0.8767 |   -0.8743 |   -0.8675 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8704 |   -0.8704 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      500.2003 |     93.00 |    -5.300 |   -0.9400 |   -0.1100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.200 |   0.03000 |     1.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03000 |    0.7598 |    0.8758 |     1.214 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.068 |     1.071 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      500.2003</span> |     93.00 |  0.004992 |    0.2809 |    0.8958 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.974 |     1.200 |   0.03000 |     1.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03000 |    0.7598 |    0.8758 |     1.214 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.068 |     1.071 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    G|    Gill Diff. |     48.88 |     2.383 |    0.1231 |    0.1986 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1571 |    -68.85 |    -20.11 |     3.616 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.292 |    0.6250 |     11.41 |    -12.48 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -9.903 |    -10.91 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    2</span>|     2737.7532 |    0.4556 |    -1.027 |   -0.9127 |   -0.8966 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8471 |   -0.1009 |   -0.6676 |   -0.9080 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8660 |   -0.8837 |    -1.001 |   -0.7285 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7601 |   -0.7489 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     2737.7532 |     42.37 |    -5.327 |   -0.9413 |   -0.1122 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.298 |     1.660 |   0.03336 |     1.176 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03038 |    0.7545 |    0.7645 |     1.383 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.185 |     1.201 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     2737.7532</span> |     42.37 |  0.004861 |    0.2806 |    0.8939 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.957 |     1.660 |   0.03336 |     1.176 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03038 |    0.7545 |    0.7645 |     1.383 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.185 |     1.201 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    3</span>|     513.07755 |    0.9456 |    -1.003 |   -0.9114 |   -0.8946 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8455 |   -0.7911 |   -0.8692 |   -0.8718 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8890 |   -0.8774 |   -0.8871 |   -0.8536 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8594 |   -0.8582 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     513.07755 |     87.94 |    -5.303 |   -0.9401 |   -0.1102 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.246 |   0.03034 |     1.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03004 |    0.7593 |    0.8646 |     1.231 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.079 |     1.084 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     513.07755</span> |     87.94 |  0.004978 |    0.2809 |    0.8956 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.972 |     1.246 |   0.03034 |     1.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03004 |    0.7593 |    0.8646 |     1.231 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.079 |     1.084 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    4</span>|     499.28604 |    0.9888 |    -1.001 |   -0.9113 |   -0.8945 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8454 |   -0.8520 |   -0.8870 |   -0.8686 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8910 |   -0.8769 |   -0.8770 |   -0.8646 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8682 |   -0.8679 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     499.28604 |     91.96 |    -5.301 |   -0.9400 |   -0.1100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.209 |   0.03007 |     1.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03001 |    0.7597 |    0.8735 |     1.218 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.070 |     1.074 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     499.28604</span> |     91.96 |  0.004989 |    0.2809 |    0.8958 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.974 |     1.209 |   0.03007 |     1.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03001 |    0.7597 |    0.8735 |     1.218 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.070 |     1.074 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -111.5 |     2.236 |   -0.2057 |    0.1035 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1971 |    -66.17 |    -21.17 |     4.107 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.586 |     1.583 |     8.991 |    -11.70 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -9.625 |    -10.49 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    5</span>|     497.76004 |     1.001 |    -1.001 |   -0.9113 |   -0.8945 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8454 |   -0.8366 |   -0.8822 |   -0.8695 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8906 |   -0.8771 |   -0.8792 |   -0.8619 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8660 |   -0.8654 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     497.76004 |     93.05 |    -5.301 |   -0.9400 |   -0.1101 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.219 |   0.03014 |     1.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03001 |    0.7595 |    0.8715 |     1.221 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.072 |     1.076 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     497.76004</span> |     93.05 |  0.004986 |    0.2809 |    0.8958 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.974 |     1.219 |   0.03014 |     1.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03001 |    0.7595 |    0.8715 |     1.221 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.072 |     1.076 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     56.91 |     2.356 |    0.1468 |    0.2109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1761 |    -64.61 |    -18.67 |     3.291 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.680 |     1.108 |     8.338 |    -11.70 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -9.518 |    -10.47 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    6</span>|     496.73820 |    0.9899 |    -1.002 |   -0.9113 |   -0.8945 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8454 |   -0.8205 |   -0.8775 |   -0.8703 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8902 |   -0.8774 |   -0.8813 |   -0.8590 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8636 |   -0.8628 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      496.7382 |     92.06 |    -5.302 |   -0.9400 |   -0.1101 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.228 |   0.03021 |     1.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03002 |    0.7593 |    0.8696 |     1.225 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.075 |     1.079 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      496.7382</span> |     92.06 |  0.004983 |    0.2809 |    0.8957 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.974 |     1.228 |   0.03021 |     1.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03002 |    0.7593 |    0.8696 |     1.225 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.075 |     1.079 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -94.52 |     2.222 |   -0.1721 |    0.1131 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1712 |    -62.76 |    -19.55 |     3.974 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.718 |     1.304 |     7.360 |    -11.47 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -9.402 |    -10.21 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    7</span>|     495.33979 |     1.001 |    -1.002 |   -0.9113 |   -0.8946 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8454 |   -0.8044 |   -0.8726 |   -0.8713 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8897 |   -0.8777 |   -0.8834 |   -0.8560 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8612 |   -0.8602 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     495.33979 |     93.05 |    -5.302 |   -0.9400 |   -0.1102 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.238 |   0.03028 |     1.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03003 |    0.7591 |    0.8679 |     1.228 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.077 |     1.082 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     495.33979</span> |     93.05 |  0.004981 |    0.2809 |    0.8957 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.974 |     1.238 |   0.03028 |     1.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03003 |    0.7591 |    0.8679 |     1.228 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.077 |     1.082 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     56.11 |     2.327 |    0.1520 |    0.2091 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1742 |    -61.13 |    -17.23 |     3.435 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.620 |     1.132 |     9.399 |    -11.44 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -9.328 |    -10.19 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    8</span>|     494.31617 |    0.9904 |    -1.003 |   -0.9113 |   -0.8946 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8455 |   -0.7881 |   -0.8680 |   -0.8722 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8893 |   -0.8780 |   -0.8859 |   -0.8530 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8587 |   -0.8575 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     494.31617 |     92.11 |    -5.303 |   -0.9400 |   -0.1102 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.248 |   0.03035 |     1.197 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03003 |    0.7589 |    0.8657 |     1.232 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.080 |     1.085 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     494.31617</span> |     92.11 |  0.004977 |    0.2809 |    0.8956 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.973 |     1.248 |   0.03035 |     1.197 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03003 |    0.7589 |    0.8657 |     1.232 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.080 |     1.085 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -86.81 |     2.198 |   -0.1566 |    0.1238 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1638 |    -59.37 |    -18.02 |     4.057 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.591 |     1.325 |     8.479 |    -11.19 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -9.201 |    -9.937 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    9</span>|     493.00423 |     1.001 |    -1.003 |   -0.9113 |   -0.8947 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8454 |   -0.7718 |   -0.8631 |   -0.8732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8888 |   -0.8783 |   -0.8883 |   -0.8499 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8562 |   -0.8548 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     493.00423 |     93.05 |    -5.303 |   -0.9400 |   -0.1103 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.258 |   0.03043 |     1.197 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03004 |    0.7586 |    0.8635 |     1.236 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.083 |     1.088 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     493.00423</span> |     93.05 |  0.004974 |    0.2809 |    0.8956 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.974 |     1.258 |   0.03043 |     1.197 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03004 |    0.7586 |    0.8635 |     1.236 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.083 |     1.088 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     54.84 |     2.286 |    0.1404 |    0.2142 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1628 |    -57.96 |    -15.94 |     3.372 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.642 |     1.184 |     9.036 |    -11.17 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -9.110 |    -9.901 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   10</span>|     491.99601 |    0.9908 |    -1.004 |   -0.9114 |   -0.8947 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8455 |   -0.7552 |   -0.8585 |   -0.8742 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8884 |   -0.8786 |   -0.8910 |   -0.8467 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8536 |   -0.8520 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     491.99601 |     92.15 |    -5.304 |   -0.9401 |   -0.1103 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.268 |   0.03050 |     1.196 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03005 |    0.7584 |    0.8612 |     1.239 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.085 |     1.091 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     491.99601</span> |     92.15 |  0.004971 |    0.2809 |    0.8956 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.973 |     1.268 |   0.03050 |     1.196 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03005 |    0.7584 |    0.8612 |     1.239 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.085 |     1.091 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -80.67 |     2.171 |   -0.1367 |    0.1256 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1610 |    -56.13 |    -16.61 |     4.047 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.607 |     1.226 |     8.142 |    -10.92 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -8.982 |    -9.644 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   11</span>|     490.76421 |     1.000 |    -1.005 |   -0.9113 |   -0.8948 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8455 |   -0.7388 |   -0.8538 |   -0.8754 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8879 |   -0.8790 |   -0.8935 |   -0.8435 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8510 |   -0.8492 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     490.76421 |     93.04 |    -5.305 |   -0.9401 |   -0.1104 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.277 |   0.03057 |     1.195 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03006 |    0.7581 |    0.8590 |     1.243 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.088 |     1.094 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     490.76421</span> |     93.04 |  0.004968 |    0.2809 |    0.8955 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.973 |     1.277 |   0.03057 |     1.195 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03006 |    0.7581 |    0.8590 |     1.243 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.088 |     1.094 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     54.34 |     2.256 |    0.1715 |    0.2224 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1653 |    -54.87 |    -14.72 |     3.299 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.777 |     1.023 |     8.651 |    -10.90 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -8.887 |    -9.594 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   12</span>|     489.76286 |    0.9913 |    -1.005 |   -0.9114 |   -0.8948 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8455 |   -0.7220 |   -0.8492 |   -0.8764 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8873 |   -0.8793 |   -0.8962 |   -0.8402 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8483 |   -0.8462 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     489.76286 |     92.19 |    -5.305 |   -0.9401 |   -0.1104 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.287 |   0.03063 |     1.195 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03006 |    0.7579 |    0.8566 |     1.247 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.091 |     1.097 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     489.76286</span> |     92.19 |  0.004965 |    0.2809 |    0.8955 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.973 |     1.287 |   0.03063 |     1.195 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03006 |    0.7579 |    0.8566 |     1.247 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.091 |     1.097 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -73.18 |     2.145 |   -0.1190 |    0.1261 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1580 |    -53.08 |    -15.27 |     4.015 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.639 |     1.110 |     7.813 |    -10.65 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -8.743 |    -9.338 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   13</span>|     488.61493 |     1.000 |    -1.006 |   -0.9114 |   -0.8949 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8455 |   -0.7053 |   -0.8446 |   -0.8776 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8867 |   -0.8796 |   -0.8989 |   -0.8368 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8455 |   -0.8433 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     488.61493 |     93.04 |    -5.306 |   -0.9401 |   -0.1105 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.297 |   0.03070 |     1.194 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03007 |    0.7577 |    0.8543 |     1.252 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.094 |     1.100 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     488.61493</span> |     93.04 |  0.004961 |    0.2809 |    0.8954 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.973 |     1.297 |   0.03070 |     1.194 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03007 |    0.7577 |    0.8543 |     1.252 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.094 |     1.100 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     54.41 |     2.230 |    0.1889 |    0.2376 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1637 |    -52.01 |    -13.54 |     3.223 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.937 |    0.8847 |     10.07 |    -10.59 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -8.650 |    -9.282 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   14</span>|     487.61806 |    0.9919 |    -1.007 |   -0.9114 |   -0.8949 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8455 |   -0.6884 |   -0.8402 |   -0.8788 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8861 |   -0.8798 |   -0.9022 |   -0.8333 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8427 |   -0.8402 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     487.61806 |     92.24 |    -5.307 |   -0.9401 |   -0.1105 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.308 |   0.03077 |     1.193 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03008 |    0.7575 |    0.8514 |     1.256 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.097 |     1.103 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     487.61806</span> |     92.24 |  0.004958 |    0.2809 |    0.8953 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.973 |     1.308 |   0.03077 |     1.193 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03008 |    0.7575 |    0.8514 |     1.256 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.097 |     1.103 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -64.97 |     2.116 |  -0.09941 |    0.1372 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1475 |    -50.35 |    -14.02 |     3.916 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.790 |    0.8979 |     9.163 |    -10.32 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -8.505 |    -9.022 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   15</span>|     486.55968 |     1.001 |    -1.008 |   -0.9114 |   -0.8950 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8455 |   -0.6717 |   -0.8358 |   -0.8800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8854 |   -0.8801 |   -0.9058 |   -0.8297 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8398 |   -0.8372 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     486.55968 |     93.05 |    -5.308 |   -0.9401 |   -0.1106 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.318 |   0.03084 |     1.193 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03009 |    0.7573 |    0.8482 |     1.260 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.100 |     1.107 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     486.55968</span> |     93.05 |  0.004954 |    0.2809 |    0.8953 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.973 |     1.318 |   0.03084 |     1.193 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03009 |    0.7573 |    0.8482 |     1.260 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.100 |     1.107 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     55.08 |     2.188 |    0.1916 |    0.2341 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1660 |    -48.95 |    -12.27 |     3.347 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.726 |    0.9589 |     6.446 |    -10.30 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -8.390 |    -8.950 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   16</span>|     485.60262 |    0.9920 |    -1.008 |   -0.9115 |   -0.8951 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8455 |   -0.6547 |   -0.8316 |   -0.8813 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8847 |   -0.8804 |   -0.9083 |   -0.8260 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8367 |   -0.8340 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     485.60262 |     92.26 |    -5.308 |   -0.9402 |   -0.1107 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.328 |   0.03090 |     1.192 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03010 |    0.7571 |    0.8460 |     1.265 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.104 |     1.110 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     485.60262</span> |     92.26 |  0.004950 |    0.2809 |    0.8952 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.973 |     1.328 |   0.03090 |     1.192 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03010 |    0.7571 |    0.8460 |     1.265 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.104 |     1.110 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -62.47 |     2.078 |  -0.09882 |    0.1294 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1587 |    -47.42 |    -12.81 |     3.926 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.683 |    0.9415 |     5.611 |    -10.02 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -8.237 |    -8.679 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   17</span>|     484.58535 |     1.000 |    -1.009 |   -0.9115 |   -0.8952 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8455 |   -0.6375 |   -0.8274 |   -0.8829 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8840 |   -0.8807 |   -0.9099 |   -0.8222 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8336 |   -0.8307 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     484.58535 |     93.02 |    -5.309 |   -0.9402 |   -0.1107 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.338 |   0.03096 |     1.191 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03011 |    0.7568 |    0.8446 |     1.269 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.107 |     1.114 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     484.58535</span> |     93.02 |  0.004946 |    0.2809 |    0.8952 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.973 |     1.338 |   0.03096 |     1.191 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03011 |    0.7568 |    0.8446 |     1.269 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.107 |     1.114 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     50.15 |     2.155 |    0.1960 |    0.2225 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1509 |    -46.14 |    -11.19 |     3.405 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.652 |     1.035 |     7.536 |    -9.951 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -8.121 |    -8.609 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   18</span>|     483.66860 |    0.9923 |    -1.010 |   -0.9115 |   -0.8952 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8455 |   -0.6203 |   -0.8235 |   -0.8844 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8833 |   -0.8810 |   -0.9123 |   -0.8182 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8303 |   -0.8273 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      483.6686 |     92.29 |    -5.310 |   -0.9402 |   -0.1108 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.348 |   0.03102 |     1.190 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03012 |    0.7565 |    0.8425 |     1.274 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.110 |     1.117 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      483.6686</span> |     92.29 |  0.004942 |    0.2809 |    0.8951 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.973 |     1.348 |   0.03102 |     1.190 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03012 |    0.7565 |    0.8425 |     1.274 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.110 |     1.117 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -57.62 |     2.059 |  -0.07549 |    0.1395 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1501 |    -44.74 |    -11.66 |     3.913 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.664 |    0.8845 |     6.781 |    -9.703 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -7.961 |    -8.325 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   19</span>|     482.71012 |     1.000 |    -1.011 |   -0.9115 |   -0.8953 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8455 |   -0.6032 |   -0.8198 |   -0.8861 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8825 |   -0.8814 |   -0.9153 |   -0.8141 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8270 |   -0.8239 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     482.71012 |     93.02 |    -5.311 |   -0.9402 |   -0.1109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.359 |   0.03108 |     1.189 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03014 |    0.7563 |    0.8399 |     1.279 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.114 |     1.121 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     482.71012</span> |     93.02 |  0.004937 |    0.2809 |    0.8950 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.973 |     1.359 |   0.03108 |     1.189 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03014 |    0.7563 |    0.8399 |     1.279 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.114 |     1.121 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     48.98 |     2.122 |    0.2043 |    0.2315 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1449 |    -43.47 |    -10.15 |     3.365 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.703 |    0.9582 |     7.160 |    -9.600 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -7.838 |    -8.245 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   20</span>|     481.83107 |    0.9926 |    -1.012 |   -0.9116 |   -0.8954 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8455 |   -0.5860 |   -0.8164 |   -0.8879 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8817 |   -0.8818 |   -0.9184 |   -0.8098 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8235 |   -0.8203 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     481.83107 |     92.31 |    -5.312 |   -0.9403 |   -0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.369 |   0.03113 |     1.188 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03015 |    0.7560 |    0.8372 |     1.284 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.118 |     1.125 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     481.83107</span> |     92.31 |  0.004932 |    0.2808 |    0.8949 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.973 |     1.369 |   0.03113 |     1.188 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03015 |    0.7560 |    0.8372 |     1.284 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.118 |     1.125 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -54.27 |     2.027 |  -0.06740 |    0.1414 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1465 |    -41.84 |    -10.48 |     3.798 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.730 |    0.8044 |     6.401 |    -9.335 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -7.662 |    -7.960 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   21</span>|     480.92878 |     1.000 |    -1.013 |   -0.9117 |   -0.8955 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8455 |   -0.5689 |   -0.8133 |   -0.8899 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8807 |   -0.8821 |   -0.9215 |   -0.8053 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8199 |   -0.8166 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.92878 |     93.01 |    -5.313 |   -0.9403 |   -0.1111 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.379 |   0.03117 |     1.187 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03016 |    0.7557 |    0.8345 |     1.290 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.122 |     1.129 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.92878</span> |     93.01 |  0.004927 |    0.2808 |    0.8948 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.973 |     1.379 |   0.03117 |     1.187 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03016 |    0.7557 |    0.8345 |     1.290 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.122 |     1.129 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     47.18 |     2.077 |    0.1996 |    0.2200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1318 |    -41.07 |    -9.197 |     3.259 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.748 |    0.9101 |     6.743 |    -9.229 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -7.511 |    -7.855 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   22</span>|     480.07574 |    0.9930 |    -1.014 |   -0.9117 |   -0.8956 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8455 |   -0.5517 |   -0.8106 |   -0.8919 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8795 |   -0.8825 |   -0.9247 |   -0.8007 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8161 |   -0.8129 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.07574 |     92.35 |    -5.314 |   -0.9404 |   -0.1112 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.390 |   0.03121 |     1.186 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03018 |    0.7555 |    0.8317 |     1.295 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.126 |     1.133 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.07574</span> |     92.35 |  0.004921 |    0.2808 |    0.8947 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.973 |     1.390 |   0.03121 |     1.186 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03018 |    0.7555 |    0.8317 |     1.295 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.126 |     1.133 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -48.22 |     1.995 |  -0.04658 |    0.1415 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1316 |    -39.56 |    -9.522 |     3.639 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.878 |    0.6154 |     4.556 |    -8.937 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -7.320 |    -7.563 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   23</span>|     479.25211 |     1.000 |    -1.015 |   -0.9118 |   -0.8957 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8454 |   -0.5343 |   -0.8082 |   -0.8940 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8781 |   -0.8826 |   -0.9261 |   -0.7958 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8122 |   -0.8090 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.25211 |     93.02 |    -5.315 |   -0.9405 |   -0.1113 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.400 |   0.03125 |     1.184 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03020 |    0.7553 |    0.8305 |     1.301 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.130 |     1.137 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.25211</span> |     93.02 |  0.004915 |    0.2808 |    0.8946 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.974 |     1.400 |   0.03125 |     1.184 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03020 |    0.7553 |    0.8305 |     1.301 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.130 |     1.137 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     48.39 |     2.048 |    0.2189 |    0.2175 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1393 |    -38.80 |    -8.286 |     3.169 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.757 |    0.9021 |     6.456 |    -8.802 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -7.165 |    -7.452 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   24</span>|     478.42395 |    0.9935 |    -1.017 |   -0.9119 |   -0.8959 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8454 |   -0.5169 |   -0.8064 |   -0.8963 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8766 |   -0.8829 |   -0.9277 |   -0.7908 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8081 |   -0.8051 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     478.42395 |     92.40 |    -5.317 |   -0.9406 |   -0.1115 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.411 |   0.03128 |     1.183 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03022 |    0.7552 |    0.8290 |     1.307 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.134 |     1.141 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     478.42395</span> |     92.40 |  0.004909 |    0.2808 |    0.8945 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.974 |     1.411 |   0.03128 |     1.183 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03022 |    0.7552 |    0.8290 |     1.307 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.134 |     1.141 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -41.65 |     1.977 |  -0.02866 |    0.1453 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1180 |    -37.67 |    -8.677 |     3.579 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.774 |    0.6894 |     5.855 |    -8.506 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -6.962 |    -7.153 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   25</span>|     477.65816 |     1.000 |    -1.018 |   -0.9120 |   -0.8960 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8454 |   -0.4997 |   -0.8051 |   -0.8988 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8750 |   -0.8832 |   -0.9313 |   -0.7859 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8042 |   -0.8013 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     477.65816 |     93.02 |    -5.318 |   -0.9406 |   -0.1116 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.421 |   0.03130 |     1.181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03025 |    0.7549 |    0.8259 |     1.313 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.138 |     1.145 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     477.65816</span> |     93.02 |  0.004901 |    0.2808 |    0.8944 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.974 |     1.421 |   0.03130 |     1.181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03025 |    0.7549 |    0.8259 |     1.313 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.138 |     1.145 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     47.50 |     2.013 |    0.2278 |    0.2145 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1415 |    -36.68 |    -7.460 |     3.039 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.807 |    0.8669 |     6.124 |    -8.373 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -6.814 |    -7.074 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   26</span>|     476.87668 |    0.9936 |    -1.020 |   -0.9121 |   -0.8962 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8454 |   -0.4825 |   -0.8047 |   -0.9014 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8732 |   -0.8836 |   -0.9351 |   -0.7808 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8001 |   -0.7975 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     476.87668 |     92.41 |    -5.320 |   -0.9408 |   -0.1117 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.431 |   0.03130 |     1.180 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03028 |    0.7546 |    0.8226 |     1.319 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.143 |     1.149 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     476.87668</span> |     92.41 |  0.004893 |    0.2807 |    0.8943 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.974 |     1.431 |   0.03130 |     1.180 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03028 |    0.7546 |    0.8226 |     1.319 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.143 |     1.149 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -40.64 |     1.935 |  -0.02164 |    0.1511 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1127 |    -35.59 |    -7.858 |     3.450 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.805 |    0.6731 |     3.945 |    -8.048 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -6.589 |    -6.756 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   27</span>|     476.16299 |     1.000 |    -1.022 |   -0.9123 |   -0.8963 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8454 |   -0.4651 |   -0.8047 |   -0.9041 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8711 |   -0.8840 |   -0.9355 |   -0.7757 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7959 |   -0.7936 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     476.16299 |     93.01 |    -5.322 |   -0.9409 |   -0.1119 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.442 |   0.03130 |     1.178 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03031 |    0.7543 |    0.8222 |     1.326 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.147 |     1.153 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     476.16299</span> |     93.01 |  0.004885 |    0.2807 |    0.8941 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.974 |     1.442 |   0.03130 |     1.178 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03031 |    0.7543 |    0.8222 |     1.326 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.147 |     1.153 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     44.38 |     1.982 |    0.2331 |    0.2213 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1467 |    -34.64 |    -6.709 |     2.894 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.888 |    0.7760 |     4.358 |    -7.901 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -6.419 |    -6.620 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   28</span>|     475.47434 |    0.9938 |    -1.024 |   -0.9125 |   -0.8966 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8454 |   -0.4477 |   -0.8058 |   -0.9069 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8685 |   -0.8844 |   -0.9328 |   -0.7706 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7919 |   -0.7898 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     475.47434 |     92.43 |    -5.324 |   -0.9411 |   -0.1122 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.452 |   0.03129 |     1.177 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03035 |    0.7540 |    0.8246 |     1.332 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.151 |     1.157 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     475.47434</span> |     92.43 |  0.004875 |    0.2807 |    0.8939 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.974 |     1.452 |   0.03129 |     1.177 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03035 |    0.7540 |    0.8246 |     1.332 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.151 |     1.157 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -38.01 |     1.932 |  -0.02542 |    0.1483 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1147 |    -33.71 |    -7.114 |     3.216 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.879 |    0.6267 |     4.067 |    -7.573 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -6.213 |    -6.338 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   29</span>|     474.82575 |    0.9998 |    -1.026 |   -0.9126 |   -0.8968 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8454 |   -0.4303 |   -0.8075 |   -0.9096 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8656 |   -0.8846 |   -0.9310 |   -0.7657 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7878 |   -0.7862 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     474.82575 |     92.98 |    -5.326 |   -0.9413 |   -0.1124 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.462 |   0.03126 |     1.175 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03039 |    0.7538 |    0.8261 |     1.338 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.156 |     1.161 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     474.82575</span> |     92.98 |  0.004864 |    0.2806 |    0.8937 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.974 |     1.462 |   0.03126 |     1.175 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03039 |    0.7538 |    0.8261 |     1.338 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.156 |     1.161 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     40.83 |     1.980 |    0.2123 |    0.2125 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1324 |    -32.65 |    -6.032 |     2.979 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.922 |    0.7692 |     4.596 |    -7.426 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -6.058 |    -6.217 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   30</span>|     474.18202 |    0.9939 |    -1.028 |   -0.9128 |   -0.8971 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8454 |   -0.4131 |   -0.8106 |   -0.9130 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8620 |   -0.8851 |   -0.9307 |   -0.7608 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7838 |   -0.7828 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     474.18202 |     92.44 |    -5.328 |   -0.9414 |   -0.1127 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.473 |   0.03121 |     1.173 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03044 |    0.7535 |    0.8264 |     1.344 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.160 |     1.165 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     474.18202</span> |     92.44 |  0.004852 |    0.2806 |    0.8935 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.974 |     1.473 |   0.03121 |     1.173 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03044 |    0.7535 |    0.8264 |     1.344 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.160 |     1.165 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -37.33 |     1.920 |  -0.03157 |    0.1462 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1145 |    -31.92 |    -6.433 |     3.018 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.919 |    0.6114 |     5.640 |    -7.155 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -5.876 |    -5.958 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   31</span>|     473.55376 |    0.9995 |    -1.031 |   -0.9130 |   -0.8973 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8454 |   -0.3971 |   -0.8148 |   -0.9162 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8581 |   -0.8855 |   -0.9368 |   -0.7563 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7800 |   -0.7797 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     473.55376 |     92.95 |    -5.331 |   -0.9416 |   -0.1129 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.482 |   0.03115 |     1.171 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03050 |    0.7531 |    0.8211 |     1.349 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.164 |     1.168 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     473.55376</span> |     92.95 |  0.004838 |    0.2806 |    0.8932 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.974 |     1.482 |   0.03115 |     1.171 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03050 |    0.7531 |    0.8211 |     1.349 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.164 |     1.168 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   32</span>|     472.90182 |    0.9995 |    -1.035 |   -0.9132 |   -0.8977 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8453 |   -0.3799 |   -0.8225 |   -0.9204 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8527 |   -0.8861 |   -0.9447 |   -0.7510 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7757 |   -0.7763 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     472.90182 |     92.96 |    -5.335 |   -0.9418 |   -0.1133 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.493 |   0.03104 |     1.168 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03058 |    0.7527 |    0.8141 |     1.356 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.169 |     1.172 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     472.90182</span> |     92.96 |  0.004819 |    0.2805 |    0.8929 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.975 |     1.493 |   0.03104 |     1.168 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03058 |    0.7527 |    0.8141 |     1.356 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.169 |     1.172 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   33</span>|     469.90339 |    0.9997 |    -1.054 |   -0.9142 |   -0.8996 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8452 |   -0.2944 |   -0.8611 |   -0.9412 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8255 |   -0.8889 |   -0.9843 |   -0.7249 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7539 |   -0.7597 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     469.90339 |     92.98 |    -5.354 |   -0.9427 |   -0.1152 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.544 |   0.03046 |     1.156 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03099 |    0.7505 |    0.7794 |     1.387 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.192 |     1.190 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     469.90339</span> |     92.98 |  0.004727 |    0.2803 |    0.8912 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.976 |     1.544 |   0.03046 |     1.156 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03099 |    0.7505 |    0.7794 |     1.387 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.192 |     1.190 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   34</span>|     461.71523 |     1.001 |    -1.134 |   -0.9184 |   -0.9072 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8447 |   0.05742 |    -1.020 |    -1.027 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7136 |   -0.9005 |    -1.147 |   -0.6175 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6642 |   -0.6913 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     461.71523 |     93.05 |    -5.434 |   -0.9467 |   -0.1228 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.301 |     1.755 |   0.02808 |     1.105 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03267 |    0.7417 |    0.6368 |     1.518 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.288 |     1.263 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     461.71523</span> |     93.05 |  0.004366 |    0.2795 |    0.8844 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.981 |     1.755 |   0.02808 |     1.105 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03267 |    0.7417 |    0.6368 |     1.518 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.288 |     1.263 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     11.20 |     1.045 |     1.020 |    0.3495 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7923 |    -15.00 |   -0.3969 |    -1.315 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -3.031 |    0.2676 |    -6.780 |    -1.078 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|  -0.09081 |    -1.463 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   35</span>|     459.94653 |     1.009 |    -1.206 |   -0.9768 |   -0.9283 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8884 |    0.4547 |    -1.238 |   -0.9419 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5030 |   -0.9080 |   -0.7892 |   -0.6450 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7313 |   -0.6959 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     459.94653 |     93.85 |    -5.506 |    -1.002 |   -0.1439 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.257 |     1.993 |   0.02480 |     1.155 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03583 |    0.7361 |    0.9504 |     1.484 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.216 |     1.258 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     459.94653</span> |     93.85 |  0.004064 |    0.2686 |    0.8660 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.554 |     1.993 |   0.02480 |     1.155 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03583 |    0.7361 |    0.9504 |     1.484 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.216 |     1.258 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     117.0 |     1.389 |    -1.721 |   -0.1200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.004314 |    -5.763 |     2.093 |     3.426 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.378 |     2.600 |     15.14 |    -1.236 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -4.018 |    -1.529 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   36</span>|     458.11528 |     1.003 |    -1.307 |   -0.9477 |   -0.9422 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9430 |    0.6906 |    -1.630 |   -0.8850 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1864 |    -1.043 |   -0.8947 |   -0.8359 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7822 |   -0.7759 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.11528 |     93.30 |    -5.607 |   -0.9742 |   -0.1578 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.202 |     2.135 |   0.01893 |     1.190 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.04058 |    0.6334 |    0.8579 |     1.253 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.162 |     1.172 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.11528</span> |     93.30 |  0.003671 |    0.2740 |    0.8540 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.046 |     2.135 |   0.01893 |     1.190 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.04058 |    0.6334 |    0.8579 |     1.253 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.162 |     1.172 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.502 |    0.8367 |   -0.1865 |   -0.1361 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6555 |    -2.945 |     1.425 |     8.523 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6882 |    -2.700 |     7.982 |    -9.961 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -6.514 |    -5.474 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   37</span>|     454.42202 |     1.008 |    -1.411 |   -0.9501 |   -0.9563 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9900 |    0.8215 |    -2.099 |    -1.032 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1520 |    -1.049 |   -0.9274 |   -0.7533 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7158 |   -0.7241 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     454.42202 |     93.74 |    -5.711 |   -0.9765 |   -0.1719 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.155 |     2.214 |   0.01188 |     1.102 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.04565 |    0.6287 |    0.8293 |     1.353 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.233 |     1.228 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     454.42202</span> |     93.74 |  0.003311 |    0.2736 |    0.8421 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.631 |     2.214 |   0.01188 |     1.102 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.04565 |    0.6287 |    0.8293 |     1.353 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.233 |     1.228 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   38</span>|     451.48622 |     1.000 |    -1.659 |   -0.9551 |   -0.9914 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.111 |     1.029 |    -2.892 |    -1.353 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9897 |    -1.076 |   -0.9627 |   -0.6147 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.5957 |   -0.6379 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     451.48622 |     93.01 |    -5.959 |   -0.9812 |   -0.2070 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.035 |     2.338 | 5.960e-07 |    0.9088 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.05822 |    0.6083 |    0.7984 |     1.521 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.361 |     1.320 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     451.48622</span> |     93.01 |  0.002583 |    0.2727 |    0.8130 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     7.651 |     2.338 | 5.960e-07 |    0.9088 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.05822 |    0.6083 |    0.7984 |     1.521 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.361 |     1.320 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -68.34 |   0.03355 |   0.09905 |    -1.306 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -5.058 |    -1.204 |  -0.09883 |    -1.076 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.726 |    -4.145 |     7.646 |   -0.5743 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.003 |     2.613 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   39</span>|     453.25472 |     1.003 |    -2.027 |    -1.069 |   -0.8933 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6960 |     1.084 |    -2.892 |    -1.695 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.689 |   -0.4882 |    -1.206 |   -0.2814 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7545 |   -0.8496 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     453.25472 |     93.31 |    -6.327 |    -1.089 |   -0.1089 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.449 |     2.371 | 5.960e-07 |    0.7037 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.08370 |     1.055 |    0.5857 |     1.926 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.191 |     1.093 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     453.25472</span> |     93.31 |  0.001787 |    0.2519 |    0.8968 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     11.58 |     2.371 | 5.960e-07 |    0.7037 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.08370 |     1.055 |    0.5857 |     1.926 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.191 |     1.093 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   40</span>|     449.59369 |     1.000 |    -1.818 |    -1.010 |   -0.9508 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9364 |     1.052 |    -2.892 |    -1.498 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.730 |   -0.8175 |    -1.073 |   -0.4671 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6691 |   -0.7270 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     449.59369 |     93.04 |    -6.118 |    -1.033 |   -0.1664 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.209 |     2.352 | 5.960e-07 |    0.8216 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.06933 |    0.8048 |    0.7021 |     1.700 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.282 |     1.225 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     449.59369</span> |     93.04 |  0.002202 |    0.2625 |    0.8467 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.106 |     2.352 | 5.960e-07 |    0.8216 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.06933 |    0.8048 |    0.7021 |     1.700 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.282 |     1.225 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -128.1 |    0.1096 |    -3.510 |   -0.1773 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1823 |    -1.358 | -0.002263 |     1.313 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.294 |     5.530 |     5.100 |     4.831 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.292 |    -3.851 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   41</span>|     472.13387 |     1.020 |    -1.985 |   -0.7592 |   -0.7822 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2976 |     1.062 |    -2.892 |    -1.866 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     3.141 |   -0.7519 |    -1.136 |   -0.6405 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7706 |   -0.2153 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     472.13387 |     94.83 |    -6.285 |   -0.7970 |  0.002176 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.848 |     2.358 | 5.960e-07 |    0.6009 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.09049 |    0.8547 |    0.6467 |     1.490 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.174 |     1.773 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     472.13387</span> |     94.83 |  0.001864 |    0.3107 |     1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     17.25 |     2.358 | 5.960e-07 |    0.6009 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.09049 |    0.8547 |    0.6467 |     1.490 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.174 |     1.773 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   42</span>|     454.13083 |     1.030 |    -1.841 |   -0.9756 |   -0.9280 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8501 |     1.054 |    -2.892 |    -1.548 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.921 |   -0.8098 |    -1.082 |   -0.4916 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6825 |   -0.6571 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     454.13083 |     95.82 |    -6.141 |    -1.000 |   -0.1436 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.295 |     2.353 | 5.960e-07 |    0.7917 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.07219 |    0.8106 |    0.6936 |     1.671 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.268 |     1.299 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     454.13083</span> |     95.82 |  0.002153 |    0.2688 |    0.8663 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.927 |     2.353 | 5.960e-07 |    0.7917 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.07219 |    0.8106 |    0.6936 |     1.671 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.268 |     1.299 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   43</span>|     454.81886 |     1.032 |    -1.823 |    -1.002 |   -0.9459 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9179 |     1.053 |    -2.892 |    -1.509 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.771 |   -0.8169 |    -1.076 |   -0.4733 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6717 |   -0.7113 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     454.81886 |     95.94 |    -6.123 |    -1.025 |   -0.1614 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.227 |     2.352 | 5.960e-07 |    0.8150 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.06994 |    0.8052 |    0.6994 |     1.693 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.280 |     1.241 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     454.81886</span> |     95.94 |  0.002192 |    0.2640 |    0.8509 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.277 |     2.352 | 5.960e-07 |    0.8150 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.06994 |    0.8052 |    0.6994 |     1.693 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.280 |     1.241 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   44</span>|     449.57011 |     1.014 |    -1.818 |    -1.010 |   -0.9508 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9364 |     1.053 |    -2.892 |    -1.499 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.730 |   -0.8181 |    -1.073 |   -0.4676 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6690 |   -0.7267 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     449.57011 |     94.27 |    -6.118 |    -1.033 |   -0.1664 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.209 |     2.352 | 5.995e-07 |    0.8215 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.06933 |    0.8044 |    0.7016 |     1.700 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.283 |     1.225 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     449.57011</span> |     94.27 |  0.002202 |    0.2626 |    0.8467 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.106 |     2.352 | 5.995e-07 |    0.8215 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.06933 |    0.8044 |    0.7016 |     1.700 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.283 |     1.225 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     126.9 |    0.1582 |    -2.201 |   0.05331 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4695 |   -0.6265 |   0.01503 |    0.4241 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.337 |     5.854 |     6.688 |     4.536 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.410 |    -4.016 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   45</span>|     449.14922 |     1.007 |    -1.818 |    -1.010 |   -0.9508 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9364 |     1.053 |    -2.892 |    -1.499 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.730 |   -0.8183 |    -1.074 |   -0.4678 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6689 |   -0.7265 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     449.14922 |     93.66 |    -6.118 |    -1.033 |   -0.1664 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.209 |     2.352 | 5.960e-07 |    0.8215 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.06933 |    0.8042 |    0.7014 |     1.699 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.283 |     1.225 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     449.14922</span> |     93.66 |  0.002202 |    0.2626 |    0.8467 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.106 |     2.352 | 5.960e-07 |    0.8215 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.06933 |    0.8042 |    0.7014 |     1.699 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.283 |     1.225 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |   -0.4066 |    0.1367 |    -2.828 |  -0.06013 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1445 |   -0.8286 |   0.01677 |    0.9213 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.224 |     4.943 |     4.017 |     5.223 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.311 |    -3.922 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   46</span>|     449.08136 |     1.007 |    -1.818 |    -1.008 |   -0.9507 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9365 |     1.053 |    -2.892 |    -1.499 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.731 |   -0.8217 |    -1.076 |   -0.4714 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6680 |   -0.7238 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     449.08136 |     93.68 |    -6.118 |    -1.031 |   -0.1663 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.209 |     2.353 | 5.960e-07 |    0.8212 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.06934 |    0.8016 |    0.6990 |     1.695 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.284 |     1.228 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     449.08136</span> |     93.68 |  0.002202 |    0.2629 |    0.8468 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.105 |     2.353 | 5.960e-07 |    0.8212 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.06934 |    0.8016 |    0.6990 |     1.695 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.284 |     1.228 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   47</span>|     448.89872 |     1.008 |    -1.819 |    -1.002 |   -0.9506 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9368 |     1.055 |    -2.892 |    -1.501 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.734 |   -0.8317 |    -1.084 |   -0.4820 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6654 |   -0.7159 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     448.89872 |     93.76 |    -6.119 |    -1.025 |   -0.1662 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.209 |     2.354 | 5.960e-07 |    0.8200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.06938 |    0.7940 |    0.6918 |     1.682 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.286 |     1.237 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     448.89872</span> |     93.76 |  0.002202 |    0.2640 |    0.8469 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.102 |     2.354 | 5.960e-07 |    0.8200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.06938 |    0.7940 |    0.6918 |     1.682 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.286 |     1.237 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   48</span>|     448.46351 |     1.011 |    -1.820 |   -0.9792 |   -0.9501 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9380 |     1.062 |    -2.892 |    -1.509 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.744 |   -0.8718 |    -1.117 |   -0.5244 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6547 |   -0.6840 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     448.46351 |     94.07 |    -6.120 |    -1.004 |   -0.1657 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.207 |     2.358 | 5.960e-07 |    0.8155 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.06953 |    0.7635 |    0.6633 |     1.631 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.298 |     1.271 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     448.46351</span> |     94.07 |  0.002199 |    0.2682 |    0.8473 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.092 |     2.358 | 5.960e-07 |    0.8155 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.06953 |    0.7635 |    0.6633 |     1.631 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.298 |     1.271 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     75.37 |    0.2597 |   -0.1569 |   0.03608 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3501 |   -0.3601 |  -0.01324 |    0.5025 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.327 |     4.308 |    0.9122 |     2.456 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.5631 |    -1.887 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   49</span>|     448.12149 |     1.008 |    -1.833 |   -0.9950 |   -0.9378 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8937 |     1.062 |    -2.892 |    -1.543 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.827 |   -0.8899 |    -1.115 |   -0.5324 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6574 |   -0.6703 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     448.12149 |     93.73 |    -6.133 |    -1.019 |   -0.1534 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.252 |     2.358 | 5.960e-07 |    0.7948 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.07077 |    0.7498 |    0.6653 |     1.621 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.295 |     1.285 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     448.12149</span> |     93.73 |  0.002169 |    0.2653 |    0.8578 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.504 |     2.358 | 5.960e-07 |    0.7948 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.07077 |    0.7498 |    0.6653 |     1.621 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.295 |     1.285 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     13.78 |    0.2222 |    -1.381 |    0.2569 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.259 |   -0.5740 |  -0.02850 |    0.1620 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.241 |     3.503 |    0.9893 |     2.205 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7124 |    -1.054 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   50</span>|     447.94443 |     1.007 |    -1.844 |   -0.9902 |   -0.9390 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9059 |     1.062 |    -2.881 |    -1.561 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.911 |   -0.9357 |    -1.119 |   -0.5167 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6474 |   -0.7008 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     447.94443 |     93.62 |    -6.144 |    -1.014 |   -0.1546 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.239 |     2.358 | 0.0001615 |    0.7838 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.07204 |    0.7150 |    0.6617 |     1.640 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.306 |     1.253 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     447.94443</span> |     93.62 |  0.002146 |    0.2662 |    0.8567 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.388 |     2.358 | 0.0001615 |    0.7838 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.07204 |    0.7150 |    0.6617 |     1.640 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.306 |     1.253 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -5.879 |    0.2071 |    -1.215 |    0.2527 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8228 |   -0.5096 |  -0.06036 |    0.4983 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.183 |     1.766 |     2.266 |     2.617 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.3179 |    -2.389 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   51</span>|     447.77390 |     1.007 |    -1.858 |    -1.003 |   -0.9470 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9285 |     1.056 |    -2.855 |    -1.583 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.006 |   -0.9406 |    -1.128 |   -0.5198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6539 |   -0.6964 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      447.7739 |     93.69 |    -6.158 |    -1.026 |   -0.1626 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.217 |     2.354 | 0.0005494 |    0.7710 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.07347 |    0.7113 |    0.6533 |     1.636 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.299 |     1.257 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      447.7739</span> |     93.69 |  0.002117 |    0.2639 |    0.8500 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.179 |     2.354 | 0.0005494 |    0.7710 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.07347 |    0.7113 |    0.6533 |     1.636 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.299 |     1.257 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   52</span>|     447.56301 |     1.006 |    -1.897 |    -1.041 |   -0.9708 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9958 |     1.039 |    -2.777 |    -1.647 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.291 |   -0.9540 |    -1.156 |   -0.5273 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6737 |   -0.6848 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     447.56301 |     93.51 |    -6.197 |    -1.061 |   -0.1864 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.150 |     2.344 |  0.001717 |    0.7326 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.07774 |    0.7011 |    0.6293 |     1.627 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.278 |     1.270 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     447.56301</span> |     93.51 |  0.002035 |    0.2570 |    0.8300 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.581 |     2.344 |  0.001717 |    0.7326 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.07774 |    0.7011 |    0.6293 |     1.627 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.278 |     1.270 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -30.60 |    0.2182 |    -4.075 |   -0.4855 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.237 |   -0.6626 |   -0.1062 |    0.6612 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6049 |     1.074 |     1.318 |     2.279 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.614 |    -1.745 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   53</span>|     447.16930 |     1.007 |    -1.971 |   -0.9627 |   -0.9652 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9709 |     1.044 |    -2.781 |    -1.732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.691 |   -0.9472 |    -1.156 |   -0.5162 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6828 |   -0.6632 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      447.1693 |     93.67 |    -6.271 |   -0.9883 |   -0.1807 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.174 |     2.347 |  0.001652 |    0.6812 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.08374 |    0.7063 |    0.6288 |     1.641 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.268 |     1.293 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      447.1693</span> |     93.67 |  0.001890 |    0.2713 |    0.8346 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.798 |     2.347 |  0.001652 |    0.6812 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.08374 |    0.7063 |    0.6288 |     1.641 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.268 |     1.293 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -3.693 |    0.1048 |    0.8560 |   -0.3278 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6115 |   -0.5188 |  -0.09596 |    0.9789 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.02585 |     1.094 |     1.548 |     2.318 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -2.079 |   -0.9259 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   54</span>|     447.68852 |     1.013 |    -2.042 |    -1.008 |   -0.9421 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9288 |     1.034 |    -2.709 |    -1.852 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     3.037 |   -0.9368 |    -1.153 |   -0.6162 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.5399 |   -0.6984 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     447.68852 |     94.21 |    -6.342 |    -1.031 |   -0.1577 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.217 |     2.341 |  0.002735 |    0.6094 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.08893 |    0.7142 |    0.6313 |     1.519 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.420 |     1.255 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     447.68852</span> |     94.21 |  0.001761 |    0.2629 |    0.8541 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.176 |     2.341 |  0.002735 |    0.6094 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.08893 |    0.7142 |    0.6313 |     1.519 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.420 |     1.255 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   55</span>|     447.15405 |     1.011 |    -1.991 |   -0.9760 |   -0.9584 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9586 |     1.042 |    -2.761 |    -1.767 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.789 |   -0.9450 |    -1.157 |   -0.5460 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6410 |   -0.6726 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     447.15405 |     94.04 |    -6.291 |    -1.001 |   -0.1740 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.187 |     2.346 |  0.001959 |    0.6605 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.08521 |    0.7080 |    0.6287 |     1.605 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.312 |     1.283 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     447.15405</span> |     94.04 |  0.001852 |    0.2688 |    0.8403 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.906 |     2.346 |  0.001959 |    0.6605 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.08521 |    0.7080 |    0.6287 |     1.605 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.312 |     1.283 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     53.59 |   0.07914 |    0.2662 |   -0.1234 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1097 |   -0.6518 |  -0.06086 |    0.6193 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.05679 |     1.445 |    0.6511 |     1.197 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1794 |    -1.326 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   56</span>|     447.09915 |     1.005 |    -2.002 |   -0.9768 |   -0.9545 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9567 |     1.042 |    -2.752 |    -1.789 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.838 |   -0.9392 |    -1.155 |   -0.5617 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6261 |   -0.6787 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     447.09915 |     93.47 |    -6.302 |    -1.002 |   -0.1701 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.189 |     2.346 |  0.002096 |    0.6471 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.08594 |    0.7123 |    0.6298 |     1.586 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.328 |     1.276 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     447.09915</span> |     93.47 |  0.001833 |    0.2686 |    0.8436 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.923 |     2.346 |  0.002096 |    0.6471 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.08594 |    0.7123 |    0.6298 |     1.586 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.328 |     1.276 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -33.97 |   0.05091 |   -0.1219 |  -0.09607 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3249 |   -0.8735 |   -0.1278 |    0.5888 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.06635 |     1.636 |    -1.923 |    0.5172 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.5270 |    -1.419 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   57</span>|     447.01951 |     1.007 |    -2.009 |   -0.9874 |   -0.9450 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9329 |     1.053 |    -2.780 |    -1.808 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.841 |   -0.9693 |    -1.155 |   -0.5556 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6514 |   -0.6721 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     447.01951 |     93.64 |    -6.309 |    -1.012 |   -0.1606 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.212 |     2.352 |  0.001680 |    0.6361 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.08598 |    0.6895 |    0.6300 |     1.593 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.301 |     1.283 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     447.01951</span> |     93.64 |  0.001820 |    0.2667 |    0.8517 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.138 |     2.352 |  0.001680 |    0.6361 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.08598 |    0.6895 |    0.6300 |     1.593 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.301 |     1.283 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -6.500 |   0.04518 |   -0.6412 |    0.1616 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3106 |   -0.6054 |  -0.07873 |   -0.1077 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1002 |    0.2460 |     1.553 |    0.4490 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8245 |    -1.132 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   58</span>|     447.04861 |     1.009 |    -2.019 |   -0.9927 |   -0.9476 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9252 |     1.054 |    -2.796 |    -1.779 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.878 |   -0.9737 |    -1.157 |   -0.5487 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6291 |   -0.6521 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     447.04861 |     93.85 |    -6.319 |    -1.017 |   -0.1632 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.220 |     2.353 |  0.001429 |    0.6532 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.08654 |    0.6862 |    0.6280 |     1.601 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.325 |     1.305 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     447.04861</span> |     93.85 |  0.001802 |    0.2657 |    0.8494 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.209 |     2.353 |  0.001429 |    0.6532 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.08654 |    0.6862 |    0.6280 |     1.601 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.325 |     1.305 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   59</span>|     447.01937 |     1.009 |    -2.012 |   -0.9891 |   -0.9459 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9304 |     1.053 |    -2.785 |    -1.798 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.853 |   -0.9708 |    -1.156 |   -0.5534 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6439 |   -0.6653 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     447.01937 |     93.80 |    -6.312 |    -1.013 |   -0.1615 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.215 |     2.353 |  0.001597 |    0.6418 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.08617 |    0.6884 |    0.6292 |     1.596 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.309 |     1.291 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     447.01937</span> |     93.80 |  0.001814 |    0.2664 |    0.8509 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.161 |     2.353 |  0.001597 |    0.6418 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.08617 |    0.6884 |    0.6292 |     1.596 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.309 |     1.291 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     15.62 |   0.04451 |   -0.6370 |    0.1658 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4720 |   -0.4967 |  -0.06076 |    0.2478 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.07878 |    0.2929 |    0.7908 |    0.7364 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2169 |   -0.8458 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   60</span>|     447.00331 |     1.007 |    -2.016 |   -0.9874 |   -0.9487 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9321 |     1.057 |    -2.790 |    -1.800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.856 |   -0.9696 |    -1.156 |   -0.5575 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6423 |   -0.6633 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     447.00331 |     93.68 |    -6.316 |    -1.012 |   -0.1643 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.213 |     2.355 |  0.001526 |    0.6406 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.08622 |    0.6893 |    0.6295 |     1.591 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.311 |     1.293 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     447.00331</span> |     93.68 |  0.001807 |    0.2667 |    0.8485 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.146 |     2.355 |  0.001526 |    0.6406 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.08622 |    0.6893 |    0.6295 |     1.591 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.311 |     1.293 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.749 |   0.03581 |   -0.6032 |   0.06997 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3775 |   -0.4012 |  -0.08782 |    0.2553 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.001918 |    0.1783 |     1.513 |    0.3567 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.3333 |   -0.7379 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   61</span>|     446.99944 |     1.008 |    -2.018 |   -0.9851 |   -0.9476 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9359 |     1.058 |    -2.787 |    -1.806 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.859 |   -0.9671 |    -1.156 |   -0.5614 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6427 |   -0.6650 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     446.99944 |     93.70 |    -6.318 |    -1.009 |   -0.1632 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.209 |     2.355 |  0.001564 |    0.6369 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.08626 |    0.6911 |    0.6291 |     1.586 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.311 |     1.291 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     446.99944</span> |     93.70 |  0.001804 |    0.2671 |    0.8494 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.111 |     2.355 |  0.001564 |    0.6369 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.08626 |    0.6911 |    0.6291 |     1.586 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.311 |     1.291 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     2.474 |   0.03546 |   -0.4554 |    0.1057 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2777 |   -0.4692 |  -0.06549 |   0.06429 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.09650 |    0.2331 |    0.6150 |    0.1626 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.3753 |   -0.8072 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   62</span>|     446.99641 |     1.007 |    -2.020 |   -0.9846 |   -0.9460 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9366 |     1.059 |    -2.790 |    -1.808 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.868 |   -0.9676 |    -1.156 |   -0.5621 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6420 |   -0.6633 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     446.99641 |     93.67 |    -6.320 |    -1.009 |   -0.1616 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.209 |     2.356 |  0.001530 |    0.6356 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.08639 |    0.6908 |    0.6287 |     1.585 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.311 |     1.293 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     446.99641</span> |     93.67 |  0.001800 |    0.2672 |    0.8508 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.105 |     2.356 |  0.001530 |    0.6356 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.08639 |    0.6908 |    0.6287 |     1.585 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.311 |     1.293 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -2.070 |   0.03317 |   -0.4443 |    0.1488 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2478 |   -0.4968 |  -0.07674 |   0.09423 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.05318 |    0.2365 |    0.6058 |    0.5619 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.3013 |   -0.7224 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   63</span>|     446.99641 |     1.007 |    -2.020 |   -0.9846 |   -0.9460 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9366 |     1.059 |    -2.790 |    -1.808 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.868 |   -0.9676 |    -1.156 |   -0.5621 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6420 |   -0.6633 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     446.99641 |     93.67 |    -6.320 |    -1.009 |   -0.1616 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.209 |     2.356 |  0.001530 |    0.6356 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.08639 |    0.6908 |    0.6287 |     1.585 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.311 |     1.293 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     446.99641</span> |     93.67 |  0.001800 |    0.2672 |    0.8508 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.105 |     2.356 |  0.001530 |    0.6356 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.08639 |    0.6908 |    0.6287 |     1.585 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.311 |     1.293 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> calculating covariance matrix</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating residuals/tables</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-in"><span class="va">f_nlmixr_dfop_sfo_saem_obs_tc</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr</a></span><span class="op">(</span><span class="va">f_mmkin_tc</span><span class="op">[</span><span class="st">"DFOP-SFO"</span>, <span class="op">]</span>, est <span class="op">=</span> <span class="st">"saem"</span>,</span>
-<span class="r-in">  error_model <span class="op">=</span> <span class="st">"obs_tc"</span><span class="op">)</span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1:    90.7519   -4.8586   -1.7674   -3.5599   -2.0059    0.5800    4.8899    1.4250    1.1400    2.6923    0.4845    0.4370   10.2815    0.0004    9.0437    0.4047</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2:    9.0763e+01  -5.2004e+00  -1.9605e+00  -4.2009e+00  -1.8767e+00   2.0341e-01   4.6454e+00   1.3537e+00   1.0830e+00   2.7771e+00   4.6027e-01   5.1181e-01   6.3469e+00   4.0033e-04   6.8216e+00   9.0448e-05</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 3:    9.0656e+01  -5.4820e+00  -2.0937e+00  -4.0962e+00  -1.5381e+00  -1.7136e-02   4.4131e+00   1.2861e+00   1.0288e+00   3.5620e+00   4.3726e-01   6.1217e-01   4.8763e+00   4.1436e-05   5.4967e+00   1.0148e-05</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 4:    9.0543e+01  -5.7744e+00  -2.1074e+00  -4.0393e+00  -1.3603e+00  -2.3531e-01   5.0162e+00   1.2218e+00   1.0041e+00   3.3839e+00   4.1540e-01   5.8157e-01   4.1029e+00   2.1466e-04   4.2869e+00   1.9419e-06</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5:    91.1621   -6.0534   -2.0430   -4.0508   -1.2523   -0.2043    6.1015    1.1715    1.1998    3.2147    0.3946    0.5525    3.5979    0.0187    3.8221    0.0271</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 6:    91.1179   -5.9892   -2.0082   -4.1363   -1.1428   -0.1930    5.7964    1.1960    1.2360    3.4525    0.3749    0.5293    3.3091    0.0209    3.6230    0.0284</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 7:    91.6680   -5.9609   -2.0492   -4.1163   -1.0919   -0.1505    5.6134    1.6750    1.3055    3.2799    0.3631    0.5028    3.2412    0.0254    3.5108    0.0371</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 8:    91.6903   -5.9346   -2.0630   -4.0790   -1.1130   -0.0963    5.3327    2.2525    1.3524    3.1159    0.3449    0.4777    2.6165    0.0445    2.8556    0.0576</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 9:    92.2564   -5.7600   -2.0460   -4.1144   -1.0635   -0.0594    5.9616    2.1399    1.2848    2.9601    0.3277    0.4538    2.3593    0.0295    2.5704    0.0491</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 10:    92.2425   -5.7990   -2.0052   -4.0409   -1.0387   -0.1141    6.9547    2.1239    1.2205    2.8121    0.3113    0.4311    2.3237    0.0323    2.4227    0.0421</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 11:    92.4404   -5.6829   -2.0866   -4.0010   -1.0214   -0.0587    6.6790    2.0177    1.1595    2.6715    0.2957    0.4096    1.9963    0.0407    2.2478    0.0501</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 12:    92.0713   -5.8051   -2.1699   -4.0539   -0.9807    0.0174    6.7885    2.1380    1.1811    2.6017    0.2810    0.3891    1.8480    0.0502    2.0516    0.0522</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 13:    91.7214   -5.6954   -2.1579   -4.0944   -0.9935   -0.0195    6.4491    2.0311    1.1221    2.4716    0.2669    0.3696    1.8299    0.0531    1.9271    0.0520</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 14:    91.1978   -5.6733   -2.1988   -4.0794   -0.9387    0.0091    6.1267    1.9295    1.1589    2.3480    0.2536    0.3511    1.6357    0.0470    1.8899    0.0639</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 15:    91.3746   -5.5864   -2.1484   -4.1356   -0.9126    0.0045    5.8203    1.8330    1.2078    2.3265    0.2409    0.3336    1.6218    0.0428    1.6558    0.0725</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 16:    91.5646   -5.4931   -2.1474   -4.1242   -0.9148    0.0179    5.5293    1.7414    1.1474    2.2733    0.2288    0.3169    1.6467    0.0377    1.6977    0.0600</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 17:    91.4767   -5.5885   -2.1424   -4.1386   -0.9308    0.0602    5.2528    1.9428    1.2141    2.1762    0.2174    0.3343    1.4916    0.0424    1.4326    0.0717</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 18:    90.9989   -5.6364   -2.1601   -4.1606   -0.9676    0.0694    4.9902    1.8457    1.2521    2.2899    0.2065    0.3279    1.4504    0.0471    1.4267    0.0700</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 19:    91.4050   -5.7347   -2.0985   -4.1476   -0.9656    0.0668    4.7407    2.3263    1.4064    2.2061    0.1962    0.3296    1.3679    0.0485    1.2735    0.0844</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 20:    91.2707   -5.7623   -2.1155   -4.1538   -0.9601    0.0425    4.5037    2.6509    1.3408    2.1958    0.1864    0.3576    1.3736    0.0452    1.3454    0.0743</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 21:    91.6878   -5.8143   -2.1261   -4.1649   -0.9325    0.0333    4.7695    2.8996    1.3244    2.1300    0.1771    0.3509    1.4793    0.0508    1.0333    0.0839</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 22:    91.5363   -5.9047   -2.1358   -4.1655   -0.9207    0.0339    4.5310    3.4408    1.3022    2.1829    0.1682    0.3378    1.4260    0.0488    0.9436    0.0877</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 23:    91.9969   -5.9565   -2.1378   -4.1609   -0.9243    0.0701    4.3045    3.7239    1.3446    2.2424    0.1598    0.3209    1.4182    0.0412    0.9205    0.0873</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 24:    92.2835   -5.9272   -2.0883   -4.1651   -0.9248    0.0834    4.0892    3.7780    1.2773    2.2187    0.1518    0.3049    1.4749    0.0427    0.9837    0.0802</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 25:    92.3389   -6.1292   -2.1033   -4.2473   -0.9069    0.0593    3.8848    4.7059    1.2740    2.4323    0.1442    0.2896    1.3656    0.0493    0.9299    0.0798</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 26:    91.9840   -5.9821   -2.1124   -4.2150   -0.9100    0.1076    3.6905    4.4706    1.2103    2.4699    0.1458    0.2751    1.3200    0.0504    0.8256    0.0912</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 27:    92.4671   -5.8765   -2.0910   -4.2169   -0.9356    0.0483    3.5060    4.2471    1.2302    2.4791    0.1385    0.2614    1.4252    0.0520    0.8191    0.0836</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 28:    92.4130   -5.9480   -2.0870   -4.2191   -0.9338    0.0866    3.3859    4.1337    1.2210    2.4685    0.1412    0.2557    1.3860    0.0484    0.9189    0.0770</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 29:    92.3064   -5.7845   -2.0759   -4.2321   -0.9270    0.0855    4.2691    3.9271    1.3369    2.5457    0.1341    0.2922    1.3848    0.0513    0.9658    0.0785</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 30:    92.2109   -5.9347   -2.0725   -4.2042   -0.9074    0.0481    5.5659    3.9234    1.2701    2.4184    0.1370    0.2776    1.2925    0.0560    0.9058    0.0806</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 31:    91.8912   -5.7466   -2.0912   -4.1504   -0.9087    0.0443    5.2876    3.7272    1.2690    2.2975    0.1301    0.2637    1.3348    0.0517    0.8672    0.0840</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 32:    92.3866   -5.8560   -2.0979   -4.1547   -0.9044    0.0335    5.0232    3.5408    1.2761    2.3185    0.1307    0.2505    1.3558    0.0487    0.9422    0.0791</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 33:    92.4555   -5.6989   -2.0956   -4.1479   -0.9038    0.0447    4.7720    3.3638    1.3253    2.2962    0.1242    0.2380    1.4321    0.0432    0.8961    0.0799</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 34:    92.6307   -5.8831   -2.0769   -4.1319   -0.8946    0.0430    4.5334    3.7151    1.2785    2.2638    0.1201    0.2261    1.4076    0.0457    0.8269    0.0813</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 35:    92.5659   -5.9100   -2.0748   -4.1482   -0.9079    0.0563    4.6634    3.7452    1.3141    2.3378    0.1168    0.2148    1.3571    0.0451    0.8317    0.0831</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 36:    92.5738   -5.8574   -2.0981   -4.1214   -0.8963    0.0302    5.3272    3.7371    1.3359    2.2776    0.1331    0.2041    1.3308    0.0480    0.8953    0.0791</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 37:    92.1615   -5.7137   -2.0922   -4.1178   -0.9035    0.0455    5.4894    3.5503    1.2691    2.2508    0.1473    0.1939    1.3692    0.0495    0.9358    0.0800</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 38:    92.4421   -5.6978   -2.0905   -4.1275   -0.9052    0.0108    5.4295    3.3728    1.2057    2.2119    0.1432    0.1842    1.3421    0.0549    0.8932    0.0781</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 39:    92.3995   -5.6121   -2.0804   -4.1275   -0.8903    0.0270    5.1580    3.2041    1.1454    2.2119    0.1360    0.1978    1.3353    0.0524    0.9062    0.0775</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 40:    92.5616   -5.7907   -2.1099   -4.1127   -0.9074    0.0501    4.9001    3.0439    1.1582    2.1723    0.1387    0.1879    1.2981    0.0510    0.9517    0.0797</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 41:    92.2655   -5.6532   -2.0631   -4.1437   -0.9128    0.0346    4.6551    2.8917    1.1202    2.1498    0.1403    0.1785    1.4500    0.0461    0.7394    0.0903</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 42:    92.3218   -5.4916   -2.1072   -4.1312   -0.9310    0.0360    4.4224    2.7471    1.1648    2.1428    0.1332    0.1695    1.4034    0.0508    0.8131    0.0854</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 43:    92.6165   -5.5145   -2.0838   -4.1239   -0.9215    0.0347    4.2012    2.6098    1.1892    2.1202    0.1463    0.1611    1.3416    0.0507    0.8213    0.0847</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 44:    92.5385   -5.5590   -2.0872   -4.1376   -0.9210    0.0672    3.9912    2.4793    1.1416    2.2253    0.1450    0.1530    1.4008    0.0352    0.8639    0.0847</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 45:    92.2152   -5.6170   -2.1014   -4.1657   -0.9331    0.0781    3.7916    2.4382    1.1250    2.2378    0.1516    0.1556    1.4612    0.0329    1.0091    0.0820</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 46:    91.8943   -5.6650   -2.1329   -4.1649   -0.9241    0.1060    3.6020    2.6448    1.0705    2.3112    0.1441    0.1549    1.5047    0.0384    1.0179    0.0805</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 47:    91.8153   -5.5910   -2.1514   -4.1919   -0.9169    0.0885    3.4219    2.5125    1.0386    2.3140    0.1506    0.1620    1.4759    0.0375    1.1021    0.0740</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 48:    91.6295   -5.5438   -2.1516   -4.1570   -0.8742    0.0851    3.2508    2.3869    1.0816    2.1983    0.1630    0.1786    1.4024    0.0405    1.1135    0.0732</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 49:    91.4363   -5.5847   -2.1732   -4.1794   -0.8634    0.0948    3.0883    2.4829    1.0865    2.2378    0.1549    0.1697    1.4314    0.0434    1.0529    0.0742</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 50:    91.3634   -5.5794   -2.1613   -4.1507   -0.8849    0.0862    2.9339    2.6404    1.0829    2.2519    0.1493    0.1612    1.3704    0.0446    1.0081    0.0736</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 51:    91.2150   -5.5503   -2.1675   -4.1432   -0.8749    0.1178    2.7872    2.7368    1.0605    2.2261    0.1418    0.1532    1.4312    0.0418    1.0380    0.0781</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 52:    91.4343   -5.6336   -2.2138   -4.1552   -0.8867    0.1391    2.6677    2.7022    1.0857    2.2588    0.1493    0.1455    1.3220    0.0469    0.9272    0.0761</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 53:    91.3310   -5.5185   -2.2340   -4.1430   -0.8855    0.1545    2.9130    2.5670    1.0720    2.3074    0.1532    0.1385    1.3224    0.0503    0.9688    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 54:    91.4342   -5.5008   -2.2086   -4.1777   -0.8720    0.1688    2.8202    2.4387    1.0624    2.4253    0.1631    0.1315    1.3186    0.0429    0.9425    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 55:    91.4983   -5.5944   -2.1709   -4.1211   -0.8638    0.1168    3.4789    2.5154    1.0549    2.3041    0.1557    0.1279    1.3800    0.0423    0.9620    0.0727</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 56:    91.5954   -5.5317   -2.1881   -4.1263   -0.8627    0.1366    4.1396    2.3896    1.0732    2.2684    0.1562    0.1221    1.3285    0.0431    1.0156    0.0708</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 57:    91.6591   -5.3839   -2.2082   -4.1122   -0.8929    0.1457    4.5073    2.2701    1.0672    2.3639    0.1539    0.1160    1.4074    0.0478    0.9796    0.0722</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 58:    91.7434   -5.3597   -2.2068   -4.1214   -0.8912    0.1437    4.2819    2.1566    1.0605    2.4496    0.1667    0.1109    1.4274    0.0450    1.0782    0.0676</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 59:    91.6597   -5.4672   -2.1918   -4.1412   -0.9021    0.1657    4.0678    2.0488    1.1306    2.5245    0.1888    0.1054    1.4745    0.0391    1.1492    0.0670</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 60:    91.7565   -5.4513   -2.2051   -4.1618   -0.9013    0.1554    3.8644    1.9464    1.1728    2.6191    0.1793    0.1001    1.4135    0.0402    1.1572    0.0665</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 61:    91.9484   -5.5390   -2.1973   -4.1611   -0.9011    0.1318    3.6712    1.8963    1.1460    2.6242    0.1704    0.0951    1.3855    0.0481    1.1109    0.0634</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 62:    92.1661   -5.4985   -2.1729   -4.1352   -0.8887    0.1327    4.3155    1.8588    1.1087    2.5551    0.1633    0.0995    1.3847    0.0444    1.0736    0.0656</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 63:    92.1727   -5.4086   -2.1610   -4.1488   -0.8998    0.1451    4.6019    1.7659    1.1413    2.6020    0.1718    0.1152    1.4299    0.0404    1.2202    0.0603</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 64:    92.4331   -5.5220   -2.1121   -4.1300   -0.9428    0.1211    5.0354    1.8121    1.0982    2.6157    0.1775    0.1209    1.4168    0.0348    1.2148    0.0625</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 65:    92.1871   -5.6089   -2.1258   -4.1412   -0.9127    0.1399    4.7836    2.1060    1.1568    2.5971    0.1757    0.1148    1.4000    0.0336    1.0305    0.0707</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 66:    92.1419   -5.7752   -2.1344   -4.1493   -0.9237    0.1494    4.5444    2.7076    1.1563    2.6439    0.1740    0.1091    1.4238    0.0341    1.0285    0.0688</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 67:    92.1187   -5.5967   -2.1430   -4.1460   -0.9074    0.1245    4.3172    2.5722    1.2052    2.6139    0.1868    0.1104    1.4100    0.0381    1.0508    0.0703</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 68:    92.1123   -5.6882   -2.1481   -4.1269   -0.9083    0.1206    4.1014    2.5548    1.2021    2.4832    0.1781    0.1462    1.3099    0.0417    1.0099    0.0713</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 69:    92.2511   -5.6923   -2.1456   -4.1315   -0.9077    0.1421    4.3551    2.6261    1.1850    2.3591    0.1692    0.1389    1.3546    0.0416    0.9516    0.0762</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 70:    92.0291   -5.7376   -2.1689   -4.1364   -0.8908    0.1407    4.1373    3.0520    1.1595    2.2495    0.1608    0.1465    1.3226    0.0436    0.9904    0.0711</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 71:    91.7690   -5.7223   -2.1773   -4.1986   -0.9036    0.1831    3.9304    2.8994    1.1015    2.4335    0.1527    0.1602    1.3135    0.0450    0.9564    0.0722</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 72:    91.4802   -5.7302   -2.2079   -4.2000   -0.8990    0.2112    3.7339    3.2965    1.1222    2.4612    0.1451    0.1622    1.3222    0.0426    1.0215    0.0717</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 73:    91.1247   -5.7803   -2.1827   -4.2214   -0.8876    0.1385    3.5472    3.2786    1.1612    2.5216    0.1650    0.1541    1.2811    0.0493    1.0076    0.0696</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 74:    90.9765   -5.6534   -2.1808   -4.2511   -0.8824    0.1308    3.8553    3.1146    1.1389    2.8140    0.1725    0.1464    1.2895    0.0518    1.0100    0.0707</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 75:    90.4805   -5.7293   -2.1481   -4.2613   -0.8704    0.1395    4.1255    2.9589    1.1623    2.7523    0.1713    0.1391    1.3112    0.0483    0.9439    0.0788</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 76:    90.3958   -5.7635   -2.1508   -4.2426   -0.8841    0.1402    4.7427    3.0223    1.2187    2.7243    0.1789    0.1321    1.3065    0.0468    0.9303    0.0758</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 77:    90.7518   -5.6517   -2.1498   -4.2773   -0.8730    0.1688    5.9340    2.8712    1.1840    2.8530    0.1828    0.1255    1.3553    0.0422    0.9755    0.0696</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 78:    91.0443   -5.7462   -2.1351   -4.2601   -0.8848    0.1746    5.6373    2.9912    1.1312    2.7659    0.1887    0.1192    1.3303    0.0410    0.8578    0.0744</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 79:    90.9631   -5.7307   -2.1618   -4.2593   -0.8965    0.1944    5.7520    3.1324    1.1206    2.8212    0.1875    0.1340    1.3519    0.0449    0.9006    0.0799</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 80:    90.8703   -5.7665   -2.1989   -4.2476   -0.9122    0.1933    5.4644    3.4527    1.1063    2.7920    0.1781    0.1273    1.3014    0.0497    0.9305    0.0792</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 81:    90.7781   -5.8702   -2.1789   -4.2821   -0.8828    0.1793    5.1912    3.7553    1.0662    3.0568    0.1807    0.1209    1.3120    0.0472    0.9808    0.0772</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 82:    90.8137   -6.1164   -2.2001   -4.3046   -0.8912    0.1916    4.9316    4.9023    1.0714    3.1363    0.1716    0.1149    1.2724    0.0539    1.0644    0.0693</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 83:    90.9900   -6.2077   -2.1695   -4.3121   -0.8924    0.1722    6.7123    5.3290    1.1120    3.2733    0.1741    0.1091    1.2822    0.0510    1.0142    0.0715</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 84:    90.8158   -6.3282   -2.1595   -4.2907   -0.9096    0.1818    6.3767    5.9725    1.1342    3.2384    0.1763    0.1037    1.2704    0.0420    0.8891    0.0834</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 85:    90.5926   -6.2052   -2.1643   -4.2780   -0.9000    0.1579    6.0579    5.6739    1.1472    3.0765    0.1751    0.0985    1.3263    0.0433    0.9484    0.0814</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 86:    90.3804   -6.2707   -2.1470   -4.2627   -0.9260    0.1650    5.7550    5.5542    1.1135    2.9227    0.1956    0.0936    1.2747    0.0447    0.9731    0.0780</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 87:    90.8425   -6.4455   -2.1135   -4.2273   -0.9294    0.1491    5.4672    6.8599    1.0716    2.7765    0.1858    0.0928    1.3204    0.0427    1.0005    0.0744</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 88:    91.2019   -6.2302   -2.1362   -4.1982   -0.9333    0.1615    5.1939    6.5169    1.1221    2.7203    0.1841    0.0928    1.2689    0.0466    0.9914    0.0771</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 89:    90.9907   -6.4173   -2.1174   -4.2493   -0.9182    0.1628    4.9342    6.2408    1.1719    2.9614    0.1749    0.1021    1.3580    0.0403    1.0171    0.0775</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 90:    91.1666   -6.5154   -2.1219   -4.2011   -0.9210    0.1594    4.6875    7.4895    1.1930    2.8133    0.1745    0.0970    1.2500    0.0432    0.9467    0.0799</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 91:    91.0165   -6.5918   -2.1465   -4.2231   -0.9079    0.1382    4.9772    7.3172    1.1706    2.6789    0.1810    0.1032    1.2958    0.0458    1.1223    0.0639</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 92:    91.5386   -6.6567   -2.1376   -4.2247   -0.9358    0.1696    5.5955    7.4188    1.1543    2.6339    0.1754    0.0980    1.2894    0.0463    0.9927    0.0764</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 93:    92.2552   -6.5096   -2.1645   -4.2340   -0.9393    0.1798    5.3157    7.0479    1.1612    2.6312    0.1718    0.0931    1.3698    0.0431    1.2649    0.0579</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 94:    91.7588   -6.4124   -2.1795   -4.2305   -0.9265    0.1842    5.8275    6.6955    1.1988    2.6403    0.1891    0.0884    1.3022    0.0481    1.0741    0.0713</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 95:    91.6694   -6.2975   -2.1946   -4.2442   -0.9392    0.2004    5.5361    6.3607    1.1399    2.7164    0.1796    0.1027    1.2519    0.0513    1.0982    0.0724</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 96:    91.2252   -6.0124   -2.2316   -4.2701   -0.8946    0.2375    5.2593    6.0427    1.0829    2.7921    0.2024    0.1147    1.3343    0.0426    1.3064    0.0560</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 97:    91.0388   -5.9178   -2.2563   -4.2703   -0.9093    0.2304    5.6825    5.7405    1.0890    2.7678    0.2113    0.1090    1.3517    0.0484    1.4160    0.0493</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 98:    90.9013   -6.1325   -2.2597   -4.2776   -0.9133    0.2447    5.7546    5.4535    1.1028    2.6806    0.2130    0.1182    1.2983    0.0451    1.2436    0.0584</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 99:    91.2086   -6.0047   -2.2719   -4.2972   -0.9136    0.2738    5.4668    5.1808    1.1776    2.7160    0.2132    0.1123    1.3301    0.0431    1.1850    0.0628</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 100:    91.3181   -5.9175   -2.2311   -4.3202   -0.8947    0.2844    5.1935    4.9218    1.1187    2.8660    0.2289    0.1471    1.3409    0.0371    0.9566    0.0806</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 101:    91.5112   -5.7721   -2.2292   -4.3060   -0.8842    0.3006    6.3617    4.6757    1.0628    2.7227    0.2174    0.1500    1.3269    0.0407    1.2002    0.0592</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 102:    91.5205   -5.9982   -2.2162   -4.3041   -0.8813    0.3065    7.7957    4.4419    1.0320    2.7176    0.2290    0.1474    1.3218    0.0392    0.9685    0.0783</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 103:    91.2416   -5.7012   -2.2204   -4.3198   -0.8610    0.3180    7.4059    4.2198    1.0242    2.5817    0.2175    0.1714    1.3003    0.0395    0.9472    0.0825</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 104:    91.2662   -5.8136   -2.2357   -4.3063   -0.8700    0.3110    7.0356    4.0088    1.0024    2.4701    0.2067    0.1939    1.2806    0.0407    1.0387    0.0743</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 105:    91.4688   -5.7922   -2.2204   -4.3319   -0.8703    0.2767    6.6838    3.8084    0.9751    2.6368    0.1963    0.1967    1.3320    0.0470    1.2043    0.0580</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 106:    91.3665   -5.8505   -2.2225   -4.3614   -0.8757    0.2922    7.1982    3.6180    1.0018    2.6378    0.1865    0.2132    1.2613    0.0464    1.0683    0.0693</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 107:    91.4934   -5.8933   -2.1993   -4.3433   -0.9112    0.3267    6.8383    3.7379    0.9616    2.5329    0.1772    0.2169    1.2911    0.0449    1.0688    0.0731</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 108:    92.1268   -5.7807   -2.1863   -4.4348   -0.9273    0.3235    6.4964    3.5510    1.0093    2.9025    0.1718    0.2061    1.3153    0.0414    0.8982    0.0796</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 109:    91.6585   -5.9778   -2.2083   -4.3550   -0.9061    0.3339    6.1715    4.5778    1.0600    2.7574    0.1755    0.2163    1.2754    0.0382    0.8100    0.0868</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 110:    92.0456   -5.6784   -2.2236   -4.3483   -0.8911    0.3022    5.8630    4.3489    1.0427    2.6195    0.1667    0.2472    1.3782    0.0336    0.7819    0.0931</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 111:    92.2862   -5.6624   -2.1985   -4.3680   -0.9178    0.3156    5.5698    4.1315    1.0149    2.5443    0.1622    0.2348    1.3541    0.0415    0.7848    0.0868</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 112:    92.5436   -5.6572   -2.2134   -4.3290   -0.9267    0.3025    5.2913    3.9249    1.0720    2.5118    0.1541    0.2231    1.3798    0.0385    0.9087    0.0799</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 113:    92.5970   -5.6263   -2.2030   -4.3044   -0.9123    0.2856    5.0268    3.7287    1.1371    2.4674    0.1764    0.2119    1.3007    0.0455    0.8651    0.0850</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 114:    92.9227   -5.6379   -2.2032   -4.2926   -0.9187    0.3282    4.7754    3.5422    1.1022    2.4105    0.1676    0.2013    1.3103    0.0439    0.9003    0.0815</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 115:    92.7678   -5.8033   -2.1888   -4.2988   -0.9406    0.3026    4.5367    3.3651    1.1127    2.5043    0.1592    0.1913    1.3643    0.0420    0.9649    0.0800</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 116:    92.0655   -5.8531   -2.2033   -4.2989   -0.9487    0.3013    4.3098    3.6792    1.1260    2.4981    0.1513    0.1817    1.2783    0.0445    0.9812    0.0818</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 117:    92.2258   -5.7861   -2.2095   -4.3393   -0.9399    0.2998    4.0943    3.4952    1.0697    2.7102    0.1437    0.1752    1.3037    0.0426    0.9067    0.0786</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 118:    92.3560   -5.8080   -2.2058   -4.3166   -0.9480    0.2743    4.6025    3.3205    1.0709    2.6198    0.1432    0.1747    1.2850    0.0481    0.9236    0.0769</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 119:    92.0489   -5.7726   -2.1972   -4.2740   -0.9087    0.2627    4.3723    3.1544    1.0892    2.4888    0.1620    0.1660    1.2659    0.0435    0.8389    0.0832</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 120:    91.9163   -5.8211   -2.2101   -4.2770   -0.9142    0.2674    4.1537    3.1700    1.0985    2.5003    0.1795    0.1577    1.2289    0.0454    0.9280    0.0826</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 121:    92.0274   -5.7601   -2.1916   -4.2686   -0.9055    0.2571    3.9460    3.1181    1.1026    2.3985    0.1908    0.1498    1.3015    0.0432    0.9363    0.0807</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 122:    92.2933   -5.7972   -2.2019   -4.2888   -0.8932    0.2679    3.7487    3.1755    1.0809    2.3976    0.1843    0.1636    1.2918    0.0433    0.8760    0.0803</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 123:    92.3361   -5.8903   -2.1745   -4.3386   -0.9157    0.2665    3.5613    3.8794    1.1308    2.5838    0.1751    0.1555    1.3219    0.0421    0.8987    0.0806</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 124:    92.5677   -6.0471   -2.1638   -4.2764   -0.9325    0.2645    3.3832    4.6152    1.1336    2.4546    0.1664    0.1526    1.2938    0.0375    0.9412    0.0798</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 125:    92.7852   -5.9841   -2.1661   -4.2825   -0.9373    0.2866    3.2141    4.3844    1.0770    2.3788    0.1581    0.1727    1.3137    0.0417    0.9228    0.0733</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 126:    92.7867   -5.9126   -2.1636   -4.2413   -0.9141    0.2756    3.0534    4.1652    1.0857    2.2921    0.1502    0.1994    1.2428    0.0450    0.8206    0.0795</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 127:    92.8733   -5.9110   -2.1546   -4.2269   -0.9258    0.2531    2.9007    3.9569    1.0914    2.3822    0.1426    0.2029    1.3043    0.0369    0.8206    0.0795</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 128:    93.0457   -5.9202   -2.1505   -4.2076   -0.9466    0.2463    3.3353    3.7591    1.0647    2.3012    0.1355    0.2180    1.3420    0.0385    0.8503    0.0803</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 129:    92.9207   -5.9882   -2.1704   -4.2204   -0.9319    0.2464    4.3160    4.4368    1.1187    2.3415    0.1386    0.2316    1.2546    0.0435    0.8777    0.0830</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 130:    92.2660   -6.2043   -2.1712   -4.2251   -0.9276    0.2186    4.4370    5.1440    1.0817    2.3426    0.1338    0.2200    1.2733    0.0476    0.9132    0.0753</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 131:    92.1286   -6.4163   -2.1634   -4.2642   -0.9223    0.2300    4.2151    6.2253    1.0614    2.5012    0.1333    0.2090    1.2690    0.0430    0.8201    0.0814</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 132:    92.0287   -6.4297   -2.1522   -4.2717   -0.9259    0.2250    4.0044    6.1979    1.0791    2.5146    0.1314    0.1985    1.2736    0.0449    0.8671    0.0784</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 133:    91.6843   -6.1415   -2.1685   -4.2553   -0.9349    0.2209    3.8041    5.8880    1.1445    2.4277    0.1248    0.2026    1.3381    0.0426    0.9129    0.0858</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 134:    91.6928   -6.1408   -2.1467   -4.2709   -0.9270    0.2648    3.6139    5.5936    1.0873    2.5602    0.1305    0.1925    1.3202    0.0416    0.7626    0.0936</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 135:    91.7984   -6.0371   -2.1673   -4.2572   -0.9497    0.2480    3.4332    5.3139    1.0649    2.4322    0.1389    0.1829    1.2592    0.0544    0.9459    0.0831</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 136:    91.9754   -6.1770   -2.1769   -4.2311   -0.9401    0.2435    3.2616    5.0482    1.0717    2.3106    0.1691    0.1737    1.3170    0.0436    1.0497    0.0816</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 137:    92.1546   -6.1216   -2.1731   -4.2278   -0.9401    0.2492    3.0985    4.9378    1.0200    2.1950    0.1606    0.1988    1.4019    0.0421    1.0200    0.0766</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 138:    92.2370   -5.9463   -2.1794   -4.1949   -0.9321    0.2460    2.9436    4.6909    1.0203    2.1200    0.1535    0.1947    1.3378    0.0425    0.9448    0.0804</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 139:    92.2025   -5.8849   -2.1820   -4.1868   -0.9200    0.2294    2.9458    4.4564    0.9875    2.1260    0.1759    0.2080    1.3244    0.0428    1.0110    0.0778</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 140:    91.8182   -5.7494   -2.1569   -4.1998   -0.9095    0.2318    2.7985    4.2336    1.0015    2.1507    0.1830    0.2113    1.3502    0.0432    0.7716    0.0922</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 141:    91.8292   -5.9568   -2.1640   -4.2109   -0.9122    0.2130    4.4608    4.0219    1.0180    2.1032    0.1739    0.2057    1.3594    0.0419    0.8088    0.0883</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 142:    91.9995   -6.0927   -2.1471   -4.2313   -0.9091    0.1875    4.5415    4.7705    1.0571    2.1286    0.1810    0.1954    1.4145    0.0406    0.8943    0.0847</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 143:    91.9160   -5.9892   -2.1546   -4.2043   -0.9355    0.1819    4.3145    4.5320    1.1337    2.1453    0.1888    0.2208    1.3449    0.0394    1.0910    0.0797</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 144:    92.0136   -5.9765   -2.1643   -4.2346   -0.9448    0.2455    4.3041    4.3054    1.1130    2.1300    0.1817    0.3253    1.3828    0.0340    1.0535    0.0873</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 145:    92.3893   -6.2224   -2.1211   -4.2316   -0.9405    0.2285    5.2279    4.9099    1.0573    2.1037    0.1727    0.3091    1.3797    0.0340    1.0160    0.0832</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 146:    92.6097   -6.2204   -2.1406   -4.2210   -0.9217    0.1762    4.9665    5.4466    1.0724    2.1658    0.1640    0.2936    1.4421    0.0408    1.2412    0.0674</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 147:    92.8499   -6.0091   -2.1165   -4.2234   -0.9682    0.1783    4.7182    5.1743    1.0188    2.1714    0.1722    0.2789    1.4545    0.0397    1.4472    0.0556</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 148:    92.6602   -5.8003   -2.1059   -4.2132   -0.9282    0.2102    4.4823    4.9156    1.0106    2.1075    0.1880    0.2684    1.4013    0.0413    1.0748    0.0758</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 149:    92.7388   -5.8727   -2.1569   -4.2185   -0.9266    0.1868    4.2581    4.6698    1.1447    2.0851    0.1786    0.2550    1.3137    0.0413    0.9963    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 150:    92.7348   -5.7926   -2.1184   -4.2257   -0.9324    0.1966    4.0452    4.4363    1.1760    2.1508    0.1697    0.2704    1.3792    0.0363    0.8204    0.0889</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 151:    92.7968   -5.7301   -2.1179   -4.1959   -0.9275    0.1927    3.8430    4.2145    1.1707    2.1702    0.1842    0.2569    1.3789    0.0387    0.7890    0.0873</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 152:    92.9011   -5.7417   -2.1622   -4.2086   -0.9215    0.1784    2.0089    3.0670    1.1984    2.2253    0.1814    0.2507    1.3431    0.0395    0.9622    0.0780</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 153:    92.8020   -5.7707   -2.1494   -4.2128   -0.9228    0.1818    2.2261    2.9648    1.1192    2.3058    0.1749    0.2548    1.3671    0.0369    0.9507    0.0744</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 154:    92.5217   -5.8043   -2.1447   -4.2074   -0.9303    0.1766    2.7638    3.1314    1.1141    2.2814    0.1811    0.2389    1.3250    0.0408    1.0538    0.0689</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 155:    92.8765   -5.5586   -2.1275   -4.1639   -0.9320    0.1434    2.4217    2.4658    1.1231    2.1314    0.1746    0.2426    1.3785    0.0407    0.9682    0.0796</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 156:    93.0074   -5.6819   -2.1343   -4.1819   -0.9382    0.1587    1.4756    2.7496    1.1200    2.1700    0.1849    0.2320    1.3643    0.0395    1.0875    0.0653</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 157:    92.7950   -5.6827   -2.1178   -4.1808   -0.9553    0.1585    1.1607    2.5156    1.1129    2.2109    0.1724    0.2366    1.4045    0.0374    1.1322    0.0676</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 158:    92.7684   -5.7106   -2.0933   -4.2149   -0.9717    0.1602    1.4884    2.6532    1.1269    2.1388    0.1838    0.2666    1.4009    0.0348    0.8728    0.0832</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 159:    93.0980   -5.8911   -2.1297   -4.2160   -0.9660    0.1407    1.6034    3.3765    1.1182    2.1661    0.1705    0.2804    1.3557    0.0430    1.0076    0.0776</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 160:    93.1849   -5.9433   -2.1177   -4.1735   -0.9436    0.1324    1.8692    4.0887    1.1252    2.0213    0.1642    0.2922    1.3251    0.0386    0.8586    0.0820</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 161:    93.4771   -5.7768   -2.0899   -4.1697   -0.9670    0.1043    2.0938    2.9110    1.1069    1.9538    0.1820    0.3046    1.3270    0.0413    0.8220    0.0855</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 162:    93.4961   -5.6516   -2.0965   -4.1633   -0.9708    0.1263    2.2053    2.3022    1.1179    1.9555    0.1758    0.2891    1.3158    0.0417    0.9943    0.0768</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 163:    93.1627   -5.5944   -2.1527   -4.1633   -0.9488    0.1452    2.9600    2.3291    1.1027    1.9555    0.1766    0.3000    1.3626    0.0376    0.9790    0.0743</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 164:    92.7951   -5.6646   -2.1539   -4.1699   -0.9447    0.1863    2.9195    2.7599    1.0794    1.9511    0.1651    0.2918    1.4091    0.0357    1.1005    0.0709</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 165:    92.6045   -5.5927   -2.1842   -4.1770   -0.9436    0.1939    2.2014    2.3213    1.0622    1.9775    0.1671    0.2899    1.3945    0.0372    1.0533    0.0694</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 166:    92.6660   -5.5922   -2.1438   -4.1611   -0.9441    0.1767    1.8951    2.3445    1.1025    2.0399    0.1802    0.2802    1.4229    0.0354    1.1552    0.0682</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 167:    92.4353   -5.5118   -2.1415   -4.1730   -0.9124    0.1827    1.5029    1.8145    1.0701    2.0090    0.1727    0.2642    1.4525    0.0362    1.0577    0.0754</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 168:    92.3761   -5.5522   -2.1625   -4.1911   -0.9114    0.1782    1.2703    2.0808    1.0814    2.0588    0.1810    0.2658    1.3973    0.0364    0.9874    0.0729</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 169:    92.5240   -5.6102   -2.1396   -4.1793   -0.8972    0.1722    1.3115    2.2750    1.0759    2.0910    0.2147    0.2549    1.3553    0.0392    0.9770    0.0759</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 170:    92.3827   -5.5942   -2.1231   -4.1847   -0.9338    0.1525    1.5707    2.3778    1.0542    2.0824    0.2042    0.2519    1.4424    0.0363    1.1270    0.0687</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 171:    92.1706   -5.6171   -2.1619   -4.1926   -0.9056    0.1390    1.0935    2.2869    1.1239    2.1697    0.1946    0.2624    1.2980    0.0411    0.9988    0.0740</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 172:    92.1491   -5.5704   -2.1447   -4.1913   -0.9285    0.1649    1.0292    2.2160    1.1231    2.1905    0.1860    0.2518    1.3154    0.0375    1.0152    0.0772</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 173:    91.9987   -5.5411   -2.1434   -4.1803   -0.9056    0.1585    0.6511    1.9989    1.0892    2.2283    0.1899    0.2413    1.3814    0.0380    1.1733    0.0693</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 174:    92.0377   -5.5419   -2.1081   -4.1928   -0.9057    0.1418    0.9013    2.0933    1.1813    2.2773    0.1756    0.2559    1.4419    0.0343    0.9942    0.0801</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 175:    92.0266   -5.5125   -2.1014   -4.1775   -0.9119    0.1371    0.7194    2.0624    1.1848    2.2328    0.1754    0.2504    1.3987    0.0353    1.0838    0.0728</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 176:    92.0365   -5.5658   -2.0914   -4.1646   -0.8956    0.1096    0.7557    2.0588    1.1642    2.1610    0.1617    0.2722    1.3658    0.0347    0.9405    0.0757</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 177:    91.8661   -5.6799   -2.0950   -4.1695   -0.9010    0.1193    0.8835    2.8899    1.1394    2.1886    0.1809    0.2777    1.3841    0.0351    0.9178    0.0794</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 178:    91.9392   -5.7853   -2.1139   -4.1711   -0.9190    0.1222    0.6673    3.0461    1.1712    2.1948    0.1576    0.2237    1.3623    0.0383    0.9060    0.0795</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 179:    92.0116   -5.7560   -2.1418   -4.1711   -0.9153    0.1034    0.4356    2.9139    1.1653    2.1948    0.1497    0.2364    1.3387    0.0392    0.9327    0.0793</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 180:    92.1013   -5.7920   -2.1368   -4.1654   -0.9059    0.0912    0.3859    3.2592    1.1357    2.1744    0.1570    0.2337    1.3622    0.0408    1.0299    0.0739</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 181:    92.1032   -5.8125   -2.1260   -4.1791   -0.9404    0.1731    0.4091    3.0817    1.1113    2.2356    0.1572    0.2742    1.4509    0.0340    0.9180    0.0865</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 182:    92.1315   -5.7991   -2.1327   -4.1737   -0.9319    0.1519    0.3145    3.0292    1.1520    2.1360    0.1661    0.2482    1.4105    0.0361    1.1338    0.0748</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 183:    92.1350   -6.0470   -2.1264   -4.1707   -0.9525    0.1787    0.2045    3.8296    1.1229    2.1369    0.1735    0.2453    1.3132    0.0350    1.0507    0.0759</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 184:    92.0539   -5.9856   -2.1289   -4.1968   -0.9281    0.1789    0.1166    3.9425    1.0558    2.2320    0.1658    0.2253    1.3390    0.0355    1.1271    0.0720</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 185:    92.0755   -6.0524   -2.1385   -4.2076   -0.9313    0.1794    0.1193    4.2430    1.0465    2.3571    0.1566    0.2152    1.3777    0.0368    1.2013    0.0631</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 186:    92.1639   -6.0663   -2.1399   -4.1869   -0.9311    0.1817    0.1392    4.4053    1.0495    2.4857    0.1532    0.2331    1.3751    0.0365    1.0497    0.0697</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 187:    92.1759   -6.3052   -2.1469   -4.2221   -0.9428    0.1970    0.1356    5.5622    0.9953    2.3367    0.1451    0.2359    1.3754    0.0367    1.0969    0.0656</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 188:    92.1744   -6.0494   -2.1406   -4.2445   -0.9595    0.2089    0.1364    4.3818    0.9882    2.4074    0.1480    0.2364    1.3709    0.0396    1.1259    0.0642</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 189:    92.1989   -6.1255   -2.1138   -4.2037   -0.9368    0.1796    0.1444    4.6756    0.9840    2.3786    0.1502    0.2303    1.3979    0.0393    1.2011    0.0630</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 190:    92.0944   -6.1343   -2.1536   -4.2106   -0.9267    0.2105    0.1288    4.4977    1.0475    2.3593    0.1443    0.2396    1.3247    0.0384    1.1304    0.0711</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 191:    92.1238   -6.0639   -2.1462   -4.2883   -0.9311    0.2155    0.1246    4.1066    1.0428    2.6836    0.1442    0.2344    1.4041    0.0344    1.1631    0.0684</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 192:    92.1328   -6.0743   -2.1232   -4.3119   -0.9586    0.2319    0.1192    4.1348    1.0020    2.7067    0.1466    0.2515    1.4039    0.0355    1.0021    0.0772</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 193:    92.0881   -5.8697   -2.1298   -4.2691   -0.9309    0.2295    0.1050    3.4476    0.9879    2.5496    0.1344    0.2348    1.4677    0.0351    1.0332    0.0705</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 194:    92.1086   -5.7557   -2.1176   -4.3249   -0.9029    0.2183    0.0702    2.7793    1.0043    2.8853    0.1460    0.2213    1.4733    0.0325    1.0929    0.0681</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 195:    92.1265   -5.8976   -2.1506   -4.2932   -0.9267    0.2511    0.0593    3.4959    0.9881    2.6806    0.1278    0.2505    1.6138    0.0350    1.1327    0.0703</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 196:    92.1316   -5.7018   -2.1627   -4.2889   -0.9278    0.2392    0.0717    2.9210    1.0160    2.7067    0.1470    0.2350    1.5063    0.0418    1.0525    0.0734</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 197:    92.1756   -5.7899   -2.1604   -4.2819   -0.9248    0.2765    0.0754    2.9991    1.0167    2.6170    0.1465    0.2314    1.4982    0.0387    1.0504    0.0774</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 198:    92.2153   -5.8248   -2.1571   -4.2245   -0.9350    0.2195    0.0651    3.0128    0.9196    2.3123    0.1451    0.2363    1.3965    0.0453    1.1561    0.0650</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 199:    92.1935   -5.8187   -2.1505   -4.2123   -0.9452    0.2406    0.0535    3.0017    0.9553    2.2771    0.1338    0.2415    1.4485    0.0407    1.0647    0.0699</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 200:    92.2308   -5.6732   -2.1578   -4.2182   -0.9324    0.2330    0.0618    2.4070    1.0687    2.2978    0.1513    0.2199    1.4101    0.0385    1.0283    0.0741</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 201:    92.2305   -5.6796   -2.1604   -4.2064   -0.9320    0.2262    0.0530    2.4360    1.0551    2.2994    0.1529    0.2219    1.3653    0.0407    1.0115    0.0757</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 202:    92.2259   -5.7201   -2.1550   -4.2058   -0.9263    0.2208    0.0461    2.6422    1.0351    2.2888    0.1520    0.2100    1.3739    0.0418    1.0474    0.0730</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 203:    92.2212   -5.7539   -2.1457   -4.2037   -0.9303    0.2163    0.0422    2.8480    1.0403    2.2723    0.1465    0.2070    1.3973    0.0404    1.0518    0.0729</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 204:    92.2164   -5.8068   -2.1371   -4.2093   -0.9300    0.2163    0.0383    3.1313    1.0426    2.2984    0.1423    0.2091    1.4025    0.0391    1.0284    0.0737</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 205:    92.2120   -5.8371   -2.1369   -4.2142   -0.9308    0.2156    0.0347    3.2786    1.0392    2.3249    0.1390    0.2048    1.4115    0.0385    1.0161    0.0733</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 206:    92.2071   -5.8681   -2.1391   -4.2176   -0.9287    0.2158    0.0336    3.4917    1.0442    2.3461    0.1388    0.2041    1.4017    0.0394    0.9977    0.0735</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 207:    92.2078   -5.8966   -2.1424   -4.2230   -0.9286    0.2181    0.0337    3.6741    1.0456    2.3743    0.1369    0.2009    1.3988    0.0397    0.9792    0.0740</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 208:    92.2077   -5.8972   -2.1459   -4.2249   -0.9300    0.2208    0.0340    3.6775    1.0460    2.3889    0.1362    0.1985    1.3969    0.0394    0.9729    0.0742</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 209:    92.2091   -5.8831   -2.1488   -4.2271   -0.9326    0.2227    0.0337    3.6019    1.0465    2.4005    0.1380    0.1967    1.3949    0.0395    0.9745    0.0741</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 210:    92.2115   -5.8806   -2.1490   -4.2282   -0.9349    0.2238    0.0324    3.5652    1.0516    2.4211    0.1386    0.1979    1.3941    0.0391    0.9820    0.0740</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 211:    92.2151   -5.8791   -2.1516   -4.2291   -0.9363    0.2258    0.0318    3.5315    1.0458    2.4465    0.1390    0.1989    1.3918    0.0394    0.9901    0.0735</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 212:    92.2189   -5.8789   -2.1532   -4.2297   -0.9380    0.2269    0.0312    3.4989    1.0434    2.4718    0.1409    0.1993    1.3884    0.0394    1.0007    0.0732</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 213:    92.2226   -5.8714   -2.1556   -4.2287   -0.9395    0.2255    0.0313    3.4460    1.0440    2.4800    0.1413    0.1993    1.3859    0.0397    1.0157    0.0722</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 214:    92.2233   -5.8706   -2.1553   -4.2279   -0.9394    0.2237    0.0309    3.4283    1.0431    2.4809    0.1414    0.2003    1.3849    0.0399    1.0186    0.0720</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 215:    92.2242   -5.8750   -2.1536   -4.2285   -0.9390    0.2212    0.0312    3.4442    1.0455    2.4800    0.1417    0.2015    1.3830    0.0401    1.0126    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 216:    92.2252   -5.8788   -2.1521   -4.2277   -0.9393    0.2192    0.0316    3.4718    1.0459    2.4791    0.1410    0.2028    1.3817    0.0402    1.0046    0.0726</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 217:    92.2255   -5.8869   -2.1516   -4.2268   -0.9400    0.2177    0.0322    3.5295    1.0456    2.4751    0.1407    0.2042    1.3814    0.0401    1.0011    0.0729</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 218:    92.2230   -5.8819   -2.1511   -4.2259   -0.9400    0.2164    0.0327    3.5004    1.0473    2.4693    0.1399    0.2051    1.3784    0.0401    0.9962    0.0731</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 219:    92.2206   -5.8761   -2.1499   -4.2269   -0.9393    0.2156    0.0325    3.4736    1.0494    2.4667    0.1399    0.2066    1.3794    0.0399    0.9925    0.0734</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 220:    92.2185   -5.8775   -2.1494   -4.2267   -0.9380    0.2150    0.0331    3.4894    1.0511    2.4596    0.1396    0.2090    1.3788    0.0398    0.9881    0.0734</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 221:    92.2180   -5.8774   -2.1499   -4.2271   -0.9369    0.2142    0.0328    3.4873    1.0518    2.4559    0.1399    0.2109    1.3776    0.0399    0.9842    0.0735</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 222:    92.2191   -5.8833   -2.1503   -4.2280   -0.9359    0.2127    0.0328    3.5384    1.0518    2.4533    0.1400    0.2114    1.3778    0.0400    0.9825    0.0735</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 223:    92.2199   -5.8831   -2.1486   -4.2288   -0.9343    0.2112    0.0326    3.5474    1.0539    2.4515    0.1396    0.2127    1.3795    0.0398    0.9806    0.0735</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 224:    92.2206   -5.8870   -2.1467   -4.2283   -0.9331    0.2093    0.0324    3.5619    1.0550    2.4482    0.1395    0.2133    1.3805    0.0395    0.9792    0.0735</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 225:    92.2218   -5.8929   -2.1459   -4.2289   -0.9328    0.2086    0.0322    3.6001    1.0548    2.4492    0.1388    0.2142    1.3804    0.0393    0.9809    0.0734</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 226:    92.2228   -5.8909   -2.1440   -4.2293   -0.9322    0.2082    0.0320    3.6007    1.0566    2.4513    0.1383    0.2143    1.3826    0.0390    0.9815    0.0737</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 227:    92.2234   -5.8838   -2.1444   -4.2304   -0.9319    0.2088    0.0319    3.5748    1.0551    2.4562    0.1381    0.2140    1.3814    0.0390    0.9836    0.0736</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 228:    92.2243   -5.8835   -2.1444   -4.2314   -0.9326    0.2093    0.0317    3.5756    1.0526    2.4607    0.1377    0.2143    1.3824    0.0389    0.9901    0.0733</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 229:    92.2251   -5.8916   -2.1442   -4.2307   -0.9330    0.2095    0.0318    3.6344    1.0501    2.4580    0.1371    0.2148    1.3850    0.0388    0.9930    0.0733</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 230:    92.2250   -5.8951   -2.1442   -4.2303   -0.9334    0.2107    0.0318    3.6533    1.0475    2.4550    0.1369    0.2150    1.3872    0.0387    0.9953    0.0734</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 231:    92.2247   -5.8964   -2.1444   -4.2300   -0.9345    0.2119    0.0322    3.6564    1.0446    2.4549    0.1368    0.2151    1.3899    0.0388    0.9991    0.0733</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 232:    92.2230   -5.8982   -2.1451   -4.2306   -0.9352    0.2132    0.0326    3.6643    1.0422    2.4591    0.1365    0.2155    1.3934    0.0388    1.0052    0.0729</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 233:    92.2223   -5.9022   -2.1455   -4.2327   -0.9354    0.2141    0.0329    3.6824    1.0396    2.4697    0.1362    0.2165    1.3970    0.0388    1.0107    0.0726</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 234:    92.2200   -5.9075   -2.1457   -4.2324   -0.9348    0.2140    0.0335    3.7056    1.0364    2.4735    0.1366    0.2174    1.3977    0.0388    1.0163    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 235:    92.2171   -5.9094   -2.1457   -4.2338   -0.9342    0.2142    0.0342    3.7222    1.0339    2.4854    0.1372    0.2189    1.3999    0.0389    1.0228    0.0721</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 236:    92.2161   -5.9195   -2.1464   -4.2368   -0.9340    0.2140    0.0346    3.7789    1.0329    2.4986    0.1376    0.2200    1.4007    0.0389    1.0281    0.0721</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 237:    92.2163   -5.9214   -2.1469   -4.2390   -0.9330    0.2143    0.0347    3.7862    1.0329    2.5074    0.1378    0.2195    1.4030    0.0389    1.0320    0.0719</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 238:    92.2165   -5.9203   -2.1473   -4.2399   -0.9326    0.2143    0.0347    3.7781    1.0336    2.5100    0.1381    0.2186    1.4031    0.0388    1.0347    0.0719</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 239:    92.2173   -5.9212   -2.1471   -4.2403   -0.9325    0.2136    0.0344    3.7908    1.0350    2.5128    0.1384    0.2175    1.4021    0.0389    1.0352    0.0718</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 240:    92.2178   -5.9217   -2.1470   -4.2394   -0.9322    0.2133    0.0342    3.8082    1.0363    2.5118    0.1384    0.2169    1.4025    0.0387    1.0345    0.0719</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 241:    92.2177   -5.9200   -2.1470   -4.2383   -0.9318    0.2136    0.0340    3.8123    1.0381    2.5110    0.1385    0.2161    1.4028    0.0387    1.0336    0.0720</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 242:    92.2170   -5.9109   -2.1478   -4.2371   -0.9308    0.2136    0.0340    3.7766    1.0387    2.5090    0.1385    0.2157    1.4027    0.0386    1.0322    0.0720</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 243:    92.2165   -5.9029   -2.1484   -4.2371   -0.9300    0.2140    0.0340    3.7433    1.0389    2.5128    0.1386    0.2150    1.4032    0.0385    1.0342    0.0720</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 244:    92.2161   -5.8998   -2.1489   -4.2374   -0.9294    0.2143    0.0340    3.7242    1.0392    2.5173    0.1386    0.2148    1.4030    0.0384    1.0343    0.0720</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 245:    92.2153   -5.9048   -2.1494   -4.2370   -0.9291    0.2149    0.0339    3.7491    1.0399    2.5182    0.1386    0.2146    1.4019    0.0383    1.0317    0.0722</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 246:    92.2147   -5.9093   -2.1498   -4.2371   -0.9289    0.2156    0.0339    3.7727    1.0409    2.5218    0.1384    0.2145    1.4016    0.0382    1.0309    0.0724</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 247:    92.2143   -5.9124   -2.1503   -4.2368   -0.9286    0.2159    0.0338    3.7972    1.0408    2.5232    0.1383    0.2144    1.4012    0.0382    1.0297    0.0725</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 248:    92.2137   -5.9122   -2.1500   -4.2367   -0.9286    0.2160    0.0336    3.8036    1.0409    2.5245    0.1382    0.2141    1.4022    0.0381    1.0272    0.0727</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 249:    92.2133   -5.9101   -2.1501   -4.2364   -0.9288    0.2160    0.0334    3.7984    1.0406    2.5268    0.1383    0.2136    1.4031    0.0381    1.0273    0.0726</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 250:    92.2139   -5.9115   -2.1502   -4.2368   -0.9289    0.2167    0.0335    3.8015    1.0412    2.5326    0.1380    0.2132    1.4035    0.0381    1.0261    0.0727</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 251:    92.2132   -5.9123   -2.1502   -4.2373   -0.9297    0.2170    0.0338    3.8015    1.0421    2.5362    0.1376    0.2126    1.4053    0.0379    1.0274    0.0727</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 252:    92.2128   -5.9106   -2.1502   -4.2366   -0.9299    0.2174    0.0338    3.7931    1.0427    2.5345    0.1371    0.2120    1.4062    0.0379    1.0262    0.0728</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 253:    92.2118   -5.9098   -2.1492   -4.2364   -0.9297    0.2173    0.0340    3.8011    1.0420    2.5338    0.1369    0.2121    1.4074    0.0378    1.0232    0.0731</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 254:    92.2107   -5.9104   -2.1479   -4.2362   -0.9296    0.2171    0.0341    3.8085    1.0398    2.5330    0.1366    0.2123    1.4093    0.0377    1.0230    0.0731</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 255:    92.2103   -5.9133   -2.1466   -4.2359   -0.9299    0.2166    0.0341    3.8219    1.0381    2.5346    0.1363    0.2122    1.4105    0.0377    1.0230    0.0731</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 256:    92.2094   -5.9171   -2.1453   -4.2356   -0.9299    0.2161    0.0341    3.8498    1.0366    2.5366    0.1362    0.2123    1.4114    0.0375    1.0239    0.0731</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 257:    92.2091   -5.9169   -2.1441   -4.2353   -0.9302    0.2153    0.0341    3.8529    1.0347    2.5385    0.1360    0.2118    1.4126    0.0375    1.0265    0.0729</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 258:    92.2089   -5.9191   -2.1431   -4.2349   -0.9301    0.2145    0.0340    3.8608    1.0331    2.5392    0.1359    0.2119    1.4126    0.0375    1.0282    0.0728</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 259:    92.2093   -5.9249   -2.1426   -4.2337   -0.9301    0.2145    0.0341    3.8853    1.0321    2.5385    0.1361    0.2120    1.4114    0.0374    1.0280    0.0728</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 260:    92.2095   -5.9297   -2.1415   -4.2328   -0.9303    0.2146    0.0339    3.9108    1.0302    2.5367    0.1358    0.2125    1.4112    0.0373    1.0265    0.0729</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 261:    92.2096   -5.9341   -2.1407   -4.2321   -0.9307    0.2147    0.0338    3.9343    1.0278    2.5343    0.1356    0.2131    1.4121    0.0373    1.0272    0.0728</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 262:    92.2094   -5.9342   -2.1398   -4.2312   -0.9310    0.2149    0.0336    3.9314    1.0254    2.5313    0.1356    0.2139    1.4115    0.0372    1.0249    0.0729</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 263:    92.2088   -5.9376   -2.1388   -4.2304   -0.9313    0.2153    0.0334    3.9452    1.0232    2.5289    0.1357    0.2143    1.4109    0.0371    1.0228    0.0731</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 264:    92.2077   -5.9410   -2.1379   -4.2301   -0.9317    0.2153    0.0333    3.9571    1.0204    2.5265    0.1356    0.2147    1.4105    0.0371    1.0224    0.0731</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 265:    92.2069   -5.9454   -2.1374   -4.2296   -0.9322    0.2154    0.0332    3.9800    1.0182    2.5230    0.1356    0.2147    1.4115    0.0371    1.0232    0.0730</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 266:    92.2067   -5.9463   -2.1370   -4.2291   -0.9324    0.2155    0.0330    3.9809    1.0165    2.5217    0.1354    0.2148    1.4121    0.0371    1.0220    0.0730</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 267:    92.2064   -5.9492   -2.1368   -4.2282   -0.9329    0.2154    0.0328    3.9874    1.0150    2.5199    0.1357    0.2150    1.4124    0.0370    1.0233    0.0729</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 268:    92.2065   -5.9468   -2.1360   -4.2276   -0.9333    0.2155    0.0328    3.9722    1.0133    2.5171    0.1362    0.2155    1.4133    0.0369    1.0243    0.0729</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 269:    92.2066   -5.9438   -2.1352   -4.2267   -0.9335    0.2151    0.0328    3.9526    1.0122    2.5142    0.1367    0.2154    1.4140    0.0369    1.0244    0.0728</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 270:    92.2067   -5.9388   -2.1351   -4.2259   -0.9339    0.2151    0.0326    3.9283    1.0112    2.5114    0.1370    0.2157    1.4137    0.0368    1.0240    0.0729</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 271:    92.2069   -5.9350   -2.1344   -4.2252   -0.9342    0.2152    0.0324    3.9072    1.0107    2.5094    0.1370    0.2158    1.4136    0.0367    1.0222    0.0730</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 272:    92.2069   -5.9317   -2.1341   -4.2246   -0.9348    0.2153    0.0321    3.8870    1.0104    2.5082    0.1372    0.2157    1.4139    0.0366    1.0219    0.0731</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 273:    92.2068   -5.9289   -2.1340   -4.2240   -0.9348    0.2152    0.0320    3.8711    1.0101    2.5075    0.1373    0.2159    1.4146    0.0366    1.0232    0.0730</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 274:    92.2067   -5.9271   -2.1337   -4.2239   -0.9350    0.2153    0.0318    3.8569    1.0101    2.5085    0.1377    0.2157    1.4144    0.0366    1.0239    0.0731</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 275:    92.2063   -5.9264   -2.1339   -4.2235   -0.9353    0.2156    0.0317    3.8476    1.0097    2.5078    0.1382    0.2157    1.4143    0.0365    1.0256    0.0730</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 276:    92.2059   -5.9271   -2.1339   -4.2231   -0.9354    0.2160    0.0316    3.8417    1.0097    2.5074    0.1387    0.2156    1.4132    0.0365    1.0260    0.0730</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 277:    92.2059   -5.9283   -2.1342   -4.2225   -0.9356    0.2163    0.0316    3.8412    1.0094    2.5073    0.1393    0.2155    1.4122    0.0364    1.0283    0.0729</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 278:    92.2062   -5.9278   -2.1341   -4.2220   -0.9361    0.2166    0.0318    3.8331    1.0081    2.5069    0.1397    0.2154    1.4117    0.0364    1.0321    0.0727</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 279:    92.2061   -5.9264   -2.1341   -4.2214   -0.9363    0.2168    0.0319    3.8188    1.0072    2.5051    0.1402    0.2155    1.4111    0.0364    1.0342    0.0726</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 280:    92.2057   -5.9263   -2.1344   -4.2211   -0.9365    0.2167    0.0321    3.8114    1.0072    2.5061    0.1406    0.2148    1.4108    0.0365    1.0361    0.0725</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 281:    92.2048   -5.9269   -2.1344   -4.2208   -0.9365    0.2166    0.0324    3.8082    1.0076    2.5077    0.1410    0.2142    1.4098    0.0365    1.0363    0.0726</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 282:    92.2045   -5.9257   -2.1347   -4.2205   -0.9366    0.2165    0.0328    3.8040    1.0082    2.5092    0.1413    0.2138    1.4090    0.0365    1.0364    0.0726</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 283:    92.2038   -5.9235   -2.1348   -4.2201   -0.9363    0.2163    0.0331    3.7955    1.0089    2.5108    0.1415    0.2134    1.4086    0.0365    1.0362    0.0726</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 284:    92.2032   -5.9234   -2.1348   -4.2198   -0.9359    0.2162    0.0335    3.7945    1.0100    2.5118    0.1420    0.2129    1.4083    0.0365    1.0362    0.0726</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 285:    92.2028   -5.9236   -2.1347   -4.2193   -0.9360    0.2161    0.0339    3.7930    1.0104    2.5124    0.1422    0.2125    1.4080    0.0365    1.0373    0.0725</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 286:    92.2020   -5.9221   -2.1344   -4.2187   -0.9355    0.2156    0.0341    3.7872    1.0104    2.5134    0.1425    0.2121    1.4074    0.0364    1.0377    0.0725</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 287:    92.2013   -5.9213   -2.1341   -4.2183   -0.9354    0.2154    0.0343    3.7888    1.0098    2.5151    0.1428    0.2119    1.4075    0.0364    1.0390    0.0724</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 288:    92.2003   -5.9195   -2.1339   -4.2182   -0.9355    0.2152    0.0343    3.7831    1.0090    2.5180    0.1430    0.2116    1.4082    0.0364    1.0408    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 289:    92.1995   -5.9186   -2.1339   -4.2182   -0.9355    0.2154    0.0343    3.7805    1.0083    2.5217    0.1429    0.2115    1.4085    0.0364    1.0426    0.0722</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 290:    92.1984   -5.9180   -2.1339   -4.2177   -0.9354    0.2153    0.0343    3.7767    1.0080    2.5219    0.1427    0.2113    1.4083    0.0365    1.0429    0.0721</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 291:    92.1975   -5.9167   -2.1334   -4.2175   -0.9355    0.2151    0.0343    3.7701    1.0080    2.5230    0.1427    0.2108    1.4083    0.0364    1.0439    0.0721</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 292:    92.1971   -5.9175   -2.1330   -4.2166   -0.9356    0.2144    0.0342    3.7722    1.0081    2.5227    0.1426    0.2107    1.4079    0.0364    1.0443    0.0721</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 293:    92.1963   -5.9188   -2.1330   -4.2157   -0.9357    0.2141    0.0342    3.7768    1.0083    2.5242    0.1425    0.2103    1.4073    0.0364    1.0439    0.0721</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 294:    92.1953   -5.9189   -2.1328   -4.2157   -0.9357    0.2136    0.0344    3.7727    1.0078    2.5294    0.1427    0.2095    1.4079    0.0364    1.0456    0.0720</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 295:    92.1946   -5.9171   -2.1329   -4.2153   -0.9355    0.2135    0.0345    3.7623    1.0081    2.5331    0.1426    0.2090    1.4080    0.0364    1.0462    0.0720</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 296:    92.1940   -5.9154   -2.1329   -4.2150   -0.9354    0.2130    0.0346    3.7523    1.0082    2.5354    0.1425    0.2084    1.4075    0.0365    1.0463    0.0720</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 297:    92.1934   -5.9128   -2.1330   -4.2145   -0.9351    0.2127    0.0349    3.7395    1.0084    2.5373    0.1423    0.2079    1.4072    0.0365    1.0453    0.0720</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 298:    92.1932   -5.9128   -2.1332   -4.2138   -0.9347    0.2120    0.0350    3.7372    1.0085    2.5368    0.1423    0.2075    1.4068    0.0365    1.0449    0.0720</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 299:    92.1927   -5.9120   -2.1333   -4.2131   -0.9344    0.2112    0.0352    3.7324    1.0088    2.5366    0.1422    0.2070    1.4059    0.0366    1.0441    0.0721</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 300:    92.1921   -5.9097   -2.1335   -4.2122   -0.9343    0.2105    0.0353    3.7211    1.0096    2.5357    0.1425    0.2066    1.4050    0.0367    1.0443    0.0721</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 301:    92.1915   -5.9081   -2.1337   -4.2116   -0.9343    0.2098    0.0355    3.7110    1.0104    2.5355    0.1427    0.2061    1.4041    0.0368    1.0448    0.0721</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 302:    92.1912   -5.9067   -2.1340   -4.2108   -0.9342    0.2092    0.0355    3.7052    1.0114    2.5348    0.1428    0.2055    1.4036    0.0369    1.0442    0.0722</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 303:    92.1913   -5.9039   -2.1342   -4.2113   -0.9342    0.2087    0.0355    3.6925    1.0123    2.5408    0.1430    0.2049    1.4028    0.0369    1.0436    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 304:    92.1920   -5.9012   -2.1342   -4.2119   -0.9342    0.2081    0.0356    3.6781    1.0129    2.5476    0.1432    0.2044    1.4026    0.0369    1.0436    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 305:    92.1923   -5.8984   -2.1343   -4.2123   -0.9341    0.2076    0.0354    3.6633    1.0134    2.5525    0.1434    0.2038    1.4022    0.0370    1.0434    0.0724</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 306:    92.1926   -5.8977   -2.1343   -4.2121   -0.9343    0.2072    0.0354    3.6603    1.0144    2.5567    0.1436    0.2030    1.4022    0.0369    1.0431    0.0724</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 307:    92.1931   -5.8970   -2.1346   -4.2124   -0.9344    0.2068    0.0353    3.6555    1.0154    2.5617    0.1438    0.2021    1.4017    0.0370    1.0431    0.0725</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 308:    92.1935   -5.8963   -2.1347   -4.2127   -0.9343    0.2062    0.0351    3.6486    1.0166    2.5665    0.1439    0.2012    1.4017    0.0370    1.0431    0.0724</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 309:    92.1934   -5.8965   -2.1349   -4.2130   -0.9344    0.2058    0.0350    3.6454    1.0180    2.5711    0.1439    0.2003    1.4013    0.0370    1.0427    0.0725</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 310:    92.1935   -5.8965   -2.1349   -4.2133   -0.9345    0.2055    0.0350    3.6401    1.0193    2.5761    0.1439    0.1993    1.4013    0.0371    1.0418    0.0725</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 311:    92.1931   -5.8976   -2.1349   -4.2139   -0.9346    0.2052    0.0349    3.6416    1.0207    2.5820    0.1437    0.1982    1.4015    0.0370    1.0411    0.0726</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 312:    92.1928   -5.8995   -2.1350   -4.2149   -0.9349    0.2053    0.0350    3.6472    1.0222    2.5902    0.1437    0.1972    1.4018    0.0370    1.0412    0.0727</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 313:    92.1922   -5.8999   -2.1350   -4.2159   -0.9352    0.2055    0.0350    3.6450    1.0236    2.5989    0.1436    0.1962    1.4017    0.0369    1.0407    0.0728</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 314:    92.1913   -5.9021   -2.1353   -4.2171   -0.9353    0.2055    0.0350    3.6532    1.0244    2.6086    0.1437    0.1951    1.4019    0.0369    1.0409    0.0728</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 315:    92.1905   -5.9033   -2.1358   -4.2178   -0.9354    0.2055    0.0351    3.6591    1.0252    2.6142    0.1438    0.1941    1.4018    0.0369    1.0420    0.0728</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 316:    92.1895   -5.9043   -2.1358   -4.2186   -0.9352    0.2053    0.0352    3.6648    1.0266    2.6207    0.1441    0.1934    1.4016    0.0369    1.0420    0.0729</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 317:    92.1888   -5.9041   -2.1359   -4.2200   -0.9352    0.2052    0.0352    3.6673    1.0278    2.6312    0.1444    0.1926    1.4014    0.0369    1.0416    0.0729</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 318:    92.1879   -5.9039   -2.1360   -4.2212   -0.9352    0.2052    0.0354    3.6670    1.0292    2.6380    0.1446    0.1919    1.4010    0.0369    1.0403    0.0730</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 319:    92.1871   -5.9046   -2.1361   -4.2222   -0.9353    0.2052    0.0354    3.6709    1.0306    2.6432    0.1448    0.1912    1.4008    0.0369    1.0394    0.0731</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 320:    92.1863   -5.9023   -2.1365   -4.2229   -0.9355    0.2052    0.0355    3.6602    1.0318    2.6466    0.1448    0.1903    1.4011    0.0369    1.0398    0.0731</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 321:    92.1855   -5.8996   -2.1368   -4.2236   -0.9354    0.2053    0.0355    3.6491    1.0329    2.6505    0.1448    0.1895    1.4017    0.0369    1.0402    0.0731</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 322:    92.1850   -5.8979   -2.1370   -4.2243   -0.9353    0.2057    0.0356    3.6399    1.0341    2.6534    0.1447    0.1888    1.4020    0.0368    1.0394    0.0732</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 323:    92.1844   -5.8984   -2.1371   -4.2251   -0.9352    0.2059    0.0356    3.6431    1.0346    2.6566    0.1447    0.1882    1.4026    0.0368    1.0389    0.0733</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 324:    92.1838   -5.8967   -2.1372   -4.2257   -0.9353    0.2062    0.0356    3.6392    1.0351    2.6598    0.1445    0.1875    1.4031    0.0368    1.0382    0.0734</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 325:    92.1835   -5.8936   -2.1374   -4.2264   -0.9351    0.2066    0.0355    3.6288    1.0352    2.6633    0.1444    0.1869    1.4043    0.0367    1.0391    0.0734</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 326:    92.1837   -5.8936   -2.1376   -4.2276   -0.9350    0.2071    0.0354    3.6296    1.0354    2.6684    0.1444    0.1865    1.4043    0.0367    1.0380    0.0735</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 327:    92.1837   -5.8943   -2.1378   -4.2289   -0.9347    0.2076    0.0354    3.6348    1.0357    2.6746    0.1444    0.1860    1.4043    0.0366    1.0380    0.0735</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 328:    92.1838   -5.8953   -2.1380   -4.2297   -0.9345    0.2081    0.0354    3.6456    1.0360    2.6776    0.1444    0.1856    1.4043    0.0366    1.0379    0.0735</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 329:    92.1836   -5.8974   -2.1383   -4.2305   -0.9342    0.2085    0.0356    3.6591    1.0361    2.6802    0.1444    0.1852    1.4043    0.0366    1.0385    0.0735</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 330:    92.1832   -5.8994   -2.1387   -4.2305   -0.9342    0.2088    0.0358    3.6715    1.0364    2.6798    0.1443    0.1849    1.4037    0.0367    1.0378    0.0736</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 331:    92.1826   -5.8983   -2.1391   -4.2302   -0.9342    0.2091    0.0357    3.6670    1.0368    2.6800    0.1442    0.1845    1.4038    0.0367    1.0379    0.0736</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 332:    92.1824   -5.8959   -2.1396   -4.2301   -0.9341    0.2097    0.0357    3.6576    1.0375    2.6792    0.1442    0.1842    1.4034    0.0367    1.0372    0.0737</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 333:    92.1826   -5.8937   -2.1401   -4.2304   -0.9340    0.2104    0.0356    3.6487    1.0383    2.6798    0.1442    0.1838    1.4029    0.0367    1.0360    0.0738</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 334:    92.1825   -5.8911   -2.1407   -4.2311   -0.9339    0.2112    0.0355    3.6378    1.0390    2.6825    0.1444    0.1834    1.4025    0.0367    1.0349    0.0739</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 335:    92.1830   -5.8887   -2.1411   -4.2317   -0.9336    0.2118    0.0355    3.6294    1.0396    2.6850    0.1444    0.1830    1.4022    0.0367    1.0338    0.0740</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 336:    92.1834   -5.8872   -2.1413   -4.2324   -0.9334    0.2124    0.0356    3.6249    1.0402    2.6880    0.1444    0.1825    1.4024    0.0367    1.0339    0.0740</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 337:    92.1835   -5.8869   -2.1416   -4.2334   -0.9332    0.2131    0.0356    3.6252    1.0411    2.6926    0.1443    0.1821    1.4029    0.0366    1.0330    0.0741</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 338:    92.1839   -5.8882   -2.1418   -4.2346   -0.9333    0.2137    0.0357    3.6332    1.0421    2.6972    0.1441    0.1815    1.4036    0.0366    1.0324    0.0742</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 339:    92.1845   -5.8869   -2.1420   -4.2358   -0.9332    0.2143    0.0357    3.6261    1.0428    2.7021    0.1439    0.1810    1.4043    0.0366    1.0322    0.0742</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 340:    92.1846   -5.8863   -2.1421   -4.2367   -0.9331    0.2147    0.0357    3.6242    1.0436    2.7060    0.1439    0.1804    1.4049    0.0365    1.0328    0.0743</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 341:    92.1848   -5.8847   -2.1421   -4.2378   -0.9330    0.2150    0.0356    3.6177    1.0442    2.7099    0.1439    0.1798    1.4056    0.0366    1.0334    0.0743</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 342:    92.1848   -5.8838   -2.1421   -4.2390   -0.9330    0.2154    0.0355    3.6114    1.0449    2.7151    0.1438    0.1792    1.4064    0.0365    1.0332    0.0743</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 343:    92.1849   -5.8840   -2.1423   -4.2398   -0.9330    0.2157    0.0353    3.6109    1.0459    2.7191    0.1438    0.1785    1.4060    0.0366    1.0334    0.0743</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 344:    92.1851   -5.8839   -2.1423   -4.2406   -0.9330    0.2159    0.0352    3.6092    1.0467    2.7229    0.1440    0.1779    1.4060    0.0365    1.0338    0.0743</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 345:    92.1850   -5.8841   -2.1424   -4.2414   -0.9331    0.2162    0.0352    3.6070    1.0472    2.7263    0.1441    0.1774    1.4056    0.0365    1.0341    0.0743</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 346:    92.1848   -5.8839   -2.1424   -4.2420   -0.9333    0.2164    0.0352    3.6048    1.0479    2.7292    0.1443    0.1769    1.4058    0.0365    1.0344    0.0743</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 347:    92.1846   -5.8835   -2.1425   -4.2427   -0.9334    0.2166    0.0353    3.6017    1.0495    2.7330    0.1444    0.1763    1.4053    0.0365    1.0343    0.0743</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 348:    92.1846   -5.8825   -2.1427   -4.2434   -0.9336    0.2168    0.0353    3.5968    1.0508    2.7362    0.1445    0.1757    1.4051    0.0365    1.0343    0.0743</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 349:    92.1846   -5.8820   -2.1432   -4.2444   -0.9337    0.2172    0.0352    3.5937    1.0519    2.7412    0.1446    0.1752    1.4047    0.0365    1.0343    0.0743</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 350:    92.1845   -5.8817   -2.1435   -4.2454   -0.9338    0.2179    0.0351    3.5899    1.0526    2.7460    0.1448    0.1747    1.4042    0.0365    1.0339    0.0744</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 351:    92.1844   -5.8824   -2.1439   -4.2468   -0.9338    0.2184    0.0350    3.5917    1.0535    2.7531    0.1448    0.1740    1.4041    0.0365    1.0336    0.0744</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 352:    92.1840   -5.8840   -2.1442   -4.2482   -0.9338    0.2189    0.0350    3.6013    1.0543    2.7603    0.1450    0.1734    1.4044    0.0364    1.0342    0.0744</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 353:    92.1838   -5.8848   -2.1445   -4.2492   -0.9336    0.2194    0.0349    3.6030    1.0552    2.7652    0.1451    0.1730    1.4043    0.0364    1.0338    0.0744</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 354:    92.1838   -5.8835   -2.1449   -4.2501   -0.9336    0.2197    0.0349    3.5961    1.0563    2.7697    0.1452    0.1723    1.4044    0.0364    1.0342    0.0744</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 355:    92.1837   -5.8826   -2.1453   -4.2511   -0.9335    0.2199    0.0348    3.5910    1.0569    2.7757    0.1451    0.1718    1.4053    0.0364    1.0348    0.0744</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 356:    92.1835   -5.8826   -2.1456   -4.2520   -0.9334    0.2198    0.0348    3.5922    1.0577    2.7815    0.1451    0.1712    1.4054    0.0364    1.0352    0.0744</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 357:    92.1834   -5.8818   -2.1457   -4.2525   -0.9334    0.2198    0.0349    3.5894    1.0588    2.7852    0.1449    0.1706    1.4058    0.0364    1.0354    0.0744</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 358:    92.1834   -5.8808   -2.1459   -4.2531   -0.9334    0.2197    0.0348    3.5861    1.0600    2.7891    0.1449    0.1701    1.4062    0.0364    1.0361    0.0744</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 359:    92.1833   -5.8799   -2.1459   -4.2533   -0.9333    0.2197    0.0350    3.5822    1.0607    2.7903    0.1448    0.1696    1.4063    0.0364    1.0360    0.0744</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 360:    92.1831   -5.8792   -2.1460   -4.2534   -0.9332    0.2196    0.0351    3.5787    1.0613    2.7914    0.1446    0.1691    1.4065    0.0364    1.0361    0.0744</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 361:    92.1831   -5.8785   -2.1460   -4.2542   -0.9331    0.2196    0.0351    3.5771    1.0620    2.7969    0.1445    0.1688    1.4072    0.0364    1.0366    0.0744</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 362:    92.1832   -5.8780   -2.1462   -4.2547   -0.9331    0.2195    0.0351    3.5750    1.0625    2.8017    0.1443    0.1683    1.4079    0.0363    1.0369    0.0744</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 363:    92.1830   -5.8788   -2.1464   -4.2551   -0.9331    0.2192    0.0351    3.5785    1.0630    2.8057    0.1443    0.1677    1.4081    0.0363    1.0377    0.0743</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 364:    92.1829   -5.8778   -2.1466   -4.2554   -0.9332    0.2193    0.0350    3.5747    1.0634    2.8092    0.1443    0.1672    1.4084    0.0363    1.0386    0.0743</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 365:    92.1830   -5.8782   -2.1466   -4.2558   -0.9332    0.2192    0.0350    3.5771    1.0638    2.8135    0.1443    0.1667    1.4086    0.0363    1.0385    0.0743</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 366:    92.1830   -5.8795   -2.1465   -4.2564   -0.9332    0.2190    0.0350    3.5838    1.0645    2.8190    0.1444    0.1661    1.4086    0.0363    1.0390    0.0743</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 367:    92.1826   -5.8801   -2.1465   -4.2569   -0.9333    0.2191    0.0349    3.5867    1.0652    2.8232    0.1445    0.1657    1.4086    0.0362    1.0388    0.0744</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 368:    92.1824   -5.8799   -2.1465   -4.2571   -0.9333    0.2193    0.0348    3.5859    1.0654    2.8252    0.1445    0.1653    1.4088    0.0362    1.0385    0.0744</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 369:    92.1822   -5.8803   -2.1467   -4.2572   -0.9333    0.2193    0.0348    3.5846    1.0653    2.8261    0.1444    0.1648    1.4086    0.0362    1.0393    0.0744</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 370:    92.1820   -5.8802   -2.1469   -4.2575   -0.9333    0.2195    0.0348    3.5822    1.0653    2.8273    0.1444    0.1645    1.4088    0.0362    1.0396    0.0744</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 371:    92.1818   -5.8806   -2.1470   -4.2575   -0.9333    0.2198    0.0348    3.5819    1.0649    2.8277    0.1443    0.1642    1.4091    0.0362    1.0403    0.0743</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 372:    92.1818   -5.8804   -2.1473   -4.2580   -0.9332    0.2202    0.0348    3.5795    1.0645    2.8300    0.1441    0.1640    1.4096    0.0362    1.0418    0.0743</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 373:    92.1816   -5.8812   -2.1475   -4.2580   -0.9332    0.2203    0.0348    3.5816    1.0644    2.8298    0.1441    0.1639    1.4100    0.0362    1.0439    0.0741</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 374:    92.1815   -5.8817   -2.1477   -4.2578   -0.9333    0.2203    0.0349    3.5819    1.0641    2.8282    0.1440    0.1641    1.4099    0.0362    1.0443    0.0741</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 375:    92.1817   -5.8828   -2.1479   -4.2574   -0.9335    0.2204    0.0351    3.5853    1.0636    2.8267    0.1440    0.1642    1.4100    0.0362    1.0457    0.0740</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 376:    92.1819   -5.8826   -2.1479   -4.2573   -0.9338    0.2206    0.0353    3.5843    1.0634    2.8246    0.1440    0.1643    1.4100    0.0362    1.0464    0.0740</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 377:    92.1819   -5.8826   -2.1480   -4.2572   -0.9339    0.2206    0.0354    3.5819    1.0633    2.8225    0.1440    0.1644    1.4097    0.0363    1.0470    0.0739</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 378:    92.1819   -5.8821   -2.1481   -4.2571   -0.9340    0.2205    0.0355    3.5777    1.0633    2.8206    0.1440    0.1644    1.4093    0.0363    1.0468    0.0739</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 379:    92.1819   -5.8823   -2.1481   -4.2573   -0.9340    0.2205    0.0356    3.5746    1.0633    2.8201    0.1439    0.1645    1.4092    0.0363    1.0468    0.0739</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 380:    92.1820   -5.8809   -2.1481   -4.2570   -0.9340    0.2205    0.0356    3.5665    1.0634    2.8178    0.1439    0.1647    1.4093    0.0363    1.0463    0.0739</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 381:    92.1823   -5.8795   -2.1481   -4.2568   -0.9338    0.2204    0.0357    3.5586    1.0635    2.8170    0.1439    0.1648    1.4095    0.0363    1.0456    0.0740</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 382:    92.1828   -5.8783   -2.1481   -4.2571   -0.9339    0.2203    0.0357    3.5514    1.0636    2.8183    0.1441    0.1649    1.4093    0.0363    1.0462    0.0740</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 383:    92.1827   -5.8773   -2.1480   -4.2569   -0.9340    0.2202    0.0357    3.5459    1.0633    2.8173    0.1441    0.1650    1.4094    0.0363    1.0465    0.0739</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 384:    92.1825   -5.8763   -2.1482   -4.2568   -0.9341    0.2202    0.0357    3.5397    1.0636    2.8165    0.1441    0.1650    1.4094    0.0363    1.0467    0.0739</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 385:    92.1822   -5.8761   -2.1483   -4.2567   -0.9341    0.2201    0.0357    3.5363    1.0641    2.8160    0.1440    0.1650    1.4094    0.0363    1.0469    0.0739</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 386:    92.1820   -5.8763   -2.1485   -4.2567   -0.9342    0.2200    0.0356    3.5365    1.0645    2.8157    0.1441    0.1648    1.4092    0.0364    1.0478    0.0738</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 387:    92.1819   -5.8767   -2.1486   -4.2567   -0.9343    0.2201    0.0358    3.5383    1.0652    2.8156    0.1442    0.1646    1.4090    0.0364    1.0480    0.0738</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 388:    92.1818   -5.8772   -2.1488   -4.2569   -0.9344    0.2202    0.0359    3.5400    1.0656    2.8153    0.1443    0.1645    1.4086    0.0364    1.0480    0.0738</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 389:    92.1816   -5.8765   -2.1489   -4.2569   -0.9344    0.2203    0.0359    3.5369    1.0660    2.8145    0.1443    0.1644    1.4083    0.0364    1.0476    0.0738</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 390:    92.1815   -5.8761   -2.1490   -4.2569   -0.9343    0.2205    0.0359    3.5349    1.0664    2.8136    0.1444    0.1644    1.4080    0.0364    1.0472    0.0738</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 391:    92.1814   -5.8749   -2.1492   -4.2572   -0.9343    0.2207    0.0359    3.5313    1.0668    2.8153    0.1445    0.1643    1.4077    0.0364    1.0465    0.0739</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 392:    92.1812   -5.8733   -2.1494   -4.2571   -0.9344    0.2210    0.0359    3.5248    1.0674    2.8141    0.1445    0.1642    1.4073    0.0364    1.0457    0.0739</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 393:    92.1811   -5.8727   -2.1497   -4.2570   -0.9345    0.2212    0.0359    3.5226    1.0679    2.8127    0.1445    0.1642    1.4071    0.0364    1.0457    0.0739</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 394:    92.1811   -5.8712   -2.1500   -4.2572   -0.9346    0.2214    0.0360    3.5174    1.0684    2.8132    0.1446    0.1641    1.4067    0.0364    1.0456    0.0739</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 395:    92.1810   -5.8714   -2.1501   -4.2579   -0.9348    0.2217    0.0361    3.5182    1.0685    2.8171    0.1445    0.1641    1.4065    0.0364    1.0452    0.0740</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 396:    92.1809   -5.8711   -2.1501   -4.2582   -0.9350    0.2220    0.0363    3.5161    1.0681    2.8184    0.1446    0.1639    1.4063    0.0364    1.0447    0.0740</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 397:    92.1809   -5.8707   -2.1503   -4.2585   -0.9352    0.2222    0.0363    3.5119    1.0684    2.8197    0.1446    0.1637    1.4065    0.0364    1.0442    0.0740</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 398:    92.1808   -5.8703   -2.1503   -4.2591   -0.9354    0.2224    0.0363    3.5084    1.0689    2.8218    0.1447    0.1634    1.4064    0.0364    1.0445    0.0740</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 399:    92.1806   -5.8711   -2.1503   -4.2596   -0.9355    0.2226    0.0364    3.5114    1.0691    2.8234    0.1447    0.1629    1.4061    0.0364    1.0447    0.0740</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 400:    92.1806   -5.8713   -2.1503   -4.2601   -0.9356    0.2229    0.0366    3.5113    1.0695    2.8253    0.1448    0.1626    1.4059    0.0364    1.0440    0.0740</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 401:    92.1804   -5.8709   -2.1503   -4.2604   -0.9357    0.2231    0.0367    3.5089    1.0696    2.8266    0.1448    0.1624    1.4058    0.0364    1.0440    0.0740</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 402:    92.1801   -5.8705   -2.1502   -4.2606   -0.9358    0.2232    0.0368    3.5071    1.0695    2.8268    0.1447    0.1621    1.4060    0.0364    1.0447    0.0740</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 403:    92.1800   -5.8708   -2.1503   -4.2610   -0.9359    0.2234    0.0369    3.5083    1.0697    2.8271    0.1447    0.1618    1.4056    0.0365    1.0448    0.0739</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 404:    92.1798   -5.8709   -2.1505   -4.2612   -0.9360    0.2237    0.0369    3.5085    1.0699    2.8262    0.1447    0.1616    1.4055    0.0365    1.0449    0.0739</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 405:    92.1795   -5.8705   -2.1503   -4.2613   -0.9360    0.2239    0.0370    3.5059    1.0698    2.8254    0.1448    0.1615    1.4052    0.0364    1.0449    0.0739</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 406:    92.1792   -5.8710   -2.1504   -4.2613   -0.9359    0.2243    0.0371    3.5069    1.0696    2.8244    0.1449    0.1614    1.4049    0.0364    1.0447    0.0739</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 407:    92.1788   -5.8718   -2.1505   -4.2614   -0.9359    0.2243    0.0372    3.5092    1.0697    2.8234    0.1449    0.1614    1.4046    0.0364    1.0448    0.0739</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 408:    92.1786   -5.8729   -2.1505   -4.2615   -0.9360    0.2245    0.0373    3.5140    1.0699    2.8223    0.1449    0.1612    1.4045    0.0365    1.0455    0.0739</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 409:    92.1784   -5.8740   -2.1506   -4.2615   -0.9360    0.2246    0.0373    3.5192    1.0700    2.8213    0.1450    0.1610    1.4042    0.0365    1.0462    0.0738</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 410:    92.1782   -5.8753   -2.1505   -4.2616   -0.9361    0.2246    0.0373    3.5248    1.0705    2.8205    0.1450    0.1607    1.4040    0.0365    1.0465    0.0738</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 411:    92.1780   -5.8758   -2.1503   -4.2619   -0.9362    0.2247    0.0374    3.5249    1.0707    2.8204    0.1449    0.1605    1.4043    0.0364    1.0467    0.0738</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 412:    92.1778   -5.8759   -2.1502   -4.2621   -0.9363    0.2248    0.0374    3.5238    1.0707    2.8209    0.1448    0.1602    1.4045    0.0364    1.0466    0.0738</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 413:    92.1779   -5.8759   -2.1501   -4.2623   -0.9364    0.2249    0.0374    3.5225    1.0711    2.8213    0.1448    0.1600    1.4043    0.0364    1.0460    0.0738</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 414:    92.1781   -5.8754   -2.1500   -4.2622   -0.9364    0.2250    0.0374    3.5205    1.0710    2.8203    0.1448    0.1599    1.4047    0.0364    1.0456    0.0738</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 415:    92.1781   -5.8750   -2.1499   -4.2623   -0.9363    0.2251    0.0374    3.5187    1.0709    2.8190    0.1448    0.1598    1.4050    0.0364    1.0448    0.0738</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 416:    92.1783   -5.8747   -2.1500   -4.2624   -0.9363    0.2252    0.0375    3.5216    1.0708    2.8187    0.1448    0.1598    1.4052    0.0364    1.0440    0.0738</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 417:    92.1783   -5.8738   -2.1500   -4.2625   -0.9362    0.2252    0.0375    3.5219    1.0712    2.8188    0.1447    0.1597    1.4055    0.0364    1.0438    0.0739</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 418:    92.1784   -5.8735   -2.1501   -4.2627   -0.9360    0.2254    0.0375    3.5222    1.0715    2.8193    0.1448    0.1596    1.4057    0.0364    1.0438    0.0739</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 419:    92.1784   -5.8725   -2.1501   -4.2629   -0.9358    0.2255    0.0376    3.5190    1.0719    2.8202    0.1447    0.1594    1.4060    0.0364    1.0436    0.0739</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 420:    92.1785   -5.8713   -2.1500   -4.2633   -0.9357    0.2256    0.0377    3.5152    1.0723    2.8213    0.1447    0.1592    1.4065    0.0364    1.0439    0.0738</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 421:    92.1786   -5.8697   -2.1500   -4.2639   -0.9355    0.2257    0.0377    3.5100    1.0728    2.8232    0.1448    0.1592    1.4068    0.0364    1.0436    0.0739</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 422:    92.1787   -5.8683   -2.1502   -4.2645   -0.9352    0.2260    0.0377    3.5051    1.0732    2.8258    0.1448    0.1592    1.4069    0.0364    1.0432    0.0739</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 423:    92.1788   -5.8667   -2.1504   -4.2653   -0.9350    0.2262    0.0377    3.4992    1.0736    2.8292    0.1448    0.1592    1.4071    0.0364    1.0435    0.0738</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 424:    92.1789   -5.8659   -2.1505   -4.2661   -0.9349    0.2263    0.0378    3.4972    1.0739    2.8324    0.1448    0.1591    1.4069    0.0364    1.0439    0.0738</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 425:    92.1788   -5.8649   -2.1505   -4.2669   -0.9349    0.2263    0.0378    3.4947    1.0740    2.8358    0.1448    0.1590    1.4071    0.0364    1.0438    0.0738</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 426:    92.1788   -5.8643   -2.1506   -4.2677   -0.9348    0.2265    0.0377    3.4922    1.0743    2.8389    0.1448    0.1589    1.4070    0.0365    1.0437    0.0738</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 427:    92.1785   -5.8637   -2.1506   -4.2683   -0.9347    0.2266    0.0378    3.4895    1.0747    2.8425    0.1448    0.1588    1.4068    0.0365    1.0434    0.0738</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 428:    92.1783   -5.8627   -2.1507   -4.2689   -0.9347    0.2268    0.0378    3.4870    1.0749    2.8464    0.1447    0.1586    1.4070    0.0365    1.0434    0.0738</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 429:    92.1782   -5.8617   -2.1509   -4.2696   -0.9347    0.2271    0.0379    3.4845    1.0752    2.8500    0.1446    0.1584    1.4072    0.0365    1.0439    0.0738</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 430:    92.1783   -5.8608   -2.1510   -4.2701   -0.9347    0.2274    0.0379    3.4830    1.0755    2.8526    0.1446    0.1582    1.4075    0.0365    1.0443    0.0738</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 431:    92.1784   -5.8606   -2.1511   -4.2704   -0.9348    0.2275    0.0379    3.4825    1.0757    2.8532    0.1446    0.1580    1.4076    0.0365    1.0439    0.0738</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 432:    92.1784   -5.8610   -2.1512   -4.2707   -0.9350    0.2275    0.0378    3.4833    1.0761    2.8539    0.1447    0.1578    1.4079    0.0365    1.0443    0.0738</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 433:    92.1783   -5.8618   -2.1512   -4.2711   -0.9351    0.2276    0.0378    3.4867    1.0768    2.8554    0.1447    0.1577    1.4078    0.0366    1.0440    0.0738</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 434:    92.1784   -5.8612   -2.1513   -4.2714   -0.9350    0.2278    0.0378    3.4858    1.0774    2.8564    0.1448    0.1577    1.4074    0.0365    1.0430    0.0739</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 435:    92.1783   -5.8610   -2.1513   -4.2718   -0.9348    0.2279    0.0377    3.4874    1.0779    2.8581    0.1448    0.1576    1.4072    0.0366    1.0423    0.0739</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 436:    92.1781   -5.8610   -2.1513   -4.2721   -0.9346    0.2280    0.0376    3.4879    1.0789    2.8598    0.1448    0.1575    1.4070    0.0365    1.0417    0.0739</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 437:    92.1781   -5.8610   -2.1511   -4.2726   -0.9345    0.2279    0.0376    3.4886    1.0794    2.8618    0.1449    0.1574    1.4071    0.0365    1.0417    0.0739</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 438:    92.1781   -5.8609   -2.1510   -4.2730   -0.9345    0.2280    0.0375    3.4893    1.0797    2.8641    0.1449    0.1573    1.4075    0.0365    1.0420    0.0739</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 439:    92.1781   -5.8608   -2.1509   -4.2734   -0.9343    0.2280    0.0374    3.4894    1.0803    2.8659    0.1449    0.1573    1.4076    0.0365    1.0421    0.0739</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 440:    92.1780   -5.8603   -2.1510   -4.2737   -0.9343    0.2279    0.0374    3.4885    1.0808    2.8674    0.1448    0.1573    1.4079    0.0365    1.0430    0.0738</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 441:    92.1779   -5.8600   -2.1510   -4.2738   -0.9342    0.2279    0.0373    3.4899    1.0811    2.8686    0.1447    0.1574    1.4086    0.0365    1.0445    0.0737</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 442:    92.1779   -5.8600   -2.1509   -4.2737   -0.9343    0.2278    0.0372    3.4925    1.0814    2.8691    0.1447    0.1575    1.4088    0.0365    1.0451    0.0737</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 443:    92.1780   -5.8598   -2.1509   -4.2739   -0.9341    0.2278    0.0372    3.4949    1.0819    2.8705    0.1447    0.1576    1.4092    0.0365    1.0453    0.0737</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 444:    92.1779   -5.8593   -2.1510   -4.2742   -0.9340    0.2277    0.0371    3.4951    1.0824    2.8717    0.1447    0.1576    1.4096    0.0365    1.0455    0.0737</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 445:    92.1777   -5.8593   -2.1511   -4.2744   -0.9338    0.2275    0.0371    3.4999    1.0828    2.8729    0.1448    0.1576    1.4100    0.0365    1.0461    0.0736</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 446:    92.1774   -5.8600   -2.1511   -4.2748   -0.9338    0.2274    0.0372    3.5051    1.0837    2.8747    0.1449    0.1576    1.4099    0.0365    1.0464    0.0736</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 447:    92.1771   -5.8604   -2.1512   -4.2751   -0.9338    0.2273    0.0373    3.5087    1.0844    2.8758    0.1449    0.1575    1.4097    0.0365    1.0467    0.0736</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 448:    92.1769   -5.8607   -2.1513   -4.2756   -0.9336    0.2272    0.0374    3.5124    1.0849    2.8778    0.1449    0.1575    1.4098    0.0365    1.0470    0.0735</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 449:    92.1768   -5.8611   -2.1515   -4.2759   -0.9337    0.2270    0.0374    3.5165    1.0853    2.8789    0.1449    0.1575    1.4101    0.0365    1.0473    0.0735</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 450:    92.1766   -5.8611   -2.1515   -4.2762   -0.9337    0.2269    0.0374    3.5158    1.0859    2.8800    0.1448    0.1576    1.4102    0.0365    1.0474    0.0735</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 451:    92.1762   -5.8615   -2.1514   -4.2765   -0.9337    0.2267    0.0374    3.5202    1.0866    2.8812    0.1447    0.1576    1.4104    0.0365    1.0477    0.0735</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 452:    92.1761   -5.8622   -2.1514   -4.2767   -0.9337    0.2264    0.0374    3.5232    1.0871    2.8824    0.1447    0.1576    1.4103    0.0365    1.0479    0.0734</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 453:    92.1760   -5.8621   -2.1514   -4.2776   -0.9336    0.2261    0.0373    3.5247    1.0878    2.8901    0.1447    0.1576    1.4103    0.0365    1.0480    0.0734</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 454:    92.1759   -5.8611   -2.1513   -4.2781   -0.9335    0.2259    0.0373    3.5206    1.0882    2.8939    0.1448    0.1575    1.4104    0.0365    1.0483    0.0734</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 455:    92.1758   -5.8599   -2.1513   -4.2788   -0.9336    0.2258    0.0372    3.5151    1.0887    2.9010    0.1448    0.1575    1.4104    0.0365    1.0485    0.0734</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 456:    92.1760   -5.8583   -2.1512   -4.2797   -0.9335    0.2258    0.0372    3.5086    1.0892    2.9081    0.1448    0.1574    1.4105    0.0365    1.0483    0.0734</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 457:    92.1761   -5.8575   -2.1513   -4.2804   -0.9334    0.2257    0.0371    3.5041    1.0896    2.9144    0.1449    0.1574    1.4103    0.0365    1.0480    0.0733</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 458:    92.1763   -5.8569   -2.1512   -4.2813   -0.9334    0.2257    0.0370    3.5014    1.0901    2.9227    0.1449    0.1574    1.4102    0.0365    1.0476    0.0734</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 459:    92.1763   -5.8565   -2.1512   -4.2816   -0.9334    0.2256    0.0370    3.4991    1.0908    2.9248    0.1449    0.1573    1.4100    0.0365    1.0471    0.0734</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 460:    92.1764   -5.8557   -2.1513   -4.2818   -0.9333    0.2256    0.0370    3.4957    1.0915    2.9258    0.1449    0.1572    1.4097    0.0365    1.0466    0.0734</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 461:    92.1765   -5.8550   -2.1515   -4.2822   -0.9333    0.2255    0.0370    3.4923    1.0921    2.9296    0.1449    0.1571    1.4097    0.0365    1.0464    0.0734</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 462:    92.1764   -5.8546   -2.1517   -4.2824   -0.9333    0.2256    0.0370    3.4897    1.0925    2.9319    0.1448    0.1569    1.4093    0.0365    1.0463    0.0733</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 463:    92.1763   -5.8539   -2.1519   -4.2830   -0.9333    0.2256    0.0369    3.4863    1.0928    2.9373    0.1449    0.1568    1.4093    0.0366    1.0463    0.0733</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 464:    92.1763   -5.8532   -2.1520   -4.2833   -0.9333    0.2257    0.0370    3.4825    1.0933    2.9405    0.1448    0.1567    1.4093    0.0366    1.0462    0.0733</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 465:    92.1762   -5.8528   -2.1521   -4.2837   -0.9335    0.2257    0.0370    3.4795    1.0938    2.9433    0.1448    0.1567    1.4093    0.0366    1.0464    0.0733</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 466:    92.1762   -5.8530   -2.1523   -4.2840   -0.9336    0.2256    0.0370    3.4780    1.0943    2.9462    0.1448    0.1566    1.4089    0.0366    1.0466    0.0733</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 467:    92.1764   -5.8529   -2.1524   -4.2846   -0.9337    0.2256    0.0371    3.4751    1.0948    2.9506    0.1447    0.1565    1.4087    0.0366    1.0465    0.0733</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 468:    92.1765   -5.8531   -2.1525   -4.2849   -0.9337    0.2255    0.0371    3.4734    1.0952    2.9523    0.1447    0.1563    1.4087    0.0366    1.0465    0.0733</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 469:    92.1767   -5.8533   -2.1525   -4.2853   -0.9337    0.2254    0.0372    3.4754    1.0958    2.9540    0.1447    0.1561    1.4090    0.0366    1.0472    0.0733</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 470:    92.1767   -5.8536   -2.1525   -4.2858   -0.9338    0.2254    0.0373    3.4780    1.0965    2.9571    0.1448    0.1559    1.4094    0.0366    1.0478    0.0732</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 471:    92.1767   -5.8540   -2.1525   -4.2864   -0.9338    0.2254    0.0373    3.4812    1.0970    2.9606    0.1447    0.1558    1.4099    0.0366    1.0482    0.0732</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 472:    92.1766   -5.8547   -2.1526   -4.2871   -0.9339    0.2255    0.0373    3.4844    1.0975    2.9645    0.1446    0.1556    1.4101    0.0366    1.0482    0.0732</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 473:    92.1764   -5.8549   -2.1527   -4.2875   -0.9339    0.2255    0.0373    3.4848    1.0977    2.9672    0.1446    0.1554    1.4100    0.0366    1.0482    0.0732</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 474:    92.1762   -5.8553   -2.1528   -4.2880   -0.9338    0.2256    0.0373    3.4855    1.0979    2.9700    0.1445    0.1552    1.4104    0.0366    1.0484    0.0732</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 475:    92.1763   -5.8561   -2.1530   -4.2884   -0.9338    0.2256    0.0374    3.4885    1.0981    2.9726    0.1445    0.1549    1.4103    0.0366    1.0484    0.0731</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 476:    92.1763   -5.8564   -2.1530   -4.2885   -0.9339    0.2258    0.0374    3.4914    1.0983    2.9734    0.1444    0.1548    1.4106    0.0366    1.0484    0.0731</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 477:    92.1763   -5.8565   -2.1531   -4.2888   -0.9340    0.2259    0.0373    3.4941    1.0985    2.9755    0.1443    0.1546    1.4108    0.0366    1.0487    0.0731</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 478:    92.1763   -5.8576   -2.1532   -4.2889   -0.9340    0.2258    0.0373    3.4995    1.0987    2.9772    0.1442    0.1544    1.4111    0.0366    1.0490    0.0731</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 479:    92.1765   -5.8593   -2.1533   -4.2890   -0.9342    0.2259    0.0372    3.5086    1.0990    2.9787    0.1442    0.1544    1.4113    0.0366    1.0495    0.0731</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 480:    92.1766   -5.8613   -2.1535   -4.2892   -0.9342    0.2260    0.0372    3.5233    1.0991    2.9800    0.1442    0.1543    1.4116    0.0367    1.0496    0.0730</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 481:    92.1768   -5.8624   -2.1536   -4.2894   -0.9341    0.2260    0.0371    3.5309    1.0993    2.9820    0.1442    0.1542    1.4117    0.0367    1.0497    0.0730</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 482:    92.1766   -5.8634   -2.1537   -4.2896   -0.9341    0.2260    0.0371    3.5393    1.0995    2.9833    0.1443    0.1542    1.4116    0.0367    1.0496    0.0730</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 483:    92.1762   -5.8645   -2.1538   -4.2896   -0.9340    0.2260    0.0372    3.5472    1.0997    2.9840    0.1443    0.1542    1.4115    0.0367    1.0498    0.0730</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 484:    92.1757   -5.8648   -2.1538   -4.2896   -0.9340    0.2261    0.0373    3.5508    1.0998    2.9843    0.1445    0.1542    1.4115    0.0366    1.0498    0.0730</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 485:    92.1752   -5.8648   -2.1539   -4.2896   -0.9340    0.2262    0.0374    3.5516    1.1000    2.9846    0.1445    0.1542    1.4115    0.0366    1.0497    0.0730</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 486:    92.1747   -5.8649   -2.1539   -4.2897   -0.9340    0.2262    0.0375    3.5517    1.1003    2.9856    0.1446    0.1541    1.4113    0.0366    1.0498    0.0730</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 487:    92.1743   -5.8649   -2.1539   -4.2896   -0.9340    0.2262    0.0376    3.5502    1.1006    2.9856    0.1447    0.1539    1.4110    0.0367    1.0498    0.0730</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 488:    92.1739   -5.8645   -2.1540   -4.2893   -0.9340    0.2261    0.0377    3.5475    1.1008    2.9848    0.1447    0.1537    1.4106    0.0367    1.0497    0.0729</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 489:    92.1736   -5.8640   -2.1539   -4.2892   -0.9340    0.2260    0.0376    3.5445    1.1011    2.9844    0.1448    0.1536    1.4105    0.0367    1.0496    0.0729</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 490:    92.1736   -5.8631   -2.1539   -4.2889   -0.9340    0.2260    0.0376    3.5402    1.1014    2.9835    0.1448    0.1534    1.4103    0.0366    1.0496    0.0729</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 491:    92.1735   -5.8617   -2.1539   -4.2887   -0.9339    0.2260    0.0375    3.5343    1.1016    2.9825    0.1449    0.1532    1.4103    0.0366    1.0497    0.0729</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 492:    92.1736   -5.8609   -2.1539   -4.2884   -0.9339    0.2259    0.0375    3.5298    1.1017    2.9816    0.1449    0.1530    1.4101    0.0367    1.0499    0.0729</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 493:    92.1737   -5.8605   -2.1539   -4.2882   -0.9339    0.2260    0.0375    3.5266    1.1018    2.9807    0.1450    0.1529    1.4098    0.0367    1.0496    0.0729</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 494:    92.1739   -5.8609   -2.1539   -4.2881   -0.9340    0.2261    0.0375    3.5273    1.1018    2.9802    0.1450    0.1528    1.4097    0.0367    1.0495    0.0729</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 495:    92.1741   -5.8614   -2.1540   -4.2880   -0.9340    0.2262    0.0376    3.5290    1.1019    2.9797    0.1450    0.1526    1.4096    0.0367    1.0492    0.0729</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 496:    92.1742   -5.8623   -2.1540   -4.2879   -0.9341    0.2262    0.0376    3.5348    1.1020    2.9791    0.1449    0.1525    1.4096    0.0367    1.0491    0.0729</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 497:    92.1742   -5.8634   -2.1541   -4.2879   -0.9341    0.2264    0.0377    3.5402    1.1020    2.9789    0.1449    0.1524    1.4097    0.0367    1.0493    0.0729</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 498:    92.1744   -5.8637   -2.1543   -4.2879   -0.9341    0.2266    0.0377    3.5406    1.1019    2.9787    0.1449    0.1524    1.4096    0.0367    1.0496    0.0729</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 499:    92.1744   -5.8635   -2.1544   -4.2880   -0.9341    0.2268    0.0376    3.5400    1.1019    2.9789    0.1450    0.1523    1.4095    0.0367    1.0500    0.0728</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 500:    92.1744   -5.8628   -2.1545   -4.2882   -0.9341    0.2270    0.0377    3.5381    1.1020    2.9795    0.1450    0.1522    1.4096    0.0367    1.0503    0.0728</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating covariance matrix</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling EBE model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Needed Covariates:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "CMT"</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating residuals/tables</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-in"><span class="va">f_nlmixr_dfop_sfo_focei_obs_tc</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr</a></span><span class="op">(</span><span class="va">f_mmkin_tc</span><span class="op">[</span><span class="st">"DFOP-SFO"</span>, <span class="op">]</span>, est <span class="op">=</span> <span class="st">"focei"</span>,</span>
-<span class="r-in">  error_model <span class="op">=</span> <span class="st">"obs_tc"</span><span class="op">)</span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate jacobian</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate sensitivities</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate ∂(f)/∂(η)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate ∂(R²)/∂(η)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in inner model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in inner model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in EBE model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in EBE model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling inner model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in FD model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in FD model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling EBE model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling events FD model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Needed Covariates:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "CMT"</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="font-weight: bold;">Key:</span> U: Unscaled Parameters; X: Back-transformed parameters; G: Gill difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> F: Forward difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> C: Central difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> M: Mixed forward and central difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Unscaled parameters for Omegas=chol(solve(omega));</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Diagonals are transformed, as specified by foceiControl(diagXform=)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |-----+---------------+-----------+-----------+-----------+-----------|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    #| Objective Fun |  parent_0 |  log_k_A1 |f_parent_qlogis |    log_k1 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    log_k2 |  g_qlogis |sigma_low_parent |rsd_high_parent |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|sigma_low_A1 |rsd_high_A1 |        o1 |        o2 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|        o3 |        o4 |        o5 |        o6 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    1</span>|     495.48573 |     1.000 |    -1.000 |   -0.9104 |   -0.9376 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9875 |   -0.8823 |   -0.8746 |   -0.8907 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8746 |   -0.8907 |   -0.8767 |   -0.8731 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8673 |   -0.8720 |   -0.8739 |   -0.8666 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     495.48573 |     91.00 |    -5.200 |   -0.8900 |    -2.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.600 |    0.4600 |    0.8300 |   0.05800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8300 |   0.05800 |    0.7311 |    0.9036 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.183 |    0.9554 |    0.8633 |     1.214 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     495.48573</span> |     91.00 |  0.005517 |    0.2911 |    0.1108 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01005 |    0.6130 |    0.8300 |   0.05800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8300 |   0.05800 |    0.7311 |    0.9036 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.183 |    0.9554 |    0.8633 |     1.214 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    G|    Gill Diff. |   -0.9648 |     2.223 |   -0.3153 |  -0.01817 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3350 |    0.6789 |    -23.42 |    -17.64 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -5.440 |    -1.950 |    0.9642 |     9.851 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -11.94 |    -1.319 |     8.578 |    -12.45 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    2</span>|     481.75012 |     1.026 |    -1.060 |   -0.9019 |   -0.9371 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9785 |   -0.9007 |   -0.2420 |   -0.4142 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7277 |   -0.8380 |   -0.9027 |    -1.139 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5448 |   -0.8364 |    -1.106 |   -0.5303 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     481.75012 |     93.37 |    -5.260 |   -0.8824 |    -2.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.591 |    0.4516 |     1.093 |   0.07182 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8910 |   0.05953 |    0.7121 |    0.6631 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.565 |    0.9895 |    0.6633 |     1.623 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     481.75012</span> |     93.37 |  0.005195 |    0.2927 |    0.1109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01014 |    0.6110 |     1.093 |   0.07182 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8910 |   0.05953 |    0.7121 |    0.6631 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.565 |    0.9895 |    0.6633 |     1.623 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     152.5 |     1.317 |     3.315 |   -0.1772 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3391 |    0.1426 |    -4.513 |     6.696 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.211 |    0.7988 |    0.6299 |    -5.324 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009964 |     3.044 |    -5.727 |    -6.694 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    3</span>|     3004.9713 |    0.2745 |    -1.093 |   -0.9147 |   -0.9360 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9762 |   -0.9095 |   0.05941 |   -0.2377 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6690 |   -0.8188 |   -0.9174 |    -1.204 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4027 |   -0.8359 |    -1.205 |   -0.3486 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     3004.9713 |     24.98 |    -5.293 |   -0.8938 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.589 |    0.4475 |     1.218 |   0.07694 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9154 |   0.06008 |    0.7014 |    0.6043 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.733 |    0.9899 |    0.5774 |     1.843 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     3004.9713</span> |     24.98 |  0.005026 |    0.2903 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01017 |    0.6100 |     1.218 |   0.07694 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9154 |   0.06008 |    0.7014 |    0.6043 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.733 |    0.9899 |    0.5774 |     1.843 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    4</span>|     491.68825 |    0.9393 |    -1.061 |   -0.9038 |   -0.9370 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9787 |   -0.9008 |   -0.2394 |   -0.4180 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7284 |   -0.8385 |   -0.9031 |    -1.136 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5448 |   -0.8381 |    -1.102 |   -0.5265 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     491.68825 |     85.47 |    -5.261 |   -0.8841 |    -2.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.591 |    0.4515 |     1.094 |   0.07171 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8907 |   0.05951 |    0.7118 |    0.6659 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.565 |    0.9878 |    0.6661 |     1.627 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     491.68825</span> |     85.47 |  0.005191 |    0.2923 |    0.1109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01014 |    0.6110 |     1.094 |   0.07171 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8907 |   0.05951 |    0.7118 |    0.6659 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.565 |    0.9878 |    0.6661 |     1.627 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    5</span>|     479.72282 |     1.001 |    -1.060 |   -0.9024 |   -0.9371 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9785 |   -0.9007 |   -0.2413 |   -0.4153 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7279 |   -0.8381 |   -0.9028 |    -1.138 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5448 |   -0.8369 |    -1.105 |   -0.5292 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.72282 |     91.11 |    -5.260 |   -0.8829 |    -2.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.591 |    0.4516 |     1.093 |   0.07179 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8909 |   0.05952 |    0.7120 |    0.6639 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.565 |    0.9890 |    0.6641 |     1.624 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.72282</span> |     91.11 |  0.005194 |    0.2926 |    0.1109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01014 |    0.6110 |     1.093 |   0.07179 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8909 |   0.05952 |    0.7120 |    0.6639 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.565 |    0.9890 |    0.6641 |     1.624 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     6.589 |     1.222 |    0.9137 |    0.1103 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3993 |    0.5206 |    -3.904 |     6.654 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9090 |     1.134 |    -1.839 |    -6.108 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.622 |     4.007 |    -4.921 |    -6.374 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    6</span>|     479.56384 |    0.9950 |    -1.061 |   -0.9037 |   -0.9373 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9792 |   -0.9012 |   -0.2438 |   -0.4298 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7309 |   -0.8403 |   -0.9001 |    -1.126 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5509 |   -0.8426 |    -1.095 |   -0.5241 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.56384 |     90.55 |    -5.261 |   -0.8840 |    -2.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.592 |    0.4513 |     1.092 |   0.07137 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8897 |   0.05946 |    0.7140 |    0.6748 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.558 |    0.9836 |    0.6729 |     1.630 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.56384</span> |     90.55 |  0.005189 |    0.2923 |    0.1108 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01014 |    0.6110 |     1.092 |   0.07137 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8897 |   0.05946 |    0.7140 |    0.6748 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.558 |    0.9836 |    0.6729 |     1.630 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -31.71 |     1.225 |    0.1963 |    0.1681 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4113 |    0.6853 |    -4.208 |     6.141 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7033 |     1.163 |    -2.029 |    -4.071 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.106 |     3.494 |    -3.921 |    -6.098 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    7</span>|     479.23599 |     1.003 |    -1.063 |   -0.9048 |   -0.9375 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9799 |   -0.9023 |   -0.2352 |   -0.4403 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7320 |   -0.8422 |   -0.8974 |    -1.117 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5472 |   -0.8484 |    -1.086 |   -0.5115 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.23599 |     91.29 |    -5.263 |   -0.8850 |    -2.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.592 |    0.4508 |     1.095 |   0.07106 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8892 |   0.05941 |    0.7160 |    0.6828 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.562 |    0.9781 |    0.6800 |     1.645 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.23599</span> |     91.29 |  0.005177 |    0.2921 |    0.1108 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01013 |    0.6108 |     1.095 |   0.07106 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8892 |   0.05941 |    0.7160 |    0.6828 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.562 |    0.9781 |    0.6800 |     1.645 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     18.36 |     1.286 |    0.8956 |   0.06941 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3942 |    0.6495 |    -3.460 |     6.349 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7828 |    0.9998 |    -1.947 |    -2.931 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1591 |     2.144 |    -3.375 |    -5.909 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    8</span>|     479.05200 |    0.9982 |    -1.066 |   -0.9056 |   -0.9376 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9803 |   -0.9037 |   -0.2181 |   -0.4407 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7304 |   -0.8427 |   -0.8951 |    -1.119 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5384 |   -0.8504 |    -1.087 |   -0.4972 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|       479.052 |     90.84 |    -5.266 |   -0.8857 |    -2.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.593 |    0.4502 |     1.102 |   0.07105 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8899 |   0.05939 |    0.7177 |    0.6818 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.572 |    0.9761 |    0.6797 |     1.663 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">       479.052</span> |     90.84 |  0.005162 |    0.2920 |    0.1108 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01012 |    0.6107 |     1.102 |   0.07105 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8899 |   0.05939 |    0.7177 |    0.6818 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.572 |    0.9761 |    0.6797 |     1.663 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    9</span>|     478.91507 |    0.9977 |    -1.070 |   -0.9061 |   -0.9377 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9807 |   -0.9051 |   -0.2002 |   -0.4395 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7284 |   -0.8431 |   -0.8930 |    -1.121 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5287 |   -0.8520 |    -1.088 |   -0.4828 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     478.91507 |     90.79 |    -5.270 |   -0.8862 |    -2.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.593 |    0.4495 |     1.110 |   0.07109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8907 |   0.05938 |    0.7192 |    0.6799 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.584 |    0.9745 |    0.6785 |     1.680 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     478.91507</span> |     90.79 |  0.005146 |    0.2919 |    0.1108 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01012 |    0.6105 |     1.110 |   0.07109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8907 |   0.05938 |    0.7192 |    0.6799 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.584 |    0.9745 |    0.6785 |     1.680 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   10</span>|     478.54700 |    0.9959 |    -1.080 |   -0.9081 |   -0.9381 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9820 |   -0.9099 |   -0.1391 |   -0.4353 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7215 |   -0.8442 |   -0.8862 |    -1.128 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4957 |   -0.8577 |    -1.093 |   -0.4342 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|       478.547 |     90.63 |    -5.280 |   -0.8880 |    -2.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.594 |    0.4473 |     1.135 |   0.07121 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8936 |   0.05935 |    0.7242 |    0.6734 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.623 |    0.9691 |    0.6746 |     1.739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">       478.547</span> |     90.63 |  0.005094 |    0.2915 |    0.1108 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01011 |    0.6100 |     1.135 |   0.07121 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8936 |   0.05935 |    0.7242 |    0.6734 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.623 |    0.9691 |    0.6746 |     1.739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   11</span>|     478.24707 |    0.9926 |    -1.098 |   -0.9118 |   -0.9388 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9843 |   -0.9186 |  -0.02735 |   -0.4276 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7088 |   -0.8464 |   -0.8736 |    -1.141 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4354 |   -0.8680 |    -1.101 |   -0.3451 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     478.24707 |     90.33 |    -5.298 |   -0.8913 |    -2.201 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.597 |    0.4433 |     1.182 |   0.07143 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8988 |   0.05929 |    0.7334 |    0.6616 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.694 |    0.9593 |    0.6674 |     1.848 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     478.24707</span> |     90.33 |  0.004999 |    0.2909 |    0.1107 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01008 |    0.6090 |     1.182 |   0.07143 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8988 |   0.05929 |    0.7334 |    0.6616 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.694 |    0.9593 |    0.6674 |     1.848 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -54.86 |     1.159 |   -0.2545 |    0.1198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4779 |     1.320 |    -1.627 |     7.719 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.488 |     1.194 |    -2.008 |    -4.434 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     3.563 |    0.8010 |    -2.393 |    -3.495 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   12</span>|     475.90466 |     1.002 |    -1.127 |   -0.9233 |   -0.9398 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9978 |   -0.9482 |  -0.05448 |   -0.6822 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7579 |   -0.8801 |   -0.8190 |    -1.095 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4550 |   -0.8498 |    -1.031 |   -0.2699 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     475.90466 |     91.18 |    -5.327 |   -0.9014 |    -2.202 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.610 |    0.4297 |     1.170 |   0.06405 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8785 |   0.05831 |    0.7733 |    0.7032 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.671 |    0.9767 |    0.7277 |     1.939 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     475.90466</span> |     91.18 |  0.004860 |    0.2888 |    0.1106 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009950 |    0.6058 |     1.170 |   0.06405 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8785 |   0.05831 |    0.7733 |    0.7032 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.671 |    0.9767 |    0.7277 |     1.939 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -5.773 |     1.281 |   0.05418 |  -0.06269 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4104 |     1.812 |    -4.981 |     3.640 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.08126 |   0.09477 |    -1.092 |    -2.966 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     5.262 |     2.712 |     3.245 |    -2.012 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   13</span>|     477.06760 |     1.030 |    -1.176 |   -0.9281 |   -0.9377 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.013 |    -1.011 |   0.09564 |   -0.8420 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7668 |   -0.8907 |   -0.7738 |    -1.073 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5619 |   -0.8693 |    -1.126 |   -0.1773 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      477.0676 |     93.71 |    -5.376 |   -0.9057 |    -2.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.625 |    0.4008 |     1.233 |   0.05941 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8748 |   0.05800 |    0.8064 |    0.7234 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.545 |    0.9581 |    0.6461 |     2.051 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      477.0676</span> |     93.71 |  0.004627 |    0.2879 |    0.1108 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009799 |    0.5989 |     1.233 |   0.05941 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8748 |   0.05800 |    0.8064 |    0.7234 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.545 |    0.9581 |    0.6461 |     2.051 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   14</span>|     477.20174 |     1.026 |    -1.143 |   -0.9246 |   -0.9391 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.003 |   -0.9695 | -0.001726 |   -0.7335 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7599 |   -0.8831 |   -0.8043 |    -1.080 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4976 |   -0.8627 |    -1.065 |   -0.2404 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     477.20174 |     93.37 |    -5.343 |   -0.9027 |    -2.201 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.615 |    0.4199 |     1.192 |   0.06256 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8776 |   0.05822 |    0.7840 |    0.7162 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.621 |    0.9644 |    0.6988 |     1.975 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     477.20174</span> |     93.37 |  0.004782 |    0.2885 |    0.1106 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009899 |    0.6035 |     1.192 |   0.06256 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8776 |   0.05822 |    0.7840 |    0.7162 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.621 |    0.9644 |    0.6988 |     1.975 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   15</span>|     476.22973 |     1.014 |    -1.129 |   -0.9234 |   -0.9397 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9986 |   -0.9521 |  -0.04396 |   -0.6899 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7577 |   -0.8803 |   -0.8167 |    -1.089 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4661 |   -0.8555 |    -1.038 |   -0.2656 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     476.22973 |     92.29 |    -5.329 |   -0.9015 |    -2.202 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.611 |    0.4279 |     1.175 |   0.06382 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8785 |   0.05830 |    0.7750 |    0.7089 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.658 |    0.9712 |    0.7218 |     1.944 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     476.22973</span> |     92.29 |  0.004847 |    0.2887 |    0.1106 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009941 |    0.6054 |     1.175 |   0.06382 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8785 |   0.05830 |    0.7750 |    0.7089 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.658 |    0.9712 |    0.7218 |     1.944 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   16</span>|     475.87776 |     1.005 |    -1.127 |   -0.9233 |   -0.9398 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9980 |   -0.9491 |  -0.05201 |   -0.6840 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7578 |   -0.8802 |   -0.8184 |    -1.093 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4576 |   -0.8511 |    -1.033 |   -0.2689 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     475.87776 |     91.44 |    -5.327 |   -0.9015 |    -2.202 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.610 |    0.4293 |     1.171 |   0.06399 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8785 |   0.05830 |    0.7737 |    0.7045 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.668 |    0.9754 |    0.7263 |     1.940 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     475.87776</span> |     91.44 |  0.004857 |    0.2887 |    0.1106 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009948 |    0.6057 |     1.171 |   0.06399 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8785 |   0.05830 |    0.7737 |    0.7045 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.668 |    0.9754 |    0.7263 |     1.940 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     16.12 |     1.298 |    0.4116 |  -0.09723 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4002 |     1.728 |    -4.991 |     3.781 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.06392 |   0.04117 |    -1.251 |   -0.6787 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     5.079 |     2.620 |     3.013 |    -2.074 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   17</span>|     475.82399 |     1.002 |    -1.128 |   -0.9234 |   -0.9397 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9982 |   -0.9501 |  -0.04950 |   -0.6866 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7580 |   -0.8803 |   -0.8177 |    -1.093 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4596 |   -0.8518 |    -1.034 |   -0.2675 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     475.82399 |     91.17 |    -5.328 |   -0.9016 |    -2.202 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.611 |    0.4288 |     1.172 |   0.06392 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8784 |   0.05830 |    0.7743 |    0.7045 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.666 |    0.9748 |    0.7251 |     1.942 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     475.82399</span> |     91.17 |  0.004853 |    0.2887 |    0.1106 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009945 |    0.6056 |     1.172 |   0.06392 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8784 |   0.05830 |    0.7743 |    0.7045 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.666 |    0.9748 |    0.7251 |     1.942 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -7.267 |     1.279 |  0.007095 |  -0.05940 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4115 |     1.783 |    -5.114 |     3.652 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1068 |    0.1083 |    -1.295 |   -0.9578 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.682 |     2.514 |     3.014 |    -2.035 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   18</span>|     475.78862 |     1.005 |    -1.129 |   -0.9235 |   -0.9397 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9985 |   -0.9512 |  -0.04657 |   -0.6892 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7580 |   -0.8804 |   -0.8168 |    -1.093 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4611 |   -0.8527 |    -1.036 |   -0.2661 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     475.78862 |     91.41 |    -5.329 |   -0.9016 |    -2.202 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.611 |    0.4283 |     1.174 |   0.06384 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8784 |   0.05830 |    0.7749 |    0.7045 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.664 |    0.9739 |    0.7236 |     1.943 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     475.78862</span> |     91.41 |  0.004849 |    0.2887 |    0.1106 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009942 |    0.6055 |     1.174 |   0.06384 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8784 |   0.05830 |    0.7749 |    0.7045 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.664 |    0.9739 |    0.7236 |     1.943 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     13.19 |     1.292 |    0.3498 |  -0.09321 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4023 |     1.703 |    -5.372 |     3.699 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1234 |   0.05429 |    -1.241 |   -0.7588 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.815 |     2.436 |     2.783 |    -2.083 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   19</span>|     475.73531 |     1.002 |    -1.130 |   -0.9236 |   -0.9397 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9987 |   -0.9524 |  -0.04361 |   -0.6921 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7582 |   -0.8806 |   -0.8159 |    -1.094 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4630 |   -0.8531 |    -1.037 |   -0.2646 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     475.73531 |     91.21 |    -5.330 |   -0.9018 |    -2.202 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.611 |    0.4278 |     1.175 |   0.06376 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8783 |   0.05829 |    0.7756 |    0.7041 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.662 |    0.9735 |    0.7222 |     1.945 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     475.73531</span> |     91.21 |  0.004845 |    0.2887 |    0.1106 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009940 |    0.6053 |     1.175 |   0.06376 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8783 |   0.05829 |    0.7756 |    0.7041 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.662 |    0.9735 |    0.7222 |     1.945 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -3.312 |     1.277 |   0.06663 |  -0.06695 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4102 |     1.736 |    -5.095 |     3.555 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.08175 |   0.08515 |    -1.253 |   -0.9364 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.836 |     2.452 |     2.739 |    -2.058 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   20</span>|     475.69941 |     1.004 |    -1.131 |   -0.9237 |   -0.9396 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9990 |   -0.9534 |  -0.04063 |   -0.6942 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7581 |   -0.8807 |   -0.8151 |    -1.093 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4658 |   -0.8545 |    -1.039 |   -0.2634 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     475.69941 |     91.39 |    -5.331 |   -0.9018 |    -2.202 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.611 |    0.4273 |     1.176 |   0.06370 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8783 |   0.05829 |    0.7761 |    0.7046 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.658 |    0.9722 |    0.7208 |     1.947 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     475.69941</span> |     91.39 |  0.004841 |    0.2887 |    0.1106 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009937 |    0.6052 |     1.176 |   0.06370 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8783 |   0.05829 |    0.7761 |    0.7046 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.658 |    0.9722 |    0.7208 |     1.947 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     11.57 |     1.287 |    0.3079 |  -0.08979 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4039 |     1.674 |    -5.153 |     3.653 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.06063 |   0.04440 |    -1.200 |   -0.7646 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.452 |     2.339 |     2.552 |    -2.095 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   21</span>|     475.66307 |     1.001 |    -1.131 |   -0.9238 |   -0.9396 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9992 |   -0.9545 |  -0.03780 |   -0.6969 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7583 |   -0.8808 |   -0.8143 |    -1.094 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4671 |   -0.8550 |    -1.041 |   -0.2620 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     475.66307 |     91.11 |    -5.331 |   -0.9019 |    -2.202 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.612 |    0.4268 |     1.177 |   0.06362 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8783 |   0.05829 |    0.7767 |    0.7044 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.657 |    0.9717 |    0.7195 |     1.948 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     475.66307</span> |     91.11 |  0.004837 |    0.2887 |    0.1106 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009935 |    0.6051 |     1.177 |   0.06362 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8783 |   0.05829 |    0.7767 |    0.7044 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.657 |    0.9717 |    0.7195 |     1.948 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -12.57 |     1.267 |  -0.09715 |  -0.05310 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4138 |     1.728 |    -5.558 |     3.438 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1081 |    0.1203 |    -1.232 |    -1.053 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.344 |     2.291 |     2.543 |    -2.059 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   22</span>|     475.61346 |     1.003 |    -1.132 |   -0.9238 |   -0.9395 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9995 |   -0.9557 |  -0.03467 |   -0.6999 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7585 |   -0.8810 |   -0.8134 |    -1.094 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4680 |   -0.8550 |    -1.042 |   -0.2607 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     475.61346 |     91.32 |    -5.332 |   -0.9020 |    -2.202 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.612 |    0.4262 |     1.179 |   0.06353 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8782 |   0.05828 |    0.7774 |    0.7040 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.656 |    0.9717 |    0.7181 |     1.950 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     475.61346</span> |     91.32 |  0.004833 |    0.2886 |    0.1106 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009932 |    0.6050 |     1.179 |   0.06353 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8782 |   0.05828 |    0.7774 |    0.7040 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.656 |    0.9717 |    0.7181 |     1.950 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     5.192 |     1.277 |    0.1967 |  -0.08157 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4060 |     1.656 |    -5.231 |     3.627 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1038 |   0.05786 |    -1.199 |   -0.8859 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.283 |     2.293 |     2.331 |    -2.101 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   23</span>|     475.58436 |     1.001 |    -1.133 |   -0.9239 |   -0.9395 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9998 |   -0.9568 |  -0.03140 |   -0.7025 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7585 |   -0.8810 |   -0.8126 |    -1.094 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4692 |   -0.8560 |    -1.044 |   -0.2594 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     475.58436 |     91.09 |    -5.333 |   -0.9021 |    -2.202 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.612 |    0.4257 |     1.180 |   0.06346 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8782 |   0.05828 |    0.7780 |    0.7043 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.654 |    0.9707 |    0.7167 |     1.952 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     475.58436</span> |     91.09 |  0.004829 |    0.2886 |    0.1106 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009929 |    0.6049 |     1.180 |   0.06346 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8782 |   0.05828 |    0.7780 |    0.7043 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.654 |    0.9707 |    0.7167 |     1.952 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -14.46 |     1.261 |   -0.1306 |  -0.05131 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4140 |     1.696 |    -5.518 |     3.404 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1407 |    0.1181 |    -1.199 |    -1.071 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.272 |     2.212 |     2.296 |    -2.075 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   24</span>|     475.53229 |     1.003 |    -1.134 |   -0.9240 |   -0.9394 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.000 |   -0.9581 |  -0.02828 |   -0.7055 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7587 |   -0.8812 |   -0.8117 |    -1.094 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4701 |   -0.8560 |    -1.045 |   -0.2580 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     475.53229 |     91.31 |    -5.334 |   -0.9021 |    -2.202 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.613 |    0.4251 |     1.181 |   0.06337 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8781 |   0.05828 |    0.7786 |    0.7039 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.653 |    0.9708 |    0.7153 |     1.953 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     475.53229</span> |     91.31 |  0.004824 |    0.2886 |    0.1106 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009926 |    0.6047 |     1.181 |   0.06337 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8781 |   0.05828 |    0.7786 |    0.7039 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.653 |    0.9708 |    0.7153 |     1.953 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     4.355 |     1.271 |    0.1786 |  -0.08149 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4055 |     1.621 |    -5.117 |     3.557 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1060 |   0.04285 |   -0.9518 |    -2.902 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.469 |     2.204 |     2.093 |    -2.115 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   25</span>|     475.50379 |     1.001 |    -1.135 |   -0.9241 |   -0.9394 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.000 |   -0.9591 |  -0.02533 |   -0.7076 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7587 |   -0.8812 |   -0.8111 |    -1.093 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4726 |   -0.8571 |    -1.047 |   -0.2568 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     475.50379 |     91.10 |    -5.335 |   -0.9022 |    -2.202 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.613 |    0.4247 |     1.182 |   0.06331 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8781 |   0.05828 |    0.7790 |    0.7053 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.650 |    0.9697 |    0.7143 |     1.955 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     475.50379</span> |     91.10 |  0.004820 |    0.2886 |    0.1106 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009924 |    0.6046 |     1.182 |   0.06331 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8781 |   0.05828 |    0.7790 |    0.7053 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.650 |    0.9697 |    0.7143 |     1.955 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -13.73 |     1.259 |   -0.1294 |  -0.05234 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4131 |     1.659 |    -5.626 |     3.356 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1179 |    0.1182 |    -1.163 |   -0.9759 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.282 |     2.162 |     2.085 |    -2.085 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   26</span>|     475.45890 |     1.004 |    -1.136 |   -0.9240 |   -0.9393 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.001 |   -0.9602 |  -0.02221 |   -0.7103 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7588 |   -0.8813 |   -0.8104 |    -1.092 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4738 |   -0.8571 |    -1.048 |   -0.2555 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      475.4589 |     91.37 |    -5.336 |   -0.9021 |    -2.202 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.613 |    0.4242 |     1.184 |   0.06323 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8781 |   0.05827 |    0.7796 |    0.7056 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.649 |    0.9697 |    0.7132 |     1.956 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      475.4589</span> |     91.37 |  0.004816 |    0.2886 |    0.1106 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009921 |    0.6045 |     1.184 |   0.06323 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8781 |   0.05827 |    0.7796 |    0.7056 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.649 |    0.9697 |    0.7132 |     1.956 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     9.388 |     1.275 |    0.2447 |  -0.08891 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4027 |     1.576 |    -4.598 |     3.539 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.08390 |   0.04261 |   -0.9004 |    -2.725 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.305 |     2.111 |     1.882 |    -2.135 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   27</span>|     475.41657 |     1.002 |    -1.137 |   -0.9241 |   -0.9392 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.001 |   -0.9615 |  -0.01910 |   -0.7133 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7590 |   -0.8814 |   -0.8097 |    -1.092 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4754 |   -0.8571 |    -1.049 |   -0.2540 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     475.41657 |     91.17 |    -5.337 |   -0.9022 |    -2.202 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.613 |    0.4236 |     1.185 |   0.06314 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8780 |   0.05827 |    0.7801 |    0.7060 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.647 |    0.9697 |    0.7121 |     1.958 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     475.41657</span> |     91.17 |  0.004811 |    0.2886 |    0.1106 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009918 |    0.6043 |     1.185 |   0.06314 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8780 |   0.05827 |    0.7801 |    0.7060 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.647 |    0.9697 |    0.7121 |     1.958 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -7.291 |     1.263 |  -0.02799 |  -0.06240 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4098 |     1.607 |    -5.561 |     3.409 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1363 |   0.09124 |    -1.126 |   -0.9025 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.264 |     2.123 |     1.858 |    -2.103 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   28</span>|     475.37603 |     1.004 |    -1.138 |   -0.9241 |   -0.9392 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.001 |   -0.9626 |  -0.01569 |   -0.7160 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7590 |   -0.8815 |   -0.8090 |    -1.092 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4775 |   -0.8574 |    -1.050 |   -0.2525 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     475.37603 |     91.35 |    -5.338 |   -0.9022 |    -2.202 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.614 |    0.4231 |     1.186 |   0.06307 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8780 |   0.05827 |    0.7806 |    0.7060 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.645 |    0.9695 |    0.7112 |     1.960 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     475.37603</span> |     91.35 |  0.004807 |    0.2886 |    0.1106 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009915 |    0.6042 |     1.186 |   0.06307 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8780 |   0.05827 |    0.7806 |    0.7060 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.645 |    0.9695 |    0.7112 |     1.960 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     7.976 |     1.271 |    0.2167 |  -0.08766 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4024 |     1.550 |    -5.132 |     3.516 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.09627 |   0.04106 |    -1.088 |   -0.7404 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.103 |     2.126 |     1.707 |    -2.133 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   29</span>|     475.34297 |     1.001 |    -1.139 |   -0.9242 |   -0.9391 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.002 |   -0.9636 |  -0.01242 |   -0.7185 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7591 |   -0.8816 |   -0.8082 |    -1.092 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4796 |   -0.8578 |    -1.051 |   -0.2511 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     475.34297 |     91.13 |    -5.339 |   -0.9023 |    -2.201 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.614 |    0.4226 |     1.188 |   0.06299 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8779 |   0.05826 |    0.7812 |    0.7056 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.642 |    0.9691 |    0.7103 |     1.962 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     475.34297</span> |     91.13 |  0.004803 |    0.2886 |    0.1106 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009912 |    0.6041 |     1.188 |   0.06299 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8779 |   0.05826 |    0.7812 |    0.7056 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.642 |    0.9691 |    0.7103 |     1.962 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -11.69 |     1.251 |  -0.09758 |  -0.05188 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4100 |     1.596 |    -5.544 |     3.338 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1288 |   0.09302 |    -1.103 |   -0.9943 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.044 |     2.113 |     1.726 |    -2.096 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   30</span>|     475.29763 |     1.004 |    -1.140 |   -0.9242 |   -0.9391 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.002 |   -0.9647 | -0.009016 |   -0.7212 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7592 |   -0.8817 |   -0.8074 |    -1.093 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4815 |   -0.8578 |    -1.052 |   -0.2496 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     475.29763 |     91.33 |    -5.340 |   -0.9023 |    -2.201 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.614 |    0.4221 |     1.189 |   0.06292 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8779 |   0.05826 |    0.7818 |    0.7049 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.640 |    0.9691 |    0.7096 |     1.964 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     475.29763</span> |     91.33 |  0.004798 |    0.2886 |    0.1106 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009909 |    0.6040 |     1.189 |   0.06292 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8779 |   0.05826 |    0.7818 |    0.7049 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.640 |    0.9691 |    0.7096 |     1.964 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     5.626 |     1.261 |    0.1834 |  -0.08674 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4019 |     1.535 |    -5.612 |     3.466 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1091 |   0.03444 |    -1.082 |   -0.8814 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     3.882 |     2.084 |     1.576 |    -2.128 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   31</span>|     475.26968 |     1.001 |    -1.140 |   -0.9243 |   -0.9390 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.002 |   -0.9656 | -0.005554 |   -0.7235 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7592 |   -0.8818 |   -0.8067 |    -1.093 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4837 |   -0.8585 |    -1.053 |   -0.2482 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     475.26968 |     91.11 |    -5.340 |   -0.9024 |    -2.201 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.615 |    0.4217 |     1.191 |   0.06285 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8779 |   0.05826 |    0.7823 |    0.7049 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.637 |    0.9684 |    0.7088 |     1.965 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     475.26968</span> |     91.11 |  0.004794 |    0.2886 |    0.1106 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009907 |    0.6039 |     1.191 |   0.06285 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8779 |   0.05826 |    0.7823 |    0.7049 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.637 |    0.9684 |    0.7088 |     1.965 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -13.71 |     1.246 |   -0.1255 |  -0.05322 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4099 |     1.581 |    -5.546 |     3.317 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1509 |    0.1096 |   -0.8594 |    -3.000 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     3.990 |     2.056 |     1.604 |    -2.087 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   32</span>|     475.22190 |     1.004 |    -1.141 |   -0.9243 |   -0.9390 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.002 |   -0.9667 | -0.002058 |   -0.7261 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7593 |   -0.8819 |   -0.8061 |    -1.093 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4854 |   -0.8583 |    -1.054 |   -0.2469 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      475.2219 |     91.33 |    -5.341 |   -0.9024 |    -2.201 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.615 |    0.4212 |     1.192 |   0.06277 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8778 |   0.05826 |    0.7828 |    0.7049 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.635 |    0.9686 |    0.7081 |     1.967 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      475.2219</span> |     91.33 |  0.004790 |    0.2886 |    0.1107 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009904 |    0.6038 |     1.192 |   0.06277 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8778 |   0.05826 |    0.7828 |    0.7049 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.635 |    0.9686 |    0.7081 |     1.967 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     5.841 |     1.258 |    0.1840 |  -0.08823 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4011 |     1.514 |    -4.992 |     3.441 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1080 |   0.03852 |    -1.043 |   -0.8514 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     3.826 |     2.019 |     1.451 |    -2.125 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   33</span>|     475.19540 |     1.001 |    -1.142 |   -0.9244 |   -0.9389 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.003 |   -0.9677 |  0.001228 |   -0.7286 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7593 |   -0.8819 |   -0.8054 |    -1.093 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4876 |   -0.8589 |    -1.055 |   -0.2455 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      475.1954 |     91.10 |    -5.342 |   -0.9025 |    -2.201 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.615 |    0.4207 |     1.193 |   0.06270 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8778 |   0.05825 |    0.7833 |    0.7050 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.633 |    0.9680 |    0.7073 |     1.969 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      475.1954</span> |     91.10 |  0.004786 |    0.2885 |    0.1107 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009901 |    0.6037 |     1.193 |   0.06270 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8778 |   0.05825 |    0.7833 |    0.7050 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.633 |    0.9680 |    0.7073 |     1.969 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -14.17 |     1.239 |   -0.1323 |  -0.05443 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4093 |     1.561 |    -5.475 |     3.262 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1301 |   0.09817 |    -1.038 |    -1.045 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     3.818 |     2.054 |     1.474 |    -2.085 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   34</span>|     475.14668 |     1.003 |    -1.143 |   -0.9244 |   -0.9388 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.003 |   -0.9688 |  0.004635 |   -0.7312 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7595 |   -0.8820 |   -0.8046 |    -1.094 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4894 |   -0.8588 |    -1.055 |   -0.2440 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     475.14668 |     91.31 |    -5.343 |   -0.9025 |    -2.201 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.615 |    0.4202 |     1.195 |   0.06262 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8778 |   0.05825 |    0.7838 |    0.7043 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.631 |    0.9681 |    0.7066 |     1.970 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     475.14668</span> |     91.31 |  0.004781 |    0.2885 |    0.1107 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009898 |    0.6035 |     1.195 |   0.06262 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8778 |   0.05825 |    0.7838 |    0.7043 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.631 |    0.9681 |    0.7066 |     1.970 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     3.838 |     1.251 |    0.1547 |  -0.08725 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4006 |     1.498 |    -4.927 |     3.416 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1219 |   0.06473 |    -1.010 |    -2.917 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     3.712 |     2.020 |     1.337 |    -2.117 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   35</span>|     475.12366 |     1.001 |    -1.144 |   -0.9245 |   -0.9388 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.003 |   -0.9698 |  0.007665 |   -0.7333 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7594 |   -0.8821 |   -0.8040 |    -1.092 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4916 |   -0.8600 |    -1.056 |   -0.2427 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     475.12366 |     91.10 |    -5.344 |   -0.9025 |    -2.201 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.616 |    0.4198 |     1.196 |   0.06256 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8778 |   0.05825 |    0.7843 |    0.7059 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.628 |    0.9669 |    0.7059 |     1.972 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     475.12366</span> |     91.10 |  0.004777 |    0.2885 |    0.1107 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009896 |    0.6034 |     1.196 |   0.06256 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8778 |   0.05825 |    0.7843 |    0.7059 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.628 |    0.9669 |    0.7059 |     1.972 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -14.75 |     1.239 |   -0.1466 |  -0.05465 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4082 |     1.541 |    -5.471 |     3.270 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1342 |   0.09829 |    -1.014 |    -1.026 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     3.624 |     1.932 |     1.359 |    -2.081 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   36</span>|     475.07465 |     1.004 |    -1.145 |   -0.9245 |   -0.9387 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.003 |   -0.9709 |   0.01108 |   -0.7360 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7595 |   -0.8821 |   -0.8033 |    -1.092 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4933 |   -0.8597 |    -1.057 |   -0.2414 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     475.07465 |     91.33 |    -5.345 |   -0.9025 |    -2.201 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.616 |    0.4193 |     1.198 |   0.06248 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8778 |   0.05825 |    0.7848 |    0.7058 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.626 |    0.9672 |    0.7053 |     1.974 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     475.07465</span> |     91.33 |  0.004773 |    0.2885 |    0.1107 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009893 |    0.6033 |     1.198 |   0.06248 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8778 |   0.05825 |    0.7848 |    0.7058 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.626 |    0.9672 |    0.7053 |     1.974 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     5.021 |     1.249 |    0.1599 |  -0.08992 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3985 |     1.471 |    -4.995 |     3.410 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1196 |   0.03779 |   -0.9990 |    -2.873 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     3.497 |     1.906 |     1.211 |    -2.119 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   37</span>|     475.04940 |     1.001 |    -1.146 |   -0.9245 |   -0.9386 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.004 |   -0.9719 |   0.01438 |   -0.7384 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7594 |   -0.8822 |   -0.8026 |    -1.091 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4953 |   -0.8604 |    -1.058 |   -0.2400 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      475.0494 |     91.11 |    -5.346 |   -0.9026 |    -2.201 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.616 |    0.4188 |     1.199 |   0.06242 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8778 |   0.05825 |    0.7853 |    0.7070 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.623 |    0.9666 |    0.7046 |     1.975 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      475.0494</span> |     91.11 |  0.004769 |    0.2885 |    0.1107 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009890 |    0.6032 |     1.199 |   0.06242 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8778 |   0.05825 |    0.7853 |    0.7070 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.623 |    0.9666 |    0.7046 |     1.975 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -14.15 |     1.235 |   -0.1370 |  -0.05688 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4085 |     1.517 |    -5.494 |     3.160 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1583 |    0.1112 |   -0.7821 |    -2.927 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     3.432 |     1.909 |     1.245 |    -2.084 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   38</span>|     475.00092 |     1.004 |    -1.147 |   -0.9245 |   -0.9386 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.004 |   -0.9731 |   0.01792 |   -0.7411 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7595 |   -0.8822 |   -0.8020 |    -1.090 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4968 |   -0.8598 |    -1.059 |   -0.2387 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     475.00092 |     91.32 |    -5.347 |   -0.9025 |    -2.201 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.617 |    0.4182 |     1.200 |   0.06234 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8778 |   0.05825 |    0.7857 |    0.7077 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.622 |    0.9671 |    0.7039 |     1.977 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     475.00092</span> |     91.32 |  0.004764 |    0.2885 |    0.1107 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009887 |    0.6031 |     1.200 |   0.06234 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8778 |   0.05825 |    0.7857 |    0.7077 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.622 |    0.9671 |    0.7039 |     1.977 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     4.379 |     1.249 |    0.1419 |  -0.08698 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3989 |     1.449 |    -4.966 |     3.395 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1055 |   0.03295 |   -0.9696 |   -0.7580 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     3.283 |     1.918 |     1.096 |    -2.115 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   39</span>|     474.98492 |     1.001 |    -1.147 |   -0.9246 |   -0.9385 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.004 |   -0.9740 |   0.02115 |   -0.7433 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7595 |   -0.8822 |   -0.8014 |    -1.089 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4989 |   -0.8610 |    -1.059 |   -0.2373 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     474.98492 |     91.07 |    -5.347 |   -0.9026 |    -2.201 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.617 |    0.4178 |     1.202 |   0.06227 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8778 |   0.05825 |    0.7862 |    0.7081 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.619 |    0.9660 |    0.7033 |     1.978 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     474.98492</span> |     91.07 |  0.004760 |    0.2885 |    0.1107 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009884 |    0.6030 |     1.202 |   0.06227 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8778 |   0.05825 |    0.7862 |    0.7081 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.619 |    0.9660 |    0.7033 |     1.978 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -17.65 |     1.231 |   -0.1920 |  -0.05242 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4084 |     1.504 |    -5.397 |     3.171 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1354 |    0.1061 |   -0.9468 |   -0.9144 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.271 |     1.859 |     1.156 |    -2.067 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   40</span>|     474.93249 |     1.004 |    -1.148 |   -0.9245 |   -0.9384 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.005 |   -0.9752 |   0.02452 |   -0.7460 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7596 |   -0.8823 |   -0.8007 |    -1.090 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5000 |   -0.8607 |    -1.060 |   -0.2361 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     474.93249 |     91.32 |    -5.348 |   -0.9026 |    -2.201 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.617 |    0.4173 |     1.203 |   0.06220 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8778 |   0.05824 |    0.7867 |    0.7073 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.618 |    0.9663 |    0.7027 |     1.980 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     474.93249</span> |     91.32 |  0.004755 |    0.2885 |    0.1107 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009881 |    0.6028 |     1.203 |   0.06220 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8778 |   0.05824 |    0.7867 |    0.7073 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.618 |    0.9663 |    0.7027 |     1.980 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     4.492 |     1.243 |    0.1448 |  -0.09052 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3963 |     1.427 |    -4.973 |     3.376 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.06414 |   0.02300 |   -0.7344 |    -2.787 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     3.083 |     1.834 |    0.9813 |    -2.110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   41</span>|     474.90355 |     1.001 |    -1.149 |   -0.9246 |   -0.9383 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.005 |   -0.9763 |   0.02806 |   -0.7486 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7596 |   -0.8823 |   -0.8002 |    -1.089 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5018 |   -0.8611 |    -1.061 |   -0.2347 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     474.90355 |     91.13 |    -5.349 |   -0.9026 |    -2.201 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.617 |    0.4168 |     1.205 |   0.06212 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8777 |   0.05824 |    0.7870 |    0.7084 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.616 |    0.9659 |    0.7020 |     1.982 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     474.90355</span> |     91.13 |  0.004751 |    0.2885 |    0.1107 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009878 |    0.6027 |     1.205 |   0.06212 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8777 |   0.05824 |    0.7870 |    0.7084 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.616 |    0.9659 |    0.7020 |     1.982 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -12.15 |     1.229 |   -0.1075 |  -0.06320 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4033 |     1.463 |    -5.606 |     3.135 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1416 |   0.07801 |   -0.9461 |    -2.867 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     3.063 |     1.817 |     1.008 |    -2.075 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   42</span>|     474.85832 |     1.003 |    -1.150 |   -0.9245 |   -0.9383 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.005 |   -0.9775 |   0.03184 |   -0.7513 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7597 |   -0.8823 |   -0.7996 |    -1.089 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5032 |   -0.8605 |    -1.061 |   -0.2334 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     474.85832 |     91.32 |    -5.350 |   -0.9026 |    -2.201 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.618 |    0.4162 |     1.206 |   0.06204 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8777 |   0.05824 |    0.7875 |    0.7089 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.614 |    0.9665 |    0.7015 |     1.983 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     474.85832</span> |     91.32 |  0.004746 |    0.2885 |    0.1107 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009875 |    0.6026 |     1.206 |   0.06204 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8777 |   0.05824 |    0.7875 |    0.7089 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.614 |    0.9665 |    0.7015 |     1.983 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     3.689 |     1.242 |    0.1265 |  -0.09001 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3949 |     1.405 |    -5.495 |     3.344 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1166 |   0.02645 |   -0.9105 |   -0.7126 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.952 |     1.861 |    0.8779 |    -2.102 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   43</span>|     474.83791 |     1.001 |    -1.151 |   -0.9246 |   -0.9382 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.006 |   -0.9784 |   0.03545 |   -0.7535 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7596 |   -0.8823 |   -0.7990 |    -1.088 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5052 |   -0.8617 |    -1.062 |   -0.2320 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     474.83791 |     91.10 |    -5.351 |   -0.9027 |    -2.201 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.618 |    0.4158 |     1.208 |   0.06198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8777 |   0.05824 |    0.7879 |    0.7094 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.612 |    0.9653 |    0.7010 |     1.985 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     474.83791</span> |     91.10 |  0.004742 |    0.2885 |    0.1107 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009872 |    0.6025 |     1.208 |   0.06198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8777 |   0.05824 |    0.7879 |    0.7094 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.612 |    0.9653 |    0.7010 |     1.985 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -15.77 |     1.225 |   -0.1616 |  -0.05944 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4032 |     1.455 |    -5.461 |     3.071 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1419 |   0.08593 |   -0.9091 |   -0.8855 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.951 |     1.791 |    0.9091 |    -2.062 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   44</span>|     474.78971 |     1.004 |    -1.152 |   -0.9246 |   -0.9381 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.006 |   -0.9794 |   0.03911 |   -0.7559 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7597 |   -0.8824 |   -0.7984 |    -1.089 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5068 |   -0.8614 |    -1.062 |   -0.2307 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     474.78971 |     91.33 |    -5.352 |   -0.9026 |    -2.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.618 |    0.4153 |     1.209 |   0.06191 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8777 |   0.05824 |    0.7883 |    0.7086 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.610 |    0.9656 |    0.7006 |     1.986 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     474.78971</span> |     91.33 |  0.004738 |    0.2885 |    0.1107 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009869 |    0.6024 |     1.209 |   0.06191 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8777 |   0.05824 |    0.7883 |    0.7086 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.610 |    0.9656 |    0.7006 |     1.986 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     4.398 |     1.237 |    0.1402 |  -0.09195 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3940 |     1.388 |    -4.885 |     3.322 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1374 |   0.01792 |   -0.6865 |    -2.709 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.810 |     1.778 |    0.8100 |    -2.095 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   45</span>|     474.76763 |     1.001 |    -1.153 |   -0.9247 |   -0.9380 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.006 |   -0.9804 |   0.04256 |   -0.7584 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7597 |   -0.8824 |   -0.7979 |    -1.088 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5086 |   -0.8621 |    -1.063 |   -0.2293 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     474.76763 |     91.11 |    -5.353 |   -0.9027 |    -2.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.619 |    0.4149 |     1.211 |   0.06184 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8777 |   0.05824 |    0.7887 |    0.7097 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.608 |    0.9650 |    0.7001 |     1.988 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     474.76763</span> |     91.11 |  0.004734 |    0.2885 |    0.1108 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009867 |    0.6023 |     1.211 |   0.06184 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8777 |   0.05824 |    0.7887 |    0.7097 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.608 |    0.9650 |    0.7001 |     1.988 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -14.92 |     1.222 |   -0.1466 |  -0.06186 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4014 |     1.433 |    -4.989 |     3.157 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1326 |   0.08284 |   -0.6789 |    -2.803 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.814 |     1.775 |    0.8327 |    -2.053 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   46</span>|     474.71973 |     1.004 |    -1.154 |   -0.9246 |   -0.9379 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.006 |   -0.9816 |   0.04617 |   -0.7611 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7597 |   -0.8824 |   -0.7975 |    -1.087 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5100 |   -0.8614 |    -1.064 |   -0.2281 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     474.71973 |     91.32 |    -5.354 |   -0.9026 |    -2.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.619 |    0.4143 |     1.212 |   0.06176 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8777 |   0.05824 |    0.7890 |    0.7100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.606 |    0.9656 |    0.6996 |     1.990 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     474.71973</span> |     91.32 |  0.004729 |    0.2885 |    0.1108 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009863 |    0.6021 |     1.212 |   0.06176 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8777 |   0.05824 |    0.7890 |    0.7100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.606 |    0.9656 |    0.6996 |     1.990 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     4.021 |     1.236 |    0.1299 |  -0.09158 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3929 |     1.368 |    -4.925 |     3.331 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.07142 |   0.08893 |   -0.6522 |    -2.634 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.639 |     1.780 |    0.7273 |    -2.085 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   47</span>|     474.70040 |     1.001 |    -1.155 |   -0.9247 |   -0.9379 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.007 |   -0.9826 |   0.04954 |   -0.7634 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7597 |   -0.8825 |   -0.7971 |    -1.086 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5117 |   -0.8624 |    -1.064 |   -0.2267 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      474.7004 |     91.10 |    -5.355 |   -0.9027 |    -2.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.619 |    0.4139 |     1.214 |   0.06169 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8777 |   0.05824 |    0.7893 |    0.7114 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.604 |    0.9647 |    0.6992 |     1.991 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      474.7004</span> |     91.10 |  0.004724 |    0.2885 |    0.1108 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009861 |    0.6020 |     1.214 |   0.06169 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8777 |   0.05824 |    0.7893 |    0.7114 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.604 |    0.9647 |    0.6992 |     1.991 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -15.76 |     1.220 |   -0.1617 |  -0.06091 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4007 |     1.415 |    -5.116 |     3.166 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1298 |   0.07714 |   -0.6701 |    -2.700 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.670 |     1.748 |    0.7590 |    -2.043 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   48</span>|     474.65116 |     1.003 |    -1.156 |   -0.9246 |   -0.9378 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.007 |   -0.9837 |   0.05321 |   -0.7662 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7598 |   -0.8825 |   -0.7967 |    -1.086 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5130 |   -0.8617 |    -1.065 |   -0.2255 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     474.65116 |     91.32 |    -5.356 |   -0.9026 |    -2.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.620 |    0.4134 |     1.215 |   0.06161 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8777 |   0.05824 |    0.7896 |    0.7116 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.603 |    0.9653 |    0.6987 |     1.993 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     474.65116</span> |     91.32 |  0.004720 |    0.2885 |    0.1108 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009857 |    0.6019 |     1.215 |   0.06161 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8777 |   0.05824 |    0.7896 |    0.7116 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.603 |    0.9653 |    0.6987 |     1.993 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     3.462 |     1.239 |    0.1107 |  -0.09136 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3915 |     1.348 |    -5.441 |     3.268 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1113 |   0.02510 |   -0.6252 |    -2.485 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.647 |     1.796 |    0.6587 |    -2.076 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   49</span>|     474.63065 |     1.001 |    -1.157 |   -0.9247 |   -0.9377 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.007 |   -0.9846 |   0.05678 |   -0.7683 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7597 |   -0.8825 |   -0.7963 |    -1.084 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5148 |   -0.8629 |    -1.065 |   -0.2241 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     474.63065 |     91.11 |    -5.357 |   -0.9027 |    -2.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.620 |    0.4129 |     1.217 |   0.06155 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8777 |   0.05824 |    0.7899 |    0.7131 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.600 |    0.9642 |    0.6984 |     1.994 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     474.63065</span> |     91.11 |  0.004716 |    0.2885 |    0.1108 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009855 |    0.6018 |     1.217 |   0.06155 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8777 |   0.05824 |    0.7899 |    0.7131 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.600 |    0.9642 |    0.6984 |     1.994 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -14.93 |     1.220 |   -0.1531 |  -0.06288 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3983 |     1.394 |    -5.436 |     3.113 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1458 |   0.07621 |   -0.8397 |   -0.6848 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.501 |     1.690 |    0.6891 |    -2.034 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   50</span>|     474.58497 |     1.004 |    -1.158 |   -0.9246 |   -0.9376 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.008 |   -0.9857 |   0.06060 |   -0.7708 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7598 |   -0.8826 |   -0.7958 |    -1.084 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5162 |   -0.8624 |    -1.065 |   -0.2230 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     474.58497 |     91.34 |    -5.358 |   -0.9027 |    -2.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.620 |    0.4125 |     1.218 |   0.06148 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8777 |   0.05824 |    0.7902 |    0.7126 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.599 |    0.9647 |    0.6980 |     1.996 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     474.58497</span> |     91.34 |  0.004711 |    0.2885 |    0.1108 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009852 |    0.6017 |     1.218 |   0.06148 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8777 |   0.05824 |    0.7902 |    0.7126 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.599 |    0.9647 |    0.6980 |     1.996 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     5.248 |     1.234 |    0.1371 |  -0.09456 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3896 |     1.328 |    -5.011 |     3.180 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1110 |  0.004022 |   -0.8103 |   -0.4964 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.349 |     1.713 |    0.5705 |    -2.061 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   51</span>|     474.55760 |     1.002 |    -1.159 |   -0.9247 |   -0.9376 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.008 |   -0.9867 |   0.06444 |   -0.7734 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7598 |   -0.8826 |   -0.7952 |    -1.085 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5178 |   -0.8626 |    -1.066 |   -0.2215 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      474.5576 |     91.15 |    -5.359 |   -0.9027 |    -2.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.620 |    0.4120 |     1.220 |   0.06140 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8777 |   0.05824 |    0.7907 |    0.7122 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.597 |    0.9645 |    0.6977 |     1.998 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      474.5576</span> |     91.15 |  0.004706 |    0.2885 |    0.1108 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009849 |    0.6016 |     1.220 |   0.06140 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8777 |   0.05824 |    0.7907 |    0.7122 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.597 |    0.9645 |    0.6977 |     1.998 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -11.86 |     1.219 |   -0.1003 |  -0.07615 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3954 |     1.369 |    -4.929 |     3.171 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1229 |   0.06540 |   -0.8183 |   -0.7141 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.360 |     1.696 |    0.6359 |    -2.032 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   52</span>|     474.51619 |     1.004 |    -1.160 |   -0.9246 |   -0.9375 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.008 |   -0.9878 |   0.06816 |   -0.7761 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7599 |   -0.8826 |   -0.7946 |    -1.086 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5193 |   -0.8622 |    -1.066 |   -0.2202 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     474.51619 |     91.33 |    -5.360 |   -0.9027 |    -2.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.621 |    0.4115 |     1.221 |   0.06132 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8776 |   0.05823 |    0.7911 |    0.7113 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.595 |    0.9648 |    0.6972 |     1.999 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     474.51619</span> |     91.33 |  0.004702 |    0.2885 |    0.1108 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009846 |    0.6014 |     1.221 |   0.06132 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8776 |   0.05823 |    0.7911 |    0.7113 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.595 |    0.9648 |    0.6972 |     1.999 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     4.413 |     1.225 |    0.1371 |  -0.09620 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3880 |     1.314 |    -5.554 |     3.197 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.07604 |  0.008867 |   -0.7931 |   -0.6282 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.148 |     1.715 |    0.5273 |    -2.052 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   53</span>|     474.48673 |     1.002 |    -1.161 |   -0.9247 |   -0.9374 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.009 |   -0.9889 |   0.07224 |   -0.7786 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7599 |   -0.8826 |   -0.7941 |    -1.086 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5208 |   -0.8625 |    -1.067 |   -0.2188 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     474.48673 |     91.17 |    -5.361 |   -0.9027 |    -2.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.621 |    0.4110 |     1.223 |   0.06125 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8776 |   0.05823 |    0.7915 |    0.7110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.593 |    0.9645 |    0.6969 |     2.001 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     474.48673</span> |     91.17 |  0.004697 |    0.2885 |    0.1108 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009843 |    0.6013 |     1.223 |   0.06125 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8776 |   0.05823 |    0.7915 |    0.7110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.593 |    0.9645 |    0.6969 |     2.001 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -10.51 |     1.211 |  -0.06554 |  -0.07429 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3932 |     1.350 |    -4.456 |     3.182 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.08901 |   0.05354 |   -0.5957 |    -2.739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.160 |     1.696 |    0.5687 |    -2.022 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   54</span>|     474.45218 |     1.004 |    -1.162 |   -0.9246 |   -0.9373 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.009 |   -0.9900 |   0.07590 |   -0.7814 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7601 |   -0.8826 |   -0.7937 |    -1.086 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5220 |   -0.8620 |    -1.067 |   -0.2177 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     474.45218 |     91.40 |    -5.362 |   -0.9027 |    -2.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.621 |    0.4105 |     1.224 |   0.06117 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8776 |   0.05823 |    0.7918 |    0.7111 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.592 |    0.9650 |    0.6965 |     2.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     474.45218</span> |     91.40 |  0.004692 |    0.2885 |    0.1108 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009840 |    0.6012 |     1.224 |   0.06117 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8776 |   0.05823 |    0.7918 |    0.7111 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.592 |    0.9650 |    0.6965 |     2.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     9.724 |     1.224 |    0.2009 |   -0.1069 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3834 |     1.279 |    -5.556 |     3.324 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1101 |  -0.01912 |   -0.5638 |    -2.553 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.043 |     1.731 |    0.4140 |    -2.044 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   55</span>|     474.41257 |     1.003 |    -1.163 |   -0.9246 |   -0.9371 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.009 |   -0.9913 |   0.07979 |   -0.7844 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7601 |   -0.8825 |   -0.7935 |    -1.085 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5231 |   -0.8612 |    -1.068 |   -0.2167 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     474.41257 |     91.25 |    -5.363 |   -0.9026 |    -2.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.622 |    0.4099 |     1.226 |   0.06108 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8775 |   0.05824 |    0.7919 |    0.7118 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.591 |    0.9658 |    0.6961 |     2.004 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     474.41257</span> |     91.25 |  0.004687 |    0.2885 |    0.1109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009836 |    0.6011 |     1.226 |   0.06108 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8775 |   0.05824 |    0.7919 |    0.7118 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.591 |    0.9658 |    0.6961 |     2.004 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -3.208 |     1.213 |   0.03978 |  -0.08693 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3876 |     1.303 |    -5.023 |     3.218 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1347 |   0.02311 |   -0.7771 |   -0.6556 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.056 |     1.822 |    0.4520 |    -2.025 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   56</span>|     474.39271 |     1.005 |    -1.164 |   -0.9246 |   -0.9371 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.010 |   -0.9922 |   0.08348 |   -0.7867 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7600 |   -0.8825 |   -0.7930 |    -1.085 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5246 |   -0.8625 |    -1.068 |   -0.2152 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     474.39271 |     91.47 |    -5.364 |   -0.9027 |    -2.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.622 |    0.4094 |     1.228 |   0.06101 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8776 |   0.05824 |    0.7923 |    0.7123 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.589 |    0.9645 |    0.6958 |     2.005 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     474.39271</span> |     91.47 |  0.004683 |    0.2885 |    0.1109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009833 |    0.6010 |     1.228 |   0.06101 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8776 |   0.05824 |    0.7923 |    0.7123 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.589 |    0.9645 |    0.6958 |     2.005 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     15.86 |     1.227 |    0.2807 |   -0.1163 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3793 |     1.243 |    -5.494 |     3.329 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.09368 |   0.01293 |   -0.5149 |    -2.407 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.979 |     1.686 |    0.3308 |    -2.044 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   57</span>|     474.34538 |     1.003 |    -1.165 |   -0.9247 |   -0.9369 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.010 |   -0.9934 |   0.08718 |   -0.7897 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7601 |   -0.8825 |   -0.7926 |    -1.086 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5258 |   -0.8620 |    -1.068 |   -0.2141 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     474.34538 |     91.27 |    -5.365 |   -0.9027 |    -2.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.622 |    0.4089 |     1.229 |   0.06093 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8775 |   0.05824 |    0.7926 |    0.7117 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.587 |    0.9650 |    0.6954 |     2.007 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     474.34538</span> |     91.27 |  0.004677 |    0.2885 |    0.1109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009830 |    0.6008 |     1.229 |   0.06093 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8775 |   0.05824 |    0.7926 |    0.7117 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.587 |    0.9650 |    0.6954 |     2.007 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -2.345 |     1.210 |   0.04189 |  -0.08986 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3855 |     1.284 |    -5.145 |     3.218 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1284 |   0.01309 |   -0.7466 |   -0.6536 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.964 |     1.743 |    0.3816 |    -2.008 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   58</span>|     474.31834 |     1.005 |    -1.166 |   -0.9247 |   -0.9369 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.010 |   -0.9944 |   0.09109 |   -0.7921 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7600 |   -0.8825 |   -0.7920 |    -1.085 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5273 |   -0.8633 |    -1.069 |   -0.2126 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     474.31834 |     91.43 |    -5.366 |   -0.9027 |    -2.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.623 |    0.4085 |     1.231 |   0.06086 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8776 |   0.05824 |    0.7930 |    0.7121 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.586 |    0.9638 |    0.6952 |     2.008 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     474.31834</span> |     91.43 |  0.004673 |    0.2885 |    0.1109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009827 |    0.6007 |     1.231 |   0.06086 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8776 |   0.05824 |    0.7930 |    0.7121 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.586 |    0.9638 |    0.6952 |     2.008 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     12.13 |     1.219 |    0.2312 |   -0.1125 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3787 |     1.238 |    -5.317 |     3.196 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.08361 |  -0.02599 |   -0.7000 |   -0.4940 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.837 |     1.637 |    0.3211 |    -2.028 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   59</span>|     474.27833 |     1.003 |    -1.167 |   -0.9247 |   -0.9368 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.010 |   -0.9955 |   0.09487 |   -0.7949 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7601 |   -0.8825 |   -0.7915 |    -1.086 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5286 |   -0.8630 |    -1.069 |   -0.2114 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     474.27833 |     91.25 |    -5.367 |   -0.9028 |    -2.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.623 |    0.4080 |     1.232 |   0.06078 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8775 |   0.05824 |    0.7934 |    0.7111 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.584 |    0.9640 |    0.6948 |     2.010 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     474.27833</span> |     91.25 |  0.004668 |    0.2885 |    0.1109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009824 |    0.6006 |     1.232 |   0.06078 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8775 |   0.05824 |    0.7934 |    0.7111 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.584 |    0.9640 |    0.6948 |     2.010 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.145 |     1.202 |   0.02355 |  -0.08919 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3842 |     1.275 |    -5.102 |     3.189 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1071 |   0.01700 |   -0.7283 |   -0.7209 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.776 |     1.686 |    0.3249 |    -1.982 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   60</span>|     474.25305 |     1.005 |    -1.168 |   -0.9247 |   -0.9367 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.011 |   -0.9965 |   0.09878 |   -0.7975 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7601 |   -0.8825 |   -0.7909 |    -1.086 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5300 |   -0.8636 |    -1.069 |   -0.2100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     474.25305 |     91.44 |    -5.368 |   -0.9028 |    -2.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.623 |    0.4075 |     1.234 |   0.06070 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8775 |   0.05824 |    0.7938 |    0.7109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.583 |    0.9635 |    0.6946 |     2.012 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     474.25305</span> |     91.44 |  0.004663 |    0.2885 |    0.1109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009821 |    0.6005 |     1.234 |   0.06070 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8775 |   0.05824 |    0.7938 |    0.7109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.583 |    0.9635 |    0.6946 |     2.012 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     13.11 |     1.213 |    0.2527 |   -0.1161 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3767 |     1.219 |    -5.003 |     3.213 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.09270 |  -0.04298 |   -0.4814 |    -2.533 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.730 |     1.613 |    0.2495 |    -2.014 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   61</span>|     474.21254 |     1.003 |    -1.169 |   -0.9248 |   -0.9365 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.011 |   -0.9977 |    0.1025 |   -0.8005 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7602 |   -0.8824 |   -0.7906 |    -1.087 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5311 |   -0.8630 |    -1.070 |   -0.2089 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     474.21254 |     91.24 |    -5.369 |   -0.9028 |    -2.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.624 |    0.4069 |     1.236 |   0.06062 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8775 |   0.05824 |    0.7941 |    0.7105 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.581 |    0.9641 |    0.6942 |     2.013 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     474.21254</span> |     91.24 |  0.004658 |    0.2885 |    0.1109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009817 |    0.6003 |     1.236 |   0.06062 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8775 |   0.05824 |    0.7941 |    0.7105 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.581 |    0.9641 |    0.6942 |     2.013 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -5.267 |     1.195 |   0.01760 |  -0.08987 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3825 |     1.260 |    -5.272 |     3.141 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1336 |  0.009843 |   -0.5186 |    -2.706 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.727 |     1.652 |    0.2904 |    -1.976 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   62</span>|     474.18171 |     1.004 |    -1.170 |   -0.9247 |   -0.9364 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.011 |   -0.9989 |    0.1065 |   -0.8033 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7602 |   -0.8823 |   -0.7904 |    -1.086 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5322 |   -0.8627 |    -1.070 |   -0.2078 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     474.18171 |     91.41 |    -5.370 |   -0.9028 |    -2.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.624 |    0.4064 |     1.237 |   0.06054 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8775 |   0.05824 |    0.7942 |    0.7112 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.580 |    0.9644 |    0.6939 |     2.014 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     474.18171</span> |     91.41 |  0.004653 |    0.2885 |    0.1109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009814 |    0.6002 |     1.237 |   0.06054 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8775 |   0.05824 |    0.7942 |    0.7112 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.580 |    0.9644 |    0.6939 |     2.014 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     9.716 |     1.206 |    0.2113 |   -0.1128 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3755 |     1.207 |    -5.399 |     3.226 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.09670 |  -0.03459 |   -0.6713 |   -0.5638 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.619 |     1.687 |    0.2005 |    -1.998 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   63</span>|     474.14509 |     1.003 |    -1.171 |   -0.9248 |   -0.9363 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.012 |    -1.000 |    0.1105 |   -0.8061 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7602 |   -0.8822 |   -0.7900 |    -1.087 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5333 |   -0.8621 |    -1.071 |   -0.2068 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     474.14509 |     91.25 |    -5.371 |   -0.9028 |    -2.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.624 |    0.4059 |     1.239 |   0.06045 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8775 |   0.05825 |    0.7945 |    0.7106 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.579 |    0.9649 |    0.6936 |     2.015 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     474.14509</span> |     91.25 |  0.004648 |    0.2885 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009811 |    0.6001 |     1.239 |   0.06045 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8775 |   0.05825 |    0.7945 |    0.7106 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.579 |    0.9649 |    0.6936 |     2.015 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -5.280 |     1.192 |   0.02321 |  -0.09129 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3799 |     1.239 |    -5.339 |     3.156 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1074 |   0.06181 |   -0.4875 |    -2.680 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.648 |     1.743 |    0.2195 |    -1.955 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   64</span>|     474.11542 |     1.005 |    -1.172 |   -0.9248 |   -0.9361 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.012 |    -1.001 |    0.1146 |   -0.8089 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7603 |   -0.8821 |   -0.7897 |    -1.086 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5344 |   -0.8621 |    -1.071 |   -0.2057 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     474.11542 |     91.42 |    -5.372 |   -0.9028 |    -2.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.625 |    0.4054 |     1.241 |   0.06037 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8774 |   0.05825 |    0.7947 |    0.7110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.577 |    0.9649 |    0.6934 |     2.017 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     474.11542</span> |     91.42 |  0.004643 |    0.2885 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009807 |    0.6000 |     1.241 |   0.06037 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8774 |   0.05825 |    0.7947 |    0.7110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.577 |    0.9649 |    0.6934 |     2.017 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     10.52 |     1.202 |    0.2258 |   -0.1162 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3725 |     1.186 |    -5.381 |     3.222 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1104 |  -0.04157 |   -0.6550 |   -0.5841 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.505 |     1.701 |    0.1753 |    -1.993 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   65</span>|     474.07794 |     1.003 |    -1.173 |   -0.9248 |   -0.9360 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.012 |    -1.002 |    0.1187 |   -0.8117 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7604 |   -0.8821 |   -0.7895 |    -1.087 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5355 |   -0.8615 |    -1.071 |   -0.2048 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     474.07794 |     91.25 |    -5.373 |   -0.9028 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.625 |    0.4048 |     1.242 |   0.06029 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8774 |   0.05825 |    0.7949 |    0.7104 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.576 |    0.9655 |    0.6932 |     2.018 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     474.07794</span> |     91.25 |  0.004638 |    0.2885 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009804 |    0.5999 |     1.242 |   0.06029 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8774 |   0.05825 |    0.7949 |    0.7104 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.576 |    0.9655 |    0.6932 |     2.018 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.801 |     1.188 |   0.03689 |  -0.09236 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3785 |     1.221 |    -5.368 |     3.104 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1066 |  0.003449 |   -0.6711 |   -0.7398 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.563 |     1.803 |    0.1884 |    -1.939 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   66</span>|     474.04951 |     1.005 |    -1.175 |   -0.9248 |   -0.9359 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.013 |    -1.003 |    0.1228 |   -0.8144 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7604 |   -0.8820 |   -0.7890 |    -1.087 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5367 |   -0.8618 |    -1.071 |   -0.2036 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     474.04951 |     91.43 |    -5.375 |   -0.9028 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.625 |    0.4044 |     1.244 |   0.06021 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8774 |   0.05825 |    0.7952 |    0.7100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.575 |    0.9652 |    0.6930 |     2.019 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     474.04951</span> |     91.43 |  0.004633 |    0.2885 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009801 |    0.5997 |     1.244 |   0.06021 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8774 |   0.05825 |    0.7952 |    0.7100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.575 |    0.9652 |    0.6930 |     2.019 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     10.86 |     1.196 |    0.2377 |   -0.1190 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3700 |     1.169 |    -5.399 |     3.259 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.09645 |  -0.03914 |   -0.4400 |    -2.610 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.421 |     1.705 |    0.1351 |    -1.978 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   67</span>|     474.01246 |     1.003 |    -1.176 |   -0.9249 |   -0.9357 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.013 |    -1.004 |    0.1268 |   -0.8173 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7606 |   -0.8819 |   -0.7888 |    -1.088 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5377 |   -0.8613 |    -1.072 |   -0.2028 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     474.01246 |     91.25 |    -5.376 |   -0.9029 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.626 |    0.4039 |     1.246 |   0.06013 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8773 |   0.05825 |    0.7954 |    0.7098 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.573 |    0.9657 |    0.6927 |     2.020 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     474.01246</span> |     91.25 |  0.004628 |    0.2885 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009798 |    0.5996 |     1.246 |   0.06013 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8773 |   0.05825 |    0.7954 |    0.7098 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.573 |    0.9657 |    0.6927 |     2.020 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -5.724 |     1.179 |   0.03043 |  -0.09493 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3750 |     1.206 |    -5.283 |     3.092 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.09863 |  -0.01543 |   -0.6573 |   -0.7980 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.377 |     1.778 |    0.1578 |    -1.944 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   68</span>|     473.98155 |     1.005 |    -1.177 |   -0.9249 |   -0.9356 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.013 |    -1.005 |    0.1310 |   -0.8202 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7607 |   -0.8818 |   -0.7885 |    -1.088 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5387 |   -0.8611 |    -1.072 |   -0.2018 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     473.98155 |     91.42 |    -5.377 |   -0.9029 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.626 |    0.4034 |     1.247 |   0.06004 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8773 |   0.05826 |    0.7956 |    0.7095 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.572 |    0.9659 |    0.6925 |     2.022 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     473.98155</span> |     91.42 |  0.004623 |    0.2885 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009794 |    0.5995 |     1.247 |   0.06004 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8773 |   0.05826 |    0.7956 |    0.7095 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.572 |    0.9659 |    0.6925 |     2.022 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     9.125 |     1.189 |    0.2215 |   -0.1183 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3680 |     1.157 |    -5.346 |     3.214 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.06695 |  -0.05805 |   -0.6245 |   -0.6678 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.365 |     1.770 |   0.07348 |    -1.960 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   69</span>|     473.94647 |     1.003 |    -1.178 |   -0.9250 |   -0.9354 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.014 |    -1.007 |    0.1350 |   -0.8231 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7608 |   -0.8817 |   -0.7881 |    -1.089 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5397 |   -0.8608 |    -1.072 |   -0.2009 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     473.94647 |     91.26 |    -5.378 |   -0.9030 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.626 |    0.4029 |     1.249 |   0.05996 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8772 |   0.05826 |    0.7959 |    0.7086 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.571 |    0.9661 |    0.6923 |     2.023 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     473.94647</span> |     91.26 |  0.004618 |    0.2884 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009791 |    0.5994 |     1.249 |   0.05996 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8772 |   0.05826 |    0.7959 |    0.7086 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.571 |    0.9661 |    0.6923 |     2.023 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -5.271 |     1.174 |   0.04603 |  -0.09766 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3725 |     1.188 |    -5.320 |     3.084 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.09768 |  -0.01918 |   -0.6378 |   -0.8469 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.360 |     1.825 |   0.08453 |    -1.920 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   70</span>|     473.91708 |     1.005 |    -1.179 |   -0.9250 |   -0.9353 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.014 |    -1.008 |    0.1391 |   -0.8259 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7609 |   -0.8816 |   -0.7877 |    -1.089 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5408 |   -0.8610 |    -1.072 |   -0.1999 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     473.91708 |     91.43 |    -5.379 |   -0.9030 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.627 |    0.4024 |     1.251 |   0.05988 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8772 |   0.05826 |    0.7962 |    0.7081 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.570 |    0.9660 |    0.6921 |     2.024 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     473.91708</span> |     91.43 |  0.004613 |    0.2884 |    0.1111 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009788 |    0.5993 |     1.251 |   0.05988 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8772 |   0.05826 |    0.7962 |    0.7081 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.570 |    0.9660 |    0.6921 |     2.024 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     9.919 |     1.183 |    0.2388 |   -0.1221 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3649 |     1.138 |    -5.295 |     3.201 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.05759 |  -0.06746 |   -0.6018 |   -0.7458 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.218 |     1.797 |   0.02666 |    -1.950 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   71</span>|     473.88166 |     1.003 |    -1.180 |   -0.9251 |   -0.9351 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.014 |    -1.009 |    0.1432 |   -0.8289 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7611 |   -0.8814 |   -0.7874 |    -1.090 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5418 |   -0.8607 |    -1.072 |   -0.1991 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     473.88166 |     91.26 |    -5.380 |   -0.9031 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.627 |    0.4019 |     1.252 |   0.05979 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8771 |   0.05827 |    0.7964 |    0.7073 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.568 |    0.9662 |    0.6919 |     2.025 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     473.88166</span> |     91.26 |  0.004608 |    0.2884 |    0.1111 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009785 |    0.5991 |     1.252 |   0.05979 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8771 |   0.05827 |    0.7964 |    0.7073 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.568 |    0.9662 |    0.6919 |     2.025 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -5.370 |     1.167 |   0.05346 |  -0.09978 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3701 |     1.174 |    -5.172 |     3.105 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.07011 |  -0.02516 |   -0.4239 |    -2.943 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.187 |     1.854 |   0.07395 |    -1.907 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   72</span>|     473.85215 |     1.005 |    -1.181 |   -0.9251 |   -0.9350 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.015 |    -1.010 |    0.1472 |   -0.8318 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7612 |   -0.8813 |   -0.7872 |    -1.090 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5427 |   -0.8608 |    -1.073 |   -0.1983 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     473.85215 |     91.44 |    -5.381 |   -0.9031 |    -2.197 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.627 |    0.4014 |     1.254 |   0.05971 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8771 |   0.05827 |    0.7965 |    0.7078 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.567 |    0.9662 |    0.6918 |     2.026 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     473.85215</span> |     91.44 |  0.004603 |    0.2884 |    0.1111 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009781 |    0.5990 |     1.254 |   0.05971 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8771 |   0.05827 |    0.7965 |    0.7078 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.567 |    0.9662 |    0.6918 |     2.026 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     10.42 |     1.179 |    0.2474 |   -0.1240 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3627 |     1.120 |    -5.345 |     3.221 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.07960 |  -0.04998 |   -0.3836 |    -2.755 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.094 |     1.797 |  -0.01014 |    -1.939 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   73</span>|     473.81514 |     1.003 |    -1.182 |   -0.9251 |   -0.9348 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.015 |    -1.011 |    0.1513 |   -0.8349 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7614 |   -0.8810 |   -0.7873 |    -1.089 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5432 |   -0.8601 |    -1.073 |   -0.1979 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     473.81514 |     91.27 |    -5.382 |   -0.9031 |    -2.197 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.628 |    0.4008 |     1.256 |   0.05962 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8770 |   0.05828 |    0.7964 |    0.7084 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.567 |    0.9669 |    0.6916 |     2.026 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     473.81514</span> |     91.27 |  0.004598 |    0.2884 |    0.1111 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009778 |    0.5989 |     1.256 |   0.05962 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8770 |   0.05828 |    0.7964 |    0.7084 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.567 |    0.9669 |    0.6916 |     2.026 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.811 |     1.166 |   0.05684 |   -0.1019 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3663 |     1.150 |    -5.276 |     3.057 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.06818 |  -0.03474 |   -0.4125 |    -2.836 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.158 |     1.851 |   0.03258 |    -1.914 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   74</span>|     473.78884 |     1.005 |    -1.183 |   -0.9252 |   -0.9346 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.015 |    -1.012 |    0.1554 |   -0.8377 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7615 |   -0.8808 |   -0.7873 |    -1.088 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5439 |   -0.8602 |    -1.073 |   -0.1971 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     473.78884 |     91.46 |    -5.383 |   -0.9031 |    -2.197 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.628 |    0.4003 |     1.257 |   0.05954 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8770 |   0.05829 |    0.7965 |    0.7095 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.566 |    0.9667 |    0.6915 |     2.027 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     473.78884</span> |     91.46 |  0.004593 |    0.2884 |    0.1111 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009775 |    0.5988 |     1.257 |   0.05954 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8770 |   0.05829 |    0.7965 |    0.7095 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.566 |    0.9667 |    0.6915 |     2.027 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     12.26 |     1.178 |    0.2628 |   -0.1282 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3579 |     1.094 |    -5.180 |     3.234 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.08932 |  -0.09780 |   -0.5652 |   -0.6340 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.031 |     1.841 |  -0.06209 |    -1.938 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   75</span>|     473.75065 |     1.003 |    -1.184 |   -0.9252 |   -0.9344 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.016 |    -1.013 |    0.1594 |   -0.8407 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7617 |   -0.8806 |   -0.7872 |    -1.088 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5446 |   -0.8597 |    -1.073 |   -0.1966 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     473.75065 |     91.27 |    -5.384 |   -0.9032 |    -2.197 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.628 |    0.3998 |     1.259 |   0.05945 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8769 |   0.05829 |    0.7965 |    0.7092 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.565 |    0.9672 |    0.6914 |     2.028 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     473.75065</span> |     91.27 |  0.004588 |    0.2884 |    0.1112 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009772 |    0.5986 |     1.259 |   0.05945 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8769 |   0.05829 |    0.7965 |    0.7092 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.565 |    0.9672 |    0.6914 |     2.028 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -5.250 |     1.164 |   0.05078 |   -0.1019 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3635 |     1.132 |    -5.308 |     3.057 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.09569 |  -0.05602 |   -0.5891 |   -0.8121 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.074 |     1.883 |  0.002586 |    -1.906 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   76</span>|     473.72028 |     1.005 |    -1.185 |   -0.9252 |   -0.9343 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.016 |    -1.014 |    0.1635 |   -0.8437 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7618 |   -0.8804 |   -0.7870 |    -1.089 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5455 |   -0.8599 |    -1.073 |   -0.1958 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     473.72028 |     91.43 |    -5.385 |   -0.9032 |    -2.197 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.629 |    0.3993 |     1.261 |   0.05936 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8768 |   0.05830 |    0.7967 |    0.7088 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.564 |    0.9671 |    0.6912 |     2.029 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     473.72028</span> |     91.43 |  0.004583 |    0.2884 |    0.1112 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009768 |    0.5985 |     1.261 |   0.05936 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8768 |   0.05830 |    0.7967 |    0.7088 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.564 |    0.9671 |    0.6912 |     2.029 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     8.975 |     1.171 |    0.2290 |   -0.1246 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3563 |     1.085 |    -5.320 |     3.173 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.08707 |   -0.1016 |   -0.5561 |   -0.7117 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9536 |     1.850 |  -0.07963 |    -1.922 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   77</span>|     473.68600 |     1.003 |    -1.186 |   -0.9253 |   -0.9341 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.016 |    -1.015 |    0.1676 |   -0.8467 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7620 |   -0.8801 |   -0.7868 |    -1.089 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5463 |   -0.8596 |    -1.073 |   -0.1952 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|       473.686 |     91.28 |    -5.386 |   -0.9033 |    -2.196 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.629 |    0.3989 |     1.263 |   0.05928 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8768 |   0.05831 |    0.7968 |    0.7081 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.563 |    0.9673 |    0.6911 |     2.030 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">       473.686</span> |     91.28 |  0.004578 |    0.2884 |    0.1112 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009765 |    0.5984 |     1.263 |   0.05928 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8768 |   0.05831 |    0.7968 |    0.7081 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.563 |    0.9673 |    0.6911 |     2.030 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -5.608 |     1.155 |   0.05506 |   -0.1036 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3607 |     1.118 |    -5.212 |     3.031 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.08611 |  -0.04336 |   -0.3798 |    -2.808 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.093 |     1.880 |  -0.02731 |    -1.896 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   78</span>|     473.65599 |     1.005 |    -1.188 |   -0.9253 |   -0.9339 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.017 |    -1.016 |    0.1718 |   -0.8497 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7621 |   -0.8799 |   -0.7868 |    -1.089 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5471 |   -0.8595 |    -1.074 |   -0.1946 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     473.65599 |     91.44 |    -5.388 |   -0.9033 |    -2.196 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.629 |    0.3984 |     1.264 |   0.05919 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8767 |   0.05831 |    0.7968 |    0.7087 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.562 |    0.9674 |    0.6910 |     2.030 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     473.65599</span> |     91.44 |  0.004573 |    0.2884 |    0.1112 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009762 |    0.5983 |     1.264 |   0.05919 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8767 |   0.05831 |    0.7968 |    0.7087 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.562 |    0.9674 |    0.6910 |     2.030 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     9.720 |     1.170 |    0.2444 |   -0.1229 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3557 |     1.067 |    -5.275 |     3.173 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.07850 |   -0.1042 |   -0.5348 |   -0.6844 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9479 |     1.915 |   -0.1018 |    -1.917 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   79</span>|     473.62083 |     1.003 |    -1.189 |   -0.9254 |   -0.9337 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.017 |    -1.017 |    0.1759 |   -0.8528 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7623 |   -0.8795 |   -0.7869 |    -1.089 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5477 |   -0.8591 |    -1.074 |   -0.1942 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     473.62083 |     91.28 |    -5.389 |   -0.9033 |    -2.196 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.630 |    0.3979 |     1.266 |   0.05910 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8766 |   0.05832 |    0.7968 |    0.7085 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.562 |    0.9678 |    0.6909 |     2.031 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     473.62083</span> |     91.28 |  0.004568 |    0.2884 |    0.1112 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009758 |    0.5982 |     1.266 |   0.05910 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8766 |   0.05832 |    0.7968 |    0.7085 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.562 |    0.9678 |    0.6909 |     2.031 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -5.323 |     1.153 |   0.05982 |   -0.1053 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3572 |     1.098 |    -4.702 |     3.058 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1117 |  -0.04281 |   -0.5573 |   -0.7999 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.039 |     1.912 |  -0.05974 |    -1.890 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   80</span>|     473.59326 |     1.005 |    -1.190 |   -0.9254 |   -0.9335 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.018 |    -1.019 |    0.1798 |   -0.8560 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7624 |   -0.8793 |   -0.7867 |    -1.090 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5486 |   -0.8595 |    -1.074 |   -0.1934 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     473.59326 |     91.46 |    -5.390 |   -0.9034 |    -2.196 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.630 |    0.3974 |     1.268 |   0.05901 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8766 |   0.05833 |    0.7969 |    0.7081 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.560 |    0.9674 |    0.6908 |     2.032 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     473.59326</span> |     91.46 |  0.004562 |    0.2884 |    0.1113 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009755 |    0.5981 |     1.268 |   0.05901 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8766 |   0.05833 |    0.7969 |    0.7081 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.560 |    0.9674 |    0.6908 |     2.032 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     11.25 |     1.162 |    0.2586 |   -0.1299 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3499 |     1.048 |    -5.229 |     3.126 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.05642 |  -0.09930 |   -0.3252 |    -2.718 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8202 |     1.876 |   -0.1477 |    -1.910 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   81</span>|     473.55541 |     1.003 |    -1.191 |   -0.9255 |   -0.9333 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.018 |    -1.020 |    0.1836 |   -0.8595 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7626 |   -0.8789 |   -0.7868 |    -1.090 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5492 |   -0.8592 |    -1.074 |   -0.1932 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     473.55541 |     91.31 |    -5.391 |   -0.9034 |    -2.196 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.630 |    0.3968 |     1.269 |   0.05891 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8765 |   0.05834 |    0.7968 |    0.7080 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.560 |    0.9677 |    0.6906 |     2.032 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     473.55541</span> |     91.31 |  0.004557 |    0.2883 |    0.1113 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009751 |    0.5979 |     1.269 |   0.05891 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8765 |   0.05834 |    0.7968 |    0.7080 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.560 |    0.9677 |    0.6906 |     2.032 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -3.062 |     1.149 |   0.09224 |   -0.1103 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3525 |     1.070 |    -5.098 |     2.994 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1095 |  -0.04877 |   -0.3504 |    -2.828 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8255 |     1.898 |   -0.1147 |    -1.889 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   82</span>|     473.53708 |     1.006 |    -1.192 |   -0.9256 |   -0.9332 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.018 |    -1.021 |    0.1873 |   -0.8616 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7625 |   -0.8789 |   -0.7866 |    -1.087 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5498 |   -0.8606 |    -1.074 |   -0.1918 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     473.53708 |     91.52 |    -5.392 |   -0.9035 |    -2.196 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.631 |    0.3964 |     1.271 |   0.05884 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8765 |   0.05834 |    0.7970 |    0.7099 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.559 |    0.9664 |    0.6907 |     2.034 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     473.53708</span> |     91.52 |  0.004553 |    0.2883 |    0.1113 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009749 |    0.5978 |     1.271 |   0.05884 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8765 |   0.05834 |    0.7970 |    0.7099 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.559 |    0.9664 |    0.6907 |     2.034 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     16.25 |     1.165 |    0.3077 |   -0.1378 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3448 |     1.019 |    -5.264 |     3.150 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.09278 |   -0.1366 |   -0.2969 |    -2.557 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7485 |     1.781 |   -0.1828 |    -1.909 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   83</span>|     473.49312 |     1.003 |    -1.193 |   -0.9256 |   -0.9329 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.018 |    -1.022 |    0.1912 |   -0.8647 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7626 |   -0.8785 |   -0.7869 |    -1.087 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5501 |   -0.8598 |    -1.074 |   -0.1918 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     473.49312 |     91.32 |    -5.393 |   -0.9035 |    -2.195 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.631 |    0.3959 |     1.272 |   0.05875 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8765 |   0.05835 |    0.7967 |    0.7106 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.559 |    0.9671 |    0.6906 |     2.034 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     473.49312</span> |     91.32 |  0.004547 |    0.2883 |    0.1113 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009745 |    0.5977 |     1.272 |   0.05875 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8765 |   0.05835 |    0.7967 |    0.7106 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.559 |    0.9671 |    0.6906 |     2.034 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -2.793 |     1.150 |   0.08391 |   -0.1095 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3500 |     1.057 |    -5.154 |     2.847 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.07414 |  -0.06025 |   -0.5244 |   -0.6779 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8291 |     1.867 |   -0.1201 |    -1.880 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   84</span>|     473.47390 |     1.006 |    -1.194 |   -0.9256 |   -0.9329 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.019 |    -1.022 |    0.1953 |   -0.8670 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7626 |   -0.8785 |   -0.7865 |    -1.086 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5507 |   -0.8613 |    -1.074 |   -0.1903 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      473.4739 |     91.52 |    -5.394 |   -0.9036 |    -2.195 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.631 |    0.3955 |     1.274 |   0.05869 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8765 |   0.05835 |    0.7970 |    0.7110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.558 |    0.9657 |    0.6907 |     2.036 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      473.4739</span> |     91.52 |  0.004543 |    0.2883 |    0.1113 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009743 |    0.5976 |     1.274 |   0.05869 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8765 |   0.05835 |    0.7970 |    0.7110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.558 |    0.9657 |    0.6907 |     2.036 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     15.82 |     1.163 |    0.3026 |   -0.1376 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3423 |     1.007 |    -4.821 |     3.176 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.06207 |   -0.1161 |   -0.4712 |   -0.4774 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7015 |     1.725 |   -0.1877 |    -1.896 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   85</span>|     473.43080 |     1.003 |    -1.195 |   -0.9257 |   -0.9326 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.019 |    -1.024 |    0.1991 |   -0.8700 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7628 |   -0.8781 |   -0.7866 |    -1.087 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5513 |   -0.8608 |    -1.074 |   -0.1899 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      473.4308 |     91.31 |    -5.395 |   -0.9036 |    -2.195 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.632 |    0.3950 |     1.276 |   0.05860 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8764 |   0.05836 |    0.7970 |    0.7105 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.557 |    0.9662 |    0.6906 |     2.036 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      473.4308</span> |     91.31 |  0.004538 |    0.2883 |    0.1114 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009739 |    0.5975 |     1.276 |   0.05860 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8764 |   0.05836 |    0.7970 |    0.7105 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.557 |    0.9662 |    0.6906 |     2.036 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -3.611 |     1.145 |   0.07564 |   -0.1091 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3478 |     1.048 |    -5.196 |     2.853 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.07176 |  -0.06153 |   -0.5100 |   -0.6862 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7701 |     1.783 |   -0.1204 |    -1.866 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   86</span>|     473.40390 |     1.005 |    -1.196 |   -0.9258 |   -0.9324 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.019 |    -1.025 |    0.2034 |   -0.8728 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7629 |   -0.8779 |   -0.7864 |    -1.087 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5520 |   -0.8612 |    -1.074 |   -0.1890 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      473.4039 |     91.48 |    -5.396 |   -0.9037 |    -2.195 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.632 |    0.3946 |     1.277 |   0.05852 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8764 |   0.05837 |    0.7971 |    0.7105 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.556 |    0.9658 |    0.6906 |     2.037 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      473.4039</span> |     91.48 |  0.004533 |    0.2883 |    0.1114 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009736 |    0.5974 |     1.277 |   0.05852 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8764 |   0.05837 |    0.7971 |    0.7105 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.556 |    0.9658 |    0.6906 |     2.037 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     11.82 |     1.154 |    0.2542 |   -0.1321 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3410 |     1.002 |    -5.150 |     3.097 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.04820 |   -0.1059 |   -0.4698 |   -0.5387 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6891 |     1.733 |   -0.1906 |    -1.879 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   87</span>|     473.36686 |     1.003 |    -1.198 |   -0.9258 |   -0.9322 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.020 |    -1.026 |    0.2076 |   -0.8758 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7631 |   -0.8776 |   -0.7865 |    -1.087 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5525 |   -0.8606 |    -1.074 |   -0.1886 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     473.36686 |     91.32 |    -5.398 |   -0.9037 |    -2.195 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.632 |    0.3941 |     1.279 |   0.05843 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8763 |   0.05838 |    0.7970 |    0.7100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.556 |    0.9664 |    0.6905 |     2.038 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     473.36686</span> |     91.32 |  0.004527 |    0.2883 |    0.1114 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009733 |    0.5973 |     1.279 |   0.05843 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8763 |   0.05838 |    0.7970 |    0.7100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.556 |    0.9664 |    0.6905 |     2.038 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -3.686 |     1.139 |   0.08028 |   -0.1103 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3450 |     1.034 |    -5.059 |     2.983 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.07022 |  -0.06541 |   -0.4954 |   -0.7148 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6839 |     1.782 |   -0.1353 |    -1.853 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   88</span>|     473.34117 |     1.005 |    -1.199 |   -0.9259 |   -0.9321 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.020 |    -1.027 |    0.2118 |   -0.8787 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7632 |   -0.8774 |   -0.7864 |    -1.087 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5531 |   -0.8611 |    -1.074 |   -0.1877 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     473.34117 |     91.49 |    -5.399 |   -0.9038 |    -2.194 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.633 |    0.3936 |     1.281 |   0.05835 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8763 |   0.05838 |    0.7971 |    0.7100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.555 |    0.9659 |    0.6904 |     2.039 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     473.34117</span> |     91.49 |  0.004522 |    0.2883 |    0.1114 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009730 |    0.5972 |     1.281 |   0.05835 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8763 |   0.05838 |    0.7971 |    0.7100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.555 |    0.9659 |    0.6904 |     2.039 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     12.62 |     1.149 |    0.2679 |   -0.1346 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3379 |    0.9875 |    -5.147 |     2.965 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.07400 |   -0.1145 |   -0.4542 |   -0.5455 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6283 |     1.734 |   -0.2097 |    -1.869 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   89</span>|     473.30326 |     1.004 |    -1.200 |   -0.9259 |   -0.9318 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.020 |    -1.028 |    0.2159 |   -0.8818 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7634 |   -0.8771 |   -0.7865 |    -1.088 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5535 |   -0.8604 |    -1.074 |   -0.1874 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     473.30326 |     91.32 |    -5.400 |   -0.9038 |    -2.194 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.633 |    0.3932 |     1.283 |   0.05826 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8762 |   0.05839 |    0.7970 |    0.7096 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.555 |    0.9665 |    0.6903 |     2.039 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     473.30326</span> |     91.32 |  0.004517 |    0.2883 |    0.1114 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009726 |    0.5970 |     1.283 |   0.05826 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8762 |   0.05839 |    0.7970 |    0.7096 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.555 |    0.9665 |    0.6903 |     2.039 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -3.609 |     1.134 |   0.08636 |   -0.1114 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3423 |     1.018 |    -5.228 |     2.880 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1079 |  -0.06580 |   -0.2896 |    -2.686 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6917 |     1.801 |   -0.1599 |    -1.842 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   90</span>|     473.27616 |     1.005 |    -1.201 |   -0.9260 |   -0.9316 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.021 |    -1.029 |    0.2202 |   -0.8845 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7634 |   -0.8768 |   -0.7866 |    -1.087 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5540 |   -0.8607 |    -1.074 |   -0.1867 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     473.27616 |     91.48 |    -5.401 |   -0.9039 |    -2.194 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.633 |    0.3927 |     1.284 |   0.05818 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8762 |   0.05840 |    0.7970 |    0.7107 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.554 |    0.9663 |    0.6904 |     2.040 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     473.27616</span> |     91.48 |  0.004512 |    0.2883 |    0.1115 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009723 |    0.5969 |     1.284 |   0.05818 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8762 |   0.05840 |    0.7970 |    0.7107 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.554 |    0.9663 |    0.6904 |     2.040 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     11.44 |     1.145 |    0.2516 |   -0.1333 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3351 |    0.9744 |    -4.753 |     3.090 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.05028 |   -0.1148 |   -0.4450 |   -0.5198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.5573 |     1.743 |   -0.2240 |    -1.858 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   91</span>|     473.24283 |     1.003 |    -1.202 |   -0.9260 |   -0.9314 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.021 |    -1.030 |    0.2240 |   -0.8877 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7636 |   -0.8764 |   -0.7870 |    -1.087 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5543 |   -0.8601 |    -1.074 |   -0.1866 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     473.24283 |     91.29 |    -5.402 |   -0.9039 |    -2.194 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.634 |    0.3922 |     1.286 |   0.05809 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8761 |   0.05841 |    0.7967 |    0.7107 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.554 |    0.9668 |    0.6903 |     2.040 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     473.24283</span> |     91.29 |  0.004506 |    0.2882 |    0.1115 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009720 |    0.5968 |     1.286 |   0.05809 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8761 |   0.05841 |    0.7967 |    0.7107 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.554 |    0.9668 |    0.6903 |     2.040 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -7.383 |     1.129 |   0.03939 |   -0.1057 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3410 |     1.013 |    -4.733 |     2.907 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.07460 |  -0.06623 |   -0.4732 |   -0.6761 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6495 |     1.823 |   -0.1637 |    -1.832 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   92</span>|     473.20973 |     1.005 |    -1.204 |   -0.9260 |   -0.9311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.021 |    -1.031 |    0.2279 |   -0.8912 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7638 |   -0.8760 |   -0.7872 |    -1.087 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5548 |   -0.8600 |    -1.075 |   -0.1862 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     473.20973 |     91.43 |    -5.404 |   -0.9039 |    -2.193 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.634 |    0.3917 |     1.288 |   0.05799 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8760 |   0.05843 |    0.7966 |    0.7102 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.553 |    0.9669 |    0.6900 |     2.040 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     473.20973</span> |     91.43 |  0.004500 |    0.2882 |    0.1115 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009716 |    0.5967 |     1.288 |   0.05799 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8760 |   0.05843 |    0.7966 |    0.7102 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.553 |    0.9669 |    0.6900 |     2.040 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   93</span>|     473.17178 |     1.005 |    -1.205 |   -0.9261 |   -0.9307 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.022 |    -1.032 |    0.2326 |   -0.8959 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7641 |   -0.8754 |   -0.7876 |    -1.088 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5554 |   -0.8593 |    -1.075 |   -0.1863 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     473.17178 |     91.43 |    -5.405 |   -0.9040 |    -2.193 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.634 |    0.3910 |     1.289 |   0.05785 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8759 |   0.05844 |    0.7963 |    0.7092 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.552 |    0.9676 |    0.6896 |     2.040 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     473.17178</span> |     91.43 |  0.004492 |    0.2882 |    0.1116 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009711 |    0.5965 |     1.289 |   0.05785 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8759 |   0.05844 |    0.7963 |    0.7092 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.552 |    0.9676 |    0.6896 |     2.040 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   94</span>|     472.97479 |     1.005 |    -1.215 |   -0.9262 |   -0.9287 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.025 |    -1.041 |    0.2575 |   -0.9212 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7657 |   -0.8720 |   -0.7899 |    -1.094 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5585 |   -0.8552 |    -1.078 |   -0.1866 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     472.97479 |     91.45 |    -5.415 |   -0.9041 |    -2.191 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.637 |    0.3872 |     1.300 |   0.05712 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8752 |   0.05854 |    0.7946 |    0.7037 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.549 |    0.9715 |    0.6874 |     2.040 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     472.97479</span> |     91.45 |  0.004449 |    0.2882 |    0.1118 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009685 |    0.5956 |     1.300 |   0.05712 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8752 |   0.05854 |    0.7946 |    0.7037 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.549 |    0.9715 |    0.6874 |     2.040 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   95</span>|     472.26932 |     1.006 |    -1.253 |   -0.9268 |   -0.9205 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.035 |    -1.074 |    0.3572 |    -1.022 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7723 |   -0.8583 |   -0.7991 |    -1.118 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5709 |   -0.8392 |    -1.088 |   -0.1877 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     472.26932 |     91.55 |    -5.453 |   -0.9046 |    -2.183 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.648 |    0.3719 |     1.341 |   0.05419 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8725 |   0.05894 |    0.7878 |    0.6819 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.534 |    0.9868 |    0.6787 |     2.039 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     472.26932</span> |     91.55 |  0.004282 |    0.2881 |    0.1127 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009582 |    0.5919 |     1.341 |   0.05419 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8725 |   0.05894 |    0.7878 |    0.6819 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.534 |    0.9868 |    0.6787 |     2.039 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   96</span>|     470.90347 |     1.012 |    -1.408 |   -0.9286 |   -0.8872 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.078 |    -1.207 |    0.7550 |    -1.427 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7985 |   -0.8024 |   -0.8343 |    -1.204 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6170 |   -0.7722 |    -1.128 |   -0.1957 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     470.90347 |     92.06 |    -5.608 |   -0.9062 |    -2.150 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.691 |    0.3105 |     1.506 |   0.04245 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8616 |   0.06056 |    0.7621 |    0.6043 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.480 |     1.051 |    0.6438 |     2.029 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     470.90347</span> |     92.06 |  0.003670 |    0.2878 |    0.1165 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009180 |    0.5770 |     1.506 |   0.04245 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8616 |   0.06056 |    0.7621 |    0.6043 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.480 |     1.051 |    0.6438 |     2.029 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     16.46 |    0.7178 |     1.137 |   -0.2842 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.06515 |    -1.058 |    -6.810 |    -2.325 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1903 |   -0.6630 |     1.424 |    -9.046 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.659 |     8.235 |    -7.307 |    -1.506 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   97</span>|     469.55066 |     1.016 |    -1.623 |   -0.9529 |   -0.8214 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.136 |    -1.353 |     1.295 |    -1.874 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8595 |   -0.6716 |   -0.9513 |    -1.204 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7087 |   -0.8316 |   -0.9499 |   -0.4083 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     469.55066 |     92.47 |    -5.823 |   -0.9279 |    -2.084 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.749 |    0.2437 |     1.731 |   0.02949 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8363 |   0.06435 |    0.6766 |    0.6043 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.371 |    0.9940 |    0.7978 |     1.771 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     469.55066</span> |     92.47 |  0.002960 |    0.2834 |    0.1245 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008661 |    0.5606 |     1.731 |   0.02949 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8363 |   0.06435 |    0.6766 |    0.6043 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.371 |    0.9940 |    0.7978 |     1.771 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -5.003 |    0.4823 |    0.6025 |   -0.2481 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.135 |    0.1015 |    -5.875 |    -8.309 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8363 |   -0.7839 |     1.711 |    -6.097 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.755 |     1.794 |     4.040 |   -0.5408 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   98</span>|     471.32255 |     1.022 |    -1.880 |    -1.043 |   -0.7426 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.107 |    -1.512 |     1.956 |    -1.964 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9773 |   -0.5429 |    -1.097 |    -1.122 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6469 |   -0.5662 |   -0.9027 |   -0.4752 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     471.32255 |     93.00 |    -6.080 |    -1.008 |    -2.005 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.719 |    0.1705 |     2.005 |   0.02687 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7874 |   0.06809 |    0.5704 |    0.6787 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.444 |     1.248 |    0.8385 |     1.690 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     471.32255</span> |     93.00 |  0.002289 |    0.2674 |    0.1347 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008922 |    0.5425 |     2.005 |   0.02687 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7874 |   0.06809 |    0.5704 |    0.6787 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.444 |     1.248 |    0.8385 |     1.690 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   99</span>|     468.82475 |     1.022 |    -1.709 |   -0.9836 |   -0.7948 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.126 |    -1.406 |     1.521 |    -1.898 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8984 |   -0.6279 |    -1.001 |    -1.172 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6845 |   -0.7440 |   -0.9371 |   -0.4303 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.82475 |     92.98 |    -5.909 |   -0.9551 |    -2.057 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.738 |    0.2191 |     1.824 |   0.02879 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8201 |   0.06562 |    0.6400 |    0.6333 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.400 |     1.078 |    0.8088 |     1.744 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.82475</span> |     92.98 |  0.002714 |    0.2779 |    0.1278 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008755 |    0.5546 |     1.824 |   0.02879 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8201 |   0.06562 |    0.6400 |    0.6333 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.400 |     1.078 |    0.8088 |     1.744 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     40.86 |    0.3767 |  -0.09575 |   -0.1893 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.313 |   -0.2132 |    -3.864 |    -4.927 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.292 |    -1.079 |   -0.1017 |    -3.329 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.248 |     8.116 |     3.916 |   -0.3530 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  100</span>|     467.75171 |     1.017 |    -1.816 |   -0.9824 |   -0.7623 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.067 |    -1.470 |     1.678 |    -1.962 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9501 |   -0.5667 |   -0.9995 |    -1.140 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5909 |   -0.8204 |    -1.012 |   -0.4878 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     467.75171 |     92.51 |    -6.016 |   -0.9541 |    -2.025 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.679 |    0.1896 |     1.889 |   0.02693 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7987 |   0.06740 |    0.6413 |    0.6622 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.510 |     1.005 |    0.7438 |     1.674 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     467.75171</span> |     92.51 |  0.002440 |    0.2781 |    0.1320 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009285 |    0.5473 |     1.889 |   0.02693 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7987 |   0.06740 |    0.6413 |    0.6622 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.510 |     1.005 |    0.7438 |     1.674 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -11.72 |    0.1617 |   -0.2966 |    0.1391 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7722 |   -0.6625 |    -3.136 |    -6.222 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.617 |   -0.9189 |   -0.2811 |    -3.105 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.05355 |     2.320 |    -3.473 |    -2.160 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  101</span>|     467.28745 |     1.018 |    -1.902 |   -0.9107 |   -0.7577 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9795 |    -1.517 |     1.777 |    -1.860 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8874 |   -0.4787 |   -0.8826 |    -1.170 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6199 |   -0.8544 |   -0.9952 |   -0.3962 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     467.28745 |     92.66 |    -6.102 |   -0.8902 |    -2.020 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.592 |    0.1682 |     1.931 |   0.02990 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8247 |   0.06995 |    0.7268 |    0.6357 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.476 |    0.9723 |    0.7587 |     1.785 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     467.28745</span> |     92.66 |  0.002239 |    0.2911 |    0.1327 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01013 |    0.5420 |     1.931 |   0.02990 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8247 |   0.06995 |    0.7268 |    0.6357 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.476 |    0.9723 |    0.7587 |     1.785 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     21.75 |    0.1003 |     2.571 |    0.1450 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5415 |    -1.471 |    -1.062 |    -1.774 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.363 |    0.7539 |     2.182 |    -3.357 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.337 |    0.2346 |    -1.234 |   -0.8531 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  102</span>|     469.68385 |    0.9954 |    -1.950 |    -1.032 |   -0.7900 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8810 |    -1.433 |     1.783 |    -1.785 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9418 |   -0.5114 |   -0.9107 |    -1.204 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6798 |   -0.8966 |    -1.014 |   -0.2565 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     469.68385 |     90.58 |    -6.150 |   -0.9984 |    -2.052 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.493 |    0.2066 |     1.933 |   0.03207 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8021 |   0.06900 |    0.7062 |    0.6043 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.405 |    0.9320 |    0.7422 |     1.955 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     469.68385</span> |     90.58 |  0.002133 |    0.2693 |    0.1284 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01118 |    0.5515 |     1.933 |   0.03207 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8021 |   0.06900 |    0.7062 |    0.6043 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.405 |    0.9320 |    0.7422 |     1.955 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  103</span>|     467.87907 |     1.005 |    -1.909 |   -0.9308 |   -0.7628 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9639 |    -1.503 |     1.779 |    -1.847 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8965 |   -0.4841 |   -0.8880 |    -1.173 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6287 |   -0.8610 |   -0.9975 |   -0.3740 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     467.87907 |     91.48 |    -6.109 |   -0.9081 |    -2.025 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.576 |    0.1745 |     1.931 |   0.03026 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8209 |   0.06979 |    0.7229 |    0.6325 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.466 |    0.9660 |    0.7566 |     1.812 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     467.87907</span> |     91.48 |  0.002222 |    0.2874 |    0.1320 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01029 |    0.5435 |     1.931 |   0.03026 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8209 |   0.06979 |    0.7229 |    0.6325 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.466 |    0.9660 |    0.7566 |     1.812 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  104</span>|     467.53566 |     1.009 |    -1.902 |   -0.9117 |   -0.7577 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9793 |    -1.516 |     1.778 |    -1.859 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8880 |   -0.4790 |   -0.8835 |    -1.168 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6194 |   -0.8545 |   -0.9947 |   -0.3959 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     467.53566 |     91.86 |    -6.102 |   -0.8912 |    -2.020 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.592 |    0.1685 |     1.931 |   0.02992 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8245 |   0.06994 |    0.7261 |    0.6369 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.477 |    0.9722 |    0.7591 |     1.786 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     467.53566</span> |     91.86 |  0.002239 |    0.2909 |    0.1326 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01013 |    0.5420 |     1.931 |   0.02992 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8245 |   0.06994 |    0.7261 |    0.6369 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.477 |    0.9722 |    0.7591 |     1.786 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  105</span>|     467.26444 |     1.016 |    -1.902 |   -0.9109 |   -0.7577 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9795 |    -1.516 |     1.777 |    -1.860 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8875 |   -0.4788 |   -0.8828 |    -1.169 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6198 |   -0.8544 |   -0.9951 |   -0.3961 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     467.26444 |     92.48 |    -6.102 |   -0.8905 |    -2.020 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.592 |    0.1683 |     1.931 |   0.02990 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8246 |   0.06995 |    0.7266 |    0.6360 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.476 |    0.9723 |    0.7588 |     1.786 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     467.26444</span> |     92.48 |  0.002239 |    0.2910 |    0.1326 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01013 |    0.5420 |     1.931 |   0.02990 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8246 |   0.06995 |    0.7266 |    0.6360 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.476 |    0.9723 |    0.7588 |     1.786 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.458 |   0.09189 |     2.450 |    0.1713 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5320 |    -1.492 |    -1.346 |    -2.030 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.374 |    0.8089 |     1.804 |    -5.854 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.303 |    0.2787 |    -1.170 |   -0.8602 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  106</span>|     467.25360 |     1.017 |    -1.902 |   -0.9116 |   -0.7577 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9793 |    -1.516 |     1.778 |    -1.859 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8879 |   -0.4790 |   -0.8833 |    -1.168 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6195 |   -0.8545 |   -0.9948 |   -0.3959 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      467.2536 |     92.51 |    -6.102 |   -0.8910 |    -2.020 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.592 |    0.1685 |     1.931 |   0.02992 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8245 |   0.06994 |    0.7263 |    0.6374 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.477 |    0.9722 |    0.7590 |     1.786 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      467.2536</span> |     92.51 |  0.002239 |    0.2909 |    0.1326 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01013 |    0.5420 |     1.931 |   0.02992 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8245 |   0.06994 |    0.7263 |    0.6374 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.477 |    0.9722 |    0.7590 |     1.786 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     3.196 |   0.08873 |     2.446 |    0.1672 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5342 |    -1.484 |    -1.056 |    -1.956 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.373 |    0.7965 |     2.077 |    -5.722 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.292 |    0.2471 |    -1.040 |   -0.8496 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  107</span>|     467.24389 |     1.015 |    -1.902 |   -0.9128 |   -0.7578 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9790 |    -1.515 |     1.778 |    -1.858 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8886 |   -0.4794 |   -0.8844 |    -1.165 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6188 |   -0.8546 |   -0.9942 |   -0.3955 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     467.24389 |     92.37 |    -6.102 |   -0.8922 |    -2.020 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.592 |    0.1688 |     1.931 |   0.02995 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8242 |   0.06993 |    0.7255 |    0.6400 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.477 |    0.9721 |    0.7595 |     1.786 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     467.24389</span> |     92.37 |  0.002239 |    0.2907 |    0.1326 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01014 |    0.5421 |     1.931 |   0.02995 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8242 |   0.06993 |    0.7255 |    0.6400 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.477 |    0.9721 |    0.7595 |     1.786 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -15.43 |   0.07336 |     2.305 |    0.1893 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5256 |    -1.495 |    -1.329 |    -2.142 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.357 |    0.9382 |     1.940 |    -3.165 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.157 |    0.2760 |   -0.9277 |   -0.8566 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  108</span>|     467.22397 |     1.017 |    -1.902 |   -0.9138 |   -0.7581 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9782 |    -1.514 |     1.778 |    -1.857 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8889 |   -0.4797 |   -0.8863 |    -1.164 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6190 |   -0.8552 |   -0.9945 |   -0.3935 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     467.22397 |     92.56 |    -6.102 |   -0.8930 |    -2.020 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.591 |    0.1692 |     1.931 |   0.02998 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8241 |   0.06992 |    0.7241 |    0.6409 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.477 |    0.9715 |    0.7593 |     1.789 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     467.22397</span> |     92.56 |  0.002238 |    0.2905 |    0.1326 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01015 |    0.5422 |     1.931 |   0.02998 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8241 |   0.06992 |    0.7241 |    0.6409 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.477 |    0.9715 |    0.7593 |     1.789 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     8.612 |   0.07601 |     2.377 |    0.1597 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5308 |    -1.476 |    -1.038 |    -1.834 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.332 |    0.7679 |     2.039 |    -3.039 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.288 |    0.2037 |    -1.093 |   -0.8313 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  109</span>|     467.20858 |     1.016 |    -1.903 |   -0.9153 |   -0.7584 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9772 |    -1.513 |     1.777 |    -1.856 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8896 |   -0.4802 |   -0.8882 |    -1.164 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6194 |   -0.8560 |   -0.9946 |   -0.3915 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     467.20858 |     92.46 |    -6.103 |   -0.8944 |    -2.021 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.590 |    0.1698 |     1.931 |   0.03001 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8238 |   0.06990 |    0.7227 |    0.6411 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.477 |    0.9707 |    0.7591 |     1.791 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     467.20858</span> |     92.46 |  0.002236 |    0.2902 |    0.1325 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01016 |    0.5423 |     1.931 |   0.03001 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8238 |   0.06990 |    0.7227 |    0.6411 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.477 |    0.9707 |    0.7591 |     1.791 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -3.651 |   0.07137 |     2.259 |    0.1744 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5171 |    -1.486 |    -1.304 |    -1.934 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.324 |    0.7910 |     1.690 |    -3.053 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.233 |    0.1999 |    -1.046 |   -0.8164 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  110</span>|     467.19548 |     1.017 |    -1.903 |   -0.9170 |   -0.7588 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9762 |    -1.512 |     1.778 |    -1.855 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8906 |   -0.4808 |   -0.8898 |    -1.163 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6195 |   -0.8567 |   -0.9946 |   -0.3897 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     467.19548 |     92.56 |    -6.103 |   -0.8959 |    -2.021 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.589 |    0.1705 |     1.931 |   0.03005 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8234 |   0.06989 |    0.7215 |    0.6417 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.477 |    0.9701 |    0.7592 |     1.793 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     467.19548</span> |     92.56 |  0.002235 |    0.2899 |    0.1325 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01017 |    0.5425 |     1.931 |   0.03005 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8234 |   0.06989 |    0.7215 |    0.6417 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.477 |    0.9701 |    0.7592 |     1.793 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     9.695 |   0.07360 |     2.256 |    0.1610 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5147 |    -1.467 |    -1.275 |    -1.734 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.307 |    0.8740 |     2.039 |    -5.568 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.898 |   -0.2550 |    -1.273 |   -0.7762 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  111</span>|     467.17845 |     1.016 |    -1.904 |   -0.9175 |   -0.7593 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9751 |    -1.510 |     1.777 |    -1.854 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8907 |   -0.4813 |   -0.8918 |    -1.161 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6194 |   -0.8571 |   -0.9949 |   -0.3876 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     467.17845 |     92.50 |    -6.104 |   -0.8963 |    -2.022 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.588 |    0.1711 |     1.930 |   0.03007 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8234 |   0.06987 |    0.7201 |    0.6433 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.477 |    0.9697 |    0.7589 |     1.796 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     467.17845</span> |     92.50 |  0.002234 |    0.2898 |    0.1324 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01018 |    0.5427 |     1.930 |   0.03007 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8234 |   0.06987 |    0.7201 |    0.6433 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.477 |    0.9697 |    0.7589 |     1.796 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     2.021 |   0.06308 |     2.194 |    0.1658 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5050 |    -1.471 |    -1.283 |    -1.811 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.304 |    0.7659 |     1.629 |    -2.860 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.234 |    0.1434 |    -1.025 |   -0.7796 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  112</span>|     467.16518 |     1.015 |    -1.904 |   -0.9192 |   -0.7596 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9742 |    -1.509 |     1.777 |    -1.853 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8917 |   -0.4819 |   -0.8931 |    -1.160 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6190 |   -0.8574 |   -0.9946 |   -0.3864 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     467.16518 |     92.39 |    -6.104 |   -0.8978 |    -2.022 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.587 |    0.1719 |     1.931 |   0.03011 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8229 |   0.06985 |    0.7191 |    0.6447 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.477 |    0.9694 |    0.7592 |     1.797 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     467.16518</span> |     92.39 |  0.002234 |    0.2895 |    0.1324 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01019 |    0.5429 |     1.931 |   0.03011 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8229 |   0.06985 |    0.7191 |    0.6447 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.477 |    0.9694 |    0.7592 |     1.797 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -11.15 |   0.05292 |     2.063 |    0.1800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4962 |    -1.473 |   -0.9311 |    -1.912 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.287 |    0.8965 |     1.659 |    -5.399 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.766 |   -0.2649 |    -1.228 |   -0.7899 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  113</span>|     467.14275 |     1.016 |    -1.904 |   -0.9201 |   -0.7599 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9736 |    -1.507 |     1.777 |    -1.851 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8922 |   -0.4826 |   -0.8948 |    -1.157 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6182 |   -0.8576 |   -0.9943 |   -0.3850 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     467.14275 |     92.43 |    -6.104 |   -0.8986 |    -2.022 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.586 |    0.1725 |     1.931 |   0.03014 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8227 |   0.06984 |    0.7179 |    0.6470 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.478 |    0.9693 |    0.7594 |     1.799 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     467.14275</span> |     92.43 |  0.002233 |    0.2893 |    0.1324 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01019 |    0.5430 |     1.931 |   0.03014 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8227 |   0.06984 |    0.7179 |    0.6470 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.478 |    0.9693 |    0.7594 |     1.799 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -6.177 |   0.04590 |     2.051 |    0.1758 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4936 |    -1.461 |   -0.9893 |    -1.828 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.266 |    0.7615 |     1.545 |    -5.206 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.792 |   -0.2512 |    -1.220 |   -0.7551 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  114</span>|     467.10820 |     1.017 |    -1.905 |   -0.9220 |   -0.7605 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9724 |    -1.505 |     1.777 |    -1.849 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8932 |   -0.4835 |   -0.8976 |    -1.151 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6164 |   -0.8576 |   -0.9935 |   -0.3828 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      467.1082 |     92.55 |    -6.105 |   -0.9003 |    -2.023 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.585 |    0.1737 |     1.930 |   0.03020 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8223 |   0.06981 |    0.7158 |    0.6522 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.480 |    0.9692 |    0.7601 |     1.802 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      467.1082</span> |     92.55 |  0.002232 |    0.2890 |    0.1323 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01020 |    0.5433 |     1.930 |   0.03020 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8223 |   0.06981 |    0.7158 |    0.6522 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.480 |    0.9692 |    0.7601 |     1.802 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     8.449 |   0.03092 |     2.044 |    0.1611 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4934 |    -1.430 |    -1.221 |    -1.596 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.229 |    0.8691 |     1.875 |    -2.123 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.605 |   -0.2558 |    -1.009 |   -0.7122 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  115</span>|     467.10106 |     1.014 |    -1.905 |   -0.9236 |   -0.7611 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9712 |    -1.502 |     1.777 |    -1.847 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8940 |   -0.4849 |   -0.9012 |    -1.147 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6148 |   -0.8578 |   -0.9928 |   -0.3803 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     467.10106 |     92.28 |    -6.105 |   -0.9017 |    -2.023 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.584 |    0.1749 |     1.930 |   0.03026 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8220 |   0.06977 |    0.7132 |    0.6562 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.482 |    0.9690 |    0.7607 |     1.805 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     467.10106</span> |     92.28 |  0.002232 |    0.2887 |    0.1322 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01022 |    0.5436 |     1.930 |   0.03026 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8220 |   0.06977 |    0.7132 |    0.6562 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.482 |    0.9690 |    0.7607 |     1.805 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -24.17 |  0.001665 |     1.814 |    0.1960 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4738 |    -1.444 |   -0.8955 |    -1.908 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.221 |    0.7910 |     1.755 |    -3.560 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.244 |   -0.2675 |   -0.8607 |   -0.7175 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  116</span>|     467.04630 |     1.016 |    -1.905 |   -0.9246 |   -0.7619 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9696 |    -1.499 |     1.777 |    -1.845 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8944 |   -0.4866 |   -0.9055 |    -1.143 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6133 |   -0.8582 |   -0.9923 |   -0.3775 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      467.0463 |     92.45 |    -6.105 |   -0.9026 |    -2.024 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.582 |    0.1762 |     1.930 |   0.03032 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8218 |   0.06972 |    0.7101 |    0.6601 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.484 |    0.9686 |    0.7611 |     1.808 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      467.0463</span> |     92.45 |  0.002231 |    0.2885 |    0.1321 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01023 |    0.5439 |     1.930 |   0.03032 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8218 |   0.06972 |    0.7101 |    0.6601 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.484 |    0.9686 |    0.7611 |     1.808 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -2.647 | -0.007887 |     1.876 |    0.1719 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4730 |    -1.410 |   -0.8701 |    -1.589 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.184 |    0.7244 |     1.544 |    -4.110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.544 |   -0.3120 |   -0.9430 |   -0.6527 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  117</span>|     467.02543 |     1.017 |    -1.906 |   -0.9263 |   -0.7625 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9684 |    -1.497 |     1.777 |    -1.843 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8954 |   -0.4879 |   -0.9089 |    -1.137 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6116 |   -0.8583 |   -0.9915 |   -0.3753 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     467.02543 |     92.58 |    -6.106 |   -0.9041 |    -2.025 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.581 |    0.1775 |     1.930 |   0.03038 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8214 |   0.06968 |    0.7076 |    0.6649 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.486 |    0.9686 |    0.7618 |     1.811 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     467.02543</span> |     92.58 |  0.002230 |    0.2882 |    0.1320 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01025 |    0.5443 |     1.930 |   0.03038 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8214 |   0.06968 |    0.7076 |    0.6649 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.486 |    0.9686 |    0.7618 |     1.811 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     12.94 |  -0.02385 |     1.879 |    0.1542 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4735 |    -1.378 |   -0.8918 |    -1.355 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.131 |    0.6590 |     1.631 |    -3.618 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.430 |   -0.2321 |   -0.8462 |   -0.6495 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  118</span>|     466.99646 |     1.016 |    -1.906 |   -0.9273 |   -0.7632 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9670 |    -1.494 |     1.776 |    -1.841 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8958 |   -0.4892 |   -0.9132 |    -1.133 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6101 |   -0.8587 |   -0.9911 |   -0.3725 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     466.99646 |     92.42 |    -6.106 |   -0.9050 |    -2.026 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.579 |    0.1788 |     1.930 |   0.03043 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8212 |   0.06964 |    0.7044 |    0.6690 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.488 |    0.9682 |    0.7622 |     1.814 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     466.99646</span> |     92.42 |  0.002230 |    0.2880 |    0.1319 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01026 |    0.5446 |     1.930 |   0.03043 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8212 |   0.06964 |    0.7044 |    0.6690 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.488 |    0.9682 |    0.7622 |     1.814 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -5.590 |  -0.05004 |     1.738 |    0.1737 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4573 |    -1.378 |   -0.8791 |    -1.534 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.117 |    0.8064 |     1.528 |    -3.141 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8220 |    0.1296 |   -0.3838 |   -0.6246 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  119</span>|     466.97639 |     1.017 |    -1.906 |   -0.9282 |   -0.7640 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9655 |    -1.490 |     1.776 |    -1.839 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8964 |   -0.4910 |   -0.9175 |    -1.129 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6092 |   -0.8593 |   -0.9910 |   -0.3693 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     466.97639 |     92.52 |    -6.106 |   -0.9059 |    -2.026 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.578 |    0.1803 |     1.930 |   0.03049 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8210 |   0.06959 |    0.7013 |    0.6726 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.489 |    0.9676 |    0.7623 |     1.818 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     466.97639</span> |     92.52 |  0.002230 |    0.2878 |    0.1318 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01028 |    0.5450 |     1.930 |   0.03049 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8210 |   0.06959 |    0.7013 |    0.6726 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.489 |    0.9676 |    0.7623 |     1.818 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     7.027 |  -0.06281 |     1.755 |    0.1604 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4507 |    -1.349 |   -0.8053 |    -1.299 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.095 |    0.6623 |     1.032 |    -2.794 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8171 |   0.07286 |   -0.3538 |   -0.5889 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  120</span>|     466.96001 |     1.015 |    -1.906 |   -0.9299 |   -0.7649 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9637 |    -1.486 |     1.775 |    -1.837 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8976 |   -0.4931 |   -0.9192 |    -1.125 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6091 |   -0.8600 |   -0.9916 |   -0.3657 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     466.96001 |     92.40 |    -6.106 |   -0.9074 |    -2.027 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.576 |    0.1824 |     1.930 |   0.03055 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8205 |   0.06953 |    0.7000 |    0.6758 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.489 |    0.9669 |    0.7618 |     1.823 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     466.96001</span> |     92.40 |  0.002230 |    0.2875 |    0.1317 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01029 |    0.5455 |     1.930 |   0.03055 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8205 |   0.06953 |    0.7000 |    0.6758 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.489 |    0.9669 |    0.7618 |     1.823 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -7.315 |  -0.08361 |     1.595 |    0.1755 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4289 |    -1.331 |   -0.8104 |    -1.428 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.055 |    0.7710 |     1.242 |    -2.826 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.346 |   -0.3282 |   -0.6399 |   -0.5709 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  121</span>|     466.94053 |     1.016 |    -1.905 |   -0.9315 |   -0.7662 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9617 |    -1.480 |     1.774 |    -1.835 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8987 |   -0.4954 |   -0.9193 |    -1.121 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6079 |   -0.8601 |   -0.9919 |   -0.3632 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     466.94053 |     92.48 |    -6.105 |   -0.9088 |    -2.029 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.574 |    0.1849 |     1.929 |   0.03061 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8200 |   0.06946 |    0.6999 |    0.6792 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.490 |    0.9668 |    0.7615 |     1.826 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     466.94053</span> |     92.48 |  0.002231 |    0.2873 |    0.1315 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01031 |    0.5461 |     1.929 |   0.03061 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8200 |   0.06946 |    0.6999 |    0.6792 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.490 |    0.9668 |    0.7615 |     1.826 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     2.448 |  -0.09452 |     1.578 |    0.1631 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4178 |    -1.275 |   -0.7880 |    -1.291 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.013 |    0.7275 |     1.336 |   -0.1176 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.297 |   -0.2757 |   -0.6338 |   -0.5586 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  122</span>|     466.92529 |     1.015 |    -1.905 |   -0.9321 |   -0.7676 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9593 |    -1.476 |     1.775 |    -1.833 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8992 |   -0.4984 |   -0.9232 |    -1.122 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6063 |   -0.8604 |   -0.9916 |   -0.3612 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     466.92529 |     92.34 |    -6.105 |   -0.9093 |    -2.030 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.572 |    0.1871 |     1.930 |   0.03066 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8198 |   0.06938 |    0.6971 |    0.6791 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.492 |    0.9666 |    0.7618 |     1.828 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     466.92529</span> |     92.34 |  0.002233 |    0.2871 |    0.1313 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01034 |    0.5466 |     1.930 |   0.03066 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8198 |   0.06938 |    0.6971 |    0.6791 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.492 |    0.9666 |    0.7618 |     1.828 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -12.72 |  -0.09950 |     1.479 |    0.1754 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4012 |    -1.242 |   -0.7866 |    -1.413 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9668 |    0.6187 |    0.9349 |    -2.630 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.260 |   -0.3489 |   -0.4063 |   -0.5476 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  123</span>|     466.89942 |     1.016 |    -1.904 |   -0.9326 |   -0.7693 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9564 |    -1.470 |     1.775 |    -1.832 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8995 |   -0.5006 |   -0.9260 |    -1.122 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6055 |   -0.8609 |   -0.9921 |   -0.3587 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     466.89942 |     92.48 |    -6.104 |   -0.9097 |    -2.032 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.569 |    0.1897 |     1.929 |   0.03069 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8197 |   0.06931 |    0.6950 |    0.6788 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.493 |    0.9661 |    0.7614 |     1.831 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     466.89942</span> |     92.48 |  0.002235 |    0.2871 |    0.1311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01037 |    0.5473 |     1.929 |   0.03069 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8197 |   0.06931 |    0.6950 |    0.6788 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.493 |    0.9661 |    0.7614 |     1.831 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     3.439 |  -0.09008 |     1.544 |    0.1555 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3845 |    -1.174 |   -0.7709 |    -1.201 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9305 |    0.6799 |     1.153 |    -2.871 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.766 |   -0.2963 |   -0.5389 |   -0.5305 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  124</span>|     466.87885 |     1.016 |    -1.902 |   -0.9335 |   -0.7713 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9544 |    -1.465 |     1.775 |    -1.831 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8988 |   -0.5028 |   -0.9291 |    -1.120 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6025 |   -0.8609 |   -0.9918 |   -0.3573 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     466.87885 |     92.42 |    -6.102 |   -0.9106 |    -2.034 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.567 |    0.1922 |     1.930 |   0.03072 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8200 |   0.06925 |    0.6928 |    0.6803 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.497 |    0.9661 |    0.7616 |     1.833 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     466.87885</span> |     92.42 |  0.002239 |    0.2869 |    0.1309 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01039 |    0.5479 |     1.930 |   0.03072 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8200 |   0.06925 |    0.6928 |    0.6803 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.497 |    0.9661 |    0.7616 |     1.833 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -3.274 |  -0.09794 |     1.477 |    0.1445 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3711 |    -1.126 |   -0.7697 |    -1.253 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9213 |    0.5797 |    0.6929 |    -2.254 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4364 |   0.05549 |  -0.07007 |   -0.5105 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  125</span>|     466.87374 |     1.017 |    -1.901 |   -0.9363 |   -0.7725 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9523 |    -1.459 |     1.775 |    -1.829 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9004 |   -0.5050 |   -0.9284 |    -1.119 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6029 |   -0.8616 |   -0.9930 |   -0.3542 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     466.87374 |     92.57 |    -6.101 |   -0.9131 |    -2.035 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.565 |    0.1945 |     1.929 |   0.03078 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8193 |   0.06919 |    0.6933 |    0.6817 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.496 |    0.9655 |    0.7605 |     1.836 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     466.87374</span> |     92.57 |  0.002241 |    0.2864 |    0.1307 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01041 |    0.5485 |     1.929 |   0.03078 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8193 |   0.06919 |    0.6933 |    0.6817 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.496 |    0.9655 |    0.7605 |     1.836 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     13.60 |  -0.09484 |     1.445 |    0.1251 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3584 |    -1.070 |   -0.7354 |    -1.048 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8551 |    0.6144 |    0.8824 | -0.009520 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4410 |   0.04024 |   -0.2274 |   -0.5153 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  126</span>|     466.85547 |     1.016 |    -1.900 |   -0.9388 |   -0.7737 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9500 |    -1.454 |     1.774 |    -1.828 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9020 |   -0.5070 |   -0.9271 |    -1.119 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6040 |   -0.8622 |   -0.9946 |   -0.3509 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     466.85547 |     92.42 |    -6.100 |   -0.9152 |    -2.036 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.562 |    0.1970 |     1.929 |   0.03083 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8186 |   0.06913 |    0.6942 |    0.6818 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.495 |    0.9648 |    0.7592 |     1.841 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     466.85547</span> |     92.42 |  0.002243 |    0.2859 |    0.1305 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01044 |    0.5491 |     1.929 |   0.03083 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8186 |   0.06913 |    0.6942 |    0.6818 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.495 |    0.9648 |    0.7592 |     1.841 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -2.539 |  -0.09755 |     1.262 |    0.1433 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3291 |    -1.038 |   -0.7482 |    -1.225 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8087 |    0.5023 |    0.8171 | -0.005344 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4685 |   0.01356 |   -0.1499 |   -0.5112 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  127</span>|     466.84409 |     1.016 |    -1.899 |   -0.9408 |   -0.7751 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9477 |    -1.449 |     1.774 |    -1.826 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9037 |   -0.5088 |   -0.9269 |    -1.119 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6048 |   -0.8630 |   -0.9960 |   -0.3473 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     466.84409 |     92.47 |    -6.099 |   -0.9171 |    -2.037 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.560 |    0.1996 |     1.929 |   0.03088 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8179 |   0.06907 |    0.6944 |    0.6816 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.494 |    0.9641 |    0.7580 |     1.845 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     466.84409</span> |     92.47 |  0.002245 |    0.2856 |    0.1304 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01046 |    0.5497 |     1.929 |   0.03088 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8179 |   0.06907 |    0.6944 |    0.6816 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.494 |    0.9641 |    0.7580 |     1.845 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     3.205 |  -0.09489 |     1.201 |    0.1289 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3142 |   -0.9889 |   -0.7667 |    -1.156 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7401 |    0.5570 |     1.301 |    -1.479 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.860 |   -0.4540 |   -0.5722 |   -0.5005 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  128</span>|     466.83069 |     1.016 |    -1.897 |   -0.9405 |   -0.7762 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9462 |    -1.443 |     1.775 |    -1.825 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9047 |   -0.5101 |   -0.9307 |    -1.119 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6046 |   -0.8636 |   -0.9969 |   -0.3435 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     466.83069 |     92.41 |    -6.097 |   -0.9168 |    -2.039 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.559 |    0.2019 |     1.929 |   0.03091 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8175 |   0.06904 |    0.6917 |    0.6819 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.494 |    0.9635 |    0.7572 |     1.850 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     466.83069</span> |     92.41 |  0.002250 |    0.2856 |    0.1302 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01048 |    0.5503 |     1.929 |   0.03091 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8175 |   0.06904 |    0.6917 |    0.6819 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.494 |    0.9635 |    0.7572 |     1.850 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  129</span>|     466.81923 |     1.016 |    -1.894 |   -0.9398 |   -0.7773 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9447 |    -1.438 |     1.775 |    -1.824 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9056 |   -0.5113 |   -0.9343 |    -1.119 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6049 |   -0.8643 |   -0.9979 |   -0.3395 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     466.81923 |     92.43 |    -6.094 |   -0.9162 |    -2.040 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.557 |    0.2043 |     1.930 |   0.03094 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8171 |   0.06900 |    0.6890 |    0.6818 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.494 |    0.9628 |    0.7563 |     1.854 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     466.81923</span> |     92.43 |  0.002255 |    0.2857 |    0.1301 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01049 |    0.5509 |     1.930 |   0.03094 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8171 |   0.06900 |    0.6890 |    0.6818 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.494 |    0.9628 |    0.7563 |     1.854 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  130</span>|     466.78977 |     1.016 |    -1.887 |   -0.9376 |   -0.7810 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9400 |    -1.422 |     1.776 |    -1.820 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9086 |   -0.5153 |   -0.9461 |    -1.119 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6057 |   -0.8666 |    -1.001 |   -0.3266 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     466.78977 |     92.47 |    -6.087 |   -0.9142 |    -2.043 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.552 |    0.2120 |     1.930 |   0.03105 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8159 |   0.06889 |    0.6804 |    0.6815 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.493 |    0.9606 |    0.7533 |     1.870 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     466.78977</span> |     92.47 |  0.002272 |    0.2861 |    0.1296 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01054 |    0.5528 |     1.930 |   0.03105 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8159 |   0.06889 |    0.6804 |    0.6815 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.493 |    0.9606 |    0.7533 |     1.870 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  131</span>|     466.76586 |     1.017 |    -1.875 |   -0.9340 |   -0.7871 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9321 |    -1.394 |     1.779 |    -1.814 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9134 |   -0.5218 |   -0.9656 |    -1.119 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6071 |   -0.8705 |    -1.007 |   -0.3052 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     466.76586 |     92.53 |    -6.075 |   -0.9110 |    -2.049 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.545 |    0.2247 |     1.931 |   0.03121 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8139 |   0.06870 |    0.6661 |    0.6811 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.491 |    0.9569 |    0.7485 |     1.896 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     466.76586</span> |     92.53 |  0.002300 |    0.2868 |    0.1288 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01062 |    0.5559 |     1.931 |   0.03121 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8139 |   0.06870 |    0.6661 |    0.6811 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.491 |    0.9569 |    0.7485 |     1.896 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     10.71 |  -0.05605 |     1.440 |   0.09508 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1459 |   -0.5070 |   -0.9907 |   -0.8007 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4069 |    0.2923 |   0.01818 |    -1.553 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.109 |   -0.3306 |   -0.3991 |   -0.2309 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  132</span>|     466.72646 |     1.014 |    -1.848 |   -0.9815 |   -0.8092 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9160 |    -1.342 |     1.783 |    -1.808 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9032 |   -0.5322 |   -0.9570 |    -1.117 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6032 |   -0.8748 |    -1.022 |   -0.2748 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     466.72646 |     92.30 |    -6.048 |   -0.9533 |    -2.072 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.528 |    0.2483 |     1.933 |   0.03140 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8182 |   0.06840 |    0.6724 |    0.6828 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.496 |    0.9528 |    0.7357 |     1.933 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     466.72646</span> |     92.30 |  0.002362 |    0.2782 |    0.1260 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01080 |    0.5618 |     1.933 |   0.03140 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8182 |   0.06840 |    0.6724 |    0.6828 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.496 |    0.9528 |    0.7357 |     1.933 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -13.11 |  -0.01237 |   -0.5306 |  0.001870 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01074 | -0.001553 |   -0.5011 |    -1.124 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.5240 |    0.3999 |    0.1337 |    0.6209 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3667 |   -0.5464 |   -0.9326 |   -0.1861 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  133</span>|     466.72714 |     1.014 |    -1.830 |   -0.9704 |   -0.8250 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9153 |    -1.342 |     1.791 |    -1.785 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9449 |   -0.5943 |   -0.9266 |    -1.116 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5962 |   -0.8737 |    -1.020 |   -0.2698 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     466.72714 |     92.32 |    -6.030 |   -0.9434 |    -2.087 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.528 |    0.2486 |     1.936 |   0.03205 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8009 |   0.06660 |    0.6946 |    0.6846 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.504 |    0.9539 |    0.7372 |     1.939 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     466.72714</span> |     92.32 |  0.002405 |    0.2802 |    0.1240 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01081 |    0.5618 |     1.936 |   0.03205 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8009 |   0.06660 |    0.6946 |    0.6846 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.504 |    0.9539 |    0.7372 |     1.939 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  134</span>|     466.69721 |     1.015 |    -1.839 |   -0.9760 |   -0.8170 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9156 |    -1.342 |     1.787 |    -1.797 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9238 |   -0.5630 |   -0.9420 |    -1.117 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5997 |   -0.8742 |    -1.021 |   -0.2723 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     466.69721 |     92.41 |    -6.039 |   -0.9484 |    -2.079 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.528 |    0.2485 |     1.935 |   0.03173 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8096 |   0.06750 |    0.6834 |    0.6836 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.500 |    0.9534 |    0.7365 |     1.936 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     466.69721</span> |     92.41 |  0.002383 |    0.2792 |    0.1250 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01080 |    0.5618 |     1.935 |   0.03173 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8096 |   0.06750 |    0.6834 |    0.6836 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.500 |    0.9534 |    0.7365 |     1.936 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    0.5723 |  0.006769 |   -0.2879 |  -0.06332 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01114 |  -0.03155 |   -0.3915 |   -0.7923 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3201 |   -0.2330 |    0.8086 |    -1.424 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3663 |   -0.4815 |   -0.7933 |   -0.2384 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  135</span>|     466.73286 |     1.017 |    -1.834 |   -0.9731 |   -0.8131 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9227 |    -1.351 |     1.796 |    -1.779 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9201 |   -0.5595 |   -0.9572 |    -1.109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6142 |   -0.8686 |    -1.016 |   -0.2440 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     466.73286 |     92.54 |    -6.034 |   -0.9458 |    -2.075 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.535 |    0.2444 |     1.938 |   0.03223 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8111 |   0.06760 |    0.6723 |    0.6901 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.483 |    0.9587 |    0.7409 |     1.970 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     466.73286</span> |     92.54 |  0.002396 |    0.2797 |    0.1255 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01072 |    0.5608 |     1.938 |   0.03223 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8111 |   0.06760 |    0.6723 |    0.6901 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.483 |    0.9587 |    0.7409 |     1.970 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  136</span>|     466.69733 |     1.015 |    -1.838 |   -0.9750 |   -0.8159 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9176 |    -1.345 |     1.790 |    -1.791 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9226 |   -0.5619 |   -0.9467 |    -1.114 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6033 |   -0.8723 |    -1.019 |   -0.2644 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     466.69733 |     92.40 |    -6.038 |   -0.9475 |    -2.078 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.530 |    0.2474 |     1.936 |   0.03188 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8101 |   0.06754 |    0.6799 |    0.6863 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.496 |    0.9552 |    0.7382 |     1.945 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     466.69733</span> |     92.40 |  0.002386 |    0.2794 |    0.1251 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01078 |    0.5615 |     1.936 |   0.03188 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8101 |   0.06754 |    0.6799 |    0.6863 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.496 |    0.9552 |    0.7382 |     1.945 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  137</span>|     466.69584 |     1.015 |    -1.839 |   -0.9754 |   -0.8165 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9164 |    -1.343 |     1.788 |    -1.794 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9231 |   -0.5623 |   -0.9445 |    -1.114 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6009 |   -0.8731 |    -1.020 |   -0.2689 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     466.69584 |     92.37 |    -6.039 |   -0.9478 |    -2.079 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.529 |    0.2480 |     1.935 |   0.03180 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8099 |   0.06752 |    0.6815 |    0.6857 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.499 |    0.9544 |    0.7377 |     1.940 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     466.69584</span> |     92.37 |  0.002384 |    0.2793 |    0.1251 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01079 |    0.5617 |     1.935 |   0.03180 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8099 |   0.06752 |    0.6815 |    0.6857 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.499 |    0.9544 |    0.7377 |     1.940 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -3.069 | -0.001275 |   -0.2861 |  -0.04975 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01387 |  -0.05203 |   -0.3437 |   -0.7173 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2694 |   -0.1778 |    0.5026 |    0.8511 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9213 |   -0.7376 |   -0.9765 |   -0.1682 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  138</span>|     466.68962 |     1.016 |    -1.839 |   -0.9764 |   -0.8159 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9167 |    -1.342 |     1.790 |    -1.793 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9191 |   -0.5597 |   -0.9461 |    -1.115 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6002 |   -0.8712 |    -1.018 |   -0.2680 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     466.68962 |     92.46 |    -6.039 |   -0.9488 |    -2.078 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.529 |    0.2485 |     1.936 |   0.03184 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8115 |   0.06760 |    0.6803 |    0.6854 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.499 |    0.9562 |    0.7392 |     1.941 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     466.68962</span> |     92.46 |  0.002385 |    0.2791 |    0.1251 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01079 |    0.5618 |     1.936 |   0.03184 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8115 |   0.06760 |    0.6803 |    0.6854 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.499 |    0.9562 |    0.7392 |     1.941 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     6.342 |  0.001592 |   -0.2787 |  -0.05882 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.004933 |  -0.02534 |   -0.4440 |   -0.5467 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1484 |  -0.03146 |    0.4648 |    -1.264 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3607 |   -0.2274 |   -0.5157 |   -0.1312 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  139</span>|     466.68314 |     1.015 |    -1.839 |   -0.9751 |   -0.8151 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9166 |    -1.339 |     1.792 |    -1.792 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9160 |   -0.5585 |   -0.9470 |    -1.115 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5998 |   -0.8691 |    -1.016 |   -0.2676 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     466.68314 |     92.36 |    -6.039 |   -0.9476 |    -2.077 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.529 |    0.2501 |     1.937 |   0.03187 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8128 |   0.06763 |    0.6797 |    0.6853 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.500 |    0.9582 |    0.7404 |     1.942 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     466.68314</span> |     92.36 |  0.002385 |    0.2794 |    0.1252 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01079 |    0.5622 |     1.937 |   0.03187 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8128 |   0.06763 |    0.6797 |    0.6853 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.500 |    0.9582 |    0.7404 |     1.942 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.258 |-0.0003497 |   -0.2788 |  -0.03383 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01408 |   0.02647 |   -0.2857 |   -0.5956 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.05089 |   0.05050 |    0.4395 |    0.8187 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2669 |  -0.06868 |   -0.3694 |   -0.1159 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  140</span>|     466.67997 |     1.015 |    -1.839 |   -0.9739 |   -0.8142 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9180 |    -1.339 |     1.794 |    -1.790 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9137 |   -0.5623 |   -0.9494 |    -1.116 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6002 |   -0.8690 |    -1.016 |   -0.2664 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     466.67997 |     92.38 |    -6.039 |   -0.9465 |    -2.077 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.531 |    0.2499 |     1.938 |   0.03191 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8138 |   0.06752 |    0.6779 |    0.6846 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.499 |    0.9583 |    0.7410 |     1.943 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     466.67997</span> |     92.38 |  0.002385 |    0.2796 |    0.1254 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01078 |    0.5622 |     1.938 |   0.03191 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8138 |   0.06752 |    0.6779 |    0.6846 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.499 |    0.9583 |    0.7410 |     1.943 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    M|   Mixed Diff. |    -1.882 |-7.391e+04 |   -0.2259 |  -0.03295 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.005718 |   0.01130 |   -0.3842 |   -0.4930 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.04972 |  -0.05953 |    0.5251 |    -1.398 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3564 |  -0.08212 |   -0.3242 |   -0.1008 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  141</span>|     466.67997 |     1.015 |    -1.839 |   -0.9739 |   -0.8142 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9180 |    -1.339 |     1.794 |    -1.790 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9137 |   -0.5623 |   -0.9494 |    -1.116 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6002 |   -0.8690 |    -1.016 |   -0.2664 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     466.67997 |     92.38 |    -6.039 |   -0.9465 |    -2.077 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.531 |    0.2499 |     1.938 |   0.03191 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8138 |   0.06752 |    0.6779 |    0.6846 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.499 |    0.9583 |    0.7410 |     1.943 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     466.67997</span> |     92.38 |  0.002385 |    0.2796 |    0.1254 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01078 |    0.5622 |     1.938 |   0.03191 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8138 |   0.06752 |    0.6779 |    0.6846 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.499 |    0.9583 |    0.7410 |     1.943 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> calculating covariance matrix</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating residuals/tables</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-in"></span>
-<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">AIC</a></span><span class="op">(</span></span>
-<span class="r-in">  <span class="va">f_nlmixr_sfo_sfo_focei_const</span><span class="op">$</span><span class="va">nm</span>,</span>
-<span class="r-in">  <span class="va">f_nlmixr_fomc_sfo_focei_const</span><span class="op">$</span><span class="va">nm</span>,</span>
-<span class="r-in">  <span class="va">f_nlmixr_dfop_sfo_focei_const</span><span class="op">$</span><span class="va">nm</span>,</span>
-<span class="r-in">  <span class="va">f_nlmixr_fomc_sfo_saem_obs</span><span class="op">$</span><span class="va">nm</span>,</span>
-<span class="r-in">  <span class="va">f_nlmixr_fomc_sfo_focei_obs</span><span class="op">$</span><span class="va">nm</span>,</span>
-<span class="r-in">  <span class="va">f_nlmixr_dfop_sfo_saem_obs</span><span class="op">$</span><span class="va">nm</span>,</span>
-<span class="r-in">  <span class="va">f_nlmixr_dfop_sfo_focei_obs</span><span class="op">$</span><span class="va">nm</span>,</span>
-<span class="r-in">  <span class="va">f_nlmixr_fomc_sfo_focei_tc</span><span class="op">$</span><span class="va">nm</span>,</span>
-<span class="r-in">  <span class="va">f_nlmixr_dfop_sfo_focei_tc</span><span class="op">$</span><span class="va">nm</span>,</span>
-<span class="r-in">  <span class="va">f_nlmixr_fomc_sfo_saem_obs_tc</span><span class="op">$</span><span class="va">nm</span>,</span>
-<span class="r-in">  <span class="va">f_nlmixr_fomc_sfo_focei_obs_tc</span><span class="op">$</span><span class="va">nm</span>,</span>
-<span class="r-in">  <span class="va">f_nlmixr_dfop_sfo_saem_obs_tc</span><span class="op">$</span><span class="va">nm</span>,</span>
-<span class="r-in">  <span class="va">f_nlmixr_dfop_sfo_focei_obs_tc</span><span class="op">$</span><span class="va">nm</span></span>
-<span class="r-in"><span class="op">)</span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating -2LL by Gaussian quadrature (nnodes=3,nsd=1.6)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating -2LL by Gaussian quadrature (nnodes=3,nsd=1.6)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating -2LL by Gaussian quadrature (nnodes=3,nsd=1.6)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating -2LL by Gaussian quadrature (nnodes=3,nsd=1.6)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                                   df       AIC</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_nlmixr_sfo_sfo_focei_const$nm    9 1082.4605</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_nlmixr_fomc_sfo_focei_const$nm  11  814.4261</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_nlmixr_dfop_sfo_focei_const$nm  13  870.2659</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_nlmixr_fomc_sfo_saem_obs$nm     12  788.8373</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_nlmixr_fomc_sfo_focei_obs$nm    12  794.5194</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_nlmixr_dfop_sfo_saem_obs$nm     14  815.0797</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_nlmixr_dfop_sfo_focei_obs$nm    14  834.8474</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_nlmixr_fomc_sfo_focei_tc$nm     12  812.3296</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_nlmixr_dfop_sfo_focei_tc$nm     14  819.4103</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_nlmixr_fomc_sfo_saem_obs_tc$nm  14  814.4248</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_nlmixr_fomc_sfo_focei_obs_tc$nm 14  787.4355</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_nlmixr_dfop_sfo_saem_obs_tc$nm  16  828.5143</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_nlmixr_dfop_sfo_focei_obs_tc$nm 16  811.1191</span>
-<span class="r-in"><span class="co"># Currently, FOMC-SFO with two-component error by variable fitted by focei gives the</span></span>
-<span class="r-in"><span class="co"># lowest AIC</span></span>
-<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_nlmixr_fomc_sfo_focei_obs_tc</span><span class="op">)</span></span>
-<span class="r-plt img"><img src="nlmixr.mmkin-2.png" alt="" width="700" height="433"></span>
-<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">f_nlmixr_fomc_sfo_focei_obs_tc</span><span class="op">)</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> nlmixr version used for fitting:    2.0.6 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> mkin version used for pre-fitting:  1.1.0 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> R version used for fitting:         4.1.2 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Date of fit:     Mon Feb 28 14:50:18 2022 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Date of summary: Mon Feb 28 14:51:57 2022 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Equations:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> d_A1/dt = + f_parent_to_A1 * (alpha/beta) * 1/((time/beta) + 1) *</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>            parent - k_A1 * A1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Data:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 170 observations of 2 variable(s) grouped in 5 datasets</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Degradation model predictions using RxODE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Fitted in 18.08 s</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Variance model: Two-component variance unique to each observed variable </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Mean of starting values for individual parameters:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>        parent_0        log_k_A1 f_parent_qlogis       log_alpha        log_beta </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         93.1168         -5.3034         -0.9442         -0.1065          2.2909 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Mean of starting values for error model parameters:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma_low_parent  rsd_high_parent     sigma_low_A1      rsd_high_A1 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>          1.15958          0.03005          1.15958          0.03005 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Fixed degradation parameter values:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> None</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Results:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Likelihood calculated by focei  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     AIC   BIC logLik</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   787.4 831.3 -379.7</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Optimised parameters:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                    est.   lower   upper</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0        93.6717 91.2552 96.0882</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k_A1        -6.3199 -8.4468 -4.1930</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_parent_qlogis -1.0089 -1.3823 -0.6356</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_alpha       -0.1616 -0.6624  0.3392</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_beta         2.2088  1.6800  2.7376</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Correlation: </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                 prnt_0 lg__A1 f_prn_ lg_lph</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k_A1         0.372                     </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_parent_qlogis -0.786 -0.409              </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_alpha        0.336  0.942 -0.306       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_beta        -0.399 -0.759  0.248 -0.555</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Random effects (omega):</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                     eta.parent_0 eta.log_k_A1 eta.f_parent_qlogis eta.log_alpha</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> eta.parent_0               4.391        0.000              0.0000        0.0000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> eta.log_k_A1               0.000        6.402              0.0000        0.0000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> eta.f_parent_qlogis        0.000        0.000              0.1584        0.0000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> eta.log_alpha              0.000        0.000              0.0000        0.3381</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> eta.log_beta               0.000        0.000              0.0000        0.0000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                     eta.log_beta</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> eta.parent_0               0.000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> eta.log_k_A1               0.000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> eta.f_parent_qlogis        0.000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> eta.log_alpha              0.000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> eta.log_beta               0.358</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Variance model:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma_low_parent  rsd_high_parent     sigma_low_A1      rsd_high_A1 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>          2.35616          0.00153          0.63564          0.08639 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Backtransformed parameters:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                   est.     lower   upper</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0       93.6717 9.126e+01 96.0882</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_A1            0.0018 2.146e-04  0.0151</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_parent_to_A1  0.2672 2.006e-01  0.3462</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> alpha           0.8508 5.156e-01  1.4038</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> beta            9.1049 5.366e+00 15.4499</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Resulting formation fractions:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                 ff</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_A1   0.2672</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_sink 0.7328</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Estimated disappearance times:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>          DT50   DT90 DT50back</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent  11.46  127.3    38.31</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> A1     385.05 1279.1       NA</span>
-<span class="r-in"><span class="co"># }</span></span>
-</code></pre></div>
-    </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.2.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/nobs.mkinfit.html b/docs/dev/reference/nobs.mkinfit.html
deleted file mode 100644
index 10f6550b..00000000
--- a/docs/dev/reference/nobs.mkinfit.html
+++ /dev/null
@@ -1,157 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Number of observations on which an mkinfit object was fitted — nobs.mkinfit • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Number of observations on which an mkinfit object was fitted — nobs.mkinfit"><meta property="og:description" content="Number of observations on which an mkinfit object was fitted"><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Number of observations on which an mkinfit object was fitted</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/nobs.mkinfit.R" class="external-link"><code>R/nobs.mkinfit.R</code></a></small>
-    <div class="hidden name"><code>nobs.mkinfit.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Number of observations on which an mkinfit object was fitted</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for mkinfit</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/stats/nobs.html" class="external-link">nobs</a></span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
-    </div>
-
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>object</dt>
-<dd><p>An mkinfit object</p></dd>
-
-
-<dt>...</dt>
-<dd><p>For compatibility with the generic method</p></dd>
-
-</dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>The number of rows in the data included in the mkinfit object</p>
-    </div>
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/parhist.html b/docs/dev/reference/parhist.html
deleted file mode 100644
index 27fc116f..00000000
--- a/docs/dev/reference/parhist.html
+++ /dev/null
@@ -1,169 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Plot parameter distributions from multistart objects — parhist • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Plot parameter distributions from multistart objects — parhist"><meta property="og:description" content="Produces a boxplot with all parameters from the multiple runs, scaled
-either by the parameters of the run with the highest likelihood,
-or by their medians as proposed in the paper by Duchesne et al. (2021)."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.1.2</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Functions and data</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Plot parameter distributions from multistart objects</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/parhist.R" class="external-link"><code>R/parhist.R</code></a></small>
-    <div class="hidden name"><code>parhist.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Produces a boxplot with all parameters from the multiple runs, scaled
-either by the parameters of the run with the highest likelihood,
-or by their medians as proposed in the paper by Duchesne et al. (2021).</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">parhist</span><span class="op">(</span></span>
-<span>  <span class="va">object</span>,</span>
-<span>  llmin <span class="op">=</span> <span class="op">-</span><span class="cn">Inf</span>,</span>
-<span>  scale <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"best"</span>, <span class="st">"median"</span><span class="op">)</span>,</span>
-<span>  lpos <span class="op">=</span> <span class="st">"bottomleft"</span>,</span>
-<span>  main <span class="op">=</span> <span class="st">""</span>,</span>
-<span>  <span class="va">...</span></span>
-<span><span class="op">)</span></span></code></pre></div>
-    </div>
-
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>object</dt>
-<dd><p>The <a href="multistart.html">multistart</a> object</p></dd>
-
-
-<dt>llmin</dt>
-<dd><p>The minimum likelihood of objects to be shown</p></dd>
-
-
-<dt>scale</dt>
-<dd><p>By default, scale parameters using the best available fit.
-If 'median', parameters are scaled using the median parameters from all fits.</p></dd>
-
-
-<dt>lpos</dt>
-<dd><p>Positioning of the legend.</p></dd>
-
-
-<dt>main</dt>
-<dd><p>Title of the plot</p></dd>
-
-
-<dt>...</dt>
-<dd><p>Passed to <a href="https://rdrr.io/r/graphics/boxplot.html" class="external-link">boxplot</a></p></dd>
-
-</dl></div>
-    <div id="references">
-    <h2>References</h2>
-    <p>Duchesne R, Guillemin A, Gandrillon O, Crauste F. Practical
-identifiability in the frame of nonlinear mixed effects models: the example
-of the in vitro erythropoiesis. BMC Bioinformatics. 2021 Oct 4;22(1):478.
-doi: 10.1186/s12859-021-04373-4.</p>
-    </div>
-    <div id="see-also">
-    <h2>See also</h2>
-    <div class="dont-index"><p><a href="multistart.html">multistart</a></p></div>
-    </div>
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/parms.html b/docs/dev/reference/parms.html
deleted file mode 100644
index d4175d41..00000000
--- a/docs/dev/reference/parms.html
+++ /dev/null
@@ -1,296 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Extract model parameters — parms • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Extract model parameters — parms"><meta property="og:description" content="This function returns degradation model parameters as well as error
-model parameters per default, in order to avoid working with a fitted model
-without considering the error structure that was assumed for the fit."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Extract model parameters</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/parms.R" class="external-link"><code>R/parms.R</code></a>, <a href="https://github.com/jranke/mkin/blob/HEAD/R/saem.R" class="external-link"><code>R/saem.R</code></a></small>
-    <div class="hidden name"><code>parms.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>This function returns degradation model parameters as well as error
-model parameters per default, in order to avoid working with a fitted model
-without considering the error structure that was assumed for the fit.</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">parms</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># S3 method for mkinfit</span></span>
-<span><span class="fu">parms</span><span class="op">(</span><span class="va">object</span>, transformed <span class="op">=</span> <span class="cn">FALSE</span>, errparms <span class="op">=</span> <span class="cn">TRUE</span>, <span class="va">...</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># S3 method for mmkin</span></span>
-<span><span class="fu">parms</span><span class="op">(</span><span class="va">object</span>, transformed <span class="op">=</span> <span class="cn">FALSE</span>, errparms <span class="op">=</span> <span class="cn">TRUE</span>, <span class="va">...</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># S3 method for multistart</span></span>
-<span><span class="fu">parms</span><span class="op">(</span><span class="va">object</span>, exclude_failed <span class="op">=</span> <span class="cn">TRUE</span>, <span class="va">...</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># S3 method for saem.mmkin</span></span>
-<span><span class="fu">parms</span><span class="op">(</span><span class="va">object</span>, ci <span class="op">=</span> <span class="cn">FALSE</span>, covariates <span class="op">=</span> <span class="cn">NULL</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
-    </div>
-
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>object</dt>
-<dd><p>A fitted model object.</p></dd>
-
-
-<dt>...</dt>
-<dd><p>Not used</p></dd>
-
-
-<dt>transformed</dt>
-<dd><p>Should the parameters be returned as used internally
-during the optimisation?</p></dd>
-
-
-<dt>errparms</dt>
-<dd><p>Should the error model parameters be returned
-in addition to the degradation parameters?</p></dd>
-
-
-<dt>exclude_failed</dt>
-<dd><p>For <a href="multistart.html">multistart</a> objects, should rows for failed fits
-be removed from the returned parameter matrix?</p></dd>
-
-
-<dt>ci</dt>
-<dd><p>Should a matrix with estimates and confidence interval boundaries
-be returned? If FALSE (default), a vector of estimates is returned if no
-covariates are given, otherwise a matrix of estimates is returned, with
-each column corresponding to a row of the data frame holding the covariates</p></dd>
-
-
-<dt>covariates</dt>
-<dd><p>A data frame holding covariate values for which to
-return parameter values. Only has an effect if 'ci' is FALSE.</p></dd>
-
-</dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>Depending on the object, a numeric vector of fitted model parameters,
-a matrix (e.g. for mmkin row objects), or a list of matrices (e.g. for
-mmkin objects with more than one row).</p>
-    </div>
-    <div id="see-also">
-    <h2>See also</h2>
-    <div class="dont-index"><p><a href="saem.html">saem</a>, <a href="multistart.html">multistart</a></p></div>
-    </div>
-
-    <div id="ref-examples">
-    <h2>Examples</h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># mkinfit objects</span></span></span>
-<span class="r-in"><span><span class="va">fit</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="va">FOCUS_2006_C</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu">parms</span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   parent_0   k_parent      sigma </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 82.4921598  0.3060633  4.6730124 </span>
-<span class="r-in"><span><span class="fu">parms</span><span class="op">(</span><span class="va">fit</span>, transformed <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     parent_0 log_k_parent        sigma </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    82.492160    -1.183963     4.673012 </span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co"># mmkin objects</span></span></span>
-<span class="r-in"><span><span class="va">ds</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="va">experimental_data_for_UBA_2019</span><span class="op">[</span><span class="fl">6</span><span class="op">:</span><span class="fl">10</span><span class="op">]</span>,</span></span>
-<span class="r-in"><span> <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fu"><a href="https://rdrr.io/r/base/subset.html" class="external-link">subset</a></span><span class="op">(</span><span class="va">x</span><span class="op">$</span><span class="va">data</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"name"</span>, <span class="st">"time"</span>, <span class="st">"value"</span><span class="op">)</span><span class="op">]</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">ds</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste</a></span><span class="op">(</span><span class="st">"Dataset"</span>, <span class="fl">6</span><span class="op">:</span><span class="fl">10</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="co"># \dontrun{</span></span></span>
-<span class="r-in"><span><span class="va">fits</span> <span class="op">&lt;-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"FOMC"</span>, <span class="st">"DFOP"</span><span class="op">)</span>, <span class="va">ds</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, cores <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu">parms</span><span class="op">(</span><span class="va">fits</span><span class="op">[</span><span class="st">"SFO"</span>, <span class="op">]</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>            Dataset 6    Dataset 7  Dataset 8  Dataset 9  Dataset 10</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0 88.52275400 82.666781678 86.8547308 91.7779306 82.14809450</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_parent  0.05794659  0.009647805  0.2102974  0.1232258  0.00720421</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma     5.15274487  7.040168584  3.6769645  6.4669234  6.50457673</span>
-<span class="r-in"><span><span class="fu">parms</span><span class="op">(</span><span class="va">fits</span><span class="op">[</span>, <span class="fl">2</span><span class="op">]</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> $SFO</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>             Dataset 7</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0 82.666781678</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_parent  0.009647805</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma     7.040168584</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> $FOMC</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>           Dataset 7</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0 92.6837649</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> alpha     0.4967832</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> beta     14.1451255</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma     1.9167519</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> $DFOP</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>             Dataset 7</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0 91.058971589</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k1        0.044946770</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k2        0.002868336</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> g         0.526942415</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma     2.221302196</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-in"><span><span class="fu">parms</span><span class="op">(</span><span class="va">fits</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> $SFO</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>            Dataset 6    Dataset 7  Dataset 8  Dataset 9  Dataset 10</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0 88.52275400 82.666781678 86.8547308 91.7779306 82.14809450</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_parent  0.05794659  0.009647805  0.2102974  0.1232258  0.00720421</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma     5.15274487  7.040168584  3.6769645  6.4669234  6.50457673</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> $FOMC</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>          Dataset 6  Dataset 7 Dataset 8 Dataset 9 Dataset 10</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0 95.558575 92.6837649 90.719787 98.383939 94.8481459</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> alpha     1.338667  0.4967832  1.639099  1.074460  0.2805272</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> beta     13.033315 14.1451255  5.007077  4.397126  6.9052224</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma     1.847671  1.9167519  1.066063  3.146056  1.6222778</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> $DFOP</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>            Dataset 6    Dataset 7   Dataset 8   Dataset 9   Dataset 10</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0 96.55213663 91.058971589 90.34509493 98.14858820 94.311323734</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k1        0.21954588  0.044946770  0.41232288  0.31697588  0.080663857</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k2        0.02957934  0.002868336  0.07581766  0.03260384  0.003425417</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> g         0.44845068  0.526942415  0.66091967  0.65322767  0.342652880</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma     1.35690468  2.221302196  1.34169076  2.87159846  1.942067831</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-in"><span><span class="fu">parms</span><span class="op">(</span><span class="va">fits</span>, transformed <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> $SFO</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>              Dataset 6 Dataset 7 Dataset 8 Dataset 9 Dataset 10</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0     88.522754 82.666782 86.854731 91.777931  82.148094</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k_parent -2.848234 -4.641025 -1.559232 -2.093737  -4.933090</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma         5.152745  7.040169  3.676964  6.466923   6.504577</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> $FOMC</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>            Dataset 6  Dataset 7  Dataset 8   Dataset 9 Dataset 10</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0  95.5585751 92.6837649 90.7197870 98.38393897  94.848146</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_alpha  0.2916741 -0.6996015  0.4941466  0.07181816  -1.271085</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_beta   2.5675088  2.6493701  1.6108523  1.48095106   1.932278</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma      1.8476712  1.9167519  1.0660627  3.14605557   1.622278</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> $DFOP</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>           Dataset 6  Dataset 7  Dataset 8  Dataset 9 Dataset 10</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0 96.5521366 91.0589716 90.3450949 98.1485882 94.3113237</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k1   -1.5161940 -3.1022764 -0.8859486 -1.1489296 -2.5174647</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k2   -3.5206791 -5.8540232 -2.5794240 -3.4233253 -5.6765322</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> g_qlogis -0.2069326  0.1078741  0.6673953  0.6332573 -0.6514943</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma     1.3569047  2.2213022  1.3416908  2.8715985  1.9420678</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-in"><span><span class="co"># }</span></span></span>
-</code></pre></div>
-    </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/parplot.html b/docs/dev/reference/parplot.html
deleted file mode 100644
index ee2ec7b2..00000000
--- a/docs/dev/reference/parplot.html
+++ /dev/null
@@ -1,205 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Plot parameter variability of multistart objects — parplot • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Plot parameter variability of multistart objects — parplot"><meta property="og:description" content="Produces a boxplot with all parameters from the multiple runs, scaled
-either by the parameters of the run with the highest likelihood,
-or by their medians as proposed in the paper by Duchesne et al. (2021)."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Plot parameter variability of multistart objects</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/parplot.R" class="external-link"><code>R/parplot.R</code></a></small>
-    <div class="hidden name"><code>parplot.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Produces a boxplot with all parameters from the multiple runs, scaled
-either by the parameters of the run with the highest likelihood,
-or by their medians as proposed in the paper by Duchesne et al. (2021).</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">parplot</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># S3 method for multistart.saem.mmkin</span></span>
-<span><span class="fu">parplot</span><span class="op">(</span></span>
-<span>  <span class="va">object</span>,</span>
-<span>  llmin <span class="op">=</span> <span class="op">-</span><span class="cn">Inf</span>,</span>
-<span>  llquant <span class="op">=</span> <span class="cn">NA</span>,</span>
-<span>  scale <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"best"</span>, <span class="st">"median"</span><span class="op">)</span>,</span>
-<span>  lpos <span class="op">=</span> <span class="st">"bottomleft"</span>,</span>
-<span>  main <span class="op">=</span> <span class="st">""</span>,</span>
-<span>  <span class="va">...</span></span>
-<span><span class="op">)</span></span></code></pre></div>
-    </div>
-
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>object</dt>
-<dd><p>The <a href="multistart.html">multistart</a> object</p></dd>
-
-
-<dt>...</dt>
-<dd><p>Passed to <a href="https://rdrr.io/r/graphics/boxplot.html" class="external-link">boxplot</a></p></dd>
-
-
-<dt>llmin</dt>
-<dd><p>The minimum likelihood of objects to be shown</p></dd>
-
-
-<dt>llquant</dt>
-<dd><p>Fractional value for selecting only the fits with higher
-likelihoods. Overrides 'llmin'.</p></dd>
-
-
-<dt>scale</dt>
-<dd><p>By default, scale parameters using the best
-available fit.
-If 'median', parameters are scaled using the median parameters from all fits.</p></dd>
-
-
-<dt>lpos</dt>
-<dd><p>Positioning of the legend.</p></dd>
-
-
-<dt>main</dt>
-<dd><p>Title of the plot</p></dd>
-
-</dl></div>
-    <div id="details">
-    <h2>Details</h2>
-    <p>Starting values of degradation model parameters and error model parameters
-are shown as green circles. The results obtained in the original run
-are shown as red circles.</p>
-    </div>
-    <div id="references">
-    <h2>References</h2>
-    <p>Duchesne R, Guillemin A, Gandrillon O, Crauste F. Practical
-identifiability in the frame of nonlinear mixed effects models: the example
-of the in vitro erythropoiesis. BMC Bioinformatics. 2021 Oct 4;22(1):478.
-doi: 10.1186/s12859-021-04373-4.</p>
-    </div>
-    <div id="see-also">
-    <h2>See also</h2>
-    <div class="dont-index"><p><a href="multistart.html">multistart</a></p></div>
-    </div>
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/plot.mixed.mmkin-1.png b/docs/dev/reference/plot.mixed.mmkin-1.png
deleted file mode 100644
index 2e145bb7..00000000
--- a/docs/dev/reference/plot.mixed.mmkin-1.png
+++ /dev/null
diff --git a/docs/dev/reference/plot.mixed.mmkin-2.png b/docs/dev/reference/plot.mixed.mmkin-2.png
deleted file mode 100644
index b22c1dbb..00000000
--- a/docs/dev/reference/plot.mixed.mmkin-2.png
+++ /dev/null
diff --git a/docs/dev/reference/plot.mixed.mmkin-3.png b/docs/dev/reference/plot.mixed.mmkin-3.png
deleted file mode 100644
index cd424bf2..00000000
--- a/docs/dev/reference/plot.mixed.mmkin-3.png
+++ /dev/null
diff --git a/docs/dev/reference/plot.mixed.mmkin-4.png b/docs/dev/reference/plot.mixed.mmkin-4.png
deleted file mode 100644
index f6ffe4d5..00000000
--- a/docs/dev/reference/plot.mixed.mmkin-4.png
+++ /dev/null
diff --git a/docs/dev/reference/plot.mixed.mmkin.html b/docs/dev/reference/plot.mixed.mmkin.html
deleted file mode 100644
index 3d902b77..00000000
--- a/docs/dev/reference/plot.mixed.mmkin.html
+++ /dev/null
@@ -1,342 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Plot predictions from a fitted nonlinear mixed model obtained via an mmkin row object — plot.mixed.mmkin • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Plot predictions from a fitted nonlinear mixed model obtained via an mmkin row object — plot.mixed.mmkin"><meta property="og:description" content="Plot predictions from a fitted nonlinear mixed model obtained via an mmkin row object"><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Plot predictions from a fitted nonlinear mixed model obtained via an mmkin row object</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/plot.mixed.mmkin.R" class="external-link"><code>R/plot.mixed.mmkin.R</code></a></small>
-    <div class="hidden name"><code>plot.mixed.mmkin.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Plot predictions from a fitted nonlinear mixed model obtained via an mmkin row object</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for mixed.mmkin</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span></span>
-<span>  <span class="va">x</span>,</span>
-<span>  i <span class="op">=</span> <span class="fl">1</span><span class="op">:</span><span class="fu"><a href="https://rdrr.io/r/base/nrow.html" class="external-link">ncol</a></span><span class="op">(</span><span class="va">x</span><span class="op">$</span><span class="va">mmkin</span><span class="op">)</span>,</span>
-<span>  obs_vars <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">x</span><span class="op">$</span><span class="va">mkinmod</span><span class="op">$</span><span class="va">map</span><span class="op">)</span>,</span>
-<span>  standardized <span class="op">=</span> <span class="cn">TRUE</span>,</span>
-<span>  covariates <span class="op">=</span> <span class="cn">NULL</span>,</span>
-<span>  covariate_quantiles <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.5</span>, <span class="fl">0.05</span>, <span class="fl">0.95</span><span class="op">)</span>,</span>
-<span>  xlab <span class="op">=</span> <span class="st">"Time"</span>,</span>
-<span>  xlim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/range.html" class="external-link">range</a></span><span class="op">(</span><span class="va">x</span><span class="op">$</span><span class="va">data</span><span class="op">$</span><span class="va">time</span><span class="op">)</span>,</span>
-<span>  resplot <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"predicted"</span>, <span class="st">"time"</span><span class="op">)</span>,</span>
-<span>  pop_curves <span class="op">=</span> <span class="st">"auto"</span>,</span>
-<span>  pred_over <span class="op">=</span> <span class="cn">NULL</span>,</span>
-<span>  test_log_parms <span class="op">=</span> <span class="cn">FALSE</span>,</span>
-<span>  conf.level <span class="op">=</span> <span class="fl">0.6</span>,</span>
-<span>  default_log_parms <span class="op">=</span> <span class="cn">NA</span>,</span>
-<span>  ymax <span class="op">=</span> <span class="st">"auto"</span>,</span>
-<span>  maxabs <span class="op">=</span> <span class="st">"auto"</span>,</span>
-<span>  ncol.legend <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html" class="external-link">ifelse</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/length.html" class="external-link">length</a></span><span class="op">(</span><span class="va">i</span><span class="op">)</span> <span class="op">&lt;=</span> <span class="fl">3</span>, <span class="fu"><a href="https://rdrr.io/r/base/length.html" class="external-link">length</a></span><span class="op">(</span><span class="va">i</span><span class="op">)</span> <span class="op">+</span> <span class="fl">1</span>, <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html" class="external-link">ifelse</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/length.html" class="external-link">length</a></span><span class="op">(</span><span class="va">i</span><span class="op">)</span> <span class="op">&lt;=</span> <span class="fl">8</span>, <span class="fl">3</span>, <span class="fl">4</span><span class="op">)</span><span class="op">)</span>,</span>
-<span>  nrow.legend <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Round.html" class="external-link">ceiling</a></span><span class="op">(</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/length.html" class="external-link">length</a></span><span class="op">(</span><span class="va">i</span><span class="op">)</span> <span class="op">+</span> <span class="fl">1</span><span class="op">)</span><span class="op">/</span><span class="va">ncol.legend</span><span class="op">)</span>,</span>
-<span>  rel.height.legend <span class="op">=</span> <span class="fl">0.02</span> <span class="op">+</span> <span class="fl">0.07</span> <span class="op">*</span> <span class="va">nrow.legend</span>,</span>
-<span>  rel.height.bottom <span class="op">=</span> <span class="fl">1.1</span>,</span>
-<span>  pch_ds <span class="op">=</span> <span class="fl">1</span><span class="op">:</span><span class="fu"><a href="https://rdrr.io/r/base/length.html" class="external-link">length</a></span><span class="op">(</span><span class="va">i</span><span class="op">)</span>,</span>
-<span>  col_ds <span class="op">=</span> <span class="va">pch_ds</span> <span class="op">+</span> <span class="fl">1</span>,</span>
-<span>  lty_ds <span class="op">=</span> <span class="va">col_ds</span>,</span>
-<span>  frame <span class="op">=</span> <span class="cn">TRUE</span>,</span>
-<span>  <span class="va">...</span></span>
-<span><span class="op">)</span></span></code></pre></div>
-    </div>
-
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>x</dt>
-<dd><p>An object of class <a href="mixed.html">mixed.mmkin</a>, <a href="saem.html">saem.mmkin</a> or <a href="nlme.mmkin.html">nlme.mmkin</a></p></dd>
-
-
-<dt>i</dt>
-<dd><p>A numeric index to select datasets for which to plot the individual predictions,
-in case plots get too large</p></dd>
-
-
-<dt>obs_vars</dt>
-<dd><p>A character vector of names of the observed variables for
-which the data and the model should be plotted. Defauls to all observed
-variables in the model.</p></dd>
-
-
-<dt>standardized</dt>
-<dd><p>Should the residuals be standardized? Only takes effect if
-<code>resplot = "time"</code>.</p></dd>
-
-
-<dt>covariates</dt>
-<dd><p>Data frame with covariate values for all variables in
-any covariate models in the object. If given, it overrides 'covariate_quantiles'.
-Each line in the data frame will result in a line drawn for the population.
-Rownames are used in the legend to label the lines.</p></dd>
-
-
-<dt>covariate_quantiles</dt>
-<dd><p>This argument only has an effect if the fitted
-object has covariate models. If so, the default is to show three population
-curves, for the 5th percentile, the 50th percentile and the 95th percentile
-of the covariate values used for fitting the model.</p></dd>
-
-
-<dt>xlab</dt>
-<dd><p>Label for the x axis.</p></dd>
-
-
-<dt>xlim</dt>
-<dd><p>Plot range in x direction.</p></dd>
-
-
-<dt>resplot</dt>
-<dd><p>Should the residuals plotted against time or against
-predicted values?</p></dd>
-
-
-<dt>pop_curves</dt>
-<dd><p>Per default, one population curve is drawn in case
-population parameters are fitted by the model, e.g. for saem objects.
-In case there is a covariate model, the behaviour depends on the value
-of 'covariates'</p></dd>
-
-
-<dt>pred_over</dt>
-<dd><p>Named list of alternative predictions as obtained
-from <a href="mkinpredict.html">mkinpredict</a> with a compatible <a href="mkinmod.html">mkinmod</a>.</p></dd>
-
-
-<dt>test_log_parms</dt>
-<dd><p>Passed to <a href="mean_degparms.html">mean_degparms</a> in the case of an
-<a href="mixed.html">mixed.mmkin</a> object</p></dd>
-
-
-<dt>conf.level</dt>
-<dd><p>Passed to <a href="mean_degparms.html">mean_degparms</a> in the case of an
-<a href="mixed.html">mixed.mmkin</a> object</p></dd>
-
-
-<dt>default_log_parms</dt>
-<dd><p>Passed to <a href="mean_degparms.html">mean_degparms</a> in the case of an
-<a href="mixed.html">mixed.mmkin</a> object</p></dd>
-
-
-<dt>ymax</dt>
-<dd><p>Vector of maximum y axis values</p></dd>
-
-
-<dt>maxabs</dt>
-<dd><p>Maximum absolute value of the residuals. This is used for the
-scaling of the y axis and defaults to "auto".</p></dd>
-
-
-<dt>ncol.legend</dt>
-<dd><p>Number of columns to use in the legend</p></dd>
-
-
-<dt>nrow.legend</dt>
-<dd><p>Number of rows to use in the legend</p></dd>
-
-
-<dt>rel.height.legend</dt>
-<dd><p>The relative height of the legend shown on top</p></dd>
-
-
-<dt>rel.height.bottom</dt>
-<dd><p>The relative height of the bottom plot row</p></dd>
-
-
-<dt>pch_ds</dt>
-<dd><p>Symbols to be used for plotting the data.</p></dd>
-
-
-<dt>col_ds</dt>
-<dd><p>Colors used for plotting the observed data and the
-corresponding model prediction lines for the different datasets.</p></dd>
-
-
-<dt>lty_ds</dt>
-<dd><p>Line types to be used for the model predictions.</p></dd>
-
-
-<dt>frame</dt>
-<dd><p>Should a frame be drawn around the plots?</p></dd>
-
-
-<dt>...</dt>
-<dd><p>Further arguments passed to <code><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></code>.</p></dd>
-
-</dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>The function is called for its side effect.</p>
-    </div>
-    <div id="note">
-    <h2>Note</h2>
-    <p>Covariate models are currently only supported for saem.mmkin objects.</p>
-    </div>
-    <div id="author">
-    <h2>Author</h2>
-    <p>Johannes Ranke</p>
-    </div>
-
-    <div id="ref-examples">
-    <h2>Examples</h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">ds</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="va">experimental_data_for_UBA_2019</span><span class="op">[</span><span class="fl">6</span><span class="op">:</span><span class="fl">10</span><span class="op">]</span>,</span></span>
-<span class="r-in"><span> <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="va">x</span><span class="op">$</span><span class="va">data</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"name"</span>, <span class="st">"time"</span>, <span class="st">"value"</span><span class="op">)</span><span class="op">]</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">ds</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span><span class="st">"ds "</span>, <span class="fl">6</span><span class="op">:</span><span class="fl">10</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">dfop_sfo</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="st">"A1"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  A1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="co"># \dontrun{</span></span></span>
-<span class="r-in"><span><span class="va">f</span> <span class="op">&lt;-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="st">"DFOP-SFO"</span> <span class="op">=</span> <span class="va">dfop_sfo</span><span class="op">)</span>, <span class="va">ds</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f</span><span class="op">[</span>, <span class="fl">3</span><span class="op">:</span><span class="fl">4</span><span class="op">]</span>, standardized <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-plt img"><img src="plot.mixed.mmkin-1.png" alt="" width="700" height="433"></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co"># For this fit we need to increase pnlsMaxiter, and we increase the</span></span></span>
-<span class="r-in"><span><span class="co"># tolerance in order to speed up the fit for this example evaluation</span></span></span>
-<span class="r-in"><span><span class="co"># It still takes 20 seconds to run</span></span></span>
-<span class="r-in"><span><span class="va">f_nlme</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a></span><span class="op">(</span><span class="va">f</span>, control <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>pnlsMaxIter <span class="op">=</span> <span class="fl">120</span>, tolerance <span class="op">=</span> <span class="fl">1e-3</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_nlme</span><span class="op">)</span></span></span>
-<span class="r-plt img"><img src="plot.mixed.mmkin-2.png" alt="" width="700" height="433"></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="va">f_saem</span> <span class="op">&lt;-</span> <span class="fu"><a href="saem.html">saem</a></span><span class="op">(</span><span class="va">f</span>, transformations <span class="op">=</span> <span class="st">"saemix"</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_saem</span><span class="op">)</span></span></span>
-<span class="r-plt img"><img src="plot.mixed.mmkin-3.png" alt="" width="700" height="433"></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="va">f_obs</span> <span class="op">&lt;-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="st">"DFOP-SFO"</span> <span class="op">=</span> <span class="va">dfop_sfo</span><span class="op">)</span>, <span class="va">ds</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, error_model <span class="op">=</span> <span class="st">"obs"</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">f_nlmix</span> <span class="op">&lt;-</span> <span class="fu">nlmix</span><span class="op">(</span><span class="va">f_obs</span><span class="op">)</span></span></span>
-<span class="r-err co"><span class="r-pr">#&gt;</span> <span class="error">Error in nlmix(f_obs):</span> could not find function "nlmix"</span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_nlmix</span><span class="op">)</span></span></span>
-<span class="r-err co"><span class="r-pr">#&gt;</span> <span class="error">Error in plot(f_nlmix):</span> object 'f_nlmix' not found</span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co"># We can overlay the two variants if we generate predictions</span></span></span>
-<span class="r-in"><span><span class="va">pred_nlme</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinpredict.html">mkinpredict</a></span><span class="op">(</span><span class="va">dfop_sfo</span>,</span></span>
-<span class="r-in"><span>  <span class="va">f_nlme</span><span class="op">$</span><span class="va">bparms.optim</span><span class="op">[</span><span class="op">-</span><span class="fl">1</span><span class="op">]</span>,</span></span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="va">f_nlme</span><span class="op">$</span><span class="va">bparms.optim</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span>, A1 <span class="op">=</span> <span class="fl">0</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/base/seq.html" class="external-link">seq</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">180</span>, by <span class="op">=</span> <span class="fl">0.2</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_saem</span>, pred_over <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>nlme <span class="op">=</span> <span class="va">pred_nlme</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-plt img"><img src="plot.mixed.mmkin-4.png" alt="" width="700" height="433"></span>
-<span class="r-in"><span><span class="co"># }</span></span></span>
-</code></pre></div>
-    </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/plot.mkinfit-1.png b/docs/dev/reference/plot.mkinfit-1.png
deleted file mode 100644
index a48ea72b..00000000
--- a/docs/dev/reference/plot.mkinfit-1.png
+++ /dev/null
diff --git a/docs/dev/reference/plot.mkinfit-2.png b/docs/dev/reference/plot.mkinfit-2.png
deleted file mode 100644
index 4bc1815c..00000000
--- a/docs/dev/reference/plot.mkinfit-2.png
+++ /dev/null
diff --git a/docs/dev/reference/plot.mkinfit-3.png b/docs/dev/reference/plot.mkinfit-3.png
deleted file mode 100644
index 8de1babc..00000000
--- a/docs/dev/reference/plot.mkinfit-3.png
+++ /dev/null
diff --git a/docs/dev/reference/plot.mkinfit-4.png b/docs/dev/reference/plot.mkinfit-4.png
deleted file mode 100644
index 4b7a5f27..00000000
--- a/docs/dev/reference/plot.mkinfit-4.png
+++ /dev/null
diff --git a/docs/dev/reference/plot.mkinfit-5.png b/docs/dev/reference/plot.mkinfit-5.png
deleted file mode 100644
index a8525aaa..00000000
--- a/docs/dev/reference/plot.mkinfit-5.png
+++ /dev/null
diff --git a/docs/dev/reference/plot.mkinfit-6.png b/docs/dev/reference/plot.mkinfit-6.png
deleted file mode 100644
index 878e3dd6..00000000
--- a/docs/dev/reference/plot.mkinfit-6.png
+++ /dev/null
diff --git a/docs/dev/reference/plot.mkinfit-7.png b/docs/dev/reference/plot.mkinfit-7.png
deleted file mode 100644
index c2537ea7..00000000
--- a/docs/dev/reference/plot.mkinfit-7.png
+++ /dev/null
diff --git a/docs/dev/reference/plot.mkinfit.html b/docs/dev/reference/plot.mkinfit.html
deleted file mode 100644
index 9cf12d82..00000000
--- a/docs/dev/reference/plot.mkinfit.html
+++ /dev/null
@@ -1,367 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Plot the observed data and the fitted model of an mkinfit object — plot.mkinfit • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Plot the observed data and the fitted model of an mkinfit object — plot.mkinfit"><meta property="og:description" content="Solves the differential equations with the optimised and fixed parameters
-from a previous successful call to mkinfit and plots the
-observed data together with the solution of the fitted model."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Plot the observed data and the fitted model of an mkinfit object</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/plot.mkinfit.R" class="external-link"><code>R/plot.mkinfit.R</code></a></small>
-    <div class="hidden name"><code>plot.mkinfit.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Solves the differential equations with the optimised and fixed parameters
-from a previous successful call to <code><a href="mkinfit.html">mkinfit</a></code> and plots the
-observed data together with the solution of the fitted model.</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for mkinfit</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span></span>
-<span>  <span class="va">x</span>,</span>
-<span>  fit <span class="op">=</span> <span class="va">x</span>,</span>
-<span>  obs_vars <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">fit</span><span class="op">$</span><span class="va">mkinmod</span><span class="op">$</span><span class="va">map</span><span class="op">)</span>,</span>
-<span>  xlab <span class="op">=</span> <span class="st">"Time"</span>,</span>
-<span>  ylab <span class="op">=</span> <span class="st">"Residue"</span>,</span>
-<span>  xlim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/range.html" class="external-link">range</a></span><span class="op">(</span><span class="va">fit</span><span class="op">$</span><span class="va">data</span><span class="op">$</span><span class="va">time</span><span class="op">)</span>,</span>
-<span>  ylim <span class="op">=</span> <span class="st">"default"</span>,</span>
-<span>  col_obs <span class="op">=</span> <span class="fl">1</span><span class="op">:</span><span class="fu"><a href="https://rdrr.io/r/base/length.html" class="external-link">length</a></span><span class="op">(</span><span class="va">obs_vars</span><span class="op">)</span>,</span>
-<span>  pch_obs <span class="op">=</span> <span class="va">col_obs</span>,</span>
-<span>  lty_obs <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/rep.html" class="external-link">rep</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fu"><a href="https://rdrr.io/r/base/length.html" class="external-link">length</a></span><span class="op">(</span><span class="va">obs_vars</span><span class="op">)</span><span class="op">)</span>,</span>
-<span>  add <span class="op">=</span> <span class="cn">FALSE</span>,</span>
-<span>  legend <span class="op">=</span> <span class="op">!</span><span class="va">add</span>,</span>
-<span>  show_residuals <span class="op">=</span> <span class="cn">FALSE</span>,</span>
-<span>  show_errplot <span class="op">=</span> <span class="cn">FALSE</span>,</span>
-<span>  maxabs <span class="op">=</span> <span class="st">"auto"</span>,</span>
-<span>  sep_obs <span class="op">=</span> <span class="cn">FALSE</span>,</span>
-<span>  rel.height.middle <span class="op">=</span> <span class="fl">0.9</span>,</span>
-<span>  row_layout <span class="op">=</span> <span class="cn">FALSE</span>,</span>
-<span>  lpos <span class="op">=</span> <span class="st">"topright"</span>,</span>
-<span>  inset <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.05</span>, <span class="fl">0.05</span><span class="op">)</span>,</span>
-<span>  show_errmin <span class="op">=</span> <span class="cn">FALSE</span>,</span>
-<span>  errmin_digits <span class="op">=</span> <span class="fl">3</span>,</span>
-<span>  frame <span class="op">=</span> <span class="cn">TRUE</span>,</span>
-<span>  <span class="va">...</span></span>
-<span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">plot_sep</span><span class="op">(</span></span>
-<span>  <span class="va">fit</span>,</span>
-<span>  show_errmin <span class="op">=</span> <span class="cn">TRUE</span>,</span>
-<span>  show_residuals <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html" class="external-link">ifelse</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/identical.html" class="external-link">identical</a></span><span class="op">(</span><span class="va">fit</span><span class="op">$</span><span class="va">err_mod</span>, <span class="st">"const"</span><span class="op">)</span>, <span class="cn">TRUE</span>, <span class="st">"standardized"</span><span class="op">)</span>,</span>
-<span>  <span class="va">...</span></span>
-<span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">plot_res</span><span class="op">(</span></span>
-<span>  <span class="va">fit</span>,</span>
-<span>  sep_obs <span class="op">=</span> <span class="cn">FALSE</span>,</span>
-<span>  show_errmin <span class="op">=</span> <span class="va">sep_obs</span>,</span>
-<span>  standardized <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html" class="external-link">ifelse</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/identical.html" class="external-link">identical</a></span><span class="op">(</span><span class="va">fit</span><span class="op">$</span><span class="va">err_mod</span>, <span class="st">"const"</span><span class="op">)</span>, <span class="cn">FALSE</span>, <span class="cn">TRUE</span><span class="op">)</span>,</span>
-<span>  <span class="va">...</span></span>
-<span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">plot_err</span><span class="op">(</span><span class="va">fit</span>, sep_obs <span class="op">=</span> <span class="cn">FALSE</span>, show_errmin <span class="op">=</span> <span class="va">sep_obs</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
-    </div>
-
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>x</dt>
-<dd><p>Alias for fit introduced for compatibility with the generic S3
-method.</p></dd>
-
-
-<dt>fit</dt>
-<dd><p>An object of class <code><a href="mkinfit.html">mkinfit</a></code>.</p></dd>
-
-
-<dt>obs_vars</dt>
-<dd><p>A character vector of names of the observed variables for
-which the data and the model should be plotted. Defauls to all observed
-variables in the model.</p></dd>
-
-
-<dt>xlab</dt>
-<dd><p>Label for the x axis.</p></dd>
-
-
-<dt>ylab</dt>
-<dd><p>Label for the y axis.</p></dd>
-
-
-<dt>xlim</dt>
-<dd><p>Plot range in x direction.</p></dd>
-
-
-<dt>ylim</dt>
-<dd><p>Plot range in y direction. If given as a list, plot ranges
-for the different plot rows can be given for row layout.</p></dd>
-
-
-<dt>col_obs</dt>
-<dd><p>Colors used for plotting the observed data and the
-corresponding model prediction lines.</p></dd>
-
-
-<dt>pch_obs</dt>
-<dd><p>Symbols to be used for plotting the data.</p></dd>
-
-
-<dt>lty_obs</dt>
-<dd><p>Line types to be used for the model predictions.</p></dd>
-
-
-<dt>add</dt>
-<dd><p>Should the plot be added to an existing plot?</p></dd>
-
-
-<dt>legend</dt>
-<dd><p>Should a legend be added to the plot?</p></dd>
-
-
-<dt>show_residuals</dt>
-<dd><p>Should residuals be shown? If only one plot of the
-fits is shown, the residual plot is in the lower third of the plot.
-Otherwise, i.e. if "sep_obs" is given, the residual plots will be located
-to the right of the plots of the fitted curves. If this is set to
-'standardized', a plot of the residuals divided by the standard deviation
-given by the fitted error model will be shown.</p></dd>
-
-
-<dt>show_errplot</dt>
-<dd><p>Should squared residuals and the error model be shown?
-If only one plot of the fits is shown, this plot is in the lower third of
-the plot.  Otherwise, i.e. if "sep_obs" is given, the residual plots will
-be located to the right of the plots of the fitted curves.</p></dd>
-
-
-<dt>maxabs</dt>
-<dd><p>Maximum absolute value of the residuals. This is used for the
-scaling of the y axis and defaults to "auto".</p></dd>
-
-
-<dt>sep_obs</dt>
-<dd><p>Should the observed variables be shown in separate subplots?
-If yes, residual plots requested by "show_residuals" will be shown next
-to, not below the plot of the fits.</p></dd>
-
-
-<dt>rel.height.middle</dt>
-<dd><p>The relative height of the middle plot, if more
-than two rows of plots are shown.</p></dd>
-
-
-<dt>row_layout</dt>
-<dd><p>Should we use a row layout where the residual plot or the
-error model plot is shown to the right?</p></dd>
-
-
-<dt>lpos</dt>
-<dd><p>Position(s) of the legend(s). Passed to <code><a href="https://rdrr.io/r/graphics/legend.html" class="external-link">legend</a></code> as
-the first argument.  If not length one, this should be of the same length
-as the obs_var argument.</p></dd>
-
-
-<dt>inset</dt>
-<dd><p>Passed to <code><a href="https://rdrr.io/r/graphics/legend.html" class="external-link">legend</a></code> if applicable.</p></dd>
-
-
-<dt>show_errmin</dt>
-<dd><p>Should the FOCUS chi2 error value be shown in the upper
-margin of the plot?</p></dd>
-
-
-<dt>errmin_digits</dt>
-<dd><p>The number of significant digits for rounding the FOCUS
-chi2 error percentage.</p></dd>
-
-
-<dt>frame</dt>
-<dd><p>Should a frame be drawn around the plots?</p></dd>
-
-
-<dt>...</dt>
-<dd><p>Further arguments passed to <code><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></code>.</p></dd>
-
-
-<dt>standardized</dt>
-<dd><p>When calling 'plot_res', should the residuals be
-standardized in the residual plot?</p></dd>
-
-</dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>The function is called for its side effect.</p>
-    </div>
-    <div id="details">
-    <h2>Details</h2>
-    <p>If the current plot device is a <code><a href="https://rdrr.io/pkg/tikzDevice/man/tikz.html" class="external-link">tikz</a></code> device, then
-latex is being used for the formatting of the chi2 error level, if
-<code>show_errmin = TRUE</code>.</p>
-    </div>
-    <div id="author">
-    <h2>Author</h2>
-    <p>Johannes Ranke</p>
-    </div>
-
-    <div id="ref-examples">
-    <h2>Examples</h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co"># One parent compound, one metabolite, both single first order, path from</span></span></span>
-<span class="r-in"><span><span class="co"># parent to sink included</span></span></span>
-<span class="r-in"><span><span class="co"># \dontrun{</span></span></span>
-<span class="r-in"><span><span class="va">SFO_SFO</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"m1"</span>, full <span class="op">=</span> <span class="st">"Parent"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>                   m1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, full <span class="op">=</span> <span class="st">"Metabolite M1"</span> <span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Temporary DLL for differentials generated and loaded</span>
-<span class="r-in"><span><span class="va">fit</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="va">FOCUS_2006_D</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>Observations with value of zero were removed from the data</span>
-<span class="r-in"><span><span class="va">fit</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="va">FOCUS_2006_D</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, error_model <span class="op">=</span> <span class="st">"tc"</span><span class="op">)</span></span></span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>Observations with value of zero were removed from the data</span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></span></span>
-<span class="r-plt img"><img src="plot.mkinfit-1.png" alt="" width="700" height="433"></span>
-<span class="r-in"><span><span class="fu">plot_res</span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></span></span>
-<span class="r-plt img"><img src="plot.mkinfit-2.png" alt="" width="700" height="433"></span>
-<span class="r-in"><span><span class="fu">plot_res</span><span class="op">(</span><span class="va">fit</span>, standardized <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></span>
-<span class="r-plt img"><img src="plot.mkinfit-3.png" alt="" width="700" height="433"></span>
-<span class="r-in"><span><span class="fu">plot_err</span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></span></span>
-<span class="r-plt img"><img src="plot.mkinfit-4.png" alt="" width="700" height="433"></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co"># Show the observed variables separately, with residuals</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">fit</span>, sep_obs <span class="op">=</span> <span class="cn">TRUE</span>, show_residuals <span class="op">=</span> <span class="cn">TRUE</span>, lpos <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"topright"</span>, <span class="st">"bottomright"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>     show_errmin <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-plt img"><img src="plot.mkinfit-5.png" alt="" width="700" height="433"></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co"># The same can be obtained with less typing, using the convenience function plot_sep</span></span></span>
-<span class="r-in"><span><span class="fu">plot_sep</span><span class="op">(</span><span class="va">fit</span>, lpos <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"topright"</span>, <span class="st">"bottomright"</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-plt img"><img src="plot.mkinfit-6.png" alt="" width="700" height="433"></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co"># Show the observed variables separately, with the error model</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">fit</span>, sep_obs <span class="op">=</span> <span class="cn">TRUE</span>, show_errplot <span class="op">=</span> <span class="cn">TRUE</span>, lpos <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"topright"</span>, <span class="st">"bottomright"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>     show_errmin <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-plt img"><img src="plot.mkinfit-7.png" alt="" width="700" height="433"></span>
-<span class="r-in"><span><span class="co"># }</span></span></span>
-<span class="r-in"><span></span></span>
-</code></pre></div>
-    </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/plot.mmkin-1.png b/docs/dev/reference/plot.mmkin-1.png
deleted file mode 100644
index c4fcb9ac..00000000
--- a/docs/dev/reference/plot.mmkin-1.png
+++ /dev/null
diff --git a/docs/dev/reference/plot.mmkin-2.png b/docs/dev/reference/plot.mmkin-2.png
deleted file mode 100644
index 8cc727c3..00000000
--- a/docs/dev/reference/plot.mmkin-2.png
+++ /dev/null
diff --git a/docs/dev/reference/plot.mmkin-3.png b/docs/dev/reference/plot.mmkin-3.png
deleted file mode 100644
index 066958c9..00000000
--- a/docs/dev/reference/plot.mmkin-3.png
+++ /dev/null
diff --git a/docs/dev/reference/plot.mmkin-4.png b/docs/dev/reference/plot.mmkin-4.png
deleted file mode 100644
index c91410fa..00000000
--- a/docs/dev/reference/plot.mmkin-4.png
+++ /dev/null
diff --git a/docs/dev/reference/plot.mmkin-5.png b/docs/dev/reference/plot.mmkin-5.png
deleted file mode 100644
index f0a03694..00000000
--- a/docs/dev/reference/plot.mmkin-5.png
+++ /dev/null
diff --git a/docs/dev/reference/plot.mmkin.html b/docs/dev/reference/plot.mmkin.html
deleted file mode 100644
index c8fde67c..00000000
--- a/docs/dev/reference/plot.mmkin.html
+++ /dev/null
@@ -1,271 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Plot model fits (observed and fitted) and the residuals for a row or column
-of an mmkin object — plot.mmkin • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Plot model fits (observed and fitted) and the residuals for a row or column
-of an mmkin object — plot.mmkin"><meta property="og:description" content="When x is a row selected from an mmkin object ([.mmkin), the
-same model fitted for at least one dataset is shown. When it is a column,
-the fit of at least one model to the same dataset is shown."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Plot model fits (observed and fitted) and the residuals for a row or column
-of an mmkin object</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/plot.mmkin.R" class="external-link"><code>R/plot.mmkin.R</code></a></small>
-    <div class="hidden name"><code>plot.mmkin.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>When x is a row selected from an mmkin object (<code><a href="Extract.mmkin.html">[.mmkin</a></code>), the
-same model fitted for at least one dataset is shown. When it is a column,
-the fit of at least one model to the same dataset is shown.</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for mmkin</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span></span>
-<span>  <span class="va">x</span>,</span>
-<span>  main <span class="op">=</span> <span class="st">"auto"</span>,</span>
-<span>  legends <span class="op">=</span> <span class="fl">1</span>,</span>
-<span>  resplot <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"time"</span>, <span class="st">"errmod"</span><span class="op">)</span>,</span>
-<span>  ylab <span class="op">=</span> <span class="st">"Residue"</span>,</span>
-<span>  standardized <span class="op">=</span> <span class="cn">FALSE</span>,</span>
-<span>  show_errmin <span class="op">=</span> <span class="cn">TRUE</span>,</span>
-<span>  errmin_var <span class="op">=</span> <span class="st">"All data"</span>,</span>
-<span>  errmin_digits <span class="op">=</span> <span class="fl">3</span>,</span>
-<span>  cex <span class="op">=</span> <span class="fl">0.7</span>,</span>
-<span>  rel.height.middle <span class="op">=</span> <span class="fl">0.9</span>,</span>
-<span>  ymax <span class="op">=</span> <span class="st">"auto"</span>,</span>
-<span>  <span class="va">...</span></span>
-<span><span class="op">)</span></span></code></pre></div>
-    </div>
-
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>x</dt>
-<dd><p>An object of class <code><a href="mmkin.html">mmkin</a></code>, with either one row or one
-column.</p></dd>
-
-
-<dt>main</dt>
-<dd><p>The main title placed on the outer margin of the plot.</p></dd>
-
-
-<dt>legends</dt>
-<dd><p>An index for the fits for which legends should be shown.</p></dd>
-
-
-<dt>resplot</dt>
-<dd><p>Should the residuals plotted against time, using
-<code><a href="mkinresplot.html">mkinresplot</a></code>, or as squared residuals against predicted
-values, with the error model, using <code><a href="mkinerrplot.html">mkinerrplot</a></code>.</p></dd>
-
-
-<dt>ylab</dt>
-<dd><p>Label for the y axis.</p></dd>
-
-
-<dt>standardized</dt>
-<dd><p>Should the residuals be standardized? This option
-is passed to <code><a href="mkinresplot.html">mkinresplot</a></code>, it only takes effect if
-<code>resplot = "time"</code>.</p></dd>
-
-
-<dt>show_errmin</dt>
-<dd><p>Should the chi2 error level be shown on top of the plots
-to the left?</p></dd>
-
-
-<dt>errmin_var</dt>
-<dd><p>The variable for which the FOCUS chi2 error value should
-be shown.</p></dd>
-
-
-<dt>errmin_digits</dt>
-<dd><p>The number of significant digits for rounding the FOCUS
-chi2 error percentage.</p></dd>
-
-
-<dt>cex</dt>
-<dd><p>Passed to the plot functions and <code><a href="https://rdrr.io/r/graphics/mtext.html" class="external-link">mtext</a></code>.</p></dd>
-
-
-<dt>rel.height.middle</dt>
-<dd><p>The relative height of the middle plot, if more
-than two rows of plots are shown.</p></dd>
-
-
-<dt>ymax</dt>
-<dd><p>Maximum y axis value for <code><a href="plot.mkinfit.html">plot.mkinfit</a></code>.</p></dd>
-
-
-<dt>...</dt>
-<dd><p>Further arguments passed to <code><a href="plot.mkinfit.html">plot.mkinfit</a></code> and
-<code><a href="mkinresplot.html">mkinresplot</a></code>.</p></dd>
-
-</dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>The function is called for its side effect.</p>
-    </div>
-    <div id="details">
-    <h2>Details</h2>
-    <p>If the current plot device is a <code><a href="https://rdrr.io/pkg/tikzDevice/man/tikz.html" class="external-link">tikz</a></code> device, then
-latex is being used for the formatting of the chi2 error level.</p>
-    </div>
-    <div id="author">
-    <h2>Author</h2>
-    <p>Johannes Ranke</p>
-    </div>
-
-    <div id="ref-examples">
-    <h2>Examples</h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span>
-<span class="r-in"><span>  <span class="co"># \dontrun{</span></span></span>
-<span class="r-in"><span>  <span class="co"># Only use one core not to offend CRAN checks</span></span></span>
-<span class="r-in"><span>  <span class="va">fits</span> <span class="op">&lt;-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"FOMC"</span>, <span class="st">"HS"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>                <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="st">"FOCUS B"</span> <span class="op">=</span> <span class="va">FOCUS_2006_B</span>, <span class="st">"FOCUS C"</span> <span class="op">=</span> <span class="va">FOCUS_2006_C</span><span class="op">)</span>, <span class="co"># named list for titles</span></span></span>
-<span class="r-in"><span>                cores <span class="op">=</span> <span class="fl">1</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, error_model <span class="op">=</span> <span class="st">"tc"</span><span class="op">)</span></span></span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>Optimisation did not converge:</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> iteration limit reached without convergence (10)</span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">fits</span><span class="op">[</span>, <span class="st">"FOCUS C"</span><span class="op">]</span><span class="op">)</span></span></span>
-<span class="r-plt img"><img src="plot.mmkin-1.png" alt="" width="700" height="433"></span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">fits</span><span class="op">[</span><span class="st">"FOMC"</span>, <span class="op">]</span><span class="op">)</span></span></span>
-<span class="r-plt img"><img src="plot.mmkin-2.png" alt="" width="700" height="433"></span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">fits</span><span class="op">[</span><span class="st">"FOMC"</span>, <span class="op">]</span>, show_errmin <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></span>
-<span class="r-plt img"><img src="plot.mmkin-3.png" alt="" width="700" height="433"></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span>  <span class="co"># We can also plot a single fit, if we like the way plot.mmkin works, but then the plot</span></span></span>
-<span class="r-in"><span>  <span class="co"># height should be smaller than the plot width (this is not possible for the html pages</span></span></span>
-<span class="r-in"><span>  <span class="co"># generated by pkgdown, as far as I know).</span></span></span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">fits</span><span class="op">[</span><span class="st">"FOMC"</span>, <span class="st">"FOCUS C"</span><span class="op">]</span><span class="op">)</span> <span class="co"># same as plot(fits[1, 2])</span></span></span>
-<span class="r-plt img"><img src="plot.mmkin-4.png" alt="" width="700" height="433"></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span>  <span class="co"># Show the error models</span></span></span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">fits</span><span class="op">[</span><span class="st">"FOMC"</span>, <span class="op">]</span>, resplot <span class="op">=</span> <span class="st">"errmod"</span><span class="op">)</span></span></span>
-<span class="r-plt img"><img src="plot.mmkin-5.png" alt="" width="700" height="433"></span>
-<span class="r-in"><span>  <span class="co"># }</span></span></span>
-<span class="r-in"><span></span></span>
-</code></pre></div>
-    </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/plot.nafta.html b/docs/dev/reference/plot.nafta.html
deleted file mode 100644
index 8acaa8b3..00000000
--- a/docs/dev/reference/plot.nafta.html
+++ /dev/null
@@ -1,175 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Plot the results of the three models used in the NAFTA scheme. — plot.nafta • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Plot the results of the three models used in the NAFTA scheme. — plot.nafta"><meta property="og:description" content="The plots are ordered with increasing complexity of the model in this
-function (SFO, then IORE, then DFOP)."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Plot the results of the three models used in the NAFTA scheme.</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/nafta.R" class="external-link"><code>R/nafta.R</code></a></small>
-    <div class="hidden name"><code>plot.nafta.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>The plots are ordered with increasing complexity of the model in this
-function (SFO, then IORE, then DFOP).</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for nafta</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">x</span>, legend <span class="op">=</span> <span class="cn">FALSE</span>, main <span class="op">=</span> <span class="st">"auto"</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
-    </div>
-
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>x</dt>
-<dd><p>An object of class <code><a href="nafta.html">nafta</a></code>.</p></dd>
-
-
-<dt>legend</dt>
-<dd><p>Should a legend be added?</p></dd>
-
-
-<dt>main</dt>
-<dd><p>Possibility to override the main title of the plot.</p></dd>
-
-
-<dt>...</dt>
-<dd><p>Further arguments passed to <code><a href="plot.mmkin.html">plot.mmkin</a></code>.</p></dd>
-
-</dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>The function is called for its side effect.</p>
-    </div>
-    <div id="details">
-    <h2>Details</h2>
-    <p>Calls <code><a href="plot.mmkin.html">plot.mmkin</a></code>.</p>
-    </div>
-    <div id="author">
-    <h2>Author</h2>
-    <p>Johannes Ranke</p>
-    </div>
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/print.mmkin.html b/docs/dev/reference/print.mmkin.html
deleted file mode 100644
index 12c67d86..00000000
--- a/docs/dev/reference/print.mmkin.html
+++ /dev/null
@@ -1,194 +0,0 @@
-<!-- Generated by pkgdown: do not edit by hand -->
-<!DOCTYPE html>
-<html lang="en">
-  <head>
-  <meta charset="utf-8">
-<meta http-equiv="X-UA-Compatible" content="IE=edge">
-<meta name="viewport" content="width=device-width, initial-scale=1.0">
-
-<title>Print method for mmkin objects — print.mmkin • mkin</title>
-
-
-<!-- jquery -->
-<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script>
-<!-- Bootstrap -->
-
-<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous" />
-
-<script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script>
-
-<!-- bootstrap-toc -->
-<link rel="stylesheet" href="../bootstrap-toc.css">
-<script src="../bootstrap-toc.js"></script>
-
-<!-- Font Awesome icons -->
-<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous" />
-<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous" />
-
-<!-- clipboard.js -->
-<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script>
-
-<!-- headroom.js -->
-<script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script>
-<script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script>
-
-<!-- pkgdown -->
-<link href="../pkgdown.css" rel="stylesheet">
-<script src="../pkgdown.js"></script>
-
-
-
-
-<meta property="og:title" content="Print method for mmkin objects — print.mmkin" />
-<meta property="og:description" content="Print method for mmkin objects" />
-
-
-<meta name="robots" content="noindex">
-
-<!-- mathjax -->
-<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script>
-<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script>
-
-<!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]-->
-
-
-
-  </head>
-
-  <body data-spy="scroll" data-target="#toc">
-    <div class="container template-reference-topic">
-      <header>
-      <div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">0.9.50.4</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav">
-        <li>
-  <a href="../reference/index.html">Functions and data</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu">
-    <li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a>
-    </li>
-  </ul>
-</li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul>
-      <ul class="nav navbar-nav navbar-right">
-        <li>
-  <a href="https://github.com/jranke/mkin/">
-    <span class="fab fa fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul>
-      
-    </div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header>
-
-<div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Print method for mmkin objects</h1>
-    <small class="dont-index">Source: <a href='https://github.com/jranke/mkin/blob/master/R/mmkin.R'><code>R/mmkin.R</code></a></small>
-    <div class="hidden name"><code>print.mmkin.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Print method for mmkin objects</p>
-    </div>
-
-    <pre class="usage"><span class='co'># S3 method for mmkin</span>
-<span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span><span class='op'>(</span><span class='va'>x</span>, <span class='va'>...</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>x</th>
-      <td><p>An <a href='mmkin.html'>mmkin</a> object.</p></td>
-    </tr>
-    <tr>
-      <th>...</th>
-      <td><p>Not used.</p></td>
-    </tr>
-    </table>
-
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
-
-
-      <footer>
-      <div class="copyright">
-  <p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p>
-</div>
-
-      </footer>
-   </div>
-
-  
-
-
-  </body>
-</html>
-
-
diff --git a/docs/dev/reference/read_spreadsheet.html b/docs/dev/reference/read_spreadsheet.html
deleted file mode 100644
index 6e7203f4..00000000
--- a/docs/dev/reference/read_spreadsheet.html
+++ /dev/null
@@ -1,196 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Read datasets and relevant meta information from a spreadsheet file — read_spreadsheet • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Read datasets and relevant meta information from a spreadsheet file — read_spreadsheet"><meta property="og:description" content="This function imports one dataset from each sheet of a spreadsheet file.
-These sheets are selected based on the contents of a sheet 'Datasets', with
-a column called 'Dataset Number', containing numbers identifying the dataset
-sheets to be read in. In the second column there must be a grouping
-variable, which will often be named 'Soil'. Optionally, time normalization
-factors can be given in columns named 'Temperature' and 'Moisture'."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Read datasets and relevant meta information from a spreadsheet file</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/read_spreadsheet.R" class="external-link"><code>R/read_spreadsheet.R</code></a></small>
-    <div class="hidden name"><code>read_spreadsheet.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>This function imports one dataset from each sheet of a spreadsheet file.
-These sheets are selected based on the contents of a sheet 'Datasets', with
-a column called 'Dataset Number', containing numbers identifying the dataset
-sheets to be read in. In the second column there must be a grouping
-variable, which will often be named 'Soil'. Optionally, time normalization
-factors can be given in columns named 'Temperature' and 'Moisture'.</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">read_spreadsheet</span><span class="op">(</span></span>
-<span>  <span class="va">path</span>,</span>
-<span>  valid_datasets <span class="op">=</span> <span class="st">"all"</span>,</span>
-<span>  parent_only <span class="op">=</span> <span class="cn">FALSE</span>,</span>
-<span>  normalize <span class="op">=</span> <span class="cn">TRUE</span></span>
-<span><span class="op">)</span></span></code></pre></div>
-    </div>
-
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>path</dt>
-<dd><p>Absolute or relative path to the spreadsheet file</p></dd>
-
-
-<dt>valid_datasets</dt>
-<dd><p>Optional numeric index of the valid datasets, default is
-to use all datasets</p></dd>
-
-
-<dt>parent_only</dt>
-<dd><p>Should only the parent data be used?</p></dd>
-
-
-<dt>normalize</dt>
-<dd><p>Should the time scale be normalized using temperature
-and moisture normalisation factors in the sheet 'Datasets'?</p></dd>
-
-</dl></div>
-    <div id="details">
-    <h2>Details</h2>
-    <p>There must be a sheet 'Compounds', with columns 'Name' and 'Acronym'.
-The first row read after the header read in from this sheet is assumed
-to contain name and acronym of the parent compound.</p>
-<p>The dataset sheets should be named using the dataset numbers read in from
-the 'Datasets' sheet, i.e. '1', '2', ... . In each dataset sheet, name
-of the observed variable (e.g. the acronym of the parent compound or
-one of its transformation products) should be in the first column,
-the time values should be in the second colum, and the observed value
-in the third column.</p>
-<p>In case relevant covariate data are available, they should be given
-in a sheet 'Covariates', containing one line for each value of the grouping
-variable specified in 'Datasets'. These values should be in the first
-column and the column must have the same name as the second column in
-'Datasets'. Covariates will be read in from columns four and higher.
-Their names should preferably not contain special characters like spaces,
-so they can be easily used for specifying covariate models.</p>
-<p>An similar data structure is defined as the R6 class <a href="mkindsg.html">mkindsg</a>, but
-is probably more complicated to use.</p>
-    </div>
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/reexports.html b/docs/dev/reference/reexports.html
deleted file mode 100644
index ee1148da..00000000
--- a/docs/dev/reference/reexports.html
+++ /dev/null
@@ -1,157 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Objects exported from other packages — reexports • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Objects exported from other packages — reexports"><meta property="og:description" content="These objects are imported from other packages. Follow the links
-below to see their documentation.
-
-  lmtest
-lrtest
-
-
-  nlme
-intervals, nlme
-
-
-"><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Objects exported from other packages</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/intervals.R" class="external-link"><code>R/intervals.R</code></a>, <a href="https://github.com/jranke/mkin/blob/HEAD/R/lrtest.mkinfit.R" class="external-link"><code>R/lrtest.mkinfit.R</code></a>, <a href="https://github.com/jranke/mkin/blob/HEAD/R/nlme.mmkin.R" class="external-link"><code>R/nlme.mmkin.R</code></a></small>
-    <div class="hidden name"><code>reexports.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>These objects are imported from other packages. Follow the links
-below to see their documentation.</p>
-<dl><dt>lmtest</dt>
-<dd><p><code><a href="https://rdrr.io/pkg/lmtest/man/lrtest.html" class="external-link">lrtest</a></code></p></dd>
-
-
-  <dt>nlme</dt>
-<dd><p><code><a href="https://rdrr.io/pkg/nlme/man/intervals.html" class="external-link">intervals</a></code>, <code><a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a></code></p></dd>
-
-
-</dl></div>
-
-
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/residuals.mkinfit.html b/docs/dev/reference/residuals.mkinfit.html
deleted file mode 100644
index 578b5066..00000000
--- a/docs/dev/reference/residuals.mkinfit.html
+++ /dev/null
@@ -1,167 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Extract residuals from an mkinfit model — residuals.mkinfit • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Extract residuals from an mkinfit model — residuals.mkinfit"><meta property="og:description" content="Extract residuals from an mkinfit model"><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Extract residuals from an mkinfit model</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/residuals.mkinfit.R" class="external-link"><code>R/residuals.mkinfit.R</code></a></small>
-    <div class="hidden name"><code>residuals.mkinfit.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Extract residuals from an mkinfit model</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for mkinfit</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/stats/residuals.html" class="external-link">residuals</a></span><span class="op">(</span><span class="va">object</span>, standardized <span class="op">=</span> <span class="cn">FALSE</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
-    </div>
-
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>object</dt>
-<dd><p>A <code><a href="mkinfit.html">mkinfit</a></code> object</p></dd>
-
-
-<dt>standardized</dt>
-<dd><p>Should the residuals be standardized by dividing by the
-standard deviation obtained from the fitted error model?</p></dd>
-
-
-<dt>...</dt>
-<dd><p>Not used</p></dd>
-
-</dl></div>
-
-    <div id="ref-examples">
-    <h2>Examples</h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">f</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="va">FOCUS_2006_C</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/residuals.html" class="external-link">residuals</a></span><span class="op">(</span><span class="va">f</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1]  0.09726374 -0.13912142 -0.15351210  0.73388322 -0.08657004 -0.93204702</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [7] -0.03269080  1.45347823 -0.88423697</span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/residuals.html" class="external-link">residuals</a></span><span class="op">(</span><span class="va">f</span>, standardized <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1]  0.13969917 -0.19981904 -0.22048826  1.05407091 -0.12433989 -1.33869208</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [7] -0.04695355  2.08761977 -1.27002287</span>
-</code></pre></div>
-    </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/saem-1.png b/docs/dev/reference/saem-1.png
deleted file mode 100644
index 1fa206c4..00000000
--- a/docs/dev/reference/saem-1.png
+++ /dev/null
diff --git a/docs/dev/reference/saem-2.png b/docs/dev/reference/saem-2.png
deleted file mode 100644
index e5c62c35..00000000
--- a/docs/dev/reference/saem-2.png
+++ /dev/null
diff --git a/docs/dev/reference/saem-3.png b/docs/dev/reference/saem-3.png
deleted file mode 100644
index f191ad3a..00000000
--- a/docs/dev/reference/saem-3.png
+++ /dev/null
diff --git a/docs/dev/reference/saem-4.png b/docs/dev/reference/saem-4.png
deleted file mode 100644
index a74e21f8..00000000
--- a/docs/dev/reference/saem-4.png
+++ /dev/null
diff --git a/docs/dev/reference/saem-5.png b/docs/dev/reference/saem-5.png
deleted file mode 100644
index d22e7285..00000000
--- a/docs/dev/reference/saem-5.png
+++ /dev/null
diff --git a/docs/dev/reference/saem-6.png b/docs/dev/reference/saem-6.png
deleted file mode 100644
index 314f9cd9..00000000
--- a/docs/dev/reference/saem-6.png
+++ /dev/null
diff --git a/docs/dev/reference/saem.html b/docs/dev/reference/saem.html
deleted file mode 100644
index 9b9a911d..00000000
--- a/docs/dev/reference/saem.html
+++ /dev/null
@@ -1,779 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Fit nonlinear mixed models with SAEM — saem • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Fit nonlinear mixed models with SAEM — saem"><meta property="og:description" content="This function uses saemix::saemix() as a backend for fitting nonlinear mixed
-effects models created from mmkin row objects using the Stochastic Approximation
-Expectation Maximisation algorithm (SAEM)."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Fit nonlinear mixed models with SAEM</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/saem.R" class="external-link"><code>R/saem.R</code></a></small>
-    <div class="hidden name"><code>saem.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>This function uses <code><a href="https://rdrr.io/pkg/saemix/man/saemix.html" class="external-link">saemix::saemix()</a></code> as a backend for fitting nonlinear mixed
-effects models created from <a href="mmkin.html">mmkin</a> row objects using the Stochastic Approximation
-Expectation Maximisation algorithm (SAEM).</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">saem</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># S3 method for mmkin</span></span>
-<span><span class="fu">saem</span><span class="op">(</span></span>
-<span>  <span class="va">object</span>,</span>
-<span>  transformations <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"mkin"</span>, <span class="st">"saemix"</span><span class="op">)</span>,</span>
-<span>  error_model <span class="op">=</span> <span class="st">"auto"</span>,</span>
-<span>  degparms_start <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/numeric.html" class="external-link">numeric</a></span><span class="op">(</span><span class="op">)</span>,</span>
-<span>  test_log_parms <span class="op">=</span> <span class="cn">TRUE</span>,</span>
-<span>  conf.level <span class="op">=</span> <span class="fl">0.6</span>,</span>
-<span>  solution_type <span class="op">=</span> <span class="st">"auto"</span>,</span>
-<span>  covariance.model <span class="op">=</span> <span class="st">"auto"</span>,</span>
-<span>  omega.init <span class="op">=</span> <span class="st">"auto"</span>,</span>
-<span>  covariates <span class="op">=</span> <span class="cn">NULL</span>,</span>
-<span>  covariate_models <span class="op">=</span> <span class="cn">NULL</span>,</span>
-<span>  no_random_effect <span class="op">=</span> <span class="cn">NULL</span>,</span>
-<span>  error.init <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">1</span><span class="op">)</span>,</span>
-<span>  nbiter.saemix <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">300</span>, <span class="fl">100</span><span class="op">)</span>,</span>
-<span>  control <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>displayProgress <span class="op">=</span> <span class="cn">FALSE</span>, print <span class="op">=</span> <span class="cn">FALSE</span>, nbiter.saemix <span class="op">=</span> <span class="va">nbiter.saemix</span>,</span>
-<span>    save <span class="op">=</span> <span class="cn">FALSE</span>, save.graphs <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span>
-<span>  verbose <span class="op">=</span> <span class="cn">FALSE</span>,</span>
-<span>  quiet <span class="op">=</span> <span class="cn">FALSE</span>,</span>
-<span>  <span class="va">...</span></span>
-<span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># S3 method for saem.mmkin</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, digits <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html" class="external-link">max</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fu"><a href="https://rdrr.io/r/base/options.html" class="external-link">getOption</a></span><span class="op">(</span><span class="st">"digits"</span><span class="op">)</span> <span class="op">-</span> <span class="fl">3</span><span class="op">)</span>, <span class="va">...</span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">saemix_model</span><span class="op">(</span></span>
-<span>  <span class="va">object</span>,</span>
-<span>  solution_type <span class="op">=</span> <span class="st">"auto"</span>,</span>
-<span>  transformations <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"mkin"</span>, <span class="st">"saemix"</span><span class="op">)</span>,</span>
-<span>  error_model <span class="op">=</span> <span class="st">"auto"</span>,</span>
-<span>  degparms_start <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/numeric.html" class="external-link">numeric</a></span><span class="op">(</span><span class="op">)</span>,</span>
-<span>  covariance.model <span class="op">=</span> <span class="st">"auto"</span>,</span>
-<span>  no_random_effect <span class="op">=</span> <span class="cn">NULL</span>,</span>
-<span>  omega.init <span class="op">=</span> <span class="st">"auto"</span>,</span>
-<span>  covariates <span class="op">=</span> <span class="cn">NULL</span>,</span>
-<span>  covariate_models <span class="op">=</span> <span class="cn">NULL</span>,</span>
-<span>  error.init <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/numeric.html" class="external-link">numeric</a></span><span class="op">(</span><span class="op">)</span>,</span>
-<span>  test_log_parms <span class="op">=</span> <span class="cn">FALSE</span>,</span>
-<span>  conf.level <span class="op">=</span> <span class="fl">0.6</span>,</span>
-<span>  verbose <span class="op">=</span> <span class="cn">FALSE</span>,</span>
-<span>  <span class="va">...</span></span>
-<span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">saemix_data</span><span class="op">(</span><span class="va">object</span>, covariates <span class="op">=</span> <span class="cn">NULL</span>, verbose <span class="op">=</span> <span class="cn">FALSE</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
-    </div>
-
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>object</dt>
-<dd><p>An <a href="mmkin.html">mmkin</a> row object containing several fits of the same
-<a href="mkinmod.html">mkinmod</a> model to different datasets</p></dd>
-
-
-<dt>...</dt>
-<dd><p>Further parameters passed to <a href="https://rdrr.io/pkg/saemix/man/saemixModel.html" class="external-link">saemix::saemixModel</a>.</p></dd>
-
-
-<dt>transformations</dt>
-<dd><p>Per default, all parameter transformations are done
-in mkin. If this argument is set to 'saemix', parameter transformations
-are done in 'saemix' for the supported cases, i.e. (as of version 1.1.2)
-SFO, FOMC, DFOP and HS without fixing <code>parent_0</code>, and SFO or DFOP with
-one SFO metabolite.</p></dd>
-
-
-<dt>error_model</dt>
-<dd><p>Possibility to override the error model used in the mmkin object</p></dd>
-
-
-<dt>degparms_start</dt>
-<dd><p>Parameter values given as a named numeric vector will
-be used to override the starting values obtained from the 'mmkin' object.</p></dd>
-
-
-<dt>test_log_parms</dt>
-<dd><p>If TRUE, an attempt is made to use more robust starting
-values for population parameters fitted as log parameters in mkin (like
-rate constants) by only considering rate constants that pass the t-test
-when calculating mean degradation parameters using <a href="mean_degparms.html">mean_degparms</a>.</p></dd>
-
-
-<dt>conf.level</dt>
-<dd><p>Possibility to adjust the required confidence level
-for parameter that are tested if requested by 'test_log_parms'.</p></dd>
-
-
-<dt>solution_type</dt>
-<dd><p>Possibility to specify the solution type in case the
-automatic choice is not desired</p></dd>
-
-
-<dt>covariance.model</dt>
-<dd><p>Will be passed to <code><a href="https://rdrr.io/pkg/saemix/man/saemixModel.html" class="external-link">saemix::saemixModel()</a></code>. Per
-default, uncorrelated random effects are specified for all degradation
-parameters.</p></dd>
-
-
-<dt>omega.init</dt>
-<dd><p>Will be passed to <code><a href="https://rdrr.io/pkg/saemix/man/saemixModel.html" class="external-link">saemix::saemixModel()</a></code>. If using
-mkin transformations and the default covariance model with optionally
-excluded random effects, the variances of the degradation parameters
-are estimated using <a href="mean_degparms.html">mean_degparms</a>, with testing of untransformed
-log parameters for significant difference from zero. If not using
-mkin transformations or a custom covariance model, the default
-initialisation of <a href="https://rdrr.io/pkg/saemix/man/saemixModel.html" class="external-link">saemix::saemixModel</a> is used for omega.init.</p></dd>
-
-
-<dt>covariates</dt>
-<dd><p>A data frame with covariate data for use in
-'covariate_models', with dataset names as row names.</p></dd>
-
-
-<dt>covariate_models</dt>
-<dd><p>A list containing linear model formulas with one explanatory
-variable, i.e. of the type 'parameter ~ covariate'. Covariates must be available
-in the 'covariates' data frame.</p></dd>
-
-
-<dt>no_random_effect</dt>
-<dd><p>Character vector of degradation parameters for
-which there should be no variability over the groups. Only used
-if the covariance model is not explicitly specified.</p></dd>
-
-
-<dt>error.init</dt>
-<dd><p>Will be passed to <code><a href="https://rdrr.io/pkg/saemix/man/saemixModel.html" class="external-link">saemix::saemixModel()</a></code>.</p></dd>
-
-
-<dt>nbiter.saemix</dt>
-<dd><p>Convenience option to increase the number of
-iterations</p></dd>
-
-
-<dt>control</dt>
-<dd><p>Passed to <a href="https://rdrr.io/pkg/saemix/man/saemix.html" class="external-link">saemix::saemix</a>.</p></dd>
-
-
-<dt>verbose</dt>
-<dd><p>Should we print information about created objects of
-type <a href="https://rdrr.io/pkg/saemix/man/SaemixModel-class.html" class="external-link">saemix::SaemixModel</a> and <a href="https://rdrr.io/pkg/saemix/man/SaemixData-class.html" class="external-link">saemix::SaemixData</a>?</p></dd>
-
-
-<dt>quiet</dt>
-<dd><p>Should we suppress the messages saemix prints at the beginning
-and the end of the optimisation process?</p></dd>
-
-
-<dt>x</dt>
-<dd><p>An saem.mmkin object to print</p></dd>
-
-
-<dt>digits</dt>
-<dd><p>Number of digits to use for printing</p></dd>
-
-</dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>An S3 object of class 'saem.mmkin', containing the fitted
-<a href="https://rdrr.io/pkg/saemix/man/SaemixObject-class.html" class="external-link">saemix::SaemixObject</a> as a list component named 'so'. The
-object also inherits from 'mixed.mmkin'.</p>
-
-
-<p>An <a href="https://rdrr.io/pkg/saemix/man/SaemixModel-class.html" class="external-link">saemix::SaemixModel</a> object.</p>
-
-
-<p>An <a href="https://rdrr.io/pkg/saemix/man/SaemixData-class.html" class="external-link">saemix::SaemixData</a> object.</p>
-    </div>
-    <div id="details">
-    <h2>Details</h2>
-    <p>An mmkin row object is essentially a list of mkinfit objects that have been
-obtained by fitting the same model to a list of datasets using <a href="mkinfit.html">mkinfit</a>.</p>
-<p>Starting values for the fixed effects (population mean parameters, argument
-psi0 of <code><a href="https://rdrr.io/pkg/saemix/man/saemixModel.html" class="external-link">saemix::saemixModel()</a></code> are the mean values of the parameters found
-using <a href="mmkin.html">mmkin</a>.</p>
-    </div>
-    <div id="see-also">
-    <h2>See also</h2>
-    <div class="dont-index"><p><a href="summary.saem.mmkin.html">summary.saem.mmkin</a> <a href="plot.mixed.mmkin.html">plot.mixed.mmkin</a></p></div>
-    </div>
-
-    <div id="ref-examples">
-    <h2>Examples</h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># \dontrun{</span></span></span>
-<span class="r-in"><span><span class="va">ds</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="va">experimental_data_for_UBA_2019</span><span class="op">[</span><span class="fl">6</span><span class="op">:</span><span class="fl">10</span><span class="op">]</span>,</span></span>
-<span class="r-in"><span> <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fu"><a href="https://rdrr.io/r/base/subset.html" class="external-link">subset</a></span><span class="op">(</span><span class="va">x</span><span class="op">$</span><span class="va">data</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"name"</span>, <span class="st">"time"</span>, <span class="st">"value"</span><span class="op">)</span><span class="op">]</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">ds</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste</a></span><span class="op">(</span><span class="st">"Dataset"</span>, <span class="fl">6</span><span class="op">:</span><span class="fl">10</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">f_mmkin_parent_p0_fixed</span> <span class="op">&lt;-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="st">"FOMC"</span>, <span class="va">ds</span>,</span></span>
-<span class="r-in"><span>  state.ini <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fl">100</span><span class="op">)</span>, fixed_initials <span class="op">=</span> <span class="st">"parent"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">f_saem_p0_fixed</span> <span class="op">&lt;-</span> <span class="fu">saem</span><span class="op">(</span><span class="va">f_mmkin_parent_p0_fixed</span><span class="op">)</span></span></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="va">f_mmkin_parent</span> <span class="op">&lt;-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"FOMC"</span>, <span class="st">"DFOP"</span><span class="op">)</span>, <span class="va">ds</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">f_saem_sfo</span> <span class="op">&lt;-</span> <span class="fu">saem</span><span class="op">(</span><span class="va">f_mmkin_parent</span><span class="op">[</span><span class="st">"SFO"</span>, <span class="op">]</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">f_saem_fomc</span> <span class="op">&lt;-</span> <span class="fu">saem</span><span class="op">(</span><span class="va">f_mmkin_parent</span><span class="op">[</span><span class="st">"FOMC"</span>, <span class="op">]</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">f_saem_dfop</span> <span class="op">&lt;-</span> <span class="fu">saem</span><span class="op">(</span><span class="va">f_mmkin_parent</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_saem_sfo</span>, <span class="va">f_saem_fomc</span>, <span class="va">f_saem_dfop</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Data: 90 observations of 1 variable(s) grouped in 5 datasets</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>             npar    AIC    BIC     Lik</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_saem_sfo     5 624.33 622.38 -307.17</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_saem_fomc    7 467.85 465.11 -226.92</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_saem_dfop    9 493.76 490.24 -237.88</span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_saem_sfo</span>, <span class="va">f_saem_dfop</span>, test <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Data: 90 observations of 1 variable(s) grouped in 5 datasets</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>             npar    AIC    BIC     Lik  Chisq Df Pr(&gt;Chisq)    </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_saem_sfo     5 624.33 622.38 -307.17                         </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_saem_dfop    9 493.76 490.24 -237.88 138.57  4  &lt; 2.2e-16 ***</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> ---</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1</span>
-<span class="r-in"><span><span class="fu"><a href="illparms.html">illparms</a></span><span class="op">(</span><span class="va">f_saem_dfop</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "sd(g_qlogis)"</span>
-<span class="r-in"><span><span class="va">f_saem_dfop_red</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_saem_dfop</span>, no_random_effect <span class="op">=</span> <span class="st">"g_qlogis"</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_saem_dfop</span>, <span class="va">f_saem_dfop_red</span>, test <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Data: 90 observations of 1 variable(s) grouped in 5 datasets</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                 npar    AIC    BIC     Lik Chisq Df Pr(&gt;Chisq)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_saem_dfop_red    8 488.68 485.55 -236.34                    </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_saem_dfop        9 493.76 490.24 -237.88     0  1          1</span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_saem_sfo</span>, <span class="va">f_saem_fomc</span>, <span class="va">f_saem_dfop</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Data: 90 observations of 1 variable(s) grouped in 5 datasets</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>             npar    AIC    BIC     Lik</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_saem_sfo     5 624.33 622.38 -307.17</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_saem_fomc    7 467.85 465.11 -226.92</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_saem_dfop    9 493.76 490.24 -237.88</span>
-<span class="r-in"><span><span class="co"># The returned saem.mmkin object contains an SaemixObject, therefore we can use</span></span></span>
-<span class="r-in"><span><span class="co"># functions from saemix</span></span></span>
-<span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va">saemix</span><span class="op">)</span></span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Loading required package: npde</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Package saemix, version 3.2</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>   please direct bugs, questions and feedback to emmanuelle.comets@inserm.fr</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Attaching package: ‘saemix’</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> The following objects are masked from ‘package:npde’:</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>     kurtosis, skewness</span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/compare.saemix.html" class="external-link">compare.saemix</a></span><span class="op">(</span><span class="va">f_saem_sfo</span><span class="op">$</span><span class="va">so</span>, <span class="va">f_saem_fomc</span><span class="op">$</span><span class="va">so</span>, <span class="va">f_saem_dfop</span><span class="op">$</span><span class="va">so</span><span class="op">)</span></span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Likelihoods calculated by importance sampling</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>        AIC      BIC</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1 624.3316 622.3788</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2 467.8472 465.1132</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 3 493.7592 490.2441</span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_saem_fomc</span><span class="op">$</span><span class="va">so</span>, plot.type <span class="op">=</span> <span class="st">"convergence"</span><span class="op">)</span></span></span>
-<span class="r-plt img"><img src="saem-1.png" alt="" width="700" height="433"></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_saem_fomc</span><span class="op">$</span><span class="va">so</span>, plot.type <span class="op">=</span> <span class="st">"individual.fit"</span><span class="op">)</span></span></span>
-<span class="r-plt img"><img src="saem-2.png" alt="" width="700" height="433"></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_saem_fomc</span><span class="op">$</span><span class="va">so</span>, plot.type <span class="op">=</span> <span class="st">"npde"</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Simulating data using nsim = 1000 simulated datasets</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Computing WRES and npde .</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Please use npdeSaemix to obtain VPC and npde</span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_saem_fomc</span><span class="op">$</span><span class="va">so</span>, plot.type <span class="op">=</span> <span class="st">"vpc"</span><span class="op">)</span></span></span>
-<span class="r-plt img"><img src="saem-3.png" alt="" width="700" height="433"></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="va">f_mmkin_parent_tc</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_mmkin_parent</span>, error_model <span class="op">=</span> <span class="st">"tc"</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">f_saem_fomc_tc</span> <span class="op">&lt;-</span> <span class="fu">saem</span><span class="op">(</span><span class="va">f_mmkin_parent_tc</span><span class="op">[</span><span class="st">"FOMC"</span>, <span class="op">]</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_saem_fomc</span>, <span class="va">f_saem_fomc_tc</span>, test <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Data: 90 observations of 1 variable(s) grouped in 5 datasets</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                npar    AIC    BIC     Lik Chisq Df Pr(&gt;Chisq)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_saem_fomc       7 467.85 465.11 -226.92                    </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_saem_fomc_tc    8 469.83 466.71 -226.92 0.015  1     0.9027</span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="va">sfo_sfo</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"A1"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  A1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Temporary DLL for differentials generated and loaded</span>
-<span class="r-in"><span><span class="va">fomc_sfo</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"FOMC"</span>, <span class="st">"A1"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  A1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Temporary DLL for differentials generated and loaded</span>
-<span class="r-in"><span><span class="va">dfop_sfo</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="st">"A1"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  A1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Temporary DLL for differentials generated and loaded</span>
-<span class="r-in"><span><span class="co"># The following fit uses analytical solutions for SFO-SFO and DFOP-SFO,</span></span></span>
-<span class="r-in"><span><span class="co"># and compiled ODEs for FOMC that are much slower</span></span></span>
-<span class="r-in"><span><span class="va">f_mmkin</span> <span class="op">&lt;-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span></span></span>
-<span class="r-in"><span>    <span class="st">"SFO-SFO"</span> <span class="op">=</span> <span class="va">sfo_sfo</span>, <span class="st">"FOMC-SFO"</span> <span class="op">=</span> <span class="va">fomc_sfo</span>, <span class="st">"DFOP-SFO"</span> <span class="op">=</span> <span class="va">dfop_sfo</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  <span class="va">ds</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="co"># saem fits of SFO-SFO and DFOP-SFO to these data take about five seconds</span></span></span>
-<span class="r-in"><span><span class="co"># each on this system, as we use analytical solutions written for saemix.</span></span></span>
-<span class="r-in"><span><span class="co"># When using the analytical solutions written for mkin this took around</span></span></span>
-<span class="r-in"><span><span class="co"># four minutes</span></span></span>
-<span class="r-in"><span><span class="va">f_saem_sfo_sfo</span> <span class="op">&lt;-</span> <span class="fu">saem</span><span class="op">(</span><span class="va">f_mmkin</span><span class="op">[</span><span class="st">"SFO-SFO"</span>, <span class="op">]</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">f_saem_dfop_sfo</span> <span class="op">&lt;-</span> <span class="fu">saem</span><span class="op">(</span><span class="va">f_mmkin</span><span class="op">[</span><span class="st">"DFOP-SFO"</span>, <span class="op">]</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="co"># We can use print, plot and summary methods to check the results</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">f_saem_dfop_sfo</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Kinetic nonlinear mixed-effects model fit by SAEM</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Structural model:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>            * parent</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> d_A1/dt = + f_parent_to_A1 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>            * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>            exp(-k2 * time))) * parent - k_A1 * A1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Data:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 170 observations of 2 variable(s) grouped in 5 datasets</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Likelihood computed by importance sampling</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     AIC   BIC logLik</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   839.2 834.1 -406.6</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Fitted parameters:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                    estimate    lower   upper</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0           93.70402 91.04104 96.3670</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k_A1           -5.83760 -7.66452 -4.0107</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_parent_qlogis    -0.95718 -1.35955 -0.5548</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k1             -2.35514 -3.39402 -1.3163</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k2             -3.79634 -5.64009 -1.9526</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> g_qlogis           -0.02108 -0.66463  0.6225</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> a.1                 1.88191  1.66491  2.0989</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> SD.parent_0         2.81628  0.78922  4.8433</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> SD.log_k_A1         1.78751  0.42105  3.1540</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> SD.f_parent_qlogis  0.45016  0.16116  0.7391</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> SD.log_k1           1.06923  0.31676  1.8217</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> SD.log_k2           2.03768  0.70938  3.3660</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> SD.g_qlogis         0.44024 -0.09262  0.9731</span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_saem_dfop_sfo</span><span class="op">)</span></span></span>
-<span class="r-plt img"><img src="saem-4.png" alt="" width="700" height="433"></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">f_saem_dfop_sfo</span>, data <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> saemix version used for fitting:      3.2 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> mkin version used for pre-fitting:  1.2.3 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> R version used for fitting:         4.2.3 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Date of fit:     Sun Apr 16 08:32:32 2023 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Date of summary: Sun Apr 16 08:32:32 2023 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Equations:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>            * parent</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> d_A1/dt = + f_parent_to_A1 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>            * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>            exp(-k2 * time))) * parent - k_A1 * A1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Data:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 170 observations of 2 variable(s) grouped in 5 datasets</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Model predictions using solution type analytical </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Fitted in 4.145 s</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Using 300, 100 iterations and 10 chains</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Variance model: Constant variance </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Starting values for degradation parameters:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>        parent_0        log_k_A1 f_parent_qlogis          log_k1          log_k2 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         93.8102         -5.3734         -0.9711         -1.8799         -4.2708 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>        g_qlogis </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>          0.1356 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Fixed degradation parameter values:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> None</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Starting values for random effects (square root of initial entries in omega):</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                 parent_0 log_k_A1 f_parent_qlogis log_k1 log_k2 g_qlogis</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0           4.941    0.000          0.0000  0.000  0.000   0.0000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k_A1           0.000    2.551          0.0000  0.000  0.000   0.0000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_parent_qlogis    0.000    0.000          0.7251  0.000  0.000   0.0000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k1             0.000    0.000          0.0000  1.449  0.000   0.0000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k2             0.000    0.000          0.0000  0.000  2.228   0.0000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> g_qlogis           0.000    0.000          0.0000  0.000  0.000   0.7814</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Starting values for error model parameters:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> a.1 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   1 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Results:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Likelihood computed by importance sampling</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     AIC   BIC logLik</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   839.2 834.1 -406.6</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Optimised parameters:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                        est.    lower   upper</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0           93.70402 91.04104 96.3670</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k_A1           -5.83760 -7.66452 -4.0107</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_parent_qlogis    -0.95718 -1.35955 -0.5548</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k1             -2.35514 -3.39402 -1.3163</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k2             -3.79634 -5.64009 -1.9526</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> g_qlogis           -0.02108 -0.66463  0.6225</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> a.1                 1.88191  1.66491  2.0989</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> SD.parent_0         2.81628  0.78922  4.8433</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> SD.log_k_A1         1.78751  0.42105  3.1540</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> SD.f_parent_qlogis  0.45016  0.16116  0.7391</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> SD.log_k1           1.06923  0.31676  1.8217</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> SD.log_k2           2.03768  0.70938  3.3660</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> SD.g_qlogis         0.44024 -0.09262  0.9731</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Correlation: </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                 parnt_0 lg_k_A1 f_prnt_ log_k1  log_k2 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k_A1        -0.0147                                </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_parent_qlogis -0.0269  0.0573                        </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k1           0.0263 -0.0011 -0.0040                </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k2           0.0020  0.0065 -0.0002 -0.0776        </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> g_qlogis        -0.0248 -0.0180 -0.0004 -0.0903 -0.0603</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Random effects:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                      est.    lower  upper</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> SD.parent_0        2.8163  0.78922 4.8433</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> SD.log_k_A1        1.7875  0.42105 3.1540</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> SD.f_parent_qlogis 0.4502  0.16116 0.7391</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> SD.log_k1          1.0692  0.31676 1.8217</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> SD.log_k2          2.0377  0.70938 3.3660</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> SD.g_qlogis        0.4402 -0.09262 0.9731</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Variance model:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>      est. lower upper</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> a.1 1.882 1.665 2.099</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Backtransformed parameters:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                     est.     lower    upper</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0       93.704015 9.104e+01 96.36699</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_A1            0.002916 4.692e-04  0.01812</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_parent_to_A1  0.277443 2.043e-01  0.36475</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k1              0.094880 3.357e-02  0.26813</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k2              0.022453 3.553e-03  0.14191</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> g               0.494731 3.397e-01  0.65078</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Resulting formation fractions:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                 ff</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_A1   0.2774</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_sink 0.7226</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Estimated disappearance times:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         DT50   DT90 DT50back DT50_k1 DT50_k2</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent  14.0  72.38    21.79   7.306   30.87</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> A1     237.7 789.68       NA      NA      NA</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Data:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>          ds   name time observed predicted residual   std standardized</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 6 parent    0     97.2  95.70025  1.49975 1.882      0.79693</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 6 parent    0     96.4  95.70025  0.69975 1.882      0.37183</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 6 parent    3     71.1  71.44670 -0.34670 1.882     -0.18423</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 6 parent    3     69.2  71.44670 -2.24670 1.882     -1.19384</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 6 parent    6     58.1  56.59283  1.50717 1.882      0.80087</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 6 parent    6     56.6  56.59283  0.00717 1.882      0.00381</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 6 parent   10     44.4  44.56648 -0.16648 1.882     -0.08847</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 6 parent   10     43.4  44.56648 -1.16648 1.882     -0.61984</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 6 parent   20     33.3  29.76020  3.53980 1.882      1.88096</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 6 parent   20     29.2  29.76020 -0.56020 1.882     -0.29767</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 6 parent   34     17.6  19.39208 -1.79208 1.882     -0.95226</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 6 parent   34     18.0  19.39208 -1.39208 1.882     -0.73971</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 6 parent   55     10.5  10.55761 -0.05761 1.882     -0.03061</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 6 parent   55      9.3  10.55761 -1.25761 1.882     -0.66826</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 6 parent   90      4.5   3.84742  0.65258 1.882      0.34676</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 6 parent   90      4.7   3.84742  0.85258 1.882      0.45304</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 6 parent  112      3.0   2.03997  0.96003 1.882      0.51013</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 6 parent  112      3.4   2.03997  1.36003 1.882      0.72268</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 6 parent  132      2.3   1.14585  1.15415 1.882      0.61328</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 6 parent  132      2.7   1.14585  1.55415 1.882      0.82583</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 6     A1    3      4.3   4.86054 -0.56054 1.882     -0.29786</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 6     A1    3      4.6   4.86054 -0.26054 1.882     -0.13844</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 6     A1    6      7.0   7.74179 -0.74179 1.882     -0.39417</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 6     A1    6      7.2   7.74179 -0.54179 1.882     -0.28789</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 6     A1   10      8.2   9.94048 -1.74048 1.882     -0.92485</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 6     A1   10      8.0   9.94048 -1.94048 1.882     -1.03112</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 6     A1   20     11.0  12.19109 -1.19109 1.882     -0.63291</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 6     A1   20     13.7  12.19109  1.50891 1.882      0.80180</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 6     A1   34     11.5  13.10706 -1.60706 1.882     -0.85395</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 6     A1   34     12.7  13.10706 -0.40706 1.882     -0.21630</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 6     A1   55     14.9  13.06131  1.83869 1.882      0.97703</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 6     A1   55     14.5  13.06131  1.43869 1.882      0.76448</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 6     A1   90     12.1  11.54495  0.55505 1.882      0.29494</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 6     A1   90     12.3  11.54495  0.75505 1.882      0.40122</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 6     A1  112      9.9  10.31533 -0.41533 1.882     -0.22070</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 6     A1  112     10.2  10.31533 -0.11533 1.882     -0.06128</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 6     A1  132      8.8   9.20222 -0.40222 1.882     -0.21373</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 6     A1  132      7.8   9.20222 -1.40222 1.882     -0.74510</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 7 parent    0     93.6  90.82357  2.77643 1.882      1.47532</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 7 parent    0     92.3  90.82357  1.47643 1.882      0.78453</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 7 parent    3     87.0  84.73448  2.26552 1.882      1.20384</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 7 parent    3     82.2  84.73448 -2.53448 1.882     -1.34675</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 7 parent    7     74.0  77.65013 -3.65013 1.882     -1.93958</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 7 parent    7     73.9  77.65013 -3.75013 1.882     -1.99272</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 7 parent   14     64.2  67.60639 -3.40639 1.882     -1.81007</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 7 parent   14     69.5  67.60639  1.89361 1.882      1.00621</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 7 parent   30     54.0  52.53663  1.46337 1.882      0.77760</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 7 parent   30     54.6  52.53663  2.06337 1.882      1.09642</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 7 parent   60     41.1  39.42728  1.67272 1.882      0.88884</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 7 parent   60     38.4  39.42728 -1.02728 1.882     -0.54587</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 7 parent   90     32.5  33.76360 -1.26360 1.882     -0.67144</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 7 parent   90     35.5  33.76360  1.73640 1.882      0.92268</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 7 parent  120     28.1  30.39975 -2.29975 1.882     -1.22203</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 7 parent  120     29.0  30.39975 -1.39975 1.882     -0.74379</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 7 parent  180     26.5  25.62379  0.87621 1.882      0.46559</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 7 parent  180     27.6  25.62379  1.97621 1.882      1.05010</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 7     A1    3      3.9   2.70005  1.19995 1.882      0.63762</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 7     A1    3      3.1   2.70005  0.39995 1.882      0.21252</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 7     A1    7      6.9   5.83475  1.06525 1.882      0.56605</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 7     A1    7      6.6   5.83475  0.76525 1.882      0.40663</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 7     A1   14     10.4  10.26142  0.13858 1.882      0.07364</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 7     A1   14      8.3  10.26142 -1.96142 1.882     -1.04225</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 7     A1   30     14.4  16.82999 -2.42999 1.882     -1.29123</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 7     A1   30     13.7  16.82999 -3.12999 1.882     -1.66319</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 7     A1   60     22.1  22.32486 -0.22486 1.882     -0.11949</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 7     A1   60     22.3  22.32486 -0.02486 1.882     -0.01321</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 7     A1   90     27.5  24.45927  3.04073 1.882      1.61576</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 7     A1   90     25.4  24.45927  0.94073 1.882      0.49988</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 7     A1  120     28.0  25.54862  2.45138 1.882      1.30260</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 7     A1  120     26.6  25.54862  1.05138 1.882      0.55868</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 7     A1  180     25.8  26.82277 -1.02277 1.882     -0.54347</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 7     A1  180     25.3  26.82277 -1.52277 1.882     -0.80916</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 8 parent    0     91.9  91.16791  0.73209 1.882      0.38901</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 8 parent    0     90.8  91.16791 -0.36791 1.882     -0.19550</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 8 parent    1     64.9  67.58358 -2.68358 1.882     -1.42598</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 8 parent    1     66.2  67.58358 -1.38358 1.882     -0.73520</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 8 parent    3     43.5  41.62086  1.87914 1.882      0.99853</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 8 parent    3     44.1  41.62086  2.47914 1.882      1.31735</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 8 parent    8     18.3  19.60116 -1.30116 1.882     -0.69140</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 8 parent    8     18.1  19.60116 -1.50116 1.882     -0.79768</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 8 parent   14     10.2  10.63101 -0.43101 1.882     -0.22903</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 8 parent   14     10.8  10.63101  0.16899 1.882      0.08980</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 8 parent   27      4.9   3.12435  1.77565 1.882      0.94354</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 8 parent   27      3.3   3.12435  0.17565 1.882      0.09334</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 8 parent   48      1.6   0.43578  1.16422 1.882      0.61864</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 8 parent   48      1.5   0.43578  1.06422 1.882      0.56550</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 8 parent   70      1.1   0.05534  1.04466 1.882      0.55510</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 8 parent   70      0.9   0.05534  0.84466 1.882      0.44883</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 8     A1    1      9.6   7.63450  1.96550 1.882      1.04442</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 8     A1    1      7.7   7.63450  0.06550 1.882      0.03481</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 8     A1    3     15.0  15.52593 -0.52593 1.882     -0.27947</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 8     A1    3     15.1  15.52593 -0.42593 1.882     -0.22633</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 8     A1    8     21.2  20.32192  0.87808 1.882      0.46659</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 8     A1    8     21.1  20.32192  0.77808 1.882      0.41345</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 8     A1   14     19.7  20.09721 -0.39721 1.882     -0.21107</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 8     A1   14     18.9  20.09721 -1.19721 1.882     -0.63617</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 8     A1   27     17.5  16.37477  1.12523 1.882      0.59792</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 8     A1   27     15.9  16.37477 -0.47477 1.882     -0.25228</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 8     A1   48      9.5  10.13141 -0.63141 1.882     -0.33551</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 8     A1   48      9.8  10.13141 -0.33141 1.882     -0.17610</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 8     A1   70      6.2   5.81827  0.38173 1.882      0.20284</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 8     A1   70      6.1   5.81827  0.28173 1.882      0.14970</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 9 parent    0     99.8  97.48728  2.31272 1.882      1.22892</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 9 parent    0     98.3  97.48728  0.81272 1.882      0.43186</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 9 parent    1     77.1  79.29476 -2.19476 1.882     -1.16624</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 9 parent    1     77.2  79.29476 -2.09476 1.882     -1.11310</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 9 parent    3     59.0  55.67060  3.32940 1.882      1.76915</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 9 parent    3     58.1  55.67060  2.42940 1.882      1.29092</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 9 parent    8     27.4  31.57871 -4.17871 1.882     -2.22046</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 9 parent    8     29.2  31.57871 -2.37871 1.882     -1.26398</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 9 parent   14     19.1  22.51546 -3.41546 1.882     -1.81489</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 9 parent   14     29.6  22.51546  7.08454 1.882      3.76454</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 9 parent   27     10.1  14.09074 -3.99074 1.882     -2.12057</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 9 parent   27     18.2  14.09074  4.10926 1.882      2.18355</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 9 parent   48      4.5   6.95747 -2.45747 1.882     -1.30584</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 9 parent   48      9.1   6.95747  2.14253 1.882      1.13848</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 9 parent   70      2.3   3.32472 -1.02472 1.882     -0.54451</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 9 parent   70      2.9   3.32472 -0.42472 1.882     -0.22569</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 9 parent   91      2.0   1.64300  0.35700 1.882      0.18970</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 9 parent   91      1.8   1.64300  0.15700 1.882      0.08343</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 9 parent  120      2.0   0.62073  1.37927 1.882      0.73291</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 9 parent  120      2.2   0.62073  1.57927 1.882      0.83918</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 9     A1    1      4.2   3.64568  0.55432 1.882      0.29455</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 9     A1    1      3.9   3.64568  0.25432 1.882      0.13514</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 9     A1    3      7.4   8.30173 -0.90173 1.882     -0.47916</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 9     A1    3      7.9   8.30173 -0.40173 1.882     -0.21347</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 9     A1    8     14.5  12.71589  1.78411 1.882      0.94803</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 9     A1    8     13.7  12.71589  0.98411 1.882      0.52293</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 9     A1   14     14.2  13.90452  0.29548 1.882      0.15701</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 9     A1   14     12.2  13.90452 -1.70452 1.882     -0.90574</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 9     A1   27     13.7  14.15523 -0.45523 1.882     -0.24190</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 9     A1   27     13.2  14.15523 -0.95523 1.882     -0.50759</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 9     A1   48     13.6  13.31038  0.28962 1.882      0.15389</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 9     A1   48     15.4  13.31038  2.08962 1.882      1.11037</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 9     A1   70     10.4  11.85965 -1.45965 1.882     -0.77562</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 9     A1   70     11.6  11.85965 -0.25965 1.882     -0.13797</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 9     A1   91     10.0  10.36294 -0.36294 1.882     -0.19286</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 9     A1   91      9.5  10.36294 -0.86294 1.882     -0.45855</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 9     A1  120      9.1   8.43003  0.66997 1.882      0.35601</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Dataset 9     A1  120      9.0   8.43003  0.56997 1.882      0.30287</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Dataset 10 parent    0     96.1  93.95603  2.14397 1.882      1.13925</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Dataset 10 parent    0     94.3  93.95603  0.34397 1.882      0.18278</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Dataset 10 parent    8     73.9  77.70592 -3.80592 1.882     -2.02237</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Dataset 10 parent    8     73.9  77.70592 -3.80592 1.882     -2.02237</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Dataset 10 parent   14     69.4  70.04570 -0.64570 1.882     -0.34311</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Dataset 10 parent   14     73.1  70.04570  3.05430 1.882      1.62298</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Dataset 10 parent   21     65.6  64.01710  1.58290 1.882      0.84111</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Dataset 10 parent   21     65.3  64.01710  1.28290 1.882      0.68170</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Dataset 10 parent   41     55.9  54.98434  0.91566 1.882      0.48656</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Dataset 10 parent   41     54.4  54.98434 -0.58434 1.882     -0.31050</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Dataset 10 parent   63     47.0  49.87137 -2.87137 1.882     -1.52577</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Dataset 10 parent   63     49.3  49.87137 -0.57137 1.882     -0.30361</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Dataset 10 parent   91     44.7  45.06727 -0.36727 1.882     -0.19516</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Dataset 10 parent   91     46.7  45.06727  1.63273 1.882      0.86759</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Dataset 10 parent  120     42.1  40.76402  1.33598 1.882      0.70991</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Dataset 10 parent  120     41.3  40.76402  0.53598 1.882      0.28481</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Dataset 10     A1    8      3.3   4.14599 -0.84599 1.882     -0.44954</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Dataset 10     A1    8      3.4   4.14599 -0.74599 1.882     -0.39640</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Dataset 10     A1   14      3.9   6.08478 -2.18478 1.882     -1.16093</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Dataset 10     A1   14      2.9   6.08478 -3.18478 1.882     -1.69231</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Dataset 10     A1   21      6.4   7.59411 -1.19411 1.882     -0.63452</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Dataset 10     A1   21      7.2   7.59411 -0.39411 1.882     -0.20942</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Dataset 10     A1   41      9.1   9.78292 -0.68292 1.882     -0.36289</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Dataset 10     A1   41      8.5   9.78292 -1.28292 1.882     -0.68171</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Dataset 10     A1   63     11.7  10.93274  0.76726 1.882      0.40770</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Dataset 10     A1   63     12.0  10.93274  1.06726 1.882      0.56711</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Dataset 10     A1   91     13.3  11.93986  1.36014 1.882      0.72274</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Dataset 10     A1   91     13.2  11.93986  1.26014 1.882      0.66961</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Dataset 10     A1  120     14.3  12.79238  1.50762 1.882      0.80111</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Dataset 10     A1  120     12.1  12.79238 -0.69238 1.882     -0.36791</span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co"># The following takes about 6 minutes</span></span></span>
-<span class="r-in"><span><span class="va">f_saem_dfop_sfo_deSolve</span> <span class="op">&lt;-</span> <span class="fu">saem</span><span class="op">(</span><span class="va">f_mmkin</span><span class="op">[</span><span class="st">"DFOP-SFO"</span>, <span class="op">]</span>, solution_type <span class="op">=</span> <span class="st">"deSolve"</span>,</span></span>
-<span class="r-in"><span>  nbiter.saemix <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">200</span>, <span class="fl">80</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> DINTDY-  T (=R1) illegal      </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> In above message, R1 = 70</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>       T not in interval TCUR - HU (= R1) to TCUR (=R2)      </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> In above message, R1 = 53.1122, R2 = 56.6407</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> DINTDY-  T (=R1) illegal      </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> In above message, R1 = 91</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>       T not in interval TCUR - HU (= R1) to TCUR (=R2)      </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> In above message, R1 = 53.1122, R2 = 56.6407</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> DLSODA-  Trouble in DINTDY.  ITASK = I1, TOUT = R1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> In above message, I1 = 1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> In above message, R1 = 91</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Error in deSolve::lsoda(y = odeini, times = outtimes, func = lsoda_func,  : </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   illegal input detected before taking any integration steps - see written message</span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co">#anova(</span></span></span>
-<span class="r-in"><span><span class="co">#  f_saem_dfop_sfo,</span></span></span>
-<span class="r-in"><span><span class="co">#  f_saem_dfop_sfo_deSolve))</span></span></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co"># If the model supports it, we can also use eigenvalue based solutions, which</span></span></span>
-<span class="r-in"><span><span class="co"># take a similar amount of time</span></span></span>
-<span class="r-in"><span><span class="co">#f_saem_sfo_sfo_eigen &lt;- saem(f_mmkin["SFO-SFO", ], solution_type = "eigen",</span></span></span>
-<span class="r-in"><span><span class="co">#  control = list(nbiter.saemix = c(200, 80), nbdisplay = 10))</span></span></span>
-<span class="r-in"><span><span class="co"># }</span></span></span>
-</code></pre></div>
-    </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/schaefer07_complex_case-1.png b/docs/dev/reference/schaefer07_complex_case-1.png
deleted file mode 100644
index 7622ae7f..00000000
--- a/docs/dev/reference/schaefer07_complex_case-1.png
+++ /dev/null
diff --git a/docs/dev/reference/schaefer07_complex_case.html b/docs/dev/reference/schaefer07_complex_case.html
deleted file mode 100644
index 880e5ac0..00000000
--- a/docs/dev/reference/schaefer07_complex_case.html
+++ /dev/null
@@ -1,221 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Metabolism data set used for checking the software quality of KinGUI — schaefer07_complex_case • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Metabolism data set used for checking the software quality of KinGUI — schaefer07_complex_case"><meta property="og:description" content="This dataset was used for a comparison of KinGUI and ModelMaker to check the
-  software quality of KinGUI in the original publication (Schäfer et al., 2007).
-  The results from the fitting are also included."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Metabolism data set used for checking the software quality of KinGUI</h1>
-    
-    <div class="hidden name"><code>schaefer07_complex_case.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>This dataset was used for a comparison of KinGUI and ModelMaker to check the
-  software quality of KinGUI in the original publication (Schäfer et al., 2007).
-  The results from the fitting are also included.</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="va">schaefer07_complex_case</span></span></code></pre></div>
-    </div>
-
-    <div id="format">
-    <h2>Format</h2>
-    <p>The data set is a data frame with 8 observations on the following 6 variables.</p><dl><dt><code>time</code></dt>
-<dd><p>a numeric vector</p></dd>
-
-    <dt><code>parent</code></dt>
-<dd><p>a numeric vector</p></dd>
-
-    <dt><code>A1</code></dt>
-<dd><p>a numeric vector</p></dd>
-
-    <dt><code>B1</code></dt>
-<dd><p>a numeric vector</p></dd>
-
-    <dt><code>C1</code></dt>
-<dd><p>a numeric vector</p></dd>
-
-    <dt><code>A2</code></dt>
-<dd><p>a numeric vector</p></dd>
-
-  
-</dl><p>The results are a data frame with 14 results for different parameter values</p>
-    </div>
-    <div id="references">
-    <h2>References</h2>
-    <p>Schäfer D, Mikolasch B, Rainbird P and Harvey B (2007). KinGUI: a new kinetic
-  software tool for evaluations according to FOCUS degradation kinetics. In: Del
-  Re AAM, Capri E, Fragoulis G and Trevisan M (Eds.). Proceedings of the XIII
-  Symposium Pesticide Chemistry, Piacenza, 2007, p. 916-923.</p>
-    </div>
-
-    <div id="ref-examples">
-    <h2>Examples</h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">data</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkin_wide_to_long.html">mkin_wide_to_long</a></span><span class="op">(</span><span class="va">schaefer07_complex_case</span>, time <span class="op">=</span> <span class="st">"time"</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">model</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span></span></span>
-<span class="r-in"><span>  parent <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span>, to <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"A1"</span>, <span class="st">"B1"</span>, <span class="st">"C1"</span><span class="op">)</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  A1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"A2"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  B1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  C1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  A2 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span>, use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Temporary DLL for differentials generated and loaded</span>
-<span class="r-in"><span>  <span class="co"># \dontrun{</span></span></span>
-<span class="r-in"><span>    <span class="va">fit</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">model</span>, <span class="va">data</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-in"><span>    <span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></span></span>
-<span class="r-plt img"><img src="schaefer07_complex_case-1.png" alt="" width="700" height="433"></span>
-<span class="r-in"><span>    <span class="fu"><a href="endpoints.html">endpoints</a></span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> $ff</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   parent_A1   parent_B1   parent_C1 parent_sink       A1_A2     A1_sink </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   0.3809620   0.1954667   0.4235713   0.0000000   0.4479619   0.5520381 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> $distimes</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>            DT50      DT90</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent 13.95078  46.34350</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> A1     49.75342 165.27728</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> B1     37.26908 123.80520</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> C1     11.23131  37.30961</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> A2     28.50624  94.69567</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-in"><span>  <span class="co"># }</span></span></span>
-<span class="r-in"><span> <span class="co"># Compare with the results obtained in the original publication</span></span></span>
-<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">schaefer07_complex_results</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         compound          parameter  KinGUI ModelMaker deviation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1         parent   degradation rate  0.0496     0.0506       2.0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2         parent               DT50 13.9900    13.6900       2.2</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 3  metabolite A1 formation fraction  0.3803     0.3696       2.9</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 4  metabolite A1   degradation rate  0.0139     0.0136       2.2</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5  metabolite A1               DT50 49.9600    50.8900       1.8</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 6  metabolite B1 formation fraction  0.1866     0.1818       2.6</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 7  metabolite B1   degradation rate  0.0175     0.0172       1.7</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 8  metabolite B1               DT50 39.6100    40.2400       1.6</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 9  metabolite C1 formation fraction  0.4331     0.4486       3.5</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 10 metabolite C1   degradation rate  0.0638     0.0700       8.9</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 11 metabolite C1               DT50 10.8700     9.9000       9.8</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 12 metabolite A2 formation fraction  0.4529     0.4559       0.7</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 13 metabolite A2   degradation rate  0.0245     0.0244       0.4</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 14 metabolite A2               DT50 28.2400    28.4500       0.7</span>
-</code></pre></div>
-    </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/set_nd_nq.html b/docs/dev/reference/set_nd_nq.html
deleted file mode 100644
index 6f4ae169..00000000
--- a/docs/dev/reference/set_nd_nq.html
+++ /dev/null
@@ -1,278 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Set non-detects and unquantified values in residue series without replicates — set_nd_nq • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Set non-detects and unquantified values in residue series without replicates — set_nd_nq"><meta property="og:description" content="This function automates replacing unquantified values in residue time and
-depth series. For time series, the function performs part of the residue
-processing proposed in the FOCUS kinetics guidance for parent compounds
-and metabolites. For two-dimensional residue series over time and depth,
-it automates the proposal of Boesten et al (2015)."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Set non-detects and unquantified values in residue series without replicates</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/set_nd_nq.R" class="external-link"><code>R/set_nd_nq.R</code></a></small>
-    <div class="hidden name"><code>set_nd_nq.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>This function automates replacing unquantified values in residue time and
-depth series. For time series, the function performs part of the residue
-processing proposed in the FOCUS kinetics guidance for parent compounds
-and metabolites. For two-dimensional residue series over time and depth,
-it automates the proposal of Boesten et al (2015).</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">set_nd_nq</span><span class="op">(</span><span class="va">res_raw</span>, <span class="va">lod</span>, loq <span class="op">=</span> <span class="cn">NA</span>, time_zero_presence <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">set_nd_nq_focus</span><span class="op">(</span></span>
-<span>  <span class="va">res_raw</span>,</span>
-<span>  <span class="va">lod</span>,</span>
-<span>  loq <span class="op">=</span> <span class="cn">NA</span>,</span>
-<span>  set_first_sample_nd <span class="op">=</span> <span class="cn">TRUE</span>,</span>
-<span>  first_sample_nd_value <span class="op">=</span> <span class="fl">0</span>,</span>
-<span>  ignore_below_loq_after_first_nd <span class="op">=</span> <span class="cn">TRUE</span></span>
-<span><span class="op">)</span></span></code></pre></div>
-    </div>
-
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>res_raw</dt>
-<dd><p>Character vector of a residue time series, or matrix of
-residue values with rows representing depth profiles for a specific sampling
-time, and columns representing time series of residues at the same depth.
-Values below the limit of detection (lod) have to be coded as "nd", values
-between the limit of detection and the limit of quantification, if any, have
-to be coded as "nq". Samples not analysed have to be coded as "na". All
-values that are not "na", "nd" or "nq" have to be coercible to numeric</p></dd>
-
-
-<dt>lod</dt>
-<dd><p>Limit of detection (numeric)</p></dd>
-
-
-<dt>loq</dt>
-<dd><p>Limit of quantification(numeric). Must be specified if the FOCUS rule to
-stop after the first non-detection is to be applied</p></dd>
-
-
-<dt>time_zero_presence</dt>
-<dd><p>Do we assume that residues occur at time zero?
-This only affects samples from the first sampling time that have been
-reported as "nd" (not detected).</p></dd>
-
-
-<dt>set_first_sample_nd</dt>
-<dd><p>Should the first sample be set to "first_sample_nd_value"
-in case it is a non-detection?</p></dd>
-
-
-<dt>first_sample_nd_value</dt>
-<dd><p>Value to be used for the first sample if it is a non-detection</p></dd>
-
-
-<dt>ignore_below_loq_after_first_nd</dt>
-<dd><p>Should we ignore values below the LOQ after the first
-non-detection that occurs after the quantified values?</p></dd>
-
-</dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>A numeric vector, if a vector was supplied, or a numeric matrix otherwise</p>
-    </div>
-    <div id="functions">
-    <h2>Functions</h2>
-    
-<ul><li><p><code>set_nd_nq_focus()</code>: Set non-detects in residue time series according to FOCUS rules</p></li>
-</ul></div>
-    <div id="references">
-    <h2>References</h2>
-    <p>Boesten, J. J. T. I., van der Linden, A. M. A., Beltman, W. H.
-J. and Pol, J. W. (2015). Leaching of plant protection products and their
-transformation products; Proposals for improving the assessment of leaching
-to groundwater in the Netherlands — Version 2. Alterra report 2630, Alterra
-Wageningen UR (University &amp; Research centre)</p>
-<p>FOCUS (2014) Generic Guidance for Estimating Persistence and Degradation
-Kinetics from Environmental Fate Studies on Pesticides in EU Registration, Version 1.1,
-18 December 2014, p. 251</p>
-    </div>
-
-    <div id="ref-examples">
-    <h2>Examples</h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># FOCUS (2014) p. 75/76 and 131/132</span></span></span>
-<span class="r-in"><span><span class="va">parent_1</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">.12</span>, <span class="fl">.09</span>, <span class="fl">.05</span>, <span class="fl">.03</span>, <span class="st">"nd"</span>, <span class="st">"nd"</span>, <span class="st">"nd"</span>, <span class="st">"nd"</span>, <span class="st">"nd"</span>, <span class="st">"nd"</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu">set_nd_nq</span><span class="op">(</span><span class="va">parent_1</span>, <span class="fl">0.02</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 0.12 0.09 0.05 0.03 0.01   NA   NA   NA   NA   NA</span>
-<span class="r-in"><span><span class="va">parent_2</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">.12</span>, <span class="fl">.09</span>, <span class="fl">.05</span>, <span class="fl">.03</span>, <span class="st">"nd"</span>, <span class="st">"nd"</span>, <span class="fl">.03</span>, <span class="st">"nd"</span>, <span class="st">"nd"</span>, <span class="st">"nd"</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu">set_nd_nq</span><span class="op">(</span><span class="va">parent_2</span>, <span class="fl">0.02</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 0.12 0.09 0.05 0.03 0.01 0.01 0.03 0.01   NA   NA</span>
-<span class="r-in"><span><span class="fu">set_nd_nq_focus</span><span class="op">(</span><span class="va">parent_2</span>, <span class="fl">0.02</span>, loq <span class="op">=</span> <span class="fl">0.05</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 0.12 0.09 0.05 0.03 0.01   NA   NA   NA   NA   NA</span>
-<span class="r-in"><span><span class="va">parent_3</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">.12</span>, <span class="fl">.09</span>, <span class="fl">.05</span>, <span class="fl">.03</span>, <span class="st">"nd"</span>, <span class="st">"nd"</span>, <span class="fl">.06</span>, <span class="st">"nd"</span>, <span class="st">"nd"</span>, <span class="st">"nd"</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu">set_nd_nq</span><span class="op">(</span><span class="va">parent_3</span>, <span class="fl">0.02</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 0.12 0.09 0.05 0.03 0.01 0.01 0.06 0.01   NA   NA</span>
-<span class="r-in"><span><span class="fu">set_nd_nq_focus</span><span class="op">(</span><span class="va">parent_3</span>, <span class="fl">0.02</span>, loq <span class="op">=</span> <span class="fl">0.05</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 0.12 0.09 0.05 0.03 0.01 0.01 0.06 0.01   NA   NA</span>
-<span class="r-in"><span><span class="va">metabolite</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"nd"</span>, <span class="st">"nd"</span>, <span class="st">"nd"</span>, <span class="fl">0.03</span>, <span class="fl">0.06</span>, <span class="fl">0.10</span>, <span class="fl">0.11</span>, <span class="fl">0.10</span>, <span class="fl">0.09</span>, <span class="fl">0.05</span>, <span class="fl">0.03</span>, <span class="st">"nd"</span>, <span class="st">"nd"</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu">set_nd_nq</span><span class="op">(</span><span class="va">metabolite</span>, <span class="fl">0.02</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [1]   NA   NA 0.01 0.03 0.06 0.10 0.11 0.10 0.09 0.05 0.03 0.01   NA</span>
-<span class="r-in"><span><span class="fu">set_nd_nq_focus</span><span class="op">(</span><span class="va">metabolite</span>, <span class="fl">0.02</span>, <span class="fl">0.05</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 0.00   NA 0.01 0.03 0.06 0.10 0.11 0.10 0.09 0.05 0.03 0.01   NA</span>
-<span class="r-in"><span><span class="co">#</span></span></span>
-<span class="r-in"><span><span class="co"># Boesten et al. (2015), p. 57/58</span></span></span>
-<span class="r-in"><span><span class="va">table_8</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/matrix.html" class="external-link">matrix</a></span><span class="op">(</span></span></span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">10</span>, <span class="fl">10</span>, <span class="fu"><a href="https://rdrr.io/r/base/rep.html" class="external-link">rep</a></span><span class="op">(</span><span class="st">"nd"</span>, <span class="fl">4</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>    <span class="fl">10</span>, <span class="fl">10</span>, <span class="fu"><a href="https://rdrr.io/r/base/rep.html" class="external-link">rep</a></span><span class="op">(</span><span class="st">"nq"</span>, <span class="fl">2</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/rep.html" class="external-link">rep</a></span><span class="op">(</span><span class="st">"nd"</span>, <span class="fl">2</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>    <span class="fl">10</span>, <span class="fl">10</span>, <span class="fl">10</span>, <span class="st">"nq"</span>, <span class="st">"nd"</span>, <span class="st">"nd"</span>,</span></span>
-<span class="r-in"><span>    <span class="st">"nq"</span>, <span class="fl">10</span>, <span class="st">"nq"</span>, <span class="fu"><a href="https://rdrr.io/r/base/rep.html" class="external-link">rep</a></span><span class="op">(</span><span class="st">"nd"</span>, <span class="fl">3</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>    <span class="st">"nd"</span>, <span class="st">"nq"</span>, <span class="st">"nq"</span>, <span class="fu"><a href="https://rdrr.io/r/base/rep.html" class="external-link">rep</a></span><span class="op">(</span><span class="st">"nd"</span>, <span class="fl">3</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>    <span class="fu"><a href="https://rdrr.io/r/base/rep.html" class="external-link">rep</a></span><span class="op">(</span><span class="st">"nd"</span>, <span class="fl">6</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/rep.html" class="external-link">rep</a></span><span class="op">(</span><span class="st">"nd"</span>, <span class="fl">6</span><span class="op">)</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  ncol <span class="op">=</span> <span class="fl">6</span>, byrow <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu">set_nd_nq</span><span class="op">(</span><span class="va">table_8</span>, <span class="fl">0.5</span>, <span class="fl">1.5</span>, time_zero_presence <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>       [,1]  [,2]  [,3] [,4] [,5] [,6]</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1,] 10.00 10.00  0.25 0.25   NA   NA</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [2,] 10.00 10.00  1.00 1.00 0.25   NA</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [3,] 10.00 10.00 10.00 1.00 0.25   NA</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [4,]  1.00 10.00  1.00 0.25   NA   NA</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [5,]  0.25  1.00  1.00 0.25   NA   NA</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [6,]    NA  0.25  0.25   NA   NA   NA</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [7,]    NA    NA    NA   NA   NA   NA</span>
-<span class="r-in"><span><span class="va">table_10</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/matrix.html" class="external-link">matrix</a></span><span class="op">(</span></span></span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">10</span>, <span class="fl">10</span>, <span class="fu"><a href="https://rdrr.io/r/base/rep.html" class="external-link">rep</a></span><span class="op">(</span><span class="st">"nd"</span>, <span class="fl">4</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>    <span class="fl">10</span>, <span class="fl">10</span>, <span class="fu"><a href="https://rdrr.io/r/base/rep.html" class="external-link">rep</a></span><span class="op">(</span><span class="st">"nd"</span>, <span class="fl">4</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>    <span class="fl">10</span>, <span class="fl">10</span>, <span class="fl">10</span>, <span class="fu"><a href="https://rdrr.io/r/base/rep.html" class="external-link">rep</a></span><span class="op">(</span><span class="st">"nd"</span>, <span class="fl">3</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>    <span class="st">"nd"</span>, <span class="fl">10</span>, <span class="fu"><a href="https://rdrr.io/r/base/rep.html" class="external-link">rep</a></span><span class="op">(</span><span class="st">"nd"</span>, <span class="fl">4</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>    <span class="fu"><a href="https://rdrr.io/r/base/rep.html" class="external-link">rep</a></span><span class="op">(</span><span class="st">"nd"</span>, <span class="fl">18</span><span class="op">)</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  ncol <span class="op">=</span> <span class="fl">6</span>, byrow <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu">set_nd_nq</span><span class="op">(</span><span class="va">table_10</span>, <span class="fl">0.5</span>, time_zero_presence <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>       [,1]  [,2]  [,3] [,4] [,5] [,6]</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1,] 10.00 10.00  0.25   NA   NA   NA</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [2,] 10.00 10.00  0.25   NA   NA   NA</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [3,] 10.00 10.00 10.00 0.25   NA   NA</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [4,]  0.25 10.00  0.25   NA   NA   NA</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [5,]    NA  0.25    NA   NA   NA   NA</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [6,]    NA    NA    NA   NA   NA   NA</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [7,]    NA    NA    NA   NA   NA   NA</span>
-</code></pre></div>
-    </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/sigma_twocomp-1.png b/docs/dev/reference/sigma_twocomp-1.png
deleted file mode 100644
index 3671c658..00000000
--- a/docs/dev/reference/sigma_twocomp-1.png
+++ /dev/null
diff --git a/docs/dev/reference/sigma_twocomp.html b/docs/dev/reference/sigma_twocomp.html
deleted file mode 100644
index 0ead0184..00000000
--- a/docs/dev/reference/sigma_twocomp.html
+++ /dev/null
@@ -1,216 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Two-component error model — sigma_twocomp • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Two-component error model — sigma_twocomp"><meta property="og:description" content="Function describing the standard deviation of the measurement error in
-dependence of the measured value \(y\):"><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Two-component error model</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/sigma_twocomp.R" class="external-link"><code>R/sigma_twocomp.R</code></a></small>
-    <div class="hidden name"><code>sigma_twocomp.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Function describing the standard deviation of the measurement error in
-dependence of the measured value \(y\):</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">sigma_twocomp</span><span class="op">(</span><span class="va">y</span>, <span class="va">sigma_low</span>, <span class="va">rsd_high</span><span class="op">)</span></span></code></pre></div>
-    </div>
-
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>y</dt>
-<dd><p>The magnitude of the observed value</p></dd>
-
-
-<dt>sigma_low</dt>
-<dd><p>The asymptotic minimum of the standard deviation for low
-observed values</p></dd>
-
-
-<dt>rsd_high</dt>
-<dd><p>The coefficient describing the increase of the standard
-deviation with the magnitude of the observed value</p></dd>
-
-</dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>The standard deviation of the response variable.</p>
-    </div>
-    <div id="details">
-    <h2>Details</h2>
-    <p>$$\sigma = \sqrt{ \sigma_{low}^2 + y^2 * {rsd}_{high}^2}$$ sigma =
-sqrt(sigma_low^2 + y^2 * rsd_high^2)</p>
-<p>This is the error model used for example by Werner et al. (1978). The model
-proposed by Rocke and Lorenzato (1995) can be written in this form as well,
-but assumes approximate lognormal distribution of errors for high values of
-y.</p>
-    </div>
-    <div id="references">
-    <h2>References</h2>
-    <p>Werner, Mario, Brooks, Samuel H., and Knott, Lancaster B. (1978)
-Additive, Multiplicative, and Mixed Analytical Errors. Clinical Chemistry
-24(11), 1895-1898.</p>
-<p>Rocke, David M. and Lorenzato, Stefan (1995) A two-component model for
-measurement error in analytical chemistry. Technometrics 37(2), 176-184.</p>
-<p>Ranke J and Meinecke S (2019) Error Models for the Kinetic Evaluation of Chemical
-Degradation Data. <em>Environments</em> 6(12) 124
-<a href="https://doi.org/10.3390/environments6120124" class="external-link">doi:10.3390/environments6120124</a>
-.</p>
-    </div>
-
-    <div id="ref-examples">
-    <h2>Examples</h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">times</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span>, <span class="fl">3</span>, <span class="fl">7</span>, <span class="fl">14</span>, <span class="fl">28</span>, <span class="fl">60</span>, <span class="fl">90</span>, <span class="fl">120</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">d_pred</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span>time <span class="op">=</span> <span class="va">times</span>, parent <span class="op">=</span> <span class="fl">100</span> <span class="op">*</span> <span class="fu"><a href="https://rdrr.io/r/base/Log.html" class="external-link">exp</a></span><span class="op">(</span><span class="op">-</span> <span class="fl">0.03</span> <span class="op">*</span> <span class="va">times</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/Random.html" class="external-link">set.seed</a></span><span class="op">(</span><span class="fl">123456</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">d_syn</span> <span class="op">&lt;-</span> <span class="fu"><a href="add_err.html">add_err</a></span><span class="op">(</span><span class="va">d_pred</span>, <span class="kw">function</span><span class="op">(</span><span class="va">y</span><span class="op">)</span> <span class="fu">sigma_twocomp</span><span class="op">(</span><span class="va">y</span>, <span class="fl">1</span>, <span class="fl">0.07</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  reps <span class="op">=</span> <span class="fl">2</span>, n <span class="op">=</span> <span class="fl">1</span><span class="op">)</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span></span></span>
-<span class="r-in"><span><span class="va">f_nls</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/nls.html" class="external-link">nls</a></span><span class="op">(</span><span class="va">value</span> <span class="op">~</span> <span class="fu"><a href="https://rdrr.io/r/stats/SSasymp.html" class="external-link">SSasymp</a></span><span class="op">(</span><span class="va">time</span>, <span class="fl">0</span>, <span class="va">parent_0</span>, <span class="va">lrc</span><span class="op">)</span>, data <span class="op">=</span> <span class="va">d_syn</span>,</span></span>
-<span class="r-in"><span> start <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>parent_0 <span class="op">=</span> <span class="fl">100</span>, lrc <span class="op">=</span> <span class="op">-</span><span class="fl">3</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://svn.r-project.org/R-packages/trunk/nlme/" class="external-link">nlme</a></span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">f_gnls</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/gnls.html" class="external-link">gnls</a></span><span class="op">(</span><span class="va">value</span> <span class="op">~</span> <span class="fu"><a href="https://rdrr.io/r/stats/SSasymp.html" class="external-link">SSasymp</a></span><span class="op">(</span><span class="va">time</span>, <span class="fl">0</span>, <span class="va">parent_0</span>, <span class="va">lrc</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  data <span class="op">=</span> <span class="va">d_syn</span>, na.action <span class="op">=</span> <span class="va">na.omit</span>,</span></span>
-<span class="r-in"><span>  start <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>parent_0 <span class="op">=</span> <span class="fl">100</span>, lrc <span class="op">=</span> <span class="op">-</span><span class="fl">3</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/length.html" class="external-link">length</a></span><span class="op">(</span><span class="fu">findFunction</span><span class="op">(</span><span class="st">"varConstProp"</span><span class="op">)</span><span class="op">)</span> <span class="op">&gt;</span> <span class="fl">0</span><span class="op">)</span> <span class="op">{</span></span></span>
-<span class="r-in"><span>  <span class="va">f_gnls_tc</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_gnls</span>, weights <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/varConstProp.html" class="external-link">varConstProp</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-in"><span>  <span class="va">f_gnls_tc_sf</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_gnls_tc</span>, control <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>sigma <span class="op">=</span> <span class="fl">1</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="op">}</span></span></span>
-<span class="r-in"><span><span class="va">f_mkin</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="va">d_syn</span>, error_model <span class="op">=</span> <span class="st">"const"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">f_mkin_tc</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="va">d_syn</span>, error_model <span class="op">=</span> <span class="st">"tc"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="plot.mkinfit.html">plot_res</a></span><span class="op">(</span><span class="va">f_mkin_tc</span>, standardized <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-plt img"><img src="sigma_twocomp-1.png" alt="" width="700" height="433"></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">AIC</a></span><span class="op">(</span><span class="va">f_nls</span>, <span class="va">f_gnls</span>, <span class="va">f_gnls_tc</span>, <span class="va">f_gnls_tc_sf</span>, <span class="va">f_mkin</span>, <span class="va">f_mkin_tc</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>              df      AIC</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_nls         3 114.4817</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_gnls        3 114.4817</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_gnls_tc     5 103.6447</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_gnls_tc_sf  4 101.6447</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_mkin        3 114.4817</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_mkin_tc     4 101.6446</span>
-</code></pre></div>
-    </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/status.html b/docs/dev/reference/status.html
deleted file mode 100644
index df3e3a44..00000000
--- a/docs/dev/reference/status.html
+++ /dev/null
@@ -1,191 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Method to get status information for fit array objects — status • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Method to get status information for fit array objects — status"><meta property="og:description" content="Method to get status information for fit array objects"><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Method to get status information for fit array objects</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/status.R" class="external-link"><code>R/status.R</code></a></small>
-    <div class="hidden name"><code>status.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Method to get status information for fit array objects</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">status</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># S3 method for mmkin</span></span>
-<span><span class="fu">status</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># S3 method for status.mmkin</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># S3 method for mhmkin</span></span>
-<span><span class="fu">status</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># S3 method for status.mhmkin</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
-    </div>
-
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>object</dt>
-<dd><p>The object to investigate</p></dd>
-
-
-<dt>...</dt>
-<dd><p>For potential future extensions</p></dd>
-
-
-<dt>x</dt>
-<dd><p>The object to be printed</p></dd>
-
-</dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>An object with the same dimensions as the fit array
-suitable printing method.</p>
-    </div>
-
-    <div id="ref-examples">
-    <h2>Examples</h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># \dontrun{</span></span></span>
-<span class="r-in"><span><span class="va">fits</span> <span class="op">&lt;-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span></span></span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"FOMC"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="st">"FOCUS A"</span> <span class="op">=</span> <span class="va">FOCUS_2006_A</span>,</span></span>
-<span class="r-in"><span>       <span class="st">"FOCUS B"</span> <span class="op">=</span> <span class="va">FOCUS_2006_C</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu">status</span><span class="op">(</span><span class="va">fits</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>       dataset</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> model  FOCUS A FOCUS B</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   SFO  OK      OK     </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   FOMC OK      OK     </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> OK: No warnings</span>
-<span class="r-in"><span><span class="co"># }</span></span></span>
-</code></pre></div>
-    </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/summary.mkinfit.html b/docs/dev/reference/summary.mkinfit.html
deleted file mode 100644
index f627c2f6..00000000
--- a/docs/dev/reference/summary.mkinfit.html
+++ /dev/null
@@ -1,334 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Summary method for class "mkinfit" — summary.mkinfit • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Summary method for class " mkinfit summary.mkinfit><meta property="og:description" content="Lists model equations, initial parameter values, optimised parameters with
-some uncertainty statistics, the chi2 error levels calculated according to
-FOCUS guidance (2006) as defined therein, formation fractions, DT50 values
-and optionally the data, consisting of observed, predicted and residual
-values."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Summary method for class "mkinfit"</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/summary.mkinfit.R" class="external-link"><code>R/summary.mkinfit.R</code></a></small>
-    <div class="hidden name"><code>summary.mkinfit.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Lists model equations, initial parameter values, optimised parameters with
-some uncertainty statistics, the chi2 error levels calculated according to
-FOCUS guidance (2006) as defined therein, formation fractions, DT50 values
-and optionally the data, consisting of observed, predicted and residual
-values.</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for mkinfit</span></span>
-<span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">object</span>, data <span class="op">=</span> <span class="cn">TRUE</span>, distimes <span class="op">=</span> <span class="cn">TRUE</span>, alpha <span class="op">=</span> <span class="fl">0.05</span>, <span class="va">...</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># S3 method for summary.mkinfit</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, digits <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html" class="external-link">max</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fu"><a href="https://rdrr.io/r/base/options.html" class="external-link">getOption</a></span><span class="op">(</span><span class="st">"digits"</span><span class="op">)</span> <span class="op">-</span> <span class="fl">3</span><span class="op">)</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
-    </div>
-
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>object</dt>
-<dd><p>an object of class <a href="mkinfit.html">mkinfit</a>.</p></dd>
-
-
-<dt>data</dt>
-<dd><p>logical, indicating whether the data should be included in the
-summary.</p></dd>
-
-
-<dt>distimes</dt>
-<dd><p>logical, indicating whether DT50 and DT90 values should be
-included.</p></dd>
-
-
-<dt>alpha</dt>
-<dd><p>error level for confidence interval estimation from t
-distribution</p></dd>
-
-
-<dt>...</dt>
-<dd><p>optional arguments passed to methods like <code>print</code>.</p></dd>
-
-
-<dt>x</dt>
-<dd><p>an object of class <code>summary.mkinfit</code>.</p></dd>
-
-
-<dt>digits</dt>
-<dd><p>Number of digits to use for printing</p></dd>
-
-</dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>The summary function returns a list with components, among others</p>
-<dl><dt>version, Rversion</dt>
-<dd><p>The mkin and R versions used</p></dd>
-
-<dt>date.fit, date.summary</dt>
-<dd><p>The dates where the fit and the summary were
-produced</p></dd>
-
-<dt>diffs</dt>
-<dd><p>The differential equations used in the model</p></dd>
-
-<dt>use_of_ff</dt>
-<dd><p>Was maximum or minimum use made of formation fractions</p></dd>
-
-<dt>bpar</dt>
-<dd><p>Optimised and backtransformed
-parameters</p></dd>
-
-<dt>data</dt>
-<dd><p>The data (see Description above).</p></dd>
-
-<dt>start</dt>
-<dd><p>The starting values and bounds, if applicable, for optimised
-parameters.</p></dd>
-
-<dt>fixed</dt>
-<dd><p>The values of fixed parameters.</p></dd>
-
-<dt>errmin </dt>
-<dd><p>The chi2 error levels for
-each observed variable.</p></dd>
-
-<dt>bparms.ode</dt>
-<dd><p>All backtransformed ODE
-parameters, for use as starting parameters for related models.</p></dd>
-
-<dt>errparms</dt>
-<dd><p>Error model parameters.</p></dd>
-
-<dt>ff</dt>
-<dd><p>The estimated formation fractions derived from the fitted
-model.</p></dd>
-
-<dt>distimes</dt>
-<dd><p>The DT50 and DT90 values for each observed variable.</p></dd>
-
-<dt>SFORB</dt>
-<dd><p>If applicable, eigenvalues and fractional eigenvector component
-g of SFORB systems in the model.</p></dd>
-
-</dl><p>The print method is called for its side effect, i.e. printing the summary.</p>
-    </div>
-    <div id="references">
-    <h2>References</h2>
-    <p>FOCUS (2006) “Guidance Document on Estimating Persistence
-and Degradation Kinetics from Environmental Fate Studies on Pesticides in
-EU Registration” Report of the FOCUS Work Group on Degradation Kinetics,
-EC Document Reference Sanco/10058/2005 version 2.0, 434 pp,
-<a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics" class="external-link">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p>
-    </div>
-    <div id="author">
-    <h2>Author</h2>
-    <p>Johannes Ranke</p>
-    </div>
-
-    <div id="ref-examples">
-    <h2>Examples</h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="va">FOCUS_2006_A</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> mkin version used for fitting:    1.2.3 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> R version used for fitting:       4.2.3 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Date of fit:     Sun Apr 16 08:34:39 2023 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Date of summary: Sun Apr 16 08:34:39 2023 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Equations:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> d_parent/dt = - k_parent * parent</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Model predictions using solution type analytical </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Fitted using 131 model solutions performed in 0.009 s</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Error model: Constant variance </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Error model algorithm: OLS </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Starting values for parameters to be optimised:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>           value   type</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0 101.24  state</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_parent   0.10 deparm</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Starting values for the transformed parameters actually optimised:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                   value lower upper</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0     101.240000  -Inf   Inf</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k_parent  -2.302585  -Inf   Inf</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Fixed parameter values:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> None</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Results:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>        AIC     BIC    logLik</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   55.28197 55.5203 -24.64099</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Optimised, transformed parameters with symmetric confidence intervals:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>              Estimate Std. Error  Lower   Upper</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0      109.200    3.70400 99.630 118.700</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k_parent   -3.291    0.09176 -3.527  -3.055</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma           5.266    1.31600  1.882   8.649</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Parameter correlation:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>               parent_0 log_k_parent     sigma</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0     1.000e+00    5.428e-01 1.642e-07</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k_parent 5.428e-01    1.000e+00 2.507e-07</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma        1.642e-07    2.507e-07 1.000e+00</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Backtransformed parameters:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Confidence intervals for internally transformed parameters are asymmetric.</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> t-test (unrealistically) based on the assumption of normal distribution</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> for estimators of untransformed parameters.</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>           Estimate t value    Pr(&gt;t)   Lower     Upper</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0 109.20000   29.47 4.218e-07 99.6300 118.70000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_parent   0.03722   10.90 5.650e-05  0.0294   0.04712</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma      5.26600    4.00 5.162e-03  1.8820   8.64900</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> FOCUS Chi2 error levels in percent:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>          err.min n.optim df</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> All data   8.385       2  6</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent     8.385       2  6</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Estimated disappearance times:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         DT50  DT90</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent 18.62 61.87</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Data:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  time variable observed predicted residual</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     0   parent   101.24   109.153  -7.9132</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     3   parent    99.27    97.622   1.6484</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     7   parent    90.11    84.119   5.9913</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    14   parent    72.19    64.826   7.3641</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    30   parent    29.71    35.738  -6.0283</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    62   parent     5.98    10.862  -4.8818</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    90   parent     1.54     3.831  -2.2911</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   118   parent     0.39     1.351  -0.9613</span>
-<span class="r-in"><span></span></span>
-</code></pre></div>
-    </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/summary.mmkin.html b/docs/dev/reference/summary.mmkin.html
deleted file mode 100644
index c27f1634..00000000
--- a/docs/dev/reference/summary.mmkin.html
+++ /dev/null
@@ -1,196 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Summary method for class "mmkin" — summary.mmkin • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Summary method for class " mmkin summary.mmkin><meta property="og:description" content="Shows status information on the mkinfit objects contained in the object
-and gives an overview of ill-defined parameters calculated by illparms."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Summary method for class "mmkin"</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/summary.mmkin.R" class="external-link"><code>R/summary.mmkin.R</code></a></small>
-    <div class="hidden name"><code>summary.mmkin.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Shows status information on the <a href="mkinfit.html">mkinfit</a> objects contained in the object
-and gives an overview of ill-defined parameters calculated by <a href="illparms.html">illparms</a>.</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for mmkin</span></span>
-<span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">object</span>, conf.level <span class="op">=</span> <span class="fl">0.95</span>, <span class="va">...</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># S3 method for summary.mmkin</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, digits <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html" class="external-link">max</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fu"><a href="https://rdrr.io/r/base/options.html" class="external-link">getOption</a></span><span class="op">(</span><span class="st">"digits"</span><span class="op">)</span> <span class="op">-</span> <span class="fl">3</span><span class="op">)</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
-    </div>
-
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>object</dt>
-<dd><p>an object of class <a href="mmkin.html">mmkin</a></p></dd>
-
-
-<dt>conf.level</dt>
-<dd><p>confidence level for testing parameters</p></dd>
-
-
-<dt>...</dt>
-<dd><p>optional arguments passed to methods like <code>print</code>.</p></dd>
-
-
-<dt>x</dt>
-<dd><p>an object of class <code>summary.mmkin</code>.</p></dd>
-
-
-<dt>digits</dt>
-<dd><p>number of digits to use for printing</p></dd>
-
-</dl></div>
-
-    <div id="ref-examples">
-    <h2>Examples</h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="va">fits</span> <span class="op">&lt;-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span></span></span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"FOMC"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="st">"FOCUS A"</span> <span class="op">=</span> <span class="va">FOCUS_2006_A</span>,</span></span>
-<span class="r-in"><span>       <span class="st">"FOCUS C"</span> <span class="op">=</span> <span class="va">FOCUS_2006_C</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  quiet <span class="op">=</span> <span class="cn">TRUE</span>, cores <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">fits</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Error model: Constant variance </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Fitted in 0.509 s</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Status:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>       dataset</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> model  FOCUS A FOCUS C</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   SFO  OK      OK     </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   FOMC OK      OK     </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> OK: No warnings</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Ill-defined parameters:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>       dataset</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> model  FOCUS A                      FOCUS C</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   SFO                                      </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   FOMC parent_0, alpha, beta, sigma        </span>
-<span class="r-in"><span></span></span>
-</code></pre></div>
-    </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/summary.nlme.mmkin.html b/docs/dev/reference/summary.nlme.mmkin.html
deleted file mode 100644
index cb2c20bd..00000000
--- a/docs/dev/reference/summary.nlme.mmkin.html
+++ /dev/null
@@ -1,435 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Summary method for class "nlme.mmkin" — summary.nlme.mmkin • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Summary method for class " nlme.mmkin summary.nlme.mmkin><meta property="og:description" content="Lists model equations, initial parameter values, optimised parameters
-for fixed effects (population), random effects (deviations from the
-population mean) and residual error model, as well as the resulting
-endpoints such as formation fractions and DT50 values. Optionally
-(default is FALSE), the data are listed in full."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Summary method for class "nlme.mmkin"</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/summary.nlme.mmkin.R" class="external-link"><code>R/summary.nlme.mmkin.R</code></a></small>
-    <div class="hidden name"><code>summary.nlme.mmkin.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Lists model equations, initial parameter values, optimised parameters
-for fixed effects (population), random effects (deviations from the
-population mean) and residual error model, as well as the resulting
-endpoints such as formation fractions and DT50 values. Optionally
-(default is FALSE), the data are listed in full.</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for nlme.mmkin</span></span>
-<span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span></span>
-<span>  <span class="va">object</span>,</span>
-<span>  data <span class="op">=</span> <span class="cn">FALSE</span>,</span>
-<span>  verbose <span class="op">=</span> <span class="cn">FALSE</span>,</span>
-<span>  distimes <span class="op">=</span> <span class="cn">TRUE</span>,</span>
-<span>  alpha <span class="op">=</span> <span class="fl">0.05</span>,</span>
-<span>  <span class="va">...</span></span>
-<span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># S3 method for summary.nlme.mmkin</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, digits <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html" class="external-link">max</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fu"><a href="https://rdrr.io/r/base/options.html" class="external-link">getOption</a></span><span class="op">(</span><span class="st">"digits"</span><span class="op">)</span> <span class="op">-</span> <span class="fl">3</span><span class="op">)</span>, verbose <span class="op">=</span> <span class="va">x</span><span class="op">$</span><span class="va">verbose</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
-    </div>
-
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>object</dt>
-<dd><p>an object of class <a href="nlme.mmkin.html">nlme.mmkin</a></p></dd>
-
-
-<dt>data</dt>
-<dd><p>logical, indicating whether the full data should be included in
-the summary.</p></dd>
-
-
-<dt>verbose</dt>
-<dd><p>Should the summary be verbose?</p></dd>
-
-
-<dt>distimes</dt>
-<dd><p>logical, indicating whether DT50 and DT90 values should be
-included.</p></dd>
-
-
-<dt>alpha</dt>
-<dd><p>error level for confidence interval estimation from the t
-distribution</p></dd>
-
-
-<dt>...</dt>
-<dd><p>optional arguments passed to methods like <code>print</code>.</p></dd>
-
-
-<dt>x</dt>
-<dd><p>an object of class summary.nlme.mmkin</p></dd>
-
-
-<dt>digits</dt>
-<dd><p>Number of digits to use for printing</p></dd>
-
-</dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>The summary function returns a list based on the <a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a> object
-obtained in the fit, with at least the following additional components</p>
-<dl><dt>nlmeversion, mkinversion, Rversion</dt>
-<dd><p>The nlme, mkin and R versions used</p></dd>
-
-<dt>date.fit, date.summary</dt>
-<dd><p>The dates where the fit and the summary were
-produced</p></dd>
-
-<dt>diffs</dt>
-<dd><p>The differential equations used in the degradation model</p></dd>
-
-<dt>use_of_ff</dt>
-<dd><p>Was maximum or minimum use made of formation fractions</p></dd>
-
-<dt>data</dt>
-<dd><p>The data</p></dd>
-
-<dt>confint_trans</dt>
-<dd><p>Transformed parameters as used in the optimisation, with confidence intervals</p></dd>
-
-<dt>confint_back</dt>
-<dd><p>Backtransformed parameters, with confidence intervals if available</p></dd>
-
-<dt>ff</dt>
-<dd><p>The estimated formation fractions derived from the fitted
-model.</p></dd>
-
-<dt>distimes</dt>
-<dd><p>The DT50 and DT90 values for each observed variable.</p></dd>
-
-<dt>SFORB</dt>
-<dd><p>If applicable, eigenvalues of SFORB components of the model.</p></dd>
-
-</dl><p>The print method is called for its side effect, i.e. printing the summary.</p>
-    </div>
-    <div id="author">
-    <h2>Author</h2>
-    <p>Johannes Ranke for the mkin specific parts
-José Pinheiro and Douglas Bates for the components inherited from nlme</p>
-    </div>
-
-    <div id="ref-examples">
-    <h2>Examples</h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co"># Generate five datasets following SFO kinetics</span></span></span>
-<span class="r-in"><span><span class="va">sampling_times</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span>, <span class="fl">3</span>, <span class="fl">7</span>, <span class="fl">14</span>, <span class="fl">28</span>, <span class="fl">60</span>, <span class="fl">90</span>, <span class="fl">120</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">dt50_sfo_in_pop</span> <span class="op">&lt;-</span> <span class="fl">50</span></span></span>
-<span class="r-in"><span><span class="va">k_in_pop</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/Log.html" class="external-link">log</a></span><span class="op">(</span><span class="fl">2</span><span class="op">)</span> <span class="op">/</span> <span class="va">dt50_sfo_in_pop</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/Random.html" class="external-link">set.seed</a></span><span class="op">(</span><span class="fl">1234</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">k_in</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/Lognormal.html" class="external-link">rlnorm</a></span><span class="op">(</span><span class="fl">5</span>, <span class="fu"><a href="https://rdrr.io/r/base/Log.html" class="external-link">log</a></span><span class="op">(</span><span class="va">k_in_pop</span><span class="op">)</span>, <span class="fl">0.5</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">SFO</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="va">pred_sfo</span> <span class="op">&lt;-</span> <span class="kw">function</span><span class="op">(</span><span class="va">k</span><span class="op">)</span> <span class="op">{</span></span></span>
-<span class="r-in"><span>  <span class="fu"><a href="mkinpredict.html">mkinpredict</a></span><span class="op">(</span><span class="va">SFO</span>,</span></span>
-<span class="r-in"><span>    <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_parent <span class="op">=</span> <span class="va">k</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>    <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fl">100</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>    <span class="va">sampling_times</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="op">}</span></span></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="va">ds_sfo_mean</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="va">k_in</span>, <span class="va">pred_sfo</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">ds_sfo_mean</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste</a></span><span class="op">(</span><span class="st">"ds"</span>, <span class="fl">1</span><span class="op">:</span><span class="fl">5</span><span class="op">)</span></span></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/Random.html" class="external-link">set.seed</a></span><span class="op">(</span><span class="fl">12345</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">ds_sfo_syn</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="va">ds_sfo_mean</span>, <span class="kw">function</span><span class="op">(</span><span class="va">ds</span><span class="op">)</span> <span class="op">{</span></span></span>
-<span class="r-in"><span>  <span class="fu"><a href="add_err.html">add_err</a></span><span class="op">(</span><span class="va">ds</span>,</span></span>
-<span class="r-in"><span>    sdfunc <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">value</span><span class="op">)</span> <span class="fu"><a href="https://rdrr.io/r/base/MathFun.html" class="external-link">sqrt</a></span><span class="op">(</span><span class="fl">1</span><span class="op">^</span><span class="fl">2</span> <span class="op">+</span> <span class="va">value</span><span class="op">^</span><span class="fl">2</span> <span class="op">*</span> <span class="fl">0.07</span><span class="op">^</span><span class="fl">2</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>    n <span class="op">=</span> <span class="fl">1</span><span class="op">)</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span></span></span>
-<span class="r-in"><span><span class="op">}</span><span class="op">)</span></span></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co"># \dontrun{</span></span></span>
-<span class="r-in"><span><span class="co"># Evaluate using mmkin and nlme</span></span></span>
-<span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://svn.r-project.org/R-packages/trunk/nlme/" class="external-link">nlme</a></span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">f_mmkin</span> <span class="op">&lt;-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="va">ds_sfo_syn</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, error_model <span class="op">=</span> <span class="st">"tc"</span>, cores <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>Optimisation did not converge:</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> iteration limit reached without convergence (10)</span>
-<span class="r-in"><span><span class="va">f_nlme</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a></span><span class="op">(</span><span class="va">f_mmkin</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">f_nlme</span>, data <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> nlme version used for fitting:      3.1.162 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> mkin version used for pre-fitting:  1.2.3 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> R version used for fitting:         4.2.3 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Date of fit:     Sun Apr 16 08:34:41 2023 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Date of summary: Sun Apr 16 08:34:41 2023 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Equations:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> d_parent/dt = - k_parent * parent</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Data:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 90 observations of 1 variable(s) grouped in 5 datasets</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Model predictions using solution type analytical </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Fitted in 0.194 s using 4 iterations</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Variance model: Two-component variance function </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Mean of starting values for individual parameters:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     parent_0 log_k_parent </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>      101.569       -4.454 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Fixed degradation parameter values:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> None</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Results:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     AIC   BIC logLik</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   584.5 599.5 -286.2</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Optimised, transformed parameters with symmetric confidence intervals:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>               lower    est.   upper</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0     99.371 101.592 103.814</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k_parent -4.973  -4.449  -3.926</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Correlation: </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>              parnt_0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k_parent 0.0507 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Random effects:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Formula: list(parent_0 ~ 1, log_k_parent ~ 1)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Level: ds</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Structure: Diagonal</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         parent_0 log_k_parent Residual</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> StdDev: 6.92e-05       0.5863        1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Variance function:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Structure: Constant plus proportion of variance covariate</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Formula: ~fitted(.) </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Parameter estimates:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>        const         prop </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 0.0001208154 0.0789968021 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Backtransformed parameters with asymmetric confidence intervals:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>              lower      est.     upper</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0 99.370882 101.59243 103.81398</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_parent  0.006923   0.01168   0.01972</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Estimated disappearance times:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         DT50  DT90</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent 59.32 197.1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Data:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    ds   name time observed predicted  residual    std standardized</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 1 parent    0    104.1   101.592   2.50757 8.0255     0.312451</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 1 parent    0    105.0   101.592   3.40757 8.0255     0.424594</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 1 parent    1     98.5   100.796  -2.29571 7.9625    -0.288313</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 1 parent    1     96.1   100.796  -4.69571 7.9625    -0.589725</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 1 parent    3    101.9    99.221   2.67904 7.8381     0.341796</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 1 parent    3     85.2    99.221 -14.02096 7.8381    -1.788812</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 1 parent    7     99.1    96.145   2.95512 7.5951     0.389081</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 1 parent    7     93.0    96.145  -3.14488 7.5951    -0.414065</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 1 parent   14     88.1    90.989  -2.88944 7.1879    -0.401987</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 1 parent   14     84.1    90.989  -6.88944 7.1879    -0.958480</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 1 parent   28     80.2    81.493  -1.29305 6.4377    -0.200857</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 1 parent   28     91.3    81.493   9.80695 6.4377     1.523364</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 1 parent   60     65.1    63.344   1.75642 5.0039     0.351008</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 1 parent   60     65.8    63.344   2.45642 5.0039     0.490898</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 1 parent   90     47.8    50.018  -2.21764 3.9512    -0.561252</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 1 parent   90     53.5    50.018   3.48236 3.9512     0.881335</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 1 parent  120     37.6    39.495  -1.89515 3.1200    -0.607423</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 1 parent  120     39.3    39.495  -0.19515 3.1200    -0.062549</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 2 parent    0    107.9   101.592   6.30757 8.0255     0.785943</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 2 parent    0    102.1   101.592   0.50757 8.0255     0.063245</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 2 parent    1    103.8   100.058   3.74159 7.9043     0.473361</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 2 parent    1    108.6   100.058   8.54159 7.9043     1.080626</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 2 parent    3     91.0    97.060  -6.05952 7.6674    -0.790297</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 2 parent    3     84.9    97.060 -12.15952 7.6674    -1.585874</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 2 parent    7     79.3    91.329 -12.02867 7.2147    -1.667251</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 2 parent    7    100.9    91.329   9.57133 7.2147     1.326647</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 2 parent   14     77.3    82.102  -4.80185 6.4858    -0.740366</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 2 parent   14     83.5    82.102   1.39815 6.4858     0.215571</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 2 parent   28     66.8    66.351   0.44945 5.2415     0.085748</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 2 parent   28     63.3    66.351  -3.05055 5.2415    -0.582002</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 2 parent   60     40.8    40.775   0.02474 3.2211     0.007679</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 2 parent   60     44.8    40.775   4.02474 3.2211     1.249485</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 2 parent   90     27.8    25.832   1.96762 2.0407     0.964198</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 2 parent   90     27.0    25.832   1.16762 2.0407     0.572171</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 2 parent  120     15.2    16.366  -1.16561 1.2928    -0.901595</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 2 parent  120     15.5    16.366  -0.86561 1.2928    -0.669547</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 3 parent    0     97.7   101.592  -3.89243 8.0255    -0.485009</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 3 parent    0     88.2   101.592 -13.39243 8.0255    -1.668739</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 3 parent    1    109.9    99.218  10.68196 7.8379     1.362858</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 3 parent    1     97.8    99.218  -1.41804 7.8379    -0.180921</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 3 parent    3    100.5    94.634   5.86555 7.4758     0.784603</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 3 parent    3     77.4    94.634 -17.23445 7.4758    -2.305360</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 3 parent    7     78.3    86.093  -7.79273 6.8011    -1.145813</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 3 parent    7     90.3    86.093   4.20727 6.8011     0.618620</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 3 parent   14     76.0    72.958   3.04222 5.7634     0.527848</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 3 parent   14     79.1    72.958   6.14222 5.7634     1.065722</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 3 parent   28     46.0    52.394  -6.39404 4.1390    -1.544842</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 3 parent   28     53.4    52.394   1.00596 4.1390     0.243046</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 3 parent   60     25.1    24.582   0.51786 1.9419     0.266676</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 3 parent   60     21.4    24.582  -3.18214 1.9419    -1.638664</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 3 parent   90     11.0    12.092  -1.09202 0.9552    -1.143199</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 3 parent   90     14.2    12.092   2.10798 0.9552     2.206776</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 3 parent  120      5.8     5.948  -0.14810 0.4699    -0.315178</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 3 parent  120      6.1     5.948   0.15190 0.4699     0.323282</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 4 parent    0     95.3   101.592  -6.29243 8.0255    -0.784057</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 4 parent    0    102.0   101.592   0.40757 8.0255     0.050784</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 4 parent    1    104.4   101.125   3.27549 7.9885     0.410025</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 4 parent    1    105.4   101.125   4.27549 7.9885     0.535205</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 4 parent    3    113.7   100.195  13.50487 7.9151     1.706218</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 4 parent    3     82.3   100.195 -17.89513 7.9151    -2.260886</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 4 parent    7     98.1    98.362  -0.26190 7.7703    -0.033706</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 4 parent    7     87.8    98.362 -10.56190 7.7703    -1.359270</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 4 parent   14     97.9    95.234   2.66590 7.5232     0.354357</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 4 parent   14    104.8    95.234   9.56590 7.5232     1.271521</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 4 parent   28     85.0    89.274  -4.27372 7.0523    -0.606001</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 4 parent   28     77.2    89.274 -12.07372 7.0523    -1.712017</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 4 parent   60     82.2    77.013   5.18660 6.0838     0.852526</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 4 parent   60     86.1    77.013   9.08660 6.0838     1.493571</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 4 parent   90     70.5    67.053   3.44692 5.2970     0.650733</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 4 parent   90     61.7    67.053  -5.35308 5.2970    -1.010591</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 4 parent  120     60.0    58.381   1.61905 4.6119     0.351058</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 4 parent  120     56.4    58.381  -1.98095 4.6119    -0.429530</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 5 parent    0     92.6   101.592  -8.99243 8.0255    -1.120485</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 5 parent    0    116.5   101.592  14.90757 8.0255     1.857531</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 5 parent    1    108.0    99.914   8.08560 7.8929     1.024413</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 5 parent    1    104.9    99.914   4.98560 7.8929     0.631655</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 5 parent    3    100.5    96.641   3.85898 7.6343     0.505477</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 5 parent    3     89.5    96.641  -7.14102 7.6343    -0.935383</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 5 parent    7     91.7    90.412   1.28752 7.1423     0.180267</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 5 parent    7     95.1    90.412   4.68752 7.1423     0.656304</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 5 parent   14     82.2    80.463   1.73715 6.3563     0.273295</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 5 parent   14     84.5    80.463   4.03715 6.3563     0.635141</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 5 parent   28     60.5    63.728  -3.22788 5.0343    -0.641178</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 5 parent   28     72.8    63.728   9.07212 5.0343     1.802062</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 5 parent   60     38.3    37.399   0.90061 2.9544     0.304835</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 5 parent   60     40.7    37.399   3.30061 2.9544     1.117174</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 5 parent   90     22.5    22.692  -0.19165 1.7926    -0.106913</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 5 parent   90     20.8    22.692  -1.89165 1.7926    -1.055273</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 5 parent  120     13.4    13.768  -0.36790 1.0876    -0.338259</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 5 parent  120     13.8    13.768   0.03210 1.0876     0.029517</span>
-<span class="r-in"><span><span class="co"># }</span></span></span>
-<span class="r-in"><span></span></span>
-</code></pre></div>
-    </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/summary.nlmixr.mmkin.html b/docs/dev/reference/summary.nlmixr.mmkin.html
deleted file mode 100644
index 31f68f3f..00000000
--- a/docs/dev/reference/summary.nlmixr.mmkin.html
+++ /dev/null
@@ -1,2914 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Summary method for class "nlmixr.mmkin" — summary.nlmixr.mmkin • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Summary method for class " nlmixr.mmkin summary.nlmixr.mmkin><meta property="og:description" content="Lists model equations, initial parameter values, optimised parameters
-for fixed effects (population), random effects (deviations from the
-population mean) and residual error model, as well as the resulting
-endpoints such as formation fractions and DT50 values. Optionally
-(default is FALSE), the data are listed in full."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.1.0</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Functions and data</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Summary method for class "nlmixr.mmkin"</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/summary.nlmixr.mmkin.R" class="external-link"><code>R/summary.nlmixr.mmkin.R</code></a></small>
-    <div class="hidden name"><code>summary.nlmixr.mmkin.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Lists model equations, initial parameter values, optimised parameters
-for fixed effects (population), random effects (deviations from the
-population mean) and residual error model, as well as the resulting
-endpoints such as formation fractions and DT50 values. Optionally
-(default is FALSE), the data are listed in full.</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="co"># S3 method for nlmixr.mmkin</span>
-<span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">object</span>, data <span class="op">=</span> <span class="cn">FALSE</span>, verbose <span class="op">=</span> <span class="cn">FALSE</span>, distimes <span class="op">=</span> <span class="cn">TRUE</span>, <span class="va">...</span><span class="op">)</span>
-
-<span class="co"># S3 method for summary.nlmixr.mmkin</span>
-<span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, digits <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html" class="external-link">max</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fu"><a href="https://rdrr.io/r/base/options.html" class="external-link">getOption</a></span><span class="op">(</span><span class="st">"digits"</span><span class="op">)</span> <span class="op">-</span> <span class="fl">3</span><span class="op">)</span>, verbose <span class="op">=</span> <span class="va">x</span><span class="op">$</span><span class="va">verbose</span>, <span class="va">...</span><span class="op">)</span></code></pre></div>
-    </div>
-
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>object</dt>
-<dd><p>an object of class <a href="nlmixr.mmkin.html">nlmixr.mmkin</a></p></dd>
-<dt>data</dt>
-<dd><p>logical, indicating whether the full data should be included in
-the summary.</p></dd>
-<dt>verbose</dt>
-<dd><p>Should the summary be verbose?</p></dd>
-<dt>distimes</dt>
-<dd><p>logical, indicating whether DT50 and DT90 values should be
-included.</p></dd>
-<dt>...</dt>
-<dd><p>optional arguments passed to methods like <code>print</code>.</p></dd>
-<dt>x</dt>
-<dd><p>an object of class summary.nlmixr.mmkin</p></dd>
-<dt>digits</dt>
-<dd><p>Number of digits to use for printing</p></dd>
-</dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    <p>The summary function returns a list obtained in the fit, with at
-least the following additional components</p>
-<dl><dt>nlmixrversion, mkinversion, Rversion</dt>
-<dd><p>The nlmixr, mkin and R versions used</p></dd>
-<dt>date.fit, date.summary</dt>
-<dd><p>The dates where the fit and the summary were
-produced</p></dd>
-<dt>diffs</dt>
-<dd><p>The differential equations used in the degradation model</p></dd>
-<dt>use_of_ff</dt>
-<dd><p>Was maximum or minimum use made of formation fractions</p></dd>
-<dt>data</dt>
-<dd><p>The data</p></dd>
-<dt>confint_back</dt>
-<dd><p>Backtransformed parameters, with confidence intervals if available</p></dd>
-<dt>ff</dt>
-<dd><p>The estimated formation fractions derived from the fitted
-model.</p></dd>
-<dt>distimes</dt>
-<dd><p>The DT50 and DT90 values for each observed variable.</p></dd>
-<dt>SFORB</dt>
-<dd><p>If applicable, eigenvalues of SFORB components of the model.</p></dd>
-</dl><p>The print method is called for its side effect, i.e. printing the summary.</p>
-    </div>
-    <div id="author">
-    <h2>Author</h2>
-    <p>Johannes Ranke for the mkin specific parts
-nlmixr authors for the parts inherited from nlmixr.</p>
-    </div>
-
-    <div id="ref-examples">
-    <h2>Examples</h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="co"># Generate five datasets following DFOP-SFO kinetics</span></span>
-<span class="r-in"><span class="va">sampling_times</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span>, <span class="fl">3</span>, <span class="fl">7</span>, <span class="fl">14</span>, <span class="fl">28</span>, <span class="fl">60</span>, <span class="fl">90</span>, <span class="fl">120</span><span class="op">)</span></span>
-<span class="r-in"><span class="va">dfop_sfo</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="st">"m1"</span><span class="op">)</span>,</span>
-<span class="r-in"> m1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
-<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/Random.html" class="external-link">set.seed</a></span><span class="op">(</span><span class="fl">1234</span><span class="op">)</span></span>
-<span class="r-in"><span class="va">k1_in</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/Lognormal.html" class="external-link">rlnorm</a></span><span class="op">(</span><span class="fl">5</span>, <span class="fu"><a href="https://rdrr.io/r/base/Log.html" class="external-link">log</a></span><span class="op">(</span><span class="fl">0.1</span><span class="op">)</span>, <span class="fl">0.3</span><span class="op">)</span></span>
-<span class="r-in"><span class="va">k2_in</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/Lognormal.html" class="external-link">rlnorm</a></span><span class="op">(</span><span class="fl">5</span>, <span class="fu"><a href="https://rdrr.io/r/base/Log.html" class="external-link">log</a></span><span class="op">(</span><span class="fl">0.02</span><span class="op">)</span>, <span class="fl">0.3</span><span class="op">)</span></span>
-<span class="r-in"><span class="va">g_in</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/Logistic.html" class="external-link">plogis</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/stats/Normal.html" class="external-link">rnorm</a></span><span class="op">(</span><span class="fl">5</span>, <span class="fu"><a href="https://rdrr.io/r/stats/Logistic.html" class="external-link">qlogis</a></span><span class="op">(</span><span class="fl">0.5</span><span class="op">)</span>, <span class="fl">0.3</span><span class="op">)</span><span class="op">)</span></span>
-<span class="r-in"><span class="va">f_parent_to_m1_in</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/Logistic.html" class="external-link">plogis</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/stats/Normal.html" class="external-link">rnorm</a></span><span class="op">(</span><span class="fl">5</span>, <span class="fu"><a href="https://rdrr.io/r/stats/Logistic.html" class="external-link">qlogis</a></span><span class="op">(</span><span class="fl">0.3</span><span class="op">)</span>, <span class="fl">0.3</span><span class="op">)</span><span class="op">)</span></span>
-<span class="r-in"><span class="va">k_m1_in</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/Lognormal.html" class="external-link">rlnorm</a></span><span class="op">(</span><span class="fl">5</span>, <span class="fu"><a href="https://rdrr.io/r/base/Log.html" class="external-link">log</a></span><span class="op">(</span><span class="fl">0.02</span><span class="op">)</span>, <span class="fl">0.3</span><span class="op">)</span></span>
-<span class="r-in"></span>
-<span class="r-in"><span class="va">pred_dfop_sfo</span> <span class="op">&lt;-</span> <span class="kw">function</span><span class="op">(</span><span class="va">k1</span>, <span class="va">k2</span>, <span class="va">g</span>, <span class="va">f_parent_to_m1</span>, <span class="va">k_m1</span><span class="op">)</span> <span class="op">{</span></span>
-<span class="r-in">  <span class="fu"><a href="mkinpredict.html">mkinpredict</a></span><span class="op">(</span><span class="va">dfop_sfo</span>,</span>
-<span class="r-in">    <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k1 <span class="op">=</span> <span class="va">k1</span>, k2 <span class="op">=</span> <span class="va">k2</span>, g <span class="op">=</span> <span class="va">g</span>, f_parent_to_m1 <span class="op">=</span> <span class="va">f_parent_to_m1</span>, k_m1 <span class="op">=</span> <span class="va">k_m1</span><span class="op">)</span>,</span>
-<span class="r-in">    <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fl">100</span>, m1 <span class="op">=</span> <span class="fl">0</span><span class="op">)</span>,</span>
-<span class="r-in">    <span class="va">sampling_times</span><span class="op">)</span></span>
-<span class="r-in"><span class="op">}</span></span>
-<span class="r-in"></span>
-<span class="r-in"><span class="va">ds_mean_dfop_sfo</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fl">5</span>, <span class="kw">function</span><span class="op">(</span><span class="va">i</span><span class="op">)</span> <span class="op">{</span></span>
-<span class="r-in">  <span class="fu"><a href="mkinpredict.html">mkinpredict</a></span><span class="op">(</span><span class="va">dfop_sfo</span>,</span>
-<span class="r-in">    <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k1 <span class="op">=</span> <span class="va">k1_in</span><span class="op">[</span><span class="va">i</span><span class="op">]</span>, k2 <span class="op">=</span> <span class="va">k2_in</span><span class="op">[</span><span class="va">i</span><span class="op">]</span>, g <span class="op">=</span> <span class="va">g_in</span><span class="op">[</span><span class="va">i</span><span class="op">]</span>,</span>
-<span class="r-in">      f_parent_to_m1 <span class="op">=</span> <span class="va">f_parent_to_m1_in</span><span class="op">[</span><span class="va">i</span><span class="op">]</span>, k_m1 <span class="op">=</span> <span class="va">k_m1_in</span><span class="op">[</span><span class="va">i</span><span class="op">]</span><span class="op">)</span>,</span>
-<span class="r-in">    <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fl">100</span>, m1 <span class="op">=</span> <span class="fl">0</span><span class="op">)</span>,</span>
-<span class="r-in">    <span class="va">sampling_times</span><span class="op">)</span></span>
-<span class="r-in"><span class="op">}</span><span class="op">)</span></span>
-<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">ds_mean_dfop_sfo</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste</a></span><span class="op">(</span><span class="st">"ds"</span>, <span class="fl">1</span><span class="op">:</span><span class="fl">5</span><span class="op">)</span></span>
-<span class="r-in"></span>
-<span class="r-in"><span class="va">ds_syn_dfop_sfo</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="va">ds_mean_dfop_sfo</span>, <span class="kw">function</span><span class="op">(</span><span class="va">ds</span><span class="op">)</span> <span class="op">{</span></span>
-<span class="r-in">  <span class="fu"><a href="add_err.html">add_err</a></span><span class="op">(</span><span class="va">ds</span>,</span>
-<span class="r-in">    sdfunc <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">value</span><span class="op">)</span> <span class="fu"><a href="https://rdrr.io/r/base/MathFun.html" class="external-link">sqrt</a></span><span class="op">(</span><span class="fl">1</span><span class="op">^</span><span class="fl">2</span> <span class="op">+</span> <span class="va">value</span><span class="op">^</span><span class="fl">2</span> <span class="op">*</span> <span class="fl">0.07</span><span class="op">^</span><span class="fl">2</span><span class="op">)</span>,</span>
-<span class="r-in">    n <span class="op">=</span> <span class="fl">1</span><span class="op">)</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span></span>
-<span class="r-in"><span class="op">}</span><span class="op">)</span></span>
-<span class="r-in"></span>
-<span class="r-in"><span class="co"># \dontrun{</span></span>
-<span class="r-in"><span class="co"># Evaluate using mmkin and nlmixr</span></span>
-<span class="r-in"><span class="va">f_mmkin_dfop_sfo</span> <span class="op">&lt;-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">dfop_sfo</span><span class="op">)</span>, <span class="va">ds_syn_dfop_sfo</span>,</span>
-<span class="r-in">  quiet <span class="op">=</span> <span class="cn">TRUE</span>, error_model <span class="op">=</span> <span class="st">"tc"</span>, cores <span class="op">=</span> <span class="fl">5</span><span class="op">)</span></span>
-<span class="r-in"><span class="va">f_saemix_dfop_sfo</span> <span class="op">&lt;-</span> <span class="fu">mkin</span><span class="fu">::</span><span class="fu"><a href="saem.html">saem</a></span><span class="op">(</span><span class="va">f_mmkin_dfop_sfo</span><span class="op">)</span></span>
-<span class="r-in"><span class="va">f_nlme_dfop_sfo</span> <span class="op">&lt;-</span> <span class="fu">mkin</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a></span><span class="op">(</span><span class="va">f_mmkin_dfop_sfo</span><span class="op">)</span></span>
-<span class="r-in"><span class="va">f_nlmixr_dfop_sfo_saem</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr</a></span><span class="op">(</span><span class="va">f_mmkin_dfop_sfo</span>, est <span class="op">=</span> <span class="st">"saem"</span><span class="op">)</span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> With est = 'saem', a different error model is required for each observed variableChanging the error model to 'obs_tc' (Two-component error for each observed variable)</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → generate SAEM model</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1:    99.5921   -3.9040   -2.1350   -3.7103   -2.0097    0.3217    6.6502    0.1520    0.5415    0.1995    0.3705    0.5588    7.9084    0.0824    7.5666    0.1777</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2:    99.9835   -3.8650   -2.3586   -4.0306   -1.6536    0.0696    6.3177    0.1590    0.5144    0.1895    0.3520    0.5308    4.7746    0.0649    4.5667    0.0865</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 3:    1.0014e+02  -4.0173e+00  -2.3869e+00  -4.0840e+00  -1.2338e+00   3.4899e-02   6.0018e+00   1.5109e-01   4.8870e-01   1.8005e-01   3.3438e-01   5.0428e-01   2.8890e+00   7.0377e-02   2.9045e+00   9.5437e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 4:    1.0024e+02  -4.1414e+00  -2.4972e+00  -4.1082e+00  -1.0291e+00  -1.0684e-02   5.7017e+00   1.4354e-01   4.6427e-01   1.7105e-01   3.1766e-01   4.7907e-01   2.1715e+00   8.9921e-02   1.8765e+00   1.0403e-01</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5:    1.0068e+02  -4.1132e+00  -2.4776e+00  -4.0576e+00  -1.0391e+00  -8.8738e-02   5.8192e+00   1.3636e-01   4.4106e-01   1.6249e-01   3.0177e-01   4.5512e-01   1.6349e+00   8.3197e-02   1.4947e+00   1.0221e-01</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 6:    1.0044e+02  -4.0883e+00  -2.3931e+00  -4.1068e+00  -9.5058e-01  -1.2696e-01   5.5283e+00   1.2954e-01   4.1900e-01   1.5437e-01   2.8669e-01   4.5581e-01   1.4126e+00   8.1219e-02   1.2286e+00   1.0043e-01</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 7:    1.0103e+02  -4.0833e+00  -2.4194e+00  -4.0894e+00  -9.8547e-01   5.2360e-03   9.8755e+00   1.2306e-01   3.9805e-01   1.4665e-01   2.7235e-01   5.3394e-01   1.3620e+00   7.9842e-02   1.0649e+00   1.0298e-01</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 8:    1.0106e+02  -4.0535e+00  -2.3489e+00  -4.0682e+00  -9.4455e-01  -9.4310e-02   9.3818e+00   1.1691e-01   3.7815e-01   1.3932e-01   2.5873e-01   5.0724e-01   1.2163e+00   7.0820e-02   1.0147e+00   9.2362e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 9:    1.0150e+02  -4.0498e+00  -2.4037e+00  -4.0719e+00  -9.3384e-01  -1.2384e-01   8.9127e+00   1.1107e-01   3.5924e-01   1.3235e-01   2.4580e-01   5.3463e-01   1.0849e+00   7.7513e-02   9.8295e-01   8.5927e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 10:    1.0089e+02  -4.0313e+00  -2.3670e+00  -4.0573e+00  -9.0269e-01  -1.6427e-01   8.4670e+00   1.0551e-01   3.4128e-01   1.2573e-01   2.3351e-01   5.4980e-01   1.1274e+00   7.6000e-02   9.8582e-01   8.9740e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 11:    1.0089e+02  -4.0664e+00  -2.3547e+00  -4.0554e+00  -9.3659e-01  -1.6900e-01   8.0437e+00   1.0024e-01   3.2422e-01   1.1945e-01   2.2183e-01   5.2231e-01   1.1513e+00   7.5801e-02   9.7638e-01   8.5324e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 12:    1.0067e+02  -4.0427e+00  -2.3729e+00  -4.0413e+00  -9.2169e-01  -2.0927e-01   8.6156e+00   9.5225e-02   3.0801e-01   1.1348e-01   2.1074e-01   6.0288e-01   1.1918e+00   7.4830e-02   9.1899e-01   8.7983e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 13:    1.0042e+02  -4.0387e+00  -2.3650e+00  -4.0623e+00  -9.3973e-01  -2.2277e-01   8.1848e+00   9.0463e-02   2.9260e-01   1.0780e-01   2.0020e-01   5.7274e-01   1.1022e+00   7.3652e-02   9.1657e-01   8.7973e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 14:    1.0020e+02  -4.0227e+00  -2.3586e+00  -4.0333e+00  -9.4451e-01  -1.8020e-01   7.7756e+00   8.5940e-02   2.7797e-01   1.0241e-01   1.9019e-01   5.4410e-01   1.0728e+00   8.0169e-02   7.8974e-01   9.0162e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 15:    1.0045e+02  -4.0209e+00  -2.3544e+00  -4.0187e+00  -8.9758e-01  -2.4348e-01   7.3868e+00   8.1643e-02   2.6408e-01   9.7291e-02   1.8068e-01   6.4534e-01   1.0287e+00   7.9387e-02   7.3357e-01   9.0858e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 16:    1.0061e+02  -4.0195e+00  -2.3678e+00  -4.0419e+00  -8.9130e-01  -2.4248e-01   7.0175e+00   7.7561e-02   2.5087e-01   9.2427e-02   1.7165e-01   6.7145e-01   1.0440e+00   7.9292e-02   8.4681e-01   8.5285e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 17:    1.0034e+02  -4.0298e+00  -2.3685e+00  -4.0637e+00  -8.9550e-01  -1.0712e-01   6.6666e+00   7.3683e-02   2.3833e-01   8.7805e-02   1.6307e-01   6.3788e-01   1.0827e+00   7.1441e-02   8.5278e-01   8.8090e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 18:    99.8774   -4.0600   -2.3805   -4.0726   -0.9545   -0.1196    6.3333    0.0700    0.2264    0.0834    0.1549    0.6060    1.0056    0.0751    0.8017    0.0877</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 19:    1.0055e+02  -4.0731e+00  -2.2711e+00  -4.0824e+00  -9.5827e-01  -5.9764e-02   6.0166e+00   6.6499e-02   2.1509e-01   7.9244e-02   1.4717e-01   5.7835e-01   9.3185e-01   7.9396e-02   8.2332e-01   9.1277e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 20:    1.0060e+02  -4.1023e+00  -2.2841e+00  -4.0757e+00  -9.7931e-01  -1.0440e-01   5.7158e+00   6.3174e-02   2.0434e-01   7.5282e-02   1.3981e-01   6.4675e-01   1.0006e+00   7.7840e-02   8.7958e-01   9.1512e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 21:    1.0095e+02  -4.0955e+00  -2.3274e+00  -4.1038e+00  -9.5611e-01  -5.2481e-02   5.8063e+00   6.0015e-02   1.9412e-01   7.1518e-02   1.3282e-01   6.4943e-01   1.0926e+00   7.2665e-02   9.0450e-01   8.6278e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 22:    1.0083e+02  -4.0789e+00  -2.3108e+00  -4.1074e+00  -9.6548e-01  -4.9665e-02   5.5160e+00   5.7014e-02   1.8441e-01   6.7942e-02   1.2618e-01   6.7304e-01   9.3297e-01   8.1042e-02   8.9206e-01   9.2336e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 23:    1.0114e+02  -4.0924e+00  -2.4027e+00  -4.0947e+00  -9.7291e-01  -5.0773e-02   5.2567e+00   5.4164e-02   1.7519e-01   6.5747e-02   1.1987e-01   7.3402e-01   9.7858e-01   8.2133e-02   8.4928e-01   8.8254e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 24:    1.0151e+02  -4.0787e+00  -2.3696e+00  -4.0712e+00  -9.5647e-01  -5.4136e-02   4.9938e+00   5.1456e-02   1.6643e-01   6.2460e-02   1.1388e-01   7.1847e-01   9.7548e-01   7.7691e-02   9.0418e-01   8.9115e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 25:    1.0158e+02  -4.0830e+00  -2.3384e+00  -4.0705e+00  -9.5727e-01  -8.4974e-02   4.7442e+00   4.8883e-02   1.5811e-01   5.9337e-02   1.0818e-01   6.8255e-01   1.0340e+00   7.4497e-02   9.0691e-01   9.0886e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 26:    1.0156e+02  -4.0784e+00  -2.3030e+00  -4.1027e+00  -9.5638e-01  -4.9456e-03   4.5069e+00   4.6439e-02   1.5021e-01   5.6370e-02   1.1189e-01   6.4842e-01   1.0537e+00   7.7612e-02   9.6725e-01   8.7824e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 27:    1.0198e+02  -4.0828e+00  -2.3006e+00  -4.0803e+00  -9.9402e-01  -8.3280e-02   4.2816e+00   4.4117e-02   1.4270e-01   5.3552e-02   1.3300e-01   6.1600e-01   9.1051e-01   7.7677e-02   9.5949e-01   8.6552e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 28:    1.0183e+02  -4.0871e+00  -2.2822e+00  -4.0596e+00  -9.9920e-01  -1.1469e-01   4.0675e+00   4.1911e-02   1.3556e-01   5.0874e-02   1.4021e-01   5.8520e-01   1.0294e+00   7.0917e-02   8.6440e-01   9.0301e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 29:    1.0171e+02  -4.0838e+00  -2.2606e+00  -4.0885e+00  -9.8937e-01  -6.6488e-02   4.3393e+00   3.9815e-02   1.2878e-01   4.8330e-02   1.3320e-01   5.5594e-01   1.0042e+00   8.1585e-02   8.5434e-01   8.6032e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 30:    1.0177e+02  -4.0861e+00  -2.2615e+00  -4.0670e+00  -9.9119e-01  -1.1322e-01   6.3058e+00   3.7825e-02   1.2942e-01   4.5914e-02   1.4223e-01   5.2814e-01   9.6301e-01   7.3336e-02   9.2366e-01   8.9276e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 31:    1.0160e+02  -4.0900e+00  -2.2455e+00  -4.0514e+00  -9.8754e-01  -2.2617e-01   5.9905e+00   3.5933e-02   1.2295e-01   4.3618e-02   1.3512e-01   5.2898e-01   1.0441e+00   7.6262e-02   1.0227e+00   8.3759e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 32:    1.0193e+02  -4.0967e+00  -2.2599e+00  -4.0295e+00  -9.8917e-01  -1.6626e-01   5.6910e+00   3.4137e-02   1.2620e-01   4.1437e-02   1.4303e-01   5.0253e-01   9.8083e-01   7.4136e-02   1.0031e+00   8.5124e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 33:    1.0215e+02  -4.0713e+00  -2.2563e+00  -4.0313e+00  -9.7361e-01  -1.8246e-01   5.4065e+00   3.2430e-02   1.1989e-01   3.9365e-02   1.5371e-01   4.7741e-01   9.6935e-01   8.0491e-02   9.7610e-01   8.0590e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 34:    1.0251e+02  -4.0707e+00  -2.2635e+00  -4.0387e+00  -9.2149e-01  -1.3452e-01   6.3940e+00   3.0808e-02   1.1389e-01   3.7397e-02   1.6720e-01   4.5354e-01   9.2449e-01   7.4355e-02   1.0505e+00   8.3049e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 35:    1.0278e+02  -4.0579e+00  -2.2877e+00  -4.0289e+00  -9.6632e-01  -1.2910e-01   6.0743e+00   2.9268e-02   1.0820e-01   3.5527e-02   1.7242e-01   4.3086e-01   1.0129e+00   7.3224e-02   1.0035e+00   7.9411e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 36:    1.0283e+02  -4.0771e+00  -2.2977e+00  -4.0108e+00  -9.5228e-01  -2.1781e-01   5.7706e+00   2.7805e-02   1.0279e-01   3.3751e-02   1.7034e-01   4.0932e-01   9.0249e-01   7.9917e-02   9.4562e-01   8.2391e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 37:    1.0228e+02  -4.0442e+00  -2.3061e+00  -4.0150e+00  -9.5376e-01  -1.4873e-01   5.4821e+00   2.6414e-02   9.7651e-02   3.2063e-02   1.7772e-01   3.8885e-01   9.0446e-01   8.1418e-02   9.7276e-01   8.3331e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 38:    1.0220e+02  -4.0454e+00  -2.3204e+00  -4.0207e+00  -9.3198e-01  -1.9324e-01   5.2080e+00   2.5094e-02   1.0946e-01   3.0460e-02   1.6883e-01   3.6941e-01   8.9159e-01   7.8645e-02   9.5848e-01   8.2734e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 39:    1.0193e+02  -4.0306e+00  -2.2926e+00  -4.0131e+00  -9.3448e-01  -2.1869e-01   6.7005e+00   2.3839e-02   1.0399e-01   2.8937e-02   1.6039e-01   3.5571e-01   9.0078e-01   7.8982e-02   9.3495e-01   8.3462e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 40:    1.0217e+02  -4.0463e+00  -2.2823e+00  -4.0209e+00  -9.2912e-01  -2.3580e-01   8.7857e+00   2.2647e-02   9.8789e-02   2.7490e-02   1.5418e-01   3.3792e-01   8.5126e-01   7.8849e-02   9.5510e-01   8.3011e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 41:    1.0210e+02  -4.0564e+00  -2.2213e+00  -4.0041e+00  -9.4137e-01  -2.4103e-01   9.2761e+00   2.1515e-02   9.5179e-02   2.6116e-02   1.4795e-01   3.9870e-01   9.8312e-01   7.1947e-02   1.0015e+00   8.1407e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 42:    1.0201e+02  -4.0204e+00  -2.2598e+00  -3.9984e+00  -9.4412e-01  -2.6838e-01   8.8123e+00   2.0439e-02   9.0420e-02   2.4810e-02   1.5013e-01   4.0334e-01   9.5583e-01   7.5617e-02   9.5131e-01   8.2343e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 43:    1.0244e+02  -4.0107e+00  -2.2178e+00  -3.9923e+00  -9.4481e-01  -3.1831e-01   8.3717e+00   1.9417e-02   9.5791e-02   2.3569e-02   1.5440e-01   4.8194e-01   9.4582e-01   7.4374e-02   9.7798e-01   8.3391e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 44:    1.0211e+02  -4.0193e+00  -2.2379e+00  -3.9784e+00  -9.2192e-01  -3.0224e-01   7.9531e+00   1.8446e-02   9.1002e-02   2.6623e-02   1.4668e-01   4.5785e-01   9.4162e-01   7.4503e-02   9.3229e-01   7.8428e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 45:    1.0183e+02  -4.0198e+00  -2.2150e+00  -3.9966e+00  -9.6219e-01  -3.0236e-01   7.5554e+00   1.7524e-02   8.6452e-02   2.8045e-02   1.4346e-01   4.3495e-01   9.3318e-01   7.5937e-02   9.4917e-01   8.2946e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 46:    1.0132e+02  -4.0245e+00  -2.2215e+00  -3.9954e+00  -9.4368e-01  -2.6878e-01   7.1777e+00   1.6648e-02   9.5839e-02   2.6909e-02   1.4633e-01   4.1321e-01   9.7698e-01   7.1947e-02   9.6599e-01   8.3812e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 47:    1.0138e+02  -4.0076e+00  -2.2741e+00  -4.0047e+00  -9.0608e-01  -2.2839e-01   6.8188e+00   1.5815e-02   9.2192e-02   2.5563e-02   1.6160e-01   3.9255e-01   9.3678e-01   7.4216e-02   1.0295e+00   7.9396e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 48:    1.0102e+02  -4.0235e+00  -2.2764e+00  -4.0123e+00  -9.1050e-01  -2.0555e-01   6.4778e+00   1.5024e-02   8.7583e-02   2.4285e-02   1.6368e-01   3.7292e-01   9.2204e-01   7.6976e-02   1.0289e+00   8.1381e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 49:    1.0074e+02  -4.0407e+00  -2.2583e+00  -4.0142e+00  -9.2682e-01  -2.5941e-01   6.1540e+00   1.4273e-02   8.3203e-02   2.3071e-02   1.8251e-01   3.5427e-01   9.0436e-01   7.4192e-02   1.0237e+00   8.2178e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 50:    1.0046e+02  -4.0626e+00  -2.2464e+00  -4.0201e+00  -9.3021e-01  -2.6983e-01   5.8463e+00   1.4303e-02   7.9043e-02   2.3395e-02   1.7339e-01   3.3846e-01   9.1334e-01   7.8046e-02   9.5881e-01   8.1223e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 51:    1.0022e+02  -4.0414e+00  -2.2373e+00  -4.0107e+00  -9.2183e-01  -2.6371e-01   5.8667e+00   1.8681e-02   7.5091e-02   2.2225e-02   1.6472e-01   3.7327e-01   9.4906e-01   8.0171e-02   1.0278e+00   8.7157e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 52:    1.0059e+02  -4.0444e+00  -2.2676e+00  -4.0263e+00  -9.1269e-01  -2.1748e-01   5.5734e+00   1.7747e-02   7.1337e-02   2.1114e-02   1.5648e-01   3.5461e-01   8.6417e-01   8.2918e-02   1.0332e+00   8.1690e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 53:    1.0071e+02  -4.0384e+00  -2.2530e+00  -4.0171e+00  -9.2997e-01  -2.5319e-01   5.2947e+00   1.6860e-02   6.7770e-02   2.0058e-02   1.6621e-01   3.3688e-01   8.9792e-01   7.8990e-02   1.0230e+00   7.9862e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 54:    1.0067e+02  -4.0271e+00  -2.2540e+00  -4.0251e+00  -9.2151e-01  -2.2197e-01   5.0300e+00   1.6017e-02   6.4381e-02   1.9055e-02   1.7025e-01   3.2003e-01   8.7999e-01   8.0187e-02   1.0487e+00   8.1091e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 55:    1.0083e+02  -4.0326e+00  -2.2090e+00  -4.0230e+00  -9.2078e-01  -2.4936e-01   4.8429e+00   1.5216e-02   6.1162e-02   1.8103e-02   1.7211e-01   3.0403e-01   8.8923e-01   8.2248e-02   1.0522e+00   8.0777e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 56:    1.0082e+02  -4.0284e+00  -2.2350e+00  -4.0083e+00  -8.9532e-01  -1.9565e-01   5.3306e+00   1.4455e-02   5.8104e-02   1.7197e-02   1.6350e-01   2.8883e-01   8.7438e-01   7.9530e-02   1.0748e+00   7.9892e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 57:    1.0089e+02  -4.0118e+00  -2.2585e+00  -3.9924e+00  -9.5279e-01  -2.7691e-01   5.4228e+00   1.3733e-02   5.5199e-02   1.6338e-02   1.9088e-01   2.7439e-01   9.3164e-01   8.0286e-02   1.1149e+00   8.3224e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 58:    1.0103e+02  -4.0117e+00  -2.2470e+00  -3.9628e+00  -9.5632e-01  -3.1385e-01   5.1517e+00   1.3046e-02   6.2482e-02   1.5521e-02   1.8744e-01   2.8144e-01   9.6857e-01   8.0586e-02   1.0149e+00   8.0615e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 59:    1.0110e+02  -4.0400e+00  -2.2319e+00  -3.9749e+00  -9.3280e-01  -3.1491e-01   4.8941e+00   1.4375e-02   7.6521e-02   1.9982e-02   1.8994e-01   2.6736e-01   9.9294e-01   7.6723e-02   9.8321e-01   8.4135e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 60:    1.0135e+02  -4.0401e+00  -2.2969e+00  -4.0006e+00  -9.3649e-01  -2.7105e-01   4.6494e+00   1.5352e-02   7.2695e-02   1.8983e-02   1.8339e-01   2.5400e-01   9.7671e-01   8.0731e-02   1.0344e+00   8.4798e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 61:    1.0139e+02  -4.0447e+00  -2.3267e+00  -4.0036e+00  -9.2609e-01  -2.4279e-01   4.4169e+00   1.4584e-02   6.9060e-02   1.8034e-02   1.7844e-01   2.4130e-01   9.4921e-01   8.1065e-02   1.0031e+00   8.2364e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 62:    1.0187e+02  -4.0605e+00  -2.2435e+00  -4.0093e+00  -9.4594e-01  -2.2911e-01   4.1961e+00   1.3855e-02   6.5607e-02   2.0229e-02   1.6951e-01   2.2923e-01   8.5507e-01   8.5043e-02   1.0003e+00   8.4881e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 63:    1.0181e+02  -4.0665e+00  -2.2106e+00  -3.9954e+00  -9.5259e-01  -2.2539e-01   3.9863e+00   1.3162e-02   6.2327e-02   1.9217e-02   2.0722e-01   2.1777e-01   9.2699e-01   7.9303e-02   9.8900e-01   8.5477e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 64:    1.0178e+02  -4.0641e+00  -2.2152e+00  -3.9904e+00  -9.7044e-01  -2.2671e-01   3.8289e+00   1.2504e-02   5.9210e-02   1.8256e-02   2.1010e-01   2.0688e-01   8.6741e-01   8.9748e-02   9.9564e-01   8.6675e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 65:    1.0149e+02  -4.0871e+00  -2.2063e+00  -4.0000e+00  -9.7253e-01  -2.0360e-01   3.6375e+00   1.1879e-02   5.8060e-02   1.7344e-02   2.3758e-01   1.9654e-01   9.1092e-01   8.6259e-02   1.0674e+00   8.6171e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 66:    1.0131e+02  -4.0863e+00  -2.2283e+00  -4.0026e+00  -9.6884e-01  -2.2153e-01   3.4556e+00   1.1285e-02   5.5157e-02   1.6476e-02   2.5128e-01   1.8671e-01   9.2512e-01   7.7687e-02   1.0847e+00   8.2689e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 67:    1.0124e+02  -4.0691e+00  -2.2261e+00  -4.0021e+00  -9.2647e-01  -2.9309e-01   3.2828e+00   1.0721e-02   5.2400e-02   1.5653e-02   2.3872e-01   1.7738e-01   8.3514e-01   8.7680e-02   1.0093e+00   8.1515e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 68:    1.0160e+02  -4.0653e+00  -2.2627e+00  -3.9685e+00  -9.5325e-01  -2.9870e-01   3.1187e+00   1.0185e-02   5.0455e-02   1.6013e-02   2.2678e-01   1.6851e-01   9.3016e-01   8.4873e-02   1.0357e+00   8.0066e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 69:    1.0154e+02  -4.0667e+00  -2.2536e+00  -3.9686e+00  -9.8329e-01  -2.1749e-01   2.9628e+00   9.6753e-03   4.7932e-02   1.6408e-02   2.1544e-01   1.6008e-01   9.4932e-01   8.5396e-02   1.0298e+00   8.2915e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 70:    1.0140e+02  -4.0718e+00  -2.2809e+00  -4.0031e+00  -9.7818e-01  -1.5912e-01   2.8146e+00   1.2376e-02   4.5536e-02   1.5606e-02   2.0467e-01   1.5208e-01   9.8171e-01   8.4966e-02   1.0881e+00   8.4583e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 71:    1.0119e+02  -4.0810e+00  -2.2832e+00  -3.9945e+00  -9.7859e-01  -1.6288e-01   2.6739e+00   1.1757e-02   4.3259e-02   2.2477e-02   1.9444e-01   1.4447e-01   9.2355e-01   8.4027e-02   1.0557e+00   8.3942e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 72:    1.0133e+02  -4.0904e+00  -2.2582e+00  -4.0034e+00  -1.0103e+00  -1.9818e-01   2.5402e+00   1.1169e-02   6.0710e-02   2.4943e-02   1.8472e-01   1.3725e-01   8.8530e-01   8.8313e-02   9.6043e-01   8.6565e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 73:    1.0081e+02  -4.0852e+00  -2.2408e+00  -4.0324e+00  -9.8147e-01  -2.0245e-01   2.4132e+00   1.0611e-02   6.0672e-02   2.3696e-02   1.7586e-01   1.5058e-01   9.8632e-01   8.0072e-02   1.0754e+00   8.4211e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 74:    1.0096e+02  -4.0784e+00  -2.2415e+00  -4.0085e+00  -9.6374e-01  -2.1820e-01   2.2925e+00   1.0080e-02   7.8175e-02   2.2511e-02   2.0451e-01   1.4305e-01   9.7218e-01   7.5856e-02   1.0703e+00   8.2684e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 75:    1.0059e+02  -4.0614e+00  -2.2571e+00  -4.0069e+00  -9.6858e-01  -2.2415e-01   2.1779e+00   9.5763e-03   7.4266e-02   2.1385e-02   2.0050e-01   1.3590e-01   9.4625e-01   8.3873e-02   1.0545e+00   8.0628e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 76:    1.0071e+02  -4.0570e+00  -2.2509e+00  -4.0111e+00  -9.4159e-01  -2.0936e-01   2.0690e+00   9.0975e-03   1.0546e-01   2.0316e-02   1.9048e-01   1.2911e-01   9.1139e-01   8.4290e-02   1.0164e+00   8.0587e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 77:    1.0056e+02  -4.0315e+00  -2.2434e+00  -4.0068e+00  -9.1913e-01  -2.0655e-01   1.9656e+00   8.6426e-03   1.2867e-01   1.9300e-02   1.8971e-01   1.5112e-01   9.1748e-01   8.0221e-02   1.0080e+00   8.2126e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 78:    1.0054e+02  -4.0350e+00  -2.2335e+00  -4.0109e+00  -9.4315e-01  -1.7472e-01   2.6310e+00   8.2105e-03   1.3206e-01   1.8867e-02   2.0835e-01   1.8443e-01   9.7893e-01   7.9595e-02   1.0622e+00   8.3669e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 79:    1.0036e+02  -4.0500e+00  -2.2739e+00  -4.0223e+00  -9.6070e-01  -1.8756e-01   2.5665e+00   7.7999e-03   1.2546e-01   1.7924e-02   1.9794e-01   2.0106e-01   9.4360e-01   8.0070e-02   1.0209e+00   8.4807e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 80:    1.0030e+02  -4.0522e+00  -2.2890e+00  -4.0149e+00  -9.2615e-01  -2.1233e-01   2.4382e+00   7.4099e-03   1.2570e-01   1.7028e-02   1.8804e-01   2.4730e-01   9.6559e-01   8.0105e-02   1.0382e+00   7.9028e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 81:    1.0004e+02  -4.0462e+00  -2.2766e+00  -4.0158e+00  -9.0841e-01  -2.3731e-01   3.0663e+00   7.0394e-03   1.2300e-01   1.6176e-02   1.7864e-01   2.3493e-01   8.8273e-01   8.0620e-02   1.0835e+00   7.9915e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 82:    1.0015e+02  -4.0106e+00  -2.2696e+00  -3.9845e+00  -9.2535e-01  -2.7387e-01   2.9130e+00   6.6875e-03   1.5058e-01   1.5368e-02   1.6971e-01   2.4826e-01   1.0039e+00   7.8035e-02   1.1181e+00   8.5136e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 83:    1.0019e+02  -4.0111e+00  -2.2266e+00  -3.9884e+00  -8.8579e-01  -2.3822e-01   2.7674e+00   6.3531e-03   1.9061e-01   1.4599e-02   1.6122e-01   2.3585e-01   1.0256e+00   8.1065e-02   1.0988e+00   8.4111e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 84:    99.9333   -4.0014   -2.2848   -3.9877   -0.8906   -0.2014    2.6290    0.0060    0.2388    0.0139    0.1532    0.2628    0.9936    0.0809    1.1059    0.0847</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 85:    99.6658   -4.0093   -2.2848   -3.9884   -0.8922   -0.2517    2.4976    0.0057    0.2269    0.0132    0.1455    0.2920    0.9705    0.0823    1.0458    0.0834</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 86:    99.8823   -4.0064   -2.2659   -4.0038   -0.9174   -0.1848    2.3727    0.0054    0.2155    0.0125    0.1382    0.2774    0.9699    0.0796    0.9808    0.0839</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 87:    1.0051e+02  -4.0156e+00  -2.2877e+00  -4.0160e+00  -8.9867e-01  -1.3463e-01   2.3477e+00   6.2688e-03   2.0474e-01   1.1891e-02   1.4859e-01   2.6352e-01   9.0742e-01   7.8361e-02   1.0082e+00   8.1020e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 88:    1.0036e+02  -4.0013e+00  -2.2738e+00  -4.0184e+00  -9.1589e-01  -1.4154e-01   3.0581e+00   7.7930e-03   2.5551e-01   1.1297e-02   1.4116e-01   2.5035e-01   9.8581e-01   7.6769e-02   9.7255e-01   8.8653e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 89:    1.0031e+02  -4.0155e+00  -2.3418e+00  -4.0080e+00  -8.8958e-01  -1.4006e-01   3.3754e+00   7.4891e-03   2.4274e-01   1.2919e-02   1.3411e-01   2.3783e-01   8.9921e-01   8.3685e-02   9.2657e-01   8.1804e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 90:    1.0044e+02  -4.0279e+00  -2.3328e+00  -4.0081e+00  -9.1868e-01  -1.3077e-01   3.6776e+00   1.1221e-02   2.3380e-01   1.2419e-02   1.4247e-01   2.2594e-01   8.8620e-01   8.0520e-02   9.0269e-01   8.2413e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 91:    1.0007e+02  -4.0329e+00  -2.3865e+00  -4.0145e+00  -8.9566e-01  -1.2400e-01   3.7450e+00   1.0660e-02   2.2211e-01   1.3667e-02   1.3534e-01   2.1539e-01   9.5638e-01   8.0235e-02   9.4346e-01   8.5828e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 92:    1.0025e+02  -4.0506e+00  -2.3252e+00  -4.0445e+00  -9.2023e-01  -5.6654e-02   3.9282e+00   1.0127e-02   2.4912e-01   1.2984e-02   1.4349e-01   2.6955e-01   9.6021e-01   7.7975e-02   9.1878e-01   8.6695e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 93:    1.0091e+02  -4.0566e+00  -2.2760e+00  -4.0422e+00  -9.2960e-01  -5.8179e-02   3.7318e+00   9.6210e-03   2.6385e-01   1.3422e-02   1.5136e-01   2.5607e-01   9.7012e-01   7.7323e-02   1.0157e+00   8.6123e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 94:    1.0045e+02  -4.0682e+00  -2.3163e+00  -4.0391e+00  -9.3398e-01  -1.0367e-01   4.0009e+00   9.1399e-03   2.5066e-01   1.2751e-02   1.6029e-01   2.9867e-01   9.1442e-01   8.0048e-02   9.7607e-01   8.5436e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 95:    1.0029e+02  -4.0790e+00  -2.2791e+00  -4.0470e+00  -9.4031e-01  -7.2610e-02   3.8008e+00   8.6829e-03   2.3813e-01   1.2114e-02   1.5227e-01   2.8373e-01   8.9105e-01   8.2168e-02   9.5542e-01   8.5308e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 96:    1.0017e+02  -4.0818e+00  -2.3204e+00  -4.0507e+00  -9.2181e-01  -7.5195e-02   3.6108e+00   8.2488e-03   2.2622e-01   1.1508e-02   1.5273e-01   2.6955e-01   9.3179e-01   7.7026e-02   1.0092e+00   8.4966e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 97:    1.0007e+02  -4.0721e+00  -2.3606e+00  -4.0453e+00  -9.4375e-01  -7.2811e-02   3.4303e+00   1.0904e-02   2.1585e-01   1.2641e-02   1.8282e-01   2.5788e-01   9.3750e-01   8.0427e-02   9.3193e-01   8.3262e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 98:    1.0024e+02  -4.1025e+00  -2.3916e+00  -4.0401e+00  -9.6711e-01  -6.7764e-02   3.2587e+00   1.0359e-02   2.0506e-01   1.8712e-02   1.9260e-01   2.4499e-01   1.0086e+00   7.7282e-02   9.6010e-01   8.1760e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 99:    1.0024e+02  -4.0755e+00  -2.3237e+00  -4.0581e+00  -9.5543e-01  -8.1716e-02   3.0958e+00   9.8408e-03   1.9481e-01   1.7973e-02   1.8297e-01   2.5653e-01   8.6589e-01   7.7940e-02   9.2919e-01   8.4396e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 100:    1.0039e+02  -4.0828e+00  -2.2976e+00  -4.0616e+00  -9.8223e-01  -8.5593e-02   3.9873e+00   9.3488e-03   1.8507e-01   1.8837e-02   1.7382e-01   2.4370e-01   8.6471e-01   8.4021e-02   9.4587e-01   8.1697e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 101:    1.0089e+02  -4.0738e+00  -2.3406e+00  -4.0465e+00  -9.4824e-01  -4.4055e-02   5.2624e+00   8.8814e-03   1.8051e-01   1.7896e-02   1.6513e-01   2.3288e-01   8.4179e-01   8.2487e-02   8.8097e-01   8.1625e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 102:    1.0090e+02  -4.0777e+00  -2.3244e+00  -4.0500e+00  -9.4069e-01  -5.2554e-02   5.5339e+00   8.4373e-03   1.7148e-01   1.7001e-02   1.5688e-01   2.2124e-01   9.0419e-01   7.8446e-02   9.4721e-01   8.0896e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 103:    1.0067e+02  -4.0658e+00  -2.3188e+00  -4.0312e+00  -9.5981e-01  -7.3735e-02   7.0281e+00   8.0154e-03   1.6291e-01   1.6151e-02   1.7019e-01   2.1018e-01   9.3631e-01   8.1516e-02   9.8773e-01   8.0587e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 104:    1.0015e+02  -4.0729e+00  -2.3830e+00  -4.0458e+00  -9.4446e-01  -5.0075e-02   6.6767e+00   7.6147e-03   1.5476e-01   1.5343e-02   1.6822e-01   1.9967e-01   8.2373e-01   8.3029e-02   9.6909e-01   7.9505e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 105:    1.0022e+02  -4.0888e+00  -2.3301e+00  -4.0411e+00  -9.5279e-01  -1.1412e-01   6.3429e+00   7.2339e-03   1.4702e-01   1.4576e-02   1.8886e-01   2.2088e-01   9.2925e-01   7.3020e-02   1.0450e+00   8.6511e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 106:    1.0013e+02  -4.0846e+00  -2.3428e+00  -4.0530e+00  -9.1649e-01  -8.1899e-02   6.0257e+00   6.8722e-03   1.3967e-01   1.8939e-02   1.8350e-01   2.1970e-01   9.4571e-01   8.6259e-02   8.8509e-01   8.1023e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 107:    1.0026e+02  -4.0954e+00  -2.3362e+00  -4.0586e+00  -9.6302e-01  -8.0898e-03   5.7244e+00   6.5286e-03   1.3269e-01   2.2595e-02   2.0043e-01   2.4151e-01   8.8882e-01   8.8155e-02   8.2466e-01   8.5467e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 108:    1.0109e+02  -4.0796e+00  -2.3980e+00  -4.0841e+00  -9.5525e-01   2.0583e-02   5.4382e+00   6.2022e-03   1.2606e-01   3.4423e-02   2.2509e-01   2.3732e-01   9.0211e-01   8.1331e-02   9.2728e-01   8.4409e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 109:    1.0091e+02  -4.0872e+00  -2.3614e+00  -4.0620e+00  -9.5950e-01  -1.8283e-02   5.1663e+00   6.1750e-03   1.1975e-01   3.2702e-02   2.1384e-01   2.2546e-01   9.7296e-01   7.7298e-02   9.7366e-01   8.3784e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 110:    1.0137e+02  -4.0637e+00  -2.3734e+00  -4.0781e+00  -9.3874e-01  -6.0140e-03   4.9080e+00   6.5148e-03   1.1376e-01   3.1066e-02   2.0314e-01   2.3943e-01   9.2451e-01   8.4473e-02   9.7311e-01   8.4958e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 111:    1.0181e+02  -4.0588e+00  -2.3200e+00  -4.0792e+00  -9.9395e-01  -3.1776e-02   4.6626e+00   9.9112e-03   1.0808e-01   4.1755e-02   1.9299e-01   2.9896e-01   1.0697e+00   7.1561e-02   1.0062e+00   8.8034e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 112:    1.0222e+02  -4.0566e+00  -2.3525e+00  -4.0607e+00  -9.6055e-01  -6.3691e-02   4.4850e+00   9.4157e-03   1.0267e-01   4.0540e-02   1.8334e-01   3.5699e-01   1.0213e+00   7.5944e-02   1.0070e+00   8.3434e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 113:    1.0189e+02  -4.0478e+00  -2.3774e+00  -4.0434e+00  -9.6638e-01  -6.7729e-02   5.1753e+00   8.9449e-03   9.7539e-02   3.8513e-02   2.0339e-01   3.4044e-01   9.5484e-01   7.7854e-02   9.8545e-01   8.1852e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 114:    1.0225e+02  -4.0334e+00  -2.3728e+00  -4.0502e+00  -9.4795e-01  -7.3569e-02   4.9165e+00   8.8152e-03   9.2662e-02   3.7310e-02   2.0002e-01   3.2342e-01   9.3586e-01   8.1063e-02   9.5652e-01   8.3067e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 115:    1.0196e+02  -4.0302e+00  -2.3599e+00  -4.0812e+00  -9.6783e-01   7.1536e-03   4.8473e+00   9.4780e-03   1.0791e-01   3.7184e-02   1.9002e-01   3.0725e-01   9.1488e-01   7.6079e-02   1.0104e+00   8.7136e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 116:    1.0150e+02  -4.0420e+00  -2.3637e+00  -4.0535e+00  -9.4545e-01  -7.6387e-02   4.6049e+00   9.6408e-03   1.0251e-01   3.8115e-02   1.8052e-01   2.9189e-01   9.0583e-01   8.2597e-02   9.5680e-01   8.1246e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 117:    1.0161e+02  -4.0606e+00  -2.3853e+00  -4.0750e+00  -9.7273e-01  -2.7338e-03   4.3747e+00   9.1587e-03   9.7386e-02   3.7863e-02   1.8701e-01   2.7729e-01   8.4511e-01   8.4164e-02   9.4149e-01   8.0525e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 118:    1.0174e+02  -4.0727e+00  -2.3776e+00  -4.0850e+00  -9.6982e-01  -1.2188e-03   4.1560e+00   8.7008e-03   9.2516e-02   4.9300e-02   2.1364e-01   2.6343e-01   9.5447e-01   8.6627e-02   9.1835e-01   8.1284e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 119:    1.0141e+02  -4.0887e+00  -2.3138e+00  -4.0772e+00  -9.9409e-01  -5.0120e-02   3.9482e+00   8.2658e-03   8.7891e-02   4.6835e-02   2.5364e-01   2.5026e-01   9.4576e-01   8.4355e-02   9.5762e-01   8.5430e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 120:    1.0116e+02  -4.1014e+00  -2.3328e+00  -4.0418e+00  -9.7139e-01  -8.9664e-02   3.7508e+00   7.8525e-03   8.3496e-02   4.4493e-02   2.4096e-01   2.3774e-01   9.2816e-01   8.5558e-02   9.7817e-01   8.5169e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 121:    1.0099e+02  -4.0876e+00  -2.3277e+00  -4.0746e+00  -9.5453e-01  -2.6752e-02   3.5632e+00   7.4598e-03   7.9321e-02   4.2269e-02   2.2891e-01   2.2586e-01   8.9613e-01   8.0488e-02   9.7590e-01   8.4798e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 122:    1.0171e+02  -4.0620e+00  -2.3349e+00  -4.0659e+00  -9.4420e-01  -1.6907e-02   3.3851e+00   7.0869e-03   8.8424e-02   4.0155e-02   2.1747e-01   2.1456e-01   9.0159e-01   7.8017e-02   9.8579e-01   8.4585e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 123:    1.0191e+02  -4.0708e+00  -2.3487e+00  -4.0827e+00  -9.5025e-01  -5.7459e-03   3.2158e+00   6.7325e-03   1.0499e-01   3.8147e-02   2.0659e-01   2.0384e-01   9.6378e-01   7.4758e-02   9.6268e-01   8.4712e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 124:    1.0224e+02  -4.0798e+00  -2.2989e+00  -4.0724e+00  -9.8452e-01  -3.1802e-02   3.0550e+00   6.3959e-03   9.9739e-02   3.6240e-02   1.9626e-01   1.9364e-01   9.4424e-01   7.4219e-02   9.9842e-01   8.4793e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 125:    1.0242e+02  -4.0710e+00  -2.3242e+00  -4.0661e+00  -1.0094e+00  -2.9970e-02   2.9023e+00   6.0761e-03   9.4752e-02   3.6130e-02   1.8645e-01   1.8396e-01   9.2576e-01   8.0533e-02   9.9407e-01   8.1660e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 126:    1.0254e+02  -4.0685e+00  -2.3356e+00  -4.0599e+00  -1.0031e+00   4.0250e-03   2.7571e+00   5.7723e-03   1.0658e-01   3.4324e-02   1.7971e-01   1.7476e-01   9.4870e-01   7.4027e-02   9.2757e-01   8.3091e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 127:    1.0270e+02  -4.0492e+00  -2.3174e+00  -4.0476e+00  -9.9898e-01  -6.3197e-02   2.6193e+00   5.4837e-03   1.0746e-01   3.2607e-02   1.7072e-01   1.6603e-01   9.0721e-01   7.7941e-02   9.4624e-01   8.1077e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 128:    1.0284e+02  -4.0582e+00  -2.2991e+00  -4.0331e+00  -9.9114e-01  -5.6800e-02   2.4883e+00   5.2095e-03   1.2321e-01   3.1304e-02   1.6219e-01   1.5772e-01   8.7403e-01   8.4147e-02   8.9187e-01   8.5089e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 129:    1.0275e+02  -4.0773e+00  -2.2935e+00  -4.0389e+00  -9.9661e-01  -7.4747e-02   2.3639e+00   4.9922e-03   1.1705e-01   2.9738e-02   1.9667e-01   1.4984e-01   9.1481e-01   8.4947e-02   9.3224e-01   8.5270e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 130:    1.0222e+02  -4.0835e+00  -2.3177e+00  -4.0343e+00  -1.0044e+00  -7.6530e-02   2.2457e+00   4.7426e-03   1.1120e-01   3.1348e-02   2.0663e-01   1.4235e-01   1.0173e+00   7.4769e-02   9.9905e-01   8.6058e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 131:    1.0202e+02  -4.0767e+00  -2.2497e+00  -4.0270e+00  -1.0015e+00  -1.4868e-01   2.1334e+00   5.5790e-03   1.0564e-01   2.9781e-02   2.1254e-01   1.3523e-01   1.0223e+00   7.7587e-02   9.4151e-01   8.4206e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 132:    1.0191e+02  -4.0756e+00  -2.2363e+00  -4.0081e+00  -9.7264e-01  -2.0991e-01   2.0268e+00   5.3000e-03   1.0184e-01   2.8291e-02   2.0191e-01   1.7667e-01   9.2404e-01   7.9813e-02   9.2266e-01   8.6307e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 133:    1.0187e+02  -4.0743e+00  -2.1951e+00  -4.0196e+00  -9.7534e-01  -1.8742e-01   1.9254e+00   5.0350e-03   9.6744e-02   2.6877e-02   1.9181e-01   1.6784e-01   1.0007e+00   8.2043e-02   1.0028e+00   8.3921e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 134:    1.0182e+02  -4.0731e+00  -2.2321e+00  -4.0041e+00  -9.2800e-01  -1.4985e-01   1.8291e+00   4.7833e-03   9.1906e-02   2.7751e-02   1.8222e-01   1.6415e-01   9.7093e-01   7.5558e-02   9.8455e-01   8.4921e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 135:    1.0188e+02  -4.0724e+00  -2.2774e+00  -4.0213e+00  -9.3930e-01  -1.7385e-01   1.7377e+00   4.5441e-03   8.7311e-02   2.6363e-02   1.7311e-01   1.5594e-01   9.4053e-01   8.3922e-02   9.8689e-01   8.1203e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 136:    1.0202e+02  -4.0712e+00  -2.2711e+00  -4.0128e+00  -9.8747e-01  -1.3610e-01   1.6508e+00   4.3169e-03   1.0383e-01   2.5045e-02   1.8004e-01   1.4814e-01   9.6497e-01   8.1397e-02   1.0782e+00   8.2539e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 137:    1.0228e+02  -4.0676e+00  -2.2403e+00  -4.0069e+00  -9.7761e-01  -1.5314e-01   1.5683e+00   4.1011e-03   1.0931e-01   2.4722e-02   1.8328e-01   1.4074e-01   9.8300e-01   7.5906e-02   1.0221e+00   8.2306e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 138:    1.0229e+02  -4.0654e+00  -2.2510e+00  -3.9908e+00  -1.0400e+00  -1.4173e-01   1.4899e+00   4.2166e-03   1.3448e-01   2.3486e-02   1.8272e-01   1.3370e-01   1.0305e+00   8.0580e-02   1.0988e+00   8.3512e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 139:    1.0258e+02  -4.0599e+00  -2.2207e+00  -3.9622e+00  -1.0058e+00  -2.6676e-01   1.4154e+00   4.0057e-03   1.5190e-01   2.2312e-02   1.7359e-01   1.2702e-01   9.6425e-01   8.6887e-02   1.0373e+00   8.3853e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 140:    1.0224e+02  -4.0665e+00  -2.2479e+00  -4.0063e+00  -1.0054e+00  -1.6109e-01   1.3446e+00   3.8054e-03   1.4430e-01   2.4389e-02   1.6697e-01   1.2066e-01   9.9245e-01   8.3900e-02   1.1218e+00   8.2527e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 141:    1.0245e+02  -4.0843e+00  -2.2024e+00  -3.9657e+00  -1.0057e+00  -2.4900e-01   1.2774e+00   3.6152e-03   1.3709e-01   2.3170e-02   1.8559e-01   1.1463e-01   9.4091e-01   8.7616e-02   1.0760e+00   8.1871e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 142:    1.0253e+02  -4.0972e+00  -2.1943e+00  -3.9649e+00  -1.0160e+00  -2.9535e-01   1.2135e+00   3.4344e-03   1.6892e-01   2.2011e-02   1.7631e-01   1.0890e-01   9.2146e-01   9.0207e-02   1.0645e+00   8.5078e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 143:    1.0252e+02  -4.1005e+00  -2.1652e+00  -3.9687e+00  -1.0445e+00  -2.4274e-01   2.0559e+00   3.9136e-03   1.6047e-01   2.0911e-02   1.6750e-01   1.0345e-01   9.0785e-01   8.5522e-02   1.1734e+00   8.5544e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 144:    1.0257e+02  -4.1199e+00  -2.1994e+00  -3.9618e+00  -1.0480e+00  -2.0099e-01   1.9531e+00   4.2739e-03   1.5745e-01   1.9865e-02   1.6053e-01   9.8282e-02   9.3710e-01   8.5496e-02   1.0597e+00   8.4335e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 145:    1.0282e+02  -4.1356e+00  -2.1489e+00  -3.9708e+00  -1.0268e+00  -2.2250e-01   1.8554e+00   4.0602e-03   1.7177e-01   1.8872e-02   1.5251e-01   9.3368e-02   9.5312e-01   8.3609e-02   1.0695e+00   8.3492e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 146:    1.0282e+02  -4.1443e+00  -2.2103e+00  -3.9821e+00  -1.0080e+00  -2.2414e-01   1.7627e+00   4.0693e-03   1.6460e-01   1.7928e-02   1.5576e-01   8.8699e-02   9.8656e-01   8.4549e-02   1.0251e+00   7.9572e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 147:    1.0295e+02  -4.1363e+00  -2.1920e+00  -3.9828e+00  -1.0567e+00  -1.9047e-01   1.6745e+00   3.8659e-03   1.6104e-01   1.7032e-02   1.6551e-01   8.4264e-02   9.4144e-01   8.7085e-02   1.0623e+00   8.1949e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 148:    1.0299e+02  -4.1336e+00  -2.1967e+00  -3.9921e+00  -1.0425e+00  -1.7466e-01   1.7992e+00   3.6726e-03   1.5298e-01   1.9937e-02   1.9453e-01   8.0051e-02   9.9498e-01   8.3001e-02   1.0884e+00   8.1599e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 149:    1.0292e+02  -4.1282e+00  -2.2741e+00  -4.0028e+00  -1.0650e+00  -1.4879e-01   2.1425e+00   3.4890e-03   1.4534e-01   1.8941e-02   2.0240e-01   7.6049e-02   1.0080e+00   7.9550e-02   1.1679e+00   7.9115e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 150:    1.0302e+02  -4.1402e+00  -2.2769e+00  -4.0056e+00  -1.0488e+00  -1.2845e-01   2.7814e+00   3.3145e-03   1.3807e-01   2.0220e-02   1.9228e-01   7.2246e-02   9.9253e-01   8.0215e-02   1.0075e+00   8.2295e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 151:    1.0304e+02  -4.1281e+00  -2.2250e+00  -4.0169e+00  -1.0448e+00  -1.4468e-01   2.6423e+00   3.1488e-03   1.3117e-01   2.1298e-02   1.8267e-01   6.8634e-02   1.0207e+00   8.0317e-02   1.0813e+00   8.5492e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 152:    1.0321e+02  -4.1291e+00  -2.2281e+00  -4.0180e+00  -1.0232e+00  -1.4990e-01   1.6327e+00   2.8585e-03   1.0498e-01   1.8944e-02   1.6146e-01   5.0784e-02   9.5663e-01   7.9113e-02   1.1344e+00   8.2687e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 153:    1.0309e+02  -4.1280e+00  -2.2030e+00  -4.0149e+00  -1.0198e+00  -1.3442e-01   1.6757e+00   3.4142e-03   9.0693e-02   2.2471e-02   1.6167e-01   3.6279e-02   9.6659e-01   8.1655e-02   1.0345e+00   8.6113e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 154:    1.0298e+02  -4.1506e+00  -2.2176e+00  -3.9851e+00  -1.0417e+00  -1.6429e-01   1.9656e+00   3.3408e-03   9.5697e-02   2.1721e-02   1.6754e-01   2.9142e-02   1.0379e+00   7.9957e-02   1.0132e+00   8.5327e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 155:    1.0328e+02  -4.1204e+00  -2.2287e+00  -3.9772e+00  -1.0135e+00  -2.0459e-01   1.9195e+00   3.3987e-03   1.0293e-01   1.6897e-02   1.9470e-01   2.0826e-02   9.4310e-01   8.0528e-02   1.0639e+00   8.1926e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 156:    1.0338e+02  -4.1272e+00  -2.1962e+00  -3.9743e+00  -1.0491e+00  -1.8971e-01   1.7194e+00   3.2287e-03   7.8468e-02   1.8220e-02   1.8015e-01   2.0956e-02   9.2992e-01   8.6295e-02   9.5939e-01   8.1945e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 157:    1.0331e+02  -4.1188e+00  -2.1783e+00  -3.9779e+00  -1.0506e+00  -1.9810e-01   1.1127e+00   2.7176e-03   6.1234e-02   1.3748e-02   2.0147e-01   3.4800e-02   9.3006e-01   8.4794e-02   1.0579e+00   8.1604e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 158:    1.0326e+02  -4.1162e+00  -2.1734e+00  -3.9827e+00  -1.0632e+00  -2.0171e-01   1.3289e+00   2.7407e-03   7.1465e-02   1.2306e-02   2.1511e-01   3.4923e-02   8.9200e-01   8.4554e-02   1.1179e+00   8.2301e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 159:    1.0358e+02  -4.1075e+00  -2.2069e+00  -3.9725e+00  -1.0445e+00  -1.8044e-01   1.6656e+00   2.5282e-03   6.4340e-02   1.3587e-02   2.0355e-01   2.7629e-02   8.9557e-01   8.3727e-02   1.0560e+00   8.0934e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 160:    1.0364e+02  -4.1182e+00  -2.1514e+00  -3.9750e+00  -1.0378e+00  -2.2449e-01   1.8413e+00   3.5609e-03   7.2072e-02   1.5648e-02   2.0999e-01   3.1283e-02   9.1468e-01   8.2340e-02   1.0016e+00   8.0955e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 161:    1.0387e+02  -4.0964e+00  -2.1926e+00  -3.9808e+00  -1.0397e+00  -2.5178e-01   2.3706e+00   2.4158e-03   7.4658e-02   1.7789e-02   1.8587e-01   3.1647e-02   8.8257e-01   8.4503e-02   9.5909e-01   8.2216e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 162:    1.0399e+02  -4.0992e+00  -2.2056e+00  -3.9647e+00  -1.0388e+00  -2.4824e-01   1.9736e+00   2.2302e-03   1.0445e-01   1.9729e-02   1.9531e-01   2.9879e-02   8.3560e-01   8.6622e-02   9.5407e-01   7.9625e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 163:    1.0366e+02  -4.0956e+00  -2.1684e+00  -3.9612e+00  -1.0058e+00  -2.6882e-01   2.5872e+00   1.6246e-03   8.1892e-02   2.2777e-02   2.2429e-01   2.8371e-02   8.4799e-01   9.0947e-02   9.5842e-01   8.2649e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 164:    1.0325e+02  -4.0878e+00  -2.1946e+00  -3.9781e+00  -9.8890e-01  -2.6917e-01   2.0805e+00   1.3401e-03   8.2477e-02   2.6550e-02   1.8386e-01   2.4255e-02   8.5728e-01   8.9778e-02   9.1439e-01   7.9784e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 165:    1.0282e+02  -4.0801e+00  -2.1928e+00  -3.9664e+00  -9.9558e-01  -2.6059e-01   1.4087e+00   1.2155e-03   5.8081e-02   2.3833e-02   2.1871e-01   2.0911e-02   8.6998e-01   8.9895e-02   9.9363e-01   8.0284e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 166:    1.0285e+02  -4.0942e+00  -2.1855e+00  -3.9889e+00  -1.0234e+00  -2.4125e-01   8.5034e-01   6.8919e-04   6.5725e-02   2.3045e-02   2.1221e-01   2.6083e-02   8.7024e-01   9.4598e-02   9.5824e-01   8.3361e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 167:    1.0267e+02  -4.0904e+00  -2.2131e+00  -3.9917e+00  -9.9055e-01  -2.1449e-01   9.1382e-01   4.6145e-04   8.1337e-02   2.2154e-02   2.1431e-01   2.7524e-02   8.4730e-01   8.3563e-02   9.9458e-01   8.0138e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 168:    1.0249e+02  -4.0954e+00  -2.2174e+00  -3.9868e+00  -9.9799e-01  -1.8848e-01   6.5737e-01   4.2757e-04   6.9428e-02   2.8833e-02   2.0256e-01   4.1573e-02   9.8269e-01   7.6264e-02   1.0011e+00   8.1357e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 169:    1.0252e+02  -4.0995e+00  -2.2132e+00  -3.9792e+00  -9.8661e-01  -2.2724e-01   5.4553e-01   3.9691e-04   4.2460e-02   2.7545e-02   2.2594e-01   4.9079e-02   9.6027e-01   8.1006e-02   1.0261e+00   8.5194e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 170:    1.0246e+02  -4.1056e+00  -2.2389e+00  -3.9785e+00  -1.0354e+00  -2.4212e-01   4.5653e-01   2.8583e-04   2.8936e-02   2.5026e-02   1.8496e-01   4.3514e-02   9.2689e-01   9.6050e-02   9.9096e-01   8.3466e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 171:    1.0238e+02  -4.1027e+00  -2.2592e+00  -3.9859e+00  -1.0106e+00  -2.3962e-01   5.1080e-01   2.6715e-04   3.2138e-02   2.7859e-02   1.6505e-01   6.2560e-02   9.1886e-01   9.0993e-02   9.4634e-01   8.3542e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 172:    1.0242e+02  -4.0998e+00  -2.2415e+00  -3.9999e+00  -1.0234e+00  -2.2610e-01   5.1154e-01   1.8668e-04   2.2768e-02   2.9930e-02   2.0056e-01   7.5570e-02   9.4948e-01   8.4742e-02   9.1357e-01   8.3596e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 173:    1.0232e+02  -4.1005e+00  -2.2382e+00  -4.0022e+00  -1.0017e+00  -1.8406e-01   3.2169e-01   2.0484e-04   2.4102e-02   2.9249e-02   2.0445e-01   1.1517e-01   9.5169e-01   8.3951e-02   9.0402e-01   8.3130e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 174:    1.0233e+02  -4.0990e+00  -2.2337e+00  -3.9975e+00  -1.0298e+00  -1.8028e-01   4.2668e-01   2.1409e-04   2.4350e-02   2.4159e-02   2.0114e-01   1.4344e-01   9.3446e-01   8.6704e-02   9.6511e-01   8.3994e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 175:    1.0236e+02  -4.0991e+00  -2.2315e+00  -3.9883e+00  -1.0110e+00  -2.2190e-01   4.1564e-01   2.5844e-04   3.5664e-02   2.1844e-02   2.0315e-01   1.2876e-01   8.9006e-01   8.3016e-02   9.2369e-01   8.0410e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 176:    1.0222e+02  -4.0983e+00  -2.1991e+00  -3.9924e+00  -1.0148e+00  -2.3133e-01   1.7684e-01   2.5312e-04   5.3516e-02   2.2568e-02   2.0010e-01   1.0721e-01   8.9884e-01   7.6042e-02   1.0110e+00   7.9890e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 177:    1.0219e+02  -4.1022e+00  -2.2129e+00  -3.9804e+00  -1.0286e+00  -1.9916e-01   1.9962e-01   3.0338e-04   6.9476e-02   2.0133e-02   1.8707e-01   1.0631e-01   8.7419e-01   8.3199e-02   9.5450e-01   8.2626e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 178:    1.0225e+02  -4.1022e+00  -2.2135e+00  -4.0057e+00  -1.0264e+00  -2.2859e-01   2.1742e-01   2.9489e-04   5.1238e-02   2.2227e-02   1.7924e-01   1.4496e-01   8.5487e-01   8.3885e-02   9.7315e-01   8.1406e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 179:    1.0234e+02  -4.1015e+00  -2.2170e+00  -3.9999e+00  -1.0177e+00  -2.0895e-01   2.1196e-01   3.0802e-04   5.7417e-02   2.6776e-02   1.7214e-01   1.5197e-01   8.8641e-01   7.7789e-02   9.2725e-01   8.1277e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 180:    1.0243e+02  -4.1034e+00  -2.2337e+00  -4.0000e+00  -1.0018e+00  -2.3492e-01   2.0521e-01   3.1261e-04   6.0162e-02   2.7277e-02   1.7524e-01   1.6673e-01   8.8472e-01   7.8939e-02   9.1311e-01   8.3794e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 181:    1.0244e+02  -4.1031e+00  -2.2415e+00  -3.9951e+00  -9.9356e-01  -1.9938e-01   1.4850e-01   2.7894e-04   5.5244e-02   3.2404e-02   2.0701e-01   2.1022e-01   8.7495e-01   8.1883e-02   9.7574e-01   8.4575e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 182:    1.0245e+02  -4.1066e+00  -2.2849e+00  -4.0083e+00  -1.0260e+00  -1.9418e-01   1.0783e-01   1.7359e-04   2.9632e-02   3.2098e-02   2.0294e-01   1.7919e-01   9.0795e-01   9.1179e-02   9.8651e-01   8.3561e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 183:    1.0248e+02  -4.1066e+00  -2.2844e+00  -4.0201e+00  -1.0102e+00  -1.5510e-01   9.2727e-02   1.4337e-04   2.8436e-02   4.3941e-02   1.9683e-01   2.1943e-01   9.0334e-01   8.2113e-02   8.9037e-01   8.4506e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 184:    1.0239e+02  -4.1032e+00  -2.2726e+00  -4.0270e+00  -1.0162e+00  -1.4921e-01   9.0062e-02   1.0820e-04   3.5099e-02   4.7338e-02   1.8488e-01   2.2174e-01   9.1525e-01   7.9986e-02   8.6762e-01   8.4376e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 185:    1.0240e+02  -4.1028e+00  -2.3201e+00  -4.0248e+00  -9.8758e-01  -1.7235e-01   9.2455e-02   8.9653e-05   4.7097e-02   4.5964e-02   1.9093e-01   1.9093e-01   9.5192e-01   7.6980e-02   9.4058e-01   8.2740e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 186:    1.0250e+02  -4.1020e+00  -2.3095e+00  -4.0326e+00  -9.9767e-01  -1.2041e-01   1.0792e-01   7.3472e-05   3.6352e-02   4.6680e-02   1.7527e-01   1.7411e-01   9.6831e-01   8.3088e-02   9.7978e-01   8.1096e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 187:    1.0254e+02  -4.1022e+00  -2.3078e+00  -4.0329e+00  -1.0166e+00  -1.1644e-01   8.2987e-02   7.9222e-05   3.8388e-02   6.0452e-02   2.3498e-01   1.7389e-01   1.0450e+00   8.0354e-02   9.7366e-01   8.2504e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 188:    1.0253e+02  -4.1022e+00  -2.3172e+00  -4.0401e+00  -1.0276e+00  -8.2442e-02   8.3913e-02   7.1884e-05   4.3025e-02   4.8383e-02   1.9392e-01   1.6073e-01   9.7466e-01   7.5977e-02   9.0319e-01   8.4648e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 189:    1.0256e+02  -4.1013e+00  -2.3037e+00  -4.0402e+00  -1.0004e+00  -1.5946e-01   9.6273e-02   8.0773e-05   4.6130e-02   5.2356e-02   2.1044e-01   2.0403e-01   9.2931e-01   8.2686e-02   9.0103e-01   8.5442e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 190:    1.0245e+02  -4.1021e+00  -2.3143e+00  -4.0314e+00  -9.9314e-01  -8.5545e-02   8.0660e-02   7.3369e-05   4.1713e-02   4.4940e-02   1.7715e-01   1.6886e-01   9.7982e-01   8.1720e-02   8.9868e-01   8.4964e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 191:    1.0251e+02  -4.1021e+00  -2.2849e+00  -4.0229e+00  -1.0346e+00  -9.0261e-02   6.1956e-02   9.4824e-05   5.8037e-02   4.0685e-02   1.8455e-01   2.2838e-01   9.9679e-01   7.7457e-02   9.4591e-01   8.5446e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 192:    1.0249e+02  -4.1012e+00  -2.2915e+00  -4.0452e+00  -1.0323e+00  -1.1123e-01   5.1409e-02   1.1452e-04   7.2457e-02   3.6376e-02   1.6706e-01   2.1050e-01   8.7970e-01   8.6881e-02   9.2230e-01   8.6563e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 193:    1.0249e+02  -4.1011e+00  -2.3104e+00  -4.0569e+00  -1.0032e+00  -2.9383e-02   4.2462e-02   9.6872e-05   4.9405e-02   4.6840e-02   1.7348e-01   1.4473e-01   9.1758e-01   8.1380e-02   1.0057e+00   8.4481e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 194:    1.0250e+02  -4.1009e+00  -2.2960e+00  -4.0567e+00  -9.9145e-01  -1.0640e-01   2.6698e-02   5.3298e-05   4.3880e-02   5.1866e-02   2.0619e-01   1.4008e-01   9.8306e-01   8.0320e-02   9.8405e-01   8.0106e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 195:    1.0252e+02  -4.1001e+00  -2.2779e+00  -4.0677e+00  -9.9960e-01  -5.5605e-02   1.9114e-02   5.6472e-05   4.5810e-02   5.1552e-02   1.8363e-01   1.2037e-01   9.9334e-01   8.2093e-02   9.9128e-01   8.3930e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 196:    1.0251e+02  -4.0988e+00  -2.2515e+00  -4.0555e+00  -9.9701e-01  -1.0235e-01   1.3362e-02   3.2648e-05   4.0337e-02   4.4117e-02   1.9469e-01   1.2907e-01   9.1150e-01   8.5071e-02   9.7929e-01   8.2761e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 197:    1.0253e+02  -4.0985e+00  -2.2589e+00  -4.0427e+00  -1.0251e+00  -9.4695e-02   1.7104e-02   2.3125e-05   4.1773e-02   3.3831e-02   2.1013e-01   1.3727e-01   9.1462e-01   8.1836e-02   9.7695e-01   8.1326e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 198:    1.0253e+02  -4.0992e+00  -2.2745e+00  -4.0327e+00  -9.8743e-01  -1.4108e-01   9.3331e-03   2.3064e-05   3.7271e-02   2.7379e-02   2.1190e-01   9.3733e-02   9.0916e-01   8.4247e-02   9.8720e-01   7.7405e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 199:    1.0251e+02  -4.0981e+00  -2.2770e+00  -4.0275e+00  -1.0216e+00  -1.3364e-01   8.3776e-03   2.3195e-05   4.6827e-02   3.2861e-02   1.9817e-01   8.7748e-02   9.1184e-01   8.3773e-02   9.5209e-01   7.9056e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 200:    1.0252e+02  -4.0992e+00  -2.2757e+00  -4.0277e+00  -9.9266e-01  -8.4583e-02   6.8073e-03   2.9414e-05   5.3404e-02   2.5942e-02   1.9915e-01   7.4937e-02   8.7168e-01   8.5197e-02   9.3606e-01   8.0739e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 201:    1.0252e+02  -4.0989e+00  -2.2706e+00  -4.0333e+00  -9.8978e-01  -8.7996e-02   5.9272e-03   2.4268e-05   5.4295e-02   2.9276e-02   2.0543e-01   7.1533e-02   9.0557e-01   8.3204e-02   9.4913e-01   8.0398e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 202:    1.0251e+02  -4.0990e+00  -2.2661e+00  -4.0324e+00  -9.8785e-01  -9.8008e-02   5.7831e-03   2.1967e-05   5.7627e-02   3.0606e-02   2.1414e-01   6.9475e-02   9.2255e-01   8.2143e-02   9.5633e-01   8.1333e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 203:    1.0251e+02  -4.0992e+00  -2.2586e+00  -4.0297e+00  -9.9017e-01  -1.1012e-01   6.4046e-03   2.2234e-05   5.7955e-02   3.0102e-02   2.0473e-01   6.8225e-02   9.2900e-01   8.3228e-02   9.5862e-01   8.1460e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 204:    1.0251e+02  -4.0995e+00  -2.2570e+00  -4.0280e+00  -9.9135e-01  -1.0718e-01   6.5386e-03   2.2043e-05   5.6949e-02   2.9096e-02   2.0562e-01   6.8687e-02   9.3306e-01   8.2703e-02   9.6581e-01   8.1671e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 205:    1.0250e+02  -4.0997e+00  -2.2569e+00  -4.0256e+00  -9.9350e-01  -1.1491e-01   6.1521e-03   2.2570e-05   5.5450e-02   2.8152e-02   2.0423e-01   7.2820e-02   9.2469e-01   8.3272e-02   9.7372e-01   8.0814e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 206:    1.0250e+02  -4.0997e+00  -2.2567e+00  -4.0269e+00  -9.9869e-01  -1.1438e-01   5.9720e-03   2.3307e-05   5.5103e-02   2.8506e-02   2.0414e-01   7.8225e-02   9.2768e-01   8.3328e-02   9.8294e-01   8.0918e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 207:    1.0250e+02  -4.0998e+00  -2.2657e+00  -4.0311e+00  -1.0000e+00  -1.0595e-01   6.0030e-03   2.4031e-05   5.4378e-02   2.9468e-02   2.0222e-01   8.1455e-02   9.3397e-01   8.3285e-02   9.8989e-01   8.0967e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 208:    1.0250e+02  -4.0999e+00  -2.2759e+00  -4.0330e+00  -1.0001e+00  -9.6916e-02   5.9586e-03   2.3690e-05   5.3811e-02   2.9674e-02   1.9979e-01   8.1206e-02   9.3593e-01   8.4094e-02   9.8891e-01   8.1107e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 209:    1.0250e+02  -4.0998e+00  -2.2836e+00  -4.0344e+00  -1.0016e+00  -8.9119e-02   5.6137e-03   2.3512e-05   5.4159e-02   2.9780e-02   1.9778e-01   8.2635e-02   9.4080e-01   8.3757e-02   9.9062e-01   8.0979e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 210:    1.0251e+02  -4.0998e+00  -2.2898e+00  -4.0334e+00  -1.0017e+00  -8.3861e-02   5.2428e-03   2.3117e-05   5.4271e-02   2.9112e-02   1.9539e-01   8.3602e-02   9.4410e-01   8.4262e-02   9.8944e-01   8.1260e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 211:    1.0251e+02  -4.0998e+00  -2.2968e+00  -4.0329e+00  -9.9953e-01  -8.2036e-02   4.9312e-03   2.2863e-05   5.5392e-02   2.8432e-02   1.9466e-01   8.3724e-02   9.5395e-01   8.4317e-02   9.9876e-01   8.1402e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 212:    1.0251e+02  -4.0997e+00  -2.2982e+00  -4.0327e+00  -1.0018e+00  -8.2942e-02   4.6622e-03   2.3364e-05   5.5864e-02   2.7552e-02   1.9313e-01   8.3815e-02   9.5374e-01   8.4223e-02   1.0049e+00   8.1479e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 213:    1.0251e+02  -4.0995e+00  -2.2996e+00  -4.0322e+00  -1.0027e+00  -8.4931e-02   4.4988e-03   2.4085e-05   5.6000e-02   2.6729e-02   1.9184e-01   8.3522e-02   9.5091e-01   8.4321e-02   1.0026e+00   8.1310e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 214:    1.0251e+02  -4.0994e+00  -2.2997e+00  -4.0326e+00  -1.0033e+00  -8.7810e-02   4.3582e-03   2.5253e-05   5.6470e-02   2.6179e-02   1.9110e-01   8.5338e-02   9.5311e-01   8.4118e-02   1.0062e+00   8.1320e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 215:    1.0251e+02  -4.0993e+00  -2.3013e+00  -4.0331e+00  -1.0042e+00  -8.5053e-02   4.3765e-03   2.6370e-05   5.6978e-02   2.5984e-02   1.8960e-01   8.6633e-02   9.5505e-01   8.3787e-02   1.0092e+00   8.1298e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 216:    1.0251e+02  -4.0993e+00  -2.3032e+00  -4.0320e+00  -1.0033e+00  -8.5521e-02   4.4127e-03   2.7607e-05   5.7092e-02   2.5514e-02   1.8916e-01   8.8986e-02   9.5571e-01   8.3655e-02   1.0106e+00   8.1177e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 217:    1.0251e+02  -4.0993e+00  -2.3050e+00  -4.0309e+00  -1.0039e+00  -8.5267e-02   4.4663e-03   2.8101e-05   5.7536e-02   2.5186e-02   1.8803e-01   9.0776e-02   9.5624e-01   8.3792e-02   1.0100e+00   8.1214e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 218:    1.0251e+02  -4.0993e+00  -2.3068e+00  -4.0309e+00  -1.0052e+00  -8.3353e-02   4.4833e-03   2.7541e-05   5.7391e-02   2.4589e-02   1.8691e-01   9.4302e-02   9.5168e-01   8.4114e-02   1.0124e+00   8.1211e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 219:    1.0251e+02  -4.0993e+00  -2.3077e+00  -4.0316e+00  -1.0049e+00  -8.0885e-02   4.4529e-03   2.6825e-05   5.7273e-02   2.3909e-02   1.8700e-01   9.6040e-02   9.4675e-01   8.4115e-02   1.0115e+00   8.1075e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 220:    1.0251e+02  -4.0993e+00  -2.3095e+00  -4.0327e+00  -1.0051e+00  -7.6386e-02   4.5217e-03   2.6724e-05   5.7342e-02   2.3648e-02   1.8752e-01   9.7466e-02   9.4316e-01   8.4134e-02   1.0099e+00   8.1206e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 221:    1.0251e+02  -4.0993e+00  -2.3120e+00  -4.0339e+00  -1.0050e+00  -7.3314e-02   4.5361e-03   2.6133e-05   5.7387e-02   2.3612e-02   1.8744e-01   9.9108e-02   9.4164e-01   8.4073e-02   1.0096e+00   8.1143e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 222:    1.0251e+02  -4.0993e+00  -2.3136e+00  -4.0355e+00  -1.0049e+00  -6.8041e-02   4.5806e-03   2.5728e-05   5.7827e-02   2.3466e-02   1.8681e-01   9.9150e-02   9.4237e-01   8.3991e-02   1.0116e+00   8.1192e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 223:    1.0251e+02  -4.0993e+00  -2.3144e+00  -4.0364e+00  -1.0047e+00  -6.6770e-02   4.5349e-03   2.5412e-05   5.8823e-02   2.3232e-02   1.8728e-01   9.9882e-02   9.4051e-01   8.3808e-02   1.0097e+00   8.1028e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 224:    1.0251e+02  -4.0992e+00  -2.3155e+00  -4.0373e+00  -1.0040e+00  -6.4487e-02   4.4985e-03   2.5094e-05   6.0294e-02   2.3022e-02   1.8652e-01   1.0114e-01   9.3800e-01   8.3696e-02   1.0099e+00   8.0990e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 225:    1.0251e+02  -4.0992e+00  -2.3167e+00  -4.0384e+00  -1.0041e+00  -6.2966e-02   4.4783e-03   2.4741e-05   6.0569e-02   2.2900e-02   1.8622e-01   1.0072e-01   9.3574e-01   8.3833e-02   1.0075e+00   8.0893e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 226:    1.0251e+02  -4.0993e+00  -2.3173e+00  -4.0395e+00  -1.0036e+00  -6.0541e-02   4.4466e-03   2.4601e-05   6.0661e-02   2.2897e-02   1.8544e-01   1.0160e-01   9.3842e-01   8.3565e-02   1.0064e+00   8.1006e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 227:    1.0251e+02  -4.0993e+00  -2.3183e+00  -4.0403e+00  -1.0028e+00  -5.9061e-02   4.4290e-03   2.4610e-05   6.1093e-02   2.2836e-02   1.8582e-01   1.0184e-01   9.3983e-01   8.3421e-02   1.0066e+00   8.1225e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 228:    1.0251e+02  -4.0993e+00  -2.3190e+00  -4.0405e+00  -1.0027e+00  -5.8994e-02   4.4377e-03   2.4228e-05   6.0957e-02   2.2540e-02   1.8424e-01   1.0104e-01   9.4056e-01   8.3559e-02   1.0085e+00   8.1266e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 229:    1.0251e+02  -4.0993e+00  -2.3206e+00  -4.0398e+00  -1.0026e+00  -5.9258e-02   4.5021e-03   2.3801e-05   6.0331e-02   2.2380e-02   1.8409e-01   9.9681e-02   9.4320e-01   8.3678e-02   1.0111e+00   8.1236e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 230:    1.0251e+02  -4.0994e+00  -2.3217e+00  -4.0401e+00  -1.0028e+00  -5.7766e-02   4.5320e-03   2.3519e-05   5.9913e-02   2.2317e-02   1.8484e-01   9.9325e-02   9.4418e-01   8.3884e-02   1.0144e+00   8.1294e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 231:    1.0251e+02  -4.0994e+00  -2.3223e+00  -4.0400e+00  -1.0036e+00  -5.7213e-02   4.6519e-03   2.3658e-05   5.9386e-02   2.2185e-02   1.8526e-01   9.8374e-02   9.4356e-01   8.4167e-02   1.0171e+00   8.1315e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 232:    1.0251e+02  -4.0994e+00  -2.3231e+00  -4.0400e+00  -1.0045e+00  -5.5248e-02   4.7067e-03   2.3117e-05   5.9583e-02   2.2248e-02   1.8574e-01   9.7396e-02   9.4340e-01   8.4212e-02   1.0179e+00   8.1401e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 233:    1.0251e+02  -4.0994e+00  -2.3237e+00  -4.0401e+00  -1.0046e+00  -5.4085e-02   4.7534e-03   2.2858e-05   5.8985e-02   2.2248e-02   1.8649e-01   9.7364e-02   9.4271e-01   8.4248e-02   1.0189e+00   8.1456e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 234:    1.0251e+02  -4.0995e+00  -2.3238e+00  -4.0400e+00  -1.0042e+00  -5.2957e-02   4.8433e-03   2.2715e-05   5.8868e-02   2.2174e-02   1.8630e-01   9.7975e-02   9.4382e-01   8.4160e-02   1.0210e+00   8.1665e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 235:    1.0251e+02  -4.0995e+00  -2.3248e+00  -4.0396e+00  -1.0044e+00  -5.3132e-02   4.8769e-03   2.2540e-05   5.9052e-02   2.2120e-02   1.8627e-01   9.8772e-02   9.4330e-01   8.4170e-02   1.0196e+00   8.1627e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 236:    1.0251e+02  -4.0995e+00  -2.3244e+00  -4.0392e+00  -1.0052e+00  -5.3786e-02   4.8860e-03   2.2664e-05   5.8981e-02   2.2095e-02   1.8771e-01   9.8852e-02   9.4172e-01   8.4214e-02   1.0191e+00   8.1698e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 237:    1.0251e+02  -4.0996e+00  -2.3231e+00  -4.0386e+00  -1.0056e+00  -5.5158e-02   4.8731e-03   2.2576e-05   5.8788e-02   2.2037e-02   1.8860e-01   9.9599e-02   9.4242e-01   8.4097e-02   1.0198e+00   8.1684e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 238:    1.0251e+02  -4.0996e+00  -2.3222e+00  -4.0384e+00  -1.0054e+00  -5.6412e-02   4.8673e-03   2.2575e-05   5.8733e-02   2.1962e-02   1.8874e-01   9.9196e-02   9.4097e-01   8.4103e-02   1.0208e+00   8.1649e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 239:    1.0251e+02  -4.0996e+00  -2.3212e+00  -4.0382e+00  -1.0068e+00  -5.7131e-02   4.8223e-03   2.2398e-05   5.8674e-02   2.1817e-02   1.8926e-01   9.9323e-02   9.4261e-01   8.4059e-02   1.0229e+00   8.1670e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 240:    1.0251e+02  -4.0996e+00  -2.3216e+00  -4.0376e+00  -1.0073e+00  -5.6962e-02   4.7984e-03   2.2260e-05   5.8558e-02   2.1632e-02   1.8892e-01   9.9232e-02   9.4434e-01   8.4130e-02   1.0237e+00   8.1679e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 241:    1.0251e+02  -4.0996e+00  -2.3216e+00  -4.0371e+00  -1.0078e+00  -5.6984e-02   4.7767e-03   2.2074e-05   5.9552e-02   2.1436e-02   1.8860e-01   9.9285e-02   9.4491e-01   8.4026e-02   1.0247e+00   8.1680e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 242:    1.0251e+02  -4.0996e+00  -2.3222e+00  -4.0372e+00  -1.0079e+00  -5.6422e-02   4.7880e-03   2.2014e-05   6.0033e-02   2.1277e-02   1.8860e-01   9.8674e-02   9.4359e-01   8.3991e-02   1.0255e+00   8.1634e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 243:    1.0251e+02  -4.0995e+00  -2.3227e+00  -4.0376e+00  -1.0079e+00  -5.4330e-02   4.7944e-03   2.1687e-05   6.0189e-02   2.1088e-02   1.8830e-01   9.7952e-02   9.4207e-01   8.4036e-02   1.0257e+00   8.1666e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 244:    1.0251e+02  -4.0995e+00  -2.3237e+00  -4.0385e+00  -1.0076e+00  -5.2834e-02   4.7916e-03   2.1467e-05   6.0432e-02   2.1062e-02   1.8810e-01   9.7065e-02   9.4039e-01   8.4075e-02   1.0248e+00   8.1599e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 245:    1.0251e+02  -4.0995e+00  -2.3249e+00  -4.0390e+00  -1.0073e+00  -5.0542e-02   4.7926e-03   2.1549e-05   6.1095e-02   2.0981e-02   1.8814e-01   9.6715e-02   9.3900e-01   8.4049e-02   1.0226e+00   8.1505e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 246:    1.0251e+02  -4.0995e+00  -2.3261e+00  -4.0398e+00  -1.0066e+00  -4.7686e-02   4.8114e-03   2.1557e-05   6.1692e-02   2.0969e-02   1.8783e-01   9.6477e-02   9.3880e-01   8.4024e-02   1.0211e+00   8.1456e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 247:    1.0251e+02  -4.0996e+00  -2.3270e+00  -4.0406e+00  -1.0063e+00  -4.5794e-02   4.7864e-03   2.1709e-05   6.1819e-02   2.0928e-02   1.8765e-01   9.5549e-02   9.3939e-01   8.4076e-02   1.0201e+00   8.1427e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 248:    1.0251e+02  -4.0995e+00  -2.3282e+00  -4.0412e+00  -1.0064e+00  -4.3009e-02   4.7307e-03   2.1707e-05   6.2309e-02   2.0970e-02   1.8795e-01   9.5221e-02   9.3898e-01   8.4080e-02   1.0197e+00   8.1501e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 249:    1.0251e+02  -4.0996e+00  -2.3298e+00  -4.0417e+00  -1.0070e+00  -4.1062e-02   4.7070e-03   2.1737e-05   6.2607e-02   2.1094e-02   1.8796e-01   9.5103e-02   9.3917e-01   8.4069e-02   1.0194e+00   8.1525e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 250:    1.0251e+02  -4.0996e+00  -2.3313e+00  -4.0420e+00  -1.0073e+00  -3.9250e-02   4.7191e-03   2.1947e-05   6.2963e-02   2.1053e-02   1.8834e-01   9.4954e-02   9.3851e-01   8.4115e-02   1.0190e+00   8.1496e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 251:    1.0251e+02  -4.0997e+00  -2.3317e+00  -4.0424e+00  -1.0078e+00  -3.7549e-02   4.7501e-03   2.2063e-05   6.3160e-02   2.0958e-02   1.8840e-01   9.4783e-02   9.3749e-01   8.4137e-02   1.0185e+00   8.1476e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 252:    1.0251e+02  -4.0997e+00  -2.3319e+00  -4.0424e+00  -1.0083e+00  -3.6657e-02   4.7441e-03   2.2081e-05   6.3250e-02   2.0891e-02   1.8911e-01   9.4954e-02   9.3708e-01   8.4108e-02   1.0180e+00   8.1479e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 253:    1.0251e+02  -4.0997e+00  -2.3323e+00  -4.0425e+00  -1.0085e+00  -3.5826e-02   4.7409e-03   2.2021e-05   6.3513e-02   2.0810e-02   1.8920e-01   9.5196e-02   9.3509e-01   8.4179e-02   1.0184e+00   8.1440e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 254:    1.0251e+02  -4.0997e+00  -2.3324e+00  -4.0426e+00  -1.0086e+00  -3.6456e-02   4.7046e-03   2.1929e-05   6.3535e-02   2.0859e-02   1.9010e-01   9.5077e-02   9.3362e-01   8.4279e-02   1.0185e+00   8.1371e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 255:    1.0251e+02  -4.0997e+00  -2.3313e+00  -4.0424e+00  -1.0088e+00  -3.7358e-02   4.6636e-03   2.1827e-05   6.3864e-02   2.0820e-02   1.9126e-01   9.4979e-02   9.3183e-01   8.4380e-02   1.0185e+00   8.1434e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 256:    1.0251e+02  -4.0997e+00  -2.3306e+00  -4.0423e+00  -1.0086e+00  -3.8558e-02   4.6418e-03   2.1776e-05   6.4176e-02   2.0845e-02   1.9175e-01   9.4890e-02   9.3144e-01   8.4436e-02   1.0188e+00   8.1420e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 257:    1.0251e+02  -4.0997e+00  -2.3297e+00  -4.0419e+00  -1.0092e+00  -3.9580e-02   4.6134e-03   2.1742e-05   6.4408e-02   2.0751e-02   1.9188e-01   9.4692e-02   9.3113e-01   8.4507e-02   1.0187e+00   8.1412e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 258:    1.0251e+02  -4.0997e+00  -2.3287e+00  -4.0415e+00  -1.0090e+00  -4.0619e-02   4.5918e-03   2.1710e-05   6.4634e-02   2.0663e-02   1.9172e-01   9.4422e-02   9.3104e-01   8.4479e-02   1.0189e+00   8.1395e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 259:    1.0251e+02  -4.0997e+00  -2.3284e+00  -4.0412e+00  -1.0086e+00  -4.1245e-02   4.5889e-03   2.1741e-05   6.5035e-02   2.0678e-02   1.9132e-01   9.3998e-02   9.2986e-01   8.4448e-02   1.0177e+00   8.1368e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 260:    1.0251e+02  -4.0997e+00  -2.3283e+00  -4.0410e+00  -1.0084e+00  -4.1907e-02   4.5818e-03   2.1877e-05   6.5755e-02   2.0691e-02   1.9113e-01   9.3631e-02   9.2955e-01   8.4378e-02   1.0172e+00   8.1346e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 261:    1.0251e+02  -4.0998e+00  -2.3282e+00  -4.0409e+00  -1.0087e+00  -4.2022e-02   4.5724e-03   2.1946e-05   6.6396e-02   2.0714e-02   1.9084e-01   9.3140e-02   9.2882e-01   8.4426e-02   1.0166e+00   8.1329e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 262:    1.0251e+02  -4.0997e+00  -2.3275e+00  -4.0408e+00  -1.0092e+00  -4.2439e-02   4.5357e-03   2.1842e-05   6.6895e-02   2.0692e-02   1.9082e-01   9.2527e-02   9.2811e-01   8.4447e-02   1.0168e+00   8.1339e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 263:    1.0251e+02  -4.0998e+00  -2.3269e+00  -4.0407e+00  -1.0097e+00  -4.2867e-02   4.5244e-03   2.1830e-05   6.7068e-02   2.0721e-02   1.9077e-01   9.2003e-02   9.2800e-01   8.4366e-02   1.0171e+00   8.1343e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 264:    1.0251e+02  -4.0998e+00  -2.3262e+00  -4.0403e+00  -1.0102e+00  -4.3066e-02   4.4987e-03   2.1812e-05   6.7458e-02   2.0718e-02   1.9065e-01   9.1528e-02   9.2710e-01   8.4389e-02   1.0163e+00   8.1355e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 265:    1.0251e+02  -4.0998e+00  -2.3264e+00  -4.0401e+00  -1.0105e+00  -4.3042e-02   4.4865e-03   2.1721e-05   6.7680e-02   2.0722e-02   1.9057e-01   9.1243e-02   9.2631e-01   8.4430e-02   1.0154e+00   8.1321e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 266:    1.0251e+02  -4.0998e+00  -2.3264e+00  -4.0397e+00  -1.0107e+00  -4.3247e-02   4.4552e-03   2.1609e-05   6.8246e-02   2.0757e-02   1.9054e-01   9.1222e-02   9.2659e-01   8.4381e-02   1.0154e+00   8.1290e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 267:    1.0251e+02  -4.0999e+00  -2.3261e+00  -4.0396e+00  -1.0107e+00  -4.4145e-02   4.4367e-03   2.1505e-05   6.8834e-02   2.0735e-02   1.9038e-01   9.1594e-02   9.2661e-01   8.4338e-02   1.0153e+00   8.1308e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 268:    1.0251e+02  -4.0999e+00  -2.3249e+00  -4.0395e+00  -1.0112e+00  -4.5394e-02   4.4239e-03   2.1411e-05   6.9776e-02   2.0749e-02   1.9017e-01   9.1916e-02   9.2711e-01   8.4286e-02   1.0158e+00   8.1400e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 269:    1.0251e+02  -4.0999e+00  -2.3236e+00  -4.0391e+00  -1.0112e+00  -4.6744e-02   4.4119e-03   2.1310e-05   7.0528e-02   2.0709e-02   1.9026e-01   9.2008e-02   9.2638e-01   8.4248e-02   1.0160e+00   8.1389e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 270:    1.0251e+02  -4.0999e+00  -2.3231e+00  -4.0388e+00  -1.0116e+00  -4.7514e-02   4.3782e-03   2.1160e-05   7.1513e-02   2.0724e-02   1.9006e-01   9.1551e-02   9.2668e-01   8.4246e-02   1.0149e+00   8.1419e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 271:    1.0251e+02  -4.0999e+00  -2.3228e+00  -4.0387e+00  -1.0117e+00  -4.7755e-02   4.3389e-03   2.1121e-05   7.1868e-02   2.0670e-02   1.8969e-01   9.1369e-02   9.2678e-01   8.4228e-02   1.0145e+00   8.1437e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 272:    1.0251e+02  -4.0999e+00  -2.3222e+00  -4.0387e+00  -1.0121e+00  -4.8163e-02   4.3080e-03   2.1146e-05   7.2226e-02   2.0710e-02   1.8983e-01   9.0962e-02   9.2639e-01   8.4261e-02   1.0132e+00   8.1523e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 273:    1.0251e+02  -4.0999e+00  -2.3216e+00  -4.0389e+00  -1.0122e+00  -4.8336e-02   4.2916e-03   2.1202e-05   7.2734e-02   2.0689e-02   1.8982e-01   9.0422e-02   9.2618e-01   8.4177e-02   1.0134e+00   8.1561e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 274:    1.0251e+02  -4.0999e+00  -2.3211e+00  -4.0388e+00  -1.0124e+00  -4.8286e-02   4.2592e-03   2.1201e-05   7.3324e-02   2.0658e-02   1.9015e-01   8.9938e-02   9.2530e-01   8.4133e-02   1.0133e+00   8.1562e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 275:    1.0251e+02  -4.0999e+00  -2.3211e+00  -4.0387e+00  -1.0128e+00  -4.8312e-02   4.2368e-03   2.1092e-05   7.3673e-02   2.0639e-02   1.9018e-01   8.9708e-02   9.2404e-01   8.4155e-02   1.0130e+00   8.1535e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 276:    1.0251e+02  -4.0999e+00  -2.3212e+00  -4.0386e+00  -1.0126e+00  -4.8249e-02   4.2070e-03   2.0985e-05   7.4089e-02   2.0674e-02   1.8992e-01   8.9341e-02   9.2339e-01   8.4163e-02   1.0121e+00   8.1515e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 277:    1.0251e+02  -4.0999e+00  -2.3212e+00  -4.0387e+00  -1.0123e+00  -4.8743e-02   4.1781e-03   2.0914e-05   7.4488e-02   2.0779e-02   1.8959e-01   8.9106e-02   9.2352e-01   8.4100e-02   1.0112e+00   8.1477e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 278:    1.0251e+02  -4.0999e+00  -2.3213e+00  -4.0385e+00  -1.0118e+00  -4.9468e-02   4.1782e-03   2.0802e-05   7.4843e-02   2.0859e-02   1.8970e-01   8.8995e-02   9.2374e-01   8.4037e-02   1.0103e+00   8.1458e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 279:    1.0251e+02  -4.0999e+00  -2.3213e+00  -4.0384e+00  -1.0115e+00  -4.9612e-02   4.1736e-03   2.0737e-05   7.5385e-02   2.0899e-02   1.8969e-01   8.8806e-02   9.2347e-01   8.3989e-02   1.0095e+00   8.1440e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 280:    1.0251e+02  -4.0999e+00  -2.3213e+00  -4.0382e+00  -1.0114e+00  -5.0460e-02   4.1900e-03   2.0708e-05   7.6051e-02   2.0933e-02   1.8968e-01   8.9243e-02   9.2293e-01   8.3972e-02   1.0085e+00   8.1467e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 281:    1.0251e+02  -4.0999e+00  -2.3215e+00  -4.0378e+00  -1.0113e+00  -5.0583e-02   4.2096e-03   2.0638e-05   7.6759e-02   2.1003e-02   1.8941e-01   8.9450e-02   9.2294e-01   8.4027e-02   1.0080e+00   8.1467e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 282:    1.0251e+02  -4.0999e+00  -2.3218e+00  -4.0380e+00  -1.0114e+00  -5.0276e-02   4.2061e-03   2.0669e-05   7.7323e-02   2.1016e-02   1.8946e-01   8.9456e-02   9.2386e-01   8.3996e-02   1.0095e+00   8.1446e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 283:    1.0251e+02  -4.0999e+00  -2.3224e+00  -4.0383e+00  -1.0115e+00  -4.9357e-02   4.2005e-03   2.0601e-05   7.7856e-02   2.1072e-02   1.8968e-01   8.9327e-02   9.2480e-01   8.3964e-02   1.0101e+00   8.1418e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 284:    1.0251e+02  -4.0999e+00  -2.3226e+00  -4.0386e+00  -1.0110e+00  -4.8703e-02   4.1977e-03   2.0549e-05   7.8183e-02   2.1122e-02   1.8956e-01   8.9430e-02   9.2470e-01   8.3898e-02   1.0098e+00   8.1428e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 285:    1.0251e+02  -4.0999e+00  -2.3232e+00  -4.0389e+00  -1.0110e+00  -4.7526e-02   4.2063e-03   2.0546e-05   7.8449e-02   2.1151e-02   1.8967e-01   8.9133e-02   9.2438e-01   8.3889e-02   1.0089e+00   8.1428e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 286:    1.0251e+02  -4.0999e+00  -2.3242e+00  -4.0389e+00  -1.0103e+00  -4.6191e-02   4.2076e-03   2.0486e-05   7.8629e-02   2.1133e-02   1.8970e-01   8.8773e-02   9.2471e-01   8.3861e-02   1.0087e+00   8.1427e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 287:    1.0251e+02  -4.0999e+00  -2.3243e+00  -4.0391e+00  -1.0105e+00  -4.5159e-02   4.1980e-03   2.0488e-05   7.8916e-02   2.1071e-02   1.8955e-01   8.8297e-02   9.2415e-01   8.3957e-02   1.0084e+00   8.1464e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 288:    1.0251e+02  -4.0999e+00  -2.3237e+00  -4.0392e+00  -1.0109e+00  -4.4970e-02   4.1921e-03   2.0495e-05   7.8764e-02   2.0953e-02   1.8988e-01   8.8160e-02   9.2539e-01   8.3963e-02   1.0103e+00   8.1466e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 289:    1.0251e+02  -4.0999e+00  -2.3232e+00  -4.0392e+00  -1.0114e+00  -4.4592e-02   4.1763e-03   2.0584e-05   7.8632e-02   2.0841e-02   1.8982e-01   8.8037e-02   9.2559e-01   8.4031e-02   1.0111e+00   8.1469e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 290:    1.0251e+02  -4.0999e+00  -2.3228e+00  -4.0393e+00  -1.0118e+00  -4.4506e-02   4.1574e-03   2.0479e-05   7.8482e-02   2.0726e-02   1.8968e-01   8.7644e-02   9.2519e-01   8.4079e-02   1.0117e+00   8.1474e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 291:    1.0251e+02  -4.0999e+00  -2.3223e+00  -4.0392e+00  -1.0120e+00  -4.4578e-02   4.1365e-03   2.0488e-05   7.8220e-02   2.0662e-02   1.8952e-01   8.7558e-02   9.2534e-01   8.4128e-02   1.0119e+00   8.1459e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 292:    1.0251e+02  -4.0999e+00  -2.3219e+00  -4.0392e+00  -1.0120e+00  -4.4486e-02   4.1205e-03   2.0514e-05   7.8288e-02   2.0601e-02   1.8947e-01   8.7669e-02   9.2591e-01   8.4106e-02   1.0124e+00   8.1443e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 293:    1.0251e+02  -4.0999e+00  -2.3217e+00  -4.0395e+00  -1.0120e+00  -4.3914e-02   4.1076e-03   2.0606e-05   7.8474e-02   2.0557e-02   1.8960e-01   8.7970e-02   9.2580e-01   8.4052e-02   1.0124e+00   8.1443e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 294:    1.0251e+02  -4.0998e+00  -2.3218e+00  -4.0399e+00  -1.0123e+00  -4.3173e-02   4.1124e-03   2.0598e-05   7.8561e-02   2.0485e-02   1.8979e-01   8.7954e-02   9.2624e-01   8.4027e-02   1.0132e+00   8.1419e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 295:    1.0251e+02  -4.0998e+00  -2.3220e+00  -4.0399e+00  -1.0123e+00  -4.2805e-02   4.1150e-03   2.0599e-05   7.8804e-02   2.0397e-02   1.8985e-01   8.8079e-02   9.2637e-01   8.3980e-02   1.0139e+00   8.1382e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 296:    1.0251e+02  -4.0998e+00  -2.3224e+00  -4.0401e+00  -1.0121e+00  -4.2813e-02   4.1342e-03   2.0618e-05   7.9093e-02   2.0359e-02   1.8980e-01   8.8328e-02   9.2555e-01   8.3955e-02   1.0137e+00   8.1344e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 297:    1.0251e+02  -4.0998e+00  -2.3225e+00  -4.0403e+00  -1.0118e+00  -4.2861e-02   4.1653e-03   2.0599e-05   7.9236e-02   2.0318e-02   1.8969e-01   8.8659e-02   9.2477e-01   8.3916e-02   1.0135e+00   8.1323e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 298:    1.0251e+02  -4.0997e+00  -2.3228e+00  -4.0404e+00  -1.0116e+00  -4.2874e-02   4.2066e-03   2.0567e-05   7.9391e-02   2.0249e-02   1.8973e-01   8.8706e-02   9.2359e-01   8.3939e-02   1.0134e+00   8.1277e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 299:    1.0251e+02  -4.0997e+00  -2.3224e+00  -4.0404e+00  -1.0113e+00  -4.3190e-02   4.2168e-03   2.0532e-05   7.9379e-02   2.0187e-02   1.8973e-01   8.8757e-02   9.2294e-01   8.3916e-02   1.0137e+00   8.1243e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 300:    1.0251e+02  -4.0997e+00  -2.3221e+00  -4.0405e+00  -1.0111e+00  -4.3480e-02   4.2289e-03   2.0490e-05   7.9314e-02   2.0140e-02   1.8983e-01   8.8620e-02   9.2242e-01   8.3900e-02   1.0137e+00   8.1210e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 301:    1.0251e+02  -4.0997e+00  -2.3213e+00  -4.0404e+00  -1.0109e+00  -4.4169e-02   4.2524e-03   2.0546e-05   7.9499e-02   2.0117e-02   1.8991e-01   8.8503e-02   9.2226e-01   8.3843e-02   1.0136e+00   8.1235e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 302:    1.0251e+02  -4.0997e+00  -2.3205e+00  -4.0403e+00  -1.0108e+00  -4.5199e-02   4.2709e-03   2.0511e-05   7.9674e-02   2.0085e-02   1.8969e-01   8.8687e-02   9.2207e-01   8.3852e-02   1.0134e+00   8.1262e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 303:    1.0251e+02  -4.0997e+00  -2.3199e+00  -4.0401e+00  -1.0106e+00  -4.6193e-02   4.2807e-03   2.0440e-05   7.9845e-02   2.0104e-02   1.8985e-01   8.8661e-02   9.2135e-01   8.3843e-02   1.0128e+00   8.1274e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 304:    1.0251e+02  -4.0997e+00  -2.3189e+00  -4.0398e+00  -1.0105e+00  -4.7180e-02   4.3058e-03   2.0450e-05   7.9808e-02   2.0159e-02   1.8987e-01   8.8660e-02   9.2076e-01   8.3856e-02   1.0120e+00   8.1277e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 305:    1.0251e+02  -4.0997e+00  -2.3180e+00  -4.0396e+00  -1.0102e+00  -4.8474e-02   4.3050e-03   2.0527e-05   7.9932e-02   2.0184e-02   1.8970e-01   8.8567e-02   9.1986e-01   8.3867e-02   1.0111e+00   8.1290e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 306:    1.0251e+02  -4.0997e+00  -2.3175e+00  -4.0393e+00  -1.0101e+00  -4.9308e-02   4.3055e-03   2.0586e-05   8.0113e-02   2.0178e-02   1.8971e-01   8.8307e-02   9.1930e-01   8.3865e-02   1.0112e+00   8.1290e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 307:    1.0251e+02  -4.0997e+00  -2.3175e+00  -4.0393e+00  -1.0103e+00  -4.9278e-02   4.3027e-03   2.0441e-05   8.0168e-02   2.0211e-02   1.8973e-01   8.7993e-02   9.1919e-01   8.3837e-02   1.0109e+00   8.1287e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 308:    1.0251e+02  -4.0998e+00  -2.3175e+00  -4.0394e+00  -1.0103e+00  -4.9430e-02   4.2899e-03   2.0384e-05   8.0417e-02   2.0187e-02   1.8983e-01   8.7757e-02   9.1937e-01   8.3778e-02   1.0106e+00   8.1289e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 309:    1.0251e+02  -4.0998e+00  -2.3175e+00  -4.0393e+00  -1.0103e+00  -4.9319e-02   4.2773e-03   2.0349e-05   8.0807e-02   2.0161e-02   1.8959e-01   8.7493e-02   9.1950e-01   8.3734e-02   1.0108e+00   8.1279e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 310:    1.0251e+02  -4.0998e+00  -2.3174e+00  -4.0393e+00  -1.0101e+00  -4.8880e-02   4.2710e-03   2.0327e-05   8.1202e-02   2.0116e-02   1.8983e-01   8.7251e-02   9.1864e-01   8.3730e-02   1.0108e+00   8.1294e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 311:    1.0251e+02  -4.0998e+00  -2.3174e+00  -4.0394e+00  -1.0100e+00  -4.8080e-02   4.2611e-03   2.0272e-05   8.1332e-02   2.0078e-02   1.9003e-01   8.6995e-02   9.1844e-01   8.3699e-02   1.0105e+00   8.1319e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 312:    1.0251e+02  -4.0998e+00  -2.3173e+00  -4.0394e+00  -1.0100e+00  -4.7724e-02   4.2663e-03   2.0239e-05   8.1553e-02   2.0021e-02   1.8982e-01   8.6721e-02   9.1838e-01   8.3721e-02   1.0100e+00   8.1312e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 313:    1.0251e+02  -4.0998e+00  -2.3170e+00  -4.0395e+00  -1.0100e+00  -4.7216e-02   4.2775e-03   2.0213e-05   8.2001e-02   1.9971e-02   1.8980e-01   8.6651e-02   9.1837e-01   8.3697e-02   1.0100e+00   8.1318e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 314:    1.0251e+02  -4.0998e+00  -2.3169e+00  -4.0398e+00  -1.0100e+00  -4.6902e-02   4.2949e-03   2.0265e-05   8.2392e-02   1.9922e-02   1.8988e-01   8.6426e-02   9.1845e-01   8.3722e-02   1.0103e+00   8.1309e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 315:    1.0251e+02  -4.0998e+00  -2.3168e+00  -4.0399e+00  -1.0102e+00  -4.6480e-02   4.3206e-03   2.0314e-05   8.2534e-02   1.9912e-02   1.8972e-01   8.6211e-02   9.1792e-01   8.3758e-02   1.0103e+00   8.1337e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 316:    1.0251e+02  -4.0998e+00  -2.3169e+00  -4.0400e+00  -1.0100e+00  -4.6238e-02   4.3456e-03   2.0323e-05   8.2729e-02   1.9936e-02   1.8975e-01   8.5856e-02   9.1759e-01   8.3814e-02   1.0095e+00   8.1353e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 317:    1.0251e+02  -4.0998e+00  -2.3169e+00  -4.0402e+00  -1.0103e+00  -4.5470e-02   4.3521e-03   2.0275e-05   8.2864e-02   1.9965e-02   1.8980e-01   8.5438e-02   9.1755e-01   8.3894e-02   1.0088e+00   8.1373e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 318:    1.0251e+02  -4.0998e+00  -2.3169e+00  -4.0402e+00  -1.0106e+00  -4.5001e-02   4.3661e-03   2.0191e-05   8.2949e-02   1.9983e-02   1.9001e-01   8.5203e-02   9.1746e-01   8.3944e-02   1.0078e+00   8.1401e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 319:    1.0251e+02  -4.0998e+00  -2.3169e+00  -4.0403e+00  -1.0108e+00  -4.4545e-02   4.3665e-03   2.0114e-05   8.2694e-02   2.0003e-02   1.9041e-01   8.4997e-02   9.1774e-01   8.3940e-02   1.0073e+00   8.1417e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 320:    1.0251e+02  -4.0998e+00  -2.3171e+00  -4.0402e+00  -1.0109e+00  -4.4419e-02   4.3600e-03   2.0043e-05   8.2516e-02   1.9999e-02   1.9044e-01   8.4873e-02   9.1798e-01   8.3932e-02   1.0070e+00   8.1420e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 321:    1.0251e+02  -4.0998e+00  -2.3170e+00  -4.0401e+00  -1.0107e+00  -4.4267e-02   4.3539e-03   1.9995e-05   8.2307e-02   1.9967e-02   1.9050e-01   8.4982e-02   9.1814e-01   8.3919e-02   1.0067e+00   8.1429e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 322:    1.0251e+02  -4.0998e+00  -2.3170e+00  -4.0400e+00  -1.0106e+00  -4.4240e-02   4.3331e-03   1.9984e-05   8.2305e-02   1.9961e-02   1.9054e-01   8.5443e-02   9.1821e-01   8.3910e-02   1.0065e+00   8.1424e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 323:    1.0251e+02  -4.0998e+00  -2.3166e+00  -4.0397e+00  -1.0105e+00  -4.4793e-02   4.3199e-03   1.9921e-05   8.2300e-02   1.9935e-02   1.9078e-01   8.6081e-02   9.1836e-01   8.3854e-02   1.0062e+00   8.1448e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 324:    1.0251e+02  -4.0998e+00  -2.3167e+00  -4.0395e+00  -1.0104e+00  -4.4862e-02   4.3167e-03   1.9926e-05   8.2063e-02   1.9919e-02   1.9086e-01   8.6736e-02   9.1865e-01   8.3834e-02   1.0060e+00   8.1439e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 325:    1.0251e+02  -4.0998e+00  -2.3166e+00  -4.0394e+00  -1.0103e+00  -4.5123e-02   4.3012e-03   1.9902e-05   8.1871e-02   1.9888e-02   1.9103e-01   8.7030e-02   9.1829e-01   8.3865e-02   1.0059e+00   8.1436e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 326:    1.0251e+02  -4.0998e+00  -2.3166e+00  -4.0393e+00  -1.0100e+00  -4.5111e-02   4.2839e-03   1.9869e-05   8.2016e-02   1.9843e-02   1.9083e-01   8.7106e-02   9.1802e-01   8.3847e-02   1.0058e+00   8.1427e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 327:    1.0251e+02  -4.0998e+00  -2.3165e+00  -4.0393e+00  -1.0101e+00  -4.5297e-02   4.2847e-03   1.9858e-05   8.2297e-02   1.9823e-02   1.9075e-01   8.7398e-02   9.1826e-01   8.3771e-02   1.0054e+00   8.1451e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 328:    1.0251e+02  -4.0998e+00  -2.3162e+00  -4.0393e+00  -1.0100e+00  -4.5633e-02   4.2864e-03   1.9877e-05   8.2486e-02   1.9809e-02   1.9060e-01   8.7863e-02   9.1822e-01   8.3706e-02   1.0049e+00   8.1482e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 329:    1.0251e+02  -4.0998e+00  -2.3163e+00  -4.0392e+00  -1.0098e+00  -4.5698e-02   4.3097e-03   1.9869e-05   8.2469e-02   1.9795e-02   1.9047e-01   8.8220e-02   9.1805e-01   8.3704e-02   1.0045e+00   8.1502e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 330:    1.0251e+02  -4.0998e+00  -2.3168e+00  -4.0390e+00  -1.0099e+00  -4.5752e-02   4.3120e-03   1.9810e-05   8.2684e-02   1.9770e-02   1.9049e-01   8.8495e-02   9.1779e-01   8.3731e-02   1.0046e+00   8.1500e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 331:    1.0251e+02  -4.0998e+00  -2.3170e+00  -4.0390e+00  -1.0099e+00  -4.5957e-02   4.3052e-03   1.9736e-05   8.2748e-02   1.9734e-02   1.9034e-01   8.8651e-02   9.1818e-01   8.3707e-02   1.0050e+00   8.1489e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 332:    1.0251e+02  -4.0998e+00  -2.3171e+00  -4.0391e+00  -1.0098e+00  -4.5788e-02   4.2994e-03   1.9733e-05   8.2906e-02   1.9711e-02   1.9035e-01   8.8647e-02   9.1835e-01   8.3660e-02   1.0052e+00   8.1490e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 333:    1.0251e+02  -4.0998e+00  -2.3173e+00  -4.0392e+00  -1.0099e+00  -4.5316e-02   4.2822e-03   1.9667e-05   8.2967e-02   1.9695e-02   1.9041e-01   8.8729e-02   9.1832e-01   8.3666e-02   1.0056e+00   8.1484e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 334:    1.0251e+02  -4.0998e+00  -2.3179e+00  -4.0394e+00  -1.0097e+00  -4.4740e-02   4.2668e-03   1.9593e-05   8.3055e-02   1.9702e-02   1.9058e-01   8.8853e-02   9.1799e-01   8.3687e-02   1.0056e+00   8.1457e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 335:    1.0251e+02  -4.0998e+00  -2.3181e+00  -4.0395e+00  -1.0095e+00  -4.4451e-02   4.2558e-03   1.9535e-05   8.3023e-02   1.9762e-02   1.9027e-01   8.9035e-02   9.1828e-01   8.3673e-02   1.0052e+00   8.1469e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 336:    1.0251e+02  -4.0998e+00  -2.3178e+00  -4.0395e+00  -1.0098e+00  -4.4500e-02   4.2571e-03   1.9546e-05   8.3051e-02   1.9779e-02   1.9024e-01   8.9486e-02   9.1868e-01   8.3611e-02   1.0053e+00   8.1515e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 337:    1.0251e+02  -4.0998e+00  -2.3179e+00  -4.0396e+00  -1.0099e+00  -4.4018e-02   4.2494e-03   1.9584e-05   8.3206e-02   1.9764e-02   1.9011e-01   8.9643e-02   9.1871e-01   8.3659e-02   1.0056e+00   8.1531e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 338:    1.0251e+02  -4.0998e+00  -2.3180e+00  -4.0396e+00  -1.0101e+00  -4.3768e-02   4.2332e-03   1.9635e-05   8.3235e-02   1.9751e-02   1.9011e-01   8.9877e-02   9.1939e-01   8.3622e-02   1.0060e+00   8.1528e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 339:    1.0251e+02  -4.0998e+00  -2.3186e+00  -4.0397e+00  -1.0098e+00  -4.3605e-02   4.2237e-03   1.9638e-05   8.3611e-02   1.9725e-02   1.9014e-01   8.9946e-02   9.2025e-01   8.3580e-02   1.0064e+00   8.1528e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 340:    1.0251e+02  -4.0998e+00  -2.3190e+00  -4.0396e+00  -1.0097e+00  -4.3585e-02   4.2095e-03   1.9630e-05   8.3986e-02   1.9677e-02   1.9019e-01   8.9920e-02   9.2124e-01   8.3559e-02   1.0073e+00   8.1516e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 341:    1.0251e+02  -4.0998e+00  -2.3191e+00  -4.0398e+00  -1.0097e+00  -4.3238e-02   4.1994e-03   1.9636e-05   8.4311e-02   1.9683e-02   1.9023e-01   8.9809e-02   9.2179e-01   8.3517e-02   1.0079e+00   8.1498e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 342:    1.0251e+02  -4.0998e+00  -2.3194e+00  -4.0400e+00  -1.0097e+00  -4.2748e-02   4.1886e-03   1.9661e-05   8.4345e-02   1.9684e-02   1.9018e-01   8.9600e-02   9.2207e-01   8.3517e-02   1.0081e+00   8.1517e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 343:    1.0251e+02  -4.0998e+00  -2.3196e+00  -4.0402e+00  -1.0096e+00  -4.2336e-02   4.1813e-03   1.9615e-05   8.4331e-02   1.9666e-02   1.9016e-01   8.9379e-02   9.2231e-01   8.3534e-02   1.0083e+00   8.1548e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 344:    1.0251e+02  -4.0998e+00  -2.3197e+00  -4.0403e+00  -1.0097e+00  -4.2462e-02   4.1751e-03   1.9621e-05   8.4176e-02   1.9673e-02   1.8996e-01   8.9217e-02   9.2236e-01   8.3540e-02   1.0083e+00   8.1557e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 345:    1.0251e+02  -4.0998e+00  -2.3200e+00  -4.0403e+00  -1.0098e+00  -4.2327e-02   4.1705e-03   1.9552e-05   8.4159e-02   1.9684e-02   1.9017e-01   8.8957e-02   9.2232e-01   8.3511e-02   1.0085e+00   8.1566e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 346:    1.0251e+02  -4.0998e+00  -2.3200e+00  -4.0403e+00  -1.0097e+00  -4.2261e-02   4.1750e-03   1.9564e-05   8.4080e-02   1.9731e-02   1.9047e-01   8.8630e-02   9.2248e-01   8.3516e-02   1.0081e+00   8.1578e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 347:    1.0251e+02  -4.0999e+00  -2.3200e+00  -4.0403e+00  -1.0096e+00  -4.2296e-02   4.1883e-03   1.9634e-05   8.4231e-02   1.9747e-02   1.9061e-01   8.8399e-02   9.2250e-01   8.3557e-02   1.0075e+00   8.1580e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 348:    1.0251e+02  -4.0999e+00  -2.3199e+00  -4.0404e+00  -1.0096e+00  -4.2345e-02   4.2129e-03   1.9692e-05   8.4295e-02   1.9719e-02   1.9068e-01   8.8210e-02   9.2212e-01   8.3568e-02   1.0075e+00   8.1586e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 349:    1.0251e+02  -4.0999e+00  -2.3198e+00  -4.0404e+00  -1.0095e+00  -4.2617e-02   4.2066e-03   1.9720e-05   8.4289e-02   1.9731e-02   1.9054e-01   8.8147e-02   9.2185e-01   8.3565e-02   1.0070e+00   8.1590e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 350:    1.0251e+02  -4.0999e+00  -2.3196e+00  -4.0405e+00  -1.0093e+00  -4.2582e-02   4.2011e-03   1.9760e-05   8.4302e-02   1.9703e-02   1.9044e-01   8.8068e-02   9.2182e-01   8.3537e-02   1.0070e+00   8.1590e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 351:    1.0251e+02  -4.0998e+00  -2.3198e+00  -4.0405e+00  -1.0093e+00  -4.2377e-02   4.1970e-03   1.9750e-05   8.4587e-02   1.9708e-02   1.9045e-01   8.8008e-02   9.2169e-01   8.3507e-02   1.0067e+00   8.1584e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 352:    1.0251e+02  -4.0998e+00  -2.3201e+00  -4.0405e+00  -1.0091e+00  -4.2159e-02   4.1973e-03   1.9819e-05   8.4804e-02   1.9678e-02   1.9040e-01   8.7894e-02   9.2113e-01   8.3528e-02   1.0061e+00   8.1554e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 353:    1.0251e+02  -4.0998e+00  -2.3202e+00  -4.0405e+00  -1.0088e+00  -4.2123e-02   4.1968e-03   1.9855e-05   8.4945e-02   1.9634e-02   1.9049e-01   8.7941e-02   9.2108e-01   8.3500e-02   1.0058e+00   8.1548e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 354:    1.0251e+02  -4.0998e+00  -2.3204e+00  -4.0404e+00  -1.0087e+00  -4.2217e-02   4.1918e-03   1.9909e-05   8.5122e-02   1.9565e-02   1.9055e-01   8.7948e-02   9.2088e-01   8.3523e-02   1.0059e+00   8.1530e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 355:    1.0251e+02  -4.0998e+00  -2.3206e+00  -4.0404e+00  -1.0086e+00  -4.2057e-02   4.1853e-03   1.9973e-05   8.5389e-02   1.9505e-02   1.9057e-01   8.8109e-02   9.2100e-01   8.3496e-02   1.0058e+00   8.1520e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 356:    1.0251e+02  -4.0998e+00  -2.3209e+00  -4.0403e+00  -1.0084e+00  -4.1880e-02   4.1796e-03   2.0058e-05   8.5446e-02   1.9451e-02   1.9063e-01   8.7944e-02   9.2086e-01   8.3483e-02   1.0061e+00   8.1488e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 357:    1.0251e+02  -4.0998e+00  -2.3210e+00  -4.0404e+00  -1.0084e+00  -4.1565e-02   4.1858e-03   2.0131e-05   8.5481e-02   1.9391e-02   1.9072e-01   8.7879e-02   9.2119e-01   8.3465e-02   1.0066e+00   8.1468e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 358:    1.0251e+02  -4.0998e+00  -2.3213e+00  -4.0404e+00  -1.0084e+00  -4.1497e-02   4.1826e-03   2.0168e-05   8.5661e-02   1.9361e-02   1.9086e-01   8.7635e-02   9.2095e-01   8.3475e-02   1.0069e+00   8.1445e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 359:    1.0251e+02  -4.0997e+00  -2.3212e+00  -4.0405e+00  -1.0083e+00  -4.1127e-02   4.1993e-03   2.0188e-05   8.5904e-02   1.9347e-02   1.9078e-01   8.7558e-02   9.2087e-01   8.3451e-02   1.0068e+00   8.1461e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 360:    1.0251e+02  -4.0997e+00  -2.3215e+00  -4.0405e+00  -1.0082e+00  -4.0919e-02   4.2006e-03   2.0173e-05   8.6133e-02   1.9333e-02   1.9071e-01   8.7479e-02   9.2061e-01   8.3439e-02   1.0067e+00   8.1453e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 361:    1.0251e+02  -4.0997e+00  -2.3218e+00  -4.0406e+00  -1.0084e+00  -4.0317e-02   4.2013e-03   2.0253e-05   8.6558e-02   1.9351e-02   1.9075e-01   8.7309e-02   9.2060e-01   8.3425e-02   1.0069e+00   8.1464e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 362:    1.0251e+02  -4.0997e+00  -2.3221e+00  -4.0407e+00  -1.0086e+00  -3.9991e-02   4.2004e-03   2.0305e-05   8.6952e-02   1.9369e-02   1.9077e-01   8.7106e-02   9.2037e-01   8.3462e-02   1.0072e+00   8.1464e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 363:    1.0251e+02  -4.0997e+00  -2.3224e+00  -4.0407e+00  -1.0086e+00  -3.9745e-02   4.1920e-03   2.0350e-05   8.7210e-02   1.9373e-02   1.9095e-01   8.6967e-02   9.2025e-01   8.3462e-02   1.0070e+00   8.1489e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 364:    1.0251e+02  -4.0997e+00  -2.3225e+00  -4.0407e+00  -1.0088e+00  -3.9585e-02   4.1826e-03   2.0355e-05   8.7449e-02   1.9353e-02   1.9099e-01   8.7119e-02   9.2081e-01   8.3468e-02   1.0070e+00   8.1514e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 365:    1.0251e+02  -4.0997e+00  -2.3224e+00  -4.0407e+00  -1.0088e+00  -3.9531e-02   4.1781e-03   2.0298e-05   8.7646e-02   1.9346e-02   1.9083e-01   8.6997e-02   9.2106e-01   8.3455e-02   1.0068e+00   8.1546e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 366:    1.0251e+02  -4.0997e+00  -2.3223e+00  -4.0407e+00  -1.0088e+00  -3.9436e-02   4.1805e-03   2.0301e-05   8.7772e-02   1.9354e-02   1.9074e-01   8.7057e-02   9.2118e-01   8.3426e-02   1.0064e+00   8.1555e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 367:    1.0251e+02  -4.0997e+00  -2.3223e+00  -4.0408e+00  -1.0088e+00  -3.9172e-02   4.1791e-03   2.0368e-05   8.7914e-02   1.9333e-02   1.9063e-01   8.6948e-02   9.2106e-01   8.3409e-02   1.0060e+00   8.1558e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 368:    1.0251e+02  -4.0997e+00  -2.3222e+00  -4.0410e+00  -1.0088e+00  -3.8640e-02   4.1857e-03   2.0343e-05   8.7937e-02   1.9334e-02   1.9051e-01   8.6857e-02   9.2143e-01   8.3379e-02   1.0058e+00   8.1564e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 369:    1.0251e+02  -4.0997e+00  -2.3223e+00  -4.0411e+00  -1.0088e+00  -3.8331e-02   4.1862e-03   2.0309e-05   8.7933e-02   1.9319e-02   1.9046e-01   8.6689e-02   9.2126e-01   8.3410e-02   1.0053e+00   8.1559e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 370:    1.0251e+02  -4.0997e+00  -2.3222e+00  -4.0413e+00  -1.0088e+00  -3.7984e-02   4.1921e-03   2.0301e-05   8.8082e-02   1.9313e-02   1.9050e-01   8.6622e-02   9.2101e-01   8.3424e-02   1.0049e+00   8.1559e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 371:    1.0251e+02  -4.0998e+00  -2.3222e+00  -4.0415e+00  -1.0087e+00  -3.7729e-02   4.1972e-03   2.0277e-05   8.8271e-02   1.9308e-02   1.9035e-01   8.6655e-02   9.2092e-01   8.3426e-02   1.0046e+00   8.1575e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 372:    1.0251e+02  -4.0998e+00  -2.3222e+00  -4.0414e+00  -1.0087e+00  -3.7571e-02   4.1922e-03   2.0292e-05   8.8651e-02   1.9317e-02   1.9025e-01   8.6528e-02   9.2126e-01   8.3440e-02   1.0046e+00   8.1576e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 373:    1.0251e+02  -4.0998e+00  -2.3223e+00  -4.0415e+00  -1.0087e+00  -3.7327e-02   4.1971e-03   2.0343e-05   8.8865e-02   1.9290e-02   1.9002e-01   8.6437e-02   9.2122e-01   8.3475e-02   1.0044e+00   8.1571e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 374:    1.0251e+02  -4.0998e+00  -2.3224e+00  -4.0415e+00  -1.0089e+00  -3.7275e-02   4.2072e-03   2.0450e-05   8.9078e-02   1.9306e-02   1.9002e-01   8.6224e-02   9.2111e-01   8.3480e-02   1.0047e+00   8.1569e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 375:    1.0251e+02  -4.0998e+00  -2.3226e+00  -4.0415e+00  -1.0089e+00  -3.7080e-02   4.2062e-03   2.0489e-05   8.9178e-02   1.9320e-02   1.9016e-01   8.6141e-02   9.2116e-01   8.3483e-02   1.0047e+00   8.1586e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 376:    1.0251e+02  -4.0998e+00  -2.3230e+00  -4.0415e+00  -1.0089e+00  -3.7043e-02   4.2105e-03   2.0517e-05   8.9291e-02   1.9325e-02   1.9018e-01   8.6033e-02   9.2114e-01   8.3474e-02   1.0046e+00   8.1572e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 377:    1.0251e+02  -4.0998e+00  -2.3234e+00  -4.0414e+00  -1.0090e+00  -3.7302e-02   4.2097e-03   2.0524e-05   8.9438e-02   1.9333e-02   1.9012e-01   8.6018e-02   9.2123e-01   8.3476e-02   1.0046e+00   8.1545e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 378:    1.0251e+02  -4.0998e+00  -2.3234e+00  -4.0413e+00  -1.0089e+00  -3.7617e-02   4.2161e-03   2.0520e-05   8.9567e-02   1.9305e-02   1.9012e-01   8.6106e-02   9.2115e-01   8.3444e-02   1.0049e+00   8.1534e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 379:    1.0251e+02  -4.0998e+00  -2.3232e+00  -4.0412e+00  -1.0088e+00  -3.8039e-02   4.2231e-03   2.0484e-05   8.9704e-02   1.9276e-02   1.9015e-01   8.5914e-02   9.2060e-01   8.3438e-02   1.0048e+00   8.1532e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 380:    1.0251e+02  -4.0998e+00  -2.3229e+00  -4.0409e+00  -1.0088e+00  -3.8613e-02   4.2221e-03   2.0452e-05   8.9751e-02   1.9293e-02   1.9018e-01   8.5907e-02   9.2032e-01   8.3428e-02   1.0045e+00   8.1545e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 381:    1.0251e+02  -4.0998e+00  -2.3228e+00  -4.0408e+00  -1.0088e+00  -3.8992e-02   4.2177e-03   2.0475e-05   8.9641e-02   1.9314e-02   1.9014e-01   8.5978e-02   9.2015e-01   8.3429e-02   1.0040e+00   8.1535e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 382:    1.0251e+02  -4.0998e+00  -2.3228e+00  -4.0408e+00  -1.0088e+00  -3.9393e-02   4.2092e-03   2.0508e-05   8.9624e-02   1.9352e-02   1.9010e-01   8.5929e-02   9.2037e-01   8.3422e-02   1.0037e+00   8.1535e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 383:    1.0251e+02  -4.0998e+00  -2.3226e+00  -4.0407e+00  -1.0089e+00  -3.9650e-02   4.2080e-03   2.0503e-05   8.9621e-02   1.9349e-02   1.9000e-01   8.5735e-02   9.2021e-01   8.3436e-02   1.0038e+00   8.1544e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 384:    1.0251e+02  -4.0998e+00  -2.3223e+00  -4.0407e+00  -1.0089e+00  -3.9709e-02   4.2111e-03   2.0489e-05   8.9690e-02   1.9347e-02   1.8985e-01   8.5516e-02   9.2025e-01   8.3420e-02   1.0037e+00   8.1541e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 385:    1.0251e+02  -4.0998e+00  -2.3225e+00  -4.0406e+00  -1.0087e+00  -3.9762e-02   4.2130e-03   2.0458e-05   8.9795e-02   1.9380e-02   1.8978e-01   8.5306e-02   9.2055e-01   8.3405e-02   1.0034e+00   8.1518e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 386:    1.0251e+02  -4.0998e+00  -2.3226e+00  -4.0407e+00  -1.0087e+00  -3.9801e-02   4.2117e-03   2.0473e-05   8.9838e-02   1.9386e-02   1.8986e-01   8.5045e-02   9.2034e-01   8.3388e-02   1.0036e+00   8.1512e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 387:    1.0251e+02  -4.0998e+00  -2.3228e+00  -4.0408e+00  -1.0088e+00  -3.9548e-02   4.2221e-03   2.0474e-05   8.9914e-02   1.9399e-02   1.9001e-01   8.4840e-02   9.2080e-01   8.3373e-02   1.0037e+00   8.1503e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 388:    1.0251e+02  -4.0998e+00  -2.3230e+00  -4.0408e+00  -1.0088e+00  -3.9408e-02   4.2272e-03   2.0464e-05   8.9977e-02   1.9383e-02   1.9000e-01   8.4644e-02   9.2070e-01   8.3377e-02   1.0034e+00   8.1494e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 389:    1.0250e+02  -4.0998e+00  -2.3229e+00  -4.0408e+00  -1.0087e+00  -3.9326e-02   4.2285e-03   2.0459e-05   8.9927e-02   1.9366e-02   1.9001e-01   8.4485e-02   9.2063e-01   8.3367e-02   1.0032e+00   8.1487e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 390:    1.0250e+02  -4.0998e+00  -2.3231e+00  -4.0409e+00  -1.0086e+00  -3.9130e-02   4.2286e-03   2.0423e-05   8.9894e-02   1.9331e-02   1.8996e-01   8.4434e-02   9.2054e-01   8.3339e-02   1.0032e+00   8.1481e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 391:    1.0250e+02  -4.0998e+00  -2.3232e+00  -4.0410e+00  -1.0086e+00  -3.8976e-02   4.2242e-03   2.0419e-05   8.9782e-02   1.9299e-02   1.8992e-01   8.4344e-02   9.2089e-01   8.3320e-02   1.0032e+00   8.1480e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 392:    1.0250e+02  -4.0998e+00  -2.3232e+00  -4.0409e+00  -1.0086e+00  -3.8856e-02   4.2179e-03   2.0396e-05   8.9773e-02   1.9263e-02   1.8984e-01   8.4469e-02   9.2084e-01   8.3309e-02   1.0034e+00   8.1489e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 393:    1.0250e+02  -4.0998e+00  -2.3234e+00  -4.0409e+00  -1.0089e+00  -3.8820e-02   4.2135e-03   2.0375e-05   8.9746e-02   1.9259e-02   1.8972e-01   8.4731e-02   9.2088e-01   8.3310e-02   1.0031e+00   8.1481e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 394:    1.0250e+02  -4.0998e+00  -2.3234e+00  -4.0410e+00  -1.0089e+00  -3.8759e-02   4.2123e-03   2.0382e-05   8.9791e-02   1.9271e-02   1.8958e-01   8.5075e-02   9.2064e-01   8.3316e-02   1.0028e+00   8.1483e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 395:    1.0250e+02  -4.0998e+00  -2.3234e+00  -4.0410e+00  -1.0090e+00  -3.8565e-02   4.2179e-03   2.0420e-05   8.9844e-02   1.9279e-02   1.8948e-01   8.5286e-02   9.2070e-01   8.3311e-02   1.0027e+00   8.1499e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 396:    1.0250e+02  -4.0998e+00  -2.3233e+00  -4.0411e+00  -1.0092e+00  -3.8505e-02   4.2207e-03   2.0445e-05   8.9863e-02   1.9281e-02   1.8944e-01   8.5452e-02   9.2053e-01   8.3326e-02   1.0028e+00   8.1524e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 397:    1.0250e+02  -4.0998e+00  -2.3232e+00  -4.0411e+00  -1.0093e+00  -3.8366e-02   4.2115e-03   2.0410e-05   8.9792e-02   1.9287e-02   1.8938e-01   8.5649e-02   9.2063e-01   8.3351e-02   1.0034e+00   8.1519e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 398:    1.0250e+02  -4.0998e+00  -2.3234e+00  -4.0411e+00  -1.0093e+00  -3.8179e-02   4.2049e-03   2.0362e-05   8.9718e-02   1.9281e-02   1.8943e-01   8.5813e-02   9.2065e-01   8.3391e-02   1.0040e+00   8.1513e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 399:    1.0250e+02  -4.0998e+00  -2.3236e+00  -4.0411e+00  -1.0093e+00  -3.8020e-02   4.2015e-03   2.0320e-05   8.9743e-02   1.9283e-02   1.8934e-01   8.5899e-02   9.2056e-01   8.3426e-02   1.0045e+00   8.1500e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 400:    1.0250e+02  -4.0998e+00  -2.3237e+00  -4.0412e+00  -1.0093e+00  -3.7796e-02   4.2017e-03   2.0296e-05   8.9684e-02   1.9297e-02   1.8932e-01   8.5849e-02   9.2107e-01   8.3413e-02   1.0051e+00   8.1502e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 401:    1.0250e+02  -4.0998e+00  -2.3238e+00  -4.0413e+00  -1.0093e+00  -3.7697e-02   4.1959e-03   2.0275e-05   8.9618e-02   1.9291e-02   1.8914e-01   8.5796e-02   9.2086e-01   8.3480e-02   1.0051e+00   8.1510e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 402:    1.0250e+02  -4.0998e+00  -2.3238e+00  -4.0414e+00  -1.0093e+00  -3.7639e-02   4.1863e-03   2.0273e-05   8.9522e-02   1.9285e-02   1.8898e-01   8.5723e-02   9.2041e-01   8.3534e-02   1.0053e+00   8.1520e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 403:    1.0250e+02  -4.0998e+00  -2.3238e+00  -4.0415e+00  -1.0093e+00  -3.7634e-02   4.1878e-03   2.0250e-05   8.9456e-02   1.9288e-02   1.8895e-01   8.5791e-02   9.1990e-01   8.3549e-02   1.0055e+00   8.1545e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 404:    1.0250e+02  -4.0999e+00  -2.3239e+00  -4.0416e+00  -1.0094e+00  -3.7581e-02   4.1854e-03   2.0292e-05   8.9326e-02   1.9326e-02   1.8902e-01   8.5928e-02   9.2016e-01   8.3542e-02   1.0053e+00   8.1565e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 405:    1.0250e+02  -4.0999e+00  -2.3239e+00  -4.0415e+00  -1.0094e+00  -3.7740e-02   4.1804e-03   2.0333e-05   8.9304e-02   1.9334e-02   1.8892e-01   8.6225e-02   9.1978e-01   8.3559e-02   1.0052e+00   8.1588e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 406:    1.0250e+02  -4.0999e+00  -2.3241e+00  -4.0415e+00  -1.0093e+00  -3.7837e-02   4.1866e-03   2.0375e-05   8.9281e-02   1.9322e-02   1.8906e-01   8.6421e-02   9.1972e-01   8.3556e-02   1.0051e+00   8.1596e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 407:    1.0250e+02  -4.0999e+00  -2.3240e+00  -4.0415e+00  -1.0092e+00  -3.8211e-02   4.1948e-03   2.0335e-05   8.9117e-02   1.9303e-02   1.8913e-01   8.6644e-02   9.1975e-01   8.3592e-02   1.0052e+00   8.1594e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 408:    1.0250e+02  -4.0999e+00  -2.3239e+00  -4.0415e+00  -1.0091e+00  -3.8144e-02   4.2081e-03   2.0320e-05   8.9057e-02   1.9287e-02   1.8912e-01   8.6825e-02   9.1992e-01   8.3572e-02   1.0054e+00   8.1577e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 409:    1.0250e+02  -4.0999e+00  -2.3239e+00  -4.0415e+00  -1.0092e+00  -3.8261e-02   4.2186e-03   2.0295e-05   8.9007e-02   1.9339e-02   1.8923e-01   8.6869e-02   9.1991e-01   8.3568e-02   1.0053e+00   8.1578e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 410:    1.0250e+02  -4.0999e+00  -2.3239e+00  -4.0414e+00  -1.0093e+00  -3.8478e-02   4.2150e-03   2.0311e-05   8.9089e-02   1.9427e-02   1.8939e-01   8.7036e-02   9.2050e-01   8.3537e-02   1.0051e+00   8.1581e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 411:    1.0250e+02  -4.0999e+00  -2.3238e+00  -4.0413e+00  -1.0092e+00  -3.8732e-02   4.2116e-03   2.0289e-05   8.9015e-02   1.9467e-02   1.8939e-01   8.7005e-02   9.2025e-01   8.3538e-02   1.0049e+00   8.1581e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 412:    1.0250e+02  -4.0999e+00  -2.3238e+00  -4.0413e+00  -1.0092e+00  -3.8793e-02   4.2085e-03   2.0295e-05   8.9020e-02   1.9477e-02   1.8946e-01   8.7080e-02   9.2047e-01   8.3532e-02   1.0049e+00   8.1575e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 413:    1.0250e+02  -4.0999e+00  -2.3236e+00  -4.0412e+00  -1.0093e+00  -3.8937e-02   4.2069e-03   2.0288e-05   8.9121e-02   1.9495e-02   1.8939e-01   8.7203e-02   9.2043e-01   8.3528e-02   1.0046e+00   8.1581e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 414:    1.0250e+02  -4.0999e+00  -2.3233e+00  -4.0412e+00  -1.0093e+00  -3.9068e-02   4.2065e-03   2.0272e-05   8.9126e-02   1.9508e-02   1.8939e-01   8.7485e-02   9.2038e-01   8.3517e-02   1.0046e+00   8.1589e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 415:    1.0250e+02  -4.0999e+00  -2.3234e+00  -4.0412e+00  -1.0092e+00  -3.9299e-02   4.2117e-03   2.0277e-05   8.8971e-02   1.9564e-02   1.8955e-01   8.7734e-02   9.2051e-01   8.3502e-02   1.0048e+00   8.1575e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 416:    1.0250e+02  -4.0999e+00  -2.3232e+00  -4.0413e+00  -1.0092e+00  -3.9575e-02   4.2122e-03   2.0269e-05   8.8909e-02   1.9609e-02   1.8954e-01   8.7910e-02   9.2061e-01   8.3488e-02   1.0045e+00   8.1561e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 417:    1.0250e+02  -4.0999e+00  -2.3228e+00  -4.0412e+00  -1.0092e+00  -3.9794e-02   4.2133e-03   2.0289e-05   8.8845e-02   1.9606e-02   1.8949e-01   8.8036e-02   9.2065e-01   8.3481e-02   1.0047e+00   8.1558e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 418:    1.0250e+02  -4.0999e+00  -2.3226e+00  -4.0412e+00  -1.0092e+00  -4.0019e-02   4.2209e-03   2.0279e-05   8.8864e-02   1.9618e-02   1.8939e-01   8.8094e-02   9.2062e-01   8.3490e-02   1.0044e+00   8.1566e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 419:    1.0250e+02  -4.0999e+00  -2.3225e+00  -4.0411e+00  -1.0092e+00  -4.0090e-02   4.2242e-03   2.0258e-05   8.9089e-02   1.9644e-02   1.8924e-01   8.8285e-02   9.2082e-01   8.3484e-02   1.0040e+00   8.1567e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 420:    1.0250e+02  -4.0999e+00  -2.3223e+00  -4.0410e+00  -1.0091e+00  -4.0260e-02   4.2298e-03   2.0249e-05   8.9215e-02   1.9640e-02   1.8919e-01   8.8683e-02   9.2127e-01   8.3454e-02   1.0041e+00   8.1564e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 421:    1.0250e+02  -4.0998e+00  -2.3222e+00  -4.0410e+00  -1.0090e+00  -4.0359e-02   4.2292e-03   2.0289e-05   8.9366e-02   1.9642e-02   1.8928e-01   8.8905e-02   9.2154e-01   8.3452e-02   1.0038e+00   8.1572e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 422:    1.0250e+02  -4.0998e+00  -2.3223e+00  -4.0410e+00  -1.0088e+00  -4.0380e-02   4.2311e-03   2.0289e-05   8.9318e-02   1.9636e-02   1.8934e-01   8.9025e-02   9.2137e-01   8.3486e-02   1.0039e+00   8.1562e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 423:    1.0250e+02  -4.0998e+00  -2.3224e+00  -4.0411e+00  -1.0088e+00  -4.0256e-02   4.2283e-03   2.0277e-05   8.9324e-02   1.9645e-02   1.8923e-01   8.9305e-02   9.2129e-01   8.3484e-02   1.0038e+00   8.1565e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 424:    1.0250e+02  -4.0998e+00  -2.3226e+00  -4.0412e+00  -1.0088e+00  -4.0075e-02   4.2333e-03   2.0245e-05   8.9193e-02   1.9657e-02   1.8914e-01   8.9480e-02   9.2122e-01   8.3487e-02   1.0039e+00   8.1587e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 425:    1.0250e+02  -4.0998e+00  -2.3226e+00  -4.0413e+00  -1.0088e+00  -3.9950e-02   4.2360e-03   2.0257e-05   8.9170e-02   1.9644e-02   1.8924e-01   8.9622e-02   9.2104e-01   8.3492e-02   1.0042e+00   8.1599e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 426:    1.0250e+02  -4.0998e+00  -2.3226e+00  -4.0413e+00  -1.0088e+00  -3.9789e-02   4.2376e-03   2.0245e-05   8.9143e-02   1.9654e-02   1.8917e-01   8.9791e-02   9.2100e-01   8.3514e-02   1.0039e+00   8.1607e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 427:    1.0250e+02  -4.0998e+00  -2.3224e+00  -4.0414e+00  -1.0087e+00  -3.9766e-02   4.2469e-03   2.0228e-05   8.9102e-02   1.9656e-02   1.8913e-01   9.0076e-02   9.2110e-01   8.3499e-02   1.0037e+00   8.1607e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 428:    1.0250e+02  -4.0998e+00  -2.3225e+00  -4.0414e+00  -1.0088e+00  -3.9662e-02   4.2522e-03   2.0205e-05   8.9151e-02   1.9669e-02   1.8912e-01   9.0312e-02   9.2102e-01   8.3483e-02   1.0036e+00   8.1599e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 429:    1.0250e+02  -4.0998e+00  -2.3225e+00  -4.0414e+00  -1.0089e+00  -3.9547e-02   4.2615e-03   2.0187e-05   8.9103e-02   1.9681e-02   1.8907e-01   9.0576e-02   9.2082e-01   8.3488e-02   1.0035e+00   8.1592e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 430:    1.0250e+02  -4.0998e+00  -2.3224e+00  -4.0414e+00  -1.0088e+00  -3.9593e-02   4.2613e-03   2.0159e-05   8.9075e-02   1.9692e-02   1.8894e-01   9.0762e-02   9.2073e-01   8.3487e-02   1.0035e+00   8.1603e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 431:    1.0250e+02  -4.0998e+00  -2.3223e+00  -4.0413e+00  -1.0088e+00  -3.9929e-02   4.2668e-03   2.0112e-05   8.8982e-02   1.9682e-02   1.8901e-01   9.0997e-02   9.2043e-01   8.3487e-02   1.0036e+00   8.1604e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 432:    1.0250e+02  -4.0998e+00  -2.3223e+00  -4.0413e+00  -1.0088e+00  -4.0182e-02   4.2693e-03   2.0089e-05   8.8860e-02   1.9659e-02   1.8898e-01   9.1110e-02   9.2024e-01   8.3490e-02   1.0036e+00   8.1585e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 433:    1.0250e+02  -4.0998e+00  -2.3222e+00  -4.0412e+00  -1.0088e+00  -4.0415e-02   4.2706e-03   2.0081e-05   8.8670e-02   1.9650e-02   1.8899e-01   9.1199e-02   9.1999e-01   8.3485e-02   1.0036e+00   8.1587e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 434:    1.0250e+02  -4.0998e+00  -2.3220e+00  -4.0412e+00  -1.0087e+00  -4.0405e-02   4.2661e-03   2.0060e-05   8.8576e-02   1.9667e-02   1.8897e-01   9.1180e-02   9.1998e-01   8.3468e-02   1.0034e+00   8.1594e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 435:    1.0250e+02  -4.0998e+00  -2.3218e+00  -4.0413e+00  -1.0087e+00  -4.0471e-02   4.2639e-03   2.0050e-05   8.8450e-02   1.9671e-02   1.8891e-01   9.1156e-02   9.1970e-01   8.3469e-02   1.0033e+00   8.1602e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 436:    1.0250e+02  -4.0998e+00  -2.3216e+00  -4.0413e+00  -1.0086e+00  -4.0478e-02   4.2614e-03   2.0025e-05   8.8470e-02   1.9677e-02   1.8892e-01   9.1234e-02   9.1948e-01   8.3484e-02   1.0033e+00   8.1608e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 437:    1.0250e+02  -4.0998e+00  -2.3214e+00  -4.0413e+00  -1.0086e+00  -4.0648e-02   4.2569e-03   2.0021e-05   8.8378e-02   1.9694e-02   1.8896e-01   9.1289e-02   9.1946e-01   8.3486e-02   1.0034e+00   8.1608e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 438:    1.0250e+02  -4.0998e+00  -2.3214e+00  -4.0413e+00  -1.0085e+00  -4.0807e-02   4.2559e-03   2.0029e-05   8.8244e-02   1.9704e-02   1.8898e-01   9.1352e-02   9.1920e-01   8.3494e-02   1.0034e+00   8.1604e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 439:    1.0250e+02  -4.0998e+00  -2.3215e+00  -4.0413e+00  -1.0084e+00  -4.0814e-02   4.2472e-03   2.0035e-05   8.8064e-02   1.9736e-02   1.8905e-01   9.1469e-02   9.1900e-01   8.3510e-02   1.0032e+00   8.1602e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 440:    1.0250e+02  -4.0998e+00  -2.3216e+00  -4.0414e+00  -1.0084e+00  -4.0845e-02   4.2385e-03   2.0044e-05   8.7953e-02   1.9775e-02   1.8906e-01   9.1482e-02   9.1902e-01   8.3529e-02   1.0029e+00   8.1605e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 441:    1.0250e+02  -4.0998e+00  -2.3218e+00  -4.0415e+00  -1.0084e+00  -4.0561e-02   4.2293e-03   2.0096e-05   8.7978e-02   1.9848e-02   1.8914e-01   9.1431e-02   9.1890e-01   8.3545e-02   1.0026e+00   8.1618e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 442:    1.0250e+02  -4.0998e+00  -2.3219e+00  -4.0415e+00  -1.0085e+00  -4.0297e-02   4.2190e-03   2.0140e-05   8.8016e-02   1.9884e-02   1.8912e-01   9.1373e-02   9.1910e-01   8.3556e-02   1.0025e+00   8.1632e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 443:    1.0250e+02  -4.0998e+00  -2.3222e+00  -4.0415e+00  -1.0086e+00  -4.0124e-02   4.2172e-03   2.0182e-05   8.8183e-02   1.9921e-02   1.8920e-01   9.1354e-02   9.1928e-01   8.3555e-02   1.0023e+00   8.1646e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 444:    1.0250e+02  -4.0999e+00  -2.3223e+00  -4.0416e+00  -1.0084e+00  -4.0120e-02   4.2226e-03   2.0208e-05   8.8142e-02   1.9980e-02   1.8916e-01   9.1269e-02   9.1934e-01   8.3540e-02   1.0020e+00   8.1653e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 445:    1.0250e+02  -4.0999e+00  -2.3223e+00  -4.0416e+00  -1.0083e+00  -4.0180e-02   4.2279e-03   2.0283e-05   8.8188e-02   2.0009e-02   1.8925e-01   9.1096e-02   9.1924e-01   8.3538e-02   1.0017e+00   8.1644e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 446:    1.0250e+02  -4.0999e+00  -2.3223e+00  -4.0417e+00  -1.0084e+00  -4.0083e-02   4.2373e-03   2.0357e-05   8.8255e-02   2.0030e-02   1.8937e-01   9.0921e-02   9.1921e-01   8.3535e-02   1.0015e+00   8.1652e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 447:    1.0250e+02  -4.0999e+00  -2.3223e+00  -4.0417e+00  -1.0083e+00  -4.0032e-02   4.2591e-03   2.0418e-05   8.8322e-02   2.0055e-02   1.8931e-01   9.0741e-02   9.1928e-01   8.3527e-02   1.0011e+00   8.1662e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 448:    1.0250e+02  -4.0999e+00  -2.3224e+00  -4.0417e+00  -1.0083e+00  -4.0042e-02   4.2759e-03   2.0499e-05   8.8372e-02   2.0083e-02   1.8935e-01   9.0554e-02   9.1942e-01   8.3524e-02   1.0010e+00   8.1661e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 449:    1.0250e+02  -4.0999e+00  -2.3224e+00  -4.0417e+00  -1.0083e+00  -4.0219e-02   4.2779e-03   2.0525e-05   8.8377e-02   2.0104e-02   1.8937e-01   9.0424e-02   9.1970e-01   8.3510e-02   1.0009e+00   8.1654e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 450:    1.0250e+02  -4.0999e+00  -2.3223e+00  -4.0416e+00  -1.0084e+00  -4.0390e-02   4.2779e-03   2.0520e-05   8.8387e-02   2.0120e-02   1.8939e-01   9.0277e-02   9.1991e-01   8.3489e-02   1.0010e+00   8.1662e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 451:    1.0250e+02  -4.0999e+00  -2.3221e+00  -4.0415e+00  -1.0085e+00  -4.0403e-02   4.2785e-03   2.0554e-05   8.8442e-02   2.0130e-02   1.8934e-01   9.0032e-02   9.2001e-01   8.3485e-02   1.0011e+00   8.1676e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 452:    1.0250e+02  -4.0999e+00  -2.3220e+00  -4.0415e+00  -1.0084e+00  -4.0503e-02   4.2730e-03   2.0537e-05   8.8507e-02   2.0135e-02   1.8926e-01   8.9777e-02   9.2031e-01   8.3473e-02   1.0013e+00   8.1671e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 453:    1.0250e+02  -4.0999e+00  -2.3221e+00  -4.0415e+00  -1.0084e+00  -4.0716e-02   4.2650e-03   2.0525e-05   8.8565e-02   2.0168e-02   1.8921e-01   8.9653e-02   9.2030e-01   8.3473e-02   1.0010e+00   8.1665e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 454:    1.0250e+02  -4.0999e+00  -2.3221e+00  -4.0415e+00  -1.0083e+00  -4.0900e-02   4.2583e-03   2.0505e-05   8.8572e-02   2.0194e-02   1.8912e-01   8.9524e-02   9.2039e-01   8.3457e-02   1.0009e+00   8.1658e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 455:    1.0250e+02  -4.0999e+00  -2.3220e+00  -4.0415e+00  -1.0083e+00  -4.0930e-02   4.2524e-03   2.0495e-05   8.8643e-02   2.0246e-02   1.8914e-01   8.9416e-02   9.2035e-01   8.3454e-02   1.0006e+00   8.1655e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 456:    1.0250e+02  -4.0999e+00  -2.3218e+00  -4.0414e+00  -1.0084e+00  -4.1008e-02   4.2455e-03   2.0482e-05   8.8831e-02   2.0290e-02   1.8904e-01   8.9438e-02   9.2042e-01   8.3448e-02   1.0005e+00   8.1672e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 457:    1.0250e+02  -4.0999e+00  -2.3216e+00  -4.0414e+00  -1.0085e+00  -4.1073e-02   4.2376e-03   2.0479e-05   8.8831e-02   2.0310e-02   1.8893e-01   8.9411e-02   9.2049e-01   8.3438e-02   1.0005e+00   8.1677e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 458:    1.0250e+02  -4.0999e+00  -2.3215e+00  -4.0414e+00  -1.0085e+00  -4.1036e-02   4.2288e-03   2.0479e-05   8.8894e-02   2.0311e-02   1.8879e-01   8.9378e-02   9.2055e-01   8.3419e-02   1.0006e+00   8.1689e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 459:    1.0250e+02  -4.0999e+00  -2.3216e+00  -4.0414e+00  -1.0085e+00  -4.1084e-02   4.2296e-03   2.0472e-05   8.8976e-02   2.0290e-02   1.8876e-01   8.9365e-02   9.2056e-01   8.3404e-02   1.0007e+00   8.1678e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 460:    1.0250e+02  -4.0999e+00  -2.3215e+00  -4.0414e+00  -1.0084e+00  -4.1065e-02   4.2257e-03   2.0473e-05   8.8978e-02   2.0254e-02   1.8871e-01   8.9287e-02   9.2061e-01   8.3386e-02   1.0007e+00   8.1663e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 461:    1.0250e+02  -4.0999e+00  -2.3213e+00  -4.0414e+00  -1.0084e+00  -4.1142e-02   4.2254e-03   2.0441e-05   8.8952e-02   2.0238e-02   1.8864e-01   8.9236e-02   9.2067e-01   8.3359e-02   1.0007e+00   8.1670e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 462:    1.0250e+02  -4.0999e+00  -2.3213e+00  -4.0413e+00  -1.0083e+00  -4.1199e-02   4.2230e-03   2.0409e-05   8.9161e-02   2.0235e-02   1.8852e-01   8.9179e-02   9.2070e-01   8.3339e-02   1.0005e+00   8.1686e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 463:    1.0250e+02  -4.0999e+00  -2.3214e+00  -4.0413e+00  -1.0084e+00  -4.1131e-02   4.2191e-03   2.0397e-05   8.9356e-02   2.0251e-02   1.8850e-01   8.9138e-02   9.2098e-01   8.3308e-02   1.0003e+00   8.1688e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 464:    1.0250e+02  -4.0999e+00  -2.3213e+00  -4.0414e+00  -1.0084e+00  -4.0937e-02   4.2187e-03   2.0395e-05   8.9553e-02   2.0237e-02   1.8848e-01   8.9097e-02   9.2108e-01   8.3293e-02   1.0001e+00   8.1684e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 465:    1.0250e+02  -4.0999e+00  -2.3213e+00  -4.0414e+00  -1.0086e+00  -4.0873e-02   4.2226e-03   2.0374e-05   8.9675e-02   2.0210e-02   1.8854e-01   8.9045e-02   9.2115e-01   8.3282e-02   1.0002e+00   8.1678e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 466:    1.0250e+02  -4.0999e+00  -2.3213e+00  -4.0414e+00  -1.0085e+00  -4.1046e-02   4.2312e-03   2.0379e-05   8.9703e-02   2.0232e-02   1.8852e-01   8.9031e-02   9.2100e-01   8.3294e-02   9.9982e-01   8.1665e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 467:    1.0250e+02  -4.0999e+00  -2.3214e+00  -4.0414e+00  -1.0086e+00  -4.1081e-02   4.2382e-03   2.0421e-05   8.9802e-02   2.0266e-02   1.8851e-01   8.9175e-02   9.2124e-01   8.3271e-02   9.9943e-01   8.1671e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 468:    1.0250e+02  -4.0999e+00  -2.3215e+00  -4.0414e+00  -1.0085e+00  -4.1107e-02   4.2412e-03   2.0446e-05   8.9937e-02   2.0281e-02   1.8857e-01   8.9238e-02   9.2125e-01   8.3255e-02   9.9912e-01   8.1659e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 469:    1.0250e+02  -4.0999e+00  -2.3214e+00  -4.0414e+00  -1.0084e+00  -4.1269e-02   4.2380e-03   2.0449e-05   8.9938e-02   2.0285e-02   1.8867e-01   8.9378e-02   9.2144e-01   8.3225e-02   9.9906e-01   8.1651e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 470:    1.0250e+02  -4.0999e+00  -2.3212e+00  -4.0413e+00  -1.0083e+00  -4.1552e-02   4.2384e-03   2.0437e-05   8.9924e-02   2.0280e-02   1.8878e-01   8.9520e-02   9.2149e-01   8.3205e-02   9.9880e-01   8.1648e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 471:    1.0250e+02  -4.0999e+00  -2.3210e+00  -4.0412e+00  -1.0082e+00  -4.2096e-02   4.2375e-03   2.0433e-05   8.9875e-02   2.0267e-02   1.8880e-01   8.9608e-02   9.2149e-01   8.3221e-02   9.9869e-01   8.1642e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 472:    1.0250e+02  -4.0999e+00  -2.3210e+00  -4.0411e+00  -1.0081e+00  -4.2380e-02   4.2446e-03   2.0435e-05   8.9823e-02   2.0262e-02   1.8885e-01   8.9602e-02   9.2156e-01   8.3233e-02   9.9868e-01   8.1643e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 473:    1.0250e+02  -4.0999e+00  -2.3210e+00  -4.0410e+00  -1.0080e+00  -4.2522e-02   4.2486e-03   2.0429e-05   8.9793e-02   2.0231e-02   1.8877e-01   8.9608e-02   9.2177e-01   8.3210e-02   9.9901e-01   8.1637e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 474:    1.0250e+02  -4.0999e+00  -2.3209e+00  -4.0410e+00  -1.0079e+00  -4.2640e-02   4.2536e-03   2.0403e-05   8.9731e-02   2.0213e-02   1.8876e-01   8.9632e-02   9.2177e-01   8.3192e-02   9.9942e-01   8.1632e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 475:    1.0250e+02  -4.0999e+00  -2.3208e+00  -4.0409e+00  -1.0079e+00  -4.2882e-02   4.2576e-03   2.0375e-05   8.9666e-02   2.0190e-02   1.8866e-01   8.9718e-02   9.2160e-01   8.3218e-02   9.9931e-01   8.1622e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 476:    1.0250e+02  -4.0999e+00  -2.3207e+00  -4.0409e+00  -1.0080e+00  -4.2983e-02   4.2599e-03   2.0368e-05   8.9563e-02   2.0171e-02   1.8859e-01   8.9777e-02   9.2162e-01   8.3236e-02   9.9932e-01   8.1620e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 477:    1.0250e+02  -4.0999e+00  -2.3205e+00  -4.0410e+00  -1.0081e+00  -4.3022e-02   4.2570e-03   2.0364e-05   8.9440e-02   2.0166e-02   1.8860e-01   8.9785e-02   9.2171e-01   8.3236e-02   9.9940e-01   8.1633e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 478:    1.0250e+02  -4.0999e+00  -2.3203e+00  -4.0410e+00  -1.0080e+00  -4.3129e-02   4.2505e-03   2.0371e-05   8.9374e-02   2.0151e-02   1.8859e-01   8.9826e-02   9.2171e-01   8.3224e-02   9.9970e-01   8.1620e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 479:    1.0250e+02  -4.1000e+00  -2.3201e+00  -4.0410e+00  -1.0080e+00  -4.3186e-02   4.2440e-03   2.0373e-05   8.9355e-02   2.0133e-02   1.8858e-01   8.9837e-02   9.2168e-01   8.3214e-02   9.9991e-01   8.1621e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 480:    1.0250e+02  -4.1000e+00  -2.3198e+00  -4.0410e+00  -1.0080e+00  -4.3477e-02   4.2424e-03   2.0440e-05   8.9311e-02   2.0121e-02   1.8857e-01   8.9848e-02   9.2158e-01   8.3221e-02   9.9995e-01   8.1627e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 481:    1.0250e+02  -4.1000e+00  -2.3194e+00  -4.0410e+00  -1.0079e+00  -4.3647e-02   4.2358e-03   2.0479e-05   8.9315e-02   2.0096e-02   1.8851e-01   8.9865e-02   9.2159e-01   8.3197e-02   1.0002e+00   8.1642e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 482:    1.0250e+02  -4.1000e+00  -2.3188e+00  -4.0409e+00  -1.0079e+00  -4.3909e-02   4.2370e-03   2.0551e-05   8.9382e-02   2.0085e-02   1.8840e-01   8.9799e-02   9.2147e-01   8.3229e-02   1.0001e+00   8.1656e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 483:    1.0250e+02  -4.1000e+00  -2.3189e+00  -4.0409e+00  -1.0078e+00  -4.4169e-02   4.2467e-03   2.0587e-05   8.9377e-02   2.0054e-02   1.8838e-01   8.9943e-02   9.2130e-01   8.3237e-02   1.0003e+00   8.1650e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 484:    1.0250e+02  -4.1000e+00  -2.3188e+00  -4.0408e+00  -1.0078e+00  -4.4346e-02   4.2627e-03   2.0605e-05   8.9450e-02   2.0020e-02   1.8840e-01   9.0090e-02   9.2114e-01   8.3237e-02   1.0004e+00   8.1639e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 485:    1.0250e+02  -4.1000e+00  -2.3185e+00  -4.0408e+00  -1.0079e+00  -4.4414e-02   4.2737e-03   2.0594e-05   8.9541e-02   1.9999e-02   1.8842e-01   9.0237e-02   9.2116e-01   8.3227e-02   1.0004e+00   8.1641e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 486:    1.0250e+02  -4.1000e+00  -2.3183e+00  -4.0408e+00  -1.0080e+00  -4.4572e-02   4.2902e-03   2.0581e-05   8.9733e-02   1.9978e-02   1.8837e-01   9.0412e-02   9.2098e-01   8.3224e-02   1.0003e+00   8.1642e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 487:    1.0250e+02  -4.1000e+00  -2.3182e+00  -4.0407e+00  -1.0081e+00  -4.4698e-02   4.2979e-03   2.0560e-05   8.9822e-02   1.9954e-02   1.8827e-01   9.0744e-02   9.2114e-01   8.3212e-02   1.0003e+00   8.1644e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 488:    1.0250e+02  -4.1000e+00  -2.3180e+00  -4.0406e+00  -1.0081e+00  -4.4892e-02   4.3035e-03   2.0549e-05   8.9849e-02   1.9928e-02   1.8816e-01   9.1000e-02   9.2118e-01   8.3206e-02   1.0003e+00   8.1644e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 489:    1.0250e+02  -4.1000e+00  -2.3180e+00  -4.0406e+00  -1.0081e+00  -4.5037e-02   4.2987e-03   2.0515e-05   8.9843e-02   1.9906e-02   1.8815e-01   9.1260e-02   9.2116e-01   8.3190e-02   1.0001e+00   8.1633e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 490:    1.0250e+02  -4.1000e+00  -2.3179e+00  -4.0405e+00  -1.0081e+00  -4.5139e-02   4.2930e-03   2.0508e-05   8.9827e-02   1.9887e-02   1.8814e-01   9.1545e-02   9.2114e-01   8.3178e-02   9.9991e-01   8.1624e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 491:    1.0250e+02  -4.1000e+00  -2.3179e+00  -4.0405e+00  -1.0081e+00  -4.5107e-02   4.2871e-03   2.0490e-05   8.9765e-02   1.9855e-02   1.8814e-01   9.1702e-02   9.2089e-01   8.3179e-02   9.9986e-01   8.1624e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 492:    1.0250e+02  -4.1000e+00  -2.3178e+00  -4.0405e+00  -1.0080e+00  -4.5192e-02   4.2818e-03   2.0470e-05   8.9791e-02   1.9831e-02   1.8810e-01   9.1846e-02   9.2086e-01   8.3164e-02   9.9977e-01   8.1630e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 493:    1.0250e+02  -4.1000e+00  -2.3179e+00  -4.0406e+00  -1.0078e+00  -4.4968e-02   4.2782e-03   2.0452e-05   8.9797e-02   1.9804e-02   1.8806e-01   9.1987e-02   9.2080e-01   8.3146e-02   9.9982e-01   8.1627e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 494:    1.0250e+02  -4.1000e+00  -2.3180e+00  -4.0406e+00  -1.0078e+00  -4.4773e-02   4.2766e-03   2.0453e-05   8.9696e-02   1.9781e-02   1.8805e-01   9.2096e-02   9.2057e-01   8.3148e-02   9.9999e-01   8.1611e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 495:    1.0250e+02  -4.1000e+00  -2.3181e+00  -4.0408e+00  -1.0078e+00  -4.4647e-02   4.2798e-03   2.0446e-05   8.9661e-02   1.9763e-02   1.8804e-01   9.2155e-02   9.2039e-01   8.3137e-02   9.9999e-01   8.1599e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 496:    1.0250e+02  -4.1000e+00  -2.3182e+00  -4.0408e+00  -1.0079e+00  -4.4482e-02   4.2851e-03   2.0434e-05   8.9630e-02   1.9750e-02   1.8796e-01   9.2258e-02   9.2042e-01   8.3124e-02   1.0000e+00   8.1601e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 497:    1.0250e+02  -4.1000e+00  -2.3184e+00  -4.0409e+00  -1.0079e+00  -4.4175e-02   4.2866e-03   2.0423e-05   8.9656e-02   1.9743e-02   1.8789e-01   9.2225e-02   9.2017e-01   8.3126e-02   1.0002e+00   8.1616e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 498:    1.0250e+02  -4.1000e+00  -2.3187e+00  -4.0409e+00  -1.0079e+00  -4.3826e-02   4.2847e-03   2.0406e-05   8.9587e-02   1.9730e-02   1.8781e-01   9.2143e-02   9.2017e-01   8.3156e-02   1.0002e+00   8.1613e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 499:    1.0250e+02  -4.1000e+00  -2.3190e+00  -4.0409e+00  -1.0078e+00  -4.3529e-02   4.2806e-03   2.0397e-05   8.9653e-02   1.9712e-02   1.8779e-01   9.2107e-02   9.2028e-01   8.3172e-02   1.0004e+00   8.1607e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 500:    1.0250e+02  -4.1000e+00  -2.3193e+00  -4.0409e+00  -1.0077e+00  -4.3241e-02   4.2787e-03   2.0390e-05   8.9759e-02   1.9689e-02   1.8773e-01   9.2021e-02   9.2029e-01   8.3203e-02   1.0006e+00   8.1613e-02</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating covariance matrix</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling EBE model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Needed Covariates:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "CMT"</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating residuals/tables</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-in"><span class="co"># The following takes a very long time but gives</span></span>
-<span class="r-in"><span class="va">f_nlmixr_dfop_sfo_focei</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr</a></span><span class="op">(</span><span class="va">f_mmkin_dfop_sfo</span>, est <span class="op">=</span> <span class="st">"focei"</span><span class="op">)</span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate jacobian</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate sensitivities</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate ∂(f)/∂(η)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate ∂(R²)/∂(η)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in inner model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in inner model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in EBE model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in EBE model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling inner model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in FD model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in FD model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling EBE model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling events FD model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Needed Covariates:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "CMT"</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="font-weight: bold;">Key:</span> U: Unscaled Parameters; X: Back-transformed parameters; G: Gill difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> F: Forward difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> C: Central difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> M: Mixed forward and central difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Unscaled parameters for Omegas=chol(solve(omega));</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Diagonals are transformed, as specified by foceiControl(diagXform=)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |-----+---------------+-----------+-----------+-----------+-----------|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    #| Objective Fun |  parent_0 |  log_k_m1 |f_parent_qlogis |    log_k1 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    log_k2 |  g_qlogis | sigma_low |  rsd_high |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|        o1 |        o2 |        o3 |        o4 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|        o5 |        o6 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    1</span>|     496.69520 |     1.000 |    -1.000 |   -0.9393 |   -0.9673 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.000 |   -0.9214 |   -0.9072 |   -0.9199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9082 |   -0.8909 |   -0.8969 |   -0.8991 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8928 |   -0.8991 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      496.6952 |     100.0 |    -4.100 |   -0.9400 |    -2.400 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.100 |  -0.01100 |    0.7300 |   0.06700 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6756 |     1.581 |     1.265 |     1.151 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.477 |     1.151 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      496.6952</span> |     100.0 |   0.01657 |    0.2809 |   0.09072 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01657 |    0.4973 |    0.7300 |   0.06700 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6756 |     1.581 |     1.265 |     1.151 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.477 |     1.151 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    G|    Gill Diff. |    -72.99 |    -3.907 |   -0.8109 |   0.02803 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.841 |  0.006841 |    -22.18 |    -32.78 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     3.538 |    -16.93 |    -12.84 |    -12.77 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -15.02 |    -9.608 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    2</span>|     1346.5436 |     1.823 |   -0.9559 |   -0.9301 |   -0.9677 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9792 |   -0.9215 |   -0.6570 |   -0.5501 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9482 |   -0.6999 |   -0.7521 |   -0.7551 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7234 |   -0.7907 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     1346.5436 |     182.3 |    -4.056 |   -0.9314 |    -2.400 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.079 |  -0.01100 |    0.8213 |   0.07939 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6486 |     1.883 |     1.449 |     1.317 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.728 |     1.276 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     1346.5436</span> |     182.3 |   0.01732 |    0.2826 |   0.09069 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01692 |    0.4972 |    0.8213 |   0.07939 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6486 |     1.883 |     1.449 |     1.317 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.728 |     1.276 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    3</span>|     512.48246 |     1.082 |   -0.9956 |   -0.9384 |   -0.9674 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9979 |   -0.9214 |   -0.8822 |   -0.8830 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9122 |   -0.8718 |   -0.8824 |   -0.8847 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8759 |   -0.8883 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     512.48246 |     108.2 |    -4.096 |   -0.9391 |    -2.400 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.098 |  -0.01100 |    0.7391 |   0.06824 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6729 |     1.611 |     1.284 |     1.167 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.502 |     1.163 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     512.48246</span> |     108.2 |   0.01665 |    0.2811 |   0.09072 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01661 |    0.4973 |    0.7391 |   0.06824 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6729 |     1.611 |     1.284 |     1.167 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.502 |     1.163 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    4</span>|     495.95762 |     1.015 |   -0.9992 |   -0.9391 |   -0.9673 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9996 |   -0.9214 |   -0.9027 |   -0.9133 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9090 |   -0.8874 |   -0.8943 |   -0.8965 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8898 |   -0.8972 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     495.95762 |     101.5 |    -4.099 |   -0.9398 |    -2.400 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.100 |  -0.01100 |    0.7316 |   0.06722 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6751 |     1.587 |     1.269 |     1.154 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.482 |     1.153 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     495.95762</span> |     101.5 |   0.01659 |    0.2809 |   0.09072 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01658 |    0.4973 |    0.7316 |   0.06722 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6751 |     1.587 |     1.269 |     1.154 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.482 |     1.153 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     41.81 |    -3.013 |  -0.08916 |   -0.3303 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.914 |  0.005472 |    -23.39 |    -29.38 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     3.465 |    -17.07 |    -12.49 |    -12.74 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -15.23 |    -9.735 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    5</span>|     495.32815 |     1.003 |   -0.9984 |   -0.9391 |   -0.9673 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9991 |   -0.9214 |   -0.8962 |   -0.9051 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9099 |   -0.8826 |   -0.8908 |   -0.8930 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8855 |   -0.8944 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     495.32815 |     100.3 |    -4.098 |   -0.9398 |    -2.400 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.099 |  -0.01100 |    0.7340 |   0.06750 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6745 |     1.594 |     1.273 |     1.158 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.488 |     1.156 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     495.32815</span> |     100.3 |   0.01660 |    0.2809 |   0.09073 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01659 |    0.4973 |    0.7340 |   0.06750 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6745 |     1.594 |     1.273 |     1.158 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.488 |     1.156 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -47.70 |    -3.739 |   -0.6694 |  -0.04580 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.870 |  0.006670 |    -22.76 |    -30.26 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     3.613 |    -16.75 |    -12.29 |    -12.38 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -15.09 |    -9.486 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    6</span>|     494.72051 |     1.016 |   -0.9974 |   -0.9389 |   -0.9672 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9986 |   -0.9214 |   -0.8902 |   -0.8971 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9109 |   -0.8782 |   -0.8876 |   -0.8897 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8816 |   -0.8919 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     494.72051 |     101.6 |    -4.097 |   -0.9397 |    -2.400 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.099 |  -0.01100 |    0.7362 |   0.06777 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6738 |     1.601 |     1.277 |     1.162 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.494 |     1.159 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     494.72051</span> |     101.6 |   0.01662 |    0.2810 |   0.09073 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01660 |    0.4973 |    0.7362 |   0.06777 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6738 |     1.601 |     1.277 |     1.162 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.494 |     1.159 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     48.34 |    -2.939 |  -0.03847 |   -0.3608 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.945 |  0.005434 |    -20.90 |    -26.17 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     3.821 |    -17.00 |    -12.53 |    -12.70 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -15.56 |    -9.587 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    7</span>|     494.15722 |     1.003 |   -0.9966 |   -0.9389 |   -0.9671 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9981 |   -0.9214 |   -0.8845 |   -0.8900 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9119 |   -0.8736 |   -0.8842 |   -0.8863 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8773 |   -0.8893 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     494.15722 |     100.3 |    -4.097 |   -0.9396 |    -2.400 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.098 |  -0.01100 |    0.7383 |   0.06800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6731 |     1.608 |     1.282 |     1.166 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.500 |     1.162 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     494.15722</span> |     100.3 |   0.01663 |    0.2810 |   0.09074 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01660 |    0.4973 |    0.7383 |   0.06800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6731 |     1.608 |     1.282 |     1.166 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.500 |     1.162 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -50.36 |    -3.770 |   -0.7003 |  -0.03307 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.882 |  0.006830 |    -20.02 |    -28.64 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     3.020 |    -16.49 |    -12.31 |    -12.33 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -14.87 |    -9.319 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    8</span>|     493.49185 |     1.015 |   -0.9955 |   -0.9387 |   -0.9671 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9975 |   -0.9214 |   -0.8787 |   -0.8818 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9128 |   -0.8688 |   -0.8806 |   -0.8827 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8730 |   -0.8866 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     493.49185 |     101.5 |    -4.096 |   -0.9395 |    -2.400 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.098 |  -0.01100 |    0.7404 |   0.06828 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6725 |     1.616 |     1.286 |     1.170 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.507 |     1.165 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     493.49185</span> |     101.5 |   0.01665 |    0.2810 |   0.09074 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01661 |    0.4973 |    0.7404 |   0.06828 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6725 |     1.616 |     1.286 |     1.170 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.507 |     1.165 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     41.03 |    -2.976 |  -0.07989 |   -0.3421 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.950 |  0.005619 |    -20.95 |    -25.58 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     3.377 |    -16.59 |    -11.95 |    -12.23 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -14.73 |    -9.398 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    9</span>|     492.95463 |     1.003 |   -0.9946 |   -0.9387 |   -0.9670 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9969 |   -0.9215 |   -0.8725 |   -0.8742 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9138 |   -0.8639 |   -0.8771 |   -0.8790 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8686 |   -0.8839 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     492.95463 |     100.3 |    -4.095 |   -0.9394 |    -2.400 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.097 |  -0.01100 |    0.7427 |   0.06853 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6718 |     1.624 |     1.291 |     1.174 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.513 |     1.168 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     492.95463</span> |     100.3 |   0.01666 |    0.2810 |   0.09075 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01662 |    0.4973 |    0.7427 |   0.06853 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6718 |     1.624 |     1.291 |     1.174 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.513 |     1.168 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -49.97 |    -3.772 |   -0.7121 |  -0.02926 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.890 |  0.006985 |    -18.86 |    -26.85 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.866 |    -16.28 |    -11.70 |    -11.82 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -14.59 |    -9.132 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   10</span>|     492.31444 |     1.015 |   -0.9935 |   -0.9385 |   -0.9670 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9964 |   -0.9215 |   -0.8668 |   -0.8662 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9147 |   -0.8589 |   -0.8735 |   -0.8755 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8642 |   -0.8811 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     492.31444 |     101.5 |    -4.094 |   -0.9393 |    -2.400 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.096 |  -0.01100 |    0.7448 |   0.06880 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6712 |     1.632 |     1.295 |     1.178 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.520 |     1.172 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     492.31444</span> |     101.5 |   0.01668 |    0.2810 |   0.09075 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01663 |    0.4972 |    0.7448 |   0.06880 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6712 |     1.632 |     1.295 |     1.178 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.520 |     1.172 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     40.47 |    -2.944 |  -0.07505 |   -0.3464 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.962 |  0.005707 |    -18.58 |    -22.75 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     3.454 |    -16.50 |    -11.93 |    -12.14 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -15.04 |    -9.193 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   11</span>|     491.79472 |     1.003 |   -0.9926 |   -0.9385 |   -0.9669 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9957 |   -0.9215 |   -0.8609 |   -0.8590 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9158 |   -0.8537 |   -0.8697 |   -0.8716 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8594 |   -0.8781 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     491.79472 |     100.3 |    -4.093 |   -0.9392 |    -2.400 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.096 |  -0.01100 |    0.7469 |   0.06904 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6705 |     1.640 |     1.300 |     1.183 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.527 |     1.175 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     491.79472</span> |     100.3 |   0.01670 |    0.2811 |   0.09076 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01664 |    0.4972 |    0.7469 |   0.06904 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6705 |     1.640 |     1.300 |     1.183 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.527 |     1.175 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -48.84 |    -3.756 |   -0.7183 |  -0.02810 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.902 |  0.007122 |    -19.51 |    -25.13 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.711 |    -16.03 |    -11.73 |    -11.80 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -14.28 |    -8.965 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   12</span>|     491.12641 |     1.014 |   -0.9913 |   -0.9383 |   -0.9669 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9951 |   -0.9215 |   -0.8546 |   -0.8511 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9167 |   -0.8483 |   -0.8658 |   -0.8677 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8546 |   -0.8752 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     491.12641 |     101.4 |    -4.091 |   -0.9390 |    -2.400 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.095 |  -0.01100 |    0.7492 |   0.06931 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6699 |     1.648 |     1.305 |     1.187 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.534 |     1.178 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     491.12641</span> |     101.4 |   0.01672 |    0.2811 |   0.09076 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01665 |    0.4972 |    0.7492 |   0.06931 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6699 |     1.648 |     1.305 |     1.187 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.534 |     1.178 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     34.57 |    -2.950 |   -0.1076 |   -0.3310 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.967 |  0.005897 |    -18.06 |    -21.71 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     3.154 |    -16.24 |    -11.62 |    -11.87 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -14.73 |    -8.995 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   13</span>|     490.65951 |     1.002 |   -0.9903 |   -0.9382 |   -0.9668 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9944 |   -0.9215 |   -0.8485 |   -0.8438 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9178 |   -0.8429 |   -0.8619 |   -0.8637 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8496 |   -0.8721 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     490.65951 |     100.2 |    -4.090 |   -0.9390 |    -2.399 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.094 |  -0.01100 |    0.7514 |   0.06955 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6692 |     1.657 |     1.310 |     1.192 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.541 |     1.182 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     490.65951</span> |     100.2 |   0.01673 |    0.2811 |   0.09077 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01667 |    0.4972 |    0.7514 |   0.06955 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6692 |     1.657 |     1.310 |     1.192 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.541 |     1.182 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -50.76 |    -3.759 |   -0.7472 |  -0.01668 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.908 |  0.007323 |    -16.70 |    -21.90 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     3.094 |    -15.72 |    -11.38 |    -11.47 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -14.04 |    -8.745 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   14</span>|     490.01300 |     1.014 |   -0.9891 |   -0.9380 |   -0.9667 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9937 |   -0.9215 |   -0.8428 |   -0.8366 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9189 |   -0.8372 |   -0.8579 |   -0.8596 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8446 |   -0.8690 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|       490.013 |     101.4 |    -4.089 |   -0.9388 |    -2.399 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.094 |  -0.01100 |    0.7535 |   0.06979 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6684 |     1.666 |     1.315 |     1.196 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.549 |     1.186 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">       490.013</span> |     101.4 |   0.01676 |    0.2811 |   0.09077 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01668 |    0.4972 |    0.7535 |   0.06979 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6684 |     1.666 |     1.315 |     1.196 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.549 |     1.186 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     34.25 |    -2.890 |  -0.09666 |   -0.3362 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.973 |  0.006003 |    -17.09 |    -20.19 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     3.007 |    -15.95 |    -11.25 |    -11.56 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -14.42 |    -8.788 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   15</span>|     489.51603 |     1.003 |   -0.9880 |   -0.9380 |   -0.9666 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9930 |   -0.9215 |   -0.8366 |   -0.8294 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9200 |   -0.8313 |   -0.8537 |   -0.8553 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8392 |   -0.8658 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     489.51603 |     100.3 |    -4.088 |   -0.9388 |    -2.399 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.093 |  -0.01100 |    0.7558 |   0.07003 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6677 |     1.675 |     1.320 |     1.201 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.556 |     1.189 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     489.51603</span> |     100.3 |   0.01677 |    0.2812 |   0.09078 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01669 |    0.4972 |    0.7558 |   0.07003 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6677 |     1.675 |     1.320 |     1.201 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.556 |     1.189 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -45.29 |    -3.673 |   -0.7179 |  -0.03084 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.907 |  0.007423 |    -17.34 |    -21.86 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.565 |    -15.52 |    -11.05 |    -11.19 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -14.28 |    -8.545 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   16</span>|     488.89410 |     1.014 |   -0.9866 |   -0.9377 |   -0.9666 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9922 |   -0.9215 |   -0.8304 |   -0.8220 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9210 |   -0.8254 |   -0.8495 |   -0.8510 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8337 |   -0.8625 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      488.8941 |     101.4 |    -4.087 |   -0.9385 |    -2.399 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.092 |  -0.01100 |    0.7580 |   0.07028 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6670 |     1.685 |     1.325 |     1.206 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.565 |     1.193 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      488.8941</span> |     101.4 |   0.01680 |    0.2812 |   0.09079 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01670 |    0.4972 |    0.7580 |   0.07028 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6670 |     1.685 |     1.325 |     1.206 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.565 |     1.193 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     32.56 |    -2.827 |  -0.09853 |   -0.3366 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.968 |  0.006143 |    -15.42 |    -18.16 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.733 |    -15.52 |    -10.94 |    -11.25 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -14.13 |    -8.583 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   17</span>|     488.48678 |     1.002 |   -0.9855 |   -0.9377 |   -0.9664 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9915 |   -0.9215 |   -0.8246 |   -0.8152 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9220 |   -0.8195 |   -0.8454 |   -0.8468 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8284 |   -0.8593 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     488.48678 |     100.2 |    -4.086 |   -0.9385 |    -2.399 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.091 |  -0.01100 |    0.7601 |   0.07051 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6663 |     1.694 |     1.331 |     1.211 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.572 |     1.197 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     488.48678</span> |     100.2 |   0.01681 |    0.2812 |   0.09080 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01671 |    0.4972 |    0.7601 |   0.07051 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6663 |     1.694 |     1.331 |     1.211 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.572 |     1.197 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -50.86 |    -3.681 |   -0.7723 |-0.0007467 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.892 |  0.007742 |    -16.40 |    -20.55 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.306 |    -15.16 |    -10.65 |    -10.82 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -14.00 |    -8.331 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   18</span>|     487.81394 |     1.012 |   -0.9841 |   -0.9374 |   -0.9664 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9907 |   -0.9215 |   -0.8186 |   -0.8083 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9229 |   -0.8132 |   -0.8411 |   -0.8424 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8225 |   -0.8558 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     487.81394 |     101.2 |    -4.084 |   -0.9383 |    -2.399 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.091 |  -0.01100 |    0.7623 |   0.07074 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6657 |     1.704 |     1.336 |     1.216 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.581 |     1.201 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     487.81394</span> |     101.2 |   0.01684 |    0.2812 |   0.09080 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01673 |    0.4972 |    0.7623 |   0.07074 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6657 |     1.704 |     1.336 |     1.216 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.581 |     1.201 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     24.73 |    -2.818 |   -0.1513 |   -0.3103 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.952 |  0.006413 |    -14.43 |    -16.89 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.774 |    -15.21 |    -10.56 |    -10.91 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -13.80 |    -8.354 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   19</span>|     487.41354 |     1.002 |   -0.9829 |   -0.9374 |   -0.9663 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9898 |   -0.9215 |   -0.8126 |   -0.8013 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9240 |   -0.8068 |   -0.8367 |   -0.8378 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8167 |   -0.8523 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     487.41354 |     100.2 |    -4.083 |   -0.9382 |    -2.399 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.090 |  -0.01100 |    0.7645 |   0.07098 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6649 |     1.714 |     1.342 |     1.221 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.590 |     1.205 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     487.41354</span> |     100.2 |   0.01686 |    0.2813 |   0.09082 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01674 |    0.4972 |    0.7645 |   0.07098 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6649 |     1.714 |     1.342 |     1.221 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.590 |     1.205 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -49.04 |    -3.605 |   -0.7738 | -0.005231 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.885 |  0.007887 |    -13.75 |    -19.14 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.153 |    -14.90 |    -10.33 |    -10.54 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -13.68 |    -8.125 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   20</span>|     486.77105 |     1.012 |   -0.9814 |   -0.9371 |   -0.9662 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9889 |   -0.9215 |   -0.8076 |   -0.7947 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9250 |   -0.7999 |   -0.8321 |   -0.8330 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8104 |   -0.8486 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     486.77105 |     101.2 |    -4.081 |   -0.9380 |    -2.399 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.089 |  -0.01100 |    0.7664 |   0.07119 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6643 |     1.725 |     1.347 |     1.227 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.599 |     1.209 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     486.77105</span> |     101.2 |   0.01688 |    0.2813 |   0.09082 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01676 |    0.4972 |    0.7664 |   0.07119 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6643 |     1.725 |     1.347 |     1.227 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.599 |     1.209 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     24.27 |    -2.713 |   -0.1438 |   -0.3142 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.939 |  0.006562 |    -14.18 |    -16.12 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.525 |    -14.86 |    -10.17 |    -10.57 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -13.46 |    -8.131 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   21</span>|     486.40106 |     1.002 |   -0.9802 |   -0.9371 |   -0.9661 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9881 |   -0.9215 |   -0.8015 |   -0.7878 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9261 |   -0.7935 |   -0.8277 |   -0.8285 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8046 |   -0.8452 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     486.40106 |     100.2 |    -4.080 |   -0.9379 |    -2.399 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.088 |  -0.01100 |    0.7686 |   0.07143 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6635 |     1.735 |     1.353 |     1.232 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.608 |     1.213 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     486.40106</span> |     100.2 |   0.01690 |    0.2813 |   0.09083 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01677 |    0.4972 |    0.7686 |   0.07143 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6635 |     1.735 |     1.353 |     1.232 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.608 |     1.213 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -49.43 |    -3.533 |   -0.7907 |  0.002583 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.861 |  0.008122 |    -12.85 |    -17.77 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.313 |    -14.52 |    -9.868 |    -10.18 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -13.34 |    -7.899 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   22</span>|     485.77865 |     1.012 |   -0.9786 |   -0.9368 |   -0.9660 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9871 |   -0.9215 |   -0.7966 |   -0.7818 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9271 |   -0.7864 |   -0.8231 |   -0.8237 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7980 |   -0.8414 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     485.77865 |     101.2 |    -4.079 |   -0.9376 |    -2.399 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.087 |  -0.01100 |    0.7704 |   0.07163 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6629 |     1.746 |     1.359 |     1.238 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.617 |     1.217 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     485.77865</span> |     101.2 |   0.01693 |    0.2814 |   0.09084 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01679 |    0.4972 |    0.7704 |   0.07163 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6629 |     1.746 |     1.359 |     1.238 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.617 |     1.217 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     23.77 |    -2.587 |   -0.1355 |   -0.3176 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.913 |  0.006717 |    -13.39 |    -15.00 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.211 |    -14.53 |    -9.747 |    -10.22 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -13.16 |    -7.915 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   23</span>|     485.42886 |     1.002 |   -0.9775 |   -0.9367 |   -0.9659 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9862 |   -0.9215 |   -0.7907 |   -0.7751 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9281 |   -0.7799 |   -0.8187 |   -0.8190 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7921 |   -0.8378 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     485.42886 |     100.2 |    -4.077 |   -0.9376 |    -2.399 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.086 |  -0.01100 |    0.7725 |   0.07185 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6622 |     1.757 |     1.364 |     1.243 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.626 |     1.221 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     485.42886</span> |     100.2 |   0.01695 |    0.2814 |   0.09085 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01680 |    0.4972 |    0.7725 |   0.07185 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6622 |     1.757 |     1.364 |     1.243 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.626 |     1.221 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -48.95 |    -3.431 |   -0.7980 |  0.007511 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.828 |  0.008340 |    -12.05 |    -16.59 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.177 |    -14.27 |    -9.567 |    -9.843 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -12.98 |    -7.655 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   24</span>|     484.82183 |     1.012 |   -0.9759 |   -0.9364 |   -0.9658 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9852 |   -0.9215 |   -0.7861 |   -0.7697 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9290 |   -0.7723 |   -0.8141 |   -0.8141 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7852 |   -0.8339 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     484.82183 |     101.2 |    -4.076 |   -0.9373 |    -2.398 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.085 |  -0.01100 |    0.7742 |   0.07203 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6616 |     1.769 |     1.370 |     1.249 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.636 |     1.226 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     484.82183</span> |     101.2 |   0.01698 |    0.2814 |   0.09086 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01682 |    0.4972 |    0.7742 |   0.07203 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6616 |     1.769 |     1.370 |     1.249 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.636 |     1.226 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     22.05 |    -2.460 |   -0.1372 |   -0.3150 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.875 |  0.006918 |    -12.51 |    -13.87 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.059 |    -14.26 |    -9.441 |    -9.910 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -12.81 |    -7.688 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   25</span>|     484.49026 |     1.002 |   -0.9747 |   -0.9363 |   -0.9656 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9843 |   -0.9215 |   -0.7802 |   -0.7632 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9300 |   -0.7655 |   -0.8095 |   -0.8093 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7791 |   -0.8302 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     484.49026 |     100.2 |    -4.075 |   -0.9372 |    -2.398 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.084 |  -0.01100 |    0.7764 |   0.07225 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6609 |     1.779 |     1.376 |     1.254 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.645 |     1.230 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     484.49026</span> |     100.2 |   0.01700 |    0.2815 |   0.09087 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01684 |    0.4972 |    0.7764 |   0.07225 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6609 |     1.779 |     1.376 |     1.254 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.645 |     1.230 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -48.40 |    -3.314 |   -0.8046 |   0.01082 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.785 |  0.008564 |    -11.34 |    -13.90 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.095 |    -13.95 |    -9.157 |    -9.483 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -12.66 |    -7.448 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   26</span>|     483.91316 |     1.012 |   -0.9731 |   -0.9360 |   -0.9656 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9832 |   -0.9215 |   -0.7758 |   -0.7593 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9309 |   -0.7575 |   -0.8048 |   -0.8042 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7719 |   -0.8261 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     483.91316 |     101.2 |    -4.073 |   -0.9369 |    -2.398 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.083 |  -0.01100 |    0.7780 |   0.07238 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6603 |     1.792 |     1.382 |     1.260 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.656 |     1.235 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     483.91316</span> |     101.2 |   0.01702 |    0.2815 |   0.09088 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01685 |    0.4972 |    0.7780 |   0.07238 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6603 |     1.792 |     1.382 |     1.260 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.656 |     1.235 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     21.71 |    -2.295 |   -0.1241 |   -0.3190 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.824 |  0.007081 |    -13.14 |    -14.20 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.713 |    -13.92 |    -9.049 |    -9.560 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -12.44 |    -7.439 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   27</span>|     483.53972 |     1.003 |   -0.9719 |   -0.9359 |   -0.9654 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9822 |   -0.9215 |   -0.7695 |   -0.7529 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9317 |   -0.7501 |   -0.8002 |   -0.7993 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7653 |   -0.8222 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     483.53972 |     100.3 |    -4.072 |   -0.9368 |    -2.398 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.082 |  -0.01100 |    0.7803 |   0.07259 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6598 |     1.804 |     1.388 |     1.266 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.666 |     1.239 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     483.53972</span> |     100.3 |   0.01705 |    0.2815 |   0.09089 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01687 |    0.4972 |    0.7803 |   0.07259 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6598 |     1.804 |     1.388 |     1.266 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.666 |     1.239 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -41.33 |    -3.086 |   -0.7421 |  -0.01550 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.732 |  0.008667 |    -12.24 |    -14.31 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.879 |    -13.57 |    -8.704 |    -9.151 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -12.31 |    -7.231 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   28</span>|     483.02129 |     1.012 |   -0.9704 |   -0.9356 |   -0.9653 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9811 |   -0.9215 |   -0.7639 |   -0.7482 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9323 |   -0.7422 |   -0.7957 |   -0.7943 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7582 |   -0.8182 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     483.02129 |     101.2 |    -4.070 |   -0.9365 |    -2.398 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.081 |  -0.01100 |    0.7823 |   0.07275 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6594 |     1.816 |     1.393 |     1.271 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.676 |     1.244 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     483.02129</span> |     101.2 |   0.01707 |    0.2816 |   0.09090 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01689 |    0.4972 |    0.7823 |   0.07275 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6594 |     1.816 |     1.393 |     1.271 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.676 |     1.244 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     24.99 |    -2.064 |  -0.07177 |   -0.3414 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.768 |  0.007186 |    -12.35 |    -13.09 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.619 |    -13.63 |    -8.749 |    -9.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -12.40 |    -7.209 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   29</span>|     482.63111 |     1.003 |   -0.9692 |   -0.9355 |   -0.9651 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9800 |   -0.9215 |   -0.7579 |   -0.7429 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9329 |   -0.7344 |   -0.7911 |   -0.7893 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7510 |   -0.8142 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     482.63111 |     100.3 |    -4.069 |   -0.9364 |    -2.398 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.080 |  -0.01100 |    0.7845 |   0.07293 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6589 |     1.829 |     1.399 |     1.277 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.687 |     1.249 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     482.63111</span> |     100.3 |   0.01709 |    0.2816 |   0.09092 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01691 |    0.4972 |    0.7845 |   0.07293 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6589 |     1.829 |     1.399 |     1.277 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.687 |     1.249 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -36.55 |    -2.873 |   -0.6941 |  -0.03251 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.664 |  0.008820 |    -11.56 |    -13.31 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.468 |    -13.22 |    -8.319 |    -8.815 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -11.93 |    -7.014 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   30</span>|     482.15860 |     1.013 |   -0.9679 |   -0.9352 |   -0.9650 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9789 |   -0.9215 |   -0.7525 |   -0.7387 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9332 |   -0.7261 |   -0.7866 |   -0.7843 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7435 |   -0.8100 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      482.1586 |     101.3 |    -4.068 |   -0.9361 |    -2.398 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.079 |  -0.01100 |    0.7865 |   0.07307 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6587 |     1.842 |     1.405 |     1.283 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.698 |     1.253 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      482.1586</span> |     101.3 |   0.01711 |    0.2817 |   0.09093 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01693 |    0.4972 |    0.7865 |   0.07307 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6587 |     1.842 |     1.405 |     1.283 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.698 |     1.253 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     26.18 |    -1.853 |  -0.03641 |   -0.3540 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.697 |  0.007343 |    -10.35 |    -10.94 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.762 |    -13.15 |    -8.194 |    -8.841 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -11.73 |    -7.013 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   31</span>|     481.77253 |     1.004 |   -0.9667 |   -0.9350 |   -0.9648 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9778 |   -0.9215 |   -0.7477 |   -0.7350 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9339 |   -0.7174 |   -0.7818 |   -0.7789 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7358 |   -0.8055 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     481.77253 |     100.4 |    -4.067 |   -0.9360 |    -2.397 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.078 |  -0.01100 |    0.7882 |   0.07320 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6583 |     1.855 |     1.411 |     1.289 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.709 |     1.259 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     481.77253</span> |     100.4 |   0.01713 |    0.2817 |   0.09095 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01695 |    0.4972 |    0.7882 |   0.07320 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6583 |     1.855 |     1.411 |     1.289 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.709 |     1.259 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -33.42 |    -2.468 |   -0.6588 |  -0.04495 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.582 |  0.009000 |    -9.278 |    -10.93 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.073 |    -12.89 |    -7.995 |    -8.451 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -11.53 |    -6.784 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   32</span>|     481.33405 |     1.012 |   -0.9656 |   -0.9347 |   -0.9646 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9766 |   -0.9215 |   -0.7446 |   -0.7329 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9352 |   -0.7079 |   -0.7769 |   -0.7733 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7275 |   -0.8008 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     481.33405 |     101.2 |    -4.066 |   -0.9357 |    -2.397 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.077 |  -0.01100 |    0.7894 |   0.07326 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6574 |     1.870 |     1.417 |     1.296 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.721 |     1.264 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     481.33405</span> |     101.2 |   0.01715 |    0.2818 |   0.09096 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01697 |    0.4972 |    0.7894 |   0.07326 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6574 |     1.870 |     1.417 |     1.296 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.721 |     1.264 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     23.32 |    -1.710 |  -0.03967 |   -0.3452 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.599 |  0.007614 |    -9.890 |    -10.44 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.650 |    -12.78 |    -7.841 |    -8.441 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -11.31 |    -6.748 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   33</span>|     480.95912 |     1.004 |   -0.9646 |   -0.9346 |   -0.9644 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9754 |   -0.9215 |   -0.7406 |   -0.7300 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9364 |   -0.6985 |   -0.7720 |   -0.7677 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7193 |   -0.7960 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.95912 |     100.4 |    -4.065 |   -0.9356 |    -2.397 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.075 |  -0.01100 |    0.7908 |   0.07336 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6566 |     1.885 |     1.424 |     1.302 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.734 |     1.270 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.95912</span> |     100.4 |   0.01717 |    0.2818 |   0.09098 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01699 |    0.4972 |    0.7908 |   0.07336 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6566 |     1.885 |     1.424 |     1.302 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.734 |     1.270 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -30.38 |    -2.273 |   -0.6168 |  -0.05646 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.481 |  0.009185 |    -8.899 |    -11.86 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.510 |    -12.49 |    -7.578 |    -8.069 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -11.13 |    -6.546 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   34</span>|     480.53887 |     1.012 |   -0.9637 |   -0.9342 |   -0.9642 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9743 |   -0.9216 |   -0.7379 |   -0.7267 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9369 |   -0.6888 |   -0.7673 |   -0.7623 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7109 |   -0.7912 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.53887 |     101.2 |    -4.064 |   -0.9352 |    -2.397 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.074 |  -0.01100 |    0.7918 |   0.07347 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6562 |     1.901 |     1.429 |     1.308 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.746 |     1.275 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.53887</span> |     101.2 |   0.01719 |    0.2819 |   0.09100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01700 |    0.4972 |    0.7918 |   0.07347 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6562 |     1.901 |     1.429 |     1.308 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.746 |     1.275 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     23.40 |    -1.545 | -0.009684 |   -0.3499 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.497 |  0.007817 |    -10.78 |    -11.16 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.267 |    -12.36 |    -7.423 |    -8.042 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -10.90 |    -6.497 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   35</span>|     480.15983 |     1.005 |   -0.9631 |   -0.9341 |   -0.9640 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9733 |   -0.9216 |   -0.7332 |   -0.7223 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9371 |   -0.6793 |   -0.7629 |   -0.7571 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7029 |   -0.7866 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.15983 |     100.5 |    -4.063 |   -0.9351 |    -2.397 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.073 |  -0.01100 |    0.7935 |   0.07362 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6561 |     1.916 |     1.435 |     1.314 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.758 |     1.280 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.15983</span> |     100.5 |   0.01720 |    0.2819 |   0.09102 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01702 |    0.4972 |    0.7935 |   0.07362 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6561 |     1.916 |     1.435 |     1.314 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.758 |     1.280 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -28.57 |    -2.381 |   -0.5845 |  -0.05882 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.378 |  0.009420 |    -9.960 |    -11.12 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.408 |    -12.08 |    -7.201 |    -7.708 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -10.66 |    -6.274 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   36</span>|     479.76525 |     1.013 |   -0.9621 |   -0.9338 |   -0.9638 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9724 |   -0.9216 |   -0.7274 |   -0.7189 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9370 |   -0.6698 |   -0.7590 |   -0.7523 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6950 |   -0.7821 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.76525 |     101.3 |    -4.062 |   -0.9349 |    -2.396 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.072 |  -0.01100 |    0.7956 |   0.07373 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6562 |     1.931 |     1.440 |     1.320 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.770 |     1.286 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.76525</span> |     101.3 |   0.01721 |    0.2819 |   0.09104 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01704 |    0.4972 |    0.7956 |   0.07373 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6562 |     1.931 |     1.440 |     1.320 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.770 |     1.286 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     25.60 |    -1.145 |   0.04744 |   -0.3638 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.405 |  0.007969 |    -8.937 |    -9.159 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.481 |    -11.97 |    -7.076 |    -7.721 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -10.46 |    -6.241 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   37</span>|     479.40619 |     1.005 |   -0.9617 |   -0.9337 |   -0.9635 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9714 |   -0.9216 |   -0.7231 |   -0.7171 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9376 |   -0.6597 |   -0.7546 |   -0.7470 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6867 |   -0.7774 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.40619 |     100.5 |    -4.062 |   -0.9348 |    -2.396 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.071 |  -0.01100 |    0.7972 |   0.07380 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6558 |     1.947 |     1.445 |     1.326 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.782 |     1.291 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.40619</span> |     100.5 |   0.01722 |    0.2820 |   0.09107 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01705 |    0.4972 |    0.7972 |   0.07380 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6558 |     1.947 |     1.445 |     1.326 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.782 |     1.291 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -26.68 |    -2.255 |   -0.5492 |  -0.06756 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.289 |  0.009576 |    -8.071 |    -10.65 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.114 |    -11.68 |    -6.840 |    -7.373 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -10.30 |    -6.055 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   38</span>|     479.03445 |     1.012 |   -0.9610 |   -0.9335 |   -0.9633 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9705 |   -0.9216 |   -0.7212 |   -0.7148 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9376 |   -0.6492 |   -0.7503 |   -0.7416 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6779 |   -0.7724 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.03445 |     101.2 |    -4.061 |   -0.9345 |    -2.396 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.071 |  -0.01100 |    0.7979 |   0.07387 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6558 |     1.963 |     1.451 |     1.332 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.795 |     1.297 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.03445</span> |     101.2 |   0.01723 |    0.2820 |   0.09109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01707 |    0.4972 |    0.7979 |   0.07387 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6558 |     1.963 |     1.451 |     1.332 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.795 |     1.297 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     22.12 |    -1.081 |   0.03630 |   -0.3485 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.311 |  0.008233 |    -8.610 |    -8.856 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.385 |    -11.53 |    -6.694 |    -7.351 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -10.05 |    -5.987 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   39</span>|     478.70046 |     1.005 |   -0.9607 |   -0.9334 |   -0.9629 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9697 |   -0.9216 |   -0.7185 |   -0.7124 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9378 |   -0.6386 |   -0.7461 |   -0.7363 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6692 |   -0.7674 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     478.70046 |     100.5 |    -4.061 |   -0.9345 |    -2.396 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.070 |  -0.01100 |    0.7989 |   0.07395 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6557 |     1.980 |     1.456 |     1.338 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.808 |     1.302 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     478.70046</span> |     100.5 |   0.01724 |    0.2820 |   0.09112 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01708 |    0.4972 |    0.7989 |   0.07395 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6557 |     1.980 |     1.456 |     1.338 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.808 |     1.302 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -26.51 |    -2.193 |   -0.5308 |  -0.05813 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.191 |  0.009843 |    -9.246 |    -10.25 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.049 |    -11.25 |    -6.459 |    -7.012 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -9.842 |    -5.790 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   40</span>|     478.33460 |     1.012 |   -0.9602 |   -0.9332 |   -0.9627 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9690 |   -0.9216 |   -0.7136 |   -0.7111 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9376 |   -0.6278 |   -0.7425 |   -0.7314 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6607 |   -0.7626 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      478.3346 |     101.2 |    -4.060 |   -0.9343 |    -2.395 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.069 |  -0.01100 |    0.8007 |   0.07400 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6558 |     1.997 |     1.461 |     1.344 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.820 |     1.308 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      478.3346</span> |     101.2 |   0.01724 |    0.2821 |   0.09114 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01709 |    0.4972 |    0.8007 |   0.07400 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6558 |     1.997 |     1.461 |     1.344 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.820 |     1.308 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     20.00 |    -1.246 |   0.04282 |   -0.3358 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.228 |  0.008460 |    -8.250 |    -8.533 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.305 |    -11.09 |    -6.335 |    -6.999 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -9.637 |    -5.739 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   41</span>|     478.02904 |     1.005 |   -0.9595 |   -0.9333 |   -0.9623 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9683 |   -0.9217 |   -0.7087 |   -0.7092 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9377 |   -0.6173 |   -0.7388 |   -0.7264 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6523 |   -0.7577 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     478.02904 |     100.5 |    -4.060 |   -0.9343 |    -2.395 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.068 |  -0.01100 |    0.8025 |   0.07406 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6557 |     2.014 |     1.466 |     1.350 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.833 |     1.314 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     478.02904</span> |     100.5 |   0.01726 |    0.2820 |   0.09117 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01711 |    0.4972 |    0.8025 |   0.07406 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6557 |     2.014 |     1.466 |     1.350 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.833 |     1.314 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -28.91 |    -2.133 |   -0.5430 |  -0.03427 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.114 |   0.01012 |    -7.324 |    -9.914 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9278 |    -10.81 |    -6.119 |    -6.673 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -9.457 |    -5.557 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   42</span>|     477.67480 |     1.012 |   -0.9587 |   -0.9331 |   -0.9620 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9677 |   -0.9217 |   -0.7077 |   -0.7075 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9374 |   -0.6060 |   -0.7354 |   -0.7215 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6433 |   -0.7527 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      477.6748 |     101.2 |    -4.059 |   -0.9342 |    -2.395 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.068 |  -0.01100 |    0.8028 |   0.07412 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6559 |     2.032 |     1.470 |     1.355 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.846 |     1.319 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      477.6748</span> |     101.2 |   0.01727 |    0.2821 |   0.09120 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01712 |    0.4972 |    0.8028 |   0.07412 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6559 |     2.032 |     1.470 |     1.355 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.846 |     1.319 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     17.58 |    -1.074 |   0.03251 |   -0.3151 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.162 |  0.008688 |    -9.226 |    -9.481 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9303 |    -10.65 |    -6.004 |    -6.660 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -9.223 |    -5.492 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   43</span>|     477.36674 |     1.005 |   -0.9586 |   -0.9332 |   -0.9617 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9672 |   -0.9217 |   -0.7028 |   -0.7029 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9362 |   -0.5954 |   -0.7323 |   -0.7170 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6350 |   -0.7479 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     477.36674 |     100.5 |    -4.059 |   -0.9343 |    -2.394 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.067 |  -0.01100 |    0.8046 |   0.07427 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6567 |     2.048 |     1.474 |     1.361 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.858 |     1.325 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     477.36674</span> |     100.5 |   0.01727 |    0.2821 |   0.09123 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01713 |    0.4972 |    0.8046 |   0.07427 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6567 |     2.048 |     1.474 |     1.361 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.858 |     1.325 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -26.86 |    -1.999 |   -0.5088 |  -0.02993 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.056 |   0.01029 |    -6.930 |    -7.943 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.123 |    -10.40 |    -5.776 |    -6.354 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -9.032 |    -5.321 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   44</span>|     477.05158 |     1.012 |   -0.9583 |   -0.9331 |   -0.9614 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9667 |   -0.9217 |   -0.6985 |   -0.7029 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9357 |   -0.5841 |   -0.7296 |   -0.7125 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6265 |   -0.7430 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     477.05158 |     101.2 |    -4.058 |   -0.9342 |    -2.394 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.067 |  -0.01100 |    0.8062 |   0.07427 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6570 |     2.066 |     1.477 |     1.366 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.871 |     1.331 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     477.05158</span> |     101.2 |   0.01728 |    0.2821 |   0.09126 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01713 |    0.4972 |    0.8062 |   0.07427 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6570 |     2.066 |     1.477 |     1.366 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.871 |     1.331 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     21.59 |   -0.9080 |    0.1072 |   -0.3377 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.120 |  0.008656 |    -8.076 |    -8.919 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8940 |    -10.23 |    -5.724 |    -6.385 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -8.830 |    -5.273 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   45</span>|     476.75116 |     1.006 |   -0.9582 |   -0.9333 |   -0.9610 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9662 |   -0.9217 |   -0.6961 |   -0.7025 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9353 |   -0.5723 |   -0.7265 |   -0.7076 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6174 |   -0.7378 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     476.75116 |     100.6 |    -4.058 |   -0.9343 |    -2.394 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.066 |  -0.01100 |    0.8070 |   0.07428 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6573 |     2.085 |     1.481 |     1.371 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.884 |     1.337 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     476.75116</span> |     100.6 |   0.01728 |    0.2820 |   0.09129 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01714 |    0.4972 |    0.8070 |   0.07428 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6573 |     2.085 |     1.481 |     1.371 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.884 |     1.337 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -19.26 |    -1.806 |   -0.3949 |  -0.06769 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.006 |   0.01017 |    -6.717 |    -7.715 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.115 |    -9.949 |    -5.529 |    -6.080 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -8.599 |    -5.083 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   46</span>|     476.47529 |     1.013 |   -0.9577 |   -0.9333 |   -0.9608 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9657 |   -0.9218 |   -0.6941 |   -0.7020 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9354 |   -0.5606 |   -0.7233 |   -0.7027 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6086 |   -0.7326 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     476.47529 |     101.3 |    -4.058 |   -0.9343 |    -2.393 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.066 |  -0.01100 |    0.8078 |   0.07430 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6573 |     2.103 |     1.485 |     1.377 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.897 |     1.342 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     476.47529</span> |     101.3 |   0.01729 |    0.2820 |   0.09132 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01715 |    0.4972 |    0.8078 |   0.07430 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6573 |     2.103 |     1.485 |     1.377 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.897 |     1.342 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     23.23 |   -0.7393 |    0.1467 |   -0.3435 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.063 |  0.008714 |    -6.650 |    -7.604 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.072 |    -9.778 |    -5.421 |    -6.068 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -8.413 |    -5.023 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   47</span>|     476.20958 |     1.007 |   -0.9575 |   -0.9335 |   -0.9604 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9651 |   -0.9218 |   -0.6949 |   -0.7035 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9364 |   -0.5488 |   -0.7198 |   -0.6977 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.5996 |   -0.7274 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     476.20958 |     100.7 |    -4.058 |   -0.9345 |    -2.393 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.065 |  -0.01100 |    0.8075 |   0.07425 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6566 |     2.122 |     1.490 |     1.383 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.910 |     1.349 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     476.20958</span> |     100.7 |   0.01729 |    0.2820 |   0.09135 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01716 |    0.4972 |    0.8075 |   0.07425 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6566 |     2.122 |     1.490 |     1.383 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.910 |     1.349 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -16.91 |    -1.652 |   -0.3580 |  -0.07063 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9392 |   0.01022 |    -6.706 |    -7.708 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9788 |    -9.498 |    -5.202 |    -5.742 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -8.208 |    -4.851 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   48</span>|     475.96032 |     1.013 |   -0.9572 |   -0.9335 |   -0.9601 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9647 |   -0.9218 |   -0.6944 |   -0.7034 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9370 |   -0.5368 |   -0.7166 |   -0.6928 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.5905 |   -0.7221 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     475.96032 |     101.3 |    -4.057 |   -0.9346 |    -2.393 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.065 |  -0.01100 |    0.8077 |   0.07425 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6562 |     2.141 |     1.494 |     1.388 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.924 |     1.355 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     475.96032</span> |     101.3 |   0.01730 |    0.2820 |   0.09138 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01717 |    0.4972 |    0.8077 |   0.07425 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6562 |     2.141 |     1.494 |     1.388 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.924 |     1.355 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     22.21 |   -0.6469 |    0.1490 |   -0.3297 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9912 |  0.008804 |    -6.697 |    -7.730 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.011 |    -9.295 |    -5.014 |    -5.737 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -7.990 |    -4.768 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   49</span>|     475.69232 |     1.007 |   -0.9579 |   -0.9338 |   -0.9597 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9645 |   -0.9219 |   -0.6939 |   -0.7032 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9375 |   -0.5243 |   -0.7149 |   -0.6885 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.5813 |   -0.7168 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     475.69232 |     100.7 |    -4.058 |   -0.9348 |    -2.392 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.064 |  -0.01100 |    0.8078 |   0.07426 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6559 |     2.161 |     1.496 |     1.393 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.937 |     1.361 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     475.69232</span> |     100.7 |   0.01729 |    0.2819 |   0.09141 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01717 |    0.4972 |    0.8078 |   0.07426 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6559 |     2.161 |     1.496 |     1.393 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.937 |     1.361 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -16.63 |    -1.703 |   -0.3428 |  -0.06069 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9074 |   0.01024 |    -7.409 |    -9.759 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6668 |    -9.063 |    -4.970 |    -5.451 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -7.786 |    -4.607 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   50</span>|     475.41012 |     1.012 |   -0.9580 |   -0.9340 |   -0.9594 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9645 |   -0.9219 |   -0.6912 |   -0.6928 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9358 |   -0.5138 |   -0.7150 |   -0.6862 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.5743 |   -0.7128 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     475.41012 |     101.2 |    -4.058 |   -0.9351 |    -2.392 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.064 |  -0.01101 |    0.8088 |   0.07461 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6570 |     2.177 |     1.496 |     1.396 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.948 |     1.365 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     475.41012</span> |     101.2 |   0.01728 |    0.2819 |   0.09144 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01717 |    0.4972 |    0.8088 |   0.07461 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6570 |     2.177 |     1.496 |     1.396 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.948 |     1.365 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     17.88 |   -0.8521 |   0.09625 |   -0.2834 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.006 |  0.008928 |    -8.320 |    -8.272 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6841 |    -8.896 |    -4.958 |    -5.482 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -7.690 |    -4.565 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   51</span>|     475.17343 |     1.006 |   -0.9580 |   -0.9344 |   -0.9592 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9643 |   -0.9219 |   -0.6806 |   -0.6840 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9328 |   -0.5062 |   -0.7153 |   -0.6852 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.5694 |   -0.7102 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     475.17343 |     100.6 |    -4.058 |   -0.9354 |    -2.392 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.064 |  -0.01101 |    0.8127 |   0.07490 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6590 |     2.189 |     1.495 |     1.397 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.955 |     1.368 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     475.17343</span> |     100.6 |   0.01728 |    0.2818 |   0.09146 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01718 |    0.4972 |    0.8127 |   0.07490 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6590 |     2.189 |     1.495 |     1.397 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.955 |     1.368 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -22.54 |    -1.885 |   -0.4332 | -0.003109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9120 |   0.01053 |    -7.129 |    -7.659 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8338 |    -8.700 |    -4.918 |    -5.365 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -7.558 |    -4.481 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   52</span>|     474.94312 |     1.012 |   -0.9570 |   -0.9345 |   -0.9591 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9638 |   -0.9220 |   -0.6676 |   -0.6825 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9307 |   -0.4984 |   -0.7146 |   -0.6834 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.5639 |   -0.7072 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     474.94312 |     101.2 |    -4.057 |   -0.9355 |    -2.392 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.064 |  -0.01101 |    0.8175 |   0.07495 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6604 |     2.202 |     1.496 |     1.399 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.963 |     1.372 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     474.94312</span> |     101.2 |   0.01730 |    0.2818 |   0.09147 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01718 |    0.4972 |    0.8175 |   0.07495 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6604 |     2.202 |     1.496 |     1.399 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.963 |     1.372 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     20.08 |   -0.5236 |    0.1181 |   -0.2986 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9599 |  0.008866 |    -5.310 |    -6.088 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8565 |    -8.707 |    -4.867 |    -5.430 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -7.458 |    -4.464 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   53</span>|     474.70657 |     1.007 |   -0.9569 |   -0.9346 |   -0.9588 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9629 |   -0.9220 |   -0.6640 |   -0.6851 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9296 |   -0.4868 |   -0.7112 |   -0.6784 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.5553 |   -0.7021 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     474.70657 |     100.7 |    -4.057 |   -0.9356 |    -2.392 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.063 |  -0.01101 |    0.8188 |   0.07487 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6612 |     2.220 |     1.500 |     1.405 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.976 |     1.378 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     474.70657</span> |     100.7 |   0.01730 |    0.2818 |   0.09149 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01720 |    0.4972 |    0.8188 |   0.07487 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6612 |     2.220 |     1.500 |     1.405 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.976 |     1.378 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -17.64 |    -1.502 |   -0.3627 |  -0.03824 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7798 |   0.01049 |    -5.965 |    -8.154 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.5631 |    -8.385 |    -4.675 |    -5.151 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -7.306 |    -4.317 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   54</span>|     474.46623 |     1.012 |   -0.9580 |   -0.9346 |   -0.9586 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9630 |   -0.9221 |   -0.6659 |   -0.6813 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9264 |   -0.4751 |   -0.7083 |   -0.6739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.5464 |   -0.6970 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     474.46623 |     101.2 |    -4.058 |   -0.9356 |    -2.391 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.063 |  -0.01101 |    0.8181 |   0.07499 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6633 |     2.239 |     1.504 |     1.410 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.989 |     1.384 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     474.46623</span> |     101.2 |   0.01728 |    0.2818 |   0.09151 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01720 |    0.4972 |    0.8181 |   0.07499 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6633 |     2.239 |     1.504 |     1.410 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.989 |     1.384 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     15.93 |   -0.7754 |   0.09266 |   -0.2754 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8835 |  0.009135 |    -6.026 |    -6.068 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8217 |    -8.210 |    -4.532 |    -5.141 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -7.011 |    -4.237 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   55</span>|     474.23394 |     1.006 |   -0.9577 |   -0.9351 |   -0.9581 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9633 |   -0.9221 |   -0.6610 |   -0.6793 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9234 |   -0.4633 |   -0.7068 |   -0.6694 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.5382 |   -0.6917 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     474.23394 |     100.6 |    -4.058 |   -0.9360 |    -2.391 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.063 |  -0.01101 |    0.8199 |   0.07506 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6654 |     2.257 |     1.506 |     1.415 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.001 |     1.390 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     474.23394</span> |     100.6 |   0.01729 |    0.2817 |   0.09156 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01719 |    0.4972 |    0.8199 |   0.07506 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6654 |     2.257 |     1.506 |     1.415 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.001 |     1.390 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -20.30 |    -1.703 |   -0.3919 | -0.002629 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8653 |   0.01064 |    -4.947 |    -7.765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7818 |    -8.010 |    -4.437 |    -4.875 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -6.938 |    -4.094 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   56</span>|     473.98878 |     1.011 |   -0.9542 |   -0.9354 |   -0.9578 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9619 |   -0.9222 |   -0.6621 |   -0.6770 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9235 |   -0.4507 |   -0.7053 |   -0.6648 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.5298 |   -0.6863 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     473.98878 |     101.1 |    -4.054 |   -0.9363 |    -2.390 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.062 |  -0.01101 |    0.8195 |   0.07514 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6653 |     2.277 |     1.508 |     1.421 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.013 |     1.396 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     473.98878</span> |     101.1 |   0.01735 |    0.2816 |   0.09159 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01722 |    0.4972 |    0.8195 |   0.07514 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6653 |     2.277 |     1.508 |     1.421 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.013 |     1.396 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   57</span>|     473.76836 |     1.011 |   -0.9500 |   -0.9359 |   -0.9574 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9601 |   -0.9223 |   -0.6653 |   -0.6763 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9234 |   -0.4357 |   -0.7046 |   -0.6601 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.5205 |   -0.6802 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     473.76836 |     101.1 |    -4.050 |   -0.9368 |    -2.390 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.060 |  -0.01101 |    0.8183 |   0.07516 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6654 |     2.301 |     1.509 |     1.426 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.027 |     1.403 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     473.76836</span> |     101.1 |   0.01742 |    0.2815 |   0.09163 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01725 |    0.4972 |    0.8183 |   0.07516 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6654 |     2.301 |     1.509 |     1.426 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.027 |     1.403 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   58</span>|     472.96708 |     1.009 |   -0.9317 |   -0.9384 |   -0.9555 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9527 |   -0.9228 |   -0.6791 |   -0.6735 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9230 |   -0.3711 |   -0.7015 |   -0.6396 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.4802 |   -0.6537 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     472.96708 |     100.9 |    -4.032 |   -0.9391 |    -2.388 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.053 |  -0.01101 |    0.8133 |   0.07525 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6656 |     2.403 |     1.513 |     1.450 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.087 |     1.433 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     472.96708</span> |     100.9 |   0.01774 |    0.2811 |   0.09180 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01737 |    0.4972 |    0.8133 |   0.07525 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6656 |     2.403 |     1.513 |     1.450 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.087 |     1.433 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   59</span>|     472.23802 |     1.005 |   -0.8811 |   -0.9451 |   -0.9502 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9323 |   -0.9240 |   -0.7172 |   -0.6658 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9220 |   -0.1924 |   -0.6930 |   -0.5829 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.3688 |   -0.5805 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     472.23802 |     100.5 |    -3.981 |   -0.9455 |    -2.383 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.032 |  -0.01103 |    0.7994 |   0.07551 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6663 |     2.686 |     1.523 |     1.515 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.251 |     1.518 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     472.23802</span> |     100.5 |   0.01866 |    0.2798 |   0.09228 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01773 |    0.4972 |    0.7994 |   0.07551 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6663 |     2.686 |     1.523 |     1.515 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.251 |     1.518 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -10.41 |     25.09 |    -1.964 |    0.8913 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.956 |   0.02208 |    -7.724 |    -8.360 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01123 |    -2.619 |    -3.185 |    -1.886 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -3.376 |    -1.727 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   60</span>|     470.34942 |     1.020 |    -1.007 |   -0.9408 |   -0.9496 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9429 |   -0.9259 |   -0.7397 |   -0.6282 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9207 |   0.06578 |   -0.6693 |   -0.4998 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2002 |   -0.4722 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     470.34942 |     102.0 |    -4.107 |   -0.9414 |    -2.382 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.043 |  -0.01105 |    0.7911 |   0.07677 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6672 |     3.094 |     1.553 |     1.610 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.500 |     1.642 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     470.34942</span> |     102.0 |   0.01646 |    0.2806 |   0.09235 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01755 |    0.4972 |    0.7911 |   0.07677 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6672 |     3.094 |     1.553 |     1.610 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.500 |     1.642 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     49.94 |    -17.10 |     1.872 |    -1.413 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.684 |  0.005064 |    -7.591 |    -5.431 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2085 |    -2.165 |    -1.196 |   -0.1732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.4074 |   -0.2957 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   61</span>|     480.76065 |    0.9913 |   -0.9789 |   -0.9482 |   -0.9314 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.207 |   -0.9279 |   -0.6546 |   -0.4866 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9168 |    0.2296 |   -0.6593 |   -0.5083 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1307 |   -0.4332 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.76065 |     99.13 |    -4.079 |   -0.9483 |    -2.364 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.307 |  -0.01107 |    0.8222 |   0.08152 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6698 |     3.353 |     1.566 |     1.601 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.603 |     1.687 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.76065</span> |     99.13 |   0.01693 |    0.2792 |   0.09404 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01347 |    0.4972 |    0.8222 |   0.08152 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6698 |     3.353 |     1.566 |     1.601 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.603 |     1.687 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   62</span>|     472.12595 |    0.9860 |   -0.9933 |   -0.9428 |   -0.9466 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9755 |   -0.9261 |   -0.7254 |   -0.6089 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9204 |   0.08558 |   -0.6674 |   -0.5007 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1921 |   -0.4676 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     472.12595 |     98.60 |    -4.093 |   -0.9433 |    -2.379 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.076 |  -0.01105 |    0.7964 |   0.07742 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6674 |     3.125 |     1.556 |     1.609 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.512 |     1.648 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     472.12595</span> |     98.60 |   0.01668 |    0.2802 |   0.09262 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01698 |    0.4972 |    0.7964 |   0.07742 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6674 |     3.125 |     1.556 |     1.609 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.512 |     1.648 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   63</span>|     470.26696 |     1.005 |    -1.002 |   -0.9414 |   -0.9491 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9444 |   -0.9259 |   -0.7373 |   -0.6265 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9207 |   0.06646 |   -0.6689 |   -0.4998 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2001 |   -0.4721 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     470.26696 |     100.5 |    -4.102 |   -0.9420 |    -2.382 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.044 |  -0.01105 |    0.7920 |   0.07683 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6672 |     3.095 |     1.554 |     1.610 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.501 |     1.642 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     470.26696</span> |     100.5 |   0.01654 |    0.2805 |   0.09239 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01752 |    0.4972 |    0.7920 |   0.07683 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6672 |     3.095 |     1.554 |     1.610 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.501 |     1.642 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -57.97 |    -21.53 |    0.3336 |   -0.1964 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.949 |   0.01388 |    -7.299 |    -5.475 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3363 |    -1.951 |    -1.200 |    0.1716 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.4705 |   -0.2755 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   64</span>|     469.85165 |     1.014 |   -0.9972 |   -0.9415 |   -0.9489 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9548 |   -0.9260 |   -0.7307 |   -0.6235 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9208 |   0.07069 |   -0.6682 |   -0.5004 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1987 |   -0.4712 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     469.85165 |     101.4 |    -4.097 |   -0.9421 |    -2.382 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.055 |  -0.01105 |    0.7944 |   0.07693 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6671 |     3.101 |     1.555 |     1.610 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.503 |     1.643 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     469.85165</span> |     101.4 |   0.01662 |    0.2805 |   0.09241 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01734 |    0.4972 |    0.7944 |   0.07693 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6671 |     3.101 |     1.555 |     1.610 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.503 |     1.643 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     9.302 |    -15.01 |     1.055 |   -0.6015 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.082 |  0.004567 |    -8.090 |    -5.650 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2579 |    -2.336 |    -1.339 |  -0.08766 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.5208 |   -0.3062 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   65</span>|     469.69998 |     1.007 |   -0.9853 |   -0.9423 |   -0.9484 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9564 |   -0.9260 |   -0.7231 |   -0.6186 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9204 |   0.07309 |   -0.6671 |   -0.5006 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1982 |   -0.4709 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     469.69998 |     100.7 |    -4.085 |   -0.9429 |    -2.381 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.056 |  -0.01105 |    0.7972 |   0.07709 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6674 |     3.105 |     1.556 |     1.610 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.503 |     1.644 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     469.69998</span> |     100.7 |   0.01682 |    0.2803 |   0.09245 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01731 |    0.4972 |    0.7972 |   0.07709 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6674 |     3.105 |     1.556 |     1.610 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.503 |     1.644 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -36.86 |    -12.39 |    0.1028 |    0.1354 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7614 |  0.008951 |    -8.542 |    -6.076 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2246 |    -2.313 |    -1.235 |    0.1010 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.5654 |   -0.2998 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   66</span>|     469.45579 |     1.013 |   -0.9821 |   -0.9425 |   -0.9491 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9562 |   -0.9260 |   -0.7099 |   -0.6134 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9182 |   0.07758 |   -0.6674 |   -0.5042 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1996 |   -0.4719 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     469.45579 |     101.3 |    -4.082 |   -0.9430 |    -2.382 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.056 |  -0.01105 |    0.8020 |   0.07727 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6689 |     3.112 |     1.556 |     1.605 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.501 |     1.643 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     469.45579</span> |     101.3 |   0.01687 |    0.2803 |   0.09239 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01731 |    0.4972 |    0.8020 |   0.07727 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6689 |     3.112 |     1.556 |     1.605 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.501 |     1.643 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     7.383 |    -7.976 |    0.5709 |   -0.2773 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4749 |  0.005686 |    -7.013 |    -4.742 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3037 |    -2.186 |    -1.089 |  -0.04880 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.5661 |   -0.3435 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   67</span>|     469.35718 |     1.007 |   -0.9760 |   -0.9430 |   -0.9493 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9561 |   -0.9261 |   -0.6977 |   -0.6075 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9166 |   0.08278 |   -0.6675 |   -0.5068 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2001 |   -0.4722 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     469.35718 |     100.7 |    -4.076 |   -0.9435 |    -2.382 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.056 |  -0.01105 |    0.8065 |   0.07747 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6700 |     3.121 |     1.556 |     1.602 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.501 |     1.642 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     469.35718</span> |     100.7 |   0.01698 |    0.2802 |   0.09237 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01732 |    0.4972 |    0.8065 |   0.07747 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6700 |     3.121 |     1.556 |     1.602 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.501 |     1.642 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -28.34 |    -7.406 |  -0.07810 |    0.1748 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6873 |  0.009156 |    -6.696 |    -4.901 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2913 |    -2.170 |    -1.086 |  -0.02443 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6503 |   -0.3607 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   68</span>|     469.19639 |     1.012 |   -0.9749 |   -0.9431 |   -0.9497 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9576 |   -0.9262 |   -0.6862 |   -0.6021 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9137 |   0.09137 |   -0.6688 |   -0.5111 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2009 |   -0.4726 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     469.19639 |     101.2 |    -4.075 |   -0.9436 |    -2.382 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.058 |  -0.01105 |    0.8107 |   0.07765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6719 |     3.134 |     1.554 |     1.597 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.499 |     1.642 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     469.19639</span> |     101.2 |   0.01699 |    0.2802 |   0.09233 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01729 |    0.4972 |    0.8107 |   0.07765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6719 |     3.134 |     1.554 |     1.597 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.499 |     1.642 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     4.760 |    -4.629 |    0.3091 |   -0.1325 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.04401 |  0.005819 |    -5.826 |    -2.903 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1562 |    -2.130 |    -1.121 |   -0.2535 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6922 |   -0.4141 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   69</span>|     469.12263 |     1.008 |   -0.9713 |   -0.9434 |   -0.9499 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9575 |   -0.9262 |   -0.6727 |   -0.6012 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9123 |   0.09974 |   -0.6678 |   -0.5134 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2010 |   -0.4726 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     469.12263 |     100.8 |    -4.071 |   -0.9438 |    -2.383 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.058 |  -0.01105 |    0.8156 |   0.07768 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6728 |     3.147 |     1.555 |     1.595 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.499 |     1.642 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     469.12263</span> |     100.8 |   0.01706 |    0.2801 |   0.09232 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01729 |    0.4972 |    0.8156 |   0.07768 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6728 |     3.147 |     1.555 |     1.595 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.499 |     1.642 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -20.86 |    -4.532 |   -0.1251 |    0.1663 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1284 |  0.008177 |    -5.101 |    -3.487 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2183 |    -2.137 |    -1.171 |   -0.2991 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6817 |   -0.4261 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   70</span>|     469.02483 |     1.011 |   -0.9711 |   -0.9434 |   -0.9500 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9569 |   -0.9263 |   -0.6616 |   -0.6038 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9105 |    0.1111 |   -0.6656 |   -0.5157 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2026 |   -0.4730 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     469.02483 |     101.1 |    -4.071 |   -0.9439 |    -2.383 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.057 |  -0.01105 |    0.8196 |   0.07759 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6741 |     3.165 |     1.558 |     1.592 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.497 |     1.641 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     469.02483</span> |     101.1 |   0.01706 |    0.2801 |   0.09230 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01730 |    0.4972 |    0.8196 |   0.07759 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6741 |     3.165 |     1.558 |     1.592 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.497 |     1.641 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     4.786 |    -2.589 |    0.2046 |   -0.1061 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1383 |  0.006373 |    -4.080 |    -2.588 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1418 |    -1.956 |   -0.9118 |   -0.4226 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7530 |   -0.4429 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   71</span>|     468.97618 |     1.009 |   -0.9710 |   -0.9439 |   -0.9494 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9625 |   -0.9265 |   -0.6573 |   -0.5998 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9085 |    0.1255 |   -0.6648 |   -0.5157 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2015 |   -0.4719 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.97618 |     100.9 |    -4.071 |   -0.9443 |    -2.382 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.063 |  -0.01106 |    0.8212 |   0.07773 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6755 |     3.188 |     1.559 |     1.592 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.498 |     1.643 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.97618</span> |     100.9 |   0.01706 |    0.2800 |   0.09236 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01721 |    0.4972 |    0.8212 |   0.07773 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6755 |     3.188 |     1.559 |     1.592 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.498 |     1.643 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -11.25 |    -3.431 |  -0.02518 |    0.1501 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.529 |  0.005347 |    -4.655 |    -3.448 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3102 |    -2.013 |   -0.8937 |   -0.3742 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7403 |   -0.4511 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   72</span>|     468.91299 |     1.010 |   -0.9702 |   -0.9444 |   -0.9496 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9534 |   -0.9266 |   -0.6524 |   -0.5929 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9051 |    0.1362 |   -0.6665 |   -0.5172 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2029 |   -0.4725 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.91299 |     101.0 |    -4.070 |   -0.9448 |    -2.382 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.053 |  -0.01106 |    0.8230 |   0.07796 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6777 |     3.205 |     1.557 |     1.590 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.496 |     1.642 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.91299</span> |     101.0 |   0.01707 |    0.2799 |   0.09234 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01736 |    0.4972 |    0.8230 |   0.07796 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6777 |     3.205 |     1.557 |     1.590 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.496 |     1.642 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -8.129 |    -2.859 |  -0.02802 | -0.004107 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.281 |  0.009120 |    -4.291 |    -2.058 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1551 |    -2.052 |    -1.121 |   -0.5699 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7547 |   -0.4743 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   73</span>|     471.25018 |     1.036 |   -0.9607 |   -0.9443 |   -0.9496 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9576 |   -0.9266 |   -0.6382 |   -0.5860 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9046 |    0.1430 |   -0.6628 |   -0.5153 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2004 |   -0.4709 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     471.25018 |     103.6 |    -4.061 |   -0.9447 |    -2.382 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.058 |  -0.01106 |    0.8282 |   0.07819 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6781 |     3.216 |     1.562 |     1.593 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.500 |     1.644 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     471.25018</span> |     103.6 |   0.01724 |    0.2800 |   0.09234 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01729 |    0.4972 |    0.8282 |   0.07819 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6781 |     3.216 |     1.562 |     1.593 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.500 |     1.644 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   74</span>|     468.90555 |     1.012 |   -0.9692 |   -0.9444 |   -0.9496 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9538 |   -0.9266 |   -0.6510 |   -0.5922 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9050 |    0.1369 |   -0.6661 |   -0.5170 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2027 |   -0.4723 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.90555 |     101.2 |    -4.069 |   -0.9448 |    -2.382 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.054 |  -0.01106 |    0.8235 |   0.07798 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6778 |     3.206 |     1.557 |     1.591 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.497 |     1.642 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.90555</span> |     101.2 |   0.01709 |    0.2799 |   0.09234 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01736 |    0.4972 |    0.8235 |   0.07798 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6778 |     3.206 |     1.557 |     1.591 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.497 |     1.642 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     11.48 |    -1.027 |    0.1960 |   -0.1655 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9827 |  0.007474 |    -3.960 |    -2.226 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2199 |    -2.046 |    -1.158 |   -0.6171 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7493 |   -0.4797 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   75</span>|     468.88608 |     1.010 |   -0.9691 |   -0.9444 |   -0.9495 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9542 |   -0.9266 |   -0.6497 |   -0.5917 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9044 |    0.1391 |   -0.6659 |   -0.5166 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2026 |   -0.4722 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.88608 |     101.0 |    -4.069 |   -0.9448 |    -2.382 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.054 |  -0.01106 |    0.8240 |   0.07799 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6782 |     3.210 |     1.558 |     1.591 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.497 |     1.642 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.88608</span> |     101.0 |   0.01709 |    0.2799 |   0.09235 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01735 |    0.4972 |    0.8240 |   0.07799 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6782 |     3.210 |     1.558 |     1.591 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.497 |     1.642 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -2.784 |    -1.944 |  0.009889 |  -0.02065 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9781 |  0.008521 |    -3.685 |    -1.489 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.08345 |    -1.963 |   -0.9408 |   -0.4978 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7271 |   -0.4658 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   76</span>|     468.87604 |     1.012 |   -0.9680 |   -0.9444 |   -0.9495 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9547 |   -0.9266 |   -0.6476 |   -0.5909 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9043 |    0.1402 |   -0.6654 |   -0.5163 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2021 |   -0.4719 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.87604 |     101.2 |    -4.068 |   -0.9448 |    -2.382 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.055 |  -0.01106 |    0.8248 |   0.07802 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6783 |     3.211 |     1.558 |     1.591 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.498 |     1.643 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.87604</span> |     101.2 |   0.01711 |    0.2799 |   0.09235 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01734 |    0.4972 |    0.8248 |   0.07802 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6783 |     3.211 |     1.558 |     1.591 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.498 |     1.643 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     9.309 |   -0.5261 |    0.1314 |   -0.1053 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6891 |  0.007435 |    -3.809 |    -2.129 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2297 |    -2.013 |    -1.108 |   -0.5855 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7533 |   -0.4780 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   77</span>|     468.85962 |     1.010 |   -0.9680 |   -0.9444 |   -0.9495 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9550 |   -0.9266 |   -0.6462 |   -0.5907 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9037 |    0.1428 |   -0.6653 |   -0.5160 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2020 |   -0.4717 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.85962 |     101.0 |    -4.068 |   -0.9448 |    -2.382 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.055 |  -0.01106 |    0.8253 |   0.07803 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6787 |     3.215 |     1.558 |     1.592 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.498 |     1.643 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.85962</span> |     101.0 |   0.01711 |    0.2799 |   0.09235 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01734 |    0.4972 |    0.8253 |   0.07803 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6787 |     3.215 |     1.558 |     1.592 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.498 |     1.643 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.556 |    -1.258 | -0.007590 |  0.001179 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7035 |  0.008243 |    -4.446 |    -2.277 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2406 |    -1.984 |    -1.075 |   -0.5476 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7588 |   -0.4741 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   78</span>|     468.84535 |     1.011 |   -0.9673 |   -0.9444 |   -0.9495 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9554 |   -0.9266 |   -0.6436 |   -0.5895 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9036 |    0.1439 |   -0.6647 |   -0.5157 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2016 |   -0.4714 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.84535 |     101.1 |    -4.067 |   -0.9448 |    -2.382 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.055 |  -0.01106 |    0.8262 |   0.07807 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6788 |     3.217 |     1.559 |     1.592 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.498 |     1.643 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.84535</span> |     101.1 |   0.01712 |    0.2799 |   0.09235 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01733 |    0.4972 |    0.8262 |   0.07807 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6788 |     3.217 |     1.559 |     1.592 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.498 |     1.643 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     5.130 |   -0.4165 |   0.05789 |  -0.04298 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.5152 |  0.007618 |    -4.342 |    -2.024 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2412 |    -1.936 |   -0.8986 |   -0.4874 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7551 |   -0.4761 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   79</span>|     468.83559 |     1.009 |   -0.9672 |   -0.9445 |   -0.9494 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9556 |   -0.9266 |   -0.6414 |   -0.5890 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9032 |    0.1455 |   -0.6647 |   -0.5153 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2012 |   -0.4711 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.83559 |     100.9 |    -4.067 |   -0.9449 |    -2.382 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.056 |  -0.01106 |    0.8270 |   0.07809 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6790 |     3.220 |     1.559 |     1.593 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.499 |     1.643 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.83559</span> |     100.9 |   0.01713 |    0.2799 |   0.09236 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01733 |    0.4972 |    0.8270 |   0.07809 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6790 |     3.220 |     1.559 |     1.593 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.499 |     1.643 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -7.676 |    -1.212 |   -0.1115 |   0.08612 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.5488 |  0.008589 |    -2.761 |    -2.165 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2456 |    -1.924 |   -0.8622 |   -0.4413 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7592 |   -0.4720 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   80</span>|     468.82320 |     1.011 |   -0.9670 |   -0.9445 |   -0.9494 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9559 |   -0.9267 |   -0.6401 |   -0.5885 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9025 |    0.1478 |   -0.6651 |   -0.5152 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2008 |   -0.4708 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      468.8232 |     101.1 |    -4.067 |   -0.9449 |    -2.382 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.056 |  -0.01106 |    0.8275 |   0.07810 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6795 |     3.223 |     1.559 |     1.593 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.499 |     1.644 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      468.8232</span> |     101.1 |   0.01713 |    0.2799 |   0.09236 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01732 |    0.4972 |    0.8275 |   0.07810 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6795 |     3.223 |     1.559 |     1.593 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.499 |     1.644 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     5.440 |   -0.1810 |   0.05193 |  -0.03082 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3483 |  0.007438 |    -3.541 |    -1.923 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2409 |    -1.947 |    -1.058 |   -0.5469 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7518 |   -0.4735 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   81</span>|     468.81154 |     1.010 |   -0.9671 |   -0.9445 |   -0.9493 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9562 |   -0.9267 |   -0.6389 |   -0.5880 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9017 |    0.1505 |   -0.6652 |   -0.5150 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2005 |   -0.4704 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.81154 |     101.0 |    -4.067 |   -0.9449 |    -2.382 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.056 |  -0.01106 |    0.8279 |   0.07812 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6800 |     3.228 |     1.559 |     1.593 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.500 |     1.644 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.81154</span> |     101.0 |   0.01713 |    0.2799 |   0.09237 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01732 |    0.4972 |    0.8279 |   0.07812 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6800 |     3.228 |     1.559 |     1.593 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.500 |     1.644 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -2.875 |   -0.7736 |  -0.05343 |   0.05244 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3113 |  0.007978 |    -4.161 |    -2.012 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2433 |    -1.896 |   -0.8853 |   -0.4539 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7524 |   -0.4694 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   82</span>|     468.80001 |     1.011 |   -0.9668 |   -0.9445 |   -0.9493 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9563 |   -0.9267 |   -0.6364 |   -0.5873 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9013 |    0.1522 |   -0.6651 |   -0.5147 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2001 |   -0.4701 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.80001 |     101.1 |    -4.067 |   -0.9449 |    -2.382 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.056 |  -0.01106 |    0.8288 |   0.07814 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6803 |     3.230 |     1.559 |     1.593 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.501 |     1.645 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.80001</span> |     101.1 |   0.01713 |    0.2799 |   0.09237 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01731 |    0.4972 |    0.8288 |   0.07814 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6803 |     3.230 |     1.559 |     1.593 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.501 |     1.645 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     6.046 |   0.03721 |   0.05257 |  -0.02595 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1943 |  0.007238 |    -2.985 |    -2.233 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3122 |    -1.891 |   -0.9609 |   -0.5652 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8019 |   -0.4712 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   83</span>|     468.79133 |     1.009 |   -0.9669 |   -0.9445 |   -0.9492 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9565 |   -0.9267 |   -0.6348 |   -0.5869 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9006 |    0.1543 |   -0.6654 |   -0.5144 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1996 |   -0.4698 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.79133 |     100.9 |    -4.067 |   -0.9449 |    -2.382 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.056 |  -0.01106 |    0.8294 |   0.07816 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6807 |     3.234 |     1.558 |     1.594 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.501 |     1.645 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.79133</span> |     100.9 |   0.01713 |    0.2799 |   0.09237 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01731 |    0.4972 |    0.8294 |   0.07816 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6807 |     3.234 |     1.558 |     1.594 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.501 |     1.645 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -7.226 |   -0.9038 |   -0.1166 |    0.1039 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2475 |  0.008246 |    -3.388 |    -1.948 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2420 |    -1.872 |   -0.8620 |   -0.4291 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7186 |   -0.4561 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   84</span>|     468.77848 |     1.011 |   -0.9668 |   -0.9446 |   -0.9492 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9568 |   -0.9267 |   -0.6338 |   -0.5863 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8997 |    0.1569 |   -0.6658 |   -0.5142 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1992 |   -0.4695 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.77848 |     101.1 |    -4.067 |   -0.9450 |    -2.382 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.057 |  -0.01106 |    0.8298 |   0.07818 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6814 |     3.238 |     1.558 |     1.594 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.502 |     1.645 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.77848</span> |     101.1 |   0.01713 |    0.2799 |   0.09238 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01730 |    0.4972 |    0.8298 |   0.07818 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6814 |     3.238 |     1.558 |     1.594 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.502 |     1.645 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     2.503 |   -0.1762 |  0.005994 |   0.01651 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.06327 |  0.007339 |    -4.407 |    -2.185 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3133 |    -1.836 |   -0.8110 |   -0.4803 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7946 |   -0.4665 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   85</span>|     468.76805 |     1.010 |   -0.9668 |   -0.9446 |   -0.9492 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9569 |   -0.9267 |   -0.6312 |   -0.5853 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8993 |    0.1583 |   -0.6657 |   -0.5139 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1987 |   -0.4691 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.76805 |     101.0 |    -4.067 |   -0.9450 |    -2.382 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.057 |  -0.01106 |    0.8307 |   0.07821 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6816 |     3.240 |     1.558 |     1.594 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.503 |     1.646 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.76805</span> |     101.0 |   0.01713 |    0.2799 |   0.09238 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01730 |    0.4972 |    0.8307 |   0.07821 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6816 |     3.240 |     1.558 |     1.594 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.503 |     1.646 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -5.980 |   -0.7098 |   -0.1045 |    0.1001 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1097 |  0.008007 |    -2.998 |    -1.799 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2421 |    -1.902 |    -1.021 |   -0.4929 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7234 |   -0.4545 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   86</span>|     468.76178 |     1.012 |   -0.9666 |   -0.9446 |   -0.9492 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9569 |   -0.9268 |   -0.6296 |   -0.5849 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8987 |    0.1602 |   -0.6657 |   -0.5136 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1982 |   -0.4689 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.76178 |     101.2 |    -4.067 |   -0.9450 |    -2.382 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.057 |  -0.01106 |    0.8313 |   0.07822 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6820 |     3.243 |     1.558 |     1.595 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.503 |     1.646 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.76178</span> |     101.2 |   0.01714 |    0.2799 |   0.09238 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01730 |    0.4972 |    0.8313 |   0.07822 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6820 |     3.243 |     1.558 |     1.595 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.503 |     1.646 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     9.381 |    0.4945 |   0.08709 |  -0.04347 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.04178 |  0.006758 |    -3.785 |    -1.947 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3007 |    -1.819 |   -0.8940 |   -0.4901 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7836 |   -0.4662 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   87</span>|     468.74724 |     1.010 |   -0.9668 |   -0.9447 |   -0.9491 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9570 |   -0.9268 |   -0.6284 |   -0.5849 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8978 |    0.1628 |   -0.6658 |   -0.5134 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1979 |   -0.4686 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.74724 |     101.0 |    -4.067 |   -0.9451 |    -2.382 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.057 |  -0.01106 |    0.8318 |   0.07822 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6827 |     3.247 |     1.558 |     1.595 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.504 |     1.646 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.74724</span> |     101.0 |   0.01713 |    0.2799 |   0.09239 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01730 |    0.4972 |    0.8318 |   0.07822 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6827 |     3.247 |     1.558 |     1.595 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.504 |     1.646 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.251 |   -0.3146 |  -0.04610 |   0.05724 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.02086 |  0.007590 |    -4.230 |    -2.098 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3136 |    -1.833 |   -0.9365 |   -0.5244 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7857 |   -0.4618 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   88</span>|     468.73570 |     1.011 |   -0.9666 |   -0.9446 |   -0.9492 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9570 |   -0.9268 |   -0.6257 |   -0.5836 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8976 |    0.1639 |   -0.6652 |   -0.5131 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1974 |   -0.4683 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      468.7357 |     101.1 |    -4.067 |   -0.9450 |    -2.382 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.057 |  -0.01106 |    0.8327 |   0.07827 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6828 |     3.249 |     1.559 |     1.595 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.504 |     1.647 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      468.7357</span> |     101.1 |   0.01714 |    0.2799 |   0.09238 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01730 |    0.4972 |    0.8327 |   0.07827 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6828 |     3.249 |     1.559 |     1.595 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.504 |     1.647 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     4.473 |    0.2106 |   0.02342 |  0.003517 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.03354 |  0.007130 |    -3.431 |    -1.904 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3114 |    -1.829 |   -0.9161 |   -0.5352 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7806 |   -0.4641 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   89</span>|     468.73562 |     1.009 |   -0.9667 |   -0.9447 |   -0.9491 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9570 |   -0.9268 |   -0.6239 |   -0.5826 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8973 |    0.1651 |   -0.6648 |   -0.5128 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1970 |   -0.4681 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.73562 |     100.9 |    -4.067 |   -0.9450 |    -2.382 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.057 |  -0.01106 |    0.8334 |   0.07830 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6830 |     3.251 |     1.559 |     1.596 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.505 |     1.647 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.73562</span> |     100.9 |   0.01713 |    0.2799 |   0.09238 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01730 |    0.4972 |    0.8334 |   0.07830 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6830 |     3.251 |     1.559 |     1.596 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.505 |     1.647 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    C| Central Diff. |    -12.26 |    -1.176 |   -0.2138 |    0.1217 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.02157 |  0.008077 |    -2.776 |    -1.301 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1367 |    -1.738 |   -0.7465 |   -0.4531 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7359 |   -0.4611 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   90</span>|     468.72331 |     1.010 |   -0.9665 |   -0.9446 |   -0.9492 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9570 |   -0.9268 |   -0.6235 |   -0.5825 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8973 |    0.1654 |   -0.6647 |   -0.5127 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1969 |   -0.4680 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.72331 |     101.0 |    -4.067 |   -0.9450 |    -2.382 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.057 |  -0.01106 |    0.8335 |   0.07831 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6830 |     3.251 |     1.559 |     1.596 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.505 |     1.647 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.72331</span> |     101.0 |   0.01714 |    0.2799 |   0.09238 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01730 |    0.4972 |    0.8335 |   0.07831 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6830 |     3.251 |     1.559 |     1.596 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.505 |     1.647 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |   -0.3583 |  -0.08989 |  -0.03828 |   0.04946 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.006154 |  0.007526 |    -3.405 |    -1.898 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3214 |    -1.836 |   -0.8751 |   -0.5118 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7778 |   -0.4622 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   91</span>|     468.71715 |     1.011 |   -0.9665 |   -0.9446 |   -0.9492 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9570 |   -0.9268 |   -0.6223 |   -0.5817 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8972 |    0.1661 |   -0.6643 |   -0.5125 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1966 |   -0.4678 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.71715 |     101.1 |    -4.067 |   -0.9450 |    -2.382 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.057 |  -0.01106 |    0.8340 |   0.07833 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6831 |     3.252 |     1.560 |     1.596 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.506 |     1.647 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.71715</span> |     101.1 |   0.01714 |    0.2799 |   0.09238 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01730 |    0.4972 |    0.8340 |   0.07833 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6831 |     3.252 |     1.560 |     1.596 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.506 |     1.647 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   92</span>|     468.70862 |     1.011 |   -0.9665 |   -0.9446 |   -0.9492 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9570 |   -0.9268 |   -0.6204 |   -0.5807 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8970 |    0.1671 |   -0.6639 |   -0.5123 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1962 |   -0.4676 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.70862 |     101.1 |    -4.066 |   -0.9450 |    -2.382 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.057 |  -0.01106 |    0.8347 |   0.07836 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6832 |     3.254 |     1.560 |     1.596 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.506 |     1.648 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.70862</span> |     101.1 |   0.01714 |    0.2799 |   0.09238 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01730 |    0.4972 |    0.8347 |   0.07836 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6832 |     3.254 |     1.560 |     1.596 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.506 |     1.648 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   93</span>|     468.69301 |     1.011 |   -0.9664 |   -0.9446 |   -0.9493 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9570 |   -0.9268 |   -0.6166 |   -0.5786 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8967 |    0.1691 |   -0.6629 |   -0.5117 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1953 |   -0.4671 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.69301 |     101.1 |    -4.066 |   -0.9449 |    -2.382 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.057 |  -0.01106 |    0.8361 |   0.07843 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6834 |     3.257 |     1.562 |     1.597 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.508 |     1.648 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.69301</span> |     101.1 |   0.01714 |    0.2799 |   0.09237 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01730 |    0.4972 |    0.8361 |   0.07843 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6834 |     3.257 |     1.562 |     1.597 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.508 |     1.648 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     5.000 |    0.4463 |   0.03173 | -0.005614 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.04729 |  0.007073 |    -3.041 |    -1.513 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3269 |    -1.766 |   -0.6347 |   -0.4208 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7611 |   -0.4631 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   94</span>|     468.66018 |     1.011 |   -0.9666 |   -0.9442 |   -0.9484 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9562 |   -0.9269 |   -0.6101 |   -0.5828 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8947 |    0.1828 |   -0.6704 |   -0.5138 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1918 |   -0.4639 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.66018 |     101.1 |    -4.067 |   -0.9446 |    -2.381 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.056 |  -0.01106 |    0.8385 |   0.07829 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6847 |     3.279 |     1.552 |     1.594 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.513 |     1.652 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.66018</span> |     101.1 |   0.01714 |    0.2800 |   0.09245 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01731 |    0.4972 |    0.8385 |   0.07829 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6847 |     3.279 |     1.552 |     1.594 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.513 |     1.652 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   95</span>|     468.61872 |     1.011 |   -0.9669 |   -0.9434 |   -0.9467 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9547 |   -0.9271 |   -0.5984 |   -0.5914 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8911 |    0.2086 |   -0.6849 |   -0.5181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1852 |   -0.4581 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.61872 |     101.1 |    -4.067 |   -0.9439 |    -2.379 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.055 |  -0.01106 |    0.8427 |   0.07801 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6872 |     3.320 |     1.534 |     1.589 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.523 |     1.658 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.61872</span> |     101.1 |   0.01713 |    0.2801 |   0.09261 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01734 |    0.4972 |    0.8427 |   0.07801 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6872 |     3.320 |     1.534 |     1.589 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.523 |     1.658 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     3.484 |    0.5384 |   0.08463 |   0.09865 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7055 |  0.007848 |    -2.909 |    -1.559 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2162 |    -1.642 |    -1.728 |   -0.7121 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.5947 |   -0.3648 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   96</span>|     468.58548 |     1.008 |   -0.9690 |   -0.9515 |   -0.9524 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9418 |   -0.9281 |   -0.5713 |   -0.6320 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8823 |    0.2961 |   -0.6589 |   -0.5284 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1884 |   -0.4503 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.58548 |     100.8 |    -4.069 |   -0.9515 |    -2.385 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.042 |  -0.01107 |    0.8526 |   0.07665 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6931 |     3.458 |     1.567 |     1.578 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.518 |     1.667 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.58548</span> |     100.8 |   0.01709 |    0.2786 |   0.09208 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01757 |    0.4972 |    0.8526 |   0.07665 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6931 |     3.458 |     1.567 |     1.578 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.518 |     1.667 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -20.63 |    -1.258 |   -0.4941 |   -0.2790 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     5.133 |   0.01399 |    -2.360 |    -3.936 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.04374 |    -1.146 |   -0.2901 |    -1.004 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6864 |   -0.2503 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   97</span>|     468.42330 |     1.011 |   -0.9777 |   -0.9596 |   -0.9476 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9583 |   -0.9295 |   -0.5827 |   -0.6211 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8760 |    0.3979 |   -0.6632 |   -0.5247 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1834 |   -0.4404 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      468.4233 |     101.1 |    -4.078 |   -0.9591 |    -2.380 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.058 |  -0.01109 |    0.8485 |   0.07701 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6974 |     3.619 |     1.561 |     1.582 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.525 |     1.679 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      468.4233</span> |     101.1 |   0.01695 |    0.2771 |   0.09253 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01728 |    0.4972 |    0.8485 |   0.07701 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6974 |     3.619 |     1.561 |     1.582 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.525 |     1.679 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -2.203 |    -3.445 |   -0.3661 |   -0.1876 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3413 |  0.004713 |    -2.617 |    -2.520 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.09908 |   -0.8887 |   -0.1336 |   -0.9981 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6593 |   -0.1712 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   98</span>|     468.44544 |     1.015 |   -0.9711 |   -0.9083 |   -0.9392 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9571 |   -0.9308 |   -0.5728 |   -0.6140 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9111 |    0.4459 |   -0.6774 |   -0.4582 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1728 |   -0.4486 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.44544 |     101.5 |    -4.071 |   -0.9109 |    -2.372 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.057 |  -0.01110 |    0.8520 |   0.07725 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6737 |     3.695 |     1.543 |     1.658 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.541 |     1.669 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.44544</span> |     101.5 |   0.01706 |    0.2868 |   0.09331 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01730 |    0.4972 |    0.8520 |   0.07725 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6737 |     3.695 |     1.543 |     1.658 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.541 |     1.669 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   99</span>|     468.45245 |     1.016 |   -0.9696 |   -0.9377 |   -0.9438 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9573 |   -0.9300 |   -0.5745 |   -0.6143 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8908 |    0.4193 |   -0.6689 |   -0.4955 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1780 |   -0.4435 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.45245 |     101.6 |    -4.070 |   -0.9385 |    -2.376 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.057 |  -0.01109 |    0.8514 |   0.07724 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6874 |     3.653 |     1.554 |     1.615 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.533 |     1.675 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.45245</span> |     101.6 |   0.01708 |    0.2812 |   0.09288 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01730 |    0.4972 |    0.8514 |   0.07724 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6874 |     3.653 |     1.554 |     1.615 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.533 |     1.675 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  100</span>|     468.50368 |     1.017 |   -0.9688 |   -0.9537 |   -0.9463 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9573 |   -0.9296 |   -0.5755 |   -0.6144 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8797 |    0.4047 |   -0.6643 |   -0.5158 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1808 |   -0.4408 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.50368 |     101.7 |    -4.069 |   -0.9536 |    -2.379 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.057 |  -0.01109 |    0.8511 |   0.07724 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6949 |     3.630 |     1.560 |     1.592 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.529 |     1.678 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.50368</span> |     101.7 |   0.01710 |    0.2782 |   0.09265 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01729 |    0.4972 |    0.8511 |   0.07724 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6949 |     3.630 |     1.560 |     1.592 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.529 |     1.678 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  101</span>|     468.41664 |     1.014 |   -0.9744 |   -0.9593 |   -0.9474 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9580 |   -0.9295 |   -0.5802 |   -0.6187 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8761 |    0.3987 |   -0.6631 |   -0.5237 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1828 |   -0.4402 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.41664 |     101.4 |    -4.074 |   -0.9588 |    -2.380 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.058 |  -0.01109 |    0.8494 |   0.07709 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6973 |     3.620 |     1.561 |     1.583 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.526 |     1.679 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.41664</span> |     101.4 |   0.01700 |    0.2771 |   0.09254 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01728 |    0.4972 |    0.8494 |   0.07709 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6973 |     3.620 |     1.561 |     1.583 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.526 |     1.679 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     15.72 |  -0.02243 |   -0.2559 |   -0.2642 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4515 |  0.003975 |    -3.178 |    -2.839 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.02889 |   -0.8526 |   -0.3239 |    -1.069 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6251 |   -0.1666 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  102</span>|     468.39161 |     1.011 |   -0.9744 |   -0.9571 |   -0.9468 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9580 |   -0.9295 |   -0.5795 |   -0.6181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8777 |    0.4014 |   -0.6637 |   -0.5208 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1824 |   -0.4404 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.39161 |     101.1 |    -4.074 |   -0.9567 |    -2.380 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.058 |  -0.01109 |    0.8496 |   0.07711 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6962 |     3.624 |     1.561 |     1.586 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.527 |     1.679 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.39161</span> |     101.1 |   0.01700 |    0.2775 |   0.09260 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01728 |    0.4972 |    0.8496 |   0.07711 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6962 |     3.624 |     1.561 |     1.586 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.527 |     1.679 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |   -0.3129 |    -1.347 |   -0.3359 |  -0.08922 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3284 |  0.005471 |    -2.643 |    -3.028 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.08821 |   -0.8778 |   -0.3083 |   -0.9349 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6501 |   -0.1781 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  103</span>|     468.37315 |     1.012 |   -0.9728 |   -0.9567 |   -0.9467 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9576 |   -0.9295 |   -0.5763 |   -0.6144 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8776 |    0.4024 |   -0.6633 |   -0.5197 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1816 |   -0.4402 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.37315 |     101.2 |    -4.073 |   -0.9564 |    -2.379 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.058 |  -0.01109 |    0.8508 |   0.07724 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6963 |     3.626 |     1.561 |     1.588 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.528 |     1.679 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.37315</span> |     101.2 |   0.01703 |    0.2776 |   0.09261 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01729 |    0.4972 |    0.8508 |   0.07724 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6963 |     3.626 |     1.561 |     1.588 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.528 |     1.679 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     3.643 |  -0.05906 |   -0.3284 |  -0.08464 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2659 |  0.005628 |    -2.441 |    -2.741 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1033 |   -0.8597 |   -0.2783 |   -0.9091 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6423 |   -0.1835 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  104</span>|     468.35774 |     1.011 |   -0.9727 |   -0.9524 |   -0.9471 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9577 |   -0.9297 |   -0.5762 |   -0.6128 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8791 |    0.4096 |   -0.6644 |   -0.5136 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1797 |   -0.4410 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.35774 |     101.1 |    -4.073 |   -0.9523 |    -2.380 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.058 |  -0.01109 |    0.8508 |   0.07729 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6953 |     3.637 |     1.560 |     1.595 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.531 |     1.678 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.35774</span> |     101.1 |   0.01703 |    0.2784 |   0.09257 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01729 |    0.4972 |    0.8508 |   0.07729 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6953 |     3.637 |     1.560 |     1.595 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.531 |     1.678 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.235 |   -0.7549 |   -0.2072 |  -0.03117 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2512 |  0.006470 |    -2.488 |    -2.805 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1485 |   -0.7903 |   -0.1532 |   -0.6368 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.5813 |   -0.1871 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  105</span>|     468.34230 |     1.012 |   -0.9728 |   -0.9537 |   -0.9473 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9580 |   -0.9300 |   -0.5748 |   -0.6111 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8817 |    0.4177 |   -0.6661 |   -0.5086 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1765 |   -0.4413 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      468.3423 |     101.2 |    -4.073 |   -0.9535 |    -2.380 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.058 |  -0.01109 |    0.8513 |   0.07735 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6935 |     3.650 |     1.558 |     1.600 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.535 |     1.678 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      468.3423</span> |     101.2 |   0.01703 |    0.2782 |   0.09256 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01728 |    0.4972 |    0.8513 |   0.07735 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6935 |     3.650 |     1.558 |     1.600 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.535 |     1.678 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     5.528 |    0.1613 |   -0.1518 |   -0.1427 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4211 |  0.005662 |    -2.339 |    -2.549 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1833 |   -0.8387 |   -0.3838 |   -0.5791 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.5586 |   -0.2081 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  106</span>|     468.32828 |     1.011 |   -0.9730 |   -0.9582 |   -0.9446 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9579 |   -0.9303 |   -0.5739 |   -0.6081 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8827 |    0.4247 |   -0.6653 |   -0.5110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1721 |   -0.4394 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.32828 |     101.1 |    -4.073 |   -0.9578 |    -2.377 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.058 |  -0.01110 |    0.8517 |   0.07745 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6929 |     3.661 |     1.559 |     1.598 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.542 |     1.680 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.32828</span> |     101.1 |   0.01703 |    0.2773 |   0.09281 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01729 |    0.4972 |    0.8517 |   0.07745 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6929 |     3.661 |     1.559 |     1.598 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.542 |     1.680 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.625 |   -0.2716 |   -0.4478 |   0.07059 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3746 |  0.006164 |    -2.097 |    -1.611 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.05708 |   -0.8042 |   -0.2658 |   -0.6338 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.5023 |   -0.1867 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  107</span>|     468.31633 |     1.012 |   -0.9727 |   -0.9524 |   -0.9447 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9580 |   -0.9305 |   -0.5707 |   -0.6067 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8839 |    0.4327 |   -0.6650 |   -0.5105 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1703 |   -0.4382 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.31633 |     101.2 |    -4.073 |   -0.9523 |    -2.377 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.058 |  -0.01110 |    0.8528 |   0.07749 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6921 |     3.674 |     1.559 |     1.598 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.545 |     1.681 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.31633</span> |     101.2 |   0.01703 |    0.2784 |   0.09280 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01728 |    0.4972 |    0.8528 |   0.07749 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6921 |     3.674 |     1.559 |     1.598 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.545 |     1.681 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     8.853 |    0.6395 |  -0.04718 |  -0.01095 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5142 |  0.005281 |    -1.984 |    -1.469 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.06825 |   -0.7616 |   -0.1275 |   -0.5767 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.4529 |   -0.1729 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  108</span>|     468.30195 |     1.011 |   -0.9730 |   -0.9497 |   -0.9482 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9586 |   -0.9308 |   -0.5675 |   -0.6075 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8868 |    0.4402 |   -0.6670 |   -0.5066 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1704 |   -0.4381 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.30195 |     101.1 |    -4.073 |   -0.9498 |    -2.381 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.059 |  -0.01110 |    0.8540 |   0.07747 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6901 |     3.686 |     1.556 |     1.603 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.544 |     1.681 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.30195</span> |     101.1 |   0.01703 |    0.2789 |   0.09247 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01727 |    0.4972 |    0.8540 |   0.07747 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6901 |     3.686 |     1.556 |     1.603 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.544 |     1.681 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.407 |   -0.4624 |  -0.02880 |   -0.1214 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5499 |  0.005628 |    -1.325 |    -1.518 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1161 |   -0.7707 |   -0.3300 |   -0.4912 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.4772 |   -0.1931 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  109</span>|     468.29210 |     1.012 |   -0.9733 |   -0.9523 |   -0.9459 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9581 |   -0.9311 |   -0.5658 |   -0.6073 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8878 |    0.4488 |   -0.6682 |   -0.5015 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1697 |   -0.4385 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      468.2921 |     101.2 |    -4.073 |   -0.9523 |    -2.379 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.058 |  -0.01111 |    0.8546 |   0.07747 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6894 |     3.699 |     1.555 |     1.608 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.546 |     1.681 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      468.2921</span> |     101.2 |   0.01702 |    0.2784 |   0.09269 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01728 |    0.4972 |    0.8546 |   0.07747 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6894 |     3.699 |     1.555 |     1.608 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.546 |     1.681 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  110</span>|     468.28540 |     1.012 |   -0.9739 |   -0.9554 |   -0.9432 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9581 |   -0.9314 |   -0.5648 |   -0.6081 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8889 |    0.4579 |   -0.6697 |   -0.4962 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1692 |   -0.4390 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      468.2854 |     101.2 |    -4.074 |   -0.9551 |    -2.376 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.058 |  -0.01111 |    0.8550 |   0.07745 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6886 |     3.714 |     1.553 |     1.615 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.546 |     1.680 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      468.2854</span> |     101.2 |   0.01701 |    0.2779 |   0.09293 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01728 |    0.4972 |    0.8550 |   0.07745 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6886 |     3.714 |     1.553 |     1.615 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.546 |     1.680 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     3.644 |   0.03668 |   -0.1986 |   0.05251 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5265 |  0.006364 |    -2.013 |    -2.999 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3077 |   -0.7577 |   -0.4283 |   -0.2217 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.4457 |   -0.1968 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  111</span>|     468.26285 |     1.012 |   -0.9738 |   -0.9546 |   -0.9455 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9580 |   -0.9321 |   -0.5638 |   -0.6053 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8894 |    0.4796 |   -0.6701 |   -0.4937 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1665 |   -0.4375 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.26285 |     101.2 |    -4.074 |   -0.9544 |    -2.378 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.058 |  -0.01112 |    0.8554 |   0.07754 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6883 |     3.748 |     1.552 |     1.617 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.550 |     1.682 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.26285</span> |     101.2 |   0.01701 |    0.2780 |   0.09272 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01728 |    0.4972 |    0.8554 |   0.07754 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6883 |     3.748 |     1.552 |     1.617 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.550 |     1.682 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  112</span>|     468.24808 |     1.012 |   -0.9737 |   -0.9538 |   -0.9478 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9581 |   -0.9328 |   -0.5632 |   -0.6032 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8900 |    0.5018 |   -0.6707 |   -0.4913 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1639 |   -0.4361 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.24808 |     101.2 |    -4.074 |   -0.9537 |    -2.380 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.058 |  -0.01113 |    0.8556 |   0.07761 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6879 |     3.783 |     1.552 |     1.620 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.554 |     1.684 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.24808</span> |     101.2 |   0.01701 |    0.2781 |   0.09251 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01728 |    0.4972 |    0.8556 |   0.07761 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6879 |     3.783 |     1.552 |     1.620 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.554 |     1.684 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     7.572 |    0.7442 |   -0.1004 |   -0.2485 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5511 |  0.005645 |    -1.896 |    -1.976 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3421 |   -0.6164 |   -0.3384 |   -0.1002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.3637 |   -0.1858 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  113</span>|     468.24925 |     1.009 |   -0.9744 |   -0.9459 |   -0.9275 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9570 |   -0.9342 |   -0.5606 |   -0.6003 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8814 |    0.5390 |   -0.6664 |   -0.4916 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1575 |   -0.4319 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.24925 |     100.9 |    -4.074 |   -0.9463 |    -2.360 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.057 |  -0.01114 |    0.8565 |   0.07771 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6937 |     3.842 |     1.557 |     1.620 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.563 |     1.689 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.24925</span> |     100.9 |   0.01700 |    0.2796 |   0.09441 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01730 |    0.4972 |    0.8565 |   0.07771 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6937 |     3.842 |     1.557 |     1.620 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.563 |     1.689 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  114</span>|     468.25666 |     1.009 |   -0.9742 |   -0.9500 |   -0.9379 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9574 |   -0.9335 |   -0.5615 |   -0.6013 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8858 |    0.5198 |   -0.6685 |   -0.4914 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1608 |   -0.4340 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.25666 |     100.9 |    -4.074 |   -0.9501 |    -2.371 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.057 |  -0.01113 |    0.8562 |   0.07768 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6908 |     3.812 |     1.554 |     1.620 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.559 |     1.686 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.25666</span> |     100.9 |   0.01701 |    0.2789 |   0.09342 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01729 |    0.4972 |    0.8562 |   0.07768 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6908 |     3.812 |     1.554 |     1.620 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.559 |     1.686 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  115</span>|     468.26483 |     1.009 |   -0.9741 |   -0.9524 |   -0.9439 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9577 |   -0.9331 |   -0.5620 |   -0.6018 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8883 |    0.5089 |   -0.6697 |   -0.4913 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1626 |   -0.4352 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.26483 |     100.9 |    -4.074 |   -0.9523 |    -2.377 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.058 |  -0.01113 |    0.8560 |   0.07766 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6891 |     3.794 |     1.553 |     1.620 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.556 |     1.685 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.26483</span> |     100.9 |   0.01701 |    0.2784 |   0.09287 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01729 |    0.4972 |    0.8560 |   0.07766 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6891 |     3.794 |     1.553 |     1.620 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.556 |     1.685 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  116</span>|     468.25961 |     1.009 |   -0.9740 |   -0.9538 |   -0.9477 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9579 |   -0.9328 |   -0.5624 |   -0.6023 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8898 |    0.5020 |   -0.6705 |   -0.4913 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1637 |   -0.4360 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.25961 |     100.9 |    -4.074 |   -0.9536 |    -2.380 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.058 |  -0.01113 |    0.8558 |   0.07764 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6880 |     3.783 |     1.552 |     1.620 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.554 |     1.684 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.25961</span> |     100.9 |   0.01701 |    0.2782 |   0.09252 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01729 |    0.4972 |    0.8558 |   0.07764 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6880 |     3.783 |     1.552 |     1.620 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.554 |     1.684 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  117</span>|     468.24351 |     1.011 |   -0.9738 |   -0.9538 |   -0.9477 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9580 |   -0.9328 |   -0.5629 |   -0.6029 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8899 |    0.5019 |   -0.6706 |   -0.4913 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1638 |   -0.4360 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.24351 |     101.1 |    -4.074 |   -0.9537 |    -2.380 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.058 |  -0.01113 |    0.8557 |   0.07762 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6880 |     3.783 |     1.552 |     1.620 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.554 |     1.684 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.24351</span> |     101.1 |   0.01701 |    0.2781 |   0.09252 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01728 |    0.4972 |    0.8557 |   0.07762 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6880 |     3.783 |     1.552 |     1.620 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.554 |     1.684 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |   -0.7570 |  -0.04882 |   -0.1901 |   -0.1704 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4436 |  0.006358 |    -1.944 |    -2.069 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3547 |   -0.6277 |   -0.2411 |  -0.07768 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.3909 |   -0.1932 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  118</span>|     468.24096 |     1.012 |   -0.9738 |   -0.9538 |   -0.9477 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9579 |   -0.9328 |   -0.5622 |   -0.6021 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8898 |    0.5021 |   -0.6705 |   -0.4913 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1637 |   -0.4360 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.24096 |     101.2 |    -4.074 |   -0.9536 |    -2.380 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.058 |  -0.01113 |    0.8559 |   0.07765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6881 |     3.784 |     1.552 |     1.620 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.554 |     1.684 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.24096</span> |     101.2 |   0.01701 |    0.2782 |   0.09252 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01729 |    0.4972 |    0.8559 |   0.07765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6881 |     3.784 |     1.552 |     1.620 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.554 |     1.684 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     1.456 |    0.1549 |   -0.1619 |   -0.1892 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4078 |  0.006267 |    -1.860 |    -2.735 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3543 |   -0.6265 |   -0.1851 |  -0.08656 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.3612 |   -0.1856 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  119</span>|     468.23716 |     1.012 |   -0.9738 |   -0.9536 |   -0.9467 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9576 |   -0.9329 |   -0.5614 |   -0.6008 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8898 |    0.5037 |   -0.6704 |   -0.4911 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1638 |   -0.4357 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.23716 |     101.2 |    -4.074 |   -0.9534 |    -2.379 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.058 |  -0.01113 |    0.8562 |   0.07769 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6881 |     3.786 |     1.552 |     1.620 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.554 |     1.684 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.23716</span> |     101.2 |   0.01701 |    0.2782 |   0.09261 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01729 |    0.4972 |    0.8562 |   0.07769 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6881 |     3.786 |     1.552 |     1.620 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.554 |     1.684 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     2.250 |    0.2329 |   -0.1405 |   -0.1467 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3393 |  0.006453 |    -1.023 |    -1.889 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3583 |   -0.6239 |   -0.2353 |  -0.09107 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.3636 |   -0.1837 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  120</span>|     468.26529 |     1.008 |   -0.9742 |   -0.9534 |   -0.9465 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9571 |   -0.9329 |   -0.5599 |   -0.5980 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8893 |    0.5046 |   -0.6700 |   -0.4910 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1633 |   -0.4354 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.26529 |     100.8 |    -4.074 |   -0.9532 |    -2.379 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.057 |  -0.01113 |    0.8568 |   0.07778 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6884 |     3.788 |     1.553 |     1.621 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.555 |     1.685 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.26529</span> |     100.8 |   0.01701 |    0.2782 |   0.09263 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01730 |    0.4972 |    0.8568 |   0.07778 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6884 |     3.788 |     1.553 |     1.621 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.555 |     1.685 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  121</span>|     468.23604 |     1.011 |   -0.9739 |   -0.9535 |   -0.9467 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9575 |   -0.9329 |   -0.5612 |   -0.6003 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8897 |    0.5039 |   -0.6703 |   -0.4911 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1637 |   -0.4357 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.23604 |     101.1 |    -4.074 |   -0.9534 |    -2.379 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.058 |  -0.01113 |    0.8563 |   0.07771 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6881 |     3.786 |     1.552 |     1.620 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.554 |     1.684 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.23604</span> |     101.1 |   0.01701 |    0.2782 |   0.09261 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01729 |    0.4972 |    0.8563 |   0.07771 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6881 |     3.786 |     1.552 |     1.620 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.554 |     1.684 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -2.188 |   -0.1927 |   -0.1868 |   -0.1045 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2665 |  0.006857 |    -1.024 |    -1.172 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2153 |   -0.6125 |   -0.1502 |  -0.07921 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.3902 |   -0.1918 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  122</span>|     468.23464 |     1.012 |   -0.9738 |   -0.9534 |   -0.9465 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9574 |   -0.9329 |   -0.5609 |   -0.6000 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8895 |    0.5043 |   -0.6702 |   -0.4911 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1635 |   -0.4356 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.23464 |     101.2 |    -4.074 |   -0.9533 |    -2.379 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.057 |  -0.01113 |    0.8564 |   0.07772 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6883 |     3.787 |     1.552 |     1.620 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.555 |     1.684 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.23464</span> |     101.2 |   0.01701 |    0.2782 |   0.09263 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01729 |    0.4972 |    0.8564 |   0.07772 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6883 |     3.787 |     1.552 |     1.620 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.555 |     1.684 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     1.857 |    0.1925 |   -0.1360 |   -0.1332 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2859 |  0.006578 |   -0.9819 |    -1.076 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2136 |   -0.6087 |   -0.2154 |  -0.09459 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.3609 |   -0.1836 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  123</span>|     468.23346 |     1.011 |   -0.9739 |   -0.9532 |   -0.9463 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9574 |   -0.9329 |   -0.5607 |   -0.5998 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8893 |    0.5049 |   -0.6701 |   -0.4911 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1633 |   -0.4355 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.23346 |     101.1 |    -4.074 |   -0.9531 |    -2.379 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.057 |  -0.01113 |    0.8565 |   0.07772 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6884 |     3.788 |     1.552 |     1.621 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.555 |     1.684 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.23346</span> |     101.1 |   0.01701 |    0.2783 |   0.09265 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01729 |    0.4972 |    0.8565 |   0.07772 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6884 |     3.788 |     1.552 |     1.621 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.555 |     1.684 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.112 |  -0.08635 |   -0.1600 |  -0.09176 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2402 |  0.006873 |   -0.9941 |    -1.084 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2133 |   -0.6080 |   -0.2006 |  -0.08423 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.3853 |   -0.1906 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  124</span>|     468.23209 |     1.012 |   -0.9738 |   -0.9531 |   -0.9461 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9573 |   -0.9329 |   -0.5604 |   -0.5995 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8891 |    0.5054 |   -0.6700 |   -0.4910 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1631 |   -0.4354 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.23209 |     101.2 |    -4.074 |   -0.9530 |    -2.379 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.057 |  -0.01113 |    0.8566 |   0.07774 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6885 |     3.789 |     1.553 |     1.621 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.555 |     1.685 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.23209</span> |     101.2 |   0.01701 |    0.2783 |   0.09266 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01730 |    0.4972 |    0.8566 |   0.07774 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6885 |     3.789 |     1.553 |     1.621 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.555 |     1.685 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  125</span>|     468.23061 |     1.012 |   -0.9738 |   -0.9526 |   -0.9453 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9573 |   -0.9330 |   -0.5604 |   -0.5995 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8885 |    0.5071 |   -0.6698 |   -0.4911 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1626 |   -0.4353 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.23061 |     101.2 |    -4.074 |   -0.9525 |    -2.378 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.057 |  -0.01113 |    0.8566 |   0.07773 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6889 |     3.791 |     1.553 |     1.620 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.556 |     1.685 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.23061</span> |     101.2 |   0.01701 |    0.2784 |   0.09274 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01730 |    0.4972 |    0.8566 |   0.07773 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6889 |     3.791 |     1.553 |     1.620 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.556 |     1.685 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  126</span>|     468.22552 |     1.012 |   -0.9738 |   -0.9506 |   -0.9419 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9573 |   -0.9333 |   -0.5606 |   -0.5998 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8861 |    0.5138 |   -0.6689 |   -0.4912 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1606 |   -0.4347 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.22552 |     101.2 |    -4.074 |   -0.9506 |    -2.375 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.057 |  -0.01113 |    0.8565 |   0.07773 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6906 |     3.802 |     1.554 |     1.620 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.559 |     1.685 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.22552</span> |     101.2 |   0.01701 |    0.2788 |   0.09305 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01730 |    0.4972 |    0.8565 |   0.07773 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6906 |     3.802 |     1.554 |     1.620 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.559 |     1.685 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  127</span>|     468.21689 |     1.012 |   -0.9737 |   -0.9426 |   -0.9286 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9574 |   -0.9344 |   -0.5614 |   -0.6009 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8763 |    0.5405 |   -0.6653 |   -0.4919 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1525 |   -0.4325 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.21689 |     101.2 |    -4.074 |   -0.9431 |    -2.361 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.057 |  -0.01114 |    0.8562 |   0.07769 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6972 |     3.844 |     1.558 |     1.620 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.571 |     1.688 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.21689</span> |     101.2 |   0.01701 |    0.2803 |   0.09430 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01729 |    0.4972 |    0.8562 |   0.07769 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6972 |     3.844 |     1.558 |     1.620 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.571 |     1.688 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     1.525 |    0.5842 |    0.4183 |    0.9468 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5439 |  0.008618 |    -1.874 |    -1.936 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2463 |   -0.5498 |   0.04440 |   -0.1490 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2038 |   -0.1015 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  128</span>|     468.19158 |     1.011 |   -0.9737 |   -0.9504 |   -0.9293 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9577 |   -0.9362 |   -0.5600 |   -0.6009 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8523 |    0.5756 |   -0.6644 |   -0.4878 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1417 |   -0.4298 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.19158 |     101.1 |    -4.074 |   -0.9504 |    -2.362 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.058 |  -0.01116 |    0.8567 |   0.07769 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7134 |     3.900 |     1.560 |     1.624 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.587 |     1.691 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.19158</span> |     101.1 |   0.01701 |    0.2788 |   0.09423 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01729 |    0.4972 |    0.8567 |   0.07769 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7134 |     3.900 |     1.560 |     1.624 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.587 |     1.691 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -7.990 |    0.8226 |  -0.02734 |    0.9308 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6358 |  0.008756 |    -3.089 |    -2.706 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.08468 |   -0.5047 |    0.1760 |  -0.08785 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|  -0.06839 |  -0.06706 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  129</span>|     468.16872 |     1.012 |   -0.9746 |   -0.9536 |   -0.9297 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9583 |   -0.9383 |   -0.5551 |   -0.6021 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8428 |    0.6190 |   -0.6652 |   -0.4874 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1424 |   -0.4276 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.16872 |     101.2 |    -4.075 |   -0.9534 |    -2.362 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.058 |  -0.01118 |    0.8585 |   0.07765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7198 |     3.968 |     1.559 |     1.625 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.586 |     1.694 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.16872</span> |     101.2 |   0.01700 |    0.2782 |   0.09420 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01728 |    0.4972 |    0.8585 |   0.07765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7198 |     3.968 |     1.559 |     1.625 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.586 |     1.694 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     1.949 |     1.224 |  -0.09904 |    0.8335 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8902 |  0.008091 |    -1.732 |    -1.975 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.02618 |   -0.4349 |    0.2067 |   -0.1419 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|  -0.09587 |  -0.05531 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  130</span>|     468.14579 |     1.011 |   -0.9759 |   -0.9465 |   -0.9356 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9575 |   -0.9407 |   -0.5519 |   -0.5999 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8420 |    0.6626 |   -0.6664 |   -0.4855 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1418 |   -0.4273 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.14579 |     101.1 |    -4.076 |   -0.9467 |    -2.368 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.057 |  -0.01121 |    0.8597 |   0.07772 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7204 |     4.037 |     1.557 |     1.627 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.587 |     1.694 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.14579</span> |     101.1 |   0.01698 |    0.2795 |   0.09364 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01729 |    0.4972 |    0.8597 |   0.07772 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7204 |     4.037 |     1.557 |     1.627 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.587 |     1.694 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -9.402 |   -0.5832 |    0.1847 |    0.5188 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4155 |  0.008564 |    -1.671 |    -1.980 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.03632 |   -0.3649 |    0.3304 |  -0.09551 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1039 |  -0.08698 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  131</span>|     468.12514 |     1.011 |   -0.9763 |   -0.9445 |   -0.9403 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9555 |   -0.9442 |   -0.5537 |   -0.5953 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8391 |    0.7059 |   -0.6668 |   -0.4805 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1376 |   -0.4223 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.12514 |     101.1 |    -4.076 |   -0.9449 |    -2.373 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.056 |  -0.01125 |    0.8590 |   0.07788 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7223 |     4.106 |     1.557 |     1.633 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.593 |     1.700 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.12514</span> |     101.1 |   0.01697 |    0.2799 |   0.09320 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01733 |    0.4972 |    0.8590 |   0.07788 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7223 |     4.106 |     1.557 |     1.633 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.593 |     1.700 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -5.823 |   -0.4010 |    0.3316 |    0.1628 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2993 |  0.008766 |    -1.373 |    -1.049 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008284 |   -0.3402 |    0.4029 |   0.04088 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|  -0.03436 |  -0.04680 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  132</span>|     468.10920 |     1.012 |   -0.9773 |   -0.9503 |   -0.9395 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9556 |   -0.9476 |   -0.5516 |   -0.5942 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8420 |    0.7499 |   -0.6684 |   -0.4836 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1347 |   -0.4216 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      468.1092 |     101.2 |    -4.077 |   -0.9504 |    -2.372 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.056 |  -0.01129 |    0.8598 |   0.07791 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7204 |     4.175 |     1.555 |     1.629 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.597 |     1.700 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      468.1092</span> |     101.2 |   0.01695 |    0.2788 |   0.09328 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01732 |    0.4972 |    0.8598 |   0.07791 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7204 |     4.175 |     1.555 |     1.629 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.597 |     1.700 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    0.4262 |  -0.09925 |    0.1213 |    0.1364 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2200 |  0.008054 |   -0.8480 |   -0.9224 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.002683 |   -0.2932 |    0.3866 |  -0.06974 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   0.01820 |  -0.05063 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  133</span>|     468.09766 |     1.011 |   -0.9774 |   -0.9506 |   -0.9392 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9564 |   -0.9508 |   -0.5497 |   -0.5929 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8318 |    0.7901 |   -0.6724 |   -0.4776 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1474 |   -0.4133 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.09766 |     101.1 |    -4.077 |   -0.9507 |    -2.372 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.056 |  -0.01132 |    0.8605 |   0.07796 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7273 |     4.239 |     1.550 |     1.636 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.578 |     1.710 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.09766</span> |     101.1 |   0.01695 |    0.2788 |   0.09331 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01731 |    0.4972 |    0.8605 |   0.07796 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7273 |     4.239 |     1.550 |     1.636 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.578 |     1.710 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.610 |   0.04645 |   0.08092 |    0.1870 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1143 |  0.008788 |   -0.7906 |   -0.9380 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03576 |   -0.2567 |    0.2790 |   0.04583 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1853 |   0.03622 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  134</span>|     468.08577 |     1.011 |   -0.9779 |   -0.9521 |   -0.9394 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9569 |   -0.9550 |   -0.5484 |   -0.5900 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8316 |    0.8332 |   -0.6752 |   -0.4781 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1428 |   -0.4231 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.08577 |     101.1 |    -4.078 |   -0.9520 |    -2.372 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.057 |  -0.01137 |    0.8610 |   0.07805 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7274 |     4.307 |     1.546 |     1.635 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.585 |     1.699 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.08577</span> |     101.1 |   0.01694 |    0.2785 |   0.09329 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01730 |    0.4972 |    0.8610 |   0.07805 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7274 |     4.307 |     1.546 |     1.635 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.585 |     1.699 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -2.835 |  -0.08271 |   0.01028 |    0.1502 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2859 |  0.008536 |   -0.7298 |   -0.7597 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.09519 |   -0.2281 |    0.1720 |  -0.01715 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1386 |   -0.1062 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  135</span>|     468.06947 |     1.011 |   -0.9795 |   -0.9573 |   -0.9407 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9575 |   -0.9652 |   -0.5443 |   -0.5834 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8515 |    0.9153 |   -0.6785 |   -0.4834 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1291 |   -0.4003 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.06947 |     101.1 |    -4.080 |   -0.9569 |    -2.373 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.057 |  -0.01148 |    0.8625 |   0.07827 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7139 |     4.437 |     1.542 |     1.629 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.605 |     1.725 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.06947</span> |     101.1 |   0.01692 |    0.2775 |   0.09316 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01729 |    0.4971 |    0.8625 |   0.07827 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7139 |     4.437 |     1.542 |     1.629 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.605 |     1.725 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -3.887 |   -0.6407 |   -0.1939 |   0.08469 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4208 |  0.006503 |    -1.301 |    -1.072 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1255 |   -0.1861 |    0.1431 |  -0.06050 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   0.01658 |    0.1111 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  136</span>|     468.05012 |     1.012 |   -0.9812 |   -0.9547 |   -0.9420 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9570 |   -0.9873 |   -0.5356 |   -0.5736 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8556 |     1.092 |   -0.6884 |   -0.4796 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1211 |   -0.3873 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.05012 |     101.2 |    -4.081 |   -0.9544 |    -2.375 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.057 |  -0.01172 |    0.8656 |   0.07860 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7112 |     4.716 |     1.529 |     1.634 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.617 |     1.740 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.05012</span> |     101.2 |   0.01689 |    0.2780 |   0.09305 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01730 |    0.4971 |    0.8656 |   0.07860 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7112 |     4.716 |     1.529 |     1.634 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.617 |     1.740 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     2.331 |   -0.3124 |   0.04539 |  -0.09325 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3359 |  0.005628 |   -0.9294 |   -0.4404 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1788 |   -0.1003 |   -0.1180 |  -0.02636 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   0.09915 |    0.1820 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  137</span>|     468.05341 |     1.012 |   -0.9836 |   -0.9687 |   -0.9317 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9507 |    -1.013 |   -0.5235 |   -0.5660 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8472 |     1.268 |   -0.6904 |   -0.4754 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1200 |   -0.3857 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.05341 |     101.2 |    -4.084 |   -0.9676 |    -2.364 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.051 |  -0.01201 |    0.8700 |   0.07886 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7168 |     4.995 |     1.527 |     1.639 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.619 |     1.742 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.05341</span> |     101.2 |   0.01685 |    0.2754 |   0.09401 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01741 |    0.4970 |    0.8700 |   0.07886 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7168 |     4.995 |     1.527 |     1.639 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.619 |     1.742 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  138</span>|     468.05012 |     1.012 |   -0.9812 |   -0.9547 |   -0.9420 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9570 |   -0.9873 |   -0.5356 |   -0.5736 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8556 |     1.092 |   -0.6884 |   -0.4796 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1211 |   -0.3873 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.05012 |     101.2 |    -4.081 |   -0.9544 |    -2.375 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.057 |  -0.01172 |    0.8656 |   0.07860 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7112 |     4.716 |     1.529 |     1.634 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.617 |     1.740 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.05012</span> |     101.2 |   0.01689 |    0.2780 |   0.09305 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01730 |    0.4971 |    0.8656 |   0.07860 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7112 |     4.716 |     1.529 |     1.634 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.617 |     1.740 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> calculating covariance matrix</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating residuals/tables</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">AIC</a></span><span class="op">(</span><span class="va">f_nlmixr_dfop_sfo_saem</span><span class="op">$</span><span class="va">nm</span>, <span class="va">f_nlmixr_dfop_sfo_focei</span><span class="op">$</span><span class="va">nm</span><span class="op">)</span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating -2LL by Gaussian quadrature (nnodes=3,nsd=1.6)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                            df       AIC</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_nlmixr_dfop_sfo_saem$nm  16 1105.3725</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_nlmixr_dfop_sfo_focei$nm 14  810.3271</span>
-<span class="r-in"><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">f_nlmixr_dfop_sfo_sfo</span>, data <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
-<span class="r-err co"><span class="r-pr">#&gt;</span> <span class="error">Error in h(simpleError(msg, call)):</span> error in evaluating the argument 'object' in selecting a method for function 'summary': object 'f_nlmixr_dfop_sfo_sfo' not found</span>
-<span class="r-in"><span class="co"># }</span></span>
-<span class="r-in"></span>
-</code></pre></div>
-    </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.2.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/summary.saem.mmkin.html b/docs/dev/reference/summary.saem.mmkin.html
deleted file mode 100644
index e434ad8d..00000000
--- a/docs/dev/reference/summary.saem.mmkin.html
+++ /dev/null
@@ -1,677 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Summary method for class "saem.mmkin" — summary.saem.mmkin • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Summary method for class " saem.mmkin summary.saem.mmkin><meta property="og:description" content="Lists model equations, initial parameter values, optimised parameters
-for fixed effects (population), random effects (deviations from the
-population mean) and residual error model, as well as the resulting
-endpoints such as formation fractions and DT50 values. Optionally
-(default is FALSE), the data are listed in full."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Summary method for class "saem.mmkin"</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/summary.saem.mmkin.R" class="external-link"><code>R/summary.saem.mmkin.R</code></a></small>
-    <div class="hidden name"><code>summary.saem.mmkin.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Lists model equations, initial parameter values, optimised parameters
-for fixed effects (population), random effects (deviations from the
-population mean) and residual error model, as well as the resulting
-endpoints such as formation fractions and DT50 values. Optionally
-(default is FALSE), the data are listed in full.</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for saem.mmkin</span></span>
-<span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span></span>
-<span>  <span class="va">object</span>,</span>
-<span>  data <span class="op">=</span> <span class="cn">FALSE</span>,</span>
-<span>  verbose <span class="op">=</span> <span class="cn">FALSE</span>,</span>
-<span>  covariates <span class="op">=</span> <span class="cn">NULL</span>,</span>
-<span>  covariate_quantile <span class="op">=</span> <span class="fl">0.5</span>,</span>
-<span>  distimes <span class="op">=</span> <span class="cn">TRUE</span>,</span>
-<span>  <span class="va">...</span></span>
-<span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># S3 method for summary.saem.mmkin</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, digits <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html" class="external-link">max</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fu"><a href="https://rdrr.io/r/base/options.html" class="external-link">getOption</a></span><span class="op">(</span><span class="st">"digits"</span><span class="op">)</span> <span class="op">-</span> <span class="fl">3</span><span class="op">)</span>, verbose <span class="op">=</span> <span class="va">x</span><span class="op">$</span><span class="va">verbose</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
-    </div>
-
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>object</dt>
-<dd><p>an object of class <a href="saem.html">saem.mmkin</a></p></dd>
-
-
-<dt>data</dt>
-<dd><p>logical, indicating whether the full data should be included in
-the summary.</p></dd>
-
-
-<dt>verbose</dt>
-<dd><p>Should the summary be verbose?</p></dd>
-
-
-<dt>covariates</dt>
-<dd><p>Numeric vector with covariate values for all variables in
-any covariate models in the object. If given, it overrides 'covariate_quantile'.</p></dd>
-
-
-<dt>covariate_quantile</dt>
-<dd><p>This argument only has an effect if the fitted
-object has covariate models. If so, the default is to show endpoints
-for the median of the covariate values (50th percentile).</p></dd>
-
-
-<dt>distimes</dt>
-<dd><p>logical, indicating whether DT50 and DT90 values should be
-included.</p></dd>
-
-
-<dt>...</dt>
-<dd><p>optional arguments passed to methods like <code>print</code>.</p></dd>
-
-
-<dt>x</dt>
-<dd><p>an object of class summary.saem.mmkin</p></dd>
-
-
-<dt>digits</dt>
-<dd><p>Number of digits to use for printing</p></dd>
-
-</dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>The summary function returns a list based on the <a href="https://rdrr.io/pkg/saemix/man/SaemixObject-class.html" class="external-link">saemix::SaemixObject</a></p>
-
-
-<p>obtained in the fit, with at least the following additional components</p>
-<dl><dt>saemixversion, mkinversion, Rversion</dt>
-<dd><p>The saemix, mkin and R versions used</p></dd>
-
-<dt>date.fit, date.summary</dt>
-<dd><p>The dates where the fit and the summary were
-produced</p></dd>
-
-<dt>diffs</dt>
-<dd><p>The differential equations used in the degradation model</p></dd>
-
-<dt>use_of_ff</dt>
-<dd><p>Was maximum or minimum use made of formation fractions</p></dd>
-
-<dt>data</dt>
-<dd><p>The data</p></dd>
-
-<dt>confint_trans</dt>
-<dd><p>Transformed parameters as used in the optimisation, with confidence intervals</p></dd>
-
-<dt>confint_back</dt>
-<dd><p>Backtransformed parameters, with confidence intervals if available</p></dd>
-
-<dt>confint_errmod</dt>
-<dd><p>Error model parameters with confidence intervals</p></dd>
-
-<dt>ff</dt>
-<dd><p>The estimated formation fractions derived from the fitted
-model.</p></dd>
-
-<dt>distimes</dt>
-<dd><p>The DT50 and DT90 values for each observed variable.</p></dd>
-
-<dt>SFORB</dt>
-<dd><p>If applicable, eigenvalues of SFORB components of the model.</p></dd>
-
-</dl><p>The print method is called for its side effect, i.e. printing the summary.</p>
-    </div>
-    <div id="author">
-    <h2>Author</h2>
-    <p>Johannes Ranke for the mkin specific parts
-saemix authors for the parts inherited from saemix.</p>
-    </div>
-
-    <div id="ref-examples">
-    <h2>Examples</h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># Generate five datasets following DFOP-SFO kinetics</span></span></span>
-<span class="r-in"><span><span class="va">sampling_times</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span>, <span class="fl">3</span>, <span class="fl">7</span>, <span class="fl">14</span>, <span class="fl">28</span>, <span class="fl">60</span>, <span class="fl">90</span>, <span class="fl">120</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">dfop_sfo</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="st">"m1"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span> m1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/Random.html" class="external-link">set.seed</a></span><span class="op">(</span><span class="fl">1234</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">k1_in</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/Lognormal.html" class="external-link">rlnorm</a></span><span class="op">(</span><span class="fl">5</span>, <span class="fu"><a href="https://rdrr.io/r/base/Log.html" class="external-link">log</a></span><span class="op">(</span><span class="fl">0.1</span><span class="op">)</span>, <span class="fl">0.3</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">k2_in</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/Lognormal.html" class="external-link">rlnorm</a></span><span class="op">(</span><span class="fl">5</span>, <span class="fu"><a href="https://rdrr.io/r/base/Log.html" class="external-link">log</a></span><span class="op">(</span><span class="fl">0.02</span><span class="op">)</span>, <span class="fl">0.3</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">g_in</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/Logistic.html" class="external-link">plogis</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/stats/Normal.html" class="external-link">rnorm</a></span><span class="op">(</span><span class="fl">5</span>, <span class="fu"><a href="https://rdrr.io/r/stats/Logistic.html" class="external-link">qlogis</a></span><span class="op">(</span><span class="fl">0.5</span><span class="op">)</span>, <span class="fl">0.3</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">f_parent_to_m1_in</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/Logistic.html" class="external-link">plogis</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/stats/Normal.html" class="external-link">rnorm</a></span><span class="op">(</span><span class="fl">5</span>, <span class="fu"><a href="https://rdrr.io/r/stats/Logistic.html" class="external-link">qlogis</a></span><span class="op">(</span><span class="fl">0.3</span><span class="op">)</span>, <span class="fl">0.3</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">k_m1_in</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/Lognormal.html" class="external-link">rlnorm</a></span><span class="op">(</span><span class="fl">5</span>, <span class="fu"><a href="https://rdrr.io/r/base/Log.html" class="external-link">log</a></span><span class="op">(</span><span class="fl">0.02</span><span class="op">)</span>, <span class="fl">0.3</span><span class="op">)</span></span></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="va">pred_dfop_sfo</span> <span class="op">&lt;-</span> <span class="kw">function</span><span class="op">(</span><span class="va">k1</span>, <span class="va">k2</span>, <span class="va">g</span>, <span class="va">f_parent_to_m1</span>, <span class="va">k_m1</span><span class="op">)</span> <span class="op">{</span></span></span>
-<span class="r-in"><span>  <span class="fu"><a href="mkinpredict.html">mkinpredict</a></span><span class="op">(</span><span class="va">dfop_sfo</span>,</span></span>
-<span class="r-in"><span>    <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k1 <span class="op">=</span> <span class="va">k1</span>, k2 <span class="op">=</span> <span class="va">k2</span>, g <span class="op">=</span> <span class="va">g</span>, f_parent_to_m1 <span class="op">=</span> <span class="va">f_parent_to_m1</span>, k_m1 <span class="op">=</span> <span class="va">k_m1</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>    <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fl">100</span>, m1 <span class="op">=</span> <span class="fl">0</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>    <span class="va">sampling_times</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="op">}</span></span></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="va">ds_mean_dfop_sfo</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fl">5</span>, <span class="kw">function</span><span class="op">(</span><span class="va">i</span><span class="op">)</span> <span class="op">{</span></span></span>
-<span class="r-in"><span>  <span class="fu"><a href="mkinpredict.html">mkinpredict</a></span><span class="op">(</span><span class="va">dfop_sfo</span>,</span></span>
-<span class="r-in"><span>    <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k1 <span class="op">=</span> <span class="va">k1_in</span><span class="op">[</span><span class="va">i</span><span class="op">]</span>, k2 <span class="op">=</span> <span class="va">k2_in</span><span class="op">[</span><span class="va">i</span><span class="op">]</span>, g <span class="op">=</span> <span class="va">g_in</span><span class="op">[</span><span class="va">i</span><span class="op">]</span>,</span></span>
-<span class="r-in"><span>      f_parent_to_m1 <span class="op">=</span> <span class="va">f_parent_to_m1_in</span><span class="op">[</span><span class="va">i</span><span class="op">]</span>, k_m1 <span class="op">=</span> <span class="va">k_m1_in</span><span class="op">[</span><span class="va">i</span><span class="op">]</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>    <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fl">100</span>, m1 <span class="op">=</span> <span class="fl">0</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>    <span class="va">sampling_times</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="op">}</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">ds_mean_dfop_sfo</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste</a></span><span class="op">(</span><span class="st">"ds"</span>, <span class="fl">1</span><span class="op">:</span><span class="fl">5</span><span class="op">)</span></span></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="va">ds_syn_dfop_sfo</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="va">ds_mean_dfop_sfo</span>, <span class="kw">function</span><span class="op">(</span><span class="va">ds</span><span class="op">)</span> <span class="op">{</span></span></span>
-<span class="r-in"><span>  <span class="fu"><a href="add_err.html">add_err</a></span><span class="op">(</span><span class="va">ds</span>,</span></span>
-<span class="r-in"><span>    sdfunc <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">value</span><span class="op">)</span> <span class="fu"><a href="https://rdrr.io/r/base/MathFun.html" class="external-link">sqrt</a></span><span class="op">(</span><span class="fl">1</span><span class="op">^</span><span class="fl">2</span> <span class="op">+</span> <span class="va">value</span><span class="op">^</span><span class="fl">2</span> <span class="op">*</span> <span class="fl">0.07</span><span class="op">^</span><span class="fl">2</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>    n <span class="op">=</span> <span class="fl">1</span><span class="op">)</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span></span></span>
-<span class="r-in"><span><span class="op">}</span><span class="op">)</span></span></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co"># \dontrun{</span></span></span>
-<span class="r-in"><span><span class="co"># Evaluate using mmkin and saem</span></span></span>
-<span class="r-in"><span><span class="va">f_mmkin_dfop_sfo</span> <span class="op">&lt;-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">dfop_sfo</span><span class="op">)</span>, <span class="va">ds_syn_dfop_sfo</span>,</span></span>
-<span class="r-in"><span>  quiet <span class="op">=</span> <span class="cn">TRUE</span>, error_model <span class="op">=</span> <span class="st">"tc"</span>, cores <span class="op">=</span> <span class="fl">5</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">f_saem_dfop_sfo</span> <span class="op">&lt;-</span> <span class="fu"><a href="saem.html">saem</a></span><span class="op">(</span><span class="va">f_mmkin_dfop_sfo</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">f_saem_dfop_sfo</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Kinetic nonlinear mixed-effects model fit by SAEM</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Structural model:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>            * parent</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> d_m1/dt = + f_parent_to_m1 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>            * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>            exp(-k2 * time))) * parent - k_m1 * m1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Data:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 171 observations of 2 variable(s) grouped in 5 datasets</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Likelihood computed by importance sampling</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     AIC   BIC logLik</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   810.8 805.4 -391.4</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Fitted parameters:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                     estimate     lower     upper</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0           100.86947  97.81542 103.92353</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k_m1            -4.06947  -4.16944  -3.96950</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_parent_qlogis     -0.93256  -1.34200  -0.52312</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k1              -2.37017  -2.72660  -2.01375</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k2              -4.06264  -4.21344  -3.91184</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> g_qlogis            -0.02174  -0.45898   0.41549</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> a.1                  0.87598   0.67275   1.07922</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> b.1                  0.07949   0.06389   0.09509</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> SD.parent_0          0.19170 -30.36286  30.74626</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> SD.log_k_m1          0.01883  -0.28736   0.32502</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> SD.f_parent_qlogis   0.44300   0.16391   0.72209</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> SD.log_k1            0.35320   0.09661   0.60978</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> SD.log_k2            0.13707   0.02359   0.25056</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> SD.g_qlogis          0.37478   0.04490   0.70467</span>
-<span class="r-in"><span><span class="fu"><a href="illparms.html">illparms</a></span><span class="op">(</span><span class="va">f_saem_dfop_sfo</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "sd(parent_0)" "sd(log_k_m1)"</span>
-<span class="r-in"><span><span class="va">f_saem_dfop_sfo_2</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_saem_dfop_sfo</span>,</span></span>
-<span class="r-in"><span>  no_random_effect <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"parent_0"</span>, <span class="st">"log_k_m1"</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="illparms.html">illparms</a></span><span class="op">(</span><span class="va">f_saem_dfop_sfo_2</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/pkg/nlme/man/intervals.html" class="external-link">intervals</a></span><span class="op">(</span><span class="va">f_saem_dfop_sfo_2</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Approximate 95% confidence intervals</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Fixed effects:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                      lower         est.        upper</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0       98.36731429 101.42508066 104.48284703</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_m1            0.01513234   0.01670094   0.01843214</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_parent_to_m1  0.20221431   0.27608850   0.36461630</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k1              0.06915073   0.09759718   0.13774560</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k2              0.01487068   0.01740389   0.02036863</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> g               0.37365671   0.48384821   0.59563299</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Random effects:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                          lower      est.     upper</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sd(f_parent_qlogis) 0.16439770 0.4427585 0.7211193</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sd(log_k1)          0.08304243 0.3345213 0.5860002</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sd(log_k2)          0.03146410 0.1490210 0.2665779</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sd(g_qlogis)        0.06216385 0.4023430 0.7425221</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>          lower       est.      upper</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> a.1 0.67696663 0.87777355 1.07858048</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> b.1 0.06363957 0.07878001 0.09392044</span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">f_saem_dfop_sfo_2</span>, data <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> saemix version used for fitting:      3.2 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> mkin version used for pre-fitting:  1.2.3 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> R version used for fitting:         4.2.3 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Date of fit:     Sun Apr 16 08:34:58 2023 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Date of summary: Sun Apr 16 08:34:58 2023 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Equations:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>            * parent</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> d_m1/dt = + f_parent_to_m1 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>            * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>            exp(-k2 * time))) * parent - k_m1 * m1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Data:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 171 observations of 2 variable(s) grouped in 5 datasets</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Model predictions using solution type analytical </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Fitted in 9.384 s</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Using 300, 100 iterations and 10 chains</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Variance model: Two-component variance function </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Starting values for degradation parameters:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>        parent_0        log_k_m1 f_parent_qlogis          log_k1          log_k2 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>       101.65645        -4.05368        -0.94311        -2.35943        -4.07006 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>        g_qlogis </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>        -0.01132 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Fixed degradation parameter values:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> None</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Starting values for random effects (square root of initial entries in omega):</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                 parent_0 log_k_m1 f_parent_qlogis log_k1 log_k2 g_qlogis</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0           6.742   0.0000          0.0000 0.0000 0.0000    0.000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k_m1           0.000   0.2236          0.0000 0.0000 0.0000    0.000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_parent_qlogis    0.000   0.0000          0.5572 0.0000 0.0000    0.000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k1             0.000   0.0000          0.0000 0.8031 0.0000    0.000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k2             0.000   0.0000          0.0000 0.0000 0.2931    0.000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> g_qlogis           0.000   0.0000          0.0000 0.0000 0.0000    0.807</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Starting values for error model parameters:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> a.1 b.1 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   1   1 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Results:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Likelihood computed by importance sampling</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   AIC   BIC logLik</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   807 802.3 -391.5</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Optimised parameters:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                         est.    lower     upper</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0           101.42508 98.36731 104.48285</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k_m1            -4.09229 -4.19092  -3.99366</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_parent_qlogis     -0.96395 -1.37251  -0.55538</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k1              -2.32691 -2.67147  -1.98235</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k2              -4.05106 -4.20836  -3.89376</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> g_qlogis            -0.06463 -0.51656   0.38730</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> a.1                  0.87777  0.67697   1.07858</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> b.1                  0.07878  0.06364   0.09392</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> SD.f_parent_qlogis   0.44276  0.16440   0.72112</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> SD.log_k1            0.33452  0.08304   0.58600</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> SD.log_k2            0.14902  0.03146   0.26658</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> SD.g_qlogis          0.40234  0.06216   0.74252</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Correlation: </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                 parnt_0 lg_k_m1 f_prnt_ log_k1  log_k2 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k_m1        -0.4693                                </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_parent_qlogis -0.2378  0.2595                        </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k1           0.1720 -0.1593 -0.0669                </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k2           0.0179  0.0594  0.0035  0.1995        </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> g_qlogis         0.1073 -0.1060 -0.0322 -0.2299 -0.3168</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Random effects:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                      est.   lower  upper</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> SD.f_parent_qlogis 0.4428 0.16440 0.7211</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> SD.log_k1          0.3345 0.08304 0.5860</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> SD.log_k2          0.1490 0.03146 0.2666</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> SD.g_qlogis        0.4023 0.06216 0.7425</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Variance model:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>        est.   lower   upper</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> a.1 0.87777 0.67697 1.07858</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> b.1 0.07878 0.06364 0.09392</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Backtransformed parameters:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                    est.    lower     upper</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0       101.4251 98.36731 104.48285</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_m1             0.0167  0.01513   0.01843</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_parent_to_m1   0.2761  0.20221   0.36462</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k1               0.0976  0.06915   0.13775</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k2               0.0174  0.01487   0.02037</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> g                0.4838  0.37366   0.59563</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Resulting formation fractions:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                 ff</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_m1   0.2761</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_sink 0.7239</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Estimated disappearance times:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         DT50   DT90 DT50back DT50_k1 DT50_k2</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent 15.54  94.33     28.4   7.102   39.83</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> m1     41.50 137.87       NA      NA      NA</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Data:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    ds   name time observed predicted  residual    std standardized</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 1 parent    0     89.8 1.014e+02 -11.62508 8.0383     -1.44620</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 1 parent    0    104.1 1.014e+02   2.67492 8.0383      0.33277</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 1 parent    1     88.7 9.650e+01  -7.80311 7.6530     -1.01961</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 1 parent    1     95.5 9.650e+01  -1.00311 7.6530     -0.13107</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 1 parent    3     81.8 8.753e+01  -5.72638 6.9510     -0.82382</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 1 parent    3     94.5 8.753e+01   6.97362 6.9510      1.00326</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 1 parent    7     71.5 7.254e+01  -1.04133 5.7818     -0.18010</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 1 parent    7     70.3 7.254e+01  -2.24133 5.7818     -0.38765</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 1 parent   14     54.2 5.349e+01   0.71029 4.3044      0.16502</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 1 parent   14     49.6 5.349e+01  -3.88971 4.3044     -0.90366</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 1 parent   28     31.5 3.167e+01  -0.16616 2.6446     -0.06283</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 1 parent   28     28.8 3.167e+01  -2.86616 2.6446     -1.08379</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 1 parent   60     12.1 1.279e+01  -0.69287 1.3365     -0.51843</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 1 parent   60     13.6 1.279e+01   0.80713 1.3365      0.60392</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 1 parent   90      6.2 6.397e+00  -0.19718 1.0122     -0.19481</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 1 parent   90      8.3 6.397e+00   1.90282 1.0122      1.87996</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 1 parent  120      2.2 3.323e+00  -1.12320 0.9160     -1.22623</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 1 parent  120      2.4 3.323e+00  -0.92320 0.9160     -1.00788</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 1     m1    1      0.3 1.179e+00  -0.87919 0.8827     -0.99605</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 1     m1    1      0.2 1.179e+00  -0.97919 0.8827     -1.10935</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 1     m1    3      2.2 3.273e+00  -1.07272 0.9149     -1.17256</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 1     m1    3      3.0 3.273e+00  -0.27272 0.9149     -0.29811</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 1     m1    7      6.5 6.559e+00  -0.05872 1.0186     -0.05765</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 1     m1    7      5.0 6.559e+00  -1.55872 1.0186     -1.53032</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 1     m1   14     10.2 1.016e+01   0.03787 1.1880      0.03188</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 1     m1   14      9.5 1.016e+01  -0.66213 1.1880     -0.55734</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 1     m1   28     12.2 1.268e+01  -0.47913 1.3297     -0.36032</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 1     m1   28     13.4 1.268e+01   0.72087 1.3297      0.54211</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 1     m1   60     11.8 1.078e+01   1.02493 1.2211      0.83936</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 1     m1   60     13.2 1.078e+01   2.42493 1.2211      1.98588</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 1     m1   90      6.6 7.705e+00  -1.10464 1.0672     -1.03509</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 1     m1   90      9.3 7.705e+00   1.59536 1.0672      1.49491</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 1     m1  120      3.5 5.236e+00  -1.73617 0.9699     -1.79010</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 1     m1  120      5.4 5.236e+00   0.16383 0.9699      0.16892</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 2 parent    0    118.0 1.014e+02  16.57492 8.0383      2.06198</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 2 parent    0     99.8 1.014e+02  -1.62508 8.0383     -0.20217</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 2 parent    1     90.2 9.599e+01  -5.79045 7.6129     -0.76061</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 2 parent    1     94.6 9.599e+01  -1.39045 7.6129     -0.18264</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 2 parent    3     96.1 8.652e+01   9.57931 6.8724      1.39388</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 2 parent    3     78.4 8.652e+01  -8.12069 6.8724     -1.18164</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 2 parent    7     77.9 7.197e+01   5.93429 5.7370      1.03439</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 2 parent    7     77.7 7.197e+01   5.73429 5.7370      0.99953</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 2 parent   14     56.0 5.555e+01   0.44657 4.4637      0.10005</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 2 parent   14     54.7 5.555e+01  -0.85343 4.4637     -0.19120</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 2 parent   28     36.6 3.853e+01  -1.93170 3.1599     -0.61132</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 2 parent   28     36.8 3.853e+01  -1.73170 3.1599     -0.54803</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 2 parent   60     22.1 2.110e+01   1.00360 1.8795      0.53396</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 2 parent   60     24.7 2.110e+01   3.60360 1.8795      1.91728</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 2 parent   90     12.4 1.250e+01  -0.09712 1.3190     -0.07363</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 2 parent   90     10.8 1.250e+01  -1.69712 1.3190     -1.28667</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 2 parent  120      6.8 7.419e+00  -0.61913 1.0546     -0.58709</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 2 parent  120      7.9 7.419e+00   0.48087 1.0546      0.45599</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 2     m1    1      1.3 1.422e+00  -0.12194 0.8849     -0.13781</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 2     m1    3      3.7 3.831e+00  -0.13149 0.9282     -0.14166</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 2     m1    3      4.7 3.831e+00   0.86851 0.9282      0.93567</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 2     m1    7      8.1 7.292e+00   0.80812 1.0490      0.77034</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 2     m1    7      7.9 7.292e+00   0.60812 1.0490      0.57969</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 2     m1   14     10.1 1.055e+01  -0.45332 1.2090     -0.37495</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 2     m1   14     10.3 1.055e+01  -0.25332 1.2090     -0.20953</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 2     m1   28     10.7 1.230e+01  -1.59960 1.3074     -1.22347</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 2     m1   28     12.2 1.230e+01  -0.09960 1.3074     -0.07618</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 2     m1   60     10.7 1.065e+01   0.05342 1.2141      0.04400</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 2     m1   60     12.5 1.065e+01   1.85342 1.2141      1.52661</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 2     m1   90      9.1 8.196e+00   0.90368 1.0897      0.82930</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 2     m1   90      7.4 8.196e+00  -0.79632 1.0897     -0.73078</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 2     m1  120      6.1 5.997e+00   0.10252 0.9969      0.10284</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 2     m1  120      4.5 5.997e+00  -1.49748 0.9969     -1.50220</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 3 parent    0    106.2 1.014e+02   4.77492 8.0383      0.59402</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 3 parent    0    106.9 1.014e+02   5.47492 8.0383      0.68110</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 3 parent    1    107.4 9.390e+01  13.49935 7.4494      1.81214</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 3 parent    1     96.1 9.390e+01   2.19935 7.4494      0.29524</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 3 parent    3     79.4 8.152e+01  -2.12307 6.4821     -0.32753</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 3 parent    3     82.6 8.152e+01   1.07693 6.4821      0.16614</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 3 parent    7     63.9 6.446e+01  -0.55834 5.1533     -0.10834</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 3 parent    7     62.4 6.446e+01  -2.05834 5.1533     -0.39942</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 3 parent   14     51.0 4.826e+01   2.74073 3.9019      0.70241</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 3 parent   14     47.1 4.826e+01  -1.15927 3.9019     -0.29711</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 3 parent   28     36.1 3.424e+01   1.86399 2.8364      0.65718</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 3 parent   28     36.6 3.424e+01   2.36399 2.8364      0.83346</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 3 parent   60     20.1 1.968e+01   0.42172 1.7815      0.23672</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 3 parent   60     19.8 1.968e+01   0.12172 1.7815      0.06833</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 3 parent   90     11.3 1.195e+01  -0.64633 1.2869     -0.50222</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 3 parent   90     10.7 1.195e+01  -1.24633 1.2869     -0.96844</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 3 parent  120      8.2 7.255e+00   0.94532 1.0474      0.90251</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 3 parent  120      7.3 7.255e+00   0.04532 1.0474      0.04327</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 3     m1    0      0.8 2.956e-11   0.80000 0.8778      0.91140</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 3     m1    1      1.8 1.758e+00   0.04187 0.8886      0.04712</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 3     m1    1      2.3 1.758e+00   0.54187 0.8886      0.60978</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 3     m1    3      4.2 4.567e+00  -0.36697 0.9486     -0.38683</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 3     m1    3      4.1 4.567e+00  -0.46697 0.9486     -0.49224</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 3     m1    7      6.8 8.151e+00  -1.35124 1.0876     -1.24242</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 3     m1    7     10.1 8.151e+00   1.94876 1.0876      1.79182</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 3     m1   14     11.4 1.083e+01   0.57098 1.2240      0.46647</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 3     m1   14     12.8 1.083e+01   1.97098 1.2240      1.61022</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 3     m1   28     11.5 1.147e+01   0.03175 1.2597      0.02520</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 3     m1   28     10.6 1.147e+01  -0.86825 1.2597     -0.68928</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 3     m1   60      7.5 9.298e+00  -1.79834 1.1433     -1.57298</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 3     m1   60      8.6 9.298e+00  -0.69834 1.1433     -0.61083</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 3     m1   90      7.3 7.038e+00   0.26249 1.0382      0.25283</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 3     m1   90      8.1 7.038e+00   1.06249 1.0382      1.02340</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 3     m1  120      5.3 5.116e+00   0.18417 0.9659      0.19068</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 3     m1  120      3.8 5.116e+00  -1.31583 0.9659     -1.36232</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 4 parent    0    104.7 1.014e+02   3.27492 8.0383      0.40741</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 4 parent    0     88.3 1.014e+02 -13.12508 8.0383     -1.63281</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 4 parent    1     94.2 9.781e+01  -3.61183 7.7555     -0.46572</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 4 parent    1     94.6 9.781e+01  -3.21183 7.7555     -0.41414</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 4 parent    3     78.1 9.110e+01 -13.00467 7.2307     -1.79853</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 4 parent    3     96.5 9.110e+01   5.39533 7.2307      0.74617</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 4 parent    7     76.2 7.951e+01  -3.30511 6.3246     -0.52258</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 4 parent    7     77.8 7.951e+01  -1.70511 6.3246     -0.26960</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 4 parent   14     70.8 6.376e+01   7.03783 5.0993      1.38016</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 4 parent   14     67.3 6.376e+01   3.53783 5.0993      0.69379</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 4 parent   28     43.1 4.340e+01  -0.30456 3.5303     -0.08627</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 4 parent   28     45.1 4.340e+01   1.69544 3.5303      0.48026</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 4 parent   60     21.3 2.142e+01  -0.12077 1.9022     -0.06349</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 4 parent   60     23.5 2.142e+01   2.07923 1.9022      1.09308</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 4 parent   90     11.8 1.207e+01  -0.26813 1.2940     -0.20721</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 4 parent   90     12.1 1.207e+01   0.03187 1.2940      0.02463</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 4 parent  120      7.0 6.954e+00   0.04554 1.0347      0.04402</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 4 parent  120      6.2 6.954e+00  -0.75446 1.0347     -0.72914</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 4     m1    0      1.6 1.990e-13   1.60000 0.8778      1.82279</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 4     m1    1      0.9 7.305e-01   0.16949 0.8797      0.19267</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 4     m1    3      3.7 2.051e+00   1.64896 0.8925      1.84753</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 4     m1    3      2.0 2.051e+00  -0.05104 0.8925     -0.05719</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 4     m1    7      3.6 4.204e+00  -0.60375 0.9382     -0.64354</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 4     m1    7      3.8 4.204e+00  -0.40375 0.9382     -0.43036</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 4     m1   14      7.1 6.760e+00   0.34021 1.0267      0.33137</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 4     m1   14      6.6 6.760e+00  -0.15979 1.0267     -0.15563</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 4     m1   28      9.5 9.011e+00   0.48856 1.1289      0.43277</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 4     m1   28      9.3 9.011e+00   0.28856 1.1289      0.25561</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 4     m1   60      8.3 8.611e+00  -0.31077 1.1093     -0.28014</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 4     m1   60      9.0 8.611e+00   0.38923 1.1093      0.35086</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 4     m1   90      6.6 6.678e+00  -0.07753 1.0233     -0.07576</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 4     m1   90      7.7 6.678e+00   1.02247 1.0233      0.99915</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 4     m1  120      3.7 4.847e+00  -1.14679 0.9572     -1.19804</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 4     m1  120      3.5 4.847e+00  -1.34679 0.9572     -1.40698</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 5 parent    0    110.4 1.014e+02   8.97492 8.0383      1.11651</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 5 parent    0    112.1 1.014e+02  10.67492 8.0383      1.32800</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 5 parent    1     93.5 9.466e+01  -1.16118 7.5089     -0.15464</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 5 parent    1     91.0 9.466e+01  -3.66118 7.5089     -0.48758</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 5 parent    3     71.0 8.302e+01 -12.01844 6.5988     -1.82130</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 5 parent    3     89.7 8.302e+01   6.68156 6.5988      1.01254</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 5 parent    7     60.4 6.563e+01  -5.22574 5.2440     -0.99652</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 5 parent    7     59.1 6.563e+01  -6.52574 5.2440     -1.24442</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 5 parent   14     56.5 4.727e+01   9.22621 3.8263      2.41128</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 5 parent   14     47.0 4.727e+01  -0.27379 3.8263     -0.07156</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 5 parent   28     30.2 3.103e+01  -0.83405 2.5977     -0.32108</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 5 parent   28     23.9 3.103e+01  -7.13405 2.5977     -2.74634</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 5 parent   60     17.0 1.800e+01  -0.99696 1.6675     -0.59787</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 5 parent   60     18.7 1.800e+01   0.70304 1.6675      0.42161</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 5 parent   90     11.3 1.167e+01  -0.36809 1.2710     -0.28961</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 5 parent   90     11.9 1.167e+01   0.23191 1.2710      0.18246</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 5 parent  120      9.0 7.595e+00   1.40496 1.0623      1.32256</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 5 parent  120      8.1 7.595e+00   0.50496 1.0623      0.47535</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 5     m1    0      0.7 0.000e+00   0.70000 0.8778      0.79747</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 5     m1    1      3.0 3.158e+00  -0.15799 0.9123     -0.17317</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 5     m1    1      2.6 3.158e+00  -0.55799 0.9123     -0.61160</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 5     m1    3      5.1 8.443e+00  -3.34286 1.1013     -3.03535</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 5     m1    3      7.5 8.443e+00  -0.94286 1.1013     -0.85613</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 5     m1    7     16.5 1.580e+01   0.69781 1.5232      0.45811</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 5     m1    7     19.0 1.580e+01   3.19781 1.5232      2.09935</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 5     m1   14     22.9 2.216e+01   0.73604 1.9543      0.37663</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 5     m1   14     23.2 2.216e+01   1.03604 1.9543      0.53014</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 5     m1   28     22.2 2.423e+01  -2.03128 2.1011     -0.96678</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 5     m1   28     24.4 2.423e+01   0.16872 2.1011      0.08030</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 5     m1   60     15.5 1.876e+01  -3.25610 1.7187     -1.89455</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 5     m1   60     19.8 1.876e+01   1.04390 1.7187      0.60739</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 5     m1   90     14.9 1.366e+01   1.23585 1.3890      0.88976</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 5     m1   90     14.2 1.366e+01   0.53585 1.3890      0.38579</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 5     m1  120     10.9 9.761e+00   1.13911 1.1670      0.97613</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  ds 5     m1  120     10.4 9.761e+00   0.63911 1.1670      0.54767</span>
-<span class="r-in"><span><span class="co"># Add a correlation between random effects of g and k2</span></span></span>
-<span class="r-in"><span><span class="va">cov_model_3</span> <span class="op">&lt;-</span> <span class="va">f_saem_dfop_sfo_2</span><span class="op">$</span><span class="va">so</span><span class="op">@</span><span class="va">model</span><span class="op">@</span><span class="va">covariance.model</span></span></span>
-<span class="r-in"><span><span class="va">cov_model_3</span><span class="op">[</span><span class="st">"log_k2"</span>, <span class="st">"g_qlogis"</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="fl">1</span></span></span>
-<span class="r-in"><span><span class="va">cov_model_3</span><span class="op">[</span><span class="st">"g_qlogis"</span>, <span class="st">"log_k2"</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="fl">1</span></span></span>
-<span class="r-in"><span><span class="va">f_saem_dfop_sfo_3</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_saem_dfop_sfo</span>,</span></span>
-<span class="r-in"><span>  covariance.model <span class="op">=</span> <span class="va">cov_model_3</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/pkg/nlme/man/intervals.html" class="external-link">intervals</a></span><span class="op">(</span><span class="va">f_saem_dfop_sfo_3</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Approximate 95% confidence intervals</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Fixed effects:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                      lower         est.        upper</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0       98.39888363 101.48951337 104.58014311</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_m1            0.01508704   0.01665986   0.01839665</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_parent_to_m1  0.20141557   0.27540583   0.36418131</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k1              0.07708759   0.10430866   0.14114200</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k2              0.01476621   0.01786384   0.02161129</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> g               0.33679867   0.45083525   0.57028162</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Random effects:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                             lower       est.      upper</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sd(f_parent_qlogis)    0.38085375  0.4441841  0.5075145</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sd(log_k1)             0.04774819  0.2660384  0.4843286</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sd(log_k2)            -0.63842736  0.1977024  1.0338321</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sd(g_qlogis)           0.22711289  0.4502227  0.6733326</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> corr(log_k2,g_qlogis) -0.83271473 -0.6176939 -0.4026730</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>          lower       est.      upper</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> a.1 0.67347568 0.87437392 1.07527216</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> b.1 0.06393032 0.07912417 0.09431802</span>
-<span class="r-in"><span><span class="co"># The correlation does not improve the fit judged by AIC and BIC, although</span></span></span>
-<span class="r-in"><span><span class="co"># the likelihood is higher with the additional parameter</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_saem_dfop_sfo</span>, <span class="va">f_saem_dfop_sfo_2</span>, <span class="va">f_saem_dfop_sfo_3</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Data: 171 observations of 2 variable(s) grouped in 5 datasets</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                   npar    AIC    BIC     Lik</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_saem_dfop_sfo_2   12 806.96 802.27 -391.48</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_saem_dfop_sfo_3   13 807.99 802.91 -391.00</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_saem_dfop_sfo     14 810.83 805.36 -391.42</span>
-<span class="r-in"><span><span class="co"># }</span></span></span>
-<span class="r-in"><span></span></span>
-</code></pre></div>
-    </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/summary_listing.html b/docs/dev/reference/summary_listing.html
deleted file mode 100644
index e584416a..00000000
--- a/docs/dev/reference/summary_listing.html
+++ /dev/null
@@ -1,164 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Display the output of a summary function according to the output format — summary_listing • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Display the output of a summary function according to the output format — summary_listing"><meta property="og:description" content='This function is intended for use in a R markdown code chunk with the chunk
-option results = "asis".'><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Display the output of a summary function according to the output format</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/summary_listing.R" class="external-link"><code>R/summary_listing.R</code></a></small>
-    <div class="hidden name"><code>summary_listing.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>This function is intended for use in a R markdown code chunk with the chunk
-option <code>results = "asis"</code>.</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">summary_listing</span><span class="op">(</span><span class="va">object</span>, caption <span class="op">=</span> <span class="cn">NULL</span>, label <span class="op">=</span> <span class="cn">NULL</span>, clearpage <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">tex_listing</span><span class="op">(</span><span class="va">object</span>, caption <span class="op">=</span> <span class="cn">NULL</span>, label <span class="op">=</span> <span class="cn">NULL</span>, clearpage <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">html_listing</span><span class="op">(</span><span class="va">object</span>, caption <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></span></code></pre></div>
-    </div>
-
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>object</dt>
-<dd><p>The object for which the summary is to be listed</p></dd>
-
-
-<dt>caption</dt>
-<dd><p>An optional caption</p></dd>
-
-
-<dt>label</dt>
-<dd><p>An optional label, ignored in html output</p></dd>
-
-
-<dt>clearpage</dt>
-<dd><p>Should a new page be started after the listing? Ignored in html output</p></dd>
-
-</dl></div>
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/synthetic_data_for_UBA_2014-1.png b/docs/dev/reference/synthetic_data_for_UBA_2014-1.png
deleted file mode 100644
index 11eae1f9..00000000
--- a/docs/dev/reference/synthetic_data_for_UBA_2014-1.png
+++ /dev/null
diff --git a/docs/dev/reference/synthetic_data_for_UBA_2014.html b/docs/dev/reference/synthetic_data_for_UBA_2014.html
deleted file mode 100644
index b8154c47..00000000
--- a/docs/dev/reference/synthetic_data_for_UBA_2014.html
+++ /dev/null
@@ -1,446 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Synthetic datasets for one parent compound with two metabolites — synthetic_data_for_UBA_2014 • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Synthetic datasets for one parent compound with two metabolites — synthetic_data_for_UBA_2014"><meta property="og:description" content="The 12 datasets were generated using four different models and three different
- variance components. The four models are either the SFO or the DFOP model with either
- two sequential or two parallel metabolites.
-Variance component 'a' is based on a normal distribution with standard deviation of 3,
- Variance component 'b' is also based on a normal distribution, but with a standard deviation of 7.
- Variance component 'c' is based on the error model from Rocke and Lorenzato (1995), with the
- minimum standard deviation (for small y values) of 0.5, and a proportionality constant of 0.07
- for the increase of the standard deviation with y. Note that this is a simplified version
- of the error model proposed by Rocke and Lorenzato (1995), as in their model the error of the
- measured values approximates lognormal distribution for high values, whereas we are using
- normally distributed error components all along.
-Initial concentrations for metabolites and all values where adding the variance component resulted
- in a value below the assumed limit of detection of 0.1 were set to NA.
-As an example, the first dataset has the title SFO_lin_a and is based on the SFO model
- with two sequential metabolites (linear pathway), with added variance component 'a'.
-Compare also the code in the example section to see the degradation models."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Synthetic datasets for one parent compound with two metabolites</h1>
-    
-    <div class="hidden name"><code>synthetic_data_for_UBA_2014.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>The 12 datasets were generated using four different models and three different
- variance components. The four models are either the SFO or the DFOP model with either
- two sequential or two parallel metabolites.</p>
-<p>Variance component 'a' is based on a normal distribution with standard deviation of 3,
- Variance component 'b' is also based on a normal distribution, but with a standard deviation of 7.
- Variance component 'c' is based on the error model from Rocke and Lorenzato (1995), with the
- minimum standard deviation (for small y values) of 0.5, and a proportionality constant of 0.07
- for the increase of the standard deviation with y. Note that this is a simplified version
- of the error model proposed by Rocke and Lorenzato (1995), as in their model the error of the
- measured values approximates lognormal distribution for high values, whereas we are using
- normally distributed error components all along.</p>
-<p>Initial concentrations for metabolites and all values where adding the variance component resulted
- in a value below the assumed limit of detection of 0.1 were set to <code>NA</code>.</p>
-<p>As an example, the first dataset has the title <code>SFO_lin_a</code> and is based on the SFO model
- with two sequential metabolites (linear pathway), with added variance component 'a'.</p>
-<p>Compare also the code in the example section to see the degradation models.</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="va">synthetic_data_for_UBA_2014</span></span></code></pre></div>
-    </div>
-
-    <div id="format">
-    <h2>Format</h2>
-    <p>A list containing twelve datasets as an R6 class defined by <code><a href="mkinds.html">mkinds</a></code>,
-  each containing, among others, the following components</p><dl><dt><code>title</code></dt>
-<dd><p>The name of the dataset, e.g. <code>SFO_lin_a</code></p></dd>
-
-    <dt><code>data</code></dt>
-<dd><p>A data frame with the data in the form expected by <code><a href="mkinfit.html">mkinfit</a></code></p></dd>
-
-  
-</dl></div>
-    <div id="source">
-    <h2>Source</h2>
-    <p>Ranke (2014) Prüfung und Validierung von Modellierungssoftware als Alternative
-  zu ModelMaker 4.0, Umweltbundesamt Projektnummer 27452</p>
-<p>Rocke, David M. und Lorenzato, Stefan (1995) A two-component model for
-  measurement error in analytical chemistry. Technometrics 37(2), 176-184.</p>
-    </div>
-
-    <div id="ref-examples">
-    <h2>Examples</h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># \dontrun{</span></span></span>
-<span class="r-in"><span><span class="co"># The data have been generated using the following kinetic models</span></span></span>
-<span class="r-in"><span><span class="va">m_synth_SFO_lin</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"M1"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>                           M1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"M2"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>                           M2 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span>, use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Temporary DLL for differentials generated and loaded</span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="va">m_synth_SFO_par</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span>, to <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"M1"</span>, <span class="st">"M2"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>                                         sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>                           M1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>                           M2 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span>, use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Temporary DLL for differentials generated and loaded</span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="va">m_synth_DFOP_lin</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"DFOP"</span>, to <span class="op">=</span> <span class="st">"M1"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>                            M1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"M2"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>                            M2 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span>, use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Temporary DLL for differentials generated and loaded</span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="va">m_synth_DFOP_par</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"DFOP"</span>, to <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"M1"</span>, <span class="st">"M2"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>                                          sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>                            M1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>                            M2 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span>, use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Temporary DLL for differentials generated and loaded</span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co"># The model predictions without intentional error were generated as follows</span></span></span>
-<span class="r-in"><span><span class="va">sampling_times</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span>, <span class="fl">3</span>, <span class="fl">7</span>, <span class="fl">14</span>, <span class="fl">28</span>, <span class="fl">60</span>, <span class="fl">90</span>, <span class="fl">120</span><span class="op">)</span></span></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="va">d_synth_SFO_lin</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinpredict.html">mkinpredict</a></span><span class="op">(</span><span class="va">m_synth_SFO_lin</span>,</span></span>
-<span class="r-in"><span>                               <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_parent <span class="op">=</span> <span class="fl">0.7</span>, f_parent_to_M1 <span class="op">=</span> <span class="fl">0.8</span>,</span></span>
-<span class="r-in"><span>                                 k_M1 <span class="op">=</span> <span class="fl">0.3</span>, f_M1_to_M2 <span class="op">=</span> <span class="fl">0.7</span>,</span></span>
-<span class="r-in"><span>                                 k_M2 <span class="op">=</span> <span class="fl">0.02</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>                               <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fl">100</span>, M1 <span class="op">=</span> <span class="fl">0</span>, M2 <span class="op">=</span> <span class="fl">0</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>                               <span class="va">sampling_times</span><span class="op">)</span></span></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="va">d_synth_DFOP_lin</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinpredict.html">mkinpredict</a></span><span class="op">(</span><span class="va">m_synth_DFOP_lin</span>,</span></span>
-<span class="r-in"><span>                                <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k1 <span class="op">=</span> <span class="fl">0.2</span>, k2 <span class="op">=</span> <span class="fl">0.02</span>, g <span class="op">=</span> <span class="fl">0.5</span>,</span></span>
-<span class="r-in"><span>                                  f_parent_to_M1 <span class="op">=</span> <span class="fl">0.5</span>, k_M1 <span class="op">=</span> <span class="fl">0.3</span>,</span></span>
-<span class="r-in"><span>                                  f_M1_to_M2 <span class="op">=</span> <span class="fl">0.7</span>, k_M2 <span class="op">=</span> <span class="fl">0.02</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>                                 <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fl">100</span>, M1 <span class="op">=</span> <span class="fl">0</span>, M2 <span class="op">=</span> <span class="fl">0</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>                                 <span class="va">sampling_times</span><span class="op">)</span></span></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="va">d_synth_SFO_par</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinpredict.html">mkinpredict</a></span><span class="op">(</span><span class="va">m_synth_SFO_par</span>,</span></span>
-<span class="r-in"><span>                               <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_parent <span class="op">=</span> <span class="fl">0.2</span>,</span></span>
-<span class="r-in"><span>                                 f_parent_to_M1 <span class="op">=</span> <span class="fl">0.8</span>, k_M1 <span class="op">=</span> <span class="fl">0.01</span>,</span></span>
-<span class="r-in"><span>                                 f_parent_to_M2 <span class="op">=</span> <span class="fl">0.2</span>, k_M2 <span class="op">=</span> <span class="fl">0.02</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>                                 <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fl">100</span>, M1 <span class="op">=</span> <span class="fl">0</span>, M2 <span class="op">=</span> <span class="fl">0</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>                                 <span class="va">sampling_times</span><span class="op">)</span></span></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="va">d_synth_DFOP_par</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinpredict.html">mkinpredict</a></span><span class="op">(</span><span class="va">m_synth_DFOP_par</span>,</span></span>
-<span class="r-in"><span>                               <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k1 <span class="op">=</span> <span class="fl">0.3</span>, k2 <span class="op">=</span> <span class="fl">0.02</span>, g <span class="op">=</span> <span class="fl">0.7</span>,</span></span>
-<span class="r-in"><span>                                 f_parent_to_M1 <span class="op">=</span> <span class="fl">0.6</span>, k_M1 <span class="op">=</span> <span class="fl">0.04</span>,</span></span>
-<span class="r-in"><span>                                 f_parent_to_M2 <span class="op">=</span> <span class="fl">0.4</span>, k_M2 <span class="op">=</span> <span class="fl">0.01</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>                                 <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fl">100</span>, M1 <span class="op">=</span> <span class="fl">0</span>, M2 <span class="op">=</span> <span class="fl">0</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>                                 <span class="va">sampling_times</span><span class="op">)</span></span></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co"># Construct names for datasets with errors</span></span></span>
-<span class="r-in"><span><span class="va">d_synth_names</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span><span class="st">"d_synth_"</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO_lin"</span>, <span class="st">"SFO_par"</span>,</span></span>
-<span class="r-in"><span>                                     <span class="st">"DFOP_lin"</span>, <span class="st">"DFOP_par"</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co"># Original function used or adding errors. The add_err function now published</span></span></span>
-<span class="r-in"><span><span class="co"># with this package is a slightly generalised version where the names of</span></span></span>
-<span class="r-in"><span><span class="co"># secondary compartments that should have an initial value of zero (M1 and M2</span></span></span>
-<span class="r-in"><span><span class="co"># in this case) are not hardcoded any more.</span></span></span>
-<span class="r-in"><span><span class="co"># add_err = function(d, sdfunc, LOD = 0.1, reps = 2, seed = 123456789)</span></span></span>
-<span class="r-in"><span><span class="co"># {</span></span></span>
-<span class="r-in"><span><span class="co">#   set.seed(seed)</span></span></span>
-<span class="r-in"><span><span class="co">#   d_long = mkin_wide_to_long(d, time = "time")</span></span></span>
-<span class="r-in"><span><span class="co">#   d_rep = data.frame(lapply(d_long, rep, each = 2))</span></span></span>
-<span class="r-in"><span><span class="co">#   d_rep$value = rnorm(length(d_rep$value), d_rep$value, sdfunc(d_rep$value))</span></span></span>
-<span class="r-in"><span><span class="co">#</span></span></span>
-<span class="r-in"><span><span class="co">#   d_rep[d_rep$time == 0 &amp; d_rep$name %in% c("M1", "M2"), "value"] &lt;- 0</span></span></span>
-<span class="r-in"><span><span class="co">#   d_NA &lt;- transform(d_rep, value = ifelse(value &lt; LOD, NA, value))</span></span></span>
-<span class="r-in"><span><span class="co">#   d_NA$value &lt;- round(d_NA$value, 1)</span></span></span>
-<span class="r-in"><span><span class="co">#   return(d_NA)</span></span></span>
-<span class="r-in"><span><span class="co"># }</span></span></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co"># The following is the simplified version of the two-component model of Rocke</span></span></span>
-<span class="r-in"><span><span class="co"># and Lorenzato (1995)</span></span></span>
-<span class="r-in"><span><span class="va">sdfunc_twocomp</span> <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">value</span>, <span class="va">sd_low</span>, <span class="va">rsd_high</span><span class="op">)</span> <span class="op">{</span></span></span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/base/MathFun.html" class="external-link">sqrt</a></span><span class="op">(</span><span class="va">sd_low</span><span class="op">^</span><span class="fl">2</span> <span class="op">+</span> <span class="va">value</span><span class="op">^</span><span class="fl">2</span> <span class="op">*</span> <span class="va">rsd_high</span><span class="op">^</span><span class="fl">2</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="op">}</span></span></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co"># Add the errors.</span></span></span>
-<span class="r-in"><span><span class="kw">for</span> <span class="op">(</span><span class="va">d_synth_name</span> <span class="kw">in</span> <span class="va">d_synth_names</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="op">{</span></span></span>
-<span class="r-in"><span>  <span class="va">d_synth</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/get.html" class="external-link">get</a></span><span class="op">(</span><span class="va">d_synth_name</span><span class="op">)</span></span></span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/base/assign.html" class="external-link">assign</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span><span class="va">d_synth_name</span>, <span class="st">"_a"</span><span class="op">)</span>, <span class="fu"><a href="add_err.html">add_err</a></span><span class="op">(</span><span class="va">d_synth</span>, <span class="kw">function</span><span class="op">(</span><span class="va">value</span><span class="op">)</span> <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/base/assign.html" class="external-link">assign</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span><span class="va">d_synth_name</span>, <span class="st">"_b"</span><span class="op">)</span>, <span class="fu"><a href="add_err.html">add_err</a></span><span class="op">(</span><span class="va">d_synth</span>, <span class="kw">function</span><span class="op">(</span><span class="va">value</span><span class="op">)</span> <span class="fl">7</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/base/assign.html" class="external-link">assign</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span><span class="va">d_synth_name</span>, <span class="st">"_c"</span><span class="op">)</span>, <span class="fu"><a href="add_err.html">add_err</a></span><span class="op">(</span><span class="va">d_synth</span>,</span></span>
-<span class="r-in"><span>                           <span class="kw">function</span><span class="op">(</span><span class="va">value</span><span class="op">)</span> <span class="fu">sdfunc_twocomp</span><span class="op">(</span><span class="va">value</span>, <span class="fl">0.5</span>, <span class="fl">0.07</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="op">}</span></span></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="va">d_synth_err_names</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span></span></span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/rep.html" class="external-link">rep</a></span><span class="op">(</span><span class="va">d_synth_names</span>, each <span class="op">=</span> <span class="fl">3</span><span class="op">)</span>, <span class="va">letters</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">3</span><span class="op">]</span>, sep <span class="op">=</span> <span class="st">"_"</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="op">)</span></span></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co"># This is just one example of an evaluation using the kinetic model used for</span></span></span>
-<span class="r-in"><span><span class="co"># the generation of the data</span></span></span>
-<span class="r-in"><span>  <span class="va">fit</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">m_synth_SFO_lin</span>, <span class="va">synthetic_data_for_UBA_2014</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">data</span>,</span></span>
-<span class="r-in"><span>                 quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-in"><span>  <span class="fu"><a href="plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></span></span>
-<span class="r-plt img"><img src="synthetic_data_for_UBA_2014-1.png" alt="" width="700" height="433"></span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> mkin version used for fitting:    1.2.3 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> R version used for fitting:       4.2.3 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Date of fit:     Sun Apr 16 08:35:08 2023 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Date of summary: Sun Apr 16 08:35:08 2023 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Equations:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> d_parent/dt = - k_parent * parent</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> d_M1/dt = + f_parent_to_M1 * k_parent * parent - k_M1 * M1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> d_M2/dt = + f_M1_to_M2 * k_M1 * M1 - k_M2 * M2</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Model predictions using solution type deSolve </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Fitted using 833 model solutions performed in 0.166 s</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Error model: Constant variance </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Error model algorithm: OLS </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Starting values for parameters to be optimised:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                   value   type</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0       101.3500  state</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_parent         0.1000 deparm</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_M1             0.1001 deparm</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_M2             0.1002 deparm</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_parent_to_M1   0.5000 deparm</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_M1_to_M2       0.5000 deparm</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Starting values for the transformed parameters actually optimised:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                      value lower upper</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0        101.350000  -Inf   Inf</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k_parent     -2.302585  -Inf   Inf</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k_M1         -2.301586  -Inf   Inf</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k_M2         -2.300587  -Inf   Inf</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_parent_qlogis   0.000000  -Inf   Inf</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_M1_qlogis       0.000000  -Inf   Inf</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Fixed parameter values:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>      value  type</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> M1_0     0 state</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> M2_0     0 state</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Results:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>        AIC      BIC    logLik</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   188.7274 200.3723 -87.36368</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Optimised, transformed parameters with symmetric confidence intervals:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                 Estimate Std. Error   Lower    Upper</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0        102.1000    1.57000 98.8600 105.3000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k_parent     -0.3020    0.03885 -0.3812  -0.2229</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k_M1         -1.2070    0.07123 -1.3520  -1.0620</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k_M2         -3.9010    0.06571 -4.0350  -3.7670</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_parent_qlogis   1.2010    0.23530  0.7216   1.6800</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_M1_qlogis       0.9589    0.24890  0.4520   1.4660</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma             2.2730    0.25740  1.7490   2.7970</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Parameter correlation:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                   parent_0 log_k_parent   log_k_M1   log_k_M2 f_parent_qlogis</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0         1.000e+00    3.933e-01 -1.605e-01  2.819e-02      -4.624e-01</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k_parent     3.933e-01    1.000e+00 -4.082e-01  7.166e-02      -5.682e-01</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k_M1        -1.605e-01   -4.082e-01  1.000e+00 -3.929e-01       7.478e-01</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k_M2         2.819e-02    7.166e-02 -3.929e-01  1.000e+00      -2.658e-01</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_parent_qlogis -4.624e-01   -5.682e-01  7.478e-01 -2.658e-01       1.000e+00</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_M1_qlogis      1.614e-01    4.102e-01 -8.109e-01  5.419e-01      -8.605e-01</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma           -2.900e-08   -8.030e-09 -2.741e-08  3.938e-08      -2.681e-08</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                 f_M1_qlogis      sigma</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0          1.614e-01 -2.900e-08</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k_parent      4.102e-01 -8.030e-09</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k_M1         -8.109e-01 -2.741e-08</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k_M2          5.419e-01  3.938e-08</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_parent_qlogis  -8.605e-01 -2.681e-08</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_M1_qlogis       1.000e+00  4.971e-08</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma             4.971e-08  1.000e+00</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Backtransformed parameters:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Confidence intervals for internally transformed parameters are asymmetric.</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> t-test (unrealistically) based on the assumption of normal distribution</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> for estimators of untransformed parameters.</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                 Estimate t value    Pr(&gt;t)    Lower     Upper</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0       102.10000  65.000 7.281e-36 98.86000 105.30000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_parent         0.73930  25.740 2.948e-23  0.68310   0.80020</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_M1             0.29920  14.040 1.577e-15  0.25880   0.34590</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_M2             0.02023  15.220 1.653e-16  0.01769   0.02312</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_parent_to_M1   0.76870  18.370 7.295e-19  0.67300   0.84290</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_M1_to_M2       0.72290  14.500 6.418e-16  0.61110   0.81240</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma            2.27300   8.832 2.161e-10  1.74900   2.79700</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> FOCUS Chi2 error levels in percent:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>          err.min n.optim df</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> All data   8.454       6 17</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent     8.660       2  6</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> M1        10.583       2  5</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> M2         3.586       2  6</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Resulting formation fractions:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                 ff</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_M1   0.7687</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_sink 0.2313</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> M1_M2       0.7229</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> M1_sink     0.2771</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Estimated disappearance times:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>           DT50    DT90</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent  0.9376   3.114</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> M1      2.3170   7.697</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> M2     34.2689 113.839</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Data:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  time variable observed  predicted residual</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     0   parent    101.5  1.021e+02 -0.56248</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     0   parent    101.2  1.021e+02 -0.86248</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     1   parent     53.9  4.873e+01  5.17118</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     1   parent     47.5  4.873e+01 -1.22882</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     3   parent     10.4  1.111e+01 -0.70773</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     3   parent      7.6  1.111e+01 -3.50773</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     7   parent      1.1  5.772e-01  0.52283</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     7   parent      0.3  5.772e-01 -0.27717</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    14   parent      3.5  3.264e-03  3.49674</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    28   parent      3.2  1.045e-07  3.20000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    90   parent      0.6  9.530e-10  0.60000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   120   parent      3.5 -5.940e-10  3.50000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     1       M1     36.4  3.479e+01  1.61088</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     1       M1     37.4  3.479e+01  2.61088</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     3       M1     34.3  3.937e+01 -5.07027</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     3       M1     39.8  3.937e+01  0.42973</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     7       M1     15.1  1.549e+01 -0.38715</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     7       M1     17.8  1.549e+01  2.31285</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    14       M1      5.8  1.995e+00  3.80469</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    14       M1      1.2  1.995e+00 -0.79531</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    60       M1      0.5  2.111e-06  0.50000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    90       M1      3.2 -9.670e-10  3.20000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   120       M1      1.5  7.670e-10  1.50000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   120       M1      0.6  7.670e-10  0.60000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     1       M2      4.8  4.455e+00  0.34517</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     3       M2     20.9  2.153e+01 -0.62527</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     3       M2     19.3  2.153e+01 -2.22527</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     7       M2     42.0  4.192e+01  0.07941</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     7       M2     43.1  4.192e+01  1.17941</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    14       M2     49.4  4.557e+01  3.83353</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    14       M2     44.3  4.557e+01 -1.26647</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    28       M2     34.6  3.547e+01 -0.87275</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    28       M2     33.0  3.547e+01 -2.47275</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    60       M2     18.8  1.858e+01  0.21837</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    60       M2     17.6  1.858e+01 -0.98163</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    90       M2     10.6  1.013e+01  0.47130</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    90       M2     10.8  1.013e+01  0.67130</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   120       M2      9.8  5.521e+00  4.27893</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   120       M2      3.3  5.521e+00 -2.22107</span>
-<span class="r-in"><span><span class="co"># }</span></span></span>
-</code></pre></div>
-    </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/test_data_from_UBA_2014-1.png b/docs/dev/reference/test_data_from_UBA_2014-1.png
deleted file mode 100644
index a007a102..00000000
--- a/docs/dev/reference/test_data_from_UBA_2014-1.png
+++ /dev/null
diff --git a/docs/dev/reference/test_data_from_UBA_2014-2.png b/docs/dev/reference/test_data_from_UBA_2014-2.png
deleted file mode 100644
index f460ac83..00000000
--- a/docs/dev/reference/test_data_from_UBA_2014-2.png
+++ /dev/null
diff --git a/docs/dev/reference/test_data_from_UBA_2014.html b/docs/dev/reference/test_data_from_UBA_2014.html
deleted file mode 100644
index 8b0194f6..00000000
--- a/docs/dev/reference/test_data_from_UBA_2014.html
+++ /dev/null
@@ -1,236 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Three experimental datasets from two water sediment systems and one soil — test_data_from_UBA_2014 • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Three experimental datasets from two water sediment systems and one soil — test_data_from_UBA_2014"><meta property="og:description" content="The datasets were used for the comparative validation of several kinetic evaluation
- software packages (Ranke, 2014)."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Three experimental datasets from two water sediment systems and one soil</h1>
-    
-    <div class="hidden name"><code>test_data_from_UBA_2014.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>The datasets were used for the comparative validation of several kinetic evaluation
- software packages (Ranke, 2014).</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="va">test_data_from_UBA_2014</span></span></code></pre></div>
-    </div>
-
-    <div id="format">
-    <h2>Format</h2>
-    <p>A list containing three datasets as an R6 class defined by <code><a href="mkinds.html">mkinds</a></code>.
-  Each dataset has, among others, the following components</p><dl><dt><code>title</code></dt>
-<dd><p>The name of the dataset, e.g. <code>UBA_2014_WS_river</code></p></dd>
-
-    <dt><code>data</code></dt>
-<dd><p>A data frame with the data in the form expected by <code><a href="mkinfit.html">mkinfit</a></code></p></dd>
-
-  
-</dl></div>
-    <div id="source">
-    <h2>Source</h2>
-    <p>Ranke (2014) Prüfung und Validierung von Modellierungssoftware als Alternative
-  zu ModelMaker 4.0, Umweltbundesamt Projektnummer 27452</p>
-    </div>
-
-    <div id="ref-examples">
-    <h2>Examples</h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span>  <span class="co"># \dontrun{</span></span></span>
-<span class="r-in"><span>  <span class="co"># This is a level P-II evaluation of the dataset according to the FOCUS kinetics</span></span></span>
-<span class="r-in"><span>  <span class="co"># guidance. Due to the strong correlation of the parameter estimates, the</span></span></span>
-<span class="r-in"><span>  <span class="co"># covariance matrix is not returned. Note that level P-II evaluations are</span></span></span>
-<span class="r-in"><span>  <span class="co"># generally considered deprecated due to the frequent occurrence of such</span></span></span>
-<span class="r-in"><span>  <span class="co"># large parameter correlations, among other reasons (e.g. the adequacy of the</span></span></span>
-<span class="r-in"><span>  <span class="co"># model).</span></span></span>
-<span class="r-in"><span>  <span class="va">m_ws</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent_w <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"parent_s"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>                  parent_s <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"parent_w"</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Temporary DLL for differentials generated and loaded</span>
-<span class="r-in"><span>  <span class="va">f_river</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">m_ws</span>, <span class="va">test_data_from_UBA_2014</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">data</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>Observations with value of zero were removed from the data</span>
-<span class="r-in"><span>  <span class="fu"><a href="plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">f_river</span><span class="op">)</span></span></span>
-<span class="r-plt img"><img src="test_data_from_UBA_2014-1.png" alt="" width="700" height="433"></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">f_river</span><span class="op">)</span><span class="op">$</span><span class="va">bpar</span></span></span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>Could not calculate correlation; no covariance matrix</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                           Estimate se_notrans t value Pr(&gt;t) Lower Upper</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_w_0             95.91998118         NA      NA     NA    NA    NA</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_parent_w              0.41145375         NA      NA     NA    NA    NA</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_parent_s              0.04663944         NA      NA     NA    NA    NA</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_parent_w_to_parent_s  0.12467894         NA      NA     NA    NA    NA</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_parent_s_to_parent_w  0.50000000         NA      NA     NA    NA    NA</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma                   3.13612618         NA      NA     NA    NA    NA</span>
-<span class="r-in"><span>  <span class="fu"><a href="mkinerrmin.html">mkinerrmin</a></span><span class="op">(</span><span class="va">f_river</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>            err.min n.optim df</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> All data 0.1090929       5  6</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_w 0.0817436       3  3</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_s 0.1619965       2  3</span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span>  <span class="co"># This is the evaluation used for the validation of software packages</span></span></span>
-<span class="r-in"><span>  <span class="co"># in the expertise from 2014</span></span></span>
-<span class="r-in"><span>  <span class="va">m_soil</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"M1"</span>, <span class="st">"M2"</span><span class="op">)</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>                    M1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"M3"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>                    M2 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"M3"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>                    M3 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>                    use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Temporary DLL for differentials generated and loaded</span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span>  <span class="va">f_soil</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">m_soil</span>, <span class="va">test_data_from_UBA_2014</span><span class="op">[[</span><span class="fl">3</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">data</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>Observations with value of zero were removed from the data</span>
-<span class="r-in"><span>  <span class="fu"><a href="plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">f_soil</span>, lpos <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"topright"</span>, <span class="st">"topright"</span>, <span class="st">"topright"</span>, <span class="st">"bottomright"</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-plt img"><img src="test_data_from_UBA_2014-2.png" alt="" width="700" height="433"></span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">f_soil</span><span class="op">)</span><span class="op">$</span><span class="va">bpar</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                   Estimate  se_notrans    t value       Pr(&gt;t)        Lower</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0       76.55425650 0.859186399 89.1008710 1.113861e-26 74.755959418</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_parent        0.12081956 0.004601918 26.2541722 1.077359e-16  0.111561575</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_M1            0.84258615 0.806160102  1.0451846 1.545268e-01  0.113779609</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_M2            0.04210880 0.017083034  2.4649483 1.170188e-02  0.018013857</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_M3            0.01122918 0.007245856  1.5497385 6.885052e-02  0.002909431</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_parent_to_M1  0.32240200 0.240783943  1.3389680 9.819076e-02           NA</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_parent_to_M2  0.16099855 0.033691952  4.7785464 6.531136e-05           NA</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_M1_to_M3      0.27921507 0.269423780  1.0363416 1.565267e-01  0.022978205</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_M2_to_M3      0.55641252 0.595119966  0.9349586 1.807707e-01  0.008002509</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma           1.14005399 0.149696423  7.6157731 1.727024e-07  0.826735778</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                      Upper</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0       78.35255358</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_parent        0.13084582</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_M1            6.23970702</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_M2            0.09843260</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_M3            0.04333992</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_parent_to_M1          NA</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_parent_to_M2          NA</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_M1_to_M3      0.86450775</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_M2_to_M3      0.99489895</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma           1.45337221</span>
-<span class="r-in"><span>  <span class="fu"><a href="mkinerrmin.html">mkinerrmin</a></span><span class="op">(</span><span class="va">f_soil</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>             err.min n.optim df</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> All data 0.09649963       9 20</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent   0.04721283       2  6</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> M1       0.26551208       2  5</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> M2       0.20327575       2  5</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> M3       0.05196550       3  4</span>
-<span class="r-in"><span>  <span class="co"># }</span></span></span>
-</code></pre></div>
-    </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/tex_listing.html b/docs/dev/reference/tex_listing.html
deleted file mode 100644
index 03bd83f2..00000000
--- a/docs/dev/reference/tex_listing.html
+++ /dev/null
@@ -1,143 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Wrap the output of a summary function in tex listing environment — tex_listing • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Wrap the output of a summary function in tex listing environment — tex_listing"><meta property="og:description" content='This function can be used in a R markdown code chunk with the chunk
-option results = "asis".'><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Functions and data</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Wrap the output of a summary function in tex listing environment</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/tex_listing.R" class="external-link"><code>R/tex_listing.R</code></a></small>
-    <div class="hidden name"><code>tex_listing.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>This function can be used in a R markdown code chunk with the chunk
-option <code>results = "asis"</code>.</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">tex_listing</span><span class="op">(</span><span class="va">object</span>, caption <span class="op">=</span> <span class="cn">NULL</span>, label <span class="op">=</span> <span class="cn">NULL</span>, clearpage <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
-    </div>
-
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>object</dt>
-<dd><p>The object for which the summary is to be listed</p></dd>
-
-
-<dt>caption</dt>
-<dd><p>An optional caption</p></dd>
-
-
-<dt>label</dt>
-<dd><p>An optional label</p></dd>
-
-
-<dt>clearpage</dt>
-<dd><p>Should a new page be started after the listing?</p></dd>
-
-</dl></div>
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/tffm0.html b/docs/dev/reference/tffm0.html
deleted file mode 100644
index 8988b5a2..00000000
--- a/docs/dev/reference/tffm0.html
+++ /dev/null
@@ -1,158 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Transform formation fractions as in the first published mkin version — tffm0 • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Transform formation fractions as in the first published mkin version — tffm0"><meta property="og:description" content="The transformed fractions can be restricted between 0 and 1 in model
-optimisations. Therefore this transformation was used originally in mkin. It
-was later replaced by the ilr transformation because the ilr transformed
-fractions can assumed to follow normal distribution. As the ilr
-transformation is not available in RxODE and can therefore not be used in
-the nlmixr modelling language, this transformation is currently used for
-translating mkin models with formation fractions to more than one target
-compartment for fitting with nlmixr in nlmixr_model. However,
-this implementation cannot be used there, as it is not accessible
-from RxODE."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.1.0</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Functions and data</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Transform formation fractions as in the first published mkin version</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/tffm0.R" class="external-link"><code>R/tffm0.R</code></a></small>
-    <div class="hidden name"><code>tffm0.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>The transformed fractions can be restricted between 0 and 1 in model
-optimisations. Therefore this transformation was used originally in mkin. It
-was later replaced by the <a href="ilr.html">ilr</a> transformation because the ilr transformed
-fractions can assumed to follow normal distribution. As the ilr
-transformation is not available in <a href="https://nlmixrdevelopment.github.io/RxODE/reference/RxODE.html" class="external-link">RxODE</a> and can therefore not be used in
-the nlmixr modelling language, this transformation is currently used for
-translating mkin models with formation fractions to more than one target
-compartment for fitting with nlmixr in <a href="nlmixr.mmkin.html">nlmixr_model</a>. However,
-this implementation cannot be used there, as it is not accessible
-from RxODE.</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">tffm0</span><span class="op">(</span><span class="va">ff</span><span class="op">)</span>
-
-<span class="fu">invtffm0</span><span class="op">(</span><span class="va">ff_trans</span><span class="op">)</span></code></pre></div>
-    </div>
-
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>ff</dt>
-<dd><p>Vector of untransformed formation fractions. The sum
-must be smaller or equal to one</p></dd>
-<dt>ff_trans</dt>
-<dd><p>Vector of transformed formation fractions that can be
-restricted to the interval from 0 to 1</p></dd>
-</dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    <p>A vector of the transformed formation fractions
-A vector of backtransformed formation fractions for natural use in degradation models</p>
-    </div>
-
-    <div id="ref-examples">
-    <h2>Examples</h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="va">ff_example</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span></span>
-<span class="r-in">  <span class="fl">0.10983681</span>, <span class="fl">0.09035905</span>, <span class="fl">0.08399383</span></span>
-<span class="r-in"><span class="op">)</span></span>
-<span class="r-in"><span class="va">ff_example_trans</span> <span class="op">&lt;-</span> <span class="fu">tffm0</span><span class="op">(</span><span class="va">ff_example</span><span class="op">)</span></span>
-<span class="r-in"><span class="fu">invtffm0</span><span class="op">(</span><span class="va">ff_example_trans</span><span class="op">)</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 0.10983681 0.09035905 0.08399383</span>
-</code></pre></div>
-    </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.2.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/transform_odeparms.html b/docs/dev/reference/transform_odeparms.html
deleted file mode 100644
index 4cb2e575..00000000
--- a/docs/dev/reference/transform_odeparms.html
+++ /dev/null
@@ -1,336 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Functions to transform and backtransform kinetic parameters for fitting — transform_odeparms • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Functions to transform and backtransform kinetic parameters for fitting — transform_odeparms"><meta property="og:description" content="The transformations are intended to map parameters that should only take on
-restricted values to the full scale of real numbers. For kinetic rate
-constants and other parameters that can only take on positive values, a
-simple log transformation is used. For compositional parameters, such as the
-formations fractions that should always sum up to 1 and can not be negative,
-the ilr transformation is used."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Functions to transform and backtransform kinetic parameters for fitting</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/transform_odeparms.R" class="external-link"><code>R/transform_odeparms.R</code></a></small>
-    <div class="hidden name"><code>transform_odeparms.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>The transformations are intended to map parameters that should only take on
-restricted values to the full scale of real numbers. For kinetic rate
-constants and other parameters that can only take on positive values, a
-simple log transformation is used. For compositional parameters, such as the
-formations fractions that should always sum up to 1 and can not be negative,
-the <a href="ilr.html">ilr</a> transformation is used.</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">transform_odeparms</span><span class="op">(</span></span>
-<span>  <span class="va">parms</span>,</span>
-<span>  <span class="va">mkinmod</span>,</span>
-<span>  transform_rates <span class="op">=</span> <span class="cn">TRUE</span>,</span>
-<span>  transform_fractions <span class="op">=</span> <span class="cn">TRUE</span></span>
-<span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">backtransform_odeparms</span><span class="op">(</span></span>
-<span>  <span class="va">transparms</span>,</span>
-<span>  <span class="va">mkinmod</span>,</span>
-<span>  transform_rates <span class="op">=</span> <span class="cn">TRUE</span>,</span>
-<span>  transform_fractions <span class="op">=</span> <span class="cn">TRUE</span></span>
-<span><span class="op">)</span></span></code></pre></div>
-    </div>
-
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>parms</dt>
-<dd><p>Parameters of kinetic models as used in the differential
-equations.</p></dd>
-
-
-<dt>mkinmod</dt>
-<dd><p>The kinetic model of class <a href="mkinmod.html">mkinmod</a>, containing
-the names of the model variables that are needed for grouping the
-formation fractions before <a href="ilr.html">ilr</a> transformation, the parameter
-names and the information if the pathway to sink is included in the model.</p></dd>
-
-
-<dt>transform_rates</dt>
-<dd><p>Boolean specifying if kinetic rate constants should
-be transformed in the model specification used in the fitting for better
-compliance with the assumption of normal distribution of the estimator. If
-TRUE, also alpha and beta parameters of the FOMC model are
-log-transformed, as well as k1 and k2 rate constants for the DFOP and HS
-models and the break point tb of the HS model.</p></dd>
-
-
-<dt>transform_fractions</dt>
-<dd><p>Boolean specifying if formation fractions
-constants should be transformed in the model specification used in the
-fitting for better compliance with the assumption of normal distribution
-of the estimator. The default (TRUE) is to do transformations.
-The g parameter of the DFOP model is also seen as a fraction.
-If a single fraction is transformed (g parameter of DFOP or only a single
-target variable e.g. a single metabolite plus a pathway to sink), a
-logistic transformation is used <code><a href="https://rdrr.io/r/stats/Logistic.html" class="external-link">stats::qlogis()</a></code>. In other cases, i.e. if
-two or more formation fractions need to be transformed whose sum cannot
-exceed one, the <a href="ilr.html">ilr</a> transformation is used.</p></dd>
-
-
-<dt>transparms</dt>
-<dd><p>Transformed parameters of kinetic models as used in the
-fitting procedure.</p></dd>
-
-</dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>A vector of transformed or backtransformed parameters</p>
-    </div>
-    <div id="details">
-    <h2>Details</h2>
-    <p>The transformation of sets of formation fractions is fragile, as it supposes
-the same ordering of the components in forward and backward transformation.
-This is no problem for the internal use in <a href="mkinfit.html">mkinfit</a>.</p>
-    </div>
-    <div id="author">
-    <h2>Author</h2>
-    <p>Johannes Ranke</p>
-    </div>
-
-    <div id="ref-examples">
-    <h2>Examples</h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="va">SFO_SFO</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span></span></span>
-<span class="r-in"><span>  parent <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"m1"</span>, sink <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  m1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span>, use_of_ff <span class="op">=</span> <span class="st">"min"</span><span class="op">)</span></span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Temporary DLL for differentials generated and loaded</span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co"># Fit the model to the FOCUS example dataset D using defaults</span></span></span>
-<span class="r-in"><span><span class="va">FOCUS_D</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/subset.html" class="external-link">subset</a></span><span class="op">(</span><span class="va">FOCUS_2006_D</span>, <span class="va">value</span> <span class="op">!=</span> <span class="fl">0</span><span class="op">)</span> <span class="co"># remove zero values to avoid warning</span></span></span>
-<span class="r-in"><span><span class="va">fit</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="va">FOCUS_D</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">fit.s</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="co"># Transformed and backtransformed parameters</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">fit.s</span><span class="op">$</span><span class="va">par</span>, <span class="fl">3</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                   Estimate Std. Error Lower  Upper</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0             99.60     1.5702 96.40 102.79</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k_parent_sink    -3.04     0.0763 -3.19  -2.88</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k_parent_m1      -2.98     0.0403 -3.06  -2.90</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k_m1_sink        -5.25     0.1332 -5.52  -4.98</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma                 3.13     0.3585  2.40   3.85</span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">fit.s</span><span class="op">$</span><span class="va">bpar</span>, <span class="fl">3</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>               Estimate se_notrans t value   Pr(&gt;t)    Lower    Upper</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0      99.59848    1.57022   63.43 2.30e-36 96.40384 102.7931</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_parent_sink  0.04792    0.00365   13.11 6.13e-15  0.04103   0.0560</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_parent_m1    0.05078    0.00205   24.80 3.27e-23  0.04678   0.0551</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_m1_sink      0.00526    0.00070    7.51 6.16e-09  0.00401   0.0069</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma          3.12550    0.35852    8.72 2.24e-10  2.39609   3.8549</span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co"># \dontrun{</span></span></span>
-<span class="r-in"><span><span class="co"># Compare to the version without transforming rate parameters (does not work</span></span></span>
-<span class="r-in"><span><span class="co"># with analytical solution, we get NA values for m1 in predictions)</span></span></span>
-<span class="r-in"><span><span class="va">fit.2</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="va">FOCUS_D</span>, transform_rates <span class="op">=</span> <span class="cn">FALSE</span>,</span></span>
-<span class="r-in"><span>  solution_type <span class="op">=</span> <span class="st">"deSolve"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">fit.2.s</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">fit.2</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">fit.2.s</span><span class="op">$</span><span class="va">par</span>, <span class="fl">3</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>               Estimate Std. Error    Lower    Upper</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0      99.59848    1.57022 96.40384 1.03e+02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_parent_sink  0.04792    0.00365  0.04049 5.54e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_parent_m1    0.05078    0.00205  0.04661 5.49e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_m1_sink      0.00526    0.00070  0.00384 6.69e-03</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma          3.12550    0.35852  2.39609 3.85e+00</span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">fit.2.s</span><span class="op">$</span><span class="va">bpar</span>, <span class="fl">3</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>               Estimate se_notrans t value   Pr(&gt;t)    Lower    Upper</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0      99.59848    1.57022   63.43 2.30e-36 96.40384 1.03e+02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_parent_sink  0.04792    0.00365   13.11 6.13e-15  0.04049 5.54e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_parent_m1    0.05078    0.00205   24.80 3.27e-23  0.04661 5.49e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_m1_sink      0.00526    0.00070    7.51 6.16e-09  0.00384 6.69e-03</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma          3.12550    0.35852    8.72 2.24e-10  2.39609 3.85e+00</span>
-<span class="r-in"><span><span class="co"># }</span></span></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="va">initials</span> <span class="op">&lt;-</span> <span class="va">fit</span><span class="op">$</span><span class="va">start</span><span class="op">$</span><span class="va">value</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">initials</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/colnames.html" class="external-link">rownames</a></span><span class="op">(</span><span class="va">fit</span><span class="op">$</span><span class="va">start</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">transformed</span> <span class="op">&lt;-</span> <span class="va">fit</span><span class="op">$</span><span class="va">start_transformed</span><span class="op">$</span><span class="va">value</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">transformed</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/colnames.html" class="external-link">rownames</a></span><span class="op">(</span><span class="va">fit</span><span class="op">$</span><span class="va">start_transformed</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu">transform_odeparms</span><span class="op">(</span><span class="va">initials</span>, <span class="va">SFO_SFO</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>          parent_0 log_k_parent_sink   log_k_parent_m1     log_k_m1_sink </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>        100.750000         -2.302585         -2.301586         -2.300587 </span>
-<span class="r-in"><span><span class="fu">backtransform_odeparms</span><span class="op">(</span><span class="va">transformed</span>, <span class="va">SFO_SFO</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>      parent_0 k_parent_sink   k_parent_m1     k_m1_sink </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>      100.7500        0.1000        0.1001        0.1002 </span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co"># \dontrun{</span></span></span>
-<span class="r-in"><span><span class="co"># The case of formation fractions (this is now the default)</span></span></span>
-<span class="r-in"><span><span class="va">SFO_SFO.ff</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span></span></span>
-<span class="r-in"><span>  parent <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"m1"</span>, sink <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  m1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Temporary DLL for differentials generated and loaded</span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="va">fit.ff</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO.ff</span>, <span class="va">FOCUS_D</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">fit.ff.s</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">fit.ff</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">fit.ff.s</span><span class="op">$</span><span class="va">par</span>, <span class="fl">3</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                 Estimate Std. Error  Lower  Upper</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0         99.5985     1.5702 96.404 102.79</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k_parent     -2.3157     0.0409 -2.399  -2.23</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k_m1         -5.2475     0.1332 -5.518  -4.98</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_parent_qlogis   0.0579     0.0893 -0.124   0.24</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma             3.1255     0.3585  2.396   3.85</span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">fit.ff.s</span><span class="op">$</span><span class="va">bpar</span>, <span class="fl">3</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                Estimate se_notrans t value   Pr(&gt;t)    Lower    Upper</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0       99.59848    1.57022   63.43 2.30e-36 96.40383 102.7931</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_parent        0.09870    0.00403   24.47 4.96e-23  0.09082   0.1073</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_m1            0.00526    0.00070    7.51 6.16e-09  0.00401   0.0069</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_parent_to_m1  0.51448    0.02230   23.07 3.10e-22  0.46912   0.5596</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma           3.12550    0.35852    8.72 2.24e-10  2.39609   3.8549</span>
-<span class="r-in"><span><span class="va">initials</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"f_parent_to_m1"</span> <span class="op">=</span> <span class="fl">0.5</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">transformed</span> <span class="op">&lt;-</span> <span class="fu">transform_odeparms</span><span class="op">(</span><span class="va">initials</span>, <span class="va">SFO_SFO.ff</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu">backtransform_odeparms</span><span class="op">(</span><span class="va">transformed</span>, <span class="va">SFO_SFO.ff</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_parent_to_m1 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>            0.5 </span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co"># And without sink</span></span></span>
-<span class="r-in"><span><span class="va">SFO_SFO.ff.2</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span></span></span>
-<span class="r-in"><span>  parent <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"m1"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  m1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Temporary DLL for differentials generated and loaded</span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="va">fit.ff.2</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO.ff.2</span>, <span class="va">FOCUS_D</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">fit.ff.2.s</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">fit.ff.2</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">fit.ff.2.s</span><span class="op">$</span><span class="va">par</span>, <span class="fl">3</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>              Estimate Std. Error Lower Upper</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0        84.79      3.012 78.67 90.91</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k_parent    -2.76      0.082 -2.92 -2.59</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k_m1        -4.21      0.123 -4.46 -3.96</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma            8.22      0.943  6.31 10.14</span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">fit.ff.2.s</span><span class="op">$</span><span class="va">bpar</span>, <span class="fl">3</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>          Estimate se_notrans t value   Pr(&gt;t)   Lower  Upper</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0  84.7916    3.01203   28.15 1.92e-25 78.6704 90.913</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_parent   0.0635    0.00521   12.19 2.91e-14  0.0538  0.075</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_m1       0.0148    0.00182    8.13 8.81e-10  0.0115  0.019</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma      8.2229    0.94323    8.72 1.73e-10  6.3060 10.140</span>
-<span class="r-in"><span><span class="co"># }</span></span></span>
-<span class="r-in"><span></span></span>
-</code></pre></div>
-    </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/reference/update.mkinfit-1.png b/docs/dev/reference/update.mkinfit-1.png
deleted file mode 100644
index 9278aefd..00000000
--- a/docs/dev/reference/update.mkinfit-1.png
+++ /dev/null
diff --git a/docs/dev/reference/update.mkinfit-2.png b/docs/dev/reference/update.mkinfit-2.png
deleted file mode 100644
index f73a6180..00000000
--- a/docs/dev/reference/update.mkinfit-2.png
+++ /dev/null
diff --git a/docs/dev/reference/update.mkinfit.html b/docs/dev/reference/update.mkinfit.html
deleted file mode 100644
index 90aa0edb..00000000
--- a/docs/dev/reference/update.mkinfit.html
+++ /dev/null
@@ -1,181 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Update an mkinfit model with different arguments — update.mkinfit • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Update an mkinfit model with different arguments — update.mkinfit"><meta property="og:description" content="This function will return an updated mkinfit object. The fitted degradation
-model parameters from the old fit are used as starting values for the
-updated fit. Values specified as 'parms.ini' and/or 'state.ini' will
-override these starting values."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Update an mkinfit model with different arguments</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/update.mkinfit.R" class="external-link"><code>R/update.mkinfit.R</code></a></small>
-    <div class="hidden name"><code>update.mkinfit.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>This function will return an updated mkinfit object. The fitted degradation
-model parameters from the old fit are used as starting values for the
-updated fit. Values specified as 'parms.ini' and/or 'state.ini' will
-override these starting values.</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for mkinfit</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span>, evaluate <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
-    </div>
-
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>object</dt>
-<dd><p>An mkinfit object to be updated</p></dd>
-
-
-<dt>...</dt>
-<dd><p>Arguments to <code><a href="mkinfit.html">mkinfit</a></code> that should replace
-the arguments from the original call. Arguments set to NULL will
-remove arguments given in the original call</p></dd>
-
-
-<dt>evaluate</dt>
-<dd><p>Should the call be evaluated or returned as a call</p></dd>
-
-</dl></div>
-
-    <div id="ref-examples">
-    <h2>Examples</h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># \dontrun{</span></span></span>
-<span class="r-in"><span><span class="va">fit</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="fu"><a href="https://rdrr.io/r/base/subset.html" class="external-link">subset</a></span><span class="op">(</span><span class="va">FOCUS_2006_D</span>, <span class="va">value</span> <span class="op">!=</span> <span class="fl">0</span><span class="op">)</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="parms.html">parms</a></span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    parent_0    k_parent       sigma </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 99.44423885  0.09793574  3.39632469 </span>
-<span class="r-in"><span><span class="fu"><a href="plot.mkinfit.html">plot_err</a></span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></span></span>
-<span class="r-plt img"><img src="update.mkinfit-1.png" alt="" width="700" height="433"></span>
-<span class="r-in"><span><span class="va">fit_2</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">fit</span>, error_model <span class="op">=</span> <span class="st">"tc"</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="parms.html">parms</a></span><span class="op">(</span><span class="va">fit_2</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     parent_0     k_parent    sigma_low     rsd_high </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1.008549e+02 1.005665e-01 3.752222e-03 6.763434e-02 </span>
-<span class="r-in"><span><span class="fu"><a href="plot.mkinfit.html">plot_err</a></span><span class="op">(</span><span class="va">fit_2</span><span class="op">)</span></span></span>
-<span class="r-plt img"><img src="update.mkinfit-2.png" alt="" width="700" height="433"></span>
-<span class="r-in"><span><span class="co"># }</span></span></span>
-</code></pre></div>
-    </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/dev/sitemap.xml b/docs/dev/sitemap.xml
deleted file mode 100644
index b3542d0b..00000000
--- a/docs/dev/sitemap.xml
+++ /dev/null
@@ -1,345 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<urlset xmlns="http://www.sitemaps.org/schemas/sitemap/0.9">
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/404.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/articles/2022_wp_1.1_dmta_parent.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/articles/FOCUS_D.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/articles/FOCUS_L.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/articles/index.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/articles/mkin.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_cyan_pathway.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_dmta_parent.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_dmta_pathway.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/articles/twa.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/articles/web_only/FOCUS_Z.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/articles/web_only/NAFTA_examples.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/articles/web_only/benchmarks.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/articles/web_only/compiled_models.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/articles/web_only/dimethenamid_2018.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/articles/web_only/multistart.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/articles/web_only/saem_benchmarks.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/authors.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/index.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/news/index.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/AIC.mmkin.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/CAKE_export.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/D24_2014.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/DFOP.solution.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/Extract.mmkin.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_DFOP_ref_A_to_B.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_FOMC_ref_A_to_F.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_HS_ref_A_to_F.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_SFO_ref_A_to_F.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_datasets.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/FOMC.solution.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/HS.solution.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/IORE.solution.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/NAFTA_SOP_2015.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/NAFTA_SOP_Attachment.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/SFO.solution.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/SFORB.solution.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/add_err.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/anova.saem.mmkin.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/aw.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/confint.mkinfit.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/convergence.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/create_deg_func.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/dimethenamid_2018.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/ds_mixed.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/endpoints.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/experimental_data_for_UBA.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/f_time_norm_focus.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/focus_soil_moisture.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/get_deg_func.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/hierarchical_kinetics.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/illparms.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/ilr.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/index.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/intervals.nlmixr.mmkin.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/intervals.saem.mmkin.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/llhist.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/loftest.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/logLik.mkinfit.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/logLik.saem.mmkin.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/logistic.solution.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/lrtest.mkinfit.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/max_twa_parent.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/mccall81_245T.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/mean_degparms.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/mhmkin.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/mixed.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/mkin_long_to_wide.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/mkin_wide_to_long.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/mkinds.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/mkindsg.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/mkinerrmin.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/mkinerrplot.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/mkinfit.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/mkinmod.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/mkinparplot.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/mkinplot.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/mkinpredict.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/mkinresplot.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/mmkin.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/multistart.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/nafta.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/nlme.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/nlme.mmkin.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/nlmixr.mmkin.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/nobs.mkinfit.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/parhist.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/parms.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/parplot.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/plot.mixed.mmkin.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/plot.mkinfit.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/plot.mmkin.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/plot.nafta.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/print.mmkin.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/read_spreadsheet.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/reexports.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/residuals.mkinfit.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/saem.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/schaefer07_complex_case.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/set_nd_nq.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/sigma_twocomp.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/status.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/summary.mkinfit.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/summary.mmkin.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/summary.nlme.mmkin.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/summary.nlmixr.mmkin.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/summary.saem.mmkin.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/summary_listing.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/synthetic_data_for_UBA_2014.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/test_data_from_UBA_2014.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/tex_listing.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/tffm0.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/transform_odeparms.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/update.mkinfit.html</loc>
-  </url>
-</urlset>
diff --git a/docs/docsearch.css b/docs/docsearch.css
deleted file mode 100644
index e5f1fe1d..00000000
--- a/docs/docsearch.css
+++ /dev/null
@@ -1,148 +0,0 @@
-/* Docsearch -------------------------------------------------------------- */
-/*
-  Source: https://github.com/algolia/docsearch/
-  License: MIT
-*/
-
-.algolia-autocomplete {
-  display: block;
-  -webkit-box-flex: 1;
-  -ms-flex: 1;
-  flex: 1
-}
-
-.algolia-autocomplete .ds-dropdown-menu {
-  width: 100%;
-  min-width: none;
-  max-width: none;
-  padding: .75rem 0;
-  background-color: #fff;
-  background-clip: padding-box;
-  border: 1px solid rgba(0, 0, 0, .1);
-  box-shadow: 0 .5rem 1rem rgba(0, 0, 0, .175);
-}
-
-@media (min-width:768px) {
-  .algolia-autocomplete .ds-dropdown-menu {
-      width: 175%
-  }
-}
-
-.algolia-autocomplete .ds-dropdown-menu::before {
-  display: none
-}
-
-.algolia-autocomplete .ds-dropdown-menu [class^=ds-dataset-] {
-  padding: 0;
-  background-color: rgb(255,255,255);
-  border: 0;
-  max-height: 80vh;
-}
-
-.algolia-autocomplete .ds-dropdown-menu .ds-suggestions {
-  margin-top: 0
-}
-
-.algolia-autocomplete .algolia-docsearch-suggestion {
-  padding: 0;
-  overflow: visible
-}
-
-.algolia-autocomplete .algolia-docsearch-suggestion--category-header {
-  padding: .125rem 1rem;
-  margin-top: 0;
-  font-size: 1.3em;
-  font-weight: 500;
-  color: #00008B;
-  border-bottom: 0
-}
-
-.algolia-autocomplete .algolia-docsearch-suggestion--wrapper {
-    float: none;
-    padding-top: 0
-}
-
-.algolia-autocomplete .algolia-docsearch-suggestion--subcategory-column {
-  float: none;
-  width: auto;
-  padding: 0;
-  text-align: left
-}
-
-.algolia-autocomplete .algolia-docsearch-suggestion--content {
-  float: none;
-  width: auto;
-  padding: 0
-}
-
-.algolia-autocomplete .algolia-docsearch-suggestion--content::before {
-  display: none
-}
-
-.algolia-autocomplete .ds-suggestion:not(:first-child) .algolia-docsearch-suggestion--category-header {
-  padding-top: .75rem;
-  margin-top: .75rem;
-  border-top: 1px solid rgba(0, 0, 0, .1)
-}
-
-.algolia-autocomplete .ds-suggestion .algolia-docsearch-suggestion--subcategory-column {
-  display: block;
-  padding: .1rem 1rem;
-  margin-bottom: 0.1;
-  font-size: 1.0em;
-  font-weight: 400
-  /* display: none */
-}
-
-.algolia-autocomplete .algolia-docsearch-suggestion--title {
-  display: block;
-  padding: .25rem 1rem;
-  margin-bottom: 0;
-  font-size: 0.9em;
-  font-weight: 400
-}
-
-.algolia-autocomplete .algolia-docsearch-suggestion--text {
-  padding: 0 1rem .5rem;
-  margin-top: -.25rem;
-  font-size: 0.8em;
-  font-weight: 400;
-  line-height: 1.25
-}
-
-.algolia-autocomplete .algolia-docsearch-footer {
-  width: 110px;
-  height: 20px;
-  z-index: 3;
-  margin-top: 10.66667px;
-  float: right;
-  font-size: 0;
-  line-height: 0;
-}
-
-.algolia-autocomplete .algolia-docsearch-footer--logo {
-  background-image: url("data:image/svg+xml;utf8,<svg viewBox='0 0 130 18' xmlns='http://www.w3.org/2000/svg'><defs><linearGradient x1='-36.868%' y1='134.936%' x2='129.432%' y2='-27.7%' id='a'><stop stop-color='%2300AEFF' offset='0%'/><stop stop-color='%233369E7' offset='100%'/></linearGradient></defs><g fill='none' fill-rule='evenodd'><path d='M59.399.022h13.299a2.372 2.372 0 0 1 2.377 2.364V15.62a2.372 2.372 0 0 1-2.377 2.364H59.399a2.372 2.372 0 0 1-2.377-2.364V2.381A2.368 2.368 0 0 1 59.399.022z' fill='url(%23a)'/><path d='M66.257 4.56c-2.815 0-5.1 2.272-5.1 5.078 0 2.806 2.284 5.072 5.1 5.072 2.815 0 5.1-2.272 5.1-5.078 0-2.806-2.279-5.072-5.1-5.072zm0 8.652c-1.983 0-3.593-1.602-3.593-3.574 0-1.972 1.61-3.574 3.593-3.574 1.983 0 3.593 1.602 3.593 3.574a3.582 3.582 0 0 1-3.593 3.574zm0-6.418v2.664c0 .076.082.131.153.093l2.377-1.226c.055-.027.071-.093.044-.147a2.96 2.96 0 0 0-2.465-1.487c-.055 0-.11.044-.11.104l.001-.001zm-3.33-1.956l-.312-.311a.783.783 0 0 0-1.106 0l-.372.37a.773.773 0 0 0 0 1.101l.307.305c.049.049.121.038.164-.011.181-.245.378-.479.597-.697.225-.223.455-.42.707-.599.055-.033.06-.109.016-.158h-.001zm5.001-.806v-.616a.781.781 0 0 0-.783-.779h-1.824a.78.78 0 0 0-.783.779v.632c0 .071.066.12.137.104a5.736 5.736 0 0 1 1.588-.223c.52 0 1.035.071 1.534.207a.106.106 0 0 0 .131-.104z' fill='%23FFF'/><path d='M102.162 13.762c0 1.455-.372 2.517-1.123 3.193-.75.676-1.895 1.013-3.44 1.013-.564 0-1.736-.109-2.673-.316l.345-1.689c.783.163 1.819.207 2.361.207.86 0 1.473-.174 1.84-.523.367-.349.548-.866.548-1.553v-.349a6.374 6.374 0 0 1-.838.316 4.151 4.151 0 0 1-1.194.158 4.515 4.515 0 0 1-1.616-.278 3.385 3.385 0 0 1-1.254-.817 3.744 3.744 0 0 1-.811-1.351c-.192-.539-.29-1.504-.29-2.212 0-.665.104-1.498.307-2.054a3.925 3.925 0 0 1 .904-1.433 4.124 4.124 0 0 1 1.441-.926 5.31 5.31 0 0 1 1.945-.365c.696 0 1.337.087 1.961.191a15.86 15.86 0 0 1 1.588.332v8.456h-.001zm-5.954-4.206c0 .893.197 1.885.592 2.299.394.414.904.621 1.528.621.34 0 .663-.049.964-.142a2.75 2.75 0 0 0 .734-.332v-5.29a8.531 8.531 0 0 0-1.413-.18c-.778-.022-1.369.294-1.786.801-.411.507-.619 1.395-.619 2.223zm16.12 0c0 .719-.104 1.264-.318 1.858a4.389 4.389 0 0 1-.904 1.52c-.389.42-.854.746-1.402.975-.548.229-1.391.36-1.813.36-.422-.005-1.26-.125-1.802-.36a4.088 4.088 0 0 1-1.397-.975 4.486 4.486 0 0 1-.909-1.52 5.037 5.037 0 0 1-.329-1.858c0-.719.099-1.411.318-1.999.219-.588.526-1.09.92-1.509.394-.42.865-.741 1.402-.97a4.547 4.547 0 0 1 1.786-.338 4.69 4.69 0 0 1 1.791.338c.548.229 1.019.55 1.402.97.389.42.69.921.909 1.509.23.588.345 1.28.345 1.999h.001zm-2.191.005c0-.921-.203-1.689-.597-2.223-.394-.539-.948-.806-1.654-.806-.707 0-1.26.267-1.654.806-.394.539-.586 1.302-.586 2.223 0 .932.197 1.558.592 2.098.394.545.948.812 1.654.812.707 0 1.26-.272 1.654-.812.394-.545.592-1.166.592-2.098h-.001zm6.962 4.707c-3.511.016-3.511-2.822-3.511-3.274L113.583.926l2.142-.338v10.003c0 .256 0 1.88 1.375 1.885v1.792h-.001zm3.774 0h-2.153V5.072l2.153-.338v9.534zm-1.079-10.542c.718 0 1.304-.578 1.304-1.291 0-.714-.581-1.291-1.304-1.291-.723 0-1.304.578-1.304 1.291 0 .714.586 1.291 1.304 1.291zm6.431 1.013c.707 0 1.304.087 1.786.262.482.174.871.42 1.156.73.285.311.488.735.608 1.182.126.447.186.937.186 1.476v5.481a25.24 25.24 0 0 1-1.495.251c-.668.098-1.419.147-2.251.147a6.829 6.829 0 0 1-1.517-.158 3.213 3.213 0 0 1-1.178-.507 2.455 2.455 0 0 1-.761-.904c-.181-.37-.274-.893-.274-1.438 0-.523.104-.855.307-1.215.208-.36.487-.654.838-.883a3.609 3.609 0 0 1 1.227-.49 7.073 7.073 0 0 1 2.202-.103c.263.027.537.076.833.147v-.349c0-.245-.027-.479-.088-.697a1.486 1.486 0 0 0-.307-.583c-.148-.169-.34-.3-.581-.392a2.536 2.536 0 0 0-.915-.163c-.493 0-.942.06-1.353.131-.411.071-.75.153-1.008.245l-.257-1.749c.268-.093.668-.185 1.183-.278a9.335 9.335 0 0 1 1.66-.142l-.001-.001zm.181 7.731c.657 0 1.145-.038 1.484-.104v-2.168a5.097 5.097 0 0 0-1.978-.104c-.241.033-.46.098-.652.191a1.167 1.167 0 0 0-.466.392c-.121.169-.175.267-.175.523 0 .501.175.79.493.981.323.196.75.289 1.293.289h.001zM84.109 4.794c.707 0 1.304.087 1.786.262.482.174.871.42 1.156.73.29.316.487.735.608 1.182.126.447.186.937.186 1.476v5.481a25.24 25.24 0 0 1-1.495.251c-.668.098-1.419.147-2.251.147a6.829 6.829 0 0 1-1.517-.158 3.213 3.213 0 0 1-1.178-.507 2.455 2.455 0 0 1-.761-.904c-.181-.37-.274-.893-.274-1.438 0-.523.104-.855.307-1.215.208-.36.487-.654.838-.883a3.609 3.609 0 0 1 1.227-.49 7.073 7.073 0 0 1 2.202-.103c.257.027.537.076.833.147v-.349c0-.245-.027-.479-.088-.697a1.486 1.486 0 0 0-.307-.583c-.148-.169-.34-.3-.581-.392a2.536 2.536 0 0 0-.915-.163c-.493 0-.942.06-1.353.131-.411.071-.75.153-1.008.245l-.257-1.749c.268-.093.668-.185 1.183-.278a8.89 8.89 0 0 1 1.66-.142l-.001-.001zm.186 7.736c.657 0 1.145-.038 1.484-.104v-2.168a5.097 5.097 0 0 0-1.978-.104c-.241.033-.46.098-.652.191a1.167 1.167 0 0 0-.466.392c-.121.169-.175.267-.175.523 0 .501.175.79.493.981.318.191.75.289 1.293.289h.001zm8.682 1.738c-3.511.016-3.511-2.822-3.511-3.274L89.461.926l2.142-.338v10.003c0 .256 0 1.88 1.375 1.885v1.792h-.001z' fill='%23182359'/><path d='M5.027 11.025c0 .698-.252 1.246-.757 1.644-.505.397-1.201.596-2.089.596-.888 0-1.615-.138-2.181-.414v-1.214c.358.168.739.301 1.141.397.403.097.778.145 1.125.145.508 0 .884-.097 1.125-.29a.945.945 0 0 0 .363-.779.978.978 0 0 0-.333-.747c-.222-.204-.68-.446-1.375-.725-.716-.29-1.221-.621-1.515-.994-.294-.372-.44-.82-.44-1.343 0-.655.233-1.171.698-1.547.466-.376 1.09-.564 1.875-.564.752 0 1.5.165 2.245.494l-.408 1.047c-.698-.294-1.321-.44-1.869-.44-.415 0-.73.09-.945.271a.89.89 0 0 0-.322.717c0 .204.043.379.129.524.086.145.227.282.424.411.197.129.551.299 1.063.51.577.24.999.464 1.268.671.269.208.466.442.591.704.125.261.188.569.188.924l-.001.002zm3.98 2.24c-.924 0-1.646-.269-2.167-.808-.521-.539-.782-1.281-.782-2.226 0-.97.242-1.733.725-2.288.483-.555 1.148-.833 1.993-.833.784 0 1.404.238 1.858.714.455.476.682 1.132.682 1.966v.682H7.357c.018.577.174 1.02.467 1.329.294.31.707.465 1.241.465.351 0 .678-.033.98-.099a5.1 5.1 0 0 0 .975-.33v1.026a3.865 3.865 0 0 1-.935.312 5.723 5.723 0 0 1-1.08.091l.002-.001zm-.231-5.199c-.401 0-.722.127-.964.381s-.386.625-.432 1.112h2.696c-.007-.491-.125-.862-.354-1.115-.229-.252-.544-.379-.945-.379l-.001.001zm7.692 5.092l-.252-.827h-.043c-.286.362-.575.608-.865.739-.29.131-.662.196-1.117.196-.584 0-1.039-.158-1.367-.473-.328-.315-.491-.761-.491-1.337 0-.612.227-1.074.682-1.386.455-.312 1.148-.482 2.079-.51l1.026-.032v-.317c0-.38-.089-.663-.266-.851-.177-.188-.452-.282-.824-.282-.304 0-.596.045-.876.134a6.68 6.68 0 0 0-.806.317l-.408-.902a4.414 4.414 0 0 1 1.058-.384 4.856 4.856 0 0 1 1.085-.132c.756 0 1.326.165 1.711.494.385.329.577.847.577 1.552v4.002h-.902l-.001-.001zm-1.88-.859c.458 0 .826-.128 1.104-.384.278-.256.416-.615.416-1.077v-.516l-.763.032c-.594.021-1.027.121-1.297.298s-.406.448-.406.814c0 .265.079.47.236.615.158.145.394.218.709.218h.001zm7.557-5.189c.254 0 .464.018.628.054l-.124 1.176a2.383 2.383 0 0 0-.559-.064c-.505 0-.914.165-1.227.494-.313.329-.47.757-.47 1.284v3.105h-1.262V7.218h.988l.167 1.047h.064c.197-.354.454-.636.771-.843a1.83 1.83 0 0 1 1.023-.312h.001zm4.125 6.155c-.899 0-1.582-.262-2.049-.787-.467-.525-.701-1.277-.701-2.259 0-.999.244-1.767.733-2.304.489-.537 1.195-.806 2.119-.806.627 0 1.191.116 1.692.349l-.381 1.015c-.534-.208-.974-.312-1.321-.312-1.028 0-1.542.682-1.542 2.046 0 .666.128 1.166.384 1.501.256.335.631.502 1.125.502a3.23 3.23 0 0 0 1.595-.419v1.101a2.53 2.53 0 0 1-.722.285 4.356 4.356 0 0 1-.932.086v.002zm8.277-.107h-1.268V9.506c0-.458-.092-.8-.277-1.026-.184-.226-.477-.338-.878-.338-.53 0-.919.158-1.168.475-.249.317-.373.848-.373 1.593v2.949h-1.262V4.801h1.262v2.122c0 .34-.021.704-.064 1.09h.081a1.76 1.76 0 0 1 .717-.666c.306-.158.663-.236 1.072-.236 1.439 0 2.159.725 2.159 2.175v3.873l-.001-.001zm7.649-6.048c.741 0 1.319.269 1.732.806.414.537.62 1.291.62 2.261 0 .974-.209 1.732-.628 2.275-.419.542-1.001.814-1.746.814-.752 0-1.336-.27-1.751-.811h-.086l-.231.704h-.945V4.801h1.262v1.987l-.021.655-.032.553h.054c.401-.591.992-.886 1.772-.886zm-.328 1.031c-.508 0-.875.149-1.098.448-.224.299-.339.799-.346 1.501v.086c0 .723.115 1.247.344 1.571.229.324.603.486 1.123.486.448 0 .787-.177 1.018-.532.231-.354.346-.867.346-1.536 0-1.35-.462-2.025-1.386-2.025l-.001.001zm3.244-.924h1.375l1.209 3.368c.183.48.304.931.365 1.354h.043c.032-.197.091-.436.177-.717.086-.281.541-1.616 1.364-4.004h1.364l-2.541 6.73c-.462 1.235-1.232 1.853-2.31 1.853-.279 0-.551-.03-.816-.091v-.999c.19.043.406.064.65.064.609 0 1.037-.353 1.284-1.058l.22-.559-2.385-5.941h.001z' fill='%231D3657'/></g></svg>");
-  background-repeat: no-repeat;
-  background-position: 50%;
-  background-size: 100%;
-  overflow: hidden;
-  text-indent: -9000px;
-  width: 100%;
-  height: 100%;
-  display: block;
-  transform: translate(-8px);
-}
-
-.algolia-autocomplete .algolia-docsearch-suggestion--highlight {
-  color: #FF8C00;
-  background: rgba(232, 189, 54, 0.1)
-}
-
-
-.algolia-autocomplete .algolia-docsearch-suggestion--text .algolia-docsearch-suggestion--highlight {
-  box-shadow: inset 0 -2px 0 0 rgba(105, 105, 105, .5)
-}
-
-.algolia-autocomplete .ds-suggestion.ds-cursor .algolia-docsearch-suggestion--content {
-  background-color: rgba(192, 192, 192, .15)
-}
diff --git a/docs/docsearch.js b/docs/docsearch.js
deleted file mode 100644
index b35504cd..00000000
--- a/docs/docsearch.js
+++ /dev/null
@@ -1,85 +0,0 @@
-$(function() {
-
-  // register a handler to move the focus to the search bar
-  // upon pressing shift + "/" (i.e. "?")
-  $(document).on('keydown', function(e) {
-    if (e.shiftKey && e.keyCode == 191) {
-      e.preventDefault();
-      $("#search-input").focus();
-    }
-  });
-
-  $(document).ready(function() {
-    // do keyword highlighting
-    /* modified from https://jsfiddle.net/julmot/bL6bb5oo/ */
-    var mark = function() {
-
-      var referrer = document.URL ;
-      var paramKey = "q" ;
-
-      if (referrer.indexOf("?") !== -1) {
-        var qs = referrer.substr(referrer.indexOf('?') + 1);
-        var qs_noanchor = qs.split('#')[0];
-        var qsa = qs_noanchor.split('&');
-        var keyword = "";
-
-        for (var i = 0; i < qsa.length; i++) {
-          var currentParam = qsa[i].split('=');
-
-          if (currentParam.length !== 2) {
-            continue;
-          }
-
-          if (currentParam[0] == paramKey) {
-            keyword = decodeURIComponent(currentParam[1].replace(/\+/g, "%20"));
-          }
-        }
-
-        if (keyword !== "") {
-          $(".contents").unmark({
-            done: function() {
-              $(".contents").mark(keyword);
-            }
-          });
-        }
-      }
-    };
-
-    mark();
-  });
-});
-
-/* Search term highlighting ------------------------------*/
-
-function matchedWords(hit) {
-  var words = [];
-
-  var hierarchy = hit._highlightResult.hierarchy;
-  // loop to fetch from lvl0, lvl1, etc.
-  for (var idx in hierarchy) {
-    words = words.concat(hierarchy[idx].matchedWords);
-  }
-
-  var content = hit._highlightResult.content;
-  if (content) {
-    words = words.concat(content.matchedWords);
-  }
-
-  // return unique words
-  var words_uniq = [...new Set(words)];
-  return words_uniq;
-}
-
-function updateHitURL(hit) {
-
-  var words = matchedWords(hit);
-  var url = "";
-
-  if (hit.anchor) {
-    url = hit.url_without_anchor + '?q=' + escape(words.join(" ")) + '#' + hit.anchor;
-  } else {
-    url = hit.url + '?q=' + escape(words.join(" "));
-  }
-
-  return url;
-}
diff --git a/docs/index.html b/docs/index.html
index eb3fe7f9..ef4fc650 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -4,148 +4,82 @@
 <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
 <meta charset="utf-8">
 <meta http-equiv="X-UA-Compatible" content="IE=edge">
-<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
 <title>Kinetic Evaluation of Chemical Degradation Data • mkin</title>
-<!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous">
-<script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="bootstrap-toc.css">
-<script src="bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous">
-<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous">
-<!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="pkgdown.css" rel="stylesheet">
-<script src="pkgdown.js"></script><meta property="og:title" content="Kinetic Evaluation of Chemical Degradation Data">
-<meta property="og:description" content="Calculation routines based on the FOCUS Kinetics Report (2006,
-  2014).  Includes a function for conveniently defining differential equation
-  models, model solution based on eigenvalues if possible or using numerical
-  solvers.  If a C compiler (on windows: Rtools) is installed, differential
-  equation models are solved using automatically generated C functions.
-  Heteroscedasticity can be taken into account using variance by variable or
-  two-component error models as described by Ranke and Meinecke (2018)
-  &lt;doi:10.3390/environments6120124&gt;.  Hierarchical degradation models can
-  be fitted using nonlinear mixed-effects model packages as a back end as
-  described by Ranke et al. (2021) &lt;doi:10.3390/environments8080071&gt;.  Please
-  note that no warranty is implied for correctness of results or fitness for a
-  particular purpose.">
-<!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]-->
+<script src="deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
+<link href="deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet">
+<script src="deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet">
+<link href="deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet">
+<script src="deps/headroom-0.11.0/headroom.min.js"></script><script src="deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="deps/search-1.0.0/fuse.min.js"></script><script src="deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="pkgdown.js"></script><meta property="og:title" content="Kinetic Evaluation of Chemical Degradation Data">
+<meta name="description" content="Calculation routines based on the FOCUS Kinetics Report (2006, 2014). Includes a function for conveniently defining differential equation models, model solution based on eigenvalues if possible or using numerical solvers. If a C compiler (on windows: Rtools) is installed, differential equation models are solved using automatically generated C functions. Non-constant errors can be taken into account using variance by variable or two-component error models &lt;doi:10.3390/environments6120124&gt;. Hierarchical degradation models can be fitted using nonlinear mixed-effects model packages as a back end &lt;doi:10.3390/environments8080071&gt;. Please note that no warranty is implied for correctness of results or fitness for a particular purpose.">
+<meta property="og:description" content="Calculation routines based on the FOCUS Kinetics Report (2006, 2014). Includes a function for conveniently defining differential equation models, model solution based on eigenvalues if possible or using numerical solvers. If a C compiler (on windows: Rtools) is installed, differential equation models are solved using automatically generated C functions. Non-constant errors can be taken into account using variance by variable or two-component error models &lt;doi:10.3390/environments6120124&gt;. Hierarchical degradation models can be fitted using nonlinear mixed-effects model packages as a back end &lt;doi:10.3390/environments8080071&gt;. Please note that no warranty is implied for correctness of results or fitness for a particular purpose.">
 </head>
-<body data-spy="scroll" data-target="#toc">
-    
+<body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
 
-    <div class="container template-home">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
 
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav">
-<li>
-  <a href="reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu">
-<li>
-      <a href="articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Performance</li>
-    <li>
-      <a href="articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto">
+<li class="nav-item"><a class="nav-link" href="reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles">
+<li><a class="dropdown-item" href="articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul>
 </li>
-<li>
-  <a href="news/index.html">News</a>
-</li>
+<li class="nav-item"><a class="nav-link" href="coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="news/index.html">News</a></li>
       </ul>
-<ul class="nav navbar-nav navbar-right">
-<li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
+<ul class="navbar-nav">
+<li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="search.json">
+</form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul>
 </div>
-<!--/.nav-collapse -->
-  </div>
-<!--/.container -->
-</div>
-<!--/.navbar -->
 
-      
 
-      </header><div class="row">
-  <div class="contents col-md-9">
-<div class="section level1">
+  </div>
+</nav><div class="container template-home">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="section level1">
 <div class="page-header"><h1 id="mkin">mkin<a class="anchor" aria-label="anchor" href="#mkin"></a>
 </h1></div>
-<p><a href="https://cran.r-project.org/package=mkin" class="external-link"><img src="https://www.r-pkg.org/badges/version/mkin"></a> <a href="https://jranke.r-universe.dev/ui/#package:mkin" class="external-link"><img src="https://jranke.r-universe.dev/badges/mkin" alt="mkin status badge"></a> <a href="https://app.travis-ci.com/github/jranke/mkin" class="external-link"><img src="https://travis-ci.com/jranke/mkin.svg?branch=main" alt="Build Status"></a> <a href="https://app.codecov.io/gh/jranke/mkin" class="external-link"><img src="https://codecov.io/github/jranke/mkin/branch/main/graphs/badge.svg" alt="codecov"></a></p>
+<!-- badges: start -->
+
 <p>The <a href="https://www.r-project.org" class="external-link">R</a> package <strong>mkin</strong> provides calculation routines for the analysis of chemical degradation data, including <b>m</b>ulticompartment <b>kin</b>etics as needed for modelling the formation and decline of transformation products, or if several degradation compartments are involved. It provides stable functionality for kinetic evaluations according to the FOCUS guidance (see below for details). In addition, it provides functionality to do hierarchical kinetics based on nonlinear mixed-effects models.</p>
 <div class="section level2">
 <h2 id="installation">Installation<a class="anchor" aria-label="anchor" href="#installation"></a>
@@ -281,10 +215,7 @@ Von Götz N, Nörtersheuser P, Richter O (1999) Population based analysis of pes
 <p>Contributions are welcome!</p>
 </div>
 </div>
-  </div>
-
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <div class="links">
+  </main><aside class="col-md-3"><div class="links">
 <h2 data-toc-skip>Links</h2>
 <ul class="list-unstyled">
 <li><a href="https://cloud.r-project.org/package=mkin" class="external-link">View on CRAN</a></li>
@@ -318,27 +249,24 @@ Von Götz N, Nörtersheuser P, Richter O (1999) Population based analysis of pes
 
 
 
-  </div>
+  </aside>
 </div>
 
 
-      <footer><div class="copyright">
-  <p></p>
-<p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p>
-<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer>
+    </footer>
 </div>
 
-  
 
 
-  
+
 
   </body>
 </html>
diff --git a/docs/katex-auto.js b/docs/katex-auto.js
new file mode 100644
index 00000000..20651d9f
--- /dev/null
+++ b/docs/katex-auto.js
@@ -0,0 +1,14 @@
+// https://github.com/jgm/pandoc/blob/29fa97ab96b8e2d62d48326e1b949a71dc41f47a/src/Text/Pandoc/Writers/HTML.hs#L332-L345
+document.addEventListener("DOMContentLoaded", function () {
+  var mathElements = document.getElementsByClassName("math");
+  var macros = [];
+  for (var i = 0; i < mathElements.length; i++) {
+    var texText = mathElements[i].firstChild;
+    if (mathElements[i].tagName == "SPAN") {
+      katex.render(texText.data, mathElements[i], {
+        displayMode: mathElements[i].classList.contains("display"),
+        throwOnError: false,
+        macros: macros,
+        fleqn: false
+      });
+    }}});
diff --git a/docs/lightswitch.js b/docs/lightswitch.js
new file mode 100644
index 00000000..9467125a
--- /dev/null
+++ b/docs/lightswitch.js
@@ -0,0 +1,85 @@
+
+/*!
+ * Color mode toggler for Bootstrap's docs (https://getbootstrap.com/)
+ * Copyright 2011-2023 The Bootstrap Authors
+ * Licensed under the Creative Commons Attribution 3.0 Unported License.
+ * Updates for {pkgdown} by the {bslib} authors, also licensed under CC-BY-3.0.
+ */
+
+const getStoredTheme = () => localStorage.getItem('theme')
+const setStoredTheme = theme => localStorage.setItem('theme', theme)
+
+const getPreferredTheme = () => {
+  const storedTheme = getStoredTheme()
+  if (storedTheme) {
+    return storedTheme
+  }
+
+  return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light'
+}
+
+const setTheme = theme => {
+  if (theme === 'auto') {
+    document.documentElement.setAttribute('data-bs-theme', (window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light'))
+  } else {
+    document.documentElement.setAttribute('data-bs-theme', theme)
+  }
+}
+
+function bsSetupThemeToggle () {
+  'use strict'
+
+  const showActiveTheme = (theme, focus = false) => {
+    var activeLabel, activeIcon;
+
+    document.querySelectorAll('[data-bs-theme-value]').forEach(element => {
+      const buttonTheme = element.getAttribute('data-bs-theme-value')
+      const isActive = buttonTheme == theme
+      
+      element.classList.toggle('active', isActive)
+      element.setAttribute('aria-pressed', isActive)
+
+      if (isActive) {
+        activeLabel = element.textContent;
+        activeIcon = element.querySelector('span').classList.value;
+      }
+    })
+
+    const themeSwitcher = document.querySelector('#dropdown-lightswitch')
+    if (!themeSwitcher) {
+      return
+    }
+
+    themeSwitcher.setAttribute('aria-label', activeLabel)
+    themeSwitcher.querySelector('span').classList.value = activeIcon;
+
+    if (focus) {
+      themeSwitcher.focus()
+    }
+  }
+
+  window.matchMedia('(prefers-color-scheme: dark)').addEventListener('change', () => {
+    const storedTheme = getStoredTheme()
+    if (storedTheme !== 'light' && storedTheme !== 'dark') {
+      setTheme(getPreferredTheme())
+    }
+  })
+
+  window.addEventListener('DOMContentLoaded', () => {
+    showActiveTheme(getPreferredTheme())
+
+    document
+      .querySelectorAll('[data-bs-theme-value]')
+      .forEach(toggle => {
+        toggle.addEventListener('click', () => {
+          const theme = toggle.getAttribute('data-bs-theme-value')
+          setTheme(theme)
+          setStoredTheme(theme)
+          showActiveTheme(theme, true)
+        })
+      })
+  })
+}
+
+setTheme(getPreferredTheme());
+bsSetupThemeToggle();
diff --git a/docs/news/index.html b/docs/news/index.html
index 01185d99..c1837d9d 100644
--- a/docs/news/index.html
+++ b/docs/news/index.html
@@ -1,132 +1,99 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Changelog • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Changelog"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Changelog • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Changelog"></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
 
-    <div class="container template-news">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
 
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="active nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
 
-      
+  </div>
+</nav><div class="container template-news">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
 
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-      <h1 data-toc-skip>Changelog <small></small></h1>
+      <h1>Changelog</h1>
       <small>Source: <a href="https://github.com/jranke/mkin/blob/HEAD/NEWS.md" class="external-link"><code>NEWS.md</code></a></small>
     </div>
 
     <div class="section level2">
-<h2 class="page-header" data-toc-text="1.2.6" id="mkin-126">mkin 1.2.6<a class="anchor" aria-label="anchor" href="#mkin-126"></a></h2>
+<h2 class="pkg-version" data-toc-text="1.2.9" id="mkin-129">mkin 1.2.9<a class="anchor" aria-label="anchor" href="#mkin-129"></a></h2>
+<ul><li><p>‘R/plot.mixed.R’: Support more than 25 datasets</p></li>
+<li><p>‘R/mkinfit.R’: Support passing the observed data as a ‘tibble’</p></li>
+<li><p>‘R/parplot.R’: Support multistart objects with covariate models and filter negative values of scaled parameters (with a warning) for plotting.</p></li>
+<li><p>’R/create_deg_func.R: Make sure that no reversible reactions are specified in the case of two observed variables, as this is not supported</p></li>
+</ul></div>
+    <div class="section level2">
+<h2 class="pkg-version" data-toc-text="1.2.8" id="mkin-128-unreleased">mkin 1.2.8 (unreleased)<a class="anchor" aria-label="anchor" href="#mkin-128-unreleased"></a></h2>
+<ul><li>‘R/{mhmkin,status}.R’: Deal with ‘saem’ fits that fail when updating an ‘mhmkin’ object</li>
+</ul></div>
+    <div class="section level2">
+<h2 class="pkg-version" data-toc-text="1.2.7" id="mkin-127-unreleased">mkin 1.2.7 (unreleased)<a class="anchor" aria-label="anchor" href="#mkin-127-unreleased"></a></h2>
+<ul><li>‘R/illparms.R’: Fix a bug that prevented an ill-defined random effect to be found if there was only one random effect in the model. Also add a test for this.</li>
+</ul></div>
+    <div class="section level2">
+<h2 class="pkg-version" data-toc-text="1.2.6" id="mkin-126-2023-10-14">mkin 1.2.6 (2023-10-14)<a class="anchor" aria-label="anchor" href="#mkin-126-2023-10-14"></a></h2>
 <ul><li>‘inst/rmarkdown/templates/hierarchical_kinetics/skeleton/skeleton.Rmd’: Fix an erroneous call to the ‘endpoints()’ function</li>
 </ul></div>
     <div class="section level2">
-<h2 class="page-header" data-toc-text="1.2.5" id="mkin-125-2023-08-09">mkin 1.2.5 (2023-08-09)<a class="anchor" aria-label="anchor" href="#mkin-125-2023-08-09"></a></h2>
+<h2 class="pkg-version" data-toc-text="1.2.5" id="mkin-125-2023-08-09">mkin 1.2.5 (2023-08-09)<a class="anchor" aria-label="anchor" href="#mkin-125-2023-08-09"></a></h2>
 <ul><li><p>‘vignettes/mesotrione_parent_2023.rnw’: Prebuilt vignette showing how covariate modelling can be done for all relevant parent degradation models.</p></li>
 <li><p>‘inst/testdata/mesotrione_soil_efsa_2016}.xlsx’: Another example spreadsheets for use with ‘read_spreadsheet()’, featuring pH dependent degradation</p></li>
 <li><p>R/illparms.R: Fix the detection of ill-defined slope or error model parameters for the case that the estimate is negative</p></li>
 </ul></div>
     <div class="section level2">
-<h2 class="page-header" data-toc-text="1.2.4" id="mkin-124-2023-05-19">mkin 1.2.4 (2023-05-19)<a class="anchor" aria-label="anchor" href="#mkin-124-2023-05-19"></a></h2>
+<h2 class="pkg-version" data-toc-text="1.2.4" id="mkin-124-2023-05-19">mkin 1.2.4 (2023-05-19)<a class="anchor" aria-label="anchor" href="#mkin-124-2023-05-19"></a></h2>
 <ul><li>R/endpoints.R: Fix the calculation of endpoints for user specified covariate values</li>
 </ul></div>
     <div class="section level2">
-<h2 class="page-header" data-toc-text="1.2.3.1" id="mkin-1231-unreleased">mkin 1.2.3.1 (unreleased)<a class="anchor" aria-label="anchor" href="#mkin-1231-unreleased"></a></h2>
+<h2 class="pkg-version" data-toc-text="1.2.3.1" id="mkin-1231-unreleased">mkin 1.2.3.1 (unreleased)<a class="anchor" aria-label="anchor" href="#mkin-1231-unreleased"></a></h2>
 <ul><li>Small fixes to get the online docs right (example code in R/hierarchical_kinetics, cluster setup in cyantraniliprole and dmta pathway vignettes, graphics and model comparison in multistart vignette), rebuild online docs</li>
 </ul></div>
     <div class="section level2">
-<h2 class="page-header" data-toc-text="1.2.3" id="mkin-123-2023-04-17">mkin 1.2.3 (2023-04-17)<a class="anchor" aria-label="anchor" href="#mkin-123-2023-04-17"></a></h2>
+<h2 class="pkg-version" data-toc-text="1.2.3" id="mkin-123-2023-04-17">mkin 1.2.3 (2023-04-17)<a class="anchor" aria-label="anchor" href="#mkin-123-2023-04-17"></a></h2>
 <ul><li><p>‘R/{endpoints,parms,plot.mixed.mmkin,summary.saem.mmkin}.R’: Calculate parameters and endpoints and plot population curves for specific covariate values, or specific percentiles of covariate values used in saem fits.</p></li>
 <li><p>Depend on current deSolve version with the possibility to avoid resolving symbols in a shared library (compiled models) over and over, thanks to Thomas Petzoldt.</p></li>
 <li><p>‘inst/rmarkdown/templates/hierarchical_kinetics/skeleton/skeleton.Rmd’: Start a new cluster after creating a model stored in the user specified location, because otherwise symbols are not found by the worker processes.</p></li>
@@ -134,7 +101,7 @@
 <li><p>‘R/mkinerrmin.R’: Fix typo in subset (use of = instead of ==), thanks to Sebastian Meyer for spotting this during his work on R 4.3.0.</p></li>
 </ul></div>
     <div class="section level2">
-<h2 class="page-header" data-toc-text="1.2.2" id="mkin-122-unreleased">mkin 1.2.2 (unreleased)<a class="anchor" aria-label="anchor" href="#mkin-122-unreleased"></a></h2>
+<h2 class="pkg-version" data-toc-text="1.2.2" id="mkin-122-unreleased">mkin 1.2.2 (unreleased)<a class="anchor" aria-label="anchor" href="#mkin-122-unreleased"></a></h2>
 <ul><li><p>‘inst/rmarkdown/templates/hierarchical_kinetics’: R markdown template to facilitate the application of hierarchical kinetic models.</p></li>
 <li><p>‘inst/testdata/{cyantraniliprole_soil_efsa_2014,lambda-cyhalothrin_soil_efsa_2014}.xlsx’: Example spreadsheets for use with ‘read_spreadsheet()’.</p></li>
 <li><p>‘R/mhmkin.R’: Allow an ‘illparms.mhmkin’ object or a list with suitable dimensions as value of the argument ‘no_random_effects’, making it possible to exclude random effects that were ill-defined in simpler variants of the set of degradation models. Remove the possibility to exclude random effects based on separate fits, as it did not work well.</p></li>
@@ -145,7 +112,7 @@
 <li><p>‘R/intervals.R’: Include correlations of random effects in the model in case there are any.</p></li>
 </ul></div>
     <div class="section level2">
-<h2 class="page-header" data-toc-text="1.2.1" id="mkin-121-2022-11-19">mkin 1.2.1 (2022-11-19)<a class="anchor" aria-label="anchor" href="#mkin-121-2022-11-19"></a></h2>
+<h2 class="pkg-version" data-toc-text="1.2.1" id="mkin-121-2022-11-19">mkin 1.2.1 (2022-11-19)<a class="anchor" aria-label="anchor" href="#mkin-121-2022-11-19"></a></h2>
 <ul><li><p>‘{data,R}/ds_mixed.rda’: Include the test data in the package instead of generating it in ‘tests/testthat/setup_script.R’. Refactor the generating code to make it consistent and update tests.</p></li>
 <li><p>‘tests/testthat/setup_script.R’: Excluded another ill-defined random effect for the DFOP fit with ‘saem’, in an attempt to avoid a platform dependence that surfaced on Fedora systems on the CRAN check farm</p></li>
 <li><p>‘tests/testthat/test_mixed.R’: Round parameters found by saemix to two significant digits before printing, to also help to avoid platform dependence of tests</p></li>
@@ -154,7 +121,7 @@
 <li><p>‘R/loglik.mkinfit.R’: Add ‘nobs’ attribute to the resulting ‘logLik’ object, in order to make test_AIC.R succeed on current R-devel</p></li>
 </ul></div>
     <div class="section level2">
-<h2 class="page-header" data-toc-text="1.2.0" id="mkin-120-2022-11-17">mkin 1.2.0 (2022-11-17)<a class="anchor" aria-label="anchor" href="#mkin-120-2022-11-17"></a></h2>
+<h2 class="pkg-version" data-toc-text="1.2.0" id="mkin-120-2022-11-17">mkin 1.2.0 (2022-11-17)<a class="anchor" aria-label="anchor" href="#mkin-120-2022-11-17"></a></h2>
 <ul><li><p>‘R/saem.R’: ‘logLik’, ‘update’ and ‘anova’ methods for ‘saem.mmkin’ objects.</p></li>
 <li><p>‘R/saem.R’: Automatic estimation of start parameters for random effects for the case of mkin transformations, nicely improving convergence and reducing problems with iterative ODE solutions.</p></li>
 <li><p>‘R/status.R’: New generic to show status information for fit array objects with methods for ‘mmkin’, ‘mhmkin’ and ‘multistart’ objects.</p></li>
@@ -168,7 +135,7 @@
 <li><p>‘R/tex_listings.R’: Conveniently include summaries of fit objects in R markdown documents that are compiled to LaTeX.</p></li>
 </ul></div>
     <div class="section level2">
-<h2 class="page-header" data-toc-text="1.1.1" id="mkin-111-2022-07-12">mkin 1.1.1 (2022-07-12)<a class="anchor" aria-label="anchor" href="#mkin-111-2022-07-12"></a></h2>
+<h2 class="pkg-version" data-toc-text="1.1.1" id="mkin-111-2022-07-12">mkin 1.1.1 (2022-07-12)<a class="anchor" aria-label="anchor" href="#mkin-111-2022-07-12"></a></h2>
 <ul><li><p>’R/{mkinmod,mkinpredict}.R: Store DLL information in mkinmod objects and use that information in mkinpredict to avoid a performance regression brought by a bugfix in R 4.2.x. Thanks to Tomas Kalibera for his analysis of the problem on the r-package-devel list and his suggestion on how to fix it.</p></li>
 <li><p>‘vignettes/FOCUS_L.rmd’: Remove an outdated note referring to a failure to calculate the covariance matrix for DFOP with the L2 dataset. Since 0.9.45.5 the covariance matrix is available</p></li>
 <li><p>‘vignettes/web_only/benchmarks.rmd’: Add the first benchmark data using my laptop system, therefore add the CPU when showing the benchmark results.</p></li>
@@ -180,7 +147,7 @@
 <li><p>‘plot.mkinfit’: Respect argument ‘maxabs’ for residual plots, and make it possible to give ylim as a list, for row layouts</p></li>
 </ul></div>
     <div class="section level2">
-<h2 class="page-header" data-toc-text="1.1.0" id="mkin-110-2022-03-14">mkin 1.1.0 (2022-03-14)<a class="anchor" aria-label="anchor" href="#mkin-110-2022-03-14"></a></h2>
+<h2 class="pkg-version" data-toc-text="1.1.0" id="mkin-110-2022-03-14">mkin 1.1.0 (2022-03-14)<a class="anchor" aria-label="anchor" href="#mkin-110-2022-03-14"></a></h2>
 <div class="section level3">
 <h3 id="mixed-effects-models-1-1-0">Mixed-effects models<a class="anchor" aria-label="anchor" href="#mixed-effects-models-1-1-0"></a></h3>
 <ul><li><p>Reintroduce the interface to saemix version 3.0 (now on CRAN), in particular the generic function ‘saem’ with a generator ‘saem.mmkin’, currently using ‘saemix_model’ and ‘saemix_data’, summary and plot methods</p></li>
@@ -191,25 +158,25 @@
 </ul></div>
 </div>
     <div class="section level2">
-<h2 class="page-header" data-toc-text="1.0.5" id="mkin-105-2021-09-15">mkin 1.0.5 (2021-09-15)<a class="anchor" aria-label="anchor" href="#mkin-105-2021-09-15"></a></h2>
+<h2 class="pkg-version" data-toc-text="1.0.5" id="mkin-105-2021-09-15">mkin 1.0.5 (2021-09-15)<a class="anchor" aria-label="anchor" href="#mkin-105-2021-09-15"></a></h2>
 <ul><li>‘dimethenamid_2018’: Correct the data for the Borstel soil. The five observations from Staudenmaier (2013) that were previously stored as “Borstel 2” are actually just a subset of the 16 observations in “Borstel 1” which is now simply “Borstel”</li>
 </ul></div>
     <div class="section level2">
-<h2 class="page-header" data-toc-text="1.0.4" id="mkin-104-2021-04-20">mkin 1.0.4 (2021-04-20)<a class="anchor" aria-label="anchor" href="#mkin-104-2021-04-20"></a></h2>
+<h2 class="pkg-version" data-toc-text="1.0.4" id="mkin-104-2021-04-20">mkin 1.0.4 (2021-04-20)<a class="anchor" aria-label="anchor" href="#mkin-104-2021-04-20"></a></h2>
 <ul><li><p>All plotting functions setting graphical parameters: Use on.exit() for resetting graphical parameters</p></li>
 <li><p>‘plot.mkinfit’: Use xlab and xlim for the residual plot if show_residuals is TRUE</p></li>
 <li><p>‘mmkin’: Use cores = 1 per default on Windows to make it easier for first time users</p></li>
 </ul></div>
     <div class="section level2">
-<h2 class="page-header" data-toc-text="1.0.3" id="mkin-103-2021-02-15">mkin 1.0.3 (2021-02-15)<a class="anchor" aria-label="anchor" href="#mkin-103-2021-02-15"></a></h2>
+<h2 class="pkg-version" data-toc-text="1.0.3" id="mkin-103-2021-02-15">mkin 1.0.3 (2021-02-15)<a class="anchor" aria-label="anchor" href="#mkin-103-2021-02-15"></a></h2>
 <ul><li>Review and update README, the ‘Introduction to mkin’ vignette and some of the help pages</li>
 </ul></div>
     <div class="section level2">
-<h2 class="page-header" data-toc-text="1.0.2" id="mkin-102-unreleased">mkin 1.0.2 (Unreleased)<a class="anchor" aria-label="anchor" href="#mkin-102-unreleased"></a></h2>
+<h2 class="pkg-version" data-toc-text="1.0.2" id="mkin-102-unreleased">mkin 1.0.2 (Unreleased)<a class="anchor" aria-label="anchor" href="#mkin-102-unreleased"></a></h2>
 <ul><li>‘mkinfit’: Keep model names stored in ‘mkinmod’ objects, avoiding their loss in ‘gmkin’</li>
 </ul></div>
     <div class="section level2">
-<h2 class="page-header" data-toc-text="1.0.1" id="mkin-101-2021-02-10">mkin 1.0.1 (2021-02-10)<a class="anchor" aria-label="anchor" href="#mkin-101-2021-02-10"></a></h2>
+<h2 class="pkg-version" data-toc-text="1.0.1" id="mkin-101-2021-02-10">mkin 1.0.1 (2021-02-10)<a class="anchor" aria-label="anchor" href="#mkin-101-2021-02-10"></a></h2>
 <ul><li><p>‘confint.mmkin’, ‘nlme.mmkin’, ‘transform_odeparms’: Fix example code in dontrun sections that failed with current defaults</p></li>
 <li><p>‘logLik.mkinfit’: Improve example code to avoid warnings and show convenient syntax</p></li>
 <li><p>‘mkinresplot’: Re-add Katrin Lindenberger as coauthor who was accidentally removed long ago</p></li>
@@ -217,7 +184,7 @@
 <li><p>Increase test tolerance for some parameter comparisons that also proved to be platform dependent</p></li>
 </ul></div>
     <div class="section level2">
-<h2 class="page-header" data-toc-text="1.0.0" id="mkin-100-2021-02-03">mkin 1.0.0 (2021-02-03)<a class="anchor" aria-label="anchor" href="#mkin-100-2021-02-03"></a></h2>
+<h2 class="pkg-version" data-toc-text="1.0.0" id="mkin-100-2021-02-03">mkin 1.0.0 (2021-02-03)<a class="anchor" aria-label="anchor" href="#mkin-100-2021-02-03"></a></h2>
 <div class="section level3">
 <h3 id="general-1-0-0">General<a class="anchor" aria-label="anchor" href="#general-1-0-0"></a></h3>
 <ul><li><p>‘mkinmod’ models gain arguments ‘name’ and ‘dll_dir’ which, in conjunction with a current version of the ‘inline’ package, make it possible to still use the DLL used for fast ODE solutions with ‘deSolve’ after saving and restoring the ‘mkinmod’ object.</p></li>
@@ -238,7 +205,7 @@
 </ul></div>
 </div>
     <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9.50.3" id="mkin-09503-2020-10-08">mkin 0.9.50.3 (2020-10-08)<a class="anchor" aria-label="anchor" href="#mkin-09503-2020-10-08"></a></h2>
+<h2 class="pkg-version" data-toc-text="0.9.50.3" id="mkin-09503-2020-10-08">mkin 0.9.50.3 (2020-10-08)<a class="anchor" aria-label="anchor" href="#mkin-09503-2020-10-08"></a></h2>
 <ul><li><p>‘parms’: Add a method for mmkin objects</p></li>
 <li><p>‘mmkin’ and ‘confint(method = ’profile’): Use all cores detected by parallel::detectCores() per default</p></li>
 <li><p>‘confint(method = ’profile’): Choose accuracy based on ‘rel_tol’ argument, relative to the bounds obtained by the quadratic approximation</p></li>
@@ -250,22 +217,22 @@
 <li><p>‘endpoints’: Back-calculate DT50 value from DT90 also for the biphasic models DFOP, HS and SFORB</p></li>
 </ul></div>
     <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9.50.2" id="mkin-09502-2020-05-12">mkin 0.9.50.2 (2020-05-12)<a class="anchor" aria-label="anchor" href="#mkin-09502-2020-05-12"></a></h2>
+<h2 class="pkg-version" data-toc-text="0.9.50.2" id="mkin-09502-2020-05-12">mkin 0.9.50.2 (2020-05-12)<a class="anchor" aria-label="anchor" href="#mkin-09502-2020-05-12"></a></h2>
 <ul><li><p>Increase tolerance for a platform specific test results on the Solaris test machine on CRAN</p></li>
 <li><p>Updates and corrections (using the spelling package) to the documentation</p></li>
 </ul></div>
     <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9.50.1" id="mkin-09501-2020-05-11">mkin 0.9.50.1 (2020-05-11)<a class="anchor" aria-label="anchor" href="#mkin-09501-2020-05-11"></a></h2>
+<h2 class="pkg-version" data-toc-text="0.9.50.1" id="mkin-09501-2020-05-11">mkin 0.9.50.1 (2020-05-11)<a class="anchor" aria-label="anchor" href="#mkin-09501-2020-05-11"></a></h2>
 <ul><li><p>Support SFORB with formation fractions</p></li>
 <li><p>‘mkinmod’: Make ‘use_of_ff’ = “max” the default</p></li>
 <li><p>Improve performance by a) avoiding expensive calls in the cost function like merge() and data.frame(), and b) by implementing analytical solutions for SFO-SFO and DFOP-SFO</p></li>
 </ul></div>
     <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9.49.11" id="mkin-094911-2020-04-20">mkin 0.9.49.11 (2020-04-20)<a class="anchor" aria-label="anchor" href="#mkin-094911-2020-04-20"></a></h2>
+<h2 class="pkg-version" data-toc-text="0.9.49.11" id="mkin-094911-2020-04-20">mkin 0.9.49.11 (2020-04-20)<a class="anchor" aria-label="anchor" href="#mkin-094911-2020-04-20"></a></h2>
 <ul><li>Increase a test tolerance to make it pass on all CRAN check machines</li>
 </ul></div>
     <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9.49.10" id="mkin-094910-2020-04-18">mkin 0.9.49.10 (2020-04-18)<a class="anchor" aria-label="anchor" href="#mkin-094910-2020-04-18"></a></h2>
+<h2 class="pkg-version" data-toc-text="0.9.49.10" id="mkin-094910-2020-04-18">mkin 0.9.49.10 (2020-04-18)<a class="anchor" aria-label="anchor" href="#mkin-094910-2020-04-18"></a></h2>
 <ul><li><p>‘nlme.mmkin’: An nlme method for mmkin row objects and an associated S3 class with print, plot, anova and endpoint methods</p></li>
 <li><p>‘mean_degparms, nlme_data, nlme_function’: Three new functions to facilitate building nlme models from mmkin row objects</p></li>
 <li><p>‘endpoints’: Don’t return the SFORB list component if it’s empty. This reduces distraction and complies with the documentation</p></li>
@@ -274,12 +241,12 @@
 <li><p>‘summary.mkinfit’: Add AIC, BIC and log likelihood to the summary</p></li>
 </ul></div>
     <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9.49.9" id="mkin-09499-2020-03-31">mkin 0.9.49.9 (2020-03-31)<a class="anchor" aria-label="anchor" href="#mkin-09499-2020-03-31"></a></h2>
+<h2 class="pkg-version" data-toc-text="0.9.49.9" id="mkin-09499-2020-03-31">mkin 0.9.49.9 (2020-03-31)<a class="anchor" aria-label="anchor" href="#mkin-09499-2020-03-31"></a></h2>
 <ul><li><p>‘mkinmod’: Use pkgbuild::has_compiler instead of Sys.which(‘gcc’), as the latter will often fail even if Rtools are installed</p></li>
 <li><p>‘mkinds’: Use roxygen for documenting fields and methods of this R6 class</p></li>
 </ul></div>
     <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9.49.8" id="mkin-09498-2020-01-09">mkin 0.9.49.8 (2020-01-09)<a class="anchor" aria-label="anchor" href="#mkin-09498-2020-01-09"></a></h2>
+<h2 class="pkg-version" data-toc-text="0.9.49.8" id="mkin-09498-2020-01-09">mkin 0.9.49.8 (2020-01-09)<a class="anchor" aria-label="anchor" href="#mkin-09498-2020-01-09"></a></h2>
 <ul><li><p>‘aw’: Generic function for calculating Akaike weights, methods for mkinfit objects and mmkin columns</p></li>
 <li><p>‘loftest’: Add a lack-of-fit test</p></li>
 <li><p>‘plot_res’, ‘plot_sep’ and ‘mkinerrplot’: Add the possibility to show standardized residuals and make it the default for fits with error models other than ‘const’</p></li>
@@ -287,12 +254,12 @@
 <li><p>‘confint.mkinfit’: Make the quadratic approximation the default, as the likelihood profiling takes a lot of time, especially if the fit has more than three parameters</p></li>
 </ul></div>
     <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9.49.7" id="mkin-09497-2019-11-01">mkin 0.9.49.7 (2019-11-01)<a class="anchor" aria-label="anchor" href="#mkin-09497-2019-11-01"></a></h2>
+<h2 class="pkg-version" data-toc-text="0.9.49.7" id="mkin-09497-2019-11-01">mkin 0.9.49.7 (2019-11-01)<a class="anchor" aria-label="anchor" href="#mkin-09497-2019-11-01"></a></h2>
 <ul><li><p>Fix a bug introduced in 0.9.49.6 that occurred if the direct optimisation yielded a higher likelihood than the three-step optimisation in the d_3 algorithm, which caused the fitted parameters of the three-step optimisation to be returned instead of the parameters of the direct optimisation</p></li>
 <li><p>Add a ‘nobs’ method for mkinfit objects, enabling the default ‘BIC’ method from the stats package. Also, add a ‘BIC’ method for mmkin column objects.</p></li>
 </ul></div>
     <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9.49.6" id="mkin-09496-2019-10-31">mkin 0.9.49.6 (2019-10-31)<a class="anchor" aria-label="anchor" href="#mkin-09496-2019-10-31"></a></h2>
+<h2 class="pkg-version" data-toc-text="0.9.49.6" id="mkin-09496-2019-10-31">mkin 0.9.49.6 (2019-10-31)<a class="anchor" aria-label="anchor" href="#mkin-09496-2019-10-31"></a></h2>
 <ul><li><p>Implement a likelihood ratio test as a method for ‘lrtest’ from the lmtest package</p></li>
 <li><p>Add an ‘update’ method for mkinfit objects which remembers fitted parameters if appropriate</p></li>
 <li><p>Add a ‘residuals’ method for mkinfit objects that supports scaling based on the error model</p></li>
@@ -308,7 +275,7 @@
 <li><p>Support summarizing ‘mkinfit’ objects generated with versions &lt; 0.9.49.5</p></li>
 </ul></div>
     <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9.49.5" id="mkin-09495-2019-07-04">mkin 0.9.49.5 (2019-07-04)<a class="anchor" aria-label="anchor" href="#mkin-09495-2019-07-04"></a></h2>
+<h2 class="pkg-version" data-toc-text="0.9.49.5" id="mkin-09495-2019-07-04">mkin 0.9.49.5 (2019-07-04)<a class="anchor" aria-label="anchor" href="#mkin-09495-2019-07-04"></a></h2>
 <ul><li><p>Several algorithms for minimization of the negative log-likelihood for non-constant error models (two-component and variance by variable). In the case the error model is constant variance, least squares is used as this is more stable. The default algorithm ‘d_3’ tries direct minimization and a three-step procedure, and returns the model with the highest likelihood.</p></li>
 <li><p>The argument ‘reweight.method’ to mkinfit and mmkin is now obsolete, use ‘error_model’ and ‘error_model_algorithm’ instead</p></li>
 <li><p>Add a test that checks if we get the best known AIC for parent only fits to 12 test datasets. Add these test datasets for this purpose.</p></li>
@@ -323,7 +290,7 @@
 <li><p>Add example datasets obtained from risk assessment reports published by the European Food Safety Agency.</p></li>
 </ul></div>
     <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9.48.1" id="mkin-09481-2019-03-04">mkin 0.9.48.1 (2019-03-04)<a class="anchor" aria-label="anchor" href="#mkin-09481-2019-03-04"></a></h2>
+<h2 class="pkg-version" data-toc-text="0.9.48.1" id="mkin-09481-2019-03-04">mkin 0.9.48.1 (2019-03-04)<a class="anchor" aria-label="anchor" href="#mkin-09481-2019-03-04"></a></h2>
 <ul><li><p>Add the function ‘logLik.mkinfit’ which makes it possible to calculate an AIC for mkinfit objects</p></li>
 <li><p>Add the function ‘AIC.mmkin’ to make it easy to compare columns of mmkin objects</p></li>
 <li><p>‘add_err’: Respect the argument giving the number of replicates in the synthetic dataset</p></li>
@@ -336,23 +303,23 @@
 <li><p>‘nafta’: Add evaluations according to the NAFTA guidance</p></li>
 </ul></div>
     <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9.47.5" id="mkin-09475-2018-09-14">mkin 0.9.47.5 (2018-09-14)<a class="anchor" aria-label="anchor" href="#mkin-09475-2018-09-14"></a></h2>
+<h2 class="pkg-version" data-toc-text="0.9.47.5" id="mkin-09475-2018-09-14">mkin 0.9.47.5 (2018-09-14)<a class="anchor" aria-label="anchor" href="#mkin-09475-2018-09-14"></a></h2>
 <ul><li><p>Make the two-component error model stop in cases where it is inadequate to avoid nls crashes on windows</p></li>
 <li><p>Move two vignettes to a location where they will not be built on CRAN (to avoid more NOTES from long execution times)</p></li>
 <li><p>Exclude more example code from testing on CRAN to avoid NOTES from long execution times</p></li>
 </ul></div>
     <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9.47.3" id="mkin-09473">mkin 0.9.47.3<a class="anchor" aria-label="anchor" href="#mkin-09473"></a></h2>
+<h2 class="pkg-version" data-toc-text="0.9.47.3" id="mkin-09473">mkin 0.9.47.3<a class="anchor" aria-label="anchor" href="#mkin-09473"></a></h2>
 <ul><li><p>‘mkinfit’: Improve fitting the error model for reweight.method = ‘tc’. Add ‘manual’ to possible arguments for ‘weight’</p></li>
 <li><p>Test that FOCUS_2006_C can be evaluated with DFOP and reweight.method = ‘tc’</p></li>
 </ul></div>
     <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9.47.2" id="mkin-09472-2018-07-19">mkin 0.9.47.2 (2018-07-19)<a class="anchor" aria-label="anchor" href="#mkin-09472-2018-07-19"></a></h2>
+<h2 class="pkg-version" data-toc-text="0.9.47.2" id="mkin-09472-2018-07-19">mkin 0.9.47.2 (2018-07-19)<a class="anchor" aria-label="anchor" href="#mkin-09472-2018-07-19"></a></h2>
 <ul><li><p>‘sigma_twocomp’: Rename ‘sigma_rl’ to ‘sigma_twocomp’ as the Rocke and Lorenzato model assumes lognormal distribution for large y. Correct references to the Rocke and Lorenzato model accordingly.</p></li>
 <li><p>‘mkinfit’: Use 1.1 as starting value for N parameter of IORE models to obtain convergence in more difficult cases. Show parameter names when ‘trace_parms’ is ‘TRUE’.</p></li>
 </ul></div>
     <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9.47.1" id="mkin-09471-2018-02-06">mkin 0.9.47.1 (2018-02-06)<a class="anchor" aria-label="anchor" href="#mkin-09471-2018-02-06"></a></h2>
+<h2 class="pkg-version" data-toc-text="0.9.47.1" id="mkin-09471-2018-02-06">mkin 0.9.47.1 (2018-02-06)<a class="anchor" aria-label="anchor" href="#mkin-09471-2018-02-06"></a></h2>
 <ul><li><p>Skip some tests on CRAN and winbuilder to avoid timeouts</p></li>
 <li><p>‘test_data_from_UBA_2014’: Added this list of datasets containing experimental data used in the expertise from 2014</p></li>
 <li><p>‘mkinfit’: Added the iterative reweighting method ‘tc’ using the two-component error model from Rocke and Lorenzato. NA values in the data are not returned any more.</p></li>
@@ -361,41 +328,41 @@
 <li><p>‘summary.mkinfit’: Show versions of mkin and R used for fitting (not the ones used for the summary) if the fit was generated with mkin &gt;= 0.9.47.1</p></li>
 </ul></div>
     <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9.46.3" id="mkin-09463-2017-11-16">mkin 0.9.46.3 (2017-11-16)<a class="anchor" aria-label="anchor" href="#mkin-09463-2017-11-16"></a></h2>
+<h2 class="pkg-version" data-toc-text="0.9.46.3" id="mkin-09463-2017-11-16">mkin 0.9.46.3 (2017-11-16)<a class="anchor" aria-label="anchor" href="#mkin-09463-2017-11-16"></a></h2>
 <ul><li><p><code>README.md</code>, <code>vignettes/mkin.Rmd</code>: URLs were updated</p></li>
 <li><p><code>synthetic_data_for_UBA</code>: Add the code used to generate the data in the interest of reproducibility</p></li>
 </ul></div>
     <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9.46.2" id="mkin-09462-2017-10-10">mkin 0.9.46.2 (2017-10-10)<a class="anchor" aria-label="anchor" href="#mkin-09462-2017-10-10"></a></h2>
+<h2 class="pkg-version" data-toc-text="0.9.46.2" id="mkin-09462-2017-10-10">mkin 0.9.46.2 (2017-10-10)<a class="anchor" aria-label="anchor" href="#mkin-09462-2017-10-10"></a></h2>
 <ul><li><p>Converted the vignette FOCUS_Z from tex/pdf to markdown/html</p></li>
 <li><p><code>DESCRIPTION</code>: Add ORCID</p></li>
 </ul></div>
     <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9.46.1" id="mkin-09461-2017-09-14">mkin 0.9.46.1 (2017-09-14)<a class="anchor" aria-label="anchor" href="#mkin-09461-2017-09-14"></a></h2>
+<h2 class="pkg-version" data-toc-text="0.9.46.1" id="mkin-09461-2017-09-14">mkin 0.9.46.1 (2017-09-14)<a class="anchor" aria-label="anchor" href="#mkin-09461-2017-09-14"></a></h2>
 <ul><li><p><code>plot.mkinfit</code>: Fix scaling of residual plots for the case of separate plots for each observed variable</p></li>
 <li><p><code>plot.mkinfit</code>: Use all data points of the fitted curve for y axis scaling for the case of separate plots for each observed variable</p></li>
 <li><p>Documentation updates</p></li>
 </ul></div>
     <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9.46" id="mkin-0946-2017-07-24">mkin 0.9.46 (2017-07-24)<a class="anchor" aria-label="anchor" href="#mkin-0946-2017-07-24"></a></h2>
+<h2 class="pkg-version" data-toc-text="0.9.46" id="mkin-0946-2017-07-24">mkin 0.9.46 (2017-07-24)<a class="anchor" aria-label="anchor" href="#mkin-0946-2017-07-24"></a></h2>
 <ul><li>Remove <code>test_FOMC_ill-defined.R</code> as it is too platform dependent</li>
 </ul></div>
     <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9.45.2" id="mkin-09452-2017-07-24">mkin 0.9.45.2 (2017-07-24)<a class="anchor" aria-label="anchor" href="#mkin-09452-2017-07-24"></a></h2>
+<h2 class="pkg-version" data-toc-text="0.9.45.2" id="mkin-09452-2017-07-24">mkin 0.9.45.2 (2017-07-24)<a class="anchor" aria-label="anchor" href="#mkin-09452-2017-07-24"></a></h2>
 <ul><li><p>Rename <code>twa</code> to <code>max_twa_parent</code> to avoid conflict with <code>twa</code> from my <code>pfm</code> package</p></li>
 <li><p>Update URLs in documentation</p></li>
 <li><p>Limit test code to one core to pass on windows</p></li>
 <li><p>Switch from <code>microbenchmark</code> to <code>rbenchmark</code> as the former is not supported on all platforms</p></li>
 </ul></div>
     <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9.45.1" id="mkin-09451-2016-12-20">mkin 0.9.45.1 (2016-12-20)<a class="anchor" aria-label="anchor" href="#mkin-09451-2016-12-20"></a></h2>
+<h2 class="pkg-version" data-toc-text="0.9.45.1" id="mkin-09451-2016-12-20">mkin 0.9.45.1 (2016-12-20)<a class="anchor" aria-label="anchor" href="#mkin-09451-2016-12-20"></a></h2>
 <div class="section level3">
 <h3 id="new-features-0-9-45-1">New features<a class="anchor" aria-label="anchor" href="#new-features-0-9-45-1"></a></h3>
 <ul><li>A <code>twa</code> function, calculating maximum time weighted average concentrations for the parent (SFO, FOMC and DFOP).</li>
 </ul></div>
 </div>
     <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9.45" id="mkin-0945-2016-12-08">mkin 0.9.45 (2016-12-08)<a class="anchor" aria-label="anchor" href="#mkin-0945-2016-12-08"></a></h2>
+<h2 class="pkg-version" data-toc-text="0.9.45" id="mkin-0945-2016-12-08">mkin 0.9.45 (2016-12-08)<a class="anchor" aria-label="anchor" href="#mkin-0945-2016-12-08"></a></h2>
 <div class="section level3">
 <h3 id="minor-changes-0-9-45">Minor changes<a class="anchor" aria-label="anchor" href="#minor-changes-0-9-45"></a></h3>
 <ul><li><p><code>plot.mkinfit</code> and <code>plot.mmkin</code>: If the plotting device is <code>tikz</code>, LaTeX markup is being used for the chi2 error in the graphs.</p></li>
@@ -404,14 +371,14 @@
 </ul></div>
 </div>
     <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9.44" id="mkin-0944-2016-06-29">mkin 0.9.44 (2016-06-29)<a class="anchor" aria-label="anchor" href="#mkin-0944-2016-06-29"></a></h2>
+<h2 class="pkg-version" data-toc-text="0.9.44" id="mkin-0944-2016-06-29">mkin 0.9.44 (2016-06-29)<a class="anchor" aria-label="anchor" href="#mkin-0944-2016-06-29"></a></h2>
 <div class="section level3">
 <h3 id="bug-fixes-0-9-44">Bug fixes<a class="anchor" aria-label="anchor" href="#bug-fixes-0-9-44"></a></h3>
 <ul><li>The test <code>test_FOMC_ill-defined</code> failed on several architectures, so the test is now skipped</li>
 </ul></div>
 </div>
     <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9.43" id="mkin-0943-2016-06-28">mkin 0.9.43 (2016-06-28)<a class="anchor" aria-label="anchor" href="#mkin-0943-2016-06-28"></a></h2>
+<h2 class="pkg-version" data-toc-text="0.9.43" id="mkin-0943-2016-06-28">mkin 0.9.43 (2016-06-28)<a class="anchor" aria-label="anchor" href="#mkin-0943-2016-06-28"></a></h2>
 <div class="section level3">
 <h3 id="major-changes-0-9-43">Major changes<a class="anchor" aria-label="anchor" href="#major-changes-0-9-43"></a></h3>
 <ul><li><p>The title was changed to <code>Kinetic evaluations of chemical degradation data</code></p></li>
@@ -439,7 +406,7 @@
 </ul></div>
 </div>
     <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9.42" id="mkin-0942-2016-03-25">mkin 0.9.42 (2016-03-25)<a class="anchor" aria-label="anchor" href="#mkin-0942-2016-03-25"></a></h2>
+<h2 class="pkg-version" data-toc-text="0.9.42" id="mkin-0942-2016-03-25">mkin 0.9.42 (2016-03-25)<a class="anchor" aria-label="anchor" href="#mkin-0942-2016-03-25"></a></h2>
 <div class="section level3">
 <h3 id="major-changes-0-9-42">Major changes<a class="anchor" aria-label="anchor" href="#major-changes-0-9-42"></a></h3>
 <ul><li>Add the argument <code>from_max_mean</code> to <code>mkinfit</code>, for fitting only the decline from the maximum observed value for models with a single observed variable</li>
@@ -452,7 +419,7 @@
 </ul></div>
 </div>
     <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9-41" id="mkin-09-41-2015-11-09">mkin 0.9-41 (2015-11-09)<a class="anchor" aria-label="anchor" href="#mkin-09-41-2015-11-09"></a></h2>
+<h2 class="pkg-version" data-toc-text="0.9-41" id="mkin-09-41-2015-11-09">mkin 0.9-41 (2015-11-09)<a class="anchor" aria-label="anchor" href="#mkin-09-41-2015-11-09"></a></h2>
 <div class="section level3">
 <h3 id="minor-changes-0-9-41">Minor changes<a class="anchor" aria-label="anchor" href="#minor-changes-0-9-41"></a></h3>
 <ul><li><p>Add an R6 class <code>mkinds</code> representing datasets with a printing method</p></li>
@@ -467,7 +434,7 @@
 </ul></div>
 </div>
     <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9-40" id="mkin-09-40-2015-07-21">mkin 0.9-40 (2015-07-21)<a class="anchor" aria-label="anchor" href="#mkin-09-40-2015-07-21"></a></h2>
+<h2 class="pkg-version" data-toc-text="0.9-40" id="mkin-09-40-2015-07-21">mkin 0.9-40 (2015-07-21)<a class="anchor" aria-label="anchor" href="#mkin-09-40-2015-07-21"></a></h2>
 <div class="section level3">
 <h3 id="bug-fixes-0-9-40">Bug fixes<a class="anchor" aria-label="anchor" href="#bug-fixes-0-9-40"></a></h3>
 <ul><li>
@@ -480,7 +447,7 @@
 </ul></div>
 </div>
     <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9-39" id="mkin-09-39-2015-06-26">mkin 0.9-39 (2015-06-26)<a class="anchor" aria-label="anchor" href="#mkin-09-39-2015-06-26"></a></h2>
+<h2 class="pkg-version" data-toc-text="0.9-39" id="mkin-09-39-2015-06-26">mkin 0.9-39 (2015-06-26)<a class="anchor" aria-label="anchor" href="#mkin-09-39-2015-06-26"></a></h2>
 <div class="section level3">
 <h3 id="major-changes-0-9-39">Major changes<a class="anchor" aria-label="anchor" href="#major-changes-0-9-39"></a></h3>
 <ul><li><p>New function <code><a href="../reference/mmkin.html">mmkin()</a></code>: This function takes a character vector of model shorthand names, or alternatively a list of mkinmod models, as well as a list of dataset as main arguments. It returns a matrix of mkinfit objects, with a row for each model and a column for each dataset. A subsetting method with single brackets is available. Fitting the models in parallel using the <code>parallel</code> package is supported.</p></li>
@@ -493,7 +460,7 @@
 </ul></div>
 </div>
     <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9-38" id="mkin-09-38-2015-06-24">mkin 0.9-38 (2015-06-24)<a class="anchor" aria-label="anchor" href="#mkin-09-38-2015-06-24"></a></h2>
+<h2 class="pkg-version" data-toc-text="0.9-38" id="mkin-09-38-2015-06-24">mkin 0.9-38 (2015-06-24)<a class="anchor" aria-label="anchor" href="#mkin-09-38-2015-06-24"></a></h2>
 <div class="section level3">
 <h3 id="minor-changes-0-9-38">Minor changes<a class="anchor" aria-label="anchor" href="#minor-changes-0-9-38"></a></h3>
 <ul><li><p><code>vignettes/compiled_models.html</code>: Show the performance improvement factor actually obtained when building the vignette, as well as mkin version, some system info and the CPU model used for building the vignette.</p></li>
@@ -506,7 +473,7 @@
 </ul></div>
 </div>
     <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9-36" id="mkin-09-36-2015-06-21">mkin 0.9-36 (2015-06-21)<a class="anchor" aria-label="anchor" href="#mkin-09-36-2015-06-21"></a></h2>
+<h2 class="pkg-version" data-toc-text="0.9-36" id="mkin-09-36-2015-06-21">mkin 0.9-36 (2015-06-21)<a class="anchor" aria-label="anchor" href="#mkin-09-36-2015-06-21"></a></h2>
 <div class="section level3">
 <h3 id="major-changes-0-9-36">Major changes<a class="anchor" aria-label="anchor" href="#major-changes-0-9-36"></a></h3>
 <ul><li><p><code><a href="../reference/summary.mkinfit.html">summary.mkinfit()</a></code>: A one-sided t-test for significant difference of untransformed parameters from zero is now always shown, based on the assumption of normal distribution for estimators of all untransformed parameters. Use with caution, as this assumption is unrealistic e.g. for rate constants in these nonlinear kinetic models.</p></li>
@@ -520,7 +487,7 @@
 </ul></div>
 </div>
     <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9-35" id="mkin-09-35-2015-05-15">mkin 0.9-35 (2015-05-15)<a class="anchor" aria-label="anchor" href="#mkin-09-35-2015-05-15"></a></h2>
+<h2 class="pkg-version" data-toc-text="0.9-35" id="mkin-09-35-2015-05-15">mkin 0.9-35 (2015-05-15)<a class="anchor" aria-label="anchor" href="#mkin-09-35-2015-05-15"></a></h2>
 <div class="section level3">
 <h3 id="major-changes-0-9-35">Major changes<a class="anchor" aria-label="anchor" href="#major-changes-0-9-35"></a></h3>
 <ul><li>Switch from RUnit to testthat for testing</li>
@@ -541,7 +508,7 @@
 </ul></div>
 </div>
     <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9-34" id="mkin-09-34-2014-11-22">mkin 0.9-34 (2014-11-22)<a class="anchor" aria-label="anchor" href="#mkin-09-34-2014-11-22"></a></h2>
+<h2 class="pkg-version" data-toc-text="0.9-34" id="mkin-09-34-2014-11-22">mkin 0.9-34 (2014-11-22)<a class="anchor" aria-label="anchor" href="#mkin-09-34-2014-11-22"></a></h2>
 <div class="section level3">
 <h3 id="new-features-0-9-34">New features<a class="anchor" aria-label="anchor" href="#new-features-0-9-34"></a></h3>
 <ul><li><p>Add the convenience function <code><a href="../reference/mkinmod.html">mkinsub()</a></code> for creating the lists used in <code><a href="../reference/mkinmod.html">mkinmod()</a></code></p></li>
@@ -555,7 +522,7 @@
 </ul></div>
 </div>
     <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9-33" id="mkin-09-33-2014-10-22">mkin 0.9-33 (2014-10-22)<a class="anchor" aria-label="anchor" href="#mkin-09-33-2014-10-22"></a></h2>
+<h2 class="pkg-version" data-toc-text="0.9-33" id="mkin-09-33-2014-10-22">mkin 0.9-33 (2014-10-22)<a class="anchor" aria-label="anchor" href="#mkin-09-33-2014-10-22"></a></h2>
 <div class="section level3">
 <h3 id="new-features-0-9-33">New features<a class="anchor" aria-label="anchor" href="#new-features-0-9-33"></a></h3>
 <ul><li><p>The initial value (state.ini) for the observed variable with the highest observed residue is set to 100 in case it has no time zero observation and <code>state.ini = "auto"</code></p></li>
@@ -577,7 +544,7 @@
 </ul></div>
 </div>
     <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9-32" id="mkin-09-32-2014-07-24">mkin 0.9-32 (2014-07-24)<a class="anchor" aria-label="anchor" href="#mkin-09-32-2014-07-24"></a></h2>
+<h2 class="pkg-version" data-toc-text="0.9-32" id="mkin-09-32-2014-07-24">mkin 0.9-32 (2014-07-24)<a class="anchor" aria-label="anchor" href="#mkin-09-32-2014-07-24"></a></h2>
 <div class="section level3">
 <h3 id="new-features-0-9-32">New features<a class="anchor" aria-label="anchor" href="#new-features-0-9-32"></a></h3>
 <ul><li><p>The number of degrees of freedom is difficult to define in the case of ilr transformation of formation fractions. Now for each source compartment the number of ilr parameters (=number of optimised parameters) is divided by the number of pathways to metabolites (=number of affected data series) which leads to fractional degrees of freedom in some cases.</p></li>
@@ -603,14 +570,14 @@
 </ul></div>
 </div>
     <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9-31" id="mkin-09-31-2014-07-14">mkin 0.9-31 (2014-07-14)<a class="anchor" aria-label="anchor" href="#mkin-09-31-2014-07-14"></a></h2>
+<h2 class="pkg-version" data-toc-text="0.9-31" id="mkin-09-31-2014-07-14">mkin 0.9-31 (2014-07-14)<a class="anchor" aria-label="anchor" href="#mkin-09-31-2014-07-14"></a></h2>
 <div class="section level3">
 <h3 id="bug-fixes-0-9-31">Bug fixes<a class="anchor" aria-label="anchor" href="#bug-fixes-0-9-31"></a></h3>
 <ul><li>The internal renaming of optimised parameters in Version 0.9-30 led to errors in the determination of the degrees of freedom for the chi2 error level calulations in <code><a href="../reference/mkinerrmin.html">mkinerrmin()</a></code> used by the summary function.</li>
 </ul></div>
 </div>
     <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9-30" id="mkin-09-30-2014-07-11">mkin 0.9-30 (2014-07-11)<a class="anchor" aria-label="anchor" href="#mkin-09-30-2014-07-11"></a></h2>
+<h2 class="pkg-version" data-toc-text="0.9-30" id="mkin-09-30-2014-07-11">mkin 0.9-30 (2014-07-11)<a class="anchor" aria-label="anchor" href="#mkin-09-30-2014-07-11"></a></h2>
 <div class="section level3">
 <h3 id="new-features-0-9-30">New features<a class="anchor" aria-label="anchor" href="#new-features-0-9-30"></a></h3>
 <ul><li>It is now possible to use formation fractions in combination with turning off the sink in <code><a href="../reference/mkinmod.html">mkinmod()</a></code>.</li>
@@ -631,19 +598,19 @@
 </ul></div>
 </div>
     <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9-29" id="mkin-09-29-2014-06-27">mkin 0.9-29 (2014-06-27)<a class="anchor" aria-label="anchor" href="#mkin-09-29-2014-06-27"></a></h2>
+<h2 class="pkg-version" data-toc-text="0.9-29" id="mkin-09-29-2014-06-27">mkin 0.9-29 (2014-06-27)<a class="anchor" aria-label="anchor" href="#mkin-09-29-2014-06-27"></a></h2>
 <ul><li><p>R/mkinresplot.R: Make it possible to specify <code>xlim</code></p></li>
 <li><p>R/geometric_mean.R, man/geometric_mean.Rd: Add geometric mean function</p></li>
 <li><p>R/endpoints.R, man/endpoints.Rd: Calculate additional (pseudo)-DT50 values for FOMC, DFOP, HS and SFORB. Avoid calculation of formation fractions from rate constants when they are directly fitted</p></li>
 </ul></div>
     <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9-28" id="mkin-09-28-2014-05-20">mkin 0.9-28 (2014-05-20)<a class="anchor" aria-label="anchor" href="#mkin-09-28-2014-05-20"></a></h2>
+<h2 class="pkg-version" data-toc-text="0.9-28" id="mkin-09-28-2014-05-20">mkin 0.9-28 (2014-05-20)<a class="anchor" aria-label="anchor" href="#mkin-09-28-2014-05-20"></a></h2>
 <ul><li><p>Do not backtransform confidence intervals for formation fractions if more than one compound is formed, as such parameters only define the pathways as a set</p></li>
 <li><p>Add historical remarks and some background to the main package vignette</p></li>
 <li><p>Correct ‘isotropic’ into ‘isometric’ for the ilr transformation</p></li>
 </ul></div>
     <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9-27" id="mkin-09-27-2014-05-10">mkin 0.9-27 (2014-05-10)<a class="anchor" aria-label="anchor" href="#mkin-09-27-2014-05-10"></a></h2>
+<h2 class="pkg-version" data-toc-text="0.9-27" id="mkin-09-27-2014-05-10">mkin 0.9-27 (2014-05-10)<a class="anchor" aria-label="anchor" href="#mkin-09-27-2014-05-10"></a></h2>
 <ul><li><p>Fork the GUI into a separate package <a href="https://github.com/jranke/gmkin" class="external-link">gmkin</a></p></li>
 <li><p>DESCRIPTION, NAMESPACE, TODO: Adapt and add copyright information</p></li>
 <li><p>Remove files belonging to the GUI</p></li>
@@ -663,13 +630,13 @@
 <li><p>Add gmkin workspace datasets FOCUS_2006_gmkin and FOCUS_2006_Z_gmkin</p></li>
 </ul></div>
     <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9-24" id="mkin-09-24-2013-11-06">mkin 0.9-24 (2013-11-06)<a class="anchor" aria-label="anchor" href="#mkin-09-24-2013-11-06"></a></h2>
+<h2 class="pkg-version" data-toc-text="0.9-24" id="mkin-09-24-2013-11-06">mkin 0.9-24 (2013-11-06)<a class="anchor" aria-label="anchor" href="#mkin-09-24-2013-11-06"></a></h2>
 <ul><li><p>Bugfix re-enabling the fixing of any combination of initial values for state variables</p></li>
 <li><p>Default values for kinetic rate constants are not all 0.1 any more but are “salted” with a small increment to avoid numeric artefacts with the eigenvalue based solutions</p></li>
 <li><p>Backtransform fixed ODE parameters for the summary</p></li>
 </ul></div>
     <div class="section level2">
-<h2 class="page-header" data-toc-text="0.9-22" id="mkin-09-22-2013-10-26">mkin 0.9-22 (2013-10-26)<a class="anchor" aria-label="anchor" href="#mkin-09-22-2013-10-26"></a></h2>
+<h2 class="pkg-version" data-toc-text="0.9-22" id="mkin-09-22-2013-10-26">mkin 0.9-22 (2013-10-26)<a class="anchor" aria-label="anchor" href="#mkin-09-22-2013-10-26"></a></h2>
 <ul><li><p>Get rid of the optimisation step in <code>mkinerrmin</code> - this was unnecessary. Thanks to KinGUII for the inspiration - actually this is equation 6-2 in FOCUS kinetics p. 91 that I had overlooked originally</p></li>
 <li><p>Fix <code>plot.mkinfit</code> as it passed graphical arguments like main to the solver</p></li>
 <li><p>Do not use <code>plot=TRUE</code> in <code><a href="../reference/mkinfit.html">mkinfit()</a></code> example</p></li>
@@ -683,29 +650,23 @@
 <li><p>Do not use 0 values at time zero for chi2 error level calculations. This is the way it is done in KinGUII and it makes sense. It does impact the chi2 error levels in the output. Generally they seem to be lower for metabolites now, presumably because the mean of the observed values is higher</p></li>
 </ul><p>For a detailed list of changes to the mkin source please consult the commit history on <a href="http://github.com/jranke/mkin" class="external-link uri">http://github.com/jranke/mkin</a></p>
 </div>
-  </div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
 
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
-</div>
+    </footer></div>
 
-      </footer></div>
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/pkgdown.css b/docs/pkgdown.css
deleted file mode 100644
index 80ea5b83..00000000
--- a/docs/pkgdown.css
+++ /dev/null
@@ -1,384 +0,0 @@
-/* Sticky footer */
-
-/**
- * Basic idea: https://philipwalton.github.io/solved-by-flexbox/demos/sticky-footer/
- * Details: https://github.com/philipwalton/solved-by-flexbox/blob/master/assets/css/components/site.css
- *
- * .Site -> body > .container
- * .Site-content -> body > .container .row
- * .footer -> footer
- *
- * Key idea seems to be to ensure that .container and __all its parents__
- * have height set to 100%
- *
- */
-
-html, body {
-  height: 100%;
-}
-
-body {
-  position: relative;
-}
-
-body > .container {
-  display: flex;
-  height: 100%;
-  flex-direction: column;
-}
-
-body > .container .row {
-  flex: 1 0 auto;
-}
-
-footer {
-  margin-top: 45px;
-  padding: 35px 0 36px;
-  border-top: 1px solid #e5e5e5;
-  color: #666;
-  display: flex;
-  flex-shrink: 0;
-}
-footer p {
-  margin-bottom: 0;
-}
-footer div {
-  flex: 1;
-}
-footer .pkgdown {
-  text-align: right;
-}
-footer p {
-  margin-bottom: 0;
-}
-
-img.icon {
-  float: right;
-}
-
-/* Ensure in-page images don't run outside their container */
-.contents img {
-  max-width: 100%;
-  height: auto;
-}
-
-/* Fix bug in bootstrap (only seen in firefox) */
-summary {
-  display: list-item;
-}
-
-/* Typographic tweaking ---------------------------------*/
-
-.contents .page-header {
-  margin-top: calc(-60px + 1em);
-}
-
-dd {
-  margin-left: 3em;
-}
-
-/* Section anchors ---------------------------------*/
-
-a.anchor {
-  display: none;
-  margin-left: 5px;
-  width: 20px;
-  height: 20px;
-
-  background-image: url(./link.svg);
-  background-repeat: no-repeat;
-  background-size: 20px 20px;
-  background-position: center center;
-}
-
-h1:hover .anchor,
-h2:hover .anchor,
-h3:hover .anchor,
-h4:hover .anchor,
-h5:hover .anchor,
-h6:hover .anchor {
-  display: inline-block;
-}
-
-/* Fixes for fixed navbar --------------------------*/
-
-.contents h1, .contents h2, .contents h3, .contents h4 {
-  padding-top: 60px;
-  margin-top: -40px;
-}
-
-/* Navbar submenu --------------------------*/
-
-.dropdown-submenu {
-  position: relative;
-}
-
-.dropdown-submenu>.dropdown-menu {
-  top: 0;
-  left: 100%;
-  margin-top: -6px;
-  margin-left: -1px;
-  border-radius: 0 6px 6px 6px;
-}
-
-.dropdown-submenu:hover>.dropdown-menu {
-  display: block;
-}
-
-.dropdown-submenu>a:after {
-  display: block;
-  content: " ";
-  float: right;
-  width: 0;
-  height: 0;
-  border-color: transparent;
-  border-style: solid;
-  border-width: 5px 0 5px 5px;
-  border-left-color: #cccccc;
-  margin-top: 5px;
-  margin-right: -10px;
-}
-
-.dropdown-submenu:hover>a:after {
-  border-left-color: #ffffff;
-}
-
-.dropdown-submenu.pull-left {
-  float: none;
-}
-
-.dropdown-submenu.pull-left>.dropdown-menu {
-  left: -100%;
-  margin-left: 10px;
-  border-radius: 6px 0 6px 6px;
-}
-
-/* Sidebar --------------------------*/
-
-#pkgdown-sidebar {
-  margin-top: 30px;
-  position: -webkit-sticky;
-  position: sticky;
-  top: 70px;
-}
-
-#pkgdown-sidebar h2 {
-  font-size: 1.5em;
-  margin-top: 1em;
-}
-
-#pkgdown-sidebar h2:first-child {
-  margin-top: 0;
-}
-
-#pkgdown-sidebar .list-unstyled li {
-  margin-bottom: 0.5em;
-}
-
-/* bootstrap-toc tweaks ------------------------------------------------------*/
-
-/* All levels of nav */
-
-nav[data-toggle='toc'] .nav > li > a {
-  padding: 4px 20px 4px 6px;
-  font-size: 1.5rem;
-  font-weight: 400;
-  color: inherit;
-}
-
-nav[data-toggle='toc'] .nav > li > a:hover,
-nav[data-toggle='toc'] .nav > li > a:focus {
-  padding-left: 5px;
-  color: inherit;
-  border-left: 1px solid #878787;
-}
-
-nav[data-toggle='toc'] .nav > .active > a,
-nav[data-toggle='toc'] .nav > .active:hover > a,
-nav[data-toggle='toc'] .nav > .active:focus > a {
-  padding-left: 5px;
-  font-size: 1.5rem;
-  font-weight: 400;
-  color: inherit;
-  border-left: 2px solid #878787;
-}
-
-/* Nav: second level (shown on .active) */
-
-nav[data-toggle='toc'] .nav .nav {
-  display: none; /* Hide by default, but at >768px, show it */
-  padding-bottom: 10px;
-}
-
-nav[data-toggle='toc'] .nav .nav > li > a {
-  padding-left: 16px;
-  font-size: 1.35rem;
-}
-
-nav[data-toggle='toc'] .nav .nav > li > a:hover,
-nav[data-toggle='toc'] .nav .nav > li > a:focus {
-  padding-left: 15px;
-}
-
-nav[data-toggle='toc'] .nav .nav > .active > a,
-nav[data-toggle='toc'] .nav .nav > .active:hover > a,
-nav[data-toggle='toc'] .nav .nav > .active:focus > a {
-  padding-left: 15px;
-  font-weight: 500;
-  font-size: 1.35rem;
-}
-
-/* orcid ------------------------------------------------------------------- */
-
-.orcid {
-  font-size: 16px;
-  color: #A6CE39;
-  /* margins are required by official ORCID trademark and display guidelines */
-  margin-left:4px;
-  margin-right:4px;
-  vertical-align: middle;
-}
-
-/* Reference index & topics ----------------------------------------------- */
-
-.ref-index th {font-weight: normal;}
-
-.ref-index td {vertical-align: top; min-width: 100px}
-.ref-index .icon {width: 40px;}
-.ref-index .alias {width: 40%;}
-.ref-index-icons .alias {width: calc(40% - 40px);}
-.ref-index .title {width: 60%;}
-
-.ref-arguments th {text-align: right; padding-right: 10px;}
-.ref-arguments th, .ref-arguments td {vertical-align: top; min-width: 100px}
-.ref-arguments .name {width: 20%;}
-.ref-arguments .desc {width: 80%;}
-
-/* Nice scrolling for wide elements --------------------------------------- */
-
-table {
-  display: block;
-  overflow: auto;
-}
-
-/* Syntax highlighting ---------------------------------------------------- */
-
-pre, code, pre code {
-  background-color: #f8f8f8;
-  color: #333;
-}
-pre, pre code {
-  white-space: pre-wrap;
-  word-break: break-all;
-  overflow-wrap: break-word;
-}
-
-pre {
-  border: 1px solid #eee;
-}
-
-pre .img, pre .r-plt {
-  margin: 5px 0;
-}
-
-pre .img img, pre .r-plt img {
-  background-color: #fff;
-}
-
-code a, pre a {
-  color: #375f84;
-}
-
-a.sourceLine:hover {
-  text-decoration: none;
-}
-
-.fl      {color: #1514b5;}
-.fu      {color: #000000;} /* function */
-.ch,.st  {color: #036a07;} /* string */
-.kw      {color: #264D66;} /* keyword */
-.co      {color: #888888;} /* comment */
-
-.error   {font-weight: bolder;}
-.warning {font-weight: bolder;}
-
-/* Clipboard --------------------------*/
-
-.hasCopyButton {
-  position: relative;
-}
-
-.btn-copy-ex {
-  position: absolute;
-  right: 0;
-  top: 0;
-  visibility: hidden;
-}
-
-.hasCopyButton:hover button.btn-copy-ex {
-  visibility: visible;
-}
-
-/* headroom.js ------------------------ */
-
-.headroom {
-  will-change: transform;
-  transition: transform 200ms linear;
-}
-.headroom--pinned {
-  transform: translateY(0%);
-}
-.headroom--unpinned {
-  transform: translateY(-100%);
-}
-
-/* mark.js ----------------------------*/
-
-mark {
-  background-color: rgba(255, 255, 51, 0.5);
-  border-bottom: 2px solid rgba(255, 153, 51, 0.3);
-  padding: 1px;
-}
-
-/* vertical spacing after htmlwidgets */
-.html-widget {
-  margin-bottom: 10px;
-}
-
-/* fontawesome ------------------------ */
-
-.fab {
-    font-family: "Font Awesome 5 Brands" !important;
-}
-
-/* don't display links in code chunks when printing */
-/* source: https://stackoverflow.com/a/10781533 */
-@media print {
-  code a:link:after, code a:visited:after {
-    content: "";
-  }
-}
-
-/* Section anchors ---------------------------------
-   Added in pandoc 2.11: https://github.com/jgm/pandoc-templates/commit/9904bf71
-*/
-
-div.csl-bib-body { }
-div.csl-entry {
-  clear: both;
-}
-.hanging-indent div.csl-entry {
-  margin-left:2em;
-  text-indent:-2em;
-}
-div.csl-left-margin {
-  min-width:2em;
-  float:left;
-}
-div.csl-right-inline {
-  margin-left:2em;
-  padding-left:1em;
-}
-div.csl-indent {
-  margin-left: 2em;
-}
diff --git a/docs/pkgdown.js b/docs/pkgdown.js
index 6f0eee40..1a99c65f 100644
--- a/docs/pkgdown.js
+++ b/docs/pkgdown.js
@@ -2,83 +2,43 @@
 (function($) {
   $(function() {
 
-    $('.navbar-fixed-top').headroom();
+    $('nav.navbar').headroom();
 
-    $('body').css('padding-top', $('.navbar').height() + 10);
-    $(window).resize(function(){
-      $('body').css('padding-top', $('.navbar').height() + 10);
+    Toc.init({
+      $nav: $("#toc"),
+      $scope: $("main h2, main h3, main h4, main h5, main h6")
     });
 
-    $('[data-toggle="tooltip"]').tooltip();
-
-    var cur_path = paths(location.pathname);
-    var links = $("#navbar ul li a");
-    var max_length = -1;
-    var pos = -1;
-    for (var i = 0; i < links.length; i++) {
-      if (links[i].getAttribute("href") === "#")
-        continue;
-      // Ignore external links
-      if (links[i].host !== location.host)
-        continue;
-
-      var nav_path = paths(links[i].pathname);
-
-      var length = prefix_length(nav_path, cur_path);
-      if (length > max_length) {
-        max_length = length;
-        pos = i;
-      }
-    }
-
-    // Add class to parent <li>, and enclosing <li> if in dropdown
-    if (pos >= 0) {
-      var menu_anchor = $(links[pos]);
-      menu_anchor.parent().addClass("active");
-      menu_anchor.closest("li.dropdown").addClass("active");
-    }
-  });
-
-  function paths(pathname) {
-    var pieces = pathname.split("/");
-    pieces.shift(); // always starts with /
-
-    var end = pieces[pieces.length - 1];
-    if (end === "index.html" || end === "")
-      pieces.pop();
-    return(pieces);
-  }
-
-  // Returns -1 if not found
-  function prefix_length(needle, haystack) {
-    if (needle.length > haystack.length)
-      return(-1);
-
-    // Special case for length-0 haystack, since for loop won't run
-    if (haystack.length === 0) {
-      return(needle.length === 0 ? 0 : -1);
+    if ($('#toc').length) {
+      $('body').scrollspy({
+        target: '#toc',
+        offset: $("nav.navbar").outerHeight() + 1
+      });
     }
 
-    for (var i = 0; i < haystack.length; i++) {
-      if (needle[i] != haystack[i])
-        return(i);
-    }
+    // Activate popovers
+    $('[data-bs-toggle="popover"]').popover({
+      container: 'body',
+      html: true,
+      trigger: 'focus',
+      placement: "top",
+      sanitize: false,
+    });
 
-    return(haystack.length);
-  }
+    $('[data-bs-toggle="tooltip"]').tooltip();
 
   /* Clipboard --------------------------*/
 
   function changeTooltipMessage(element, msg) {
-    var tooltipOriginalTitle=element.getAttribute('data-original-title');
-    element.setAttribute('data-original-title', msg);
+    var tooltipOriginalTitle=element.getAttribute('data-bs-original-title');
+    element.setAttribute('data-bs-original-title', msg);
     $(element).tooltip('show');
-    element.setAttribute('data-original-title', tooltipOriginalTitle);
+    element.setAttribute('data-bs-original-title', tooltipOriginalTitle);
   }
 
   if(ClipboardJS.isSupported()) {
     $(document).ready(function() {
-      var copyButton = "<button type='button' class='btn btn-primary btn-copy-ex' type = 'submit' title='Copy to clipboard' aria-label='Copy to clipboard' data-toggle='tooltip' data-placement='left auto' data-trigger='hover' data-clipboard-copy><i class='fa fa-copy'></i></button>";
+      var copyButton = "<button type='button' class='btn btn-primary btn-copy-ex' title='Copy to clipboard' aria-label='Copy to clipboard' data-toggle='tooltip' data-placement='left' data-trigger='hover' data-clipboard-copy><i class='fa fa-copy'></i></button>";
 
       $("div.sourceCode").addClass("hasCopyButton");
 
@@ -89,20 +49,114 @@
       $('.btn-copy-ex').tooltip({container: 'body'});
 
       // Initialize clipboard:
-      var clipboardBtnCopies = new ClipboardJS('[data-clipboard-copy]', {
+      var clipboard = new ClipboardJS('[data-clipboard-copy]', {
         text: function(trigger) {
           return trigger.parentNode.textContent.replace(/\n#>[^\n]*/g, "");
         }
       });
 
-      clipboardBtnCopies.on('success', function(e) {
+      clipboard.on('success', function(e) {
         changeTooltipMessage(e.trigger, 'Copied!');
         e.clearSelection();
       });
 
-      clipboardBtnCopies.on('error', function() {
+      clipboard.on('error', function(e) {
         changeTooltipMessage(e.trigger,'Press Ctrl+C or Command+C to copy');
       });
+
     });
   }
+
+    /* Search marking --------------------------*/
+    var url = new URL(window.location.href);
+    var toMark = url.searchParams.get("q");
+    var mark = new Mark("main#main");
+    if (toMark) {
+      mark.mark(toMark, {
+        accuracy: {
+          value: "complementary",
+          limiters: [",", ".", ":", "/"],
+        }
+      });
+    }
+
+  /* Search --------------------------*/
+  /* Adapted from https://github.com/rstudio/bookdown/blob/2d692ba4b61f1e466c92e78fd712b0ab08c11d31/inst/resources/bs4_book/bs4_book.js#L25 */
+    // Initialise search index on focus
+  var fuse;
+  $("#search-input").focus(async function(e) {
+    if (fuse) {
+      return;
+    }
+
+    $(e.target).addClass("loading");
+    var response = await fetch($("#search-input").data("search-index"));
+    var data = await response.json();
+
+    var options = {
+      keys: ["what", "text", "code"],
+      ignoreLocation: true,
+      threshold: 0.1,
+      includeMatches: true,
+      includeScore: true,
+    };
+    fuse = new Fuse(data, options);
+
+    $(e.target).removeClass("loading");
+  });
+
+  // Use algolia autocomplete
+  var options = {
+    autoselect: true,
+    debug: true,
+    hint: false,
+    minLength: 2,
+  };
+  var q;
+async function searchFuse(query, callback) {
+  await fuse;
+
+  var items;
+  if (!fuse) {
+    items = [];
+  } else {
+    q = query;
+    var results = fuse.search(query, { limit: 20 });
+    items = results
+      .filter((x) => x.score <= 0.75)
+      .map((x) => x.item);
+    if (items.length === 0) {
+      items = [{dir:"Sorry 😿",previous_headings:"",title:"No results found.",what:"No results found.",path:window.location.href}];
+    }
+  }
+  callback(items);
+}
+  $("#search-input").autocomplete(options, [
+    {
+      name: "content",
+      source: searchFuse,
+      templates: {
+        suggestion: (s) => {
+          if (s.title == s.what) {
+            return `${s.dir} >	<div class="search-details"> ${s.title}</div>`;
+          } else if (s.previous_headings == "") {
+            return `${s.dir} >	<div class="search-details"> ${s.title}</div> > ${s.what}`;
+          } else {
+            return `${s.dir} >	<div class="search-details"> ${s.title}</div> > ${s.previous_headings} > ${s.what}`;
+          }
+        },
+      },
+    },
+  ]).on('autocomplete:selected', function(event, s) {
+    window.location.href = s.path + "?q=" + q + "#" + s.id;
+  });
+  });
 })(window.jQuery || window.$)
+
+document.addEventListener('keydown', function(event) {
+  // Check if the pressed key is '/'
+  if (event.key === '/') {
+    event.preventDefault();  // Prevent any default action associated with the '/' key
+    document.getElementById('search-input').focus();  // Set focus to the search input
+  }
+});
diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml
index ed32dc77..ad6c5809 100644
--- a/docs/pkgdown.yml
+++ b/docs/pkgdown.yml
@@ -1,24 +1,23 @@
 pandoc: 2.17.1.1
-pkgdown: 2.0.7
+pkgdown: 2.1.1
 pkgdown_sha: ~
 articles:
+  prebuilt/2022_cyan_pathway: prebuilt/2022_cyan_pathway.html
+  prebuilt/2022_dmta_parent: prebuilt/2022_dmta_parent.html
+  prebuilt/2022_dmta_pathway: prebuilt/2022_dmta_pathway.html
+  prebuilt/2023_mesotrione_parent: prebuilt/2023_mesotrione_parent.html
+  web_only/benchmarks: web_only/benchmarks.html
+  web_only/compiled_models: web_only/compiled_models.html
+  web_only/dimethenamid_2018: web_only/dimethenamid_2018.html
   FOCUS_D: FOCUS_D.html
   FOCUS_L: FOCUS_L.html
+  web_only/FOCUS_Z: web_only/FOCUS_Z.html
   mkin: mkin.html
-  2022_cyan_pathway: prebuilt/2022_cyan_pathway.html
-  2022_dmta_parent: prebuilt/2022_dmta_parent.html
-  2022_dmta_pathway: prebuilt/2022_dmta_pathway.html
-  2023_mesotrione_parent: prebuilt/2023_mesotrione_parent.html
+  web_only/multistart: web_only/multistart.html
+  web_only/NAFTA_examples: web_only/NAFTA_examples.html
+  web_only/saem_benchmarks: web_only/saem_benchmarks.html
   twa: twa.html
-  FOCUS_Z: web_only/FOCUS_Z.html
-  NAFTA_examples: web_only/NAFTA_examples.html
-  benchmarks: web_only/benchmarks.html
-  compiled_models: web_only/compiled_models.html
-  dimethenamid_2018: web_only/dimethenamid_2018.html
-  multistart: web_only/multistart.html
-  saem_benchmarks: web_only/saem_benchmarks.html
-last_built: 2023-11-02T12:21Z
+last_built: 2025-02-13T13:50Z
 urls:
   reference: https://pkgdown.jrwb.de/mkin/reference
   article: https://pkgdown.jrwb.de/mkin/articles
-
diff --git a/docs/reference/AIC.mmkin.html b/docs/reference/AIC.mmkin.html
index 78296f28..516e6009 100644
--- a/docs/reference/AIC.mmkin.html
+++ b/docs/reference/AIC.mmkin.html
@@ -1,156 +1,110 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Calculate the AIC for a column of an mmkin object — AIC.mmkin • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Calculate the AIC for a column of an mmkin object — AIC.mmkin"><meta property="og:description" content="Provides a convenient way to compare different kinetic models fitted to the
-same dataset."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Calculate the AIC for a column of an mmkin object — AIC.mmkin • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Calculate the AIC for a column of an mmkin object — AIC.mmkin"><meta name="description" content="Provides a convenient way to compare different kinetic models fitted to the
+same dataset."><meta property="og:description" content="Provides a convenient way to compare different kinetic models fitted to the
+same dataset."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Calculate the AIC for a column of an mmkin object</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/AIC.mmkin.R" class="external-link"><code>R/AIC.mmkin.R</code></a></small>
-    <div class="hidden name"><code>AIC.mmkin.Rd</code></div>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Calculate the AIC for a column of an mmkin object</h1>
+      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/AIC.mmkin.R" class="external-link"><code>R/AIC.mmkin.R</code></a></small>
+      <div class="d-none name"><code>AIC.mmkin.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Provides a convenient way to compare different kinetic models fitted to the
 same dataset.</p>
     </div>
 
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for mmkin</span></span>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'mmkin'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">AIC</a></span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span>, k <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for mmkin</span></span>
+<span><span class="co"># S3 method for class 'mmkin'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">BIC</a></span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>object</dt>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
 <dd><p>An object of class <code><a href="mmkin.html">mmkin</a></code>, containing only one
 column.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>For compatibility with the generic method</p></dd>
 
 
-<dt>k</dt>
+<dt id="arg-k">k<a class="anchor" aria-label="anchor" href="#arg-k"></a></dt>
 <dd><p>As in the generic method</p></dd>
 
 </dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>As in the generic method (a numeric value for single fits, or a
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>As in the generic method (a numeric value for single fits, or a
 dataframe if there are several fits in the column).</p>
     </div>
-    <div id="author">
-    <h2>Author</h2>
+    <div class="section level2">
+    <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a></h2>
     <p>Johannes Ranke</p>
     </div>
 
-    <div id="ref-examples">
-    <h2>Examples</h2>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span>
 <span class="r-in"><span>   <span class="co"># skip, as it takes &gt; 10 s on winbuilder</span></span></span>
 <span class="r-in"><span>  <span class="va">f</span> <span class="op">&lt;-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"FOMC"</span>, <span class="st">"DFOP"</span><span class="op">)</span>,</span></span>
@@ -199,27 +153,23 @@ dataframe if there are several fits in the column).</p>
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/BIC.mmkin.html b/docs/reference/BIC.mmkin.html
new file mode 100644
index 00000000..191b50a5
--- /dev/null
+++ b/docs/reference/BIC.mmkin.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/AIC.mmkin.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/AIC.mmkin.html">
+  </head>
+</html>
+
diff --git a/docs/reference/CAKE_export.html b/docs/reference/CAKE_export.html
index 92cb2755..3dd5cafe 100644
--- a/docs/reference/CAKE_export.html
+++ b/docs/reference/CAKE_export.html
@@ -1,120 +1,74 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Export a list of datasets format to a CAKE study file — CAKE_export • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Export a list of datasets format to a CAKE study file — CAKE_export"><meta property="og:description" content="In addition to the datasets, the pathways in the degradation model can be
-specified as well."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Export a list of datasets format to a CAKE study file — CAKE_export • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Export a list of datasets format to a CAKE study file — CAKE_export"><meta name="description" content="In addition to the datasets, the pathways in the degradation model can be
+specified as well."><meta property="og:description" content="In addition to the datasets, the pathways in the degradation model can be
+specified as well."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Export a list of datasets format to a CAKE study file</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/CAKE_export.R" class="external-link"><code>R/CAKE_export.R</code></a></small>
-    <div class="hidden name"><code>CAKE_export.Rd</code></div>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Export a list of datasets format to a CAKE study file</h1>
+      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/CAKE_export.R" class="external-link"><code>R/CAKE_export.R</code></a></small>
+      <div class="d-none name"><code>CAKE_export.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>In addition to the datasets, the pathways in the degradation model can be
 specified as well.</p>
     </div>
 
-    <div id="ref-usage">
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">CAKE_export</span><span class="op">(</span></span>
 <span>  <span class="va">ds</span>,</span>
 <span>  map <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="st">"Parent"</span><span class="op">)</span>,</span>
@@ -132,97 +86,93 @@ specified as well.</p>
 <span><span class="op">)</span></span></code></pre></div>
     </div>
 
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>ds</dt>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-ds">ds<a class="anchor" aria-label="anchor" href="#arg-ds"></a></dt>
 <dd><p>A named list of datasets in long format as compatible with
 <code><a href="mkinfit.html">mkinfit</a></code>.</p></dd>
 
 
-<dt>map</dt>
+<dt id="arg-map">map<a class="anchor" aria-label="anchor" href="#arg-map"></a></dt>
 <dd><p>A character vector with CAKE compartment names (Parent, A1, ...),
 named with the names used in the list of datasets.</p></dd>
 
 
-<dt>links</dt>
+<dt id="arg-links">links<a class="anchor" aria-label="anchor" href="#arg-links"></a></dt>
 <dd><p>An optional character vector of target compartments, named with
 the names of the source compartments. In order to make this easier, the
 names are used as in the datasets supplied.</p></dd>
 
 
-<dt>filename</dt>
+<dt id="arg-filename">filename<a class="anchor" aria-label="anchor" href="#arg-filename"></a></dt>
 <dd><p>Where to write the result. Should end in .csf in order to be
 compatible with CAKE.</p></dd>
 
 
-<dt>path</dt>
+<dt id="arg-path">path<a class="anchor" aria-label="anchor" href="#arg-path"></a></dt>
 <dd><p>An optional path to the output file.</p></dd>
 
 
-<dt>overwrite</dt>
+<dt id="arg-overwrite">overwrite<a class="anchor" aria-label="anchor" href="#arg-overwrite"></a></dt>
 <dd><p>If TRUE, existing files are overwritten.</p></dd>
 
 
-<dt>study</dt>
+<dt id="arg-study">study<a class="anchor" aria-label="anchor" href="#arg-study"></a></dt>
 <dd><p>The name of the study.</p></dd>
 
 
-<dt>description</dt>
+<dt id="arg-description">description<a class="anchor" aria-label="anchor" href="#arg-description"></a></dt>
 <dd><p>An optional description.</p></dd>
 
 
-<dt>time_unit</dt>
+<dt id="arg-time-unit">time_unit<a class="anchor" aria-label="anchor" href="#arg-time-unit"></a></dt>
 <dd><p>The time unit for the residue data.</p></dd>
 
 
-<dt>res_unit</dt>
+<dt id="arg-res-unit">res_unit<a class="anchor" aria-label="anchor" href="#arg-res-unit"></a></dt>
 <dd><p>The unit used for the residues.</p></dd>
 
 
-<dt>comment</dt>
+<dt id="arg-comment">comment<a class="anchor" aria-label="anchor" href="#arg-comment"></a></dt>
 <dd><p>An optional comment.</p></dd>
 
 
-<dt>date</dt>
+<dt id="arg-date">date<a class="anchor" aria-label="anchor" href="#arg-date"></a></dt>
 <dd><p>The date of file creation.</p></dd>
 
 
-<dt>optimiser</dt>
+<dt id="arg-optimiser">optimiser<a class="anchor" aria-label="anchor" href="#arg-optimiser"></a></dt>
 <dd><p>Can be OLS or IRLS.</p></dd>
 
 </dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>The function is called for its side effect.</p>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>The function is called for its side effect.</p>
     </div>
-    <div id="author">
-    <h2>Author</h2>
+    <div class="section level2">
+    <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a></h2>
     <p>Johannes Ranke</p>
     </div>
 
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/D24_2014.html b/docs/reference/D24_2014.html
index 1e35e864..8bb74b1d 100644
--- a/docs/reference/D24_2014.html
+++ b/docs/reference/D24_2014.html
@@ -1,116 +1,76 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Aerobic soil degradation data on 2,4-D from the EU assessment in 2014 — D24_2014 • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Aerobic soil degradation data on 2,4-D from the EU assessment in 2014 — D24_2014"><meta property="og:description" content="The five datasets were extracted from the active substance evaluation dossier
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Aerobic soil degradation data on 2,4-D from the EU assessment in 2014 — D24_2014 • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Aerobic soil degradation data on 2,4-D from the EU assessment in 2014 — D24_2014"><meta name="description" content="The five datasets were extracted from the active substance evaluation dossier
 published by EFSA. Kinetic evaluations shown for these datasets are intended
 to illustrate and advance kinetic modelling. The fact that these data and
 some results are shown here does not imply a license to use them in the
 context of pesticide  registrations, as the use of the data may be
-constrained by data protection regulations."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.5</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+constrained by data protection regulations."><meta property="og:description" content="The five datasets were extracted from the active substance evaluation dossier
+published by EFSA. Kinetic evaluations shown for these datasets are intended
+to illustrate and advance kinetic modelling. The fact that these data and
+some results are shown here does not imply a license to use them in the
+context of pesticide  registrations, as the use of the data may be
+constrained by data protection regulations."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
 
-      
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
 
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Aerobic soil degradation data on 2,4-D from the EU assessment in 2014</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/D24_2014.R" class="external-link"><code>R/D24_2014.R</code></a></small>
-    <div class="hidden name"><code>D24_2014.Rd</code></div>
+      <h1>Aerobic soil degradation data on 2,4-D from the EU assessment in 2014</h1>
+      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/D24_2014.R" class="external-link"><code>R/D24_2014.R</code></a></small>
+      <div class="d-none name"><code>D24_2014.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>The five datasets were extracted from the active substance evaluation dossier
 published by EFSA. Kinetic evaluations shown for these datasets are intended
 to illustrate and advance kinetic modelling. The fact that these data and
@@ -119,23 +79,24 @@ context of pesticide  registrations, as the use of the data may be
 constrained by data protection regulations.</p>
     </div>
 
-    <div id="ref-usage">
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="va">D24_2014</span></span></code></pre></div>
     </div>
 
-    <div id="format">
-    <h2>Format</h2>
+    <div class="section level2">
+    <h2 id="format">Format<a class="anchor" aria-label="anchor" href="#format"></a></h2>
     <p>An <a href="mkindsg.html">mkindsg</a> object grouping five datasets</p>
     </div>
-    <div id="source">
-    <h2>Source</h2>
+    <div class="section level2">
+    <h2 id="source">Source<a class="anchor" aria-label="anchor" href="#source"></a></h2>
     <p>Hellenic Ministry of Rural Development and Agriculture (2014)
 Final addendum to the Renewal Assessment Report - public version - 2,4-D
 Volume 3 Annex B.8 Fate and behaviour in the environment
 https://open.efsa.europa.eu/study-inventory/EFSA-Q-2013-00811</p>
     </div>
-    <div id="details">
-    <h2>Details</h2>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>Data for the first dataset are from p. 685. Data for the other four
 datasets were used in the preprocessed versions given in the kinetics
 section (p. 761ff.), with the exception of residues smaller than 1 for DCP
@@ -145,8 +106,8 @@ in the 'dataset_generation' directory. In the code, page numbers are given for
 specific pieces of information in the comments.</p>
     </div>
 
-    <div id="ref-examples">
-    <h2>Examples</h2>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">D24_2014</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> &lt;mkindsg&gt; holding 5 mkinds objects</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Title $title:  Aerobic soil degradation data on 2,4-D from the EU assessment in 2014 </span>
@@ -235,27 +196,23 @@ specific pieces of information in the comments.</p>
 <span class="r-in"><span><span class="co"># }</span></span></span>
 </code></pre></div>
     </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/DFOP.solution-1.png b/docs/reference/DFOP.solution-1.png
index 5ebba336..6b78836f 100644
--- a/docs/reference/DFOP.solution-1.png
+++ b/docs/reference/DFOP.solution-1.png
diff --git a/docs/reference/DFOP.solution.html b/docs/reference/DFOP.solution.html
index b526ac9e..ab31d4e1 100644
--- a/docs/reference/DFOP.solution.html
+++ b/docs/reference/DFOP.solution.html
@@ -1,154 +1,108 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Double First-Order in Parallel kinetics — DFOP.solution • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Double First-Order in Parallel kinetics — DFOP.solution"><meta property="og:description" content="Function describing decline from a defined starting value using the sum of
-two exponential decline functions."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Double First-Order in Parallel kinetics — DFOP.solution • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Double First-Order in Parallel kinetics — DFOP.solution"><meta name="description" content="Function describing decline from a defined starting value using the sum of
+two exponential decline functions."><meta property="og:description" content="Function describing decline from a defined starting value using the sum of
+two exponential decline functions."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Double First-Order in Parallel kinetics</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/parent_solutions.R" class="external-link"><code>R/parent_solutions.R</code></a></small>
-    <div class="hidden name"><code>DFOP.solution.Rd</code></div>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Double First-Order in Parallel kinetics</h1>
+      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/parent_solutions.R" class="external-link"><code>R/parent_solutions.R</code></a></small>
+      <div class="d-none name"><code>DFOP.solution.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Function describing decline from a defined starting value using the sum of
 two exponential decline functions.</p>
     </div>
 
-    <div id="ref-usage">
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">DFOP.solution</span><span class="op">(</span><span class="va">t</span>, <span class="va">parent_0</span>, <span class="va">k1</span>, <span class="va">k2</span>, <span class="va">g</span><span class="op">)</span></span></code></pre></div>
     </div>
 
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>t</dt>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-t">t<a class="anchor" aria-label="anchor" href="#arg-t"></a></dt>
 <dd><p>Time.</p></dd>
 
 
-<dt>parent_0</dt>
+<dt id="arg-parent-">parent_0<a class="anchor" aria-label="anchor" href="#arg-parent-"></a></dt>
 <dd><p>Starting value for the response variable at time zero.</p></dd>
 
 
-<dt>k1</dt>
+<dt id="arg-k-">k1<a class="anchor" aria-label="anchor" href="#arg-k-"></a></dt>
 <dd><p>First kinetic constant.</p></dd>
 
 
-<dt>k2</dt>
+<dt id="arg-k-">k2<a class="anchor" aria-label="anchor" href="#arg-k-"></a></dt>
 <dd><p>Second kinetic constant.</p></dd>
 
 
-<dt>g</dt>
+<dt id="arg-g">g<a class="anchor" aria-label="anchor" href="#arg-g"></a></dt>
 <dd><p>Fraction of the starting value declining according to the first
 kinetic constant.</p></dd>
 
 </dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>The value of the response variable at time <code>t</code>.</p>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>The value of the response variable at time <code>t</code>.</p>
     </div>
-    <div id="references">
-    <h2>References</h2>
+    <div class="section level2">
+    <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
     <p>FOCUS (2006) “Guidance Document on Estimating Persistence
 and Degradation Kinetics from Environmental Fate Studies on Pesticides in
 EU Registration” Report of the FOCUS Work Group on Degradation Kinetics,
@@ -160,9 +114,9 @@ EU Registration” Report of the FOCUS Work Group on Degradation Kinetics,
 Version 1.1, 18 December 2014
 <a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics" class="external-link">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p>
     </div>
-    <div id="see-also">
-    <h2>See also</h2>
-    <div class="dont-index"><p>Other parent solutions: 
+    <div class="section level2">
+    <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
+    <div class="dont-index"><p>Other parent solutions:
 <code><a href="FOMC.solution.html">FOMC.solution</a>()</code>,
 <code><a href="HS.solution.html">HS.solution</a>()</code>,
 <code><a href="IORE.solution.html">IORE.solution</a>()</code>,
@@ -171,35 +125,31 @@ Version 1.1, 18 December 2014
 <code><a href="logistic.solution.html">logistic.solution</a>()</code></p></div>
     </div>
 
-    <div id="ref-examples">
-    <h2>Examples</h2>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span>
 <span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fu">DFOP.solution</span><span class="op">(</span><span class="va">x</span>, <span class="fl">100</span>, <span class="fl">5</span>, <span class="fl">0.5</span>, <span class="fl">0.3</span><span class="op">)</span>, <span class="fl">0</span>, <span class="fl">4</span>, ylim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">100</span><span class="op">)</span><span class="op">)</span></span></span>
 <span class="r-plt img"><img src="DFOP.solution-1.png" alt="" width="700" height="433"></span>
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/Extract.mmkin.html b/docs/reference/Extract.mmkin.html
index 76a2cda8..d0af31c7 100644
--- a/docs/reference/Extract.mmkin.html
+++ b/docs/reference/Extract.mmkin.html
@@ -1,158 +1,111 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Subsetting method for mmkin objects — [.mmkin • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Subsetting method for mmkin objects — [.mmkin"><meta property="og:description" content="Subsetting method for mmkin objects"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Subsetting method for mmkin objects — [.mmkin • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Subsetting method for mmkin objects — [.mmkin"><meta name="description" content="Subsetting method for mmkin objects"><meta property="og:description" content="Subsetting method for mmkin objects"></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Subsetting method for mmkin objects</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/mmkin.R" class="external-link"><code>R/mmkin.R</code></a></small>
-    <div class="hidden name"><code>Extract.mmkin.Rd</code></div>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Subsetting method for mmkin objects</h1>
+      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/mmkin.R" class="external-link"><code>R/mmkin.R</code></a></small>
+      <div class="d-none name"><code>Extract.mmkin.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Subsetting method for mmkin objects</p>
     </div>
 
-    <div id="ref-usage">
-    <div class="sourceCode"><pre><code># S3 method for mmkin
-[(x, i, j, ..., drop = FALSE)</code></pre></div>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'mmkin'</span></span>
+<span><span class="va">x</span><span class="op">[</span><span class="va">i</span>, <span class="va">j</span>, <span class="va">...</span>, drop <span class="op">=</span> <span class="cn">FALSE</span><span class="op">]</span></span></code></pre></div>
     </div>
 
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>x</dt>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>An <code><a href="mmkin.html">mmkin</a> object</code></p></dd>
 
 
-<dt>i</dt>
+<dt id="arg-i">i<a class="anchor" aria-label="anchor" href="#arg-i"></a></dt>
 <dd><p>Row index selecting the fits for specific models</p></dd>
 
 
-<dt>j</dt>
+<dt id="arg-j">j<a class="anchor" aria-label="anchor" href="#arg-j"></a></dt>
 <dd><p>Column index selecting the fits to specific datasets</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Not used, only there to satisfy the generic method definition</p></dd>
 
 
-<dt>drop</dt>
+<dt id="arg-drop">drop<a class="anchor" aria-label="anchor" href="#arg-drop"></a></dt>
 <dd><p>If FALSE, the method always returns an mmkin object, otherwise
 either a list of mkinfit objects or a single mkinfit object.</p></dd>
 
 </dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>An object of class <code><a href="mmkin.html">mmkin</a></code>.</p>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>An object of class <code><a href="mmkin.html">mmkin</a></code>.</p>
     </div>
-    <div id="author">
-    <h2>Author</h2>
+    <div class="section level2">
+    <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a></h2>
     <p>Johannes Ranke</p>
     </div>
 
-    <div id="ref-examples">
-    <h2>Examples</h2>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span>
 <span class="r-in"><span>  <span class="co"># Only use one core, to pass R CMD check --as-cran</span></span></span>
 <span class="r-in"><span>  <span class="va">fits</span> <span class="op">&lt;-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"FOMC"</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>B <span class="op">=</span> <span class="va">FOCUS_2006_B</span>, C <span class="op">=</span> <span class="va">FOCUS_2006_C</span><span class="op">)</span>,</span></span>
@@ -212,27 +165,23 @@ either a list of mkinfit objects or a single mkinfit object.</p></dd>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 </code></pre></div>
     </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/FOCUS_2006_A.html b/docs/reference/FOCUS_2006_A.html
new file mode 100644
index 00000000..76954866
--- /dev/null
+++ b/docs/reference/FOCUS_2006_A.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_datasets.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_datasets.html">
+  </head>
+</html>
+
diff --git a/docs/reference/FOCUS_2006_B.html b/docs/reference/FOCUS_2006_B.html
new file mode 100644
index 00000000..76954866
--- /dev/null
+++ b/docs/reference/FOCUS_2006_B.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_datasets.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_datasets.html">
+  </head>
+</html>
+
diff --git a/docs/reference/FOCUS_2006_C.html b/docs/reference/FOCUS_2006_C.html
new file mode 100644
index 00000000..76954866
--- /dev/null
+++ b/docs/reference/FOCUS_2006_C.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_datasets.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_datasets.html">
+  </head>
+</html>
+
diff --git a/docs/reference/FOCUS_2006_D.html b/docs/reference/FOCUS_2006_D.html
new file mode 100644
index 00000000..76954866
--- /dev/null
+++ b/docs/reference/FOCUS_2006_D.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_datasets.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_datasets.html">
+  </head>
+</html>
+
diff --git a/docs/reference/FOCUS_2006_DFOP_ref_A_to_B.html b/docs/reference/FOCUS_2006_DFOP_ref_A_to_B.html
index 2483a632..1b9f38ac 100644
--- a/docs/reference/FOCUS_2006_DFOP_ref_A_to_B.html
+++ b/docs/reference/FOCUS_2006_DFOP_ref_A_to_B.html
@@ -1,118 +1,74 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Results of fitting the DFOP model to Datasets A to B of FOCUS (2006) — FOCUS_2006_DFOP_ref_A_to_B • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Results of fitting the DFOP model to Datasets A to B of FOCUS (2006) — FOCUS_2006_DFOP_ref_A_to_B"><meta property="og:description" content="A table with the fitted parameters and the resulting DT50 and DT90 values
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Results of fitting the DFOP model to Datasets A to B of FOCUS (2006) — FOCUS_2006_DFOP_ref_A_to_B • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Results of fitting the DFOP model to Datasets A to B of FOCUS (2006) — FOCUS_2006_DFOP_ref_A_to_B"><meta name="description" content="A table with the fitted parameters and the resulting DT50 and DT90 values
 generated with different software packages. Taken directly from FOCUS (2006).
 The results from fitting the data with the Topfit software was removed, as
 the initial concentration of the parent compound was fixed to a value of 100
-in this fit."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+in this fit."><meta property="og:description" content="A table with the fitted parameters and the resulting DT50 and DT90 values
+generated with different software packages. Taken directly from FOCUS (2006).
+The results from fitting the data with the Topfit software was removed, as
+the initial concentration of the parent compound was fixed to a value of 100
+in this fit."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Results of fitting the DFOP model to Datasets A to B of FOCUS (2006)</h1>
-    
-    <div class="hidden name"><code>FOCUS_2006_DFOP_ref_A_to_B.Rd</code></div>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Results of fitting the DFOP model to Datasets A to B of FOCUS (2006)</h1>
+
+      <div class="d-none name"><code>FOCUS_2006_DFOP_ref_A_to_B.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>A table with the fitted parameters and the resulting DT50 and DT90 values
 generated with different software packages. Taken directly from FOCUS (2006).
 The results from fitting the data with the Topfit software was removed, as
@@ -120,12 +76,13 @@ the initial concentration of the parent compound was fixed to a value of 100
 in this fit.</p>
     </div>
 
-    <div id="ref-usage">
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="va">FOCUS_2006_DFOP_ref_A_to_B</span></span></code></pre></div>
     </div>
 
-    <div id="format">
-    <h2>Format</h2>
+    <div class="section level2">
+    <h2 id="format">Format<a class="anchor" aria-label="anchor" href="#format"></a></h2>
     <p>A data frame containing the following variables.</p><dl><dt><code>package</code></dt>
 <dd><p>a factor giving the name of the software package</p></dd>
 
@@ -150,10 +107,10 @@ in this fit.</p>
     <dt><code>dataset</code></dt>
 <dd><p>The FOCUS dataset that was used</p></dd>
 
-  
+
 </dl></div>
-    <div id="source">
-    <h2>Source</h2>
+    <div class="section level2">
+    <h2 id="source">Source<a class="anchor" aria-label="anchor" href="#source"></a></h2>
     <p>FOCUS (2006) “Guidance Document on Estimating Persistence and
   Degradation Kinetics from Environmental Fate Studies on Pesticides in EU
   Registration” Report of the FOCUS Work Group on Degradation Kinetics,
@@ -161,32 +118,28 @@ in this fit.</p>
   <a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics" class="external-link">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p>
     </div>
 
-    <div id="ref-examples">
-    <h2>Examples</h2>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/utils/data.html" class="external-link">data</a></span><span class="op">(</span><span class="va">FOCUS_2006_DFOP_ref_A_to_B</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/FOCUS_2006_E.html b/docs/reference/FOCUS_2006_E.html
new file mode 100644
index 00000000..76954866
--- /dev/null
+++ b/docs/reference/FOCUS_2006_E.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_datasets.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_datasets.html">
+  </head>
+</html>
+
diff --git a/docs/reference/FOCUS_2006_F.html b/docs/reference/FOCUS_2006_F.html
new file mode 100644
index 00000000..76954866
--- /dev/null
+++ b/docs/reference/FOCUS_2006_F.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_datasets.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_datasets.html">
+  </head>
+</html>
+
diff --git a/docs/reference/FOCUS_2006_FOMC_ref_A_to_F.html b/docs/reference/FOCUS_2006_FOMC_ref_A_to_F.html
index d3ee5aba..568bb125 100644
--- a/docs/reference/FOCUS_2006_FOMC_ref_A_to_F.html
+++ b/docs/reference/FOCUS_2006_FOMC_ref_A_to_F.html
@@ -1,118 +1,74 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Results of fitting the FOMC model to Datasets A to F of FOCUS (2006) — FOCUS_2006_FOMC_ref_A_to_F • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Results of fitting the FOMC model to Datasets A to F of FOCUS (2006) — FOCUS_2006_FOMC_ref_A_to_F"><meta property="og:description" content="A table with the fitted parameters and the resulting DT50 and DT90 values
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Results of fitting the FOMC model to Datasets A to F of FOCUS (2006) — FOCUS_2006_FOMC_ref_A_to_F • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Results of fitting the FOMC model to Datasets A to F of FOCUS (2006) — FOCUS_2006_FOMC_ref_A_to_F"><meta name="description" content="A table with the fitted parameters and the resulting DT50 and DT90 values
 generated with different software packages. Taken directly from FOCUS (2006).
 The results from fitting the data with the Topfit software was removed, as
 the initial concentration of the parent compound was fixed to a value of 100
-in this fit."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+in this fit."><meta property="og:description" content="A table with the fitted parameters and the resulting DT50 and DT90 values
+generated with different software packages. Taken directly from FOCUS (2006).
+The results from fitting the data with the Topfit software was removed, as
+the initial concentration of the parent compound was fixed to a value of 100
+in this fit."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Results of fitting the FOMC model to Datasets A to F of FOCUS (2006)</h1>
-    
-    <div class="hidden name"><code>FOCUS_2006_FOMC_ref_A_to_F.Rd</code></div>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Results of fitting the FOMC model to Datasets A to F of FOCUS (2006)</h1>
+
+      <div class="d-none name"><code>FOCUS_2006_FOMC_ref_A_to_F.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>A table with the fitted parameters and the resulting DT50 and DT90 values
 generated with different software packages. Taken directly from FOCUS (2006).
 The results from fitting the data with the Topfit software was removed, as
@@ -120,12 +76,13 @@ the initial concentration of the parent compound was fixed to a value of 100
 in this fit.</p>
     </div>
 
-    <div id="ref-usage">
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="va">FOCUS_2006_FOMC_ref_A_to_F</span></span></code></pre></div>
     </div>
 
-    <div id="format">
-    <h2>Format</h2>
+    <div class="section level2">
+    <h2 id="format">Format<a class="anchor" aria-label="anchor" href="#format"></a></h2>
     <p>A data frame containing the following variables.</p><dl><dt><code>package</code></dt>
 <dd><p>a factor giving the name of the software package</p></dd>
 
@@ -147,10 +104,10 @@ in this fit.</p>
     <dt><code>dataset</code></dt>
 <dd><p>The FOCUS dataset that was used</p></dd>
 
-  
+
 </dl></div>
-    <div id="source">
-    <h2>Source</h2>
+    <div class="section level2">
+    <h2 id="source">Source<a class="anchor" aria-label="anchor" href="#source"></a></h2>
     <p>FOCUS (2006) “Guidance Document on Estimating Persistence and
   Degradation Kinetics from Environmental Fate Studies on Pesticides in EU
   Registration” Report of the FOCUS Work Group on Degradation Kinetics,
@@ -158,32 +115,28 @@ in this fit.</p>
   <a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics" class="external-link">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p>
     </div>
 
-    <div id="ref-examples">
-    <h2>Examples</h2>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/utils/data.html" class="external-link">data</a></span><span class="op">(</span><span class="va">FOCUS_2006_FOMC_ref_A_to_F</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/FOCUS_2006_HS_ref_A_to_F.html b/docs/reference/FOCUS_2006_HS_ref_A_to_F.html
index cb563cdf..73d2cc42 100644
--- a/docs/reference/FOCUS_2006_HS_ref_A_to_F.html
+++ b/docs/reference/FOCUS_2006_HS_ref_A_to_F.html
@@ -1,118 +1,74 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Results of fitting the HS model to Datasets A to F of FOCUS (2006) — FOCUS_2006_HS_ref_A_to_F • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Results of fitting the HS model to Datasets A to F of FOCUS (2006) — FOCUS_2006_HS_ref_A_to_F"><meta property="og:description" content="A table with the fitted parameters and the resulting DT50 and DT90 values
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Results of fitting the HS model to Datasets A to F of FOCUS (2006) — FOCUS_2006_HS_ref_A_to_F • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Results of fitting the HS model to Datasets A to F of FOCUS (2006) — FOCUS_2006_HS_ref_A_to_F"><meta name="description" content="A table with the fitted parameters and the resulting DT50 and DT90 values
 generated with different software packages. Taken directly from FOCUS (2006).
 The results from fitting the data with the Topfit software was removed, as
 the initial concentration of the parent compound was fixed to a value of 100
-in this fit."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+in this fit."><meta property="og:description" content="A table with the fitted parameters and the resulting DT50 and DT90 values
+generated with different software packages. Taken directly from FOCUS (2006).
+The results from fitting the data with the Topfit software was removed, as
+the initial concentration of the parent compound was fixed to a value of 100
+in this fit."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Results of fitting the HS model to Datasets A to F of FOCUS (2006)</h1>
-    
-    <div class="hidden name"><code>FOCUS_2006_HS_ref_A_to_F.Rd</code></div>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Results of fitting the HS model to Datasets A to F of FOCUS (2006)</h1>
+
+      <div class="d-none name"><code>FOCUS_2006_HS_ref_A_to_F.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>A table with the fitted parameters and the resulting DT50 and DT90 values
 generated with different software packages. Taken directly from FOCUS (2006).
 The results from fitting the data with the Topfit software was removed, as
@@ -120,12 +76,13 @@ the initial concentration of the parent compound was fixed to a value of 100
 in this fit.</p>
     </div>
 
-    <div id="ref-usage">
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="va">FOCUS_2006_HS_ref_A_to_F</span></span></code></pre></div>
     </div>
 
-    <div id="format">
-    <h2>Format</h2>
+    <div class="section level2">
+    <h2 id="format">Format<a class="anchor" aria-label="anchor" href="#format"></a></h2>
     <p>A data frame containing the following variables.</p><dl><dt><code>package</code></dt>
 <dd><p>a factor giving the name of the software package</p></dd>
 
@@ -150,10 +107,10 @@ in this fit.</p>
     <dt><code>dataset</code></dt>
 <dd><p>The FOCUS dataset that was used</p></dd>
 
-  
+
 </dl></div>
-    <div id="source">
-    <h2>Source</h2>
+    <div class="section level2">
+    <h2 id="source">Source<a class="anchor" aria-label="anchor" href="#source"></a></h2>
     <p>FOCUS (2006) “Guidance Document on Estimating Persistence and
   Degradation Kinetics from Environmental Fate Studies on Pesticides in EU
   Registration” Report of the FOCUS Work Group on Degradation Kinetics,
@@ -161,32 +118,28 @@ in this fit.</p>
   <a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics" class="external-link">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p>
     </div>
 
-    <div id="ref-examples">
-    <h2>Examples</h2>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/utils/data.html" class="external-link">data</a></span><span class="op">(</span><span class="va">FOCUS_2006_HS_ref_A_to_F</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/FOCUS_2006_SFO_ref_A_to_F.html b/docs/reference/FOCUS_2006_SFO_ref_A_to_F.html
index 93fc9030..69b92c4f 100644
--- a/docs/reference/FOCUS_2006_SFO_ref_A_to_F.html
+++ b/docs/reference/FOCUS_2006_SFO_ref_A_to_F.html
@@ -1,118 +1,74 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Results of fitting the SFO model to Datasets A to F of FOCUS (2006) — FOCUS_2006_SFO_ref_A_to_F • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Results of fitting the SFO model to Datasets A to F of FOCUS (2006) — FOCUS_2006_SFO_ref_A_to_F"><meta property="og:description" content="A table with the fitted parameters and the resulting DT50 and DT90 values
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Results of fitting the SFO model to Datasets A to F of FOCUS (2006) — FOCUS_2006_SFO_ref_A_to_F • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Results of fitting the SFO model to Datasets A to F of FOCUS (2006) — FOCUS_2006_SFO_ref_A_to_F"><meta name="description" content="A table with the fitted parameters and the resulting DT50 and DT90 values
 generated with different software packages. Taken directly from FOCUS (2006).
 The results from fitting the data with the Topfit software was removed, as
 the initial concentration of the parent compound was fixed to a value of 100
-in this fit."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+in this fit."><meta property="og:description" content="A table with the fitted parameters and the resulting DT50 and DT90 values
+generated with different software packages. Taken directly from FOCUS (2006).
+The results from fitting the data with the Topfit software was removed, as
+the initial concentration of the parent compound was fixed to a value of 100
+in this fit."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Results of fitting the SFO model to Datasets A to F of FOCUS (2006)</h1>
-    
-    <div class="hidden name"><code>FOCUS_2006_SFO_ref_A_to_F.Rd</code></div>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Results of fitting the SFO model to Datasets A to F of FOCUS (2006)</h1>
+
+      <div class="d-none name"><code>FOCUS_2006_SFO_ref_A_to_F.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>A table with the fitted parameters and the resulting DT50 and DT90 values
 generated with different software packages. Taken directly from FOCUS (2006).
 The results from fitting the data with the Topfit software was removed, as
@@ -120,12 +76,13 @@ the initial concentration of the parent compound was fixed to a value of 100
 in this fit.</p>
     </div>
 
-    <div id="ref-usage">
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="va">FOCUS_2006_SFO_ref_A_to_F</span></span></code></pre></div>
     </div>
 
-    <div id="format">
-    <h2>Format</h2>
+    <div class="section level2">
+    <h2 id="format">Format<a class="anchor" aria-label="anchor" href="#format"></a></h2>
     <p>A data frame containing the following variables.</p><dl><dt><code>package</code></dt>
 <dd><p>a factor giving the name of the software package</p></dd>
 
@@ -144,10 +101,10 @@ in this fit.</p>
     <dt><code>dataset</code></dt>
 <dd><p>The FOCUS dataset that was used</p></dd>
 
-  
+
 </dl></div>
-    <div id="source">
-    <h2>Source</h2>
+    <div class="section level2">
+    <h2 id="source">Source<a class="anchor" aria-label="anchor" href="#source"></a></h2>
     <p>FOCUS (2006) “Guidance Document on Estimating Persistence and
   Degradation Kinetics from Environmental Fate Studies on Pesticides in EU
   Registration” Report of the FOCUS Work Group on Degradation Kinetics,
@@ -155,32 +112,28 @@ in this fit.</p>
   <a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics" class="external-link">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p>
     </div>
 
-    <div id="ref-examples">
-    <h2>Examples</h2>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/utils/data.html" class="external-link">data</a></span><span class="op">(</span><span class="va">FOCUS_2006_SFO_ref_A_to_F</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/FOCUS_2006_datasets.html b/docs/reference/FOCUS_2006_datasets.html
index f1343c5c..f6283e46 100644
--- a/docs/reference/FOCUS_2006_datasets.html
+++ b/docs/reference/FOCUS_2006_datasets.html
@@ -1,118 +1,71 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Datasets A to F from the FOCUS Kinetics report from 2006 — FOCUS_2006_datasets • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Datasets A to F from the FOCUS Kinetics report from 2006 — FOCUS_2006_datasets"><meta property="og:description" content="Data taken from FOCUS (2006), p. 258."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Datasets A to F from the FOCUS Kinetics report from 2006 — FOCUS_2006_datasets • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Datasets A to F from the FOCUS Kinetics report from 2006 — FOCUS_2006_datasets"><meta name="description" content="Data taken from FOCUS (2006), p. 258."><meta property="og:description" content="Data taken from FOCUS (2006), p. 258."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Datasets A to F from the FOCUS Kinetics report from 2006</h1>
-    
-    <div class="hidden name"><code>FOCUS_2006_datasets.Rd</code></div>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Datasets A to F from the FOCUS Kinetics report from 2006</h1>
+
+      <div class="d-none name"><code>FOCUS_2006_datasets.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Data taken from FOCUS (2006), p. 258.</p>
     </div>
 
-    <div id="ref-usage">
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="va">FOCUS_2006_A</span></span>
 <span>  <span class="va">FOCUS_2006_B</span></span>
 <span>  <span class="va">FOCUS_2006_C</span></span>
@@ -121,8 +74,8 @@
 <span>  <span class="va">FOCUS_2006_F</span></span></code></pre></div>
     </div>
 
-    <div id="format">
-    <h2>Format</h2>
+    <div class="section level2">
+    <h2 id="format">Format<a class="anchor" aria-label="anchor" href="#format"></a></h2>
     <p>6 datasets with observations on the following variables.</p><dl><dt><code>name</code></dt>
 <dd><p>a factor containing the name of the observed variable</p></dd>
 
@@ -132,10 +85,10 @@
     <dt><code>value</code></dt>
 <dd><p>a numeric vector containing concentrations in percent of applied radioactivity</p></dd>
 
-  
+
 </dl></div>
-    <div id="source">
-    <h2>Source</h2>
+    <div class="section level2">
+    <h2 id="source">Source<a class="anchor" aria-label="anchor" href="#source"></a></h2>
     <p>FOCUS (2006) “Guidance Document on Estimating Persistence and
   Degradation Kinetics from Environmental Fate Studies on Pesticides in EU
   Registration” Report of the FOCUS Work Group on Degradation Kinetics,
@@ -143,8 +96,8 @@
   <a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics" class="external-link">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p>
     </div>
 
-    <div id="ref-examples">
-    <h2>Examples</h2>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">FOCUS_2006_C</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>     name time value</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> 1 parent    0  85.1</span>
@@ -158,27 +111,23 @@
 <span class="r-out co"><span class="r-pr">#&gt;</span> 9 parent  119   0.6</span>
 </code></pre></div>
     </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/FOMC.solution-1.png b/docs/reference/FOMC.solution-1.png
index 9d222d42..18a4b586 100644
--- a/docs/reference/FOMC.solution-1.png
+++ b/docs/reference/FOMC.solution-1.png
diff --git a/docs/reference/FOMC.solution.html b/docs/reference/FOMC.solution.html
index e5087117..4878da60 100644
--- a/docs/reference/FOMC.solution.html
+++ b/docs/reference/FOMC.solution.html
@@ -1,162 +1,116 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>First-Order Multi-Compartment kinetics — FOMC.solution • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="First-Order Multi-Compartment kinetics — FOMC.solution"><meta property="og:description" content="Function describing exponential decline from a defined starting value, with
-a decreasing rate constant."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>First-Order Multi-Compartment kinetics — FOMC.solution • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="First-Order Multi-Compartment kinetics — FOMC.solution"><meta name="description" content="Function describing exponential decline from a defined starting value, with
+a decreasing rate constant."><meta property="og:description" content="Function describing exponential decline from a defined starting value, with
+a decreasing rate constant."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>First-Order Multi-Compartment kinetics</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/parent_solutions.R" class="external-link"><code>R/parent_solutions.R</code></a></small>
-    <div class="hidden name"><code>FOMC.solution.Rd</code></div>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>First-Order Multi-Compartment kinetics</h1>
+      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/parent_solutions.R" class="external-link"><code>R/parent_solutions.R</code></a></small>
+      <div class="d-none name"><code>FOMC.solution.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Function describing exponential decline from a defined starting value, with
 a decreasing rate constant.</p>
     </div>
 
-    <div id="ref-usage">
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">FOMC.solution</span><span class="op">(</span><span class="va">t</span>, <span class="va">parent_0</span>, <span class="va">alpha</span>, <span class="va">beta</span><span class="op">)</span></span></code></pre></div>
     </div>
 
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>t</dt>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-t">t<a class="anchor" aria-label="anchor" href="#arg-t"></a></dt>
 <dd><p>Time.</p></dd>
 
 
-<dt>parent_0</dt>
+<dt id="arg-parent-">parent_0<a class="anchor" aria-label="anchor" href="#arg-parent-"></a></dt>
 <dd><p>Starting value for the response variable at time zero.</p></dd>
 
 
-<dt>alpha</dt>
+<dt id="arg-alpha">alpha<a class="anchor" aria-label="anchor" href="#arg-alpha"></a></dt>
 <dd><p>Shape parameter determined by coefficient of variation of rate
 constant values.</p></dd>
 
 
-<dt>beta</dt>
+<dt id="arg-beta">beta<a class="anchor" aria-label="anchor" href="#arg-beta"></a></dt>
 <dd><p>Location parameter.</p></dd>
 
 </dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>The value of the response variable at time <code>t</code>.</p>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>The value of the response variable at time <code>t</code>.</p>
     </div>
-    <div id="details">
-    <h2>Details</h2>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>The form given here differs slightly from the original reference by
 Gustafson and Holden (1990). The parameter <code>beta</code> corresponds to 1/beta
 in the original equation.</p>
     </div>
-    <div id="note">
-    <h2>Note</h2>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>The solution of the FOMC kinetic model reduces to the
 <code><a href="SFO.solution.html">SFO.solution</a></code> for large values of <code>alpha</code> and <code>beta</code>
 with \(k = \frac{\beta}{\alpha}\).</p>
     </div>
-    <div id="references">
-    <h2>References</h2>
+    <div class="section level2">
+    <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
     <p>FOCUS (2006) “Guidance Document on Estimating Persistence
 and Degradation Kinetics from Environmental Fate Studies on Pesticides in
 EU Registration” Report of the FOCUS Work Group on Degradation Kinetics,
@@ -171,9 +125,9 @@ Version 1.1, 18 December 2014
 A new model based on spatial variability. <em>Environmental Science and
 Technology</em> <b>24</b>, 1032-1038</p>
     </div>
-    <div id="see-also">
-    <h2>See also</h2>
-    <div class="dont-index"><p>Other parent solutions: 
+    <div class="section level2">
+    <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
+    <div class="dont-index"><p>Other parent solutions:
 <code><a href="DFOP.solution.html">DFOP.solution</a>()</code>,
 <code><a href="HS.solution.html">HS.solution</a>()</code>,
 <code><a href="IORE.solution.html">IORE.solution</a>()</code>,
@@ -182,35 +136,31 @@ Technology</em> <b>24</b>, 1032-1038</p>
 <code><a href="logistic.solution.html">logistic.solution</a>()</code></p></div>
     </div>
 
-    <div id="ref-examples">
-    <h2>Examples</h2>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span>
 <span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fu">FOMC.solution</span><span class="op">(</span><span class="va">x</span>, <span class="fl">100</span>, <span class="fl">10</span>, <span class="fl">2</span><span class="op">)</span>, <span class="fl">0</span>, <span class="fl">2</span>, ylim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">100</span><span class="op">)</span><span class="op">)</span></span></span>
 <span class="r-plt img"><img src="FOMC.solution-1.png" alt="" width="700" height="433"></span>
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/HS.solution-1.png b/docs/reference/HS.solution-1.png
index dd7a76c8..61d89dbc 100644
--- a/docs/reference/HS.solution-1.png
+++ b/docs/reference/HS.solution-1.png
diff --git a/docs/reference/HS.solution.html b/docs/reference/HS.solution.html
index d24015a0..0420545d 100644
--- a/docs/reference/HS.solution.html
+++ b/docs/reference/HS.solution.html
@@ -1,155 +1,109 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Hockey-Stick kinetics — HS.solution • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Hockey-Stick kinetics — HS.solution"><meta property="og:description" content="Function describing two exponential decline functions with a break point
-between them."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Hockey-Stick kinetics — HS.solution • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Hockey-Stick kinetics — HS.solution"><meta name="description" content="Function describing two exponential decline functions with a break point
+between them."><meta property="og:description" content="Function describing two exponential decline functions with a break point
+between them."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Hockey-Stick kinetics</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/parent_solutions.R" class="external-link"><code>R/parent_solutions.R</code></a></small>
-    <div class="hidden name"><code>HS.solution.Rd</code></div>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Hockey-Stick kinetics</h1>
+      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/parent_solutions.R" class="external-link"><code>R/parent_solutions.R</code></a></small>
+      <div class="d-none name"><code>HS.solution.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Function describing two exponential decline functions with a break point
 between them.</p>
     </div>
 
-    <div id="ref-usage">
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">HS.solution</span><span class="op">(</span><span class="va">t</span>, <span class="va">parent_0</span>, <span class="va">k1</span>, <span class="va">k2</span>, <span class="va">tb</span><span class="op">)</span></span></code></pre></div>
     </div>
 
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>t</dt>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-t">t<a class="anchor" aria-label="anchor" href="#arg-t"></a></dt>
 <dd><p>Time.</p></dd>
 
 
-<dt>parent_0</dt>
+<dt id="arg-parent-">parent_0<a class="anchor" aria-label="anchor" href="#arg-parent-"></a></dt>
 <dd><p>Starting value for the response variable at time zero.</p></dd>
 
 
-<dt>k1</dt>
+<dt id="arg-k-">k1<a class="anchor" aria-label="anchor" href="#arg-k-"></a></dt>
 <dd><p>First kinetic constant.</p></dd>
 
 
-<dt>k2</dt>
+<dt id="arg-k-">k2<a class="anchor" aria-label="anchor" href="#arg-k-"></a></dt>
 <dd><p>Second kinetic constant.</p></dd>
 
 
-<dt>tb</dt>
+<dt id="arg-tb">tb<a class="anchor" aria-label="anchor" href="#arg-tb"></a></dt>
 <dd><p>Break point. Before this time, exponential decline according to
 <code>k1</code> is calculated, after this time, exponential decline proceeds
 according to <code>k2</code>.</p></dd>
 
 </dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>The value of the response variable at time <code>t</code>.</p>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>The value of the response variable at time <code>t</code>.</p>
     </div>
-    <div id="references">
-    <h2>References</h2>
+    <div class="section level2">
+    <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
     <p>FOCUS (2006) “Guidance Document on Estimating Persistence
 and Degradation Kinetics from Environmental Fate Studies on Pesticides in
 EU Registration” Report of the FOCUS Work Group on Degradation Kinetics,
@@ -161,9 +115,9 @@ EU Registration” Report of the FOCUS Work Group on Degradation Kinetics,
 Version 1.1, 18 December 2014
 <a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics" class="external-link">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p>
     </div>
-    <div id="see-also">
-    <h2>See also</h2>
-    <div class="dont-index"><p>Other parent solutions: 
+    <div class="section level2">
+    <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
+    <div class="dont-index"><p>Other parent solutions:
 <code><a href="DFOP.solution.html">DFOP.solution</a>()</code>,
 <code><a href="FOMC.solution.html">FOMC.solution</a>()</code>,
 <code><a href="IORE.solution.html">IORE.solution</a>()</code>,
@@ -172,35 +126,31 @@ Version 1.1, 18 December 2014
 <code><a href="logistic.solution.html">logistic.solution</a>()</code></p></div>
     </div>
 
-    <div id="ref-examples">
-    <h2>Examples</h2>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span>
 <span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fu">HS.solution</span><span class="op">(</span><span class="va">x</span>, <span class="fl">100</span>, <span class="fl">2</span>, <span class="fl">0.3</span>, <span class="fl">0.5</span><span class="op">)</span>, <span class="fl">0</span>, <span class="fl">2</span>, ylim<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">100</span><span class="op">)</span><span class="op">)</span></span></span>
 <span class="r-plt img"><img src="HS.solution-1.png" alt="" width="700" height="433"></span>
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/IORE.solution-1.png b/docs/reference/IORE.solution-1.png
index 9b6ab58f..54c9dcae 100644
--- a/docs/reference/IORE.solution-1.png
+++ b/docs/reference/IORE.solution-1.png
diff --git a/docs/reference/IORE.solution.html b/docs/reference/IORE.solution.html
index 0ab484cd..13aacdce 100644
--- a/docs/reference/IORE.solution.html
+++ b/docs/reference/IORE.solution.html
@@ -1,163 +1,117 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Indeterminate order rate equation kinetics — IORE.solution • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Indeterminate order rate equation kinetics — IORE.solution"><meta property="og:description" content="Function describing exponential decline from a defined starting value, with
-a concentration dependent rate constant."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Indeterminate order rate equation kinetics — IORE.solution • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Indeterminate order rate equation kinetics — IORE.solution"><meta name="description" content="Function describing exponential decline from a defined starting value, with
+a concentration dependent rate constant."><meta property="og:description" content="Function describing exponential decline from a defined starting value, with
+a concentration dependent rate constant."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Indeterminate order rate equation kinetics</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/parent_solutions.R" class="external-link"><code>R/parent_solutions.R</code></a></small>
-    <div class="hidden name"><code>IORE.solution.Rd</code></div>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Indeterminate order rate equation kinetics</h1>
+      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/parent_solutions.R" class="external-link"><code>R/parent_solutions.R</code></a></small>
+      <div class="d-none name"><code>IORE.solution.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Function describing exponential decline from a defined starting value, with
 a concentration dependent rate constant.</p>
     </div>
 
-    <div id="ref-usage">
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">IORE.solution</span><span class="op">(</span><span class="va">t</span>, <span class="va">parent_0</span>, <span class="va">k__iore</span>, <span class="va">N</span><span class="op">)</span></span></code></pre></div>
     </div>
 
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>t</dt>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-t">t<a class="anchor" aria-label="anchor" href="#arg-t"></a></dt>
 <dd><p>Time.</p></dd>
 
 
-<dt>parent_0</dt>
+<dt id="arg-parent-">parent_0<a class="anchor" aria-label="anchor" href="#arg-parent-"></a></dt>
 <dd><p>Starting value for the response variable at time zero.</p></dd>
 
 
-<dt>k__iore</dt>
+<dt id="arg-k-iore">k__iore<a class="anchor" aria-label="anchor" href="#arg-k-iore"></a></dt>
 <dd><p>Rate constant. Note that this depends on the concentration
 units used.</p></dd>
 
 
-<dt>N</dt>
+<dt id="arg-n">N<a class="anchor" aria-label="anchor" href="#arg-n"></a></dt>
 <dd><p>Exponent describing the nonlinearity of the rate equation</p></dd>
 
 </dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>The value of the response variable at time <code>t</code>.</p>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>The value of the response variable at time <code>t</code>.</p>
     </div>
-    <div id="note">
-    <h2>Note</h2>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>The solution of the IORE kinetic model reduces to the
 <code><a href="SFO.solution.html">SFO.solution</a></code> if N = 1.  The parameters of the IORE model can
 be transformed to equivalent parameters of the FOMC mode - see the NAFTA
 guidance for details.</p>
     </div>
-    <div id="references">
-    <h2>References</h2>
+    <div class="section level2">
+    <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
     <p>NAFTA Technical Working Group on Pesticides (not dated) Guidance
 for Evaluating and Calculating Degradation Kinetics in Environmental Media</p>
     </div>
-    <div id="see-also">
-    <h2>See also</h2>
-    <div class="dont-index"><p>Other parent solutions: 
+    <div class="section level2">
+    <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
+    <div class="dont-index"><p>Other parent solutions:
 <code><a href="DFOP.solution.html">DFOP.solution</a>()</code>,
 <code><a href="FOMC.solution.html">FOMC.solution</a>()</code>,
 <code><a href="HS.solution.html">HS.solution</a>()</code>,
@@ -166,8 +120,8 @@ for Evaluating and Calculating Degradation Kinetics in Environmental Media</p>
 <code><a href="logistic.solution.html">logistic.solution</a>()</code></p></div>
     </div>
 
-    <div id="ref-examples">
-    <h2>Examples</h2>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span>
 <span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fu">IORE.solution</span><span class="op">(</span><span class="va">x</span>, <span class="fl">100</span>, <span class="fl">0.2</span>, <span class="fl">1.3</span><span class="op">)</span>, <span class="fl">0</span>, <span class="fl">2</span>, ylim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">100</span><span class="op">)</span><span class="op">)</span></span></span>
 <span class="r-plt img"><img src="IORE.solution-1.png" alt="" width="700" height="433"></span>
@@ -195,27 +149,23 @@ for Evaluating and Calculating Degradation Kinetics in Environmental Media</p>
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/NAFTA_SOP_2015-1.png b/docs/reference/NAFTA_SOP_2015-1.png
index 5d2d434b..98d4246c 100644
--- a/docs/reference/NAFTA_SOP_2015-1.png
+++ b/docs/reference/NAFTA_SOP_2015-1.png
diff --git a/docs/reference/NAFTA_SOP_2015.html b/docs/reference/NAFTA_SOP_2015.html
index 9a4f668e..ebe30cbf 100644
--- a/docs/reference/NAFTA_SOP_2015.html
+++ b/docs/reference/NAFTA_SOP_2015.html
@@ -1,124 +1,77 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Example datasets from the NAFTA SOP published 2015 — NAFTA_SOP_2015 • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Example datasets from the NAFTA SOP published 2015 — NAFTA_SOP_2015"><meta property="og:description" content="Data taken from US EPA (2015), p. 19 and 23."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Example datasets from the NAFTA SOP published 2015 — NAFTA_SOP_2015 • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Example datasets from the NAFTA SOP published 2015 — NAFTA_SOP_2015"><meta name="description" content="Data taken from US EPA (2015), p. 19 and 23."><meta property="og:description" content="Data taken from US EPA (2015), p. 19 and 23."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Example datasets from the NAFTA SOP published 2015</h1>
-    
-    <div class="hidden name"><code>NAFTA_SOP_2015.Rd</code></div>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Example datasets from the NAFTA SOP published 2015</h1>
+
+      <div class="d-none name"><code>NAFTA_SOP_2015.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Data taken from US EPA (2015), p. 19 and 23.</p>
     </div>
 
-    <div id="ref-usage">
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="va">NAFTA_SOP_Appendix_B</span></span>
 <span>  <span class="va">NAFTA_SOP_Appendix_D</span></span></code></pre></div>
     </div>
 
-    <div id="format">
-    <h2>Format</h2>
+    <div class="section level2">
+    <h2 id="format">Format<a class="anchor" aria-label="anchor" href="#format"></a></h2>
     <p>2 datasets with observations on the following variables.</p><dl><dt><code>name</code></dt>
 <dd><p>a factor containing the name of the observed variable</p></dd>
 
@@ -128,10 +81,10 @@
     <dt><code>value</code></dt>
 <dd><p>a numeric vector containing concentrations</p></dd>
 
-  
+
 </dl></div>
-    <div id="source">
-    <h2>Source</h2>
+    <div class="section level2">
+    <h2 id="source">Source<a class="anchor" aria-label="anchor" href="#source"></a></h2>
     <p>NAFTA (2011) Guidance for evaluating and calculating degradation kinetics
   in environmental media. NAFTA Technical Working Group on Pesticides
   <a href="https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/guidance-evaluating-and-calculating-degradation" class="external-link">https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/guidance-evaluating-and-calculating-degradation</a>
@@ -142,8 +95,8 @@
   <a href="https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/standard-operating-procedure-using-nafta-guidance" class="external-link">https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/standard-operating-procedure-using-nafta-guidance</a></p>
     </div>
 
-    <div id="ref-examples">
-    <h2>Examples</h2>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span>  <span class="va">nafta_evaluation</span> <span class="op">&lt;-</span> <span class="fu"><a href="nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Appendix_D</span>, cores <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> The representative half-life of the IORE model is longer than the one corresponding</span>
@@ -192,27 +145,23 @@
 <span class="r-plt img"><img src="NAFTA_SOP_2015-1.png" alt="" width="700" height="433"></span>
 </code></pre></div>
     </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/NAFTA_SOP_Appendix_B.html b/docs/reference/NAFTA_SOP_Appendix_B.html
new file mode 100644
index 00000000..0ba2fb91
--- /dev/null
+++ b/docs/reference/NAFTA_SOP_Appendix_B.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/NAFTA_SOP_2015.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/NAFTA_SOP_2015.html">
+  </head>
+</html>
+
diff --git a/docs/reference/NAFTA_SOP_Appendix_D.html b/docs/reference/NAFTA_SOP_Appendix_D.html
new file mode 100644
index 00000000..0ba2fb91
--- /dev/null
+++ b/docs/reference/NAFTA_SOP_Appendix_D.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/NAFTA_SOP_2015.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/NAFTA_SOP_2015.html">
+  </head>
+</html>
+
diff --git a/docs/reference/NAFTA_SOP_Attachment-1.png b/docs/reference/NAFTA_SOP_Attachment-1.png
index d8951fc3..a6066441 100644
--- a/docs/reference/NAFTA_SOP_Attachment-1.png
+++ b/docs/reference/NAFTA_SOP_Attachment-1.png
diff --git a/docs/reference/NAFTA_SOP_Attachment.html b/docs/reference/NAFTA_SOP_Attachment.html
index 02b33e7d..f922430b 100644
--- a/docs/reference/NAFTA_SOP_Attachment.html
+++ b/docs/reference/NAFTA_SOP_Attachment.html
@@ -1,128 +1,81 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Example datasets from Attachment 1 to the NAFTA SOP published 2015 — NAFTA_SOP_Attachment • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Example datasets from Attachment 1 to the NAFTA SOP published 2015 — NAFTA_SOP_Attachment"><meta property="og:description" content="Data taken from from Attachment 1 of the SOP."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Example datasets from Attachment 1 to the NAFTA SOP published 2015 — NAFTA_SOP_Attachment • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Example datasets from Attachment 1 to the NAFTA SOP published 2015 — NAFTA_SOP_Attachment"><meta name="description" content="Data taken from from Attachment 1 of the SOP."><meta property="og:description" content="Data taken from from Attachment 1 of the SOP."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Example datasets from Attachment 1 to the NAFTA SOP published 2015</h1>
-    
-    <div class="hidden name"><code>NAFTA_SOP_Attachment.Rd</code></div>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Example datasets from Attachment 1 to the NAFTA SOP published 2015</h1>
+
+      <div class="d-none name"><code>NAFTA_SOP_Attachment.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Data taken from from Attachment 1 of the SOP.</p>
     </div>
 
-    <div id="ref-usage">
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="va">NAFTA_SOP_Attachment</span></span></code></pre></div>
     </div>
 
-    <div id="format">
-    <h2>Format</h2>
+    <div class="section level2">
+    <h2 id="format">Format<a class="anchor" aria-label="anchor" href="#format"></a></h2>
     <p>A list (NAFTA_SOP_Attachment) containing 16 datasets suitable
   for the evaluation with <code><a href="nafta.html">nafta</a></code></p>
     </div>
-    <div id="source">
-    <h2>Source</h2>
+    <div class="section level2">
+    <h2 id="source">Source<a class="anchor" aria-label="anchor" href="#source"></a></h2>
     <p>NAFTA (2011) Guidance for evaluating and calculating degradation kinetics
   in environmental media. NAFTA Technical Working Group on Pesticides
   <a href="https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/guidance-evaluating-and-calculating-degradation" class="external-link">https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/guidance-evaluating-and-calculating-degradation</a>
@@ -133,8 +86,8 @@
   <a href="https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/standard-operating-procedure-using-nafta-guidance" class="external-link">https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/standard-operating-procedure-using-nafta-guidance</a></p>
     </div>
 
-    <div id="ref-examples">
-    <h2>Examples</h2>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span>  <span class="va">nafta_att_p5a</span> <span class="op">&lt;-</span> <span class="fu"><a href="nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p5a"</span><span class="op">]</span><span class="op">]</span>, cores <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> The half-life obtained from the IORE model may be used</span>
@@ -181,27 +134,23 @@
 <span class="r-plt img"><img src="NAFTA_SOP_Attachment-1.png" alt="" width="700" height="433"></span>
 </code></pre></div>
     </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/Rplot001.png b/docs/reference/Rplot001.png
deleted file mode 100644
index ca982688..00000000
--- a/docs/reference/Rplot001.png
+++ /dev/null
diff --git a/docs/reference/Rplot002.png b/docs/reference/Rplot002.png
deleted file mode 100644
index de2d61aa..00000000
--- a/docs/reference/Rplot002.png
+++ /dev/null
diff --git a/docs/reference/Rplot003.png b/docs/reference/Rplot003.png
deleted file mode 100644
index 8fd02b8e..00000000
--- a/docs/reference/Rplot003.png
+++ /dev/null
diff --git a/docs/reference/Rplot004.png b/docs/reference/Rplot004.png
deleted file mode 100644
index 2c12ceb1..00000000
--- a/docs/reference/Rplot004.png
+++ /dev/null
diff --git a/docs/reference/Rplot005.png b/docs/reference/Rplot005.png
deleted file mode 100644
index 1d28b587..00000000
--- a/docs/reference/Rplot005.png
+++ /dev/null
diff --git a/docs/reference/Rplot006.png b/docs/reference/Rplot006.png
deleted file mode 100644
index 48f5bbd8..00000000
--- a/docs/reference/Rplot006.png
+++ /dev/null
diff --git a/docs/reference/Rplot007.png b/docs/reference/Rplot007.png
deleted file mode 100644
index 21a6ea76..00000000
--- a/docs/reference/Rplot007.png
+++ /dev/null
diff --git a/docs/reference/SFO.solution-1.png b/docs/reference/SFO.solution-1.png
index a00499cb..34fdd460 100644
--- a/docs/reference/SFO.solution-1.png
+++ b/docs/reference/SFO.solution-1.png
diff --git a/docs/reference/SFO.solution.html b/docs/reference/SFO.solution.html
index 67c7abc7..8e8deb83 100644
--- a/docs/reference/SFO.solution.html
+++ b/docs/reference/SFO.solution.html
@@ -1,143 +1,96 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Single First-Order kinetics — SFO.solution • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Single First-Order kinetics — SFO.solution"><meta property="og:description" content="Function describing exponential decline from a defined starting value."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Single First-Order kinetics — SFO.solution • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Single First-Order kinetics — SFO.solution"><meta name="description" content="Function describing exponential decline from a defined starting value."><meta property="og:description" content="Function describing exponential decline from a defined starting value."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Single First-Order kinetics</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/parent_solutions.R" class="external-link"><code>R/parent_solutions.R</code></a></small>
-    <div class="hidden name"><code>SFO.solution.Rd</code></div>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Single First-Order kinetics</h1>
+      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/parent_solutions.R" class="external-link"><code>R/parent_solutions.R</code></a></small>
+      <div class="d-none name"><code>SFO.solution.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Function describing exponential decline from a defined starting value.</p>
     </div>
 
-    <div id="ref-usage">
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">SFO.solution</span><span class="op">(</span><span class="va">t</span>, <span class="va">parent_0</span>, <span class="va">k</span><span class="op">)</span></span></code></pre></div>
     </div>
 
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>t</dt>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-t">t<a class="anchor" aria-label="anchor" href="#arg-t"></a></dt>
 <dd><p>Time.</p></dd>
 
 
-<dt>parent_0</dt>
+<dt id="arg-parent-">parent_0<a class="anchor" aria-label="anchor" href="#arg-parent-"></a></dt>
 <dd><p>Starting value for the response variable at time zero.</p></dd>
 
 
-<dt>k</dt>
+<dt id="arg-k">k<a class="anchor" aria-label="anchor" href="#arg-k"></a></dt>
 <dd><p>Kinetic rate constant.</p></dd>
 
 </dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>The value of the response variable at time <code>t</code>.</p>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>The value of the response variable at time <code>t</code>.</p>
     </div>
-    <div id="references">
-    <h2>References</h2>
+    <div class="section level2">
+    <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
     <p>FOCUS (2006) “Guidance Document on Estimating Persistence
 and Degradation Kinetics from Environmental Fate Studies on Pesticides in
 EU Registration” Report of the FOCUS Work Group on Degradation Kinetics,
@@ -149,9 +102,9 @@ EU Registration” Report of the FOCUS Work Group on Degradation Kinetics,
 Version 1.1, 18 December 2014
 <a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics" class="external-link">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p>
     </div>
-    <div id="see-also">
-    <h2>See also</h2>
-    <div class="dont-index"><p>Other parent solutions: 
+    <div class="section level2">
+    <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
+    <div class="dont-index"><p>Other parent solutions:
 <code><a href="DFOP.solution.html">DFOP.solution</a>()</code>,
 <code><a href="FOMC.solution.html">FOMC.solution</a>()</code>,
 <code><a href="HS.solution.html">HS.solution</a>()</code>,
@@ -160,35 +113,31 @@ Version 1.1, 18 December 2014
 <code><a href="logistic.solution.html">logistic.solution</a>()</code></p></div>
     </div>
 
-    <div id="ref-examples">
-    <h2>Examples</h2>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span>
 <span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fu">SFO.solution</span><span class="op">(</span><span class="va">x</span>, <span class="fl">100</span>, <span class="fl">3</span><span class="op">)</span>, <span class="fl">0</span>, <span class="fl">2</span><span class="op">)</span></span></span>
 <span class="r-plt img"><img src="SFO.solution-1.png" alt="" width="700" height="433"></span>
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/SFORB.solution-1.png b/docs/reference/SFORB.solution-1.png
index 7bea3b78..08d25616 100644
--- a/docs/reference/SFORB.solution-1.png
+++ b/docs/reference/SFORB.solution-1.png
diff --git a/docs/reference/SFORB.solution.html b/docs/reference/SFORB.solution.html
index a99deac7..50d15ac2 100644
--- a/docs/reference/SFORB.solution.html
+++ b/docs/reference/SFORB.solution.html
@@ -1,118 +1,74 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Single First-Order Reversible Binding kinetics — SFORB.solution • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Single First-Order Reversible Binding kinetics — SFORB.solution"><meta property="og:description" content="Function describing the solution of the differential equations describing
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Single First-Order Reversible Binding kinetics — SFORB.solution • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Single First-Order Reversible Binding kinetics — SFORB.solution"><meta name="description" content="Function describing the solution of the differential equations describing
 the kinetic model with first-order terms for a two-way transfer from a free
 to a bound fraction, and a first-order degradation term for the free
 fraction.  The initial condition is a defined amount in the free fraction
-and no substance in the bound fraction."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+and no substance in the bound fraction."><meta property="og:description" content="Function describing the solution of the differential equations describing
+the kinetic model with first-order terms for a two-way transfer from a free
+to a bound fraction, and a first-order degradation term for the free
+fraction.  The initial condition is a defined amount in the free fraction
+and no substance in the bound fraction."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Single First-Order Reversible Binding kinetics</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/parent_solutions.R" class="external-link"><code>R/parent_solutions.R</code></a></small>
-    <div class="hidden name"><code>SFORB.solution.Rd</code></div>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Single First-Order Reversible Binding kinetics</h1>
+      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/parent_solutions.R" class="external-link"><code>R/parent_solutions.R</code></a></small>
+      <div class="d-none name"><code>SFORB.solution.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Function describing the solution of the differential equations describing
 the kinetic model with first-order terms for a two-way transfer from a free
 to a bound fraction, and a first-order degradation term for the free
@@ -120,42 +76,43 @@ fraction.  The initial condition is a defined amount in the free fraction
 and no substance in the bound fraction.</p>
     </div>
 
-    <div id="ref-usage">
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">SFORB.solution</span><span class="op">(</span><span class="va">t</span>, <span class="va">parent_0</span>, <span class="va">k_12</span>, <span class="va">k_21</span>, <span class="va">k_1output</span><span class="op">)</span></span></code></pre></div>
     </div>
 
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>t</dt>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-t">t<a class="anchor" aria-label="anchor" href="#arg-t"></a></dt>
 <dd><p>Time.</p></dd>
 
 
-<dt>parent_0</dt>
+<dt id="arg-parent-">parent_0<a class="anchor" aria-label="anchor" href="#arg-parent-"></a></dt>
 <dd><p>Starting value for the response variable at time zero.</p></dd>
 
 
-<dt>k_12</dt>
+<dt id="arg-k-">k_12<a class="anchor" aria-label="anchor" href="#arg-k-"></a></dt>
 <dd><p>Kinetic constant describing transfer from free to bound.</p></dd>
 
 
-<dt>k_21</dt>
+<dt id="arg-k-">k_21<a class="anchor" aria-label="anchor" href="#arg-k-"></a></dt>
 <dd><p>Kinetic constant describing transfer from bound to free.</p></dd>
 
 
-<dt>k_1output</dt>
+<dt id="arg-k-output">k_1output<a class="anchor" aria-label="anchor" href="#arg-k-output"></a></dt>
 <dd><p>Kinetic constant describing degradation of the free
 fraction.</p></dd>
 
 </dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>The value of the response variable, which is the sum of free and
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>The value of the response variable, which is the sum of free and
 bound fractions at time <code>t</code>.</p>
     </div>
-    <div id="references">
-    <h2>References</h2>
+    <div class="section level2">
+    <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
     <p>FOCUS (2006) “Guidance Document on Estimating Persistence
 and Degradation Kinetics from Environmental Fate Studies on Pesticides in
 EU Registration” Report of the FOCUS Work Group on Degradation Kinetics,
@@ -167,9 +124,9 @@ EU Registration” Report of the FOCUS Work Group on Degradation Kinetics,
 Version 1.1, 18 December 2014
 <a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics" class="external-link">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p>
     </div>
-    <div id="see-also">
-    <h2>See also</h2>
-    <div class="dont-index"><p>Other parent solutions: 
+    <div class="section level2">
+    <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
+    <div class="dont-index"><p>Other parent solutions:
 <code><a href="DFOP.solution.html">DFOP.solution</a>()</code>,
 <code><a href="FOMC.solution.html">FOMC.solution</a>()</code>,
 <code><a href="HS.solution.html">HS.solution</a>()</code>,
@@ -178,35 +135,31 @@ Version 1.1, 18 December 2014
 <code><a href="logistic.solution.html">logistic.solution</a>()</code></p></div>
     </div>
 
-    <div id="ref-examples">
-    <h2>Examples</h2>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span>
 <span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fu">SFORB.solution</span><span class="op">(</span><span class="va">x</span>, <span class="fl">100</span>, <span class="fl">0.5</span>, <span class="fl">2</span>, <span class="fl">3</span><span class="op">)</span>, <span class="fl">0</span>, <span class="fl">2</span><span class="op">)</span></span></span>
 <span class="r-plt img"><img src="SFORB.solution-1.png" alt="" width="700" height="433"></span>
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/[.mhmkin.html b/docs/reference/[.mhmkin.html
new file mode 100644
index 00000000..4be1ad9e
--- /dev/null
+++ b/docs/reference/[.mhmkin.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/mhmkin.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/mhmkin.html">
+  </head>
+</html>
+
diff --git a/docs/reference/add_err-1.png b/docs/reference/add_err-1.png
index 4a3b4062..1bcea3d5 100644
--- a/docs/reference/add_err-1.png
+++ b/docs/reference/add_err-1.png
diff --git a/docs/reference/add_err-2.png b/docs/reference/add_err-2.png
index 5aec1744..67c083ea 100644
--- a/docs/reference/add_err-2.png
+++ b/docs/reference/add_err-2.png
diff --git a/docs/reference/add_err-3.png b/docs/reference/add_err-3.png
index 2e71f02f..f25ae5e7 100644
--- a/docs/reference/add_err-3.png
+++ b/docs/reference/add_err-3.png
diff --git a/docs/reference/add_err.html b/docs/reference/add_err.html
index 4db51d10..4fd6326c 100644
--- a/docs/reference/add_err.html
+++ b/docs/reference/add_err.html
@@ -1,122 +1,77 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Add normally distributed errors to simulated kinetic degradation data — add_err • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Add normally distributed errors to simulated kinetic degradation data — add_err"><meta property="og:description" content="Normally distributed errors are added to data predicted for a specific
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Add normally distributed errors to simulated kinetic degradation data — add_err • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Add normally distributed errors to simulated kinetic degradation data — add_err"><meta name="description" content="Normally distributed errors are added to data predicted for a specific
 degradation model using mkinpredict. The variance of the error
-may depend on the predicted value and is specified as a standard deviation."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+may depend on the predicted value and is specified as a standard deviation."><meta property="og:description" content="Normally distributed errors are added to data predicted for a specific
+degradation model using mkinpredict. The variance of the error
+may depend on the predicted value and is specified as a standard deviation."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Add normally distributed errors to simulated kinetic degradation data</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/add_err.R" class="external-link"><code>R/add_err.R</code></a></small>
-    <div class="hidden name"><code>add_err.Rd</code></div>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Add normally distributed errors to simulated kinetic degradation data</h1>
+      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/add_err.R" class="external-link"><code>R/add_err.R</code></a></small>
+      <div class="d-none name"><code>add_err.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Normally distributed errors are added to data predicted for a specific
 degradation model using <code><a href="mkinpredict.html">mkinpredict</a></code>. The variance of the error
 may depend on the predicted value and is specified as a standard deviation.</p>
     </div>
 
-    <div id="ref-usage">
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">add_err</span><span class="op">(</span></span>
 <span>  <span class="va">prediction</span>,</span>
 <span>  <span class="va">sdfunc</span>,</span>
@@ -129,67 +84,67 @@ may depend on the predicted value and is specified as a standard deviation.</p>
 <span><span class="op">)</span></span></code></pre></div>
     </div>
 
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>prediction</dt>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-prediction">prediction<a class="anchor" aria-label="anchor" href="#arg-prediction"></a></dt>
 <dd><p>A prediction from a kinetic model as produced by
 <code><a href="mkinpredict.html">mkinpredict</a></code>.</p></dd>
 
 
-<dt>sdfunc</dt>
+<dt id="arg-sdfunc">sdfunc<a class="anchor" aria-label="anchor" href="#arg-sdfunc"></a></dt>
 <dd><p>A function taking the predicted value as its only argument and
 returning a standard deviation that should be used for generating the
 random error terms for this value.</p></dd>
 
 
-<dt>secondary</dt>
+<dt id="arg-secondary">secondary<a class="anchor" aria-label="anchor" href="#arg-secondary"></a></dt>
 <dd><p>The names of state variables that should have an initial
 value of zero</p></dd>
 
 
-<dt>n</dt>
+<dt id="arg-n">n<a class="anchor" aria-label="anchor" href="#arg-n"></a></dt>
 <dd><p>The number of datasets to be generated.</p></dd>
 
 
-<dt>LOD</dt>
+<dt id="arg-lod">LOD<a class="anchor" aria-label="anchor" href="#arg-lod"></a></dt>
 <dd><p>The limit of detection (LOD). Values that are below the LOD after
 adding the random error will be set to NA.</p></dd>
 
 
-<dt>reps</dt>
+<dt id="arg-reps">reps<a class="anchor" aria-label="anchor" href="#arg-reps"></a></dt>
 <dd><p>The number of replicates to be generated within the datasets.</p></dd>
 
 
-<dt>digits</dt>
+<dt id="arg-digits">digits<a class="anchor" aria-label="anchor" href="#arg-digits"></a></dt>
 <dd><p>The number of digits to which the values will be rounded.</p></dd>
 
 
-<dt>seed</dt>
+<dt id="arg-seed">seed<a class="anchor" aria-label="anchor" href="#arg-seed"></a></dt>
 <dd><p>The seed used for the generation of random numbers. If NA, the
 seed is not set.</p></dd>
 
 </dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>A list of datasets compatible with <code><a href="mmkin.html">mmkin</a></code>, i.e. the
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>A list of datasets compatible with <code><a href="mmkin.html">mmkin</a></code>, i.e. the
 components of the list are datasets compatible with <code><a href="mkinfit.html">mkinfit</a></code>.</p>
     </div>
-    <div id="references">
-    <h2>References</h2>
+    <div class="section level2">
+    <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
     <p>Ranke J and Lehmann R (2015) To t-test or not to t-test, that is
 the question. XV Symposium on Pesticide Chemistry 2-4 September 2015,
 Piacenza, Italy
 https://jrwb.de/posters/piacenza_2015.pdf</p>
     </div>
-    <div id="author">
-    <h2>Author</h2>
+    <div class="section level2">
+    <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a></h2>
     <p>Johannes Ranke</p>
     </div>
 
-    <div id="ref-examples">
-    <h2>Examples</h2>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># The kinetic model</span></span></span>
 <span class="r-in"><span><span class="va">m_SFO_SFO</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"M1"</span><span class="op">)</span>,</span></span>
@@ -239,27 +194,23 @@ https://jrwb.de/posters/piacenza_2015.pdf</p>
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/anova.saem.mmkin.html b/docs/reference/anova.saem.mmkin.html
index 03ca588d..9fb05d5c 100644
--- a/docs/reference/anova.saem.mmkin.html
+++ b/docs/reference/anova.saem.mmkin.html
@@ -1,125 +1,81 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Anova method for saem.mmkin objects — anova.saem.mmkin • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Anova method for saem.mmkin objects — anova.saem.mmkin"><meta property="og:description" content="Generate an anova object. The method to calculate the BIC is that from the
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Anova method for saem.mmkin objects — anova.saem.mmkin • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Anova method for saem.mmkin objects — anova.saem.mmkin"><meta name="description" content="Generate an anova object. The method to calculate the BIC is that from the
 saemix package. As in other prominent anova methods, models are sorted by
 number of parameters, and the tests (if requested) are always relative to
-the model on the previous line."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+the model on the previous line."><meta property="og:description" content="Generate an anova object. The method to calculate the BIC is that from the
+saemix package. As in other prominent anova methods, models are sorted by
+number of parameters, and the tests (if requested) are always relative to
+the model on the previous line."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Anova method for saem.mmkin objects</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/anova.saem.mmkin.R" class="external-link"><code>R/anova.saem.mmkin.R</code></a></small>
-    <div class="hidden name"><code>anova.saem.mmkin.Rd</code></div>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Anova method for saem.mmkin objects</h1>
+      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/anova.saem.mmkin.R" class="external-link"><code>R/anova.saem.mmkin.R</code></a></small>
+      <div class="d-none name"><code>anova.saem.mmkin.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Generate an anova object. The method to calculate the BIC is that from the
 saemix package. As in other prominent anova methods, models are sorted by
 number of parameters, and the tests (if requested) are always relative to
 the model on the previous line.</p>
     </div>
 
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for saem.mmkin</span></span>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'saem.mmkin'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span></span>
 <span>  <span class="va">object</span>,</span>
 <span>  <span class="va">...</span>,</span>
@@ -129,60 +85,56 @@ the model on the previous line.</p>
 <span><span class="op">)</span></span></code></pre></div>
     </div>
 
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>object</dt>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
 <dd><p>An <a href="saem.html">saem.mmkin</a> object</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>further such objects</p></dd>
 
 
-<dt>method</dt>
+<dt id="arg-method">method<a class="anchor" aria-label="anchor" href="#arg-method"></a></dt>
 <dd><p>Method for likelihood calculation: "is" (importance sampling),
 "lin" (linear approximation), or "gq" (Gaussian quadrature). Passed
 to <a href="https://rdrr.io/pkg/saemix/man/logLik.html" class="external-link">saemix::logLik.SaemixObject</a></p></dd>
 
 
-<dt>test</dt>
+<dt id="arg-test">test<a class="anchor" aria-label="anchor" href="#arg-test"></a></dt>
 <dd><p>Should a likelihood ratio test be performed? If TRUE,
 the alternative models are tested against the first model. Should
 only be done for nested models.</p></dd>
 
 
-<dt>model.names</dt>
+<dt id="arg-model-names">model.names<a class="anchor" aria-label="anchor" href="#arg-model-names"></a></dt>
 <dd><p>Optional character vector of model names</p></dd>
 
 </dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>an "anova" data frame; the traditional (S3) result of anova()</p>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>an "anova" data frame; the traditional (S3) result of anova()</p>
     </div>
 
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/aw.html b/docs/reference/aw.html
index 5740d67d..3405e6f2 100644
--- a/docs/reference/aw.html
+++ b/docs/reference/aw.html
@@ -1,140 +1,97 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Calculate Akaike weights for model averaging — aw • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Calculate Akaike weights for model averaging — aw"><meta property="og:description" content="Akaike weights are calculated based on the relative
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Calculate Akaike weights for model averaging — aw • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Calculate Akaike weights for model averaging — aw"><meta name="description" content="Akaike weights are calculated based on the relative
 expected Kullback-Leibler information as specified
-by Burnham and Anderson (2004)."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+by Burnham and Anderson (2004)."><meta property="og:description" content="Akaike weights are calculated based on the relative
+expected Kullback-Leibler information as specified
+by Burnham and Anderson (2004)."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Calculate Akaike weights for model averaging</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/aw.R" class="external-link"><code>R/aw.R</code></a></small>
-    <div class="hidden name"><code>aw.Rd</code></div>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Calculate Akaike weights for model averaging</h1>
+      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/aw.R" class="external-link"><code>R/aw.R</code></a></small>
+      <div class="d-none name"><code>aw.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Akaike weights are calculated based on the relative
 expected Kullback-Leibler information as specified
 by Burnham and Anderson (2004).</p>
     </div>
 
-    <div id="ref-usage">
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">aw</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for mkinfit</span></span>
+<span><span class="co"># S3 method for class 'mkinfit'</span></span>
 <span><span class="fu">aw</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for mmkin</span></span>
+<span><span class="co"># S3 method for class 'mmkin'</span></span>
 <span><span class="fu">aw</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for mixed.mmkin</span></span>
+<span><span class="co"># S3 method for class 'mixed.mmkin'</span></span>
 <span><span class="fu">aw</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for multistart</span></span>
+<span><span class="co"># S3 method for class 'multistart'</span></span>
 <span><span class="fu">aw</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>object</dt>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
 <dd><p>An <a href="mmkin.html">mmkin</a> column object, containing two or more
 <a href="mkinfit.html">mkinfit</a> models that have been fitted to the same data,
 or an mkinfit object. In the latter case, further mkinfit
@@ -142,20 +99,20 @@ objects fitted to the same data should be specified
 as dots arguments.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Not used in the method for <a href="mmkin.html">mmkin</a> column objects,
 further <a href="mkinfit.html">mkinfit</a> objects in the method for mkinfit objects.</p></dd>
 
 </dl></div>
-    <div id="references">
-    <h2>References</h2>
+    <div class="section level2">
+    <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
     <p>Burnham KP and Anderson DR (2004) Multimodel
 Inference: Understanding AIC and BIC in Model Selection.
 <em>Sociological Methods &amp; Research</em> <strong>33</strong>(2) 261-304</p>
     </div>
 
-    <div id="ref-examples">
-    <h2>Examples</h2>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># \dontrun{</span></span></span>
 <span class="r-in"><span><span class="va">f_sfo</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="va">FOCUS_2006_D</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">f_dfop</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="va">FOCUS_2006_D</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
@@ -174,27 +131,23 @@ Inference: Understanding AIC and BIC in Model Selection.
 <span class="r-in"><span><span class="co"># }</span></span></span>
 </code></pre></div>
     </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/aw.mixed.mmkin.html b/docs/reference/aw.mixed.mmkin.html
new file mode 100644
index 00000000..9e9a4d03
--- /dev/null
+++ b/docs/reference/aw.mixed.mmkin.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/aw.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/aw.html">
+  </head>
+</html>
+
diff --git a/docs/reference/aw.mkinfit.html b/docs/reference/aw.mkinfit.html
new file mode 100644
index 00000000..9e9a4d03
--- /dev/null
+++ b/docs/reference/aw.mkinfit.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/aw.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/aw.html">
+  </head>
+</html>
+
diff --git a/docs/reference/aw.mmkin.html b/docs/reference/aw.mmkin.html
new file mode 100644
index 00000000..9e9a4d03
--- /dev/null
+++ b/docs/reference/aw.mmkin.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/aw.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/aw.html">
+  </head>
+</html>
+
diff --git a/docs/reference/aw.multistart.html b/docs/reference/aw.multistart.html
new file mode 100644
index 00000000..9e9a4d03
--- /dev/null
+++ b/docs/reference/aw.multistart.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/aw.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/aw.html">
+  </head>
+</html>
+
diff --git a/docs/reference/backtransform_odeparms.html b/docs/reference/backtransform_odeparms.html
new file mode 100644
index 00000000..6cbb805d
--- /dev/null
+++ b/docs/reference/backtransform_odeparms.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/transform_odeparms.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/transform_odeparms.html">
+  </head>
+</html>
+
diff --git a/docs/reference/best.default.html b/docs/reference/best.default.html
new file mode 100644
index 00000000..9700ef05
--- /dev/null
+++ b/docs/reference/best.default.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/multistart.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/multistart.html">
+  </head>
+</html>
+
diff --git a/docs/reference/best.html b/docs/reference/best.html
new file mode 100644
index 00000000..9700ef05
--- /dev/null
+++ b/docs/reference/best.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/multistart.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/multistart.html">
+  </head>
+</html>
+
diff --git a/docs/reference/check_failed.html b/docs/reference/check_failed.html
new file mode 100644
index 00000000..d6fd6dfe
--- /dev/null
+++ b/docs/reference/check_failed.html
@@ -0,0 +1,100 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Check if fit within an mhmkin object failed — check_failed • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Check if fit within an mhmkin object failed — check_failed"><meta name="description" content="Check if fit within an mhmkin object failed"><meta property="og:description" content="Check if fit within an mhmkin object failed"></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Check if fit within an mhmkin object failed</h1>
+      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/mhmkin.R" class="external-link"><code>R/mhmkin.R</code></a></small>
+      <div class="d-none name"><code>check_failed.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>Check if fit within an mhmkin object failed</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">check_failed</span><span class="op">(</span><span class="va">x</span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
+<dd><p>The object to be checked</p></dd>
+
+</dl></div>
+
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
+</div>
+
+    </footer></div>
+
+
+
+
+
+  </body></html>
+
diff --git a/docs/reference/confint.mkinfit.html b/docs/reference/confint.mkinfit.html
index 48240abc..195ad75b 100644
--- a/docs/reference/confint.mkinfit.html
+++ b/docs/reference/confint.mkinfit.html
@@ -1,121 +1,80 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Confidence intervals for parameters of mkinfit objects — confint.mkinfit • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Confidence intervals for parameters of mkinfit objects — confint.mkinfit"><meta property="og:description" content="The default method 'quadratic' is based on the quadratic approximation of
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Confidence intervals for parameters of mkinfit objects — confint.mkinfit • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Confidence intervals for parameters of mkinfit objects — confint.mkinfit"><meta name="description" content="The default method 'quadratic' is based on the quadratic approximation of
 the curvature of the likelihood function at the maximum likelihood parameter
 estimates.
 The alternative method 'profile' is based on the profile likelihood for each
 parameter. The 'profile' method uses two nested optimisations and can take a
 very long time, even if parallelized by specifying 'cores' on unixoid
 platforms. The speed of the method could likely be improved by using the
-method of Venzon and Moolgavkar (1988)."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+method of Venzon and Moolgavkar (1988)."><meta property="og:description" content="The default method 'quadratic' is based on the quadratic approximation of
+the curvature of the likelihood function at the maximum likelihood parameter
+estimates.
+The alternative method 'profile' is based on the profile likelihood for each
+parameter. The 'profile' method uses two nested optimisations and can take a
+very long time, even if parallelized by specifying 'cores' on unixoid
+platforms. The speed of the method could likely be improved by using the
+method of Venzon and Moolgavkar (1988)."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Confidence intervals for parameters of mkinfit objects</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/confint.mkinfit.R" class="external-link"><code>R/confint.mkinfit.R</code></a></small>
-    <div class="hidden name"><code>confint.mkinfit.Rd</code></div>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Confidence intervals for parameters of mkinfit objects</h1>
+      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/confint.mkinfit.R" class="external-link"><code>R/confint.mkinfit.R</code></a></small>
+      <div class="d-none name"><code>confint.mkinfit.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>The default method 'quadratic' is based on the quadratic approximation of
 the curvature of the likelihood function at the maximum likelihood parameter
 estimates.
@@ -126,8 +85,9 @@ platforms. The speed of the method could likely be improved by using the
 method of Venzon and Moolgavkar (1988).</p>
     </div>
 
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for mkinfit</span></span>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'mkinfit'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/stats/confint.html" class="external-link">confint</a></span><span class="op">(</span></span>
 <span>  <span class="va">object</span>,</span>
 <span>  <span class="va">parm</span>,</span>
@@ -144,32 +104,34 @@ method of Venzon and Moolgavkar (1988).</p>
 <span><span class="op">)</span></span></code></pre></div>
     </div>
 
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>object</dt>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
 <dd><p>An <code><a href="mkinfit.html">mkinfit</a></code> object</p></dd>
 
 
-<dt>parm</dt>
+<dt id="arg-parm">parm<a class="anchor" aria-label="anchor" href="#arg-parm"></a></dt>
 <dd><p>A vector of names of the parameters which are to be given
 confidence intervals. If missing, all parameters are considered.</p></dd>
 
 
-<dt>level</dt>
+<dt id="arg-level">level<a class="anchor" aria-label="anchor" href="#arg-level"></a></dt>
 <dd><p>The confidence level required</p></dd>
 
 
-<dt>alpha</dt>
+<dt id="arg-alpha">alpha<a class="anchor" aria-label="anchor" href="#arg-alpha"></a></dt>
 <dd><p>The allowed error probability, overrides 'level' if specified.</p></dd>
 
 
-<dt>cutoff</dt>
+<dt id="arg-cutoff">cutoff<a class="anchor" aria-label="anchor" href="#arg-cutoff"></a></dt>
 <dd><p>Possibility to specify an alternative cutoff for the difference
 in the log-likelihoods at the confidence boundary. Specifying an explicit
 cutoff value overrides arguments 'level' and 'alpha'</p></dd>
 
 
-<dt>method</dt>
+<dt id="arg-method">method<a class="anchor" aria-label="anchor" href="#arg-method"></a></dt>
 <dd><p>The 'quadratic' method approximates the likelihood function at
 the optimised parameters using the second term of the Taylor expansion,
 using a second derivative (hessian) contained in the object.
@@ -177,45 +139,43 @@ The 'profile' method searches the parameter space for the
 cutoff of the confidence intervals by means of a likelihood ratio test.</p></dd>
 
 
-<dt>transformed</dt>
+<dt id="arg-transformed">transformed<a class="anchor" aria-label="anchor" href="#arg-transformed"></a></dt>
 <dd><p>If the quadratic approximation is used, should it be
 applied to the likelihood based on the transformed parameters?</p></dd>
 
 
-<dt>backtransform</dt>
+<dt id="arg-backtransform">backtransform<a class="anchor" aria-label="anchor" href="#arg-backtransform"></a></dt>
 <dd><p>If we approximate the likelihood in terms of the
 transformed parameters, should we backtransform the parameters with
 their confidence intervals?</p></dd>
 
 
-<dt>cores</dt>
+<dt id="arg-cores">cores<a class="anchor" aria-label="anchor" href="#arg-cores"></a></dt>
 <dd><p>The number of cores to be used for multicore processing.
 On Windows machines, cores &gt; 1 is currently not supported.</p></dd>
 
 
-<dt>rel_tol</dt>
+<dt id="arg-rel-tol">rel_tol<a class="anchor" aria-label="anchor" href="#arg-rel-tol"></a></dt>
 <dd><p>If the method is 'profile', what should be the accuracy
 of the lower and upper bounds, relative to the estimate obtained from
 the quadratic method?</p></dd>
 
 
-<dt>quiet</dt>
+<dt id="arg-quiet">quiet<a class="anchor" aria-label="anchor" href="#arg-quiet"></a></dt>
 <dd><p>Should we suppress the message "Profiling the likelihood"</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Not used</p></dd>
 
 </dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>A matrix with columns giving lower and upper confidence limits for
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>A matrix with columns giving lower and upper confidence limits for
 each parameter.</p>
     </div>
-    <div id="references">
-    <h2>References</h2>
+    <div class="section level2">
+    <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
     <p>Bates DM and Watts GW (1988) Nonlinear regression analysis &amp; its applications</p>
 <p>Pawitan Y (2013) In all likelihood - Statistical modelling and
 inference using likelihood. Clarendon Press, Oxford.</p>
@@ -224,8 +184,8 @@ Profile-Likelihood Based Confidence Intervals, Applied Statistics, 37,
 87–94.</p>
     </div>
 
-    <div id="ref-examples">
-    <h2>Examples</h2>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">f</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="va">FOCUS_2006_C</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/confint.html" class="external-link">confint</a></span><span class="op">(</span><span class="va">f</span>, method <span class="op">=</span> <span class="st">"quadratic"</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                2.5%      97.5%</span>
@@ -257,7 +217,7 @@ Profile-Likelihood Based Confidence Intervals, Applied Statistics, 37,
 <span class="r-in"><span><span class="va">f_d_1</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="fu"><a href="https://rdrr.io/r/base/subset.html" class="external-link">subset</a></span><span class="op">(</span><span class="va">FOCUS_2006_D</span>, <span class="va">value</span> <span class="op">!=</span> <span class="fl">0</span><span class="op">)</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/system.time.html" class="external-link">system.time</a></span><span class="op">(</span><span class="va">ci_profile</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/confint.html" class="external-link">confint</a></span><span class="op">(</span><span class="va">f_d_1</span>, method <span class="op">=</span> <span class="st">"profile"</span>, cores <span class="op">=</span> <span class="fl">1</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>    user  system elapsed </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   2.578   0.005   2.599 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   1.206   0.000   1.206 </span>
 <span class="r-in"><span><span class="co"># Using more cores does not save much time here, as parent_0 takes up most of the time</span></span></span>
 <span class="r-in"><span><span class="co"># If we additionally exclude parent_0 (the confidence of which is often of</span></span></span>
 <span class="r-in"><span><span class="co"># minor interest), we get a nice performance improvement if we use at least 4 cores</span></span></span>
@@ -265,7 +225,7 @@ Profile-Likelihood Based Confidence Intervals, Applied Statistics, 37,
 <span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"k_parent_sink"</span>, <span class="st">"k_parent_m1"</span>, <span class="st">"k_m1_sink"</span>, <span class="st">"sigma"</span><span class="op">)</span>, cores <span class="op">=</span> <span class="va">n_cores</span><span class="op">)</span><span class="op">)</span></span></span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> Profiling the likelihood</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>    user  system elapsed </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   0.963   0.255   0.636 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.406   0.172   0.317 </span>
 <span class="r-in"><span><span class="va">ci_profile</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                       2.5%        97.5%</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> parent_0      96.456003640 1.027703e+02</span>
@@ -400,27 +360,23 @@ Profile-Likelihood Based Confidence Intervals, Applied Statistics, 37,
 <span class="r-in"><span><span class="co"># }</span></span></span>
 </code></pre></div>
     </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/convergence.html b/docs/reference/convergence.html
deleted file mode 100644
index 3c7d5536..00000000
--- a/docs/reference/convergence.html
+++ /dev/null
@@ -1,163 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Method to get convergence information — convergence • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Method to get convergence information — convergence"><meta property="og:description" content="Method to get convergence information"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.2</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Functions and data</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Method to get convergence information</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/convergence.R" class="external-link"><code>R/convergence.R</code></a></small>
-    <div class="hidden name"><code>convergence.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Method to get convergence information</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">convergence</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># S3 method for mmkin</span></span>
-<span><span class="fu">convergence</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># S3 method for convergence.mmkin</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
-    </div>
-
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>object</dt>
-<dd><p>The object to investigate</p></dd>
-
-
-<dt>...</dt>
-<dd><p>For potential future extensions</p></dd>
-
-
-<dt>x</dt>
-<dd><p>The object to be printed</p></dd>
-
-</dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>For <a href="mkinfit.html">mkinfit</a> objects, a character vector containing
-For <a href="mmkin.html">mmkin</a> objects, an object of class 'convergence.mmkin' with a
-suitable printing method.</p>
-    </div>
-
-    <div id="ref-examples">
-    <h2>Examples</h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># \dontrun{</span></span></span>
-<span class="r-in"><span><span class="va">fits</span> <span class="op">&lt;-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span></span></span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"FOMC"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="st">"FOCUS A"</span> <span class="op">=</span> <span class="va">FOCUS_2006_A</span>,</span></span>
-<span class="r-in"><span>       <span class="st">"FOCUS B"</span> <span class="op">=</span> <span class="va">FOCUS_2006_C</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu">convergence</span><span class="op">(</span><span class="va">fits</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>       dataset</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> model  FOCUS A FOCUS B</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   SFO  OK      OK     </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   FOMC OK      OK     </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> OK: No warnings</span>
-<span class="r-in"><span><span class="co"># }</span></span></span>
-</code></pre></div>
-    </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.5.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/reference/create_deg_func.html b/docs/reference/create_deg_func.html
index bf02afe3..be11b498 100644
--- a/docs/reference/create_deg_func.html
+++ b/docs/reference/create_deg_func.html
@@ -1,140 +1,93 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Create degradation functions for known analytical solutions — create_deg_func • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Create degradation functions for known analytical solutions — create_deg_func"><meta property="og:description" content="Create degradation functions for known analytical solutions"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Create degradation functions for known analytical solutions — create_deg_func • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Create degradation functions for known analytical solutions — create_deg_func"><meta name="description" content="Create degradation functions for known analytical solutions"><meta property="og:description" content="Create degradation functions for known analytical solutions"></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Create degradation functions for known analytical solutions</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/create_deg_func.R" class="external-link"><code>R/create_deg_func.R</code></a></small>
-    <div class="hidden name"><code>create_deg_func.Rd</code></div>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Create degradation functions for known analytical solutions</h1>
+      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/create_deg_func.R" class="external-link"><code>R/create_deg_func.R</code></a></small>
+      <div class="d-none name"><code>create_deg_func.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Create degradation functions for known analytical solutions</p>
     </div>
 
-    <div id="ref-usage">
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">create_deg_func</span><span class="op">(</span><span class="va">spec</span>, use_of_ff <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"min"</span>, <span class="st">"max"</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
     </div>
 
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>spec</dt>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-spec">spec<a class="anchor" aria-label="anchor" href="#arg-spec"></a></dt>
 <dd><p>List of model specifications as contained in mkinmod objects</p></dd>
 
 
-<dt>use_of_ff</dt>
+<dt id="arg-use-of-ff">use_of_ff<a class="anchor" aria-label="anchor" href="#arg-use-of-ff"></a></dt>
 <dd><p>Minimum or maximum use of formation fractions</p></dd>
 
 </dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>Degradation function to be attached to mkinmod objects</p>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>Degradation function to be attached to mkinmod objects</p>
     </div>
 
-    <div id="ref-examples">
-    <h2>Examples</h2>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="va">SFO_SFO</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span></span></span>
 <span class="r-in"><span>  parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"m1"</span><span class="op">)</span>,</span></span>
@@ -151,8 +104,8 @@
 <span class="r-in"><span>    replications <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span></span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> Loading required package: rbenchmark</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>         test replications elapsed relative user.self sys.self user.child</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1 analytical            2   0.421    1.000     0.412    0.000          0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2    deSolve            2   0.601    1.428     0.566    0.024          0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 1 analytical            2    0.24    1.000     0.240        0          0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2    deSolve            2    0.31    1.292     0.309        0          0</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>   sys.child</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> 1         0</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> 2         0</span>
@@ -165,35 +118,31 @@
 <span class="r-in"><span>    deSolve <span class="op">=</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">DFOP_SFO</span>, <span class="va">FOCUS_D</span>, solution_type <span class="op">=</span> <span class="st">"deSolve"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>,</span></span>
 <span class="r-in"><span>    replications <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>         test replications elapsed relative user.self sys.self user.child</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1 analytical            2   0.762    1.000     0.758    0.004          0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2    deSolve            2   1.194    1.567     1.140    0.052          0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 1 analytical            2   0.403    1.000     0.402        0          0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2    deSolve            2   0.539    1.337     0.538        0          0</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>   sys.child</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> 1         0</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> 2         0</span>
 <span class="r-in"><span><span class="co"># }</span></span></span>
 </code></pre></div>
     </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/dimethenamid_2018-1.png b/docs/reference/dimethenamid_2018-1.png
index e570a766..3a509a7f 100644
--- a/docs/reference/dimethenamid_2018-1.png
+++ b/docs/reference/dimethenamid_2018-1.png
diff --git a/docs/reference/dimethenamid_2018-2.png b/docs/reference/dimethenamid_2018-2.png
deleted file mode 100644
index cf5f03f7..00000000
--- a/docs/reference/dimethenamid_2018-2.png
+++ /dev/null
diff --git a/docs/reference/dimethenamid_2018-3.png b/docs/reference/dimethenamid_2018-3.png
deleted file mode 100644
index 7c876208..00000000
--- a/docs/reference/dimethenamid_2018-3.png
+++ /dev/null
diff --git a/docs/reference/dimethenamid_2018.html b/docs/reference/dimethenamid_2018.html
index 961b28d2..2311a67e 100644
--- a/docs/reference/dimethenamid_2018.html
+++ b/docs/reference/dimethenamid_2018.html
@@ -1,116 +1,76 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Aerobic soil degradation data on dimethenamid and dimethenamid-P from the EU assessment in 2018 — dimethenamid_2018 • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Aerobic soil degradation data on dimethenamid and dimethenamid-P from the EU assessment in 2018 — dimethenamid_2018"><meta property="og:description" content="The datasets were extracted from the active substance evaluation dossier
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Aerobic soil degradation data on dimethenamid and dimethenamid-P from the EU assessment in 2018 — dimethenamid_2018 • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Aerobic soil degradation data on dimethenamid and dimethenamid-P from the EU assessment in 2018 — dimethenamid_2018"><meta name="description" content="The datasets were extracted from the active substance evaluation dossier
 published by EFSA. Kinetic evaluations shown for these datasets are intended
 to illustrate and advance kinetic modelling. The fact that these data and
 some results are shown here does not imply a license to use them in the
 context of pesticide  registrations, as the use of the data may be
-constrained by data protection regulations."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
+constrained by data protection regulations."><meta property="og:description" content="The datasets were extracted from the active substance evaluation dossier
+published by EFSA. Kinetic evaluations shown for these datasets are intended
+to illustrate and advance kinetic modelling. The fact that these data and
+some results are shown here does not imply a license to use them in the
+context of pesticide  registrations, as the use of the data may be
+constrained by data protection regulations."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
 
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.5</span>
-      </span>
-    </div>
 
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
 
-      
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
 
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Aerobic soil degradation data on dimethenamid and dimethenamid-P from the EU assessment in 2018</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/dimethenamid_2018.R" class="external-link"><code>R/dimethenamid_2018.R</code></a></small>
-    <div class="hidden name"><code>dimethenamid_2018.Rd</code></div>
+      <h1>Aerobic soil degradation data on dimethenamid and dimethenamid-P from the EU assessment in 2018</h1>
+      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/dimethenamid_2018.R" class="external-link"><code>R/dimethenamid_2018.R</code></a></small>
+      <div class="d-none name"><code>dimethenamid_2018.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>The datasets were extracted from the active substance evaluation dossier
 published by EFSA. Kinetic evaluations shown for these datasets are intended
 to illustrate and advance kinetic modelling. The fact that these data and
@@ -119,30 +79,31 @@ context of pesticide  registrations, as the use of the data may be
 constrained by data protection regulations.</p>
     </div>
 
-    <div id="ref-usage">
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="va">dimethenamid_2018</span></span></code></pre></div>
     </div>
 
-    <div id="format">
-    <h2>Format</h2>
+    <div class="section level2">
+    <h2 id="format">Format<a class="anchor" aria-label="anchor" href="#format"></a></h2>
     <p>An <a href="mkindsg.html">mkindsg</a> object grouping seven datasets with some meta information</p>
     </div>
-    <div id="source">
-    <h2>Source</h2>
+    <div class="section level2">
+    <h2 id="source">Source<a class="anchor" aria-label="anchor" href="#source"></a></h2>
     <p>Rapporteur Member State Germany, Co-Rapporteur Member State Bulgaria (2018)
 Renewal Assessment Report Dimethenamid-P Volume 3 - B.8 Environmental fate and behaviour
 Rev. 2 - November 2017
 https://open.efsa.europa.eu/study-inventory/EFSA-Q-2014-00716</p>
     </div>
-    <div id="details">
-    <h2>Details</h2>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>The R code used to create this data object is installed with this package
 in the 'dataset_generation' directory. In the code, page numbers are given for
 specific pieces of information in the comments.</p>
     </div>
 
-    <div id="ref-examples">
-    <h2>Examples</h2>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">dimethenamid_2018</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> &lt;mkindsg&gt; holding 7 mkinds objects</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Title $title:  Aerobic soil degradation data on dimethenamid-P from the EU assessment in 2018 </span>
@@ -203,11 +164,11 @@ specific pieces of information in the comments.</p>
 <span class="r-in"><span><span class="co"># influence of ill-defined rate constants that have</span></span></span>
 <span class="r-in"><span><span class="co"># extremely small values:</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="fu"><a href="mixed.html">mixed</a></span><span class="op">(</span><span class="va">dmta_sfo_sfo3p_tc</span><span class="op">)</span>, test_log_parms <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></span>
+<span class="r-plt img"><img src="dimethenamid_2018-1.png" alt="" width="700" height="433"></span>
 <span class="r-in"><span><span class="co"># If we disregards ill-defined rate constants, the results</span></span></span>
 <span class="r-in"><span><span class="co"># look more plausible, but the truth is likely to be in</span></span></span>
 <span class="r-in"><span><span class="co"># between these variants</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="fu"><a href="mixed.html">mixed</a></span><span class="op">(</span><span class="va">dmta_sfo_sfo3p_tc</span><span class="op">)</span>, test_log_parms <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-plt img"><img src="dimethenamid_2018-1.png" alt="" width="700" height="433"></span>
 <span class="r-in"><span><span class="co"># We can also specify a default value for the failing</span></span></span>
 <span class="r-in"><span><span class="co"># log parameters, to mimic FOCUS guidance</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="fu"><a href="mixed.html">mixed</a></span><span class="op">(</span><span class="va">dmta_sfo_sfo3p_tc</span><span class="op">)</span>, test_log_parms <span class="op">=</span> <span class="cn">TRUE</span>,</span></span>
@@ -221,11 +182,11 @@ specific pieces of information in the comments.</p>
 <span class="r-in"><span><span class="co"># graphics device used)</span></span></span>
 <span class="r-in"><span><span class="co">#saemix::plot(f_dmta_saem_tc$so, plot.type = "convergence")</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">f_dmta_saem_tc</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> saemix version used for fitting:      3.2 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> mkin version used for pre-fitting:  1.2.5 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> R version used for fitting:         4.3.1 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Date of fit:     Wed Aug  9 17:55:36 2023 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Date of summary: Wed Aug  9 17:55:36 2023 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> saemix version used for fitting:      3.3 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> mkin version used for pre-fitting:  1.2.9 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> R version used for fitting:         4.4.2 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Date of fit:     Thu Feb 13 14:56:10 2025 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Date of summary: Thu Feb 13 14:56:10 2025 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Equations:</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> d_DMTA/dt = - k_DMTA * DMTA</span>
@@ -238,7 +199,7 @@ specific pieces of information in the comments.</p>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Model predictions using solution type deSolve </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Fitted in 796.123 s</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Fitted in 295.43 s</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Using 300, 100 iterations and 9 chains</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Variance model: Two-component variance function </span>
@@ -292,9 +253,9 @@ specific pieces of information in the comments.</p>
 <span class="r-out co"><span class="r-pr">#&gt;</span> f_DMTA_ilr_1     0.1264 -0.2186  0.4714</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> f_DMTA_ilr_2     0.1509 -0.2547  0.5565</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> f_DMTA_ilr_3    -1.3891 -1.6962 -1.0819</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> a.1              0.9196  0.8231  1.0161</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> b.1              0.1377  0.1203  0.1551</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> SD.DMTA_0        3.5956 -0.8154  8.0066</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> a.1              0.9196  0.8307  1.0085</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> b.1              0.1377  0.1205  0.1549</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> SD.DMTA_0        3.5956 -0.8167  8.0078</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> SD.log_k_DMTA    0.6437  0.2784  1.0091</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> SD.log_k_M23     0.9929  0.3719  1.6139</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> SD.log_k_M27     0.4530  0.1522  0.7537</span>
@@ -315,7 +276,7 @@ specific pieces of information in the comments.</p>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Random effects:</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                   est.   lower  upper</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> SD.DMTA_0       3.5956 -0.8154 8.0066</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> SD.DMTA_0       3.5956 -0.8167 8.0078</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> SD.log_k_DMTA   0.6437  0.2784 1.0091</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> SD.log_k_M23    0.9929  0.3719 1.6139</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> SD.log_k_M27    0.4530  0.1522 0.7537</span>
@@ -326,8 +287,8 @@ specific pieces of information in the comments.</p>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Variance model:</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>       est.  lower  upper</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> a.1 0.9196 0.8231 1.0161</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> b.1 0.1377 0.1203 0.1551</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> a.1 0.9196 0.8307 1.0085</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> b.1 0.1377 0.1205 0.1549</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Backtransformed parameters:</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                   est.     lower    upper</span>
@@ -364,27 +325,23 @@ specific pieces of information in the comments.</p>
 <span class="r-in"><span><span class="co"># }</span></span></span>
 </code></pre></div>
     </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/ds_dfop.html b/docs/reference/ds_dfop.html
new file mode 100644
index 00000000..6b8c7d09
--- /dev/null
+++ b/docs/reference/ds_dfop.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/ds_mixed.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/ds_mixed.html">
+  </head>
+</html>
+
diff --git a/docs/reference/ds_dfop_sfo.html b/docs/reference/ds_dfop_sfo.html
new file mode 100644
index 00000000..6b8c7d09
--- /dev/null
+++ b/docs/reference/ds_dfop_sfo.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/ds_mixed.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/ds_mixed.html">
+  </head>
+</html>
+
diff --git a/docs/reference/ds_fomc.html b/docs/reference/ds_fomc.html
new file mode 100644
index 00000000..6b8c7d09
--- /dev/null
+++ b/docs/reference/ds_fomc.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/ds_mixed.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/ds_mixed.html">
+  </head>
+</html>
+
diff --git a/docs/reference/ds_hs.html b/docs/reference/ds_hs.html
new file mode 100644
index 00000000..6b8c7d09
--- /dev/null
+++ b/docs/reference/ds_hs.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/ds_mixed.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/ds_mixed.html">
+  </head>
+</html>
+
diff --git a/docs/reference/ds_mixed-1.png b/docs/reference/ds_mixed-1.png
index 053a7253..7968f1c9 100644
--- a/docs/reference/ds_mixed-1.png
+++ b/docs/reference/ds_mixed-1.png
diff --git a/docs/reference/ds_mixed.html b/docs/reference/ds_mixed.html
index fdf72593..37c49191 100644
--- a/docs/reference/ds_mixed.html
+++ b/docs/reference/ds_mixed.html
@@ -1,123 +1,76 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Synthetic data for hierarchical kinetic degradation models — ds_mixed • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Synthetic data for hierarchical kinetic degradation models — ds_mixed"><meta property="og:description" content="The R code used to create this data object is installed with this package in
-the 'dataset_generation' directory."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Synthetic data for hierarchical kinetic degradation models — ds_mixed • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Synthetic data for hierarchical kinetic degradation models — ds_mixed"><meta name="description" content="The R code used to create this data object is installed with this package in
+the 'dataset_generation' directory."><meta property="og:description" content="The R code used to create this data object is installed with this package in
+the 'dataset_generation' directory."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
 
-      
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
 
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Synthetic data for hierarchical kinetic degradation models</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/ds_mixed.R" class="external-link"><code>R/ds_mixed.R</code></a></small>
-    <div class="hidden name"><code>ds_mixed.Rd</code></div>
+      <h1>Synthetic data for hierarchical kinetic degradation models</h1>
+      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/ds_mixed.R" class="external-link"><code>R/ds_mixed.R</code></a></small>
+      <div class="d-none name"><code>ds_mixed.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>The R code used to create this data object is installed with this package in
 the 'dataset_generation' directory.</p>
     </div>
 
 
 
-    <div id="ref-examples">
-    <h2>Examples</h2>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># \dontrun{</span></span></span>
 <span class="r-in"><span>  <span class="va">sfo_mmkin</span> <span class="op">&lt;-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="va">ds_sfo</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, error_model <span class="op">=</span> <span class="st">"tc"</span>, cores <span class="op">=</span> <span class="fl">15</span><span class="op">)</span></span></span>
 <span class="r-in"><span>  <span class="va">sfo_saem</span> <span class="op">&lt;-</span> <span class="fu"><a href="saem.html">saem</a></span><span class="op">(</span><span class="va">sfo_mmkin</span>, no_random_effect <span class="op">=</span> <span class="st">"parent_0"</span><span class="op">)</span></span></span>
@@ -234,27 +187,22 @@ the 'dataset_generation' directory.</p>
 <span class="r-out co"><span class="r-pr">#&gt;</span> #save(ds_sfo, ds_fomc, ds_dfop, ds_hs, ds_dfop_sfo, file = "data/ds_mixed.rda", version = 2)</span>
 </code></pre></div>
     </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/ds_sfo.html b/docs/reference/ds_sfo.html
new file mode 100644
index 00000000..6b8c7d09
--- /dev/null
+++ b/docs/reference/ds_sfo.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/ds_mixed.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/ds_mixed.html">
+  </head>
+</html>
+
diff --git a/docs/reference/endpoints.html b/docs/reference/endpoints.html
index 4ce6a8ca..23c550ca 100644
--- a/docs/reference/endpoints.html
+++ b/docs/reference/endpoints.html
@@ -1,121 +1,74 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Function to calculate endpoints for further use from kinetic models fitted
-with mkinfit — endpoints • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Function to calculate endpoints for further use from kinetic models fitted
-with mkinfit — endpoints"><meta property="og:description" content="This function calculates DT50 and DT90 values as well as formation fractions
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Function to calculate endpoints for further use from kinetic models fitted with mkinfit — endpoints • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Function to calculate endpoints for further use from kinetic models fitted with mkinfit — endpoints"><meta name="description" content="This function calculates DT50 and DT90 values as well as formation fractions
 from kinetic models fitted with mkinfit. If the SFORB model was specified
 for one of the parents or metabolites, the Eigenvalues are returned. These
 are equivalent to the rate constants of the DFOP model, but with the
-advantage that the SFORB model can also be used for metabolites."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+advantage that the SFORB model can also be used for metabolites."><meta property="og:description" content="This function calculates DT50 and DT90 values as well as formation fractions
+from kinetic models fitted with mkinfit. If the SFORB model was specified
+for one of the parents or metabolites, the Eigenvalues are returned. These
+are equivalent to the rate constants of the DFOP model, but with the
+advantage that the SFORB model can also be used for metabolites."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Function to calculate endpoints for further use from kinetic models fitted
-with mkinfit</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/endpoints.R" class="external-link"><code>R/endpoints.R</code></a></small>
-    <div class="hidden name"><code>endpoints.Rd</code></div>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Function to calculate endpoints for further use from kinetic models fitted with mkinfit</h1>
+      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/endpoints.R" class="external-link"><code>R/endpoints.R</code></a></small>
+      <div class="d-none name"><code>endpoints.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>This function calculates DT50 and DT90 values as well as formation fractions
 from kinetic models fitted with mkinfit. If the SFORB model was specified
 for one of the parents or metabolites, the Eigenvalues are returned. These
@@ -123,56 +76,57 @@ are equivalent to the rate constants of the DFOP model, but with the
 advantage that the SFORB model can also be used for metabolites.</p>
     </div>
 
-    <div id="ref-usage">
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">endpoints</span><span class="op">(</span><span class="va">fit</span>, covariates <span class="op">=</span> <span class="cn">NULL</span>, covariate_quantile <span class="op">=</span> <span class="fl">0.5</span><span class="op">)</span></span></code></pre></div>
     </div>
 
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>fit</dt>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-fit">fit<a class="anchor" aria-label="anchor" href="#arg-fit"></a></dt>
 <dd><p>An object of class <a href="mkinfit.html">mkinfit</a>, <a href="nlme.mmkin.html">nlme.mmkin</a> or <a href="saem.html">saem.mmkin</a>, or
 another object that has list components mkinmod containing an <a href="mkinmod.html">mkinmod</a>
 degradation model, and two numeric vectors, bparms.optim and bparms.fixed,
 that contain parameter values for that model.</p></dd>
 
 
-<dt>covariates</dt>
+<dt id="arg-covariates">covariates<a class="anchor" aria-label="anchor" href="#arg-covariates"></a></dt>
 <dd><p>Numeric vector with covariate values for all variables in
 any covariate models in the object. If given, it overrides 'covariate_quantile'.</p></dd>
 
 
-<dt>covariate_quantile</dt>
+<dt id="arg-covariate-quantile">covariate_quantile<a class="anchor" aria-label="anchor" href="#arg-covariate-quantile"></a></dt>
 <dd><p>This argument only has an effect if the fitted
 object has covariate models. If so, the default is to show endpoints
 for the median of the covariate values (50th percentile).</p></dd>
 
 </dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>A list with a matrix of dissipation times named distimes, and, if
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>A list with a matrix of dissipation times named distimes, and, if
 applicable, a vector of formation fractions named ff and, if the SFORB model
 was in use, a vector of eigenvalues of these SFORB models, equivalent to
 DFOP rate constants</p>
     </div>
-    <div id="details">
-    <h2>Details</h2>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>Additional DT50 values are calculated from the FOMC DT90 and k1 and k2 from
 HS and DFOP, as well as from Eigenvalues b1 and b2 of any SFORB models</p>
     </div>
-    <div id="note">
-    <h2>Note</h2>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>The function is used internally by <a href="summary.mkinfit.html">summary.mkinfit</a>,
 <a href="summary.nlme.mmkin.html">summary.nlme.mmkin</a> and <a href="summary.saem.mmkin.html">summary.saem.mmkin</a>.</p>
     </div>
-    <div id="author">
-    <h2>Author</h2>
+    <div class="section level2">
+    <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a></h2>
     <p>Johannes Ranke</p>
     </div>
 
-    <div id="ref-examples">
-    <h2>Examples</h2>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span>
 <span class="r-in"><span>  <span class="va">fit</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"FOMC"</span>, <span class="va">FOCUS_2006_C</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
 <span class="r-in"><span>  <span class="fu">endpoints</span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></span></span>
@@ -205,27 +159,23 @@ HS and DFOP, as well as from Eigenvalues b1 and b2 of any SFORB models</p>
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/experimental_data_for_UBA-1.png b/docs/reference/experimental_data_for_UBA-1.png
index 4de80e3a..07c1b33e 100644
--- a/docs/reference/experimental_data_for_UBA-1.png
+++ b/docs/reference/experimental_data_for_UBA-1.png
diff --git a/docs/reference/experimental_data_for_UBA.html b/docs/reference/experimental_data_for_UBA.html
index 6a02f430..d1329e21 100644
--- a/docs/reference/experimental_data_for_UBA.html
+++ b/docs/reference/experimental_data_for_UBA.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Experimental datasets used for development and testing of error models — experimental_data_for_UBA_2019 • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Experimental datasets used for development and testing of error models — experimental_data_for_UBA_2019"><meta property="og:description" content="The 12 datasets were extracted from active substance evaluation dossiers published
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Experimental datasets used for development and testing of error models — experimental_data_for_UBA_2019 • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Experimental datasets used for development and testing of error models — experimental_data_for_UBA_2019"><meta name="description" content="The 12 datasets were extracted from active substance evaluation dossiers published
  by EFSA. Kinetic evaluations shown for these datasets are intended to illustrate
  and advance error model specifications. The fact that these data and some
  results are shown here do not imply a license to use them in the context of
@@ -27,116 +27,96 @@ Dataset 11 is from the Renewal Assessment Report (RAR) for 2,4-D
  the exception of the day three sampling of metabolite A2, which was set
  to one half of the LOD reported to be 1% AR.
 Dataset 12 is from the Renewal Assessment Report (RAR) for thifensulfuron-methyl
- (United Kingdom, 2014, p. 81)."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
+ (United Kingdom, 2014, p. 81)."><meta property="og:description" content="The 12 datasets were extracted from active substance evaluation dossiers published
+ by EFSA. Kinetic evaluations shown for these datasets are intended to illustrate
+ and advance error model specifications. The fact that these data and some
+ results are shown here do not imply a license to use them in the context of
+ pesticide registrations, as the use of the data may be constrained by
+ data protection regulations.
+Preprocessing of data was performed based on the recommendations of the FOCUS
+ kinetics workgroup (FOCUS, 2014) as described below.
+Datasets 1 and 2 are from the Renewal Assessment Report (RAR) for imazamox
+ (France, 2015, p. 15). For setting values reported as zero, an LOQ of 0.1
+ was assumed. Metabolite residues reported for day zero were added to the
+ parent compound residues.
+Datasets 3 and 4 are from the Renewal Assessment Report (RAR) for isofetamid
+ (Belgium, 2014, p. 8) and show the data for two different radiolabels. For
+ dataset 4, the value given for the metabolite in the day zero sampling
+ in replicate B was added to the parent compound, following the respective
+ FOCUS recommendation.
+Dataset 5 is from the Renewal Assessment Report (RAR) for ethofumesate
+ (Austria, 2015, p. 16).
+Datasets 6 to 10 are from the Renewal Assessment Report (RAR) for glyphosate
+ (Germany, 2013, pages 8, 28, 50, 51). For the initial sampling,
+ the residues given for the metabolite were added to the parent
+ value, following the recommendation of the FOCUS kinetics workgroup.
+Dataset 11 is from the Renewal Assessment Report (RAR) for 2,4-D
+ (Hellas, 2013, p. 644). Values reported as zero were set to NA, with
+ the exception of the day three sampling of metabolite A2, which was set
+ to one half of the LOD reported to be 1% AR.
+Dataset 12 is from the Renewal Assessment Report (RAR) for thifensulfuron-methyl
+ (United Kingdom, 2014, p. 81)."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
 
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
 
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
 
-      
+      <h1>Experimental datasets used for development and testing of error models</h1>
 
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Experimental datasets used for development and testing of error models</h1>
-    
-    <div class="hidden name"><code>experimental_data_for_UBA.Rd</code></div>
+      <div class="d-none name"><code>experimental_data_for_UBA.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>The 12 datasets were extracted from active substance evaluation dossiers published
  by EFSA. Kinetic evaluations shown for these datasets are intended to illustrate
  and advance error model specifications. The fact that these data and some
@@ -168,12 +148,13 @@ Dataset 12 is from the Renewal Assessment Report (RAR) for thifensulfuron-methyl
  (United Kingdom, 2014, p. 81).</p>
     </div>
 
-    <div id="ref-usage">
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="va">experimental_data_for_UBA_2019</span></span></code></pre></div>
     </div>
 
-    <div id="format">
-    <h2>Format</h2>
+    <div class="section level2">
+    <h2 id="format">Format<a class="anchor" aria-label="anchor" href="#format"></a></h2>
     <p>A list containing twelve datasets as an R6 class defined by <code><a href="mkinds.html">mkinds</a></code>,
   each containing, among others, the following components</p><dl><dt><code>title</code></dt>
 <dd><p>The name of the dataset, e.g. <code>Soil 1</code></p></dd>
@@ -181,11 +162,11 @@ Dataset 12 is from the Renewal Assessment Report (RAR) for thifensulfuron-methyl
     <dt><code>data</code></dt>
 <dd><p>A data frame with the data in the form expected by <code><a href="mkinfit.html">mkinfit</a></code></p></dd>
 
-  
+
 </dl></div>
-    <div id="source">
-    <h2>Source</h2>
-    
+    <div class="section level2">
+    <h2 id="source">Source<a class="anchor" aria-label="anchor" href="#source"></a></h2>
+
 
 <p>Austria (2015). Ethofumesate Renewal Assessment Report Volume 3 Annex B.8 (AS)</p>
 <p>Belgium (2014). Isofetamid (IKF-5411) Draft Assessment Report Volume 3 Annex B.8 (AS)</p>
@@ -206,8 +187,8 @@ Dataset 12 is from the Renewal Assessment Report (RAR) for thifensulfuron-methyl
   1141/2010 for renewal of an active substance</p>
     </div>
 
-    <div id="ref-examples">
-    <h2>Examples</h2>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># \dontrun{</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># Model definitions</span></span></span>
@@ -252,27 +233,23 @@ Dataset 12 is from the Renewal Assessment Report (RAR) for thifensulfuron-methyl
 <span class="r-in"><span><span class="co"># }</span></span></span>
 </code></pre></div>
     </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/f_time_norm_focus.html b/docs/reference/f_time_norm_focus.html
index ace7c028..c318b762 100644
--- a/docs/reference/f_time_norm_focus.html
+++ b/docs/reference/f_time_norm_focus.html
@@ -1,123 +1,77 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Normalisation factors for aerobic soil degradation according to FOCUS guidance — f_time_norm_focus • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Normalisation factors for aerobic soil degradation according to FOCUS guidance — f_time_norm_focus"><meta property="og:description" content="Time step normalisation factors for aerobic soil degradation as described
-in Appendix 8 to the FOCUS kinetics guidance (FOCUS 2014, p. 369)."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Normalisation factors for aerobic soil degradation according to FOCUS guidance — f_time_norm_focus • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Normalisation factors for aerobic soil degradation according to FOCUS guidance — f_time_norm_focus"><meta name="description" content="Time step normalisation factors for aerobic soil degradation as described
+in Appendix 8 to the FOCUS kinetics guidance (FOCUS 2014, p. 369)."><meta property="og:description" content="Time step normalisation factors for aerobic soil degradation as described
+in Appendix 8 to the FOCUS kinetics guidance (FOCUS 2014, p. 369)."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Normalisation factors for aerobic soil degradation according to FOCUS guidance</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/f_time_norm_focus.R" class="external-link"><code>R/f_time_norm_focus.R</code></a></small>
-    <div class="hidden name"><code>f_time_norm_focus.Rd</code></div>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Normalisation factors for aerobic soil degradation according to FOCUS guidance</h1>
+      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/f_time_norm_focus.R" class="external-link"><code>R/f_time_norm_focus.R</code></a></small>
+      <div class="d-none name"><code>f_time_norm_focus.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Time step normalisation factors for aerobic soil degradation as described
 in Appendix 8 to the FOCUS kinetics guidance (FOCUS 2014, p. 369).</p>
     </div>
 
-    <div id="ref-usage">
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">f_time_norm_focus</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for numeric</span></span>
+<span><span class="co"># S3 method for class 'numeric'</span></span>
 <span><span class="fu">f_time_norm_focus</span><span class="op">(</span></span>
 <span>  <span class="va">object</span>,</span>
 <span>  moisture <span class="op">=</span> <span class="cn">NA</span>,</span>
@@ -129,7 +83,7 @@ in Appendix 8 to the FOCUS kinetics guidance (FOCUS 2014, p. 369).</p>
 <span>  <span class="va">...</span></span>
 <span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for mkindsg</span></span>
+<span><span class="co"># S3 method for class 'mkindsg'</span></span>
 <span><span class="fu">f_time_norm_focus</span><span class="op">(</span></span>
 <span>  <span class="va">object</span>,</span>
 <span>  study_moisture_ref_source <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"auto"</span>, <span class="st">"meta"</span>, <span class="st">"focus"</span><span class="op">)</span>,</span>
@@ -140,51 +94,53 @@ in Appendix 8 to the FOCUS kinetics guidance (FOCUS 2014, p. 369).</p>
 <span><span class="op">)</span></span></code></pre></div>
     </div>
 
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>object</dt>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
 <dd><p>An object containing information used for the calculations</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Currently not used</p></dd>
 
 
-<dt>moisture</dt>
+<dt id="arg-moisture">moisture<a class="anchor" aria-label="anchor" href="#arg-moisture"></a></dt>
 <dd><p>Numeric vector of moisture contents in \% w/w</p></dd>
 
 
-<dt>field_moisture</dt>
+<dt id="arg-field-moisture">field_moisture<a class="anchor" aria-label="anchor" href="#arg-field-moisture"></a></dt>
 <dd><p>Numeric vector of moisture contents at field capacity
 (pF2) in \% w/w</p></dd>
 
 
-<dt>temperature</dt>
+<dt id="arg-temperature">temperature<a class="anchor" aria-label="anchor" href="#arg-temperature"></a></dt>
 <dd><p>Numeric vector of temperatures in °C</p></dd>
 
 
-<dt>Q10</dt>
+<dt id="arg-q-">Q10<a class="anchor" aria-label="anchor" href="#arg-q-"></a></dt>
 <dd><p>The Q10 value used for temperature normalisation</p></dd>
 
 
-<dt>walker</dt>
+<dt id="arg-walker">walker<a class="anchor" aria-label="anchor" href="#arg-walker"></a></dt>
 <dd><p>The Walker exponent used for moisture normalisation</p></dd>
 
 
-<dt>f_na</dt>
+<dt id="arg-f-na">f_na<a class="anchor" aria-label="anchor" href="#arg-f-na"></a></dt>
 <dd><p>The factor to use for NA values. If set to NA, only factors
 for complete cases will be returned.</p></dd>
 
 
-<dt>study_moisture_ref_source</dt>
+<dt id="arg-study-moisture-ref-source">study_moisture_ref_source<a class="anchor" aria-label="anchor" href="#arg-study-moisture-ref-source"></a></dt>
 <dd><p>Source for the reference value
 used to calculate the study moisture. If 'auto', preference is given
 to a reference moisture given in the meta information, otherwise
 the focus soil moisture for the soil class is used</p></dd>
 
 </dl></div>
-    <div id="references">
-    <h2>References</h2>
+    <div class="section level2">
+    <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
     <p>FOCUS (2006) “Guidance Document on Estimating Persistence
 and Degradation Kinetics from Environmental Fate Studies on Pesticides in
 EU Registration” Report of the FOCUS Work Group on Degradation Kinetics,
@@ -196,13 +152,13 @@ EU Registration” Report of the FOCUS Work Group on Degradation Kinetics,
 Version 1.1, 18 December 2014
 <a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics" class="external-link">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p>
     </div>
-    <div id="see-also">
-    <h2>See also</h2>
+    <div class="section level2">
+    <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
     <div class="dont-index"><p><a href="focus_soil_moisture.html">focus_soil_moisture</a></p></div>
     </div>
 
-    <div id="ref-examples">
-    <h2>Examples</h2>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="fu">f_time_norm_focus</span><span class="op">(</span><span class="fl">25</span>, <span class="fl">20</span>, <span class="fl">25</span><span class="op">)</span> <span class="co"># 1.37, compare FOCUS 2014 p. 184</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] 1.373956</span>
 <span class="r-in"><span></span></span>
@@ -225,27 +181,23 @@ Version 1.1, 18 December 2014
 <span class="r-msg co"><span class="r-pr">#&gt;</span> $f_time_norm was (re)set to normalised values</span>
 </code></pre></div>
     </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/f_time_norm_focus.mkindsg.html b/docs/reference/f_time_norm_focus.mkindsg.html
new file mode 100644
index 00000000..f6530f49
--- /dev/null
+++ b/docs/reference/f_time_norm_focus.mkindsg.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/f_time_norm_focus.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/f_time_norm_focus.html">
+  </head>
+</html>
+
diff --git a/docs/reference/f_time_norm_focus.numeric.html b/docs/reference/f_time_norm_focus.numeric.html
new file mode 100644
index 00000000..f6530f49
--- /dev/null
+++ b/docs/reference/f_time_norm_focus.numeric.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/f_time_norm_focus.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/f_time_norm_focus.html">
+  </head>
+</html>
+
diff --git a/docs/reference/focus_soil_moisture.html b/docs/reference/focus_soil_moisture.html
index 4cb95b77..3b07a5ea 100644
--- a/docs/reference/focus_soil_moisture.html
+++ b/docs/reference/focus_soil_moisture.html
@@ -1,136 +1,90 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>FOCUS default values for soil moisture contents at field capacity, MWHC and 1/3 bar — focus_soil_moisture • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="FOCUS default values for soil moisture contents at field capacity, MWHC and 1/3 bar — focus_soil_moisture"><meta property="og:description" content="The value were transcribed from p. 36. The table assumes field capacity
-corresponds to pF2, MWHC to pF 1 and 1/3 bar to pF 2.5."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>FOCUS default values for soil moisture contents at field capacity, MWHC and 1/3 bar — focus_soil_moisture • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="FOCUS default values for soil moisture contents at field capacity, MWHC and 1/3 bar — focus_soil_moisture"><meta name="description" content="The value were transcribed from p. 36. The table assumes field capacity
+corresponds to pF2, MWHC to pF 1 and 1/3 bar to pF 2.5."><meta property="og:description" content="The value were transcribed from p. 36. The table assumes field capacity
+corresponds to pF2, MWHC to pF 1 and 1/3 bar to pF 2.5."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>FOCUS default values for soil moisture contents at field capacity, MWHC and 1/3 bar</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/focus_soil_moisture.R" class="external-link"><code>R/focus_soil_moisture.R</code></a></small>
-    <div class="hidden name"><code>focus_soil_moisture.Rd</code></div>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>FOCUS default values for soil moisture contents at field capacity, MWHC and 1/3 bar</h1>
+      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/focus_soil_moisture.R" class="external-link"><code>R/focus_soil_moisture.R</code></a></small>
+      <div class="d-none name"><code>focus_soil_moisture.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>The value were transcribed from p. 36. The table assumes field capacity
 corresponds to pF2, MWHC to pF 1 and 1/3 bar to pF 2.5.</p>
     </div>
 
-    <div id="ref-usage">
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="va">focus_soil_moisture</span></span></code></pre></div>
     </div>
 
-    <div id="format">
-    <h2>Format</h2>
+    <div class="section level2">
+    <h2 id="format">Format<a class="anchor" aria-label="anchor" href="#format"></a></h2>
     <p>A matrix with upper case USDA soil classes as row names, and water tension
 ('pF1', 'pF2', 'pF 2.5') as column names</p>
     </div>
-    <div id="source">
-    <h2>Source</h2>
+    <div class="section level2">
+    <h2 id="source">Source<a class="anchor" aria-label="anchor" href="#source"></a></h2>
     <p>Anonymous (2014) Generic Guidance for Tier 1 FOCUS Ground Water Assessment
 Version 2.2, May 2014 <a href="https://esdac.jrc.ec.europa.eu/projects/ground-water" class="external-link">https://esdac.jrc.ec.europa.eu/projects/ground-water</a></p>
     </div>
 
-    <div id="ref-examples">
-    <h2>Examples</h2>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">focus_soil_moisture</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                 pF1 pF2 pF2.5</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Sand             24  12     7</span>
@@ -147,27 +101,23 @@ Version 2.2, May 2014 <a href="https://esdac.jrc.ec.europa.eu/projects/ground-wa
 <span class="r-out co"><span class="r-pr">#&gt;</span> Clay             53  48    43</span>
 </code></pre></div>
     </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/get_deg_func.html b/docs/reference/get_deg_func.html
index 79c88c54..6b296b87 100644
--- a/docs/reference/get_deg_func.html
+++ b/docs/reference/get_deg_func.html
@@ -1,150 +1,97 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Retrieve a degradation function from the mmkin namespace — get_deg_func • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Retrieve a degradation function from the mmkin namespace — get_deg_func"><meta property="og:description" content="Retrieve a degradation function from the mmkin namespace"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Retrieve a degradation function from the mmkin namespace — get_deg_func • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Retrieve a degradation function from the mmkin namespace — get_deg_func"><meta name="description" content="Retrieve a degradation function from the mmkin namespace"><meta property="og:description" content="Retrieve a degradation function from the mmkin namespace"></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Retrieve a degradation function from the mmkin namespace</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/nlme.mmkin.R" class="external-link"><code>R/nlme.mmkin.R</code></a></small>
-    <div class="hidden name"><code>get_deg_func.Rd</code></div>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Retrieve a degradation function from the mmkin namespace</h1>
+      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/nlme.mmkin.R" class="external-link"><code>R/nlme.mmkin.R</code></a></small>
+      <div class="d-none name"><code>get_deg_func.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Retrieve a degradation function from the mmkin namespace</p>
     </div>
 
-    <div id="ref-usage">
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">get_deg_func</span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
     </div>
 
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>A function that was likely previously assigned from within
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>A function that was likely previously assigned from within
 nlme.mmkin</p>
     </div>
 
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/hierarchical_kinetics.html b/docs/reference/hierarchical_kinetics.html
index ab0b3d04..85cd2644 100644
--- a/docs/reference/hierarchical_kinetics.html
+++ b/docs/reference/hierarchical_kinetics.html
@@ -1,121 +1,80 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Hierarchical kinetics template — hierarchical_kinetics • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Hierarchical kinetics template — hierarchical_kinetics"><meta property="og:description" content='R markdown format for setting up hierarchical kinetics based on a template
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Hierarchical kinetics template — hierarchical_kinetics • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Hierarchical kinetics template — hierarchical_kinetics"><meta name="description" content='R markdown format for setting up hierarchical kinetics based on a template
 provided with the mkin package. This format is based on rmarkdown::pdf_document.
 Chunk options are adapted. Echoing R code from code chunks and caching are
 turned on per default. character for prepending output from code chunks is
 set to the empty string, code tidying is off, figure alignment defaults to
 centering, and positioning of figures is set to "H", which means that
 figures will not move around in the document, but stay where the user
-includes them.'><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+includes them.'><meta property="og:description" content='R markdown format for setting up hierarchical kinetics based on a template
+provided with the mkin package. This format is based on rmarkdown::pdf_document.
+Chunk options are adapted. Echoing R code from code chunks and caching are
+turned on per default. character for prepending output from code chunks is
+set to the empty string, code tidying is off, figure alignment defaults to
+centering, and positioning of figures is set to "H", which means that
+figures will not move around in the document, but stay where the user
+includes them.'></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Hierarchical kinetics template</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/hierarchical_kinetics.R" class="external-link"><code>R/hierarchical_kinetics.R</code></a></small>
-    <div class="hidden name"><code>hierarchical_kinetics.Rd</code></div>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Hierarchical kinetics template</h1>
+      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/hierarchical_kinetics.R" class="external-link"><code>R/hierarchical_kinetics.R</code></a></small>
+      <div class="d-none name"><code>hierarchical_kinetics.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>R markdown format for setting up hierarchical kinetics based on a template
 provided with the mkin package. This format is based on <a href="https://pkgs.rstudio.com/rmarkdown/reference/pdf_document.html" class="external-link">rmarkdown::pdf_document</a>.
 Chunk options are adapted. Echoing R code from code chunks and caching are
@@ -126,31 +85,34 @@ figures will not move around in the document, but stay where the user
 includes them.</p>
     </div>
 
-    <div id="ref-usage">
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">hierarchical_kinetics</span><span class="op">(</span><span class="va">...</span>, keep_tex <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div>
     </div>
 
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>...</dt>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Arguments to <code><a href="https://pkgs.rstudio.com/rmarkdown/reference/pdf_document.html" class="external-link">rmarkdown::pdf_document</a></code></p></dd>
 
 
-<dt>keep_tex</dt>
-<dd><p>Keep the intermediate tex file used in the conversion to PDF</p></dd>
+<dt id="arg-keep-tex">keep_tex<a class="anchor" aria-label="anchor" href="#arg-keep-tex"></a></dt>
+<dd><p>Keep the intermediate tex file used in the conversion to PDF.
+Note that this argument does not control whether to keep the auxiliary
+files (e.g., <code class="file">.aux</code>) generated by LaTeX when compiling <code class="file">.tex</code> to
+<code class="file">.pdf</code>. To keep these files, you may set <code>options(tinytex.clean =
+FALSE)</code>.</p></dd>
 
 </dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>R Markdown output format to pass to
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>R Markdown output format to pass to
 <code><a href="https://pkgs.rstudio.com/rmarkdown/reference/render.html" class="external-link">render</a></code></p>
-
-
     </div>
-    <div id="details">
-    <h2>Details</h2>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>The latter feature (positioning the figures with "H") depends on the LaTeX
 package 'float'. In addition, the LaTeX package 'listing' is used in the
 template for showing model fit summaries in the Appendix. This means that
@@ -162,8 +124,8 @@ is present before) is to install the 'tinytex' R package, to run
 and then to run 'tinytex::tlmgr_install(c("float", "listing"))'.</p>
     </div>
 
-    <div id="ref-examples">
-    <h2>Examples</h2>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># \dontrun{</span></span></span>
 <span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/rstudio/rmarkdown" class="external-link">rmarkdown</a></span><span class="op">)</span></span></span>
@@ -174,27 +136,23 @@ and then to run 'tinytex::tlmgr_install(c("float", "listing"))'.</p>
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/html_listing.html b/docs/reference/html_listing.html
new file mode 100644
index 00000000..879363c3
--- /dev/null
+++ b/docs/reference/html_listing.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/summary_listing.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/summary_listing.html">
+  </head>
+</html>
+
diff --git a/docs/reference/illparms.html b/docs/reference/illparms.html
index bd90c0ed..cbe48623 100644
--- a/docs/reference/illparms.html
+++ b/docs/reference/illparms.html
@@ -1,118 +1,74 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Method to get the names of ill-defined parameters — illparms • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Method to get the names of ill-defined parameters — illparms"><meta property="og:description" content="The method for generalised nonlinear regression fits as obtained
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Method to get the names of ill-defined parameters — illparms • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Method to get the names of ill-defined parameters — illparms"><meta name="description" content="The method for generalised nonlinear regression fits as obtained
 with mkinfit and mmkin checks if the degradation parameters
 pass the Wald test (in degradation kinetics often simply called t-test) for
 significant difference from zero. For this test, the parameterisation
-without parameter transformations is used."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+without parameter transformations is used."><meta property="og:description" content="The method for generalised nonlinear regression fits as obtained
+with mkinfit and mmkin checks if the degradation parameters
+pass the Wald test (in degradation kinetics often simply called t-test) for
+significant difference from zero. For this test, the parameterisation
+without parameter transformations is used."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Method to get the names of ill-defined parameters</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/illparms.R" class="external-link"><code>R/illparms.R</code></a></small>
-    <div class="hidden name"><code>illparms.Rd</code></div>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Method to get the names of ill-defined parameters</h1>
+      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/illparms.R" class="external-link"><code>R/illparms.R</code></a></small>
+      <div class="d-none name"><code>illparms.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>The method for generalised nonlinear regression fits as obtained
 with <a href="mkinfit.html">mkinfit</a> and <a href="mmkin.html">mmkin</a> checks if the degradation parameters
 pass the Wald test (in degradation kinetics often simply called t-test) for
@@ -120,22 +76,23 @@ significant difference from zero. For this test, the parameterisation
 without parameter transformations is used.</p>
     </div>
 
-    <div id="ref-usage">
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">illparms</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for mkinfit</span></span>
+<span><span class="co"># S3 method for class 'mkinfit'</span></span>
 <span><span class="fu">illparms</span><span class="op">(</span><span class="va">object</span>, conf.level <span class="op">=</span> <span class="fl">0.95</span>, <span class="va">...</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for illparms.mkinfit</span></span>
+<span><span class="co"># S3 method for class 'illparms.mkinfit'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for mmkin</span></span>
+<span><span class="co"># S3 method for class 'mmkin'</span></span>
 <span><span class="fu">illparms</span><span class="op">(</span><span class="va">object</span>, conf.level <span class="op">=</span> <span class="fl">0.95</span>, <span class="va">...</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for illparms.mmkin</span></span>
+<span><span class="co"># S3 method for class 'illparms.mmkin'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for saem.mmkin</span></span>
+<span><span class="co"># S3 method for class 'saem.mmkin'</span></span>
 <span><span class="fu">illparms</span><span class="op">(</span></span>
 <span>  <span class="va">object</span>,</span>
 <span>  conf.level <span class="op">=</span> <span class="fl">0.95</span>,</span>
@@ -145,59 +102,59 @@ without parameter transformations is used.</p>
 <span>  <span class="va">...</span></span>
 <span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for illparms.saem.mmkin</span></span>
+<span><span class="co"># S3 method for class 'illparms.saem.mmkin'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for mhmkin</span></span>
+<span><span class="co"># S3 method for class 'mhmkin'</span></span>
 <span><span class="fu">illparms</span><span class="op">(</span><span class="va">object</span>, conf.level <span class="op">=</span> <span class="fl">0.95</span>, random <span class="op">=</span> <span class="cn">TRUE</span>, errmod <span class="op">=</span> <span class="cn">TRUE</span>, <span class="va">...</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for illparms.mhmkin</span></span>
+<span><span class="co"># S3 method for class 'illparms.mhmkin'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>object</dt>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
 <dd><p>The object to investigate</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>For potential future extensions</p></dd>
 
 
-<dt>conf.level</dt>
+<dt id="arg-conf-level">conf.level<a class="anchor" aria-label="anchor" href="#arg-conf-level"></a></dt>
 <dd><p>The confidence level for checking p values</p></dd>
 
 
-<dt>x</dt>
+<dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>The object to be printed</p></dd>
 
 
-<dt>random</dt>
+<dt id="arg-random">random<a class="anchor" aria-label="anchor" href="#arg-random"></a></dt>
 <dd><p>For hierarchical fits, should random effects be tested?</p></dd>
 
 
-<dt>errmod</dt>
+<dt id="arg-errmod">errmod<a class="anchor" aria-label="anchor" href="#arg-errmod"></a></dt>
 <dd><p>For hierarchical fits, should error model parameters be
 tested?</p></dd>
 
 
-<dt>slopes</dt>
+<dt id="arg-slopes">slopes<a class="anchor" aria-label="anchor" href="#arg-slopes"></a></dt>
 <dd><p>For hierarchical <a href="saem.html">saem</a> fits using saemix as backend,
 should slope parameters in the covariate model(starting with 'beta_') be
 tested?</p></dd>
 
 </dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>For <a href="mkinfit.html">mkinfit</a> or <a href="saem.html">saem</a> objects, a character vector of parameter
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>For <a href="mkinfit.html">mkinfit</a> or <a href="saem.html">saem</a> objects, a character vector of parameter
 names. For <a href="mmkin.html">mmkin</a> or <a href="mhmkin.html">mhmkin</a> objects, a matrix like object of class
 'illparms.mmkin' or 'illparms.mhmkin'.</p>
     </div>
-    <div id="details">
-    <h2>Details</h2>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>The method for hierarchical model fits, also known as nonlinear
 mixed-effects model fits as obtained with <a href="saem.html">saem</a> and <a href="mhmkin.html">mhmkin</a>
 checks if any of the confidence intervals for the random
@@ -205,14 +162,14 @@ effects expressed as standard deviations include zero, and if
 the confidence intervals for the error model parameters include
 zero.</p>
     </div>
-    <div id="note">
-    <h2>Note</h2>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>All return objects have printing methods. For the single fits, printing
 does not output anything in the case no ill-defined parameters are found.</p>
     </div>
 
-    <div id="ref-examples">
-    <h2>Examples</h2>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">fit</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"FOMC"</span>, <span class="va">FOCUS_2006_A</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>Optimisation did not converge:</span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> false convergence (8)</span>
@@ -232,27 +189,23 @@ does not output anything in the case no ill-defined parameters are found.</p>
 <span class="r-in"><span><span class="co"># }</span></span></span>
 </code></pre></div>
     </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/illparms.mhmkin.html b/docs/reference/illparms.mhmkin.html
new file mode 100644
index 00000000..5c70c3f7
--- /dev/null
+++ b/docs/reference/illparms.mhmkin.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/illparms.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/illparms.html">
+  </head>
+</html>
+
diff --git a/docs/reference/illparms.mkinfit.html b/docs/reference/illparms.mkinfit.html
new file mode 100644
index 00000000..5c70c3f7
--- /dev/null
+++ b/docs/reference/illparms.mkinfit.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/illparms.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/illparms.html">
+  </head>
+</html>
+
diff --git a/docs/reference/illparms.mmkin.html b/docs/reference/illparms.mmkin.html
new file mode 100644
index 00000000..5c70c3f7
--- /dev/null
+++ b/docs/reference/illparms.mmkin.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/illparms.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/illparms.html">
+  </head>
+</html>
+
diff --git a/docs/reference/illparms.saem.mmkin.html b/docs/reference/illparms.saem.mmkin.html
new file mode 100644
index 00000000..5c70c3f7
--- /dev/null
+++ b/docs/reference/illparms.saem.mmkin.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/illparms.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/illparms.html">
+  </head>
+</html>
+
diff --git a/docs/reference/ilr.html b/docs/reference/ilr.html
index 7937d770..7aa6379f 100644
--- a/docs/reference/ilr.html
+++ b/docs/reference/ilr.html
@@ -1,157 +1,111 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Function to perform isometric log-ratio transformation — ilr • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Function to perform isometric log-ratio transformation — ilr"><meta property="og:description" content="This implementation is a special case of the class of isometric log-ratio
-transformations."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Function to perform isometric log-ratio transformation — ilr • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Function to perform isometric log-ratio transformation — ilr"><meta name="description" content="This implementation is a special case of the class of isometric log-ratio
+transformations."><meta property="og:description" content="This implementation is a special case of the class of isometric log-ratio
+transformations."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Function to perform isometric log-ratio transformation</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/ilr.R" class="external-link"><code>R/ilr.R</code></a></small>
-    <div class="hidden name"><code>ilr.Rd</code></div>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Function to perform isometric log-ratio transformation</h1>
+      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/ilr.R" class="external-link"><code>R/ilr.R</code></a></small>
+      <div class="d-none name"><code>ilr.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>This implementation is a special case of the class of isometric log-ratio
 transformations.</p>
     </div>
 
-    <div id="ref-usage">
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">ilr</span><span class="op">(</span><span class="va">x</span><span class="op">)</span></span>
 <span></span>
 <span><span class="fu">invilr</span><span class="op">(</span><span class="va">x</span><span class="op">)</span></span></code></pre></div>
     </div>
 
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>x</dt>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>A numeric vector. Naturally, the forward transformation is only
 sensible for vectors with all elements being greater than zero.</p></dd>
 
 </dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>The result of the forward or backward transformation. The returned
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>The result of the forward or backward transformation. The returned
 components always sum to 1 for the case of the inverse log-ratio
 transformation.</p>
     </div>
-    <div id="references">
-    <h2>References</h2>
+    <div class="section level2">
+    <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
     <p>Peter Filzmoser, Karel Hron (2008) Outlier Detection for
 Compositional Data Using Robust Methods. Math Geosci 40 233-248</p>
     </div>
-    <div id="see-also">
-    <h2>See also</h2>
+    <div class="section level2">
+    <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
     <div class="dont-index"><p>Another implementation can be found in R package
 <code>robCompositions</code>.</p></div>
     </div>
-    <div id="author">
-    <h2>Author</h2>
+    <div class="section level2">
+    <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a></h2>
     <p>René Lehmann and Johannes Ranke</p>
     </div>
 
-    <div id="ref-examples">
-    <h2>Examples</h2>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># Order matters</span></span></span>
 <span class="r-in"><span><span class="fu">ilr</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.1</span>, <span class="fl">1</span>, <span class="fl">10</span><span class="op">)</span><span class="op">)</span></span></span>
@@ -187,27 +141,23 @@ Compositional Data Using Robust Methods. Math Geosci 40 233-248</p>
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/index.html b/docs/reference/index.html
index a7f02199..191fbe04 100644
--- a/docs/reference/index.html
+++ b/docs/reference/index.html
@@ -1,531 +1,735 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Function reference • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Function reference"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-index">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Package index • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Package index"></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
 
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="contents col-md-9">
-    <div class="page-header">
-      <h1>Reference</h1>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-index">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Package index</h1>
     </div>
 
-    <table class="ref-index"><colgroup><col class="alias"><col class="title"></colgroup><tbody><tr><th colspan="2">
-          <h2 id="main-functions">Main functions <a href="#main-functions" class="anchor" aria-hidden="true"></a></h2>
-          <p class="section-desc"></p><p>Essential functionality</p>
-        </th>
-      </tr></tbody><tbody><tr><td>
-          <p><code><a href="mkinmod.html">mkinmod()</a></code> <code><a href="mkinmod.html">print(<i>&lt;mkinmod&gt;</i>)</a></code> <code><a href="mkinmod.html">mkinsub()</a></code> </p>
-        </td>
-        <td><p>Function to set up a kinetic model with one or more state variables</p></td>
-      </tr><tr><td>
-          <p><code><a href="mkinfit.html">mkinfit()</a></code> </p>
-        </td>
-        <td><p>Fit a kinetic model to data with one or more state variables</p></td>
-      </tr><tr><td>
-          <p><code><a href="mmkin.html">mmkin()</a></code> <code><a href="mmkin.html">print(<i>&lt;mmkin&gt;</i>)</a></code> </p>
-        </td>
-        <td><p>Fit one or more kinetic models with one or more state variables to one or
-more datasets</p></td>
-      </tr><tr><td>
-          <p><code><a href="mhmkin.html">mhmkin()</a></code> <code><a href="mhmkin.html">`[`(<i>&lt;mhmkin&gt;</i>)</a></code> <code><a href="mhmkin.html">print(<i>&lt;mhmkin&gt;</i>)</a></code> </p>
-        </td>
-        <td><p>Fit nonlinear mixed-effects models built from one or more kinetic
-degradation models and one or more error models</p></td>
-      </tr></tbody><tbody><tr><th colspan="2">
-          <h2 id="generics">Generics <a href="#generics" class="anchor" aria-hidden="true"></a></h2>
-          <p class="section-desc"></p><p>Generic functions introduced by the package</p>
-        </th>
-      </tr></tbody><tbody><tr><td>
-          <p><code><a href="parms.html">parms()</a></code> </p>
-        </td>
-        <td><p>Extract model parameters</p></td>
-      </tr><tr><td>
-          <p><code><a href="status.html">status()</a></code> <code><a href="status.html">print(<i>&lt;status.mmkin&gt;</i>)</a></code> <code><a href="status.html">print(<i>&lt;status.mhmkin&gt;</i>)</a></code> </p>
-        </td>
-        <td><p>Method to get status information for fit array objects</p></td>
-      </tr><tr><td>
-          <p><code><a href="illparms.html">illparms()</a></code> <code><a href="illparms.html">print(<i>&lt;illparms.mkinfit&gt;</i>)</a></code> <code><a href="illparms.html">print(<i>&lt;illparms.mmkin&gt;</i>)</a></code> <code><a href="illparms.html">print(<i>&lt;illparms.saem.mmkin&gt;</i>)</a></code> <code><a href="illparms.html">print(<i>&lt;illparms.mhmkin&gt;</i>)</a></code> </p>
-        </td>
-        <td><p>Method to get the names of ill-defined parameters</p></td>
-      </tr><tr><td>
-          <p><code><a href="endpoints.html">endpoints()</a></code> </p>
-        </td>
-        <td><p>Function to calculate endpoints for further use from kinetic models fitted
-with mkinfit</p></td>
-      </tr><tr><td>
-          <p><code><a href="aw.html">aw()</a></code> </p>
-        </td>
-        <td><p>Calculate Akaike weights for model averaging</p></td>
-      </tr></tbody><tbody><tr><th colspan="2">
-          <h2 id="show-results">Show results <a href="#show-results" class="anchor" aria-hidden="true"></a></h2>
-          <p class="section-desc"></p><p>Functions working with mkinfit objects</p>
-        </th>
-      </tr></tbody><tbody><tr><td>
-          <p><code><a href="plot.mkinfit.html">plot(<i>&lt;mkinfit&gt;</i>)</a></code> <code><a href="plot.mkinfit.html">plot_sep()</a></code> <code><a href="plot.mkinfit.html">plot_res()</a></code> <code><a href="plot.mkinfit.html">plot_err()</a></code> </p>
-        </td>
-        <td><p>Plot the observed data and the fitted model of an mkinfit object</p></td>
-      </tr><tr><td>
-          <p><code><a href="summary.mkinfit.html">summary(<i>&lt;mkinfit&gt;</i>)</a></code> <code><a href="summary.mkinfit.html">print(<i>&lt;summary.mkinfit&gt;</i>)</a></code> </p>
-        </td>
-        <td><p>Summary method for class "mkinfit"</p></td>
-      </tr><tr><td>
-          <p><code><a href="confint.mkinfit.html">confint(<i>&lt;mkinfit&gt;</i>)</a></code> </p>
-        </td>
-        <td><p>Confidence intervals for parameters of mkinfit objects</p></td>
-      </tr><tr><td>
-          <p><code><a href="update.mkinfit.html">update(<i>&lt;mkinfit&gt;</i>)</a></code> </p>
-        </td>
-        <td><p>Update an mkinfit model with different arguments</p></td>
-      </tr><tr><td>
-          <p><code><a href="lrtest.mkinfit.html">lrtest(<i>&lt;mkinfit&gt;</i>)</a></code> <code><a href="lrtest.mkinfit.html">lrtest(<i>&lt;mmkin&gt;</i>)</a></code> </p>
-        </td>
-        <td><p>Likelihood ratio test for mkinfit models</p></td>
-      </tr><tr><td>
-          <p><code><a href="loftest.html">loftest()</a></code> </p>
-        </td>
-        <td><p>Lack-of-fit test for models fitted to data with replicates</p></td>
-      </tr><tr><td>
-          <p><code><a href="mkinerrmin.html">mkinerrmin()</a></code> </p>
-        </td>
-        <td><p>Calculate the minimum error to assume in order to pass the variance test</p></td>
-      </tr><tr><td>
-          <p><code><a href="CAKE_export.html">CAKE_export()</a></code> </p>
-        </td>
-        <td><p>Export a list of datasets format to a CAKE study file</p></td>
-      </tr></tbody><tbody><tr><th colspan="2">
-          <h2 id="work-with-mmkin-objects">Work with mmkin objects <a href="#work-with-mmkin-objects" class="anchor" aria-hidden="true"></a></h2>
-          <p class="section-desc"></p><p>Functions working with aggregated results</p>
-        </th>
-      </tr></tbody><tbody><tr><td>
-          <p><code><a href="Extract.mmkin.html">`[`(<i>&lt;mmkin&gt;</i>)</a></code> </p>
-        </td>
-        <td><p>Subsetting method for mmkin objects</p></td>
-      </tr><tr><td>
-          <p><code><a href="plot.mmkin.html">plot(<i>&lt;mmkin&gt;</i>)</a></code> </p>
-        </td>
-        <td><p>Plot model fits (observed and fitted) and the residuals for a row or column
-of an mmkin object</p></td>
-      </tr><tr><td>
-          <p><code><a href="AIC.mmkin.html">AIC(<i>&lt;mmkin&gt;</i>)</a></code> <code><a href="AIC.mmkin.html">BIC(<i>&lt;mmkin&gt;</i>)</a></code> </p>
-        </td>
-        <td><p>Calculate the AIC for a column of an mmkin object</p></td>
-      </tr><tr><td>
-          <p><code><a href="summary.mmkin.html">summary(<i>&lt;mmkin&gt;</i>)</a></code> <code><a href="summary.mmkin.html">print(<i>&lt;summary.mmkin&gt;</i>)</a></code> </p>
-        </td>
-        <td><p>Summary method for class "mmkin"</p></td>
-      </tr></tbody><tbody><tr><th colspan="2">
-          <h2 id="mixed-models">Mixed models <a href="#mixed-models" class="anchor" aria-hidden="true"></a></h2>
-          <p class="section-desc"></p><p>Create and work with nonlinear hierarchical models</p>
-        </th>
-      </tr></tbody><tbody><tr><td>
-          <p><code><a href="hierarchical_kinetics.html">hierarchical_kinetics()</a></code> </p>
-        </td>
-        <td><p>Hierarchical kinetics template</p></td>
-      </tr><tr><td>
-          <p><code><a href="read_spreadsheet.html">read_spreadsheet()</a></code> </p>
-        </td>
-        <td><p>Read datasets and relevant meta information from a spreadsheet file</p></td>
-      </tr><tr><td>
-          <p><code><a href="nlme.mmkin.html">nlme(<i>&lt;mmkin&gt;</i>)</a></code> <code><a href="nlme.mmkin.html">print(<i>&lt;nlme.mmkin&gt;</i>)</a></code> <code><a href="nlme.mmkin.html">update(<i>&lt;nlme.mmkin&gt;</i>)</a></code> </p>
-        </td>
-        <td><p>Create an nlme model for an mmkin row object</p></td>
-      </tr><tr><td>
-          <p><code><a href="saem.html">saem()</a></code> <code><a href="saem.html">print(<i>&lt;saem.mmkin&gt;</i>)</a></code> <code><a href="saem.html">saemix_model()</a></code> <code><a href="saem.html">saemix_data()</a></code> </p>
-        </td>
-        <td><p>Fit nonlinear mixed models with SAEM</p></td>
-      </tr><tr><td>
-          <p><code><a href="mhmkin.html">mhmkin()</a></code> <code><a href="mhmkin.html">`[`(<i>&lt;mhmkin&gt;</i>)</a></code> <code><a href="mhmkin.html">print(<i>&lt;mhmkin&gt;</i>)</a></code> </p>
-        </td>
-        <td><p>Fit nonlinear mixed-effects models built from one or more kinetic
-degradation models and one or more error models</p></td>
-      </tr><tr><td>
-          <p><code><a href="plot.mixed.mmkin.html">plot(<i>&lt;mixed.mmkin&gt;</i>)</a></code> </p>
-        </td>
-        <td><p>Plot predictions from a fitted nonlinear mixed model obtained via an mmkin row object</p></td>
-      </tr><tr><td>
-          <p><code><a href="summary.nlme.mmkin.html">summary(<i>&lt;nlme.mmkin&gt;</i>)</a></code> <code><a href="summary.nlme.mmkin.html">print(<i>&lt;summary.nlme.mmkin&gt;</i>)</a></code> </p>
-        </td>
-        <td><p>Summary method for class "nlme.mmkin"</p></td>
-      </tr><tr><td>
-          <p><code><a href="summary.saem.mmkin.html">summary(<i>&lt;saem.mmkin&gt;</i>)</a></code> <code><a href="summary.saem.mmkin.html">print(<i>&lt;summary.saem.mmkin&gt;</i>)</a></code> </p>
-        </td>
-        <td><p>Summary method for class "saem.mmkin"</p></td>
-      </tr><tr><td>
-          <p><code><a href="anova.saem.mmkin.html">anova(<i>&lt;saem.mmkin&gt;</i>)</a></code> </p>
-        </td>
-        <td><p>Anova method for saem.mmkin objects</p></td>
-      </tr><tr><td>
-          <p><code><a href="logLik.saem.mmkin.html">logLik(<i>&lt;saem.mmkin&gt;</i>)</a></code> </p>
-        </td>
-        <td><p>logLik method for saem.mmkin objects</p></td>
-      </tr><tr><td>
-          <p><code><a href="nlme.html">nlme_function()</a></code> <code><a href="nlme.html">nlme_data()</a></code> </p>
-        </td>
-        <td><p>Helper functions to create nlme models from mmkin row objects</p></td>
-      </tr><tr><td>
-          <p><code><a href="get_deg_func.html">get_deg_func()</a></code> </p>
-        </td>
-        <td><p>Retrieve a degradation function from the mmkin namespace</p></td>
-      </tr><tr><td>
-          <p><code><a href="mixed.html">mixed()</a></code> <code><a href="mixed.html">print(<i>&lt;mixed.mmkin&gt;</i>)</a></code> </p>
-        </td>
-        <td><p>Create a mixed effects model from an mmkin row object</p></td>
-      </tr><tr><td>
-          <p><code><a href="reexports.html">reexports</a></code> <code><a href="reexports.html">intervals</a></code> <code><a href="reexports.html">lrtest</a></code> <code><a href="reexports.html">nlme</a></code> </p>
-        </td>
-        <td><p>Objects exported from other packages</p></td>
-      </tr><tr><td>
-          <p><code><a href="intervals.saem.mmkin.html">intervals(<i>&lt;saem.mmkin&gt;</i>)</a></code> </p>
-        </td>
-        <td><p>Confidence intervals for parameters in saem.mmkin objects</p></td>
-      </tr><tr><td>
-          <p><code><a href="multistart.html">multistart()</a></code> <code><a href="multistart.html">print(<i>&lt;multistart&gt;</i>)</a></code> <code><a href="multistart.html">best()</a></code> <code><a href="multistart.html">which.best()</a></code> </p>
-        </td>
-        <td><p>Perform a hierarchical model fit with multiple starting values</p></td>
-      </tr><tr><td>
-          <p><code><a href="llhist.html">llhist()</a></code> </p>
-        </td>
-        <td><p>Plot the distribution of log likelihoods from multistart objects</p></td>
-      </tr><tr><td>
-          <p><code><a href="parplot.html">parplot()</a></code> </p>
-        </td>
-        <td><p>Plot parameter variability of multistart objects</p></td>
-      </tr></tbody><tbody><tr><th colspan="2">
-          <h2 id="datasets-and-known-results">Datasets and known results <a href="#datasets-and-known-results" class="anchor" aria-hidden="true"></a></h2>
-          <p class="section-desc"></p>
-        </th>
-      </tr></tbody><tbody><tr><td>
-          <p><code><a href="ds_mixed.html">ds_mixed</a></code> <code><a href="ds_mixed.html">ds_sfo</a></code> <code><a href="ds_mixed.html">ds_fomc</a></code> <code><a href="ds_mixed.html">ds_dfop</a></code> <code><a href="ds_mixed.html">ds_hs</a></code> <code><a href="ds_mixed.html">ds_dfop_sfo</a></code> </p>
-        </td>
-        <td><p>Synthetic data for hierarchical kinetic degradation models</p></td>
-      </tr><tr><td>
-          <p><code><a href="D24_2014.html">D24_2014</a></code> </p>
-        </td>
-        <td><p>Aerobic soil degradation data on 2,4-D from the EU assessment in 2014</p></td>
-      </tr><tr><td>
-          <p><code><a href="dimethenamid_2018.html">dimethenamid_2018</a></code> </p>
-        </td>
-        <td><p>Aerobic soil degradation data on dimethenamid and dimethenamid-P from the EU assessment in 2018</p></td>
-      </tr><tr><td>
-          <p><code><a href="FOCUS_2006_datasets.html">FOCUS_2006_A</a></code> <code><a href="FOCUS_2006_datasets.html">FOCUS_2006_B</a></code> <code><a href="FOCUS_2006_datasets.html">FOCUS_2006_C</a></code> <code><a href="FOCUS_2006_datasets.html">FOCUS_2006_D</a></code> <code><a href="FOCUS_2006_datasets.html">FOCUS_2006_E</a></code> <code><a href="FOCUS_2006_datasets.html">FOCUS_2006_F</a></code> </p>
-        </td>
-        <td><p>Datasets A to F from the FOCUS Kinetics report from 2006</p></td>
-      </tr><tr><td>
-          <p><code><a href="FOCUS_2006_SFO_ref_A_to_F.html">FOCUS_2006_SFO_ref_A_to_F</a></code> </p>
-        </td>
-        <td><p>Results of fitting the SFO model to Datasets A to F of FOCUS (2006)</p></td>
-      </tr><tr><td>
-          <p><code><a href="FOCUS_2006_FOMC_ref_A_to_F.html">FOCUS_2006_FOMC_ref_A_to_F</a></code> </p>
-        </td>
-        <td><p>Results of fitting the FOMC model to Datasets A to F of FOCUS (2006)</p></td>
-      </tr><tr><td>
-          <p><code><a href="FOCUS_2006_HS_ref_A_to_F.html">FOCUS_2006_HS_ref_A_to_F</a></code> </p>
-        </td>
-        <td><p>Results of fitting the HS model to Datasets A to F of FOCUS (2006)</p></td>
-      </tr><tr><td>
-          <p><code><a href="FOCUS_2006_DFOP_ref_A_to_B.html">FOCUS_2006_DFOP_ref_A_to_B</a></code> </p>
-        </td>
-        <td><p>Results of fitting the DFOP model to Datasets A to B of FOCUS (2006)</p></td>
-      </tr><tr><td>
-          <p><code><a href="NAFTA_SOP_2015.html">NAFTA_SOP_Appendix_B</a></code> <code><a href="NAFTA_SOP_2015.html">NAFTA_SOP_Appendix_D</a></code> </p>
-        </td>
-        <td><p>Example datasets from the NAFTA SOP published 2015</p></td>
-      </tr><tr><td>
-          <p><code><a href="NAFTA_SOP_Attachment.html">NAFTA_SOP_Attachment</a></code> </p>
-        </td>
-        <td><p>Example datasets from Attachment 1 to the NAFTA SOP published 2015</p></td>
-      </tr><tr><td>
-          <p><code><a href="mccall81_245T.html">mccall81_245T</a></code> </p>
-        </td>
-        <td><p>Datasets on aerobic soil metabolism of 2,4,5-T in six soils</p></td>
-      </tr><tr><td>
-          <p><code><a href="schaefer07_complex_case.html">schaefer07_complex_case</a></code> </p>
-        </td>
-        <td><p>Metabolism data set used for checking the software quality of KinGUI</p></td>
-      </tr><tr><td>
-          <p><code><a href="synthetic_data_for_UBA_2014.html">synthetic_data_for_UBA_2014</a></code> </p>
-        </td>
-        <td><p>Synthetic datasets for one parent compound with two metabolites</p></td>
-      </tr><tr><td>
-          <p><code><a href="experimental_data_for_UBA.html">experimental_data_for_UBA_2019</a></code> </p>
-        </td>
-        <td><p>Experimental datasets used for development and testing of error models</p></td>
-      </tr><tr><td>
-          <p><code><a href="test_data_from_UBA_2014.html">test_data_from_UBA_2014</a></code> </p>
-        </td>
-        <td><p>Three experimental datasets from two water sediment systems and one soil</p></td>
-      </tr><tr><td>
-          <p><code><a href="focus_soil_moisture.html">focus_soil_moisture</a></code> </p>
-        </td>
-        <td><p>FOCUS default values for soil moisture contents at field capacity, MWHC and 1/3 bar</p></td>
-      </tr><tr><td>
-          <p><code><a href="mkinds.html">print(<i>&lt;mkinds&gt;</i>)</a></code> </p>
-        </td>
-        <td><p>A dataset class for mkin</p></td>
-      </tr><tr><td>
-          <p><code><a href="mkindsg.html">print(<i>&lt;mkindsg&gt;</i>)</a></code> </p>
-        </td>
-        <td><p>A class for dataset groups for mkin</p></td>
-      </tr></tbody><tbody><tr><th colspan="2">
-          <h2 id="nafta-guidance">NAFTA guidance <a href="#nafta-guidance" class="anchor" aria-hidden="true"></a></h2>
-          <p class="section-desc"></p>
-        </th>
-      </tr></tbody><tbody><tr><td>
-          <p><code><a href="nafta.html">nafta()</a></code> <code><a href="nafta.html">print(<i>&lt;nafta&gt;</i>)</a></code> </p>
-        </td>
-        <td><p>Evaluate parent kinetics using the NAFTA guidance</p></td>
-      </tr><tr><td>
-          <p><code><a href="plot.nafta.html">plot(<i>&lt;nafta&gt;</i>)</a></code> </p>
-        </td>
-        <td><p>Plot the results of the three models used in the NAFTA scheme.</p></td>
-      </tr></tbody><tbody><tr><th colspan="2">
-          <h2 id="utility-functions">Utility functions <a href="#utility-functions" class="anchor" aria-hidden="true"></a></h2>
-          <p class="section-desc"></p>
-        </th>
-      </tr></tbody><tbody><tr><td>
-          <p><code><a href="summary_listing.html">summary_listing()</a></code> <code><a href="summary_listing.html">tex_listing()</a></code> <code><a href="summary_listing.html">html_listing()</a></code> </p>
-        </td>
-        <td><p>Display the output of a summary function according to the output format</p></td>
-      </tr><tr><td>
-          <p><code><a href="f_time_norm_focus.html">f_time_norm_focus()</a></code> </p>
-        </td>
-        <td><p>Normalisation factors for aerobic soil degradation according to FOCUS guidance</p></td>
-      </tr><tr><td>
-          <p><code><a href="set_nd_nq.html">set_nd_nq()</a></code> <code><a href="set_nd_nq.html">set_nd_nq_focus()</a></code> </p>
-        </td>
-        <td><p>Set non-detects and unquantified values in residue series without replicates</p></td>
-      </tr><tr><td>
-          <p><code><a href="max_twa_parent.html">max_twa_parent()</a></code> <code><a href="max_twa_parent.html">max_twa_sfo()</a></code> <code><a href="max_twa_parent.html">max_twa_fomc()</a></code> <code><a href="max_twa_parent.html">max_twa_dfop()</a></code> <code><a href="max_twa_parent.html">max_twa_hs()</a></code> </p>
-        </td>
-        <td><p>Function to calculate maximum time weighted average concentrations from
-kinetic models fitted with mkinfit</p></td>
-      </tr><tr><td>
-          <p><code><a href="mkin_wide_to_long.html">mkin_wide_to_long()</a></code> </p>
-        </td>
-        <td><p>Convert a dataframe with observations over time into long format</p></td>
-      </tr><tr><td>
-          <p><code><a href="mkin_long_to_wide.html">mkin_long_to_wide()</a></code> </p>
-        </td>
-        <td><p>Convert a dataframe from long to wide format</p></td>
-      </tr></tbody><tbody><tr><th colspan="2">
-          <h2 id="helper-functions-mainly-used-internally">Helper functions mainly used internally <a href="#helper-functions-mainly-used-internally" class="anchor" aria-hidden="true"></a></h2>
-          <p class="section-desc"></p>
-        </th>
-      </tr></tbody><tbody><tr><td>
-          <p><code><a href="mkinpredict.html">mkinpredict()</a></code> </p>
-        </td>
-        <td><p>Produce predictions from a kinetic model using specific parameters</p></td>
-      </tr><tr><td>
-          <p><code><a href="transform_odeparms.html">transform_odeparms()</a></code> <code><a href="transform_odeparms.html">backtransform_odeparms()</a></code> </p>
-        </td>
-        <td><p>Functions to transform and backtransform kinetic parameters for fitting</p></td>
-      </tr><tr><td>
-          <p><code><a href="ilr.html">ilr()</a></code> <code><a href="ilr.html">invilr()</a></code> </p>
-        </td>
-        <td><p>Function to perform isometric log-ratio transformation</p></td>
-      </tr><tr><td>
-          <p><code><a href="logLik.mkinfit.html">logLik(<i>&lt;mkinfit&gt;</i>)</a></code> </p>
-        </td>
-        <td><p>Calculated the log-likelihood of a fitted mkinfit object</p></td>
-      </tr><tr><td>
-          <p><code><a href="residuals.mkinfit.html">residuals(<i>&lt;mkinfit&gt;</i>)</a></code> </p>
-        </td>
-        <td><p>Extract residuals from an mkinfit model</p></td>
-      </tr><tr><td>
-          <p><code><a href="nobs.mkinfit.html">nobs(<i>&lt;mkinfit&gt;</i>)</a></code> </p>
-        </td>
-        <td><p>Number of observations on which an mkinfit object was fitted</p></td>
-      </tr><tr><td>
-          <p><code><a href="mkinresplot.html">mkinresplot()</a></code> </p>
-        </td>
-        <td><p>Function to plot residuals stored in an mkin object</p></td>
-      </tr><tr><td>
-          <p><code><a href="mkinparplot.html">mkinparplot()</a></code> </p>
-        </td>
-        <td><p>Function to plot the confidence intervals obtained using mkinfit</p></td>
-      </tr><tr><td>
-          <p><code><a href="mkinerrplot.html">mkinerrplot()</a></code> </p>
-        </td>
-        <td><p>Function to plot squared residuals and the error model for an mkin object</p></td>
-      </tr><tr><td>
-          <p><code><a href="mean_degparms.html">mean_degparms()</a></code> </p>
-        </td>
-        <td><p>Calculate mean degradation parameters for an mmkin row object</p></td>
-      </tr><tr><td>
-          <p><code><a href="create_deg_func.html">create_deg_func()</a></code> </p>
-        </td>
-        <td><p>Create degradation functions for known analytical solutions</p></td>
-      </tr></tbody><tbody><tr><th colspan="2">
-          <h2 id="analytical-solutions">Analytical solutions <a href="#analytical-solutions" class="anchor" aria-hidden="true"></a></h2>
-          <p class="section-desc"></p><p>Parent only model solutions</p>
-        </th>
-      </tr></tbody><tbody><tr><td>
-          <p><code><a href="SFO.solution.html">SFO.solution()</a></code> </p>
-        </td>
-        <td><p>Single First-Order kinetics</p></td>
-      </tr><tr><td>
-          <p><code><a href="FOMC.solution.html">FOMC.solution()</a></code> </p>
-        </td>
-        <td><p>First-Order Multi-Compartment kinetics</p></td>
-      </tr><tr><td>
-          <p><code><a href="DFOP.solution.html">DFOP.solution()</a></code> </p>
-        </td>
-        <td><p>Double First-Order in Parallel kinetics</p></td>
-      </tr><tr><td>
-          <p><code><a href="SFORB.solution.html">SFORB.solution()</a></code> </p>
-        </td>
-        <td><p>Single First-Order Reversible Binding kinetics</p></td>
-      </tr><tr><td>
-          <p><code><a href="HS.solution.html">HS.solution()</a></code> </p>
-        </td>
-        <td><p>Hockey-Stick kinetics</p></td>
-      </tr><tr><td>
-          <p><code><a href="IORE.solution.html">IORE.solution()</a></code> </p>
-        </td>
-        <td><p>Indeterminate order rate equation kinetics</p></td>
-      </tr><tr><td>
-          <p><code><a href="logistic.solution.html">logistic.solution()</a></code> </p>
-        </td>
-        <td><p>Logistic kinetics</p></td>
-      </tr></tbody><tbody><tr><th colspan="2">
-          <h2 id="generate-synthetic-datasets">Generate synthetic datasets <a href="#generate-synthetic-datasets" class="anchor" aria-hidden="true"></a></h2>
-          <p class="section-desc"></p>
-        </th>
-      </tr></tbody><tbody><tr><td>
-          <p><code><a href="add_err.html">add_err()</a></code> </p>
-        </td>
-        <td><p>Add normally distributed errors to simulated kinetic degradation data</p></td>
-      </tr><tr><td>
-          <p><code><a href="sigma_twocomp.html">sigma_twocomp()</a></code> </p>
-        </td>
-        <td><p>Two-component error model</p></td>
-      </tr></tbody><tbody><tr><th colspan="2">
-          <h2 id="deprecated-functions">Deprecated functions <a href="#deprecated-functions" class="anchor" aria-hidden="true"></a></h2>
-          <p class="section-desc"></p><p>Functions that have been superseded</p>
-        </th>
-      </tr></tbody><tbody><tr><td>
-          <p><code><a href="mkinplot.html">mkinplot()</a></code> </p>
-        </td>
-        <td><p>Plot the observed data and the fitted model of an mkinfit object</p></td>
-      </tr></tbody></table></div>
-
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+    <div class="section level2">
+      <h2 id="main-functions">Main functions<a class="anchor" aria-label="anchor" href="#main-functions"></a></h2>
+
+      <div class="section-desc"><p>Essential functionality</p></div>
+
+
+    </div><div class="section level2">
+
+
+
+
+      <dl><dt>
+
+          <code><a href="mkinmod.html">mkinmod()</a></code> <code><a href="mkinmod.html">print(<i>&lt;mkinmod&gt;</i>)</a></code> <code><a href="mkinmod.html">mkinsub()</a></code>
+
+        </dt>
+        <dd>Function to set up a kinetic model with one or more state variables</dd>
+      </dl><dl><dt>
+
+          <code><a href="mkinfit.html">mkinfit()</a></code>
+
+        </dt>
+        <dd>Fit a kinetic model to data with one or more state variables</dd>
+      </dl><dl><dt>
+
+          <code><a href="mmkin.html">mmkin()</a></code> <code><a href="mmkin.html">print(<i>&lt;mmkin&gt;</i>)</a></code>
+
+        </dt>
+        <dd>Fit one or more kinetic models with one or more state variables to one or more datasets</dd>
+      </dl><dl><dt>
+
+          <code><a href="mhmkin.html">mhmkin()</a></code> <code><a href="mhmkin.html">`[`(<i>&lt;mhmkin&gt;</i>)</a></code> <code><a href="mhmkin.html">print(<i>&lt;mhmkin&gt;</i>)</a></code>
+
+        </dt>
+        <dd>Fit nonlinear mixed-effects models built from one or more kinetic degradation models and one or more error models</dd>
+      </dl></div><div class="section level2">
+      <h2 id="generics">Generics<a class="anchor" aria-label="anchor" href="#generics"></a></h2>
+
+      <div class="section-desc"><p>Generic functions introduced by the package</p></div>
+
+
+    </div><div class="section level2">
+
+
+
+
+      <dl><dt>
+
+          <code><a href="parms.html">parms()</a></code>
+
+        </dt>
+        <dd>Extract model parameters</dd>
+      </dl><dl><dt>
+
+          <code><a href="status.html">status()</a></code> <code><a href="status.html">print(<i>&lt;status.mmkin&gt;</i>)</a></code> <code><a href="status.html">print(<i>&lt;status.mhmkin&gt;</i>)</a></code>
+
+        </dt>
+        <dd>Method to get status information for fit array objects</dd>
+      </dl><dl><dt>
+
+          <code><a href="illparms.html">illparms()</a></code> <code><a href="illparms.html">print(<i>&lt;illparms.mkinfit&gt;</i>)</a></code> <code><a href="illparms.html">print(<i>&lt;illparms.mmkin&gt;</i>)</a></code> <code><a href="illparms.html">print(<i>&lt;illparms.saem.mmkin&gt;</i>)</a></code> <code><a href="illparms.html">print(<i>&lt;illparms.mhmkin&gt;</i>)</a></code>
+
+        </dt>
+        <dd>Method to get the names of ill-defined parameters</dd>
+      </dl><dl><dt>
+
+          <code><a href="endpoints.html">endpoints()</a></code>
+
+        </dt>
+        <dd>Function to calculate endpoints for further use from kinetic models fitted with mkinfit</dd>
+      </dl><dl><dt>
+
+          <code><a href="aw.html">aw()</a></code>
+
+        </dt>
+        <dd>Calculate Akaike weights for model averaging</dd>
+      </dl></div><div class="section level2">
+      <h2 id="show-results">Show results<a class="anchor" aria-label="anchor" href="#show-results"></a></h2>
+
+      <div class="section-desc"><p>Functions working with mkinfit objects</p></div>
+
+
+    </div><div class="section level2">
+
+
+
+
+      <dl><dt>
+
+          <code><a href="plot.mkinfit.html">plot(<i>&lt;mkinfit&gt;</i>)</a></code> <code><a href="plot.mkinfit.html">plot_sep()</a></code> <code><a href="plot.mkinfit.html">plot_res()</a></code> <code><a href="plot.mkinfit.html">plot_err()</a></code>
+
+        </dt>
+        <dd>Plot the observed data and the fitted model of an mkinfit object</dd>
+      </dl><dl><dt>
+
+          <code><a href="summary.mkinfit.html">summary(<i>&lt;mkinfit&gt;</i>)</a></code> <code><a href="summary.mkinfit.html">print(<i>&lt;summary.mkinfit&gt;</i>)</a></code>
+
+        </dt>
+        <dd>Summary method for class "mkinfit"</dd>
+      </dl><dl><dt>
+
+          <code><a href="confint.mkinfit.html">confint(<i>&lt;mkinfit&gt;</i>)</a></code>
+
+        </dt>
+        <dd>Confidence intervals for parameters of mkinfit objects</dd>
+      </dl><dl><dt>
+
+          <code><a href="update.mkinfit.html">update(<i>&lt;mkinfit&gt;</i>)</a></code>
+
+        </dt>
+        <dd>Update an mkinfit model with different arguments</dd>
+      </dl><dl><dt>
+
+          <code><a href="lrtest.mkinfit.html">lrtest(<i>&lt;mkinfit&gt;</i>)</a></code> <code><a href="lrtest.mkinfit.html">lrtest(<i>&lt;mmkin&gt;</i>)</a></code>
+
+        </dt>
+        <dd>Likelihood ratio test for mkinfit models</dd>
+      </dl><dl><dt>
+
+          <code><a href="loftest.html">loftest()</a></code>
+
+        </dt>
+        <dd>Lack-of-fit test for models fitted to data with replicates</dd>
+      </dl><dl><dt>
+
+          <code><a href="mkinerrmin.html">mkinerrmin()</a></code>
+
+        </dt>
+        <dd>Calculate the minimum error to assume in order to pass the variance test</dd>
+      </dl><dl><dt>
+
+          <code><a href="CAKE_export.html">CAKE_export()</a></code>
+
+        </dt>
+        <dd>Export a list of datasets format to a CAKE study file</dd>
+      </dl></div><div class="section level2">
+      <h2 id="work-with-mmkin-objects">Work with mmkin objects<a class="anchor" aria-label="anchor" href="#work-with-mmkin-objects"></a></h2>
+
+      <div class="section-desc"><p>Functions working with aggregated results</p></div>
+
+
+    </div><div class="section level2">
+
+
+
+
+      <dl><dt>
+
+          <code><a href="Extract.mmkin.html">`[`(<i>&lt;mmkin&gt;</i>)</a></code>
+
+        </dt>
+        <dd>Subsetting method for mmkin objects</dd>
+      </dl><dl><dt>
+
+          <code><a href="plot.mmkin.html">plot(<i>&lt;mmkin&gt;</i>)</a></code>
+
+        </dt>
+        <dd>Plot model fits (observed and fitted) and the residuals for a row or column of an mmkin object</dd>
+      </dl><dl><dt>
+
+          <code><a href="AIC.mmkin.html">AIC(<i>&lt;mmkin&gt;</i>)</a></code> <code><a href="AIC.mmkin.html">BIC(<i>&lt;mmkin&gt;</i>)</a></code>
+
+        </dt>
+        <dd>Calculate the AIC for a column of an mmkin object</dd>
+      </dl><dl><dt>
+
+          <code><a href="summary.mmkin.html">summary(<i>&lt;mmkin&gt;</i>)</a></code> <code><a href="summary.mmkin.html">print(<i>&lt;summary.mmkin&gt;</i>)</a></code>
+
+        </dt>
+        <dd>Summary method for class "mmkin"</dd>
+      </dl></div><div class="section level2">
+      <h2 id="mixed-models">Mixed models<a class="anchor" aria-label="anchor" href="#mixed-models"></a></h2>
+
+      <div class="section-desc"><p>Create and work with nonlinear hierarchical models</p></div>
+
+
+    </div><div class="section level2">
+
+
+
+
+      <dl><dt>
+
+          <code><a href="hierarchical_kinetics.html">hierarchical_kinetics()</a></code>
+
+        </dt>
+        <dd>Hierarchical kinetics template</dd>
+      </dl><dl><dt>
+
+          <code><a href="read_spreadsheet.html">read_spreadsheet()</a></code>
+
+        </dt>
+        <dd>Read datasets and relevant meta information from a spreadsheet file</dd>
+      </dl><dl><dt>
+
+          <code><a href="nlme.mmkin.html">nlme(<i>&lt;mmkin&gt;</i>)</a></code> <code><a href="nlme.mmkin.html">print(<i>&lt;nlme.mmkin&gt;</i>)</a></code> <code><a href="nlme.mmkin.html">update(<i>&lt;nlme.mmkin&gt;</i>)</a></code>
+
+        </dt>
+        <dd>Create an nlme model for an mmkin row object</dd>
+      </dl><dl><dt>
+
+          <code><a href="saem.html">saem()</a></code> <code><a href="saem.html">print(<i>&lt;saem.mmkin&gt;</i>)</a></code> <code><a href="saem.html">saemix_model()</a></code> <code><a href="saem.html">saemix_data()</a></code>
+
+        </dt>
+        <dd>Fit nonlinear mixed models with SAEM</dd>
+      </dl><dl><dt>
+
+          <code><a href="mhmkin.html">mhmkin()</a></code> <code><a href="mhmkin.html">`[`(<i>&lt;mhmkin&gt;</i>)</a></code> <code><a href="mhmkin.html">print(<i>&lt;mhmkin&gt;</i>)</a></code>
+
+        </dt>
+        <dd>Fit nonlinear mixed-effects models built from one or more kinetic degradation models and one or more error models</dd>
+      </dl><dl><dt>
+
+          <code><a href="plot.mixed.mmkin.html">plot(<i>&lt;mixed.mmkin&gt;</i>)</a></code>
+
+        </dt>
+        <dd>Plot predictions from a fitted nonlinear mixed model obtained via an mmkin row object</dd>
+      </dl><dl><dt>
+
+          <code><a href="summary.nlme.mmkin.html">summary(<i>&lt;nlme.mmkin&gt;</i>)</a></code> <code><a href="summary.nlme.mmkin.html">print(<i>&lt;summary.nlme.mmkin&gt;</i>)</a></code>
+
+        </dt>
+        <dd>Summary method for class "nlme.mmkin"</dd>
+      </dl><dl><dt>
+
+          <code><a href="summary.saem.mmkin.html">summary(<i>&lt;saem.mmkin&gt;</i>)</a></code> <code><a href="summary.saem.mmkin.html">print(<i>&lt;summary.saem.mmkin&gt;</i>)</a></code>
+
+        </dt>
+        <dd>Summary method for class "saem.mmkin"</dd>
+      </dl><dl><dt>
+
+          <code><a href="anova.saem.mmkin.html">anova(<i>&lt;saem.mmkin&gt;</i>)</a></code>
+
+        </dt>
+        <dd>Anova method for saem.mmkin objects</dd>
+      </dl><dl><dt>
+
+          <code><a href="logLik.saem.mmkin.html">logLik(<i>&lt;saem.mmkin&gt;</i>)</a></code>
+
+        </dt>
+        <dd>logLik method for saem.mmkin objects</dd>
+      </dl><dl><dt>
+
+          <code><a href="nlme.html">nlme_function()</a></code> <code><a href="nlme.html">nlme_data()</a></code>
+
+        </dt>
+        <dd>Helper functions to create nlme models from mmkin row objects</dd>
+      </dl><dl><dt>
+
+          <code><a href="get_deg_func.html">get_deg_func()</a></code>
+
+        </dt>
+        <dd>Retrieve a degradation function from the mmkin namespace</dd>
+      </dl><dl><dt>
+
+          <code><a href="mixed.html">mixed()</a></code> <code><a href="mixed.html">print(<i>&lt;mixed.mmkin&gt;</i>)</a></code>
+
+        </dt>
+        <dd>Create a mixed effects model from an mmkin row object</dd>
+      </dl><dl><dt>
+
+          <code><a href="reexports.html">reexports</a></code> <code><a href="reexports.html">intervals</a></code> <code><a href="reexports.html">lrtest</a></code> <code><a href="reexports.html">nlme</a></code>
+
+        </dt>
+        <dd>Objects exported from other packages</dd>
+      </dl><dl><dt>
+
+          <code><a href="intervals.saem.mmkin.html">intervals(<i>&lt;saem.mmkin&gt;</i>)</a></code>
+
+        </dt>
+        <dd>Confidence intervals for parameters in saem.mmkin objects</dd>
+      </dl><dl><dt>
+
+          <code><a href="multistart.html">multistart()</a></code> <code><a href="multistart.html">print(<i>&lt;multistart&gt;</i>)</a></code> <code><a href="multistart.html">best()</a></code> <code><a href="multistart.html">which.best()</a></code>
+
+        </dt>
+        <dd>Perform a hierarchical model fit with multiple starting values</dd>
+      </dl><dl><dt>
+
+          <code><a href="llhist.html">llhist()</a></code>
+
+        </dt>
+        <dd>Plot the distribution of log likelihoods from multistart objects</dd>
+      </dl><dl><dt>
+
+          <code><a href="parplot.html">parplot()</a></code>
+
+        </dt>
+        <dd>Plot parameter variability of multistart objects</dd>
+      </dl><dl><dt>
+
+          <code><a href="check_failed.html">check_failed()</a></code>
+
+        </dt>
+        <dd>Check if fit within an mhmkin object failed</dd>
+      </dl></div><div class="section level2">
+      <h2 id="datasets-and-known-results">Datasets and known results<a class="anchor" aria-label="anchor" href="#datasets-and-known-results"></a></h2>
+
+
+
+
+    </div><div class="section level2">
+
+
+
+
+      <dl><dt>
+
+          <code><a href="ds_mixed.html">ds_mixed</a></code> <code><a href="ds_mixed.html">ds_sfo</a></code> <code><a href="ds_mixed.html">ds_fomc</a></code> <code><a href="ds_mixed.html">ds_dfop</a></code> <code><a href="ds_mixed.html">ds_hs</a></code> <code><a href="ds_mixed.html">ds_dfop_sfo</a></code>
+
+        </dt>
+        <dd>Synthetic data for hierarchical kinetic degradation models</dd>
+      </dl><dl><dt>
+
+          <code><a href="D24_2014.html">D24_2014</a></code>
+
+        </dt>
+        <dd>Aerobic soil degradation data on 2,4-D from the EU assessment in 2014</dd>
+      </dl><dl><dt>
 
+          <code><a href="dimethenamid_2018.html">dimethenamid_2018</a></code>
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+        </dt>
+        <dd>Aerobic soil degradation data on dimethenamid and dimethenamid-P from the EU assessment in 2018</dd>
+      </dl><dl><dt>
+
+          <code><a href="FOCUS_2006_datasets.html">FOCUS_2006_A</a></code> <code><a href="FOCUS_2006_datasets.html">FOCUS_2006_B</a></code> <code><a href="FOCUS_2006_datasets.html">FOCUS_2006_C</a></code> <code><a href="FOCUS_2006_datasets.html">FOCUS_2006_D</a></code> <code><a href="FOCUS_2006_datasets.html">FOCUS_2006_E</a></code> <code><a href="FOCUS_2006_datasets.html">FOCUS_2006_F</a></code>
+
+        </dt>
+        <dd>Datasets A to F from the FOCUS Kinetics report from 2006</dd>
+      </dl><dl><dt>
+
+          <code><a href="FOCUS_2006_SFO_ref_A_to_F.html">FOCUS_2006_SFO_ref_A_to_F</a></code>
+
+        </dt>
+        <dd>Results of fitting the SFO model to Datasets A to F of FOCUS (2006)</dd>
+      </dl><dl><dt>
+
+          <code><a href="FOCUS_2006_FOMC_ref_A_to_F.html">FOCUS_2006_FOMC_ref_A_to_F</a></code>
+
+        </dt>
+        <dd>Results of fitting the FOMC model to Datasets A to F of FOCUS (2006)</dd>
+      </dl><dl><dt>
+
+          <code><a href="FOCUS_2006_HS_ref_A_to_F.html">FOCUS_2006_HS_ref_A_to_F</a></code>
+
+        </dt>
+        <dd>Results of fitting the HS model to Datasets A to F of FOCUS (2006)</dd>
+      </dl><dl><dt>
+
+          <code><a href="FOCUS_2006_DFOP_ref_A_to_B.html">FOCUS_2006_DFOP_ref_A_to_B</a></code>
+
+        </dt>
+        <dd>Results of fitting the DFOP model to Datasets A to B of FOCUS (2006)</dd>
+      </dl><dl><dt>
+
+          <code><a href="NAFTA_SOP_2015.html">NAFTA_SOP_Appendix_B</a></code> <code><a href="NAFTA_SOP_2015.html">NAFTA_SOP_Appendix_D</a></code>
+
+        </dt>
+        <dd>Example datasets from the NAFTA SOP published 2015</dd>
+      </dl><dl><dt>
+
+          <code><a href="NAFTA_SOP_Attachment.html">NAFTA_SOP_Attachment</a></code>
+
+        </dt>
+        <dd>Example datasets from Attachment 1 to the NAFTA SOP published 2015</dd>
+      </dl><dl><dt>
+
+          <code><a href="mccall81_245T.html">mccall81_245T</a></code>
+
+        </dt>
+        <dd>Datasets on aerobic soil metabolism of 2,4,5-T in six soils</dd>
+      </dl><dl><dt>
+
+          <code><a href="schaefer07_complex_case.html">schaefer07_complex_case</a></code>
+
+        </dt>
+        <dd>Metabolism data set used for checking the software quality of KinGUI</dd>
+      </dl><dl><dt>
+
+          <code><a href="synthetic_data_for_UBA_2014.html">synthetic_data_for_UBA_2014</a></code>
+
+        </dt>
+        <dd>Synthetic datasets for one parent compound with two metabolites</dd>
+      </dl><dl><dt>
+
+          <code><a href="experimental_data_for_UBA.html">experimental_data_for_UBA_2019</a></code>
+
+        </dt>
+        <dd>Experimental datasets used for development and testing of error models</dd>
+      </dl><dl><dt>
+
+          <code><a href="test_data_from_UBA_2014.html">test_data_from_UBA_2014</a></code>
+
+        </dt>
+        <dd>Three experimental datasets from two water sediment systems and one soil</dd>
+      </dl><dl><dt>
+
+          <code><a href="focus_soil_moisture.html">focus_soil_moisture</a></code>
+
+        </dt>
+        <dd>FOCUS default values for soil moisture contents at field capacity, MWHC and 1/3 bar</dd>
+      </dl><dl><dt>
+
+          <code><a href="mkinds.html">print(<i>&lt;mkinds&gt;</i>)</a></code>
+
+        </dt>
+        <dd>A dataset class for mkin</dd>
+      </dl><dl><dt>
+
+          <code><a href="mkindsg.html">print(<i>&lt;mkindsg&gt;</i>)</a></code>
+
+        </dt>
+        <dd>A class for dataset groups for mkin</dd>
+      </dl></div><div class="section level2">
+      <h2 id="nafta-guidance">NAFTA guidance<a class="anchor" aria-label="anchor" href="#nafta-guidance"></a></h2>
+
+
+
+
+    </div><div class="section level2">
+
+
+
+
+      <dl><dt>
+
+          <code><a href="nafta.html">nafta()</a></code> <code><a href="nafta.html">print(<i>&lt;nafta&gt;</i>)</a></code>
+
+        </dt>
+        <dd>Evaluate parent kinetics using the NAFTA guidance</dd>
+      </dl><dl><dt>
+
+          <code><a href="plot.nafta.html">plot(<i>&lt;nafta&gt;</i>)</a></code>
+
+        </dt>
+        <dd>Plot the results of the three models used in the NAFTA scheme.</dd>
+      </dl></div><div class="section level2">
+      <h2 id="utility-functions">Utility functions<a class="anchor" aria-label="anchor" href="#utility-functions"></a></h2>
+
+
+
+
+    </div><div class="section level2">
+
+
+
+
+      <dl><dt>
+
+          <code><a href="summary_listing.html">summary_listing()</a></code> <code><a href="summary_listing.html">tex_listing()</a></code> <code><a href="summary_listing.html">html_listing()</a></code>
+
+        </dt>
+        <dd>Display the output of a summary function according to the output format</dd>
+      </dl><dl><dt>
+
+          <code><a href="f_time_norm_focus.html">f_time_norm_focus()</a></code>
+
+        </dt>
+        <dd>Normalisation factors for aerobic soil degradation according to FOCUS guidance</dd>
+      </dl><dl><dt>
+
+          <code><a href="set_nd_nq.html">set_nd_nq()</a></code> <code><a href="set_nd_nq.html">set_nd_nq_focus()</a></code>
+
+        </dt>
+        <dd>Set non-detects and unquantified values in residue series without replicates</dd>
+      </dl><dl><dt>
+
+          <code><a href="max_twa_parent.html">max_twa_parent()</a></code> <code><a href="max_twa_parent.html">max_twa_sfo()</a></code> <code><a href="max_twa_parent.html">max_twa_fomc()</a></code> <code><a href="max_twa_parent.html">max_twa_dfop()</a></code> <code><a href="max_twa_parent.html">max_twa_hs()</a></code>
+
+        </dt>
+        <dd>Function to calculate maximum time weighted average concentrations from kinetic models fitted with mkinfit</dd>
+      </dl><dl><dt>
+
+          <code><a href="mkin_wide_to_long.html">mkin_wide_to_long()</a></code>
+
+        </dt>
+        <dd>Convert a dataframe with observations over time into long format</dd>
+      </dl><dl><dt>
+
+          <code><a href="mkin_long_to_wide.html">mkin_long_to_wide()</a></code>
+
+        </dt>
+        <dd>Convert a dataframe from long to wide format</dd>
+      </dl></div><div class="section level2">
+      <h2 id="helper-functions-mainly-used-internally">Helper functions mainly used internally<a class="anchor" aria-label="anchor" href="#helper-functions-mainly-used-internally"></a></h2>
+
+
+
+
+    </div><div class="section level2">
+
+
+
+
+      <dl><dt>
+
+          <code><a href="mkinpredict.html">mkinpredict()</a></code>
+
+        </dt>
+        <dd>Produce predictions from a kinetic model using specific parameters</dd>
+      </dl><dl><dt>
+
+          <code><a href="transform_odeparms.html">transform_odeparms()</a></code> <code><a href="transform_odeparms.html">backtransform_odeparms()</a></code>
+
+        </dt>
+        <dd>Functions to transform and backtransform kinetic parameters for fitting</dd>
+      </dl><dl><dt>
+
+          <code><a href="ilr.html">ilr()</a></code> <code><a href="ilr.html">invilr()</a></code>
+
+        </dt>
+        <dd>Function to perform isometric log-ratio transformation</dd>
+      </dl><dl><dt>
+
+          <code><a href="logLik.mkinfit.html">logLik(<i>&lt;mkinfit&gt;</i>)</a></code>
+
+        </dt>
+        <dd>Calculated the log-likelihood of a fitted mkinfit object</dd>
+      </dl><dl><dt>
+
+          <code><a href="residuals.mkinfit.html">residuals(<i>&lt;mkinfit&gt;</i>)</a></code>
+
+        </dt>
+        <dd>Extract residuals from an mkinfit model</dd>
+      </dl><dl><dt>
+
+          <code><a href="nobs.mkinfit.html">nobs(<i>&lt;mkinfit&gt;</i>)</a></code>
+
+        </dt>
+        <dd>Number of observations on which an mkinfit object was fitted</dd>
+      </dl><dl><dt>
+
+          <code><a href="mkinresplot.html">mkinresplot()</a></code>
+
+        </dt>
+        <dd>Function to plot residuals stored in an mkin object</dd>
+      </dl><dl><dt>
+
+          <code><a href="mkinparplot.html">mkinparplot()</a></code>
+
+        </dt>
+        <dd>Function to plot the confidence intervals obtained using mkinfit</dd>
+      </dl><dl><dt>
+
+          <code><a href="mkinerrplot.html">mkinerrplot()</a></code>
+
+        </dt>
+        <dd>Function to plot squared residuals and the error model for an mkin object</dd>
+      </dl><dl><dt>
+
+          <code><a href="mean_degparms.html">mean_degparms()</a></code>
+
+        </dt>
+        <dd>Calculate mean degradation parameters for an mmkin row object</dd>
+      </dl><dl><dt>
+
+          <code><a href="create_deg_func.html">create_deg_func()</a></code>
+
+        </dt>
+        <dd>Create degradation functions for known analytical solutions</dd>
+      </dl></div><div class="section level2">
+      <h2 id="analytical-solutions">Analytical solutions<a class="anchor" aria-label="anchor" href="#analytical-solutions"></a></h2>
+
+      <div class="section-desc"><p>Parent only model solutions</p></div>
+
+
+    </div><div class="section level2">
+
+
+
+
+      <dl><dt>
+
+          <code><a href="SFO.solution.html">SFO.solution()</a></code>
+
+        </dt>
+        <dd>Single First-Order kinetics</dd>
+      </dl><dl><dt>
+
+          <code><a href="FOMC.solution.html">FOMC.solution()</a></code>
+
+        </dt>
+        <dd>First-Order Multi-Compartment kinetics</dd>
+      </dl><dl><dt>
+
+          <code><a href="DFOP.solution.html">DFOP.solution()</a></code>
+
+        </dt>
+        <dd>Double First-Order in Parallel kinetics</dd>
+      </dl><dl><dt>
+
+          <code><a href="SFORB.solution.html">SFORB.solution()</a></code>
+
+        </dt>
+        <dd>Single First-Order Reversible Binding kinetics</dd>
+      </dl><dl><dt>
+
+          <code><a href="HS.solution.html">HS.solution()</a></code>
+
+        </dt>
+        <dd>Hockey-Stick kinetics</dd>
+      </dl><dl><dt>
+
+          <code><a href="IORE.solution.html">IORE.solution()</a></code>
+
+        </dt>
+        <dd>Indeterminate order rate equation kinetics</dd>
+      </dl><dl><dt>
+
+          <code><a href="logistic.solution.html">logistic.solution()</a></code>
+
+        </dt>
+        <dd>Logistic kinetics</dd>
+      </dl></div><div class="section level2">
+      <h2 id="generate-synthetic-datasets">Generate synthetic datasets<a class="anchor" aria-label="anchor" href="#generate-synthetic-datasets"></a></h2>
+
+
+
+
+    </div><div class="section level2">
+
+
+
+
+      <dl><dt>
+
+          <code><a href="add_err.html">add_err()</a></code>
+
+        </dt>
+        <dd>Add normally distributed errors to simulated kinetic degradation data</dd>
+      </dl><dl><dt>
+
+          <code><a href="sigma_twocomp.html">sigma_twocomp()</a></code>
+
+        </dt>
+        <dd>Two-component error model</dd>
+      </dl></div><div class="section level2">
+      <h2 id="deprecated-functions">Deprecated functions<a class="anchor" aria-label="anchor" href="#deprecated-functions"></a></h2>
+
+      <div class="section-desc"><p>Functions that have been superseded</p></div>
+
+
+    </div><div class="section level2">
+
+
+
+
+      <dl><dt>
+
+          <code><a href="mkinplot.html">mkinplot()</a></code>
+
+        </dt>
+        <dd>Plot the observed data and the fitted model of an mkinfit object</dd>
+      </dl></div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/intervals.html b/docs/reference/intervals.html
new file mode 100644
index 00000000..ef2e29e9
--- /dev/null
+++ b/docs/reference/intervals.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/reexports.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/reexports.html">
+  </head>
+</html>
+
diff --git a/docs/reference/intervals.nlmixr.mmkin.html b/docs/reference/intervals.nlmixr.mmkin.html
deleted file mode 100644
index 87906c07..00000000
--- a/docs/reference/intervals.nlmixr.mmkin.html
+++ /dev/null
@@ -1,135 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Confidence intervals for parameters in nlmixr.mmkin objects — intervals.nlmixr.mmkin • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Confidence intervals for parameters in nlmixr.mmkin objects — intervals.nlmixr.mmkin"><meta property="og:description" content="Confidence intervals for parameters in nlmixr.mmkin objects"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Functions and data</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Confidence intervals for parameters in nlmixr.mmkin objects</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/intervals.R" class="external-link"><code>R/intervals.R</code></a></small>
-    <div class="hidden name"><code>intervals.nlmixr.mmkin.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Confidence intervals for parameters in nlmixr.mmkin objects</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="co"># S3 method for nlmixr.mmkin</span>
-<span class="fu"><a href="https://rdrr.io/pkg/nlme/man/intervals.html" class="external-link">intervals</a></span><span class="op">(</span><span class="va">object</span>, level <span class="op">=</span> <span class="fl">0.95</span>, backtransform <span class="op">=</span> <span class="cn">TRUE</span>, <span class="va">...</span><span class="op">)</span></code></pre></div>
-    </div>
-
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>object</dt>
-<dd><p>The fitted saem.mmkin object</p></dd>
-<dt>level</dt>
-<dd><p>The confidence level.</p></dd>
-<dt>backtransform</dt>
-<dd><p>Should we backtransform the parameters where a one to
-one correlation between transformed and backtransformed parameters exists?</p></dd>
-<dt>...</dt>
-<dd><p>For compatibility with the generic method</p></dd>
-</dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    <p>An object with 'intervals.saem.mmkin' and 'intervals.lme' in the
-class attribute</p>
-    </div>
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.2.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/reference/intervals.saem.mmkin.html b/docs/reference/intervals.saem.mmkin.html
index d52d8501..3a8cdb5d 100644
--- a/docs/reference/intervals.saem.mmkin.html
+++ b/docs/reference/intervals.saem.mmkin.html
@@ -1,172 +1,121 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Confidence intervals for parameters in saem.mmkin objects — intervals.saem.mmkin • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Confidence intervals for parameters in saem.mmkin objects — intervals.saem.mmkin"><meta property="og:description" content="Confidence intervals for parameters in saem.mmkin objects"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Confidence intervals for parameters in saem.mmkin objects — intervals.saem.mmkin • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Confidence intervals for parameters in saem.mmkin objects — intervals.saem.mmkin"><meta name="description" content="Confidence intervals for parameters in saem.mmkin objects"><meta property="og:description" content="Confidence intervals for parameters in saem.mmkin objects"></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Confidence intervals for parameters in saem.mmkin objects</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/intervals.R" class="external-link"><code>R/intervals.R</code></a></small>
-    <div class="hidden name"><code>intervals.saem.mmkin.Rd</code></div>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Confidence intervals for parameters in saem.mmkin objects</h1>
+      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/intervals.R" class="external-link"><code>R/intervals.R</code></a></small>
+      <div class="d-none name"><code>intervals.saem.mmkin.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Confidence intervals for parameters in saem.mmkin objects</p>
     </div>
 
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for saem.mmkin</span></span>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'saem.mmkin'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/pkg/nlme/man/intervals.html" class="external-link">intervals</a></span><span class="op">(</span><span class="va">object</span>, level <span class="op">=</span> <span class="fl">0.95</span>, backtransform <span class="op">=</span> <span class="cn">TRUE</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>object</dt>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
 <dd><p>The fitted saem.mmkin object</p></dd>
 
 
-<dt>level</dt>
+<dt id="arg-level">level<a class="anchor" aria-label="anchor" href="#arg-level"></a></dt>
 <dd><p>The confidence level. Must be the default of 0.95 as this is what
 is available in the saemix object</p></dd>
 
 
-<dt>backtransform</dt>
+<dt id="arg-backtransform">backtransform<a class="anchor" aria-label="anchor" href="#arg-backtransform"></a></dt>
 <dd><p>In case the model was fitted with mkin transformations,
 should we backtransform the parameters where a one to one correlation
 between transformed and backtransformed parameters exists?</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>For compatibility with the generic method</p></dd>
 
 </dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>An object with 'intervals.saem.mmkin' and 'intervals.lme' in the
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>An object with 'intervals.saem.mmkin' and 'intervals.lme' in the
 class attribute</p>
     </div>
 
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/invilr.html b/docs/reference/invilr.html
new file mode 100644
index 00000000..53dd75ca
--- /dev/null
+++ b/docs/reference/invilr.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/ilr.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/ilr.html">
+  </head>
+</html>
+
diff --git a/docs/reference/llhist.html b/docs/reference/llhist.html
index d53d6319..01299b04 100644
--- a/docs/reference/llhist.html
+++ b/docs/reference/llhist.html
@@ -1,171 +1,123 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Plot the distribution of log likelihoods from multistart objects — llhist • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Plot the distribution of log likelihoods from multistart objects — llhist"><meta property="og:description" content="Produces a histogram of log-likelihoods. In addition, the likelihood of the
-original fit is shown as a red vertical line."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Plot the distribution of log likelihoods from multistart objects — llhist • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Plot the distribution of log likelihoods from multistart objects — llhist"><meta name="description" content="Produces a histogram of log-likelihoods. In addition, the likelihood of the
+original fit is shown as a red vertical line."><meta property="og:description" content="Produces a histogram of log-likelihoods. In addition, the likelihood of the
+original fit is shown as a red vertical line."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Plot the distribution of log likelihoods from multistart objects</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/llhist.R" class="external-link"><code>R/llhist.R</code></a></small>
-    <div class="hidden name"><code>llhist.Rd</code></div>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Plot the distribution of log likelihoods from multistart objects</h1>
+      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/llhist.R" class="external-link"><code>R/llhist.R</code></a></small>
+      <div class="d-none name"><code>llhist.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Produces a histogram of log-likelihoods. In addition, the likelihood of the
 original fit is shown as a red vertical line.</p>
     </div>
 
-    <div id="ref-usage">
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">llhist</span><span class="op">(</span><span class="va">object</span>, breaks <span class="op">=</span> <span class="st">"Sturges"</span>, lpos <span class="op">=</span> <span class="st">"topleft"</span>, main <span class="op">=</span> <span class="st">""</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>object</dt>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
 <dd><p>The <a href="multistart.html">multistart</a> object</p></dd>
 
 
-<dt>breaks</dt>
+<dt id="arg-breaks">breaks<a class="anchor" aria-label="anchor" href="#arg-breaks"></a></dt>
 <dd><p>Passed to <a href="https://rdrr.io/r/graphics/hist.html" class="external-link">hist</a></p></dd>
 
 
-<dt>lpos</dt>
+<dt id="arg-lpos">lpos<a class="anchor" aria-label="anchor" href="#arg-lpos"></a></dt>
 <dd><p>Positioning of the legend.</p></dd>
 
 
-<dt>main</dt>
+<dt id="arg-main">main<a class="anchor" aria-label="anchor" href="#arg-main"></a></dt>
 <dd><p>Title of the plot</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Passed to <a href="https://rdrr.io/r/graphics/hist.html" class="external-link">hist</a></p></dd>
 
 </dl></div>
-    <div id="see-also">
-    <h2>See also</h2>
+    <div class="section level2">
+    <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
     <div class="dont-index"><p><a href="multistart.html">multistart</a></p></div>
     </div>
 
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/loftest-1.png b/docs/reference/loftest-1.png
index f1dc5fa7..e6281140 100644
--- a/docs/reference/loftest-1.png
+++ b/docs/reference/loftest-1.png
diff --git a/docs/reference/loftest-2.png b/docs/reference/loftest-2.png
index 3f1015a9..32842c7d 100644
--- a/docs/reference/loftest-2.png
+++ b/docs/reference/loftest-2.png
diff --git a/docs/reference/loftest-3.png b/docs/reference/loftest-3.png
index e876cbab..e2a5663d 100644
--- a/docs/reference/loftest-3.png
+++ b/docs/reference/loftest-3.png
diff --git a/docs/reference/loftest-4.png b/docs/reference/loftest-4.png
index ac44c162..a052626b 100644
--- a/docs/reference/loftest-4.png
+++ b/docs/reference/loftest-4.png
diff --git a/docs/reference/loftest-5.png b/docs/reference/loftest-5.png
index 14537feb..168aa062 100644
--- a/docs/reference/loftest-5.png
+++ b/docs/reference/loftest-5.png
diff --git a/docs/reference/loftest.html b/docs/reference/loftest.html
index b474ad20..97b48751 100644
--- a/docs/reference/loftest.html
+++ b/docs/reference/loftest.html
@@ -1,154 +1,112 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Lack-of-fit test for models fitted to data with replicates — loftest • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Lack-of-fit test for models fitted to data with replicates — loftest"><meta property="og:description" content="This is a generic function with a method currently only defined for mkinfit
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Lack-of-fit test for models fitted to data with replicates — loftest • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Lack-of-fit test for models fitted to data with replicates — loftest"><meta name="description" content="This is a generic function with a method currently only defined for mkinfit
 objects. It fits an anova model to the data contained in the object and
 compares the likelihoods using the likelihood ratio test
-lrtest.default from the lmtest package."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
+lrtest.default from the lmtest package."><meta property="og:description" content="This is a generic function with a method currently only defined for mkinfit
+objects. It fits an anova model to the data contained in the object and
+compares the likelihoods using the likelihood ratio test
+lrtest.default from the lmtest package."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
 
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
 
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
 
-      
 
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Lack-of-fit test for models fitted to data with replicates</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/loftest.R" class="external-link"><code>R/loftest.R</code></a></small>
-    <div class="hidden name"><code>loftest.Rd</code></div>
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Lack-of-fit test for models fitted to data with replicates</h1>
+      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/loftest.R" class="external-link"><code>R/loftest.R</code></a></small>
+      <div class="d-none name"><code>loftest.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>This is a generic function with a method currently only defined for mkinfit
 objects. It fits an anova model to the data contained in the object and
 compares the likelihoods using the likelihood ratio test
 <code><a href="https://rdrr.io/pkg/lmtest/man/lrtest.html" class="external-link">lrtest.default</a></code> from the lmtest package.</p>
     </div>
 
-    <div id="ref-usage">
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">loftest</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for mkinfit</span></span>
+<span><span class="co"># S3 method for class 'mkinfit'</span></span>
 <span><span class="fu">loftest</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>object</dt>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
 <dd><p>A model object with a defined loftest method</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Not used</p></dd>
 
 </dl></div>
-    <div id="details">
-    <h2>Details</h2>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>The anova model is interpreted as the simplest form of an mkinfit model,
 assuming only a constant variance about the means, but not enforcing any
 structure of the means, so we have one model parameter for every mean
 of replicate samples.</p>
     </div>
-    <div id="see-also">
-    <h2>See also</h2>
+    <div class="section level2">
+    <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
     <div class="dont-index"><p>lrtest</p></div>
     </div>
 
-    <div id="ref-examples">
-    <h2>Examples</h2>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># \dontrun{</span></span></span>
 <span class="r-in"><span><span class="va">test_data</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/subset.html" class="external-link">subset</a></span><span class="op">(</span><span class="va">synthetic_data_for_UBA_2014</span><span class="op">[[</span><span class="fl">12</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">data</span>, <span class="va">name</span> <span class="op">==</span> <span class="st">"parent"</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">sfo_fit</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="va">test_data</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
@@ -319,27 +277,23 @@ of replicate samples.</p>
 <span class="r-in"><span><span class="co"># }</span></span></span>
 </code></pre></div>
     </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/loftest.mkinfit.html b/docs/reference/loftest.mkinfit.html
new file mode 100644
index 00000000..cd9934b8
--- /dev/null
+++ b/docs/reference/loftest.mkinfit.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/loftest.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/loftest.html">
+  </head>
+</html>
+
diff --git a/docs/reference/logLik.mkinfit.html b/docs/reference/logLik.mkinfit.html
index 07884f26..eddf1f8f 100644
--- a/docs/reference/logLik.mkinfit.html
+++ b/docs/reference/logLik.mkinfit.html
@@ -1,118 +1,74 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Calculated the log-likelihood of a fitted mkinfit object — logLik.mkinfit • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Calculated the log-likelihood of a fitted mkinfit object — logLik.mkinfit"><meta property="og:description" content="This function returns the product of the likelihood densities of each
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Calculated the log-likelihood of a fitted mkinfit object — logLik.mkinfit • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Calculated the log-likelihood of a fitted mkinfit object — logLik.mkinfit"><meta name="description" content="This function returns the product of the likelihood densities of each
 observed value, as calculated as part of the fitting procedure using
 dnorm, i.e. assuming normal distribution, and with the means
 predicted by the degradation model, and the standard deviations predicted by
-the error model."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+the error model."><meta property="og:description" content="This function returns the product of the likelihood densities of each
+observed value, as calculated as part of the fitting procedure using
+dnorm, i.e. assuming normal distribution, and with the means
+predicted by the degradation model, and the standard deviations predicted by
+the error model."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Calculated the log-likelihood of a fitted mkinfit object</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/logLik.mkinfit.R" class="external-link"><code>R/logLik.mkinfit.R</code></a></small>
-    <div class="hidden name"><code>logLik.mkinfit.Rd</code></div>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Calculated the log-likelihood of a fitted mkinfit object</h1>
+      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/logLik.mkinfit.R" class="external-link"><code>R/logLik.mkinfit.R</code></a></small>
+      <div class="d-none name"><code>logLik.mkinfit.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>This function returns the product of the likelihood densities of each
 observed value, as calculated as part of the fitting procedure using
 <code><a href="https://rdrr.io/r/stats/Normal.html" class="external-link">dnorm</a></code>, i.e. assuming normal distribution, and with the means
@@ -120,47 +76,48 @@ predicted by the degradation model, and the standard deviations predicted by
 the error model.</p>
     </div>
 
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for mkinfit</span></span>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'mkinfit'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/stats/logLik.html" class="external-link">logLik</a></span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>object</dt>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
 <dd><p>An object of class <code><a href="mkinfit.html">mkinfit</a></code>.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>For compatibility with the generic method</p></dd>
 
 </dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>An object of class <code><a href="https://rdrr.io/r/stats/logLik.html" class="external-link">logLik</a></code> with the number of estimated
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>An object of class <code><a href="https://rdrr.io/r/stats/logLik.html" class="external-link">logLik</a></code> with the number of estimated
 parameters (degradation model parameters plus variance model parameters)
 as attribute.</p>
     </div>
-    <div id="details">
-    <h2>Details</h2>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>The total number of estimated parameters returned with the value of the
 likelihood is calculated as the sum of fitted degradation model parameters
 and the fitted error model parameters.</p>
     </div>
-    <div id="see-also">
-    <h2>See also</h2>
+    <div class="section level2">
+    <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
     <div class="dont-index"><p>Compare the AIC of columns of <code><a href="mmkin.html">mmkin</a></code> objects using
 <code><a href="AIC.mmkin.html">AIC.mmkin</a></code>.</p></div>
     </div>
-    <div id="author">
-    <h2>Author</h2>
+    <div class="section level2">
+    <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a></h2>
     <p>Johannes Ranke</p>
     </div>
 
-    <div id="ref-examples">
-    <h2>Examples</h2>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span>
 <span class="r-in"><span>  <span class="co"># \dontrun{</span></span></span>
 <span class="r-in"><span>  <span class="va">sfo_sfo</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span></span></span>
@@ -181,27 +138,23 @@ and the fitted error model parameters.</p>
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/logLik.saem.mmkin.html b/docs/reference/logLik.saem.mmkin.html
index 92d14aed..8cc985a8 100644
--- a/docs/reference/logLik.saem.mmkin.html
+++ b/docs/reference/logLik.saem.mmkin.html
@@ -1,158 +1,109 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>logLik method for saem.mmkin objects — logLik.saem.mmkin • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="logLik method for saem.mmkin objects — logLik.saem.mmkin"><meta property="og:description" content="logLik method for saem.mmkin objects"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>logLik method for saem.mmkin objects — logLik.saem.mmkin • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="logLik method for saem.mmkin objects — logLik.saem.mmkin"><meta name="description" content="logLik method for saem.mmkin objects"><meta property="og:description" content="logLik method for saem.mmkin objects"></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>logLik method for saem.mmkin objects</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/saem.R" class="external-link"><code>R/saem.R</code></a></small>
-    <div class="hidden name"><code>logLik.saem.mmkin.Rd</code></div>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>logLik method for saem.mmkin objects</h1>
+      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/saem.R" class="external-link"><code>R/saem.R</code></a></small>
+      <div class="d-none name"><code>logLik.saem.mmkin.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>logLik method for saem.mmkin objects</p>
     </div>
 
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for saem.mmkin</span></span>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'saem.mmkin'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/stats/logLik.html" class="external-link">logLik</a></span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span>, method <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"is"</span>, <span class="st">"lin"</span>, <span class="st">"gq"</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
     </div>
 
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>object</dt>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
 <dd><p>The fitted <a href="saem.html">saem.mmkin</a> object</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Passed to <a href="https://rdrr.io/pkg/saemix/man/logLik.html" class="external-link">saemix::logLik.SaemixObject</a></p></dd>
 
 
-<dt>method</dt>
+<dt id="arg-method">method<a class="anchor" aria-label="anchor" href="#arg-method"></a></dt>
 <dd><p>Passed to <a href="https://rdrr.io/pkg/saemix/man/logLik.html" class="external-link">saemix::logLik.SaemixObject</a></p></dd>
 
 </dl></div>
 
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/logistic.solution-1.png b/docs/reference/logistic.solution-1.png
index 73dad0a4..65739bb7 100644
--- a/docs/reference/logistic.solution-1.png
+++ b/docs/reference/logistic.solution-1.png
diff --git a/docs/reference/logistic.solution-2.png b/docs/reference/logistic.solution-2.png
index 8d2514a3..e737621e 100644
--- a/docs/reference/logistic.solution-2.png
+++ b/docs/reference/logistic.solution-2.png
diff --git a/docs/reference/logistic.solution.html b/docs/reference/logistic.solution.html
index 412a981c..f03f0c70 100644
--- a/docs/reference/logistic.solution.html
+++ b/docs/reference/logistic.solution.html
@@ -1,158 +1,112 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Logistic kinetics — logistic.solution • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Logistic kinetics — logistic.solution"><meta property="og:description" content="Function describing exponential decline from a defined starting value, with
-an increasing rate constant, supposedly caused by microbial growth"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Logistic kinetics — logistic.solution • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Logistic kinetics — logistic.solution"><meta name="description" content="Function describing exponential decline from a defined starting value, with
+an increasing rate constant, supposedly caused by microbial growth"><meta property="og:description" content="Function describing exponential decline from a defined starting value, with
+an increasing rate constant, supposedly caused by microbial growth"></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Logistic kinetics</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/parent_solutions.R" class="external-link"><code>R/parent_solutions.R</code></a></small>
-    <div class="hidden name"><code>logistic.solution.Rd</code></div>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Logistic kinetics</h1>
+      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/parent_solutions.R" class="external-link"><code>R/parent_solutions.R</code></a></small>
+      <div class="d-none name"><code>logistic.solution.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Function describing exponential decline from a defined starting value, with
 an increasing rate constant, supposedly caused by microbial growth</p>
     </div>
 
-    <div id="ref-usage">
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">logistic.solution</span><span class="op">(</span><span class="va">t</span>, <span class="va">parent_0</span>, <span class="va">kmax</span>, <span class="va">k0</span>, <span class="va">r</span><span class="op">)</span></span></code></pre></div>
     </div>
 
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>t</dt>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-t">t<a class="anchor" aria-label="anchor" href="#arg-t"></a></dt>
 <dd><p>Time.</p></dd>
 
 
-<dt>parent_0</dt>
+<dt id="arg-parent-">parent_0<a class="anchor" aria-label="anchor" href="#arg-parent-"></a></dt>
 <dd><p>Starting value for the response variable at time zero.</p></dd>
 
 
-<dt>kmax</dt>
+<dt id="arg-kmax">kmax<a class="anchor" aria-label="anchor" href="#arg-kmax"></a></dt>
 <dd><p>Maximum rate constant.</p></dd>
 
 
-<dt>k0</dt>
+<dt id="arg-k-">k0<a class="anchor" aria-label="anchor" href="#arg-k-"></a></dt>
 <dd><p>Minimum rate constant effective at time zero.</p></dd>
 
 
-<dt>r</dt>
+<dt id="arg-r">r<a class="anchor" aria-label="anchor" href="#arg-r"></a></dt>
 <dd><p>Growth rate of the increase in the rate constant.</p></dd>
 
 </dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>The value of the response variable at time <code>t</code>.</p>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>The value of the response variable at time <code>t</code>.</p>
     </div>
-    <div id="note">
-    <h2>Note</h2>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>The solution of the logistic model reduces to the
 <code><a href="SFO.solution.html">SFO.solution</a></code> if <code>k0</code> is equal to <code>kmax</code>.</p>
     </div>
-    <div id="references">
-    <h2>References</h2>
+    <div class="section level2">
+    <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
     <p>FOCUS (2006) “Guidance Document on Estimating Persistence
 and Degradation Kinetics from Environmental Fate Studies on Pesticides in
 EU Registration” Report of the FOCUS Work Group on Degradation Kinetics,
@@ -164,9 +118,9 @@ EU Registration” Report of the FOCUS Work Group on Degradation Kinetics,
 Version 1.1, 18 December 2014
 <a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics" class="external-link">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p>
     </div>
-    <div id="see-also">
-    <h2>See also</h2>
-    <div class="dont-index"><p>Other parent solutions: 
+    <div class="section level2">
+    <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
+    <div class="dont-index"><p>Other parent solutions:
 <code><a href="DFOP.solution.html">DFOP.solution</a>()</code>,
 <code><a href="FOMC.solution.html">FOMC.solution</a>()</code>,
 <code><a href="HS.solution.html">HS.solution</a>()</code>,
@@ -175,8 +129,8 @@ Version 1.1, 18 December 2014
 <code><a href="SFORB.solution.html">SFORB.solution</a>()</code></p></div>
     </div>
 
-    <div id="ref-examples">
-    <h2>Examples</h2>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span>
 <span class="r-in"><span>  <span class="co"># Reproduce the plot on page 57 of FOCUS (2014)</span></span></span>
 <span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fu">logistic.solution</span><span class="op">(</span><span class="va">x</span>, <span class="fl">100</span>, <span class="fl">0.08</span>, <span class="fl">0.0001</span>, <span class="fl">0.2</span><span class="op">)</span>,</span></span>
@@ -230,27 +184,23 @@ Version 1.1, 18 December 2014
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/lrtest.html b/docs/reference/lrtest.html
new file mode 100644
index 00000000..ef2e29e9
--- /dev/null
+++ b/docs/reference/lrtest.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/reexports.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/reexports.html">
+  </head>
+</html>
+
diff --git a/docs/reference/lrtest.mkinfit.html b/docs/reference/lrtest.mkinfit.html
index 45466262..17ed11c6 100644
--- a/docs/reference/lrtest.mkinfit.html
+++ b/docs/reference/lrtest.mkinfit.html
@@ -1,118 +1,74 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Likelihood ratio test for mkinfit models — lrtest.mkinfit • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Likelihood ratio test for mkinfit models — lrtest.mkinfit"><meta property="og:description" content="Compare two mkinfit models based on their likelihood. If two fitted
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Likelihood ratio test for mkinfit models — lrtest.mkinfit • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Likelihood ratio test for mkinfit models — lrtest.mkinfit"><meta name="description" content="Compare two mkinfit models based on their likelihood. If two fitted
 mkinfit objects are given as arguments, it is checked if they have been
 fitted to the same data. It is the responsibility of the user to make sure
 that the models are nested, i.e. one of them has less degrees of freedom
-and can be expressed by fixing the parameters of the other."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+and can be expressed by fixing the parameters of the other."><meta property="og:description" content="Compare two mkinfit models based on their likelihood. If two fitted
+mkinfit objects are given as arguments, it is checked if they have been
+fitted to the same data. It is the responsibility of the user to make sure
+that the models are nested, i.e. one of them has less degrees of freedom
+and can be expressed by fixing the parameters of the other."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Likelihood ratio test for mkinfit models</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/lrtest.mkinfit.R" class="external-link"><code>R/lrtest.mkinfit.R</code></a></small>
-    <div class="hidden name"><code>lrtest.mkinfit.Rd</code></div>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Likelihood ratio test for mkinfit models</h1>
+      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/lrtest.mkinfit.R" class="external-link"><code>R/lrtest.mkinfit.R</code></a></small>
+      <div class="d-none name"><code>lrtest.mkinfit.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Compare two mkinfit models based on their likelihood. If two fitted
 mkinfit objects are given as arguments, it is checked if they have been
 fitted to the same data. It is the responsibility of the user to make sure
@@ -120,32 +76,35 @@ that the models are nested, i.e. one of them has less degrees of freedom
 and can be expressed by fixing the parameters of the other.</p>
     </div>
 
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for mkinfit</span></span>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'mkinfit'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/pkg/lmtest/man/lrtest.html" class="external-link">lrtest</a></span><span class="op">(</span><span class="va">object</span>, object_2 <span class="op">=</span> <span class="cn">NULL</span>, <span class="va">...</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for mmkin</span></span>
+<span><span class="co"># S3 method for class 'mmkin'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/pkg/lmtest/man/lrtest.html" class="external-link">lrtest</a></span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>object</dt>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
 <dd><p>An <code><a href="mkinfit.html">mkinfit</a></code> object, or an <code><a href="mmkin.html">mmkin</a></code> column
 object containing two fits to the same data.</p></dd>
 
 
-<dt>object_2</dt>
+<dt id="arg-object-">object_2<a class="anchor" aria-label="anchor" href="#arg-object-"></a></dt>
 <dd><p>Optionally, another mkinfit object fitted to the same data.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Argument to <code><a href="mkinfit.html">mkinfit</a></code>, passed to
 <code><a href="update.mkinfit.html">update.mkinfit</a></code> for creating the alternative fitted object.</p></dd>
 
 </dl></div>
-    <div id="details">
-    <h2>Details</h2>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>Alternatively, an argument to mkinfit can be given which is then passed
 to <code><a href="update.mkinfit.html">update.mkinfit</a></code> to obtain the alternative model.</p>
 <p>The comparison is then made by the <code><a href="https://rdrr.io/pkg/lmtest/man/lrtest.html" class="external-link">lrtest.default</a></code>
@@ -154,8 +113,8 @@ parameters (alternative hypothesis) is listed first, then the model with the
 lower number of fitted parameters (null hypothesis).</p>
     </div>
 
-    <div id="ref-examples">
-    <h2>Examples</h2>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># \dontrun{</span></span></span>
 <span class="r-in"><span><span class="va">test_data</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/subset.html" class="external-link">subset</a></span><span class="op">(</span><span class="va">synthetic_data_for_UBA_2014</span><span class="op">[[</span><span class="fl">12</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">data</span>, <span class="va">name</span> <span class="op">==</span> <span class="st">"parent"</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">sfo_fit</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="va">test_data</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
@@ -210,27 +169,23 @@ lower number of fitted parameters (null hypothesis).</p>
 <span class="r-in"><span><span class="co"># }</span></span></span>
 </code></pre></div>
     </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/lrtest.mmkin.html b/docs/reference/lrtest.mmkin.html
new file mode 100644
index 00000000..132a24ed
--- /dev/null
+++ b/docs/reference/lrtest.mmkin.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/lrtest.mkinfit.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/lrtest.mkinfit.html">
+  </head>
+</html>
+
diff --git a/docs/reference/max_twa_dfop.html b/docs/reference/max_twa_dfop.html
new file mode 100644
index 00000000..2b5bae2f
--- /dev/null
+++ b/docs/reference/max_twa_dfop.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/max_twa_parent.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/max_twa_parent.html">
+  </head>
+</html>
+
diff --git a/docs/reference/max_twa_fomc.html b/docs/reference/max_twa_fomc.html
new file mode 100644
index 00000000..2b5bae2f
--- /dev/null
+++ b/docs/reference/max_twa_fomc.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/max_twa_parent.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/max_twa_parent.html">
+  </head>
+</html>
+
diff --git a/docs/reference/max_twa_hs.html b/docs/reference/max_twa_hs.html
new file mode 100644
index 00000000..2b5bae2f
--- /dev/null
+++ b/docs/reference/max_twa_hs.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/max_twa_parent.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/max_twa_parent.html">
+  </head>
+</html>
+
diff --git a/docs/reference/max_twa_parent.html b/docs/reference/max_twa_parent.html
index 892aa4e1..453f801a 100644
--- a/docs/reference/max_twa_parent.html
+++ b/docs/reference/max_twa_parent.html
@@ -1,121 +1,74 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Function to calculate maximum time weighted average concentrations from
-kinetic models fitted with mkinfit — max_twa_parent • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Function to calculate maximum time weighted average concentrations from
-kinetic models fitted with mkinfit — max_twa_parent"><meta property="og:description" content="This function calculates maximum moving window time weighted average
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Function to calculate maximum time weighted average concentrations from kinetic models fitted with mkinfit — max_twa_parent • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Function to calculate maximum time weighted average concentrations from kinetic models fitted with mkinfit — max_twa_parent"><meta name="description" content="This function calculates maximum moving window time weighted average
 concentrations (TWAs) for kinetic models fitted with mkinfit.
 Currently, only calculations for the parent are implemented for the SFO,
 FOMC, DFOP and HS models, using the analytical formulas given in the PEC
-soil section of the FOCUS guidance."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+soil section of the FOCUS guidance."><meta property="og:description" content="This function calculates maximum moving window time weighted average
+concentrations (TWAs) for kinetic models fitted with mkinfit.
+Currently, only calculations for the parent are implemented for the SFO,
+FOMC, DFOP and HS models, using the analytical formulas given in the PEC
+soil section of the FOCUS guidance."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Function to calculate maximum time weighted average concentrations from
-kinetic models fitted with mkinfit</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/max_twa_parent.R" class="external-link"><code>R/max_twa_parent.R</code></a></small>
-    <div class="hidden name"><code>max_twa_parent.Rd</code></div>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Function to calculate maximum time weighted average concentrations from kinetic models fitted with mkinfit</h1>
+      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/max_twa_parent.R" class="external-link"><code>R/max_twa_parent.R</code></a></small>
+      <div class="d-none name"><code>max_twa_parent.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>This function calculates maximum moving window time weighted average
 concentrations (TWAs) for kinetic models fitted with <code><a href="mkinfit.html">mkinfit</a></code>.
 Currently, only calculations for the parent are implemented for the SFO,
@@ -123,7 +76,8 @@ FOMC, DFOP and HS models, using the analytical formulas given in the PEC
 soil section of the FOCUS guidance.</p>
     </div>
 
-    <div id="ref-usage">
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">max_twa_parent</span><span class="op">(</span><span class="va">fit</span>, <span class="va">windows</span><span class="op">)</span></span>
 <span></span>
 <span><span class="fu">max_twa_sfo</span><span class="op">(</span>M0 <span class="op">=</span> <span class="fl">1</span>, <span class="va">k</span>, <span class="va">t</span><span class="op">)</span></span>
@@ -135,79 +89,79 @@ soil section of the FOCUS guidance.</p>
 <span><span class="fu">max_twa_hs</span><span class="op">(</span>M0 <span class="op">=</span> <span class="fl">1</span>, <span class="va">k1</span>, <span class="va">k2</span>, <span class="va">tb</span>, <span class="va">t</span><span class="op">)</span></span></code></pre></div>
     </div>
 
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>fit</dt>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-fit">fit<a class="anchor" aria-label="anchor" href="#arg-fit"></a></dt>
 <dd><p>An object of class <code><a href="mkinfit.html">mkinfit</a></code>.</p></dd>
 
 
-<dt>windows</dt>
+<dt id="arg-windows">windows<a class="anchor" aria-label="anchor" href="#arg-windows"></a></dt>
 <dd><p>The width of the time windows for which the TWAs should be
 calculated.</p></dd>
 
 
-<dt>M0</dt>
+<dt id="arg-m-">M0<a class="anchor" aria-label="anchor" href="#arg-m-"></a></dt>
 <dd><p>The initial concentration for which the maximum time weighted
 average over the decline curve should be calculated. The default is to use
 a value of 1, which means that a relative maximum time weighted average
 factor (f_twa) is calculated.</p></dd>
 
 
-<dt>k</dt>
+<dt id="arg-k">k<a class="anchor" aria-label="anchor" href="#arg-k"></a></dt>
 <dd><p>The rate constant in the case of SFO kinetics.</p></dd>
 
 
-<dt>t</dt>
+<dt id="arg-t">t<a class="anchor" aria-label="anchor" href="#arg-t"></a></dt>
 <dd><p>The width of the time window.</p></dd>
 
 
-<dt>alpha</dt>
+<dt id="arg-alpha">alpha<a class="anchor" aria-label="anchor" href="#arg-alpha"></a></dt>
 <dd><p>Parameter of the FOMC model.</p></dd>
 
 
-<dt>beta</dt>
+<dt id="arg-beta">beta<a class="anchor" aria-label="anchor" href="#arg-beta"></a></dt>
 <dd><p>Parameter of the FOMC model.</p></dd>
 
 
-<dt>k1</dt>
+<dt id="arg-k-">k1<a class="anchor" aria-label="anchor" href="#arg-k-"></a></dt>
 <dd><p>The first rate constant of the DFOP or the HS kinetics.</p></dd>
 
 
-<dt>k2</dt>
+<dt id="arg-k-">k2<a class="anchor" aria-label="anchor" href="#arg-k-"></a></dt>
 <dd><p>The second rate constant of the DFOP or the HS kinetics.</p></dd>
 
 
-<dt>g</dt>
+<dt id="arg-g">g<a class="anchor" aria-label="anchor" href="#arg-g"></a></dt>
 <dd><p>Parameter of the DFOP model.</p></dd>
 
 
-<dt>tb</dt>
+<dt id="arg-tb">tb<a class="anchor" aria-label="anchor" href="#arg-tb"></a></dt>
 <dd><p>Parameter of the HS model.</p></dd>
 
 </dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>For <code>max_twa_parent</code>, a numeric vector, named using the
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>For <code>max_twa_parent</code>, a numeric vector, named using the
 <code>windows</code> argument.  For the other functions, a numeric vector of
 length one (also known as 'a number').</p>
     </div>
-    <div id="references">
-    <h2>References</h2>
+    <div class="section level2">
+    <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
     <p>FOCUS (2006) “Guidance Document on Estimating Persistence
 and Degradation Kinetics from Environmental Fate Studies on Pesticides in
 EU Registration” Report of the FOCUS Work Group on Degradation Kinetics,
 EC Document Reference Sanco/10058/2005 version 2.0, 434 pp,
 <a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics" class="external-link">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p>
     </div>
-    <div id="author">
-    <h2>Author</h2>
+    <div class="section level2">
+    <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a></h2>
     <p>Johannes Ranke</p>
     </div>
 
-    <div id="ref-examples">
-    <h2>Examples</h2>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span>
 <span class="r-in"><span>  <span class="va">fit</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"FOMC"</span>, <span class="va">FOCUS_2006_C</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
 <span class="r-in"><span>  <span class="fu">max_twa_parent</span><span class="op">(</span><span class="va">fit</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">7</span>, <span class="fl">21</span><span class="op">)</span><span class="op">)</span></span></span>
@@ -216,27 +170,23 @@ EC Document Reference Sanco/10058/2005 version 2.0, 434 pp,
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/max_twa_sfo.html b/docs/reference/max_twa_sfo.html
new file mode 100644
index 00000000..2b5bae2f
--- /dev/null
+++ b/docs/reference/max_twa_sfo.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/max_twa_parent.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/max_twa_parent.html">
+  </head>
+</html>
+
diff --git a/docs/reference/mccall81_245T-1.png b/docs/reference/mccall81_245T-1.png
index 79c45fe6..3bb35cbd 100644
--- a/docs/reference/mccall81_245T-1.png
+++ b/docs/reference/mccall81_245T-1.png
diff --git a/docs/reference/mccall81_245T.html b/docs/reference/mccall81_245T.html
index 6b057c9d..bb0e36cf 100644
--- a/docs/reference/mccall81_245T.html
+++ b/docs/reference/mccall81_245T.html
@@ -1,127 +1,82 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Datasets on aerobic soil metabolism of 2,4,5-T in six soils — mccall81_245T • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Datasets on aerobic soil metabolism of 2,4,5-T in six soils — mccall81_245T"><meta property="og:description" content="Time course of 2,4,5-trichlorophenoxyacetic acid, and the corresponding
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Datasets on aerobic soil metabolism of 2,4,5-T in six soils — mccall81_245T • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Datasets on aerobic soil metabolism of 2,4,5-T in six soils — mccall81_245T"><meta name="description" content="Time course of 2,4,5-trichlorophenoxyacetic acid, and the corresponding
  2,4,5-trichlorophenol and 2,4,5-trichloroanisole as recovered in diethylether
- extracts."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+ extracts."><meta property="og:description" content="Time course of 2,4,5-trichlorophenoxyacetic acid, and the corresponding
+ 2,4,5-trichlorophenol and 2,4,5-trichloroanisole as recovered in diethylether
+ extracts."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Datasets on aerobic soil metabolism of 2,4,5-T in six soils</h1>
-    
-    <div class="hidden name"><code>mccall81_245T.Rd</code></div>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Datasets on aerobic soil metabolism of 2,4,5-T in six soils</h1>
+
+      <div class="d-none name"><code>mccall81_245T.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Time course of 2,4,5-trichlorophenoxyacetic acid, and the corresponding
  2,4,5-trichlorophenol and 2,4,5-trichloroanisole as recovered in diethylether
  extracts.</p>
     </div>
 
-    <div id="ref-usage">
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="va">mccall81_245T</span></span></code></pre></div>
     </div>
 
-    <div id="format">
-    <h2>Format</h2>
+    <div class="section level2">
+    <h2 id="format">Format<a class="anchor" aria-label="anchor" href="#format"></a></h2>
     <p>A dataframe containing the following variables.</p><dl><dt><code>name</code></dt>
 <dd><p>the name of the compound observed. Note that T245 is used as
                        an acronym for 2,4,5-T. T245 is a legitimate object name
@@ -138,16 +93,16 @@
     <dt><code>soil</code></dt>
 <dd><p>a factor containing the name of the soil</p></dd>
 
-  
+
 </dl></div>
-    <div id="source">
-    <h2>Source</h2>
+    <div class="section level2">
+    <h2 id="source">Source<a class="anchor" aria-label="anchor" href="#source"></a></h2>
     <p>McCall P, Vrona SA, Kelley SS (1981) Fate of uniformly carbon-14 ring labelled 2,4,5-Trichlorophenoxyacetic acid and 2,4-dichlorophenoxyacetic acid. J Agric Chem 29, 100-107
   <a href="https://doi.org/10.1021/jf00103a026" class="external-link">doi:10.1021/jf00103a026</a></p>
     </div>
 
-    <div id="ref-examples">
-    <h2>Examples</h2>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span>  <span class="va">SFO_SFO_SFO</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>T245 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"phenol"</span><span class="op">)</span>,</span></span>
 <span class="r-in"><span>    phenol <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"anisole"</span><span class="op">)</span>,</span></span>
 <span class="r-in"><span>    anisole <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span></span>
@@ -224,27 +179,23 @@
 <span class="r-in"><span>  <span class="co"># }</span></span></span>
 </code></pre></div>
     </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/mean_degparms.html b/docs/reference/mean_degparms.html
index 6e17045a..e9c78767 100644
--- a/docs/reference/mean_degparms.html
+++ b/docs/reference/mean_degparms.html
@@ -1,118 +1,71 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Calculate mean degradation parameters for an mmkin row object — mean_degparms • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Calculate mean degradation parameters for an mmkin row object — mean_degparms"><meta property="og:description" content="Calculate mean degradation parameters for an mmkin row object"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Calculate mean degradation parameters for an mmkin row object — mean_degparms • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Calculate mean degradation parameters for an mmkin row object — mean_degparms"><meta name="description" content="Calculate mean degradation parameters for an mmkin row object"><meta property="og:description" content="Calculate mean degradation parameters for an mmkin row object"></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Calculate mean degradation parameters for an mmkin row object</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/mean_degparms.R" class="external-link"><code>R/mean_degparms.R</code></a></small>
-    <div class="hidden name"><code>mean_degparms.Rd</code></div>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Calculate mean degradation parameters for an mmkin row object</h1>
+      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/mean_degparms.R" class="external-link"><code>R/mean_degparms.R</code></a></small>
+      <div class="d-none name"><code>mean_degparms.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Calculate mean degradation parameters for an mmkin row object</p>
     </div>
 
-    <div id="ref-usage">
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">mean_degparms</span><span class="op">(</span></span>
 <span>  <span class="va">object</span>,</span>
 <span>  random <span class="op">=</span> <span class="cn">FALSE</span>,</span>
@@ -122,64 +75,60 @@
 <span><span class="op">)</span></span></code></pre></div>
     </div>
 
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>object</dt>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
 <dd><p>An mmkin row object containing several fits of the same model to different datasets</p></dd>
 
 
-<dt>random</dt>
+<dt id="arg-random">random<a class="anchor" aria-label="anchor" href="#arg-random"></a></dt>
 <dd><p>Should a list with fixed and random effects be returned?</p></dd>
 
 
-<dt>test_log_parms</dt>
+<dt id="arg-test-log-parms">test_log_parms<a class="anchor" aria-label="anchor" href="#arg-test-log-parms"></a></dt>
 <dd><p>If TRUE, log parameters are only considered in
 the mean calculations if their untransformed counterparts (most likely
 rate constants) pass the t-test for significant difference from zero.</p></dd>
 
 
-<dt>conf.level</dt>
+<dt id="arg-conf-level">conf.level<a class="anchor" aria-label="anchor" href="#arg-conf-level"></a></dt>
 <dd><p>Possibility to adjust the required confidence level
 for parameter that are tested if requested by 'test_log_parms'.</p></dd>
 
 
-<dt>default_log_parms</dt>
+<dt id="arg-default-log-parms">default_log_parms<a class="anchor" aria-label="anchor" href="#arg-default-log-parms"></a></dt>
 <dd><p>If set to a numeric value, this is used
 as a default value for the tested log parameters that failed the
 t-test.</p></dd>
 
 </dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>If random is FALSE (default), a named vector containing mean values
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>If random is FALSE (default), a named vector containing mean values
 of the fitted degradation model parameters. If random is TRUE, a list with
 fixed and random effects, in the format required by the start argument of
 nlme for the case of a single grouping variable ds.</p>
     </div>
 
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/mhmkin-1.png b/docs/reference/mhmkin-1.png
index 2ecb6759..1c99aead 100644
--- a/docs/reference/mhmkin-1.png
+++ b/docs/reference/mhmkin-1.png
diff --git a/docs/reference/mhmkin-2.png b/docs/reference/mhmkin-2.png
index 70cd7723..7311206d 100644
--- a/docs/reference/mhmkin-2.png
+++ b/docs/reference/mhmkin-2.png
diff --git a/docs/reference/mhmkin.html b/docs/reference/mhmkin.html
index 7fcb98fb..26bfd34b 100644
--- a/docs/reference/mhmkin.html
+++ b/docs/reference/mhmkin.html
@@ -1,174 +1,129 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Fit nonlinear mixed-effects models built from one or more kinetic
-degradation models and one or more error models — mhmkin • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Fit nonlinear mixed-effects models built from one or more kinetic
-degradation models and one or more error models — mhmkin"><meta property="og:description" content="The name of the methods expresses that (multiple) hierarchichal
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Fit nonlinear mixed-effects models built from one or more kinetic degradation models and one or more error models — mhmkin • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Fit nonlinear mixed-effects models built from one or more kinetic degradation models and one or more error models — mhmkin"><meta name="description" content="The name of the methods expresses that (multiple) hierarchichal
 (also known as multilevel) multicompartment kinetic models are
 fitted. Our kinetic models are nonlinear, so we can use various nonlinear
-mixed-effects model fitting functions."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+mixed-effects model fitting functions."><meta property="og:description" content="The name of the methods expresses that (multiple) hierarchichal
+(also known as multilevel) multicompartment kinetic models are
+fitted. Our kinetic models are nonlinear, so we can use various nonlinear
+mixed-effects model fitting functions."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Fit nonlinear mixed-effects models built from one or more kinetic
-degradation models and one or more error models</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/mhmkin.R" class="external-link"><code>R/mhmkin.R</code></a></small>
-    <div class="hidden name"><code>mhmkin.Rd</code></div>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Fit nonlinear mixed-effects models built from one or more kinetic degradation models and one or more error models</h1>
+      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/mhmkin.R" class="external-link"><code>R/mhmkin.R</code></a></small>
+      <div class="d-none name"><code>mhmkin.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>The name of the methods expresses that (<strong>m</strong>ultiple) <strong>h</strong>ierarchichal
 (also known as multilevel) <strong>m</strong>ulticompartment <strong>kin</strong>etic models are
 fitted. Our kinetic models are nonlinear, so we can use various nonlinear
 mixed-effects model fitting functions.</p>
     </div>
 
-    <div id="ref-usage">
-    <div class="sourceCode"><pre><code>mhmkin(objects, ...)
-
-# S3 method for mmkin
-mhmkin(objects, ...)
-
-# S3 method for list
-mhmkin(
-  objects,
-  backend = "saemix",
-  algorithm = "saem",
-  no_random_effect = NULL,
-  ...,
-  cores = if (Sys.info()["sysname"] == "Windows") 1 else parallel::detectCores(),
-  cluster = NULL
-)
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">mhmkin</span><span class="op">(</span><span class="va">objects</span>, <span class="va">...</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># S3 method for class 'mmkin'</span></span>
+<span><span class="fu">mhmkin</span><span class="op">(</span><span class="va">objects</span>, <span class="va">...</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># S3 method for class 'list'</span></span>
+<span><span class="fu">mhmkin</span><span class="op">(</span></span>
+<span>  <span class="va">objects</span>,</span>
+<span>  backend <span class="op">=</span> <span class="st">"saemix"</span>,</span>
+<span>  algorithm <span class="op">=</span> <span class="st">"saem"</span>,</span>
+<span>  no_random_effect <span class="op">=</span> <span class="cn">NULL</span>,</span>
+<span>  <span class="va">...</span>,</span>
+<span>  cores <span class="op">=</span> <span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/Sys.info.html" class="external-link">Sys.info</a></span><span class="op">(</span><span class="op">)</span><span class="op">[</span><span class="st">"sysname"</span><span class="op">]</span> <span class="op">==</span> <span class="st">"Windows"</span><span class="op">)</span> <span class="fl">1</span> <span class="kw">else</span> <span class="fu">parallel</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/r/parallel/detectCores.html" class="external-link">detectCores</a></span><span class="op">(</span><span class="op">)</span>,</span>
+<span>  cluster <span class="op">=</span> <span class="cn">NULL</span></span>
+<span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># S3 method for class 'mhmkin'</span></span>
+<span><span class="va">x</span><span class="op">[</span><span class="va">i</span>, <span class="va">j</span>, <span class="va">...</span>, drop <span class="op">=</span> <span class="cn">FALSE</span><span class="op">]</span></span>
+<span></span>
+<span><span class="co"># S3 method for class 'mhmkin'</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
+    </div>
 
-# S3 method for mhmkin
-[(x, i, j, ..., drop = FALSE)
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
 
-# S3 method for mhmkin
-print(x, ...)</code></pre></div>
-    </div>
 
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>objects</dt>
+<dl><dt id="arg-objects">objects<a class="anchor" aria-label="anchor" href="#arg-objects"></a></dt>
 <dd><p>A list of <a href="mmkin.html">mmkin</a> objects containing fits of the same
 degradation models to the same data, but using different error models.
 Alternatively, a single <a href="mmkin.html">mmkin</a> object containing fits of several
 degradation models to the same data</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Further arguments that will be passed to the nonlinear mixed-effects
 model fitting function.</p></dd>
 
 
-<dt>backend</dt>
+<dt id="arg-backend">backend<a class="anchor" aria-label="anchor" href="#arg-backend"></a></dt>
 <dd><p>The backend to be used for fitting. Currently, only saemix is
 supported</p></dd>
 
 
-<dt>algorithm</dt>
+<dt id="arg-algorithm">algorithm<a class="anchor" aria-label="anchor" href="#arg-algorithm"></a></dt>
 <dd><p>The algorithm to be used for fitting (currently not used)</p></dd>
 
 
-<dt>no_random_effect</dt>
+<dt id="arg-no-random-effect">no_random_effect<a class="anchor" aria-label="anchor" href="#arg-no-random-effect"></a></dt>
 <dd><p>Default is NULL and will be passed to <a href="saem.html">saem</a>. If a
 character vector is supplied, it will be passed to all calls to <a href="saem.html">saem</a>,
 which will exclude random effects for all matching parameters. Alternatively,
@@ -179,7 +134,7 @@ of degradation models in the mmkin object(s), and the number of columns must
 match the number of error models used in the mmkin object(s).</p></dd>
 
 
-<dt>cores</dt>
+<dt id="arg-cores">cores<a class="anchor" aria-label="anchor" href="#arg-cores"></a></dt>
 <dd><p>The number of cores to be used for multicore processing. This
 is only used when the <code>cluster</code> argument is <code>NULL</code>. On Windows
 machines, cores &gt; 1 is not supported, you need to use the <code>cluster</code>
@@ -188,51 +143,47 @@ by <code><a href="https://rdrr.io/r/parallel/detectCores.html" class="external-l
 is 1.</p></dd>
 
 
-<dt>cluster</dt>
+<dt id="arg-cluster">cluster<a class="anchor" aria-label="anchor" href="#arg-cluster"></a></dt>
 <dd><p>A cluster as returned by makeCluster to be used for
 parallel execution.</p></dd>
 
 
-<dt>x</dt>
+<dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>An mhmkin object.</p></dd>
 
 
-<dt>i</dt>
+<dt id="arg-i">i<a class="anchor" aria-label="anchor" href="#arg-i"></a></dt>
 <dd><p>Row index selecting the fits for specific models</p></dd>
 
 
-<dt>j</dt>
+<dt id="arg-j">j<a class="anchor" aria-label="anchor" href="#arg-j"></a></dt>
 <dd><p>Column index selecting the fits to specific datasets</p></dd>
 
 
-<dt>drop</dt>
+<dt id="arg-drop">drop<a class="anchor" aria-label="anchor" href="#arg-drop"></a></dt>
 <dd><p>If FALSE, the method always returns an mhmkin object, otherwise
 either a list of fit objects or a single fit object.</p></dd>
 
 </dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>A two-dimensional <a href="https://rdrr.io/r/base/array.html" class="external-link">array</a> of fit objects and/or try-errors that can
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>A two-dimensional <a href="https://rdrr.io/r/base/array.html" class="external-link">array</a> of fit objects and/or try-errors that can
 be indexed using the degradation model names for the first index (row index)
 and the error model names for the second index (column index), with class
 attribute 'mhmkin'.</p>
-
-
 <p>An object inheriting from <code>mhmkin</code>.</p>
     </div>
-    <div id="see-also">
-    <h2>See also</h2>
+    <div class="section level2">
+    <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
     <div class="dont-index"><p><code>[.mhmkin</code> for subsetting mhmkin objects</p></div>
     </div>
-    <div id="author">
-    <h2>Author</h2>
+    <div class="section level2">
+    <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a></h2>
     <p>Johannes Ranke</p>
     </div>
 
-    <div id="ref-examples">
-    <h2>Examples</h2>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># \dontrun{</span></span></span>
 <span class="r-in"><span><span class="co"># We start with separate evaluations of all the first six datasets with two</span></span></span>
 <span class="r-in"><span><span class="co"># degradation models and two error models</span></span></span>
@@ -313,27 +264,23 @@ attribute 'mhmkin'.</p>
 <span class="r-in"><span><span class="co"># }</span></span></span>
 </code></pre></div>
     </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/mhmkin.list.html b/docs/reference/mhmkin.list.html
new file mode 100644
index 00000000..4be1ad9e
--- /dev/null
+++ b/docs/reference/mhmkin.list.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/mhmkin.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/mhmkin.html">
+  </head>
+</html>
+
diff --git a/docs/reference/mhmkin.mmkin.html b/docs/reference/mhmkin.mmkin.html
new file mode 100644
index 00000000..4be1ad9e
--- /dev/null
+++ b/docs/reference/mhmkin.mmkin.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/mhmkin.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/mhmkin.html">
+  </head>
+</html>
+
diff --git a/docs/reference/mixed-1.png b/docs/reference/mixed-1.png
index 3992b681..d46a2485 100644
--- a/docs/reference/mixed-1.png
+++ b/docs/reference/mixed-1.png
diff --git a/docs/reference/mixed.html b/docs/reference/mixed.html
index b0cce30b..9bdd620d 100644
--- a/docs/reference/mixed.html
+++ b/docs/reference/mixed.html
@@ -1,159 +1,112 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Create a mixed effects model from an mmkin row object — mixed • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Create a mixed effects model from an mmkin row object — mixed"><meta property="og:description" content="Create a mixed effects model from an mmkin row object"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Create a mixed effects model from an mmkin row object — mixed • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Create a mixed effects model from an mmkin row object — mixed"><meta name="description" content="Create a mixed effects model from an mmkin row object"><meta property="og:description" content="Create a mixed effects model from an mmkin row object"></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Create a mixed effects model from an mmkin row object</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/mixed.mmkin.R" class="external-link"><code>R/mixed.mmkin.R</code></a></small>
-    <div class="hidden name"><code>mixed.Rd</code></div>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Create a mixed effects model from an mmkin row object</h1>
+      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/mixed.mmkin.R" class="external-link"><code>R/mixed.mmkin.R</code></a></small>
+      <div class="d-none name"><code>mixed.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Create a mixed effects model from an mmkin row object</p>
     </div>
 
-    <div id="ref-usage">
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">mixed</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for mmkin</span></span>
+<span><span class="co"># S3 method for class 'mmkin'</span></span>
 <span><span class="fu">mixed</span><span class="op">(</span><span class="va">object</span>, method <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"none"</span><span class="op">)</span>, <span class="va">...</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for mixed.mmkin</span></span>
+<span><span class="co"># S3 method for class 'mixed.mmkin'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, digits <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html" class="external-link">max</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fu"><a href="https://rdrr.io/r/base/options.html" class="external-link">getOption</a></span><span class="op">(</span><span class="st">"digits"</span><span class="op">)</span> <span class="op">-</span> <span class="fl">3</span><span class="op">)</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>object</dt>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
 <dd><p>An <a href="mmkin.html">mmkin</a> row object</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Currently not used</p></dd>
 
 
-<dt>method</dt>
+<dt id="arg-method">method<a class="anchor" aria-label="anchor" href="#arg-method"></a></dt>
 <dd><p>The method to be used</p></dd>
 
 
-<dt>x</dt>
+<dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>A mixed.mmkin object to print</p></dd>
 
 
-<dt>digits</dt>
+<dt id="arg-digits">digits<a class="anchor" aria-label="anchor" href="#arg-digits"></a></dt>
 <dd><p>Number of digits to use for printing.</p></dd>
 
 </dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>An object of class 'mixed.mmkin' which has the observed data in a
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>An object of class 'mixed.mmkin' which has the observed data in a
 single dataframe which is convenient for plotting</p>
     </div>
 
-    <div id="ref-examples">
-    <h2>Examples</h2>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">sampling_times</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span>, <span class="fl">3</span>, <span class="fl">7</span>, <span class="fl">14</span>, <span class="fl">28</span>, <span class="fl">60</span>, <span class="fl">90</span>, <span class="fl">120</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">n_biphasic</span> <span class="op">&lt;-</span> <span class="fl">8</span></span></span>
 <span class="r-in"><span><span class="va">err_1</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>const <span class="op">=</span> <span class="fl">1</span>, prop <span class="op">=</span> <span class="fl">0.07</span><span class="op">)</span></span></span>
@@ -224,27 +177,23 @@ single dataframe which is convenient for plotting</p>
 <span class="r-in"><span><span class="co"># }</span></span></span>
 </code></pre></div>
     </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/mixed.mmkin.html b/docs/reference/mixed.mmkin.html
new file mode 100644
index 00000000..6a5f7aef
--- /dev/null
+++ b/docs/reference/mixed.mmkin.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/mixed.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/mixed.html">
+  </head>
+</html>
+
diff --git a/docs/reference/mkin_long_to_wide.html b/docs/reference/mkin_long_to_wide.html
index 9e1487bd..4bdbbc64 100644
--- a/docs/reference/mkin_long_to_wide.html
+++ b/docs/reference/mkin_long_to_wide.html
@@ -1,155 +1,110 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Convert a dataframe from long to wide format — mkin_long_to_wide • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Convert a dataframe from long to wide format — mkin_long_to_wide"><meta property="og:description" content="This function takes a dataframe in the long form, i.e. with a row for each
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Convert a dataframe from long to wide format — mkin_long_to_wide • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Convert a dataframe from long to wide format — mkin_long_to_wide"><meta name="description" content="This function takes a dataframe in the long form, i.e. with a row for each
 observed value, and converts it into a dataframe with one independent
-variable and several dependent variables as columns."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+variable and several dependent variables as columns."><meta property="og:description" content="This function takes a dataframe in the long form, i.e. with a row for each
+observed value, and converts it into a dataframe with one independent
+variable and several dependent variables as columns."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Convert a dataframe from long to wide format</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/mkin_long_to_wide.R" class="external-link"><code>R/mkin_long_to_wide.R</code></a></small>
-    <div class="hidden name"><code>mkin_long_to_wide.Rd</code></div>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Convert a dataframe from long to wide format</h1>
+      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/mkin_long_to_wide.R" class="external-link"><code>R/mkin_long_to_wide.R</code></a></small>
+      <div class="d-none name"><code>mkin_long_to_wide.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>This function takes a dataframe in the long form, i.e. with a row for each
 observed value, and converts it into a dataframe with one independent
 variable and several dependent variables as columns.</p>
     </div>
 
-    <div id="ref-usage">
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">mkin_long_to_wide</span><span class="op">(</span><span class="va">long_data</span>, time <span class="op">=</span> <span class="st">"time"</span>, outtime <span class="op">=</span> <span class="st">"time"</span><span class="op">)</span></span></code></pre></div>
     </div>
 
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>long_data</dt>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-long-data">long_data<a class="anchor" aria-label="anchor" href="#arg-long-data"></a></dt>
 <dd><p>The dataframe must contain one variable called "time" with
 the time values specified by the <code>time</code> argument, one column called
 "name" with the grouping of the observed values, and finally one column of
 observed values called "value".</p></dd>
 
 
-<dt>time</dt>
+<dt id="arg-time">time<a class="anchor" aria-label="anchor" href="#arg-time"></a></dt>
 <dd><p>The name of the time variable in the long input data.</p></dd>
 
 
-<dt>outtime</dt>
+<dt id="arg-outtime">outtime<a class="anchor" aria-label="anchor" href="#arg-outtime"></a></dt>
 <dd><p>The name of the time variable in the wide output data.</p></dd>
 
 </dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>Dataframe in wide format.</p>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>Dataframe in wide format.</p>
     </div>
-    <div id="author">
-    <h2>Author</h2>
+    <div class="section level2">
+    <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a></h2>
     <p>Johannes Ranke</p>
     </div>
 
-    <div id="ref-examples">
-    <h2>Examples</h2>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="fu">mkin_long_to_wide</span><span class="op">(</span><span class="va">FOCUS_2006_D</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>    time parent    m1</span>
@@ -178,27 +133,23 @@ observed values called "value".</p></dd>
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/mkin_wide_to_long.html b/docs/reference/mkin_wide_to_long.html
index 8bf04bdf..bcc303d1 100644
--- a/docs/reference/mkin_wide_to_long.html
+++ b/docs/reference/mkin_wide_to_long.html
@@ -1,150 +1,105 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Convert a dataframe with observations over time into long format — mkin_wide_to_long • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Convert a dataframe with observations over time into long format — mkin_wide_to_long"><meta property="og:description" content="This function simply takes a dataframe with one independent variable and
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Convert a dataframe with observations over time into long format — mkin_wide_to_long • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Convert a dataframe with observations over time into long format — mkin_wide_to_long"><meta name="description" content="This function simply takes a dataframe with one independent variable and
 several dependent variable and converts it into the long form as required by
-mkinfit."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+mkinfit."><meta property="og:description" content="This function simply takes a dataframe with one independent variable and
+several dependent variable and converts it into the long form as required by
+mkinfit."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Convert a dataframe with observations over time into long format</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/mkin_wide_to_long.R" class="external-link"><code>R/mkin_wide_to_long.R</code></a></small>
-    <div class="hidden name"><code>mkin_wide_to_long.Rd</code></div>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Convert a dataframe with observations over time into long format</h1>
+      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/mkin_wide_to_long.R" class="external-link"><code>R/mkin_wide_to_long.R</code></a></small>
+      <div class="d-none name"><code>mkin_wide_to_long.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>This function simply takes a dataframe with one independent variable and
 several dependent variable and converts it into the long form as required by
 <code><a href="mkinfit.html">mkinfit</a></code>.</p>
     </div>
 
-    <div id="ref-usage">
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">mkin_wide_to_long</span><span class="op">(</span><span class="va">wide_data</span>, time <span class="op">=</span> <span class="st">"t"</span><span class="op">)</span></span></code></pre></div>
     </div>
 
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>wide_data</dt>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-wide-data">wide_data<a class="anchor" aria-label="anchor" href="#arg-wide-data"></a></dt>
 <dd><p>The dataframe must contain one variable with the time
 values specified by the <code>time</code> argument and usually more than one
 column of observed values.</p></dd>
 
 
-<dt>time</dt>
+<dt id="arg-time">time<a class="anchor" aria-label="anchor" href="#arg-time"></a></dt>
 <dd><p>The name of the time variable.</p></dd>
 
 </dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>Dataframe in long format as needed for <code><a href="mkinfit.html">mkinfit</a></code>.</p>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>Dataframe in long format as needed for <code><a href="mkinfit.html">mkinfit</a></code>.</p>
     </div>
-    <div id="author">
-    <h2>Author</h2>
+    <div class="section level2">
+    <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a></h2>
     <p>Johannes Ranke</p>
     </div>
 
-    <div id="ref-examples">
-    <h2>Examples</h2>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="va">wide</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span>t <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>,<span class="fl">2</span>,<span class="fl">3</span><span class="op">)</span>, x <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>,<span class="fl">4</span>,<span class="fl">7</span><span class="op">)</span>, y <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>,<span class="fl">4</span>,<span class="fl">5</span><span class="op">)</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu">mkin_wide_to_long</span><span class="op">(</span><span class="va">wide</span><span class="op">)</span></span></span>
@@ -158,27 +113,23 @@ column of observed values.</p></dd>
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/mkinds.html b/docs/reference/mkinds.html
index dda44620..488c0c2f 100644
--- a/docs/reference/mkinds.html
+++ b/docs/reference/mkinds.html
@@ -1,144 +1,102 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>A dataset class for mkin — mkinds • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="A dataset class for mkin — mkinds"><meta property="og:description" content="At the moment this dataset class is hardly used in mkin. For example,
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>A dataset class for mkin — mkinds • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="A dataset class for mkin — mkinds"><meta name="description" content="At the moment this dataset class is hardly used in mkin. For example,
 mkinfit does not take mkinds datasets as argument, but works with dataframes
 such as the on contained in the data field of mkinds objects. Some datasets
-provided by this package come as mkinds objects nevertheless."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+provided by this package come as mkinds objects nevertheless."><meta property="og:description" content="At the moment this dataset class is hardly used in mkin. For example,
+mkinfit does not take mkinds datasets as argument, but works with dataframes
+such as the on contained in the data field of mkinds objects. Some datasets
+provided by this package come as mkinds objects nevertheless."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>A dataset class for mkin</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/mkinds.R" class="external-link"><code>R/mkinds.R</code></a></small>
-    <div class="hidden name"><code>mkinds.Rd</code></div>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>A dataset class for mkin</h1>
+      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/mkinds.R" class="external-link"><code>R/mkinds.R</code></a></small>
+      <div class="d-none name"><code>mkinds.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>At the moment this dataset class is hardly used in mkin. For example,
 mkinfit does not take mkinds datasets as argument, but works with dataframes
 such as the on contained in the data field of mkinds objects. Some datasets
 provided by this package come as mkinds objects nevertheless.</p>
     </div>
 
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for mkinds</span></span>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'mkinds'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, data <span class="op">=</span> <span class="cn">FALSE</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>x</dt>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>An mkinds object.</p></dd>
 
 
-<dt>data</dt>
+<dt id="arg-data">data<a class="anchor" aria-label="anchor" href="#arg-data"></a></dt>
 <dd><p>Should the data be printed?</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Not used.</p></dd>
 
 </dl></div>
-    <div id="public-fields">
-    <h2>Public fields</h2>
+    <div class="section level2">
+    <h2 id="public-fields">Public fields<a class="anchor" aria-label="anchor" href="#public-fields"></a></h2>
     <p></p><div class="r6-fields"><dl><dt><code>title</code></dt>
 <dd><p>A full title for the dataset</p></dd>
 
@@ -170,9 +128,9 @@ and value in order to be compatible with mkinfit</p></dd>
 
 </dl><p></p></div>
     </div>
-    <div id="methods">
-    <h2>Methods</h2>
-    
+    <div class="section level2">
+    <h2 id="methods">Methods<a class="anchor" aria-label="anchor" href="#methods"></a></h2>
+
 <div class="section">
 <h3 id="public-methods">Public methods<a class="anchor" aria-label="anchor" href="#public-methods"></a></h3>
 
@@ -186,7 +144,7 @@ and value in order to be compatible with mkinfit</p></dd>
 </div>
 
 <div class="section">
-<h4 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h4>
+<h4 id="arguments-1">Arguments<a class="anchor" aria-label="anchor" href="#arguments-1"></a></h4>
 <p></p><div class="arguments"><dl><dt><code>title</code></dt>
 <dd><p>The dataset title</p></dd>
 
@@ -214,7 +172,7 @@ and value in order to be compatible with mkinfit</p></dd>
 </div>
 
 <div class="section">
-<h4 id="arguments-1">Arguments<a class="anchor" aria-label="anchor" href="#arguments-1"></a></h4>
+<h4 id="arguments-2">Arguments<a class="anchor" aria-label="anchor" href="#arguments-2"></a></h4>
 <p></p><div class="arguments"><dl><dt><code>deep</code></dt>
 <dd><p>Whether to make a deep clone.</p></dd>
 
@@ -226,8 +184,8 @@ and value in order to be compatible with mkinfit</p></dd>
 
     </div>
 
-    <div id="ref-examples">
-    <h2>Examples</h2>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="va">mds</span> <span class="op">&lt;-</span> <span class="va">mkinds</span><span class="op">$</span><span class="fu">new</span><span class="op">(</span><span class="st">"FOCUS A"</span>, <span class="va">FOCUS_2006_A</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">mds</span><span class="op">)</span></span></span>
@@ -239,27 +197,23 @@ and value in order to be compatible with mkinfit</p></dd>
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/mkindsg.html b/docs/reference/mkindsg.html
index ac6a2471..b314a5f8 100644
--- a/docs/reference/mkindsg.html
+++ b/docs/reference/mkindsg.html
@@ -1,148 +1,106 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>A class for dataset groups for mkin — mkindsg • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="A class for dataset groups for mkin — mkindsg"><meta property="og:description" content="A container for working with datasets that share at least one compound,
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>A class for dataset groups for mkin — mkindsg • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="A class for dataset groups for mkin — mkindsg"><meta name="description" content="A container for working with datasets that share at least one compound,
 so that combined evaluations are desirable.
 Time normalisation factors are initialised with a value of 1 for each
-dataset if no data are supplied."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+dataset if no data are supplied."><meta property="og:description" content="A container for working with datasets that share at least one compound,
+so that combined evaluations are desirable.
+Time normalisation factors are initialised with a value of 1 for each
+dataset if no data are supplied."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>A class for dataset groups for mkin</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/mkinds.R" class="external-link"><code>R/mkinds.R</code></a></small>
-    <div class="hidden name"><code>mkindsg.Rd</code></div>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>A class for dataset groups for mkin</h1>
+      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/mkinds.R" class="external-link"><code>R/mkinds.R</code></a></small>
+      <div class="d-none name"><code>mkindsg.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>A container for working with datasets that share at least one compound,
 so that combined evaluations are desirable.</p>
 <p>Time normalisation factors are initialised with a value of 1 for each
 dataset if no data are supplied.</p>
     </div>
 
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for mkindsg</span></span>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'mkindsg'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, data <span class="op">=</span> <span class="cn">FALSE</span>, verbose <span class="op">=</span> <span class="va">data</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>x</dt>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>An mkindsg object.</p></dd>
 
 
-<dt>data</dt>
+<dt id="arg-data">data<a class="anchor" aria-label="anchor" href="#arg-data"></a></dt>
 <dd><p>Should the mkinds objects be printed with their data?</p></dd>
 
 
-<dt>verbose</dt>
+<dt id="arg-verbose">verbose<a class="anchor" aria-label="anchor" href="#arg-verbose"></a></dt>
 <dd><p>Should the mkinds objects be printed?</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Not used.</p></dd>
 
 </dl></div>
-    <div id="public-fields">
-    <h2>Public fields</h2>
+    <div class="section level2">
+    <h2 id="public-fields">Public fields<a class="anchor" aria-label="anchor" href="#public-fields"></a></h2>
     <p></p><div class="r6-fields"><dl><dt><code>title</code></dt>
 <dd><p>A title for the dataset group</p></dd>
 
@@ -169,9 +127,9 @@ or covariates like soil pH).</p></dd>
 
 </dl><p></p></div>
     </div>
-    <div id="methods">
-    <h2>Methods</h2>
-    
+    <div class="section level2">
+    <h2 id="methods">Methods<a class="anchor" aria-label="anchor" href="#methods"></a></h2>
+
 <div class="section">
 <h3 id="public-methods">Public methods<a class="anchor" aria-label="anchor" href="#public-methods"></a></h3>
 
@@ -185,7 +143,7 @@ or covariates like soil pH).</p></dd>
 </div>
 
 <div class="section">
-<h4 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h4>
+<h4 id="arguments-1">Arguments<a class="anchor" aria-label="anchor" href="#arguments-1"></a></h4>
 <p></p><div class="arguments"><dl><dt><code>title</code></dt>
 <dd><p>The title</p></dd>
 
@@ -213,7 +171,7 @@ or covariates like soil pH).</p></dd>
 </div>
 
 <div class="section">
-<h4 id="arguments-1">Arguments<a class="anchor" aria-label="anchor" href="#arguments-1"></a></h4>
+<h4 id="arguments-2">Arguments<a class="anchor" aria-label="anchor" href="#arguments-2"></a></h4>
 <p></p><div class="arguments"><dl><dt><code>deep</code></dt>
 <dd><p>Whether to make a deep clone.</p></dd>
 
@@ -225,8 +183,8 @@ or covariates like soil pH).</p></dd>
 
     </div>
 
-    <div id="ref-examples">
-    <h2>Examples</h2>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="va">mdsg</span> <span class="op">&lt;-</span> <span class="va">mkindsg</span><span class="op">$</span><span class="fu">new</span><span class="op">(</span><span class="st">"Experimental X"</span>, <span class="va">experimental_data_for_UBA_2019</span><span class="op">[</span><span class="fl">6</span><span class="op">:</span><span class="fl">10</span><span class="op">]</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">mdsg</span><span class="op">)</span></span></span>
@@ -427,27 +385,23 @@ or covariates like soil pH).</p></dd>
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/mkinerrmin.html b/docs/reference/mkinerrmin.html
index 11837e63..26cc19f7 100644
--- a/docs/reference/mkinerrmin.html
+++ b/docs/reference/mkinerrmin.html
@@ -1,138 +1,92 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Calculate the minimum error to assume in order to pass the variance test — mkinerrmin • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Calculate the minimum error to assume in order to pass the variance test — mkinerrmin"><meta property="og:description" content="This function finds the smallest relative error still resulting in passing
-the chi-squared test as defined in the FOCUS kinetics report from 2006."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Calculate the minimum error to assume in order to pass the variance test — mkinerrmin • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Calculate the minimum error to assume in order to pass the variance test — mkinerrmin"><meta name="description" content="This function finds the smallest relative error still resulting in passing
+the chi-squared test as defined in the FOCUS kinetics report from 2006."><meta property="og:description" content="This function finds the smallest relative error still resulting in passing
+the chi-squared test as defined in the FOCUS kinetics report from 2006."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Calculate the minimum error to assume in order to pass the variance test</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/mkinerrmin.R" class="external-link"><code>R/mkinerrmin.R</code></a></small>
-    <div class="hidden name"><code>mkinerrmin.Rd</code></div>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Calculate the minimum error to assume in order to pass the variance test</h1>
+      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/mkinerrmin.R" class="external-link"><code>R/mkinerrmin.R</code></a></small>
+      <div class="d-none name"><code>mkinerrmin.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>This function finds the smallest relative error still resulting in passing
 the chi-squared test as defined in the FOCUS kinetics report from 2006.</p>
     </div>
 
-    <div id="ref-usage">
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">mkinerrmin</span><span class="op">(</span><span class="va">fit</span>, alpha <span class="op">=</span> <span class="fl">0.05</span><span class="op">)</span></span></code></pre></div>
     </div>
 
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>fit</dt>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-fit">fit<a class="anchor" aria-label="anchor" href="#arg-fit"></a></dt>
 <dd><p>an object of class <code><a href="mkinfit.html">mkinfit</a></code>.</p></dd>
 
 
-<dt>alpha</dt>
+<dt id="arg-alpha">alpha<a class="anchor" aria-label="anchor" href="#arg-alpha"></a></dt>
 <dd><p>The confidence level chosen for the chi-squared test.</p></dd>
 
 </dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>A dataframe with the following components:</p>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>A dataframe with the following components:</p>
 <dl><dt>err.min</dt>
 <dd><p>The
 relative error, expressed as a fraction.</p></dd>
@@ -148,12 +102,12 @@ point with observed values in the series only counts one time.</p></dd>
 dataframe has one row for the total dataset and one further row for each
 observed state variable in the model.</p>
     </div>
-    <div id="details">
-    <h2>Details</h2>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>This function is used internally by <code><a href="summary.mkinfit.html">summary.mkinfit</a></code>.</p>
     </div>
-    <div id="references">
-    <h2>References</h2>
+    <div class="section level2">
+    <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
     <p>FOCUS (2006) “Guidance Document on Estimating Persistence
 and Degradation Kinetics from Environmental Fate Studies on Pesticides in EU
 Registration” Report of the FOCUS Work Group on Degradation Kinetics, EC
@@ -161,8 +115,8 @@ Document Reference Sanco/10058/2005 version 2.0, 434 pp,
 <a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics" class="external-link">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p>
     </div>
 
-    <div id="ref-examples">
-    <h2>Examples</h2>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="va">SFO_SFO</span> <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"m1"</span><span class="op">)</span>,</span></span>
 <span class="r-in"><span>                  m1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span></span>
@@ -187,27 +141,23 @@ Document Reference Sanco/10058/2005 version 2.0, 434 pp,
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/mkinerrplot-1.png b/docs/reference/mkinerrplot-1.png
index 49bb1c0e..03a054c8 100644
--- a/docs/reference/mkinerrplot-1.png
+++ b/docs/reference/mkinerrplot-1.png
diff --git a/docs/reference/mkinerrplot.html b/docs/reference/mkinerrplot.html
index 6996eb86..bcd4285b 100644
--- a/docs/reference/mkinerrplot.html
+++ b/docs/reference/mkinerrplot.html
@@ -1,118 +1,74 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Function to plot squared residuals and the error model for an mkin object — mkinerrplot • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Function to plot squared residuals and the error model for an mkin object — mkinerrplot"><meta property="og:description" content="This function plots the squared residuals for the specified subset of the
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Function to plot squared residuals and the error model for an mkin object — mkinerrplot • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Function to plot squared residuals and the error model for an mkin object — mkinerrplot"><meta name="description" content="This function plots the squared residuals for the specified subset of the
 observed variables from an mkinfit object. In addition, one or more dashed
 line(s) show the fitted error model.  A combined plot of the fitted model
 and this error model plot can be obtained with plot.mkinfit
-using the argument show_errplot = TRUE."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+using the argument show_errplot = TRUE."><meta property="og:description" content="This function plots the squared residuals for the specified subset of the
+observed variables from an mkinfit object. In addition, one or more dashed
+line(s) show the fitted error model.  A combined plot of the fitted model
+and this error model plot can be obtained with plot.mkinfit
+using the argument show_errplot = TRUE."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Function to plot squared residuals and the error model for an mkin object</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/mkinerrplot.R" class="external-link"><code>R/mkinerrplot.R</code></a></small>
-    <div class="hidden name"><code>mkinerrplot.Rd</code></div>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Function to plot squared residuals and the error model for an mkin object</h1>
+      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/mkinerrplot.R" class="external-link"><code>R/mkinerrplot.R</code></a></small>
+      <div class="d-none name"><code>mkinerrplot.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>This function plots the squared residuals for the specified subset of the
 observed variables from an mkinfit object. In addition, one or more dashed
 line(s) show the fitted error model.  A combined plot of the fitted model
@@ -120,7 +76,8 @@ and this error model plot can be obtained with <code><a href="plot.mkinfit.html"
 using the argument <code>show_errplot = TRUE</code>.</p>
     </div>
 
-    <div id="ref-usage">
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">mkinerrplot</span><span class="op">(</span></span>
 <span>  <span class="va">object</span>,</span>
 <span>  obs_vars <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">object</span><span class="op">$</span><span class="va">mkinmod</span><span class="op">$</span><span class="va">map</span><span class="op">)</span>,</span>
@@ -137,79 +94,79 @@ using the argument <code>show_errplot = TRUE</code>.</p>
 <span><span class="op">)</span></span></code></pre></div>
     </div>
 
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>object</dt>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
 <dd><p>A fit represented in an <code><a href="mkinfit.html">mkinfit</a></code> object.</p></dd>
 
 
-<dt>obs_vars</dt>
+<dt id="arg-obs-vars">obs_vars<a class="anchor" aria-label="anchor" href="#arg-obs-vars"></a></dt>
 <dd><p>A character vector of names of the observed variables for
 which residuals should be plotted. Defaults to all observed variables in
 the model</p></dd>
 
 
-<dt>xlim</dt>
+<dt id="arg-xlim">xlim<a class="anchor" aria-label="anchor" href="#arg-xlim"></a></dt>
 <dd><p>plot range in x direction.</p></dd>
 
 
-<dt>xlab</dt>
+<dt id="arg-xlab">xlab<a class="anchor" aria-label="anchor" href="#arg-xlab"></a></dt>
 <dd><p>Label for the x axis.</p></dd>
 
 
-<dt>ylab</dt>
+<dt id="arg-ylab">ylab<a class="anchor" aria-label="anchor" href="#arg-ylab"></a></dt>
 <dd><p>Label for the y axis.</p></dd>
 
 
-<dt>maxy</dt>
+<dt id="arg-maxy">maxy<a class="anchor" aria-label="anchor" href="#arg-maxy"></a></dt>
 <dd><p>Maximum value of the residuals. This is used for the scaling of
 the y axis and defaults to "auto".</p></dd>
 
 
-<dt>legend</dt>
+<dt id="arg-legend">legend<a class="anchor" aria-label="anchor" href="#arg-legend"></a></dt>
 <dd><p>Should a legend be plotted?</p></dd>
 
 
-<dt>lpos</dt>
+<dt id="arg-lpos">lpos<a class="anchor" aria-label="anchor" href="#arg-lpos"></a></dt>
 <dd><p>Where should the legend be placed? Default is "topright". Will
 be passed on to <code><a href="https://rdrr.io/r/graphics/legend.html" class="external-link">legend</a></code>.</p></dd>
 
 
-<dt>col_obs</dt>
+<dt id="arg-col-obs">col_obs<a class="anchor" aria-label="anchor" href="#arg-col-obs"></a></dt>
 <dd><p>Colors for the observed variables.</p></dd>
 
 
-<dt>pch_obs</dt>
+<dt id="arg-pch-obs">pch_obs<a class="anchor" aria-label="anchor" href="#arg-pch-obs"></a></dt>
 <dd><p>Symbols to be used for the observed variables.</p></dd>
 
 
-<dt>frame</dt>
+<dt id="arg-frame">frame<a class="anchor" aria-label="anchor" href="#arg-frame"></a></dt>
 <dd><p>Should a frame be drawn around the plots?</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>further arguments passed to <code><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></code>.</p></dd>
 
 </dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>Nothing is returned by this function, as it is called for its side
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>Nothing is returned by this function, as it is called for its side
 effect, namely to produce a plot.</p>
     </div>
-    <div id="see-also">
-    <h2>See also</h2>
+    <div class="section level2">
+    <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
     <div class="dont-index"><p><code><a href="mkinplot.html">mkinplot</a></code>, for a way to plot the data and the fitted
 lines of the mkinfit object.</p></div>
     </div>
-    <div id="author">
-    <h2>Author</h2>
+    <div class="section level2">
+    <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a></h2>
     <p>Johannes Ranke</p>
     </div>
 
-    <div id="ref-examples">
-    <h2>Examples</h2>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># \dontrun{</span></span></span>
 <span class="r-in"><span><span class="va">model</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"m1"</span><span class="op">)</span>, m1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span></span>
@@ -222,27 +179,23 @@ lines of the mkinfit object.</p></div>
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/mkinfit-1.png b/docs/reference/mkinfit-1.png
index 7c51deb6..77f6e65c 100644
--- a/docs/reference/mkinfit-1.png
+++ b/docs/reference/mkinfit-1.png
diff --git a/docs/reference/mkinfit.html b/docs/reference/mkinfit.html
index 865bdc28..5af8eaaf 100644
--- a/docs/reference/mkinfit.html
+++ b/docs/reference/mkinfit.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Fit a kinetic model to data with one or more state variables — mkinfit • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Fit a kinetic model to data with one or more state variables — mkinfit"><meta property="og:description" content="This function maximises the likelihood of the observed data using the Port
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Fit a kinetic model to data with one or more state variables — mkinfit • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Fit a kinetic model to data with one or more state variables — mkinfit"><meta name="description" content="This function maximises the likelihood of the observed data using the Port
 algorithm stats::nlminb(), and the specified initial or fixed
 parameters and starting values.  In each step of the optimisation, the
 kinetic model is solved using the function mkinpredict(), except
@@ -7,116 +7,76 @@ if an analytical solution is implemented, in which case the model is solved
 using the degradation function in the mkinmod object. The
 parameters of the selected error model are fitted simultaneously with the
 degradation model parameters, as both of them are arguments of the
-likelihood function."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+likelihood function."><meta property="og:description" content="This function maximises the likelihood of the observed data using the Port
+algorithm stats::nlminb(), and the specified initial or fixed
+parameters and starting values.  In each step of the optimisation, the
+kinetic model is solved using the function mkinpredict(), except
+if an analytical solution is implemented, in which case the model is solved
+using the degradation function in the mkinmod object. The
+parameters of the selected error model are fitted simultaneously with the
+degradation model parameters, as both of them are arguments of the
+likelihood function."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Fit a kinetic model to data with one or more state variables</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/mkinfit.R" class="external-link"><code>R/mkinfit.R</code></a></small>
-    <div class="hidden name"><code>mkinfit.Rd</code></div>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Fit a kinetic model to data with one or more state variables</h1>
+      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/mkinfit.R" class="external-link"><code>R/mkinfit.R</code></a></small>
+      <div class="d-none name"><code>mkinfit.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>This function maximises the likelihood of the observed data using the Port
 algorithm <code><a href="https://rdrr.io/r/stats/nlminb.html" class="external-link">stats::nlminb()</a></code>, and the specified initial or fixed
 parameters and starting values.  In each step of the optimisation, the
@@ -128,7 +88,8 @@ degradation model parameters, as both of them are arguments of the
 likelihood function.</p>
     </div>
 
-    <div id="ref-usage">
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">mkinfit</span><span class="op">(</span></span>
 <span>  <span class="va">mkinmod</span>,</span>
 <span>  <span class="va">observed</span>,</span>
@@ -158,9 +119,11 @@ likelihood function.</p>
 <span><span class="op">)</span></span></code></pre></div>
     </div>
 
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>mkinmod</dt>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-mkinmod">mkinmod<a class="anchor" aria-label="anchor" href="#arg-mkinmod"></a></dt>
 <dd><p>A list of class <a href="mkinmod.html">mkinmod</a>, containing the kinetic
 model to be fitted to the data, or one of the shorthand names ("SFO",
 "FOMC", "DFOP", "HS", "SFORB", "IORE"). If a shorthand name is given, a
@@ -168,8 +131,9 @@ parent only degradation model is generated for the variable with the
 highest value in <code>observed</code>.</p></dd>
 
 
-<dt>observed</dt>
-<dd><p>A dataframe with the observed data.  The first column called
+<dt id="arg-observed">observed<a class="anchor" aria-label="anchor" href="#arg-observed"></a></dt>
+<dd><p>A dataframe or an object coercible to a dataframe
+(e.g. a <code>tibble</code>) with the observed data.  The first column called
 "name" must contain the name of the observed variable for each data point.
 The second column must contain the times of observation, named "time".
 The third column must be named "value" and contain the observed values.
@@ -180,7 +144,7 @@ not observed in degradation data, because there is a lower limit of
 detection.</p></dd>
 
 
-<dt>parms.ini</dt>
+<dt id="arg-parms-ini">parms.ini<a class="anchor" aria-label="anchor" href="#arg-parms-ini"></a></dt>
 <dd><p>A named vector of initial values for the parameters,
 including parameters to be optimised and potentially also fixed parameters
 as indicated by <code>fixed_parms</code>.  If set to "auto", initial values for
@@ -193,7 +157,7 @@ this model.  This works nicely if the models are nested. An example is
 given below.</p></dd>
 
 
-<dt>state.ini</dt>
+<dt id="arg-state-ini">state.ini<a class="anchor" aria-label="anchor" href="#arg-state-ini"></a></dt>
 <dd><p>A named vector of initial values for the state variables of
 the model. In case the observed variables are represented by more than one
 model variable, the names will differ from the names of the observed
@@ -204,27 +168,27 @@ others to 0.  If this variable has no time zero observations, its initial
 value is set to 100.</p></dd>
 
 
-<dt>err.ini</dt>
+<dt id="arg-err-ini">err.ini<a class="anchor" aria-label="anchor" href="#arg-err-ini"></a></dt>
 <dd><p>A named vector of initial values for the error model
 parameters to be optimised.  If set to "auto", initial values are set to
 default values.  Otherwise, inital values for all error model parameters
 must be given.</p></dd>
 
 
-<dt>fixed_parms</dt>
+<dt id="arg-fixed-parms">fixed_parms<a class="anchor" aria-label="anchor" href="#arg-fixed-parms"></a></dt>
 <dd><p>The names of parameters that should not be optimised but
 rather kept at the values specified in <code>parms.ini</code>. Alternatively,
 a named numeric vector of parameters to be fixed, regardless of the values
 in parms.ini.</p></dd>
 
 
-<dt>fixed_initials</dt>
+<dt id="arg-fixed-initials">fixed_initials<a class="anchor" aria-label="anchor" href="#arg-fixed-initials"></a></dt>
 <dd><p>The names of model variables for which the initial
 state at time 0 should be excluded from the optimisation. Defaults to all
 state variables except for the first one.</p></dd>
 
 
-<dt>from_max_mean</dt>
+<dt id="arg-from-max-mean">from_max_mean<a class="anchor" aria-label="anchor" href="#arg-from-max-mean"></a></dt>
 <dd><p>If this is set to TRUE, and the model has only one
 observed variable, then data before the time of the maximum observed value
 (after averaging for each sampling time) are discarded, and this time is
@@ -232,7 +196,7 @@ subtracted from all remaining time values, so the time of the maximum
 observed mean value is the new time zero.</p></dd>
 
 
-<dt>solution_type</dt>
+<dt id="arg-solution-type">solution_type<a class="anchor" aria-label="anchor" href="#arg-solution-type"></a></dt>
 <dd><p>If set to "eigen", the solution of the system of
 differential equations is based on the spectral decomposition of the
 coefficient matrix in cases that this is possible. If set to "deSolve", a
@@ -244,23 +208,23 @@ compiler is present, and "eigen" if no compiler is present and the model
 can be expressed using eigenvalues and eigenvectors.</p></dd>
 
 
-<dt>method.ode</dt>
+<dt id="arg-method-ode">method.ode<a class="anchor" aria-label="anchor" href="#arg-method-ode"></a></dt>
 <dd><p>The solution method passed via <code><a href="mkinpredict.html">mkinpredict()</a></code>
 to <code><a href="https://rdrr.io/pkg/deSolve/man/ode.html" class="external-link">deSolve::ode()</a></code> in case the solution type is "deSolve". The default
 "lsoda" is performant, but sometimes fails to converge.</p></dd>
 
 
-<dt>use_compiled</dt>
+<dt id="arg-use-compiled">use_compiled<a class="anchor" aria-label="anchor" href="#arg-use-compiled"></a></dt>
 <dd><p>If set to <code>FALSE</code>, no compiled version of the
 <a href="mkinmod.html">mkinmod</a> model is used in the calls to <code><a href="mkinpredict.html">mkinpredict()</a></code> even if a compiled
 version is present.</p></dd>
 
 
-<dt>control</dt>
+<dt id="arg-control">control<a class="anchor" aria-label="anchor" href="#arg-control"></a></dt>
 <dd><p>A list of control arguments passed to <code><a href="https://rdrr.io/r/stats/nlminb.html" class="external-link">stats::nlminb()</a></code>.</p></dd>
 
 
-<dt>transform_rates</dt>
+<dt id="arg-transform-rates">transform_rates<a class="anchor" aria-label="anchor" href="#arg-transform-rates"></a></dt>
 <dd><p>Boolean specifying if kinetic rate constants should
 be transformed in the model specification used in the fitting for better
 compliance with the assumption of normal distribution of the estimator. If
@@ -270,7 +234,7 @@ models and the break point tb of the HS model.  If FALSE, zero is used as
 a lower bound for the rates in the optimisation.</p></dd>
 
 
-<dt>transform_fractions</dt>
+<dt id="arg-transform-fractions">transform_fractions<a class="anchor" aria-label="anchor" href="#arg-transform-fractions"></a></dt>
 <dd><p>Boolean specifying if formation fractions
 should be transformed in the model specification used in the fitting for
 better compliance with the assumption of normal distribution of the
@@ -279,23 +243,23 @@ the g parameter of the DFOP model is also transformed. Transformations
 are described in <a href="transform_odeparms.html">transform_odeparms</a>.</p></dd>
 
 
-<dt>quiet</dt>
+<dt id="arg-quiet">quiet<a class="anchor" aria-label="anchor" href="#arg-quiet"></a></dt>
 <dd><p>Suppress printing out the current value of the negative
 log-likelihood after each improvement?</p></dd>
 
 
-<dt>atol</dt>
+<dt id="arg-atol">atol<a class="anchor" aria-label="anchor" href="#arg-atol"></a></dt>
 <dd><p>Absolute error tolerance, passed to <code><a href="https://rdrr.io/pkg/deSolve/man/ode.html" class="external-link">deSolve::ode()</a></code>. Default
 is 1e-8, which is lower than the default in the <code><a href="https://rdrr.io/pkg/deSolve/man/lsoda.html" class="external-link">deSolve::lsoda()</a></code>
 function which is used per default.</p></dd>
 
 
-<dt>rtol</dt>
+<dt id="arg-rtol">rtol<a class="anchor" aria-label="anchor" href="#arg-rtol"></a></dt>
 <dd><p>Absolute error tolerance, passed to <code><a href="https://rdrr.io/pkg/deSolve/man/ode.html" class="external-link">deSolve::ode()</a></code>. Default
 is 1e-10, much lower than in <code><a href="https://rdrr.io/pkg/deSolve/man/lsoda.html" class="external-link">deSolve::lsoda()</a></code>.</p></dd>
 
 
-<dt>error_model</dt>
+<dt id="arg-error-model">error_model<a class="anchor" aria-label="anchor" href="#arg-error-model"></a></dt>
 <dd><p>If the error model is "const", a constant standard
 deviation is assumed.</p>
 <p>If the error model is "obs", each observed variable is assumed to have its
@@ -308,7 +272,7 @@ that the errors follow a lognormal distribution for large values, not a
 normal distribution as assumed by this method.</p></dd>
 
 
-<dt>error_model_algorithm</dt>
+<dt id="arg-error-model-algorithm">error_model_algorithm<a class="anchor" aria-label="anchor" href="#arg-error-model-algorithm"></a></dt>
 <dd><p>If "auto", the selected algorithm depends on
 the error model.  If the error model is "const", unweighted nonlinear
 least squares fitting ("OLS") is selected. If the error model is "obs", or
@@ -335,49 +299,47 @@ using those error model parameters, until the error model parameters
 converge.</p></dd>
 
 
-<dt>reweight.tol</dt>
+<dt id="arg-reweight-tol">reweight.tol<a class="anchor" aria-label="anchor" href="#arg-reweight-tol"></a></dt>
 <dd><p>Tolerance for the convergence criterion calculated from
 the error model parameters in IRLS fits.</p></dd>
 
 
-<dt>reweight.max.iter</dt>
+<dt id="arg-reweight-max-iter">reweight.max.iter<a class="anchor" aria-label="anchor" href="#arg-reweight-max-iter"></a></dt>
 <dd><p>Maximum number of iterations in IRLS fits.</p></dd>
 
 
-<dt>trace_parms</dt>
+<dt id="arg-trace-parms">trace_parms<a class="anchor" aria-label="anchor" href="#arg-trace-parms"></a></dt>
 <dd><p>Should a trace of the parameter values be listed?</p></dd>
 
 
-<dt>test_residuals</dt>
+<dt id="arg-test-residuals">test_residuals<a class="anchor" aria-label="anchor" href="#arg-test-residuals"></a></dt>
 <dd><p>Should the residuals be tested for normal distribution?</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Further arguments that will be passed on to
 <code><a href="https://rdrr.io/pkg/deSolve/man/ode.html" class="external-link">deSolve::ode()</a></code>.</p></dd>
 
 </dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>A list with "mkinfit" in the class attribute.</p>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>A list with "mkinfit" in the class attribute.</p>
     </div>
-    <div id="details">
-    <h2>Details</h2>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>Per default, parameters in the kinetic models are internally transformed in
 order to better satisfy the assumption of a normal distribution of their
 estimators.</p>
     </div>
-    <div id="note">
-    <h2>Note</h2>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>When using the "IORE" submodel for metabolites, fitting with
 "transform_rates = TRUE" (the default) often leads to failures of the
 numerical ODE solver. In this situation it may help to switch off the
 internal rate transformation.</p>
     </div>
-    <div id="references">
-    <h2>References</h2>
+    <div class="section level2">
+    <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
     <p>Rocke DM and Lorenzato S (1995) A two-component model
 for measurement error in analytical chemistry. <em>Technometrics</em> 37(2), 176-184.</p>
 <p>Ranke J and Meinecke S (2019) Error Models for the Kinetic Evaluation of Chemical
@@ -385,36 +347,36 @@ Degradation Data. <em>Environments</em> 6(12) 124
 <a href="https://doi.org/10.3390/environments6120124" class="external-link">doi:10.3390/environments6120124</a>
 .</p>
     </div>
-    <div id="see-also">
-    <h2>See also</h2>
+    <div class="section level2">
+    <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
     <div class="dont-index"><p><a href="summary.mkinfit.html">summary.mkinfit</a>, <a href="plot.mkinfit.html">plot.mkinfit</a>, <a href="parms.html">parms</a> and <a href="https://rdrr.io/pkg/lmtest/man/lrtest.html" class="external-link">lrtest</a>.</p>
 <p>Comparisons of models fitted to the same data can be made using
 <code><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">AIC</a></code> by virtue of the method <code><a href="logLik.mkinfit.html">logLik.mkinfit</a></code>.</p>
 <p>Fitting of several models to several datasets in a single call to
 <code><a href="mmkin.html">mmkin</a></code>.</p></div>
     </div>
-    <div id="author">
-    <h2>Author</h2>
+    <div class="section level2">
+    <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a></h2>
     <p>Johannes Ranke</p>
     </div>
 
-    <div id="ref-examples">
-    <h2>Examples</h2>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># Use shorthand notation for parent only degradation</span></span></span>
 <span class="r-in"><span><span class="va">fit</span> <span class="op">&lt;-</span> <span class="fu">mkinfit</span><span class="op">(</span><span class="st">"FOMC"</span>, <span class="va">FOCUS_2006_C</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> mkin version used for fitting:    1.2.6 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> R version used for fitting:       4.3.1 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Date of fit:     Mon Oct 30 09:30:09 2023 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Date of summary: Mon Oct 30 09:30:09 2023 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> mkin version used for fitting:    1.2.9 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> R version used for fitting:       4.4.2 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Date of fit:     Thu Feb 13 14:56:49 2025 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Date of summary: Thu Feb 13 14:56:49 2025 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Equations:</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Model predictions using solution type analytical </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Fitted using 222 model solutions performed in 0.031 s</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Fitted using 222 model solutions performed in 0.014 s</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Error model: Constant variance </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
@@ -555,9 +517,9 @@ Degradation Data. <em>Environments</em> 6(12) 124
 <span class="r-in"><span>      solution_type <span class="op">=</span> <span class="st">"analytical"</span><span class="op">)</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>               test relative elapsed</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 3       analytical    1.000   0.465</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2            eigen    2.260   1.051</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1 deSolve_compiled    2.282   1.061</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 3       analytical    1.000   0.227</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2            eigen    1.930   0.438</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 1 deSolve_compiled    1.991   0.452</span>
 <span class="r-in"><span><span class="co"># }</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># Use stepwise fitting, using optimised parameters from parent only fit, FOMC-SFO</span></span></span>
@@ -582,12 +544,11 @@ Degradation Data. <em>Environments</em> 6(12) 124
 <span class="r-in"><span><span class="co"># and beta indicate overparameterisation</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">fit.FOMC_SFO.tc</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>NaNs produced</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>NaNs produced</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>diag(.) had 0 or NA entries; non-finite result is doubtful</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> mkin version used for fitting:    1.2.6 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> R version used for fitting:       4.3.1 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Date of fit:     Mon Oct 30 09:30:19 2023 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Date of summary: Mon Oct 30 09:30:19 2023 </span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>diag(V) had non-positive or NA entries; the non-finite result may be dubious</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> mkin version used for fitting:    1.2.9 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> R version used for fitting:       4.4.2 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Date of fit:     Thu Feb 13 14:56:53 2025 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Date of summary: Thu Feb 13 14:56:53 2025 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Equations:</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent</span>
@@ -596,7 +557,7 @@ Degradation Data. <em>Environments</em> 6(12) 124
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Model predictions using solution type deSolve </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Fitted using 4062 model solutions performed in 1.981 s</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Fitted using 4062 model solutions performed in 0.751 s</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Error model: Two-component variance function </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
@@ -696,27 +657,23 @@ Degradation Data. <em>Environments</em> 6(12) 124
 <span class="r-in"><span><span class="co"># }</span></span></span>
 </code></pre></div>
     </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/mkinmod.html b/docs/reference/mkinmod.html
index 11a87acc..9ddbdc59 100644
--- a/docs/reference/mkinmod.html
+++ b/docs/reference/mkinmod.html
@@ -1,118 +1,74 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Function to set up a kinetic model with one or more state variables — mkinmod • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Function to set up a kinetic model with one or more state variables — mkinmod"><meta property="og:description" content="This function is usually called using a call to mkinsub() for each observed
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Function to set up a kinetic model with one or more state variables — mkinmod • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Function to set up a kinetic model with one or more state variables — mkinmod"><meta name="description" content="This function is usually called using a call to mkinsub() for each observed
 variable, specifying the corresponding submodel as well as outgoing pathways
 (see examples).
 Print mkinmod objects in a way that the user finds his way to get to its
-components."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+components."><meta property="og:description" content="This function is usually called using a call to mkinsub() for each observed
+variable, specifying the corresponding submodel as well as outgoing pathways
+(see examples).
+Print mkinmod objects in a way that the user finds his way to get to its
+components."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Function to set up a kinetic model with one or more state variables</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/mkinmod.R" class="external-link"><code>R/mkinmod.R</code></a>, <a href="https://github.com/jranke/mkin/blob/HEAD/R/mkinsub.R" class="external-link"><code>R/mkinsub.R</code></a></small>
-    <div class="hidden name"><code>mkinmod.Rd</code></div>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Function to set up a kinetic model with one or more state variables</h1>
+      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/mkinmod.R" class="external-link"><code>R/mkinmod.R</code></a>, <a href="https://github.com/jranke/mkin/blob/HEAD/R/mkinsub.R" class="external-link"><code>R/mkinsub.R</code></a></small>
+      <div class="d-none name"><code>mkinmod.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>This function is usually called using a call to <code>mkinsub()</code> for each observed
 variable, specifying the corresponding submodel as well as outgoing pathways
 (see examples).</p>
@@ -120,7 +76,8 @@ variable, specifying the corresponding submodel as well as outgoing pathways
 components.</p>
     </div>
 
-    <div id="ref-usage">
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">mkinmod</span><span class="op">(</span></span>
 <span>  <span class="va">...</span>,</span>
 <span>  use_of_ff <span class="op">=</span> <span class="st">"max"</span>,</span>
@@ -133,15 +90,17 @@ components.</p>
 <span>  overwrite <span class="op">=</span> <span class="cn">FALSE</span></span>
 <span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for mkinmod</span></span>
+<span><span class="co"># S3 method for class 'mkinmod'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span></span>
 <span></span>
 <span><span class="fu">mkinsub</span><span class="op">(</span><span class="va">submodel</span>, to <span class="op">=</span> <span class="cn">NULL</span>, sink <span class="op">=</span> <span class="cn">TRUE</span>, full_name <span class="op">=</span> <span class="cn">NA</span><span class="op">)</span></span></code></pre></div>
     </div>
 
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>...</dt>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>For each observed variable, a list as obtained by <code>mkinsub()</code>
 has to be specified as an argument (see examples).  Currently, single
 first order kinetics "SFO", indeterminate order rate equation kinetics
@@ -158,7 +117,7 @@ fixing the flux to sink to zero.
 In print.mkinmod, this argument is currently not used.</p></dd>
 
 
-<dt>use_of_ff</dt>
+<dt id="arg-use-of-ff">use_of_ff<a class="anchor" aria-label="anchor" href="#arg-use-of-ff"></a></dt>
 <dd><p>Specification of the use of formation fractions in the
 model equations and, if applicable, the coefficient matrix.  If "max",
 formation fractions are always used (default).  If "min", a minimum use of
@@ -166,26 +125,26 @@ formation fractions is made, i.e. each first-order pathway to a metabolite
 has its own rate constant.</p></dd>
 
 
-<dt>name</dt>
+<dt id="arg-name">name<a class="anchor" aria-label="anchor" href="#arg-name"></a></dt>
 <dd><p>A name for the model. Should be a valid R object name.</p></dd>
 
 
-<dt>speclist</dt>
+<dt id="arg-speclist">speclist<a class="anchor" aria-label="anchor" href="#arg-speclist"></a></dt>
 <dd><p>The specification of the observed variables and their
 submodel types and pathways can be given as a single list using this
 argument. Default is NULL.</p></dd>
 
 
-<dt>quiet</dt>
+<dt id="arg-quiet">quiet<a class="anchor" aria-label="anchor" href="#arg-quiet"></a></dt>
 <dd><p>Should messages be suppressed?</p></dd>
 
 
-<dt>verbose</dt>
+<dt id="arg-verbose">verbose<a class="anchor" aria-label="anchor" href="#arg-verbose"></a></dt>
 <dd><p>If <code>TRUE</code>, passed to <code><a href="https://rdrr.io/pkg/inline/man/cfunction.html" class="external-link">inline::cfunction()</a></code> if
 applicable to give detailed information about the C function being built.</p></dd>
 
 
-<dt>dll_dir</dt>
+<dt id="arg-dll-dir">dll_dir<a class="anchor" aria-label="anchor" href="#arg-dll-dir"></a></dt>
 <dd><p>Directory where an DLL object, if generated internally by
 <code><a href="https://rdrr.io/pkg/inline/man/cfunction.html" class="external-link">inline::cfunction()</a></code>, should be saved.  The DLL will only be stored in a
 permanent location for use in future sessions, if 'dll_dir' and 'name'
@@ -194,47 +153,45 @@ as the cache remains functional across sessions if the DLL is stored in
 a user defined location.</p></dd>
 
 
-<dt>unload</dt>
+<dt id="arg-unload">unload<a class="anchor" aria-label="anchor" href="#arg-unload"></a></dt>
 <dd><p>If a DLL from the target location in 'dll_dir' is already
 loaded, should that be unloaded first?</p></dd>
 
 
-<dt>overwrite</dt>
+<dt id="arg-overwrite">overwrite<a class="anchor" aria-label="anchor" href="#arg-overwrite"></a></dt>
 <dd><p>If a file exists at the target DLL location in 'dll_dir',
 should this be overwritten?</p></dd>
 
 
-<dt>x</dt>
+<dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>An <code>mkinmod</code> object.</p></dd>
 
 
-<dt>submodel</dt>
+<dt id="arg-submodel">submodel<a class="anchor" aria-label="anchor" href="#arg-submodel"></a></dt>
 <dd><p>Character vector of length one to specify the submodel type.
 See <code>mkinmod</code> for the list of allowed submodel names.</p></dd>
 
 
-<dt>to</dt>
+<dt id="arg-to">to<a class="anchor" aria-label="anchor" href="#arg-to"></a></dt>
 <dd><p>Vector of the names of the state variable to which a
 transformation shall be included in the model.</p></dd>
 
 
-<dt>sink</dt>
+<dt id="arg-sink">sink<a class="anchor" aria-label="anchor" href="#arg-sink"></a></dt>
 <dd><p>Should a pathway to sink be included in the model in addition to
 the pathways to other state variables?</p></dd>
 
 
-<dt>full_name</dt>
+<dt id="arg-full-name">full_name<a class="anchor" aria-label="anchor" href="#arg-full-name"></a></dt>
 <dd><p>An optional name to be used e.g. for plotting fits
 performed with the model.  You can use non-ASCII characters here, but then
 your R code will not be portable, <em>i.e.</em> may produce unintended plot
 results on other operating systems or system configurations.</p></dd>
 
 </dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>A list of class <code>mkinmod</code> for use with <code><a href="mkinfit.html">mkinfit()</a></code>,
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>A list of class <code>mkinmod</code> for use with <code><a href="mkinfit.html">mkinfit()</a></code>,
 containing, among others,</p>
 <dl><dt>diffs</dt>
 <dd><p>A vector of string representations of differential equations, one for
@@ -262,8 +219,8 @@ returned by cfunction.</p></dd>
 
 </dl><p>A list for use with <code>mkinmod</code>.</p>
     </div>
-    <div id="details">
-    <h2>Details</h2>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>For the definition of model types and their parameters, the equations given
 in the FOCUS and NAFTA guidance documents are used.</p>
 <p>For kinetic models with more than one observed variable, a symbolic solution
@@ -274,14 +231,14 @@ is more than one observed variable in the specification, C code is generated
 for evaluating the differential equations, compiled using
 <code><a href="https://rdrr.io/pkg/inline/man/cfunction.html" class="external-link">inline::cfunction()</a></code> and added to the resulting mkinmod object.</p>
     </div>
-    <div id="note">
-    <h2>Note</h2>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>The IORE submodel is not well tested for metabolites. When using this
 model for metabolites, you may want to read the note in the help
 page to <a href="mkinfit.html">mkinfit</a>.</p>
     </div>
-    <div id="references">
-    <h2>References</h2>
+    <div class="section level2">
+    <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
     <p>FOCUS (2006) “Guidance Document on Estimating Persistence
 and Degradation Kinetics from Environmental Fate Studies on Pesticides in
 EU Registration” Report of the FOCUS Work Group on Degradation Kinetics,
@@ -290,13 +247,13 @@ EC Document Reference Sanco/10058/2005 version 2.0, 434 pp,
 <p>NAFTA Technical Working Group on Pesticides (not dated) Guidance for
 Evaluating and Calculating Degradation Kinetics in Environmental Media</p>
     </div>
-    <div id="author">
-    <h2>Author</h2>
+    <div class="section level2">
+    <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a></h2>
     <p>Johannes Ranke</p>
     </div>
 
-    <div id="ref-examples">
-    <h2>Examples</h2>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># Specify the SFO model (this is not needed any more, as we can now mkinfit("SFO", ...)</span></span></span>
 <span class="r-in"><span><span class="va">SFO</span> <span class="op">&lt;-</span> <span class="fu">mkinmod</span><span class="op">(</span>parent <span class="op">=</span> <span class="fu">mkinsub</span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span></span>
@@ -333,7 +290,7 @@ Evaluating and Calculating Degradation Kinetics in Environmental Media</p>
 <span class="r-in"><span>   m1 <span class="op">=</span> <span class="fu">mkinsub</span><span class="op">(</span><span class="st">"SFO"</span>, full_name <span class="op">=</span> <span class="st">"Metabolite M1"</span><span class="op">)</span>,</span></span>
 <span class="r-in"><span>   name <span class="op">=</span> <span class="st">"SFO_SFO"</span>, dll_dir <span class="op">=</span> <span class="va">DLL_dir</span>, unload <span class="op">=</span> <span class="cn">TRUE</span>, overwrite <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> Temporary DLL for differentials generated and loaded</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Copied DLL from /tmp/RtmpkUSV6I/file244bcd2ba24b1a.so to /home/agsad.admin.ch/f80868656/.local/share/mkin/SFO_SFO.so</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> Copied DLL from /tmp/RtmpUBdk0y/file210eb9601bdd53.so to /home/jranke/.local/share/mkin/SFO_SFO.so</span>
 <span class="r-in"><span><span class="co"># Now we can save the model and restore it in a new session</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/readRDS.html" class="external-link">saveRDS</a></span><span class="op">(</span><span class="va">SFO_SFO.2</span>, file <span class="op">=</span> <span class="st">"~/SFO_SFO.rds"</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="co"># Terminate the R session here if you would like to check, and then do</span></span></span>
@@ -386,7 +343,7 @@ Evaluating and Calculating Degradation Kinetics in Environmental Media</p>
 <span class="r-out co"><span class="r-pr">#&gt;</span>     })</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>     return(predicted)</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> }</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> &lt;environment: 0x560630960080&gt;</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> &lt;environment: 0x55555a37aab8&gt;</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># If we have several parallel metabolites</span></span></span>
 <span class="r-in"><span><span class="co"># (compare tests/testthat/test_synthetic_data_for_UBA_2014.R)</span></span></span>
@@ -403,27 +360,23 @@ Evaluating and Calculating Degradation Kinetics in Environmental Media</p>
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/mkinparplot-1.png b/docs/reference/mkinparplot-1.png
index 5d6fde87..8d34b451 100644
--- a/docs/reference/mkinparplot-1.png
+++ b/docs/reference/mkinparplot-1.png
diff --git a/docs/reference/mkinparplot.html b/docs/reference/mkinparplot.html
index 71957650..57f536e0 100644
--- a/docs/reference/mkinparplot.html
+++ b/docs/reference/mkinparplot.html
@@ -1,143 +1,97 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Function to plot the confidence intervals obtained using mkinfit — mkinparplot • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Function to plot the confidence intervals obtained using mkinfit — mkinparplot"><meta property="og:description" content="This function plots the confidence intervals for the parameters fitted using
-mkinfit."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Function to plot the confidence intervals obtained using mkinfit — mkinparplot • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Function to plot the confidence intervals obtained using mkinfit — mkinparplot"><meta name="description" content="This function plots the confidence intervals for the parameters fitted using
+mkinfit."><meta property="og:description" content="This function plots the confidence intervals for the parameters fitted using
+mkinfit."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Function to plot the confidence intervals obtained using mkinfit</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/mkinparplot.R" class="external-link"><code>R/mkinparplot.R</code></a></small>
-    <div class="hidden name"><code>mkinparplot.Rd</code></div>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Function to plot the confidence intervals obtained using mkinfit</h1>
+      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/mkinparplot.R" class="external-link"><code>R/mkinparplot.R</code></a></small>
+      <div class="d-none name"><code>mkinparplot.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>This function plots the confidence intervals for the parameters fitted using
 <code><a href="mkinfit.html">mkinfit</a></code>.</p>
     </div>
 
-    <div id="ref-usage">
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">mkinparplot</span><span class="op">(</span><span class="va">object</span><span class="op">)</span></span></code></pre></div>
     </div>
 
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>object</dt>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
 <dd><p>A fit represented in an <code><a href="mkinfit.html">mkinfit</a></code> object.</p></dd>
 
 </dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>Nothing is returned by this function, as it is called for its side
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>Nothing is returned by this function, as it is called for its side
 effect, namely to produce a plot.</p>
     </div>
-    <div id="author">
-    <h2>Author</h2>
+    <div class="section level2">
+    <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a></h2>
     <p>Johannes Ranke</p>
     </div>
 
-    <div id="ref-examples">
-    <h2>Examples</h2>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># \dontrun{</span></span></span>
 <span class="r-in"><span><span class="va">model</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span></span></span>
@@ -152,27 +106,23 @@ effect, namely to produce a plot.</p>
 <span class="r-in"><span><span class="co"># }</span></span></span>
 </code></pre></div>
     </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/mkinplot.html b/docs/reference/mkinplot.html
index da047bf7..b5c84645 100644
--- a/docs/reference/mkinplot.html
+++ b/docs/reference/mkinplot.html
@@ -1,165 +1,115 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Plot the observed data and the fitted model of an mkinfit object — mkinplot • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Plot the observed data and the fitted model of an mkinfit object — mkinplot"><meta property="og:description" content="Deprecated function. It now only calls the plot method
-plot.mkinfit."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Plot the observed data and the fitted model of an mkinfit object — mkinplot • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Plot the observed data and the fitted model of an mkinfit object — mkinplot"><meta name="description" content="Deprecated function. It now only calls the plot method
+plot.mkinfit."><meta property="og:description" content="Deprecated function. It now only calls the plot method
+plot.mkinfit."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Plot the observed data and the fitted model of an mkinfit object</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/plot.mkinfit.R" class="external-link"><code>R/plot.mkinfit.R</code></a></small>
-    <div class="hidden name"><code>mkinplot.Rd</code></div>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Plot the observed data and the fitted model of an mkinfit object</h1>
+      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/plot.mkinfit.R" class="external-link"><code>R/plot.mkinfit.R</code></a></small>
+      <div class="d-none name"><code>mkinplot.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Deprecated function. It now only calls the plot method
 <code><a href="plot.mkinfit.html">plot.mkinfit</a></code>.</p>
     </div>
 
-    <div id="ref-usage">
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">mkinplot</span><span class="op">(</span><span class="va">fit</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>fit</dt>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-fit">fit<a class="anchor" aria-label="anchor" href="#arg-fit"></a></dt>
 <dd><p>an object of class <code><a href="mkinfit.html">mkinfit</a></code>.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>further arguments passed to <code><a href="plot.mkinfit.html">plot.mkinfit</a></code>.</p></dd>
 
 </dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>The function is called for its side effect.</p>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>The function is called for its side effect.</p>
     </div>
-    <div id="author">
-    <h2>Author</h2>
+    <div class="section level2">
+    <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a></h2>
     <p>Johannes Ranke</p>
     </div>
 
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/mkinpredict.html b/docs/reference/mkinpredict.html
index d52e57cc..fc656e43 100644
--- a/docs/reference/mkinpredict.html
+++ b/docs/reference/mkinpredict.html
@@ -1,125 +1,80 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Produce predictions from a kinetic model using specific parameters — mkinpredict • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Produce predictions from a kinetic model using specific parameters — mkinpredict"><meta property="og:description" content="This function produces a time series for all the observed variables in a
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Produce predictions from a kinetic model using specific parameters — mkinpredict • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Produce predictions from a kinetic model using specific parameters — mkinpredict"><meta name="description" content="This function produces a time series for all the observed variables in a
 kinetic model as specified by mkinmod, using a specific set of
-kinetic parameters and initial values for the state variables."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+kinetic parameters and initial values for the state variables."><meta property="og:description" content="This function produces a time series for all the observed variables in a
+kinetic model as specified by mkinmod, using a specific set of
+kinetic parameters and initial values for the state variables."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Produce predictions from a kinetic model using specific parameters</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/mkinpredict.R" class="external-link"><code>R/mkinpredict.R</code></a></small>
-    <div class="hidden name"><code>mkinpredict.Rd</code></div>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Produce predictions from a kinetic model using specific parameters</h1>
+      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/mkinpredict.R" class="external-link"><code>R/mkinpredict.R</code></a></small>
+      <div class="d-none name"><code>mkinpredict.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>This function produces a time series for all the observed variables in a
 kinetic model as specified by <a href="mkinmod.html">mkinmod</a>, using a specific set of
 kinetic parameters and initial values for the state variables.</p>
     </div>
 
-    <div id="ref-usage">
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">mkinpredict</span><span class="op">(</span><span class="va">x</span>, <span class="va">odeparms</span>, <span class="va">odeini</span>, <span class="va">outtimes</span>, <span class="va">...</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for mkinmod</span></span>
+<span><span class="co"># S3 method for class 'mkinmod'</span></span>
 <span><span class="fu">mkinpredict</span><span class="op">(</span></span>
 <span>  <span class="va">x</span>,</span>
 <span>  odeparms <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_parent_sink <span class="op">=</span> <span class="fl">0.1</span><span class="op">)</span>,</span>
@@ -137,7 +92,7 @@ kinetic parameters and initial values for the state variables.</p>
 <span>  <span class="va">...</span></span>
 <span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for mkinfit</span></span>
+<span><span class="co"># S3 method for class 'mkinfit'</span></span>
 <span><span class="fu">mkinpredict</span><span class="op">(</span></span>
 <span>  <span class="va">x</span>,</span>
 <span>  odeparms <span class="op">=</span> <span class="va">x</span><span class="op">$</span><span class="va">bparms.ode</span>,</span>
@@ -153,37 +108,39 @@ kinetic parameters and initial values for the state variables.</p>
 <span><span class="op">)</span></span></code></pre></div>
     </div>
 
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>x</dt>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>A kinetic model as produced by <a href="mkinmod.html">mkinmod</a>, or a kinetic fit as
 fitted by <a href="mkinfit.html">mkinfit</a>. In the latter case, the fitted parameters are used for
 the prediction.</p></dd>
 
 
-<dt>odeparms</dt>
+<dt id="arg-odeparms">odeparms<a class="anchor" aria-label="anchor" href="#arg-odeparms"></a></dt>
 <dd><p>A numeric vector specifying the parameters used in the
 kinetic model, which is generally defined as a set of ordinary differential
 equations.</p></dd>
 
 
-<dt>odeini</dt>
+<dt id="arg-odeini">odeini<a class="anchor" aria-label="anchor" href="#arg-odeini"></a></dt>
 <dd><p>A numeric vector containing the initial values of the state
 variables of the model. Note that the state variables can differ from the
 observed variables, for example in the case of the SFORB model.</p></dd>
 
 
-<dt>outtimes</dt>
+<dt id="arg-outtimes">outtimes<a class="anchor" aria-label="anchor" href="#arg-outtimes"></a></dt>
 <dd><p>A numeric vector specifying the time points for which model
 predictions should be generated.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Further arguments passed to the ode solver in case such a
 solver is used.</p></dd>
 
 
-<dt>solution_type</dt>
+<dt id="arg-solution-type">solution_type<a class="anchor" aria-label="anchor" href="#arg-solution-type"></a></dt>
 <dd><p>The method that should be used for producing the
 predictions. This should generally be "analytical" if there is only one
 observed variable, and usually "deSolve" in the case of several observed
@@ -192,58 +149,56 @@ ODE models, but not applicable to some models, e.g. using FOMC for the
 parent compound.</p></dd>
 
 
-<dt>use_compiled</dt>
+<dt id="arg-use-compiled">use_compiled<a class="anchor" aria-label="anchor" href="#arg-use-compiled"></a></dt>
 <dd><p>If set to <code>FALSE</code>, no compiled version of the
 <a href="mkinmod.html">mkinmod</a> model is used, even if is present.</p></dd>
 
 
-<dt>use_symbols</dt>
+<dt id="arg-use-symbols">use_symbols<a class="anchor" aria-label="anchor" href="#arg-use-symbols"></a></dt>
 <dd><p>If set to <code>TRUE</code> (default), symbol info present in
 the <a href="mkinmod.html">mkinmod</a> object is used if available for accessing compiled code</p></dd>
 
 
-<dt>method.ode</dt>
-<dd><p>The solution method passed via mkinpredict to ode] in
+<dt id="arg-method-ode">method.ode<a class="anchor" aria-label="anchor" href="#arg-method-ode"></a></dt>
+<dd><p>The solution method passed via mkinpredict to <code><a href="https://rdrr.io/pkg/deSolve/man/ode.html" class="external-link">deSolve::ode()</a></code> in
 case the solution type is "deSolve" and we are not using compiled code.
 When using compiled code, only lsoda is supported.</p></dd>
 
 
-<dt>atol</dt>
+<dt id="arg-atol">atol<a class="anchor" aria-label="anchor" href="#arg-atol"></a></dt>
 <dd><p>Absolute error tolerance, passed to the ode solver.</p></dd>
 
 
-<dt>rtol</dt>
+<dt id="arg-rtol">rtol<a class="anchor" aria-label="anchor" href="#arg-rtol"></a></dt>
 <dd><p>Absolute error tolerance, passed to the ode solver.</p></dd>
 
 
-<dt>maxsteps</dt>
+<dt id="arg-maxsteps">maxsteps<a class="anchor" aria-label="anchor" href="#arg-maxsteps"></a></dt>
 <dd><p>Maximum number of steps, passed to the ode solver.</p></dd>
 
 
-<dt>map_output</dt>
+<dt id="arg-map-output">map_output<a class="anchor" aria-label="anchor" href="#arg-map-output"></a></dt>
 <dd><p>Boolean to specify if the output should list values for
 the observed variables (default) or for all state variables (if set to
 FALSE). Setting this to FALSE has no effect for analytical solutions,
 as these always return mapped output.</p></dd>
 
 
-<dt>na_stop</dt>
-<dd><p>Should it be an error if ode returns NaN values</p></dd>
+<dt id="arg-na-stop">na_stop<a class="anchor" aria-label="anchor" href="#arg-na-stop"></a></dt>
+<dd><p>Should it be an error if <code><a href="https://rdrr.io/pkg/deSolve/man/ode.html" class="external-link">deSolve::ode()</a></code> returns NaN values</p></dd>
 
 </dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>A matrix with the numeric solution in wide format</p>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>A matrix with the numeric solution in wide format</p>
     </div>
-    <div id="author">
-    <h2>Author</h2>
+    <div class="section level2">
+    <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a></h2>
     <p>Johannes Ranke</p>
     </div>
 
-    <div id="ref-examples">
-    <h2>Examples</h2>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="va">SFO</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>degradinol <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="co"># Compare solution types</span></span></span>
@@ -398,10 +353,10 @@ as these always return mapped output.</p></dd>
 <span class="r-in"><span>      solution_type <span class="op">=</span> <span class="st">"analytical"</span>, use_compiled <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span><span class="op">[</span><span class="fl">201</span>,<span class="op">]</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>               test relative elapsed</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2 deSolve_compiled      1.0   0.005</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 4       analytical      1.0   0.005</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1            eigen      3.2   0.016</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 3          deSolve     24.4   0.122</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2 deSolve_compiled        1   0.002</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 4       analytical        1   0.002</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 1            eigen        4   0.008</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 3          deSolve       32   0.064</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># \dontrun{</span></span></span>
 <span class="r-in"><span>  <span class="co"># Predict from a fitted model</span></span></span>
@@ -413,27 +368,23 @@ as these always return mapped output.</p></dd>
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/mkinpredict.mkinfit.html b/docs/reference/mkinpredict.mkinfit.html
new file mode 100644
index 00000000..3078db1d
--- /dev/null
+++ b/docs/reference/mkinpredict.mkinfit.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/mkinpredict.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/mkinpredict.html">
+  </head>
+</html>
+
diff --git a/docs/reference/mkinpredict.mkinmod.html b/docs/reference/mkinpredict.mkinmod.html
new file mode 100644
index 00000000..3078db1d
--- /dev/null
+++ b/docs/reference/mkinpredict.mkinmod.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/mkinpredict.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/mkinpredict.html">
+  </head>
+</html>
+
diff --git a/docs/reference/mkinresplot-1.png b/docs/reference/mkinresplot-1.png
index 97ccd762..29da1fda 100644
--- a/docs/reference/mkinresplot-1.png
+++ b/docs/reference/mkinresplot-1.png
diff --git a/docs/reference/mkinresplot.html b/docs/reference/mkinresplot.html
index 4bfd033e..97b24dbf 100644
--- a/docs/reference/mkinresplot.html
+++ b/docs/reference/mkinresplot.html
@@ -1,124 +1,80 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Function to plot residuals stored in an mkin object — mkinresplot • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Function to plot residuals stored in an mkin object — mkinresplot"><meta property="og:description" content="This function plots the residuals for the specified subset of the observed
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Function to plot residuals stored in an mkin object — mkinresplot • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Function to plot residuals stored in an mkin object — mkinresplot"><meta name="description" content="This function plots the residuals for the specified subset of the observed
 variables from an mkinfit object. A combined plot of the fitted model and
 the residuals can be obtained using plot.mkinfit using the
-argument show_residuals = TRUE."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+argument show_residuals = TRUE."><meta property="og:description" content="This function plots the residuals for the specified subset of the observed
+variables from an mkinfit object. A combined plot of the fitted model and
+the residuals can be obtained using plot.mkinfit using the
+argument show_residuals = TRUE."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Function to plot residuals stored in an mkin object</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/mkinresplot.R" class="external-link"><code>R/mkinresplot.R</code></a></small>
-    <div class="hidden name"><code>mkinresplot.Rd</code></div>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Function to plot residuals stored in an mkin object</h1>
+      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/mkinresplot.R" class="external-link"><code>R/mkinresplot.R</code></a></small>
+      <div class="d-none name"><code>mkinresplot.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>This function plots the residuals for the specified subset of the observed
 variables from an mkinfit object. A combined plot of the fitted model and
 the residuals can be obtained using <code><a href="plot.mkinfit.html">plot.mkinfit</a></code> using the
 argument <code>show_residuals = TRUE</code>.</p>
     </div>
 
-    <div id="ref-usage">
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">mkinresplot</span><span class="op">(</span></span>
 <span>  <span class="va">object</span>,</span>
 <span>  obs_vars <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">object</span><span class="op">$</span><span class="va">mkinmod</span><span class="op">$</span><span class="va">map</span><span class="op">)</span>,</span>
@@ -136,85 +92,85 @@ argument <code>show_residuals = TRUE</code>.</p>
 <span><span class="op">)</span></span></code></pre></div>
     </div>
 
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>object</dt>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
 <dd><p>A fit represented in an <code><a href="mkinfit.html">mkinfit</a></code> object.</p></dd>
 
 
-<dt>obs_vars</dt>
+<dt id="arg-obs-vars">obs_vars<a class="anchor" aria-label="anchor" href="#arg-obs-vars"></a></dt>
 <dd><p>A character vector of names of the observed variables for
 which residuals should be plotted. Defaults to all observed variables in
 the model</p></dd>
 
 
-<dt>xlim</dt>
+<dt id="arg-xlim">xlim<a class="anchor" aria-label="anchor" href="#arg-xlim"></a></dt>
 <dd><p>plot range in x direction.</p></dd>
 
 
-<dt>standardized</dt>
+<dt id="arg-standardized">standardized<a class="anchor" aria-label="anchor" href="#arg-standardized"></a></dt>
 <dd><p>Should the residuals be standardized by dividing by the
 standard deviation given by the error model of the fit?</p></dd>
 
 
-<dt>xlab</dt>
+<dt id="arg-xlab">xlab<a class="anchor" aria-label="anchor" href="#arg-xlab"></a></dt>
 <dd><p>Label for the x axis.</p></dd>
 
 
-<dt>ylab</dt>
+<dt id="arg-ylab">ylab<a class="anchor" aria-label="anchor" href="#arg-ylab"></a></dt>
 <dd><p>Label for the y axis.</p></dd>
 
 
-<dt>maxabs</dt>
+<dt id="arg-maxabs">maxabs<a class="anchor" aria-label="anchor" href="#arg-maxabs"></a></dt>
 <dd><p>Maximum absolute value of the residuals. This is used for the
 scaling of the y axis and defaults to "auto".</p></dd>
 
 
-<dt>legend</dt>
+<dt id="arg-legend">legend<a class="anchor" aria-label="anchor" href="#arg-legend"></a></dt>
 <dd><p>Should a legend be plotted?</p></dd>
 
 
-<dt>lpos</dt>
+<dt id="arg-lpos">lpos<a class="anchor" aria-label="anchor" href="#arg-lpos"></a></dt>
 <dd><p>Where should the legend be placed? Default is "topright". Will
 be passed on to <code><a href="https://rdrr.io/r/graphics/legend.html" class="external-link">legend</a></code>.</p></dd>
 
 
-<dt>col_obs</dt>
+<dt id="arg-col-obs">col_obs<a class="anchor" aria-label="anchor" href="#arg-col-obs"></a></dt>
 <dd><p>Colors for the observed variables.</p></dd>
 
 
-<dt>pch_obs</dt>
+<dt id="arg-pch-obs">pch_obs<a class="anchor" aria-label="anchor" href="#arg-pch-obs"></a></dt>
 <dd><p>Symbols to be used for the observed variables.</p></dd>
 
 
-<dt>frame</dt>
+<dt id="arg-frame">frame<a class="anchor" aria-label="anchor" href="#arg-frame"></a></dt>
 <dd><p>Should a frame be drawn around the plots?</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>further arguments passed to <code><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></code>.</p></dd>
 
 </dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>Nothing is returned by this function, as it is called for its side
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>Nothing is returned by this function, as it is called for its side
 effect, namely to produce a plot.</p>
     </div>
-    <div id="see-also">
-    <h2>See also</h2>
+    <div class="section level2">
+    <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
     <div class="dont-index"><p><code><a href="mkinplot.html">mkinplot</a></code>, for a way to plot the data and the fitted
 lines of the mkinfit object, and <code><a href="plot.mkinfit.html">plot_res</a></code> for a function
 combining the plot of the fit and the residual plot.</p></div>
     </div>
-    <div id="author">
-    <h2>Author</h2>
+    <div class="section level2">
+    <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a></h2>
     <p>Johannes Ranke and Katrin Lindenberger</p>
     </div>
 
-    <div id="ref-examples">
-    <h2>Examples</h2>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="va">model</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"m1"</span><span class="op">)</span>, m1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span></span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> Temporary DLL for differentials generated and loaded</span>
@@ -225,27 +181,23 @@ combining the plot of the fit and the residual plot.</p></div>
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/mkinsub.html b/docs/reference/mkinsub.html
index 77ee41f8..9c52fb21 100644
--- a/docs/reference/mkinsub.html
+++ b/docs/reference/mkinsub.html
@@ -1,234 +1,8 @@
-<!-- Generated by pkgdown: do not edit by hand -->
-<!DOCTYPE html>
-<html lang="en">
+<html>
   <head>
-  <meta charset="utf-8">
-<meta http-equiv="X-UA-Compatible" content="IE=edge">
-<meta name="viewport" content="width=device-width, initial-scale=1.0">
-
-<title>Function to set up a kinetic submodel for one state variable — mkinsub • mkin</title>
-
-
-<!-- jquery -->
-<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script>
-<!-- Bootstrap -->
-
-<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous" />
-
-<script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script>
-
-<!-- bootstrap-toc -->
-<link rel="stylesheet" href="../bootstrap-toc.css">
-<script src="../bootstrap-toc.js"></script>
-
-<!-- Font Awesome icons -->
-<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous" />
-<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous" />
-
-<!-- clipboard.js -->
-<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script>
-
-<!-- headroom.js -->
-<script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script>
-<script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script>
-
-<!-- pkgdown -->
-<link href="../pkgdown.css" rel="stylesheet">
-<script src="../pkgdown.js"></script>
-
-
-
-
-<meta property="og:title" content="Function to set up a kinetic submodel for one state variable — mkinsub" />
-<meta property="og:description" content="This is a convenience function to set up the lists used as arguments for
-mkinmod." />
-
-
-
-
-<!-- mathjax -->
-<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script>
-<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script>
-
-<!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]-->
-
-
-
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/mkinmod.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/mkinmod.html">
   </head>
-
-  <body data-spy="scroll" data-target="#toc">
-    <div class="container template-reference-topic">
-      <header>
-      <div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.9.50.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav">
-        <li>
-  <a href="../reference/index.html">Functions and data</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu">
-    <li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a>
-    </li>
-  </ul>
-</li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul>
-      <ul class="nav navbar-nav navbar-right">
-        <li>
-  <a href="https://github.com/jranke/mkin/">
-    <span class="fab fa fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul>
-      
-    </div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header>
-
-<div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Function to set up a kinetic submodel for one state variable</h1>
-    <small class="dont-index">Source: <a href='https://github.com/jranke/mkin/blob/master/R/mkinsub.R'><code>R/mkinsub.R</code></a></small>
-    <div class="hidden name"><code>mkinsub.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>This is a convenience function to set up the lists used as arguments for
-<code><a href='mkinmod.html'>mkinmod</a></code>.</p>
-    </div>
-
-    <pre class="usage"><span class='fu'>mkinsub</span><span class='op'>(</span><span class='va'>submodel</span>, to <span class='op'>=</span> <span class='cn'>NULL</span>, sink <span class='op'>=</span> <span class='cn'>TRUE</span>, full_name <span class='op'>=</span> <span class='cn'>NA</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>submodel</th>
-      <td><p>Character vector of length one to specify the submodel type.
-See <code><a href='mkinmod.html'>mkinmod</a></code> for the list of allowed submodel names.</p></td>
-    </tr>
-    <tr>
-      <th>to</th>
-      <td><p>Vector of the names of the state variable to which a
-transformation shall be included in the model.</p></td>
-    </tr>
-    <tr>
-      <th>sink</th>
-      <td><p>Should a pathway to sink be included in the model in addition to
-the pathways to other state variables?</p></td>
-    </tr>
-    <tr>
-      <th>full_name</th>
-      <td><p>An optional name to be used e.g. for plotting fits
-performed with the model.  You can use non-ASCII characters here, but then
-your R code will not be portable, <em>i.e.</em> may produce unintended plot
-results on other operating systems or system configurations.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
-
-    <p>A list for use with <code><a href='mkinmod.html'>mkinmod</a></code>.</p>
-    <h2 class="hasAnchor" id="author"><a class="anchor" href="#author"></a>Author</h2>
-
-    <p>Johannes Ranke</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'>
-<span class='co'># One parent compound, one metabolite, both single first order.</span>
-<span class='va'>SFO_SFO</span> <span class='op'>&lt;-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span><span class='op'>(</span>
-  parent <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span>type <span class='op'>=</span> <span class='st'>"SFO"</span>, to <span class='op'>=</span> <span class='st'>"m1"</span><span class='op'>)</span>,
-  m1 <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span>type <span class='op'>=</span> <span class='st'>"SFO"</span><span class='op'>)</span><span class='op'>)</span>
-</div><div class='output co'>#&gt; <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'>
-<span class='co'># The same model using mkinsub</span>
-<span class='va'>SFO_SFO.2</span> <span class='op'>&lt;-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span><span class='op'>(</span>
-  parent <span class='op'>=</span> <span class='fu'>mkinsub</span><span class='op'>(</span><span class='st'>"SFO"</span>, <span class='st'>"m1"</span><span class='op'>)</span>,
-  m1 <span class='op'>=</span> <span class='fu'>mkinsub</span><span class='op'>(</span><span class='st'>"SFO"</span><span class='op'>)</span><span class='op'>)</span>
-</div><div class='output co'>#&gt; <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'>
-<span class='co'># \dontrun{</span>
-  <span class='co'># Now supplying full names</span>
-  <span class='va'>SFO_SFO.2</span> <span class='op'>&lt;-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span><span class='op'>(</span>
-    parent <span class='op'>=</span> <span class='fu'>mkinsub</span><span class='op'>(</span><span class='st'>"SFO"</span>, <span class='st'>"m1"</span>, full_name <span class='op'>=</span> <span class='st'>"Test compound"</span><span class='op'>)</span>,
-    m1 <span class='op'>=</span> <span class='fu'>mkinsub</span><span class='op'>(</span><span class='st'>"SFO"</span>, full_name <span class='op'>=</span> <span class='st'>"Metabolite M1"</span><span class='op'>)</span><span class='op'>)</span>
-</div><div class='output co'>#&gt; <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'> <span class='co'># }</span>
-</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
-
-
-      <footer>
-      <div class="copyright">
-  <p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p>
-</div>
-
-      </footer>
-   </div>
-
-  
-
-
-  </body>
 </html>
 
-
diff --git a/docs/reference/mmkin-1.png b/docs/reference/mmkin-1.png
index 2361d3dc..060775ff 100644
--- a/docs/reference/mmkin-1.png
+++ b/docs/reference/mmkin-1.png
diff --git a/docs/reference/mmkin-2.png b/docs/reference/mmkin-2.png
index 6ce2010f..242a50fa 100644
--- a/docs/reference/mmkin-2.png
+++ b/docs/reference/mmkin-2.png
diff --git a/docs/reference/mmkin-3.png b/docs/reference/mmkin-3.png
index 5d56da86..ef013dea 100644
--- a/docs/reference/mmkin-3.png
+++ b/docs/reference/mmkin-3.png
diff --git a/docs/reference/mmkin-4.png b/docs/reference/mmkin-4.png
index 132380a8..c08d231d 100644
--- a/docs/reference/mmkin-4.png
+++ b/docs/reference/mmkin-4.png
diff --git a/docs/reference/mmkin-5.png b/docs/reference/mmkin-5.png
index 4bfcc55e..fe5797b4 100644
--- a/docs/reference/mmkin-5.png
+++ b/docs/reference/mmkin-5.png
diff --git a/docs/reference/mmkin.html b/docs/reference/mmkin.html
index 57039604..66ea275d 100644
--- a/docs/reference/mmkin.html
+++ b/docs/reference/mmkin.html
@@ -1,123 +1,74 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Fit one or more kinetic models with one or more state variables to one or
-more datasets — mmkin • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Fit one or more kinetic models with one or more state variables to one or
-more datasets — mmkin"><meta property="og:description" content="This function calls mkinfit on all combinations of models and
-datasets specified in its first two arguments."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Fit one or more kinetic models with one or more state variables to one or more datasets — mmkin • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Fit one or more kinetic models with one or more state variables to one or more datasets — mmkin"><meta name="description" content="This function calls mkinfit on all combinations of models and
+datasets specified in its first two arguments."><meta property="og:description" content="This function calls mkinfit on all combinations of models and
+datasets specified in its first two arguments."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Fit one or more kinetic models with one or more state variables to one or
-more datasets</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/mmkin.R" class="external-link"><code>R/mmkin.R</code></a></small>
-    <div class="hidden name"><code>mmkin.Rd</code></div>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Fit one or more kinetic models with one or more state variables to one or more datasets</h1>
+      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/mmkin.R" class="external-link"><code>R/mmkin.R</code></a></small>
+      <div class="d-none name"><code>mmkin.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>This function calls <code><a href="mkinfit.html">mkinfit</a></code> on all combinations of models and
 datasets specified in its first two arguments.</p>
     </div>
 
-    <div id="ref-usage">
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">mmkin</span><span class="op">(</span></span>
 <span>  models <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"FOMC"</span>, <span class="st">"DFOP"</span><span class="op">)</span>,</span>
 <span>  <span class="va">datasets</span>,</span>
@@ -126,24 +77,26 @@ datasets specified in its first two arguments.</p>
 <span>  <span class="va">...</span></span>
 <span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for mmkin</span></span>
+<span><span class="co"># S3 method for class 'mmkin'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>models</dt>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-models">models<a class="anchor" aria-label="anchor" href="#arg-models"></a></dt>
 <dd><p>Either a character vector of shorthand names like
 <code>c("SFO", "FOMC", "DFOP", "HS", "SFORB")</code>, or an optionally named
 list of <code><a href="mkinmod.html">mkinmod</a></code> objects.</p></dd>
 
 
-<dt>datasets</dt>
+<dt id="arg-datasets">datasets<a class="anchor" aria-label="anchor" href="#arg-datasets"></a></dt>
 <dd><p>An optionally named list of datasets suitable as observed
 data for <code><a href="mkinfit.html">mkinfit</a></code>.</p></dd>
 
 
-<dt>cores</dt>
+<dt id="arg-cores">cores<a class="anchor" aria-label="anchor" href="#arg-cores"></a></dt>
 <dd><p>The number of cores to be used for multicore processing. This
 is only used when the <code>cluster</code> argument is <code>NULL</code>. On Windows
 machines, cores &gt; 1 is not supported, you need to use the <code>cluster</code>
@@ -152,42 +105,38 @@ detected by <code><a href="https://rdrr.io/r/parallel/detectCores.html" class="e
 the default is 1.</p></dd>
 
 
-<dt>cluster</dt>
+<dt id="arg-cluster">cluster<a class="anchor" aria-label="anchor" href="#arg-cluster"></a></dt>
 <dd><p>A cluster as returned by <code>makeCluster</code> to be used
 for parallel execution.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Not used.</p></dd>
 
 
-<dt>x</dt>
+<dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>An mmkin object.</p></dd>
 
 </dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>A two-dimensional <code><a href="https://rdrr.io/r/base/array.html" class="external-link">array</a></code> of <code><a href="mkinfit.html">mkinfit</a></code></p>
-
-
-<p>objects and/or try-errors that can be indexed using the model names for the
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>A two-dimensional <code><a href="https://rdrr.io/r/base/array.html" class="external-link">array</a></code> of <code><a href="mkinfit.html">mkinfit</a></code>
+objects and/or try-errors that can be indexed using the model names for the
 first index (row index) and the dataset names for the second index (column
 index).</p>
     </div>
-    <div id="see-also">
-    <h2>See also</h2>
+    <div class="section level2">
+    <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
     <div class="dont-index"><p><code><a href="Extract.mmkin.html">[.mmkin</a></code> for subsetting, <code><a href="plot.mmkin.html">plot.mmkin</a></code> for
 plotting.</p></div>
     </div>
-    <div id="author">
-    <h2>Author</h2>
+    <div class="section level2">
+    <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a></h2>
     <p>Johannes Ranke</p>
     </div>
 
-    <div id="ref-examples">
-    <h2>Examples</h2>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># \dontrun{</span></span></span>
 <span class="r-in"><span><span class="va">m_synth_SFO_lin</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"M1"</span><span class="op">)</span>,</span></span>
@@ -209,10 +158,10 @@ plotting.</p></div>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="va">time_default</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>    user  system elapsed </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   3.540   1.287   1.429 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   1.608   0.866   0.732 </span>
 <span class="r-in"><span><span class="va">time_1</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>    user  system elapsed </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   4.074   0.037   4.142 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   1.964   0.016   1.980 </span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="fu"><a href="endpoints.html">endpoints</a></span><span class="op">(</span><span class="va">fits.0</span><span class="op">[[</span><span class="st">"SFO_lin"</span>, <span class="fl">2</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $ff</span>
@@ -268,27 +217,23 @@ plotting.</p></div>
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/multistart-1.png b/docs/reference/multistart-1.png
index cf3cdca3..d326b0c4 100644
--- a/docs/reference/multistart-1.png
+++ b/docs/reference/multistart-1.png
diff --git a/docs/reference/multistart-2.png b/docs/reference/multistart-2.png
index 05a160d0..b6ae3051 100644
--- a/docs/reference/multistart-2.png
+++ b/docs/reference/multistart-2.png
diff --git a/docs/reference/multistart.html b/docs/reference/multistart.html
index 840f0e01..af5f614e 100644
--- a/docs/reference/multistart.html
+++ b/docs/reference/multistart.html
@@ -1,119 +1,76 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Perform a hierarchical model fit with multiple starting values — multistart • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Perform a hierarchical model fit with multiple starting values — multistart"><meta property="og:description" content="The purpose of this method is to check if a certain algorithm for fitting
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Perform a hierarchical model fit with multiple starting values — multistart • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Perform a hierarchical model fit with multiple starting values — multistart"><meta name="description" content="The purpose of this method is to check if a certain algorithm for fitting
 nonlinear hierarchical models (also known as nonlinear mixed-effects models)
 will reliably yield results that are sufficiently similar to each other, if
 started with a certain range of reasonable starting parameters. It is
 inspired by the article on practical identifiabiliy in the frame of nonlinear
-mixed-effects models by Duchesne et al (2021)."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+mixed-effects models by Duchesne et al (2021)."><meta property="og:description" content="The purpose of this method is to check if a certain algorithm for fitting
+nonlinear hierarchical models (also known as nonlinear mixed-effects models)
+will reliably yield results that are sufficiently similar to each other, if
+started with a certain range of reasonable starting parameters. It is
+inspired by the article on practical identifiabiliy in the frame of nonlinear
+mixed-effects models by Duchesne et al (2021)."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Perform a hierarchical model fit with multiple starting values</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/multistart.R" class="external-link"><code>R/multistart.R</code></a></small>
-    <div class="hidden name"><code>multistart.Rd</code></div>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Perform a hierarchical model fit with multiple starting values</h1>
+      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/multistart.R" class="external-link"><code>R/multistart.R</code></a></small>
+      <div class="d-none name"><code>multistart.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>The purpose of this method is to check if a certain algorithm for fitting
 nonlinear hierarchical models (also known as nonlinear mixed-effects models)
 will reliably yield results that are sufficiently similar to each other, if
@@ -122,7 +79,8 @@ inspired by the article on practical identifiabiliy in the frame of nonlinear
 mixed-effects models by Duchesne et al (2021).</p>
     </div>
 
-    <div id="ref-usage">
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">multistart</span><span class="op">(</span></span>
 <span>  <span class="va">object</span>,</span>
 <span>  n <span class="op">=</span> <span class="fl">50</span>,</span>
@@ -131,78 +89,74 @@ mixed-effects models by Duchesne et al (2021).</p>
 <span>  <span class="va">...</span></span>
 <span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for saem.mmkin</span></span>
+<span><span class="co"># S3 method for class 'saem.mmkin'</span></span>
 <span><span class="fu">multistart</span><span class="op">(</span><span class="va">object</span>, n <span class="op">=</span> <span class="fl">50</span>, cores <span class="op">=</span> <span class="fl">1</span>, cluster <span class="op">=</span> <span class="cn">NULL</span>, <span class="va">...</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for multistart</span></span>
+<span><span class="co"># S3 method for class 'multistart'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span></span>
 <span></span>
 <span><span class="fu">best</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for default</span></span>
+<span><span class="co"># Default S3 method</span></span>
 <span><span class="fu">best</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span>
 <span></span>
 <span><span class="fu">which.best</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for default</span></span>
+<span><span class="co"># Default S3 method</span></span>
 <span><span class="fu">which.best</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>object</dt>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
 <dd><p>The fit object to work with</p></dd>
 
 
-<dt>n</dt>
+<dt id="arg-n">n<a class="anchor" aria-label="anchor" href="#arg-n"></a></dt>
 <dd><p>How many different combinations of starting parameters should be
 used?</p></dd>
 
 
-<dt>cores</dt>
+<dt id="arg-cores">cores<a class="anchor" aria-label="anchor" href="#arg-cores"></a></dt>
 <dd><p>How many fits should be run in parallel (only on posix platforms)?</p></dd>
 
 
-<dt>cluster</dt>
+<dt id="arg-cluster">cluster<a class="anchor" aria-label="anchor" href="#arg-cluster"></a></dt>
 <dd><p>A cluster as returned by <a href="https://rdrr.io/r/parallel/makeCluster.html" class="external-link">parallel::makeCluster</a> to be used
 for parallel execution.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Passed to the update function.</p></dd>
 
 
-<dt>x</dt>
+<dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>The multistart object to print</p></dd>
 
 </dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>A list of <a href="saem.html">saem.mmkin</a> objects, with class attributes
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>A list of <a href="saem.html">saem.mmkin</a> objects, with class attributes
 'multistart.saem.mmkin' and 'multistart'.</p>
-
-
 <p>The object with the highest likelihood</p>
-
-
 <p>The index of the object with the highest likelihood</p>
     </div>
-    <div id="references">
-    <h2>References</h2>
+    <div class="section level2">
+    <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
     <p>Duchesne R, Guillemin A, Gandrillon O, Crauste F. Practical
 identifiability in the frame of nonlinear mixed effects models: the example
 of the in vitro erythropoiesis. BMC Bioinformatics. 2021 Oct 4;22(1):478.
 doi: 10.1186/s12859-021-04373-4.</p>
     </div>
-    <div id="see-also">
-    <h2>See also</h2>
+    <div class="section level2">
+    <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
     <div class="dont-index"><p><a href="parplot.html">parplot</a>, <a href="llhist.html">llhist</a></p></div>
     </div>
 
-    <div id="ref-examples">
-    <h2>Examples</h2>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># \dontrun{</span></span></span>
 <span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://pkgdown.jrwb.de/mkin/">mkin</a></span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">dmta_ds</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fl">7</span>, <span class="kw">function</span><span class="op">(</span><span class="va">i</span><span class="op">)</span> <span class="op">{</span></span></span>
@@ -218,7 +172,7 @@ doi: 10.1186/s12859-021-04373-4.</p>
 <span class="r-in"><span><span class="va">f_mmkin</span> <span class="op">&lt;-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="va">dmta_ds</span>, error_model <span class="op">=</span> <span class="st">"tc"</span>, cores <span class="op">=</span> <span class="fl">7</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">f_saem_full</span> <span class="op">&lt;-</span> <span class="fu"><a href="saem.html">saem</a></span><span class="op">(</span><span class="va">f_mmkin</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">f_saem_full_multi</span> <span class="op">&lt;-</span> <span class="fu">multistart</span><span class="op">(</span><span class="va">f_saem_full</span>, n <span class="op">=</span> <span class="fl">16</span>, cores <span class="op">=</span> <span class="fl">16</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="parplot.html">parplot</a></span><span class="op">(</span><span class="va">f_saem_full_multi</span>, lpos <span class="op">=</span> <span class="st">"topleft"</span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="fu"><a href="parplot.html">parplot</a></span><span class="op">(</span><span class="va">f_saem_full_multi</span>, lpos <span class="op">=</span> <span class="st">"topleft"</span>, las <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span></span>
 <span class="r-plt img"><img src="multistart-1.png" alt="" width="700" height="433"></span>
 <span class="r-in"><span><span class="fu"><a href="illparms.html">illparms</a></span><span class="op">(</span><span class="va">f_saem_full</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] "sd(log_k2)"</span>
@@ -230,33 +184,29 @@ doi: 10.1186/s12859-021-04373-4.</p>
 <span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va">parallel</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">cl</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/parallel/makeCluster.html" class="external-link">makePSOCKcluster</a></span><span class="op">(</span><span class="fl">12</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">f_saem_reduced_multi</span> <span class="op">&lt;-</span> <span class="fu">multistart</span><span class="op">(</span><span class="va">f_saem_reduced</span>, n <span class="op">=</span> <span class="fl">16</span>, cluster <span class="op">=</span> <span class="va">cl</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="parplot.html">parplot</a></span><span class="op">(</span><span class="va">f_saem_reduced_multi</span>, lpos <span class="op">=</span> <span class="st">"topright"</span>, ylim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.5</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="fu"><a href="parplot.html">parplot</a></span><span class="op">(</span><span class="va">f_saem_reduced_multi</span>, lpos <span class="op">=</span> <span class="st">"topright"</span>, ylim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.5</span>, <span class="fl">2</span><span class="op">)</span>, las <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span></span>
 <span class="r-plt img"><img src="multistart-2.png" alt="" width="700" height="433"></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/parallel/makeCluster.html" class="external-link">stopCluster</a></span><span class="op">(</span><span class="va">cl</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="co"># }</span></span></span>
 </code></pre></div>
     </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/multistart.saem.mmkin.html b/docs/reference/multistart.saem.mmkin.html
new file mode 100644
index 00000000..9700ef05
--- /dev/null
+++ b/docs/reference/multistart.saem.mmkin.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/multistart.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/multistart.html">
+  </head>
+</html>
+
diff --git a/docs/reference/nafta-1.png b/docs/reference/nafta-1.png
index 5d2d434b..98d4246c 100644
--- a/docs/reference/nafta-1.png
+++ b/docs/reference/nafta-1.png
diff --git a/docs/reference/nafta.html b/docs/reference/nafta.html
index 011b766a..07dc0f42 100644
--- a/docs/reference/nafta.html
+++ b/docs/reference/nafta.html
@@ -1,118 +1,74 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Evaluate parent kinetics using the NAFTA guidance — nafta • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Evaluate parent kinetics using the NAFTA guidance — nafta"><meta property="og:description" content="The function fits the SFO, IORE and DFOP models using mmkin
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Evaluate parent kinetics using the NAFTA guidance — nafta • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Evaluate parent kinetics using the NAFTA guidance — nafta"><meta name="description" content="The function fits the SFO, IORE and DFOP models using mmkin
 and returns an object of class nafta that has methods for printing
 and plotting.
 Print nafta objects. The results for the three models are printed in the
-order of increasing model complexity, i.e. SFO, then IORE, and finally DFOP."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+order of increasing model complexity, i.e. SFO, then IORE, and finally DFOP."><meta property="og:description" content="The function fits the SFO, IORE and DFOP models using mmkin
+and returns an object of class nafta that has methods for printing
+and plotting.
+Print nafta objects. The results for the three models are printed in the
+order of increasing model complexity, i.e. SFO, then IORE, and finally DFOP."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Evaluate parent kinetics using the NAFTA guidance</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/nafta.R" class="external-link"><code>R/nafta.R</code></a></small>
-    <div class="hidden name"><code>nafta.Rd</code></div>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Evaluate parent kinetics using the NAFTA guidance</h1>
+      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/nafta.R" class="external-link"><code>R/nafta.R</code></a></small>
+      <div class="d-none name"><code>nafta.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>The function fits the SFO, IORE and DFOP models using <code><a href="mmkin.html">mmkin</a></code>
 and returns an object of class <code>nafta</code> that has methods for printing
 and plotting.</p>
@@ -120,15 +76,16 @@ and plotting.</p>
 order of increasing model complexity, i.e. SFO, then IORE, and finally DFOP.</p>
     </div>
 
-    <div id="ref-usage">
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">nafta</span><span class="op">(</span><span class="va">ds</span>, title <span class="op">=</span> <span class="cn">NA</span>, quiet <span class="op">=</span> <span class="cn">FALSE</span>, <span class="va">...</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for nafta</span></span>
+<span><span class="co"># S3 method for class 'nafta'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, digits <span class="op">=</span> <span class="fl">3</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
-    <div id="source">
-    <h2>Source</h2>
+    <div class="section level2">
+    <h2 id="source">Source<a class="anchor" aria-label="anchor" href="#source"></a></h2>
     <p>NAFTA (2011) Guidance for evaluating and calculating degradation
 kinetics in environmental media. NAFTA Technical Working Group on
 Pesticides
@@ -139,53 +96,53 @@ Calculate Representative Half-life Values and Characterizing Pesticide
 Degradation
 <a href="https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/standard-operating-procedure-using-nafta-guidance" class="external-link">https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/standard-operating-procedure-using-nafta-guidance</a></p>
     </div>
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>ds</dt>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-ds">ds<a class="anchor" aria-label="anchor" href="#arg-ds"></a></dt>
 <dd><p>A dataframe that must contain one variable called "time" with the
 time values specified by the <code>time</code> argument, one column called
 "name" with the grouping of the observed values, and finally one column of
 observed values called "value".</p></dd>
 
 
-<dt>title</dt>
+<dt id="arg-title">title<a class="anchor" aria-label="anchor" href="#arg-title"></a></dt>
 <dd><p>Optional title of the dataset</p></dd>
 
 
-<dt>quiet</dt>
+<dt id="arg-quiet">quiet<a class="anchor" aria-label="anchor" href="#arg-quiet"></a></dt>
 <dd><p>Should the evaluation text be shown?</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Further arguments passed to <code><a href="mmkin.html">mmkin</a></code> (not for the
 printing method).</p></dd>
 
 
-<dt>x</dt>
+<dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>An <code>nafta</code> object.</p></dd>
 
 
-<dt>digits</dt>
+<dt id="arg-digits">digits<a class="anchor" aria-label="anchor" href="#arg-digits"></a></dt>
 <dd><p>Number of digits to be used for printing parameters and
 dissipation times.</p></dd>
 
 </dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>An list of class <code>nafta</code>. The list element named "mmkin" is the
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>An list of class <code>nafta</code>. The list element named "mmkin" is the
 <code><a href="mmkin.html">mmkin</a></code> object containing the fits of the three models. The
 list element named "title" contains the title of the dataset used. The
 list element "data" contains the dataset used in the fits.</p>
     </div>
-    <div id="author">
-    <h2>Author</h2>
+    <div class="section level2">
+    <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a></h2>
     <p>Johannes Ranke</p>
     </div>
 
-    <div id="ref-examples">
-    <h2>Examples</h2>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span>
 <span class="r-in"><span>  <span class="va">nafta_evaluation</span> <span class="op">&lt;-</span> <span class="fu">nafta</span><span class="op">(</span><span class="va">NAFTA_SOP_Appendix_D</span>, cores <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span>
@@ -236,27 +193,23 @@ list element "data" contains the dataset used in the fits.</p>
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/nlme-1.png b/docs/reference/nlme-1.png
index 0134107f..9583da2a 100644
--- a/docs/reference/nlme-1.png
+++ b/docs/reference/nlme-1.png
diff --git a/docs/reference/nlme-2.png b/docs/reference/nlme-2.png
index 95739890..08e3b642 100644
--- a/docs/reference/nlme-2.png
+++ b/docs/reference/nlme-2.png
diff --git a/docs/reference/nlme.html b/docs/reference/nlme.html
index 26c56e54..8bf352af 100644
--- a/docs/reference/nlme.html
+++ b/docs/reference/nlme.html
@@ -1,151 +1,105 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Helper functions to create nlme models from mmkin row objects — nlme_function • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Helper functions to create nlme models from mmkin row objects — nlme_function"><meta property="og:description" content="These functions facilitate setting up a nonlinear mixed effects model for
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Helper functions to create nlme models from mmkin row objects — nlme_function • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Helper functions to create nlme models from mmkin row objects — nlme_function"><meta name="description" content="These functions facilitate setting up a nonlinear mixed effects model for
 an mmkin row object. An mmkin row object is essentially a list of mkinfit
 objects that have been obtained by fitting the same model to a list of
-datasets. They are used internally by the nlme.mmkin() method."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+datasets. They are used internally by the nlme.mmkin() method."><meta property="og:description" content="These functions facilitate setting up a nonlinear mixed effects model for
+an mmkin row object. An mmkin row object is essentially a list of mkinfit
+objects that have been obtained by fitting the same model to a list of
+datasets. They are used internally by the nlme.mmkin() method."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Helper functions to create nlme models from mmkin row objects</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/nlme.R" class="external-link"><code>R/nlme.R</code></a></small>
-    <div class="hidden name"><code>nlme.Rd</code></div>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Helper functions to create nlme models from mmkin row objects</h1>
+      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/nlme.R" class="external-link"><code>R/nlme.R</code></a></small>
+      <div class="d-none name"><code>nlme.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>These functions facilitate setting up a nonlinear mixed effects model for
 an mmkin row object. An mmkin row object is essentially a list of mkinfit
 objects that have been obtained by fitting the same model to a list of
 datasets. They are used internally by the <code><a href="nlme.mmkin.html">nlme.mmkin()</a></code> method.</p>
     </div>
 
-    <div id="ref-usage">
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">nlme_function</span><span class="op">(</span><span class="va">object</span><span class="op">)</span></span>
 <span></span>
 <span><span class="fu">nlme_data</span><span class="op">(</span><span class="va">object</span><span class="op">)</span></span></code></pre></div>
     </div>
 
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>object</dt>
-<dd><p>An mmkin row object containing several fits of the same model to different datasets</p></dd>
-
-</dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
 
-<p>A function that can be used with nlme</p>
 
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
+<dd><p>An mmkin row object containing several fits of the same model to different datasets</p></dd>
 
-<p>A <code><a href="https://rdrr.io/pkg/nlme/man/groupedData.html" class="external-link">groupedData</a></code> object</p>
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>A function that can be used with nlme</p>
+<p>A <code><a href="https://rdrr.io/pkg/nlme/man/groupedData.html" class="external-link">nlme::groupedData</a></code> object</p>
     </div>
-    <div id="see-also">
-    <h2>See also</h2>
+    <div class="section level2">
+    <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
     <div class="dont-index"><p><code><a href="nlme.mmkin.html">nlme.mmkin</a></code></p></div>
     </div>
 
-    <div id="ref-examples">
-    <h2>Examples</h2>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">sampling_times</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span>, <span class="fl">3</span>, <span class="fl">7</span>, <span class="fl">14</span>, <span class="fl">28</span>, <span class="fl">60</span>, <span class="fl">90</span>, <span class="fl">120</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">m_SFO</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">d_SFO_1</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinpredict.html">mkinpredict</a></span><span class="op">(</span><span class="va">m_SFO</span>,</span></span>
@@ -221,27 +175,23 @@ datasets. They are used internally by the <code><a href="nlme.mmkin.html">nlme.m
 <span class="r-plt img"><img src="nlme-2.png" alt="" width="700" height="433"></span>
 </code></pre></div>
     </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/nlme.mmkin-1.png b/docs/reference/nlme.mmkin-1.png
index 7cabb086..a940da0c 100644
--- a/docs/reference/nlme.mmkin-1.png
+++ b/docs/reference/nlme.mmkin-1.png
diff --git a/docs/reference/nlme.mmkin-2.png b/docs/reference/nlme.mmkin-2.png
index b9a68e92..b12ddb73 100644
--- a/docs/reference/nlme.mmkin-2.png
+++ b/docs/reference/nlme.mmkin-2.png
diff --git a/docs/reference/nlme.mmkin-3.png b/docs/reference/nlme.mmkin-3.png
index ce7999bd..629fe7d2 100644
--- a/docs/reference/nlme.mmkin-3.png
+++ b/docs/reference/nlme.mmkin-3.png
diff --git a/docs/reference/nlme.mmkin-4.png b/docs/reference/nlme.mmkin-4.png
deleted file mode 100644
index d7c68dd5..00000000
--- a/docs/reference/nlme.mmkin-4.png
+++ /dev/null
diff --git a/docs/reference/nlme.mmkin-5.png b/docs/reference/nlme.mmkin-5.png
deleted file mode 100644
index 2c299b08..00000000
--- a/docs/reference/nlme.mmkin-5.png
+++ /dev/null
diff --git a/docs/reference/nlme.mmkin-6.png b/docs/reference/nlme.mmkin-6.png
deleted file mode 100644
index 2bcb9cec..00000000
--- a/docs/reference/nlme.mmkin-6.png
+++ /dev/null
diff --git a/docs/reference/nlme.mmkin-7.png b/docs/reference/nlme.mmkin-7.png
deleted file mode 100644
index 30e2d351..00000000
--- a/docs/reference/nlme.mmkin-7.png
+++ /dev/null
diff --git a/docs/reference/nlme.mmkin.html b/docs/reference/nlme.mmkin.html
index 05fbac5c..4f124b07 100644
--- a/docs/reference/nlme.mmkin.html
+++ b/docs/reference/nlme.mmkin.html
@@ -1,123 +1,78 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Create an nlme model for an mmkin row object — nlme.mmkin • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Create an nlme model for an mmkin row object — nlme.mmkin"><meta property="og:description" content="This functions sets up a nonlinear mixed effects model for an mmkin row
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Create an nlme model for an mmkin row object — nlme.mmkin • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Create an nlme model for an mmkin row object — nlme.mmkin"><meta name="description" content="This functions sets up a nonlinear mixed effects model for an mmkin row
 object. An mmkin row object is essentially a list of mkinfit objects that
-have been obtained by fitting the same model to a list of datasets."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+have been obtained by fitting the same model to a list of datasets."><meta property="og:description" content="This functions sets up a nonlinear mixed effects model for an mmkin row
+object. An mmkin row object is essentially a list of mkinfit objects that
+have been obtained by fitting the same model to a list of datasets."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Create an nlme model for an mmkin row object</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/nlme.mmkin.R" class="external-link"><code>R/nlme.mmkin.R</code></a></small>
-    <div class="hidden name"><code>nlme.mmkin.Rd</code></div>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Create an nlme model for an mmkin row object</h1>
+      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/nlme.mmkin.R" class="external-link"><code>R/nlme.mmkin.R</code></a></small>
+      <div class="d-none name"><code>nlme.mmkin.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>This functions sets up a nonlinear mixed effects model for an mmkin row
 object. An mmkin row object is essentially a list of mkinfit objects that
 have been obtained by fitting the same model to a list of datasets.</p>
     </div>
 
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for mmkin</span></span>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'mmkin'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a></span><span class="op">(</span></span>
 <span>  <span class="va">model</span>,</span>
 <span>  data <span class="op">=</span> <span class="st">"auto"</span>,</span>
@@ -136,118 +91,118 @@ have been obtained by fitting the same model to a list of datasets.</p>
 <span>  verbose <span class="op">=</span> <span class="cn">FALSE</span></span>
 <span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for nlme.mmkin</span></span>
+<span><span class="co"># S3 method for class 'nlme.mmkin'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, digits <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html" class="external-link">max</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fu"><a href="https://rdrr.io/r/base/options.html" class="external-link">getOption</a></span><span class="op">(</span><span class="st">"digits"</span><span class="op">)</span> <span class="op">-</span> <span class="fl">3</span><span class="op">)</span>, <span class="va">...</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for nlme.mmkin</span></span>
+<span><span class="co"># S3 method for class 'nlme.mmkin'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>model</dt>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-model">model<a class="anchor" aria-label="anchor" href="#arg-model"></a></dt>
 <dd><p>An <a href="mmkin.html">mmkin</a> row object.</p></dd>
 
 
-<dt>data</dt>
+<dt id="arg-data">data<a class="anchor" aria-label="anchor" href="#arg-data"></a></dt>
 <dd><p>Ignored, data are taken from the mmkin model</p></dd>
 
 
-<dt>fixed</dt>
+<dt id="arg-fixed">fixed<a class="anchor" aria-label="anchor" href="#arg-fixed"></a></dt>
 <dd><p>Ignored, all degradation parameters fitted in the
 mmkin model are used as fixed parameters</p></dd>
 
 
-<dt>random</dt>
+<dt id="arg-random">random<a class="anchor" aria-label="anchor" href="#arg-random"></a></dt>
 <dd><p>If not specified, no correlations between random effects are
 set up for the optimised degradation model parameters. This is
 achieved by using the <a href="https://rdrr.io/pkg/nlme/man/pdDiag.html" class="external-link">nlme::pdDiag</a> method.</p></dd>
 
 
-<dt>groups</dt>
+<dt id="arg-groups">groups<a class="anchor" aria-label="anchor" href="#arg-groups"></a></dt>
 <dd><p>See the documentation of nlme</p></dd>
 
 
-<dt>start</dt>
+<dt id="arg-start">start<a class="anchor" aria-label="anchor" href="#arg-start"></a></dt>
 <dd><p>If not specified, mean values of the fitted degradation
 parameters taken from the mmkin object are used</p></dd>
 
 
-<dt>correlation</dt>
+<dt id="arg-correlation">correlation<a class="anchor" aria-label="anchor" href="#arg-correlation"></a></dt>
 <dd><p>See the documentation of nlme</p></dd>
 
 
-<dt>weights</dt>
+<dt id="arg-weights">weights<a class="anchor" aria-label="anchor" href="#arg-weights"></a></dt>
 <dd><p>passed to nlme</p></dd>
 
 
-<dt>subset</dt>
+<dt id="arg-subset">subset<a class="anchor" aria-label="anchor" href="#arg-subset"></a></dt>
 <dd><p>passed to nlme</p></dd>
 
 
-<dt>method</dt>
+<dt id="arg-method">method<a class="anchor" aria-label="anchor" href="#arg-method"></a></dt>
 <dd><p>passed to nlme</p></dd>
 
 
-<dt>na.action</dt>
+<dt id="arg-na-action">na.action<a class="anchor" aria-label="anchor" href="#arg-na-action"></a></dt>
 <dd><p>passed to nlme</p></dd>
 
 
-<dt>naPattern</dt>
+<dt id="arg-napattern">naPattern<a class="anchor" aria-label="anchor" href="#arg-napattern"></a></dt>
 <dd><p>passed to nlme</p></dd>
 
 
-<dt>control</dt>
+<dt id="arg-control">control<a class="anchor" aria-label="anchor" href="#arg-control"></a></dt>
 <dd><p>passed to nlme</p></dd>
 
 
-<dt>verbose</dt>
+<dt id="arg-verbose">verbose<a class="anchor" aria-label="anchor" href="#arg-verbose"></a></dt>
 <dd><p>passed to nlme</p></dd>
 
 
-<dt>x</dt>
+<dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>An nlme.mmkin object to print</p></dd>
 
 
-<dt>digits</dt>
+<dt id="arg-digits">digits<a class="anchor" aria-label="anchor" href="#arg-digits"></a></dt>
 <dd><p>Number of digits to use for printing</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Update specifications passed to update.nlme</p></dd>
 
 
-<dt>object</dt>
+<dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
 <dd><p>An nlme.mmkin object to update</p></dd>
 
 </dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>Upon success, a fitted 'nlme.mmkin' object, which is an nlme object
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>Upon success, a fitted 'nlme.mmkin' object, which is an nlme object
 with additional elements. It also inherits from 'mixed.mmkin'.</p>
     </div>
-    <div id="details">
-    <h2>Details</h2>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>Note that the convergence of the nlme algorithms depends on the quality
 of the data. In degradation kinetics, we often only have few datasets
 (e.g. data for few soils) and complicated degradation models, which may
 make it impossible to obtain convergence with nlme.</p>
     </div>
-    <div id="note">
-    <h2>Note</h2>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>As the object inherits from <a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme::nlme</a>, there is a wealth of
 methods that will automatically work on 'nlme.mmkin' objects, such as
 <code><a href="https://rdrr.io/pkg/nlme/man/intervals.html" class="external-link">nlme::intervals()</a></code>, <code><a href="https://rdrr.io/pkg/nlme/man/anova.lme.html" class="external-link">nlme::anova.lme()</a></code> and <code><a href="https://rdrr.io/pkg/nlme/man/coef.lme.html" class="external-link">nlme::coef.lme()</a></code>.</p>
     </div>
-    <div id="see-also">
-    <h2>See also</h2>
+    <div class="section level2">
+    <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
     <div class="dont-index"><p><code><a href="nlme.html">nlme_function()</a></code>, <a href="plot.mixed.mmkin.html">plot.mixed.mmkin</a>, <a href="summary.nlme.mmkin.html">summary.nlme.mmkin</a></p></div>
     </div>
 
-    <div id="ref-examples">
-    <h2>Examples</h2>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">ds</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="va">experimental_data_for_UBA_2019</span><span class="op">[</span><span class="fl">6</span><span class="op">:</span><span class="fl">10</span><span class="op">]</span>,</span></span>
 <span class="r-in"><span> <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fu"><a href="https://rdrr.io/r/base/subset.html" class="external-link">subset</a></span><span class="op">(</span><span class="va">x</span><span class="op">$</span><span class="va">data</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"name"</span>, <span class="st">"time"</span>, <span class="st">"value"</span><span class="op">)</span><span class="op">]</span>, <span class="va">name</span> <span class="op">==</span> <span class="st">"parent"</span><span class="op">)</span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
@@ -438,27 +393,23 @@ methods that will automatically work on 'nlme.mmkin' objects, such as
 <span class="r-in"><span><span class="co"># }</span></span></span>
 </code></pre></div>
     </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/nlme_data.html b/docs/reference/nlme_data.html
new file mode 100644
index 00000000..57c00862
--- /dev/null
+++ b/docs/reference/nlme_data.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/nlme.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/nlme.html">
+  </head>
+</html>
+
diff --git a/docs/reference/nlmixr.mmkin-1.png b/docs/reference/nlmixr.mmkin-1.png
deleted file mode 100644
index 42a266e5..00000000
--- a/docs/reference/nlmixr.mmkin-1.png
+++ /dev/null
diff --git a/docs/reference/nlmixr.mmkin-2.png b/docs/reference/nlmixr.mmkin-2.png
deleted file mode 100644
index 0c440fa8..00000000
--- a/docs/reference/nlmixr.mmkin-2.png
+++ /dev/null
diff --git a/docs/reference/nlmixr.mmkin.html b/docs/reference/nlmixr.mmkin.html
deleted file mode 100644
index deb8bacc..00000000
--- a/docs/reference/nlmixr.mmkin.html
+++ /dev/null
@@ -1,15455 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Fit nonlinear mixed models using nlmixr — nlmixr.mmkin • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Fit nonlinear mixed models using nlmixr — nlmixr.mmkin"><meta property="og:description" content="This function uses nlmixr::nlmixr() as a backend for fitting nonlinear mixed
-effects models created from mmkin row objects using the Stochastic Approximation
-Expectation Maximisation algorithm (SAEM) or First Order Conditional
-Estimation with Interaction (FOCEI)."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Functions and data</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Fit nonlinear mixed models using nlmixr</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/nlmixr.R" class="external-link"><code>R/nlmixr.R</code></a></small>
-    <div class="hidden name"><code>nlmixr.mmkin.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>This function uses <code><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr::nlmixr()</a></code> as a backend for fitting nonlinear mixed
-effects models created from <a href="mmkin.html">mmkin</a> row objects using the Stochastic Approximation
-Expectation Maximisation algorithm (SAEM) or First Order Conditional
-Estimation with Interaction (FOCEI).</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="co"># S3 method for mmkin</span>
-<span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr</a></span><span class="op">(</span>
-  <span class="va">object</span>,
-  data <span class="op">=</span> <span class="cn">NULL</span>,
-  est <span class="op">=</span> <span class="cn">NULL</span>,
-  control <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="op">)</span>,
-  table <span class="op">=</span> <span class="fu">tableControl</span><span class="op">(</span><span class="op">)</span>,
-  error_model <span class="op">=</span> <span class="va">object</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">err_mod</span>,
-  test_log_parms <span class="op">=</span> <span class="cn">TRUE</span>,
-  conf.level <span class="op">=</span> <span class="fl">0.6</span>,
-  degparms_start <span class="op">=</span> <span class="st">"auto"</span>,
-  eta_start <span class="op">=</span> <span class="st">"auto"</span>,
-  <span class="va">...</span>,
-  save <span class="op">=</span> <span class="cn">NULL</span>,
-  envir <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sys.parent.html" class="external-link">parent.frame</a></span><span class="op">(</span><span class="op">)</span>
-<span class="op">)</span>
-
-<span class="co"># S3 method for nlmixr.mmkin</span>
-<span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, digits <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html" class="external-link">max</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fu"><a href="https://rdrr.io/r/base/options.html" class="external-link">getOption</a></span><span class="op">(</span><span class="st">"digits"</span><span class="op">)</span> <span class="op">-</span> <span class="fl">3</span><span class="op">)</span>, <span class="va">...</span><span class="op">)</span>
-
-<span class="fu">nlmixr_model</span><span class="op">(</span>
-  <span class="va">object</span>,
-  est <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"saem"</span>, <span class="st">"focei"</span><span class="op">)</span>,
-  degparms_start <span class="op">=</span> <span class="st">"auto"</span>,
-  eta_start <span class="op">=</span> <span class="st">"auto"</span>,
-  test_log_parms <span class="op">=</span> <span class="cn">TRUE</span>,
-  conf.level <span class="op">=</span> <span class="fl">0.6</span>,
-  error_model <span class="op">=</span> <span class="va">object</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">err_mod</span>,
-  add_attributes <span class="op">=</span> <span class="cn">FALSE</span>
-<span class="op">)</span>
-
-<span class="fu">nlmixr_data</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></code></pre></div>
-    </div>
-
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>object</dt>
-<dd><p>An <a href="mmkin.html">mmkin</a> row object containing several fits of the same
-<a href="mkinmod.html">mkinmod</a> model to different datasets</p></dd>
-<dt>data</dt>
-<dd><p>Not used, as the data are extracted from the mmkin row object</p></dd>
-<dt>est</dt>
-<dd><p>Estimation method passed to <a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr::nlmixr</a></p></dd>
-<dt>control</dt>
-<dd><p>Passed to <a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr::nlmixr</a></p></dd>
-<dt>table</dt>
-<dd><p>Passed to <a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr::nlmixr</a></p></dd>
-<dt>error_model</dt>
-<dd><p>Optional argument to override the error model which is
-being set based on the error model used in the mmkin row object.</p></dd>
-<dt>test_log_parms</dt>
-<dd><p>If TRUE, an attempt is made to use more robust starting
-values for population parameters fitted as log parameters in mkin (like
-rate constants) by only considering rate constants that pass the t-test
-when calculating mean degradation parameters using <a href="mean_degparms.html">mean_degparms</a>.</p></dd>
-<dt>conf.level</dt>
-<dd><p>Possibility to adjust the required confidence level
-for parameter that are tested if requested by 'test_log_parms'.</p></dd>
-<dt>degparms_start</dt>
-<dd><p>Parameter values given as a named numeric vector will
-be used to override the starting values obtained from the 'mmkin' object.</p></dd>
-<dt>eta_start</dt>
-<dd><p>Standard deviations on the transformed scale given as a
-named numeric vector will be used to override the starting values obtained
-from the 'mmkin' object.</p></dd>
-<dt>...</dt>
-<dd><p>Passed to nlmixr_model</p></dd>
-<dt>save</dt>
-<dd><p>Passed to <a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr::nlmixr</a></p></dd>
-<dt>envir</dt>
-<dd><p>Passed to <a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr::nlmixr</a></p></dd>
-<dt>x</dt>
-<dd><p>An nlmixr.mmkin object to print</p></dd>
-<dt>digits</dt>
-<dd><p>Number of digits to use for printing</p></dd>
-<dt>add_attributes</dt>
-<dd><p>Should the starting values used for degradation model
-parameters and their distribution and for the error model parameters
-be returned as attributes?</p></dd>
-</dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    <p>An S3 object of class 'nlmixr.mmkin', containing the fitted
-<a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr::nlmixr</a> object as a list component named 'nm'. The
-object also inherits from 'mixed.mmkin'.
-An function defining a model suitable for fitting with <a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr::nlmixr</a>.
-An dataframe suitable for use with <a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr::nlmixr</a></p>
-    </div>
-    <div id="details">
-    <h2>Details</h2>
-    <p>An mmkin row object is essentially a list of mkinfit objects that have been
-obtained by fitting the same model to a list of datasets using <a href="mkinfit.html">mkinfit</a>.</p>
-    </div>
-    <div id="see-also">
-    <h2>See also</h2>
-    <div class="dont-index"><p><a href="summary.nlmixr.mmkin.html">summary.nlmixr.mmkin</a> <a href="plot.mixed.mmkin.html">plot.mixed.mmkin</a></p></div>
-    </div>
-
-    <div id="ref-examples">
-    <h2>Examples</h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="co"># \dontrun{</span></span>
-<span class="r-in"><span class="va">ds</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="va">experimental_data_for_UBA_2019</span><span class="op">[</span><span class="fl">6</span><span class="op">:</span><span class="fl">10</span><span class="op">]</span>,</span>
-<span class="r-in"> <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fu"><a href="https://rdrr.io/r/base/subset.html" class="external-link">subset</a></span><span class="op">(</span><span class="va">x</span><span class="op">$</span><span class="va">data</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"name"</span>, <span class="st">"time"</span>, <span class="st">"value"</span><span class="op">)</span><span class="op">]</span><span class="op">)</span><span class="op">)</span></span>
-<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">ds</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste</a></span><span class="op">(</span><span class="st">"Dataset"</span>, <span class="fl">6</span><span class="op">:</span><span class="fl">10</span><span class="op">)</span></span>
-<span class="r-in"></span>
-<span class="r-in"><span class="va">f_mmkin_parent</span> <span class="op">&lt;-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"FOMC"</span>, <span class="st">"DFOP"</span>, <span class="st">"HS"</span><span class="op">)</span>, <span class="va">ds</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, cores <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span>
-<span class="r-in"><span class="va">f_mmkin_parent_tc</span> <span class="op">&lt;-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"FOMC"</span>, <span class="st">"DFOP"</span><span class="op">)</span>, <span class="va">ds</span>, error_model <span class="op">=</span> <span class="st">"tc"</span>,</span>
-<span class="r-in">  cores <span class="op">=</span> <span class="fl">1</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
-<span class="r-in"></span>
-<span class="r-in"><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/nlmixrdevelopment/nlmixr" class="external-link">nlmixr</a></span><span class="op">)</span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Attaching package: ‘nlmixr’</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> The following object is masked from ‘package:mkin’:</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>     saem</span>
-<span class="r-in"><span class="va">f_nlmixr_sfo_saem</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr</a></span><span class="op">(</span><span class="va">f_mmkin_parent</span><span class="op">[</span><span class="st">"SFO"</span>, <span class="op">]</span>, est <span class="op">=</span> <span class="st">"saem"</span>,</span>
-<span class="r-in">  control <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/saemControl.html" class="external-link">saemControl</a></span><span class="op">(</span>print <span class="op">=</span> <span class="fl">0</span><span class="op">)</span><span class="op">)</span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → generate SAEM model</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> RxODE 1.1.4 using 8 threads (see ?getRxThreads)</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>   no cache: create with `rxCreateCache()`</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating covariance matrix</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling EBE model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating residuals/tables</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-in"><span class="va">f_nlmixr_sfo_focei</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr</a></span><span class="op">(</span><span class="va">f_mmkin_parent</span><span class="op">[</span><span class="st">"SFO"</span>, <span class="op">]</span>, est <span class="op">=</span> <span class="st">"focei"</span>,</span>
-<span class="r-in">  control <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/foceiControl.html" class="external-link">foceiControl</a></span><span class="op">(</span>print <span class="op">=</span> <span class="fl">0</span><span class="op">)</span><span class="op">)</span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate jacobian</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate sensitivities</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate ∂(f)/∂(η)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate ∂(R²)/∂(η)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in inner model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in inner model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in EBE model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in EBE model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling inner model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in FD model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling EBE model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling events FD model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> calculating covariance matrix</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating residuals/tables</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>initial ETAs were nudged; (can control by foceiControl(etaNudge=., etaNudge2=))</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>ETAs were reset to zero during optimization; (Can control by foceiControl(resetEtaP=.))</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>gradient problems with initial estimate and covariance; see $scaleInfo</span>
-<span class="r-in"></span>
-<span class="r-in"><span class="va">f_nlmixr_fomc_saem</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr</a></span><span class="op">(</span><span class="va">f_mmkin_parent</span><span class="op">[</span><span class="st">"FOMC"</span>, <span class="op">]</span>, est <span class="op">=</span> <span class="st">"saem"</span>,</span>
-<span class="r-in">  control <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/saemControl.html" class="external-link">saemControl</a></span><span class="op">(</span>print <span class="op">=</span> <span class="fl">0</span><span class="op">)</span><span class="op">)</span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → generate SAEM model</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating covariance matrix</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling EBE model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating residuals/tables</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-in"><span class="va">f_nlmixr_fomc_focei</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr</a></span><span class="op">(</span><span class="va">f_mmkin_parent</span><span class="op">[</span><span class="st">"FOMC"</span>, <span class="op">]</span>, est <span class="op">=</span> <span class="st">"focei"</span>,</span>
-<span class="r-in">  control <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/foceiControl.html" class="external-link">foceiControl</a></span><span class="op">(</span>print <span class="op">=</span> <span class="fl">0</span><span class="op">)</span><span class="op">)</span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate jacobian</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate sensitivities</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate ∂(f)/∂(η)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate ∂(R²)/∂(η)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in inner model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in inner model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in EBE model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in EBE model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling inner model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in FD model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in FD model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling EBE model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling events FD model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> calculating covariance matrix</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating residuals/tables</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>initial ETAs were nudged; (can control by foceiControl(etaNudge=., etaNudge2=))</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>ETAs were reset to zero during optimization; (Can control by foceiControl(resetEtaP=.))</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>last objective function was not at minimum, possible problems in optimization</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>gradient problems with initial estimate and covariance; see $scaleInfo</span>
-<span class="r-in"></span>
-<span class="r-in"><span class="va">f_nlmixr_dfop_saem</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr</a></span><span class="op">(</span><span class="va">f_mmkin_parent</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span>, est <span class="op">=</span> <span class="st">"saem"</span>,</span>
-<span class="r-in">  control <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/saemControl.html" class="external-link">saemControl</a></span><span class="op">(</span>print <span class="op">=</span> <span class="fl">0</span><span class="op">)</span><span class="op">)</span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → generate SAEM model</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating covariance matrix</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling EBE model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating residuals/tables</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-in"><span class="va">f_nlmixr_dfop_focei</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr</a></span><span class="op">(</span><span class="va">f_mmkin_parent</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span>, est <span class="op">=</span> <span class="st">"focei"</span>,</span>
-<span class="r-in">  control <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/foceiControl.html" class="external-link">foceiControl</a></span><span class="op">(</span>print <span class="op">=</span> <span class="fl">0</span><span class="op">)</span><span class="op">)</span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate jacobian</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate sensitivities</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate ∂(f)/∂(η)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate ∂(R²)/∂(η)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in inner model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in inner model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in EBE model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in EBE model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling inner model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in FD model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in FD model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling EBE model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling events FD model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> calculating covariance matrix</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating residuals/tables</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>initial ETAs were nudged; (can control by foceiControl(etaNudge=., etaNudge2=))</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>ETAs were reset to zero during optimization; (Can control by foceiControl(resetEtaP=.))</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>gradient problems with initial estimate and covariance; see $scaleInfo</span>
-<span class="r-in"></span>
-<span class="r-in"><span class="va">f_nlmixr_hs_saem</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr</a></span><span class="op">(</span><span class="va">f_mmkin_parent</span><span class="op">[</span><span class="st">"HS"</span>, <span class="op">]</span>, est <span class="op">=</span> <span class="st">"saem"</span>,</span>
-<span class="r-in">  control <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/saemControl.html" class="external-link">saemControl</a></span><span class="op">(</span>print <span class="op">=</span> <span class="fl">0</span><span class="op">)</span><span class="op">)</span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → generate SAEM model</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating covariance matrix</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling EBE model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating residuals/tables</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-in"><span class="va">f_nlmixr_hs_focei</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr</a></span><span class="op">(</span><span class="va">f_mmkin_parent</span><span class="op">[</span><span class="st">"HS"</span>, <span class="op">]</span>, est <span class="op">=</span> <span class="st">"focei"</span>,</span>
-<span class="r-in">  control <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/foceiControl.html" class="external-link">foceiControl</a></span><span class="op">(</span>print <span class="op">=</span> <span class="fl">0</span><span class="op">)</span><span class="op">)</span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate jacobian</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate sensitivities</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate ∂(f)/∂(η)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate ∂(R²)/∂(η)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in inner model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in inner model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in EBE model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in EBE model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling inner model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in FD model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in FD model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling EBE model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling events FD model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> calculating covariance matrix</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating residuals/tables</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>initial ETAs were nudged; (can control by foceiControl(etaNudge=., etaNudge2=))</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>ETAs were reset to zero during optimization; (Can control by foceiControl(resetEtaP=.))</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>last objective function was not at minimum, possible problems in optimization</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>using S matrix to calculate covariance, can check sandwich or R matrix with $covRS and $covR</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>gradient problems with initial estimate and covariance; see $scaleInfo</span>
-<span class="r-in"></span>
-<span class="r-in"><span class="va">f_nlmixr_fomc_saem_tc</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr</a></span><span class="op">(</span><span class="va">f_mmkin_parent_tc</span><span class="op">[</span><span class="st">"FOMC"</span>, <span class="op">]</span>, est <span class="op">=</span> <span class="st">"saem"</span>,</span>
-<span class="r-in">  control <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/saemControl.html" class="external-link">saemControl</a></span><span class="op">(</span>print <span class="op">=</span> <span class="fl">0</span><span class="op">)</span><span class="op">)</span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating covariance matrix</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling EBE model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating residuals/tables</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-in"><span class="va">f_nlmixr_fomc_focei_tc</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr</a></span><span class="op">(</span><span class="va">f_mmkin_parent_tc</span><span class="op">[</span><span class="st">"FOMC"</span>, <span class="op">]</span>, est <span class="op">=</span> <span class="st">"focei"</span>,</span>
-<span class="r-in">  control <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/foceiControl.html" class="external-link">foceiControl</a></span><span class="op">(</span>print <span class="op">=</span> <span class="fl">0</span><span class="op">)</span><span class="op">)</span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate jacobian</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate sensitivities</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate ∂(f)/∂(η)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate ∂(R²)/∂(η)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in inner model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in inner model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in EBE model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in EBE model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling inner model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in FD model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in FD model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling EBE model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling events FD model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating residuals/tables</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>initial ETAs were nudged; (can control by foceiControl(etaNudge=., etaNudge2=))</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>ETAs were reset to zero during optimization; (Can control by foceiControl(resetEtaP=.))</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>last objective function was not at minimum, possible problems in optimization</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>parameter estimate near boundary; covariance not calculated:</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span>    "rsd_high" </span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span>  use 'getVarCov' to calculate anyway</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>gradient problems with initial estimate; see $scaleInfo</span>
-<span class="r-in"></span>
-<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">AIC</a></span><span class="op">(</span></span>
-<span class="r-in">  <span class="va">f_nlmixr_sfo_saem</span><span class="op">$</span><span class="va">nm</span>, <span class="va">f_nlmixr_sfo_focei</span><span class="op">$</span><span class="va">nm</span>,</span>
-<span class="r-in">  <span class="va">f_nlmixr_fomc_saem</span><span class="op">$</span><span class="va">nm</span>, <span class="va">f_nlmixr_fomc_focei</span><span class="op">$</span><span class="va">nm</span>,</span>
-<span class="r-in">  <span class="va">f_nlmixr_dfop_saem</span><span class="op">$</span><span class="va">nm</span>, <span class="va">f_nlmixr_dfop_focei</span><span class="op">$</span><span class="va">nm</span>,</span>
-<span class="r-in">  <span class="va">f_nlmixr_hs_saem</span><span class="op">$</span><span class="va">nm</span>, <span class="va">f_nlmixr_hs_focei</span><span class="op">$</span><span class="va">nm</span>,</span>
-<span class="r-in">  <span class="va">f_nlmixr_fomc_saem_tc</span><span class="op">$</span><span class="va">nm</span>, <span class="va">f_nlmixr_fomc_focei_tc</span><span class="op">$</span><span class="va">nm</span><span class="op">)</span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating -2LL by Gaussian quadrature (nnodes=3,nsd=1.6)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating -2LL by Gaussian quadrature (nnodes=3,nsd=1.6)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating -2LL by Gaussian quadrature (nnodes=3,nsd=1.6)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating -2LL by Gaussian quadrature (nnodes=3,nsd=1.6)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating -2LL by Gaussian quadrature (nnodes=3,nsd=1.6)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                           df      AIC</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_nlmixr_sfo_saem$nm       5 624.9492</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_nlmixr_sfo_focei$nm      5 625.0695</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_nlmixr_fomc_saem$nm      7 463.7577</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_nlmixr_fomc_focei$nm     7 468.0861</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_nlmixr_dfop_saem$nm      9 495.1980</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_nlmixr_dfop_focei$nm     9 495.1072</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_nlmixr_hs_saem$nm        9 531.0689</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_nlmixr_hs_focei$nm       9 545.6728</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_nlmixr_fomc_saem_tc$nm   8 462.1411</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_nlmixr_fomc_focei_tc$nm  8 470.0745</span>
-<span class="r-in"></span>
-<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">AIC</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a></span><span class="op">(</span><span class="va">f_mmkin_parent</span><span class="op">[</span><span class="st">"FOMC"</span>, <span class="op">]</span><span class="op">)</span><span class="op">)</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 468.0781</span>
-<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">AIC</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a></span><span class="op">(</span><span class="va">f_mmkin_parent</span><span class="op">[</span><span class="st">"HS"</span>, <span class="op">]</span><span class="op">)</span><span class="op">)</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 535.609</span>
-<span class="r-in"></span>
-<span class="r-in"><span class="co"># The FOCEI fit of FOMC with constant variance or the tc error model is best</span></span>
-<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_nlmixr_fomc_saem_tc</span><span class="op">)</span></span>
-<span class="r-plt img"><img src="nlmixr.mmkin-1.png" alt="" width="700" height="433"></span>
-<span class="r-in"></span>
-<span class="r-in"><span class="va">sfo_sfo</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"A1"</span><span class="op">)</span>,</span>
-<span class="r-in">  A1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
-<span class="r-in"><span class="va">fomc_sfo</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"FOMC"</span>, <span class="st">"A1"</span><span class="op">)</span>,</span>
-<span class="r-in">  A1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
-<span class="r-in"><span class="va">dfop_sfo</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="st">"A1"</span><span class="op">)</span>,</span>
-<span class="r-in">  A1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
-<span class="r-in"></span>
-<span class="r-in"><span class="va">f_mmkin_const</span> <span class="op">&lt;-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span></span>
-<span class="r-in">    <span class="st">"SFO-SFO"</span> <span class="op">=</span> <span class="va">sfo_sfo</span>, <span class="st">"FOMC-SFO"</span> <span class="op">=</span> <span class="va">fomc_sfo</span>, <span class="st">"DFOP-SFO"</span> <span class="op">=</span> <span class="va">dfop_sfo</span><span class="op">)</span>,</span>
-<span class="r-in">  <span class="va">ds</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, error_model <span class="op">=</span> <span class="st">"const"</span><span class="op">)</span></span>
-<span class="r-in"><span class="va">f_mmkin_obs</span> <span class="op">&lt;-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span></span>
-<span class="r-in">    <span class="st">"SFO-SFO"</span> <span class="op">=</span> <span class="va">sfo_sfo</span>, <span class="st">"FOMC-SFO"</span> <span class="op">=</span> <span class="va">fomc_sfo</span>, <span class="st">"DFOP-SFO"</span> <span class="op">=</span> <span class="va">dfop_sfo</span><span class="op">)</span>,</span>
-<span class="r-in">  <span class="va">ds</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, error_model <span class="op">=</span> <span class="st">"obs"</span><span class="op">)</span></span>
-<span class="r-in"><span class="va">f_mmkin_tc</span> <span class="op">&lt;-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span></span>
-<span class="r-in">    <span class="st">"SFO-SFO"</span> <span class="op">=</span> <span class="va">sfo_sfo</span>, <span class="st">"FOMC-SFO"</span> <span class="op">=</span> <span class="va">fomc_sfo</span>, <span class="st">"DFOP-SFO"</span> <span class="op">=</span> <span class="va">dfop_sfo</span><span class="op">)</span>,</span>
-<span class="r-in">  <span class="va">ds</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, error_model <span class="op">=</span> <span class="st">"tc"</span><span class="op">)</span></span>
-<span class="r-in"></span>
-<span class="r-in"><span class="fu">nlmixr_model</span><span class="op">(</span><span class="va">f_mmkin_const</span><span class="op">[</span><span class="st">"SFO-SFO"</span>, <span class="op">]</span><span class="op">)</span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Constant variance for more than one variable is not supported for est = 'saem'</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Changing the error model to 'obs' (variance by observed variable)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> function () </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> {</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     ini({</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         parent_0 = 87</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         eta.parent_0 ~ 4.2</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         log_k_parent = -3.2</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         eta.log_k_parent ~ 1.5</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         log_k_A1 = -4.6</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         eta.log_k_A1 ~ 0.56</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         f_parent_qlogis = -0.33</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         eta.f_parent_qlogis ~ 1.1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         sigma_parent = 4.3</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         sigma_A1 = 4.3</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     })</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     model({</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         parent_0_model = parent_0 + eta.parent_0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         parent(0) = parent_0_model</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         k_parent = exp(log_k_parent + eta.log_k_parent)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         k_A1 = exp(log_k_A1 + eta.log_k_A1)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         f_parent_to_A1 = expit(f_parent_qlogis + eta.f_parent_qlogis)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         d/dt(parent) = -k_parent * parent</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         d/dt(A1) = +f_parent_to_A1 * k_parent * parent - k_A1 * </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>             A1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         parent ~ add(sigma_parent)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         A1 ~ add(sigma_A1)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     })</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> }</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> &lt;environment: 0x555564a5b090&gt;</span>
-<span class="r-in"></span>
-<span class="r-in"><span class="co"># A single constant variance is currently only possible with est = 'focei' in nlmixr</span></span>
-<span class="r-in"><span class="va">f_nlmixr_sfo_sfo_focei_const</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr</a></span><span class="op">(</span><span class="va">f_mmkin_const</span><span class="op">[</span><span class="st">"SFO-SFO"</span>, <span class="op">]</span>, est <span class="op">=</span> <span class="st">"focei"</span><span class="op">)</span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate jacobian</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate sensitivities</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate ∂(f)/∂(η)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate ∂(R²)/∂(η)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in inner model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in inner model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in EBE model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in EBE model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling inner model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in FD model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in FD model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling EBE model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling events FD model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Needed Covariates:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "CMT"</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="font-weight: bold;">Key:</span> U: Unscaled Parameters; X: Back-transformed parameters; G: Gill difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> F: Forward difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> C: Central difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> M: Mixed forward and central difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Unscaled parameters for Omegas=chol(solve(omega));</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Diagonals are transformed, as specified by foceiControl(diagXform=)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |-----+---------------+-----------+-----------+-----------+-----------|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    #| Objective Fun |  parent_0 |log_k_parent |  log_k_A1 |f_parent_qlogis |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     sigma |        o1 |        o2 |        o3 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|        o4 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    1</span>|     756.61847 |     1.000 |   -0.9694 |    -1.000 |   -0.9068 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8057 |   -0.8843 |   -0.8798 |   -0.8743 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8782 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     756.61847 |     87.00 |    -3.200 |    -4.600 |   -0.3300 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.300 |    0.6985 |    0.9036 |     1.156 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.9765 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     756.61847</span> |     87.00 |   0.04076 |   0.01005 |    0.4182 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.300 |    0.6985 |    0.9036 |     1.156 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.9765 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    G|    Gill Diff. |     104.1 |   0.02915 |    0.3320 |    0.4427 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -29.46 |     6.499 |     3.260 |    -8.158 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -5.501 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    2</span>|     4014.8405 |   0.04387 |   -0.9697 |    -1.003 |   -0.9108 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5352 |   -0.9440 |   -0.9098 |   -0.7994 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8277 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     4014.8405 |     3.817 |    -3.200 |    -4.603 |   -0.3313 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.882 |    0.6569 |    0.8766 |     1.243 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.026 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     4014.8405</span> |     3.817 |   0.04075 |   0.01002 |    0.4179 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.882 |    0.6569 |    0.8766 |     1.243 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.026 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    3</span>|     784.09766 |    0.9044 |   -0.9695 |    -1.000 |   -0.9072 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7786 |   -0.8903 |   -0.8828 |   -0.8668 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8732 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     784.09766 |     78.68 |    -3.200 |    -4.600 |   -0.3301 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.358 |    0.6944 |    0.9009 |     1.165 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.9814 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     784.09766</span> |     78.68 |   0.04076 |   0.01005 |    0.4182 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.358 |    0.6944 |    0.9009 |     1.165 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.9814 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    4</span>|     755.85897 |    0.9864 |   -0.9694 |    -1.000 |   -0.9068 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8018 |   -0.8852 |   -0.8803 |   -0.8733 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8775 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     755.85897 |     85.82 |    -3.200 |    -4.600 |   -0.3300 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.308 |    0.6979 |    0.9032 |     1.157 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.9772 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     755.85897</span> |     85.82 |   0.04076 |   0.01005 |    0.4182 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.308 |    0.6979 |    0.9032 |     1.157 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.9772 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -10.67 |    0.1182 |    0.2197 |    0.3686 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -28.82 |     3.860 |     3.200 |    -8.294 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -5.254 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    5</span>|     755.50135 |    0.9911 |   -0.9695 |    -1.000 |   -0.9070 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7893 |   -0.8868 |   -0.8816 |   -0.8697 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8752 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     755.50135 |     86.22 |    -3.200 |    -4.600 |   -0.3301 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.335 |    0.6968 |    0.9020 |     1.161 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.9794 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     755.50135</span> |     86.22 |   0.04076 |   0.01005 |    0.4182 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.335 |    0.6968 |    0.9020 |     1.161 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.9794 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    6</span>|     755.34910 |    0.9966 |   -0.9695 |    -1.000 |   -0.9072 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7744 |   -0.8888 |   -0.8833 |   -0.8654 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8725 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      755.3491 |     86.70 |    -3.200 |    -4.600 |   -0.3301 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.367 |    0.6954 |    0.9005 |     1.166 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.9820 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      755.3491</span> |     86.70 |   0.04076 |   0.01005 |    0.4182 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.367 |    0.6954 |    0.9005 |     1.166 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.9820 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     73.92 |   0.04965 |    0.3063 |    0.4373 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -23.46 |     5.143 |     2.934 |    -7.746 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -5.165 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    7</span>|     754.44379 |    0.9831 |   -0.9697 |    -1.001 |   -0.9076 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7489 |   -0.8930 |   -0.8865 |   -0.8566 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8669 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     754.44379 |     85.53 |    -3.200 |    -4.601 |   -0.3303 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.422 |    0.6925 |    0.8975 |     1.176 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.9875 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     754.44379</span> |     85.53 |   0.04075 |   0.01005 |    0.4182 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.422 |    0.6925 |    0.8975 |     1.176 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.9875 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -36.39 |    0.1354 |    0.1953 |    0.3724 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -19.20 |     3.020 |     2.621 |    -7.506 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -4.671 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    8</span>|     753.82350 |    0.9908 |   -0.9699 |    -1.001 |   -0.9085 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7249 |   -0.8992 |   -0.8910 |   -0.8427 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8580 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      753.8235 |     86.20 |    -3.200 |    -4.601 |   -0.3306 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.474 |    0.6881 |    0.8935 |     1.193 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.9962 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      753.8235</span> |     86.20 |   0.04074 |   0.01004 |    0.4181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.474 |    0.6881 |    0.8935 |     1.193 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.9962 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     25.33 |   0.08213 |    0.2542 |    0.4339 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -14.89 |     3.322 |     2.230 |    -6.934 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -4.324 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    9</span>|     753.42397 |    0.9836 |   -0.9702 |    -1.002 |   -0.9101 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7094 |   -0.9058 |   -0.8962 |   -0.8215 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8457 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     753.42397 |     85.57 |    -3.201 |    -4.602 |   -0.3311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.507 |    0.6835 |    0.8888 |     1.217 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.008 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     753.42397</span> |     85.57 |   0.04073 |   0.01003 |    0.4180 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.507 |    0.6835 |    0.8888 |     1.217 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.008 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -31.38 |    0.1220 |    0.1752 |    0.4111 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -12.58 |     2.402 |     1.769 |    -6.044 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -3.541 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   10</span>|     753.10125 |    0.9902 |   -0.9707 |    -1.003 |   -0.9128 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7033 |   -0.9147 |   -0.8999 |   -0.7966 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8328 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     753.10125 |     86.15 |    -3.201 |    -4.603 |   -0.3320 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.520 |    0.6773 |    0.8855 |     1.246 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.021 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     753.10125</span> |     86.15 |   0.04071 |   0.01002 |    0.4178 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.520 |    0.6773 |    0.8855 |     1.246 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.021 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     18.19 |   0.07644 |    0.2005 |    0.4693 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -11.34 |     2.660 |     1.470 |    -4.866 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -2.908 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   11</span>|     752.83558 |    0.9856 |   -0.9716 |    -1.004 |   -0.9194 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6931 |   -0.9294 |   -0.8999 |   -0.7740 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8246 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.83558 |     85.75 |    -3.202 |    -4.604 |   -0.3342 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.542 |    0.6671 |    0.8855 |     1.272 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.029 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.83558</span> |     85.75 |   0.04067 |   0.01001 |    0.4172 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.542 |    0.6671 |    0.8855 |     1.272 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.029 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -17.42 |   0.09700 |    0.1175 |    0.4453 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -9.793 |     1.829 |     1.489 |    -3.997 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -2.337 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   12</span>|     752.64046 |    0.9898 |   -0.9731 |    -1.005 |   -0.9331 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6718 |   -0.9413 |   -0.9101 |   -0.7759 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8318 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.64046 |     86.12 |    -3.204 |    -4.605 |   -0.3387 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.588 |    0.6587 |    0.8763 |     1.270 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.022 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.64046</span> |     86.12 |   0.04061 |   0.01000 |    0.4161 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.588 |    0.6587 |    0.8763 |     1.270 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.022 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     12.96 |   0.06018 |    0.1556 |    0.4415 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -6.445 |     1.822 |    0.6282 |    -4.092 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -2.761 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   13</span>|     752.52461 |    0.9874 |   -0.9741 |    -1.006 |   -0.9430 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6755 |   -0.9483 |   -0.9069 |   -0.7553 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8127 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.52461 |     85.90 |    -3.205 |    -4.606 |   -0.3420 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.580 |    0.6538 |    0.8792 |     1.294 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.040 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.52461</span> |     85.90 |   0.04057 |  0.009996 |    0.4153 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.580 |    0.6538 |    0.8792 |     1.294 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.040 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -5.319 |   0.06758 |    0.1323 |    0.4528 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -7.018 |     1.348 |    0.9128 |    -3.312 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.706 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   14</span>|     752.47650 |    0.9920 |   -0.9750 |    -1.008 |   -0.9578 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6735 |   -0.9481 |   -0.9014 |   -0.7293 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8100 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      752.4765 |     86.30 |    -3.206 |    -4.608 |   -0.3468 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.584 |    0.6539 |    0.8841 |     1.324 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.043 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      752.4765</span> |     86.30 |   0.04054 |  0.009974 |    0.4142 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.584 |    0.6539 |    0.8841 |     1.324 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.043 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     26.38 |   0.03719 |   0.08701 |    0.4621 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -6.642 |     1.808 |     1.401 |    -2.463 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.595 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   15</span>|     752.37074 |    0.9880 |   -0.9759 |    -1.009 |   -0.9778 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6665 |   -0.9465 |   -0.9156 |   -0.7192 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8239 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.37074 |     85.96 |    -3.206 |    -4.609 |   -0.3534 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.599 |    0.6551 |    0.8713 |     1.335 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.030 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.37074</span> |     85.96 |   0.04050 |  0.009963 |    0.4125 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.599 |    0.6551 |    0.8713 |     1.335 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.030 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |   -0.8866 |   0.05090 |  -0.01541 |    0.3812 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -5.712 |     1.436 |    0.1646 |    -2.191 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -2.247 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   16</span>|     752.26949 |    0.9921 |   -0.9761 |    -1.009 |   -0.9796 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6402 |   -0.9531 |   -0.9164 |   -0.7091 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8135 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.26949 |     86.31 |    -3.207 |    -4.609 |   -0.3540 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.656 |    0.6505 |    0.8706 |     1.347 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.040 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.26949</span> |     86.31 |   0.04049 |  0.009963 |    0.4124 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.656 |    0.6505 |    0.8706 |     1.347 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.040 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     26.82 |   0.02710 |   0.03892 |    0.4267 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.005 |     1.500 |    0.1022 |    -1.862 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.773 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   17</span>|     752.20271 |    0.9873 |   -0.9768 |    -1.006 |   -0.9927 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6361 |   -0.9677 |   -0.9007 |   -0.7162 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7982 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.20271 |     85.89 |    -3.207 |    -4.606 |   -0.3584 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.664 |    0.6403 |    0.8848 |     1.339 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.055 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.20271</span> |     85.89 |   0.04046 |  0.009992 |    0.4114 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.664 |    0.6403 |    0.8848 |     1.339 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.055 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -7.259 |   0.05081 |    0.1320 |    0.4166 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.497 |    0.4663 |     1.471 |    -1.984 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.9590 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   18</span>|     752.16826 |    0.9883 |   -0.9778 |    -1.006 |    -1.005 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6437 |   -0.9795 |   -0.9210 |   -0.7076 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7873 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.16826 |     85.98 |    -3.208 |    -4.606 |   -0.3626 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.648 |    0.6320 |    0.8664 |     1.349 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.065 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.16826</span> |     85.98 |   0.04042 |  0.009989 |    0.4103 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.648 |    0.6320 |    0.8664 |     1.349 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.065 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.732 |   0.03428 |    0.1666 |    0.4265 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.413 |    0.2526 |   -0.2557 |    -1.689 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.3553 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   19</span>|     752.25223 |    0.9952 |   -0.9788 |    -1.010 |    -1.023 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6370 |   -0.9826 |   -0.9156 |   -0.6853 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7912 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.25223 |     86.58 |    -3.209 |    -4.610 |   -0.3684 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.663 |    0.6299 |    0.8713 |     1.374 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.061 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.25223</span> |     86.58 |   0.04038 |  0.009949 |    0.4089 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.663 |    0.6299 |    0.8713 |     1.374 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.061 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   20</span>|     752.18605 |    0.9920 |   -0.9778 |    -1.007 |    -1.006 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6387 |   -0.9801 |   -0.9204 |   -0.7040 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7866 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.18605 |     86.30 |    -3.208 |    -4.607 |   -0.3629 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.659 |    0.6316 |    0.8669 |     1.353 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.066 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.18605</span> |     86.30 |   0.04042 |  0.009985 |    0.4103 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.659 |    0.6316 |    0.8669 |     1.353 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.066 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   21</span>|     752.16428 |    0.9894 |   -0.9778 |    -1.006 |    -1.006 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6422 |   -0.9797 |   -0.9208 |   -0.7065 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7871 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.16428 |     86.08 |    -3.208 |    -4.606 |   -0.3627 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.651 |    0.6319 |    0.8666 |     1.350 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.065 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.16428</span> |     86.08 |   0.04042 |  0.009988 |    0.4103 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.651 |    0.6319 |    0.8666 |     1.350 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.065 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     5.144 |   0.02886 |    0.1726 |    0.4333 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.192 |    0.3285 |   -0.2407 |    -1.661 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.3568 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   22</span>|     752.15919 |    0.9884 |   -0.9779 |    -1.007 |    -1.007 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6420 |   -0.9798 |   -0.9206 |   -0.7051 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7874 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.15919 |     85.99 |    -3.208 |    -4.607 |   -0.3630 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.652 |    0.6319 |    0.8668 |     1.352 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.065 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.15919</span> |     85.99 |   0.04042 |  0.009985 |    0.4102 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.652 |    0.6319 |    0.8668 |     1.352 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.065 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.486 |   0.03361 |    0.1549 |    0.4244 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.177 |    0.2364 |   -0.2213 |    -1.616 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.3570 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   23</span>|     752.15545 |    0.9894 |   -0.9779 |    -1.007 |    -1.007 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6405 |   -0.9799 |   -0.9204 |   -0.7040 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7872 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.15545 |     86.07 |    -3.208 |    -4.607 |   -0.3631 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.655 |    0.6318 |    0.8669 |     1.353 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.065 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.15545</span> |     86.07 |   0.04042 |  0.009984 |    0.4102 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.655 |    0.6318 |    0.8669 |     1.353 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.065 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     4.936 |   0.02854 |    0.1600 |    0.4306 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.962 |    0.3065 |   -0.2063 |    -1.586 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.3570 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   24</span>|     752.15077 |    0.9884 |   -0.9779 |    -1.007 |    -1.008 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6403 |   -0.9800 |   -0.9202 |   -0.7026 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7875 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.15077 |     85.99 |    -3.209 |    -4.607 |   -0.3635 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.656 |    0.6317 |    0.8672 |     1.355 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.065 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.15077</span> |     85.99 |   0.04042 |  0.009981 |    0.4101 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.656 |    0.6317 |    0.8672 |     1.355 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.065 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.303 |   0.03292 |    0.1430 |    0.4220 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.955 |    0.2207 |   -0.1874 |    -1.542 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.3581 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   25</span>|     752.14731 |    0.9893 |   -0.9780 |    -1.007 |    -1.009 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6389 |   -0.9801 |   -0.9200 |   -0.7014 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7873 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.14731 |     86.07 |    -3.209 |    -4.607 |   -0.3637 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.659 |    0.6316 |    0.8673 |     1.356 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.065 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.14731</span> |     86.07 |   0.04042 |  0.009980 |    0.4101 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.659 |    0.6316 |    0.8673 |     1.356 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.065 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     4.764 |   0.02806 |    0.1473 |    0.4277 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.749 |    0.2865 |   -0.1727 |    -1.510 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.3572 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   26</span>|     752.14299 |    0.9884 |   -0.9780 |    -1.007 |    -1.010 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6388 |   -0.9801 |   -0.9198 |   -0.7000 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7876 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.14299 |     85.99 |    -3.209 |    -4.607 |   -0.3641 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.659 |    0.6316 |    0.8675 |     1.358 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.065 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.14299</span> |     85.99 |   0.04041 |  0.009978 |    0.4100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.659 |    0.6316 |    0.8675 |     1.358 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.065 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.130 |   0.03192 |    0.1311 |    0.4194 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.750 |    0.2064 |   -0.1542 |    -1.466 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.3587 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   27</span>|     752.13982 |    0.9893 |   -0.9781 |    -1.008 |    -1.010 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6374 |   -0.9803 |   -0.9196 |   -0.6987 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7873 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.13982 |     86.07 |    -3.209 |    -4.608 |   -0.3642 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.662 |    0.6315 |    0.8676 |     1.359 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.065 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.13982</span> |     86.07 |   0.04041 |  0.009977 |    0.4099 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.662 |    0.6315 |    0.8676 |     1.359 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.065 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     4.635 |   0.02760 |    0.1350 |    0.4248 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.550 |    0.2683 |   -0.1407 |    -1.433 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.3557 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   28</span>|     752.13580 |    0.9884 |   -0.9782 |    -1.008 |    -1.012 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6373 |   -0.9803 |   -0.9194 |   -0.6973 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7876 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      752.1358 |     85.99 |    -3.209 |    -4.608 |   -0.3647 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.662 |    0.6315 |    0.8678 |     1.361 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.065 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      752.1358</span> |     85.99 |   0.04041 |  0.009975 |    0.4098 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.662 |    0.6315 |    0.8678 |     1.361 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.065 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |   -0.9657 |   0.03147 |    0.1207 |    0.4166 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.557 |    0.1931 |   -0.1227 |    -1.391 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.3574 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   29</span>|     752.13295 |    0.9893 |   -0.9782 |    -1.008 |    -1.012 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6359 |   -0.9805 |   -0.9193 |   -0.6961 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7873 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.13295 |     86.07 |    -3.209 |    -4.608 |   -0.3648 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.665 |    0.6314 |    0.8679 |     1.362 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.065 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.13295</span> |     86.07 |   0.04041 |  0.009973 |    0.4098 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.665 |    0.6314 |    0.8679 |     1.362 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.065 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     4.554 |   0.02702 |    0.1245 |    0.4220 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.357 |    0.2511 |   -0.1114 |    -1.356 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.3512 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   30</span>|     752.12919 |    0.9885 |   -0.9783 |    -1.008 |    -1.014 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6359 |   -0.9804 |   -0.9191 |   -0.6947 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7876 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.12919 |     86.00 |    -3.209 |    -4.608 |   -0.3653 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.665 |    0.6314 |    0.8681 |     1.364 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.065 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.12919</span> |     86.00 |   0.04040 |  0.009972 |    0.4097 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.665 |    0.6314 |    0.8681 |     1.364 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.065 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |   -0.8077 |   0.03076 |    0.1104 |    0.4140 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.370 |    0.1799 |  -0.09417 |    -1.307 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.3529 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   31</span>|     752.12656 |    0.9893 |   -0.9783 |    -1.008 |    -1.014 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6345 |   -0.9806 |   -0.9190 |   -0.6934 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7872 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.12656 |     86.07 |    -3.209 |    -4.608 |   -0.3654 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.668 |    0.6313 |    0.8682 |     1.365 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.065 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.12656</span> |     86.07 |   0.04040 |  0.009970 |    0.4096 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.668 |    0.6313 |    0.8682 |     1.365 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.065 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     4.375 |   0.02651 |    0.1140 |    0.4191 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.173 |    0.2330 |  -0.08481 |    -1.281 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.3435 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   32</span>|     752.12310 |    0.9885 |   -0.9784 |    -1.008 |    -1.016 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6345 |   -0.9806 |   -0.9188 |   -0.6922 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7875 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      752.1231 |     86.00 |    -3.209 |    -4.608 |   -0.3659 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.668 |    0.6313 |    0.8684 |     1.367 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.065 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      752.1231</span> |     86.00 |   0.04040 |  0.009969 |    0.4095 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.668 |    0.6313 |    0.8684 |     1.367 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.065 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |   -0.6515 |   0.02994 |    0.1012 |    0.4113 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.192 |    0.1669 |  -0.06829 |    -1.233 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.3455 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   33</span>|     752.12055 |    0.9892 |   -0.9784 |    -1.008 |    -1.016 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6332 |   -0.9808 |   -0.9187 |   -0.6908 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7871 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.12055 |     86.06 |    -3.209 |    -4.608 |   -0.3661 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.671 |    0.6312 |    0.8685 |     1.368 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.065 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.12055</span> |     86.06 |   0.04040 |  0.009968 |    0.4095 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.671 |    0.6312 |    0.8685 |     1.368 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.065 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     3.998 |   0.02610 |    0.1041 |    0.4159 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.002 |    0.2127 |  -0.06061 |    -1.206 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.3333 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   34</span>|     752.11747 |    0.9885 |   -0.9785 |    -1.009 |    -1.018 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6332 |   -0.9807 |   -0.9185 |   -0.6896 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7874 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.11747 |     86.00 |    -3.209 |    -4.609 |   -0.3666 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.671 |    0.6312 |    0.8687 |     1.370 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.065 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.11747</span> |     86.00 |   0.04039 |  0.009966 |    0.4094 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.671 |    0.6312 |    0.8687 |     1.370 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.065 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |   -0.5772 |   0.02918 |   0.09258 |    0.4085 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.024 |    0.1535 |  -0.04467 |    -1.159 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.3360 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   35</span>|     752.11522 |    0.9892 |   -0.9785 |    -1.009 |    -1.018 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6319 |   -0.9809 |   -0.9185 |   -0.6881 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7870 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.11522 |     86.06 |    -3.209 |    -4.609 |   -0.3668 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.674 |    0.6311 |    0.8687 |     1.371 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.066 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.11522</span> |     86.06 |   0.04039 |  0.009965 |    0.4093 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.674 |    0.6311 |    0.8687 |     1.371 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.066 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     3.959 |   0.02535 |   0.09612 |    0.4130 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8451 |    0.1980 |  -0.03883 |    -1.121 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.3219 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   36</span>|     752.11234 |    0.9885 |   -0.9786 |    -1.009 |    -1.020 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6320 |   -0.9808 |   -0.9183 |   -0.6870 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7872 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.11234 |     86.00 |    -3.209 |    -4.609 |   -0.3673 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.673 |    0.6311 |    0.8689 |     1.373 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.065 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.11234</span> |     86.00 |   0.04039 |  0.009964 |    0.4092 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.673 |    0.6311 |    0.8689 |     1.373 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.065 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |   -0.5042 |   0.02791 |   0.08363 |    0.4056 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8741 |    0.1402 |  -0.02542 |    -1.088 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.3257 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   37</span>|     752.11033 |    0.9892 |   -0.9787 |    -1.009 |    -1.020 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6308 |   -0.9810 |   -0.9182 |   -0.6855 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7868 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.11033 |     86.06 |    -3.209 |    -4.609 |   -0.3675 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.676 |    0.6310 |    0.8689 |     1.374 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.066 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.11033</span> |     86.06 |   0.04039 |  0.009963 |    0.4091 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.676 |    0.6310 |    0.8689 |     1.374 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.066 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     3.874 |   0.02465 |   0.08719 |    0.4100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7032 |    0.1835 |  -0.01978 |    -1.047 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.3084 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   38</span>|     752.10764 |    0.9885 |   -0.9787 |    -1.009 |    -1.022 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6309 |   -0.9810 |   -0.9181 |   -0.6844 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7870 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.10764 |     86.00 |    -3.209 |    -4.609 |   -0.3681 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.676 |    0.6310 |    0.8691 |     1.376 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.066 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.10764</span> |     86.00 |   0.04038 |  0.009962 |    0.4090 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.676 |    0.6310 |    0.8691 |     1.376 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.066 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |   -0.4131 |   0.02699 |   0.07721 |    0.4026 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7354 |    0.1282 | -0.007503 |    -1.011 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.3125 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   39</span>|     752.10572 |    0.9892 |   -0.9788 |    -1.009 |    -1.023 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6298 |   -0.9812 |   -0.9181 |   -0.6829 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7865 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.10572 |     86.06 |    -3.209 |    -4.609 |   -0.3683 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.678 |    0.6309 |    0.8691 |     1.377 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.066 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.10572</span> |     86.06 |   0.04038 |  0.009960 |    0.4090 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.678 |    0.6309 |    0.8691 |     1.377 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.066 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     3.580 |   0.02411 |   0.07834 |    0.4067 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5755 |    0.1666 | -0.003596 |   -0.9604 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2924 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   40</span>|     752.10333 |    0.9885 |   -0.9789 |    -1.009 |    -1.024 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6300 |   -0.9811 |   -0.9179 |   -0.6819 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7867 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.10333 |     86.00 |    -3.209 |    -4.609 |   -0.3688 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.678 |    0.6309 |    0.8692 |     1.378 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.066 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.10333</span> |     86.00 |   0.04038 |  0.009959 |    0.4088 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.678 |    0.6309 |    0.8692 |     1.378 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.066 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |   -0.3772 |   0.02648 |   0.06875 |    0.3997 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6082 |    0.1162 |  0.008208 |   -0.9328 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2962 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   41</span>|     752.10169 |    0.9892 |   -0.9789 |    -1.009 |    -1.025 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6289 |   -0.9813 |   -0.9179 |   -0.6803 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7862 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.10169 |     86.06 |    -3.209 |    -4.609 |   -0.3691 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.680 |    0.6308 |    0.8692 |     1.380 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.066 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.10169</span> |     86.06 |   0.04038 |  0.009958 |    0.4088 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.680 |    0.6308 |    0.8692 |     1.380 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.066 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     3.644 |   0.02338 |   0.07127 |    0.4037 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4618 |    0.1554 |  0.009391 |   -0.8908 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2755 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   42</span>|     752.09938 |    0.9886 |   -0.9790 |    -1.009 |    -1.027 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6291 |   -0.9812 |   -0.9178 |   -0.6794 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7864 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.09938 |     86.00 |    -3.210 |    -4.609 |   -0.3697 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.680 |    0.6308 |    0.8693 |     1.381 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.066 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.09938</span> |     86.00 |   0.04037 |  0.009958 |    0.4086 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.680 |    0.6308 |    0.8693 |     1.381 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.066 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |   -0.2627 |   0.02548 |   0.06256 |    0.3967 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4955 |    0.1055 |   0.02027 |   -0.8653 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2789 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   43</span>|     752.09757 |    0.9890 |   -0.9791 |    -1.010 |    -1.028 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6281 |   -0.9814 |   -0.9178 |   -0.6778 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7859 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.09757 |     86.05 |    -3.210 |    -4.610 |   -0.3699 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.682 |    0.6307 |    0.8693 |     1.383 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.067 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.09757</span> |     86.05 |   0.04037 |  0.009956 |    0.4086 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.682 |    0.6307 |    0.8693 |     1.383 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.067 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     2.867 |   0.02296 |   0.06369 |    0.3997 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3645 |    0.1327 |   0.01894 |   -0.8216 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2549 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   44</span>|     752.09570 |    0.9886 |   -0.9791 |    -1.010 |    -1.030 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6283 |   -0.9814 |   -0.9177 |   -0.6770 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7861 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      752.0957 |     86.00 |    -3.210 |    -4.610 |   -0.3705 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.681 |    0.6307 |    0.8694 |     1.384 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.066 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      752.0957</span> |     86.00 |   0.04037 |  0.009956 |    0.4084 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.681 |    0.6307 |    0.8694 |     1.384 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.066 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |   -0.2846 |   0.02474 |   0.05675 |    0.3935 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3960 |   0.09353 |   0.02976 |   -0.7961 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2595 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   45</span>|     752.09434 |    0.9891 |   -0.9792 |    -1.010 |    -1.030 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6275 |   -0.9816 |   -0.9177 |   -0.6754 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7856 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.09434 |     86.05 |    -3.210 |    -4.610 |   -0.3708 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.683 |    0.6306 |    0.8694 |     1.386 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.067 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.09434</span> |     86.05 |   0.04037 |  0.009955 |    0.4083 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.683 |    0.6306 |    0.8694 |     1.386 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.067 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     3.348 |   0.02147 |   0.05816 |    0.3967 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2833 |    0.1286 |   0.02562 |   -0.7529 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2380 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   46</span>|     752.09236 |    0.9886 |   -0.9793 |    -1.010 |    -1.032 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6277 |   -0.9815 |   -0.9176 |   -0.6746 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7858 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.09236 |     86.01 |    -3.210 |    -4.610 |   -0.3715 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.683 |    0.6306 |    0.8695 |     1.387 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.067 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.09236</span> |     86.01 |   0.04036 |  0.009954 |    0.4082 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.683 |    0.6306 |    0.8695 |     1.387 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.067 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |   -0.1856 |   0.02378 |   0.05144 |    0.3902 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3147 |   0.08386 |   0.03545 |   -0.7433 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2408 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   47</span>|     752.09077 |    0.9890 |   -0.9793 |    -1.010 |    -1.033 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6269 |   -0.9817 |   -0.9177 |   -0.6729 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7852 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.09077 |     86.04 |    -3.210 |    -4.610 |   -0.3717 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.684 |    0.6305 |    0.8694 |     1.389 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.067 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.09077</span> |     86.04 |   0.04036 |  0.009953 |    0.4081 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.684 |    0.6305 |    0.8694 |     1.389 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.067 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   48</span>|     752.08937 |    0.9892 |   -0.9795 |    -1.010 |    -1.036 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6267 |   -0.9818 |   -0.9176 |   -0.6714 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7852 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.08937 |     86.06 |    -3.210 |    -4.610 |   -0.3725 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.685 |    0.6304 |    0.8695 |     1.391 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.067 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.08937</span> |     86.06 |   0.04036 |  0.009952 |    0.4079 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.685 |    0.6304 |    0.8695 |     1.391 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.067 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     3.945 |   0.01952 |   0.05064 |    0.3906 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1866 |    0.1233 |   0.03740 |   -0.6554 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2138 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   49</span>|     752.08593 |    0.9886 |   -0.9796 |    -1.010 |    -1.040 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6271 |   -0.9817 |   -0.9174 |   -0.6701 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7856 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.08593 |     86.01 |    -3.210 |    -4.610 |   -0.3740 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.684 |    0.6305 |    0.8697 |     1.392 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.067 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.08593</span> |     86.01 |   0.04035 |  0.009951 |    0.4076 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.684 |    0.6305 |    0.8697 |     1.392 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.067 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |  -0.04707 |   0.02143 |   0.04311 |    0.3795 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2514 |   0.07631 |   0.05739 |   -0.6184 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2313 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   50</span>|     752.08243 |    0.9889 |   -0.9798 |    -1.010 |    -1.042 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6256 |   -0.9821 |   -0.9177 |   -0.6665 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7842 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.08243 |     86.03 |    -3.210 |    -4.610 |   -0.3748 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.687 |    0.6302 |    0.8694 |     1.396 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.068 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.08243</span> |     86.03 |   0.04034 |  0.009949 |    0.4074 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.687 |    0.6302 |    0.8694 |     1.396 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.068 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   51</span>|     752.07616 |    0.9895 |   -0.9803 |    -1.011 |    -1.054 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6231 |   -0.9827 |   -0.9181 |   -0.6582 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7817 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.07616 |     86.08 |    -3.211 |    -4.611 |   -0.3786 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.693 |    0.6298 |    0.8690 |     1.406 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.071 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.07616</span> |     86.08 |   0.04032 |  0.009942 |    0.4065 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.693 |    0.6298 |    0.8690 |     1.406 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.071 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     5.128 |   0.01286 |   0.03023 |    0.3726 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2708 |   0.07184 |  -0.02487 |   -0.2959 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|  -0.03882 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   52</span>|     752.06706 |    0.9888 |   -0.9810 |    -1.011 |    -1.073 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6241 |   -0.9834 |   -0.9174 |   -0.6562 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7830 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.06706 |     86.03 |    -3.212 |    -4.611 |   -0.3848 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.690 |    0.6293 |    0.8696 |     1.408 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.069 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.06706</span> |     86.03 |   0.04030 |  0.009938 |    0.4050 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.690 |    0.6293 |    0.8696 |     1.408 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.069 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   53</span>|     752.05726 |    0.9892 |   -0.9821 |    -1.012 |    -1.106 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6257 |   -0.9847 |   -0.9162 |   -0.6528 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7853 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.05726 |     86.06 |    -3.213 |    -4.612 |   -0.3958 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.687 |    0.6284 |    0.8707 |     1.412 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.067 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.05726</span> |     86.06 |   0.04025 |  0.009929 |    0.4023 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.687 |    0.6284 |    0.8707 |     1.412 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.067 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   54</span>|     752.04392 |    0.9898 |   -0.9854 |    -1.015 |    -1.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6305 |   -0.9883 |   -0.9128 |   -0.6430 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7918 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.04392 |     86.11 |    -3.216 |    -4.615 |   -0.4269 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.677 |    0.6259 |    0.8738 |     1.423 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.061 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.04392</span> |     86.11 |   0.04012 |  0.009906 |    0.3949 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.677 |    0.6259 |    0.8738 |     1.423 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.061 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     5.566 |  -0.02004 |   0.02472 |    0.1678 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6767 | -0.003606 |    0.4773 |   0.08205 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6933 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   55</span>|     752.05860 |    0.9880 |   -0.9900 |    -1.027 |    -1.486 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6186 |   -0.9338 |   -0.9168 |   -0.6653 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7443 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      752.0586 |     85.96 |    -3.221 |    -4.627 |   -0.5212 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.702 |    0.6640 |    0.8702 |     1.398 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.107 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      752.0586</span> |     85.96 |   0.03993 |  0.009780 |    0.3726 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.702 |    0.6640 |    0.8702 |     1.398 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.107 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   56</span>|     752.02137 |    0.9885 |   -0.9874 |    -1.020 |    -1.325 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6252 |   -0.9645 |   -0.9146 |   -0.6528 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7710 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.02137 |     86.00 |    -3.218 |    -4.620 |   -0.4681 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.688 |    0.6425 |    0.8722 |     1.412 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.081 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.02137</span> |     86.00 |   0.04004 |  0.009850 |    0.3851 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.688 |    0.6425 |    0.8722 |     1.412 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.081 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.728 |  -0.02729 |   0.08518 |   0.04185 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2367 |    0.4063 |    0.2432 |   0.01436 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|  -0.07563 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   57</span>|     752.06656 |    0.9886 |   -0.9792 |    -1.053 |    -1.433 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6165 |    -1.020 |   -0.9133 |   -0.6801 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7607 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.06656 |     86.01 |    -3.210 |    -4.653 |   -0.5037 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.707 |    0.6037 |    0.8734 |     1.380 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.091 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.06656</span> |     86.01 |   0.04036 |  0.009533 |    0.3767 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.707 |    0.6037 |    0.8734 |     1.380 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.091 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   58</span>|     752.02137 |    0.9885 |   -0.9874 |    -1.020 |    -1.325 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6252 |   -0.9645 |   -0.9146 |   -0.6528 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7710 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     752.02137 |     86.00 |    -3.218 |    -4.620 |   -0.4681 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.688 |    0.6425 |    0.8722 |     1.412 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.081 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     752.02137</span> |     86.00 |   0.04004 |  0.009850 |    0.3851 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.688 |    0.6425 |    0.8722 |     1.412 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.081 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> calculating covariance matrix</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating residuals/tables</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>initial ETAs were nudged; (can control by foceiControl(etaNudge=., etaNudge2=))</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>ETAs were reset to zero during optimization; (Can control by foceiControl(resetEtaP=.))</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>gradient problems with initial estimate and covariance; see $scaleInfo</span>
-<span class="r-in"><span class="va">f_nlmixr_fomc_sfo_focei_const</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr</a></span><span class="op">(</span><span class="va">f_mmkin_const</span><span class="op">[</span><span class="st">"FOMC-SFO"</span>, <span class="op">]</span>, est <span class="op">=</span> <span class="st">"focei"</span><span class="op">)</span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate jacobian</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate sensitivities</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate ∂(f)/∂(η)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate ∂(R²)/∂(η)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in inner model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in inner model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in EBE model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in EBE model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling inner model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in FD model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in FD model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling EBE model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling events FD model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Needed Covariates:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "CMT"</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="font-weight: bold;">Key:</span> U: Unscaled Parameters; X: Back-transformed parameters; G: Gill difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> F: Forward difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> C: Central difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> M: Mixed forward and central difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Unscaled parameters for Omegas=chol(solve(omega));</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Diagonals are transformed, as specified by foceiControl(diagXform=)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |-----+---------------+-----------+-----------+-----------+-----------|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    #| Objective Fun |  parent_0 |  log_k_A1 |f_parent_qlogis | log_alpha |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  log_beta |     sigma |        o1 |        o2 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|        o3 |        o4 |        o5 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    1</span>|     494.78160 |     1.000 |    -1.000 |   -0.9115 |   -0.8954 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8491 |   -0.8592 |   -0.8761 |   -0.8740 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8674 |   -0.8694 |   -0.8683 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      494.7816 |     94.00 |    -5.400 |    -1.000 |   -0.2000 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.100 |     1.600 |    0.7598 |    0.8633 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.189 |     1.089 |     1.146 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      494.7816</span> |     94.00 |  0.004517 |    0.2689 |    0.8187 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.166 |     1.600 |    0.7598 |    0.8633 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.189 |     1.089 |     1.146 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    G|    Gill Diff. |    -28.01 |     1.933 |   -0.2086 |   0.01492 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1687 |    -59.79 |     10.74 |     9.966 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -11.82 |    -8.862 |    -10.52 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    2</span>|     1353.4477 |     1.400 |    -1.028 |   -0.9085 |   -0.8956 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8467 | -0.005584 |    -1.029 |    -1.016 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6987 |   -0.7429 |   -0.7181 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     1353.4477 |     131.6 |    -5.428 |   -0.9970 |   -0.2002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.102 |     2.283 |    0.6433 |    0.7405 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.390 |     1.227 |     1.318 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     1353.4477</span> |     131.6 |  0.004394 |    0.2695 |    0.8186 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.186 |     2.283 |    0.6433 |    0.7405 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.390 |     1.227 |     1.318 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    3</span>|     504.82409 |     1.040 |    -1.003 |   -0.9112 |   -0.8954 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8489 |   -0.7738 |   -0.8914 |   -0.8882 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8506 |   -0.8568 |   -0.8533 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     504.82409 |     97.76 |    -5.403 |   -0.9997 |   -0.2000 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.100 |     1.668 |    0.7482 |    0.8511 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.209 |     1.103 |     1.163 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     504.82409</span> |     97.76 |  0.004504 |    0.2690 |    0.8187 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.168 |     1.668 |    0.7482 |    0.8511 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.209 |     1.103 |     1.163 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    4</span>|     494.10898 |     1.008 |    -1.001 |   -0.9114 |   -0.8954 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8490 |   -0.8416 |   -0.8792 |   -0.8769 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8640 |   -0.8668 |   -0.8652 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     494.10898 |     94.77 |    -5.401 |   -0.9999 |   -0.2000 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.100 |     1.614 |    0.7574 |    0.8608 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.193 |     1.092 |     1.149 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     494.10898</span> |     94.77 |  0.004514 |    0.2690 |    0.8187 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.167 |     1.614 |    0.7574 |    0.8608 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.193 |     1.092 |     1.149 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     147.0 |     1.955 |  -0.08761 |   0.06834 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.05948 |    -55.61 |     11.89 |     8.304 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -12.01 |    -8.510 |    -10.24 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    5</span>|     492.58255 |    0.9992 |    -1.001 |   -0.9114 |   -0.8954 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8490 |   -0.8245 |   -0.8825 |   -0.8797 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8605 |   -0.8643 |   -0.8621 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     492.58255 |     93.93 |    -5.401 |   -0.9999 |   -0.2000 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.100 |     1.628 |    0.7550 |    0.8584 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.197 |     1.095 |     1.153 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     492.58255</span> |     93.93 |  0.004511 |    0.2690 |    0.8187 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.167 |     1.628 |    0.7550 |    0.8584 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.197 |     1.095 |     1.153 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -43.22 |     1.882 |   -0.2206 | -0.004719 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1970 |    -51.78 |     10.22 |     6.620 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -11.81 |    -8.346 |    -10.04 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    6</span>|     491.66702 |     1.006 |    -1.002 |   -0.9113 |   -0.8954 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8490 |   -0.8063 |   -0.8860 |   -0.8822 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8565 |   -0.8614 |   -0.8588 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     491.66702 |     94.52 |    -5.402 |   -0.9998 |   -0.2000 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.100 |     1.642 |    0.7523 |    0.8562 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.202 |     1.098 |     1.157 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     491.66702</span> |     94.52 |  0.004509 |    0.2690 |    0.8187 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.167 |     1.642 |    0.7523 |    0.8562 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.202 |     1.098 |     1.157 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     87.79 |     1.893 |   -0.1269 |   0.04418 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.01699 |    -47.85 |     10.34 |     7.758 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -11.67 |    -8.213 |    -9.916 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    7</span>|     490.57489 |    0.9987 |    -1.002 |   -0.9112 |   -0.8954 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8489 |   -0.7885 |   -0.8895 |   -0.8851 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8525 |   -0.8586 |   -0.8553 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     490.57489 |     93.88 |    -5.402 |   -0.9998 |   -0.2000 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.100 |     1.657 |    0.7496 |    0.8537 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.207 |     1.101 |     1.161 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     490.57489</span> |     93.88 |  0.004506 |    0.2690 |    0.8187 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.168 |     1.657 |    0.7496 |    0.8537 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.207 |     1.101 |     1.161 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -52.56 |     1.834 |   -0.2285 |  -0.01046 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2159 |    -44.44 |     9.379 |     7.248 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -11.47 |    -8.044 |    -9.710 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    8</span>|     489.67804 |     1.004 |    -1.003 |   -0.9112 |   -0.8954 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8489 |   -0.7706 |   -0.8932 |   -0.8884 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8482 |   -0.8554 |   -0.8516 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     489.67804 |     94.42 |    -5.403 |   -0.9997 |   -0.2000 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.100 |     1.671 |    0.7468 |    0.8509 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.212 |     1.105 |     1.165 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     489.67804</span> |     94.42 |  0.004503 |    0.2690 |    0.8187 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.168 |     1.671 |    0.7468 |    0.8509 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.212 |     1.105 |     1.165 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     63.10 |     1.841 |   -0.1396 |   0.03713 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.05177 |    -40.65 |     9.345 |     6.018 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -11.29 |    -7.879 |    -9.567 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    9</span>|     488.80457 |    0.9984 |    -1.004 |   -0.9111 |   -0.8954 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8488 |   -0.7529 |   -0.8970 |   -0.8913 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8435 |   -0.8521 |   -0.8475 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     488.80457 |     93.85 |    -5.404 |   -0.9996 |   -0.2000 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.100 |     1.685 |    0.7439 |    0.8484 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.218 |     1.108 |     1.170 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     488.80457</span> |     93.85 |  0.004499 |    0.2690 |    0.8187 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.168 |     1.685 |    0.7439 |    0.8484 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.218 |     1.108 |     1.170 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -56.52 |     1.788 |   -0.2313 |  -0.01570 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2287 |    -37.43 |     8.740 |     5.512 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -11.08 |    -7.680 |    -9.353 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   10</span>|     487.98244 |     1.004 |    -1.005 |   -0.9110 |   -0.8954 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8487 |   -0.7356 |   -0.9012 |   -0.8939 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8380 |   -0.8482 |   -0.8428 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     487.98244 |     94.36 |    -5.405 |   -0.9995 |   -0.2000 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.100 |     1.699 |    0.7407 |    0.8461 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.224 |     1.112 |     1.175 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     487.98244</span> |     94.36 |  0.004495 |    0.2690 |    0.8187 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.169 |     1.699 |    0.7407 |    0.8461 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.224 |     1.112 |     1.175 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     49.57 |     1.794 |   -0.1466 |   0.03517 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.07178 |    -34.12 |     8.494 |     6.684 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -10.82 |    -7.482 |    -9.140 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   11</span>|     487.23587 |    0.9987 |    -1.006 |   -0.9109 |   -0.8954 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8487 |   -0.7192 |   -0.9058 |   -0.8980 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8316 |   -0.8438 |   -0.8374 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     487.23587 |     93.88 |    -5.406 |   -0.9994 |   -0.2001 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.100 |     1.712 |    0.7372 |    0.8426 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.232 |     1.117 |     1.181 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     487.23587</span> |     93.88 |  0.004490 |    0.2691 |    0.8187 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.170 |     1.712 |    0.7372 |    0.8426 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.232 |     1.117 |     1.181 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -49.22 |     1.745 |   -0.2274 | -0.009194 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2301 |    -31.48 |     7.992 |     6.132 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -10.64 |    -7.269 |    -8.903 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   12</span>|     486.53337 |     1.004 |    -1.007 |   -0.9107 |   -0.8954 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8485 |   -0.7047 |   -0.9109 |   -0.9037 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8240 |   -0.8386 |   -0.8310 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     486.53337 |     94.34 |    -5.407 |   -0.9993 |   -0.2001 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.101 |     1.724 |    0.7334 |    0.8376 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.241 |     1.123 |     1.189 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     486.53337</span> |     94.34 |  0.004484 |    0.2691 |    0.8187 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.171 |     1.724 |    0.7334 |    0.8376 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.241 |     1.123 |     1.189 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     43.47 |     1.742 |   -0.1424 |   0.02918 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.07089 |    -28.76 |     7.629 |     4.806 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -10.24 |    -6.953 |    -8.584 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   13</span>|     485.91669 |    0.9989 |    -1.009 |   -0.9105 |   -0.8955 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8484 |   -0.6920 |   -0.9165 |   -0.9072 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8145 |   -0.8325 |   -0.8231 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     485.91669 |     93.90 |    -5.409 |   -0.9991 |   -0.2001 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.101 |     1.734 |    0.7291 |    0.8346 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.252 |     1.130 |     1.198 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     485.91669</span> |     93.90 |  0.004476 |    0.2691 |    0.8187 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.172 |     1.734 |    0.7291 |    0.8346 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.252 |     1.130 |     1.198 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -44.30 |     1.699 |   -0.2182 |  0.001936 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2284 |    -26.86 |     7.286 |     5.487 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -9.898 |    -6.659 |    -8.234 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   14</span>|     485.33976 |     1.003 |    -1.011 |   -0.9103 |   -0.8955 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8481 |   -0.6819 |   -0.9228 |   -0.9110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8035 |   -0.8257 |   -0.8143 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     485.33976 |     94.29 |    -5.411 |   -0.9988 |   -0.2001 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.101 |     1.742 |    0.7243 |    0.8314 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.265 |     1.137 |     1.208 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     485.33976</span> |     94.29 |  0.004467 |    0.2692 |    0.8186 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.174 |     1.742 |    0.7243 |    0.8314 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.265 |     1.137 |     1.208 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   15</span>|     484.77317 |     1.003 |    -1.014 |   -0.9100 |   -0.8956 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8479 |   -0.6718 |   -0.9302 |   -0.9153 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7902 |   -0.8175 |   -0.8035 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     484.77317 |     94.32 |    -5.414 |   -0.9986 |   -0.2002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.101 |     1.750 |    0.7187 |    0.8276 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.281 |     1.146 |     1.220 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     484.77317</span> |     94.32 |  0.004455 |    0.2692 |    0.8186 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.176 |     1.750 |    0.7187 |    0.8276 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.281 |     1.146 |     1.220 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   16</span>|     483.17588 |     1.004 |    -1.023 |   -0.9091 |   -0.8958 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8469 |   -0.6384 |   -0.9549 |   -0.9296 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7463 |   -0.7903 |   -0.7681 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     483.17588 |     94.41 |    -5.423 |   -0.9976 |   -0.2004 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.102 |     1.777 |    0.6999 |    0.8153 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.333 |     1.176 |     1.261 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     483.17588</span> |     94.41 |  0.004415 |    0.2694 |    0.8184 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.184 |     1.777 |    0.6999 |    0.8153 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.333 |     1.176 |     1.261 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   17</span>|     481.21481 |     1.006 |    -1.040 |   -0.9072 |   -0.8961 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8451 |   -0.5721 |    -1.004 |   -0.9579 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6592 |   -0.7365 |   -0.6977 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     481.21481 |     94.58 |    -5.440 |   -0.9958 |   -0.2008 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.104 |     1.830 |    0.6628 |    0.7909 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.437 |     1.234 |     1.341 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     481.21481</span> |     94.58 |  0.004338 |    0.2698 |    0.8181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.199 |     1.830 |    0.6628 |    0.7909 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.437 |     1.234 |     1.341 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     62.86 |     1.476 |   0.06730 |   0.05792 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1123 |    -8.977 |     1.055 |     2.914 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -3.044 |    -1.094 |    -2.299 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   18</span>|     481.29476 |     1.000 |    -1.145 |   -0.9129 |   -0.8997 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8485 |   -0.5378 |   -0.8985 |    -1.168 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6228 |   -0.8149 |   -0.6969 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     481.29476 |     94.03 |    -5.545 |    -1.001 |   -0.2043 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.101 |     1.857 |    0.7428 |    0.6099 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.480 |     1.149 |     1.342 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     481.29476</span> |     94.03 |  0.003907 |    0.2687 |    0.8152 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.171 |     1.857 |    0.7428 |    0.6099 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.480 |     1.149 |     1.342 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   19</span>|     480.77138 |     1.000 |    -1.090 |   -0.9099 |   -0.8978 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8467 |   -0.5555 |   -0.9542 |    -1.057 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6419 |   -0.7733 |   -0.6972 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.77138 |     94.01 |    -5.490 |   -0.9984 |   -0.2025 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.102 |     1.843 |    0.7004 |    0.7055 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.457 |     1.194 |     1.342 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.77138</span> |     94.01 |  0.004129 |    0.2693 |    0.8167 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.186 |     1.843 |    0.7004 |    0.7055 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.457 |     1.194 |     1.342 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -25.72 |     1.151 |   0.08772 | -0.007897 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.07232 |    -6.930 |     3.330 |    -2.491 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -2.626 |    -3.111 |    -2.500 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   20</span>|     480.61717 |     1.001 |    -1.203 |   -0.9227 |   -0.9002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8503 |   -0.5418 |   -0.9405 |    -1.001 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6281 |   -0.7705 |   -0.6908 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.61717 |     94.09 |    -5.603 |    -1.011 |   -0.2049 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.099 |     1.854 |    0.7108 |    0.7537 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.474 |     1.197 |     1.349 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.61717</span> |     94.09 |  0.003688 |    0.2667 |    0.8147 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.156 |     1.854 |    0.7108 |    0.7537 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.474 |     1.197 |     1.349 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -10.61 |    0.9224 |   -0.5881 |  -0.06621 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1838 |    -5.929 |     3.775 |     2.798 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.936 |    -2.993 |    -2.208 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   21</span>|     480.72130 |     1.009 |    -1.314 |   -0.9169 |   -0.9003 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8494 |   -0.5516 |   -0.9873 |    -1.003 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6340 |   -0.7292 |   -0.6819 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      480.7213 |     94.81 |    -5.714 |    -1.005 |   -0.2049 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.100 |     1.846 |    0.6753 |    0.7523 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.467 |     1.242 |     1.359 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      480.7213</span> |     94.81 |  0.003299 |    0.2679 |    0.8147 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.163 |     1.846 |    0.6753 |    0.7523 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.467 |     1.242 |     1.359 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   22</span>|     480.86254 |     1.009 |    -1.247 |   -0.9202 |   -0.9002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8499 |   -0.5429 |   -0.9605 |    -1.003 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6295 |   -0.7530 |   -0.6863 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.86254 |     94.84 |    -5.647 |    -1.009 |   -0.2048 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.099 |     1.853 |    0.6957 |    0.7520 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.472 |     1.216 |     1.354 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.86254</span> |     94.84 |  0.003530 |    0.2672 |    0.8148 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.160 |     1.853 |    0.6957 |    0.7520 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.472 |     1.216 |     1.354 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   23</span>|     480.98412 |     1.009 |    -1.209 |   -0.9220 |   -0.9002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8501 |   -0.5381 |   -0.9458 |    -1.003 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6270 |   -0.7661 |   -0.6887 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.98412 |     94.85 |    -5.609 |    -1.010 |   -0.2048 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.099 |     1.857 |    0.7069 |    0.7519 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.475 |     1.202 |     1.352 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.98412</span> |     94.85 |  0.003664 |    0.2669 |    0.8148 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.158 |     1.857 |    0.7069 |    0.7519 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.475 |     1.202 |     1.352 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   24</span>|     480.60990 |     1.003 |    -1.203 |   -0.9226 |   -0.9002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8503 |   -0.5410 |   -0.9411 |    -1.001 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6278 |   -0.7701 |   -0.6905 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      480.6099 |     94.24 |    -5.603 |    -1.011 |   -0.2049 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.099 |     1.855 |    0.7104 |    0.7534 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.474 |     1.198 |     1.350 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      480.6099</span> |     94.24 |  0.003688 |    0.2668 |    0.8148 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.156 |     1.855 |    0.7104 |    0.7534 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.474 |     1.198 |     1.350 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     15.29 |    0.9278 |   -0.5289 |  -0.04762 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1043 |    -5.468 |     3.876 |    0.6273 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.897 |    -2.941 |    -2.183 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   25</span>|     480.59557 |     1.001 |    -1.204 |   -0.9225 |   -0.9002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8503 |   -0.5407 |   -0.9419 |    -1.001 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6277 |   -0.7694 |   -0.6902 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.59557 |     94.13 |    -5.604 |    -1.011 |   -0.2049 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.099 |     1.855 |    0.7098 |    0.7533 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.474 |     1.198 |     1.350 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.59557</span> |     94.13 |  0.003683 |    0.2668 |    0.8148 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.157 |     1.855 |    0.7098 |    0.7533 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.474 |     1.198 |     1.350 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.733 |    0.9212 |   -0.5619 |  -0.05855 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1664 |    -5.388 |     3.833 |    0.5854 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.924 |    -2.927 |    -2.102 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   26</span>|     480.58581 |     1.002 |    -1.204 |   -0.9224 |   -0.9002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8502 |   -0.5395 |   -0.9428 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6273 |   -0.7688 |   -0.6897 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.58581 |     94.23 |    -5.604 |    -1.011 |   -0.2048 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.099 |     1.856 |    0.7092 |    0.7532 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.475 |     1.199 |     1.351 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.58581</span> |     94.23 |  0.003683 |    0.2668 |    0.8148 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.157 |     1.856 |    0.7092 |    0.7532 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.475 |     1.199 |     1.351 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     12.31 |    0.9226 |   -0.5246 |  -0.05019 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1134 |    -5.293 |     3.771 |    0.5822 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.938 |    -2.904 |    -2.175 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   27</span>|     480.57360 |     1.001 |    -1.205 |   -0.9222 |   -0.9002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8502 |   -0.5392 |   -0.9437 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6271 |   -0.7681 |   -0.6894 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      480.5736 |     94.13 |    -5.605 |    -1.011 |   -0.2048 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.099 |     1.856 |    0.7085 |    0.7532 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.475 |     1.200 |     1.351 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      480.5736</span> |     94.13 |  0.003678 |    0.2668 |    0.8148 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.157 |     1.856 |    0.7085 |    0.7532 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.475 |     1.200 |     1.351 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.864 |    0.9151 |   -0.5515 |  -0.06112 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1679 |    -5.184 |     3.617 |    0.5746 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.832 |    -2.849 |    -2.141 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   28</span>|     480.56429 |     1.002 |    -1.206 |   -0.9220 |   -0.9002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8502 |   -0.5382 |   -0.9446 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6268 |   -0.7674 |   -0.6889 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.56429 |     94.23 |    -5.606 |    -1.011 |   -0.2048 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.099 |     1.857 |    0.7078 |    0.7531 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.475 |     1.201 |     1.351 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.56429</span> |     94.23 |  0.003676 |    0.2669 |    0.8148 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.158 |     1.857 |    0.7078 |    0.7531 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.475 |     1.201 |     1.351 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     12.18 |    0.9169 |   -0.5105 |  -0.05061 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1125 |    -5.131 |     3.532 |    0.5638 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.931 |    -2.821 |    -2.132 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   29</span>|     480.55246 |     1.001 |    -1.207 |   -0.9218 |   -0.9002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8501 |   -0.5380 |   -0.9454 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6266 |   -0.7667 |   -0.6886 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.55246 |     94.13 |    -5.607 |    -1.010 |   -0.2048 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.099 |     1.857 |    0.7071 |    0.7532 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.476 |     1.201 |     1.352 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.55246</span> |     94.13 |  0.003672 |    0.2669 |    0.8148 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.158 |     1.857 |    0.7071 |    0.7532 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.476 |     1.201 |     1.352 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.480 |    0.9098 |   -0.5353 |  -0.06055 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1647 |    -5.053 |     3.625 |    0.5654 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.860 |    -2.782 |    -2.112 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   30</span>|     480.54335 |     1.002 |    -1.207 |   -0.9217 |   -0.9002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8501 |   -0.5368 |   -0.9463 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6262 |   -0.7660 |   -0.6881 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.54335 |     94.23 |    -5.607 |    -1.010 |   -0.2048 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.099 |     1.858 |    0.7065 |    0.7530 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.476 |     1.202 |     1.352 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.54335</span> |     94.23 |  0.003671 |    0.2669 |    0.8148 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.158 |     1.858 |    0.7065 |    0.7530 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.476 |     1.202 |     1.352 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     12.09 |    0.9120 |   -0.4955 |  -0.05014 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1107 |    -4.912 |     3.583 |    0.5835 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.814 |    -2.702 |    -2.057 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   31</span>|     480.53185 |     1.001 |    -1.209 |   -0.9215 |   -0.9002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8500 |   -0.5366 |   -0.9472 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6260 |   -0.7654 |   -0.6878 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.53185 |     94.13 |    -5.609 |    -1.010 |   -0.2048 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.099 |     1.858 |    0.7058 |    0.7531 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.476 |     1.203 |     1.353 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.53185</span> |     94.13 |  0.003666 |    0.2670 |    0.8148 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.158 |     1.858 |    0.7058 |    0.7531 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.476 |     1.203 |     1.353 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.509 |    0.9048 |   -0.5214 |  -0.06027 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1665 |    -4.870 |     3.525 |    0.5641 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.813 |    -2.699 |    -2.069 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   32</span>|     480.52276 |     1.002 |    -1.209 |   -0.9214 |   -0.9001 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8500 |   -0.5356 |   -0.9481 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6256 |   -0.7647 |   -0.6873 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.52276 |     94.22 |    -5.609 |    -1.010 |   -0.2048 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.099 |     1.859 |    0.7051 |    0.7530 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.477 |     1.203 |     1.353 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.52276</span> |     94.22 |  0.003664 |    0.2670 |    0.8148 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.159 |     1.859 |    0.7051 |    0.7530 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.477 |     1.203 |     1.353 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     11.38 |    0.9060 |   -0.4821 |  -0.05014 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1110 |    -4.747 |     3.479 |    0.5798 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.777 |    -2.636 |    -2.020 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   33</span>|     480.51194 |     1.001 |    -1.211 |   -0.9212 |   -0.9001 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8500 |   -0.5355 |   -0.9490 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6255 |   -0.7641 |   -0.6869 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.51194 |     94.13 |    -5.611 |    -1.010 |   -0.2048 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.099 |     1.859 |    0.7044 |    0.7530 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.477 |     1.204 |     1.354 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.51194</span> |     94.13 |  0.003659 |    0.2670 |    0.8148 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.159 |     1.859 |    0.7044 |    0.7530 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.477 |     1.204 |     1.354 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.485 |    0.8991 |   -0.5064 |  -0.06148 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1643 |    -4.758 |     3.403 |    0.5432 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.772 |    -2.633 |    -2.022 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   34</span>|     480.50302 |     1.002 |    -1.211 |   -0.9210 |   -0.9001 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8499 |   -0.5345 |   -0.9500 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6251 |   -0.7633 |   -0.6864 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.50302 |     94.22 |    -5.611 |    -1.010 |   -0.2047 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.099 |     1.860 |    0.7037 |    0.7529 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.477 |     1.205 |     1.354 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.50302</span> |     94.22 |  0.003656 |    0.2671 |    0.8149 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.159 |     1.860 |    0.7037 |    0.7529 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.477 |     1.205 |     1.354 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     10.83 |    0.8997 |   -0.4680 |  -0.05021 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1106 |    -4.703 |     3.332 |    0.5674 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.765 |    -2.559 |    -1.986 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   35</span>|     480.49281 |     1.001 |    -1.213 |   -0.9209 |   -0.9001 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8499 |   -0.5344 |   -0.9509 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6250 |   -0.7627 |   -0.6860 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.49281 |     94.13 |    -5.613 |    -1.009 |   -0.2047 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.099 |     1.860 |    0.7030 |    0.7529 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.478 |     1.206 |     1.355 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.49281</span> |     94.13 |  0.003652 |    0.2671 |    0.8149 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.160 |     1.860 |    0.7030 |    0.7529 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.478 |     1.206 |     1.355 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.447 |    0.8934 |   -0.4900 |  -0.06030 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1577 |    -4.708 |     3.266 |    0.5080 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.782 |    -2.544 |    -1.983 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   36</span>|     480.48415 |     1.002 |    -1.213 |   -0.9207 |   -0.9001 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8498 |   -0.5335 |   -0.9518 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6246 |   -0.7620 |   -0.6855 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.48415 |     94.22 |    -5.613 |    -1.009 |   -0.2047 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.099 |     1.861 |    0.7023 |    0.7528 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.478 |     1.206 |     1.355 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.48415</span> |     94.22 |  0.003649 |    0.2671 |    0.8149 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.160 |     1.861 |    0.7023 |    0.7528 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.478 |     1.206 |     1.355 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     10.47 |    0.8932 |   -0.4526 |  -0.04997 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1093 |    -4.502 |     3.250 |    0.5653 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.769 |    -2.486 |    -1.944 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   37</span>|     480.47437 |     1.001 |    -1.215 |   -0.9205 |   -0.9001 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8498 |   -0.5334 |   -0.9527 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6244 |   -0.7613 |   -0.6852 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.47437 |     94.13 |    -5.615 |    -1.009 |   -0.2047 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.099 |     1.861 |    0.7016 |    0.7528 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.478 |     1.207 |     1.356 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.47437</span> |     94.13 |  0.003644 |    0.2672 |    0.8149 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.161 |     1.861 |    0.7016 |    0.7528 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.478 |     1.207 |     1.356 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.431 |    0.8869 |   -0.4735 |  -0.05983 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1554 |    -4.492 |     3.189 |   -0.6996 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.700 |    -2.467 |    -1.937 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   38</span>|     480.46651 |     1.002 |    -1.216 |   -0.9203 |   -0.9000 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8497 |   -0.5326 |   -0.9537 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6240 |   -0.7606 |   -0.6847 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.46651 |     94.21 |    -5.616 |    -1.009 |   -0.2047 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.099 |     1.861 |    0.7009 |    0.7530 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.479 |     1.208 |     1.356 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.46651</span> |     94.21 |  0.003640 |    0.2672 |    0.8149 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.161 |     1.861 |    0.7009 |    0.7530 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.479 |     1.208 |     1.356 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     9.502 |    0.8860 |   -0.4381 |  -0.05035 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1103 |    -4.453 |     3.114 |    0.5384 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.770 |    -2.407 |    -1.897 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   39</span>|     480.45755 |     1.001 |    -1.217 |   -0.9200 |   -0.9000 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8497 |   -0.5325 |   -0.9546 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6238 |   -0.7600 |   -0.6843 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.45755 |     94.13 |    -5.617 |    -1.009 |   -0.2047 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.099 |     1.861 |    0.7002 |    0.7530 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.479 |     1.209 |     1.357 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.45755</span> |     94.13 |  0.003635 |    0.2673 |    0.8149 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.162 |     1.861 |    0.7002 |    0.7530 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.479 |     1.209 |     1.357 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.779 |    0.8798 |   -0.4544 |  -0.05816 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1541 |    -4.432 |     3.060 |    0.5236 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.719 |    -2.365 |    -1.874 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   40</span>|     480.44943 |     1.002 |    -1.218 |   -0.9199 |   -0.9000 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8496 |   -0.5319 |   -0.9555 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6235 |   -0.7592 |   -0.6838 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.44943 |     94.21 |    -5.618 |    -1.008 |   -0.2046 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.099 |     1.862 |    0.6995 |    0.7529 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.479 |     1.209 |     1.357 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.44943</span> |     94.21 |  0.003631 |    0.2673 |    0.8149 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.162 |     1.862 |    0.6995 |    0.7529 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.479 |     1.209 |     1.357 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     8.806 |    0.8785 |   -0.4202 |  -0.05024 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1124 |    -4.289 |     3.039 |    0.5974 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.651 |    -2.322 |    -1.843 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   41</span>|     480.44100 |     1.001 |    -1.220 |   -0.9197 |   -0.9000 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8496 |   -0.5318 |   -0.9564 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6233 |   -0.7586 |   -0.6835 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|       480.441 |     94.13 |    -5.620 |    -1.008 |   -0.2046 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.100 |     1.862 |    0.6988 |    0.7529 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.480 |     1.210 |     1.358 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">       480.441</span> |     94.13 |  0.003626 |    0.2673 |    0.8150 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.162 |     1.862 |    0.6988 |    0.7529 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.480 |     1.210 |     1.358 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.662 |    0.8724 |   -0.4382 |  -0.05735 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1541 |    -4.192 |     3.008 |    0.5718 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.633 |    -2.283 |    -1.828 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   42</span>|     480.43316 |     1.002 |    -1.221 |   -0.9195 |   -0.9000 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8495 |   -0.5313 |   -0.9573 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6230 |   -0.7579 |   -0.6830 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.43316 |     94.21 |    -5.621 |    -1.008 |   -0.2046 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.100 |     1.862 |    0.6981 |    0.7529 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.480 |     1.211 |     1.358 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.43316</span> |     94.21 |  0.003622 |    0.2674 |    0.8150 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.163 |     1.862 |    0.6981 |    0.7529 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.480 |     1.211 |     1.358 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     8.374 |    0.8709 |   -0.4057 |  -0.04981 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1077 |    -4.274 |     2.898 |    0.5670 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.709 |    -2.249 |    -1.806 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   43</span>|     480.42514 |     1.001 |    -1.222 |   -0.9194 |   -0.8999 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8495 |   -0.5312 |   -0.9582 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6227 |   -0.7573 |   -0.6826 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.42514 |     94.13 |    -5.622 |    -1.008 |   -0.2046 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.100 |     1.862 |    0.6974 |    0.7529 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.480 |     1.212 |     1.359 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.42514</span> |     94.13 |  0.003617 |    0.2674 |    0.8150 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.163 |     1.862 |    0.6974 |    0.7529 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.480 |     1.212 |     1.359 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.560 |    0.8648 |   -0.4238 |  -0.05673 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1505 |    -4.130 |     2.894 |    0.5588 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.637 |    -2.210 |    -1.785 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   44</span>|     480.41757 |     1.002 |    -1.223 |   -0.9192 |   -0.8999 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8494 |   -0.5307 |   -0.9591 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6223 |   -0.7566 |   -0.6821 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.41757 |     94.21 |    -5.623 |    -1.008 |   -0.2046 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.100 |     1.863 |    0.6967 |    0.7528 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.481 |     1.212 |     1.359 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.41757</span> |     94.21 |  0.003613 |    0.2674 |    0.8150 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.164 |     1.863 |    0.6967 |    0.7528 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.481 |     1.212 |     1.359 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     8.260 |    0.8632 |   -0.3905 |  -0.04898 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1050 |    -4.163 |     2.793 |    0.5626 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.680 |    -2.170 |    -1.767 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   45</span>|     480.40986 |     1.001 |    -1.225 |   -0.9190 |   -0.8999 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8494 |   -0.5306 |   -0.9600 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6221 |   -0.7560 |   -0.6818 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.40986 |     94.13 |    -5.625 |    -1.008 |   -0.2045 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.100 |     1.863 |    0.6961 |    0.7528 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.481 |     1.213 |     1.360 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.40986</span> |     94.13 |  0.003607 |    0.2675 |    0.8150 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.164 |     1.863 |    0.6961 |    0.7528 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.481 |     1.213 |     1.360 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.433 |    0.8570 |   -0.4082 |  -0.05598 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1439 |    -4.083 |     2.776 |   -0.7191 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.623 |    -2.134 |    -1.744 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   46</span>|     480.40309 |     1.002 |    -1.226 |   -0.9188 |   -0.8999 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8493 |   -0.5301 |   -0.9609 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6217 |   -0.7553 |   -0.6813 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.40309 |     94.20 |    -5.626 |    -1.007 |   -0.2045 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.100 |     1.863 |    0.6954 |    0.7529 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.481 |     1.214 |     1.360 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.40309</span> |     94.20 |  0.003603 |    0.2675 |    0.8151 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.165 |     1.863 |    0.6954 |    0.7529 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.481 |     1.214 |     1.360 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     7.640 |    0.8551 |   -0.3719 |  -0.04853 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1037 |    -4.111 |     2.689 |    0.5674 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.653 |    -2.095 |    -1.726 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   47</span>|     480.39597 |     1.001 |    -1.227 |   -0.9185 |   -0.8998 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8492 |   -0.5300 |   -0.9618 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6215 |   -0.7547 |   -0.6809 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.39597 |     94.13 |    -5.627 |    -1.007 |   -0.2045 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.100 |     1.863 |    0.6947 |    0.7530 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.482 |     1.214 |     1.361 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.39597</span> |     94.13 |  0.003598 |    0.2676 |    0.8151 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.165 |     1.863 |    0.6947 |    0.7530 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.482 |     1.214 |     1.361 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.827 |    0.8487 |   -0.3865 |  -0.05558 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1453 |    -3.997 |     2.662 |    0.5449 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.580 |    -2.061 |    -1.701 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   48</span>|     480.38900 |     1.002 |    -1.229 |   -0.9184 |   -0.8998 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8491 |   -0.5297 |   -0.9626 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6211 |   -0.7540 |   -0.6805 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|       480.389 |     94.20 |    -5.629 |    -1.007 |   -0.2044 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.100 |     1.864 |    0.6940 |    0.7529 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.482 |     1.215 |     1.361 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">       480.389</span> |     94.20 |  0.003593 |    0.2676 |    0.8151 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.166 |     1.864 |    0.6940 |    0.7529 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.482 |     1.215 |     1.361 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     7.052 |    0.8465 |   -0.3544 |  -0.04836 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1022 |    -3.917 |     2.636 |    0.5926 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.553 |    -2.027 |    -1.689 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   49</span>|     480.38219 |     1.001 |    -1.230 |   -0.9183 |   -0.8998 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8491 |   -0.5296 |   -0.9634 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6209 |   -0.7534 |   -0.6801 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.38219 |     94.13 |    -5.630 |    -1.007 |   -0.2044 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.100 |     1.864 |    0.6934 |    0.7529 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.482 |     1.216 |     1.362 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.38219</span> |     94.13 |  0.003587 |    0.2676 |    0.8151 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.166 |     1.864 |    0.6934 |    0.7529 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.482 |     1.216 |     1.362 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.619 |    0.8406 |   -0.3725 |  -0.05439 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1411 |    -3.915 |     2.594 |   -0.6808 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.519 |    -1.986 |    -1.659 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   50</span>|     480.37598 |     1.002 |    -1.232 |   -0.9180 |   -0.8998 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8490 |   -0.5292 |   -0.9644 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6206 |   -0.7528 |   -0.6796 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.37598 |     94.20 |    -5.632 |    -1.007 |   -0.2044 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.100 |     1.864 |    0.6927 |    0.7530 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.483 |     1.216 |     1.362 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.37598</span> |     94.20 |  0.003582 |    0.2677 |    0.8152 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.167 |     1.864 |    0.6927 |    0.7530 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.483 |     1.216 |     1.362 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     6.566 |    0.8383 |   -0.3380 |  -0.04700 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.09943 |    -3.949 |     2.377 |    0.5751 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.561 |    -1.951 |    -1.641 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   51</span>|     480.36982 |     1.001 |    -1.233 |   -0.9178 |   -0.8997 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8489 |   -0.5291 |   -0.9651 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6204 |   -0.7522 |   -0.6792 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.36982 |     94.12 |    -5.633 |    -1.006 |   -0.2043 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.100 |     1.864 |    0.6922 |    0.7530 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.483 |     1.217 |     1.363 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.36982</span> |     94.12 |  0.003577 |    0.2677 |    0.8152 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.167 |     1.864 |    0.6922 |    0.7530 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.483 |     1.217 |     1.363 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -5.243 |    0.8317 |   -0.3515 |  -0.05392 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1358 |    -3.830 |     2.493 |    0.5823 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.510 |    -1.918 |    -1.620 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   52</span>|     480.36331 |     1.002 |    -1.235 |   -0.9176 |   -0.8997 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8489 |   -0.5289 |   -0.9659 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6201 |   -0.7516 |   -0.6788 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.36331 |     94.19 |    -5.635 |    -1.006 |   -0.2043 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.100 |     1.864 |    0.6916 |    0.7530 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.483 |     1.218 |     1.363 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.36331</span> |     94.19 |  0.003572 |    0.2677 |    0.8152 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.168 |     1.864 |    0.6916 |    0.7530 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.483 |     1.218 |     1.363 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     5.492 |    0.8288 |   -0.3226 |  -0.04732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.09956 |    -3.829 |     2.435 |    0.5676 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.517 |    -1.882 |    -1.604 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   53</span>|     480.35731 |     1.001 |    -1.236 |   -0.9175 |   -0.8996 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8488 |   -0.5288 |   -0.9666 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6198 |   -0.7510 |   -0.6783 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.35731 |     94.12 |    -5.636 |    -1.006 |   -0.2043 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.100 |     1.864 |    0.6910 |    0.7529 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.484 |     1.218 |     1.364 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.35731</span> |     94.12 |  0.003566 |    0.2678 |    0.8152 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.168 |     1.864 |    0.6910 |    0.7529 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.484 |     1.218 |     1.364 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -5.442 |    0.8230 |   -0.3401 |  -0.05270 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1329 |    -3.818 |     2.394 |   -0.6865 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.462 |    -1.826 |    -1.568 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   54</span>|     480.35158 |     1.002 |    -1.238 |   -0.9173 |   -0.8996 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8487 |   -0.5286 |   -0.9675 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6197 |   -0.7504 |   -0.6779 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.35158 |     94.19 |    -5.638 |    -1.006 |   -0.2042 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.100 |     1.864 |    0.6904 |    0.7530 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.484 |     1.219 |     1.364 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.35158</span> |     94.19 |  0.003561 |    0.2678 |    0.8153 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.169 |     1.864 |    0.6904 |    0.7530 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.484 |     1.219 |     1.364 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     5.119 |    0.8198 |   -0.3066 |  -0.04724 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1005 |    -3.787 |     2.338 |    0.5896 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.465 |    -1.812 |    -1.559 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   55</span>|     480.34603 |     1.001 |    -1.239 |   -0.9170 |   -0.8996 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8486 |   -0.5283 |   -0.9683 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6195 |   -0.7499 |   -0.6775 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.34603 |     94.12 |    -5.639 |    -1.006 |   -0.2042 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.100 |     1.865 |    0.6897 |    0.7530 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.484 |     1.220 |     1.364 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.34603</span> |     94.12 |  0.003555 |    0.2679 |    0.8153 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.170 |     1.865 |    0.6897 |    0.7530 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.484 |     1.220 |     1.364 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -5.669 |    0.8137 |   -0.3182 |  -0.05158 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1292 |    -3.845 |     2.251 |    0.5349 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.532 |    -1.781 |    -1.537 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   56</span>|     480.33997 |     1.002 |    -1.241 |   -0.9169 |   -0.8995 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8486 |   -0.5282 |   -0.9690 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6192 |   -0.7493 |   -0.6772 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.33997 |     94.19 |    -5.641 |    -1.005 |   -0.2041 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.101 |     1.865 |    0.6892 |    0.7529 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.484 |     1.220 |     1.365 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.33997</span> |     94.19 |  0.003550 |    0.2679 |    0.8153 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.170 |     1.865 |    0.6892 |    0.7529 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.484 |     1.220 |     1.365 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     4.944 |    0.8109 |   -0.2900 |  -0.04418 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.09220 |    -3.817 |     2.207 |    0.5423 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.530 |    -1.750 |    -1.524 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   57</span>|     480.33440 |     1.001 |    -1.242 |   -0.9168 |   -0.8995 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8485 |   -0.5280 |   -0.9697 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6188 |   -0.7487 |   -0.6768 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      480.3344 |     94.12 |    -5.642 |    -1.005 |   -0.2041 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.101 |     1.865 |    0.6887 |    0.7529 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.485 |     1.221 |     1.365 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      480.3344</span> |     94.12 |  0.003544 |    0.2679 |    0.8154 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.171 |     1.865 |    0.6887 |    0.7529 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.485 |     1.221 |     1.365 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -5.630 |    0.8048 |   -0.3070 |  -0.04965 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1246 |    -3.770 |     2.179 |    0.5400 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.465 |    -1.710 |    -1.500 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   58</span>|     480.32852 |     1.002 |    -1.244 |   -0.9167 |   -0.8994 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8485 |   -0.5278 |   -0.9703 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6184 |   -0.7482 |   -0.6764 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.32852 |     94.19 |    -5.644 |    -1.005 |   -0.2041 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.101 |     1.865 |    0.6882 |    0.7528 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.485 |     1.221 |     1.366 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.32852</span> |     94.19 |  0.003538 |    0.2679 |    0.8154 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.171 |     1.865 |    0.6882 |    0.7528 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.485 |     1.221 |     1.366 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     4.819 |    0.8018 |   -0.2789 |  -0.04260 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.08823 |    -3.653 |     2.177 |   -0.6593 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.384 |    -1.678 |    -1.478 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   59</span>|     480.32382 |     1.001 |    -1.246 |   -0.9164 |   -0.8994 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8484 |   -0.5276 |   -0.9711 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6183 |   -0.7477 |   -0.6761 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.32382 |     94.12 |    -5.646 |    -1.005 |   -0.2040 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.101 |     1.865 |    0.6876 |    0.7529 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.486 |     1.222 |     1.366 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.32382</span> |     94.12 |  0.003533 |    0.2680 |    0.8155 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.172 |     1.865 |    0.6876 |    0.7529 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.486 |     1.222 |     1.366 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -5.225 |    0.7951 |   -0.2896 |  -0.04843 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1194 |    -3.720 |     2.091 |   -0.7072 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.430 |    -1.641 |    -1.454 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   60</span>|     480.31902 |     1.002 |    -1.247 |   -0.9160 |   -0.8993 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8482 |   -0.5273 |   -0.9719 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6182 |   -0.7473 |   -0.6757 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.31902 |     94.19 |    -5.647 |    -1.005 |   -0.2039 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.101 |     1.865 |    0.6870 |    0.7531 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.486 |     1.222 |     1.367 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.31902</span> |     94.19 |  0.003527 |    0.2681 |    0.8155 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.173 |     1.865 |    0.6870 |    0.7531 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.486 |     1.222 |     1.367 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     4.904 |    0.7922 |   -0.2469 |  -0.04034 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.08539 |    -3.545 |     2.094 |   -0.6237 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.331 |    -1.619 |    -1.454 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   61</span>|     480.31471 |     1.001 |    -1.249 |   -0.9156 |   -0.8993 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8481 |   -0.5271 |   -0.9727 |    -1.001 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6181 |   -0.7468 |   -0.6753 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.31471 |     94.12 |    -5.649 |    -1.004 |   -0.2039 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.101 |     1.866 |    0.6864 |    0.7533 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.486 |     1.223 |     1.367 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.31471</span> |     94.12 |  0.003522 |    0.2681 |    0.8156 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.174 |     1.866 |    0.6864 |    0.7533 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.486 |     1.223 |     1.367 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -5.295 |    0.7853 |   -0.2507 |  -0.04627 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1126 |    -3.760 |     1.942 |   -0.7288 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.482 |    -1.617 |    -1.436 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   62</span>|     480.31013 |     1.002 |    -1.250 |   -0.9152 |   -0.8992 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8480 |   -0.5269 |   -0.9734 |    -1.001 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6180 |   -0.7464 |   -0.6750 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.31013 |     94.19 |    -5.650 |    -1.004 |   -0.2038 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.101 |     1.866 |    0.6859 |    0.7535 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.486 |     1.223 |     1.367 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.31013</span> |     94.19 |  0.003516 |    0.2682 |    0.8156 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.175 |     1.866 |    0.6859 |    0.7535 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.486 |     1.223 |     1.367 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     4.435 |    0.7821 |   -0.2118 |  -0.03855 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.07590 |    -3.495 |     2.000 |   -0.6027 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.382 |    -1.576 |    -1.413 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   63</span>|     480.30581 |     1.001 |    -1.252 |   -0.9149 |   -0.8991 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8479 |   -0.5266 |   -0.9741 |    -1.001 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6178 |   -0.7459 |   -0.6746 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.30581 |     94.12 |    -5.652 |    -1.003 |   -0.2037 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.101 |     1.866 |    0.6854 |    0.7536 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.486 |     1.224 |     1.368 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.30581</span> |     94.12 |  0.003510 |    0.2683 |    0.8157 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.176 |     1.866 |    0.6854 |    0.7536 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.486 |     1.224 |     1.368 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -5.315 |    0.7789 |   -0.2090 |  -0.03720 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.09944 |    -3.577 |     1.911 |   -0.6518 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.378 |    -1.528 |    -1.376 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   64</span>|     480.30125 |     1.002 |    -1.254 |   -0.9146 |   -0.8990 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8478 |   -0.5264 |   -0.9748 |    -1.001 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6176 |   -0.7454 |   -0.6743 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.30125 |     94.19 |    -5.654 |    -1.003 |   -0.2037 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.101 |     1.866 |    0.6848 |    0.7538 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.486 |     1.224 |     1.368 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.30125</span> |     94.19 |  0.003505 |    0.2683 |    0.8157 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.177 |     1.866 |    0.6848 |    0.7538 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.486 |     1.224 |     1.368 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     4.353 |    0.7723 |   -0.1834 |  -0.03622 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.07047 |    -3.672 |     1.822 |   -0.6713 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.445 |    -1.532 |    -1.389 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   65</span>|     480.29714 |     1.001 |    -1.255 |   -0.9143 |   -0.8990 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8476 |   -0.5261 |   -0.9754 |    -1.001 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6174 |   -0.7450 |   -0.6740 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.29714 |     94.12 |    -5.655 |    -1.003 |   -0.2036 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.101 |     1.866 |    0.6843 |    0.7539 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.487 |     1.225 |     1.369 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.29714</span> |     94.12 |  0.003499 |    0.2684 |    0.8158 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.178 |     1.866 |    0.6843 |    0.7539 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.487 |     1.225 |     1.369 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -5.435 |    0.7663 |   -0.1878 |  -0.03986 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1019 |    -3.343 |     1.893 |    0.6711 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.269 |    -1.487 |    -1.354 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   66</span>|     480.29215 |     1.002 |    -1.257 |   -0.9143 |   -0.8989 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8476 |   -0.5260 |   -0.9760 |    -1.001 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6172 |   -0.7444 |   -0.6736 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.29215 |     94.18 |    -5.657 |    -1.003 |   -0.2036 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.102 |     1.867 |    0.6839 |    0.7538 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.487 |     1.226 |     1.369 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.29215</span> |     94.18 |  0.003493 |    0.2684 |    0.8158 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.179 |     1.867 |    0.6839 |    0.7538 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.487 |     1.226 |     1.369 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     4.161 |    0.7621 |   -0.1686 |  -0.03452 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.06632 |    -3.552 |     1.775 |    0.6057 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.379 |    -1.473 |    -1.364 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   67</span>|     480.28721 |     1.001 |    -1.259 |   -0.9145 |   -0.8989 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8476 |   -0.5257 |   -0.9765 |    -1.001 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6170 |   -0.7439 |   -0.6732 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.28721 |     94.12 |    -5.659 |    -1.003 |   -0.2035 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.102 |     1.867 |    0.6836 |    0.7537 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.487 |     1.226 |     1.369 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.28721</span> |     94.12 |  0.003487 |    0.2684 |    0.8159 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.179 |     1.867 |    0.6836 |    0.7537 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.487 |     1.226 |     1.369 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.925 |    0.7584 |   -0.1913 |  -0.03513 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.09132 |    -3.418 |     1.781 |   -0.6203 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.349 |    -1.417 |    -1.313 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   68</span>|     480.28278 |     1.002 |    -1.260 |   -0.9144 |   -0.8988 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8475 |   -0.5255 |   -0.9770 |    -1.001 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6167 |   -0.7434 |   -0.6728 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.28278 |     94.19 |    -5.660 |    -1.003 |   -0.2035 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.102 |     1.867 |    0.6832 |    0.7537 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.487 |     1.227 |     1.370 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.28278</span> |     94.19 |  0.003481 |    0.2684 |    0.8159 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.179 |     1.867 |    0.6832 |    0.7537 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.487 |     1.227 |     1.370 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     4.442 |    0.7524 |   -0.1734 |  -0.03276 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.06267 |    -3.400 |     1.746 |   -0.5960 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.307 |    -1.411 |    -1.323 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   69</span>|     480.27897 |     1.001 |    -1.262 |   -0.9141 |   -0.8988 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8474 |   -0.5252 |   -0.9776 |    -1.001 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6165 |   -0.7431 |   -0.6724 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.27897 |     94.13 |    -5.662 |    -1.003 |   -0.2034 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.102 |     1.867 |    0.6827 |    0.7539 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.488 |     1.227 |     1.370 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.27897</span> |     94.13 |  0.003475 |    0.2684 |    0.8159 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.180 |     1.867 |    0.6827 |    0.7539 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.488 |     1.227 |     1.370 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.732 |    0.7463 |   -0.1764 |  -0.03625 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.08933 |    -3.375 |     1.716 |    0.6486 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.273 |    -1.374 |    -1.283 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   70</span>|     480.27440 |     1.002 |    -1.264 |   -0.9140 |   -0.8987 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8473 |   -0.5250 |   -0.9782 |    -1.001 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6164 |   -0.7427 |   -0.6721 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      480.2744 |     94.19 |    -5.664 |    -1.003 |   -0.2034 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.102 |     1.867 |    0.6823 |    0.7538 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.488 |     1.227 |     1.371 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      480.2744</span> |     94.19 |  0.003469 |    0.2684 |    0.8160 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.181 |     1.867 |    0.6823 |    0.7538 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.488 |     1.227 |     1.371 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     4.305 |    0.7431 |   -0.1535 |  -0.02884 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.05624 |    -3.424 |     1.641 |   -0.6166 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.366 |    -1.368 |    -1.291 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   71</span>|     480.27043 |     1.001 |    -1.266 |   -0.9140 |   -0.8987 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8473 |   -0.5247 |   -0.9786 |    -1.001 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6161 |   -0.7423 |   -0.6718 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.27043 |     94.13 |    -5.666 |    -1.002 |   -0.2033 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.102 |     1.868 |    0.6819 |    0.7539 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.488 |     1.228 |     1.371 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.27043</span> |     94.13 |  0.003463 |    0.2685 |    0.8160 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.181 |     1.868 |    0.6819 |    0.7539 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.488 |     1.228 |     1.371 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.306 |    0.7372 |   -0.1665 |  -0.03186 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.08221 |    -3.333 |     1.631 |   -0.6040 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.265 |    -1.322 |    -1.255 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   72</span>|     480.26665 |     1.002 |    -1.267 |   -0.9136 |   -0.8986 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8471 |   -0.5244 |   -0.9791 |    -1.001 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6160 |   -0.7419 |   -0.6714 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.26665 |     94.19 |    -5.667 |    -1.002 |   -0.2032 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.102 |     1.868 |    0.6815 |    0.7540 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.488 |     1.228 |     1.371 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.26665</span> |     94.19 |  0.003457 |    0.2685 |    0.8161 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.182 |     1.868 |    0.6815 |    0.7540 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.488 |     1.228 |     1.371 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     4.654 |    0.7327 |   -0.1326 |  -0.02617 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.04981 |    -3.394 |     1.559 |   -0.6139 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.358 |    -1.341 |    -1.268 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   73</span>|     480.26300 |     1.001 |    -1.269 |   -0.9132 |   -0.8985 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8470 |   -0.5241 |   -0.9796 |    -1.000 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6158 |   -0.7416 |   -0.6711 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|       480.263 |     94.13 |    -5.669 |    -1.002 |   -0.2032 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.102 |     1.868 |    0.6812 |    0.7542 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.488 |     1.229 |     1.372 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">       480.263</span> |     94.13 |  0.003451 |    0.2686 |    0.8162 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.183 |     1.868 |    0.6812 |    0.7542 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.488 |     1.229 |     1.372 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.009 |    0.7258 |   -0.1343 |  -0.03035 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.07619 |    -3.164 |     1.609 |   -0.5397 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.251 |    -1.282 |    -1.219 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   74</span>|     480.25930 |     1.002 |    -1.271 |   -0.9130 |   -0.8985 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8469 |   -0.5239 |   -0.9802 |    -1.000 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6155 |   -0.7412 |   -0.6708 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      480.2593 |     94.19 |    -5.671 |    -1.002 |   -0.2031 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.102 |     1.868 |    0.6807 |    0.7543 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.489 |     1.229 |     1.372 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      480.2593</span> |     94.19 |  0.003445 |    0.2686 |    0.8162 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.184 |     1.868 |    0.6807 |    0.7543 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.489 |     1.229 |     1.372 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     4.851 |    0.7223 |   -0.1045 |  -0.02290 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.04316 |    -3.376 |     1.478 |    0.6193 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.343 |    -1.302 |    -1.232 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   75</span>|     480.25486 |     1.001 |    -1.273 |   -0.9131 |   -0.8984 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8469 |   -0.5237 |   -0.9805 |    -1.000 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6152 |   -0.7408 |   -0.6705 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.25486 |     94.14 |    -5.673 |    -1.002 |   -0.2030 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.102 |     1.868 |    0.6805 |    0.7542 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.489 |     1.230 |     1.373 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.25486</span> |     94.14 |  0.003439 |    0.2686 |    0.8162 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.184 |     1.868 |    0.6805 |    0.7542 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.489 |     1.230 |     1.373 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -3.282 |    0.7167 |   -0.1236 |  -0.02586 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.06793 |    -3.294 |     1.470 |    0.6693 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.247 |    -1.242 |    -1.194 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   76</span>|     480.25000 |     1.002 |    -1.274 |   -0.9134 |   -0.8984 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8469 |   -0.5231 |   -0.9806 |    -1.001 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6147 |   -0.7402 |   -0.6701 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|        480.25 |     94.20 |    -5.674 |    -1.002 |   -0.2030 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.102 |     1.869 |    0.6804 |    0.7539 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.490 |     1.230 |     1.373 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">        480.25</span> |     94.20 |  0.003434 |    0.2686 |    0.8163 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.184 |     1.869 |    0.6804 |    0.7539 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.490 |     1.230 |     1.373 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     5.823 |    0.7134 |   -0.1173 |  -0.02040 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.04062 |    -3.151 |     1.491 |   -0.5685 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.243 |    -1.237 |    -1.210 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   77</span>|     480.24598 |     1.001 |    -1.276 |   -0.9134 |   -0.8984 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8469 |   -0.5229 |   -0.9809 |    -1.001 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6145 |   -0.7399 |   -0.6698 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.24598 |     94.13 |    -5.676 |    -1.002 |   -0.2030 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.102 |     1.869 |    0.6802 |    0.7539 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.490 |     1.231 |     1.373 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.24598</span> |     94.13 |  0.003427 |    0.2686 |    0.8163 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.184 |     1.869 |    0.6802 |    0.7539 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.490 |     1.231 |     1.373 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -3.294 |    0.7076 |   -0.1362 |  -0.02383 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.06566 |    -3.147 |     1.461 |    0.6663 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.244 |    -1.185 |    -1.155 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   78</span>|     480.24152 |     1.002 |    -1.278 |   -0.9134 |   -0.8983 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8469 |   -0.5225 |   -0.9813 |    -1.001 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6142 |   -0.7395 |   -0.6694 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.24152 |     94.19 |    -5.678 |    -1.002 |   -0.2030 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.102 |     1.869 |    0.6799 |    0.7538 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.490 |     1.231 |     1.374 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.24152</span> |     94.19 |  0.003421 |    0.2686 |    0.8163 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.184 |     1.869 |    0.6799 |    0.7538 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.490 |     1.231 |     1.374 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     4.573 |    0.7031 |   -0.1214 |  -0.01999 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.04022 |    -3.129 |     1.425 |    0.6287 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.274 |    -1.197 |    -1.174 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   79</span>|     480.23703 |     1.001 |    -1.280 |   -0.9136 |   -0.8983 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8469 |   -0.5223 |   -0.9815 |    -1.001 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6136 |   -0.7392 |   -0.6691 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.23703 |     94.13 |    -5.680 |    -1.002 |   -0.2030 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.102 |     1.869 |    0.6797 |    0.7536 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.491 |     1.231 |     1.374 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.23703</span> |     94.13 |  0.003415 |    0.2685 |    0.8163 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.184 |     1.869 |    0.6797 |    0.7536 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.491 |     1.231 |     1.374 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -3.496 |    0.6977 |   -0.1464 |  -0.02500 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.06997 |    -3.031 |     1.316 |    0.6455 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.198 |    -1.144 |    -1.124 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   80</span>|     480.23246 |     1.002 |    -1.281 |   -0.9138 |   -0.8983 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8469 |   -0.5219 |   -0.9816 |    -1.001 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6131 |   -0.7387 |   -0.6687 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.23246 |     94.19 |    -5.681 |    -1.002 |   -0.2030 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.102 |     1.870 |    0.6796 |    0.7533 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.492 |     1.232 |     1.375 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.23246</span> |     94.19 |  0.003409 |    0.2685 |    0.8163 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.184 |     1.870 |    0.6796 |    0.7533 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.492 |     1.232 |     1.375 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     4.825 |    0.6940 |   -0.1347 |  -0.01919 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.03939 |    -3.118 |     1.378 |    0.5922 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.219 |    -1.136 |    -1.127 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   81</span>|     480.22809 |     1.001 |    -1.283 |   -0.9138 |   -0.8983 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8469 |   -0.5217 |   -0.9816 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6127 |   -0.7384 |   -0.6684 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.22809 |     94.14 |    -5.683 |    -1.002 |   -0.2029 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.102 |     1.870 |    0.6796 |    0.7531 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.492 |     1.232 |     1.375 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.22809</span> |     94.14 |  0.003403 |    0.2685 |    0.8163 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.184 |     1.870 |    0.6796 |    0.7531 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.492 |     1.232 |     1.375 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -3.001 |    0.6885 |   -0.1518 |  -0.02256 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.06414 |    -2.892 |     1.443 |    0.6415 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.085 |    -1.098 |    -1.101 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   82</span>|     480.22387 |     1.002 |    -1.285 |   -0.9139 |   -0.8983 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8469 |   -0.5212 |   -0.9821 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6124 |   -0.7381 |   -0.6680 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.22387 |     94.19 |    -5.685 |    -1.002 |   -0.2029 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.102 |     1.870 |    0.6793 |    0.7529 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.492 |     1.232 |     1.375 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.22387</span> |     94.19 |  0.003397 |    0.2685 |    0.8163 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.184 |     1.870 |    0.6793 |    0.7529 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.492 |     1.232 |     1.375 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     5.360 |    0.6846 |   -0.1392 |  -0.01938 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.04006 |    -2.853 |     1.421 |   -0.5715 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.064 |    -1.117 |    -1.118 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   83</span>|     480.22054 |     1.001 |    -1.287 |   -0.9139 |   -0.8983 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8469 |   -0.5210 |   -0.9826 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6125 |   -0.7380 |   -0.6676 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.22054 |     94.14 |    -5.687 |    -1.002 |   -0.2029 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.102 |     1.871 |    0.6789 |    0.7530 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.492 |     1.233 |     1.376 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.22054</span> |     94.14 |  0.003391 |    0.2685 |    0.8163 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.184 |     1.871 |    0.6789 |    0.7530 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.492 |     1.233 |     1.376 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -2.774 |    0.6781 |   -0.1521 |  -0.02419 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.06697 |    -2.800 |     1.392 |    0.6467 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.111 |    -1.074 |    -1.064 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   84</span>|     480.21670 |     1.002 |    -1.288 |   -0.9138 |   -0.8983 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8469 |   -0.5206 |   -0.9832 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6123 |   -0.7378 |   -0.6672 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      480.2167 |     94.19 |    -5.688 |    -1.002 |   -0.2029 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.102 |     1.871 |    0.6784 |    0.7529 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.493 |     1.233 |     1.376 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      480.2167</span> |     94.19 |  0.003385 |    0.2685 |    0.8163 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.184 |     1.871 |    0.6784 |    0.7529 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.493 |     1.233 |     1.376 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     4.519 |    0.6730 |   -0.1351 |  -0.02088 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.04118 |    -2.856 |     1.311 |    0.6094 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.165 |    -1.062 |    -1.048 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   85</span>|     480.21269 |     1.001 |    -1.290 |   -0.9139 |   -0.8983 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8469 |   -0.5205 |   -0.9835 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6119 |   -0.7376 |   -0.6670 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.21269 |     94.14 |    -5.690 |    -1.002 |   -0.2029 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.102 |     1.871 |    0.6782 |    0.7527 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.493 |     1.233 |     1.377 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.21269</span> |     94.14 |  0.003379 |    0.2685 |    0.8163 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.184 |     1.871 |    0.6782 |    0.7527 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.493 |     1.233 |     1.377 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -2.933 |    0.6686 |   -0.1517 |  -0.01805 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.06304 |    -2.806 |     1.298 |    0.6045 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.073 |    -1.045 |    -1.026 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   86</span>|     480.20865 |     1.002 |    -1.292 |   -0.9139 |   -0.8983 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8469 |   -0.5201 |   -0.9839 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6115 |   -0.7373 |   -0.6667 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.20865 |     94.19 |    -5.692 |    -1.002 |   -0.2029 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.102 |     1.871 |    0.6779 |    0.7525 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.494 |     1.233 |     1.377 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.20865</span> |     94.19 |  0.003373 |    0.2685 |    0.8163 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.184 |     1.871 |    0.6779 |    0.7525 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.494 |     1.233 |     1.377 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     4.802 |    0.6647 |   -0.1367 |  -0.01906 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.03994 |    -2.699 |     1.311 |   -0.5807 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.078 |    -1.030 |    -1.030 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   87</span>|     480.20558 |     1.001 |    -1.294 |   -0.9137 |   -0.8983 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8468 |   -0.5199 |   -0.9842 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6113 |   -0.7372 |   -0.6665 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.20558 |     94.14 |    -5.694 |    -1.002 |   -0.2029 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.102 |     1.871 |    0.6776 |    0.7525 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.494 |     1.233 |     1.377 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.20558</span> |     94.14 |  0.003367 |    0.2685 |    0.8163 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.185 |     1.871 |    0.6776 |    0.7525 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.494 |     1.233 |     1.377 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -2.911 |    0.6576 |   -0.1438 |  -0.02051 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.06613 |    -2.751 |     1.246 |   -0.6218 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.053 |    -1.017 |    -1.010 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   88</span>|     480.20291 |     1.002 |    -1.296 |   -0.9132 |   -0.8982 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8467 |   -0.5195 |   -0.9848 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6112 |   -0.7370 |   -0.6662 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.20291 |     94.19 |    -5.696 |    -1.002 |   -0.2029 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.102 |     1.872 |    0.6772 |    0.7527 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.494 |     1.234 |     1.377 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.20291</span> |     94.19 |  0.003361 |    0.2686 |    0.8164 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.186 |     1.872 |    0.6772 |    0.7527 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.494 |     1.234 |     1.377 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     4.296 |    0.6533 |   -0.1055 |  -0.01383 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.03435 |    -2.750 |     1.192 |   -0.6155 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.121 |    -1.037 |    -1.054 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   89</span>|     480.20010 |     1.001 |    -1.297 |   -0.9127 |   -0.8982 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8466 |   -0.5193 |   -0.9850 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6110 |   -0.7368 |   -0.6658 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      480.2001 |     94.14 |    -5.697 |    -1.001 |   -0.2028 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.103 |     1.872 |    0.6771 |    0.7528 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.494 |     1.234 |     1.378 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      480.2001</span> |     94.14 |  0.003355 |    0.2687 |    0.8164 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.187 |     1.872 |    0.6771 |    0.7528 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.494 |     1.234 |     1.378 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -2.841 |    0.6461 |  -0.09617 |  -0.01892 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.05600 |    -2.503 |     1.255 |    0.6797 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.9587 |    -1.006 |   -0.9794 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   90</span>|     480.19652 |     1.002 |    -1.299 |   -0.9127 |   -0.8982 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8466 |   -0.5191 |   -0.9853 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6108 |   -0.7364 |   -0.6653 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.19652 |     94.19 |    -5.699 |    -1.001 |   -0.2028 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.103 |     1.872 |    0.6768 |    0.7526 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.494 |     1.234 |     1.378 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.19652</span> |     94.19 |  0.003349 |    0.2687 |    0.8164 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.187 |     1.872 |    0.6768 |    0.7526 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.494 |     1.234 |     1.378 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   91</span>|     480.19461 |     1.002 |    -1.301 |   -0.9128 |   -0.8982 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8466 |   -0.5193 |   -0.9855 |    -1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6107 |   -0.7361 |   -0.6650 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.19461 |     94.18 |    -5.701 |    -1.001 |   -0.2028 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.103 |     1.872 |    0.6767 |    0.7526 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.494 |     1.235 |     1.379 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.19461</span> |     94.18 |  0.003342 |    0.2687 |    0.8164 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.187 |     1.872 |    0.6767 |    0.7526 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.494 |     1.235 |     1.379 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   92</span>|     480.18441 |     1.002 |    -1.313 |   -0.9134 |   -0.8983 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8466 |   -0.5209 |   -0.9862 |    -1.003 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6104 |   -0.7344 |   -0.6628 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.18441 |     94.17 |    -5.713 |    -1.002 |   -0.2029 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.103 |     1.871 |    0.6762 |    0.7522 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.495 |     1.237 |     1.381 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.18441</span> |     94.17 |  0.003303 |    0.2686 |    0.8164 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.187 |     1.871 |    0.6762 |    0.7522 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.495 |     1.237 |     1.381 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   93</span>|     480.15712 |     1.001 |    -1.360 |   -0.9157 |   -0.8984 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8466 |   -0.5271 |   -0.9890 |    -1.004 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6089 |   -0.7277 |   -0.6540 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.15712 |     94.12 |    -5.760 |    -1.004 |   -0.2031 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.103 |     1.866 |    0.6740 |    0.7509 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.497 |     1.244 |     1.391 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.15712</span> |     94.12 |  0.003151 |    0.2681 |    0.8162 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.187 |     1.866 |    0.6740 |    0.7509 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.497 |     1.244 |     1.391 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   94</span>|     480.23418 |    0.9997 |    -1.543 |   -0.9246 |   -0.8991 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8465 |   -0.5509 |    -1.000 |    -1.011 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6032 |   -0.7017 |   -0.6198 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.23418 |     93.97 |    -5.943 |    -1.013 |   -0.2037 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.103 |     1.847 |    0.6655 |    0.7454 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.503 |     1.272 |     1.431 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.23418</span> |     93.97 |  0.002624 |    0.2664 |    0.8157 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.187 |     1.847 |    0.6655 |    0.7454 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.503 |     1.272 |     1.431 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -6.048 |    0.4781 |   -0.2230 |  -0.02217 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.05480 |    -3.577 |    0.9301 |    0.6494 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.028 |   -0.4525 |   -0.4232 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   95</span>|     480.10748 |     1.002 |    -1.612 |   -0.8919 |   -0.8955 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8419 |   -0.5057 |    -1.005 |    -1.011 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6089 |   -0.7402 |   -0.6757 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.10748 |     94.20 |    -6.012 |   -0.9804 |   -0.2001 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.107 |     1.883 |    0.6618 |    0.7449 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.497 |     1.230 |     1.367 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.10748</span> |     94.20 |  0.002450 |    0.2728 |    0.8186 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.225 |     1.883 |    0.6618 |    0.7449 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.497 |     1.230 |     1.367 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     5.588 |   -0.2032 |     1.050 |   0.05863 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1556 |    -1.013 |   -0.1797 |    0.1430 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.088 |    -1.192 |    -1.504 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   96</span>|     480.44664 |     1.003 |    -1.804 |   -0.9880 |   -0.8940 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8524 |   -0.4601 |   -0.9127 |    -1.040 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5485 |   -0.7099 |   -0.6226 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.44664 |     94.31 |    -6.204 |    -1.077 |   -0.1987 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.097 |     1.919 |    0.7320 |    0.7196 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.568 |     1.263 |     1.427 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.44664</span> |     94.31 |  0.002022 |    0.2542 |    0.8198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.139 |     1.919 |    0.7320 |    0.7196 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.568 |     1.263 |     1.427 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   97</span>|     480.05051 |     1.002 |    -1.657 |   -0.9147 |   -0.8952 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8444 |   -0.4949 |   -0.9832 |    -1.018 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5946 |   -0.7329 |   -0.6630 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.05051 |     94.19 |    -6.057 |    -1.003 |   -0.1998 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.105 |     1.891 |    0.6784 |    0.7389 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.514 |     1.238 |     1.381 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.05051</span> |     94.19 |  0.002341 |    0.2683 |    0.8189 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.205 |     1.891 |    0.6784 |    0.7389 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.514 |     1.238 |     1.381 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     3.048 |   -0.2764 |  -0.02525 |   0.07726 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1002 |    0.1555 |     1.072 |    0.1458 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.5013 |   -0.7465 |   -0.9442 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   98</span>|     480.05873 |     1.002 |    -1.641 |   -0.9145 |   -0.9051 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8597 |   -0.5017 |   -0.9961 |    -1.019 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5552 |   -0.7600 |   -0.6392 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.05873 |     94.21 |    -6.041 |    -1.003 |   -0.2097 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.089 |     1.886 |    0.6686 |    0.7381 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.561 |     1.209 |     1.408 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.05873</span> |     94.21 |  0.002380 |    0.2684 |    0.8108 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.080 |     1.886 |    0.6686 |    0.7381 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.561 |     1.209 |     1.408 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   99</span>|     480.03299 |     1.002 |    -1.650 |   -0.9146 |   -0.8993 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8508 |   -0.4977 |   -0.9887 |    -1.018 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5780 |   -0.7442 |   -0.6529 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.03299 |     94.17 |    -6.050 |    -1.003 |   -0.2040 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.098 |     1.889 |    0.6742 |    0.7386 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.533 |     1.226 |     1.393 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.03299</span> |     94.17 |  0.002357 |    0.2683 |    0.8155 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.152 |     1.889 |    0.6742 |    0.7386 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.533 |     1.226 |     1.393 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |  -0.07884 |   -0.2508 |  -0.03233 |  -0.02314 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1302 |   0.06288 |    0.7629 |    0.2261 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.3850 |    -1.277 |   -0.6610 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  100</span>|     480.00970 |     1.003 |    -1.641 |   -0.9125 |   -0.9005 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8500 |   -0.4980 |   -0.9983 |    -1.021 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5841 |   -0.7275 |   -0.6414 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      480.0097 |     94.26 |    -6.041 |    -1.001 |   -0.2051 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.099 |     1.889 |    0.6670 |    0.7367 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.526 |     1.244 |     1.406 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      480.0097</span> |     94.26 |  0.002380 |    0.2687 |    0.8145 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.159 |     1.889 |    0.6670 |    0.7367 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.526 |     1.244 |     1.406 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     12.51 |   -0.2151 |    0.1066 |  -0.04414 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.06212 |   -0.2557 |   0.09002 |  -0.09728 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   0.06582 |   -0.3883 |   0.07016 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  101</span>|     480.02569 |     1.000 |    -1.627 |   -0.9257 |   -0.9015 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8496 |   -0.4974 |    -1.010 |    -1.023 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5876 |   -0.7200 |   -0.6493 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.02569 |     94.03 |    -6.027 |    -1.014 |   -0.2061 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.099 |     1.889 |    0.6581 |    0.7348 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.522 |     1.252 |     1.397 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.02569</span> |     94.03 |  0.002413 |    0.2662 |    0.8138 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.162 |     1.889 |    0.6581 |    0.7348 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.522 |     1.252 |     1.397 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  102</span>|     480.01783 |     1.000 |    -1.636 |   -0.9171 |   -0.9008 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8499 |   -0.4978 |    -1.002 |    -1.021 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5853 |   -0.7249 |   -0.6441 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.01783 |     94.03 |    -6.036 |    -1.006 |   -0.2055 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.099 |     1.889 |    0.6639 |    0.7361 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.525 |     1.247 |     1.403 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.01783</span> |     94.03 |  0.002392 |    0.2678 |    0.8143 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.160 |     1.889 |    0.6639 |    0.7361 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.525 |     1.247 |     1.403 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  103</span>|     480.01762 |     1.000 |    -1.639 |   -0.9140 |   -0.9006 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8500 |   -0.4979 |   -0.9995 |    -1.021 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5844 |   -0.7266 |   -0.6422 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.01762 |     94.04 |    -6.039 |    -1.002 |   -0.2052 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.099 |     1.889 |    0.6660 |    0.7365 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.526 |     1.245 |     1.405 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.01762</span> |     94.04 |  0.002384 |    0.2685 |    0.8145 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.159 |     1.889 |    0.6660 |    0.7365 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.526 |     1.245 |     1.405 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  104</span>|     480.00603 |     1.001 |    -1.641 |   -0.9125 |   -0.9005 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8500 |   -0.4980 |   -0.9983 |    -1.021 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5841 |   -0.7275 |   -0.6414 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.00603 |     94.12 |    -6.041 |    -1.001 |   -0.2051 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.099 |     1.889 |    0.6670 |    0.7367 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.526 |     1.244 |     1.406 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.00603</span> |     94.12 |  0.002380 |    0.2687 |    0.8145 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.159 |     1.889 |    0.6670 |    0.7367 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.526 |     1.244 |     1.406 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -6.885 |   -0.2187 |   0.06031 |  -0.05864 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1316 |   -0.6942 |  -0.07940 |    0.7419 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2628 |   -0.6303 |   -0.1748 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  105</span>|     480.00355 |     1.002 |    -1.640 |   -0.9125 |   -0.9004 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8497 |   -0.4981 |   -0.9983 |    -1.021 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5835 |   -0.7267 |   -0.6420 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.00355 |     94.17 |    -6.040 |    -1.001 |   -0.2050 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.099 |     1.889 |    0.6669 |    0.7366 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.527 |     1.245 |     1.405 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.00355</span> |     94.17 |  0.002382 |    0.2687 |    0.8147 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.162 |     1.889 |    0.6669 |    0.7366 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.527 |     1.245 |     1.405 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |   -0.1332 |   -0.2152 |   0.07761 |  -0.04946 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.09731 |  -0.07704 |    0.1258 |    0.9492 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   0.08309 |   -0.3432 |   0.03979 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  106</span>|     480.00003 |     1.002 |    -1.640 |   -0.9126 |   -0.9003 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8495 |   -0.4980 |   -0.9985 |    -1.022 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5836 |   -0.7262 |   -0.6420 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.00003 |     94.19 |    -6.040 |    -1.001 |   -0.2049 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.100 |     1.889 |    0.6668 |    0.7355 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.527 |     1.245 |     1.405 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.00003</span> |     94.19 |  0.002382 |    0.2687 |    0.8147 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.163 |     1.889 |    0.6668 |    0.7355 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.527 |     1.245 |     1.405 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     2.294 |   -0.2073 |   0.08146 |  -0.04541 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.07807 |   -0.2293 |   0.05730 |    0.8075 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................| -0.008164 |   -0.3171 |   0.03808 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  107</span>|     479.99578 |     1.002 |    -1.638 |   -0.9109 |   -0.8997 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8483 |   -0.4984 |   -0.9975 |    -1.023 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5809 |   -0.7230 |   -0.6440 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.99578 |     94.17 |    -6.038 |   -0.9994 |   -0.2043 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.101 |     1.889 |    0.6675 |    0.7343 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.530 |     1.249 |     1.403 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.99578</span> |     94.17 |  0.002387 |    0.2691 |    0.8152 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.172 |     1.889 |    0.6675 |    0.7343 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.530 |     1.249 |     1.403 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  108</span>|     479.99301 |     1.002 |    -1.632 |   -0.9055 |   -0.8980 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8447 |   -0.4999 |   -0.9947 |    -1.027 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5728 |   -0.7134 |   -0.6498 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.99301 |     94.16 |    -6.032 |   -0.9941 |   -0.2026 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.104 |     1.887 |    0.6697 |    0.7310 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.540 |     1.259 |     1.396 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.99301</span> |     94.16 |  0.002402 |    0.2701 |    0.8166 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.202 |     1.887 |    0.6697 |    0.7310 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.540 |     1.259 |     1.396 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |   -0.5972 |   -0.1625 |    0.4650 |  0.009686 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.08636 |    -1.694 |   -0.1652 |    -1.042 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1517 |    0.4204 |   -0.3659 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  109</span>|     479.98697 |     1.002 |    -1.611 |   -0.9101 |   -0.8945 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8385 |   -0.4966 |   -0.9980 |    -1.027 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5750 |   -0.7140 |   -0.6459 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.98697 |     94.17 |    -6.011 |   -0.9986 |   -0.1991 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.111 |     1.890 |    0.6672 |    0.7309 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.537 |     1.259 |     1.401 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.98697</span> |     94.17 |  0.002451 |    0.2692 |    0.8195 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.253 |     1.890 |    0.6672 |    0.7309 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.537 |     1.259 |     1.401 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. | -0.005684 |   -0.1115 |    0.2082 |   0.08938 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3008 |   0.01411 |    0.1052 |   -0.7061 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.4111 |    0.4010 |   -0.1199 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  110</span>|     479.98697 |     1.002 |    -1.611 |   -0.9101 |   -0.8945 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8385 |   -0.4966 |   -0.9980 |    -1.027 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5750 |   -0.7140 |   -0.6459 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.98697 |     94.17 |    -6.011 |   -0.9986 |   -0.1991 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.111 |     1.890 |    0.6672 |    0.7309 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.537 |     1.259 |     1.401 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.98697</span> |     94.17 |  0.002451 |    0.2692 |    0.8195 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.253 |     1.890 |    0.6672 |    0.7309 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.537 |     1.259 |     1.401 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> calculating covariance matrix</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating residuals/tables</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>initial ETAs were nudged; (can control by foceiControl(etaNudge=., etaNudge2=))</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>ETAs were reset to zero during optimization; (Can control by foceiControl(resetEtaP=.))</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>gradient problems with initial estimate and covariance; see $scaleInfo</span>
-<span class="r-in"><span class="va">f_nlmixr_dfop_sfo_focei_const</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr</a></span><span class="op">(</span><span class="va">f_mmkin_const</span><span class="op">[</span><span class="st">"DFOP-SFO"</span>, <span class="op">]</span>, est <span class="op">=</span> <span class="st">"focei"</span><span class="op">)</span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate jacobian</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate sensitivities</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate ∂(f)/∂(η)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate ∂(R²)/∂(η)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in inner model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in inner model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in EBE model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in EBE model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling inner model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in FD model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in FD model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling EBE model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling events FD model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Needed Covariates:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "CMT"</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="font-weight: bold;">Key:</span> U: Unscaled Parameters; X: Back-transformed parameters; G: Gill difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> F: Forward difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> C: Central difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> M: Mixed forward and central difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Unscaled parameters for Omegas=chol(solve(omega));</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Diagonals are transformed, as specified by foceiControl(diagXform=)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |-----+---------------+-----------+-----------+-----------+-----------|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    #| Objective Fun |  parent_0 |  log_k_A1 |f_parent_qlogis |    log_k1 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    log_k2 |  g_qlogis |     sigma |        o1 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|        o2 |        o3 |        o4 |        o5 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|        o6 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    1</span>|     517.20934 |     1.000 |    -1.000 |   -0.9109 |   -0.9296 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9779 |   -0.8885 |   -0.8592 |   -0.8766 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8740 |   -0.8675 |   -0.8712 |   -0.8735 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8680 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     517.20934 |     94.00 |    -5.400 |   -0.9700 |    -1.900 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.300 |    0.1400 |     1.600 |    0.7311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8633 |     1.183 |     1.000 |    0.8891 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.161 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     517.20934</span> |     94.00 |  0.004517 |    0.2749 |    0.1496 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01357 |    0.5349 |     1.600 |    0.7311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8633 |     1.183 |     1.000 |    0.8891 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.161 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    G|    Gill Diff. |     64.43 |     1.648 |  -0.07882 |   -0.2050 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4304 |   0.05992 |    -56.51 |     17.73 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.983 |    -11.00 |    -3.771 |     3.593 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -10.58 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    2</span>|     2737.3115 |    0.2806 |    -1.018 |   -0.9100 |   -0.9273 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9731 |   -0.8892 |   -0.2282 |    -1.075 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9854 |   -0.7447 |   -0.8291 |   -0.9136 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7499 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     2737.3115 |     26.38 |    -5.418 |   -0.9691 |    -1.898 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.295 |    0.1399 |     2.105 |    0.5864 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7671 |     1.329 |     1.042 |    0.8535 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.298 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     2737.3115</span> |     26.38 |  0.004434 |    0.2751 |    0.1499 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01363 |    0.5349 |     2.105 |    0.5864 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7671 |     1.329 |     1.042 |    0.8535 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.298 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    3</span>|     569.57476 |    0.9281 |    -1.002 |   -0.9108 |   -0.9293 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9774 |   -0.8886 |   -0.7961 |   -0.8964 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8851 |   -0.8553 |   -0.8670 |   -0.8775 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8562 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     569.57476 |     87.24 |    -5.402 |   -0.9699 |    -1.900 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.300 |    0.1400 |     1.650 |    0.7166 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8537 |     1.198 |     1.004 |    0.8856 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.175 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     569.57476</span> |     87.24 |  0.004508 |    0.2749 |    0.1496 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01358 |    0.5349 |     1.650 |    0.7166 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8537 |     1.198 |     1.004 |    0.8856 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.175 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    4</span>|     531.41065 |    0.9928 |    -1.000 |   -0.9109 |   -0.9296 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9778 |   -0.8885 |   -0.8528 |   -0.8786 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8751 |   -0.8663 |   -0.8708 |   -0.8739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8668 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     531.41065 |     93.32 |    -5.400 |   -0.9700 |    -1.900 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.300 |    0.1400 |     1.605 |    0.7297 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8624 |     1.185 |     1.000 |    0.8888 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.163 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     531.41065</span> |     93.32 |  0.004516 |    0.2749 |    0.1496 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01357 |    0.5349 |     1.605 |    0.7297 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8624 |     1.185 |     1.000 |    0.8888 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.163 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    5</span>|     531.74000 |    0.9993 |    -1.000 |   -0.9109 |   -0.9296 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9779 |   -0.8885 |   -0.8585 |   -0.8768 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8741 |   -0.8674 |   -0.8712 |   -0.8735 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8679 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|        531.74 |     93.93 |    -5.400 |   -0.9700 |    -1.900 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.300 |    0.1400 |     1.601 |    0.7310 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8632 |     1.183 |     1.000 |    0.8891 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.161 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">        531.74</span> |     93.93 |  0.004516 |    0.2749 |    0.1496 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01357 |    0.5349 |     1.601 |    0.7310 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8632 |     1.183 |     1.000 |    0.8891 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.161 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    6</span>|     531.81753 |    0.9999 |    -1.000 |   -0.9109 |   -0.9296 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9779 |   -0.8885 |   -0.8591 |   -0.8767 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8740 |   -0.8675 |   -0.8712 |   -0.8735 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8680 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     531.81753 |     93.99 |    -5.400 |   -0.9700 |    -1.900 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.300 |    0.1400 |     1.600 |    0.7311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8633 |     1.183 |     1.000 |    0.8891 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.161 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     531.81753</span> |     93.99 |  0.004517 |    0.2749 |    0.1496 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01357 |    0.5349 |     1.600 |    0.7311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8633 |     1.183 |     1.000 |    0.8891 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.161 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    7</span>|     531.82573 |     1.000 |    -1.000 |   -0.9109 |   -0.9296 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9779 |   -0.8885 |   -0.8591 |   -0.8766 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8740 |   -0.8675 |   -0.8712 |   -0.8735 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8680 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     531.82573 |     94.00 |    -5.400 |   -0.9700 |    -1.900 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.300 |    0.1400 |     1.600 |    0.7311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8633 |     1.183 |     1.000 |    0.8891 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.161 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     531.82573</span> |     94.00 |  0.004517 |    0.2749 |    0.1496 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01357 |    0.5349 |     1.600 |    0.7311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8633 |     1.183 |     1.000 |    0.8891 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.161 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    8</span>|     531.82668 |     1.000 |    -1.000 |   -0.9109 |   -0.9296 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9779 |   -0.8885 |   -0.8592 |   -0.8766 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8740 |   -0.8675 |   -0.8712 |   -0.8735 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8680 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     531.82668 |     94.00 |    -5.400 |   -0.9700 |    -1.900 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.300 |    0.1400 |     1.600 |    0.7311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8633 |     1.183 |     1.000 |    0.8891 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.161 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     531.82668</span> |     94.00 |  0.004517 |    0.2749 |    0.1496 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01357 |    0.5349 |     1.600 |    0.7311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8633 |     1.183 |     1.000 |    0.8891 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.161 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    9</span>|     531.82678 |     1.000 |    -1.000 |   -0.9109 |   -0.9296 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9779 |   -0.8885 |   -0.8592 |   -0.8766 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8740 |   -0.8675 |   -0.8712 |   -0.8735 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8680 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     531.82678 |     94.00 |    -5.400 |   -0.9700 |    -1.900 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.300 |    0.1400 |     1.600 |    0.7311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8633 |     1.183 |     1.000 |    0.8891 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.161 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     531.82678</span> |     94.00 |  0.004517 |    0.2749 |    0.1496 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01357 |    0.5349 |     1.600 |    0.7311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8633 |     1.183 |     1.000 |    0.8891 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.161 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   10</span>|     531.82679 |     1.000 |    -1.000 |   -0.9109 |   -0.9296 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9779 |   -0.8885 |   -0.8592 |   -0.8766 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8740 |   -0.8675 |   -0.8712 |   -0.8735 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8680 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     531.82679 |     94.00 |    -5.400 |   -0.9700 |    -1.900 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.300 |    0.1400 |     1.600 |    0.7311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8633 |     1.183 |     1.000 |    0.8891 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.161 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     531.82679</span> |     94.00 |  0.004517 |    0.2749 |    0.1496 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01357 |    0.5349 |     1.600 |    0.7311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8633 |     1.183 |     1.000 |    0.8891 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.161 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   11</span>|     531.82679 |     1.000 |    -1.000 |   -0.9109 |   -0.9296 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9779 |   -0.8885 |   -0.8592 |   -0.8766 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8740 |   -0.8675 |   -0.8712 |   -0.8735 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8680 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     531.82679 |     94.00 |    -5.400 |   -0.9700 |    -1.900 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.300 |    0.1400 |     1.600 |    0.7311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8633 |     1.183 |     1.000 |    0.8891 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.161 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     531.82679</span> |     94.00 |  0.004517 |    0.2749 |    0.1496 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01357 |    0.5349 |     1.600 |    0.7311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8633 |     1.183 |     1.000 |    0.8891 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.161 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   12</span>|     531.82678 |     1.000 |    -1.000 |   -0.9109 |   -0.9296 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9779 |   -0.8885 |   -0.8592 |   -0.8766 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8740 |   -0.8675 |   -0.8712 |   -0.8735 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8680 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     531.82678 |     94.00 |    -5.400 |   -0.9700 |    -1.900 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.300 |    0.1400 |     1.600 |    0.7311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8633 |     1.183 |     1.000 |    0.8891 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.161 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     531.82678</span> |     94.00 |  0.004517 |    0.2749 |    0.1496 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01357 |    0.5349 |     1.600 |    0.7311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8633 |     1.183 |     1.000 |    0.8891 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.161 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   13</span>|     531.82679 |     1.000 |    -1.000 |   -0.9109 |   -0.9296 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9779 |   -0.8885 |   -0.8592 |   -0.8766 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8740 |   -0.8675 |   -0.8712 |   -0.8735 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8680 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     531.82679 |     94.00 |    -5.400 |   -0.9700 |    -1.900 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.300 |    0.1400 |     1.600 |    0.7311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8633 |     1.183 |     1.000 |    0.8891 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.161 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     531.82679</span> |     94.00 |  0.004517 |    0.2749 |    0.1496 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01357 |    0.5349 |     1.600 |    0.7311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8633 |     1.183 |     1.000 |    0.8891 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.161 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   14</span>|     531.82679 |     1.000 |    -1.000 |   -0.9109 |   -0.9296 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9779 |   -0.8885 |   -0.8592 |   -0.8766 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8740 |   -0.8675 |   -0.8712 |   -0.8735 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8680 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     531.82679 |     94.00 |    -5.400 |   -0.9700 |    -1.900 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.300 |    0.1400 |     1.600 |    0.7311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8633 |     1.183 |     1.000 |    0.8891 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.161 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     531.82679</span> |     94.00 |  0.004517 |    0.2749 |    0.1496 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01357 |    0.5349 |     1.600 |    0.7311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8633 |     1.183 |     1.000 |    0.8891 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.161 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   15</span>|     531.82679 |     1.000 |    -1.000 |   -0.9109 |   -0.9296 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9779 |   -0.8885 |   -0.8592 |   -0.8766 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8740 |   -0.8675 |   -0.8712 |   -0.8735 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8680 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     531.82679 |     94.00 |    -5.400 |   -0.9700 |    -1.900 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.300 |    0.1400 |     1.600 |    0.7311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8633 |     1.183 |     1.000 |    0.8891 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.161 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     531.82679</span> |     94.00 |  0.004517 |    0.2749 |    0.1496 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01357 |    0.5349 |     1.600 |    0.7311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8633 |     1.183 |     1.000 |    0.8891 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.161 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   16</span>|     531.82679 |     1.000 |    -1.000 |   -0.9109 |   -0.9296 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9779 |   -0.8885 |   -0.8592 |   -0.8766 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8740 |   -0.8675 |   -0.8712 |   -0.8735 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8680 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     531.82679 |     94.00 |    -5.400 |   -0.9700 |    -1.900 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.300 |    0.1400 |     1.600 |    0.7311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8633 |     1.183 |     1.000 |    0.8891 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.161 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     531.82679</span> |     94.00 |  0.004517 |    0.2749 |    0.1496 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01357 |    0.5349 |     1.600 |    0.7311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8633 |     1.183 |     1.000 |    0.8891 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.161 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   17</span>|     531.82679 |     1.000 |    -1.000 |   -0.9109 |   -0.9296 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9779 |   -0.8885 |   -0.8592 |   -0.8766 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8740 |   -0.8675 |   -0.8712 |   -0.8735 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8680 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     531.82679 |     94.00 |    -5.400 |   -0.9700 |    -1.900 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.300 |    0.1400 |     1.600 |    0.7311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8633 |     1.183 |     1.000 |    0.8891 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.161 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     531.82679</span> |     94.00 |  0.004517 |    0.2749 |    0.1496 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01357 |    0.5349 |     1.600 |    0.7311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8633 |     1.183 |     1.000 |    0.8891 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.161 |...........|...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> calculating covariance matrix</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating residuals/tables</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>initial ETAs were nudged; (can control by foceiControl(etaNudge=., etaNudge2=))</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>last objective function was not at minimum, possible problems in optimization</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>using R matrix to calculate covariance, can check sandwich or S matrix with $covRS and $covS</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>gradient problems with initial estimate and covariance; see $scaleInfo</span>
-<span class="r-in"></span>
-<span class="r-in"><span class="co"># Variance by variable is supported by 'saem' and 'focei'</span></span>
-<span class="r-in"><span class="va">f_nlmixr_fomc_sfo_saem_obs</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr</a></span><span class="op">(</span><span class="va">f_mmkin_obs</span><span class="op">[</span><span class="st">"FOMC-SFO"</span>, <span class="op">]</span>, est <span class="op">=</span> <span class="st">"saem"</span><span class="op">)</span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → generate SAEM model</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1:    93.4067   -5.7935   -0.0604    2.2993   -1.1624    2.9450    1.7342    0.6650    0.5890    0.4750   14.5215    9.1023</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2:    93.8811   -5.7873   -0.0289    2.3640   -1.0762    2.7977    2.0710    0.6317    0.5595    0.4512   11.1033    4.6425</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 3:    94.0397   -5.9934    0.0119    2.4035   -1.0703    3.0693    2.4524    0.6124    0.5316    0.4287   10.0698    3.4243</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 4:    93.8834   -6.0401    0.0041    2.3944   -1.0097    2.9159    3.1645    0.6120    0.5050    0.4073    9.2013    3.2162</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5:    94.0163   -5.8381   -0.0267    2.3580   -1.0239    2.7701    3.0063    0.5814    0.4797    0.3869    9.0330    3.0330</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 6:    93.9753   -5.8371   -0.0315    2.3598   -1.0052    2.6316    2.8559    0.5708    0.4558    0.3675    8.6051    2.6518</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 7:    93.6109   -5.8741   -0.0401    2.3570   -1.0025    2.5000    2.7131    0.5691    0.4330    0.3492    8.4407    2.4701</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 8:    93.2480   -6.0361   -0.0523    2.3504   -1.0028    2.3750    3.1584    0.5407    0.4113    0.3317    8.6121    2.2437</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 9:    93.2245   -6.0431   -0.0503    2.3552   -0.9828    2.2562    3.9790    0.5395    0.3908    0.3151    8.6609    2.1129</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 10:    93.3040   -6.1080   -0.0503    2.3613   -0.9784    2.1434    4.8606    0.5401    0.3712    0.2994    8.6497    2.0865</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 11:    93.4509   -5.9532   -0.0503    2.3444   -0.9823    3.0948    4.6176    0.5679    0.3527    0.2844    8.1651    2.0310</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 12:    93.5099   -6.1699   -0.0503    2.3408   -0.9746    3.2814    4.3867    0.5679    0.3350    0.2702    8.1716    1.9862</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 13:    93.8052   -6.1984   -0.0474    2.3233   -0.9922    3.1173    4.6967    0.5644    0.3183    0.2567    8.2982    2.0040</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 14:    93.6510   -6.0090   -0.0429    2.3601   -0.9885    2.9615    4.4618    0.5526    0.3024    0.2438    8.3254    2.0605</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 15:    93.8952   -6.3354   -0.0394    2.3580   -0.9792    2.8134    5.2117    0.5657    0.2872    0.2316    8.1329    2.0520</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 16:    93.4703   -6.0722   -0.0434    2.3386   -1.0026    2.6727    4.9512    0.5781    0.2729    0.2201    8.0866    2.0994</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 17:    93.4238   -6.2132   -0.0755    2.3120   -1.0119    2.5391    4.7036    0.5495    0.2592    0.2091    7.5958    2.2864</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 18:    93.5288   -6.2747   -0.0616    2.3223   -1.0105    2.4121    4.4684    0.5467    0.2463    0.1986    7.1910    1.9828</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 19:    93.2607   -6.3635   -0.0631    2.3206   -1.0045    3.6271    4.8142    0.5405    0.2340    0.1928    7.3672    1.8187</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 20:    93.3918   -6.3241   -0.0742    2.2941   -1.0140    3.4457    4.9242    0.5596    0.2223    0.1950    7.1427    1.8754</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 21:    93.6794   -6.1336   -0.0758    2.3000   -1.0048    3.2734    4.6780    0.5565    0.2111    0.1852    7.0989    1.9232</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 22:    94.0006   -6.1882   -0.0800    2.3099   -1.0252    3.1098    4.4441    0.5354    0.2006    0.1870    7.0038    1.9920</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 23:    93.6433   -6.2626   -0.0841    2.2791   -1.0183    4.8893    4.4476    0.5276    0.1906    0.1798    6.3698    1.8787</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 24:    93.9545   -6.3772   -0.0816    2.2887   -1.0019    4.6448    5.1698    0.5293    0.1810    0.1779    6.5903    1.9474</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 25:    94.2280   -6.3235   -0.0839    2.2600   -0.9932    5.2801    4.9113    0.5262    0.1720    0.1806    6.5267    1.9807</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 26:    94.2022   -6.2830   -0.0883    2.2643   -1.0012    5.9595    4.6658    0.5225    0.1634    0.1795    6.3678    1.9659</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 27:    94.4398   -6.1769   -0.0894    2.2564   -1.0177    9.0771    4.4325    0.5207    0.1552    0.1880    6.4522    1.8590</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 28:    94.2586   -6.1652   -0.0882    2.2574   -1.0226    8.6233    4.2108    0.5158    0.1475    0.1881    6.3701    1.7882</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 29:    94.3490   -6.1505   -0.0854    2.2615   -1.0081    9.6333    4.0003    0.5109    0.1423    0.1833    6.3601    1.8485</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 30:    94.6929   -6.0285   -0.0909    2.2610   -1.0082    9.1517    3.8003    0.5117    0.1401    0.2097    6.2461    1.8606</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 31:    94.2553   -6.0390   -0.0896    2.2625   -1.0078    8.6941    3.6103    0.5072    0.1378    0.2088    6.3337    1.8220</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 32:    94.3096   -5.7252   -0.0886    2.2677   -0.9967    9.0244    3.4298    0.5051    0.1325    0.2015    6.4601    1.8880</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 33:    94.8327   -5.8775   -0.0865    2.2684   -0.9976    8.6305    3.2583    0.4920    0.1333    0.1993    6.4804    1.8203</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 34:    94.3527   -5.9488   -0.0826    2.2879   -0.9989    8.1989    3.1538    0.4969    0.1388    0.1984    6.4201    1.7696</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 35:    94.4411   -6.1171   -0.0826    2.2913   -0.9964    8.4377    3.7937    0.4969    0.1387    0.1972    6.3878    1.7612</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 36:    94.2058   -6.1151   -0.0844    2.2920   -1.0069    8.0158    3.7963    0.4962    0.1395    0.1938    6.2469    1.6680</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 37:    93.7449   -6.1251   -0.0925    2.2736   -1.0019    7.6150    3.8477    0.5101    0.1325    0.1841    6.1375    1.7472</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 38:    93.6861   -6.0575   -0.0934    2.2803   -1.0097    7.2343    3.6553    0.5119    0.1259    0.1862    6.0729    1.7608</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 39:    93.9767   -6.0314   -0.0999    2.2548   -1.0231    6.8726    3.4725    0.5195    0.1234    0.1951    6.1947    1.8276</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 40:    94.0297   -6.1559   -0.0989    2.2440   -1.0374    6.5290    3.8559    0.5199    0.1279    0.1891    6.0195    1.8906</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 41:    94.2069   -6.3055   -0.0820    2.2710   -1.0275    6.2025    4.7866    0.5304    0.1215    0.1858    6.1777    1.8541</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 42:    94.2400   -6.3179   -0.0790    2.2783   -1.0379    5.8924    4.6915    0.5253    0.1154    0.1794    6.0530    1.8960</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 43:    93.9851   -6.4096   -0.0784    2.2832   -1.0341    5.5978    5.0608    0.5163    0.1097    0.1773    6.0057    1.8136</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 44:    94.1440   -6.2214   -0.0746    2.2969   -1.0262    5.3179    4.8077    0.5130    0.1079    0.1851    6.1182    1.8390</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 45:    93.8847   -6.3883   -0.0745    2.3059   -1.0132    6.8202    5.1378    0.5130    0.1174    0.1769    6.1000    1.8391</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 46:    93.7228   -6.3305   -0.0794    2.3033   -1.0107    6.4792    4.8809    0.5095    0.1196    0.1783    6.2794    1.7304</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 47:    93.7031   -6.4232   -0.0796    2.3006   -1.0028    6.1552    5.5756    0.5092    0.1243    0.1887    6.1716    1.7279</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 48:    93.5210   -6.2770   -0.0794    2.2948   -1.0122    5.8475    5.2968    0.5098    0.1247    0.1863    5.9847    1.7994</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 49:    93.3676   -6.4486   -0.0754    2.3079   -1.0061    5.5551    5.4356    0.5018    0.1198    0.1858    6.1108    1.7598</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 50:    93.8573   -6.3944   -0.0755    2.3057   -1.0038    5.2773    5.2487    0.5063    0.1193    0.1843    6.0935    1.7725</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 51:    93.7004   -6.2783   -0.0854    2.2836   -1.0074    5.0135    4.9863    0.4932    0.1269    0.1858    6.1630    1.8063</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 52:    93.4843   -6.3763   -0.0962    2.2731   -1.0073    4.7628    5.0969    0.4815    0.1345    0.1924    6.0823    1.8013</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 53:    93.6971   -6.5002   -0.0887    2.2774   -1.0111    4.5246    5.7428    0.4726    0.1346    0.1982    6.1744    1.7695</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 54:    93.6176   -6.4928   -0.0917    2.2648   -1.0220    4.2984    5.4557    0.4765    0.1419    0.2017    6.3732    1.8195</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 55:    93.7072   -6.5760   -0.0833    2.2865   -1.0282    4.0835    5.9775    0.4732    0.1434    0.1963    6.3653    1.7028</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 56:    94.1360   -6.6854   -0.0901    2.2941   -1.0244    3.8793    7.2562    0.4637    0.1393    0.1935    6.3001    1.7878</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 57:    93.4627   -6.6255   -0.1049    2.2502   -1.0233    3.6854    6.8934    0.4847    0.1323    0.1878    6.2357    1.8480</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 58:    93.8066   -6.6603   -0.1049    2.2362   -1.0280    3.5011    6.9334    0.4847    0.1397    0.1872    6.3582    1.7787</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 59:    93.8599   -6.7837   -0.1046    2.2450   -1.0274    3.3260    8.4672    0.4853    0.1332    0.1833    6.1248    1.8066</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 60:    93.6190   -6.5618   -0.1055    2.2381   -1.0244    3.1597    8.0438    0.4742    0.1390    0.1858    6.2589    1.7881</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 61:    93.7045   -6.6482   -0.1159    2.2413   -1.0272    3.2942    7.8485    0.4655    0.1406    0.1827    5.8425    1.7744</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 62:    93.5232   -6.5027   -0.1168    2.2418   -1.0187    3.3810    7.4560    0.4766    0.1414    0.1790    5.9349    1.7717</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 63:    93.4884   -6.4221   -0.1164    2.2505   -1.0062    3.2119    7.0832    0.4768    0.1422    0.1752    6.0193    1.7434</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 64:    93.2305   -6.4456   -0.1153    2.2573   -1.0062    4.1468    6.7291    0.4776    0.1395    0.1753    5.8355    1.7529</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 65:    93.3743   -6.3237   -0.1227    2.2484   -1.0102    3.9395    6.3926    0.4864    0.1374    0.1800    5.6731    1.7808</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 66:    93.7132   -6.3178   -0.1186    2.2389   -0.9894    4.0557    6.0730    0.4909    0.1426    0.1807    5.7099    1.7283</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 67:    93.7490   -6.3514   -0.1155    2.2445   -0.9946    3.8529    5.7693    0.4938    0.1379    0.1830    5.7366    1.7847</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 68:    93.7617   -6.1181   -0.1251    2.2487   -0.9841    3.7650    5.4809    0.4816    0.1549    0.1769    5.6569    1.7415</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 69:    93.4342   -6.3588   -0.1301    2.2350   -0.9813    4.5688    5.2068    0.4745    0.1624    0.1736    5.5771    1.7091</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 70:    93.5303   -6.3266   -0.1330    2.2384   -0.9753    4.3404    4.9465    0.4734    0.1563    0.1699    5.5332    1.7256</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 71:    93.4733   -6.2859   -0.1364    2.2170   -0.9781    4.1234    4.6992    0.4701    0.1604    0.1649    5.6661    1.7335</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 72:    93.3055   -6.2502   -0.1462    2.2156   -0.9724    3.9172    4.7693    0.4519    0.1607    0.1582    5.4776    1.7679</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 73:    93.3010   -6.6844   -0.1490    2.2262   -0.9777    3.7213    6.9975    0.4426    0.1848    0.1640    5.7066    1.7588</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 74:    93.1104   -6.6720   -0.1484    2.2079   -0.9982    3.5353    6.7811    0.4436    0.1807    0.1732    5.7700    1.7343</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 75:    93.4534   -6.9644   -0.1480    2.2078   -0.9930    3.3585    8.7027    0.4497    0.1717    0.1708    5.5371    1.7098</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 76:    93.5886   -6.3503   -0.1491    2.1958   -0.9884    3.7136    8.2676    0.4509    0.1731    0.1706    5.3943    1.7340</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 77:    93.4345   -6.4976   -0.1531    2.1831   -0.9928    4.5945    7.8542    0.4592    0.1741    0.1703    5.5564    1.7164</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 78:    93.6007   -6.4885   -0.1538    2.1850   -0.9919    4.3647    7.4615    0.4607    0.1803    0.1692    5.4698    1.7354</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 79:    93.2897   -7.0329   -0.1518    2.1935   -0.9863    4.6922   10.9870    0.4569    0.1790    0.1608    5.3799    1.7484</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 80:    93.4130   -6.6634   -0.1531    2.1865   -0.9839    5.4720   10.4377    0.4585    0.1852    0.1543    5.4298    1.7237</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 81:    93.5828   -6.7204   -0.1563    2.1914   -0.9882    5.1984    9.9158    0.4548    0.1973    0.1600    5.4425    1.7741</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 82:    93.4450   -6.7357   -0.1537    2.1964   -0.9958    4.9384   10.1331    0.4577    0.1874    0.1632    5.6874    1.7789</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 83:    93.6109   -6.9249   -0.1493    2.2061   -0.9945    4.6915   11.1537    0.4474    0.1781    0.1686    5.4249    1.7317</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 84:    93.7133   -6.8029   -0.1493    2.2016   -0.9886    4.4569   10.9568    0.4474    0.1715    0.1689    5.5426    1.7227</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 85:    93.8040   -6.6434   -0.1483    2.2032   -0.9861    5.4991   10.4090    0.4330    0.1749    0.1726    5.4570    1.7332</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 86:    93.9029   -6.7750   -0.1472    2.2066   -0.9892    5.2241   11.7325    0.4352    0.1819    0.1644    5.5652    1.6802</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 87:    93.8127   -6.7015   -0.1499    2.2019   -0.9891    4.9629   11.1459    0.4292    0.1977    0.1661    5.7122    1.6713</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 88:    93.6777   -6.7044   -0.1440    2.2074   -1.0050    4.7148   10.5886    0.4379    0.1878    0.1750    5.6084    1.7096</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 89:    94.0481   -6.2990   -0.1443    2.2085   -0.9869    4.4790   10.0591    0.4355    0.1951    0.1688    5.4280    1.8093</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 90:    93.6399   -6.3965   -0.1429    2.2138   -0.9737    5.1306    9.5562    0.4367    0.1917    0.1604    5.5652    1.7458</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 91:    93.8670   -6.3075   -0.1426    2.2128   -0.9856    5.1368    9.0784    0.4427    0.1993    0.1546    5.3927    1.8246</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 92:    93.7332   -6.4793   -0.1426    2.2091   -0.9835    5.0102    8.6245    0.4427    0.1986    0.1585    5.4463    1.7343</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 93:    93.8211   -6.3270   -0.1416    2.2123   -0.9941    4.7597    8.1932    0.4431    0.1908    0.1689    5.5213    1.7093</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 94:    93.7499   -6.0880   -0.1390    2.2158   -0.9960    5.1992    7.7836    0.4444    0.1958    0.1733    5.5329    1.7880</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 95:    93.6253   -6.2196   -0.1436    2.2126   -1.0053    4.9392    7.3944    0.4383    0.2011    0.1717    5.6042    1.7460</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 96:    93.8862   -6.1475   -0.1408    2.2211   -0.9922    4.6923    7.0247    0.4347    0.2077    0.1669    5.6807    1.6943</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 97:    93.7610   -6.2409   -0.1368    2.2281   -0.9864    4.4576    6.6734    0.4313    0.2084    0.1723    5.5387    1.7075</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 98:    93.5362   -6.3378   -0.1368    2.2294   -0.9813    4.2348    6.3398    0.4313    0.2127    0.1877    5.5850    1.6627</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 99:    93.5044   -6.2557   -0.1311    2.2282   -0.9993    4.0230    6.0228    0.4461    0.2167    0.1879    5.6437    1.7076</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 100:    93.3102   -6.3602   -0.1311    2.2368   -1.0040    3.8219    5.7216    0.4461    0.2139    0.1875    5.8029    1.7592</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 101:    93.4687   -6.0385   -0.1241    2.2347   -1.0031    3.6308    5.4356    0.4483    0.2035    0.1816    6.0097    1.7002</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 102:    93.6536   -6.2867   -0.1299    2.2421   -1.0002    3.4492    5.1743    0.4434    0.1993    0.1872    5.8540    1.7162</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 103:    93.9532   -6.2261   -0.1277    2.2361   -0.9931    3.2768    5.0546    0.4450    0.2069    0.1884    5.6688    1.7324</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 104:    93.9839   -6.1980   -0.1287    2.2286   -1.0081    3.1129    5.0671    0.4475    0.1997    0.1985    5.7690    1.7636</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 105:    94.1682   -6.1671   -0.1283    2.2217   -1.0154    2.9573    4.8137    0.4481    0.1976    0.1965    5.9277    1.7386</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 106:    94.2778   -6.1839   -0.1243    2.2323   -1.0022    3.5381    4.5730    0.4707    0.1980    0.1932    5.7059    1.7184</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 107:    94.3667   -5.9941   -0.1182    2.2283   -1.0191    3.3612    4.3444    0.4753    0.1984    0.1986    5.7813    1.7446</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 108:    94.2722   -6.1869   -0.1171    2.2293   -1.0027    3.6659    4.6329    0.4742    0.1986    0.2011    5.7827    1.7074</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 109:    94.1997   -6.2385   -0.1172    2.2241   -1.0083    3.4826    4.7954    0.4721    0.2027    0.2020    5.8339    1.7650</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 110:    94.3017   -6.3774   -0.1291    2.2229   -0.9857    3.7634    5.8516    0.4810    0.2126    0.1984    5.7961    1.6706</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 111:    93.9803   -6.0240   -0.1258    2.2273   -0.9879    3.5752    5.5590    0.4749    0.2060    0.1976    5.6243    1.7082</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 112:    94.1307   -6.0036   -0.1253    2.2365   -0.9886    4.0368    5.2810    0.4760    0.2060    0.1975    5.5732    1.7063</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 113:    93.8676   -6.2496   -0.1118    2.2600   -1.0080    3.8350    5.0170    0.4855    0.2109    0.2006    5.6406    1.7357</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 114:    93.5949   -6.3200   -0.1044    2.2449   -1.0172    3.6472    4.7661    0.4868    0.2095    0.2131    5.7690    1.7428</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 115:    93.6997   -6.3282   -0.1046    2.2567   -1.0135    3.4648    4.8622    0.4876    0.2264    0.2134    5.8853    1.7823</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 116:    93.8191   -6.0802   -0.1087    2.2535   -1.0011    3.4347    4.6191    0.4786    0.2176    0.2108    5.6553    1.7802</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 117:    93.8575   -6.0930   -0.1022    2.2498   -0.9898    3.3071    4.3881    0.4822    0.2163    0.2075    5.7806    1.8150</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 118:    93.9164   -5.9787   -0.1133    2.2535   -0.9861    4.2578    4.1687    0.4687    0.2198    0.2088    5.4441    1.8411</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 119:    93.8748   -6.0108   -0.1165    2.2488   -0.9775    4.0449    3.9603    0.4653    0.2271    0.2032    5.6119    1.7501</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 120:    93.6001   -6.0447   -0.1144    2.2477   -0.9821    3.8426    3.7623    0.4641    0.2223    0.2055    5.6454    1.7244</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 121:    93.6712   -5.9851   -0.1195    2.2484   -0.9917    3.6505    3.5742    0.4600    0.2143    0.2010    5.4083    1.7965</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 122:    93.6859   -6.0390   -0.1145    2.2497   -0.9888    3.4680    3.5595    0.4618    0.2136    0.1986    5.4111    1.7519</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 123:    93.6014   -5.8383   -0.1145    2.2584   -0.9893    3.6047    3.3815    0.4618    0.2155    0.2045    5.3624    1.7023</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 124:    93.6333   -5.7861   -0.1131    2.2556   -0.9872    3.4245    3.2125    0.4621    0.2153    0.2025    5.3930    1.7036</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 125:    93.4504   -5.9483   -0.1154    2.2531   -0.9924    3.2533    3.0518    0.4640    0.2175    0.2030    5.5097    1.6830</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 126:    93.5693   -5.8818   -0.1120    2.2506   -0.9960    3.0906    2.8992    0.4606    0.2267    0.2016    5.4583    1.6650</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 127:    93.7074   -5.8191   -0.1178    2.2412   -0.9891    2.9361    2.7543    0.4688    0.2234    0.2039    5.4861    1.8125</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 128:    93.5959   -5.8842   -0.1179    2.2544   -0.9985    3.0224    2.6166    0.4700    0.2237    0.2019    5.6417    1.8454</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 129:    93.5600   -5.8683   -0.1161    2.2365   -0.9987    3.4186    2.4857    0.4721    0.2182    0.1918    5.4391    1.8145</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 130:    93.4104   -5.8226   -0.1126    2.2355   -0.9892    3.3648    2.4231    0.4763    0.2212    0.1873    5.3999    1.7457</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 131:    93.5045   -5.7255   -0.1118    2.2486   -0.9918    4.1951    2.3020    0.4776    0.2121    0.1927    5.4342    1.7744</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 132:    93.2626   -5.8379   -0.1097    2.2510   -0.9933    3.9853    2.5572    0.4763    0.2114    0.1966    5.2979    1.7239</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 133:    93.4370   -5.9097   -0.1049    2.2578   -0.9939    4.2190    2.8246    0.4738    0.2036    0.2020    5.2853    1.6765</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 134:    93.8665   -5.9439   -0.1035    2.2654   -0.9832    4.0081    3.1228    0.4756    0.2102    0.1961    5.3467    1.7177</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 135:    93.6301   -5.8062   -0.1031    2.2702   -0.9737    3.9024    2.9667    0.4748    0.2101    0.2003    5.3053    1.6977</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 136:    93.7744   -5.9328   -0.1055    2.2685   -0.9764    3.7072    2.9952    0.4721    0.2096    0.1949    5.4319    1.6864</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 137:    93.6734   -5.9886   -0.1107    2.2517   -0.9732    3.8811    3.4962    0.4642    0.2204    0.1924    5.4294    1.6684</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 138:    93.7128   -5.9927   -0.1119    2.2517   -0.9775    3.6870    3.4543    0.4667    0.2204    0.1953    5.3912    1.7060</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 139:    93.6530   -6.1296   -0.1210    2.2456   -0.9929    3.5027    4.6472    0.4527    0.2296    0.1855    5.4953    1.7242</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 140:    93.9344   -6.2519   -0.1384    2.2304   -0.9970    3.6814    4.4707    0.4337    0.2182    0.1762    5.5643    1.7596</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 141:    93.7356   -6.2804   -0.1296    2.2549   -0.9921    3.9811    4.4876    0.4218    0.2361    0.1674    5.3531    1.7175</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 142:    93.7399   -6.0023   -0.1098    2.2611   -0.9815    3.7821    4.2632    0.4395    0.2421    0.1807    5.3139    1.7191</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 143:    93.4732   -6.0376   -0.1108    2.2689   -0.9734    3.5930    4.0500    0.4396    0.2541    0.1718    5.2930    1.6229</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 144:    93.4900   -5.9517   -0.1093    2.2876   -0.9675    3.4133    3.8475    0.4337    0.2542    0.1711    5.4258    1.5660</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 145:    93.4090   -5.9596   -0.1000    2.2942   -0.9756    3.2426    3.6551    0.4274    0.2422    0.1671    5.3539    1.6971</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 146:    93.4142   -5.9549   -0.0982    2.2846   -0.9704    3.0805    3.6092    0.4226    0.2339    0.1745    5.5092    1.6696</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 147:    93.4409   -6.0720   -0.0971    2.2942   -0.9891    2.9265    3.9922    0.4224    0.2373    0.1781    5.5599    1.6080</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 148:    93.4504   -6.2201   -0.0980    2.2855   -0.9832    2.7802    4.6985    0.4104    0.2464    0.1856    5.5016    1.5877</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 149:    93.4240   -6.2122   -0.1005    2.2728   -0.9881    3.7659    4.7755    0.4082    0.2642    0.1885    5.4942    1.5534</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 150:    93.5094   -6.1295   -0.1087    2.2717   -0.9941    3.5776    4.5367    0.4109    0.2611    0.1928    5.3468    1.5585</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 151:    93.4038   -6.2751   -0.1130    2.2643   -0.9892    3.3987    4.9866    0.4172    0.2638    0.1905    5.4955    1.6256</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 152:    93.5072   -6.3361   -0.1147    2.2627   -0.9988    2.3580    5.3824    0.4175    0.2656    0.1819    5.7685    1.6126</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 153:    93.3582   -6.2019   -0.1227    2.2526   -0.9929    2.5874    4.7052    0.4348    0.2621    0.1810    5.5149    1.6181</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 154:    93.1890   -6.3537   -0.1263    2.2446   -0.9871    2.4073    5.7070    0.4351    0.2586    0.1829    5.3136    1.6272</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 155:    93.1706   -6.4117   -0.1260    2.2484   -0.9845    2.3035    6.0004    0.4463    0.2586    0.1781    5.4260    1.6494</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 156:    93.3240   -6.3931   -0.1259    2.2469   -0.9824    2.5659    5.7375    0.4466    0.2781    0.1754    5.6202    1.6591</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 157:    93.3239   -6.1812   -0.1242    2.2541   -0.9743    1.6054    4.5906    0.4478    0.2657    0.1758    5.6806    1.6367</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 158:    93.3756   -6.2562   -0.1264    2.2706   -0.9888    1.5329    4.6790    0.4458    0.2532    0.1728    5.7756    1.6248</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 159:    93.3034   -6.3291   -0.1217    2.2524   -0.9954    1.7774    5.7204    0.4532    0.2642    0.1715    5.8189    1.6830</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 160:    93.4387   -6.5115   -0.1196    2.2488   -0.9846    2.2219    6.4960    0.4555    0.2728    0.1689    5.5273    1.6332</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 161:    93.7646   -6.3820   -0.1231    2.2520   -0.9837    2.8322    5.7269    0.4498    0.2712    0.1730    5.3659    1.5787</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 162:    93.6252   -6.4563   -0.1243    2.2472   -0.9867    2.8322    5.9119    0.4502    0.2671    0.1726    5.4519    1.5819</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 163:    93.6787   -6.6444   -0.1292    2.2366   -0.9899    2.0520    7.4835    0.4556    0.2705    0.1689    5.4095    1.5883</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 164:    93.7458   -6.9330   -0.1257    2.2430   -0.9872    1.7825    9.4340    0.4495    0.2701    0.1593    5.4517    1.6116</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 165:    93.7370   -6.9118   -0.1250    2.2421   -0.9832    1.9949   10.6549    0.4499    0.2685    0.1622    5.6272    1.6075</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 166:    94.0889   -6.8704   -0.1276    2.2409   -0.9872    1.5618   10.4435    0.4482    0.2608    0.1648    5.5891    1.6208</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 167:    94.1319   -7.2779   -0.1206    2.2505   -0.9894    1.5063   13.4312    0.4393    0.2549    0.1661    5.6013    1.6043</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 168:    93.8341   -6.9310   -0.1140    2.2495   -0.9844    1.7329   11.3224    0.4463    0.2540    0.1629    5.8366    1.6765</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 169:    93.8923   -6.6547   -0.1173    2.2620   -0.9872    1.4531    8.4608    0.4419    0.2414    0.1666    5.8784    1.6268</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 170:    94.0072   -6.3970   -0.1173    2.2606   -0.9861    1.4164    6.9237    0.4419    0.2468    0.1656    5.8793    1.6387</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 171:    93.8690   -6.5151   -0.1079    2.2482   -0.9880    1.9225    7.7681    0.4547    0.2404    0.1523    6.0158    1.6281</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 172:    93.6847   -6.3416   -0.1095    2.2438   -0.9849    2.0143    5.5739    0.4498    0.2450    0.1534    6.1355    1.6422</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 173:    93.4817   -6.3165   -0.1115    2.2573   -0.9885    1.6855    5.5626    0.4440    0.2471    0.1542    6.1343    1.6337</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 174:    93.6781   -6.2722   -0.1113    2.2521   -0.9958    1.9186    5.3383    0.4471    0.2427    0.1565    6.1081    1.6358</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 175:    93.7764   -6.1664   -0.1113    2.2468   -0.9864    1.6286    4.6233    0.4471    0.2426    0.1596    5.8892    1.6375</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 176:    93.9246   -6.2164   -0.1160    2.2529   -0.9853    1.0357    4.8013    0.4422    0.2471    0.1756    5.7340    1.6016</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 177:    93.9711   -6.1274   -0.1112    2.2540   -0.9883    1.2079    4.1536    0.4415    0.2492    0.1783    5.8399    1.6291</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 178:    93.9212   -6.0532   -0.1116    2.2593   -0.9742    1.2409    3.6443    0.4489    0.2458    0.1683    5.8422    1.6290</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 179:    94.0137   -6.0739   -0.1095    2.2664   -0.9778    1.5060    3.9878    0.4506    0.2370    0.1746    6.0349    1.6326</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 180:    93.9247   -6.0681   -0.1130    2.2660   -0.9934    1.9619    3.9582    0.4474    0.2328    0.1773    5.8082    1.6740</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 181:    93.7150   -6.0191   -0.1153    2.2558   -0.9915    2.6849    3.7075    0.4491    0.2283    0.1759    5.7187    1.6842</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 182:    93.5908   -6.1098   -0.1111    2.2769   -0.9942    3.0096    3.9325    0.4643    0.2275    0.1736    5.9243    1.6466</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 183:    93.3386   -6.0987   -0.1131    2.2630   -0.9962    3.5457    4.1285    0.4693    0.2373    0.1751    5.6948    1.7222</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 184:    93.4889   -6.3097   -0.1134    2.2660   -0.9720    3.0855    5.2642    0.4648    0.2255    0.1585    5.6827    1.7444</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 185:    93.6387   -6.1883   -0.1188    2.2622   -0.9603    3.3568    4.8291    0.4554    0.2223    0.1681    5.7089    1.8164</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 186:    93.3420   -6.2909   -0.1195    2.2656   -0.9835    3.2124    4.8317    0.4541    0.2286    0.1531    5.7574    1.7708</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 187:    93.4395   -6.0358   -0.1165    2.2528   -0.9917    3.8299    3.3301    0.4518    0.2370    0.1593    5.8508    1.6988</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 188:    93.5358   -6.0105   -0.1161    2.2540   -0.9813    5.1249    3.3448    0.4522    0.2361    0.1660    5.8700    1.6525</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 189:    93.4932   -6.1199   -0.1129    2.2636   -0.9812    4.5430    4.3213    0.4428    0.2359    0.1907    5.6970    1.7268</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 190:    93.4754   -5.9088   -0.1171    2.2564   -0.9614    4.6253    3.2590    0.4410    0.2399    0.1864    5.7116    1.8140</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 191:    93.4709   -5.9676   -0.1171    2.2568   -0.9748    4.8326    3.6704    0.4410    0.2428    0.1812    5.5925    1.7267</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 192:    93.3895   -5.9940   -0.1191    2.2523   -0.9691    4.3019    3.5174    0.4360    0.2484    0.1591    5.4631    1.7057</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 193:    93.4904   -6.0400   -0.1173    2.2519   -0.9697    4.6476    3.6255    0.4389    0.2388    0.1694    5.5362    1.7174</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 194:    93.4591   -5.9642   -0.1245    2.2626   -0.9559    5.3125    3.8133    0.4297    0.2550    0.1660    5.7591    1.7344</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 195:    93.6610   -6.2211   -0.1226    2.2580   -0.9669    5.2051    4.9271    0.4318    0.2414    0.1811    5.7010    1.7710</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 196:    93.4249   -5.9570   -0.1068    2.2735   -0.9727    5.1049    3.4872    0.4442    0.2429    0.1815    5.7753    1.7379</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 197:    93.4082   -6.0568   -0.1054    2.2754   -0.9865    5.2827    3.9305    0.4554    0.2408    0.1925    5.7514    1.7163</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 198:    93.3856   -5.8616   -0.1087    2.2708   -0.9686    4.1369    3.0197    0.4530    0.2473    0.1878    5.6920    1.7043</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 199:    93.5488   -6.0494   -0.1167    2.2682   -0.9719    3.8010    3.8897    0.4600    0.2445    0.1860    5.7126    1.6605</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 200:    93.3779   -5.9779   -0.1110    2.2761   -0.9780    3.5955    3.4721    0.4595    0.2468    0.1941    5.7539    1.6736</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 201:    93.4946   -6.0259   -0.1102    2.2676   -0.9755    3.0583    3.7305    0.4592    0.2554    0.1921    5.8877    1.6730</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 202:    93.4698   -6.0522   -0.1110    2.2685   -0.9703    2.9421    3.8718    0.4579    0.2595    0.1906    5.8527    1.6701</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 203:    93.4625   -6.0744   -0.1132    2.2642   -0.9696    3.1854    4.0983    0.4589    0.2596    0.1886    5.7532    1.6655</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 204:    93.4984   -6.0853   -0.1138    2.2589   -0.9718    3.2826    4.1667    0.4581    0.2588    0.1851    5.7274    1.6694</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 205:    93.5279   -6.1054   -0.1151    2.2562   -0.9742    3.3257    4.2680    0.4569    0.2584    0.1832    5.6976    1.6777</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 206:    93.6025   -6.1087   -0.1174    2.2518   -0.9767    3.2399    4.2443    0.4582    0.2589    0.1822    5.6809    1.6775</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 207:    93.6382   -6.0990   -0.1204    2.2481   -0.9801    3.2768    4.1473    0.4579    0.2591    0.1823    5.6460    1.6819</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 208:    93.6250   -6.0878   -0.1224    2.2438   -0.9812    3.1815    4.0872    0.4579    0.2577    0.1818    5.6256    1.6887</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 209:    93.6102   -6.0740   -0.1255    2.2394   -0.9803    3.1716    3.9968    0.4561    0.2573    0.1812    5.6063    1.6886</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 210:    93.6005   -6.0571   -0.1277    2.2348   -0.9799    3.2408    3.8923    0.4548    0.2572    0.1798    5.5849    1.6912</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 211:    93.6270   -6.0425   -0.1306    2.2292   -0.9807    3.3500    3.8267    0.4538    0.2586    0.1792    5.5578    1.6992</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 212:    93.6641   -6.0403   -0.1331    2.2253   -0.9806    3.4487    3.8366    0.4529    0.2596    0.1786    5.5422    1.7022</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 213:    93.6743   -6.0344   -0.1354    2.2214   -0.9800    3.5484    3.8260    0.4518    0.2606    0.1781    5.5250    1.7069</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 214:    93.6719   -6.0405   -0.1377    2.2179   -0.9804    3.5538    3.8785    0.4506    0.2612    0.1769    5.5148    1.7087</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 215:    93.6743   -6.0403   -0.1396    2.2146   -0.9801    3.5578    3.9180    0.4496    0.2615    0.1761    5.5118    1.7094</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 216:    93.6666   -6.0436   -0.1413    2.2115   -0.9796    3.5848    3.9484    0.4488    0.2624    0.1755    5.5015    1.7116</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 217:    93.6715   -6.0438   -0.1430    2.2086   -0.9794    3.6188    3.9603    0.4478    0.2631    0.1748    5.4884    1.7132</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 218:    93.6765   -6.0488   -0.1441    2.2060   -0.9796    3.6126    3.9885    0.4471    0.2632    0.1746    5.4714    1.7156</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 219:    93.6714   -6.0557   -0.1453    2.2038   -0.9798    3.6603    4.0118    0.4463    0.2632    0.1735    5.4593    1.7235</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 220:    93.6728   -6.0711   -0.1462    2.2027   -0.9794    3.7244    4.0910    0.4457    0.2639    0.1730    5.4531    1.7241</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 221:    93.6723   -6.0822   -0.1470    2.2015   -0.9788    3.7754    4.1554    0.4450    0.2647    0.1724    5.4511    1.7247</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 222:    93.6789   -6.0745   -0.1480    2.1998   -0.9780    3.8735    4.1186    0.4442    0.2662    0.1718    5.4419    1.7258</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 223:    93.6891   -6.0700   -0.1488    2.1984   -0.9778    3.9353    4.0998    0.4434    0.2681    0.1715    5.4375    1.7251</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 224:    93.7125   -6.0705   -0.1496    2.1976   -0.9774    4.0220    4.0861    0.4427    0.2697    0.1711    5.4378    1.7230</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 225:    93.7332   -6.0695   -0.1502    2.1966   -0.9775    4.0553    4.0669    0.4422    0.2712    0.1711    5.4355    1.7205</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 226:    93.7631   -6.0712   -0.1508    2.1951   -0.9779    4.0755    4.0572    0.4417    0.2728    0.1712    5.4347    1.7190</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 227:    93.7912   -6.0687   -0.1512    2.1938   -0.9785    4.0621    4.0439    0.4409    0.2742    0.1716    5.4325    1.7189</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 228:    93.8077   -6.0644   -0.1517    2.1927   -0.9791    4.0246    4.0190    0.4400    0.2755    0.1722    5.4269    1.7193</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 229:    93.8255   -6.0661   -0.1521    2.1909   -0.9796    3.9958    4.0166    0.4392    0.2769    0.1725    5.4214    1.7214</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 230:    93.8403   -6.0766   -0.1530    2.1895   -0.9802    4.0152    4.0548    0.4380    0.2788    0.1730    5.4214    1.7232</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 231:    93.8549   -6.0768   -0.1541    2.1877   -0.9810    4.0690    4.0542    0.4368    0.2803    0.1734    5.4157    1.7236</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 232:    93.8666   -6.0728   -0.1550    2.1858   -0.9816    4.0852    4.0337    0.4356    0.2818    0.1736    5.4136    1.7224</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 233:    93.8728   -6.0672   -0.1557    2.1844   -0.9820    4.1001    4.0000    0.4346    0.2828    0.1738    5.4028    1.7243</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 234:    93.8862   -6.0646   -0.1563    2.1830   -0.9825    4.1303    3.9850    0.4337    0.2838    0.1737    5.3924    1.7222</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 235:    93.8862   -6.0632   -0.1570    2.1819   -0.9827    4.1149    3.9735    0.4329    0.2847    0.1737    5.3846    1.7225</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 236:    93.8827   -6.0639   -0.1577    2.1814   -0.9834    4.1004    3.9711    0.4322    0.2852    0.1739    5.3838    1.7220</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 237:    93.8729   -6.0680   -0.1582    2.1808   -0.9840    4.0606    3.9866    0.4316    0.2857    0.1741    5.3806    1.7213</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 238:    93.8739   -6.0733   -0.1587    2.1806   -0.9845    4.0331    4.0011    0.4311    0.2859    0.1742    5.3775    1.7199</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 239:    93.8732   -6.0729   -0.1591    2.1802   -0.9853    3.9850    3.9892    0.4307    0.2859    0.1744    5.3782    1.7216</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 240:    93.8760   -6.0754   -0.1595    2.1796   -0.9854    3.9349    3.9867    0.4303    0.2858    0.1747    5.3781    1.7232</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 241:    93.8779   -6.0749   -0.1599    2.1791   -0.9853    3.9241    3.9801    0.4299    0.2858    0.1748    5.3757    1.7228</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 242:    93.8842   -6.0716   -0.1602    2.1786   -0.9852    3.9394    3.9651    0.4297    0.2859    0.1749    5.3726    1.7224</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 243:    93.8884   -6.0719   -0.1606    2.1778   -0.9851    3.9310    3.9741    0.4295    0.2857    0.1750    5.3705    1.7224</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 244:    93.8910   -6.0712   -0.1610    2.1771   -0.9850    3.9173    3.9819    0.4294    0.2856    0.1749    5.3700    1.7222</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 245:    93.9054   -6.0736   -0.1614    2.1764   -0.9854    3.9106    3.9984    0.4293    0.2856    0.1748    5.3711    1.7217</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 246:    93.9209   -6.0753   -0.1617    2.1757   -0.9859    3.9080    4.0071    0.4291    0.2852    0.1746    5.3711    1.7215</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 247:    93.9273   -6.0846   -0.1621    2.1752   -0.9861    3.8790    4.0580    0.4286    0.2851    0.1745    5.3755    1.7206</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 248:    93.9265   -6.0884   -0.1625    2.1749   -0.9865    3.8613    4.0784    0.4286    0.2848    0.1744    5.3760    1.7198</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 249:    93.9286   -6.0926   -0.1627    2.1746   -0.9872    3.8663    4.1008    0.4287    0.2845    0.1745    5.3755    1.7195</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 250:    93.9287   -6.0968   -0.1629    2.1744   -0.9878    3.8822    4.1269    0.4289    0.2844    0.1743    5.3755    1.7201</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 251:    93.9314   -6.1017   -0.1630    2.1739   -0.9882    3.8878    4.1495    0.4291    0.2843    0.1744    5.3729    1.7200</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 252:    93.9351   -6.1036   -0.1632    2.1734   -0.9885    3.8908    4.1545    0.4293    0.2843    0.1746    5.3684    1.7191</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 253:    93.9415   -6.1053   -0.1634    2.1729   -0.9889    3.8727    4.1602    0.4294    0.2842    0.1747    5.3650    1.7196</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 254:    93.9473   -6.1088   -0.1636    2.1723   -0.9891    3.8657    4.1769    0.4296    0.2843    0.1749    5.3666    1.7190</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 255:    93.9505   -6.1087   -0.1639    2.1717   -0.9888    3.8457    4.1720    0.4298    0.2841    0.1749    5.3617    1.7191</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 256:    93.9497   -6.1054   -0.1642    2.1715   -0.9885    3.8395    4.1559    0.4299    0.2839    0.1749    5.3598    1.7192</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 257:    93.9477   -6.1008   -0.1643    2.1713   -0.9882    3.8383    4.1360    0.4299    0.2836    0.1749    5.3567    1.7196</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 258:    93.9402   -6.0998   -0.1645    2.1713   -0.9880    3.8516    4.1288    0.4300    0.2832    0.1749    5.3565    1.7190</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 259:    93.9318   -6.0979   -0.1646    2.1711   -0.9879    3.8396    4.1182    0.4301    0.2829    0.1750    5.3583    1.7188</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 260:    93.9277   -6.1002   -0.1646    2.1713   -0.9879    3.8173    4.1273    0.4304    0.2826    0.1751    5.3611    1.7186</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 261:    93.9251   -6.0973   -0.1646    2.1714   -0.9879    3.8140    4.1105    0.4306    0.2822    0.1750    5.3588    1.7192</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 262:    93.9186   -6.1009   -0.1647    2.1715   -0.9880    3.8177    4.1303    0.4308    0.2822    0.1750    5.3604    1.7194</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 263:    93.9130   -6.1033   -0.1646    2.1715   -0.9880    3.8029    4.1529    0.4309    0.2822    0.1748    5.3647    1.7188</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 264:    93.9040   -6.1077   -0.1645    2.1717   -0.9879    3.7950    4.1820    0.4310    0.2822    0.1747    5.3699    1.7184</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 265:    93.9012   -6.1071   -0.1645    2.1714   -0.9879    3.7834    4.1895    0.4310    0.2822    0.1746    5.3755    1.7191</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 266:    93.8988   -6.1051   -0.1644    2.1714   -0.9879    3.7788    4.1822    0.4311    0.2822    0.1745    5.3765    1.7203</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 267:    93.8964   -6.1059   -0.1643    2.1714   -0.9877    3.7774    4.1896    0.4311    0.2822    0.1744    5.3792    1.7197</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 268:    93.8901   -6.1092   -0.1643    2.1715   -0.9876    3.7790    4.2198    0.4310    0.2822    0.1742    5.3811    1.7200</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 269:    93.8842   -6.1105   -0.1643    2.1717   -0.9875    3.7620    4.2336    0.4310    0.2823    0.1742    5.3838    1.7193</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 270:    93.8760   -6.1125   -0.1643    2.1721   -0.9874    3.7668    4.2483    0.4308    0.2823    0.1741    5.3852    1.7181</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 271:    93.8705   -6.1149   -0.1643    2.1722   -0.9873    3.7785    4.2625    0.4306    0.2823    0.1742    5.3853    1.7172</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 272:    93.8674   -6.1149   -0.1643    2.1723   -0.9871    3.7829    4.2581    0.4304    0.2823    0.1743    5.3836    1.7162</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 273:    93.8644   -6.1159   -0.1642    2.1725   -0.9870    3.7910    4.2631    0.4303    0.2825    0.1743    5.3818    1.7154</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 274:    93.8585   -6.1158   -0.1640    2.1728   -0.9869    3.7926    4.2612    0.4302    0.2825    0.1743    5.3816    1.7147</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 275:    93.8564   -6.1151   -0.1639    2.1732   -0.9867    3.8053    4.2581    0.4301    0.2826    0.1743    5.3804    1.7143</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 276:    93.8564   -6.1132   -0.1638    2.1736   -0.9866    3.7958    4.2486    0.4300    0.2827    0.1745    5.3810    1.7144</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 277:    93.8564   -6.1120   -0.1637    2.1741   -0.9867    3.7952    4.2426    0.4298    0.2829    0.1747    5.3808    1.7148</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 278:    93.8528   -6.1113   -0.1636    2.1743   -0.9867    3.7922    4.2352    0.4297    0.2832    0.1750    5.3819    1.7144</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 279:    93.8503   -6.1124   -0.1636    2.1744   -0.9867    3.7930    4.2390    0.4298    0.2834    0.1753    5.3826    1.7144</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 280:    93.8466   -6.1146   -0.1636    2.1743   -0.9867    3.7946    4.2470    0.4299    0.2838    0.1755    5.3832    1.7142</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 281:    93.8435   -6.1165   -0.1638    2.1743   -0.9867    3.7994    4.2552    0.4298    0.2840    0.1756    5.3828    1.7140</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 282:    93.8421   -6.1162   -0.1639    2.1741   -0.9868    3.7967    4.2496    0.4296    0.2843    0.1758    5.3816    1.7137</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 283:    93.8382   -6.1146   -0.1641    2.1740   -0.9866    3.7957    4.2387    0.4294    0.2845    0.1760    5.3796    1.7135</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 284:    93.8356   -6.1131   -0.1641    2.1740   -0.9865    3.7904    4.2263    0.4292    0.2848    0.1762    5.3776    1.7127</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 285:    93.8349   -6.1113   -0.1642    2.1740   -0.9865    3.7841    4.2129    0.4291    0.2850    0.1765    5.3763    1.7130</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 286:    93.8372   -6.1095   -0.1643    2.1741   -0.9864    3.7801    4.2035    0.4289    0.2853    0.1769    5.3779    1.7130</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 287:    93.8393   -6.1077   -0.1643    2.1741   -0.9864    3.7804    4.1908    0.4287    0.2857    0.1771    5.3785    1.7132</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 288:    93.8395   -6.1071   -0.1644    2.1740   -0.9866    3.7714    4.1834    0.4284    0.2859    0.1772    5.3798    1.7120</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 289:    93.8398   -6.1065   -0.1645    2.1738   -0.9865    3.7635    4.1765    0.4282    0.2861    0.1774    5.3821    1.7111</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 290:    93.8376   -6.1089   -0.1647    2.1737   -0.9865    3.7495    4.1853    0.4281    0.2863    0.1776    5.3852    1.7106</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 291:    93.8341   -6.1091   -0.1647    2.1738   -0.9863    3.7340    4.1854    0.4278    0.2865    0.1776    5.3868    1.7098</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 292:    93.8329   -6.1080   -0.1647    2.1741   -0.9863    3.7189    4.1760    0.4275    0.2868    0.1777    5.3885    1.7091</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 293:    93.8312   -6.1065   -0.1647    2.1743   -0.9862    3.7091    4.1651    0.4272    0.2871    0.1778    5.3896    1.7086</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 294:    93.8310   -6.1042   -0.1647    2.1745   -0.9861    3.6946    4.1521    0.4269    0.2874    0.1780    5.3908    1.7077</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 295:    93.8299   -6.1033   -0.1648    2.1747   -0.9861    3.6968    4.1433    0.4265    0.2880    0.1781    5.3904    1.7066</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 296:    93.8276   -6.1027   -0.1648    2.1749   -0.9862    3.7072    4.1361    0.4261    0.2885    0.1782    5.3910    1.7056</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 297:    93.8212   -6.1015   -0.1647    2.1750   -0.9859    3.7253    4.1278    0.4256    0.2891    0.1783    5.3927    1.7051</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 298:    93.8173   -6.0980   -0.1647    2.1751   -0.9858    3.7552    4.1115    0.4251    0.2897    0.1784    5.3932    1.7045</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 299:    93.8168   -6.0984   -0.1646    2.1754   -0.9856    3.7687    4.1143    0.4246    0.2902    0.1786    5.3949    1.7037</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 300:    93.8154   -6.0967   -0.1646    2.1756   -0.9855    3.7888    4.1072    0.4241    0.2906    0.1788    5.3962    1.7030</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 301:    93.8129   -6.0946   -0.1646    2.1757   -0.9852    3.8097    4.1006    0.4236    0.2911    0.1791    5.3971    1.7026</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 302:    93.8120   -6.0932   -0.1646    2.1759   -0.9849    3.8403    4.0955    0.4231    0.2917    0.1792    5.3971    1.7021</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 303:    93.8115   -6.0942   -0.1646    2.1762   -0.9850    3.8602    4.1004    0.4227    0.2922    0.1795    5.3976    1.7020</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 304:    93.8106   -6.0979   -0.1645    2.1765   -0.9850    3.8898    4.1217    0.4222    0.2926    0.1798    5.3975    1.7024</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 305:    93.8091   -6.1009   -0.1644    2.1767   -0.9851    3.9165    4.1374    0.4218    0.2931    0.1801    5.3989    1.7024</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 306:    93.8090   -6.1043   -0.1644    2.1770   -0.9850    3.9485    4.1617    0.4214    0.2936    0.1803    5.3998    1.7021</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 307:    93.8082   -6.1063   -0.1644    2.1772   -0.9850    3.9730    4.1797    0.4210    0.2940    0.1803    5.3998    1.7017</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 308:    93.8093   -6.1090   -0.1644    2.1775   -0.9850    3.9874    4.2026    0.4205    0.2945    0.1804    5.3996    1.7006</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 309:    93.8092   -6.1122   -0.1643    2.1777   -0.9849    3.9948    4.2297    0.4201    0.2948    0.1804    5.4001    1.6998</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 310:    93.8080   -6.1142   -0.1642    2.1780   -0.9850    3.9976    4.2436    0.4197    0.2951    0.1803    5.4016    1.6989</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 311:    93.8094   -6.1164   -0.1641    2.1784   -0.9851    4.0015    4.2552    0.4194    0.2952    0.1803    5.4033    1.6978</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 312:    93.8107   -6.1184   -0.1640    2.1788   -0.9851    4.0006    4.2628    0.4190    0.2954    0.1802    5.4042    1.6972</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 313:    93.8118   -6.1190   -0.1640    2.1789   -0.9851    3.9991    4.2638    0.4186    0.2955    0.1802    5.4053    1.6967</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 314:    93.8139   -6.1188   -0.1639    2.1791   -0.9851    4.0019    4.2619    0.4183    0.2956    0.1802    5.4049    1.6966</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 315:    93.8152   -6.1173   -0.1639    2.1792   -0.9851    4.0111    4.2553    0.4179    0.2957    0.1802    5.4052    1.6966</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 316:    93.8178   -6.1158   -0.1639    2.1792   -0.9851    4.0073    4.2498    0.4175    0.2957    0.1802    5.4050    1.6966</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 317:    93.8205   -6.1155   -0.1639    2.1792   -0.9851    3.9999    4.2491    0.4172    0.2957    0.1802    5.4048    1.6963</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 318:    93.8216   -6.1145   -0.1639    2.1792   -0.9850    3.9961    4.2438    0.4168    0.2957    0.1802    5.4031    1.6961</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 319:    93.8241   -6.1143   -0.1639    2.1792   -0.9849    4.0009    4.2412    0.4164    0.2958    0.1801    5.4004    1.6956</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 320:    93.8257   -6.1142   -0.1639    2.1792   -0.9849    4.0018    4.2380    0.4160    0.2958    0.1801    5.3986    1.6952</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 321:    93.8280   -6.1134   -0.1639    2.1792   -0.9849    4.0055    4.2339    0.4156    0.2959    0.1802    5.3966    1.6950</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 322:    93.8299   -6.1122   -0.1639    2.1793   -0.9848    4.0075    4.2274    0.4152    0.2959    0.1802    5.3969    1.6948</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 323:    93.8318   -6.1123   -0.1639    2.1794   -0.9848    4.0087    4.2257    0.4149    0.2960    0.1802    5.3980    1.6941</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 324:    93.8352   -6.1098   -0.1639    2.1795   -0.9848    4.0123    4.2136    0.4145    0.2960    0.1802    5.3988    1.6936</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 325:    93.8374   -6.1072   -0.1638    2.1796   -0.9848    4.0230    4.2005    0.4142    0.2961    0.1802    5.3991    1.6933</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 326:    93.8410   -6.1050   -0.1637    2.1796   -0.9849    4.0327    4.1891    0.4139    0.2963    0.1802    5.4004    1.6927</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 327:    93.8457   -6.1023   -0.1637    2.1796   -0.9849    4.0327    4.1767    0.4135    0.2964    0.1802    5.4013    1.6922</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 328:    93.8493   -6.1017   -0.1637    2.1797   -0.9850    4.0440    4.1730    0.4131    0.2964    0.1802    5.4019    1.6915</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 329:    93.8515   -6.1001   -0.1637    2.1799   -0.9851    4.0556    4.1648    0.4128    0.2963    0.1803    5.4017    1.6912</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 330:    93.8541   -6.1001   -0.1636    2.1800   -0.9852    4.0606    4.1631    0.4124    0.2962    0.1804    5.4025    1.6912</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 331:    93.8539   -6.0994   -0.1635    2.1801   -0.9854    4.0654    4.1584    0.4120    0.2961    0.1805    5.4025    1.6907</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 332:    93.8536   -6.0999   -0.1634    2.1803   -0.9854    4.0696    4.1601    0.4116    0.2960    0.1808    5.4027    1.6904</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 333:    93.8531   -6.1002   -0.1633    2.1806   -0.9853    4.0682    4.1646    0.4112    0.2960    0.1810    5.4038    1.6893</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 334:    93.8543   -6.1005   -0.1632    2.1809   -0.9852    4.0771    4.1690    0.4108    0.2960    0.1812    5.4040    1.6883</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 335:    93.8552   -6.1010   -0.1631    2.1813   -0.9851    4.0888    4.1775    0.4104    0.2960    0.1813    5.4044    1.6878</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 336:    93.8555   -6.1016   -0.1630    2.1817   -0.9850    4.0969    4.1858    0.4099    0.2961    0.1814    5.4046    1.6873</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 337:    93.8553   -6.1025   -0.1628    2.1820   -0.9849    4.1152    4.1921    0.4094    0.2962    0.1815    5.4070    1.6863</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 338:    93.8553   -6.1018   -0.1626    2.1824   -0.9848    4.1314    4.1904    0.4090    0.2963    0.1816    5.4080    1.6852</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 339:    93.8567   -6.1004   -0.1625    2.1828   -0.9847    4.1449    4.1855    0.4087    0.2964    0.1817    5.4087    1.6841</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 340:    93.8582   -6.0987   -0.1623    2.1832   -0.9846    4.1603    4.1799    0.4083    0.2965    0.1819    5.4086    1.6832</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 341:    93.8589   -6.0962   -0.1620    2.1836   -0.9844    4.1692    4.1694    0.4079    0.2965    0.1820    5.4102    1.6828</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 342:    93.8583   -6.0932   -0.1618    2.1841   -0.9844    4.1729    4.1563    0.4075    0.2966    0.1821    5.4117    1.6821</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 343:    93.8590   -6.0906   -0.1615    2.1845   -0.9844    4.1840    4.1447    0.4071    0.2966    0.1822    5.4125    1.6819</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 344:    93.8582   -6.0890   -0.1613    2.1851   -0.9845    4.1860    4.1347    0.4068    0.2968    0.1826    5.4135    1.6814</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 345:    93.8576   -6.0876   -0.1610    2.1857   -0.9846    4.1889    4.1252    0.4064    0.2969    0.1829    5.4143    1.6810</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 346:    93.8549   -6.0853   -0.1608    2.1862   -0.9845    4.1925    4.1121    0.4061    0.2969    0.1832    5.4157    1.6803</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 347:    93.8540   -6.0829   -0.1605    2.1867   -0.9845    4.2031    4.0990    0.4058    0.2970    0.1834    5.4158    1.6803</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 348:    93.8516   -6.0814   -0.1602    2.1873   -0.9845    4.2169    4.0885    0.4055    0.2971    0.1836    5.4174    1.6801</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 349:    93.8505   -6.0797   -0.1600    2.1879   -0.9845    4.2253    4.0779    0.4052    0.2971    0.1837    5.4190    1.6799</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 350:    93.8512   -6.0773   -0.1597    2.1885   -0.9845    4.2268    4.0657    0.4049    0.2972    0.1839    5.4209    1.6797</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 351:    93.8507   -6.0754   -0.1595    2.1890   -0.9845    4.2245    4.0559    0.4046    0.2972    0.1840    5.4230    1.6794</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 352:    93.8490   -6.0736   -0.1592    2.1896   -0.9845    4.2296    4.0474    0.4044    0.2973    0.1841    5.4252    1.6790</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 353:    93.8460   -6.0718   -0.1589    2.1901   -0.9845    4.2356    4.0374    0.4042    0.2973    0.1843    5.4272    1.6790</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 354:    93.8437   -6.0695   -0.1586    2.1906   -0.9845    4.2408    4.0255    0.4041    0.2973    0.1844    5.4303    1.6787</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 355:    93.8420   -6.0679   -0.1584    2.1912   -0.9844    4.2428    4.0166    0.4040    0.2973    0.1845    5.4342    1.6785</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 356:    93.8413   -6.0666   -0.1581    2.1916   -0.9843    4.2418    4.0072    0.4040    0.2973    0.1845    5.4348    1.6792</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 357:    93.8406   -6.0650   -0.1578    2.1921   -0.9844    4.2531    3.9973    0.4040    0.2973    0.1846    5.4350    1.6798</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 358:    93.8404   -6.0639   -0.1575    2.1926   -0.9844    4.2596    3.9901    0.4040    0.2973    0.1846    5.4357    1.6805</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 359:    93.8373   -6.0630   -0.1572    2.1931   -0.9845    4.2724    3.9820    0.4039    0.2973    0.1848    5.4368    1.6816</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 360:    93.8347   -6.0622   -0.1569    2.1936   -0.9845    4.2788    3.9747    0.4039    0.2973    0.1849    5.4377    1.6824</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 361:    93.8327   -6.0626   -0.1566    2.1942   -0.9846    4.2919    3.9749    0.4038    0.2973    0.1850    5.4382    1.6831</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 362:    93.8326   -6.0629   -0.1562    2.1947   -0.9846    4.2989    3.9737    0.4038    0.2973    0.1850    5.4408    1.6838</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 363:    93.8316   -6.0629   -0.1559    2.1953   -0.9846    4.3007    3.9725    0.4037    0.2972    0.1850    5.4420    1.6842</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 364:    93.8317   -6.0629   -0.1556    2.1957   -0.9846    4.2910    3.9739    0.4038    0.2971    0.1850    5.4430    1.6840</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 365:    93.8317   -6.0629   -0.1553    2.1962   -0.9846    4.2878    3.9759    0.4040    0.2967    0.1849    5.4441    1.6839</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 366:    93.8319   -6.0633   -0.1549    2.1966   -0.9845    4.2870    3.9823    0.4042    0.2963    0.1849    5.4461    1.6839</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 367:    93.8320   -6.0636   -0.1546    2.1971   -0.9845    4.2828    3.9850    0.4042    0.2959    0.1849    5.4479    1.6843</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 368:    93.8312   -6.0635   -0.1544    2.1975   -0.9844    4.2781    3.9859    0.4043    0.2955    0.1849    5.4475    1.6841</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 369:    93.8301   -6.0640   -0.1541    2.1979   -0.9844    4.2760    3.9891    0.4043    0.2953    0.1850    5.4472    1.6835</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 370:    93.8306   -6.0651   -0.1539    2.1983   -0.9844    4.2690    3.9967    0.4042    0.2950    0.1850    5.4484    1.6829</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 371:    93.8312   -6.0669   -0.1536    2.1988   -0.9845    4.2613    4.0111    0.4042    0.2947    0.1851    5.4494    1.6822</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 372:    93.8312   -6.0681   -0.1533    2.1992   -0.9845    4.2585    4.0252    0.4042    0.2945    0.1852    5.4513    1.6817</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 373:    93.8307   -6.0681   -0.1531    2.1996   -0.9846    4.2605    4.0284    0.4042    0.2943    0.1853    5.4523    1.6815</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 374:    93.8305   -6.0678   -0.1529    2.1999   -0.9846    4.2676    4.0279    0.4040    0.2942    0.1853    5.4533    1.6816</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 375:    93.8313   -6.0673   -0.1528    2.2002   -0.9846    4.2708    4.0232    0.4038    0.2940    0.1854    5.4543    1.6813</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 376:    93.8310   -6.0672   -0.1527    2.2004   -0.9846    4.2775    4.0214    0.4037    0.2938    0.1855    5.4538    1.6809</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 377:    93.8298   -6.0666   -0.1526    2.2007   -0.9846    4.2787    4.0166    0.4035    0.2937    0.1856    5.4532    1.6806</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 378:    93.8276   -6.0664   -0.1525    2.2009   -0.9846    4.2781    4.0135    0.4033    0.2935    0.1857    5.4538    1.6801</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 379:    93.8262   -6.0671   -0.1524    2.2012   -0.9846    4.2800    4.0157    0.4031    0.2932    0.1857    5.4530    1.6803</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 380:    93.8243   -6.0675   -0.1523    2.2015   -0.9846    4.2735    4.0168    0.4029    0.2929    0.1857    5.4523    1.6803</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 381:    93.8233   -6.0677   -0.1522    2.2017   -0.9845    4.2670    4.0189    0.4027    0.2926    0.1858    5.4517    1.6798</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 382:    93.8229   -6.0674   -0.1521    2.2019   -0.9845    4.2655    4.0211    0.4025    0.2924    0.1858    5.4510    1.6794</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 383:    93.8196   -6.0681   -0.1521    2.2021   -0.9843    4.2696    4.0291    0.4023    0.2922    0.1859    5.4504    1.6788</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 384:    93.8174   -6.0688   -0.1520    2.2023   -0.9842    4.2851    4.0333    0.4021    0.2919    0.1860    5.4500    1.6783</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 385:    93.8156   -6.0682   -0.1518    2.2028   -0.9840    4.3056    4.0305    0.4019    0.2920    0.1862    5.4503    1.6774</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 386:    93.8145   -6.0684   -0.1516    2.2032   -0.9838    4.3195    4.0302    0.4016    0.2920    0.1863    5.4499    1.6765</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 387:    93.8121   -6.0679   -0.1514    2.2036   -0.9837    4.3290    4.0272    0.4014    0.2920    0.1864    5.4501    1.6756</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 388:    93.8105   -6.0676   -0.1513    2.2040   -0.9835    4.3393    4.0267    0.4011    0.2920    0.1865    5.4509    1.6751</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 389:    93.8089   -6.0664   -0.1510    2.2045   -0.9833    4.3458    4.0224    0.4009    0.2920    0.1865    5.4512    1.6746</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 390:    93.8080   -6.0658   -0.1508    2.2049   -0.9832    4.3422    4.0199    0.4007    0.2920    0.1866    5.4514    1.6744</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 391:    93.8077   -6.0669   -0.1507    2.2053   -0.9831    4.3500    4.0277    0.4007    0.2920    0.1867    5.4511    1.6740</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 392:    93.8069   -6.0676   -0.1505    2.2056   -0.9831    4.3525    4.0326    0.4006    0.2920    0.1868    5.4500    1.6733</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 393:    93.8072   -6.0678   -0.1504    2.2059   -0.9830    4.3562    4.0344    0.4005    0.2920    0.1868    5.4491    1.6725</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 394:    93.8083   -6.0684   -0.1503    2.2061   -0.9829    4.3577    4.0386    0.4004    0.2920    0.1869    5.4493    1.6716</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 395:    93.8086   -6.0685   -0.1501    2.2064   -0.9828    4.3574    4.0394    0.4003    0.2920    0.1869    5.4493    1.6709</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 396:    93.8078   -6.0677   -0.1500    2.2067   -0.9827    4.3591    4.0355    0.4002    0.2921    0.1870    5.4493    1.6707</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 397:    93.8064   -6.0668   -0.1499    2.2071   -0.9825    4.3621    4.0317    0.4000    0.2922    0.1871    5.4495    1.6704</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 398:    93.8058   -6.0661   -0.1499    2.2073   -0.9823    4.3701    4.0285    0.3999    0.2923    0.1872    5.4491    1.6699</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 399:    93.8057   -6.0651   -0.1498    2.2075   -0.9822    4.3803    4.0243    0.3997    0.2924    0.1872    5.4485    1.6699</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 400:    93.8057   -6.0647   -0.1498    2.2076   -0.9820    4.3854    4.0225    0.3996    0.2924    0.1872    5.4488    1.6699</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 401:    93.8059   -6.0635   -0.1498    2.2078   -0.9819    4.3939    4.0178    0.3995    0.2925    0.1873    5.4491    1.6702</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 402:    93.8063   -6.0621   -0.1498    2.2079   -0.9818    4.4033    4.0120    0.3993    0.2926    0.1875    5.4492    1.6704</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 403:    93.8055   -6.0609   -0.1498    2.2080   -0.9816    4.4098    4.0069    0.3992    0.2926    0.1876    5.4494    1.6703</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 404:    93.8040   -6.0608   -0.1499    2.2081   -0.9815    4.4153    4.0050    0.3991    0.2927    0.1877    5.4494    1.6701</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 405:    93.8030   -6.0604   -0.1499    2.2082   -0.9814    4.4181    4.0047    0.3990    0.2928    0.1879    5.4491    1.6700</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 406:    93.8002   -6.0597   -0.1499    2.2083   -0.9812    4.4257    4.0025    0.3989    0.2928    0.1880    5.4498    1.6702</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 407:    93.7969   -6.0593   -0.1500    2.2085   -0.9810    4.4343    3.9994    0.3988    0.2928    0.1881    5.4514    1.6703</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 408:    93.7947   -6.0582   -0.1499    2.2087   -0.9809    4.4509    3.9936    0.3987    0.2927    0.1882    5.4526    1.6704</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 409:    93.7929   -6.0579   -0.1499    2.2088   -0.9807    4.4584    3.9918    0.3987    0.2926    0.1882    5.4537    1.6707</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 410:    93.7896   -6.0593   -0.1499    2.2089   -0.9807    4.4664    4.0002    0.3987    0.2925    0.1883    5.4560    1.6705</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 411:    93.7875   -6.0601   -0.1499    2.2090   -0.9807    4.4626    4.0057    0.3987    0.2924    0.1883    5.4567    1.6705</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 412:    93.7862   -6.0602   -0.1499    2.2091   -0.9808    4.4574    4.0095    0.3986    0.2923    0.1884    5.4565    1.6706</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 413:    93.7858   -6.0606   -0.1498    2.2092   -0.9808    4.4527    4.0146    0.3986    0.2922    0.1885    5.4561    1.6708</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 414:    93.7859   -6.0615   -0.1498    2.2093   -0.9808    4.4451    4.0211    0.3987    0.2921    0.1885    5.4574    1.6708</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 415:    93.7864   -6.0629   -0.1498    2.2092   -0.9808    4.4443    4.0298    0.3986    0.2921    0.1885    5.4576    1.6707</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 416:    93.7851   -6.0639   -0.1498    2.2092   -0.9808    4.4462    4.0362    0.3985    0.2920    0.1884    5.4575    1.6706</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 417:    93.7820   -6.0644   -0.1499    2.2092   -0.9808    4.4425    4.0387    0.3985    0.2920    0.1884    5.4577    1.6703</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 418:    93.7801   -6.0652   -0.1499    2.2093   -0.9808    4.4365    4.0420    0.3985    0.2920    0.1883    5.4589    1.6702</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 419:    93.7799   -6.0648   -0.1499    2.2094   -0.9807    4.4303    4.0418    0.3984    0.2921    0.1883    5.4596    1.6703</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 420:    93.7797   -6.0641   -0.1498    2.2095   -0.9807    4.4216    4.0383    0.3983    0.2921    0.1884    5.4607    1.6702</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 421:    93.7803   -6.0641   -0.1498    2.2096   -0.9808    4.4120    4.0396    0.3983    0.2922    0.1885    5.4621    1.6702</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 422:    93.7806   -6.0638   -0.1498    2.2097   -0.9809    4.4017    4.0373    0.3981    0.2923    0.1885    5.4635    1.6702</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 423:    93.7800   -6.0636   -0.1498    2.2098   -0.9810    4.3948    4.0339    0.3981    0.2923    0.1885    5.4640    1.6701</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 424:    93.7789   -6.0638   -0.1498    2.2099   -0.9810    4.3884    4.0336    0.3980    0.2924    0.1885    5.4651    1.6700</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 425:    93.7774   -6.0628   -0.1498    2.2101   -0.9810    4.3814    4.0271    0.3978    0.2925    0.1884    5.4666    1.6699</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 426:    93.7755   -6.0619   -0.1498    2.2102   -0.9811    4.3841    4.0221    0.3977    0.2925    0.1884    5.4693    1.6697</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 427:    93.7753   -6.0612   -0.1498    2.2103   -0.9810    4.3785    4.0167    0.3975    0.2926    0.1883    5.4712    1.6696</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 428:    93.7741   -6.0613   -0.1498    2.2104   -0.9810    4.3767    4.0161    0.3973    0.2926    0.1882    5.4729    1.6696</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 429:    93.7717   -6.0613   -0.1498    2.2105   -0.9810    4.3744    4.0158    0.3971    0.2926    0.1880    5.4743    1.6694</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 430:    93.7690   -6.0619   -0.1498    2.2106   -0.9809    4.3744    4.0189    0.3969    0.2926    0.1879    5.4753    1.6692</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 431:    93.7668   -6.0615   -0.1498    2.2107   -0.9809    4.3709    4.0174    0.3967    0.2925    0.1878    5.4762    1.6691</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 432:    93.7656   -6.0619   -0.1498    2.2108   -0.9809    4.3710    4.0173    0.3966    0.2926    0.1878    5.4770    1.6688</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 433:    93.7632   -6.0625   -0.1498    2.2108   -0.9809    4.3689    4.0204    0.3964    0.2926    0.1877    5.4774    1.6687</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 434:    93.7624   -6.0621   -0.1498    2.2109   -0.9808    4.3687    4.0178    0.3962    0.2926    0.1876    5.4774    1.6683</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 435:    93.7608   -6.0622   -0.1497    2.2111   -0.9808    4.3714    4.0182    0.3961    0.2927    0.1875    5.4777    1.6679</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 436:    93.7586   -6.0622   -0.1498    2.2112   -0.9808    4.3695    4.0178    0.3959    0.2928    0.1874    5.4784    1.6679</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 437:    93.7558   -6.0622   -0.1498    2.2114   -0.9809    4.3658    4.0167    0.3957    0.2928    0.1873    5.4795    1.6678</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 438:    93.7519   -6.0627   -0.1499    2.2115   -0.9808    4.3601    4.0183    0.3955    0.2928    0.1872    5.4807    1.6674</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 439:    93.7478   -6.0618   -0.1499    2.2115   -0.9807    4.3538    4.0133    0.3952    0.2928    0.1871    5.4814    1.6672</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 440:    93.7449   -6.0612   -0.1499    2.2117   -0.9805    4.3504    4.0103    0.3951    0.2928    0.1870    5.4820    1.6669</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 441:    93.7433   -6.0608   -0.1499    2.2117   -0.9805    4.3438    4.0077    0.3949    0.2928    0.1869    5.4818    1.6666</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 442:    93.7417   -6.0604   -0.1500    2.2118   -0.9805    4.3354    4.0058    0.3947    0.2927    0.1868    5.4824    1.6666</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 443:    93.7407   -6.0604   -0.1500    2.2119   -0.9805    4.3281    4.0046    0.3946    0.2927    0.1867    5.4832    1.6667</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 444:    93.7397   -6.0608   -0.1500    2.2118   -0.9805    4.3180    4.0069    0.3944    0.2928    0.1866    5.4857    1.6664</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 445:    93.7387   -6.0613   -0.1501    2.2118   -0.9805    4.3092    4.0085    0.3942    0.2929    0.1866    5.4866    1.6663</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 446:    93.7376   -6.0612   -0.1502    2.2117   -0.9805    4.3022    4.0075    0.3939    0.2930    0.1865    5.4866    1.6660</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 447:    93.7375   -6.0613   -0.1503    2.2116   -0.9806    4.2981    4.0076    0.3937    0.2931    0.1865    5.4863    1.6658</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 448:    93.7388   -6.0617   -0.1504    2.2115   -0.9806    4.2970    4.0087    0.3935    0.2932    0.1864    5.4870    1.6658</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 449:    93.7400   -6.0617   -0.1504    2.2115   -0.9807    4.2904    4.0069    0.3933    0.2933    0.1864    5.4880    1.6655</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 450:    93.7414   -6.0623   -0.1505    2.2113   -0.9808    4.2829    4.0095    0.3930    0.2935    0.1863    5.4880    1.6653</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 451:    93.7426   -6.0634   -0.1506    2.2112   -0.9808    4.2797    4.0146    0.3928    0.2936    0.1863    5.4877    1.6655</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 452:    93.7447   -6.0642   -0.1507    2.2110   -0.9809    4.2770    4.0171    0.3926    0.2938    0.1862    5.4876    1.6657</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 453:    93.7465   -6.0644   -0.1508    2.2108   -0.9810    4.2698    4.0184    0.3924    0.2940    0.1862    5.4877    1.6652</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 454:    93.7478   -6.0645   -0.1509    2.2106   -0.9810    4.2657    4.0182    0.3922    0.2941    0.1861    5.4874    1.6648</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 455:    93.7486   -6.0650   -0.1511    2.2104   -0.9811    4.2656    4.0203    0.3921    0.2943    0.1861    5.4871    1.6644</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 456:    93.7485   -6.0659   -0.1512    2.2102   -0.9812    4.2646    4.0240    0.3920    0.2945    0.1860    5.4869    1.6641</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 457:    93.7487   -6.0668   -0.1514    2.2099   -0.9813    4.2613    4.0277    0.3919    0.2946    0.1860    5.4866    1.6640</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 458:    93.7484   -6.0667   -0.1515    2.2097   -0.9812    4.2586    4.0263    0.3918    0.2947    0.1859    5.4854    1.6639</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 459:    93.7468   -6.0661   -0.1517    2.2095   -0.9812    4.2565    4.0225    0.3917    0.2948    0.1858    5.4853    1.6639</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 460:    93.7458   -6.0652   -0.1518    2.2093   -0.9812    4.2548    4.0171    0.3916    0.2950    0.1858    5.4843    1.6638</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 461:    93.7435   -6.0645   -0.1519    2.2091   -0.9811    4.2554    4.0121    0.3914    0.2951    0.1858    5.4843    1.6637</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 462:    93.7421   -6.0637   -0.1521    2.2089   -0.9811    4.2509    4.0079    0.3913    0.2953    0.1857    5.4840    1.6638</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 463:    93.7406   -6.0630   -0.1522    2.2088   -0.9810    4.2534    4.0036    0.3912    0.2955    0.1857    5.4834    1.6640</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 464:    93.7406   -6.0622   -0.1524    2.2086   -0.9810    4.2548    3.9986    0.3911    0.2956    0.1857    5.4828    1.6639</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 465:    93.7404   -6.0617   -0.1525    2.2084   -0.9810    4.2511    3.9955    0.3910    0.2958    0.1857    5.4828    1.6638</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 466:    93.7414   -6.0610   -0.1527    2.2080   -0.9809    4.2468    3.9918    0.3910    0.2959    0.1857    5.4812    1.6640</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 467:    93.7419   -6.0606   -0.1529    2.2077   -0.9810    4.2446    3.9885    0.3909    0.2960    0.1858    5.4799    1.6642</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 468:    93.7427   -6.0604   -0.1531    2.2074   -0.9809    4.2473    3.9872    0.3909    0.2961    0.1858    5.4790    1.6643</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 469:    93.7435   -6.0608   -0.1532    2.2073   -0.9810    4.2457    3.9891    0.3907    0.2963    0.1858    5.4789    1.6641</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 470:    93.7442   -6.0611   -0.1533    2.2071   -0.9811    4.2443    3.9895    0.3905    0.2965    0.1858    5.4785    1.6640</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 471:    93.7443   -6.0618   -0.1534    2.2070   -0.9812    4.2415    3.9932    0.3903    0.2967    0.1858    5.4773    1.6639</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 472:    93.7433   -6.0624   -0.1535    2.2069   -0.9812    4.2414    3.9958    0.3901    0.2969    0.1858    5.4765    1.6637</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 473:    93.7421   -6.0621   -0.1536    2.2067   -0.9811    4.2330    3.9962    0.3899    0.2970    0.1858    5.4762    1.6637</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 474:    93.7415   -6.0621   -0.1537    2.2066   -0.9812    4.2263    3.9968    0.3897    0.2972    0.1858    5.4755    1.6638</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 475:    93.7396   -6.0621   -0.1538    2.2066   -0.9812    4.2261    3.9956    0.3895    0.2973    0.1859    5.4751    1.6637</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 476:    93.7373   -6.0626   -0.1539    2.2065   -0.9811    4.2265    3.9976    0.3893    0.2975    0.1859    5.4743    1.6634</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 477:    93.7357   -6.0623   -0.1540    2.2063   -0.9811    4.2222    3.9953    0.3891    0.2976    0.1859    5.4741    1.6635</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 478:    93.7345   -6.0620   -0.1541    2.2062   -0.9811    4.2165    3.9939    0.3889    0.2978    0.1860    5.4745    1.6638</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 479:    93.7341   -6.0615   -0.1542    2.2062   -0.9812    4.2122    3.9916    0.3887    0.2979    0.1860    5.4751    1.6637</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 480:    93.7338   -6.0619   -0.1543    2.2061   -0.9812    4.2084    3.9932    0.3885    0.2980    0.1860    5.4753    1.6635</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 481:    93.7336   -6.0618   -0.1544    2.2060   -0.9812    4.2066    3.9945    0.3883    0.2981    0.1860    5.4748    1.6637</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 482:    93.7340   -6.0622   -0.1545    2.2058   -0.9812    4.2069    3.9969    0.3882    0.2983    0.1860    5.4741    1.6636</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 483:    93.7328   -6.0627   -0.1547    2.2057   -0.9813    4.2062    3.9992    0.3879    0.2985    0.1861    5.4739    1.6636</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 484:    93.7323   -6.0629   -0.1547    2.2056   -0.9813    4.2075    3.9995    0.3877    0.2987    0.1861    5.4730    1.6638</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 485:    93.7326   -6.0634   -0.1548    2.2055   -0.9814    4.2045    4.0013    0.3875    0.2989    0.1862    5.4727    1.6637</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 486:    93.7320   -6.0632   -0.1549    2.2055   -0.9814    4.2084    3.9989    0.3872    0.2991    0.1863    5.4718    1.6635</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 487:    93.7321   -6.0625   -0.1549    2.2054   -0.9814    4.2105    3.9949    0.3870    0.2993    0.1864    5.4712    1.6634</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 488:    93.7326   -6.0623   -0.1550    2.2054   -0.9814    4.2104    3.9935    0.3868    0.2996    0.1865    5.4721    1.6635</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 489:    93.7342   -6.0615   -0.1551    2.2054   -0.9815    4.2103    3.9893    0.3866    0.2998    0.1866    5.4725    1.6635</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 490:    93.7360   -6.0610   -0.1551    2.2053   -0.9815    4.2116    3.9857    0.3864    0.3001    0.1867    5.4730    1.6636</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 491:    93.7374   -6.0601   -0.1551    2.2052   -0.9815    4.2128    3.9804    0.3862    0.3003    0.1868    5.4722    1.6638</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 492:    93.7386   -6.0597   -0.1552    2.2050   -0.9816    4.2171    3.9774    0.3861    0.3006    0.1869    5.4714    1.6642</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 493:    93.7399   -6.0597   -0.1553    2.2049   -0.9816    4.2262    3.9766    0.3859    0.3008    0.1869    5.4712    1.6643</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 494:    93.7404   -6.0596   -0.1553    2.2047   -0.9816    4.2347    3.9746    0.3857    0.3011    0.1870    5.4711    1.6646</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 495:    93.7406   -6.0590   -0.1554    2.2046   -0.9817    4.2395    3.9711    0.3855    0.3014    0.1871    5.4705    1.6650</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 496:    93.7416   -6.0582   -0.1554    2.2045   -0.9817    4.2456    3.9660    0.3853    0.3017    0.1871    5.4707    1.6656</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 497:    93.7433   -6.0575   -0.1555    2.2044   -0.9818    4.2511    3.9631    0.3851    0.3020    0.1871    5.4714    1.6658</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 498:    93.7444   -6.0574   -0.1556    2.2042   -0.9817    4.2516    3.9632    0.3848    0.3023    0.1871    5.4721    1.6660</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 499:    93.7457   -6.0576   -0.1557    2.2041   -0.9817    4.2518    3.9660    0.3846    0.3025    0.1871    5.4728    1.6660</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 500:    93.7469   -6.0586   -0.1557    2.2040   -0.9817    4.2481    3.9757    0.3844    0.3028    0.1871    5.4738    1.6661</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating covariance matrix</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling EBE model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Needed Covariates:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "CMT"</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating residuals/tables</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-in"><span class="va">f_nlmixr_fomc_sfo_focei_obs</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr</a></span><span class="op">(</span><span class="va">f_mmkin_obs</span><span class="op">[</span><span class="st">"FOMC-SFO"</span>, <span class="op">]</span>, est <span class="op">=</span> <span class="st">"focei"</span><span class="op">)</span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate jacobian</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate sensitivities</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate ∂(f)/∂(η)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate ∂(R²)/∂(η)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in inner model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in inner model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in EBE model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in EBE model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling inner model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in FD model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in FD model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling EBE model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling events FD model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Needed Covariates:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "CMT"</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="font-weight: bold;">Key:</span> U: Unscaled Parameters; X: Back-transformed parameters; G: Gill difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> F: Forward difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> C: Central difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> M: Mixed forward and central difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Unscaled parameters for Omegas=chol(solve(omega));</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Diagonals are transformed, as specified by foceiControl(diagXform=)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |-----+---------------+-----------+-----------+-----------+-----------|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    #| Objective Fun |  parent_0 |  log_k_A1 |f_parent_qlogis | log_alpha |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  log_beta |sigma_parent |  sigma_A1 |        o1 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|        o2 |        o3 |        o4 |        o5 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    1</span>|     468.02617 |     1.000 |    -1.000 |   -0.9113 |   -0.8954 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8491 |   -0.8511 |   -0.8672 |   -0.8762 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8737 |   -0.8674 |   -0.8694 |   -0.8687 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.02617 |     94.00 |    -5.400 |   -0.9900 |   -0.2000 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.100 |     2.000 |     1.200 |    0.7536 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8758 |     1.189 |     1.093 |     1.127 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.02617</span> |     94.00 |  0.004517 |    0.2709 |    0.8187 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.166 |     2.000 |     1.200 |    0.7536 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8758 |     1.189 |     1.093 |     1.127 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    G|    Gill Diff. |     49.30 |     2.016 |   -0.2473 |   -0.3737 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.227 |    -27.89 |    -10.29 |     8.753 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     11.17 |    -12.52 |    -9.819 |    -8.910 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    2</span>|     4021.4865 |    0.2059 |    -1.032 |   -0.9073 |   -0.8894 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8293 |   -0.4019 |   -0.7014 |    -1.017 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.054 |   -0.6658 |   -0.7112 |   -0.7251 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     4021.4865 |     19.35 |    -5.432 |   -0.9861 |   -0.1940 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.120 |     2.449 |     1.299 |    0.6474 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7182 |     1.429 |     1.266 |     1.289 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     4021.4865</span> |     19.35 |  0.004372 |    0.2717 |    0.8237 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.329 |     2.449 |     1.299 |    0.6474 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7182 |     1.429 |     1.266 |     1.289 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    3</span>|     518.20369 |    0.9206 |    -1.003 |   -0.9109 |   -0.8948 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8471 |   -0.8062 |   -0.8506 |   -0.8903 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8917 |   -0.8473 |   -0.8535 |   -0.8543 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     518.20369 |     86.53 |    -5.403 |   -0.9896 |   -0.1994 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.102 |     2.045 |     1.210 |    0.7430 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8600 |     1.213 |     1.111 |     1.143 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     518.20369</span> |     86.53 |  0.004502 |    0.2710 |    0.8192 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.182 |     2.045 |     1.210 |    0.7430 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8600 |     1.213 |     1.111 |     1.143 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    4</span>|     467.99742 |    0.9921 |    -1.000 |   -0.9112 |   -0.8953 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8489 |   -0.8466 |   -0.8655 |   -0.8776 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8755 |   -0.8654 |   -0.8678 |   -0.8672 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     467.99742 |     93.25 |    -5.400 |   -0.9900 |   -0.1999 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.100 |     2.004 |     1.201 |    0.7526 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8742 |     1.192 |     1.095 |     1.129 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     467.99742</span> |     93.25 |  0.004515 |    0.2709 |    0.8188 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.168 |     2.004 |     1.201 |    0.7526 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8742 |     1.192 |     1.095 |     1.129 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -98.28 |     1.929 |   -0.4044 |   -0.4503 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.484 |    -29.27 |    -9.987 |     8.922 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.417 |    -11.79 |    -9.521 |    -8.343 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    5</span>|     467.67344 |    0.9967 |    -1.000 |   -0.9112 |   -0.8953 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8488 |   -0.8452 |   -0.8651 |   -0.8780 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8760 |   -0.8648 |   -0.8673 |   -0.8668 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     467.67344 |     93.69 |    -5.400 |   -0.9899 |   -0.1999 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.100 |     2.006 |     1.201 |    0.7523 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8738 |     1.192 |     1.095 |     1.129 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     467.67344</span> |     93.69 |  0.004515 |    0.2709 |    0.8188 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.168 |     2.006 |     1.201 |    0.7523 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8738 |     1.192 |     1.095 |     1.129 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -11.92 |     1.963 |   -0.3242 |   -0.4184 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.350 |    -28.16 |    -10.02 |     8.541 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.305 |    -11.80 |    -9.512 |    -8.408 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    6</span>|     467.50396 |    0.9983 |    -1.001 |   -0.9112 |   -0.8952 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8487 |   -0.8416 |   -0.8638 |   -0.8791 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8771 |   -0.8633 |   -0.8661 |   -0.8658 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     467.50396 |     93.84 |    -5.401 |   -0.9899 |   -0.1999 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.100 |     2.010 |     1.202 |    0.7514 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8729 |     1.194 |     1.097 |     1.130 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     467.50396</span> |     93.84 |  0.004514 |    0.2709 |    0.8188 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.170 |     2.010 |     1.202 |    0.7514 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8729 |     1.194 |     1.097 |     1.130 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    7</span>|     467.27231 |     1.003 |    -1.001 |   -0.9110 |   -0.8951 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8481 |   -0.8306 |   -0.8599 |   -0.8825 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8803 |   -0.8587 |   -0.8624 |   -0.8625 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     467.27231 |     94.27 |    -5.401 |   -0.9898 |   -0.1997 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.101 |     2.021 |     1.204 |    0.7489 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8700 |     1.200 |     1.101 |     1.134 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     467.27231</span> |     94.27 |  0.004510 |    0.2710 |    0.8190 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.174 |     2.021 |     1.204 |    0.7489 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8700 |     1.200 |     1.101 |     1.134 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     101.6 |     1.991 |   -0.1997 |   -0.3688 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.177 |    -25.72 |    -9.853 |     9.249 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.335 |    -11.58 |    -9.288 |    -8.277 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    8</span>|     466.36087 |    0.9961 |    -1.002 |   -0.9109 |   -0.8949 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8474 |   -0.8165 |   -0.8547 |   -0.8873 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8853 |   -0.8526 |   -0.8575 |   -0.8582 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     466.36087 |     93.63 |    -5.402 |   -0.9896 |   -0.1995 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.102 |     2.035 |     1.207 |    0.7453 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8657 |     1.207 |     1.106 |     1.139 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     466.36087</span> |     93.63 |  0.004506 |    0.2710 |    0.8191 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.180 |     2.035 |     1.207 |    0.7453 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8657 |     1.207 |     1.106 |     1.139 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -21.78 |     1.909 |   -0.3215 |   -0.4291 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.401 |    -25.38 |    -9.315 |     7.655 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     10.17 |    -11.26 |    -9.035 |    -7.894 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    9</span>|     465.79764 |     1.000 |    -1.004 |   -0.9107 |   -0.8946 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8467 |   -0.8024 |   -0.8495 |   -0.8920 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8917 |   -0.8462 |   -0.8524 |   -0.8537 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     465.79764 |     94.04 |    -5.404 |   -0.9895 |   -0.1993 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.102 |     2.049 |     1.211 |    0.7417 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8600 |     1.214 |     1.112 |     1.144 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     465.79764</span> |     94.04 |  0.004501 |    0.2710 |    0.8193 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.186 |     2.049 |     1.211 |    0.7417 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8600 |     1.214 |     1.112 |     1.144 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     54.81 |     1.910 |   -0.2489 |   -0.4009 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.285 |    -22.17 |    -8.389 |     8.020 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     7.340 |    -11.02 |    -8.786 |    -7.720 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   10</span>|     465.18897 |    0.9945 |    -1.005 |   -0.9105 |   -0.8943 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8457 |   -0.7893 |   -0.8445 |   -0.8971 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8975 |   -0.8390 |   -0.8467 |   -0.8487 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     465.18897 |     93.48 |    -5.405 |   -0.9893 |   -0.1990 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.103 |     2.062 |     1.214 |    0.7379 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8549 |     1.223 |     1.118 |     1.149 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     465.18897</span> |     93.48 |  0.004495 |    0.2711 |    0.8196 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.194 |     2.062 |     1.214 |    0.7379 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8549 |     1.223 |     1.118 |     1.149 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -47.52 |     1.834 |   -0.3684 |   -0.4503 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.489 |    -22.39 |    -7.996 |     7.294 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.249 |    -10.90 |    -8.741 |    -7.575 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   11</span>|     464.56229 |    0.9983 |    -1.006 |   -0.9102 |   -0.8940 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8445 |   -0.7772 |   -0.8401 |   -0.9030 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9029 |   -0.8305 |   -0.8400 |   -0.8430 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     464.56229 |     93.84 |    -5.406 |   -0.9890 |   -0.1986 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.105 |     2.074 |     1.216 |    0.7334 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8503 |     1.233 |     1.125 |     1.156 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     464.56229</span> |     93.84 |  0.004488 |    0.2711 |    0.8199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.204 |     2.074 |     1.216 |    0.7334 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8503 |     1.233 |     1.125 |     1.156 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   12</span>|     463.71730 |    0.9982 |    -1.009 |   -0.9098 |   -0.8933 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8424 |   -0.7583 |   -0.8332 |   -0.9128 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9112 |   -0.8167 |   -0.8291 |   -0.8337 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      463.7173 |     93.83 |    -5.409 |   -0.9885 |   -0.1980 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.107 |     2.093 |     1.220 |    0.7260 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8430 |     1.249 |     1.137 |     1.166 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      463.7173</span> |     93.83 |  0.004478 |    0.2712 |    0.8204 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.221 |     2.093 |     1.220 |    0.7260 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8430 |     1.249 |     1.137 |     1.166 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   13</span>|     461.03699 |    0.9978 |    -1.018 |   -0.9080 |   -0.8909 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8343 |   -0.6839 |   -0.8059 |   -0.9516 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9441 |   -0.7622 |   -0.7862 |   -0.7969 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     461.03699 |     93.79 |    -5.418 |   -0.9868 |   -0.1955 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.115 |     2.167 |     1.237 |    0.6968 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8141 |     1.314 |     1.184 |     1.208 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     461.03699</span> |     93.79 |  0.004435 |    0.2716 |    0.8224 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.288 |     2.167 |     1.237 |    0.6968 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8141 |     1.314 |     1.184 |     1.208 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   14</span>|     458.83693 |    0.9972 |    -1.033 |   -0.9052 |   -0.8871 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8218 |   -0.5692 |   -0.7639 |    -1.011 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9948 |   -0.6782 |   -0.7201 |   -0.7403 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.83693 |     93.74 |    -5.433 |   -0.9840 |   -0.1917 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.127 |     2.282 |     1.262 |    0.6519 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7697 |     1.414 |     1.256 |     1.272 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.83693</span> |     93.74 |  0.004371 |    0.2721 |    0.8255 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.393 |     2.282 |     1.262 |    0.6519 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7697 |     1.414 |     1.256 |     1.272 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |   0.05416 |     1.397 |   -0.2200 |   -0.5344 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.585 |    -3.387 |    -1.306 |   -0.2250 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.392 |    -3.452 |    -2.065 |    -1.405 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   15</span>|     459.30045 |    0.9957 |    -1.166 |   -0.8845 |   -0.8313 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6584 |   -0.5528 |   -0.7505 |   -0.8569 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.017 |   -0.4560 |   -0.6245 |   -0.7036 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     459.30045 |     93.60 |    -5.566 |   -0.9635 |   -0.1360 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.291 |     2.298 |     1.270 |    0.7682 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7505 |     1.678 |     1.361 |     1.313 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     459.30045</span> |     93.60 |  0.003827 |    0.2762 |    0.8729 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.881 |     2.298 |     1.270 |    0.7682 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7505 |     1.678 |     1.361 |     1.313 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   16</span>|     458.36319 |    0.9960 |    -1.071 |   -0.8992 |   -0.8719 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7770 |   -0.5045 |   -0.7383 |   -0.9927 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.023 |   -0.5931 |   -0.6727 |   -0.7107 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.36319 |     93.63 |    -5.471 |   -0.9780 |   -0.1766 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.172 |     2.347 |     1.277 |    0.6658 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7452 |     1.515 |     1.308 |     1.305 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.36319</span> |     93.63 |  0.004206 |    0.2733 |    0.8381 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.777 |     2.347 |     1.277 |    0.6658 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7452 |     1.515 |     1.308 |     1.305 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -12.30 |     1.214 |   0.06343 |   -0.2616 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6069 |   0.08029 |    0.4273 |   0.07297 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3937 |    0.3470 |    0.5201 |   0.09241 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   17</span>|     458.37724 |    0.9977 |    -1.183 |   -0.9046 |   -0.8476 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7196 |   -0.4765 |   -0.7567 |    -1.014 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9850 |   -0.5955 |   -0.6985 |   -0.7030 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.37724 |     93.79 |    -5.583 |   -0.9834 |   -0.1522 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.229 |     2.375 |     1.266 |    0.6497 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7783 |     1.513 |     1.280 |     1.314 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.37724</span> |     93.79 |  0.003762 |    0.2722 |    0.8588 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.295 |     2.375 |     1.266 |    0.6497 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7783 |     1.513 |     1.280 |     1.314 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   18</span>|     458.32800 |    0.9976 |    -1.124 |   -0.9017 |   -0.8605 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7499 |   -0.4913 |   -0.7470 |    -1.003 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.005 |   -0.5943 |   -0.6849 |   -0.7071 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|       458.328 |     93.78 |    -5.524 |   -0.9806 |   -0.1651 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.199 |     2.360 |     1.272 |    0.6583 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7608 |     1.514 |     1.295 |     1.309 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">       458.328</span> |     93.78 |  0.003990 |    0.2728 |    0.8478 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.017 |     2.360 |     1.272 |    0.6583 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7608 |     1.514 |     1.295 |     1.309 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     9.757 |     1.093 |  -0.06310 |   0.02237 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.09811 |    0.3115 |   -0.3381 |   -0.2098 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8196 |    0.7052 |   0.04425 |    0.2610 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   19</span>|     458.25889 |    0.9968 |    -1.183 |   -0.9011 |   -0.8543 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7363 |   -0.4871 |   -0.7422 |    -1.003 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.023 |   -0.6115 |   -0.6911 |   -0.7106 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.25889 |     93.70 |    -5.583 |   -0.9799 |   -0.1589 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.213 |     2.364 |     1.275 |    0.6583 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7450 |     1.494 |     1.288 |     1.305 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.25889</span> |     93.70 |  0.003760 |    0.2729 |    0.8531 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.141 |     2.364 |     1.275 |    0.6583 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7450 |     1.494 |     1.288 |     1.305 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |   -0.6125 |    0.8824 |  -0.01905 |    0.1697 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4368 |    0.4743 |   0.05191 |   -0.6440 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4868 |   -0.9136 |   -0.3225 |  -0.06220 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   20</span>|     458.18570 |    0.9978 |    -1.246 |   -0.9006 |   -0.8585 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7495 |   -0.4978 |   -0.7385 |   -0.9895 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.024 |   -0.6046 |   -0.6874 |   -0.7124 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      458.1857 |     93.79 |    -5.646 |   -0.9794 |   -0.1632 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.200 |     2.353 |     1.277 |    0.6683 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7440 |     1.502 |     1.292 |     1.303 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      458.1857</span> |     93.79 |  0.003532 |    0.2730 |    0.8495 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.022 |     2.353 |     1.277 |    0.6683 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7440 |     1.502 |     1.292 |     1.303 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   21</span>|     458.13464 |    0.9963 |    -1.435 |   -0.8992 |   -0.8705 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7875 |   -0.5278 |   -0.7264 |   -0.9531 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.031 |   -0.5901 |   -0.6784 |   -0.7184 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.13464 |     93.65 |    -5.835 |   -0.9781 |   -0.1751 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.162 |     2.323 |     1.284 |    0.6957 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7378 |     1.519 |     1.302 |     1.296 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.13464</span> |     93.65 |  0.002922 |    0.2733 |    0.8394 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.685 |     2.323 |     1.284 |    0.6957 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7378 |     1.519 |     1.302 |     1.296 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -15.15 |    0.2290 |    0.2476 |   -0.1455 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7111 |    -1.138 |    0.8895 |     1.880 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.837 |    0.2029 |    0.3485 |   -0.6531 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   22</span>|     458.67841 |    0.9992 |    -1.651 |   -0.9027 |   -0.9245 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8733 |   -0.5209 |   -0.7232 |    -1.036 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.001 |   -0.6164 |   -0.6125 |   -0.6771 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.67841 |     93.92 |    -6.051 |   -0.9815 |   -0.2291 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.076 |     2.330 |     1.286 |    0.6333 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7647 |     1.488 |     1.374 |     1.343 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.67841</span> |     93.92 |  0.002355 |    0.2726 |    0.7952 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     7.971 |     2.330 |     1.286 |    0.6333 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7647 |     1.488 |     1.374 |     1.343 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   23</span>|     458.25487 |     1.002 |    -1.469 |   -0.8998 |   -0.8789 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8006 |   -0.5263 |   -0.7262 |   -0.9666 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5942 |   -0.6683 |   -0.7117 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.25487 |     94.17 |    -5.869 |   -0.9787 |   -0.1835 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.148 |     2.325 |     1.285 |    0.6855 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7425 |     1.514 |     1.313 |     1.304 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.25487</span> |     94.17 |  0.002825 |    0.2732 |    0.8324 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.572 |     2.325 |     1.285 |    0.6855 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7425 |     1.514 |     1.313 |     1.304 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   24</span>|     458.28425 |     1.002 |    -1.442 |   -0.8994 |   -0.8721 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7899 |   -0.5271 |   -0.7267 |   -0.9564 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.030 |   -0.5910 |   -0.6765 |   -0.7168 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.28425 |     94.21 |    -5.842 |   -0.9783 |   -0.1768 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.159 |     2.324 |     1.284 |    0.6932 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7392 |     1.518 |     1.304 |     1.298 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.28425</span> |     94.21 |  0.002902 |    0.2732 |    0.8380 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.664 |     2.324 |     1.284 |    0.6932 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7392 |     1.518 |     1.304 |     1.298 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   25</span>|     458.13208 |    0.9983 |    -1.435 |   -0.8993 |   -0.8705 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7874 |   -0.5276 |   -0.7266 |   -0.9533 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.031 |   -0.5901 |   -0.6785 |   -0.7183 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.13208 |     93.84 |    -5.835 |   -0.9781 |   -0.1751 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.162 |     2.323 |     1.284 |    0.6955 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7380 |     1.519 |     1.302 |     1.296 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.13208</span> |     93.84 |  0.002922 |    0.2733 |    0.8394 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.686 |     2.323 |     1.284 |    0.6955 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7380 |     1.519 |     1.302 |     1.296 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     13.75 |    0.2391 |    0.3431 |   -0.1129 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6079 |    -1.684 |    0.2301 |     1.587 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9924 |   -0.4675 |    0.3927 |   -0.7567 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   26</span>|     458.12484 |    0.9973 |    -1.435 |   -0.8993 |   -0.8705 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7874 |   -0.5275 |   -0.7266 |   -0.9535 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.031 |   -0.5900 |   -0.6785 |   -0.7182 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.12484 |     93.75 |    -5.835 |   -0.9782 |   -0.1751 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.162 |     2.324 |     1.284 |    0.6954 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7379 |     1.519 |     1.302 |     1.297 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.12484</span> |     93.75 |  0.002922 |    0.2733 |    0.8394 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.686 |     2.324 |     1.284 |    0.6954 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7379 |     1.519 |     1.302 |     1.297 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |   -0.4576 |    0.2336 |    0.2904 |   -0.1274 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6585 |    -1.014 |    0.9040 |     1.932 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.695 |    0.2950 |    0.3980 |   -0.7211 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   27</span>|     458.12349 |    0.9975 |    -1.436 |   -0.8994 |   -0.8704 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7872 |   -0.5272 |   -0.7269 |   -0.9542 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.031 |   -0.5901 |   -0.6787 |   -0.7180 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.12349 |     93.76 |    -5.836 |   -0.9783 |   -0.1751 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.162 |     2.324 |     1.284 |    0.6949 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7384 |     1.519 |     1.302 |     1.297 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.12349</span> |     93.76 |  0.002922 |    0.2732 |    0.8394 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.688 |     2.324 |     1.284 |    0.6949 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7384 |     1.519 |     1.302 |     1.297 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     1.734 |    0.2328 |    0.2907 |   -0.1244 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6480 |   -0.5259 |     1.203 |     2.086 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.687 |    0.2714 |    0.3976 |   -0.7167 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   28</span>|     458.12069 |    0.9972 |    -1.436 |   -0.8995 |   -0.8704 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7868 |   -0.5270 |   -0.7278 |   -0.9557 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.030 |   -0.5902 |   -0.6785 |   -0.7174 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.12069 |     93.74 |    -5.836 |   -0.9784 |   -0.1750 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.162 |     2.324 |     1.284 |    0.6937 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7392 |     1.519 |     1.302 |     1.297 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.12069</span> |     93.74 |  0.002921 |    0.2732 |    0.8394 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.691 |     2.324 |     1.284 |    0.6937 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7392 |     1.519 |     1.302 |     1.297 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.995 |    0.2265 |    0.2648 |   -0.1319 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6577 |    -1.056 |    0.5532 |     1.629 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4851 |    0.2582 |    0.3806 |   -0.6784 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   29</span>|     458.12793 |    0.9986 |    -1.436 |   -0.8997 |   -0.8703 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7863 |   -0.5263 |   -0.7282 |   -0.9568 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.029 |   -0.5904 |   -0.6788 |   -0.7169 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.12793 |     93.87 |    -5.836 |   -0.9785 |   -0.1749 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.163 |     2.325 |     1.283 |    0.6929 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7395 |     1.519 |     1.302 |     1.298 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.12793</span> |     93.87 |  0.002920 |    0.2732 |    0.8395 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.695 |     2.325 |     1.283 |    0.6929 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7395 |     1.519 |     1.302 |     1.298 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   30</span>|     458.12002 |    0.9975 |    -1.436 |   -0.8996 |   -0.8704 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7866 |   -0.5269 |   -0.7279 |   -0.9559 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.030 |   -0.5903 |   -0.6786 |   -0.7173 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.12002 |     93.77 |    -5.836 |   -0.9784 |   -0.1750 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.162 |     2.324 |     1.284 |    0.6935 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7392 |     1.519 |     1.302 |     1.298 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.12002</span> |     93.77 |  0.002921 |    0.2732 |    0.8395 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.692 |     2.324 |     1.284 |    0.6935 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7392 |     1.519 |     1.302 |     1.298 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     2.647 |    0.2293 |    0.2815 |   -0.1267 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6372 |   -0.9129 |    0.8290 |     1.823 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.624 |    0.2659 |    0.4184 |   -0.6478 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   31</span>|     458.11922 |    0.9973 |    -1.436 |   -0.8996 |   -0.8704 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7866 |   -0.5268 |   -0.7280 |   -0.9563 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.029 |   -0.5903 |   -0.6786 |   -0.7171 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.11922 |     93.74 |    -5.836 |   -0.9784 |   -0.1750 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.163 |     2.324 |     1.284 |    0.6933 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7394 |     1.519 |     1.302 |     1.298 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.11922</span> |     93.74 |  0.002920 |    0.2732 |    0.8395 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.693 |     2.324 |     1.284 |    0.6933 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7394 |     1.519 |     1.302 |     1.298 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |   -0.8024 |    0.2274 |    0.2665 |   -0.1307 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6475 |    -1.019 |    0.5594 |     1.618 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.681 |    0.3232 |    0.3907 |   -0.6658 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   32</span>|     458.11869 |    0.9974 |    -1.436 |   -0.8996 |   -0.8703 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7864 |   -0.5266 |   -0.7281 |   -0.9565 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.029 |   -0.5904 |   -0.6787 |   -0.7170 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.11869 |     93.76 |    -5.836 |   -0.9785 |   -0.1750 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.163 |     2.324 |     1.283 |    0.6931 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7397 |     1.519 |     1.302 |     1.298 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.11869</span> |     93.76 |  0.002920 |    0.2732 |    0.8395 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.694 |     2.324 |     1.283 |    0.6931 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7397 |     1.519 |     1.302 |     1.298 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     1.112 |    0.2271 |    0.2694 |   -0.1286 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6401 |    -1.529 |    0.3516 |     1.832 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3653 |    0.2635 |    0.3774 |   -0.6589 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   33</span>|     458.11797 |    0.9972 |    -1.436 |   -0.8997 |   -0.8703 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7863 |   -0.5263 |   -0.7282 |   -0.9569 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.029 |   -0.5904 |   -0.6788 |   -0.7169 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.11797 |     93.74 |    -5.836 |   -0.9785 |   -0.1749 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.163 |     2.325 |     1.283 |    0.6928 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7397 |     1.519 |     1.302 |     1.298 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.11797</span> |     93.74 |  0.002920 |    0.2732 |    0.8395 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.695 |     2.325 |     1.283 |    0.6928 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7397 |     1.519 |     1.302 |     1.298 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.528 |    0.2260 |    0.2581 |   -0.1311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6478 |    -1.074 |    0.7096 |     1.705 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.609 |    0.2690 |    0.3809 |   -0.6419 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   34</span>|     458.11744 |    0.9975 |    -1.436 |   -0.8997 |   -0.8703 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7862 |   -0.5262 |   -0.7283 |   -0.9571 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.029 |   -0.5904 |   -0.6788 |   -0.7168 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.11744 |     93.76 |    -5.836 |   -0.9786 |   -0.1749 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.163 |     2.325 |     1.283 |    0.6926 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7399 |     1.519 |     1.302 |     1.298 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.11744</span> |     93.76 |  0.002920 |    0.2732 |    0.8395 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.696 |     2.325 |     1.283 |    0.6926 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7399 |     1.519 |     1.302 |     1.298 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     1.737 |    0.2262 |    0.2659 |   -0.1276 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6346 |   -0.9458 |    0.5100 |     1.567 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.640 |    0.3111 |    0.3526 |   -0.6225 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   35</span>|     458.11714 |    0.9972 |    -1.436 |   -0.8998 |   -0.8703 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7861 |   -0.5260 |   -0.7284 |   -0.9574 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.029 |   -0.5905 |   -0.6789 |   -0.7167 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.11714 |     93.74 |    -5.836 |   -0.9786 |   -0.1749 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.163 |     2.325 |     1.283 |    0.6924 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7402 |     1.519 |     1.302 |     1.298 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.11714</span> |     93.74 |  0.002920 |    0.2732 |    0.8395 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.697 |     2.325 |     1.283 |    0.6924 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7402 |     1.519 |     1.302 |     1.298 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.976 |    0.2241 |    0.2491 |   -0.1309 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6467 |   -0.8649 |    0.8521 |     1.757 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.585 |    0.2641 |    0.3618 |   -0.6092 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   36</span>|     458.11663 |    0.9975 |    -1.436 |   -0.8998 |   -0.8703 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7860 |   -0.5259 |   -0.7285 |   -0.9576 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.028 |   -0.5905 |   -0.6789 |   -0.7166 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.11663 |     93.76 |    -5.836 |   -0.9787 |   -0.1749 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.163 |     2.325 |     1.283 |    0.6923 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7404 |     1.518 |     1.301 |     1.298 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.11663</span> |     93.76 |  0.002920 |    0.2732 |    0.8396 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.698 |     2.325 |     1.283 |    0.6923 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7404 |     1.518 |     1.301 |     1.298 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     2.001 |    0.2249 |    0.2609 |   -0.1271 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6310 |   -0.8317 |    0.8110 |     1.745 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3162 |    0.2564 |    0.3586 |   -0.6097 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   37</span>|     458.11610 |    0.9971 |    -1.436 |   -0.8999 |   -0.8702 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7859 |   -0.5258 |   -0.7286 |   -0.9579 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.028 |   -0.5906 |   -0.6790 |   -0.7165 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      458.1161 |     93.73 |    -5.836 |   -0.9787 |   -0.1749 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.163 |     2.325 |     1.283 |    0.6920 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7404 |     1.518 |     1.301 |     1.298 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      458.1161</span> |     93.73 |  0.002920 |    0.2732 |    0.8396 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.699 |     2.325 |     1.283 |    0.6920 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7404 |     1.518 |     1.301 |     1.298 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -2.471 |    0.2228 |    0.2417 |   -0.1310 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6447 |    -1.605 |  0.009434 |     1.531 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.615 |    0.2909 |    0.3664 |   -0.6093 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   38</span>|     458.11531 |    0.9974 |    -1.436 |   -0.8999 |   -0.8702 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7859 |   -0.5256 |   -0.7287 |   -0.9582 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.028 |   -0.5905 |   -0.6791 |   -0.7164 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.11531 |     93.76 |    -5.836 |   -0.9788 |   -0.1749 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.163 |     2.326 |     1.283 |    0.6918 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7405 |     1.518 |     1.301 |     1.299 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.11531</span> |     93.76 |  0.002919 |    0.2731 |    0.8396 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.699 |     2.326 |     1.283 |    0.6918 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7405 |     1.518 |     1.301 |     1.299 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     1.540 |    0.2236 |    0.2522 |   -0.1268 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6308 |   -0.9638 |    0.7239 |     1.714 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.540 |    0.2567 |    0.3599 |   -0.5942 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   39</span>|     458.11496 |    0.9972 |    -1.436 |   -0.8999 |   -0.8702 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7858 |   -0.5254 |   -0.7288 |   -0.9585 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.028 |   -0.5906 |   -0.6791 |   -0.7163 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.11496 |     93.74 |    -5.836 |   -0.9788 |   -0.1748 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.163 |     2.326 |     1.283 |    0.6916 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7408 |     1.518 |     1.301 |     1.299 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.11496</span> |     93.74 |  0.002919 |    0.2731 |    0.8396 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.700 |     2.326 |     1.283 |    0.6916 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7408 |     1.518 |     1.301 |     1.299 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.772 |    0.2217 |    0.2376 |   -0.1305 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6412 |    -1.736 |    0.1210 |     1.307 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.540 |    0.2780 |    0.3483 |   -0.5916 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   40</span>|     458.11458 |    0.9975 |    -1.436 |   -0.9000 |   -0.8702 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7857 |   -0.5252 |   -0.7289 |   -0.9587 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.028 |   -0.5906 |   -0.6792 |   -0.7163 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.11458 |     93.76 |    -5.836 |   -0.9788 |   -0.1748 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.163 |     2.326 |     1.283 |    0.6915 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7410 |     1.518 |     1.301 |     1.299 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.11458</span> |     93.76 |  0.002919 |    0.2731 |    0.8396 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.701 |     2.326 |     1.283 |    0.6915 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7410 |     1.518 |     1.301 |     1.299 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     1.896 |    0.2223 |    0.2476 |   -0.1262 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6268 |   -0.4531 |    0.7600 |     1.698 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.483 |    0.2984 |    0.3374 |   -0.6006 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   41</span>|     458.11430 |    0.9972 |    -1.436 |   -0.9000 |   -0.8702 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7856 |   -0.5251 |   -0.7290 |   -0.9589 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.027 |   -0.5907 |   -0.6792 |   -0.7162 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      458.1143 |     93.73 |    -5.836 |   -0.9789 |   -0.1748 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.164 |     2.326 |     1.283 |    0.6913 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7412 |     1.518 |     1.301 |     1.299 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      458.1143</span> |     93.73 |  0.002919 |    0.2731 |    0.8396 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.702 |     2.326 |     1.283 |    0.6913 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7412 |     1.518 |     1.301 |     1.299 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -2.121 |    0.2201 |    0.2306 |   -0.1307 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6392 |   -0.8820 |    0.4853 |     1.475 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.568 |    0.2856 |    0.3442 |   -0.5942 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   42</span>|     458.11392 |    0.9975 |    -1.437 |   -0.9001 |   -0.8702 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7855 |   -0.5250 |   -0.7290 |   -0.9592 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.027 |   -0.5907 |   -0.6793 |   -0.7161 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.11392 |     93.76 |    -5.837 |   -0.9789 |   -0.1748 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.164 |     2.326 |     1.283 |    0.6911 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7414 |     1.518 |     1.301 |     1.299 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.11392</span> |     93.76 |  0.002919 |    0.2731 |    0.8396 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.702 |     2.326 |     1.283 |    0.6911 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7414 |     1.518 |     1.301 |     1.299 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     2.218 |    0.2203 |    0.2415 |   -0.1269 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6242 |    -1.700 |   -0.1298 |     1.090 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.097 |   -0.4639 |    0.3406 |   -0.5940 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   43</span>|     458.11292 |    0.9972 |    -1.437 |   -0.9001 |   -0.8702 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7854 |   -0.5247 |   -0.7291 |   -0.9594 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.027 |   -0.5906 |   -0.6794 |   -0.7160 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.11292 |     93.74 |    -5.837 |   -0.9790 |   -0.1748 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.164 |     2.326 |     1.283 |    0.6909 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7412 |     1.518 |     1.301 |     1.299 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.11292</span> |     93.74 |  0.002919 |    0.2731 |    0.8396 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.703 |     2.326 |     1.283 |    0.6909 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7412 |     1.518 |     1.301 |     1.299 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.114 |    0.2202 |    0.2288 |   -0.1307 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6328 |   -0.7727 |    0.7964 |     1.664 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2569 |    0.2827 |    0.3132 |   -0.5835 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   44</span>|     458.11219 |    0.9975 |    -1.437 |   -0.9002 |   -0.8701 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7853 |   -0.5246 |   -0.7293 |   -0.9597 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.027 |   -0.5907 |   -0.6795 |   -0.7158 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.11219 |     93.76 |    -5.837 |   -0.9790 |   -0.1748 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.164 |     2.327 |     1.283 |    0.6907 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7413 |     1.518 |     1.301 |     1.299 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.11219</span> |     93.76 |  0.002919 |    0.2731 |    0.8397 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.704 |     2.327 |     1.283 |    0.6907 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7413 |     1.518 |     1.301 |     1.299 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     1.924 |    0.2207 |    0.2357 |   -0.1254 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6202 |   -0.4451 |    0.8288 |     1.643 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.561 |    0.2786 |    0.3167 |   -0.5797 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   45</span>|     458.11161 |    0.9972 |    -1.437 |   -0.9002 |   -0.8701 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7852 |   -0.5246 |   -0.7295 |   -0.9601 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.027 |   -0.5907 |   -0.6795 |   -0.7157 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.11161 |     93.74 |    -5.837 |   -0.9791 |   -0.1747 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.164 |     2.327 |     1.283 |    0.6904 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7414 |     1.518 |     1.301 |     1.299 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.11161</span> |     93.74 |  0.002918 |    0.2731 |    0.8397 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.705 |     2.327 |     1.283 |    0.6904 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7414 |     1.518 |     1.301 |     1.299 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.221 |    0.2188 |    0.2217 |   -0.1291 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6301 |    -1.629 |    0.1332 |     1.223 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.491 |    0.2969 |    0.3215 |   -0.5516 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   46</span>|     458.11130 |    0.9974 |    -1.437 |   -0.9003 |   -0.8701 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7851 |   -0.5243 |   -0.7295 |   -0.9603 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.027 |   -0.5907 |   -0.6796 |   -0.7156 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      458.1113 |     93.76 |    -5.837 |   -0.9791 |   -0.1747 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.164 |     2.327 |     1.283 |    0.6902 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7416 |     1.518 |     1.301 |     1.299 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      458.1113</span> |     93.76 |  0.002918 |    0.2731 |    0.8397 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.706 |     2.327 |     1.283 |    0.6902 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7416 |     1.518 |     1.301 |     1.299 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     1.605 |    0.2188 |    0.2279 |   -0.1262 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6191 |    -1.596 |    0.1216 |     1.254 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.158 |   -0.4084 |    0.3005 |   -0.5648 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   47</span>|     458.11059 |    0.9972 |    -1.437 |   -0.9003 |   -0.8701 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7850 |   -0.5240 |   -0.7295 |   -0.9605 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.027 |   -0.5906 |   -0.6796 |   -0.7155 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.11059 |     93.73 |    -5.837 |   -0.9791 |   -0.1747 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.164 |     2.327 |     1.283 |    0.6901 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7415 |     1.518 |     1.301 |     1.300 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.11059</span> |     93.73 |  0.002918 |    0.2731 |    0.8397 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.707 |     2.327 |     1.283 |    0.6901 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7415 |     1.518 |     1.301 |     1.300 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -2.022 |    0.2179 |    0.2140 |   -0.1317 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6294 |   -0.4112 |    0.8588 |     1.629 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.524 |    0.3410 |    0.3226 |   -0.5400 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   48</span>|     458.10995 |    0.9975 |    -1.437 |   -0.9003 |   -0.8701 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7849 |   -0.5239 |   -0.7297 |   -0.9608 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.027 |   -0.5906 |   -0.6797 |   -0.7154 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.10995 |     93.76 |    -5.837 |   -0.9792 |   -0.1747 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.164 |     2.327 |     1.283 |    0.6899 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7416 |     1.518 |     1.301 |     1.300 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.10995</span> |     93.76 |  0.002918 |    0.2731 |    0.8397 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.708 |     2.327 |     1.283 |    0.6899 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7416 |     1.518 |     1.301 |     1.300 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     1.872 |    0.2188 |    0.2245 |   -0.1278 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6141 |    -1.055 |    0.5121 |     1.433 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.504 |    0.3641 |    0.3235 |   -0.5302 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   49</span>|     458.10978 |    0.9972 |    -1.437 |   -0.9004 |   -0.8700 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7848 |   -0.5238 |   -0.7297 |   -0.9610 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.027 |   -0.5907 |   -0.6797 |   -0.7153 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.10978 |     93.73 |    -5.837 |   -0.9792 |   -0.1747 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.164 |     2.327 |     1.282 |    0.6897 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7418 |     1.518 |     1.301 |     1.300 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.10978</span> |     93.73 |  0.002918 |    0.2730 |    0.8397 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.709 |     2.327 |     1.282 |    0.6897 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7418 |     1.518 |     1.301 |     1.300 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    C| Central Diff. |    -2.410 |    0.2142 |    0.1743 |   -0.1457 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6441 |   -0.7806 |    0.7426 |     1.188 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.06181 |   -0.3659 |    0.5158 |   -0.5913 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   50</span>|     458.10914 |    0.9975 |    -1.437 |   -0.9004 |   -0.8700 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7847 |   -0.5236 |   -0.7299 |   -0.9612 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.027 |   -0.5906 |   -0.6798 |   -0.7152 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.10914 |     93.77 |    -5.837 |   -0.9792 |   -0.1747 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.164 |     2.327 |     1.282 |    0.6895 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7418 |     1.518 |     1.300 |     1.300 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.10914</span> |     93.77 |  0.002918 |    0.2730 |    0.8397 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.709 |     2.327 |     1.282 |    0.6895 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7418 |     1.518 |     1.300 |     1.300 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     2.690 |    0.2171 |    0.2203 |   -0.1291 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6099 |   -0.7940 |    0.4528 |     1.377 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.452 |    0.2831 |    0.2911 |   -0.5300 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   51</span>|     458.10832 |    0.9972 |    -1.437 |   -0.9004 |   -0.8700 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7846 |   -0.5235 |   -0.7300 |   -0.9615 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.027 |   -0.5905 |   -0.6799 |   -0.7151 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.10832 |     93.74 |    -5.837 |   -0.9793 |   -0.1747 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.164 |     2.328 |     1.282 |    0.6893 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7418 |     1.518 |     1.300 |     1.300 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.10832</span> |     93.74 |  0.002918 |    0.2730 |    0.8397 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.710 |     2.328 |     1.282 |    0.6893 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7418 |     1.518 |     1.300 |     1.300 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.386 |    0.2164 |    0.2066 |   -0.1317 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6210 |   -0.6390 |    0.8493 |     1.598 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.491 |    0.2964 |    0.2278 |   -0.4988 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   52</span>|     458.10801 |    0.9974 |    -1.437 |   -0.9005 |   -0.8700 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7845 |   -0.5234 |   -0.7302 |   -0.9618 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5906 |   -0.6800 |   -0.7150 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.10801 |     93.76 |    -5.837 |   -0.9793 |   -0.1746 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.165 |     2.328 |     1.282 |    0.6891 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7421 |     1.518 |     1.300 |     1.300 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.10801</span> |     93.76 |  0.002918 |    0.2730 |    0.8398 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.711 |     2.328 |     1.282 |    0.6891 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7421 |     1.518 |     1.300 |     1.300 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     1.854 |    0.2167 |    0.2150 |   -0.1282 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6083 |   -0.7461 |    0.7029 |     1.539 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1981 |    0.2733 |    0.2717 |   -0.5257 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   53</span>|     458.10765 |    0.9971 |    -1.437 |   -0.9005 |   -0.8700 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7844 |   -0.5232 |   -0.7303 |   -0.9621 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5906 |   -0.6800 |   -0.7149 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.10765 |     93.73 |    -5.837 |   -0.9794 |   -0.1746 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.165 |     2.328 |     1.282 |    0.6889 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7421 |     1.518 |     1.300 |     1.300 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.10765</span> |     93.73 |  0.002917 |    0.2730 |    0.8398 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.712 |     2.328 |     1.282 |    0.6889 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7421 |     1.518 |     1.300 |     1.300 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -2.678 |    0.2148 |    0.1964 |   -0.1329 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6222 |   -0.7140 |    0.5004 |     1.353 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2647 |    0.3470 |    0.3026 |   -0.5041 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   54</span>|     458.10677 |    0.9974 |    -1.437 |   -0.9006 |   -0.8700 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7843 |   -0.5231 |   -0.7305 |   -0.9624 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5907 |   -0.6801 |   -0.7148 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.10677 |     93.76 |    -5.837 |   -0.9794 |   -0.1746 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.165 |     2.328 |     1.282 |    0.6887 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7421 |     1.518 |     1.300 |     1.300 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.10677</span> |     93.76 |  0.002917 |    0.2730 |    0.8398 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.713 |     2.328 |     1.282 |    0.6887 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7421 |     1.518 |     1.300 |     1.300 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     1.911 |    0.2164 |    0.2105 |   -0.1281 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6034 |   -0.7920 |    0.6471 |     1.488 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2445 |    0.2110 |    0.2380 |   -0.4469 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   55</span>|     458.10609 |    0.9972 |    -1.437 |   -0.9006 |   -0.8700 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7842 |   -0.5230 |   -0.7306 |   -0.9627 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5907 |   -0.6801 |   -0.7147 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.10609 |     93.74 |    -5.837 |   -0.9794 |   -0.1746 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.165 |     2.328 |     1.282 |    0.6884 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7421 |     1.518 |     1.300 |     1.300 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.10609</span> |     93.74 |  0.002917 |    0.2730 |    0.8398 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.714 |     2.328 |     1.282 |    0.6884 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7421 |     1.518 |     1.300 |     1.300 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.589 |    0.2148 |    0.1951 |   -0.1322 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6145 |   -0.8575 |    0.5942 |     1.427 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.492 |    0.2699 |    0.1872 |   -0.4796 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   56</span>|     458.10567 |    0.9974 |    -1.437 |   -0.9006 |   -0.8699 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7841 |   -0.5228 |   -0.7308 |   -0.9630 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5907 |   -0.6801 |   -0.7146 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.10567 |     93.76 |    -5.837 |   -0.9795 |   -0.1746 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.165 |     2.328 |     1.282 |    0.6882 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7422 |     1.518 |     1.300 |     1.301 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.10567</span> |     93.76 |  0.002917 |    0.2730 |    0.8398 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.715 |     2.328 |     1.282 |    0.6882 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7422 |     1.518 |     1.300 |     1.301 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     1.760 |    0.2153 |    0.2043 |   -0.1283 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6005 |   -0.7529 |    0.6292 |     1.454 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2107 |    0.2534 |    0.2280 |   -0.4318 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   57</span>|     458.10540 |    0.9971 |    -1.437 |   -0.9007 |   -0.8699 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7840 |   -0.5227 |   -0.7309 |   -0.9633 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5907 |   -0.6801 |   -0.7146 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      458.1054 |     93.73 |    -5.837 |   -0.9795 |   -0.1745 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.165 |     2.328 |     1.282 |    0.6880 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.518 |     1.300 |     1.301 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      458.1054</span> |     93.73 |  0.002917 |    0.2730 |    0.8398 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.716 |     2.328 |     1.282 |    0.6880 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.518 |     1.300 |     1.301 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -2.789 |    0.2130 |    0.1850 |   -0.1339 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6160 |   -0.6903 |    0.7130 |     1.481 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1975 |    0.2824 |    0.2646 |   -0.4868 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   58</span>|     458.10444 |    0.9974 |    -1.437 |   -0.9007 |   -0.8699 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7839 |   -0.5226 |   -0.7311 |   -0.9636 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5907 |   -0.6801 |   -0.7145 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.10444 |     93.76 |    -5.837 |   -0.9796 |   -0.1745 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.165 |     2.329 |     1.282 |    0.6878 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.518 |     1.300 |     1.301 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.10444</span> |     93.76 |  0.002916 |    0.2730 |    0.8398 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.717 |     2.329 |     1.282 |    0.6878 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.518 |     1.300 |     1.301 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     1.285 |    0.2143 |    0.1972 |   -0.1290 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5987 |   -0.7355 |    0.6064 |     1.421 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.460 |    0.2526 |    0.2188 |   -0.4206 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   59</span>|     458.10435 |    0.9972 |    -1.437 |   -0.9008 |   -0.8699 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7838 |   -0.5224 |   -0.7312 |   -0.9638 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5908 |   -0.6802 |   -0.7144 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.10435 |     93.73 |    -5.837 |   -0.9796 |   -0.1745 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.165 |     2.329 |     1.282 |    0.6876 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7425 |     1.518 |     1.300 |     1.301 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.10435</span> |     93.73 |  0.002916 |    0.2730 |    0.8399 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.718 |     2.329 |     1.282 |    0.6876 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7425 |     1.518 |     1.300 |     1.301 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    C| Central Diff. |    -2.184 |    0.2097 |    0.1486 |   -0.1446 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6255 |   -0.7028 |    0.6444 |     1.495 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.416 |    0.3010 |    0.4942 |   -0.5303 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   60</span>|     458.10393 |    0.9975 |    -1.438 |   -0.9008 |   -0.8699 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7837 |   -0.5223 |   -0.7313 |   -0.9641 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5908 |   -0.6802 |   -0.7143 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.10393 |     93.76 |    -5.838 |   -0.9796 |   -0.1745 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.165 |     2.329 |     1.282 |    0.6873 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7426 |     1.518 |     1.300 |     1.301 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.10393</span> |     93.76 |  0.002916 |    0.2730 |    0.8399 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.718 |     2.329 |     1.282 |    0.6873 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7426 |     1.518 |     1.300 |     1.301 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     2.045 |    0.2119 |    0.1940 |   -0.1305 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5955 |   -0.7252 |    0.6285 |     1.413 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2070 |    0.2194 |    0.2208 |   -0.3980 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   61</span>|     458.10364 |    0.9971 |    -1.438 |   -0.9008 |   -0.8699 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7836 |   -0.5222 |   -0.7314 |   -0.9644 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5908 |   -0.6803 |   -0.7142 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.10364 |     93.73 |    -5.838 |   -0.9796 |   -0.1745 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.166 |     2.329 |     1.281 |    0.6871 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7427 |     1.518 |     1.300 |     1.301 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.10364</span> |     93.73 |  0.002916 |    0.2730 |    0.8399 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.719 |     2.329 |     1.281 |    0.6871 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7427 |     1.518 |     1.300 |     1.301 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -2.727 |    0.2108 |    0.1771 |   -0.1339 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6094 |   -0.7304 |    0.6317 |     1.376 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.443 |    0.2644 |    0.2580 |   -0.4629 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   62</span>|     458.10286 |    0.9974 |    -1.438 |   -0.9008 |   -0.8699 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7835 |   -0.5221 |   -0.7316 |   -0.9647 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5908 |   -0.6803 |   -0.7141 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.10286 |     93.75 |    -5.838 |   -0.9796 |   -0.1745 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.166 |     2.329 |     1.281 |    0.6869 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7427 |     1.518 |     1.300 |     1.301 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.10286</span> |     93.75 |  0.002915 |    0.2730 |    0.8399 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.720 |     2.329 |     1.281 |    0.6869 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7427 |     1.518 |     1.300 |     1.301 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     1.225 |    0.2118 |    0.1898 |   -0.1294 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5932 |   -0.6493 |    0.6517 |     1.411 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1402 |    0.3233 |    0.2428 |   -0.3732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   63</span>|     458.10253 |    0.9971 |    -1.438 |   -0.9009 |   -0.8698 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7834 |   -0.5219 |   -0.7318 |   -0.9650 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5909 |   -0.6803 |   -0.7141 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.10253 |     93.73 |    -5.838 |   -0.9797 |   -0.1745 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.166 |     2.329 |     1.281 |    0.6867 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7428 |     1.518 |     1.300 |     1.301 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.10253</span> |     93.73 |  0.002915 |    0.2730 |    0.8399 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.721 |     2.329 |     1.281 |    0.6867 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7428 |     1.518 |     1.300 |     1.301 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -2.449 |    0.2099 |    0.1736 |   -0.1338 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6049 |   -0.7015 |    0.6330 |     1.384 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1863 |    0.2615 |    0.2563 |   -0.4532 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   64</span>|     458.10167 |    0.9974 |    -1.438 |   -0.9009 |   -0.8698 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7832 |   -0.5218 |   -0.7320 |   -0.9653 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5909 |   -0.6804 |   -0.7140 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.10167 |     93.76 |    -5.838 |   -0.9797 |   -0.1744 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.166 |     2.329 |     1.281 |    0.6864 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7427 |     1.518 |     1.300 |     1.301 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.10167</span> |     93.76 |  0.002915 |    0.2729 |    0.8399 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.722 |     2.329 |     1.281 |    0.6864 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7427 |     1.518 |     1.300 |     1.301 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     1.422 |    0.2110 |    0.1849 |   -0.1293 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5889 |   -0.5964 |    0.6731 |     1.405 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1535 |    0.3114 |    0.2496 |   -0.4521 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   65</span>|     458.10133 |    0.9971 |    -1.438 |   -0.9009 |   -0.8698 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7831 |   -0.5217 |   -0.7321 |   -0.9657 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5909 |   -0.6804 |   -0.7139 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.10133 |     93.73 |    -5.838 |   -0.9798 |   -0.1744 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.166 |     2.329 |     1.281 |    0.6862 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7428 |     1.518 |     1.300 |     1.301 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.10133</span> |     93.73 |  0.002915 |    0.2729 |    0.8400 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.723 |     2.329 |     1.281 |    0.6862 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7428 |     1.518 |     1.300 |     1.301 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -2.461 |    0.2094 |    0.1688 |   -0.1332 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6003 |   -0.7109 |    0.5914 |     1.349 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.404 |    0.2951 |    0.2552 |   -0.4331 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   66</span>|     458.10059 |    0.9974 |    -1.438 |   -0.9009 |   -0.8698 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7830 |   -0.5215 |   -0.7323 |   -0.9660 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5909 |   -0.6804 |   -0.7138 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.10059 |     93.75 |    -5.838 |   -0.9798 |   -0.1744 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.166 |     2.330 |     1.281 |    0.6860 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7428 |     1.518 |     1.300 |     1.302 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.10059</span> |     93.75 |  0.002915 |    0.2729 |    0.8400 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.724 |     2.330 |     1.281 |    0.6860 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7428 |     1.518 |     1.300 |     1.302 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     1.228 |    0.2101 |    0.1802 |   -0.1294 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5857 |   -0.2287 |    0.9339 |     1.558 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.376 |    0.3488 |    0.2665 |   -0.4324 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   67</span>|     458.10050 |    0.9972 |    -1.438 |   -0.9010 |   -0.8697 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7829 |   -0.5215 |   -0.7325 |   -0.9663 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.025 |   -0.5910 |   -0.6804 |   -0.7137 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      458.1005 |     93.73 |    -5.838 |   -0.9798 |   -0.1744 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.166 |     2.330 |     1.281 |    0.6857 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7430 |     1.518 |     1.300 |     1.302 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      458.1005</span> |     93.73 |  0.002915 |    0.2729 |    0.8400 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.725 |     2.330 |     1.281 |    0.6857 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7430 |     1.518 |     1.300 |     1.302 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    C| Central Diff. |    -2.045 |    0.2058 |    0.1327 |   -0.1461 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6105 |   -0.6040 |    0.6130 |    0.9518 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01902 |    0.3702 |    0.4676 |   -0.4886 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   68</span>|     458.10013 |    0.9974 |    -1.438 |   -0.9010 |   -0.8697 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7829 |   -0.5214 |   -0.7325 |   -0.9664 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.025 |   -0.5910 |   -0.6805 |   -0.7136 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.10013 |     93.75 |    -5.838 |   -0.9798 |   -0.1744 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.166 |     2.330 |     1.281 |    0.6857 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7430 |     1.518 |     1.300 |     1.302 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.10013</span> |     93.75 |  0.002914 |    0.2729 |    0.8400 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.725 |     2.330 |     1.281 |    0.6857 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7430 |     1.518 |     1.300 |     1.302 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    0.8557 |    0.2088 |    0.1747 |   -0.1300 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5847 |   -0.2393 |    0.9114 |     1.536 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.367 |    0.3016 |    0.2557 |   -0.4123 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   69</span>|     458.10002 |    0.9973 |    -1.438 |   -0.9010 |   -0.8697 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7828 |   -0.5214 |   -0.7326 |   -0.9665 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.025 |   -0.5911 |   -0.6805 |   -0.7136 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.10002 |     93.74 |    -5.838 |   -0.9798 |   -0.1744 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.166 |     2.330 |     1.281 |    0.6856 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7432 |     1.518 |     1.300 |     1.302 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.10002</span> |     93.74 |  0.002914 |    0.2729 |    0.8400 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.726 |     2.330 |     1.281 |    0.6856 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7432 |     1.518 |     1.300 |     1.302 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    C| Central Diff. |   -0.5131 |    0.2058 |    0.1358 |   -0.1441 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6029 |   -0.6262 |    0.5998 |     1.158 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.373 |    0.3470 |    0.4566 |   -0.4872 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   70</span>|     458.09992 |    0.9973 |    -1.438 |   -0.9010 |   -0.8697 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7827 |   -0.5214 |   -0.7327 |   -0.9666 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.025 |   -0.5911 |   -0.6806 |   -0.7135 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.09992 |     93.75 |    -5.838 |   -0.9799 |   -0.1743 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.166 |     2.330 |     1.281 |    0.6855 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7433 |     1.518 |     1.300 |     1.302 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.09992</span> |     93.75 |  0.002914 |    0.2729 |    0.8400 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.726 |     2.330 |     1.281 |    0.6855 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7433 |     1.518 |     1.300 |     1.302 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    C| Central Diff. |    0.2584 |    0.2056 |    0.1371 |   -0.1438 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5991 |   -0.6198 |    0.6082 |    0.9676 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.295 |    0.3221 |    0.4533 |   -0.4750 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   71</span>|     458.09944 |    0.9973 |    -1.438 |   -0.9010 |   -0.8697 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7827 |   -0.5213 |   -0.7328 |   -0.9668 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.025 |   -0.5911 |   -0.6806 |   -0.7135 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.09944 |     93.75 |    -5.838 |   -0.9799 |   -0.1743 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.166 |     2.330 |     1.281 |    0.6854 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7431 |     1.518 |     1.300 |     1.302 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.09944</span> |     93.75 |  0.002914 |    0.2729 |    0.8400 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.727 |     2.330 |     1.281 |    0.6854 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7431 |     1.518 |     1.300 |     1.302 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   72</span>|     458.09825 |    0.9974 |    -1.438 |   -0.9011 |   -0.8697 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7824 |   -0.5210 |   -0.7332 |   -0.9672 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.025 |   -0.5911 |   -0.6808 |   -0.7131 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.09825 |     93.76 |    -5.838 |   -0.9799 |   -0.1743 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.167 |     2.330 |     1.280 |    0.6851 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7430 |     1.518 |     1.299 |     1.302 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.09825</span> |     93.76 |  0.002914 |    0.2729 |    0.8401 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.729 |     2.330 |     1.280 |    0.6851 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7430 |     1.518 |     1.299 |     1.302 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   73</span>|     458.09793 |    0.9981 |    -1.438 |   -0.9013 |   -0.8695 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7815 |   -0.5202 |   -0.7349 |   -0.9686 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5909 |   -0.6812 |   -0.7117 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.09793 |     93.82 |    -5.838 |   -0.9801 |   -0.1741 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.168 |     2.331 |     1.279 |    0.6840 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7428 |     1.518 |     1.299 |     1.304 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.09793</span> |     93.82 |  0.002913 |    0.2729 |    0.8402 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.737 |     2.331 |     1.279 |    0.6840 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7428 |     1.518 |     1.299 |     1.304 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     10.76 |    0.2116 |    0.1923 |   -0.1162 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5255 |   -0.5796 |    0.3777 |     1.210 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.409 |    0.2855 |    0.1875 |   -0.3085 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   74</span>|     458.08987 |    0.9971 |    -1.438 |   -0.9013 |   -0.8695 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7808 |   -0.5187 |   -0.7369 |   -0.9692 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5907 |   -0.6832 |   -0.7095 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.08987 |     93.72 |    -5.838 |   -0.9802 |   -0.1741 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.168 |     2.332 |     1.278 |    0.6836 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7427 |     1.518 |     1.297 |     1.306 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.08987</span> |     93.72 |  0.002914 |    0.2729 |    0.8402 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.743 |     2.332 |     1.278 |    0.6836 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7427 |     1.518 |     1.297 |     1.306 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -2.512 |    0.2078 |    0.1434 |   -0.1372 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5667 |   -0.8061 |    0.1467 |     1.033 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.202 |   -0.4065 |   0.09417 |   -0.1935 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   75</span>|     458.08564 |    0.9973 |    -1.438 |   -0.9016 |   -0.8695 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7801 |   -0.5170 |   -0.7384 |   -0.9704 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5898 |   -0.6859 |   -0.7082 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.08564 |     93.74 |    -5.838 |   -0.9804 |   -0.1741 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.169 |     2.334 |     1.277 |    0.6827 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7424 |     1.519 |     1.294 |     1.308 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.08564</span> |     93.74 |  0.002914 |    0.2728 |    0.8402 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.749 |     2.334 |     1.277 |    0.6827 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7424 |     1.519 |     1.294 |     1.308 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   76</span>|     458.08078 |    0.9972 |    -1.438 |   -0.9021 |   -0.8695 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7788 |   -0.5136 |   -0.7416 |   -0.9727 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.027 |   -0.5879 |   -0.6916 |   -0.7053 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.08078 |     93.73 |    -5.838 |   -0.9809 |   -0.1742 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.170 |     2.338 |     1.275 |    0.6809 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7420 |     1.522 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.08078</span> |     93.73 |  0.002915 |    0.2727 |    0.8402 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.761 |     2.338 |     1.275 |    0.6809 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7420 |     1.522 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |   -0.8456 |    0.2109 |    0.1052 |   -0.1453 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5381 |   -0.2225 |   -0.1274 |    0.8336 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2653 |    0.4698 |   -0.4321 |   0.07917 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   77</span>|     458.08109 |    0.9983 |    -1.445 |   -0.9066 |   -0.8702 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7727 |   -0.5143 |   -0.7362 |   -0.9770 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.027 |   -0.5885 |   -0.6915 |   -0.7058 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.08109 |     93.84 |    -5.845 |   -0.9853 |   -0.1748 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.176 |     2.337 |     1.279 |    0.6777 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7418 |     1.521 |     1.288 |     1.310 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.08109</span> |     93.84 |  0.002894 |    0.2718 |    0.8396 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.815 |     2.337 |     1.279 |    0.6777 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7418 |     1.521 |     1.288 |     1.310 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   78</span>|     458.08775 |    0.9985 |    -1.441 |   -0.9040 |   -0.8696 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7757 |   -0.5136 |   -0.7393 |   -0.9753 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5887 |   -0.6911 |   -0.7056 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.08775 |     93.86 |    -5.841 |   -0.9828 |   -0.1743 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.173 |     2.338 |     1.277 |    0.6789 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7421 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.08775</span> |     93.86 |  0.002906 |    0.2723 |    0.8401 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.788 |     2.338 |     1.277 |    0.6789 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7421 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   79</span>|     458.08407 |    0.9980 |    -1.438 |   -0.9022 |   -0.8694 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7783 |   -0.5133 |   -0.7415 |   -0.9736 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5884 |   -0.6912 |   -0.7054 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.08407 |     93.81 |    -5.838 |   -0.9810 |   -0.1740 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.171 |     2.338 |     1.275 |    0.6802 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7422 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.08407</span> |     93.81 |  0.002915 |    0.2727 |    0.8403 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.766 |     2.338 |     1.275 |    0.6802 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7422 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   80</span>|     458.08069 |    0.9973 |    -1.438 |   -0.9021 |   -0.8695 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7787 |   -0.5135 |   -0.7416 |   -0.9729 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5880 |   -0.6915 |   -0.7053 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.08069 |     93.75 |    -5.838 |   -0.9809 |   -0.1741 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.170 |     2.338 |     1.275 |    0.6807 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7420 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.08069</span> |     93.75 |  0.002915 |    0.2727 |    0.8402 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.762 |     2.338 |     1.275 |    0.6807 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7420 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     1.424 |    0.2112 |    0.1114 |   -0.1426 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5267 |   -0.3290 |  -0.02249 |    0.9218 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.516 |    0.4273 |   -0.4325 |   0.09748 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   81</span>|     458.08076 |    0.9972 |    -1.438 |   -0.9021 |   -0.8695 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7786 |   -0.5134 |   -0.7416 |   -0.9730 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5880 |   -0.6914 |   -0.7055 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.08076 |     93.74 |    -5.838 |   -0.9810 |   -0.1741 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.170 |     2.338 |     1.275 |    0.6807 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7421 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.08076</span> |     93.74 |  0.002915 |    0.2727 |    0.8402 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.763 |     2.338 |     1.275 |    0.6807 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7421 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   82</span>|     458.08078 |    0.9973 |    -1.438 |   -0.9021 |   -0.8695 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7787 |   -0.5135 |   -0.7416 |   -0.9730 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5880 |   -0.6915 |   -0.7053 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.08078 |     93.74 |    -5.838 |   -0.9809 |   -0.1741 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.170 |     2.338 |     1.275 |    0.6807 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7421 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.08078</span> |     93.74 |  0.002915 |    0.2727 |    0.8402 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.762 |     2.338 |     1.275 |    0.6807 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7421 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   83</span>|     458.08068 |    0.9973 |    -1.438 |   -0.9021 |   -0.8695 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7787 |   -0.5135 |   -0.7416 |   -0.9729 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5880 |   -0.6915 |   -0.7053 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.08068 |     93.75 |    -5.838 |   -0.9809 |   -0.1741 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.170 |     2.338 |     1.275 |    0.6807 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7420 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.08068</span> |     93.75 |  0.002915 |    0.2727 |    0.8402 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.762 |     2.338 |     1.275 |    0.6807 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7420 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    C| Central Diff. |    0.7458 |    0.2086 |   0.07146 |   -0.1568 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5453 |   -0.1723 |    0.1066 |    0.6006 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.05957 |    0.1717 |   -0.3314 |   0.06442 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   84</span>|     458.08065 |    0.9973 |    -1.438 |   -0.9021 |   -0.8695 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7787 |   -0.5135 |   -0.7416 |   -0.9729 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5880 |   -0.6915 |   -0.7053 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.08065 |     93.74 |    -5.838 |   -0.9809 |   -0.1741 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.170 |     2.338 |     1.275 |    0.6807 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7420 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.08065</span> |     93.74 |  0.002915 |    0.2727 |    0.8402 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.762 |     2.338 |     1.275 |    0.6807 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7420 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    C| Central Diff. |    0.5271 |    0.2085 |   0.07055 |   -0.1570 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5454 |   -0.3138 |   0.09492 |    0.7765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.192 |    0.4934 |   -0.3382 |    0.1071 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   85</span>|     458.08062 |    0.9973 |    -1.438 |   -0.9021 |   -0.8695 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7786 |   -0.5135 |   -0.7416 |   -0.9730 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5880 |   -0.6915 |   -0.7054 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.08062 |     93.74 |    -5.838 |   -0.9809 |   -0.1741 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.170 |     2.338 |     1.275 |    0.6807 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7420 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.08062</span> |     93.74 |  0.002915 |    0.2727 |    0.8402 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.762 |     2.338 |     1.275 |    0.6807 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7420 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    C| Central Diff. |    0.1077 |    0.2083 |   0.06880 |   -0.1572 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5469 |   -0.1727 |    0.1219 |    0.9565 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.457 |    0.5128 |   -0.3399 |    0.1022 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   86</span>|     458.08061 |    0.9972 |    -1.438 |   -0.9021 |   -0.8695 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7786 |   -0.5135 |   -0.7416 |   -0.9730 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5881 |   -0.6914 |   -0.7054 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.08061 |     93.74 |    -5.838 |   -0.9809 |   -0.1741 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.170 |     2.338 |     1.275 |    0.6807 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7421 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.08061</span> |     93.74 |  0.002915 |    0.2727 |    0.8402 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.763 |     2.338 |     1.275 |    0.6807 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7421 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    C| Central Diff. |   -0.6930 |    0.2078 |   0.06712 |   -0.1577 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5484 |   -0.1767 |    0.1148 |    0.9863 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2038 |    0.5855 |   -0.3296 |    0.1002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   87</span>|     458.08058 |    0.9972 |    -1.438 |   -0.9021 |   -0.8695 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7786 |   -0.5135 |   -0.7416 |   -0.9731 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5881 |   -0.6914 |   -0.7054 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.08058 |     93.74 |    -5.838 |   -0.9809 |   -0.1741 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.171 |     2.338 |     1.275 |    0.6806 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7421 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.08058</span> |     93.74 |  0.002915 |    0.2727 |    0.8402 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.763 |     2.338 |     1.275 |    0.6806 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7421 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    C| Central Diff. |   -0.3701 |    0.2079 |   0.06808 |   -0.1573 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5470 |   -0.1729 |  0.005725 |    0.9526 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.465 |    0.5101 |   -0.1466 |  -0.02770 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   88</span>|     458.08052 |    0.9973 |    -1.438 |   -0.9021 |   -0.8695 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7785 |   -0.5135 |   -0.7416 |   -0.9731 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5881 |   -0.6914 |   -0.7053 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.08052 |     93.74 |    -5.838 |   -0.9809 |   -0.1741 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.171 |     2.338 |     1.275 |    0.6806 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7421 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.08052</span> |     93.74 |  0.002915 |    0.2727 |    0.8402 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.763 |     2.338 |     1.275 |    0.6806 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7421 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    C| Central Diff. |    0.2062 |    0.2080 |   0.06837 |   -0.1566 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5439 |   -0.1704 |   0.09858 |    0.5928 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.05984 |    0.5131 |   -0.3447 |   0.06046 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   89</span>|     458.08047 |    0.9972 |    -1.438 |   -0.9021 |   -0.8694 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7785 |   -0.5134 |   -0.7416 |   -0.9732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5882 |   -0.6914 |   -0.7054 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.08047 |     93.74 |    -5.838 |   -0.9809 |   -0.1741 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.171 |     2.338 |     1.275 |    0.6805 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7421 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.08047</span> |     93.74 |  0.002915 |    0.2727 |    0.8402 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.764 |     2.338 |     1.275 |    0.6805 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7421 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    C| Central Diff. |  -0.06148 |    0.2077 |   0.06828 |   -0.1566 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5434 |   -0.1748 |    0.1045 |    0.7316 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7607 |    0.4924 |   -0.3350 |    0.1046 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   90</span>|     458.08041 |    0.9972 |    -1.438 |   -0.9021 |   -0.8694 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7784 |   -0.5134 |   -0.7417 |   -0.9733 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5883 |   -0.6913 |   -0.7054 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.08041 |     93.74 |    -5.838 |   -0.9810 |   -0.1740 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.171 |     2.338 |     1.275 |    0.6804 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7421 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.08041</span> |     93.74 |  0.002915 |    0.2727 |    0.8403 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.765 |     2.338 |     1.275 |    0.6804 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7421 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    C| Central Diff. |   -0.5076 |    0.2073 |   0.06528 |   -0.1564 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5454 |   -0.1802 |    0.1061 |    0.9740 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1992 |    0.5430 |   -0.2714 |    0.1031 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   91</span>|     458.08026 |    0.9972 |    -1.438 |   -0.9022 |   -0.8694 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7782 |   -0.5133 |   -0.7417 |   -0.9736 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5884 |   -0.6912 |   -0.7055 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.08026 |     93.74 |    -5.838 |   -0.9810 |   -0.1740 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.171 |     2.338 |     1.275 |    0.6802 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7422 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.08026</span> |     93.74 |  0.002914 |    0.2727 |    0.8403 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.766 |     2.338 |     1.275 |    0.6802 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7422 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    C| Central Diff. |   -0.3779 |    0.2092 |   0.07455 |   -0.1547 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5349 |    0.4172 |   -0.5362 |    0.7903 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.451 |    0.5182 |   -0.2163 |   -0.4361 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   92</span>|     458.08039 |    0.9975 |    -1.438 |   -0.9022 |   -0.8693 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7778 |   -0.5133 |   -0.7416 |   -0.9742 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5887 |   -0.6910 |   -0.7056 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.08039 |     93.76 |    -5.838 |   -0.9811 |   -0.1739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.171 |     2.338 |     1.275 |    0.6798 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7424 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.08039</span> |     93.76 |  0.002914 |    0.2727 |    0.8404 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.769 |     2.338 |     1.275 |    0.6798 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7424 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   93</span>|     458.08025 |    0.9973 |    -1.438 |   -0.9022 |   -0.8693 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7780 |   -0.5133 |   -0.7416 |   -0.9739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5886 |   -0.6911 |   -0.7055 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.08025 |     93.75 |    -5.838 |   -0.9810 |   -0.1739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.171 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.08025</span> |     93.75 |  0.002914 |    0.2727 |    0.8403 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.768 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    C| Central Diff. |     1.019 |    0.2066 |   0.06601 |   -0.1530 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5270 |   -0.1637 |    0.1163 |    0.9184 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.439 |    0.4715 |   -0.2217 |   0.03756 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   94</span>|     458.08029 |    0.9972 |    -1.438 |   -0.9022 |   -0.8693 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7780 |   -0.5133 |   -0.7416 |   -0.9739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5886 |   -0.6911 |   -0.7055 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.08029 |     93.74 |    -5.838 |   -0.9810 |   -0.1739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.171 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7424 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.08029</span> |     93.74 |  0.002914 |    0.2727 |    0.8404 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.768 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7424 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   95</span>|     458.08025 |    0.9973 |    -1.438 |   -0.9022 |   -0.8693 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7780 |   -0.5133 |   -0.7416 |   -0.9739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5886 |   -0.6911 |   -0.7055 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.08025 |     93.74 |    -5.838 |   -0.9810 |   -0.1739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.171 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7424 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.08025</span> |     93.74 |  0.002914 |    0.2727 |    0.8404 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.768 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7424 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   96</span>|     458.08029 |    0.9973 |    -1.438 |   -0.9022 |   -0.8693 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7780 |   -0.5133 |   -0.7416 |   -0.9739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5886 |   -0.6911 |   -0.7055 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.08029 |     93.75 |    -5.838 |   -0.9810 |   -0.1739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.171 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.08029</span> |     93.75 |  0.002914 |    0.2727 |    0.8403 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.768 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   97</span>|     458.08024 |    0.9973 |    -1.438 |   -0.9022 |   -0.8693 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7780 |   -0.5133 |   -0.7416 |   -0.9739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5886 |   -0.6911 |   -0.7055 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.08024 |     93.75 |    -5.838 |   -0.9810 |   -0.1739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.171 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.08024</span> |     93.75 |  0.002914 |    0.2727 |    0.8403 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.768 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    C| Central Diff. |    0.9275 |    0.2065 |   0.06562 |   -0.1530 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5278 |   -0.1682 |    0.1168 |    0.9523 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1838 |    0.4888 |   -0.3154 |   0.09539 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   98</span>|     458.08024 |    0.9973 |    -1.438 |   -0.9022 |   -0.8693 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7780 |   -0.5133 |   -0.7416 |   -0.9739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5886 |   -0.6911 |   -0.7055 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.08024 |     93.75 |    -5.838 |   -0.9810 |   -0.1739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.171 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.08024</span> |     93.75 |  0.002914 |    0.2727 |    0.8403 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.768 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    C| Central Diff. |    0.8715 |    0.2065 |   0.06540 |   -0.1531 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5308 |  -0.07756 |    0.1164 |    0.5595 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.04465 |   -0.2577 |   -0.3240 |    0.1066 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   99</span>|     458.08023 |    0.9973 |    -1.438 |   -0.9022 |   -0.8693 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7780 |   -0.5133 |   -0.7416 |   -0.9739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5886 |   -0.6911 |   -0.7055 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.08023 |     93.75 |    -5.838 |   -0.9810 |   -0.1739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.171 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.08023</span> |     93.75 |  0.002914 |    0.2727 |    0.8403 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.768 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  100</span>|     458.08013 |    0.9973 |    -1.438 |   -0.9022 |   -0.8693 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7780 |   -0.5133 |   -0.7416 |   -0.9739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5886 |   -0.6911 |   -0.7055 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.08013 |     93.75 |    -5.838 |   -0.9810 |   -0.1739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.171 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.08013</span> |     93.75 |  0.002914 |    0.2727 |    0.8403 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.768 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    C| Central Diff. |    0.7255 |    0.2064 |   0.06482 |   -0.1531 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5283 |   -0.1617 |    0.1081 |    0.9192 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.441 |    0.4906 |   -0.2900 |    0.1161 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  101</span>|     458.08017 |    0.9973 |    -1.438 |   -0.9022 |   -0.8693 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7780 |   -0.5133 |   -0.7416 |   -0.9739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5885 |   -0.6911 |   -0.7055 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.08017 |     93.74 |    -5.838 |   -0.9810 |   -0.1739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.171 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.08017</span> |     93.74 |  0.002914 |    0.2727 |    0.8404 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.768 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  102</span>|     458.08021 |    0.9973 |    -1.438 |   -0.9022 |   -0.8693 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7780 |   -0.5133 |   -0.7416 |   -0.9739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5886 |   -0.6911 |   -0.7055 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.08021 |     93.74 |    -5.838 |   -0.9810 |   -0.1739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.171 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.08021</span> |     93.74 |  0.002914 |    0.2727 |    0.8404 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.768 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  103</span>|     458.08029 |    0.9973 |    -1.438 |   -0.9022 |   -0.8693 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7780 |   -0.5133 |   -0.7416 |   -0.9739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5886 |   -0.6911 |   -0.7055 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.08029 |     93.75 |    -5.838 |   -0.9810 |   -0.1739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.171 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.08029</span> |     93.75 |  0.002914 |    0.2727 |    0.8404 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.768 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  104</span>|     458.08032 |    0.9973 |    -1.438 |   -0.9022 |   -0.8693 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7780 |   -0.5133 |   -0.7416 |   -0.9739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5886 |   -0.6911 |   -0.7055 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.08032 |     93.75 |    -5.838 |   -0.9810 |   -0.1739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.171 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.08032</span> |     93.75 |  0.002914 |    0.2727 |    0.8403 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.768 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  105</span>|     458.08033 |    0.9973 |    -1.438 |   -0.9022 |   -0.8693 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7780 |   -0.5133 |   -0.7416 |   -0.9739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5886 |   -0.6911 |   -0.7055 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.08033 |     93.75 |    -5.838 |   -0.9810 |   -0.1739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.171 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.08033</span> |     93.75 |  0.002914 |    0.2727 |    0.8403 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.768 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  106</span>|     458.08032 |    0.9973 |    -1.438 |   -0.9022 |   -0.8693 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7780 |   -0.5133 |   -0.7416 |   -0.9739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5886 |   -0.6911 |   -0.7055 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.08032 |     93.75 |    -5.838 |   -0.9810 |   -0.1739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.171 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.08032</span> |     93.75 |  0.002914 |    0.2727 |    0.8403 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.768 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  107</span>|     458.08032 |    0.9973 |    -1.438 |   -0.9022 |   -0.8693 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7780 |   -0.5133 |   -0.7416 |   -0.9739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5886 |   -0.6911 |   -0.7055 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.08032 |     93.75 |    -5.838 |   -0.9810 |   -0.1739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.171 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.08032</span> |     93.75 |  0.002914 |    0.2727 |    0.8403 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.768 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  108</span>|     458.08032 |    0.9973 |    -1.438 |   -0.9022 |   -0.8693 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7780 |   -0.5133 |   -0.7416 |   -0.9739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5886 |   -0.6911 |   -0.7055 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.08032 |     93.75 |    -5.838 |   -0.9810 |   -0.1739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.171 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.08032</span> |     93.75 |  0.002914 |    0.2727 |    0.8403 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.768 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  109</span>|     458.08032 |    0.9973 |    -1.438 |   -0.9022 |   -0.8693 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7780 |   -0.5133 |   -0.7416 |   -0.9739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5886 |   -0.6911 |   -0.7055 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.08032 |     93.75 |    -5.838 |   -0.9810 |   -0.1739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.171 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.08032</span> |     93.75 |  0.002914 |    0.2727 |    0.8403 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.768 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  110</span>|     458.08032 |    0.9973 |    -1.438 |   -0.9022 |   -0.8693 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7780 |   -0.5133 |   -0.7416 |   -0.9739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5886 |   -0.6911 |   -0.7055 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.08032 |     93.75 |    -5.838 |   -0.9810 |   -0.1739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.171 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.08032</span> |     93.75 |  0.002914 |    0.2727 |    0.8403 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.768 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  111</span>|     458.08032 |    0.9973 |    -1.438 |   -0.9022 |   -0.8693 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7780 |   -0.5133 |   -0.7416 |   -0.9739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5886 |   -0.6911 |   -0.7055 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.08032 |     93.75 |    -5.838 |   -0.9810 |   -0.1739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.171 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.08032</span> |     93.75 |  0.002914 |    0.2727 |    0.8403 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.768 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  112</span>|     458.08032 |    0.9973 |    -1.438 |   -0.9022 |   -0.8693 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7780 |   -0.5133 |   -0.7416 |   -0.9739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.5886 |   -0.6911 |   -0.7055 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.08032 |     93.75 |    -5.838 |   -0.9810 |   -0.1739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.171 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.08032</span> |     93.75 |  0.002914 |    0.2727 |    0.8403 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.768 |     2.338 |     1.275 |    0.6800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7423 |     1.521 |     1.288 |     1.311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> calculating covariance matrix</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating residuals/tables</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>initial ETAs were nudged; (can control by foceiControl(etaNudge=., etaNudge2=))</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>ETAs were reset to zero during optimization; (Can control by foceiControl(resetEtaP=.))</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>last objective function was not at minimum, possible problems in optimization</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>gradient problems with initial estimate and covariance; see $scaleInfo</span>
-<span class="r-in"><span class="va">f_nlmixr_dfop_sfo_saem_obs</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr</a></span><span class="op">(</span><span class="va">f_mmkin_obs</span><span class="op">[</span><span class="st">"DFOP-SFO"</span>, <span class="op">]</span>, est <span class="op">=</span> <span class="st">"saem"</span><span class="op">)</span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → generate SAEM model</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1:    93.0952   -5.6337   -1.8988   -4.1294   -1.2035    0.1038    5.2152    1.6150    1.0450    2.6377    0.5035    0.5225   20.0768   11.4566</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2:    93.1464   -5.6948   -1.8729   -4.2029   -1.1302    0.1295    5.5478    1.5342    0.9927    2.5059    0.4783    0.5147   11.2089    8.1577</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 3:    93.1719   -5.7029   -1.8810   -4.2024   -1.0686    0.1063    5.2764    1.5217    0.9431    2.3806    0.4544    0.5587    9.3753    5.7166</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 4:    92.8647   -5.8139   -1.8979   -4.1992   -1.0168    0.1122    5.4892    1.7893    0.8960    2.2615    0.4317    0.5307    9.4134    4.7166</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5:    92.7759   -5.8627   -1.8873   -4.1402   -1.0347    0.1243    6.2028    2.1819    0.8795    2.1485    0.4101    0.5042    9.5964    4.7340</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 6:    92.8080   -5.8954   -1.9186   -4.1772   -0.9727    0.1311    5.8926    2.0728    0.8633    2.0410    0.3896    0.4790    8.7334    4.1957</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 7:    92.9042   -5.9034   -1.9603   -4.1854   -0.9717    0.1630    5.5980    2.2068    0.8672    1.9390    0.3701    0.4550    8.7213    3.3409</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 8:    92.9302   -5.8545   -1.9240   -4.2343   -0.9748    0.1686    5.3181    2.3934    0.8239    1.9533    0.3516    0.4323    7.6950    2.9174</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 9:    92.9518   -5.5561   -1.9734   -4.1856   -0.9322    0.2129    5.0522    2.2738    0.7830    2.0138    0.3340    0.4689    7.9230    2.1930</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 10:    93.0672   -5.5564   -1.9633   -4.1456   -0.9343    0.2049    4.7996    2.1601    0.7438    2.0517    0.3173    0.4771    8.0644    2.0682</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 11:    92.6849   -5.6187   -1.9773   -4.1793   -0.9342    0.2290    4.5596    2.0521    0.7123    2.0774    0.3015    0.5052    8.2355    1.9349</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 12:    92.6425   -5.6989   -2.0144   -4.1395   -0.9342    0.2669    4.3316    2.2634    0.7142    2.3008    0.2864    0.5728    7.5188    1.8028</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 13:    92.2996   -5.6700   -1.9885   -4.0443   -0.9202    0.2750    4.5787    2.1608    0.7019    2.7515    0.2721    0.6641    7.0744    1.8958</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 14:    92.2457   -5.5825   -2.0314   -3.9788   -0.9142    0.2908    6.3744    2.0527    0.7018    3.2703    0.2585    0.6387    6.9499    1.8421</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 15:    92.1741   -5.5333   -2.0318   -3.9664   -0.9059    0.2894    6.0557    1.9501    0.7179    3.2869    0.2455    0.6068    7.0951    1.6824</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 16:    92.5772   -5.4982   -2.0352   -3.9471   -0.9063    0.2930    5.7529    1.8526    0.7244    3.6994    0.2333    0.5764    7.2138    1.7042</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 17:    92.7024   -5.4902   -2.0438   -4.0347   -0.9079    0.2959    5.4652    1.7600    0.7393    3.5144    0.2216    0.5765    7.0258    1.6793</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 18:    92.0240   -5.5539   -2.0520   -3.9601   -0.9079    0.2884    6.5142    1.9232    0.7320    3.9052    0.2157    0.5521    7.2568    1.6151</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 19:    92.6532   -5.6325   -2.0456   -3.9091   -0.9179    0.3047    6.5743    2.3363    0.7405    4.5825    0.2049    0.5425    7.6201    1.6565</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 20:    92.3488   -5.6617   -2.0554   -3.9501   -0.9216    0.3056    6.7080    2.7562    0.7218    4.7569    0.2083    0.5432    7.3481    1.8059</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 21:    92.5679   -5.7469   -2.0614   -4.0829   -0.8890    0.3158    6.6510    3.1993    0.7136    4.5191    0.1978    0.5281    7.3723    1.7461</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 22:    92.4785   -5.9527   -2.0657   -4.0580   -0.8971    0.3084    6.3184    4.1791    0.7144    4.2931    0.1929    0.5157    7.1922    1.6984</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 23:    92.5594   -5.8590   -2.0723   -4.0580   -0.9020    0.2952    6.0025    3.9702    0.7335    4.0784    0.1833    0.5129    7.7560    1.6302</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 24:    92.6152   -5.9488   -2.0658   -4.1187   -0.9050    0.2997    5.7024    4.5381    0.7215    3.8745    0.1875    0.5129    7.6182    1.6362</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 25:    92.4658   -5.8715   -2.0791   -4.0949   -0.8922    0.3074    6.1428    4.3112    0.7353    3.6808    0.1896    0.5157    7.2703    1.5633</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 26:    92.2507   -5.9797   -2.0707   -4.0834   -0.8868    0.3266    6.4124    4.7125    0.7334    3.5368    0.1851    0.5217    7.3085    1.5828</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 27:    92.7055   -5.9792   -2.0779   -4.2066   -0.8887    0.2993    6.0918    4.8624    0.7659    3.7456    0.1841    0.4956    7.3534    1.5240</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 28:    92.3971   -6.0253   -2.0648   -4.1406   -0.9042    0.2897    5.7872    4.7690    0.7449    3.5583    0.1823    0.4709    7.2479    1.5008</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 29:    92.4045   -5.9045   -2.0730   -4.1407   -0.8969    0.3103    5.6683    4.5306    0.7349    3.3816    0.1849    0.5054    7.1719    1.6004</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 30:    92.1714   -5.8598   -2.0645   -4.0913   -0.8964    0.2743    5.3849    4.3040    0.7538    3.3509    0.1757    0.4801    7.3739    1.5736</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 31:    91.9134   -5.7867   -2.0367   -4.0563   -0.8957    0.2280    5.1156    4.0888    0.7299    3.3452    0.1749    0.4673    6.5991    1.5909</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 32:    92.3743   -5.9967   -2.0255   -4.0668   -0.9025    0.2247    4.8599    4.6666    0.7256    3.3271    0.1714    0.5008    6.4693    1.5458</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 33:    92.5239   -5.9301   -2.0387   -4.0365   -0.9033    0.2558    4.6169    4.4333    0.7492    3.6109    0.1688    0.5424    6.7730    1.5771</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 34:    92.6892   -6.1135   -2.0587   -4.0280   -0.8993    0.2430    4.3860    6.0227    0.7798    3.5725    0.1672    0.5153    6.8110    1.5417</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 35:    92.7276   -6.0815   -2.0629   -4.0833   -0.8943    0.2375    4.6811    5.7216    0.8010    3.3939    0.1715    0.4895    6.6812    1.5478</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 36:    92.7316   -6.0691   -2.0882   -4.0626   -0.9030    0.2405    4.4614    5.4355    0.8355    3.2242    0.1789    0.4650    6.8443    1.4806</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 37:    92.6685   -5.7905   -2.0666   -4.0663   -0.8998    0.2784    5.1916    5.1637    0.7937    3.0641    0.1823    0.4418    6.5421    1.6284</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 38:    93.0325   -5.6829   -2.0674   -4.0772   -0.9167    0.2499    4.9320    4.9055    0.7768    3.1213    0.1887    0.4290    6.8220    1.5224</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 39:    93.0378   -5.5554   -2.0743   -4.0772   -0.9189    0.2361    4.6854    4.6603    0.7822    3.1213    0.1813    0.4242    7.1137    1.5021</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 40:    93.3297   -5.6270   -2.0591   -4.1200   -0.9167    0.2035    4.4511    4.4272    0.8128    2.9653    0.1829    0.4030    7.3894    1.5064</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 41:    93.3408   -5.5437   -2.0344   -4.1042   -0.9078    0.1744    4.2286    4.2059    0.8228    2.8170    0.1881    0.3829    7.2734    1.5519</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 42:    93.1691   -5.4436   -2.0551   -4.1048   -0.8984    0.1732    4.0172    3.9956    0.7816    2.6762    0.1853    0.3637    6.9712    1.5332</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 43:    93.2443   -5.5247   -2.0722   -4.0980   -0.8992    0.1756    3.8163    3.7958    0.7781    2.7200    0.1956    0.3455    6.7012    1.5344</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 44:    92.9509   -5.5020   -2.0495   -4.0961   -0.8964    0.1777    3.6255    3.6060    0.7555    2.8172    0.1994    0.3283    6.2180    1.6137</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 45:    92.8898   -5.4913   -2.0462   -4.1079   -0.9003    0.1701    4.1177    3.4257    0.7491    2.8491    0.1989    0.3372    6.2876    1.6205</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 46:    92.6044   -5.6429   -2.0499   -4.1079   -0.9069    0.1999    4.2103    3.2544    0.7657    2.8491    0.1921    0.3532    6.2261    1.6435</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 47:    92.7740   -5.6128   -2.0804   -4.1186   -0.8976    0.1864    5.2188    3.0917    0.7381    2.9036    0.2030    0.3391    6.6803    1.6177</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 48:    92.4691   -5.6645   -2.0600   -4.1288   -0.8796    0.1913    6.2045    2.9371    0.7774    2.9223    0.1966    0.3396    6.7169    1.6215</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 49:    92.4128   -5.7079   -2.0802   -4.1166   -0.8798    0.1741    5.8943    3.1446    0.7854    2.8715    0.1935    0.3369    6.9151    1.4834</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 50:    91.8883   -5.7713   -2.0932   -4.0899   -0.8886    0.1758    5.5996    3.5478    0.8033    2.8192    0.2014    0.3201    7.0775    1.4635</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 51:    92.1187   -5.7306   -2.0903   -4.0878   -0.8923    0.2024    5.3196    3.6534    0.7823    2.7891    0.1969    0.3140    6.9879    1.5430</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 52:    92.3209   -5.7897   -2.0903   -4.1622   -0.9072    0.2261    5.3781    3.4707    0.8001    3.0801    0.2063    0.3088    6.7047    1.4499</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 53:    92.4318   -5.6954   -2.0950   -4.1866   -0.9082    0.2131    7.1200    3.2972    0.7711    3.3398    0.2034    0.3186    6.6152    1.5123</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 54:    92.5380   -5.6975   -2.0782   -4.2394   -0.8976    0.2118    6.7640    3.1323    0.7744    3.5385    0.2175    0.3296    6.4402    1.5403</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 55:    92.7213   -5.6663   -2.0427   -4.3288   -0.9028    0.2032    8.1796    2.9757    0.7748    4.4495    0.2142    0.3316    6.4111    1.5369</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 56:    92.7219   -5.6865   -2.0557   -4.3434   -0.9084    0.2021    7.7706    2.8269    0.7989    4.6691    0.2174    0.3433    6.4256    1.4639</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 57:    92.8932   -5.5736   -2.0497   -4.3955   -0.9216    0.1694    7.3821    2.6856    0.8205    4.9791    0.2142    0.3500    6.5378    1.5406</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 58:    92.9219   -5.5789   -2.0547   -4.3094   -0.9183    0.1838    7.0130    2.5513    0.7958    4.7302    0.2193    0.3495    6.2662    1.4940</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 59:    93.0432   -5.6201   -2.0395   -4.2527   -0.9199    0.2302    6.6623    2.4237    0.8241    4.4937    0.2241    0.3321    5.8693    1.6176</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 60:    92.9724   -5.5971   -2.0537   -4.3509   -0.9219    0.2118    7.5905    2.3026    0.8418    4.4546    0.2177    0.3155    5.5960    1.5040</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 61:    93.1235   -5.6332   -2.0671   -4.3572   -0.9219    0.1917    7.2521    2.3037    0.8527    4.5707    0.2230    0.2997    5.8136    1.4516</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 62:    93.5431   -5.6373   -2.0371   -4.3384   -0.9276    0.1997    8.5187    2.3895    0.8575    4.4378    0.2258    0.3041    5.5746    1.4572</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 63:    93.5009   -5.5634   -2.0224   -4.3560   -0.9339    0.2167    8.0927    2.2700    0.8462    4.4449    0.2266    0.3162    5.4922    1.5233</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 64:    93.7369   -5.6032   -2.0362   -4.2323   -0.9355    0.2159    9.6684    2.1949    0.8514    4.2227    0.2300    0.3318    5.7597    1.4866</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 65:    93.5021   -5.5769   -2.0311   -4.3204   -0.9375    0.1927    9.5537    2.0852    0.8559    4.6151    0.2208    0.3550    5.6975    1.5031</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 66:    93.4211   -5.7184   -2.0384   -4.3041   -0.9228    0.2121    9.0760    2.6390    0.8430    4.3843    0.2098    0.3755    5.8990    1.5133</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 67:    93.3435   -5.6579   -2.0456   -4.2222   -0.9183    0.1825    8.6222    2.5071    0.8719    4.1651    0.2152    0.3567    5.9004    1.4874</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 68:    93.3914   -5.6554   -2.0420   -4.1618   -0.9303    0.1714    8.1911    2.3817    0.8856    3.9569    0.2272    0.3388    6.1793    1.4917</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 69:    93.5112   -5.6834   -2.0349   -4.1591   -0.9331    0.1759    8.9900    2.5191    0.9184    3.7590    0.2236    0.3219    6.1618    1.4697</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 70:    93.4289   -5.6576   -2.0273   -4.1763   -0.9399    0.1738    8.5405    2.4302    0.8725    3.5711    0.2149    0.3288    6.5324    1.5410</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 71:    93.2936   -5.6699   -2.0256   -4.1405   -0.9370    0.1922    8.1134    2.3831    0.8289    3.3925    0.2080    0.3548    5.9794    1.5904</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 72:    92.9152   -5.6742   -2.0382   -4.1523   -0.9411    0.1991    7.7078    2.4322    0.7875    3.2916    0.2007    0.3456    6.0999    1.6022</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 73:    92.8129   -5.8099   -2.0372   -4.1735   -0.9377    0.1605    8.2752    2.8931    0.7737    3.4015    0.2057    0.3283    6.0140    1.5472</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 74:    92.7806   -5.7269   -2.0315   -4.1877   -0.9399    0.1675    8.4688    2.7484    0.7773    3.4620    0.2174    0.3249    5.8495    1.5779</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 75:    92.9128   -5.8680   -2.0304   -4.1459   -0.9387    0.1567    8.0454    3.2365    0.7681    3.2935    0.2198    0.3187    5.8539    1.5815</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 76:    92.8931   -5.8483   -2.0253   -4.1815   -0.9387    0.1540    9.6800    3.0747    0.8007    3.5838    0.2198    0.3291    5.9005    1.5053</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 77:    92.7474   -5.7995   -2.0202   -4.1904   -0.9333    0.1724   10.0488    2.9210    0.7841    3.7255    0.2148    0.3465    5.9418    1.5569</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 78:    92.8150   -5.9608   -2.0025   -4.1583   -0.9511    0.1566    9.5464    3.5249    0.7858    3.6988    0.2246    0.3292    5.7737    1.5904</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 79:    92.6663   -6.0485   -2.0245   -4.1437   -0.9446    0.1786    9.0690    4.1252    0.7803    3.6526    0.2155    0.3335    5.9109    1.5521</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 80:    92.5932   -6.1001   -2.0304   -4.1969   -0.9409    0.1424    8.6156    4.6478    0.8007    4.0379    0.2114    0.3168    6.1732    1.5544</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 81:    92.6789   -6.3510   -2.0062   -4.2272   -0.9541    0.1360    8.3363    5.9861    0.7719    3.8800    0.2202    0.3010    5.6841    1.6317</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 82:    92.9996   -6.5680   -2.0048   -4.1609   -0.9578    0.1367   10.6901    7.4391    0.7962    3.6860    0.2092    0.2859    5.7335    1.6391</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 83:    93.1087   -6.5298   -1.9757   -4.1694   -0.9596    0.1323   10.5524    7.4660    0.8237    3.8009    0.2134    0.2716    5.9664    1.6316</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 84:    93.1929   -6.5844   -2.0191   -4.2021   -0.9616    0.1384   10.0248    7.7313    0.8230    3.6109    0.2110    0.2580    5.7854    1.5609</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 85:    92.9161   -6.4934   -2.0281   -4.2008   -0.9600    0.1107    9.5236    7.4269    0.8448    3.5630    0.2110    0.2451    5.6111    1.5333</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 86:    92.9816   -6.5397   -2.0234   -4.1830   -0.9694    0.1111    9.1945    7.4382    0.8481    3.3849    0.2166    0.2329    5.8375    1.5263</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 87:    93.0930   -6.5749   -2.0168   -4.1986   -0.9816    0.0926    8.7348    8.4431    0.8646    3.3032    0.2155    0.2212    5.7542    1.5736</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 88:    93.0765   -6.4914   -2.0274   -4.2274   -0.9870    0.1148    8.4666    8.0209    0.8666    3.5292    0.2048    0.2102    5.8988    1.5329</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 89:    92.8699   -6.6431   -2.0199   -4.2175   -0.9463    0.1011    8.4648    7.7973    0.8908    3.3980    0.1945    0.2102    5.6876    1.5177</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 90:    92.8225   -6.6932   -2.0211   -4.2175   -0.9505    0.0887   10.1862    8.7787    0.8730    3.3980    0.1877    0.1997    5.9135    1.4821</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 91:    92.7874   -6.7007   -2.0420   -4.2936   -0.9468    0.0927   11.2275    8.3397    0.8600    3.9490    0.1885    0.2147    6.1108    1.4315</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 92:    93.2320   -6.8088   -2.0341   -4.2817   -0.9582    0.1270   12.4029    9.6204    0.8503    3.7516    0.1950    0.2070    5.9434    1.5251</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 93:    93.8996   -6.5471   -2.0440   -4.2123   -0.9543    0.1140   11.7828    9.1394    0.8493    3.5640    0.1988    0.2017    6.1302    1.5568</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 94:    93.4416   -6.4623   -2.0448   -4.2188   -0.9595    0.1174   12.1277    8.6824    0.8568    3.3858    0.1975    0.1938    5.9173    1.5204</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 95:    93.2953   -6.1521   -2.0477   -4.2216   -0.9551    0.1152   11.5213    8.2483    0.8356    3.2165    0.1979    0.1855    5.8298    1.5357</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 96:    92.9577   -6.0477   -2.0579   -4.2534   -0.9465    0.1284   10.9452    7.8359    0.8202    3.3357    0.1937    0.1917    5.8590    1.5738</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 97:    92.8703   -6.1037   -2.0745   -4.1960   -0.9405    0.1417   10.4858    7.4441    0.8488    3.1690    0.1969    0.2016    5.7948    1.4759</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 98:    92.9728   -6.3534   -2.0868   -4.2155   -0.9444    0.1069    9.9615    7.1788    0.8736    3.1913    0.1998    0.2114    5.6533    1.4322</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 99:    93.4116   -6.1712   -2.0837   -4.2280   -0.9473    0.1197    9.4634    6.8198    0.8959    3.1806    0.1899    0.2167    5.9149    1.3768</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 100:    93.2598   -6.1345   -2.0645   -4.2237   -0.9575    0.1099    8.9903    6.4788    0.9204    3.2429    0.1903    0.2058    5.7085    1.4133</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 101:    93.3082   -6.1574   -2.0474   -4.2315   -0.9619    0.1161    9.0819    6.1549    0.8744    3.2049    0.1891    0.2278    5.6493    1.4894</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 102:    93.5741   -6.2923   -2.0484   -4.2853   -0.9665    0.1325   10.4108    5.8522    0.8804    3.5370    0.2024    0.2324    5.6995    1.4594</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 103:    92.9199   -6.1797   -2.0522   -4.2940   -0.9568    0.1289    9.8903    5.5596    0.8722    3.6682    0.1975    0.2398    5.5536    1.4510</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 104:    93.1139   -6.1630   -2.0546   -4.2912   -0.9613    0.1115    9.3958    5.2816    0.8648    3.6673    0.2010    0.2278    5.5768    1.4812</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 105:    93.4085   -6.0359   -2.0450   -4.2889   -0.9591    0.1258    8.9260    5.0175    0.8412    3.7286    0.1917    0.2171    5.6780    1.5203</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 106:    93.3103   -6.1029   -2.0425   -4.2948   -0.9579    0.0934    8.4797    4.7667    0.8539    3.6947    0.1947    0.2234    5.7210    1.4760</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 107:    93.6389   -5.9750   -2.0309   -4.2630   -0.9660    0.1110    8.0557    4.5283    0.8470    3.5100    0.2044    0.2299    5.6280    1.5525</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 108:    93.8641   -5.8551   -2.0360   -4.2311   -0.9629    0.0900    7.6529    4.3019    0.8567    3.3345    0.2014    0.2302    5.7841    1.5978</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 109:    93.6274   -6.0268   -2.0445   -4.3047   -0.9488    0.0979    7.2703    4.7807    0.8565    3.7665    0.1929    0.2271    5.7941    1.5580</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 110:    93.6320   -5.8321   -2.0413   -4.2855   -0.9477    0.1143    6.9068    4.5417    0.8412    3.6165    0.1901    0.2199    5.8169    1.5477</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 111:    93.6245   -5.8256   -2.0157   -4.2797   -0.9612    0.0940    6.5774    4.3146    0.8460    3.5782    0.1837    0.2182    5.6157    1.6424</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 112:    93.8512   -5.9045   -2.0116   -4.2409   -0.9708    0.0883    6.2486    4.0989    0.8658    3.5059    0.1761    0.2073    5.8852    1.6073</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 113:    93.7080   -5.9935   -2.0306   -4.1884   -0.9690    0.0740    5.9361    4.0088    0.9072    3.3400    0.1957    0.2169    6.3792    1.4770</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 114:    93.8574   -5.9185   -2.0233   -4.2030   -0.9588    0.1137    5.6393    3.8084    0.9322    3.2878    0.1939    0.2098    5.8891    1.5325</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 115:    93.7414   -5.8789   -2.0183   -4.2256   -0.9701    0.1105    5.6148    3.6180    0.9222    3.5507    0.1921    0.1993    5.6441    1.5458</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 116:    93.4104   -5.9704   -2.0428   -4.2091   -0.9807    0.1099    5.3341    4.1157    0.9363    3.4004    0.1968    0.2134    5.7764    1.4617</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 117:    93.5239   -5.9057   -2.0518   -4.2494   -0.9812    0.1127    6.8839    3.9099    0.9151    3.6604    0.1921    0.2095    5.4753    1.4249</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 118:    93.7599   -5.9418   -2.0482   -4.2272   -0.9822    0.1094    6.8133    3.7144    0.9198    3.5160    0.1971    0.2039    5.6467    1.4116</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 119:    93.6617   -6.0020   -2.0483   -4.2146   -0.9816    0.1003    6.4727    3.8251    0.9103    3.4716    0.1950    0.2109    5.8513    1.4268</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 120:    93.5436   -5.9804   -2.0458   -4.1906   -0.9819    0.1065    6.1490    3.6756    0.9088    3.2980    0.1989    0.2123    5.8268    1.4689</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 121:    93.4880   -5.9047   -2.0452   -4.1957   -0.9640    0.1349    5.8416    3.4918    0.8824    3.2437    0.1889    0.2017    5.7152    1.4382</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 122:    93.7406   -5.9844   -2.0596   -4.2328   -0.9558    0.1563    5.5495    3.7606    0.8489    3.3043    0.1795    0.2015    5.5095    1.5112</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 123:    93.6728   -6.0394   -2.0372   -4.2812   -0.9592    0.1507    5.2720    4.2004    0.8341    3.5274    0.1817    0.2008    5.6936    1.6011</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 124:    93.9591   -6.0483   -2.0280   -4.2613   -0.9594    0.1463    5.3846    4.1913    0.8351    3.4341    0.1870    0.2193    5.5694    1.5684</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 125:    94.0201   -5.9102   -2.0507   -4.2686   -0.9697    0.1455    5.1154    3.9818    0.8512    3.3475    0.1859    0.2165    5.6224    1.5643</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 126:    93.8825   -5.8970   -2.0543   -4.2569   -0.9663    0.1493    4.9144    3.7827    0.8907    3.3030    0.1876    0.2180    5.7351    1.4722</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 127:    93.9893   -5.8955   -2.0624   -4.2430   -0.9802    0.1476    7.2413    3.5935    0.8915    3.2740    0.1851    0.2110    5.7614    1.4305</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 128:    94.1849   -5.9123   -2.0624   -4.2385   -0.9786    0.1523    7.9575    3.4139    0.9094    3.2656    0.1807    0.2198    5.7366    1.4264</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 129:    94.0812   -6.0044   -2.0696   -4.3056   -0.9770    0.1693    8.7809    3.7526    0.9111    3.6172    0.1859    0.2255    5.8064    1.4718</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 130:    93.6046   -6.1387   -2.0718   -4.3056   -0.9821    0.1477    8.3419    4.3029    0.9177    3.6172    0.1867    0.2143    5.8893    1.4447</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 131:    93.5216   -6.1347   -2.0740   -4.3114   -0.9766    0.1288    8.2937    4.3407    0.9250    3.5298    0.1881    0.2293    5.8054    1.4219</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 132:    93.6142   -6.2789   -2.0786   -4.3297   -0.9716    0.1288    8.3731    5.1225    0.9236    3.6683    0.1929    0.2295    5.8064    1.4194</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 133:    93.4410   -5.9177   -2.0916   -4.3557   -0.9798    0.1066    7.9544    4.8663    0.9537    3.7076    0.1937    0.2279    5.9844    1.4297</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 134:    93.4716   -5.9152   -2.0838   -4.3611   -0.9818    0.1332    7.5567    4.6230    0.9161    3.7833    0.2017    0.2308    6.0611    1.5717</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 135:    93.3787   -6.0381   -2.0728   -4.2627   -0.9719    0.1051    7.2396    4.3919    0.8970    3.5941    0.1916    0.2193    5.8837    1.6057</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 136:    93.4339   -5.9876   -2.0801   -4.3002   -0.9690    0.1214    6.8776    4.1723    0.8888    3.4144    0.2002    0.2227    6.0141    1.5231</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 137:    93.7639   -6.0411   -2.0803   -4.2799   -0.9646    0.1484    6.5337    3.9765    0.8969    3.2437    0.1995    0.2115    5.9404    1.6402</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 138:    93.6414   -6.0122   -2.0714   -4.2666   -0.9755    0.1506    7.4057    3.7962    0.9242    3.1524    0.1934    0.2010    6.0666    1.5001</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 139:    93.7743   -5.7966   -2.0613   -4.2289   -0.9722    0.1383    7.9358    3.6064    0.9015    2.9948    0.1946    0.2029    5.9655    1.5250</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 140:    93.1082   -5.7994   -2.0388   -4.2289   -0.9659    0.1382    8.0282    3.4261    0.9053    2.9079    0.1909    0.2138    5.9183    1.5191</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 141:    93.2122   -6.0181   -2.0396   -4.2398   -0.9587    0.1016    8.9769    3.9426    0.9028    2.9136    0.1941    0.2195    6.1560    1.4902</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 142:    93.4684   -6.1438   -2.0273   -4.2541   -0.9508    0.0848    8.5281    4.8727    0.9056    2.9875    0.1901    0.2233    6.2546    1.4695</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 143:    93.0059   -6.0964   -2.0145   -4.2760   -0.9563    0.0826    8.1017    4.6291    0.9312    3.1063    0.1850    0.2138    6.5768    1.4391</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 144:    93.0612   -6.1127   -1.9951   -4.2589   -0.9539    0.0904    7.6966    4.6160    0.9623    3.1681    0.1824    0.2032    6.1506    1.4497</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 145:    93.5170   -6.1066   -1.9951   -4.3574   -0.9478    0.1040    7.3118    4.6263    0.9639    3.6914    0.1986    0.2292    5.9389    1.4867</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 146:    93.4915   -6.3235   -2.0006   -4.3866   -0.9579    0.1202    6.9462    6.0529    0.9514    3.9899    0.1887    0.2335    5.9265    1.4978</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 147:    93.8963   -6.1119   -2.0055   -4.3446   -0.9682    0.1242    7.1711    5.7503    0.9315    3.7904    0.1923    0.2332    5.9346    1.5021</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 148:    93.6758   -5.9705   -2.0137   -4.2906   -0.9614    0.1125    6.8125    5.4628    0.9506    3.6009    0.1913    0.2371    6.2579    1.4384</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 149:    93.6499   -6.0049   -2.0246   -4.2730   -0.9776    0.0996    6.4719    5.1896    0.9736    3.4209    0.1817    0.2252    6.3224    1.3878</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 150:    94.0242   -5.9760   -2.0176   -4.2182   -0.9773    0.1032    6.1483    4.9301    0.9944    3.2498    0.1807    0.2290    6.5662    1.3618</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 151:    93.9234   -5.8772   -2.0132   -4.2362   -0.9651    0.1058    7.2453    4.6836    0.9824    3.4860    0.1806    0.2175    6.2575    1.4370</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 152:    94.2513   -5.9391   -2.0814   -4.2278   -0.9753    0.1221    4.9753    3.4013    0.9808    3.3265    0.1836    0.1897    6.6966    1.3457</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 153:    93.9434   -6.1294   -2.0570   -4.2447   -0.9761    0.1527    5.0454    4.3115    0.9494    3.3663    0.1684    0.1680    5.9106    1.4777</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 154:    93.8141   -6.2749   -2.0366   -4.2383   -0.9835    0.1440    5.5466    5.3351    0.9320    3.3337    0.1731    0.1913    5.8842    1.4325</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 155:    94.1987   -6.1029   -2.0252   -4.2520   -0.9780    0.1132    7.1508    4.2713    0.9457    3.3428    0.1727    0.1691    6.1632    1.4658</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 156:    94.0626   -6.2640   -2.0263   -4.2461   -0.9854    0.1346    5.6296    5.1645    0.9423    3.3987    0.1697    0.1778    5.9631    1.4483</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 157:    93.9319   -6.1392   -2.0388   -4.2294   -0.9921    0.1265    5.6768    4.7366    0.9210    3.3673    0.1733    0.1789    5.9114    1.4976</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 158:    93.9123   -6.1970   -2.0165   -4.2241   -0.9924    0.1540    7.2552    4.7537    0.9369    3.2727    0.1805    0.1923    5.9324    1.5351</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 159:    93.9704   -6.4018   -2.0455   -4.2127   -0.9919    0.1413    7.7561    6.0732    0.9802    3.3376    0.1806    0.2260    6.4511    1.4227</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 160:    94.1967   -6.2985   -2.0412   -4.2264   -0.9795    0.1206    8.3836    6.1319    0.9689    3.4685    0.1792    0.2150    6.5116    1.4706</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 161:    94.3500   -6.1427   -2.0189   -4.2242   -1.0022    0.0837    8.0282    4.5505    0.9524    3.3269    0.1859    0.1816    6.0642    1.4746</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 162:    94.2711   -5.9578   -2.0215   -4.2078   -1.0110    0.0946    7.8634    3.3072    0.9559    3.2374    0.1876    0.1791    6.0798    1.4903</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 163:    93.9824   -5.8794   -2.0409   -4.2367   -0.9970    0.1150    9.3872    3.0498    0.9830    3.3179    0.1880    0.1828    5.8091    1.4852</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 164:    94.2013   -5.8651   -2.0122   -4.2257   -0.9906    0.1267    7.1953    3.0510    0.9697    3.2713    0.1871    0.1832    5.8741    1.5313</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 165:    94.1804   -5.7868   -2.0200   -4.2053   -0.9812    0.1219    6.7375    2.4769    0.9688    3.2706    0.1910    0.1859    5.7890    1.5188</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 166:    93.9790   -5.8156   -2.0311   -4.2438   -0.9784    0.1247    5.7617    2.6907    0.9533    3.5342    0.1953    0.1872    5.8816    1.5243</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 167:    93.2524   -5.8603   -2.0497   -4.2594   -0.9787    0.1265    4.7086    2.9121    0.9117    3.4696    0.1943    0.1832    5.9672    1.4567</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 168:    93.2924   -6.0371   -2.0528   -4.2607   -0.9727    0.1201    5.5273    4.0286    0.9177    3.4501    0.1918    0.1908    5.7790    1.4701</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 169:    93.4838   -6.1497   -2.0389   -4.2716   -0.9639    0.1127    5.4524    4.2700    0.9544    3.4329    0.1940    0.1871    5.7795    1.4575</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 170:    93.3951   -6.2298   -2.0438   -4.4133   -0.9939    0.1088    6.1460    4.6552    0.9645    4.5240    0.1978    0.2091    5.7549    1.5233</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 171:    93.4113   -6.1187   -2.0536   -4.4019   -0.9771    0.0899    6.8123    4.0073    0.9531    4.4290    0.1878    0.1970    5.9067    1.4857</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 172:    93.1140   -5.9515   -2.0530   -4.3250   -0.9723    0.1094    5.1247    3.4502    0.9572    3.8504    0.1954    0.1944    5.8583    1.4867</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 173:    92.9782   -6.0415   -2.0633   -4.2887   -0.9608    0.1081    4.1020    3.8967    0.9478    3.7222    0.1890    0.1812    5.9473    1.4583</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 174:    92.9661   -5.9295   -2.0457   -4.2907   -0.9626    0.0991    5.7954    3.3581    0.9785    3.7311    0.1867    0.2026    5.8087    1.4797</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 175:    93.2577   -5.8895   -2.0281   -4.2845   -0.9560    0.0829    7.3434    3.1501    0.9975    3.6334    0.1920    0.2151    5.4717    1.4832</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 176:    93.1210   -5.9567   -2.0370   -4.2848   -0.9488    0.0787    6.8946    3.4999    0.9983    3.6159    0.1922    0.2233    5.8426    1.4096</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 177:    92.5456   -6.1797   -2.0319   -4.2684   -0.9401    0.0873    6.9744    5.2939    0.9928    3.4880    0.1989    0.2213    5.9613    1.4367</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 178:    92.6854   -6.1483   -2.0278   -4.2705   -0.9504    0.0630    5.0582    5.0622    0.9953    3.4915    0.1930    0.2238    5.9775    1.4263</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 179:    93.1323   -6.1739   -2.0353   -4.2590   -0.9455    0.0584    4.9914    4.7898    0.9817    3.4163    0.1899    0.2124    5.9579    1.4242</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 180:    93.0611   -6.2228   -2.0441   -4.2977   -0.9387    0.0320    4.0323    5.5685    0.9890    3.7202    0.1940    0.2335    6.2224    1.4194</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 181:    92.7741   -6.1462   -2.0477   -4.3335   -0.9454    0.1011    3.7007    5.1590    0.9807    3.8469    0.1939    0.2463    5.9703    1.4343</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 182:    93.0775   -6.0640   -2.0496   -4.3171   -0.9444    0.0897    5.0266    4.5597    0.9792    3.7741    0.1931    0.2186    5.6727    1.4858</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 183:    93.2566   -6.1757   -2.0368   -4.2888   -0.9560    0.0809    5.8284    5.2504    0.9636    3.7078    0.1939    0.2262    5.5170    1.4560</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 184:    93.0357   -6.1158   -2.0217   -4.3111   -0.9453    0.0901    6.7209    5.4048    0.9503    3.8949    0.1967    0.2209    5.3578    1.4704</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 185:    93.0173   -6.1998   -2.0371   -4.3713   -0.9451    0.0752    6.1040    5.8272    0.9333    4.4038    0.1951    0.2238    5.5896    1.4202</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 186:    93.2835   -6.1217   -2.0383   -4.3308   -0.9574    0.1110    6.0519    4.7669    0.9400    4.0265    0.1972    0.2274    5.4560    1.4602</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 187:    93.5312   -6.3356   -2.0253   -4.3418   -0.9583    0.1166    6.7561    5.8784    0.9346    4.0264    0.2038    0.2281    5.5024    1.4994</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 188:    93.6460   -5.8426   -2.0237   -4.4519   -0.9594    0.1283    6.3492    3.4189    0.9091    4.9358    0.2086    0.2348    5.4301    1.5893</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 189:    93.8538   -6.0183   -2.0178   -4.3911   -0.9797    0.1262    8.7939    3.7358    0.9008    4.4894    0.2125    0.2199    5.6613    1.5073</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 190:    93.1543   -6.1364   -2.0451   -4.4389   -0.9708    0.1584    9.9803    4.2747    0.8922    4.8507    0.2084    0.2607    5.9136    1.4572</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 191:    93.4334   -6.1466   -2.0389   -4.4661   -0.9678    0.1565    7.8390    4.4393    0.9022    4.7857    0.2105    0.2634    5.7161    1.5325</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 192:    93.3623   -6.0940   -2.0240   -4.4569   -0.9673    0.1420    8.0856    4.3185    0.8895    4.4721    0.2113    0.2350    5.5282    1.5221</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 193:    93.1990   -5.9864   -2.0301   -4.4538   -0.9563    0.1515    8.4425    3.7598    0.8814    4.4376    0.2013    0.2257    5.4205    1.4820</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 194:    93.3165   -6.0045   -2.0353   -4.4314   -0.9525    0.1486    8.2370    3.6742    0.8947    4.4594    0.1960    0.2248    5.4579    1.4767</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 195:    93.1288   -6.1006   -2.0551   -4.5184   -0.9503    0.1583    9.6259    4.2294    0.9040    5.1981    0.1950    0.1962    5.5602    1.4254</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 196:    93.1943   -5.9871   -2.0607   -4.4728   -0.9446    0.1482    9.3401    3.5579    0.8925    4.7901    0.1892    0.1879    5.7296    1.4172</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 197:    93.5803   -5.9131   -2.0522   -4.3675   -0.9476    0.1571    7.2599    3.4241    0.8857    3.8551    0.1886    0.1793    5.4832    1.6006</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 198:    93.5703   -5.9980   -2.0550   -4.3578   -0.9519    0.1491    7.0416    3.8805    0.8563    3.7930    0.1882    0.1896    5.4355    1.5402</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 199:    93.2909   -5.8288   -2.0532   -4.3605   -0.9518    0.1692    8.3926    3.0173    0.8566    3.8610    0.1902    0.2033    5.5735    1.5647</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 200:    93.4049   -5.7474   -2.0447   -4.3548   -0.9517    0.1812    7.4977    2.8256    0.8520    3.8236    0.1897    0.2060    5.5092    1.5699</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 201:    93.3386   -5.8209   -2.0398   -4.4174   -0.9597    0.1738    6.3555    3.0871    0.8431    4.3211    0.1853    0.2062    5.5460    1.5925</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 202:    93.3397   -5.8313   -2.0387   -4.4364   -0.9580    0.1669    6.0946    3.1018    0.8460    4.5093    0.1821    0.2068    5.6622    1.5740</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 203:    93.3071   -5.8276   -2.0384   -4.4758   -0.9571    0.1639    5.7815    3.0575    0.8608    4.9441    0.1826    0.2052    5.6855    1.5570</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 204:    93.3138   -5.8477   -2.0368   -4.4714   -0.9541    0.1658    5.7526    3.1322    0.8704    4.9129    0.1816    0.2057    5.6642    1.5606</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 205:    93.3066   -5.8537   -2.0395   -4.4842   -0.9521    0.1642    5.6045    3.1633    0.8748    5.0053    0.1805    0.2036    5.6633    1.5550</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 206:    93.3042   -5.8790   -2.0453   -4.4977   -0.9501    0.1633    5.7219    3.3320    0.8807    5.1121    0.1793    0.1999    5.6888    1.5413</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 207:    93.3281   -5.9005   -2.0504   -4.5109   -0.9480    0.1629    5.8004    3.4696    0.8865    5.2248    0.1789    0.1961    5.7206    1.5357</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 208:    93.3437   -5.8972   -2.0569   -4.5200   -0.9452    0.1641    5.7523    3.4604    0.8875    5.2848    0.1787    0.1933    5.7450    1.5288</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 209:    93.3265   -5.8864   -2.0628   -4.5092   -0.9440    0.1639    5.5355    3.4138    0.8882    5.1586    0.1791    0.1916    5.7744    1.5283</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 210:    93.3087   -5.8812   -2.0671   -4.5004   -0.9426    0.1677    5.3488    3.3975    0.8895    5.0589    0.1798    0.1924    5.7781    1.5307</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 211:    93.2807   -5.8760   -2.0703   -4.5009   -0.9413    0.1709    5.2654    3.3770    0.8894    5.0377    0.1805    0.1927    5.7808    1.5282</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 212:    93.2815   -5.8637   -2.0711   -4.4955   -0.9409    0.1708    5.3028    3.3250    0.8914    4.9702    0.1819    0.1941    5.7827    1.5274</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 213:    93.2828   -5.8481   -2.0709   -4.4895   -0.9396    0.1702    5.3840    3.2614    0.8913    4.9108    0.1826    0.1953    5.7744    1.5301</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 214:    93.2645   -5.8422   -2.0704   -4.4882   -0.9384    0.1710    5.3939    3.2358    0.8931    4.8944    0.1828    0.1955    5.7797    1.5351</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 215:    93.2591   -5.8519   -2.0709   -4.4858   -0.9380    0.1713    5.5142    3.2959    0.8953    4.8587    0.1822    0.1960    5.7853    1.5369</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 216:    93.2595   -5.8523   -2.0715   -4.4827   -0.9376    0.1723    5.5563    3.3077    0.8964    4.8306    0.1817    0.1973    5.7975    1.5396</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 217:    93.2503   -5.8512   -2.0732   -4.4737   -0.9373    0.1734    5.5036    3.3182    0.8959    4.7582    0.1817    0.1992    5.7940    1.5365</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 218:    93.2345   -5.8463   -2.0749   -4.4639   -0.9372    0.1747    5.5046    3.2959    0.8959    4.6771    0.1817    0.2008    5.7889    1.5340</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 219:    93.2264   -5.8439   -2.0757   -4.4549   -0.9364    0.1759    5.4970    3.2802    0.8966    4.6015    0.1819    0.2027    5.7804    1.5321</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 220:    93.2259   -5.8464   -2.0758   -4.4493   -0.9363    0.1771    5.4793    3.2944    0.8950    4.5498    0.1823    0.2049    5.7745    1.5344</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 221:    93.2243   -5.8505   -2.0768   -4.4443   -0.9367    0.1785    5.5360    3.3132    0.8924    4.5028    0.1829    0.2068    5.7556    1.5329</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 222:    93.2385   -5.8603   -2.0776   -4.4371   -0.9376    0.1800    5.5401    3.3878    0.8903    4.4416    0.1834    0.2096    5.7522    1.5317</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 223:    93.2339   -5.8634   -2.0780   -4.4309   -0.9377    0.1803    5.5522    3.4132    0.8882    4.3844    0.1838    0.2118    5.7457    1.5323</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 224:    93.2379   -5.8686   -2.0778   -4.4262   -0.9374    0.1803    5.5469    3.4421    0.8848    4.3375    0.1842    0.2137    5.7429    1.5323</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 225:    93.2329   -5.8654   -2.0784   -4.4222   -0.9373    0.1811    5.5553    3.4255    0.8843    4.2952    0.1848    0.2160    5.7452    1.5295</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 226:    93.2330   -5.8621   -2.0789   -4.4182   -0.9366    0.1816    5.5838    3.4123    0.8838    4.2565    0.1858    0.2176    5.7453    1.5284</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 227:    93.2309   -5.8549   -2.0794   -4.4153   -0.9365    0.1823    5.6720    3.3787    0.8827    4.2227    0.1866    0.2181    5.7339    1.5287</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 228:    93.2248   -5.8556   -2.0794   -4.4116   -0.9372    0.1832    5.7344    3.3780    0.8830    4.1863    0.1873    0.2200    5.7232    1.5308</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 229:    93.2215   -5.8615   -2.0798   -4.4081   -0.9373    0.1844    5.8478    3.4087    0.8821    4.1558    0.1878    0.2217    5.7174    1.5283</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 230:    93.2122   -5.8646   -2.0807   -4.4053   -0.9372    0.1858    5.8987    3.4233    0.8800    4.1288    0.1881    0.2233    5.7073    1.5272</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 231:    93.2080   -5.8665   -2.0816   -4.4025   -0.9372    0.1872    5.9544    3.4381    0.8782    4.1008    0.1883    0.2250    5.7006    1.5283</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 232:    93.1921   -5.8677   -2.0829   -4.3997   -0.9370    0.1887    5.9768    3.4440    0.8770    4.0748    0.1883    0.2268    5.7012    1.5261</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 233:    93.1794   -5.8674   -2.0840   -4.3972   -0.9363    0.1892    6.0074    3.4397    0.8757    4.0495    0.1884    0.2281    5.6997    1.5235</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 234:    93.1623   -5.8677   -2.0853   -4.3959   -0.9358    0.1898    5.9759    3.4442    0.8750    4.0330    0.1887    0.2295    5.7000    1.5223</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 235:    93.1499   -5.8709   -2.0862   -4.3918   -0.9356    0.1900    5.9951    3.4709    0.8747    4.0020    0.1891    0.2309    5.7002    1.5219</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 236:    93.1408   -5.8764   -2.0875   -4.3879   -0.9349    0.1898    6.0359    3.5027    0.8752    3.9720    0.1895    0.2321    5.7098    1.5196</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 237:    93.1307   -5.8766   -2.0887   -4.3843   -0.9344    0.1896    6.0589    3.5108    0.8755    3.9437    0.1900    0.2330    5.7176    1.5174</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 238:    93.1233   -5.8767   -2.0889   -4.3806   -0.9341    0.1891    6.0959    3.5158    0.8745    3.9173    0.1907    0.2339    5.7198    1.5169</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 239:    93.1245   -5.8810   -2.0889   -4.3775   -0.9337    0.1885    6.1196    3.5614    0.8746    3.8935    0.1915    0.2349    5.7177    1.5172</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 240:    93.1180   -5.8836   -2.0893   -4.3745   -0.9332    0.1883    6.1647    3.6004    0.8741    3.8709    0.1921    0.2360    5.7150    1.5192</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 241:    93.1096   -5.8838   -2.0898   -4.3714   -0.9327    0.1883    6.2283    3.6196    0.8743    3.8487    0.1927    0.2366    5.7177    1.5202</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 242:    93.1058   -5.8804   -2.0912   -4.3684   -0.9320    0.1888    6.2553    3.6018    0.8723    3.8274    0.1933    0.2378    5.7265    1.5198</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 243:    93.0953   -5.8762   -2.0924   -4.3668   -0.9315    0.1892    6.2692    3.5785    0.8705    3.8130    0.1940    0.2391    5.7353    1.5205</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 244:    93.0840   -5.8758   -2.0928   -4.3658   -0.9310    0.1889    6.2702    3.5746    0.8695    3.8007    0.1947    0.2401    5.7382    1.5205</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 245:    93.0720   -5.8795   -2.0933   -4.3647   -0.9306    0.1895    6.3022    3.5971    0.8685    3.7886    0.1953    0.2407    5.7367    1.5192</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 246:    93.0626   -5.8798   -2.0933   -4.3637   -0.9301    0.1898    6.2987    3.5992    0.8680    3.7781    0.1957    0.2410    5.7331    1.5184</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 247:    93.0526   -5.8805   -2.0934   -4.3610   -0.9298    0.1903    6.3067    3.6060    0.8682    3.7618    0.1963    0.2414    5.7329    1.5189</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 248:    93.0481   -5.8780   -2.0933   -4.3583   -0.9296    0.1911    6.3135    3.6007    0.8683    3.7460    0.1967    0.2420    5.7344    1.5191</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 249:    93.0483   -5.8762   -2.0933   -4.3558   -0.9294    0.1913    6.3095    3.5961    0.8685    3.7298    0.1970    0.2422    5.7414    1.5179</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 250:    93.0520   -5.8768   -2.0931   -4.3538   -0.9292    0.1912    6.2900    3.6003    0.8692    3.7176    0.1973    0.2424    5.7547    1.5148</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 251:    93.0430   -5.8769   -2.0930   -4.3516   -0.9291    0.1905    6.2815    3.6041    0.8704    3.7045    0.1975    0.2427    5.7653    1.5123</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 252:    93.0300   -5.8743   -2.0928   -4.3490   -0.9291    0.1901    6.2896    3.5885    0.8716    3.6919    0.1978    0.2428    5.7797    1.5106</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 253:    93.0217   -5.8740   -2.0926   -4.3468   -0.9289    0.1898    6.3238    3.5875    0.8731    3.6817    0.1981    0.2429    5.7885    1.5102</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 254:    93.0147   -5.8729   -2.0924   -4.3439   -0.9289    0.1892    6.3418    3.5857    0.8732    3.6683    0.1980    0.2426    5.7912    1.5102</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 255:    93.0144   -5.8743   -2.0922   -4.3407   -0.9290    0.1885    6.3755    3.5933    0.8735    3.6580    0.1979    0.2420    5.7932    1.5086</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 256:    93.0136   -5.8778   -2.0919   -4.3376   -0.9290    0.1876    6.3932    3.6240    0.8741    3.6481    0.1980    0.2418    5.7969    1.5066</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 257:    93.0116   -5.8792   -2.0917   -4.3345   -0.9291    0.1862    6.4096    3.6459    0.8744    3.6385    0.1980    0.2414    5.7990    1.5065</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 258:    93.0084   -5.8812   -2.0913   -4.3319   -0.9290    0.1842    6.4231    3.6686    0.8753    3.6281    0.1980    0.2414    5.8024    1.5050</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 259:    93.0090   -5.8866   -2.0909   -4.3293   -0.9287    0.1825    6.4361    3.7063    0.8762    3.6181    0.1981    0.2413    5.8106    1.5030</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 260:    93.0067   -5.8911   -2.0902   -4.3265   -0.9283    0.1811    6.4128    3.7384    0.8765    3.6076    0.1981    0.2412    5.8102    1.5026</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 261:    93.0060   -5.8933   -2.0894   -4.3237   -0.9284    0.1799    6.4253    3.7604    0.8765    3.5968    0.1981    0.2410    5.8080    1.5026</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 262:    93.0051   -5.8934   -2.0884   -4.3208   -0.9285    0.1789    6.4008    3.7597    0.8762    3.5855    0.1981    0.2412    5.8046    1.5020</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 263:    93.0019   -5.8945   -2.0875   -4.3182   -0.9287    0.1781    6.3788    3.7644    0.8758    3.5756    0.1982    0.2411    5.8048    1.5023</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 264:    93.0021   -5.8959   -2.0870   -4.3158   -0.9293    0.1773    6.3614    3.7682    0.8749    3.5667    0.1983    0.2410    5.8017    1.5021</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 265:    93.0053   -5.8989   -2.0866   -4.3130   -0.9296    0.1766    6.3506    3.7814    0.8739    3.5567    0.1982    0.2409    5.7995    1.5018</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 266:    93.0061   -5.8992   -2.0864   -4.3104   -0.9300    0.1757    6.3307    3.7730    0.8733    3.5471    0.1982    0.2408    5.7994    1.5012</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 267:    93.0098   -5.9009   -2.0861   -4.3077   -0.9302    0.1749    6.3326    3.7738    0.8730    3.5388    0.1983    0.2407    5.7964    1.5004</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 268:    93.0144   -5.9000   -2.0853   -4.3054   -0.9304    0.1740    6.3487    3.7623    0.8729    3.5290    0.1985    0.2405    5.7897    1.5014</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 269:    93.0146   -5.8984   -2.0847   -4.3032   -0.9306    0.1731    6.3716    3.7485    0.8735    3.5197    0.1985    0.2402    5.7867    1.5016</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 270:    93.0159   -5.8950   -2.0842   -4.3013   -0.9307    0.1722    6.3630    3.7266    0.8743    3.5098    0.1986    0.2400    5.7837    1.5016</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 271:    93.0161   -5.8925   -2.0837   -4.2995   -0.9309    0.1715    6.3539    3.7068    0.8744    3.5001    0.1986    0.2400    5.7843    1.5020</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 272:    93.0195   -5.8919   -2.0837   -4.2977   -0.9314    0.1710    6.3430    3.6964    0.8744    3.4922    0.1985    0.2400    5.7859    1.5031</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 273:    93.0184   -5.8923   -2.0837   -4.2961   -0.9318    0.1705    6.3531    3.6914    0.8743    3.4854    0.1984    0.2402    5.7880    1.5030</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 274:    93.0178   -5.8927   -2.0834   -4.2951   -0.9320    0.1701    6.3818    3.6865    0.8749    3.4824    0.1984    0.2403    5.7928    1.5020</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 275:    93.0204   -5.8937   -2.0833   -4.2942   -0.9324    0.1700    6.3961    3.6842    0.8754    3.4790    0.1984    0.2402    5.7949    1.5011</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 276:    93.0228   -5.8958   -2.0833   -4.2932   -0.9327    0.1699    6.3965    3.6870    0.8760    3.4752    0.1984    0.2402    5.7934    1.5002</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 277:    93.0251   -5.9000   -2.0833   -4.2921   -0.9329    0.1695    6.4076    3.7032    0.8766    3.4712    0.1985    0.2400    5.7979    1.4989</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 278:    93.0297   -5.9021   -2.0833   -4.2912   -0.9331    0.1690    6.4279    3.7073    0.8771    3.4671    0.1986    0.2399    5.7978    1.4981</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 279:    93.0273   -5.9021   -2.0832   -4.2902   -0.9332    0.1684    6.4349    3.7003    0.8779    3.4622    0.1988    0.2400    5.7993    1.4972</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 280:    93.0236   -5.9030   -2.0831   -4.2892   -0.9334    0.1674    6.4776    3.6986    0.8787    3.4576    0.1990    0.2401    5.8026    1.4963</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 281:    93.0172   -5.9037   -2.0829   -4.2881   -0.9337    0.1665    6.4942    3.6984    0.8797    3.4536    0.1992    0.2402    5.8056    1.4955</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 282:    93.0147   -5.9055   -2.0827   -4.2872   -0.9339    0.1656    6.4861    3.7031    0.8810    3.4492    0.1992    0.2403    5.8107    1.4954</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 283:    93.0138   -5.9053   -2.0824   -4.2865   -0.9341    0.1646    6.4962    3.6967    0.8822    3.4451    0.1993    0.2406    5.8142    1.4959</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 284:    93.0132   -5.9067   -2.0821   -4.2864   -0.9343    0.1636    6.5021    3.7013    0.8838    3.4437    0.1993    0.2406    5.8146    1.4955</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 285:    93.0138   -5.9081   -2.0819   -4.2859   -0.9343    0.1628    6.5037    3.7043    0.8851    3.4406    0.1994    0.2406    5.8146    1.4945</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 286:    93.0119   -5.9086   -2.0815   -4.2858   -0.9342    0.1620    6.5068    3.7037    0.8864    3.4395    0.1994    0.2404    5.8133    1.4936</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 287:    93.0122   -5.9089   -2.0813   -4.2860   -0.9342    0.1614    6.5202    3.7044    0.8872    3.4399    0.1997    0.2401    5.8096    1.4929</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 288:    93.0104   -5.9083   -2.0812   -4.2858   -0.9342    0.1609    6.5237    3.6997    0.8876    3.4376    0.1999    0.2398    5.8041    1.4924</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 289:    93.0076   -5.9066   -2.0812   -4.2854   -0.9342    0.1604    6.5121    3.6893    0.8881    3.4342    0.1999    0.2394    5.8021    1.4922</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 290:    93.0064   -5.9052   -2.0813   -4.2851   -0.9343    0.1602    6.5106    3.6772    0.8886    3.4309    0.2000    0.2389    5.7988    1.4915</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 291:    93.0071   -5.9031   -2.0813   -4.2849   -0.9345    0.1601    6.5031    3.6628    0.8891    3.4284    0.2000    0.2384    5.7959    1.4908</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 292:    93.0114   -5.9023   -2.0809   -4.2841   -0.9346    0.1594    6.4930    3.6538    0.8894    3.4237    0.2001    0.2383    5.7923    1.4902</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 293:    93.0148   -5.9032   -2.0807   -4.2834   -0.9348    0.1589    6.4836    3.6542    0.8898    3.4206    0.2002    0.2380    5.7893    1.4893</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 294:    93.0161   -5.9026   -2.0806   -4.2826   -0.9349    0.1582    6.4719    3.6469    0.8901    3.4176    0.2005    0.2375    5.7898    1.4886</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 295:    93.0212   -5.9008   -2.0806   -4.2817   -0.9350    0.1576    6.4649    3.6372    0.8904    3.4145    0.2007    0.2370    5.7885    1.4890</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 296:    93.0270   -5.8989   -2.0805   -4.2809   -0.9351    0.1569    6.4835    3.6279    0.8911    3.4124    0.2010    0.2366    5.7886    1.4884</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 297:    93.0291   -5.8969   -2.0803   -4.2800   -0.9352    0.1560    6.5014    3.6177    0.8915    3.4094    0.2012    0.2364    5.7873    1.4880</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 298:    93.0332   -5.8963   -2.0801   -4.2790   -0.9352    0.1551    6.5168    3.6111    0.8919    3.4065    0.2014    0.2361    5.7855    1.4875</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 299:    93.0339   -5.8961   -2.0799   -4.2782   -0.9352    0.1542    6.5288    3.6081    0.8927    3.4038    0.2015    0.2358    5.7844    1.4869</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 300:    93.0335   -5.8971   -2.0797   -4.2772   -0.9351    0.1533    6.5409    3.6097    0.8937    3.4009    0.2017    0.2356    5.7844    1.4859</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 301:    93.0320   -5.8979   -2.0796   -4.2762   -0.9350    0.1522    6.5553    3.6126    0.8945    3.3977    0.2020    0.2354    5.7898    1.4847</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 302:    93.0321   -5.8997   -2.0795   -4.2751   -0.9351    0.1513    6.5737    3.6195    0.8956    3.3943    0.2023    0.2351    5.7917    1.4835</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 303:    93.0328   -5.8984   -2.0792   -4.2739   -0.9351    0.1504    6.5888    3.6111    0.8964    3.3912    0.2025    0.2350    5.7915    1.4825</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 304:    93.0357   -5.8969   -2.0790   -4.2728   -0.9350    0.1494    6.5907    3.6018    0.8975    3.3882    0.2026    0.2348    5.7929    1.4813</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 305:    93.0351   -5.8953   -2.0786   -4.2718   -0.9349    0.1484    6.5764    3.5916    0.8986    3.3858    0.2027    0.2345    5.7937    1.4816</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 306:    93.0352   -5.8950   -2.0784   -4.2707   -0.9350    0.1475    6.5727    3.5890    0.8999    3.3835    0.2028    0.2341    5.7981    1.4810</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 307:    93.0354   -5.8947   -2.0784   -4.2699   -0.9351    0.1466    6.5759    3.5853    0.9010    3.3820    0.2030    0.2339    5.8028    1.4799</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 308:    93.0336   -5.8938   -2.0783   -4.2690   -0.9351    0.1459    6.5855    3.5776    0.9022    3.3809    0.2031    0.2333    5.8014    1.4788</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 309:    93.0311   -5.8931   -2.0780   -4.2683   -0.9351    0.1452    6.5799    3.5717    0.9038    3.3805    0.2033    0.2328    5.8048    1.4779</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 310:    93.0303   -5.8915   -2.0778   -4.2675   -0.9352    0.1447    6.5716    3.5609    0.9046    3.3797    0.2033    0.2323    5.8060    1.4774</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 311:    93.0275   -5.8915   -2.0776   -4.2668   -0.9352    0.1441    6.5679    3.5581    0.9052    3.3788    0.2034    0.2319    5.8056    1.4770</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 312:    93.0258   -5.8920   -2.0775   -4.2659   -0.9352    0.1435    6.5573    3.5571    0.9058    3.3775    0.2033    0.2314    5.8053    1.4766</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 313:    93.0234   -5.8928   -2.0773   -4.2649   -0.9353    0.1431    6.5510    3.5573    0.9065    3.3761    0.2033    0.2309    5.8046    1.4757</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 314:    93.0237   -5.8937   -2.0771   -4.2639   -0.9355    0.1425    6.5488    3.5578    0.9074    3.3747    0.2033    0.2303    5.8029    1.4751</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 315:    93.0237   -5.8940   -2.0769   -4.2629   -0.9357    0.1420    6.5480    3.5565    0.9081    3.3730    0.2034    0.2299    5.8010    1.4746</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 316:    93.0218   -5.8936   -2.0767   -4.2617   -0.9358    0.1413    6.5424    3.5517    0.9087    3.3705    0.2034    0.2296    5.7992    1.4741</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 317:    93.0218   -5.8932   -2.0766   -4.2606   -0.9359    0.1406    6.5576    3.5474    0.9091    3.3679    0.2034    0.2292    5.7965    1.4736</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 318:    93.0215   -5.8927   -2.0765   -4.2597   -0.9361    0.1402    6.5884    3.5420    0.9096    3.3661    0.2034    0.2288    5.7960    1.4727</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 319:    93.0201   -5.8938   -2.0764   -4.2588   -0.9361    0.1397    6.6095    3.5439    0.9101    3.3642    0.2034    0.2283    5.7943    1.4719</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 320:    93.0188   -5.8926   -2.0763   -4.2579   -0.9361    0.1392    6.6170    3.5368    0.9103    3.3622    0.2034    0.2282    5.7930    1.4711</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 321:    93.0155   -5.8908   -2.0762   -4.2569   -0.9361    0.1385    6.6328    3.5268    0.9105    3.3598    0.2033    0.2282    5.7921    1.4702</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 322:    93.0133   -5.8894   -2.0760   -4.2561   -0.9360    0.1378    6.6415    3.5192    0.9110    3.3580    0.2032    0.2282    5.7903    1.4698</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 323:    93.0089   -5.8888   -2.0759   -4.2555   -0.9360    0.1372    6.6480    3.5131    0.9107    3.3563    0.2031    0.2285    5.7915    1.4691</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 324:    93.0038   -5.8881   -2.0758   -4.2547   -0.9359    0.1364    6.6639    3.5076    0.9106    3.3547    0.2029    0.2287    5.7912    1.4687</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 325:    93.0011   -5.8871   -2.0756   -4.2540   -0.9359    0.1361    6.6587    3.5005    0.9102    3.3531    0.2028    0.2289    5.7896    1.4686</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 326:    93.0033   -5.8874   -2.0755   -4.2535   -0.9360    0.1360    6.6621    3.4995    0.9098    3.3510    0.2026    0.2294    5.7883    1.4686</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 327:    93.0039   -5.8885   -2.0754   -4.2532   -0.9361    0.1359    6.6589    3.5051    0.9093    3.3497    0.2025    0.2297    5.7869    1.4687</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 328:    93.0061   -5.8896   -2.0753   -4.2529   -0.9362    0.1355    6.6671    3.5132    0.9088    3.3484    0.2024    0.2299    5.7854    1.4687</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 329:    93.0077   -5.8915   -2.0755   -4.2525   -0.9363    0.1352    6.6719    3.5255    0.9085    3.3464    0.2024    0.2301    5.7850    1.4682</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 330:    93.0061   -5.8942   -2.0757   -4.2518   -0.9365    0.1351    6.6696    3.5399    0.9083    3.3438    0.2023    0.2302    5.7840    1.4680</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 331:    93.0032   -5.8953   -2.0759   -4.2513   -0.9366    0.1348    6.6554    3.5433    0.9080    3.3411    0.2022    0.2302    5.7839    1.4677</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 332:    93.0013   -5.8956   -2.0761   -4.2507   -0.9367    0.1347    6.6298    3.5423    0.9079    3.3385    0.2021    0.2302    5.7851    1.4672</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 333:    93.0026   -5.8950   -2.0764   -4.2502   -0.9368    0.1346    6.6207    3.5365    0.9078    3.3357    0.2021    0.2303    5.7849    1.4667</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 334:    93.0019   -5.8933   -2.0767   -4.2497   -0.9369    0.1348    6.6145    3.5259    0.9077    3.3330    0.2021    0.2302    5.7856    1.4662</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 335:    93.0038   -5.8930   -2.0768   -4.2492   -0.9370    0.1348    6.6200    3.5227    0.9078    3.3307    0.2020    0.2303    5.7845    1.4654</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 336:    93.0038   -5.8923   -2.0765   -4.2488   -0.9371    0.1348    6.6316    3.5180    0.9081    3.3291    0.2019    0.2304    5.7837    1.4654</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 337:    93.0074   -5.8927   -2.0764   -4.2485   -0.9373    0.1349    6.6509    3.5187    0.9083    3.3275    0.2018    0.2304    5.7808    1.4655</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 338:    93.0117   -5.8950   -2.0761   -4.2483   -0.9377    0.1349    6.6559    3.5291    0.9087    3.3263    0.2018    0.2303    5.7770    1.4657</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 339:    93.0172   -5.8960   -2.0759   -4.2482   -0.9380    0.1349    6.6610    3.5304    0.9090    3.3260    0.2017    0.2302    5.7744    1.4657</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 340:    93.0179   -5.8977   -2.0757   -4.2481   -0.9383    0.1349    6.6583    3.5340    0.9093    3.3263    0.2017    0.2301    5.7749    1.4650</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 341:    93.0201   -5.8986   -2.0755   -4.2484   -0.9386    0.1348    6.6603    3.5337    0.9092    3.3283    0.2018    0.2300    5.7738    1.4650</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 342:    93.0245   -5.8990   -2.0752   -4.2484   -0.9389    0.1348    6.6680    3.5324    0.9093    3.3297    0.2018    0.2300    5.7727    1.4649</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 343:    93.0302   -5.9006   -2.0751   -4.2484   -0.9391    0.1347    6.6715    3.5367    0.9093    3.3313    0.2017    0.2300    5.7729    1.4645</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 344:    93.0340   -5.9026   -2.0749   -4.2484   -0.9394    0.1346    6.6793    3.5438    0.9093    3.3326    0.2017    0.2301    5.7709    1.4646</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 345:    93.0372   -5.9049   -2.0746   -4.2484   -0.9397    0.1347    6.6874    3.5515    0.9090    3.3340    0.2017    0.2301    5.7688    1.4648</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 346:    93.0372   -5.9063   -2.0743   -4.2483   -0.9399    0.1348    6.6963    3.5592    0.9090    3.3348    0.2018    0.2299    5.7680    1.4656</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 347:    93.0383   -5.9075   -2.0742   -4.2481   -0.9402    0.1350    6.7101    3.5658    0.9093    3.3353    0.2018    0.2299    5.7672    1.4649</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 348:    93.0412   -5.9084   -2.0742   -4.2479   -0.9405    0.1351    6.7183    3.5707    0.9095    3.3356    0.2019    0.2297    5.7657    1.4645</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 349:    93.0436   -5.9097   -2.0742   -4.2477   -0.9407    0.1351    6.7143    3.5783    0.9098    3.3359    0.2019    0.2295    5.7646    1.4643</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 350:    93.0476   -5.9105   -2.0742   -4.2474   -0.9409    0.1351    6.7239    3.5808    0.9100    3.3354    0.2019    0.2294    5.7628    1.4639</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 351:    93.0506   -5.9113   -2.0741   -4.2473   -0.9411    0.1352    6.7270    3.5825    0.9103    3.3356    0.2019    0.2292    5.7604    1.4637</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 352:    93.0529   -5.9127   -2.0740   -4.2471   -0.9413    0.1353    6.7312    3.5886    0.9107    3.3358    0.2019    0.2290    5.7594    1.4634</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 353:    93.0580   -5.9139   -2.0739   -4.2470   -0.9415    0.1354    6.7315    3.5922    0.9111    3.3357    0.2019    0.2288    5.7571    1.4636</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 354:    93.0639   -5.9129   -2.0738   -4.2468   -0.9417    0.1354    6.7390    3.5876    0.9112    3.3356    0.2018    0.2286    5.7541    1.4642</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 355:    93.0671   -5.9131   -2.0737   -4.2467   -0.9417    0.1353    6.7348    3.5906    0.9113    3.3354    0.2017    0.2284    5.7520    1.4648</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 356:    93.0682   -5.9134   -2.0737   -4.2465   -0.9418    0.1352    6.7329    3.5962    0.9113    3.3353    0.2017    0.2283    5.7505    1.4649</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 357:    93.0698   -5.9128   -2.0738   -4.2471   -0.9418    0.1354    6.7397    3.5933    0.9115    3.3388    0.2016    0.2280    5.7512    1.4651</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 358:    93.0709   -5.9129   -2.0740   -4.2475   -0.9419    0.1355    6.7379    3.5915    0.9119    3.3416    0.2016    0.2278    5.7526    1.4644</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 359:    93.0718   -5.9128   -2.0742   -4.2478   -0.9419    0.1358    6.7428    3.5886    0.9123    3.3432    0.2015    0.2275    5.7516    1.4641</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 360:    93.0693   -5.9136   -2.0743   -4.2481   -0.9419    0.1360    6.7385    3.5930    0.9125    3.3443    0.2015    0.2272    5.7511    1.4636</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 361:    93.0674   -5.9148   -2.0744   -4.2484   -0.9419    0.1361    6.7230    3.6002    0.9127    3.3458    0.2015    0.2270    5.7514    1.4634</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 362:    93.0660   -5.9168   -2.0745   -4.2486   -0.9420    0.1361    6.7313    3.6117    0.9130    3.3473    0.2014    0.2270    5.7506    1.4636</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 363:    93.0635   -5.9196   -2.0746   -4.2490   -0.9421    0.1360    6.7388    3.6275    0.9132    3.3500    0.2014    0.2269    5.7493    1.4636</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 364:    93.0631   -5.9210   -2.0747   -4.2497   -0.9421    0.1361    6.7383    3.6323    0.9135    3.3548    0.2015    0.2268    5.7483    1.4634</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 365:    93.0635   -5.9219   -2.0747   -4.2504   -0.9421    0.1361    6.7402    3.6341    0.9137    3.3590    0.2015    0.2268    5.7461    1.4635</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 366:    93.0640   -5.9232   -2.0746   -4.2511   -0.9422    0.1362    6.7477    3.6409    0.9142    3.3624    0.2015    0.2267    5.7451    1.4641</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 367:    93.0616   -5.9247   -2.0746   -4.2518   -0.9422    0.1364    6.7557    3.6473    0.9148    3.3653    0.2015    0.2269    5.7472    1.4640</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 368:    93.0601   -5.9247   -2.0746   -4.2522   -0.9422    0.1366    6.7632    3.6452    0.9150    3.3678    0.2015    0.2270    5.7482    1.4639</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 369:    93.0583   -5.9240   -2.0748   -4.2527   -0.9423    0.1369    6.7737    3.6395    0.9148    3.3695    0.2015    0.2273    5.7499    1.4636</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 370:    93.0591   -5.9236   -2.0752   -4.2532   -0.9423    0.1373    6.7721    3.6352    0.9145    3.3718    0.2015    0.2276    5.7513    1.4636</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 371:    93.0607   -5.9235   -2.0755   -4.2540   -0.9424    0.1378    6.7682    3.6330    0.9143    3.3754    0.2015    0.2280    5.7535    1.4635</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 372:    93.0615   -5.9229   -2.0759   -4.2549   -0.9424    0.1382    6.7640    3.6288    0.9142    3.3795    0.2014    0.2284    5.7553    1.4633</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 373:    93.0612   -5.9237   -2.0763   -4.2557   -0.9424    0.1385    6.7641    3.6327    0.9140    3.3832    0.2012    0.2288    5.7570    1.4629</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 374:    93.0611   -5.9240   -2.0766   -4.2565   -0.9424    0.1389    6.7701    3.6341    0.9137    3.3872    0.2011    0.2293    5.7599    1.4625</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 375:    93.0615   -5.9247   -2.0770   -4.2573   -0.9424    0.1393    6.7729    3.6362    0.9134    3.3912    0.2009    0.2296    5.7629    1.4620</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 376:    93.0621   -5.9248   -2.0772   -4.2578   -0.9425    0.1397    6.7732    3.6371    0.9132    3.3931    0.2008    0.2298    5.7654    1.4613</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 377:    93.0622   -5.9255   -2.0774   -4.2582   -0.9426    0.1401    6.7681    3.6389    0.9130    3.3953    0.2007    0.2300    5.7678    1.4607</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 378:    93.0609   -5.9256   -2.0775   -4.2585   -0.9426    0.1402    6.7705    3.6381    0.9128    3.3972    0.2006    0.2301    5.7673    1.4602</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 379:    93.0590   -5.9262   -2.0776   -4.2589   -0.9426    0.1404    6.7777    3.6382    0.9127    3.3991    0.2005    0.2303    5.7668    1.4599</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 380:    93.0607   -5.9258   -2.0777   -4.2595   -0.9427    0.1407    6.7836    3.6350    0.9127    3.4031    0.2004    0.2305    5.7665    1.4598</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 381:    93.0617   -5.9251   -2.0777   -4.2596   -0.9427    0.1410    6.7880    3.6294    0.9125    3.4030    0.2003    0.2307    5.7651    1.4601</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 382:    93.0631   -5.9252   -2.0777   -4.2599   -0.9428    0.1413    6.7912    3.6278    0.9123    3.4038    0.2002    0.2310    5.7649    1.4606</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 383:    93.0621   -5.9253   -2.0778   -4.2602   -0.9429    0.1415    6.7834    3.6279    0.9119    3.4053    0.2000    0.2312    5.7657    1.4607</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 384:    93.0614   -5.9254   -2.0779   -4.2604   -0.9430    0.1416    6.7853    3.6280    0.9115    3.4066    0.1999    0.2313    5.7662    1.4608</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 385:    93.0613   -5.9259   -2.0780   -4.2605   -0.9430    0.1418    6.7757    3.6301    0.9112    3.4066    0.1997    0.2315    5.7678    1.4609</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 386:    93.0614   -5.9264   -2.0780   -4.2607   -0.9431    0.1418    6.7610    3.6331    0.9110    3.4073    0.1995    0.2317    5.7696    1.4612</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 387:    93.0631   -5.9276   -2.0780   -4.2610   -0.9432    0.1420    6.7595    3.6397    0.9108    3.4085    0.1993    0.2318    5.7716    1.4612</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 388:    93.0650   -5.9282   -2.0779   -4.2613   -0.9433    0.1421    6.7552    3.6445    0.9106    3.4092    0.1992    0.2318    5.7731    1.4612</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 389:    93.0644   -5.9286   -2.0779   -4.2616   -0.9434    0.1422    6.7488    3.6471    0.9104    3.4098    0.1991    0.2317    5.7724    1.4614</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 390:    93.0653   -5.9297   -2.0778   -4.2619   -0.9435    0.1423    6.7412    3.6524    0.9101    3.4103    0.1990    0.2317    5.7722    1.4615</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 391:    93.0647   -5.9297   -2.0778   -4.2623   -0.9435    0.1425    6.7508    3.6524    0.9101    3.4115    0.1989    0.2317    5.7729    1.4621</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 392:    93.0637   -5.9293   -2.0778   -4.2624   -0.9436    0.1427    6.7572    3.6498    0.9102    3.4109    0.1988    0.2317    5.7727    1.4623</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 393:    93.0657   -5.9294   -2.0778   -4.2632   -0.9436    0.1429    6.7607    3.6496    0.9104    3.4148    0.1987    0.2318    5.7719    1.4621</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 394:    93.0689   -5.9293   -2.0779   -4.2635   -0.9438    0.1431    6.7635    3.6489    0.9108    3.4141    0.1987    0.2318    5.7724    1.4623</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 395:    93.0705   -5.9295   -2.0780   -4.2640   -0.9438    0.1433    6.7753    3.6500    0.9110    3.4145    0.1986    0.2319    5.7724    1.4622</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 396:    93.0704   -5.9296   -2.0780   -4.2648   -0.9438    0.1435    6.7793    3.6511    0.9112    3.4175    0.1985    0.2319    5.7720    1.4618</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 397:    93.0715   -5.9302   -2.0781   -4.2656   -0.9438    0.1437    6.7782    3.6530    0.9114    3.4206    0.1985    0.2320    5.7706    1.4617</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 398:    93.0719   -5.9297   -2.0781   -4.2666   -0.9438    0.1439    6.7774    3.6510    0.9120    3.4258    0.1984    0.2319    5.7709    1.4616</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 399:    93.0720   -5.9296   -2.0781   -4.2678   -0.9438    0.1441    6.7819    3.6526    0.9126    3.4317    0.1984    0.2319    5.7717    1.4615</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 400:    93.0730   -5.9296   -2.0783   -4.2691   -0.9438    0.1443    6.7786    3.6538    0.9129    3.4389    0.1983    0.2319    5.7716    1.4614</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 401:    93.0728   -5.9292   -2.0783   -4.2706   -0.9437    0.1445    6.7731    3.6521    0.9133    3.4478    0.1982    0.2319    5.7701    1.4615</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 402:    93.0732   -5.9289   -2.0784   -4.2718   -0.9438    0.1447    6.7698    3.6517    0.9137    3.4542    0.1981    0.2319    5.7689    1.4618</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 403:    93.0732   -5.9301   -2.0785   -4.2730   -0.9438    0.1450    6.7640    3.6576    0.9142    3.4593    0.1980    0.2320    5.7693    1.4615</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 404:    93.0710   -5.9316   -2.0787   -4.2740   -0.9439    0.1453    6.7544    3.6647    0.9147    3.4644    0.1979    0.2320    5.7708    1.4611</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 405:    93.0687   -5.9322   -2.0788   -4.2750   -0.9440    0.1454    6.7547    3.6663    0.9153    3.4693    0.1978    0.2322    5.7714    1.4608</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 406:    93.0673   -5.9337   -2.0789   -4.2760   -0.9440    0.1456    6.7563    3.6726    0.9163    3.4742    0.1977    0.2324    5.7718    1.4603</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 407:    93.0653   -5.9345   -2.0791   -4.2769   -0.9440    0.1456    6.7601    3.6756    0.9169    3.4786    0.1976    0.2327    5.7732    1.4598</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 408:    93.0640   -5.9357   -2.0793   -4.2779   -0.9440    0.1455    6.7547    3.6821    0.9177    3.4838    0.1974    0.2329    5.7751    1.4592</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 409:    93.0646   -5.9371   -2.0795   -4.2784   -0.9440    0.1456    6.7486    3.6915    0.9184    3.4863    0.1973    0.2330    5.7766    1.4588</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 410:    93.0639   -5.9391   -2.0797   -4.2791   -0.9441    0.1457    6.7523    3.7048    0.9192    3.4889    0.1972    0.2329    5.7770    1.4582</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 411:    93.0628   -5.9401   -2.0799   -4.2799   -0.9442    0.1457    6.7529    3.7113    0.9199    3.4929    0.1972    0.2329    5.7761    1.4580</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 412:    93.0620   -5.9402   -2.0801   -4.2808   -0.9443    0.1458    6.7502    3.7099    0.9207    3.4972    0.1971    0.2328    5.7767    1.4577</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 413:    93.0628   -5.9403   -2.0804   -4.2816   -0.9443    0.1461    6.7466    3.7091    0.9214    3.5007    0.1970    0.2326    5.7771    1.4572</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 414:    93.0627   -5.9405   -2.0807   -4.2825   -0.9443    0.1463    6.7480    3.7089    0.9221    3.5048    0.1969    0.2325    5.7774    1.4567</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 415:    93.0614   -5.9402   -2.0810   -4.2836   -0.9443    0.1465    6.7474    3.7066    0.9226    3.5098    0.1969    0.2323    5.7780    1.4564</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 416:    93.0610   -5.9408   -2.0813   -4.2848   -0.9443    0.1469    6.7544    3.7116    0.9229    3.5157    0.1968    0.2321    5.7786    1.4559</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 417:    93.0601   -5.9413   -2.0815   -4.2860   -0.9443    0.1471    6.7592    3.7158    0.9234    3.5206    0.1967    0.2319    5.7793    1.4556</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 418:    93.0606   -5.9424   -2.0817   -4.2868   -0.9444    0.1473    6.7589    3.7214    0.9238    3.5237    0.1966    0.2318    5.7787    1.4553</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 419:    93.0605   -5.9433   -2.0818   -4.2877   -0.9444    0.1476    6.7641    3.7253    0.9242    3.5274    0.1965    0.2316    5.7780    1.4552</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 420:    93.0623   -5.9427   -2.0819   -4.2886   -0.9445    0.1478    6.7593    3.7216    0.9246    3.5310    0.1965    0.2314    5.7772    1.4551</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 421:    93.0630   -5.9418   -2.0819   -4.2895   -0.9445    0.1480    6.7489    3.7166    0.9250    3.5337    0.1964    0.2312    5.7764    1.4552</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 422:    93.0626   -5.9410   -2.0820   -4.2900   -0.9445    0.1482    6.7452    3.7124    0.9253    3.5350    0.1963    0.2311    5.7758    1.4549</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 423:    93.0629   -5.9407   -2.0822   -4.2906   -0.9446    0.1484    6.7409    3.7095    0.9256    3.5360    0.1963    0.2309    5.7753    1.4546</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 424:    93.0637   -5.9404   -2.0821   -4.2912   -0.9446    0.1485    6.7387    3.7073    0.9258    3.5370    0.1962    0.2308    5.7733    1.4546</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 425:    93.0625   -5.9402   -2.0821   -4.2917   -0.9447    0.1486    6.7330    3.7061    0.9258    3.5381    0.1961    0.2306    5.7722    1.4548</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 426:    93.0624   -5.9399   -2.0820   -4.2921   -0.9447    0.1487    6.7256    3.7034    0.9259    3.5387    0.1961    0.2304    5.7719    1.4545</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 427:    93.0606   -5.9397   -2.0821   -4.2925   -0.9447    0.1488    6.7139    3.7010    0.9257    3.5394    0.1961    0.2301    5.7730    1.4545</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 428:    93.0601   -5.9394   -2.0822   -4.2929   -0.9447    0.1489    6.7062    3.6984    0.9256    3.5405    0.1961    0.2298    5.7723    1.4543</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 429:    93.0615   -5.9391   -2.0821   -4.2933   -0.9447    0.1490    6.7040    3.6956    0.9254    3.5412    0.1960    0.2297    5.7714    1.4544</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 430:    93.0654   -5.9389   -2.0820   -4.2934   -0.9448    0.1491    6.7016    3.6934    0.9253    3.5412    0.1960    0.2295    5.7710    1.4546</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 431:    93.0674   -5.9392   -2.0819   -4.2936   -0.9449    0.1491    6.6946    3.6935    0.9252    3.5414    0.1960    0.2294    5.7699    1.4547</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 432:    93.0683   -5.9401   -2.0818   -4.2938   -0.9450    0.1491    6.6895    3.6960    0.9250    3.5417    0.1961    0.2292    5.7687    1.4549</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 433:    93.0693   -5.9411   -2.0815   -4.2942   -0.9451    0.1491    6.6826    3.7003    0.9254    3.5433    0.1961    0.2291    5.7679    1.4549</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 434:    93.0720   -5.9410   -2.0813   -4.2945   -0.9452    0.1491    6.6842    3.6998    0.9258    3.5440    0.1960    0.2290    5.7662    1.4548</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 435:    93.0735   -5.9417   -2.0811   -4.2947   -0.9453    0.1490    6.6907    3.7029    0.9261    3.5446    0.1960    0.2290    5.7651    1.4548</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 436:    93.0752   -5.9430   -2.0809   -4.2949   -0.9454    0.1489    6.6939    3.7083    0.9266    3.5459    0.1959    0.2290    5.7638    1.4547</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 437:    93.0774   -5.9442   -2.0807   -4.2952   -0.9455    0.1487    6.7016    3.7147    0.9270    3.5474    0.1960    0.2290    5.7624    1.4550</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 438:    93.0795   -5.9453   -2.0805   -4.2954   -0.9456    0.1485    6.7089    3.7212    0.9275    3.5491    0.1959    0.2291    5.7614    1.4551</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 439:    93.0816   -5.9467   -2.0802   -4.2956   -0.9457    0.1484    6.7230    3.7291    0.9281    3.5503    0.1959    0.2293    5.7616    1.4551</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 440:    93.0834   -5.9475   -2.0800   -4.2957   -0.9458    0.1480    6.7306    3.7340    0.9287    3.5512    0.1960    0.2295    5.7631    1.4551</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 441:    93.0855   -5.9480   -2.0797   -4.2961   -0.9459    0.1478    6.7436    3.7373    0.9292    3.5534    0.1960    0.2299    5.7642    1.4553</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 442:    93.0885   -5.9488   -2.0793   -4.2963   -0.9460    0.1474    6.7533    3.7419    0.9297    3.5546    0.1960    0.2304    5.7630    1.4554</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 443:    93.0907   -5.9497   -2.0789   -4.2967   -0.9461    0.1471    6.7641    3.7469    0.9304    3.5570    0.1960    0.2308    5.7616    1.4554</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 444:    93.0905   -5.9504   -2.0785   -4.2972   -0.9462    0.1467    6.7570    3.7486    0.9312    3.5601    0.1960    0.2311    5.7604    1.4553</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 445:    93.0903   -5.9515   -2.0782   -4.2977   -0.9462    0.1462    6.7547    3.7543    0.9319    3.5635    0.1960    0.2314    5.7595    1.4550</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 446:    93.0902   -5.9530   -2.0778   -4.2982   -0.9462    0.1459    6.7562    3.7615    0.9325    3.5664    0.1960    0.2316    5.7580    1.4548</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 447:    93.0905   -5.9541   -2.0775   -4.2990   -0.9463    0.1455    6.7639    3.7668    0.9333    3.5719    0.1960    0.2318    5.7574    1.4543</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 448:    93.0912   -5.9555   -2.0772   -4.2999   -0.9463    0.1452    6.7671    3.7736    0.9340    3.5783    0.1960    0.2322    5.7572    1.4540</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 449:    93.0918   -5.9570   -2.0769   -4.3004   -0.9464    0.1448    6.7824    3.7802    0.9345    3.5809    0.1960    0.2326    5.7561    1.4537</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 450:    93.0901   -5.9579   -2.0766   -4.3011   -0.9464    0.1444    6.7866    3.7829    0.9351    3.5853    0.1959    0.2329    5.7551    1.4536</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 451:    93.0899   -5.9594   -2.0763   -4.3016   -0.9465    0.1439    6.7875    3.7896    0.9356    3.5888    0.1959    0.2332    5.7550    1.4537</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 452:    93.0902   -5.9603   -2.0759   -4.3023   -0.9465    0.1435    6.7953    3.7926    0.9363    3.5927    0.1958    0.2335    5.7537    1.4538</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 453:    93.0906   -5.9615   -2.0755   -4.3026   -0.9465    0.1430    6.7996    3.7982    0.9372    3.5950    0.1958    0.2338    5.7531    1.4539</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 454:    93.0909   -5.9614   -2.0751   -4.3029   -0.9465    0.1425    6.8016    3.7969    0.9381    3.5976    0.1958    0.2340    5.7532    1.4540</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 455:    93.0909   -5.9610   -2.0749   -4.3035   -0.9464    0.1420    6.8022    3.7940    0.9391    3.6022    0.1957    0.2342    5.7532    1.4539</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 456:    93.0916   -5.9600   -2.0746   -4.3045   -0.9463    0.1416    6.7942    3.7893    0.9398    3.6104    0.1957    0.2344    5.7540    1.4537</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 457:    93.0905   -5.9595   -2.0744   -4.3058   -0.9463    0.1411    6.7931    3.7866    0.9407    3.6210    0.1956    0.2345    5.7551    1.4534</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 458:    93.0905   -5.9597   -2.0742   -4.3071   -0.9462    0.1407    6.7877    3.7882    0.9416    3.6327    0.1955    0.2347    5.7566    1.4532</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 459:    93.0895   -5.9598   -2.0741   -4.3078   -0.9461    0.1402    6.7884    3.7889    0.9425    3.6383    0.1955    0.2349    5.7601    1.4527</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 460:    93.0871   -5.9602   -2.0741   -4.3086   -0.9460    0.1398    6.7889    3.7912    0.9434    3.6439    0.1954    0.2350    5.7619    1.4522</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 461:    93.0854   -5.9613   -2.0739   -4.3091   -0.9459    0.1393    6.7813    3.7973    0.9440    3.6481    0.1953    0.2351    5.7620    1.4520</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 462:    93.0838   -5.9623   -2.0737   -4.3093   -0.9458    0.1388    6.7791    3.8037    0.9445    3.6498    0.1953    0.2353    5.7616    1.4518</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 463:    93.0816   -5.9629   -2.0734   -4.3095   -0.9457    0.1384    6.7733    3.8070    0.9451    3.6508    0.1952    0.2355    5.7626    1.4519</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 464:    93.0792   -5.9631   -2.0731   -4.3095   -0.9457    0.1379    6.7697    3.8081    0.9460    3.6507    0.1951    0.2358    5.7638    1.4518</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 465:    93.0775   -5.9633   -2.0728   -4.3095   -0.9456    0.1374    6.7717    3.8095    0.9466    3.6506    0.1950    0.2361    5.7647    1.4519</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 466:    93.0774   -5.9640   -2.0725   -4.3097   -0.9455    0.1368    6.7686    3.8141    0.9473    3.6516    0.1949    0.2363    5.7662    1.4516</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 467:    93.0773   -5.9646   -2.0722   -4.3100   -0.9454    0.1364    6.7668    3.8172    0.9480    3.6535    0.1948    0.2366    5.7671    1.4516</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 468:    93.0777   -5.9653   -2.0719   -4.3106   -0.9453    0.1358    6.7648    3.8226    0.9487    3.6566    0.1947    0.2371    5.7681    1.4514</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 469:    93.0778   -5.9657   -2.0717   -4.3112   -0.9453    0.1353    6.7617    3.8253    0.9495    3.6588    0.1947    0.2375    5.7686    1.4510</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 470:    93.0769   -5.9663   -2.0715   -4.3117   -0.9452    0.1347    6.7650    3.8278    0.9503    3.6613    0.1947    0.2379    5.7688    1.4506</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 471:    93.0749   -5.9664   -2.0714   -4.3123   -0.9451    0.1342    6.7708    3.8280    0.9510    3.6643    0.1947    0.2383    5.7699    1.4505</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 472:    93.0721   -5.9668   -2.0713   -4.3127   -0.9450    0.1337    6.7756    3.8284    0.9517    3.6665    0.1947    0.2386    5.7710    1.4501</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 473:    93.0696   -5.9670   -2.0713   -4.3133   -0.9449    0.1333    6.7784    3.8280    0.9522    3.6698    0.1947    0.2388    5.7716    1.4498</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 474:    93.0674   -5.9673   -2.0714   -4.3138   -0.9448    0.1329    6.7761    3.8281    0.9527    3.6731    0.1947    0.2390    5.7729    1.4496</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 475:    93.0655   -5.9679   -2.0714   -4.3143   -0.9447    0.1324    6.7779    3.8302    0.9531    3.6762    0.1947    0.2391    5.7743    1.4493</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 476:    93.0643   -5.9682   -2.0713   -4.3144   -0.9447    0.1321    6.7776    3.8314    0.9536    3.6768    0.1946    0.2391    5.7762    1.4492</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 477:    93.0631   -5.9684   -2.0714   -4.3146   -0.9446    0.1317    6.7725    3.8326    0.9540    3.6787    0.1945    0.2392    5.7768    1.4492</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 478:    93.0630   -5.9693   -2.0715   -4.3148   -0.9447    0.1314    6.7626    3.8365    0.9543    3.6788    0.1944    0.2395    5.7785    1.4492</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 479:    93.0634   -5.9710   -2.0714   -4.3148   -0.9447    0.1310    6.7499    3.8462    0.9547    3.6778    0.1943    0.2399    5.7798    1.4490</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 480:    93.0651   -5.9732   -2.0715   -4.3147   -0.9447    0.1307    6.7463    3.8616    0.9548    3.6770    0.1943    0.2403    5.7810    1.4488</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 481:    93.0664   -5.9747   -2.0715   -4.3149   -0.9447    0.1304    6.7442    3.8722    0.9550    3.6780    0.1941    0.2409    5.7811    1.4487</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 482:    93.0647   -5.9762   -2.0714   -4.3151   -0.9447    0.1300    6.7389    3.8834    0.9554    3.6784    0.1941    0.2414    5.7816    1.4484</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 483:    93.0628   -5.9778   -2.0714   -4.3153   -0.9447    0.1295    6.7352    3.8943    0.9557    3.6783    0.1940    0.2419    5.7820    1.4483</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 484:    93.0614   -5.9787   -2.0715   -4.3153   -0.9447    0.1291    6.7314    3.8999    0.9559    3.6774    0.1939    0.2424    5.7819    1.4481</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 485:    93.0593   -5.9789   -2.0715   -4.3153   -0.9447    0.1288    6.7293    3.9007    0.9561    3.6765    0.1938    0.2430    5.7815    1.4480</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 486:    93.0575   -5.9793   -2.0716   -4.3152   -0.9447    0.1284    6.7331    3.9021    0.9563    3.6753    0.1938    0.2434    5.7806    1.4480</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 487:    93.0555   -5.9796   -2.0716   -4.3152   -0.9447    0.1281    6.7342    3.9036    0.9566    3.6741    0.1937    0.2439    5.7805    1.4479</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 488:    93.0545   -5.9795   -2.0716   -4.3152   -0.9447    0.1277    6.7365    3.9024    0.9569    3.6729    0.1937    0.2444    5.7795    1.4480</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 489:    93.0550   -5.9800   -2.0716   -4.3152   -0.9447    0.1274    6.7336    3.9046    0.9571    3.6719    0.1937    0.2448    5.7778    1.4481</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 490:    93.0571   -5.9793   -2.0715   -4.3152   -0.9447    0.1272    6.7331    3.9009    0.9573    3.6704    0.1936    0.2450    5.7767    1.4484</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 491:    93.0584   -5.9787   -2.0714   -4.3151   -0.9447    0.1270    6.7280    3.8980    0.9575    3.6688    0.1936    0.2452    5.7764    1.4487</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 492:    93.0589   -5.9786   -2.0714   -4.3150   -0.9447    0.1267    6.7264    3.8971    0.9578    3.6675    0.1936    0.2455    5.7759    1.4489</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 493:    93.0593   -5.9790   -2.0711   -4.3149   -0.9447    0.1265    6.7273    3.8982    0.9584    3.6660    0.1935    0.2456    5.7759    1.4490</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 494:    93.0594   -5.9805   -2.0710   -4.3149   -0.9448    0.1263    6.7291    3.9066    0.9590    3.6650    0.1935    0.2457    5.7758    1.4489</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 495:    93.0596   -5.9816   -2.0709   -4.3148   -0.9448    0.1261    6.7281    3.9123    0.9593    3.6639    0.1936    0.2459    5.7745    1.4490</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 496:    93.0590   -5.9829   -2.0707   -4.3148   -0.9448    0.1259    6.7319    3.9227    0.9597    3.6628    0.1936    0.2461    5.7740    1.4491</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 497:    93.0580   -5.9842   -2.0706   -4.3148   -0.9448    0.1257    6.7388    3.9326    0.9600    3.6623    0.1936    0.2463    5.7732    1.4492</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 498:    93.0578   -5.9848   -2.0705   -4.3148   -0.9448    0.1255    6.7447    3.9368    0.9605    3.6618    0.1936    0.2464    5.7726    1.4494</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 499:    93.0568   -5.9843   -2.0704   -4.3147   -0.9447    0.1253    6.7475    3.9341    0.9609    3.6612    0.1937    0.2467    5.7718    1.4495</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 500:    93.0563   -5.9831   -2.0703   -4.3147   -0.9447    0.1251    6.7500    3.9292    0.9614    3.6607    0.1937    0.2469    5.7712    1.4496</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating covariance matrix</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling EBE model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Needed Covariates:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "CMT"</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating residuals/tables</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-in"><span class="va">f_nlmixr_dfop_sfo_focei_obs</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr</a></span><span class="op">(</span><span class="va">f_mmkin_obs</span><span class="op">[</span><span class="st">"DFOP-SFO"</span>, <span class="op">]</span>, est <span class="op">=</span> <span class="st">"focei"</span><span class="op">)</span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate jacobian</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate sensitivities</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate ∂(f)/∂(η)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate ∂(R²)/∂(η)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in inner model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in inner model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in EBE model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in EBE model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling inner model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in FD model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in FD model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling EBE model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling events FD model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Needed Covariates:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "CMT"</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="font-weight: bold;">Key:</span> U: Unscaled Parameters; X: Back-transformed parameters; G: Gill difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> F: Forward difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> C: Central difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> M: Mixed forward and central difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Unscaled parameters for Omegas=chol(solve(omega));</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Diagonals are transformed, as specified by foceiControl(diagXform=)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |-----+---------------+-----------+-----------+-----------+-----------|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    #| Objective Fun |  parent_0 |  log_k_A1 |f_parent_qlogis |    log_k1 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    log_k2 |  g_qlogis |sigma_parent |  sigma_A1 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|        o1 |        o2 |        o3 |        o4 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|        o5 |        o6 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    1</span>|     488.98943 |     1.000 |    -1.000 |   -0.9115 |   -0.9329 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9817 |   -0.8883 |   -0.8515 |   -0.8698 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8775 |   -0.8743 |   -0.8683 |   -0.8723 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8746 |   -0.8685 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     488.98943 |     93.00 |    -5.300 |   -0.9500 |    -2.000 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.400 |    0.1900 |     2.000 |     1.100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7210 |    0.8758 |     1.172 |    0.9765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.8633 |     1.161 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     488.98943</span> |     93.00 |  0.004992 |    0.2789 |    0.1353 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01228 |    0.5474 |     2.000 |     1.100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7210 |    0.8758 |     1.172 |    0.9765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.8633 |     1.161 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    G|    Gill Diff. |    -27.68 |     2.403 |   -0.1248 |   -0.3242 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3705 |   0.07384 |    -31.92 |    -15.13 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     14.74 |     13.03 |    -12.01 |    -2.072 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     5.553 |    -10.09 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    2</span>|     1205.4696 |     1.534 |    -1.046 |   -0.9091 |   -0.9266 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9745 |   -0.8897 |   -0.2358 |   -0.5780 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.162 |    -1.126 |   -0.6367 |   -0.8323 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.9817 |   -0.6738 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     1205.4696 |     142.6 |    -5.346 |   -0.9477 |    -1.994 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.393 |    0.1897 |     2.616 |     1.260 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.5160 |    0.6557 |     1.444 |     1.015 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.7709 |     1.387 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     1205.4696</span> |     142.6 |  0.004766 |    0.2793 |    0.1362 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01237 |    0.5473 |     2.616 |     1.260 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.5160 |    0.6557 |     1.444 |     1.015 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.7709 |     1.387 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    3</span>|     510.70816 |     1.053 |    -1.005 |   -0.9113 |   -0.9322 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9810 |   -0.8884 |   -0.7899 |   -0.8406 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9059 |   -0.8995 |   -0.8452 |   -0.8683 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8853 |   -0.8491 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     510.70816 |     97.96 |    -5.305 |   -0.9498 |    -1.999 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.399 |    0.1900 |     2.062 |     1.116 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7005 |    0.8538 |     1.199 |    0.9804 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.8541 |     1.184 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     510.70816</span> |     97.96 |  0.004969 |    0.2789 |    0.1354 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01229 |    0.5474 |     2.062 |     1.116 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7005 |    0.8538 |     1.199 |    0.9804 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.8541 |     1.184 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    4</span>|     494.11470 |     1.005 |    -1.000 |   -0.9115 |   -0.9328 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9816 |   -0.8883 |   -0.8453 |   -0.8669 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8803 |   -0.8769 |   -0.8660 |   -0.8719 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8757 |   -0.8666 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      494.1147 |     93.50 |    -5.300 |   -0.9500 |    -2.000 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.400 |    0.1900 |     2.006 |     1.102 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7190 |    0.8736 |     1.175 |    0.9768 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.8624 |     1.163 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      494.1147</span> |     93.50 |  0.004989 |    0.2789 |    0.1353 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01228 |    0.5474 |     2.006 |     1.102 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7190 |    0.8736 |     1.175 |    0.9768 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.8624 |     1.163 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    5</span>|     494.35784 |     1.001 |    -1.000 |   -0.9115 |   -0.9329 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9817 |   -0.8883 |   -0.8509 |   -0.8695 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8778 |   -0.8746 |   -0.8681 |   -0.8723 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8747 |   -0.8683 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     494.35784 |     93.05 |    -5.300 |   -0.9500 |    -2.000 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.400 |    0.1900 |     2.001 |     1.100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7208 |    0.8755 |     1.172 |    0.9765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.8632 |     1.161 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     494.35784</span> |     93.05 |  0.004991 |    0.2789 |    0.1353 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01228 |    0.5474 |     2.001 |     1.100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7208 |    0.8755 |     1.172 |    0.9765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.8632 |     1.161 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    6</span>|     494.40319 |     1.000 |    -1.000 |   -0.9115 |   -0.9329 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9817 |   -0.8883 |   -0.8514 |   -0.8698 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8775 |   -0.8744 |   -0.8683 |   -0.8723 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8746 |   -0.8685 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     494.40319 |     93.00 |    -5.300 |   -0.9500 |    -2.000 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.400 |    0.1900 |     2.000 |     1.100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7210 |    0.8757 |     1.172 |    0.9765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.8633 |     1.161 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     494.40319</span> |     93.00 |  0.004992 |    0.2789 |    0.1353 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01228 |    0.5474 |     2.000 |     1.100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7210 |    0.8757 |     1.172 |    0.9765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.8633 |     1.161 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    7</span>|     494.40793 |     1.000 |    -1.000 |   -0.9115 |   -0.9329 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9817 |   -0.8883 |   -0.8515 |   -0.8698 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8775 |   -0.8744 |   -0.8683 |   -0.8723 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8746 |   -0.8685 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     494.40793 |     93.00 |    -5.300 |   -0.9500 |    -2.000 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.400 |    0.1900 |     2.000 |     1.100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7210 |    0.8758 |     1.172 |    0.9765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.8633 |     1.161 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     494.40793</span> |     93.00 |  0.004992 |    0.2789 |    0.1353 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01228 |    0.5474 |     2.000 |     1.100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7210 |    0.8758 |     1.172 |    0.9765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.8633 |     1.161 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    8</span>|     494.40830 |     1.000 |    -1.000 |   -0.9115 |   -0.9329 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9817 |   -0.8883 |   -0.8515 |   -0.8698 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8775 |   -0.8743 |   -0.8683 |   -0.8723 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8746 |   -0.8685 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      494.4083 |     93.00 |    -5.300 |   -0.9500 |    -2.000 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.400 |    0.1900 |     2.000 |     1.100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7210 |    0.8758 |     1.172 |    0.9765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.8633 |     1.161 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      494.4083</span> |     93.00 |  0.004992 |    0.2789 |    0.1353 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01228 |    0.5474 |     2.000 |     1.100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7210 |    0.8758 |     1.172 |    0.9765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.8633 |     1.161 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    9</span>|     494.40832 |     1.000 |    -1.000 |   -0.9115 |   -0.9329 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9817 |   -0.8883 |   -0.8515 |   -0.8698 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8775 |   -0.8743 |   -0.8683 |   -0.8723 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8746 |   -0.8685 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     494.40832 |     93.00 |    -5.300 |   -0.9500 |    -2.000 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.400 |    0.1900 |     2.000 |     1.100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7210 |    0.8758 |     1.172 |    0.9765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.8633 |     1.161 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     494.40832</span> |     93.00 |  0.004992 |    0.2789 |    0.1353 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01228 |    0.5474 |     2.000 |     1.100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7210 |    0.8758 |     1.172 |    0.9765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.8633 |     1.161 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   10</span>|     494.40832 |     1.000 |    -1.000 |   -0.9115 |   -0.9329 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9817 |   -0.8883 |   -0.8515 |   -0.8698 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8775 |   -0.8743 |   -0.8683 |   -0.8723 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8746 |   -0.8685 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     494.40832 |     93.00 |    -5.300 |   -0.9500 |    -2.000 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.400 |    0.1900 |     2.000 |     1.100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7210 |    0.8758 |     1.172 |    0.9765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.8633 |     1.161 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     494.40832</span> |     93.00 |  0.004992 |    0.2789 |    0.1353 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01228 |    0.5474 |     2.000 |     1.100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7210 |    0.8758 |     1.172 |    0.9765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.8633 |     1.161 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   11</span>|     494.40832 |     1.000 |    -1.000 |   -0.9115 |   -0.9329 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9817 |   -0.8883 |   -0.8515 |   -0.8698 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8775 |   -0.8743 |   -0.8683 |   -0.8723 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8746 |   -0.8685 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     494.40832 |     93.00 |    -5.300 |   -0.9500 |    -2.000 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.400 |    0.1900 |     2.000 |     1.100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7210 |    0.8758 |     1.172 |    0.9765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.8633 |     1.161 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     494.40832</span> |     93.00 |  0.004992 |    0.2789 |    0.1353 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01228 |    0.5474 |     2.000 |     1.100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7210 |    0.8758 |     1.172 |    0.9765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.8633 |     1.161 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   12</span>|     494.40832 |     1.000 |    -1.000 |   -0.9115 |   -0.9329 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9817 |   -0.8883 |   -0.8515 |   -0.8698 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8775 |   -0.8743 |   -0.8683 |   -0.8723 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8746 |   -0.8685 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     494.40832 |     93.00 |    -5.300 |   -0.9500 |    -2.000 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.400 |    0.1900 |     2.000 |     1.100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7210 |    0.8758 |     1.172 |    0.9765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.8633 |     1.161 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     494.40832</span> |     93.00 |  0.004992 |    0.2789 |    0.1353 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01228 |    0.5474 |     2.000 |     1.100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7210 |    0.8758 |     1.172 |    0.9765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.8633 |     1.161 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   13</span>|     494.40832 |     1.000 |    -1.000 |   -0.9115 |   -0.9329 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9817 |   -0.8883 |   -0.8515 |   -0.8698 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8775 |   -0.8743 |   -0.8683 |   -0.8723 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8746 |   -0.8685 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     494.40832 |     93.00 |    -5.300 |   -0.9500 |    -2.000 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.400 |    0.1900 |     2.000 |     1.100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7210 |    0.8758 |     1.172 |    0.9765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.8633 |     1.161 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     494.40832</span> |     93.00 |  0.004992 |    0.2789 |    0.1353 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01228 |    0.5474 |     2.000 |     1.100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7210 |    0.8758 |     1.172 |    0.9765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.8633 |     1.161 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   14</span>|     494.40832 |     1.000 |    -1.000 |   -0.9115 |   -0.9329 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9817 |   -0.8883 |   -0.8515 |   -0.8698 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8775 |   -0.8743 |   -0.8683 |   -0.8723 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8746 |   -0.8685 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     494.40832 |     93.00 |    -5.300 |   -0.9500 |    -2.000 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.400 |    0.1900 |     2.000 |     1.100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7210 |    0.8758 |     1.172 |    0.9765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.8633 |     1.161 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     494.40832</span> |     93.00 |  0.004992 |    0.2789 |    0.1353 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01228 |    0.5474 |     2.000 |     1.100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7210 |    0.8758 |     1.172 |    0.9765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.8633 |     1.161 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   15</span>|     494.40832 |     1.000 |    -1.000 |   -0.9115 |   -0.9329 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9817 |   -0.8883 |   -0.8515 |   -0.8698 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8775 |   -0.8743 |   -0.8683 |   -0.8723 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8746 |   -0.8685 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     494.40832 |     93.00 |    -5.300 |   -0.9500 |    -2.000 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.400 |    0.1900 |     2.000 |     1.100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7210 |    0.8758 |     1.172 |    0.9765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.8633 |     1.161 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     494.40832</span> |     93.00 |  0.004992 |    0.2789 |    0.1353 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01228 |    0.5474 |     2.000 |     1.100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7210 |    0.8758 |     1.172 |    0.9765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.8633 |     1.161 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   16</span>|     494.40832 |     1.000 |    -1.000 |   -0.9115 |   -0.9329 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9817 |   -0.8883 |   -0.8515 |   -0.8698 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8775 |   -0.8743 |   -0.8683 |   -0.8723 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8746 |   -0.8685 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     494.40832 |     93.00 |    -5.300 |   -0.9500 |    -2.000 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.400 |    0.1900 |     2.000 |     1.100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7210 |    0.8758 |     1.172 |    0.9765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.8633 |     1.161 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     494.40832</span> |     93.00 |  0.004992 |    0.2789 |    0.1353 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01228 |    0.5474 |     2.000 |     1.100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7210 |    0.8758 |     1.172 |    0.9765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.8633 |     1.161 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   17</span>|     494.40832 |     1.000 |    -1.000 |   -0.9115 |   -0.9329 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9817 |   -0.8883 |   -0.8515 |   -0.8698 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8775 |   -0.8743 |   -0.8683 |   -0.8723 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8746 |   -0.8685 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     494.40832 |     93.00 |    -5.300 |   -0.9500 |    -2.000 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.400 |    0.1900 |     2.000 |     1.100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7210 |    0.8758 |     1.172 |    0.9765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.8633 |     1.161 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     494.40832</span> |     93.00 |  0.004992 |    0.2789 |    0.1353 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01228 |    0.5474 |     2.000 |     1.100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7210 |    0.8758 |     1.172 |    0.9765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.8633 |     1.161 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> calculating covariance matrix</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating residuals/tables</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>initial ETAs were nudged; (can control by foceiControl(etaNudge=., etaNudge2=))</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>last objective function was not at minimum, possible problems in optimization</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>using R matrix to calculate covariance, can check sandwich or S matrix with $covRS and $covS</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>gradient problems with initial estimate and covariance; see $scaleInfo</span>
-<span class="r-in"></span>
-<span class="r-in"><span class="co"># Identical two-component error for all variables is only possible with</span></span>
-<span class="r-in"><span class="co"># est = 'focei' in nlmixr</span></span>
-<span class="r-in"><span class="va">f_nlmixr_fomc_sfo_focei_tc</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr</a></span><span class="op">(</span><span class="va">f_mmkin_tc</span><span class="op">[</span><span class="st">"FOMC-SFO"</span>, <span class="op">]</span>, est <span class="op">=</span> <span class="st">"focei"</span><span class="op">)</span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate jacobian</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate sensitivities</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate ∂(f)/∂(η)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate ∂(R²)/∂(η)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in inner model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in inner model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in EBE model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in EBE model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling inner model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in FD model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in FD model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling EBE model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling events FD model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Needed Covariates:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "CMT"</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="font-weight: bold;">Key:</span> U: Unscaled Parameters; X: Back-transformed parameters; G: Gill difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> F: Forward difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> C: Central difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> M: Mixed forward and central difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Unscaled parameters for Omegas=chol(solve(omega));</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Diagonals are transformed, as specified by foceiControl(diagXform=)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |-----+---------------+-----------+-----------+-----------+-----------|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    #| Objective Fun |  parent_0 |  log_k_A1 |f_parent_qlogis | log_alpha |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  log_beta | sigma_low |  rsd_high |        o1 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|        o2 |        o3 |        o4 |        o5 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    1</span>|     500.20030 |     1.000 |    -1.000 |   -0.9113 |   -0.8944 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8454 |   -0.8678 |   -0.8916 |   -0.8767 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8743 |   -0.8675 |   -0.8704 |   -0.8704 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      500.2003 |     93.00 |    -5.300 |   -0.9400 |   -0.1100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.200 |   0.03000 |    0.7598 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8758 |     1.214 |     1.068 |     1.071 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      500.2003</span> |     93.00 |  0.004992 |    0.2809 |    0.8958 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.974 |     1.200 |   0.03000 |    0.7598 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8758 |     1.214 |     1.068 |     1.071 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    G|    Gill Diff. |     48.88 |     2.383 |    0.1231 |    0.1986 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1571 |    -64.31 |    -21.89 |    0.6250 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     11.41 |    -12.48 |    -9.903 |    -10.91 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    2</span>|     2909.4393 |    0.4361 |    -1.027 |   -0.9127 |   -0.8967 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8472 |   -0.1258 |   -0.6390 |   -0.8839 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.006 |   -0.7235 |   -0.7562 |   -0.7445 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     2909.4393 |     40.56 |    -5.327 |   -0.9413 |   -0.1123 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.298 |     1.645 |   0.03379 |    0.7544 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7605 |     1.389 |     1.189 |     1.206 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     2909.4393</span> |     40.56 |  0.004856 |    0.2806 |    0.8938 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.956 |     1.645 |   0.03379 |    0.7544 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7605 |     1.389 |     1.189 |     1.206 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    3</span>|     515.24373 |    0.9436 |    -1.003 |   -0.9114 |   -0.8946 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8456 |   -0.7936 |   -0.8663 |   -0.8774 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8875 |   -0.8531 |   -0.8590 |   -0.8578 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     515.24373 |     87.76 |    -5.303 |   -0.9401 |   -0.1102 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.245 |   0.03038 |    0.7593 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8642 |     1.232 |     1.080 |     1.084 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     515.24373</span> |     87.76 |  0.004978 |    0.2809 |    0.8956 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.972 |     1.245 |   0.03038 |    0.7593 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8642 |     1.232 |     1.080 |     1.084 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    4</span>|     499.40695 |    0.9897 |    -1.001 |   -0.9113 |   -0.8944 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8454 |   -0.8542 |   -0.8869 |   -0.8768 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8768 |   -0.8648 |   -0.8684 |   -0.8681 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     499.40695 |     92.04 |    -5.301 |   -0.9400 |   -0.1100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.208 |   0.03007 |    0.7597 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8737 |     1.217 |     1.070 |     1.073 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     499.40695</span> |     92.04 |  0.004989 |    0.2809 |    0.8958 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.974 |     1.208 |   0.03007 |    0.7597 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8737 |     1.217 |     1.070 |     1.073 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -99.71 |     2.245 |   -0.1707 |    0.1202 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1546 |    -61.69 |    -22.54 |     1.475 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     7.677 |    -11.72 |    -9.584 |    -10.53 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    5</span>|     498.17135 |     1.001 |    -1.001 |   -0.9113 |   -0.8945 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8454 |   -0.8410 |   -0.8822 |   -0.8771 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8786 |   -0.8623 |   -0.8663 |   -0.8658 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     498.17135 |     93.06 |    -5.301 |   -0.9400 |   -0.1101 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.216 |   0.03014 |    0.7595 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8720 |     1.221 |     1.072 |     1.076 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     498.17135</span> |     93.06 |  0.004987 |    0.2809 |    0.8958 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.974 |     1.216 |   0.03014 |    0.7595 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8720 |     1.221 |     1.072 |     1.076 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     55.25 |     2.349 |    0.1359 |    0.2180 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1993 |    -60.48 |    -20.28 |     1.106 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.662 |    -11.74 |    -9.501 |    -10.55 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    6</span>|     497.33758 |    0.9906 |    -1.002 |   -0.9113 |   -0.8945 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8454 |   -0.8273 |   -0.8776 |   -0.8773 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8808 |   -0.8596 |   -0.8642 |   -0.8634 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     497.33758 |     92.12 |    -5.302 |   -0.9400 |   -0.1101 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.224 |   0.03021 |    0.7594 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8701 |     1.224 |     1.074 |     1.078 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     497.33758</span> |     92.12 |  0.004984 |    0.2809 |    0.8957 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.974 |     1.224 |   0.03021 |    0.7594 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8701 |     1.224 |     1.074 |     1.078 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -88.13 |     2.220 |   -0.1371 |    0.1382 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1101 |    -57.44 |    -20.90 |     1.111 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     7.358 |    -11.52 |    -9.371 |    -10.34 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    7</span>|     496.20963 |     1.001 |    -1.002 |   -0.9113 |   -0.8946 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8454 |   -0.8137 |   -0.8728 |   -0.8776 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8827 |   -0.8569 |   -0.8620 |   -0.8610 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     496.20963 |     93.07 |    -5.302 |   -0.9400 |   -0.1102 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.232 |   0.03028 |    0.7592 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8684 |     1.227 |     1.077 |     1.081 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     496.20963</span> |     93.07 |  0.004981 |    0.2809 |    0.8957 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.974 |     1.232 |   0.03028 |    0.7592 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8684 |     1.227 |     1.077 |     1.081 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     55.31 |     2.316 |    0.1573 |    0.2363 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2327 |    -56.27 |    -18.79 |    0.9054 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.277 |    -11.53 |    -9.283 |    -10.35 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    8</span>|     495.35926 |    0.9914 |    -1.003 |   -0.9113 |   -0.8946 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8455 |   -0.7996 |   -0.8680 |   -0.8778 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8850 |   -0.8541 |   -0.8597 |   -0.8584 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     495.35926 |     92.20 |    -5.303 |   -0.9401 |   -0.1102 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.241 |   0.03035 |    0.7590 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8664 |     1.231 |     1.079 |     1.084 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     495.35926</span> |     92.20 |  0.004979 |    0.2809 |    0.8956 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.973 |     1.241 |   0.03035 |    0.7590 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8664 |     1.231 |     1.079 |     1.084 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -77.78 |     2.194 |   -0.1077 |    0.1552 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.06714 |    -52.97 |    -19.27 |    0.8916 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     7.037 |    -11.29 |    -9.130 |    -10.15 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    9</span>|     494.33654 |     1.001 |    -1.003 |   -0.9113 |   -0.8947 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8455 |   -0.7857 |   -0.8631 |   -0.8780 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8870 |   -0.8511 |   -0.8573 |   -0.8558 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     494.33654 |     93.09 |    -5.303 |   -0.9400 |   -0.1103 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.249 |   0.03043 |    0.7588 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8647 |     1.234 |     1.082 |     1.087 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     494.33654</span> |     93.09 |  0.004976 |    0.2809 |    0.8956 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.973 |     1.249 |   0.03043 |    0.7588 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8647 |     1.234 |     1.082 |     1.087 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     55.95 |     2.282 |    0.1850 |    0.2507 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2656 |    -51.81 |    -17.28 |    0.8212 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     7.543 |    -11.27 |    -9.029 |    -10.13 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   10</span>|     493.47922 |    0.9922 |    -1.004 |   -0.9114 |   -0.8947 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8455 |   -0.7714 |   -0.8582 |   -0.8782 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8891 |   -0.8480 |   -0.8548 |   -0.8530 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     493.47922 |     92.28 |    -5.304 |   -0.9401 |   -0.1103 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.258 |   0.03050 |    0.7587 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8629 |     1.238 |     1.084 |     1.090 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     493.47922</span> |     92.28 |  0.004973 |    0.2809 |    0.8955 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.973 |     1.258 |   0.03050 |    0.7587 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8629 |     1.238 |     1.084 |     1.090 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -67.18 |     2.173 |  -0.07166 |    0.1818 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.01313 |    -49.19 |    -17.69 |    0.7047 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.114 |    -11.03 |    -8.882 |    -9.923 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   11</span>|     492.53845 |     1.001 |    -1.004 |   -0.9114 |   -0.8948 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8456 |   -0.7572 |   -0.8532 |   -0.8784 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8914 |   -0.8448 |   -0.8522 |   -0.8501 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     492.53845 |     93.09 |    -5.304 |   -0.9401 |   -0.1104 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.266 |   0.03057 |    0.7585 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8608 |     1.242 |     1.087 |     1.093 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     492.53845</span> |     93.09 |  0.004969 |    0.2809 |    0.8955 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.972 |     1.266 |   0.03057 |    0.7585 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8608 |     1.242 |     1.087 |     1.093 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     53.38 |     2.243 |    0.1941 |    0.2566 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2879 |    -47.96 |    -15.83 |    0.7595 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.537 |    -10.98 |    -8.771 |    -9.890 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   12</span>|     491.72645 |    0.9926 |    -1.005 |   -0.9114 |   -0.8949 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8456 |   -0.7429 |   -0.8484 |   -0.8786 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8941 |   -0.8415 |   -0.8496 |   -0.8471 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     491.72645 |     92.31 |    -5.305 |   -0.9401 |   -0.1105 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.275 |   0.03065 |    0.7584 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8584 |     1.246 |     1.090 |     1.096 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     491.72645</span> |     92.31 |  0.004966 |    0.2809 |    0.8954 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.972 |     1.275 |   0.03065 |    0.7584 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8584 |     1.246 |     1.090 |     1.096 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -63.56 |     2.131 |  -0.05387 |    0.1833 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................| 0.0009134 |    -45.55 |    -16.30 |    0.3872 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.447 |    -10.76 |    -8.612 |    -9.684 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   13</span>|     490.83850 |     1.001 |    -1.006 |   -0.9114 |   -0.8949 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8457 |   -0.7286 |   -0.8435 |   -0.8787 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8974 |   -0.8380 |   -0.8468 |   -0.8440 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      490.8385 |     93.07 |    -5.306 |   -0.9401 |   -0.1105 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.283 |   0.03072 |    0.7583 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8556 |     1.250 |     1.093 |     1.099 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      490.8385</span> |     93.07 |  0.004963 |    0.2809 |    0.8954 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.971 |     1.283 |   0.03072 |    0.7583 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8556 |     1.250 |     1.093 |     1.099 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     49.01 |     2.198 |    0.2057 |    0.2666 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3102 |    -44.06 |    -14.52 |    0.5614 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.039 |    -10.72 |    -8.482 |    -9.628 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   14</span>|     490.09324 |    0.9928 |    -1.007 |   -0.9115 |   -0.8950 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8458 |   -0.7143 |   -0.8387 |   -0.8788 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9004 |   -0.8343 |   -0.8439 |   -0.8407 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     490.09324 |     92.33 |    -5.307 |   -0.9402 |   -0.1106 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.292 |   0.03079 |    0.7582 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8529 |     1.254 |     1.096 |     1.103 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     490.09324</span> |     92.33 |  0.004959 |    0.2809 |    0.8953 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.970 |     1.292 |   0.03079 |    0.7582 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8529 |     1.254 |     1.096 |     1.103 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -62.13 |     2.095 |  -0.03472 |    0.1999 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03562 |    -41.55 |    -14.84 |    0.5236 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     7.264 |    -10.46 |    -8.310 |    -9.412 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   15</span>|     489.25271 |     1.001 |    -1.007 |   -0.9115 |   -0.8951 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8458 |   -0.7001 |   -0.8338 |   -0.8789 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9032 |   -0.8304 |   -0.8408 |   -0.8372 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     489.25271 |     93.06 |    -5.307 |   -0.9402 |   -0.1107 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.301 |   0.03087 |    0.7581 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8505 |     1.259 |     1.099 |     1.107 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     489.25271</span> |     93.06 |  0.004955 |    0.2809 |    0.8952 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.970 |     1.301 |   0.03087 |    0.7581 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8505 |     1.259 |     1.099 |     1.107 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     44.98 |     2.155 |    0.2191 |    0.2769 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3339 |    -40.42 |    -13.24 |    0.4473 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     7.595 |    -10.35 |    -8.165 |    -9.335 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   16</span>|     488.54089 |    0.9934 |    -1.008 |   -0.9116 |   -0.8952 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8459 |   -0.6857 |   -0.8290 |   -0.8790 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9061 |   -0.8262 |   -0.8376 |   -0.8335 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     488.54089 |     92.38 |    -5.308 |   -0.9403 |   -0.1108 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.299 |     1.309 |   0.03094 |    0.7581 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8479 |     1.264 |     1.103 |     1.111 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     488.54089</span> |     92.38 |  0.004951 |    0.2808 |    0.8951 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.968 |     1.309 |   0.03094 |    0.7581 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8479 |     1.264 |     1.103 |     1.111 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -55.81 |     2.061 |  -0.02096 |    0.2050 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.07367 |    -38.08 |    -13.50 |    0.4111 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     6.904 |    -10.08 |    -7.981 |    -9.106 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   17</span>|     487.77387 |     1.001 |    -1.009 |   -0.9116 |   -0.8953 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8461 |   -0.6716 |   -0.8243 |   -0.8791 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9092 |   -0.8218 |   -0.8341 |   -0.8294 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     487.77387 |     93.07 |    -5.309 |   -0.9403 |   -0.1109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.299 |     1.318 |   0.03101 |    0.7580 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8453 |     1.270 |     1.106 |     1.115 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     487.77387</span> |     93.07 |  0.004946 |    0.2808 |    0.8950 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.967 |     1.318 |   0.03101 |    0.7580 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8453 |     1.270 |     1.106 |     1.115 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     43.45 |     2.114 |    0.2402 |    0.2940 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3672 |    -36.87 |    -12.03 |    0.3081 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     7.143 |    -10.02 |    -7.799 |    -9.000 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   18</span>|     487.09815 |    0.9939 |    -1.010 |   -0.9118 |   -0.8955 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8463 |   -0.6575 |   -0.8197 |   -0.8791 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9124 |   -0.8169 |   -0.8304 |   -0.8251 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     487.09815 |     92.43 |    -5.310 |   -0.9404 |   -0.1111 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.299 |     1.326 |   0.03108 |    0.7580 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8425 |     1.276 |     1.110 |     1.120 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     487.09815</span> |     92.43 |  0.004941 |    0.2808 |    0.8948 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.965 |     1.326 |   0.03108 |    0.7580 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8425 |     1.276 |     1.110 |     1.120 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -50.03 |     2.024 |  0.005993 |    0.2193 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1119 |    -34.69 |    -12.26 |    0.2072 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     6.453 |    -9.722 |    -7.597 |    -8.758 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   19</span>|     486.39882 |     1.001 |    -1.011 |   -0.9119 |   -0.8957 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8465 |   -0.6438 |   -0.8151 |   -0.8789 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9156 |   -0.8116 |   -0.8264 |   -0.8203 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     486.39882 |     93.07 |    -5.311 |   -0.9405 |   -0.1113 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.299 |     1.334 |   0.03115 |    0.7582 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8396 |     1.282 |     1.114 |     1.125 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     486.39882</span> |     93.07 |  0.004935 |    0.2808 |    0.8947 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.963 |     1.334 |   0.03115 |    0.7582 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8396 |     1.282 |     1.114 |     1.125 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     41.24 |     2.069 |    0.2581 |    0.3038 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3965 |    -33.71 |    -10.93 |    0.1523 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     5.279 |    -9.597 |    -7.397 |    -8.617 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   20</span>|     485.77269 |    0.9943 |    -1.013 |   -0.9120 |   -0.8959 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8467 |   -0.6301 |   -0.8107 |   -0.8786 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9175 |   -0.8058 |   -0.8222 |   -0.8151 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     485.77269 |     92.47 |    -5.313 |   -0.9407 |   -0.1115 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.299 |     1.343 |   0.03121 |    0.7584 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8380 |     1.289 |     1.119 |     1.130 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     485.77269</span> |     92.47 |  0.004928 |    0.2808 |    0.8945 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.960 |     1.343 |   0.03121 |    0.7584 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8380 |     1.289 |     1.119 |     1.130 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -45.38 |     1.992 |   0.03513 |    0.2356 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1513 |    -31.62 |    -11.12 |   0.08802 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     6.113 |    -9.278 |    -7.167 |    -8.332 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   21</span>|     485.13787 |     1.001 |    -1.014 |   -0.9122 |   -0.8962 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8471 |   -0.6169 |   -0.8065 |   -0.8780 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9196 |   -0.7993 |   -0.8176 |   -0.8094 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     485.13787 |     93.06 |    -5.314 |   -0.9409 |   -0.1118 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.298 |     1.351 |   0.03128 |    0.7588 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8361 |     1.297 |     1.124 |     1.136 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     485.13787</span> |     93.06 |  0.004921 |    0.2807 |    0.8942 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.957 |     1.351 |   0.03128 |    0.7588 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8361 |     1.297 |     1.124 |     1.136 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     37.95 |     2.033 |    0.2726 |    0.3147 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4223 |    -30.68 |    -9.906 |   0.05100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.975 |    -9.039 |    -6.928 |    -8.144 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   22</span>|     484.56781 |    0.9947 |    -1.016 |   -0.9125 |   -0.8965 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8476 |   -0.6040 |   -0.8026 |   -0.8774 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9219 |   -0.7925 |   -0.8127 |   -0.8032 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     484.56781 |     92.51 |    -5.316 |   -0.9411 |   -0.1121 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.298 |     1.358 |   0.03133 |    0.7593 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8341 |     1.305 |     1.129 |     1.143 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     484.56781</span> |     92.51 |  0.004912 |    0.2807 |    0.8939 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.952 |     1.358 |   0.03133 |    0.7593 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8341 |     1.305 |     1.129 |     1.143 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -42.28 |     1.959 |   0.05438 |    0.2483 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1829 |    -28.95 |    -10.12 |  -0.04344 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.379 |    -8.677 |    -6.653 |    -7.817 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   23</span>|     484.00832 |     1.000 |    -1.018 |   -0.9128 |   -0.8970 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8481 |   -0.5915 |   -0.7988 |   -0.8764 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9214 |   -0.7852 |   -0.8076 |   -0.7966 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     484.00832 |     93.05 |    -5.318 |   -0.9414 |   -0.1125 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.297 |     1.366 |   0.03139 |    0.7601 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8346 |     1.314 |     1.135 |     1.150 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     484.00832</span> |     93.05 |  0.004901 |    0.2806 |    0.8936 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.947 |     1.366 |   0.03139 |    0.7601 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8346 |     1.314 |     1.135 |     1.150 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     34.54 |     2.001 |    0.2786 |    0.3182 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4381 |    -28.02 |    -9.026 |  -0.09975 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     6.146 |    -8.496 |    -6.417 |    -7.602 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   24</span>|     483.48726 |    0.9952 |    -1.021 |   -0.9132 |   -0.8975 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8489 |   -0.5798 |   -0.7955 |   -0.8750 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9256 |   -0.7775 |   -0.8025 |   -0.7898 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     483.48726 |     92.55 |    -5.321 |   -0.9418 |   -0.1131 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.296 |     1.373 |   0.03144 |    0.7611 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8309 |     1.323 |     1.140 |     1.157 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     483.48726</span> |     92.55 |  0.004889 |    0.2805 |    0.8931 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.939 |     1.373 |   0.03144 |    0.7611 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8309 |     1.323 |     1.140 |     1.157 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -37.52 |     1.926 |   0.07054 |    0.2526 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2102 |    -26.43 |    -9.184 |   -0.1123 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     5.591 |    -8.057 |    -6.119 |    -7.239 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   25</span>|     482.99669 |     1.001 |    -1.023 |   -0.9136 |   -0.8980 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8497 |   -0.5700 |   -0.7928 |   -0.8735 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9342 |   -0.7702 |   -0.7978 |   -0.7834 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     482.99669 |     93.05 |    -5.323 |   -0.9422 |   -0.1136 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.296 |     1.379 |   0.03148 |    0.7623 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8234 |     1.332 |     1.145 |     1.164 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     482.99669</span> |     93.05 |  0.004876 |    0.2805 |    0.8926 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.932 |     1.379 |   0.03148 |    0.7623 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8234 |     1.332 |     1.145 |     1.164 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     33.56 |     1.934 |    0.3091 |    0.3219 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4700 |    -25.85 |    -8.255 |  -0.09267 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     5.467 |    -7.833 |    -5.883 |    -7.031 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   26</span>|     482.53338 |    0.9957 |    -1.027 |   -0.9143 |   -0.8987 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8507 |   -0.5600 |   -0.7904 |   -0.8719 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9423 |   -0.7626 |   -0.7931 |   -0.7767 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     482.53338 |     92.60 |    -5.327 |   -0.9428 |   -0.1143 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.295 |     1.385 |   0.03152 |    0.7635 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8163 |     1.342 |     1.150 |     1.171 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     482.53338</span> |     92.60 |  0.004861 |    0.2803 |    0.8920 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.921 |     1.385 |   0.03152 |    0.7635 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8163 |     1.342 |     1.150 |     1.171 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -33.71 |     1.852 |    0.1405 |    0.2615 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2476 |    -24.53 |    -8.462 |   -0.2274 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.657 |    -7.455 |    -5.599 |    -6.689 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   27</span>|     482.07760 |     1.001 |    -1.030 |   -0.9154 |   -0.8996 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8522 |   -0.5495 |   -0.7881 |   -0.8694 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9481 |   -0.7546 |   -0.7885 |   -0.7696 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      482.0776 |     93.06 |    -5.330 |   -0.9439 |   -0.1152 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.293 |     1.391 |   0.03155 |    0.7654 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8112 |     1.351 |     1.155 |     1.179 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      482.0776</span> |     93.06 |  0.004842 |    0.2801 |    0.8912 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.906 |     1.391 |   0.03155 |    0.7654 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8112 |     1.351 |     1.155 |     1.179 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     31.51 |     1.862 |    0.3171 |    0.3253 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4890 |    -23.74 |    -7.570 |   -0.1627 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.673 |    -7.176 |    -5.365 |    -6.441 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   28</span>|     481.65018 |    0.9960 |    -1.035 |   -0.9168 |   -0.9007 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8541 |   -0.5386 |   -0.7859 |   -0.8662 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9515 |   -0.7465 |   -0.7840 |   -0.7624 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     481.65018 |     92.63 |    -5.335 |   -0.9452 |   -0.1163 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.291 |     1.398 |   0.03158 |    0.7678 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8082 |     1.361 |     1.160 |     1.187 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     481.65018</span> |     92.63 |  0.004820 |    0.2799 |    0.8902 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.887 |     1.398 |   0.03158 |    0.7678 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8082 |     1.361 |     1.160 |     1.187 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -31.18 |     1.794 |    0.1244 |    0.2518 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2479 |    -22.54 |    -7.736 |   -0.2537 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.155 |    -6.794 |    -5.094 |    -6.088 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   29</span>|     481.23911 |     1.000 |    -1.041 |   -0.9182 |   -0.9020 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8564 |   -0.5274 |   -0.7840 |   -0.8624 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9539 |   -0.7386 |   -0.7800 |   -0.7556 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     481.23911 |     93.04 |    -5.341 |   -0.9465 |   -0.1176 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.289 |     1.404 |   0.03161 |    0.7707 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8061 |     1.371 |     1.164 |     1.194 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     481.23911</span> |     93.04 |  0.004792 |    0.2796 |    0.8890 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.865 |     1.404 |   0.03161 |    0.7707 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8061 |     1.371 |     1.164 |     1.194 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   30</span>|     480.84332 |     1.001 |    -1.048 |   -0.9201 |   -0.9037 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8593 |   -0.5164 |   -0.7829 |   -0.8573 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9562 |   -0.7293 |   -0.7754 |   -0.7475 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.84332 |     93.06 |    -5.348 |   -0.9483 |   -0.1193 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.286 |     1.411 |   0.03163 |    0.7746 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8041 |     1.382 |     1.169 |     1.203 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.84332</span> |     93.06 |  0.004757 |    0.2792 |    0.8875 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.836 |     1.411 |   0.03163 |    0.7746 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8041 |     1.382 |     1.169 |     1.203 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   31</span>|     479.47395 |     1.001 |    -1.077 |   -0.9274 |   -0.9105 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8708 |   -0.4737 |   -0.7785 |   -0.8375 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9655 |   -0.6927 |   -0.7575 |   -0.7158 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.47395 |     93.12 |    -5.377 |   -0.9551 |   -0.1261 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.275 |     1.436 |   0.03170 |    0.7896 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7959 |     1.426 |     1.188 |     1.237 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.47395</span> |     93.12 |  0.004620 |    0.2779 |    0.8816 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.723 |     1.436 |   0.03170 |    0.7896 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7959 |     1.426 |     1.188 |     1.237 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   32</span>|     477.01144 |     1.003 |    -1.162 |   -0.9485 |   -0.9300 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9044 |   -0.3493 |   -0.7656 |   -0.7799 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9925 |   -0.5865 |   -0.7057 |   -0.6237 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     477.01144 |     93.31 |    -5.462 |   -0.9750 |   -0.1456 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.241 |     1.511 |   0.03189 |    0.8334 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7723 |     1.555 |     1.243 |     1.335 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     477.01144</span> |     93.31 |  0.004245 |    0.2739 |    0.8645 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.403 |     1.511 |   0.03189 |    0.8334 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7723 |     1.555 |     1.243 |     1.335 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     48.38 |     1.353 |   -0.3553 |  -0.05530 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................| -0.006768 |    -8.661 |    -2.325 |   -0.1540 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.503 |   -0.2811 |   -0.6438 |   -0.5229 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   33</span>|     477.99463 |     1.002 |    -1.284 |   -0.8933 |   -0.9162 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8846 |   -0.1951 |   -0.7385 |   -0.7367 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.093 |   -0.7697 |   -0.8046 |   -0.7596 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     477.99463 |     93.20 |    -5.584 |   -0.9231 |   -0.1318 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.261 |     1.604 |   0.03230 |    0.8662 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6840 |     1.333 |     1.138 |     1.190 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     477.99463</span> |     93.20 |  0.003757 |    0.2843 |    0.8766 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.591 |     1.604 |   0.03230 |    0.8662 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6840 |     1.333 |     1.138 |     1.190 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   34</span>|     476.67952 |     1.000 |    -1.201 |   -0.9310 |   -0.9256 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8981 |   -0.3000 |   -0.7569 |   -0.7662 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.025 |   -0.6445 |   -0.7370 |   -0.6668 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     476.67952 |     93.04 |    -5.501 |   -0.9585 |   -0.1412 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.247 |     1.541 |   0.03202 |    0.8438 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7442 |     1.485 |     1.210 |     1.289 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     476.67952</span> |     93.04 |  0.004084 |    0.2772 |    0.8683 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.462 |     1.541 |   0.03202 |    0.8438 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7442 |     1.485 |     1.210 |     1.289 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     3.308 |     1.138 |    0.3206 |  0.007327 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................| -0.005220 |    -6.420 |    -1.952 |   -0.8085 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4295 |    -3.531 |    -2.349 |    -2.485 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   35</span>|     477.00015 |    0.9939 |    -1.268 |   -0.9234 |   -0.9152 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8823 |   -0.2647 |   -0.7585 |   -0.7168 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9725 |   -0.6365 |   -0.7310 |   -0.6853 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     477.00015 |     92.43 |    -5.568 |   -0.9514 |   -0.1308 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.263 |     1.562 |   0.03200 |    0.8813 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7898 |     1.495 |     1.216 |     1.269 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     477.00015</span> |     92.43 |  0.003818 |    0.2786 |    0.8774 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.613 |     1.562 |   0.03200 |    0.8813 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7898 |     1.495 |     1.216 |     1.269 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   36</span>|     476.87328 |    0.9941 |    -1.216 |   -0.9300 |   -0.9236 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8950 |   -0.2832 |   -0.7542 |   -0.7554 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.014 |   -0.6375 |   -0.7322 |   -0.6666 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     476.87328 |     92.45 |    -5.516 |   -0.9576 |   -0.1392 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.250 |     1.551 |   0.03206 |    0.8520 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7536 |     1.493 |     1.215 |     1.289 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     476.87328</span> |     92.45 |  0.004024 |    0.2774 |    0.8700 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.491 |     1.551 |   0.03206 |    0.8520 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7536 |     1.493 |     1.215 |     1.289 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   37</span>|     476.68202 |    0.9977 |    -1.202 |   -0.9313 |   -0.9256 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8981 |   -0.2947 |   -0.7553 |   -0.7655 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.024 |   -0.6416 |   -0.7351 |   -0.6648 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     476.68202 |     92.79 |    -5.502 |   -0.9588 |   -0.1412 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.247 |     1.544 |   0.03204 |    0.8443 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7445 |     1.488 |     1.212 |     1.291 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     476.68202</span> |     92.79 |  0.004080 |    0.2771 |    0.8683 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.462 |     1.544 |   0.03204 |    0.8443 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7445 |     1.488 |     1.212 |     1.291 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   38</span>|     476.66620 |    0.9991 |    -1.201 |   -0.9311 |   -0.9256 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8981 |   -0.2974 |   -0.7561 |   -0.7659 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.024 |   -0.6431 |   -0.7361 |   -0.6658 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      476.6662 |     92.92 |    -5.501 |   -0.9587 |   -0.1412 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.247 |     1.542 |   0.03203 |    0.8441 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7443 |     1.487 |     1.211 |     1.290 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      476.6662</span> |     92.92 |  0.004082 |    0.2771 |    0.8683 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.462 |     1.542 |   0.03203 |    0.8441 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7443 |     1.487 |     1.211 |     1.290 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -16.67 |     1.127 |    0.2138 |  -0.01630 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1134 |    -4.869 |    -1.703 |  -0.03057 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.03000 |    -2.848 |    -2.302 |    -2.432 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   39</span>|     476.65034 |     1.001 |    -1.204 |   -0.9308 |   -0.9253 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8974 |   -0.2967 |   -0.7561 |   -0.7660 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.024 |   -0.6432 |   -0.7350 |   -0.6655 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     476.65034 |     93.06 |    -5.504 |   -0.9584 |   -0.1409 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.248 |     1.543 |   0.03203 |    0.8440 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7448 |     1.487 |     1.212 |     1.290 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     476.65034</span> |     93.06 |  0.004070 |    0.2772 |    0.8686 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.468 |     1.543 |   0.03203 |    0.8440 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7448 |     1.487 |     1.212 |     1.290 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     7.111 |     1.131 |    0.3498 |   0.02199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03596 |    -6.336 |    -1.893 |   -0.8646 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4089 |    -3.511 |    -2.253 |    -2.437 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   40</span>|     476.63921 |    0.9998 |    -1.207 |   -0.9306 |   -0.9249 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8968 |   -0.2957 |   -0.7561 |   -0.7661 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.024 |   -0.6430 |   -0.7338 |   -0.6650 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     476.63921 |     92.98 |    -5.507 |   -0.9581 |   -0.1405 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.249 |     1.543 |   0.03203 |    0.8439 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7451 |     1.487 |     1.213 |     1.291 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     476.63921</span> |     92.98 |  0.004058 |    0.2773 |    0.8689 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.474 |     1.543 |   0.03203 |    0.8439 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7451 |     1.487 |     1.213 |     1.291 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -6.837 |     1.116 |    0.2875 |   0.01149 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.02542 |    -4.741 |    -1.572 |   0.01643 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.427 |    -2.805 |    -2.162 |    -2.393 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   41</span>|     476.63321 |     1.001 |    -1.209 |   -0.9304 |   -0.9247 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8965 |   -0.2943 |   -0.7557 |   -0.7669 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.022 |   -0.6432 |   -0.7330 |   -0.6644 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     476.63321 |     93.07 |    -5.509 |   -0.9580 |   -0.1403 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.249 |     1.544 |   0.03204 |    0.8433 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7468 |     1.487 |     1.214 |     1.292 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     476.63321</span> |     93.07 |  0.004050 |    0.2773 |    0.8691 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.477 |     1.544 |   0.03204 |    0.8433 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7468 |     1.487 |     1.214 |     1.292 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     8.780 |     1.119 |    0.3715 |   0.03544 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.06786 |    -4.773 |    -1.470 |  -0.02124 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.265 |    -2.850 |    -2.132 |    -2.390 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   42</span>|     476.62737 |    0.9998 |    -1.211 |   -0.9303 |   -0.9246 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8963 |   -0.2932 |   -0.7554 |   -0.7683 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.020 |   -0.6436 |   -0.7322 |   -0.6637 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     476.62737 |     92.98 |    -5.511 |   -0.9578 |   -0.1402 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.249 |     1.545 |   0.03204 |    0.8422 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7485 |     1.486 |     1.215 |     1.292 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     476.62737</span> |     92.98 |  0.004043 |    0.2773 |    0.8692 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.479 |     1.545 |   0.03204 |    0.8422 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7485 |     1.486 |     1.215 |     1.292 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -5.468 |     1.108 |    0.2964 |   0.01787 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.01029 |    -4.584 |    -1.512 |  -0.04750 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.233 |    -2.839 |    -2.097 |    -2.338 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   43</span>|     476.62183 |     1.001 |    -1.213 |   -0.9301 |   -0.9245 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8960 |   -0.2918 |   -0.7549 |   -0.7696 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.018 |   -0.6438 |   -0.7313 |   -0.6628 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     476.62183 |     93.07 |    -5.513 |   -0.9577 |   -0.1401 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.249 |     1.546 |   0.03205 |    0.8412 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7501 |     1.486 |     1.216 |     1.293 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     476.62183</span> |     93.07 |  0.004035 |    0.2773 |    0.8693 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.481 |     1.546 |   0.03205 |    0.8412 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7501 |     1.486 |     1.216 |     1.293 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     8.726 |     1.111 |    0.3721 |   0.03969 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.07339 |    -4.588 |    -1.391 |  -0.04583 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.059 |    -2.851 |    -2.052 |    -2.306 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   44</span>|     476.61645 |    0.9998 |    -1.215 |   -0.9300 |   -0.9243 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8958 |   -0.2908 |   -0.7546 |   -0.7711 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.016 |   -0.6442 |   -0.7306 |   -0.6621 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     476.61645 |     92.98 |    -5.515 |   -0.9576 |   -0.1399 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.250 |     1.546 |   0.03205 |    0.8401 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7517 |     1.485 |     1.217 |     1.294 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     476.61645</span> |     92.98 |  0.004027 |    0.2774 |    0.8694 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.484 |     1.546 |   0.03205 |    0.8401 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7517 |     1.485 |     1.217 |     1.294 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -5.224 |     1.099 |    0.2980 |   0.02349 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................| -0.002638 |    -4.438 |    -1.447 |  -0.09166 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4490 |    -2.896 |    -2.021 |    -2.267 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   45</span>|     476.60491 |     1.001 |    -1.217 |   -0.9300 |   -0.9242 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8956 |   -0.2899 |   -0.7543 |   -0.7729 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.016 |   -0.6437 |   -0.7294 |   -0.6608 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     476.60491 |     93.08 |    -5.517 |   -0.9576 |   -0.1398 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.250 |     1.547 |   0.03206 |    0.8387 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7514 |     1.486 |     1.218 |     1.295 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     476.60491</span> |     93.08 |  0.004018 |    0.2774 |    0.8695 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.485 |     1.547 |   0.03206 |    0.8387 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7514 |     1.486 |     1.218 |     1.295 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     9.891 |     1.101 |    0.3805 |   0.04781 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.09121 |    -4.602 |    -1.355 |   -0.1211 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9908 |    -2.875 |    -1.954 |    -2.212 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   46</span>|     476.59275 |    0.9999 |    -1.219 |   -0.9301 |   -0.9241 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8954 |   -0.2896 |   -0.7542 |   -0.7748 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.017 |   -0.6434 |   -0.7284 |   -0.6597 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     476.59275 |     92.99 |    -5.519 |   -0.9576 |   -0.1397 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.250 |     1.547 |   0.03206 |    0.8373 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7512 |     1.486 |     1.219 |     1.297 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     476.59275</span> |     92.99 |  0.004009 |    0.2774 |    0.8696 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.487 |     1.547 |   0.03206 |    0.8373 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7512 |     1.486 |     1.219 |     1.297 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.741 |     1.086 |    0.3018 |   0.02978 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01334 |    -4.300 |    -1.393 |  -0.08082 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4335 |    -2.821 |    -1.884 |    -2.156 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   47</span>|     476.58049 |     1.001 |    -1.222 |   -0.9302 |   -0.9241 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8953 |   -0.2889 |   -0.7541 |   -0.7767 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.017 |   -0.6427 |   -0.7275 |   -0.6585 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     476.58049 |     93.06 |    -5.522 |   -0.9577 |   -0.1397 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.250 |     1.547 |   0.03206 |    0.8359 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7507 |     1.487 |     1.220 |     1.298 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     476.58049</span> |     93.06 |  0.003999 |    0.2773 |    0.8697 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.488 |     1.547 |   0.03206 |    0.8359 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7507 |     1.487 |     1.220 |     1.298 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   48</span>|     476.57085 |     1.001 |    -1.225 |   -0.9302 |   -0.9239 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8951 |   -0.2891 |   -0.7542 |   -0.7796 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.018 |   -0.6424 |   -0.7265 |   -0.6573 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     476.57085 |     93.06 |    -5.525 |   -0.9578 |   -0.1395 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.250 |     1.547 |   0.03206 |    0.8336 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7502 |     1.488 |     1.221 |     1.299 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     476.57085</span> |     93.06 |  0.003985 |    0.2773 |    0.8698 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.490 |     1.547 |   0.03206 |    0.8336 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7502 |     1.488 |     1.221 |     1.299 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   49</span>|     476.52700 |     1.000 |    -1.243 |   -0.9306 |   -0.9233 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8940 |   -0.2898 |   -0.7549 |   -0.7948 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.021 |   -0.6409 |   -0.7216 |   -0.6509 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|       476.527 |     93.02 |    -5.543 |   -0.9582 |   -0.1389 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.251 |     1.547 |   0.03205 |    0.8221 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7475 |     1.489 |     1.226 |     1.306 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">       476.527</span> |     93.02 |  0.003915 |    0.2772 |    0.8703 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.501 |     1.547 |   0.03205 |    0.8221 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7475 |     1.489 |     1.226 |     1.306 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   50</span>|     476.45166 |    0.9988 |    -1.314 |   -0.9321 |   -0.9209 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8895 |   -0.2927 |   -0.7576 |   -0.8554 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.033 |   -0.6351 |   -0.7022 |   -0.6254 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     476.45166 |     92.89 |    -5.614 |   -0.9596 |   -0.1365 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.256 |     1.545 |   0.03201 |    0.7760 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7365 |     1.496 |     1.247 |     1.333 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     476.45166</span> |     92.89 |  0.003648 |    0.2770 |    0.8724 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.543 |     1.545 |   0.03201 |    0.7760 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7365 |     1.496 |     1.247 |     1.333 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -21.31 |    0.8191 |    0.2022 |    0.1018 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1327 |    -4.505 |    -1.303 |    -1.202 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.080 |    -2.304 |   -0.3706 |   -0.4948 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   51</span>|     476.56836 |     1.004 |    -1.424 |   -0.9336 |   -0.9183 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8856 |   -0.2810 |   -0.7637 |   -0.9206 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.004 |   -0.6303 |   -0.6962 |   -0.6148 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     476.56836 |     93.36 |    -5.724 |   -0.9609 |   -0.1338 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.260 |     1.552 |   0.03192 |    0.7265 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7619 |     1.502 |     1.254 |     1.345 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     476.56836</span> |     93.36 |  0.003266 |    0.2767 |    0.8747 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.581 |     1.552 |   0.03192 |    0.7265 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7619 |     1.502 |     1.254 |     1.345 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   52</span>|     476.44457 |     1.002 |    -1.351 |   -0.9326 |   -0.9200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8882 |   -0.2885 |   -0.7595 |   -0.8773 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.024 |   -0.6333 |   -0.7001 |   -0.6218 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     476.44457 |     93.15 |    -5.651 |   -0.9600 |   -0.1356 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.257 |     1.548 |   0.03198 |    0.7594 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7451 |     1.499 |     1.249 |     1.337 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     476.44457</span> |     93.15 |  0.003514 |    0.2769 |    0.8732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.556 |     1.548 |   0.03198 |    0.7594 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7451 |     1.499 |     1.249 |     1.337 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     15.82 |    0.7276 |    0.3571 |    0.1746 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4004 |    -4.436 |   -0.9222 |    -1.572 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.287 |    -2.164 |   -0.2873 |   -0.3883 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   53</span>|     476.40417 |     1.000 |    -1.371 |   -0.9349 |   -0.9209 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8904 |   -0.2869 |   -0.7634 |   -0.8782 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.024 |   -0.6262 |   -0.7046 |   -0.6250 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     476.40417 |     93.02 |    -5.671 |   -0.9622 |   -0.1365 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.255 |     1.549 |   0.03192 |    0.7587 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7444 |     1.507 |     1.245 |     1.334 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     476.40417</span> |     93.02 |  0.003446 |    0.2764 |    0.8724 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.535 |     1.549 |   0.03192 |    0.7587 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7444 |     1.507 |     1.245 |     1.334 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   54</span>|     476.37918 |     1.000 |    -1.391 |   -0.9372 |   -0.9218 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8927 |   -0.2856 |   -0.7675 |   -0.8792 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.025 |   -0.6191 |   -0.7092 |   -0.6283 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     476.37918 |     93.03 |    -5.691 |   -0.9644 |   -0.1374 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.253 |     1.549 |   0.03186 |    0.7580 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7434 |     1.516 |     1.240 |     1.330 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     476.37918</span> |     93.03 |  0.003376 |    0.2760 |    0.8716 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.513 |     1.549 |   0.03186 |    0.7580 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7434 |     1.516 |     1.240 |     1.330 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   55</span>|     476.33357 |     1.001 |    -1.461 |   -0.9453 |   -0.9249 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9004 |   -0.2814 |   -0.7818 |   -0.8827 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.029 |   -0.5944 |   -0.7253 |   -0.6399 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     476.33357 |     93.07 |    -5.761 |   -0.9719 |   -0.1405 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.245 |     1.552 |   0.03165 |    0.7553 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7403 |     1.546 |     1.222 |     1.318 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     476.33357</span> |     93.07 |  0.003146 |    0.2745 |    0.8689 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.440 |     1.552 |   0.03165 |    0.7553 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7403 |     1.546 |     1.222 |     1.318 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.553 |    0.4475 |   -0.2195 |   0.07220 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.05655 |    -4.350 |    -1.061 |    -1.750 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1436 |   -0.4527 |    -1.673 |    -1.245 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   56</span>|     476.20746 |     1.002 |    -1.571 |   -0.9452 |   -0.9313 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9123 |   -0.2680 |   -0.8139 |   -0.8382 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.032 |   -0.6009 |   -0.7081 |   -0.6381 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     476.20746 |     93.20 |    -5.871 |   -0.9719 |   -0.1469 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.233 |     1.560 |   0.03117 |    0.7891 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7373 |     1.538 |     1.241 |     1.320 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     476.20746</span> |     93.20 |  0.002821 |    0.2745 |    0.8634 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.328 |     1.560 |   0.03117 |    0.7891 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7373 |     1.538 |     1.241 |     1.320 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     12.15 |    0.1791 |  -0.01894 | -0.004675 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.06593 |    -5.850 |    -1.512 |    -1.794 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.396 |    -1.647 |   -0.7085 |    -1.309 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   57</span>|     476.15444 |     1.000 |    -1.669 |   -0.9317 |   -0.9229 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8889 |   -0.2480 |   -0.8424 |   -0.7961 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.030 |   -0.6117 |   -0.7275 |   -0.5959 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     476.15444 |     93.03 |    -5.969 |   -0.9591 |   -0.1385 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.256 |     1.572 |   0.03074 |    0.8211 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7395 |     1.525 |     1.220 |     1.365 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     476.15444</span> |     93.03 |  0.002558 |    0.2771 |    0.8707 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.549 |     1.572 |   0.03074 |    0.8211 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7395 |     1.525 |     1.220 |     1.365 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -13.97 |  -0.09883 |    0.5245 |    0.1410 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.5845 |    -3.407 |    -1.272 |   -0.2800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9652 |    -1.487 |    -1.684 |    0.3572 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   58</span>|     476.18235 |     1.000 |    -1.690 |   -0.9781 |   -0.8961 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8818 |   -0.1915 |   -0.8548 |   -0.7635 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.041 |   -0.6068 |   -0.6702 |   -0.6625 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     476.18235 |     93.04 |    -5.990 |    -1.003 |   -0.1117 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.264 |     1.606 |   0.03055 |    0.8458 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7299 |     1.531 |     1.281 |     1.294 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     476.18235</span> |     93.04 |  0.002505 |    0.2684 |    0.8943 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.617 |     1.606 |   0.03055 |    0.8458 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7299 |     1.531 |     1.281 |     1.294 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   59</span>|     476.08231 |     1.003 |    -1.678 |   -0.9530 |   -0.9107 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8858 |   -0.2215 |   -0.8479 |   -0.7811 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.035 |   -0.6092 |   -0.7010 |   -0.6265 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     476.08231 |     93.25 |    -5.978 |   -0.9792 |   -0.1263 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.260 |     1.588 |   0.03066 |    0.8325 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7350 |     1.528 |     1.248 |     1.332 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     476.08231</span> |     93.25 |  0.002533 |    0.2730 |    0.8814 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.579 |     1.588 |   0.03066 |    0.8325 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7350 |     1.528 |     1.248 |     1.332 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     18.25 |  -0.08933 |   -0.2508 |    0.5157 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8830 |    -3.835 |    -1.133 |    -1.026 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.010 |    -2.153 |   -0.2439 |    -1.007 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   60</span>|     476.03808 |     1.001 |    -1.672 |   -0.9320 |   -0.9310 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9291 |   -0.2079 |   -0.8419 |   -0.7733 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.039 |   -0.6026 |   -0.6913 |   -0.6380 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     476.03808 |     93.13 |    -5.972 |   -0.9594 |   -0.1466 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.216 |     1.596 |   0.03074 |    0.8384 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7314 |     1.536 |     1.259 |     1.320 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     476.03808</span> |     93.13 |  0.002548 |    0.2770 |    0.8636 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.173 |     1.596 |   0.03074 |    0.8384 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7314 |     1.536 |     1.259 |     1.320 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -5.407 |  -0.06545 |    0.6727 |   0.05373 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4541 |    -1.330 |   -0.5709 |   -0.1224 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8702 |    -1.228 |    0.2640 |    -1.506 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   61</span>|     476.03300 |     1.003 |    -1.664 |   -0.9621 |   -0.9598 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9520 |   -0.2020 |   -0.8337 |   -0.7650 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.042 |   -0.6106 |   -0.7076 |   -0.6154 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|       476.033 |     93.29 |    -5.964 |   -0.9878 |   -0.1754 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.193 |     1.599 |   0.03087 |    0.8447 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7289 |     1.526 |     1.241 |     1.344 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">       476.033</span> |     93.29 |  0.002569 |    0.2714 |    0.8391 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.966 |     1.599 |   0.03087 |    0.8447 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7289 |     1.526 |     1.241 |     1.344 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     15.25 | -0.008049 |   -0.5780 |   -0.5632 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9601 |   -0.9870 |   -0.1614 |  -0.08306 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3446 |    -1.564 |   -0.5735 |   -0.5777 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   62</span>|     475.98457 |     1.001 |    -1.657 |   -0.9620 |   -0.9650 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9284 |   -0.2016 |   -0.8312 |   -0.7567 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.045 |   -0.6004 |   -0.7091 |   -0.6133 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     475.98457 |     93.13 |    -5.957 |   -0.9877 |   -0.1806 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.217 |     1.600 |   0.03091 |    0.8511 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7262 |     1.538 |     1.240 |     1.346 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     475.98457</span> |     93.13 |  0.002588 |    0.2714 |    0.8347 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.180 |     1.600 |   0.03091 |    0.8511 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7262 |     1.538 |     1.240 |     1.346 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   63</span>|     475.96536 |     1.003 |    -1.649 |   -0.9620 |   -0.9705 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9043 |   -0.2014 |   -0.8286 |   -0.7480 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.048 |   -0.5903 |   -0.7109 |   -0.6113 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     475.96536 |     93.24 |    -5.949 |   -0.9876 |   -0.1861 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.241 |     1.600 |   0.03094 |    0.8576 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7235 |     1.551 |     1.238 |     1.349 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     475.96536</span> |     93.24 |  0.002608 |    0.2714 |    0.8302 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.403 |     1.600 |   0.03094 |    0.8576 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7235 |     1.551 |     1.238 |     1.349 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   64</span>|     476.05821 |     1.003 |    -1.627 |   -0.9618 |   -0.9866 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8332 |   -0.2007 |   -0.8211 |   -0.7226 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.057 |   -0.5602 |   -0.7159 |   -0.6052 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     476.05821 |     93.28 |    -5.927 |   -0.9874 |   -0.2022 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.312 |     1.600 |   0.03106 |    0.8769 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7156 |     1.587 |     1.233 |     1.355 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     476.05821</span> |     93.28 |  0.002666 |    0.2714 |    0.8169 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     10.10 |     1.600 |   0.03106 |    0.8769 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7156 |     1.587 |     1.233 |     1.355 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     11.90 |   0.05098 |   -0.6601 |   -0.8657 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4347 |   -0.9523 |   -0.3178 |   -0.1283 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1024 |   -0.7800 |   -0.7284 |   -0.4981 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   65</span>|     476.00714 |     1.001 |    -1.643 |   -0.9512 |   -0.8390 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8932 |   -0.2051 |   -0.7956 |   -0.7365 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.045 |   -0.5599 |   -0.7127 |   -0.5744 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     476.00714 |     93.11 |    -5.943 |   -0.9775 |  -0.05460 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.252 |     1.598 |   0.03144 |    0.8663 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7264 |     1.588 |     1.236 |     1.388 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     476.00714</span> |     93.11 |  0.002624 |    0.2734 |    0.9469 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.508 |     1.598 |   0.03144 |    0.8663 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7264 |     1.588 |     1.236 |     1.388 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   66</span>|     475.93814 |     1.000 |    -1.647 |   -0.9575 |   -0.9171 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8999 |   -0.2027 |   -0.8152 |   -0.7434 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.047 |   -0.5779 |   -0.7115 |   -0.5963 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     475.93814 |     93.01 |    -5.947 |   -0.9834 |   -0.1327 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.245 |     1.599 |   0.03115 |    0.8612 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7247 |     1.566 |     1.237 |     1.365 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     475.93814</span> |     93.01 |  0.002614 |    0.2722 |    0.8757 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.445 |     1.599 |   0.03115 |    0.8612 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7247 |     1.566 |     1.237 |     1.365 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -23.67 |   0.03897 |   -0.7058 |    0.2979 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2507 |   -0.5051 |   -0.4675 |  -0.05050 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.329 |   -0.1594 |   -0.7628 |    0.1885 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   67</span>|     475.98668 |     1.003 |    -1.653 |   -0.9119 |   -0.8938 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8940 |   -0.2050 |   -0.7980 |   -0.7479 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.040 |   -0.5617 |   -0.7056 |   -0.5853 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     475.98668 |     93.26 |    -5.953 |   -0.9406 |   -0.1094 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.251 |     1.598 |   0.03140 |    0.8577 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7310 |     1.586 |     1.243 |     1.376 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     475.98668</span> |     93.26 |  0.002598 |    0.2808 |    0.8964 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.501 |     1.598 |   0.03140 |    0.8577 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7310 |     1.586 |     1.243 |     1.376 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   68</span>|     475.93401 |     1.003 |    -1.649 |   -0.9419 |   -0.9092 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8979 |   -0.2035 |   -0.8093 |   -0.7449 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.044 |   -0.5723 |   -0.7094 |   -0.5925 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     475.93401 |     93.26 |    -5.949 |   -0.9687 |   -0.1248 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.247 |     1.599 |   0.03123 |    0.8600 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7270 |     1.573 |     1.239 |     1.369 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     475.93401</span> |     93.26 |  0.002609 |    0.2751 |    0.8827 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.464 |     1.599 |   0.03123 |    0.8600 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7270 |     1.573 |     1.239 |     1.369 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     21.62 |   0.03755 |    0.3553 |    0.5344 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.5780 |   -0.8226 |   -0.2181 |   -0.2167 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.049 |  0.002537 |   -0.7279 |    0.2347 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   69</span>|     475.92580 |     1.001 |    -1.653 |   -0.9417 |   -0.9124 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8966 |   -0.2037 |   -0.8058 |   -0.7501 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.041 |   -0.5739 |   -0.7048 |   -0.5926 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      475.9258 |     93.12 |    -5.953 |   -0.9686 |   -0.1280 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.249 |     1.598 |   0.03129 |    0.8560 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7297 |     1.571 |     1.244 |     1.369 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      475.9258</span> |     93.12 |  0.002598 |    0.2752 |    0.8798 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.476 |     1.598 |   0.03129 |    0.8560 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7297 |     1.571 |     1.244 |     1.369 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.238 |  0.003180 |    0.2025 |    0.4289 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4493 |   -0.2935 |   -0.2270 |   -0.1209 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3327 |  -0.04065 |   -0.4864 |    0.2705 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   70</span>|     475.92421 |     1.002 |    -1.655 |   -0.9475 |   -0.9180 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8971 |   -0.2051 |   -0.8017 |   -0.7499 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.041 |   -0.5760 |   -0.7024 |   -0.5951 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     475.92421 |     93.14 |    -5.955 |   -0.9740 |   -0.1336 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.248 |     1.598 |   0.03135 |    0.8562 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7299 |     1.568 |     1.247 |     1.366 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     475.92421</span> |     93.14 |  0.002593 |    0.2741 |    0.8749 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.472 |     1.598 |   0.03135 |    0.8562 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7299 |     1.568 |     1.247 |     1.366 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     1.572 | 0.0005078 |  -0.07939 |    0.3039 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4520 |   -0.5123 |   -0.2298 |   -0.1955 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3506 |   -0.1759 |   -0.3471 |    0.1717 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   71</span>|     475.91356 |     1.001 |    -1.654 |   -0.9476 |   -0.9223 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8992 |   -0.2071 |   -0.7965 |   -0.7440 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.043 |   -0.5749 |   -0.7035 |   -0.5974 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     475.91356 |     93.12 |    -5.954 |   -0.9741 |   -0.1379 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.246 |     1.596 |   0.03143 |    0.8607 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7278 |     1.569 |     1.246 |     1.363 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     475.91356</span> |     93.12 |  0.002596 |    0.2741 |    0.8712 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.452 |     1.596 |   0.03143 |    0.8607 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7278 |     1.569 |     1.246 |     1.363 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   72</span>|     475.89054 |     1.001 |    -1.650 |   -0.9479 |   -0.9349 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9054 |   -0.2136 |   -0.7808 |   -0.7261 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.051 |   -0.5716 |   -0.7071 |   -0.6043 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     475.89054 |     93.11 |    -5.950 |   -0.9744 |   -0.1505 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.240 |     1.592 |   0.03166 |    0.8742 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7214 |     1.574 |     1.242 |     1.356 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     475.89054</span> |     93.11 |  0.002606 |    0.2740 |    0.8602 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.393 |     1.592 |   0.03166 |    0.8742 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7214 |     1.574 |     1.242 |     1.356 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -3.467 |   0.05330 |   -0.1133 |   -0.1045 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1362 |   -0.3304 |   -0.2172 |   0.04726 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.416 |   0.08323 |   -0.4566 |   -0.2851 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   73</span>|     476.06529 |     1.002 |    -1.688 |   -0.8959 |   -0.9784 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9076 |   -0.2354 |   -0.6823 |   -0.7302 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.035 |   -0.5603 |   -0.6700 |   -0.6079 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     476.06529 |     93.20 |    -5.988 |   -0.9255 |   -0.1940 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.238 |     1.579 |   0.03314 |    0.8712 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7347 |     1.587 |     1.281 |     1.352 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     476.06529</span> |     93.20 |  0.002510 |    0.2838 |    0.8236 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.372 |     1.579 |   0.03314 |    0.8712 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7347 |     1.587 |     1.281 |     1.352 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   74</span>|     475.89054 |     1.001 |    -1.650 |   -0.9479 |   -0.9349 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9054 |   -0.2136 |   -0.7808 |   -0.7261 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.051 |   -0.5716 |   -0.7071 |   -0.6043 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     475.89054 |     93.11 |    -5.950 |   -0.9744 |   -0.1505 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.240 |     1.592 |   0.03166 |    0.8742 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7214 |     1.574 |     1.242 |     1.356 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     475.89054</span> |     93.11 |  0.002606 |    0.2740 |    0.8602 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.393 |     1.592 |   0.03166 |    0.8742 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7214 |     1.574 |     1.242 |     1.356 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> calculating covariance matrix</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating residuals/tables</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>initial ETAs were nudged; (can control by foceiControl(etaNudge=., etaNudge2=))</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>ETAs were reset to zero during optimization; (Can control by foceiControl(resetEtaP=.))</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>gradient problems with initial estimate and covariance; see $scaleInfo</span>
-<span class="r-in"><span class="va">f_nlmixr_dfop_sfo_focei_tc</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr</a></span><span class="op">(</span><span class="va">f_mmkin_tc</span><span class="op">[</span><span class="st">"DFOP-SFO"</span>, <span class="op">]</span>, est <span class="op">=</span> <span class="st">"focei"</span><span class="op">)</span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate jacobian</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate sensitivities</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate ∂(f)/∂(η)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate ∂(R²)/∂(η)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in inner model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in inner model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in EBE model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in EBE model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling inner model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in FD model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in FD model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling EBE model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling events FD model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Needed Covariates:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "CMT"</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="font-weight: bold;">Key:</span> U: Unscaled Parameters; X: Back-transformed parameters; G: Gill difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> F: Forward difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> C: Central difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> M: Mixed forward and central difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Unscaled parameters for Omegas=chol(solve(omega));</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Diagonals are transformed, as specified by foceiControl(diagXform=)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |-----+---------------+-----------+-----------+-----------+-----------|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    #| Objective Fun |  parent_0 |  log_k_A1 |f_parent_qlogis |    log_k1 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    log_k2 |  g_qlogis | sigma_low |  rsd_high |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|        o1 |        o2 |        o3 |        o4 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|        o5 |        o6 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    1</span>|     495.48573 |     1.000 |    -1.000 |   -0.9104 |   -0.9376 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9875 |   -0.8823 |   -0.8746 |   -0.8907 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8767 |   -0.8731 |   -0.8673 |   -0.8720 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8739 |   -0.8666 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     495.48573 |     91.00 |    -5.200 |   -0.8900 |    -2.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.600 |    0.4600 |    0.8300 |   0.05800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7311 |    0.9036 |     1.183 |    0.9554 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.8633 |     1.214 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     495.48573</span> |     91.00 |  0.005517 |    0.2911 |    0.1108 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01005 |    0.6130 |    0.8300 |   0.05800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7311 |    0.9036 |     1.183 |    0.9554 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.8633 |     1.214 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    G|    Gill Diff. |   -0.9650 |     2.223 |   -0.3153 |  -0.01817 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3350 |    0.6789 |    -29.17 |    -19.58 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9642 |     9.851 |    -11.94 |    -1.319 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     8.578 |    -12.45 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    2</span>|     487.27153 |     1.023 |    -1.054 |   -0.9028 |   -0.9372 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9794 |   -0.8987 |   -0.1695 |   -0.4175 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9000 |    -1.111 |   -0.5788 |   -0.8401 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.081 |   -0.5657 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     487.27153 |     93.12 |    -5.254 |   -0.8832 |    -2.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.592 |    0.4525 |     1.123 |   0.07172 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7141 |    0.6884 |     1.525 |    0.9859 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6843 |     1.580 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     487.27153</span> |     93.12 |  0.005228 |    0.2925 |    0.1109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01013 |    0.6112 |     1.123 |   0.07172 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7141 |    0.6884 |     1.525 |    0.9859 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6843 |     1.580 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     131.2 |     1.375 |     2.844 |   -0.2311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2724 |    0.4206 |     9.234 |     14.82 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3432 |    -1.547 |    -2.137 |     2.699 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -4.845 |    -6.389 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    3</span>|     3806.3530 |    0.1967 |    -1.082 |   -0.9181 |   -0.9356 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9782 |   -0.9073 |   0.02635 |   -0.3409 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9062 |    -1.204 |   -0.4610 |   -0.8458 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.125 |   -0.4164 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      3806.353 |     17.90 |    -5.282 |   -0.8969 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.591 |    0.4485 |     1.204 |   0.07394 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7095 |    0.6043 |     1.664 |    0.9805 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6463 |     1.761 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      3806.353</span> |     17.90 |  0.005083 |    0.2897 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01015 |    0.6103 |     1.204 |   0.07394 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7095 |    0.6043 |     1.664 |    0.9805 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6463 |     1.761 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    4</span>|     498.29847 |    0.9363 |    -1.055 |   -0.9047 |   -0.9370 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9796 |   -0.8990 |   -0.1756 |   -0.4273 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8998 |    -1.110 |   -0.5773 |   -0.8419 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.078 |   -0.5615 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     498.29847 |     85.20 |    -5.255 |   -0.8849 |    -2.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.592 |    0.4523 |     1.120 |   0.07144 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7142 |    0.6894 |     1.526 |    0.9842 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6871 |     1.585 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     498.29847</span> |     85.20 |  0.005223 |    0.2922 |    0.1109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01013 |    0.6112 |     1.120 |   0.07144 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7142 |    0.6894 |     1.526 |    0.9842 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6871 |     1.585 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    5</span>|     485.66266 |     1.001 |    -1.054 |   -0.9033 |   -0.9372 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9795 |   -0.8988 |   -0.1711 |   -0.4200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8999 |    -1.111 |   -0.5784 |   -0.8406 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.080 |   -0.5646 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     485.66266 |     91.08 |    -5.254 |   -0.8836 |    -2.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.592 |    0.4524 |     1.122 |   0.07165 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7141 |    0.6887 |     1.525 |    0.9855 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6850 |     1.581 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     485.66266</span> |     91.08 |  0.005227 |    0.2924 |    0.1109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01013 |    0.6112 |     1.122 |   0.07165 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7141 |    0.6887 |     1.525 |    0.9855 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6850 |     1.581 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     5.221 |     1.276 |    0.8286 |   0.07146 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3378 |    0.6177 |     8.950 |     14.42 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.232 |    -2.934 |    -2.090 |     2.822 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -4.031 |    -6.085 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    6</span>|     485.32609 |    0.9950 |    -1.055 |   -0.9042 |   -0.9372 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9799 |   -0.8995 |   -0.1813 |   -0.4364 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8974 |    -1.108 |   -0.5760 |   -0.8438 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.076 |   -0.5577 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     485.32609 |     90.54 |    -5.255 |   -0.8845 |    -2.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.592 |    0.4521 |     1.118 |   0.07117 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7160 |    0.6917 |     1.528 |    0.9824 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6890 |     1.589 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     485.32609</span> |     90.54 |  0.005219 |    0.2922 |    0.1108 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01013 |    0.6111 |     1.118 |   0.07117 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7160 |    0.6917 |     1.528 |    0.9824 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6890 |     1.589 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -30.72 |     1.256 |    0.2407 |    0.1468 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3576 |    0.6982 |     8.550 |     13.79 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.152 |    -3.092 |    -1.869 |     2.672 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -3.399 |    -5.827 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    7</span>|     484.94767 |     1.003 |    -1.055 |   -0.9045 |   -0.9373 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9801 |   -0.8996 |   -0.1956 |   -0.4497 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8963 |    -1.103 |   -0.5795 |   -0.8455 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.071 |   -0.5595 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     484.94767 |     91.28 |    -5.255 |   -0.8848 |    -2.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.593 |    0.4521 |     1.112 |   0.07079 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7168 |    0.6961 |     1.524 |    0.9808 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6930 |     1.587 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     484.94767</span> |     91.28 |  0.005220 |    0.2922 |    0.1108 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01013 |    0.6111 |     1.112 |   0.07079 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7168 |    0.6961 |     1.524 |    0.9808 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6930 |     1.587 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     16.95 |     1.308 |    0.9181 |   0.03760 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3308 |    0.6549 |     8.124 |     13.74 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.975 |    -2.369 |    -2.107 |     2.437 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -3.334 |    -5.928 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    8</span>|     484.63747 |    0.9965 |    -1.055 |   -0.9051 |   -0.9373 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9805 |   -0.8998 |   -0.2100 |   -0.4642 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8952 |    -1.098 |   -0.5823 |   -0.8472 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.067 |   -0.5602 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     484.63747 |     90.68 |    -5.255 |   -0.8853 |    -2.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.593 |    0.4520 |     1.106 |   0.07037 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7176 |    0.7002 |     1.521 |    0.9792 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6968 |     1.586 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     484.63747</span> |     90.68 |  0.005220 |    0.2921 |    0.1108 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01012 |    0.6111 |     1.106 |   0.07037 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7176 |    0.7002 |     1.521 |    0.9792 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6968 |     1.586 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -22.67 |     1.288 |    0.2773 |    0.1211 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3459 |    0.7203 |     8.028 |     13.24 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.967 |    -2.555 |    -2.117 |     2.333 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -2.789 |    -5.812 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    9</span>|     484.31288 |     1.003 |    -1.055 |   -0.9054 |   -0.9374 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9808 |   -0.9000 |   -0.2248 |   -0.4783 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8942 |    -1.094 |   -0.5856 |   -0.8486 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.063 |   -0.5617 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     484.31288 |     91.27 |    -5.255 |   -0.8855 |    -2.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.593 |    0.4519 |     1.100 |   0.06996 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7183 |    0.7043 |     1.517 |    0.9778 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.7003 |     1.585 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     484.31288</span> |     91.27 |  0.005220 |    0.2920 |    0.1108 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01012 |    0.6111 |     1.100 |   0.06996 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7183 |    0.7043 |     1.517 |    0.9778 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.7003 |     1.585 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     16.32 |     1.333 |    0.8110 |   0.03416 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3225 |    0.6774 |     7.502 |     12.95 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.990 |    -1.849 |    -2.190 |     2.193 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -2.714 |    -5.891 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   10</span>|     484.03445 |    0.9966 |    -1.055 |   -0.9058 |   -0.9374 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9811 |   -0.9003 |   -0.2393 |   -0.4932 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8929 |    -1.090 |   -0.5881 |   -0.8501 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.059 |   -0.5620 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     484.03445 |     90.69 |    -5.255 |   -0.8859 |    -2.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.594 |    0.4517 |     1.094 |   0.06953 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7192 |    0.7079 |     1.514 |    0.9764 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.7034 |     1.584 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     484.03445</span> |     90.69 |  0.005219 |    0.2919 |    0.1108 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01012 |    0.6110 |     1.094 |   0.06953 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7192 |    0.7079 |     1.514 |    0.9764 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.7034 |     1.584 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -23.34 |     1.311 |    0.1997 |    0.1136 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3397 |    0.7422 |     7.043 |     12.17 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.843 |    -2.075 |    -2.300 |     2.141 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -2.235 |    -5.778 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   11</span>|     483.72794 |     1.003 |    -1.056 |   -0.9061 |   -0.9374 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9815 |   -0.9008 |   -0.2540 |   -0.5081 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8918 |    -1.086 |   -0.5906 |   -0.8514 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.056 |   -0.5623 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     483.72794 |     91.28 |    -5.256 |   -0.8861 |    -2.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.594 |    0.4515 |     1.088 |   0.06909 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7200 |    0.7113 |     1.511 |    0.9751 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.7061 |     1.584 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     483.72794</span> |     91.28 |  0.005217 |    0.2919 |    0.1108 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01011 |    0.6110 |     1.088 |   0.06909 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7200 |    0.7113 |     1.511 |    0.9751 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.7061 |     1.584 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     16.56 |     1.355 |    0.7367 |   0.03002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3166 |    0.7021 |     6.528 |     11.89 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.880 |    -1.512 |    -2.404 |     1.917 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -2.215 |    -5.846 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   12</span>|     483.44272 |    0.9974 |    -1.057 |   -0.9064 |   -0.9375 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9820 |   -0.9016 |   -0.2678 |   -0.5243 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8904 |    -1.083 |   -0.5914 |   -0.8528 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.054 |   -0.5601 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     483.44272 |     90.76 |    -5.257 |   -0.8865 |    -2.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.594 |    0.4511 |     1.082 |   0.06862 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7211 |    0.7141 |     1.510 |    0.9738 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.7081 |     1.587 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     483.44272</span> |     90.76 |  0.005212 |    0.2918 |    0.1108 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01011 |    0.6109 |     1.082 |   0.06862 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7211 |    0.7141 |     1.510 |    0.9738 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.7081 |     1.587 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -19.50 |     1.332 |    0.2061 |    0.1022 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3336 |    0.7519 |     5.944 |     11.09 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.878 |    -1.675 |    -2.404 |     1.054 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.833 |    -5.715 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   13</span>|     483.13758 |     1.003 |    -1.059 |   -0.9067 |   -0.9375 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9826 |   -0.9029 |   -0.2791 |   -0.5415 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8881 |    -1.081 |   -0.5896 |   -0.8508 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.053 |   -0.5541 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     483.13758 |     91.30 |    -5.259 |   -0.8867 |    -2.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.595 |    0.4505 |     1.077 |   0.06813 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7227 |    0.7157 |     1.512 |    0.9758 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.7089 |     1.594 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     483.13758</span> |     91.30 |  0.005202 |    0.2918 |    0.1108 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01010 |    0.6108 |     1.077 |   0.06813 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7227 |    0.7157 |     1.512 |    0.9758 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.7089 |     1.594 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     16.83 |     1.368 |    0.7006 |   0.02276 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3159 |    0.7144 |     5.487 |     10.74 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.773 |    -1.125 |    -2.251 |     1.956 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.882 |    -5.671 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   14</span>|     482.85180 |    0.9981 |    -1.062 |   -0.9072 |   -0.9376 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9834 |   -0.9050 |   -0.2850 |   -0.5582 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8853 |    -1.082 |   -0.5852 |   -0.8503 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.053 |   -0.5428 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      482.8518 |     90.83 |    -5.262 |   -0.8872 |    -2.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.596 |    0.4496 |     1.075 |   0.06764 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7248 |    0.7152 |     1.517 |    0.9762 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.7084 |     1.607 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      482.8518</span> |     90.83 |  0.005184 |    0.2917 |    0.1108 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01009 |    0.6105 |     1.075 |   0.06764 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7248 |    0.7152 |     1.517 |    0.9762 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.7084 |     1.607 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -16.64 |     1.337 |    0.2421 |   0.09047 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3389 |    0.7643 |     5.051 |     10.06 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.787 |    -1.445 |    -2.047 |     2.073 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.627 |    -5.410 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   15</span>|     482.60290 |     1.003 |    -1.066 |   -0.9079 |   -0.9377 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9844 |   -0.9075 |   -0.2858 |   -0.5723 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8822 |    -1.083 |   -0.5806 |   -0.8571 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.055 |   -0.5294 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      482.6029 |     91.28 |    -5.266 |   -0.8878 |    -2.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.597 |    0.4484 |     1.074 |   0.06723 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7271 |    0.7136 |     1.523 |    0.9697 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.7072 |     1.624 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      482.6029</span> |     91.28 |  0.005162 |    0.2916 |    0.1108 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01008 |    0.6103 |     1.074 |   0.06723 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7271 |    0.7136 |     1.523 |    0.9697 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.7072 |     1.624 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     14.50 |     1.352 |    0.6742 |   0.02192 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3317 |    0.7297 |     4.803 |     9.894 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.694 |    -1.220 |    -2.031 |     1.506 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.722 |    -5.269 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   16</span>|     482.37953 |    0.9987 |    -1.071 |   -0.9090 |   -0.9377 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9857 |   -0.9106 |   -0.2840 |   -0.5862 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8787 |    -1.084 |   -0.5758 |   -0.8611 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.056 |   -0.5150 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     482.37953 |     90.88 |    -5.271 |   -0.8888 |    -2.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.598 |    0.4470 |     1.075 |   0.06683 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7296 |    0.7132 |     1.528 |    0.9659 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.7058 |     1.641 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     482.37953</span> |     90.88 |  0.005137 |    0.2914 |    0.1108 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01007 |    0.6099 |     1.075 |   0.06683 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7296 |    0.7132 |     1.528 |    0.9659 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.7058 |     1.641 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   17</span>|     482.17662 |    0.9986 |    -1.077 |   -0.9102 |   -0.9378 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9871 |   -0.9141 |   -0.2800 |   -0.5994 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8750 |    -1.085 |   -0.5707 |   -0.8654 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.059 |   -0.4996 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     482.17662 |     90.88 |    -5.277 |   -0.8898 |    -2.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.600 |    0.4454 |     1.077 |   0.06645 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7323 |    0.7123 |     1.534 |    0.9618 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.7037 |     1.660 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     482.17662</span> |     90.88 |  0.005108 |    0.2911 |    0.1108 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01006 |    0.6095 |     1.077 |   0.06645 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7323 |    0.7123 |     1.534 |    0.9618 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.7037 |     1.660 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   18</span>|     481.54428 |    0.9984 |    -1.097 |   -0.9142 |   -0.9379 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9921 |   -0.9262 |   -0.2660 |   -0.6456 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8623 |    -1.088 |   -0.5530 |   -0.8805 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.068 |   -0.4457 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     481.54428 |     90.86 |    -5.297 |   -0.8934 |    -2.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.605 |    0.4398 |     1.083 |   0.06511 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7417 |    0.7091 |     1.555 |    0.9473 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6961 |     1.725 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     481.54428</span> |     90.86 |  0.005009 |    0.2904 |    0.1108 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01001 |    0.6082 |     1.083 |   0.06511 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7417 |    0.7091 |     1.555 |    0.9473 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6961 |     1.725 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   19</span>|     480.42060 |    0.9980 |    -1.149 |   -0.9249 |   -0.9384 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.006 |   -0.9588 |   -0.2285 |   -0.7695 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8280 |    -1.098 |   -0.5055 |   -0.9211 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.091 |   -0.3011 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      480.4206 |     90.81 |    -5.349 |   -0.9029 |    -2.201 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.618 |    0.4248 |     1.098 |   0.06151 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7667 |    0.7005 |     1.611 |    0.9085 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6759 |     1.901 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      480.4206</span> |     90.81 |  0.004753 |    0.2885 |    0.1107 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009872 |    0.6046 |     1.098 |   0.06151 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7667 |    0.7005 |     1.611 |    0.9085 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6759 |     1.901 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -36.54 |     1.160 |   -0.4282 |   0.02550 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4248 |    0.7330 |     2.572 |     4.769 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.047 |    -1.982 |     1.244 |    -3.305 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.792 |    -2.494 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   20</span>|     480.52888 |     1.003 |    -1.232 |   -0.9300 |   -0.9345 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.037 |    -1.013 |   -0.2660 |   -0.9542 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7770 |    -1.021 |   -0.6177 |   -0.7306 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.035 |   -0.1927 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.52888 |     91.28 |    -5.432 |   -0.9075 |    -2.197 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.649 |    0.3997 |     1.083 |   0.05616 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8040 |    0.7699 |     1.479 |     1.091 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.7247 |     2.033 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.52888</span> |     91.28 |  0.004374 |    0.2875 |    0.1111 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009571 |    0.5986 |     1.083 |   0.05616 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8040 |    0.7699 |     1.479 |     1.091 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.7247 |     2.033 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   21</span>|     479.69850 |     1.004 |    -1.189 |   -0.9273 |   -0.9365 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.020 |   -0.9850 |   -0.2467 |   -0.8583 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8035 |    -1.061 |   -0.5593 |   -0.8297 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.064 |   -0.2490 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      479.6985 |     91.34 |    -5.389 |   -0.9050 |    -2.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.633 |    0.4128 |     1.091 |   0.05894 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7846 |    0.7338 |     1.548 |    0.9959 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6994 |     1.964 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      479.6985</span> |     91.34 |  0.004567 |    0.2880 |    0.1109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009727 |    0.6018 |     1.091 |   0.05894 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7846 |    0.7338 |     1.548 |    0.9959 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6994 |     1.964 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     5.264 |     1.163 |  -0.05494 |  -0.06753 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3641 |    0.6248 |    0.1998 |     1.877 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5696 |    0.3809 |    0.8989 |     3.758 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2123 |    -1.578 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   22</span>|     479.87893 |     1.001 |    -1.256 |   -0.9150 |   -0.9312 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.045 |    -1.024 |   -0.2268 |   -0.8946 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7799 |    -1.052 |   -0.6825 |   -0.8629 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.056 |   -0.2060 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.87893 |     91.06 |    -5.456 |   -0.8941 |    -2.194 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.658 |    0.3949 |     1.099 |   0.05789 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8019 |    0.7416 |     1.402 |    0.9642 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.7063 |     2.016 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.87893</span> |     91.06 |  0.004269 |    0.2903 |    0.1115 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009490 |    0.5975 |     1.099 |   0.05789 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8019 |    0.7416 |     1.402 |    0.9642 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.7063 |     2.016 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   23</span>|     479.70356 |    0.9996 |    -1.214 |   -0.9229 |   -0.9346 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.029 |   -0.9992 |   -0.2397 |   -0.8723 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7947 |    -1.058 |   -0.6038 |   -0.8437 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.061 |   -0.2328 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.70356 |     90.96 |    -5.414 |   -0.9011 |    -2.197 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.642 |    0.4062 |     1.093 |   0.05853 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7910 |    0.7364 |     1.495 |    0.9825 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.7017 |     1.984 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.70356</span> |     90.96 |  0.004455 |    0.2888 |    0.1111 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009639 |    0.6002 |     1.093 |   0.05853 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7910 |    0.7364 |     1.495 |    0.9825 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.7017 |     1.984 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   24</span>|     479.71523 |    0.9993 |    -1.201 |   -0.9253 |   -0.9356 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.025 |   -0.9918 |   -0.2436 |   -0.8656 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7992 |    -1.060 |   -0.5801 |   -0.8379 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.063 |   -0.2408 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.71523 |     90.93 |    -5.401 |   -0.9032 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.637 |    0.4096 |     1.092 |   0.05873 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7877 |    0.7349 |     1.523 |    0.9880 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.7004 |     1.974 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.71523</span> |     90.93 |  0.004513 |    0.2884 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009685 |    0.6010 |     1.092 |   0.05873 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7877 |    0.7349 |     1.523 |    0.9880 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.7004 |     1.974 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   25</span>|     479.73471 |    0.9991 |    -1.194 |   -0.9266 |   -0.9362 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.022 |   -0.9877 |   -0.2457 |   -0.8619 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8017 |    -1.061 |   -0.5670 |   -0.8347 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.064 |   -0.2453 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.73471 |     90.92 |    -5.394 |   -0.9044 |    -2.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.635 |    0.4115 |     1.091 |   0.05884 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7859 |    0.7340 |     1.539 |    0.9911 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6996 |     1.969 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.73471</span> |     90.92 |  0.004545 |    0.2881 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009710 |    0.6015 |     1.091 |   0.05884 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7859 |    0.7340 |     1.539 |    0.9911 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6996 |     1.969 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   26</span>|     479.71271 |     1.001 |    -1.190 |   -0.9273 |   -0.9365 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.021 |   -0.9854 |   -0.2468 |   -0.8594 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8032 |    -1.061 |   -0.5599 |   -0.8320 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.064 |   -0.2481 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.71271 |     91.05 |    -5.390 |   -0.9050 |    -2.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.633 |    0.4126 |     1.091 |   0.05891 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7849 |    0.7336 |     1.547 |    0.9937 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6993 |     1.965 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.71271</span> |     91.05 |  0.004564 |    0.2880 |    0.1109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009725 |    0.6017 |     1.091 |   0.05891 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7849 |    0.7336 |     1.547 |    0.9937 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6993 |     1.965 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   27</span>|     479.69386 |     1.003 |    -1.189 |   -0.9273 |   -0.9365 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.020 |   -0.9851 |   -0.2467 |   -0.8587 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8034 |    -1.061 |   -0.5595 |   -0.8305 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.064 |   -0.2487 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.69386 |     91.24 |    -5.389 |   -0.9050 |    -2.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.633 |    0.4127 |     1.091 |   0.05893 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7847 |    0.7338 |     1.548 |    0.9951 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6994 |     1.965 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.69386</span> |     91.24 |  0.004566 |    0.2880 |    0.1109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009726 |    0.6017 |     1.091 |   0.05893 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7847 |    0.7338 |     1.548 |    0.9951 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6994 |     1.965 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -3.722 |     1.155 |   -0.1645 |  -0.05121 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3669 |    0.6376 |   0.06785 |     1.772 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5854 |    0.2936 |    0.9229 |     3.709 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2520 |    -1.579 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   28</span>|     479.68901 |     1.004 |    -1.189 |   -0.9273 |   -0.9365 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.021 |   -0.9853 |   -0.2467 |   -0.8591 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8033 |    -1.061 |   -0.5597 |   -0.8314 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.064 |   -0.2483 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.68901 |     91.32 |    -5.389 |   -0.9050 |    -2.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.633 |    0.4126 |     1.091 |   0.05892 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7848 |    0.7337 |     1.547 |    0.9942 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6993 |     1.965 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.68901</span> |     91.32 |  0.004565 |    0.2880 |    0.1109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009725 |    0.6017 |     1.091 |   0.05892 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7848 |    0.7337 |     1.547 |    0.9942 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6993 |     1.965 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     3.772 |     1.160 |  -0.07003 |  -0.06455 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3646 |    0.6238 |   0.08418 |     1.806 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5672 |    0.3711 |    0.9250 |     3.651 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2161 |    -1.579 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   29</span>|     479.68474 |     1.003 |    -1.190 |   -0.9272 |   -0.9365 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.021 |   -0.9854 |   -0.2468 |   -0.8595 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8031 |    -1.061 |   -0.5600 |   -0.8323 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.064 |   -0.2479 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.68474 |     91.24 |    -5.390 |   -0.9050 |    -2.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.633 |    0.4126 |     1.091 |   0.05890 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7849 |    0.7336 |     1.547 |    0.9934 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6993 |     1.966 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.68474</span> |     91.24 |  0.004563 |    0.2880 |    0.1109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009724 |    0.6017 |     1.091 |   0.05890 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7849 |    0.7336 |     1.547 |    0.9934 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6993 |     1.966 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -3.834 |     1.153 |   -0.1599 |  -0.05010 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3669 |    0.6355 |   0.09191 |     1.764 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5796 |    0.2889 |     1.006 |     3.561 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2610 |    -1.565 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   30</span>|     479.68016 |     1.004 |    -1.190 |   -0.9272 |   -0.9365 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.021 |   -0.9856 |   -0.2468 |   -0.8600 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8030 |    -1.061 |   -0.5602 |   -0.8332 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.064 |   -0.2475 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.68016 |     91.32 |    -5.390 |   -0.9050 |    -2.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.633 |    0.4125 |     1.091 |   0.05889 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7850 |    0.7336 |     1.547 |    0.9926 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6992 |     1.966 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.68016</span> |     91.32 |  0.004562 |    0.2880 |    0.1109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009723 |    0.6017 |     1.091 |   0.05889 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7850 |    0.7336 |     1.547 |    0.9926 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6992 |     1.966 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     3.891 |     1.158 |  -0.06530 |  -0.06447 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3645 |    0.6207 |    0.1821 |     1.812 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5486 |    0.3656 |     1.002 |     3.562 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2233 |    -1.574 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   31</span>|     479.67614 |     1.003 |    -1.190 |   -0.9272 |   -0.9365 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.021 |   -0.9857 |   -0.2468 |   -0.8604 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8028 |    -1.061 |   -0.5604 |   -0.8341 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.064 |   -0.2472 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.67614 |     91.24 |    -5.390 |   -0.9049 |    -2.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.633 |    0.4124 |     1.091 |   0.05888 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7851 |    0.7335 |     1.546 |    0.9917 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6992 |     1.966 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.67614</span> |     91.24 |  0.004561 |    0.2880 |    0.1109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009722 |    0.6017 |     1.091 |   0.05888 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7851 |    0.7335 |     1.546 |    0.9917 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6992 |     1.966 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -3.915 |     1.151 |   -0.1574 |  -0.04977 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3672 |    0.6321 |   0.06058 |     1.705 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5691 |    0.2601 |    0.8724 |     3.420 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2638 |    -1.562 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   32</span>|     479.67180 |     1.004 |    -1.191 |   -0.9271 |   -0.9364 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.021 |   -0.9859 |   -0.2469 |   -0.8608 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8027 |    -1.061 |   -0.5607 |   -0.8349 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.064 |   -0.2468 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      479.6718 |     91.33 |    -5.391 |   -0.9049 |    -2.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.633 |    0.4124 |     1.091 |   0.05887 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7852 |    0.7334 |     1.546 |    0.9909 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6991 |     1.967 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      479.6718</span> |     91.33 |  0.004560 |    0.2880 |    0.1109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009722 |    0.6017 |     1.091 |   0.05887 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7852 |    0.7334 |     1.546 |    0.9909 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6991 |     1.967 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     4.074 |     1.157 |  -0.05990 |  -0.06452 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3645 |    0.6170 |    0.1286 |     1.744 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5576 |    0.3161 |    0.9007 |     3.345 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2216 |    -1.572 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   33</span>|     479.66809 |     1.003 |    -1.191 |   -0.9271 |   -0.9364 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.021 |   -0.9860 |   -0.2469 |   -0.8613 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8026 |    -1.062 |   -0.5609 |   -0.8357 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.064 |   -0.2464 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.66809 |     91.23 |    -5.391 |   -0.9049 |    -2.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.634 |    0.4123 |     1.091 |   0.05885 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7853 |    0.7334 |     1.546 |    0.9901 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6991 |     1.967 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.66809</span> |     91.23 |  0.004558 |    0.2880 |    0.1109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009721 |    0.6016 |     1.091 |   0.05885 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7853 |    0.7334 |     1.546 |    0.9901 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6991 |     1.967 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.161 |     1.149 |   -0.1570 |  -0.04911 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3674 |    0.6293 |   0.04215 |     1.668 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5621 |    0.2625 |    0.8940 |     3.313 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2611 |    -1.561 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   34</span>|     479.66397 |     1.004 |    -1.191 |   -0.9271 |   -0.9364 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.021 |   -0.9862 |   -0.2469 |   -0.8617 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8024 |    -1.062 |   -0.5611 |   -0.8366 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.064 |   -0.2460 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.66397 |     91.33 |    -5.391 |   -0.9049 |    -2.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.634 |    0.4122 |     1.091 |   0.05884 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7854 |    0.7333 |     1.546 |    0.9893 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6990 |     1.968 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.66397</span> |     91.33 |  0.004557 |    0.2881 |    0.1109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009720 |    0.6016 |     1.091 |   0.05884 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7854 |    0.7333 |     1.546 |    0.9893 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6990 |     1.968 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     4.197 |     1.155 |  -0.05534 |  -0.06448 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3644 |    0.6140 |    0.1526 |     1.765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5340 |    0.3555 |    0.9900 |     3.325 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2199 |    -1.571 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   35</span>|     479.66043 |     1.003 |    -1.191 |   -0.9271 |   -0.9364 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.021 |   -0.9863 |   -0.2469 |   -0.8621 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8023 |    -1.062 |   -0.5613 |   -0.8374 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.064 |   -0.2456 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.66043 |     91.23 |    -5.391 |   -0.9048 |    -2.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.634 |    0.4122 |     1.090 |   0.05883 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7855 |    0.7332 |     1.545 |    0.9886 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6989 |     1.968 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.66043</span> |     91.23 |  0.004556 |    0.2881 |    0.1109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009719 |    0.6016 |     1.090 |   0.05883 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7855 |    0.7332 |     1.545 |    0.9886 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6989 |     1.968 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.161 |     1.147 |   -0.1538 |  -0.04891 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3674 |    0.6262 |   0.06581 |     1.677 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5453 |    0.2828 |     1.029 |     3.251 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2683 |    -1.555 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   36</span>|     479.65652 |     1.004 |    -1.192 |   -0.9270 |   -0.9364 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.021 |   -0.9865 |   -0.2469 |   -0.8625 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8022 |    -1.062 |   -0.5616 |   -0.8382 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.064 |   -0.2452 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.65652 |     91.33 |    -5.392 |   -0.9048 |    -2.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.634 |    0.4121 |     1.090 |   0.05882 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7856 |    0.7332 |     1.545 |    0.9878 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6989 |     1.969 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.65652</span> |     91.33 |  0.004554 |    0.2881 |    0.1109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009718 |    0.6016 |     1.090 |   0.05882 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7856 |    0.7332 |     1.545 |    0.9878 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6989 |     1.969 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     4.221 |     1.153 |  -0.05203 |  -0.06424 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3643 |    0.6104 |   0.04854 |     1.680 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5365 |    0.2999 |    0.7877 |     3.080 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2284 |    -1.566 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   37</span>|     479.65328 |     1.003 |    -1.192 |   -0.9270 |   -0.9364 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.021 |   -0.9866 |   -0.2470 |   -0.8629 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8020 |    -1.062 |   -0.5618 |   -0.8389 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.064 |   -0.2448 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.65328 |     91.23 |    -5.392 |   -0.9048 |    -2.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.634 |    0.4120 |     1.090 |   0.05880 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7857 |    0.7331 |     1.545 |    0.9871 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6988 |     1.969 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.65328</span> |     91.23 |  0.004553 |    0.2881 |    0.1109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009717 |    0.6016 |     1.090 |   0.05880 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7857 |    0.7331 |     1.545 |    0.9871 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6988 |     1.969 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.438 |     1.145 |   -0.1538 |  -0.04822 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3675 |    0.6235 | 0.0004265 |     1.625 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5487 |    0.2329 |    0.8499 |     3.058 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2658 |    -1.554 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   38</span>|     479.64923 |     1.004 |    -1.192 |   -0.9270 |   -0.9364 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.022 |   -0.9868 |   -0.2469 |   -0.8633 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8019 |    -1.062 |   -0.5621 |   -0.8396 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.065 |   -0.2444 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.64923 |     91.32 |    -5.392 |   -0.9048 |    -2.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.634 |    0.4119 |     1.091 |   0.05879 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7858 |    0.7330 |     1.544 |    0.9864 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6988 |     1.970 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.64923</span> |     91.32 |  0.004551 |    0.2881 |    0.1109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009716 |    0.6015 |     1.091 |   0.05879 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7858 |    0.7330 |     1.544 |    0.9864 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6988 |     1.970 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     3.878 |     1.150 |  -0.05277 |  -0.06333 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3643 |    0.6074 |   0.07271 |     1.651 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5369 |    0.2992 |    0.8896 |     2.970 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2314 |    -1.562 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   39</span>|     479.64621 |     1.003 |    -1.193 |   -0.9270 |   -0.9363 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.022 |   -0.9870 |   -0.2469 |   -0.8638 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8017 |    -1.062 |   -0.5623 |   -0.8404 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.065 |   -0.2440 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.64621 |     91.23 |    -5.393 |   -0.9047 |    -2.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.634 |    0.4119 |     1.091 |   0.05878 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7859 |    0.7330 |     1.544 |    0.9856 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6987 |     1.970 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.64621</span> |     91.23 |  0.004550 |    0.2881 |    0.1109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009715 |    0.6015 |     1.091 |   0.05878 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7859 |    0.7330 |     1.544 |    0.9856 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6987 |     1.970 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.471 |     1.143 |   -0.1506 |  -0.04802 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3674 |    0.6203 | -0.003354 |     1.603 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5429 |    0.2243 |    0.9025 |     2.920 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2732 |    -1.548 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   40</span>|     479.64228 |     1.004 |    -1.193 |   -0.9269 |   -0.9363 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.022 |   -0.9872 |   -0.2468 |   -0.8642 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8016 |    -1.062 |   -0.5627 |   -0.8411 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.065 |   -0.2435 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.64228 |     91.32 |    -5.393 |   -0.9047 |    -2.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.634 |    0.4118 |     1.091 |   0.05877 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7860 |    0.7329 |     1.544 |    0.9850 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6987 |     1.971 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.64228</span> |     91.32 |  0.004548 |    0.2881 |    0.1109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009714 |    0.6015 |     1.091 |   0.05877 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7860 |    0.7329 |     1.544 |    0.9850 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6987 |     1.971 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     3.744 |     1.148 |  -0.05088 |  -0.06289 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3642 |    0.6037 |   0.03047 |     1.629 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5287 |    0.2733 |    0.8334 |     2.850 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2290 |    -1.558 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   41</span>|     479.63935 |     1.003 |    -1.193 |   -0.9269 |   -0.9363 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.022 |   -0.9874 |   -0.2468 |   -0.8646 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8014 |    -1.062 |   -0.5630 |   -0.8419 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.065 |   -0.2431 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.63935 |     91.23 |    -5.393 |   -0.9047 |    -2.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.634 |    0.4117 |     1.091 |   0.05876 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7861 |    0.7329 |     1.543 |    0.9843 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6986 |     1.971 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.63935</span> |     91.23 |  0.004547 |    0.2881 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009713 |    0.6015 |     1.091 |   0.05876 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7861 |    0.7329 |     1.543 |    0.9843 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6986 |     1.971 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.432 |     1.140 |   -0.1464 |  -0.04798 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3672 |    0.6157 |  -0.06295 |     1.547 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5341 |    0.1919 |    0.7582 |     2.801 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2698 |    -1.544 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   42</span>|     479.63543 |     1.004 |    -1.194 |   -0.9268 |   -0.9363 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.022 |   -0.9876 |   -0.2467 |   -0.8650 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8013 |    -1.062 |   -0.5633 |   -0.8425 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.065 |   -0.2426 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.63543 |     91.32 |    -5.394 |   -0.9046 |    -2.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.634 |    0.4116 |     1.091 |   0.05874 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7863 |    0.7328 |     1.543 |    0.9837 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6985 |     1.972 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.63543</span> |     91.32 |  0.004545 |    0.2881 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009712 |    0.6015 |     1.091 |   0.05874 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7863 |    0.7328 |     1.543 |    0.9837 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6985 |     1.972 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     3.612 |     1.146 |  -0.04876 |  -0.06242 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3640 |    0.6002 |   0.04743 |     1.619 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5199 |    0.2573 |    0.7734 |     2.739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2298 |    -1.553 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   43</span>|     479.63248 |     1.003 |    -1.194 |   -0.9268 |   -0.9363 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.022 |   -0.9878 |   -0.2466 |   -0.8655 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8011 |    -1.062 |   -0.5636 |   -0.8432 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.065 |   -0.2421 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.63248 |     91.23 |    -5.394 |   -0.9046 |    -2.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.635 |    0.4115 |     1.091 |   0.05873 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7864 |    0.7328 |     1.543 |    0.9830 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6985 |     1.973 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.63248</span> |     91.23 |  0.004543 |    0.2881 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009710 |    0.6014 |     1.091 |   0.05873 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7864 |    0.7328 |     1.543 |    0.9830 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6985 |     1.973 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.237 |     1.138 |   -0.1400 |  -0.04830 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3668 |    0.6108 |  -0.07665 |     1.521 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5353 |    0.1893 |    0.7456 |     2.704 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2668 |    -1.543 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   44</span>|     479.62869 |     1.004 |    -1.195 |   -0.9268 |   -0.9362 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.022 |   -0.9880 |   -0.2464 |   -0.8659 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8009 |    -1.062 |   -0.5639 |   -0.8438 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.065 |   -0.2415 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.62869 |     91.32 |    -5.395 |   -0.9046 |    -2.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.635 |    0.4114 |     1.091 |   0.05872 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7865 |    0.7327 |     1.542 |    0.9824 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6984 |     1.973 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.62869</span> |     91.32 |  0.004541 |    0.2881 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009709 |    0.6014 |     1.091 |   0.05872 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7865 |    0.7327 |     1.542 |    0.9824 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6984 |     1.973 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     3.552 |     1.143 |  -0.04554 |  -0.06221 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3637 |    0.5952 |  -0.01323 |     1.563 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3448 |    0.2470 |    0.7924 |     2.643 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2272 |    -1.550 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   45</span>|     479.62584 |     1.003 |    -1.195 |   -0.9268 |   -0.9362 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.022 |   -0.9883 |   -0.2463 |   -0.8663 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8008 |    -1.062 |   -0.5642 |   -0.8446 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.065 |   -0.2410 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.62584 |     91.24 |    -5.395 |   -0.9046 |    -2.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.635 |    0.4113 |     1.091 |   0.05871 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7866 |    0.7326 |     1.542 |    0.9817 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6983 |     1.974 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.62584</span> |     91.24 |  0.004539 |    0.2881 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009708 |    0.6014 |     1.091 |   0.05871 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7866 |    0.7326 |     1.542 |    0.9817 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6983 |     1.974 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.148 |     1.135 |   -0.1348 |  -0.04846 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3664 |    0.6063 | -0.009834 |     1.510 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5271 |    0.1803 |    0.7932 |     2.587 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2684 |    -1.536 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   46</span>|     479.62216 |     1.004 |    -1.195 |   -0.9267 |   -0.9362 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.023 |   -0.9885 |   -0.2462 |   -0.8667 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8007 |    -1.062 |   -0.5646 |   -0.8451 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.065 |   -0.2404 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.62216 |     91.32 |    -5.395 |   -0.9045 |    -2.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.635 |    0.4112 |     1.091 |   0.05870 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7867 |    0.7326 |     1.541 |    0.9811 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6983 |     1.975 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.62216</span> |     91.32 |  0.004537 |    0.2881 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009706 |    0.6014 |     1.091 |   0.05870 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7867 |    0.7326 |     1.541 |    0.9811 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6983 |     1.975 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     3.404 |     1.140 |  -0.04564 |  -0.06187 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3633 |    0.5903 |  -0.01970 |     1.541 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5054 |    0.2303 |    0.6483 |     2.528 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2288 |    -1.542 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   47</span>|     479.61938 |     1.003 |    -1.196 |   -0.9267 |   -0.9362 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.023 |   -0.9887 |   -0.2461 |   -0.8672 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8005 |    -1.062 |   -0.5649 |   -0.8458 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.065 |   -0.2398 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.61938 |     91.24 |    -5.396 |   -0.9045 |    -2.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.635 |    0.4111 |     1.091 |   0.05868 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7868 |    0.7325 |     1.541 |    0.9805 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6982 |     1.975 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.61938</span> |     91.24 |  0.004535 |    0.2881 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009705 |    0.6013 |     1.091 |   0.05868 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7868 |    0.7325 |     1.541 |    0.9805 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6982 |     1.975 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.197 |     1.132 |   -0.1312 |  -0.04839 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3660 |    0.6012 |   0.05916 |     1.560 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4869 |    0.2015 |    0.8305 |     2.573 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2666 |    -1.531 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   48</span>|     479.61563 |     1.003 |    -1.196 |   -0.9266 |   -0.9361 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.023 |   -0.9890 |   -0.2459 |   -0.8676 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8003 |    -1.062 |   -0.5653 |   -0.8464 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.065 |   -0.2392 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.61563 |     91.32 |    -5.396 |   -0.9044 |    -2.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.635 |    0.4109 |     1.091 |   0.05867 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7870 |    0.7325 |     1.541 |    0.9799 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6981 |     1.976 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.61563</span> |     91.32 |  0.004533 |    0.2881 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009703 |    0.6013 |     1.091 |   0.05867 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7870 |    0.7325 |     1.541 |    0.9799 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6981 |     1.976 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     3.024 |     1.137 |  -0.04591 |  -0.06118 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3629 |    0.5856 |  0.004035 |     1.536 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4978 |    0.2329 |    0.6617 |     2.436 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2318 |    -1.535 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   49</span>|     479.61337 |     1.003 |    -1.197 |   -0.9266 |   -0.9361 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.023 |   -0.9892 |   -0.2459 |   -0.8680 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8001 |    -1.063 |   -0.5655 |   -0.8471 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.065 |   -0.2388 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.61337 |     91.23 |    -5.397 |   -0.9044 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.635 |    0.4109 |     1.091 |   0.05866 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7871 |    0.7324 |     1.540 |    0.9792 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6980 |     1.977 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.61337</span> |     91.23 |  0.004531 |    0.2881 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009702 |    0.6013 |     1.091 |   0.05866 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7871 |    0.7324 |     1.540 |    0.9792 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6980 |     1.977 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.815 |     1.129 |   -0.1342 |  -0.04706 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3660 |    0.5971 |  -0.07697 |     1.438 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5059 |    0.1462 |    0.6633 |     2.381 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2676 |    -1.522 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   50</span>|     479.60932 |     1.003 |    -1.197 |   -0.9266 |   -0.9361 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.023 |   -0.9894 |   -0.2456 |   -0.8684 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8000 |    -1.063 |   -0.5659 |   -0.8476 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.066 |   -0.2381 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.60932 |     91.31 |    -5.397 |   -0.9044 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.636 |    0.4107 |     1.091 |   0.05865 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7872 |    0.7324 |     1.540 |    0.9788 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6979 |     1.977 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.60932</span> |     91.31 |  0.004529 |    0.2882 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009700 |    0.6013 |     1.091 |   0.05865 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7872 |    0.7324 |     1.540 |    0.9788 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6979 |     1.977 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     2.699 |     1.134 |  -0.04549 |  -0.06017 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3630 |    0.5814 |  -0.04512 |     1.484 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4915 |    0.2556 |    0.7248 |     2.322 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2317 |    -1.529 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   51</span>|     479.60706 |     1.003 |    -1.198 |   -0.9265 |   -0.9361 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.023 |   -0.9896 |   -0.2455 |   -0.8689 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7998 |    -1.063 |   -0.5661 |   -0.8484 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.066 |   -0.2376 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.60706 |     91.23 |    -5.398 |   -0.9044 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.636 |    0.4106 |     1.091 |   0.05863 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7873 |    0.7323 |     1.540 |    0.9781 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6979 |     1.978 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.60706</span> |     91.23 |  0.004527 |    0.2882 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009699 |    0.6012 |     1.091 |   0.05863 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7873 |    0.7323 |     1.540 |    0.9781 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6979 |     1.978 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.618 |     1.127 |   -0.1276 |  -0.04760 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3656 |    0.5915 |  -0.08622 |     1.415 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5531 |    0.1512 |    0.6569 |     2.298 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2620 |    -1.540 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   52</span>|     479.60314 |     1.003 |    -1.198 |   -0.9265 |   -0.9360 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.023 |   -0.9899 |   -0.2452 |   -0.8692 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7996 |    -1.063 |   -0.5665 |   -0.8488 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.066 |   -0.2369 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.60314 |     91.31 |    -5.398 |   -0.9043 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.636 |    0.4105 |     1.091 |   0.05862 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7875 |    0.7322 |     1.539 |    0.9776 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6978 |     1.979 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.60314</span> |     91.31 |  0.004525 |    0.2882 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009698 |    0.6012 |     1.091 |   0.05862 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7875 |    0.7322 |     1.539 |    0.9776 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6978 |     1.979 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     2.641 |     1.131 |  -0.04213 |  -0.06002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3627 |    0.5770 |  -0.05114 |     1.464 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4913 |    0.1735 |    0.5980 |     2.238 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2116 |    -1.521 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   53</span>|     479.60093 |     1.003 |    -1.199 |   -0.9265 |   -0.9360 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.024 |   -0.9901 |   -0.2452 |   -0.8697 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7994 |    -1.063 |   -0.5667 |   -0.8495 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.066 |   -0.2363 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.60093 |     91.23 |    -5.399 |   -0.9043 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.636 |    0.4104 |     1.091 |   0.05861 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7876 |    0.7322 |     1.539 |    0.9769 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6977 |     1.980 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.60093</span> |     91.23 |  0.004523 |    0.2882 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009696 |    0.6012 |     1.091 |   0.05861 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7876 |    0.7322 |     1.539 |    0.9769 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6977 |     1.980 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.657 |     1.124 |   -0.1240 |  -0.04721 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3655 |    0.5872 |  -0.09119 |     1.382 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4948 |    0.1646 |    0.6965 |     2.186 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2747 |    -1.512 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   54</span>|     479.59701 |     1.003 |    -1.199 |   -0.9264 |   -0.9360 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.024 |   -0.9904 |   -0.2448 |   -0.8700 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7992 |    -1.063 |   -0.5670 |   -0.8499 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.066 |   -0.2356 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.59701 |     91.31 |    -5.399 |   -0.9043 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.636 |    0.4103 |     1.091 |   0.05860 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7877 |    0.7321 |     1.539 |    0.9766 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6976 |     1.980 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.59701</span> |     91.31 |  0.004520 |    0.2882 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009695 |    0.6012 |     1.091 |   0.05860 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7877 |    0.7321 |     1.539 |    0.9766 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6976 |     1.980 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     2.447 |     1.128 |  -0.03826 |  -0.05934 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3628 |    0.5721 |  -0.04376 |     1.440 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4844 |    0.2233 |   -0.3355 |     1.410 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.3677 |    -1.539 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   55</span>|     479.59436 |     1.003 |    -1.200 |   -0.9264 |   -0.9360 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.024 |   -0.9907 |   -0.2446 |   -0.8705 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7990 |    -1.063 |   -0.5669 |   -0.8504 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.066 |   -0.2349 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.59436 |     91.23 |    -5.400 |   -0.9042 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.636 |    0.4102 |     1.091 |   0.05858 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7879 |    0.7320 |     1.539 |    0.9761 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6977 |     1.981 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.59436</span> |     91.23 |  0.004518 |    0.2882 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009693 |    0.6011 |     1.091 |   0.05858 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7879 |    0.7320 |     1.539 |    0.9761 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6977 |     1.981 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.478 |     1.121 |   -0.1165 |  -0.04761 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3651 |    0.5835 |  -0.09604 |     1.373 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4943 |    0.1086 |   -0.4551 |     1.395 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.3088 |    -1.502 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   56</span>|     479.58979 |     1.003 |    -1.200 |   -0.9263 |   -0.9359 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.024 |   -0.9910 |   -0.2442 |   -0.8708 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7988 |    -1.063 |   -0.5665 |   -0.8506 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.066 |   -0.2341 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.58979 |     91.30 |    -5.400 |   -0.9042 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.637 |    0.4100 |     1.092 |   0.05858 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7880 |    0.7319 |     1.539 |    0.9759 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6978 |     1.982 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.58979</span> |     91.30 |  0.004516 |    0.2882 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009691 |    0.6011 |     1.092 |   0.05858 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7880 |    0.7319 |     1.539 |    0.9759 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6978 |     1.982 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     1.813 |     1.125 |  -0.03904 |  -0.05862 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3624 |    0.5728 | -0.008587 |     1.448 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2657 |    0.1639 |    0.6610 |     2.108 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2622 |    -1.501 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   57</span>|     479.58727 |     1.003 |    -1.201 |   -0.9263 |   -0.9359 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.024 |   -0.9912 |   -0.2441 |   -0.8713 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7987 |    -1.063 |   -0.5668 |   -0.8514 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.066 |   -0.2335 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.58727 |     91.24 |    -5.401 |   -0.9042 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.637 |    0.4099 |     1.092 |   0.05856 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7881 |    0.7318 |     1.539 |    0.9751 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6978 |     1.983 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.58727</span> |     91.24 |  0.004513 |    0.2882 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009690 |    0.6011 |     1.092 |   0.05856 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7881 |    0.7318 |     1.539 |    0.9751 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6978 |     1.983 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -3.987 |     1.119 |   -0.1055 |  -0.04878 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3644 |    0.5797 |  -0.03160 |     1.359 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4809 |   0.09403 |   -0.4184 |     1.337 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2770 |    -1.489 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   58</span>|     479.58366 |     1.004 |    -1.201 |   -0.9263 |   -0.9359 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.024 |   -0.9915 |   -0.2438 |   -0.8717 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7986 |    -1.063 |   -0.5667 |   -0.8517 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.066 |   -0.2327 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.58366 |     91.32 |    -5.401 |   -0.9041 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.637 |    0.4098 |     1.092 |   0.05855 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7882 |    0.7317 |     1.539 |    0.9749 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6978 |     1.984 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.58366</span> |     91.32 |  0.004511 |    0.2882 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009688 |    0.6010 |     1.092 |   0.05855 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7882 |    0.7317 |     1.539 |    0.9749 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6978 |     1.984 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     3.434 |     1.122 |  -0.01954 |  -0.06326 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3597 |    0.5645 |  -0.03484 |     1.404 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4782 |    0.1697 |  -0.05991 |     1.573 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2504 |    -1.489 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   59</span>|     479.57994 |     1.003 |    -1.202 |   -0.9262 |   -0.9358 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.025 |   -0.9918 |   -0.2433 |   -0.8720 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7984 |    -1.063 |   -0.5664 |   -0.8520 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.066 |   -0.2319 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.57994 |     91.26 |    -5.402 |   -0.9041 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.637 |    0.4096 |     1.092 |   0.05854 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7884 |    0.7316 |     1.539 |    0.9746 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6977 |     1.985 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.57994</span> |     91.26 |  0.004508 |    0.2882 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009686 |    0.6010 |     1.092 |   0.05854 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7884 |    0.7316 |     1.539 |    0.9746 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6977 |     1.985 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -2.328 |     1.117 |  -0.07952 |  -0.05137 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3636 |    0.5738 |   0.02815 |     1.418 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4669 |    0.1345 |    0.7258 |     2.078 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2949 |    -1.476 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   60</span>|     479.57683 |     1.004 |    -1.202 |   -0.9262 |   -0.9358 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.025 |   -0.9921 |   -0.2431 |   -0.8724 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7982 |    -1.064 |   -0.5667 |   -0.8526 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.066 |   -0.2311 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.57683 |     91.32 |    -5.402 |   -0.9041 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.637 |    0.4095 |     1.092 |   0.05853 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7885 |    0.7315 |     1.539 |    0.9740 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6976 |     1.986 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.57683</span> |     91.32 |  0.004505 |    0.2882 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009684 |    0.6010 |     1.092 |   0.05853 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7885 |    0.7315 |     1.539 |    0.9740 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6976 |     1.986 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     3.369 |     1.121 |  -0.01236 |  -0.06033 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3618 |    0.5608 | -0.009614 |     1.424 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4677 |    0.1416 |    0.6194 |     1.932 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2634 |    -1.483 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   61</span>|     479.57433 |     1.003 |    -1.203 |   -0.9262 |   -0.9358 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.025 |   -0.9924 |   -0.2429 |   -0.8728 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7980 |    -1.064 |   -0.5671 |   -0.8530 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.066 |   -0.2304 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.57433 |     91.25 |    -5.403 |   -0.9041 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.637 |    0.4094 |     1.092 |   0.05852 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7886 |    0.7315 |     1.539 |    0.9736 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6975 |     1.987 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.57433</span> |     91.25 |  0.004503 |    0.2882 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009683 |    0.6009 |     1.092 |   0.05852 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7886 |    0.7315 |     1.539 |    0.9736 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6975 |     1.987 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -3.507 |     1.111 |  -0.09183 |  -0.05192 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3614 |    0.5665 |  -0.03887 |     1.349 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4753 |   0.07826 |    0.5528 |     1.905 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2844 |    -1.468 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   62</span>|     479.57116 |     1.003 |    -1.204 |   -0.9262 |   -0.9357 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.025 |   -0.9927 |   -0.2425 |   -0.8732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7978 |    -1.064 |   -0.5674 |   -0.8534 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.066 |   -0.2297 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.57116 |     91.32 |    -5.404 |   -0.9040 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.638 |    0.4092 |     1.092 |   0.05851 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7888 |    0.7315 |     1.538 |    0.9732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6973 |     1.988 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.57116</span> |     91.32 |  0.004500 |    0.2882 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009681 |    0.6009 |     1.092 |   0.05851 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7888 |    0.7315 |     1.538 |    0.9732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6973 |     1.988 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     2.953 |     1.117 |  -0.01332 |  -0.05913 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3618 |    0.5550 |  -0.01942 |     1.377 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4588 |    0.1396 |    0.5378 |     1.901 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2564 |    -1.475 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   63</span>|     479.56857 |     1.003 |    -1.204 |   -0.9261 |   -0.9357 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.025 |   -0.9930 |   -0.2422 |   -0.8735 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7976 |    -1.064 |   -0.5677 |   -0.8539 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.066 |   -0.2289 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.56857 |     91.25 |    -5.404 |   -0.9040 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.638 |    0.4091 |     1.092 |   0.05850 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7889 |    0.7314 |     1.538 |    0.9728 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6972 |     1.989 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.56857</span> |     91.25 |  0.004497 |    0.2882 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009679 |    0.6009 |     1.092 |   0.05850 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7889 |    0.7314 |     1.538 |    0.9728 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6972 |     1.989 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -3.279 |     1.109 |  -0.08378 |  -0.05051 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3625 |    0.5609 |  -0.06234 |     1.320 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5093 |   0.06989 |    -1.266 |     1.852 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2753 |    -1.463 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   64</span>|     479.56430 |     1.003 |    -1.205 |   -0.9261 |   -0.9357 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.9933 |   -0.2419 |   -0.8738 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7974 |    -1.064 |   -0.5672 |   -0.8543 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.067 |   -0.2281 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      479.5643 |     91.31 |    -5.405 |   -0.9040 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.638 |    0.4090 |     1.093 |   0.05849 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7891 |    0.7314 |     1.538 |    0.9724 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6971 |     1.990 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      479.5643</span> |     91.31 |  0.004495 |    0.2882 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009677 |    0.6008 |     1.093 |   0.05849 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7891 |    0.7314 |     1.538 |    0.9724 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6971 |     1.990 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     2.181 |     1.113 |  -0.01711 |  -0.05862 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3610 |    0.5490 |  -0.06295 |     1.399 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4495 |    0.1474 |    0.6781 |     1.913 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2455 |    -1.468 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   65</span>|     479.56239 |     1.003 |    -1.205 |   -0.9261 |   -0.9356 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.9935 |   -0.2417 |   -0.8744 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7972 |    -1.064 |   -0.5674 |   -0.8549 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.067 |   -0.2275 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.56239 |     91.24 |    -5.405 |   -0.9040 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.638 |    0.4089 |     1.093 |   0.05847 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7892 |    0.7313 |     1.538 |    0.9718 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6970 |     1.990 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.56239</span> |     91.24 |  0.004493 |    0.2882 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009675 |    0.6008 |     1.093 |   0.05847 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7892 |    0.7313 |     1.538 |    0.9718 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6970 |     1.990 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.516 |     1.106 |  -0.09308 |  -0.04714 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3636 |    0.5580 |  -0.04406 |     1.488 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4662 |   0.05187 |    0.6104 |     1.787 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2769 |    -1.453 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   66</span>|     479.55877 |     1.003 |    -1.206 |   -0.9261 |   -0.9356 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.9938 |   -0.2414 |   -0.8747 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7970 |    -1.064 |   -0.5678 |   -0.8553 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.067 |   -0.2268 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.55877 |     91.31 |    -5.406 |   -0.9039 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.638 |    0.4087 |     1.093 |   0.05846 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7894 |    0.7313 |     1.538 |    0.9714 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6969 |     1.991 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.55877</span> |     91.31 |  0.004490 |    0.2882 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009674 |    0.6008 |     1.093 |   0.05846 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7894 |    0.7313 |     1.538 |    0.9714 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6969 |     1.991 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     2.254 |     1.111 |  -0.01255 |  -0.05783 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3613 |    0.5437 |  -0.01079 |     1.366 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4954 |    0.1048 |    0.5565 |     1.745 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2417 |    -1.458 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   67</span>|     479.55696 |     1.003 |    -1.206 |   -0.9261 |   -0.9356 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.9940 |   -0.2413 |   -0.8753 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7968 |    -1.064 |   -0.5681 |   -0.8559 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.067 |   -0.2262 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.55696 |     91.24 |    -5.406 |   -0.9039 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.639 |    0.4086 |     1.093 |   0.05845 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7895 |    0.7312 |     1.537 |    0.9708 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6968 |     1.992 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.55696</span> |     91.24 |  0.004488 |    0.2882 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009672 |    0.6008 |     1.093 |   0.05845 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7895 |    0.7312 |     1.537 |    0.9708 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6968 |     1.992 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.420 |     1.104 |  -0.08938 |  -0.04652 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3638 |    0.5524 |  -0.03600 |     1.474 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4537 |   0.08714 |    0.5943 |     1.686 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2777 |    -1.443 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   68</span>|     479.55331 |     1.003 |    -1.207 |   -0.9260 |   -0.9356 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.9943 |   -0.2409 |   -0.8756 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7966 |    -1.064 |   -0.5684 |   -0.8562 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.067 |   -0.2254 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.55331 |     91.31 |    -5.407 |   -0.9039 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.639 |    0.4085 |     1.093 |   0.05844 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7897 |    0.7312 |     1.537 |    0.9706 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6966 |     1.993 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.55331</span> |     91.31 |  0.004485 |    0.2882 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009670 |    0.6007 |     1.093 |   0.05844 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7897 |    0.7312 |     1.537 |    0.9706 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6966 |     1.993 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     1.940 |     1.107 |  -0.01326 |  -0.05734 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3610 |    0.5380 |  -0.03050 |     1.334 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4419 |   0.09953 |    0.4758 |     1.660 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2363 |    -1.448 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   69</span>|     479.55178 |     1.003 |    -1.208 |   -0.9260 |   -0.9355 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.9945 |   -0.2409 |   -0.8762 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7964 |    -1.064 |   -0.5686 |   -0.8569 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.067 |   -0.2248 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.55178 |     91.23 |    -5.408 |   -0.9039 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.639 |    0.4084 |     1.093 |   0.05842 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7898 |    0.7311 |     1.537 |    0.9699 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6965 |     1.994 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.55178</span> |     91.23 |  0.004483 |    0.2882 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009668 |    0.6007 |     1.093 |   0.05842 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7898 |    0.7311 |     1.537 |    0.9699 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6965 |     1.994 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.846 |     1.100 |  -0.09130 |  -0.04553 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3638 |    0.5474 |  -0.02936 |     1.430 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4494 |   0.05105 |    0.6245 |     1.611 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2724 |    -1.435 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   70</span>|     479.54790 |     1.003 |    -1.208 |   -0.9260 |   -0.9355 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.027 |   -0.9948 |   -0.2405 |   -0.8765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7962 |    -1.064 |   -0.5690 |   -0.8571 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.067 |   -0.2240 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      479.5479 |     91.30 |    -5.408 |   -0.9039 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.639 |    0.4083 |     1.093 |   0.05841 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7899 |    0.7311 |     1.536 |    0.9697 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6964 |     1.995 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      479.5479</span> |     91.30 |  0.004480 |    0.2883 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009667 |    0.6007 |     1.093 |   0.05841 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7899 |    0.7311 |     1.536 |    0.9697 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6964 |     1.995 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     1.570 |     1.104 |  -0.01594 |  -0.05674 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3607 |    0.5327 |  -0.02577 |     1.253 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4401 |   0.07544 |   -0.5163 |    0.8741 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.3456 |    -1.445 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   71</span>|     479.54587 |     1.003 |    -1.209 |   -0.9260 |   -0.9355 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.027 |   -0.9951 |   -0.2405 |   -0.8771 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7960 |    -1.064 |   -0.5687 |   -0.8576 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.067 |   -0.2233 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.54587 |     91.23 |    -5.409 |   -0.9039 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.639 |    0.4081 |     1.093 |   0.05839 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7901 |    0.7310 |     1.537 |    0.9693 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6965 |     1.995 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.54587</span> |     91.23 |  0.004478 |    0.2883 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009665 |    0.6006 |     1.093 |   0.05839 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7901 |    0.7310 |     1.537 |    0.9693 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6965 |     1.995 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.902 |     1.097 |  -0.08880 |  -0.04568 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3633 |    0.5436 |  -0.03820 |     1.421 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4438 |   0.04451 |    0.5507 |     1.558 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2862 |    -1.423 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   72</span>|     479.54157 |     1.003 |    -1.209 |   -0.9260 |   -0.9354 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.027 |   -0.9954 |   -0.2399 |   -0.8773 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7958 |    -1.064 |   -0.5687 |   -0.8577 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.067 |   -0.2225 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.54157 |     91.30 |    -5.409 |   -0.9038 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.639 |    0.4080 |     1.093 |   0.05839 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7902 |    0.7309 |     1.537 |    0.9691 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6965 |     1.996 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.54157</span> |     91.30 |  0.004475 |    0.2883 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009663 |    0.6006 |     1.093 |   0.05839 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7902 |    0.7309 |     1.537 |    0.9691 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6965 |     1.996 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     1.063 |     1.100 |  -0.01768 |  -0.05631 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3603 |    0.5311 |  -0.05904 |     1.468 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4400 |   0.06384 |    0.5216 |     1.564 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2548 |    -1.433 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   73</span>|     479.53906 |     1.003 |    -1.210 |   -0.9259 |   -0.9354 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.027 |   -0.9956 |   -0.2399 |   -0.8780 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7956 |    -1.064 |   -0.5689 |   -0.8584 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.067 |   -0.2218 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.53906 |     91.25 |    -5.410 |   -0.9038 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.640 |    0.4079 |     1.093 |   0.05837 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7904 |    0.7309 |     1.536 |    0.9684 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6964 |     1.997 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.53906</span> |     91.25 |  0.004472 |    0.2883 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009661 |    0.6006 |     1.093 |   0.05837 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7904 |    0.7309 |     1.536 |    0.9684 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6964 |     1.997 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -3.131 |     1.095 |  -0.06506 |  -0.04908 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3620 |    0.5356 | -0.007514 |     1.441 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4274 |   0.08578 |   -0.3543 |    0.8441 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2944 |    -1.418 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   74</span>|     479.53616 |     1.004 |    -1.210 |   -0.9259 |   -0.9354 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.027 |   -0.9959 |   -0.2396 |   -0.8785 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7954 |    -1.064 |   -0.5688 |   -0.8586 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.067 |   -0.2210 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.53616 |     91.33 |    -5.410 |   -0.9038 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.640 |    0.4077 |     1.094 |   0.05835 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7905 |    0.7308 |     1.537 |    0.9682 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6965 |     1.998 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.53616</span> |     91.33 |  0.004470 |    0.2883 |    0.1111 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009659 |    0.6005 |     1.094 |   0.05835 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7905 |    0.7308 |     1.537 |    0.9682 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6965 |     1.998 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     3.979 |     1.099 |   0.01619 |  -0.06233 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3580 |    0.5217 |  -0.09749 |     1.245 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4289 |    0.1115 |   -0.5282 |    0.7580 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.3266 |    -1.417 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   75</span>|     479.53242 |     1.003 |    -1.211 |   -0.9259 |   -0.9353 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.028 |   -0.9962 |   -0.2391 |   -0.8787 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7953 |    -1.065 |   -0.5685 |   -0.8588 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.067 |   -0.2202 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.53242 |     91.27 |    -5.411 |   -0.9038 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.640 |    0.4076 |     1.094 |   0.05835 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7906 |    0.7307 |     1.537 |    0.9681 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6966 |     1.999 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.53242</span> |     91.27 |  0.004467 |    0.2883 |    0.1111 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009657 |    0.6005 |     1.094 |   0.05835 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7906 |    0.7307 |     1.537 |    0.9681 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6966 |     1.999 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.679 |     1.093 |  -0.04308 |  -0.05224 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3605 |    0.5318 | -0.002555 |     1.446 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4215 |   0.05786 |    0.6079 |     1.538 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2997 |    -1.401 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   76</span>|     479.52958 |     1.004 |    -1.212 |   -0.9259 |   -0.9353 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.028 |   -0.9965 |   -0.2388 |   -0.8793 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7951 |    -1.065 |   -0.5686 |   -0.8593 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.067 |   -0.2194 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.52958 |     91.33 |    -5.412 |   -0.9038 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.640 |    0.4075 |     1.094 |   0.05833 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7908 |    0.7306 |     1.537 |    0.9676 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6965 |     2.000 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.52958</span> |     91.33 |  0.004464 |    0.2883 |    0.1111 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009655 |    0.6005 |     1.094 |   0.05833 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7908 |    0.7306 |     1.537 |    0.9676 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6965 |     2.000 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     3.139 |     1.094 |   0.01046 |  -0.06226 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3570 |    0.5194 |  -0.09592 |     1.421 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4323 |   0.06052 |   -0.5454 |    0.7071 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2962 |    -1.410 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   77</span>|     479.52646 |     1.003 |    -1.212 |   -0.9259 |   -0.9353 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.028 |   -0.9968 |   -0.2384 |   -0.8797 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7949 |    -1.065 |   -0.5684 |   -0.8594 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.067 |   -0.2186 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.52646 |     91.26 |    -5.412 |   -0.9038 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.640 |    0.4073 |     1.094 |   0.05832 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7909 |    0.7304 |     1.537 |    0.9675 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6966 |     2.001 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.52646</span> |     91.26 |  0.004461 |    0.2883 |    0.1111 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009653 |    0.6005 |     1.094 |   0.05832 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7909 |    0.7304 |     1.537 |    0.9675 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6966 |     2.001 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -2.758 |     1.088 |  -0.05217 |  -0.05158 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3593 |    0.5291 |  -0.05980 |     1.185 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4227 |    -2.218 |   -0.3659 |    0.8223 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2423 |    -1.392 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   78</span>|     479.52294 |     1.003 |    -1.213 |   -0.9258 |   -0.9352 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.028 |   -0.9971 |   -0.2379 |   -0.8800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7947 |    -1.064 |   -0.5681 |   -0.8596 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.067 |   -0.2178 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.52294 |     91.30 |    -5.413 |   -0.9037 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.641 |    0.4072 |     1.094 |   0.05831 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7910 |    0.7311 |     1.537 |    0.9673 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6967 |     2.002 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.52294</span> |     91.30 |  0.004458 |    0.2883 |    0.1111 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009652 |    0.6004 |     1.094 |   0.05831 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7910 |    0.7311 |     1.537 |    0.9673 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6967 |     2.002 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    0.7571 |     1.091 |  -0.01328 |  -0.05733 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3573 |    0.5211 |  0.006105 |     1.423 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4119 |    0.1268 |    0.6771 |     1.465 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.3146 |    -1.386 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   79</span>|     479.52041 |     1.003 |    -1.213 |   -0.9258 |   -0.9352 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.028 |   -0.9973 |   -0.2380 |   -0.8807 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7945 |    -1.064 |   -0.5684 |   -0.8604 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.067 |   -0.2172 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.52041 |     91.27 |    -5.413 |   -0.9037 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.641 |    0.4071 |     1.094 |   0.05829 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7912 |    0.7311 |     1.537 |    0.9666 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6965 |     2.003 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.52041</span> |     91.27 |  0.004456 |    0.2883 |    0.1111 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009650 |    0.6004 |     1.094 |   0.05829 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7912 |    0.7311 |     1.537 |    0.9666 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6965 |     2.003 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -2.400 |     1.087 |  -0.04852 |  -0.05189 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3585 |    0.5236 |  -0.05100 |     1.183 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4319 |   0.01564 |    0.3566 |     1.324 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.3221 |    -1.379 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   80</span>|     479.51911 |     1.004 |    -1.214 |   -0.9258 |   -0.9352 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.029 |   -0.9976 |   -0.2379 |   -0.8812 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7944 |    -1.064 |   -0.5686 |   -0.8609 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.067 |   -0.2166 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.51911 |     91.35 |    -5.414 |   -0.9037 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.641 |    0.4070 |     1.094 |   0.05828 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7913 |    0.7310 |     1.537 |    0.9661 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6964 |     2.004 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.51911</span> |     91.35 |  0.004454 |    0.2883 |    0.1111 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009648 |    0.6004 |     1.094 |   0.05828 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7913 |    0.7310 |     1.537 |    0.9661 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6964 |     2.004 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     5.458 |     1.092 |   0.04306 |  -0.06507 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3552 |    0.5068 |  -0.06807 |     1.418 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4090 |    0.1358 |   -0.5109 |    0.5676 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2804 |    -1.390 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   81</span>|     479.51487 |     1.003 |    -1.215 |   -0.9258 |   -0.9352 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.029 |   -0.9978 |   -0.2377 |   -0.8817 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7942 |    -1.064 |   -0.5685 |   -0.8612 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.067 |   -0.2158 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.51487 |     91.27 |    -5.415 |   -0.9037 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.641 |    0.4069 |     1.094 |   0.05826 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7914 |    0.7307 |     1.537 |    0.9658 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6963 |     2.005 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.51487</span> |     91.27 |  0.004451 |    0.2883 |    0.1111 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009647 |    0.6003 |     1.094 |   0.05826 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7914 |    0.7307 |     1.537 |    0.9658 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6963 |     2.005 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.582 |     1.084 |  -0.03533 |  -0.05340 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3581 |    0.5175 |  -0.06480 |     1.157 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4224 |   0.03489 |   -0.5536 |    0.6041 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2386 |    -1.371 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   82</span>|     479.51208 |     1.004 |    -1.215 |   -0.9258 |   -0.9351 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.029 |   -0.9981 |   -0.2375 |   -0.8823 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7939 |    -1.065 |   -0.5682 |   -0.8615 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.067 |   -0.2150 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.51208 |     91.34 |    -5.415 |   -0.9037 |    -2.197 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.641 |    0.4067 |     1.094 |   0.05824 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7916 |    0.7306 |     1.537 |    0.9655 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6964 |     2.006 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.51208</span> |     91.34 |  0.004449 |    0.2883 |    0.1111 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009645 |    0.6003 |     1.094 |   0.05824 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7916 |    0.7306 |     1.537 |    0.9655 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6964 |     2.006 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     3.943 |     1.087 |   0.02817 |  -0.06279 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3555 |    0.5065 |  -0.06060 |     1.305 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4115 |   0.06912 |    0.4865 |     1.240 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2990 |    -1.371 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   83</span>|     479.50842 |     1.003 |    -1.216 |   -0.9258 |   -0.9351 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.029 |   -0.9984 |   -0.2372 |   -0.8827 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7937 |    -1.065 |   -0.5683 |   -0.8618 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.067 |   -0.2141 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.50842 |     91.29 |    -5.416 |   -0.9037 |    -2.197 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.642 |    0.4066 |     1.095 |   0.05823 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7918 |    0.7302 |     1.537 |    0.9652 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6962 |     2.007 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.50842</span> |     91.29 |  0.004446 |    0.2883 |    0.1111 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009643 |    0.6003 |     1.095 |   0.05823 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7918 |    0.7302 |     1.537 |    0.9652 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6962 |     2.007 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |   -0.7463 |     1.081 |  -0.02105 |  -0.05532 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3572 |    0.5118 |  -0.07839 |     1.134 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4206 |   0.01709 |   -0.5616 |    0.5218 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2429 |    -1.361 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   84</span>|     479.50515 |     1.004 |    -1.216 |   -0.9258 |   -0.9351 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.029 |   -0.9987 |   -0.2372 |   -0.8834 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7935 |    -1.065 |   -0.5680 |   -0.8621 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.067 |   -0.2133 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.50515 |     91.33 |    -5.416 |   -0.9037 |    -2.197 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.642 |    0.4065 |     1.095 |   0.05821 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7919 |    0.7302 |     1.537 |    0.9649 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6964 |     2.008 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.50515</span> |     91.33 |  0.004443 |    0.2883 |    0.1111 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009641 |    0.6002 |     1.095 |   0.05821 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7919 |    0.7302 |     1.537 |    0.9649 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6964 |     2.008 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   85</span>|     479.49887 |     1.004 |    -1.219 |   -0.9257 |   -0.9350 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.030 |   -0.9997 |   -0.2363 |   -0.8851 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7928 |    -1.066 |   -0.5675 |   -0.8630 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.067 |   -0.2104 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.49887 |     91.39 |    -5.419 |   -0.9037 |    -2.197 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.642 |    0.4060 |     1.095 |   0.05816 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7925 |    0.7293 |     1.538 |    0.9641 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6963 |     2.011 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.49887</span> |     91.39 |  0.004434 |    0.2883 |    0.1111 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009634 |    0.6001 |     1.095 |   0.05816 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7925 |    0.7293 |     1.538 |    0.9641 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6963 |     2.011 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     8.922 |     1.081 |    0.1004 |  -0.07252 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3520 |    0.4871 |    0.2543 |     1.578 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3854 |   0.03465 |   -0.5794 |    0.4083 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2550 |    -1.344 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   86</span>|     479.48147 |     1.003 |    -1.221 |   -0.9257 |   -0.9348 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.031 |    -1.001 |   -0.2344 |   -0.8861 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7921 |    -1.067 |   -0.5658 |   -0.8636 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.067 |   -0.2068 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.48147 |     91.29 |    -5.421 |   -0.9036 |    -2.197 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.643 |    0.4054 |     1.096 |   0.05813 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7930 |    0.7283 |     1.540 |    0.9635 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6963 |     2.016 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.48147</span> |     91.29 |  0.004421 |    0.2883 |    0.1111 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009626 |    0.6000 |     1.096 |   0.05813 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7930 |    0.7283 |     1.540 |    0.9635 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6963 |     2.016 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   87</span>|     479.46291 |     1.003 |    -1.226 |   -0.9256 |   -0.9345 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.032 |    -1.003 |   -0.2312 |   -0.8874 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7910 |    -1.069 |   -0.5631 |   -0.8644 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.067 |   -0.2012 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.46291 |     91.29 |    -5.426 |   -0.9035 |    -2.197 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.645 |    0.4045 |     1.097 |   0.05809 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7937 |    0.7267 |     1.543 |    0.9627 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6964 |     2.022 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.46291</span> |     91.29 |  0.004402 |    0.2883 |    0.1111 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009612 |    0.5998 |     1.097 |   0.05809 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7937 |    0.7267 |     1.543 |    0.9627 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6964 |     2.022 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   88</span>|     479.38653 |     1.003 |    -1.248 |   -0.9250 |   -0.9332 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.039 |    -1.013 |   -0.2154 |   -0.8940 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7859 |    -1.078 |   -0.5497 |   -0.8687 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.067 |   -0.1734 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.38653 |     91.26 |    -5.448 |   -0.9030 |    -2.196 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.652 |    0.4000 |     1.104 |   0.05790 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7975 |    0.7188 |     1.559 |    0.9587 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6966 |     2.056 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.38653</span> |     91.26 |  0.004306 |    0.2884 |    0.1113 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009547 |    0.5987 |     1.104 |   0.05790 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7975 |    0.7188 |     1.559 |    0.9587 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6966 |     2.056 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   89</span>|     479.33405 |     1.002 |    -1.333 |   -0.9226 |   -0.9281 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.066 |    -1.051 |   -0.1533 |   -0.9198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7659 |    -1.112 |   -0.4971 |   -0.8853 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.066 |  -0.06439 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.33405 |     91.15 |    -5.533 |   -0.9008 |    -2.190 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.678 |    0.3823 |     1.129 |   0.05715 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8121 |    0.6876 |     1.621 |    0.9427 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6975 |     2.188 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.33405</span> |     91.15 |  0.003953 |    0.2889 |    0.1119 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009294 |    0.5944 |     1.129 |   0.05715 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8121 |    0.6876 |     1.621 |    0.9427 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6975 |     2.188 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -22.35 |    0.8049 |    0.3265 |  -0.06456 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3038 |    0.2091 |     1.763 |     1.473 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5535 |    -2.463 |     1.244 |   -0.6274 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.702 |   -0.2760 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   90</span>|     479.70817 |     1.005 |    -1.492 |   -0.9504 |   -0.9122 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.121 |    -1.106 |   -0.1774 |   -0.9510 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7305 |    -1.145 |   -0.4385 |   -0.8800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.177 |  -0.01012 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.70817 |     91.47 |    -5.692 |   -0.9256 |    -2.175 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.734 |    0.3572 |     1.119 |   0.05625 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8380 |    0.6579 |     1.691 |    0.9478 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6020 |     2.254 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.70817</span> |     91.47 |  0.003371 |    0.2838 |    0.1137 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008794 |    0.5884 |     1.119 |   0.05625 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8380 |    0.6579 |     1.691 |    0.9478 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6020 |     2.254 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   91</span>|     479.27063 |     1.005 |    -1.375 |   -0.9298 |   -0.9240 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.080 |    -1.065 |   -0.1597 |   -0.9281 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7567 |    -1.120 |   -0.4820 |   -0.8839 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.095 |  -0.05030 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.27063 |     91.42 |    -5.575 |   -0.9073 |    -2.186 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.693 |    0.3758 |     1.127 |   0.05692 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8189 |    0.6801 |     1.639 |    0.9441 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6726 |     2.206 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.27063</span> |     91.42 |  0.003793 |    0.2876 |    0.1123 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009162 |    0.5929 |     1.127 |   0.05692 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8189 |    0.6801 |     1.639 |    0.9441 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6726 |     2.206 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     2.862 |    0.7516 |    0.3576 |  -0.07893 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2806 |   -0.4298 |     1.369 |     1.213 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5227 |    -2.562 |     2.801 |    -1.010 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.234 |   -0.5388 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   92</span>|     479.18239 |     1.005 |    -1.423 |   -0.9401 |   -0.9189 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.098 |    -1.064 |   -0.1729 |   -0.9195 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7414 |    -1.117 |   -0.4967 |   -0.8806 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.083 |  -0.04375 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.18239 |     91.43 |    -5.623 |   -0.9165 |    -2.181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.710 |    0.3764 |     1.121 |   0.05716 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8300 |    0.6834 |     1.622 |    0.9472 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6830 |     2.213 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.18239</span> |     91.43 |  0.003613 |    0.2857 |    0.1129 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009001 |    0.5930 |     1.121 |   0.05716 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8300 |    0.6834 |     1.622 |    0.9472 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6830 |     2.213 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     2.797 |    0.6564 |   -0.1913 |  -0.04289 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2359 |   -0.3012 |     1.209 |     1.124 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4349 |    -2.322 |     2.160 |   -0.7215 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1578 |   -0.4171 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   93</span>|     479.53483 |    0.9938 |    -1.508 |   -0.8927 |   -0.9141 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.127 |    -1.054 |   -0.1802 |   -0.9016 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7110 |    -1.089 |   -0.5260 |   -0.8567 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.093 |  -0.01567 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.53483 |     90.44 |    -5.708 |   -0.8742 |    -2.177 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.740 |    0.3812 |     1.118 |   0.05768 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8523 |    0.7081 |     1.587 |    0.9700 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6741 |     2.248 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.53483</span> |     90.44 |  0.003320 |    0.2944 |    0.1134 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008741 |    0.5942 |     1.118 |   0.05768 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8523 |    0.7081 |     1.587 |    0.9700 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6741 |     2.248 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   94</span>|     479.57437 |    0.9943 |    -1.436 |   -0.9336 |   -0.9181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.102 |    -1.062 |   -0.1777 |   -0.9209 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7362 |    -1.106 |   -0.5073 |   -0.8753 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.084 |  -0.03890 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.57437 |     90.49 |    -5.636 |   -0.9106 |    -2.181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.715 |    0.3775 |     1.119 |   0.05712 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8338 |    0.6933 |     1.609 |    0.9523 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6824 |     2.219 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.57437</span> |     90.49 |  0.003567 |    0.2869 |    0.1130 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008961 |    0.5933 |     1.119 |   0.05712 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8338 |    0.6933 |     1.609 |    0.9523 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6824 |     2.219 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   95</span>|     479.18328 |     1.003 |    -1.424 |   -0.9400 |   -0.9189 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.098 |    -1.064 |   -0.1736 |   -0.9201 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7412 |    -1.115 |   -0.4980 |   -0.8802 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.083 |  -0.04351 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.18328 |     91.28 |    -5.624 |   -0.9164 |    -2.181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.711 |    0.3765 |     1.121 |   0.05715 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8302 |    0.6847 |     1.620 |    0.9476 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6831 |     2.214 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.18328</span> |     91.28 |  0.003612 |    0.2857 |    0.1129 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009000 |    0.5930 |     1.121 |   0.05715 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8302 |    0.6847 |     1.620 |    0.9476 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6831 |     2.214 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   96</span>|     479.17990 |     1.004 |    -1.423 |   -0.9401 |   -0.9189 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.098 |    -1.064 |   -0.1732 |   -0.9198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7413 |    -1.116 |   -0.4973 |   -0.8805 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.083 |  -0.04364 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      479.1799 |     91.36 |    -5.623 |   -0.9164 |    -2.181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.710 |    0.3764 |     1.121 |   0.05716 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8301 |    0.6840 |     1.621 |    0.9474 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6831 |     2.214 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      479.1799</span> |     91.36 |  0.003612 |    0.2857 |    0.1129 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009001 |    0.5930 |     1.121 |   0.05716 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8301 |    0.6840 |     1.621 |    0.9474 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6831 |     2.214 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -3.933 |    0.6514 |   -0.2757 |  -0.03244 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2382 |   -0.2823 |     1.183 |     1.090 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4397 |    -2.345 |     2.145 |   -0.7620 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1240 |   -0.3947 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   97</span>|     479.17667 |     1.005 |    -1.424 |   -0.9399 |   -0.9189 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.098 |    -1.064 |   -0.1735 |   -0.9200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7411 |    -1.116 |   -0.4978 |   -0.8802 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.083 |  -0.04350 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.17667 |     91.44 |    -5.624 |   -0.9162 |    -2.181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.711 |    0.3765 |     1.121 |   0.05715 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8302 |    0.6845 |     1.621 |    0.9476 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6831 |     2.214 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.17667</span> |     91.44 |  0.003611 |    0.2857 |    0.1129 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009000 |    0.5930 |     1.121 |   0.05715 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8302 |    0.6845 |     1.621 |    0.9476 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6831 |     2.214 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     3.521 |    0.6547 |   -0.1751 |  -0.04313 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2353 |   -0.2995 |     1.118 |     1.121 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4506 |    -2.323 |     2.084 |   -0.7259 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1653 |   -0.4503 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   98</span>|     479.17418 |     1.004 |    -1.424 |   -0.9398 |   -0.9188 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.098 |    -1.064 |   -0.1738 |   -0.9202 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7410 |    -1.115 |   -0.4983 |   -0.8800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.083 |  -0.04335 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.17418 |     91.36 |    -5.624 |   -0.9161 |    -2.181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.711 |    0.3765 |     1.121 |   0.05714 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8303 |    0.6850 |     1.620 |    0.9478 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6831 |     2.214 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.17418</span> |     91.36 |  0.003610 |    0.2857 |    0.1129 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008999 |    0.5930 |     1.121 |   0.05714 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8303 |    0.6850 |     1.620 |    0.9478 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6831 |     2.214 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -3.859 |    0.6491 |   -0.2708 |  -0.03145 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2380 |   -0.2786 |     1.113 |     1.074 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4387 |    -2.285 |     2.045 |   -0.7498 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1354 |   -0.4011 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   99</span>|     479.17107 |     1.005 |    -1.424 |   -0.9396 |   -0.9188 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.098 |    -1.064 |   -0.1740 |   -0.9204 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7408 |    -1.114 |   -0.4988 |   -0.8798 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.083 |  -0.04320 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.17107 |     91.44 |    -5.624 |   -0.9160 |    -2.181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.711 |    0.3766 |     1.121 |   0.05714 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8305 |    0.6855 |     1.619 |    0.9480 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6831 |     2.214 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.17107</span> |     91.44 |  0.003609 |    0.2858 |    0.1129 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008997 |    0.5930 |     1.121 |   0.05714 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8305 |    0.6855 |     1.619 |    0.9480 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6831 |     2.214 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     3.569 |    0.6522 |   -0.1642 |  -0.04243 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2349 |   -0.2958 |     1.101 |     1.106 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4222 |    -2.201 |     1.058 |   -0.2096 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2358 |   -0.4015 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  100</span>|     479.16873 |     1.004 |    -1.425 |   -0.9393 |   -0.9188 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.099 |    -1.064 |   -0.1743 |   -0.9206 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7406 |    -1.114 |   -0.4991 |   -0.8797 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.083 |  -0.04303 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.16873 |     91.36 |    -5.625 |   -0.9158 |    -2.181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.711 |    0.3766 |     1.121 |   0.05713 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8306 |    0.6860 |     1.619 |    0.9481 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6830 |     2.214 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.16873</span> |     91.36 |  0.003607 |    0.2858 |    0.1129 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008996 |    0.5931 |     1.121 |   0.05713 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8306 |    0.6860 |     1.619 |    0.9481 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6830 |     2.214 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -3.833 |    0.6464 |   -0.2551 |  -0.03020 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2388 |   -0.2745 |     1.092 |     1.036 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4378 |    -2.238 |     1.100 |   -0.2036 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2823 |   -0.3907 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  101</span>|     479.16547 |     1.005 |    -1.426 |   -0.9390 |   -0.9187 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.099 |    -1.063 |   -0.1745 |   -0.9206 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7403 |    -1.113 |   -0.4993 |   -0.8795 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.083 |  -0.04283 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.16547 |     91.42 |    -5.626 |   -0.9155 |    -2.181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.711 |    0.3767 |     1.121 |   0.05713 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8308 |    0.6865 |     1.619 |    0.9483 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6830 |     2.215 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.16547</span> |     91.42 |  0.003605 |    0.2859 |    0.1129 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008994 |    0.5931 |     1.121 |   0.05713 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8308 |    0.6865 |     1.619 |    0.9483 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6830 |     2.215 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     2.367 |    0.6482 |   -0.1602 |  -0.03907 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2347 |   -0.2874 |     1.147 |     1.057 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4142 |    -2.141 |     1.081 |   -0.1748 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2414 |   -0.4049 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  102</span>|     479.16316 |     1.004 |    -1.426 |   -0.9389 |   -0.9187 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.099 |    -1.063 |   -0.1748 |   -0.9208 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7401 |    -1.113 |   -0.4996 |   -0.8794 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.083 |  -0.04264 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.16316 |     91.36 |    -5.626 |   -0.9154 |    -2.181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.711 |    0.3768 |     1.120 |   0.05713 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8310 |    0.6872 |     1.618 |    0.9484 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6829 |     2.215 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.16316</span> |     91.36 |  0.003603 |    0.2859 |    0.1129 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008992 |    0.5931 |     1.120 |   0.05713 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8310 |    0.6872 |     1.618 |    0.9484 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6829 |     2.215 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -3.810 |    0.6431 |   -0.2376 |  -0.02872 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2384 |   -0.2689 |     1.073 |     1.021 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4195 |    -2.179 |     2.025 |   -0.6985 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1403 |   -0.3873 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  103</span>|     479.15976 |     1.005 |    -1.427 |   -0.9385 |   -0.9187 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.099 |    -1.063 |   -0.1751 |   -0.9208 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7399 |    -1.112 |   -0.5000 |   -0.8792 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.083 |  -0.04242 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.15976 |     91.41 |    -5.627 |   -0.9150 |    -2.181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.712 |    0.3768 |     1.120 |   0.05713 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8311 |    0.6876 |     1.618 |    0.9486 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6829 |     2.215 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.15976</span> |     91.41 |  0.003601 |    0.2860 |    0.1129 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008990 |    0.5931 |     1.120 |   0.05713 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8311 |    0.6876 |     1.618 |    0.9486 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6829 |     2.215 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     1.385 |    0.6444 |   -0.1513 |  -0.03575 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2363 |   -0.2788 |     1.057 |     1.037 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4304 |    -2.135 |     1.940 |   -0.6607 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1552 |   -0.4034 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  104</span>|     479.15697 |     1.004 |    -1.427 |   -0.9385 |   -0.9187 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.099 |    -1.063 |   -0.1754 |   -0.9212 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7397 |    -1.111 |   -0.5007 |   -0.8790 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.083 |  -0.04228 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.15697 |     91.37 |    -5.627 |   -0.9150 |    -2.181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.712 |    0.3769 |     1.120 |   0.05712 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8312 |    0.6883 |     1.617 |    0.9488 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6829 |     2.215 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.15697</span> |     91.37 |  0.003600 |    0.2860 |    0.1129 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008990 |    0.5931 |     1.120 |   0.05712 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8312 |    0.6883 |     1.617 |    0.9488 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6829 |     2.215 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -2.828 |    0.6406 |   -0.2090 |  -0.02895 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2376 |   -0.2659 |     1.059 |    0.9695 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4112 |    -2.078 |     1.041 |   -0.1304 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2608 |   -0.3859 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  105</span>|     479.15524 |     1.005 |    -1.427 |   -0.9384 |   -0.9187 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.099 |    -1.063 |   -0.1758 |   -0.9215 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7396 |    -1.111 |   -0.5010 |   -0.8789 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.083 |  -0.04216 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.15524 |     91.45 |    -5.627 |   -0.9149 |    -2.181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.712 |    0.3769 |     1.120 |   0.05711 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8313 |    0.6888 |     1.617 |    0.9489 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6828 |     2.215 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.15524</span> |     91.45 |  0.003599 |    0.2860 |    0.1129 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008989 |    0.5931 |     1.120 |   0.05711 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8313 |    0.6888 |     1.617 |    0.9489 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6828 |     2.215 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     5.191 |    0.6447 |   -0.1017 |  -0.04048 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2348 |   -0.2858 |     1.095 |     1.009 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3975 |    -1.983 |     1.956 |   -0.5702 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1591 |   -0.3996 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  106</span>|     479.15192 |     1.004 |    -1.428 |   -0.9381 |   -0.9186 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.100 |    -1.063 |   -0.1760 |   -0.9214 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7393 |    -1.110 |   -0.5013 |   -0.8787 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.083 |  -0.04194 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.15192 |     91.38 |    -5.628 |   -0.9146 |    -2.181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.712 |    0.3770 |     1.120 |   0.05711 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8315 |    0.6892 |     1.616 |    0.9490 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6828 |     2.216 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.15192</span> |     91.38 |  0.003597 |    0.2861 |    0.1129 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008987 |    0.5932 |     1.120 |   0.05711 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8315 |    0.6892 |     1.616 |    0.9490 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6828 |     2.216 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.012 |    0.6390 |   -0.1711 |  -0.03027 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2366 |   -0.2653 |     1.098 |    0.9640 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4101 |    -2.024 |    0.9749 |   -0.1142 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2645 |   -0.3871 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  107</span>|     479.14897 |     1.005 |    -1.428 |   -0.9380 |   -0.9186 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.100 |    -1.063 |   -0.1764 |   -0.9218 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7392 |    -1.110 |   -0.5017 |   -0.8787 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.083 |  -0.04178 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.14897 |     91.42 |    -5.628 |   -0.9146 |    -2.181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.712 |    0.3771 |     1.120 |   0.05710 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8317 |    0.6900 |     1.616 |    0.9491 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6827 |     2.216 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.14897</span> |     91.42 |  0.003596 |    0.2861 |    0.1129 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008986 |    0.5932 |     1.120 |   0.05710 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8317 |    0.6900 |     1.616 |    0.9491 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6827 |     2.216 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  108</span>|     479.14773 |     1.005 |    -1.428 |   -0.9379 |   -0.9186 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.100 |    -1.062 |   -0.1770 |   -0.9223 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7390 |    -1.109 |   -0.5022 |   -0.8786 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.083 |  -0.04159 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.14773 |     91.47 |    -5.628 |   -0.9145 |    -2.181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.712 |    0.3771 |     1.120 |   0.05708 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8318 |    0.6909 |     1.615 |    0.9491 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6826 |     2.216 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.14773</span> |     91.47 |  0.003595 |    0.2861 |    0.1129 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008985 |    0.5932 |     1.120 |   0.05708 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8318 |    0.6909 |     1.615 |    0.9491 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6826 |     2.216 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     7.333 |    0.6414 |  -0.06030 |  -0.04156 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2338 |   -0.2836 |     1.014 |     1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3897 |    -1.922 |    0.8816 |   -0.1288 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.1940 |   -0.4011 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  109</span>|     479.14119 |     1.004 |    -1.430 |   -0.9374 |   -0.9185 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.100 |    -1.062 |   -0.1775 |   -0.9218 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7383 |    -1.108 |   -0.5019 |   -0.8783 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.083 |  -0.04112 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.14119 |     91.39 |    -5.630 |   -0.9140 |    -2.181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.713 |    0.3774 |     1.119 |   0.05710 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8323 |    0.6917 |     1.616 |    0.9495 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6826 |     2.217 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.14119</span> |     91.39 |  0.003588 |    0.2862 |    0.1129 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008979 |    0.5933 |     1.119 |   0.05710 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8323 |    0.6917 |     1.616 |    0.9495 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6826 |     2.217 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |   -0.4299 |    0.6321 |   -0.1435 |  -0.02760 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2360 |   -0.2479 |     1.019 |    0.9518 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3967 |    -1.860 |     1.900 |   -0.5922 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1599 |   -0.3806 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  110</span>|     479.13501 |     1.005 |    -1.431 |   -0.9373 |   -0.9185 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.101 |    -1.062 |   -0.1783 |   -0.9226 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7379 |    -1.106 |   -0.5035 |   -0.8778 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.083 |  -0.04080 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.13501 |     91.42 |    -5.631 |   -0.9139 |    -2.181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.713 |    0.3775 |     1.119 |   0.05707 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8326 |    0.6932 |     1.614 |    0.9500 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6827 |     2.217 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.13501</span> |     91.42 |  0.003586 |    0.2862 |    0.1129 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008977 |    0.5933 |     1.119 |   0.05707 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8326 |    0.6932 |     1.614 |    0.9500 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6827 |     2.217 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     2.960 |    0.6324 |   -0.1010 |  -0.03176 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2344 |   -0.2516 |    0.9454 |    0.8792 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3822 |    -1.742 |    0.9146 |  -0.04263 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2425 |   -0.3771 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  111</span>|     479.13244 |     1.004 |    -1.432 |   -0.9368 |   -0.9184 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.101 |    -1.061 |   -0.1790 |   -0.9224 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7372 |    -1.105 |   -0.5036 |   -0.8775 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.083 |  -0.04025 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.13244 |     91.33 |    -5.632 |   -0.9135 |    -2.181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.714 |    0.3778 |     1.119 |   0.05708 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8331 |    0.6941 |     1.614 |    0.9502 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6825 |     2.218 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.13244</span> |     91.33 |  0.003580 |    0.2863 |    0.1130 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008971 |    0.5933 |     1.119 |   0.05708 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8331 |    0.6941 |     1.614 |    0.9502 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6825 |     2.218 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -5.808 |    0.6219 |   -0.1958 |  -0.01683 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2369 |   -0.2179 |    0.9347 |    0.8364 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3987 |    -1.797 |    0.8150 |  -0.05765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2618 |   -0.3704 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  112</span>|     479.12666 |     1.004 |    -1.434 |   -0.9365 |   -0.9183 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.102 |    -1.060 |   -0.1796 |   -0.9220 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7365 |    -1.104 |   -0.5033 |   -0.8772 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.083 |  -0.03975 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.12666 |     91.40 |    -5.634 |   -0.9132 |    -2.181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.714 |    0.3782 |     1.118 |   0.05709 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8336 |    0.6951 |     1.614 |    0.9505 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6824 |     2.218 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.12666</span> |     91.40 |  0.003574 |    0.2863 |    0.1130 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008965 |    0.5934 |     1.118 |   0.05709 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8336 |    0.6951 |     1.614 |    0.9505 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6824 |     2.218 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    0.9955 |    0.6218 |  -0.09617 |  -0.02448 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2342 |   -0.2176 |    0.9924 |    0.8560 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3772 |    -1.645 |     1.883 |   -0.4422 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1320 |   -0.3750 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  113</span>|     479.12255 |     1.004 |    -1.436 |   -0.9361 |   -0.9182 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.103 |    -1.060 |   -0.1805 |   -0.9221 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7358 |    -1.103 |   -0.5043 |   -0.8768 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.083 |  -0.03920 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.12255 |     91.34 |    -5.636 |   -0.9129 |    -2.181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.715 |    0.3784 |     1.118 |   0.05709 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8341 |    0.6962 |     1.613 |    0.9509 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6824 |     2.219 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.12255</span> |     91.34 |  0.003569 |    0.2864 |    0.1130 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008960 |    0.5935 |     1.118 |   0.05709 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8341 |    0.6962 |     1.613 |    0.9509 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6824 |     2.219 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.522 |    0.6133 |   -0.1571 |  -0.01492 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2353 |   -0.1923 |    0.9045 |    0.7846 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3572 |    -1.629 |    0.8508 |   0.03564 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2811 |   -0.3561 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  114</span>|     479.11855 |     1.005 |    -1.437 |   -0.9356 |   -0.9182 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.103 |    -1.059 |   -0.1812 |   -0.9217 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7351 |    -1.102 |   -0.5043 |   -0.8766 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.084 |  -0.03859 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.11855 |     91.41 |    -5.637 |   -0.9124 |    -2.181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.716 |    0.3787 |     1.118 |   0.05710 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8346 |    0.6968 |     1.613 |    0.9510 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6823 |     2.220 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.11855</span> |     91.41 |  0.003562 |    0.2865 |    0.1130 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008953 |    0.5936 |     1.118 |   0.05710 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8346 |    0.6968 |     1.613 |    0.9510 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6823 |     2.220 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     2.813 |    0.6131 |  -0.03713 |  -0.02450 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2319 |   -0.1976 |    0.8863 |    0.8390 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3589 |    -1.516 |    0.8806 |   -0.4426 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1647 |   -0.3784 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  115</span>|     479.11487 |     1.004 |    -1.439 |   -0.9352 |   -0.9181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.104 |    -1.058 |   -0.1819 |   -0.9213 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7344 |    -1.101 |   -0.5040 |   -0.8763 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.084 |  -0.03810 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.11487 |     91.37 |    -5.639 |   -0.9121 |    -2.180 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.716 |    0.3790 |     1.117 |   0.05711 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8352 |    0.6977 |     1.613 |    0.9514 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6823 |     2.220 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.11487</span> |     91.37 |  0.003555 |    0.2866 |    0.1130 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008947 |    0.5936 |     1.117 |   0.05711 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8352 |    0.6977 |     1.613 |    0.9514 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6823 |     2.220 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.684 |    0.6054 |  -0.08346 |  -0.01559 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2328 |   -0.1708 |    0.9518 |    0.8099 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3621 |    -1.485 |    0.9267 |   -0.3776 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1041 |   -0.3537 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  116</span>|     479.11139 |     1.005 |    -1.441 |   -0.9347 |   -0.9181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.105 |    -1.058 |   -0.1829 |   -0.9211 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7337 |    -1.100 |   -0.5039 |   -0.8760 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.083 |  -0.03771 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.11139 |     91.41 |    -5.641 |   -0.9117 |    -2.180 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.717 |    0.3792 |     1.117 |   0.05712 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8357 |    0.6988 |     1.613 |    0.9517 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6824 |     2.221 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.11139</span> |     91.41 |  0.003549 |    0.2867 |    0.1130 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008941 |    0.5937 |     1.117 |   0.05712 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8357 |    0.6988 |     1.613 |    0.9517 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6824 |     2.221 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     3.241 |    0.6036 | 0.0009951 |  -0.02154 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2300 |   -0.1727 |    0.8524 |    0.8311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3642 |    -1.386 |     1.866 |   -0.3472 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1024 |   -0.3634 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  117</span>|     479.10851 |     1.004 |    -1.443 |   -0.9342 |   -0.9180 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.105 |    -1.058 |   -0.1837 |   -0.9207 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7329 |    -1.100 |   -0.5043 |   -0.8759 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.084 |  -0.03710 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.10851 |     91.37 |    -5.643 |   -0.9112 |    -2.180 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.718 |    0.3794 |     1.117 |   0.05713 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8363 |    0.6988 |     1.613 |    0.9517 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6823 |     2.222 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.10851</span> |     91.37 |  0.003542 |    0.2868 |    0.1130 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008933 |    0.5937 |     1.117 |   0.05713 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8363 |    0.6988 |     1.613 |    0.9517 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6823 |     2.222 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.500 |    0.5956 |  -0.03344 |  -0.01385 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2306 |   -0.1498 |    0.8910 |    0.8240 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3570 |    -1.405 |    0.8461 |   -0.3572 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|  -0.08808 |   -0.3487 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  118</span>|     479.10606 |     1.005 |    -1.445 |   -0.9338 |   -0.9180 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.106 |    -1.057 |   -0.1846 |   -0.9207 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7320 |    -1.099 |   -0.5047 |   -0.8757 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.084 |  -0.03650 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.10606 |     91.43 |    -5.645 |   -0.9108 |    -2.180 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.719 |    0.3796 |     1.116 |   0.05713 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8369 |    0.6994 |     1.612 |    0.9519 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6822 |     2.222 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.10606</span> |     91.43 |  0.003535 |    0.2868 |    0.1130 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008927 |    0.5938 |     1.116 |   0.05713 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8369 |    0.6994 |     1.612 |    0.9519 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6822 |     2.222 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     5.260 |    0.5953 |   0.07226 |  -0.02302 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2275 |   -0.1573 |    0.8150 |    0.8159 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3410 |    -1.340 |    0.8208 |    0.1422 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2750 |   -0.3500 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  119</span>|     479.10199 |     1.004 |    -1.447 |   -0.9337 |   -0.9181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.107 |    -1.057 |   -0.1854 |   -0.9204 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7311 |    -1.099 |   -0.5045 |   -0.8757 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.084 |  -0.03604 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.10199 |     91.37 |    -5.647 |   -0.9107 |    -2.180 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.720 |    0.3799 |     1.116 |   0.05714 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8375 |    0.6999 |     1.613 |    0.9520 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6821 |     2.223 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.10199</span> |     91.37 |  0.003528 |    0.2869 |    0.1130 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008919 |    0.5938 |     1.116 |   0.05714 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8375 |    0.6999 |     1.613 |    0.9520 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6821 |     2.223 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |   -0.7698 |    0.5858 | -0.004343 |  -0.01418 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2278 |   -0.1298 |    0.8244 |    0.7686 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3575 |    -1.372 |    0.8320 |   -0.3359 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2977 |   -0.3384 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  120</span>|     479.09844 |     1.005 |    -1.448 |   -0.9337 |   -0.9180 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.108 |    -1.056 |   -0.1863 |   -0.9208 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7304 |    -1.097 |   -0.5051 |   -0.8752 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.084 |  -0.03534 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.09844 |     91.43 |    -5.648 |   -0.9108 |    -2.180 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.720 |    0.3801 |     1.116 |   0.05713 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8381 |    0.7011 |     1.612 |    0.9524 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6817 |     2.224 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.09844</span> |     91.43 |  0.003523 |    0.2868 |    0.1130 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008914 |    0.5939 |     1.116 |   0.05713 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8381 |    0.7011 |     1.612 |    0.9524 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6817 |     2.224 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     4.894 |    0.5851 |   0.05981 |  -0.02077 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2261 |   -0.1389 |    0.7622 |    0.7617 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3411 |    -1.236 |    0.7863 |    0.1583 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2091 |   -0.3951 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  121</span>|     479.09485 |     1.004 |    -1.450 |   -0.9338 |   -0.9180 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.108 |    -1.056 |   -0.1867 |   -0.9202 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7295 |    -1.097 |   -0.5051 |   -0.8750 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.085 |  -0.03445 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.09485 |     91.37 |    -5.650 |   -0.9108 |    -2.180 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.721 |    0.3803 |     1.116 |   0.05714 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8388 |    0.7016 |     1.612 |    0.9526 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6813 |     2.225 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.09485</span> |     91.37 |  0.003517 |    0.2868 |    0.1130 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008907 |    0.5939 |     1.116 |   0.05714 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8388 |    0.7016 |     1.612 |    0.9526 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6813 |     2.225 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.077 |    0.5762 |  -0.02502 |  -0.01122 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2275 |   -0.1137 |    0.7953 |    0.7347 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3473 |    -1.249 |    0.8683 |    0.1895 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2333 |   -0.3625 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  122</span>|     479.09211 |     1.005 |    -1.452 |   -0.9337 |   -0.9180 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.109 |    -1.055 |   -0.1878 |   -0.9202 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7286 |    -1.096 |   -0.5055 |   -0.8752 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.085 |  -0.03372 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.09211 |     91.41 |    -5.652 |   -0.9107 |    -2.180 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.722 |    0.3805 |     1.115 |   0.05714 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8394 |    0.7021 |     1.612 |    0.9524 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6810 |     2.226 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.09211</span> |     91.41 |  0.003510 |    0.2868 |    0.1130 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008900 |    0.5940 |     1.115 |   0.05714 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8394 |    0.7021 |     1.612 |    0.9524 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6810 |     2.226 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     3.651 |    0.5754 |   0.04071 |  -0.01634 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2261 |   -0.1150 |    0.8084 |    0.7089 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3268 |    -1.156 |    0.8610 |    0.1793 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2137 |   -0.3471 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  123</span>|     479.08947 |     1.004 |    -1.454 |   -0.9335 |   -0.9181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.110 |    -1.055 |   -0.1890 |   -0.9199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7277 |    -1.096 |   -0.5056 |   -0.8754 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.085 |  -0.03307 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.08947 |     91.37 |    -5.654 |   -0.9105 |    -2.180 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.722 |    0.3808 |     1.115 |   0.05715 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8401 |    0.7021 |     1.611 |    0.9522 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6809 |     2.226 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.08947</span> |     91.37 |  0.003504 |    0.2869 |    0.1130 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008893 |    0.5941 |     1.115 |   0.05715 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8401 |    0.7021 |     1.611 |    0.9522 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6809 |     2.226 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |   -0.8529 |    0.5679 | -0.006947 | -0.009666 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2263 |  -0.09399 |    0.7582 |    0.6848 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3382 |    -1.232 |    0.7736 |   -0.3452 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1950 |   -0.3796 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  124</span>|     479.08673 |     1.005 |    -1.456 |   -0.9331 |   -0.9181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.111 |    -1.054 |   -0.1901 |   -0.9198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7269 |    -1.095 |   -0.5060 |   -0.8751 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.085 |  -0.03242 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.08673 |     91.42 |    -5.656 |   -0.9102 |    -2.180 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.723 |    0.3808 |     1.114 |   0.05716 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8406 |    0.7030 |     1.611 |    0.9525 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6810 |     2.227 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.08673</span> |     91.42 |  0.003498 |    0.2870 |    0.1130 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008887 |    0.5941 |     1.114 |   0.05716 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8406 |    0.7030 |     1.611 |    0.9525 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6810 |     2.227 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     4.055 |    0.5667 |   0.07240 |  -0.01542 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2241 |   -0.1033 |    0.7160 |    0.6904 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3260 |    -1.123 |    0.7675 |    0.1856 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.1986 |   -0.4973 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  125</span>|     479.08385 |     1.004 |    -1.457 |   -0.9328 |   -0.9181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.112 |    -1.054 |   -0.1908 |   -0.9191 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7261 |    -1.095 |   -0.5059 |   -0.8749 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.085 |  -0.03140 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.08385 |     91.37 |    -5.657 |   -0.9100 |    -2.180 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.724 |    0.3808 |     1.114 |   0.05718 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8412 |    0.7034 |     1.611 |    0.9527 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6808 |     2.228 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.08385</span> |     91.37 |  0.003491 |    0.2870 |    0.1130 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008879 |    0.5941 |     1.114 |   0.05718 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8412 |    0.7034 |     1.611 |    0.9527 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6808 |     2.228 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |   -0.7735 |    0.5583 |   0.01997 | -0.008335 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2243 |  -0.08958 |    0.6952 |    0.6337 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3199 |    -1.102 |    0.8222 |    0.2014 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2310 |   -0.4538 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  126</span>|     479.08143 |     1.005 |    -1.459 |   -0.9328 |   -0.9181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.112 |    -1.054 |   -0.1918 |   -0.9191 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7254 |    -1.094 |   -0.5066 |   -0.8749 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.086 |  -0.03018 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.08143 |     91.42 |    -5.659 |   -0.9100 |    -2.180 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.725 |    0.3810 |     1.113 |   0.05718 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8417 |    0.7042 |     1.610 |    0.9527 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6804 |     2.230 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.08143</span> |     91.42 |  0.003486 |    0.2870 |    0.1130 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008874 |    0.5941 |     1.113 |   0.05718 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8417 |    0.7042 |     1.610 |    0.9527 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6804 |     2.230 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     4.589 |    0.5568 |   0.08446 |  -0.01637 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2217 |  -0.09943 |    0.6530 |    0.6713 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3160 |    -1.044 |    0.6971 |   -0.3035 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2323 |   -0.3746 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  127</span>|     479.07858 |     1.004 |    -1.461 |   -0.9326 |   -0.9181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.113 |    -1.054 |   -0.1923 |   -0.9184 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7246 |    -1.093 |   -0.5066 |   -0.8747 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.086 |  -0.02916 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.07858 |     91.36 |    -5.661 |   -0.9098 |    -2.181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.725 |    0.3811 |     1.113 |   0.05720 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8423 |    0.7045 |     1.610 |    0.9529 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6802 |     2.231 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.07858</span> |     91.36 |  0.003479 |    0.2870 |    0.1130 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008866 |    0.5941 |     1.113 |   0.05720 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8423 |    0.7045 |     1.610 |    0.9529 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6802 |     2.231 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.147 |    0.5485 |   0.01974 | -0.006990 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2231 |  -0.08394 |    0.6583 |    0.6424 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3232 |    -1.114 |    0.8219 |    0.1886 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.1911 |   -0.3375 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  128</span>|     479.07618 |     1.004 |    -1.463 |   -0.9323 |   -0.9182 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.114 |    -1.054 |   -0.1929 |   -0.9181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7237 |    -1.093 |   -0.5068 |   -0.8746 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.086 |  -0.02850 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.07618 |     91.40 |    -5.663 |   -0.9095 |    -2.181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.726 |    0.3811 |     1.113 |   0.05721 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8430 |    0.7050 |     1.610 |    0.9530 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6800 |     2.232 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.07618</span> |     91.40 |  0.003472 |    0.2871 |    0.1130 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008858 |    0.5941 |     1.113 |   0.05721 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8430 |    0.7050 |     1.610 |    0.9530 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6800 |     2.232 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     2.586 |    0.5458 |   0.07904 |  -0.01251 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2203 |  -0.08960 |    0.6458 |    0.6586 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3031 |   -0.9906 |    0.7687 |    0.2646 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.1721 |   -0.3284 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  129</span>|     479.07405 |     1.004 |    -1.465 |   -0.9322 |   -0.9183 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.115 |    -1.053 |   -0.1937 |   -0.9179 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7228 |    -1.093 |   -0.5070 |   -0.8747 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.087 |  -0.02798 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.07405 |     91.36 |    -5.665 |   -0.9094 |    -2.181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.727 |    0.3813 |     1.113 |   0.05721 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8437 |    0.7050 |     1.610 |    0.9529 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6797 |     2.233 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.07405</span> |     91.36 |  0.003465 |    0.2871 |    0.1130 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008850 |    0.5942 |     1.113 |   0.05721 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8437 |    0.7050 |     1.610 |    0.9529 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6797 |     2.233 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.296 |    0.5389 |   0.03360 | -0.006173 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2208 |  -0.07255 |    0.6853 |    0.5995 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2906 |    -1.015 |    0.7685 |    0.2327 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.1765 |   -0.3245 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  130</span>|     479.07209 |     1.004 |    -1.467 |   -0.9321 |   -0.9183 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.116 |    -1.053 |   -0.1949 |   -0.9178 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7220 |    -1.093 |   -0.5073 |   -0.8750 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.087 |  -0.02746 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.07209 |     91.39 |    -5.667 |   -0.9093 |    -2.181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.728 |    0.3814 |     1.112 |   0.05721 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8442 |    0.7050 |     1.609 |    0.9526 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6796 |     2.233 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.07209</span> |     91.39 |  0.003458 |    0.2871 |    0.1130 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008841 |    0.5942 |     1.112 |   0.05721 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8442 |    0.7050 |     1.609 |    0.9526 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6796 |     2.233 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     2.160 |    0.5372 |   0.08826 |  -0.01153 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2184 |  -0.07756 |    0.6386 |    0.6404 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2977 |   -0.9812 |    0.7463 |    0.2047 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.1647 |   -0.3238 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  131</span>|     479.07029 |     1.004 |    -1.469 |   -0.9320 |   -0.9184 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.117 |    -1.053 |   -0.1960 |   -0.9175 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7213 |    -1.093 |   -0.5075 |   -0.8752 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.087 |  -0.02689 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.07029 |     91.35 |    -5.669 |   -0.9092 |    -2.181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.729 |    0.3815 |     1.112 |   0.05722 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8447 |    0.7049 |     1.609 |    0.9524 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6795 |     2.234 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.07029</span> |     91.35 |  0.003451 |    0.2872 |    0.1130 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008833 |    0.5942 |     1.112 |   0.05722 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8447 |    0.7049 |     1.609 |    0.9524 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6795 |     2.234 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.487 |    0.5301 |   0.04502 | -0.006786 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2180 |  -0.06576 |    0.6433 |    0.5523 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2822 |    -1.039 |     1.637 |   -0.2973 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2152 |   -0.3175 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  132</span>|     479.06833 |     1.004 |    -1.471 |   -0.9316 |   -0.9185 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.118 |    -1.053 |   -0.1965 |   -0.9170 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7207 |    -1.093 |   -0.5082 |   -0.8751 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.087 |  -0.02616 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.06833 |     91.38 |    -5.671 |   -0.9088 |    -2.181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.730 |    0.3814 |     1.111 |   0.05724 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8452 |    0.7048 |     1.608 |    0.9525 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6792 |     2.235 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.06833</span> |     91.38 |  0.003444 |    0.2872 |    0.1129 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008825 |    0.5942 |     1.111 |   0.05724 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8452 |    0.7048 |     1.608 |    0.9525 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6792 |     2.235 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    0.6466 |    0.5255 |   0.09166 |  -0.01288 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2142 |  -0.07904 |    0.5622 |    0.5865 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2779 |    -1.008 |    0.7011 |    0.1998 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.1404 |   -0.3181 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  133</span>|     479.06868 |     1.003 |    -1.472 |   -0.9317 |   -0.9185 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.118 |    -1.053 |   -0.1973 |   -0.9180 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7202 |    -1.092 |   -0.5093 |   -0.8754 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.087 |  -0.02567 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.06868 |     91.29 |    -5.672 |   -0.9090 |    -2.181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.731 |    0.3814 |     1.111 |   0.05721 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8455 |    0.7062 |     1.607 |    0.9522 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6790 |     2.235 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.06868</span> |     91.29 |  0.003441 |    0.2872 |    0.1129 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008822 |    0.5942 |     1.111 |   0.05721 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8455 |    0.7062 |     1.607 |    0.9522 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6790 |     2.235 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  134</span>|     479.06735 |     1.004 |    -1.472 |   -0.9316 |   -0.9185 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.118 |    -1.053 |   -0.1969 |   -0.9175 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7205 |    -1.092 |   -0.5087 |   -0.8753 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.087 |  -0.02593 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.06735 |     91.33 |    -5.672 |   -0.9089 |    -2.181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.730 |    0.3814 |     1.111 |   0.05722 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8453 |    0.7054 |     1.608 |    0.9523 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6791 |     2.235 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.06735</span> |     91.33 |  0.003443 |    0.2872 |    0.1129 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008823 |    0.5942 |     1.111 |   0.05722 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8453 |    0.7054 |     1.608 |    0.9523 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6791 |     2.235 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -3.476 |    0.5223 |   0.03269 | -0.003763 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2173 |  -0.06616 |    0.5281 |    0.5229 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3041 |    -1.023 |     1.607 |   -0.2773 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2325 |   -0.3192 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  135</span>|     479.06543 |     1.004 |    -1.472 |   -0.9319 |   -0.9185 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.118 |    -1.053 |   -0.1968 |   -0.9175 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7201 |    -1.092 |   -0.5088 |   -0.8751 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.088 |  -0.02567 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.06543 |     91.36 |    -5.672 |   -0.9091 |    -2.181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.731 |    0.3815 |     1.111 |   0.05722 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8456 |    0.7056 |     1.608 |    0.9525 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6790 |     2.235 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.06543</span> |     91.36 |  0.003439 |    0.2872 |    0.1129 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008820 |    0.5942 |     1.111 |   0.05722 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8456 |    0.7056 |     1.608 |    0.9525 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6790 |     2.235 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  136</span>|     479.06390 |     1.004 |    -1.474 |   -0.9324 |   -0.9185 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.119 |    -1.053 |   -0.1965 |   -0.9174 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7194 |    -1.092 |   -0.5089 |   -0.8750 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.088 |  -0.02528 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      479.0639 |     91.36 |    -5.674 |   -0.9095 |    -2.181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.732 |    0.3816 |     1.111 |   0.05723 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8461 |    0.7059 |     1.607 |    0.9526 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6786 |     2.236 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      479.0639</span> |     91.36 |  0.003434 |    0.2871 |    0.1129 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008813 |    0.5943 |     1.111 |   0.05723 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8461 |    0.7059 |     1.607 |    0.9526 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6786 |     2.236 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  137</span>|     479.05708 |     1.004 |    -1.481 |   -0.9346 |   -0.9186 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.123 |    -1.051 |   -0.1952 |   -0.9171 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7164 |    -1.090 |   -0.5089 |   -0.8741 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.090 |  -0.02338 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.05708 |     91.37 |    -5.681 |   -0.9115 |    -2.181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.735 |    0.3824 |     1.112 |   0.05724 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8483 |    0.7072 |     1.607 |    0.9535 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6769 |     2.238 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.05708</span> |     91.37 |  0.003409 |    0.2867 |    0.1129 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008783 |    0.5944 |     1.112 |   0.05724 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8483 |    0.7072 |     1.607 |    0.9535 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6769 |     2.238 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  138</span>|     479.03987 |     1.004 |    -1.510 |   -0.9434 |   -0.9188 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.136 |    -1.045 |   -0.1899 |   -0.9157 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7043 |    -1.085 |   -0.5092 |   -0.8705 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.098 |  -0.01577 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.03987 |     91.37 |    -5.710 |   -0.9193 |    -2.181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.749 |    0.3852 |     1.114 |   0.05728 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8571 |    0.7123 |     1.607 |    0.9569 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6702 |     2.247 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.03987</span> |     91.37 |  0.003311 |    0.2851 |    0.1129 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008662 |    0.5951 |     1.114 |   0.05728 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8571 |    0.7123 |     1.607 |    0.9569 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6702 |     2.247 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |   -0.3651 |    0.4285 |   -0.5283 | -0.001988 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1991 |   0.06799 |    0.8278 |    0.7155 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2362 |   -0.3851 |    0.6274 |    0.1204 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.9270 |   -0.3308 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  139</span>|     479.01407 |     1.005 |    -1.553 |   -0.9371 |   -0.9187 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.158 |    -1.041 |   -0.1978 |   -0.9162 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6881 |    -1.086 |   -0.5088 |   -0.8721 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.097 |  -0.01436 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.01407 |     91.44 |    -5.753 |   -0.9138 |    -2.181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.770 |    0.3869 |     1.111 |   0.05726 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8690 |    0.7117 |     1.608 |    0.9554 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6704 |     2.249 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.01407</span> |     91.44 |  0.003172 |    0.2862 |    0.1129 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008479 |    0.5955 |     1.111 |   0.05726 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8690 |    0.7117 |     1.608 |    0.9554 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6704 |     2.249 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     7.322 |    0.3385 |  -0.09736 | -0.005783 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1607 |    0.1322 |    0.5556 |    0.5176 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1562 |   -0.2690 |     1.549 |  -0.03161 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8578 |   -0.3240 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  140</span>|     478.99946 |     1.003 |    -1.595 |   -0.9294 |   -0.9194 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.179 |    -1.045 |   -0.1992 |   -0.9136 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6715 |    -1.093 |   -0.5132 |   -0.8724 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.100 | -0.009543 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     478.99946 |     91.31 |    -5.795 |   -0.9069 |    -2.182 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.791 |    0.3851 |     1.110 |   0.05734 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8811 |    0.7049 |     1.602 |    0.9551 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6678 |     2.255 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     478.99946</span> |     91.31 |  0.003042 |    0.2876 |    0.1128 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008303 |    0.5951 |     1.110 |   0.05734 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8811 |    0.7049 |     1.602 |    0.9551 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6678 |     2.255 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -5.440 |    0.2525 |    0.1677 |  0.001517 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1272 |   0.05864 |    0.6253 |    0.4352 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1457 |   -0.7321 |     1.353 | 0.0009973 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.018 |   -0.3498 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  141</span>|     479.00064 |     1.004 |    -1.626 |   -0.9429 |   -0.9240 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.201 |    -1.064 |   -0.2165 |   -0.9169 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6612 |    -1.100 |   -0.5189 |   -0.8809 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.100 | -0.001744 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.00064 |     91.38 |    -5.826 |   -0.9189 |    -2.186 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.814 |    0.3763 |     1.103 |   0.05724 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8887 |    0.6986 |     1.596 |    0.9470 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6683 |     2.264 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.00064</span> |     91.38 |  0.002950 |    0.2852 |    0.1123 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008117 |    0.5930 |     1.103 |   0.05724 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8887 |    0.6986 |     1.596 |    0.9470 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6683 |     2.264 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  142</span>|     478.99385 |     1.004 |    -1.610 |   -0.9359 |   -0.9216 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.190 |    -1.054 |   -0.2077 |   -0.9152 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6665 |    -1.096 |   -0.5161 |   -0.8765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.100 | -0.005738 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     478.99385 |     91.39 |    -5.810 |   -0.9127 |    -2.184 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.802 |    0.3808 |     1.107 |   0.05729 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8848 |    0.7019 |     1.599 |    0.9512 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6681 |     2.260 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     478.99385</span> |     91.39 |  0.002997 |    0.2864 |    0.1126 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008212 |    0.5941 |     1.107 |   0.05729 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8848 |    0.7019 |     1.599 |    0.9512 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6681 |     2.260 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     2.499 |    0.2382 |  -0.03703 |  -0.03958 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.09629 |   -0.2111 |    0.3210 |    0.2393 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1233 |   -0.7892 |     1.126 |   -0.4047 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.051 |   -0.3984 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  143</span>|     478.98455 |     1.004 |    -1.625 |   -0.9347 |   -0.9200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.200 |    -1.054 |   -0.2131 |   -0.9088 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6653 |    -1.094 |   -0.5167 |   -0.8757 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.099 |  0.008183 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     478.98455 |     91.36 |    -5.825 |   -0.9116 |    -2.182 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.813 |    0.3812 |     1.105 |   0.05748 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8857 |    0.7042 |     1.598 |    0.9520 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6689 |     2.277 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     478.98455</span> |     91.36 |  0.002951 |    0.2867 |    0.1128 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008125 |    0.5942 |     1.105 |   0.05748 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8857 |    0.7042 |     1.598 |    0.9520 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6689 |     2.277 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |   -0.2799 |    0.1926 |  -0.02074 |  -0.02333 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.08052 |   -0.1468 |    0.2817 |    0.3348 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1470 |   -0.6425 |     1.062 |   -0.3208 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8181 |   -0.3186 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  144</span>|     478.97736 |     1.005 |    -1.639 |   -0.9325 |   -0.9174 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.210 |    -1.043 |   -0.2100 |   -0.9166 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6557 |    -1.093 |   -0.5206 |   -0.8754 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.097 |   0.01446 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     478.97736 |     91.42 |    -5.839 |   -0.9097 |    -2.180 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.822 |    0.3861 |     1.106 |   0.05725 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8927 |    0.7053 |     1.594 |    0.9522 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6711 |     2.284 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     478.97736</span> |     91.42 |  0.002912 |    0.2871 |    0.1131 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008047 |    0.5953 |     1.106 |   0.05725 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8927 |    0.7053 |     1.594 |    0.9522 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6711 |     2.284 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     5.719 |    0.1644 |    0.1764 | 0.0002018 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.06754 |    0.1935 |  -0.08189 |   -0.1310 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.08678 |    -2.826 |    0.8338 |   -0.2552 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.4561 |   -0.1956 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  145</span>|     478.97194 |     1.004 |    -1.652 |   -0.9304 |   -0.9153 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.221 |    -1.039 |   -0.2044 |   -0.9254 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6443 |    -1.087 |   -0.5238 |   -0.8740 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.092 |   0.01749 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     478.97194 |     91.40 |    -5.852 |   -0.9078 |    -2.178 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.833 |    0.3880 |     1.108 |   0.05699 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9010 |    0.7106 |     1.590 |    0.9536 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6749 |     2.288 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     478.97194</span> |     91.40 |  0.002874 |    0.2875 |    0.1133 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.007961 |    0.5958 |     1.108 |   0.05699 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9010 |    0.7106 |     1.590 |    0.9536 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6749 |     2.288 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     2.925 |    0.1117 |    0.2312 |   0.02461 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.04286 |    0.3818 |    0.1548 |   -0.1592 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.04325 |   -0.1382 |    0.7430 |   -0.2262 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|  -0.01070 |   -0.1316 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  146</span>|     479.08778 |    0.9990 |    -1.662 |   -0.9298 |   -0.9158 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.224 |    -1.050 |   -0.2095 |   -0.9175 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6325 |    -1.087 |   -0.5258 |   -0.8715 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.094 |   0.02816 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.08778 |     90.91 |    -5.862 |   -0.9073 |    -2.178 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.836 |    0.3828 |     1.106 |   0.05722 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9096 |    0.7103 |     1.587 |    0.9559 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6733 |     2.301 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.08778</span> |     90.91 |  0.002845 |    0.2876 |    0.1132 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.007936 |    0.5946 |     1.106 |   0.05722 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9096 |    0.7103 |     1.587 |    0.9559 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6733 |     2.301 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  147</span>|     478.99303 |     1.002 |    -1.652 |   -0.9306 |   -0.9153 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.221 |    -1.039 |   -0.2045 |   -0.9253 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6442 |    -1.087 |   -0.5244 |   -0.8738 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.092 |   0.01760 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     478.99303 |     91.18 |    -5.852 |   -0.9080 |    -2.178 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.833 |    0.3879 |     1.108 |   0.05700 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9011 |    0.7107 |     1.589 |    0.9537 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6749 |     2.288 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     478.99303</span> |     91.18 |  0.002874 |    0.2874 |    0.1133 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.007961 |    0.5958 |     1.108 |   0.05700 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9011 |    0.7107 |     1.589 |    0.9537 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6749 |     2.288 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  148</span>|     478.97158 |     1.004 |    -1.652 |   -0.9304 |   -0.9153 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.221 |    -1.039 |   -0.2044 |   -0.9254 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6443 |    -1.087 |   -0.5239 |   -0.8740 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.092 |   0.01751 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     478.97158 |     91.37 |    -5.852 |   -0.9078 |    -2.178 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.833 |    0.3880 |     1.108 |   0.05699 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9010 |    0.7106 |     1.590 |    0.9536 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6749 |     2.288 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     478.97158</span> |     91.37 |  0.002874 |    0.2874 |    0.1133 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.007961 |    0.5958 |     1.108 |   0.05699 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9010 |    0.7106 |     1.590 |    0.9536 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6749 |     2.288 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |   -0.1518 |    0.1094 |    0.1916 |   0.02921 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.04385 |    0.3896 |   -0.2077 |   -0.4379 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.03471 |    -2.421 |    0.7262 |   -0.1589 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   0.01110 |   -0.1305 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  149</span>|     478.97152 |     1.004 |    -1.652 |   -0.9304 |   -0.9153 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.221 |    -1.039 |   -0.2044 |   -0.9254 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6443 |    -1.086 |   -0.5240 |   -0.8739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.092 |   0.01753 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     478.97152 |     91.37 |    -5.852 |   -0.9078 |    -2.178 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.833 |    0.3880 |     1.108 |   0.05699 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9010 |    0.7109 |     1.590 |    0.9536 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6749 |     2.288 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     478.97152</span> |     91.37 |  0.002874 |    0.2874 |    0.1133 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.007961 |    0.5958 |     1.108 |   0.05699 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9010 |    0.7109 |     1.590 |    0.9536 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6749 |     2.288 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    C| Central Diff. |   -0.2246 |    0.1050 |    0.1574 |   0.02036 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.04103 |    0.3470 |   -0.3387 |   -0.1919 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1172 |   -0.1870 |    0.6243 |   -0.2341 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|  -0.06769 |   -0.1265 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  150</span>|     478.97142 |     1.004 |    -1.652 |   -0.9305 |   -0.9153 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.221 |    -1.039 |   -0.2042 |   -0.9253 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6442 |    -1.086 |   -0.5242 |   -0.8739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.092 |   0.01757 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     478.97142 |     91.38 |    -5.852 |   -0.9079 |    -2.178 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.833 |    0.3879 |     1.108 |   0.05700 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9011 |    0.7110 |     1.589 |    0.9537 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6749 |     2.288 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     478.97142</span> |     91.38 |  0.002874 |    0.2874 |    0.1133 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.007961 |    0.5958 |     1.108 |   0.05700 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9011 |    0.7110 |     1.589 |    0.9537 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6749 |     2.288 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    C| Central Diff. |    0.5593 |    0.1052 |    0.1640 |   0.01900 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.04052 |    0.3419 |   -0.3227 |   -0.1646 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1158 |   -0.1768 |    0.1839 |   -0.2151 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|  -0.04116 |   -0.1006 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  151</span>|     478.97143 |     1.004 |    -1.652 |   -0.9306 |   -0.9153 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.221 |    -1.039 |   -0.2041 |   -0.9252 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6442 |    -1.086 |   -0.5243 |   -0.8738 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.092 |   0.01761 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     478.97143 |     91.36 |    -5.852 |   -0.9079 |    -2.178 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.833 |    0.3879 |     1.108 |   0.05700 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9011 |    0.7110 |     1.589 |    0.9538 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6749 |     2.288 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     478.97143</span> |     91.36 |  0.002874 |    0.2874 |    0.1133 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.007961 |    0.5958 |     1.108 |   0.05700 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9011 |    0.7110 |     1.589 |    0.9538 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6749 |     2.288 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  152</span>|     478.97137 |     1.004 |    -1.652 |   -0.9305 |   -0.9153 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.221 |    -1.039 |   -0.2042 |   -0.9253 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6442 |    -1.086 |   -0.5242 |   -0.8738 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.092 |   0.01759 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     478.97137 |     91.37 |    -5.852 |   -0.9079 |    -2.178 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.833 |    0.3879 |     1.108 |   0.05700 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9011 |    0.7110 |     1.589 |    0.9537 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6749 |     2.288 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     478.97137</span> |     91.37 |  0.002874 |    0.2874 |    0.1133 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.007961 |    0.5958 |     1.108 |   0.05700 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9011 |    0.7110 |     1.589 |    0.9537 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6749 |     2.288 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    C| Central Diff. |   -0.4660 |    0.1043 |    0.1497 |   0.02048 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.04080 |    0.3434 |   -0.3050 |   -0.1804 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2015 |    -1.954 |    0.1460 |   -0.2263 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|  -0.03239 |   -0.1147 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  153</span>|     478.97135 |     1.004 |    -1.652 |   -0.9305 |   -0.9153 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.221 |    -1.039 |   -0.2041 |   -0.9253 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6441 |    -1.086 |   -0.5242 |   -0.8738 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.092 |   0.01760 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     478.97135 |     91.36 |    -5.852 |   -0.9079 |    -2.178 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.833 |    0.3879 |     1.108 |   0.05700 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9012 |    0.7110 |     1.589 |    0.9538 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6749 |     2.288 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     478.97135</span> |     91.36 |  0.002874 |    0.2874 |    0.1133 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.007961 |    0.5958 |     1.108 |   0.05700 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9012 |    0.7110 |     1.589 |    0.9538 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6749 |     2.288 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    C| Central Diff. |   -0.8144 |    0.1038 |    0.1446 |   0.02101 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.04083 |    0.3431 |   -0.3133 |   -0.1844 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.05440 |    -1.096 |    0.1463 |   -0.2004 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.2780 |   -0.1269 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  154</span>|     478.97125 |     1.004 |    -1.652 |   -0.9305 |   -0.9153 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.221 |    -1.039 |   -0.2041 |   -0.9252 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6441 |    -1.086 |   -0.5243 |   -0.8738 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.092 |   0.01762 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     478.97125 |     91.37 |    -5.852 |   -0.9079 |    -2.178 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.833 |    0.3879 |     1.108 |   0.05700 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9012 |    0.7111 |     1.589 |    0.9538 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6749 |     2.288 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     478.97125</span> |     91.37 |  0.002874 |    0.2874 |    0.1133 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.007961 |    0.5958 |     1.108 |   0.05700 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9012 |    0.7111 |     1.589 |    0.9538 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6749 |     2.288 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    C| Central Diff. |   0.03985 |    0.1039 |    0.1540 |   0.01970 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.04056 |    0.3392 |   -0.3415 |   -0.1666 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1386 |   -0.1574 |   0.08901 |   -0.2003 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|  -0.04000 |   -0.1187 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  155</span>|     478.97118 |     1.004 |    -1.652 |   -0.9306 |   -0.9153 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.221 |    -1.039 |   -0.2040 |   -0.9252 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6440 |    -1.086 |   -0.5243 |   -0.8737 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.092 |   0.01765 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     478.97118 |     91.37 |    -5.852 |   -0.9080 |    -2.178 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.833 |    0.3878 |     1.108 |   0.05700 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9012 |    0.7112 |     1.589 |    0.9538 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6749 |     2.288 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     478.97118</span> |     91.37 |  0.002874 |    0.2874 |    0.1133 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.007961 |    0.5958 |     1.108 |   0.05700 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9012 |    0.7112 |     1.589 |    0.9538 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6749 |     2.288 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    C| Central Diff. |  -0.05720 |    0.1036 |    0.1507 |   0.01972 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.04048 |    0.3375 |   -0.2940 |   -0.1684 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1979 |    -1.920 |    0.1339 |   -0.2027 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|  -0.04399 |   -0.1216 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  156</span>|     478.97118 |     1.004 |    -1.652 |   -0.9306 |   -0.9153 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.221 |    -1.039 |   -0.2040 |   -0.9252 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6440 |    -1.086 |   -0.5243 |   -0.8737 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.092 |   0.01765 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     478.97118 |     91.37 |    -5.852 |   -0.9080 |    -2.178 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.833 |    0.3878 |     1.108 |   0.05700 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9012 |    0.7112 |     1.589 |    0.9538 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6749 |     2.288 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     478.97118</span> |     91.37 |  0.002874 |    0.2874 |    0.1133 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.007961 |    0.5958 |     1.108 |   0.05700 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9012 |    0.7112 |     1.589 |    0.9538 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.6749 |     2.288 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> calculating covariance matrix</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating residuals/tables</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>initial ETAs were nudged; (can control by foceiControl(etaNudge=., etaNudge2=))</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>ETAs were reset to zero during optimization; (Can control by foceiControl(resetEtaP=.))</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>last objective function was not at minimum, possible problems in optimization</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>using R matrix to calculate covariance, can check sandwich or S matrix with $covRS and $covS</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>gradient problems with initial estimate and covariance; see $scaleInfo</span>
-<span class="r-in"></span>
-<span class="r-in"><span class="co"># Two-component error by variable is possible with both estimation methods</span></span>
-<span class="r-in"><span class="co"># Variance by variable is supported by 'saem' and 'focei'</span></span>
-<span class="r-in"><span class="va">f_nlmixr_fomc_sfo_saem_obs_tc</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr</a></span><span class="op">(</span><span class="va">f_mmkin_tc</span><span class="op">[</span><span class="st">"FOMC-SFO"</span>, <span class="op">]</span>, est <span class="op">=</span> <span class="st">"saem"</span>,</span>
-<span class="r-in">  error_model <span class="op">=</span> <span class="st">"obs_tc"</span><span class="op">)</span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1:    92.0624   -5.2854    0.1952    1.9494   -1.9431    2.8500    1.6150    0.7315    0.7220    0.4370    6.8425    0.4265    7.3797    0.5659</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2:    92.7371   -5.3819    0.0345    2.0839   -2.0310    2.7075    1.5342    0.6949    0.8358    0.4151    7.2043    0.0003    8.1096    0.0003</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 3:    9.2941e+01  -5.7880e+00   1.0336e-01   2.5772e+00  -1.5152e+00   2.5721e+00   1.4575e+00   6.6018e-01   7.9403e-01   3.9439e-01   4.5749e+00   1.5986e-05   5.1354e+00   2.8796e-05</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 4:    92.6277   -5.8599    0.0858    2.5068   -1.3253    2.4435    1.3847    0.6272    0.7543    0.3747    3.4165    0.0001    3.9071    0.0016</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5:    93.0289   -6.0258    0.0528    2.4932   -1.1961    2.3213    1.3154    0.5958    0.7166    0.3559    3.2552    0.0069    3.3744    0.0170</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 6:    93.2107   -6.2881    0.0143    2.4052   -1.1930    2.2053    2.2853    0.5660    0.6808    0.3381    2.8020    0.0086    3.1436    0.0256</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 7:    93.0563   -6.3624   -0.0104    2.3989   -1.1521    2.0950    2.4414    0.5377    0.6467    0.3212    2.6528    0.0209    2.8462    0.0289</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 8:    93.0567   -6.2699   -0.0303    2.3700   -1.0941    1.9903    2.5004    0.5204    0.6144    0.3052    2.3448    0.0314    2.5026    0.0349</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 9:    92.8366   -6.2013   -0.0507    2.3798   -1.0963    1.8907    2.6552    0.4943    0.5837    0.2899    2.2219    0.0368    2.2604    0.0448</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 10:    92.9069   -6.2499   -0.1116    2.3100   -1.0557    1.9625    3.9231    0.4696    0.5545    0.2754    2.0980    0.0359    2.1413    0.0325</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 11:    92.9942   -6.2134   -0.1037    2.3109   -1.0441    1.8643    3.7270    0.4755    0.5268    0.2616    1.9711    0.0372    1.9901    0.0355</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 12:    92.9262   -6.2511   -0.1133    2.2707   -1.0465    1.8430    4.3304    0.4697    0.5004    0.2486    1.8083    0.0346    1.9184    0.0356</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 13:    93.0761   -6.3559   -0.1106    2.2534   -1.0162    2.4826    5.2857    0.4685    0.4754    0.2361    1.7896    0.0331    1.9865    0.0342</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 14:    93.0929   -6.1852   -0.1058    2.2972   -1.0175    2.9964    5.0214    0.4451    0.4516    0.2243    1.8316    0.0309    1.9112    0.0348</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 15:    93.3343   -6.3408   -0.1025    2.2905   -0.9962    3.1132    4.9630    0.4594    0.4291    0.2131    1.8198    0.0338    1.9154    0.0372</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 16:    92.9959   -6.2227   -0.1016    2.2869   -1.0103    3.3338    4.7149    0.4598    0.4076    0.2025    1.8726    0.0327    1.9617    0.0361</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 17:    93.0394   -6.2586   -0.1027    2.2932   -0.9991    3.1671    4.8730    0.4591    0.3872    0.1923    1.8405    0.0336    2.0192    0.0242</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 18:    93.2374   -6.3410   -0.1110    2.2965   -0.9689    3.0087    5.4325    0.4632    0.3679    0.1827    1.9051    0.0351    1.9247    0.0277</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 19:    93.0557   -6.4503   -0.1011    2.2941   -0.9884    2.8583    6.2872    0.4742    0.3495    0.1736    1.8054    0.0345    1.9173    0.0260</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 20:    92.9280   -6.4945   -0.1018    2.2902   -0.9864    3.5934    6.8737    0.4751    0.3320    0.1649    1.7097    0.0372    1.9483    0.0225</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 21:    93.2023   -6.3418   -0.1033    2.2846   -0.9863    3.4138    6.5300    0.4710    0.3154    0.1567    1.7507    0.0318    1.9030    0.0249</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 22:    93.5494   -6.1985   -0.1078    2.2991   -1.0171    3.2431    6.2035    0.4475    0.2996    0.1488    1.7370    0.0342    1.8124    0.0282</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 23:    93.3588   -6.2577   -0.1084    2.2654   -0.9956    3.0809    5.8933    0.4393    0.2869    0.1481    1.6928    0.0380    1.8288    0.0254</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 24:    93.5705   -6.4876   -0.1083    2.2606   -1.0062    3.7095    6.7801    0.4459    0.2726    0.1684    1.7001    0.0377    1.9012    0.0249</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 25:    93.9726   -6.5638   -0.1239    2.2374   -1.0071    5.1357    6.6571    0.4608    0.2755    0.1600    1.6615    0.0382    1.8846    0.0213</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 26:    93.8894   -6.5073   -0.1120    2.2645   -1.0327    4.8789    6.3243    0.4473    0.3014    0.1520    1.7152    0.0340    1.8990    0.0254</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 27:    94.5348   -6.3868   -0.0891    2.3139   -1.0361    5.3856    6.0081    0.4250    0.3080    0.1453    1.7028    0.0385    1.7853    0.0304</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 28:    93.9221   -6.1747   -0.1039    2.3013   -1.0087    5.1163    5.7077    0.4214    0.3136    0.1634    1.6823    0.0340    1.7487    0.0353</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 29:    93.4946   -6.0264   -0.0940    2.3124   -1.0166    5.3744    5.4223    0.4404    0.3114    0.1813    1.6310    0.0370    1.7646    0.0367</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 30:    93.9287   -5.9114   -0.0967    2.3078   -1.0090    5.2017    5.1512    0.4381    0.3048    0.2091    1.5825    0.0393    1.7431    0.0312</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 31:    93.7065   -6.0174   -0.0928    2.2917   -0.9880    5.1935    4.8936    0.4352    0.3109    0.1986    1.5876    0.0392    1.8465    0.0280</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 32:    93.7675   -5.6444   -0.0918    2.3145   -0.9664    4.9338    4.6489    0.4332    0.3090    0.1918    1.6874    0.0332    1.7501    0.0331</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 33:    94.2589   -5.7637   -0.0865    2.3130   -0.9733    4.6871    4.4165    0.4235    0.2935    0.1865    1.7173    0.0355    1.7365    0.0334</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 34:    94.0788   -5.7432   -0.1022    2.3049   -0.9681    4.4528    4.1957    0.4319    0.2962    0.1772    1.6357    0.0361    1.4755    0.0574</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 35:    94.0798   -5.8549   -0.0929    2.2856   -0.9849    4.2301    3.9859    0.4391    0.2937    0.1734    1.6460    0.0275    1.7739    0.0379</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 36:    93.9997   -5.8708   -0.0890    2.2870   -1.0134    4.0186    3.7866    0.4268    0.2819    0.1840    1.5830    0.0258    1.8922    0.0320</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 37:    93.8186   -5.8979   -0.0973    2.2807   -1.0197    3.8177    3.5973    0.4280    0.2778    0.1799    1.5783    0.0282    1.7697    0.0404</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 38:    94.0728   -5.9601   -0.0936    2.3011   -1.0381    3.6268    3.4174    0.4186    0.2944    0.1864    1.5780    0.0294    1.5210    0.0558</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 39:    94.0504   -5.8302   -0.0995    2.2847   -1.0418    3.4455    3.2465    0.4240    0.2796    0.1771    1.6888    0.0242    1.7744    0.0366</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 40:    93.8918   -5.9418   -0.0988    2.2925   -1.0613    3.2732    3.0842    0.4144    0.2672    0.1837    1.7275    0.0232    1.7840    0.0357</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 41:    93.7961   -6.0741   -0.0896    2.2932   -1.0359    3.1095    3.2890    0.4236    0.2606    0.1782    1.7591    0.0212    1.8920    0.0273</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 42:    93.9458   -6.0319   -0.0909    2.3114   -1.0281    3.0544    3.1246    0.4196    0.2598    0.1839    1.6854    0.0310    1.7586    0.0308</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 43:    93.7623   -6.0165   -0.1056    2.2837   -1.0378    3.9092    3.2100    0.4298    0.2468    0.1747    1.7064    0.0286    1.6676    0.0397</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 44:    93.4300   -5.9019   -0.1114    2.2716   -1.0167    4.4100    3.0495    0.4476    0.2345    0.1687    1.7989    0.0253    1.7741    0.0299</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 45:    93.3475   -5.9962   -0.1231    2.2546   -1.0007    6.4879    3.3965    0.4528    0.2452    0.1602    1.7283    0.0276    1.7219    0.0379</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 46:    93.3100   -6.0073   -0.1255    2.2483   -0.9962    6.6009    3.5942    0.4520    0.2533    0.1568    1.6991    0.0258    1.1783    0.0706</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 47:    93.4422   -5.8442   -0.1286    2.2578   -0.9819    6.2709    3.4145    0.4492    0.2637    0.1694    1.6754    0.0317    1.3442    0.0599</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 48:    93.0996   -5.5881   -0.1307    2.2541   -0.9882    5.9573    3.2438    0.4480    0.2665    0.1695    1.7040    0.0285    1.6825    0.0426</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 49:    93.3649   -5.8011   -0.1260    2.2595   -0.9704    5.7230    3.0816    0.4399    0.2646    0.1715    1.6544    0.0297    1.6414    0.0418</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 50:    93.9331   -5.8731   -0.1195    2.2615   -0.9837    5.4368    2.9275    0.4453    0.2735    0.1940    1.6316    0.0289    1.6885    0.0392</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 51:    93.6092   -5.8721   -0.1237    2.2599   -1.0015    5.1650    2.7811    0.4413    0.2865    0.1939    1.6492    0.0272    1.7686    0.0358</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 52:    93.3008   -5.9775   -0.1277    2.2628   -0.9962    4.9067    3.1139    0.4438    0.2946    0.1963    1.5642    0.0337    1.7391    0.0335</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 53:    93.6347   -6.0805   -0.1189    2.2663   -1.0177    4.6614    3.5169    0.4335    0.2962    0.2043    1.5275    0.0343    1.7417    0.0366</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 54:    93.5781   -6.0412   -0.1166    2.2573   -1.0015    4.4283    3.3411    0.4395    0.2912    0.2080    1.5464    0.0346    1.7584    0.0359</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 55:    93.9675   -6.0867   -0.0952    2.2970   -0.9987    4.2069    3.6762    0.4550    0.2780    0.1988    1.5138    0.0405    1.6251    0.0472</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 56:    94.4069   -6.1375   -0.0943    2.2975   -1.0190    3.9966    4.2280    0.4550    0.2806    0.1945    1.5294    0.0425    1.6443    0.0468</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 57:    93.8076   -6.1618   -0.0936    2.2687   -1.0237    3.7967    4.3085    0.4524    0.2666    0.1897    1.5227    0.0414    1.6955    0.0438</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 58:    93.9188   -6.2272   -0.0947    2.2767   -1.0408    3.6069    4.6707    0.4536    0.2676    0.1873    1.5201    0.0425    1.5336    0.0550</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 59:    93.9572   -6.3460   -0.1074    2.2746   -1.0069    3.4266    5.8058    0.4614    0.2718    0.1919    1.5533    0.0417    1.6227    0.0460</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 60:    93.5430   -6.1622   -0.1048    2.2861   -0.9959    3.2552    5.5155    0.4535    0.2848    0.1823    1.4745    0.0431    1.5242    0.0522</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 61:    93.8016   -6.4079   -0.1065    2.2897   -1.0325    3.0925    5.9252    0.4583    0.2901    0.1732    1.5364    0.0365    1.4623    0.0550</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 62:    93.4249   -6.2284   -0.0967    2.3126   -1.0208    2.9378    5.6289    0.4429    0.2756    0.1645    1.6111    0.0325    1.3996    0.0618</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 63:    93.2335   -6.0996   -0.1051    2.3047   -0.9976    2.7909    5.3475    0.4292    0.2719    0.1633    1.6010    0.0326    1.5115    0.0494</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 64:    92.7091   -6.1109   -0.1041    2.3032   -0.9746    3.2610    5.0801    0.4301    0.2793    0.1751    1.5979    0.0302    1.3867    0.0575</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 65:    92.6059   -6.0049   -0.1077    2.3062   -0.9853    3.0979    4.8261    0.4316    0.2826    0.1714    1.6357    0.0272    1.3931    0.0543</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 66:    92.7778   -6.0856   -0.1040    2.3057   -0.9828    2.9430    4.5848    0.4362    0.2888    0.1628    1.6126    0.0316    1.4340    0.0505</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 67:    93.0325   -6.1775   -0.1085    2.3122   -0.9714    2.7959    4.3790    0.4464    0.2843    0.1547    1.6090    0.0354    1.4966    0.0448</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 68:    92.8987   -5.8741   -0.1089    2.3184   -0.9567    2.6561    4.1601    0.4389    0.2866    0.1470    1.5888    0.0347    1.4492    0.0507</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 69:    92.7300   -6.0473   -0.1070    2.3082   -0.9739    2.5233    4.0183    0.4371    0.3158    0.1527    1.5904    0.0298    1.5387    0.0477</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 70:    92.5285   -6.0962   -0.1115    2.3179   -0.9785    2.4856    4.0612    0.4380    0.3059    0.1504    1.5237    0.0402    1.4541    0.0463</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 71:    92.3975   -6.2090   -0.1269    2.2819   -0.9713    2.3613    4.3811    0.4173    0.2906    0.1520    1.5388    0.0316    1.3713    0.0584</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 72:    92.1593   -6.2214   -0.1217    2.2950   -0.9481    2.2433    5.1205    0.4227    0.2760    0.1605    1.5431    0.0312    1.5797    0.0459</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 73:    92.1195   -6.6667   -0.1244    2.3076   -0.9642    2.1311    7.6538    0.4124    0.2622    0.1525    1.4505    0.0375    1.2810    0.0595</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 74:    92.2191   -6.6665   -0.1278    2.2989   -0.9703    2.0246    7.2711    0.4098    0.2491    0.1666    1.4309    0.0384    1.2717    0.0638</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 75:    92.4656   -6.8881   -0.1274    2.3011   -0.9595    1.9233    8.3835    0.4147    0.2367    0.1656    1.4181    0.0424    1.2498    0.0620</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 76:    92.4593   -6.2323   -0.1203    2.2912   -0.9799    1.8272    7.9643    0.3967    0.2301    0.1596    1.5448    0.0336    1.6758    0.0359</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 77:    92.4501   -6.1123   -0.1219    2.2889   -0.9426    1.7358    7.5661    0.4049    0.2299    0.1594    1.5380    0.0327    1.6947    0.0364</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 78:    92.5059   -6.0339   -0.1198    2.2985   -0.9544    1.6490    7.1878    0.4007    0.2422    0.1568    1.5722    0.0302    1.5665    0.0415</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 79:    92.4302   -6.6428   -0.1141    2.3119   -0.9421    1.5666    9.0889    0.3986    0.2340    0.1489    1.5029    0.0306    1.1894    0.0638</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 80:    92.5305   -6.3036   -0.1127    2.3099   -0.9406    1.9062    8.6344    0.4091    0.2327    0.1648    1.5558    0.0308    1.0136    0.0749</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 81:    92.6652   -6.0832   -0.1167    2.2994   -0.9552    2.3641    8.2027    0.4142    0.2348    0.1566    1.6232    0.0300    1.1320    0.0712</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 82:    92.5109   -6.1524   -0.1100    2.3037   -0.9686    2.2459    7.7926    0.4145    0.2381    0.1695    1.6128    0.0306    1.6145    0.0413</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 83:    92.6455   -6.3979   -0.1059    2.3245   -0.9573    2.1336    8.1077    0.4060    0.2348    0.1667    1.6039    0.0304    1.6251    0.0419</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 84:    92.5768   -6.3469   -0.0979    2.3177   -0.9346    2.0269    8.2125    0.4141    0.2439    0.1720    1.6334    0.0302    1.5803    0.0450</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 85:    92.7920   -6.3536   -0.0969    2.3363   -0.9389    1.9256    8.1820    0.4035    0.2512    0.1713    1.5815    0.0338    1.1193    0.0719</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 86:    92.8571   -6.2567   -0.0911    2.3426   -0.9633    1.8293    8.1211    0.4008    0.2659    0.1627    1.5840    0.0356    1.2425    0.0639</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 87:    92.9101   -6.1557   -0.0937    2.3326   -0.9624    1.7378    7.7151    0.3950    0.2839    0.1546    1.6017    0.0354    1.0600    0.0766</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 88:    92.7649   -6.2151   -0.0881    2.3275   -0.9967    1.6509    7.3293    0.4046    0.2697    0.1469    1.6092    0.0302    1.1839    0.0693</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 89:    92.9350   -5.9222   -0.0903    2.3372   -0.9612    1.5684    6.9628    0.4058    0.2818    0.1395    1.5957    0.0335    1.5450    0.0444</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 90:    92.7895   -5.9348   -0.0890    2.3457   -0.9601    1.4900    6.6147    0.4070    0.2705    0.1430    1.5716    0.0378    1.4918    0.0439</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 91:    93.0113   -5.9302   -0.0997    2.3316   -0.9489    1.4155    6.2840    0.4424    0.2929    0.1410    1.5582    0.0377    1.2459    0.0582</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 92:    92.8603   -6.3366   -0.0918    2.3356   -0.9938    1.3447    5.9698    0.4287    0.2929    0.1531    1.4906    0.0398    1.2421    0.0605</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 93:    92.8094   -6.1097   -0.0953    2.3382   -0.9996    1.2775    5.6713    0.4309    0.2783    0.1597    1.4317    0.0468    1.2694    0.0584</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 94:    92.7100   -6.1043   -0.0909    2.3287   -0.9928    1.3828    5.3877    0.4337    0.2698    0.1517    1.4173    0.0489    1.4335    0.0476</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 95:    92.2055   -6.1792   -0.0889    2.3553   -0.9983    1.3137    5.1183    0.4209    0.3273    0.1566    1.4865    0.0417    1.2485    0.0681</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 96:    92.3225   -6.2710   -0.0811    2.3734   -1.0043    1.3428    4.8624    0.4121    0.3110    0.1532    1.5817    0.0374    1.5209    0.0532</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 97:    92.2983   -6.3023   -0.0776    2.3615   -0.9801    1.2757    4.6193    0.4221    0.3018    0.1455    1.5971    0.0337    1.2137    0.0719</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 98:    92.1433   -6.3114   -0.0935    2.3269   -0.9866    1.2227    4.8121    0.4327    0.2907    0.1694    1.5524    0.0303    1.1779    0.0727</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 99:    92.1631   -6.3090   -0.0908    2.3197   -0.9891    1.1615    4.9271    0.4169    0.2859    0.1736    1.5303    0.0334    1.6227    0.0511</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 100:    92.1185   -6.3603   -0.0950    2.3280   -1.0209    1.1035    5.0957    0.4213    0.2924    0.1817    1.5991    0.0330    1.7197    0.0445</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 101:    92.3964   -6.0447   -0.0898    2.3347   -1.0164    1.0483    4.8409    0.4251    0.2778    0.1727    1.5708    0.0422    1.6197    0.0461</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 102:    92.6597   -6.2545   -0.0954    2.3264   -1.0206    0.9959    4.5989    0.4218    0.2736    0.1640    1.5853    0.0408    1.5912    0.0455</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 103:    92.7866   -6.3292   -0.0933    2.3148   -1.0013    0.9461    4.8935    0.4234    0.2599    0.1688    1.6299    0.0399    1.5921    0.0445</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 104:    92.8098   -6.2608   -0.1009    2.3014   -0.9947    0.8988    4.7750    0.4306    0.2548    0.1813    1.6363    0.0378    1.6713    0.0427</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 105:    92.8300   -5.9827   -0.1028    2.3155   -0.9896    0.8538    4.5362    0.4339    0.2664    0.1722    1.6079    0.0397    1.3691    0.0573</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 106:    92.8218   -6.1138   -0.1024    2.3462   -0.9750    0.8299    4.3094    0.4494    0.2530    0.1636    1.5761    0.0389    1.2857    0.0595</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 107:    92.9304   -6.0063   -0.1002    2.3269   -0.9778    0.7884    4.0939    0.4600    0.2404    0.1620    1.6499    0.0373    1.2101    0.0675</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 108:    92.9072   -6.0928   -0.0991    2.3183   -0.9732    0.7489    4.4394    0.4584    0.2518    0.1539    1.6509    0.0332    1.2346    0.0670</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 109:    92.7504   -6.1545   -0.0862    2.3423   -0.9618    0.7115    4.7501    0.4371    0.2766    0.1465    1.5755    0.0349    1.1552    0.0701</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 110:    92.9277   -6.3786   -0.0904    2.3468   -0.9402    0.6759    5.8620    0.4328    0.2821    0.1703    1.4661    0.0439    1.3292    0.0609</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 111:    92.8023   -5.8686   -0.0886    2.3653   -0.9417    0.7635    5.5689    0.4288    0.2680    0.1618    1.4278    0.0502    1.4715    0.0468</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 112:    92.9411   -5.8095   -0.0883    2.3625   -0.9322    0.8592    5.2905    0.4562    0.2586    0.1537    1.3920    0.0513    1.1766    0.0648</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 113:    92.9845   -6.0499   -0.0761    2.3626   -0.9533    0.8163    5.0259    0.4403    0.2595    0.1532    1.4306    0.0450    1.1416    0.0645</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 114:    92.7735   -6.0605   -0.0694    2.3497   -0.9646    0.7754    4.7746    0.4394    0.2682    0.1576    1.4866    0.0344    1.1127    0.0745</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 115:    92.7048   -6.1327   -0.0702    2.3427   -0.9910    0.7367    4.5359    0.4257    0.2736    0.1497    1.5045    0.0413    1.2783    0.0677</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 116:    92.7131   -6.0115   -0.0751    2.3370   -0.9899    0.6998    4.3091    0.4187    0.2638    0.1422    1.6036    0.0325    1.1109    0.0748</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 117:    92.7720   -5.9163   -0.0709    2.3503   -0.9566    0.6648    4.0937    0.4185    0.2555    0.1417    1.6016    0.0297    1.0778    0.0774</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 118:    92.9182   -5.9312   -0.0812    2.3436   -0.9731    0.6316    3.8890    0.4084    0.2667    0.1400    1.5698    0.0320    1.2364    0.0665</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 119:    92.9546   -5.9622   -0.0870    2.3330   -0.9934    0.6000    3.6945    0.4035    0.2867    0.1486    1.5364    0.0343    1.5892    0.0489</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 120:    92.9168   -6.0966   -0.0905    2.3301   -1.0045    0.5700    3.6677    0.3988    0.2871    0.1567    1.5378    0.0349    1.0886    0.0796</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 121:    92.7767   -6.1038   -0.0802    2.3396   -0.9815    0.5981    4.3032    0.3803    0.2884    0.1684    1.4788    0.0335    1.0886    0.0796</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 122:    92.8439   -6.2425   -0.0799    2.3323   -0.9987    0.6537    4.5745    0.3884    0.2834    0.1600    1.4786    0.0346    1.2146    0.0682</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 123:    92.8219   -5.7869   -0.0872    2.3478   -0.9619    0.7886    4.3458    0.3951    0.2887    0.1658    1.4827    0.0341    1.3911    0.0559</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 124:    92.7930   -5.7551   -0.0819    2.3559   -0.9809    0.8922    4.1285    0.3907    0.2972    0.1575    1.5329    0.0330    1.3824    0.0594</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 125:    92.7488   -5.9406   -0.0819    2.3550   -0.9797    0.8476    3.9221    0.3907    0.2996    0.1733    1.5096    0.0333    1.3148    0.0667</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 126:    92.7926   -5.7106   -0.0824    2.3560   -0.9653    0.8052    3.7260    0.3918    0.3018    0.1854    1.4237    0.0381    1.2607    0.0639</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 127:    92.9153   -5.6682   -0.0717    2.3654   -0.9645    0.7649    3.5397    0.3722    0.2948    0.1784    1.4679    0.0415    1.2310    0.0695</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 128:    92.8827   -5.7984   -0.0783    2.3813   -1.0047    1.1012    3.3627    0.3802    0.2997    0.1694    1.5187    0.0442    1.2884    0.0649</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 129:    92.8727   -5.7191   -0.0785    2.3426   -0.9857    1.0461    3.1946    0.3784    0.2984    0.1610    1.4757    0.0398    1.1695    0.0775</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 130:    92.8689   -5.8036   -0.0935    2.3123   -0.9783    0.9938    3.0348    0.4184    0.2980    0.1723    1.5143    0.0319    1.2298    0.0738</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 131:    92.8733   -5.6707   -0.0988    2.3045   -0.9936    1.3313    2.8831    0.4277    0.2831    0.1816    1.5919    0.0329    1.5260    0.0533</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 132:    92.5761   -5.7773   -0.0925    2.3211   -0.9661    1.2647    2.7389    0.4216    0.2815    0.1995    1.5306    0.0321    1.5863    0.0520</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 133:    92.4512   -5.7800   -0.0920    2.3269   -0.9625    1.2015    2.6020    0.4242    0.2674    0.1955    1.5669    0.0288    1.4087    0.0606</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 134:    92.5625   -5.7968   -0.0965    2.3271   -0.9728    1.1414    2.4719    0.4201    0.2753    0.1945    1.5935    0.0311    1.5348    0.0534</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 135:    92.3448   -5.6624   -0.0961    2.3303   -0.9600    1.0843    2.3483    0.4193    0.2728    0.1983    1.6245    0.0305    1.6281    0.0506</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 136:    92.3407   -5.7523   -0.0932    2.3208   -0.9897    1.0301    2.3267    0.4107    0.2668    0.1884    1.5933    0.0358    1.2970    0.0657</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 137:    92.3940   -6.0148   -0.1046    2.2981   -0.9866    0.9786    3.1382    0.4166    0.3012    0.1790    1.5077    0.0385    1.1944    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 138:    92.3556   -5.9912   -0.1039    2.2969   -0.9920    0.9297    3.2144    0.4208    0.3039    0.1703    1.5042    0.0378    1.2588    0.0717</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 139:    92.5548   -5.9253   -0.1177    2.2723   -1.0079    0.8832    3.2648    0.4049    0.3263    0.1618    1.5558    0.0382    1.2673    0.0698</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 140:    92.6546   -6.0596   -0.1368    2.2659   -0.9922    0.8390    3.3893    0.3846    0.3100    0.1537    1.5475    0.0355    1.3190    0.0629</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 141:    92.6661   -5.9744   -0.1379    2.2793   -0.9754    0.8287    3.2725    0.3800    0.3178    0.1528    1.5076    0.0361    1.4087    0.0540</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 142:    92.5681   -5.7650   -0.1033    2.3249   -0.9613    0.7872    3.1089    0.3610    0.3564    0.1701    1.4733    0.0332    1.2603    0.0720</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 143:    92.4184   -5.6070   -0.1033    2.3520   -0.9764    0.7479    2.9534    0.3591    0.3758    0.1674    1.5420    0.0390    1.3280    0.0614</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 144:    92.5354   -5.6144   -0.0978    2.3801   -0.9425    0.7105    2.8058    0.3538    0.3606    0.1722    1.4884    0.0400    1.3455    0.0620</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 145:    92.4207   -5.6541   -0.0746    2.3873   -0.9633    0.6750    2.6655    0.3361    0.3426    0.1718    1.4981    0.0416    1.2440    0.0685</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 146:    92.3058   -5.6608   -0.0731    2.3755   -0.9732    0.6412    2.5322    0.3193    0.3304    0.1719    1.6391    0.0348    1.1788    0.0743</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 147:    92.4067   -5.7615   -0.0746    2.3775   -0.9903    0.6091    2.4056    0.3148    0.3311    0.1709    1.6695    0.0314    1.2931    0.0676</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 148:    92.3739   -5.8812   -0.0820    2.3644   -0.9823    0.5787    2.7260    0.3332    0.3296    0.1794    1.6168    0.0303    1.3206    0.0670</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 149:    92.4456   -5.8277   -0.0921    2.3508   -0.9924    0.5498    2.8750    0.3368    0.3305    0.1917    1.5602    0.0302    1.3622    0.0608</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 150:    92.5049   -5.7964   -0.1003    2.3353   -0.9809    0.5223    2.7312    0.3291    0.3634    0.1874    1.5035    0.0301    1.4002    0.0615</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 151:    92.3292   -6.0377   -0.1039    2.3301   -0.9866    0.4962    3.3342    0.3348    0.3626    0.1780    1.4819    0.0298    1.3217    0.0672</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 152:    92.3747   -6.0460   -0.1023    2.3154   -1.0242    0.5079    3.4530    0.3371    0.3518    0.1674    1.6034    0.0296    1.2304    0.0749</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 153:    92.3909   -5.8707   -0.1008    2.3284   -0.9897    0.5623    2.8180    0.3480    0.3792    0.1749    1.4953    0.0291    1.3442    0.0677</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 154:    92.2532   -5.9313   -0.0991    2.3232   -0.9839    0.5340    3.3946    0.3502    0.3723    0.1729    1.4837    0.0280    1.1452    0.0776</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 155:    92.1782   -6.1012   -0.0988    2.3339   -0.9600    0.3900    3.9676    0.3593    0.3704    0.1531    1.4927    0.0286    1.1848    0.0730</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 156:    92.2225   -5.8387   -0.0991    2.3393   -0.9459    0.3294    3.0685    0.3598    0.3808    0.1568    1.5540    0.0318    1.3419    0.0671</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 157:    92.2411   -5.7045   -0.1038    2.3578   -0.9252    0.2797    2.5238    0.3716    0.3579    0.1653    1.4704    0.0373    1.1959    0.0686</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 158:    92.2865   -5.6692   -0.1009    2.3771   -0.9532    0.2840    2.4144    0.3592    0.3610    0.1641    1.5065    0.0389    1.1780    0.0674</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 159:    92.2771   -5.7526   -0.0782    2.3632   -0.9771    0.2996    2.7295    0.3357    0.3779    0.1606    1.5818    0.0366    1.1512    0.0781</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 160:    92.3400   -5.8039   -0.0811    2.3615   -0.9453    0.2707    2.7305    0.3373    0.3834    0.1528    1.4765    0.0370    1.1427    0.0756</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 161:    92.4180   -5.7448   -0.0961    2.3653   -0.9508    0.2965    2.6051    0.3666    0.3833    0.1647    1.4581    0.0364    0.9817    0.0827</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 162:    92.4487   -5.8952   -0.0889    2.3460   -0.9646    0.2370    3.1413    0.3460    0.3801    0.1571    1.4528    0.0346    1.0006    0.0798</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 163:    92.5251   -5.8567   -0.0987    2.3342   -0.9615    0.1453    2.9917    0.3535    0.3735    0.1452    1.4521    0.0330    1.0498    0.0766</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 164:    92.5949   -6.1580   -0.1008    2.3332   -0.9749    0.1095    4.1797    0.3586    0.3767    0.1355    1.4659    0.0310    1.0218    0.0797</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 165:    92.5794   -6.0542   -0.1001    2.3321   -0.9795    0.1332    4.2924    0.3588    0.3752    0.1386    1.4858    0.0313    0.9836    0.0812</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 166:    92.6686   -6.0065   -0.0960    2.3623   -0.9532    0.1465    4.0810    0.3439    0.3683    0.1647    1.3929    0.0372    0.9354    0.0837</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 167:    92.7086   -6.2096   -0.0895    2.3656   -0.9552    0.1938    4.6857    0.3325    0.3519    0.1681    1.4281    0.0362    1.0329    0.0784</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 168:    92.6096   -6.0709   -0.0880    2.3595   -0.9474    0.2141    4.1277    0.3379    0.3527    0.1620    1.4264    0.0347    0.9627    0.0836</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 169:    92.5365   -5.9418   -0.0919    2.3546   -0.9575    0.3620    3.7767    0.3489    0.3589    0.1609    1.4761    0.0360    1.1348    0.0745</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 170:    92.6270   -5.8404   -0.0935    2.3562   -0.9517    0.3312    3.2506    0.3552    0.3607    0.1560    1.4506    0.0383    1.0731    0.0722</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 171:    92.5606   -5.9213   -0.0899    2.3513   -0.9554    0.4399    3.5587    0.3599    0.3646    0.1385    1.4779    0.0349    0.9844    0.0775</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 172:    92.3630   -5.7066   -0.0765    2.3798   -0.9314    0.5208    2.5578    0.3458    0.3657    0.1547    1.4900    0.0348    1.0485    0.0765</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 173:    92.2527   -5.5811   -0.0713    2.4173   -0.9221    0.8674    2.2100    0.3448    0.3370    0.1513    1.5403    0.0445    1.4403    0.0518</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 174:    92.3852   -5.5348   -0.0712    2.4066   -0.9033    0.7543    2.1568    0.3457    0.3324    0.1482    1.5322    0.0407    1.2744    0.0623</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 175:    92.4798   -5.5494   -0.0712    2.4004   -0.9100    0.5675    2.0147    0.3457    0.3333    0.1629    1.5229    0.0383    1.2917    0.0655</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 176:    92.5881   -5.5613   -0.0675    2.4180   -0.9188    0.4011    2.0736    0.3413    0.3487    0.1781    1.5149    0.0389    1.1499    0.0669</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 177:    92.6015   -5.4951   -0.0494    2.4324   -0.9525    0.4992    1.9009    0.3629    0.3353    0.1919    1.4881    0.0393    1.2289    0.0650</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 178:    92.6317   -5.4943   -0.0505    2.4373   -0.9298    0.5087    1.6116    0.3631    0.3324    0.1906    1.4475    0.0454    1.0764    0.0701</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 179:    92.7043   -5.5326   -0.0419    2.4386   -0.9376    0.4350    1.8241    0.3577    0.3757    0.1991    1.4445    0.0424    0.9626    0.0833</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 180:    92.7457   -5.5591   -0.0371    2.4684   -0.9450    0.3973    1.7797    0.3507    0.3707    0.1944    1.4392    0.0415    1.0784    0.0764</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 181:    92.6287   -5.5741   -0.0368    2.4489   -0.9459    0.4744    1.8016    0.3502    0.3544    0.1970    1.4317    0.0392    0.9225    0.0848</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 182:    92.6121   -5.5593   -0.0316    2.4610   -0.9345    0.6054    1.9206    0.3492    0.3538    0.1855    1.4410    0.0367    0.8977    0.0878</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 183:    92.4258   -5.4737   -0.0393    2.4592   -0.9319    0.8062    1.7845    0.3528    0.3544    0.1649    1.4545    0.0409    1.0723    0.0751</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 184:    92.3939   -5.5961   -0.0479    2.4268   -0.9435    1.0246    2.2534    0.3497    0.3289    0.1363    1.5022    0.0370    1.1058    0.0729</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 185:    92.4673   -5.5415   -0.0525    2.4157   -0.9058    0.8296    2.2848    0.3406    0.3314    0.1706    1.4935    0.0367    1.1362    0.0722</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 186:    92.4122   -5.6594   -0.0574    2.4466   -0.9430    0.9133    2.3350    0.3327    0.3596    0.1506    1.4450    0.0404    1.2273    0.0629</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 187:    92.5416   -5.5472   -0.0521    2.4393   -0.9261    0.9731    1.9228    0.3208    0.3673    0.1421    1.4890    0.0409    1.2553    0.0626</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 188:    92.5502   -5.6425   -0.0591    2.4345   -0.9246    0.9315    2.2041    0.3184    0.3639    0.1319    1.4896    0.0392    1.0986    0.0684</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 189:    92.4180   -5.6737   -0.0504    2.4448   -0.9191    0.9006    2.4647    0.3102    0.3743    0.1648    1.4649    0.0397    1.1320    0.0742</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 190:    92.5821   -5.5823   -0.0363    2.4588   -0.9205    0.9862    2.2692    0.2820    0.4087    0.1459    1.3938    0.0412    0.9562    0.0815</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 191:    92.3708   -5.5825   -0.0418    2.4621   -0.9231    0.9867    2.4743    0.2890    0.4196    0.1445    1.4619    0.0403    0.9630    0.0811</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 192:    92.2628   -5.5337   -0.0366    2.4392   -0.9194    0.6944    2.2771    0.2909    0.4169    0.1279    1.4745    0.0363    0.8601    0.0892</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 193:    92.4854   -5.6303   -0.0352    2.4484   -0.9316    0.4403    2.3253    0.2928    0.4109    0.1282    1.4768    0.0399    0.8886    0.0871</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 194:    92.4900   -5.5824   -0.0372    2.4648   -0.9451    0.4891    2.6428    0.2899    0.4160    0.1331    1.5124    0.0412    0.9340    0.0853</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 195:    92.4685   -5.7658   -0.0357    2.4659   -0.9217    0.4549    3.3767    0.2921    0.4171    0.1552    1.5062    0.0396    1.0336    0.0817</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 196:    92.4125   -5.7544   -0.0255    2.4834   -0.9230    0.4146    3.1963    0.3000    0.4369    0.1501    1.4560    0.0430    0.9942    0.0813</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 197:    92.3284   -5.9347   -0.0305    2.4860   -0.9448    0.4115    3.5642    0.3129    0.4314    0.1740    1.3441    0.0473    1.0954    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 198:    92.3428   -5.9255   -0.0266    2.4822   -0.9463    0.3258    3.4818    0.3185    0.4163    0.1716    1.3868    0.0447    0.9452    0.0788</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 199:    92.1692   -6.0683   -0.0227    2.4860   -0.9495    0.2685    4.4044    0.3149    0.4064    0.1824    1.3558    0.0478    1.0294    0.0749</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 200:    92.0965   -6.1541   -0.0261    2.4793   -0.9288    0.2210    4.4026    0.3265    0.4200    0.1772    1.3348    0.0441    1.0018    0.0761</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 201:    92.1362   -6.1170   -0.0244    2.4776   -0.9237    0.1801    4.4160    0.3276    0.4303    0.1732    1.3479    0.0418    0.9419    0.0809</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 202:    92.1114   -6.0898   -0.0233    2.4862   -0.9253    0.1506    4.4494    0.3286    0.4305    0.1656    1.3473    0.0440    0.9394    0.0802</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 203:    92.0893   -6.1307   -0.0233    2.4894   -0.9258    0.1510    4.7958    0.3310    0.4233    0.1673    1.3396    0.0457    0.9617    0.0788</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 204:    92.0870   -6.0914   -0.0230    2.4867   -0.9234    0.1589    4.6265    0.3316    0.4237    0.1643    1.3478    0.0456    0.9509    0.0793</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 205:    92.0800   -6.0960   -0.0228    2.4867   -0.9256    0.1613    4.6319    0.3321    0.4226    0.1623    1.3399    0.0463    0.9547    0.0789</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 206:    92.1037   -6.0869   -0.0238    2.4833   -0.9264    0.1648    4.5547    0.3341    0.4186    0.1606    1.3351    0.0466    0.9453    0.0797</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 207:    92.1212   -6.0561   -0.0247    2.4821   -0.9299    0.1677    4.3736    0.3363    0.4150    0.1602    1.3394    0.0467    0.9552    0.0790</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 208:    92.1203   -6.0351   -0.0260    2.4804   -0.9309    0.1616    4.2520    0.3369    0.4120    0.1613    1.3366    0.0466    0.9667    0.0785</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 209:    92.1169   -6.0133   -0.0277    2.4792   -0.9300    0.1596    4.1237    0.3352    0.4111    0.1616    1.3344    0.0464    0.9741    0.0778</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 210:    92.1195   -5.9866   -0.0289    2.4752   -0.9287    0.1586    3.9853    0.3352    0.4091    0.1602    1.3410    0.0461    0.9708    0.0778</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 211:    92.1243   -5.9458   -0.0316    2.4687   -0.9287    0.1606    3.8383    0.3366    0.4094    0.1607    1.3525    0.0455    0.9759    0.0778</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 212:    92.1384   -5.9338   -0.0345    2.4640   -0.9280    0.1635    3.7875    0.3375    0.4105    0.1594    1.3589    0.0454    0.9800    0.0776</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 213:    92.1481   -5.9191   -0.0379    2.4587   -0.9269    0.1629    3.7150    0.3375    0.4100    0.1586    1.3673    0.0449    0.9839    0.0775</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 214:    92.1523   -5.9189   -0.0408    2.4542   -0.9270    0.1593    3.7061    0.3375    0.4092    0.1579    1.3731    0.0446    0.9847    0.0773</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 215:    92.1548   -5.9183   -0.0429    2.4499   -0.9269    0.1604    3.7075    0.3369    0.4095    0.1574    1.3765    0.0442    0.9821    0.0775</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 216:    92.1547   -5.9171   -0.0450    2.4455   -0.9270    0.1600    3.6967    0.3368    0.4100    0.1571    1.3811    0.0438    0.9830    0.0776</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 217:    92.1556   -5.9141   -0.0468    2.4422   -0.9281    0.1580    3.6732    0.3361    0.4102    0.1578    1.3864    0.0435    0.9995    0.0770</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 218:    92.1587   -5.9173   -0.0477    2.4395   -0.9287    0.1545    3.6822    0.3351    0.4112    0.1580    1.3882    0.0432    1.0001    0.0772</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 219:    92.1590   -5.9188   -0.0483    2.4374   -0.9303    0.1540    3.6595    0.3341    0.4132    0.1578    1.3889    0.0430    0.9983    0.0775</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 220:    92.1614   -5.9303   -0.0493    2.4353   -0.9321    0.1559    3.6914    0.3336    0.4141    0.1581    1.3914    0.0427    1.0003    0.0775</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 221:    92.1657   -5.9455   -0.0504    2.4328   -0.9344    0.1585    3.7569    0.3329    0.4140    0.1591    1.3938    0.0424    1.0064    0.0773</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 222:    92.1697   -5.9320   -0.0515    2.4306   -0.9358    0.1617    3.6783    0.3321    0.4145    0.1600    1.3992    0.0421    1.0107    0.0772</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 223:    92.1754   -5.9205   -0.0525    2.4280   -0.9375    0.1630    3.6233    0.3313    0.4158    0.1604    1.4072    0.0416    1.0216    0.0770</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 224:    92.1809   -5.9149   -0.0534    2.4264   -0.9386    0.1623    3.5812    0.3306    0.4167    0.1607    1.4130    0.0413    1.0349    0.0764</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 225:    92.1872   -5.9099   -0.0543    2.4243   -0.9390    0.1619    3.5446    0.3299    0.4174    0.1607    1.4152    0.0411    1.0348    0.0766</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 226:    92.1935   -5.9046   -0.0554    2.4224   -0.9396    0.1631    3.5046    0.3299    0.4183    0.1608    1.4157    0.0409    1.0375    0.0766</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 227:    92.2026   -5.8964   -0.0567    2.4198   -0.9400    0.1637    3.4591    0.3303    0.4183    0.1614    1.4144    0.0408    1.0444    0.0763</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 228:    92.2069   -5.8852   -0.0578    2.4175   -0.9407    0.1633    3.4091    0.3308    0.4185    0.1621    1.4144    0.0407    1.0485    0.0763</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 229:    92.2122   -5.8844   -0.0591    2.4145   -0.9418    0.1631    3.3899    0.3314    0.4194    0.1622    1.4173    0.0404    1.0514    0.0763</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 230:    92.2183   -5.8907   -0.0604    2.4116   -0.9416    0.1640    3.4053    0.3319    0.4202    0.1616    1.4200    0.0401    1.0527    0.0764</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 231:    92.2246   -5.8895   -0.0619    2.4087   -0.9422    0.1651    3.3985    0.3323    0.4208    0.1609    1.4234    0.0400    1.0560    0.0762</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 232:    92.2282   -5.8840   -0.0632    2.4058   -0.9423    0.1641    3.3771    0.3327    0.4215    0.1609    1.4266    0.0398    1.0585    0.0762</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 233:    92.2294   -5.8775   -0.0645    2.4032   -0.9424    0.1620    3.3481    0.3335    0.4216    0.1607    1.4311    0.0395    1.0600    0.0763</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 234:    92.2311   -5.8709   -0.0658    2.4006   -0.9421    0.1626    3.3242    0.3343    0.4220    0.1602    1.4340    0.0393    1.0604    0.0764</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 235:    92.2312   -5.8656   -0.0668    2.3985   -0.9420    0.1608    3.3023    0.3350    0.4225    0.1596    1.4381    0.0391    1.0600    0.0765</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 236:    92.2289   -5.8633   -0.0675    2.3974   -0.9423    0.1599    3.2811    0.3352    0.4227    0.1589    1.4392    0.0390    1.0594    0.0765</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 237:    92.2251   -5.8641   -0.0683    2.3960   -0.9435    0.1586    3.2734    0.3351    0.4226    0.1588    1.4403    0.0389    1.0623    0.0765</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 238:    92.2242   -5.8633   -0.0690    2.3949   -0.9443    0.1579    3.2547    0.3349    0.4230    0.1590    1.4409    0.0389    1.0659    0.0765</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 239:    92.2250   -5.8612   -0.0693    2.3951   -0.9449    0.1558    3.2309    0.3346    0.4234    0.1599    1.4396    0.0389    1.0740    0.0762</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 240:    92.2264   -5.8597   -0.0696    2.3949   -0.9446    0.1550    3.2124    0.3342    0.4234    0.1603    1.4401    0.0388    1.0791    0.0760</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 241:    92.2281   -5.8562   -0.0699    2.3948   -0.9449    0.1544    3.1933    0.3339    0.4236    0.1604    1.4411    0.0388    1.0825    0.0760</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 242:    92.2289   -5.8512   -0.0703    2.3942   -0.9450    0.1534    3.1709    0.3338    0.4235    0.1606    1.4421    0.0388    1.0870    0.0760</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 243:    92.2296   -5.8489   -0.0708    2.3934   -0.9446    0.1531    3.1689    0.3342    0.4230    0.1605    1.4423    0.0387    1.0876    0.0760</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 244:    92.2311   -5.8446   -0.0715    2.3929   -0.9443    0.1528    3.1630    0.3349    0.4228    0.1604    1.4429    0.0387    1.0916    0.0759</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 245:    92.2338   -5.8421   -0.0718    2.3924   -0.9443    0.1528    3.1616    0.3352    0.4224    0.1610    1.4438    0.0387    1.0980    0.0755</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 246:    92.2361   -5.8389   -0.0721    2.3918   -0.9439    0.1525    3.1525    0.3354    0.4216    0.1620    1.4438    0.0388    1.1018    0.0753</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 247:    92.2374   -5.8388   -0.0724    2.3917   -0.9434    0.1510    3.1605    0.3356    0.4212    0.1629    1.4438    0.0389    1.1050    0.0751</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 248:    92.2360   -5.8367   -0.0728    2.3918   -0.9432    0.1505    3.1559    0.3360    0.4207    0.1638    1.4437    0.0389    1.1090    0.0748</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 249:    92.2361   -5.8351   -0.0732    2.3914   -0.9435    0.1499    3.1521    0.3363    0.4204    0.1646    1.4433    0.0389    1.1117    0.0748</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 250:    92.2353   -5.8349   -0.0733    2.3906   -0.9436    0.1502    3.1607    0.3365    0.4202    0.1646    1.4457    0.0388    1.1107    0.0749</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 251:    92.2343   -5.8318   -0.0736    2.3903   -0.9430    0.1494    3.1513    0.3367    0.4201    0.1648    1.4453    0.0387    1.1093    0.0750</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 252:    92.2356   -5.8244   -0.0739    2.3895   -0.9424    0.1477    3.1240    0.3369    0.4200    0.1651    1.4460    0.0386    1.1083    0.0750</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 253:    92.2367   -5.8188   -0.0742    2.3890   -0.9423    0.1465    3.1025    0.3369    0.4200    0.1649    1.4477    0.0385    1.1092    0.0750</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 254:    92.2392   -5.8154   -0.0747    2.3880   -0.9421    0.1458    3.0888    0.3372    0.4195    0.1644    1.4494    0.0384    1.1080    0.0750</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 255:    92.2404   -5.8131   -0.0751    2.3870   -0.9417    0.1451    3.0778    0.3375    0.4191    0.1639    1.4501    0.0383    1.1070    0.0751</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 256:    92.2408   -5.8109   -0.0753    2.3867   -0.9413    0.1445    3.0740    0.3376    0.4192    0.1633    1.4510    0.0382    1.1064    0.0751</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 257:    92.2413   -5.8066   -0.0754    2.3866   -0.9409    0.1446    3.0636    0.3376    0.4191    0.1628    1.4522    0.0382    1.1080    0.0750</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 258:    92.2412   -5.8035   -0.0754    2.3869   -0.9404    0.1436    3.0502    0.3372    0.4191    0.1623    1.4534    0.0381    1.1057    0.0751</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 259:    92.2399   -5.7996   -0.0753    2.3871   -0.9399    0.1427    3.0357    0.3370    0.4195    0.1621    1.4540    0.0380    1.1037    0.0752</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 260:    92.2389   -5.7955   -0.0755    2.3872   -0.9391    0.1420    3.0251    0.3366    0.4199    0.1619    1.4555    0.0380    1.1041    0.0752</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 261:    92.2378   -5.7941   -0.0755    2.3875   -0.9385    0.1412    3.0240    0.3362    0.4201    0.1613    1.4575    0.0380    1.1048    0.0751</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 262:    92.2361   -5.7920   -0.0756    2.3879   -0.9379    0.1407    3.0182    0.3358    0.4205    0.1607    1.4581    0.0381    1.1047    0.0750</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 263:    92.2338   -5.7891   -0.0754    2.3883   -0.9375    0.1407    3.0072    0.3356    0.4211    0.1603    1.4585    0.0381    1.1041    0.0750</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 264:    92.2313   -5.7882   -0.0752    2.3886   -0.9371    0.1407    3.0009    0.3355    0.4217    0.1601    1.4574    0.0381    1.1036    0.0750</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 265:    92.2312   -5.7843   -0.0752    2.3883   -0.9372    0.1402    2.9872    0.3358    0.4220    0.1599    1.4577    0.0381    1.1026    0.0750</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 266:    92.2312   -5.7800   -0.0753    2.3880   -0.9370    0.1396    2.9708    0.3363    0.4223    0.1597    1.4584    0.0381    1.1019    0.0750</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 267:    92.2306   -5.7785   -0.0755    2.3878   -0.9372    0.1397    2.9619    0.3368    0.4227    0.1595    1.4578    0.0381    1.1021    0.0750</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 268:    92.2287   -5.7803   -0.0758    2.3870   -0.9372    0.1394    2.9777    0.3375    0.4226    0.1594    1.4578    0.0380    1.1032    0.0749</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 269:    92.2265   -5.7804   -0.0761    2.3865   -0.9371    0.1399    2.9816    0.3382    0.4226    0.1592    1.4589    0.0380    1.1058    0.0747</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 270:    92.2236   -5.7811   -0.0765    2.3860   -0.9369    0.1411    2.9893    0.3386    0.4227    0.1591    1.4597    0.0380    1.1081    0.0745</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 271:    92.2211   -5.7793   -0.0769    2.3852   -0.9365    0.1421    2.9888    0.3390    0.4228    0.1591    1.4606    0.0379    1.1083    0.0745</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 272:    92.2187   -5.7773   -0.0773    2.3845   -0.9361    0.1423    2.9810    0.3396    0.4232    0.1589    1.4617    0.0378    1.1076    0.0746</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 273:    92.2164   -5.7769   -0.0776    2.3838   -0.9356    0.1427    2.9809    0.3402    0.4240    0.1589    1.4615    0.0378    1.1073    0.0746</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 274:    92.2145   -5.7763   -0.0778    2.3836   -0.9355    0.1434    2.9795    0.3407    0.4248    0.1589    1.4615    0.0378    1.1069    0.0746</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 275:    92.2133   -5.7762   -0.0779    2.3836   -0.9352    0.1436    2.9837    0.3410    0.4253    0.1589    1.4621    0.0378    1.1064    0.0746</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 276:    92.2123   -5.7764   -0.0781    2.3835   -0.9348    0.1431    2.9889    0.3414    0.4255    0.1589    1.4632    0.0378    1.1073    0.0746</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 277:    92.2113   -5.7771   -0.0781    2.3839   -0.9347    0.1423    2.9925    0.3423    0.4264    0.1589    1.4630    0.0378    1.1104    0.0744</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 278:    92.2099   -5.7774   -0.0780    2.3841   -0.9349    0.1418    2.9927    0.3429    0.4270    0.1590    1.4634    0.0378    1.1127    0.0742</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 279:    92.2087   -5.7798   -0.0779    2.3844   -0.9352    0.1413    2.9997    0.3437    0.4276    0.1590    1.4630    0.0378    1.1133    0.0742</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 280:    92.2077   -5.7802   -0.0778    2.3841   -0.9358    0.1407    2.9971    0.3445    0.4284    0.1586    1.4634    0.0378    1.1136    0.0742</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 281:    92.2061   -5.7814   -0.0777    2.3837   -0.9362    0.1401    3.0004    0.3452    0.4291    0.1582    1.4629    0.0378    1.1133    0.0743</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 282:    92.2058   -5.7802   -0.0777    2.3834   -0.9363    0.1386    2.9994    0.3459    0.4297    0.1579    1.4634    0.0378    1.1126    0.0743</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 283:    92.2057   -5.7780   -0.0778    2.3828   -0.9361    0.1376    2.9917    0.3469    0.4298    0.1576    1.4645    0.0378    1.1147    0.0742</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 284:    92.2051   -5.7788   -0.0780    2.3824   -0.9360    0.1367    2.9968    0.3479    0.4300    0.1573    1.4647    0.0378    1.1149    0.0741</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 285:    92.2041   -5.7793   -0.0782    2.3821   -0.9362    0.1359    2.9941    0.3487    0.4302    0.1573    1.4653    0.0378    1.1164    0.0740</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 286:    92.2040   -5.7803   -0.0783    2.3819   -0.9364    0.1352    2.9957    0.3495    0.4304    0.1573    1.4655    0.0378    1.1180    0.0738</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 287:    92.2044   -5.7809   -0.0784    2.3817   -0.9365    0.1348    2.9961    0.3502    0.4304    0.1572    1.4657    0.0379    1.1178    0.0738</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 288:    92.2043   -5.7835   -0.0785    2.3814   -0.9366    0.1350    3.0073    0.3509    0.4304    0.1572    1.4658    0.0379    1.1184    0.0738</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 289:    92.2041   -5.7845   -0.0787    2.3809   -0.9364    0.1347    3.0126    0.3516    0.4304    0.1571    1.4653    0.0379    1.1190    0.0737</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 290:    92.2032   -5.7852   -0.0788    2.3807   -0.9363    0.1349    3.0161    0.3521    0.4304    0.1568    1.4656    0.0379    1.1191    0.0737</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 291:    92.2019   -5.7848   -0.0790    2.3802   -0.9363    0.1356    3.0155    0.3528    0.4300    0.1565    1.4664    0.0380    1.1210    0.0736</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 292:    92.2012   -5.7846   -0.0792    2.3799   -0.9363    0.1361    3.0167    0.3535    0.4296    0.1563    1.4670    0.0380    1.1223    0.0735</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 293:    92.2007   -5.7845   -0.0793    2.3795   -0.9363    0.1365    3.0175    0.3542    0.4291    0.1561    1.4681    0.0380    1.1236    0.0734</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 294:    92.2009   -5.7850   -0.0795    2.3791   -0.9360    0.1365    3.0189    0.3550    0.4287    0.1560    1.4682    0.0380    1.1243    0.0733</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 295:    92.2009   -5.7865   -0.0797    2.3786   -0.9360    0.1360    3.0279    0.3556    0.4286    0.1558    1.4689    0.0380    1.1243    0.0734</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 296:    92.2007   -5.7878   -0.0798    2.3781   -0.9362    0.1358    3.0345    0.3562    0.4283    0.1555    1.4694    0.0379    1.1247    0.0734</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 297:    92.1992   -5.7891   -0.0801    2.3774   -0.9364    0.1358    3.0403    0.3568    0.4278    0.1554    1.4699    0.0379    1.1267    0.0734</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 298:    92.1978   -5.7892   -0.0803    2.3768   -0.9366    0.1357    3.0383    0.3573    0.4274    0.1553    1.4706    0.0378    1.1276    0.0734</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 299:    92.1968   -5.7906   -0.0805    2.3763   -0.9369    0.1353    3.0408    0.3579    0.4269    0.1553    1.4712    0.0378    1.1282    0.0733</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 300:    92.1954   -5.7929   -0.0807    2.3760   -0.9369    0.1352    3.0477    0.3583    0.4265    0.1551    1.4716    0.0379    1.1286    0.0733</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 301:    92.1941   -5.7934   -0.0809    2.3757   -0.9369    0.1352    3.0483    0.3588    0.4261    0.1548    1.4727    0.0378    1.1288    0.0732</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 302:    92.1929   -5.7949   -0.0811    2.3754   -0.9370    0.1354    3.0560    0.3592    0.4256    0.1548    1.4728    0.0379    1.1296    0.0731</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 303:    92.1919   -5.7972   -0.0813    2.3751   -0.9368    0.1352    3.0671    0.3597    0.4250    0.1550    1.4730    0.0379    1.1302    0.0731</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 304:    92.1906   -5.8018   -0.0814    2.3750   -0.9368    0.1349    3.0935    0.3602    0.4245    0.1552    1.4731    0.0379    1.1314    0.0730</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 305:    92.1897   -5.8063   -0.0817    2.3744   -0.9370    0.1350    3.1211    0.3606    0.4238    0.1554    1.4727    0.0379    1.1323    0.0730</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 306:    92.1896   -5.8116   -0.0820    2.3740   -0.9373    0.1347    3.1571    0.3610    0.4233    0.1555    1.4727    0.0379    1.1351    0.0728</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 307:    92.1895   -5.8156   -0.0822    2.3735   -0.9373    0.1341    3.1826    0.3613    0.4226    0.1552    1.4741    0.0379    1.1374    0.0726</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 308:    92.1899   -5.8202   -0.0824    2.3732   -0.9376    0.1338    3.2124    0.3617    0.4220    0.1554    1.4745    0.0379    1.1408    0.0724</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 309:    92.1903   -5.8232   -0.0827    2.3728   -0.9377    0.1337    3.2330    0.3620    0.4213    0.1554    1.4749    0.0379    1.1419    0.0724</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 310:    92.1902   -5.8249   -0.0828    2.3727   -0.9378    0.1335    3.2463    0.3621    0.4207    0.1554    1.4752    0.0379    1.1435    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 311:    92.1910   -5.8267   -0.0828    2.3726   -0.9378    0.1335    3.2581    0.3623    0.4200    0.1554    1.4754    0.0379    1.1441    0.0722</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 312:    92.1918   -5.8271   -0.0829    2.3726   -0.9378    0.1333    3.2567    0.3624    0.4195    0.1552    1.4751    0.0380    1.1432    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 313:    92.1926   -5.8260   -0.0829    2.3725   -0.9380    0.1334    3.2497    0.3626    0.4190    0.1552    1.4751    0.0380    1.1434    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 314:    92.1934   -5.8256   -0.0829    2.3724   -0.9381    0.1330    3.2426    0.3628    0.4185    0.1553    1.4746    0.0380    1.1441    0.0722</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 315:    92.1938   -5.8237   -0.0829    2.3724   -0.9384    0.1326    3.2327    0.3630    0.4179    0.1555    1.4746    0.0380    1.1468    0.0721</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 316:    92.1950   -5.8235   -0.0829    2.3722   -0.9385    0.1324    3.2283    0.3631    0.4172    0.1557    1.4745    0.0380    1.1476    0.0721</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 317:    92.1963   -5.8239   -0.0829    2.3722   -0.9385    0.1322    3.2260    0.3633    0.4167    0.1560    1.4741    0.0380    1.1481    0.0721</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 318:    92.1975   -5.8242   -0.0829    2.3722   -0.9387    0.1321    3.2240    0.3634    0.4163    0.1559    1.4740    0.0380    1.1477    0.0722</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 319:    92.1990   -5.8250   -0.0829    2.3721   -0.9387    0.1320    3.2215    0.3634    0.4159    0.1560    1.4736    0.0381    1.1496    0.0720</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 320:    92.2003   -5.8256   -0.0829    2.3722   -0.9386    0.1322    3.2199    0.3635    0.4155    0.1561    1.4730    0.0381    1.1508    0.0719</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 321:    92.2018   -5.8249   -0.0829    2.3723   -0.9386    0.1326    3.2150    0.3635    0.4151    0.1562    1.4727    0.0381    1.1514    0.0718</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 322:    92.2031   -5.8239   -0.0829    2.3724   -0.9386    0.1333    3.2081    0.3635    0.4147    0.1561    1.4729    0.0381    1.1527    0.0717</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 323:    92.2045   -5.8244   -0.0829    2.3725   -0.9384    0.1336    3.2065    0.3635    0.4143    0.1562    1.4729    0.0381    1.1541    0.0716</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 324:    92.2060   -5.8228   -0.0828    2.3726   -0.9383    0.1339    3.1996    0.3635    0.4139    0.1562    1.4729    0.0382    1.1562    0.0714</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 325:    92.2071   -5.8212   -0.0828    2.3727   -0.9383    0.1339    3.1921    0.3636    0.4135    0.1563    1.4733    0.0382    1.1580    0.0713</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 326:    92.2083   -5.8201   -0.0826    2.3728   -0.9382    0.1340    3.1888    0.3639    0.4134    0.1562    1.4734    0.0382    1.1587    0.0713</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 327:    92.2095   -5.8185   -0.0825    2.3729   -0.9379    0.1340    3.1831    0.3641    0.4134    0.1562    1.4737    0.0382    1.1592    0.0713</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 328:    92.2105   -5.8188   -0.0823    2.3731   -0.9379    0.1338    3.1836    0.3643    0.4132    0.1561    1.4736    0.0383    1.1589    0.0713</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 329:    92.2115   -5.8182   -0.0821    2.3732   -0.9381    0.1339    3.1807    0.3646    0.4129    0.1561    1.4737    0.0383    1.1585    0.0713</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 330:    92.2127   -5.8181   -0.0819    2.3734   -0.9381    0.1338    3.1827    0.3648    0.4127    0.1563    1.4735    0.0383    1.1578    0.0714</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 331:    92.2134   -5.8180   -0.0815    2.3739   -0.9381    0.1340    3.1837    0.3647    0.4128    0.1566    1.4735    0.0383    1.1575    0.0714</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 332:    92.2136   -5.8184   -0.0811    2.3746   -0.9379    0.1340    3.1847    0.3647    0.4130    0.1570    1.4728    0.0384    1.1577    0.0714</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 333:    92.2133   -5.8180   -0.0807    2.3753   -0.9376    0.1340    3.1853    0.3646    0.4132    0.1572    1.4725    0.0384    1.1573    0.0714</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 334:    92.2136   -5.8173   -0.0803    2.3760   -0.9374    0.1342    3.1838    0.3645    0.4133    0.1576    1.4720    0.0385    1.1570    0.0714</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 335:    92.2139   -5.8187   -0.0800    2.3769   -0.9372    0.1344    3.1947    0.3643    0.4135    0.1576    1.4714    0.0386    1.1567    0.0714</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 336:    92.2134   -5.8198   -0.0796    2.3777   -0.9370    0.1348    3.2008    0.3641    0.4135    0.1577    1.4706    0.0387    1.1557    0.0714</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 337:    92.2130   -5.8201   -0.0792    2.3784   -0.9370    0.1357    3.2050    0.3640    0.4137    0.1579    1.4703    0.0388    1.1558    0.0714</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 338:    92.2130   -5.8190   -0.0787    2.3791   -0.9368    0.1362    3.2036    0.3638    0.4139    0.1580    1.4708    0.0388    1.1558    0.0714</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 339:    92.2132   -5.8177   -0.0783    2.3798   -0.9368    0.1369    3.2006    0.3637    0.4142    0.1581    1.4712    0.0388    1.1551    0.0714</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 340:    92.2137   -5.8167   -0.0778    2.3806   -0.9366    0.1376    3.1984    0.3636    0.4143    0.1581    1.4712    0.0388    1.1540    0.0715</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 341:    92.2141   -5.8145   -0.0773    2.3814   -0.9364    0.1378    3.1916    0.3635    0.4142    0.1581    1.4712    0.0389    1.1529    0.0715</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 342:    92.2142   -5.8123   -0.0769    2.3821   -0.9364    0.1383    3.1840    0.3634    0.4142    0.1581    1.4718    0.0389    1.1513    0.0716</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 343:    92.2139   -5.8109   -0.0764    2.3830   -0.9364    0.1389    3.1806    0.3632    0.4142    0.1581    1.4721    0.0389    1.1501    0.0716</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 344:    92.2137   -5.8117   -0.0759    2.3839   -0.9366    0.1390    3.1830    0.3631    0.4144    0.1582    1.4711    0.0390    1.1492    0.0717</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 345:    92.2133   -5.8118   -0.0754    2.3849   -0.9368    0.1391    3.1827    0.3630    0.4146    0.1582    1.4703    0.0391    1.1488    0.0717</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 346:    92.2127   -5.8113   -0.0748    2.3858   -0.9369    0.1389    3.1793    0.3629    0.4147    0.1581    1.4700    0.0391    1.1475    0.0717</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 347:    92.2121   -5.8107   -0.0743    2.3867   -0.9371    0.1386    3.1748    0.3628    0.4149    0.1579    1.4701    0.0392    1.1463    0.0718</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 348:    92.2106   -5.8109   -0.0738    2.3876   -0.9374    0.1385    3.1726    0.3626    0.4151    0.1577    1.4704    0.0392    1.1453    0.0719</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 349:    92.2096   -5.8111   -0.0732    2.3883   -0.9377    0.1382    3.1703    0.3626    0.4151    0.1575    1.4705    0.0392    1.1448    0.0719</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 350:    92.2088   -5.8108   -0.0727    2.3890   -0.9378    0.1380    3.1674    0.3625    0.4152    0.1574    1.4704    0.0392    1.1439    0.0720</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 351:    92.2077   -5.8103   -0.0722    2.3899   -0.9379    0.1379    3.1634    0.3623    0.4154    0.1572    1.4701    0.0393    1.1432    0.0720</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 352:    92.2069   -5.8103   -0.0718    2.3906   -0.9381    0.1380    3.1626    0.3623    0.4154    0.1570    1.4701    0.0393    1.1425    0.0721</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 353:    92.2058   -5.8107   -0.0714    2.3913   -0.9381    0.1382    3.1629    0.3624    0.4154    0.1570    1.4695    0.0394    1.1426    0.0720</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 354:    92.2046   -5.8102   -0.0710    2.3921   -0.9381    0.1384    3.1576    0.3624    0.4154    0.1571    1.4691    0.0394    1.1424    0.0720</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 355:    92.2034   -5.8084   -0.0707    2.3928   -0.9381    0.1388    3.1501    0.3624    0.4154    0.1570    1.4686    0.0395    1.1414    0.0721</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 356:    92.2027   -5.8079   -0.0703    2.3935   -0.9382    0.1392    3.1463    0.3626    0.4155    0.1569    1.4682    0.0396    1.1405    0.0721</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 357:    92.2019   -5.8084   -0.0698    2.3943   -0.9382    0.1390    3.1444    0.3628    0.4156    0.1569    1.4665    0.0397    1.1403    0.0721</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 358:    92.2014   -5.8085   -0.0694    2.3950   -0.9381    0.1389    3.1413    0.3630    0.4158    0.1569    1.4662    0.0398    1.1398    0.0722</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 359:    92.2005   -5.8091   -0.0689    2.3956   -0.9383    0.1387    3.1393    0.3633    0.4159    0.1570    1.4664    0.0398    1.1401    0.0722</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 360:    92.1994   -5.8091   -0.0685    2.3962   -0.9385    0.1385    3.1362    0.3635    0.4159    0.1572    1.4664    0.0398    1.1413    0.0722</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 361:    92.1983   -5.8094   -0.0682    2.3966   -0.9386    0.1384    3.1340    0.3638    0.4159    0.1573    1.4669    0.0398    1.1420    0.0722</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 362:    92.1976   -5.8086   -0.0678    2.3971   -0.9389    0.1380    3.1277    0.3639    0.4160    0.1573    1.4671    0.0398    1.1414    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 363:    92.1967   -5.8081   -0.0675    2.3976   -0.9389    0.1377    3.1239    0.3641    0.4161    0.1573    1.4669    0.0399    1.1412    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 364:    92.1960   -5.8073   -0.0671    2.3982   -0.9390    0.1373    3.1208    0.3643    0.4161    0.1572    1.4668    0.0399    1.1411    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 365:    92.1958   -5.8066   -0.0667    2.3988   -0.9389    0.1369    3.1164    0.3645    0.4160    0.1572    1.4672    0.0399    1.1416    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 366:    92.1956   -5.8063   -0.0664    2.3992   -0.9390    0.1364    3.1127    0.3650    0.4156    0.1573    1.4674    0.0399    1.1425    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 367:    92.1952   -5.8056   -0.0661    2.3996   -0.9389    0.1361    3.1082    0.3652    0.4155    0.1574    1.4675    0.0399    1.1416    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 368:    92.1948   -5.8059   -0.0658    2.4001   -0.9389    0.1359    3.1068    0.3655    0.4154    0.1575    1.4671    0.0399    1.1406    0.0724</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 369:    92.1948   -5.8064   -0.0655    2.4005   -0.9388    0.1360    3.1055    0.3658    0.4152    0.1576    1.4669    0.0399    1.1408    0.0724</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 370:    92.1951   -5.8072   -0.0652    2.4010   -0.9389    0.1361    3.1060    0.3660    0.4151    0.1576    1.4669    0.0399    1.1406    0.0724</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 371:    92.1956   -5.8080   -0.0649    2.4015   -0.9390    0.1362    3.1095    0.3662    0.4150    0.1576    1.4669    0.0400    1.1411    0.0724</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 372:    92.1962   -5.8096   -0.0645    2.4020   -0.9390    0.1363    3.1168    0.3665    0.4149    0.1576    1.4667    0.0400    1.1411    0.0724</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 373:    92.1968   -5.8098   -0.0642    2.4025   -0.9392    0.1363    3.1154    0.3666    0.4147    0.1576    1.4664    0.0400    1.1418    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 374:    92.1972   -5.8098   -0.0640    2.4029   -0.9395    0.1363    3.1117    0.3667    0.4146    0.1576    1.4661    0.0401    1.1421    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 375:    92.1979   -5.8101   -0.0637    2.4032   -0.9397    0.1364    3.1090    0.3668    0.4143    0.1577    1.4654    0.0401    1.1425    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 376:    92.1981   -5.8106   -0.0635    2.4035   -0.9399    0.1363    3.1084    0.3669    0.4142    0.1576    1.4650    0.0401    1.1423    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 377:    92.1986   -5.8104   -0.0633    2.4039   -0.9399    0.1360    3.1053    0.3670    0.4142    0.1576    1.4645    0.0402    1.1424    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 378:    92.1989   -5.8104   -0.0631    2.4043   -0.9399    0.1358    3.1026    0.3671    0.4142    0.1577    1.4638    0.0402    1.1421    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 379:    92.1990   -5.8114   -0.0628    2.4047   -0.9399    0.1355    3.1068    0.3671    0.4141    0.1577    1.4636    0.0402    1.1413    0.0724</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 380:    92.1989   -5.8119   -0.0627    2.4048   -0.9400    0.1351    3.1095    0.3673    0.4136    0.1577    1.4634    0.0402    1.1413    0.0724</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 381:    92.1989   -5.8127   -0.0627    2.4048   -0.9400    0.1349    3.1152    0.3676    0.4131    0.1577    1.4631    0.0403    1.1408    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 382:    92.1994   -5.8126   -0.0627    2.4049   -0.9399    0.1348    3.1188    0.3678    0.4126    0.1576    1.4635    0.0403    1.1403    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 383:    92.2000   -5.8129   -0.0626    2.4049   -0.9397    0.1346    3.1256    0.3681    0.4120    0.1576    1.4637    0.0403    1.1396    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 384:    92.2006   -5.8133   -0.0626    2.4050   -0.9396    0.1347    3.1290    0.3683    0.4115    0.1575    1.4641    0.0402    1.1389    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 385:    92.2015   -5.8131   -0.0626    2.4050   -0.9394    0.1350    3.1287    0.3686    0.4110    0.1575    1.4642    0.0402    1.1382    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 386:    92.2023   -5.8140   -0.0625    2.4049   -0.9391    0.1349    3.1324    0.3690    0.4104    0.1574    1.4647    0.0402    1.1374    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 387:    92.2030   -5.8147   -0.0625    2.4050   -0.9390    0.1349    3.1345    0.3693    0.4099    0.1572    1.4652    0.0402    1.1366    0.0724</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 388:    92.2040   -5.8157   -0.0625    2.4050   -0.9389    0.1349    3.1381    0.3696    0.4094    0.1570    1.4652    0.0402    1.1362    0.0724</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 389:    92.2051   -5.8156   -0.0625    2.4051   -0.9387    0.1349    3.1373    0.3699    0.4090    0.1570    1.4653    0.0402    1.1354    0.0724</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 390:    92.2063   -5.8152   -0.0625    2.4051   -0.9386    0.1349    3.1350    0.3702    0.4087    0.1570    1.4655    0.0402    1.1345    0.0725</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 391:    92.2076   -5.8160   -0.0625    2.4051   -0.9384    0.1350    3.1380    0.3705    0.4083    0.1571    1.4656    0.0402    1.1341    0.0725</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 392:    92.2086   -5.8166   -0.0626    2.4049   -0.9384    0.1349    3.1397    0.3707    0.4081    0.1572    1.4658    0.0402    1.1345    0.0725</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 393:    92.2095   -5.8168   -0.0626    2.4047   -0.9383    0.1348    3.1405    0.3708    0.4080    0.1573    1.4659    0.0402    1.1344    0.0725</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 394:    92.2107   -5.8174   -0.0627    2.4045   -0.9382    0.1347    3.1433    0.3710    0.4079    0.1575    1.4660    0.0401    1.1342    0.0725</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 395:    92.2118   -5.8174   -0.0628    2.4043   -0.9380    0.1346    3.1437    0.3711    0.4078    0.1575    1.4661    0.0401    1.1343    0.0725</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 396:    92.2127   -5.8170   -0.0629    2.4041   -0.9379    0.1344    3.1424    0.3712    0.4077    0.1575    1.4664    0.0401    1.1342    0.0725</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 397:    92.2137   -5.8161   -0.0630    2.4040   -0.9376    0.1342    3.1386    0.3713    0.4076    0.1577    1.4667    0.0401    1.1341    0.0725</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 398:    92.2146   -5.8152   -0.0633    2.4036   -0.9374    0.1341    3.1344    0.3713    0.4075    0.1578    1.4671    0.0401    1.1334    0.0725</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 399:    92.2157   -5.8138   -0.0635    2.4032   -0.9373    0.1340    3.1296    0.3714    0.4074    0.1578    1.4678    0.0401    1.1336    0.0726</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 400:    92.2165   -5.8131   -0.0638    2.4027   -0.9372    0.1340    3.1262    0.3715    0.4072    0.1579    1.4681    0.0400    1.1332    0.0726</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 401:    92.2173   -5.8118   -0.0641    2.4022   -0.9372    0.1341    3.1220    0.3716    0.4069    0.1579    1.4686    0.0400    1.1339    0.0726</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 402:    92.2181   -5.8107   -0.0643    2.4018   -0.9370    0.1344    3.1192    0.3718    0.4065    0.1580    1.4694    0.0400    1.1338    0.0726</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 403:    92.2190   -5.8099   -0.0646    2.4013   -0.9371    0.1348    3.1166    0.3720    0.4061    0.1581    1.4700    0.0400    1.1344    0.0725</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 404:    92.2198   -5.8104   -0.0649    2.4008   -0.9372    0.1348    3.1161    0.3723    0.4058    0.1582    1.4704    0.0399    1.1357    0.0725</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 405:    92.2203   -5.8107   -0.0652    2.4004   -0.9372    0.1348    3.1177    0.3725    0.4055    0.1582    1.4705    0.0400    1.1358    0.0724</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 406:    92.2204   -5.8103   -0.0653    2.4002   -0.9371    0.1348    3.1157    0.3724    0.4051    0.1582    1.4709    0.0400    1.1361    0.0724</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 407:    92.2201   -5.8094   -0.0655    2.4001   -0.9369    0.1348    3.1121    0.3724    0.4048    0.1583    1.4713    0.0400    1.1360    0.0724</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 408:    92.2201   -5.8087   -0.0657    2.3999   -0.9368    0.1350    3.1085    0.3724    0.4044    0.1582    1.4714    0.0400    1.1360    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 409:    92.2201   -5.8083   -0.0658    2.3998   -0.9365    0.1355    3.1073    0.3724    0.4043    0.1582    1.4713    0.0401    1.1358    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 410:    92.2196   -5.8094   -0.0659    2.3996   -0.9365    0.1361    3.1123    0.3724    0.4041    0.1582    1.4712    0.0401    1.1357    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 411:    92.2192   -5.8099   -0.0661    2.3994   -0.9366    0.1363    3.1168    0.3724    0.4038    0.1582    1.4715    0.0401    1.1354    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 412:    92.2187   -5.8105   -0.0662    2.3993   -0.9365    0.1365    3.1215    0.3725    0.4035    0.1582    1.4716    0.0401    1.1359    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 413:    92.2185   -5.8109   -0.0663    2.3992   -0.9365    0.1367    3.1268    0.3725    0.4031    0.1583    1.4719    0.0401    1.1357    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 414:    92.2183   -5.8111   -0.0665    2.3987   -0.9365    0.1370    3.1286    0.3727    0.4026    0.1582    1.4724    0.0400    1.1349    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 415:    92.2181   -5.8122   -0.0667    2.3983   -0.9366    0.1370    3.1351    0.3729    0.4021    0.1581    1.4726    0.0400    1.1341    0.0724</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 416:    92.2180   -5.8133   -0.0669    2.3979   -0.9367    0.1372    3.1409    0.3731    0.4015    0.1578    1.4734    0.0400    1.1333    0.0724</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 417:    92.2176   -5.8138   -0.0671    2.3976   -0.9368    0.1374    3.1426    0.3733    0.4009    0.1576    1.4739    0.0400    1.1325    0.0724</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 418:    92.2173   -5.8150   -0.0673    2.3973   -0.9369    0.1376    3.1479    0.3735    0.4003    0.1575    1.4740    0.0400    1.1316    0.0725</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 419:    92.2174   -5.8154   -0.0674    2.3970   -0.9369    0.1375    3.1497    0.3736    0.3998    0.1574    1.4741    0.0400    1.1314    0.0724</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 420:    92.2175   -5.8154   -0.0676    2.3968   -0.9369    0.1375    3.1489    0.3737    0.3993    0.1574    1.4742    0.0400    1.1315    0.0724</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 421:    92.2176   -5.8154   -0.0678    2.3964   -0.9369    0.1374    3.1485    0.3739    0.3989    0.1573    1.4743    0.0400    1.1312    0.0724</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 422:    92.2174   -5.8155   -0.0680    2.3961   -0.9370    0.1375    3.1483    0.3741    0.3985    0.1572    1.4744    0.0400    1.1309    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 423:    92.2170   -5.8156   -0.0681    2.3958   -0.9371    0.1375    3.1481    0.3742    0.3980    0.1571    1.4741    0.0400    1.1308    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 424:    92.2169   -5.8168   -0.0683    2.3956   -0.9372    0.1374    3.1524    0.3744    0.3976    0.1571    1.4740    0.0400    1.1316    0.0722</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 425:    92.2167   -5.8171   -0.0685    2.3954   -0.9372    0.1373    3.1520    0.3744    0.3972    0.1571    1.4736    0.0401    1.1317    0.0722</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 426:    92.2164   -5.8170   -0.0687    2.3951   -0.9372    0.1373    3.1502    0.3745    0.3968    0.1570    1.4734    0.0401    1.1320    0.0722</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 427:    92.2166   -5.8169   -0.0688    2.3949   -0.9372    0.1372    3.1480    0.3745    0.3964    0.1570    1.4734    0.0401    1.1317    0.0721</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 428:    92.2166   -5.8170   -0.0689    2.3948   -0.9374    0.1371    3.1460    0.3745    0.3959    0.1570    1.4735    0.0401    1.1315    0.0721</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 429:    92.2162   -5.8171   -0.0691    2.3946   -0.9374    0.1369    3.1446    0.3745    0.3954    0.1570    1.4736    0.0401    1.1316    0.0721</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 430:    92.2158   -5.8178   -0.0692    2.3944   -0.9375    0.1370    3.1464    0.3745    0.3950    0.1570    1.4736    0.0401    1.1314    0.0721</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 431:    92.2153   -5.8180   -0.0693    2.3942   -0.9375    0.1371    3.1470    0.3745    0.3946    0.1570    1.4735    0.0401    1.1314    0.0721</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 432:    92.2150   -5.8182   -0.0695    2.3940   -0.9375    0.1372    3.1477    0.3746    0.3942    0.1570    1.4735    0.0401    1.1315    0.0721</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 433:    92.2145   -5.8189   -0.0696    2.3938   -0.9375    0.1374    3.1512    0.3746    0.3938    0.1571    1.4736    0.0400    1.1323    0.0720</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 434:    92.2141   -5.8186   -0.0697    2.3936   -0.9373    0.1376    3.1530    0.3746    0.3933    0.1571    1.4736    0.0400    1.1330    0.0719</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 435:    92.2133   -5.8187   -0.0699    2.3934   -0.9373    0.1381    3.1571    0.3747    0.3929    0.1570    1.4738    0.0400    1.1325    0.0720</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 436:    92.2129   -5.8180   -0.0700    2.3932   -0.9374    0.1380    3.1544    0.3746    0.3925    0.1570    1.4740    0.0400    1.1332    0.0719</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 437:    92.2122   -5.8186   -0.0702    2.3930   -0.9375    0.1380    3.1574    0.3746    0.3921    0.1570    1.4739    0.0400    1.1342    0.0718</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 438:    92.2114   -5.8187   -0.0703    2.3928   -0.9376    0.1380    3.1583    0.3745    0.3918    0.1569    1.4740    0.0400    1.1346    0.0718</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 439:    92.2104   -5.8181   -0.0705    2.3925   -0.9377    0.1381    3.1568    0.3744    0.3914    0.1568    1.4743    0.0400    1.1352    0.0718</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 440:    92.2095   -5.8178   -0.0706    2.3923   -0.9377    0.1381    3.1555    0.3743    0.3910    0.1566    1.4745    0.0400    1.1349    0.0718</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 441:    92.2088   -5.8176   -0.0707    2.3923   -0.9378    0.1381    3.1559    0.3742    0.3907    0.1565    1.4748    0.0400    1.1349    0.0718</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 442:    92.2081   -5.8172   -0.0708    2.3921   -0.9379    0.1383    3.1539    0.3741    0.3903    0.1564    1.4754    0.0400    1.1350    0.0717</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 443:    92.2074   -5.8171   -0.0709    2.3920   -0.9380    0.1387    3.1526    0.3740    0.3901    0.1563    1.4756    0.0400    1.1349    0.0717</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 444:    92.2068   -5.8175   -0.0711    2.3918   -0.9380    0.1390    3.1533    0.3739    0.3898    0.1562    1.4758    0.0400    1.1353    0.0717</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 445:    92.2061   -5.8180   -0.0712    2.3915   -0.9382    0.1394    3.1529    0.3737    0.3896    0.1562    1.4758    0.0400    1.1355    0.0717</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 446:    92.2054   -5.8177   -0.0714    2.3912   -0.9384    0.1398    3.1496    0.3735    0.3894    0.1562    1.4760    0.0399    1.1367    0.0716</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 447:    92.2051   -5.8180   -0.0716    2.3910   -0.9385    0.1400    3.1484    0.3734    0.3891    0.1563    1.4762    0.0399    1.1370    0.0715</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 448:    92.2053   -5.8189   -0.0717    2.3909   -0.9387    0.1405    3.1499    0.3732    0.3889    0.1563    1.4764    0.0399    1.1377    0.0715</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 449:    92.2054   -5.8195   -0.0718    2.3908   -0.9388    0.1411    3.1497    0.3730    0.3887    0.1562    1.4768    0.0399    1.1382    0.0715</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 450:    92.2054   -5.8205   -0.0719    2.3906   -0.9390    0.1417    3.1528    0.3728    0.3885    0.1562    1.4769    0.0399    1.1378    0.0715</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 451:    92.2053   -5.8213   -0.0720    2.3905   -0.9391    0.1423    3.1544    0.3725    0.3883    0.1561    1.4774    0.0399    1.1379    0.0715</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 452:    92.2054   -5.8218   -0.0720    2.3905   -0.9393    0.1426    3.1541    0.3722    0.3882    0.1560    1.4779    0.0398    1.1378    0.0715</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 453:    92.2054   -5.8217   -0.0721    2.3903   -0.9395    0.1428    3.1530    0.3721    0.3880    0.1559    1.4785    0.0398    1.1378    0.0715</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 454:    92.2053   -5.8214   -0.0722    2.3901   -0.9395    0.1431    3.1508    0.3720    0.3878    0.1559    1.4786    0.0398    1.1375    0.0715</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 455:    92.2052   -5.8220   -0.0724    2.3898   -0.9396    0.1433    3.1519    0.3720    0.3875    0.1559    1.4791    0.0398    1.1379    0.0715</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 456:    92.2053   -5.8230   -0.0727    2.3894   -0.9397    0.1434    3.1544    0.3720    0.3873    0.1560    1.4793    0.0398    1.1384    0.0714</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 457:    92.2053   -5.8240   -0.0729    2.3889   -0.9398    0.1437    3.1568    0.3720    0.3869    0.1559    1.4793    0.0398    1.1393    0.0714</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 458:    92.2051   -5.8245   -0.0731    2.3885   -0.9399    0.1440    3.1571    0.3721    0.3865    0.1558    1.4797    0.0397    1.1404    0.0713</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 459:    92.2045   -5.8246   -0.0732    2.3882   -0.9402    0.1442    3.1562    0.3721    0.3862    0.1558    1.4801    0.0397    1.1407    0.0713</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 460:    92.2040   -5.8244   -0.0734    2.3878   -0.9403    0.1442    3.1536    0.3722    0.3859    0.1558    1.4806    0.0397    1.1406    0.0713</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 461:    92.2030   -5.8245   -0.0736    2.3874   -0.9404    0.1444    3.1517    0.3722    0.3856    0.1557    1.4811    0.0397    1.1412    0.0713</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 462:    92.2022   -5.8253   -0.0738    2.3870   -0.9405    0.1445    3.1531    0.3723    0.3853    0.1556    1.4817    0.0396    1.1425    0.0712</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 463:    92.2014   -5.8260   -0.0740    2.3866   -0.9405    0.1449    3.1545    0.3724    0.3849    0.1556    1.4823    0.0396    1.1441    0.0711</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 464:    92.2008   -5.8257   -0.0742    2.3862   -0.9405    0.1453    3.1522    0.3726    0.3845    0.1555    1.4828    0.0396    1.1451    0.0711</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 465:    92.2002   -5.8256   -0.0744    2.3859   -0.9404    0.1458    3.1511    0.3727    0.3842    0.1555    1.4830    0.0396    1.1459    0.0710</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 466:    92.1997   -5.8256   -0.0747    2.3855   -0.9403    0.1463    3.1516    0.3728    0.3839    0.1555    1.4834    0.0396    1.1476    0.0709</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 467:    92.1993   -5.8258   -0.0749    2.3850   -0.9404    0.1468    3.1521    0.3730    0.3836    0.1555    1.4835    0.0395    1.1490    0.0708</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 468:    92.1990   -5.8259   -0.0752    2.3846   -0.9404    0.1473    3.1546    0.3731    0.3834    0.1555    1.4837    0.0395    1.1500    0.0708</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 469:    92.1987   -5.8263   -0.0753    2.3844   -0.9403    0.1479    3.1598    0.3731    0.3831    0.1555    1.4839    0.0395    1.1504    0.0707</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 470:    92.1987   -5.8267   -0.0755    2.3841   -0.9403    0.1482    3.1611    0.3730    0.3829    0.1555    1.4839    0.0395    1.1496    0.0708</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 471:    92.1987   -5.8269   -0.0756    2.3839   -0.9404    0.1482    3.1627    0.3730    0.3826    0.1555    1.4839    0.0395    1.1492    0.0708</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 472:    92.1983   -5.8269   -0.0758    2.3838   -0.9403    0.1480    3.1618    0.3729    0.3823    0.1555    1.4839    0.0395    1.1493    0.0708</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 473:    92.1980   -5.8264   -0.0760    2.3836   -0.9402    0.1478    3.1602    0.3728    0.3820    0.1554    1.4839    0.0395    1.1489    0.0708</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 474:    92.1976   -5.8256   -0.0762    2.3834   -0.9402    0.1475    3.1565    0.3727    0.3818    0.1554    1.4842    0.0395    1.1487    0.0708</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 475:    92.1971   -5.8252   -0.0763    2.3832   -0.9402    0.1473    3.1543    0.3726    0.3816    0.1553    1.4845    0.0395    1.1487    0.0707</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 476:    92.1965   -5.8250   -0.0765    2.3830   -0.9402    0.1469    3.1523    0.3725    0.3814    0.1552    1.4846    0.0395    1.1484    0.0707</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 477:    92.1960   -5.8242   -0.0766    2.3827   -0.9402    0.1465    3.1483    0.3724    0.3811    0.1552    1.4849    0.0395    1.1483    0.0708</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 478:    92.1955   -5.8236   -0.0767    2.3826   -0.9402    0.1463    3.1447    0.3722    0.3808    0.1553    1.4854    0.0395    1.1481    0.0708</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 479:    92.1952   -5.8232   -0.0769    2.3824   -0.9401    0.1462    3.1421    0.3722    0.3805    0.1554    1.4857    0.0395    1.1478    0.0708</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 480:    92.1948   -5.8235   -0.0770    2.3822   -0.9400    0.1461    3.1426    0.3721    0.3803    0.1554    1.4862    0.0395    1.1478    0.0708</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 481:    92.1947   -5.8240   -0.0772    2.3820   -0.9399    0.1459    3.1455    0.3721    0.3801    0.1554    1.4868    0.0395    1.1483    0.0708</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 482:    92.1948   -5.8244   -0.0774    2.3817   -0.9399    0.1456    3.1476    0.3720    0.3799    0.1553    1.4873    0.0395    1.1488    0.0708</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 483:    92.1944   -5.8247   -0.0776    2.3815   -0.9397    0.1455    3.1487    0.3719    0.3797    0.1553    1.4876    0.0394    1.1494    0.0708</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 484:    92.1941   -5.8249   -0.0778    2.3811   -0.9396    0.1454    3.1493    0.3719    0.3795    0.1554    1.4879    0.0394    1.1501    0.0707</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 485:    92.1940   -5.8252   -0.0780    2.3809   -0.9396    0.1453    3.1501    0.3718    0.3793    0.1554    1.4881    0.0394    1.1503    0.0707</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 486:    92.1936   -5.8249   -0.0781    2.3807   -0.9395    0.1453    3.1486    0.3717    0.3792    0.1554    1.4884    0.0394    1.1508    0.0707</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 487:    92.1935   -5.8248   -0.0783    2.3804   -0.9394    0.1453    3.1485    0.3716    0.3791    0.1553    1.4887    0.0393    1.1507    0.0707</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 488:    92.1932   -5.8246   -0.0785    2.3800   -0.9394    0.1454    3.1478    0.3715    0.3788    0.1552    1.4892    0.0393    1.1510    0.0707</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 489:    92.1931   -5.8241   -0.0787    2.3796   -0.9394    0.1455    3.1468    0.3715    0.3787    0.1551    1.4895    0.0393    1.1514    0.0706</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 490:    92.1934   -5.8242   -0.0790    2.3793   -0.9393    0.1456    3.1478    0.3715    0.3786    0.1551    1.4898    0.0393    1.1528    0.0706</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 491:    92.1936   -5.8241   -0.0792    2.3788   -0.9392    0.1455    3.1472    0.3715    0.3785    0.1551    1.4900    0.0393    1.1533    0.0706</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 492:    92.1940   -5.8234   -0.0794    2.3783   -0.9391    0.1455    3.1449    0.3714    0.3783    0.1551    1.4903    0.0392    1.1538    0.0705</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 493:    92.1943   -5.8230   -0.0797    2.3779   -0.9390    0.1455    3.1426    0.3714    0.3783    0.1552    1.4907    0.0392    1.1541    0.0705</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 494:    92.1946   -5.8226   -0.0799    2.3774   -0.9390    0.1458    3.1405    0.3713    0.3782    0.1551    1.4911    0.0392    1.1541    0.0706</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 495:    92.1948   -5.8219   -0.0802    2.3770   -0.9391    0.1459    3.1366    0.3712    0.3782    0.1551    1.4916    0.0392    1.1543    0.0706</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 496:    92.1948   -5.8213   -0.0804    2.3766   -0.9392    0.1460    3.1331    0.3711    0.3781    0.1552    1.4920    0.0392    1.1556    0.0705</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 497:    92.1949   -5.8213   -0.0806    2.3762   -0.9393    0.1462    3.1326    0.3711    0.3780    0.1553    1.4923    0.0391    1.1564    0.0705</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 498:    92.1950   -5.8215   -0.0808    2.3758   -0.9394    0.1462    3.1331    0.3710    0.3779    0.1553    1.4926    0.0391    1.1568    0.0705</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 499:    92.1953   -5.8219   -0.0810    2.3754   -0.9395    0.1461    3.1343    0.3709    0.3778    0.1554    1.4929    0.0391    1.1567    0.0705</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 500:    92.1957   -5.8232   -0.0812    2.3751   -0.9395    0.1459    3.1411    0.3709    0.3776    0.1554    1.4931    0.0391    1.1575    0.0705</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating covariance matrix</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling EBE model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Needed Covariates:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "CMT"</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating residuals/tables</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-in"><span class="va">f_nlmixr_fomc_sfo_focei_obs_tc</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr</a></span><span class="op">(</span><span class="va">f_mmkin_tc</span><span class="op">[</span><span class="st">"FOMC-SFO"</span>, <span class="op">]</span>, est <span class="op">=</span> <span class="st">"focei"</span>,</span>
-<span class="r-in">  error_model <span class="op">=</span> <span class="st">"obs_tc"</span><span class="op">)</span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate jacobian</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate sensitivities</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate ∂(f)/∂(η)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate ∂(R²)/∂(η)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in inner model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in inner model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in EBE model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in EBE model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling inner model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in FD model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in FD model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling EBE model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling events FD model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Needed Covariates:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "CMT"</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="font-weight: bold;">Key:</span> U: Unscaled Parameters; X: Back-transformed parameters; G: Gill difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> F: Forward difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> C: Central difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> M: Mixed forward and central difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Unscaled parameters for Omegas=chol(solve(omega));</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Diagonals are transformed, as specified by foceiControl(diagXform=)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |-----+---------------+-----------+-----------+-----------+-----------|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    #| Objective Fun |  parent_0 |  log_k_A1 |f_parent_qlogis | log_alpha |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  log_beta |sigma_low_parent |rsd_high_parent |sigma_low_A1 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|rsd_high_A1 |        o1 |        o2 |        o3 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|        o4 |        o5 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    1</span>|     500.20030 |     1.000 |    -1.000 |   -0.9113 |   -0.8944 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8454 |   -0.8678 |   -0.8916 |   -0.8678 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8916 |   -0.8767 |   -0.8743 |   -0.8675 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8704 |   -0.8704 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      500.2003 |     93.00 |    -5.300 |   -0.9400 |   -0.1100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.200 |   0.03000 |     1.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03000 |    0.7598 |    0.8758 |     1.214 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.068 |     1.071 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      500.2003</span> |     93.00 |  0.004992 |    0.2809 |    0.8958 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.974 |     1.200 |   0.03000 |     1.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03000 |    0.7598 |    0.8758 |     1.214 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.068 |     1.071 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    G|    Gill Diff. |     48.88 |     2.383 |    0.1231 |    0.1986 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1571 |    -68.85 |    -20.11 |     3.616 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.292 |    0.6250 |     11.41 |    -12.48 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -9.903 |    -10.91 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    2</span>|     2737.7532 |    0.4556 |    -1.027 |   -0.9127 |   -0.8966 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8471 |   -0.1009 |   -0.6676 |   -0.9080 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8660 |   -0.8837 |    -1.001 |   -0.7285 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7601 |   -0.7489 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     2737.7532 |     42.37 |    -5.327 |   -0.9413 |   -0.1122 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.298 |     1.660 |   0.03336 |     1.176 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03038 |    0.7545 |    0.7645 |     1.383 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.185 |     1.201 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     2737.7532</span> |     42.37 |  0.004861 |    0.2806 |    0.8939 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.957 |     1.660 |   0.03336 |     1.176 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03038 |    0.7545 |    0.7645 |     1.383 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.185 |     1.201 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    3</span>|     513.07755 |    0.9456 |    -1.003 |   -0.9114 |   -0.8946 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8455 |   -0.7911 |   -0.8692 |   -0.8718 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8890 |   -0.8774 |   -0.8871 |   -0.8536 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8594 |   -0.8582 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     513.07755 |     87.94 |    -5.303 |   -0.9401 |   -0.1102 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.246 |   0.03034 |     1.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03004 |    0.7593 |    0.8646 |     1.231 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.079 |     1.084 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     513.07755</span> |     87.94 |  0.004978 |    0.2809 |    0.8956 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.972 |     1.246 |   0.03034 |     1.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03004 |    0.7593 |    0.8646 |     1.231 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.079 |     1.084 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    4</span>|     499.28604 |    0.9888 |    -1.001 |   -0.9113 |   -0.8945 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8454 |   -0.8520 |   -0.8870 |   -0.8686 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8910 |   -0.8769 |   -0.8770 |   -0.8646 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8682 |   -0.8679 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     499.28604 |     91.96 |    -5.301 |   -0.9400 |   -0.1100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.209 |   0.03007 |     1.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03001 |    0.7597 |    0.8735 |     1.218 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.070 |     1.074 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     499.28604</span> |     91.96 |  0.004989 |    0.2809 |    0.8958 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.974 |     1.209 |   0.03007 |     1.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03001 |    0.7597 |    0.8735 |     1.218 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.070 |     1.074 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -111.5 |     2.236 |   -0.2057 |    0.1035 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1971 |    -66.17 |    -21.17 |     4.107 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.586 |     1.583 |     8.991 |    -11.70 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -9.625 |    -10.49 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    5</span>|     497.76004 |     1.001 |    -1.001 |   -0.9113 |   -0.8945 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8454 |   -0.8366 |   -0.8822 |   -0.8695 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8906 |   -0.8771 |   -0.8792 |   -0.8619 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8660 |   -0.8654 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     497.76004 |     93.05 |    -5.301 |   -0.9400 |   -0.1101 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.219 |   0.03014 |     1.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03001 |    0.7595 |    0.8715 |     1.221 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.072 |     1.076 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     497.76004</span> |     93.05 |  0.004986 |    0.2809 |    0.8958 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.974 |     1.219 |   0.03014 |     1.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03001 |    0.7595 |    0.8715 |     1.221 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.072 |     1.076 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     56.91 |     2.356 |    0.1468 |    0.2109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1761 |    -64.61 |    -18.67 |     3.291 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.680 |     1.108 |     8.338 |    -11.70 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -9.518 |    -10.47 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    6</span>|     496.73820 |    0.9899 |    -1.002 |   -0.9113 |   -0.8945 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8454 |   -0.8205 |   -0.8775 |   -0.8703 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8902 |   -0.8774 |   -0.8813 |   -0.8590 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8636 |   -0.8628 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      496.7382 |     92.06 |    -5.302 |   -0.9400 |   -0.1101 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.228 |   0.03021 |     1.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03002 |    0.7593 |    0.8696 |     1.225 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.075 |     1.079 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      496.7382</span> |     92.06 |  0.004983 |    0.2809 |    0.8957 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.974 |     1.228 |   0.03021 |     1.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03002 |    0.7593 |    0.8696 |     1.225 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.075 |     1.079 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -94.52 |     2.222 |   -0.1721 |    0.1131 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1712 |    -62.76 |    -19.55 |     3.974 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.718 |     1.304 |     7.360 |    -11.47 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -9.402 |    -10.21 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    7</span>|     495.33979 |     1.001 |    -1.002 |   -0.9113 |   -0.8946 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8454 |   -0.8044 |   -0.8726 |   -0.8713 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8897 |   -0.8777 |   -0.8834 |   -0.8560 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8612 |   -0.8602 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     495.33979 |     93.05 |    -5.302 |   -0.9400 |   -0.1102 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.238 |   0.03028 |     1.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03003 |    0.7591 |    0.8679 |     1.228 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.077 |     1.082 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     495.33979</span> |     93.05 |  0.004981 |    0.2809 |    0.8957 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.974 |     1.238 |   0.03028 |     1.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03003 |    0.7591 |    0.8679 |     1.228 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.077 |     1.082 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     56.11 |     2.327 |    0.1520 |    0.2091 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1742 |    -61.13 |    -17.23 |     3.435 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.620 |     1.132 |     9.399 |    -11.44 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -9.328 |    -10.19 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    8</span>|     494.31617 |    0.9904 |    -1.003 |   -0.9113 |   -0.8946 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8455 |   -0.7881 |   -0.8680 |   -0.8722 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8893 |   -0.8780 |   -0.8859 |   -0.8530 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8587 |   -0.8575 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     494.31617 |     92.11 |    -5.303 |   -0.9400 |   -0.1102 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.248 |   0.03035 |     1.197 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03003 |    0.7589 |    0.8657 |     1.232 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.080 |     1.085 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     494.31617</span> |     92.11 |  0.004977 |    0.2809 |    0.8956 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.973 |     1.248 |   0.03035 |     1.197 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03003 |    0.7589 |    0.8657 |     1.232 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.080 |     1.085 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -86.81 |     2.198 |   -0.1566 |    0.1238 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1638 |    -59.37 |    -18.02 |     4.057 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.591 |     1.325 |     8.479 |    -11.19 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -9.201 |    -9.937 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    9</span>|     493.00423 |     1.001 |    -1.003 |   -0.9113 |   -0.8947 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8454 |   -0.7718 |   -0.8631 |   -0.8732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8888 |   -0.8783 |   -0.8883 |   -0.8499 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8562 |   -0.8548 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     493.00423 |     93.05 |    -5.303 |   -0.9400 |   -0.1103 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.258 |   0.03043 |     1.197 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03004 |    0.7586 |    0.8635 |     1.236 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.083 |     1.088 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     493.00423</span> |     93.05 |  0.004974 |    0.2809 |    0.8956 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.974 |     1.258 |   0.03043 |     1.197 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03004 |    0.7586 |    0.8635 |     1.236 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.083 |     1.088 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     54.84 |     2.286 |    0.1404 |    0.2142 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1628 |    -57.96 |    -15.94 |     3.372 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.642 |     1.184 |     9.036 |    -11.17 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -9.110 |    -9.901 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   10</span>|     491.99601 |    0.9908 |    -1.004 |   -0.9114 |   -0.8947 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8455 |   -0.7552 |   -0.8585 |   -0.8742 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8884 |   -0.8786 |   -0.8910 |   -0.8467 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8536 |   -0.8520 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     491.99601 |     92.15 |    -5.304 |   -0.9401 |   -0.1103 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.268 |   0.03050 |     1.196 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03005 |    0.7584 |    0.8612 |     1.239 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.085 |     1.091 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     491.99601</span> |     92.15 |  0.004971 |    0.2809 |    0.8956 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.973 |     1.268 |   0.03050 |     1.196 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03005 |    0.7584 |    0.8612 |     1.239 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.085 |     1.091 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -80.67 |     2.171 |   -0.1367 |    0.1256 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1610 |    -56.13 |    -16.61 |     4.047 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.607 |     1.226 |     8.142 |    -10.92 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -8.982 |    -9.644 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   11</span>|     490.76421 |     1.000 |    -1.005 |   -0.9113 |   -0.8948 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8455 |   -0.7388 |   -0.8538 |   -0.8754 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8879 |   -0.8790 |   -0.8935 |   -0.8435 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8510 |   -0.8492 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     490.76421 |     93.04 |    -5.305 |   -0.9401 |   -0.1104 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.277 |   0.03057 |     1.195 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03006 |    0.7581 |    0.8590 |     1.243 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.088 |     1.094 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     490.76421</span> |     93.04 |  0.004968 |    0.2809 |    0.8955 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.973 |     1.277 |   0.03057 |     1.195 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03006 |    0.7581 |    0.8590 |     1.243 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.088 |     1.094 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     54.34 |     2.256 |    0.1715 |    0.2224 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1653 |    -54.87 |    -14.72 |     3.299 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.777 |     1.023 |     8.651 |    -10.90 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -8.887 |    -9.594 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   12</span>|     489.76286 |    0.9913 |    -1.005 |   -0.9114 |   -0.8948 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8455 |   -0.7220 |   -0.8492 |   -0.8764 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8873 |   -0.8793 |   -0.8962 |   -0.8402 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8483 |   -0.8462 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     489.76286 |     92.19 |    -5.305 |   -0.9401 |   -0.1104 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.287 |   0.03063 |     1.195 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03006 |    0.7579 |    0.8566 |     1.247 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.091 |     1.097 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     489.76286</span> |     92.19 |  0.004965 |    0.2809 |    0.8955 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.973 |     1.287 |   0.03063 |     1.195 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03006 |    0.7579 |    0.8566 |     1.247 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.091 |     1.097 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -73.18 |     2.145 |   -0.1190 |    0.1261 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1580 |    -53.08 |    -15.27 |     4.015 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.639 |     1.110 |     7.813 |    -10.65 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -8.743 |    -9.338 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   13</span>|     488.61493 |     1.000 |    -1.006 |   -0.9114 |   -0.8949 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8455 |   -0.7053 |   -0.8446 |   -0.8776 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8867 |   -0.8796 |   -0.8989 |   -0.8368 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8455 |   -0.8433 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     488.61493 |     93.04 |    -5.306 |   -0.9401 |   -0.1105 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.297 |   0.03070 |     1.194 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03007 |    0.7577 |    0.8543 |     1.252 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.094 |     1.100 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     488.61493</span> |     93.04 |  0.004961 |    0.2809 |    0.8954 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.973 |     1.297 |   0.03070 |     1.194 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03007 |    0.7577 |    0.8543 |     1.252 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.094 |     1.100 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     54.41 |     2.230 |    0.1889 |    0.2376 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1637 |    -52.01 |    -13.54 |     3.223 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.937 |    0.8847 |     10.07 |    -10.59 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -8.650 |    -9.282 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   14</span>|     487.61806 |    0.9919 |    -1.007 |   -0.9114 |   -0.8949 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8455 |   -0.6884 |   -0.8402 |   -0.8788 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8861 |   -0.8798 |   -0.9022 |   -0.8333 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8427 |   -0.8402 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     487.61806 |     92.24 |    -5.307 |   -0.9401 |   -0.1105 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.308 |   0.03077 |     1.193 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03008 |    0.7575 |    0.8514 |     1.256 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.097 |     1.103 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     487.61806</span> |     92.24 |  0.004958 |    0.2809 |    0.8953 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.973 |     1.308 |   0.03077 |     1.193 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03008 |    0.7575 |    0.8514 |     1.256 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.097 |     1.103 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -64.97 |     2.116 |  -0.09941 |    0.1372 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1475 |    -50.35 |    -14.02 |     3.916 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.790 |    0.8979 |     9.163 |    -10.32 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -8.505 |    -9.022 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   15</span>|     486.55968 |     1.001 |    -1.008 |   -0.9114 |   -0.8950 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8455 |   -0.6717 |   -0.8358 |   -0.8800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8854 |   -0.8801 |   -0.9058 |   -0.8297 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8398 |   -0.8372 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     486.55968 |     93.05 |    -5.308 |   -0.9401 |   -0.1106 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.318 |   0.03084 |     1.193 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03009 |    0.7573 |    0.8482 |     1.260 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.100 |     1.107 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     486.55968</span> |     93.05 |  0.004954 |    0.2809 |    0.8953 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.973 |     1.318 |   0.03084 |     1.193 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03009 |    0.7573 |    0.8482 |     1.260 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.100 |     1.107 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     55.08 |     2.188 |    0.1916 |    0.2341 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1660 |    -48.95 |    -12.27 |     3.347 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.726 |    0.9589 |     6.446 |    -10.30 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -8.390 |    -8.950 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   16</span>|     485.60262 |    0.9920 |    -1.008 |   -0.9115 |   -0.8951 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8455 |   -0.6547 |   -0.8316 |   -0.8813 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8847 |   -0.8804 |   -0.9083 |   -0.8260 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8367 |   -0.8340 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     485.60262 |     92.26 |    -5.308 |   -0.9402 |   -0.1107 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.328 |   0.03090 |     1.192 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03010 |    0.7571 |    0.8460 |     1.265 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.104 |     1.110 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     485.60262</span> |     92.26 |  0.004950 |    0.2809 |    0.8952 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.973 |     1.328 |   0.03090 |     1.192 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03010 |    0.7571 |    0.8460 |     1.265 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.104 |     1.110 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -62.47 |     2.078 |  -0.09882 |    0.1294 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1587 |    -47.42 |    -12.81 |     3.926 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.683 |    0.9415 |     5.611 |    -10.02 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -8.237 |    -8.679 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   17</span>|     484.58535 |     1.000 |    -1.009 |   -0.9115 |   -0.8952 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8455 |   -0.6375 |   -0.8274 |   -0.8829 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8840 |   -0.8807 |   -0.9099 |   -0.8222 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8336 |   -0.8307 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     484.58535 |     93.02 |    -5.309 |   -0.9402 |   -0.1107 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.338 |   0.03096 |     1.191 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03011 |    0.7568 |    0.8446 |     1.269 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.107 |     1.114 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     484.58535</span> |     93.02 |  0.004946 |    0.2809 |    0.8952 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.973 |     1.338 |   0.03096 |     1.191 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03011 |    0.7568 |    0.8446 |     1.269 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.107 |     1.114 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     50.15 |     2.155 |    0.1960 |    0.2225 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1509 |    -46.14 |    -11.19 |     3.405 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.652 |     1.035 |     7.536 |    -9.951 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -8.121 |    -8.609 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   18</span>|     483.66860 |    0.9923 |    -1.010 |   -0.9115 |   -0.8952 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8455 |   -0.6203 |   -0.8235 |   -0.8844 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8833 |   -0.8810 |   -0.9123 |   -0.8182 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8303 |   -0.8273 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      483.6686 |     92.29 |    -5.310 |   -0.9402 |   -0.1108 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.348 |   0.03102 |     1.190 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03012 |    0.7565 |    0.8425 |     1.274 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.110 |     1.117 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      483.6686</span> |     92.29 |  0.004942 |    0.2809 |    0.8951 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.973 |     1.348 |   0.03102 |     1.190 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03012 |    0.7565 |    0.8425 |     1.274 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.110 |     1.117 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -57.62 |     2.059 |  -0.07549 |    0.1395 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1501 |    -44.74 |    -11.66 |     3.913 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.664 |    0.8845 |     6.781 |    -9.703 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -7.961 |    -8.325 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   19</span>|     482.71012 |     1.000 |    -1.011 |   -0.9115 |   -0.8953 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8455 |   -0.6032 |   -0.8198 |   -0.8861 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8825 |   -0.8814 |   -0.9153 |   -0.8141 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8270 |   -0.8239 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     482.71012 |     93.02 |    -5.311 |   -0.9402 |   -0.1109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.359 |   0.03108 |     1.189 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03014 |    0.7563 |    0.8399 |     1.279 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.114 |     1.121 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     482.71012</span> |     93.02 |  0.004937 |    0.2809 |    0.8950 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.973 |     1.359 |   0.03108 |     1.189 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03014 |    0.7563 |    0.8399 |     1.279 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.114 |     1.121 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     48.98 |     2.122 |    0.2043 |    0.2315 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1449 |    -43.47 |    -10.15 |     3.365 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.703 |    0.9582 |     7.160 |    -9.600 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -7.838 |    -8.245 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   20</span>|     481.83107 |    0.9926 |    -1.012 |   -0.9116 |   -0.8954 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8455 |   -0.5860 |   -0.8164 |   -0.8879 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8817 |   -0.8818 |   -0.9184 |   -0.8098 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8235 |   -0.8203 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     481.83107 |     92.31 |    -5.312 |   -0.9403 |   -0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.369 |   0.03113 |     1.188 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03015 |    0.7560 |    0.8372 |     1.284 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.118 |     1.125 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     481.83107</span> |     92.31 |  0.004932 |    0.2808 |    0.8949 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.973 |     1.369 |   0.03113 |     1.188 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03015 |    0.7560 |    0.8372 |     1.284 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.118 |     1.125 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -54.27 |     2.027 |  -0.06740 |    0.1414 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1465 |    -41.84 |    -10.48 |     3.798 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.730 |    0.8044 |     6.401 |    -9.335 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -7.662 |    -7.960 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   21</span>|     480.92878 |     1.000 |    -1.013 |   -0.9117 |   -0.8955 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8455 |   -0.5689 |   -0.8133 |   -0.8899 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8807 |   -0.8821 |   -0.9215 |   -0.8053 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8199 |   -0.8166 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.92878 |     93.01 |    -5.313 |   -0.9403 |   -0.1111 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.379 |   0.03117 |     1.187 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03016 |    0.7557 |    0.8345 |     1.290 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.122 |     1.129 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.92878</span> |     93.01 |  0.004927 |    0.2808 |    0.8948 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.973 |     1.379 |   0.03117 |     1.187 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03016 |    0.7557 |    0.8345 |     1.290 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.122 |     1.129 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     47.18 |     2.077 |    0.1996 |    0.2200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1318 |    -41.07 |    -9.197 |     3.259 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.748 |    0.9101 |     6.743 |    -9.229 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -7.511 |    -7.855 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   22</span>|     480.07574 |    0.9930 |    -1.014 |   -0.9117 |   -0.8956 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8455 |   -0.5517 |   -0.8106 |   -0.8919 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8795 |   -0.8825 |   -0.9247 |   -0.8007 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8161 |   -0.8129 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.07574 |     92.35 |    -5.314 |   -0.9404 |   -0.1112 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.390 |   0.03121 |     1.186 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03018 |    0.7555 |    0.8317 |     1.295 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.126 |     1.133 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.07574</span> |     92.35 |  0.004921 |    0.2808 |    0.8947 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.973 |     1.390 |   0.03121 |     1.186 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03018 |    0.7555 |    0.8317 |     1.295 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.126 |     1.133 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -48.22 |     1.995 |  -0.04658 |    0.1415 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1316 |    -39.56 |    -9.522 |     3.639 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.878 |    0.6154 |     4.556 |    -8.937 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -7.320 |    -7.563 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   23</span>|     479.25211 |     1.000 |    -1.015 |   -0.9118 |   -0.8957 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8454 |   -0.5343 |   -0.8082 |   -0.8940 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8781 |   -0.8826 |   -0.9261 |   -0.7958 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8122 |   -0.8090 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.25211 |     93.02 |    -5.315 |   -0.9405 |   -0.1113 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.400 |   0.03125 |     1.184 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03020 |    0.7553 |    0.8305 |     1.301 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.130 |     1.137 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.25211</span> |     93.02 |  0.004915 |    0.2808 |    0.8946 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.974 |     1.400 |   0.03125 |     1.184 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03020 |    0.7553 |    0.8305 |     1.301 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.130 |     1.137 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     48.39 |     2.048 |    0.2189 |    0.2175 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1393 |    -38.80 |    -8.286 |     3.169 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.757 |    0.9021 |     6.456 |    -8.802 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -7.165 |    -7.452 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   24</span>|     478.42395 |    0.9935 |    -1.017 |   -0.9119 |   -0.8959 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8454 |   -0.5169 |   -0.8064 |   -0.8963 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8766 |   -0.8829 |   -0.9277 |   -0.7908 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8081 |   -0.8051 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     478.42395 |     92.40 |    -5.317 |   -0.9406 |   -0.1115 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.411 |   0.03128 |     1.183 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03022 |    0.7552 |    0.8290 |     1.307 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.134 |     1.141 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     478.42395</span> |     92.40 |  0.004909 |    0.2808 |    0.8945 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.974 |     1.411 |   0.03128 |     1.183 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03022 |    0.7552 |    0.8290 |     1.307 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.134 |     1.141 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -41.65 |     1.977 |  -0.02866 |    0.1453 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1180 |    -37.67 |    -8.677 |     3.579 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.774 |    0.6894 |     5.855 |    -8.506 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -6.962 |    -7.153 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   25</span>|     477.65816 |     1.000 |    -1.018 |   -0.9120 |   -0.8960 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8454 |   -0.4997 |   -0.8051 |   -0.8988 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8750 |   -0.8832 |   -0.9313 |   -0.7859 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8042 |   -0.8013 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     477.65816 |     93.02 |    -5.318 |   -0.9406 |   -0.1116 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.421 |   0.03130 |     1.181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03025 |    0.7549 |    0.8259 |     1.313 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.138 |     1.145 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     477.65816</span> |     93.02 |  0.004901 |    0.2808 |    0.8944 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.974 |     1.421 |   0.03130 |     1.181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03025 |    0.7549 |    0.8259 |     1.313 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.138 |     1.145 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     47.50 |     2.013 |    0.2278 |    0.2145 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1415 |    -36.68 |    -7.460 |     3.039 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.807 |    0.8669 |     6.124 |    -8.373 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -6.814 |    -7.074 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   26</span>|     476.87668 |    0.9936 |    -1.020 |   -0.9121 |   -0.8962 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8454 |   -0.4825 |   -0.8047 |   -0.9014 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8732 |   -0.8836 |   -0.9351 |   -0.7808 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8001 |   -0.7975 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     476.87668 |     92.41 |    -5.320 |   -0.9408 |   -0.1117 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.431 |   0.03130 |     1.180 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03028 |    0.7546 |    0.8226 |     1.319 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.143 |     1.149 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     476.87668</span> |     92.41 |  0.004893 |    0.2807 |    0.8943 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.974 |     1.431 |   0.03130 |     1.180 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03028 |    0.7546 |    0.8226 |     1.319 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.143 |     1.149 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -40.64 |     1.935 |  -0.02164 |    0.1511 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1127 |    -35.59 |    -7.858 |     3.450 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.805 |    0.6731 |     3.945 |    -8.048 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -6.589 |    -6.756 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   27</span>|     476.16299 |     1.000 |    -1.022 |   -0.9123 |   -0.8963 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8454 |   -0.4651 |   -0.8047 |   -0.9041 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8711 |   -0.8840 |   -0.9355 |   -0.7757 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7959 |   -0.7936 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     476.16299 |     93.01 |    -5.322 |   -0.9409 |   -0.1119 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.442 |   0.03130 |     1.178 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03031 |    0.7543 |    0.8222 |     1.326 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.147 |     1.153 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     476.16299</span> |     93.01 |  0.004885 |    0.2807 |    0.8941 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.974 |     1.442 |   0.03130 |     1.178 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03031 |    0.7543 |    0.8222 |     1.326 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.147 |     1.153 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     44.38 |     1.982 |    0.2331 |    0.2213 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1467 |    -34.64 |    -6.709 |     2.894 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.888 |    0.7760 |     4.358 |    -7.901 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -6.419 |    -6.620 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   28</span>|     475.47434 |    0.9938 |    -1.024 |   -0.9125 |   -0.8966 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8454 |   -0.4477 |   -0.8058 |   -0.9069 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8685 |   -0.8844 |   -0.9328 |   -0.7706 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7919 |   -0.7898 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     475.47434 |     92.43 |    -5.324 |   -0.9411 |   -0.1122 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.452 |   0.03129 |     1.177 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03035 |    0.7540 |    0.8246 |     1.332 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.151 |     1.157 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     475.47434</span> |     92.43 |  0.004875 |    0.2807 |    0.8939 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.974 |     1.452 |   0.03129 |     1.177 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03035 |    0.7540 |    0.8246 |     1.332 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.151 |     1.157 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -38.01 |     1.932 |  -0.02542 |    0.1483 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1147 |    -33.71 |    -7.114 |     3.216 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.879 |    0.6267 |     4.067 |    -7.573 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -6.213 |    -6.338 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   29</span>|     474.82575 |    0.9998 |    -1.026 |   -0.9126 |   -0.8968 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8454 |   -0.4303 |   -0.8075 |   -0.9096 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8656 |   -0.8846 |   -0.9310 |   -0.7657 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7878 |   -0.7862 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     474.82575 |     92.98 |    -5.326 |   -0.9413 |   -0.1124 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.462 |   0.03126 |     1.175 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03039 |    0.7538 |    0.8261 |     1.338 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.156 |     1.161 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     474.82575</span> |     92.98 |  0.004864 |    0.2806 |    0.8937 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.974 |     1.462 |   0.03126 |     1.175 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03039 |    0.7538 |    0.8261 |     1.338 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.156 |     1.161 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     40.83 |     1.980 |    0.2123 |    0.2125 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1324 |    -32.65 |    -6.032 |     2.979 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.922 |    0.7692 |     4.596 |    -7.426 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -6.058 |    -6.217 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   30</span>|     474.18202 |    0.9939 |    -1.028 |   -0.9128 |   -0.8971 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8454 |   -0.4131 |   -0.8106 |   -0.9130 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8620 |   -0.8851 |   -0.9307 |   -0.7608 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7838 |   -0.7828 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     474.18202 |     92.44 |    -5.328 |   -0.9414 |   -0.1127 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.473 |   0.03121 |     1.173 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03044 |    0.7535 |    0.8264 |     1.344 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.160 |     1.165 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     474.18202</span> |     92.44 |  0.004852 |    0.2806 |    0.8935 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.974 |     1.473 |   0.03121 |     1.173 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03044 |    0.7535 |    0.8264 |     1.344 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.160 |     1.165 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -37.33 |     1.920 |  -0.03157 |    0.1462 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1145 |    -31.92 |    -6.433 |     3.018 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.919 |    0.6114 |     5.640 |    -7.155 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -5.876 |    -5.958 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   31</span>|     473.55376 |    0.9995 |    -1.031 |   -0.9130 |   -0.8973 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8454 |   -0.3971 |   -0.8148 |   -0.9162 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8581 |   -0.8855 |   -0.9368 |   -0.7563 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7800 |   -0.7797 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     473.55376 |     92.95 |    -5.331 |   -0.9416 |   -0.1129 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.482 |   0.03115 |     1.171 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03050 |    0.7531 |    0.8211 |     1.349 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.164 |     1.168 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     473.55376</span> |     92.95 |  0.004838 |    0.2806 |    0.8932 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.974 |     1.482 |   0.03115 |     1.171 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03050 |    0.7531 |    0.8211 |     1.349 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.164 |     1.168 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   32</span>|     472.90182 |    0.9995 |    -1.035 |   -0.9132 |   -0.8977 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8453 |   -0.3799 |   -0.8225 |   -0.9204 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8527 |   -0.8861 |   -0.9447 |   -0.7510 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7757 |   -0.7763 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     472.90182 |     92.96 |    -5.335 |   -0.9418 |   -0.1133 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.493 |   0.03104 |     1.168 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03058 |    0.7527 |    0.8141 |     1.356 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.169 |     1.172 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     472.90182</span> |     92.96 |  0.004819 |    0.2805 |    0.8929 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.975 |     1.493 |   0.03104 |     1.168 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03058 |    0.7527 |    0.8141 |     1.356 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.169 |     1.172 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   33</span>|     469.90339 |    0.9997 |    -1.054 |   -0.9142 |   -0.8996 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8452 |   -0.2944 |   -0.8611 |   -0.9412 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8255 |   -0.8889 |   -0.9843 |   -0.7249 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7539 |   -0.7597 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     469.90339 |     92.98 |    -5.354 |   -0.9427 |   -0.1152 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.300 |     1.544 |   0.03046 |     1.156 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03099 |    0.7505 |    0.7794 |     1.387 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.192 |     1.190 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     469.90339</span> |     92.98 |  0.004727 |    0.2803 |    0.8912 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.976 |     1.544 |   0.03046 |     1.156 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03099 |    0.7505 |    0.7794 |     1.387 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.192 |     1.190 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   34</span>|     461.71523 |     1.001 |    -1.134 |   -0.9184 |   -0.9072 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8447 |   0.05742 |    -1.020 |    -1.027 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7136 |   -0.9005 |    -1.147 |   -0.6175 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6642 |   -0.6913 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     461.71523 |     93.05 |    -5.434 |   -0.9467 |   -0.1228 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.301 |     1.755 |   0.02808 |     1.105 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03267 |    0.7417 |    0.6368 |     1.518 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.288 |     1.263 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     461.71523</span> |     93.05 |  0.004366 |    0.2795 |    0.8844 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.981 |     1.755 |   0.02808 |     1.105 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03267 |    0.7417 |    0.6368 |     1.518 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.288 |     1.263 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     11.20 |     1.045 |     1.020 |    0.3495 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7923 |    -15.00 |   -0.3969 |    -1.315 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -3.031 |    0.2676 |    -6.780 |    -1.078 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|  -0.09081 |    -1.463 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   35</span>|     459.94653 |     1.009 |    -1.206 |   -0.9768 |   -0.9283 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8884 |    0.4547 |    -1.238 |   -0.9419 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5030 |   -0.9080 |   -0.7892 |   -0.6450 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7313 |   -0.6959 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     459.94653 |     93.85 |    -5.506 |    -1.002 |   -0.1439 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.257 |     1.993 |   0.02480 |     1.155 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03583 |    0.7361 |    0.9504 |     1.484 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.216 |     1.258 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     459.94653</span> |     93.85 |  0.004064 |    0.2686 |    0.8660 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.554 |     1.993 |   0.02480 |     1.155 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03583 |    0.7361 |    0.9504 |     1.484 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.216 |     1.258 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     117.0 |     1.389 |    -1.721 |   -0.1200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.004314 |    -5.763 |     2.093 |     3.426 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.378 |     2.600 |     15.14 |    -1.236 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -4.018 |    -1.529 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   36</span>|     458.11528 |     1.003 |    -1.307 |   -0.9477 |   -0.9422 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9430 |    0.6906 |    -1.630 |   -0.8850 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1864 |    -1.043 |   -0.8947 |   -0.8359 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7822 |   -0.7759 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     458.11528 |     93.30 |    -5.607 |   -0.9742 |   -0.1578 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.202 |     2.135 |   0.01893 |     1.190 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.04058 |    0.6334 |    0.8579 |     1.253 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.162 |     1.172 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     458.11528</span> |     93.30 |  0.003671 |    0.2740 |    0.8540 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.046 |     2.135 |   0.01893 |     1.190 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.04058 |    0.6334 |    0.8579 |     1.253 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.162 |     1.172 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.502 |    0.8367 |   -0.1865 |   -0.1361 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6555 |    -2.945 |     1.425 |     8.523 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6882 |    -2.700 |     7.982 |    -9.961 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -6.514 |    -5.474 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   37</span>|     454.42202 |     1.008 |    -1.411 |   -0.9501 |   -0.9563 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9900 |    0.8215 |    -2.099 |    -1.032 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1520 |    -1.049 |   -0.9274 |   -0.7533 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7158 |   -0.7241 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     454.42202 |     93.74 |    -5.711 |   -0.9765 |   -0.1719 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.155 |     2.214 |   0.01188 |     1.102 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.04565 |    0.6287 |    0.8293 |     1.353 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.233 |     1.228 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     454.42202</span> |     93.74 |  0.003311 |    0.2736 |    0.8421 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.631 |     2.214 |   0.01188 |     1.102 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.04565 |    0.6287 |    0.8293 |     1.353 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.233 |     1.228 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   38</span>|     451.48622 |     1.000 |    -1.659 |   -0.9551 |   -0.9914 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.111 |     1.029 |    -2.892 |    -1.353 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9897 |    -1.076 |   -0.9627 |   -0.6147 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.5957 |   -0.6379 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     451.48622 |     93.01 |    -5.959 |   -0.9812 |   -0.2070 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.035 |     2.338 | 5.960e-07 |    0.9088 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.05822 |    0.6083 |    0.7984 |     1.521 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.361 |     1.320 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     451.48622</span> |     93.01 |  0.002583 |    0.2727 |    0.8130 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     7.651 |     2.338 | 5.960e-07 |    0.9088 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.05822 |    0.6083 |    0.7984 |     1.521 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.361 |     1.320 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -68.34 |   0.03355 |   0.09905 |    -1.306 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -5.058 |    -1.204 |  -0.09883 |    -1.076 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.726 |    -4.145 |     7.646 |   -0.5743 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.003 |     2.613 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   39</span>|     453.25472 |     1.003 |    -2.027 |    -1.069 |   -0.8933 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6960 |     1.084 |    -2.892 |    -1.695 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.689 |   -0.4882 |    -1.206 |   -0.2814 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7545 |   -0.8496 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     453.25472 |     93.31 |    -6.327 |    -1.089 |   -0.1089 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.449 |     2.371 | 5.960e-07 |    0.7037 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.08370 |     1.055 |    0.5857 |     1.926 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.191 |     1.093 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     453.25472</span> |     93.31 |  0.001787 |    0.2519 |    0.8968 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     11.58 |     2.371 | 5.960e-07 |    0.7037 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.08370 |     1.055 |    0.5857 |     1.926 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.191 |     1.093 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   40</span>|     449.59369 |     1.000 |    -1.818 |    -1.010 |   -0.9508 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9364 |     1.052 |    -2.892 |    -1.498 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.730 |   -0.8175 |    -1.073 |   -0.4671 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6691 |   -0.7270 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     449.59369 |     93.04 |    -6.118 |    -1.033 |   -0.1664 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.209 |     2.352 | 5.960e-07 |    0.8216 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.06933 |    0.8048 |    0.7021 |     1.700 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.282 |     1.225 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     449.59369</span> |     93.04 |  0.002202 |    0.2625 |    0.8467 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.106 |     2.352 | 5.960e-07 |    0.8216 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.06933 |    0.8048 |    0.7021 |     1.700 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.282 |     1.225 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -128.1 |    0.1096 |    -3.510 |   -0.1773 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1823 |    -1.358 | -0.002263 |     1.313 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.294 |     5.530 |     5.100 |     4.831 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.292 |    -3.851 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   41</span>|     472.13387 |     1.020 |    -1.985 |   -0.7592 |   -0.7822 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2976 |     1.062 |    -2.892 |    -1.866 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     3.141 |   -0.7519 |    -1.136 |   -0.6405 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7706 |   -0.2153 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     472.13387 |     94.83 |    -6.285 |   -0.7970 |  0.002176 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.848 |     2.358 | 5.960e-07 |    0.6009 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.09049 |    0.8547 |    0.6467 |     1.490 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.174 |     1.773 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     472.13387</span> |     94.83 |  0.001864 |    0.3107 |     1.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     17.25 |     2.358 | 5.960e-07 |    0.6009 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.09049 |    0.8547 |    0.6467 |     1.490 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.174 |     1.773 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   42</span>|     454.13083 |     1.030 |    -1.841 |   -0.9756 |   -0.9280 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8501 |     1.054 |    -2.892 |    -1.548 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.921 |   -0.8098 |    -1.082 |   -0.4916 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6825 |   -0.6571 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     454.13083 |     95.82 |    -6.141 |    -1.000 |   -0.1436 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.295 |     2.353 | 5.960e-07 |    0.7917 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.07219 |    0.8106 |    0.6936 |     1.671 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.268 |     1.299 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     454.13083</span> |     95.82 |  0.002153 |    0.2688 |    0.8663 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.927 |     2.353 | 5.960e-07 |    0.7917 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.07219 |    0.8106 |    0.6936 |     1.671 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.268 |     1.299 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   43</span>|     454.81886 |     1.032 |    -1.823 |    -1.002 |   -0.9459 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9179 |     1.053 |    -2.892 |    -1.509 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.771 |   -0.8169 |    -1.076 |   -0.4733 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6717 |   -0.7113 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     454.81886 |     95.94 |    -6.123 |    -1.025 |   -0.1614 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.227 |     2.352 | 5.960e-07 |    0.8150 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.06994 |    0.8052 |    0.6994 |     1.693 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.280 |     1.241 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     454.81886</span> |     95.94 |  0.002192 |    0.2640 |    0.8509 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.277 |     2.352 | 5.960e-07 |    0.8150 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.06994 |    0.8052 |    0.6994 |     1.693 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.280 |     1.241 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   44</span>|     449.57011 |     1.014 |    -1.818 |    -1.010 |   -0.9508 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9364 |     1.053 |    -2.892 |    -1.499 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.730 |   -0.8181 |    -1.073 |   -0.4676 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6690 |   -0.7267 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     449.57011 |     94.27 |    -6.118 |    -1.033 |   -0.1664 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.209 |     2.352 | 5.995e-07 |    0.8215 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.06933 |    0.8044 |    0.7016 |     1.700 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.283 |     1.225 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     449.57011</span> |     94.27 |  0.002202 |    0.2626 |    0.8467 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.106 |     2.352 | 5.995e-07 |    0.8215 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.06933 |    0.8044 |    0.7016 |     1.700 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.283 |     1.225 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     126.9 |    0.1582 |    -2.201 |   0.05331 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4695 |   -0.6265 |   0.01503 |    0.4241 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.337 |     5.854 |     6.688 |     4.536 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.410 |    -4.016 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   45</span>|     449.14922 |     1.007 |    -1.818 |    -1.010 |   -0.9508 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9364 |     1.053 |    -2.892 |    -1.499 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.730 |   -0.8183 |    -1.074 |   -0.4678 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6689 |   -0.7265 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     449.14922 |     93.66 |    -6.118 |    -1.033 |   -0.1664 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.209 |     2.352 | 5.960e-07 |    0.8215 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.06933 |    0.8042 |    0.7014 |     1.699 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.283 |     1.225 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     449.14922</span> |     93.66 |  0.002202 |    0.2626 |    0.8467 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.106 |     2.352 | 5.960e-07 |    0.8215 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.06933 |    0.8042 |    0.7014 |     1.699 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.283 |     1.225 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |   -0.4066 |    0.1367 |    -2.828 |  -0.06013 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1445 |   -0.8286 |   0.01677 |    0.9213 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.224 |     4.943 |     4.017 |     5.223 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.311 |    -3.922 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   46</span>|     449.08136 |     1.007 |    -1.818 |    -1.008 |   -0.9507 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9365 |     1.053 |    -2.892 |    -1.499 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.731 |   -0.8217 |    -1.076 |   -0.4714 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6680 |   -0.7238 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     449.08136 |     93.68 |    -6.118 |    -1.031 |   -0.1663 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.209 |     2.353 | 5.960e-07 |    0.8212 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.06934 |    0.8016 |    0.6990 |     1.695 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.284 |     1.228 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     449.08136</span> |     93.68 |  0.002202 |    0.2629 |    0.8468 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.105 |     2.353 | 5.960e-07 |    0.8212 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.06934 |    0.8016 |    0.6990 |     1.695 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.284 |     1.228 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   47</span>|     448.89872 |     1.008 |    -1.819 |    -1.002 |   -0.9506 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9368 |     1.055 |    -2.892 |    -1.501 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.734 |   -0.8317 |    -1.084 |   -0.4820 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6654 |   -0.7159 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     448.89872 |     93.76 |    -6.119 |    -1.025 |   -0.1662 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.209 |     2.354 | 5.960e-07 |    0.8200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.06938 |    0.7940 |    0.6918 |     1.682 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.286 |     1.237 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     448.89872</span> |     93.76 |  0.002202 |    0.2640 |    0.8469 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.102 |     2.354 | 5.960e-07 |    0.8200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.06938 |    0.7940 |    0.6918 |     1.682 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.286 |     1.237 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   48</span>|     448.46351 |     1.011 |    -1.820 |   -0.9792 |   -0.9501 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9380 |     1.062 |    -2.892 |    -1.509 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.744 |   -0.8718 |    -1.117 |   -0.5244 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6547 |   -0.6840 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     448.46351 |     94.07 |    -6.120 |    -1.004 |   -0.1657 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.207 |     2.358 | 5.960e-07 |    0.8155 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.06953 |    0.7635 |    0.6633 |     1.631 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.298 |     1.271 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     448.46351</span> |     94.07 |  0.002199 |    0.2682 |    0.8473 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.092 |     2.358 | 5.960e-07 |    0.8155 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.06953 |    0.7635 |    0.6633 |     1.631 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.298 |     1.271 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     75.37 |    0.2597 |   -0.1569 |   0.03608 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3501 |   -0.3601 |  -0.01324 |    0.5025 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.327 |     4.308 |    0.9122 |     2.456 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.5631 |    -1.887 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   49</span>|     448.12149 |     1.008 |    -1.833 |   -0.9950 |   -0.9378 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8937 |     1.062 |    -2.892 |    -1.543 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.827 |   -0.8899 |    -1.115 |   -0.5324 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6574 |   -0.6703 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     448.12149 |     93.73 |    -6.133 |    -1.019 |   -0.1534 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.252 |     2.358 | 5.960e-07 |    0.7948 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.07077 |    0.7498 |    0.6653 |     1.621 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.295 |     1.285 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     448.12149</span> |     93.73 |  0.002169 |    0.2653 |    0.8578 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.504 |     2.358 | 5.960e-07 |    0.7948 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.07077 |    0.7498 |    0.6653 |     1.621 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.295 |     1.285 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     13.78 |    0.2222 |    -1.381 |    0.2569 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.259 |   -0.5740 |  -0.02850 |    0.1620 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.241 |     3.503 |    0.9893 |     2.205 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7124 |    -1.054 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   50</span>|     447.94443 |     1.007 |    -1.844 |   -0.9902 |   -0.9390 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9059 |     1.062 |    -2.881 |    -1.561 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.911 |   -0.9357 |    -1.119 |   -0.5167 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6474 |   -0.7008 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     447.94443 |     93.62 |    -6.144 |    -1.014 |   -0.1546 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.239 |     2.358 | 0.0001615 |    0.7838 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.07204 |    0.7150 |    0.6617 |     1.640 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.306 |     1.253 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     447.94443</span> |     93.62 |  0.002146 |    0.2662 |    0.8567 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.388 |     2.358 | 0.0001615 |    0.7838 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.07204 |    0.7150 |    0.6617 |     1.640 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.306 |     1.253 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -5.879 |    0.2071 |    -1.215 |    0.2527 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8228 |   -0.5096 |  -0.06036 |    0.4983 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.183 |     1.766 |     2.266 |     2.617 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.3179 |    -2.389 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   51</span>|     447.77390 |     1.007 |    -1.858 |    -1.003 |   -0.9470 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9285 |     1.056 |    -2.855 |    -1.583 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.006 |   -0.9406 |    -1.128 |   -0.5198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6539 |   -0.6964 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      447.7739 |     93.69 |    -6.158 |    -1.026 |   -0.1626 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.217 |     2.354 | 0.0005494 |    0.7710 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.07347 |    0.7113 |    0.6533 |     1.636 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.299 |     1.257 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      447.7739</span> |     93.69 |  0.002117 |    0.2639 |    0.8500 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.179 |     2.354 | 0.0005494 |    0.7710 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.07347 |    0.7113 |    0.6533 |     1.636 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.299 |     1.257 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   52</span>|     447.56301 |     1.006 |    -1.897 |    -1.041 |   -0.9708 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9958 |     1.039 |    -2.777 |    -1.647 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.291 |   -0.9540 |    -1.156 |   -0.5273 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6737 |   -0.6848 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     447.56301 |     93.51 |    -6.197 |    -1.061 |   -0.1864 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.150 |     2.344 |  0.001717 |    0.7326 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.07774 |    0.7011 |    0.6293 |     1.627 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.278 |     1.270 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     447.56301</span> |     93.51 |  0.002035 |    0.2570 |    0.8300 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.581 |     2.344 |  0.001717 |    0.7326 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.07774 |    0.7011 |    0.6293 |     1.627 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.278 |     1.270 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -30.60 |    0.2182 |    -4.075 |   -0.4855 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.237 |   -0.6626 |   -0.1062 |    0.6612 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6049 |     1.074 |     1.318 |     2.279 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.614 |    -1.745 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   53</span>|     447.16930 |     1.007 |    -1.971 |   -0.9627 |   -0.9652 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9709 |     1.044 |    -2.781 |    -1.732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.691 |   -0.9472 |    -1.156 |   -0.5162 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6828 |   -0.6632 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      447.1693 |     93.67 |    -6.271 |   -0.9883 |   -0.1807 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.174 |     2.347 |  0.001652 |    0.6812 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.08374 |    0.7063 |    0.6288 |     1.641 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.268 |     1.293 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      447.1693</span> |     93.67 |  0.001890 |    0.2713 |    0.8346 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.798 |     2.347 |  0.001652 |    0.6812 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.08374 |    0.7063 |    0.6288 |     1.641 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.268 |     1.293 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -3.693 |    0.1048 |    0.8560 |   -0.3278 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6115 |   -0.5188 |  -0.09596 |    0.9789 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.02585 |     1.094 |     1.548 |     2.318 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -2.079 |   -0.9259 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   54</span>|     447.68852 |     1.013 |    -2.042 |    -1.008 |   -0.9421 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9288 |     1.034 |    -2.709 |    -1.852 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     3.037 |   -0.9368 |    -1.153 |   -0.6162 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.5399 |   -0.6984 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     447.68852 |     94.21 |    -6.342 |    -1.031 |   -0.1577 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.217 |     2.341 |  0.002735 |    0.6094 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.08893 |    0.7142 |    0.6313 |     1.519 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.420 |     1.255 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     447.68852</span> |     94.21 |  0.001761 |    0.2629 |    0.8541 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.176 |     2.341 |  0.002735 |    0.6094 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.08893 |    0.7142 |    0.6313 |     1.519 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.420 |     1.255 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   55</span>|     447.15405 |     1.011 |    -1.991 |   -0.9760 |   -0.9584 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9586 |     1.042 |    -2.761 |    -1.767 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.789 |   -0.9450 |    -1.157 |   -0.5460 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6410 |   -0.6726 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     447.15405 |     94.04 |    -6.291 |    -1.001 |   -0.1740 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.187 |     2.346 |  0.001959 |    0.6605 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.08521 |    0.7080 |    0.6287 |     1.605 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.312 |     1.283 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     447.15405</span> |     94.04 |  0.001852 |    0.2688 |    0.8403 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.906 |     2.346 |  0.001959 |    0.6605 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.08521 |    0.7080 |    0.6287 |     1.605 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.312 |     1.283 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     53.59 |   0.07914 |    0.2662 |   -0.1234 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1097 |   -0.6518 |  -0.06086 |    0.6193 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.05679 |     1.445 |    0.6511 |     1.197 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1794 |    -1.326 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   56</span>|     447.09915 |     1.005 |    -2.002 |   -0.9768 |   -0.9545 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9567 |     1.042 |    -2.752 |    -1.789 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.838 |   -0.9392 |    -1.155 |   -0.5617 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6261 |   -0.6787 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     447.09915 |     93.47 |    -6.302 |    -1.002 |   -0.1701 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.189 |     2.346 |  0.002096 |    0.6471 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.08594 |    0.7123 |    0.6298 |     1.586 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.328 |     1.276 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     447.09915</span> |     93.47 |  0.001833 |    0.2686 |    0.8436 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     8.923 |     2.346 |  0.002096 |    0.6471 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.08594 |    0.7123 |    0.6298 |     1.586 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.328 |     1.276 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -33.97 |   0.05091 |   -0.1219 |  -0.09607 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3249 |   -0.8735 |   -0.1278 |    0.5888 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.06635 |     1.636 |    -1.923 |    0.5172 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.5270 |    -1.419 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   57</span>|     447.01951 |     1.007 |    -2.009 |   -0.9874 |   -0.9450 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9329 |     1.053 |    -2.780 |    -1.808 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.841 |   -0.9693 |    -1.155 |   -0.5556 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6514 |   -0.6721 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     447.01951 |     93.64 |    -6.309 |    -1.012 |   -0.1606 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.212 |     2.352 |  0.001680 |    0.6361 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.08598 |    0.6895 |    0.6300 |     1.593 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.301 |     1.283 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     447.01951</span> |     93.64 |  0.001820 |    0.2667 |    0.8517 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.138 |     2.352 |  0.001680 |    0.6361 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.08598 |    0.6895 |    0.6300 |     1.593 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.301 |     1.283 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -6.500 |   0.04518 |   -0.6412 |    0.1616 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3106 |   -0.6054 |  -0.07873 |   -0.1077 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1002 |    0.2460 |     1.553 |    0.4490 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8245 |    -1.132 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   58</span>|     447.04861 |     1.009 |    -2.019 |   -0.9927 |   -0.9476 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9252 |     1.054 |    -2.796 |    -1.779 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.878 |   -0.9737 |    -1.157 |   -0.5487 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6291 |   -0.6521 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     447.04861 |     93.85 |    -6.319 |    -1.017 |   -0.1632 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.220 |     2.353 |  0.001429 |    0.6532 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.08654 |    0.6862 |    0.6280 |     1.601 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.325 |     1.305 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     447.04861</span> |     93.85 |  0.001802 |    0.2657 |    0.8494 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.209 |     2.353 |  0.001429 |    0.6532 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.08654 |    0.6862 |    0.6280 |     1.601 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.325 |     1.305 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   59</span>|     447.01937 |     1.009 |    -2.012 |   -0.9891 |   -0.9459 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9304 |     1.053 |    -2.785 |    -1.798 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.853 |   -0.9708 |    -1.156 |   -0.5534 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6439 |   -0.6653 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     447.01937 |     93.80 |    -6.312 |    -1.013 |   -0.1615 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.215 |     2.353 |  0.001597 |    0.6418 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.08617 |    0.6884 |    0.6292 |     1.596 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.309 |     1.291 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     447.01937</span> |     93.80 |  0.001814 |    0.2664 |    0.8509 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.161 |     2.353 |  0.001597 |    0.6418 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.08617 |    0.6884 |    0.6292 |     1.596 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.309 |     1.291 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     15.62 |   0.04451 |   -0.6370 |    0.1658 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4720 |   -0.4967 |  -0.06076 |    0.2478 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.07878 |    0.2929 |    0.7908 |    0.7364 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2169 |   -0.8458 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   60</span>|     447.00331 |     1.007 |    -2.016 |   -0.9874 |   -0.9487 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9321 |     1.057 |    -2.790 |    -1.800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.856 |   -0.9696 |    -1.156 |   -0.5575 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6423 |   -0.6633 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     447.00331 |     93.68 |    -6.316 |    -1.012 |   -0.1643 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.213 |     2.355 |  0.001526 |    0.6406 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.08622 |    0.6893 |    0.6295 |     1.591 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.311 |     1.293 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     447.00331</span> |     93.68 |  0.001807 |    0.2667 |    0.8485 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.146 |     2.355 |  0.001526 |    0.6406 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.08622 |    0.6893 |    0.6295 |     1.591 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.311 |     1.293 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.749 |   0.03581 |   -0.6032 |   0.06997 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3775 |   -0.4012 |  -0.08782 |    0.2553 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.001918 |    0.1783 |     1.513 |    0.3567 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.3333 |   -0.7379 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   61</span>|     446.99944 |     1.008 |    -2.018 |   -0.9851 |   -0.9476 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9359 |     1.058 |    -2.787 |    -1.806 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.859 |   -0.9671 |    -1.156 |   -0.5614 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6427 |   -0.6650 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     446.99944 |     93.70 |    -6.318 |    -1.009 |   -0.1632 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.209 |     2.355 |  0.001564 |    0.6369 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.08626 |    0.6911 |    0.6291 |     1.586 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.311 |     1.291 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     446.99944</span> |     93.70 |  0.001804 |    0.2671 |    0.8494 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.111 |     2.355 |  0.001564 |    0.6369 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.08626 |    0.6911 |    0.6291 |     1.586 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.311 |     1.291 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     2.474 |   0.03546 |   -0.4554 |    0.1057 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2777 |   -0.4692 |  -0.06549 |   0.06429 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.09650 |    0.2331 |    0.6150 |    0.1626 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.3753 |   -0.8072 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   62</span>|     446.99641 |     1.007 |    -2.020 |   -0.9846 |   -0.9460 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9366 |     1.059 |    -2.790 |    -1.808 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.868 |   -0.9676 |    -1.156 |   -0.5621 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6420 |   -0.6633 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     446.99641 |     93.67 |    -6.320 |    -1.009 |   -0.1616 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.209 |     2.356 |  0.001530 |    0.6356 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.08639 |    0.6908 |    0.6287 |     1.585 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.311 |     1.293 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     446.99641</span> |     93.67 |  0.001800 |    0.2672 |    0.8508 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.105 |     2.356 |  0.001530 |    0.6356 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.08639 |    0.6908 |    0.6287 |     1.585 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.311 |     1.293 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -2.070 |   0.03317 |   -0.4443 |    0.1488 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2478 |   -0.4968 |  -0.07674 |   0.09423 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.05318 |    0.2365 |    0.6058 |    0.5619 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.3013 |   -0.7224 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   63</span>|     446.99641 |     1.007 |    -2.020 |   -0.9846 |   -0.9460 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9366 |     1.059 |    -2.790 |    -1.808 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.868 |   -0.9676 |    -1.156 |   -0.5621 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6420 |   -0.6633 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     446.99641 |     93.67 |    -6.320 |    -1.009 |   -0.1616 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.209 |     2.356 |  0.001530 |    0.6356 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.08639 |    0.6908 |    0.6287 |     1.585 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.311 |     1.293 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     446.99641</span> |     93.67 |  0.001800 |    0.2672 |    0.8508 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     9.105 |     2.356 |  0.001530 |    0.6356 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.08639 |    0.6908 |    0.6287 |     1.585 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.311 |     1.293 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> calculating covariance matrix</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating residuals/tables</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>initial ETAs were nudged; (can control by foceiControl(etaNudge=., etaNudge2=))</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>ETAs were reset to zero during optimization; (Can control by foceiControl(resetEtaP=.))</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>gradient problems with initial estimate and covariance; see $scaleInfo</span>
-<span class="r-in"><span class="va">f_nlmixr_dfop_sfo_saem_obs_tc</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr</a></span><span class="op">(</span><span class="va">f_mmkin_tc</span><span class="op">[</span><span class="st">"DFOP-SFO"</span>, <span class="op">]</span>, est <span class="op">=</span> <span class="st">"saem"</span>,</span>
-<span class="r-in">  error_model <span class="op">=</span> <span class="st">"obs_tc"</span><span class="op">)</span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1:    90.7519   -4.8586   -1.7674   -3.5599   -2.0059    0.5800    4.8899    1.4250    1.1400    2.6923    0.4845    0.4370   10.2815    0.0004    9.0437    0.4047</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2:    9.0763e+01  -5.2004e+00  -1.9605e+00  -4.2009e+00  -1.8767e+00   2.0341e-01   4.6454e+00   1.3537e+00   1.0830e+00   2.7771e+00   4.6027e-01   5.1181e-01   6.3469e+00   4.0033e-04   6.8216e+00   9.0448e-05</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 3:    9.0656e+01  -5.4820e+00  -2.0937e+00  -4.0962e+00  -1.5381e+00  -1.7136e-02   4.4131e+00   1.2861e+00   1.0288e+00   3.5620e+00   4.3726e-01   6.1217e-01   4.8763e+00   4.1436e-05   5.4967e+00   1.0148e-05</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 4:    9.0543e+01  -5.7744e+00  -2.1074e+00  -4.0393e+00  -1.3603e+00  -2.3531e-01   5.0162e+00   1.2218e+00   1.0041e+00   3.3839e+00   4.1540e-01   5.8157e-01   4.1029e+00   2.1466e-04   4.2869e+00   1.9419e-06</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5:    91.1621   -6.0534   -2.0430   -4.0508   -1.2523   -0.2043    6.1015    1.1715    1.1998    3.2147    0.3946    0.5525    3.5979    0.0187    3.8221    0.0271</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 6:    91.1179   -5.9892   -2.0082   -4.1363   -1.1428   -0.1930    5.7964    1.1960    1.2360    3.4525    0.3749    0.5293    3.3091    0.0209    3.6230    0.0284</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 7:    91.6680   -5.9609   -2.0492   -4.1163   -1.0919   -0.1505    5.6134    1.6750    1.3055    3.2799    0.3631    0.5028    3.2412    0.0254    3.5108    0.0371</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 8:    91.6903   -5.9346   -2.0630   -4.0790   -1.1130   -0.0963    5.3327    2.2525    1.3524    3.1159    0.3449    0.4777    2.6165    0.0445    2.8556    0.0576</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 9:    92.2564   -5.7600   -2.0460   -4.1144   -1.0635   -0.0594    5.9616    2.1399    1.2848    2.9601    0.3277    0.4538    2.3593    0.0295    2.5704    0.0491</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 10:    92.2425   -5.7990   -2.0052   -4.0409   -1.0387   -0.1141    6.9547    2.1239    1.2205    2.8121    0.3113    0.4311    2.3237    0.0323    2.4227    0.0421</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 11:    92.4404   -5.6829   -2.0866   -4.0010   -1.0214   -0.0587    6.6790    2.0177    1.1595    2.6715    0.2957    0.4096    1.9963    0.0407    2.2478    0.0501</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 12:    92.0713   -5.8051   -2.1699   -4.0539   -0.9807    0.0174    6.7885    2.1380    1.1811    2.6017    0.2810    0.3891    1.8480    0.0502    2.0516    0.0522</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 13:    91.7214   -5.6954   -2.1579   -4.0944   -0.9935   -0.0195    6.4491    2.0311    1.1221    2.4716    0.2669    0.3696    1.8299    0.0531    1.9271    0.0520</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 14:    91.1978   -5.6733   -2.1988   -4.0794   -0.9387    0.0091    6.1267    1.9295    1.1589    2.3480    0.2536    0.3511    1.6357    0.0470    1.8899    0.0639</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 15:    91.3746   -5.5864   -2.1484   -4.1356   -0.9126    0.0045    5.8203    1.8330    1.2078    2.3265    0.2409    0.3336    1.6218    0.0428    1.6558    0.0725</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 16:    91.5646   -5.4931   -2.1474   -4.1242   -0.9148    0.0179    5.5293    1.7414    1.1474    2.2733    0.2288    0.3169    1.6467    0.0377    1.6977    0.0600</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 17:    91.4767   -5.5885   -2.1424   -4.1386   -0.9308    0.0602    5.2528    1.9428    1.2141    2.1762    0.2174    0.3343    1.4916    0.0424    1.4326    0.0717</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 18:    90.9989   -5.6364   -2.1601   -4.1606   -0.9676    0.0694    4.9902    1.8457    1.2521    2.2899    0.2065    0.3279    1.4504    0.0471    1.4267    0.0700</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 19:    91.4050   -5.7347   -2.0985   -4.1476   -0.9656    0.0668    4.7407    2.3263    1.4064    2.2061    0.1962    0.3296    1.3679    0.0485    1.2735    0.0844</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 20:    91.2707   -5.7623   -2.1155   -4.1538   -0.9601    0.0425    4.5037    2.6509    1.3408    2.1958    0.1864    0.3576    1.3736    0.0452    1.3454    0.0743</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 21:    91.6878   -5.8143   -2.1261   -4.1649   -0.9325    0.0333    4.7695    2.8996    1.3244    2.1300    0.1771    0.3509    1.4793    0.0508    1.0333    0.0839</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 22:    91.5363   -5.9047   -2.1358   -4.1655   -0.9207    0.0339    4.5310    3.4408    1.3022    2.1829    0.1682    0.3378    1.4260    0.0488    0.9436    0.0877</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 23:    91.9969   -5.9565   -2.1378   -4.1609   -0.9243    0.0701    4.3045    3.7239    1.3446    2.2424    0.1598    0.3209    1.4182    0.0412    0.9205    0.0873</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 24:    92.2835   -5.9272   -2.0883   -4.1651   -0.9248    0.0834    4.0892    3.7780    1.2773    2.2187    0.1518    0.3049    1.4749    0.0427    0.9837    0.0802</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 25:    92.3389   -6.1292   -2.1033   -4.2473   -0.9069    0.0593    3.8848    4.7059    1.2740    2.4323    0.1442    0.2896    1.3656    0.0493    0.9299    0.0798</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 26:    91.9840   -5.9821   -2.1124   -4.2150   -0.9100    0.1076    3.6905    4.4706    1.2103    2.4699    0.1458    0.2751    1.3200    0.0504    0.8256    0.0912</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 27:    92.4671   -5.8765   -2.0910   -4.2169   -0.9356    0.0483    3.5060    4.2471    1.2302    2.4791    0.1385    0.2614    1.4252    0.0520    0.8191    0.0836</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 28:    92.4130   -5.9480   -2.0870   -4.2191   -0.9338    0.0866    3.3859    4.1337    1.2210    2.4685    0.1412    0.2557    1.3860    0.0484    0.9189    0.0770</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 29:    92.3064   -5.7845   -2.0759   -4.2321   -0.9270    0.0855    4.2691    3.9271    1.3369    2.5457    0.1341    0.2922    1.3848    0.0513    0.9658    0.0785</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 30:    92.2109   -5.9347   -2.0725   -4.2042   -0.9074    0.0481    5.5659    3.9234    1.2701    2.4184    0.1370    0.2776    1.2925    0.0560    0.9058    0.0806</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 31:    91.8912   -5.7466   -2.0912   -4.1504   -0.9087    0.0443    5.2876    3.7272    1.2690    2.2975    0.1301    0.2637    1.3348    0.0517    0.8672    0.0840</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 32:    92.3866   -5.8560   -2.0979   -4.1547   -0.9044    0.0335    5.0232    3.5408    1.2761    2.3185    0.1307    0.2505    1.3558    0.0487    0.9422    0.0791</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 33:    92.4555   -5.6989   -2.0956   -4.1479   -0.9038    0.0447    4.7720    3.3638    1.3253    2.2962    0.1242    0.2380    1.4321    0.0432    0.8961    0.0799</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 34:    92.6307   -5.8831   -2.0769   -4.1319   -0.8946    0.0430    4.5334    3.7151    1.2785    2.2638    0.1201    0.2261    1.4076    0.0457    0.8269    0.0813</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 35:    92.5659   -5.9100   -2.0748   -4.1482   -0.9079    0.0563    4.6634    3.7452    1.3141    2.3378    0.1168    0.2148    1.3571    0.0451    0.8317    0.0831</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 36:    92.5738   -5.8574   -2.0981   -4.1214   -0.8963    0.0302    5.3272    3.7371    1.3359    2.2776    0.1331    0.2041    1.3308    0.0480    0.8953    0.0791</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 37:    92.1615   -5.7137   -2.0922   -4.1178   -0.9035    0.0455    5.4894    3.5503    1.2691    2.2508    0.1473    0.1939    1.3692    0.0495    0.9358    0.0800</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 38:    92.4421   -5.6978   -2.0905   -4.1275   -0.9052    0.0108    5.4295    3.3728    1.2057    2.2119    0.1432    0.1842    1.3421    0.0549    0.8932    0.0781</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 39:    92.3995   -5.6121   -2.0804   -4.1275   -0.8903    0.0270    5.1580    3.2041    1.1454    2.2119    0.1360    0.1978    1.3353    0.0524    0.9062    0.0775</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 40:    92.5616   -5.7907   -2.1099   -4.1127   -0.9074    0.0501    4.9001    3.0439    1.1582    2.1723    0.1387    0.1879    1.2981    0.0510    0.9517    0.0797</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 41:    92.2655   -5.6532   -2.0631   -4.1437   -0.9128    0.0346    4.6551    2.8917    1.1202    2.1498    0.1403    0.1785    1.4500    0.0461    0.7394    0.0903</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 42:    92.3218   -5.4916   -2.1072   -4.1312   -0.9310    0.0360    4.4224    2.7471    1.1648    2.1428    0.1332    0.1695    1.4034    0.0508    0.8131    0.0854</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 43:    92.6165   -5.5145   -2.0838   -4.1239   -0.9215    0.0347    4.2012    2.6098    1.1892    2.1202    0.1463    0.1611    1.3416    0.0507    0.8213    0.0847</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 44:    92.5385   -5.5590   -2.0872   -4.1376   -0.9210    0.0672    3.9912    2.4793    1.1416    2.2253    0.1450    0.1530    1.4008    0.0352    0.8639    0.0847</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 45:    92.2152   -5.6170   -2.1014   -4.1657   -0.9331    0.0781    3.7916    2.4382    1.1250    2.2378    0.1516    0.1556    1.4612    0.0329    1.0091    0.0820</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 46:    91.8943   -5.6650   -2.1329   -4.1649   -0.9241    0.1060    3.6020    2.6448    1.0705    2.3112    0.1441    0.1549    1.5047    0.0384    1.0179    0.0805</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 47:    91.8153   -5.5910   -2.1514   -4.1919   -0.9169    0.0885    3.4219    2.5125    1.0386    2.3140    0.1506    0.1620    1.4759    0.0375    1.1021    0.0740</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 48:    91.6295   -5.5438   -2.1516   -4.1570   -0.8742    0.0851    3.2508    2.3869    1.0816    2.1983    0.1630    0.1786    1.4024    0.0405    1.1135    0.0732</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 49:    91.4363   -5.5847   -2.1732   -4.1794   -0.8634    0.0948    3.0883    2.4829    1.0865    2.2378    0.1549    0.1697    1.4314    0.0434    1.0529    0.0742</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 50:    91.3634   -5.5794   -2.1613   -4.1507   -0.8849    0.0862    2.9339    2.6404    1.0829    2.2519    0.1493    0.1612    1.3704    0.0446    1.0081    0.0736</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 51:    91.2150   -5.5503   -2.1675   -4.1432   -0.8749    0.1178    2.7872    2.7368    1.0605    2.2261    0.1418    0.1532    1.4312    0.0418    1.0380    0.0781</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 52:    91.4343   -5.6336   -2.2138   -4.1552   -0.8867    0.1391    2.6677    2.7022    1.0857    2.2588    0.1493    0.1455    1.3220    0.0469    0.9272    0.0761</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 53:    91.3310   -5.5185   -2.2340   -4.1430   -0.8855    0.1545    2.9130    2.5670    1.0720    2.3074    0.1532    0.1385    1.3224    0.0503    0.9688    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 54:    91.4342   -5.5008   -2.2086   -4.1777   -0.8720    0.1688    2.8202    2.4387    1.0624    2.4253    0.1631    0.1315    1.3186    0.0429    0.9425    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 55:    91.4983   -5.5944   -2.1709   -4.1211   -0.8638    0.1168    3.4789    2.5154    1.0549    2.3041    0.1557    0.1279    1.3800    0.0423    0.9620    0.0727</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 56:    91.5954   -5.5317   -2.1881   -4.1263   -0.8627    0.1366    4.1396    2.3896    1.0732    2.2684    0.1562    0.1221    1.3285    0.0431    1.0156    0.0708</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 57:    91.6591   -5.3839   -2.2082   -4.1122   -0.8929    0.1457    4.5073    2.2701    1.0672    2.3639    0.1539    0.1160    1.4074    0.0478    0.9796    0.0722</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 58:    91.7434   -5.3597   -2.2068   -4.1214   -0.8912    0.1437    4.2819    2.1566    1.0605    2.4496    0.1667    0.1109    1.4274    0.0450    1.0782    0.0676</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 59:    91.6597   -5.4672   -2.1918   -4.1412   -0.9021    0.1657    4.0678    2.0488    1.1306    2.5245    0.1888    0.1054    1.4745    0.0391    1.1492    0.0670</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 60:    91.7565   -5.4513   -2.2051   -4.1618   -0.9013    0.1554    3.8644    1.9464    1.1728    2.6191    0.1793    0.1001    1.4135    0.0402    1.1572    0.0665</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 61:    91.9484   -5.5390   -2.1973   -4.1611   -0.9011    0.1318    3.6712    1.8963    1.1460    2.6242    0.1704    0.0951    1.3855    0.0481    1.1109    0.0634</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 62:    92.1661   -5.4985   -2.1729   -4.1352   -0.8887    0.1327    4.3155    1.8588    1.1087    2.5551    0.1633    0.0995    1.3847    0.0444    1.0736    0.0656</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 63:    92.1727   -5.4086   -2.1610   -4.1488   -0.8998    0.1451    4.6019    1.7659    1.1413    2.6020    0.1718    0.1152    1.4299    0.0404    1.2202    0.0603</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 64:    92.4331   -5.5220   -2.1121   -4.1300   -0.9428    0.1211    5.0354    1.8121    1.0982    2.6157    0.1775    0.1209    1.4168    0.0348    1.2148    0.0625</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 65:    92.1871   -5.6089   -2.1258   -4.1412   -0.9127    0.1399    4.7836    2.1060    1.1568    2.5971    0.1757    0.1148    1.4000    0.0336    1.0305    0.0707</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 66:    92.1419   -5.7752   -2.1344   -4.1493   -0.9237    0.1494    4.5444    2.7076    1.1563    2.6439    0.1740    0.1091    1.4238    0.0341    1.0285    0.0688</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 67:    92.1187   -5.5967   -2.1430   -4.1460   -0.9074    0.1245    4.3172    2.5722    1.2052    2.6139    0.1868    0.1104    1.4100    0.0381    1.0508    0.0703</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 68:    92.1123   -5.6882   -2.1481   -4.1269   -0.9083    0.1206    4.1014    2.5548    1.2021    2.4832    0.1781    0.1462    1.3099    0.0417    1.0099    0.0713</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 69:    92.2511   -5.6923   -2.1456   -4.1315   -0.9077    0.1421    4.3551    2.6261    1.1850    2.3591    0.1692    0.1389    1.3546    0.0416    0.9516    0.0762</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 70:    92.0291   -5.7376   -2.1689   -4.1364   -0.8908    0.1407    4.1373    3.0520    1.1595    2.2495    0.1608    0.1465    1.3226    0.0436    0.9904    0.0711</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 71:    91.7690   -5.7223   -2.1773   -4.1986   -0.9036    0.1831    3.9304    2.8994    1.1015    2.4335    0.1527    0.1602    1.3135    0.0450    0.9564    0.0722</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 72:    91.4802   -5.7302   -2.2079   -4.2000   -0.8990    0.2112    3.7339    3.2965    1.1222    2.4612    0.1451    0.1622    1.3222    0.0426    1.0215    0.0717</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 73:    91.1247   -5.7803   -2.1827   -4.2214   -0.8876    0.1385    3.5472    3.2786    1.1612    2.5216    0.1650    0.1541    1.2811    0.0493    1.0076    0.0696</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 74:    90.9765   -5.6534   -2.1808   -4.2511   -0.8824    0.1308    3.8553    3.1146    1.1389    2.8140    0.1725    0.1464    1.2895    0.0518    1.0100    0.0707</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 75:    90.4805   -5.7293   -2.1481   -4.2613   -0.8704    0.1395    4.1255    2.9589    1.1623    2.7523    0.1713    0.1391    1.3112    0.0483    0.9439    0.0788</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 76:    90.3958   -5.7635   -2.1508   -4.2426   -0.8841    0.1402    4.7427    3.0223    1.2187    2.7243    0.1789    0.1321    1.3065    0.0468    0.9303    0.0758</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 77:    90.7518   -5.6517   -2.1498   -4.2773   -0.8730    0.1688    5.9340    2.8712    1.1840    2.8530    0.1828    0.1255    1.3553    0.0422    0.9755    0.0696</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 78:    91.0443   -5.7462   -2.1351   -4.2601   -0.8848    0.1746    5.6373    2.9912    1.1312    2.7659    0.1887    0.1192    1.3303    0.0410    0.8578    0.0744</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 79:    90.9631   -5.7307   -2.1618   -4.2593   -0.8965    0.1944    5.7520    3.1324    1.1206    2.8212    0.1875    0.1340    1.3519    0.0449    0.9006    0.0799</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 80:    90.8703   -5.7665   -2.1989   -4.2476   -0.9122    0.1933    5.4644    3.4527    1.1063    2.7920    0.1781    0.1273    1.3014    0.0497    0.9305    0.0792</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 81:    90.7781   -5.8702   -2.1789   -4.2821   -0.8828    0.1793    5.1912    3.7553    1.0662    3.0568    0.1807    0.1209    1.3120    0.0472    0.9808    0.0772</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 82:    90.8137   -6.1164   -2.2001   -4.3046   -0.8912    0.1916    4.9316    4.9023    1.0714    3.1363    0.1716    0.1149    1.2724    0.0539    1.0644    0.0693</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 83:    90.9900   -6.2077   -2.1695   -4.3121   -0.8924    0.1722    6.7123    5.3290    1.1120    3.2733    0.1741    0.1091    1.2822    0.0510    1.0142    0.0715</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 84:    90.8158   -6.3282   -2.1595   -4.2907   -0.9096    0.1818    6.3767    5.9725    1.1342    3.2384    0.1763    0.1037    1.2704    0.0420    0.8891    0.0834</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 85:    90.5926   -6.2052   -2.1643   -4.2780   -0.9000    0.1579    6.0579    5.6739    1.1472    3.0765    0.1751    0.0985    1.3263    0.0433    0.9484    0.0814</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 86:    90.3804   -6.2707   -2.1470   -4.2627   -0.9260    0.1650    5.7550    5.5542    1.1135    2.9227    0.1956    0.0936    1.2747    0.0447    0.9731    0.0780</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 87:    90.8425   -6.4455   -2.1135   -4.2273   -0.9294    0.1491    5.4672    6.8599    1.0716    2.7765    0.1858    0.0928    1.3204    0.0427    1.0005    0.0744</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 88:    91.2019   -6.2302   -2.1362   -4.1982   -0.9333    0.1615    5.1939    6.5169    1.1221    2.7203    0.1841    0.0928    1.2689    0.0466    0.9914    0.0771</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 89:    90.9907   -6.4173   -2.1174   -4.2493   -0.9182    0.1628    4.9342    6.2408    1.1719    2.9614    0.1749    0.1021    1.3580    0.0403    1.0171    0.0775</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 90:    91.1666   -6.5154   -2.1219   -4.2011   -0.9210    0.1594    4.6875    7.4895    1.1930    2.8133    0.1745    0.0970    1.2500    0.0432    0.9467    0.0799</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 91:    91.0165   -6.5918   -2.1465   -4.2231   -0.9079    0.1382    4.9772    7.3172    1.1706    2.6789    0.1810    0.1032    1.2958    0.0458    1.1223    0.0639</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 92:    91.5386   -6.6567   -2.1376   -4.2247   -0.9358    0.1696    5.5955    7.4188    1.1543    2.6339    0.1754    0.0980    1.2894    0.0463    0.9927    0.0764</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 93:    92.2552   -6.5096   -2.1645   -4.2340   -0.9393    0.1798    5.3157    7.0479    1.1612    2.6312    0.1718    0.0931    1.3698    0.0431    1.2649    0.0579</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 94:    91.7588   -6.4124   -2.1795   -4.2305   -0.9265    0.1842    5.8275    6.6955    1.1988    2.6403    0.1891    0.0884    1.3022    0.0481    1.0741    0.0713</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 95:    91.6694   -6.2975   -2.1946   -4.2442   -0.9392    0.2004    5.5361    6.3607    1.1399    2.7164    0.1796    0.1027    1.2519    0.0513    1.0982    0.0724</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 96:    91.2252   -6.0124   -2.2316   -4.2701   -0.8946    0.2375    5.2593    6.0427    1.0829    2.7921    0.2024    0.1147    1.3343    0.0426    1.3064    0.0560</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 97:    91.0388   -5.9178   -2.2563   -4.2703   -0.9093    0.2304    5.6825    5.7405    1.0890    2.7678    0.2113    0.1090    1.3517    0.0484    1.4160    0.0493</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 98:    90.9013   -6.1325   -2.2597   -4.2776   -0.9133    0.2447    5.7546    5.4535    1.1028    2.6806    0.2130    0.1182    1.2983    0.0451    1.2436    0.0584</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 99:    91.2086   -6.0047   -2.2719   -4.2972   -0.9136    0.2738    5.4668    5.1808    1.1776    2.7160    0.2132    0.1123    1.3301    0.0431    1.1850    0.0628</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 100:    91.3181   -5.9175   -2.2311   -4.3202   -0.8947    0.2844    5.1935    4.9218    1.1187    2.8660    0.2289    0.1471    1.3409    0.0371    0.9566    0.0806</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 101:    91.5112   -5.7721   -2.2292   -4.3060   -0.8842    0.3006    6.3617    4.6757    1.0628    2.7227    0.2174    0.1500    1.3269    0.0407    1.2002    0.0592</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 102:    91.5205   -5.9982   -2.2162   -4.3041   -0.8813    0.3065    7.7957    4.4419    1.0320    2.7176    0.2290    0.1474    1.3218    0.0392    0.9685    0.0783</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 103:    91.2416   -5.7012   -2.2204   -4.3198   -0.8610    0.3180    7.4059    4.2198    1.0242    2.5817    0.2175    0.1714    1.3003    0.0395    0.9472    0.0825</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 104:    91.2662   -5.8136   -2.2357   -4.3063   -0.8700    0.3110    7.0356    4.0088    1.0024    2.4701    0.2067    0.1939    1.2806    0.0407    1.0387    0.0743</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 105:    91.4688   -5.7922   -2.2204   -4.3319   -0.8703    0.2767    6.6838    3.8084    0.9751    2.6368    0.1963    0.1967    1.3320    0.0470    1.2043    0.0580</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 106:    91.3665   -5.8505   -2.2225   -4.3614   -0.8757    0.2922    7.1982    3.6180    1.0018    2.6378    0.1865    0.2132    1.2613    0.0464    1.0683    0.0693</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 107:    91.4934   -5.8933   -2.1993   -4.3433   -0.9112    0.3267    6.8383    3.7379    0.9616    2.5329    0.1772    0.2169    1.2911    0.0449    1.0688    0.0731</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 108:    92.1268   -5.7807   -2.1863   -4.4348   -0.9273    0.3235    6.4964    3.5510    1.0093    2.9025    0.1718    0.2061    1.3153    0.0414    0.8982    0.0796</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 109:    91.6585   -5.9778   -2.2083   -4.3550   -0.9061    0.3339    6.1715    4.5778    1.0600    2.7574    0.1755    0.2163    1.2754    0.0382    0.8100    0.0868</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 110:    92.0456   -5.6784   -2.2236   -4.3483   -0.8911    0.3022    5.8630    4.3489    1.0427    2.6195    0.1667    0.2472    1.3782    0.0336    0.7819    0.0931</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 111:    92.2862   -5.6624   -2.1985   -4.3680   -0.9178    0.3156    5.5698    4.1315    1.0149    2.5443    0.1622    0.2348    1.3541    0.0415    0.7848    0.0868</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 112:    92.5436   -5.6572   -2.2134   -4.3290   -0.9267    0.3025    5.2913    3.9249    1.0720    2.5118    0.1541    0.2231    1.3798    0.0385    0.9087    0.0799</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 113:    92.5970   -5.6263   -2.2030   -4.3044   -0.9123    0.2856    5.0268    3.7287    1.1371    2.4674    0.1764    0.2119    1.3007    0.0455    0.8651    0.0850</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 114:    92.9227   -5.6379   -2.2032   -4.2926   -0.9187    0.3282    4.7754    3.5422    1.1022    2.4105    0.1676    0.2013    1.3103    0.0439    0.9003    0.0815</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 115:    92.7678   -5.8033   -2.1888   -4.2988   -0.9406    0.3026    4.5367    3.3651    1.1127    2.5043    0.1592    0.1913    1.3643    0.0420    0.9649    0.0800</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 116:    92.0655   -5.8531   -2.2033   -4.2989   -0.9487    0.3013    4.3098    3.6792    1.1260    2.4981    0.1513    0.1817    1.2783    0.0445    0.9812    0.0818</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 117:    92.2258   -5.7861   -2.2095   -4.3393   -0.9399    0.2998    4.0943    3.4952    1.0697    2.7102    0.1437    0.1752    1.3037    0.0426    0.9067    0.0786</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 118:    92.3560   -5.8080   -2.2058   -4.3166   -0.9480    0.2743    4.6025    3.3205    1.0709    2.6198    0.1432    0.1747    1.2850    0.0481    0.9236    0.0769</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 119:    92.0489   -5.7726   -2.1972   -4.2740   -0.9087    0.2627    4.3723    3.1544    1.0892    2.4888    0.1620    0.1660    1.2659    0.0435    0.8389    0.0832</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 120:    91.9163   -5.8211   -2.2101   -4.2770   -0.9142    0.2674    4.1537    3.1700    1.0985    2.5003    0.1795    0.1577    1.2289    0.0454    0.9280    0.0826</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 121:    92.0274   -5.7601   -2.1916   -4.2686   -0.9055    0.2571    3.9460    3.1181    1.1026    2.3985    0.1908    0.1498    1.3015    0.0432    0.9363    0.0807</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 122:    92.2933   -5.7972   -2.2019   -4.2888   -0.8932    0.2679    3.7487    3.1755    1.0809    2.3976    0.1843    0.1636    1.2918    0.0433    0.8760    0.0803</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 123:    92.3361   -5.8903   -2.1745   -4.3386   -0.9157    0.2665    3.5613    3.8794    1.1308    2.5838    0.1751    0.1555    1.3219    0.0421    0.8987    0.0806</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 124:    92.5677   -6.0471   -2.1638   -4.2764   -0.9325    0.2645    3.3832    4.6152    1.1336    2.4546    0.1664    0.1526    1.2938    0.0375    0.9412    0.0798</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 125:    92.7852   -5.9841   -2.1661   -4.2825   -0.9373    0.2866    3.2141    4.3844    1.0770    2.3788    0.1581    0.1727    1.3137    0.0417    0.9228    0.0733</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 126:    92.7867   -5.9126   -2.1636   -4.2413   -0.9141    0.2756    3.0534    4.1652    1.0857    2.2921    0.1502    0.1994    1.2428    0.0450    0.8206    0.0795</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 127:    92.8733   -5.9110   -2.1546   -4.2269   -0.9258    0.2531    2.9007    3.9569    1.0914    2.3822    0.1426    0.2029    1.3043    0.0369    0.8206    0.0795</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 128:    93.0457   -5.9202   -2.1505   -4.2076   -0.9466    0.2463    3.3353    3.7591    1.0647    2.3012    0.1355    0.2180    1.3420    0.0385    0.8503    0.0803</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 129:    92.9207   -5.9882   -2.1704   -4.2204   -0.9319    0.2464    4.3160    4.4368    1.1187    2.3415    0.1386    0.2316    1.2546    0.0435    0.8777    0.0830</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 130:    92.2660   -6.2043   -2.1712   -4.2251   -0.9276    0.2186    4.4370    5.1440    1.0817    2.3426    0.1338    0.2200    1.2733    0.0476    0.9132    0.0753</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 131:    92.1286   -6.4163   -2.1634   -4.2642   -0.9223    0.2300    4.2151    6.2253    1.0614    2.5012    0.1333    0.2090    1.2690    0.0430    0.8201    0.0814</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 132:    92.0287   -6.4297   -2.1522   -4.2717   -0.9259    0.2250    4.0044    6.1979    1.0791    2.5146    0.1314    0.1985    1.2736    0.0449    0.8671    0.0784</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 133:    91.6843   -6.1415   -2.1685   -4.2553   -0.9349    0.2209    3.8041    5.8880    1.1445    2.4277    0.1248    0.2026    1.3381    0.0426    0.9129    0.0858</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 134:    91.6928   -6.1408   -2.1467   -4.2709   -0.9270    0.2648    3.6139    5.5936    1.0873    2.5602    0.1305    0.1925    1.3202    0.0416    0.7626    0.0936</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 135:    91.7984   -6.0371   -2.1673   -4.2572   -0.9497    0.2480    3.4332    5.3139    1.0649    2.4322    0.1389    0.1829    1.2592    0.0544    0.9459    0.0831</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 136:    91.9754   -6.1770   -2.1769   -4.2311   -0.9401    0.2435    3.2616    5.0482    1.0717    2.3106    0.1691    0.1737    1.3170    0.0436    1.0497    0.0816</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 137:    92.1546   -6.1216   -2.1731   -4.2278   -0.9401    0.2492    3.0985    4.9378    1.0200    2.1950    0.1606    0.1988    1.4019    0.0421    1.0200    0.0766</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 138:    92.2370   -5.9463   -2.1794   -4.1949   -0.9321    0.2460    2.9436    4.6909    1.0203    2.1200    0.1535    0.1947    1.3378    0.0425    0.9448    0.0804</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 139:    92.2025   -5.8849   -2.1820   -4.1868   -0.9200    0.2294    2.9458    4.4564    0.9875    2.1260    0.1759    0.2080    1.3244    0.0428    1.0110    0.0778</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 140:    91.8182   -5.7494   -2.1569   -4.1998   -0.9095    0.2318    2.7985    4.2336    1.0015    2.1507    0.1830    0.2113    1.3502    0.0432    0.7716    0.0922</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 141:    91.8292   -5.9568   -2.1640   -4.2109   -0.9122    0.2130    4.4608    4.0219    1.0180    2.1032    0.1739    0.2057    1.3594    0.0419    0.8088    0.0883</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 142:    91.9995   -6.0927   -2.1471   -4.2313   -0.9091    0.1875    4.5415    4.7705    1.0571    2.1286    0.1810    0.1954    1.4145    0.0406    0.8943    0.0847</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 143:    91.9160   -5.9892   -2.1546   -4.2043   -0.9355    0.1819    4.3145    4.5320    1.1337    2.1453    0.1888    0.2208    1.3449    0.0394    1.0910    0.0797</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 144:    92.0136   -5.9765   -2.1643   -4.2346   -0.9448    0.2455    4.3041    4.3054    1.1130    2.1300    0.1817    0.3253    1.3828    0.0340    1.0535    0.0873</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 145:    92.3893   -6.2224   -2.1211   -4.2316   -0.9405    0.2285    5.2279    4.9099    1.0573    2.1037    0.1727    0.3091    1.3797    0.0340    1.0160    0.0832</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 146:    92.6097   -6.2204   -2.1406   -4.2210   -0.9217    0.1762    4.9665    5.4466    1.0724    2.1658    0.1640    0.2936    1.4421    0.0408    1.2412    0.0674</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 147:    92.8499   -6.0091   -2.1165   -4.2234   -0.9682    0.1783    4.7182    5.1743    1.0188    2.1714    0.1722    0.2789    1.4545    0.0397    1.4472    0.0556</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 148:    92.6602   -5.8003   -2.1059   -4.2132   -0.9282    0.2102    4.4823    4.9156    1.0106    2.1075    0.1880    0.2684    1.4013    0.0413    1.0748    0.0758</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 149:    92.7388   -5.8727   -2.1569   -4.2185   -0.9266    0.1868    4.2581    4.6698    1.1447    2.0851    0.1786    0.2550    1.3137    0.0413    0.9963    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 150:    92.7348   -5.7926   -2.1184   -4.2257   -0.9324    0.1966    4.0452    4.4363    1.1760    2.1508    0.1697    0.2704    1.3792    0.0363    0.8204    0.0889</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 151:    92.7968   -5.7301   -2.1179   -4.1959   -0.9275    0.1927    3.8430    4.2145    1.1707    2.1702    0.1842    0.2569    1.3789    0.0387    0.7890    0.0873</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 152:    92.9011   -5.7417   -2.1622   -4.2086   -0.9215    0.1784    2.0089    3.0670    1.1984    2.2253    0.1814    0.2507    1.3431    0.0395    0.9622    0.0780</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 153:    92.8020   -5.7707   -2.1494   -4.2128   -0.9228    0.1818    2.2261    2.9648    1.1192    2.3058    0.1749    0.2548    1.3671    0.0369    0.9507    0.0744</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 154:    92.5217   -5.8043   -2.1447   -4.2074   -0.9303    0.1766    2.7638    3.1314    1.1141    2.2814    0.1811    0.2389    1.3250    0.0408    1.0538    0.0689</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 155:    92.8765   -5.5586   -2.1275   -4.1639   -0.9320    0.1434    2.4217    2.4658    1.1231    2.1314    0.1746    0.2426    1.3785    0.0407    0.9682    0.0796</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 156:    93.0074   -5.6819   -2.1343   -4.1819   -0.9382    0.1587    1.4756    2.7496    1.1200    2.1700    0.1849    0.2320    1.3643    0.0395    1.0875    0.0653</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 157:    92.7950   -5.6827   -2.1178   -4.1808   -0.9553    0.1585    1.1607    2.5156    1.1129    2.2109    0.1724    0.2366    1.4045    0.0374    1.1322    0.0676</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 158:    92.7684   -5.7106   -2.0933   -4.2149   -0.9717    0.1602    1.4884    2.6532    1.1269    2.1388    0.1838    0.2666    1.4009    0.0348    0.8728    0.0832</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 159:    93.0980   -5.8911   -2.1297   -4.2160   -0.9660    0.1407    1.6034    3.3765    1.1182    2.1661    0.1705    0.2804    1.3557    0.0430    1.0076    0.0776</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 160:    93.1849   -5.9433   -2.1177   -4.1735   -0.9436    0.1324    1.8692    4.0887    1.1252    2.0213    0.1642    0.2922    1.3251    0.0386    0.8586    0.0820</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 161:    93.4771   -5.7768   -2.0899   -4.1697   -0.9670    0.1043    2.0938    2.9110    1.1069    1.9538    0.1820    0.3046    1.3270    0.0413    0.8220    0.0855</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 162:    93.4961   -5.6516   -2.0965   -4.1633   -0.9708    0.1263    2.2053    2.3022    1.1179    1.9555    0.1758    0.2891    1.3158    0.0417    0.9943    0.0768</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 163:    93.1627   -5.5944   -2.1527   -4.1633   -0.9488    0.1452    2.9600    2.3291    1.1027    1.9555    0.1766    0.3000    1.3626    0.0376    0.9790    0.0743</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 164:    92.7951   -5.6646   -2.1539   -4.1699   -0.9447    0.1863    2.9195    2.7599    1.0794    1.9511    0.1651    0.2918    1.4091    0.0357    1.1005    0.0709</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 165:    92.6045   -5.5927   -2.1842   -4.1770   -0.9436    0.1939    2.2014    2.3213    1.0622    1.9775    0.1671    0.2899    1.3945    0.0372    1.0533    0.0694</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 166:    92.6660   -5.5922   -2.1438   -4.1611   -0.9441    0.1767    1.8951    2.3445    1.1025    2.0399    0.1802    0.2802    1.4229    0.0354    1.1552    0.0682</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 167:    92.4353   -5.5118   -2.1415   -4.1730   -0.9124    0.1827    1.5029    1.8145    1.0701    2.0090    0.1727    0.2642    1.4525    0.0362    1.0577    0.0754</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 168:    92.3761   -5.5522   -2.1625   -4.1911   -0.9114    0.1782    1.2703    2.0808    1.0814    2.0588    0.1810    0.2658    1.3973    0.0364    0.9874    0.0729</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 169:    92.5240   -5.6102   -2.1396   -4.1793   -0.8972    0.1722    1.3115    2.2750    1.0759    2.0910    0.2147    0.2549    1.3553    0.0392    0.9770    0.0759</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 170:    92.3827   -5.5942   -2.1231   -4.1847   -0.9338    0.1525    1.5707    2.3778    1.0542    2.0824    0.2042    0.2519    1.4424    0.0363    1.1270    0.0687</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 171:    92.1706   -5.6171   -2.1619   -4.1926   -0.9056    0.1390    1.0935    2.2869    1.1239    2.1697    0.1946    0.2624    1.2980    0.0411    0.9988    0.0740</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 172:    92.1491   -5.5704   -2.1447   -4.1913   -0.9285    0.1649    1.0292    2.2160    1.1231    2.1905    0.1860    0.2518    1.3154    0.0375    1.0152    0.0772</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 173:    91.9987   -5.5411   -2.1434   -4.1803   -0.9056    0.1585    0.6511    1.9989    1.0892    2.2283    0.1899    0.2413    1.3814    0.0380    1.1733    0.0693</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 174:    92.0377   -5.5419   -2.1081   -4.1928   -0.9057    0.1418    0.9013    2.0933    1.1813    2.2773    0.1756    0.2559    1.4419    0.0343    0.9942    0.0801</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 175:    92.0266   -5.5125   -2.1014   -4.1775   -0.9119    0.1371    0.7194    2.0624    1.1848    2.2328    0.1754    0.2504    1.3987    0.0353    1.0838    0.0728</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 176:    92.0365   -5.5658   -2.0914   -4.1646   -0.8956    0.1096    0.7557    2.0588    1.1642    2.1610    0.1617    0.2722    1.3658    0.0347    0.9405    0.0757</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 177:    91.8661   -5.6799   -2.0950   -4.1695   -0.9010    0.1193    0.8835    2.8899    1.1394    2.1886    0.1809    0.2777    1.3841    0.0351    0.9178    0.0794</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 178:    91.9392   -5.7853   -2.1139   -4.1711   -0.9190    0.1222    0.6673    3.0461    1.1712    2.1948    0.1576    0.2237    1.3623    0.0383    0.9060    0.0795</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 179:    92.0116   -5.7560   -2.1418   -4.1711   -0.9153    0.1034    0.4356    2.9139    1.1653    2.1948    0.1497    0.2364    1.3387    0.0392    0.9327    0.0793</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 180:    92.1013   -5.7920   -2.1368   -4.1654   -0.9059    0.0912    0.3859    3.2592    1.1357    2.1744    0.1570    0.2337    1.3622    0.0408    1.0299    0.0739</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 181:    92.1032   -5.8125   -2.1260   -4.1791   -0.9404    0.1731    0.4091    3.0817    1.1113    2.2356    0.1572    0.2742    1.4509    0.0340    0.9180    0.0865</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 182:    92.1315   -5.7991   -2.1327   -4.1737   -0.9319    0.1519    0.3145    3.0292    1.1520    2.1360    0.1661    0.2482    1.4105    0.0361    1.1338    0.0748</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 183:    92.1350   -6.0470   -2.1264   -4.1707   -0.9525    0.1787    0.2045    3.8296    1.1229    2.1369    0.1735    0.2453    1.3132    0.0350    1.0507    0.0759</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 184:    92.0539   -5.9856   -2.1289   -4.1968   -0.9281    0.1789    0.1166    3.9425    1.0558    2.2320    0.1658    0.2253    1.3390    0.0355    1.1271    0.0720</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 185:    92.0755   -6.0524   -2.1385   -4.2076   -0.9313    0.1794    0.1193    4.2430    1.0465    2.3571    0.1566    0.2152    1.3777    0.0368    1.2013    0.0631</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 186:    92.1639   -6.0663   -2.1399   -4.1869   -0.9311    0.1817    0.1392    4.4053    1.0495    2.4857    0.1532    0.2331    1.3751    0.0365    1.0497    0.0697</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 187:    92.1759   -6.3052   -2.1469   -4.2221   -0.9428    0.1970    0.1356    5.5622    0.9953    2.3367    0.1451    0.2359    1.3754    0.0367    1.0969    0.0656</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 188:    92.1744   -6.0494   -2.1406   -4.2445   -0.9595    0.2089    0.1364    4.3818    0.9882    2.4074    0.1480    0.2364    1.3709    0.0396    1.1259    0.0642</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 189:    92.1989   -6.1255   -2.1138   -4.2037   -0.9368    0.1796    0.1444    4.6756    0.9840    2.3786    0.1502    0.2303    1.3979    0.0393    1.2011    0.0630</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 190:    92.0944   -6.1343   -2.1536   -4.2106   -0.9267    0.2105    0.1288    4.4977    1.0475    2.3593    0.1443    0.2396    1.3247    0.0384    1.1304    0.0711</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 191:    92.1238   -6.0639   -2.1462   -4.2883   -0.9311    0.2155    0.1246    4.1066    1.0428    2.6836    0.1442    0.2344    1.4041    0.0344    1.1631    0.0684</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 192:    92.1328   -6.0743   -2.1232   -4.3119   -0.9586    0.2319    0.1192    4.1348    1.0020    2.7067    0.1466    0.2515    1.4039    0.0355    1.0021    0.0772</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 193:    92.0881   -5.8697   -2.1298   -4.2691   -0.9309    0.2295    0.1050    3.4476    0.9879    2.5496    0.1344    0.2348    1.4677    0.0351    1.0332    0.0705</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 194:    92.1086   -5.7557   -2.1176   -4.3249   -0.9029    0.2183    0.0702    2.7793    1.0043    2.8853    0.1460    0.2213    1.4733    0.0325    1.0929    0.0681</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 195:    92.1265   -5.8976   -2.1506   -4.2932   -0.9267    0.2511    0.0593    3.4959    0.9881    2.6806    0.1278    0.2505    1.6138    0.0350    1.1327    0.0703</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 196:    92.1316   -5.7018   -2.1627   -4.2889   -0.9278    0.2392    0.0717    2.9210    1.0160    2.7067    0.1470    0.2350    1.5063    0.0418    1.0525    0.0734</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 197:    92.1756   -5.7899   -2.1604   -4.2819   -0.9248    0.2765    0.0754    2.9991    1.0167    2.6170    0.1465    0.2314    1.4982    0.0387    1.0504    0.0774</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 198:    92.2153   -5.8248   -2.1571   -4.2245   -0.9350    0.2195    0.0651    3.0128    0.9196    2.3123    0.1451    0.2363    1.3965    0.0453    1.1561    0.0650</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 199:    92.1935   -5.8187   -2.1505   -4.2123   -0.9452    0.2406    0.0535    3.0017    0.9553    2.2771    0.1338    0.2415    1.4485    0.0407    1.0647    0.0699</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 200:    92.2308   -5.6732   -2.1578   -4.2182   -0.9324    0.2330    0.0618    2.4070    1.0687    2.2978    0.1513    0.2199    1.4101    0.0385    1.0283    0.0741</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 201:    92.2305   -5.6796   -2.1604   -4.2064   -0.9320    0.2262    0.0530    2.4360    1.0551    2.2994    0.1529    0.2219    1.3653    0.0407    1.0115    0.0757</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 202:    92.2259   -5.7201   -2.1550   -4.2058   -0.9263    0.2208    0.0461    2.6422    1.0351    2.2888    0.1520    0.2100    1.3739    0.0418    1.0474    0.0730</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 203:    92.2212   -5.7539   -2.1457   -4.2037   -0.9303    0.2163    0.0422    2.8480    1.0403    2.2723    0.1465    0.2070    1.3973    0.0404    1.0518    0.0729</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 204:    92.2164   -5.8068   -2.1371   -4.2093   -0.9300    0.2163    0.0383    3.1313    1.0426    2.2984    0.1423    0.2091    1.4025    0.0391    1.0284    0.0737</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 205:    92.2120   -5.8371   -2.1369   -4.2142   -0.9308    0.2156    0.0347    3.2786    1.0392    2.3249    0.1390    0.2048    1.4115    0.0385    1.0161    0.0733</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 206:    92.2071   -5.8681   -2.1391   -4.2176   -0.9287    0.2158    0.0336    3.4917    1.0442    2.3461    0.1388    0.2041    1.4017    0.0394    0.9977    0.0735</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 207:    92.2078   -5.8966   -2.1424   -4.2230   -0.9286    0.2181    0.0337    3.6741    1.0456    2.3743    0.1369    0.2009    1.3988    0.0397    0.9792    0.0740</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 208:    92.2077   -5.8972   -2.1459   -4.2249   -0.9300    0.2208    0.0340    3.6775    1.0460    2.3889    0.1362    0.1985    1.3969    0.0394    0.9729    0.0742</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 209:    92.2091   -5.8831   -2.1488   -4.2271   -0.9326    0.2227    0.0337    3.6019    1.0465    2.4005    0.1380    0.1967    1.3949    0.0395    0.9745    0.0741</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 210:    92.2115   -5.8806   -2.1490   -4.2282   -0.9349    0.2238    0.0324    3.5652    1.0516    2.4211    0.1386    0.1979    1.3941    0.0391    0.9820    0.0740</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 211:    92.2151   -5.8791   -2.1516   -4.2291   -0.9363    0.2258    0.0318    3.5315    1.0458    2.4465    0.1390    0.1989    1.3918    0.0394    0.9901    0.0735</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 212:    92.2189   -5.8789   -2.1532   -4.2297   -0.9380    0.2269    0.0312    3.4989    1.0434    2.4718    0.1409    0.1993    1.3884    0.0394    1.0007    0.0732</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 213:    92.2226   -5.8714   -2.1556   -4.2287   -0.9395    0.2255    0.0313    3.4460    1.0440    2.4800    0.1413    0.1993    1.3859    0.0397    1.0157    0.0722</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 214:    92.2233   -5.8706   -2.1553   -4.2279   -0.9394    0.2237    0.0309    3.4283    1.0431    2.4809    0.1414    0.2003    1.3849    0.0399    1.0186    0.0720</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 215:    92.2242   -5.8750   -2.1536   -4.2285   -0.9390    0.2212    0.0312    3.4442    1.0455    2.4800    0.1417    0.2015    1.3830    0.0401    1.0126    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 216:    92.2252   -5.8788   -2.1521   -4.2277   -0.9393    0.2192    0.0316    3.4718    1.0459    2.4791    0.1410    0.2028    1.3817    0.0402    1.0046    0.0726</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 217:    92.2255   -5.8869   -2.1516   -4.2268   -0.9400    0.2177    0.0322    3.5295    1.0456    2.4751    0.1407    0.2042    1.3814    0.0401    1.0011    0.0729</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 218:    92.2230   -5.8819   -2.1511   -4.2259   -0.9400    0.2164    0.0327    3.5004    1.0473    2.4693    0.1399    0.2051    1.3784    0.0401    0.9962    0.0731</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 219:    92.2206   -5.8761   -2.1499   -4.2269   -0.9393    0.2156    0.0325    3.4736    1.0494    2.4667    0.1399    0.2066    1.3794    0.0399    0.9925    0.0734</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 220:    92.2185   -5.8775   -2.1494   -4.2267   -0.9380    0.2150    0.0331    3.4894    1.0511    2.4596    0.1396    0.2090    1.3788    0.0398    0.9881    0.0734</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 221:    92.2180   -5.8774   -2.1499   -4.2271   -0.9369    0.2142    0.0328    3.4873    1.0518    2.4559    0.1399    0.2109    1.3776    0.0399    0.9842    0.0735</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 222:    92.2191   -5.8833   -2.1503   -4.2280   -0.9359    0.2127    0.0328    3.5384    1.0518    2.4533    0.1400    0.2114    1.3778    0.0400    0.9825    0.0735</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 223:    92.2199   -5.8831   -2.1486   -4.2288   -0.9343    0.2112    0.0326    3.5474    1.0539    2.4515    0.1396    0.2127    1.3795    0.0398    0.9806    0.0735</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 224:    92.2206   -5.8870   -2.1467   -4.2283   -0.9331    0.2093    0.0324    3.5619    1.0550    2.4482    0.1395    0.2133    1.3805    0.0395    0.9792    0.0735</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 225:    92.2218   -5.8929   -2.1459   -4.2289   -0.9328    0.2086    0.0322    3.6001    1.0548    2.4492    0.1388    0.2142    1.3804    0.0393    0.9809    0.0734</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 226:    92.2228   -5.8909   -2.1440   -4.2293   -0.9322    0.2082    0.0320    3.6007    1.0566    2.4513    0.1383    0.2143    1.3826    0.0390    0.9815    0.0737</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 227:    92.2234   -5.8838   -2.1444   -4.2304   -0.9319    0.2088    0.0319    3.5748    1.0551    2.4562    0.1381    0.2140    1.3814    0.0390    0.9836    0.0736</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 228:    92.2243   -5.8835   -2.1444   -4.2314   -0.9326    0.2093    0.0317    3.5756    1.0526    2.4607    0.1377    0.2143    1.3824    0.0389    0.9901    0.0733</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 229:    92.2251   -5.8916   -2.1442   -4.2307   -0.9330    0.2095    0.0318    3.6344    1.0501    2.4580    0.1371    0.2148    1.3850    0.0388    0.9930    0.0733</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 230:    92.2250   -5.8951   -2.1442   -4.2303   -0.9334    0.2107    0.0318    3.6533    1.0475    2.4550    0.1369    0.2150    1.3872    0.0387    0.9953    0.0734</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 231:    92.2247   -5.8964   -2.1444   -4.2300   -0.9345    0.2119    0.0322    3.6564    1.0446    2.4549    0.1368    0.2151    1.3899    0.0388    0.9991    0.0733</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 232:    92.2230   -5.8982   -2.1451   -4.2306   -0.9352    0.2132    0.0326    3.6643    1.0422    2.4591    0.1365    0.2155    1.3934    0.0388    1.0052    0.0729</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 233:    92.2223   -5.9022   -2.1455   -4.2327   -0.9354    0.2141    0.0329    3.6824    1.0396    2.4697    0.1362    0.2165    1.3970    0.0388    1.0107    0.0726</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 234:    92.2200   -5.9075   -2.1457   -4.2324   -0.9348    0.2140    0.0335    3.7056    1.0364    2.4735    0.1366    0.2174    1.3977    0.0388    1.0163    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 235:    92.2171   -5.9094   -2.1457   -4.2338   -0.9342    0.2142    0.0342    3.7222    1.0339    2.4854    0.1372    0.2189    1.3999    0.0389    1.0228    0.0721</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 236:    92.2161   -5.9195   -2.1464   -4.2368   -0.9340    0.2140    0.0346    3.7789    1.0329    2.4986    0.1376    0.2200    1.4007    0.0389    1.0281    0.0721</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 237:    92.2163   -5.9214   -2.1469   -4.2390   -0.9330    0.2143    0.0347    3.7862    1.0329    2.5074    0.1378    0.2195    1.4030    0.0389    1.0320    0.0719</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 238:    92.2165   -5.9203   -2.1473   -4.2399   -0.9326    0.2143    0.0347    3.7781    1.0336    2.5100    0.1381    0.2186    1.4031    0.0388    1.0347    0.0719</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 239:    92.2173   -5.9212   -2.1471   -4.2403   -0.9325    0.2136    0.0344    3.7908    1.0350    2.5128    0.1384    0.2175    1.4021    0.0389    1.0352    0.0718</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 240:    92.2178   -5.9217   -2.1470   -4.2394   -0.9322    0.2133    0.0342    3.8082    1.0363    2.5118    0.1384    0.2169    1.4025    0.0387    1.0345    0.0719</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 241:    92.2177   -5.9200   -2.1470   -4.2383   -0.9318    0.2136    0.0340    3.8123    1.0381    2.5110    0.1385    0.2161    1.4028    0.0387    1.0336    0.0720</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 242:    92.2170   -5.9109   -2.1478   -4.2371   -0.9308    0.2136    0.0340    3.7766    1.0387    2.5090    0.1385    0.2157    1.4027    0.0386    1.0322    0.0720</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 243:    92.2165   -5.9029   -2.1484   -4.2371   -0.9300    0.2140    0.0340    3.7433    1.0389    2.5128    0.1386    0.2150    1.4032    0.0385    1.0342    0.0720</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 244:    92.2161   -5.8998   -2.1489   -4.2374   -0.9294    0.2143    0.0340    3.7242    1.0392    2.5173    0.1386    0.2148    1.4030    0.0384    1.0343    0.0720</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 245:    92.2153   -5.9048   -2.1494   -4.2370   -0.9291    0.2149    0.0339    3.7491    1.0399    2.5182    0.1386    0.2146    1.4019    0.0383    1.0317    0.0722</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 246:    92.2147   -5.9093   -2.1498   -4.2371   -0.9289    0.2156    0.0339    3.7727    1.0409    2.5218    0.1384    0.2145    1.4016    0.0382    1.0309    0.0724</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 247:    92.2143   -5.9124   -2.1503   -4.2368   -0.9286    0.2159    0.0338    3.7972    1.0408    2.5232    0.1383    0.2144    1.4012    0.0382    1.0297    0.0725</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 248:    92.2137   -5.9122   -2.1500   -4.2367   -0.9286    0.2160    0.0336    3.8036    1.0409    2.5245    0.1382    0.2141    1.4022    0.0381    1.0272    0.0727</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 249:    92.2133   -5.9101   -2.1501   -4.2364   -0.9288    0.2160    0.0334    3.7984    1.0406    2.5268    0.1383    0.2136    1.4031    0.0381    1.0273    0.0726</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 250:    92.2139   -5.9115   -2.1502   -4.2368   -0.9289    0.2167    0.0335    3.8015    1.0412    2.5326    0.1380    0.2132    1.4035    0.0381    1.0261    0.0727</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 251:    92.2132   -5.9123   -2.1502   -4.2373   -0.9297    0.2170    0.0338    3.8015    1.0421    2.5362    0.1376    0.2126    1.4053    0.0379    1.0274    0.0727</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 252:    92.2128   -5.9106   -2.1502   -4.2366   -0.9299    0.2174    0.0338    3.7931    1.0427    2.5345    0.1371    0.2120    1.4062    0.0379    1.0262    0.0728</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 253:    92.2118   -5.9098   -2.1492   -4.2364   -0.9297    0.2173    0.0340    3.8011    1.0420    2.5338    0.1369    0.2121    1.4074    0.0378    1.0232    0.0731</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 254:    92.2107   -5.9104   -2.1479   -4.2362   -0.9296    0.2171    0.0341    3.8085    1.0398    2.5330    0.1366    0.2123    1.4093    0.0377    1.0230    0.0731</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 255:    92.2103   -5.9133   -2.1466   -4.2359   -0.9299    0.2166    0.0341    3.8219    1.0381    2.5346    0.1363    0.2122    1.4105    0.0377    1.0230    0.0731</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 256:    92.2094   -5.9171   -2.1453   -4.2356   -0.9299    0.2161    0.0341    3.8498    1.0366    2.5366    0.1362    0.2123    1.4114    0.0375    1.0239    0.0731</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 257:    92.2091   -5.9169   -2.1441   -4.2353   -0.9302    0.2153    0.0341    3.8529    1.0347    2.5385    0.1360    0.2118    1.4126    0.0375    1.0265    0.0729</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 258:    92.2089   -5.9191   -2.1431   -4.2349   -0.9301    0.2145    0.0340    3.8608    1.0331    2.5392    0.1359    0.2119    1.4126    0.0375    1.0282    0.0728</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 259:    92.2093   -5.9249   -2.1426   -4.2337   -0.9301    0.2145    0.0341    3.8853    1.0321    2.5385    0.1361    0.2120    1.4114    0.0374    1.0280    0.0728</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 260:    92.2095   -5.9297   -2.1415   -4.2328   -0.9303    0.2146    0.0339    3.9108    1.0302    2.5367    0.1358    0.2125    1.4112    0.0373    1.0265    0.0729</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 261:    92.2096   -5.9341   -2.1407   -4.2321   -0.9307    0.2147    0.0338    3.9343    1.0278    2.5343    0.1356    0.2131    1.4121    0.0373    1.0272    0.0728</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 262:    92.2094   -5.9342   -2.1398   -4.2312   -0.9310    0.2149    0.0336    3.9314    1.0254    2.5313    0.1356    0.2139    1.4115    0.0372    1.0249    0.0729</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 263:    92.2088   -5.9376   -2.1388   -4.2304   -0.9313    0.2153    0.0334    3.9452    1.0232    2.5289    0.1357    0.2143    1.4109    0.0371    1.0228    0.0731</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 264:    92.2077   -5.9410   -2.1379   -4.2301   -0.9317    0.2153    0.0333    3.9571    1.0204    2.5265    0.1356    0.2147    1.4105    0.0371    1.0224    0.0731</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 265:    92.2069   -5.9454   -2.1374   -4.2296   -0.9322    0.2154    0.0332    3.9800    1.0182    2.5230    0.1356    0.2147    1.4115    0.0371    1.0232    0.0730</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 266:    92.2067   -5.9463   -2.1370   -4.2291   -0.9324    0.2155    0.0330    3.9809    1.0165    2.5217    0.1354    0.2148    1.4121    0.0371    1.0220    0.0730</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 267:    92.2064   -5.9492   -2.1368   -4.2282   -0.9329    0.2154    0.0328    3.9874    1.0150    2.5199    0.1357    0.2150    1.4124    0.0370    1.0233    0.0729</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 268:    92.2065   -5.9468   -2.1360   -4.2276   -0.9333    0.2155    0.0328    3.9722    1.0133    2.5171    0.1362    0.2155    1.4133    0.0369    1.0243    0.0729</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 269:    92.2066   -5.9438   -2.1352   -4.2267   -0.9335    0.2151    0.0328    3.9526    1.0122    2.5142    0.1367    0.2154    1.4140    0.0369    1.0244    0.0728</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 270:    92.2067   -5.9388   -2.1351   -4.2259   -0.9339    0.2151    0.0326    3.9283    1.0112    2.5114    0.1370    0.2157    1.4137    0.0368    1.0240    0.0729</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 271:    92.2069   -5.9350   -2.1344   -4.2252   -0.9342    0.2152    0.0324    3.9072    1.0107    2.5094    0.1370    0.2158    1.4136    0.0367    1.0222    0.0730</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 272:    92.2069   -5.9317   -2.1341   -4.2246   -0.9348    0.2153    0.0321    3.8870    1.0104    2.5082    0.1372    0.2157    1.4139    0.0366    1.0219    0.0731</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 273:    92.2068   -5.9289   -2.1340   -4.2240   -0.9348    0.2152    0.0320    3.8711    1.0101    2.5075    0.1373    0.2159    1.4146    0.0366    1.0232    0.0730</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 274:    92.2067   -5.9271   -2.1337   -4.2239   -0.9350    0.2153    0.0318    3.8569    1.0101    2.5085    0.1377    0.2157    1.4144    0.0366    1.0239    0.0731</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 275:    92.2063   -5.9264   -2.1339   -4.2235   -0.9353    0.2156    0.0317    3.8476    1.0097    2.5078    0.1382    0.2157    1.4143    0.0365    1.0256    0.0730</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 276:    92.2059   -5.9271   -2.1339   -4.2231   -0.9354    0.2160    0.0316    3.8417    1.0097    2.5074    0.1387    0.2156    1.4132    0.0365    1.0260    0.0730</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 277:    92.2059   -5.9283   -2.1342   -4.2225   -0.9356    0.2163    0.0316    3.8412    1.0094    2.5073    0.1393    0.2155    1.4122    0.0364    1.0283    0.0729</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 278:    92.2062   -5.9278   -2.1341   -4.2220   -0.9361    0.2166    0.0318    3.8331    1.0081    2.5069    0.1397    0.2154    1.4117    0.0364    1.0321    0.0727</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 279:    92.2061   -5.9264   -2.1341   -4.2214   -0.9363    0.2168    0.0319    3.8188    1.0072    2.5051    0.1402    0.2155    1.4111    0.0364    1.0342    0.0726</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 280:    92.2057   -5.9263   -2.1344   -4.2211   -0.9365    0.2167    0.0321    3.8114    1.0072    2.5061    0.1406    0.2148    1.4108    0.0365    1.0361    0.0725</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 281:    92.2048   -5.9269   -2.1344   -4.2208   -0.9365    0.2166    0.0324    3.8082    1.0076    2.5077    0.1410    0.2142    1.4098    0.0365    1.0363    0.0726</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 282:    92.2045   -5.9257   -2.1347   -4.2205   -0.9366    0.2165    0.0328    3.8040    1.0082    2.5092    0.1413    0.2138    1.4090    0.0365    1.0364    0.0726</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 283:    92.2038   -5.9235   -2.1348   -4.2201   -0.9363    0.2163    0.0331    3.7955    1.0089    2.5108    0.1415    0.2134    1.4086    0.0365    1.0362    0.0726</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 284:    92.2032   -5.9234   -2.1348   -4.2198   -0.9359    0.2162    0.0335    3.7945    1.0100    2.5118    0.1420    0.2129    1.4083    0.0365    1.0362    0.0726</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 285:    92.2028   -5.9236   -2.1347   -4.2193   -0.9360    0.2161    0.0339    3.7930    1.0104    2.5124    0.1422    0.2125    1.4080    0.0365    1.0373    0.0725</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 286:    92.2020   -5.9221   -2.1344   -4.2187   -0.9355    0.2156    0.0341    3.7872    1.0104    2.5134    0.1425    0.2121    1.4074    0.0364    1.0377    0.0725</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 287:    92.2013   -5.9213   -2.1341   -4.2183   -0.9354    0.2154    0.0343    3.7888    1.0098    2.5151    0.1428    0.2119    1.4075    0.0364    1.0390    0.0724</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 288:    92.2003   -5.9195   -2.1339   -4.2182   -0.9355    0.2152    0.0343    3.7831    1.0090    2.5180    0.1430    0.2116    1.4082    0.0364    1.0408    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 289:    92.1995   -5.9186   -2.1339   -4.2182   -0.9355    0.2154    0.0343    3.7805    1.0083    2.5217    0.1429    0.2115    1.4085    0.0364    1.0426    0.0722</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 290:    92.1984   -5.9180   -2.1339   -4.2177   -0.9354    0.2153    0.0343    3.7767    1.0080    2.5219    0.1427    0.2113    1.4083    0.0365    1.0429    0.0721</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 291:    92.1975   -5.9167   -2.1334   -4.2175   -0.9355    0.2151    0.0343    3.7701    1.0080    2.5230    0.1427    0.2108    1.4083    0.0364    1.0439    0.0721</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 292:    92.1971   -5.9175   -2.1330   -4.2166   -0.9356    0.2144    0.0342    3.7722    1.0081    2.5227    0.1426    0.2107    1.4079    0.0364    1.0443    0.0721</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 293:    92.1963   -5.9188   -2.1330   -4.2157   -0.9357    0.2141    0.0342    3.7768    1.0083    2.5242    0.1425    0.2103    1.4073    0.0364    1.0439    0.0721</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 294:    92.1953   -5.9189   -2.1328   -4.2157   -0.9357    0.2136    0.0344    3.7727    1.0078    2.5294    0.1427    0.2095    1.4079    0.0364    1.0456    0.0720</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 295:    92.1946   -5.9171   -2.1329   -4.2153   -0.9355    0.2135    0.0345    3.7623    1.0081    2.5331    0.1426    0.2090    1.4080    0.0364    1.0462    0.0720</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 296:    92.1940   -5.9154   -2.1329   -4.2150   -0.9354    0.2130    0.0346    3.7523    1.0082    2.5354    0.1425    0.2084    1.4075    0.0365    1.0463    0.0720</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 297:    92.1934   -5.9128   -2.1330   -4.2145   -0.9351    0.2127    0.0349    3.7395    1.0084    2.5373    0.1423    0.2079    1.4072    0.0365    1.0453    0.0720</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 298:    92.1932   -5.9128   -2.1332   -4.2138   -0.9347    0.2120    0.0350    3.7372    1.0085    2.5368    0.1423    0.2075    1.4068    0.0365    1.0449    0.0720</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 299:    92.1927   -5.9120   -2.1333   -4.2131   -0.9344    0.2112    0.0352    3.7324    1.0088    2.5366    0.1422    0.2070    1.4059    0.0366    1.0441    0.0721</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 300:    92.1921   -5.9097   -2.1335   -4.2122   -0.9343    0.2105    0.0353    3.7211    1.0096    2.5357    0.1425    0.2066    1.4050    0.0367    1.0443    0.0721</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 301:    92.1915   -5.9081   -2.1337   -4.2116   -0.9343    0.2098    0.0355    3.7110    1.0104    2.5355    0.1427    0.2061    1.4041    0.0368    1.0448    0.0721</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 302:    92.1912   -5.9067   -2.1340   -4.2108   -0.9342    0.2092    0.0355    3.7052    1.0114    2.5348    0.1428    0.2055    1.4036    0.0369    1.0442    0.0722</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 303:    92.1913   -5.9039   -2.1342   -4.2113   -0.9342    0.2087    0.0355    3.6925    1.0123    2.5408    0.1430    0.2049    1.4028    0.0369    1.0436    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 304:    92.1920   -5.9012   -2.1342   -4.2119   -0.9342    0.2081    0.0356    3.6781    1.0129    2.5476    0.1432    0.2044    1.4026    0.0369    1.0436    0.0723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 305:    92.1923   -5.8984   -2.1343   -4.2123   -0.9341    0.2076    0.0354    3.6633    1.0134    2.5525    0.1434    0.2038    1.4022    0.0370    1.0434    0.0724</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 306:    92.1926   -5.8977   -2.1343   -4.2121   -0.9343    0.2072    0.0354    3.6603    1.0144    2.5567    0.1436    0.2030    1.4022    0.0369    1.0431    0.0724</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 307:    92.1931   -5.8970   -2.1346   -4.2124   -0.9344    0.2068    0.0353    3.6555    1.0154    2.5617    0.1438    0.2021    1.4017    0.0370    1.0431    0.0725</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 308:    92.1935   -5.8963   -2.1347   -4.2127   -0.9343    0.2062    0.0351    3.6486    1.0166    2.5665    0.1439    0.2012    1.4017    0.0370    1.0431    0.0724</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 309:    92.1934   -5.8965   -2.1349   -4.2130   -0.9344    0.2058    0.0350    3.6454    1.0180    2.5711    0.1439    0.2003    1.4013    0.0370    1.0427    0.0725</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 310:    92.1935   -5.8965   -2.1349   -4.2133   -0.9345    0.2055    0.0350    3.6401    1.0193    2.5761    0.1439    0.1993    1.4013    0.0371    1.0418    0.0725</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 311:    92.1931   -5.8976   -2.1349   -4.2139   -0.9346    0.2052    0.0349    3.6416    1.0207    2.5820    0.1437    0.1982    1.4015    0.0370    1.0411    0.0726</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 312:    92.1928   -5.8995   -2.1350   -4.2149   -0.9349    0.2053    0.0350    3.6472    1.0222    2.5902    0.1437    0.1972    1.4018    0.0370    1.0412    0.0727</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 313:    92.1922   -5.8999   -2.1350   -4.2159   -0.9352    0.2055    0.0350    3.6450    1.0236    2.5989    0.1436    0.1962    1.4017    0.0369    1.0407    0.0728</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 314:    92.1913   -5.9021   -2.1353   -4.2171   -0.9353    0.2055    0.0350    3.6532    1.0244    2.6086    0.1437    0.1951    1.4019    0.0369    1.0409    0.0728</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 315:    92.1905   -5.9033   -2.1358   -4.2178   -0.9354    0.2055    0.0351    3.6591    1.0252    2.6142    0.1438    0.1941    1.4018    0.0369    1.0420    0.0728</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 316:    92.1895   -5.9043   -2.1358   -4.2186   -0.9352    0.2053    0.0352    3.6648    1.0266    2.6207    0.1441    0.1934    1.4016    0.0369    1.0420    0.0729</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 317:    92.1888   -5.9041   -2.1359   -4.2200   -0.9352    0.2052    0.0352    3.6673    1.0278    2.6312    0.1444    0.1926    1.4014    0.0369    1.0416    0.0729</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 318:    92.1879   -5.9039   -2.1360   -4.2212   -0.9352    0.2052    0.0354    3.6670    1.0292    2.6380    0.1446    0.1919    1.4010    0.0369    1.0403    0.0730</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 319:    92.1871   -5.9046   -2.1361   -4.2222   -0.9353    0.2052    0.0354    3.6709    1.0306    2.6432    0.1448    0.1912    1.4008    0.0369    1.0394    0.0731</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 320:    92.1863   -5.9023   -2.1365   -4.2229   -0.9355    0.2052    0.0355    3.6602    1.0318    2.6466    0.1448    0.1903    1.4011    0.0369    1.0398    0.0731</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 321:    92.1855   -5.8996   -2.1368   -4.2236   -0.9354    0.2053    0.0355    3.6491    1.0329    2.6505    0.1448    0.1895    1.4017    0.0369    1.0402    0.0731</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 322:    92.1850   -5.8979   -2.1370   -4.2243   -0.9353    0.2057    0.0356    3.6399    1.0341    2.6534    0.1447    0.1888    1.4020    0.0368    1.0394    0.0732</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 323:    92.1844   -5.8984   -2.1371   -4.2251   -0.9352    0.2059    0.0356    3.6431    1.0346    2.6566    0.1447    0.1882    1.4026    0.0368    1.0389    0.0733</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 324:    92.1838   -5.8967   -2.1372   -4.2257   -0.9353    0.2062    0.0356    3.6392    1.0351    2.6598    0.1445    0.1875    1.4031    0.0368    1.0382    0.0734</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 325:    92.1835   -5.8936   -2.1374   -4.2264   -0.9351    0.2066    0.0355    3.6288    1.0352    2.6633    0.1444    0.1869    1.4043    0.0367    1.0391    0.0734</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 326:    92.1837   -5.8936   -2.1376   -4.2276   -0.9350    0.2071    0.0354    3.6296    1.0354    2.6684    0.1444    0.1865    1.4043    0.0367    1.0380    0.0735</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 327:    92.1837   -5.8943   -2.1378   -4.2289   -0.9347    0.2076    0.0354    3.6348    1.0357    2.6746    0.1444    0.1860    1.4043    0.0366    1.0380    0.0735</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 328:    92.1838   -5.8953   -2.1380   -4.2297   -0.9345    0.2081    0.0354    3.6456    1.0360    2.6776    0.1444    0.1856    1.4043    0.0366    1.0379    0.0735</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 329:    92.1836   -5.8974   -2.1383   -4.2305   -0.9342    0.2085    0.0356    3.6591    1.0361    2.6802    0.1444    0.1852    1.4043    0.0366    1.0385    0.0735</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 330:    92.1832   -5.8994   -2.1387   -4.2305   -0.9342    0.2088    0.0358    3.6715    1.0364    2.6798    0.1443    0.1849    1.4037    0.0367    1.0378    0.0736</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 331:    92.1826   -5.8983   -2.1391   -4.2302   -0.9342    0.2091    0.0357    3.6670    1.0368    2.6800    0.1442    0.1845    1.4038    0.0367    1.0379    0.0736</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 332:    92.1824   -5.8959   -2.1396   -4.2301   -0.9341    0.2097    0.0357    3.6576    1.0375    2.6792    0.1442    0.1842    1.4034    0.0367    1.0372    0.0737</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 333:    92.1826   -5.8937   -2.1401   -4.2304   -0.9340    0.2104    0.0356    3.6487    1.0383    2.6798    0.1442    0.1838    1.4029    0.0367    1.0360    0.0738</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 334:    92.1825   -5.8911   -2.1407   -4.2311   -0.9339    0.2112    0.0355    3.6378    1.0390    2.6825    0.1444    0.1834    1.4025    0.0367    1.0349    0.0739</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 335:    92.1830   -5.8887   -2.1411   -4.2317   -0.9336    0.2118    0.0355    3.6294    1.0396    2.6850    0.1444    0.1830    1.4022    0.0367    1.0338    0.0740</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 336:    92.1834   -5.8872   -2.1413   -4.2324   -0.9334    0.2124    0.0356    3.6249    1.0402    2.6880    0.1444    0.1825    1.4024    0.0367    1.0339    0.0740</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 337:    92.1835   -5.8869   -2.1416   -4.2334   -0.9332    0.2131    0.0356    3.6252    1.0411    2.6926    0.1443    0.1821    1.4029    0.0366    1.0330    0.0741</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 338:    92.1839   -5.8882   -2.1418   -4.2346   -0.9333    0.2137    0.0357    3.6332    1.0421    2.6972    0.1441    0.1815    1.4036    0.0366    1.0324    0.0742</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 339:    92.1845   -5.8869   -2.1420   -4.2358   -0.9332    0.2143    0.0357    3.6261    1.0428    2.7021    0.1439    0.1810    1.4043    0.0366    1.0322    0.0742</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 340:    92.1846   -5.8863   -2.1421   -4.2367   -0.9331    0.2147    0.0357    3.6242    1.0436    2.7060    0.1439    0.1804    1.4049    0.0365    1.0328    0.0743</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 341:    92.1848   -5.8847   -2.1421   -4.2378   -0.9330    0.2150    0.0356    3.6177    1.0442    2.7099    0.1439    0.1798    1.4056    0.0366    1.0334    0.0743</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 342:    92.1848   -5.8838   -2.1421   -4.2390   -0.9330    0.2154    0.0355    3.6114    1.0449    2.7151    0.1438    0.1792    1.4064    0.0365    1.0332    0.0743</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 343:    92.1849   -5.8840   -2.1423   -4.2398   -0.9330    0.2157    0.0353    3.6109    1.0459    2.7191    0.1438    0.1785    1.4060    0.0366    1.0334    0.0743</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 344:    92.1851   -5.8839   -2.1423   -4.2406   -0.9330    0.2159    0.0352    3.6092    1.0467    2.7229    0.1440    0.1779    1.4060    0.0365    1.0338    0.0743</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 345:    92.1850   -5.8841   -2.1424   -4.2414   -0.9331    0.2162    0.0352    3.6070    1.0472    2.7263    0.1441    0.1774    1.4056    0.0365    1.0341    0.0743</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 346:    92.1848   -5.8839   -2.1424   -4.2420   -0.9333    0.2164    0.0352    3.6048    1.0479    2.7292    0.1443    0.1769    1.4058    0.0365    1.0344    0.0743</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 347:    92.1846   -5.8835   -2.1425   -4.2427   -0.9334    0.2166    0.0353    3.6017    1.0495    2.7330    0.1444    0.1763    1.4053    0.0365    1.0343    0.0743</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 348:    92.1846   -5.8825   -2.1427   -4.2434   -0.9336    0.2168    0.0353    3.5968    1.0508    2.7362    0.1445    0.1757    1.4051    0.0365    1.0343    0.0743</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 349:    92.1846   -5.8820   -2.1432   -4.2444   -0.9337    0.2172    0.0352    3.5937    1.0519    2.7412    0.1446    0.1752    1.4047    0.0365    1.0343    0.0743</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 350:    92.1845   -5.8817   -2.1435   -4.2454   -0.9338    0.2179    0.0351    3.5899    1.0526    2.7460    0.1448    0.1747    1.4042    0.0365    1.0339    0.0744</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 351:    92.1844   -5.8824   -2.1439   -4.2468   -0.9338    0.2184    0.0350    3.5917    1.0535    2.7531    0.1448    0.1740    1.4041    0.0365    1.0336    0.0744</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 352:    92.1840   -5.8840   -2.1442   -4.2482   -0.9338    0.2189    0.0350    3.6013    1.0543    2.7603    0.1450    0.1734    1.4044    0.0364    1.0342    0.0744</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 353:    92.1838   -5.8848   -2.1445   -4.2492   -0.9336    0.2194    0.0349    3.6030    1.0552    2.7652    0.1451    0.1730    1.4043    0.0364    1.0338    0.0744</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 354:    92.1838   -5.8835   -2.1449   -4.2501   -0.9336    0.2197    0.0349    3.5961    1.0563    2.7697    0.1452    0.1723    1.4044    0.0364    1.0342    0.0744</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 355:    92.1837   -5.8826   -2.1453   -4.2511   -0.9335    0.2199    0.0348    3.5910    1.0569    2.7757    0.1451    0.1718    1.4053    0.0364    1.0348    0.0744</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 356:    92.1835   -5.8826   -2.1456   -4.2520   -0.9334    0.2198    0.0348    3.5922    1.0577    2.7815    0.1451    0.1712    1.4054    0.0364    1.0352    0.0744</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 357:    92.1834   -5.8818   -2.1457   -4.2525   -0.9334    0.2198    0.0349    3.5894    1.0588    2.7852    0.1449    0.1706    1.4058    0.0364    1.0354    0.0744</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 358:    92.1834   -5.8808   -2.1459   -4.2531   -0.9334    0.2197    0.0348    3.5861    1.0600    2.7891    0.1449    0.1701    1.4062    0.0364    1.0361    0.0744</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 359:    92.1833   -5.8799   -2.1459   -4.2533   -0.9333    0.2197    0.0350    3.5822    1.0607    2.7903    0.1448    0.1696    1.4063    0.0364    1.0360    0.0744</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 360:    92.1831   -5.8792   -2.1460   -4.2534   -0.9332    0.2196    0.0351    3.5787    1.0613    2.7914    0.1446    0.1691    1.4065    0.0364    1.0361    0.0744</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 361:    92.1831   -5.8785   -2.1460   -4.2542   -0.9331    0.2196    0.0351    3.5771    1.0620    2.7969    0.1445    0.1688    1.4072    0.0364    1.0366    0.0744</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 362:    92.1832   -5.8780   -2.1462   -4.2547   -0.9331    0.2195    0.0351    3.5750    1.0625    2.8017    0.1443    0.1683    1.4079    0.0363    1.0369    0.0744</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 363:    92.1830   -5.8788   -2.1464   -4.2551   -0.9331    0.2192    0.0351    3.5785    1.0630    2.8057    0.1443    0.1677    1.4081    0.0363    1.0377    0.0743</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 364:    92.1829   -5.8778   -2.1466   -4.2554   -0.9332    0.2193    0.0350    3.5747    1.0634    2.8092    0.1443    0.1672    1.4084    0.0363    1.0386    0.0743</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 365:    92.1830   -5.8782   -2.1466   -4.2558   -0.9332    0.2192    0.0350    3.5771    1.0638    2.8135    0.1443    0.1667    1.4086    0.0363    1.0385    0.0743</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 366:    92.1830   -5.8795   -2.1465   -4.2564   -0.9332    0.2190    0.0350    3.5838    1.0645    2.8190    0.1444    0.1661    1.4086    0.0363    1.0390    0.0743</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 367:    92.1826   -5.8801   -2.1465   -4.2569   -0.9333    0.2191    0.0349    3.5867    1.0652    2.8232    0.1445    0.1657    1.4086    0.0362    1.0388    0.0744</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 368:    92.1824   -5.8799   -2.1465   -4.2571   -0.9333    0.2193    0.0348    3.5859    1.0654    2.8252    0.1445    0.1653    1.4088    0.0362    1.0385    0.0744</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 369:    92.1822   -5.8803   -2.1467   -4.2572   -0.9333    0.2193    0.0348    3.5846    1.0653    2.8261    0.1444    0.1648    1.4086    0.0362    1.0393    0.0744</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 370:    92.1820   -5.8802   -2.1469   -4.2575   -0.9333    0.2195    0.0348    3.5822    1.0653    2.8273    0.1444    0.1645    1.4088    0.0362    1.0396    0.0744</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 371:    92.1818   -5.8806   -2.1470   -4.2575   -0.9333    0.2198    0.0348    3.5819    1.0649    2.8277    0.1443    0.1642    1.4091    0.0362    1.0403    0.0743</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 372:    92.1818   -5.8804   -2.1473   -4.2580   -0.9332    0.2202    0.0348    3.5795    1.0645    2.8300    0.1441    0.1640    1.4096    0.0362    1.0418    0.0743</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 373:    92.1816   -5.8812   -2.1475   -4.2580   -0.9332    0.2203    0.0348    3.5816    1.0644    2.8298    0.1441    0.1639    1.4100    0.0362    1.0439    0.0741</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 374:    92.1815   -5.8817   -2.1477   -4.2578   -0.9333    0.2203    0.0349    3.5819    1.0641    2.8282    0.1440    0.1641    1.4099    0.0362    1.0443    0.0741</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 375:    92.1817   -5.8828   -2.1479   -4.2574   -0.9335    0.2204    0.0351    3.5853    1.0636    2.8267    0.1440    0.1642    1.4100    0.0362    1.0457    0.0740</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 376:    92.1819   -5.8826   -2.1479   -4.2573   -0.9338    0.2206    0.0353    3.5843    1.0634    2.8246    0.1440    0.1643    1.4100    0.0362    1.0464    0.0740</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 377:    92.1819   -5.8826   -2.1480   -4.2572   -0.9339    0.2206    0.0354    3.5819    1.0633    2.8225    0.1440    0.1644    1.4097    0.0363    1.0470    0.0739</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 378:    92.1819   -5.8821   -2.1481   -4.2571   -0.9340    0.2205    0.0355    3.5777    1.0633    2.8206    0.1440    0.1644    1.4093    0.0363    1.0468    0.0739</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 379:    92.1819   -5.8823   -2.1481   -4.2573   -0.9340    0.2205    0.0356    3.5746    1.0633    2.8201    0.1439    0.1645    1.4092    0.0363    1.0468    0.0739</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 380:    92.1820   -5.8809   -2.1481   -4.2570   -0.9340    0.2205    0.0356    3.5665    1.0634    2.8178    0.1439    0.1647    1.4093    0.0363    1.0463    0.0739</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 381:    92.1823   -5.8795   -2.1481   -4.2568   -0.9338    0.2204    0.0357    3.5586    1.0635    2.8170    0.1439    0.1648    1.4095    0.0363    1.0456    0.0740</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 382:    92.1828   -5.8783   -2.1481   -4.2571   -0.9339    0.2203    0.0357    3.5514    1.0636    2.8183    0.1441    0.1649    1.4093    0.0363    1.0462    0.0740</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 383:    92.1827   -5.8773   -2.1480   -4.2569   -0.9340    0.2202    0.0357    3.5459    1.0633    2.8173    0.1441    0.1650    1.4094    0.0363    1.0465    0.0739</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 384:    92.1825   -5.8763   -2.1482   -4.2568   -0.9341    0.2202    0.0357    3.5397    1.0636    2.8165    0.1441    0.1650    1.4094    0.0363    1.0467    0.0739</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 385:    92.1822   -5.8761   -2.1483   -4.2567   -0.9341    0.2201    0.0357    3.5363    1.0641    2.8160    0.1440    0.1650    1.4094    0.0363    1.0469    0.0739</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 386:    92.1820   -5.8763   -2.1485   -4.2567   -0.9342    0.2200    0.0356    3.5365    1.0645    2.8157    0.1441    0.1648    1.4092    0.0364    1.0478    0.0738</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 387:    92.1819   -5.8767   -2.1486   -4.2567   -0.9343    0.2201    0.0358    3.5383    1.0652    2.8156    0.1442    0.1646    1.4090    0.0364    1.0480    0.0738</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 388:    92.1818   -5.8772   -2.1488   -4.2569   -0.9344    0.2202    0.0359    3.5400    1.0656    2.8153    0.1443    0.1645    1.4086    0.0364    1.0480    0.0738</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 389:    92.1816   -5.8765   -2.1489   -4.2569   -0.9344    0.2203    0.0359    3.5369    1.0660    2.8145    0.1443    0.1644    1.4083    0.0364    1.0476    0.0738</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 390:    92.1815   -5.8761   -2.1490   -4.2569   -0.9343    0.2205    0.0359    3.5349    1.0664    2.8136    0.1444    0.1644    1.4080    0.0364    1.0472    0.0738</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 391:    92.1814   -5.8749   -2.1492   -4.2572   -0.9343    0.2207    0.0359    3.5313    1.0668    2.8153    0.1445    0.1643    1.4077    0.0364    1.0465    0.0739</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 392:    92.1812   -5.8733   -2.1494   -4.2571   -0.9344    0.2210    0.0359    3.5248    1.0674    2.8141    0.1445    0.1642    1.4073    0.0364    1.0457    0.0739</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 393:    92.1811   -5.8727   -2.1497   -4.2570   -0.9345    0.2212    0.0359    3.5226    1.0679    2.8127    0.1445    0.1642    1.4071    0.0364    1.0457    0.0739</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 394:    92.1811   -5.8712   -2.1500   -4.2572   -0.9346    0.2214    0.0360    3.5174    1.0684    2.8132    0.1446    0.1641    1.4067    0.0364    1.0456    0.0739</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 395:    92.1810   -5.8714   -2.1501   -4.2579   -0.9348    0.2217    0.0361    3.5182    1.0685    2.8171    0.1445    0.1641    1.4065    0.0364    1.0452    0.0740</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 396:    92.1809   -5.8711   -2.1501   -4.2582   -0.9350    0.2220    0.0363    3.5161    1.0681    2.8184    0.1446    0.1639    1.4063    0.0364    1.0447    0.0740</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 397:    92.1809   -5.8707   -2.1503   -4.2585   -0.9352    0.2222    0.0363    3.5119    1.0684    2.8197    0.1446    0.1637    1.4065    0.0364    1.0442    0.0740</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 398:    92.1808   -5.8703   -2.1503   -4.2591   -0.9354    0.2224    0.0363    3.5084    1.0689    2.8218    0.1447    0.1634    1.4064    0.0364    1.0445    0.0740</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 399:    92.1806   -5.8711   -2.1503   -4.2596   -0.9355    0.2226    0.0364    3.5114    1.0691    2.8234    0.1447    0.1629    1.4061    0.0364    1.0447    0.0740</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 400:    92.1806   -5.8713   -2.1503   -4.2601   -0.9356    0.2229    0.0366    3.5113    1.0695    2.8253    0.1448    0.1626    1.4059    0.0364    1.0440    0.0740</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 401:    92.1804   -5.8709   -2.1503   -4.2604   -0.9357    0.2231    0.0367    3.5089    1.0696    2.8266    0.1448    0.1624    1.4058    0.0364    1.0440    0.0740</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 402:    92.1801   -5.8705   -2.1502   -4.2606   -0.9358    0.2232    0.0368    3.5071    1.0695    2.8268    0.1447    0.1621    1.4060    0.0364    1.0447    0.0740</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 403:    92.1800   -5.8708   -2.1503   -4.2610   -0.9359    0.2234    0.0369    3.5083    1.0697    2.8271    0.1447    0.1618    1.4056    0.0365    1.0448    0.0739</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 404:    92.1798   -5.8709   -2.1505   -4.2612   -0.9360    0.2237    0.0369    3.5085    1.0699    2.8262    0.1447    0.1616    1.4055    0.0365    1.0449    0.0739</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 405:    92.1795   -5.8705   -2.1503   -4.2613   -0.9360    0.2239    0.0370    3.5059    1.0698    2.8254    0.1448    0.1615    1.4052    0.0364    1.0449    0.0739</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 406:    92.1792   -5.8710   -2.1504   -4.2613   -0.9359    0.2243    0.0371    3.5069    1.0696    2.8244    0.1449    0.1614    1.4049    0.0364    1.0447    0.0739</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 407:    92.1788   -5.8718   -2.1505   -4.2614   -0.9359    0.2243    0.0372    3.5092    1.0697    2.8234    0.1449    0.1614    1.4046    0.0364    1.0448    0.0739</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 408:    92.1786   -5.8729   -2.1505   -4.2615   -0.9360    0.2245    0.0373    3.5140    1.0699    2.8223    0.1449    0.1612    1.4045    0.0365    1.0455    0.0739</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 409:    92.1784   -5.8740   -2.1506   -4.2615   -0.9360    0.2246    0.0373    3.5192    1.0700    2.8213    0.1450    0.1610    1.4042    0.0365    1.0462    0.0738</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 410:    92.1782   -5.8753   -2.1505   -4.2616   -0.9361    0.2246    0.0373    3.5248    1.0705    2.8205    0.1450    0.1607    1.4040    0.0365    1.0465    0.0738</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 411:    92.1780   -5.8758   -2.1503   -4.2619   -0.9362    0.2247    0.0374    3.5249    1.0707    2.8204    0.1449    0.1605    1.4043    0.0364    1.0467    0.0738</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 412:    92.1778   -5.8759   -2.1502   -4.2621   -0.9363    0.2248    0.0374    3.5238    1.0707    2.8209    0.1448    0.1602    1.4045    0.0364    1.0466    0.0738</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 413:    92.1779   -5.8759   -2.1501   -4.2623   -0.9364    0.2249    0.0374    3.5225    1.0711    2.8213    0.1448    0.1600    1.4043    0.0364    1.0460    0.0738</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 414:    92.1781   -5.8754   -2.1500   -4.2622   -0.9364    0.2250    0.0374    3.5205    1.0710    2.8203    0.1448    0.1599    1.4047    0.0364    1.0456    0.0738</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 415:    92.1781   -5.8750   -2.1499   -4.2623   -0.9363    0.2251    0.0374    3.5187    1.0709    2.8190    0.1448    0.1598    1.4050    0.0364    1.0448    0.0738</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 416:    92.1783   -5.8747   -2.1500   -4.2624   -0.9363    0.2252    0.0375    3.5216    1.0708    2.8187    0.1448    0.1598    1.4052    0.0364    1.0440    0.0738</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 417:    92.1783   -5.8738   -2.1500   -4.2625   -0.9362    0.2252    0.0375    3.5219    1.0712    2.8188    0.1447    0.1597    1.4055    0.0364    1.0438    0.0739</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 418:    92.1784   -5.8735   -2.1501   -4.2627   -0.9360    0.2254    0.0375    3.5222    1.0715    2.8193    0.1448    0.1596    1.4057    0.0364    1.0438    0.0739</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 419:    92.1784   -5.8725   -2.1501   -4.2629   -0.9358    0.2255    0.0376    3.5190    1.0719    2.8202    0.1447    0.1594    1.4060    0.0364    1.0436    0.0739</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 420:    92.1785   -5.8713   -2.1500   -4.2633   -0.9357    0.2256    0.0377    3.5152    1.0723    2.8213    0.1447    0.1592    1.4065    0.0364    1.0439    0.0738</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 421:    92.1786   -5.8697   -2.1500   -4.2639   -0.9355    0.2257    0.0377    3.5100    1.0728    2.8232    0.1448    0.1592    1.4068    0.0364    1.0436    0.0739</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 422:    92.1787   -5.8683   -2.1502   -4.2645   -0.9352    0.2260    0.0377    3.5051    1.0732    2.8258    0.1448    0.1592    1.4069    0.0364    1.0432    0.0739</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 423:    92.1788   -5.8667   -2.1504   -4.2653   -0.9350    0.2262    0.0377    3.4992    1.0736    2.8292    0.1448    0.1592    1.4071    0.0364    1.0435    0.0738</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 424:    92.1789   -5.8659   -2.1505   -4.2661   -0.9349    0.2263    0.0378    3.4972    1.0739    2.8324    0.1448    0.1591    1.4069    0.0364    1.0439    0.0738</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 425:    92.1788   -5.8649   -2.1505   -4.2669   -0.9349    0.2263    0.0378    3.4947    1.0740    2.8358    0.1448    0.1590    1.4071    0.0364    1.0438    0.0738</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 426:    92.1788   -5.8643   -2.1506   -4.2677   -0.9348    0.2265    0.0377    3.4922    1.0743    2.8389    0.1448    0.1589    1.4070    0.0365    1.0437    0.0738</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 427:    92.1785   -5.8637   -2.1506   -4.2683   -0.9347    0.2266    0.0378    3.4895    1.0747    2.8425    0.1448    0.1588    1.4068    0.0365    1.0434    0.0738</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 428:    92.1783   -5.8627   -2.1507   -4.2689   -0.9347    0.2268    0.0378    3.4870    1.0749    2.8464    0.1447    0.1586    1.4070    0.0365    1.0434    0.0738</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 429:    92.1782   -5.8617   -2.1509   -4.2696   -0.9347    0.2271    0.0379    3.4845    1.0752    2.8500    0.1446    0.1584    1.4072    0.0365    1.0439    0.0738</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 430:    92.1783   -5.8608   -2.1510   -4.2701   -0.9347    0.2274    0.0379    3.4830    1.0755    2.8526    0.1446    0.1582    1.4075    0.0365    1.0443    0.0738</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 431:    92.1784   -5.8606   -2.1511   -4.2704   -0.9348    0.2275    0.0379    3.4825    1.0757    2.8532    0.1446    0.1580    1.4076    0.0365    1.0439    0.0738</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 432:    92.1784   -5.8610   -2.1512   -4.2707   -0.9350    0.2275    0.0378    3.4833    1.0761    2.8539    0.1447    0.1578    1.4079    0.0365    1.0443    0.0738</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 433:    92.1783   -5.8618   -2.1512   -4.2711   -0.9351    0.2276    0.0378    3.4867    1.0768    2.8554    0.1447    0.1577    1.4078    0.0366    1.0440    0.0738</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 434:    92.1784   -5.8612   -2.1513   -4.2714   -0.9350    0.2278    0.0378    3.4858    1.0774    2.8564    0.1448    0.1577    1.4074    0.0365    1.0430    0.0739</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 435:    92.1783   -5.8610   -2.1513   -4.2718   -0.9348    0.2279    0.0377    3.4874    1.0779    2.8581    0.1448    0.1576    1.4072    0.0366    1.0423    0.0739</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 436:    92.1781   -5.8610   -2.1513   -4.2721   -0.9346    0.2280    0.0376    3.4879    1.0789    2.8598    0.1448    0.1575    1.4070    0.0365    1.0417    0.0739</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 437:    92.1781   -5.8610   -2.1511   -4.2726   -0.9345    0.2279    0.0376    3.4886    1.0794    2.8618    0.1449    0.1574    1.4071    0.0365    1.0417    0.0739</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 438:    92.1781   -5.8609   -2.1510   -4.2730   -0.9345    0.2280    0.0375    3.4893    1.0797    2.8641    0.1449    0.1573    1.4075    0.0365    1.0420    0.0739</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 439:    92.1781   -5.8608   -2.1509   -4.2734   -0.9343    0.2280    0.0374    3.4894    1.0803    2.8659    0.1449    0.1573    1.4076    0.0365    1.0421    0.0739</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 440:    92.1780   -5.8603   -2.1510   -4.2737   -0.9343    0.2279    0.0374    3.4885    1.0808    2.8674    0.1448    0.1573    1.4079    0.0365    1.0430    0.0738</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 441:    92.1779   -5.8600   -2.1510   -4.2738   -0.9342    0.2279    0.0373    3.4899    1.0811    2.8686    0.1447    0.1574    1.4086    0.0365    1.0445    0.0737</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 442:    92.1779   -5.8600   -2.1509   -4.2737   -0.9343    0.2278    0.0372    3.4925    1.0814    2.8691    0.1447    0.1575    1.4088    0.0365    1.0451    0.0737</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 443:    92.1780   -5.8598   -2.1509   -4.2739   -0.9341    0.2278    0.0372    3.4949    1.0819    2.8705    0.1447    0.1576    1.4092    0.0365    1.0453    0.0737</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 444:    92.1779   -5.8593   -2.1510   -4.2742   -0.9340    0.2277    0.0371    3.4951    1.0824    2.8717    0.1447    0.1576    1.4096    0.0365    1.0455    0.0737</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 445:    92.1777   -5.8593   -2.1511   -4.2744   -0.9338    0.2275    0.0371    3.4999    1.0828    2.8729    0.1448    0.1576    1.4100    0.0365    1.0461    0.0736</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 446:    92.1774   -5.8600   -2.1511   -4.2748   -0.9338    0.2274    0.0372    3.5051    1.0837    2.8747    0.1449    0.1576    1.4099    0.0365    1.0464    0.0736</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 447:    92.1771   -5.8604   -2.1512   -4.2751   -0.9338    0.2273    0.0373    3.5087    1.0844    2.8758    0.1449    0.1575    1.4097    0.0365    1.0467    0.0736</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 448:    92.1769   -5.8607   -2.1513   -4.2756   -0.9336    0.2272    0.0374    3.5124    1.0849    2.8778    0.1449    0.1575    1.4098    0.0365    1.0470    0.0735</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 449:    92.1768   -5.8611   -2.1515   -4.2759   -0.9337    0.2270    0.0374    3.5165    1.0853    2.8789    0.1449    0.1575    1.4101    0.0365    1.0473    0.0735</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 450:    92.1766   -5.8611   -2.1515   -4.2762   -0.9337    0.2269    0.0374    3.5158    1.0859    2.8800    0.1448    0.1576    1.4102    0.0365    1.0474    0.0735</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 451:    92.1762   -5.8615   -2.1514   -4.2765   -0.9337    0.2267    0.0374    3.5202    1.0866    2.8812    0.1447    0.1576    1.4104    0.0365    1.0477    0.0735</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 452:    92.1761   -5.8622   -2.1514   -4.2767   -0.9337    0.2264    0.0374    3.5232    1.0871    2.8824    0.1447    0.1576    1.4103    0.0365    1.0479    0.0734</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 453:    92.1760   -5.8621   -2.1514   -4.2776   -0.9336    0.2261    0.0373    3.5247    1.0878    2.8901    0.1447    0.1576    1.4103    0.0365    1.0480    0.0734</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 454:    92.1759   -5.8611   -2.1513   -4.2781   -0.9335    0.2259    0.0373    3.5206    1.0882    2.8939    0.1448    0.1575    1.4104    0.0365    1.0483    0.0734</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 455:    92.1758   -5.8599   -2.1513   -4.2788   -0.9336    0.2258    0.0372    3.5151    1.0887    2.9010    0.1448    0.1575    1.4104    0.0365    1.0485    0.0734</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 456:    92.1760   -5.8583   -2.1512   -4.2797   -0.9335    0.2258    0.0372    3.5086    1.0892    2.9081    0.1448    0.1574    1.4105    0.0365    1.0483    0.0734</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 457:    92.1761   -5.8575   -2.1513   -4.2804   -0.9334    0.2257    0.0371    3.5041    1.0896    2.9144    0.1449    0.1574    1.4103    0.0365    1.0480    0.0733</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 458:    92.1763   -5.8569   -2.1512   -4.2813   -0.9334    0.2257    0.0370    3.5014    1.0901    2.9227    0.1449    0.1574    1.4102    0.0365    1.0476    0.0734</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 459:    92.1763   -5.8565   -2.1512   -4.2816   -0.9334    0.2256    0.0370    3.4991    1.0908    2.9248    0.1449    0.1573    1.4100    0.0365    1.0471    0.0734</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 460:    92.1764   -5.8557   -2.1513   -4.2818   -0.9333    0.2256    0.0370    3.4957    1.0915    2.9258    0.1449    0.1572    1.4097    0.0365    1.0466    0.0734</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 461:    92.1765   -5.8550   -2.1515   -4.2822   -0.9333    0.2255    0.0370    3.4923    1.0921    2.9296    0.1449    0.1571    1.4097    0.0365    1.0464    0.0734</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 462:    92.1764   -5.8546   -2.1517   -4.2824   -0.9333    0.2256    0.0370    3.4897    1.0925    2.9319    0.1448    0.1569    1.4093    0.0365    1.0463    0.0733</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 463:    92.1763   -5.8539   -2.1519   -4.2830   -0.9333    0.2256    0.0369    3.4863    1.0928    2.9373    0.1449    0.1568    1.4093    0.0366    1.0463    0.0733</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 464:    92.1763   -5.8532   -2.1520   -4.2833   -0.9333    0.2257    0.0370    3.4825    1.0933    2.9405    0.1448    0.1567    1.4093    0.0366    1.0462    0.0733</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 465:    92.1762   -5.8528   -2.1521   -4.2837   -0.9335    0.2257    0.0370    3.4795    1.0938    2.9433    0.1448    0.1567    1.4093    0.0366    1.0464    0.0733</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 466:    92.1762   -5.8530   -2.1523   -4.2840   -0.9336    0.2256    0.0370    3.4780    1.0943    2.9462    0.1448    0.1566    1.4089    0.0366    1.0466    0.0733</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 467:    92.1764   -5.8529   -2.1524   -4.2846   -0.9337    0.2256    0.0371    3.4751    1.0948    2.9506    0.1447    0.1565    1.4087    0.0366    1.0465    0.0733</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 468:    92.1765   -5.8531   -2.1525   -4.2849   -0.9337    0.2255    0.0371    3.4734    1.0952    2.9523    0.1447    0.1563    1.4087    0.0366    1.0465    0.0733</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 469:    92.1767   -5.8533   -2.1525   -4.2853   -0.9337    0.2254    0.0372    3.4754    1.0958    2.9540    0.1447    0.1561    1.4090    0.0366    1.0472    0.0733</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 470:    92.1767   -5.8536   -2.1525   -4.2858   -0.9338    0.2254    0.0373    3.4780    1.0965    2.9571    0.1448    0.1559    1.4094    0.0366    1.0478    0.0732</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 471:    92.1767   -5.8540   -2.1525   -4.2864   -0.9338    0.2254    0.0373    3.4812    1.0970    2.9606    0.1447    0.1558    1.4099    0.0366    1.0482    0.0732</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 472:    92.1766   -5.8547   -2.1526   -4.2871   -0.9339    0.2255    0.0373    3.4844    1.0975    2.9645    0.1446    0.1556    1.4101    0.0366    1.0482    0.0732</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 473:    92.1764   -5.8549   -2.1527   -4.2875   -0.9339    0.2255    0.0373    3.4848    1.0977    2.9672    0.1446    0.1554    1.4100    0.0366    1.0482    0.0732</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 474:    92.1762   -5.8553   -2.1528   -4.2880   -0.9338    0.2256    0.0373    3.4855    1.0979    2.9700    0.1445    0.1552    1.4104    0.0366    1.0484    0.0732</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 475:    92.1763   -5.8561   -2.1530   -4.2884   -0.9338    0.2256    0.0374    3.4885    1.0981    2.9726    0.1445    0.1549    1.4103    0.0366    1.0484    0.0731</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 476:    92.1763   -5.8564   -2.1530   -4.2885   -0.9339    0.2258    0.0374    3.4914    1.0983    2.9734    0.1444    0.1548    1.4106    0.0366    1.0484    0.0731</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 477:    92.1763   -5.8565   -2.1531   -4.2888   -0.9340    0.2259    0.0373    3.4941    1.0985    2.9755    0.1443    0.1546    1.4108    0.0366    1.0487    0.0731</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 478:    92.1763   -5.8576   -2.1532   -4.2889   -0.9340    0.2258    0.0373    3.4995    1.0987    2.9772    0.1442    0.1544    1.4111    0.0366    1.0490    0.0731</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 479:    92.1765   -5.8593   -2.1533   -4.2890   -0.9342    0.2259    0.0372    3.5086    1.0990    2.9787    0.1442    0.1544    1.4113    0.0366    1.0495    0.0731</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 480:    92.1766   -5.8613   -2.1535   -4.2892   -0.9342    0.2260    0.0372    3.5233    1.0991    2.9800    0.1442    0.1543    1.4116    0.0367    1.0496    0.0730</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 481:    92.1768   -5.8624   -2.1536   -4.2894   -0.9341    0.2260    0.0371    3.5309    1.0993    2.9820    0.1442    0.1542    1.4117    0.0367    1.0497    0.0730</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 482:    92.1766   -5.8634   -2.1537   -4.2896   -0.9341    0.2260    0.0371    3.5393    1.0995    2.9833    0.1443    0.1542    1.4116    0.0367    1.0496    0.0730</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 483:    92.1762   -5.8645   -2.1538   -4.2896   -0.9340    0.2260    0.0372    3.5472    1.0997    2.9840    0.1443    0.1542    1.4115    0.0367    1.0498    0.0730</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 484:    92.1757   -5.8648   -2.1538   -4.2896   -0.9340    0.2261    0.0373    3.5508    1.0998    2.9843    0.1445    0.1542    1.4115    0.0366    1.0498    0.0730</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 485:    92.1752   -5.8648   -2.1539   -4.2896   -0.9340    0.2262    0.0374    3.5516    1.1000    2.9846    0.1445    0.1542    1.4115    0.0366    1.0497    0.0730</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 486:    92.1747   -5.8649   -2.1539   -4.2897   -0.9340    0.2262    0.0375    3.5517    1.1003    2.9856    0.1446    0.1541    1.4113    0.0366    1.0498    0.0730</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 487:    92.1743   -5.8649   -2.1539   -4.2896   -0.9340    0.2262    0.0376    3.5502    1.1006    2.9856    0.1447    0.1539    1.4110    0.0367    1.0498    0.0730</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 488:    92.1739   -5.8645   -2.1540   -4.2893   -0.9340    0.2261    0.0377    3.5475    1.1008    2.9848    0.1447    0.1537    1.4106    0.0367    1.0497    0.0729</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 489:    92.1736   -5.8640   -2.1539   -4.2892   -0.9340    0.2260    0.0376    3.5445    1.1011    2.9844    0.1448    0.1536    1.4105    0.0367    1.0496    0.0729</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 490:    92.1736   -5.8631   -2.1539   -4.2889   -0.9340    0.2260    0.0376    3.5402    1.1014    2.9835    0.1448    0.1534    1.4103    0.0366    1.0496    0.0729</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 491:    92.1735   -5.8617   -2.1539   -4.2887   -0.9339    0.2260    0.0375    3.5343    1.1016    2.9825    0.1449    0.1532    1.4103    0.0366    1.0497    0.0729</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 492:    92.1736   -5.8609   -2.1539   -4.2884   -0.9339    0.2259    0.0375    3.5298    1.1017    2.9816    0.1449    0.1530    1.4101    0.0367    1.0499    0.0729</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 493:    92.1737   -5.8605   -2.1539   -4.2882   -0.9339    0.2260    0.0375    3.5266    1.1018    2.9807    0.1450    0.1529    1.4098    0.0367    1.0496    0.0729</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 494:    92.1739   -5.8609   -2.1539   -4.2881   -0.9340    0.2261    0.0375    3.5273    1.1018    2.9802    0.1450    0.1528    1.4097    0.0367    1.0495    0.0729</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 495:    92.1741   -5.8614   -2.1540   -4.2880   -0.9340    0.2262    0.0376    3.5290    1.1019    2.9797    0.1450    0.1526    1.4096    0.0367    1.0492    0.0729</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 496:    92.1742   -5.8623   -2.1540   -4.2879   -0.9341    0.2262    0.0376    3.5348    1.1020    2.9791    0.1449    0.1525    1.4096    0.0367    1.0491    0.0729</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 497:    92.1742   -5.8634   -2.1541   -4.2879   -0.9341    0.2264    0.0377    3.5402    1.1020    2.9789    0.1449    0.1524    1.4097    0.0367    1.0493    0.0729</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 498:    92.1744   -5.8637   -2.1543   -4.2879   -0.9341    0.2266    0.0377    3.5406    1.1019    2.9787    0.1449    0.1524    1.4096    0.0367    1.0496    0.0729</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 499:    92.1744   -5.8635   -2.1544   -4.2880   -0.9341    0.2268    0.0376    3.5400    1.1019    2.9789    0.1450    0.1523    1.4095    0.0367    1.0500    0.0728</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 500:    92.1744   -5.8628   -2.1545   -4.2882   -0.9341    0.2270    0.0377    3.5381    1.1020    2.9795    0.1450    0.1522    1.4096    0.0367    1.0503    0.0728</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating covariance matrix</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling EBE model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Needed Covariates:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "CMT"</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating residuals/tables</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-in"><span class="va">f_nlmixr_dfop_sfo_focei_obs_tc</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr</a></span><span class="op">(</span><span class="va">f_mmkin_tc</span><span class="op">[</span><span class="st">"DFOP-SFO"</span>, <span class="op">]</span>, est <span class="op">=</span> <span class="st">"focei"</span>,</span>
-<span class="r-in">  error_model <span class="op">=</span> <span class="st">"obs_tc"</span><span class="op">)</span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate jacobian</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate sensitivities</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate ∂(f)/∂(η)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate ∂(R²)/∂(η)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in inner model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in inner model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in EBE model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in EBE model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling inner model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in FD model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in FD model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling EBE model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling events FD model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Needed Covariates:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "CMT"</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="font-weight: bold;">Key:</span> U: Unscaled Parameters; X: Back-transformed parameters; G: Gill difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> F: Forward difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> C: Central difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> M: Mixed forward and central difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Unscaled parameters for Omegas=chol(solve(omega));</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Diagonals are transformed, as specified by foceiControl(diagXform=)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |-----+---------------+-----------+-----------+-----------+-----------|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    #| Objective Fun |  parent_0 |  log_k_A1 |f_parent_qlogis |    log_k1 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    log_k2 |  g_qlogis |sigma_low_parent |rsd_high_parent |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|sigma_low_A1 |rsd_high_A1 |        o1 |        o2 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|        o3 |        o4 |        o5 |        o6 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    1</span>|     495.48573 |     1.000 |    -1.000 |   -0.9104 |   -0.9376 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9875 |   -0.8823 |   -0.8746 |   -0.8907 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8746 |   -0.8907 |   -0.8767 |   -0.8731 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8673 |   -0.8720 |   -0.8739 |   -0.8666 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     495.48573 |     91.00 |    -5.200 |   -0.8900 |    -2.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.600 |    0.4600 |    0.8300 |   0.05800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8300 |   0.05800 |    0.7311 |    0.9036 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.183 |    0.9554 |    0.8633 |     1.214 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     495.48573</span> |     91.00 |  0.005517 |    0.2911 |    0.1108 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01005 |    0.6130 |    0.8300 |   0.05800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8300 |   0.05800 |    0.7311 |    0.9036 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.183 |    0.9554 |    0.8633 |     1.214 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    G|    Gill Diff. |   -0.9648 |     2.223 |   -0.3153 |  -0.01817 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3350 |    0.6789 |    -23.42 |    -17.64 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -5.440 |    -1.950 |    0.9642 |     9.851 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -11.94 |    -1.319 |     8.578 |    -12.45 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    2</span>|     481.75012 |     1.026 |    -1.060 |   -0.9019 |   -0.9371 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9785 |   -0.9007 |   -0.2420 |   -0.4142 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7277 |   -0.8380 |   -0.9027 |    -1.139 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5448 |   -0.8364 |    -1.106 |   -0.5303 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     481.75012 |     93.37 |    -5.260 |   -0.8824 |    -2.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.591 |    0.4516 |     1.093 |   0.07182 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8910 |   0.05953 |    0.7121 |    0.6631 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.565 |    0.9895 |    0.6633 |     1.623 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     481.75012</span> |     93.37 |  0.005195 |    0.2927 |    0.1109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01014 |    0.6110 |     1.093 |   0.07182 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8910 |   0.05953 |    0.7121 |    0.6631 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.565 |    0.9895 |    0.6633 |     1.623 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     152.5 |     1.317 |     3.315 |   -0.1772 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3391 |    0.1426 |    -4.513 |     6.696 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.211 |    0.7988 |    0.6299 |    -5.324 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009964 |     3.044 |    -5.727 |    -6.694 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    3</span>|     3004.9713 |    0.2745 |    -1.093 |   -0.9147 |   -0.9360 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9762 |   -0.9095 |   0.05941 |   -0.2377 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6690 |   -0.8188 |   -0.9174 |    -1.204 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4027 |   -0.8359 |    -1.205 |   -0.3486 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     3004.9713 |     24.98 |    -5.293 |   -0.8938 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.589 |    0.4475 |     1.218 |   0.07694 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9154 |   0.06008 |    0.7014 |    0.6043 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.733 |    0.9899 |    0.5774 |     1.843 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     3004.9713</span> |     24.98 |  0.005026 |    0.2903 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01017 |    0.6100 |     1.218 |   0.07694 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9154 |   0.06008 |    0.7014 |    0.6043 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.733 |    0.9899 |    0.5774 |     1.843 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    4</span>|     491.68825 |    0.9393 |    -1.061 |   -0.9038 |   -0.9370 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9787 |   -0.9008 |   -0.2394 |   -0.4180 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7284 |   -0.8385 |   -0.9031 |    -1.136 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5448 |   -0.8381 |    -1.102 |   -0.5265 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     491.68825 |     85.47 |    -5.261 |   -0.8841 |    -2.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.591 |    0.4515 |     1.094 |   0.07171 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8907 |   0.05951 |    0.7118 |    0.6659 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.565 |    0.9878 |    0.6661 |     1.627 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     491.68825</span> |     85.47 |  0.005191 |    0.2923 |    0.1109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01014 |    0.6110 |     1.094 |   0.07171 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8907 |   0.05951 |    0.7118 |    0.6659 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.565 |    0.9878 |    0.6661 |     1.627 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    5</span>|     479.72282 |     1.001 |    -1.060 |   -0.9024 |   -0.9371 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9785 |   -0.9007 |   -0.2413 |   -0.4153 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7279 |   -0.8381 |   -0.9028 |    -1.138 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5448 |   -0.8369 |    -1.105 |   -0.5292 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.72282 |     91.11 |    -5.260 |   -0.8829 |    -2.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.591 |    0.4516 |     1.093 |   0.07179 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8909 |   0.05952 |    0.7120 |    0.6639 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.565 |    0.9890 |    0.6641 |     1.624 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.72282</span> |     91.11 |  0.005194 |    0.2926 |    0.1109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01014 |    0.6110 |     1.093 |   0.07179 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8909 |   0.05952 |    0.7120 |    0.6639 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.565 |    0.9890 |    0.6641 |     1.624 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     6.589 |     1.222 |    0.9137 |    0.1103 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3993 |    0.5206 |    -3.904 |     6.654 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9090 |     1.134 |    -1.839 |    -6.108 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.622 |     4.007 |    -4.921 |    -6.374 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    6</span>|     479.56384 |    0.9950 |    -1.061 |   -0.9037 |   -0.9373 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9792 |   -0.9012 |   -0.2438 |   -0.4298 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7309 |   -0.8403 |   -0.9001 |    -1.126 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5509 |   -0.8426 |    -1.095 |   -0.5241 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.56384 |     90.55 |    -5.261 |   -0.8840 |    -2.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.592 |    0.4513 |     1.092 |   0.07137 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8897 |   0.05946 |    0.7140 |    0.6748 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.558 |    0.9836 |    0.6729 |     1.630 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.56384</span> |     90.55 |  0.005189 |    0.2923 |    0.1108 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01014 |    0.6110 |     1.092 |   0.07137 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8897 |   0.05946 |    0.7140 |    0.6748 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.558 |    0.9836 |    0.6729 |     1.630 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -31.71 |     1.225 |    0.1963 |    0.1681 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4113 |    0.6853 |    -4.208 |     6.141 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7033 |     1.163 |    -2.029 |    -4.071 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.106 |     3.494 |    -3.921 |    -6.098 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    7</span>|     479.23599 |     1.003 |    -1.063 |   -0.9048 |   -0.9375 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9799 |   -0.9023 |   -0.2352 |   -0.4403 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7320 |   -0.8422 |   -0.8974 |    -1.117 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5472 |   -0.8484 |    -1.086 |   -0.5115 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.23599 |     91.29 |    -5.263 |   -0.8850 |    -2.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.592 |    0.4508 |     1.095 |   0.07106 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8892 |   0.05941 |    0.7160 |    0.6828 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.562 |    0.9781 |    0.6800 |     1.645 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.23599</span> |     91.29 |  0.005177 |    0.2921 |    0.1108 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01013 |    0.6108 |     1.095 |   0.07106 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8892 |   0.05941 |    0.7160 |    0.6828 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.562 |    0.9781 |    0.6800 |     1.645 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     18.36 |     1.286 |    0.8956 |   0.06941 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3942 |    0.6495 |    -3.460 |     6.349 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7828 |    0.9998 |    -1.947 |    -2.931 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1591 |     2.144 |    -3.375 |    -5.909 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    8</span>|     479.05200 |    0.9982 |    -1.066 |   -0.9056 |   -0.9376 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9803 |   -0.9037 |   -0.2181 |   -0.4407 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7304 |   -0.8427 |   -0.8951 |    -1.119 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5384 |   -0.8504 |    -1.087 |   -0.4972 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|       479.052 |     90.84 |    -5.266 |   -0.8857 |    -2.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.593 |    0.4502 |     1.102 |   0.07105 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8899 |   0.05939 |    0.7177 |    0.6818 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.572 |    0.9761 |    0.6797 |     1.663 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">       479.052</span> |     90.84 |  0.005162 |    0.2920 |    0.1108 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01012 |    0.6107 |     1.102 |   0.07105 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8899 |   0.05939 |    0.7177 |    0.6818 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.572 |    0.9761 |    0.6797 |     1.663 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    9</span>|     478.91507 |    0.9977 |    -1.070 |   -0.9061 |   -0.9377 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9807 |   -0.9051 |   -0.2002 |   -0.4395 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7284 |   -0.8431 |   -0.8930 |    -1.121 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5287 |   -0.8520 |    -1.088 |   -0.4828 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     478.91507 |     90.79 |    -5.270 |   -0.8862 |    -2.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.593 |    0.4495 |     1.110 |   0.07109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8907 |   0.05938 |    0.7192 |    0.6799 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.584 |    0.9745 |    0.6785 |     1.680 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     478.91507</span> |     90.79 |  0.005146 |    0.2919 |    0.1108 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01012 |    0.6105 |     1.110 |   0.07109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8907 |   0.05938 |    0.7192 |    0.6799 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.584 |    0.9745 |    0.6785 |     1.680 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   10</span>|     478.54700 |    0.9959 |    -1.080 |   -0.9081 |   -0.9381 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9820 |   -0.9099 |   -0.1391 |   -0.4353 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7215 |   -0.8442 |   -0.8862 |    -1.128 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4957 |   -0.8577 |    -1.093 |   -0.4342 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|       478.547 |     90.63 |    -5.280 |   -0.8880 |    -2.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.594 |    0.4473 |     1.135 |   0.07121 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8936 |   0.05935 |    0.7242 |    0.6734 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.623 |    0.9691 |    0.6746 |     1.739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">       478.547</span> |     90.63 |  0.005094 |    0.2915 |    0.1108 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01011 |    0.6100 |     1.135 |   0.07121 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8936 |   0.05935 |    0.7242 |    0.6734 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.623 |    0.9691 |    0.6746 |     1.739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   11</span>|     478.24707 |    0.9926 |    -1.098 |   -0.9118 |   -0.9388 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9843 |   -0.9186 |  -0.02735 |   -0.4276 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7088 |   -0.8464 |   -0.8736 |    -1.141 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4354 |   -0.8680 |    -1.101 |   -0.3451 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     478.24707 |     90.33 |    -5.298 |   -0.8913 |    -2.201 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.597 |    0.4433 |     1.182 |   0.07143 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8988 |   0.05929 |    0.7334 |    0.6616 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.694 |    0.9593 |    0.6674 |     1.848 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     478.24707</span> |     90.33 |  0.004999 |    0.2909 |    0.1107 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01008 |    0.6090 |     1.182 |   0.07143 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8988 |   0.05929 |    0.7334 |    0.6616 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.694 |    0.9593 |    0.6674 |     1.848 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -54.86 |     1.159 |   -0.2545 |    0.1198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4779 |     1.320 |    -1.627 |     7.719 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.488 |     1.194 |    -2.008 |    -4.434 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     3.563 |    0.8010 |    -2.393 |    -3.495 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   12</span>|     475.90466 |     1.002 |    -1.127 |   -0.9233 |   -0.9398 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9978 |   -0.9482 |  -0.05448 |   -0.6822 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7579 |   -0.8801 |   -0.8190 |    -1.095 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4550 |   -0.8498 |    -1.031 |   -0.2699 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     475.90466 |     91.18 |    -5.327 |   -0.9014 |    -2.202 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.610 |    0.4297 |     1.170 |   0.06405 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8785 |   0.05831 |    0.7733 |    0.7032 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.671 |    0.9767 |    0.7277 |     1.939 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     475.90466</span> |     91.18 |  0.004860 |    0.2888 |    0.1106 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009950 |    0.6058 |     1.170 |   0.06405 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8785 |   0.05831 |    0.7733 |    0.7032 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.671 |    0.9767 |    0.7277 |     1.939 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -5.773 |     1.281 |   0.05418 |  -0.06269 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4104 |     1.812 |    -4.981 |     3.640 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.08126 |   0.09477 |    -1.092 |    -2.966 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     5.262 |     2.712 |     3.245 |    -2.012 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   13</span>|     477.06760 |     1.030 |    -1.176 |   -0.9281 |   -0.9377 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.013 |    -1.011 |   0.09564 |   -0.8420 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7668 |   -0.8907 |   -0.7738 |    -1.073 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5619 |   -0.8693 |    -1.126 |   -0.1773 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      477.0676 |     93.71 |    -5.376 |   -0.9057 |    -2.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.625 |    0.4008 |     1.233 |   0.05941 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8748 |   0.05800 |    0.8064 |    0.7234 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.545 |    0.9581 |    0.6461 |     2.051 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      477.0676</span> |     93.71 |  0.004627 |    0.2879 |    0.1108 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009799 |    0.5989 |     1.233 |   0.05941 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8748 |   0.05800 |    0.8064 |    0.7234 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.545 |    0.9581 |    0.6461 |     2.051 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   14</span>|     477.20174 |     1.026 |    -1.143 |   -0.9246 |   -0.9391 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.003 |   -0.9695 | -0.001726 |   -0.7335 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7599 |   -0.8831 |   -0.8043 |    -1.080 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4976 |   -0.8627 |    -1.065 |   -0.2404 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     477.20174 |     93.37 |    -5.343 |   -0.9027 |    -2.201 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.615 |    0.4199 |     1.192 |   0.06256 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8776 |   0.05822 |    0.7840 |    0.7162 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.621 |    0.9644 |    0.6988 |     1.975 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     477.20174</span> |     93.37 |  0.004782 |    0.2885 |    0.1106 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009899 |    0.6035 |     1.192 |   0.06256 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8776 |   0.05822 |    0.7840 |    0.7162 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.621 |    0.9644 |    0.6988 |     1.975 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   15</span>|     476.22973 |     1.014 |    -1.129 |   -0.9234 |   -0.9397 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9986 |   -0.9521 |  -0.04396 |   -0.6899 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7577 |   -0.8803 |   -0.8167 |    -1.089 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4661 |   -0.8555 |    -1.038 |   -0.2656 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     476.22973 |     92.29 |    -5.329 |   -0.9015 |    -2.202 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.611 |    0.4279 |     1.175 |   0.06382 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8785 |   0.05830 |    0.7750 |    0.7089 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.658 |    0.9712 |    0.7218 |     1.944 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     476.22973</span> |     92.29 |  0.004847 |    0.2887 |    0.1106 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009941 |    0.6054 |     1.175 |   0.06382 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8785 |   0.05830 |    0.7750 |    0.7089 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.658 |    0.9712 |    0.7218 |     1.944 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   16</span>|     475.87776 |     1.005 |    -1.127 |   -0.9233 |   -0.9398 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9980 |   -0.9491 |  -0.05201 |   -0.6840 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7578 |   -0.8802 |   -0.8184 |    -1.093 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4576 |   -0.8511 |    -1.033 |   -0.2689 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     475.87776 |     91.44 |    -5.327 |   -0.9015 |    -2.202 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.610 |    0.4293 |     1.171 |   0.06399 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8785 |   0.05830 |    0.7737 |    0.7045 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.668 |    0.9754 |    0.7263 |     1.940 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     475.87776</span> |     91.44 |  0.004857 |    0.2887 |    0.1106 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009948 |    0.6057 |     1.171 |   0.06399 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8785 |   0.05830 |    0.7737 |    0.7045 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.668 |    0.9754 |    0.7263 |     1.940 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     16.12 |     1.298 |    0.4116 |  -0.09723 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4002 |     1.728 |    -4.991 |     3.781 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.06392 |   0.04117 |    -1.251 |   -0.6787 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     5.079 |     2.620 |     3.013 |    -2.074 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   17</span>|     475.82399 |     1.002 |    -1.128 |   -0.9234 |   -0.9397 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9982 |   -0.9501 |  -0.04950 |   -0.6866 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7580 |   -0.8803 |   -0.8177 |    -1.093 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4596 |   -0.8518 |    -1.034 |   -0.2675 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     475.82399 |     91.17 |    -5.328 |   -0.9016 |    -2.202 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.611 |    0.4288 |     1.172 |   0.06392 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8784 |   0.05830 |    0.7743 |    0.7045 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.666 |    0.9748 |    0.7251 |     1.942 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     475.82399</span> |     91.17 |  0.004853 |    0.2887 |    0.1106 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009945 |    0.6056 |     1.172 |   0.06392 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8784 |   0.05830 |    0.7743 |    0.7045 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.666 |    0.9748 |    0.7251 |     1.942 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -7.267 |     1.279 |  0.007095 |  -0.05940 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4115 |     1.783 |    -5.114 |     3.652 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1068 |    0.1083 |    -1.295 |   -0.9578 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.682 |     2.514 |     3.014 |    -2.035 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   18</span>|     475.78862 |     1.005 |    -1.129 |   -0.9235 |   -0.9397 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9985 |   -0.9512 |  -0.04657 |   -0.6892 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7580 |   -0.8804 |   -0.8168 |    -1.093 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4611 |   -0.8527 |    -1.036 |   -0.2661 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     475.78862 |     91.41 |    -5.329 |   -0.9016 |    -2.202 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.611 |    0.4283 |     1.174 |   0.06384 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8784 |   0.05830 |    0.7749 |    0.7045 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.664 |    0.9739 |    0.7236 |     1.943 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     475.78862</span> |     91.41 |  0.004849 |    0.2887 |    0.1106 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009942 |    0.6055 |     1.174 |   0.06384 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8784 |   0.05830 |    0.7749 |    0.7045 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.664 |    0.9739 |    0.7236 |     1.943 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     13.19 |     1.292 |    0.3498 |  -0.09321 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4023 |     1.703 |    -5.372 |     3.699 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1234 |   0.05429 |    -1.241 |   -0.7588 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.815 |     2.436 |     2.783 |    -2.083 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   19</span>|     475.73531 |     1.002 |    -1.130 |   -0.9236 |   -0.9397 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9987 |   -0.9524 |  -0.04361 |   -0.6921 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7582 |   -0.8806 |   -0.8159 |    -1.094 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4630 |   -0.8531 |    -1.037 |   -0.2646 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     475.73531 |     91.21 |    -5.330 |   -0.9018 |    -2.202 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.611 |    0.4278 |     1.175 |   0.06376 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8783 |   0.05829 |    0.7756 |    0.7041 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.662 |    0.9735 |    0.7222 |     1.945 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     475.73531</span> |     91.21 |  0.004845 |    0.2887 |    0.1106 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009940 |    0.6053 |     1.175 |   0.06376 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8783 |   0.05829 |    0.7756 |    0.7041 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.662 |    0.9735 |    0.7222 |     1.945 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -3.312 |     1.277 |   0.06663 |  -0.06695 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4102 |     1.736 |    -5.095 |     3.555 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.08175 |   0.08515 |    -1.253 |   -0.9364 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.836 |     2.452 |     2.739 |    -2.058 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   20</span>|     475.69941 |     1.004 |    -1.131 |   -0.9237 |   -0.9396 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9990 |   -0.9534 |  -0.04063 |   -0.6942 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7581 |   -0.8807 |   -0.8151 |    -1.093 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4658 |   -0.8545 |    -1.039 |   -0.2634 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     475.69941 |     91.39 |    -5.331 |   -0.9018 |    -2.202 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.611 |    0.4273 |     1.176 |   0.06370 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8783 |   0.05829 |    0.7761 |    0.7046 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.658 |    0.9722 |    0.7208 |     1.947 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     475.69941</span> |     91.39 |  0.004841 |    0.2887 |    0.1106 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009937 |    0.6052 |     1.176 |   0.06370 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8783 |   0.05829 |    0.7761 |    0.7046 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.658 |    0.9722 |    0.7208 |     1.947 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     11.57 |     1.287 |    0.3079 |  -0.08979 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4039 |     1.674 |    -5.153 |     3.653 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.06063 |   0.04440 |    -1.200 |   -0.7646 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.452 |     2.339 |     2.552 |    -2.095 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   21</span>|     475.66307 |     1.001 |    -1.131 |   -0.9238 |   -0.9396 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9992 |   -0.9545 |  -0.03780 |   -0.6969 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7583 |   -0.8808 |   -0.8143 |    -1.094 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4671 |   -0.8550 |    -1.041 |   -0.2620 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     475.66307 |     91.11 |    -5.331 |   -0.9019 |    -2.202 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.612 |    0.4268 |     1.177 |   0.06362 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8783 |   0.05829 |    0.7767 |    0.7044 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.657 |    0.9717 |    0.7195 |     1.948 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     475.66307</span> |     91.11 |  0.004837 |    0.2887 |    0.1106 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009935 |    0.6051 |     1.177 |   0.06362 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8783 |   0.05829 |    0.7767 |    0.7044 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.657 |    0.9717 |    0.7195 |     1.948 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -12.57 |     1.267 |  -0.09715 |  -0.05310 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4138 |     1.728 |    -5.558 |     3.438 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1081 |    0.1203 |    -1.232 |    -1.053 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.344 |     2.291 |     2.543 |    -2.059 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   22</span>|     475.61346 |     1.003 |    -1.132 |   -0.9238 |   -0.9395 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9995 |   -0.9557 |  -0.03467 |   -0.6999 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7585 |   -0.8810 |   -0.8134 |    -1.094 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4680 |   -0.8550 |    -1.042 |   -0.2607 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     475.61346 |     91.32 |    -5.332 |   -0.9020 |    -2.202 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.612 |    0.4262 |     1.179 |   0.06353 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8782 |   0.05828 |    0.7774 |    0.7040 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.656 |    0.9717 |    0.7181 |     1.950 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     475.61346</span> |     91.32 |  0.004833 |    0.2886 |    0.1106 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009932 |    0.6050 |     1.179 |   0.06353 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8782 |   0.05828 |    0.7774 |    0.7040 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.656 |    0.9717 |    0.7181 |     1.950 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     5.192 |     1.277 |    0.1967 |  -0.08157 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4060 |     1.656 |    -5.231 |     3.627 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1038 |   0.05786 |    -1.199 |   -0.8859 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.283 |     2.293 |     2.331 |    -2.101 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   23</span>|     475.58436 |     1.001 |    -1.133 |   -0.9239 |   -0.9395 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9998 |   -0.9568 |  -0.03140 |   -0.7025 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7585 |   -0.8810 |   -0.8126 |    -1.094 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4692 |   -0.8560 |    -1.044 |   -0.2594 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     475.58436 |     91.09 |    -5.333 |   -0.9021 |    -2.202 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.612 |    0.4257 |     1.180 |   0.06346 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8782 |   0.05828 |    0.7780 |    0.7043 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.654 |    0.9707 |    0.7167 |     1.952 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     475.58436</span> |     91.09 |  0.004829 |    0.2886 |    0.1106 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009929 |    0.6049 |     1.180 |   0.06346 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8782 |   0.05828 |    0.7780 |    0.7043 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.654 |    0.9707 |    0.7167 |     1.952 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -14.46 |     1.261 |   -0.1306 |  -0.05131 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4140 |     1.696 |    -5.518 |     3.404 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1407 |    0.1181 |    -1.199 |    -1.071 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.272 |     2.212 |     2.296 |    -2.075 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   24</span>|     475.53229 |     1.003 |    -1.134 |   -0.9240 |   -0.9394 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.000 |   -0.9581 |  -0.02828 |   -0.7055 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7587 |   -0.8812 |   -0.8117 |    -1.094 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4701 |   -0.8560 |    -1.045 |   -0.2580 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     475.53229 |     91.31 |    -5.334 |   -0.9021 |    -2.202 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.613 |    0.4251 |     1.181 |   0.06337 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8781 |   0.05828 |    0.7786 |    0.7039 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.653 |    0.9708 |    0.7153 |     1.953 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     475.53229</span> |     91.31 |  0.004824 |    0.2886 |    0.1106 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009926 |    0.6047 |     1.181 |   0.06337 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8781 |   0.05828 |    0.7786 |    0.7039 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.653 |    0.9708 |    0.7153 |     1.953 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     4.355 |     1.271 |    0.1786 |  -0.08149 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4055 |     1.621 |    -5.117 |     3.557 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1060 |   0.04285 |   -0.9518 |    -2.902 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.469 |     2.204 |     2.093 |    -2.115 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   25</span>|     475.50379 |     1.001 |    -1.135 |   -0.9241 |   -0.9394 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.000 |   -0.9591 |  -0.02533 |   -0.7076 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7587 |   -0.8812 |   -0.8111 |    -1.093 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4726 |   -0.8571 |    -1.047 |   -0.2568 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     475.50379 |     91.10 |    -5.335 |   -0.9022 |    -2.202 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.613 |    0.4247 |     1.182 |   0.06331 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8781 |   0.05828 |    0.7790 |    0.7053 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.650 |    0.9697 |    0.7143 |     1.955 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     475.50379</span> |     91.10 |  0.004820 |    0.2886 |    0.1106 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009924 |    0.6046 |     1.182 |   0.06331 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8781 |   0.05828 |    0.7790 |    0.7053 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.650 |    0.9697 |    0.7143 |     1.955 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -13.73 |     1.259 |   -0.1294 |  -0.05234 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4131 |     1.659 |    -5.626 |     3.356 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1179 |    0.1182 |    -1.163 |   -0.9759 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.282 |     2.162 |     2.085 |    -2.085 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   26</span>|     475.45890 |     1.004 |    -1.136 |   -0.9240 |   -0.9393 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.001 |   -0.9602 |  -0.02221 |   -0.7103 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7588 |   -0.8813 |   -0.8104 |    -1.092 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4738 |   -0.8571 |    -1.048 |   -0.2555 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      475.4589 |     91.37 |    -5.336 |   -0.9021 |    -2.202 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.613 |    0.4242 |     1.184 |   0.06323 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8781 |   0.05827 |    0.7796 |    0.7056 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.649 |    0.9697 |    0.7132 |     1.956 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      475.4589</span> |     91.37 |  0.004816 |    0.2886 |    0.1106 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009921 |    0.6045 |     1.184 |   0.06323 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8781 |   0.05827 |    0.7796 |    0.7056 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.649 |    0.9697 |    0.7132 |     1.956 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     9.388 |     1.275 |    0.2447 |  -0.08891 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4027 |     1.576 |    -4.598 |     3.539 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.08390 |   0.04261 |   -0.9004 |    -2.725 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.305 |     2.111 |     1.882 |    -2.135 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   27</span>|     475.41657 |     1.002 |    -1.137 |   -0.9241 |   -0.9392 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.001 |   -0.9615 |  -0.01910 |   -0.7133 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7590 |   -0.8814 |   -0.8097 |    -1.092 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4754 |   -0.8571 |    -1.049 |   -0.2540 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     475.41657 |     91.17 |    -5.337 |   -0.9022 |    -2.202 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.613 |    0.4236 |     1.185 |   0.06314 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8780 |   0.05827 |    0.7801 |    0.7060 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.647 |    0.9697 |    0.7121 |     1.958 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     475.41657</span> |     91.17 |  0.004811 |    0.2886 |    0.1106 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009918 |    0.6043 |     1.185 |   0.06314 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8780 |   0.05827 |    0.7801 |    0.7060 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.647 |    0.9697 |    0.7121 |     1.958 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -7.291 |     1.263 |  -0.02799 |  -0.06240 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4098 |     1.607 |    -5.561 |     3.409 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1363 |   0.09124 |    -1.126 |   -0.9025 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.264 |     2.123 |     1.858 |    -2.103 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   28</span>|     475.37603 |     1.004 |    -1.138 |   -0.9241 |   -0.9392 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.001 |   -0.9626 |  -0.01569 |   -0.7160 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7590 |   -0.8815 |   -0.8090 |    -1.092 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4775 |   -0.8574 |    -1.050 |   -0.2525 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     475.37603 |     91.35 |    -5.338 |   -0.9022 |    -2.202 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.614 |    0.4231 |     1.186 |   0.06307 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8780 |   0.05827 |    0.7806 |    0.7060 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.645 |    0.9695 |    0.7112 |     1.960 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     475.37603</span> |     91.35 |  0.004807 |    0.2886 |    0.1106 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009915 |    0.6042 |     1.186 |   0.06307 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8780 |   0.05827 |    0.7806 |    0.7060 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.645 |    0.9695 |    0.7112 |     1.960 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     7.976 |     1.271 |    0.2167 |  -0.08766 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4024 |     1.550 |    -5.132 |     3.516 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.09627 |   0.04106 |    -1.088 |   -0.7404 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.103 |     2.126 |     1.707 |    -2.133 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   29</span>|     475.34297 |     1.001 |    -1.139 |   -0.9242 |   -0.9391 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.002 |   -0.9636 |  -0.01242 |   -0.7185 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7591 |   -0.8816 |   -0.8082 |    -1.092 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4796 |   -0.8578 |    -1.051 |   -0.2511 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     475.34297 |     91.13 |    -5.339 |   -0.9023 |    -2.201 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.614 |    0.4226 |     1.188 |   0.06299 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8779 |   0.05826 |    0.7812 |    0.7056 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.642 |    0.9691 |    0.7103 |     1.962 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     475.34297</span> |     91.13 |  0.004803 |    0.2886 |    0.1106 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009912 |    0.6041 |     1.188 |   0.06299 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8779 |   0.05826 |    0.7812 |    0.7056 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.642 |    0.9691 |    0.7103 |     1.962 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -11.69 |     1.251 |  -0.09758 |  -0.05188 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4100 |     1.596 |    -5.544 |     3.338 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1288 |   0.09302 |    -1.103 |   -0.9943 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.044 |     2.113 |     1.726 |    -2.096 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   30</span>|     475.29763 |     1.004 |    -1.140 |   -0.9242 |   -0.9391 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.002 |   -0.9647 | -0.009016 |   -0.7212 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7592 |   -0.8817 |   -0.8074 |    -1.093 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4815 |   -0.8578 |    -1.052 |   -0.2496 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     475.29763 |     91.33 |    -5.340 |   -0.9023 |    -2.201 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.614 |    0.4221 |     1.189 |   0.06292 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8779 |   0.05826 |    0.7818 |    0.7049 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.640 |    0.9691 |    0.7096 |     1.964 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     475.29763</span> |     91.33 |  0.004798 |    0.2886 |    0.1106 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009909 |    0.6040 |     1.189 |   0.06292 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8779 |   0.05826 |    0.7818 |    0.7049 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.640 |    0.9691 |    0.7096 |     1.964 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     5.626 |     1.261 |    0.1834 |  -0.08674 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4019 |     1.535 |    -5.612 |     3.466 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1091 |   0.03444 |    -1.082 |   -0.8814 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     3.882 |     2.084 |     1.576 |    -2.128 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   31</span>|     475.26968 |     1.001 |    -1.140 |   -0.9243 |   -0.9390 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.002 |   -0.9656 | -0.005554 |   -0.7235 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7592 |   -0.8818 |   -0.8067 |    -1.093 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4837 |   -0.8585 |    -1.053 |   -0.2482 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     475.26968 |     91.11 |    -5.340 |   -0.9024 |    -2.201 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.615 |    0.4217 |     1.191 |   0.06285 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8779 |   0.05826 |    0.7823 |    0.7049 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.637 |    0.9684 |    0.7088 |     1.965 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     475.26968</span> |     91.11 |  0.004794 |    0.2886 |    0.1106 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009907 |    0.6039 |     1.191 |   0.06285 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8779 |   0.05826 |    0.7823 |    0.7049 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.637 |    0.9684 |    0.7088 |     1.965 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -13.71 |     1.246 |   -0.1255 |  -0.05322 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4099 |     1.581 |    -5.546 |     3.317 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1509 |    0.1096 |   -0.8594 |    -3.000 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     3.990 |     2.056 |     1.604 |    -2.087 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   32</span>|     475.22190 |     1.004 |    -1.141 |   -0.9243 |   -0.9390 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.002 |   -0.9667 | -0.002058 |   -0.7261 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7593 |   -0.8819 |   -0.8061 |    -1.093 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4854 |   -0.8583 |    -1.054 |   -0.2469 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      475.2219 |     91.33 |    -5.341 |   -0.9024 |    -2.201 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.615 |    0.4212 |     1.192 |   0.06277 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8778 |   0.05826 |    0.7828 |    0.7049 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.635 |    0.9686 |    0.7081 |     1.967 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      475.2219</span> |     91.33 |  0.004790 |    0.2886 |    0.1107 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009904 |    0.6038 |     1.192 |   0.06277 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8778 |   0.05826 |    0.7828 |    0.7049 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.635 |    0.9686 |    0.7081 |     1.967 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     5.841 |     1.258 |    0.1840 |  -0.08823 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4011 |     1.514 |    -4.992 |     3.441 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1080 |   0.03852 |    -1.043 |   -0.8514 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     3.826 |     2.019 |     1.451 |    -2.125 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   33</span>|     475.19540 |     1.001 |    -1.142 |   -0.9244 |   -0.9389 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.003 |   -0.9677 |  0.001228 |   -0.7286 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7593 |   -0.8819 |   -0.8054 |    -1.093 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4876 |   -0.8589 |    -1.055 |   -0.2455 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      475.1954 |     91.10 |    -5.342 |   -0.9025 |    -2.201 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.615 |    0.4207 |     1.193 |   0.06270 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8778 |   0.05825 |    0.7833 |    0.7050 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.633 |    0.9680 |    0.7073 |     1.969 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      475.1954</span> |     91.10 |  0.004786 |    0.2885 |    0.1107 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009901 |    0.6037 |     1.193 |   0.06270 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8778 |   0.05825 |    0.7833 |    0.7050 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.633 |    0.9680 |    0.7073 |     1.969 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -14.17 |     1.239 |   -0.1323 |  -0.05443 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4093 |     1.561 |    -5.475 |     3.262 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1301 |   0.09817 |    -1.038 |    -1.045 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     3.818 |     2.054 |     1.474 |    -2.085 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   34</span>|     475.14668 |     1.003 |    -1.143 |   -0.9244 |   -0.9388 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.003 |   -0.9688 |  0.004635 |   -0.7312 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7595 |   -0.8820 |   -0.8046 |    -1.094 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4894 |   -0.8588 |    -1.055 |   -0.2440 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     475.14668 |     91.31 |    -5.343 |   -0.9025 |    -2.201 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.615 |    0.4202 |     1.195 |   0.06262 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8778 |   0.05825 |    0.7838 |    0.7043 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.631 |    0.9681 |    0.7066 |     1.970 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     475.14668</span> |     91.31 |  0.004781 |    0.2885 |    0.1107 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009898 |    0.6035 |     1.195 |   0.06262 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8778 |   0.05825 |    0.7838 |    0.7043 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.631 |    0.9681 |    0.7066 |     1.970 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     3.838 |     1.251 |    0.1547 |  -0.08725 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4006 |     1.498 |    -4.927 |     3.416 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1219 |   0.06473 |    -1.010 |    -2.917 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     3.712 |     2.020 |     1.337 |    -2.117 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   35</span>|     475.12366 |     1.001 |    -1.144 |   -0.9245 |   -0.9388 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.003 |   -0.9698 |  0.007665 |   -0.7333 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7594 |   -0.8821 |   -0.8040 |    -1.092 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4916 |   -0.8600 |    -1.056 |   -0.2427 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     475.12366 |     91.10 |    -5.344 |   -0.9025 |    -2.201 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.616 |    0.4198 |     1.196 |   0.06256 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8778 |   0.05825 |    0.7843 |    0.7059 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.628 |    0.9669 |    0.7059 |     1.972 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     475.12366</span> |     91.10 |  0.004777 |    0.2885 |    0.1107 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009896 |    0.6034 |     1.196 |   0.06256 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8778 |   0.05825 |    0.7843 |    0.7059 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.628 |    0.9669 |    0.7059 |     1.972 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -14.75 |     1.239 |   -0.1466 |  -0.05465 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4082 |     1.541 |    -5.471 |     3.270 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1342 |   0.09829 |    -1.014 |    -1.026 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     3.624 |     1.932 |     1.359 |    -2.081 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   36</span>|     475.07465 |     1.004 |    -1.145 |   -0.9245 |   -0.9387 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.003 |   -0.9709 |   0.01108 |   -0.7360 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7595 |   -0.8821 |   -0.8033 |    -1.092 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4933 |   -0.8597 |    -1.057 |   -0.2414 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     475.07465 |     91.33 |    -5.345 |   -0.9025 |    -2.201 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.616 |    0.4193 |     1.198 |   0.06248 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8778 |   0.05825 |    0.7848 |    0.7058 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.626 |    0.9672 |    0.7053 |     1.974 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     475.07465</span> |     91.33 |  0.004773 |    0.2885 |    0.1107 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009893 |    0.6033 |     1.198 |   0.06248 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8778 |   0.05825 |    0.7848 |    0.7058 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.626 |    0.9672 |    0.7053 |     1.974 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     5.021 |     1.249 |    0.1599 |  -0.08992 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3985 |     1.471 |    -4.995 |     3.410 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1196 |   0.03779 |   -0.9990 |    -2.873 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     3.497 |     1.906 |     1.211 |    -2.119 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   37</span>|     475.04940 |     1.001 |    -1.146 |   -0.9245 |   -0.9386 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.004 |   -0.9719 |   0.01438 |   -0.7384 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7594 |   -0.8822 |   -0.8026 |    -1.091 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4953 |   -0.8604 |    -1.058 |   -0.2400 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      475.0494 |     91.11 |    -5.346 |   -0.9026 |    -2.201 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.616 |    0.4188 |     1.199 |   0.06242 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8778 |   0.05825 |    0.7853 |    0.7070 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.623 |    0.9666 |    0.7046 |     1.975 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      475.0494</span> |     91.11 |  0.004769 |    0.2885 |    0.1107 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009890 |    0.6032 |     1.199 |   0.06242 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8778 |   0.05825 |    0.7853 |    0.7070 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.623 |    0.9666 |    0.7046 |     1.975 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -14.15 |     1.235 |   -0.1370 |  -0.05688 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4085 |     1.517 |    -5.494 |     3.160 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1583 |    0.1112 |   -0.7821 |    -2.927 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     3.432 |     1.909 |     1.245 |    -2.084 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   38</span>|     475.00092 |     1.004 |    -1.147 |   -0.9245 |   -0.9386 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.004 |   -0.9731 |   0.01792 |   -0.7411 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7595 |   -0.8822 |   -0.8020 |    -1.090 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4968 |   -0.8598 |    -1.059 |   -0.2387 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     475.00092 |     91.32 |    -5.347 |   -0.9025 |    -2.201 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.617 |    0.4182 |     1.200 |   0.06234 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8778 |   0.05825 |    0.7857 |    0.7077 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.622 |    0.9671 |    0.7039 |     1.977 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     475.00092</span> |     91.32 |  0.004764 |    0.2885 |    0.1107 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009887 |    0.6031 |     1.200 |   0.06234 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8778 |   0.05825 |    0.7857 |    0.7077 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.622 |    0.9671 |    0.7039 |     1.977 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     4.379 |     1.249 |    0.1419 |  -0.08698 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3989 |     1.449 |    -4.966 |     3.395 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1055 |   0.03295 |   -0.9696 |   -0.7580 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     3.283 |     1.918 |     1.096 |    -2.115 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   39</span>|     474.98492 |     1.001 |    -1.147 |   -0.9246 |   -0.9385 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.004 |   -0.9740 |   0.02115 |   -0.7433 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7595 |   -0.8822 |   -0.8014 |    -1.089 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4989 |   -0.8610 |    -1.059 |   -0.2373 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     474.98492 |     91.07 |    -5.347 |   -0.9026 |    -2.201 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.617 |    0.4178 |     1.202 |   0.06227 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8778 |   0.05825 |    0.7862 |    0.7081 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.619 |    0.9660 |    0.7033 |     1.978 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     474.98492</span> |     91.07 |  0.004760 |    0.2885 |    0.1107 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009884 |    0.6030 |     1.202 |   0.06227 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8778 |   0.05825 |    0.7862 |    0.7081 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.619 |    0.9660 |    0.7033 |     1.978 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -17.65 |     1.231 |   -0.1920 |  -0.05242 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4084 |     1.504 |    -5.397 |     3.171 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1354 |    0.1061 |   -0.9468 |   -0.9144 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.271 |     1.859 |     1.156 |    -2.067 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   40</span>|     474.93249 |     1.004 |    -1.148 |   -0.9245 |   -0.9384 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.005 |   -0.9752 |   0.02452 |   -0.7460 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7596 |   -0.8823 |   -0.8007 |    -1.090 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5000 |   -0.8607 |    -1.060 |   -0.2361 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     474.93249 |     91.32 |    -5.348 |   -0.9026 |    -2.201 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.617 |    0.4173 |     1.203 |   0.06220 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8778 |   0.05824 |    0.7867 |    0.7073 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.618 |    0.9663 |    0.7027 |     1.980 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     474.93249</span> |     91.32 |  0.004755 |    0.2885 |    0.1107 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009881 |    0.6028 |     1.203 |   0.06220 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8778 |   0.05824 |    0.7867 |    0.7073 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.618 |    0.9663 |    0.7027 |     1.980 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     4.492 |     1.243 |    0.1448 |  -0.09052 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3963 |     1.427 |    -4.973 |     3.376 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.06414 |   0.02300 |   -0.7344 |    -2.787 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     3.083 |     1.834 |    0.9813 |    -2.110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   41</span>|     474.90355 |     1.001 |    -1.149 |   -0.9246 |   -0.9383 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.005 |   -0.9763 |   0.02806 |   -0.7486 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7596 |   -0.8823 |   -0.8002 |    -1.089 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5018 |   -0.8611 |    -1.061 |   -0.2347 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     474.90355 |     91.13 |    -5.349 |   -0.9026 |    -2.201 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.617 |    0.4168 |     1.205 |   0.06212 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8777 |   0.05824 |    0.7870 |    0.7084 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.616 |    0.9659 |    0.7020 |     1.982 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     474.90355</span> |     91.13 |  0.004751 |    0.2885 |    0.1107 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009878 |    0.6027 |     1.205 |   0.06212 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8777 |   0.05824 |    0.7870 |    0.7084 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.616 |    0.9659 |    0.7020 |     1.982 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -12.15 |     1.229 |   -0.1075 |  -0.06320 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4033 |     1.463 |    -5.606 |     3.135 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1416 |   0.07801 |   -0.9461 |    -2.867 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     3.063 |     1.817 |     1.008 |    -2.075 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   42</span>|     474.85832 |     1.003 |    -1.150 |   -0.9245 |   -0.9383 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.005 |   -0.9775 |   0.03184 |   -0.7513 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7597 |   -0.8823 |   -0.7996 |    -1.089 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5032 |   -0.8605 |    -1.061 |   -0.2334 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     474.85832 |     91.32 |    -5.350 |   -0.9026 |    -2.201 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.618 |    0.4162 |     1.206 |   0.06204 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8777 |   0.05824 |    0.7875 |    0.7089 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.614 |    0.9665 |    0.7015 |     1.983 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     474.85832</span> |     91.32 |  0.004746 |    0.2885 |    0.1107 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009875 |    0.6026 |     1.206 |   0.06204 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8777 |   0.05824 |    0.7875 |    0.7089 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.614 |    0.9665 |    0.7015 |     1.983 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     3.689 |     1.242 |    0.1265 |  -0.09001 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3949 |     1.405 |    -5.495 |     3.344 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1166 |   0.02645 |   -0.9105 |   -0.7126 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.952 |     1.861 |    0.8779 |    -2.102 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   43</span>|     474.83791 |     1.001 |    -1.151 |   -0.9246 |   -0.9382 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.006 |   -0.9784 |   0.03545 |   -0.7535 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7596 |   -0.8823 |   -0.7990 |    -1.088 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5052 |   -0.8617 |    -1.062 |   -0.2320 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     474.83791 |     91.10 |    -5.351 |   -0.9027 |    -2.201 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.618 |    0.4158 |     1.208 |   0.06198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8777 |   0.05824 |    0.7879 |    0.7094 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.612 |    0.9653 |    0.7010 |     1.985 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     474.83791</span> |     91.10 |  0.004742 |    0.2885 |    0.1107 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009872 |    0.6025 |     1.208 |   0.06198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8777 |   0.05824 |    0.7879 |    0.7094 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.612 |    0.9653 |    0.7010 |     1.985 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -15.77 |     1.225 |   -0.1616 |  -0.05944 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4032 |     1.455 |    -5.461 |     3.071 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1419 |   0.08593 |   -0.9091 |   -0.8855 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.951 |     1.791 |    0.9091 |    -2.062 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   44</span>|     474.78971 |     1.004 |    -1.152 |   -0.9246 |   -0.9381 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.006 |   -0.9794 |   0.03911 |   -0.7559 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7597 |   -0.8824 |   -0.7984 |    -1.089 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5068 |   -0.8614 |    -1.062 |   -0.2307 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     474.78971 |     91.33 |    -5.352 |   -0.9026 |    -2.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.618 |    0.4153 |     1.209 |   0.06191 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8777 |   0.05824 |    0.7883 |    0.7086 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.610 |    0.9656 |    0.7006 |     1.986 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     474.78971</span> |     91.33 |  0.004738 |    0.2885 |    0.1107 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009869 |    0.6024 |     1.209 |   0.06191 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8777 |   0.05824 |    0.7883 |    0.7086 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.610 |    0.9656 |    0.7006 |     1.986 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     4.398 |     1.237 |    0.1402 |  -0.09195 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3940 |     1.388 |    -4.885 |     3.322 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1374 |   0.01792 |   -0.6865 |    -2.709 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.810 |     1.778 |    0.8100 |    -2.095 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   45</span>|     474.76763 |     1.001 |    -1.153 |   -0.9247 |   -0.9380 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.006 |   -0.9804 |   0.04256 |   -0.7584 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7597 |   -0.8824 |   -0.7979 |    -1.088 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5086 |   -0.8621 |    -1.063 |   -0.2293 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     474.76763 |     91.11 |    -5.353 |   -0.9027 |    -2.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.619 |    0.4149 |     1.211 |   0.06184 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8777 |   0.05824 |    0.7887 |    0.7097 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.608 |    0.9650 |    0.7001 |     1.988 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     474.76763</span> |     91.11 |  0.004734 |    0.2885 |    0.1108 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009867 |    0.6023 |     1.211 |   0.06184 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8777 |   0.05824 |    0.7887 |    0.7097 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.608 |    0.9650 |    0.7001 |     1.988 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -14.92 |     1.222 |   -0.1466 |  -0.06186 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4014 |     1.433 |    -4.989 |     3.157 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1326 |   0.08284 |   -0.6789 |    -2.803 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.814 |     1.775 |    0.8327 |    -2.053 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   46</span>|     474.71973 |     1.004 |    -1.154 |   -0.9246 |   -0.9379 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.006 |   -0.9816 |   0.04617 |   -0.7611 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7597 |   -0.8824 |   -0.7975 |    -1.087 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5100 |   -0.8614 |    -1.064 |   -0.2281 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     474.71973 |     91.32 |    -5.354 |   -0.9026 |    -2.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.619 |    0.4143 |     1.212 |   0.06176 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8777 |   0.05824 |    0.7890 |    0.7100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.606 |    0.9656 |    0.6996 |     1.990 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     474.71973</span> |     91.32 |  0.004729 |    0.2885 |    0.1108 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009863 |    0.6021 |     1.212 |   0.06176 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8777 |   0.05824 |    0.7890 |    0.7100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.606 |    0.9656 |    0.6996 |     1.990 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     4.021 |     1.236 |    0.1299 |  -0.09158 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3929 |     1.368 |    -4.925 |     3.331 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.07142 |   0.08893 |   -0.6522 |    -2.634 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.639 |     1.780 |    0.7273 |    -2.085 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   47</span>|     474.70040 |     1.001 |    -1.155 |   -0.9247 |   -0.9379 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.007 |   -0.9826 |   0.04954 |   -0.7634 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7597 |   -0.8825 |   -0.7971 |    -1.086 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5117 |   -0.8624 |    -1.064 |   -0.2267 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      474.7004 |     91.10 |    -5.355 |   -0.9027 |    -2.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.619 |    0.4139 |     1.214 |   0.06169 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8777 |   0.05824 |    0.7893 |    0.7114 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.604 |    0.9647 |    0.6992 |     1.991 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      474.7004</span> |     91.10 |  0.004724 |    0.2885 |    0.1108 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009861 |    0.6020 |     1.214 |   0.06169 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8777 |   0.05824 |    0.7893 |    0.7114 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.604 |    0.9647 |    0.6992 |     1.991 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -15.76 |     1.220 |   -0.1617 |  -0.06091 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4007 |     1.415 |    -5.116 |     3.166 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1298 |   0.07714 |   -0.6701 |    -2.700 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.670 |     1.748 |    0.7590 |    -2.043 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   48</span>|     474.65116 |     1.003 |    -1.156 |   -0.9246 |   -0.9378 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.007 |   -0.9837 |   0.05321 |   -0.7662 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7598 |   -0.8825 |   -0.7967 |    -1.086 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5130 |   -0.8617 |    -1.065 |   -0.2255 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     474.65116 |     91.32 |    -5.356 |   -0.9026 |    -2.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.620 |    0.4134 |     1.215 |   0.06161 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8777 |   0.05824 |    0.7896 |    0.7116 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.603 |    0.9653 |    0.6987 |     1.993 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     474.65116</span> |     91.32 |  0.004720 |    0.2885 |    0.1108 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009857 |    0.6019 |     1.215 |   0.06161 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8777 |   0.05824 |    0.7896 |    0.7116 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.603 |    0.9653 |    0.6987 |     1.993 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     3.462 |     1.239 |    0.1107 |  -0.09136 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3915 |     1.348 |    -5.441 |     3.268 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1113 |   0.02510 |   -0.6252 |    -2.485 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.647 |     1.796 |    0.6587 |    -2.076 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   49</span>|     474.63065 |     1.001 |    -1.157 |   -0.9247 |   -0.9377 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.007 |   -0.9846 |   0.05678 |   -0.7683 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7597 |   -0.8825 |   -0.7963 |    -1.084 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5148 |   -0.8629 |    -1.065 |   -0.2241 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     474.63065 |     91.11 |    -5.357 |   -0.9027 |    -2.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.620 |    0.4129 |     1.217 |   0.06155 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8777 |   0.05824 |    0.7899 |    0.7131 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.600 |    0.9642 |    0.6984 |     1.994 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     474.63065</span> |     91.11 |  0.004716 |    0.2885 |    0.1108 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009855 |    0.6018 |     1.217 |   0.06155 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8777 |   0.05824 |    0.7899 |    0.7131 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.600 |    0.9642 |    0.6984 |     1.994 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -14.93 |     1.220 |   -0.1531 |  -0.06288 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3983 |     1.394 |    -5.436 |     3.113 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1458 |   0.07621 |   -0.8397 |   -0.6848 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.501 |     1.690 |    0.6891 |    -2.034 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   50</span>|     474.58497 |     1.004 |    -1.158 |   -0.9246 |   -0.9376 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.008 |   -0.9857 |   0.06060 |   -0.7708 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7598 |   -0.8826 |   -0.7958 |    -1.084 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5162 |   -0.8624 |    -1.065 |   -0.2230 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     474.58497 |     91.34 |    -5.358 |   -0.9027 |    -2.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.620 |    0.4125 |     1.218 |   0.06148 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8777 |   0.05824 |    0.7902 |    0.7126 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.599 |    0.9647 |    0.6980 |     1.996 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     474.58497</span> |     91.34 |  0.004711 |    0.2885 |    0.1108 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009852 |    0.6017 |     1.218 |   0.06148 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8777 |   0.05824 |    0.7902 |    0.7126 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.599 |    0.9647 |    0.6980 |     1.996 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     5.248 |     1.234 |    0.1371 |  -0.09456 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3896 |     1.328 |    -5.011 |     3.180 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1110 |  0.004022 |   -0.8103 |   -0.4964 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.349 |     1.713 |    0.5705 |    -2.061 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   51</span>|     474.55760 |     1.002 |    -1.159 |   -0.9247 |   -0.9376 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.008 |   -0.9867 |   0.06444 |   -0.7734 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7598 |   -0.8826 |   -0.7952 |    -1.085 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5178 |   -0.8626 |    -1.066 |   -0.2215 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      474.5576 |     91.15 |    -5.359 |   -0.9027 |    -2.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.620 |    0.4120 |     1.220 |   0.06140 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8777 |   0.05824 |    0.7907 |    0.7122 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.597 |    0.9645 |    0.6977 |     1.998 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      474.5576</span> |     91.15 |  0.004706 |    0.2885 |    0.1108 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009849 |    0.6016 |     1.220 |   0.06140 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8777 |   0.05824 |    0.7907 |    0.7122 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.597 |    0.9645 |    0.6977 |     1.998 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -11.86 |     1.219 |   -0.1003 |  -0.07615 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3954 |     1.369 |    -4.929 |     3.171 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1229 |   0.06540 |   -0.8183 |   -0.7141 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.360 |     1.696 |    0.6359 |    -2.032 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   52</span>|     474.51619 |     1.004 |    -1.160 |   -0.9246 |   -0.9375 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.008 |   -0.9878 |   0.06816 |   -0.7761 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7599 |   -0.8826 |   -0.7946 |    -1.086 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5193 |   -0.8622 |    -1.066 |   -0.2202 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     474.51619 |     91.33 |    -5.360 |   -0.9027 |    -2.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.621 |    0.4115 |     1.221 |   0.06132 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8776 |   0.05823 |    0.7911 |    0.7113 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.595 |    0.9648 |    0.6972 |     1.999 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     474.51619</span> |     91.33 |  0.004702 |    0.2885 |    0.1108 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009846 |    0.6014 |     1.221 |   0.06132 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8776 |   0.05823 |    0.7911 |    0.7113 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.595 |    0.9648 |    0.6972 |     1.999 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     4.413 |     1.225 |    0.1371 |  -0.09620 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3880 |     1.314 |    -5.554 |     3.197 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.07604 |  0.008867 |   -0.7931 |   -0.6282 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.148 |     1.715 |    0.5273 |    -2.052 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   53</span>|     474.48673 |     1.002 |    -1.161 |   -0.9247 |   -0.9374 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.009 |   -0.9889 |   0.07224 |   -0.7786 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7599 |   -0.8826 |   -0.7941 |    -1.086 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5208 |   -0.8625 |    -1.067 |   -0.2188 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     474.48673 |     91.17 |    -5.361 |   -0.9027 |    -2.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.621 |    0.4110 |     1.223 |   0.06125 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8776 |   0.05823 |    0.7915 |    0.7110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.593 |    0.9645 |    0.6969 |     2.001 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     474.48673</span> |     91.17 |  0.004697 |    0.2885 |    0.1108 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009843 |    0.6013 |     1.223 |   0.06125 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8776 |   0.05823 |    0.7915 |    0.7110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.593 |    0.9645 |    0.6969 |     2.001 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -10.51 |     1.211 |  -0.06554 |  -0.07429 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3932 |     1.350 |    -4.456 |     3.182 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.08901 |   0.05354 |   -0.5957 |    -2.739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.160 |     1.696 |    0.5687 |    -2.022 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   54</span>|     474.45218 |     1.004 |    -1.162 |   -0.9246 |   -0.9373 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.009 |   -0.9900 |   0.07590 |   -0.7814 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7601 |   -0.8826 |   -0.7937 |    -1.086 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5220 |   -0.8620 |    -1.067 |   -0.2177 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     474.45218 |     91.40 |    -5.362 |   -0.9027 |    -2.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.621 |    0.4105 |     1.224 |   0.06117 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8776 |   0.05823 |    0.7918 |    0.7111 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.592 |    0.9650 |    0.6965 |     2.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     474.45218</span> |     91.40 |  0.004692 |    0.2885 |    0.1108 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009840 |    0.6012 |     1.224 |   0.06117 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8776 |   0.05823 |    0.7918 |    0.7111 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.592 |    0.9650 |    0.6965 |     2.002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     9.724 |     1.224 |    0.2009 |   -0.1069 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3834 |     1.279 |    -5.556 |     3.324 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1101 |  -0.01912 |   -0.5638 |    -2.553 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.043 |     1.731 |    0.4140 |    -2.044 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   55</span>|     474.41257 |     1.003 |    -1.163 |   -0.9246 |   -0.9371 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.009 |   -0.9913 |   0.07979 |   -0.7844 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7601 |   -0.8825 |   -0.7935 |    -1.085 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5231 |   -0.8612 |    -1.068 |   -0.2167 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     474.41257 |     91.25 |    -5.363 |   -0.9026 |    -2.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.622 |    0.4099 |     1.226 |   0.06108 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8775 |   0.05824 |    0.7919 |    0.7118 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.591 |    0.9658 |    0.6961 |     2.004 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     474.41257</span> |     91.25 |  0.004687 |    0.2885 |    0.1109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009836 |    0.6011 |     1.226 |   0.06108 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8775 |   0.05824 |    0.7919 |    0.7118 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.591 |    0.9658 |    0.6961 |     2.004 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -3.208 |     1.213 |   0.03978 |  -0.08693 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3876 |     1.303 |    -5.023 |     3.218 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1347 |   0.02311 |   -0.7771 |   -0.6556 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.056 |     1.822 |    0.4520 |    -2.025 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   56</span>|     474.39271 |     1.005 |    -1.164 |   -0.9246 |   -0.9371 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.010 |   -0.9922 |   0.08348 |   -0.7867 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7600 |   -0.8825 |   -0.7930 |    -1.085 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5246 |   -0.8625 |    -1.068 |   -0.2152 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     474.39271 |     91.47 |    -5.364 |   -0.9027 |    -2.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.622 |    0.4094 |     1.228 |   0.06101 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8776 |   0.05824 |    0.7923 |    0.7123 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.589 |    0.9645 |    0.6958 |     2.005 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     474.39271</span> |     91.47 |  0.004683 |    0.2885 |    0.1109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009833 |    0.6010 |     1.228 |   0.06101 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8776 |   0.05824 |    0.7923 |    0.7123 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.589 |    0.9645 |    0.6958 |     2.005 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     15.86 |     1.227 |    0.2807 |   -0.1163 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3793 |     1.243 |    -5.494 |     3.329 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.09368 |   0.01293 |   -0.5149 |    -2.407 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.979 |     1.686 |    0.3308 |    -2.044 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   57</span>|     474.34538 |     1.003 |    -1.165 |   -0.9247 |   -0.9369 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.010 |   -0.9934 |   0.08718 |   -0.7897 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7601 |   -0.8825 |   -0.7926 |    -1.086 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5258 |   -0.8620 |    -1.068 |   -0.2141 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     474.34538 |     91.27 |    -5.365 |   -0.9027 |    -2.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.622 |    0.4089 |     1.229 |   0.06093 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8775 |   0.05824 |    0.7926 |    0.7117 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.587 |    0.9650 |    0.6954 |     2.007 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     474.34538</span> |     91.27 |  0.004677 |    0.2885 |    0.1109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009830 |    0.6008 |     1.229 |   0.06093 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8775 |   0.05824 |    0.7926 |    0.7117 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.587 |    0.9650 |    0.6954 |     2.007 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -2.345 |     1.210 |   0.04189 |  -0.08986 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3855 |     1.284 |    -5.145 |     3.218 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1284 |   0.01309 |   -0.7466 |   -0.6536 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.964 |     1.743 |    0.3816 |    -2.008 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   58</span>|     474.31834 |     1.005 |    -1.166 |   -0.9247 |   -0.9369 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.010 |   -0.9944 |   0.09109 |   -0.7921 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7600 |   -0.8825 |   -0.7920 |    -1.085 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5273 |   -0.8633 |    -1.069 |   -0.2126 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     474.31834 |     91.43 |    -5.366 |   -0.9027 |    -2.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.623 |    0.4085 |     1.231 |   0.06086 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8776 |   0.05824 |    0.7930 |    0.7121 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.586 |    0.9638 |    0.6952 |     2.008 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     474.31834</span> |     91.43 |  0.004673 |    0.2885 |    0.1109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009827 |    0.6007 |     1.231 |   0.06086 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8776 |   0.05824 |    0.7930 |    0.7121 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.586 |    0.9638 |    0.6952 |     2.008 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     12.13 |     1.219 |    0.2312 |   -0.1125 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3787 |     1.238 |    -5.317 |     3.196 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.08361 |  -0.02599 |   -0.7000 |   -0.4940 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.837 |     1.637 |    0.3211 |    -2.028 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   59</span>|     474.27833 |     1.003 |    -1.167 |   -0.9247 |   -0.9368 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.010 |   -0.9955 |   0.09487 |   -0.7949 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7601 |   -0.8825 |   -0.7915 |    -1.086 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5286 |   -0.8630 |    -1.069 |   -0.2114 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     474.27833 |     91.25 |    -5.367 |   -0.9028 |    -2.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.623 |    0.4080 |     1.232 |   0.06078 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8775 |   0.05824 |    0.7934 |    0.7111 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.584 |    0.9640 |    0.6948 |     2.010 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     474.27833</span> |     91.25 |  0.004668 |    0.2885 |    0.1109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009824 |    0.6006 |     1.232 |   0.06078 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8775 |   0.05824 |    0.7934 |    0.7111 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.584 |    0.9640 |    0.6948 |     2.010 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.145 |     1.202 |   0.02355 |  -0.08919 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3842 |     1.275 |    -5.102 |     3.189 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1071 |   0.01700 |   -0.7283 |   -0.7209 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.776 |     1.686 |    0.3249 |    -1.982 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   60</span>|     474.25305 |     1.005 |    -1.168 |   -0.9247 |   -0.9367 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.011 |   -0.9965 |   0.09878 |   -0.7975 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7601 |   -0.8825 |   -0.7909 |    -1.086 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5300 |   -0.8636 |    -1.069 |   -0.2100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     474.25305 |     91.44 |    -5.368 |   -0.9028 |    -2.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.623 |    0.4075 |     1.234 |   0.06070 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8775 |   0.05824 |    0.7938 |    0.7109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.583 |    0.9635 |    0.6946 |     2.012 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     474.25305</span> |     91.44 |  0.004663 |    0.2885 |    0.1109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009821 |    0.6005 |     1.234 |   0.06070 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8775 |   0.05824 |    0.7938 |    0.7109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.583 |    0.9635 |    0.6946 |     2.012 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     13.11 |     1.213 |    0.2527 |   -0.1161 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3767 |     1.219 |    -5.003 |     3.213 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.09270 |  -0.04298 |   -0.4814 |    -2.533 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.730 |     1.613 |    0.2495 |    -2.014 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   61</span>|     474.21254 |     1.003 |    -1.169 |   -0.9248 |   -0.9365 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.011 |   -0.9977 |    0.1025 |   -0.8005 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7602 |   -0.8824 |   -0.7906 |    -1.087 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5311 |   -0.8630 |    -1.070 |   -0.2089 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     474.21254 |     91.24 |    -5.369 |   -0.9028 |    -2.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.624 |    0.4069 |     1.236 |   0.06062 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8775 |   0.05824 |    0.7941 |    0.7105 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.581 |    0.9641 |    0.6942 |     2.013 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     474.21254</span> |     91.24 |  0.004658 |    0.2885 |    0.1109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009817 |    0.6003 |     1.236 |   0.06062 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8775 |   0.05824 |    0.7941 |    0.7105 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.581 |    0.9641 |    0.6942 |     2.013 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -5.267 |     1.195 |   0.01760 |  -0.08987 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3825 |     1.260 |    -5.272 |     3.141 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1336 |  0.009843 |   -0.5186 |    -2.706 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.727 |     1.652 |    0.2904 |    -1.976 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   62</span>|     474.18171 |     1.004 |    -1.170 |   -0.9247 |   -0.9364 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.011 |   -0.9989 |    0.1065 |   -0.8033 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7602 |   -0.8823 |   -0.7904 |    -1.086 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5322 |   -0.8627 |    -1.070 |   -0.2078 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     474.18171 |     91.41 |    -5.370 |   -0.9028 |    -2.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.624 |    0.4064 |     1.237 |   0.06054 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8775 |   0.05824 |    0.7942 |    0.7112 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.580 |    0.9644 |    0.6939 |     2.014 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     474.18171</span> |     91.41 |  0.004653 |    0.2885 |    0.1109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009814 |    0.6002 |     1.237 |   0.06054 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8775 |   0.05824 |    0.7942 |    0.7112 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.580 |    0.9644 |    0.6939 |     2.014 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     9.716 |     1.206 |    0.2113 |   -0.1128 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3755 |     1.207 |    -5.399 |     3.226 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.09670 |  -0.03459 |   -0.6713 |   -0.5638 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.619 |     1.687 |    0.2005 |    -1.998 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   63</span>|     474.14509 |     1.003 |    -1.171 |   -0.9248 |   -0.9363 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.012 |    -1.000 |    0.1105 |   -0.8061 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7602 |   -0.8822 |   -0.7900 |    -1.087 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5333 |   -0.8621 |    -1.071 |   -0.2068 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     474.14509 |     91.25 |    -5.371 |   -0.9028 |    -2.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.624 |    0.4059 |     1.239 |   0.06045 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8775 |   0.05825 |    0.7945 |    0.7106 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.579 |    0.9649 |    0.6936 |     2.015 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     474.14509</span> |     91.25 |  0.004648 |    0.2885 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009811 |    0.6001 |     1.239 |   0.06045 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8775 |   0.05825 |    0.7945 |    0.7106 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.579 |    0.9649 |    0.6936 |     2.015 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -5.280 |     1.192 |   0.02321 |  -0.09129 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3799 |     1.239 |    -5.339 |     3.156 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1074 |   0.06181 |   -0.4875 |    -2.680 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.648 |     1.743 |    0.2195 |    -1.955 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   64</span>|     474.11542 |     1.005 |    -1.172 |   -0.9248 |   -0.9361 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.012 |    -1.001 |    0.1146 |   -0.8089 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7603 |   -0.8821 |   -0.7897 |    -1.086 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5344 |   -0.8621 |    -1.071 |   -0.2057 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     474.11542 |     91.42 |    -5.372 |   -0.9028 |    -2.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.625 |    0.4054 |     1.241 |   0.06037 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8774 |   0.05825 |    0.7947 |    0.7110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.577 |    0.9649 |    0.6934 |     2.017 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     474.11542</span> |     91.42 |  0.004643 |    0.2885 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009807 |    0.6000 |     1.241 |   0.06037 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8774 |   0.05825 |    0.7947 |    0.7110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.577 |    0.9649 |    0.6934 |     2.017 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     10.52 |     1.202 |    0.2258 |   -0.1162 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3725 |     1.186 |    -5.381 |     3.222 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1104 |  -0.04157 |   -0.6550 |   -0.5841 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.505 |     1.701 |    0.1753 |    -1.993 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   65</span>|     474.07794 |     1.003 |    -1.173 |   -0.9248 |   -0.9360 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.012 |    -1.002 |    0.1187 |   -0.8117 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7604 |   -0.8821 |   -0.7895 |    -1.087 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5355 |   -0.8615 |    -1.071 |   -0.2048 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     474.07794 |     91.25 |    -5.373 |   -0.9028 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.625 |    0.4048 |     1.242 |   0.06029 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8774 |   0.05825 |    0.7949 |    0.7104 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.576 |    0.9655 |    0.6932 |     2.018 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     474.07794</span> |     91.25 |  0.004638 |    0.2885 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009804 |    0.5999 |     1.242 |   0.06029 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8774 |   0.05825 |    0.7949 |    0.7104 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.576 |    0.9655 |    0.6932 |     2.018 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.801 |     1.188 |   0.03689 |  -0.09236 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3785 |     1.221 |    -5.368 |     3.104 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1066 |  0.003449 |   -0.6711 |   -0.7398 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.563 |     1.803 |    0.1884 |    -1.939 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   66</span>|     474.04951 |     1.005 |    -1.175 |   -0.9248 |   -0.9359 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.013 |    -1.003 |    0.1228 |   -0.8144 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7604 |   -0.8820 |   -0.7890 |    -1.087 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5367 |   -0.8618 |    -1.071 |   -0.2036 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     474.04951 |     91.43 |    -5.375 |   -0.9028 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.625 |    0.4044 |     1.244 |   0.06021 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8774 |   0.05825 |    0.7952 |    0.7100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.575 |    0.9652 |    0.6930 |     2.019 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     474.04951</span> |     91.43 |  0.004633 |    0.2885 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009801 |    0.5997 |     1.244 |   0.06021 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8774 |   0.05825 |    0.7952 |    0.7100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.575 |    0.9652 |    0.6930 |     2.019 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     10.86 |     1.196 |    0.2377 |   -0.1190 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3700 |     1.169 |    -5.399 |     3.259 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.09645 |  -0.03914 |   -0.4400 |    -2.610 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.421 |     1.705 |    0.1351 |    -1.978 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   67</span>|     474.01246 |     1.003 |    -1.176 |   -0.9249 |   -0.9357 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.013 |    -1.004 |    0.1268 |   -0.8173 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7606 |   -0.8819 |   -0.7888 |    -1.088 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5377 |   -0.8613 |    -1.072 |   -0.2028 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     474.01246 |     91.25 |    -5.376 |   -0.9029 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.626 |    0.4039 |     1.246 |   0.06013 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8773 |   0.05825 |    0.7954 |    0.7098 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.573 |    0.9657 |    0.6927 |     2.020 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     474.01246</span> |     91.25 |  0.004628 |    0.2885 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009798 |    0.5996 |     1.246 |   0.06013 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8773 |   0.05825 |    0.7954 |    0.7098 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.573 |    0.9657 |    0.6927 |     2.020 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -5.724 |     1.179 |   0.03043 |  -0.09493 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3750 |     1.206 |    -5.283 |     3.092 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.09863 |  -0.01543 |   -0.6573 |   -0.7980 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.377 |     1.778 |    0.1578 |    -1.944 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   68</span>|     473.98155 |     1.005 |    -1.177 |   -0.9249 |   -0.9356 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.013 |    -1.005 |    0.1310 |   -0.8202 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7607 |   -0.8818 |   -0.7885 |    -1.088 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5387 |   -0.8611 |    -1.072 |   -0.2018 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     473.98155 |     91.42 |    -5.377 |   -0.9029 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.626 |    0.4034 |     1.247 |   0.06004 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8773 |   0.05826 |    0.7956 |    0.7095 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.572 |    0.9659 |    0.6925 |     2.022 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     473.98155</span> |     91.42 |  0.004623 |    0.2885 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009794 |    0.5995 |     1.247 |   0.06004 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8773 |   0.05826 |    0.7956 |    0.7095 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.572 |    0.9659 |    0.6925 |     2.022 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     9.125 |     1.189 |    0.2215 |   -0.1183 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3680 |     1.157 |    -5.346 |     3.214 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.06695 |  -0.05805 |   -0.6245 |   -0.6678 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.365 |     1.770 |   0.07348 |    -1.960 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   69</span>|     473.94647 |     1.003 |    -1.178 |   -0.9250 |   -0.9354 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.014 |    -1.007 |    0.1350 |   -0.8231 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7608 |   -0.8817 |   -0.7881 |    -1.089 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5397 |   -0.8608 |    -1.072 |   -0.2009 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     473.94647 |     91.26 |    -5.378 |   -0.9030 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.626 |    0.4029 |     1.249 |   0.05996 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8772 |   0.05826 |    0.7959 |    0.7086 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.571 |    0.9661 |    0.6923 |     2.023 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     473.94647</span> |     91.26 |  0.004618 |    0.2884 |    0.1110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009791 |    0.5994 |     1.249 |   0.05996 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8772 |   0.05826 |    0.7959 |    0.7086 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.571 |    0.9661 |    0.6923 |     2.023 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -5.271 |     1.174 |   0.04603 |  -0.09766 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3725 |     1.188 |    -5.320 |     3.084 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.09768 |  -0.01918 |   -0.6378 |   -0.8469 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.360 |     1.825 |   0.08453 |    -1.920 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   70</span>|     473.91708 |     1.005 |    -1.179 |   -0.9250 |   -0.9353 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.014 |    -1.008 |    0.1391 |   -0.8259 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7609 |   -0.8816 |   -0.7877 |    -1.089 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5408 |   -0.8610 |    -1.072 |   -0.1999 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     473.91708 |     91.43 |    -5.379 |   -0.9030 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.627 |    0.4024 |     1.251 |   0.05988 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8772 |   0.05826 |    0.7962 |    0.7081 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.570 |    0.9660 |    0.6921 |     2.024 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     473.91708</span> |     91.43 |  0.004613 |    0.2884 |    0.1111 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009788 |    0.5993 |     1.251 |   0.05988 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8772 |   0.05826 |    0.7962 |    0.7081 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.570 |    0.9660 |    0.6921 |     2.024 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     9.919 |     1.183 |    0.2388 |   -0.1221 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3649 |     1.138 |    -5.295 |     3.201 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.05759 |  -0.06746 |   -0.6018 |   -0.7458 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.218 |     1.797 |   0.02666 |    -1.950 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   71</span>|     473.88166 |     1.003 |    -1.180 |   -0.9251 |   -0.9351 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.014 |    -1.009 |    0.1432 |   -0.8289 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7611 |   -0.8814 |   -0.7874 |    -1.090 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5418 |   -0.8607 |    -1.072 |   -0.1991 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     473.88166 |     91.26 |    -5.380 |   -0.9031 |    -2.198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.627 |    0.4019 |     1.252 |   0.05979 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8771 |   0.05827 |    0.7964 |    0.7073 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.568 |    0.9662 |    0.6919 |     2.025 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     473.88166</span> |     91.26 |  0.004608 |    0.2884 |    0.1111 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009785 |    0.5991 |     1.252 |   0.05979 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8771 |   0.05827 |    0.7964 |    0.7073 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.568 |    0.9662 |    0.6919 |     2.025 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -5.370 |     1.167 |   0.05346 |  -0.09978 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3701 |     1.174 |    -5.172 |     3.105 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.07011 |  -0.02516 |   -0.4239 |    -2.943 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.187 |     1.854 |   0.07395 |    -1.907 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   72</span>|     473.85215 |     1.005 |    -1.181 |   -0.9251 |   -0.9350 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.015 |    -1.010 |    0.1472 |   -0.8318 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7612 |   -0.8813 |   -0.7872 |    -1.090 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5427 |   -0.8608 |    -1.073 |   -0.1983 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     473.85215 |     91.44 |    -5.381 |   -0.9031 |    -2.197 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.627 |    0.4014 |     1.254 |   0.05971 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8771 |   0.05827 |    0.7965 |    0.7078 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.567 |    0.9662 |    0.6918 |     2.026 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     473.85215</span> |     91.44 |  0.004603 |    0.2884 |    0.1111 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009781 |    0.5990 |     1.254 |   0.05971 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8771 |   0.05827 |    0.7965 |    0.7078 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.567 |    0.9662 |    0.6918 |     2.026 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     10.42 |     1.179 |    0.2474 |   -0.1240 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3627 |     1.120 |    -5.345 |     3.221 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.07960 |  -0.04998 |   -0.3836 |    -2.755 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.094 |     1.797 |  -0.01014 |    -1.939 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   73</span>|     473.81514 |     1.003 |    -1.182 |   -0.9251 |   -0.9348 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.015 |    -1.011 |    0.1513 |   -0.8349 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7614 |   -0.8810 |   -0.7873 |    -1.089 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5432 |   -0.8601 |    -1.073 |   -0.1979 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     473.81514 |     91.27 |    -5.382 |   -0.9031 |    -2.197 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.628 |    0.4008 |     1.256 |   0.05962 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8770 |   0.05828 |    0.7964 |    0.7084 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.567 |    0.9669 |    0.6916 |     2.026 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     473.81514</span> |     91.27 |  0.004598 |    0.2884 |    0.1111 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009778 |    0.5989 |     1.256 |   0.05962 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8770 |   0.05828 |    0.7964 |    0.7084 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.567 |    0.9669 |    0.6916 |     2.026 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.811 |     1.166 |   0.05684 |   -0.1019 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3663 |     1.150 |    -5.276 |     3.057 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.06818 |  -0.03474 |   -0.4125 |    -2.836 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.158 |     1.851 |   0.03258 |    -1.914 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   74</span>|     473.78884 |     1.005 |    -1.183 |   -0.9252 |   -0.9346 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.015 |    -1.012 |    0.1554 |   -0.8377 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7615 |   -0.8808 |   -0.7873 |    -1.088 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5439 |   -0.8602 |    -1.073 |   -0.1971 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     473.78884 |     91.46 |    -5.383 |   -0.9031 |    -2.197 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.628 |    0.4003 |     1.257 |   0.05954 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8770 |   0.05829 |    0.7965 |    0.7095 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.566 |    0.9667 |    0.6915 |     2.027 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     473.78884</span> |     91.46 |  0.004593 |    0.2884 |    0.1111 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009775 |    0.5988 |     1.257 |   0.05954 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8770 |   0.05829 |    0.7965 |    0.7095 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.566 |    0.9667 |    0.6915 |     2.027 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     12.26 |     1.178 |    0.2628 |   -0.1282 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3579 |     1.094 |    -5.180 |     3.234 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.08932 |  -0.09780 |   -0.5652 |   -0.6340 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.031 |     1.841 |  -0.06209 |    -1.938 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   75</span>|     473.75065 |     1.003 |    -1.184 |   -0.9252 |   -0.9344 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.016 |    -1.013 |    0.1594 |   -0.8407 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7617 |   -0.8806 |   -0.7872 |    -1.088 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5446 |   -0.8597 |    -1.073 |   -0.1966 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     473.75065 |     91.27 |    -5.384 |   -0.9032 |    -2.197 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.628 |    0.3998 |     1.259 |   0.05945 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8769 |   0.05829 |    0.7965 |    0.7092 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.565 |    0.9672 |    0.6914 |     2.028 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     473.75065</span> |     91.27 |  0.004588 |    0.2884 |    0.1112 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009772 |    0.5986 |     1.259 |   0.05945 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8769 |   0.05829 |    0.7965 |    0.7092 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.565 |    0.9672 |    0.6914 |     2.028 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -5.250 |     1.164 |   0.05078 |   -0.1019 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3635 |     1.132 |    -5.308 |     3.057 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.09569 |  -0.05602 |   -0.5891 |   -0.8121 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.074 |     1.883 |  0.002586 |    -1.906 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   76</span>|     473.72028 |     1.005 |    -1.185 |   -0.9252 |   -0.9343 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.016 |    -1.014 |    0.1635 |   -0.8437 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7618 |   -0.8804 |   -0.7870 |    -1.089 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5455 |   -0.8599 |    -1.073 |   -0.1958 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     473.72028 |     91.43 |    -5.385 |   -0.9032 |    -2.197 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.629 |    0.3993 |     1.261 |   0.05936 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8768 |   0.05830 |    0.7967 |    0.7088 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.564 |    0.9671 |    0.6912 |     2.029 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     473.72028</span> |     91.43 |  0.004583 |    0.2884 |    0.1112 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009768 |    0.5985 |     1.261 |   0.05936 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8768 |   0.05830 |    0.7967 |    0.7088 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.564 |    0.9671 |    0.6912 |     2.029 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     8.975 |     1.171 |    0.2290 |   -0.1246 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3563 |     1.085 |    -5.320 |     3.173 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.08707 |   -0.1016 |   -0.5561 |   -0.7117 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9536 |     1.850 |  -0.07963 |    -1.922 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   77</span>|     473.68600 |     1.003 |    -1.186 |   -0.9253 |   -0.9341 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.016 |    -1.015 |    0.1676 |   -0.8467 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7620 |   -0.8801 |   -0.7868 |    -1.089 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5463 |   -0.8596 |    -1.073 |   -0.1952 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|       473.686 |     91.28 |    -5.386 |   -0.9033 |    -2.196 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.629 |    0.3989 |     1.263 |   0.05928 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8768 |   0.05831 |    0.7968 |    0.7081 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.563 |    0.9673 |    0.6911 |     2.030 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">       473.686</span> |     91.28 |  0.004578 |    0.2884 |    0.1112 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009765 |    0.5984 |     1.263 |   0.05928 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8768 |   0.05831 |    0.7968 |    0.7081 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.563 |    0.9673 |    0.6911 |     2.030 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -5.608 |     1.155 |   0.05506 |   -0.1036 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3607 |     1.118 |    -5.212 |     3.031 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.08611 |  -0.04336 |   -0.3798 |    -2.808 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.093 |     1.880 |  -0.02731 |    -1.896 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   78</span>|     473.65599 |     1.005 |    -1.188 |   -0.9253 |   -0.9339 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.017 |    -1.016 |    0.1718 |   -0.8497 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7621 |   -0.8799 |   -0.7868 |    -1.089 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5471 |   -0.8595 |    -1.074 |   -0.1946 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     473.65599 |     91.44 |    -5.388 |   -0.9033 |    -2.196 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.629 |    0.3984 |     1.264 |   0.05919 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8767 |   0.05831 |    0.7968 |    0.7087 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.562 |    0.9674 |    0.6910 |     2.030 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     473.65599</span> |     91.44 |  0.004573 |    0.2884 |    0.1112 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009762 |    0.5983 |     1.264 |   0.05919 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8767 |   0.05831 |    0.7968 |    0.7087 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.562 |    0.9674 |    0.6910 |     2.030 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     9.720 |     1.170 |    0.2444 |   -0.1229 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3557 |     1.067 |    -5.275 |     3.173 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.07850 |   -0.1042 |   -0.5348 |   -0.6844 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9479 |     1.915 |   -0.1018 |    -1.917 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   79</span>|     473.62083 |     1.003 |    -1.189 |   -0.9254 |   -0.9337 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.017 |    -1.017 |    0.1759 |   -0.8528 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7623 |   -0.8795 |   -0.7869 |    -1.089 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5477 |   -0.8591 |    -1.074 |   -0.1942 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     473.62083 |     91.28 |    -5.389 |   -0.9033 |    -2.196 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.630 |    0.3979 |     1.266 |   0.05910 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8766 |   0.05832 |    0.7968 |    0.7085 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.562 |    0.9678 |    0.6909 |     2.031 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     473.62083</span> |     91.28 |  0.004568 |    0.2884 |    0.1112 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009758 |    0.5982 |     1.266 |   0.05910 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8766 |   0.05832 |    0.7968 |    0.7085 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.562 |    0.9678 |    0.6909 |     2.031 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -5.323 |     1.153 |   0.05982 |   -0.1053 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3572 |     1.098 |    -4.702 |     3.058 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1117 |  -0.04281 |   -0.5573 |   -0.7999 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.039 |     1.912 |  -0.05974 |    -1.890 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   80</span>|     473.59326 |     1.005 |    -1.190 |   -0.9254 |   -0.9335 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.018 |    -1.019 |    0.1798 |   -0.8560 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7624 |   -0.8793 |   -0.7867 |    -1.090 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5486 |   -0.8595 |    -1.074 |   -0.1934 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     473.59326 |     91.46 |    -5.390 |   -0.9034 |    -2.196 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.630 |    0.3974 |     1.268 |   0.05901 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8766 |   0.05833 |    0.7969 |    0.7081 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.560 |    0.9674 |    0.6908 |     2.032 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     473.59326</span> |     91.46 |  0.004562 |    0.2884 |    0.1113 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009755 |    0.5981 |     1.268 |   0.05901 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8766 |   0.05833 |    0.7969 |    0.7081 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.560 |    0.9674 |    0.6908 |     2.032 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     11.25 |     1.162 |    0.2586 |   -0.1299 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3499 |     1.048 |    -5.229 |     3.126 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.05642 |  -0.09930 |   -0.3252 |    -2.718 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8202 |     1.876 |   -0.1477 |    -1.910 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   81</span>|     473.55541 |     1.003 |    -1.191 |   -0.9255 |   -0.9333 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.018 |    -1.020 |    0.1836 |   -0.8595 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7626 |   -0.8789 |   -0.7868 |    -1.090 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5492 |   -0.8592 |    -1.074 |   -0.1932 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     473.55541 |     91.31 |    -5.391 |   -0.9034 |    -2.196 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.630 |    0.3968 |     1.269 |   0.05891 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8765 |   0.05834 |    0.7968 |    0.7080 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.560 |    0.9677 |    0.6906 |     2.032 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     473.55541</span> |     91.31 |  0.004557 |    0.2883 |    0.1113 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009751 |    0.5979 |     1.269 |   0.05891 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8765 |   0.05834 |    0.7968 |    0.7080 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.560 |    0.9677 |    0.6906 |     2.032 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -3.062 |     1.149 |   0.09224 |   -0.1103 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3525 |     1.070 |    -5.098 |     2.994 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1095 |  -0.04877 |   -0.3504 |    -2.828 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8255 |     1.898 |   -0.1147 |    -1.889 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   82</span>|     473.53708 |     1.006 |    -1.192 |   -0.9256 |   -0.9332 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.018 |    -1.021 |    0.1873 |   -0.8616 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7625 |   -0.8789 |   -0.7866 |    -1.087 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5498 |   -0.8606 |    -1.074 |   -0.1918 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     473.53708 |     91.52 |    -5.392 |   -0.9035 |    -2.196 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.631 |    0.3964 |     1.271 |   0.05884 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8765 |   0.05834 |    0.7970 |    0.7099 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.559 |    0.9664 |    0.6907 |     2.034 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     473.53708</span> |     91.52 |  0.004553 |    0.2883 |    0.1113 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009749 |    0.5978 |     1.271 |   0.05884 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8765 |   0.05834 |    0.7970 |    0.7099 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.559 |    0.9664 |    0.6907 |     2.034 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     16.25 |     1.165 |    0.3077 |   -0.1378 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3448 |     1.019 |    -5.264 |     3.150 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.09278 |   -0.1366 |   -0.2969 |    -2.557 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7485 |     1.781 |   -0.1828 |    -1.909 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   83</span>|     473.49312 |     1.003 |    -1.193 |   -0.9256 |   -0.9329 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.018 |    -1.022 |    0.1912 |   -0.8647 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7626 |   -0.8785 |   -0.7869 |    -1.087 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5501 |   -0.8598 |    -1.074 |   -0.1918 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     473.49312 |     91.32 |    -5.393 |   -0.9035 |    -2.195 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.631 |    0.3959 |     1.272 |   0.05875 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8765 |   0.05835 |    0.7967 |    0.7106 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.559 |    0.9671 |    0.6906 |     2.034 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     473.49312</span> |     91.32 |  0.004547 |    0.2883 |    0.1113 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009745 |    0.5977 |     1.272 |   0.05875 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8765 |   0.05835 |    0.7967 |    0.7106 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.559 |    0.9671 |    0.6906 |     2.034 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -2.793 |     1.150 |   0.08391 |   -0.1095 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3500 |     1.057 |    -5.154 |     2.847 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.07414 |  -0.06025 |   -0.5244 |   -0.6779 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8291 |     1.867 |   -0.1201 |    -1.880 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   84</span>|     473.47390 |     1.006 |    -1.194 |   -0.9256 |   -0.9329 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.019 |    -1.022 |    0.1953 |   -0.8670 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7626 |   -0.8785 |   -0.7865 |    -1.086 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5507 |   -0.8613 |    -1.074 |   -0.1903 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      473.4739 |     91.52 |    -5.394 |   -0.9036 |    -2.195 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.631 |    0.3955 |     1.274 |   0.05869 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8765 |   0.05835 |    0.7970 |    0.7110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.558 |    0.9657 |    0.6907 |     2.036 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      473.4739</span> |     91.52 |  0.004543 |    0.2883 |    0.1113 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009743 |    0.5976 |     1.274 |   0.05869 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8765 |   0.05835 |    0.7970 |    0.7110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.558 |    0.9657 |    0.6907 |     2.036 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     15.82 |     1.163 |    0.3026 |   -0.1376 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3423 |     1.007 |    -4.821 |     3.176 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.06207 |   -0.1161 |   -0.4712 |   -0.4774 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7015 |     1.725 |   -0.1877 |    -1.896 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   85</span>|     473.43080 |     1.003 |    -1.195 |   -0.9257 |   -0.9326 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.019 |    -1.024 |    0.1991 |   -0.8700 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7628 |   -0.8781 |   -0.7866 |    -1.087 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5513 |   -0.8608 |    -1.074 |   -0.1899 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      473.4308 |     91.31 |    -5.395 |   -0.9036 |    -2.195 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.632 |    0.3950 |     1.276 |   0.05860 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8764 |   0.05836 |    0.7970 |    0.7105 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.557 |    0.9662 |    0.6906 |     2.036 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      473.4308</span> |     91.31 |  0.004538 |    0.2883 |    0.1114 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009739 |    0.5975 |     1.276 |   0.05860 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8764 |   0.05836 |    0.7970 |    0.7105 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.557 |    0.9662 |    0.6906 |     2.036 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -3.611 |     1.145 |   0.07564 |   -0.1091 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3478 |     1.048 |    -5.196 |     2.853 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.07176 |  -0.06153 |   -0.5100 |   -0.6862 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7701 |     1.783 |   -0.1204 |    -1.866 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   86</span>|     473.40390 |     1.005 |    -1.196 |   -0.9258 |   -0.9324 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.019 |    -1.025 |    0.2034 |   -0.8728 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7629 |   -0.8779 |   -0.7864 |    -1.087 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5520 |   -0.8612 |    -1.074 |   -0.1890 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      473.4039 |     91.48 |    -5.396 |   -0.9037 |    -2.195 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.632 |    0.3946 |     1.277 |   0.05852 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8764 |   0.05837 |    0.7971 |    0.7105 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.556 |    0.9658 |    0.6906 |     2.037 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      473.4039</span> |     91.48 |  0.004533 |    0.2883 |    0.1114 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009736 |    0.5974 |     1.277 |   0.05852 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8764 |   0.05837 |    0.7971 |    0.7105 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.556 |    0.9658 |    0.6906 |     2.037 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     11.82 |     1.154 |    0.2542 |   -0.1321 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3410 |     1.002 |    -5.150 |     3.097 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.04820 |   -0.1059 |   -0.4698 |   -0.5387 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6891 |     1.733 |   -0.1906 |    -1.879 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   87</span>|     473.36686 |     1.003 |    -1.198 |   -0.9258 |   -0.9322 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.020 |    -1.026 |    0.2076 |   -0.8758 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7631 |   -0.8776 |   -0.7865 |    -1.087 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5525 |   -0.8606 |    -1.074 |   -0.1886 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     473.36686 |     91.32 |    -5.398 |   -0.9037 |    -2.195 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.632 |    0.3941 |     1.279 |   0.05843 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8763 |   0.05838 |    0.7970 |    0.7100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.556 |    0.9664 |    0.6905 |     2.038 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     473.36686</span> |     91.32 |  0.004527 |    0.2883 |    0.1114 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009733 |    0.5973 |     1.279 |   0.05843 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8763 |   0.05838 |    0.7970 |    0.7100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.556 |    0.9664 |    0.6905 |     2.038 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -3.686 |     1.139 |   0.08028 |   -0.1103 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3450 |     1.034 |    -5.059 |     2.983 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.07022 |  -0.06541 |   -0.4954 |   -0.7148 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6839 |     1.782 |   -0.1353 |    -1.853 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   88</span>|     473.34117 |     1.005 |    -1.199 |   -0.9259 |   -0.9321 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.020 |    -1.027 |    0.2118 |   -0.8787 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7632 |   -0.8774 |   -0.7864 |    -1.087 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5531 |   -0.8611 |    -1.074 |   -0.1877 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     473.34117 |     91.49 |    -5.399 |   -0.9038 |    -2.194 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.633 |    0.3936 |     1.281 |   0.05835 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8763 |   0.05838 |    0.7971 |    0.7100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.555 |    0.9659 |    0.6904 |     2.039 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     473.34117</span> |     91.49 |  0.004522 |    0.2883 |    0.1114 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009730 |    0.5972 |     1.281 |   0.05835 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8763 |   0.05838 |    0.7971 |    0.7100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.555 |    0.9659 |    0.6904 |     2.039 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     12.62 |     1.149 |    0.2679 |   -0.1346 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3379 |    0.9875 |    -5.147 |     2.965 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.07400 |   -0.1145 |   -0.4542 |   -0.5455 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6283 |     1.734 |   -0.2097 |    -1.869 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   89</span>|     473.30326 |     1.004 |    -1.200 |   -0.9259 |   -0.9318 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.020 |    -1.028 |    0.2159 |   -0.8818 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7634 |   -0.8771 |   -0.7865 |    -1.088 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5535 |   -0.8604 |    -1.074 |   -0.1874 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     473.30326 |     91.32 |    -5.400 |   -0.9038 |    -2.194 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.633 |    0.3932 |     1.283 |   0.05826 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8762 |   0.05839 |    0.7970 |    0.7096 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.555 |    0.9665 |    0.6903 |     2.039 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     473.30326</span> |     91.32 |  0.004517 |    0.2883 |    0.1114 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009726 |    0.5970 |     1.283 |   0.05826 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8762 |   0.05839 |    0.7970 |    0.7096 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.555 |    0.9665 |    0.6903 |     2.039 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -3.609 |     1.134 |   0.08636 |   -0.1114 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3423 |     1.018 |    -5.228 |     2.880 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1079 |  -0.06580 |   -0.2896 |    -2.686 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6917 |     1.801 |   -0.1599 |    -1.842 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   90</span>|     473.27616 |     1.005 |    -1.201 |   -0.9260 |   -0.9316 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.021 |    -1.029 |    0.2202 |   -0.8845 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7634 |   -0.8768 |   -0.7866 |    -1.087 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5540 |   -0.8607 |    -1.074 |   -0.1867 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     473.27616 |     91.48 |    -5.401 |   -0.9039 |    -2.194 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.633 |    0.3927 |     1.284 |   0.05818 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8762 |   0.05840 |    0.7970 |    0.7107 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.554 |    0.9663 |    0.6904 |     2.040 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     473.27616</span> |     91.48 |  0.004512 |    0.2883 |    0.1115 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009723 |    0.5969 |     1.284 |   0.05818 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8762 |   0.05840 |    0.7970 |    0.7107 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.554 |    0.9663 |    0.6904 |     2.040 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     11.44 |     1.145 |    0.2516 |   -0.1333 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3351 |    0.9744 |    -4.753 |     3.090 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.05028 |   -0.1148 |   -0.4450 |   -0.5198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.5573 |     1.743 |   -0.2240 |    -1.858 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   91</span>|     473.24283 |     1.003 |    -1.202 |   -0.9260 |   -0.9314 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.021 |    -1.030 |    0.2240 |   -0.8877 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7636 |   -0.8764 |   -0.7870 |    -1.087 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5543 |   -0.8601 |    -1.074 |   -0.1866 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     473.24283 |     91.29 |    -5.402 |   -0.9039 |    -2.194 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.634 |    0.3922 |     1.286 |   0.05809 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8761 |   0.05841 |    0.7967 |    0.7107 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.554 |    0.9668 |    0.6903 |     2.040 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     473.24283</span> |     91.29 |  0.004506 |    0.2882 |    0.1115 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009720 |    0.5968 |     1.286 |   0.05809 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8761 |   0.05841 |    0.7967 |    0.7107 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.554 |    0.9668 |    0.6903 |     2.040 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -7.383 |     1.129 |   0.03939 |   -0.1057 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3410 |     1.013 |    -4.733 |     2.907 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.07460 |  -0.06623 |   -0.4732 |   -0.6761 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6495 |     1.823 |   -0.1637 |    -1.832 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   92</span>|     473.20973 |     1.005 |    -1.204 |   -0.9260 |   -0.9311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.021 |    -1.031 |    0.2279 |   -0.8912 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7638 |   -0.8760 |   -0.7872 |    -1.087 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5548 |   -0.8600 |    -1.075 |   -0.1862 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     473.20973 |     91.43 |    -5.404 |   -0.9039 |    -2.193 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.634 |    0.3917 |     1.288 |   0.05799 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8760 |   0.05843 |    0.7966 |    0.7102 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.553 |    0.9669 |    0.6900 |     2.040 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     473.20973</span> |     91.43 |  0.004500 |    0.2882 |    0.1115 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009716 |    0.5967 |     1.288 |   0.05799 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8760 |   0.05843 |    0.7966 |    0.7102 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.553 |    0.9669 |    0.6900 |     2.040 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   93</span>|     473.17178 |     1.005 |    -1.205 |   -0.9261 |   -0.9307 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.022 |    -1.032 |    0.2326 |   -0.8959 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7641 |   -0.8754 |   -0.7876 |    -1.088 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5554 |   -0.8593 |    -1.075 |   -0.1863 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     473.17178 |     91.43 |    -5.405 |   -0.9040 |    -2.193 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.634 |    0.3910 |     1.289 |   0.05785 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8759 |   0.05844 |    0.7963 |    0.7092 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.552 |    0.9676 |    0.6896 |     2.040 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     473.17178</span> |     91.43 |  0.004492 |    0.2882 |    0.1116 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009711 |    0.5965 |     1.289 |   0.05785 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8759 |   0.05844 |    0.7963 |    0.7092 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.552 |    0.9676 |    0.6896 |     2.040 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   94</span>|     472.97479 |     1.005 |    -1.215 |   -0.9262 |   -0.9287 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.025 |    -1.041 |    0.2575 |   -0.9212 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7657 |   -0.8720 |   -0.7899 |    -1.094 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5585 |   -0.8552 |    -1.078 |   -0.1866 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     472.97479 |     91.45 |    -5.415 |   -0.9041 |    -2.191 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.637 |    0.3872 |     1.300 |   0.05712 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8752 |   0.05854 |    0.7946 |    0.7037 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.549 |    0.9715 |    0.6874 |     2.040 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     472.97479</span> |     91.45 |  0.004449 |    0.2882 |    0.1118 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009685 |    0.5956 |     1.300 |   0.05712 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8752 |   0.05854 |    0.7946 |    0.7037 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.549 |    0.9715 |    0.6874 |     2.040 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   95</span>|     472.26932 |     1.006 |    -1.253 |   -0.9268 |   -0.9205 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.035 |    -1.074 |    0.3572 |    -1.022 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7723 |   -0.8583 |   -0.7991 |    -1.118 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5709 |   -0.8392 |    -1.088 |   -0.1877 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     472.26932 |     91.55 |    -5.453 |   -0.9046 |    -2.183 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.648 |    0.3719 |     1.341 |   0.05419 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8725 |   0.05894 |    0.7878 |    0.6819 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.534 |    0.9868 |    0.6787 |     2.039 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     472.26932</span> |     91.55 |  0.004282 |    0.2881 |    0.1127 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009582 |    0.5919 |     1.341 |   0.05419 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8725 |   0.05894 |    0.7878 |    0.6819 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.534 |    0.9868 |    0.6787 |     2.039 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   96</span>|     470.90347 |     1.012 |    -1.408 |   -0.9286 |   -0.8872 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.078 |    -1.207 |    0.7550 |    -1.427 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7985 |   -0.8024 |   -0.8343 |    -1.204 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6170 |   -0.7722 |    -1.128 |   -0.1957 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     470.90347 |     92.06 |    -5.608 |   -0.9062 |    -2.150 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.691 |    0.3105 |     1.506 |   0.04245 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8616 |   0.06056 |    0.7621 |    0.6043 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.480 |     1.051 |    0.6438 |     2.029 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     470.90347</span> |     92.06 |  0.003670 |    0.2878 |    0.1165 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009180 |    0.5770 |     1.506 |   0.04245 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8616 |   0.06056 |    0.7621 |    0.6043 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.480 |     1.051 |    0.6438 |     2.029 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     16.46 |    0.7178 |     1.137 |   -0.2842 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.06515 |    -1.058 |    -6.810 |    -2.325 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1903 |   -0.6630 |     1.424 |    -9.046 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.659 |     8.235 |    -7.307 |    -1.506 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   97</span>|     469.55066 |     1.016 |    -1.623 |   -0.9529 |   -0.8214 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.136 |    -1.353 |     1.295 |    -1.874 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8595 |   -0.6716 |   -0.9513 |    -1.204 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7087 |   -0.8316 |   -0.9499 |   -0.4083 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     469.55066 |     92.47 |    -5.823 |   -0.9279 |    -2.084 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.749 |    0.2437 |     1.731 |   0.02949 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8363 |   0.06435 |    0.6766 |    0.6043 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.371 |    0.9940 |    0.7978 |     1.771 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     469.55066</span> |     92.47 |  0.002960 |    0.2834 |    0.1245 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008661 |    0.5606 |     1.731 |   0.02949 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8363 |   0.06435 |    0.6766 |    0.6043 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.371 |    0.9940 |    0.7978 |     1.771 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -5.003 |    0.4823 |    0.6025 |   -0.2481 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.135 |    0.1015 |    -5.875 |    -8.309 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8363 |   -0.7839 |     1.711 |    -6.097 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.755 |     1.794 |     4.040 |   -0.5408 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   98</span>|     471.32255 |     1.022 |    -1.880 |    -1.043 |   -0.7426 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.107 |    -1.512 |     1.956 |    -1.964 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9773 |   -0.5429 |    -1.097 |    -1.122 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6469 |   -0.5662 |   -0.9027 |   -0.4752 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     471.32255 |     93.00 |    -6.080 |    -1.008 |    -2.005 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.719 |    0.1705 |     2.005 |   0.02687 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7874 |   0.06809 |    0.5704 |    0.6787 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.444 |     1.248 |    0.8385 |     1.690 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     471.32255</span> |     93.00 |  0.002289 |    0.2674 |    0.1347 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008922 |    0.5425 |     2.005 |   0.02687 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7874 |   0.06809 |    0.5704 |    0.6787 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.444 |     1.248 |    0.8385 |     1.690 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   99</span>|     468.82475 |     1.022 |    -1.709 |   -0.9836 |   -0.7948 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.126 |    -1.406 |     1.521 |    -1.898 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8984 |   -0.6279 |    -1.001 |    -1.172 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6845 |   -0.7440 |   -0.9371 |   -0.4303 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.82475 |     92.98 |    -5.909 |   -0.9551 |    -2.057 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.738 |    0.2191 |     1.824 |   0.02879 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8201 |   0.06562 |    0.6400 |    0.6333 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.400 |     1.078 |    0.8088 |     1.744 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.82475</span> |     92.98 |  0.002714 |    0.2779 |    0.1278 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008755 |    0.5546 |     1.824 |   0.02879 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8201 |   0.06562 |    0.6400 |    0.6333 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.400 |     1.078 |    0.8088 |     1.744 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     40.86 |    0.3767 |  -0.09575 |   -0.1893 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.313 |   -0.2132 |    -3.864 |    -4.927 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.292 |    -1.079 |   -0.1017 |    -3.329 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.248 |     8.116 |     3.916 |   -0.3530 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  100</span>|     467.75171 |     1.017 |    -1.816 |   -0.9824 |   -0.7623 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.067 |    -1.470 |     1.678 |    -1.962 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9501 |   -0.5667 |   -0.9995 |    -1.140 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5909 |   -0.8204 |    -1.012 |   -0.4878 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     467.75171 |     92.51 |    -6.016 |   -0.9541 |    -2.025 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.679 |    0.1896 |     1.889 |   0.02693 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7987 |   0.06740 |    0.6413 |    0.6622 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.510 |     1.005 |    0.7438 |     1.674 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     467.75171</span> |     92.51 |  0.002440 |    0.2781 |    0.1320 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.009285 |    0.5473 |     1.889 |   0.02693 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7987 |   0.06740 |    0.6413 |    0.6622 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.510 |     1.005 |    0.7438 |     1.674 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -11.72 |    0.1617 |   -0.2966 |    0.1391 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7722 |   -0.6625 |    -3.136 |    -6.222 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.617 |   -0.9189 |   -0.2811 |    -3.105 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.05355 |     2.320 |    -3.473 |    -2.160 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  101</span>|     467.28745 |     1.018 |    -1.902 |   -0.9107 |   -0.7577 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9795 |    -1.517 |     1.777 |    -1.860 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8874 |   -0.4787 |   -0.8826 |    -1.170 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6199 |   -0.8544 |   -0.9952 |   -0.3962 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     467.28745 |     92.66 |    -6.102 |   -0.8902 |    -2.020 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.592 |    0.1682 |     1.931 |   0.02990 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8247 |   0.06995 |    0.7268 |    0.6357 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.476 |    0.9723 |    0.7587 |     1.785 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     467.28745</span> |     92.66 |  0.002239 |    0.2911 |    0.1327 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01013 |    0.5420 |     1.931 |   0.02990 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8247 |   0.06995 |    0.7268 |    0.6357 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.476 |    0.9723 |    0.7587 |     1.785 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     21.75 |    0.1003 |     2.571 |    0.1450 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5415 |    -1.471 |    -1.062 |    -1.774 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.363 |    0.7539 |     2.182 |    -3.357 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.337 |    0.2346 |    -1.234 |   -0.8531 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  102</span>|     469.68385 |    0.9954 |    -1.950 |    -1.032 |   -0.7900 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8810 |    -1.433 |     1.783 |    -1.785 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9418 |   -0.5114 |   -0.9107 |    -1.204 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6798 |   -0.8966 |    -1.014 |   -0.2565 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     469.68385 |     90.58 |    -6.150 |   -0.9984 |    -2.052 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.493 |    0.2066 |     1.933 |   0.03207 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8021 |   0.06900 |    0.7062 |    0.6043 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.405 |    0.9320 |    0.7422 |     1.955 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     469.68385</span> |     90.58 |  0.002133 |    0.2693 |    0.1284 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01118 |    0.5515 |     1.933 |   0.03207 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8021 |   0.06900 |    0.7062 |    0.6043 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.405 |    0.9320 |    0.7422 |     1.955 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  103</span>|     467.87907 |     1.005 |    -1.909 |   -0.9308 |   -0.7628 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9639 |    -1.503 |     1.779 |    -1.847 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8965 |   -0.4841 |   -0.8880 |    -1.173 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6287 |   -0.8610 |   -0.9975 |   -0.3740 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     467.87907 |     91.48 |    -6.109 |   -0.9081 |    -2.025 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.576 |    0.1745 |     1.931 |   0.03026 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8209 |   0.06979 |    0.7229 |    0.6325 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.466 |    0.9660 |    0.7566 |     1.812 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     467.87907</span> |     91.48 |  0.002222 |    0.2874 |    0.1320 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01029 |    0.5435 |     1.931 |   0.03026 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8209 |   0.06979 |    0.7229 |    0.6325 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.466 |    0.9660 |    0.7566 |     1.812 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  104</span>|     467.53566 |     1.009 |    -1.902 |   -0.9117 |   -0.7577 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9793 |    -1.516 |     1.778 |    -1.859 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8880 |   -0.4790 |   -0.8835 |    -1.168 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6194 |   -0.8545 |   -0.9947 |   -0.3959 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     467.53566 |     91.86 |    -6.102 |   -0.8912 |    -2.020 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.592 |    0.1685 |     1.931 |   0.02992 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8245 |   0.06994 |    0.7261 |    0.6369 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.477 |    0.9722 |    0.7591 |     1.786 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     467.53566</span> |     91.86 |  0.002239 |    0.2909 |    0.1326 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01013 |    0.5420 |     1.931 |   0.02992 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8245 |   0.06994 |    0.7261 |    0.6369 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.477 |    0.9722 |    0.7591 |     1.786 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  105</span>|     467.26444 |     1.016 |    -1.902 |   -0.9109 |   -0.7577 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9795 |    -1.516 |     1.777 |    -1.860 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8875 |   -0.4788 |   -0.8828 |    -1.169 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6198 |   -0.8544 |   -0.9951 |   -0.3961 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     467.26444 |     92.48 |    -6.102 |   -0.8905 |    -2.020 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.592 |    0.1683 |     1.931 |   0.02990 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8246 |   0.06995 |    0.7266 |    0.6360 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.476 |    0.9723 |    0.7588 |     1.786 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     467.26444</span> |     92.48 |  0.002239 |    0.2910 |    0.1326 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01013 |    0.5420 |     1.931 |   0.02990 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8246 |   0.06995 |    0.7266 |    0.6360 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.476 |    0.9723 |    0.7588 |     1.786 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.458 |   0.09189 |     2.450 |    0.1713 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5320 |    -1.492 |    -1.346 |    -2.030 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.374 |    0.8089 |     1.804 |    -5.854 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.303 |    0.2787 |    -1.170 |   -0.8602 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  106</span>|     467.25360 |     1.017 |    -1.902 |   -0.9116 |   -0.7577 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9793 |    -1.516 |     1.778 |    -1.859 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8879 |   -0.4790 |   -0.8833 |    -1.168 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6195 |   -0.8545 |   -0.9948 |   -0.3959 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      467.2536 |     92.51 |    -6.102 |   -0.8910 |    -2.020 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.592 |    0.1685 |     1.931 |   0.02992 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8245 |   0.06994 |    0.7263 |    0.6374 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.477 |    0.9722 |    0.7590 |     1.786 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      467.2536</span> |     92.51 |  0.002239 |    0.2909 |    0.1326 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01013 |    0.5420 |     1.931 |   0.02992 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8245 |   0.06994 |    0.7263 |    0.6374 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.477 |    0.9722 |    0.7590 |     1.786 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     3.196 |   0.08873 |     2.446 |    0.1672 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5342 |    -1.484 |    -1.056 |    -1.956 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.373 |    0.7965 |     2.077 |    -5.722 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.292 |    0.2471 |    -1.040 |   -0.8496 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  107</span>|     467.24389 |     1.015 |    -1.902 |   -0.9128 |   -0.7578 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9790 |    -1.515 |     1.778 |    -1.858 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8886 |   -0.4794 |   -0.8844 |    -1.165 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6188 |   -0.8546 |   -0.9942 |   -0.3955 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     467.24389 |     92.37 |    -6.102 |   -0.8922 |    -2.020 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.592 |    0.1688 |     1.931 |   0.02995 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8242 |   0.06993 |    0.7255 |    0.6400 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.477 |    0.9721 |    0.7595 |     1.786 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     467.24389</span> |     92.37 |  0.002239 |    0.2907 |    0.1326 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01014 |    0.5421 |     1.931 |   0.02995 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8242 |   0.06993 |    0.7255 |    0.6400 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.477 |    0.9721 |    0.7595 |     1.786 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -15.43 |   0.07336 |     2.305 |    0.1893 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5256 |    -1.495 |    -1.329 |    -2.142 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.357 |    0.9382 |     1.940 |    -3.165 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.157 |    0.2760 |   -0.9277 |   -0.8566 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  108</span>|     467.22397 |     1.017 |    -1.902 |   -0.9138 |   -0.7581 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9782 |    -1.514 |     1.778 |    -1.857 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8889 |   -0.4797 |   -0.8863 |    -1.164 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6190 |   -0.8552 |   -0.9945 |   -0.3935 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     467.22397 |     92.56 |    -6.102 |   -0.8930 |    -2.020 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.591 |    0.1692 |     1.931 |   0.02998 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8241 |   0.06992 |    0.7241 |    0.6409 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.477 |    0.9715 |    0.7593 |     1.789 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     467.22397</span> |     92.56 |  0.002238 |    0.2905 |    0.1326 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01015 |    0.5422 |     1.931 |   0.02998 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8241 |   0.06992 |    0.7241 |    0.6409 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.477 |    0.9715 |    0.7593 |     1.789 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     8.612 |   0.07601 |     2.377 |    0.1597 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5308 |    -1.476 |    -1.038 |    -1.834 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.332 |    0.7679 |     2.039 |    -3.039 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.288 |    0.2037 |    -1.093 |   -0.8313 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  109</span>|     467.20858 |     1.016 |    -1.903 |   -0.9153 |   -0.7584 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9772 |    -1.513 |     1.777 |    -1.856 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8896 |   -0.4802 |   -0.8882 |    -1.164 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6194 |   -0.8560 |   -0.9946 |   -0.3915 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     467.20858 |     92.46 |    -6.103 |   -0.8944 |    -2.021 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.590 |    0.1698 |     1.931 |   0.03001 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8238 |   0.06990 |    0.7227 |    0.6411 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.477 |    0.9707 |    0.7591 |     1.791 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     467.20858</span> |     92.46 |  0.002236 |    0.2902 |    0.1325 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01016 |    0.5423 |     1.931 |   0.03001 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8238 |   0.06990 |    0.7227 |    0.6411 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.477 |    0.9707 |    0.7591 |     1.791 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -3.651 |   0.07137 |     2.259 |    0.1744 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5171 |    -1.486 |    -1.304 |    -1.934 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.324 |    0.7910 |     1.690 |    -3.053 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.233 |    0.1999 |    -1.046 |   -0.8164 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  110</span>|     467.19548 |     1.017 |    -1.903 |   -0.9170 |   -0.7588 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9762 |    -1.512 |     1.778 |    -1.855 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8906 |   -0.4808 |   -0.8898 |    -1.163 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6195 |   -0.8567 |   -0.9946 |   -0.3897 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     467.19548 |     92.56 |    -6.103 |   -0.8959 |    -2.021 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.589 |    0.1705 |     1.931 |   0.03005 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8234 |   0.06989 |    0.7215 |    0.6417 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.477 |    0.9701 |    0.7592 |     1.793 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     467.19548</span> |     92.56 |  0.002235 |    0.2899 |    0.1325 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01017 |    0.5425 |     1.931 |   0.03005 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8234 |   0.06989 |    0.7215 |    0.6417 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.477 |    0.9701 |    0.7592 |     1.793 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     9.695 |   0.07360 |     2.256 |    0.1610 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5147 |    -1.467 |    -1.275 |    -1.734 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.307 |    0.8740 |     2.039 |    -5.568 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.898 |   -0.2550 |    -1.273 |   -0.7762 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  111</span>|     467.17845 |     1.016 |    -1.904 |   -0.9175 |   -0.7593 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9751 |    -1.510 |     1.777 |    -1.854 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8907 |   -0.4813 |   -0.8918 |    -1.161 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6194 |   -0.8571 |   -0.9949 |   -0.3876 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     467.17845 |     92.50 |    -6.104 |   -0.8963 |    -2.022 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.588 |    0.1711 |     1.930 |   0.03007 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8234 |   0.06987 |    0.7201 |    0.6433 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.477 |    0.9697 |    0.7589 |     1.796 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     467.17845</span> |     92.50 |  0.002234 |    0.2898 |    0.1324 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01018 |    0.5427 |     1.930 |   0.03007 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8234 |   0.06987 |    0.7201 |    0.6433 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.477 |    0.9697 |    0.7589 |     1.796 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     2.021 |   0.06308 |     2.194 |    0.1658 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5050 |    -1.471 |    -1.283 |    -1.811 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.304 |    0.7659 |     1.629 |    -2.860 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.234 |    0.1434 |    -1.025 |   -0.7796 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  112</span>|     467.16518 |     1.015 |    -1.904 |   -0.9192 |   -0.7596 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9742 |    -1.509 |     1.777 |    -1.853 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8917 |   -0.4819 |   -0.8931 |    -1.160 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6190 |   -0.8574 |   -0.9946 |   -0.3864 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     467.16518 |     92.39 |    -6.104 |   -0.8978 |    -2.022 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.587 |    0.1719 |     1.931 |   0.03011 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8229 |   0.06985 |    0.7191 |    0.6447 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.477 |    0.9694 |    0.7592 |     1.797 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     467.16518</span> |     92.39 |  0.002234 |    0.2895 |    0.1324 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01019 |    0.5429 |     1.931 |   0.03011 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8229 |   0.06985 |    0.7191 |    0.6447 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.477 |    0.9694 |    0.7592 |     1.797 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -11.15 |   0.05292 |     2.063 |    0.1800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4962 |    -1.473 |   -0.9311 |    -1.912 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.287 |    0.8965 |     1.659 |    -5.399 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.766 |   -0.2649 |    -1.228 |   -0.7899 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  113</span>|     467.14275 |     1.016 |    -1.904 |   -0.9201 |   -0.7599 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9736 |    -1.507 |     1.777 |    -1.851 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8922 |   -0.4826 |   -0.8948 |    -1.157 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6182 |   -0.8576 |   -0.9943 |   -0.3850 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     467.14275 |     92.43 |    -6.104 |   -0.8986 |    -2.022 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.586 |    0.1725 |     1.931 |   0.03014 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8227 |   0.06984 |    0.7179 |    0.6470 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.478 |    0.9693 |    0.7594 |     1.799 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     467.14275</span> |     92.43 |  0.002233 |    0.2893 |    0.1324 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01019 |    0.5430 |     1.931 |   0.03014 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8227 |   0.06984 |    0.7179 |    0.6470 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.478 |    0.9693 |    0.7594 |     1.799 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -6.177 |   0.04590 |     2.051 |    0.1758 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4936 |    -1.461 |   -0.9893 |    -1.828 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.266 |    0.7615 |     1.545 |    -5.206 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.792 |   -0.2512 |    -1.220 |   -0.7551 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  114</span>|     467.10820 |     1.017 |    -1.905 |   -0.9220 |   -0.7605 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9724 |    -1.505 |     1.777 |    -1.849 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8932 |   -0.4835 |   -0.8976 |    -1.151 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6164 |   -0.8576 |   -0.9935 |   -0.3828 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      467.1082 |     92.55 |    -6.105 |   -0.9003 |    -2.023 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.585 |    0.1737 |     1.930 |   0.03020 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8223 |   0.06981 |    0.7158 |    0.6522 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.480 |    0.9692 |    0.7601 |     1.802 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      467.1082</span> |     92.55 |  0.002232 |    0.2890 |    0.1323 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01020 |    0.5433 |     1.930 |   0.03020 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8223 |   0.06981 |    0.7158 |    0.6522 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.480 |    0.9692 |    0.7601 |     1.802 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     8.449 |   0.03092 |     2.044 |    0.1611 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4934 |    -1.430 |    -1.221 |    -1.596 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.229 |    0.8691 |     1.875 |    -2.123 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.605 |   -0.2558 |    -1.009 |   -0.7122 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  115</span>|     467.10106 |     1.014 |    -1.905 |   -0.9236 |   -0.7611 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9712 |    -1.502 |     1.777 |    -1.847 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8940 |   -0.4849 |   -0.9012 |    -1.147 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6148 |   -0.8578 |   -0.9928 |   -0.3803 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     467.10106 |     92.28 |    -6.105 |   -0.9017 |    -2.023 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.584 |    0.1749 |     1.930 |   0.03026 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8220 |   0.06977 |    0.7132 |    0.6562 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.482 |    0.9690 |    0.7607 |     1.805 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     467.10106</span> |     92.28 |  0.002232 |    0.2887 |    0.1322 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01022 |    0.5436 |     1.930 |   0.03026 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8220 |   0.06977 |    0.7132 |    0.6562 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.482 |    0.9690 |    0.7607 |     1.805 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -24.17 |  0.001665 |     1.814 |    0.1960 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4738 |    -1.444 |   -0.8955 |    -1.908 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.221 |    0.7910 |     1.755 |    -3.560 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.244 |   -0.2675 |   -0.8607 |   -0.7175 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  116</span>|     467.04630 |     1.016 |    -1.905 |   -0.9246 |   -0.7619 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9696 |    -1.499 |     1.777 |    -1.845 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8944 |   -0.4866 |   -0.9055 |    -1.143 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6133 |   -0.8582 |   -0.9923 |   -0.3775 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      467.0463 |     92.45 |    -6.105 |   -0.9026 |    -2.024 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.582 |    0.1762 |     1.930 |   0.03032 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8218 |   0.06972 |    0.7101 |    0.6601 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.484 |    0.9686 |    0.7611 |     1.808 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      467.0463</span> |     92.45 |  0.002231 |    0.2885 |    0.1321 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01023 |    0.5439 |     1.930 |   0.03032 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8218 |   0.06972 |    0.7101 |    0.6601 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.484 |    0.9686 |    0.7611 |     1.808 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -2.647 | -0.007887 |     1.876 |    0.1719 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4730 |    -1.410 |   -0.8701 |    -1.589 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.184 |    0.7244 |     1.544 |    -4.110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.544 |   -0.3120 |   -0.9430 |   -0.6527 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  117</span>|     467.02543 |     1.017 |    -1.906 |   -0.9263 |   -0.7625 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9684 |    -1.497 |     1.777 |    -1.843 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8954 |   -0.4879 |   -0.9089 |    -1.137 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6116 |   -0.8583 |   -0.9915 |   -0.3753 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     467.02543 |     92.58 |    -6.106 |   -0.9041 |    -2.025 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.581 |    0.1775 |     1.930 |   0.03038 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8214 |   0.06968 |    0.7076 |    0.6649 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.486 |    0.9686 |    0.7618 |     1.811 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     467.02543</span> |     92.58 |  0.002230 |    0.2882 |    0.1320 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01025 |    0.5443 |     1.930 |   0.03038 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8214 |   0.06968 |    0.7076 |    0.6649 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.486 |    0.9686 |    0.7618 |     1.811 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     12.94 |  -0.02385 |     1.879 |    0.1542 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4735 |    -1.378 |   -0.8918 |    -1.355 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.131 |    0.6590 |     1.631 |    -3.618 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.430 |   -0.2321 |   -0.8462 |   -0.6495 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  118</span>|     466.99646 |     1.016 |    -1.906 |   -0.9273 |   -0.7632 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9670 |    -1.494 |     1.776 |    -1.841 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8958 |   -0.4892 |   -0.9132 |    -1.133 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6101 |   -0.8587 |   -0.9911 |   -0.3725 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     466.99646 |     92.42 |    -6.106 |   -0.9050 |    -2.026 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.579 |    0.1788 |     1.930 |   0.03043 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8212 |   0.06964 |    0.7044 |    0.6690 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.488 |    0.9682 |    0.7622 |     1.814 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     466.99646</span> |     92.42 |  0.002230 |    0.2880 |    0.1319 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01026 |    0.5446 |     1.930 |   0.03043 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8212 |   0.06964 |    0.7044 |    0.6690 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.488 |    0.9682 |    0.7622 |     1.814 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -5.590 |  -0.05004 |     1.738 |    0.1737 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4573 |    -1.378 |   -0.8791 |    -1.534 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.117 |    0.8064 |     1.528 |    -3.141 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8220 |    0.1296 |   -0.3838 |   -0.6246 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  119</span>|     466.97639 |     1.017 |    -1.906 |   -0.9282 |   -0.7640 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9655 |    -1.490 |     1.776 |    -1.839 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8964 |   -0.4910 |   -0.9175 |    -1.129 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6092 |   -0.8593 |   -0.9910 |   -0.3693 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     466.97639 |     92.52 |    -6.106 |   -0.9059 |    -2.026 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.578 |    0.1803 |     1.930 |   0.03049 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8210 |   0.06959 |    0.7013 |    0.6726 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.489 |    0.9676 |    0.7623 |     1.818 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     466.97639</span> |     92.52 |  0.002230 |    0.2878 |    0.1318 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01028 |    0.5450 |     1.930 |   0.03049 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8210 |   0.06959 |    0.7013 |    0.6726 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.489 |    0.9676 |    0.7623 |     1.818 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     7.027 |  -0.06281 |     1.755 |    0.1604 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4507 |    -1.349 |   -0.8053 |    -1.299 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.095 |    0.6623 |     1.032 |    -2.794 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8171 |   0.07286 |   -0.3538 |   -0.5889 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  120</span>|     466.96001 |     1.015 |    -1.906 |   -0.9299 |   -0.7649 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9637 |    -1.486 |     1.775 |    -1.837 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8976 |   -0.4931 |   -0.9192 |    -1.125 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6091 |   -0.8600 |   -0.9916 |   -0.3657 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     466.96001 |     92.40 |    -6.106 |   -0.9074 |    -2.027 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.576 |    0.1824 |     1.930 |   0.03055 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8205 |   0.06953 |    0.7000 |    0.6758 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.489 |    0.9669 |    0.7618 |     1.823 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     466.96001</span> |     92.40 |  0.002230 |    0.2875 |    0.1317 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01029 |    0.5455 |     1.930 |   0.03055 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8205 |   0.06953 |    0.7000 |    0.6758 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.489 |    0.9669 |    0.7618 |     1.823 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -7.315 |  -0.08361 |     1.595 |    0.1755 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4289 |    -1.331 |   -0.8104 |    -1.428 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.055 |    0.7710 |     1.242 |    -2.826 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.346 |   -0.3282 |   -0.6399 |   -0.5709 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  121</span>|     466.94053 |     1.016 |    -1.905 |   -0.9315 |   -0.7662 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9617 |    -1.480 |     1.774 |    -1.835 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8987 |   -0.4954 |   -0.9193 |    -1.121 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6079 |   -0.8601 |   -0.9919 |   -0.3632 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     466.94053 |     92.48 |    -6.105 |   -0.9088 |    -2.029 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.574 |    0.1849 |     1.929 |   0.03061 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8200 |   0.06946 |    0.6999 |    0.6792 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.490 |    0.9668 |    0.7615 |     1.826 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     466.94053</span> |     92.48 |  0.002231 |    0.2873 |    0.1315 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01031 |    0.5461 |     1.929 |   0.03061 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8200 |   0.06946 |    0.6999 |    0.6792 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.490 |    0.9668 |    0.7615 |     1.826 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     2.448 |  -0.09452 |     1.578 |    0.1631 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4178 |    -1.275 |   -0.7880 |    -1.291 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.013 |    0.7275 |     1.336 |   -0.1176 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.297 |   -0.2757 |   -0.6338 |   -0.5586 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  122</span>|     466.92529 |     1.015 |    -1.905 |   -0.9321 |   -0.7676 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9593 |    -1.476 |     1.775 |    -1.833 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8992 |   -0.4984 |   -0.9232 |    -1.122 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6063 |   -0.8604 |   -0.9916 |   -0.3612 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     466.92529 |     92.34 |    -6.105 |   -0.9093 |    -2.030 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.572 |    0.1871 |     1.930 |   0.03066 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8198 |   0.06938 |    0.6971 |    0.6791 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.492 |    0.9666 |    0.7618 |     1.828 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     466.92529</span> |     92.34 |  0.002233 |    0.2871 |    0.1313 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01034 |    0.5466 |     1.930 |   0.03066 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8198 |   0.06938 |    0.6971 |    0.6791 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.492 |    0.9666 |    0.7618 |     1.828 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -12.72 |  -0.09950 |     1.479 |    0.1754 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4012 |    -1.242 |   -0.7866 |    -1.413 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9668 |    0.6187 |    0.9349 |    -2.630 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.260 |   -0.3489 |   -0.4063 |   -0.5476 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  123</span>|     466.89942 |     1.016 |    -1.904 |   -0.9326 |   -0.7693 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9564 |    -1.470 |     1.775 |    -1.832 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8995 |   -0.5006 |   -0.9260 |    -1.122 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6055 |   -0.8609 |   -0.9921 |   -0.3587 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     466.89942 |     92.48 |    -6.104 |   -0.9097 |    -2.032 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.569 |    0.1897 |     1.929 |   0.03069 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8197 |   0.06931 |    0.6950 |    0.6788 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.493 |    0.9661 |    0.7614 |     1.831 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     466.89942</span> |     92.48 |  0.002235 |    0.2871 |    0.1311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01037 |    0.5473 |     1.929 |   0.03069 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8197 |   0.06931 |    0.6950 |    0.6788 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.493 |    0.9661 |    0.7614 |     1.831 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     3.439 |  -0.09008 |     1.544 |    0.1555 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3845 |    -1.174 |   -0.7709 |    -1.201 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9305 |    0.6799 |     1.153 |    -2.871 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.766 |   -0.2963 |   -0.5389 |   -0.5305 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  124</span>|     466.87885 |     1.016 |    -1.902 |   -0.9335 |   -0.7713 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9544 |    -1.465 |     1.775 |    -1.831 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8988 |   -0.5028 |   -0.9291 |    -1.120 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6025 |   -0.8609 |   -0.9918 |   -0.3573 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     466.87885 |     92.42 |    -6.102 |   -0.9106 |    -2.034 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.567 |    0.1922 |     1.930 |   0.03072 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8200 |   0.06925 |    0.6928 |    0.6803 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.497 |    0.9661 |    0.7616 |     1.833 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     466.87885</span> |     92.42 |  0.002239 |    0.2869 |    0.1309 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01039 |    0.5479 |     1.930 |   0.03072 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8200 |   0.06925 |    0.6928 |    0.6803 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.497 |    0.9661 |    0.7616 |     1.833 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -3.274 |  -0.09794 |     1.477 |    0.1445 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3711 |    -1.126 |   -0.7697 |    -1.253 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9213 |    0.5797 |    0.6929 |    -2.254 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4364 |   0.05549 |  -0.07007 |   -0.5105 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  125</span>|     466.87374 |     1.017 |    -1.901 |   -0.9363 |   -0.7725 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9523 |    -1.459 |     1.775 |    -1.829 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9004 |   -0.5050 |   -0.9284 |    -1.119 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6029 |   -0.8616 |   -0.9930 |   -0.3542 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     466.87374 |     92.57 |    -6.101 |   -0.9131 |    -2.035 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.565 |    0.1945 |     1.929 |   0.03078 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8193 |   0.06919 |    0.6933 |    0.6817 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.496 |    0.9655 |    0.7605 |     1.836 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     466.87374</span> |     92.57 |  0.002241 |    0.2864 |    0.1307 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01041 |    0.5485 |     1.929 |   0.03078 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8193 |   0.06919 |    0.6933 |    0.6817 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.496 |    0.9655 |    0.7605 |     1.836 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     13.60 |  -0.09484 |     1.445 |    0.1251 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3584 |    -1.070 |   -0.7354 |    -1.048 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8551 |    0.6144 |    0.8824 | -0.009520 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4410 |   0.04024 |   -0.2274 |   -0.5153 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  126</span>|     466.85547 |     1.016 |    -1.900 |   -0.9388 |   -0.7737 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9500 |    -1.454 |     1.774 |    -1.828 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9020 |   -0.5070 |   -0.9271 |    -1.119 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6040 |   -0.8622 |   -0.9946 |   -0.3509 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     466.85547 |     92.42 |    -6.100 |   -0.9152 |    -2.036 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.562 |    0.1970 |     1.929 |   0.03083 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8186 |   0.06913 |    0.6942 |    0.6818 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.495 |    0.9648 |    0.7592 |     1.841 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     466.85547</span> |     92.42 |  0.002243 |    0.2859 |    0.1305 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01044 |    0.5491 |     1.929 |   0.03083 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8186 |   0.06913 |    0.6942 |    0.6818 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.495 |    0.9648 |    0.7592 |     1.841 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -2.539 |  -0.09755 |     1.262 |    0.1433 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3291 |    -1.038 |   -0.7482 |    -1.225 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8087 |    0.5023 |    0.8171 | -0.005344 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4685 |   0.01356 |   -0.1499 |   -0.5112 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  127</span>|     466.84409 |     1.016 |    -1.899 |   -0.9408 |   -0.7751 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9477 |    -1.449 |     1.774 |    -1.826 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9037 |   -0.5088 |   -0.9269 |    -1.119 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6048 |   -0.8630 |   -0.9960 |   -0.3473 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     466.84409 |     92.47 |    -6.099 |   -0.9171 |    -2.037 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.560 |    0.1996 |     1.929 |   0.03088 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8179 |   0.06907 |    0.6944 |    0.6816 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.494 |    0.9641 |    0.7580 |     1.845 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     466.84409</span> |     92.47 |  0.002245 |    0.2856 |    0.1304 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01046 |    0.5497 |     1.929 |   0.03088 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8179 |   0.06907 |    0.6944 |    0.6816 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.494 |    0.9641 |    0.7580 |     1.845 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     3.205 |  -0.09489 |     1.201 |    0.1289 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3142 |   -0.9889 |   -0.7667 |    -1.156 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7401 |    0.5570 |     1.301 |    -1.479 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.860 |   -0.4540 |   -0.5722 |   -0.5005 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  128</span>|     466.83069 |     1.016 |    -1.897 |   -0.9405 |   -0.7762 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9462 |    -1.443 |     1.775 |    -1.825 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9047 |   -0.5101 |   -0.9307 |    -1.119 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6046 |   -0.8636 |   -0.9969 |   -0.3435 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     466.83069 |     92.41 |    -6.097 |   -0.9168 |    -2.039 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.559 |    0.2019 |     1.929 |   0.03091 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8175 |   0.06904 |    0.6917 |    0.6819 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.494 |    0.9635 |    0.7572 |     1.850 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     466.83069</span> |     92.41 |  0.002250 |    0.2856 |    0.1302 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01048 |    0.5503 |     1.929 |   0.03091 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8175 |   0.06904 |    0.6917 |    0.6819 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.494 |    0.9635 |    0.7572 |     1.850 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  129</span>|     466.81923 |     1.016 |    -1.894 |   -0.9398 |   -0.7773 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9447 |    -1.438 |     1.775 |    -1.824 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9056 |   -0.5113 |   -0.9343 |    -1.119 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6049 |   -0.8643 |   -0.9979 |   -0.3395 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     466.81923 |     92.43 |    -6.094 |   -0.9162 |    -2.040 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.557 |    0.2043 |     1.930 |   0.03094 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8171 |   0.06900 |    0.6890 |    0.6818 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.494 |    0.9628 |    0.7563 |     1.854 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     466.81923</span> |     92.43 |  0.002255 |    0.2857 |    0.1301 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01049 |    0.5509 |     1.930 |   0.03094 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8171 |   0.06900 |    0.6890 |    0.6818 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.494 |    0.9628 |    0.7563 |     1.854 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  130</span>|     466.78977 |     1.016 |    -1.887 |   -0.9376 |   -0.7810 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9400 |    -1.422 |     1.776 |    -1.820 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9086 |   -0.5153 |   -0.9461 |    -1.119 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6057 |   -0.8666 |    -1.001 |   -0.3266 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     466.78977 |     92.47 |    -6.087 |   -0.9142 |    -2.043 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.552 |    0.2120 |     1.930 |   0.03105 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8159 |   0.06889 |    0.6804 |    0.6815 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.493 |    0.9606 |    0.7533 |     1.870 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     466.78977</span> |     92.47 |  0.002272 |    0.2861 |    0.1296 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01054 |    0.5528 |     1.930 |   0.03105 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8159 |   0.06889 |    0.6804 |    0.6815 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.493 |    0.9606 |    0.7533 |     1.870 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  131</span>|     466.76586 |     1.017 |    -1.875 |   -0.9340 |   -0.7871 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9321 |    -1.394 |     1.779 |    -1.814 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9134 |   -0.5218 |   -0.9656 |    -1.119 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6071 |   -0.8705 |    -1.007 |   -0.3052 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     466.76586 |     92.53 |    -6.075 |   -0.9110 |    -2.049 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.545 |    0.2247 |     1.931 |   0.03121 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8139 |   0.06870 |    0.6661 |    0.6811 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.491 |    0.9569 |    0.7485 |     1.896 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     466.76586</span> |     92.53 |  0.002300 |    0.2868 |    0.1288 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01062 |    0.5559 |     1.931 |   0.03121 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8139 |   0.06870 |    0.6661 |    0.6811 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.491 |    0.9569 |    0.7485 |     1.896 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     10.71 |  -0.05605 |     1.440 |   0.09508 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1459 |   -0.5070 |   -0.9907 |   -0.8007 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4069 |    0.2923 |   0.01818 |    -1.553 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.109 |   -0.3306 |   -0.3991 |   -0.2309 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  132</span>|     466.72646 |     1.014 |    -1.848 |   -0.9815 |   -0.8092 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9160 |    -1.342 |     1.783 |    -1.808 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9032 |   -0.5322 |   -0.9570 |    -1.117 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6032 |   -0.8748 |    -1.022 |   -0.2748 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     466.72646 |     92.30 |    -6.048 |   -0.9533 |    -2.072 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.528 |    0.2483 |     1.933 |   0.03140 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8182 |   0.06840 |    0.6724 |    0.6828 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.496 |    0.9528 |    0.7357 |     1.933 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     466.72646</span> |     92.30 |  0.002362 |    0.2782 |    0.1260 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01080 |    0.5618 |     1.933 |   0.03140 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8182 |   0.06840 |    0.6724 |    0.6828 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.496 |    0.9528 |    0.7357 |     1.933 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -13.11 |  -0.01237 |   -0.5306 |  0.001870 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01074 | -0.001553 |   -0.5011 |    -1.124 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.5240 |    0.3999 |    0.1337 |    0.6209 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3667 |   -0.5464 |   -0.9326 |   -0.1861 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  133</span>|     466.72714 |     1.014 |    -1.830 |   -0.9704 |   -0.8250 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9153 |    -1.342 |     1.791 |    -1.785 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9449 |   -0.5943 |   -0.9266 |    -1.116 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5962 |   -0.8737 |    -1.020 |   -0.2698 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     466.72714 |     92.32 |    -6.030 |   -0.9434 |    -2.087 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.528 |    0.2486 |     1.936 |   0.03205 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8009 |   0.06660 |    0.6946 |    0.6846 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.504 |    0.9539 |    0.7372 |     1.939 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     466.72714</span> |     92.32 |  0.002405 |    0.2802 |    0.1240 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01081 |    0.5618 |     1.936 |   0.03205 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8009 |   0.06660 |    0.6946 |    0.6846 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.504 |    0.9539 |    0.7372 |     1.939 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  134</span>|     466.69721 |     1.015 |    -1.839 |   -0.9760 |   -0.8170 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9156 |    -1.342 |     1.787 |    -1.797 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9238 |   -0.5630 |   -0.9420 |    -1.117 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5997 |   -0.8742 |    -1.021 |   -0.2723 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     466.69721 |     92.41 |    -6.039 |   -0.9484 |    -2.079 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.528 |    0.2485 |     1.935 |   0.03173 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8096 |   0.06750 |    0.6834 |    0.6836 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.500 |    0.9534 |    0.7365 |     1.936 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     466.69721</span> |     92.41 |  0.002383 |    0.2792 |    0.1250 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01080 |    0.5618 |     1.935 |   0.03173 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8096 |   0.06750 |    0.6834 |    0.6836 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.500 |    0.9534 |    0.7365 |     1.936 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    0.5723 |  0.006769 |   -0.2879 |  -0.06332 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01114 |  -0.03155 |   -0.3915 |   -0.7923 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3201 |   -0.2330 |    0.8086 |    -1.424 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3663 |   -0.4815 |   -0.7933 |   -0.2384 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  135</span>|     466.73286 |     1.017 |    -1.834 |   -0.9731 |   -0.8131 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9227 |    -1.351 |     1.796 |    -1.779 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9201 |   -0.5595 |   -0.9572 |    -1.109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6142 |   -0.8686 |    -1.016 |   -0.2440 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     466.73286 |     92.54 |    -6.034 |   -0.9458 |    -2.075 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.535 |    0.2444 |     1.938 |   0.03223 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8111 |   0.06760 |    0.6723 |    0.6901 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.483 |    0.9587 |    0.7409 |     1.970 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     466.73286</span> |     92.54 |  0.002396 |    0.2797 |    0.1255 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01072 |    0.5608 |     1.938 |   0.03223 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8111 |   0.06760 |    0.6723 |    0.6901 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.483 |    0.9587 |    0.7409 |     1.970 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  136</span>|     466.69733 |     1.015 |    -1.838 |   -0.9750 |   -0.8159 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9176 |    -1.345 |     1.790 |    -1.791 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9226 |   -0.5619 |   -0.9467 |    -1.114 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6033 |   -0.8723 |    -1.019 |   -0.2644 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     466.69733 |     92.40 |    -6.038 |   -0.9475 |    -2.078 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.530 |    0.2474 |     1.936 |   0.03188 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8101 |   0.06754 |    0.6799 |    0.6863 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.496 |    0.9552 |    0.7382 |     1.945 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     466.69733</span> |     92.40 |  0.002386 |    0.2794 |    0.1251 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01078 |    0.5615 |     1.936 |   0.03188 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8101 |   0.06754 |    0.6799 |    0.6863 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.496 |    0.9552 |    0.7382 |     1.945 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  137</span>|     466.69584 |     1.015 |    -1.839 |   -0.9754 |   -0.8165 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9164 |    -1.343 |     1.788 |    -1.794 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9231 |   -0.5623 |   -0.9445 |    -1.114 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6009 |   -0.8731 |    -1.020 |   -0.2689 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     466.69584 |     92.37 |    -6.039 |   -0.9478 |    -2.079 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.529 |    0.2480 |     1.935 |   0.03180 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8099 |   0.06752 |    0.6815 |    0.6857 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.499 |    0.9544 |    0.7377 |     1.940 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     466.69584</span> |     92.37 |  0.002384 |    0.2793 |    0.1251 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01079 |    0.5617 |     1.935 |   0.03180 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8099 |   0.06752 |    0.6815 |    0.6857 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.499 |    0.9544 |    0.7377 |     1.940 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -3.069 | -0.001275 |   -0.2861 |  -0.04975 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01387 |  -0.05203 |   -0.3437 |   -0.7173 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2694 |   -0.1778 |    0.5026 |    0.8511 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9213 |   -0.7376 |   -0.9765 |   -0.1682 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  138</span>|     466.68962 |     1.016 |    -1.839 |   -0.9764 |   -0.8159 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9167 |    -1.342 |     1.790 |    -1.793 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9191 |   -0.5597 |   -0.9461 |    -1.115 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6002 |   -0.8712 |    -1.018 |   -0.2680 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     466.68962 |     92.46 |    -6.039 |   -0.9488 |    -2.078 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.529 |    0.2485 |     1.936 |   0.03184 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8115 |   0.06760 |    0.6803 |    0.6854 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.499 |    0.9562 |    0.7392 |     1.941 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     466.68962</span> |     92.46 |  0.002385 |    0.2791 |    0.1251 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01079 |    0.5618 |     1.936 |   0.03184 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8115 |   0.06760 |    0.6803 |    0.6854 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.499 |    0.9562 |    0.7392 |     1.941 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     6.342 |  0.001592 |   -0.2787 |  -0.05882 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.004933 |  -0.02534 |   -0.4440 |   -0.5467 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1484 |  -0.03146 |    0.4648 |    -1.264 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3607 |   -0.2274 |   -0.5157 |   -0.1312 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  139</span>|     466.68314 |     1.015 |    -1.839 |   -0.9751 |   -0.8151 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9166 |    -1.339 |     1.792 |    -1.792 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9160 |   -0.5585 |   -0.9470 |    -1.115 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5998 |   -0.8691 |    -1.016 |   -0.2676 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     466.68314 |     92.36 |    -6.039 |   -0.9476 |    -2.077 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.529 |    0.2501 |     1.937 |   0.03187 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8128 |   0.06763 |    0.6797 |    0.6853 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.500 |    0.9582 |    0.7404 |     1.942 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     466.68314</span> |     92.36 |  0.002385 |    0.2794 |    0.1252 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01079 |    0.5622 |     1.937 |   0.03187 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8128 |   0.06763 |    0.6797 |    0.6853 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.500 |    0.9582 |    0.7404 |     1.942 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.258 |-0.0003497 |   -0.2788 |  -0.03383 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01408 |   0.02647 |   -0.2857 |   -0.5956 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.05089 |   0.05050 |    0.4395 |    0.8187 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2669 |  -0.06868 |   -0.3694 |   -0.1159 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  140</span>|     466.67997 |     1.015 |    -1.839 |   -0.9739 |   -0.8142 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9180 |    -1.339 |     1.794 |    -1.790 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9137 |   -0.5623 |   -0.9494 |    -1.116 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6002 |   -0.8690 |    -1.016 |   -0.2664 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     466.67997 |     92.38 |    -6.039 |   -0.9465 |    -2.077 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.531 |    0.2499 |     1.938 |   0.03191 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8138 |   0.06752 |    0.6779 |    0.6846 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.499 |    0.9583 |    0.7410 |     1.943 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     466.67997</span> |     92.38 |  0.002385 |    0.2796 |    0.1254 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01078 |    0.5622 |     1.938 |   0.03191 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8138 |   0.06752 |    0.6779 |    0.6846 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.499 |    0.9583 |    0.7410 |     1.943 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    M|   Mixed Diff. |    -1.882 |-7.391e+04 |   -0.2259 |  -0.03295 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.005718 |   0.01130 |   -0.3842 |   -0.4930 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.04972 |  -0.05953 |    0.5251 |    -1.398 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3564 |  -0.08212 |   -0.3242 |   -0.1008 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  141</span>|     466.67997 |     1.015 |    -1.839 |   -0.9739 |   -0.8142 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9180 |    -1.339 |     1.794 |    -1.790 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9137 |   -0.5623 |   -0.9494 |    -1.116 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6002 |   -0.8690 |    -1.016 |   -0.2664 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     466.67997 |     92.38 |    -6.039 |   -0.9465 |    -2.077 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.531 |    0.2499 |     1.938 |   0.03191 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8138 |   0.06752 |    0.6779 |    0.6846 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.499 |    0.9583 |    0.7410 |     1.943 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     466.67997</span> |     92.38 |  0.002385 |    0.2796 |    0.1254 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01078 |    0.5622 |     1.938 |   0.03191 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8138 |   0.06752 |    0.6779 |    0.6846 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.499 |    0.9583 |    0.7410 |     1.943 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> calculating covariance matrix</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating residuals/tables</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>initial ETAs were nudged; (can control by foceiControl(etaNudge=., etaNudge2=))</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>ETAs were reset to zero during optimization; (Can control by foceiControl(resetEtaP=.))</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>using R matrix to calculate covariance, can check sandwich or S matrix with $covRS and $covS</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>gradient problems with initial estimate and covariance; see $scaleInfo</span>
-<span class="r-in"></span>
-<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">AIC</a></span><span class="op">(</span></span>
-<span class="r-in">  <span class="va">f_nlmixr_sfo_sfo_focei_const</span><span class="op">$</span><span class="va">nm</span>,</span>
-<span class="r-in">  <span class="va">f_nlmixr_fomc_sfo_focei_const</span><span class="op">$</span><span class="va">nm</span>,</span>
-<span class="r-in">  <span class="va">f_nlmixr_dfop_sfo_focei_const</span><span class="op">$</span><span class="va">nm</span>,</span>
-<span class="r-in">  <span class="va">f_nlmixr_fomc_sfo_saem_obs</span><span class="op">$</span><span class="va">nm</span>,</span>
-<span class="r-in">  <span class="va">f_nlmixr_fomc_sfo_focei_obs</span><span class="op">$</span><span class="va">nm</span>,</span>
-<span class="r-in">  <span class="va">f_nlmixr_dfop_sfo_saem_obs</span><span class="op">$</span><span class="va">nm</span>,</span>
-<span class="r-in">  <span class="va">f_nlmixr_dfop_sfo_focei_obs</span><span class="op">$</span><span class="va">nm</span>,</span>
-<span class="r-in">  <span class="va">f_nlmixr_fomc_sfo_focei_tc</span><span class="op">$</span><span class="va">nm</span>,</span>
-<span class="r-in">  <span class="va">f_nlmixr_dfop_sfo_focei_tc</span><span class="op">$</span><span class="va">nm</span>,</span>
-<span class="r-in">  <span class="va">f_nlmixr_fomc_sfo_saem_obs_tc</span><span class="op">$</span><span class="va">nm</span>,</span>
-<span class="r-in">  <span class="va">f_nlmixr_fomc_sfo_focei_obs_tc</span><span class="op">$</span><span class="va">nm</span>,</span>
-<span class="r-in">  <span class="va">f_nlmixr_dfop_sfo_saem_obs_tc</span><span class="op">$</span><span class="va">nm</span>,</span>
-<span class="r-in">  <span class="va">f_nlmixr_dfop_sfo_focei_obs_tc</span><span class="op">$</span><span class="va">nm</span></span>
-<span class="r-in"><span class="op">)</span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating -2LL by Gaussian quadrature (nnodes=3,nsd=1.6)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating -2LL by Gaussian quadrature (nnodes=3,nsd=1.6)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating -2LL by Gaussian quadrature (nnodes=3,nsd=1.6)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating -2LL by Gaussian quadrature (nnodes=3,nsd=1.6)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                                   df       AIC</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_nlmixr_sfo_sfo_focei_const$nm    9 1082.4605</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_nlmixr_fomc_sfo_focei_const$nm  11  814.4261</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_nlmixr_dfop_sfo_focei_const$nm  13  870.2659</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_nlmixr_fomc_sfo_saem_obs$nm     12  788.8373</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_nlmixr_fomc_sfo_focei_obs$nm    12  794.5194</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_nlmixr_dfop_sfo_saem_obs$nm     14  815.0797</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_nlmixr_dfop_sfo_focei_obs$nm    14  834.8474</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_nlmixr_fomc_sfo_focei_tc$nm     12  812.3296</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_nlmixr_dfop_sfo_focei_tc$nm     14  819.4103</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_nlmixr_fomc_sfo_saem_obs_tc$nm  14  814.4248</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_nlmixr_fomc_sfo_focei_obs_tc$nm 14  787.4355</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_nlmixr_dfop_sfo_saem_obs_tc$nm  16  828.5143</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_nlmixr_dfop_sfo_focei_obs_tc$nm 16  811.1191</span>
-<span class="r-in"><span class="co"># Currently, FOMC-SFO with two-component error by variable fitted by focei gives the</span></span>
-<span class="r-in"><span class="co"># lowest AIC</span></span>
-<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_nlmixr_fomc_sfo_focei_obs_tc</span><span class="op">)</span></span>
-<span class="r-plt img"><img src="nlmixr.mmkin-2.png" alt="" width="700" height="433"></span>
-<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">f_nlmixr_fomc_sfo_focei_obs_tc</span><span class="op">)</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> nlmixr version used for fitting:    2.0.6 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> mkin version used for pre-fitting:  1.1.0 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> R version used for fitting:         4.1.2 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Date of fit:     Wed Mar  2 18:01:07 2022 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Date of summary: Wed Mar  2 18:02:45 2022 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Equations:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> d_A1/dt = + f_parent_to_A1 * (alpha/beta) * 1/((time/beta) + 1) *</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>            parent - k_A1 * A1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Data:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 170 observations of 2 variable(s) grouped in 5 datasets</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Degradation model predictions using RxODE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Fitted in 17.315 s</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Variance model: Two-component variance unique to each observed variable </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Mean of starting values for individual parameters:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>        parent_0        log_k_A1 f_parent_qlogis       log_alpha        log_beta </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         93.1168         -5.3034         -0.9442         -0.1065          2.2909 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Mean of starting values for error model parameters:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma_low_parent  rsd_high_parent     sigma_low_A1      rsd_high_A1 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>          1.15958          0.03005          1.15958          0.03005 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Fixed degradation parameter values:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> None</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Results:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Likelihood calculated by focei  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     AIC   BIC logLik</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   787.4 831.3 -379.7</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Optimised parameters:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                    est.   lower   upper</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0        93.6717 91.2552 96.0882</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k_A1        -6.3199 -8.4468 -4.1930</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_parent_qlogis -1.0089 -1.3823 -0.6356</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_alpha       -0.1616 -0.6624  0.3392</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_beta         2.2088  1.6800  2.7376</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Correlation: </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                 parnt_0 lg_k_A1 f_prnt_ log_lph</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k_A1         0.3717                        </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_parent_qlogis -0.7856 -0.4088                </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_alpha        0.3361  0.9419 -0.3063        </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_beta        -0.3985 -0.7595  0.2484 -0.5549</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Random effects (omega):</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                     eta.parent_0 eta.log_k_A1 eta.f_parent_qlogis eta.log_alpha</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> eta.parent_0               4.391        0.000              0.0000        0.0000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> eta.log_k_A1               0.000        6.402              0.0000        0.0000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> eta.f_parent_qlogis        0.000        0.000              0.1584        0.0000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> eta.log_alpha              0.000        0.000              0.0000        0.3381</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> eta.log_beta               0.000        0.000              0.0000        0.0000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                     eta.log_beta</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> eta.parent_0               0.000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> eta.log_k_A1               0.000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> eta.f_parent_qlogis        0.000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> eta.log_alpha              0.000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> eta.log_beta               0.358</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Variance model:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma_low_parent  rsd_high_parent     sigma_low_A1      rsd_high_A1 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>          2.35616          0.00153          0.63564          0.08639 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Backtransformed parameters:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                   est.     lower   upper</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0       93.6717 9.126e+01 96.0882</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_A1            0.0018 2.146e-04  0.0151</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_parent_to_A1  0.2672 2.006e-01  0.3462</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> alpha           0.8508 5.156e-01  1.4038</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> beta            9.1049 5.366e+00 15.4499</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Resulting formation fractions:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                 ff</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_A1   0.2672</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_sink 0.7328</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Estimated disappearance times:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>          DT50   DT90 DT50back</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent  11.46  127.3    38.31</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> A1     385.05 1279.1       NA</span>
-<span class="r-in"></span>
-<span class="r-in"><span class="co"># Two parallel metabolites</span></span>
-<span class="r-in"><span class="va">dmta_ds</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fl">7</span>, <span class="kw">function</span><span class="op">(</span><span class="va">i</span><span class="op">)</span> <span class="op">{</span></span>
-<span class="r-in">  <span class="va">ds_i</span> <span class="op">&lt;-</span> <span class="va">dimethenamid_2018</span><span class="op">$</span><span class="va">ds</span><span class="op">[[</span><span class="va">i</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">data</span></span>
-<span class="r-in">  <span class="va">ds_i</span><span class="op">[</span><span class="va">ds_i</span><span class="op">$</span><span class="va">name</span> <span class="op">==</span> <span class="st">"DMTAP"</span>, <span class="st">"name"</span><span class="op">]</span> <span class="op">&lt;-</span>  <span class="st">"DMTA"</span></span>
-<span class="r-in">  <span class="va">ds_i</span><span class="op">$</span><span class="va">time</span> <span class="op">&lt;-</span> <span class="va">ds_i</span><span class="op">$</span><span class="va">time</span> <span class="op">*</span> <span class="va">dimethenamid_2018</span><span class="op">$</span><span class="va">f_time_norm</span><span class="op">[</span><span class="va">i</span><span class="op">]</span></span>
-<span class="r-in">  <span class="va">ds_i</span></span>
-<span class="r-in"><span class="op">}</span><span class="op">)</span></span>
-<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">dmta_ds</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">sapply</a></span><span class="op">(</span><span class="va">dimethenamid_2018</span><span class="op">$</span><span class="va">ds</span>, <span class="kw">function</span><span class="op">(</span><span class="va">ds</span><span class="op">)</span> <span class="va">ds</span><span class="op">$</span><span class="va">title</span><span class="op">)</span></span>
-<span class="r-in"><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot"</span><span class="op">]</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/cbind.html" class="external-link">rbind</a></span><span class="op">(</span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 1"</span><span class="op">]</span><span class="op">]</span>, <span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 2"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span>
-<span class="r-in"><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 1"</span><span class="op">]</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="cn">NULL</span></span>
-<span class="r-in"><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 2"</span><span class="op">]</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="cn">NULL</span></span>
-<span class="r-in"><span class="va">sfo_sfo2</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span></span>
-<span class="r-in">  DMTA <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"M23"</span>, <span class="st">"M27"</span><span class="op">)</span><span class="op">)</span>,</span>
-<span class="r-in">  M23 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span>
-<span class="r-in">  M27 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span>
-<span class="r-in">  quiet <span class="op">=</span> <span class="cn">TRUE</span></span>
-<span class="r-in"><span class="op">)</span></span>
-<span class="r-in"><span class="va">f_dmta_sfo_sfo2</span> <span class="op">&lt;-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span></span>
-<span class="r-in">  <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="st">"SFO-SFO2"</span> <span class="op">=</span> <span class="va">sfo_sfo2</span><span class="op">)</span>,</span>
-<span class="r-in">  <span class="va">dmta_ds</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, error_model <span class="op">=</span> <span class="st">"obs"</span><span class="op">)</span></span>
-<span class="r-in"><span class="fu">nlmixr_model</span><span class="op">(</span><span class="va">f_dmta_sfo_sfo2</span><span class="op">)</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> function () </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> {</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     ini({</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         DMTA_0 = 97</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         eta.DMTA_0 ~ 2</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         log_k_DMTA = -2.9</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         eta.log_k_DMTA ~ 0.66</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         log_k_M23 = -3.8</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         eta.log_k_M23 ~ 0.52</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         log_k_M27 = -5.7</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         eta.log_k_M27 ~ 2</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         f_DMTA_tffm0_1_qlogis = -2.1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         eta.f_DMTA_tffm0_1_qlogis ~ 0.3</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         f_DMTA_tffm0_2_qlogis = -2</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         eta.f_DMTA_tffm0_2_qlogis ~ 0.3</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         sigma_DMTA = 2.1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         sigma_M23 = 0.71</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         sigma_M27 = 0.56</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     })</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     model({</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         DMTA_0_model = DMTA_0 + eta.DMTA_0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         DMTA(0) = DMTA_0_model</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         k_DMTA = exp(log_k_DMTA + eta.log_k_DMTA)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         k_M23 = exp(log_k_M23 + eta.log_k_M23)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         k_M27 = exp(log_k_M27 + eta.log_k_M27)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         f_DMTA_tffm0_1 = expit(f_DMTA_tffm0_1_qlogis + eta.f_DMTA_tffm0_1_qlogis)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         f_DMTA_tffm0_2 = expit(f_DMTA_tffm0_2_qlogis + eta.f_DMTA_tffm0_2_qlogis)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         f_DMTA_to_M23 = f_DMTA_tffm0_1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         f_DMTA_to_M27 = f_DMTA_tffm0_2 * (1 - f_DMTA_tffm0_1)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         d/dt(DMTA) = -k_DMTA * DMTA</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         d/dt(M23) = +f_DMTA_to_M23 * k_DMTA * DMTA - k_M23 * </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>             M23</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         d/dt(M27) = +f_DMTA_to_M27 * k_DMTA * DMTA - k_M27 * </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>             M27</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         DMTA ~ add(sigma_DMTA)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         M23 ~ add(sigma_M23)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         M27 ~ add(sigma_M27)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     })</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> }</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> &lt;environment: 0x555564ac3818&gt;</span>
-<span class="r-in"><span class="va">nlmixr_focei_dmta_sfo_sfo2</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr</a></span><span class="op">(</span><span class="va">f_dmta_sfo_sfo2</span>, est <span class="op">=</span> <span class="st">"focei"</span><span class="op">)</span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate jacobian</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate sensitivities</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate ∂(f)/∂(η)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate ∂(R²)/∂(η)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in inner model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in inner model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in EBE model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in EBE model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling inner model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in FD model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in FD model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling EBE model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling events FD model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Needed Covariates:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "CMT"</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="font-weight: bold;">Key:</span> U: Unscaled Parameters; X: Back-transformed parameters; G: Gill difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> F: Forward difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> C: Central difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> M: Mixed forward and central difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Unscaled parameters for Omegas=chol(solve(omega));</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Diagonals are transformed, as specified by foceiControl(diagXform=)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |-----+---------------+-----------+-----------+-----------+-----------|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    #| Objective Fun |    DMTA_0 |log_k_DMTA | log_k_M23 | log_k_M27 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|f_DMTA_tffm0_1_qlogis |f_DMTA_tffm0_2_qlogis |sigma_DMTA | sigma_M23 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................| sigma_M27 |        o1 |        o2 |        o3 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|        o4 |        o5 |        o6 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    1</span>|     833.04051 |     1.000 |   -0.9455 |   -0.9630 |    -1.000 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9299 |   -0.9279 |   -0.8481 |   -0.8752 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8781 |   -0.8726 |   -0.8674 |   -0.8661 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8726 |   -0.8627 |   -0.8627 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     833.04051 |     97.00 |    -2.900 |    -3.800 |    -5.700 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.100 |    -2.000 |     2.100 |    0.7100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.5600 |    0.8409 |     1.109 |     1.178 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8409 |     1.351 |     1.351 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     833.04051</span> |     97.00 |   0.05502 |   0.02237 |  0.003346 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1091 |    0.1192 |     2.100 |    0.7100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.5600 |    0.8409 |     1.109 |     1.178 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.8409 |     1.351 |     1.351 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    G|    Gill Diff. |     6.064 |   -0.2569 |     9.159 |    -2.987 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -5.463 |     1.428 |    -91.01 |    -64.30 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -105.9 |     2.473 |    -10.56 |     26.93 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -2.864 |    -10.73 |    -1.863 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    2</span>|     790.03872 |    0.9615 |   -0.9438 |    -1.021 |   -0.9810 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8952 |   -0.9370 |   -0.2698 |   -0.4666 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2051 |   -0.8883 |   -0.8003 |    -1.037 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8544 |   -0.7945 |   -0.8508 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     790.03872 |     93.26 |    -2.898 |    -3.858 |    -5.681 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.027 |    -2.018 |     2.707 |    0.8550 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7484 |    0.8277 |     1.184 |    0.9761 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8562 |     1.443 |     1.367 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     790.03872</span> |     93.26 |   0.05511 |   0.02111 |  0.003410 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1164 |    0.1173 |     2.707 |    0.8550 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7484 |    0.8277 |     1.184 |    0.9761 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.8562 |     1.443 |     1.367 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1203. |    0.1766 |     3.828 |    -3.699 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.277 |    -3.309 |    -7.028 |    -3.626 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.785 |     54.50 |    -5.905 |     3.230 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -3.377 |    -8.165 |    -1.842 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    3</span>|     1615.1823 |     1.259 |   -0.9424 |    -1.076 |   -0.9624 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8623 |   -0.9445 |    0.2695 |  -0.08596 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4207 |   -0.9180 |   -0.7364 |    -1.197 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8366 |   -0.7289 |   -0.8393 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     1615.1823 |     122.1 |    -2.897 |    -3.913 |    -5.662 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.958 |    -2.033 |     3.274 |    0.9902 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9237 |    0.8027 |     1.255 |    0.7875 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8712 |     1.532 |     1.383 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     1615.1823</span> |     122.1 |   0.05519 |   0.01997 |  0.003474 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1237 |    0.1158 |     3.274 |    0.9902 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9237 |    0.8027 |     1.255 |    0.7875 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.8712 |     1.532 |     1.383 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    4</span>|     959.79005 |     1.112 |   -0.9439 |    -1.022 |   -0.9806 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8949 |   -0.9366 |   -0.2689 |   -0.4661 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2049 |   -0.8952 |   -0.7996 |    -1.038 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8540 |   -0.7935 |   -0.8506 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     959.79005 |     107.9 |    -2.898 |    -3.859 |    -5.681 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.027 |    -2.017 |     2.708 |    0.8552 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7485 |    0.8219 |     1.185 |    0.9756 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8566 |     1.445 |     1.368 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     959.79005</span> |     107.9 |   0.05511 |   0.02110 |  0.003412 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1164 |    0.1174 |     2.708 |    0.8552 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7485 |    0.8219 |     1.185 |    0.9756 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.8566 |     1.445 |     1.368 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    5</span>|     766.97120 |     1.000 |   -0.9438 |    -1.021 |   -0.9809 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8951 |   -0.9369 |   -0.2695 |   -0.4665 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2050 |   -0.8901 |   -0.8001 |    -1.037 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8543 |   -0.7943 |   -0.8508 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      766.9712 |     97.04 |    -2.898 |    -3.858 |    -5.681 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.027 |    -2.018 |     2.707 |    0.8551 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7485 |    0.8262 |     1.184 |    0.9760 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8563 |     1.444 |     1.367 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      766.9712</span> |     97.04 |   0.05511 |   0.02110 |  0.003410 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1164 |    0.1173 |     2.707 |    0.8551 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7485 |    0.8262 |     1.184 |    0.9760 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.8563 |     1.444 |     1.367 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     17.19 |   -0.3226 |     4.073 |    -3.551 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.344 |   -0.4208 |     1.861 |    -4.402 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -3.892 |   -0.6542 |    -5.926 |     4.557 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -3.217 |    -8.034 |    -1.874 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    6</span>|     766.81060 |    0.9993 |   -0.9437 |    -1.024 |   -0.9793 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8941 |   -0.9371 |   -0.2482 |   -0.4501 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1789 |   -0.8907 |   -0.7962 |    -1.045 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8529 |   -0.7898 |   -0.8499 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      766.8106 |     96.93 |    -2.898 |    -3.861 |    -5.679 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.025 |    -2.018 |     2.730 |    0.8609 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7558 |    0.8257 |     1.188 |    0.9671 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8575 |     1.450 |     1.368 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      766.8106</span> |     96.93 |   0.05512 |   0.02104 |  0.003416 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1166 |    0.1173 |     2.730 |    0.8609 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7558 |    0.8257 |     1.188 |    0.9671 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.8575 |     1.450 |     1.368 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -15.39 |   -0.3115 |     3.911 |    -3.585 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.518 |   -0.5604 |     3.636 |    -2.887 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.137 |   -0.8386 |    -5.861 |     3.544 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -3.178 |    -7.806 |    -1.844 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    7</span>|     766.54512 |     1.003 |   -0.9428 |    -1.036 |   -0.9690 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8990 |   -0.9355 |   -0.2617 |   -0.4439 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1747 |   -0.8835 |   -0.7794 |    -1.053 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8438 |   -0.7677 |   -0.8446 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     766.54512 |     97.33 |    -2.897 |    -3.873 |    -5.669 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.035 |    -2.015 |     2.716 |    0.8631 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7570 |    0.8318 |     1.207 |    0.9569 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8651 |     1.479 |     1.376 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     766.54512</span> |     97.33 |   0.05517 |   0.02081 |  0.003451 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1156 |    0.1176 |     2.716 |    0.8631 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7570 |    0.8318 |     1.207 |    0.9569 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.8651 |     1.479 |     1.376 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     111.5 |   -0.3718 |     3.702 |    -3.687 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8687 |  -0.06529 |     3.022 |    -2.224 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.821 |    0.2102 |    -4.533 |     2.766 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -2.768 |    -6.628 |    -1.505 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    8</span>|     766.09473 |    0.9987 |   -0.9419 |    -1.044 |   -0.9594 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9033 |   -0.9340 |   -0.2855 |   -0.4478 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1861 |   -0.8834 |   -0.7664 |    -1.057 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8361 |   -0.7496 |   -0.8403 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     766.09473 |     96.88 |    -2.896 |    -3.881 |    -5.659 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.044 |    -2.012 |     2.691 |    0.8617 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7538 |    0.8318 |     1.221 |    0.9529 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8716 |     1.504 |     1.381 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     766.09473</span> |     96.88 |   0.05522 |   0.02062 |  0.003485 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1146 |    0.1179 |     2.691 |    0.8617 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7538 |    0.8318 |     1.221 |    0.9529 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.8716 |     1.504 |     1.381 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -37.62 |   -0.3039 |     3.548 |    -3.796 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7130 |   -0.2251 |    0.3449 |    -2.743 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.783 |   -0.4584 |    -3.845 |     2.208 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -2.677 |    -5.610 |    -1.259 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    9</span>|     765.84632 |     1.001 |   -0.9407 |    -1.057 |   -0.9442 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9020 |   -0.9332 |   -0.3051 |   -0.4515 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1945 |   -0.8828 |   -0.7538 |    -1.058 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8263 |   -0.7302 |   -0.8359 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     765.84632 |     97.08 |    -2.895 |    -3.894 |    -5.644 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.041 |    -2.011 |     2.670 |    0.8604 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7514 |    0.8324 |     1.235 |    0.9520 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8799 |     1.530 |     1.387 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     765.84632</span> |     97.08 |   0.05529 |   0.02037 |  0.003538 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1149 |    0.1181 |     2.670 |    0.8604 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7514 |    0.8324 |     1.235 |    0.9520 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.8799 |     1.530 |     1.387 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     27.16 |   -0.3150 |     3.428 |    -3.883 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2016 |  -0.01587 |   -0.9475 |    -2.944 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -3.512 |   -0.2026 |    -2.920 |     2.169 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -2.296 |    -4.549 |   -0.9950 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   10</span>|     765.58952 |    0.9994 |   -0.9389 |    -1.074 |   -0.9188 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8956 |   -0.9340 |   -0.2957 |   -0.4491 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1815 |   -0.8759 |   -0.7498 |    -1.052 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8170 |   -0.7204 |   -0.8341 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     765.58952 |     96.94 |    -2.893 |    -3.911 |    -5.619 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.028 |    -2.012 |     2.680 |    0.8613 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7550 |    0.8382 |     1.240 |    0.9583 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8877 |     1.543 |     1.390 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     765.58952</span> |     96.94 |   0.05539 |   0.02003 |  0.003629 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1163 |    0.1179 |     2.680 |    0.8613 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7550 |    0.8382 |     1.240 |    0.9583 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.8877 |     1.543 |     1.390 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   11</span>|     765.32464 |    0.9995 |   -0.9365 |    -1.096 |   -0.8851 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8870 |   -0.9350 |   -0.2832 |   -0.4460 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1644 |   -0.8666 |   -0.7446 |    -1.045 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8047 |   -0.7076 |   -0.8318 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     765.32464 |     96.95 |    -2.891 |    -3.933 |    -5.585 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.010 |    -2.014 |     2.693 |    0.8623 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7598 |    0.8459 |     1.246 |    0.9668 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8980 |     1.561 |     1.393 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     765.32464</span> |     96.95 |   0.05552 |   0.01958 |  0.003753 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1182 |    0.1177 |     2.693 |    0.8623 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7598 |    0.8459 |     1.246 |    0.9668 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.8980 |     1.561 |     1.393 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -12.84 |   -0.1780 |     3.152 |    -3.943 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     3.881 |   -0.7435 |    0.7708 |    -2.561 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.120 |    0.1753 |    -2.364 |     2.891 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.893 |    -3.031 |   -0.9792 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   12</span>|     764.89127 |     1.001 |   -0.9331 |    -1.138 |   -0.8186 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9173 |   -0.9304 |   -0.2709 |   -0.4365 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1500 |   -0.8575 |   -0.7404 |    -1.047 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7849 |   -0.6971 |   -0.8273 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     764.89127 |     97.05 |    -2.888 |    -3.975 |    -5.519 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.073 |    -2.005 |     2.706 |    0.8657 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7639 |    0.8536 |     1.250 |    0.9649 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9146 |     1.575 |     1.399 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     764.89127</span> |     97.05 |   0.05571 |   0.01878 |  0.004011 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1117 |    0.1187 |     2.706 |    0.8657 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7639 |    0.8536 |     1.250 |    0.9649 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.9146 |     1.575 |     1.399 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     19.26 |   -0.1091 |     2.782 |    -3.871 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -5.068 |  -0.03848 |     1.779 |    -1.635 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5035 |    0.4763 |    -2.083 |     2.396 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -2.295 |    -2.519 |   -0.9166 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   13</span>|     764.86909 |    0.9949 |   -0.9319 |    -1.178 |   -0.7471 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9138 |   -0.9236 |   -0.2803 |   -0.4250 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1592 |   -0.8601 |   -0.7408 |    -1.068 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7627 |   -0.7028 |   -0.8216 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     764.86909 |     96.51 |    -2.886 |    -4.015 |    -5.447 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.066 |    -1.991 |     2.696 |    0.8698 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7613 |    0.8514 |     1.250 |    0.9401 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9333 |     1.567 |     1.407 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     764.86909</span> |     96.51 |   0.05578 |   0.01803 |  0.004309 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1124 |    0.1201 |     2.696 |    0.8698 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7613 |    0.8514 |     1.250 |    0.9401 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.9333 |     1.567 |     1.407 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -169.3 |   0.02233 |     2.185 |    -3.786 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.443 |    0.4519 |    0.3349 |   -0.6672 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.014 |     1.036 |    -2.128 |   -0.1909 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -2.017 |    -2.598 |   -0.8552 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   14</span>|     764.27620 |     1.001 |   -0.9315 |    -1.198 |   -0.7106 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9112 |   -0.9215 |   -0.2834 |   -0.4198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1627 |   -0.8638 |   -0.7423 |    -1.077 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7523 |   -0.7079 |   -0.8190 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      764.2762 |     97.07 |    -2.886 |    -4.035 |    -5.411 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.061 |    -1.987 |     2.693 |    0.8716 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7603 |    0.8483 |     1.248 |    0.9297 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9421 |     1.560 |     1.410 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      764.2762</span> |     97.07 |   0.05580 |   0.01769 |  0.004469 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1130 |    0.1206 |     2.693 |    0.8716 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7603 |    0.8483 |     1.248 |    0.9297 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.9421 |     1.560 |     1.410 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     26.99 |  -0.06591 |     1.982 |    -3.655 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.737 |     1.224 |    0.5238 |   -0.1462 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.477 |    0.3446 |    -2.258 |    -1.151 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -2.063 |    -3.016 |   -0.8065 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   15</span>|     764.08600 |    0.9998 |   -0.9318 |    -1.216 |   -0.6718 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9105 |   -0.9239 |   -0.2868 |   -0.4186 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1661 |   -0.8701 |   -0.7420 |    -1.077 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7408 |   -0.7119 |   -0.8158 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|       764.086 |     96.98 |    -2.886 |    -4.053 |    -5.372 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.059 |    -1.992 |     2.689 |    0.8721 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7594 |    0.8430 |     1.249 |    0.9293 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9517 |     1.555 |     1.415 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">       764.086</span> |     96.98 |   0.05578 |   0.01738 |  0.004646 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1131 |    0.1201 |     2.689 |    0.8721 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7594 |    0.8430 |     1.249 |    0.9293 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.9517 |     1.555 |     1.415 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   16</span>|     763.68333 |    0.9997 |   -0.9325 |    -1.262 |   -0.5698 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9089 |   -0.9302 |   -0.2957 |   -0.4154 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1752 |   -0.8867 |   -0.7413 |    -1.078 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7108 |   -0.7228 |   -0.8074 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     763.68333 |     96.97 |    -2.887 |    -4.099 |    -5.270 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.056 |    -2.004 |     2.680 |    0.8732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7568 |    0.8290 |     1.249 |    0.9281 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9770 |     1.540 |     1.426 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     763.68333</span> |     96.97 |   0.05574 |   0.01659 |  0.005145 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1134 |    0.1187 |     2.680 |    0.8732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7568 |    0.8290 |     1.249 |    0.9281 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    0.9770 |     1.540 |     1.426 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   17</span>|     763.23003 |    0.9993 |   -0.9340 |    -1.363 |   -0.3486 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9055 |   -0.9438 |   -0.3150 |   -0.4083 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1949 |   -0.9227 |   -0.7397 |    -1.080 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6457 |   -0.7464 |   -0.7892 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     763.23003 |     96.93 |    -2.889 |    -4.200 |    -5.049 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.049 |    -2.032 |     2.660 |    0.8757 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7513 |    0.7988 |     1.251 |    0.9256 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.032 |     1.508 |     1.451 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     763.23003</span> |     96.93 |   0.05566 |   0.01500 |  0.006419 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1142 |    0.1159 |     2.660 |    0.8757 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7513 |    0.7988 |     1.251 |    0.9256 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.032 |     1.508 |     1.451 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -22.00 |   -0.1117 |    0.5708 |    -1.597 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5912 |    -4.959 |    -1.502 |     1.095 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.055 |    -1.786 |    -1.932 |    -2.056 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -2.026 |    -5.307 |    0.1331 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   18</span>|     765.10598 |    0.9988 |   -0.9419 |    -1.481 | -0.006130 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9009 |   -0.7845 |   -0.3455 |   -0.4625 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1991 |   -0.9538 |   -0.7488 |    -1.046 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5215 |   -0.7432 |   -0.7830 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     765.10598 |     96.88 |    -2.896 |    -4.318 |    -4.706 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.039 |    -1.713 |     2.628 |    0.8565 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7501 |    0.7726 |     1.241 |    0.9658 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.136 |     1.513 |     1.459 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     765.10598</span> |     96.88 |   0.05522 |   0.01333 |  0.009040 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1152 |    0.1528 |     2.628 |    0.8565 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7501 |    0.7726 |     1.241 |    0.9658 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.136 |     1.513 |     1.459 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   19</span>|     763.02578 |    0.9999 |   -0.9358 |    -1.391 |   -0.2684 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9044 |   -0.9064 |   -0.3221 |   -0.4211 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1957 |   -0.9299 |   -0.7418 |    -1.072 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6165 |   -0.7455 |   -0.7877 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     763.02578 |     96.99 |    -2.890 |    -4.228 |    -4.968 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.046 |    -1.957 |     2.652 |    0.8712 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7511 |    0.7927 |     1.249 |    0.9351 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.056 |     1.510 |     1.452 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     763.02578</span> |     96.99 |   0.05556 |   0.01459 |  0.006954 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1144 |    0.1238 |     2.652 |    0.8712 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7511 |    0.7927 |     1.249 |    0.9351 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.056 |     1.510 |     1.452 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.557 |   -0.1614 |    0.3205 |   -0.9685 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3267 |     2.395 |    -2.389 |   -0.1069 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.595 |    -2.113 |    -2.097 |    -1.406 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.821 |    -5.218 |   -0.7119 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   20</span>|     766.17797 |     1.015 |   -0.9359 |    -1.390 |   -0.2436 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9043 |   -0.9341 |   -0.3009 |   -0.4312 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1485 |   -0.9049 |   -0.7235 |    -1.053 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5884 |   -0.6890 |   -0.7788 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     766.17797 |     98.50 |    -2.890 |    -4.227 |    -4.944 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.046 |    -2.012 |     2.675 |    0.8676 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7643 |    0.8138 |     1.269 |    0.9575 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.080 |     1.586 |     1.465 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     766.17797</span> |     98.50 |   0.05555 |   0.01460 |  0.007129 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1144 |    0.1179 |     2.675 |    0.8676 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7643 |    0.8138 |     1.269 |    0.9575 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.080 |     1.586 |     1.465 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   21</span>|     762.99023 |     1.002 |   -0.9356 |    -1.391 |   -0.2672 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9040 |   -0.9092 |   -0.3192 |   -0.4209 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1902 |   -0.9274 |   -0.7393 |    -1.070 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6144 |   -0.7393 |   -0.7869 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.99023 |     97.17 |    -2.890 |    -4.228 |    -4.967 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.046 |    -1.963 |     2.655 |    0.8713 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7526 |    0.7948 |     1.252 |    0.9371 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.058 |     1.518 |     1.454 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.99023</span> |     97.17 |   0.05557 |   0.01458 |  0.006962 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1145 |    0.1232 |     2.655 |    0.8713 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7526 |    0.7948 |     1.252 |    0.9371 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.058 |     1.518 |     1.454 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     51.43 |   -0.1841 |    0.3204 |   -0.9603 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.07939 |     1.931 |    -1.965 |   -0.1875 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.362 |    -1.812 |    -1.918 |    -2.236 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.746 |    -4.882 |   -0.6715 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   22</span>|     762.91633 |     1.000 |   -0.9363 |    -1.389 |   -0.2610 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9054 |   -0.9109 |   -0.3206 |   -0.4247 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1891 |   -0.9263 |   -0.7404 |    -1.066 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6100 |   -0.7370 |   -0.7858 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.91633 |     97.00 |    -2.891 |    -4.226 |    -4.961 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.049 |    -1.966 |     2.654 |    0.8699 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7529 |    0.7958 |     1.250 |    0.9417 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.062 |     1.521 |     1.455 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.91633</span> |     97.00 |   0.05553 |   0.01461 |  0.007006 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1142 |    0.1228 |     2.654 |    0.8699 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7529 |    0.7958 |     1.250 |    0.9417 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.062 |     1.521 |     1.455 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |   -0.4218 |   -0.1782 |    0.3230 |   -0.9273 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6605 |     1.389 |    -2.249 |   -0.4385 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -3.981 |    -1.888 |    -1.963 |   -0.6447 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.620 |    -4.721 |   -0.6578 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   23</span>|     762.84890 |     1.001 |   -0.9361 |    -1.390 |   -0.2598 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9045 |   -0.9128 |   -0.3175 |   -0.4241 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1836 |   -0.9236 |   -0.7377 |    -1.066 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6077 |   -0.7305 |   -0.7849 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      762.8489 |     97.05 |    -2.891 |    -4.227 |    -4.960 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.047 |    -1.970 |     2.657 |    0.8701 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7544 |    0.7980 |     1.253 |    0.9427 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.064 |     1.530 |     1.456 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      762.8489</span> |     97.05 |   0.05554 |   0.01460 |  0.007015 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1144 |    0.1224 |     2.657 |    0.8701 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7544 |    0.7980 |     1.253 |    0.9427 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.064 |     1.530 |     1.456 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   24</span>|     762.76203 |     1.002 |   -0.9355 |    -1.391 |   -0.2569 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9025 |   -0.9170 |   -0.3107 |   -0.4228 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1716 |   -0.9179 |   -0.7317 |    -1.064 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6028 |   -0.7161 |   -0.7829 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.76203 |     97.18 |    -2.890 |    -4.228 |    -4.957 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.042 |    -1.978 |     2.664 |    0.8706 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7578 |    0.8028 |     1.260 |    0.9450 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.068 |     1.549 |     1.459 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.76203</span> |     97.18 |   0.05557 |   0.01459 |  0.007034 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1148 |    0.1215 |     2.664 |    0.8706 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7578 |    0.8028 |     1.260 |    0.9450 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.068 |     1.549 |     1.459 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     54.31 |   -0.1887 |    0.3021 |   -0.9307 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2254 |    0.1583 |    -1.328 |   -0.4242 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.868 |    -1.362 |    -1.371 |  -0.08091 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.408 |    -3.948 |   -0.5076 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   25</span>|     762.67102 |    0.9998 |   -0.9349 |    -1.387 |   -0.2432 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9003 |   -0.9168 |   -0.3029 |   -0.4253 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1712 |   -0.9066 |   -0.7353 |    -1.085 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5940 |   -0.7040 |   -0.7794 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.67102 |     96.98 |    -2.889 |    -4.224 |    -4.943 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.038 |    -1.978 |     2.672 |    0.8697 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7579 |    0.8123 |     1.256 |    0.9195 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.075 |     1.566 |     1.464 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.67102</span> |     96.98 |   0.05561 |   0.01464 |  0.007132 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1153 |    0.1216 |     2.672 |    0.8697 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7579 |    0.8123 |     1.256 |    0.9195 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.075 |     1.566 |     1.464 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.974 |   -0.1351 |    0.3572 |   -0.8415 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6736 |   -0.1073 |   -0.7732 |   -0.4654 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.903 |    -1.199 |    -1.653 |    -3.516 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.220 |    -3.098 |   -0.3193 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   26</span>|     762.64017 |    0.9999 |   -0.9344 |    -1.376 |   -0.2262 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9040 |   -0.9165 |   -0.3113 |   -0.4148 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1768 |   -0.8962 |   -0.7385 |    -1.074 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5804 |   -0.6983 |   -0.7733 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.64017 |     96.99 |    -2.889 |    -4.213 |    -4.926 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.046 |    -1.977 |     2.664 |    0.8734 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7564 |    0.8211 |     1.252 |    0.9329 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.087 |     1.573 |     1.472 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.64017</span> |     96.99 |   0.05564 |   0.01480 |  0.007254 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1145 |    0.1216 |     2.664 |    0.8734 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7564 |    0.8211 |     1.252 |    0.9329 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.087 |     1.573 |     1.472 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.599 |   -0.1184 |    0.4302 |   -0.7157 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5570 |   -0.2072 |    -1.823 |    0.1166 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -3.571 |   -0.7852 |    -1.969 |    -1.423 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.5877 |    -2.399 |  -0.03479 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   27</span>|     763.13274 |     1.006 |   -0.9334 |    -1.371 |   -0.2107 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9029 |   -0.9149 |   -0.3061 |   -0.4144 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1571 |   -0.8894 |   -0.7216 |    -1.068 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5763 |   -0.6939 |   -0.7717 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     763.13274 |     97.59 |    -2.888 |    -4.208 |    -4.911 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.043 |    -1.974 |     2.669 |    0.8736 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7619 |    0.8268 |     1.271 |    0.9397 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.090 |     1.579 |     1.474 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     763.13274</span> |     97.59 |   0.05569 |   0.01488 |  0.007367 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1147 |    0.1220 |     2.669 |    0.8736 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7619 |    0.8268 |     1.271 |    0.9397 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.090 |     1.579 |     1.474 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   28</span>|     762.63967 |     1.001 |   -0.9343 |    -1.377 |   -0.2257 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9036 |   -0.9164 |   -0.3100 |   -0.4149 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1742 |   -0.8956 |   -0.7371 |    -1.073 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5800 |   -0.6966 |   -0.7733 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.63967 |     97.10 |    -2.889 |    -4.214 |    -4.926 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.045 |    -1.977 |     2.665 |    0.8734 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7571 |    0.8216 |     1.254 |    0.9341 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.087 |     1.576 |     1.472 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.63967</span> |     97.10 |   0.05564 |   0.01479 |  0.007258 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1146 |    0.1217 |     2.665 |    0.8734 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7571 |    0.8216 |     1.254 |    0.9341 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.087 |     1.576 |     1.472 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     34.39 |   -0.1342 |    0.4309 |   -0.7057 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3094 |  -0.07498 |    -1.436 |    0.2859 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -3.195 |   -0.5698 |    -1.754 |    -1.179 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7834 |    -2.640 |  -0.03401 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   29</span>|     762.61838 |    0.9999 |   -0.9342 |    -1.376 |   -0.2247 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9037 |   -0.9164 |   -0.3101 |   -0.4150 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1736 |   -0.8955 |   -0.7362 |    -1.073 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5799 |   -0.6970 |   -0.7733 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.61838 |     96.99 |    -2.889 |    -4.213 |    -4.925 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.045 |    -1.977 |     2.665 |    0.8733 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7573 |    0.8216 |     1.255 |    0.9344 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.087 |     1.575 |     1.472 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.61838</span> |     96.99 |   0.05564 |   0.01480 |  0.007265 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1146 |    0.1217 |     2.665 |    0.8733 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7573 |    0.8216 |     1.255 |    0.9344 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.087 |     1.575 |     1.472 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.755 |   -0.1162 |    0.4295 |   -0.7030 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4902 |   -0.1819 |    -1.508 |    0.3366 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -3.097 |   -0.6862 |    -1.700 |    -1.138 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.9036 |    -2.963 |  -0.05834 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   30</span>|     762.61305 |     1.000 |   -0.9342 |    -1.376 |   -0.2240 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9037 |   -0.9164 |   -0.3097 |   -0.4155 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1725 |   -0.8955 |   -0.7354 |    -1.072 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5794 |   -0.6961 |   -0.7733 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.61305 |     97.04 |    -2.889 |    -4.213 |    -4.924 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.045 |    -1.977 |     2.665 |    0.8732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7576 |    0.8217 |     1.256 |    0.9348 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.087 |     1.576 |     1.472 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.61305</span> |     97.04 |   0.05565 |   0.01480 |  0.007270 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1146 |    0.1217 |     2.665 |    0.8732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7576 |    0.8217 |     1.256 |    0.9348 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.087 |     1.576 |     1.472 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     14.76 |   -0.1238 |    0.4322 |   -0.6935 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4217 |   -0.1389 |    -1.290 |    0.3261 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -3.236 |   -0.7060 |    -1.644 |    -1.160 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7164 |    -2.626 |  -0.06595 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   31</span>|     762.60623 |    0.9999 |   -0.9342 |    -1.376 |   -0.2227 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9039 |   -0.9165 |   -0.3102 |   -0.4162 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1716 |   -0.8955 |   -0.7347 |    -1.072 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5791 |   -0.6961 |   -0.7733 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.60623 |     96.99 |    -2.889 |    -4.213 |    -4.923 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.045 |    -1.977 |     2.665 |    0.8729 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7578 |    0.8216 |     1.257 |    0.9348 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.088 |     1.576 |     1.472 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.60623</span> |     96.99 |   0.05565 |   0.01481 |  0.007279 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1145 |    0.1216 |     2.665 |    0.8729 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7578 |    0.8216 |     1.257 |    0.9348 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.088 |     1.576 |     1.472 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.846 |   -0.1151 |    0.4344 |   -0.6824 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5544 |   -0.2171 |    -1.375 |    0.3027 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.857 |   -0.6587 |    -1.590 |    -1.961 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8624 |    -2.600 |  -0.06827 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   32</span>|     762.60133 |     1.000 |   -0.9341 |    -1.375 |   -0.2218 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9039 |   -0.9164 |   -0.3101 |   -0.4167 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1705 |   -0.8954 |   -0.7339 |    -1.072 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5787 |   -0.6957 |   -0.7733 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.60133 |     97.04 |    -2.889 |    -4.212 |    -4.922 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.045 |    -1.977 |     2.665 |    0.8727 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7581 |    0.8217 |     1.258 |    0.9357 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.088 |     1.577 |     1.472 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.60133</span> |     97.04 |   0.05565 |   0.01481 |  0.007286 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1145 |    0.1216 |     2.665 |    0.8727 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7581 |    0.8217 |     1.258 |    0.9357 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.088 |     1.577 |     1.472 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     13.48 |   -0.1210 |    0.4371 |   -0.6694 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4912 |   -0.1711 |    -1.470 |    0.1448 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.873 |   -0.6387 |    -1.543 |    -1.867 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7020 |    -2.876 |  -0.07344 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   33</span>|     762.59465 |    0.9999 |   -0.9341 |    -1.375 |   -0.2207 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9040 |   -0.9164 |   -0.3095 |   -0.4173 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1704 |   -0.8960 |   -0.7329 |    -1.071 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5790 |   -0.6956 |   -0.7733 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.59465 |     96.99 |    -2.889 |    -4.212 |    -4.921 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.046 |    -1.977 |     2.666 |    0.8726 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7582 |    0.8213 |     1.259 |    0.9366 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.088 |     1.577 |     1.472 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.59465</span> |     96.99 |   0.05565 |   0.01482 |  0.007294 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1145 |    0.1216 |     2.666 |    0.8726 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7582 |    0.8213 |     1.259 |    0.9366 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.088 |     1.577 |     1.472 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.775 |   -0.1124 |    0.4366 |   -0.6552 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5921 |   -0.2206 |    -1.396 |    0.1541 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.894 |   -0.7001 |    -1.461 |   -0.9904 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8842 |    -2.566 |  -0.07283 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   34</span>|     762.59066 |     1.000 |   -0.9340 |    -1.375 |   -0.2198 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9038 |   -0.9163 |   -0.3089 |   -0.4174 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1692 |   -0.8959 |   -0.7322 |    -1.070 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5787 |   -0.6947 |   -0.7733 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.59066 |     97.04 |    -2.889 |    -4.212 |    -4.920 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.045 |    -1.977 |     2.666 |    0.8725 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7585 |    0.8213 |     1.259 |    0.9371 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.088 |     1.578 |     1.472 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.59066</span> |     97.04 |   0.05566 |   0.01482 |  0.007300 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1145 |    0.1217 |     2.666 |    0.8725 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7585 |    0.8213 |     1.259 |    0.9371 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.088 |     1.578 |     1.472 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     16.18 |   -0.1194 |    0.4391 |   -0.6416 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4865 |   -0.1542 |    -1.319 |    0.1335 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.691 |   -0.6283 |    -1.400 |   -0.9220 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7113 |    -2.508 |  -0.07019 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   35</span>|     762.58429 |    0.9999 |   -0.9339 |    -1.375 |   -0.2184 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9040 |   -0.9163 |   -0.3092 |   -0.4175 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1684 |   -0.8956 |   -0.7319 |    -1.071 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5779 |   -0.6949 |   -0.7734 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.58429 |     96.99 |    -2.888 |    -4.212 |    -4.918 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.046 |    -1.977 |     2.666 |    0.8725 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7587 |    0.8216 |     1.260 |    0.9368 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.089 |     1.578 |     1.472 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.58429</span> |     96.99 |   0.05566 |   0.01482 |  0.007311 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1145 |    0.1217 |     2.666 |    0.8725 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7587 |    0.8216 |     1.260 |    0.9368 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.089 |     1.578 |     1.472 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -2.054 |   -0.1077 |    0.4392 |   -0.6327 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5920 |   -0.2170 |    -1.368 |    0.1202 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.662 |   -0.6439 |    -1.388 |    -1.754 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8733 |    -2.532 |   -0.1003 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   36</span>|     762.57982 |     1.000 |   -0.9338 |    -1.375 |   -0.2170 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9039 |   -0.9162 |   -0.3086 |   -0.4176 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1676 |   -0.8956 |   -0.7312 |    -1.070 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5773 |   -0.6947 |   -0.7733 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.57982 |     97.02 |    -2.888 |    -4.212 |    -4.917 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.046 |    -1.977 |     2.666 |    0.8724 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7589 |    0.8216 |     1.261 |    0.9374 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.089 |     1.578 |     1.472 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.57982</span> |     97.02 |   0.05567 |   0.01482 |  0.007321 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1145 |    0.1217 |     2.666 |    0.8724 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7589 |    0.8216 |     1.261 |    0.9374 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.089 |     1.578 |     1.472 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     9.173 |   -0.1105 |    0.4413 |   -0.6162 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5474 |   -0.1732 |    -1.159 |    0.1830 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.846 |   -0.7034 |    -1.343 |   -0.9047 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6908 |    -2.510 |  -0.09661 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   37</span>|     762.57572 |    0.9999 |   -0.9337 |    -1.375 |   -0.2157 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9040 |   -0.9162 |   -0.3092 |   -0.4178 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1663 |   -0.8953 |   -0.7312 |    -1.070 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5771 |   -0.6947 |   -0.7731 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.57572 |     96.99 |    -2.888 |    -4.212 |    -4.916 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.046 |    -1.977 |     2.666 |    0.8724 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7593 |    0.8218 |     1.261 |    0.9374 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.089 |     1.578 |     1.472 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.57572</span> |     96.99 |   0.05567 |   0.01482 |  0.007331 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1145 |    0.1217 |     2.666 |    0.8724 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7593 |    0.8218 |     1.261 |    0.9374 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.089 |     1.578 |     1.472 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -2.338 |   -0.1030 |    0.4402 |   -0.6028 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6140 |   -0.2137 |    -1.433 |   0.08084 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.656 |   -0.6680 |    -1.346 |    -1.715 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8965 |    -2.853 |   -0.1247 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   38</span>|     762.57150 |     1.000 |   -0.9337 |    -1.375 |   -0.2145 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9040 |   -0.9162 |   -0.3094 |   -0.4180 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1650 |   -0.8953 |   -0.7314 |    -1.070 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5771 |   -0.6938 |   -0.7730 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      762.5715 |     97.02 |    -2.888 |    -4.212 |    -4.915 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.046 |    -1.977 |     2.666 |    0.8723 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7597 |    0.8218 |     1.260 |    0.9374 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.089 |     1.579 |     1.472 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      762.5715</span> |     97.02 |   0.05568 |   0.01482 |  0.007339 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1145 |    0.1217 |     2.666 |    0.8723 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7597 |    0.8218 |     1.260 |    0.9374 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.089 |     1.579 |     1.472 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     9.917 |   -0.1081 |    0.4404 |   -0.5845 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5548 |   -0.1795 |    -1.372 |   0.08332 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.404 |   -0.6107 |    -1.343 |    -1.668 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7053 |    -2.471 |  -0.08527 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   39</span>|     762.56652 |    0.9998 |   -0.9337 |    -1.375 |   -0.2137 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9039 |   -0.9161 |   -0.3083 |   -0.4179 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1654 |   -0.8958 |   -0.7313 |    -1.070 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5775 |   -0.6927 |   -0.7729 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.56652 |     96.98 |    -2.888 |    -4.212 |    -4.914 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.045 |    -1.976 |     2.667 |    0.8723 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7596 |    0.8214 |     1.260 |    0.9380 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.089 |     1.581 |     1.472 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.56652</span> |     96.98 |   0.05568 |   0.01482 |  0.007345 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1145 |    0.1217 |     2.667 |    0.8723 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7596 |    0.8214 |     1.260 |    0.9380 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.089 |     1.581 |     1.472 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.202 |  -0.09986 |    0.4343 |   -0.5734 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5963 |   -0.2059 |    -1.230 |    0.1366 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.439 |   -0.6793 |    -1.344 |    -1.610 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8864 |    -2.392 |  -0.08042 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   40</span>|     762.56246 |     1.000 |   -0.9336 |    -1.375 |   -0.2125 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9039 |   -0.9160 |   -0.3073 |   -0.4180 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1656 |   -0.8955 |   -0.7303 |    -1.069 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5773 |   -0.6925 |   -0.7730 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.56246 |     97.01 |    -2.888 |    -4.212 |    -4.913 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.045 |    -1.976 |     2.668 |    0.8723 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7595 |    0.8216 |     1.262 |    0.9386 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.089 |     1.581 |     1.472 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.56246</span> |     97.01 |   0.05568 |   0.01481 |  0.007354 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1145 |    0.1217 |     2.668 |    0.8723 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7595 |    0.8216 |     1.262 |    0.9386 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.089 |     1.581 |     1.472 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     5.335 |   -0.1021 |    0.4320 |   -0.5553 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5609 |   -0.1614 |    -1.231 |   0.02142 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.620 |   -0.6772 |    -1.305 |    -1.580 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7609 |    -2.720 |   -0.1132 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   41</span>|     762.55730 |    0.9999 |   -0.9335 |    -1.376 |   -0.2115 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9037 |   -0.9159 |   -0.3071 |   -0.4174 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1649 |   -0.8958 |   -0.7301 |    -1.069 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5781 |   -0.6915 |   -0.7731 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      762.5573 |     96.99 |    -2.888 |    -4.213 |    -4.912 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.045 |    -1.976 |     2.668 |    0.8725 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7597 |    0.8214 |     1.262 |    0.9390 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.089 |     1.583 |     1.472 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      762.5573</span> |     96.99 |   0.05568 |   0.01481 |  0.007361 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1146 |    0.1218 |     2.668 |    0.8725 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7597 |    0.8214 |     1.262 |    0.9390 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.089 |     1.583 |     1.472 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   42</span>|     762.55308 |     1.000 |   -0.9335 |    -1.376 |   -0.2104 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9036 |   -0.9158 |   -0.3069 |   -0.4167 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1644 |   -0.8961 |   -0.7300 |    -1.068 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5790 |   -0.6904 |   -0.7733 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.55308 |     97.00 |    -2.888 |    -4.213 |    -4.910 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.045 |    -1.976 |     2.668 |    0.8727 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7598 |    0.8211 |     1.262 |    0.9393 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.088 |     1.584 |     1.472 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.55308</span> |     97.00 |   0.05568 |   0.01480 |  0.007369 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1146 |    0.1218 |     2.668 |    0.8727 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7598 |    0.8211 |     1.262 |    0.9393 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.088 |     1.584 |     1.472 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   43</span>|     762.54129 |     1.000 |   -0.9334 |    -1.378 |   -0.2065 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9033 |   -0.9153 |   -0.3062 |   -0.4144 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1625 |   -0.8972 |   -0.7297 |    -1.067 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5824 |   -0.6868 |   -0.7739 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.54129 |     97.02 |    -2.888 |    -4.215 |    -4.907 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.044 |    -1.975 |     2.669 |    0.8736 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7604 |    0.8202 |     1.262 |    0.9405 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.085 |     1.589 |     1.471 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.54129</span> |     97.02 |   0.05569 |   0.01477 |  0.007398 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1147 |    0.1219 |     2.669 |    0.8736 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7604 |    0.8202 |     1.262 |    0.9405 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.085 |     1.589 |     1.471 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   44</span>|     762.53205 |     1.001 |   -0.9333 |    -1.381 |   -0.1995 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9026 |   -0.9144 |   -0.3049 |   -0.4101 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1590 |   -0.8993 |   -0.7291 |    -1.066 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5885 |   -0.6801 |   -0.7749 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.53205 |     97.07 |    -2.888 |    -4.218 |    -4.899 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.043 |    -1.973 |     2.670 |    0.8751 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7613 |    0.8185 |     1.263 |    0.9425 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.080 |     1.598 |     1.470 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.53205</span> |     97.07 |   0.05570 |   0.01472 |  0.007451 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1148 |    0.1221 |     2.670 |    0.8751 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7613 |    0.8185 |     1.263 |    0.9425 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.080 |     1.598 |     1.470 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     23.65 |   -0.1039 |    0.3653 |   -0.2919 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1880 |    0.2896 |   -0.9107 |    0.7013 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.202 |   -0.8082 |    -1.198 |    -1.067 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.416 |    -1.756 |   -0.2090 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   45</span>|     762.48327 |    0.9998 |   -0.9329 |    -1.391 |   -0.1822 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9041 |   -0.9140 |   -0.3019 |   -0.4150 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1512 |   -0.8995 |   -0.7234 |    -1.064 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5784 |   -0.6773 |   -0.7751 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.48327 |     96.98 |    -2.887 |    -4.228 |    -4.882 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.046 |    -1.972 |     2.674 |    0.8734 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7635 |    0.8183 |     1.269 |    0.9450 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.088 |     1.602 |     1.470 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.48327</span> |     96.98 |   0.05572 |   0.01458 |  0.007580 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1145 |    0.1222 |     2.674 |    0.8734 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7635 |    0.8183 |     1.269 |    0.9450 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.088 |     1.602 |     1.470 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -2.920 |  -0.08029 |    0.2756 |   -0.1231 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7850 |    0.1611 |   -0.6966 |    0.2967 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.718 |   -0.8812 |   -0.8270 |   -0.2301 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.116 |    -1.224 |   -0.3843 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   46</span>|     762.47327 |     1.000 |   -0.9318 |    -1.401 |   -0.1764 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9027 |   -0.9109 |   -0.3019 |   -0.4099 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1415 |   -0.8907 |   -0.7247 |    -1.067 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5685 |   -0.6781 |   -0.7620 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.47327 |     97.01 |    -2.886 |    -4.238 |    -4.876 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.043 |    -1.966 |     2.673 |    0.8752 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7662 |    0.8257 |     1.268 |    0.9409 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.097 |     1.601 |     1.487 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.47327</span> |     97.01 |   0.05578 |   0.01443 |  0.007624 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1148 |    0.1228 |     2.673 |    0.8752 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7662 |    0.8257 |     1.268 |    0.9409 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.097 |     1.601 |     1.487 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     4.304 |  -0.04991 |    0.1861 |   -0.1521 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2758 |    0.6987 |   -0.6385 |    0.6999 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.104 |   -0.4937 |   -0.9023 |    -1.218 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -1.007 |    -1.649 |    0.3798 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   47</span>|     762.45236 |     1.000 |   -0.9314 |    -1.416 |   -0.1739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9025 |   -0.9126 |   -0.3007 |   -0.4145 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1407 |   -0.8864 |   -0.7274 |    -1.072 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5562 |   -0.6663 |   -0.7693 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.45236 |     97.00 |    -2.886 |    -4.253 |    -4.874 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.042 |    -1.969 |     2.675 |    0.8735 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7665 |    0.8293 |     1.265 |    0.9352 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.107 |     1.617 |     1.477 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.45236</span> |     97.00 |   0.05580 |   0.01423 |  0.007643 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1148 |    0.1225 |     2.675 |    0.8735 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7665 |    0.8293 |     1.265 |    0.9352 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.107 |     1.617 |     1.477 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     4.090 |  -0.03836 |   0.07211 |   -0.1660 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2360 |    0.2575 |   -0.5787 |    0.3648 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.053 |   -0.3652 |    -1.069 |    -1.349 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.4799 |    -1.042 |   -0.1902 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   48</span>|     762.47705 |    0.9990 |   -0.9326 |    -1.425 |   -0.1717 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9039 |   -0.9116 |   -0.2960 |   -0.4278 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1499 |   -0.8783 |   -0.6879 |    -1.071 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5536 |   -0.6497 |   -0.7536 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.47705 |     96.90 |    -2.887 |    -4.262 |    -4.872 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.045 |    -1.967 |     2.680 |    0.8688 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7639 |    0.8361 |     1.309 |    0.9365 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.109 |     1.639 |     1.499 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.47705</span> |     96.90 |   0.05574 |   0.01410 |  0.007660 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1145 |    0.1227 |     2.680 |    0.8688 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7639 |    0.8361 |     1.309 |    0.9365 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.109 |     1.639 |     1.499 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   49</span>|     762.47045 |    0.9985 |   -0.9319 |    -1.419 |   -0.1731 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9029 |   -0.9123 |   -0.2988 |   -0.4194 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1437 |   -0.8834 |   -0.7128 |    -1.071 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5551 |   -0.6600 |   -0.7636 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.47045 |     96.85 |    -2.886 |    -4.256 |    -4.873 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.043 |    -1.969 |     2.677 |    0.8718 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7656 |    0.8318 |     1.281 |    0.9361 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.108 |     1.625 |     1.485 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.47045</span> |     96.85 |   0.05578 |   0.01418 |  0.007650 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1147 |    0.1225 |     2.677 |    0.8718 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7656 |    0.8318 |     1.281 |    0.9361 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.108 |     1.625 |     1.485 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   50</span>|     762.49004 |    0.9982 |   -0.9315 |    -1.417 |   -0.1736 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9025 |   -0.9126 |   -0.3000 |   -0.4160 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1412 |   -0.8855 |   -0.7230 |    -1.071 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5558 |   -0.6642 |   -0.7677 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.49004 |     96.83 |    -2.886 |    -4.254 |    -4.874 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.043 |    -1.969 |     2.675 |    0.8730 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7663 |    0.8301 |     1.270 |    0.9360 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.107 |     1.619 |     1.480 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.49004</span> |     96.83 |   0.05579 |   0.01421 |  0.007645 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1148 |    0.1225 |     2.675 |    0.8730 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7663 |    0.8301 |     1.270 |    0.9360 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.107 |     1.619 |     1.480 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   51</span>|     762.45852 |    0.9993 |   -0.9314 |    -1.416 |   -0.1739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9024 |   -0.9127 |   -0.3006 |   -0.4146 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1405 |   -0.8864 |   -0.7272 |    -1.072 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5561 |   -0.6661 |   -0.7693 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.45852 |     96.93 |    -2.886 |    -4.253 |    -4.874 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.042 |    -1.969 |     2.675 |    0.8735 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7665 |    0.8293 |     1.265 |    0.9355 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.107 |     1.617 |     1.477 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.45852</span> |     96.93 |   0.05580 |   0.01423 |  0.007643 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1148 |    0.1224 |     2.675 |    0.8735 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7665 |    0.8293 |     1.265 |    0.9355 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.107 |     1.617 |     1.477 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   52</span>|     762.45201 |    0.9999 |   -0.9314 |    -1.416 |   -0.1739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9025 |   -0.9126 |   -0.3007 |   -0.4145 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1407 |   -0.8864 |   -0.7274 |    -1.072 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5562 |   -0.6663 |   -0.7693 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.45201 |     96.99 |    -2.886 |    -4.253 |    -4.874 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.042 |    -1.969 |     2.675 |    0.8735 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7665 |    0.8293 |     1.265 |    0.9352 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.107 |     1.617 |     1.477 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.45201</span> |     96.99 |   0.05580 |   0.01423 |  0.007643 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1148 |    0.1225 |     2.675 |    0.8735 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7665 |    0.8293 |     1.265 |    0.9352 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.107 |     1.617 |     1.477 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.157 |  -0.03565 |   0.07096 |   -0.1676 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2565 |    0.2382 |   -0.5340 |    0.4141 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.202 |   -0.3942 |    -1.048 |    -1.363 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.4633 |    -1.025 |   -0.1715 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   53</span>|     762.45163 |     1.000 |   -0.9314 |    -1.416 |   -0.1739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9025 |   -0.9127 |   -0.3007 |   -0.4145 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1406 |   -0.8864 |   -0.7273 |    -1.072 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5562 |   -0.6662 |   -0.7693 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.45163 |     97.00 |    -2.886 |    -4.253 |    -4.874 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.042 |    -1.969 |     2.675 |    0.8735 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7665 |    0.8293 |     1.265 |    0.9353 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.107 |     1.617 |     1.477 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.45163</span> |     97.00 |   0.05580 |   0.01423 |  0.007643 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1148 |    0.1225 |     2.675 |    0.8735 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7665 |    0.8293 |     1.265 |    0.9353 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.107 |     1.617 |     1.477 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     1.387 |  -0.03697 |   0.07095 |   -0.1670 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2405 |    0.2417 |   -0.4946 |    0.4249 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9410 |   -0.3603 |    -1.076 |   -0.6985 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.5549 |   -0.6934 |   -0.1964 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   54</span>|     762.45142 |    0.9999 |   -0.9314 |    -1.416 |   -0.1739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9024 |   -0.9127 |   -0.3006 |   -0.4146 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1405 |   -0.8864 |   -0.7272 |    -1.072 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5561 |   -0.6662 |   -0.7693 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.45142 |     96.99 |    -2.886 |    -4.253 |    -4.874 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.042 |    -1.969 |     2.675 |    0.8735 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7665 |    0.8293 |     1.265 |    0.9354 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.107 |     1.617 |     1.477 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.45142</span> |     96.99 |   0.05580 |   0.01423 |  0.007643 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1148 |    0.1224 |     2.675 |    0.8735 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7665 |    0.8293 |     1.265 |    0.9354 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.107 |     1.617 |     1.477 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -2.093 |  -0.03511 |   0.07006 |   -0.1682 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2501 |    0.2258 |   -0.7319 |    0.2456 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.092 |   -0.3868 |    -1.095 |   -0.7218 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.5798 |   -0.7078 |   -0.2098 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   55</span>|     762.45106 |     1.000 |   -0.9314 |    -1.416 |   -0.1739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9024 |   -0.9127 |   -0.3006 |   -0.4146 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1405 |   -0.8863 |   -0.7271 |    -1.072 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5561 |   -0.6661 |   -0.7693 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.45106 |     97.00 |    -2.886 |    -4.253 |    -4.874 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.042 |    -1.969 |     2.675 |    0.8735 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7665 |    0.8294 |     1.265 |    0.9355 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.107 |     1.617 |     1.477 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.45106</span> |     97.00 |   0.05580 |   0.01423 |  0.007644 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1148 |    0.1224 |     2.675 |    0.8735 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7665 |    0.8294 |     1.265 |    0.9355 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.107 |     1.617 |     1.477 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     1.558 |  -0.03666 |   0.07079 |   -0.1670 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2292 |    0.2352 |   -0.6822 |    0.2833 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9877 |   -0.3599 |    -1.086 |   -0.7040 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.5476 |   -0.6798 |   -0.1950 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   56</span>|     762.45087 |    0.9999 |   -0.9314 |    -1.416 |   -0.1739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9024 |   -0.9127 |   -0.3005 |   -0.4146 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1404 |   -0.8863 |   -0.7270 |    -1.072 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5561 |   -0.6661 |   -0.7693 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.45087 |     96.99 |    -2.886 |    -4.253 |    -4.874 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.042 |    -1.970 |     2.675 |    0.8735 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7666 |    0.8294 |     1.265 |    0.9355 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.107 |     1.617 |     1.477 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.45087</span> |     96.99 |   0.05580 |   0.01423 |  0.007644 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1148 |    0.1224 |     2.675 |    0.8735 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7666 |    0.8294 |     1.265 |    0.9355 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.107 |     1.617 |     1.477 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    C| Central Diff. |    -2.701 |  -0.06138 |   0.06188 |   -0.1824 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3275 |    0.1556 |   -0.5644 |    0.4107 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9451 |   -0.3602 |    -1.050 |   -0.9219 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7692 |   -0.8400 |   -0.2110 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   57</span>|     762.45052 |     1.000 |   -0.9314 |    -1.416 |   -0.1739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9024 |   -0.9127 |   -0.3005 |   -0.4146 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1404 |   -0.8863 |   -0.7269 |    -1.072 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5560 |   -0.6660 |   -0.7693 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.45052 |     97.00 |    -2.886 |    -4.253 |    -4.874 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.042 |    -1.970 |     2.675 |    0.8735 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7666 |    0.8294 |     1.265 |    0.9356 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.107 |     1.617 |     1.477 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.45052</span> |     97.00 |   0.05580 |   0.01423 |  0.007644 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1148 |    0.1224 |     2.675 |    0.8735 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7666 |    0.8294 |     1.265 |    0.9356 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.107 |     1.617 |     1.477 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     1.310 |  -0.03660 |   0.07022 |   -0.1677 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2216 |    0.2281 |   -0.6223 |    0.3362 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.101 |   -0.3801 |    -1.032 |    -1.332 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.4655 |    -1.018 |   -0.1777 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   58</span>|     762.45025 |    0.9999 |   -0.9314 |    -1.416 |   -0.1739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9024 |   -0.9127 |   -0.3005 |   -0.4146 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1403 |   -0.8862 |   -0.7268 |    -1.072 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5560 |   -0.6659 |   -0.7692 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.45025 |     96.99 |    -2.886 |    -4.253 |    -4.874 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.042 |    -1.970 |     2.675 |    0.8735 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7666 |    0.8294 |     1.265 |    0.9357 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.107 |     1.617 |     1.477 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.45025</span> |     96.99 |   0.05580 |   0.01423 |  0.007644 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1148 |    0.1224 |     2.675 |    0.8735 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7666 |    0.8294 |     1.265 |    0.9357 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.107 |     1.617 |     1.477 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.564 |  -0.03511 |   0.06930 |   -0.1687 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2301 |    0.2148 |   -0.6958 |    0.2956 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.074 |   -0.3641 |    -1.039 |   -0.6647 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.5492 |   -0.6891 |   -0.2028 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   59</span>|     762.44994 |     1.000 |   -0.9314 |    -1.416 |   -0.1738 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9024 |   -0.9127 |   -0.3004 |   -0.4147 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1402 |   -0.8862 |   -0.7268 |    -1.071 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5559 |   -0.6659 |   -0.7692 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.44994 |     97.00 |    -2.886 |    -4.253 |    -4.874 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.042 |    -1.970 |     2.675 |    0.8735 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7666 |    0.8295 |     1.266 |    0.9357 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.107 |     1.617 |     1.477 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.44994</span> |     97.00 |   0.05580 |   0.01423 |  0.007644 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1148 |    0.1224 |     2.675 |    0.8735 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7666 |    0.8295 |     1.266 |    0.9357 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.107 |     1.617 |     1.477 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     2.130 |  -0.03693 |   0.06970 |   -0.1678 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2087 |    0.2222 |   -0.4767 |    0.4303 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.005 |   -0.3788 |    -1.043 |    -1.360 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.5474 |   -0.6760 |   -0.1925 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   60</span>|     762.44969 |    0.9999 |   -0.9314 |    -1.416 |   -0.1738 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9024 |   -0.9127 |   -0.3004 |   -0.4147 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1402 |   -0.8862 |   -0.7266 |    -1.071 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5559 |   -0.6658 |   -0.7692 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.44969 |     96.99 |    -2.886 |    -4.253 |    -4.874 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.042 |    -1.970 |     2.675 |    0.8735 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7666 |    0.8295 |     1.266 |    0.9358 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.107 |     1.617 |     1.477 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.44969</span> |     96.99 |   0.05580 |   0.01423 |  0.007644 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1148 |    0.1224 |     2.675 |    0.8735 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7666 |    0.8295 |     1.266 |    0.9358 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.107 |     1.617 |     1.477 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.483 |  -0.03492 |   0.06900 |   -0.1687 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2211 |    0.2086 |   -0.7093 |    0.2512 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9848 |   -0.3396 |    -1.026 |    -1.317 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.5572 |   -0.6715 |   -0.1875 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   61</span>|     762.44936 |     1.000 |   -0.9314 |    -1.416 |   -0.1738 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9023 |   -0.9128 |   -0.3003 |   -0.4147 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1401 |   -0.8862 |   -0.7266 |    -1.071 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5558 |   -0.6658 |   -0.7692 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.44936 |     97.00 |    -2.886 |    -4.253 |    -4.874 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.042 |    -1.970 |     2.675 |    0.8735 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7666 |    0.8295 |     1.266 |    0.9359 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.107 |     1.617 |     1.478 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.44936</span> |     97.00 |   0.05580 |   0.01423 |  0.007644 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1148 |    0.1224 |     2.675 |    0.8735 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7666 |    0.8295 |     1.266 |    0.9359 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.107 |     1.617 |     1.478 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     1.828 |  -0.03668 |   0.06906 |   -0.1681 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2025 |    0.2148 |   -0.4016 |    0.4562 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7985 |   -0.3199 |    -1.005 |   -0.6242 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.4649 |    -1.017 |   -0.1851 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   62</span>|     762.44920 |    0.9998 |   -0.9314 |    -1.416 |   -0.1738 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9023 |   -0.9128 |   -0.3003 |   -0.4147 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1401 |   -0.8861 |   -0.7265 |    -1.071 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5558 |   -0.6657 |   -0.7692 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      762.4492 |     96.99 |    -2.886 |    -4.253 |    -4.874 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.042 |    -1.970 |     2.675 |    0.8735 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7667 |    0.8295 |     1.266 |    0.9360 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.107 |     1.617 |     1.478 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      762.4492</span> |     96.99 |   0.05580 |   0.01423 |  0.007644 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1149 |    0.1224 |     2.675 |    0.8735 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7667 |    0.8295 |     1.266 |    0.9360 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.107 |     1.617 |     1.478 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    C| Central Diff. |    -2.892 |  -0.06089 |   0.06027 |   -0.1834 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3033 |    0.1350 |   -0.5226 |    0.3403 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9268 |   -0.3802 |    -1.004 |   -0.8849 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7517 |   -0.8245 |   -0.1920 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   63</span>|     762.44881 |     1.000 |   -0.9314 |    -1.416 |   -0.1738 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9023 |   -0.9128 |   -0.3003 |   -0.4147 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1400 |   -0.8861 |   -0.7264 |    -1.071 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5558 |   -0.6656 |   -0.7692 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.44881 |     97.00 |    -2.886 |    -4.253 |    -4.874 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.042 |    -1.970 |     2.675 |    0.8735 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7667 |    0.8296 |     1.266 |    0.9360 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.107 |     1.617 |     1.478 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.44881</span> |     97.00 |   0.05580 |   0.01423 |  0.007644 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1149 |    0.1224 |     2.675 |    0.8735 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7667 |    0.8296 |     1.266 |    0.9360 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.107 |     1.617 |     1.478 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     1.278 |  -0.03606 |   0.06873 |   -0.1684 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1982 |    0.2076 |   -0.7410 |    0.2864 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9371 |   -0.3600 |   -0.9981 |   -0.6148 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.4567 |   -0.9956 |   -0.1730 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   64</span>|     762.44861 |    0.9999 |   -0.9314 |    -1.416 |   -0.1738 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9023 |   -0.9128 |   -0.3002 |   -0.4148 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1400 |   -0.8861 |   -0.7263 |    -1.071 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5557 |   -0.6656 |   -0.7691 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.44861 |     96.99 |    -2.886 |    -4.253 |    -4.874 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.042 |    -1.970 |     2.675 |    0.8734 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7667 |    0.8296 |     1.266 |    0.9361 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.107 |     1.618 |     1.478 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.44861</span> |     96.99 |   0.05580 |   0.01423 |  0.007644 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1149 |    0.1224 |     2.675 |    0.8734 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7667 |    0.8296 |     1.266 |    0.9361 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.107 |     1.618 |     1.478 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.918 |  -0.03426 |   0.06810 |   -0.1693 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2072 |    0.1939 |   -0.5108 |    0.4012 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.125 |   -0.3956 |    -1.016 |   -0.6465 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.5398 |   -0.6520 |   -0.1791 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   65</span>|     762.44831 |     1.000 |   -0.9314 |    -1.416 |   -0.1738 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9023 |   -0.9128 |   -0.3002 |   -0.4148 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1399 |   -0.8861 |   -0.7262 |    -1.071 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5557 |   -0.6655 |   -0.7691 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.44831 |     97.00 |    -2.886 |    -4.253 |    -4.874 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.042 |    -1.970 |     2.675 |    0.8734 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7667 |    0.8296 |     1.266 |    0.9361 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.107 |     1.618 |     1.478 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.44831</span> |     97.00 |   0.05580 |   0.01423 |  0.007645 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1149 |    0.1224 |     2.675 |    0.8734 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7667 |    0.8296 |     1.266 |    0.9361 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.107 |     1.618 |     1.478 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     2.263 |  -0.03657 |   0.06844 |   -0.1684 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1851 |    0.2030 |   -0.3956 |    0.5081 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.042 |   -0.3769 |   -0.9733 |   -0.5979 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.4511 |   -0.9881 |   -0.1717 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   66</span>|     762.44807 |    0.9999 |   -0.9314 |    -1.416 |   -0.1737 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9023 |   -0.9128 |   -0.3002 |   -0.4148 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1398 |   -0.8860 |   -0.7261 |    -1.071 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5557 |   -0.6655 |   -0.7691 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.44807 |     96.99 |    -2.886 |    -4.253 |    -4.874 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.042 |    -1.970 |     2.675 |    0.8734 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7667 |    0.8296 |     1.266 |    0.9361 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.107 |     1.618 |     1.478 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.44807</span> |     96.99 |   0.05580 |   0.01423 |  0.007645 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1149 |    0.1224 |     2.675 |    0.8734 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7667 |    0.8296 |     1.266 |    0.9361 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.107 |     1.618 |     1.478 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.676 |  -0.03430 |   0.06789 |   -0.1692 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2001 |    0.1879 |   -0.6345 |    0.2243 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9985 |   -0.3646 |   -0.9895 |   -0.6233 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.5267 |   -0.6582 |   -0.1873 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   67</span>|     762.44780 |     1.000 |   -0.9314 |    -1.416 |   -0.1737 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9023 |   -0.9128 |   -0.3001 |   -0.4149 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1398 |   -0.8860 |   -0.7261 |    -1.071 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5556 |   -0.6654 |   -0.7691 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      762.4478 |     97.00 |    -2.886 |    -4.253 |    -4.874 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.042 |    -1.970 |     2.675 |    0.8734 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7667 |    0.8297 |     1.266 |    0.9362 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.107 |     1.618 |     1.478 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      762.4478</span> |     97.00 |   0.05580 |   0.01423 |  0.007645 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1149 |    0.1224 |     2.675 |    0.8734 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7667 |    0.8297 |     1.266 |    0.9362 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.107 |     1.618 |     1.478 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     2.318 |  -0.03631 |   0.06808 |   -0.1685 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1789 |    0.1959 |   -0.7173 |    0.2475 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9453 |   -0.3416 |   -0.9621 |   -0.5903 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.4826 |   -0.9880 |   -0.1748 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   68</span>|     762.44757 |    0.9999 |   -0.9314 |    -1.416 |   -0.1737 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9023 |   -0.9128 |   -0.3001 |   -0.4149 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1397 |   -0.8860 |   -0.7260 |    -1.071 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5556 |   -0.6653 |   -0.7691 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.44757 |     96.99 |    -2.886 |    -4.253 |    -4.874 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.042 |    -1.970 |     2.675 |    0.8734 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7667 |    0.8297 |     1.266 |    0.9362 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.107 |     1.618 |     1.478 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.44757</span> |     96.99 |   0.05580 |   0.01423 |  0.007645 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1149 |    0.1224 |     2.675 |    0.8734 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7667 |    0.8297 |     1.266 |    0.9362 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.107 |     1.618 |     1.478 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.403 |  -0.03410 |   0.06766 |   -0.1693 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1923 |    0.1827 |   -0.5754 |    0.3333 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.026 |   -0.3445 |   -0.9634 |   -0.5940 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.4852 |   -0.9871 |   -0.1745 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   69</span>|     762.44726 |     1.000 |   -0.9314 |    -1.416 |   -0.1737 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9022 |   -0.9129 |   -0.3000 |   -0.4149 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1396 |   -0.8859 |   -0.7259 |    -1.071 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5555 |   -0.6653 |   -0.7691 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.44726 |     97.00 |    -2.886 |    -4.253 |    -4.874 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.042 |    -1.970 |     2.675 |    0.8734 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7668 |    0.8297 |     1.266 |    0.9363 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.108 |     1.618 |     1.478 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.44726</span> |     97.00 |   0.05580 |   0.01423 |  0.007645 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1149 |    0.1224 |     2.675 |    0.8734 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7668 |    0.8297 |     1.266 |    0.9363 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.108 |     1.618 |     1.478 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     2.060 |  -0.03580 |   0.06800 |   -0.1685 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1728 |    0.1899 |   -0.4704 |    0.4284 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9227 |   -0.3505 |   -0.9813 |   -0.6228 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.5193 |   -0.6388 |   -0.1848 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   70</span>|     762.44712 |    0.9998 |   -0.9314 |    -1.416 |   -0.1737 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9022 |   -0.9129 |   -0.3000 |   -0.4149 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1396 |   -0.8859 |   -0.7258 |    -1.071 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5555 |   -0.6652 |   -0.7691 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.44712 |     96.99 |    -2.886 |    -4.253 |    -4.874 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.042 |    -1.970 |     2.676 |    0.8734 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7668 |    0.8297 |     1.267 |    0.9363 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.108 |     1.618 |     1.478 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.44712</span> |     96.99 |   0.05580 |   0.01423 |  0.007645 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1149 |    0.1224 |     2.676 |    0.8734 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7668 |    0.8297 |     1.267 |    0.9363 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.108 |     1.618 |     1.478 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    C| Central Diff. |    -2.853 |  -0.06042 |   0.05902 |   -0.1840 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2766 |    0.1092 |   -0.5247 |    0.3320 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8590 |   -0.3265 |   -0.9669 |   -0.8543 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7362 |   -0.6424 |   -0.1982 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   71</span>|     762.44677 |     1.000 |   -0.9314 |    -1.416 |   -0.1736 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9022 |   -0.9129 |   -0.3000 |   -0.4149 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1396 |   -0.8859 |   -0.7257 |    -1.071 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5555 |   -0.6652 |   -0.7690 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.44677 |     97.00 |    -2.886 |    -4.253 |    -4.874 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.042 |    -1.970 |     2.676 |    0.8734 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7668 |    0.8297 |     1.267 |    0.9363 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.108 |     1.618 |     1.478 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.44677</span> |     97.00 |   0.05580 |   0.01423 |  0.007645 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1149 |    0.1224 |     2.676 |    0.8734 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7668 |    0.8297 |     1.267 |    0.9363 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.108 |     1.618 |     1.478 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     1.229 |  -0.03534 |   0.06769 |   -0.1687 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1722 |    0.1825 |   -0.6577 |    0.2295 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9683 |   -0.3540 |   -0.9833 |   -0.6305 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.5492 |   -0.6462 |   -0.1881 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   72</span>|     762.44660 |    0.9999 |   -0.9314 |    -1.416 |   -0.1736 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9022 |   -0.9129 |   -0.2999 |   -0.4149 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1395 |   -0.8858 |   -0.7256 |    -1.071 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5554 |   -0.6651 |   -0.7690 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      762.4466 |     96.99 |    -2.886 |    -4.253 |    -4.874 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.042 |    -1.970 |     2.676 |    0.8734 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7668 |    0.8298 |     1.267 |    0.9364 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.108 |     1.618 |     1.478 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      762.4466</span> |     96.99 |   0.05580 |   0.01423 |  0.007646 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1149 |    0.1224 |     2.676 |    0.8734 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7668 |    0.8298 |     1.267 |    0.9364 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.108 |     1.618 |     1.478 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    C| Central Diff. |    -2.619 |  -0.06037 |   0.05887 |   -0.1840 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2697 |    0.1047 |   -0.4880 |    0.3107 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8777 |   -0.3347 |   -0.9510 |   -0.8415 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8070 |   -0.6380 |   -0.2003 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   73</span>|     762.44631 |     1.000 |   -0.9314 |    -1.416 |   -0.1736 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9022 |   -0.9129 |   -0.2999 |   -0.4149 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1395 |   -0.8858 |   -0.7255 |    -1.071 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5554 |   -0.6651 |   -0.7690 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.44631 |     97.00 |    -2.886 |    -4.253 |    -4.874 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.042 |    -1.970 |     2.676 |    0.8734 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7668 |    0.8298 |     1.267 |    0.9364 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.108 |     1.618 |     1.478 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.44631</span> |     97.00 |   0.05580 |   0.01423 |  0.007646 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1149 |    0.1224 |     2.676 |    0.8734 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7668 |    0.8298 |     1.267 |    0.9364 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.108 |     1.618 |     1.478 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     1.239 |  -0.03523 |   0.06754 |   -0.1690 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1652 |    0.1779 |   -0.5810 |    0.3500 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9310 |   -0.3299 |   -0.9168 |   -0.5544 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.4590 |   -0.9780 |   -0.1676 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   74</span>|     762.44612 |    0.9999 |   -0.9314 |    -1.416 |   -0.1736 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9022 |   -0.9129 |   -0.2998 |   -0.4150 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1394 |   -0.8858 |   -0.7254 |    -1.071 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5553 |   -0.6650 |   -0.7690 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.44612 |     96.99 |    -2.886 |    -4.253 |    -4.874 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.042 |    -1.970 |     2.676 |    0.8734 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7668 |    0.8298 |     1.267 |    0.9365 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.108 |     1.618 |     1.478 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.44612</span> |     96.99 |   0.05580 |   0.01423 |  0.007646 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1149 |    0.1224 |     2.676 |    0.8734 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7668 |    0.8298 |     1.267 |    0.9365 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.108 |     1.618 |     1.478 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    C| Central Diff. |    -2.595 |  -0.06025 |   0.05873 |   -0.1842 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2631 |    0.1003 |   -0.4975 |    0.3199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8775 |   -0.3517 |   -0.9324 |   -0.8365 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7469 |   -0.7872 |   -0.1921 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   75</span>|     762.44579 |     1.000 |   -0.9314 |    -1.416 |   -0.1736 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9022 |   -0.9129 |   -0.2998 |   -0.4150 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1394 |   -0.8858 |   -0.7253 |    -1.071 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5553 |   -0.6649 |   -0.7690 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.44579 |     97.00 |    -2.886 |    -4.253 |    -4.874 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.042 |    -1.970 |     2.676 |    0.8734 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7668 |    0.8298 |     1.267 |    0.9365 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.108 |     1.618 |     1.478 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.44579</span> |     97.00 |   0.05580 |   0.01423 |  0.007646 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1149 |    0.1224 |     2.676 |    0.8734 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7668 |    0.8298 |     1.267 |    0.9365 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.108 |     1.618 |     1.478 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     1.332 |  -0.03503 |   0.06745 |   -0.1690 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1595 |    0.1733 |   -0.6521 |    0.2265 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.099 |   -0.3762 |    -1.004 |   -0.8055 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.5808 |   -0.6379 |   -0.1715 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   76</span>|     762.44562 |    0.9999 |   -0.9314 |    -1.416 |   -0.1736 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9022 |   -0.9129 |   -0.2997 |   -0.4150 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1393 |   -0.8857 |   -0.7252 |    -1.071 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5552 |   -0.6649 |   -0.7690 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.44562 |     96.99 |    -2.886 |    -4.253 |    -4.874 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.042 |    -1.970 |     2.676 |    0.8734 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7669 |    0.8299 |     1.267 |    0.9366 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.108 |     1.618 |     1.478 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.44562</span> |     96.99 |   0.05580 |   0.01423 |  0.007646 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1149 |    0.1224 |     2.676 |    0.8734 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7669 |    0.8299 |     1.267 |    0.9366 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.108 |     1.618 |     1.478 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    C| Central Diff. |    -2.540 |  -0.06014 |   0.05853 |   -0.1843 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2582 |   0.09555 |   -0.4805 |    0.3174 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8832 |   -0.3610 |   -0.9182 |   -0.8220 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7344 |   -0.7852 |   -0.1863 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   77</span>|     762.44533 |     1.000 |   -0.9314 |    -1.416 |   -0.1735 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9022 |   -0.9129 |   -0.2997 |   -0.4150 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1393 |   -0.8857 |   -0.7251 |    -1.071 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5552 |   -0.6648 |   -0.7690 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.44533 |     97.00 |    -2.886 |    -4.253 |    -4.874 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.042 |    -1.970 |     2.676 |    0.8734 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7669 |    0.8299 |     1.267 |    0.9366 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.108 |     1.619 |     1.478 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.44533</span> |     97.00 |   0.05580 |   0.01423 |  0.007646 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1149 |    0.1224 |     2.676 |    0.8734 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7669 |    0.8299 |     1.267 |    0.9366 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.108 |     1.619 |     1.478 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     1.707 |  -0.03519 |   0.06722 |   -0.1691 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1517 |    0.1695 |   -0.2998 |    0.5346 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9157 |   -0.3444 |   -0.9296 |   -0.7244 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.4238 |   -0.9503 |   -0.1555 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   78</span>|     762.44512 |    0.9999 |   -0.9314 |    -1.416 |   -0.1735 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9022 |   -0.9129 |   -0.2997 |   -0.4151 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1392 |   -0.8857 |   -0.7250 |    -1.071 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5551 |   -0.6647 |   -0.7690 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.44512 |     96.99 |    -2.886 |    -4.253 |    -4.874 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.042 |    -1.970 |     2.676 |    0.8733 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7669 |    0.8299 |     1.267 |    0.9367 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.108 |     1.619 |     1.478 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.44512</span> |     96.99 |   0.05580 |   0.01423 |  0.007646 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1149 |    0.1224 |     2.676 |    0.8733 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7669 |    0.8299 |     1.267 |    0.9367 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.108 |     1.619 |     1.478 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.725 |  -0.03313 |   0.06674 |   -0.1698 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1647 |    0.1564 |   -0.4671 |    0.3596 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.122 |   -0.3749 |   -0.8900 |   -0.5488 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.4408 |   -0.9435 |   -0.1591 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   79</span>|     762.44482 |     1.000 |   -0.9314 |    -1.416 |   -0.1735 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9021 |   -0.9129 |   -0.2997 |   -0.4151 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1391 |   -0.8856 |   -0.7249 |    -1.071 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5551 |   -0.6647 |   -0.7689 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.44482 |     97.00 |    -2.886 |    -4.253 |    -4.873 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.042 |    -1.970 |     2.676 |    0.8733 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7669 |    0.8299 |     1.268 |    0.9367 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.108 |     1.619 |     1.478 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.44482</span> |     97.00 |   0.05580 |   0.01423 |  0.007647 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1149 |    0.1224 |     2.676 |    0.8733 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7669 |    0.8299 |     1.268 |    0.9367 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.108 |     1.619 |     1.478 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     2.165 |  -0.03526 |   0.06705 |   -0.1689 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1452 |    0.1649 |   -0.6916 |    0.2043 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.053 |   -0.3782 |   -0.9414 |    -1.376 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.4339 |   -0.9640 |   -0.1678 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   80</span>|     762.44457 |    0.9999 |   -0.9314 |    -1.416 |   -0.1735 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9021 |   -0.9129 |   -0.2997 |   -0.4151 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1391 |   -0.8856 |   -0.7248 |    -1.071 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5551 |   -0.6646 |   -0.7689 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.44457 |     96.99 |    -2.886 |    -4.253 |    -4.873 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.042 |    -1.970 |     2.676 |    0.8733 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7669 |    0.8300 |     1.268 |    0.9368 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.108 |     1.619 |     1.478 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.44457</span> |     96.99 |   0.05580 |   0.01423 |  0.007647 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1149 |    0.1224 |     2.676 |    0.8733 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7669 |    0.8300 |     1.268 |    0.9368 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.108 |     1.619 |     1.478 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.241 |  -0.03355 |   0.06646 |   -0.1694 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1600 |    0.1522 |   -0.4229 |    0.3779 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8707 |   -0.3333 |   -0.8636 |   -0.5164 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.4114 |   -0.9432 |   -0.1630 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   81</span>|     762.44429 |     1.000 |   -0.9314 |    -1.416 |   -0.1734 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9021 |   -0.9130 |   -0.2996 |   -0.4151 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1390 |   -0.8856 |   -0.7247 |    -1.071 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5551 |   -0.6645 |   -0.7689 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.44429 |     97.00 |    -2.886 |    -4.253 |    -4.873 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.042 |    -1.970 |     2.676 |    0.8733 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7669 |    0.8300 |     1.268 |    0.9368 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.108 |     1.619 |     1.478 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.44429</span> |     97.00 |   0.05580 |   0.01423 |  0.007647 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1149 |    0.1224 |     2.676 |    0.8733 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7669 |    0.8300 |     1.268 |    0.9368 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.108 |     1.619 |     1.478 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     2.194 |  -0.03535 |   0.06668 |   -0.1687 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1413 |    0.1590 |   -0.5714 |    0.3381 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9309 |   -0.3230 |   -0.9277 |   -0.7053 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.4161 |   -0.9324 |   -0.1528 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   82</span>|     762.44403 |    0.9999 |   -0.9314 |    -1.416 |   -0.1734 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9021 |   -0.9130 |   -0.2996 |   -0.4152 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1390 |   -0.8855 |   -0.7246 |    -1.070 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5550 |   -0.6645 |   -0.7689 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.44403 |     96.99 |    -2.886 |    -4.253 |    -4.873 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.042 |    -1.970 |     2.676 |    0.8733 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7670 |    0.8300 |     1.268 |    0.9369 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.108 |     1.619 |     1.478 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.44403</span> |     96.99 |   0.05580 |   0.01423 |  0.007647 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1149 |    0.1224 |     2.676 |    0.8733 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7670 |    0.8300 |     1.268 |    0.9369 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.108 |     1.619 |     1.478 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.197 |  -0.03347 |   0.06628 |   -0.1692 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1562 |    0.1463 |   -0.5532 |    0.2989 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9647 |   -0.3192 |   -0.9128 |   -0.7175 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.4587 |   -0.9463 |   -0.1607 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   83</span>|     762.44374 |     1.000 |   -0.9314 |    -1.416 |   -0.1734 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9021 |   -0.9130 |   -0.2996 |   -0.4152 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1389 |   -0.8855 |   -0.7245 |    -1.070 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5550 |   -0.6644 |   -0.7689 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.44374 |     97.00 |    -2.886 |    -4.253 |    -4.873 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.042 |    -1.970 |     2.676 |    0.8733 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7670 |    0.8301 |     1.268 |    0.9369 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.108 |     1.619 |     1.478 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.44374</span> |     97.00 |   0.05580 |   0.01423 |  0.007647 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1149 |    0.1224 |     2.676 |    0.8733 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7670 |    0.8301 |     1.268 |    0.9369 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.108 |     1.619 |     1.478 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     1.963 |  -0.03502 |   0.06649 |   -0.1685 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1388 |    0.1528 |   -0.5508 |    0.3111 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7878 |   -0.3058 |   -0.9061 |   -0.7073 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.4580 |   -0.9428 |   -0.1693 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   84</span>|     762.44359 |    0.9999 |   -0.9314 |    -1.416 |   -0.1734 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9021 |   -0.9130 |   -0.2995 |   -0.4152 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1389 |   -0.8855 |   -0.7244 |    -1.070 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5550 |   -0.6643 |   -0.7689 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.44359 |     96.99 |    -2.886 |    -4.253 |    -4.873 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.042 |    -1.970 |     2.676 |    0.8733 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7670 |    0.8301 |     1.268 |    0.9370 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.108 |     1.619 |     1.478 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.44359</span> |     96.99 |   0.05580 |   0.01423 |  0.007647 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1149 |    0.1224 |     2.676 |    0.8733 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7670 |    0.8301 |     1.268 |    0.9370 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.108 |     1.619 |     1.478 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    C| Central Diff. |    -2.684 |  -0.05996 |   0.05741 |   -0.1839 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2433 |   0.07318 |   -0.4825 |    0.3221 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8461 |   -0.3440 |   -0.8674 |   -0.7797 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7318 |   -0.7544 |   -0.1758 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   85</span>|     762.44327 |     1.000 |   -0.9314 |    -1.416 |   -0.1734 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9021 |   -0.9130 |   -0.2995 |   -0.4153 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1389 |   -0.8855 |   -0.7243 |    -1.070 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5549 |   -0.6642 |   -0.7688 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.44327 |     97.00 |    -2.886 |    -4.253 |    -4.873 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.042 |    -1.970 |     2.676 |    0.8733 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7670 |    0.8301 |     1.268 |    0.9370 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.108 |     1.619 |     1.478 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.44327</span> |     97.00 |   0.05580 |   0.01423 |  0.007648 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1149 |    0.1224 |     2.676 |    0.8733 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7670 |    0.8301 |     1.268 |    0.9370 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.108 |     1.619 |     1.478 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     1.433 |  -0.03457 |   0.06633 |   -0.1687 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1378 |    0.1478 |   -0.5721 |    0.2715 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9861 |   -0.3308 |   -0.8969 |   -0.7064 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.4388 |   -0.9204 |   -0.1570 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   86</span>|     762.44310 |    0.9999 |   -0.9314 |    -1.416 |   -0.1733 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9021 |   -0.9130 |   -0.2994 |   -0.4153 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1388 |   -0.8854 |   -0.7242 |    -1.070 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5549 |   -0.6642 |   -0.7688 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      762.4431 |     96.99 |    -2.886 |    -4.253 |    -4.873 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.042 |    -1.970 |     2.676 |    0.8733 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7670 |    0.8301 |     1.268 |    0.9371 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.108 |     1.619 |     1.478 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      762.4431</span> |     96.99 |   0.05580 |   0.01423 |  0.007648 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1149 |    0.1224 |     2.676 |    0.8733 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7670 |    0.8301 |     1.268 |    0.9371 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.108 |     1.619 |     1.478 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    C| Central Diff. |    -2.488 |  -0.05983 |   0.05732 |   -0.1840 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2388 |   0.07017 |   -0.4805 |    0.5027 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8122 |   -0.3072 |   -0.8597 |   -0.7873 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7191 |   -0.5849 |   -0.1893 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   87</span>|     762.44280 |     1.000 |   -0.9314 |    -1.416 |   -0.1733 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9021 |   -0.9130 |   -0.2994 |   -0.4153 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1388 |   -0.8854 |   -0.7241 |    -1.070 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5548 |   -0.6641 |   -0.7688 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      762.4428 |     97.00 |    -2.886 |    -4.253 |    -4.873 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.042 |    -1.970 |     2.676 |    0.8732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7670 |    0.8301 |     1.268 |    0.9371 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.108 |     1.620 |     1.478 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      762.4428</span> |     97.00 |   0.05580 |   0.01423 |  0.007648 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1149 |    0.1224 |     2.676 |    0.8732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7670 |    0.8301 |     1.268 |    0.9371 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.108 |     1.620 |     1.478 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     1.618 |  -0.03464 |   0.06615 |   -0.1687 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1343 |    0.1439 |   -0.4410 |    0.3116 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7529 |   -0.3320 |   -0.8865 |   -0.6978 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.4222 |   -0.9084 |   -0.1490 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   88</span>|     762.44259 |    0.9999 |   -0.9314 |    -1.416 |   -0.1733 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9021 |   -0.9130 |   -0.2994 |   -0.4154 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1387 |   -0.8854 |   -0.7240 |    -1.070 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5548 |   -0.6640 |   -0.7687 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.44259 |     96.99 |    -2.886 |    -4.253 |    -4.873 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.042 |    -1.970 |     2.676 |    0.8732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7670 |    0.8302 |     1.269 |    0.9371 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.108 |     1.620 |     1.478 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.44259</span> |     96.99 |   0.05580 |   0.01423 |  0.007648 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1149 |    0.1224 |     2.676 |    0.8732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7670 |    0.8302 |     1.269 |    0.9371 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.108 |     1.620 |     1.478 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.481 |  -0.03300 |   0.06550 |   -0.1695 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1475 |    0.1316 |   -0.5909 |    0.2411 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9770 |   -0.3768 |   -0.8670 |    -1.331 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.4247 |   -0.9107 |   -0.1525 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   89</span>|     762.44225 |     1.000 |   -0.9314 |    -1.416 |   -0.1733 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9021 |   -0.9130 |   -0.2993 |   -0.4154 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1387 |   -0.8853 |   -0.7239 |    -1.070 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5548 |   -0.6639 |   -0.7687 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.44225 |     97.00 |    -2.886 |    -4.253 |    -4.873 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.042 |    -1.970 |     2.676 |    0.8732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7670 |    0.8302 |     1.269 |    0.9372 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.108 |     1.620 |     1.478 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.44225</span> |     97.00 |   0.05580 |   0.01423 |  0.007648 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1149 |    0.1224 |     2.676 |    0.8732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7670 |    0.8302 |     1.269 |    0.9372 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.108 |     1.620 |     1.478 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     1.420 |  -0.03457 |   0.06572 |   -0.1685 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1336 |    0.1386 |   -0.4202 |    0.2852 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9190 |   -0.3266 |   -0.8656 |   -0.6768 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.4245 |   -0.9083 |   -0.1511 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   90</span>|     762.44213 |    0.9998 |   -0.9314 |    -1.416 |   -0.1732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9021 |   -0.9130 |   -0.2993 |   -0.4154 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1386 |   -0.8853 |   -0.7238 |    -1.070 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5547 |   -0.6639 |   -0.7687 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.44213 |     96.99 |    -2.886 |    -4.253 |    -4.873 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.042 |    -1.970 |     2.676 |    0.8732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7670 |    0.8302 |     1.269 |    0.9373 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.108 |     1.620 |     1.478 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.44213</span> |     96.99 |   0.05580 |   0.01423 |  0.007649 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1149 |    0.1224 |     2.676 |    0.8732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7670 |    0.8302 |     1.269 |    0.9373 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.108 |     1.620 |     1.478 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    C| Central Diff. |    -2.863 |  -0.05945 |   0.05668 |   -0.1838 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2350 |   0.05997 |   -0.4163 |    0.3221 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8675 |   -0.3570 |   -0.8347 |   -0.7546 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7945 |   -0.5713 |   -0.1981 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   91</span>|     762.44181 |     1.000 |   -0.9314 |    -1.416 |   -0.1732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9021 |   -0.9130 |   -0.2993 |   -0.4154 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1386 |   -0.8853 |   -0.7237 |    -1.070 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5547 |   -0.6638 |   -0.7687 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.44181 |     97.00 |    -2.886 |    -4.253 |    -4.873 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.042 |    -1.970 |     2.676 |    0.8732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7671 |    0.8302 |     1.269 |    0.9373 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.108 |     1.620 |     1.478 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.44181</span> |     97.00 |   0.05580 |   0.01423 |  0.007649 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1149 |    0.1224 |     2.676 |    0.8732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7671 |    0.8302 |     1.269 |    0.9373 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.108 |     1.620 |     1.478 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     1.253 |  -0.03424 |   0.06574 |   -0.1683 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1309 |    0.1341 |   -0.5651 |    0.1807 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.016 |   -0.3557 |   -0.8957 |   -0.7064 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.5151 |   -0.5648 |   -0.1753 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   92</span>|     762.44169 |    0.9999 |   -0.9314 |    -1.416 |   -0.1732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9020 |   -0.9130 |   -0.2993 |   -0.4154 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1385 |   -0.8853 |   -0.7236 |    -1.070 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5547 |   -0.6638 |   -0.7687 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.44169 |     96.99 |    -2.886 |    -4.253 |    -4.873 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.042 |    -1.970 |     2.676 |    0.8732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7671 |    0.8303 |     1.269 |    0.9374 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.108 |     1.620 |     1.478 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.44169</span> |     96.99 |   0.05581 |   0.01423 |  0.007649 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1149 |    0.1224 |     2.676 |    0.8732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7671 |    0.8303 |     1.269 |    0.9374 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.108 |     1.620 |     1.478 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    C| Central Diff. |    -2.696 |  -0.05943 |   0.05667 |   -0.1838 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2311 |   0.05629 |   -0.4589 |    0.3239 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8257 |   -0.3368 |   -0.8102 |   -0.7513 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7185 |   -0.5600 |   -0.1932 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   93</span>|     762.44142 |     1.000 |   -0.9314 |    -1.416 |   -0.1731 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9020 |   -0.9130 |   -0.2992 |   -0.4154 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1385 |   -0.8852 |   -0.7235 |    -1.070 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5546 |   -0.6637 |   -0.7687 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.44142 |     97.00 |    -2.886 |    -4.253 |    -4.873 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.042 |    -1.970 |     2.676 |    0.8732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7671 |    0.8303 |     1.269 |    0.9374 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.108 |     1.620 |     1.478 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.44142</span> |     97.00 |   0.05581 |   0.01423 |  0.007649 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1149 |    0.1224 |     2.676 |    0.8732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7671 |    0.8303 |     1.269 |    0.9374 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.108 |     1.620 |     1.478 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     1.335 |  -0.03441 |   0.06556 |   -0.1684 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1266 |    0.1302 |   -0.6103 |    0.1783 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.044 |   -0.3387 |   -0.8638 |    -1.338 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.5275 |   -0.5670 |   -0.1757 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   94</span>|     762.44125 |    0.9999 |   -0.9314 |    -1.415 |   -0.1731 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9020 |   -0.9131 |   -0.2992 |   -0.4154 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1384 |   -0.8852 |   -0.7234 |    -1.070 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5545 |   -0.6637 |   -0.7687 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.44125 |     96.99 |    -2.886 |    -4.252 |    -4.873 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.042 |    -1.970 |     2.676 |    0.8732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7671 |    0.8303 |     1.269 |    0.9375 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.108 |     1.620 |     1.478 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.44125</span> |     96.99 |   0.05581 |   0.01423 |  0.007649 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1149 |    0.1224 |     2.676 |    0.8732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7671 |    0.8303 |     1.269 |    0.9375 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.108 |     1.620 |     1.478 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    C| Central Diff. |    -2.098 |  -0.05964 |   0.05680 |   -0.1834 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2254 |   0.05544 |   -0.4561 |    0.3185 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8411 |   -0.2737 |   -0.7922 |   -0.7469 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7250 |   -0.5635 |   -0.1902 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   95</span>|     762.44108 |     1.000 |   -0.9314 |    -1.415 |   -0.1731 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9020 |   -0.9131 |   -0.2992 |   -0.4154 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1384 |   -0.8852 |   -0.7233 |    -1.070 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5545 |   -0.6636 |   -0.7687 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.44108 |     97.00 |    -2.886 |    -4.252 |    -4.873 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.041 |    -1.970 |     2.676 |    0.8732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7671 |    0.8303 |     1.269 |    0.9376 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.108 |     1.620 |     1.478 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.44108</span> |     97.00 |   0.05581 |   0.01423 |  0.007650 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1149 |    0.1224 |     2.676 |    0.8732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7671 |    0.8303 |     1.269 |    0.9376 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.108 |     1.620 |     1.478 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    C| Central Diff. |     2.161 |  -0.06174 |   0.05743 |   -0.1824 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2028 |   0.06759 |   -0.4445 |    0.3255 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8755 |   -0.3449 |   -0.7930 |   -0.7294 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7121 |   -0.5628 |   -0.1899 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   96</span>|     762.44087 |    0.9999 |   -0.9313 |    -1.415 |   -0.1731 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9020 |   -0.9131 |   -0.2991 |   -0.4155 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1383 |   -0.8852 |   -0.7232 |    -1.070 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5544 |   -0.6636 |   -0.7686 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.44087 |     96.99 |    -2.886 |    -4.252 |    -4.873 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.041 |    -1.970 |     2.676 |    0.8732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7671 |    0.8303 |     1.269 |    0.9376 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.108 |     1.620 |     1.478 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.44087</span> |     96.99 |   0.05581 |   0.01423 |  0.007650 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1149 |    0.1224 |     2.676 |    0.8732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7671 |    0.8303 |     1.269 |    0.9376 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.108 |     1.620 |     1.478 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |   -0.8905 |  -0.03259 |   0.06518 |   -0.1692 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1290 |    0.1190 |   -0.3929 |    0.3938 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7338 |   -0.2959 |   -0.8056 |    -1.276 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.4089 |   -0.8806 |   -0.1497 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   97</span>|     762.44057 |     1.000 |   -0.9313 |    -1.415 |   -0.1731 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9020 |   -0.9131 |   -0.2991 |   -0.4155 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1383 |   -0.8852 |   -0.7232 |    -1.070 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5544 |   -0.6635 |   -0.7686 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.44057 |     97.00 |    -2.886 |    -4.252 |    -4.873 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.041 |    -1.970 |     2.676 |    0.8732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7672 |    0.8304 |     1.269 |    0.9377 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.109 |     1.620 |     1.478 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.44057</span> |     97.00 |   0.05581 |   0.01423 |  0.007650 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1149 |    0.1224 |     2.676 |    0.8732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7672 |    0.8304 |     1.269 |    0.9377 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.109 |     1.620 |     1.478 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     1.554 |  -0.03385 |   0.06512 |   -0.1686 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1157 |    0.1237 |   -0.6002 |    0.2113 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9800 |   -0.3598 |   -0.8363 |   -0.6768 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.4936 |   -0.5534 |   -0.1651 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   98</span>|     762.44050 |    0.9998 |   -0.9313 |    -1.415 |   -0.1730 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9020 |   -0.9131 |   -0.2991 |   -0.4155 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1382 |   -0.8851 |   -0.7231 |    -1.070 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5543 |   -0.6635 |   -0.7686 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      762.4405 |     96.98 |    -2.886 |    -4.252 |    -4.873 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.041 |    -1.970 |     2.676 |    0.8732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7672 |    0.8304 |     1.270 |    0.9378 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.109 |     1.620 |     1.478 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      762.4405</span> |     96.98 |   0.05581 |   0.01423 |  0.007650 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1149 |    0.1224 |     2.676 |    0.8732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7672 |    0.8304 |     1.270 |    0.9378 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.109 |     1.620 |     1.478 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    C| Central Diff. |    -3.014 |  -0.05852 |   0.05608 |   -0.1841 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2187 |   0.04463 |   -0.4314 |    0.3139 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8150 |   -0.2938 |   -0.7775 |   -0.7094 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7099 |   -0.5560 |   -0.1738 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   99</span>|     762.44022 |     1.000 |   -0.9313 |    -1.415 |   -0.1730 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9020 |   -0.9131 |   -0.2990 |   -0.4155 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1382 |   -0.8851 |   -0.7230 |    -1.070 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5543 |   -0.6634 |   -0.7686 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.44022 |     97.00 |    -2.886 |    -4.252 |    -4.873 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.041 |    -1.970 |     2.677 |    0.8732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7672 |    0.8304 |     1.270 |    0.9378 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.109 |     1.620 |     1.478 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.44022</span> |     97.00 |   0.05581 |   0.01423 |  0.007650 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1149 |    0.1224 |     2.677 |    0.8732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7672 |    0.8304 |     1.270 |    0.9378 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.109 |     1.620 |     1.478 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     1.178 |  -0.03363 |   0.06507 |   -0.1685 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1131 |    0.1194 |   -0.3125 |    0.3718 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7445 |   -0.2864 |   -0.7962 |    -1.246 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.4203 |   -0.8863 |   -0.1505 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  100</span>|     762.44004 |    0.9999 |   -0.9313 |    -1.415 |   -0.1730 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9020 |   -0.9131 |   -0.2990 |   -0.4155 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1381 |   -0.8851 |   -0.7229 |    -1.070 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5542 |   -0.6634 |   -0.7686 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.44004 |     96.99 |    -2.886 |    -4.252 |    -4.873 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.041 |    -1.970 |     2.677 |    0.8732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7672 |    0.8304 |     1.270 |    0.9379 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.109 |     1.621 |     1.478 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.44004</span> |     96.99 |   0.05581 |   0.01423 |  0.007651 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1149 |    0.1224 |     2.677 |    0.8732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7672 |    0.8304 |     1.270 |    0.9379 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.109 |     1.621 |     1.478 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    C| Central Diff. |    -2.333 |  -0.05890 |   0.05611 |   -0.1836 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2128 |   0.04378 |   -0.4032 |    0.2695 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7985 |   -0.3486 |   -0.7543 |   -0.7042 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7059 |   -0.5388 |   -0.1896 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  101</span>|     762.43981 |     1.000 |   -0.9313 |    -1.415 |   -0.1729 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9020 |   -0.9131 |   -0.2990 |   -0.4155 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1381 |   -0.8850 |   -0.7228 |    -1.070 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5542 |   -0.6633 |   -0.7686 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.43981 |     97.00 |    -2.886 |    -4.252 |    -4.873 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.041 |    -1.970 |     2.677 |    0.8732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7672 |    0.8305 |     1.270 |    0.9379 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.109 |     1.621 |     1.478 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.43981</span> |     97.00 |   0.05581 |   0.01423 |  0.007651 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1149 |    0.1224 |     2.677 |    0.8732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7672 |    0.8305 |     1.270 |    0.9379 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.109 |     1.621 |     1.478 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     2.028 |  -0.03404 |   0.06496 |   -0.1681 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1065 |    0.1184 |   -0.4168 |    0.2498 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9058 |   -0.3159 |   -0.7884 |    -1.255 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.4119 |   -0.8796 |   -0.1400 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  102</span>|     762.43962 |    0.9999 |   -0.9313 |    -1.415 |   -0.1729 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9020 |   -0.9131 |   -0.2990 |   -0.4155 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1380 |   -0.8850 |   -0.7226 |    -1.070 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5542 |   -0.6633 |   -0.7686 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.43962 |     96.99 |    -2.886 |    -4.252 |    -4.873 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.041 |    -1.970 |     2.677 |    0.8732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7672 |    0.8305 |     1.270 |    0.9380 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.109 |     1.621 |     1.478 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.43962</span> |     96.99 |   0.05581 |   0.01423 |  0.007651 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1149 |    0.1224 |     2.677 |    0.8732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7672 |    0.8305 |     1.270 |    0.9380 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.109 |     1.621 |     1.478 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    C| Central Diff. |    -1.383 |  -0.05944 |   0.05652 |   -0.1827 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2071 |   0.04458 |   -0.4356 |    0.2597 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6953 |   -0.3158 |   -0.7477 |   -0.7025 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6996 |   -0.5341 |   -0.1909 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  103</span>|     762.43950 |     1.000 |   -0.9313 |    -1.415 |   -0.1729 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9020 |   -0.9131 |   -0.2989 |   -0.4155 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1380 |   -0.8850 |   -0.7226 |    -1.069 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5541 |   -0.6632 |   -0.7686 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      762.4395 |     97.00 |    -2.886 |    -4.252 |    -4.873 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.041 |    -1.970 |     2.677 |    0.8732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7672 |    0.8305 |     1.270 |    0.9381 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.109 |     1.621 |     1.478 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      762.4395</span> |     97.00 |   0.05581 |   0.01423 |  0.007651 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1149 |    0.1224 |     2.677 |    0.8732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7672 |    0.8305 |     1.270 |    0.9381 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.109 |     1.621 |     1.478 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    C| Central Diff. |     2.549 |  -0.06132 |   0.05688 |   -0.1819 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1843 |   0.05521 |   -0.4401 |    0.2903 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8733 |   -0.3487 |   -0.7274 |   -0.6827 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7019 |   -0.6971 |   -0.1749 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  104</span>|     762.43927 |    0.9999 |   -0.9313 |    -1.415 |   -0.1728 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9019 |   -0.9131 |   -0.2989 |   -0.4155 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1379 |   -0.8849 |   -0.7225 |    -1.069 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5540 |   -0.6632 |   -0.7686 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.43927 |     96.99 |    -2.886 |    -4.252 |    -4.873 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.041 |    -1.970 |     2.677 |    0.8732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7672 |    0.8305 |     1.270 |    0.9381 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.109 |     1.621 |     1.478 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.43927</span> |     96.99 |   0.05581 |   0.01423 |  0.007652 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1149 |    0.1224 |     2.677 |    0.8732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7672 |    0.8305 |     1.270 |    0.9381 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.109 |     1.621 |     1.478 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |   -0.3204 |  -0.03231 |   0.06473 |   -0.1685 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1091 |    0.1080 |   -0.5711 |    0.1928 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9793 |   -0.3347 |   -0.7909 |   -0.6262 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.4835 |   -0.5228 |   -0.1576 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  105</span>|     762.43906 |    0.9999 |   -0.9313 |    -1.415 |   -0.1728 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9019 |   -0.9131 |   -0.2988 |   -0.4156 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1378 |   -0.8849 |   -0.7224 |    -1.069 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5540 |   -0.6631 |   -0.7686 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.43906 |     96.99 |    -2.886 |    -4.252 |    -4.873 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.041 |    -1.970 |     2.677 |    0.8732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7673 |    0.8306 |     1.270 |    0.9382 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.109 |     1.621 |     1.478 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.43906</span> |     96.99 |   0.05581 |   0.01423 |  0.007652 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1149 |    0.1224 |     2.677 |    0.8732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7673 |    0.8306 |     1.270 |    0.9382 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.109 |     1.621 |     1.478 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |   -0.5587 |  -0.06228 |   0.03863 |   -0.1950 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1923 |   0.02120 |   -0.5716 |    0.1497 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.063 |   -0.3909 |   -0.8335 |   -0.6812 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.5632 |   -0.5998 |   -0.1754 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  106</span>|     762.43877 |     1.000 |   -0.9313 |    -1.415 |   -0.1728 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9019 |   -0.9131 |   -0.2987 |   -0.4156 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1376 |   -0.8848 |   -0.7222 |    -1.069 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5539 |   -0.6630 |   -0.7686 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.43877 |     97.01 |    -2.886 |    -4.252 |    -4.873 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.041 |    -1.970 |     2.677 |    0.8732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7673 |    0.8306 |     1.271 |    0.9383 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.109 |     1.621 |     1.478 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.43877</span> |     97.01 |   0.05581 |   0.01423 |  0.007652 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1149 |    0.1224 |     2.677 |    0.8732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7673 |    0.8306 |     1.271 |    0.9383 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.109 |     1.621 |     1.478 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     4.291 |  -0.03430 |   0.06450 |   -0.1679 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.07426 |    0.1162 |   -0.3841 |    0.3404 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7902 |   -0.2891 |   -0.7321 |    -1.195 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.4125 |   -0.8681 |   -0.1491 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  107</span>|     762.43824 |    0.9999 |   -0.9312 |    -1.415 |   -0.1727 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9019 |   -0.9131 |   -0.2986 |   -0.4155 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1375 |   -0.8848 |   -0.7220 |    -1.069 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5537 |   -0.6629 |   -0.7686 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.43824 |     96.99 |    -2.886 |    -4.252 |    -4.873 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.041 |    -1.970 |     2.677 |    0.8732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7674 |    0.8307 |     1.271 |    0.9384 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.109 |     1.621 |     1.478 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.43824</span> |     96.99 |   0.05581 |   0.01423 |  0.007653 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1149 |    0.1223 |     2.677 |    0.8732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7674 |    0.8307 |     1.271 |    0.9384 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.109 |     1.621 |     1.478 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    0.2143 |  -0.03008 |   0.06460 |   -0.1683 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.09063 |   0.09635 |   -0.3662 |    0.3094 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8321 |   -0.3196 |   -0.7371 |    -1.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.3971 |   -0.8686 |   -0.1637 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  108</span>|     762.43774 |    0.9999 |   -0.9312 |    -1.415 |   -0.1727 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9019 |   -0.9132 |   -0.2986 |   -0.4156 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1373 |   -0.8847 |   -0.7219 |    -1.069 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5537 |   -0.6628 |   -0.7686 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.43774 |     96.99 |    -2.886 |    -4.252 |    -4.873 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.041 |    -1.970 |     2.677 |    0.8731 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7674 |    0.8307 |     1.271 |    0.9387 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.109 |     1.621 |     1.478 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.43774</span> |     96.99 |   0.05581 |   0.01423 |  0.007653 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1149 |    0.1223 |     2.677 |    0.8731 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7674 |    0.8307 |     1.271 |    0.9387 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.109 |     1.621 |     1.478 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.093 |  -0.02921 |   0.06344 |   -0.1689 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.09286 |   0.08671 |   -0.5952 |    0.1702 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9595 |   -0.3554 |   -0.7565 |    -1.200 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.4954 |   -0.5121 |   -0.1787 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  109</span>|     762.43688 |     1.000 |   -0.9312 |    -1.415 |   -0.1726 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9018 |   -0.9132 |   -0.2983 |   -0.4156 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1370 |   -0.8845 |   -0.7215 |    -1.069 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5534 |   -0.6626 |   -0.7686 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.43688 |     97.00 |    -2.886 |    -4.252 |    -4.873 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.041 |    -1.970 |     2.677 |    0.8732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7675 |    0.8309 |     1.271 |    0.9392 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.109 |     1.622 |     1.478 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.43688</span> |     97.00 |   0.05581 |   0.01423 |  0.007654 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1150 |    0.1223 |     2.677 |    0.8732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7675 |    0.8309 |     1.271 |    0.9392 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.109 |     1.622 |     1.478 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     1.631 |  -0.02940 |   0.06277 |   -0.1685 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.07102 |   0.08313 |   -0.3454 |    0.3850 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6531 |   -0.2578 |   -0.6895 |   -0.5122 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.3743 |   -0.8283 |   -0.1580 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  110</span>|     762.43834 |    0.9996 |   -0.9312 |    -1.415 |   -0.1725 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9018 |   -0.9132 |   -0.2981 |   -0.4156 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1367 |   -0.8844 |   -0.7212 |    -1.068 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5532 |   -0.6623 |   -0.7686 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.43834 |     96.96 |    -2.886 |    -4.252 |    -4.872 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.041 |    -1.971 |     2.677 |    0.8731 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7676 |    0.8310 |     1.272 |    0.9395 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.109 |     1.622 |     1.478 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.43834</span> |     96.96 |   0.05582 |   0.01423 |  0.007654 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1150 |    0.1223 |     2.677 |    0.8731 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7676 |    0.8310 |     1.272 |    0.9395 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.109 |     1.622 |     1.478 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  111</span>|     762.43689 |    0.9998 |   -0.9312 |    -1.415 |   -0.1726 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9018 |   -0.9132 |   -0.2983 |   -0.4156 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1369 |   -0.8845 |   -0.7215 |    -1.068 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5534 |   -0.6625 |   -0.7686 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.43689 |     96.98 |    -2.886 |    -4.252 |    -4.873 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.041 |    -1.971 |     2.677 |    0.8731 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7675 |    0.8309 |     1.271 |    0.9392 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.109 |     1.622 |     1.478 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.43689</span> |     96.98 |   0.05581 |   0.01423 |  0.007654 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1150 |    0.1223 |     2.677 |    0.8731 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7675 |    0.8309 |     1.271 |    0.9392 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.109 |     1.622 |     1.478 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  112</span>|     762.43680 |    0.9999 |   -0.9312 |    -1.415 |   -0.1726 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9018 |   -0.9132 |   -0.2983 |   -0.4156 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1369 |   -0.8845 |   -0.7215 |    -1.068 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5534 |   -0.6625 |   -0.7686 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      762.4368 |     96.99 |    -2.886 |    -4.252 |    -4.873 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.041 |    -1.971 |     2.677 |    0.8732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7675 |    0.8309 |     1.271 |    0.9392 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.109 |     1.622 |     1.478 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      762.4368</span> |     96.99 |   0.05581 |   0.01423 |  0.007654 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1150 |    0.1223 |     2.677 |    0.8732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7675 |    0.8309 |     1.271 |    0.9392 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.109 |     1.622 |     1.478 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    C| Central Diff. |    -1.290 |  -0.05938 |   0.05401 |   -0.1835 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1692 |   0.01048 |   -0.3277 |    0.2904 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7941 |   -0.3134 |   -0.6583 |   -0.6001 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6796 |   -0.4935 |   -0.1985 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  113</span>|     762.43669 |     1.000 |   -0.9312 |    -1.415 |   -0.1726 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9018 |   -0.9132 |   -0.2983 |   -0.4156 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1369 |   -0.8845 |   -0.7215 |    -1.068 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5534 |   -0.6625 |   -0.7686 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.43669 |     97.00 |    -2.886 |    -4.252 |    -4.873 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.041 |    -1.971 |     2.677 |    0.8731 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7675 |    0.8309 |     1.271 |    0.9393 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.109 |     1.622 |     1.478 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.43669</span> |     97.00 |   0.05581 |   0.01423 |  0.007654 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1150 |    0.1223 |     2.677 |    0.8731 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7675 |    0.8309 |     1.271 |    0.9393 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.109 |     1.622 |     1.478 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    C| Central Diff. |    0.7530 |  -0.06037 |   0.05419 |   -0.1831 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1576 |   0.01628 |   -0.3476 |    0.2756 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7793 |   -0.2989 |   -0.6525 |   -0.5921 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6883 |   -0.6531 |   -0.1740 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  114</span>|     762.43657 |    0.9999 |   -0.9312 |    -1.415 |   -0.1726 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9018 |   -0.9132 |   -0.2983 |   -0.4156 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1369 |   -0.8845 |   -0.7214 |    -1.068 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5533 |   -0.6625 |   -0.7686 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.43657 |     96.99 |    -2.886 |    -4.252 |    -4.873 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.041 |    -1.971 |     2.677 |    0.8731 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7675 |    0.8309 |     1.271 |    0.9393 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.109 |     1.622 |     1.478 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.43657</span> |     96.99 |   0.05581 |   0.01423 |  0.007654 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1150 |    0.1223 |     2.677 |    0.8731 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7675 |    0.8309 |     1.271 |    0.9393 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.109 |     1.622 |     1.478 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    C| Central Diff. |   -0.6017 |  -0.05955 |   0.05380 |   -0.1836 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1610 |   0.01132 |   -0.3419 |    0.2571 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5470 |   -0.3329 |   -0.6445 |   -0.5910 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6872 |   -0.4903 |   -0.2027 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  115</span>|     762.43648 |     1.000 |   -0.9312 |    -1.415 |   -0.1725 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9018 |   -0.9132 |   -0.2983 |   -0.4156 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1368 |   -0.8845 |   -0.7214 |    -1.068 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5533 |   -0.6625 |   -0.7686 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.43648 |     97.00 |    -2.886 |    -4.252 |    -4.873 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.041 |    -1.971 |     2.677 |    0.8731 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7676 |    0.8309 |     1.271 |    0.9393 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.109 |     1.622 |     1.478 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.43648</span> |     97.00 |   0.05582 |   0.01423 |  0.007654 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1150 |    0.1223 |     2.677 |    0.8731 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7676 |    0.8309 |     1.271 |    0.9393 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.109 |     1.622 |     1.478 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    C| Central Diff. |    0.6240 |  -0.06006 |   0.05383 |   -0.1835 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1526 |   0.01440 |   -0.3766 |    0.2889 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6299 |   -0.2748 |   -0.6469 |   -0.5850 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6758 |   -0.4890 |   -0.2032 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  116</span>|     762.43640 |    0.9999 |   -0.9312 |    -1.415 |   -0.1725 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9018 |   -0.9132 |   -0.2982 |   -0.4157 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1368 |   -0.8845 |   -0.7214 |    -1.068 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5533 |   -0.6624 |   -0.7686 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      762.4364 |     96.99 |    -2.886 |    -4.252 |    -4.873 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.041 |    -1.971 |     2.677 |    0.8731 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7676 |    0.8309 |     1.271 |    0.9394 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.109 |     1.622 |     1.478 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      762.4364</span> |     96.99 |   0.05582 |   0.01423 |  0.007654 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1150 |    0.1223 |     2.677 |    0.8731 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7676 |    0.8309 |     1.271 |    0.9394 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.109 |     1.622 |     1.478 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    C| Central Diff. |   -0.6134 |  -0.05930 |   0.05345 |   -0.1840 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1552 |  0.009772 |   -0.3787 |    0.2674 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7680 |   -0.3180 |   -0.6329 |   -0.5960 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6746 |   -0.4920 |   -0.1810 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  117</span>|     762.43627 |     1.000 |   -0.9312 |    -1.415 |   -0.1725 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9018 |   -0.9132 |   -0.2982 |   -0.4157 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1367 |   -0.8844 |   -0.7213 |    -1.068 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5532 |   -0.6624 |   -0.7686 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.43627 |     97.00 |    -2.886 |    -4.252 |    -4.873 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.041 |    -1.971 |     2.677 |    0.8731 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7676 |    0.8310 |     1.272 |    0.9394 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.109 |     1.622 |     1.478 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.43627</span> |     97.00 |   0.05582 |   0.01423 |  0.007654 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1150 |    0.1223 |     2.677 |    0.8731 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7676 |    0.8310 |     1.272 |    0.9394 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.109 |     1.622 |     1.478 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  118</span>|     762.43603 |     1.000 |   -0.9312 |    -1.415 |   -0.1725 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9018 |   -0.9132 |   -0.2982 |   -0.4157 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1367 |   -0.8844 |   -0.7213 |    -1.068 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5532 |   -0.6624 |   -0.7685 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.43603 |     97.00 |    -2.886 |    -4.252 |    -4.873 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.041 |    -1.971 |     2.677 |    0.8731 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7676 |    0.8310 |     1.272 |    0.9395 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.110 |     1.622 |     1.478 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.43603</span> |     97.00 |   0.05582 |   0.01423 |  0.007654 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1150 |    0.1223 |     2.677 |    0.8731 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7676 |    0.8310 |     1.272 |    0.9395 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.110 |     1.622 |     1.478 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  119</span>|     762.43566 |     1.000 |   -0.9311 |    -1.415 |   -0.1724 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9017 |   -0.9132 |   -0.2979 |   -0.4156 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1365 |   -0.8844 |   -0.7210 |    -1.068 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5529 |   -0.6622 |   -0.7686 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.43566 |     97.00 |    -2.886 |    -4.252 |    -4.872 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.041 |    -1.971 |     2.678 |    0.8731 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7677 |    0.8310 |     1.272 |    0.9398 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.110 |     1.622 |     1.478 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.43566</span> |     97.00 |   0.05582 |   0.01424 |  0.007655 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1150 |    0.1223 |     2.678 |    0.8731 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7677 |    0.8310 |     1.272 |    0.9398 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.110 |     1.622 |     1.478 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     2.185 |  -0.02630 |   0.06197 |   -0.1690 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.04569 |   0.06766 |   -0.1965 |    0.3760 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8361 |   -0.3368 |   -0.6862 |    -1.092 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.4397 |   -0.4588 |   -0.1669 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  120</span>|     762.43438 |     1.000 |   -0.9309 |    -1.415 |   -0.1720 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9017 |   -0.9133 |   -0.2975 |   -0.4155 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1356 |   -0.8841 |   -0.7203 |    -1.067 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5523 |   -0.6619 |   -0.7688 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.43438 |     97.00 |    -2.885 |    -4.252 |    -4.872 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.041 |    -1.971 |     2.678 |    0.8732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7679 |    0.8313 |     1.273 |    0.9407 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.110 |     1.623 |     1.478 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.43438</span> |     97.00 |   0.05583 |   0.01424 |  0.007658 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1150 |    0.1223 |     2.678 |    0.8732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7679 |    0.8313 |     1.273 |    0.9407 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.110 |     1.623 |     1.478 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     3.044 |  -0.02148 |   0.06186 |   -0.1669 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.02022 |   0.04124 |   -0.2960 |    0.3008 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7013 |   -0.2777 |   -0.6002 |   -0.9685 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.3508 |   -0.7933 |   -0.1801 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  121</span>|     762.45137 |    0.9988 |   -0.9307 |    -1.414 |   -0.1714 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9015 |   -0.9135 |   -0.2962 |   -0.4154 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1345 |   -0.8836 |   -0.7191 |    -1.066 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5512 |   -0.6609 |   -0.7687 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.45137 |     96.89 |    -2.885 |    -4.251 |    -4.871 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.040 |    -1.971 |     2.679 |    0.8732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7682 |    0.8316 |     1.274 |    0.9425 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.111 |     1.624 |     1.478 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.45137</span> |     96.89 |   0.05584 |   0.01425 |  0.007663 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1150 |    0.1223 |     2.679 |    0.8732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7682 |    0.8316 |     1.274 |    0.9425 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.111 |     1.624 |     1.478 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  122</span>|     762.43671 |    0.9995 |   -0.9309 |    -1.415 |   -0.1720 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9017 |   -0.9133 |   -0.2974 |   -0.4156 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1355 |   -0.8840 |   -0.7202 |    -1.067 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5523 |   -0.6617 |   -0.7687 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.43671 |     96.95 |    -2.885 |    -4.252 |    -4.872 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.041 |    -1.971 |     2.678 |    0.8732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7679 |    0.8313 |     1.273 |    0.9409 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.110 |     1.623 |     1.478 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.43671</span> |     96.95 |   0.05583 |   0.01424 |  0.007658 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1150 |    0.1223 |     2.678 |    0.8732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7679 |    0.8313 |     1.273 |    0.9409 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.110 |     1.623 |     1.478 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  123</span>|     762.43423 |    0.9999 |   -0.9309 |    -1.415 |   -0.1720 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9017 |   -0.9133 |   -0.2975 |   -0.4155 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1356 |   -0.8841 |   -0.7202 |    -1.067 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5523 |   -0.6618 |   -0.7688 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.43423 |     96.99 |    -2.885 |    -4.252 |    -4.872 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.041 |    -1.971 |     2.678 |    0.8732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7679 |    0.8313 |     1.273 |    0.9408 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.110 |     1.623 |     1.478 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.43423</span> |     96.99 |   0.05583 |   0.01424 |  0.007658 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1150 |    0.1223 |     2.678 |    0.8732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7679 |    0.8313 |     1.273 |    0.9408 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.110 |     1.623 |     1.478 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |   -0.5170 |  -0.01948 |   0.06121 |   -0.1679 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.03590 |   0.02978 |   -0.3666 |    0.2413 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7570 |   -0.2885 |   -0.6014 |   -0.3882 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.3403 |   -0.8088 |   -0.1895 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  124</span>|     762.43413 |    0.9999 |   -0.9309 |    -1.415 |   -0.1720 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9017 |   -0.9133 |   -0.2974 |   -0.4155 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1356 |   -0.8840 |   -0.7202 |    -1.067 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5523 |   -0.6618 |   -0.7687 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.43413 |     96.99 |    -2.885 |    -4.252 |    -4.872 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.041 |    -1.971 |     2.678 |    0.8732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7679 |    0.8313 |     1.273 |    0.9408 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.110 |     1.623 |     1.478 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.43413</span> |     96.99 |   0.05583 |   0.01424 |  0.007658 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1150 |    0.1223 |     2.678 |    0.8732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7679 |    0.8313 |     1.273 |    0.9408 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.110 |     1.623 |     1.478 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    C| Central Diff. |    0.1909 |  -0.05140 |   0.05299 |   -0.1819 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1179 |  -0.03105 |   -0.2940 |    0.3229 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7019 |   -0.3052 |   -0.5670 |   -0.4669 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.5306 |   -0.6218 |   -0.1957 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  125</span>|     762.43390 |    0.9999 |   -0.9309 |    -1.415 |   -0.1719 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9016 |   -0.9133 |   -0.2974 |   -0.4156 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1354 |   -0.8840 |   -0.7201 |    -1.067 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5522 |   -0.6617 |   -0.7687 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      762.4339 |     96.99 |    -2.885 |    -4.252 |    -4.872 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.041 |    -1.971 |     2.678 |    0.8731 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7679 |    0.8313 |     1.273 |    0.9409 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.110 |     1.623 |     1.478 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      762.4339</span> |     96.99 |   0.05583 |   0.01424 |  0.007659 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1150 |    0.1223 |     2.678 |    0.8731 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7679 |    0.8313 |     1.273 |    0.9409 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.110 |     1.623 |     1.478 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -2.114 |  -0.06011 |   0.02465 |   -0.2051 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1552 |  -0.09095 |   -0.5672 |   0.02114 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8958 |   -0.3459 |   -0.7191 |   -0.5113 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.5884 |   -0.9931 |   -0.1777 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  126</span>|     762.43345 |    0.9998 |   -0.9309 |    -1.415 |   -0.1719 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9016 |   -0.9133 |   -0.2973 |   -0.4157 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1352 |   -0.8839 |   -0.7199 |    -1.067 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5520 |   -0.6614 |   -0.7686 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.43345 |     96.98 |    -2.885 |    -4.252 |    -4.872 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.041 |    -1.971 |     2.678 |    0.8731 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7680 |    0.8314 |     1.273 |    0.9411 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.110 |     1.623 |     1.478 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.43345</span> |     96.98 |   0.05583 |   0.01424 |  0.007659 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1150 |    0.1223 |     2.678 |    0.8731 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7680 |    0.8314 |     1.273 |    0.9411 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.110 |     1.623 |     1.478 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -2.199 |  -0.01754 |   0.05950 |   -0.1696 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.02539 |   0.02275 |   -0.4402 |    0.1842 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6341 |   -0.3071 |   -0.5996 |   -0.4400 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.3790 |   -0.4298 |   -0.1917 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  127</span>|     762.44283 |     1.001 |   -0.9309 |    -1.415 |   -0.1718 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9016 |   -0.9133 |   -0.2971 |   -0.4158 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1349 |   -0.8837 |   -0.7196 |    -1.067 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5519 |   -0.6613 |   -0.7686 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.44283 |     97.07 |    -2.885 |    -4.252 |    -4.872 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.041 |    -1.971 |     2.679 |    0.8731 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7681 |    0.8315 |     1.273 |    0.9413 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.111 |     1.623 |     1.478 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.44283</span> |     97.07 |   0.05583 |   0.01424 |  0.007660 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1150 |    0.1223 |     2.679 |    0.8731 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7681 |    0.8315 |     1.273 |    0.9413 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.111 |     1.623 |     1.478 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  128</span>|     762.43327 |    0.9999 |   -0.9309 |    -1.415 |   -0.1719 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9016 |   -0.9133 |   -0.2972 |   -0.4157 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1352 |   -0.8839 |   -0.7199 |    -1.067 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5520 |   -0.6614 |   -0.7686 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.43327 |     96.99 |    -2.885 |    -4.252 |    -4.872 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.041 |    -1.971 |     2.678 |    0.8731 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7680 |    0.8314 |     1.273 |    0.9411 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.111 |     1.623 |     1.478 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.43327</span> |     96.99 |   0.05583 |   0.01424 |  0.007659 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1150 |    0.1223 |     2.678 |    0.8731 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7680 |    0.8314 |     1.273 |    0.9411 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.111 |     1.623 |     1.478 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    0.8515 |  -0.01931 |   0.05976 |   -0.1688 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.01220 |   0.03131 |   -0.3546 |    0.1731 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7860 |   -0.3212 |   -0.6045 |   -0.3881 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.4306 |   -0.4501 |   -0.2053 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  129</span>|     762.43323 |    0.9999 |   -0.9309 |    -1.415 |   -0.1718 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9016 |   -0.9133 |   -0.2972 |   -0.4157 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1351 |   -0.8838 |   -0.7198 |    -1.067 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5520 |   -0.6614 |   -0.7686 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.43323 |     96.99 |    -2.885 |    -4.252 |    -4.872 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.041 |    -1.971 |     2.678 |    0.8731 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7680 |    0.8315 |     1.273 |    0.9411 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.111 |     1.623 |     1.478 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.43323</span> |     96.99 |   0.05583 |   0.01424 |  0.007659 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1150 |    0.1223 |     2.678 |    0.8731 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7680 |    0.8315 |     1.273 |    0.9411 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.111 |     1.623 |     1.478 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    C| Central Diff. |    -1.674 |  -0.04944 |   0.05118 |   -0.1837 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1087 |  -0.03856 |   -0.2663 |    0.2605 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5380 |   -0.2892 |   -0.5353 |   -0.4315 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.5100 |   -0.4392 |   -0.2141 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  130</span>|     762.43310 |    0.9999 |   -0.9309 |    -1.415 |   -0.1718 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9016 |   -0.9133 |   -0.2972 |   -0.4158 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1351 |   -0.8838 |   -0.7198 |    -1.067 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5519 |   -0.6614 |   -0.7686 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      762.4331 |     96.99 |    -2.885 |    -4.252 |    -4.872 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.041 |    -1.971 |     2.678 |    0.8731 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7680 |    0.8315 |     1.273 |    0.9412 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.111 |     1.623 |     1.478 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      762.4331</span> |     96.99 |   0.05583 |   0.01424 |  0.007660 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1150 |    0.1223 |     2.678 |    0.8731 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7680 |    0.8315 |     1.273 |    0.9412 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.111 |     1.623 |     1.478 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    C| Central Diff. |    0.5279 |  -0.05046 |   0.05140 |   -0.1826 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.09758 |  -0.03208 |   -0.2630 |    0.2539 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6786 |   -0.2981 |   -0.5397 |   -0.4304 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.4336 |   -0.5936 |   -0.1903 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  131</span>|     762.43324 |    0.9999 |   -0.9309 |    -1.415 |   -0.1718 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9016 |   -0.9133 |   -0.2972 |   -0.4158 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1351 |   -0.8838 |   -0.7198 |    -1.067 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5519 |   -0.6614 |   -0.7686 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     762.43324 |     96.99 |    -2.885 |    -4.252 |    -4.872 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -2.041 |    -1.971 |     2.678 |    0.8731 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7680 |    0.8315 |     1.273 |    0.9412 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.111 |     1.623 |     1.478 |...........|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     762.43324</span> |     96.99 |   0.05583 |   0.01424 |  0.007660 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1150 |    0.1223 |     2.678 |    0.8731 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7680 |    0.8315 |     1.273 |    0.9412 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.111 |     1.623 |     1.478 |...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> calculating covariance matrix</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating residuals/tables</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>initial ETAs were nudged; (can control by foceiControl(etaNudge=., etaNudge2=))</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>ETAs were reset to zero during optimization; (Can control by foceiControl(resetEtaP=.))</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>last objective function was not at minimum, possible problems in optimization</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>using R matrix to calculate covariance, can check sandwich or S matrix with $covRS and $covS</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>gradient problems with initial estimate and covariance; see $scaleInfo</span>
-<span class="r-in"><span class="co"># }</span></span>
-</code></pre></div>
-    </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.2.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/reference/nobs.mkinfit.html b/docs/reference/nobs.mkinfit.html
index c3e24a08..3cdfc619 100644
--- a/docs/reference/nobs.mkinfit.html
+++ b/docs/reference/nobs.mkinfit.html
@@ -1,160 +1,109 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Number of observations on which an mkinfit object was fitted — nobs.mkinfit • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Number of observations on which an mkinfit object was fitted — nobs.mkinfit"><meta property="og:description" content="Number of observations on which an mkinfit object was fitted"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Number of observations on which an mkinfit object was fitted — nobs.mkinfit • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Number of observations on which an mkinfit object was fitted — nobs.mkinfit"><meta name="description" content="Number of observations on which an mkinfit object was fitted"><meta property="og:description" content="Number of observations on which an mkinfit object was fitted"></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Number of observations on which an mkinfit object was fitted</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/nobs.mkinfit.R" class="external-link"><code>R/nobs.mkinfit.R</code></a></small>
-    <div class="hidden name"><code>nobs.mkinfit.Rd</code></div>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Number of observations on which an mkinfit object was fitted</h1>
+      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/nobs.mkinfit.R" class="external-link"><code>R/nobs.mkinfit.R</code></a></small>
+      <div class="d-none name"><code>nobs.mkinfit.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Number of observations on which an mkinfit object was fitted</p>
     </div>
 
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for mkinfit</span></span>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'mkinfit'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/stats/nobs.html" class="external-link">nobs</a></span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>object</dt>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
 <dd><p>An mkinfit object</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>For compatibility with the generic method</p></dd>
 
 </dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>The number of rows in the data included in the mkinfit object</p>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>The number of rows in the data included in the mkinfit object</p>
     </div>
 
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/parms.html b/docs/reference/parms.html
index 89831adb..46b0bb31 100644
--- a/docs/reference/parms.html
+++ b/docs/reference/parms.html
@@ -1,189 +1,144 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Extract model parameters — parms • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Extract model parameters — parms"><meta property="og:description" content="This function returns degradation model parameters as well as error
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Extract model parameters — parms • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Extract model parameters — parms"><meta name="description" content="This function returns degradation model parameters as well as error
 model parameters per default, in order to avoid working with a fitted model
-without considering the error structure that was assumed for the fit."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+without considering the error structure that was assumed for the fit."><meta property="og:description" content="This function returns degradation model parameters as well as error
+model parameters per default, in order to avoid working with a fitted model
+without considering the error structure that was assumed for the fit."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Extract model parameters</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/parms.R" class="external-link"><code>R/parms.R</code></a>, <a href="https://github.com/jranke/mkin/blob/HEAD/R/saem.R" class="external-link"><code>R/saem.R</code></a></small>
-    <div class="hidden name"><code>parms.Rd</code></div>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Extract model parameters</h1>
+      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/parms.R" class="external-link"><code>R/parms.R</code></a>, <a href="https://github.com/jranke/mkin/blob/HEAD/R/saem.R" class="external-link"><code>R/saem.R</code></a></small>
+      <div class="d-none name"><code>parms.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>This function returns degradation model parameters as well as error
 model parameters per default, in order to avoid working with a fitted model
 without considering the error structure that was assumed for the fit.</p>
     </div>
 
-    <div id="ref-usage">
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">parms</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for mkinfit</span></span>
+<span><span class="co"># S3 method for class 'mkinfit'</span></span>
 <span><span class="fu">parms</span><span class="op">(</span><span class="va">object</span>, transformed <span class="op">=</span> <span class="cn">FALSE</span>, errparms <span class="op">=</span> <span class="cn">TRUE</span>, <span class="va">...</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for mmkin</span></span>
+<span><span class="co"># S3 method for class 'mmkin'</span></span>
 <span><span class="fu">parms</span><span class="op">(</span><span class="va">object</span>, transformed <span class="op">=</span> <span class="cn">FALSE</span>, errparms <span class="op">=</span> <span class="cn">TRUE</span>, <span class="va">...</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for multistart</span></span>
+<span><span class="co"># S3 method for class 'multistart'</span></span>
 <span><span class="fu">parms</span><span class="op">(</span><span class="va">object</span>, exclude_failed <span class="op">=</span> <span class="cn">TRUE</span>, <span class="va">...</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for saem.mmkin</span></span>
+<span><span class="co"># S3 method for class 'saem.mmkin'</span></span>
 <span><span class="fu">parms</span><span class="op">(</span><span class="va">object</span>, ci <span class="op">=</span> <span class="cn">FALSE</span>, covariates <span class="op">=</span> <span class="cn">NULL</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>object</dt>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
 <dd><p>A fitted model object.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Not used</p></dd>
 
 
-<dt>transformed</dt>
+<dt id="arg-transformed">transformed<a class="anchor" aria-label="anchor" href="#arg-transformed"></a></dt>
 <dd><p>Should the parameters be returned as used internally
 during the optimisation?</p></dd>
 
 
-<dt>errparms</dt>
+<dt id="arg-errparms">errparms<a class="anchor" aria-label="anchor" href="#arg-errparms"></a></dt>
 <dd><p>Should the error model parameters be returned
 in addition to the degradation parameters?</p></dd>
 
 
-<dt>exclude_failed</dt>
+<dt id="arg-exclude-failed">exclude_failed<a class="anchor" aria-label="anchor" href="#arg-exclude-failed"></a></dt>
 <dd><p>For <a href="multistart.html">multistart</a> objects, should rows for failed fits
 be removed from the returned parameter matrix?</p></dd>
 
 
-<dt>ci</dt>
+<dt id="arg-ci">ci<a class="anchor" aria-label="anchor" href="#arg-ci"></a></dt>
 <dd><p>Should a matrix with estimates and confidence interval boundaries
 be returned? If FALSE (default), a vector of estimates is returned if no
 covariates are given, otherwise a matrix of estimates is returned, with
 each column corresponding to a row of the data frame holding the covariates</p></dd>
 
 
-<dt>covariates</dt>
+<dt id="arg-covariates">covariates<a class="anchor" aria-label="anchor" href="#arg-covariates"></a></dt>
 <dd><p>A data frame holding covariate values for which to
 return parameter values. Only has an effect if 'ci' is FALSE.</p></dd>
 
 </dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>Depending on the object, a numeric vector of fitted model parameters,
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>Depending on the object, a numeric vector of fitted model parameters,
 a matrix (e.g. for mmkin row objects), or a list of matrices (e.g. for
 mmkin objects with more than one row).</p>
     </div>
-    <div id="see-also">
-    <h2>See also</h2>
+    <div class="section level2">
+    <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
     <div class="dont-index"><p><a href="saem.html">saem</a>, <a href="multistart.html">multistart</a></p></div>
     </div>
 
-    <div id="ref-examples">
-    <h2>Examples</h2>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># mkinfit objects</span></span></span>
 <span class="r-in"><span><span class="va">fit</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="va">FOCUS_2006_C</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu">parms</span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></span></span>
@@ -273,27 +228,23 @@ mmkin objects with more than one row).</p>
 <span class="r-in"><span><span class="co"># }</span></span></span>
 </code></pre></div>
     </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/parms.mkinfit.html b/docs/reference/parms.mkinfit.html
new file mode 100644
index 00000000..49c3938c
--- /dev/null
+++ b/docs/reference/parms.mkinfit.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/parms.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/parms.html">
+  </head>
+</html>
+
diff --git a/docs/reference/parms.mmkin.html b/docs/reference/parms.mmkin.html
new file mode 100644
index 00000000..49c3938c
--- /dev/null
+++ b/docs/reference/parms.mmkin.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/parms.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/parms.html">
+  </head>
+</html>
+
diff --git a/docs/reference/parms.multistart.html b/docs/reference/parms.multistart.html
new file mode 100644
index 00000000..49c3938c
--- /dev/null
+++ b/docs/reference/parms.multistart.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/parms.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/parms.html">
+  </head>
+</html>
+
diff --git a/docs/reference/parms.saem.mmkin.html b/docs/reference/parms.saem.mmkin.html
new file mode 100644
index 00000000..49c3938c
--- /dev/null
+++ b/docs/reference/parms.saem.mmkin.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/parms.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/parms.html">
+  </head>
+</html>
+
diff --git a/docs/reference/parplot.html b/docs/reference/parplot.html
index 2ae8d39f..30523983 100644
--- a/docs/reference/parplot.html
+++ b/docs/reference/parplot.html
@@ -1,125 +1,80 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Plot parameter variability of multistart objects — parplot • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Plot parameter variability of multistart objects — parplot"><meta property="og:description" content="Produces a boxplot with all parameters from the multiple runs, scaled
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Plot parameter variability of multistart objects — parplot • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Plot parameter variability of multistart objects — parplot"><meta name="description" content="Produces a boxplot with all parameters from the multiple runs, scaled
 either by the parameters of the run with the highest likelihood,
-or by their medians as proposed in the paper by Duchesne et al. (2021)."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+or by their medians as proposed in the paper by Duchesne et al. (2021)."><meta property="og:description" content="Produces a boxplot with all parameters from the multiple runs, scaled
+either by the parameters of the run with the highest likelihood,
+or by their medians as proposed in the paper by Duchesne et al. (2021)."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Plot parameter variability of multistart objects</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/parplot.R" class="external-link"><code>R/parplot.R</code></a></small>
-    <div class="hidden name"><code>parplot.Rd</code></div>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Plot parameter variability of multistart objects</h1>
+      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/parplot.R" class="external-link"><code>R/parplot.R</code></a></small>
+      <div class="d-none name"><code>parplot.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Produces a boxplot with all parameters from the multiple runs, scaled
 either by the parameters of the run with the highest likelihood,
 or by their medians as proposed in the paper by Duchesne et al. (2021).</p>
     </div>
 
-    <div id="ref-usage">
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">parplot</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for multistart.saem.mmkin</span></span>
+<span><span class="co"># S3 method for class 'multistart.saem.mmkin'</span></span>
 <span><span class="fu">parplot</span><span class="op">(</span></span>
 <span>  <span class="va">object</span>,</span>
 <span>  llmin <span class="op">=</span> <span class="op">-</span><span class="cn">Inf</span>,</span>
@@ -131,78 +86,76 @@ or by their medians as proposed in the paper by Duchesne et al. (2021).</p>
 <span><span class="op">)</span></span></code></pre></div>
     </div>
 
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>object</dt>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
 <dd><p>The <a href="multistart.html">multistart</a> object</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Passed to <a href="https://rdrr.io/r/graphics/boxplot.html" class="external-link">boxplot</a></p></dd>
 
 
-<dt>llmin</dt>
+<dt id="arg-llmin">llmin<a class="anchor" aria-label="anchor" href="#arg-llmin"></a></dt>
 <dd><p>The minimum likelihood of objects to be shown</p></dd>
 
 
-<dt>llquant</dt>
+<dt id="arg-llquant">llquant<a class="anchor" aria-label="anchor" href="#arg-llquant"></a></dt>
 <dd><p>Fractional value for selecting only the fits with higher
 likelihoods. Overrides 'llmin'.</p></dd>
 
 
-<dt>scale</dt>
+<dt id="arg-scale">scale<a class="anchor" aria-label="anchor" href="#arg-scale"></a></dt>
 <dd><p>By default, scale parameters using the best
 available fit.
 If 'median', parameters are scaled using the median parameters from all fits.</p></dd>
 
 
-<dt>lpos</dt>
+<dt id="arg-lpos">lpos<a class="anchor" aria-label="anchor" href="#arg-lpos"></a></dt>
 <dd><p>Positioning of the legend.</p></dd>
 
 
-<dt>main</dt>
+<dt id="arg-main">main<a class="anchor" aria-label="anchor" href="#arg-main"></a></dt>
 <dd><p>Title of the plot</p></dd>
 
 </dl></div>
-    <div id="details">
-    <h2>Details</h2>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>Starting values of degradation model parameters and error model parameters
 are shown as green circles. The results obtained in the original run
 are shown as red circles.</p>
     </div>
-    <div id="references">
-    <h2>References</h2>
+    <div class="section level2">
+    <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
     <p>Duchesne R, Guillemin A, Gandrillon O, Crauste F. Practical
 identifiability in the frame of nonlinear mixed effects models: the example
 of the in vitro erythropoiesis. BMC Bioinformatics. 2021 Oct 4;22(1):478.
 doi: 10.1186/s12859-021-04373-4.</p>
     </div>
-    <div id="see-also">
-    <h2>See also</h2>
+    <div class="section level2">
+    <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
     <div class="dont-index"><p><a href="multistart.html">multistart</a></p></div>
     </div>
 
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/parplot.multistart.saem.mmkin.html b/docs/reference/parplot.multistart.saem.mmkin.html
new file mode 100644
index 00000000..b22f2a6f
--- /dev/null
+++ b/docs/reference/parplot.multistart.saem.mmkin.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/parplot.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/parplot.html">
+  </head>
+</html>
+
diff --git a/docs/reference/plot.mixed.mmkin-1.png b/docs/reference/plot.mixed.mmkin-1.png
index 8a09a167..61b47f93 100644
--- a/docs/reference/plot.mixed.mmkin-1.png
+++ b/docs/reference/plot.mixed.mmkin-1.png
diff --git a/docs/reference/plot.mixed.mmkin-2.png b/docs/reference/plot.mixed.mmkin-2.png
index 68a76b47..9229163a 100644
--- a/docs/reference/plot.mixed.mmkin-2.png
+++ b/docs/reference/plot.mixed.mmkin-2.png
diff --git a/docs/reference/plot.mixed.mmkin-3.png b/docs/reference/plot.mixed.mmkin-3.png
index e18a0da1..749efd4b 100644
--- a/docs/reference/plot.mixed.mmkin-3.png
+++ b/docs/reference/plot.mixed.mmkin-3.png
diff --git a/docs/reference/plot.mixed.mmkin-4.png b/docs/reference/plot.mixed.mmkin-4.png
index f574fdd9..336a80de 100644
--- a/docs/reference/plot.mixed.mmkin-4.png
+++ b/docs/reference/plot.mixed.mmkin-4.png
diff --git a/docs/reference/plot.mixed.mmkin.html b/docs/reference/plot.mixed.mmkin.html
index b0e0f159..838265d8 100644
--- a/docs/reference/plot.mixed.mmkin.html
+++ b/docs/reference/plot.mixed.mmkin.html
@@ -1,119 +1,72 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Plot predictions from a fitted nonlinear mixed model obtained via an mmkin row object — plot.mixed.mmkin • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Plot predictions from a fitted nonlinear mixed model obtained via an mmkin row object — plot.mixed.mmkin"><meta property="og:description" content="Plot predictions from a fitted nonlinear mixed model obtained via an mmkin row object"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Plot predictions from a fitted nonlinear mixed model obtained via an mmkin row object — plot.mixed.mmkin • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Plot predictions from a fitted nonlinear mixed model obtained via an mmkin row object — plot.mixed.mmkin"><meta name="description" content="Plot predictions from a fitted nonlinear mixed model obtained via an mmkin row object"><meta property="og:description" content="Plot predictions from a fitted nonlinear mixed model obtained via an mmkin row object"></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Plot predictions from a fitted nonlinear mixed model obtained via an mmkin row object</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/plot.mixed.mmkin.R" class="external-link"><code>R/plot.mixed.mmkin.R</code></a></small>
-    <div class="hidden name"><code>plot.mixed.mmkin.Rd</code></div>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Plot predictions from a fitted nonlinear mixed model obtained via an mmkin row object</h1>
+      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/plot.mixed.mmkin.R" class="external-link"><code>R/plot.mixed.mmkin.R</code></a></small>
+      <div class="d-none name"><code>plot.mixed.mmkin.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Plot predictions from a fitted nonlinear mixed model obtained via an mmkin row object</p>
     </div>
 
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for mixed.mmkin</span></span>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'mixed.mmkin'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span></span>
 <span>  <span class="va">x</span>,</span>
 <span>  i <span class="op">=</span> <span class="fl">1</span><span class="op">:</span><span class="fu"><a href="https://rdrr.io/r/base/nrow.html" class="external-link">ncol</a></span><span class="op">(</span><span class="va">x</span><span class="op">$</span><span class="va">mmkin</span><span class="op">)</span>,</span>
@@ -135,7 +88,7 @@
 <span>  nrow.legend <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Round.html" class="external-link">ceiling</a></span><span class="op">(</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/length.html" class="external-link">length</a></span><span class="op">(</span><span class="va">i</span><span class="op">)</span> <span class="op">+</span> <span class="fl">1</span><span class="op">)</span><span class="op">/</span><span class="va">ncol.legend</span><span class="op">)</span>,</span>
 <span>  rel.height.legend <span class="op">=</span> <span class="fl">0.02</span> <span class="op">+</span> <span class="fl">0.07</span> <span class="op">*</span> <span class="va">nrow.legend</span>,</span>
 <span>  rel.height.bottom <span class="op">=</span> <span class="fl">1.1</span>,</span>
-<span>  pch_ds <span class="op">=</span> <span class="fl">1</span><span class="op">:</span><span class="fu"><a href="https://rdrr.io/r/base/length.html" class="external-link">length</a></span><span class="op">(</span><span class="va">i</span><span class="op">)</span>,</span>
+<span>  pch_ds <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fl">25</span>, <span class="fl">33</span>, <span class="fl">35</span><span class="op">:</span><span class="fl">38</span>, <span class="fl">40</span><span class="op">:</span><span class="fl">41</span>, <span class="fl">47</span><span class="op">:</span><span class="fl">57</span>, <span class="fl">60</span><span class="op">:</span><span class="fl">90</span><span class="op">)</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fu"><a href="https://rdrr.io/r/base/length.html" class="external-link">length</a></span><span class="op">(</span><span class="va">i</span><span class="op">)</span><span class="op">]</span>,</span>
 <span>  col_ds <span class="op">=</span> <span class="va">pch_ds</span> <span class="op">+</span> <span class="fl">1</span>,</span>
 <span>  lty_ds <span class="op">=</span> <span class="va">col_ds</span>,</span>
 <span>  frame <span class="op">=</span> <span class="cn">TRUE</span>,</span>
@@ -143,145 +96,145 @@
 <span><span class="op">)</span></span></code></pre></div>
     </div>
 
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>x</dt>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>An object of class <a href="mixed.html">mixed.mmkin</a>, <a href="saem.html">saem.mmkin</a> or <a href="nlme.mmkin.html">nlme.mmkin</a></p></dd>
 
 
-<dt>i</dt>
+<dt id="arg-i">i<a class="anchor" aria-label="anchor" href="#arg-i"></a></dt>
 <dd><p>A numeric index to select datasets for which to plot the individual predictions,
 in case plots get too large</p></dd>
 
 
-<dt>obs_vars</dt>
+<dt id="arg-obs-vars">obs_vars<a class="anchor" aria-label="anchor" href="#arg-obs-vars"></a></dt>
 <dd><p>A character vector of names of the observed variables for
 which the data and the model should be plotted. Defauls to all observed
 variables in the model.</p></dd>
 
 
-<dt>standardized</dt>
+<dt id="arg-standardized">standardized<a class="anchor" aria-label="anchor" href="#arg-standardized"></a></dt>
 <dd><p>Should the residuals be standardized? Only takes effect if
 <code>resplot = "time"</code>.</p></dd>
 
 
-<dt>covariates</dt>
+<dt id="arg-covariates">covariates<a class="anchor" aria-label="anchor" href="#arg-covariates"></a></dt>
 <dd><p>Data frame with covariate values for all variables in
 any covariate models in the object. If given, it overrides 'covariate_quantiles'.
 Each line in the data frame will result in a line drawn for the population.
 Rownames are used in the legend to label the lines.</p></dd>
 
 
-<dt>covariate_quantiles</dt>
+<dt id="arg-covariate-quantiles">covariate_quantiles<a class="anchor" aria-label="anchor" href="#arg-covariate-quantiles"></a></dt>
 <dd><p>This argument only has an effect if the fitted
 object has covariate models. If so, the default is to show three population
 curves, for the 5th percentile, the 50th percentile and the 95th percentile
 of the covariate values used for fitting the model.</p></dd>
 
 
-<dt>xlab</dt>
+<dt id="arg-xlab">xlab<a class="anchor" aria-label="anchor" href="#arg-xlab"></a></dt>
 <dd><p>Label for the x axis.</p></dd>
 
 
-<dt>xlim</dt>
+<dt id="arg-xlim">xlim<a class="anchor" aria-label="anchor" href="#arg-xlim"></a></dt>
 <dd><p>Plot range in x direction.</p></dd>
 
 
-<dt>resplot</dt>
+<dt id="arg-resplot">resplot<a class="anchor" aria-label="anchor" href="#arg-resplot"></a></dt>
 <dd><p>Should the residuals plotted against time or against
 predicted values?</p></dd>
 
 
-<dt>pop_curves</dt>
+<dt id="arg-pop-curves">pop_curves<a class="anchor" aria-label="anchor" href="#arg-pop-curves"></a></dt>
 <dd><p>Per default, one population curve is drawn in case
 population parameters are fitted by the model, e.g. for saem objects.
 In case there is a covariate model, the behaviour depends on the value
 of 'covariates'</p></dd>
 
 
-<dt>pred_over</dt>
+<dt id="arg-pred-over">pred_over<a class="anchor" aria-label="anchor" href="#arg-pred-over"></a></dt>
 <dd><p>Named list of alternative predictions as obtained
 from <a href="mkinpredict.html">mkinpredict</a> with a compatible <a href="mkinmod.html">mkinmod</a>.</p></dd>
 
 
-<dt>test_log_parms</dt>
+<dt id="arg-test-log-parms">test_log_parms<a class="anchor" aria-label="anchor" href="#arg-test-log-parms"></a></dt>
 <dd><p>Passed to <a href="mean_degparms.html">mean_degparms</a> in the case of an
 <a href="mixed.html">mixed.mmkin</a> object</p></dd>
 
 
-<dt>conf.level</dt>
+<dt id="arg-conf-level">conf.level<a class="anchor" aria-label="anchor" href="#arg-conf-level"></a></dt>
 <dd><p>Passed to <a href="mean_degparms.html">mean_degparms</a> in the case of an
 <a href="mixed.html">mixed.mmkin</a> object</p></dd>
 
 
-<dt>default_log_parms</dt>
+<dt id="arg-default-log-parms">default_log_parms<a class="anchor" aria-label="anchor" href="#arg-default-log-parms"></a></dt>
 <dd><p>Passed to <a href="mean_degparms.html">mean_degparms</a> in the case of an
 <a href="mixed.html">mixed.mmkin</a> object</p></dd>
 
 
-<dt>ymax</dt>
+<dt id="arg-ymax">ymax<a class="anchor" aria-label="anchor" href="#arg-ymax"></a></dt>
 <dd><p>Vector of maximum y axis values</p></dd>
 
 
-<dt>maxabs</dt>
+<dt id="arg-maxabs">maxabs<a class="anchor" aria-label="anchor" href="#arg-maxabs"></a></dt>
 <dd><p>Maximum absolute value of the residuals. This is used for the
 scaling of the y axis and defaults to "auto".</p></dd>
 
 
-<dt>ncol.legend</dt>
+<dt id="arg-ncol-legend">ncol.legend<a class="anchor" aria-label="anchor" href="#arg-ncol-legend"></a></dt>
 <dd><p>Number of columns to use in the legend</p></dd>
 
 
-<dt>nrow.legend</dt>
+<dt id="arg-nrow-legend">nrow.legend<a class="anchor" aria-label="anchor" href="#arg-nrow-legend"></a></dt>
 <dd><p>Number of rows to use in the legend</p></dd>
 
 
-<dt>rel.height.legend</dt>
+<dt id="arg-rel-height-legend">rel.height.legend<a class="anchor" aria-label="anchor" href="#arg-rel-height-legend"></a></dt>
 <dd><p>The relative height of the legend shown on top</p></dd>
 
 
-<dt>rel.height.bottom</dt>
+<dt id="arg-rel-height-bottom">rel.height.bottom<a class="anchor" aria-label="anchor" href="#arg-rel-height-bottom"></a></dt>
 <dd><p>The relative height of the bottom plot row</p></dd>
 
 
-<dt>pch_ds</dt>
+<dt id="arg-pch-ds">pch_ds<a class="anchor" aria-label="anchor" href="#arg-pch-ds"></a></dt>
 <dd><p>Symbols to be used for plotting the data.</p></dd>
 
 
-<dt>col_ds</dt>
+<dt id="arg-col-ds">col_ds<a class="anchor" aria-label="anchor" href="#arg-col-ds"></a></dt>
 <dd><p>Colors used for plotting the observed data and the
 corresponding model prediction lines for the different datasets.</p></dd>
 
 
-<dt>lty_ds</dt>
+<dt id="arg-lty-ds">lty_ds<a class="anchor" aria-label="anchor" href="#arg-lty-ds"></a></dt>
 <dd><p>Line types to be used for the model predictions.</p></dd>
 
 
-<dt>frame</dt>
+<dt id="arg-frame">frame<a class="anchor" aria-label="anchor" href="#arg-frame"></a></dt>
 <dd><p>Should a frame be drawn around the plots?</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Further arguments passed to <code><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></code>.</p></dd>
 
 </dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>The function is called for its side effect.</p>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>The function is called for its side effect.</p>
     </div>
-    <div id="note">
-    <h2>Note</h2>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>Covariate models are currently only supported for saem.mmkin objects.</p>
     </div>
-    <div id="author">
-    <h2>Author</h2>
+    <div class="section level2">
+    <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a></h2>
     <p>Johannes Ranke</p>
     </div>
 
-    <div id="ref-examples">
-    <h2>Examples</h2>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">ds</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="va">experimental_data_for_UBA_2019</span><span class="op">[</span><span class="fl">6</span><span class="op">:</span><span class="fl">10</span><span class="op">]</span>,</span></span>
 <span class="r-in"><span> <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="va">x</span><span class="op">$</span><span class="va">data</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"name"</span>, <span class="st">"time"</span>, <span class="st">"value"</span><span class="op">)</span><span class="op">]</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">ds</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span><span class="st">"ds "</span>, <span class="fl">6</span><span class="op">:</span><span class="fl">10</span><span class="op">)</span></span></span>
@@ -307,7 +260,7 @@ corresponding model prediction lines for the different datasets.</p></dd>
 <span class="r-in"><span><span class="va">f_nlmix</span> <span class="op">&lt;-</span> <span class="fu">nlmix</span><span class="op">(</span><span class="va">f_obs</span><span class="op">)</span></span></span>
 <span class="r-err co"><span class="r-pr">#&gt;</span> <span class="error">Error in nlmix(f_obs):</span> could not find function "nlmix"</span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_nlmix</span><span class="op">)</span></span></span>
-<span class="r-err co"><span class="r-pr">#&gt;</span> <span class="error">Error in eval(expr, envir, enclos):</span> object 'f_nlmix' not found</span>
+<span class="r-err co"><span class="r-pr">#&gt;</span> <span class="error">Error:</span> object 'f_nlmix' not found</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># We can overlay the two variants if we generate predictions</span></span></span>
 <span class="r-in"><span><span class="va">pred_nlme</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinpredict.html">mkinpredict</a></span><span class="op">(</span><span class="va">dfop_sfo</span>,</span></span>
@@ -319,27 +272,23 @@ corresponding model prediction lines for the different datasets.</p></dd>
 <span class="r-in"><span><span class="co"># }</span></span></span>
 </code></pre></div>
     </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/plot.mkinfit-1.png b/docs/reference/plot.mkinfit-1.png
index ea1032fb..0a120916 100644
--- a/docs/reference/plot.mkinfit-1.png
+++ b/docs/reference/plot.mkinfit-1.png
diff --git a/docs/reference/plot.mkinfit-2.png b/docs/reference/plot.mkinfit-2.png
index 4a3ddf29..d3a39657 100644
--- a/docs/reference/plot.mkinfit-2.png
+++ b/docs/reference/plot.mkinfit-2.png
diff --git a/docs/reference/plot.mkinfit-3.png b/docs/reference/plot.mkinfit-3.png
index 8a9dbd13..3a74e51e 100644
--- a/docs/reference/plot.mkinfit-3.png
+++ b/docs/reference/plot.mkinfit-3.png
diff --git a/docs/reference/plot.mkinfit-4.png b/docs/reference/plot.mkinfit-4.png
index a7164caa..0027f3eb 100644
--- a/docs/reference/plot.mkinfit-4.png
+++ b/docs/reference/plot.mkinfit-4.png
diff --git a/docs/reference/plot.mkinfit-5.png b/docs/reference/plot.mkinfit-5.png
index 3545b8d8..9112cbb9 100644
--- a/docs/reference/plot.mkinfit-5.png
+++ b/docs/reference/plot.mkinfit-5.png
diff --git a/docs/reference/plot.mkinfit-6.png b/docs/reference/plot.mkinfit-6.png
index 3d0fb25e..6127d2f7 100644
--- a/docs/reference/plot.mkinfit-6.png
+++ b/docs/reference/plot.mkinfit-6.png
diff --git a/docs/reference/plot.mkinfit-7.png b/docs/reference/plot.mkinfit-7.png
index 85045bc3..2b92c212 100644
--- a/docs/reference/plot.mkinfit-7.png
+++ b/docs/reference/plot.mkinfit-7.png
diff --git a/docs/reference/plot.mkinfit.html b/docs/reference/plot.mkinfit.html
index bc4c743f..4ce5a914 100644
--- a/docs/reference/plot.mkinfit.html
+++ b/docs/reference/plot.mkinfit.html
@@ -1,123 +1,78 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Plot the observed data and the fitted model of an mkinfit object — plot.mkinfit • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Plot the observed data and the fitted model of an mkinfit object — plot.mkinfit"><meta property="og:description" content="Solves the differential equations with the optimised and fixed parameters
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Plot the observed data and the fitted model of an mkinfit object — plot.mkinfit • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Plot the observed data and the fitted model of an mkinfit object — plot.mkinfit"><meta name="description" content="Solves the differential equations with the optimised and fixed parameters
 from a previous successful call to mkinfit and plots the
-observed data together with the solution of the fitted model."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+observed data together with the solution of the fitted model."><meta property="og:description" content="Solves the differential equations with the optimised and fixed parameters
+from a previous successful call to mkinfit and plots the
+observed data together with the solution of the fitted model."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Plot the observed data and the fitted model of an mkinfit object</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/plot.mkinfit.R" class="external-link"><code>R/plot.mkinfit.R</code></a></small>
-    <div class="hidden name"><code>plot.mkinfit.Rd</code></div>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Plot the observed data and the fitted model of an mkinfit object</h1>
+      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/plot.mkinfit.R" class="external-link"><code>R/plot.mkinfit.R</code></a></small>
+      <div class="d-none name"><code>plot.mkinfit.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Solves the differential equations with the optimised and fixed parameters
 from a previous successful call to <code><a href="mkinfit.html">mkinfit</a></code> and plots the
 observed data together with the solution of the fitted model.</p>
     </div>
 
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for mkinfit</span></span>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'mkinfit'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span></span>
 <span>  <span class="va">x</span>,</span>
 <span>  fit <span class="op">=</span> <span class="va">x</span>,</span>
@@ -163,62 +118,64 @@ observed data together with the solution of the fitted model.</p>
 <span><span class="fu">plot_err</span><span class="op">(</span><span class="va">fit</span>, sep_obs <span class="op">=</span> <span class="cn">FALSE</span>, show_errmin <span class="op">=</span> <span class="va">sep_obs</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>x</dt>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>Alias for fit introduced for compatibility with the generic S3
 method.</p></dd>
 
 
-<dt>fit</dt>
+<dt id="arg-fit">fit<a class="anchor" aria-label="anchor" href="#arg-fit"></a></dt>
 <dd><p>An object of class <code><a href="mkinfit.html">mkinfit</a></code>.</p></dd>
 
 
-<dt>obs_vars</dt>
+<dt id="arg-obs-vars">obs_vars<a class="anchor" aria-label="anchor" href="#arg-obs-vars"></a></dt>
 <dd><p>A character vector of names of the observed variables for
 which the data and the model should be plotted. Defauls to all observed
 variables in the model.</p></dd>
 
 
-<dt>xlab</dt>
+<dt id="arg-xlab">xlab<a class="anchor" aria-label="anchor" href="#arg-xlab"></a></dt>
 <dd><p>Label for the x axis.</p></dd>
 
 
-<dt>ylab</dt>
+<dt id="arg-ylab">ylab<a class="anchor" aria-label="anchor" href="#arg-ylab"></a></dt>
 <dd><p>Label for the y axis.</p></dd>
 
 
-<dt>xlim</dt>
+<dt id="arg-xlim">xlim<a class="anchor" aria-label="anchor" href="#arg-xlim"></a></dt>
 <dd><p>Plot range in x direction.</p></dd>
 
 
-<dt>ylim</dt>
+<dt id="arg-ylim">ylim<a class="anchor" aria-label="anchor" href="#arg-ylim"></a></dt>
 <dd><p>Plot range in y direction. If given as a list, plot ranges
 for the different plot rows can be given for row layout.</p></dd>
 
 
-<dt>col_obs</dt>
+<dt id="arg-col-obs">col_obs<a class="anchor" aria-label="anchor" href="#arg-col-obs"></a></dt>
 <dd><p>Colors used for plotting the observed data and the
 corresponding model prediction lines.</p></dd>
 
 
-<dt>pch_obs</dt>
+<dt id="arg-pch-obs">pch_obs<a class="anchor" aria-label="anchor" href="#arg-pch-obs"></a></dt>
 <dd><p>Symbols to be used for plotting the data.</p></dd>
 
 
-<dt>lty_obs</dt>
+<dt id="arg-lty-obs">lty_obs<a class="anchor" aria-label="anchor" href="#arg-lty-obs"></a></dt>
 <dd><p>Line types to be used for the model predictions.</p></dd>
 
 
-<dt>add</dt>
+<dt id="arg-add">add<a class="anchor" aria-label="anchor" href="#arg-add"></a></dt>
 <dd><p>Should the plot be added to an existing plot?</p></dd>
 
 
-<dt>legend</dt>
+<dt id="arg-legend">legend<a class="anchor" aria-label="anchor" href="#arg-legend"></a></dt>
 <dd><p>Should a legend be added to the plot?</p></dd>
 
 
-<dt>show_residuals</dt>
+<dt id="arg-show-residuals">show_residuals<a class="anchor" aria-label="anchor" href="#arg-show-residuals"></a></dt>
 <dd><p>Should residuals be shown? If only one plot of the
 fits is shown, the residual plot is in the lower third of the plot.
 Otherwise, i.e. if "sep_obs" is given, the residual plots will be located
@@ -227,86 +184,84 @@ to the right of the plots of the fitted curves. If this is set to
 given by the fitted error model will be shown.</p></dd>
 
 
-<dt>show_errplot</dt>
+<dt id="arg-show-errplot">show_errplot<a class="anchor" aria-label="anchor" href="#arg-show-errplot"></a></dt>
 <dd><p>Should squared residuals and the error model be shown?
 If only one plot of the fits is shown, this plot is in the lower third of
 the plot.  Otherwise, i.e. if "sep_obs" is given, the residual plots will
 be located to the right of the plots of the fitted curves.</p></dd>
 
 
-<dt>maxabs</dt>
+<dt id="arg-maxabs">maxabs<a class="anchor" aria-label="anchor" href="#arg-maxabs"></a></dt>
 <dd><p>Maximum absolute value of the residuals. This is used for the
 scaling of the y axis and defaults to "auto".</p></dd>
 
 
-<dt>sep_obs</dt>
+<dt id="arg-sep-obs">sep_obs<a class="anchor" aria-label="anchor" href="#arg-sep-obs"></a></dt>
 <dd><p>Should the observed variables be shown in separate subplots?
 If yes, residual plots requested by "show_residuals" will be shown next
 to, not below the plot of the fits.</p></dd>
 
 
-<dt>rel.height.middle</dt>
+<dt id="arg-rel-height-middle">rel.height.middle<a class="anchor" aria-label="anchor" href="#arg-rel-height-middle"></a></dt>
 <dd><p>The relative height of the middle plot, if more
 than two rows of plots are shown.</p></dd>
 
 
-<dt>row_layout</dt>
+<dt id="arg-row-layout">row_layout<a class="anchor" aria-label="anchor" href="#arg-row-layout"></a></dt>
 <dd><p>Should we use a row layout where the residual plot or the
 error model plot is shown to the right?</p></dd>
 
 
-<dt>lpos</dt>
+<dt id="arg-lpos">lpos<a class="anchor" aria-label="anchor" href="#arg-lpos"></a></dt>
 <dd><p>Position(s) of the legend(s). Passed to <code><a href="https://rdrr.io/r/graphics/legend.html" class="external-link">legend</a></code> as
 the first argument.  If not length one, this should be of the same length
 as the obs_var argument.</p></dd>
 
 
-<dt>inset</dt>
+<dt id="arg-inset">inset<a class="anchor" aria-label="anchor" href="#arg-inset"></a></dt>
 <dd><p>Passed to <code><a href="https://rdrr.io/r/graphics/legend.html" class="external-link">legend</a></code> if applicable.</p></dd>
 
 
-<dt>show_errmin</dt>
+<dt id="arg-show-errmin">show_errmin<a class="anchor" aria-label="anchor" href="#arg-show-errmin"></a></dt>
 <dd><p>Should the FOCUS chi2 error value be shown in the upper
 margin of the plot?</p></dd>
 
 
-<dt>errmin_digits</dt>
+<dt id="arg-errmin-digits">errmin_digits<a class="anchor" aria-label="anchor" href="#arg-errmin-digits"></a></dt>
 <dd><p>The number of significant digits for rounding the FOCUS
 chi2 error percentage.</p></dd>
 
 
-<dt>frame</dt>
+<dt id="arg-frame">frame<a class="anchor" aria-label="anchor" href="#arg-frame"></a></dt>
 <dd><p>Should a frame be drawn around the plots?</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Further arguments passed to <code><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></code>.</p></dd>
 
 
-<dt>standardized</dt>
+<dt id="arg-standardized">standardized<a class="anchor" aria-label="anchor" href="#arg-standardized"></a></dt>
 <dd><p>When calling 'plot_res', should the residuals be
 standardized in the residual plot?</p></dd>
 
 </dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>The function is called for its side effect.</p>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>The function is called for its side effect.</p>
     </div>
-    <div id="details">
-    <h2>Details</h2>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>If the current plot device is a <code><a href="https://rdrr.io/pkg/tikzDevice/man/tikz.html" class="external-link">tikz</a></code> device, then
 latex is being used for the formatting of the chi2 error level, if
 <code>show_errmin = TRUE</code>.</p>
     </div>
-    <div id="author">
-    <h2>Author</h2>
+    <div class="section level2">
+    <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a></h2>
     <p>Johannes Ranke</p>
     </div>
 
-    <div id="ref-examples">
-    <h2>Examples</h2>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># One parent compound, one metabolite, both single first order, path from</span></span></span>
 <span class="r-in"><span><span class="co"># parent to sink included</span></span></span>
@@ -344,27 +299,23 @@ latex is being used for the formatting of the chi2 error level, if
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/plot.mmkin-1.png b/docs/reference/plot.mmkin-1.png
index 235e33a7..58051871 100644
--- a/docs/reference/plot.mmkin-1.png
+++ b/docs/reference/plot.mmkin-1.png
diff --git a/docs/reference/plot.mmkin-2.png b/docs/reference/plot.mmkin-2.png
index 7af84edf..eff04808 100644
--- a/docs/reference/plot.mmkin-2.png
+++ b/docs/reference/plot.mmkin-2.png
diff --git a/docs/reference/plot.mmkin-3.png b/docs/reference/plot.mmkin-3.png
index 56bfac50..781c6351 100644
--- a/docs/reference/plot.mmkin-3.png
+++ b/docs/reference/plot.mmkin-3.png
diff --git a/docs/reference/plot.mmkin-4.png b/docs/reference/plot.mmkin-4.png
index 29439156..ffbc6cab 100644
--- a/docs/reference/plot.mmkin-4.png
+++ b/docs/reference/plot.mmkin-4.png
diff --git a/docs/reference/plot.mmkin-5.png b/docs/reference/plot.mmkin-5.png
index 77aa611d..37bbac8b 100644
--- a/docs/reference/plot.mmkin-5.png
+++ b/docs/reference/plot.mmkin-5.png
diff --git a/docs/reference/plot.mmkin.html b/docs/reference/plot.mmkin.html
index f3d56cd2..572731a7 100644
--- a/docs/reference/plot.mmkin.html
+++ b/docs/reference/plot.mmkin.html
@@ -1,126 +1,78 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Plot model fits (observed and fitted) and the residuals for a row or column
-of an mmkin object — plot.mmkin • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Plot model fits (observed and fitted) and the residuals for a row or column
-of an mmkin object — plot.mmkin"><meta property="og:description" content="When x is a row selected from an mmkin object ([.mmkin), the
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Plot model fits (observed and fitted) and the residuals for a row or column of an mmkin object — plot.mmkin • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Plot model fits (observed and fitted) and the residuals for a row or column of an mmkin object — plot.mmkin"><meta name="description" content="When x is a row selected from an mmkin object ([.mmkin), the
 same model fitted for at least one dataset is shown. When it is a column,
-the fit of at least one model to the same dataset is shown."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+the fit of at least one model to the same dataset is shown."><meta property="og:description" content="When x is a row selected from an mmkin object ([.mmkin), the
+same model fitted for at least one dataset is shown. When it is a column,
+the fit of at least one model to the same dataset is shown."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Plot model fits (observed and fitted) and the residuals for a row or column
-of an mmkin object</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/plot.mmkin.R" class="external-link"><code>R/plot.mmkin.R</code></a></small>
-    <div class="hidden name"><code>plot.mmkin.Rd</code></div>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Plot model fits (observed and fitted) and the residuals for a row or column of an mmkin object</h1>
+      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/plot.mmkin.R" class="external-link"><code>R/plot.mmkin.R</code></a></small>
+      <div class="d-none name"><code>plot.mmkin.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>When x is a row selected from an mmkin object (<code><a href="Extract.mmkin.html">[.mmkin</a></code>), the
 same model fitted for at least one dataset is shown. When it is a column,
 the fit of at least one model to the same dataset is shown.</p>
     </div>
 
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for mmkin</span></span>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'mmkin'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span></span>
 <span>  <span class="va">x</span>,</span>
 <span>  main <span class="op">=</span> <span class="st">"auto"</span>,</span>
@@ -138,88 +90,88 @@ the fit of at least one model to the same dataset is shown.</p>
 <span><span class="op">)</span></span></code></pre></div>
     </div>
 
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>x</dt>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>An object of class <code><a href="mmkin.html">mmkin</a></code>, with either one row or one
 column.</p></dd>
 
 
-<dt>main</dt>
+<dt id="arg-main">main<a class="anchor" aria-label="anchor" href="#arg-main"></a></dt>
 <dd><p>The main title placed on the outer margin of the plot.</p></dd>
 
 
-<dt>legends</dt>
+<dt id="arg-legends">legends<a class="anchor" aria-label="anchor" href="#arg-legends"></a></dt>
 <dd><p>An index for the fits for which legends should be shown.</p></dd>
 
 
-<dt>resplot</dt>
+<dt id="arg-resplot">resplot<a class="anchor" aria-label="anchor" href="#arg-resplot"></a></dt>
 <dd><p>Should the residuals plotted against time, using
 <code><a href="mkinresplot.html">mkinresplot</a></code>, or as squared residuals against predicted
 values, with the error model, using <code><a href="mkinerrplot.html">mkinerrplot</a></code>.</p></dd>
 
 
-<dt>ylab</dt>
+<dt id="arg-ylab">ylab<a class="anchor" aria-label="anchor" href="#arg-ylab"></a></dt>
 <dd><p>Label for the y axis.</p></dd>
 
 
-<dt>standardized</dt>
+<dt id="arg-standardized">standardized<a class="anchor" aria-label="anchor" href="#arg-standardized"></a></dt>
 <dd><p>Should the residuals be standardized? This option
 is passed to <code><a href="mkinresplot.html">mkinresplot</a></code>, it only takes effect if
 <code>resplot = "time"</code>.</p></dd>
 
 
-<dt>show_errmin</dt>
+<dt id="arg-show-errmin">show_errmin<a class="anchor" aria-label="anchor" href="#arg-show-errmin"></a></dt>
 <dd><p>Should the chi2 error level be shown on top of the plots
 to the left?</p></dd>
 
 
-<dt>errmin_var</dt>
+<dt id="arg-errmin-var">errmin_var<a class="anchor" aria-label="anchor" href="#arg-errmin-var"></a></dt>
 <dd><p>The variable for which the FOCUS chi2 error value should
 be shown.</p></dd>
 
 
-<dt>errmin_digits</dt>
+<dt id="arg-errmin-digits">errmin_digits<a class="anchor" aria-label="anchor" href="#arg-errmin-digits"></a></dt>
 <dd><p>The number of significant digits for rounding the FOCUS
 chi2 error percentage.</p></dd>
 
 
-<dt>cex</dt>
+<dt id="arg-cex">cex<a class="anchor" aria-label="anchor" href="#arg-cex"></a></dt>
 <dd><p>Passed to the plot functions and <code><a href="https://rdrr.io/r/graphics/mtext.html" class="external-link">mtext</a></code>.</p></dd>
 
 
-<dt>rel.height.middle</dt>
+<dt id="arg-rel-height-middle">rel.height.middle<a class="anchor" aria-label="anchor" href="#arg-rel-height-middle"></a></dt>
 <dd><p>The relative height of the middle plot, if more
 than two rows of plots are shown.</p></dd>
 
 
-<dt>ymax</dt>
+<dt id="arg-ymax">ymax<a class="anchor" aria-label="anchor" href="#arg-ymax"></a></dt>
 <dd><p>Maximum y axis value for <code><a href="plot.mkinfit.html">plot.mkinfit</a></code>.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Further arguments passed to <code><a href="plot.mkinfit.html">plot.mkinfit</a></code> and
 <code><a href="mkinresplot.html">mkinresplot</a></code>.</p></dd>
 
 </dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>The function is called for its side effect.</p>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>The function is called for its side effect.</p>
     </div>
-    <div id="details">
-    <h2>Details</h2>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>If the current plot device is a <code><a href="https://rdrr.io/pkg/tikzDevice/man/tikz.html" class="external-link">tikz</a></code> device, then
 latex is being used for the formatting of the chi2 error level.</p>
     </div>
-    <div id="author">
-    <h2>Author</h2>
+    <div class="section level2">
+    <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a></h2>
     <p>Johannes Ranke</p>
     </div>
 
-    <div id="ref-examples">
-    <h2>Examples</h2>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span>
 <span class="r-in"><span>  <span class="co"># \dontrun{</span></span></span>
 <span class="r-in"><span>  <span class="co"># Only use one core not to offend CRAN checks</span></span></span>
@@ -248,27 +200,23 @@ latex is being used for the formatting of the chi2 error level.</p>
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/plot.nafta.html b/docs/reference/plot.nafta.html
index 81cfd7b2..2b2338c6 100644
--- a/docs/reference/plot.nafta.html
+++ b/docs/reference/plot.nafta.html
@@ -1,178 +1,128 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Plot the results of the three models used in the NAFTA scheme. — plot.nafta • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Plot the results of the three models used in the NAFTA scheme. — plot.nafta"><meta property="og:description" content="The plots are ordered with increasing complexity of the model in this
-function (SFO, then IORE, then DFOP)."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Plot the results of the three models used in the NAFTA scheme. — plot.nafta • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Plot the results of the three models used in the NAFTA scheme. — plot.nafta"><meta name="description" content="The plots are ordered with increasing complexity of the model in this
+function (SFO, then IORE, then DFOP)."><meta property="og:description" content="The plots are ordered with increasing complexity of the model in this
+function (SFO, then IORE, then DFOP)."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Plot the results of the three models used in the NAFTA scheme.</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/nafta.R" class="external-link"><code>R/nafta.R</code></a></small>
-    <div class="hidden name"><code>plot.nafta.Rd</code></div>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Plot the results of the three models used in the NAFTA scheme.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/nafta.R" class="external-link"><code>R/nafta.R</code></a></small>
+      <div class="d-none name"><code>plot.nafta.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>The plots are ordered with increasing complexity of the model in this
 function (SFO, then IORE, then DFOP).</p>
     </div>
 
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for nafta</span></span>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'nafta'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">x</span>, legend <span class="op">=</span> <span class="cn">FALSE</span>, main <span class="op">=</span> <span class="st">"auto"</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>x</dt>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>An object of class <code><a href="nafta.html">nafta</a></code>.</p></dd>
 
 
-<dt>legend</dt>
+<dt id="arg-legend">legend<a class="anchor" aria-label="anchor" href="#arg-legend"></a></dt>
 <dd><p>Should a legend be added?</p></dd>
 
 
-<dt>main</dt>
+<dt id="arg-main">main<a class="anchor" aria-label="anchor" href="#arg-main"></a></dt>
 <dd><p>Possibility to override the main title of the plot.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Further arguments passed to <code><a href="plot.mmkin.html">plot.mmkin</a></code>.</p></dd>
 
 </dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>The function is called for its side effect.</p>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>The function is called for its side effect.</p>
     </div>
-    <div id="details">
-    <h2>Details</h2>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>Calls <code><a href="plot.mmkin.html">plot.mmkin</a></code>.</p>
     </div>
-    <div id="author">
-    <h2>Author</h2>
+    <div class="section level2">
+    <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a></h2>
     <p>Johannes Ranke</p>
     </div>
 
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/plot.nlme.mmkin-1.png b/docs/reference/plot.nlme.mmkin-1.png
deleted file mode 100644
index 63ea381c..00000000
--- a/docs/reference/plot.nlme.mmkin-1.png
+++ /dev/null
diff --git a/docs/reference/plot.nlme.mmkin-2.png b/docs/reference/plot.nlme.mmkin-2.png
deleted file mode 100644
index df4c955e..00000000
--- a/docs/reference/plot.nlme.mmkin-2.png
+++ /dev/null
diff --git a/docs/reference/plot.nlme.mmkin.html b/docs/reference/plot.nlme.mmkin.html
deleted file mode 100644
index 82d73dfb..00000000
--- a/docs/reference/plot.nlme.mmkin.html
+++ /dev/null
@@ -1,280 +0,0 @@
-<!-- Generated by pkgdown: do not edit by hand -->
-<!DOCTYPE html>
-<html lang="en">
-  <head>
-  <meta charset="utf-8">
-<meta http-equiv="X-UA-Compatible" content="IE=edge">
-<meta name="viewport" content="width=device-width, initial-scale=1.0">
-
-<title>Plot a fitted nonlinear mixed model obtained via an mmkin row object — plot.nlme.mmkin • mkin</title>
-
-
-<!-- jquery -->
-<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script>
-<!-- Bootstrap -->
-
-<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous" />
-
-<script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script>
-
-<!-- bootstrap-toc -->
-<link rel="stylesheet" href="../bootstrap-toc.css">
-<script src="../bootstrap-toc.js"></script>
-
-<!-- Font Awesome icons -->
-<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous" />
-<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous" />
-
-<!-- clipboard.js -->
-<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script>
-
-<!-- headroom.js -->
-<script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script>
-<script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script>
-
-<!-- pkgdown -->
-<link href="../pkgdown.css" rel="stylesheet">
-<script src="../pkgdown.js"></script>
-
-
-
-
-<meta property="og:title" content="Plot a fitted nonlinear mixed model obtained via an mmkin row object — plot.nlme.mmkin" />
-<meta property="og:description" content="Plot a fitted nonlinear mixed model obtained via an mmkin row object" />
-
-
-
-
-<!-- mathjax -->
-<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script>
-<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script>
-
-<!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]-->
-
-
-
-  </head>
-
-  <body data-spy="scroll" data-target="#toc">
-    <div class="container template-reference-topic">
-      <header>
-      <div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.9.50.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav">
-        <li>
-  <a href="../reference/index.html">Functions and data</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu">
-    <li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a>
-    </li>
-  </ul>
-</li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul>
-      <ul class="nav navbar-nav navbar-right">
-        <li>
-  <a href="https://github.com/jranke/mkin/">
-    <span class="fab fa fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul>
-      
-    </div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header>
-
-<div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Plot a fitted nonlinear mixed model obtained via an mmkin row object</h1>
-    <small class="dont-index">Source: <a href='https://github.com/jranke/mkin/blob/master/R/plot.nlme.mmkin.R'><code>R/plot.nlme.mmkin.R</code></a></small>
-    <div class="hidden name"><code>plot.nlme.mmkin.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Plot a fitted nonlinear mixed model obtained via an mmkin row object</p>
-    </div>
-
-    <pre class="usage"><span class='co'># S3 method for nlme.mmkin</span>
-<span class='fu'><a href='https://rdrr.io/r/graphics/plot.default.html'>plot</a></span><span class='op'>(</span>
-  <span class='va'>x</span>,
-  i <span class='op'>=</span> <span class='fl'>1</span><span class='op'>:</span><span class='fu'><a href='https://rdrr.io/r/base/nrow.html'>ncol</a></span><span class='op'>(</span><span class='va'>x</span><span class='op'>$</span><span class='va'>mmkin_orig</span><span class='op'>)</span>,
-  main <span class='op'>=</span> <span class='st'>"auto"</span>,
-  legends <span class='op'>=</span> <span class='fl'>1</span>,
-  resplot <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='st'>"time"</span>, <span class='st'>"errmod"</span><span class='op'>)</span>,
-  standardized <span class='op'>=</span> <span class='cn'>FALSE</span>,
-  show_errmin <span class='op'>=</span> <span class='cn'>TRUE</span>,
-  errmin_var <span class='op'>=</span> <span class='st'>"All data"</span>,
-  errmin_digits <span class='op'>=</span> <span class='fl'>3</span>,
-  cex <span class='op'>=</span> <span class='fl'>0.7</span>,
-  rel.height.middle <span class='op'>=</span> <span class='fl'>0.9</span>,
-  ymax <span class='op'>=</span> <span class='st'>"auto"</span>,
-  <span class='va'>...</span>
-<span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>x</th>
-      <td><p>An object of class <code><a href='nlme.mmkin.html'>nlme.mmkin</a></code></p></td>
-    </tr>
-    <tr>
-      <th>i</th>
-      <td><p>A numeric index to select datasets for which to plot the nlme fit,
-in case plots get too large</p></td>
-    </tr>
-    <tr>
-      <th>main</th>
-      <td><p>The main title placed on the outer margin of the plot.</p></td>
-    </tr>
-    <tr>
-      <th>legends</th>
-      <td><p>An index for the fits for which legends should be shown.</p></td>
-    </tr>
-    <tr>
-      <th>resplot</th>
-      <td><p>Should the residuals plotted against time, using
-<code><a href='mkinresplot.html'>mkinresplot</a></code>, or as squared residuals against predicted
-values, with the error model, using <code><a href='mkinerrplot.html'>mkinerrplot</a></code>.</p></td>
-    </tr>
-    <tr>
-      <th>standardized</th>
-      <td><p>Should the residuals be standardized? This option
-is passed to <code><a href='mkinresplot.html'>mkinresplot</a></code>, it only takes effect if
-<code>resplot = "time"</code>.</p></td>
-    </tr>
-    <tr>
-      <th>show_errmin</th>
-      <td><p>Should the chi2 error level be shown on top of the plots
-to the left?</p></td>
-    </tr>
-    <tr>
-      <th>errmin_var</th>
-      <td><p>The variable for which the FOCUS chi2 error value should
-be shown.</p></td>
-    </tr>
-    <tr>
-      <th>errmin_digits</th>
-      <td><p>The number of significant digits for rounding the FOCUS
-chi2 error percentage.</p></td>
-    </tr>
-    <tr>
-      <th>cex</th>
-      <td><p>Passed to the plot functions and <code><a href='https://rdrr.io/r/graphics/mtext.html'>mtext</a></code>.</p></td>
-    </tr>
-    <tr>
-      <th>rel.height.middle</th>
-      <td><p>The relative height of the middle plot, if more
-than two rows of plots are shown.</p></td>
-    </tr>
-    <tr>
-      <th>ymax</th>
-      <td><p>Maximum y axis value for <code><a href='plot.mkinfit.html'>plot.mkinfit</a></code>.</p></td>
-    </tr>
-    <tr>
-      <th>...</th>
-      <td><p>Further arguments passed to <code><a href='plot.mkinfit.html'>plot.mkinfit</a></code> and
-<code><a href='mkinresplot.html'>mkinresplot</a></code>.</p></td>
-    </tr>
-    </table>
-
-    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
-
-    <p>The function is called for its side effect.</p>
-    <h2 class="hasAnchor" id="author"><a class="anchor" href="#author"></a>Author</h2>
-
-    <p>Johannes Ranke</p>
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='va'>ds</span> <span class='op'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/r/base/lapply.html'>lapply</a></span><span class='op'>(</span><span class='va'>experimental_data_for_UBA_2019</span><span class='op'>[</span><span class='fl'>6</span><span class='op'>:</span><span class='fl'>10</span><span class='op'>]</span>,
- <span class='kw'>function</span><span class='op'>(</span><span class='va'>x</span><span class='op'>)</span> <span class='fu'><a href='https://rdrr.io/r/base/subset.html'>subset</a></span><span class='op'>(</span><span class='va'>x</span><span class='op'>$</span><span class='va'>data</span><span class='op'>[</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='st'>"name"</span>, <span class='st'>"time"</span>, <span class='st'>"value"</span><span class='op'>)</span><span class='op'>]</span>, <span class='va'>name</span> <span class='op'>==</span> <span class='st'>"parent"</span><span class='op'>)</span><span class='op'>)</span>
-<span class='va'>f</span> <span class='op'>&lt;-</span> <span class='fu'><a href='mmkin.html'>mmkin</a></span><span class='op'>(</span><span class='st'>"SFO"</span>, <span class='va'>ds</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span>, cores <span class='op'>=</span> <span class='fl'>1</span><span class='op'>)</span>
-</div><div class='output co'>#&gt; <span class='warning'>Warning: Shapiro-Wilk test for standardized residuals: p =  0.0195</span></div><div class='output co'>#&gt; <span class='warning'>Warning: Shapiro-Wilk test for standardized residuals: p =  0.011</span></div><div class='input'><span class='co'>#plot(f) # too many panels for pkgdown</span>
-<span class='fu'><a href='https://rdrr.io/r/graphics/plot.default.html'>plot</a></span><span class='op'>(</span><span class='va'>f</span><span class='op'>[</span>, <span class='fl'>3</span><span class='op'>:</span><span class='fl'>4</span><span class='op'>]</span><span class='op'>)</span>
-</div><div class='img'><img src='plot.nlme.mmkin-1.png' alt='' width='700' height='433' /></div><div class='input'><span class='kw'><a href='https://rdrr.io/r/base/library.html'>library</a></span><span class='op'>(</span><span class='va'><a href='https://svn.r-project.org/R-packages/trunk/nlme'>nlme</a></span><span class='op'>)</span>
-<span class='va'>f_nlme</span> <span class='op'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlme/man/nlme.html'>nlme</a></span><span class='op'>(</span><span class='va'>f</span><span class='op'>)</span>
-
-<span class='co'>#plot(f_nlme) # too many panels for pkgdown</span>
-<span class='fu'><a href='https://rdrr.io/r/graphics/plot.default.html'>plot</a></span><span class='op'>(</span><span class='va'>f_nlme</span>, <span class='fl'>3</span><span class='op'>:</span><span class='fl'>4</span><span class='op'>)</span>
-</div><div class='img'><img src='plot.nlme.mmkin-2.png' alt='' width='700' height='433' /></div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
-
-
-      <footer>
-      <div class="copyright">
-  <p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p>
-</div>
-
-      </footer>
-   </div>
-
-  
-
-
-  </body>
-</html>
-
-
diff --git a/docs/reference/plot_err.html b/docs/reference/plot_err.html
new file mode 100644
index 00000000..a189b474
--- /dev/null
+++ b/docs/reference/plot_err.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/plot.mkinfit.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/plot.mkinfit.html">
+  </head>
+</html>
+
diff --git a/docs/reference/plot_res.html b/docs/reference/plot_res.html
new file mode 100644
index 00000000..a189b474
--- /dev/null
+++ b/docs/reference/plot_res.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/plot.mkinfit.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/plot.mkinfit.html">
+  </head>
+</html>
+
diff --git a/docs/reference/plot_sep.html b/docs/reference/plot_sep.html
new file mode 100644
index 00000000..a189b474
--- /dev/null
+++ b/docs/reference/plot_sep.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/plot.mkinfit.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/plot.mkinfit.html">
+  </head>
+</html>
+
diff --git a/docs/reference/print.illparms.mhmkin.html b/docs/reference/print.illparms.mhmkin.html
new file mode 100644
index 00000000..5c70c3f7
--- /dev/null
+++ b/docs/reference/print.illparms.mhmkin.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/illparms.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/illparms.html">
+  </head>
+</html>
+
diff --git a/docs/reference/print.illparms.mkinfit.html b/docs/reference/print.illparms.mkinfit.html
new file mode 100644
index 00000000..5c70c3f7
--- /dev/null
+++ b/docs/reference/print.illparms.mkinfit.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/illparms.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/illparms.html">
+  </head>
+</html>
+
diff --git a/docs/reference/print.illparms.mmkin.html b/docs/reference/print.illparms.mmkin.html
new file mode 100644
index 00000000..5c70c3f7
--- /dev/null
+++ b/docs/reference/print.illparms.mmkin.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/illparms.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/illparms.html">
+  </head>
+</html>
+
diff --git a/docs/reference/print.illparms.saem.mmkin.html b/docs/reference/print.illparms.saem.mmkin.html
new file mode 100644
index 00000000..5c70c3f7
--- /dev/null
+++ b/docs/reference/print.illparms.saem.mmkin.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/illparms.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/illparms.html">
+  </head>
+</html>
+
diff --git a/docs/reference/print.mhmkin.html b/docs/reference/print.mhmkin.html
new file mode 100644
index 00000000..4be1ad9e
--- /dev/null
+++ b/docs/reference/print.mhmkin.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/mhmkin.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/mhmkin.html">
+  </head>
+</html>
+
diff --git a/docs/reference/print.mixed.mmkin.html b/docs/reference/print.mixed.mmkin.html
new file mode 100644
index 00000000..6a5f7aef
--- /dev/null
+++ b/docs/reference/print.mixed.mmkin.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/mixed.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/mixed.html">
+  </head>
+</html>
+
diff --git a/docs/reference/print.mkinds.html b/docs/reference/print.mkinds.html
index 7c7f6647..4055d42c 100644
--- a/docs/reference/print.mkinds.html
+++ b/docs/reference/print.mkinds.html
@@ -1,194 +1,8 @@
-<!-- Generated by pkgdown: do not edit by hand -->
-<!DOCTYPE html>
-<html lang="en">
+<html>
   <head>
-  <meta charset="utf-8">
-<meta http-equiv="X-UA-Compatible" content="IE=edge">
-<meta name="viewport" content="width=device-width, initial-scale=1.0">
-
-<title>Print mkinds objects — print.mkinds • mkin</title>
-
-
-<!-- jquery -->
-<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script>
-<!-- Bootstrap -->
-
-<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous" />
-
-<script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script>
-
-<!-- bootstrap-toc -->
-<link rel="stylesheet" href="../bootstrap-toc.css">
-<script src="../bootstrap-toc.js"></script>
-
-<!-- Font Awesome icons -->
-<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous" />
-<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous" />
-
-<!-- clipboard.js -->
-<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script>
-
-<!-- headroom.js -->
-<script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script>
-<script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script>
-
-<!-- pkgdown -->
-<link href="../pkgdown.css" rel="stylesheet">
-<script src="../pkgdown.js"></script>
-
-
-
-
-<meta property="og:title" content="Print mkinds objects — print.mkinds" />
-<meta property="og:description" content="Print mkinds objects" />
-
-
-
-
-<!-- mathjax -->
-<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script>
-<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script>
-
-<!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]-->
-
-
-
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/mkinds.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/mkinds.html">
   </head>
-
-  <body data-spy="scroll" data-target="#toc">
-    <div class="container template-reference-topic">
-      <header>
-      <div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.9.50.3</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav">
-        <li>
-  <a href="../reference/index.html">Functions and data</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu">
-    <li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a>
-    </li>
-  </ul>
-</li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul>
-      <ul class="nav navbar-nav navbar-right">
-        <li>
-  <a href="https://github.com/jranke/mkin/">
-    <span class="fab fa fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul>
-      
-    </div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header>
-
-<div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Print mkinds objects</h1>
-    <small class="dont-index">Source: <a href='https://github.com/jranke/mkin/blob/master/R/mkinds.R'><code>R/mkinds.R</code></a></small>
-    <div class="hidden name"><code>print.mkinds.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Print mkinds objects</p>
-    </div>
-
-    <pre class="usage"><span class='co'># S3 method for mkinds</span>
-<span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span><span class='op'>(</span><span class='va'>x</span>, <span class='va'>...</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>x</th>
-      <td><p>An <code><a href='mkinds.html'>mkinds</a></code> object.</p></td>
-    </tr>
-    <tr>
-      <th>...</th>
-      <td><p>Not used.</p></td>
-    </tr>
-    </table>
-
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
-
-
-      <footer>
-      <div class="copyright">
-  <p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p>
-</div>
-
-      </footer>
-   </div>
-
-  
-
-
-  </body>
 </html>
 
-
diff --git a/docs/reference/print.mkindsg.html b/docs/reference/print.mkindsg.html
new file mode 100644
index 00000000..02429abf
--- /dev/null
+++ b/docs/reference/print.mkindsg.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/mkindsg.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/mkindsg.html">
+  </head>
+</html>
+
diff --git a/docs/reference/print.mkinmod.html b/docs/reference/print.mkinmod.html
index 6622decf..9c52fb21 100644
--- a/docs/reference/print.mkinmod.html
+++ b/docs/reference/print.mkinmod.html
@@ -1,214 +1,8 @@
-<!-- Generated by pkgdown: do not edit by hand -->
-<!DOCTYPE html>
-<html lang="en">
+<html>
   <head>
-  <meta charset="utf-8">
-<meta http-equiv="X-UA-Compatible" content="IE=edge">
-<meta name="viewport" content="width=device-width, initial-scale=1.0">
-
-<title>Print mkinmod objects — print.mkinmod • mkin</title>
-
-
-<!-- jquery -->
-<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script>
-<!-- Bootstrap -->
-
-<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous" />
-
-<script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script>
-
-<!-- bootstrap-toc -->
-<link rel="stylesheet" href="../bootstrap-toc.css">
-<script src="../bootstrap-toc.js"></script>
-
-<!-- Font Awesome icons -->
-<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous" />
-<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous" />
-
-<!-- clipboard.js -->
-<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script>
-
-<!-- headroom.js -->
-<script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script>
-<script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script>
-
-<!-- pkgdown -->
-<link href="../pkgdown.css" rel="stylesheet">
-<script src="../pkgdown.js"></script>
-
-
-
-
-<meta property="og:title" content="Print mkinmod objects — print.mkinmod" />
-<meta property="og:description" content="Print mkinmod objects in a way that the user finds his way to get to its
-components." />
-
-
-
-
-<!-- mathjax -->
-<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script>
-<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script>
-
-<!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]-->
-
-
-
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/mkinmod.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/mkinmod.html">
   </head>
-
-  <body data-spy="scroll" data-target="#toc">
-    <div class="container template-reference-topic">
-      <header>
-      <div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.9.50.2</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav">
-        <li>
-  <a href="../reference/index.html">Functions and data</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu">
-    <li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-  </ul>
-</li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul>
-      <ul class="nav navbar-nav navbar-right">
-        <li>
-  <a href="http://github.com/jranke/mkin/">
-    <span class="fab fa fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul>
-      
-    </div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header>
-
-<div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Print mkinmod objects</h1>
-    <small class="dont-index">Source: <a href='http://github.com/jranke/mkin/blob/master/R/mkinmod.R'><code>R/mkinmod.R</code></a></small>
-    <div class="hidden name"><code>print.mkinmod.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Print mkinmod objects in a way that the user finds his way to get to its
-components.</p>
-    </div>
-
-    <pre class="usage"><span class='co'># S3 method for mkinmod</span>
-<span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span>(<span class='no'>x</span>, <span class='no'>...</span>)</pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>x</th>
-      <td><p>An <code><a href='mkinmod.html'>mkinmod</a></code> object.</p></td>
-    </tr>
-    <tr>
-      <th>...</th>
-      <td><p>Not used.</p></td>
-    </tr>
-    </table>
-
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'>
-  <span class='no'>m_synth_SFO_lin</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span>(<span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>, <span class='kw'>to</span> <span class='kw'>=</span> <span class='st'>"M1"</span>),
-                             <span class='kw'>M1</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>, <span class='kw'>to</span> <span class='kw'>=</span> <span class='st'>"M2"</span>),
-                             <span class='kw'>M2</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>), <span class='kw'>use_of_ff</span> <span class='kw'>=</span> <span class='st'>"max"</span>)</div><div class='output co'>#&gt; <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'>
-  <span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span>(<span class='no'>m_synth_SFO_lin</span>)</div><div class='output co'>#&gt; &lt;mkinmod&gt; model generated with
-#&gt; Use of formation fractions $use_of_ff: max 
-#&gt; Specification $spec:
-#&gt; $parent
-#&gt; $type: SFO; $to: M1; $sink: TRUE
-#&gt; $M1
-#&gt; $type: SFO; $to: M2; $sink: TRUE
-#&gt; $M2
-#&gt; $type: SFO; $sink: TRUE
-#&gt; Coefficient matrix $coefmat available
-#&gt; Compiled model $cf available
-#&gt; Differential equations:
-#&gt; d_parent/dt = - k_parent * parent
-#&gt; d_M1/dt = + f_parent_to_M1 * k_parent * parent - k_M1 * M1
-#&gt; d_M2/dt = + f_M1_to_M2 * k_M1 * M1 - k_M2 * M2</div><div class='input'>
-</div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
-
-
-      <footer>
-      <div class="copyright">
-  <p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.5.1.</p>
-</div>
-
-      </footer>
-   </div>
-
-  
-
-
-  </body>
 </html>
 
-
diff --git a/docs/reference/print.mmkin.html b/docs/reference/print.mmkin.html
index 0c094bfc..a15ee4df 100644
--- a/docs/reference/print.mmkin.html
+++ b/docs/reference/print.mmkin.html
@@ -1,194 +1,8 @@
-<!-- Generated by pkgdown: do not edit by hand -->
-<!DOCTYPE html>
-<html lang="en">
+<html>
   <head>
-  <meta charset="utf-8">
-<meta http-equiv="X-UA-Compatible" content="IE=edge">
-<meta name="viewport" content="width=device-width, initial-scale=1.0">
-
-<title>Print method for mmkin objects — print.mmkin • mkin</title>
-
-
-<!-- jquery -->
-<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script>
-<!-- Bootstrap -->
-
-<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous" />
-
-<script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script>
-
-<!-- bootstrap-toc -->
-<link rel="stylesheet" href="../bootstrap-toc.css">
-<script src="../bootstrap-toc.js"></script>
-
-<!-- Font Awesome icons -->
-<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous" />
-<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous" />
-
-<!-- clipboard.js -->
-<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script>
-
-<!-- headroom.js -->
-<script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script>
-<script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script>
-
-<!-- pkgdown -->
-<link href="../pkgdown.css" rel="stylesheet">
-<script src="../pkgdown.js"></script>
-
-
-
-
-<meta property="og:title" content="Print method for mmkin objects — print.mmkin" />
-<meta property="og:description" content="Print method for mmkin objects" />
-
-
-
-
-<!-- mathjax -->
-<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script>
-<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script>
-
-<!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]-->
-
-
-
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/mmkin.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/mmkin.html">
   </head>
-
-  <body data-spy="scroll" data-target="#toc">
-    <div class="container template-reference-topic">
-      <header>
-      <div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.0</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav">
-        <li>
-  <a href="../reference/index.html">Functions and data</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu">
-    <li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a>
-    </li>
-  </ul>
-</li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul>
-      <ul class="nav navbar-nav navbar-right">
-        <li>
-  <a href="https://github.com/jranke/mkin/">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul>
-      
-    </div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header>
-
-<div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Print method for mmkin objects</h1>
-    <small class="dont-index">Source: <a href='https://github.com/jranke/mkin/blob/master/R/mmkin.R'><code>R/mmkin.R</code></a></small>
-    <div class="hidden name"><code>print.mmkin.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Print method for mmkin objects</p>
-    </div>
-
-    <pre class="usage"><span class='co'># S3 method for mmkin</span>
-<span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span><span class='op'>(</span><span class='va'>x</span>, <span class='va'>...</span><span class='op'>)</span></pre>
-
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>x</th>
-      <td><p>An <a href='mmkin.html'>mmkin</a> object.</p></td>
-    </tr>
-    <tr>
-      <th>...</th>
-      <td><p>Not used.</p></td>
-    </tr>
-    </table>
-
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top">
-      <h2 data-toc-skip>Contents</h2>
-    </nav>
-  </div>
-</div>
-
-
-      <footer>
-      <div class="copyright">
-  <p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p>
-</div>
-
-      </footer>
-   </div>
-
-  
-
-
-  </body>
 </html>
 
-
diff --git a/docs/reference/print.multistart.html b/docs/reference/print.multistart.html
new file mode 100644
index 00000000..9700ef05
--- /dev/null
+++ b/docs/reference/print.multistart.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/multistart.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/multistart.html">
+  </head>
+</html>
+
diff --git a/docs/reference/print.nafta.html b/docs/reference/print.nafta.html
new file mode 100644
index 00000000..7d546151
--- /dev/null
+++ b/docs/reference/print.nafta.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/nafta.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/nafta.html">
+  </head>
+</html>
+
diff --git a/docs/reference/print.nlme.mmkin.html b/docs/reference/print.nlme.mmkin.html
new file mode 100644
index 00000000..dd383acd
--- /dev/null
+++ b/docs/reference/print.nlme.mmkin.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/nlme.mmkin.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/nlme.mmkin.html">
+  </head>
+</html>
+
diff --git a/docs/reference/print.saem.mmkin.html b/docs/reference/print.saem.mmkin.html
new file mode 100644
index 00000000..65220667
--- /dev/null
+++ b/docs/reference/print.saem.mmkin.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/saem.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/saem.html">
+  </head>
+</html>
+
diff --git a/docs/reference/print.status.mhmkin.html b/docs/reference/print.status.mhmkin.html
new file mode 100644
index 00000000..dc236170
--- /dev/null
+++ b/docs/reference/print.status.mhmkin.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/status.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/status.html">
+  </head>
+</html>
+
diff --git a/docs/reference/print.status.mmkin.html b/docs/reference/print.status.mmkin.html
new file mode 100644
index 00000000..dc236170
--- /dev/null
+++ b/docs/reference/print.status.mmkin.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/status.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/status.html">
+  </head>
+</html>
+
diff --git a/docs/reference/print.summary.mkinfit.html b/docs/reference/print.summary.mkinfit.html
new file mode 100644
index 00000000..9241f3cb
--- /dev/null
+++ b/docs/reference/print.summary.mkinfit.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/summary.mkinfit.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/summary.mkinfit.html">
+  </head>
+</html>
+
diff --git a/docs/reference/print.summary.mmkin.html b/docs/reference/print.summary.mmkin.html
new file mode 100644
index 00000000..b3607a52
--- /dev/null
+++ b/docs/reference/print.summary.mmkin.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/summary.mmkin.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/summary.mmkin.html">
+  </head>
+</html>
+
diff --git a/docs/reference/print.summary.nlme.mmkin.html b/docs/reference/print.summary.nlme.mmkin.html
new file mode 100644
index 00000000..b23966af
--- /dev/null
+++ b/docs/reference/print.summary.nlme.mmkin.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/summary.nlme.mmkin.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/summary.nlme.mmkin.html">
+  </head>
+</html>
+
diff --git a/docs/reference/print.summary.saem.mmkin.html b/docs/reference/print.summary.saem.mmkin.html
new file mode 100644
index 00000000..57cf3b97
--- /dev/null
+++ b/docs/reference/print.summary.saem.mmkin.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/summary.saem.mmkin.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/summary.saem.mmkin.html">
+  </head>
+</html>
+
diff --git a/docs/reference/read_spreadsheet.html b/docs/reference/read_spreadsheet.html
index 7603cee2..173ee608 100644
--- a/docs/reference/read_spreadsheet.html
+++ b/docs/reference/read_spreadsheet.html
@@ -1,116 +1,76 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Read datasets and relevant meta information from a spreadsheet file — read_spreadsheet • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Read datasets and relevant meta information from a spreadsheet file — read_spreadsheet"><meta property="og:description" content="This function imports one dataset from each sheet of a spreadsheet file.
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Read datasets and relevant meta information from a spreadsheet file — read_spreadsheet • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Read datasets and relevant meta information from a spreadsheet file — read_spreadsheet"><meta name="description" content="This function imports one dataset from each sheet of a spreadsheet file.
 These sheets are selected based on the contents of a sheet 'Datasets', with
 a column called 'Dataset Number', containing numbers identifying the dataset
 sheets to be read in. In the second column there must be a grouping
 variable, which will often be named 'Soil'. Optionally, time normalization
-factors can be given in columns named 'Temperature' and 'Moisture'."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.5</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+factors can be given in columns named 'Temperature' and 'Moisture'."><meta property="og:description" content="This function imports one dataset from each sheet of a spreadsheet file.
+These sheets are selected based on the contents of a sheet 'Datasets', with
+a column called 'Dataset Number', containing numbers identifying the dataset
+sheets to be read in. In the second column there must be a grouping
+variable, which will often be named 'Soil'. Optionally, time normalization
+factors can be given in columns named 'Temperature' and 'Moisture'."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Read datasets and relevant meta information from a spreadsheet file</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/read_spreadsheet.R" class="external-link"><code>R/read_spreadsheet.R</code></a></small>
-    <div class="hidden name"><code>read_spreadsheet.Rd</code></div>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Read datasets and relevant meta information from a spreadsheet file</h1>
+      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/read_spreadsheet.R" class="external-link"><code>R/read_spreadsheet.R</code></a></small>
+      <div class="d-none name"><code>read_spreadsheet.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>This function imports one dataset from each sheet of a spreadsheet file.
 These sheets are selected based on the contents of a sheet 'Datasets', with
 a column called 'Dataset Number', containing numbers identifying the dataset
@@ -119,7 +79,8 @@ variable, which will often be named 'Soil'. Optionally, time normalization
 factors can be given in columns named 'Temperature' and 'Moisture'.</p>
     </div>
 
-    <div id="ref-usage">
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">read_spreadsheet</span><span class="op">(</span></span>
 <span>  <span class="va">path</span>,</span>
 <span>  valid_datasets <span class="op">=</span> <span class="st">"all"</span>,</span>
@@ -128,28 +89,30 @@ factors can be given in columns named 'Temperature' and 'Moisture'.</p>
 <span><span class="op">)</span></span></code></pre></div>
     </div>
 
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>path</dt>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-path">path<a class="anchor" aria-label="anchor" href="#arg-path"></a></dt>
 <dd><p>Absolute or relative path to the spreadsheet file</p></dd>
 
 
-<dt>valid_datasets</dt>
+<dt id="arg-valid-datasets">valid_datasets<a class="anchor" aria-label="anchor" href="#arg-valid-datasets"></a></dt>
 <dd><p>Optional numeric index of the valid datasets, default is
 to use all datasets</p></dd>
 
 
-<dt>parent_only</dt>
+<dt id="arg-parent-only">parent_only<a class="anchor" aria-label="anchor" href="#arg-parent-only"></a></dt>
 <dd><p>Should only the parent data be used?</p></dd>
 
 
-<dt>normalize</dt>
+<dt id="arg-normalize">normalize<a class="anchor" aria-label="anchor" href="#arg-normalize"></a></dt>
 <dd><p>Should the time scale be normalized using temperature
 and moisture normalisation factors in the sheet 'Datasets'?</p></dd>
 
 </dl></div>
-    <div id="details">
-    <h2>Details</h2>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>There must be a sheet 'Compounds', with columns 'Name' and 'Acronym'.
 The first row read after the header read in from this sheet is assumed
 to contain name and acronym of the parent compound.</p>
@@ -170,27 +133,23 @@ so they can be easily used for specifying covariate models.</p>
 is probably more complicated to use.</p>
     </div>
 
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/reexports.html b/docs/reference/reexports.html
index 847be2cb..94ee6f14 100644
--- a/docs/reference/reexports.html
+++ b/docs/reference/reexports.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Objects exported from other packages — reexports • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Objects exported from other packages — reexports"><meta property="og:description" content="These objects are imported from other packages. Follow the links
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Objects exported from other packages — reexports • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Objects exported from other packages — reexports"><meta name="description" content="These objects are imported from other packages. Follow the links
 below to see their documentation.
 
   lmtest
@@ -10,116 +10,79 @@ lrtest
 intervals, nlme
 
 
-"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
+"><meta property="og:description" content="These objects are imported from other packages. Follow the links
+below to see their documentation.
+
+  lmtest
+lrtest
+
+
+  nlme
+intervals, nlme
 
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+
+"></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Objects exported from other packages</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/intervals.R" class="external-link"><code>R/intervals.R</code></a>, <a href="https://github.com/jranke/mkin/blob/HEAD/R/lrtest.mkinfit.R" class="external-link"><code>R/lrtest.mkinfit.R</code></a>, <a href="https://github.com/jranke/mkin/blob/HEAD/R/nlme.mmkin.R" class="external-link"><code>R/nlme.mmkin.R</code></a></small>
-    <div class="hidden name"><code>reexports.Rd</code></div>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Objects exported from other packages</h1>
+      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/intervals.R" class="external-link"><code>R/intervals.R</code></a>, <a href="https://github.com/jranke/mkin/blob/HEAD/R/lrtest.mkinfit.R" class="external-link"><code>R/lrtest.mkinfit.R</code></a>, <a href="https://github.com/jranke/mkin/blob/HEAD/R/nlme.mmkin.R" class="external-link"><code>R/nlme.mmkin.R</code></a></small>
+      <div class="d-none name"><code>reexports.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>These objects are imported from other packages. Follow the links
 below to see their documentation.</p>
 <dl><dt>lmtest</dt>
@@ -134,27 +97,22 @@ below to see their documentation.</p>
 
 
 
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/residuals.mkinfit.html b/docs/reference/residuals.mkinfit.html
index 0115cc3f..cf2f62bb 100644
--- a/docs/reference/residuals.mkinfit.html
+++ b/docs/reference/residuals.mkinfit.html
@@ -1,140 +1,95 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Extract residuals from an mkinfit model — residuals.mkinfit • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Extract residuals from an mkinfit model — residuals.mkinfit"><meta property="og:description" content="Extract residuals from an mkinfit model"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Extract residuals from an mkinfit model — residuals.mkinfit • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Extract residuals from an mkinfit model — residuals.mkinfit"><meta name="description" content="Extract residuals from an mkinfit model"><meta property="og:description" content="Extract residuals from an mkinfit model"></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Extract residuals from an mkinfit model</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/residuals.mkinfit.R" class="external-link"><code>R/residuals.mkinfit.R</code></a></small>
-    <div class="hidden name"><code>residuals.mkinfit.Rd</code></div>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Extract residuals from an mkinfit model</h1>
+      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/residuals.mkinfit.R" class="external-link"><code>R/residuals.mkinfit.R</code></a></small>
+      <div class="d-none name"><code>residuals.mkinfit.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Extract residuals from an mkinfit model</p>
     </div>
 
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for mkinfit</span></span>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'mkinfit'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/stats/residuals.html" class="external-link">residuals</a></span><span class="op">(</span><span class="va">object</span>, standardized <span class="op">=</span> <span class="cn">FALSE</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>object</dt>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
 <dd><p>A <code><a href="mkinfit.html">mkinfit</a></code> object</p></dd>
 
 
-<dt>standardized</dt>
+<dt id="arg-standardized">standardized<a class="anchor" aria-label="anchor" href="#arg-standardized"></a></dt>
 <dd><p>Should the residuals be standardized by dividing by the
 standard deviation obtained from the fitted error model?</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Not used</p></dd>
 
 </dl></div>
 
-    <div id="ref-examples">
-    <h2>Examples</h2>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">f</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="va">FOCUS_2006_C</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/residuals.html" class="external-link">residuals</a></span><span class="op">(</span><span class="va">f</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1]  0.09726374 -0.13912142 -0.15351210  0.73388322 -0.08657004 -0.93204702</span>
@@ -144,27 +99,23 @@ standard deviation obtained from the fitted error model?</p></dd>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [7] -0.04695355  2.08761977 -1.27002287</span>
 </code></pre></div>
     </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/saem-1.png b/docs/reference/saem-1.png
index 9e310252..1fa206c4 100644
--- a/docs/reference/saem-1.png
+++ b/docs/reference/saem-1.png
diff --git a/docs/reference/saem-2.png b/docs/reference/saem-2.png
index de1bcf57..e5c62c35 100644
--- a/docs/reference/saem-2.png
+++ b/docs/reference/saem-2.png
diff --git a/docs/reference/saem-3.png b/docs/reference/saem-3.png
index 8667ef06..09eb2d40 100644
--- a/docs/reference/saem-3.png
+++ b/docs/reference/saem-3.png
diff --git a/docs/reference/saem-4.png b/docs/reference/saem-4.png
index cd464533..87ebc56f 100644
--- a/docs/reference/saem-4.png
+++ b/docs/reference/saem-4.png
diff --git a/docs/reference/saem.html b/docs/reference/saem.html
index 527718ef..7146875f 100644
--- a/docs/reference/saem.html
+++ b/docs/reference/saem.html
@@ -1,125 +1,80 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Fit nonlinear mixed models with SAEM — saem • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Fit nonlinear mixed models with SAEM — saem"><meta property="og:description" content="This function uses saemix::saemix() as a backend for fitting nonlinear mixed
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Fit nonlinear mixed models with SAEM — saem • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Fit nonlinear mixed models with SAEM — saem"><meta name="description" content="This function uses saemix::saemix() as a backend for fitting nonlinear mixed
 effects models created from mmkin row objects using the Stochastic Approximation
-Expectation Maximisation algorithm (SAEM)."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+Expectation Maximisation algorithm (SAEM)."><meta property="og:description" content="This function uses saemix::saemix() as a backend for fitting nonlinear mixed
+effects models created from mmkin row objects using the Stochastic Approximation
+Expectation Maximisation algorithm (SAEM)."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Fit nonlinear mixed models with SAEM</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/saem.R" class="external-link"><code>R/saem.R</code></a></small>
-    <div class="hidden name"><code>saem.Rd</code></div>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Fit nonlinear mixed models with SAEM</h1>
+      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/saem.R" class="external-link"><code>R/saem.R</code></a></small>
+      <div class="d-none name"><code>saem.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>This function uses <code><a href="https://rdrr.io/pkg/saemix/man/saemix.html" class="external-link">saemix::saemix()</a></code> as a backend for fitting nonlinear mixed
 effects models created from <a href="mmkin.html">mmkin</a> row objects using the Stochastic Approximation
 Expectation Maximisation algorithm (SAEM).</p>
     </div>
 
-    <div id="ref-usage">
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">saem</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for mmkin</span></span>
+<span><span class="co"># S3 method for class 'mmkin'</span></span>
 <span><span class="fu">saem</span><span class="op">(</span></span>
 <span>  <span class="va">object</span>,</span>
 <span>  transformations <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"mkin"</span>, <span class="st">"saemix"</span><span class="op">)</span>,</span>
@@ -142,7 +97,7 @@ Expectation Maximisation algorithm (SAEM).</p>
 <span>  <span class="va">...</span></span>
 <span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for saem.mmkin</span></span>
+<span><span class="co"># S3 method for class 'saem.mmkin'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, digits <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html" class="external-link">max</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fu"><a href="https://rdrr.io/r/base/options.html" class="external-link">getOption</a></span><span class="op">(</span><span class="st">"digits"</span><span class="op">)</span> <span class="op">-</span> <span class="fl">3</span><span class="op">)</span>, <span class="va">...</span><span class="op">)</span></span>
 <span></span>
 <span><span class="fu">saemix_model</span><span class="op">(</span></span>
@@ -166,18 +121,20 @@ Expectation Maximisation algorithm (SAEM).</p>
 <span><span class="fu">saemix_data</span><span class="op">(</span><span class="va">object</span>, covariates <span class="op">=</span> <span class="cn">NULL</span>, verbose <span class="op">=</span> <span class="cn">FALSE</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>object</dt>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
 <dd><p>An <a href="mmkin.html">mmkin</a> row object containing several fits of the same
 <a href="mkinmod.html">mkinmod</a> model to different datasets</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Further parameters passed to <a href="https://rdrr.io/pkg/saemix/man/saemixModel.html" class="external-link">saemix::saemixModel</a>.</p></dd>
 
 
-<dt>transformations</dt>
+<dt id="arg-transformations">transformations<a class="anchor" aria-label="anchor" href="#arg-transformations"></a></dt>
 <dd><p>Per default, all parameter transformations are done
 in mkin. If this argument is set to 'saemix', parameter transformations
 are done in 'saemix' for the supported cases, i.e. (as of version 1.1.2)
@@ -185,39 +142,39 @@ SFO, FOMC, DFOP and HS without fixing <code>parent_0</code>, and SFO or DFOP wit
 one SFO metabolite.</p></dd>
 
 
-<dt>error_model</dt>
+<dt id="arg-error-model">error_model<a class="anchor" aria-label="anchor" href="#arg-error-model"></a></dt>
 <dd><p>Possibility to override the error model used in the mmkin object</p></dd>
 
 
-<dt>degparms_start</dt>
+<dt id="arg-degparms-start">degparms_start<a class="anchor" aria-label="anchor" href="#arg-degparms-start"></a></dt>
 <dd><p>Parameter values given as a named numeric vector will
 be used to override the starting values obtained from the 'mmkin' object.</p></dd>
 
 
-<dt>test_log_parms</dt>
+<dt id="arg-test-log-parms">test_log_parms<a class="anchor" aria-label="anchor" href="#arg-test-log-parms"></a></dt>
 <dd><p>If TRUE, an attempt is made to use more robust starting
 values for population parameters fitted as log parameters in mkin (like
 rate constants) by only considering rate constants that pass the t-test
 when calculating mean degradation parameters using <a href="mean_degparms.html">mean_degparms</a>.</p></dd>
 
 
-<dt>conf.level</dt>
+<dt id="arg-conf-level">conf.level<a class="anchor" aria-label="anchor" href="#arg-conf-level"></a></dt>
 <dd><p>Possibility to adjust the required confidence level
 for parameter that are tested if requested by 'test_log_parms'.</p></dd>
 
 
-<dt>solution_type</dt>
+<dt id="arg-solution-type">solution_type<a class="anchor" aria-label="anchor" href="#arg-solution-type"></a></dt>
 <dd><p>Possibility to specify the solution type in case the
 automatic choice is not desired</p></dd>
 
 
-<dt>covariance.model</dt>
+<dt id="arg-covariance-model">covariance.model<a class="anchor" aria-label="anchor" href="#arg-covariance-model"></a></dt>
 <dd><p>Will be passed to <code><a href="https://rdrr.io/pkg/saemix/man/saemixModel.html" class="external-link">saemix::saemixModel()</a></code>. Per
 default, uncorrelated random effects are specified for all degradation
 parameters.</p></dd>
 
 
-<dt>omega.init</dt>
+<dt id="arg-omega-init">omega.init<a class="anchor" aria-label="anchor" href="#arg-omega-init"></a></dt>
 <dd><p>Will be passed to <code><a href="https://rdrr.io/pkg/saemix/man/saemixModel.html" class="external-link">saemix::saemixModel()</a></code>. If using
 mkin transformations and the default covariance model with optionally
 excluded random effects, the variances of the degradation parameters
@@ -227,83 +184,77 @@ mkin transformations or a custom covariance model, the default
 initialisation of <a href="https://rdrr.io/pkg/saemix/man/saemixModel.html" class="external-link">saemix::saemixModel</a> is used for omega.init.</p></dd>
 
 
-<dt>covariates</dt>
+<dt id="arg-covariates">covariates<a class="anchor" aria-label="anchor" href="#arg-covariates"></a></dt>
 <dd><p>A data frame with covariate data for use in
 'covariate_models', with dataset names as row names.</p></dd>
 
 
-<dt>covariate_models</dt>
+<dt id="arg-covariate-models">covariate_models<a class="anchor" aria-label="anchor" href="#arg-covariate-models"></a></dt>
 <dd><p>A list containing linear model formulas with one explanatory
 variable, i.e. of the type 'parameter ~ covariate'. Covariates must be available
 in the 'covariates' data frame.</p></dd>
 
 
-<dt>no_random_effect</dt>
+<dt id="arg-no-random-effect">no_random_effect<a class="anchor" aria-label="anchor" href="#arg-no-random-effect"></a></dt>
 <dd><p>Character vector of degradation parameters for
 which there should be no variability over the groups. Only used
 if the covariance model is not explicitly specified.</p></dd>
 
 
-<dt>error.init</dt>
+<dt id="arg-error-init">error.init<a class="anchor" aria-label="anchor" href="#arg-error-init"></a></dt>
 <dd><p>Will be passed to <code><a href="https://rdrr.io/pkg/saemix/man/saemixModel.html" class="external-link">saemix::saemixModel()</a></code>.</p></dd>
 
 
-<dt>nbiter.saemix</dt>
+<dt id="arg-nbiter-saemix">nbiter.saemix<a class="anchor" aria-label="anchor" href="#arg-nbiter-saemix"></a></dt>
 <dd><p>Convenience option to increase the number of
 iterations</p></dd>
 
 
-<dt>control</dt>
+<dt id="arg-control">control<a class="anchor" aria-label="anchor" href="#arg-control"></a></dt>
 <dd><p>Passed to <a href="https://rdrr.io/pkg/saemix/man/saemix.html" class="external-link">saemix::saemix</a>.</p></dd>
 
 
-<dt>verbose</dt>
+<dt id="arg-verbose">verbose<a class="anchor" aria-label="anchor" href="#arg-verbose"></a></dt>
 <dd><p>Should we print information about created objects of
 type <a href="https://rdrr.io/pkg/saemix/man/SaemixModel-class.html" class="external-link">saemix::SaemixModel</a> and <a href="https://rdrr.io/pkg/saemix/man/SaemixData-class.html" class="external-link">saemix::SaemixData</a>?</p></dd>
 
 
-<dt>quiet</dt>
+<dt id="arg-quiet">quiet<a class="anchor" aria-label="anchor" href="#arg-quiet"></a></dt>
 <dd><p>Should we suppress the messages saemix prints at the beginning
 and the end of the optimisation process?</p></dd>
 
 
-<dt>x</dt>
+<dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>An saem.mmkin object to print</p></dd>
 
 
-<dt>digits</dt>
+<dt id="arg-digits">digits<a class="anchor" aria-label="anchor" href="#arg-digits"></a></dt>
 <dd><p>Number of digits to use for printing</p></dd>
 
 </dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>An S3 object of class 'saem.mmkin', containing the fitted
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>An S3 object of class 'saem.mmkin', containing the fitted
 <a href="https://rdrr.io/pkg/saemix/man/SaemixObject-class.html" class="external-link">saemix::SaemixObject</a> as a list component named 'so'. The
 object also inherits from 'mixed.mmkin'.</p>
-
-
 <p>An <a href="https://rdrr.io/pkg/saemix/man/SaemixModel-class.html" class="external-link">saemix::SaemixModel</a> object.</p>
-
-
 <p>An <a href="https://rdrr.io/pkg/saemix/man/SaemixData-class.html" class="external-link">saemix::SaemixData</a> object.</p>
     </div>
-    <div id="details">
-    <h2>Details</h2>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>An mmkin row object is essentially a list of mkinfit objects that have been
 obtained by fitting the same model to a list of datasets using <a href="mkinfit.html">mkinfit</a>.</p>
 <p>Starting values for the fixed effects (population mean parameters, argument
 psi0 of <code><a href="https://rdrr.io/pkg/saemix/man/saemixModel.html" class="external-link">saemix::saemixModel()</a></code> are the mean values of the parameters found
 using <a href="mmkin.html">mmkin</a>.</p>
     </div>
-    <div id="see-also">
-    <h2>See also</h2>
+    <div class="section level2">
+    <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
     <div class="dont-index"><p><a href="summary.saem.mmkin.html">summary.saem.mmkin</a> <a href="plot.mixed.mmkin.html">plot.mixed.mmkin</a></p></div>
     </div>
 
-    <div id="ref-examples">
-    <h2>Examples</h2>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># \dontrun{</span></span></span>
 <span class="r-in"><span><span class="va">ds</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="va">experimental_data_for_UBA_2019</span><span class="op">[</span><span class="fl">6</span><span class="op">:</span><span class="fl">10</span><span class="op">]</span>,</span></span>
 <span class="r-in"><span> <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fu"><a href="https://rdrr.io/r/base/subset.html" class="external-link">subset</a></span><span class="op">(</span><span class="va">x</span><span class="op">$</span><span class="va">data</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"name"</span>, <span class="st">"time"</span>, <span class="st">"value"</span><span class="op">)</span><span class="op">]</span><span class="op">)</span><span class="op">)</span></span></span>
@@ -352,7 +303,7 @@ using <a href="mmkin.html">mmkin</a>.</p>
 <span class="r-in"><span><span class="co"># functions from saemix</span></span></span>
 <span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va">saemix</span><span class="op">)</span></span></span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> Loading required package: npde</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Package saemix, version 3.2</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> Package saemix, version 3.3, March 2024</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span>   please direct bugs, questions and feedback to emmanuelle.comets@inserm.fr</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> Attaching package: ‘saemix’</span>
@@ -443,11 +394,11 @@ using <a href="mmkin.html">mmkin</a>.</p>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_saem_dfop_sfo</span><span class="op">)</span></span></span>
 <span class="r-plt img"><img src="saem-4.png" alt="" width="700" height="433"></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">f_saem_dfop_sfo</span>, data <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> saemix version used for fitting:      3.2 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> mkin version used for pre-fitting:  1.2.6 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> R version used for fitting:         4.3.1 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Date of fit:     Mon Oct 30 09:34:35 2023 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Date of summary: Mon Oct 30 09:34:35 2023 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> saemix version used for fitting:      3.3 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> mkin version used for pre-fitting:  1.2.9 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> R version used for fitting:         4.4.2 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Date of fit:     Thu Feb 13 14:59:07 2025 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Date of summary: Thu Feb 13 14:59:07 2025 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Equations:</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *</span>
@@ -462,7 +413,7 @@ using <a href="mmkin.html">mmkin</a>.</p>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Model predictions using solution type analytical </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Fitted in 8.745 s</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Fitted in 3.96 s</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Using 300, 100 iterations and 10 chains</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Variance model: Constant variance </span>
@@ -758,27 +709,23 @@ using <a href="mmkin.html">mmkin</a>.</p>
 <span class="r-in"><span><span class="co"># }</span></span></span>
 </code></pre></div>
     </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/saem.mmkin.html b/docs/reference/saem.mmkin.html
new file mode 100644
index 00000000..65220667
--- /dev/null
+++ b/docs/reference/saem.mmkin.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/saem.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/saem.html">
+  </head>
+</html>
+
diff --git a/docs/reference/saemix_data.html b/docs/reference/saemix_data.html
new file mode 100644
index 00000000..65220667
--- /dev/null
+++ b/docs/reference/saemix_data.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/saem.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/saem.html">
+  </head>
+</html>
+
diff --git a/docs/reference/saemix_model.html b/docs/reference/saemix_model.html
new file mode 100644
index 00000000..65220667
--- /dev/null
+++ b/docs/reference/saemix_model.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/saem.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/saem.html">
+  </head>
+</html>
+
diff --git a/docs/reference/schaefer07_complex_case-1.png b/docs/reference/schaefer07_complex_case-1.png
index a1d7d2f2..4a44d324 100644
--- a/docs/reference/schaefer07_complex_case-1.png
+++ b/docs/reference/schaefer07_complex_case-1.png
diff --git a/docs/reference/schaefer07_complex_case.html b/docs/reference/schaefer07_complex_case.html
index abef385d..c11e4ec5 100644
--- a/docs/reference/schaefer07_complex_case.html
+++ b/docs/reference/schaefer07_complex_case.html
@@ -1,127 +1,82 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Metabolism data set used for checking the software quality of KinGUI — schaefer07_complex_case • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Metabolism data set used for checking the software quality of KinGUI — schaefer07_complex_case"><meta property="og:description" content="This dataset was used for a comparison of KinGUI and ModelMaker to check the
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Metabolism data set used for checking the software quality of KinGUI — schaefer07_complex_case • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Metabolism data set used for checking the software quality of KinGUI — schaefer07_complex_case"><meta name="description" content="This dataset was used for a comparison of KinGUI and ModelMaker to check the
   software quality of KinGUI in the original publication (Schäfer et al., 2007).
-  The results from the fitting are also included."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+  The results from the fitting are also included."><meta property="og:description" content="This dataset was used for a comparison of KinGUI and ModelMaker to check the
+  software quality of KinGUI in the original publication (Schäfer et al., 2007).
+  The results from the fitting are also included."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Metabolism data set used for checking the software quality of KinGUI</h1>
-    
-    <div class="hidden name"><code>schaefer07_complex_case.Rd</code></div>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Metabolism data set used for checking the software quality of KinGUI</h1>
+
+      <div class="d-none name"><code>schaefer07_complex_case.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>This dataset was used for a comparison of KinGUI and ModelMaker to check the
   software quality of KinGUI in the original publication (Schäfer et al., 2007).
   The results from the fitting are also included.</p>
     </div>
 
-    <div id="ref-usage">
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="va">schaefer07_complex_case</span></span></code></pre></div>
     </div>
 
-    <div id="format">
-    <h2>Format</h2>
+    <div class="section level2">
+    <h2 id="format">Format<a class="anchor" aria-label="anchor" href="#format"></a></h2>
     <p>The data set is a data frame with 8 observations on the following 6 variables.</p><dl><dt><code>time</code></dt>
 <dd><p>a numeric vector</p></dd>
 
@@ -140,19 +95,19 @@
     <dt><code>A2</code></dt>
 <dd><p>a numeric vector</p></dd>
 
-  
+
 </dl><p>The results are a data frame with 14 results for different parameter values</p>
     </div>
-    <div id="references">
-    <h2>References</h2>
+    <div class="section level2">
+    <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
     <p>Schäfer D, Mikolasch B, Rainbird P and Harvey B (2007). KinGUI: a new kinetic
   software tool for evaluations according to FOCUS degradation kinetics. In: Del
   Re AAM, Capri E, Fragoulis G and Trevisan M (Eds.). Proceedings of the XIII
   Symposium Pesticide Chemistry, Piacenza, 2007, p. 916-923.</p>
     </div>
 
-    <div id="ref-examples">
-    <h2>Examples</h2>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">data</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkin_wide_to_long.html">mkin_wide_to_long</a></span><span class="op">(</span><span class="va">schaefer07_complex_case</span>, time <span class="op">=</span> <span class="st">"time"</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">model</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span></span></span>
 <span class="r-in"><span>  parent <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span>, to <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"A1"</span>, <span class="st">"B1"</span>, <span class="st">"C1"</span><span class="op">)</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span></span>
@@ -198,27 +153,23 @@
 <span class="r-out co"><span class="r-pr">#&gt;</span> 14 metabolite A2               DT50 28.2400    28.4500       0.7</span>
 </code></pre></div>
     </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/schaefer07_complex_results.html b/docs/reference/schaefer07_complex_results.html
new file mode 100644
index 00000000..31797672
--- /dev/null
+++ b/docs/reference/schaefer07_complex_results.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/schaefer07_complex_case.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/schaefer07_complex_case.html">
+  </head>
+</html>
+
diff --git a/docs/reference/set_nd_nq.html b/docs/reference/set_nd_nq.html
index 249a7600..3ee21a41 100644
--- a/docs/reference/set_nd_nq.html
+++ b/docs/reference/set_nd_nq.html
@@ -1,118 +1,74 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Set non-detects and unquantified values in residue series without replicates — set_nd_nq • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Set non-detects and unquantified values in residue series without replicates — set_nd_nq"><meta property="og:description" content="This function automates replacing unquantified values in residue time and
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Set non-detects and unquantified values in residue series without replicates — set_nd_nq • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Set non-detects and unquantified values in residue series without replicates — set_nd_nq"><meta name="description" content="This function automates replacing unquantified values in residue time and
 depth series. For time series, the function performs part of the residue
 processing proposed in the FOCUS kinetics guidance for parent compounds
 and metabolites. For two-dimensional residue series over time and depth,
-it automates the proposal of Boesten et al (2015)."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+it automates the proposal of Boesten et al (2015)."><meta property="og:description" content="This function automates replacing unquantified values in residue time and
+depth series. For time series, the function performs part of the residue
+processing proposed in the FOCUS kinetics guidance for parent compounds
+and metabolites. For two-dimensional residue series over time and depth,
+it automates the proposal of Boesten et al (2015)."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Set non-detects and unquantified values in residue series without replicates</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/set_nd_nq.R" class="external-link"><code>R/set_nd_nq.R</code></a></small>
-    <div class="hidden name"><code>set_nd_nq.Rd</code></div>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Set non-detects and unquantified values in residue series without replicates</h1>
+      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/set_nd_nq.R" class="external-link"><code>R/set_nd_nq.R</code></a></small>
+      <div class="d-none name"><code>set_nd_nq.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>This function automates replacing unquantified values in residue time and
 depth series. For time series, the function performs part of the residue
 processing proposed in the FOCUS kinetics guidance for parent compounds
@@ -120,7 +76,8 @@ and metabolites. For two-dimensional residue series over time and depth,
 it automates the proposal of Boesten et al (2015).</p>
     </div>
 
-    <div id="ref-usage">
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">set_nd_nq</span><span class="op">(</span><span class="va">res_raw</span>, <span class="va">lod</span>, loq <span class="op">=</span> <span class="cn">NA</span>, time_zero_presence <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span>
 <span></span>
 <span><span class="fu">set_nd_nq_focus</span><span class="op">(</span></span>
@@ -133,9 +90,11 @@ it automates the proposal of Boesten et al (2015).</p>
 <span><span class="op">)</span></span></code></pre></div>
     </div>
 
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>res_raw</dt>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-res-raw">res_raw<a class="anchor" aria-label="anchor" href="#arg-res-raw"></a></dt>
 <dd><p>Character vector of a residue time series, or matrix of
 residue values with rows representing depth profiles for a specific sampling
 time, and columns representing time series of residues at the same depth.
@@ -145,48 +104,46 @@ to be coded as "nq". Samples not analysed have to be coded as "na". All
 values that are not "na", "nd" or "nq" have to be coercible to numeric</p></dd>
 
 
-<dt>lod</dt>
+<dt id="arg-lod">lod<a class="anchor" aria-label="anchor" href="#arg-lod"></a></dt>
 <dd><p>Limit of detection (numeric)</p></dd>
 
 
-<dt>loq</dt>
+<dt id="arg-loq">loq<a class="anchor" aria-label="anchor" href="#arg-loq"></a></dt>
 <dd><p>Limit of quantification(numeric). Must be specified if the FOCUS rule to
 stop after the first non-detection is to be applied</p></dd>
 
 
-<dt>time_zero_presence</dt>
+<dt id="arg-time-zero-presence">time_zero_presence<a class="anchor" aria-label="anchor" href="#arg-time-zero-presence"></a></dt>
 <dd><p>Do we assume that residues occur at time zero?
 This only affects samples from the first sampling time that have been
 reported as "nd" (not detected).</p></dd>
 
 
-<dt>set_first_sample_nd</dt>
+<dt id="arg-set-first-sample-nd">set_first_sample_nd<a class="anchor" aria-label="anchor" href="#arg-set-first-sample-nd"></a></dt>
 <dd><p>Should the first sample be set to "first_sample_nd_value"
 in case it is a non-detection?</p></dd>
 
 
-<dt>first_sample_nd_value</dt>
+<dt id="arg-first-sample-nd-value">first_sample_nd_value<a class="anchor" aria-label="anchor" href="#arg-first-sample-nd-value"></a></dt>
 <dd><p>Value to be used for the first sample if it is a non-detection</p></dd>
 
 
-<dt>ignore_below_loq_after_first_nd</dt>
+<dt id="arg-ignore-below-loq-after-first-nd">ignore_below_loq_after_first_nd<a class="anchor" aria-label="anchor" href="#arg-ignore-below-loq-after-first-nd"></a></dt>
 <dd><p>Should we ignore values below the LOQ after the first
 non-detection that occurs after the quantified values?</p></dd>
 
 </dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>A numeric vector, if a vector was supplied, or a numeric matrix otherwise</p>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>A numeric vector, if a vector was supplied, or a numeric matrix otherwise</p>
     </div>
-    <div id="functions">
-    <h2>Functions</h2>
-    
+    <div class="section level2">
+    <h2 id="functions">Functions<a class="anchor" aria-label="anchor" href="#functions"></a></h2>
+
 <ul><li><p><code>set_nd_nq_focus()</code>: Set non-detects in residue time series according to FOCUS rules</p></li>
 </ul></div>
-    <div id="references">
-    <h2>References</h2>
+    <div class="section level2">
+    <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
     <p>Boesten, J. J. T. I., van der Linden, A. M. A., Beltman, W. H.
 J. and Pol, J. W. (2015). Leaching of plant protection products and their
 transformation products; Proposals for improving the assessment of leaching
@@ -197,8 +154,8 @@ Kinetics from Environmental Fate Studies on Pesticides in EU Registration, Versi
 18 December 2014, p. 251</p>
     </div>
 
-    <div id="ref-examples">
-    <h2>Examples</h2>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># FOCUS (2014) p. 75/76 and 131/132</span></span></span>
 <span class="r-in"><span><span class="va">parent_1</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">.12</span>, <span class="fl">.09</span>, <span class="fl">.05</span>, <span class="fl">.03</span>, <span class="st">"nd"</span>, <span class="st">"nd"</span>, <span class="st">"nd"</span>, <span class="st">"nd"</span>, <span class="st">"nd"</span>, <span class="st">"nd"</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu">set_nd_nq</span><span class="op">(</span><span class="va">parent_1</span>, <span class="fl">0.02</span><span class="op">)</span></span></span>
@@ -255,27 +212,23 @@ Kinetics from Environmental Fate Studies on Pesticides in EU Registration, Versi
 <span class="r-out co"><span class="r-pr">#&gt;</span> [7,]    NA    NA    NA   NA   NA   NA</span>
 </code></pre></div>
     </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/set_nd_nq_focus.html b/docs/reference/set_nd_nq_focus.html
new file mode 100644
index 00000000..4a0d21cd
--- /dev/null
+++ b/docs/reference/set_nd_nq_focus.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/set_nd_nq.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/set_nd_nq.html">
+  </head>
+</html>
+
diff --git a/docs/reference/sigma_twocomp-1.png b/docs/reference/sigma_twocomp-1.png
index 0353b72c..89500ee7 100644
--- a/docs/reference/sigma_twocomp-1.png
+++ b/docs/reference/sigma_twocomp-1.png
diff --git a/docs/reference/sigma_twocomp.html b/docs/reference/sigma_twocomp.html
index 06bae61f..5c9fb8b8 100644
--- a/docs/reference/sigma_twocomp.html
+++ b/docs/reference/sigma_twocomp.html
@@ -1,147 +1,101 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Two-component error model — sigma_twocomp • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Two-component error model — sigma_twocomp"><meta property="og:description" content="Function describing the standard deviation of the measurement error in
-dependence of the measured value \(y\):"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Two-component error model — sigma_twocomp • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Two-component error model — sigma_twocomp"><meta name="description" content="Function describing the standard deviation of the measurement error in
+dependence of the measured value \(y\):"><meta property="og:description" content="Function describing the standard deviation of the measurement error in
+dependence of the measured value \(y\):"></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Two-component error model</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/sigma_twocomp.R" class="external-link"><code>R/sigma_twocomp.R</code></a></small>
-    <div class="hidden name"><code>sigma_twocomp.Rd</code></div>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Two-component error model</h1>
+      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/sigma_twocomp.R" class="external-link"><code>R/sigma_twocomp.R</code></a></small>
+      <div class="d-none name"><code>sigma_twocomp.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Function describing the standard deviation of the measurement error in
 dependence of the measured value \(y\):</p>
     </div>
 
-    <div id="ref-usage">
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">sigma_twocomp</span><span class="op">(</span><span class="va">y</span>, <span class="va">sigma_low</span>, <span class="va">rsd_high</span><span class="op">)</span></span></code></pre></div>
     </div>
 
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>y</dt>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-y">y<a class="anchor" aria-label="anchor" href="#arg-y"></a></dt>
 <dd><p>The magnitude of the observed value</p></dd>
 
 
-<dt>sigma_low</dt>
+<dt id="arg-sigma-low">sigma_low<a class="anchor" aria-label="anchor" href="#arg-sigma-low"></a></dt>
 <dd><p>The asymptotic minimum of the standard deviation for low
 observed values</p></dd>
 
 
-<dt>rsd_high</dt>
+<dt id="arg-rsd-high">rsd_high<a class="anchor" aria-label="anchor" href="#arg-rsd-high"></a></dt>
 <dd><p>The coefficient describing the increase of the standard
 deviation with the magnitude of the observed value</p></dd>
 
 </dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>The standard deviation of the response variable.</p>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>The standard deviation of the response variable.</p>
     </div>
-    <div id="details">
-    <h2>Details</h2>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>$$\sigma = \sqrt{ \sigma_{low}^2 + y^2 * {rsd}_{high}^2}$$ sigma =
 sqrt(sigma_low^2 + y^2 * rsd_high^2)</p>
 <p>This is the error model used for example by Werner et al. (1978). The model
@@ -149,8 +103,8 @@ proposed by Rocke and Lorenzato (1995) can be written in this form as well,
 but assumes approximate lognormal distribution of errors for high values of
 y.</p>
     </div>
-    <div id="references">
-    <h2>References</h2>
+    <div class="section level2">
+    <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
     <p>Werner, Mario, Brooks, Samuel H., and Knott, Lancaster B. (1978)
 Additive, Multiplicative, and Mixed Analytical Errors. Clinical Chemistry
 24(11), 1895-1898.</p>
@@ -162,8 +116,8 @@ Degradation Data. <em>Environments</em> 6(12) 124
 .</p>
     </div>
 
-    <div id="ref-examples">
-    <h2>Examples</h2>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">times</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span>, <span class="fl">3</span>, <span class="fl">7</span>, <span class="fl">14</span>, <span class="fl">28</span>, <span class="fl">60</span>, <span class="fl">90</span>, <span class="fl">120</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">d_pred</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span>time <span class="op">=</span> <span class="va">times</span>, parent <span class="op">=</span> <span class="fl">100</span> <span class="op">*</span> <span class="fu"><a href="https://rdrr.io/r/base/Log.html" class="external-link">exp</a></span><span class="op">(</span><span class="op">-</span> <span class="fl">0.03</span> <span class="op">*</span> <span class="va">times</span><span class="op">)</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/Random.html" class="external-link">set.seed</a></span><span class="op">(</span><span class="fl">123456</span><span class="op">)</span></span></span>
@@ -193,27 +147,23 @@ Degradation Data. <em>Environments</em> 6(12) 124
 <span class="r-out co"><span class="r-pr">#&gt;</span> f_mkin_tc     4 101.6446</span>
 </code></pre></div>
     </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/status.html b/docs/reference/status.html
index 2ff35423..bf77c36d 100644
--- a/docs/reference/status.html
+++ b/docs/reference/status.html
@@ -1,157 +1,110 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Method to get status information for fit array objects — status • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Method to get status information for fit array objects — status"><meta property="og:description" content="Method to get status information for fit array objects"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Method to get status information for fit array objects — status • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Method to get status information for fit array objects — status"><meta name="description" content="Method to get status information for fit array objects"><meta property="og:description" content="Method to get status information for fit array objects"></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Method to get status information for fit array objects</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/status.R" class="external-link"><code>R/status.R</code></a></small>
-    <div class="hidden name"><code>status.Rd</code></div>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Method to get status information for fit array objects</h1>
+      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/status.R" class="external-link"><code>R/status.R</code></a></small>
+      <div class="d-none name"><code>status.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Method to get status information for fit array objects</p>
     </div>
 
-    <div id="ref-usage">
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">status</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for mmkin</span></span>
+<span><span class="co"># S3 method for class 'mmkin'</span></span>
 <span><span class="fu">status</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for status.mmkin</span></span>
+<span><span class="co"># S3 method for class 'status.mmkin'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for mhmkin</span></span>
+<span><span class="co"># S3 method for class 'mhmkin'</span></span>
 <span><span class="fu">status</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for status.mhmkin</span></span>
+<span><span class="co"># S3 method for class 'status.mhmkin'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>object</dt>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
 <dd><p>The object to investigate</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>For potential future extensions</p></dd>
 
 
-<dt>x</dt>
+<dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>The object to be printed</p></dd>
 
 </dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>An object with the same dimensions as the fit array
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>An object with the same dimensions as the fit array
 suitable printing method.</p>
     </div>
 
-    <div id="ref-examples">
-    <h2>Examples</h2>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># \dontrun{</span></span></span>
 <span class="r-in"><span><span class="va">fits</span> <span class="op">&lt;-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span></span></span>
 <span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"FOMC"</span><span class="op">)</span>,</span></span>
@@ -170,27 +123,23 @@ suitable printing method.</p>
 <span class="r-in"><span><span class="co"># }</span></span></span>
 </code></pre></div>
     </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/status.mhmkin.html b/docs/reference/status.mhmkin.html
new file mode 100644
index 00000000..dc236170
--- /dev/null
+++ b/docs/reference/status.mhmkin.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/status.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/status.html">
+  </head>
+</html>
+
diff --git a/docs/reference/status.mmkin.html b/docs/reference/status.mmkin.html
new file mode 100644
index 00000000..dc236170
--- /dev/null
+++ b/docs/reference/status.mmkin.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/status.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/status.html">
+  </head>
+</html>
+
diff --git a/docs/reference/summary.mkinfit.html b/docs/reference/summary.mkinfit.html
index fe24f3a4..86663890 100644
--- a/docs/reference/summary.mkinfit.html
+++ b/docs/reference/summary.mkinfit.html
@@ -1,118 +1,74 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Summary method for class "mkinfit" — summary.mkinfit • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Summary method for class " mkinfit summary.mkinfit><meta property="og:description" content="Lists model equations, initial parameter values, optimised parameters with
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Summary method for class "mkinfit" — summary.mkinfit • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Summary method for class " mkinfit summary.mkinfit><meta name="description" content="Lists model equations, initial parameter values, optimised parameters with
 some uncertainty statistics, the chi2 error levels calculated according to
 FOCUS guidance (2006) as defined therein, formation fractions, DT50 values
 and optionally the data, consisting of observed, predicted and residual
-values."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+values."><meta property="og:description" content="Lists model equations, initial parameter values, optimised parameters with
+some uncertainty statistics, the chi2 error levels calculated according to
+FOCUS guidance (2006) as defined therein, formation fractions, DT50 values
+and optionally the data, consisting of observed, predicted and residual
+values."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Summary method for class "mkinfit"</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/summary.mkinfit.R" class="external-link"><code>R/summary.mkinfit.R</code></a></small>
-    <div class="hidden name"><code>summary.mkinfit.Rd</code></div>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Summary method for class "mkinfit"</h1>
+      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/summary.mkinfit.R" class="external-link"><code>R/summary.mkinfit.R</code></a></small>
+      <div class="d-none name"><code>summary.mkinfit.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Lists model equations, initial parameter values, optimised parameters with
 some uncertainty statistics, the chi2 error levels calculated according to
 FOCUS guidance (2006) as defined therein, formation fractions, DT50 values
@@ -120,52 +76,53 @@ and optionally the data, consisting of observed, predicted and residual
 values.</p>
     </div>
 
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for mkinfit</span></span>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'mkinfit'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">object</span>, data <span class="op">=</span> <span class="cn">TRUE</span>, distimes <span class="op">=</span> <span class="cn">TRUE</span>, alpha <span class="op">=</span> <span class="fl">0.05</span>, <span class="va">...</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for summary.mkinfit</span></span>
+<span><span class="co"># S3 method for class 'summary.mkinfit'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, digits <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html" class="external-link">max</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fu"><a href="https://rdrr.io/r/base/options.html" class="external-link">getOption</a></span><span class="op">(</span><span class="st">"digits"</span><span class="op">)</span> <span class="op">-</span> <span class="fl">3</span><span class="op">)</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>object</dt>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
 <dd><p>an object of class <a href="mkinfit.html">mkinfit</a>.</p></dd>
 
 
-<dt>data</dt>
+<dt id="arg-data">data<a class="anchor" aria-label="anchor" href="#arg-data"></a></dt>
 <dd><p>logical, indicating whether the data should be included in the
 summary.</p></dd>
 
 
-<dt>distimes</dt>
+<dt id="arg-distimes">distimes<a class="anchor" aria-label="anchor" href="#arg-distimes"></a></dt>
 <dd><p>logical, indicating whether DT50 and DT90 values should be
 included.</p></dd>
 
 
-<dt>alpha</dt>
+<dt id="arg-alpha">alpha<a class="anchor" aria-label="anchor" href="#arg-alpha"></a></dt>
 <dd><p>error level for confidence interval estimation from t
 distribution</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>optional arguments passed to methods like <code>print</code>.</p></dd>
 
 
-<dt>x</dt>
+<dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>an object of class <code>summary.mkinfit</code>.</p></dd>
 
 
-<dt>digits</dt>
+<dt id="arg-digits">digits<a class="anchor" aria-label="anchor" href="#arg-digits"></a></dt>
 <dd><p>Number of digits to use for printing</p></dd>
 
 </dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>The summary function returns a list with components, among others</p>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>The summary function returns a list with components, among others</p>
 <dl><dt>version, Rversion</dt>
 <dd><p>The mkin and R versions used</p></dd>
 
@@ -217,34 +174,34 @@ g of SFORB systems in the model.</p></dd>
 
 </dl><p>The print method is called for its side effect, i.e. printing the summary.</p>
     </div>
-    <div id="references">
-    <h2>References</h2>
+    <div class="section level2">
+    <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
     <p>FOCUS (2006) “Guidance Document on Estimating Persistence
 and Degradation Kinetics from Environmental Fate Studies on Pesticides in
 EU Registration” Report of the FOCUS Work Group on Degradation Kinetics,
 EC Document Reference Sanco/10058/2005 version 2.0, 434 pp,
 <a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics" class="external-link">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p>
     </div>
-    <div id="author">
-    <h2>Author</h2>
+    <div class="section level2">
+    <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a></h2>
     <p>Johannes Ranke</p>
     </div>
 
-    <div id="ref-examples">
-    <h2>Examples</h2>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span>
 <span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="va">FOCUS_2006_A</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> mkin version used for fitting:    1.2.6 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> R version used for fitting:       4.3.1 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Date of fit:     Mon Oct 30 09:39:49 2023 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Date of summary: Mon Oct 30 09:39:49 2023 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> mkin version used for fitting:    1.2.9 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> R version used for fitting:       4.4.2 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Date of fit:     Thu Feb 13 15:01:12 2025 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Date of summary: Thu Feb 13 15:01:12 2025 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Equations:</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> d_parent/dt = - k_parent * parent</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Model predictions using solution type analytical </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Fitted using 131 model solutions performed in 0.02 s</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Fitted using 131 model solutions performed in 0.01 s</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Error model: Constant variance </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
@@ -311,27 +268,23 @@ EC Document Reference Sanco/10058/2005 version 2.0, 434 pp,
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/summary.mmkin.html b/docs/reference/summary.mmkin.html
index 9b10a415..bf48a2a6 100644
--- a/docs/reference/summary.mmkin.html
+++ b/docs/reference/summary.mmkin.html
@@ -1,152 +1,108 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Summary method for class "mmkin" — summary.mmkin • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Summary method for class " mmkin summary.mmkin><meta property="og:description" content="Shows status information on the mkinfit objects contained in the object
-and gives an overview of ill-defined parameters calculated by illparms."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Summary method for class "mmkin" — summary.mmkin • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Summary method for class " mmkin summary.mmkin><meta name="description" content="Shows status information on the mkinfit objects contained in the object
+and gives an overview of ill-defined parameters calculated by illparms."><meta property="og:description" content="Shows status information on the mkinfit objects contained in the object
+and gives an overview of ill-defined parameters calculated by illparms."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Summary method for class "mmkin"</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/summary.mmkin.R" class="external-link"><code>R/summary.mmkin.R</code></a></small>
-    <div class="hidden name"><code>summary.mmkin.Rd</code></div>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Summary method for class "mmkin"</h1>
+      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/summary.mmkin.R" class="external-link"><code>R/summary.mmkin.R</code></a></small>
+      <div class="d-none name"><code>summary.mmkin.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Shows status information on the <a href="mkinfit.html">mkinfit</a> objects contained in the object
 and gives an overview of ill-defined parameters calculated by <a href="illparms.html">illparms</a>.</p>
     </div>
 
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for mmkin</span></span>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'mmkin'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">object</span>, conf.level <span class="op">=</span> <span class="fl">0.95</span>, <span class="va">...</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for summary.mmkin</span></span>
+<span><span class="co"># S3 method for class 'summary.mmkin'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, digits <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html" class="external-link">max</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fu"><a href="https://rdrr.io/r/base/options.html" class="external-link">getOption</a></span><span class="op">(</span><span class="st">"digits"</span><span class="op">)</span> <span class="op">-</span> <span class="fl">3</span><span class="op">)</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>object</dt>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
 <dd><p>an object of class <a href="mmkin.html">mmkin</a></p></dd>
 
 
-<dt>conf.level</dt>
+<dt id="arg-conf-level">conf.level<a class="anchor" aria-label="anchor" href="#arg-conf-level"></a></dt>
 <dd><p>confidence level for testing parameters</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>optional arguments passed to methods like <code>print</code>.</p></dd>
 
 
-<dt>x</dt>
+<dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>an object of class <code>summary.mmkin</code>.</p></dd>
 
 
-<dt>digits</dt>
+<dt id="arg-digits">digits<a class="anchor" aria-label="anchor" href="#arg-digits"></a></dt>
 <dd><p>number of digits to use for printing</p></dd>
 
 </dl></div>
 
-    <div id="ref-examples">
-    <h2>Examples</h2>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="va">fits</span> <span class="op">&lt;-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span></span></span>
 <span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"FOMC"</span><span class="op">)</span>,</span></span>
@@ -157,7 +113,7 @@ and gives an overview of ill-defined parameters calculated by <a href="illparms.
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> false convergence (8)</span>
 <span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">fits</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Error model: Constant variance </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Fitted in 0.705 s</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Fitted in 0.478 s</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Status:</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>       dataset</span>
@@ -177,27 +133,23 @@ and gives an overview of ill-defined parameters calculated by <a href="illparms.
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/summary.nlme.mmkin.html b/docs/reference/summary.nlme.mmkin.html
index 58eb3e21..22cecb0d 100644
--- a/docs/reference/summary.nlme.mmkin.html
+++ b/docs/reference/summary.nlme.mmkin.html
@@ -1,118 +1,74 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Summary method for class "nlme.mmkin" — summary.nlme.mmkin • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Summary method for class " nlme.mmkin summary.nlme.mmkin><meta property="og:description" content="Lists model equations, initial parameter values, optimised parameters
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Summary method for class "nlme.mmkin" — summary.nlme.mmkin • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Summary method for class " nlme.mmkin summary.nlme.mmkin><meta name="description" content="Lists model equations, initial parameter values, optimised parameters
 for fixed effects (population), random effects (deviations from the
 population mean) and residual error model, as well as the resulting
 endpoints such as formation fractions and DT50 values. Optionally
-(default is FALSE), the data are listed in full."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+(default is FALSE), the data are listed in full."><meta property="og:description" content="Lists model equations, initial parameter values, optimised parameters
+for fixed effects (population), random effects (deviations from the
+population mean) and residual error model, as well as the resulting
+endpoints such as formation fractions and DT50 values. Optionally
+(default is FALSE), the data are listed in full."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Summary method for class "nlme.mmkin"</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/summary.nlme.mmkin.R" class="external-link"><code>R/summary.nlme.mmkin.R</code></a></small>
-    <div class="hidden name"><code>summary.nlme.mmkin.Rd</code></div>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Summary method for class "nlme.mmkin"</h1>
+      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/summary.nlme.mmkin.R" class="external-link"><code>R/summary.nlme.mmkin.R</code></a></small>
+      <div class="d-none name"><code>summary.nlme.mmkin.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Lists model equations, initial parameter values, optimised parameters
 for fixed effects (population), random effects (deviations from the
 population mean) and residual error model, as well as the resulting
@@ -120,8 +76,9 @@ endpoints such as formation fractions and DT50 values. Optionally
 (default is FALSE), the data are listed in full.</p>
     </div>
 
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for nlme.mmkin</span></span>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'nlme.mmkin'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span></span>
 <span>  <span class="va">object</span>,</span>
 <span>  data <span class="op">=</span> <span class="cn">FALSE</span>,</span>
@@ -131,52 +88,52 @@ endpoints such as formation fractions and DT50 values. Optionally
 <span>  <span class="va">...</span></span>
 <span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for summary.nlme.mmkin</span></span>
+<span><span class="co"># S3 method for class 'summary.nlme.mmkin'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, digits <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html" class="external-link">max</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fu"><a href="https://rdrr.io/r/base/options.html" class="external-link">getOption</a></span><span class="op">(</span><span class="st">"digits"</span><span class="op">)</span> <span class="op">-</span> <span class="fl">3</span><span class="op">)</span>, verbose <span class="op">=</span> <span class="va">x</span><span class="op">$</span><span class="va">verbose</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>object</dt>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
 <dd><p>an object of class <a href="nlme.mmkin.html">nlme.mmkin</a></p></dd>
 
 
-<dt>data</dt>
+<dt id="arg-data">data<a class="anchor" aria-label="anchor" href="#arg-data"></a></dt>
 <dd><p>logical, indicating whether the full data should be included in
 the summary.</p></dd>
 
 
-<dt>verbose</dt>
+<dt id="arg-verbose">verbose<a class="anchor" aria-label="anchor" href="#arg-verbose"></a></dt>
 <dd><p>Should the summary be verbose?</p></dd>
 
 
-<dt>distimes</dt>
+<dt id="arg-distimes">distimes<a class="anchor" aria-label="anchor" href="#arg-distimes"></a></dt>
 <dd><p>logical, indicating whether DT50 and DT90 values should be
 included.</p></dd>
 
 
-<dt>alpha</dt>
+<dt id="arg-alpha">alpha<a class="anchor" aria-label="anchor" href="#arg-alpha"></a></dt>
 <dd><p>error level for confidence interval estimation from the t
 distribution</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>optional arguments passed to methods like <code>print</code>.</p></dd>
 
 
-<dt>x</dt>
+<dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>an object of class summary.nlme.mmkin</p></dd>
 
 
-<dt>digits</dt>
+<dt id="arg-digits">digits<a class="anchor" aria-label="anchor" href="#arg-digits"></a></dt>
 <dd><p>Number of digits to use for printing</p></dd>
 
 </dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>The summary function returns a list based on the <a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a> object
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>The summary function returns a list based on the <a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a> object
 obtained in the fit, with at least the following additional components</p>
 <dl><dt>nlmeversion, mkinversion, Rversion</dt>
 <dd><p>The nlme, mkin and R versions used</p></dd>
@@ -212,14 +169,14 @@ model.</p></dd>
 
 </dl><p>The print method is called for its side effect, i.e. printing the summary.</p>
     </div>
-    <div id="author">
-    <h2>Author</h2>
+    <div class="section level2">
+    <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a></h2>
     <p>Johannes Ranke for the mkin specific parts
 José Pinheiro and Douglas Bates for the components inherited from nlme</p>
     </div>
 
-    <div id="ref-examples">
-    <h2>Examples</h2>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># Generate five datasets following SFO kinetics</span></span></span>
 <span class="r-in"><span><span class="va">sampling_times</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span>, <span class="fl">3</span>, <span class="fl">7</span>, <span class="fl">14</span>, <span class="fl">28</span>, <span class="fl">60</span>, <span class="fl">90</span>, <span class="fl">120</span><span class="op">)</span></span></span>
@@ -254,11 +211,11 @@ José Pinheiro and Douglas Bates for the components inherited from nlme</p>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> iteration limit reached without convergence (10)</span>
 <span class="r-in"><span><span class="va">f_nlme</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a></span><span class="op">(</span><span class="va">f_mmkin</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">f_nlme</span>, data <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> nlme version used for fitting:      3.1.163 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> mkin version used for pre-fitting:  1.2.6 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> R version used for fitting:         4.3.1 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Date of fit:     Mon Oct 30 09:39:53 2023 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Date of summary: Mon Oct 30 09:39:53 2023 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> nlme version used for fitting:      3.1.166 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> mkin version used for pre-fitting:  1.2.9 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> R version used for fitting:         4.4.2 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Date of fit:     Thu Feb 13 15:01:14 2025 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Date of summary: Thu Feb 13 15:01:14 2025 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Equations:</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> d_parent/dt = - k_parent * parent</span>
@@ -268,7 +225,7 @@ José Pinheiro and Douglas Bates for the components inherited from nlme</p>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Model predictions using solution type analytical </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Fitted in 0.451 s using 4 iterations</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Fitted in 0.185 s using 4 iterations</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Variance model: Two-component variance function </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
@@ -412,27 +369,23 @@ José Pinheiro and Douglas Bates for the components inherited from nlme</p>
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/summary.nlmixr.mmkin.html b/docs/reference/summary.nlmixr.mmkin.html
deleted file mode 100644
index 57541950..00000000
--- a/docs/reference/summary.nlmixr.mmkin.html
+++ /dev/null
@@ -1,2917 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Summary method for class "nlmixr.mmkin" — summary.nlmixr.mmkin • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Summary method for class " nlmixr.mmkin summary.nlmixr.mmkin><meta property="og:description" content="Lists model equations, initial parameter values, optimised parameters
-for fixed effects (population), random effects (deviations from the
-population mean) and residual error model, as well as the resulting
-endpoints such as formation fractions and DT50 values. Optionally
-(default is FALSE), the data are listed in full."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Functions and data</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Summary method for class "nlmixr.mmkin"</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/summary.nlmixr.mmkin.R" class="external-link"><code>R/summary.nlmixr.mmkin.R</code></a></small>
-    <div class="hidden name"><code>summary.nlmixr.mmkin.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>Lists model equations, initial parameter values, optimised parameters
-for fixed effects (population), random effects (deviations from the
-population mean) and residual error model, as well as the resulting
-endpoints such as formation fractions and DT50 values. Optionally
-(default is FALSE), the data are listed in full.</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="co"># S3 method for nlmixr.mmkin</span>
-<span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">object</span>, data <span class="op">=</span> <span class="cn">FALSE</span>, verbose <span class="op">=</span> <span class="cn">FALSE</span>, distimes <span class="op">=</span> <span class="cn">TRUE</span>, <span class="va">...</span><span class="op">)</span>
-
-<span class="co"># S3 method for summary.nlmixr.mmkin</span>
-<span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, digits <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html" class="external-link">max</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fu"><a href="https://rdrr.io/r/base/options.html" class="external-link">getOption</a></span><span class="op">(</span><span class="st">"digits"</span><span class="op">)</span> <span class="op">-</span> <span class="fl">3</span><span class="op">)</span>, verbose <span class="op">=</span> <span class="va">x</span><span class="op">$</span><span class="va">verbose</span>, <span class="va">...</span><span class="op">)</span></code></pre></div>
-    </div>
-
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>object</dt>
-<dd><p>an object of class <a href="nlmixr.mmkin.html">nlmixr.mmkin</a></p></dd>
-<dt>data</dt>
-<dd><p>logical, indicating whether the full data should be included in
-the summary.</p></dd>
-<dt>verbose</dt>
-<dd><p>Should the summary be verbose?</p></dd>
-<dt>distimes</dt>
-<dd><p>logical, indicating whether DT50 and DT90 values should be
-included.</p></dd>
-<dt>...</dt>
-<dd><p>optional arguments passed to methods like <code>print</code>.</p></dd>
-<dt>x</dt>
-<dd><p>an object of class summary.nlmixr.mmkin</p></dd>
-<dt>digits</dt>
-<dd><p>Number of digits to use for printing</p></dd>
-</dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    <p>The summary function returns a list obtained in the fit, with at
-least the following additional components</p>
-<dl><dt>nlmixrversion, mkinversion, Rversion</dt>
-<dd><p>The nlmixr, mkin and R versions used</p></dd>
-<dt>date.fit, date.summary</dt>
-<dd><p>The dates where the fit and the summary were
-produced</p></dd>
-<dt>diffs</dt>
-<dd><p>The differential equations used in the degradation model</p></dd>
-<dt>use_of_ff</dt>
-<dd><p>Was maximum or minimum use made of formation fractions</p></dd>
-<dt>data</dt>
-<dd><p>The data</p></dd>
-<dt>confint_back</dt>
-<dd><p>Backtransformed parameters, with confidence intervals if available</p></dd>
-<dt>ff</dt>
-<dd><p>The estimated formation fractions derived from the fitted
-model.</p></dd>
-<dt>distimes</dt>
-<dd><p>The DT50 and DT90 values for each observed variable.</p></dd>
-<dt>SFORB</dt>
-<dd><p>If applicable, eigenvalues of SFORB components of the model.</p></dd>
-</dl><p>The print method is called for its side effect, i.e. printing the summary.</p>
-    </div>
-    <div id="author">
-    <h2>Author</h2>
-    <p>Johannes Ranke for the mkin specific parts
-nlmixr authors for the parts inherited from nlmixr.</p>
-    </div>
-
-    <div id="ref-examples">
-    <h2>Examples</h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="co"># Generate five datasets following DFOP-SFO kinetics</span></span>
-<span class="r-in"><span class="va">sampling_times</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span>, <span class="fl">3</span>, <span class="fl">7</span>, <span class="fl">14</span>, <span class="fl">28</span>, <span class="fl">60</span>, <span class="fl">90</span>, <span class="fl">120</span><span class="op">)</span></span>
-<span class="r-in"><span class="va">dfop_sfo</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="st">"m1"</span><span class="op">)</span>,</span>
-<span class="r-in"> m1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
-<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/Random.html" class="external-link">set.seed</a></span><span class="op">(</span><span class="fl">1234</span><span class="op">)</span></span>
-<span class="r-in"><span class="va">k1_in</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/Lognormal.html" class="external-link">rlnorm</a></span><span class="op">(</span><span class="fl">5</span>, <span class="fu"><a href="https://rdrr.io/r/base/Log.html" class="external-link">log</a></span><span class="op">(</span><span class="fl">0.1</span><span class="op">)</span>, <span class="fl">0.3</span><span class="op">)</span></span>
-<span class="r-in"><span class="va">k2_in</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/Lognormal.html" class="external-link">rlnorm</a></span><span class="op">(</span><span class="fl">5</span>, <span class="fu"><a href="https://rdrr.io/r/base/Log.html" class="external-link">log</a></span><span class="op">(</span><span class="fl">0.02</span><span class="op">)</span>, <span class="fl">0.3</span><span class="op">)</span></span>
-<span class="r-in"><span class="va">g_in</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/Logistic.html" class="external-link">plogis</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/stats/Normal.html" class="external-link">rnorm</a></span><span class="op">(</span><span class="fl">5</span>, <span class="fu"><a href="https://rdrr.io/r/stats/Logistic.html" class="external-link">qlogis</a></span><span class="op">(</span><span class="fl">0.5</span><span class="op">)</span>, <span class="fl">0.3</span><span class="op">)</span><span class="op">)</span></span>
-<span class="r-in"><span class="va">f_parent_to_m1_in</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/Logistic.html" class="external-link">plogis</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/stats/Normal.html" class="external-link">rnorm</a></span><span class="op">(</span><span class="fl">5</span>, <span class="fu"><a href="https://rdrr.io/r/stats/Logistic.html" class="external-link">qlogis</a></span><span class="op">(</span><span class="fl">0.3</span><span class="op">)</span>, <span class="fl">0.3</span><span class="op">)</span><span class="op">)</span></span>
-<span class="r-in"><span class="va">k_m1_in</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/Lognormal.html" class="external-link">rlnorm</a></span><span class="op">(</span><span class="fl">5</span>, <span class="fu"><a href="https://rdrr.io/r/base/Log.html" class="external-link">log</a></span><span class="op">(</span><span class="fl">0.02</span><span class="op">)</span>, <span class="fl">0.3</span><span class="op">)</span></span>
-<span class="r-in"></span>
-<span class="r-in"><span class="va">pred_dfop_sfo</span> <span class="op">&lt;-</span> <span class="kw">function</span><span class="op">(</span><span class="va">k1</span>, <span class="va">k2</span>, <span class="va">g</span>, <span class="va">f_parent_to_m1</span>, <span class="va">k_m1</span><span class="op">)</span> <span class="op">{</span></span>
-<span class="r-in">  <span class="fu"><a href="mkinpredict.html">mkinpredict</a></span><span class="op">(</span><span class="va">dfop_sfo</span>,</span>
-<span class="r-in">    <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k1 <span class="op">=</span> <span class="va">k1</span>, k2 <span class="op">=</span> <span class="va">k2</span>, g <span class="op">=</span> <span class="va">g</span>, f_parent_to_m1 <span class="op">=</span> <span class="va">f_parent_to_m1</span>, k_m1 <span class="op">=</span> <span class="va">k_m1</span><span class="op">)</span>,</span>
-<span class="r-in">    <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fl">100</span>, m1 <span class="op">=</span> <span class="fl">0</span><span class="op">)</span>,</span>
-<span class="r-in">    <span class="va">sampling_times</span><span class="op">)</span></span>
-<span class="r-in"><span class="op">}</span></span>
-<span class="r-in"></span>
-<span class="r-in"><span class="va">ds_mean_dfop_sfo</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fl">5</span>, <span class="kw">function</span><span class="op">(</span><span class="va">i</span><span class="op">)</span> <span class="op">{</span></span>
-<span class="r-in">  <span class="fu"><a href="mkinpredict.html">mkinpredict</a></span><span class="op">(</span><span class="va">dfop_sfo</span>,</span>
-<span class="r-in">    <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k1 <span class="op">=</span> <span class="va">k1_in</span><span class="op">[</span><span class="va">i</span><span class="op">]</span>, k2 <span class="op">=</span> <span class="va">k2_in</span><span class="op">[</span><span class="va">i</span><span class="op">]</span>, g <span class="op">=</span> <span class="va">g_in</span><span class="op">[</span><span class="va">i</span><span class="op">]</span>,</span>
-<span class="r-in">      f_parent_to_m1 <span class="op">=</span> <span class="va">f_parent_to_m1_in</span><span class="op">[</span><span class="va">i</span><span class="op">]</span>, k_m1 <span class="op">=</span> <span class="va">k_m1_in</span><span class="op">[</span><span class="va">i</span><span class="op">]</span><span class="op">)</span>,</span>
-<span class="r-in">    <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fl">100</span>, m1 <span class="op">=</span> <span class="fl">0</span><span class="op">)</span>,</span>
-<span class="r-in">    <span class="va">sampling_times</span><span class="op">)</span></span>
-<span class="r-in"><span class="op">}</span><span class="op">)</span></span>
-<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">ds_mean_dfop_sfo</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste</a></span><span class="op">(</span><span class="st">"ds"</span>, <span class="fl">1</span><span class="op">:</span><span class="fl">5</span><span class="op">)</span></span>
-<span class="r-in"></span>
-<span class="r-in"><span class="va">ds_syn_dfop_sfo</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="va">ds_mean_dfop_sfo</span>, <span class="kw">function</span><span class="op">(</span><span class="va">ds</span><span class="op">)</span> <span class="op">{</span></span>
-<span class="r-in">  <span class="fu"><a href="add_err.html">add_err</a></span><span class="op">(</span><span class="va">ds</span>,</span>
-<span class="r-in">    sdfunc <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">value</span><span class="op">)</span> <span class="fu"><a href="https://rdrr.io/r/base/MathFun.html" class="external-link">sqrt</a></span><span class="op">(</span><span class="fl">1</span><span class="op">^</span><span class="fl">2</span> <span class="op">+</span> <span class="va">value</span><span class="op">^</span><span class="fl">2</span> <span class="op">*</span> <span class="fl">0.07</span><span class="op">^</span><span class="fl">2</span><span class="op">)</span>,</span>
-<span class="r-in">    n <span class="op">=</span> <span class="fl">1</span><span class="op">)</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span></span>
-<span class="r-in"><span class="op">}</span><span class="op">)</span></span>
-<span class="r-in"></span>
-<span class="r-in"><span class="co"># \dontrun{</span></span>
-<span class="r-in"><span class="co"># Evaluate using mmkin and nlmixr</span></span>
-<span class="r-in"><span class="va">f_mmkin_dfop_sfo</span> <span class="op">&lt;-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">dfop_sfo</span><span class="op">)</span>, <span class="va">ds_syn_dfop_sfo</span>,</span>
-<span class="r-in">  quiet <span class="op">=</span> <span class="cn">TRUE</span>, error_model <span class="op">=</span> <span class="st">"tc"</span>, cores <span class="op">=</span> <span class="fl">5</span><span class="op">)</span></span>
-<span class="r-in"><span class="va">f_saemix_dfop_sfo</span> <span class="op">&lt;-</span> <span class="fu">mkin</span><span class="fu">::</span><span class="fu"><a href="saem.html">saem</a></span><span class="op">(</span><span class="va">f_mmkin_dfop_sfo</span><span class="op">)</span></span>
-<span class="r-in"><span class="va">f_nlme_dfop_sfo</span> <span class="op">&lt;-</span> <span class="fu">mkin</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a></span><span class="op">(</span><span class="va">f_mmkin_dfop_sfo</span><span class="op">)</span></span>
-<span class="r-in"><span class="va">f_nlmixr_dfop_sfo_saem</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr</a></span><span class="op">(</span><span class="va">f_mmkin_dfop_sfo</span>, est <span class="op">=</span> <span class="st">"saem"</span><span class="op">)</span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> With est = 'saem', a different error model is required for each observed variableChanging the error model to 'obs_tc' (Two-component error for each observed variable)</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → generate SAEM model</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1:    99.5921   -3.9040   -2.1350   -3.7103   -2.0097    0.3217    6.6502    0.1520    0.5415    0.1995    0.3705    0.5588    7.9084    0.0824    7.5666    0.1777</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2:    99.9835   -3.8650   -2.3586   -4.0306   -1.6536    0.0696    6.3177    0.1590    0.5144    0.1895    0.3520    0.5308    4.7746    0.0649    4.5667    0.0865</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 3:    1.0014e+02  -4.0173e+00  -2.3869e+00  -4.0840e+00  -1.2338e+00   3.4899e-02   6.0018e+00   1.5109e-01   4.8870e-01   1.8005e-01   3.3438e-01   5.0428e-01   2.8890e+00   7.0377e-02   2.9045e+00   9.5437e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 4:    1.0024e+02  -4.1414e+00  -2.4972e+00  -4.1082e+00  -1.0291e+00  -1.0684e-02   5.7017e+00   1.4354e-01   4.6427e-01   1.7105e-01   3.1766e-01   4.7907e-01   2.1715e+00   8.9921e-02   1.8765e+00   1.0403e-01</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5:    1.0068e+02  -4.1132e+00  -2.4776e+00  -4.0576e+00  -1.0391e+00  -8.8738e-02   5.8192e+00   1.3636e-01   4.4106e-01   1.6249e-01   3.0177e-01   4.5512e-01   1.6349e+00   8.3197e-02   1.4947e+00   1.0221e-01</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 6:    1.0044e+02  -4.0883e+00  -2.3931e+00  -4.1068e+00  -9.5058e-01  -1.2696e-01   5.5283e+00   1.2954e-01   4.1900e-01   1.5437e-01   2.8669e-01   4.5581e-01   1.4126e+00   8.1219e-02   1.2286e+00   1.0043e-01</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 7:    1.0103e+02  -4.0833e+00  -2.4194e+00  -4.0894e+00  -9.8547e-01   5.2360e-03   9.8755e+00   1.2306e-01   3.9805e-01   1.4665e-01   2.7235e-01   5.3394e-01   1.3620e+00   7.9842e-02   1.0649e+00   1.0298e-01</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 8:    1.0106e+02  -4.0535e+00  -2.3489e+00  -4.0682e+00  -9.4455e-01  -9.4310e-02   9.3818e+00   1.1691e-01   3.7815e-01   1.3932e-01   2.5873e-01   5.0724e-01   1.2163e+00   7.0820e-02   1.0147e+00   9.2362e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 9:    1.0150e+02  -4.0498e+00  -2.4037e+00  -4.0719e+00  -9.3384e-01  -1.2384e-01   8.9127e+00   1.1107e-01   3.5924e-01   1.3235e-01   2.4580e-01   5.3463e-01   1.0849e+00   7.7513e-02   9.8295e-01   8.5927e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 10:    1.0089e+02  -4.0313e+00  -2.3670e+00  -4.0573e+00  -9.0269e-01  -1.6427e-01   8.4670e+00   1.0551e-01   3.4128e-01   1.2573e-01   2.3351e-01   5.4980e-01   1.1274e+00   7.6000e-02   9.8582e-01   8.9740e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 11:    1.0089e+02  -4.0664e+00  -2.3547e+00  -4.0554e+00  -9.3659e-01  -1.6900e-01   8.0437e+00   1.0024e-01   3.2422e-01   1.1945e-01   2.2183e-01   5.2231e-01   1.1513e+00   7.5801e-02   9.7638e-01   8.5324e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 12:    1.0067e+02  -4.0427e+00  -2.3729e+00  -4.0413e+00  -9.2169e-01  -2.0927e-01   8.6156e+00   9.5225e-02   3.0801e-01   1.1348e-01   2.1074e-01   6.0288e-01   1.1918e+00   7.4830e-02   9.1899e-01   8.7983e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 13:    1.0042e+02  -4.0387e+00  -2.3650e+00  -4.0623e+00  -9.3973e-01  -2.2277e-01   8.1848e+00   9.0463e-02   2.9260e-01   1.0780e-01   2.0020e-01   5.7274e-01   1.1022e+00   7.3652e-02   9.1657e-01   8.7973e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 14:    1.0020e+02  -4.0227e+00  -2.3586e+00  -4.0333e+00  -9.4451e-01  -1.8020e-01   7.7756e+00   8.5940e-02   2.7797e-01   1.0241e-01   1.9019e-01   5.4410e-01   1.0728e+00   8.0169e-02   7.8974e-01   9.0162e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 15:    1.0045e+02  -4.0209e+00  -2.3544e+00  -4.0187e+00  -8.9758e-01  -2.4348e-01   7.3868e+00   8.1643e-02   2.6408e-01   9.7291e-02   1.8068e-01   6.4534e-01   1.0287e+00   7.9387e-02   7.3357e-01   9.0858e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 16:    1.0061e+02  -4.0195e+00  -2.3678e+00  -4.0419e+00  -8.9130e-01  -2.4248e-01   7.0175e+00   7.7561e-02   2.5087e-01   9.2427e-02   1.7165e-01   6.7145e-01   1.0440e+00   7.9292e-02   8.4681e-01   8.5285e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 17:    1.0034e+02  -4.0298e+00  -2.3685e+00  -4.0637e+00  -8.9550e-01  -1.0712e-01   6.6666e+00   7.3683e-02   2.3833e-01   8.7805e-02   1.6307e-01   6.3788e-01   1.0827e+00   7.1441e-02   8.5278e-01   8.8090e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 18:    99.8774   -4.0600   -2.3805   -4.0726   -0.9545   -0.1196    6.3333    0.0700    0.2264    0.0834    0.1549    0.6060    1.0056    0.0751    0.8017    0.0877</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 19:    1.0055e+02  -4.0731e+00  -2.2711e+00  -4.0824e+00  -9.5827e-01  -5.9764e-02   6.0166e+00   6.6499e-02   2.1509e-01   7.9244e-02   1.4717e-01   5.7835e-01   9.3185e-01   7.9396e-02   8.2332e-01   9.1277e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 20:    1.0060e+02  -4.1023e+00  -2.2841e+00  -4.0757e+00  -9.7931e-01  -1.0440e-01   5.7158e+00   6.3174e-02   2.0434e-01   7.5282e-02   1.3981e-01   6.4675e-01   1.0006e+00   7.7840e-02   8.7958e-01   9.1512e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 21:    1.0095e+02  -4.0955e+00  -2.3274e+00  -4.1038e+00  -9.5611e-01  -5.2481e-02   5.8063e+00   6.0015e-02   1.9412e-01   7.1518e-02   1.3282e-01   6.4943e-01   1.0926e+00   7.2665e-02   9.0450e-01   8.6278e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 22:    1.0083e+02  -4.0789e+00  -2.3108e+00  -4.1074e+00  -9.6548e-01  -4.9665e-02   5.5160e+00   5.7014e-02   1.8441e-01   6.7942e-02   1.2618e-01   6.7304e-01   9.3297e-01   8.1042e-02   8.9206e-01   9.2336e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 23:    1.0114e+02  -4.0924e+00  -2.4027e+00  -4.0947e+00  -9.7291e-01  -5.0773e-02   5.2567e+00   5.4164e-02   1.7519e-01   6.5747e-02   1.1987e-01   7.3402e-01   9.7858e-01   8.2133e-02   8.4928e-01   8.8254e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 24:    1.0151e+02  -4.0787e+00  -2.3696e+00  -4.0712e+00  -9.5647e-01  -5.4136e-02   4.9938e+00   5.1456e-02   1.6643e-01   6.2460e-02   1.1388e-01   7.1847e-01   9.7548e-01   7.7691e-02   9.0418e-01   8.9115e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 25:    1.0158e+02  -4.0830e+00  -2.3384e+00  -4.0705e+00  -9.5727e-01  -8.4974e-02   4.7442e+00   4.8883e-02   1.5811e-01   5.9337e-02   1.0818e-01   6.8255e-01   1.0340e+00   7.4497e-02   9.0691e-01   9.0886e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 26:    1.0156e+02  -4.0784e+00  -2.3030e+00  -4.1027e+00  -9.5638e-01  -4.9456e-03   4.5069e+00   4.6439e-02   1.5021e-01   5.6370e-02   1.1189e-01   6.4842e-01   1.0537e+00   7.7612e-02   9.6725e-01   8.7824e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 27:    1.0198e+02  -4.0828e+00  -2.3006e+00  -4.0803e+00  -9.9402e-01  -8.3280e-02   4.2816e+00   4.4117e-02   1.4270e-01   5.3552e-02   1.3300e-01   6.1600e-01   9.1051e-01   7.7677e-02   9.5949e-01   8.6552e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 28:    1.0183e+02  -4.0871e+00  -2.2822e+00  -4.0596e+00  -9.9920e-01  -1.1469e-01   4.0675e+00   4.1911e-02   1.3556e-01   5.0874e-02   1.4021e-01   5.8520e-01   1.0294e+00   7.0917e-02   8.6440e-01   9.0301e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 29:    1.0171e+02  -4.0838e+00  -2.2606e+00  -4.0885e+00  -9.8937e-01  -6.6488e-02   4.3393e+00   3.9815e-02   1.2878e-01   4.8330e-02   1.3320e-01   5.5594e-01   1.0042e+00   8.1585e-02   8.5434e-01   8.6032e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 30:    1.0177e+02  -4.0861e+00  -2.2615e+00  -4.0670e+00  -9.9119e-01  -1.1322e-01   6.3058e+00   3.7825e-02   1.2942e-01   4.5914e-02   1.4223e-01   5.2814e-01   9.6301e-01   7.3336e-02   9.2366e-01   8.9276e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 31:    1.0160e+02  -4.0900e+00  -2.2455e+00  -4.0514e+00  -9.8754e-01  -2.2617e-01   5.9905e+00   3.5933e-02   1.2295e-01   4.3618e-02   1.3512e-01   5.2898e-01   1.0441e+00   7.6262e-02   1.0227e+00   8.3759e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 32:    1.0193e+02  -4.0967e+00  -2.2599e+00  -4.0295e+00  -9.8917e-01  -1.6626e-01   5.6910e+00   3.4137e-02   1.2620e-01   4.1437e-02   1.4303e-01   5.0253e-01   9.8083e-01   7.4136e-02   1.0031e+00   8.5124e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 33:    1.0215e+02  -4.0713e+00  -2.2563e+00  -4.0313e+00  -9.7361e-01  -1.8246e-01   5.4065e+00   3.2430e-02   1.1989e-01   3.9365e-02   1.5371e-01   4.7741e-01   9.6935e-01   8.0491e-02   9.7610e-01   8.0590e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 34:    1.0251e+02  -4.0707e+00  -2.2635e+00  -4.0387e+00  -9.2149e-01  -1.3452e-01   6.3940e+00   3.0808e-02   1.1389e-01   3.7397e-02   1.6720e-01   4.5354e-01   9.2449e-01   7.4355e-02   1.0505e+00   8.3049e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 35:    1.0278e+02  -4.0579e+00  -2.2877e+00  -4.0289e+00  -9.6632e-01  -1.2910e-01   6.0743e+00   2.9268e-02   1.0820e-01   3.5527e-02   1.7242e-01   4.3086e-01   1.0129e+00   7.3224e-02   1.0035e+00   7.9411e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 36:    1.0283e+02  -4.0771e+00  -2.2977e+00  -4.0108e+00  -9.5228e-01  -2.1781e-01   5.7706e+00   2.7805e-02   1.0279e-01   3.3751e-02   1.7034e-01   4.0932e-01   9.0249e-01   7.9917e-02   9.4562e-01   8.2391e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 37:    1.0228e+02  -4.0442e+00  -2.3061e+00  -4.0150e+00  -9.5376e-01  -1.4873e-01   5.4821e+00   2.6414e-02   9.7651e-02   3.2063e-02   1.7772e-01   3.8885e-01   9.0446e-01   8.1418e-02   9.7276e-01   8.3331e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 38:    1.0220e+02  -4.0454e+00  -2.3204e+00  -4.0207e+00  -9.3198e-01  -1.9324e-01   5.2080e+00   2.5094e-02   1.0946e-01   3.0460e-02   1.6883e-01   3.6941e-01   8.9159e-01   7.8645e-02   9.5848e-01   8.2734e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 39:    1.0193e+02  -4.0306e+00  -2.2926e+00  -4.0131e+00  -9.3448e-01  -2.1869e-01   6.7005e+00   2.3839e-02   1.0399e-01   2.8937e-02   1.6039e-01   3.5571e-01   9.0078e-01   7.8982e-02   9.3495e-01   8.3462e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 40:    1.0217e+02  -4.0463e+00  -2.2823e+00  -4.0209e+00  -9.2912e-01  -2.3580e-01   8.7857e+00   2.2647e-02   9.8789e-02   2.7490e-02   1.5418e-01   3.3792e-01   8.5126e-01   7.8849e-02   9.5510e-01   8.3011e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 41:    1.0210e+02  -4.0564e+00  -2.2213e+00  -4.0041e+00  -9.4137e-01  -2.4103e-01   9.2761e+00   2.1515e-02   9.5179e-02   2.6116e-02   1.4795e-01   3.9870e-01   9.8312e-01   7.1947e-02   1.0015e+00   8.1407e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 42:    1.0201e+02  -4.0204e+00  -2.2598e+00  -3.9984e+00  -9.4412e-01  -2.6838e-01   8.8123e+00   2.0439e-02   9.0420e-02   2.4810e-02   1.5013e-01   4.0334e-01   9.5583e-01   7.5617e-02   9.5131e-01   8.2343e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 43:    1.0244e+02  -4.0107e+00  -2.2178e+00  -3.9923e+00  -9.4481e-01  -3.1831e-01   8.3717e+00   1.9417e-02   9.5791e-02   2.3569e-02   1.5440e-01   4.8194e-01   9.4582e-01   7.4374e-02   9.7798e-01   8.3391e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 44:    1.0211e+02  -4.0193e+00  -2.2379e+00  -3.9784e+00  -9.2192e-01  -3.0224e-01   7.9531e+00   1.8446e-02   9.1002e-02   2.6623e-02   1.4668e-01   4.5785e-01   9.4162e-01   7.4503e-02   9.3229e-01   7.8428e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 45:    1.0183e+02  -4.0198e+00  -2.2150e+00  -3.9966e+00  -9.6219e-01  -3.0236e-01   7.5554e+00   1.7524e-02   8.6452e-02   2.8045e-02   1.4346e-01   4.3495e-01   9.3318e-01   7.5937e-02   9.4917e-01   8.2946e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 46:    1.0132e+02  -4.0245e+00  -2.2215e+00  -3.9954e+00  -9.4368e-01  -2.6878e-01   7.1777e+00   1.6648e-02   9.5839e-02   2.6909e-02   1.4633e-01   4.1321e-01   9.7698e-01   7.1947e-02   9.6599e-01   8.3812e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 47:    1.0138e+02  -4.0076e+00  -2.2741e+00  -4.0047e+00  -9.0608e-01  -2.2839e-01   6.8188e+00   1.5815e-02   9.2192e-02   2.5563e-02   1.6160e-01   3.9255e-01   9.3678e-01   7.4216e-02   1.0295e+00   7.9396e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 48:    1.0102e+02  -4.0235e+00  -2.2764e+00  -4.0123e+00  -9.1050e-01  -2.0555e-01   6.4778e+00   1.5024e-02   8.7583e-02   2.4285e-02   1.6368e-01   3.7292e-01   9.2204e-01   7.6976e-02   1.0289e+00   8.1381e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 49:    1.0074e+02  -4.0407e+00  -2.2583e+00  -4.0142e+00  -9.2682e-01  -2.5941e-01   6.1540e+00   1.4273e-02   8.3203e-02   2.3071e-02   1.8251e-01   3.5427e-01   9.0436e-01   7.4192e-02   1.0237e+00   8.2178e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 50:    1.0046e+02  -4.0626e+00  -2.2464e+00  -4.0201e+00  -9.3021e-01  -2.6983e-01   5.8463e+00   1.4303e-02   7.9043e-02   2.3395e-02   1.7339e-01   3.3846e-01   9.1334e-01   7.8046e-02   9.5881e-01   8.1223e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 51:    1.0022e+02  -4.0414e+00  -2.2373e+00  -4.0107e+00  -9.2183e-01  -2.6371e-01   5.8667e+00   1.8681e-02   7.5091e-02   2.2225e-02   1.6472e-01   3.7327e-01   9.4906e-01   8.0171e-02   1.0278e+00   8.7157e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 52:    1.0059e+02  -4.0444e+00  -2.2676e+00  -4.0263e+00  -9.1269e-01  -2.1748e-01   5.5734e+00   1.7747e-02   7.1337e-02   2.1114e-02   1.5648e-01   3.5461e-01   8.6417e-01   8.2918e-02   1.0332e+00   8.1690e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 53:    1.0071e+02  -4.0384e+00  -2.2530e+00  -4.0171e+00  -9.2997e-01  -2.5319e-01   5.2947e+00   1.6860e-02   6.7770e-02   2.0058e-02   1.6621e-01   3.3688e-01   8.9792e-01   7.8990e-02   1.0230e+00   7.9862e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 54:    1.0067e+02  -4.0271e+00  -2.2540e+00  -4.0251e+00  -9.2151e-01  -2.2197e-01   5.0300e+00   1.6017e-02   6.4381e-02   1.9055e-02   1.7025e-01   3.2003e-01   8.7999e-01   8.0187e-02   1.0487e+00   8.1091e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 55:    1.0083e+02  -4.0326e+00  -2.2090e+00  -4.0230e+00  -9.2078e-01  -2.4936e-01   4.8429e+00   1.5216e-02   6.1162e-02   1.8103e-02   1.7211e-01   3.0403e-01   8.8923e-01   8.2248e-02   1.0522e+00   8.0777e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 56:    1.0082e+02  -4.0284e+00  -2.2350e+00  -4.0083e+00  -8.9532e-01  -1.9565e-01   5.3306e+00   1.4455e-02   5.8104e-02   1.7197e-02   1.6350e-01   2.8883e-01   8.7438e-01   7.9530e-02   1.0748e+00   7.9892e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 57:    1.0089e+02  -4.0118e+00  -2.2585e+00  -3.9924e+00  -9.5279e-01  -2.7691e-01   5.4228e+00   1.3733e-02   5.5199e-02   1.6338e-02   1.9088e-01   2.7439e-01   9.3164e-01   8.0286e-02   1.1149e+00   8.3224e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 58:    1.0103e+02  -4.0117e+00  -2.2470e+00  -3.9628e+00  -9.5632e-01  -3.1385e-01   5.1517e+00   1.3046e-02   6.2482e-02   1.5521e-02   1.8744e-01   2.8144e-01   9.6857e-01   8.0586e-02   1.0149e+00   8.0615e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 59:    1.0110e+02  -4.0400e+00  -2.2319e+00  -3.9749e+00  -9.3280e-01  -3.1491e-01   4.8941e+00   1.4375e-02   7.6521e-02   1.9982e-02   1.8994e-01   2.6736e-01   9.9294e-01   7.6723e-02   9.8321e-01   8.4135e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 60:    1.0135e+02  -4.0401e+00  -2.2969e+00  -4.0006e+00  -9.3649e-01  -2.7105e-01   4.6494e+00   1.5352e-02   7.2695e-02   1.8983e-02   1.8339e-01   2.5400e-01   9.7671e-01   8.0731e-02   1.0344e+00   8.4798e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 61:    1.0139e+02  -4.0447e+00  -2.3267e+00  -4.0036e+00  -9.2609e-01  -2.4279e-01   4.4169e+00   1.4584e-02   6.9060e-02   1.8034e-02   1.7844e-01   2.4130e-01   9.4921e-01   8.1065e-02   1.0031e+00   8.2364e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 62:    1.0187e+02  -4.0605e+00  -2.2435e+00  -4.0093e+00  -9.4594e-01  -2.2911e-01   4.1961e+00   1.3855e-02   6.5607e-02   2.0229e-02   1.6951e-01   2.2923e-01   8.5507e-01   8.5043e-02   1.0003e+00   8.4881e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 63:    1.0181e+02  -4.0665e+00  -2.2106e+00  -3.9954e+00  -9.5259e-01  -2.2539e-01   3.9863e+00   1.3162e-02   6.2327e-02   1.9217e-02   2.0722e-01   2.1777e-01   9.2699e-01   7.9303e-02   9.8900e-01   8.5477e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 64:    1.0178e+02  -4.0641e+00  -2.2152e+00  -3.9904e+00  -9.7044e-01  -2.2671e-01   3.8289e+00   1.2504e-02   5.9210e-02   1.8256e-02   2.1010e-01   2.0688e-01   8.6741e-01   8.9748e-02   9.9564e-01   8.6675e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 65:    1.0149e+02  -4.0871e+00  -2.2063e+00  -4.0000e+00  -9.7253e-01  -2.0360e-01   3.6375e+00   1.1879e-02   5.8060e-02   1.7344e-02   2.3758e-01   1.9654e-01   9.1092e-01   8.6259e-02   1.0674e+00   8.6171e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 66:    1.0131e+02  -4.0863e+00  -2.2283e+00  -4.0026e+00  -9.6884e-01  -2.2153e-01   3.4556e+00   1.1285e-02   5.5157e-02   1.6476e-02   2.5128e-01   1.8671e-01   9.2512e-01   7.7687e-02   1.0847e+00   8.2689e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 67:    1.0124e+02  -4.0691e+00  -2.2261e+00  -4.0021e+00  -9.2647e-01  -2.9309e-01   3.2828e+00   1.0721e-02   5.2400e-02   1.5653e-02   2.3872e-01   1.7738e-01   8.3514e-01   8.7680e-02   1.0093e+00   8.1515e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 68:    1.0160e+02  -4.0653e+00  -2.2627e+00  -3.9685e+00  -9.5325e-01  -2.9870e-01   3.1187e+00   1.0185e-02   5.0455e-02   1.6013e-02   2.2678e-01   1.6851e-01   9.3016e-01   8.4873e-02   1.0357e+00   8.0066e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 69:    1.0154e+02  -4.0667e+00  -2.2536e+00  -3.9686e+00  -9.8329e-01  -2.1749e-01   2.9628e+00   9.6753e-03   4.7932e-02   1.6408e-02   2.1544e-01   1.6008e-01   9.4932e-01   8.5396e-02   1.0298e+00   8.2915e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 70:    1.0140e+02  -4.0718e+00  -2.2809e+00  -4.0031e+00  -9.7818e-01  -1.5912e-01   2.8146e+00   1.2376e-02   4.5536e-02   1.5606e-02   2.0467e-01   1.5208e-01   9.8171e-01   8.4966e-02   1.0881e+00   8.4583e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 71:    1.0119e+02  -4.0810e+00  -2.2832e+00  -3.9945e+00  -9.7859e-01  -1.6288e-01   2.6739e+00   1.1757e-02   4.3259e-02   2.2477e-02   1.9444e-01   1.4447e-01   9.2355e-01   8.4027e-02   1.0557e+00   8.3942e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 72:    1.0133e+02  -4.0904e+00  -2.2582e+00  -4.0034e+00  -1.0103e+00  -1.9818e-01   2.5402e+00   1.1169e-02   6.0710e-02   2.4943e-02   1.8472e-01   1.3725e-01   8.8530e-01   8.8313e-02   9.6043e-01   8.6565e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 73:    1.0081e+02  -4.0852e+00  -2.2408e+00  -4.0324e+00  -9.8147e-01  -2.0245e-01   2.4132e+00   1.0611e-02   6.0672e-02   2.3696e-02   1.7586e-01   1.5058e-01   9.8632e-01   8.0072e-02   1.0754e+00   8.4211e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 74:    1.0096e+02  -4.0784e+00  -2.2415e+00  -4.0085e+00  -9.6374e-01  -2.1820e-01   2.2925e+00   1.0080e-02   7.8175e-02   2.2511e-02   2.0451e-01   1.4305e-01   9.7218e-01   7.5856e-02   1.0703e+00   8.2684e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 75:    1.0059e+02  -4.0614e+00  -2.2571e+00  -4.0069e+00  -9.6858e-01  -2.2415e-01   2.1779e+00   9.5763e-03   7.4266e-02   2.1385e-02   2.0050e-01   1.3590e-01   9.4625e-01   8.3873e-02   1.0545e+00   8.0628e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 76:    1.0071e+02  -4.0570e+00  -2.2509e+00  -4.0111e+00  -9.4159e-01  -2.0936e-01   2.0690e+00   9.0975e-03   1.0546e-01   2.0316e-02   1.9048e-01   1.2911e-01   9.1139e-01   8.4290e-02   1.0164e+00   8.0587e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 77:    1.0056e+02  -4.0315e+00  -2.2434e+00  -4.0068e+00  -9.1913e-01  -2.0655e-01   1.9656e+00   8.6426e-03   1.2867e-01   1.9300e-02   1.8971e-01   1.5112e-01   9.1748e-01   8.0221e-02   1.0080e+00   8.2126e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 78:    1.0054e+02  -4.0350e+00  -2.2335e+00  -4.0109e+00  -9.4315e-01  -1.7472e-01   2.6310e+00   8.2105e-03   1.3206e-01   1.8867e-02   2.0835e-01   1.8443e-01   9.7893e-01   7.9595e-02   1.0622e+00   8.3669e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 79:    1.0036e+02  -4.0500e+00  -2.2739e+00  -4.0223e+00  -9.6070e-01  -1.8756e-01   2.5665e+00   7.7999e-03   1.2546e-01   1.7924e-02   1.9794e-01   2.0106e-01   9.4360e-01   8.0070e-02   1.0209e+00   8.4807e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 80:    1.0030e+02  -4.0522e+00  -2.2890e+00  -4.0149e+00  -9.2615e-01  -2.1233e-01   2.4382e+00   7.4099e-03   1.2570e-01   1.7028e-02   1.8804e-01   2.4730e-01   9.6559e-01   8.0105e-02   1.0382e+00   7.9028e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 81:    1.0004e+02  -4.0462e+00  -2.2766e+00  -4.0158e+00  -9.0841e-01  -2.3731e-01   3.0663e+00   7.0394e-03   1.2300e-01   1.6176e-02   1.7864e-01   2.3493e-01   8.8273e-01   8.0620e-02   1.0835e+00   7.9915e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 82:    1.0015e+02  -4.0106e+00  -2.2696e+00  -3.9845e+00  -9.2535e-01  -2.7387e-01   2.9130e+00   6.6875e-03   1.5058e-01   1.5368e-02   1.6971e-01   2.4826e-01   1.0039e+00   7.8035e-02   1.1181e+00   8.5136e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 83:    1.0019e+02  -4.0111e+00  -2.2266e+00  -3.9884e+00  -8.8579e-01  -2.3822e-01   2.7674e+00   6.3531e-03   1.9061e-01   1.4599e-02   1.6122e-01   2.3585e-01   1.0256e+00   8.1065e-02   1.0988e+00   8.4111e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 84:    99.9333   -4.0014   -2.2848   -3.9877   -0.8906   -0.2014    2.6290    0.0060    0.2388    0.0139    0.1532    0.2628    0.9936    0.0809    1.1059    0.0847</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 85:    99.6658   -4.0093   -2.2848   -3.9884   -0.8922   -0.2517    2.4976    0.0057    0.2269    0.0132    0.1455    0.2920    0.9705    0.0823    1.0458    0.0834</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 86:    99.8823   -4.0064   -2.2659   -4.0038   -0.9174   -0.1848    2.3727    0.0054    0.2155    0.0125    0.1382    0.2774    0.9699    0.0796    0.9808    0.0839</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 87:    1.0051e+02  -4.0156e+00  -2.2877e+00  -4.0160e+00  -8.9867e-01  -1.3463e-01   2.3477e+00   6.2688e-03   2.0474e-01   1.1891e-02   1.4859e-01   2.6352e-01   9.0742e-01   7.8361e-02   1.0082e+00   8.1020e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 88:    1.0036e+02  -4.0013e+00  -2.2738e+00  -4.0184e+00  -9.1589e-01  -1.4154e-01   3.0581e+00   7.7930e-03   2.5551e-01   1.1297e-02   1.4116e-01   2.5035e-01   9.8581e-01   7.6769e-02   9.7255e-01   8.8653e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 89:    1.0031e+02  -4.0155e+00  -2.3418e+00  -4.0080e+00  -8.8958e-01  -1.4006e-01   3.3754e+00   7.4891e-03   2.4274e-01   1.2919e-02   1.3411e-01   2.3783e-01   8.9921e-01   8.3685e-02   9.2657e-01   8.1804e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 90:    1.0044e+02  -4.0279e+00  -2.3328e+00  -4.0081e+00  -9.1868e-01  -1.3077e-01   3.6776e+00   1.1221e-02   2.3380e-01   1.2419e-02   1.4247e-01   2.2594e-01   8.8620e-01   8.0520e-02   9.0269e-01   8.2413e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 91:    1.0007e+02  -4.0329e+00  -2.3865e+00  -4.0145e+00  -8.9566e-01  -1.2400e-01   3.7450e+00   1.0660e-02   2.2211e-01   1.3667e-02   1.3534e-01   2.1539e-01   9.5638e-01   8.0235e-02   9.4346e-01   8.5828e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 92:    1.0025e+02  -4.0506e+00  -2.3252e+00  -4.0445e+00  -9.2023e-01  -5.6654e-02   3.9282e+00   1.0127e-02   2.4912e-01   1.2984e-02   1.4349e-01   2.6955e-01   9.6021e-01   7.7975e-02   9.1878e-01   8.6695e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 93:    1.0091e+02  -4.0566e+00  -2.2760e+00  -4.0422e+00  -9.2960e-01  -5.8179e-02   3.7318e+00   9.6210e-03   2.6385e-01   1.3422e-02   1.5136e-01   2.5607e-01   9.7012e-01   7.7323e-02   1.0157e+00   8.6123e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 94:    1.0045e+02  -4.0682e+00  -2.3163e+00  -4.0391e+00  -9.3398e-01  -1.0367e-01   4.0009e+00   9.1399e-03   2.5066e-01   1.2751e-02   1.6029e-01   2.9867e-01   9.1442e-01   8.0048e-02   9.7607e-01   8.5436e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 95:    1.0029e+02  -4.0790e+00  -2.2791e+00  -4.0470e+00  -9.4031e-01  -7.2610e-02   3.8008e+00   8.6829e-03   2.3813e-01   1.2114e-02   1.5227e-01   2.8373e-01   8.9105e-01   8.2168e-02   9.5542e-01   8.5308e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 96:    1.0017e+02  -4.0818e+00  -2.3204e+00  -4.0507e+00  -9.2181e-01  -7.5195e-02   3.6108e+00   8.2488e-03   2.2622e-01   1.1508e-02   1.5273e-01   2.6955e-01   9.3179e-01   7.7026e-02   1.0092e+00   8.4966e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 97:    1.0007e+02  -4.0721e+00  -2.3606e+00  -4.0453e+00  -9.4375e-01  -7.2811e-02   3.4303e+00   1.0904e-02   2.1585e-01   1.2641e-02   1.8282e-01   2.5788e-01   9.3750e-01   8.0427e-02   9.3193e-01   8.3262e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 98:    1.0024e+02  -4.1025e+00  -2.3916e+00  -4.0401e+00  -9.6711e-01  -6.7764e-02   3.2587e+00   1.0359e-02   2.0506e-01   1.8712e-02   1.9260e-01   2.4499e-01   1.0086e+00   7.7282e-02   9.6010e-01   8.1760e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 99:    1.0024e+02  -4.0755e+00  -2.3237e+00  -4.0581e+00  -9.5543e-01  -8.1716e-02   3.0958e+00   9.8408e-03   1.9481e-01   1.7973e-02   1.8297e-01   2.5653e-01   8.6589e-01   7.7940e-02   9.2919e-01   8.4396e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 100:    1.0039e+02  -4.0828e+00  -2.2976e+00  -4.0616e+00  -9.8223e-01  -8.5593e-02   3.9873e+00   9.3488e-03   1.8507e-01   1.8837e-02   1.7382e-01   2.4370e-01   8.6471e-01   8.4021e-02   9.4587e-01   8.1697e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 101:    1.0089e+02  -4.0738e+00  -2.3406e+00  -4.0465e+00  -9.4824e-01  -4.4055e-02   5.2624e+00   8.8814e-03   1.8051e-01   1.7896e-02   1.6513e-01   2.3288e-01   8.4179e-01   8.2487e-02   8.8097e-01   8.1625e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 102:    1.0090e+02  -4.0777e+00  -2.3244e+00  -4.0500e+00  -9.4069e-01  -5.2554e-02   5.5339e+00   8.4373e-03   1.7148e-01   1.7001e-02   1.5688e-01   2.2124e-01   9.0419e-01   7.8446e-02   9.4721e-01   8.0896e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 103:    1.0067e+02  -4.0658e+00  -2.3188e+00  -4.0312e+00  -9.5981e-01  -7.3735e-02   7.0281e+00   8.0154e-03   1.6291e-01   1.6151e-02   1.7019e-01   2.1018e-01   9.3631e-01   8.1516e-02   9.8773e-01   8.0587e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 104:    1.0015e+02  -4.0729e+00  -2.3830e+00  -4.0458e+00  -9.4446e-01  -5.0075e-02   6.6767e+00   7.6147e-03   1.5476e-01   1.5343e-02   1.6822e-01   1.9967e-01   8.2373e-01   8.3029e-02   9.6909e-01   7.9505e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 105:    1.0022e+02  -4.0888e+00  -2.3301e+00  -4.0411e+00  -9.5279e-01  -1.1412e-01   6.3429e+00   7.2339e-03   1.4702e-01   1.4576e-02   1.8886e-01   2.2088e-01   9.2925e-01   7.3020e-02   1.0450e+00   8.6511e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 106:    1.0013e+02  -4.0846e+00  -2.3428e+00  -4.0530e+00  -9.1649e-01  -8.1899e-02   6.0257e+00   6.8722e-03   1.3967e-01   1.8939e-02   1.8350e-01   2.1970e-01   9.4571e-01   8.6259e-02   8.8509e-01   8.1023e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 107:    1.0026e+02  -4.0954e+00  -2.3362e+00  -4.0586e+00  -9.6302e-01  -8.0898e-03   5.7244e+00   6.5286e-03   1.3269e-01   2.2595e-02   2.0043e-01   2.4151e-01   8.8882e-01   8.8155e-02   8.2466e-01   8.5467e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 108:    1.0109e+02  -4.0796e+00  -2.3980e+00  -4.0841e+00  -9.5525e-01   2.0583e-02   5.4382e+00   6.2022e-03   1.2606e-01   3.4423e-02   2.2509e-01   2.3732e-01   9.0211e-01   8.1331e-02   9.2728e-01   8.4409e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 109:    1.0091e+02  -4.0872e+00  -2.3614e+00  -4.0620e+00  -9.5950e-01  -1.8283e-02   5.1663e+00   6.1750e-03   1.1975e-01   3.2702e-02   2.1384e-01   2.2546e-01   9.7296e-01   7.7298e-02   9.7366e-01   8.3784e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 110:    1.0137e+02  -4.0637e+00  -2.3734e+00  -4.0781e+00  -9.3874e-01  -6.0140e-03   4.9080e+00   6.5148e-03   1.1376e-01   3.1066e-02   2.0314e-01   2.3943e-01   9.2451e-01   8.4473e-02   9.7311e-01   8.4958e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 111:    1.0181e+02  -4.0588e+00  -2.3200e+00  -4.0792e+00  -9.9395e-01  -3.1776e-02   4.6626e+00   9.9112e-03   1.0808e-01   4.1755e-02   1.9299e-01   2.9896e-01   1.0697e+00   7.1561e-02   1.0062e+00   8.8034e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 112:    1.0222e+02  -4.0566e+00  -2.3525e+00  -4.0607e+00  -9.6055e-01  -6.3691e-02   4.4850e+00   9.4157e-03   1.0267e-01   4.0540e-02   1.8334e-01   3.5699e-01   1.0213e+00   7.5944e-02   1.0070e+00   8.3434e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 113:    1.0189e+02  -4.0478e+00  -2.3774e+00  -4.0434e+00  -9.6638e-01  -6.7729e-02   5.1753e+00   8.9449e-03   9.7539e-02   3.8513e-02   2.0339e-01   3.4044e-01   9.5484e-01   7.7854e-02   9.8545e-01   8.1852e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 114:    1.0225e+02  -4.0334e+00  -2.3728e+00  -4.0502e+00  -9.4795e-01  -7.3569e-02   4.9165e+00   8.8152e-03   9.2662e-02   3.7310e-02   2.0002e-01   3.2342e-01   9.3586e-01   8.1063e-02   9.5652e-01   8.3067e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 115:    1.0196e+02  -4.0302e+00  -2.3599e+00  -4.0812e+00  -9.6783e-01   7.1536e-03   4.8473e+00   9.4780e-03   1.0791e-01   3.7184e-02   1.9002e-01   3.0725e-01   9.1488e-01   7.6079e-02   1.0104e+00   8.7136e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 116:    1.0150e+02  -4.0420e+00  -2.3637e+00  -4.0535e+00  -9.4545e-01  -7.6387e-02   4.6049e+00   9.6408e-03   1.0251e-01   3.8115e-02   1.8052e-01   2.9189e-01   9.0583e-01   8.2597e-02   9.5680e-01   8.1246e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 117:    1.0161e+02  -4.0606e+00  -2.3853e+00  -4.0750e+00  -9.7273e-01  -2.7338e-03   4.3747e+00   9.1587e-03   9.7386e-02   3.7863e-02   1.8701e-01   2.7729e-01   8.4511e-01   8.4164e-02   9.4149e-01   8.0525e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 118:    1.0174e+02  -4.0727e+00  -2.3776e+00  -4.0850e+00  -9.6982e-01  -1.2188e-03   4.1560e+00   8.7008e-03   9.2516e-02   4.9300e-02   2.1364e-01   2.6343e-01   9.5447e-01   8.6627e-02   9.1835e-01   8.1284e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 119:    1.0141e+02  -4.0887e+00  -2.3138e+00  -4.0772e+00  -9.9409e-01  -5.0120e-02   3.9482e+00   8.2658e-03   8.7891e-02   4.6835e-02   2.5364e-01   2.5026e-01   9.4576e-01   8.4355e-02   9.5762e-01   8.5430e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 120:    1.0116e+02  -4.1014e+00  -2.3328e+00  -4.0418e+00  -9.7139e-01  -8.9664e-02   3.7508e+00   7.8525e-03   8.3496e-02   4.4493e-02   2.4096e-01   2.3774e-01   9.2816e-01   8.5558e-02   9.7817e-01   8.5169e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 121:    1.0099e+02  -4.0876e+00  -2.3277e+00  -4.0746e+00  -9.5453e-01  -2.6752e-02   3.5632e+00   7.4598e-03   7.9321e-02   4.2269e-02   2.2891e-01   2.2586e-01   8.9613e-01   8.0488e-02   9.7590e-01   8.4798e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 122:    1.0171e+02  -4.0620e+00  -2.3349e+00  -4.0659e+00  -9.4420e-01  -1.6907e-02   3.3851e+00   7.0869e-03   8.8424e-02   4.0155e-02   2.1747e-01   2.1456e-01   9.0159e-01   7.8017e-02   9.8579e-01   8.4585e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 123:    1.0191e+02  -4.0708e+00  -2.3487e+00  -4.0827e+00  -9.5025e-01  -5.7459e-03   3.2158e+00   6.7325e-03   1.0499e-01   3.8147e-02   2.0659e-01   2.0384e-01   9.6378e-01   7.4758e-02   9.6268e-01   8.4712e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 124:    1.0224e+02  -4.0798e+00  -2.2989e+00  -4.0724e+00  -9.8452e-01  -3.1802e-02   3.0550e+00   6.3959e-03   9.9739e-02   3.6240e-02   1.9626e-01   1.9364e-01   9.4424e-01   7.4219e-02   9.9842e-01   8.4793e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 125:    1.0242e+02  -4.0710e+00  -2.3242e+00  -4.0661e+00  -1.0094e+00  -2.9970e-02   2.9023e+00   6.0761e-03   9.4752e-02   3.6130e-02   1.8645e-01   1.8396e-01   9.2576e-01   8.0533e-02   9.9407e-01   8.1660e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 126:    1.0254e+02  -4.0685e+00  -2.3356e+00  -4.0599e+00  -1.0031e+00   4.0250e-03   2.7571e+00   5.7723e-03   1.0658e-01   3.4324e-02   1.7971e-01   1.7476e-01   9.4870e-01   7.4027e-02   9.2757e-01   8.3091e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 127:    1.0270e+02  -4.0492e+00  -2.3174e+00  -4.0476e+00  -9.9898e-01  -6.3197e-02   2.6193e+00   5.4837e-03   1.0746e-01   3.2607e-02   1.7072e-01   1.6603e-01   9.0721e-01   7.7941e-02   9.4624e-01   8.1077e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 128:    1.0284e+02  -4.0582e+00  -2.2991e+00  -4.0331e+00  -9.9114e-01  -5.6800e-02   2.4883e+00   5.2095e-03   1.2321e-01   3.1304e-02   1.6219e-01   1.5772e-01   8.7403e-01   8.4147e-02   8.9187e-01   8.5089e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 129:    1.0275e+02  -4.0773e+00  -2.2935e+00  -4.0389e+00  -9.9661e-01  -7.4747e-02   2.3639e+00   4.9922e-03   1.1705e-01   2.9738e-02   1.9667e-01   1.4984e-01   9.1481e-01   8.4947e-02   9.3224e-01   8.5270e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 130:    1.0222e+02  -4.0835e+00  -2.3177e+00  -4.0343e+00  -1.0044e+00  -7.6530e-02   2.2457e+00   4.7426e-03   1.1120e-01   3.1348e-02   2.0663e-01   1.4235e-01   1.0173e+00   7.4769e-02   9.9905e-01   8.6058e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 131:    1.0202e+02  -4.0767e+00  -2.2497e+00  -4.0270e+00  -1.0015e+00  -1.4868e-01   2.1334e+00   5.5790e-03   1.0564e-01   2.9781e-02   2.1254e-01   1.3523e-01   1.0223e+00   7.7587e-02   9.4151e-01   8.4206e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 132:    1.0191e+02  -4.0756e+00  -2.2363e+00  -4.0081e+00  -9.7264e-01  -2.0991e-01   2.0268e+00   5.3000e-03   1.0184e-01   2.8291e-02   2.0191e-01   1.7667e-01   9.2404e-01   7.9813e-02   9.2266e-01   8.6307e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 133:    1.0187e+02  -4.0743e+00  -2.1951e+00  -4.0196e+00  -9.7534e-01  -1.8742e-01   1.9254e+00   5.0350e-03   9.6744e-02   2.6877e-02   1.9181e-01   1.6784e-01   1.0007e+00   8.2043e-02   1.0028e+00   8.3921e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 134:    1.0182e+02  -4.0731e+00  -2.2321e+00  -4.0041e+00  -9.2800e-01  -1.4985e-01   1.8291e+00   4.7833e-03   9.1906e-02   2.7751e-02   1.8222e-01   1.6415e-01   9.7093e-01   7.5558e-02   9.8455e-01   8.4921e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 135:    1.0188e+02  -4.0724e+00  -2.2774e+00  -4.0213e+00  -9.3930e-01  -1.7385e-01   1.7377e+00   4.5441e-03   8.7311e-02   2.6363e-02   1.7311e-01   1.5594e-01   9.4053e-01   8.3922e-02   9.8689e-01   8.1203e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 136:    1.0202e+02  -4.0712e+00  -2.2711e+00  -4.0128e+00  -9.8747e-01  -1.3610e-01   1.6508e+00   4.3169e-03   1.0383e-01   2.5045e-02   1.8004e-01   1.4814e-01   9.6497e-01   8.1397e-02   1.0782e+00   8.2539e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 137:    1.0228e+02  -4.0676e+00  -2.2403e+00  -4.0069e+00  -9.7761e-01  -1.5314e-01   1.5683e+00   4.1011e-03   1.0931e-01   2.4722e-02   1.8328e-01   1.4074e-01   9.8300e-01   7.5906e-02   1.0221e+00   8.2306e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 138:    1.0229e+02  -4.0654e+00  -2.2510e+00  -3.9908e+00  -1.0400e+00  -1.4173e-01   1.4899e+00   4.2166e-03   1.3448e-01   2.3486e-02   1.8272e-01   1.3370e-01   1.0305e+00   8.0580e-02   1.0988e+00   8.3512e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 139:    1.0258e+02  -4.0599e+00  -2.2207e+00  -3.9622e+00  -1.0058e+00  -2.6676e-01   1.4154e+00   4.0057e-03   1.5190e-01   2.2312e-02   1.7359e-01   1.2702e-01   9.6425e-01   8.6887e-02   1.0373e+00   8.3853e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 140:    1.0224e+02  -4.0665e+00  -2.2479e+00  -4.0063e+00  -1.0054e+00  -1.6109e-01   1.3446e+00   3.8054e-03   1.4430e-01   2.4389e-02   1.6697e-01   1.2066e-01   9.9245e-01   8.3900e-02   1.1218e+00   8.2527e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 141:    1.0245e+02  -4.0843e+00  -2.2024e+00  -3.9657e+00  -1.0057e+00  -2.4900e-01   1.2774e+00   3.6152e-03   1.3709e-01   2.3170e-02   1.8559e-01   1.1463e-01   9.4091e-01   8.7616e-02   1.0760e+00   8.1871e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 142:    1.0253e+02  -4.0972e+00  -2.1943e+00  -3.9649e+00  -1.0160e+00  -2.9535e-01   1.2135e+00   3.4344e-03   1.6892e-01   2.2011e-02   1.7631e-01   1.0890e-01   9.2146e-01   9.0207e-02   1.0645e+00   8.5078e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 143:    1.0252e+02  -4.1005e+00  -2.1652e+00  -3.9687e+00  -1.0445e+00  -2.4274e-01   2.0559e+00   3.9136e-03   1.6047e-01   2.0911e-02   1.6750e-01   1.0345e-01   9.0785e-01   8.5522e-02   1.1734e+00   8.5544e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 144:    1.0257e+02  -4.1199e+00  -2.1994e+00  -3.9618e+00  -1.0480e+00  -2.0099e-01   1.9531e+00   4.2739e-03   1.5745e-01   1.9865e-02   1.6053e-01   9.8282e-02   9.3710e-01   8.5496e-02   1.0597e+00   8.4335e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 145:    1.0282e+02  -4.1356e+00  -2.1489e+00  -3.9708e+00  -1.0268e+00  -2.2250e-01   1.8554e+00   4.0602e-03   1.7177e-01   1.8872e-02   1.5251e-01   9.3368e-02   9.5312e-01   8.3609e-02   1.0695e+00   8.3492e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 146:    1.0282e+02  -4.1443e+00  -2.2103e+00  -3.9821e+00  -1.0080e+00  -2.2414e-01   1.7627e+00   4.0693e-03   1.6460e-01   1.7928e-02   1.5576e-01   8.8699e-02   9.8656e-01   8.4549e-02   1.0251e+00   7.9572e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 147:    1.0295e+02  -4.1363e+00  -2.1920e+00  -3.9828e+00  -1.0567e+00  -1.9047e-01   1.6745e+00   3.8659e-03   1.6104e-01   1.7032e-02   1.6551e-01   8.4264e-02   9.4144e-01   8.7085e-02   1.0623e+00   8.1949e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 148:    1.0299e+02  -4.1336e+00  -2.1967e+00  -3.9921e+00  -1.0425e+00  -1.7466e-01   1.7992e+00   3.6726e-03   1.5298e-01   1.9937e-02   1.9453e-01   8.0051e-02   9.9498e-01   8.3001e-02   1.0884e+00   8.1599e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 149:    1.0292e+02  -4.1282e+00  -2.2741e+00  -4.0028e+00  -1.0650e+00  -1.4879e-01   2.1425e+00   3.4890e-03   1.4534e-01   1.8941e-02   2.0240e-01   7.6049e-02   1.0080e+00   7.9550e-02   1.1679e+00   7.9115e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 150:    1.0302e+02  -4.1402e+00  -2.2769e+00  -4.0056e+00  -1.0488e+00  -1.2845e-01   2.7814e+00   3.3145e-03   1.3807e-01   2.0220e-02   1.9228e-01   7.2246e-02   9.9253e-01   8.0215e-02   1.0075e+00   8.2295e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 151:    1.0304e+02  -4.1281e+00  -2.2250e+00  -4.0169e+00  -1.0448e+00  -1.4468e-01   2.6423e+00   3.1488e-03   1.3117e-01   2.1298e-02   1.8267e-01   6.8634e-02   1.0207e+00   8.0317e-02   1.0813e+00   8.5492e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 152:    1.0321e+02  -4.1291e+00  -2.2281e+00  -4.0180e+00  -1.0232e+00  -1.4990e-01   1.6327e+00   2.8585e-03   1.0498e-01   1.8944e-02   1.6146e-01   5.0784e-02   9.5663e-01   7.9113e-02   1.1344e+00   8.2687e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 153:    1.0309e+02  -4.1280e+00  -2.2030e+00  -4.0149e+00  -1.0198e+00  -1.3442e-01   1.6757e+00   3.4142e-03   9.0693e-02   2.2471e-02   1.6167e-01   3.6279e-02   9.6659e-01   8.1655e-02   1.0345e+00   8.6113e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 154:    1.0298e+02  -4.1506e+00  -2.2176e+00  -3.9851e+00  -1.0417e+00  -1.6429e-01   1.9656e+00   3.3408e-03   9.5697e-02   2.1721e-02   1.6754e-01   2.9142e-02   1.0379e+00   7.9957e-02   1.0132e+00   8.5327e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 155:    1.0328e+02  -4.1204e+00  -2.2287e+00  -3.9772e+00  -1.0135e+00  -2.0459e-01   1.9195e+00   3.3987e-03   1.0293e-01   1.6897e-02   1.9470e-01   2.0826e-02   9.4310e-01   8.0528e-02   1.0639e+00   8.1926e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 156:    1.0338e+02  -4.1272e+00  -2.1962e+00  -3.9743e+00  -1.0491e+00  -1.8971e-01   1.7194e+00   3.2287e-03   7.8468e-02   1.8220e-02   1.8015e-01   2.0956e-02   9.2992e-01   8.6295e-02   9.5939e-01   8.1945e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 157:    1.0331e+02  -4.1188e+00  -2.1783e+00  -3.9779e+00  -1.0506e+00  -1.9810e-01   1.1127e+00   2.7176e-03   6.1234e-02   1.3748e-02   2.0147e-01   3.4800e-02   9.3006e-01   8.4794e-02   1.0579e+00   8.1604e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 158:    1.0326e+02  -4.1162e+00  -2.1734e+00  -3.9827e+00  -1.0632e+00  -2.0171e-01   1.3289e+00   2.7407e-03   7.1465e-02   1.2306e-02   2.1511e-01   3.4923e-02   8.9200e-01   8.4554e-02   1.1179e+00   8.2301e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 159:    1.0358e+02  -4.1075e+00  -2.2069e+00  -3.9725e+00  -1.0445e+00  -1.8044e-01   1.6656e+00   2.5282e-03   6.4340e-02   1.3587e-02   2.0355e-01   2.7629e-02   8.9557e-01   8.3727e-02   1.0560e+00   8.0934e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 160:    1.0364e+02  -4.1182e+00  -2.1514e+00  -3.9750e+00  -1.0378e+00  -2.2449e-01   1.8413e+00   3.5609e-03   7.2072e-02   1.5648e-02   2.0999e-01   3.1283e-02   9.1468e-01   8.2340e-02   1.0016e+00   8.0955e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 161:    1.0387e+02  -4.0964e+00  -2.1926e+00  -3.9808e+00  -1.0397e+00  -2.5178e-01   2.3706e+00   2.4158e-03   7.4658e-02   1.7789e-02   1.8587e-01   3.1647e-02   8.8257e-01   8.4503e-02   9.5909e-01   8.2216e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 162:    1.0399e+02  -4.0992e+00  -2.2056e+00  -3.9647e+00  -1.0388e+00  -2.4824e-01   1.9736e+00   2.2302e-03   1.0445e-01   1.9729e-02   1.9531e-01   2.9879e-02   8.3560e-01   8.6622e-02   9.5407e-01   7.9625e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 163:    1.0366e+02  -4.0956e+00  -2.1684e+00  -3.9612e+00  -1.0058e+00  -2.6882e-01   2.5872e+00   1.6246e-03   8.1892e-02   2.2777e-02   2.2429e-01   2.8371e-02   8.4799e-01   9.0947e-02   9.5842e-01   8.2649e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 164:    1.0325e+02  -4.0878e+00  -2.1946e+00  -3.9781e+00  -9.8890e-01  -2.6917e-01   2.0805e+00   1.3401e-03   8.2477e-02   2.6550e-02   1.8386e-01   2.4255e-02   8.5728e-01   8.9778e-02   9.1439e-01   7.9784e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 165:    1.0282e+02  -4.0801e+00  -2.1928e+00  -3.9664e+00  -9.9558e-01  -2.6059e-01   1.4087e+00   1.2155e-03   5.8081e-02   2.3833e-02   2.1871e-01   2.0911e-02   8.6998e-01   8.9895e-02   9.9363e-01   8.0284e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 166:    1.0285e+02  -4.0942e+00  -2.1855e+00  -3.9889e+00  -1.0234e+00  -2.4125e-01   8.5034e-01   6.8919e-04   6.5725e-02   2.3045e-02   2.1221e-01   2.6083e-02   8.7024e-01   9.4598e-02   9.5824e-01   8.3361e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 167:    1.0267e+02  -4.0904e+00  -2.2131e+00  -3.9917e+00  -9.9055e-01  -2.1449e-01   9.1382e-01   4.6145e-04   8.1337e-02   2.2154e-02   2.1431e-01   2.7524e-02   8.4730e-01   8.3563e-02   9.9458e-01   8.0138e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 168:    1.0249e+02  -4.0954e+00  -2.2174e+00  -3.9868e+00  -9.9799e-01  -1.8848e-01   6.5737e-01   4.2757e-04   6.9428e-02   2.8833e-02   2.0256e-01   4.1573e-02   9.8269e-01   7.6264e-02   1.0011e+00   8.1357e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 169:    1.0252e+02  -4.0995e+00  -2.2132e+00  -3.9792e+00  -9.8661e-01  -2.2724e-01   5.4553e-01   3.9691e-04   4.2460e-02   2.7545e-02   2.2594e-01   4.9079e-02   9.6027e-01   8.1006e-02   1.0261e+00   8.5194e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 170:    1.0246e+02  -4.1056e+00  -2.2389e+00  -3.9785e+00  -1.0354e+00  -2.4212e-01   4.5653e-01   2.8583e-04   2.8936e-02   2.5026e-02   1.8496e-01   4.3514e-02   9.2689e-01   9.6050e-02   9.9096e-01   8.3466e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 171:    1.0238e+02  -4.1027e+00  -2.2592e+00  -3.9859e+00  -1.0106e+00  -2.3962e-01   5.1080e-01   2.6715e-04   3.2138e-02   2.7859e-02   1.6505e-01   6.2560e-02   9.1886e-01   9.0993e-02   9.4634e-01   8.3542e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 172:    1.0242e+02  -4.0998e+00  -2.2415e+00  -3.9999e+00  -1.0234e+00  -2.2610e-01   5.1154e-01   1.8668e-04   2.2768e-02   2.9930e-02   2.0056e-01   7.5570e-02   9.4948e-01   8.4742e-02   9.1357e-01   8.3596e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 173:    1.0232e+02  -4.1005e+00  -2.2382e+00  -4.0022e+00  -1.0017e+00  -1.8406e-01   3.2169e-01   2.0484e-04   2.4102e-02   2.9249e-02   2.0445e-01   1.1517e-01   9.5169e-01   8.3951e-02   9.0402e-01   8.3130e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 174:    1.0233e+02  -4.0990e+00  -2.2337e+00  -3.9975e+00  -1.0298e+00  -1.8028e-01   4.2668e-01   2.1409e-04   2.4350e-02   2.4159e-02   2.0114e-01   1.4344e-01   9.3446e-01   8.6704e-02   9.6511e-01   8.3994e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 175:    1.0236e+02  -4.0991e+00  -2.2315e+00  -3.9883e+00  -1.0110e+00  -2.2190e-01   4.1564e-01   2.5844e-04   3.5664e-02   2.1844e-02   2.0315e-01   1.2876e-01   8.9006e-01   8.3016e-02   9.2369e-01   8.0410e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 176:    1.0222e+02  -4.0983e+00  -2.1991e+00  -3.9924e+00  -1.0148e+00  -2.3133e-01   1.7684e-01   2.5312e-04   5.3516e-02   2.2568e-02   2.0010e-01   1.0721e-01   8.9884e-01   7.6042e-02   1.0110e+00   7.9890e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 177:    1.0219e+02  -4.1022e+00  -2.2129e+00  -3.9804e+00  -1.0286e+00  -1.9916e-01   1.9962e-01   3.0338e-04   6.9476e-02   2.0133e-02   1.8707e-01   1.0631e-01   8.7419e-01   8.3199e-02   9.5450e-01   8.2626e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 178:    1.0225e+02  -4.1022e+00  -2.2135e+00  -4.0057e+00  -1.0264e+00  -2.2859e-01   2.1742e-01   2.9489e-04   5.1238e-02   2.2227e-02   1.7924e-01   1.4496e-01   8.5487e-01   8.3885e-02   9.7315e-01   8.1406e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 179:    1.0234e+02  -4.1015e+00  -2.2170e+00  -3.9999e+00  -1.0177e+00  -2.0895e-01   2.1196e-01   3.0802e-04   5.7417e-02   2.6776e-02   1.7214e-01   1.5197e-01   8.8641e-01   7.7789e-02   9.2725e-01   8.1277e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 180:    1.0243e+02  -4.1034e+00  -2.2337e+00  -4.0000e+00  -1.0018e+00  -2.3492e-01   2.0521e-01   3.1261e-04   6.0162e-02   2.7277e-02   1.7524e-01   1.6673e-01   8.8472e-01   7.8939e-02   9.1311e-01   8.3794e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 181:    1.0244e+02  -4.1031e+00  -2.2415e+00  -3.9951e+00  -9.9356e-01  -1.9938e-01   1.4850e-01   2.7894e-04   5.5244e-02   3.2404e-02   2.0701e-01   2.1022e-01   8.7495e-01   8.1883e-02   9.7574e-01   8.4575e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 182:    1.0245e+02  -4.1066e+00  -2.2849e+00  -4.0083e+00  -1.0260e+00  -1.9418e-01   1.0783e-01   1.7359e-04   2.9632e-02   3.2098e-02   2.0294e-01   1.7919e-01   9.0795e-01   9.1179e-02   9.8651e-01   8.3561e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 183:    1.0248e+02  -4.1066e+00  -2.2844e+00  -4.0201e+00  -1.0102e+00  -1.5510e-01   9.2727e-02   1.4337e-04   2.8436e-02   4.3941e-02   1.9683e-01   2.1943e-01   9.0334e-01   8.2113e-02   8.9037e-01   8.4506e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 184:    1.0239e+02  -4.1032e+00  -2.2726e+00  -4.0270e+00  -1.0162e+00  -1.4921e-01   9.0062e-02   1.0820e-04   3.5099e-02   4.7338e-02   1.8488e-01   2.2174e-01   9.1525e-01   7.9986e-02   8.6762e-01   8.4376e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 185:    1.0240e+02  -4.1028e+00  -2.3201e+00  -4.0248e+00  -9.8758e-01  -1.7235e-01   9.2455e-02   8.9653e-05   4.7097e-02   4.5964e-02   1.9093e-01   1.9093e-01   9.5192e-01   7.6980e-02   9.4058e-01   8.2740e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 186:    1.0250e+02  -4.1020e+00  -2.3095e+00  -4.0326e+00  -9.9767e-01  -1.2041e-01   1.0792e-01   7.3472e-05   3.6352e-02   4.6680e-02   1.7527e-01   1.7411e-01   9.6831e-01   8.3088e-02   9.7978e-01   8.1096e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 187:    1.0254e+02  -4.1022e+00  -2.3078e+00  -4.0329e+00  -1.0166e+00  -1.1644e-01   8.2987e-02   7.9222e-05   3.8388e-02   6.0452e-02   2.3498e-01   1.7389e-01   1.0450e+00   8.0354e-02   9.7366e-01   8.2504e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 188:    1.0253e+02  -4.1022e+00  -2.3172e+00  -4.0401e+00  -1.0276e+00  -8.2442e-02   8.3913e-02   7.1884e-05   4.3025e-02   4.8383e-02   1.9392e-01   1.6073e-01   9.7466e-01   7.5977e-02   9.0319e-01   8.4648e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 189:    1.0256e+02  -4.1013e+00  -2.3037e+00  -4.0402e+00  -1.0004e+00  -1.5946e-01   9.6273e-02   8.0773e-05   4.6130e-02   5.2356e-02   2.1044e-01   2.0403e-01   9.2931e-01   8.2686e-02   9.0103e-01   8.5442e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 190:    1.0245e+02  -4.1021e+00  -2.3143e+00  -4.0314e+00  -9.9314e-01  -8.5545e-02   8.0660e-02   7.3369e-05   4.1713e-02   4.4940e-02   1.7715e-01   1.6886e-01   9.7982e-01   8.1720e-02   8.9868e-01   8.4964e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 191:    1.0251e+02  -4.1021e+00  -2.2849e+00  -4.0229e+00  -1.0346e+00  -9.0261e-02   6.1956e-02   9.4824e-05   5.8037e-02   4.0685e-02   1.8455e-01   2.2838e-01   9.9679e-01   7.7457e-02   9.4591e-01   8.5446e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 192:    1.0249e+02  -4.1012e+00  -2.2915e+00  -4.0452e+00  -1.0323e+00  -1.1123e-01   5.1409e-02   1.1452e-04   7.2457e-02   3.6376e-02   1.6706e-01   2.1050e-01   8.7970e-01   8.6881e-02   9.2230e-01   8.6563e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 193:    1.0249e+02  -4.1011e+00  -2.3104e+00  -4.0569e+00  -1.0032e+00  -2.9383e-02   4.2462e-02   9.6872e-05   4.9405e-02   4.6840e-02   1.7348e-01   1.4473e-01   9.1758e-01   8.1380e-02   1.0057e+00   8.4481e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 194:    1.0250e+02  -4.1009e+00  -2.2960e+00  -4.0567e+00  -9.9145e-01  -1.0640e-01   2.6698e-02   5.3298e-05   4.3880e-02   5.1866e-02   2.0619e-01   1.4008e-01   9.8306e-01   8.0320e-02   9.8405e-01   8.0106e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 195:    1.0252e+02  -4.1001e+00  -2.2779e+00  -4.0677e+00  -9.9960e-01  -5.5605e-02   1.9114e-02   5.6472e-05   4.5810e-02   5.1552e-02   1.8363e-01   1.2037e-01   9.9334e-01   8.2093e-02   9.9128e-01   8.3930e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 196:    1.0251e+02  -4.0988e+00  -2.2515e+00  -4.0555e+00  -9.9701e-01  -1.0235e-01   1.3362e-02   3.2648e-05   4.0337e-02   4.4117e-02   1.9469e-01   1.2907e-01   9.1150e-01   8.5071e-02   9.7929e-01   8.2761e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 197:    1.0253e+02  -4.0985e+00  -2.2589e+00  -4.0427e+00  -1.0251e+00  -9.4695e-02   1.7104e-02   2.3125e-05   4.1773e-02   3.3831e-02   2.1013e-01   1.3727e-01   9.1462e-01   8.1836e-02   9.7695e-01   8.1326e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 198:    1.0253e+02  -4.0992e+00  -2.2745e+00  -4.0327e+00  -9.8743e-01  -1.4108e-01   9.3331e-03   2.3064e-05   3.7271e-02   2.7379e-02   2.1190e-01   9.3733e-02   9.0916e-01   8.4247e-02   9.8720e-01   7.7405e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 199:    1.0251e+02  -4.0981e+00  -2.2770e+00  -4.0275e+00  -1.0216e+00  -1.3364e-01   8.3776e-03   2.3195e-05   4.6827e-02   3.2861e-02   1.9817e-01   8.7748e-02   9.1184e-01   8.3773e-02   9.5209e-01   7.9056e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 200:    1.0252e+02  -4.0992e+00  -2.2757e+00  -4.0277e+00  -9.9266e-01  -8.4583e-02   6.8073e-03   2.9414e-05   5.3404e-02   2.5942e-02   1.9915e-01   7.4937e-02   8.7168e-01   8.5197e-02   9.3606e-01   8.0739e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 201:    1.0252e+02  -4.0989e+00  -2.2706e+00  -4.0333e+00  -9.8978e-01  -8.7996e-02   5.9272e-03   2.4268e-05   5.4295e-02   2.9276e-02   2.0543e-01   7.1533e-02   9.0557e-01   8.3204e-02   9.4913e-01   8.0398e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 202:    1.0251e+02  -4.0990e+00  -2.2661e+00  -4.0324e+00  -9.8785e-01  -9.8008e-02   5.7831e-03   2.1967e-05   5.7627e-02   3.0606e-02   2.1414e-01   6.9475e-02   9.2255e-01   8.2143e-02   9.5633e-01   8.1333e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 203:    1.0251e+02  -4.0992e+00  -2.2586e+00  -4.0297e+00  -9.9017e-01  -1.1012e-01   6.4046e-03   2.2234e-05   5.7955e-02   3.0102e-02   2.0473e-01   6.8225e-02   9.2900e-01   8.3228e-02   9.5862e-01   8.1460e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 204:    1.0251e+02  -4.0995e+00  -2.2570e+00  -4.0280e+00  -9.9135e-01  -1.0718e-01   6.5386e-03   2.2043e-05   5.6949e-02   2.9096e-02   2.0562e-01   6.8687e-02   9.3306e-01   8.2703e-02   9.6581e-01   8.1671e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 205:    1.0250e+02  -4.0997e+00  -2.2569e+00  -4.0256e+00  -9.9350e-01  -1.1491e-01   6.1521e-03   2.2570e-05   5.5450e-02   2.8152e-02   2.0423e-01   7.2820e-02   9.2469e-01   8.3272e-02   9.7372e-01   8.0814e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 206:    1.0250e+02  -4.0997e+00  -2.2567e+00  -4.0269e+00  -9.9869e-01  -1.1438e-01   5.9720e-03   2.3307e-05   5.5103e-02   2.8506e-02   2.0414e-01   7.8225e-02   9.2768e-01   8.3328e-02   9.8294e-01   8.0918e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 207:    1.0250e+02  -4.0998e+00  -2.2657e+00  -4.0311e+00  -1.0000e+00  -1.0595e-01   6.0030e-03   2.4031e-05   5.4378e-02   2.9468e-02   2.0222e-01   8.1455e-02   9.3397e-01   8.3285e-02   9.8989e-01   8.0967e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 208:    1.0250e+02  -4.0999e+00  -2.2759e+00  -4.0330e+00  -1.0001e+00  -9.6916e-02   5.9586e-03   2.3690e-05   5.3811e-02   2.9674e-02   1.9979e-01   8.1206e-02   9.3593e-01   8.4094e-02   9.8891e-01   8.1107e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 209:    1.0250e+02  -4.0998e+00  -2.2836e+00  -4.0344e+00  -1.0016e+00  -8.9119e-02   5.6137e-03   2.3512e-05   5.4159e-02   2.9780e-02   1.9778e-01   8.2635e-02   9.4080e-01   8.3757e-02   9.9062e-01   8.0979e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 210:    1.0251e+02  -4.0998e+00  -2.2898e+00  -4.0334e+00  -1.0017e+00  -8.3861e-02   5.2428e-03   2.3117e-05   5.4271e-02   2.9112e-02   1.9539e-01   8.3602e-02   9.4410e-01   8.4262e-02   9.8944e-01   8.1260e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 211:    1.0251e+02  -4.0998e+00  -2.2968e+00  -4.0329e+00  -9.9953e-01  -8.2036e-02   4.9312e-03   2.2863e-05   5.5392e-02   2.8432e-02   1.9466e-01   8.3724e-02   9.5395e-01   8.4317e-02   9.9876e-01   8.1402e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 212:    1.0251e+02  -4.0997e+00  -2.2982e+00  -4.0327e+00  -1.0018e+00  -8.2942e-02   4.6622e-03   2.3364e-05   5.5864e-02   2.7552e-02   1.9313e-01   8.3815e-02   9.5374e-01   8.4223e-02   1.0049e+00   8.1479e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 213:    1.0251e+02  -4.0995e+00  -2.2996e+00  -4.0322e+00  -1.0027e+00  -8.4931e-02   4.4988e-03   2.4085e-05   5.6000e-02   2.6729e-02   1.9184e-01   8.3522e-02   9.5091e-01   8.4321e-02   1.0026e+00   8.1310e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 214:    1.0251e+02  -4.0994e+00  -2.2997e+00  -4.0326e+00  -1.0033e+00  -8.7810e-02   4.3582e-03   2.5253e-05   5.6470e-02   2.6179e-02   1.9110e-01   8.5338e-02   9.5311e-01   8.4118e-02   1.0062e+00   8.1320e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 215:    1.0251e+02  -4.0993e+00  -2.3013e+00  -4.0331e+00  -1.0042e+00  -8.5053e-02   4.3765e-03   2.6370e-05   5.6978e-02   2.5984e-02   1.8960e-01   8.6633e-02   9.5505e-01   8.3787e-02   1.0092e+00   8.1298e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 216:    1.0251e+02  -4.0993e+00  -2.3032e+00  -4.0320e+00  -1.0033e+00  -8.5521e-02   4.4127e-03   2.7607e-05   5.7092e-02   2.5514e-02   1.8916e-01   8.8986e-02   9.5571e-01   8.3655e-02   1.0106e+00   8.1177e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 217:    1.0251e+02  -4.0993e+00  -2.3050e+00  -4.0309e+00  -1.0039e+00  -8.5267e-02   4.4663e-03   2.8101e-05   5.7536e-02   2.5186e-02   1.8803e-01   9.0776e-02   9.5624e-01   8.3792e-02   1.0100e+00   8.1214e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 218:    1.0251e+02  -4.0993e+00  -2.3068e+00  -4.0309e+00  -1.0052e+00  -8.3353e-02   4.4833e-03   2.7541e-05   5.7391e-02   2.4589e-02   1.8691e-01   9.4302e-02   9.5168e-01   8.4114e-02   1.0124e+00   8.1211e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 219:    1.0251e+02  -4.0993e+00  -2.3077e+00  -4.0316e+00  -1.0049e+00  -8.0885e-02   4.4529e-03   2.6825e-05   5.7273e-02   2.3909e-02   1.8700e-01   9.6040e-02   9.4675e-01   8.4115e-02   1.0115e+00   8.1075e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 220:    1.0251e+02  -4.0993e+00  -2.3095e+00  -4.0327e+00  -1.0051e+00  -7.6386e-02   4.5217e-03   2.6724e-05   5.7342e-02   2.3648e-02   1.8752e-01   9.7466e-02   9.4316e-01   8.4134e-02   1.0099e+00   8.1206e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 221:    1.0251e+02  -4.0993e+00  -2.3120e+00  -4.0339e+00  -1.0050e+00  -7.3314e-02   4.5361e-03   2.6133e-05   5.7387e-02   2.3612e-02   1.8744e-01   9.9108e-02   9.4164e-01   8.4073e-02   1.0096e+00   8.1143e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 222:    1.0251e+02  -4.0993e+00  -2.3136e+00  -4.0355e+00  -1.0049e+00  -6.8041e-02   4.5806e-03   2.5728e-05   5.7827e-02   2.3466e-02   1.8681e-01   9.9150e-02   9.4237e-01   8.3991e-02   1.0116e+00   8.1192e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 223:    1.0251e+02  -4.0993e+00  -2.3144e+00  -4.0364e+00  -1.0047e+00  -6.6770e-02   4.5349e-03   2.5412e-05   5.8823e-02   2.3232e-02   1.8728e-01   9.9882e-02   9.4051e-01   8.3808e-02   1.0097e+00   8.1028e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 224:    1.0251e+02  -4.0992e+00  -2.3155e+00  -4.0373e+00  -1.0040e+00  -6.4487e-02   4.4985e-03   2.5094e-05   6.0294e-02   2.3022e-02   1.8652e-01   1.0114e-01   9.3800e-01   8.3696e-02   1.0099e+00   8.0990e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 225:    1.0251e+02  -4.0992e+00  -2.3167e+00  -4.0384e+00  -1.0041e+00  -6.2966e-02   4.4783e-03   2.4741e-05   6.0569e-02   2.2900e-02   1.8622e-01   1.0072e-01   9.3574e-01   8.3833e-02   1.0075e+00   8.0893e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 226:    1.0251e+02  -4.0993e+00  -2.3173e+00  -4.0395e+00  -1.0036e+00  -6.0541e-02   4.4466e-03   2.4601e-05   6.0661e-02   2.2897e-02   1.8544e-01   1.0160e-01   9.3842e-01   8.3565e-02   1.0064e+00   8.1006e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 227:    1.0251e+02  -4.0993e+00  -2.3183e+00  -4.0403e+00  -1.0028e+00  -5.9061e-02   4.4290e-03   2.4610e-05   6.1093e-02   2.2836e-02   1.8582e-01   1.0184e-01   9.3983e-01   8.3421e-02   1.0066e+00   8.1225e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 228:    1.0251e+02  -4.0993e+00  -2.3190e+00  -4.0405e+00  -1.0027e+00  -5.8994e-02   4.4377e-03   2.4228e-05   6.0957e-02   2.2540e-02   1.8424e-01   1.0104e-01   9.4056e-01   8.3559e-02   1.0085e+00   8.1266e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 229:    1.0251e+02  -4.0993e+00  -2.3206e+00  -4.0398e+00  -1.0026e+00  -5.9258e-02   4.5021e-03   2.3801e-05   6.0331e-02   2.2380e-02   1.8409e-01   9.9681e-02   9.4320e-01   8.3678e-02   1.0111e+00   8.1236e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 230:    1.0251e+02  -4.0994e+00  -2.3217e+00  -4.0401e+00  -1.0028e+00  -5.7766e-02   4.5320e-03   2.3519e-05   5.9913e-02   2.2317e-02   1.8484e-01   9.9325e-02   9.4418e-01   8.3884e-02   1.0144e+00   8.1294e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 231:    1.0251e+02  -4.0994e+00  -2.3223e+00  -4.0400e+00  -1.0036e+00  -5.7213e-02   4.6519e-03   2.3658e-05   5.9386e-02   2.2185e-02   1.8526e-01   9.8374e-02   9.4356e-01   8.4167e-02   1.0171e+00   8.1315e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 232:    1.0251e+02  -4.0994e+00  -2.3231e+00  -4.0400e+00  -1.0045e+00  -5.5248e-02   4.7067e-03   2.3117e-05   5.9583e-02   2.2248e-02   1.8574e-01   9.7396e-02   9.4340e-01   8.4212e-02   1.0179e+00   8.1401e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 233:    1.0251e+02  -4.0994e+00  -2.3237e+00  -4.0401e+00  -1.0046e+00  -5.4085e-02   4.7534e-03   2.2858e-05   5.8985e-02   2.2248e-02   1.8649e-01   9.7364e-02   9.4271e-01   8.4248e-02   1.0189e+00   8.1456e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 234:    1.0251e+02  -4.0995e+00  -2.3238e+00  -4.0400e+00  -1.0042e+00  -5.2957e-02   4.8433e-03   2.2715e-05   5.8868e-02   2.2174e-02   1.8630e-01   9.7975e-02   9.4382e-01   8.4160e-02   1.0210e+00   8.1665e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 235:    1.0251e+02  -4.0995e+00  -2.3248e+00  -4.0396e+00  -1.0044e+00  -5.3132e-02   4.8769e-03   2.2540e-05   5.9052e-02   2.2120e-02   1.8627e-01   9.8772e-02   9.4330e-01   8.4170e-02   1.0196e+00   8.1627e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 236:    1.0251e+02  -4.0995e+00  -2.3244e+00  -4.0392e+00  -1.0052e+00  -5.3786e-02   4.8860e-03   2.2664e-05   5.8981e-02   2.2095e-02   1.8771e-01   9.8852e-02   9.4172e-01   8.4214e-02   1.0191e+00   8.1698e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 237:    1.0251e+02  -4.0996e+00  -2.3231e+00  -4.0386e+00  -1.0056e+00  -5.5158e-02   4.8731e-03   2.2576e-05   5.8788e-02   2.2037e-02   1.8860e-01   9.9599e-02   9.4242e-01   8.4097e-02   1.0198e+00   8.1684e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 238:    1.0251e+02  -4.0996e+00  -2.3222e+00  -4.0384e+00  -1.0054e+00  -5.6412e-02   4.8673e-03   2.2575e-05   5.8733e-02   2.1962e-02   1.8874e-01   9.9196e-02   9.4097e-01   8.4103e-02   1.0208e+00   8.1649e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 239:    1.0251e+02  -4.0996e+00  -2.3212e+00  -4.0382e+00  -1.0068e+00  -5.7131e-02   4.8223e-03   2.2398e-05   5.8674e-02   2.1817e-02   1.8926e-01   9.9323e-02   9.4261e-01   8.4059e-02   1.0229e+00   8.1670e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 240:    1.0251e+02  -4.0996e+00  -2.3216e+00  -4.0376e+00  -1.0073e+00  -5.6962e-02   4.7984e-03   2.2260e-05   5.8558e-02   2.1632e-02   1.8892e-01   9.9232e-02   9.4434e-01   8.4130e-02   1.0237e+00   8.1679e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 241:    1.0251e+02  -4.0996e+00  -2.3216e+00  -4.0371e+00  -1.0078e+00  -5.6984e-02   4.7767e-03   2.2074e-05   5.9552e-02   2.1436e-02   1.8860e-01   9.9285e-02   9.4491e-01   8.4026e-02   1.0247e+00   8.1680e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 242:    1.0251e+02  -4.0996e+00  -2.3222e+00  -4.0372e+00  -1.0079e+00  -5.6422e-02   4.7880e-03   2.2014e-05   6.0033e-02   2.1277e-02   1.8860e-01   9.8674e-02   9.4359e-01   8.3991e-02   1.0255e+00   8.1634e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 243:    1.0251e+02  -4.0995e+00  -2.3227e+00  -4.0376e+00  -1.0079e+00  -5.4330e-02   4.7944e-03   2.1687e-05   6.0189e-02   2.1088e-02   1.8830e-01   9.7952e-02   9.4207e-01   8.4036e-02   1.0257e+00   8.1666e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 244:    1.0251e+02  -4.0995e+00  -2.3237e+00  -4.0385e+00  -1.0076e+00  -5.2834e-02   4.7916e-03   2.1467e-05   6.0432e-02   2.1062e-02   1.8810e-01   9.7065e-02   9.4039e-01   8.4075e-02   1.0248e+00   8.1599e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 245:    1.0251e+02  -4.0995e+00  -2.3249e+00  -4.0390e+00  -1.0073e+00  -5.0542e-02   4.7926e-03   2.1549e-05   6.1095e-02   2.0981e-02   1.8814e-01   9.6715e-02   9.3900e-01   8.4049e-02   1.0226e+00   8.1505e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 246:    1.0251e+02  -4.0995e+00  -2.3261e+00  -4.0398e+00  -1.0066e+00  -4.7686e-02   4.8114e-03   2.1557e-05   6.1692e-02   2.0969e-02   1.8783e-01   9.6477e-02   9.3880e-01   8.4024e-02   1.0211e+00   8.1456e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 247:    1.0251e+02  -4.0996e+00  -2.3270e+00  -4.0406e+00  -1.0063e+00  -4.5794e-02   4.7864e-03   2.1709e-05   6.1819e-02   2.0928e-02   1.8765e-01   9.5549e-02   9.3939e-01   8.4076e-02   1.0201e+00   8.1427e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 248:    1.0251e+02  -4.0995e+00  -2.3282e+00  -4.0412e+00  -1.0064e+00  -4.3009e-02   4.7307e-03   2.1707e-05   6.2309e-02   2.0970e-02   1.8795e-01   9.5221e-02   9.3898e-01   8.4080e-02   1.0197e+00   8.1501e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 249:    1.0251e+02  -4.0996e+00  -2.3298e+00  -4.0417e+00  -1.0070e+00  -4.1062e-02   4.7070e-03   2.1737e-05   6.2607e-02   2.1094e-02   1.8796e-01   9.5103e-02   9.3917e-01   8.4069e-02   1.0194e+00   8.1525e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 250:    1.0251e+02  -4.0996e+00  -2.3313e+00  -4.0420e+00  -1.0073e+00  -3.9250e-02   4.7191e-03   2.1947e-05   6.2963e-02   2.1053e-02   1.8834e-01   9.4954e-02   9.3851e-01   8.4115e-02   1.0190e+00   8.1496e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 251:    1.0251e+02  -4.0997e+00  -2.3317e+00  -4.0424e+00  -1.0078e+00  -3.7549e-02   4.7501e-03   2.2063e-05   6.3160e-02   2.0958e-02   1.8840e-01   9.4783e-02   9.3749e-01   8.4137e-02   1.0185e+00   8.1476e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 252:    1.0251e+02  -4.0997e+00  -2.3319e+00  -4.0424e+00  -1.0083e+00  -3.6657e-02   4.7441e-03   2.2081e-05   6.3250e-02   2.0891e-02   1.8911e-01   9.4954e-02   9.3708e-01   8.4108e-02   1.0180e+00   8.1479e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 253:    1.0251e+02  -4.0997e+00  -2.3323e+00  -4.0425e+00  -1.0085e+00  -3.5826e-02   4.7409e-03   2.2021e-05   6.3513e-02   2.0810e-02   1.8920e-01   9.5196e-02   9.3509e-01   8.4179e-02   1.0184e+00   8.1440e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 254:    1.0251e+02  -4.0997e+00  -2.3324e+00  -4.0426e+00  -1.0086e+00  -3.6456e-02   4.7046e-03   2.1929e-05   6.3535e-02   2.0859e-02   1.9010e-01   9.5077e-02   9.3362e-01   8.4279e-02   1.0185e+00   8.1371e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 255:    1.0251e+02  -4.0997e+00  -2.3313e+00  -4.0424e+00  -1.0088e+00  -3.7358e-02   4.6636e-03   2.1827e-05   6.3864e-02   2.0820e-02   1.9126e-01   9.4979e-02   9.3183e-01   8.4380e-02   1.0185e+00   8.1434e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 256:    1.0251e+02  -4.0997e+00  -2.3306e+00  -4.0423e+00  -1.0086e+00  -3.8558e-02   4.6418e-03   2.1776e-05   6.4176e-02   2.0845e-02   1.9175e-01   9.4890e-02   9.3144e-01   8.4436e-02   1.0188e+00   8.1420e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 257:    1.0251e+02  -4.0997e+00  -2.3297e+00  -4.0419e+00  -1.0092e+00  -3.9580e-02   4.6134e-03   2.1742e-05   6.4408e-02   2.0751e-02   1.9188e-01   9.4692e-02   9.3113e-01   8.4507e-02   1.0187e+00   8.1412e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 258:    1.0251e+02  -4.0997e+00  -2.3287e+00  -4.0415e+00  -1.0090e+00  -4.0619e-02   4.5918e-03   2.1710e-05   6.4634e-02   2.0663e-02   1.9172e-01   9.4422e-02   9.3104e-01   8.4479e-02   1.0189e+00   8.1395e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 259:    1.0251e+02  -4.0997e+00  -2.3284e+00  -4.0412e+00  -1.0086e+00  -4.1245e-02   4.5889e-03   2.1741e-05   6.5035e-02   2.0678e-02   1.9132e-01   9.3998e-02   9.2986e-01   8.4448e-02   1.0177e+00   8.1368e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 260:    1.0251e+02  -4.0997e+00  -2.3283e+00  -4.0410e+00  -1.0084e+00  -4.1907e-02   4.5818e-03   2.1877e-05   6.5755e-02   2.0691e-02   1.9113e-01   9.3631e-02   9.2955e-01   8.4378e-02   1.0172e+00   8.1346e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 261:    1.0251e+02  -4.0998e+00  -2.3282e+00  -4.0409e+00  -1.0087e+00  -4.2022e-02   4.5724e-03   2.1946e-05   6.6396e-02   2.0714e-02   1.9084e-01   9.3140e-02   9.2882e-01   8.4426e-02   1.0166e+00   8.1329e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 262:    1.0251e+02  -4.0997e+00  -2.3275e+00  -4.0408e+00  -1.0092e+00  -4.2439e-02   4.5357e-03   2.1842e-05   6.6895e-02   2.0692e-02   1.9082e-01   9.2527e-02   9.2811e-01   8.4447e-02   1.0168e+00   8.1339e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 263:    1.0251e+02  -4.0998e+00  -2.3269e+00  -4.0407e+00  -1.0097e+00  -4.2867e-02   4.5244e-03   2.1830e-05   6.7068e-02   2.0721e-02   1.9077e-01   9.2003e-02   9.2800e-01   8.4366e-02   1.0171e+00   8.1343e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 264:    1.0251e+02  -4.0998e+00  -2.3262e+00  -4.0403e+00  -1.0102e+00  -4.3066e-02   4.4987e-03   2.1812e-05   6.7458e-02   2.0718e-02   1.9065e-01   9.1528e-02   9.2710e-01   8.4389e-02   1.0163e+00   8.1355e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 265:    1.0251e+02  -4.0998e+00  -2.3264e+00  -4.0401e+00  -1.0105e+00  -4.3042e-02   4.4865e-03   2.1721e-05   6.7680e-02   2.0722e-02   1.9057e-01   9.1243e-02   9.2631e-01   8.4430e-02   1.0154e+00   8.1321e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 266:    1.0251e+02  -4.0998e+00  -2.3264e+00  -4.0397e+00  -1.0107e+00  -4.3247e-02   4.4552e-03   2.1609e-05   6.8246e-02   2.0757e-02   1.9054e-01   9.1222e-02   9.2659e-01   8.4381e-02   1.0154e+00   8.1290e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 267:    1.0251e+02  -4.0999e+00  -2.3261e+00  -4.0396e+00  -1.0107e+00  -4.4145e-02   4.4367e-03   2.1505e-05   6.8834e-02   2.0735e-02   1.9038e-01   9.1594e-02   9.2661e-01   8.4338e-02   1.0153e+00   8.1308e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 268:    1.0251e+02  -4.0999e+00  -2.3249e+00  -4.0395e+00  -1.0112e+00  -4.5394e-02   4.4239e-03   2.1411e-05   6.9776e-02   2.0749e-02   1.9017e-01   9.1916e-02   9.2711e-01   8.4286e-02   1.0158e+00   8.1400e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 269:    1.0251e+02  -4.0999e+00  -2.3236e+00  -4.0391e+00  -1.0112e+00  -4.6744e-02   4.4119e-03   2.1310e-05   7.0528e-02   2.0709e-02   1.9026e-01   9.2008e-02   9.2638e-01   8.4248e-02   1.0160e+00   8.1389e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 270:    1.0251e+02  -4.0999e+00  -2.3231e+00  -4.0388e+00  -1.0116e+00  -4.7514e-02   4.3782e-03   2.1160e-05   7.1513e-02   2.0724e-02   1.9006e-01   9.1551e-02   9.2668e-01   8.4246e-02   1.0149e+00   8.1419e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 271:    1.0251e+02  -4.0999e+00  -2.3228e+00  -4.0387e+00  -1.0117e+00  -4.7755e-02   4.3389e-03   2.1121e-05   7.1868e-02   2.0670e-02   1.8969e-01   9.1369e-02   9.2678e-01   8.4228e-02   1.0145e+00   8.1437e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 272:    1.0251e+02  -4.0999e+00  -2.3222e+00  -4.0387e+00  -1.0121e+00  -4.8163e-02   4.3080e-03   2.1146e-05   7.2226e-02   2.0710e-02   1.8983e-01   9.0962e-02   9.2639e-01   8.4261e-02   1.0132e+00   8.1523e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 273:    1.0251e+02  -4.0999e+00  -2.3216e+00  -4.0389e+00  -1.0122e+00  -4.8336e-02   4.2916e-03   2.1202e-05   7.2734e-02   2.0689e-02   1.8982e-01   9.0422e-02   9.2618e-01   8.4177e-02   1.0134e+00   8.1561e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 274:    1.0251e+02  -4.0999e+00  -2.3211e+00  -4.0388e+00  -1.0124e+00  -4.8286e-02   4.2592e-03   2.1201e-05   7.3324e-02   2.0658e-02   1.9015e-01   8.9938e-02   9.2530e-01   8.4133e-02   1.0133e+00   8.1562e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 275:    1.0251e+02  -4.0999e+00  -2.3211e+00  -4.0387e+00  -1.0128e+00  -4.8312e-02   4.2368e-03   2.1092e-05   7.3673e-02   2.0639e-02   1.9018e-01   8.9708e-02   9.2404e-01   8.4155e-02   1.0130e+00   8.1535e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 276:    1.0251e+02  -4.0999e+00  -2.3212e+00  -4.0386e+00  -1.0126e+00  -4.8249e-02   4.2070e-03   2.0985e-05   7.4089e-02   2.0674e-02   1.8992e-01   8.9341e-02   9.2339e-01   8.4163e-02   1.0121e+00   8.1515e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 277:    1.0251e+02  -4.0999e+00  -2.3212e+00  -4.0387e+00  -1.0123e+00  -4.8743e-02   4.1781e-03   2.0914e-05   7.4488e-02   2.0779e-02   1.8959e-01   8.9106e-02   9.2352e-01   8.4100e-02   1.0112e+00   8.1477e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 278:    1.0251e+02  -4.0999e+00  -2.3213e+00  -4.0385e+00  -1.0118e+00  -4.9468e-02   4.1782e-03   2.0802e-05   7.4843e-02   2.0859e-02   1.8970e-01   8.8995e-02   9.2374e-01   8.4037e-02   1.0103e+00   8.1458e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 279:    1.0251e+02  -4.0999e+00  -2.3213e+00  -4.0384e+00  -1.0115e+00  -4.9612e-02   4.1736e-03   2.0737e-05   7.5385e-02   2.0899e-02   1.8969e-01   8.8806e-02   9.2347e-01   8.3989e-02   1.0095e+00   8.1440e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 280:    1.0251e+02  -4.0999e+00  -2.3213e+00  -4.0382e+00  -1.0114e+00  -5.0460e-02   4.1900e-03   2.0708e-05   7.6051e-02   2.0933e-02   1.8968e-01   8.9243e-02   9.2293e-01   8.3972e-02   1.0085e+00   8.1467e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 281:    1.0251e+02  -4.0999e+00  -2.3215e+00  -4.0378e+00  -1.0113e+00  -5.0583e-02   4.2096e-03   2.0638e-05   7.6759e-02   2.1003e-02   1.8941e-01   8.9450e-02   9.2294e-01   8.4027e-02   1.0080e+00   8.1467e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 282:    1.0251e+02  -4.0999e+00  -2.3218e+00  -4.0380e+00  -1.0114e+00  -5.0276e-02   4.2061e-03   2.0669e-05   7.7323e-02   2.1016e-02   1.8946e-01   8.9456e-02   9.2386e-01   8.3996e-02   1.0095e+00   8.1446e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 283:    1.0251e+02  -4.0999e+00  -2.3224e+00  -4.0383e+00  -1.0115e+00  -4.9357e-02   4.2005e-03   2.0601e-05   7.7856e-02   2.1072e-02   1.8968e-01   8.9327e-02   9.2480e-01   8.3964e-02   1.0101e+00   8.1418e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 284:    1.0251e+02  -4.0999e+00  -2.3226e+00  -4.0386e+00  -1.0110e+00  -4.8703e-02   4.1977e-03   2.0549e-05   7.8183e-02   2.1122e-02   1.8956e-01   8.9430e-02   9.2470e-01   8.3898e-02   1.0098e+00   8.1428e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 285:    1.0251e+02  -4.0999e+00  -2.3232e+00  -4.0389e+00  -1.0110e+00  -4.7526e-02   4.2063e-03   2.0546e-05   7.8449e-02   2.1151e-02   1.8967e-01   8.9133e-02   9.2438e-01   8.3889e-02   1.0089e+00   8.1428e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 286:    1.0251e+02  -4.0999e+00  -2.3242e+00  -4.0389e+00  -1.0103e+00  -4.6191e-02   4.2076e-03   2.0486e-05   7.8629e-02   2.1133e-02   1.8970e-01   8.8773e-02   9.2471e-01   8.3861e-02   1.0087e+00   8.1427e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 287:    1.0251e+02  -4.0999e+00  -2.3243e+00  -4.0391e+00  -1.0105e+00  -4.5159e-02   4.1980e-03   2.0488e-05   7.8916e-02   2.1071e-02   1.8955e-01   8.8297e-02   9.2415e-01   8.3957e-02   1.0084e+00   8.1464e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 288:    1.0251e+02  -4.0999e+00  -2.3237e+00  -4.0392e+00  -1.0109e+00  -4.4970e-02   4.1921e-03   2.0495e-05   7.8764e-02   2.0953e-02   1.8988e-01   8.8160e-02   9.2539e-01   8.3963e-02   1.0103e+00   8.1466e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 289:    1.0251e+02  -4.0999e+00  -2.3232e+00  -4.0392e+00  -1.0114e+00  -4.4592e-02   4.1763e-03   2.0584e-05   7.8632e-02   2.0841e-02   1.8982e-01   8.8037e-02   9.2559e-01   8.4031e-02   1.0111e+00   8.1469e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 290:    1.0251e+02  -4.0999e+00  -2.3228e+00  -4.0393e+00  -1.0118e+00  -4.4506e-02   4.1574e-03   2.0479e-05   7.8482e-02   2.0726e-02   1.8968e-01   8.7644e-02   9.2519e-01   8.4079e-02   1.0117e+00   8.1474e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 291:    1.0251e+02  -4.0999e+00  -2.3223e+00  -4.0392e+00  -1.0120e+00  -4.4578e-02   4.1365e-03   2.0488e-05   7.8220e-02   2.0662e-02   1.8952e-01   8.7558e-02   9.2534e-01   8.4128e-02   1.0119e+00   8.1459e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 292:    1.0251e+02  -4.0999e+00  -2.3219e+00  -4.0392e+00  -1.0120e+00  -4.4486e-02   4.1205e-03   2.0514e-05   7.8288e-02   2.0601e-02   1.8947e-01   8.7669e-02   9.2591e-01   8.4106e-02   1.0124e+00   8.1443e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 293:    1.0251e+02  -4.0999e+00  -2.3217e+00  -4.0395e+00  -1.0120e+00  -4.3914e-02   4.1076e-03   2.0606e-05   7.8474e-02   2.0557e-02   1.8960e-01   8.7970e-02   9.2580e-01   8.4052e-02   1.0124e+00   8.1443e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 294:    1.0251e+02  -4.0998e+00  -2.3218e+00  -4.0399e+00  -1.0123e+00  -4.3173e-02   4.1124e-03   2.0598e-05   7.8561e-02   2.0485e-02   1.8979e-01   8.7954e-02   9.2624e-01   8.4027e-02   1.0132e+00   8.1419e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 295:    1.0251e+02  -4.0998e+00  -2.3220e+00  -4.0399e+00  -1.0123e+00  -4.2805e-02   4.1150e-03   2.0599e-05   7.8804e-02   2.0397e-02   1.8985e-01   8.8079e-02   9.2637e-01   8.3980e-02   1.0139e+00   8.1382e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 296:    1.0251e+02  -4.0998e+00  -2.3224e+00  -4.0401e+00  -1.0121e+00  -4.2813e-02   4.1342e-03   2.0618e-05   7.9093e-02   2.0359e-02   1.8980e-01   8.8328e-02   9.2555e-01   8.3955e-02   1.0137e+00   8.1344e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 297:    1.0251e+02  -4.0998e+00  -2.3225e+00  -4.0403e+00  -1.0118e+00  -4.2861e-02   4.1653e-03   2.0599e-05   7.9236e-02   2.0318e-02   1.8969e-01   8.8659e-02   9.2477e-01   8.3916e-02   1.0135e+00   8.1323e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 298:    1.0251e+02  -4.0997e+00  -2.3228e+00  -4.0404e+00  -1.0116e+00  -4.2874e-02   4.2066e-03   2.0567e-05   7.9391e-02   2.0249e-02   1.8973e-01   8.8706e-02   9.2359e-01   8.3939e-02   1.0134e+00   8.1277e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 299:    1.0251e+02  -4.0997e+00  -2.3224e+00  -4.0404e+00  -1.0113e+00  -4.3190e-02   4.2168e-03   2.0532e-05   7.9379e-02   2.0187e-02   1.8973e-01   8.8757e-02   9.2294e-01   8.3916e-02   1.0137e+00   8.1243e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 300:    1.0251e+02  -4.0997e+00  -2.3221e+00  -4.0405e+00  -1.0111e+00  -4.3480e-02   4.2289e-03   2.0490e-05   7.9314e-02   2.0140e-02   1.8983e-01   8.8620e-02   9.2242e-01   8.3900e-02   1.0137e+00   8.1210e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 301:    1.0251e+02  -4.0997e+00  -2.3213e+00  -4.0404e+00  -1.0109e+00  -4.4169e-02   4.2524e-03   2.0546e-05   7.9499e-02   2.0117e-02   1.8991e-01   8.8503e-02   9.2226e-01   8.3843e-02   1.0136e+00   8.1235e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 302:    1.0251e+02  -4.0997e+00  -2.3205e+00  -4.0403e+00  -1.0108e+00  -4.5199e-02   4.2709e-03   2.0511e-05   7.9674e-02   2.0085e-02   1.8969e-01   8.8687e-02   9.2207e-01   8.3852e-02   1.0134e+00   8.1262e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 303:    1.0251e+02  -4.0997e+00  -2.3199e+00  -4.0401e+00  -1.0106e+00  -4.6193e-02   4.2807e-03   2.0440e-05   7.9845e-02   2.0104e-02   1.8985e-01   8.8661e-02   9.2135e-01   8.3843e-02   1.0128e+00   8.1274e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 304:    1.0251e+02  -4.0997e+00  -2.3189e+00  -4.0398e+00  -1.0105e+00  -4.7180e-02   4.3058e-03   2.0450e-05   7.9808e-02   2.0159e-02   1.8987e-01   8.8660e-02   9.2076e-01   8.3856e-02   1.0120e+00   8.1277e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 305:    1.0251e+02  -4.0997e+00  -2.3180e+00  -4.0396e+00  -1.0102e+00  -4.8474e-02   4.3050e-03   2.0527e-05   7.9932e-02   2.0184e-02   1.8970e-01   8.8567e-02   9.1986e-01   8.3867e-02   1.0111e+00   8.1290e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 306:    1.0251e+02  -4.0997e+00  -2.3175e+00  -4.0393e+00  -1.0101e+00  -4.9308e-02   4.3055e-03   2.0586e-05   8.0113e-02   2.0178e-02   1.8971e-01   8.8307e-02   9.1930e-01   8.3865e-02   1.0112e+00   8.1290e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 307:    1.0251e+02  -4.0997e+00  -2.3175e+00  -4.0393e+00  -1.0103e+00  -4.9278e-02   4.3027e-03   2.0441e-05   8.0168e-02   2.0211e-02   1.8973e-01   8.7993e-02   9.1919e-01   8.3837e-02   1.0109e+00   8.1287e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 308:    1.0251e+02  -4.0998e+00  -2.3175e+00  -4.0394e+00  -1.0103e+00  -4.9430e-02   4.2899e-03   2.0384e-05   8.0417e-02   2.0187e-02   1.8983e-01   8.7757e-02   9.1937e-01   8.3778e-02   1.0106e+00   8.1289e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 309:    1.0251e+02  -4.0998e+00  -2.3175e+00  -4.0393e+00  -1.0103e+00  -4.9319e-02   4.2773e-03   2.0349e-05   8.0807e-02   2.0161e-02   1.8959e-01   8.7493e-02   9.1950e-01   8.3734e-02   1.0108e+00   8.1279e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 310:    1.0251e+02  -4.0998e+00  -2.3174e+00  -4.0393e+00  -1.0101e+00  -4.8880e-02   4.2710e-03   2.0327e-05   8.1202e-02   2.0116e-02   1.8983e-01   8.7251e-02   9.1864e-01   8.3730e-02   1.0108e+00   8.1294e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 311:    1.0251e+02  -4.0998e+00  -2.3174e+00  -4.0394e+00  -1.0100e+00  -4.8080e-02   4.2611e-03   2.0272e-05   8.1332e-02   2.0078e-02   1.9003e-01   8.6995e-02   9.1844e-01   8.3699e-02   1.0105e+00   8.1319e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 312:    1.0251e+02  -4.0998e+00  -2.3173e+00  -4.0394e+00  -1.0100e+00  -4.7724e-02   4.2663e-03   2.0239e-05   8.1553e-02   2.0021e-02   1.8982e-01   8.6721e-02   9.1838e-01   8.3721e-02   1.0100e+00   8.1312e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 313:    1.0251e+02  -4.0998e+00  -2.3170e+00  -4.0395e+00  -1.0100e+00  -4.7216e-02   4.2775e-03   2.0213e-05   8.2001e-02   1.9971e-02   1.8980e-01   8.6651e-02   9.1837e-01   8.3697e-02   1.0100e+00   8.1318e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 314:    1.0251e+02  -4.0998e+00  -2.3169e+00  -4.0398e+00  -1.0100e+00  -4.6902e-02   4.2949e-03   2.0265e-05   8.2392e-02   1.9922e-02   1.8988e-01   8.6426e-02   9.1845e-01   8.3722e-02   1.0103e+00   8.1309e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 315:    1.0251e+02  -4.0998e+00  -2.3168e+00  -4.0399e+00  -1.0102e+00  -4.6480e-02   4.3206e-03   2.0314e-05   8.2534e-02   1.9912e-02   1.8972e-01   8.6211e-02   9.1792e-01   8.3758e-02   1.0103e+00   8.1337e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 316:    1.0251e+02  -4.0998e+00  -2.3169e+00  -4.0400e+00  -1.0100e+00  -4.6238e-02   4.3456e-03   2.0323e-05   8.2729e-02   1.9936e-02   1.8975e-01   8.5856e-02   9.1759e-01   8.3814e-02   1.0095e+00   8.1353e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 317:    1.0251e+02  -4.0998e+00  -2.3169e+00  -4.0402e+00  -1.0103e+00  -4.5470e-02   4.3521e-03   2.0275e-05   8.2864e-02   1.9965e-02   1.8980e-01   8.5438e-02   9.1755e-01   8.3894e-02   1.0088e+00   8.1373e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 318:    1.0251e+02  -4.0998e+00  -2.3169e+00  -4.0402e+00  -1.0106e+00  -4.5001e-02   4.3661e-03   2.0191e-05   8.2949e-02   1.9983e-02   1.9001e-01   8.5203e-02   9.1746e-01   8.3944e-02   1.0078e+00   8.1401e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 319:    1.0251e+02  -4.0998e+00  -2.3169e+00  -4.0403e+00  -1.0108e+00  -4.4545e-02   4.3665e-03   2.0114e-05   8.2694e-02   2.0003e-02   1.9041e-01   8.4997e-02   9.1774e-01   8.3940e-02   1.0073e+00   8.1417e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 320:    1.0251e+02  -4.0998e+00  -2.3171e+00  -4.0402e+00  -1.0109e+00  -4.4419e-02   4.3600e-03   2.0043e-05   8.2516e-02   1.9999e-02   1.9044e-01   8.4873e-02   9.1798e-01   8.3932e-02   1.0070e+00   8.1420e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 321:    1.0251e+02  -4.0998e+00  -2.3170e+00  -4.0401e+00  -1.0107e+00  -4.4267e-02   4.3539e-03   1.9995e-05   8.2307e-02   1.9967e-02   1.9050e-01   8.4982e-02   9.1814e-01   8.3919e-02   1.0067e+00   8.1429e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 322:    1.0251e+02  -4.0998e+00  -2.3170e+00  -4.0400e+00  -1.0106e+00  -4.4240e-02   4.3331e-03   1.9984e-05   8.2305e-02   1.9961e-02   1.9054e-01   8.5443e-02   9.1821e-01   8.3910e-02   1.0065e+00   8.1424e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 323:    1.0251e+02  -4.0998e+00  -2.3166e+00  -4.0397e+00  -1.0105e+00  -4.4793e-02   4.3199e-03   1.9921e-05   8.2300e-02   1.9935e-02   1.9078e-01   8.6081e-02   9.1836e-01   8.3854e-02   1.0062e+00   8.1448e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 324:    1.0251e+02  -4.0998e+00  -2.3167e+00  -4.0395e+00  -1.0104e+00  -4.4862e-02   4.3167e-03   1.9926e-05   8.2063e-02   1.9919e-02   1.9086e-01   8.6736e-02   9.1865e-01   8.3834e-02   1.0060e+00   8.1439e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 325:    1.0251e+02  -4.0998e+00  -2.3166e+00  -4.0394e+00  -1.0103e+00  -4.5123e-02   4.3012e-03   1.9902e-05   8.1871e-02   1.9888e-02   1.9103e-01   8.7030e-02   9.1829e-01   8.3865e-02   1.0059e+00   8.1436e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 326:    1.0251e+02  -4.0998e+00  -2.3166e+00  -4.0393e+00  -1.0100e+00  -4.5111e-02   4.2839e-03   1.9869e-05   8.2016e-02   1.9843e-02   1.9083e-01   8.7106e-02   9.1802e-01   8.3847e-02   1.0058e+00   8.1427e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 327:    1.0251e+02  -4.0998e+00  -2.3165e+00  -4.0393e+00  -1.0101e+00  -4.5297e-02   4.2847e-03   1.9858e-05   8.2297e-02   1.9823e-02   1.9075e-01   8.7398e-02   9.1826e-01   8.3771e-02   1.0054e+00   8.1451e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 328:    1.0251e+02  -4.0998e+00  -2.3162e+00  -4.0393e+00  -1.0100e+00  -4.5633e-02   4.2864e-03   1.9877e-05   8.2486e-02   1.9809e-02   1.9060e-01   8.7863e-02   9.1822e-01   8.3706e-02   1.0049e+00   8.1482e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 329:    1.0251e+02  -4.0998e+00  -2.3163e+00  -4.0392e+00  -1.0098e+00  -4.5698e-02   4.3097e-03   1.9869e-05   8.2469e-02   1.9795e-02   1.9047e-01   8.8220e-02   9.1805e-01   8.3704e-02   1.0045e+00   8.1502e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 330:    1.0251e+02  -4.0998e+00  -2.3168e+00  -4.0390e+00  -1.0099e+00  -4.5752e-02   4.3120e-03   1.9810e-05   8.2684e-02   1.9770e-02   1.9049e-01   8.8495e-02   9.1779e-01   8.3731e-02   1.0046e+00   8.1500e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 331:    1.0251e+02  -4.0998e+00  -2.3170e+00  -4.0390e+00  -1.0099e+00  -4.5957e-02   4.3052e-03   1.9736e-05   8.2748e-02   1.9734e-02   1.9034e-01   8.8651e-02   9.1818e-01   8.3707e-02   1.0050e+00   8.1489e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 332:    1.0251e+02  -4.0998e+00  -2.3171e+00  -4.0391e+00  -1.0098e+00  -4.5788e-02   4.2994e-03   1.9733e-05   8.2906e-02   1.9711e-02   1.9035e-01   8.8647e-02   9.1835e-01   8.3660e-02   1.0052e+00   8.1490e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 333:    1.0251e+02  -4.0998e+00  -2.3173e+00  -4.0392e+00  -1.0099e+00  -4.5316e-02   4.2822e-03   1.9667e-05   8.2967e-02   1.9695e-02   1.9041e-01   8.8729e-02   9.1832e-01   8.3666e-02   1.0056e+00   8.1484e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 334:    1.0251e+02  -4.0998e+00  -2.3179e+00  -4.0394e+00  -1.0097e+00  -4.4740e-02   4.2668e-03   1.9593e-05   8.3055e-02   1.9702e-02   1.9058e-01   8.8853e-02   9.1799e-01   8.3687e-02   1.0056e+00   8.1457e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 335:    1.0251e+02  -4.0998e+00  -2.3181e+00  -4.0395e+00  -1.0095e+00  -4.4451e-02   4.2558e-03   1.9535e-05   8.3023e-02   1.9762e-02   1.9027e-01   8.9035e-02   9.1828e-01   8.3673e-02   1.0052e+00   8.1469e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 336:    1.0251e+02  -4.0998e+00  -2.3178e+00  -4.0395e+00  -1.0098e+00  -4.4500e-02   4.2571e-03   1.9546e-05   8.3051e-02   1.9779e-02   1.9024e-01   8.9486e-02   9.1868e-01   8.3611e-02   1.0053e+00   8.1515e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 337:    1.0251e+02  -4.0998e+00  -2.3179e+00  -4.0396e+00  -1.0099e+00  -4.4018e-02   4.2494e-03   1.9584e-05   8.3206e-02   1.9764e-02   1.9011e-01   8.9643e-02   9.1871e-01   8.3659e-02   1.0056e+00   8.1531e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 338:    1.0251e+02  -4.0998e+00  -2.3180e+00  -4.0396e+00  -1.0101e+00  -4.3768e-02   4.2332e-03   1.9635e-05   8.3235e-02   1.9751e-02   1.9011e-01   8.9877e-02   9.1939e-01   8.3622e-02   1.0060e+00   8.1528e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 339:    1.0251e+02  -4.0998e+00  -2.3186e+00  -4.0397e+00  -1.0098e+00  -4.3605e-02   4.2237e-03   1.9638e-05   8.3611e-02   1.9725e-02   1.9014e-01   8.9946e-02   9.2025e-01   8.3580e-02   1.0064e+00   8.1528e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 340:    1.0251e+02  -4.0998e+00  -2.3190e+00  -4.0396e+00  -1.0097e+00  -4.3585e-02   4.2095e-03   1.9630e-05   8.3986e-02   1.9677e-02   1.9019e-01   8.9920e-02   9.2124e-01   8.3559e-02   1.0073e+00   8.1516e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 341:    1.0251e+02  -4.0998e+00  -2.3191e+00  -4.0398e+00  -1.0097e+00  -4.3238e-02   4.1994e-03   1.9636e-05   8.4311e-02   1.9683e-02   1.9023e-01   8.9809e-02   9.2179e-01   8.3517e-02   1.0079e+00   8.1498e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 342:    1.0251e+02  -4.0998e+00  -2.3194e+00  -4.0400e+00  -1.0097e+00  -4.2748e-02   4.1886e-03   1.9661e-05   8.4345e-02   1.9684e-02   1.9018e-01   8.9600e-02   9.2207e-01   8.3517e-02   1.0081e+00   8.1517e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 343:    1.0251e+02  -4.0998e+00  -2.3196e+00  -4.0402e+00  -1.0096e+00  -4.2336e-02   4.1813e-03   1.9615e-05   8.4331e-02   1.9666e-02   1.9016e-01   8.9379e-02   9.2231e-01   8.3534e-02   1.0083e+00   8.1548e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 344:    1.0251e+02  -4.0998e+00  -2.3197e+00  -4.0403e+00  -1.0097e+00  -4.2462e-02   4.1751e-03   1.9621e-05   8.4176e-02   1.9673e-02   1.8996e-01   8.9217e-02   9.2236e-01   8.3540e-02   1.0083e+00   8.1557e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 345:    1.0251e+02  -4.0998e+00  -2.3200e+00  -4.0403e+00  -1.0098e+00  -4.2327e-02   4.1705e-03   1.9552e-05   8.4159e-02   1.9684e-02   1.9017e-01   8.8957e-02   9.2232e-01   8.3511e-02   1.0085e+00   8.1566e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 346:    1.0251e+02  -4.0998e+00  -2.3200e+00  -4.0403e+00  -1.0097e+00  -4.2261e-02   4.1750e-03   1.9564e-05   8.4080e-02   1.9731e-02   1.9047e-01   8.8630e-02   9.2248e-01   8.3516e-02   1.0081e+00   8.1578e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 347:    1.0251e+02  -4.0999e+00  -2.3200e+00  -4.0403e+00  -1.0096e+00  -4.2296e-02   4.1883e-03   1.9634e-05   8.4231e-02   1.9747e-02   1.9061e-01   8.8399e-02   9.2250e-01   8.3557e-02   1.0075e+00   8.1580e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 348:    1.0251e+02  -4.0999e+00  -2.3199e+00  -4.0404e+00  -1.0096e+00  -4.2345e-02   4.2129e-03   1.9692e-05   8.4295e-02   1.9719e-02   1.9068e-01   8.8210e-02   9.2212e-01   8.3568e-02   1.0075e+00   8.1586e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 349:    1.0251e+02  -4.0999e+00  -2.3198e+00  -4.0404e+00  -1.0095e+00  -4.2617e-02   4.2066e-03   1.9720e-05   8.4289e-02   1.9731e-02   1.9054e-01   8.8147e-02   9.2185e-01   8.3565e-02   1.0070e+00   8.1590e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 350:    1.0251e+02  -4.0999e+00  -2.3196e+00  -4.0405e+00  -1.0093e+00  -4.2582e-02   4.2011e-03   1.9760e-05   8.4302e-02   1.9703e-02   1.9044e-01   8.8068e-02   9.2182e-01   8.3537e-02   1.0070e+00   8.1590e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 351:    1.0251e+02  -4.0998e+00  -2.3198e+00  -4.0405e+00  -1.0093e+00  -4.2377e-02   4.1970e-03   1.9750e-05   8.4587e-02   1.9708e-02   1.9045e-01   8.8008e-02   9.2169e-01   8.3507e-02   1.0067e+00   8.1584e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 352:    1.0251e+02  -4.0998e+00  -2.3201e+00  -4.0405e+00  -1.0091e+00  -4.2159e-02   4.1973e-03   1.9819e-05   8.4804e-02   1.9678e-02   1.9040e-01   8.7894e-02   9.2113e-01   8.3528e-02   1.0061e+00   8.1554e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 353:    1.0251e+02  -4.0998e+00  -2.3202e+00  -4.0405e+00  -1.0088e+00  -4.2123e-02   4.1968e-03   1.9855e-05   8.4945e-02   1.9634e-02   1.9049e-01   8.7941e-02   9.2108e-01   8.3500e-02   1.0058e+00   8.1548e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 354:    1.0251e+02  -4.0998e+00  -2.3204e+00  -4.0404e+00  -1.0087e+00  -4.2217e-02   4.1918e-03   1.9909e-05   8.5122e-02   1.9565e-02   1.9055e-01   8.7948e-02   9.2088e-01   8.3523e-02   1.0059e+00   8.1530e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 355:    1.0251e+02  -4.0998e+00  -2.3206e+00  -4.0404e+00  -1.0086e+00  -4.2057e-02   4.1853e-03   1.9973e-05   8.5389e-02   1.9505e-02   1.9057e-01   8.8109e-02   9.2100e-01   8.3496e-02   1.0058e+00   8.1520e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 356:    1.0251e+02  -4.0998e+00  -2.3209e+00  -4.0403e+00  -1.0084e+00  -4.1880e-02   4.1796e-03   2.0058e-05   8.5446e-02   1.9451e-02   1.9063e-01   8.7944e-02   9.2086e-01   8.3483e-02   1.0061e+00   8.1488e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 357:    1.0251e+02  -4.0998e+00  -2.3210e+00  -4.0404e+00  -1.0084e+00  -4.1565e-02   4.1858e-03   2.0131e-05   8.5481e-02   1.9391e-02   1.9072e-01   8.7879e-02   9.2119e-01   8.3465e-02   1.0066e+00   8.1468e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 358:    1.0251e+02  -4.0998e+00  -2.3213e+00  -4.0404e+00  -1.0084e+00  -4.1497e-02   4.1826e-03   2.0168e-05   8.5661e-02   1.9361e-02   1.9086e-01   8.7635e-02   9.2095e-01   8.3475e-02   1.0069e+00   8.1445e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 359:    1.0251e+02  -4.0997e+00  -2.3212e+00  -4.0405e+00  -1.0083e+00  -4.1127e-02   4.1993e-03   2.0188e-05   8.5904e-02   1.9347e-02   1.9078e-01   8.7558e-02   9.2087e-01   8.3451e-02   1.0068e+00   8.1461e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 360:    1.0251e+02  -4.0997e+00  -2.3215e+00  -4.0405e+00  -1.0082e+00  -4.0919e-02   4.2006e-03   2.0173e-05   8.6133e-02   1.9333e-02   1.9071e-01   8.7479e-02   9.2061e-01   8.3439e-02   1.0067e+00   8.1453e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 361:    1.0251e+02  -4.0997e+00  -2.3218e+00  -4.0406e+00  -1.0084e+00  -4.0317e-02   4.2013e-03   2.0253e-05   8.6558e-02   1.9351e-02   1.9075e-01   8.7309e-02   9.2060e-01   8.3425e-02   1.0069e+00   8.1464e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 362:    1.0251e+02  -4.0997e+00  -2.3221e+00  -4.0407e+00  -1.0086e+00  -3.9991e-02   4.2004e-03   2.0305e-05   8.6952e-02   1.9369e-02   1.9077e-01   8.7106e-02   9.2037e-01   8.3462e-02   1.0072e+00   8.1464e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 363:    1.0251e+02  -4.0997e+00  -2.3224e+00  -4.0407e+00  -1.0086e+00  -3.9745e-02   4.1920e-03   2.0350e-05   8.7210e-02   1.9373e-02   1.9095e-01   8.6967e-02   9.2025e-01   8.3462e-02   1.0070e+00   8.1489e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 364:    1.0251e+02  -4.0997e+00  -2.3225e+00  -4.0407e+00  -1.0088e+00  -3.9585e-02   4.1826e-03   2.0355e-05   8.7449e-02   1.9353e-02   1.9099e-01   8.7119e-02   9.2081e-01   8.3468e-02   1.0070e+00   8.1514e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 365:    1.0251e+02  -4.0997e+00  -2.3224e+00  -4.0407e+00  -1.0088e+00  -3.9531e-02   4.1781e-03   2.0298e-05   8.7646e-02   1.9346e-02   1.9083e-01   8.6997e-02   9.2106e-01   8.3455e-02   1.0068e+00   8.1546e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 366:    1.0251e+02  -4.0997e+00  -2.3223e+00  -4.0407e+00  -1.0088e+00  -3.9436e-02   4.1805e-03   2.0301e-05   8.7772e-02   1.9354e-02   1.9074e-01   8.7057e-02   9.2118e-01   8.3426e-02   1.0064e+00   8.1555e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 367:    1.0251e+02  -4.0997e+00  -2.3223e+00  -4.0408e+00  -1.0088e+00  -3.9172e-02   4.1791e-03   2.0368e-05   8.7914e-02   1.9333e-02   1.9063e-01   8.6948e-02   9.2106e-01   8.3409e-02   1.0060e+00   8.1558e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 368:    1.0251e+02  -4.0997e+00  -2.3222e+00  -4.0410e+00  -1.0088e+00  -3.8640e-02   4.1857e-03   2.0343e-05   8.7937e-02   1.9334e-02   1.9051e-01   8.6857e-02   9.2143e-01   8.3379e-02   1.0058e+00   8.1564e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 369:    1.0251e+02  -4.0997e+00  -2.3223e+00  -4.0411e+00  -1.0088e+00  -3.8331e-02   4.1862e-03   2.0309e-05   8.7933e-02   1.9319e-02   1.9046e-01   8.6689e-02   9.2126e-01   8.3410e-02   1.0053e+00   8.1559e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 370:    1.0251e+02  -4.0997e+00  -2.3222e+00  -4.0413e+00  -1.0088e+00  -3.7984e-02   4.1921e-03   2.0301e-05   8.8082e-02   1.9313e-02   1.9050e-01   8.6622e-02   9.2101e-01   8.3424e-02   1.0049e+00   8.1559e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 371:    1.0251e+02  -4.0998e+00  -2.3222e+00  -4.0415e+00  -1.0087e+00  -3.7729e-02   4.1972e-03   2.0277e-05   8.8271e-02   1.9308e-02   1.9035e-01   8.6655e-02   9.2092e-01   8.3426e-02   1.0046e+00   8.1575e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 372:    1.0251e+02  -4.0998e+00  -2.3222e+00  -4.0414e+00  -1.0087e+00  -3.7571e-02   4.1922e-03   2.0292e-05   8.8651e-02   1.9317e-02   1.9025e-01   8.6528e-02   9.2126e-01   8.3440e-02   1.0046e+00   8.1576e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 373:    1.0251e+02  -4.0998e+00  -2.3223e+00  -4.0415e+00  -1.0087e+00  -3.7327e-02   4.1971e-03   2.0343e-05   8.8865e-02   1.9290e-02   1.9002e-01   8.6437e-02   9.2122e-01   8.3475e-02   1.0044e+00   8.1571e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 374:    1.0251e+02  -4.0998e+00  -2.3224e+00  -4.0415e+00  -1.0089e+00  -3.7275e-02   4.2072e-03   2.0450e-05   8.9078e-02   1.9306e-02   1.9002e-01   8.6224e-02   9.2111e-01   8.3480e-02   1.0047e+00   8.1569e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 375:    1.0251e+02  -4.0998e+00  -2.3226e+00  -4.0415e+00  -1.0089e+00  -3.7080e-02   4.2062e-03   2.0489e-05   8.9178e-02   1.9320e-02   1.9016e-01   8.6141e-02   9.2116e-01   8.3483e-02   1.0047e+00   8.1586e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 376:    1.0251e+02  -4.0998e+00  -2.3230e+00  -4.0415e+00  -1.0089e+00  -3.7043e-02   4.2105e-03   2.0517e-05   8.9291e-02   1.9325e-02   1.9018e-01   8.6033e-02   9.2114e-01   8.3474e-02   1.0046e+00   8.1572e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 377:    1.0251e+02  -4.0998e+00  -2.3234e+00  -4.0414e+00  -1.0090e+00  -3.7302e-02   4.2097e-03   2.0524e-05   8.9438e-02   1.9333e-02   1.9012e-01   8.6018e-02   9.2123e-01   8.3476e-02   1.0046e+00   8.1545e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 378:    1.0251e+02  -4.0998e+00  -2.3234e+00  -4.0413e+00  -1.0089e+00  -3.7617e-02   4.2161e-03   2.0520e-05   8.9567e-02   1.9305e-02   1.9012e-01   8.6106e-02   9.2115e-01   8.3444e-02   1.0049e+00   8.1534e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 379:    1.0251e+02  -4.0998e+00  -2.3232e+00  -4.0412e+00  -1.0088e+00  -3.8039e-02   4.2231e-03   2.0484e-05   8.9704e-02   1.9276e-02   1.9015e-01   8.5914e-02   9.2060e-01   8.3438e-02   1.0048e+00   8.1532e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 380:    1.0251e+02  -4.0998e+00  -2.3229e+00  -4.0409e+00  -1.0088e+00  -3.8613e-02   4.2221e-03   2.0452e-05   8.9751e-02   1.9293e-02   1.9018e-01   8.5907e-02   9.2032e-01   8.3428e-02   1.0045e+00   8.1545e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 381:    1.0251e+02  -4.0998e+00  -2.3228e+00  -4.0408e+00  -1.0088e+00  -3.8992e-02   4.2177e-03   2.0475e-05   8.9641e-02   1.9314e-02   1.9014e-01   8.5978e-02   9.2015e-01   8.3429e-02   1.0040e+00   8.1535e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 382:    1.0251e+02  -4.0998e+00  -2.3228e+00  -4.0408e+00  -1.0088e+00  -3.9393e-02   4.2092e-03   2.0508e-05   8.9624e-02   1.9352e-02   1.9010e-01   8.5929e-02   9.2037e-01   8.3422e-02   1.0037e+00   8.1535e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 383:    1.0251e+02  -4.0998e+00  -2.3226e+00  -4.0407e+00  -1.0089e+00  -3.9650e-02   4.2080e-03   2.0503e-05   8.9621e-02   1.9349e-02   1.9000e-01   8.5735e-02   9.2021e-01   8.3436e-02   1.0038e+00   8.1544e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 384:    1.0251e+02  -4.0998e+00  -2.3223e+00  -4.0407e+00  -1.0089e+00  -3.9709e-02   4.2111e-03   2.0489e-05   8.9690e-02   1.9347e-02   1.8985e-01   8.5516e-02   9.2025e-01   8.3420e-02   1.0037e+00   8.1541e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 385:    1.0251e+02  -4.0998e+00  -2.3225e+00  -4.0406e+00  -1.0087e+00  -3.9762e-02   4.2130e-03   2.0458e-05   8.9795e-02   1.9380e-02   1.8978e-01   8.5306e-02   9.2055e-01   8.3405e-02   1.0034e+00   8.1518e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 386:    1.0251e+02  -4.0998e+00  -2.3226e+00  -4.0407e+00  -1.0087e+00  -3.9801e-02   4.2117e-03   2.0473e-05   8.9838e-02   1.9386e-02   1.8986e-01   8.5045e-02   9.2034e-01   8.3388e-02   1.0036e+00   8.1512e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 387:    1.0251e+02  -4.0998e+00  -2.3228e+00  -4.0408e+00  -1.0088e+00  -3.9548e-02   4.2221e-03   2.0474e-05   8.9914e-02   1.9399e-02   1.9001e-01   8.4840e-02   9.2080e-01   8.3373e-02   1.0037e+00   8.1503e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 388:    1.0251e+02  -4.0998e+00  -2.3230e+00  -4.0408e+00  -1.0088e+00  -3.9408e-02   4.2272e-03   2.0464e-05   8.9977e-02   1.9383e-02   1.9000e-01   8.4644e-02   9.2070e-01   8.3377e-02   1.0034e+00   8.1494e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 389:    1.0250e+02  -4.0998e+00  -2.3229e+00  -4.0408e+00  -1.0087e+00  -3.9326e-02   4.2285e-03   2.0459e-05   8.9927e-02   1.9366e-02   1.9001e-01   8.4485e-02   9.2063e-01   8.3367e-02   1.0032e+00   8.1487e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 390:    1.0250e+02  -4.0998e+00  -2.3231e+00  -4.0409e+00  -1.0086e+00  -3.9130e-02   4.2286e-03   2.0423e-05   8.9894e-02   1.9331e-02   1.8996e-01   8.4434e-02   9.2054e-01   8.3339e-02   1.0032e+00   8.1481e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 391:    1.0250e+02  -4.0998e+00  -2.3232e+00  -4.0410e+00  -1.0086e+00  -3.8976e-02   4.2242e-03   2.0419e-05   8.9782e-02   1.9299e-02   1.8992e-01   8.4344e-02   9.2089e-01   8.3320e-02   1.0032e+00   8.1480e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 392:    1.0250e+02  -4.0998e+00  -2.3232e+00  -4.0409e+00  -1.0086e+00  -3.8856e-02   4.2179e-03   2.0396e-05   8.9773e-02   1.9263e-02   1.8984e-01   8.4469e-02   9.2084e-01   8.3309e-02   1.0034e+00   8.1489e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 393:    1.0250e+02  -4.0998e+00  -2.3234e+00  -4.0409e+00  -1.0089e+00  -3.8820e-02   4.2135e-03   2.0375e-05   8.9746e-02   1.9259e-02   1.8972e-01   8.4731e-02   9.2088e-01   8.3310e-02   1.0031e+00   8.1481e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 394:    1.0250e+02  -4.0998e+00  -2.3234e+00  -4.0410e+00  -1.0089e+00  -3.8759e-02   4.2123e-03   2.0382e-05   8.9791e-02   1.9271e-02   1.8958e-01   8.5075e-02   9.2064e-01   8.3316e-02   1.0028e+00   8.1483e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 395:    1.0250e+02  -4.0998e+00  -2.3234e+00  -4.0410e+00  -1.0090e+00  -3.8565e-02   4.2179e-03   2.0420e-05   8.9844e-02   1.9279e-02   1.8948e-01   8.5286e-02   9.2070e-01   8.3311e-02   1.0027e+00   8.1499e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 396:    1.0250e+02  -4.0998e+00  -2.3233e+00  -4.0411e+00  -1.0092e+00  -3.8505e-02   4.2207e-03   2.0445e-05   8.9863e-02   1.9281e-02   1.8944e-01   8.5452e-02   9.2053e-01   8.3326e-02   1.0028e+00   8.1524e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 397:    1.0250e+02  -4.0998e+00  -2.3232e+00  -4.0411e+00  -1.0093e+00  -3.8366e-02   4.2115e-03   2.0410e-05   8.9792e-02   1.9287e-02   1.8938e-01   8.5649e-02   9.2063e-01   8.3351e-02   1.0034e+00   8.1519e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 398:    1.0250e+02  -4.0998e+00  -2.3234e+00  -4.0411e+00  -1.0093e+00  -3.8179e-02   4.2049e-03   2.0362e-05   8.9718e-02   1.9281e-02   1.8943e-01   8.5813e-02   9.2065e-01   8.3391e-02   1.0040e+00   8.1513e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 399:    1.0250e+02  -4.0998e+00  -2.3236e+00  -4.0411e+00  -1.0093e+00  -3.8020e-02   4.2015e-03   2.0320e-05   8.9743e-02   1.9283e-02   1.8934e-01   8.5899e-02   9.2056e-01   8.3426e-02   1.0045e+00   8.1500e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 400:    1.0250e+02  -4.0998e+00  -2.3237e+00  -4.0412e+00  -1.0093e+00  -3.7796e-02   4.2017e-03   2.0296e-05   8.9684e-02   1.9297e-02   1.8932e-01   8.5849e-02   9.2107e-01   8.3413e-02   1.0051e+00   8.1502e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 401:    1.0250e+02  -4.0998e+00  -2.3238e+00  -4.0413e+00  -1.0093e+00  -3.7697e-02   4.1959e-03   2.0275e-05   8.9618e-02   1.9291e-02   1.8914e-01   8.5796e-02   9.2086e-01   8.3480e-02   1.0051e+00   8.1510e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 402:    1.0250e+02  -4.0998e+00  -2.3238e+00  -4.0414e+00  -1.0093e+00  -3.7639e-02   4.1863e-03   2.0273e-05   8.9522e-02   1.9285e-02   1.8898e-01   8.5723e-02   9.2041e-01   8.3534e-02   1.0053e+00   8.1520e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 403:    1.0250e+02  -4.0998e+00  -2.3238e+00  -4.0415e+00  -1.0093e+00  -3.7634e-02   4.1878e-03   2.0250e-05   8.9456e-02   1.9288e-02   1.8895e-01   8.5791e-02   9.1990e-01   8.3549e-02   1.0055e+00   8.1545e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 404:    1.0250e+02  -4.0999e+00  -2.3239e+00  -4.0416e+00  -1.0094e+00  -3.7581e-02   4.1854e-03   2.0292e-05   8.9326e-02   1.9326e-02   1.8902e-01   8.5928e-02   9.2016e-01   8.3542e-02   1.0053e+00   8.1565e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 405:    1.0250e+02  -4.0999e+00  -2.3239e+00  -4.0415e+00  -1.0094e+00  -3.7740e-02   4.1804e-03   2.0333e-05   8.9304e-02   1.9334e-02   1.8892e-01   8.6225e-02   9.1978e-01   8.3559e-02   1.0052e+00   8.1588e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 406:    1.0250e+02  -4.0999e+00  -2.3241e+00  -4.0415e+00  -1.0093e+00  -3.7837e-02   4.1866e-03   2.0375e-05   8.9281e-02   1.9322e-02   1.8906e-01   8.6421e-02   9.1972e-01   8.3556e-02   1.0051e+00   8.1596e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 407:    1.0250e+02  -4.0999e+00  -2.3240e+00  -4.0415e+00  -1.0092e+00  -3.8211e-02   4.1948e-03   2.0335e-05   8.9117e-02   1.9303e-02   1.8913e-01   8.6644e-02   9.1975e-01   8.3592e-02   1.0052e+00   8.1594e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 408:    1.0250e+02  -4.0999e+00  -2.3239e+00  -4.0415e+00  -1.0091e+00  -3.8144e-02   4.2081e-03   2.0320e-05   8.9057e-02   1.9287e-02   1.8912e-01   8.6825e-02   9.1992e-01   8.3572e-02   1.0054e+00   8.1577e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 409:    1.0250e+02  -4.0999e+00  -2.3239e+00  -4.0415e+00  -1.0092e+00  -3.8261e-02   4.2186e-03   2.0295e-05   8.9007e-02   1.9339e-02   1.8923e-01   8.6869e-02   9.1991e-01   8.3568e-02   1.0053e+00   8.1578e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 410:    1.0250e+02  -4.0999e+00  -2.3239e+00  -4.0414e+00  -1.0093e+00  -3.8478e-02   4.2150e-03   2.0311e-05   8.9089e-02   1.9427e-02   1.8939e-01   8.7036e-02   9.2050e-01   8.3537e-02   1.0051e+00   8.1581e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 411:    1.0250e+02  -4.0999e+00  -2.3238e+00  -4.0413e+00  -1.0092e+00  -3.8732e-02   4.2116e-03   2.0289e-05   8.9015e-02   1.9467e-02   1.8939e-01   8.7005e-02   9.2025e-01   8.3538e-02   1.0049e+00   8.1581e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 412:    1.0250e+02  -4.0999e+00  -2.3238e+00  -4.0413e+00  -1.0092e+00  -3.8793e-02   4.2085e-03   2.0295e-05   8.9020e-02   1.9477e-02   1.8946e-01   8.7080e-02   9.2047e-01   8.3532e-02   1.0049e+00   8.1575e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 413:    1.0250e+02  -4.0999e+00  -2.3236e+00  -4.0412e+00  -1.0093e+00  -3.8937e-02   4.2069e-03   2.0288e-05   8.9121e-02   1.9495e-02   1.8939e-01   8.7203e-02   9.2043e-01   8.3528e-02   1.0046e+00   8.1581e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 414:    1.0250e+02  -4.0999e+00  -2.3233e+00  -4.0412e+00  -1.0093e+00  -3.9068e-02   4.2065e-03   2.0272e-05   8.9126e-02   1.9508e-02   1.8939e-01   8.7485e-02   9.2038e-01   8.3517e-02   1.0046e+00   8.1589e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 415:    1.0250e+02  -4.0999e+00  -2.3234e+00  -4.0412e+00  -1.0092e+00  -3.9299e-02   4.2117e-03   2.0277e-05   8.8971e-02   1.9564e-02   1.8955e-01   8.7734e-02   9.2051e-01   8.3502e-02   1.0048e+00   8.1575e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 416:    1.0250e+02  -4.0999e+00  -2.3232e+00  -4.0413e+00  -1.0092e+00  -3.9575e-02   4.2122e-03   2.0269e-05   8.8909e-02   1.9609e-02   1.8954e-01   8.7910e-02   9.2061e-01   8.3488e-02   1.0045e+00   8.1561e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 417:    1.0250e+02  -4.0999e+00  -2.3228e+00  -4.0412e+00  -1.0092e+00  -3.9794e-02   4.2133e-03   2.0289e-05   8.8845e-02   1.9606e-02   1.8949e-01   8.8036e-02   9.2065e-01   8.3481e-02   1.0047e+00   8.1558e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 418:    1.0250e+02  -4.0999e+00  -2.3226e+00  -4.0412e+00  -1.0092e+00  -4.0019e-02   4.2209e-03   2.0279e-05   8.8864e-02   1.9618e-02   1.8939e-01   8.8094e-02   9.2062e-01   8.3490e-02   1.0044e+00   8.1566e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 419:    1.0250e+02  -4.0999e+00  -2.3225e+00  -4.0411e+00  -1.0092e+00  -4.0090e-02   4.2242e-03   2.0258e-05   8.9089e-02   1.9644e-02   1.8924e-01   8.8285e-02   9.2082e-01   8.3484e-02   1.0040e+00   8.1567e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 420:    1.0250e+02  -4.0999e+00  -2.3223e+00  -4.0410e+00  -1.0091e+00  -4.0260e-02   4.2298e-03   2.0249e-05   8.9215e-02   1.9640e-02   1.8919e-01   8.8683e-02   9.2127e-01   8.3454e-02   1.0041e+00   8.1564e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 421:    1.0250e+02  -4.0998e+00  -2.3222e+00  -4.0410e+00  -1.0090e+00  -4.0359e-02   4.2292e-03   2.0289e-05   8.9366e-02   1.9642e-02   1.8928e-01   8.8905e-02   9.2154e-01   8.3452e-02   1.0038e+00   8.1572e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 422:    1.0250e+02  -4.0998e+00  -2.3223e+00  -4.0410e+00  -1.0088e+00  -4.0380e-02   4.2311e-03   2.0289e-05   8.9318e-02   1.9636e-02   1.8934e-01   8.9025e-02   9.2137e-01   8.3486e-02   1.0039e+00   8.1562e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 423:    1.0250e+02  -4.0998e+00  -2.3224e+00  -4.0411e+00  -1.0088e+00  -4.0256e-02   4.2283e-03   2.0277e-05   8.9324e-02   1.9645e-02   1.8923e-01   8.9305e-02   9.2129e-01   8.3484e-02   1.0038e+00   8.1565e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 424:    1.0250e+02  -4.0998e+00  -2.3226e+00  -4.0412e+00  -1.0088e+00  -4.0075e-02   4.2333e-03   2.0245e-05   8.9193e-02   1.9657e-02   1.8914e-01   8.9480e-02   9.2122e-01   8.3487e-02   1.0039e+00   8.1587e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 425:    1.0250e+02  -4.0998e+00  -2.3226e+00  -4.0413e+00  -1.0088e+00  -3.9950e-02   4.2360e-03   2.0257e-05   8.9170e-02   1.9644e-02   1.8924e-01   8.9622e-02   9.2104e-01   8.3492e-02   1.0042e+00   8.1599e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 426:    1.0250e+02  -4.0998e+00  -2.3226e+00  -4.0413e+00  -1.0088e+00  -3.9789e-02   4.2376e-03   2.0245e-05   8.9143e-02   1.9654e-02   1.8917e-01   8.9791e-02   9.2100e-01   8.3514e-02   1.0039e+00   8.1607e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 427:    1.0250e+02  -4.0998e+00  -2.3224e+00  -4.0414e+00  -1.0087e+00  -3.9766e-02   4.2469e-03   2.0228e-05   8.9102e-02   1.9656e-02   1.8913e-01   9.0076e-02   9.2110e-01   8.3499e-02   1.0037e+00   8.1607e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 428:    1.0250e+02  -4.0998e+00  -2.3225e+00  -4.0414e+00  -1.0088e+00  -3.9662e-02   4.2522e-03   2.0205e-05   8.9151e-02   1.9669e-02   1.8912e-01   9.0312e-02   9.2102e-01   8.3483e-02   1.0036e+00   8.1599e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 429:    1.0250e+02  -4.0998e+00  -2.3225e+00  -4.0414e+00  -1.0089e+00  -3.9547e-02   4.2615e-03   2.0187e-05   8.9103e-02   1.9681e-02   1.8907e-01   9.0576e-02   9.2082e-01   8.3488e-02   1.0035e+00   8.1592e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 430:    1.0250e+02  -4.0998e+00  -2.3224e+00  -4.0414e+00  -1.0088e+00  -3.9593e-02   4.2613e-03   2.0159e-05   8.9075e-02   1.9692e-02   1.8894e-01   9.0762e-02   9.2073e-01   8.3487e-02   1.0035e+00   8.1603e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 431:    1.0250e+02  -4.0998e+00  -2.3223e+00  -4.0413e+00  -1.0088e+00  -3.9929e-02   4.2668e-03   2.0112e-05   8.8982e-02   1.9682e-02   1.8901e-01   9.0997e-02   9.2043e-01   8.3487e-02   1.0036e+00   8.1604e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 432:    1.0250e+02  -4.0998e+00  -2.3223e+00  -4.0413e+00  -1.0088e+00  -4.0182e-02   4.2693e-03   2.0089e-05   8.8860e-02   1.9659e-02   1.8898e-01   9.1110e-02   9.2024e-01   8.3490e-02   1.0036e+00   8.1585e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 433:    1.0250e+02  -4.0998e+00  -2.3222e+00  -4.0412e+00  -1.0088e+00  -4.0415e-02   4.2706e-03   2.0081e-05   8.8670e-02   1.9650e-02   1.8899e-01   9.1199e-02   9.1999e-01   8.3485e-02   1.0036e+00   8.1587e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 434:    1.0250e+02  -4.0998e+00  -2.3220e+00  -4.0412e+00  -1.0087e+00  -4.0405e-02   4.2661e-03   2.0060e-05   8.8576e-02   1.9667e-02   1.8897e-01   9.1180e-02   9.1998e-01   8.3468e-02   1.0034e+00   8.1594e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 435:    1.0250e+02  -4.0998e+00  -2.3218e+00  -4.0413e+00  -1.0087e+00  -4.0471e-02   4.2639e-03   2.0050e-05   8.8450e-02   1.9671e-02   1.8891e-01   9.1156e-02   9.1970e-01   8.3469e-02   1.0033e+00   8.1602e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 436:    1.0250e+02  -4.0998e+00  -2.3216e+00  -4.0413e+00  -1.0086e+00  -4.0478e-02   4.2614e-03   2.0025e-05   8.8470e-02   1.9677e-02   1.8892e-01   9.1234e-02   9.1948e-01   8.3484e-02   1.0033e+00   8.1608e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 437:    1.0250e+02  -4.0998e+00  -2.3214e+00  -4.0413e+00  -1.0086e+00  -4.0648e-02   4.2569e-03   2.0021e-05   8.8378e-02   1.9694e-02   1.8896e-01   9.1289e-02   9.1946e-01   8.3486e-02   1.0034e+00   8.1608e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 438:    1.0250e+02  -4.0998e+00  -2.3214e+00  -4.0413e+00  -1.0085e+00  -4.0807e-02   4.2559e-03   2.0029e-05   8.8244e-02   1.9704e-02   1.8898e-01   9.1352e-02   9.1920e-01   8.3494e-02   1.0034e+00   8.1604e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 439:    1.0250e+02  -4.0998e+00  -2.3215e+00  -4.0413e+00  -1.0084e+00  -4.0814e-02   4.2472e-03   2.0035e-05   8.8064e-02   1.9736e-02   1.8905e-01   9.1469e-02   9.1900e-01   8.3510e-02   1.0032e+00   8.1602e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 440:    1.0250e+02  -4.0998e+00  -2.3216e+00  -4.0414e+00  -1.0084e+00  -4.0845e-02   4.2385e-03   2.0044e-05   8.7953e-02   1.9775e-02   1.8906e-01   9.1482e-02   9.1902e-01   8.3529e-02   1.0029e+00   8.1605e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 441:    1.0250e+02  -4.0998e+00  -2.3218e+00  -4.0415e+00  -1.0084e+00  -4.0561e-02   4.2293e-03   2.0096e-05   8.7978e-02   1.9848e-02   1.8914e-01   9.1431e-02   9.1890e-01   8.3545e-02   1.0026e+00   8.1618e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 442:    1.0250e+02  -4.0998e+00  -2.3219e+00  -4.0415e+00  -1.0085e+00  -4.0297e-02   4.2190e-03   2.0140e-05   8.8016e-02   1.9884e-02   1.8912e-01   9.1373e-02   9.1910e-01   8.3556e-02   1.0025e+00   8.1632e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 443:    1.0250e+02  -4.0998e+00  -2.3222e+00  -4.0415e+00  -1.0086e+00  -4.0124e-02   4.2172e-03   2.0182e-05   8.8183e-02   1.9921e-02   1.8920e-01   9.1354e-02   9.1928e-01   8.3555e-02   1.0023e+00   8.1646e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 444:    1.0250e+02  -4.0999e+00  -2.3223e+00  -4.0416e+00  -1.0084e+00  -4.0120e-02   4.2226e-03   2.0208e-05   8.8142e-02   1.9980e-02   1.8916e-01   9.1269e-02   9.1934e-01   8.3540e-02   1.0020e+00   8.1653e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 445:    1.0250e+02  -4.0999e+00  -2.3223e+00  -4.0416e+00  -1.0083e+00  -4.0180e-02   4.2279e-03   2.0283e-05   8.8188e-02   2.0009e-02   1.8925e-01   9.1096e-02   9.1924e-01   8.3538e-02   1.0017e+00   8.1644e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 446:    1.0250e+02  -4.0999e+00  -2.3223e+00  -4.0417e+00  -1.0084e+00  -4.0083e-02   4.2373e-03   2.0357e-05   8.8255e-02   2.0030e-02   1.8937e-01   9.0921e-02   9.1921e-01   8.3535e-02   1.0015e+00   8.1652e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 447:    1.0250e+02  -4.0999e+00  -2.3223e+00  -4.0417e+00  -1.0083e+00  -4.0032e-02   4.2591e-03   2.0418e-05   8.8322e-02   2.0055e-02   1.8931e-01   9.0741e-02   9.1928e-01   8.3527e-02   1.0011e+00   8.1662e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 448:    1.0250e+02  -4.0999e+00  -2.3224e+00  -4.0417e+00  -1.0083e+00  -4.0042e-02   4.2759e-03   2.0499e-05   8.8372e-02   2.0083e-02   1.8935e-01   9.0554e-02   9.1942e-01   8.3524e-02   1.0010e+00   8.1661e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 449:    1.0250e+02  -4.0999e+00  -2.3224e+00  -4.0417e+00  -1.0083e+00  -4.0219e-02   4.2779e-03   2.0525e-05   8.8377e-02   2.0104e-02   1.8937e-01   9.0424e-02   9.1970e-01   8.3510e-02   1.0009e+00   8.1654e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 450:    1.0250e+02  -4.0999e+00  -2.3223e+00  -4.0416e+00  -1.0084e+00  -4.0390e-02   4.2779e-03   2.0520e-05   8.8387e-02   2.0120e-02   1.8939e-01   9.0277e-02   9.1991e-01   8.3489e-02   1.0010e+00   8.1662e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 451:    1.0250e+02  -4.0999e+00  -2.3221e+00  -4.0415e+00  -1.0085e+00  -4.0403e-02   4.2785e-03   2.0554e-05   8.8442e-02   2.0130e-02   1.8934e-01   9.0032e-02   9.2001e-01   8.3485e-02   1.0011e+00   8.1676e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 452:    1.0250e+02  -4.0999e+00  -2.3220e+00  -4.0415e+00  -1.0084e+00  -4.0503e-02   4.2730e-03   2.0537e-05   8.8507e-02   2.0135e-02   1.8926e-01   8.9777e-02   9.2031e-01   8.3473e-02   1.0013e+00   8.1671e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 453:    1.0250e+02  -4.0999e+00  -2.3221e+00  -4.0415e+00  -1.0084e+00  -4.0716e-02   4.2650e-03   2.0525e-05   8.8565e-02   2.0168e-02   1.8921e-01   8.9653e-02   9.2030e-01   8.3473e-02   1.0010e+00   8.1665e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 454:    1.0250e+02  -4.0999e+00  -2.3221e+00  -4.0415e+00  -1.0083e+00  -4.0900e-02   4.2583e-03   2.0505e-05   8.8572e-02   2.0194e-02   1.8912e-01   8.9524e-02   9.2039e-01   8.3457e-02   1.0009e+00   8.1658e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 455:    1.0250e+02  -4.0999e+00  -2.3220e+00  -4.0415e+00  -1.0083e+00  -4.0930e-02   4.2524e-03   2.0495e-05   8.8643e-02   2.0246e-02   1.8914e-01   8.9416e-02   9.2035e-01   8.3454e-02   1.0006e+00   8.1655e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 456:    1.0250e+02  -4.0999e+00  -2.3218e+00  -4.0414e+00  -1.0084e+00  -4.1008e-02   4.2455e-03   2.0482e-05   8.8831e-02   2.0290e-02   1.8904e-01   8.9438e-02   9.2042e-01   8.3448e-02   1.0005e+00   8.1672e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 457:    1.0250e+02  -4.0999e+00  -2.3216e+00  -4.0414e+00  -1.0085e+00  -4.1073e-02   4.2376e-03   2.0479e-05   8.8831e-02   2.0310e-02   1.8893e-01   8.9411e-02   9.2049e-01   8.3438e-02   1.0005e+00   8.1677e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 458:    1.0250e+02  -4.0999e+00  -2.3215e+00  -4.0414e+00  -1.0085e+00  -4.1036e-02   4.2288e-03   2.0479e-05   8.8894e-02   2.0311e-02   1.8879e-01   8.9378e-02   9.2055e-01   8.3419e-02   1.0006e+00   8.1689e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 459:    1.0250e+02  -4.0999e+00  -2.3216e+00  -4.0414e+00  -1.0085e+00  -4.1084e-02   4.2296e-03   2.0472e-05   8.8976e-02   2.0290e-02   1.8876e-01   8.9365e-02   9.2056e-01   8.3404e-02   1.0007e+00   8.1678e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 460:    1.0250e+02  -4.0999e+00  -2.3215e+00  -4.0414e+00  -1.0084e+00  -4.1065e-02   4.2257e-03   2.0473e-05   8.8978e-02   2.0254e-02   1.8871e-01   8.9287e-02   9.2061e-01   8.3386e-02   1.0007e+00   8.1663e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 461:    1.0250e+02  -4.0999e+00  -2.3213e+00  -4.0414e+00  -1.0084e+00  -4.1142e-02   4.2254e-03   2.0441e-05   8.8952e-02   2.0238e-02   1.8864e-01   8.9236e-02   9.2067e-01   8.3359e-02   1.0007e+00   8.1670e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 462:    1.0250e+02  -4.0999e+00  -2.3213e+00  -4.0413e+00  -1.0083e+00  -4.1199e-02   4.2230e-03   2.0409e-05   8.9161e-02   2.0235e-02   1.8852e-01   8.9179e-02   9.2070e-01   8.3339e-02   1.0005e+00   8.1686e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 463:    1.0250e+02  -4.0999e+00  -2.3214e+00  -4.0413e+00  -1.0084e+00  -4.1131e-02   4.2191e-03   2.0397e-05   8.9356e-02   2.0251e-02   1.8850e-01   8.9138e-02   9.2098e-01   8.3308e-02   1.0003e+00   8.1688e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 464:    1.0250e+02  -4.0999e+00  -2.3213e+00  -4.0414e+00  -1.0084e+00  -4.0937e-02   4.2187e-03   2.0395e-05   8.9553e-02   2.0237e-02   1.8848e-01   8.9097e-02   9.2108e-01   8.3293e-02   1.0001e+00   8.1684e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 465:    1.0250e+02  -4.0999e+00  -2.3213e+00  -4.0414e+00  -1.0086e+00  -4.0873e-02   4.2226e-03   2.0374e-05   8.9675e-02   2.0210e-02   1.8854e-01   8.9045e-02   9.2115e-01   8.3282e-02   1.0002e+00   8.1678e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 466:    1.0250e+02  -4.0999e+00  -2.3213e+00  -4.0414e+00  -1.0085e+00  -4.1046e-02   4.2312e-03   2.0379e-05   8.9703e-02   2.0232e-02   1.8852e-01   8.9031e-02   9.2100e-01   8.3294e-02   9.9982e-01   8.1665e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 467:    1.0250e+02  -4.0999e+00  -2.3214e+00  -4.0414e+00  -1.0086e+00  -4.1081e-02   4.2382e-03   2.0421e-05   8.9802e-02   2.0266e-02   1.8851e-01   8.9175e-02   9.2124e-01   8.3271e-02   9.9943e-01   8.1671e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 468:    1.0250e+02  -4.0999e+00  -2.3215e+00  -4.0414e+00  -1.0085e+00  -4.1107e-02   4.2412e-03   2.0446e-05   8.9937e-02   2.0281e-02   1.8857e-01   8.9238e-02   9.2125e-01   8.3255e-02   9.9912e-01   8.1659e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 469:    1.0250e+02  -4.0999e+00  -2.3214e+00  -4.0414e+00  -1.0084e+00  -4.1269e-02   4.2380e-03   2.0449e-05   8.9938e-02   2.0285e-02   1.8867e-01   8.9378e-02   9.2144e-01   8.3225e-02   9.9906e-01   8.1651e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 470:    1.0250e+02  -4.0999e+00  -2.3212e+00  -4.0413e+00  -1.0083e+00  -4.1552e-02   4.2384e-03   2.0437e-05   8.9924e-02   2.0280e-02   1.8878e-01   8.9520e-02   9.2149e-01   8.3205e-02   9.9880e-01   8.1648e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 471:    1.0250e+02  -4.0999e+00  -2.3210e+00  -4.0412e+00  -1.0082e+00  -4.2096e-02   4.2375e-03   2.0433e-05   8.9875e-02   2.0267e-02   1.8880e-01   8.9608e-02   9.2149e-01   8.3221e-02   9.9869e-01   8.1642e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 472:    1.0250e+02  -4.0999e+00  -2.3210e+00  -4.0411e+00  -1.0081e+00  -4.2380e-02   4.2446e-03   2.0435e-05   8.9823e-02   2.0262e-02   1.8885e-01   8.9602e-02   9.2156e-01   8.3233e-02   9.9868e-01   8.1643e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 473:    1.0250e+02  -4.0999e+00  -2.3210e+00  -4.0410e+00  -1.0080e+00  -4.2522e-02   4.2486e-03   2.0429e-05   8.9793e-02   2.0231e-02   1.8877e-01   8.9608e-02   9.2177e-01   8.3210e-02   9.9901e-01   8.1637e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 474:    1.0250e+02  -4.0999e+00  -2.3209e+00  -4.0410e+00  -1.0079e+00  -4.2640e-02   4.2536e-03   2.0403e-05   8.9731e-02   2.0213e-02   1.8876e-01   8.9632e-02   9.2177e-01   8.3192e-02   9.9942e-01   8.1632e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 475:    1.0250e+02  -4.0999e+00  -2.3208e+00  -4.0409e+00  -1.0079e+00  -4.2882e-02   4.2576e-03   2.0375e-05   8.9666e-02   2.0190e-02   1.8866e-01   8.9718e-02   9.2160e-01   8.3218e-02   9.9931e-01   8.1622e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 476:    1.0250e+02  -4.0999e+00  -2.3207e+00  -4.0409e+00  -1.0080e+00  -4.2983e-02   4.2599e-03   2.0368e-05   8.9563e-02   2.0171e-02   1.8859e-01   8.9777e-02   9.2162e-01   8.3236e-02   9.9932e-01   8.1620e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 477:    1.0250e+02  -4.0999e+00  -2.3205e+00  -4.0410e+00  -1.0081e+00  -4.3022e-02   4.2570e-03   2.0364e-05   8.9440e-02   2.0166e-02   1.8860e-01   8.9785e-02   9.2171e-01   8.3236e-02   9.9940e-01   8.1633e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 478:    1.0250e+02  -4.0999e+00  -2.3203e+00  -4.0410e+00  -1.0080e+00  -4.3129e-02   4.2505e-03   2.0371e-05   8.9374e-02   2.0151e-02   1.8859e-01   8.9826e-02   9.2171e-01   8.3224e-02   9.9970e-01   8.1620e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 479:    1.0250e+02  -4.1000e+00  -2.3201e+00  -4.0410e+00  -1.0080e+00  -4.3186e-02   4.2440e-03   2.0373e-05   8.9355e-02   2.0133e-02   1.8858e-01   8.9837e-02   9.2168e-01   8.3214e-02   9.9991e-01   8.1621e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 480:    1.0250e+02  -4.1000e+00  -2.3198e+00  -4.0410e+00  -1.0080e+00  -4.3477e-02   4.2424e-03   2.0440e-05   8.9311e-02   2.0121e-02   1.8857e-01   8.9848e-02   9.2158e-01   8.3221e-02   9.9995e-01   8.1627e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 481:    1.0250e+02  -4.1000e+00  -2.3194e+00  -4.0410e+00  -1.0079e+00  -4.3647e-02   4.2358e-03   2.0479e-05   8.9315e-02   2.0096e-02   1.8851e-01   8.9865e-02   9.2159e-01   8.3197e-02   1.0002e+00   8.1642e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 482:    1.0250e+02  -4.1000e+00  -2.3188e+00  -4.0409e+00  -1.0079e+00  -4.3909e-02   4.2370e-03   2.0551e-05   8.9382e-02   2.0085e-02   1.8840e-01   8.9799e-02   9.2147e-01   8.3229e-02   1.0001e+00   8.1656e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 483:    1.0250e+02  -4.1000e+00  -2.3189e+00  -4.0409e+00  -1.0078e+00  -4.4169e-02   4.2467e-03   2.0587e-05   8.9377e-02   2.0054e-02   1.8838e-01   8.9943e-02   9.2130e-01   8.3237e-02   1.0003e+00   8.1650e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 484:    1.0250e+02  -4.1000e+00  -2.3188e+00  -4.0408e+00  -1.0078e+00  -4.4346e-02   4.2627e-03   2.0605e-05   8.9450e-02   2.0020e-02   1.8840e-01   9.0090e-02   9.2114e-01   8.3237e-02   1.0004e+00   8.1639e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 485:    1.0250e+02  -4.1000e+00  -2.3185e+00  -4.0408e+00  -1.0079e+00  -4.4414e-02   4.2737e-03   2.0594e-05   8.9541e-02   1.9999e-02   1.8842e-01   9.0237e-02   9.2116e-01   8.3227e-02   1.0004e+00   8.1641e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 486:    1.0250e+02  -4.1000e+00  -2.3183e+00  -4.0408e+00  -1.0080e+00  -4.4572e-02   4.2902e-03   2.0581e-05   8.9733e-02   1.9978e-02   1.8837e-01   9.0412e-02   9.2098e-01   8.3224e-02   1.0003e+00   8.1642e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 487:    1.0250e+02  -4.1000e+00  -2.3182e+00  -4.0407e+00  -1.0081e+00  -4.4698e-02   4.2979e-03   2.0560e-05   8.9822e-02   1.9954e-02   1.8827e-01   9.0744e-02   9.2114e-01   8.3212e-02   1.0003e+00   8.1644e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 488:    1.0250e+02  -4.1000e+00  -2.3180e+00  -4.0406e+00  -1.0081e+00  -4.4892e-02   4.3035e-03   2.0549e-05   8.9849e-02   1.9928e-02   1.8816e-01   9.1000e-02   9.2118e-01   8.3206e-02   1.0003e+00   8.1644e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 489:    1.0250e+02  -4.1000e+00  -2.3180e+00  -4.0406e+00  -1.0081e+00  -4.5037e-02   4.2987e-03   2.0515e-05   8.9843e-02   1.9906e-02   1.8815e-01   9.1260e-02   9.2116e-01   8.3190e-02   1.0001e+00   8.1633e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 490:    1.0250e+02  -4.1000e+00  -2.3179e+00  -4.0405e+00  -1.0081e+00  -4.5139e-02   4.2930e-03   2.0508e-05   8.9827e-02   1.9887e-02   1.8814e-01   9.1545e-02   9.2114e-01   8.3178e-02   9.9991e-01   8.1624e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 491:    1.0250e+02  -4.1000e+00  -2.3179e+00  -4.0405e+00  -1.0081e+00  -4.5107e-02   4.2871e-03   2.0490e-05   8.9765e-02   1.9855e-02   1.8814e-01   9.1702e-02   9.2089e-01   8.3179e-02   9.9986e-01   8.1624e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 492:    1.0250e+02  -4.1000e+00  -2.3178e+00  -4.0405e+00  -1.0080e+00  -4.5192e-02   4.2818e-03   2.0470e-05   8.9791e-02   1.9831e-02   1.8810e-01   9.1846e-02   9.2086e-01   8.3164e-02   9.9977e-01   8.1630e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 493:    1.0250e+02  -4.1000e+00  -2.3179e+00  -4.0406e+00  -1.0078e+00  -4.4968e-02   4.2782e-03   2.0452e-05   8.9797e-02   1.9804e-02   1.8806e-01   9.1987e-02   9.2080e-01   8.3146e-02   9.9982e-01   8.1627e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 494:    1.0250e+02  -4.1000e+00  -2.3180e+00  -4.0406e+00  -1.0078e+00  -4.4773e-02   4.2766e-03   2.0453e-05   8.9696e-02   1.9781e-02   1.8805e-01   9.2096e-02   9.2057e-01   8.3148e-02   9.9999e-01   8.1611e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 495:    1.0250e+02  -4.1000e+00  -2.3181e+00  -4.0408e+00  -1.0078e+00  -4.4647e-02   4.2798e-03   2.0446e-05   8.9661e-02   1.9763e-02   1.8804e-01   9.2155e-02   9.2039e-01   8.3137e-02   9.9999e-01   8.1599e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 496:    1.0250e+02  -4.1000e+00  -2.3182e+00  -4.0408e+00  -1.0079e+00  -4.4482e-02   4.2851e-03   2.0434e-05   8.9630e-02   1.9750e-02   1.8796e-01   9.2258e-02   9.2042e-01   8.3124e-02   1.0000e+00   8.1601e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 497:    1.0250e+02  -4.1000e+00  -2.3184e+00  -4.0409e+00  -1.0079e+00  -4.4175e-02   4.2866e-03   2.0423e-05   8.9656e-02   1.9743e-02   1.8789e-01   9.2225e-02   9.2017e-01   8.3126e-02   1.0002e+00   8.1616e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 498:    1.0250e+02  -4.1000e+00  -2.3187e+00  -4.0409e+00  -1.0079e+00  -4.3826e-02   4.2847e-03   2.0406e-05   8.9587e-02   1.9730e-02   1.8781e-01   9.2143e-02   9.2017e-01   8.3156e-02   1.0002e+00   8.1613e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 499:    1.0250e+02  -4.1000e+00  -2.3190e+00  -4.0409e+00  -1.0078e+00  -4.3529e-02   4.2806e-03   2.0397e-05   8.9653e-02   1.9712e-02   1.8779e-01   9.2107e-02   9.2028e-01   8.3172e-02   1.0004e+00   8.1607e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 500:    1.0250e+02  -4.1000e+00  -2.3193e+00  -4.0409e+00  -1.0077e+00  -4.3241e-02   4.2787e-03   2.0390e-05   8.9759e-02   1.9689e-02   1.8773e-01   9.2021e-02   9.2029e-01   8.3203e-02   1.0006e+00   8.1613e-02</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating covariance matrix</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling EBE model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Needed Covariates:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "CMT"</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating residuals/tables</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-in"><span class="co"># The following takes a very long time but gives</span></span>
-<span class="r-in"><span class="va">f_nlmixr_dfop_sfo_focei</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr</a></span><span class="op">(</span><span class="va">f_mmkin_dfop_sfo</span>, est <span class="op">=</span> <span class="st">"focei"</span><span class="op">)</span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → creating full model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → pruning branches (`if`/`else`)...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate jacobian</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate sensitivities</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate ∂(f)/∂(η)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → calculate ∂(R²)/∂(η)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in inner model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in inner model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in EBE model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in EBE model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling inner model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → finding duplicate expressions in FD model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → optimizing duplicate expressions in FD model...</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling EBE model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> → compiling events FD model...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">✔</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Needed Covariates:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "CMT"</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="font-weight: bold;">Key:</span> U: Unscaled Parameters; X: Back-transformed parameters; G: Gill difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> F: Forward difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> C: Central difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> M: Mixed forward and central difference gradient approximation</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Unscaled parameters for Omegas=chol(solve(omega));</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Diagonals are transformed, as specified by foceiControl(diagXform=)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |-----+---------------+-----------+-----------+-----------+-----------|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    #| Objective Fun |  parent_0 |  log_k_m1 |f_parent_qlogis |    log_k1 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    log_k2 |  g_qlogis | sigma_low |  rsd_high |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|        o1 |        o2 |        o3 |        o4 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|        o5 |        o6 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    1</span>|     496.69520 |     1.000 |    -1.000 |   -0.9393 |   -0.9673 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.000 |   -0.9214 |   -0.9072 |   -0.9199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9082 |   -0.8909 |   -0.8969 |   -0.8991 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8928 |   -0.8991 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      496.6952 |     100.0 |    -4.100 |   -0.9400 |    -2.400 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.100 |  -0.01100 |    0.7300 |   0.06700 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6756 |     1.581 |     1.265 |     1.151 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.477 |     1.151 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      496.6952</span> |     100.0 |   0.01657 |    0.2809 |   0.09072 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01657 |    0.4973 |    0.7300 |   0.06700 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6756 |     1.581 |     1.265 |     1.151 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.477 |     1.151 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    G|    Gill Diff. |    -72.99 |    -3.907 |   -0.8109 |   0.02803 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.841 |  0.006841 |    -22.18 |    -32.78 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     3.538 |    -16.93 |    -12.84 |    -12.77 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -15.02 |    -9.608 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    2</span>|     1346.5436 |     1.823 |   -0.9559 |   -0.9301 |   -0.9677 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9792 |   -0.9215 |   -0.6570 |   -0.5501 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9482 |   -0.6999 |   -0.7521 |   -0.7551 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7234 |   -0.7907 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     1346.5436 |     182.3 |    -4.056 |   -0.9314 |    -2.400 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.079 |  -0.01100 |    0.8213 |   0.07939 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6486 |     1.883 |     1.449 |     1.317 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.728 |     1.276 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     1346.5436</span> |     182.3 |   0.01732 |    0.2826 |   0.09069 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01692 |    0.4972 |    0.8213 |   0.07939 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6486 |     1.883 |     1.449 |     1.317 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.728 |     1.276 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    3</span>|     512.48246 |     1.082 |   -0.9956 |   -0.9384 |   -0.9674 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9979 |   -0.9214 |   -0.8822 |   -0.8830 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9122 |   -0.8718 |   -0.8824 |   -0.8847 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8759 |   -0.8883 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     512.48246 |     108.2 |    -4.096 |   -0.9391 |    -2.400 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.098 |  -0.01100 |    0.7391 |   0.06824 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6729 |     1.611 |     1.284 |     1.167 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.502 |     1.163 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     512.48246</span> |     108.2 |   0.01665 |    0.2811 |   0.09072 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01661 |    0.4973 |    0.7391 |   0.06824 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6729 |     1.611 |     1.284 |     1.167 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.502 |     1.163 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    4</span>|     495.95762 |     1.015 |   -0.9992 |   -0.9391 |   -0.9673 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9996 |   -0.9214 |   -0.9027 |   -0.9133 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9090 |   -0.8874 |   -0.8943 |   -0.8965 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8898 |   -0.8972 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     495.95762 |     101.5 |    -4.099 |   -0.9398 |    -2.400 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.100 |  -0.01100 |    0.7316 |   0.06722 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6751 |     1.587 |     1.269 |     1.154 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.482 |     1.153 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     495.95762</span> |     101.5 |   0.01659 |    0.2809 |   0.09072 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01658 |    0.4973 |    0.7316 |   0.06722 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6751 |     1.587 |     1.269 |     1.154 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.482 |     1.153 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     41.81 |    -3.013 |  -0.08916 |   -0.3303 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.914 |  0.005472 |    -23.39 |    -29.38 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     3.465 |    -17.07 |    -12.49 |    -12.74 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -15.23 |    -9.735 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    5</span>|     495.32815 |     1.003 |   -0.9984 |   -0.9391 |   -0.9673 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9991 |   -0.9214 |   -0.8962 |   -0.9051 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9099 |   -0.8826 |   -0.8908 |   -0.8930 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8855 |   -0.8944 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     495.32815 |     100.3 |    -4.098 |   -0.9398 |    -2.400 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.099 |  -0.01100 |    0.7340 |   0.06750 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6745 |     1.594 |     1.273 |     1.158 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.488 |     1.156 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     495.32815</span> |     100.3 |   0.01660 |    0.2809 |   0.09073 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01659 |    0.4973 |    0.7340 |   0.06750 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6745 |     1.594 |     1.273 |     1.158 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.488 |     1.156 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -47.70 |    -3.739 |   -0.6694 |  -0.04580 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.870 |  0.006670 |    -22.76 |    -30.26 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     3.613 |    -16.75 |    -12.29 |    -12.38 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -15.09 |    -9.486 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    6</span>|     494.72051 |     1.016 |   -0.9974 |   -0.9389 |   -0.9672 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9986 |   -0.9214 |   -0.8902 |   -0.8971 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9109 |   -0.8782 |   -0.8876 |   -0.8897 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8816 |   -0.8919 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     494.72051 |     101.6 |    -4.097 |   -0.9397 |    -2.400 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.099 |  -0.01100 |    0.7362 |   0.06777 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6738 |     1.601 |     1.277 |     1.162 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.494 |     1.159 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     494.72051</span> |     101.6 |   0.01662 |    0.2810 |   0.09073 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01660 |    0.4973 |    0.7362 |   0.06777 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6738 |     1.601 |     1.277 |     1.162 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.494 |     1.159 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     48.34 |    -2.939 |  -0.03847 |   -0.3608 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.945 |  0.005434 |    -20.90 |    -26.17 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     3.821 |    -17.00 |    -12.53 |    -12.70 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -15.56 |    -9.587 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    7</span>|     494.15722 |     1.003 |   -0.9966 |   -0.9389 |   -0.9671 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9981 |   -0.9214 |   -0.8845 |   -0.8900 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9119 |   -0.8736 |   -0.8842 |   -0.8863 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8773 |   -0.8893 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     494.15722 |     100.3 |    -4.097 |   -0.9396 |    -2.400 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.098 |  -0.01100 |    0.7383 |   0.06800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6731 |     1.608 |     1.282 |     1.166 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.500 |     1.162 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     494.15722</span> |     100.3 |   0.01663 |    0.2810 |   0.09074 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01660 |    0.4973 |    0.7383 |   0.06800 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6731 |     1.608 |     1.282 |     1.166 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.500 |     1.162 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -50.36 |    -3.770 |   -0.7003 |  -0.03307 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.882 |  0.006830 |    -20.02 |    -28.64 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     3.020 |    -16.49 |    -12.31 |    -12.33 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -14.87 |    -9.319 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    8</span>|     493.49185 |     1.015 |   -0.9955 |   -0.9387 |   -0.9671 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9975 |   -0.9214 |   -0.8787 |   -0.8818 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9128 |   -0.8688 |   -0.8806 |   -0.8827 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8730 |   -0.8866 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     493.49185 |     101.5 |    -4.096 |   -0.9395 |    -2.400 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.098 |  -0.01100 |    0.7404 |   0.06828 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6725 |     1.616 |     1.286 |     1.170 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.507 |     1.165 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     493.49185</span> |     101.5 |   0.01665 |    0.2810 |   0.09074 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01661 |    0.4973 |    0.7404 |   0.06828 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6725 |     1.616 |     1.286 |     1.170 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.507 |     1.165 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     41.03 |    -2.976 |  -0.07989 |   -0.3421 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.950 |  0.005619 |    -20.95 |    -25.58 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     3.377 |    -16.59 |    -11.95 |    -12.23 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -14.73 |    -9.398 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">    9</span>|     492.95463 |     1.003 |   -0.9946 |   -0.9387 |   -0.9670 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9969 |   -0.9215 |   -0.8725 |   -0.8742 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9138 |   -0.8639 |   -0.8771 |   -0.8790 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8686 |   -0.8839 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     492.95463 |     100.3 |    -4.095 |   -0.9394 |    -2.400 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.097 |  -0.01100 |    0.7427 |   0.06853 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6718 |     1.624 |     1.291 |     1.174 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.513 |     1.168 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     492.95463</span> |     100.3 |   0.01666 |    0.2810 |   0.09075 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01662 |    0.4973 |    0.7427 |   0.06853 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6718 |     1.624 |     1.291 |     1.174 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.513 |     1.168 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -49.97 |    -3.772 |   -0.7121 |  -0.02926 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.890 |  0.006985 |    -18.86 |    -26.85 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.866 |    -16.28 |    -11.70 |    -11.82 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -14.59 |    -9.132 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   10</span>|     492.31444 |     1.015 |   -0.9935 |   -0.9385 |   -0.9670 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9964 |   -0.9215 |   -0.8668 |   -0.8662 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9147 |   -0.8589 |   -0.8735 |   -0.8755 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8642 |   -0.8811 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     492.31444 |     101.5 |    -4.094 |   -0.9393 |    -2.400 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.096 |  -0.01100 |    0.7448 |   0.06880 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6712 |     1.632 |     1.295 |     1.178 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.520 |     1.172 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     492.31444</span> |     101.5 |   0.01668 |    0.2810 |   0.09075 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01663 |    0.4972 |    0.7448 |   0.06880 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6712 |     1.632 |     1.295 |     1.178 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.520 |     1.172 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     40.47 |    -2.944 |  -0.07505 |   -0.3464 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.962 |  0.005707 |    -18.58 |    -22.75 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     3.454 |    -16.50 |    -11.93 |    -12.14 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -15.04 |    -9.193 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   11</span>|     491.79472 |     1.003 |   -0.9926 |   -0.9385 |   -0.9669 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9957 |   -0.9215 |   -0.8609 |   -0.8590 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9158 |   -0.8537 |   -0.8697 |   -0.8716 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8594 |   -0.8781 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     491.79472 |     100.3 |    -4.093 |   -0.9392 |    -2.400 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.096 |  -0.01100 |    0.7469 |   0.06904 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6705 |     1.640 |     1.300 |     1.183 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.527 |     1.175 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     491.79472</span> |     100.3 |   0.01670 |    0.2811 |   0.09076 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01664 |    0.4972 |    0.7469 |   0.06904 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6705 |     1.640 |     1.300 |     1.183 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.527 |     1.175 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -48.84 |    -3.756 |   -0.7183 |  -0.02810 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.902 |  0.007122 |    -19.51 |    -25.13 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.711 |    -16.03 |    -11.73 |    -11.80 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -14.28 |    -8.965 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   12</span>|     491.12641 |     1.014 |   -0.9913 |   -0.9383 |   -0.9669 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9951 |   -0.9215 |   -0.8546 |   -0.8511 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9167 |   -0.8483 |   -0.8658 |   -0.8677 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8546 |   -0.8752 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     491.12641 |     101.4 |    -4.091 |   -0.9390 |    -2.400 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.095 |  -0.01100 |    0.7492 |   0.06931 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6699 |     1.648 |     1.305 |     1.187 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.534 |     1.178 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     491.12641</span> |     101.4 |   0.01672 |    0.2811 |   0.09076 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01665 |    0.4972 |    0.7492 |   0.06931 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6699 |     1.648 |     1.305 |     1.187 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.534 |     1.178 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     34.57 |    -2.950 |   -0.1076 |   -0.3310 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.967 |  0.005897 |    -18.06 |    -21.71 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     3.154 |    -16.24 |    -11.62 |    -11.87 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -14.73 |    -8.995 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   13</span>|     490.65951 |     1.002 |   -0.9903 |   -0.9382 |   -0.9668 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9944 |   -0.9215 |   -0.8485 |   -0.8438 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9178 |   -0.8429 |   -0.8619 |   -0.8637 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8496 |   -0.8721 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     490.65951 |     100.2 |    -4.090 |   -0.9390 |    -2.399 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.094 |  -0.01100 |    0.7514 |   0.06955 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6692 |     1.657 |     1.310 |     1.192 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.541 |     1.182 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     490.65951</span> |     100.2 |   0.01673 |    0.2811 |   0.09077 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01667 |    0.4972 |    0.7514 |   0.06955 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6692 |     1.657 |     1.310 |     1.192 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.541 |     1.182 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -50.76 |    -3.759 |   -0.7472 |  -0.01668 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.908 |  0.007323 |    -16.70 |    -21.90 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     3.094 |    -15.72 |    -11.38 |    -11.47 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -14.04 |    -8.745 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   14</span>|     490.01300 |     1.014 |   -0.9891 |   -0.9380 |   -0.9667 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9937 |   -0.9215 |   -0.8428 |   -0.8366 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9189 |   -0.8372 |   -0.8579 |   -0.8596 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8446 |   -0.8690 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|       490.013 |     101.4 |    -4.089 |   -0.9388 |    -2.399 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.094 |  -0.01100 |    0.7535 |   0.06979 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6684 |     1.666 |     1.315 |     1.196 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.549 |     1.186 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">       490.013</span> |     101.4 |   0.01676 |    0.2811 |   0.09077 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01668 |    0.4972 |    0.7535 |   0.06979 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6684 |     1.666 |     1.315 |     1.196 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.549 |     1.186 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     34.25 |    -2.890 |  -0.09666 |   -0.3362 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.973 |  0.006003 |    -17.09 |    -20.19 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     3.007 |    -15.95 |    -11.25 |    -11.56 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -14.42 |    -8.788 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   15</span>|     489.51603 |     1.003 |   -0.9880 |   -0.9380 |   -0.9666 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9930 |   -0.9215 |   -0.8366 |   -0.8294 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9200 |   -0.8313 |   -0.8537 |   -0.8553 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8392 |   -0.8658 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     489.51603 |     100.3 |    -4.088 |   -0.9388 |    -2.399 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.093 |  -0.01100 |    0.7558 |   0.07003 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6677 |     1.675 |     1.320 |     1.201 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.556 |     1.189 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     489.51603</span> |     100.3 |   0.01677 |    0.2812 |   0.09078 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01669 |    0.4972 |    0.7558 |   0.07003 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6677 |     1.675 |     1.320 |     1.201 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.556 |     1.189 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -45.29 |    -3.673 |   -0.7179 |  -0.03084 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.907 |  0.007423 |    -17.34 |    -21.86 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.565 |    -15.52 |    -11.05 |    -11.19 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -14.28 |    -8.545 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   16</span>|     488.89410 |     1.014 |   -0.9866 |   -0.9377 |   -0.9666 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9922 |   -0.9215 |   -0.8304 |   -0.8220 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9210 |   -0.8254 |   -0.8495 |   -0.8510 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8337 |   -0.8625 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      488.8941 |     101.4 |    -4.087 |   -0.9385 |    -2.399 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.092 |  -0.01100 |    0.7580 |   0.07028 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6670 |     1.685 |     1.325 |     1.206 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.565 |     1.193 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      488.8941</span> |     101.4 |   0.01680 |    0.2812 |   0.09079 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01670 |    0.4972 |    0.7580 |   0.07028 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6670 |     1.685 |     1.325 |     1.206 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.565 |     1.193 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     32.56 |    -2.827 |  -0.09853 |   -0.3366 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.968 |  0.006143 |    -15.42 |    -18.16 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.733 |    -15.52 |    -10.94 |    -11.25 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -14.13 |    -8.583 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   17</span>|     488.48678 |     1.002 |   -0.9855 |   -0.9377 |   -0.9664 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9915 |   -0.9215 |   -0.8246 |   -0.8152 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9220 |   -0.8195 |   -0.8454 |   -0.8468 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8284 |   -0.8593 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     488.48678 |     100.2 |    -4.086 |   -0.9385 |    -2.399 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.091 |  -0.01100 |    0.7601 |   0.07051 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6663 |     1.694 |     1.331 |     1.211 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.572 |     1.197 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     488.48678</span> |     100.2 |   0.01681 |    0.2812 |   0.09080 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01671 |    0.4972 |    0.7601 |   0.07051 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6663 |     1.694 |     1.331 |     1.211 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.572 |     1.197 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -50.86 |    -3.681 |   -0.7723 |-0.0007467 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.892 |  0.007742 |    -16.40 |    -20.55 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.306 |    -15.16 |    -10.65 |    -10.82 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -14.00 |    -8.331 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   18</span>|     487.81394 |     1.012 |   -0.9841 |   -0.9374 |   -0.9664 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9907 |   -0.9215 |   -0.8186 |   -0.8083 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9229 |   -0.8132 |   -0.8411 |   -0.8424 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8225 |   -0.8558 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     487.81394 |     101.2 |    -4.084 |   -0.9383 |    -2.399 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.091 |  -0.01100 |    0.7623 |   0.07074 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6657 |     1.704 |     1.336 |     1.216 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.581 |     1.201 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     487.81394</span> |     101.2 |   0.01684 |    0.2812 |   0.09080 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01673 |    0.4972 |    0.7623 |   0.07074 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6657 |     1.704 |     1.336 |     1.216 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.581 |     1.201 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     24.73 |    -2.818 |   -0.1513 |   -0.3103 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.952 |  0.006413 |    -14.43 |    -16.89 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.774 |    -15.21 |    -10.56 |    -10.91 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -13.80 |    -8.354 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   19</span>|     487.41354 |     1.002 |   -0.9829 |   -0.9374 |   -0.9663 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9898 |   -0.9215 |   -0.8126 |   -0.8013 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9240 |   -0.8068 |   -0.8367 |   -0.8378 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8167 |   -0.8523 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     487.41354 |     100.2 |    -4.083 |   -0.9382 |    -2.399 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.090 |  -0.01100 |    0.7645 |   0.07098 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6649 |     1.714 |     1.342 |     1.221 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.590 |     1.205 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     487.41354</span> |     100.2 |   0.01686 |    0.2813 |   0.09082 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01674 |    0.4972 |    0.7645 |   0.07098 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6649 |     1.714 |     1.342 |     1.221 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.590 |     1.205 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -49.04 |    -3.605 |   -0.7738 | -0.005231 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.885 |  0.007887 |    -13.75 |    -19.14 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.153 |    -14.90 |    -10.33 |    -10.54 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -13.68 |    -8.125 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   20</span>|     486.77105 |     1.012 |   -0.9814 |   -0.9371 |   -0.9662 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9889 |   -0.9215 |   -0.8076 |   -0.7947 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9250 |   -0.7999 |   -0.8321 |   -0.8330 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8104 |   -0.8486 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     486.77105 |     101.2 |    -4.081 |   -0.9380 |    -2.399 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.089 |  -0.01100 |    0.7664 |   0.07119 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6643 |     1.725 |     1.347 |     1.227 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.599 |     1.209 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     486.77105</span> |     101.2 |   0.01688 |    0.2813 |   0.09082 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01676 |    0.4972 |    0.7664 |   0.07119 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6643 |     1.725 |     1.347 |     1.227 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.599 |     1.209 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     24.27 |    -2.713 |   -0.1438 |   -0.3142 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.939 |  0.006562 |    -14.18 |    -16.12 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.525 |    -14.86 |    -10.17 |    -10.57 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -13.46 |    -8.131 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   21</span>|     486.40106 |     1.002 |   -0.9802 |   -0.9371 |   -0.9661 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9881 |   -0.9215 |   -0.8015 |   -0.7878 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9261 |   -0.7935 |   -0.8277 |   -0.8285 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8046 |   -0.8452 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     486.40106 |     100.2 |    -4.080 |   -0.9379 |    -2.399 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.088 |  -0.01100 |    0.7686 |   0.07143 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6635 |     1.735 |     1.353 |     1.232 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.608 |     1.213 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     486.40106</span> |     100.2 |   0.01690 |    0.2813 |   0.09083 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01677 |    0.4972 |    0.7686 |   0.07143 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6635 |     1.735 |     1.353 |     1.232 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.608 |     1.213 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -49.43 |    -3.533 |   -0.7907 |  0.002583 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.861 |  0.008122 |    -12.85 |    -17.77 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.313 |    -14.52 |    -9.868 |    -10.18 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -13.34 |    -7.899 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   22</span>|     485.77865 |     1.012 |   -0.9786 |   -0.9368 |   -0.9660 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9871 |   -0.9215 |   -0.7966 |   -0.7818 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9271 |   -0.7864 |   -0.8231 |   -0.8237 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7980 |   -0.8414 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     485.77865 |     101.2 |    -4.079 |   -0.9376 |    -2.399 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.087 |  -0.01100 |    0.7704 |   0.07163 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6629 |     1.746 |     1.359 |     1.238 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.617 |     1.217 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     485.77865</span> |     101.2 |   0.01693 |    0.2814 |   0.09084 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01679 |    0.4972 |    0.7704 |   0.07163 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6629 |     1.746 |     1.359 |     1.238 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.617 |     1.217 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     23.77 |    -2.587 |   -0.1355 |   -0.3176 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.913 |  0.006717 |    -13.39 |    -15.00 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.211 |    -14.53 |    -9.747 |    -10.22 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -13.16 |    -7.915 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   23</span>|     485.42886 |     1.002 |   -0.9775 |   -0.9367 |   -0.9659 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9862 |   -0.9215 |   -0.7907 |   -0.7751 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9281 |   -0.7799 |   -0.8187 |   -0.8190 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7921 |   -0.8378 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     485.42886 |     100.2 |    -4.077 |   -0.9376 |    -2.399 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.086 |  -0.01100 |    0.7725 |   0.07185 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6622 |     1.757 |     1.364 |     1.243 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.626 |     1.221 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     485.42886</span> |     100.2 |   0.01695 |    0.2814 |   0.09085 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01680 |    0.4972 |    0.7725 |   0.07185 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6622 |     1.757 |     1.364 |     1.243 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.626 |     1.221 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -48.95 |    -3.431 |   -0.7980 |  0.007511 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.828 |  0.008340 |    -12.05 |    -16.59 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.177 |    -14.27 |    -9.567 |    -9.843 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -12.98 |    -7.655 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   24</span>|     484.82183 |     1.012 |   -0.9759 |   -0.9364 |   -0.9658 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9852 |   -0.9215 |   -0.7861 |   -0.7697 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9290 |   -0.7723 |   -0.8141 |   -0.8141 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7852 |   -0.8339 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     484.82183 |     101.2 |    -4.076 |   -0.9373 |    -2.398 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.085 |  -0.01100 |    0.7742 |   0.07203 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6616 |     1.769 |     1.370 |     1.249 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.636 |     1.226 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     484.82183</span> |     101.2 |   0.01698 |    0.2814 |   0.09086 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01682 |    0.4972 |    0.7742 |   0.07203 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6616 |     1.769 |     1.370 |     1.249 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.636 |     1.226 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     22.05 |    -2.460 |   -0.1372 |   -0.3150 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.875 |  0.006918 |    -12.51 |    -13.87 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.059 |    -14.26 |    -9.441 |    -9.910 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -12.81 |    -7.688 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   25</span>|     484.49026 |     1.002 |   -0.9747 |   -0.9363 |   -0.9656 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9843 |   -0.9215 |   -0.7802 |   -0.7632 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9300 |   -0.7655 |   -0.8095 |   -0.8093 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7791 |   -0.8302 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     484.49026 |     100.2 |    -4.075 |   -0.9372 |    -2.398 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.084 |  -0.01100 |    0.7764 |   0.07225 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6609 |     1.779 |     1.376 |     1.254 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.645 |     1.230 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     484.49026</span> |     100.2 |   0.01700 |    0.2815 |   0.09087 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01684 |    0.4972 |    0.7764 |   0.07225 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6609 |     1.779 |     1.376 |     1.254 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.645 |     1.230 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -48.40 |    -3.314 |   -0.8046 |   0.01082 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.785 |  0.008564 |    -11.34 |    -13.90 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.095 |    -13.95 |    -9.157 |    -9.483 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -12.66 |    -7.448 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   26</span>|     483.91316 |     1.012 |   -0.9731 |   -0.9360 |   -0.9656 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9832 |   -0.9215 |   -0.7758 |   -0.7593 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9309 |   -0.7575 |   -0.8048 |   -0.8042 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7719 |   -0.8261 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     483.91316 |     101.2 |    -4.073 |   -0.9369 |    -2.398 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.083 |  -0.01100 |    0.7780 |   0.07238 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6603 |     1.792 |     1.382 |     1.260 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.656 |     1.235 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     483.91316</span> |     101.2 |   0.01702 |    0.2815 |   0.09088 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01685 |    0.4972 |    0.7780 |   0.07238 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6603 |     1.792 |     1.382 |     1.260 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.656 |     1.235 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     21.71 |    -2.295 |   -0.1241 |   -0.3190 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.824 |  0.007081 |    -13.14 |    -14.20 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.713 |    -13.92 |    -9.049 |    -9.560 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -12.44 |    -7.439 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   27</span>|     483.53972 |     1.003 |   -0.9719 |   -0.9359 |   -0.9654 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9822 |   -0.9215 |   -0.7695 |   -0.7529 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9317 |   -0.7501 |   -0.8002 |   -0.7993 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7653 |   -0.8222 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     483.53972 |     100.3 |    -4.072 |   -0.9368 |    -2.398 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.082 |  -0.01100 |    0.7803 |   0.07259 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6598 |     1.804 |     1.388 |     1.266 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.666 |     1.239 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     483.53972</span> |     100.3 |   0.01705 |    0.2815 |   0.09089 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01687 |    0.4972 |    0.7803 |   0.07259 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6598 |     1.804 |     1.388 |     1.266 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.666 |     1.239 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -41.33 |    -3.086 |   -0.7421 |  -0.01550 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.732 |  0.008667 |    -12.24 |    -14.31 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.879 |    -13.57 |    -8.704 |    -9.151 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -12.31 |    -7.231 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   28</span>|     483.02129 |     1.012 |   -0.9704 |   -0.9356 |   -0.9653 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9811 |   -0.9215 |   -0.7639 |   -0.7482 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9323 |   -0.7422 |   -0.7957 |   -0.7943 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7582 |   -0.8182 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     483.02129 |     101.2 |    -4.070 |   -0.9365 |    -2.398 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.081 |  -0.01100 |    0.7823 |   0.07275 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6594 |     1.816 |     1.393 |     1.271 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.676 |     1.244 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     483.02129</span> |     101.2 |   0.01707 |    0.2816 |   0.09090 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01689 |    0.4972 |    0.7823 |   0.07275 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6594 |     1.816 |     1.393 |     1.271 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.676 |     1.244 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     24.99 |    -2.064 |  -0.07177 |   -0.3414 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.768 |  0.007186 |    -12.35 |    -13.09 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.619 |    -13.63 |    -8.749 |    -9.199 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -12.40 |    -7.209 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   29</span>|     482.63111 |     1.003 |   -0.9692 |   -0.9355 |   -0.9651 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9800 |   -0.9215 |   -0.7579 |   -0.7429 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9329 |   -0.7344 |   -0.7911 |   -0.7893 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7510 |   -0.8142 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     482.63111 |     100.3 |    -4.069 |   -0.9364 |    -2.398 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.080 |  -0.01100 |    0.7845 |   0.07293 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6589 |     1.829 |     1.399 |     1.277 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.687 |     1.249 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     482.63111</span> |     100.3 |   0.01709 |    0.2816 |   0.09092 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01691 |    0.4972 |    0.7845 |   0.07293 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6589 |     1.829 |     1.399 |     1.277 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.687 |     1.249 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -36.55 |    -2.873 |   -0.6941 |  -0.03251 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.664 |  0.008820 |    -11.56 |    -13.31 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.468 |    -13.22 |    -8.319 |    -8.815 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -11.93 |    -7.014 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   30</span>|     482.15860 |     1.013 |   -0.9679 |   -0.9352 |   -0.9650 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9789 |   -0.9215 |   -0.7525 |   -0.7387 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9332 |   -0.7261 |   -0.7866 |   -0.7843 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7435 |   -0.8100 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      482.1586 |     101.3 |    -4.068 |   -0.9361 |    -2.398 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.079 |  -0.01100 |    0.7865 |   0.07307 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6587 |     1.842 |     1.405 |     1.283 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.698 |     1.253 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      482.1586</span> |     101.3 |   0.01711 |    0.2817 |   0.09093 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01693 |    0.4972 |    0.7865 |   0.07307 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6587 |     1.842 |     1.405 |     1.283 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.698 |     1.253 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     26.18 |    -1.853 |  -0.03641 |   -0.3540 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.697 |  0.007343 |    -10.35 |    -10.94 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.762 |    -13.15 |    -8.194 |    -8.841 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -11.73 |    -7.013 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   31</span>|     481.77253 |     1.004 |   -0.9667 |   -0.9350 |   -0.9648 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9778 |   -0.9215 |   -0.7477 |   -0.7350 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9339 |   -0.7174 |   -0.7818 |   -0.7789 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7358 |   -0.8055 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     481.77253 |     100.4 |    -4.067 |   -0.9360 |    -2.397 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.078 |  -0.01100 |    0.7882 |   0.07320 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6583 |     1.855 |     1.411 |     1.289 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.709 |     1.259 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     481.77253</span> |     100.4 |   0.01713 |    0.2817 |   0.09095 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01695 |    0.4972 |    0.7882 |   0.07320 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6583 |     1.855 |     1.411 |     1.289 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.709 |     1.259 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -33.42 |    -2.468 |   -0.6588 |  -0.04495 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.582 |  0.009000 |    -9.278 |    -10.93 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     2.073 |    -12.89 |    -7.995 |    -8.451 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -11.53 |    -6.784 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   32</span>|     481.33405 |     1.012 |   -0.9656 |   -0.9347 |   -0.9646 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9766 |   -0.9215 |   -0.7446 |   -0.7329 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9352 |   -0.7079 |   -0.7769 |   -0.7733 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7275 |   -0.8008 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     481.33405 |     101.2 |    -4.066 |   -0.9357 |    -2.397 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.077 |  -0.01100 |    0.7894 |   0.07326 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6574 |     1.870 |     1.417 |     1.296 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.721 |     1.264 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     481.33405</span> |     101.2 |   0.01715 |    0.2818 |   0.09096 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01697 |    0.4972 |    0.7894 |   0.07326 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6574 |     1.870 |     1.417 |     1.296 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.721 |     1.264 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     23.32 |    -1.710 |  -0.03967 |   -0.3452 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.599 |  0.007614 |    -9.890 |    -10.44 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.650 |    -12.78 |    -7.841 |    -8.441 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -11.31 |    -6.748 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   33</span>|     480.95912 |     1.004 |   -0.9646 |   -0.9346 |   -0.9644 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9754 |   -0.9215 |   -0.7406 |   -0.7300 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9364 |   -0.6985 |   -0.7720 |   -0.7677 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7193 |   -0.7960 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.95912 |     100.4 |    -4.065 |   -0.9356 |    -2.397 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.075 |  -0.01100 |    0.7908 |   0.07336 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6566 |     1.885 |     1.424 |     1.302 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.734 |     1.270 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.95912</span> |     100.4 |   0.01717 |    0.2818 |   0.09098 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01699 |    0.4972 |    0.7908 |   0.07336 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6566 |     1.885 |     1.424 |     1.302 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.734 |     1.270 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -30.38 |    -2.273 |   -0.6168 |  -0.05646 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.481 |  0.009185 |    -8.899 |    -11.86 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.510 |    -12.49 |    -7.578 |    -8.069 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -11.13 |    -6.546 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   34</span>|     480.53887 |     1.012 |   -0.9637 |   -0.9342 |   -0.9642 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9743 |   -0.9216 |   -0.7379 |   -0.7267 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9369 |   -0.6888 |   -0.7673 |   -0.7623 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7109 |   -0.7912 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.53887 |     101.2 |    -4.064 |   -0.9352 |    -2.397 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.074 |  -0.01100 |    0.7918 |   0.07347 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6562 |     1.901 |     1.429 |     1.308 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.746 |     1.275 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.53887</span> |     101.2 |   0.01719 |    0.2819 |   0.09100 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01700 |    0.4972 |    0.7918 |   0.07347 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6562 |     1.901 |     1.429 |     1.308 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.746 |     1.275 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     23.40 |    -1.545 | -0.009684 |   -0.3499 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.497 |  0.007817 |    -10.78 |    -11.16 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.267 |    -12.36 |    -7.423 |    -8.042 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -10.90 |    -6.497 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   35</span>|     480.15983 |     1.005 |   -0.9631 |   -0.9341 |   -0.9640 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9733 |   -0.9216 |   -0.7332 |   -0.7223 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9371 |   -0.6793 |   -0.7629 |   -0.7571 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7029 |   -0.7866 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.15983 |     100.5 |    -4.063 |   -0.9351 |    -2.397 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.073 |  -0.01100 |    0.7935 |   0.07362 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6561 |     1.916 |     1.435 |     1.314 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.758 |     1.280 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.15983</span> |     100.5 |   0.01720 |    0.2819 |   0.09102 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01702 |    0.4972 |    0.7935 |   0.07362 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6561 |     1.916 |     1.435 |     1.314 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.758 |     1.280 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -28.57 |    -2.381 |   -0.5845 |  -0.05882 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.378 |  0.009420 |    -9.960 |    -11.12 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.408 |    -12.08 |    -7.201 |    -7.708 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -10.66 |    -6.274 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   36</span>|     479.76525 |     1.013 |   -0.9621 |   -0.9338 |   -0.9638 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9724 |   -0.9216 |   -0.7274 |   -0.7189 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9370 |   -0.6698 |   -0.7590 |   -0.7523 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6950 |   -0.7821 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.76525 |     101.3 |    -4.062 |   -0.9349 |    -2.396 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.072 |  -0.01100 |    0.7956 |   0.07373 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6562 |     1.931 |     1.440 |     1.320 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.770 |     1.286 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.76525</span> |     101.3 |   0.01721 |    0.2819 |   0.09104 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01704 |    0.4972 |    0.7956 |   0.07373 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6562 |     1.931 |     1.440 |     1.320 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.770 |     1.286 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     25.60 |    -1.145 |   0.04744 |   -0.3638 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.405 |  0.007969 |    -8.937 |    -9.159 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.481 |    -11.97 |    -7.076 |    -7.721 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -10.46 |    -6.241 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   37</span>|     479.40619 |     1.005 |   -0.9617 |   -0.9337 |   -0.9635 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9714 |   -0.9216 |   -0.7231 |   -0.7171 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9376 |   -0.6597 |   -0.7546 |   -0.7470 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6867 |   -0.7774 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.40619 |     100.5 |    -4.062 |   -0.9348 |    -2.396 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.071 |  -0.01100 |    0.7972 |   0.07380 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6558 |     1.947 |     1.445 |     1.326 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.782 |     1.291 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.40619</span> |     100.5 |   0.01722 |    0.2820 |   0.09107 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01705 |    0.4972 |    0.7972 |   0.07380 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6558 |     1.947 |     1.445 |     1.326 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.782 |     1.291 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -26.68 |    -2.255 |   -0.5492 |  -0.06756 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.289 |  0.009576 |    -8.071 |    -10.65 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.114 |    -11.68 |    -6.840 |    -7.373 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -10.30 |    -6.055 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   38</span>|     479.03445 |     1.012 |   -0.9610 |   -0.9335 |   -0.9633 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9705 |   -0.9216 |   -0.7212 |   -0.7148 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9376 |   -0.6492 |   -0.7503 |   -0.7416 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6779 |   -0.7724 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     479.03445 |     101.2 |    -4.061 |   -0.9345 |    -2.396 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.071 |  -0.01100 |    0.7979 |   0.07387 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6558 |     1.963 |     1.451 |     1.332 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.795 |     1.297 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     479.03445</span> |     101.2 |   0.01723 |    0.2820 |   0.09109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01707 |    0.4972 |    0.7979 |   0.07387 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6558 |     1.963 |     1.451 |     1.332 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.795 |     1.297 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     22.12 |    -1.081 |   0.03630 |   -0.3485 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.311 |  0.008233 |    -8.610 |    -8.856 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.385 |    -11.53 |    -6.694 |    -7.351 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -10.05 |    -5.987 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   39</span>|     478.70046 |     1.005 |   -0.9607 |   -0.9334 |   -0.9629 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9697 |   -0.9216 |   -0.7185 |   -0.7124 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9378 |   -0.6386 |   -0.7461 |   -0.7363 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6692 |   -0.7674 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     478.70046 |     100.5 |    -4.061 |   -0.9345 |    -2.396 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.070 |  -0.01100 |    0.7989 |   0.07395 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6557 |     1.980 |     1.456 |     1.338 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.808 |     1.302 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     478.70046</span> |     100.5 |   0.01724 |    0.2820 |   0.09112 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01708 |    0.4972 |    0.7989 |   0.07395 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6557 |     1.980 |     1.456 |     1.338 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.808 |     1.302 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -26.51 |    -2.193 |   -0.5308 |  -0.05813 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.191 |  0.009843 |    -9.246 |    -10.25 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.049 |    -11.25 |    -6.459 |    -7.012 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -9.842 |    -5.790 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   40</span>|     478.33460 |     1.012 |   -0.9602 |   -0.9332 |   -0.9627 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9690 |   -0.9216 |   -0.7136 |   -0.7111 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9376 |   -0.6278 |   -0.7425 |   -0.7314 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6607 |   -0.7626 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      478.3346 |     101.2 |    -4.060 |   -0.9343 |    -2.395 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.069 |  -0.01100 |    0.8007 |   0.07400 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6558 |     1.997 |     1.461 |     1.344 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.820 |     1.308 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      478.3346</span> |     101.2 |   0.01724 |    0.2821 |   0.09114 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01709 |    0.4972 |    0.8007 |   0.07400 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6558 |     1.997 |     1.461 |     1.344 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.820 |     1.308 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     20.00 |    -1.246 |   0.04282 |   -0.3358 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.228 |  0.008460 |    -8.250 |    -8.533 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.305 |    -11.09 |    -6.335 |    -6.999 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -9.637 |    -5.739 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   41</span>|     478.02904 |     1.005 |   -0.9595 |   -0.9333 |   -0.9623 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9683 |   -0.9217 |   -0.7087 |   -0.7092 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9377 |   -0.6173 |   -0.7388 |   -0.7264 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6523 |   -0.7577 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     478.02904 |     100.5 |    -4.060 |   -0.9343 |    -2.395 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.068 |  -0.01100 |    0.8025 |   0.07406 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6557 |     2.014 |     1.466 |     1.350 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.833 |     1.314 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     478.02904</span> |     100.5 |   0.01726 |    0.2820 |   0.09117 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01711 |    0.4972 |    0.8025 |   0.07406 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6557 |     2.014 |     1.466 |     1.350 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.833 |     1.314 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -28.91 |    -2.133 |   -0.5430 |  -0.03427 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.114 |   0.01012 |    -7.324 |    -9.914 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9278 |    -10.81 |    -6.119 |    -6.673 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -9.457 |    -5.557 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   42</span>|     477.67480 |     1.012 |   -0.9587 |   -0.9331 |   -0.9620 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9677 |   -0.9217 |   -0.7077 |   -0.7075 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9374 |   -0.6060 |   -0.7354 |   -0.7215 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6433 |   -0.7527 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      477.6748 |     101.2 |    -4.059 |   -0.9342 |    -2.395 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.068 |  -0.01100 |    0.8028 |   0.07412 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6559 |     2.032 |     1.470 |     1.355 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.846 |     1.319 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      477.6748</span> |     101.2 |   0.01727 |    0.2821 |   0.09120 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01712 |    0.4972 |    0.8028 |   0.07412 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6559 |     2.032 |     1.470 |     1.355 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.846 |     1.319 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     17.58 |    -1.074 |   0.03251 |   -0.3151 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.162 |  0.008688 |    -9.226 |    -9.481 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9303 |    -10.65 |    -6.004 |    -6.660 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -9.223 |    -5.492 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   43</span>|     477.36674 |     1.005 |   -0.9586 |   -0.9332 |   -0.9617 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9672 |   -0.9217 |   -0.7028 |   -0.7029 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9362 |   -0.5954 |   -0.7323 |   -0.7170 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6350 |   -0.7479 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     477.36674 |     100.5 |    -4.059 |   -0.9343 |    -2.394 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.067 |  -0.01100 |    0.8046 |   0.07427 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6567 |     2.048 |     1.474 |     1.361 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.858 |     1.325 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     477.36674</span> |     100.5 |   0.01727 |    0.2821 |   0.09123 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01713 |    0.4972 |    0.8046 |   0.07427 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6567 |     2.048 |     1.474 |     1.361 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.858 |     1.325 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -26.86 |    -1.999 |   -0.5088 |  -0.02993 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.056 |   0.01029 |    -6.930 |    -7.943 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.123 |    -10.40 |    -5.776 |    -6.354 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -9.032 |    -5.321 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   44</span>|     477.05158 |     1.012 |   -0.9583 |   -0.9331 |   -0.9614 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9667 |   -0.9217 |   -0.6985 |   -0.7029 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9357 |   -0.5841 |   -0.7296 |   -0.7125 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6265 |   -0.7430 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     477.05158 |     101.2 |    -4.058 |   -0.9342 |    -2.394 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.067 |  -0.01100 |    0.8062 |   0.07427 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6570 |     2.066 |     1.477 |     1.366 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.871 |     1.331 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     477.05158</span> |     101.2 |   0.01728 |    0.2821 |   0.09126 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01713 |    0.4972 |    0.8062 |   0.07427 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6570 |     2.066 |     1.477 |     1.366 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.871 |     1.331 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     21.59 |   -0.9080 |    0.1072 |   -0.3377 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.120 |  0.008656 |    -8.076 |    -8.919 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8940 |    -10.23 |    -5.724 |    -6.385 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -8.830 |    -5.273 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   45</span>|     476.75116 |     1.006 |   -0.9582 |   -0.9333 |   -0.9610 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9662 |   -0.9217 |   -0.6961 |   -0.7025 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9353 |   -0.5723 |   -0.7265 |   -0.7076 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6174 |   -0.7378 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     476.75116 |     100.6 |    -4.058 |   -0.9343 |    -2.394 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.066 |  -0.01100 |    0.8070 |   0.07428 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6573 |     2.085 |     1.481 |     1.371 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.884 |     1.337 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     476.75116</span> |     100.6 |   0.01728 |    0.2820 |   0.09129 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01714 |    0.4972 |    0.8070 |   0.07428 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6573 |     2.085 |     1.481 |     1.371 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.884 |     1.337 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -19.26 |    -1.806 |   -0.3949 |  -0.06769 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.006 |   0.01017 |    -6.717 |    -7.715 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.115 |    -9.949 |    -5.529 |    -6.080 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -8.599 |    -5.083 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   46</span>|     476.47529 |     1.013 |   -0.9577 |   -0.9333 |   -0.9608 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9657 |   -0.9218 |   -0.6941 |   -0.7020 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9354 |   -0.5606 |   -0.7233 |   -0.7027 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6086 |   -0.7326 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     476.47529 |     101.3 |    -4.058 |   -0.9343 |    -2.393 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.066 |  -0.01100 |    0.8078 |   0.07430 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6573 |     2.103 |     1.485 |     1.377 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.897 |     1.342 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     476.47529</span> |     101.3 |   0.01729 |    0.2820 |   0.09132 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01715 |    0.4972 |    0.8078 |   0.07430 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6573 |     2.103 |     1.485 |     1.377 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.897 |     1.342 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     23.23 |   -0.7393 |    0.1467 |   -0.3435 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.063 |  0.008714 |    -6.650 |    -7.604 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.072 |    -9.778 |    -5.421 |    -6.068 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -8.413 |    -5.023 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   47</span>|     476.20958 |     1.007 |   -0.9575 |   -0.9335 |   -0.9604 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9651 |   -0.9218 |   -0.6949 |   -0.7035 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9364 |   -0.5488 |   -0.7198 |   -0.6977 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.5996 |   -0.7274 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     476.20958 |     100.7 |    -4.058 |   -0.9345 |    -2.393 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.065 |  -0.01100 |    0.8075 |   0.07425 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6566 |     2.122 |     1.490 |     1.383 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.910 |     1.349 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     476.20958</span> |     100.7 |   0.01729 |    0.2820 |   0.09135 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01716 |    0.4972 |    0.8075 |   0.07425 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6566 |     2.122 |     1.490 |     1.383 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.910 |     1.349 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -16.91 |    -1.652 |   -0.3580 |  -0.07063 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9392 |   0.01022 |    -6.706 |    -7.708 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9788 |    -9.498 |    -5.202 |    -5.742 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -8.208 |    -4.851 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   48</span>|     475.96032 |     1.013 |   -0.9572 |   -0.9335 |   -0.9601 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9647 |   -0.9218 |   -0.6944 |   -0.7034 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9370 |   -0.5368 |   -0.7166 |   -0.6928 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.5905 |   -0.7221 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     475.96032 |     101.3 |    -4.057 |   -0.9346 |    -2.393 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.065 |  -0.01100 |    0.8077 |   0.07425 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6562 |     2.141 |     1.494 |     1.388 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.924 |     1.355 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     475.96032</span> |     101.3 |   0.01730 |    0.2820 |   0.09138 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01717 |    0.4972 |    0.8077 |   0.07425 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6562 |     2.141 |     1.494 |     1.388 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.924 |     1.355 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     22.21 |   -0.6469 |    0.1490 |   -0.3297 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9912 |  0.008804 |    -6.697 |    -7.730 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.011 |    -9.295 |    -5.014 |    -5.737 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -7.990 |    -4.768 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   49</span>|     475.69232 |     1.007 |   -0.9579 |   -0.9338 |   -0.9597 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9645 |   -0.9219 |   -0.6939 |   -0.7032 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9375 |   -0.5243 |   -0.7149 |   -0.6885 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.5813 |   -0.7168 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     475.69232 |     100.7 |    -4.058 |   -0.9348 |    -2.392 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.064 |  -0.01100 |    0.8078 |   0.07426 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6559 |     2.161 |     1.496 |     1.393 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.937 |     1.361 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     475.69232</span> |     100.7 |   0.01729 |    0.2819 |   0.09141 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01717 |    0.4972 |    0.8078 |   0.07426 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6559 |     2.161 |     1.496 |     1.393 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.937 |     1.361 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -16.63 |    -1.703 |   -0.3428 |  -0.06069 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9074 |   0.01024 |    -7.409 |    -9.759 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6668 |    -9.063 |    -4.970 |    -5.451 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -7.786 |    -4.607 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   50</span>|     475.41012 |     1.012 |   -0.9580 |   -0.9340 |   -0.9594 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9645 |   -0.9219 |   -0.6912 |   -0.6928 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9358 |   -0.5138 |   -0.7150 |   -0.6862 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.5743 |   -0.7128 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     475.41012 |     101.2 |    -4.058 |   -0.9351 |    -2.392 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.064 |  -0.01101 |    0.8088 |   0.07461 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6570 |     2.177 |     1.496 |     1.396 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.948 |     1.365 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     475.41012</span> |     101.2 |   0.01728 |    0.2819 |   0.09144 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01717 |    0.4972 |    0.8088 |   0.07461 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6570 |     2.177 |     1.496 |     1.396 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.948 |     1.365 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     17.88 |   -0.8521 |   0.09625 |   -0.2834 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.006 |  0.008928 |    -8.320 |    -8.272 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6841 |    -8.896 |    -4.958 |    -5.482 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -7.690 |    -4.565 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   51</span>|     475.17343 |     1.006 |   -0.9580 |   -0.9344 |   -0.9592 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9643 |   -0.9219 |   -0.6806 |   -0.6840 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9328 |   -0.5062 |   -0.7153 |   -0.6852 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.5694 |   -0.7102 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     475.17343 |     100.6 |    -4.058 |   -0.9354 |    -2.392 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.064 |  -0.01101 |    0.8127 |   0.07490 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6590 |     2.189 |     1.495 |     1.397 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.955 |     1.368 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     475.17343</span> |     100.6 |   0.01728 |    0.2818 |   0.09146 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01718 |    0.4972 |    0.8127 |   0.07490 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6590 |     2.189 |     1.495 |     1.397 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.955 |     1.368 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -22.54 |    -1.885 |   -0.4332 | -0.003109 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9120 |   0.01053 |    -7.129 |    -7.659 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8338 |    -8.700 |    -4.918 |    -5.365 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -7.558 |    -4.481 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   52</span>|     474.94312 |     1.012 |   -0.9570 |   -0.9345 |   -0.9591 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9638 |   -0.9220 |   -0.6676 |   -0.6825 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9307 |   -0.4984 |   -0.7146 |   -0.6834 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.5639 |   -0.7072 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     474.94312 |     101.2 |    -4.057 |   -0.9355 |    -2.392 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.064 |  -0.01101 |    0.8175 |   0.07495 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6604 |     2.202 |     1.496 |     1.399 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.963 |     1.372 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     474.94312</span> |     101.2 |   0.01730 |    0.2818 |   0.09147 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01718 |    0.4972 |    0.8175 |   0.07495 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6604 |     2.202 |     1.496 |     1.399 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.963 |     1.372 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     20.08 |   -0.5236 |    0.1181 |   -0.2986 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9599 |  0.008866 |    -5.310 |    -6.088 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8565 |    -8.707 |    -4.867 |    -5.430 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -7.458 |    -4.464 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   53</span>|     474.70657 |     1.007 |   -0.9569 |   -0.9346 |   -0.9588 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9629 |   -0.9220 |   -0.6640 |   -0.6851 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9296 |   -0.4868 |   -0.7112 |   -0.6784 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.5553 |   -0.7021 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     474.70657 |     100.7 |    -4.057 |   -0.9356 |    -2.392 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.063 |  -0.01101 |    0.8188 |   0.07487 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6612 |     2.220 |     1.500 |     1.405 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.976 |     1.378 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     474.70657</span> |     100.7 |   0.01730 |    0.2818 |   0.09149 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01720 |    0.4972 |    0.8188 |   0.07487 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6612 |     2.220 |     1.500 |     1.405 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.976 |     1.378 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -17.64 |    -1.502 |   -0.3627 |  -0.03824 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.7798 |   0.01049 |    -5.965 |    -8.154 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.5631 |    -8.385 |    -4.675 |    -5.151 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -7.306 |    -4.317 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   54</span>|     474.46623 |     1.012 |   -0.9580 |   -0.9346 |   -0.9586 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9630 |   -0.9221 |   -0.6659 |   -0.6813 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9264 |   -0.4751 |   -0.7083 |   -0.6739 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.5464 |   -0.6970 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     474.46623 |     101.2 |    -4.058 |   -0.9356 |    -2.391 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.063 |  -0.01101 |    0.8181 |   0.07499 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6633 |     2.239 |     1.504 |     1.410 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.989 |     1.384 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     474.46623</span> |     101.2 |   0.01728 |    0.2818 |   0.09151 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01720 |    0.4972 |    0.8181 |   0.07499 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6633 |     2.239 |     1.504 |     1.410 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     1.989 |     1.384 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     15.93 |   -0.7754 |   0.09266 |   -0.2754 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8835 |  0.009135 |    -6.026 |    -6.068 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.8217 |    -8.210 |    -4.532 |    -5.141 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -7.011 |    -4.237 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   55</span>|     474.23394 |     1.006 |   -0.9577 |   -0.9351 |   -0.9581 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9633 |   -0.9221 |   -0.6610 |   -0.6793 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9234 |   -0.4633 |   -0.7068 |   -0.6694 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.5382 |   -0.6917 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     474.23394 |     100.6 |    -4.058 |   -0.9360 |    -2.391 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.063 |  -0.01101 |    0.8199 |   0.07506 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6654 |     2.257 |     1.506 |     1.415 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.001 |     1.390 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     474.23394</span> |     100.6 |   0.01729 |    0.2817 |   0.09156 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01719 |    0.4972 |    0.8199 |   0.07506 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6654 |     2.257 |     1.506 |     1.415 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.001 |     1.390 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -20.30 |    -1.703 |   -0.3919 | -0.002629 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8653 |   0.01064 |    -4.947 |    -7.765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7818 |    -8.010 |    -4.437 |    -4.875 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -6.938 |    -4.094 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   56</span>|     473.98878 |     1.011 |   -0.9542 |   -0.9354 |   -0.9578 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9619 |   -0.9222 |   -0.6621 |   -0.6770 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9235 |   -0.4507 |   -0.7053 |   -0.6648 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.5298 |   -0.6863 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     473.98878 |     101.1 |    -4.054 |   -0.9363 |    -2.390 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.062 |  -0.01101 |    0.8195 |   0.07514 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6653 |     2.277 |     1.508 |     1.421 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.013 |     1.396 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     473.98878</span> |     101.1 |   0.01735 |    0.2816 |   0.09159 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01722 |    0.4972 |    0.8195 |   0.07514 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6653 |     2.277 |     1.508 |     1.421 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.013 |     1.396 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   57</span>|     473.76836 |     1.011 |   -0.9500 |   -0.9359 |   -0.9574 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9601 |   -0.9223 |   -0.6653 |   -0.6763 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9234 |   -0.4357 |   -0.7046 |   -0.6601 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.5205 |   -0.6802 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     473.76836 |     101.1 |    -4.050 |   -0.9368 |    -2.390 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.060 |  -0.01101 |    0.8183 |   0.07516 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6654 |     2.301 |     1.509 |     1.426 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.027 |     1.403 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     473.76836</span> |     101.1 |   0.01742 |    0.2815 |   0.09163 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01725 |    0.4972 |    0.8183 |   0.07516 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6654 |     2.301 |     1.509 |     1.426 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.027 |     1.403 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   58</span>|     472.96708 |     1.009 |   -0.9317 |   -0.9384 |   -0.9555 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9527 |   -0.9228 |   -0.6791 |   -0.6735 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9230 |   -0.3711 |   -0.7015 |   -0.6396 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.4802 |   -0.6537 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     472.96708 |     100.9 |    -4.032 |   -0.9391 |    -2.388 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.053 |  -0.01101 |    0.8133 |   0.07525 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6656 |     2.403 |     1.513 |     1.450 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.087 |     1.433 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     472.96708</span> |     100.9 |   0.01774 |    0.2811 |   0.09180 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01737 |    0.4972 |    0.8133 |   0.07525 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6656 |     2.403 |     1.513 |     1.450 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.087 |     1.433 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   59</span>|     472.23802 |     1.005 |   -0.8811 |   -0.9451 |   -0.9502 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9323 |   -0.9240 |   -0.7172 |   -0.6658 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9220 |   -0.1924 |   -0.6930 |   -0.5829 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.3688 |   -0.5805 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     472.23802 |     100.5 |    -3.981 |   -0.9455 |    -2.383 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.032 |  -0.01103 |    0.7994 |   0.07551 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6663 |     2.686 |     1.523 |     1.515 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.251 |     1.518 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     472.23802</span> |     100.5 |   0.01866 |    0.2798 |   0.09228 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01773 |    0.4972 |    0.7994 |   0.07551 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6663 |     2.686 |     1.523 |     1.515 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.251 |     1.518 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -10.41 |     25.09 |    -1.964 |    0.8913 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.956 |   0.02208 |    -7.724 |    -8.360 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01123 |    -2.619 |    -3.185 |    -1.886 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|    -3.376 |    -1.727 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   60</span>|     470.34942 |     1.020 |    -1.007 |   -0.9408 |   -0.9496 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9429 |   -0.9259 |   -0.7397 |   -0.6282 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9207 |   0.06578 |   -0.6693 |   -0.4998 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2002 |   -0.4722 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     470.34942 |     102.0 |    -4.107 |   -0.9414 |    -2.382 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.043 |  -0.01105 |    0.7911 |   0.07677 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6672 |     3.094 |     1.553 |     1.610 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.500 |     1.642 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     470.34942</span> |     102.0 |   0.01646 |    0.2806 |   0.09235 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01755 |    0.4972 |    0.7911 |   0.07677 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6672 |     3.094 |     1.553 |     1.610 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.500 |     1.642 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     49.94 |    -17.10 |     1.872 |    -1.413 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.684 |  0.005064 |    -7.591 |    -5.431 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2085 |    -2.165 |    -1.196 |   -0.1732 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.4074 |   -0.2957 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   61</span>|     480.76065 |    0.9913 |   -0.9789 |   -0.9482 |   -0.9314 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.207 |   -0.9279 |   -0.6546 |   -0.4866 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9168 |    0.2296 |   -0.6593 |   -0.5083 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1307 |   -0.4332 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     480.76065 |     99.13 |    -4.079 |   -0.9483 |    -2.364 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.307 |  -0.01107 |    0.8222 |   0.08152 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6698 |     3.353 |     1.566 |     1.601 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.603 |     1.687 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     480.76065</span> |     99.13 |   0.01693 |    0.2792 |   0.09404 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01347 |    0.4972 |    0.8222 |   0.08152 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6698 |     3.353 |     1.566 |     1.601 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.603 |     1.687 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   62</span>|     472.12595 |    0.9860 |   -0.9933 |   -0.9428 |   -0.9466 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9755 |   -0.9261 |   -0.7254 |   -0.6089 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9204 |   0.08558 |   -0.6674 |   -0.5007 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1921 |   -0.4676 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     472.12595 |     98.60 |    -4.093 |   -0.9433 |    -2.379 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.076 |  -0.01105 |    0.7964 |   0.07742 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6674 |     3.125 |     1.556 |     1.609 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.512 |     1.648 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     472.12595</span> |     98.60 |   0.01668 |    0.2802 |   0.09262 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01698 |    0.4972 |    0.7964 |   0.07742 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6674 |     3.125 |     1.556 |     1.609 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.512 |     1.648 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   63</span>|     470.26696 |     1.005 |    -1.002 |   -0.9414 |   -0.9491 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9444 |   -0.9259 |   -0.7373 |   -0.6265 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9207 |   0.06646 |   -0.6689 |   -0.4998 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2001 |   -0.4721 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     470.26696 |     100.5 |    -4.102 |   -0.9420 |    -2.382 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.044 |  -0.01105 |    0.7920 |   0.07683 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6672 |     3.095 |     1.554 |     1.610 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.501 |     1.642 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     470.26696</span> |     100.5 |   0.01654 |    0.2805 |   0.09239 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01752 |    0.4972 |    0.7920 |   0.07683 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6672 |     3.095 |     1.554 |     1.610 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.501 |     1.642 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -57.97 |    -21.53 |    0.3336 |   -0.1964 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     4.949 |   0.01388 |    -7.299 |    -5.475 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3363 |    -1.951 |    -1.200 |    0.1716 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.4705 |   -0.2755 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   64</span>|     469.85165 |     1.014 |   -0.9972 |   -0.9415 |   -0.9489 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9548 |   -0.9260 |   -0.7307 |   -0.6235 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9208 |   0.07069 |   -0.6682 |   -0.5004 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1987 |   -0.4712 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     469.85165 |     101.4 |    -4.097 |   -0.9421 |    -2.382 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.055 |  -0.01105 |    0.7944 |   0.07693 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6671 |     3.101 |     1.555 |     1.610 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.503 |     1.643 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     469.85165</span> |     101.4 |   0.01662 |    0.2805 |   0.09241 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01734 |    0.4972 |    0.7944 |   0.07693 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6671 |     3.101 |     1.555 |     1.610 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.503 |     1.643 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     9.302 |    -15.01 |     1.055 |   -0.6015 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.082 |  0.004567 |    -8.090 |    -5.650 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2579 |    -2.336 |    -1.339 |  -0.08766 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.5208 |   -0.3062 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   65</span>|     469.69998 |     1.007 |   -0.9853 |   -0.9423 |   -0.9484 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9564 |   -0.9260 |   -0.7231 |   -0.6186 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9204 |   0.07309 |   -0.6671 |   -0.5006 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1982 |   -0.4709 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     469.69998 |     100.7 |    -4.085 |   -0.9429 |    -2.381 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.056 |  -0.01105 |    0.7972 |   0.07709 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6674 |     3.105 |     1.556 |     1.610 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.503 |     1.644 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     469.69998</span> |     100.7 |   0.01682 |    0.2803 |   0.09245 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01731 |    0.4972 |    0.7972 |   0.07709 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6674 |     3.105 |     1.556 |     1.610 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.503 |     1.644 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -36.86 |    -12.39 |    0.1028 |    0.1354 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7614 |  0.008951 |    -8.542 |    -6.076 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2246 |    -2.313 |    -1.235 |    0.1010 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.5654 |   -0.2998 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   66</span>|     469.45579 |     1.013 |   -0.9821 |   -0.9425 |   -0.9491 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9562 |   -0.9260 |   -0.7099 |   -0.6134 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9182 |   0.07758 |   -0.6674 |   -0.5042 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1996 |   -0.4719 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     469.45579 |     101.3 |    -4.082 |   -0.9430 |    -2.382 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.056 |  -0.01105 |    0.8020 |   0.07727 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6689 |     3.112 |     1.556 |     1.605 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.501 |     1.643 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     469.45579</span> |     101.3 |   0.01687 |    0.2803 |   0.09239 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01731 |    0.4972 |    0.8020 |   0.07727 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6689 |     3.112 |     1.556 |     1.605 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.501 |     1.643 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     7.383 |    -7.976 |    0.5709 |   -0.2773 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.4749 |  0.005686 |    -7.013 |    -4.742 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3037 |    -2.186 |    -1.089 |  -0.04880 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.5661 |   -0.3435 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   67</span>|     469.35718 |     1.007 |   -0.9760 |   -0.9430 |   -0.9493 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9561 |   -0.9261 |   -0.6977 |   -0.6075 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9166 |   0.08278 |   -0.6675 |   -0.5068 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2001 |   -0.4722 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     469.35718 |     100.7 |    -4.076 |   -0.9435 |    -2.382 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.056 |  -0.01105 |    0.8065 |   0.07747 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6700 |     3.121 |     1.556 |     1.602 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.501 |     1.642 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     469.35718</span> |     100.7 |   0.01698 |    0.2802 |   0.09237 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01732 |    0.4972 |    0.8065 |   0.07747 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6700 |     3.121 |     1.556 |     1.602 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.501 |     1.642 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -28.34 |    -7.406 |  -0.07810 |    0.1748 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6873 |  0.009156 |    -6.696 |    -4.901 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2913 |    -2.170 |    -1.086 |  -0.02443 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6503 |   -0.3607 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   68</span>|     469.19639 |     1.012 |   -0.9749 |   -0.9431 |   -0.9497 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9576 |   -0.9262 |   -0.6862 |   -0.6021 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9137 |   0.09137 |   -0.6688 |   -0.5111 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2009 |   -0.4726 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     469.19639 |     101.2 |    -4.075 |   -0.9436 |    -2.382 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.058 |  -0.01105 |    0.8107 |   0.07765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6719 |     3.134 |     1.554 |     1.597 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.499 |     1.642 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     469.19639</span> |     101.2 |   0.01699 |    0.2802 |   0.09233 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01729 |    0.4972 |    0.8107 |   0.07765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6719 |     3.134 |     1.554 |     1.597 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.499 |     1.642 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     4.760 |    -4.629 |    0.3091 |   -0.1325 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.04401 |  0.005819 |    -5.826 |    -2.903 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1562 |    -2.130 |    -1.121 |   -0.2535 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6922 |   -0.4141 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   69</span>|     469.12263 |     1.008 |   -0.9713 |   -0.9434 |   -0.9499 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9575 |   -0.9262 |   -0.6727 |   -0.6012 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9123 |   0.09974 |   -0.6678 |   -0.5134 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2010 |   -0.4726 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     469.12263 |     100.8 |    -4.071 |   -0.9438 |    -2.383 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.058 |  -0.01105 |    0.8156 |   0.07768 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6728 |     3.147 |     1.555 |     1.595 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.499 |     1.642 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     469.12263</span> |     100.8 |   0.01706 |    0.2801 |   0.09232 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01729 |    0.4972 |    0.8156 |   0.07768 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6728 |     3.147 |     1.555 |     1.595 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.499 |     1.642 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -20.86 |    -4.532 |   -0.1251 |    0.1663 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1284 |  0.008177 |    -5.101 |    -3.487 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2183 |    -2.137 |    -1.171 |   -0.2991 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6817 |   -0.4261 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   70</span>|     469.02483 |     1.011 |   -0.9711 |   -0.9434 |   -0.9500 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9569 |   -0.9263 |   -0.6616 |   -0.6038 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9105 |    0.1111 |   -0.6656 |   -0.5157 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2026 |   -0.4730 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     469.02483 |     101.1 |    -4.071 |   -0.9439 |    -2.383 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.057 |  -0.01105 |    0.8196 |   0.07759 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6741 |     3.165 |     1.558 |     1.592 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.497 |     1.641 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     469.02483</span> |     101.1 |   0.01706 |    0.2801 |   0.09230 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01730 |    0.4972 |    0.8196 |   0.07759 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6741 |     3.165 |     1.558 |     1.592 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.497 |     1.641 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     4.786 |    -2.589 |    0.2046 |   -0.1061 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1383 |  0.006373 |    -4.080 |    -2.588 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1418 |    -1.956 |   -0.9118 |   -0.4226 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7530 |   -0.4429 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   71</span>|     468.97618 |     1.009 |   -0.9710 |   -0.9439 |   -0.9494 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9625 |   -0.9265 |   -0.6573 |   -0.5998 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9085 |    0.1255 |   -0.6648 |   -0.5157 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2015 |   -0.4719 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.97618 |     100.9 |    -4.071 |   -0.9443 |    -2.382 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.063 |  -0.01106 |    0.8212 |   0.07773 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6755 |     3.188 |     1.559 |     1.592 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.498 |     1.643 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.97618</span> |     100.9 |   0.01706 |    0.2800 |   0.09236 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01721 |    0.4972 |    0.8212 |   0.07773 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6755 |     3.188 |     1.559 |     1.592 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.498 |     1.643 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -11.25 |    -3.431 |  -0.02518 |    0.1501 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -1.529 |  0.005347 |    -4.655 |    -3.448 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3102 |    -2.013 |   -0.8937 |   -0.3742 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7403 |   -0.4511 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   72</span>|     468.91299 |     1.010 |   -0.9702 |   -0.9444 |   -0.9496 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9534 |   -0.9266 |   -0.6524 |   -0.5929 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9051 |    0.1362 |   -0.6665 |   -0.5172 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2029 |   -0.4725 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.91299 |     101.0 |    -4.070 |   -0.9448 |    -2.382 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.053 |  -0.01106 |    0.8230 |   0.07796 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6777 |     3.205 |     1.557 |     1.590 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.496 |     1.642 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.91299</span> |     101.0 |   0.01707 |    0.2799 |   0.09234 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01736 |    0.4972 |    0.8230 |   0.07796 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6777 |     3.205 |     1.557 |     1.590 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.496 |     1.642 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -8.129 |    -2.859 |  -0.02802 | -0.004107 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     1.281 |  0.009120 |    -4.291 |    -2.058 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1551 |    -2.052 |    -1.121 |   -0.5699 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7547 |   -0.4743 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   73</span>|     471.25018 |     1.036 |   -0.9607 |   -0.9443 |   -0.9496 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9576 |   -0.9266 |   -0.6382 |   -0.5860 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9046 |    0.1430 |   -0.6628 |   -0.5153 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2004 |   -0.4709 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     471.25018 |     103.6 |    -4.061 |   -0.9447 |    -2.382 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.058 |  -0.01106 |    0.8282 |   0.07819 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6781 |     3.216 |     1.562 |     1.593 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.500 |     1.644 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     471.25018</span> |     103.6 |   0.01724 |    0.2800 |   0.09234 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01729 |    0.4972 |    0.8282 |   0.07819 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6781 |     3.216 |     1.562 |     1.593 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.500 |     1.644 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   74</span>|     468.90555 |     1.012 |   -0.9692 |   -0.9444 |   -0.9496 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9538 |   -0.9266 |   -0.6510 |   -0.5922 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9050 |    0.1369 |   -0.6661 |   -0.5170 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2027 |   -0.4723 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.90555 |     101.2 |    -4.069 |   -0.9448 |    -2.382 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.054 |  -0.01106 |    0.8235 |   0.07798 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6778 |     3.206 |     1.557 |     1.591 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.497 |     1.642 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.90555</span> |     101.2 |   0.01709 |    0.2799 |   0.09234 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01736 |    0.4972 |    0.8235 |   0.07798 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6778 |     3.206 |     1.557 |     1.591 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.497 |     1.642 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     11.48 |    -1.027 |    0.1960 |   -0.1655 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9827 |  0.007474 |    -3.960 |    -2.226 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2199 |    -2.046 |    -1.158 |   -0.6171 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7493 |   -0.4797 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   75</span>|     468.88608 |     1.010 |   -0.9691 |   -0.9444 |   -0.9495 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9542 |   -0.9266 |   -0.6497 |   -0.5917 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9044 |    0.1391 |   -0.6659 |   -0.5166 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2026 |   -0.4722 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.88608 |     101.0 |    -4.069 |   -0.9448 |    -2.382 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.054 |  -0.01106 |    0.8240 |   0.07799 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6782 |     3.210 |     1.558 |     1.591 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.497 |     1.642 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.88608</span> |     101.0 |   0.01709 |    0.2799 |   0.09235 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01735 |    0.4972 |    0.8240 |   0.07799 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6782 |     3.210 |     1.558 |     1.591 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.497 |     1.642 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -2.784 |    -1.944 |  0.009889 |  -0.02065 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.9781 |  0.008521 |    -3.685 |    -1.489 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.08345 |    -1.963 |   -0.9408 |   -0.4978 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7271 |   -0.4658 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   76</span>|     468.87604 |     1.012 |   -0.9680 |   -0.9444 |   -0.9495 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9547 |   -0.9266 |   -0.6476 |   -0.5909 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9043 |    0.1402 |   -0.6654 |   -0.5163 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2021 |   -0.4719 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.87604 |     101.2 |    -4.068 |   -0.9448 |    -2.382 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.055 |  -0.01106 |    0.8248 |   0.07802 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6783 |     3.211 |     1.558 |     1.591 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.498 |     1.643 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.87604</span> |     101.2 |   0.01711 |    0.2799 |   0.09235 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01734 |    0.4972 |    0.8248 |   0.07802 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6783 |     3.211 |     1.558 |     1.591 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.498 |     1.643 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     9.309 |   -0.5261 |    0.1314 |   -0.1053 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6891 |  0.007435 |    -3.809 |    -2.129 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2297 |    -2.013 |    -1.108 |   -0.5855 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7533 |   -0.4780 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   77</span>|     468.85962 |     1.010 |   -0.9680 |   -0.9444 |   -0.9495 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9550 |   -0.9266 |   -0.6462 |   -0.5907 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9037 |    0.1428 |   -0.6653 |   -0.5160 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2020 |   -0.4717 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.85962 |     101.0 |    -4.068 |   -0.9448 |    -2.382 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.055 |  -0.01106 |    0.8253 |   0.07803 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6787 |     3.215 |     1.558 |     1.592 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.498 |     1.643 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.85962</span> |     101.0 |   0.01711 |    0.2799 |   0.09235 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01734 |    0.4972 |    0.8253 |   0.07803 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6787 |     3.215 |     1.558 |     1.592 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.498 |     1.643 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.556 |    -1.258 | -0.007590 |  0.001179 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7035 |  0.008243 |    -4.446 |    -2.277 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2406 |    -1.984 |    -1.075 |   -0.5476 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7588 |   -0.4741 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   78</span>|     468.84535 |     1.011 |   -0.9673 |   -0.9444 |   -0.9495 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9554 |   -0.9266 |   -0.6436 |   -0.5895 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9036 |    0.1439 |   -0.6647 |   -0.5157 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2016 |   -0.4714 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.84535 |     101.1 |    -4.067 |   -0.9448 |    -2.382 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.055 |  -0.01106 |    0.8262 |   0.07807 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6788 |     3.217 |     1.559 |     1.592 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.498 |     1.643 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.84535</span> |     101.1 |   0.01712 |    0.2799 |   0.09235 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01733 |    0.4972 |    0.8262 |   0.07807 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6788 |     3.217 |     1.559 |     1.592 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.498 |     1.643 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     5.130 |   -0.4165 |   0.05789 |  -0.04298 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.5152 |  0.007618 |    -4.342 |    -2.024 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2412 |    -1.936 |   -0.8986 |   -0.4874 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7551 |   -0.4761 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   79</span>|     468.83559 |     1.009 |   -0.9672 |   -0.9445 |   -0.9494 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9556 |   -0.9266 |   -0.6414 |   -0.5890 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9032 |    0.1455 |   -0.6647 |   -0.5153 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2012 |   -0.4711 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.83559 |     100.9 |    -4.067 |   -0.9449 |    -2.382 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.056 |  -0.01106 |    0.8270 |   0.07809 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6790 |     3.220 |     1.559 |     1.593 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.499 |     1.643 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.83559</span> |     100.9 |   0.01713 |    0.2799 |   0.09236 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01733 |    0.4972 |    0.8270 |   0.07809 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6790 |     3.220 |     1.559 |     1.593 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.499 |     1.643 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -7.676 |    -1.212 |   -0.1115 |   0.08612 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.5488 |  0.008589 |    -2.761 |    -2.165 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2456 |    -1.924 |   -0.8622 |   -0.4413 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7592 |   -0.4720 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   80</span>|     468.82320 |     1.011 |   -0.9670 |   -0.9445 |   -0.9494 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9559 |   -0.9267 |   -0.6401 |   -0.5885 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9025 |    0.1478 |   -0.6651 |   -0.5152 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2008 |   -0.4708 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      468.8232 |     101.1 |    -4.067 |   -0.9449 |    -2.382 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.056 |  -0.01106 |    0.8275 |   0.07810 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6795 |     3.223 |     1.559 |     1.593 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.499 |     1.644 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      468.8232</span> |     101.1 |   0.01713 |    0.2799 |   0.09236 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01732 |    0.4972 |    0.8275 |   0.07810 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6795 |     3.223 |     1.559 |     1.593 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.499 |     1.644 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     5.440 |   -0.1810 |   0.05193 |  -0.03082 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3483 |  0.007438 |    -3.541 |    -1.923 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2409 |    -1.947 |    -1.058 |   -0.5469 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7518 |   -0.4735 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   81</span>|     468.81154 |     1.010 |   -0.9671 |   -0.9445 |   -0.9493 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9562 |   -0.9267 |   -0.6389 |   -0.5880 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9017 |    0.1505 |   -0.6652 |   -0.5150 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2005 |   -0.4704 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.81154 |     101.0 |    -4.067 |   -0.9449 |    -2.382 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.056 |  -0.01106 |    0.8279 |   0.07812 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6800 |     3.228 |     1.559 |     1.593 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.500 |     1.644 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.81154</span> |     101.0 |   0.01713 |    0.2799 |   0.09237 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01732 |    0.4972 |    0.8279 |   0.07812 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6800 |     3.228 |     1.559 |     1.593 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.500 |     1.644 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -2.875 |   -0.7736 |  -0.05343 |   0.05244 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.3113 |  0.007978 |    -4.161 |    -2.012 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2433 |    -1.896 |   -0.8853 |   -0.4539 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7524 |   -0.4694 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   82</span>|     468.80001 |     1.011 |   -0.9668 |   -0.9445 |   -0.9493 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9563 |   -0.9267 |   -0.6364 |   -0.5873 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9013 |    0.1522 |   -0.6651 |   -0.5147 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2001 |   -0.4701 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.80001 |     101.1 |    -4.067 |   -0.9449 |    -2.382 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.056 |  -0.01106 |    0.8288 |   0.07814 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6803 |     3.230 |     1.559 |     1.593 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.501 |     1.645 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.80001</span> |     101.1 |   0.01713 |    0.2799 |   0.09237 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01731 |    0.4972 |    0.8288 |   0.07814 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6803 |     3.230 |     1.559 |     1.593 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.501 |     1.645 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     6.046 |   0.03721 |   0.05257 |  -0.02595 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1943 |  0.007238 |    -2.985 |    -2.233 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3122 |    -1.891 |   -0.9609 |   -0.5652 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.8019 |   -0.4712 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   83</span>|     468.79133 |     1.009 |   -0.9669 |   -0.9445 |   -0.9492 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9565 |   -0.9267 |   -0.6348 |   -0.5869 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9006 |    0.1543 |   -0.6654 |   -0.5144 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1996 |   -0.4698 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.79133 |     100.9 |    -4.067 |   -0.9449 |    -2.382 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.056 |  -0.01106 |    0.8294 |   0.07816 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6807 |     3.234 |     1.558 |     1.594 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.501 |     1.645 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.79133</span> |     100.9 |   0.01713 |    0.2799 |   0.09237 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01731 |    0.4972 |    0.8294 |   0.07816 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6807 |     3.234 |     1.558 |     1.594 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.501 |     1.645 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -7.226 |   -0.9038 |   -0.1166 |    0.1039 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2475 |  0.008246 |    -3.388 |    -1.948 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2420 |    -1.872 |   -0.8620 |   -0.4291 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7186 |   -0.4561 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   84</span>|     468.77848 |     1.011 |   -0.9668 |   -0.9446 |   -0.9492 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9568 |   -0.9267 |   -0.6338 |   -0.5863 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8997 |    0.1569 |   -0.6658 |   -0.5142 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1992 |   -0.4695 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.77848 |     101.1 |    -4.067 |   -0.9450 |    -2.382 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.057 |  -0.01106 |    0.8298 |   0.07818 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6814 |     3.238 |     1.558 |     1.594 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.502 |     1.645 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.77848</span> |     101.1 |   0.01713 |    0.2799 |   0.09238 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01730 |    0.4972 |    0.8298 |   0.07818 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6814 |     3.238 |     1.558 |     1.594 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.502 |     1.645 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     2.503 |   -0.1762 |  0.005994 |   0.01651 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.06327 |  0.007339 |    -4.407 |    -2.185 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3133 |    -1.836 |   -0.8110 |   -0.4803 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7946 |   -0.4665 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   85</span>|     468.76805 |     1.010 |   -0.9668 |   -0.9446 |   -0.9492 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9569 |   -0.9267 |   -0.6312 |   -0.5853 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8993 |    0.1583 |   -0.6657 |   -0.5139 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1987 |   -0.4691 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.76805 |     101.0 |    -4.067 |   -0.9450 |    -2.382 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.057 |  -0.01106 |    0.8307 |   0.07821 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6816 |     3.240 |     1.558 |     1.594 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.503 |     1.646 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.76805</span> |     101.0 |   0.01713 |    0.2799 |   0.09238 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01730 |    0.4972 |    0.8307 |   0.07821 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6816 |     3.240 |     1.558 |     1.594 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.503 |     1.646 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -5.980 |   -0.7098 |   -0.1045 |    0.1001 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.1097 |  0.008007 |    -2.998 |    -1.799 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2421 |    -1.902 |    -1.021 |   -0.4929 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7234 |   -0.4545 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   86</span>|     468.76178 |     1.012 |   -0.9666 |   -0.9446 |   -0.9492 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9569 |   -0.9268 |   -0.6296 |   -0.5849 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8987 |    0.1602 |   -0.6657 |   -0.5136 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1982 |   -0.4689 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.76178 |     101.2 |    -4.067 |   -0.9450 |    -2.382 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.057 |  -0.01106 |    0.8313 |   0.07822 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6820 |     3.243 |     1.558 |     1.595 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.503 |     1.646 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.76178</span> |     101.2 |   0.01714 |    0.2799 |   0.09238 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01730 |    0.4972 |    0.8313 |   0.07822 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6820 |     3.243 |     1.558 |     1.595 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.503 |     1.646 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     9.381 |    0.4945 |   0.08709 |  -0.04347 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.04178 |  0.006758 |    -3.785 |    -1.947 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3007 |    -1.819 |   -0.8940 |   -0.4901 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7836 |   -0.4662 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   87</span>|     468.74724 |     1.010 |   -0.9668 |   -0.9447 |   -0.9491 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9570 |   -0.9268 |   -0.6284 |   -0.5849 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8978 |    0.1628 |   -0.6658 |   -0.5134 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1979 |   -0.4686 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.74724 |     101.0 |    -4.067 |   -0.9451 |    -2.382 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.057 |  -0.01106 |    0.8318 |   0.07822 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6827 |     3.247 |     1.558 |     1.595 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.504 |     1.646 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.74724</span> |     101.0 |   0.01713 |    0.2799 |   0.09239 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01730 |    0.4972 |    0.8318 |   0.07822 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6827 |     3.247 |     1.558 |     1.595 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.504 |     1.646 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.251 |   -0.3146 |  -0.04610 |   0.05724 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.02086 |  0.007590 |    -4.230 |    -2.098 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3136 |    -1.833 |   -0.9365 |   -0.5244 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7857 |   -0.4618 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   88</span>|     468.73570 |     1.011 |   -0.9666 |   -0.9446 |   -0.9492 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9570 |   -0.9268 |   -0.6257 |   -0.5836 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8976 |    0.1639 |   -0.6652 |   -0.5131 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1974 |   -0.4683 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      468.7357 |     101.1 |    -4.067 |   -0.9450 |    -2.382 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.057 |  -0.01106 |    0.8327 |   0.07827 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6828 |     3.249 |     1.559 |     1.595 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.504 |     1.647 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      468.7357</span> |     101.1 |   0.01714 |    0.2799 |   0.09238 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01730 |    0.4972 |    0.8327 |   0.07827 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6828 |     3.249 |     1.559 |     1.595 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.504 |     1.647 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     4.473 |    0.2106 |   0.02342 |  0.003517 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.03354 |  0.007130 |    -3.431 |    -1.904 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3114 |    -1.829 |   -0.9161 |   -0.5352 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7806 |   -0.4641 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   89</span>|     468.73562 |     1.009 |   -0.9667 |   -0.9447 |   -0.9491 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9570 |   -0.9268 |   -0.6239 |   -0.5826 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8973 |    0.1651 |   -0.6648 |   -0.5128 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1970 |   -0.4681 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.73562 |     100.9 |    -4.067 |   -0.9450 |    -2.382 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.057 |  -0.01106 |    0.8334 |   0.07830 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6830 |     3.251 |     1.559 |     1.596 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.505 |     1.647 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.73562</span> |     100.9 |   0.01713 |    0.2799 |   0.09238 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01730 |    0.4972 |    0.8334 |   0.07830 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6830 |     3.251 |     1.559 |     1.596 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.505 |     1.647 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    C| Central Diff. |    -12.26 |    -1.176 |   -0.2138 |    0.1217 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.02157 |  0.008077 |    -2.776 |    -1.301 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1367 |    -1.738 |   -0.7465 |   -0.4531 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7359 |   -0.4611 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   90</span>|     468.72331 |     1.010 |   -0.9665 |   -0.9446 |   -0.9492 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9570 |   -0.9268 |   -0.6235 |   -0.5825 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8973 |    0.1654 |   -0.6647 |   -0.5127 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1969 |   -0.4680 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.72331 |     101.0 |    -4.067 |   -0.9450 |    -2.382 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.057 |  -0.01106 |    0.8335 |   0.07831 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6830 |     3.251 |     1.559 |     1.596 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.505 |     1.647 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.72331</span> |     101.0 |   0.01714 |    0.2799 |   0.09238 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01730 |    0.4972 |    0.8335 |   0.07831 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6830 |     3.251 |     1.559 |     1.596 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.505 |     1.647 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |   -0.3583 |  -0.08989 |  -0.03828 |   0.04946 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.006154 |  0.007526 |    -3.405 |    -1.898 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3214 |    -1.836 |   -0.8751 |   -0.5118 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7778 |   -0.4622 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   91</span>|     468.71715 |     1.011 |   -0.9665 |   -0.9446 |   -0.9492 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9570 |   -0.9268 |   -0.6223 |   -0.5817 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8972 |    0.1661 |   -0.6643 |   -0.5125 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1966 |   -0.4678 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.71715 |     101.1 |    -4.067 |   -0.9450 |    -2.382 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.057 |  -0.01106 |    0.8340 |   0.07833 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6831 |     3.252 |     1.560 |     1.596 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.506 |     1.647 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.71715</span> |     101.1 |   0.01714 |    0.2799 |   0.09238 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01730 |    0.4972 |    0.8340 |   0.07833 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6831 |     3.252 |     1.560 |     1.596 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.506 |     1.647 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   92</span>|     468.70862 |     1.011 |   -0.9665 |   -0.9446 |   -0.9492 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9570 |   -0.9268 |   -0.6204 |   -0.5807 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8970 |    0.1671 |   -0.6639 |   -0.5123 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1962 |   -0.4676 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.70862 |     101.1 |    -4.066 |   -0.9450 |    -2.382 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.057 |  -0.01106 |    0.8347 |   0.07836 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6832 |     3.254 |     1.560 |     1.596 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.506 |     1.648 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.70862</span> |     101.1 |   0.01714 |    0.2799 |   0.09238 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01730 |    0.4972 |    0.8347 |   0.07836 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6832 |     3.254 |     1.560 |     1.596 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.506 |     1.648 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   93</span>|     468.69301 |     1.011 |   -0.9664 |   -0.9446 |   -0.9493 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9570 |   -0.9268 |   -0.6166 |   -0.5786 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8967 |    0.1691 |   -0.6629 |   -0.5117 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1953 |   -0.4671 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.69301 |     101.1 |    -4.066 |   -0.9449 |    -2.382 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.057 |  -0.01106 |    0.8361 |   0.07843 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6834 |     3.257 |     1.562 |     1.597 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.508 |     1.648 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.69301</span> |     101.1 |   0.01714 |    0.2799 |   0.09237 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01730 |    0.4972 |    0.8361 |   0.07843 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6834 |     3.257 |     1.562 |     1.597 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.508 |     1.648 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     5.000 |    0.4463 |   0.03173 | -0.005614 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.04729 |  0.007073 |    -3.041 |    -1.513 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3269 |    -1.766 |   -0.6347 |   -0.4208 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.7611 |   -0.4631 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   94</span>|     468.66018 |     1.011 |   -0.9666 |   -0.9442 |   -0.9484 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9562 |   -0.9269 |   -0.6101 |   -0.5828 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8947 |    0.1828 |   -0.6704 |   -0.5138 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1918 |   -0.4639 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.66018 |     101.1 |    -4.067 |   -0.9446 |    -2.381 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.056 |  -0.01106 |    0.8385 |   0.07829 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6847 |     3.279 |     1.552 |     1.594 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.513 |     1.652 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.66018</span> |     101.1 |   0.01714 |    0.2800 |   0.09245 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01731 |    0.4972 |    0.8385 |   0.07829 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6847 |     3.279 |     1.552 |     1.594 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.513 |     1.652 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   95</span>|     468.61872 |     1.011 |   -0.9669 |   -0.9434 |   -0.9467 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9547 |   -0.9271 |   -0.5984 |   -0.5914 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8911 |    0.2086 |   -0.6849 |   -0.5181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1852 |   -0.4581 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.61872 |     101.1 |    -4.067 |   -0.9439 |    -2.379 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.055 |  -0.01106 |    0.8427 |   0.07801 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6872 |     3.320 |     1.534 |     1.589 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.523 |     1.658 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.61872</span> |     101.1 |   0.01713 |    0.2801 |   0.09261 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01734 |    0.4972 |    0.8427 |   0.07801 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6872 |     3.320 |     1.534 |     1.589 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.523 |     1.658 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     3.484 |    0.5384 |   0.08463 |   0.09865 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7055 |  0.007848 |    -2.909 |    -1.559 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2162 |    -1.642 |    -1.728 |   -0.7121 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.5947 |   -0.3648 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   96</span>|     468.58548 |     1.008 |   -0.9690 |   -0.9515 |   -0.9524 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9418 |   -0.9281 |   -0.5713 |   -0.6320 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8823 |    0.2961 |   -0.6589 |   -0.5284 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1884 |   -0.4503 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.58548 |     100.8 |    -4.069 |   -0.9515 |    -2.385 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.042 |  -0.01107 |    0.8526 |   0.07665 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6931 |     3.458 |     1.567 |     1.578 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.518 |     1.667 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.58548</span> |     100.8 |   0.01709 |    0.2786 |   0.09208 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01757 |    0.4972 |    0.8526 |   0.07665 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6931 |     3.458 |     1.567 |     1.578 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.518 |     1.667 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -20.63 |    -1.258 |   -0.4941 |   -0.2790 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|     5.133 |   0.01399 |    -2.360 |    -3.936 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.04374 |    -1.146 |   -0.2901 |    -1.004 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6864 |   -0.2503 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   97</span>|     468.42330 |     1.011 |   -0.9777 |   -0.9596 |   -0.9476 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9583 |   -0.9295 |   -0.5827 |   -0.6211 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8760 |    0.3979 |   -0.6632 |   -0.5247 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1834 |   -0.4404 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      468.4233 |     101.1 |    -4.078 |   -0.9591 |    -2.380 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.058 |  -0.01109 |    0.8485 |   0.07701 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6974 |     3.619 |     1.561 |     1.582 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.525 |     1.679 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      468.4233</span> |     101.1 |   0.01695 |    0.2771 |   0.09253 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01728 |    0.4972 |    0.8485 |   0.07701 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6974 |     3.619 |     1.561 |     1.582 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.525 |     1.679 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -2.203 |    -3.445 |   -0.3661 |   -0.1876 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3413 |  0.004713 |    -2.617 |    -2.520 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.09908 |   -0.8887 |   -0.1336 |   -0.9981 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6593 |   -0.1712 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   98</span>|     468.44544 |     1.015 |   -0.9711 |   -0.9083 |   -0.9392 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9571 |   -0.9308 |   -0.5728 |   -0.6140 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9111 |    0.4459 |   -0.6774 |   -0.4582 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1728 |   -0.4486 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.44544 |     101.5 |    -4.071 |   -0.9109 |    -2.372 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.057 |  -0.01110 |    0.8520 |   0.07725 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6737 |     3.695 |     1.543 |     1.658 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.541 |     1.669 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.44544</span> |     101.5 |   0.01706 |    0.2868 |   0.09331 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01730 |    0.4972 |    0.8520 |   0.07725 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6737 |     3.695 |     1.543 |     1.658 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.541 |     1.669 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">   99</span>|     468.45245 |     1.016 |   -0.9696 |   -0.9377 |   -0.9438 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9573 |   -0.9300 |   -0.5745 |   -0.6143 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8908 |    0.4193 |   -0.6689 |   -0.4955 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1780 |   -0.4435 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.45245 |     101.6 |    -4.070 |   -0.9385 |    -2.376 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.057 |  -0.01109 |    0.8514 |   0.07724 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6874 |     3.653 |     1.554 |     1.615 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.533 |     1.675 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.45245</span> |     101.6 |   0.01708 |    0.2812 |   0.09288 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01730 |    0.4972 |    0.8514 |   0.07724 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6874 |     3.653 |     1.554 |     1.615 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.533 |     1.675 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  100</span>|     468.50368 |     1.017 |   -0.9688 |   -0.9537 |   -0.9463 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9573 |   -0.9296 |   -0.5755 |   -0.6144 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8797 |    0.4047 |   -0.6643 |   -0.5158 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1808 |   -0.4408 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.50368 |     101.7 |    -4.069 |   -0.9536 |    -2.379 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.057 |  -0.01109 |    0.8511 |   0.07724 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6949 |     3.630 |     1.560 |     1.592 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.529 |     1.678 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.50368</span> |     101.7 |   0.01710 |    0.2782 |   0.09265 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01729 |    0.4972 |    0.8511 |   0.07724 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6949 |     3.630 |     1.560 |     1.592 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.529 |     1.678 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  101</span>|     468.41664 |     1.014 |   -0.9744 |   -0.9593 |   -0.9474 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9580 |   -0.9295 |   -0.5802 |   -0.6187 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8761 |    0.3987 |   -0.6631 |   -0.5237 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1828 |   -0.4402 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.41664 |     101.4 |    -4.074 |   -0.9588 |    -2.380 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.058 |  -0.01109 |    0.8494 |   0.07709 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6973 |     3.620 |     1.561 |     1.583 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.526 |     1.679 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.41664</span> |     101.4 |   0.01700 |    0.2771 |   0.09254 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01728 |    0.4972 |    0.8494 |   0.07709 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6973 |     3.620 |     1.561 |     1.583 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.526 |     1.679 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     15.72 |  -0.02243 |   -0.2559 |   -0.2642 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4515 |  0.003975 |    -3.178 |    -2.839 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.02889 |   -0.8526 |   -0.3239 |    -1.069 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6251 |   -0.1666 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  102</span>|     468.39161 |     1.011 |   -0.9744 |   -0.9571 |   -0.9468 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9580 |   -0.9295 |   -0.5795 |   -0.6181 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8777 |    0.4014 |   -0.6637 |   -0.5208 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1824 |   -0.4404 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.39161 |     101.1 |    -4.074 |   -0.9567 |    -2.380 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.058 |  -0.01109 |    0.8496 |   0.07711 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6962 |     3.624 |     1.561 |     1.586 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.527 |     1.679 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.39161</span> |     101.1 |   0.01700 |    0.2775 |   0.09260 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01728 |    0.4972 |    0.8496 |   0.07711 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6962 |     3.624 |     1.561 |     1.586 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.527 |     1.679 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |   -0.3129 |    -1.347 |   -0.3359 |  -0.08922 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3284 |  0.005471 |    -2.643 |    -3.028 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.08821 |   -0.8778 |   -0.3083 |   -0.9349 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6501 |   -0.1781 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  103</span>|     468.37315 |     1.012 |   -0.9728 |   -0.9567 |   -0.9467 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9576 |   -0.9295 |   -0.5763 |   -0.6144 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8776 |    0.4024 |   -0.6633 |   -0.5197 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1816 |   -0.4402 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.37315 |     101.2 |    -4.073 |   -0.9564 |    -2.379 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.058 |  -0.01109 |    0.8508 |   0.07724 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6963 |     3.626 |     1.561 |     1.588 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.528 |     1.679 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.37315</span> |     101.2 |   0.01703 |    0.2776 |   0.09261 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01729 |    0.4972 |    0.8508 |   0.07724 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6963 |     3.626 |     1.561 |     1.588 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.528 |     1.679 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     3.643 |  -0.05906 |   -0.3284 |  -0.08464 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2659 |  0.005628 |    -2.441 |    -2.741 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1033 |   -0.8597 |   -0.2783 |   -0.9091 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.6423 |   -0.1835 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  104</span>|     468.35774 |     1.011 |   -0.9727 |   -0.9524 |   -0.9471 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9577 |   -0.9297 |   -0.5762 |   -0.6128 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8791 |    0.4096 |   -0.6644 |   -0.5136 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1797 |   -0.4410 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.35774 |     101.1 |    -4.073 |   -0.9523 |    -2.380 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.058 |  -0.01109 |    0.8508 |   0.07729 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6953 |     3.637 |     1.560 |     1.595 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.531 |     1.678 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.35774</span> |     101.1 |   0.01703 |    0.2784 |   0.09257 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01729 |    0.4972 |    0.8508 |   0.07729 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6953 |     3.637 |     1.560 |     1.595 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.531 |     1.678 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -4.235 |   -0.7549 |   -0.2072 |  -0.03117 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2512 |  0.006470 |    -2.488 |    -2.805 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1485 |   -0.7903 |   -0.1532 |   -0.6368 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.5813 |   -0.1871 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  105</span>|     468.34230 |     1.012 |   -0.9728 |   -0.9537 |   -0.9473 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9580 |   -0.9300 |   -0.5748 |   -0.6111 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8817 |    0.4177 |   -0.6661 |   -0.5086 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1765 |   -0.4413 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      468.3423 |     101.2 |    -4.073 |   -0.9535 |    -2.380 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.058 |  -0.01109 |    0.8513 |   0.07735 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6935 |     3.650 |     1.558 |     1.600 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.535 |     1.678 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      468.3423</span> |     101.2 |   0.01703 |    0.2782 |   0.09256 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01728 |    0.4972 |    0.8513 |   0.07735 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6935 |     3.650 |     1.558 |     1.600 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.535 |     1.678 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     5.528 |    0.1613 |   -0.1518 |   -0.1427 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4211 |  0.005662 |    -2.339 |    -2.549 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1833 |   -0.8387 |   -0.3838 |   -0.5791 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.5586 |   -0.2081 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  106</span>|     468.32828 |     1.011 |   -0.9730 |   -0.9582 |   -0.9446 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9579 |   -0.9303 |   -0.5739 |   -0.6081 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8827 |    0.4247 |   -0.6653 |   -0.5110 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1721 |   -0.4394 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.32828 |     101.1 |    -4.073 |   -0.9578 |    -2.377 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.058 |  -0.01110 |    0.8517 |   0.07745 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6929 |     3.661 |     1.559 |     1.598 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.542 |     1.680 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.32828</span> |     101.1 |   0.01703 |    0.2773 |   0.09281 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01729 |    0.4972 |    0.8517 |   0.07745 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6929 |     3.661 |     1.559 |     1.598 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.542 |     1.680 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.625 |   -0.2716 |   -0.4478 |   0.07059 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3746 |  0.006164 |    -2.097 |    -1.611 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.05708 |   -0.8042 |   -0.2658 |   -0.6338 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.5023 |   -0.1867 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  107</span>|     468.31633 |     1.012 |   -0.9727 |   -0.9524 |   -0.9447 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9580 |   -0.9305 |   -0.5707 |   -0.6067 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8839 |    0.4327 |   -0.6650 |   -0.5105 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1703 |   -0.4382 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.31633 |     101.2 |    -4.073 |   -0.9523 |    -2.377 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.058 |  -0.01110 |    0.8528 |   0.07749 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6921 |     3.674 |     1.559 |     1.598 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.545 |     1.681 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.31633</span> |     101.2 |   0.01703 |    0.2784 |   0.09280 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01728 |    0.4972 |    0.8528 |   0.07749 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6921 |     3.674 |     1.559 |     1.598 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.545 |     1.681 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     8.853 |    0.6395 |  -0.04718 |  -0.01095 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5142 |  0.005281 |    -1.984 |    -1.469 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.06825 |   -0.7616 |   -0.1275 |   -0.5767 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.4529 |   -0.1729 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  108</span>|     468.30195 |     1.011 |   -0.9730 |   -0.9497 |   -0.9482 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9586 |   -0.9308 |   -0.5675 |   -0.6075 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8868 |    0.4402 |   -0.6670 |   -0.5066 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1704 |   -0.4381 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.30195 |     101.1 |    -4.073 |   -0.9498 |    -2.381 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.059 |  -0.01110 |    0.8540 |   0.07747 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6901 |     3.686 |     1.556 |     1.603 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.544 |     1.681 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.30195</span> |     101.1 |   0.01703 |    0.2789 |   0.09247 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01727 |    0.4972 |    0.8540 |   0.07747 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6901 |     3.686 |     1.556 |     1.603 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.544 |     1.681 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.407 |   -0.4624 |  -0.02880 |   -0.1214 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5499 |  0.005628 |    -1.325 |    -1.518 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1161 |   -0.7707 |   -0.3300 |   -0.4912 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.4772 |   -0.1931 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  109</span>|     468.29210 |     1.012 |   -0.9733 |   -0.9523 |   -0.9459 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9581 |   -0.9311 |   -0.5658 |   -0.6073 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8878 |    0.4488 |   -0.6682 |   -0.5015 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1697 |   -0.4385 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      468.2921 |     101.2 |    -4.073 |   -0.9523 |    -2.379 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.058 |  -0.01111 |    0.8546 |   0.07747 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6894 |     3.699 |     1.555 |     1.608 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.546 |     1.681 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      468.2921</span> |     101.2 |   0.01702 |    0.2784 |   0.09269 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01728 |    0.4972 |    0.8546 |   0.07747 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6894 |     3.699 |     1.555 |     1.608 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.546 |     1.681 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  110</span>|     468.28540 |     1.012 |   -0.9739 |   -0.9554 |   -0.9432 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9581 |   -0.9314 |   -0.5648 |   -0.6081 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8889 |    0.4579 |   -0.6697 |   -0.4962 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1692 |   -0.4390 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      468.2854 |     101.2 |    -4.074 |   -0.9551 |    -2.376 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.058 |  -0.01111 |    0.8550 |   0.07745 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6886 |     3.714 |     1.553 |     1.615 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.546 |     1.680 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      468.2854</span> |     101.2 |   0.01701 |    0.2779 |   0.09293 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01728 |    0.4972 |    0.8550 |   0.07745 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6886 |     3.714 |     1.553 |     1.615 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.546 |     1.680 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     3.644 |   0.03668 |   -0.1986 |   0.05251 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5265 |  0.006364 |    -2.013 |    -2.999 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3077 |   -0.7577 |   -0.4283 |   -0.2217 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.4457 |   -0.1968 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  111</span>|     468.26285 |     1.012 |   -0.9738 |   -0.9546 |   -0.9455 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9580 |   -0.9321 |   -0.5638 |   -0.6053 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8894 |    0.4796 |   -0.6701 |   -0.4937 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1665 |   -0.4375 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.26285 |     101.2 |    -4.074 |   -0.9544 |    -2.378 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.058 |  -0.01112 |    0.8554 |   0.07754 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6883 |     3.748 |     1.552 |     1.617 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.550 |     1.682 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.26285</span> |     101.2 |   0.01701 |    0.2780 |   0.09272 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01728 |    0.4972 |    0.8554 |   0.07754 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6883 |     3.748 |     1.552 |     1.617 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.550 |     1.682 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  112</span>|     468.24808 |     1.012 |   -0.9737 |   -0.9538 |   -0.9478 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9581 |   -0.9328 |   -0.5632 |   -0.6032 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8900 |    0.5018 |   -0.6707 |   -0.4913 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1639 |   -0.4361 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.24808 |     101.2 |    -4.074 |   -0.9537 |    -2.380 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.058 |  -0.01113 |    0.8556 |   0.07761 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6879 |     3.783 |     1.552 |     1.620 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.554 |     1.684 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.24808</span> |     101.2 |   0.01701 |    0.2781 |   0.09251 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01728 |    0.4972 |    0.8556 |   0.07761 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6879 |     3.783 |     1.552 |     1.620 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.554 |     1.684 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     7.572 |    0.7442 |   -0.1004 |   -0.2485 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5511 |  0.005645 |    -1.896 |    -1.976 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3421 |   -0.6164 |   -0.3384 |   -0.1002 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.3637 |   -0.1858 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  113</span>|     468.24925 |     1.009 |   -0.9744 |   -0.9459 |   -0.9275 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9570 |   -0.9342 |   -0.5606 |   -0.6003 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8814 |    0.5390 |   -0.6664 |   -0.4916 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1575 |   -0.4319 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.24925 |     100.9 |    -4.074 |   -0.9463 |    -2.360 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.057 |  -0.01114 |    0.8565 |   0.07771 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6937 |     3.842 |     1.557 |     1.620 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.563 |     1.689 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.24925</span> |     100.9 |   0.01700 |    0.2796 |   0.09441 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01730 |    0.4972 |    0.8565 |   0.07771 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6937 |     3.842 |     1.557 |     1.620 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.563 |     1.689 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  114</span>|     468.25666 |     1.009 |   -0.9742 |   -0.9500 |   -0.9379 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9574 |   -0.9335 |   -0.5615 |   -0.6013 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8858 |    0.5198 |   -0.6685 |   -0.4914 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1608 |   -0.4340 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.25666 |     100.9 |    -4.074 |   -0.9501 |    -2.371 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.057 |  -0.01113 |    0.8562 |   0.07768 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6908 |     3.812 |     1.554 |     1.620 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.559 |     1.686 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.25666</span> |     100.9 |   0.01701 |    0.2789 |   0.09342 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01729 |    0.4972 |    0.8562 |   0.07768 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6908 |     3.812 |     1.554 |     1.620 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.559 |     1.686 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  115</span>|     468.26483 |     1.009 |   -0.9741 |   -0.9524 |   -0.9439 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9577 |   -0.9331 |   -0.5620 |   -0.6018 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8883 |    0.5089 |   -0.6697 |   -0.4913 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1626 |   -0.4352 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.26483 |     100.9 |    -4.074 |   -0.9523 |    -2.377 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.058 |  -0.01113 |    0.8560 |   0.07766 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6891 |     3.794 |     1.553 |     1.620 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.556 |     1.685 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.26483</span> |     100.9 |   0.01701 |    0.2784 |   0.09287 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01729 |    0.4972 |    0.8560 |   0.07766 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6891 |     3.794 |     1.553 |     1.620 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.556 |     1.685 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  116</span>|     468.25961 |     1.009 |   -0.9740 |   -0.9538 |   -0.9477 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9579 |   -0.9328 |   -0.5624 |   -0.6023 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8898 |    0.5020 |   -0.6705 |   -0.4913 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1637 |   -0.4360 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.25961 |     100.9 |    -4.074 |   -0.9536 |    -2.380 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.058 |  -0.01113 |    0.8558 |   0.07764 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6880 |     3.783 |     1.552 |     1.620 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.554 |     1.684 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.25961</span> |     100.9 |   0.01701 |    0.2782 |   0.09252 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01729 |    0.4972 |    0.8558 |   0.07764 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6880 |     3.783 |     1.552 |     1.620 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.554 |     1.684 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  117</span>|     468.24351 |     1.011 |   -0.9738 |   -0.9538 |   -0.9477 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9580 |   -0.9328 |   -0.5629 |   -0.6029 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8899 |    0.5019 |   -0.6706 |   -0.4913 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1638 |   -0.4360 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.24351 |     101.1 |    -4.074 |   -0.9537 |    -2.380 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.058 |  -0.01113 |    0.8557 |   0.07762 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6880 |     3.783 |     1.552 |     1.620 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.554 |     1.684 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.24351</span> |     101.1 |   0.01701 |    0.2781 |   0.09252 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01728 |    0.4972 |    0.8557 |   0.07762 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6880 |     3.783 |     1.552 |     1.620 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.554 |     1.684 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |   -0.7570 |  -0.04882 |   -0.1901 |   -0.1704 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4436 |  0.006358 |    -1.944 |    -2.069 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3547 |   -0.6277 |   -0.2411 |  -0.07768 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.3909 |   -0.1932 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  118</span>|     468.24096 |     1.012 |   -0.9738 |   -0.9538 |   -0.9477 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9579 |   -0.9328 |   -0.5622 |   -0.6021 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8898 |    0.5021 |   -0.6705 |   -0.4913 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1637 |   -0.4360 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.24096 |     101.2 |    -4.074 |   -0.9536 |    -2.380 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.058 |  -0.01113 |    0.8559 |   0.07765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6881 |     3.784 |     1.552 |     1.620 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.554 |     1.684 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.24096</span> |     101.2 |   0.01701 |    0.2782 |   0.09252 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01729 |    0.4972 |    0.8559 |   0.07765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6881 |     3.784 |     1.552 |     1.620 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.554 |     1.684 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     1.456 |    0.1549 |   -0.1619 |   -0.1892 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4078 |  0.006267 |    -1.860 |    -2.735 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3543 |   -0.6265 |   -0.1851 |  -0.08656 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.3612 |   -0.1856 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  119</span>|     468.23716 |     1.012 |   -0.9738 |   -0.9536 |   -0.9467 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9576 |   -0.9329 |   -0.5614 |   -0.6008 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8898 |    0.5037 |   -0.6704 |   -0.4911 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1638 |   -0.4357 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.23716 |     101.2 |    -4.074 |   -0.9534 |    -2.379 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.058 |  -0.01113 |    0.8562 |   0.07769 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6881 |     3.786 |     1.552 |     1.620 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.554 |     1.684 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.23716</span> |     101.2 |   0.01701 |    0.2782 |   0.09261 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01729 |    0.4972 |    0.8562 |   0.07769 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6881 |     3.786 |     1.552 |     1.620 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.554 |     1.684 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     2.250 |    0.2329 |   -0.1405 |   -0.1467 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3393 |  0.006453 |    -1.023 |    -1.889 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3583 |   -0.6239 |   -0.2353 |  -0.09107 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.3636 |   -0.1837 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  120</span>|     468.26529 |     1.008 |   -0.9742 |   -0.9534 |   -0.9465 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9571 |   -0.9329 |   -0.5599 |   -0.5980 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8893 |    0.5046 |   -0.6700 |   -0.4910 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1633 |   -0.4354 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.26529 |     100.8 |    -4.074 |   -0.9532 |    -2.379 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.057 |  -0.01113 |    0.8568 |   0.07778 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6884 |     3.788 |     1.553 |     1.621 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.555 |     1.685 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.26529</span> |     100.8 |   0.01701 |    0.2782 |   0.09263 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01730 |    0.4972 |    0.8568 |   0.07778 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6884 |     3.788 |     1.553 |     1.621 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.555 |     1.685 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  121</span>|     468.23604 |     1.011 |   -0.9739 |   -0.9535 |   -0.9467 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9575 |   -0.9329 |   -0.5612 |   -0.6003 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8897 |    0.5039 |   -0.6703 |   -0.4911 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1637 |   -0.4357 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.23604 |     101.1 |    -4.074 |   -0.9534 |    -2.379 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.058 |  -0.01113 |    0.8563 |   0.07771 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6881 |     3.786 |     1.552 |     1.620 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.554 |     1.684 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.23604</span> |     101.1 |   0.01701 |    0.2782 |   0.09261 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01729 |    0.4972 |    0.8563 |   0.07771 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6881 |     3.786 |     1.552 |     1.620 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.554 |     1.684 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -2.188 |   -0.1927 |   -0.1868 |   -0.1045 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2665 |  0.006857 |    -1.024 |    -1.172 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2153 |   -0.6125 |   -0.1502 |  -0.07921 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.3902 |   -0.1918 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  122</span>|     468.23464 |     1.012 |   -0.9738 |   -0.9534 |   -0.9465 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9574 |   -0.9329 |   -0.5609 |   -0.6000 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8895 |    0.5043 |   -0.6702 |   -0.4911 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1635 |   -0.4356 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.23464 |     101.2 |    -4.074 |   -0.9533 |    -2.379 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.057 |  -0.01113 |    0.8564 |   0.07772 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6883 |     3.787 |     1.552 |     1.620 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.555 |     1.684 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.23464</span> |     101.2 |   0.01701 |    0.2782 |   0.09263 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01729 |    0.4972 |    0.8564 |   0.07772 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6883 |     3.787 |     1.552 |     1.620 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.555 |     1.684 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     1.857 |    0.1925 |   -0.1360 |   -0.1332 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2859 |  0.006578 |   -0.9819 |    -1.076 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2136 |   -0.6087 |   -0.2154 |  -0.09459 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.3609 |   -0.1836 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  123</span>|     468.23346 |     1.011 |   -0.9739 |   -0.9532 |   -0.9463 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9574 |   -0.9329 |   -0.5607 |   -0.5998 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8893 |    0.5049 |   -0.6701 |   -0.4911 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1633 |   -0.4355 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.23346 |     101.1 |    -4.074 |   -0.9531 |    -2.379 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.057 |  -0.01113 |    0.8565 |   0.07772 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6884 |     3.788 |     1.552 |     1.621 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.555 |     1.684 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.23346</span> |     101.1 |   0.01701 |    0.2783 |   0.09265 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01729 |    0.4972 |    0.8565 |   0.07772 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6884 |     3.788 |     1.552 |     1.621 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.555 |     1.684 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.112 |  -0.08635 |   -0.1600 |  -0.09176 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2402 |  0.006873 |   -0.9941 |    -1.084 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2133 |   -0.6080 |   -0.2006 |  -0.08423 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.3853 |   -0.1906 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  124</span>|     468.23209 |     1.012 |   -0.9738 |   -0.9531 |   -0.9461 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9573 |   -0.9329 |   -0.5604 |   -0.5995 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8891 |    0.5054 |   -0.6700 |   -0.4910 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1631 |   -0.4354 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.23209 |     101.2 |    -4.074 |   -0.9530 |    -2.379 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.057 |  -0.01113 |    0.8566 |   0.07774 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6885 |     3.789 |     1.553 |     1.621 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.555 |     1.685 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.23209</span> |     101.2 |   0.01701 |    0.2783 |   0.09266 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01730 |    0.4972 |    0.8566 |   0.07774 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6885 |     3.789 |     1.553 |     1.621 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.555 |     1.685 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  125</span>|     468.23061 |     1.012 |   -0.9738 |   -0.9526 |   -0.9453 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9573 |   -0.9330 |   -0.5604 |   -0.5995 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8885 |    0.5071 |   -0.6698 |   -0.4911 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1626 |   -0.4353 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.23061 |     101.2 |    -4.074 |   -0.9525 |    -2.378 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.057 |  -0.01113 |    0.8566 |   0.07773 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6889 |     3.791 |     1.553 |     1.620 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.556 |     1.685 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.23061</span> |     101.2 |   0.01701 |    0.2784 |   0.09274 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01730 |    0.4972 |    0.8566 |   0.07773 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6889 |     3.791 |     1.553 |     1.620 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.556 |     1.685 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  126</span>|     468.22552 |     1.012 |   -0.9738 |   -0.9506 |   -0.9419 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9573 |   -0.9333 |   -0.5606 |   -0.5998 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8861 |    0.5138 |   -0.6689 |   -0.4912 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1606 |   -0.4347 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.22552 |     101.2 |    -4.074 |   -0.9506 |    -2.375 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.057 |  -0.01113 |    0.8565 |   0.07773 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6906 |     3.802 |     1.554 |     1.620 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.559 |     1.685 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.22552</span> |     101.2 |   0.01701 |    0.2788 |   0.09305 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01730 |    0.4972 |    0.8565 |   0.07773 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6906 |     3.802 |     1.554 |     1.620 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.559 |     1.685 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  127</span>|     468.21689 |     1.012 |   -0.9737 |   -0.9426 |   -0.9286 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9574 |   -0.9344 |   -0.5614 |   -0.6009 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8763 |    0.5405 |   -0.6653 |   -0.4919 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1525 |   -0.4325 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.21689 |     101.2 |    -4.074 |   -0.9431 |    -2.361 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.057 |  -0.01114 |    0.8562 |   0.07769 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6972 |     3.844 |     1.558 |     1.620 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.571 |     1.688 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.21689</span> |     101.2 |   0.01701 |    0.2803 |   0.09430 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01729 |    0.4972 |    0.8562 |   0.07769 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.6972 |     3.844 |     1.558 |     1.620 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.571 |     1.688 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     1.525 |    0.5842 |    0.4183 |    0.9468 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.5439 |  0.008618 |    -1.874 |    -1.936 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2463 |   -0.5498 |   0.04440 |   -0.1490 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.2038 |   -0.1015 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  128</span>|     468.19158 |     1.011 |   -0.9737 |   -0.9504 |   -0.9293 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9577 |   -0.9362 |   -0.5600 |   -0.6009 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8523 |    0.5756 |   -0.6644 |   -0.4878 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1417 |   -0.4298 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.19158 |     101.1 |    -4.074 |   -0.9504 |    -2.362 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.058 |  -0.01116 |    0.8567 |   0.07769 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7134 |     3.900 |     1.560 |     1.624 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.587 |     1.691 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.19158</span> |     101.1 |   0.01701 |    0.2788 |   0.09423 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01729 |    0.4972 |    0.8567 |   0.07769 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7134 |     3.900 |     1.560 |     1.624 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.587 |     1.691 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -7.990 |    0.8226 |  -0.02734 |    0.9308 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.6358 |  0.008756 |    -3.089 |    -2.706 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.08468 |   -0.5047 |    0.1760 |  -0.08785 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|  -0.06839 |  -0.06706 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  129</span>|     468.16872 |     1.012 |   -0.9746 |   -0.9536 |   -0.9297 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9583 |   -0.9383 |   -0.5551 |   -0.6021 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8428 |    0.6190 |   -0.6652 |   -0.4874 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1424 |   -0.4276 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.16872 |     101.2 |    -4.075 |   -0.9534 |    -2.362 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.058 |  -0.01118 |    0.8585 |   0.07765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7198 |     3.968 |     1.559 |     1.625 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.586 |     1.694 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.16872</span> |     101.2 |   0.01700 |    0.2782 |   0.09420 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01728 |    0.4972 |    0.8585 |   0.07765 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7198 |     3.968 |     1.559 |     1.625 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.586 |     1.694 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     1.949 |     1.224 |  -0.09904 |    0.8335 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8902 |  0.008091 |    -1.732 |    -1.975 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.02618 |   -0.4349 |    0.2067 |   -0.1419 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|  -0.09587 |  -0.05531 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  130</span>|     468.14579 |     1.011 |   -0.9759 |   -0.9465 |   -0.9356 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9575 |   -0.9407 |   -0.5519 |   -0.5999 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8420 |    0.6626 |   -0.6664 |   -0.4855 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1418 |   -0.4273 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.14579 |     101.1 |    -4.076 |   -0.9467 |    -2.368 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.057 |  -0.01121 |    0.8597 |   0.07772 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7204 |     4.037 |     1.557 |     1.627 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.587 |     1.694 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.14579</span> |     101.1 |   0.01698 |    0.2795 |   0.09364 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01729 |    0.4972 |    0.8597 |   0.07772 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7204 |     4.037 |     1.557 |     1.627 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.587 |     1.694 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -9.402 |   -0.5832 |    0.1847 |    0.5188 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4155 |  0.008564 |    -1.671 |    -1.980 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  -0.03632 |   -0.3649 |    0.3304 |  -0.09551 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1039 |  -0.08698 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  131</span>|     468.12514 |     1.011 |   -0.9763 |   -0.9445 |   -0.9403 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9555 |   -0.9442 |   -0.5537 |   -0.5953 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8391 |    0.7059 |   -0.6668 |   -0.4805 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1376 |   -0.4223 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.12514 |     101.1 |    -4.076 |   -0.9449 |    -2.373 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.056 |  -0.01125 |    0.8590 |   0.07788 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7223 |     4.106 |     1.557 |     1.633 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.593 |     1.700 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.12514</span> |     101.1 |   0.01697 |    0.2799 |   0.09320 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01733 |    0.4972 |    0.8590 |   0.07788 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7223 |     4.106 |     1.557 |     1.633 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.593 |     1.700 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -5.823 |   -0.4010 |    0.3316 |    0.1628 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2993 |  0.008766 |    -1.373 |    -1.049 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.008284 |   -0.3402 |    0.4029 |   0.04088 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|  -0.03436 |  -0.04680 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  132</span>|     468.10920 |     1.012 |   -0.9773 |   -0.9503 |   -0.9395 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9556 |   -0.9476 |   -0.5516 |   -0.5942 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8420 |    0.7499 |   -0.6684 |   -0.4836 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1347 |   -0.4216 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|      468.1092 |     101.2 |    -4.077 |   -0.9504 |    -2.372 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.056 |  -0.01129 |    0.8598 |   0.07791 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7204 |     4.175 |     1.555 |     1.629 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.597 |     1.700 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">      468.1092</span> |     101.2 |   0.01695 |    0.2788 |   0.09328 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01732 |    0.4972 |    0.8598 |   0.07791 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7204 |     4.175 |     1.555 |     1.629 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.597 |     1.700 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    0.4262 |  -0.09925 |    0.1213 |    0.1364 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.2200 |  0.008054 |   -0.8480 |   -0.9224 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|  0.002683 |   -0.2932 |    0.3866 |  -0.06974 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   0.01820 |  -0.05063 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  133</span>|     468.09766 |     1.011 |   -0.9774 |   -0.9506 |   -0.9392 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9564 |   -0.9508 |   -0.5497 |   -0.5929 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8318 |    0.7901 |   -0.6724 |   -0.4776 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1474 |   -0.4133 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.09766 |     101.1 |    -4.077 |   -0.9507 |    -2.372 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.056 |  -0.01132 |    0.8605 |   0.07796 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7273 |     4.239 |     1.550 |     1.636 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.578 |     1.710 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.09766</span> |     101.1 |   0.01695 |    0.2788 |   0.09331 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01731 |    0.4972 |    0.8605 |   0.07796 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7273 |     4.239 |     1.550 |     1.636 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.578 |     1.710 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -1.610 |   0.04645 |   0.08092 |    0.1870 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1143 |  0.008788 |   -0.7906 |   -0.9380 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.03576 |   -0.2567 |    0.2790 |   0.04583 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1853 |   0.03622 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  134</span>|     468.08577 |     1.011 |   -0.9779 |   -0.9521 |   -0.9394 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9569 |   -0.9550 |   -0.5484 |   -0.5900 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8316 |    0.8332 |   -0.6752 |   -0.4781 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1428 |   -0.4231 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.08577 |     101.1 |    -4.078 |   -0.9520 |    -2.372 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.057 |  -0.01137 |    0.8610 |   0.07805 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7274 |     4.307 |     1.546 |     1.635 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.585 |     1.699 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.08577</span> |     101.1 |   0.01694 |    0.2785 |   0.09329 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01730 |    0.4972 |    0.8610 |   0.07805 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7274 |     4.307 |     1.546 |     1.635 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.585 |     1.699 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -2.835 |  -0.08271 |   0.01028 |    0.1502 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.2859 |  0.008536 |   -0.7298 |   -0.7597 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.09519 |   -0.2281 |    0.1720 |  -0.01715 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1386 |   -0.1062 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  135</span>|     468.06947 |     1.011 |   -0.9795 |   -0.9573 |   -0.9407 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9575 |   -0.9652 |   -0.5443 |   -0.5834 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8515 |    0.9153 |   -0.6785 |   -0.4834 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1291 |   -0.4003 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.06947 |     101.1 |    -4.080 |   -0.9569 |    -2.373 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.057 |  -0.01148 |    0.8625 |   0.07827 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7139 |     4.437 |     1.542 |     1.629 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.605 |     1.725 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.06947</span> |     101.1 |   0.01692 |    0.2775 |   0.09316 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01729 |    0.4971 |    0.8625 |   0.07827 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7139 |     4.437 |     1.542 |     1.629 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.605 |     1.725 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |    -3.887 |   -0.6407 |   -0.1939 |   0.08469 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.4208 |  0.006503 |    -1.301 |    -1.072 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1255 |   -0.1861 |    0.1431 |  -0.06050 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   0.01658 |    0.1111 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  136</span>|     468.05012 |     1.012 |   -0.9812 |   -0.9547 |   -0.9420 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9570 |   -0.9873 |   -0.5356 |   -0.5736 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8556 |     1.092 |   -0.6884 |   -0.4796 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1211 |   -0.3873 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.05012 |     101.2 |    -4.081 |   -0.9544 |    -2.375 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.057 |  -0.01172 |    0.8656 |   0.07860 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7112 |     4.716 |     1.529 |     1.634 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.617 |     1.740 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.05012</span> |     101.2 |   0.01689 |    0.2780 |   0.09305 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01730 |    0.4971 |    0.8656 |   0.07860 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7112 |     4.716 |     1.529 |     1.634 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.617 |     1.740 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    F| Forward Diff. |     2.331 |   -0.3124 |   0.04539 |  -0.09325 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.3359 |  0.005628 |   -0.9294 |   -0.4404 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.1788 |   -0.1003 |   -0.1180 |  -0.02636 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   0.09915 |    0.1820 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  137</span>|     468.05341 |     1.012 |   -0.9836 |   -0.9687 |   -0.9317 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9507 |    -1.013 |   -0.5235 |   -0.5660 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8472 |     1.268 |   -0.6904 |   -0.4754 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1200 |   -0.3857 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.05341 |     101.2 |    -4.084 |   -0.9676 |    -2.364 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.051 |  -0.01201 |    0.8700 |   0.07886 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7168 |     4.995 |     1.527 |     1.639 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.619 |     1.742 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.05341</span> |     101.2 |   0.01685 |    0.2754 |   0.09401 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01741 |    0.4970 |    0.8700 |   0.07886 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7168 |     4.995 |     1.527 |     1.639 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.619 |     1.742 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |<span style="font-weight: bold;">  138</span>|     468.05012 |     1.012 |   -0.9812 |   -0.9547 |   -0.9420 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.9570 |   -0.9873 |   -0.5356 |   -0.5736 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   -0.8556 |     1.092 |   -0.6884 |   -0.4796 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|   -0.1211 |   -0.3873 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    U|     468.05012 |     101.2 |    -4.081 |   -0.9544 |    -2.375 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    -4.057 |  -0.01172 |    0.8656 |   0.07860 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7112 |     4.716 |     1.529 |     1.634 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.617 |     1.740 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |    X|<span style="font-weight: bold;">     468.05012</span> |     101.2 |   0.01689 |    0.2780 |   0.09305 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|   0.01730 |    0.4971 |    0.8656 |   0.07860 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |.....................|    0.7112 |     4.716 |     1.529 |     1.634 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="text-decoration: underline;">|.....................|     2.617 |     1.740 |...........|...........|</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> calculating covariance matrix</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating residuals/tables</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> done</span>
-<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">AIC</a></span><span class="op">(</span><span class="va">f_nlmixr_dfop_sfo_saem</span><span class="op">$</span><span class="va">nm</span>, <span class="va">f_nlmixr_dfop_sfo_focei</span><span class="op">$</span><span class="va">nm</span><span class="op">)</span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> Calculating -2LL by Gaussian quadrature (nnodes=3,nsd=1.6)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                            df       AIC</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_nlmixr_dfop_sfo_saem$nm  16 1105.3725</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_nlmixr_dfop_sfo_focei$nm 14  810.3271</span>
-<span class="r-in"><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">f_nlmixr_dfop_sfo_sfo</span>, data <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
-<span class="r-err co"><span class="r-pr">#&gt;</span> <span class="error">Error in h(simpleError(msg, call)):</span> error in evaluating the argument 'object' in selecting a method for function 'summary': object 'f_nlmixr_dfop_sfo_sfo' not found</span>
-<span class="r-in"><span class="co"># }</span></span>
-<span class="r-in"></span>
-</code></pre></div>
-    </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.2.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/reference/summary.saem.mmkin.html b/docs/reference/summary.saem.mmkin.html
index 54e5260b..4c9f0d17 100644
--- a/docs/reference/summary.saem.mmkin.html
+++ b/docs/reference/summary.saem.mmkin.html
@@ -1,118 +1,74 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Summary method for class "saem.mmkin" — summary.saem.mmkin • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Summary method for class " saem.mmkin summary.saem.mmkin><meta property="og:description" content="Lists model equations, initial parameter values, optimised parameters
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Summary method for class "saem.mmkin" — summary.saem.mmkin • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Summary method for class " saem.mmkin summary.saem.mmkin><meta name="description" content="Lists model equations, initial parameter values, optimised parameters
 for fixed effects (population), random effects (deviations from the
 population mean) and residual error model, as well as the resulting
 endpoints such as formation fractions and DT50 values. Optionally
-(default is FALSE), the data are listed in full."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+(default is FALSE), the data are listed in full."><meta property="og:description" content="Lists model equations, initial parameter values, optimised parameters
+for fixed effects (population), random effects (deviations from the
+population mean) and residual error model, as well as the resulting
+endpoints such as formation fractions and DT50 values. Optionally
+(default is FALSE), the data are listed in full."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Summary method for class "saem.mmkin"</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/summary.saem.mmkin.R" class="external-link"><code>R/summary.saem.mmkin.R</code></a></small>
-    <div class="hidden name"><code>summary.saem.mmkin.Rd</code></div>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Summary method for class "saem.mmkin"</h1>
+      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/summary.saem.mmkin.R" class="external-link"><code>R/summary.saem.mmkin.R</code></a></small>
+      <div class="d-none name"><code>summary.saem.mmkin.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>Lists model equations, initial parameter values, optimised parameters
 for fixed effects (population), random effects (deviations from the
 population mean) and residual error model, as well as the resulting
@@ -120,8 +76,9 @@ endpoints such as formation fractions and DT50 values. Optionally
 (default is FALSE), the data are listed in full.</p>
     </div>
 
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for saem.mmkin</span></span>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'saem.mmkin'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span></span>
 <span>  <span class="va">object</span>,</span>
 <span>  data <span class="op">=</span> <span class="cn">FALSE</span>,</span>
@@ -132,61 +89,59 @@ endpoints such as formation fractions and DT50 values. Optionally
 <span>  <span class="va">...</span></span>
 <span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S3 method for summary.saem.mmkin</span></span>
+<span><span class="co"># S3 method for class 'summary.saem.mmkin'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, digits <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html" class="external-link">max</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fu"><a href="https://rdrr.io/r/base/options.html" class="external-link">getOption</a></span><span class="op">(</span><span class="st">"digits"</span><span class="op">)</span> <span class="op">-</span> <span class="fl">3</span><span class="op">)</span>, verbose <span class="op">=</span> <span class="va">x</span><span class="op">$</span><span class="va">verbose</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>object</dt>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
 <dd><p>an object of class <a href="saem.html">saem.mmkin</a></p></dd>
 
 
-<dt>data</dt>
+<dt id="arg-data">data<a class="anchor" aria-label="anchor" href="#arg-data"></a></dt>
 <dd><p>logical, indicating whether the full data should be included in
 the summary.</p></dd>
 
 
-<dt>verbose</dt>
+<dt id="arg-verbose">verbose<a class="anchor" aria-label="anchor" href="#arg-verbose"></a></dt>
 <dd><p>Should the summary be verbose?</p></dd>
 
 
-<dt>covariates</dt>
+<dt id="arg-covariates">covariates<a class="anchor" aria-label="anchor" href="#arg-covariates"></a></dt>
 <dd><p>Numeric vector with covariate values for all variables in
 any covariate models in the object. If given, it overrides 'covariate_quantile'.</p></dd>
 
 
-<dt>covariate_quantile</dt>
+<dt id="arg-covariate-quantile">covariate_quantile<a class="anchor" aria-label="anchor" href="#arg-covariate-quantile"></a></dt>
 <dd><p>This argument only has an effect if the fitted
 object has covariate models. If so, the default is to show endpoints
 for the median of the covariate values (50th percentile).</p></dd>
 
 
-<dt>distimes</dt>
+<dt id="arg-distimes">distimes<a class="anchor" aria-label="anchor" href="#arg-distimes"></a></dt>
 <dd><p>logical, indicating whether DT50 and DT90 values should be
 included.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>optional arguments passed to methods like <code>print</code>.</p></dd>
 
 
-<dt>x</dt>
+<dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>an object of class summary.saem.mmkin</p></dd>
 
 
-<dt>digits</dt>
+<dt id="arg-digits">digits<a class="anchor" aria-label="anchor" href="#arg-digits"></a></dt>
 <dd><p>Number of digits to use for printing</p></dd>
 
 </dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>The summary function returns a list based on the <a href="https://rdrr.io/pkg/saemix/man/SaemixObject-class.html" class="external-link">saemix::SaemixObject</a></p>
-
-
-<p>obtained in the fit, with at least the following additional components</p>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>The summary function returns a list based on the <a href="https://rdrr.io/pkg/saemix/man/SaemixObject-class.html" class="external-link">saemix::SaemixObject</a>
+obtained in the fit, with at least the following additional components</p>
 <dl><dt>saemixversion, mkinversion, Rversion</dt>
 <dd><p>The saemix, mkin and R versions used</p></dd>
 
@@ -224,14 +179,14 @@ model.</p></dd>
 
 </dl><p>The print method is called for its side effect, i.e. printing the summary.</p>
     </div>
-    <div id="author">
-    <h2>Author</h2>
+    <div class="section level2">
+    <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a></h2>
     <p>Johannes Ranke for the mkin specific parts
 saemix authors for the parts inherited from saemix.</p>
     </div>
 
-    <div id="ref-examples">
-    <h2>Examples</h2>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># Generate five datasets following DFOP-SFO kinetics</span></span></span>
 <span class="r-in"><span><span class="va">sampling_times</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span>, <span class="fl">3</span>, <span class="fl">7</span>, <span class="fl">14</span>, <span class="fl">28</span>, <span class="fl">60</span>, <span class="fl">90</span>, <span class="fl">120</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">dfop_sfo</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="st">"m1"</span><span class="op">)</span>,</span></span>
@@ -288,21 +243,21 @@ saemix authors for the parts inherited from saemix.</p>
 <span class="r-out co"><span class="r-pr">#&gt;</span>   810.8 805.4 -391.4</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Fitted parameters:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                      estimate     lower    upper</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0           100.966822  97.90584 104.0278</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k_m1            -4.076164  -4.17485  -3.9775</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_parent_qlogis     -0.940902  -1.35358  -0.5282</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k1              -2.363988  -2.71690  -2.0111</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k2              -4.060016  -4.21743  -3.9026</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> g_qlogis            -0.029999  -0.44766   0.3877</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> a.1                  0.876272   0.67308   1.0795</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> b.1                  0.079594   0.06399   0.0952</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> SD.parent_0          0.076322 -76.47330  76.6259</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> SD.log_k_m1          0.005052  -1.09071   1.1008</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> SD.f_parent_qlogis   0.446968   0.16577   0.7282</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> SD.log_k1            0.348786   0.09502   0.6025</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> SD.log_k2            0.147456   0.03111   0.2638</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> SD.g_qlogis          0.348244   0.02794   0.6686</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>                      estimate     lower     upper</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0           100.966822  97.90584 104.02780</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> log_k_m1            -4.076164  -4.17485  -3.97748</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> f_parent_qlogis     -0.940902  -1.35358  -0.52823</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> log_k1              -2.363988  -2.71690  -2.01107</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> log_k2              -4.060016  -4.21743  -3.90260</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> g_qlogis            -0.029999  -0.44766   0.38766</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> a.1                  0.876272   0.67790   1.07464</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> b.1                  0.079594   0.06521   0.09398</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> SD.parent_0          0.076322 -76.45825  76.61089</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> SD.log_k_m1          0.005052  -1.08943   1.09953</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> SD.f_parent_qlogis   0.446968   0.16577   0.72816</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> SD.log_k1            0.348786   0.09502   0.60255</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> SD.log_k2            0.147456   0.03111   0.26380</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> SD.g_qlogis          0.348244   0.02794   0.66854</span>
 <span class="r-in"><span><span class="fu"><a href="illparms.html">illparms</a></span><span class="op">(</span><span class="va">f_saem_dfop_sfo</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] "sd(parent_0)" "sd(log_k_m1)"</span>
 <span class="r-in"><span><span class="va">f_saem_dfop_sfo_2</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_saem_dfop_sfo</span>,</span></span>
@@ -322,21 +277,21 @@ saemix authors for the parts inherited from saemix.</p>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>  Random effects:</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                          lower      est.     upper</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sd(f_parent_qlogis) 0.16515100 0.4448330 0.7245149</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sd(log_k1)          0.08982372 0.3447403 0.5996568</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sd(log_k2)          0.02806589 0.1419560 0.2558462</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sd(g_qlogis)        0.04908160 0.3801993 0.7113170</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> sd(f_parent_qlogis) 0.16515113 0.4448330 0.7245148</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> sd(log_k1)          0.08982399 0.3447403 0.5996565</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> sd(log_k2)          0.02806780 0.1419560 0.2558443</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> sd(g_qlogis)        0.04908644 0.3801993 0.7113121</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>  </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>          lower       est.      upper</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> a.1 0.67539922 0.87630147 1.07720371</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> b.1 0.06401324 0.07920531 0.09439739</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> a.1 0.67993373 0.87630147 1.07266921</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> b.1 0.06522297 0.07920531 0.09318766</span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">f_saem_dfop_sfo_2</span>, data <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> saemix version used for fitting:      3.2 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> mkin version used for pre-fitting:  1.2.6 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> R version used for fitting:         4.3.1 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Date of fit:     Mon Oct 30 09:40:27 2023 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Date of summary: Mon Oct 30 09:40:27 2023 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> saemix version used for fitting:      3.3 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> mkin version used for pre-fitting:  1.2.9 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> R version used for fitting:         4.4.2 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Date of fit:     Thu Feb 13 15:01:30 2025 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Date of summary: Thu Feb 13 15:01:30 2025 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Equations:</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *</span>
@@ -351,7 +306,7 @@ saemix authors for the parts inherited from saemix.</p>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Model predictions using solution type analytical </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Fitted in 19.763 s</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Fitted in 9.035 s</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Using 300, 100 iterations and 10 chains</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Variance model: Two-component variance function </span>
@@ -385,19 +340,19 @@ saemix authors for the parts inherited from saemix.</p>
 <span class="r-out co"><span class="r-pr">#&gt;</span>   806.9 802.2 -391.5</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Optimised parameters:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                         est.    lower    upper</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0           101.09951 98.04247 104.1565</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k_m1            -4.08178 -4.18057  -3.9830</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_parent_qlogis     -0.94779 -1.35855  -0.5370</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k1              -2.33940 -2.69122  -1.9876</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> log_k2              -4.05027 -4.20262  -3.8979</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> g_qlogis            -0.06529 -0.50361   0.3730</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> a.1                  0.87630  0.67540   1.0772</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> b.1                  0.07921  0.06401   0.0944</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> SD.f_parent_qlogis   0.44483  0.16515   0.7245</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> SD.log_k1            0.34474  0.08982   0.5997</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> SD.log_k2            0.14196  0.02807   0.2558</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> SD.g_qlogis          0.38020  0.04908   0.7113</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>                         est.    lower     upper</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> parent_0           101.09951 98.04247 104.15655</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> log_k_m1            -4.08178 -4.18057  -3.98300</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> f_parent_qlogis     -0.94779 -1.35855  -0.53704</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> log_k1              -2.33940 -2.69122  -1.98759</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> log_k2              -4.05027 -4.20262  -3.89791</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> g_qlogis            -0.06529 -0.50361   0.37303</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> a.1                  0.87630  0.67993   1.07267</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> b.1                  0.07921  0.06522   0.09319</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> SD.f_parent_qlogis   0.44483  0.16515   0.72451</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> SD.log_k1            0.34474  0.08982   0.59966</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> SD.log_k2            0.14196  0.02807   0.25584</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> SD.g_qlogis          0.38020  0.04909   0.71131</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Correlation: </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                 parnt_0 lg_k_m1 f_prnt_ log_k1  log_k2 </span>
@@ -412,12 +367,12 @@ saemix authors for the parts inherited from saemix.</p>
 <span class="r-out co"><span class="r-pr">#&gt;</span> SD.f_parent_qlogis 0.4448 0.16515 0.7245</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> SD.log_k1          0.3447 0.08982 0.5997</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> SD.log_k2          0.1420 0.02807 0.2558</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> SD.g_qlogis        0.3802 0.04908 0.7113</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> SD.g_qlogis        0.3802 0.04909 0.7113</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Variance model:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>        est.   lower  upper</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> a.1 0.87630 0.67540 1.0772</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> b.1 0.07921 0.06401 0.0944</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>        est.   lower   upper</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> a.1 0.87630 0.67993 1.07267</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> b.1 0.07921 0.06522 0.09319</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Backtransformed parameters:</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                     est.    lower     upper</span>
@@ -630,17 +585,17 @@ saemix authors for the parts inherited from saemix.</p>
 <span class="r-out co"><span class="r-pr">#&gt;</span> g               0.35506898   0.46263682   0.57379888</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>  Random effects:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                            lower       est.      upper</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sd(f_parent_qlogis)    0.3827416  0.4435866  0.5044315</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sd(log_k1)             0.1226277  0.2981783  0.4737289</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sd(log_k2)            -0.5457764  0.1912531  0.9282825</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sd(g_qlogis)           0.1483976  0.3997298  0.6510619</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> corr(log_k2,g_qlogis) -0.8537145 -0.5845703 -0.3154261</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>                             lower       est.     upper</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> sd(f_parent_qlogis)    0.16472883  0.4435866 0.7224443</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> sd(log_k1)             0.05323856  0.2981783 0.5431180</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> sd(log_k2)             0.05013379  0.1912531 0.3323723</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> sd(g_qlogis)           0.04710647  0.3997298 0.7523531</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> corr(log_k2,g_qlogis) -1.31087397 -0.5845703 0.1417334</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         lower       est.     upper</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> a.1 0.6732869 0.87421677 1.0751467</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> b.1 0.0640392 0.07925135 0.0944635</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>          lower       est.      upper</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> a.1 0.67769608 0.87421677 1.07073746</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> b.1 0.06525119 0.07925135 0.09325151</span>
 <span class="r-in"><span><span class="co"># The correlation does not improve the fit judged by AIC and BIC, although</span></span></span>
 <span class="r-in"><span><span class="co"># the likelihood is higher with the additional parameter</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_saem_dfop_sfo</span>, <span class="va">f_saem_dfop_sfo_2</span>, <span class="va">f_saem_dfop_sfo_3</span><span class="op">)</span></span></span>
@@ -654,27 +609,23 @@ saemix authors for the parts inherited from saemix.</p>
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/summary_listing.html b/docs/reference/summary_listing.html
index 315f7192..01ddec57 100644
--- a/docs/reference/summary_listing.html
+++ b/docs/reference/summary_listing.html
@@ -1,120 +1,74 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Display the output of a summary function according to the output format — summary_listing • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Display the output of a summary function according to the output format — summary_listing"><meta property="og:description" content='This function is intended for use in a R markdown code chunk with the chunk
-option results = "asis".'><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Display the output of a summary function according to the output format — summary_listing • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Display the output of a summary function according to the output format — summary_listing"><meta name="description" content='This function is intended for use in a R markdown code chunk with the chunk
+option results = "asis".'><meta property="og:description" content='This function is intended for use in a R markdown code chunk with the chunk
+option results = "asis".'></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Display the output of a summary function according to the output format</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/summary_listing.R" class="external-link"><code>R/summary_listing.R</code></a></small>
-    <div class="hidden name"><code>summary_listing.Rd</code></div>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Display the output of a summary function according to the output format</h1>
+      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/summary_listing.R" class="external-link"><code>R/summary_listing.R</code></a></small>
+      <div class="d-none name"><code>summary_listing.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>This function is intended for use in a R markdown code chunk with the chunk
 option <code>results = "asis"</code>.</p>
     </div>
 
-    <div id="ref-usage">
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">summary_listing</span><span class="op">(</span><span class="va">object</span>, caption <span class="op">=</span> <span class="cn">NULL</span>, label <span class="op">=</span> <span class="cn">NULL</span>, clearpage <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
 <span></span>
 <span><span class="fu">tex_listing</span><span class="op">(</span><span class="va">object</span>, caption <span class="op">=</span> <span class="cn">NULL</span>, label <span class="op">=</span> <span class="cn">NULL</span>, clearpage <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
@@ -122,46 +76,44 @@ option <code>results = "asis"</code>.</p>
 <span><span class="fu">html_listing</span><span class="op">(</span><span class="va">object</span>, caption <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></span></code></pre></div>
     </div>
 
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>object</dt>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
 <dd><p>The object for which the summary is to be listed</p></dd>
 
 
-<dt>caption</dt>
+<dt id="arg-caption">caption<a class="anchor" aria-label="anchor" href="#arg-caption"></a></dt>
 <dd><p>An optional caption</p></dd>
 
 
-<dt>label</dt>
+<dt id="arg-label">label<a class="anchor" aria-label="anchor" href="#arg-label"></a></dt>
 <dd><p>An optional label, ignored in html output</p></dd>
 
 
-<dt>clearpage</dt>
+<dt id="arg-clearpage">clearpage<a class="anchor" aria-label="anchor" href="#arg-clearpage"></a></dt>
 <dd><p>Should a new page be started after the listing? Ignored in html output</p></dd>
 
 </dl></div>
 
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/synthetic_data_for_UBA_2014-1.png b/docs/reference/synthetic_data_for_UBA_2014-1.png
index 132380a8..c08d231d 100644
--- a/docs/reference/synthetic_data_for_UBA_2014-1.png
+++ b/docs/reference/synthetic_data_for_UBA_2014-1.png
diff --git a/docs/reference/synthetic_data_for_UBA_2014.html b/docs/reference/synthetic_data_for_UBA_2014.html
index ee71a4dd..b81bbea4 100644
--- a/docs/reference/synthetic_data_for_UBA_2014.html
+++ b/docs/reference/synthetic_data_for_UBA_2014.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Synthetic datasets for one parent compound with two metabolites — synthetic_data_for_UBA_2014 • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Synthetic datasets for one parent compound with two metabolites — synthetic_data_for_UBA_2014"><meta property="og:description" content="The 12 datasets were generated using four different models and three different
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Synthetic datasets for one parent compound with two metabolites — synthetic_data_for_UBA_2014 • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Synthetic datasets for one parent compound with two metabolites — synthetic_data_for_UBA_2014"><meta name="description" content="The 12 datasets were generated using four different models and three different
  variance components. The four models are either the SFO or the DFOP model with either
  two sequential or two parallel metabolites.
 Variance component 'a' is based on a normal distribution with standard deviation of 3,
@@ -14,116 +14,83 @@ Initial concentrations for metabolites and all values where adding the variance
  in a value below the assumed limit of detection of 0.1 were set to NA.
 As an example, the first dataset has the title SFO_lin_a and is based on the SFO model
  with two sequential metabolites (linear pathway), with added variance component 'a'.
-Compare also the code in the example section to see the degradation models."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
+Compare also the code in the example section to see the degradation models."><meta property="og:description" content="The 12 datasets were generated using four different models and three different
+ variance components. The four models are either the SFO or the DFOP model with either
+ two sequential or two parallel metabolites.
+Variance component 'a' is based on a normal distribution with standard deviation of 3,
+ Variance component 'b' is also based on a normal distribution, but with a standard deviation of 7.
+ Variance component 'c' is based on the error model from Rocke and Lorenzato (1995), with the
+ minimum standard deviation (for small y values) of 0.5, and a proportionality constant of 0.07
+ for the increase of the standard deviation with y. Note that this is a simplified version
+ of the error model proposed by Rocke and Lorenzato (1995), as in their model the error of the
+ measured values approximates lognormal distribution for high values, whereas we are using
+ normally distributed error components all along.
+Initial concentrations for metabolites and all values where adding the variance component resulted
+ in a value below the assumed limit of detection of 0.1 were set to NA.
+As an example, the first dataset has the title SFO_lin_a and is based on the SFO model
+ with two sequential metabolites (linear pathway), with added variance component 'a'.
+Compare also the code in the example section to see the degradation models."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
 
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
 
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
 
-      
 
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Synthetic datasets for one parent compound with two metabolites</h1>
-    
-    <div class="hidden name"><code>synthetic_data_for_UBA_2014.Rd</code></div>
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Synthetic datasets for one parent compound with two metabolites</h1>
+
+      <div class="d-none name"><code>synthetic_data_for_UBA_2014.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>The 12 datasets were generated using four different models and three different
  variance components. The four models are either the SFO or the DFOP model with either
  two sequential or two parallel metabolites.</p>
@@ -142,12 +109,13 @@ Compare also the code in the example section to see the degradation models."><!-
 <p>Compare also the code in the example section to see the degradation models.</p>
     </div>
 
-    <div id="ref-usage">
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="va">synthetic_data_for_UBA_2014</span></span></code></pre></div>
     </div>
 
-    <div id="format">
-    <h2>Format</h2>
+    <div class="section level2">
+    <h2 id="format">Format<a class="anchor" aria-label="anchor" href="#format"></a></h2>
     <p>A list containing twelve datasets as an R6 class defined by <code><a href="mkinds.html">mkinds</a></code>,
   each containing, among others, the following components</p><dl><dt><code>title</code></dt>
 <dd><p>The name of the dataset, e.g. <code>SFO_lin_a</code></p></dd>
@@ -155,18 +123,18 @@ Compare also the code in the example section to see the degradation models."><!-
     <dt><code>data</code></dt>
 <dd><p>A data frame with the data in the form expected by <code><a href="mkinfit.html">mkinfit</a></code></p></dd>
 
-  
+
 </dl></div>
-    <div id="source">
-    <h2>Source</h2>
+    <div class="section level2">
+    <h2 id="source">Source<a class="anchor" aria-label="anchor" href="#source"></a></h2>
     <p>Ranke (2014) Prüfung und Validierung von Modellierungssoftware als Alternative
   zu ModelMaker 4.0, Umweltbundesamt Projektnummer 27452</p>
 <p>Rocke, David M. und Lorenzato, Stefan (1995) A two-component model for
   measurement error in analytical chemistry. Technometrics 37(2), 176-184.</p>
     </div>
 
-    <div id="ref-examples">
-    <h2>Examples</h2>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># \dontrun{</span></span></span>
 <span class="r-in"><span><span class="co"># The data have been generated using the following kinetic models</span></span></span>
 <span class="r-in"><span><span class="va">m_synth_SFO_lin</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"M1"</span><span class="op">)</span>,</span></span>
@@ -272,10 +240,10 @@ Compare also the code in the example section to see the degradation models."><!-
 <span class="r-in"><span>  <span class="fu"><a href="plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></span></span>
 <span class="r-plt img"><img src="synthetic_data_for_UBA_2014-1.png" alt="" width="700" height="433"></span>
 <span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> mkin version used for fitting:    1.2.6 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> R version used for fitting:       4.3.1 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Date of fit:     Mon Oct 30 09:40:49 2023 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Date of summary: Mon Oct 30 09:40:49 2023 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> mkin version used for fitting:    1.2.9 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> R version used for fitting:       4.4.2 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Date of fit:     Thu Feb 13 15:01:40 2025 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Date of summary: Thu Feb 13 15:01:40 2025 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Equations:</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> d_parent/dt = - k_parent * parent</span>
@@ -284,7 +252,7 @@ Compare also the code in the example section to see the degradation models."><!-
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Model predictions using solution type deSolve </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Fitted using 848 model solutions performed in 0.408 s</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Fitted using 848 model solutions performed in 0.165 s</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Error model: Constant variance </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
@@ -423,27 +391,23 @@ Compare also the code in the example section to see the degradation models."><!-
 <span class="r-in"><span><span class="co"># }</span></span></span>
 </code></pre></div>
     </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/test_data_from_UBA_2014-1.png b/docs/reference/test_data_from_UBA_2014-1.png
index e4fc2a4c..db29d16d 100644
--- a/docs/reference/test_data_from_UBA_2014-1.png
+++ b/docs/reference/test_data_from_UBA_2014-1.png
diff --git a/docs/reference/test_data_from_UBA_2014-2.png b/docs/reference/test_data_from_UBA_2014-2.png
index 4ce36561..2bfb77f7 100644
--- a/docs/reference/test_data_from_UBA_2014-2.png
+++ b/docs/reference/test_data_from_UBA_2014-2.png
diff --git a/docs/reference/test_data_from_UBA_2014.html b/docs/reference/test_data_from_UBA_2014.html
index 67acdb54..2ebbbd96 100644
--- a/docs/reference/test_data_from_UBA_2014.html
+++ b/docs/reference/test_data_from_UBA_2014.html
@@ -1,125 +1,79 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Three experimental datasets from two water sediment systems and one soil — test_data_from_UBA_2014 • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Three experimental datasets from two water sediment systems and one soil — test_data_from_UBA_2014"><meta property="og:description" content="The datasets were used for the comparative validation of several kinetic evaluation
- software packages (Ranke, 2014)."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Three experimental datasets from two water sediment systems and one soil — test_data_from_UBA_2014 • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Three experimental datasets from two water sediment systems and one soil — test_data_from_UBA_2014"><meta name="description" content="The datasets were used for the comparative validation of several kinetic evaluation
+ software packages (Ranke, 2014)."><meta property="og:description" content="The datasets were used for the comparative validation of several kinetic evaluation
+ software packages (Ranke, 2014)."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Three experimental datasets from two water sediment systems and one soil</h1>
-    
-    <div class="hidden name"><code>test_data_from_UBA_2014.Rd</code></div>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Three experimental datasets from two water sediment systems and one soil</h1>
+
+      <div class="d-none name"><code>test_data_from_UBA_2014.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>The datasets were used for the comparative validation of several kinetic evaluation
  software packages (Ranke, 2014).</p>
     </div>
 
-    <div id="ref-usage">
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="va">test_data_from_UBA_2014</span></span></code></pre></div>
     </div>
 
-    <div id="format">
-    <h2>Format</h2>
+    <div class="section level2">
+    <h2 id="format">Format<a class="anchor" aria-label="anchor" href="#format"></a></h2>
     <p>A list containing three datasets as an R6 class defined by <code><a href="mkinds.html">mkinds</a></code>.
   Each dataset has, among others, the following components</p><dl><dt><code>title</code></dt>
 <dd><p>The name of the dataset, e.g. <code>UBA_2014_WS_river</code></p></dd>
@@ -127,16 +81,16 @@
     <dt><code>data</code></dt>
 <dd><p>A data frame with the data in the form expected by <code><a href="mkinfit.html">mkinfit</a></code></p></dd>
 
-  
+
 </dl></div>
-    <div id="source">
-    <h2>Source</h2>
+    <div class="section level2">
+    <h2 id="source">Source<a class="anchor" aria-label="anchor" href="#source"></a></h2>
     <p>Ranke (2014) Prüfung und Validierung von Modellierungssoftware als Alternative
   zu ModelMaker 4.0, Umweltbundesamt Projektnummer 27452</p>
     </div>
 
-    <div id="ref-examples">
-    <h2>Examples</h2>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span>  <span class="co"># \dontrun{</span></span></span>
 <span class="r-in"><span>  <span class="co"># This is a level P-II evaluation of the dataset according to the FOCUS kinetics</span></span></span>
 <span class="r-in"><span>  <span class="co"># guidance. Due to the strong correlation of the parameter estimates, the</span></span></span>
@@ -153,19 +107,25 @@
 <span class="r-plt img"><img src="test_data_from_UBA_2014-1.png" alt="" width="700" height="433"></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">f_river</span><span class="op">)</span><span class="op">$</span><span class="va">bpar</span></span></span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>Could not calculate correlation; no covariance matrix</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                           Estimate se_notrans t value Pr(&gt;t) Lower Upper</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_w_0             95.91998118         NA      NA     NA    NA    NA</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_parent_w              0.41145375         NA      NA     NA    NA    NA</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> k_parent_s              0.04663944         NA      NA     NA    NA    NA</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_parent_w_to_parent_s  0.12467894         NA      NA     NA    NA    NA</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> f_parent_s_to_parent_w  0.50000000         NA      NA     NA    NA    NA</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma                   3.13612618         NA      NA     NA    NA    NA</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>                           Estimate se_notrans    t value       Pr(&gt;t)</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> parent_w_0             95.98567441 2.16285684 44.3791159 1.245593e-17</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> k_parent_w              0.42068803 0.05573864  7.5475120 8.752928e-07</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> k_parent_s              0.07419672 0.10108562  0.7339987 2.371337e-01</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> f_parent_w_to_parent_s  0.14336920 0.05809346  2.4679062 1.305295e-02</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> f_parent_s_to_parent_w  1.00000000 3.13868615  0.3186046 3.772097e-01</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> sigma                   2.98287858 0.68923267  4.3278253 2.987160e-04</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>                              Lower       Upper</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> parent_w_0             91.48420501 100.4871438</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> k_parent_w              0.36593946   0.4836276</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> k_parent_s              0.02289956   0.2404043</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> f_parent_w_to_parent_s  0.08180934   0.2391848</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> f_parent_s_to_parent_w  0.00000000   1.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> sigma                   2.00184022   3.9639169</span>
 <span class="r-in"><span>  <span class="fu"><a href="mkinerrmin.html">mkinerrmin</a></span><span class="op">(</span><span class="va">f_river</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>            err.min n.optim df</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> All data 0.1090929       5  6</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_w 0.0817436       3  3</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> parent_s 0.1619965       2  3</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>             err.min n.optim df</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> All data 0.09246946       5  6</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> parent_w 0.06377096       3  3</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> parent_s 0.20882325       2  3</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span>  <span class="co"># This is the evaluation used for the validation of software packages</span></span></span>
 <span class="r-in"><span>  <span class="co"># in the expertise from 2014</span></span></span>
@@ -213,27 +173,23 @@
 <span class="r-in"><span>  <span class="co"># }</span></span></span>
 </code></pre></div>
     </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/tex_listing.html b/docs/reference/tex_listing.html
index 4b8736c3..879363c3 100644
--- a/docs/reference/tex_listing.html
+++ b/docs/reference/tex_listing.html
@@ -1,143 +1,8 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Wrap the output of a summary function in tex listing environment — tex_listing • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Wrap the output of a summary function in tex listing environment — tex_listing"><meta property="og:description" content='This function can be used in a R markdown code chunk with the chunk
-option results = "asis".'><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Functions and data</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Wrap the output of a summary function in tex listing environment</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/tex_listing.R" class="external-link"><code>R/tex_listing.R</code></a></small>
-    <div class="hidden name"><code>tex_listing.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>This function can be used in a R markdown code chunk with the chunk
-option <code>results = "asis"</code>.</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">tex_listing</span><span class="op">(</span><span class="va">object</span>, caption <span class="op">=</span> <span class="cn">NULL</span>, label <span class="op">=</span> <span class="cn">NULL</span>, clearpage <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
-    </div>
-
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>object</dt>
-<dd><p>The object for which the summary is to be listed</p></dd>
-
-
-<dt>caption</dt>
-<dd><p>An optional caption</p></dd>
-
-
-<dt>label</dt>
-<dd><p>An optional label</p></dd>
-
-
-<dt>clearpage</dt>
-<dd><p>Should a new page be started after the listing?</p></dd>
-
-</dl></div>
-
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/summary_listing.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/summary_listing.html">
+  </head>
+</html>
 
diff --git a/docs/reference/tffm0.html b/docs/reference/tffm0.html
deleted file mode 100644
index bc033fb3..00000000
--- a/docs/reference/tffm0.html
+++ /dev/null
@@ -1,165 +0,0 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Transform formation fractions as in the first published mkin version — tffm0 • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Transform formation fractions as in the first published mkin version — tffm0"><meta property="og:description" content="This transformation was used originally in mkin, in order to implement a
-constraint for the sum of formation fractions to be smaller than 1.  It was
-later replaced by the ilr transformation because the ilr transformed
-fractions can assumed to follow normal distribution. As the ilr
-transformation is not available in RxODE and can therefore not be used in
-the nlmixr modelling language, the original transformation is currently used
-for translating mkin models with formation fractions to more than one target
-compartment for fitting with nlmixr in nlmixr_model. However, this
-implementation cannot be used there, as it is not accessible from RxODE."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Functions and data</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a>
-    </li>
-  </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Transform formation fractions as in the first published mkin version</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/tffm0.R" class="external-link"><code>R/tffm0.R</code></a></small>
-    <div class="hidden name"><code>tffm0.Rd</code></div>
-    </div>
-
-    <div class="ref-description">
-    <p>This transformation was used originally in mkin, in order to implement a
-constraint for the sum of formation fractions to be smaller than 1.  It was
-later replaced by the <a href="ilr.html">ilr</a> transformation because the ilr transformed
-fractions can assumed to follow normal distribution. As the ilr
-transformation is not available in <a href="https://nlmixrdevelopment.github.io/RxODE/reference/RxODE.html" class="external-link">RxODE</a> and can therefore not be used in
-the nlmixr modelling language, the original transformation is currently used
-for translating mkin models with formation fractions to more than one target
-compartment for fitting with nlmixr in <a href="nlmixr.mmkin.html">nlmixr_model</a>. However, this
-implementation cannot be used there, as it is not accessible from RxODE.</p>
-    </div>
-
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">tffm0</span><span class="op">(</span><span class="va">ff</span><span class="op">)</span>
-
-<span class="fu">invtffm0</span><span class="op">(</span><span class="va">ff_trans</span><span class="op">)</span></code></pre></div>
-    </div>
-
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>ff</dt>
-<dd><p>Vector of untransformed formation fractions. The sum
-must be smaller or equal to one</p></dd>
-<dt>ff_trans</dt>
-<dd><p>Vector of transformed formation fractions that can be
-restricted to the interval from 0 to 1</p></dd>
-</dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    <p>A vector of the transformed formation fractions
-A vector of backtransformed formation fractions for natural use in degradation models</p>
-    </div>
-    <div id="details">
-    <h2>Details</h2>
-    <p>If the transformed formation fractions are restricted to the interval
-between 0 and 1, the sum of backtransformed values is restricted
-to this interval.</p>
-    </div>
-
-    <div id="ref-examples">
-    <h2>Examples</h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="va">ff_example</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span></span>
-<span class="r-in">  <span class="fl">0.10983681</span>, <span class="fl">0.09035905</span>, <span class="fl">0.08399383</span></span>
-<span class="r-in"><span class="op">)</span></span>
-<span class="r-in"><span class="va">ff_example_trans</span> <span class="op">&lt;-</span> <span class="fu">tffm0</span><span class="op">(</span><span class="va">ff_example</span><span class="op">)</span></span>
-<span class="r-in"><span class="fu">invtffm0</span><span class="op">(</span><span class="va">ff_example_trans</span><span class="op">)</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 0.10983681 0.09035905 0.08399383</span>
-</code></pre></div>
-    </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
-
-
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
-</div>
-
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.2.</p>
-</div>
-
-      </footer></div>
-
-  
-
-
-  
-
-  </body></html>
-
diff --git a/docs/reference/transform_odeparms.html b/docs/reference/transform_odeparms.html
index aade8415..feaf0917 100644
--- a/docs/reference/transform_odeparms.html
+++ b/docs/reference/transform_odeparms.html
@@ -1,119 +1,76 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Functions to transform and backtransform kinetic parameters for fitting — transform_odeparms • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Functions to transform and backtransform kinetic parameters for fitting — transform_odeparms"><meta property="og:description" content="The transformations are intended to map parameters that should only take on
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Functions to transform and backtransform kinetic parameters for fitting — transform_odeparms • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Functions to transform and backtransform kinetic parameters for fitting — transform_odeparms"><meta name="description" content="The transformations are intended to map parameters that should only take on
 restricted values to the full scale of real numbers. For kinetic rate
 constants and other parameters that can only take on positive values, a
 simple log transformation is used. For compositional parameters, such as the
 formations fractions that should always sum up to 1 and can not be negative,
-the ilr transformation is used."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
+the ilr transformation is used."><meta property="og:description" content="The transformations are intended to map parameters that should only take on
+restricted values to the full scale of real numbers. For kinetic rate
+constants and other parameters that can only take on positive values, a
+simple log transformation is used. For compositional parameters, such as the
+formations fractions that should always sum up to 1 and can not be negative,
+the ilr transformation is used."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
 
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Functions to transform and backtransform kinetic parameters for fitting</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/transform_odeparms.R" class="external-link"><code>R/transform_odeparms.R</code></a></small>
-    <div class="hidden name"><code>transform_odeparms.Rd</code></div>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Functions to transform and backtransform kinetic parameters for fitting</h1>
+      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/transform_odeparms.R" class="external-link"><code>R/transform_odeparms.R</code></a></small>
+      <div class="d-none name"><code>transform_odeparms.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>The transformations are intended to map parameters that should only take on
 restricted values to the full scale of real numbers. For kinetic rate
 constants and other parameters that can only take on positive values, a
@@ -122,7 +79,8 @@ formations fractions that should always sum up to 1 and can not be negative,
 the <a href="ilr.html">ilr</a> transformation is used.</p>
     </div>
 
-    <div id="ref-usage">
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">transform_odeparms</span><span class="op">(</span></span>
 <span>  <span class="va">parms</span>,</span>
 <span>  <span class="va">mkinmod</span>,</span>
@@ -138,21 +96,23 @@ the <a href="ilr.html">ilr</a> transformation is used.</p>
 <span><span class="op">)</span></span></code></pre></div>
     </div>
 
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>parms</dt>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-parms">parms<a class="anchor" aria-label="anchor" href="#arg-parms"></a></dt>
 <dd><p>Parameters of kinetic models as used in the differential
 equations.</p></dd>
 
 
-<dt>mkinmod</dt>
+<dt id="arg-mkinmod">mkinmod<a class="anchor" aria-label="anchor" href="#arg-mkinmod"></a></dt>
 <dd><p>The kinetic model of class <a href="mkinmod.html">mkinmod</a>, containing
 the names of the model variables that are needed for grouping the
 formation fractions before <a href="ilr.html">ilr</a> transformation, the parameter
 names and the information if the pathway to sink is included in the model.</p></dd>
 
 
-<dt>transform_rates</dt>
+<dt id="arg-transform-rates">transform_rates<a class="anchor" aria-label="anchor" href="#arg-transform-rates"></a></dt>
 <dd><p>Boolean specifying if kinetic rate constants should
 be transformed in the model specification used in the fitting for better
 compliance with the assumption of normal distribution of the estimator. If
@@ -161,7 +121,7 @@ log-transformed, as well as k1 and k2 rate constants for the DFOP and HS
 models and the break point tb of the HS model.</p></dd>
 
 
-<dt>transform_fractions</dt>
+<dt id="arg-transform-fractions">transform_fractions<a class="anchor" aria-label="anchor" href="#arg-transform-fractions"></a></dt>
 <dd><p>Boolean specifying if formation fractions
 constants should be transformed in the model specification used in the
 fitting for better compliance with the assumption of normal distribution
@@ -174,30 +134,28 @@ two or more formation fractions need to be transformed whose sum cannot
 exceed one, the <a href="ilr.html">ilr</a> transformation is used.</p></dd>
 
 
-<dt>transparms</dt>
+<dt id="arg-transparms">transparms<a class="anchor" aria-label="anchor" href="#arg-transparms"></a></dt>
 <dd><p>Transformed parameters of kinetic models as used in the
 fitting procedure.</p></dd>
 
 </dl></div>
-    <div id="value">
-    <h2>Value</h2>
-    
-
-<p>A vector of transformed or backtransformed parameters</p>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>A vector of transformed or backtransformed parameters</p>
     </div>
-    <div id="details">
-    <h2>Details</h2>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>The transformation of sets of formation fractions is fragile, as it supposes
 the same ordering of the components in forward and backward transformation.
 This is no problem for the internal use in <a href="mkinfit.html">mkinfit</a>.</p>
     </div>
-    <div id="author">
-    <h2>Author</h2>
+    <div class="section level2">
+    <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a></h2>
     <p>Johannes Ranke</p>
     </div>
 
-    <div id="ref-examples">
-    <h2>Examples</h2>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="va">SFO_SFO</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span></span></span>
 <span class="r-in"><span>  parent <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"m1"</span>, sink <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>,</span></span>
@@ -313,27 +271,23 @@ This is no problem for the internal use in <a href="mkinfit.html">mkinfit</a>.</
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/update.mkinfit-1.png b/docs/reference/update.mkinfit-1.png
index 12fe1f5b..bc818a4c 100644
--- a/docs/reference/update.mkinfit-1.png
+++ b/docs/reference/update.mkinfit-1.png
diff --git a/docs/reference/update.mkinfit-2.png b/docs/reference/update.mkinfit-2.png
index 21817f94..bbd2b9b7 100644
--- a/docs/reference/update.mkinfit-2.png
+++ b/docs/reference/update.mkinfit-2.png
diff --git a/docs/reference/update.mkinfit.html b/docs/reference/update.mkinfit.html
index 10f9e8a2..e4a2de34 100644
--- a/docs/reference/update.mkinfit.html
+++ b/docs/reference/update.mkinfit.html
@@ -1,147 +1,105 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Update an mkinfit model with different arguments — update.mkinfit • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Update an mkinfit model with different arguments — update.mkinfit"><meta property="og:description" content="This function will return an updated mkinfit object. The fitted degradation
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Update an mkinfit model with different arguments — update.mkinfit • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Update an mkinfit model with different arguments — update.mkinfit"><meta name="description" content="This function will return an updated mkinfit object. The fitted degradation
 model parameters from the old fit are used as starting values for the
 updated fit. Values specified as 'parms.ini' and/or 'state.ini' will
-override these starting values."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-    
-
-    <div class="container template-reference-topic">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.6</span>
-      </span>
-    </div>
-
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav"><li>
-  <a href="../reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
-     
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu"><li>
-      <a href="../articles/mkin.html">Introduction to mkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li>
-    <li>
-      <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
-    </li>
-    <li>
-      <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
-    </li>
-    <li>
-      <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Performance</li>
-    <li>
-      <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
-    </li>
-    <li class="divider">
-    <li class="dropdown-header">Miscellaneous</li>
-    <li>
-      <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
-    </li>
-    <li>
-      <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
-    </li>
+override these starting values."><meta property="og:description" content="This function will return an updated mkinfit object. The fitted degradation
+model parameters from the old fit are used as starting values for the
+updated fit. Values specified as 'parms.ini' and/or 'state.ini' will
+override these starting values."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="default" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">mkin</a>
+
+    <small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.9</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li>
+    <li><a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+    <li><a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li>
+    <li><a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li>
+    <li><a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li>
+    <li><a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li>
   </ul></li>
-<li>
-  <a href="../news/index.html">News</a>
-</li>
-      </ul><ul class="nav navbar-nav navbar-right"><li>
-  <a href="https://github.com/jranke/mkin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
-      </ul></div><!--/.nav-collapse -->
-  </div><!--/.container -->
-</div><!--/.navbar -->
-
-      
-
-      </header><div class="row">
-  <div class="col-md-9 contents">
-    <div class="page-header">
-    <h1>Update an mkinfit model with different arguments</h1>
-    <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/update.mkinfit.R" class="external-link"><code>R/update.mkinfit.R</code></a></small>
-    <div class="hidden name"><code>update.mkinfit.Rd</code></div>
+<li class="nav-item"><a class="nav-link" href="../coverage/coverage.html">Test coverage</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">News</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Update an mkinfit model with different arguments</h1>
+      <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/update.mkinfit.R" class="external-link"><code>R/update.mkinfit.R</code></a></small>
+      <div class="d-none name"><code>update.mkinfit.Rd</code></div>
     </div>
 
-    <div class="ref-description">
+    <div class="ref-description section level2">
     <p>This function will return an updated mkinfit object. The fitted degradation
 model parameters from the old fit are used as starting values for the
 updated fit. Values specified as 'parms.ini' and/or 'state.ini' will
 override these starting values.</p>
     </div>
 
-    <div id="ref-usage">
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for mkinfit</span></span>
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'mkinfit'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span>, evaluate <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
     </div>
 
-    <div id="arguments">
-    <h2>Arguments</h2>
-    <dl><dt>object</dt>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
 <dd><p>An mkinfit object to be updated</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Arguments to <code><a href="mkinfit.html">mkinfit</a></code> that should replace
 the arguments from the original call. Arguments set to NULL will
 remove arguments given in the original call</p></dd>
 
 
-<dt>evaluate</dt>
+<dt id="arg-evaluate">evaluate<a class="anchor" aria-label="anchor" href="#arg-evaluate"></a></dt>
 <dd><p>Should the call be evaluated or returned as a call</p></dd>
 
 </dl></div>
 
-    <div id="ref-examples">
-    <h2>Examples</h2>
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># \dontrun{</span></span></span>
 <span class="r-in"><span><span class="va">fit</span> <span class="op">&lt;-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="fu"><a href="https://rdrr.io/r/base/subset.html" class="external-link">subset</a></span><span class="op">(</span><span class="va">FOCUS_2006_D</span>, <span class="va">value</span> <span class="op">!=</span> <span class="fl">0</span><span class="op">)</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="parms.html">parms</a></span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></span></span>
@@ -158,27 +116,23 @@ remove arguments given in the original call</p></dd>
 <span class="r-in"><span><span class="co"># }</span></span></span>
 </code></pre></div>
     </div>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Johannes Ranke.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Johannes Ranke.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
-      </footer></div>
+    </footer></div>
+
 
-  
 
 
-  
 
   </body></html>
 
diff --git a/docs/reference/update.nlme.mmkin.html b/docs/reference/update.nlme.mmkin.html
new file mode 100644
index 00000000..dd383acd
--- /dev/null
+++ b/docs/reference/update.nlme.mmkin.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/nlme.mmkin.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/nlme.mmkin.html">
+  </head>
+</html>
+
diff --git a/docs/reference/which.best.default.html b/docs/reference/which.best.default.html
new file mode 100644
index 00000000..9700ef05
--- /dev/null
+++ b/docs/reference/which.best.default.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/multistart.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/multistart.html">
+  </head>
+</html>
+
diff --git a/docs/reference/which.best.html b/docs/reference/which.best.html
new file mode 100644
index 00000000..9700ef05
--- /dev/null
+++ b/docs/reference/which.best.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://pkgdown.jrwb.de/mkin/reference/multistart.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://pkgdown.jrwb.de/mkin/reference/multistart.html">
+  </head>
+</html>
+
diff --git a/docs/search.json b/docs/search.json
new file mode 100644
index 00000000..de1251f1
--- /dev/null
+++ b/docs/search.json
@@ -0,0 +1 @@
+[{"path":"https://pkgdown.jrwb.de/mkin/articles/FOCUS_L.html","id":"laboratory-data-l1","dir":"Articles","previous_headings":"","what":"Laboratory Data L1","title":"Example evaluation of FOCUS Laboratory Data L1 to L3","text":"following code defines example dataset L1 FOCUS kinetics report, p. 284: use assumptions simple first order (SFO), case declining rate constant time (FOMC) case two different phases kinetics (DFOP). detailed discussion models, please see FOCUS kinetics report. Since mkin version 0.9-32 (July 2014), can use shorthand notation like \"SFO\" parent degradation models. following two lines fit model produce summary report model fit. covers numerical analysis given FOCUS report. plot fit obtained plot function mkinfit objects.  residual plot can easily obtained  comparison, FOMC model fitted well, χ2\\chi^2 error level checked.  get warning default optimisation algorithm Port converge, indication model overparameterised, .e. contains many parameters ill-defined consequence. fact, due higher number parameters, lower number degrees freedom fit, χ2\\chi^2 error level actually higher FOMC model (3.6%) SFO model (3.4%). Additionally, parameters log_alpha log_beta internally fitted model excessive confidence intervals, span 25 orders magnitude (!) backtransformed scale alpha beta. Also, t-test significant difference zero indicate significant difference, p-values greater 0.1, finally, parameter correlation log_alpha log_beta 1.000, clearly indicating model overparameterised. χ2\\chi^2 error levels reported Appendix 3 Appendix 7 FOCUS kinetics report rounded integer percentages partly deviate one percentage point results calculated mkin. reason known. However, mkin gives χ2\\chi^2 error levels kinfit package calculation routines kinfit package extensively compared results obtained KinGUI software, documented kinfit package vignette. KinGUI first widely used standard package field. Also, calculation χ2\\chi^2 error levels compared KinGUII, CAKE DegKin manager project sponsored German Umweltbundesamt (Ranke 2014).","code":"library(\"mkin\", quietly = TRUE) FOCUS_2006_L1 = data.frame(   t = rep(c(0, 1, 2, 3, 5, 7, 14, 21, 30), each = 2),   parent = c(88.3, 91.4, 85.6, 84.5, 78.9, 77.6,              72.0, 71.9, 50.3, 59.4, 47.0, 45.1,              27.7, 27.3, 10.0, 10.4, 2.9, 4.0)) FOCUS_2006_L1_mkin <- mkin_wide_to_long(FOCUS_2006_L1) m.L1.SFO <- mkinfit(\"SFO\", FOCUS_2006_L1_mkin, quiet = TRUE) summary(m.L1.SFO) ## mkin version used for fitting:    1.2.9  ## R version used for fitting:       4.4.2  ## Date of fit:     Thu Feb 13 15:49:54 2025  ## Date of summary: Thu Feb 13 15:49:54 2025  ##  ## Equations: ## d_parent/dt = - k_parent * parent ##  ## Model predictions using solution type analytical  ##  ## Fitted using 133 model solutions performed in 0.011 s ##  ## Error model: Constant variance  ##  ## Error model algorithm: OLS  ##  ## Starting values for parameters to be optimised: ##          value   type ## parent_0 89.85  state ## k_parent  0.10 deparm ##  ## Starting values for the transformed parameters actually optimised: ##                  value lower upper ## parent_0     89.850000  -Inf   Inf ## log_k_parent -2.302585  -Inf   Inf ##  ## Fixed parameter values: ## None ##  ## Results: ##  ##        AIC     BIC    logLik ##   93.88778 96.5589 -43.94389 ##  ## Optimised, transformed parameters with symmetric confidence intervals: ##              Estimate Std. Error  Lower  Upper ## parent_0       92.470    1.28200 89.740 95.200 ## log_k_parent   -2.347    0.03763 -2.428 -2.267 ## sigma           2.780    0.46330  1.792  3.767 ##  ## Parameter correlation: ##                parent_0 log_k_parent      sigma ## parent_0      1.000e+00    6.186e-01 -1.516e-09 ## log_k_parent  6.186e-01    1.000e+00 -3.124e-09 ## sigma        -1.516e-09   -3.124e-09  1.000e+00 ##  ## Backtransformed parameters: ## Confidence intervals for internally transformed parameters are asymmetric. ## t-test (unrealistically) based on the assumption of normal distribution ## for estimators of untransformed parameters. ##          Estimate t value    Pr(>t)    Lower   Upper ## parent_0 92.47000   72.13 8.824e-21 89.74000 95.2000 ## k_parent  0.09561   26.57 2.487e-14  0.08824  0.1036 ## sigma     2.78000    6.00 1.216e-05  1.79200  3.7670 ##  ## FOCUS Chi2 error levels in percent: ##          err.min n.optim df ## All data   3.424       2  7 ## parent     3.424       2  7 ##  ## Estimated disappearance times: ##         DT50  DT90 ## parent 7.249 24.08 ##  ## Data: ##  time variable observed predicted residual ##     0   parent     88.3    92.471  -4.1710 ##     0   parent     91.4    92.471  -1.0710 ##     1   parent     85.6    84.039   1.5610 ##     1   parent     84.5    84.039   0.4610 ##     2   parent     78.9    76.376   2.5241 ##     2   parent     77.6    76.376   1.2241 ##     3   parent     72.0    69.412   2.5884 ##     3   parent     71.9    69.412   2.4884 ##     5   parent     50.3    57.330  -7.0301 ##     5   parent     59.4    57.330   2.0699 ##     7   parent     47.0    47.352  -0.3515 ##     7   parent     45.1    47.352  -2.2515 ##    14   parent     27.7    24.247   3.4528 ##    14   parent     27.3    24.247   3.0528 ##    21   parent     10.0    12.416  -2.4163 ##    21   parent     10.4    12.416  -2.0163 ##    30   parent      2.9     5.251  -2.3513 ##    30   parent      4.0     5.251  -1.2513 plot(m.L1.SFO, show_errmin = TRUE, main = \"FOCUS L1 - SFO\") mkinresplot(m.L1.SFO, ylab = \"Observed\", xlab = \"Time\") m.L1.FOMC <- mkinfit(\"FOMC\", FOCUS_2006_L1_mkin, quiet=TRUE) ## Warning in mkinfit(\"FOMC\", FOCUS_2006_L1_mkin, quiet = TRUE): Optimisation did not converge: ## false convergence (8) plot(m.L1.FOMC, show_errmin = TRUE, main = \"FOCUS L1 - FOMC\") summary(m.L1.FOMC, data = FALSE) ## Warning in sqrt(diag(covar)): NaNs produced ## Warning in cov2cor(ans$covar): diag(V) had non-positive or NA entries; the ## non-finite result may be dubious ## mkin version used for fitting:    1.2.9  ## R version used for fitting:       4.4.2  ## Date of fit:     Thu Feb 13 15:49:55 2025  ## Date of summary: Thu Feb 13 15:49:55 2025  ##  ## Equations: ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent ##  ## Model predictions using solution type analytical  ##  ## Fitted using 342 model solutions performed in 0.023 s ##  ## Error model: Constant variance  ##  ## Error model algorithm: OLS  ##  ## Starting values for parameters to be optimised: ##          value   type ## parent_0 89.85  state ## alpha     1.00 deparm ## beta     10.00 deparm ##  ## Starting values for the transformed parameters actually optimised: ##               value lower upper ## parent_0  89.850000  -Inf   Inf ## log_alpha  0.000000  -Inf   Inf ## log_beta   2.302585  -Inf   Inf ##  ## Fixed parameter values: ## None ##  ##  ## Warning(s):  ## Optimisation did not converge: ## false convergence (8) ##  ## Results: ##  ##        AIC      BIC    logLik ##   95.88782 99.44931 -43.94391 ##  ## Optimised, transformed parameters with symmetric confidence intervals: ##           Estimate Std. Error  Lower  Upper ## parent_0     92.47     1.2820 89.720 95.220 ## log_alpha    13.20        NaN    NaN    NaN ## log_beta     15.54        NaN    NaN    NaN ## sigma         2.78     0.4607  1.792  3.768 ##  ## Parameter correlation: ##           parent_0 log_alpha log_beta    sigma ## parent_0  1.000000       NaN      NaN 0.000603 ## log_alpha      NaN         1      NaN      NaN ## log_beta       NaN       NaN        1      NaN ## sigma     0.000603       NaN      NaN 1.000000 ##  ## Backtransformed parameters: ## Confidence intervals for internally transformed parameters are asymmetric. ## t-test (unrealistically) based on the assumption of normal distribution ## for estimators of untransformed parameters. ##           Estimate t value Pr(>t)  Lower  Upper ## parent_0 9.247e+01      NA     NA 89.720 95.220 ## alpha    5.386e+05      NA     NA     NA     NA ## beta     5.633e+06      NA     NA     NA     NA ## sigma    2.780e+00      NA     NA  1.792  3.768 ##  ## FOCUS Chi2 error levels in percent: ##          err.min n.optim df ## All data   3.619       3  6 ## parent     3.619       3  6 ##  ## Estimated disappearance times: ##         DT50  DT90 DT50back ## parent 7.249 24.08    7.249"},{"path":"https://pkgdown.jrwb.de/mkin/articles/FOCUS_L.html","id":"laboratory-data-l2","dir":"Articles","previous_headings":"","what":"Laboratory Data L2","title":"Example evaluation of FOCUS Laboratory Data L1 to L3","text":"following code defines example dataset L2 FOCUS kinetics report, p. 287:","code":"FOCUS_2006_L2 = data.frame(   t = rep(c(0, 1, 3, 7, 14, 28), each = 2),   parent = c(96.1, 91.8, 41.4, 38.7,              19.3, 22.3, 4.6, 4.6,              2.6, 1.2, 0.3, 0.6)) FOCUS_2006_L2_mkin <- mkin_wide_to_long(FOCUS_2006_L2)"},{"path":"https://pkgdown.jrwb.de/mkin/articles/FOCUS_L.html","id":"sfo-fit-for-l2","dir":"Articles","previous_headings":"Laboratory Data L2","what":"SFO fit for L2","title":"Example evaluation of FOCUS Laboratory Data L1 to L3","text":", SFO model fitted result plotted. residual plot can obtained simply adding argument show_residuals plot command.  χ2\\chi^2 error level 14% suggests model fit well. also obvious plots fit, included residual plot. FOCUS kinetics report, stated apparent systematic error observed residual plot measured DT90 (approximately day 5), underestimation beyond point. may add difficult judge random nature residuals just three samplings days 0, 1 3. Also, clear priori consistent underestimation approximate DT90 irrelevant. However, can rationalised fact FOCUS fate models generally implement SFO kinetics.","code":"m.L2.SFO <- mkinfit(\"SFO\", FOCUS_2006_L2_mkin, quiet=TRUE) plot(m.L2.SFO, show_residuals = TRUE, show_errmin = TRUE,      main = \"FOCUS L2 - SFO\")"},{"path":"https://pkgdown.jrwb.de/mkin/articles/FOCUS_L.html","id":"fomc-fit-for-l2","dir":"Articles","previous_headings":"Laboratory Data L2","what":"FOMC fit for L2","title":"Example evaluation of FOCUS Laboratory Data L1 to L3","text":"comparison, FOMC model fitted well, χ2\\chi^2 error level checked.  error level χ2\\chi^2 test passes much lower case. Therefore, FOMC model provides better description data, less experimental error assumed order explain data.","code":"m.L2.FOMC <- mkinfit(\"FOMC\", FOCUS_2006_L2_mkin, quiet = TRUE) plot(m.L2.FOMC, show_residuals = TRUE,      main = \"FOCUS L2 - FOMC\") summary(m.L2.FOMC, data = FALSE) ## mkin version used for fitting:    1.2.9  ## R version used for fitting:       4.4.2  ## Date of fit:     Thu Feb 13 15:49:55 2025  ## Date of summary: Thu Feb 13 15:49:55 2025  ##  ## Equations: ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent ##  ## Model predictions using solution type analytical  ##  ## Fitted using 239 model solutions performed in 0.015 s ##  ## Error model: Constant variance  ##  ## Error model algorithm: OLS  ##  ## Starting values for parameters to be optimised: ##          value   type ## parent_0 93.95  state ## alpha     1.00 deparm ## beta     10.00 deparm ##  ## Starting values for the transformed parameters actually optimised: ##               value lower upper ## parent_0  93.950000  -Inf   Inf ## log_alpha  0.000000  -Inf   Inf ## log_beta   2.302585  -Inf   Inf ##  ## Fixed parameter values: ## None ##  ## Results: ##  ##        AIC      BIC    logLik ##   61.78966 63.72928 -26.89483 ##  ## Optimised, transformed parameters with symmetric confidence intervals: ##           Estimate Std. Error    Lower   Upper ## parent_0   93.7700     1.6130 90.05000 97.4900 ## log_alpha   0.3180     0.1559 -0.04149  0.6776 ## log_beta    0.2102     0.2493 -0.36460  0.7850 ## sigma       2.2760     0.4645  1.20500  3.3470 ##  ## Parameter correlation: ##             parent_0  log_alpha   log_beta      sigma ## parent_0   1.000e+00 -1.151e-01 -2.085e-01 -7.436e-09 ## log_alpha -1.151e-01  1.000e+00  9.741e-01 -1.617e-07 ## log_beta  -2.085e-01  9.741e-01  1.000e+00 -1.386e-07 ## sigma     -7.436e-09 -1.617e-07 -1.386e-07  1.000e+00 ##  ## Backtransformed parameters: ## Confidence intervals for internally transformed parameters are asymmetric. ## t-test (unrealistically) based on the assumption of normal distribution ## for estimators of untransformed parameters. ##          Estimate t value    Pr(>t)   Lower  Upper ## parent_0   93.770  58.120 4.267e-12 90.0500 97.490 ## alpha       1.374   6.414 1.030e-04  0.9594  1.969 ## beta        1.234   4.012 1.942e-03  0.6945  2.192 ## sigma       2.276   4.899 5.977e-04  1.2050  3.347 ##  ## FOCUS Chi2 error levels in percent: ##          err.min n.optim df ## All data   6.205       3  3 ## parent     6.205       3  3 ##  ## Estimated disappearance times: ##          DT50  DT90 DT50back ## parent 0.8092 5.356    1.612"},{"path":"https://pkgdown.jrwb.de/mkin/articles/FOCUS_L.html","id":"dfop-fit-for-l2","dir":"Articles","previous_headings":"Laboratory Data L2","what":"DFOP fit for L2","title":"Example evaluation of FOCUS Laboratory Data L1 to L3","text":"Fitting four parameter DFOP model reduces χ2\\chi^2 error level.  , DFOP model clearly best-fit model dataset L2 based chi^2 error level criterion.","code":"m.L2.DFOP <- mkinfit(\"DFOP\", FOCUS_2006_L2_mkin, quiet = TRUE) plot(m.L2.DFOP, show_residuals = TRUE, show_errmin = TRUE,      main = \"FOCUS L2 - DFOP\") summary(m.L2.DFOP, data = FALSE) ## mkin version used for fitting:    1.2.9  ## R version used for fitting:       4.4.2  ## Date of fit:     Thu Feb 13 15:49:55 2025  ## Date of summary: Thu Feb 13 15:49:55 2025  ##  ## Equations: ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * ##            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) ##            * parent ##  ## Model predictions using solution type analytical  ##  ## Fitted using 581 model solutions performed in 0.043 s ##  ## Error model: Constant variance  ##  ## Error model algorithm: OLS  ##  ## Starting values for parameters to be optimised: ##          value   type ## parent_0 93.95  state ## k1        0.10 deparm ## k2        0.01 deparm ## g         0.50 deparm ##  ## Starting values for the transformed parameters actually optimised: ##              value lower upper ## parent_0 93.950000  -Inf   Inf ## log_k1   -2.302585  -Inf   Inf ## log_k2   -4.605170  -Inf   Inf ## g_qlogis  0.000000  -Inf   Inf ##  ## Fixed parameter values: ## None ##  ## Results: ##  ##        AIC      BIC    logLik ##   52.36695 54.79148 -21.18347 ##  ## Optimised, transformed parameters with symmetric confidence intervals: ##          Estimate Std. Error      Lower     Upper ## parent_0   93.950  9.998e-01    91.5900   96.3100 ## log_k1      3.113  1.849e+03 -4369.0000 4375.0000 ## log_k2     -1.088  6.285e-02    -1.2370   -0.9394 ## g_qlogis   -0.399  9.946e-02    -0.6342   -0.1638 ## sigma       1.414  2.886e-01     0.7314    2.0960 ##  ## Parameter correlation: ##            parent_0     log_k1     log_k2   g_qlogis      sigma ## parent_0  1.000e+00  6.763e-07 -8.944e-10  2.665e-01 -1.083e-09 ## log_k1    6.763e-07  1.000e+00  1.112e-04 -2.187e-04 -1.027e-05 ## log_k2   -8.944e-10  1.112e-04  1.000e+00 -7.903e-01  9.464e-09 ## g_qlogis  2.665e-01 -2.187e-04 -7.903e-01  1.000e+00 -1.532e-08 ## sigma    -1.083e-09 -1.027e-05  9.464e-09 -1.532e-08  1.000e+00 ##  ## Backtransformed parameters: ## Confidence intervals for internally transformed parameters are asymmetric. ## t-test (unrealistically) based on the assumption of normal distribution ## for estimators of untransformed parameters. ##          Estimate   t value    Pr(>t)   Lower   Upper ## parent_0  93.9500 9.397e+01 2.036e-12 91.5900 96.3100 ## k1        22.4900 5.533e-04 4.998e-01  0.0000     Inf ## k2         0.3369 1.591e+01 4.697e-07  0.2904  0.3909 ## g          0.4016 1.680e+01 3.238e-07  0.3466  0.4591 ## sigma      1.4140 4.899e+00 8.776e-04  0.7314  2.0960 ##  ## FOCUS Chi2 error levels in percent: ##          err.min n.optim df ## All data    2.53       4  2 ## parent      2.53       4  2 ##  ## Estimated disappearance times: ##          DT50  DT90 DT50back DT50_k1 DT50_k2 ## parent 0.5335 5.311    1.599 0.03083   2.058"},{"path":"https://pkgdown.jrwb.de/mkin/articles/FOCUS_L.html","id":"laboratory-data-l3","dir":"Articles","previous_headings":"","what":"Laboratory Data L3","title":"Example evaluation of FOCUS Laboratory Data L1 to L3","text":"following code defines example dataset L3 FOCUS kinetics report, p. 290.","code":"FOCUS_2006_L3 = data.frame(   t = c(0, 3, 7, 14, 30, 60, 91, 120),   parent = c(97.8, 60, 51, 43, 35, 22, 15, 12)) FOCUS_2006_L3_mkin <- mkin_wide_to_long(FOCUS_2006_L3)"},{"path":"https://pkgdown.jrwb.de/mkin/articles/FOCUS_L.html","id":"fit-multiple-models","dir":"Articles","previous_headings":"Laboratory Data L3","what":"Fit multiple models","title":"Example evaluation of FOCUS Laboratory Data L1 to L3","text":"mkin version 0.9-39 (June 2015), can fit several models one datasets one call function mmkin. datasets passed list, case named list holding L3 dataset prepared .  χ2\\chi^2 error level 21% well plot suggest SFO model fit well. FOMC model performs better, error level χ2\\chi^2 test passes 7%. Fitting four parameter DFOP model reduces χ2\\chi^2 error level considerably.","code":"# Only use one core here, not to offend the CRAN checks mm.L3 <- mmkin(c(\"SFO\", \"FOMC\", \"DFOP\"), cores = 1,                list(\"FOCUS L3\" = FOCUS_2006_L3_mkin), quiet = TRUE) plot(mm.L3)"},{"path":"https://pkgdown.jrwb.de/mkin/articles/FOCUS_L.html","id":"accessing-mmkin-objects","dir":"Articles","previous_headings":"Laboratory Data L3","what":"Accessing mmkin objects","title":"Example evaluation of FOCUS Laboratory Data L1 to L3","text":"objects returned mmkin arranged like matrix, models row index datasets column index. can extract summary plot e.g. DFOP fit, using square brackets indexing result use summary plot functions working mkinfit objects.  , look model plot, confidence intervals parameters correlation matrix suggest parameter estimates reliable, DFOP model can used best-fit model based χ2\\chi^2 error level criterion laboratory data L3. also example standard t-test parameter g_ilr misleading, tests significant difference zero. case, zero appears correct value parameter, confidence interval backtransformed parameter g quite narrow.","code":"summary(mm.L3[[\"DFOP\", 1]]) ## mkin version used for fitting:    1.2.9  ## R version used for fitting:       4.4.2  ## Date of fit:     Thu Feb 13 15:49:56 2025  ## Date of summary: Thu Feb 13 15:49:56 2025  ##  ## Equations: ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * ##            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) ##            * parent ##  ## Model predictions using solution type analytical  ##  ## Fitted using 376 model solutions performed in 0.024 s ##  ## Error model: Constant variance  ##  ## Error model algorithm: OLS  ##  ## Starting values for parameters to be optimised: ##          value   type ## parent_0 97.80  state ## k1        0.10 deparm ## k2        0.01 deparm ## g         0.50 deparm ##  ## Starting values for the transformed parameters actually optimised: ##              value lower upper ## parent_0 97.800000  -Inf   Inf ## log_k1   -2.302585  -Inf   Inf ## log_k2   -4.605170  -Inf   Inf ## g_qlogis  0.000000  -Inf   Inf ##  ## Fixed parameter values: ## None ##  ## Results: ##  ##        AIC      BIC    logLik ##   32.97732 33.37453 -11.48866 ##  ## Optimised, transformed parameters with symmetric confidence intervals: ##          Estimate Std. Error   Lower      Upper ## parent_0  97.7500    1.01900 94.5000 101.000000 ## log_k1    -0.6612    0.10050 -0.9812  -0.341300 ## log_k2    -4.2860    0.04322 -4.4230  -4.148000 ## g_qlogis  -0.1739    0.05270 -0.3416  -0.006142 ## sigma      1.0170    0.25430  0.2079   1.827000 ##  ## Parameter correlation: ##            parent_0     log_k1     log_k2   g_qlogis      sigma ## parent_0  1.000e+00  1.732e-01  2.282e-02  4.009e-01 -9.696e-08 ## log_k1    1.732e-01  1.000e+00  4.945e-01 -5.809e-01  7.148e-07 ## log_k2    2.282e-02  4.945e-01  1.000e+00 -6.812e-01  1.022e-06 ## g_qlogis  4.009e-01 -5.809e-01 -6.812e-01  1.000e+00 -7.930e-07 ## sigma    -9.696e-08  7.148e-07  1.022e-06 -7.930e-07  1.000e+00 ##  ## Backtransformed parameters: ## Confidence intervals for internally transformed parameters are asymmetric. ## t-test (unrealistically) based on the assumption of normal distribution ## for estimators of untransformed parameters. ##          Estimate t value    Pr(>t)    Lower     Upper ## parent_0 97.75000  95.960 1.248e-06 94.50000 101.00000 ## k1        0.51620   9.947 1.081e-03  0.37490   0.71090 ## k2        0.01376  23.140 8.840e-05  0.01199   0.01579 ## g         0.45660  34.920 2.581e-05  0.41540   0.49850 ## sigma     1.01700   4.000 1.400e-02  0.20790   1.82700 ##  ## FOCUS Chi2 error levels in percent: ##          err.min n.optim df ## All data   2.225       4  4 ## parent     2.225       4  4 ##  ## Estimated disappearance times: ##         DT50 DT90 DT50back DT50_k1 DT50_k2 ## parent 7.464  123    37.03   1.343   50.37 ##  ## Data: ##  time variable observed predicted residual ##     0   parent     97.8     97.75  0.05396 ##     3   parent     60.0     60.45 -0.44933 ##     7   parent     51.0     49.44  1.56338 ##    14   parent     43.0     43.84 -0.83632 ##    30   parent     35.0     35.15 -0.14707 ##    60   parent     22.0     23.26 -1.25919 ##    91   parent     15.0     15.18 -0.18181 ##   120   parent     12.0     10.19  1.81395 plot(mm.L3[[\"DFOP\", 1]], show_errmin = TRUE)"},{"path":"https://pkgdown.jrwb.de/mkin/articles/FOCUS_L.html","id":"laboratory-data-l4","dir":"Articles","previous_headings":"","what":"Laboratory Data L4","title":"Example evaluation of FOCUS Laboratory Data L1 to L3","text":"following code defines example dataset L4 FOCUS kinetics report, p. 293: Fits SFO FOMC models, plots summaries produced :  χ2\\chi^2 error level 3.3% well plot suggest SFO model fits well. error level χ2\\chi^2 test passes slightly lower FOMC model. However, difference appears negligible.","code":"FOCUS_2006_L4 = data.frame(   t = c(0, 3, 7, 14, 30, 60, 91, 120),   parent = c(96.6, 96.3, 94.3, 88.8, 74.9, 59.9, 53.5, 49.0)) FOCUS_2006_L4_mkin <- mkin_wide_to_long(FOCUS_2006_L4) # Only use one core here, not to offend the CRAN checks mm.L4 <- mmkin(c(\"SFO\", \"FOMC\"), cores = 1,                list(\"FOCUS L4\" = FOCUS_2006_L4_mkin),                quiet = TRUE) plot(mm.L4) summary(mm.L4[[\"SFO\", 1]], data = FALSE) ## mkin version used for fitting:    1.2.9  ## R version used for fitting:       4.4.2  ## Date of fit:     Thu Feb 13 15:49:56 2025  ## Date of summary: Thu Feb 13 15:49:56 2025  ##  ## Equations: ## d_parent/dt = - k_parent * parent ##  ## Model predictions using solution type analytical  ##  ## Fitted using 142 model solutions performed in 0.009 s ##  ## Error model: Constant variance  ##  ## Error model algorithm: OLS  ##  ## Starting values for parameters to be optimised: ##          value   type ## parent_0  96.6  state ## k_parent   0.1 deparm ##  ## Starting values for the transformed parameters actually optimised: ##                  value lower upper ## parent_0     96.600000  -Inf   Inf ## log_k_parent -2.302585  -Inf   Inf ##  ## Fixed parameter values: ## None ##  ## Results: ##  ##        AIC      BIC    logLik ##   47.12133 47.35966 -20.56067 ##  ## Optimised, transformed parameters with symmetric confidence intervals: ##              Estimate Std. Error  Lower   Upper ## parent_0       96.440    1.69900 92.070 100.800 ## log_k_parent   -5.030    0.07059 -5.211  -4.848 ## sigma           3.162    0.79050  1.130   5.194 ##  ## Parameter correlation: ##               parent_0 log_k_parent     sigma ## parent_0     1.000e+00    5.938e-01 3.430e-07 ## log_k_parent 5.938e-01    1.000e+00 5.885e-07 ## sigma        3.430e-07    5.885e-07 1.000e+00 ##  ## Backtransformed parameters: ## Confidence intervals for internally transformed parameters are asymmetric. ## t-test (unrealistically) based on the assumption of normal distribution ## for estimators of untransformed parameters. ##           Estimate t value    Pr(>t)     Lower     Upper ## parent_0 96.440000   56.77 1.604e-08 92.070000 1.008e+02 ## k_parent  0.006541   14.17 1.578e-05  0.005455 7.842e-03 ## sigma     3.162000    4.00 5.162e-03  1.130000 5.194e+00 ##  ## FOCUS Chi2 error levels in percent: ##          err.min n.optim df ## All data   3.287       2  6 ## parent     3.287       2  6 ##  ## Estimated disappearance times: ##        DT50 DT90 ## parent  106  352 summary(mm.L4[[\"FOMC\", 1]], data = FALSE) ## mkin version used for fitting:    1.2.9  ## R version used for fitting:       4.4.2  ## Date of fit:     Thu Feb 13 15:49:56 2025  ## Date of summary: Thu Feb 13 15:49:56 2025  ##  ## Equations: ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent ##  ## Model predictions using solution type analytical  ##  ## Fitted using 224 model solutions performed in 0.013 s ##  ## Error model: Constant variance  ##  ## Error model algorithm: OLS  ##  ## Starting values for parameters to be optimised: ##          value   type ## parent_0  96.6  state ## alpha      1.0 deparm ## beta      10.0 deparm ##  ## Starting values for the transformed parameters actually optimised: ##               value lower upper ## parent_0  96.600000  -Inf   Inf ## log_alpha  0.000000  -Inf   Inf ## log_beta   2.302585  -Inf   Inf ##  ## Fixed parameter values: ## None ##  ## Results: ##  ##        AIC      BIC    logLik ##   40.37255 40.69032 -16.18628 ##  ## Optimised, transformed parameters with symmetric confidence intervals: ##           Estimate Std. Error   Lower    Upper ## parent_0   99.1400     1.2670 95.6300 102.7000 ## log_alpha  -0.3506     0.2616 -1.0770   0.3756 ## log_beta    4.1740     0.3938  3.0810   5.2670 ## sigma       1.8300     0.4575  0.5598   3.1000 ##  ## Parameter correlation: ##             parent_0  log_alpha   log_beta      sigma ## parent_0   1.000e+00 -4.696e-01 -5.543e-01 -2.447e-07 ## log_alpha -4.696e-01  1.000e+00  9.889e-01  2.198e-08 ## log_beta  -5.543e-01  9.889e-01  1.000e+00  4.923e-08 ## sigma     -2.447e-07  2.198e-08  4.923e-08  1.000e+00 ##  ## Backtransformed parameters: ## Confidence intervals for internally transformed parameters are asymmetric. ## t-test (unrealistically) based on the assumption of normal distribution ## for estimators of untransformed parameters. ##          Estimate t value    Pr(>t)   Lower   Upper ## parent_0  99.1400  78.250 7.993e-08 95.6300 102.700 ## alpha      0.7042   3.823 9.365e-03  0.3407   1.456 ## beta      64.9800   2.540 3.201e-02 21.7800 193.900 ## sigma      1.8300   4.000 8.065e-03  0.5598   3.100 ##  ## FOCUS Chi2 error levels in percent: ##          err.min n.optim df ## All data   2.029       3  5 ## parent     2.029       3  5 ##  ## Estimated disappearance times: ##         DT50 DT90 DT50back ## parent 108.9 1644    494.9"},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/articles/mkin.html","id":null,"dir":"Articles","previous_headings":"","what":"Abstract","title":"Short introduction to mkin","text":"regulatory evaluation chemical substances like plant protection products (pesticides), biocides chemicals, degradation data play important role. evaluation pesticide degradation experiments, detailed guidance developed, based nonlinear optimisation. R add-package mkin implements fitting models recommended guidance within R calculates statistical measures data series within one compartments, parent metabolites.","code":"library(\"mkin\", quietly = TRUE) # Define the kinetic model m_SFO_SFO_SFO <- mkinmod(parent = mkinsub(\"SFO\", \"M1\"),                          M1 = mkinsub(\"SFO\", \"M2\"),                          M2 = mkinsub(\"SFO\"),                          use_of_ff = \"max\", quiet = TRUE)   # Produce model predictions using some arbitrary parameters sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120) d_SFO_SFO_SFO <- mkinpredict(m_SFO_SFO_SFO,   c(k_parent = 0.03,     f_parent_to_M1 = 0.5, k_M1 = log(2)/100,     f_M1_to_M2 = 0.9, k_M2 = log(2)/50),   c(parent = 100, M1 = 0, M2 = 0),   sampling_times)  # Generate a dataset by adding normally distributed errors with # standard deviation 3, for two replicates at each sampling time d_SFO_SFO_SFO_err <- add_err(d_SFO_SFO_SFO, reps = 2,                              sdfunc = function(x) 3,                              n = 1, seed = 123456789 )  # Fit the model to the dataset f_SFO_SFO_SFO <- mkinfit(m_SFO_SFO_SFO, d_SFO_SFO_SFO_err[[1]], quiet = TRUE)  # Plot the results separately for parent and metabolites plot_sep(f_SFO_SFO_SFO, lpos = c(\"topright\", \"bottomright\", \"bottomright\"))"},{"path":"https://pkgdown.jrwb.de/mkin/articles/mkin.html","id":null,"dir":"Articles","previous_headings":"","what":"Background","title":"Short introduction to mkin","text":"mkin package (J. Ranke 2021) implements approach degradation kinetics recommended kinetics report provided FOrum Co-ordination pesticide fate models USe (FOCUS Work Group Degradation Kinetics 2006, 2014). covers data series describing decline one compound, data series transformation products (commonly termed metabolites) data series one compartment. possible include back reactions. Therefore, equilibrium reactions equilibrium partitioning can specified, although often leads overparameterisation model. first mkin code published 2010, commonly used tools fitting complex kinetic degradation models experimental data KinGUI (Schäfer et al. 2007), MATLAB based tool graphical user interface specifically tailored task included output proposed FOCUS Kinetics Workgroup, ModelMaker, general purpose compartment based tool providing infrastructure fitting dynamic simulation models based differential equations data. ‘mkin’ code first uploaded BerliOS development platform. taken , version control history imported R-Forge site (see e.g. initial commit 11 May 2010), code still updated. time, R package FME (Flexible Modelling Environment) (Soetaert Petzoldt 2010) already available, provided good basis developing package specifically tailored task. remaining challenge make easy possible users (including author vignette) specify system differential equations include output requested FOCUS guidance, χ2\\chi^2 error level defined guidance. Also, mkin introduced using analytical solutions parent kinetics improved optimization speed. Later, Eigenvalue based solutions introduced mkin case linear differential equations (.e. FOMC DFOP models used parent compound), greatly improving optimization speed cases. , become somehow obsolete, use compiled code described gives even faster execution times. possibility specify back-reactions biphasic model (SFORB) metabolites present mkin beginning.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/articles/mkin.html","id":null,"dir":"Articles","previous_headings":"","what":"Derived software tools","title":"Short introduction to mkin","text":"Soon publication mkin, two derived tools published, namely KinGUII (developed Bayer Crop Science) CAKE (commissioned Tessella Syngenta), added graphical user interface (GUI), added fitting iteratively reweighted least squares (IRLS) characterisation likely parameter distributions Markov Chain Monte Carlo (MCMC) sampling. CAKE focuses smooth use experience, sacrificing flexibility model definition, originally allowing two primary metabolites parallel. current version 3.4 CAKE released May 2020 uses scheme six metabolites flexible arrangement supports biphasic modelling metabolites, support back-reactions (non-instantaneous equilibria). KinGUI offers even flexible widget specifying complex kinetic models. Back-reactions (non-instantaneous equilibria) supported early , 2014, simple first-order models specified transformation products. Starting KinGUII version 2.1, biphasic modelling metabolites also available KinGUII. graphical user interface (GUI) recently brought decent degree maturity browser based GUI named gmkin. Please see documentation page manual information. comparison scope, usability numerical results obtained tools recently published Johannes Ranke, Wöltjen, Meinecke (2018).","code":""},{"path":"https://pkgdown.jrwb.de/mkin/articles/mkin.html","id":null,"dir":"Articles","previous_headings":"","what":"Unique features","title":"Short introduction to mkin","text":"Currently, main unique features available mkin speed increase using compiled code compiler present, parallel model fitting multicore machines using mmkin function, estimation parameter confidence intervals based transformed parameters (see ) possibility use two-component error model iteratively reweighted least squares fitting different variances variable introduced Gao et al. (2011) available mkin since version 0.9-22. release 0.9.49.5, IRLS algorithm complemented direct step-wise maximisation likelihood function, makes possible fit variance variable error model also two-component error model inspired error models developed analytical chemistry (Johannes Ranke Meinecke 2019).","code":""},{"path":"https://pkgdown.jrwb.de/mkin/articles/mkin.html","id":null,"dir":"Articles","previous_headings":"","what":"Internal parameter transformations","title":"Short introduction to mkin","text":"rate constants, log transformation used, proposed Bates Watts (1988, 77, 149). Approximate intervals constructed transformed rate constants (compare Bates Watts 1988, 135), .e. logarithms. Confidence intervals rate constants obtained using appropriate backtransformation using exponential function. first version mkin allowing specifying models using formation fractions, home-made reparameterisation used order ensure sum formation fractions exceed unity. method still used current version KinGUII (v2.1 April 2014), modification allows fixing pathway sink zero. CAKE uses penalties objective function order enforce constraint. 2012, alternative reparameterisation formation fractions proposed together René Lehmann (J. Ranke Lehmann 2012), based isometric logratio transformation (ILR). aim improve validity linear approximation objective function parameter estimation procedure well subsequent calculation parameter confidence intervals. current version mkin, logit transformation used parameters bound 0 1, g parameter DFOP model.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/articles/mkin.html","id":null,"dir":"Articles","previous_headings":"","what":"Confidence intervals based on transformed parameters","title":"Short introduction to mkin","text":"first attempt providing improved parameter confidence intervals introduced mkin 2013, confidence intervals obtained FME transformed parameters simply backtransformed one one yield asymmetric confidence intervals backtransformed parameters. However, 1:1 relation rate constants model transformed parameters fitted model, parameters obtained isometric logratio transformation calculated set formation fractions quantify paths compounds formed specific parent compound, 1:1 relation exists. Therefore, parameter confidence intervals formation fractions obtained method appear valid case single transformation product, currently logit transformation used formation fraction. confidence intervals obtained backtransformation cases 1:1 relation transformed original parameter exist considered author vignette accurate obtained using re-estimation Hessian matrix backtransformation, implemented FME package.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/articles/mkin.html","id":null,"dir":"Articles","previous_headings":"","what":"Parameter t-test based on untransformed parameters","title":"Short introduction to mkin","text":"standard output many nonlinear regression software packages includes results test significant difference zero parameters. test also recommended check validity rate constants FOCUS guidance (FOCUS Work Group Degradation Kinetics 2014, 96ff). argued precondition test, .e. normal distribution estimator parameters, fulfilled case nonlinear regression (J. Ranke Lehmann 2015). However, test commonly used industry, consultants national authorities order decide reliability parameter estimates, based FOCUS guidance mentioned . Therefore, results one-sided t-test included summary output mkin. reasonable test significant difference transformed parameters (e.g. log(k)log(k)) zero, t-test calculated based model definition parameter transformation, .e. similar way packages apply internal parameter transformation. note included mkin output, pointing fact t-test based unjustified assumption normal distribution parameter estimators.","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/FOCUS_Z.html","id":"the-data","dir":"Articles > Web_only","previous_headings":"","what":"The data","title":"Example evaluation of FOCUS dataset Z","text":"following code defines example dataset Appendix 7 FOCUS kinetics report (FOCUS Work Group Degradation Kinetics 2014, 354).","code":"library(mkin, quietly = TRUE) LOD = 0.5 FOCUS_2006_Z = data.frame(   t = c(0, 0.04, 0.125, 0.29, 0.54, 1, 2, 3, 4, 7, 10, 14, 21,         42, 61, 96, 124),   Z0 = c(100, 81.7, 70.4, 51.1, 41.2, 6.6, 4.6, 3.9, 4.6, 4.3, 6.8,          2.9, 3.5, 5.3, 4.4, 1.2, 0.7),   Z1 = c(0, 18.3, 29.6, 46.3, 55.1, 65.7, 39.1, 36, 15.3, 5.6, 1.1,          1.6, 0.6, 0.5 * LOD, NA, NA, NA),   Z2 = c(0, NA, 0.5 * LOD, 2.6, 3.8, 15.3, 37.2, 31.7, 35.6, 14.5,          0.8, 2.1, 1.9, 0.5 * LOD, NA, NA, NA),   Z3 = c(0, NA, NA, NA, NA, 0.5 * LOD, 9.2, 13.1, 22.3, 28.4, 32.5,          25.2, 17.2, 4.8, 4.5, 2.8, 4.4))  FOCUS_2006_Z_mkin <- mkin_wide_to_long(FOCUS_2006_Z)"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/FOCUS_Z.html","id":"parent-and-one-metabolite","dir":"Articles > Web_only","previous_headings":"","what":"Parent and one metabolite","title":"Example evaluation of FOCUS dataset Z","text":"next step set models used kinetic analysis. simultaneous fit parent first metabolite usually straightforward, Step 1 (SFO parent ) skipped . start model 2a, formation decline metabolite Z1 pathway parent directly sink included (default mkin).  obvious parameter summary (component summary), kinetic rate constant parent compound Z sink small t-test parameter suggests significantly different zero. suggests, agreement analysis FOCUS kinetics report, simplify model removing pathway sink. similar result can obtained formation fractions used model formulation:  , ilr transformed formation fraction fitted model takes large value, backtransformed formation fraction parent Z Z1 practically unity. , covariance matrix used calculation confidence intervals returned model overparameterised. simplified model obtained removing pathway sink. following, use parameterisation formation fractions order able compare results FOCUS guidance, makes easier use parameters obtained previous fit adding metabolite.  one transformation product Z0 pathway sink, formation fraction internally fixed unity.","code":"Z.2a <- mkinmod(Z0 = mkinsub(\"SFO\", \"Z1\"),                 Z1 = mkinsub(\"SFO\")) ## Temporary DLL for differentials generated and loaded m.Z.2a <- mkinfit(Z.2a, FOCUS_2006_Z_mkin, quiet = TRUE) ## Warning in mkinfit(Z.2a, FOCUS_2006_Z_mkin, quiet = TRUE): Observations with ## value of zero were removed from the data plot_sep(m.Z.2a) summary(m.Z.2a, data = FALSE)$bpar ##            Estimate se_notrans t value     Pr(>t)    Lower    Upper ## Z0_0       97.01488   3.301084 29.3888 3.2971e-21 91.66556 102.3642 ## k_Z0        2.23601   0.207078 10.7979 3.3309e-11  1.95303   2.5600 ## k_Z1        0.48212   0.063265  7.6207 2.8154e-08  0.40341   0.5762 ## f_Z0_to_Z1  1.00000   0.094764 10.5525 5.3560e-11  0.00000   1.0000 ## sigma       4.80411   0.635638  7.5579 3.2592e-08  3.52677   6.0815 Z.2a.ff <- mkinmod(Z0 = mkinsub(\"SFO\", \"Z1\"),                    Z1 = mkinsub(\"SFO\"),                    use_of_ff = \"max\") ## Temporary DLL for differentials generated and loaded m.Z.2a.ff <- mkinfit(Z.2a.ff, FOCUS_2006_Z_mkin, quiet = TRUE) ## Warning in mkinfit(Z.2a.ff, FOCUS_2006_Z_mkin, quiet = TRUE): Observations with ## value of zero were removed from the data plot_sep(m.Z.2a.ff) summary(m.Z.2a.ff, data = FALSE)$bpar ##            Estimate se_notrans t value     Pr(>t)    Lower    Upper ## Z0_0       97.01488   3.301084 29.3888 3.2971e-21 91.66556 102.3642 ## k_Z0        2.23601   0.207078 10.7979 3.3309e-11  1.95303   2.5600 ## k_Z1        0.48212   0.063265  7.6207 2.8154e-08  0.40341   0.5762 ## f_Z0_to_Z1  1.00000   0.094764 10.5525 5.3560e-11  0.00000   1.0000 ## sigma       4.80411   0.635638  7.5579 3.2592e-08  3.52677   6.0815 Z.3 <- mkinmod(Z0 = mkinsub(\"SFO\", \"Z1\", sink = FALSE),                Z1 = mkinsub(\"SFO\"), use_of_ff = \"max\") ## Temporary DLL for differentials generated and loaded m.Z.3 <- mkinfit(Z.3, FOCUS_2006_Z_mkin, quiet = TRUE) ## Warning in mkinfit(Z.3, FOCUS_2006_Z_mkin, quiet = TRUE): Observations with ## value of zero were removed from the data plot_sep(m.Z.3) summary(m.Z.3, data = FALSE)$bpar ##       Estimate se_notrans t value     Pr(>t)    Lower    Upper ## Z0_0  97.01488   2.597342  37.352 2.0106e-24 91.67597 102.3538 ## k_Z0   2.23601   0.146904  15.221 9.1477e-15  1.95354   2.5593 ## k_Z1   0.48212   0.041727  11.554 4.8268e-12  0.40355   0.5760 ## sigma  4.80411   0.620208   7.746 1.6110e-08  3.52925   6.0790"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/FOCUS_Z.html","id":"metabolites-z2-and-z3","dir":"Articles > Web_only","previous_headings":"","what":"Metabolites Z2 and Z3","title":"Example evaluation of FOCUS dataset Z","text":"suggested FOCUS report, pathway sink removed metabolite Z1 well next step. step appears questionable basis results, followed purpose comparison. Also, FOCUS report, assumed additional empirical evidence Z1 quickly exclusively hydrolyses Z2.  Finally, metabolite Z3 added model. use optimised differential equation parameter values previous fit order accelerate optimization.  fit corresponds final result chosen Appendix 7 FOCUS report. Confidence intervals returned mkin based internally transformed parameters, however.","code":"Z.5 <- mkinmod(Z0 = mkinsub(\"SFO\", \"Z1\", sink = FALSE),                Z1 = mkinsub(\"SFO\", \"Z2\", sink = FALSE),                Z2 = mkinsub(\"SFO\"), use_of_ff = \"max\") ## Temporary DLL for differentials generated and loaded m.Z.5 <- mkinfit(Z.5, FOCUS_2006_Z_mkin, quiet = TRUE) ## Warning in mkinfit(Z.5, FOCUS_2006_Z_mkin, quiet = TRUE): Observations with ## value of zero were removed from the data plot_sep(m.Z.5) Z.FOCUS <- mkinmod(Z0 = mkinsub(\"SFO\", \"Z1\", sink = FALSE),                    Z1 = mkinsub(\"SFO\", \"Z2\", sink = FALSE),                    Z2 = mkinsub(\"SFO\", \"Z3\"),                    Z3 = mkinsub(\"SFO\"),                    use_of_ff = \"max\") ## Temporary DLL for differentials generated and loaded m.Z.FOCUS <- mkinfit(Z.FOCUS, FOCUS_2006_Z_mkin,                      parms.ini = m.Z.5$bparms.ode,                      quiet = TRUE) ## Warning in mkinfit(Z.FOCUS, FOCUS_2006_Z_mkin, parms.ini = m.Z.5$bparms.ode, : ## Observations with value of zero were removed from the data ## Warning in mkinfit(Z.FOCUS, FOCUS_2006_Z_mkin, parms.ini = m.Z.5$bparms.ode, : Optimisation did not converge: ## false convergence (8) plot_sep(m.Z.FOCUS) summary(m.Z.FOCUS, data = FALSE)$bpar ##             Estimate se_notrans t value     Pr(>t)     Lower      Upper ## Z0_0       96.842440   1.994291 48.5598 4.0226e-42 92.830421 100.854459 ## k_Z0        2.215425   0.118457 18.7023 1.0404e-23  1.989490   2.467019 ## k_Z1        0.478307   0.028257 16.9272 6.2332e-22  0.424709   0.538669 ## k_Z2        0.451642   0.042139 10.7178 1.6304e-14  0.374348   0.544894 ## k_Z3        0.058692   0.015245  3.8499 1.7803e-04  0.034804   0.098975 ## f_Z2_to_Z3  0.471483   0.058348  8.0806 9.6585e-11  0.357720   0.588287 ## sigma       3.984431   0.383402 10.3923 4.5576e-14  3.213126   4.755737 endpoints(m.Z.FOCUS) ## $ff ##   Z2_Z3 Z2_sink  ## 0.47148 0.52852  ##  ## $distimes ##        DT50    DT90 ## Z0  0.31287  1.0393 ## Z1  1.44917  4.8140 ## Z2  1.53473  5.0983 ## Z3 11.80991 39.2317"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/FOCUS_Z.html","id":"using-the-sforb-model","dir":"Articles > Web_only","previous_headings":"","what":"Using the SFORB model","title":"Example evaluation of FOCUS dataset Z","text":"FOCUS report states, certain tailing time course metabolite Z3. Also, time course parent compound fitted well using SFO model, residues certain low level remain. Therefore, additional model offered , using single first-order reversible binding (SFORB) model metabolite Z3. expected, χ2\\chi^2 error level lower metabolite Z3 using model graphical fit Z3 improved. However, covariance matrix returned.  Therefore, stepwise model building performed starting stage parent two metabolites, starting assumption model fit parent compound can improved using SFORB model.  results much better representation behaviour parent compound Z0. Finally, Z3 added well. models appear overparameterised (covariance matrix returned) sink Z1 left models.  error level fit, especially metabolite Z3, can improved SFORB model chosen metabolite, model capable representing tailing metabolite decline phase.  summary view backtransformed parameters shows get confidence intervals due overparameterisation. optimized excessively small, seems reasonable fix zero.  expected, residual plots Z0 Z3 random case SFO model shown . conclusion, model proposed best-fit model dataset Appendix 7 FOCUS report. graphical representation confidence intervals can finally obtained.  endpoints obtained model clear degradation rate Z3 towards end experiment low DT50_Z3_b2 (second Eigenvalue system two differential equations representing SFORB system Z3, corresponding slower rate constant DFOP model) reported infinity. However, appears feature data.","code":"Z.mkin.1 <- mkinmod(Z0 = mkinsub(\"SFO\", \"Z1\", sink = FALSE),                     Z1 = mkinsub(\"SFO\", \"Z2\", sink = FALSE),                     Z2 = mkinsub(\"SFO\", \"Z3\"),                     Z3 = mkinsub(\"SFORB\")) ## Temporary DLL for differentials generated and loaded m.Z.mkin.1 <- mkinfit(Z.mkin.1, FOCUS_2006_Z_mkin, quiet = TRUE) ## Warning in mkinfit(Z.mkin.1, FOCUS_2006_Z_mkin, quiet = TRUE): Observations ## with value of zero were removed from the data plot_sep(m.Z.mkin.1) summary(m.Z.mkin.1, data = FALSE)$cov.unscaled ## NULL Z.mkin.3 <- mkinmod(Z0 = mkinsub(\"SFORB\", \"Z1\", sink = FALSE),                     Z1 = mkinsub(\"SFO\", \"Z2\", sink = FALSE),                     Z2 = mkinsub(\"SFO\")) ## Temporary DLL for differentials generated and loaded m.Z.mkin.3 <- mkinfit(Z.mkin.3, FOCUS_2006_Z_mkin, quiet = TRUE) ## Warning in mkinfit(Z.mkin.3, FOCUS_2006_Z_mkin, quiet = TRUE): Observations ## with value of zero were removed from the data plot_sep(m.Z.mkin.3) Z.mkin.4 <- mkinmod(Z0 = mkinsub(\"SFORB\", \"Z1\", sink = FALSE),                     Z1 = mkinsub(\"SFO\", \"Z2\", sink = FALSE),                     Z2 = mkinsub(\"SFO\", \"Z3\"),                     Z3 = mkinsub(\"SFO\")) ## Temporary DLL for differentials generated and loaded m.Z.mkin.4 <- mkinfit(Z.mkin.4, FOCUS_2006_Z_mkin,                       parms.ini = m.Z.mkin.3$bparms.ode,                       quiet = TRUE) ## Warning in mkinfit(Z.mkin.4, FOCUS_2006_Z_mkin, parms.ini = ## m.Z.mkin.3$bparms.ode, : Observations with value of zero were removed from the ## data plot_sep(m.Z.mkin.4) Z.mkin.5 <- mkinmod(Z0 = mkinsub(\"SFORB\", \"Z1\", sink = FALSE),                     Z1 = mkinsub(\"SFO\", \"Z2\", sink = FALSE),                     Z2 = mkinsub(\"SFO\", \"Z3\"),                     Z3 = mkinsub(\"SFORB\")) ## Temporary DLL for differentials generated and loaded m.Z.mkin.5 <- mkinfit(Z.mkin.5, FOCUS_2006_Z_mkin,                       parms.ini = m.Z.mkin.4$bparms.ode[1:4],                       quiet = TRUE) ## Warning in mkinfit(Z.mkin.5, FOCUS_2006_Z_mkin, parms.ini = ## m.Z.mkin.4$bparms.ode[1:4], : Observations with value of zero were removed from ## the data plot_sep(m.Z.mkin.5) m.Z.mkin.5a <- mkinfit(Z.mkin.5, FOCUS_2006_Z_mkin,                        parms.ini = c(m.Z.mkin.5$bparms.ode[1:7],                                      k_Z3_bound_free = 0),                        fixed_parms = \"k_Z3_bound_free\",                        quiet = TRUE) ## Warning in mkinfit(Z.mkin.5, FOCUS_2006_Z_mkin, parms.ini = ## c(m.Z.mkin.5$bparms.ode[1:7], : Observations with value of zero were removed ## from the data plot_sep(m.Z.mkin.5a) mkinparplot(m.Z.mkin.5a) endpoints(m.Z.mkin.5a) ## $ff ## Z0_free   Z2_Z3 Z2_sink Z3_free  ## 1.00000 0.53656 0.46344 1.00000  ##  ## $SFORB ##     Z0_b1     Z0_b2      Z0_g     Z3_b1     Z3_b2      Z3_g  ## 2.4471342 0.0075124 0.9519866 0.0800071 0.0000000 0.9347816  ##  ## $distimes ##      DT50   DT90 DT50back DT50_Z0_b1 DT50_Z0_b2 DT50_Z3_b1 DT50_Z3_b2 ## Z0 0.3043 1.1848  0.35666    0.28325     92.267         NA         NA ## Z1 1.5148 5.0320       NA         NA         NA         NA         NA ## Z2 1.6414 5.4526       NA         NA         NA         NA         NA ## Z3     NA     NA       NA         NA         NA     8.6636        Inf"},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/NAFTA_examples.html","id":"introduction","dir":"Articles > Web_only","previous_headings":"","what":"Introduction","title":"Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance","text":"document, example evaluations provided Attachment 1 SOP US EPA using NAFTA guidance (US EPA 2015) repeated using mkin. original evaluations reported attachment performed using PestDF version 0.8.4. Note PestDF 0.8.13 version distributed US EPA website today (2019-02-26). datasets now distributed mkin package.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/NAFTA_examples.html","id":"examples-where-dfop-did-not-converge-with-pestdf-0-8-4","dir":"Articles > Web_only","previous_headings":"","what":"Examples where DFOP did not converge with PestDF 0.8.4","title":"Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance","text":"attachment 1, reported DFOP model converge datasets PestDF 0.8.4 used. four datasets, DFOP model can fitted mkin (see ). negative half-life given PestDF 0.8.4 fits appears result bug. results two models (SFO IORE) .","code":""},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/NAFTA_examples.html","id":"example-on-page-5-upper-panel","dir":"Articles > Web_only","previous_headings":"Examples where DFOP did not converge with PestDF 0.8.4","what":"Example on page 5, upper panel","title":"Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance","text":"","code":"p5a <- nafta(NAFTA_SOP_Attachment[[\"p5a\"]]) ## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c ## The half-life obtained from the IORE model may be used plot(p5a) print(p5a) ## Sums of squares: ##       SFO      IORE      DFOP  ## 465.21753  56.27506  32.06401  ##  ## Critical sum of squares for checking the SFO model: ## [1] 64.4304 ##  ## Parameters: ## $SFO ##          Estimate   Pr(>t)  Lower   Upper ## parent_0  95.8401 4.67e-21 92.245 99.4357 ## k_parent   0.0102 3.92e-12  0.009  0.0117 ## sigma      4.8230 3.81e-06  3.214  6.4318 ##  ## $IORE ##                Estimate Pr(>t)    Lower    Upper ## parent_0       1.01e+02     NA 9.91e+01 1.02e+02 ## k__iore_parent 1.54e-05     NA 4.08e-06 5.84e-05 ## N_parent       2.57e+00     NA 2.25e+00 2.89e+00 ## sigma          1.68e+00     NA 1.12e+00 2.24e+00 ##  ## $DFOP ##          Estimate   Pr(>t)   Lower    Upper ## parent_0 9.99e+01 1.41e-26 98.8116 101.0810 ## k1       2.67e-02 5.05e-06  0.0243   0.0295 ## k2       3.41e-12 5.00e-01  0.0000      Inf ## g        6.47e-01 3.67e-06  0.6248   0.6677 ## sigma    1.27e+00 8.91e-06  0.8395   1.6929 ##  ##  ## DTx values: ##      DT50     DT90 DT50_rep ## SFO  67.7 2.25e+02 6.77e+01 ## IORE 58.2 1.07e+03 3.22e+02 ## DFOP 55.5 3.70e+11 2.03e+11 ##  ## Representative half-life: ## [1] 321.51"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/NAFTA_examples.html","id":"example-on-page-5-lower-panel","dir":"Articles > Web_only","previous_headings":"Examples where DFOP did not converge with PestDF 0.8.4","what":"Example on page 5, lower panel","title":"Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance","text":"","code":"p5b <- nafta(NAFTA_SOP_Attachment[[\"p5b\"]]) ## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c ## The half-life obtained from the IORE model may be used plot(p5b) print(p5b) ## Sums of squares: ##      SFO     IORE     DFOP  ## 94.81123 10.10936  7.55871  ##  ## Critical sum of squares for checking the SFO model: ## [1] 11.77879 ##  ## Parameters: ## $SFO ##          Estimate   Pr(>t)    Lower    Upper ## parent_0   96.497 2.32e-24 94.85271 98.14155 ## k_parent    0.008 3.42e-14  0.00737  0.00869 ## sigma       2.295 1.22e-05  1.47976  3.11036 ##  ## $IORE ##                Estimate   Pr(>t)    Lower    Upper ## parent_0       9.85e+01 1.17e-28 9.79e+01 9.92e+01 ## k__iore_parent 1.53e-04 6.50e-03 7.21e-05 3.26e-04 ## N_parent       1.94e+00 5.88e-13 1.76e+00 2.12e+00 ## sigma          7.49e-01 1.63e-05 4.82e-01 1.02e+00 ##  ## $DFOP ##          Estimate   Pr(>t)   Lower   Upper ## parent_0 9.84e+01 1.24e-27 97.8078 98.9187 ## k1       1.55e-02 4.10e-04  0.0143  0.0167 ## k2       9.07e-12 5.00e-01  0.0000     Inf ## g        6.89e-01 2.92e-03  0.6626  0.7142 ## sigma    6.48e-01 2.38e-05  0.4147  0.8813 ##  ##  ## DTx values: ##      DT50     DT90 DT50_rep ## SFO  86.6 2.88e+02 8.66e+01 ## IORE 85.5 7.17e+02 2.16e+02 ## DFOP 83.6 1.25e+11 7.64e+10 ##  ## Representative half-life: ## [1] 215.87"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/NAFTA_examples.html","id":"example-on-page-6","dir":"Articles > Web_only","previous_headings":"Examples where DFOP did not converge with PestDF 0.8.4","what":"Example on page 6","title":"Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance","text":"","code":"p6 <- nafta(NAFTA_SOP_Attachment[[\"p6\"]]) ## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c ## The half-life obtained from the IORE model may be used plot(p6) print(p6) ## Sums of squares: ##       SFO      IORE      DFOP  ## 188.45361  51.00699  42.46931  ##  ## Critical sum of squares for checking the SFO model: ## [1] 58.39888 ##  ## Parameters: ## $SFO ##          Estimate   Pr(>t)   Lower   Upper ## parent_0  94.7759 7.29e-24 92.3478 97.2039 ## k_parent   0.0179 8.02e-16  0.0166  0.0194 ## sigma      3.0696 3.81e-06  2.0456  4.0936 ##  ## $IORE ##                Estimate   Pr(>t)    Lower    Upper ## parent_0       97.12446 2.63e-26 95.62461 98.62431 ## k__iore_parent  0.00252 1.95e-03  0.00134  0.00472 ## N_parent        1.49587 4.07e-13  1.33896  1.65279 ## sigma           1.59698 5.05e-06  1.06169  2.13227 ##  ## $DFOP ##          Estimate   Pr(>t)   Lower   Upper ## parent_0 9.66e+01 1.57e-25 95.3476 97.8979 ## k1       2.55e-02 7.33e-06  0.0233  0.0278 ## k2       3.84e-11 5.00e-01  0.0000     Inf ## g        8.61e-01 7.55e-06  0.8314  0.8867 ## sigma    1.46e+00 6.93e-06  0.9661  1.9483 ##  ##  ## DTx values: ##      DT50     DT90 DT50_rep ## SFO  38.6 1.28e+02 3.86e+01 ## IORE 34.0 1.77e+02 5.32e+01 ## DFOP 34.1 8.50e+09 1.80e+10 ##  ## Representative half-life: ## [1] 53.17"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/NAFTA_examples.html","id":"example-on-page-7","dir":"Articles > Web_only","previous_headings":"Examples where DFOP did not converge with PestDF 0.8.4","what":"Example on page 7","title":"Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance","text":"","code":"p7 <- nafta(NAFTA_SOP_Attachment[[\"p7\"]]) ## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c ## The half-life obtained from the IORE model may be used plot(p7) print(p7) ## Sums of squares: ##      SFO     IORE     DFOP  ## 3661.661 3195.030 3174.145  ##  ## Critical sum of squares for checking the SFO model: ## [1] 3334.194 ##  ## Parameters: ## $SFO ##          Estimate   Pr(>t)    Lower    Upper ## parent_0 96.41796 4.80e-53 93.32245 99.51347 ## k_parent  0.00735 7.64e-21  0.00641  0.00843 ## sigma     7.94557 1.83e-15  6.46713  9.42401 ##  ## $IORE ##                Estimate Pr(>t)    Lower    Upper ## parent_0       9.92e+01     NA 9.55e+01 1.03e+02 ## k__iore_parent 1.60e-05     NA 1.45e-07 1.77e-03 ## N_parent       2.45e+00     NA 1.35e+00 3.54e+00 ## sigma          7.42e+00     NA 6.04e+00 8.80e+00 ##  ## $DFOP ##          Estimate   Pr(>t)   Lower    Upper ## parent_0 9.89e+01 9.44e-49 95.4640 102.2573 ## k1       1.81e-02 1.75e-01  0.0116   0.0281 ## k2       3.62e-10 5.00e-01  0.0000      Inf ## g        6.06e-01 2.19e-01  0.4826   0.7178 ## sigma    7.40e+00 2.97e-15  6.0201   8.7754 ##  ##  ## DTx values: ##      DT50     DT90 DT50_rep ## SFO  94.3 3.13e+02 9.43e+01 ## IORE 96.7 1.51e+03 4.55e+02 ## DFOP 96.4 3.79e+09 1.92e+09 ##  ## Representative half-life: ## [1] 454.55"},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/NAFTA_examples.html","id":"example-on-page-8","dir":"Articles > Web_only","previous_headings":"Examples where the representative half-life deviates from the observed DT50","what":"Example on page 8","title":"Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance","text":"dataset, IORE fit converge default starting values used mkin IORE model used. Therefore, lower value rate constant used .","code":"p8 <- nafta(NAFTA_SOP_Attachment[[\"p8\"]], parms.ini = c(k__iore_parent = 1e-3)) ## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c ## The half-life obtained from the IORE model may be used plot(p8) print(p8) ## Sums of squares: ##       SFO      IORE      DFOP  ## 1996.9408  444.9237  547.5616  ##  ## Critical sum of squares for checking the SFO model: ## [1] 477.4924 ##  ## Parameters: ## $SFO ##          Estimate   Pr(>t)    Lower    Upper ## parent_0 88.16549 6.53e-29 83.37344 92.95754 ## k_parent  0.00803 1.67e-13  0.00674  0.00957 ## sigma     7.44786 4.17e-10  5.66209  9.23363 ##  ## $IORE ##                Estimate   Pr(>t)    Lower    Upper ## parent_0       9.77e+01 7.03e-35 9.44e+01 1.01e+02 ## k__iore_parent 6.14e-05 3.20e-02 2.12e-05 1.78e-04 ## N_parent       2.27e+00 4.23e-18 2.00e+00 2.54e+00 ## sigma          3.52e+00 5.36e-10 2.67e+00 4.36e+00 ##  ## $DFOP ##          Estimate   Pr(>t)    Lower    Upper ## parent_0 95.70619 8.99e-32 91.87941 99.53298 ## k1        0.02500 5.25e-04  0.01422  0.04394 ## k2        0.00273 6.84e-03  0.00125  0.00597 ## g         0.58835 2.84e-06  0.36595  0.77970 ## sigma     3.90001 6.94e-10  2.96260  4.83741 ##  ##  ## DTx values: ##      DT50 DT90 DT50_rep ## SFO  86.3  287     86.3 ## IORE 53.4  668    201.0 ## DFOP 55.6  517    253.0 ##  ## Representative half-life: ## [1] 201.03"},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/NAFTA_examples.html","id":"example-on-page-9-upper-panel","dir":"Articles > Web_only","previous_headings":"Examples where SFO was not selected for an abiotic study","what":"Example on page 9, upper panel","title":"Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance","text":"example, residuals SFO indicate lack fit model, even abiotic experiment, data suggest simple exponential decline.","code":"p9a <- nafta(NAFTA_SOP_Attachment[[\"p9a\"]]) ## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c ## The half-life obtained from the IORE model may be used plot(p9a) print(p9a) ## Sums of squares: ##       SFO      IORE      DFOP  ## 839.35238  88.57064   9.93363  ##  ## Critical sum of squares for checking the SFO model: ## [1] 105.5678 ##  ## Parameters: ## $SFO ##          Estimate   Pr(>t)   Lower   Upper ## parent_0  88.1933 3.06e-12 79.9447 96.4419 ## k_parent   0.0409 2.07e-07  0.0324  0.0516 ## sigma      7.2429 3.92e-05  4.4768 10.0090 ##  ## $IORE ##                Estimate   Pr(>t)    Lower    Upper ## parent_0       9.89e+01 1.12e-16 9.54e+01 1.02e+02 ## k__iore_parent 1.93e-05 1.13e-01 3.49e-06 1.06e-04 ## N_parent       2.91e+00 1.45e-09 2.50e+00 3.32e+00 ## sigma          2.35e+00 5.31e-05 1.45e+00 3.26e+00 ##  ## $DFOP ##          Estimate   Pr(>t)  Lower  Upper ## parent_0 9.85e+01 2.54e-20 97.390 99.672 ## k1       1.38e-01 3.52e-05  0.131  0.146 ## k2       9.02e-13 5.00e-01  0.000    Inf ## g        6.52e-01 8.13e-06  0.642  0.661 ## sigma    7.88e-01 6.13e-02  0.481  1.095 ##  ##  ## DTx values: ##      DT50     DT90 DT50_rep ## SFO  16.9 5.63e+01 1.69e+01 ## IORE 11.6 3.37e+02 1.01e+02 ## DFOP 10.5 1.38e+12 7.68e+11 ##  ## Representative half-life: ## [1] 101.43"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/NAFTA_examples.html","id":"example-on-page-9-lower-panel","dir":"Articles > Web_only","previous_headings":"Examples where SFO was not selected for an abiotic study","what":"Example on page 9, lower panel","title":"Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance","text":", mkin gives longer slow DT50 DFOP model (17.8 days) PestDF (13.5 days). Presumably, related fact PestDF gives negative value proportion fast degradation 0 1, inclusive. parameter called f PestDF g mkin. mkin, restricted interval 0 1.","code":"p9b <- nafta(NAFTA_SOP_Attachment[[\"p9b\"]]) ## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c ## The half-life obtained from the IORE model may be used plot(p9b) print(p9b) ## Sums of squares: ##      SFO     IORE     DFOP  ## 35.64867 23.22334 35.64867  ##  ## Critical sum of squares for checking the SFO model: ## [1] 28.54188 ##  ## Parameters: ## $SFO ##          Estimate   Pr(>t)  Lower   Upper ## parent_0  94.7123 2.15e-19 93.178 96.2464 ## k_parent   0.0389 4.47e-14  0.037  0.0408 ## sigma      1.5957 1.28e-04  0.932  2.2595 ##  ## $IORE ##                Estimate   Pr(>t)   Lower  Upper ## parent_0         93.863 2.32e-18 92.4565 95.269 ## k__iore_parent    0.127 1.85e-02  0.0504  0.321 ## N_parent          0.711 1.88e-05  0.4843  0.937 ## sigma             1.288 1.76e-04  0.7456  1.830 ##  ## $DFOP ##          Estimate   Pr(>t)   Lower   Upper ## parent_0  94.7123 1.61e-16 93.1355 96.2891 ## k1         0.0389 1.08e-04  0.0266  0.0569 ## k2         0.0389 2.24e-04  0.0255  0.0592 ## g          0.5256 5.00e-01  0.0000  1.0000 ## sigma      1.5957 2.50e-04  0.9135  2.2779 ##  ##  ## DTx values: ##      DT50 DT90 DT50_rep ## SFO  17.8 59.2     17.8 ## IORE 18.4 49.2     14.8 ## DFOP 17.8 59.2     17.8 ##  ## Representative half-life: ## [1] 14.8"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/NAFTA_examples.html","id":"example-on-page-10","dir":"Articles > Web_only","previous_headings":"Examples where SFO was not selected for an abiotic study","what":"Example on page 10","title":"Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance","text":", value N given IORE model, data suggests faster decline towards end experiment, appears physically rather unlikely case photolysis study. seems PestDF constrain N values zero, thus slight difference IORE model parameters PestDF mkin.","code":"p10 <- nafta(NAFTA_SOP_Attachment[[\"p10\"]]) ## Warning in sqrt(diag(covar_notrans)): NaNs produced ## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c ## The half-life obtained from the IORE model may be used plot(p10) print(p10) ## Sums of squares: ##      SFO     IORE     DFOP  ## 899.4089 336.4348 899.4089  ##  ## Critical sum of squares for checking the SFO model: ## [1] 413.4841 ##  ## Parameters: ## $SFO ##          Estimate   Pr(>t)   Lower    Upper ## parent_0 101.7315 6.42e-11 91.9259 111.5371 ## k_parent   0.0495 1.70e-07  0.0404   0.0607 ## sigma      8.0152 1.28e-04  4.6813  11.3491 ##  ## $IORE ##                Estimate   Pr(>t)  Lower   Upper ## parent_0          96.86 3.32e-12 90.848 102.863 ## k__iore_parent     2.96 7.91e-02  0.687  12.761 ## N_parent           0.00 5.00e-01 -0.372   0.372 ## sigma              4.90 1.77e-04  2.837   6.968 ##  ## $DFOP ##          Estimate   Pr(>t)   Lower   Upper ## parent_0 101.7315 1.41e-09 91.6534 111.810 ## k1         0.0495 3.04e-03  0.0188   0.131 ## k2         0.0495 4.92e-04  0.0197   0.124 ## g          0.4487      NaN  0.0000   1.000 ## sigma      8.0152 2.50e-04  4.5886  11.442 ##  ##  ## DTx values: ##      DT50 DT90 DT50_rep ## SFO  14.0 46.5    14.00 ## IORE 16.4 29.4     8.86 ## DFOP 14.0 46.5    14.00 ##  ## Representative half-life: ## [1] 8.86"},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/NAFTA_examples.html","id":"example-on-page-11","dir":"Articles > Web_only","previous_headings":"The DT50 was not observed during the study","what":"Example on page 11","title":"Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance","text":"case, DFOP fit reported PestDF resulted negative value slower rate constant, possible mkin. results agreement.","code":"p11 <- nafta(NAFTA_SOP_Attachment[[\"p11\"]]) ## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c ## The half-life obtained from the IORE model may be used plot(p11) print(p11) ## Sums of squares: ##      SFO     IORE     DFOP  ## 579.6805 204.7932 144.7783  ##  ## Critical sum of squares for checking the SFO model: ## [1] 251.6944 ##  ## Parameters: ## $SFO ##          Estimate   Pr(>t)    Lower    Upper ## parent_0 96.15820 4.83e-13 90.24934 1.02e+02 ## k_parent  0.00321 4.71e-05  0.00222 4.64e-03 ## sigma     6.43473 1.28e-04  3.75822 9.11e+00 ##  ## $IORE ##                Estimate Pr(>t)    Lower    Upper ## parent_0       1.05e+02     NA 9.90e+01 1.10e+02 ## k__iore_parent 3.11e-17     NA 1.35e-20 7.18e-14 ## N_parent       8.36e+00     NA 6.62e+00 1.01e+01 ## sigma          3.82e+00     NA 2.21e+00 5.44e+00 ##  ## $DFOP ##          Estimate   Pr(>t)    Lower    Upper ## parent_0 1.05e+02 9.47e-13  99.9990 109.1224 ## k1       4.41e-02 5.95e-03   0.0296   0.0658 ## k2       9.94e-13 5.00e-01   0.0000      Inf ## g        3.22e-01 1.45e-03   0.2814   0.3650 ## sigma    3.22e+00 3.52e-04   1.8410   4.5906 ##  ##  ## DTx values: ##          DT50     DT90 DT50_rep ## SFO  2.16e+02 7.18e+02 2.16e+02 ## IORE 9.73e+02 1.37e+08 4.11e+07 ## DFOP 3.07e+11 1.93e+12 6.98e+11 ##  ## Representative half-life: ## [1] 41148169"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/NAFTA_examples.html","id":"n-is-less-than-1-and-the-dfop-rate-constants-are-like-the-sfo-rate-constant","dir":"Articles > Web_only","previous_headings":"","what":"N is less than 1 and the DFOP rate constants are like the SFO rate constant","title":"Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance","text":"following three examples, results obtained mkin reported PestDF. case page 10, N values 1 deemed unrealistic appear result overparameterisation.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/NAFTA_examples.html","id":"example-on-page-12-upper-panel","dir":"Articles > Web_only","previous_headings":"N is less than 1 and the DFOP rate constants are like the SFO rate constant","what":"Example on page 12, upper panel","title":"Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance","text":"","code":"p12a <- nafta(NAFTA_SOP_Attachment[[\"p12a\"]]) ## Warning in summary.mkinfit(x): Could not calculate correlation; no covariance ## matrix ## Warning in sqrt(diag(covar_notrans)): NaNs produced ## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c ## The half-life obtained from the IORE model may be used plot(p12a) print(p12a) ## Sums of squares: ##      SFO     IORE     DFOP  ## 695.4440 220.0685 695.4440  ##  ## Critical sum of squares for checking the SFO model: ## [1] 270.4679 ##  ## Parameters: ## $SFO ##          Estimate   Pr(>t)  Lower   Upper ## parent_0  100.521 8.75e-12 92.461 108.581 ## k_parent    0.124 3.61e-08  0.104   0.148 ## sigma       7.048 1.28e-04  4.116   9.980 ##  ## $IORE ##                Estimate Pr(>t) Lower Upper ## parent_0         96.823     NA    NA    NA ## k__iore_parent    2.436     NA    NA    NA ## N_parent          0.263     NA    NA    NA ## sigma             3.965     NA    NA    NA ##  ## $DFOP ##          Estimate   Pr(>t)   Lower   Upper ## parent_0  100.521 2.74e-10 92.2366 108.805 ## k1          0.124 2.53e-05  0.0908   0.170 ## k2          0.124 2.52e-02  0.0456   0.339 ## g           0.793      NaN  0.0000   1.000 ## sigma       7.048 2.50e-04  4.0349  10.061 ##  ##  ## DTx values: ##      DT50 DT90 DT50_rep ## SFO  5.58 18.5     5.58 ## IORE 6.49 13.2     3.99 ## DFOP 5.58 18.5     5.58 ##  ## Representative half-life: ## [1] 3.99"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/NAFTA_examples.html","id":"example-on-page-12-lower-panel","dir":"Articles > Web_only","previous_headings":"N is less than 1 and the DFOP rate constants are like the SFO rate constant","what":"Example on page 12, lower panel","title":"Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance","text":"","code":"p12b <- nafta(NAFTA_SOP_Attachment[[\"p12b\"]]) ## Warning in sqrt(diag(covar)): NaNs produced ## Warning in qt(alpha/2, rdf): NaNs produced ## Warning in qt(1 - alpha/2, rdf): NaNs produced ## Warning in pt(abs(tval), rdf, lower.tail = FALSE): NaNs produced ## Warning in cov2cor(ans$covar): diag(V) had non-positive or NA entries; the ## non-finite result may be dubious ## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c ## The half-life obtained from the IORE model may be used plot(p12b) print(p12b) ## Sums of squares: ##      SFO     IORE     DFOP  ## 58.90242 19.06353 58.90242  ##  ## Critical sum of squares for checking the SFO model: ## [1] 51.51756 ##  ## Parameters: ## $SFO ##          Estimate  Pr(>t)   Lower    Upper ## parent_0  97.6840 0.00039 85.9388 109.4292 ## k_parent   0.0589 0.00261  0.0431   0.0805 ## sigma      3.4323 0.04356 -1.2377   8.1023 ##  ## $IORE ##                Estimate Pr(>t)     Lower  Upper ## parent_0         95.523 0.0055 74.539157 116.51 ## k__iore_parent    0.333 0.1433  0.000717 154.57 ## N_parent          0.568 0.0677 -0.989464   2.13 ## sigma             1.953 0.0975 -5.893100   9.80 ##  ## $DFOP ##          Estimate Pr(>t) Lower Upper ## parent_0  97.6840    NaN   NaN   NaN ## k1         0.0589    NaN    NA    NA ## k2         0.0589    NaN    NA    NA ## g          0.6473    NaN    NA    NA ## sigma      3.4323    NaN   NaN   NaN ##  ##  ## DTx values: ##      DT50 DT90 DT50_rep ## SFO  11.8 39.1    11.80 ## IORE 12.9 31.4     9.46 ## DFOP 11.8 39.1    11.80 ##  ## Representative half-life: ## [1] 9.46"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/NAFTA_examples.html","id":"example-on-page-13","dir":"Articles > Web_only","previous_headings":"N is less than 1 and the DFOP rate constants are like the SFO rate constant","what":"Example on page 13","title":"Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance","text":"","code":"p13 <- nafta(NAFTA_SOP_Attachment[[\"p13\"]]) ## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c ## The half-life obtained from the IORE model may be used plot(p13) print(p13) ## Sums of squares: ##      SFO     IORE     DFOP  ## 174.5971 142.3951 174.5971  ##  ## Critical sum of squares for checking the SFO model: ## [1] 172.131 ##  ## Parameters: ## $SFO ##          Estimate   Pr(>t)    Lower    Upper ## parent_0 92.73500 5.99e-17 89.61936 95.85065 ## k_parent  0.00258 2.42e-09  0.00223  0.00299 ## sigma     3.41172 7.07e-05  2.05455  4.76888 ##  ## $IORE ##                Estimate   Pr(>t)    Lower  Upper ## parent_0        91.6016 6.34e-16 88.53086 94.672 ## k__iore_parent   0.0396 2.36e-01  0.00207  0.759 ## N_parent         0.3541 1.46e-01 -0.35153  1.060 ## sigma            3.0811 9.64e-05  1.84296  4.319 ##  ## $DFOP ##          Estimate Pr(>t)    Lower    Upper ## parent_0 92.73500     NA 8.95e+01 95.92118 ## k1        0.00258     NA 4.18e-04  0.01592 ## k2        0.00258     NA 1.75e-03  0.00381 ## g         0.16452     NA 0.00e+00  1.00000 ## sigma     3.41172     NA 2.02e+00  4.79960 ##  ##  ## DTx values: ##      DT50 DT90 DT50_rep ## SFO   269  892      269 ## IORE  261  560      169 ## DFOP  269  892      269 ##  ## Representative half-life: ## [1] 168.51"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/NAFTA_examples.html","id":"dt50-not-observed-in-the-study-and-dfop-problems-in-pestdf","dir":"Articles > Web_only","previous_headings":"","what":"DT50 not observed in the study and DFOP problems in PestDF","title":"Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance","text":"slower rate constant reported PestDF negative, physically realistic, possible mkin. fits give results mkin PestDF.","code":"p14 <- nafta(NAFTA_SOP_Attachment[[\"p14\"]]) ## Warning in sqrt(diag(covar)): NaNs produced ## Warning in cov2cor(ans$covar): diag(V) had non-positive or NA entries; the ## non-finite result may be dubious ## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c ## The half-life obtained from the IORE model may be used plot(p14) print(p14) ## Sums of squares: ##      SFO     IORE     DFOP  ## 48.43249 28.67746 27.26248  ##  ## Critical sum of squares for checking the SFO model: ## [1] 32.83337 ##  ## Parameters: ## $SFO ##          Estimate   Pr(>t)    Lower    Upper ## parent_0 99.47124 2.06e-30 98.42254 1.01e+02 ## k_parent  0.00279 3.75e-15  0.00256 3.04e-03 ## sigma     1.55616 3.81e-06  1.03704 2.08e+00 ##  ## $IORE ##                Estimate Pr(>t) Lower Upper ## parent_0       1.00e+02     NA   NaN   NaN ## k__iore_parent 9.44e-08     NA   NaN   NaN ## N_parent       3.31e+00     NA   NaN   NaN ## sigma          1.20e+00     NA 0.796   1.6 ##  ## $DFOP ##          Estimate   Pr(>t)    Lower    Upper ## parent_0 1.00e+02 2.96e-28 99.40280 101.2768 ## k1       9.53e-03 1.20e-01  0.00638   0.0143 ## k2       5.21e-12 5.00e-01  0.00000      Inf ## g        3.98e-01 2.19e-01  0.30481   0.4998 ## sigma    1.17e+00 7.68e-06  0.77406   1.5610 ##  ##  ## DTx values: ##          DT50     DT90 DT50_rep ## SFO  2.48e+02 8.25e+02 2.48e+02 ## IORE 4.34e+02 2.22e+04 6.70e+03 ## DFOP 3.55e+10 3.44e+11 1.33e+11 ##  ## Representative half-life: ## [1] 6697.44"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/NAFTA_examples.html","id":"n-is-less-than-1-and-dfop-fraction-parameter-is-below-zero","dir":"Articles > Web_only","previous_headings":"","what":"N is less than 1 and DFOP fraction parameter is below zero","title":"Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance","text":"mkin, IORE fit affected (deemed unrealistic), fraction parameter DFOP model restricted interval 0 1 mkin. SFO fits give results mkin PestDF.","code":"p15a <- nafta(NAFTA_SOP_Attachment[[\"p15a\"]]) ## Warning in sqrt(diag(covar)): NaNs produced ## Warning in cov2cor(ans$covar): diag(V) had non-positive or NA entries; the ## non-finite result may be dubious ## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c ## The half-life obtained from the IORE model may be used plot(p15a) print(p15a) ## Sums of squares: ##      SFO     IORE     DFOP  ## 245.5248 135.0132 245.5248  ##  ## Critical sum of squares for checking the SFO model: ## [1] 165.9335 ##  ## Parameters: ## $SFO ##          Estimate   Pr(>t)    Lower   Upper ## parent_0 97.96751 2.00e-15 94.32049 101.615 ## k_parent  0.00952 4.93e-09  0.00824   0.011 ## sigma     4.18778 1.28e-04  2.44588   5.930 ##  ## $IORE ##                Estimate   Pr(>t)  Lower  Upper ## parent_0         95.874 2.94e-15 92.937 98.811 ## k__iore_parent    0.629 2.11e-01  0.044  8.982 ## N_parent          0.000 5.00e-01 -0.642  0.642 ## sigma             3.105 1.78e-04  1.795  4.416 ##  ## $DFOP ##          Estimate   Pr(>t)    Lower    Upper ## parent_0 97.96751 2.85e-13 94.21913 101.7159 ## k1        0.00952 6.28e-02  0.00260   0.0349 ## k2        0.00952 1.27e-04  0.00652   0.0139 ## g         0.21241 5.00e-01       NA       NA ## sigma     4.18778 2.50e-04  2.39747   5.9781 ##  ##  ## DTx values: ##      DT50 DT90 DT50_rep ## SFO  72.8  242     72.8 ## IORE 76.3  137     41.3 ## DFOP 72.8  242     72.8 ##  ## Representative half-life: ## [1] 41.33 p15b <- nafta(NAFTA_SOP_Attachment[[\"p15b\"]]) ## Warning in summary.mkinfit(x): Could not calculate correlation; no covariance ## matrix ## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c ## The half-life obtained from the IORE model may be used plot(p15b) print(p15b) ## Sums of squares: ##       SFO      IORE      DFOP  ## 106.91629  68.55574 106.91629  ##  ## Critical sum of squares for checking the SFO model: ## [1] 84.25618 ##  ## Parameters: ## $SFO ##          Estimate   Pr(>t)    Lower    Upper ## parent_0 1.01e+02 3.06e-17 98.31594 1.03e+02 ## k_parent 4.86e-03 2.48e-10  0.00435 5.42e-03 ## sigma    2.76e+00 1.28e-04  1.61402 3.91e+00 ##  ## $IORE ##                Estimate   Pr(>t)    Lower  Upper ## parent_0          99.83 1.81e-16 97.51348 102.14 ## k__iore_parent     0.38 3.22e-01  0.00352  41.05 ## N_parent           0.00 5.00e-01 -1.07696   1.08 ## sigma              2.21 2.57e-04  1.23245   3.19 ##  ## $DFOP ##          Estimate Pr(>t) Lower Upper ## parent_0 1.01e+02     NA    NA    NA ## k1       4.86e-03     NA    NA    NA ## k2       4.86e-03     NA    NA    NA ## g        1.88e-01     NA    NA    NA ## sigma    2.76e+00     NA    NA    NA ##  ##  ## DTx values: ##      DT50 DT90 DT50_rep ## SFO   143  474    143.0 ## IORE  131  236     71.2 ## DFOP  143  474    143.0 ##  ## Representative half-life: ## [1] 71.18"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/NAFTA_examples.html","id":"the-dfop-fraction-parameter-is-greater-than-1","dir":"Articles > Web_only","previous_headings":"","what":"The DFOP fraction parameter is greater than 1","title":"Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance","text":"PestDF, DFOP fit seems stuck local minimum, mkin finds solution much lower χ2\\chi^2 error level. half-life slower rate constant DFOP model larger IORE derived half-life, NAFTA recommendation obtained mkin use DFOP representative half-life 8.9 days.","code":"p16 <- nafta(NAFTA_SOP_Attachment[[\"p16\"]]) ## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c ## The representative half-life of the IORE model is longer than the one corresponding ## to the terminal degradation rate found with the DFOP model. ## The representative half-life obtained from the DFOP model may be used plot(p16) print(p16) ## Sums of squares: ##      SFO     IORE     DFOP  ## 3831.804 2062.008 1550.980  ##  ## Critical sum of squares for checking the SFO model: ## [1] 2247.348 ##  ## Parameters: ## $SFO ##          Estimate   Pr(>t)  Lower Upper ## parent_0   71.953 2.33e-13 60.509 83.40 ## k_parent    0.159 4.86e-05  0.102  0.25 ## sigma      11.302 1.25e-08  8.308 14.30 ##  ## $IORE ##                Estimate   Pr(>t)    Lower    Upper ## parent_0       8.74e+01 2.48e-16 7.72e+01 97.52972 ## k__iore_parent 4.55e-04 2.16e-01 3.48e-05  0.00595 ## N_parent       2.70e+00 1.21e-08 1.99e+00  3.40046 ## sigma          8.29e+00 1.61e-08 6.09e+00 10.49062 ##  ## $DFOP ##          Estimate   Pr(>t)   Lower  Upper ## parent_0  88.5333 7.40e-18 79.9836 97.083 ## k1        18.8461 5.00e-01  0.0000    Inf ## k2         0.0776 1.41e-05  0.0518  0.116 ## g          0.4733 1.41e-09  0.3674  0.582 ## sigma      7.1902 2.11e-08  5.2785  9.102 ##  ##  ## DTx values: ##      DT50 DT90 DT50_rep ## SFO  4.35 14.4     4.35 ## IORE 1.48 32.1     9.67 ## DFOP 0.67 21.4     8.93 ##  ## Representative half-life: ## [1] 8.93"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/NAFTA_examples.html","id":"conclusions","dir":"Articles > Web_only","previous_headings":"","what":"Conclusions","title":"Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance","text":"results obtained mkin deviate results obtained PestDF either cases one interpretive rules apply, .e. IORE parameter N less one DFOP k values obtained PestDF equal SFO k values, cases DFOP model converge, often lead negative rate constants returned PestDF. Therefore, mkin appears suitable kinetic evaluations according NAFTA guidance.","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/benchmarks.html","id":"test-cases","dir":"Articles > Web_only","previous_headings":"","what":"Test cases","title":"Benchmark timings for mkin","text":"Parent : One metabolite: Two metabolites, synthetic data:","code":"FOCUS_C <- FOCUS_2006_C FOCUS_D <- subset(FOCUS_2006_D, value != 0) parent_datasets <- list(FOCUS_C, FOCUS_D)   t1 <- system.time(mmkin_bench(c(\"SFO\", \"FOMC\", \"DFOP\", \"HS\"), parent_datasets))[[\"elapsed\"]] t2 <- system.time(mmkin_bench(c(\"SFO\", \"FOMC\", \"DFOP\", \"HS\"), parent_datasets,     error_model = \"tc\"))[[\"elapsed\"]] SFO_SFO <- mkinmod(   parent = mkinsub(\"SFO\", \"m1\"),   m1 = mkinsub(\"SFO\")) FOMC_SFO <- mkinmod(   parent = mkinsub(\"FOMC\", \"m1\"),   m1 = mkinsub(\"SFO\")) DFOP_SFO <- mkinmod(   parent = mkinsub(\"FOMC\", \"m1\"), # erroneously used FOMC twice, not fixed for consistency   m1 = mkinsub(\"SFO\")) t3 <- system.time(mmkin_bench(list(SFO_SFO, FOMC_SFO, DFOP_SFO), list(FOCUS_D)))[[\"elapsed\"]] t4 <- system.time(mmkin_bench(list(SFO_SFO, FOMC_SFO, DFOP_SFO), list(FOCUS_D),     error_model = \"tc\"))[[\"elapsed\"]] t5 <- system.time(mmkin_bench(list(SFO_SFO, FOMC_SFO, DFOP_SFO), list(FOCUS_D),     error_model = \"obs\"))[[\"elapsed\"]] m_synth_SFO_lin <- mkinmod(parent = mkinsub(\"SFO\", \"M1\"),                            M1 = mkinsub(\"SFO\", \"M2\"),                            M2 = mkinsub(\"SFO\"),                            use_of_ff = \"max\", quiet = TRUE)  m_synth_DFOP_par <- mkinmod(parent = mkinsub(\"DFOP\", c(\"M1\", \"M2\")),                            M1 = mkinsub(\"SFO\"),                            M2 = mkinsub(\"SFO\"),                            use_of_ff = \"max\", quiet = TRUE)  SFO_lin_a <- synthetic_data_for_UBA_2014[[1]]$data  DFOP_par_c <- synthetic_data_for_UBA_2014[[12]]$data  t6 <- system.time(mmkin_bench(list(m_synth_SFO_lin), list(SFO_lin_a)))[[\"elapsed\"]] t7 <- system.time(mmkin_bench(list(m_synth_DFOP_par), list(DFOP_par_c)))[[\"elapsed\"]]  t8 <- system.time(mmkin_bench(list(m_synth_SFO_lin), list(SFO_lin_a),     error_model = \"tc\"))[[\"elapsed\"]] t9 <- system.time(mmkin_bench(list(m_synth_DFOP_par), list(DFOP_par_c),     error_model = \"tc\"))[[\"elapsed\"]]  t10 <- system.time(mmkin_bench(list(m_synth_SFO_lin), list(SFO_lin_a),     error_model = \"obs\"))[[\"elapsed\"]] t11 <- system.time(mmkin_bench(list(m_synth_DFOP_par), list(DFOP_par_c),     error_model = \"obs\"))[[\"elapsed\"]]"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/benchmarks.html","id":"results","dir":"Articles > Web_only","previous_headings":"","what":"Results","title":"Benchmark timings for mkin","text":"Benchmarks available error models shown. intended improving mkin, comparing CPUs operating systems. trademarks belong respective owners.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/benchmarks.html","id":"parent-only","dir":"Articles > Web_only","previous_headings":"Results","what":"Parent only","title":"Benchmark timings for mkin","text":"Constant variance (t1) two-component error model (t2) four models fitted two datasets, .e. eight fits test.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/benchmarks.html","id":"one-metabolite","dir":"Articles > Web_only","previous_headings":"Results","what":"One metabolite","title":"Benchmark timings for mkin","text":"Constant variance (t3), two-component error model (t4), variance variable (t5) three models fitted one dataset, .e. three fits test.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/benchmarks.html","id":"two-metabolites","dir":"Articles > Web_only","previous_headings":"Results","what":"Two metabolites","title":"Benchmark timings for mkin","text":"Constant variance (t6 t7), two-component error model (t8 t9), variance variable (t10 t11) one model fitted one dataset, .e. one fit test.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/compiled_models.html","id":"how-to-benefit-from-compiled-models","dir":"Articles > Web_only","previous_headings":"","what":"How to benefit from compiled models","title":"Performance benefit by using compiled model definitions in mkin","text":"using mkin version equal greater 0.9-36 C compiler available, see message model compiled autogenerated C code defining model using mkinmod. Starting version 0.9.49.9, mkinmod() function checks presence compiler using previous versions, used Sys.(\"gcc\") check. Linux, need essential build tools like make gcc clang installed. Debian based linux distributions, pulled installing build-essential package. MacOS, use personally, reports compiler available default. Windows, need install Rtools path bin directory PATH variable. need modify PATH variable installing Rtools. Instead, recommend put line .Rprofile startup file. just text file R code executed R session starts. named .Rprofile located home directory, generally Documents folder. can check location home directory used R issuing","code":"pkgbuild::has_compiler() Sys.setenv(PATH = paste(\"C:/Rtools/bin\", Sys.getenv(\"PATH\"), sep=\";\")) Sys.getenv(\"HOME\")"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/compiled_models.html","id":"comparison-with-other-solution-methods","dir":"Articles > Web_only","previous_headings":"","what":"Comparison with other solution methods","title":"Performance benefit by using compiled model definitions in mkin","text":"First, build simple degradation model parent compound one metabolite, remove zero values dataset. can compare performance Eigenvalue based solution compiled version R implementation differential equations using benchmark package. output code, warnings zero removed FOCUS D dataset suppressed. Since mkin version 0.9.49.11, analytical solution also implemented, included tests . see using compiled model factor 10 faster using deSolve without compiled code.","code":"library(\"mkin\", quietly = TRUE) SFO_SFO <- mkinmod(   parent = mkinsub(\"SFO\", \"m1\"),   m1 = mkinsub(\"SFO\")) ## Temporary DLL for differentials generated and loaded FOCUS_D <- subset(FOCUS_2006_D, value != 0) if (require(rbenchmark)) {   b.1 <- benchmark(     \"deSolve, not compiled\" = mkinfit(SFO_SFO, FOCUS_D,        solution_type = \"deSolve\",        use_compiled = FALSE, quiet = TRUE),     \"Eigenvalue based\" = mkinfit(SFO_SFO, FOCUS_D,        solution_type = \"eigen\", quiet = TRUE),     \"deSolve, compiled\" = mkinfit(SFO_SFO, FOCUS_D,        solution_type = \"deSolve\", quiet = TRUE),     \"analytical\" = mkinfit(SFO_SFO, FOCUS_D,        solution_type = \"analytical\",        use_compiled = FALSE, quiet = TRUE),     replications = 1, order = \"relative\",     columns = c(\"test\", \"replications\", \"relative\", \"elapsed\"))   print(b.1) } else {   print(\"R package rbenchmark is not available\") } ##                    test replications relative elapsed ## 4            analytical            1    1.000   0.105 ## 3     deSolve, compiled            1    1.333   0.140 ## 2      Eigenvalue based            1    1.667   0.175 ## 1 deSolve, not compiled            1   22.486   2.361"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/compiled_models.html","id":"model-without-analytical-solution","dir":"Articles > Web_only","previous_headings":"","what":"Model without analytical solution","title":"Performance benefit by using compiled model definitions in mkin","text":"evaluation also taken example section mkinfit. analytical solution available system, now Eigenvalue based solution possible, deSolve using without compiled code available. get performance benefit factor 24 using version differential equation model compiled C code! vignette built mkin 1.2.9 ","code":"if (require(rbenchmark)) {   FOMC_SFO <- mkinmod(     parent = mkinsub(\"FOMC\", \"m1\"),     m1 = mkinsub( \"SFO\"))    b.2 <- benchmark(     \"deSolve, not compiled\" = mkinfit(FOMC_SFO, FOCUS_D,                                       use_compiled = FALSE, quiet = TRUE),     \"deSolve, compiled\" = mkinfit(FOMC_SFO, FOCUS_D, quiet = TRUE),     replications = 1, order = \"relative\",     columns = c(\"test\", \"replications\", \"relative\", \"elapsed\"))   print(b.2)   factor_FOMC_SFO <- round(b.2[\"1\", \"relative\"]) } else {   factor_FOMC_SFO <- NA   print(\"R package benchmark is not available\") } ## Temporary DLL for differentials generated and loaded ##                    test replications relative elapsed ## 2     deSolve, compiled            1    1.000   0.175 ## 1 deSolve, not compiled            1   23.937   4.189 ## R version 4.4.2 (2024-10-31) ## Platform: x86_64-pc-linux-gnu ## Running under: Debian GNU/Linux 12 (bookworm) ## CPU model: AMD Ryzen 9 7950X 16-Core Processor"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/dimethenamid_2018.html","id":"introduction","dir":"Articles > Web_only","previous_headings":"","what":"Introduction","title":"Example evaluations of the dimethenamid data from 2018","text":"first analysis data analysed presented recent journal article nonlinear mixed-effects models degradation kinetics (Ranke et al. 2021). analysis based nlme package development version saemix package unpublished time. Meanwhile, version 3.0 saemix package available CRAN repository. Also, turned error handling Borstel data mkin package time, leading duplication data points soil. dataset mkin package corrected, interface saemix mkin package updated use released version. vignette intended present date analysis data, using corrected dataset released versions mkin saemix.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/dimethenamid_2018.html","id":"data","dir":"Articles > Web_only","previous_headings":"","what":"Data","title":"Example evaluations of the dimethenamid data from 2018","text":"Residue data forming basis endpoints derived conclusion peer review pesticide risk assessment dimethenamid-P published European Food Safety Authority (EFSA) 2018 (EFSA 2018) transcribed risk assessment report (Rapporteur Member State Germany, Co-Rapporteur Member State Bulgaria 2018) can downloaded Open EFSA repository https://open.efsa.europa.eu/study-inventory/EFSA-Q-2014-00716. data available mkin package. following code (hidden default, please use button right show ) treats data available racemic mixture dimethenamid (DMTA) enantiomer dimethenamid-P (DMTAP) way, difference degradation behaviour identified EU risk assessment. observation times dataset multiplied corresponding normalisation factor also available dataset, order make possible describe datasets single set parameters. Also, datasets observed soil merged, resulting dimethenamid (DMTA) data six soils.","code":"library(mkin, quietly = TRUE) dmta_ds <- lapply(1:7, function(i) {   ds_i <- dimethenamid_2018$ds[[i]]$data   ds_i[ds_i$name == \"DMTAP\", \"name\"] <-  \"DMTA\"   ds_i$time <- ds_i$time * dimethenamid_2018$f_time_norm[i]   ds_i }) names(dmta_ds) <- sapply(dimethenamid_2018$ds, function(ds) ds$title) dmta_ds[[\"Elliot\"]] <- rbind(dmta_ds[[\"Elliot 1\"]], dmta_ds[[\"Elliot 2\"]]) dmta_ds[[\"Elliot 1\"]] <- NULL dmta_ds[[\"Elliot 2\"]] <- NULL"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/dimethenamid_2018.html","id":"parent-degradation","dir":"Articles > Web_only","previous_headings":"","what":"Parent degradation","title":"Example evaluations of the dimethenamid data from 2018","text":"evaluate observed degradation parent compound using simple exponential decline (SFO) biexponential decline (DFOP), using constant variance (const) two-component variance (tc) error models.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/dimethenamid_2018.html","id":"separate-evaluations","dir":"Articles > Web_only","previous_headings":"Parent degradation","what":"Separate evaluations","title":"Example evaluations of the dimethenamid data from 2018","text":"first step, get visual impression fit different models, separate evaluations soil using mmkin function mkin package: plot individual SFO fits shown suggests least datasets degradation slows towards later time points, scatter residuals error smaller smaller values (panel right):  Using biexponential decline (DFOP) results slightly random scatter residuals:  population curve (bold line) plot results taking mean individual transformed parameters, .e. log k1 log k2, well logit g parameter DFOP model). , procedure result parameters represent degradation well, datasets fitted value k2 extremely close zero, leading log k2 value dominates average. alleviated rate constants pass t-test significant difference zero (untransformed scale) considered averaging:  visually much satisfactory, average procedure introduce bias, results individual fits enter population curve weight. nonlinear mixed-effects models can help treating datasets equally fitting parameter distribution model together degradation model error model (see ). remaining trend residuals higher higher predicted residues reduced using two-component error model:  However, note case using error model, fits Flaach BBA 2.3 datasets appear ill-defined, indicated fact converge:","code":"f_parent_mkin_const <- mmkin(c(\"SFO\", \"DFOP\"), dmta_ds,   error_model = \"const\", quiet = TRUE) f_parent_mkin_tc <- mmkin(c(\"SFO\", \"DFOP\"), dmta_ds,   error_model = \"tc\", quiet = TRUE) plot(mixed(f_parent_mkin_const[\"SFO\", ])) plot(mixed(f_parent_mkin_const[\"DFOP\", ])) plot(mixed(f_parent_mkin_const[\"DFOP\", ]), test_log_parms = TRUE) plot(mixed(f_parent_mkin_tc[\"DFOP\", ]), test_log_parms = TRUE) print(f_parent_mkin_tc[\"DFOP\", ]) <mmkin> object Status of individual fits:        dataset model  Calke Borstel Flaach BBA 2.2 BBA 2.3 Elliot   DFOP OK    OK      OK     OK      C       OK      C: Optimisation did not converge: iteration limit reached without convergence (10) OK: No warnings"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/dimethenamid_2018.html","id":"nonlinear-mixed-effects-models","dir":"Articles > Web_only","previous_headings":"Parent degradation","what":"Nonlinear mixed-effects models","title":"Example evaluations of the dimethenamid data from 2018","text":"Instead taking model selection decision individual fits, fit nonlinear mixed-effects models (using different fitting algorithms implemented different packages) model selection using available data time. order make sure decisions unduly influenced type algorithm used, implementation details use wrong control parameters, compare model selection results obtained different R packages, different algorithms checking control parameters.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/dimethenamid_2018.html","id":"nlme","dir":"Articles > Web_only","previous_headings":"Parent degradation > Nonlinear mixed-effects models","what":"nlme","title":"Example evaluations of the dimethenamid data from 2018","text":"nlme package first R extension providing facilities fit nonlinear mixed-effects models. like model selection four combinations degradation models error models based AIC. However, fitting DFOP model constant variance using default control parameters results error, signalling maximum number 50 iterations reached, potentially indicating overparameterisation. Nevertheless, algorithm converges two-component error model used combination DFOP model. can explained fact smaller residues observed later sampling times get weight using two-component error model counteract tendency algorithm try parameter combinations unsuitable fitting data. Note certain degree overparameterisation also indicated warning obtained fitting DFOP two-component error model (‘false convergence’ ‘LME step’ iteration 3). However, warning occur later iterations, specifically last 5 iterations, can ignore warning. model comparison function nlme package can directly applied fits showing much lower AIC DFOP model fitted two-component error model. Also, likelihood ratio test indicates difference significant p-value 0.0001. addition fits, attempts also made include correlations random effects using log Cholesky parameterisation matrix specifying . code used attempts can made visible . SFO variants converge fast, additional parameters introduced lead convergence warnings DFOP model. model comparison clearly show adding correlations random effects improve fits. selected model (DFOP two-component error) fitted data assuming correlations random effects shown .","code":"library(nlme) f_parent_nlme_sfo_const <- nlme(f_parent_mkin_const[\"SFO\", ]) # f_parent_nlme_dfop_const <- nlme(f_parent_mkin_const[\"DFOP\", ]) f_parent_nlme_sfo_tc <- nlme(f_parent_mkin_tc[\"SFO\", ]) f_parent_nlme_dfop_tc <- nlme(f_parent_mkin_tc[\"DFOP\", ]) anova(   f_parent_nlme_sfo_const, f_parent_nlme_sfo_tc, f_parent_nlme_dfop_tc ) Model df    AIC    BIC  logLik   Test L.Ratio p-value f_parent_nlme_sfo_const     1  5 796.60 811.82 -393.30                        f_parent_nlme_sfo_tc        2  6 798.60 816.86 -393.30 1 vs 2    0.00   0.998 f_parent_nlme_dfop_tc       3 10 671.91 702.34 -325.95 2 vs 3  134.69  <.0001 f_parent_nlme_sfo_const_logchol <- nlme(f_parent_mkin_const[\"SFO\", ],   random = nlme::pdLogChol(list(DMTA_0 ~ 1, log_k_DMTA ~ 1))) anova(f_parent_nlme_sfo_const, f_parent_nlme_sfo_const_logchol) f_parent_nlme_sfo_tc_logchol <- nlme(f_parent_mkin_tc[\"SFO\", ],   random = nlme::pdLogChol(list(DMTA_0 ~ 1, log_k_DMTA ~ 1))) anova(f_parent_nlme_sfo_tc, f_parent_nlme_sfo_tc_logchol) f_parent_nlme_dfop_tc_logchol <- nlme(f_parent_mkin_const[\"DFOP\", ],   random = nlme::pdLogChol(list(DMTA_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1))) anova(f_parent_nlme_dfop_tc, f_parent_nlme_dfop_tc_logchol) plot(f_parent_nlme_dfop_tc)"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/dimethenamid_2018.html","id":"saemix","dir":"Articles > Web_only","previous_headings":"Parent degradation > Nonlinear mixed-effects models","what":"saemix","title":"Example evaluations of the dimethenamid data from 2018","text":"saemix package provided first Open Source implementation Stochastic Approximation Expectation Maximisation (SAEM) algorithm. SAEM fits degradation models can conveniently performed using interface saemix package available current development versions mkin package. corresponding SAEM fits four combinations degradation error models fitted . convergence criterion implemented saemix package, convergence plots need manually checked every fit. define control settings work well parent data fits shown vignette. convergence plot SFO model using constant variance shown .  Obviously selected number iterations sufficient reach convergence. can also said SFO fit using two-component error model.  fitting DFOP model constant variance (see ), parameter convergence unambiguous.  parameters converge credible values, variance k2 (omega2.k2) converges small value. printout saem.mmkin model shows estimated standard deviation k2 across population soils (SD.k2) ill-defined, indicating overparameterisation model. DFOP model fitted two-component error model, also observe estimated variance k2 becomes small, ill-defined, illustrated excessive confidence interval SD.k2.  Doubling number iterations first phase algorithm leads slightly lower likelihood, therefore slightly higher AIC BIC values. even iterations, algorithm stops error message. related variance k2 approximating zero submitted bug saemix package, algorithm converge case. alternative way fit DFOP combination two-component error model use model formulation transformed parameters used per default mkin. using option, convergence slower, eventually algorithm stops well error message. four combinations (SFO/const, SFO/tc, DFOP/const DFOP/tc) version increased iterations can compared using model comparison function saemix package: order check influence likelihood calculation algorithms implemented saemix, likelihood Gaussian quadrature added best fit, AIC values obtained three methods compared. AIC values based importance sampling Gaussian quadrature similar. Using linearisation known less accurate, still gives similar value. order illustrate comparison three method depends degree convergence obtained fit, comparison shown fit using defaults number iterations number MCMC chains. using OpenBlas linear algebra, large difference values obtained Gaussian quadrature, larger number iterations makes lot difference. using LAPACK version coming Debian Bullseye, AIC based Gaussian quadrature almost one obtained methods, also using defaults fit.","code":"library(saemix) Loading required package: npde Package saemix, version 3.3, March 2024   please direct bugs, questions and feedback to emmanuelle.comets@inserm.fr Attaching package: 'saemix' The following objects are masked from 'package:npde':      kurtosis, skewness saemix_control <- saemixControl(nbiter.saemix = c(800, 300), nb.chains = 15,     print = FALSE, save = FALSE, save.graphs = FALSE, displayProgress = FALSE) saemix_control_moreiter <- saemixControl(nbiter.saemix = c(1600, 300), nb.chains = 15,     print = FALSE, save = FALSE, save.graphs = FALSE, displayProgress = FALSE) saemix_control_10k <- saemixControl(nbiter.saemix = c(10000, 300), nb.chains = 15,     print = FALSE, save = FALSE, save.graphs = FALSE, displayProgress = FALSE) f_parent_saemix_sfo_const <- mkin::saem(f_parent_mkin_const[\"SFO\", ], quiet = TRUE,   control = saemix_control, transformations = \"saemix\") plot(f_parent_saemix_sfo_const$so, plot.type = \"convergence\") f_parent_saemix_sfo_tc <- mkin::saem(f_parent_mkin_tc[\"SFO\", ], quiet = TRUE,   control = saemix_control, transformations = \"saemix\") plot(f_parent_saemix_sfo_tc$so, plot.type = \"convergence\") f_parent_saemix_dfop_const <- mkin::saem(f_parent_mkin_const[\"DFOP\", ], quiet = TRUE,   control = saemix_control, transformations = \"saemix\") plot(f_parent_saemix_dfop_const$so, plot.type = \"convergence\") print(f_parent_saemix_dfop_const) Kinetic nonlinear mixed-effects model fit by SAEM Structural model: d_DMTA/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))            * DMTA  Data: 155 observations of 1 variable(s) grouped in 6 datasets  Likelihood computed by importance sampling   AIC BIC logLik   706 704   -344  Fitted parameters:           estimate    lower   upper DMTA_0    97.99583 96.50079 99.4909 k1         0.06377  0.03432  0.0932 k2         0.00848  0.00444  0.0125 g          0.95701  0.91313  1.0009 a.1        1.82141  1.60516  2.0377 SD.DMTA_0  1.64787  0.45729  2.8384 SD.k1      0.57439  0.24731  0.9015 SD.k2      0.03296 -2.50524  2.5712 SD.g       1.10266  0.32354  1.8818 f_parent_saemix_dfop_tc <- mkin::saem(f_parent_mkin_tc[\"DFOP\", ], quiet = TRUE,   control = saemix_control, transformations = \"saemix\") f_parent_saemix_dfop_tc_moreiter <- mkin::saem(f_parent_mkin_tc[\"DFOP\", ], quiet = TRUE,   control = saemix_control_moreiter, transformations = \"saemix\") plot(f_parent_saemix_dfop_tc$so, plot.type = \"convergence\") print(f_parent_saemix_dfop_tc) Kinetic nonlinear mixed-effects model fit by SAEM Structural model: d_DMTA/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))            * DMTA  Data: 155 observations of 1 variable(s) grouped in 6 datasets  Likelihood computed by importance sampling   AIC BIC logLik   666 664   -323  Fitted parameters:           estimate     lower    upper DMTA_0    98.24165  96.29190 100.1914 k1         0.06421   0.03352   0.0949 k2         0.00866   0.00617   0.0111 g          0.95340   0.91218   0.9946 a.1        1.06463   0.87979   1.2495 b.1        0.02964   0.02266   0.0366 SD.DMTA_0  2.03611   0.40361   3.6686 SD.k1      0.59534   0.25692   0.9338 SD.k2      0.00042 -73.00540  73.0062 SD.g       1.04234   0.37189   1.7128 AIC_parent_saemix <- saemix::compare.saemix(   f_parent_saemix_sfo_const$so,   f_parent_saemix_sfo_tc$so,   f_parent_saemix_dfop_const$so,   f_parent_saemix_dfop_tc$so,   f_parent_saemix_dfop_tc_moreiter$so) Likelihoods calculated by importance sampling rownames(AIC_parent_saemix) <- c(   \"SFO const\", \"SFO tc\", \"DFOP const\", \"DFOP tc\", \"DFOP tc more iterations\") print(AIC_parent_saemix) AIC    BIC SFO const               796.38 795.34 SFO tc                  798.38 797.13 DFOP const              705.75 703.88 DFOP tc                 665.67 663.59 DFOP tc more iterations 665.85 663.76 f_parent_saemix_dfop_tc$so <-   saemix::llgq.saemix(f_parent_saemix_dfop_tc$so) AIC_parent_saemix_methods <- c(   is = AIC(f_parent_saemix_dfop_tc$so, method = \"is\"),   gq = AIC(f_parent_saemix_dfop_tc$so, method = \"gq\"),   lin = AIC(f_parent_saemix_dfop_tc$so, method = \"lin\") ) print(AIC_parent_saemix_methods) is     gq    lin  665.67 665.74 665.13 f_parent_saemix_dfop_tc_defaults <- mkin::saem(f_parent_mkin_tc[\"DFOP\", ]) f_parent_saemix_dfop_tc_defaults$so <-   saemix::llgq.saemix(f_parent_saemix_dfop_tc_defaults$so) AIC_parent_saemix_methods_defaults <- c(   is = AIC(f_parent_saemix_dfop_tc_defaults$so, method = \"is\"),   gq = AIC(f_parent_saemix_dfop_tc_defaults$so, method = \"gq\"),   lin = AIC(f_parent_saemix_dfop_tc_defaults$so, method = \"lin\") ) print(AIC_parent_saemix_methods_defaults) is     gq    lin  670.09 669.37 671.29"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/dimethenamid_2018.html","id":"comparison","dir":"Articles > Web_only","previous_headings":"Parent degradation","what":"Comparison","title":"Example evaluations of the dimethenamid data from 2018","text":"following table gives AIC values obtained backend packages using control parameters (800 iterations burn-, 300 iterations second phase, 15 chains).","code":"AIC_all <- data.frame(   check.names = FALSE,   \"Degradation model\" = c(\"SFO\", \"SFO\", \"DFOP\", \"DFOP\"),   \"Error model\" = c(\"const\", \"tc\", \"const\", \"tc\"),   nlme = c(AIC(f_parent_nlme_sfo_const), AIC(f_parent_nlme_sfo_tc), NA, AIC(f_parent_nlme_dfop_tc)),   saemix_lin = sapply(list(f_parent_saemix_sfo_const$so, f_parent_saemix_sfo_tc$so,     f_parent_saemix_dfop_const$so, f_parent_saemix_dfop_tc$so), AIC, method = \"lin\"),   saemix_is = sapply(list(f_parent_saemix_sfo_const$so, f_parent_saemix_sfo_tc$so,     f_parent_saemix_dfop_const$so, f_parent_saemix_dfop_tc$so), AIC, method = \"is\") ) kable(AIC_all)"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/dimethenamid_2018.html","id":"conclusion","dir":"Articles > Web_only","previous_headings":"","what":"Conclusion","title":"Example evaluations of the dimethenamid data from 2018","text":"detailed analysis dimethenamid dataset confirmed DFOP model provides appropriate description decline parent compound data. hand, closer inspection results revealed variability k2 parameter across population soils ill-defined. coincides observation parameter robustly quantified soils. Regarding regulatory use data, claimed improved characterisation mean parameter values across population obtained using nonlinear mixed-effects models presented . However, attempts quantify variability slower rate constant biphasic decline dimethenamid indicate data sufficient characterise variability satisfactory precision.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/dimethenamid_2018.html","id":"session-info","dir":"Articles > Web_only","previous_headings":"","what":"Session Info","title":"Example evaluations of the dimethenamid data from 2018","text":"","code":"sessionInfo() R version 4.4.2 (2024-10-31) Platform: x86_64-pc-linux-gnu Running under: Debian GNU/Linux 12 (bookworm)  Matrix products: default BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.11.0  LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.11.0  locale:  [1] LC_CTYPE=de_DE.UTF-8       LC_NUMERIC=C                [3] LC_TIME=C                  LC_COLLATE=de_DE.UTF-8      [5] LC_MONETARY=de_DE.UTF-8    LC_MESSAGES=de_DE.UTF-8     [7] LC_PAPER=de_DE.UTF-8       LC_NAME=C                   [9] LC_ADDRESS=C               LC_TELEPHONE=C             [11] LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=C         time zone: Europe/Berlin tzcode source: system (glibc)  attached base packages: [1] stats     graphics  grDevices utils     datasets  methods   base       other attached packages: [1] saemix_3.3   npde_3.5     nlme_3.1-166 mkin_1.2.9   knitr_1.49    loaded via a namespace (and not attached):  [1] gtable_0.3.6      jsonlite_1.8.9    dplyr_1.1.4       compiler_4.4.2     [5] tidyselect_1.2.1  parallel_4.4.2    gridExtra_2.3     jquerylib_0.1.4    [9] systemfonts_1.1.0 scales_1.3.0      textshaping_0.4.1 yaml_2.3.10       [13] fastmap_1.2.0     lattice_0.22-6    ggplot2_3.5.1     R6_2.5.1          [17] generics_0.1.3    lmtest_0.9-40     MASS_7.3-61       htmlwidgets_1.6.4 [21] tibble_3.2.1      desc_1.4.3        munsell_0.5.1     bslib_0.8.0       [25] pillar_1.9.0      rlang_1.1.4       utf8_1.2.4        cachem_1.1.0      [29] xfun_0.49         fs_1.6.5          sass_0.4.9        cli_3.6.3         [33] pkgdown_2.1.1     magrittr_2.0.3    digest_0.6.37     grid_4.4.2        [37] mclust_6.1.1      lifecycle_1.0.4   vctrs_0.6.5       evaluate_1.0.1    [41] glue_1.8.0        codetools_0.2-20  ragg_1.3.3        zoo_1.8-12        [45] fansi_1.0.6       colorspace_2.1-1  rmarkdown_2.29    pkgconfig_2.0.3   [49] tools_4.4.2       htmltools_0.5.8.1"},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/saem_benchmarks.html","id":"test-data","dir":"Articles > Web_only","previous_headings":"","what":"Test data","title":"Benchmark timings for saem.mmkin","text":"Please refer vignette dimethenamid_2018 explanation following preprocessing.","code":"dmta_ds <- lapply(1:7, function(i) {   ds_i <- dimethenamid_2018$ds[[i]]$data   ds_i[ds_i$name == \"DMTAP\", \"name\"] <-  \"DMTA\"   ds_i$time <- ds_i$time * dimethenamid_2018$f_time_norm[i]   ds_i }) names(dmta_ds) <- sapply(dimethenamid_2018$ds, function(ds) ds$title) dmta_ds[[\"Elliot\"]] <- rbind(dmta_ds[[\"Elliot 1\"]], dmta_ds[[\"Elliot 2\"]]) dmta_ds[[\"Elliot 1\"]] <- NULL dmta_ds[[\"Elliot 2\"]] <- NULL"},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/saem_benchmarks.html","id":"parent-only","dir":"Articles > Web_only","previous_headings":"Test cases","what":"Parent only","title":"Benchmark timings for saem.mmkin","text":"model comparison suggests use SFORB model two-component error. comparison, keep DFOP model two-component error, competes SFORB biphasic curves. two models, random effects transformed parameters k2 k_DMTA_bound_free quantified.","code":"parent_mods <- c(\"SFO\", \"DFOP\", \"SFORB\", \"HS\") parent_sep_const <- mmkin(parent_mods, dmta_ds, quiet = TRUE, cores = n_cores) parent_sep_tc <- update(parent_sep_const, error_model = \"tc\")  t1 <- system.time(sfo_const <- saem(parent_sep_const[\"SFO\", ]))[[\"elapsed\"]] t2 <- system.time(dfop_const <- saem(parent_sep_const[\"DFOP\", ]))[[\"elapsed\"]] t3 <- system.time(sforb_const <- saem(parent_sep_const[\"SFORB\", ]))[[\"elapsed\"]] t4 <- system.time(hs_const <- saem(parent_sep_const[\"HS\", ]))[[\"elapsed\"]] t5 <- system.time(sfo_tc <- saem(parent_sep_tc[\"SFO\", ]))[[\"elapsed\"]] t6 <- system.time(dfop_tc <- saem(parent_sep_tc[\"DFOP\", ]))[[\"elapsed\"]] t7 <- system.time(sforb_tc <- saem(parent_sep_tc[\"SFORB\", ]))[[\"elapsed\"]] t8 <- system.time(hs_tc <- saem(parent_sep_tc[\"HS\", ]))[[\"elapsed\"]] anova(   sfo_const, dfop_const, sforb_const, hs_const,   sfo_tc, dfop_tc, sforb_tc, hs_tc) |> kable(, digits = 1) illparms(dfop_tc) ## [1] \"sd(log_k2)\" illparms(sforb_tc) ## [1] \"sd(log_k_DMTA_bound_free)\""},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/saem_benchmarks.html","id":"one-metabolite","dir":"Articles > Web_only","previous_headings":"Test cases","what":"One metabolite","title":"Benchmark timings for saem.mmkin","text":"remove parameters found ill-defined parent fits.","code":"one_met_mods <- list(   DFOP_SFO = mkinmod(     DMTA = mkinsub(\"DFOP\", \"M23\"),     M23 = mkinsub(\"SFO\")),   SFORB_SFO = mkinmod(     DMTA = mkinsub(\"SFORB\", \"M23\"),     M23 = mkinsub(\"SFO\")))  one_met_sep_const <- mmkin(one_met_mods, dmta_ds, error_model = \"const\",   cores = n_cores, quiet = TRUE) one_met_sep_tc <- mmkin(one_met_mods, dmta_ds, error_model = \"tc\",   cores = n_cores, quiet = TRUE)  t9 <- system.time(dfop_sfo_tc <- saem(one_met_sep_tc[\"DFOP_SFO\", ],     no_random_effect = \"log_k2\"))[[\"elapsed\"]] t10 <- system.time(sforb_sfo_tc <- saem(one_met_sep_tc[\"SFORB_SFO\", ],     no_random_effect = \"log_k_DMTA_bound_free\"))[[\"elapsed\"]]"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/saem_benchmarks.html","id":"three-metabolites","dir":"Articles > Web_only","previous_headings":"Test cases","what":"Three metabolites","title":"Benchmark timings for saem.mmkin","text":"case three metabolites, keep SFORB model order limit time compiling vignette, fitting parallel may disturb benchmark. , include random effects ill-defined previous fits subsets degradation model.","code":"illparms(sforb_sfo_tc) three_met_mods <- list(   SFORB_SFO3_plus = mkinmod(     DMTA = mkinsub(\"SFORB\", c(\"M23\", \"M27\", \"M31\")),     M23 = mkinsub(\"SFO\"),     M27 = mkinsub(\"SFO\"),     M31 = mkinsub(\"SFO\", \"M27\", sink = FALSE)))  three_met_sep_tc <- mmkin(three_met_mods, dmta_ds, error_model = \"tc\",   cores = n_cores, quiet = TRUE)  t11 <- system.time(sforb_sfo3_plus_const <- saem(three_met_sep_tc[\"SFORB_SFO3_plus\", ],     no_random_effect = \"log_k_DMTA_bound_free\"))[[\"elapsed\"]]"},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/saem_benchmarks.html","id":"results","dir":"Articles > Web_only","previous_headings":"","what":"Results","title":"Benchmark timings for saem.mmkin","text":"Benchmarks available error models shown. intended improving mkin, comparing CPUs operating systems. trademarks belong respective owners.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/saem_benchmarks.html","id":"parent-only-1","dir":"Articles > Web_only","previous_headings":"Results","what":"Parent only","title":"Benchmark timings for saem.mmkin","text":"Constant variance SFO, DFOP, SFORB HS. Two-component error fits SFO, DFOP, SFORB HS.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/saem_benchmarks.html","id":"one-metabolite-1","dir":"Articles > Web_only","previous_headings":"Results","what":"One metabolite","title":"Benchmark timings for saem.mmkin","text":"Two-component error DFOP-SFO SFORB-SFO.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/articles/web_only/saem_benchmarks.html","id":"three-metabolites-1","dir":"Articles > Web_only","previous_headings":"Results","what":"Three metabolites","title":"Benchmark timings for saem.mmkin","text":"Two-component error SFORB-SFO3-plus","code":""},{"path":"https://pkgdown.jrwb.de/mkin/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Johannes Ranke. Author, maintainer, copyright holder. Katrin Lindenberger. Contributor.           contributed mkinresplot() René Lehmann. Contributor.           ilr() invilr() Eurofins Regulatory AG. Copyright holder.           copyright contributions JR 2012-2014","code":""},{"path":"https://pkgdown.jrwb.de/mkin/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Ranke J (2025). mkin: Kinetic Evaluation Chemical Degradation Data. R package version 1.2.9, https://pkgdown.jrwb.de/mkin/.","code":"@Manual{,   title = {mkin: Kinetic Evaluation of Chemical Degradation Data},   author = {Johannes Ranke},   year = {2025},   note = {R package version 1.2.9},   url = {https://pkgdown.jrwb.de/mkin/}, }"},{"path":"https://pkgdown.jrwb.de/mkin/index.html","id":"mkin","dir":"","previous_headings":"","what":"Kinetic Evaluation of Chemical Degradation Data","title":"Kinetic Evaluation of Chemical Degradation Data","text":"R package mkin provides calculation routines analysis chemical degradation data, including multicompartment kinetics needed modelling formation decline transformation products, several degradation compartments involved. provides stable functionality kinetic evaluations according FOCUS guidance (see details). addition, provides functionality hierarchical kinetics based nonlinear mixed-effects models.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Kinetic Evaluation of Chemical Degradation Data","text":"can install latest released version CRAN within R:","code":"install.packages(\"mkin\")"},{"path":"https://pkgdown.jrwb.de/mkin/index.html","id":"background","dir":"","previous_headings":"","what":"Background","title":"Kinetic Evaluation of Chemical Degradation Data","text":"regulatory evaluation chemical substances like plant protection products (pesticides), biocides chemicals, degradation data play important role. evaluation pesticide degradation experiments, detailed guidance various helpful tools developed detailed ‘Credits historical remarks’ . package aims provide one stop solution degradation kinetics, addressing modellers willing , even prefer work R.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/index.html","id":"basic-usage","dir":"","previous_headings":"","what":"Basic usage","title":"Kinetic Evaluation of Chemical Degradation Data","text":"start, look code examples provided plot.mkinfit plot.mmkin, package vignettes FOCUS L FOCUS D.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/index.html","id":"documentation","dir":"","previous_headings":"","what":"Documentation","title":"Kinetic Evaluation of Chemical Degradation Data","text":"HTML documentation latest version released CRAN available jrwb.de github. Documentation development version found ‘dev’ subdirectory. articles section documentation, can also find demonstrations application nonlinear hierarchical models, also known nonlinear mixed-effects models, complex data, including transformation products covariates.","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/index.html","id":"general","dir":"","previous_headings":"Features","what":"General","title":"Kinetic Evaluation of Chemical Degradation Data","text":"Highly flexible model specification using mkinmod, including equilibrium reactions using single first-order reversible binding (SFORB) model, automatically create two state variables observed variable. Model solution (forward modelling) function mkinpredict performed either using analytical solution case parent degradation simple models involving single transformation product, , eigenvalue based solution simple first-order (SFO) SFORB kinetics used model, using numeric solver deSolve package (default lsoda). usual one-sided t-test significant difference zero shown based estimators untransformed parameters. Summary plotting functions. summary mkinfit object fact full report give enough information able approximately reproduce fit tools. chi-squared error level defined FOCUS kinetics guidance (see ) calculated observed variable. ‘variance variable’ error model often fitted using Iteratively Reweighted Least Squares (IRLS) can specified error_model = \"obs\".","code":""},{"path":"https://pkgdown.jrwb.de/mkin/index.html","id":"unique-in-mkin","dir":"","previous_headings":"Features","what":"Unique in mkin","title":"Kinetic Evaluation of Chemical Degradation Data","text":"Three different error models can selected using argument error_model mkinfit function. two-component error model similar one proposed Rocke Lorenzato can selected using argument error_model = \"tc\". Model comparisons using Akaike Information Criterion (AIC) supported can also used non-constant variance. cases FOCUS chi-squared error level meaningful. default, kinetic rate constants kinetic formation fractions transformed internally using transform_odeparms estimators can reasonably expected follow normal distribution. parameter estimates backtransformed match model definition, confidence intervals calculated standard errors also backtransformed correct scale, include meaningless values like negative rate constants formation fractions adding 1, occur single experiment single defined radiolabel position. metabolite decline phase described well SFO kinetics, SFORB kinetics can used metabolite. Mathematically, SFORB model equivalent DFOP model. However, SFORB model advantage mechanistic interpretation model parameters. Nonlinear mixed-effects models (hierarchical models) can created fits degradation model different datasets compound using nlme.mmkin saem.mmkin methods. Note convergence nlme fits depends quality data. Convergence better simple models data many groups (e.g. soils). saem method uses saemix package backend. Analytical solutions suitable use package implemented parent models important models including one metabolite (SFO-SFO DFOP-SFO). Fitting models saem.mmkin, makes use compiled ODE models mkin provides, longer run times (couple minutes hour).","code":""},{"path":"https://pkgdown.jrwb.de/mkin/index.html","id":"performance","dir":"","previous_headings":"Features","what":"Performance","title":"Kinetic Evaluation of Chemical Degradation Data","text":"Parallel fitting several models several datasets supported, see example plot.mmkin. C compiler installed, kinetic models compiled automatically generated C code, see vignette compiled_models. autogeneration C code inspired ccSolve package. Thanks Karline Soetaert work . Even compiler installed, many degradation models still give good performance, current versions mkin also analytical solutions models one metabolite, SFO SFORB used parent compound, Eigenvalue based solutions degradation model available.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/index.html","id":"gui","dir":"","previous_headings":"","what":"GUI","title":"Kinetic Evaluation of Chemical Degradation Data","text":"graphical user interface may useful. Please refer documentation page installation instructions manual. supports evaluations using (generalised) nonlinear regression, simultaneous fits using nonlinear mixed-effects models.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/index.html","id":"news","dir":"","previous_headings":"","what":"News","title":"Kinetic Evaluation of Chemical Degradation Data","text":"list changes latest CRAN release one github branch, e.g. main branch.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/index.html","id":"credits-and-historical-remarks","dir":"","previous_headings":"","what":"Credits and historical remarks","title":"Kinetic Evaluation of Chemical Degradation Data","text":"mkin possible without underlying software stack consisting , among others, R package deSolve. previous version, mkin also using functionality FME package. Please refer package page CRAN full list imported suggested R packages. Also, Debian Linux, vim editor Nvim-R plugin invaluable development. mkin written without introduced regulatory fate modelling pesticides Adrian Gurney time Harlan Laboratories Ltd (formerly RCC Ltd). mkin greatly profits largely follows work done FOCUS Degradation Kinetics Workgroup, detailed guidance document 2006, slightly updated 2011 2014. Also, inspired first version KinGUI developed BayerCropScience, based MatLab runtime environment. companion package kinfit (now deprecated) started 2008 first published CRAN 01 May 2010. first mkin code published 11 May 2010 first CRAN version 18 May 2010. 2011, Bayer Crop Science started distribute R based successor KinGUI named KinGUII whose R code based mkin, added, among refinements, closed source graphical user interface (GUI), iteratively reweighted least squares (IRLS) optimisation variance observed variables, Markov Chain Monte Carlo (MCMC) simulation functionality, similar available e.g. FME package. Somewhat parallel, Syngenta sponsored development mkin KinGUII based GUI application called CAKE, also adds IRLS MCMC, limited model formulation, puts weight usability. CAKE available download CAKE website, can also find zip archive R scripts derived mkin, published GPL license. Finally, KineticEval, contains development scripts used KinGUII. Thanks René Lehmann, formerly working Umweltbundesamt, nice cooperation parameter transformations, especially isometric log-ratio transformation now used formation fractions case two transformation targets. Many inspirations improvements mkin resulted kinetic evaluations degradation data clients working Harlan Laboratories Eurofins Regulatory AG, now independent consultant. Funding received Umweltbundesamt course projects Project Number 27452 (Testing validation modelling software alternative ModelMaker 4.0, 2014-2015) Project Number 56703 (Optimization gmkin routine use Umweltbundesamt, 2015) Project Number 92570 (Update Project Number 27452, 2017-2018) Project Number 112407 (Testing feasibility using error model according Rocke Lorenzato realistic parameter estimates kinetic evaluation degradation data, 2018-2019) Project Number 120667 (Development objective criteria evaluation visual fit kinetic evaluation degradation data, 2019-2020) Project Number 146839 (Checking feasibility using mixed-effects models derivation kinetic modelling parameters degradation studies, 2020-2021) Project Number 173340 (Application nonlinear hierarchical models kinetic evaluation chemical degradation data) Thanks everyone involved collaboration support! Thanks due also Emmanuelle Comets, maintainer saemix package, interest support using SAEM algorithm implementation saemix evaluation chemical degradation data. Regarding application nonlinear mixed-effects models degradation data, von Götz et al (1999) already proposed use technique context environmental risk assessments pesticides. However, work apparently followed , independently arrive idea missed cite previous work topic first publications.","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/index.html","id":"development","dir":"","previous_headings":"","what":"Development","title":"Kinetic Evaluation of Chemical Degradation Data","text":"Contributions welcome!","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/AIC.mmkin.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate the AIC for a column of an mmkin object — AIC.mmkin","title":"Calculate the AIC for a column of an mmkin object — AIC.mmkin","text":"Provides convenient way compare different kinetic models fitted dataset.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/AIC.mmkin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate the AIC for a column of an mmkin object — AIC.mmkin","text":"","code":"# S3 method for class 'mmkin' AIC(object, ..., k = 2)  # S3 method for class 'mmkin' BIC(object, ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/AIC.mmkin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate the AIC for a column of an mmkin object — AIC.mmkin","text":"object object class mmkin, containing one column. ... compatibility generic method k generic method","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/AIC.mmkin.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate the AIC for a column of an mmkin object — AIC.mmkin","text":"generic method (numeric value single fits, dataframe several fits column).","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/AIC.mmkin.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calculate the AIC for a column of an mmkin object — AIC.mmkin","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/AIC.mmkin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate the AIC for a column of an mmkin object — AIC.mmkin","text":"","code":"# skip, as it takes > 10 s on winbuilder   f <- mmkin(c(\"SFO\", \"FOMC\", \"DFOP\"),     list(\"FOCUS A\" = FOCUS_2006_A,          \"FOCUS C\" = FOCUS_2006_C), cores = 1, quiet = TRUE) #> Warning: Optimisation did not converge: #> false convergence (8)   # We get a warning because the FOMC model does not converge for the   # FOCUS A dataset, as it is well described by SFO    AIC(f[\"SFO\", \"FOCUS A\"]) # We get a single number for a single fit #> [1] 55.28197   AIC(f[[\"SFO\", \"FOCUS A\"]]) # or when extracting an mkinfit object #> [1] 55.28197    # For FOCUS A, the models fit almost equally well, so the higher the number   # of parameters, the higher (worse) the AIC   AIC(f[, \"FOCUS A\"]) #>      df      AIC #> SFO   3 55.28197 #> FOMC  4 57.28198 #> DFOP  5 59.28197   AIC(f[, \"FOCUS A\"], k = 0) # If we do not penalize additional parameters, we get nearly the same #>      df      AIC #> SFO   3 49.28197 #> FOMC  4 49.28198 #> DFOP  5 49.28197   BIC(f[, \"FOCUS A\"])        # Comparing the BIC gives a very similar picture #>      df      BIC #> SFO   3 55.52030 #> FOMC  4 57.59974 #> DFOP  5 59.67918    # For FOCUS C, the more complex models fit better   AIC(f[, \"FOCUS C\"]) #>      df      AIC #> SFO   3 59.29336 #> FOMC  4 44.68652 #> DFOP  5 29.02372   BIC(f[, \"FOCUS C\"]) #>      df      BIC #> SFO   3 59.88504 #> FOMC  4 45.47542 #> DFOP  5 30.00984"},{"path":"https://pkgdown.jrwb.de/mkin/reference/CAKE_export.html","id":null,"dir":"Reference","previous_headings":"","what":"Export a list of datasets format to a CAKE study file — CAKE_export","title":"Export a list of datasets format to a CAKE study file — CAKE_export","text":"addition datasets, pathways degradation model can specified well.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/CAKE_export.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Export a list of datasets format to a CAKE study file — CAKE_export","text":"","code":"CAKE_export(   ds,   map = c(parent = \"Parent\"),   links = NA,   filename = \"CAKE_export.csf\",   path = \".\",   overwrite = FALSE,   study = \"Degradinol aerobic soil degradation\",   description = \"\",   time_unit = \"days\",   res_unit = \"% AR\",   comment = \"\",   date = Sys.Date(),   optimiser = \"IRLS\" )"},{"path":"https://pkgdown.jrwb.de/mkin/reference/CAKE_export.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Export a list of datasets format to a CAKE study file — CAKE_export","text":"ds named list datasets long format compatible mkinfit. map character vector CAKE compartment names (Parent, A1, ...), named names used list datasets. links optional character vector target compartments, named names source compartments. order make easier, names used datasets supplied. filename write result. end .csf order compatible CAKE. path optional path output file. overwrite TRUE, existing files overwritten. study name study. description optional description. time_unit time unit residue data. res_unit unit used residues. comment optional comment. date date file creation. optimiser Can OLS IRLS.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/CAKE_export.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Export a list of datasets format to a CAKE study file — CAKE_export","text":"function called side effect.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/CAKE_export.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Export a list of datasets format to a CAKE study file — CAKE_export","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/D24_2014.html","id":null,"dir":"Reference","previous_headings":"","what":"Aerobic soil degradation data on 2,4-D from the EU assessment in 2014 — D24_2014","title":"Aerobic soil degradation data on 2,4-D from the EU assessment in 2014 — D24_2014","text":"five datasets extracted active substance evaluation dossier published EFSA. Kinetic evaluations shown datasets intended illustrate advance kinetic modelling. fact data results shown imply license use context pesticide  registrations, use data may constrained data protection regulations.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/D24_2014.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Aerobic soil degradation data on 2,4-D from the EU assessment in 2014 — D24_2014","text":"","code":"D24_2014"},{"path":"https://pkgdown.jrwb.de/mkin/reference/D24_2014.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Aerobic soil degradation data on 2,4-D from the EU assessment in 2014 — D24_2014","text":"mkindsg object grouping five datasets","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/D24_2014.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Aerobic soil degradation data on 2,4-D from the EU assessment in 2014 — D24_2014","text":"Hellenic Ministry Rural Development Agriculture (2014) Final addendum Renewal Assessment Report - public version - 2,4-D Volume 3 Annex B.8 Fate behaviour environment https://open.efsa.europa.eu/study-inventory/EFSA-Q-2013-00811","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/D24_2014.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Aerobic soil degradation data on 2,4-D from the EU assessment in 2014 — D24_2014","text":"Data first dataset p. 685. Data four datasets used preprocessed versions given kinetics section (p. 761ff.), exception residues smaller 1 DCP soil Site I2, values given p. 694 used. R code used create data object installed package 'dataset_generation' directory. code, page numbers given specific pieces information comments.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/D24_2014.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Aerobic soil degradation data on 2,4-D from the EU assessment in 2014 — D24_2014","text":"","code":"print(D24_2014) #> <mkindsg> holding 5 mkinds objects #> Title $title:  Aerobic soil degradation data on 2,4-D from the EU assessment in 2014  #> Occurrence of observed compounds $observed_n: #> D24 DCP DCA  #>   5   4   4  #> Time normalisation factors $f_time_norm: #> [1] 1.6062378 0.7118732 0.7156063 0.7156063 0.8977124 #> Meta information $meta: #>                                 study usda_soil_type study_moisture_ref_type #> Mississippi                Cohen 1991      Silt loam                    <NA> #> Fayette     Liu and Adelfinskaya 2011      Silt loam                     pF1 #> RefSol 03-G Liu and Adelfinskaya 2011           Loam                     pF1 #> Site E1     Liu and Adelfinskaya 2011           Loam                     pF1 #> Site I2     Liu and Adelfinskaya 2011     Loamy sand                     pF1 #>             rel_moisture temperature #> Mississippi           NA          25 #> Fayette              0.5          20 #> RefSol 03-G          0.5          20 #> Site E1              0.5          20 #> Site I2              0.5          20 # \\dontrun{ print(D24_2014$ds[[1]], data = TRUE) #> <mkinds> with $title:  Mississippi  #> Observed compounds $observed:  D24  #> Sampling times $sampling_times: #> 0, 2, 4, 7, 15, 24, 35, 56, 71, 114, 183, 273, 365  #> With a maximum of  1  replicates #>    time  D24 #> 1     0 96.8 #> 2     2 81.0 #> 3     4 81.7 #> 4     7 88.2 #> 5    15 66.3 #> 6    24 72.9 #> 7    35 62.6 #> 8    56 54.6 #> 9    71 35.2 #> 10  114 18.0 #> 11  183 11.3 #> 12  273  9.9 #> 13  365  6.3 m_D24 = mkinmod(D24 = mkinsub(\"SFO\", to = \"DCP\"),   DCP = mkinsub(\"SFO\", to = \"DCA\"),   DCA = mkinsub(\"SFO\")) #> Temporary DLL for differentials generated and loaded print(m_D24) #> <mkinmod> model generated with #> Use of formation fractions $use_of_ff: max  #> Specification $spec: #> $D24 #> $type: SFO; $to: DCP; $sink: TRUE #> $DCP #> $type: SFO; $to: DCA; $sink: TRUE #> $DCA #> $type: SFO; $sink: TRUE #> Coefficient matrix $coefmat available #> Compiled model $cf available #> Differential equations: #> d_D24/dt = - k_D24 * D24 #> d_DCP/dt = + f_D24_to_DCP * k_D24 * D24 - k_DCP * DCP #> d_DCA/dt = + f_DCP_to_DCA * k_DCP * DCP - k_DCA * DCA m_D24_2 = mkinmod(D24 = mkinsub(\"DFOP\", to = \"DCP\"),   DCP = mkinsub(\"SFO\", to = \"DCA\"),   DCA = mkinsub(\"SFO\")) #> Temporary DLL for differentials generated and loaded print(m_D24_2) #> <mkinmod> model generated with #> Use of formation fractions $use_of_ff: max  #> Specification $spec: #> $D24 #> $type: DFOP; $to: DCP; $sink: TRUE #> $DCP #> $type: SFO; $to: DCA; $sink: TRUE #> $DCA #> $type: SFO; $sink: TRUE #> Compiled model $cf available #> Differential equations: #> d_D24/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * #>            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) #>            * D24 #> d_DCP/dt = + f_D24_to_DCP * ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * #>            exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 #>            * time))) * D24 - k_DCP * DCP #> d_DCA/dt = + f_DCP_to_DCA * k_DCP * DCP - k_DCA * DCA # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/DFOP.solution.html","id":null,"dir":"Reference","previous_headings":"","what":"Double First-Order in Parallel kinetics — DFOP.solution","title":"Double First-Order in Parallel kinetics — DFOP.solution","text":"Function describing decline defined starting value using sum two exponential decline functions.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/DFOP.solution.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Double First-Order in Parallel kinetics — DFOP.solution","text":"","code":"DFOP.solution(t, parent_0, k1, k2, g)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/DFOP.solution.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Double First-Order in Parallel kinetics — DFOP.solution","text":"t Time. parent_0 Starting value response variable time zero. k1 First kinetic constant. k2 Second kinetic constant. g Fraction starting value declining according first kinetic constant.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/DFOP.solution.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Double First-Order in Parallel kinetics — DFOP.solution","text":"value response variable time t.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/DFOP.solution.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Double First-Order in Parallel kinetics — DFOP.solution","text":"FOCUS (2006) “Guidance Document Estimating Persistence Degradation Kinetics Environmental Fate Studies Pesticides EU Registration” Report FOCUS Work Group Degradation Kinetics, EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics FOCUS (2014) “Generic guidance Estimating Persistence Degradation Kinetics Environmental Fate Studies Pesticides EU Registration” Report FOCUS Work Group Degradation Kinetics, Version 1.1, 18 December 2014 http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/reference/DFOP.solution.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Double First-Order in Parallel kinetics — DFOP.solution","text":"","code":"plot(function(x) DFOP.solution(x, 100, 5, 0.5, 0.3), 0, 4, ylim = c(0,100))"},{"path":"https://pkgdown.jrwb.de/mkin/reference/Extract.mmkin.html","id":null,"dir":"Reference","previous_headings":"","what":"Subsetting method for mmkin objects — [.mmkin","title":"Subsetting method for mmkin objects — [.mmkin","text":"Subsetting method mmkin objects","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/Extract.mmkin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Subsetting method for mmkin objects — [.mmkin","text":"","code":"# S3 method for class 'mmkin' x[i, j, ..., drop = FALSE]"},{"path":"https://pkgdown.jrwb.de/mkin/reference/Extract.mmkin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Subsetting method for mmkin objects — [.mmkin","text":"x mmkin object Row index selecting fits specific models j Column index selecting fits specific datasets ... used, satisfy generic method definition drop FALSE, method always returns mmkin object, otherwise either list mkinfit objects single mkinfit object.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/Extract.mmkin.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Subsetting method for mmkin objects — [.mmkin","text":"object class mmkin.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/Extract.mmkin.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Subsetting method for mmkin objects — [.mmkin","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/Extract.mmkin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Subsetting method for mmkin objects — [.mmkin","text":"","code":"# Only use one core, to pass R CMD check --as-cran   fits <- mmkin(c(\"SFO\", \"FOMC\"), list(B = FOCUS_2006_B, C = FOCUS_2006_C),                 cores = 1, quiet = TRUE)   fits[\"FOMC\", ] #> <mmkin> object #> Status of individual fits: #>  #>       dataset #> model  B  C  #>   FOMC OK OK #>  #> OK: No warnings   fits[, \"B\"] #> <mmkin> object #> Status of individual fits: #>  #>       dataset #> model  B  #>   SFO  OK #>   FOMC OK #>  #> OK: No warnings   fits[\"SFO\", \"B\"] #> <mmkin> object #> Status of individual fits: #>  #>      dataset #> model B  #>   SFO OK #>  #> OK: No warnings    head(     # This extracts an mkinfit object with lots of components     fits[[\"FOMC\", \"B\"]]   ) #> $par #>  parent_0 log_alpha  log_beta     sigma  #> 99.666192  2.549850  5.050587  1.890202  #>  #> $objective #> [1] 28.58291 #>  #> $convergence #> [1] 0 #>  #> $iterations #> [1] 21 #>  #> $evaluations #> function gradient  #>       25       78  #>  #> $message #> [1] \"both X-convergence and relative convergence (5)\" #>"},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_DFOP_ref_A_to_B.html","id":null,"dir":"Reference","previous_headings":"","what":"Results of fitting the DFOP model to Datasets A to B of FOCUS (2006) — FOCUS_2006_DFOP_ref_A_to_B","title":"Results of fitting the DFOP model to Datasets A to B of FOCUS (2006) — FOCUS_2006_DFOP_ref_A_to_B","text":"table fitted parameters resulting DT50 DT90 values generated different software packages. Taken directly FOCUS (2006). results fitting data Topfit software removed, initial concentration parent compound fixed value 100 fit.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_DFOP_ref_A_to_B.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Results of fitting the DFOP model to Datasets A to B of FOCUS (2006) — FOCUS_2006_DFOP_ref_A_to_B","text":"","code":"FOCUS_2006_DFOP_ref_A_to_B"},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_DFOP_ref_A_to_B.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Results of fitting the DFOP model to Datasets A to B of FOCUS (2006) — FOCUS_2006_DFOP_ref_A_to_B","text":"data frame containing following variables. package factor giving name software package M0 fitted initial concentration parent compound f fitted f parameter k1 fitted k1 parameter k2 fitted k2 parameter DT50 resulting half-life parent compound DT90 resulting DT90 parent compound dataset FOCUS dataset used","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_DFOP_ref_A_to_B.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Results of fitting the DFOP model to Datasets A to B of FOCUS (2006) — FOCUS_2006_DFOP_ref_A_to_B","text":"FOCUS (2006) “Guidance Document Estimating Persistence   Degradation Kinetics Environmental Fate Studies Pesticides EU   Registration” Report FOCUS Work Group Degradation Kinetics,   EC Document Reference Sanco/10058/2005 version 2.0, 434 pp,   http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_DFOP_ref_A_to_B.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Results of fitting the DFOP model to Datasets A to B of FOCUS (2006) — FOCUS_2006_DFOP_ref_A_to_B","text":"","code":"data(FOCUS_2006_DFOP_ref_A_to_B)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_FOMC_ref_A_to_F.html","id":null,"dir":"Reference","previous_headings":"","what":"Results of fitting the FOMC model to Datasets A to F of FOCUS (2006) — FOCUS_2006_FOMC_ref_A_to_F","title":"Results of fitting the FOMC model to Datasets A to F of FOCUS (2006) — FOCUS_2006_FOMC_ref_A_to_F","text":"table fitted parameters resulting DT50 DT90 values generated different software packages. Taken directly FOCUS (2006). results fitting data Topfit software removed, initial concentration parent compound fixed value 100 fit.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_FOMC_ref_A_to_F.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Results of fitting the FOMC model to Datasets A to F of FOCUS (2006) — FOCUS_2006_FOMC_ref_A_to_F","text":"","code":"FOCUS_2006_FOMC_ref_A_to_F"},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_FOMC_ref_A_to_F.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Results of fitting the FOMC model to Datasets A to F of FOCUS (2006) — FOCUS_2006_FOMC_ref_A_to_F","text":"data frame containing following variables. package factor giving name software package M0 fitted initial concentration parent compound alpha fitted alpha parameter beta fitted beta parameter DT50 resulting half-life parent compound DT90 resulting DT90 parent compound dataset FOCUS dataset used","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_FOMC_ref_A_to_F.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Results of fitting the FOMC model to Datasets A to F of FOCUS (2006) — FOCUS_2006_FOMC_ref_A_to_F","text":"FOCUS (2006) “Guidance Document Estimating Persistence   Degradation Kinetics Environmental Fate Studies Pesticides EU   Registration” Report FOCUS Work Group Degradation Kinetics,   EC Document Reference Sanco/10058/2005 version 2.0, 434 pp,   http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_FOMC_ref_A_to_F.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Results of fitting the FOMC model to Datasets A to F of FOCUS (2006) — FOCUS_2006_FOMC_ref_A_to_F","text":"","code":"data(FOCUS_2006_FOMC_ref_A_to_F)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_HS_ref_A_to_F.html","id":null,"dir":"Reference","previous_headings":"","what":"Results of fitting the HS model to Datasets A to F of FOCUS (2006) — FOCUS_2006_HS_ref_A_to_F","title":"Results of fitting the HS model to Datasets A to F of FOCUS (2006) — FOCUS_2006_HS_ref_A_to_F","text":"table fitted parameters resulting DT50 DT90 values generated different software packages. Taken directly FOCUS (2006). results fitting data Topfit software removed, initial concentration parent compound fixed value 100 fit.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_HS_ref_A_to_F.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Results of fitting the HS model to Datasets A to F of FOCUS (2006) — FOCUS_2006_HS_ref_A_to_F","text":"","code":"FOCUS_2006_HS_ref_A_to_F"},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_HS_ref_A_to_F.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Results of fitting the HS model to Datasets A to F of FOCUS (2006) — FOCUS_2006_HS_ref_A_to_F","text":"data frame containing following variables. package factor giving name software package M0 fitted initial concentration parent compound tb fitted tb parameter k1 fitted k1 parameter k2 fitted k2 parameter DT50 resulting half-life parent compound DT90 resulting DT90 parent compound dataset FOCUS dataset used","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_HS_ref_A_to_F.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Results of fitting the HS model to Datasets A to F of FOCUS (2006) — FOCUS_2006_HS_ref_A_to_F","text":"FOCUS (2006) “Guidance Document Estimating Persistence   Degradation Kinetics Environmental Fate Studies Pesticides EU   Registration” Report FOCUS Work Group Degradation Kinetics,   EC Document Reference Sanco/10058/2005 version 2.0, 434 pp,   http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_HS_ref_A_to_F.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Results of fitting the HS model to Datasets A to F of FOCUS (2006) — FOCUS_2006_HS_ref_A_to_F","text":"","code":"data(FOCUS_2006_HS_ref_A_to_F)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_SFO_ref_A_to_F.html","id":null,"dir":"Reference","previous_headings":"","what":"Results of fitting the SFO model to Datasets A to F of FOCUS (2006) — FOCUS_2006_SFO_ref_A_to_F","title":"Results of fitting the SFO model to Datasets A to F of FOCUS (2006) — FOCUS_2006_SFO_ref_A_to_F","text":"table fitted parameters resulting DT50 DT90 values generated different software packages. Taken directly FOCUS (2006). results fitting data Topfit software removed, initial concentration parent compound fixed value 100 fit.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_SFO_ref_A_to_F.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Results of fitting the SFO model to Datasets A to F of FOCUS (2006) — FOCUS_2006_SFO_ref_A_to_F","text":"","code":"FOCUS_2006_SFO_ref_A_to_F"},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_SFO_ref_A_to_F.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Results of fitting the SFO model to Datasets A to F of FOCUS (2006) — FOCUS_2006_SFO_ref_A_to_F","text":"data frame containing following variables. package factor giving name software package M0 fitted initial concentration parent compound k fitted first-order degradation rate constant DT50 resulting half-life parent compound DT90 resulting DT90 parent compound dataset FOCUS dataset used","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_SFO_ref_A_to_F.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Results of fitting the SFO model to Datasets A to F of FOCUS (2006) — FOCUS_2006_SFO_ref_A_to_F","text":"FOCUS (2006) “Guidance Document Estimating Persistence   Degradation Kinetics Environmental Fate Studies Pesticides EU   Registration” Report FOCUS Work Group Degradation Kinetics,   EC Document Reference Sanco/10058/2005 version 2.0, 434 pp,   http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_SFO_ref_A_to_F.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Results of fitting the SFO model to Datasets A to F of FOCUS (2006) — FOCUS_2006_SFO_ref_A_to_F","text":"","code":"data(FOCUS_2006_SFO_ref_A_to_F)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_datasets.html","id":null,"dir":"Reference","previous_headings":"","what":"Datasets A to F from the FOCUS Kinetics report from 2006 — FOCUS_2006_datasets","title":"Datasets A to F from the FOCUS Kinetics report from 2006 — FOCUS_2006_datasets","text":"Data taken FOCUS (2006), p. 258.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_datasets.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Datasets A to F from the FOCUS Kinetics report from 2006 — FOCUS_2006_datasets","text":"","code":"FOCUS_2006_A   FOCUS_2006_B   FOCUS_2006_C   FOCUS_2006_D   FOCUS_2006_E   FOCUS_2006_F"},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_datasets.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Datasets A to F from the FOCUS Kinetics report from 2006 — FOCUS_2006_datasets","text":"6 datasets observations following variables. name factor containing name observed variable time numeric vector containing time points value numeric vector containing concentrations percent applied radioactivity","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_datasets.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Datasets A to F from the FOCUS Kinetics report from 2006 — FOCUS_2006_datasets","text":"FOCUS (2006) “Guidance Document Estimating Persistence   Degradation Kinetics Environmental Fate Studies Pesticides EU   Registration” Report FOCUS Work Group Degradation Kinetics,   EC Document Reference Sanco/10058/2005 version 2.0, 434 pp,   http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_datasets.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Datasets A to F from the FOCUS Kinetics report from 2006 — FOCUS_2006_datasets","text":"","code":"FOCUS_2006_C #>     name time value #> 1 parent    0  85.1 #> 2 parent    1  57.9 #> 3 parent    3  29.9 #> 4 parent    7  14.6 #> 5 parent   14   9.7 #> 6 parent   28   6.6 #> 7 parent   63   4.0 #> 8 parent   91   3.9 #> 9 parent  119   0.6"},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOMC.solution.html","id":null,"dir":"Reference","previous_headings":"","what":"First-Order Multi-Compartment kinetics — FOMC.solution","title":"First-Order Multi-Compartment kinetics — FOMC.solution","text":"Function describing exponential decline defined starting value, decreasing rate constant.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOMC.solution.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"First-Order Multi-Compartment kinetics — FOMC.solution","text":"","code":"FOMC.solution(t, parent_0, alpha, beta)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOMC.solution.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"First-Order Multi-Compartment kinetics — FOMC.solution","text":"t Time. parent_0 Starting value response variable time zero. alpha Shape parameter determined coefficient variation rate constant values. beta Location parameter.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOMC.solution.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"First-Order Multi-Compartment kinetics — FOMC.solution","text":"value response variable time t.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOMC.solution.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"First-Order Multi-Compartment kinetics — FOMC.solution","text":"form given differs slightly original reference Gustafson Holden (1990). parameter beta corresponds 1/beta original equation.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOMC.solution.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"First-Order Multi-Compartment kinetics — FOMC.solution","text":"solution FOMC kinetic model reduces SFO.solution large values alpha beta \\(k = \\frac{\\beta}{\\alpha}\\).","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOMC.solution.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"First-Order Multi-Compartment kinetics — FOMC.solution","text":"FOCUS (2006) “Guidance Document Estimating Persistence Degradation Kinetics Environmental Fate Studies Pesticides EU Registration” Report FOCUS Work Group Degradation Kinetics, EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics FOCUS (2014) “Generic guidance Estimating Persistence Degradation Kinetics Environmental Fate Studies Pesticides EU Registration” Report FOCUS Work Group Degradation Kinetics, Version 1.1, 18 December 2014 http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics Gustafson DI Holden LR (1990) Nonlinear pesticide dissipation soil: new model based spatial variability. Environmental Science Technology 24, 1032-1038","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/reference/FOMC.solution.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"First-Order Multi-Compartment kinetics — FOMC.solution","text":"","code":"plot(function(x) FOMC.solution(x, 100, 10, 2), 0, 2, ylim = c(0, 100))"},{"path":"https://pkgdown.jrwb.de/mkin/reference/HS.solution.html","id":null,"dir":"Reference","previous_headings":"","what":"Hockey-Stick kinetics — HS.solution","title":"Hockey-Stick kinetics — HS.solution","text":"Function describing two exponential decline functions break point .","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/HS.solution.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hockey-Stick kinetics — HS.solution","text":"","code":"HS.solution(t, parent_0, k1, k2, tb)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/HS.solution.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hockey-Stick kinetics — HS.solution","text":"t Time. parent_0 Starting value response variable time zero. k1 First kinetic constant. k2 Second kinetic constant. tb Break point. time, exponential decline according k1 calculated, time, exponential decline proceeds according k2.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/HS.solution.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Hockey-Stick kinetics — HS.solution","text":"value response variable time t.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/HS.solution.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Hockey-Stick kinetics — HS.solution","text":"FOCUS (2006) “Guidance Document Estimating Persistence Degradation Kinetics Environmental Fate Studies Pesticides EU Registration” Report FOCUS Work Group Degradation Kinetics, EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics FOCUS (2014) “Generic guidance Estimating Persistence Degradation Kinetics Environmental Fate Studies Pesticides EU Registration” Report FOCUS Work Group Degradation Kinetics, Version 1.1, 18 December 2014 http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/reference/HS.solution.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Hockey-Stick kinetics — HS.solution","text":"","code":"plot(function(x) HS.solution(x, 100, 2, 0.3, 0.5), 0, 2, ylim=c(0,100))"},{"path":"https://pkgdown.jrwb.de/mkin/reference/IORE.solution.html","id":null,"dir":"Reference","previous_headings":"","what":"Indeterminate order rate equation kinetics — IORE.solution","title":"Indeterminate order rate equation kinetics — IORE.solution","text":"Function describing exponential decline defined starting value, concentration dependent rate constant.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/IORE.solution.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Indeterminate order rate equation kinetics — IORE.solution","text":"","code":"IORE.solution(t, parent_0, k__iore, N)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/IORE.solution.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Indeterminate order rate equation kinetics — IORE.solution","text":"t Time. parent_0 Starting value response variable time zero. k__iore Rate constant. Note depends concentration units used. N Exponent describing nonlinearity rate equation","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/IORE.solution.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Indeterminate order rate equation kinetics — IORE.solution","text":"value response variable time t.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/IORE.solution.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Indeterminate order rate equation kinetics — IORE.solution","text":"solution IORE kinetic model reduces SFO.solution N = 1.  parameters IORE model can transformed equivalent parameters FOMC mode - see NAFTA guidance details.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/IORE.solution.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Indeterminate order rate equation kinetics — IORE.solution","text":"NAFTA Technical Working Group Pesticides (dated) Guidance Evaluating Calculating Degradation Kinetics Environmental Media","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/reference/IORE.solution.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Indeterminate order rate equation kinetics — IORE.solution","text":"","code":"plot(function(x) IORE.solution(x, 100, 0.2, 1.3), 0, 2, ylim = c(0, 100))    # \\dontrun{     fit.fomc <- mkinfit(\"FOMC\", FOCUS_2006_C, quiet = TRUE)     fit.iore <- mkinfit(\"IORE\", FOCUS_2006_C, quiet = TRUE)     fit.iore.deS <- mkinfit(\"IORE\", FOCUS_2006_C, solution_type = \"deSolve\", quiet = TRUE) #> Error in is.loaded(initfunc, PACKAGE = dllname, type = \"\") :  #>   invalid 'PACKAGE' argument      print(data.frame(fit.fomc$par, fit.iore$par, fit.iore.deS$par,                      row.names = paste(\"model par\", 1:4))) #>             fit.fomc.par fit.iore.par fit.iore.deS.par #> model par 1  85.87489063    85.874890        85.874890 #> model par 2   0.05192238    -4.826631        -4.826631 #> model par 3   0.65096665     1.949403         1.949403 #> model par 4   1.85744396     1.857444         1.857444     print(rbind(fomc = endpoints(fit.fomc)$distimes, iore = endpoints(fit.iore)$distimes,                 iore.deS = endpoints(fit.iore)$distimes)) #>              DT50    DT90 DT50back #> fomc     1.785233 15.1479 4.559973 #> iore     1.785233 15.1479 4.559973 #> iore.deS 1.785233 15.1479 4.559973   # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/NAFTA_SOP_2015.html","id":null,"dir":"Reference","previous_headings":"","what":"Example datasets from the NAFTA SOP published 2015 — NAFTA_SOP_2015","title":"Example datasets from the NAFTA SOP published 2015 — NAFTA_SOP_2015","text":"Data taken US EPA (2015), p. 19 23.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/NAFTA_SOP_2015.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Example datasets from the NAFTA SOP published 2015 — NAFTA_SOP_2015","text":"","code":"NAFTA_SOP_Appendix_B   NAFTA_SOP_Appendix_D"},{"path":"https://pkgdown.jrwb.de/mkin/reference/NAFTA_SOP_2015.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Example datasets from the NAFTA SOP published 2015 — NAFTA_SOP_2015","text":"2 datasets observations following variables. name factor containing name observed variable time numeric vector containing time points value numeric vector containing concentrations","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/NAFTA_SOP_2015.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Example datasets from the NAFTA SOP published 2015 — NAFTA_SOP_2015","text":"NAFTA (2011) Guidance evaluating calculating degradation kinetics   environmental media. NAFTA Technical Working Group Pesticides   https://www.epa.gov/pesticide-science--assessing-pesticide-risks/guidance-evaluating--calculating-degradation   accessed 2019-02-22 US EPA (2015) Standard Operating Procedure Using NAFTA Guidance   Calculate Representative Half-life Values Characterizing Pesticide   Degradation   https://www.epa.gov/pesticide-science--assessing-pesticide-risks/standard-operating-procedure-using-nafta-guidance","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/NAFTA_SOP_2015.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Example datasets from the NAFTA SOP published 2015 — NAFTA_SOP_2015","text":"","code":"nafta_evaluation <- nafta(NAFTA_SOP_Appendix_D, cores = 1) #> The SFO model is rejected as S_SFO is equal or higher than the critical value S_c #> The representative half-life of the IORE model is longer than the one corresponding #> to the terminal degradation rate found with the DFOP model. #> The representative half-life obtained from the DFOP model may be used   print(nafta_evaluation) #> Sums of squares: #>       SFO      IORE      DFOP  #> 1378.6832  615.7730  517.8836  #>  #> Critical sum of squares for checking the SFO model: #> [1] 717.4598 #>  #> Parameters: #> $SFO #>          Estimate   Pr(>t)    Lower   Upper #> parent_0  83.7558 1.80e-14 77.18268 90.3288 #> k_parent   0.0017 7.43e-05  0.00112  0.0026 #> sigma      8.7518 1.22e-05  5.64278 11.8608 #>  #> $IORE #>                Estimate Pr(>t)    Lower    Upper #> parent_0       9.69e+01     NA 8.88e+01 1.05e+02 #> k__iore_parent 8.40e-14     NA 1.79e-18 3.94e-09 #> N_parent       6.68e+00     NA 4.19e+00 9.17e+00 #> sigma          5.85e+00     NA 3.76e+00 7.94e+00 #>  #> $DFOP #>          Estimate   Pr(>t)    Lower    Upper #> parent_0 9.76e+01 1.94e-13 9.02e+01 1.05e+02 #> k1       4.24e-02 5.92e-03 2.03e-02 8.88e-02 #> k2       8.24e-04 6.48e-03 3.89e-04 1.75e-03 #> g        2.88e-01 2.47e-05 1.95e-01 4.03e-01 #> sigma    5.36e+00 2.22e-05 3.43e+00 7.30e+00 #>  #>  #> DTx values: #>      DT50    DT90 DT50_rep #> SFO   407    1350      407 #> IORE  541 5190000  1560000 #> DFOP  429    2380      841 #>  #> Representative half-life: #> [1] 841.41   plot(nafta_evaluation)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/NAFTA_SOP_Attachment.html","id":null,"dir":"Reference","previous_headings":"","what":"Example datasets from Attachment 1 to the NAFTA SOP published 2015 — NAFTA_SOP_Attachment","title":"Example datasets from Attachment 1 to the NAFTA SOP published 2015 — NAFTA_SOP_Attachment","text":"Data taken Attachment 1 SOP.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/NAFTA_SOP_Attachment.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Example datasets from Attachment 1 to the NAFTA SOP published 2015 — NAFTA_SOP_Attachment","text":"","code":"NAFTA_SOP_Attachment"},{"path":"https://pkgdown.jrwb.de/mkin/reference/NAFTA_SOP_Attachment.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Example datasets from Attachment 1 to the NAFTA SOP published 2015 — NAFTA_SOP_Attachment","text":"list (NAFTA_SOP_Attachment) containing 16 datasets suitable   evaluation nafta","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/NAFTA_SOP_Attachment.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Example datasets from Attachment 1 to the NAFTA SOP published 2015 — NAFTA_SOP_Attachment","text":"NAFTA (2011) Guidance evaluating calculating degradation kinetics   environmental media. NAFTA Technical Working Group Pesticides   https://www.epa.gov/pesticide-science--assessing-pesticide-risks/guidance-evaluating--calculating-degradation   accessed 2019-02-22 US EPA (2015) Standard Operating Procedure Using NAFTA Guidance   Calculate Representative Half-life Values Characterizing Pesticide   Degradation   https://www.epa.gov/pesticide-science--assessing-pesticide-risks/standard-operating-procedure-using-nafta-guidance","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/NAFTA_SOP_Attachment.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Example datasets from Attachment 1 to the NAFTA SOP published 2015 — NAFTA_SOP_Attachment","text":"","code":"nafta_att_p5a <- nafta(NAFTA_SOP_Attachment[[\"p5a\"]], cores = 1) #> The SFO model is rejected as S_SFO is equal or higher than the critical value S_c #> The half-life obtained from the IORE model may be used   print(nafta_att_p5a) #> Sums of squares: #>       SFO      IORE      DFOP  #> 465.21753  56.27506  32.06401  #>  #> Critical sum of squares for checking the SFO model: #> [1] 64.4304 #>  #> Parameters: #> $SFO #>          Estimate   Pr(>t)  Lower   Upper #> parent_0  95.8401 4.67e-21 92.245 99.4357 #> k_parent   0.0102 3.92e-12  0.009  0.0117 #> sigma      4.8230 3.81e-06  3.214  6.4318 #>  #> $IORE #>                Estimate Pr(>t)    Lower    Upper #> parent_0       1.01e+02     NA 9.91e+01 1.02e+02 #> k__iore_parent 1.54e-05     NA 4.08e-06 5.84e-05 #> N_parent       2.57e+00     NA 2.25e+00 2.89e+00 #> sigma          1.68e+00     NA 1.12e+00 2.24e+00 #>  #> $DFOP #>          Estimate   Pr(>t)   Lower    Upper #> parent_0 9.99e+01 1.41e-26 98.8116 101.0810 #> k1       2.67e-02 5.05e-06  0.0243   0.0295 #> k2       3.41e-12 5.00e-01  0.0000      Inf #> g        6.47e-01 3.67e-06  0.6248   0.6677 #> sigma    1.27e+00 8.91e-06  0.8395   1.6929 #>  #>  #> DTx values: #>      DT50     DT90 DT50_rep #> SFO  67.7 2.25e+02 6.77e+01 #> IORE 58.2 1.07e+03 3.22e+02 #> DFOP 55.5 3.70e+11 2.03e+11 #>  #> Representative half-life: #> [1] 321.51   plot(nafta_att_p5a)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/SFO.solution.html","id":null,"dir":"Reference","previous_headings":"","what":"Single First-Order kinetics — SFO.solution","title":"Single First-Order kinetics — SFO.solution","text":"Function describing exponential decline defined starting value.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/SFO.solution.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Single First-Order kinetics — SFO.solution","text":"","code":"SFO.solution(t, parent_0, k)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/SFO.solution.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Single First-Order kinetics — SFO.solution","text":"t Time. parent_0 Starting value response variable time zero. k Kinetic rate constant.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/SFO.solution.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Single First-Order kinetics — SFO.solution","text":"value response variable time t.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/SFO.solution.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Single First-Order kinetics — SFO.solution","text":"FOCUS (2006) “Guidance Document Estimating Persistence Degradation Kinetics Environmental Fate Studies Pesticides EU Registration” Report FOCUS Work Group Degradation Kinetics, EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics FOCUS (2014) “Generic guidance Estimating Persistence Degradation Kinetics Environmental Fate Studies Pesticides EU Registration” Report FOCUS Work Group Degradation Kinetics, Version 1.1, 18 December 2014 http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/reference/SFO.solution.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Single First-Order kinetics — SFO.solution","text":"","code":"plot(function(x) SFO.solution(x, 100, 3), 0, 2)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/SFORB.solution.html","id":null,"dir":"Reference","previous_headings":"","what":"Single First-Order Reversible Binding kinetics — SFORB.solution","title":"Single First-Order Reversible Binding kinetics — SFORB.solution","text":"Function describing solution differential equations describing kinetic model first-order terms two-way transfer free bound fraction, first-order degradation term free fraction.  initial condition defined amount free fraction substance bound fraction.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/SFORB.solution.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Single First-Order Reversible Binding kinetics — SFORB.solution","text":"","code":"SFORB.solution(t, parent_0, k_12, k_21, k_1output)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/SFORB.solution.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Single First-Order Reversible Binding kinetics — SFORB.solution","text":"t Time. parent_0 Starting value response variable time zero. k_12 Kinetic constant describing transfer free bound. k_21 Kinetic constant describing transfer bound free. k_1output Kinetic constant describing degradation free fraction.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/SFORB.solution.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Single First-Order Reversible Binding kinetics — SFORB.solution","text":"value response variable, sum free bound fractions time t.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/SFORB.solution.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Single First-Order Reversible Binding kinetics — SFORB.solution","text":"FOCUS (2006) “Guidance Document Estimating Persistence Degradation Kinetics Environmental Fate Studies Pesticides EU Registration” Report FOCUS Work Group Degradation Kinetics, EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics FOCUS (2014) “Generic guidance Estimating Persistence Degradation Kinetics Environmental Fate Studies Pesticides EU Registration” Report FOCUS Work Group Degradation Kinetics, Version 1.1, 18 December 2014 http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/reference/SFORB.solution.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Single First-Order Reversible Binding kinetics — SFORB.solution","text":"","code":"plot(function(x) SFORB.solution(x, 100, 0.5, 2, 3), 0, 2)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/add_err.html","id":null,"dir":"Reference","previous_headings":"","what":"Add normally distributed errors to simulated kinetic degradation data — add_err","title":"Add normally distributed errors to simulated kinetic degradation data — add_err","text":"Normally distributed errors added data predicted specific degradation model using mkinpredict. variance error may depend predicted value specified standard deviation.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/add_err.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add normally distributed errors to simulated kinetic degradation data — add_err","text":"","code":"add_err(   prediction,   sdfunc,   secondary = c(\"M1\", \"M2\"),   n = 10,   LOD = 0.1,   reps = 2,   digits = 1,   seed = NA )"},{"path":"https://pkgdown.jrwb.de/mkin/reference/add_err.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add normally distributed errors to simulated kinetic degradation data — add_err","text":"prediction prediction kinetic model produced mkinpredict. sdfunc function taking predicted value argument returning standard deviation used generating random error terms value. secondary names state variables initial value zero n number datasets generated. LOD limit detection (LOD). Values LOD adding random error set NA. reps number replicates generated within datasets. digits number digits values rounded. seed seed used generation random numbers. NA, seed set.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/add_err.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add normally distributed errors to simulated kinetic degradation data — add_err","text":"list datasets compatible mmkin, .e. components list datasets compatible mkinfit.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/add_err.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Add normally distributed errors to simulated kinetic degradation data — add_err","text":"Ranke J Lehmann R (2015) t-test t-test, question. XV Symposium Pesticide Chemistry 2-4 September 2015, Piacenza, Italy https://jrwb.de/posters/piacenza_2015.pdf","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/add_err.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Add normally distributed errors to simulated kinetic degradation data — add_err","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/add_err.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add normally distributed errors to simulated kinetic degradation data — add_err","text":"","code":"# The kinetic model m_SFO_SFO <- mkinmod(parent = mkinsub(\"SFO\", \"M1\"),                      M1 = mkinsub(\"SFO\"), use_of_ff = \"max\") #> Temporary DLL for differentials generated and loaded  # Generate a prediction for a specific set of parameters sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)  # This is the prediction used for the \"Type 2 datasets\" on the Piacenza poster # from 2015 d_SFO_SFO <- mkinpredict(m_SFO_SFO,                          c(k_parent = 0.1, f_parent_to_M1 = 0.5,                            k_M1 = log(2)/1000),                          c(parent = 100, M1 = 0),                          sampling_times)  # Add an error term with a constant (independent of the value) standard deviation # of 10, and generate three datasets d_SFO_SFO_err <- add_err(d_SFO_SFO, function(x) 10, n = 3, seed = 123456789 )  # Name the datasets for nicer plotting names(d_SFO_SFO_err) <- paste(\"Dataset\", 1:3)  # Name the model in the list of models (with only one member in this case) for # nicer plotting later on.  Be quiet and use only one core not to offend CRAN # checks # \\dontrun{ f_SFO_SFO <- mmkin(list(\"SFO-SFO\" = m_SFO_SFO),                    d_SFO_SFO_err, cores = 1,                    quiet = TRUE)  plot(f_SFO_SFO)   # We would like to inspect the fit for dataset 3 more closely # Using double brackets makes the returned object an mkinfit object # instead of a list of mkinfit objects, so plot.mkinfit is used plot(f_SFO_SFO[[3]], show_residuals = TRUE)   # If we use single brackets, we should give two indices (model and dataset), # and plot.mmkin is used plot(f_SFO_SFO[1, 3])  # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/anova.saem.mmkin.html","id":null,"dir":"Reference","previous_headings":"","what":"Anova method for saem.mmkin objects — anova.saem.mmkin","title":"Anova method for saem.mmkin objects — anova.saem.mmkin","text":"Generate anova object. method calculate BIC saemix package. prominent anova methods, models sorted number parameters, tests (requested) always relative model previous line.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/anova.saem.mmkin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Anova method for saem.mmkin objects — anova.saem.mmkin","text":"","code":"# S3 method for class 'saem.mmkin' anova(   object,   ...,   method = c(\"is\", \"lin\", \"gq\"),   test = FALSE,   model.names = NULL )"},{"path":"https://pkgdown.jrwb.de/mkin/reference/anova.saem.mmkin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Anova method for saem.mmkin objects — anova.saem.mmkin","text":"object saem.mmkin object ... objects method Method likelihood calculation: \"\" (importance sampling), \"lin\" (linear approximation), \"gq\" (Gaussian quadrature). Passed saemix::logLik.SaemixObject test likelihood ratio test performed? TRUE, alternative models tested first model. done nested models. model.names Optional character vector model names","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/anova.saem.mmkin.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Anova method for saem.mmkin objects — anova.saem.mmkin","text":"\"anova\" data frame; traditional (S3) result anova()","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/aw.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Akaike weights for model averaging — aw","title":"Calculate Akaike weights for model averaging — aw","text":"Akaike weights calculated based relative expected Kullback-Leibler information specified Burnham Anderson (2004).","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/aw.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Akaike weights for model averaging — aw","text":"","code":"aw(object, ...)  # S3 method for class 'mkinfit' aw(object, ...)  # S3 method for class 'mmkin' aw(object, ...)  # S3 method for class 'mixed.mmkin' aw(object, ...)  # S3 method for class 'multistart' aw(object, ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/aw.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Akaike weights for model averaging — aw","text":"object mmkin column object, containing two mkinfit models fitted data, mkinfit object. latter case, mkinfit objects fitted data specified dots arguments. ... used method mmkin column objects, mkinfit objects method mkinfit objects.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/aw.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Calculate Akaike weights for model averaging — aw","text":"Burnham KP Anderson DR (2004) Multimodel Inference: Understanding AIC BIC Model Selection. Sociological Methods & Research 33(2) 261-304","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/aw.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Akaike weights for model averaging — aw","text":"","code":"# \\dontrun{ f_sfo <- mkinfit(\"SFO\", FOCUS_2006_D, quiet = TRUE) f_dfop <- mkinfit(\"DFOP\", FOCUS_2006_D, quiet = TRUE) aw_sfo_dfop <- aw(f_sfo, f_dfop) sum(aw_sfo_dfop) #> [1] 1 aw_sfo_dfop # SFO gets more weight as it has less parameters and a similar fit #> [1] 0.5970258 0.4029742 f <- mmkin(c(\"SFO\", \"FOMC\", \"DFOP\"), list(\"FOCUS D\" = FOCUS_2006_D), cores = 1, quiet = TRUE) aw(f) #> [1] 0.4808722 0.1945539 0.3245740 sum(aw(f)) #> [1] 1 aw(f[c(\"SFO\", \"DFOP\")]) #> [1] 0.5970258 0.4029742 # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/check_failed.html","id":null,"dir":"Reference","previous_headings":"","what":"Check if fit within an mhmkin object failed — check_failed","title":"Check if fit within an mhmkin object failed — check_failed","text":"Check fit within mhmkin object failed","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/check_failed.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check if fit within an mhmkin object failed — check_failed","text":"","code":"check_failed(x)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/check_failed.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check if fit within an mhmkin object failed — check_failed","text":"x object checked","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/confint.mkinfit.html","id":null,"dir":"Reference","previous_headings":"","what":"Confidence intervals for parameters of mkinfit objects — confint.mkinfit","title":"Confidence intervals for parameters of mkinfit objects — confint.mkinfit","text":"default method 'quadratic' based quadratic approximation curvature likelihood function maximum likelihood parameter estimates. alternative method 'profile' based profile likelihood parameter. 'profile' method uses two nested optimisations can take long time, even parallelized specifying 'cores' unixoid platforms. speed method likely improved using method Venzon Moolgavkar (1988).","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/confint.mkinfit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Confidence intervals for parameters of mkinfit objects — confint.mkinfit","text":"","code":"# S3 method for class 'mkinfit' confint(   object,   parm,   level = 0.95,   alpha = 1 - level,   cutoff,   method = c(\"quadratic\", \"profile\"),   transformed = TRUE,   backtransform = TRUE,   cores = parallel::detectCores(),   rel_tol = 0.01,   quiet = FALSE,   ... )"},{"path":"https://pkgdown.jrwb.de/mkin/reference/confint.mkinfit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Confidence intervals for parameters of mkinfit objects — confint.mkinfit","text":"object mkinfit object parm vector names parameters given confidence intervals. missing, parameters considered. level confidence level required alpha allowed error probability, overrides 'level' specified. cutoff Possibility specify alternative cutoff difference log-likelihoods confidence boundary. Specifying explicit cutoff value overrides arguments 'level' 'alpha' method 'quadratic' method approximates likelihood function optimised parameters using second term Taylor expansion, using second derivative (hessian) contained object. 'profile' method searches parameter space cutoff confidence intervals means likelihood ratio test. transformed quadratic approximation used, applied likelihood based transformed parameters? backtransform approximate likelihood terms transformed parameters, backtransform parameters confidence intervals? cores number cores used multicore processing. Windows machines, cores > 1 currently supported. rel_tol method 'profile', accuracy lower upper bounds, relative estimate obtained quadratic method? quiet suppress message \"Profiling likelihood\" ... used","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/confint.mkinfit.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Confidence intervals for parameters of mkinfit objects — confint.mkinfit","text":"matrix columns giving lower upper confidence limits parameter.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/confint.mkinfit.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Confidence intervals for parameters of mkinfit objects — confint.mkinfit","text":"Bates DM Watts GW (1988) Nonlinear regression analysis & applications Pawitan Y (2013) likelihood - Statistical modelling inference using likelihood. Clarendon Press, Oxford. Venzon DJ Moolgavkar SH (1988) Method Computing Profile-Likelihood Based Confidence Intervals, Applied Statistics, 37, 87–94.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/confint.mkinfit.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Confidence intervals for parameters of mkinfit objects — confint.mkinfit","text":"","code":"f <- mkinfit(\"SFO\", FOCUS_2006_C, quiet = TRUE) confint(f, method = \"quadratic\") #>                2.5%      97.5% #> parent_0 71.8242430 93.1600766 #> k_parent  0.2109541  0.4440528 #> sigma     1.9778868  7.3681380  # \\dontrun{ confint(f, method = \"profile\") #> Profiling the likelihood #>                2.5%      97.5% #> parent_0 73.0641834 92.1392181 #> k_parent  0.2170293  0.4235348 #> sigma     3.1307772  8.0628314  # Set the number of cores for the profiling method for further examples if (identical(Sys.getenv(\"NOT_CRAN\"), \"true\")) {   n_cores <- parallel::detectCores() - 1 } else {   n_cores <- 1 } if (Sys.getenv(\"TRAVIS\") != \"\") n_cores = 1 if (Sys.info()[\"sysname\"] == \"Windows\") n_cores = 1  SFO_SFO <- mkinmod(parent = mkinsub(\"SFO\", \"m1\"), m1 = mkinsub(\"SFO\"),   use_of_ff = \"min\", quiet = TRUE) SFO_SFO.ff <- mkinmod(parent = mkinsub(\"SFO\", \"m1\"), m1 = mkinsub(\"SFO\"),   use_of_ff = \"max\", quiet = TRUE) f_d_1 <- mkinfit(SFO_SFO, subset(FOCUS_2006_D, value != 0), quiet = TRUE) system.time(ci_profile <- confint(f_d_1, method = \"profile\", cores = 1, quiet = TRUE)) #>    user  system elapsed  #>   1.206   0.000   1.206  # Using more cores does not save much time here, as parent_0 takes up most of the time # If we additionally exclude parent_0 (the confidence of which is often of # minor interest), we get a nice performance improvement if we use at least 4 cores system.time(ci_profile_no_parent_0 <- confint(f_d_1, method = \"profile\",   c(\"k_parent_sink\", \"k_parent_m1\", \"k_m1_sink\", \"sigma\"), cores = n_cores)) #> Profiling the likelihood #>    user  system elapsed  #>   0.406   0.172   0.317  ci_profile #>                       2.5%        97.5% #> parent_0      96.456003640 1.027703e+02 #> k_parent_sink  0.040762501 5.549764e-02 #> k_parent_m1    0.046786482 5.500879e-02 #> k_m1_sink      0.003892605 6.702778e-03 #> sigma          2.535612399 3.985263e+00 ci_quadratic_transformed <- confint(f_d_1, method = \"quadratic\") ci_quadratic_transformed #>                       2.5%        97.5% #> parent_0      96.403841640 1.027931e+02 #> k_parent_sink  0.041033378 5.596269e-02 #> k_parent_m1    0.046777902 5.511931e-02 #> k_m1_sink      0.004012217 6.897547e-03 #> sigma          2.396089689 3.854918e+00 ci_quadratic_untransformed <- confint(f_d_1, method = \"quadratic\", transformed = FALSE) ci_quadratic_untransformed #>                       2.5%        97.5% #> parent_0      96.403841645 102.79312449 #> k_parent_sink  0.040485331   0.05535491 #> k_parent_m1    0.046611582   0.05494364 #> k_m1_sink      0.003835483   0.00668582 #> sigma          2.396089689   3.85491806 # Against the expectation based on Bates and Watts (1988), the confidence # intervals based on the internal parameter transformation are less # congruent with the likelihood based intervals. Note the superiority of the # interval based on the untransformed fit for k_m1_sink rel_diffs_transformed <- abs((ci_quadratic_transformed - ci_profile)/ci_profile) rel_diffs_untransformed <- abs((ci_quadratic_untransformed - ci_profile)/ci_profile) rel_diffs_transformed < rel_diffs_untransformed #>                2.5% 97.5% #> parent_0      FALSE FALSE #> k_parent_sink  TRUE FALSE #> k_parent_m1    TRUE FALSE #> k_m1_sink     FALSE FALSE #> sigma         FALSE FALSE signif(rel_diffs_transformed, 3) #>                   2.5%    97.5% #> parent_0      0.000541 0.000222 #> k_parent_sink 0.006650 0.008380 #> k_parent_m1   0.000183 0.002010 #> k_m1_sink     0.030700 0.029100 #> sigma         0.055000 0.032700 signif(rel_diffs_untransformed, 3) #>                   2.5%    97.5% #> parent_0      0.000541 0.000222 #> k_parent_sink 0.006800 0.002570 #> k_parent_m1   0.003740 0.001180 #> k_m1_sink     0.014700 0.002530 #> sigma         0.055000 0.032700   # Investigate a case with formation fractions f_d_2 <- mkinfit(SFO_SFO.ff, subset(FOCUS_2006_D, value != 0), quiet = TRUE) ci_profile_ff <- confint(f_d_2, method = \"profile\", cores = n_cores) #> Profiling the likelihood ci_profile_ff #>                        2.5%        97.5% #> parent_0       96.456003640 1.027703e+02 #> k_parent        0.090911032 1.071578e-01 #> k_m1            0.003892606 6.702775e-03 #> f_parent_to_m1  0.471328495 5.611550e-01 #> sigma           2.535612399 3.985263e+00 ci_quadratic_transformed_ff <- confint(f_d_2, method = \"quadratic\") ci_quadratic_transformed_ff #>                        2.5%        97.5% #> parent_0       96.403833581 102.79311649 #> k_parent        0.090823771   0.10725430 #> k_m1            0.004012219   0.00689755 #> f_parent_to_m1  0.469118824   0.55959615 #> sigma           2.396089689   3.85491806 ci_quadratic_untransformed_ff <- confint(f_d_2, method = \"quadratic\", transformed = FALSE) ci_quadratic_untransformed_ff #>                        2.5%        97.5% #> parent_0       96.403833586 1.027931e+02 #> k_parent        0.090491913 1.069035e-01 #> k_m1            0.003835485 6.685823e-03 #> f_parent_to_m1  0.469113477 5.598387e-01 #> sigma           2.396089689 3.854918e+00 rel_diffs_transformed_ff <- abs((ci_quadratic_transformed_ff - ci_profile_ff)/ci_profile_ff) rel_diffs_untransformed_ff <- abs((ci_quadratic_untransformed_ff - ci_profile_ff)/ci_profile_ff) # While the confidence interval for the parent rate constant is closer to # the profile based interval when using the internal parameter # transformation, the interval for the metabolite rate constant is 'better # without internal parameter transformation. rel_diffs_transformed_ff < rel_diffs_untransformed_ff #>                 2.5% 97.5% #> parent_0       FALSE FALSE #> k_parent        TRUE  TRUE #> k_m1           FALSE FALSE #> f_parent_to_m1  TRUE FALSE #> sigma           TRUE FALSE rel_diffs_transformed_ff #>                        2.5%        97.5% #> parent_0       0.0005408690 0.0002217233 #> k_parent       0.0009598532 0.0009001864 #> k_m1           0.0307283045 0.0290588367 #> f_parent_to_m1 0.0046881768 0.0027780062 #> sigma          0.0550252516 0.0327066836 rel_diffs_untransformed_ff #>                        2.5%        97.5% #> parent_0       0.0005408689 0.0002217233 #> k_parent       0.0046102155 0.0023732280 #> k_m1           0.0146740687 0.0025291815 #> f_parent_to_m1 0.0046995210 0.0023457712 #> sigma          0.0550252516 0.0327066836  # The profiling for the following fit does not finish in a reasonable time, # therefore we use the quadratic approximation m_synth_DFOP_par <- mkinmod(parent = mkinsub(\"DFOP\", c(\"M1\", \"M2\")),   M1 = mkinsub(\"SFO\"),   M2 = mkinsub(\"SFO\"),   use_of_ff = \"max\", quiet = TRUE) DFOP_par_c <- synthetic_data_for_UBA_2014[[12]]$data f_tc_2 <- mkinfit(m_synth_DFOP_par, DFOP_par_c, error_model = \"tc\",   error_model_algorithm = \"direct\", quiet = TRUE) confint(f_tc_2, method = \"quadratic\") #>                        2.5%        97.5% #> parent_0       94.596181875 106.19936592 #> k_M1            0.037605432   0.04490757 #> k_M2            0.008568745   0.01087675 #> f_parent_to_M1  0.021464676   0.62023880 #> f_parent_to_M2  0.015167158   0.37975350 #> k1              0.273897535   0.33388072 #> k2              0.018614555   0.02250379 #> g               0.671943738   0.73583261 #> sigma_low       0.251283679   0.83992102 #> rsd_high        0.040411022   0.07662008 confint(f_tc_2, \"parent_0\", method = \"quadratic\") #>              2.5%    97.5% #> parent_0 94.59618 106.1994 # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/create_deg_func.html","id":null,"dir":"Reference","previous_headings":"","what":"Create degradation functions for known analytical solutions — create_deg_func","title":"Create degradation functions for known analytical solutions — create_deg_func","text":"Create degradation functions known analytical solutions","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/create_deg_func.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create degradation functions for known analytical solutions — create_deg_func","text":"","code":"create_deg_func(spec, use_of_ff = c(\"min\", \"max\"))"},{"path":"https://pkgdown.jrwb.de/mkin/reference/create_deg_func.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create degradation functions for known analytical solutions — create_deg_func","text":"spec List model specifications contained mkinmod objects use_of_ff Minimum maximum use formation fractions","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/create_deg_func.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create degradation functions for known analytical solutions — create_deg_func","text":"Degradation function attached mkinmod objects","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/create_deg_func.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create degradation functions for known analytical solutions — create_deg_func","text":"","code":"SFO_SFO <- mkinmod(   parent = mkinsub(\"SFO\", \"m1\"),   m1 = mkinsub(\"SFO\")) #> Temporary DLL for differentials generated and loaded FOCUS_D <- subset(FOCUS_2006_D, value != 0) # to avoid warnings fit_1 <- mkinfit(SFO_SFO, FOCUS_D, solution_type = \"analytical\", quiet = TRUE) # \\dontrun{ fit_2 <- mkinfit(SFO_SFO, FOCUS_D, solution_type = \"deSolve\", quiet = TRUE) if (require(rbenchmark))   benchmark(     analytical = mkinfit(SFO_SFO, FOCUS_D, solution_type = \"analytical\", quiet = TRUE),     deSolve = mkinfit(SFO_SFO, FOCUS_D, solution_type = \"deSolve\", quiet = TRUE),     replications = 2) #> Loading required package: rbenchmark #>         test replications elapsed relative user.self sys.self user.child #> 1 analytical            2    0.24    1.000     0.240        0          0 #> 2    deSolve            2    0.31    1.292     0.309        0          0 #>   sys.child #> 1         0 #> 2         0   DFOP_SFO <- mkinmod(     parent = mkinsub(\"DFOP\", \"m1\"),     m1 = mkinsub(\"SFO\")) #> Temporary DLL for differentials generated and loaded   benchmark(     analytical = mkinfit(DFOP_SFO, FOCUS_D, solution_type = \"analytical\", quiet = TRUE),     deSolve = mkinfit(DFOP_SFO, FOCUS_D, solution_type = \"deSolve\", quiet = TRUE),     replications = 2) #>         test replications elapsed relative user.self sys.self user.child #> 1 analytical            2   0.403    1.000     0.402        0          0 #> 2    deSolve            2   0.539    1.337     0.538        0          0 #>   sys.child #> 1         0 #> 2         0 # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/dimethenamid_2018.html","id":null,"dir":"Reference","previous_headings":"","what":"Aerobic soil degradation data on dimethenamid and dimethenamid-P from the EU assessment in 2018 — dimethenamid_2018","title":"Aerobic soil degradation data on dimethenamid and dimethenamid-P from the EU assessment in 2018 — dimethenamid_2018","text":"datasets extracted active substance evaluation dossier published EFSA. Kinetic evaluations shown datasets intended illustrate advance kinetic modelling. fact data results shown imply license use context pesticide  registrations, use data may constrained data protection regulations.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/dimethenamid_2018.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Aerobic soil degradation data on dimethenamid and dimethenamid-P from the EU assessment in 2018 — dimethenamid_2018","text":"","code":"dimethenamid_2018"},{"path":"https://pkgdown.jrwb.de/mkin/reference/dimethenamid_2018.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Aerobic soil degradation data on dimethenamid and dimethenamid-P from the EU assessment in 2018 — dimethenamid_2018","text":"mkindsg object grouping seven datasets meta information","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/dimethenamid_2018.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Aerobic soil degradation data on dimethenamid and dimethenamid-P from the EU assessment in 2018 — dimethenamid_2018","text":"Rapporteur Member State Germany, Co-Rapporteur Member State Bulgaria (2018) Renewal Assessment Report Dimethenamid-P Volume 3 - B.8 Environmental fate behaviour Rev. 2 - November 2017 https://open.efsa.europa.eu/study-inventory/EFSA-Q-2014-00716","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/dimethenamid_2018.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Aerobic soil degradation data on dimethenamid and dimethenamid-P from the EU assessment in 2018 — dimethenamid_2018","text":"R code used create data object installed package 'dataset_generation' directory. code, page numbers given specific pieces information comments.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/dimethenamid_2018.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Aerobic soil degradation data on dimethenamid and dimethenamid-P from the EU assessment in 2018 — dimethenamid_2018","text":"","code":"print(dimethenamid_2018) #> <mkindsg> holding 7 mkinds objects #> Title $title:  Aerobic soil degradation data on dimethenamid-P from the EU assessment in 2018  #> Occurrence of observed compounds $observed_n: #> DMTAP   M23   M27   M31  DMTA  #>     3     7     7     7     4  #> Time normalisation factors $f_time_norm: #> [1] 1.0000000 0.9706477 1.2284784 1.2284784 0.6233856 0.7678922 0.6733938 #> Meta information $meta: #>                      study  usda_soil_type study_moisture_ref_type rel_moisture #> Calke        Unsworth 2014      Sandy loam                     pF2         1.00 #> Borstel  Staudenmaier 2009            Sand                     pF1         0.50 #> Elliot 1        Wendt 1997       Clay loam                   pF2.5         0.75 #> Elliot 2        Wendt 1997       Clay loam                   pF2.5         0.75 #> Flaach          König 1996 Sandy clay loam                     pF1         0.40 #> BBA 2.2         König 1995      Loamy sand                     pF1         0.40 #> BBA 2.3         König 1995      Sandy loam                     pF1         0.40 #>          study_ref_moisture temperature #> Calke                    NA          20 #> Borstel               23.00          20 #> Elliot 1              33.37          23 #> Elliot 2              33.37          23 #> Flaach                   NA          20 #> BBA 2.2                  NA          20 #> BBA 2.3                  NA          20 dmta_ds <- lapply(1:7, function(i) {   ds_i <- dimethenamid_2018$ds[[i]]$data   ds_i[ds_i$name == \"DMTAP\", \"name\"] <-  \"DMTA\"   ds_i$time <- ds_i$time * dimethenamid_2018$f_time_norm[i]   ds_i }) names(dmta_ds) <- sapply(dimethenamid_2018$ds, function(ds) ds$title) dmta_ds[[\"Elliot\"]] <- rbind(dmta_ds[[\"Elliot 1\"]], dmta_ds[[\"Elliot 2\"]]) dmta_ds[[\"Elliot 1\"]] <- NULL dmta_ds[[\"Elliot 2\"]] <- NULL # \\dontrun{ # We don't use DFOP for the parent compound, as this gives numerical # instabilities in the fits sfo_sfo3p <- mkinmod(  DMTA = mkinsub(\"SFO\", c(\"M23\", \"M27\", \"M31\")),  M23 = mkinsub(\"SFO\"),  M27 = mkinsub(\"SFO\"),  M31 = mkinsub(\"SFO\", \"M27\", sink = FALSE),  quiet = TRUE ) dmta_sfo_sfo3p_tc <- mmkin(list(\"SFO-SFO3+\" = sfo_sfo3p),   dmta_ds, error_model = \"tc\", quiet = TRUE) print(dmta_sfo_sfo3p_tc) #> <mmkin> object #> Status of individual fits: #>  #>            dataset #> model       Calke Borstel Flaach BBA 2.2 BBA 2.3 Elliot #>   SFO-SFO3+ OK    OK      OK     OK      OK      OK     #>  #> OK: No warnings # The default (test_log_parms = FALSE) gives an undue # influence of ill-defined rate constants that have # extremely small values: plot(mixed(dmta_sfo_sfo3p_tc), test_log_parms = FALSE)  # If we disregards ill-defined rate constants, the results # look more plausible, but the truth is likely to be in # between these variants plot(mixed(dmta_sfo_sfo3p_tc), test_log_parms = TRUE) # We can also specify a default value for the failing # log parameters, to mimic FOCUS guidance plot(mixed(dmta_sfo_sfo3p_tc), test_log_parms = TRUE,   default_log_parms = log(2)/1000) # As these attempts are not satisfying, we use nonlinear mixed-effects models # f_dmta_nlme_tc <- nlme(dmta_sfo_sfo3p_tc) # nlme reaches maxIter = 50 without convergence f_dmta_saem_tc <- saem(dmta_sfo_sfo3p_tc) # I am commenting out the convergence plot as rendering them # with pkgdown fails (at least without further tweaks to the # graphics device used) #saemix::plot(f_dmta_saem_tc$so, plot.type = \"convergence\") summary(f_dmta_saem_tc) #> saemix version used for fitting:      3.3  #> mkin version used for pre-fitting:  1.2.9  #> R version used for fitting:         4.4.2  #> Date of fit:     Thu Feb 13 14:56:10 2025  #> Date of summary: Thu Feb 13 14:56:10 2025  #>  #> Equations: #> d_DMTA/dt = - k_DMTA * DMTA #> d_M23/dt = + f_DMTA_to_M23 * k_DMTA * DMTA - k_M23 * M23 #> d_M27/dt = + f_DMTA_to_M27 * k_DMTA * DMTA - k_M27 * M27 + k_M31 * M31 #> d_M31/dt = + f_DMTA_to_M31 * k_DMTA * DMTA - k_M31 * M31 #>  #> Data: #> 563 observations of 4 variable(s) grouped in 6 datasets #>  #> Model predictions using solution type deSolve  #>  #> Fitted in 295.43 s #> Using 300, 100 iterations and 9 chains #>  #> Variance model: Two-component variance function  #>  #> Starting values for degradation parameters: #>       DMTA_0   log_k_DMTA    log_k_M23    log_k_M27    log_k_M31 f_DMTA_ilr_1  #>      95.5662      -2.9048      -3.8130      -4.1600      -4.1486       0.1341  #> f_DMTA_ilr_2 f_DMTA_ilr_3  #>       0.1385      -1.6700  #>  #> Fixed degradation parameter values: #> None #>  #> Starting values for random effects (square root of initial entries in omega): #>              DMTA_0 log_k_DMTA log_k_M23 log_k_M27 log_k_M31 f_DMTA_ilr_1 #> DMTA_0        4.802     0.0000    0.0000     0.000    0.0000       0.0000 #> log_k_DMTA    0.000     0.9834    0.0000     0.000    0.0000       0.0000 #> log_k_M23     0.000     0.0000    0.6983     0.000    0.0000       0.0000 #> log_k_M27     0.000     0.0000    0.0000     1.028    0.0000       0.0000 #> log_k_M31     0.000     0.0000    0.0000     0.000    0.9841       0.0000 #> f_DMTA_ilr_1  0.000     0.0000    0.0000     0.000    0.0000       0.7185 #> f_DMTA_ilr_2  0.000     0.0000    0.0000     0.000    0.0000       0.0000 #> f_DMTA_ilr_3  0.000     0.0000    0.0000     0.000    0.0000       0.0000 #>              f_DMTA_ilr_2 f_DMTA_ilr_3 #> DMTA_0             0.0000       0.0000 #> log_k_DMTA         0.0000       0.0000 #> log_k_M23          0.0000       0.0000 #> log_k_M27          0.0000       0.0000 #> log_k_M31          0.0000       0.0000 #> f_DMTA_ilr_1       0.0000       0.0000 #> f_DMTA_ilr_2       0.7378       0.0000 #> f_DMTA_ilr_3       0.0000       0.4451 #>  #> Starting values for error model parameters: #> a.1 b.1  #>   1   1  #>  #> Results: #>  #> Likelihood computed by importance sampling #>    AIC  BIC logLik #>   2276 2273  -1120 #>  #> Optimised parameters: #>                    est.   lower   upper #> DMTA_0          88.4862 84.1127 92.8598 #> log_k_DMTA      -3.0512 -3.5674 -2.5351 #> log_k_M23       -4.0576 -4.9013 -3.2139 #> log_k_M27       -3.8584 -4.2572 -3.4595 #> log_k_M31       -3.9779 -4.4844 -3.4714 #> f_DMTA_ilr_1     0.1264 -0.2186  0.4714 #> f_DMTA_ilr_2     0.1509 -0.2547  0.5565 #> f_DMTA_ilr_3    -1.3891 -1.6962 -1.0819 #> a.1              0.9196  0.8307  1.0085 #> b.1              0.1377  0.1205  0.1549 #> SD.DMTA_0        3.5956 -0.8167  8.0078 #> SD.log_k_DMTA    0.6437  0.2784  1.0091 #> SD.log_k_M23     0.9929  0.3719  1.6139 #> SD.log_k_M27     0.4530  0.1522  0.7537 #> SD.log_k_M31     0.5773  0.1952  0.9595 #> SD.f_DMTA_ilr_1  0.4063  0.1505  0.6621 #> SD.f_DMTA_ilr_2  0.4800  0.1817  0.7783 #> SD.f_DMTA_ilr_3  0.3582  0.1350  0.5814 #>  #> Correlation:  #>              DMTA_0  l__DMTA lg__M23 lg__M27 lg__M31 f_DMTA__1 f_DMTA__2 #> log_k_DMTA    0.0306                                                     #> log_k_M23    -0.0234 -0.0032                                             #> log_k_M27    -0.0380 -0.0049  0.0041                                     #> log_k_M31    -0.0247 -0.0031  0.0022  0.0817                             #> f_DMTA_ilr_1 -0.0046 -0.0006  0.0425 -0.0438  0.0319                     #> f_DMTA_ilr_2 -0.0008 -0.0002  0.0216 -0.0267 -0.0890 -0.0349             #> f_DMTA_ilr_3 -0.1805 -0.0136  0.0434  0.0791  0.0390 -0.0061    0.0053   #>  #> Random effects: #>                   est.   lower  upper #> SD.DMTA_0       3.5956 -0.8167 8.0078 #> SD.log_k_DMTA   0.6437  0.2784 1.0091 #> SD.log_k_M23    0.9929  0.3719 1.6139 #> SD.log_k_M27    0.4530  0.1522 0.7537 #> SD.log_k_M31    0.5773  0.1952 0.9595 #> SD.f_DMTA_ilr_1 0.4063  0.1505 0.6621 #> SD.f_DMTA_ilr_2 0.4800  0.1817 0.7783 #> SD.f_DMTA_ilr_3 0.3582  0.1350 0.5814 #>  #> Variance model: #>       est.  lower  upper #> a.1 0.9196 0.8307 1.0085 #> b.1 0.1377 0.1205 0.1549 #>  #> Backtransformed parameters: #>                   est.     lower    upper #> DMTA_0        88.48621 84.112654 92.85977 #> k_DMTA         0.04730  0.028230  0.07926 #> k_M23          0.01729  0.007437  0.04020 #> k_M27          0.02110  0.014162  0.03144 #> k_M31          0.01872  0.011283  0.03107 #> f_DMTA_to_M23  0.14551        NA       NA #> f_DMTA_to_M27  0.12169        NA       NA #> f_DMTA_to_M31  0.11062        NA       NA #>  #> Resulting formation fractions: #>               ff #> DMTA_M23  0.1455 #> DMTA_M27  0.1217 #> DMTA_M31  0.1106 #> DMTA_sink 0.6222 #>  #> Estimated disappearance times: #>       DT50   DT90 #> DMTA 14.65  48.68 #> M23  40.09 133.17 #> M27  32.85 109.11 #> M31  37.02 122.97 # As the confidence interval for the random effects of DMTA_0 # includes zero, we could try an alternative model without # such random effects # f_dmta_saem_tc_2 <- saem(dmta_sfo_sfo3p_tc, #   covariance.model = diag(c(0, rep(1, 7)))) # saemix::plot(f_dmta_saem_tc_2$so, plot.type = \"convergence\") # This does not perform better judged by AIC and BIC # saemix::compare.saemix(f_dmta_saem_tc$so, f_dmta_saem_tc_2$so) # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/ds_mixed.html","id":null,"dir":"Reference","previous_headings":"","what":"Synthetic data for hierarchical kinetic degradation models — ds_mixed","title":"Synthetic data for hierarchical kinetic degradation models — ds_mixed","text":"R code used create data object installed package 'dataset_generation' directory.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/ds_mixed.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Synthetic data for hierarchical kinetic degradation models — ds_mixed","text":"","code":"# \\dontrun{   sfo_mmkin <- mmkin(\"SFO\", ds_sfo, quiet = TRUE, error_model = \"tc\", cores = 15)   sfo_saem <- saem(sfo_mmkin, no_random_effect = \"parent_0\")   plot(sfo_saem)  # }  # This is the code used to generate the datasets cat(readLines(system.file(\"dataset_generation/ds_mixed.R\", package = \"mkin\")), sep = \"\\n\") #> # Synthetic data for hierarchical kinetic models #> # Refactored version of the code previously in tests/testthat/setup_script.R #> # The number of datasets was 3 for FOMC, and 10 for HS in that script, now it #> # is always 15 for consistency #>  #> library(mkin)  # We use mkinmod and mkinpredict #> sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120) #> n <- 15 #> log_sd <- 0.3 #> err_1 = list(const = 1, prop = 0.05) #> tc <- function(value) sigma_twocomp(value, err_1$const, err_1$prop) #> const <- function(value) 2 #>  #> set.seed(123456) #> SFO <- mkinmod(parent = mkinsub(\"SFO\")) #> sfo_pop <- list(parent_0 = 100, k_parent = 0.03) #> sfo_parms <- as.matrix(data.frame( #>     k_parent = rlnorm(n, log(sfo_pop$k_parent), log_sd))) #> set.seed(123456) #> ds_sfo <- lapply(1:n, function(i) { #>   ds_mean <- mkinpredict(SFO, sfo_parms[i, ], #>     c(parent = sfo_pop$parent_0), sampling_times) #>   add_err(ds_mean, tc, n = 1)[[1]] #> }) #> attr(ds_sfo, \"pop\") <- sfo_pop #> attr(ds_sfo, \"parms\") <- sfo_parms #>  #> set.seed(123456) #> FOMC <- mkinmod(parent = mkinsub(\"FOMC\")) #> fomc_pop <- list(parent_0 = 100, alpha = 2, beta = 8) #> fomc_parms <- as.matrix(data.frame( #>     alpha = rlnorm(n, log(fomc_pop$alpha), 0.4), #>     beta = rlnorm(n, log(fomc_pop$beta), 0.2))) #> set.seed(123456) #> ds_fomc <- lapply(1:n, function(i) { #>   ds_mean <- mkinpredict(FOMC, fomc_parms[i, ], #>     c(parent = fomc_pop$parent_0), sampling_times) #>   add_err(ds_mean, tc, n = 1)[[1]] #> }) #> attr(ds_fomc, \"pop\") <- fomc_pop #> attr(ds_fomc, \"parms\") <- fomc_parms #>  #> set.seed(123456) #> DFOP <- mkinmod(parent = mkinsub(\"DFOP\")) #> dfop_pop <- list(parent_0 = 100, k1 = 0.06, k2 = 0.015, g = 0.4) #> dfop_parms <- as.matrix(data.frame( #>   k1 = rlnorm(n, log(dfop_pop$k1), log_sd), #>   k2 = rlnorm(n, log(dfop_pop$k2), log_sd), #>   g = plogis(rnorm(n, qlogis(dfop_pop$g), log_sd)))) #> set.seed(123456) #> ds_dfop <- lapply(1:n, function(i) { #>   ds_mean <- mkinpredict(DFOP, dfop_parms[i, ], #>     c(parent = dfop_pop$parent_0), sampling_times) #>   add_err(ds_mean, tc, n = 1)[[1]] #> }) #> attr(ds_dfop, \"pop\") <- dfop_pop #> attr(ds_dfop, \"parms\") <- dfop_parms #>  #> set.seed(123456) #> HS <- mkinmod(parent = mkinsub(\"HS\")) #> hs_pop <- list(parent_0 = 100, k1 = 0.08, k2 = 0.01, tb = 15) #> hs_parms <- as.matrix(data.frame( #>   k1 = rlnorm(n, log(hs_pop$k1), log_sd), #>   k2 = rlnorm(n, log(hs_pop$k2), log_sd), #>   tb = rlnorm(n, log(hs_pop$tb), 0.1))) #> set.seed(123456) #> ds_hs <- lapply(1:n, function(i) { #>   ds_mean <- mkinpredict(HS, hs_parms[i, ], #>     c(parent = hs_pop$parent_0), sampling_times) #>   add_err(ds_mean, const, n = 1)[[1]] #> }) #> attr(ds_hs, \"pop\") <- hs_pop #> attr(ds_hs, \"parms\") <- hs_parms #>  #> set.seed(123456) #> DFOP_SFO <- mkinmod( #>   parent = mkinsub(\"DFOP\", \"m1\"), #>   m1 = mkinsub(\"SFO\"), #>   quiet = TRUE) #> dfop_sfo_pop <- list(parent_0 = 100, #>   k_m1 = 0.007, f_parent_to_m1 = 0.5, #>   k1 = 0.1, k2 = 0.02, g = 0.5) #> dfop_sfo_parms <- as.matrix(data.frame( #>   k1 = rlnorm(n, log(dfop_sfo_pop$k1), log_sd), #>   k2 = rlnorm(n, log(dfop_sfo_pop$k2), log_sd), #>   g = plogis(rnorm(n, qlogis(dfop_sfo_pop$g), log_sd)), #>   f_parent_to_m1 = plogis(rnorm(n, #>       qlogis(dfop_sfo_pop$f_parent_to_m1), log_sd)), #>   k_m1 = rlnorm(n, log(dfop_sfo_pop$k_m1), log_sd))) #> ds_dfop_sfo_mean <- lapply(1:n, #>   function(i) { #>     mkinpredict(DFOP_SFO, dfop_sfo_parms[i, ], #>       c(parent = dfop_sfo_pop$parent_0, m1 = 0), sampling_times) #>   } #> ) #> set.seed(123456) #> ds_dfop_sfo <- lapply(ds_dfop_sfo_mean, function(ds) { #>   add_err(ds, #>     sdfunc = function(value) sqrt(err_1$const^2 + value^2 * err_1$prop^2), #>     n = 1, secondary = \"m1\")[[1]] #> }) #> attr(ds_dfop_sfo, \"pop\") <- dfop_sfo_pop #> attr(ds_dfop_sfo, \"parms\") <- dfop_sfo_parms #>  #> #save(ds_sfo, ds_fomc, ds_dfop, ds_hs, ds_dfop_sfo, file = \"data/ds_mixed.rda\", version = 2)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/endpoints.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to calculate endpoints for further use from kinetic models fitted with mkinfit — endpoints","title":"Function to calculate endpoints for further use from kinetic models fitted with mkinfit — endpoints","text":"function calculates DT50 DT90 values well formation fractions kinetic models fitted mkinfit. SFORB model specified one parents metabolites, Eigenvalues returned. equivalent rate constants DFOP model, advantage SFORB model can also used metabolites.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/endpoints.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to calculate endpoints for further use from kinetic models fitted with mkinfit — endpoints","text":"","code":"endpoints(fit, covariates = NULL, covariate_quantile = 0.5)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/endpoints.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to calculate endpoints for further use from kinetic models fitted with mkinfit — endpoints","text":"fit object class mkinfit, nlme.mmkin saem.mmkin, another object list components mkinmod containing mkinmod degradation model, two numeric vectors, bparms.optim bparms.fixed, contain parameter values model. covariates Numeric vector covariate values variables covariate models object. given, overrides 'covariate_quantile'. covariate_quantile argument effect fitted object covariate models. , default show endpoints median covariate values (50th percentile).","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/endpoints.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to calculate endpoints for further use from kinetic models fitted with mkinfit — endpoints","text":"list matrix dissipation times named distimes, , applicable, vector formation fractions named ff , SFORB model use, vector eigenvalues SFORB models, equivalent DFOP rate constants","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/endpoints.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Function to calculate endpoints for further use from kinetic models fitted with mkinfit — endpoints","text":"Additional DT50 values calculated FOMC DT90 k1 k2 HS DFOP, well Eigenvalues b1 b2 SFORB models","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/endpoints.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Function to calculate endpoints for further use from kinetic models fitted with mkinfit — endpoints","text":"function used internally summary.mkinfit, summary.nlme.mmkin summary.saem.mmkin.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/endpoints.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Function to calculate endpoints for further use from kinetic models fitted with mkinfit — endpoints","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/endpoints.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function to calculate endpoints for further use from kinetic models fitted with mkinfit — endpoints","text":"","code":"fit <- mkinfit(\"FOMC\", FOCUS_2006_C, quiet = TRUE)   endpoints(fit) #> $distimes #>            DT50    DT90 DT50back #> parent 1.785233 15.1479 4.559973 #>    # \\dontrun{     fit_2 <- mkinfit(\"DFOP\", FOCUS_2006_C, quiet = TRUE)     endpoints(fit_2) #> $distimes #>            DT50     DT90 DT50back  DT50_k1  DT50_k2 #> parent 1.886925 21.25106 6.397207 1.508293 38.83438 #>      fit_3 <- mkinfit(\"SFORB\", FOCUS_2006_C, quiet = TRUE)     endpoints(fit_3) #> $ff #> parent_free  #>           1  #>  #> $SFORB #> parent_b1 parent_b2  parent_g  #> 0.4595574 0.0178488 0.8539454  #>  #> $distimes #>            DT50     DT90 DT50back DT50_parent_b1 DT50_parent_b2 #> parent 1.886925 21.25106 6.397208       1.508293       38.83438 #>    # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/experimental_data_for_UBA.html","id":null,"dir":"Reference","previous_headings":"","what":"Experimental datasets used for development and testing of error models — experimental_data_for_UBA_2019","title":"Experimental datasets used for development and testing of error models — experimental_data_for_UBA_2019","text":"12 datasets extracted active substance evaluation dossiers published  EFSA. Kinetic evaluations shown datasets intended illustrate  advance error model specifications. fact data  results shown imply license use context  pesticide registrations, use data may constrained  data protection regulations. Preprocessing data performed based recommendations FOCUS  kinetics workgroup (FOCUS, 2014) described . Datasets 1 2 Renewal Assessment Report (RAR) imazamox  (France, 2015, p. 15). setting values reported zero, LOQ 0.1  assumed. Metabolite residues reported day zero added  parent compound residues. Datasets 3 4 Renewal Assessment Report (RAR) isofetamid  (Belgium, 2014, p. 8) show data two different radiolabels.  dataset 4, value given metabolite day zero sampling  replicate B added parent compound, following respective  FOCUS recommendation. Dataset 5 Renewal Assessment Report (RAR) ethofumesate  (Austria, 2015, p. 16). Datasets 6 10 Renewal Assessment Report (RAR) glyphosate  (Germany, 2013, pages 8, 28, 50, 51). initial sampling,  residues given metabolite added parent  value, following recommendation FOCUS kinetics workgroup. Dataset 11 Renewal Assessment Report (RAR) 2,4-D  (Hellas, 2013, p. 644). Values reported zero set NA,  exception day three sampling metabolite A2, set  one half LOD reported 1% AR. Dataset 12 Renewal Assessment Report (RAR) thifensulfuron-methyl  (United Kingdom, 2014, p. 81).","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/experimental_data_for_UBA.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Experimental datasets used for development and testing of error models — experimental_data_for_UBA_2019","text":"","code":"experimental_data_for_UBA_2019"},{"path":"https://pkgdown.jrwb.de/mkin/reference/experimental_data_for_UBA.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Experimental datasets used for development and testing of error models — experimental_data_for_UBA_2019","text":"list containing twelve datasets R6 class defined mkinds,   containing, among others, following components title name dataset, e.g. Soil 1 data data frame data form expected mkinfit","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/experimental_data_for_UBA.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Experimental datasets used for development and testing of error models — experimental_data_for_UBA_2019","text":"Austria (2015). Ethofumesate Renewal Assessment Report Volume 3 Annex B.8 () Belgium (2014). Isofetamid (IKF-5411) Draft Assessment Report Volume 3 Annex B.8 () France (2015). Imazamox Draft Renewal Assessment Report Volume 3 Annex B.8 () FOCUS (2014) “Generic guidance Estimating Persistence   Degradation Kinetics Environmental Fate Studies Pesticides EU   Registration” Report FOCUS Work Group Degradation Kinetics,   Version 1.1, 18 December 2014   http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics Germany (2013). Renewal Assessment Report Glyphosate Volume 3 Annex B.8: Environmental Fate   Behaviour Hellas (2013). Renewal Assessment Report 2,4-D Volume 3 Annex B.8: Fate behaviour   environment Ranke (2019) Documentation results obtained error model expertise   written German Umweltbundesamt. United Kingdom (2014). Thifensulfuron-methyl - Annex B.8 (Volume 3) Report Proposed   Decision United Kingdom made European Commission Regulation (EC) .   1141/2010 renewal active substance","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/experimental_data_for_UBA.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Experimental datasets used for development and testing of error models — experimental_data_for_UBA_2019","text":"","code":"# \\dontrun{  # Model definitions sfo_sfo <- mkinmod(   parent = mkinsub(\"SFO\", to = \"A1\"),   A1 = mkinsub(\"SFO\"),   use_of_ff = \"max\" ) #> Temporary DLL for differentials generated and loaded  dfop_sfo <- mkinmod(   parent = mkinsub(\"DFOP\", to = \"A1\"),   A1 = mkinsub(\"SFO\"),   use_of_ff = \"max\" ) #> Temporary DLL for differentials generated and loaded  sfo_sfo_sfo <- mkinmod(   parent = mkinsub(\"SFO\", to = \"A1\"),   A1 = mkinsub(\"SFO\", to = \"A2\"),   A2 = mkinsub(\"SFO\"),   use_of_ff = \"max\" ) #> Temporary DLL for differentials generated and loaded  dfop_sfo_sfo <- mkinmod(   parent = mkinsub(\"DFOP\", to = \"A1\"),   A1 = mkinsub(\"SFO\", to = \"A2\"),   A2 = mkinsub(\"SFO\"),   use_of_ff = \"max\" ) #> Temporary DLL for differentials generated and loaded d_1_2 <- lapply(experimental_data_for_UBA_2019[1:2], function(x) x$data) names(d_1_2) <- paste(\"Soil\", 1:2)   f_1_2_tc <- mmkin(list(\"DFOP-SFO-SFO\" = dfop_sfo_sfo), d_1_2, error_model = \"tc\")  plot(f_1_2_tc, resplot = \"errmod\")   # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/f_time_norm_focus.html","id":null,"dir":"Reference","previous_headings":"","what":"Normalisation factors for aerobic soil degradation according to FOCUS guidance — f_time_norm_focus","title":"Normalisation factors for aerobic soil degradation according to FOCUS guidance — f_time_norm_focus","text":"Time step normalisation factors aerobic soil degradation described Appendix 8 FOCUS kinetics guidance (FOCUS 2014, p. 369).","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/f_time_norm_focus.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Normalisation factors for aerobic soil degradation according to FOCUS guidance — f_time_norm_focus","text":"","code":"f_time_norm_focus(object, ...)  # S3 method for class 'numeric' f_time_norm_focus(   object,   moisture = NA,   field_moisture = NA,   temperature = object,   Q10 = 2.58,   walker = 0.7,   f_na = NA,   ... )  # S3 method for class 'mkindsg' f_time_norm_focus(   object,   study_moisture_ref_source = c(\"auto\", \"meta\", \"focus\"),   Q10 = 2.58,   walker = 0.7,   f_na = NA,   ... )"},{"path":"https://pkgdown.jrwb.de/mkin/reference/f_time_norm_focus.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Normalisation factors for aerobic soil degradation according to FOCUS guidance — f_time_norm_focus","text":"object object containing information used calculations ... Currently used moisture Numeric vector moisture contents \\% w/w field_moisture Numeric vector moisture contents field capacity (pF2) \\% w/w temperature Numeric vector temperatures °C Q10 Q10 value used temperature normalisation walker Walker exponent used moisture normalisation f_na factor use NA values. set NA, factors complete cases returned. study_moisture_ref_source Source reference value used calculate study moisture. 'auto', preference given reference moisture given meta information, otherwise focus soil moisture soil class used","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/f_time_norm_focus.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Normalisation factors for aerobic soil degradation according to FOCUS guidance — f_time_norm_focus","text":"FOCUS (2006) “Guidance Document Estimating Persistence Degradation Kinetics Environmental Fate Studies Pesticides EU Registration” Report FOCUS Work Group Degradation Kinetics, EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics FOCUS (2014) “Generic guidance Estimating Persistence Degradation Kinetics Environmental Fate Studies Pesticides EU Registration” Report FOCUS Work Group Degradation Kinetics, Version 1.1, 18 December 2014 http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/reference/f_time_norm_focus.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Normalisation factors for aerobic soil degradation according to FOCUS guidance — f_time_norm_focus","text":"","code":"f_time_norm_focus(25, 20, 25) # 1.37, compare FOCUS 2014 p. 184 #> [1] 1.373956  D24_2014$meta #>                                 study usda_soil_type study_moisture_ref_type #> Mississippi                Cohen 1991      Silt loam                    <NA> #> Fayette     Liu and Adelfinskaya 2011      Silt loam                     pF1 #> RefSol 03-G Liu and Adelfinskaya 2011           Loam                     pF1 #> Site E1     Liu and Adelfinskaya 2011           Loam                     pF1 #> Site I2     Liu and Adelfinskaya 2011     Loamy sand                     pF1 #>             rel_moisture temperature #> Mississippi           NA          25 #> Fayette              0.5          20 #> RefSol 03-G          0.5          20 #> Site E1              0.5          20 #> Site I2              0.5          20 # No moisture normalisation in the first dataset, so we use f_na = 1 to get # temperature only normalisation as in the EU evaluation f_time_norm_focus(D24_2014, study_moisture_ref_source = \"focus\", f_na = 1) #> $f_time_norm was (re)set to normalised values"},{"path":"https://pkgdown.jrwb.de/mkin/reference/focus_soil_moisture.html","id":null,"dir":"Reference","previous_headings":"","what":"FOCUS default values for soil moisture contents at field capacity, MWHC and 1/3 bar — focus_soil_moisture","title":"FOCUS default values for soil moisture contents at field capacity, MWHC and 1/3 bar — focus_soil_moisture","text":"value transcribed p. 36. table assumes field capacity corresponds pF2, MWHC pF 1 1/3 bar pF 2.5.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/focus_soil_moisture.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"FOCUS default values for soil moisture contents at field capacity, MWHC and 1/3 bar — focus_soil_moisture","text":"","code":"focus_soil_moisture"},{"path":"https://pkgdown.jrwb.de/mkin/reference/focus_soil_moisture.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"FOCUS default values for soil moisture contents at field capacity, MWHC and 1/3 bar — focus_soil_moisture","text":"matrix upper case USDA soil classes row names, water tension ('pF1', 'pF2', 'pF 2.5') column names","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/focus_soil_moisture.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"FOCUS default values for soil moisture contents at field capacity, MWHC and 1/3 bar — focus_soil_moisture","text":"Anonymous (2014) Generic Guidance Tier 1 FOCUS Ground Water Assessment Version 2.2, May 2014 https://esdac.jrc.ec.europa.eu/projects/ground-water","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/focus_soil_moisture.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"FOCUS default values for soil moisture contents at field capacity, MWHC and 1/3 bar — focus_soil_moisture","text":"","code":"focus_soil_moisture #>                 pF1 pF2 pF2.5 #> Sand             24  12     7 #> Loamy sand       24  14     9 #> Sandy loam       27  19    15 #> Sandy clay loam  28  22    18 #> Clay loam        32  28    25 #> Loam             31  25    21 #> Silt loam        32  26    21 #> Silty clay loam  34  30    27 #> Silt             31  27    21 #> Sandy clay       41  35    31 #> Silty clay       44  40    36 #> Clay             53  48    43"},{"path":"https://pkgdown.jrwb.de/mkin/reference/get_deg_func.html","id":null,"dir":"Reference","previous_headings":"","what":"Retrieve a degradation function from the mmkin namespace — get_deg_func","title":"Retrieve a degradation function from the mmkin namespace — get_deg_func","text":"Retrieve degradation function mmkin namespace","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/get_deg_func.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Retrieve a degradation function from the mmkin namespace — get_deg_func","text":"","code":"get_deg_func()"},{"path":"https://pkgdown.jrwb.de/mkin/reference/get_deg_func.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Retrieve a degradation function from the mmkin namespace — get_deg_func","text":"function likely previously assigned within nlme.mmkin","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/hierarchical_kinetics.html","id":null,"dir":"Reference","previous_headings":"","what":"Hierarchical kinetics template — hierarchical_kinetics","title":"Hierarchical kinetics template — hierarchical_kinetics","text":"R markdown format setting hierarchical kinetics based template provided mkin package. format based rmarkdown::pdf_document. Chunk options adapted. Echoing R code code chunks caching turned per default. character prepending output code chunks set empty string, code tidying , figure alignment defaults centering, positioning figures set \"H\", means figures move around document, stay user includes .","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/hierarchical_kinetics.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hierarchical kinetics template — hierarchical_kinetics","text":"","code":"hierarchical_kinetics(..., keep_tex = FALSE)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/hierarchical_kinetics.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hierarchical kinetics template — hierarchical_kinetics","text":"... Arguments rmarkdown::pdf_document keep_tex Keep intermediate tex file used conversion PDF. Note argument control whether keep auxiliary files (e.g., .aux) generated LaTeX compiling .tex .pdf. keep files, may set options(tinytex.clean = FALSE).","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/hierarchical_kinetics.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Hierarchical kinetics template — hierarchical_kinetics","text":"R Markdown output format pass render","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/hierarchical_kinetics.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Hierarchical kinetics template — hierarchical_kinetics","text":"latter feature (positioning figures \"H\") depends LaTeX package 'float'. addition, LaTeX package 'listing' used template showing model fit summaries Appendix. means LaTeX packages 'float' 'listing' need installed TeX distribution used. Windows, easiest way achieve (TeX distribution present ) install 'tinytex' R package, run 'tinytex::install_tinytex()' get basic tiny Tex distribution, run 'tinytex::tlmgr_install(c(\"float\", \"listing\"))'.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/hierarchical_kinetics.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Hierarchical kinetics template — hierarchical_kinetics","text":"","code":"# \\dontrun{ library(rmarkdown) # The following is now commented out after the relase of v1.2.3 for the generation # of online docs, as the command creates a directory and opens an editor #draft(\"example_analysis.rmd\", template = \"hierarchical_kinetics\", package = \"mkin\") # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/illparms.html","id":null,"dir":"Reference","previous_headings":"","what":"Method to get the names of ill-defined parameters — illparms","title":"Method to get the names of ill-defined parameters — illparms","text":"method generalised nonlinear regression fits obtained mkinfit mmkin checks degradation parameters pass Wald test (degradation kinetics often simply called t-test) significant difference zero. test, parameterisation without parameter transformations used.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/illparms.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Method to get the names of ill-defined parameters — illparms","text":"","code":"illparms(object, ...)  # S3 method for class 'mkinfit' illparms(object, conf.level = 0.95, ...)  # S3 method for class 'illparms.mkinfit' print(x, ...)  # S3 method for class 'mmkin' illparms(object, conf.level = 0.95, ...)  # S3 method for class 'illparms.mmkin' print(x, ...)  # S3 method for class 'saem.mmkin' illparms(   object,   conf.level = 0.95,   random = TRUE,   errmod = TRUE,   slopes = TRUE,   ... )  # S3 method for class 'illparms.saem.mmkin' print(x, ...)  # S3 method for class 'mhmkin' illparms(object, conf.level = 0.95, random = TRUE, errmod = TRUE, ...)  # S3 method for class 'illparms.mhmkin' print(x, ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/illparms.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Method to get the names of ill-defined parameters — illparms","text":"object object investigate ... potential future extensions conf.level confidence level checking p values x object printed random hierarchical fits, random effects tested? errmod hierarchical fits, error model parameters tested? slopes hierarchical saem fits using saemix backend, slope parameters covariate model(starting 'beta_') tested?","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/illparms.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Method to get the names of ill-defined parameters — illparms","text":"mkinfit saem objects, character vector parameter names. mmkin mhmkin objects, matrix like object class 'illparms.mmkin' 'illparms.mhmkin'.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/illparms.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Method to get the names of ill-defined parameters — illparms","text":"method hierarchical model fits, also known nonlinear mixed-effects model fits obtained saem mhmkin checks confidence intervals random effects expressed standard deviations include zero, confidence intervals error model parameters include zero.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/illparms.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Method to get the names of ill-defined parameters — illparms","text":"return objects printing methods. single fits, printing output anything case ill-defined parameters found.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/illparms.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Method to get the names of ill-defined parameters — illparms","text":"","code":"fit <- mkinfit(\"FOMC\", FOCUS_2006_A, quiet = TRUE) #> Warning: Optimisation did not converge: #> false convergence (8) illparms(fit) #> [1] \"parent_0\" \"alpha\"    \"beta\"     \"sigma\"    # \\dontrun{ fits <- mmkin(   c(\"SFO\", \"FOMC\"),   list(\"FOCUS A\" = FOCUS_2006_A,        \"FOCUS C\" = FOCUS_2006_C),   quiet = TRUE) illparms(fits) #>       dataset #> model  FOCUS A                      FOCUS C #>   SFO                                       #>   FOMC parent_0, alpha, beta, sigma         # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/ilr.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to perform isometric log-ratio transformation — ilr","title":"Function to perform isometric log-ratio transformation — ilr","text":"implementation special case class isometric log-ratio transformations.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/ilr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to perform isometric log-ratio transformation — ilr","text":"","code":"ilr(x)  invilr(x)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/ilr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to perform isometric log-ratio transformation — ilr","text":"x numeric vector. Naturally, forward transformation sensible vectors elements greater zero.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/ilr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to perform isometric log-ratio transformation — ilr","text":"result forward backward transformation. returned components always sum 1 case inverse log-ratio transformation.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/ilr.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Function to perform isometric log-ratio transformation — ilr","text":"Peter Filzmoser, Karel Hron (2008) Outlier Detection Compositional Data Using Robust Methods. Math Geosci 40 233-248","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/reference/ilr.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Function to perform isometric log-ratio transformation — ilr","text":"René Lehmann Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/ilr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function to perform isometric log-ratio transformation — ilr","text":"","code":"# Order matters ilr(c(0.1, 1, 10)) #> [1] -1.628174 -2.820079 ilr(c(10, 1, 0.1)) #> [1] 1.628174 2.820079 # Equal entries give ilr transformations with zeros as elements ilr(c(3, 3, 3)) #> [1] 0 0 # Almost equal entries give small numbers ilr(c(0.3, 0.4, 0.3)) #> [1] -0.2034219  0.1174457 # Only the ratio between the numbers counts, not their sum invilr(ilr(c(0.7, 0.29, 0.01))) #> [1] 0.70 0.29 0.01 invilr(ilr(2.1 * c(0.7, 0.29, 0.01))) #> [1] 0.70 0.29 0.01 # Inverse transformation of larger numbers gives unequal elements invilr(-10) #> [1] 7.213536e-07 9.999993e-01 invilr(c(-10, 0)) #> [1] 7.207415e-07 9.991507e-01 8.486044e-04 # The sum of the elements of the inverse ilr is 1 sum(invilr(c(-10, 0))) #> [1] 1 # This is why we do not need all elements of the inverse transformation to go back: a <- c(0.1, 0.3, 0.5) b <- invilr(a) length(b) # Four elements #> [1] 4 ilr(c(b[1:3], 1 - sum(b[1:3]))) # Gives c(0.1, 0.3, 0.5) #> [1] 0.1 0.3 0.5"},{"path":"https://pkgdown.jrwb.de/mkin/reference/intervals.saem.mmkin.html","id":null,"dir":"Reference","previous_headings":"","what":"Confidence intervals for parameters in saem.mmkin objects — intervals.saem.mmkin","title":"Confidence intervals for parameters in saem.mmkin objects — intervals.saem.mmkin","text":"Confidence intervals parameters saem.mmkin objects","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/intervals.saem.mmkin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Confidence intervals for parameters in saem.mmkin objects — intervals.saem.mmkin","text":"","code":"# S3 method for class 'saem.mmkin' intervals(object, level = 0.95, backtransform = TRUE, ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/intervals.saem.mmkin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Confidence intervals for parameters in saem.mmkin objects — intervals.saem.mmkin","text":"object fitted saem.mmkin object level confidence level. Must default 0.95 available saemix object backtransform case model fitted mkin transformations, backtransform parameters one one correlation transformed backtransformed parameters exists? ... compatibility generic method","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/intervals.saem.mmkin.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Confidence intervals for parameters in saem.mmkin objects — intervals.saem.mmkin","text":"object 'intervals.saem.mmkin' 'intervals.lme' class attribute","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/llhist.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot the distribution of log likelihoods from multistart objects — llhist","title":"Plot the distribution of log likelihoods from multistart objects — llhist","text":"Produces histogram log-likelihoods. addition, likelihood original fit shown red vertical line.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/llhist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot the distribution of log likelihoods from multistart objects — llhist","text":"","code":"llhist(object, breaks = \"Sturges\", lpos = \"topleft\", main = \"\", ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/llhist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot the distribution of log likelihoods from multistart objects — llhist","text":"object multistart object breaks Passed hist lpos Positioning legend. main Title plot ... Passed hist","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/reference/loftest.html","id":null,"dir":"Reference","previous_headings":"","what":"Lack-of-fit test for models fitted to data with replicates — loftest","title":"Lack-of-fit test for models fitted to data with replicates — loftest","text":"generic function method currently defined mkinfit objects. fits anova model data contained object compares likelihoods using likelihood ratio test lrtest.default lmtest package.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/loftest.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Lack-of-fit test for models fitted to data with replicates — loftest","text":"","code":"loftest(object, ...)  # S3 method for class 'mkinfit' loftest(object, ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/loftest.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Lack-of-fit test for models fitted to data with replicates — loftest","text":"object model object defined loftest method ... used","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/loftest.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Lack-of-fit test for models fitted to data with replicates — loftest","text":"anova model interpreted simplest form mkinfit model, assuming constant variance means, enforcing structure means, one model parameter every mean replicate samples.","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/reference/loftest.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Lack-of-fit test for models fitted to data with replicates — loftest","text":"","code":"# \\dontrun{ test_data <- subset(synthetic_data_for_UBA_2014[[12]]$data, name == \"parent\") sfo_fit <- mkinfit(\"SFO\", test_data, quiet = TRUE) plot_res(sfo_fit) # We see a clear pattern in the residuals  loftest(sfo_fit)  # We have a clear lack of fit #> Likelihood ratio test #>  #> Model 1: ANOVA with error model const #> Model 2: SFO with error model const #>   #Df  LogLik Df  Chisq Pr(>Chisq)     #> 1  10 -40.710                          #> 2   3 -63.954 -7 46.487  7.027e-08 *** #> --- #> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 # # We try a different model (the one that was used to generate the data) dfop_fit <- mkinfit(\"DFOP\", test_data, quiet = TRUE) plot_res(dfop_fit) # We don't see systematic deviations, but heteroscedastic residuals  # therefore we should consider adapting the error model, although we have loftest(dfop_fit) # no lack of fit #> Likelihood ratio test #>  #> Model 1: ANOVA with error model const #> Model 2: DFOP with error model const #>   #Df  LogLik Df Chisq Pr(>Chisq) #> 1  10 -40.710                     #> 2   5 -42.453 -5 3.485     0.6257 # # This is the anova model used internally for the comparison test_data_anova <- test_data test_data_anova$time <- as.factor(test_data_anova$time) anova_fit <- lm(value ~ time, data = test_data_anova) summary(anova_fit) #>  #> Call: #> lm(formula = value ~ time, data = test_data_anova) #>  #> Residuals: #>     Min      1Q  Median      3Q     Max  #> -6.1000 -0.5625  0.0000  0.5625  6.1000  #>  #> Coefficients: #>             Estimate Std. Error t value Pr(>|t|)     #> (Intercept)  103.150      2.323  44.409 7.44e-12 *** #> time1        -19.950      3.285  -6.073 0.000185 *** #> time3        -50.800      3.285 -15.465 8.65e-08 *** #> time7        -68.500      3.285 -20.854 6.28e-09 *** #> time14       -79.750      3.285 -24.278 1.63e-09 *** #> time28       -86.000      3.285 -26.181 8.35e-10 *** #> time60       -94.900      3.285 -28.891 3.48e-10 *** #> time90       -98.500      3.285 -29.986 2.49e-10 *** #> time120     -100.450      3.285 -30.580 2.09e-10 *** #> --- #> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 #>  #> Residual standard error: 3.285 on 9 degrees of freedom #> Multiple R-squared:  0.9953,\tAdjusted R-squared:  0.9912  #> F-statistic: 240.5 on 8 and 9 DF,  p-value: 1.417e-09 #>  logLik(anova_fit) # We get the same likelihood and degrees of freedom #> 'log Lik.' -40.71015 (df=10) # test_data_2 <- synthetic_data_for_UBA_2014[[12]]$data m_synth_SFO_lin <- mkinmod(parent = list(type = \"SFO\", to = \"M1\"),   M1 = list(type = \"SFO\", to = \"M2\"),   M2 = list(type = \"SFO\"), use_of_ff = \"max\") #> Temporary DLL for differentials generated and loaded sfo_lin_fit <- mkinfit(m_synth_SFO_lin, test_data_2, quiet = TRUE) plot_res(sfo_lin_fit) # not a good model, we try parallel formation  loftest(sfo_lin_fit) #> Likelihood ratio test #>  #> Model 1: ANOVA with error model const #> Model 2: m_synth_SFO_lin with error model const and fixed parameter(s) M1_0, M2_0 #>   #Df   LogLik  Df  Chisq Pr(>Chisq)     #> 1  28  -93.606                           #> 2   7 -171.927 -21 156.64  < 2.2e-16 *** #> --- #> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 # m_synth_SFO_par <- mkinmod(parent = list(type = \"SFO\", to = c(\"M1\", \"M2\")),   M1 = list(type = \"SFO\"),   M2 = list(type = \"SFO\"), use_of_ff = \"max\") #> Temporary DLL for differentials generated and loaded sfo_par_fit <- mkinfit(m_synth_SFO_par, test_data_2, quiet = TRUE) plot_res(sfo_par_fit) # much better for metabolites  loftest(sfo_par_fit) #> Likelihood ratio test #>  #> Model 1: ANOVA with error model const #> Model 2: m_synth_SFO_par with error model const and fixed parameter(s) M1_0, M2_0 #>   #Df   LogLik  Df  Chisq Pr(>Chisq)     #> 1  28  -93.606                           #> 2   7 -156.331 -21 125.45  < 2.2e-16 *** #> --- #> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 # m_synth_DFOP_par <- mkinmod(parent = list(type = \"DFOP\", to = c(\"M1\", \"M2\")),   M1 = list(type = \"SFO\"),   M2 = list(type = \"SFO\"), use_of_ff = \"max\") #> Temporary DLL for differentials generated and loaded dfop_par_fit <- mkinfit(m_synth_DFOP_par, test_data_2, quiet = TRUE) plot_res(dfop_par_fit) # No visual lack of fit  loftest(dfop_par_fit)  # no lack of fit found by the test #> Likelihood ratio test #>  #> Model 1: ANOVA with error model const #> Model 2: m_synth_DFOP_par with error model const and fixed parameter(s) M1_0, M2_0 #>   #Df   LogLik  Df  Chisq Pr(>Chisq) #> 1  28  -93.606                       #> 2   9 -102.763 -19 18.313     0.5016 # # The anova model used for comparison in the case of transformation products test_data_anova_2 <- dfop_par_fit$data test_data_anova_2$variable <- as.factor(test_data_anova_2$variable) test_data_anova_2$time <- as.factor(test_data_anova_2$time) anova_fit_2 <- lm(observed ~ time:variable - 1, data = test_data_anova_2) summary(anova_fit_2) #>  #> Call: #> lm(formula = observed ~ time:variable - 1, data = test_data_anova_2) #>  #> Residuals: #>     Min      1Q  Median      3Q     Max  #> -6.1000 -0.5875  0.0000  0.5875  6.1000  #>  #> Coefficients: (2 not defined because of singularities) #>                        Estimate Std. Error t value Pr(>|t|)     #> time0:variableparent    103.150      1.573  65.562  < 2e-16 *** #> time1:variableparent     83.200      1.573  52.882  < 2e-16 *** #> time3:variableparent     52.350      1.573  33.274  < 2e-16 *** #> time7:variableparent     34.650      1.573  22.024  < 2e-16 *** #> time14:variableparent    23.400      1.573  14.873 6.35e-14 *** #> time28:variableparent    17.150      1.573  10.901 5.47e-11 *** #> time60:variableparent     8.250      1.573   5.244 1.99e-05 *** #> time90:variableparent     4.650      1.573   2.956 0.006717 **  #> time120:variableparent    2.700      1.573   1.716 0.098507 .   #> time0:variableM1             NA         NA      NA       NA     #> time1:variableM1         11.850      1.573   7.532 6.93e-08 *** #> time3:variableM1         22.700      1.573  14.428 1.26e-13 *** #> time7:variableM1         33.050      1.573  21.007  < 2e-16 *** #> time14:variableM1        31.250      1.573  19.863  < 2e-16 *** #> time28:variableM1        18.900      1.573  12.013 7.02e-12 *** #> time60:variableM1         7.550      1.573   4.799 6.28e-05 *** #> time90:variableM1         3.850      1.573   2.447 0.021772 *   #> time120:variableM1        2.050      1.573   1.303 0.204454     #> time0:variableM2             NA         NA      NA       NA     #> time1:variableM2          6.700      1.573   4.259 0.000254 *** #> time3:variableM2         16.750      1.573  10.646 8.93e-11 *** #> time7:variableM2         25.800      1.573  16.399 6.89e-15 *** #> time14:variableM2        28.600      1.573  18.178 6.35e-16 *** #> time28:variableM2        25.400      1.573  16.144 9.85e-15 *** #> time60:variableM2        21.600      1.573  13.729 3.81e-13 *** #> time90:variableM2        17.800      1.573  11.314 2.51e-11 *** #> time120:variableM2       14.100      1.573   8.962 2.79e-09 *** #> --- #> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 #>  #> Residual standard error: 2.225 on 25 degrees of freedom #> Multiple R-squared:  0.9979,\tAdjusted R-squared:  0.9957  #> F-statistic: 469.2 on 25 and 25 DF,  p-value: < 2.2e-16 #>  # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/logLik.mkinfit.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculated the log-likelihood of a fitted mkinfit object — logLik.mkinfit","title":"Calculated the log-likelihood of a fitted mkinfit object — logLik.mkinfit","text":"function returns product likelihood densities observed value, calculated part fitting procedure using dnorm, .e. assuming normal distribution, means predicted degradation model, standard deviations predicted error model.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/logLik.mkinfit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculated the log-likelihood of a fitted mkinfit object — logLik.mkinfit","text":"","code":"# S3 method for class 'mkinfit' logLik(object, ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/logLik.mkinfit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculated the log-likelihood of a fitted mkinfit object — logLik.mkinfit","text":"object object class mkinfit. ... compatibility generic method","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/logLik.mkinfit.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculated the log-likelihood of a fitted mkinfit object — logLik.mkinfit","text":"object class logLik number estimated parameters (degradation model parameters plus variance model parameters) attribute.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/logLik.mkinfit.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculated the log-likelihood of a fitted mkinfit object — logLik.mkinfit","text":"total number estimated parameters returned value likelihood calculated sum fitted degradation model parameters fitted error model parameters.","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/reference/logLik.mkinfit.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calculated the log-likelihood of a fitted mkinfit object — logLik.mkinfit","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/logLik.mkinfit.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculated the log-likelihood of a fitted mkinfit object — logLik.mkinfit","text":"","code":"# \\dontrun{   sfo_sfo <- mkinmod(     parent = mkinsub(\"SFO\", to = \"m1\"),     m1 = mkinsub(\"SFO\")   ) #> Temporary DLL for differentials generated and loaded   d_t <- subset(FOCUS_2006_D, value != 0)   f_nw <- mkinfit(sfo_sfo, d_t, quiet = TRUE) # no weighting (weights are unity)   f_obs <- update(f_nw, error_model = \"obs\")   f_tc <- update(f_nw, error_model = \"tc\")   AIC(f_nw, f_obs, f_tc) #>       df      AIC #> f_nw   5 204.4486 #> f_obs  6 205.8727 #> f_tc   6 141.9656   # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/logLik.saem.mmkin.html","id":null,"dir":"Reference","previous_headings":"","what":"logLik method for saem.mmkin objects — logLik.saem.mmkin","title":"logLik method for saem.mmkin objects — logLik.saem.mmkin","text":"logLik method saem.mmkin objects","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/logLik.saem.mmkin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"logLik method for saem.mmkin objects — logLik.saem.mmkin","text":"","code":"# S3 method for class 'saem.mmkin' logLik(object, ..., method = c(\"is\", \"lin\", \"gq\"))"},{"path":"https://pkgdown.jrwb.de/mkin/reference/logLik.saem.mmkin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"logLik method for saem.mmkin objects — logLik.saem.mmkin","text":"object fitted saem.mmkin object ... Passed saemix::logLik.SaemixObject method Passed saemix::logLik.SaemixObject","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/logistic.solution.html","id":null,"dir":"Reference","previous_headings":"","what":"Logistic kinetics — logistic.solution","title":"Logistic kinetics — logistic.solution","text":"Function describing exponential decline defined starting value, increasing rate constant, supposedly caused microbial growth","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/logistic.solution.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Logistic kinetics — logistic.solution","text":"","code":"logistic.solution(t, parent_0, kmax, k0, r)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/logistic.solution.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Logistic kinetics — logistic.solution","text":"t Time. parent_0 Starting value response variable time zero. kmax Maximum rate constant. k0 Minimum rate constant effective time zero. r Growth rate increase rate constant.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/logistic.solution.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Logistic kinetics — logistic.solution","text":"value response variable time t.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/logistic.solution.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Logistic kinetics — logistic.solution","text":"solution logistic model reduces SFO.solution k0 equal kmax.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/logistic.solution.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Logistic kinetics — logistic.solution","text":"FOCUS (2006) “Guidance Document Estimating Persistence Degradation Kinetics Environmental Fate Studies Pesticides EU Registration” Report FOCUS Work Group Degradation Kinetics, EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics FOCUS (2014) “Generic guidance Estimating Persistence Degradation Kinetics Environmental Fate Studies Pesticides EU Registration” Report FOCUS Work Group Degradation Kinetics, Version 1.1, 18 December 2014 http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/reference/logistic.solution.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Logistic kinetics — logistic.solution","text":"","code":"# Reproduce the plot on page 57 of FOCUS (2014)   plot(function(x) logistic.solution(x, 100, 0.08, 0.0001, 0.2),        from = 0, to = 100, ylim = c(0, 100),        xlab = \"Time\", ylab = \"Residue\")   plot(function(x) logistic.solution(x, 100, 0.08, 0.0001, 0.4),        from = 0, to = 100, add = TRUE, lty = 2, col = 2)   plot(function(x) logistic.solution(x, 100, 0.08, 0.0001, 0.8),        from = 0, to = 100, add = TRUE, lty = 3, col = 3)   plot(function(x) logistic.solution(x, 100, 0.08, 0.001, 0.2),        from = 0, to = 100, add = TRUE, lty = 4, col = 4)   plot(function(x) logistic.solution(x, 100, 0.08, 0.08, 0.2),        from = 0, to = 100, add = TRUE, lty = 5, col = 5)   legend(\"topright\", inset = 0.05,          legend = paste0(\"k0 = \", c(0.0001, 0.0001, 0.0001, 0.001, 0.08),                          \", r = \", c(0.2, 0.4, 0.8, 0.2, 0.2)),          lty = 1:5, col = 1:5)     # Fit with synthetic data   logistic <- mkinmod(parent = mkinsub(\"logistic\"))    sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)   parms_logistic <- c(kmax = 0.08, k0 = 0.0001, r = 0.2)   d_logistic <- mkinpredict(logistic,     parms_logistic, c(parent = 100),     sampling_times)   d_2_1 <- add_err(d_logistic,     sdfunc = function(x) sigma_twocomp(x, 0.5, 0.07),     n = 1, reps = 2, digits = 5, LOD = 0.1, seed = 123456)[[1]]    m <- mkinfit(\"logistic\", d_2_1, quiet = TRUE)   plot_sep(m)    summary(m)$bpar #>              Estimate   se_notrans   t value       Pr(>t)        Lower #> parent_0 1.057896e+02 1.9023449604 55.610120 3.768360e-16 1.016451e+02 #> kmax     6.398190e-02 0.0143201030  4.467978 3.841828e-04 3.929235e-02 #> k0       1.612775e-04 0.0005866813  0.274898 3.940351e-01 5.846688e-08 #> r        2.263946e-01 0.1718110664  1.317695 1.061043e-01 4.335843e-02 #> sigma    5.332935e+00 0.9145907310  5.830952 4.036926e-05 3.340213e+00 #>                Upper #> parent_0 109.9341588 #> kmax       0.1041853 #> k0         0.4448749 #> r          1.1821120 #> sigma      7.3256566   endpoints(m)$distimes #>            DT50     DT90  DT50_k0 DT50_kmax #> parent 36.86533 62.41511 4297.853  10.83349"},{"path":"https://pkgdown.jrwb.de/mkin/reference/lrtest.mkinfit.html","id":null,"dir":"Reference","previous_headings":"","what":"Likelihood ratio test for mkinfit models — lrtest.mkinfit","title":"Likelihood ratio test for mkinfit models — lrtest.mkinfit","text":"Compare two mkinfit models based likelihood. two fitted mkinfit objects given arguments, checked fitted data. responsibility user make sure models nested, .e. one less degrees freedom can expressed fixing parameters .","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/lrtest.mkinfit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Likelihood ratio test for mkinfit models — lrtest.mkinfit","text":"","code":"# S3 method for class 'mkinfit' lrtest(object, object_2 = NULL, ...)  # S3 method for class 'mmkin' lrtest(object, ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/lrtest.mkinfit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Likelihood ratio test for mkinfit models — lrtest.mkinfit","text":"object mkinfit object, mmkin column object containing two fits data. object_2 Optionally, another mkinfit object fitted data. ... Argument mkinfit, passed update.mkinfit creating alternative fitted object.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/lrtest.mkinfit.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Likelihood ratio test for mkinfit models — lrtest.mkinfit","text":"Alternatively, argument mkinfit can given passed update.mkinfit obtain alternative model. comparison made lrtest.default method lmtest package. model higher number fitted parameters (alternative hypothesis) listed first, model lower number fitted parameters (null hypothesis).","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/lrtest.mkinfit.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Likelihood ratio test for mkinfit models — lrtest.mkinfit","text":"","code":"# \\dontrun{ test_data <- subset(synthetic_data_for_UBA_2014[[12]]$data, name == \"parent\") sfo_fit <- mkinfit(\"SFO\", test_data, quiet = TRUE) dfop_fit <- mkinfit(\"DFOP\", test_data, quiet = TRUE) lrtest(dfop_fit, sfo_fit) #> Likelihood ratio test #>  #> Model 1: DFOP with error model const #> Model 2: SFO with error model const #>   #Df  LogLik Df  Chisq Pr(>Chisq)     #> 1   5 -42.453                          #> 2   3 -63.954 -2 43.002  4.594e-10 *** #> --- #> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 lrtest(sfo_fit, dfop_fit) #> Likelihood ratio test #>  #> Model 1: DFOP with error model const #> Model 2: SFO with error model const #>   #Df  LogLik Df  Chisq Pr(>Chisq)     #> 1   5 -42.453                          #> 2   3 -63.954 -2 43.002  4.594e-10 *** #> --- #> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1  # The following two examples are commented out as they fail during # generation of the static help pages by pkgdown #lrtest(dfop_fit, error_model = \"tc\") #lrtest(dfop_fit, fixed_parms = c(k2 = 0))  # However, this equivalent syntax also works for static help pages lrtest(dfop_fit, update(dfop_fit, error_model = \"tc\")) #> Likelihood ratio test #>  #> Model 1: DFOP with error model tc #> Model 2: DFOP with error model const #>   #Df  LogLik Df  Chisq Pr(>Chisq)     #> 1   6 -34.587                          #> 2   5 -42.453 -1 15.731  7.302e-05 *** #> --- #> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 lrtest(dfop_fit, update(dfop_fit, fixed_parms = c(k2 = 0))) #> Likelihood ratio test #>  #> Model 1: DFOP with error model const #> Model 2: DFOP with error model const and fixed parameter(s) k2 #>   #Df  LogLik Df  Chisq Pr(>Chisq)     #> 1   5 -42.453                          #> 2   4 -57.340 -1 29.776  4.851e-08 *** #> --- #> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/max_twa_parent.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to calculate maximum time weighted average concentrations from kinetic models fitted with mkinfit — max_twa_parent","title":"Function to calculate maximum time weighted average concentrations from kinetic models fitted with mkinfit — max_twa_parent","text":"function calculates maximum moving window time weighted average concentrations (TWAs) kinetic models fitted mkinfit. Currently, calculations parent implemented SFO, FOMC, DFOP HS models, using analytical formulas given PEC soil section FOCUS guidance.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/max_twa_parent.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to calculate maximum time weighted average concentrations from kinetic models fitted with mkinfit — max_twa_parent","text":"","code":"max_twa_parent(fit, windows)  max_twa_sfo(M0 = 1, k, t)  max_twa_fomc(M0 = 1, alpha, beta, t)  max_twa_dfop(M0 = 1, k1, k2, g, t)  max_twa_hs(M0 = 1, k1, k2, tb, t)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/max_twa_parent.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to calculate maximum time weighted average concentrations from kinetic models fitted with mkinfit — max_twa_parent","text":"fit object class mkinfit. windows width time windows TWAs calculated. M0 initial concentration maximum time weighted average decline curve calculated. default use value 1, means relative maximum time weighted average factor (f_twa) calculated. k rate constant case SFO kinetics. t width time window. alpha Parameter FOMC model. beta Parameter FOMC model. k1 first rate constant DFOP HS kinetics. k2 second rate constant DFOP HS kinetics. g Parameter DFOP model. tb Parameter HS model.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/max_twa_parent.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to calculate maximum time weighted average concentrations from kinetic models fitted with mkinfit — max_twa_parent","text":"max_twa_parent, numeric vector, named using windows argument.  functions, numeric vector length one (also known 'number').","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/max_twa_parent.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Function to calculate maximum time weighted average concentrations from kinetic models fitted with mkinfit — max_twa_parent","text":"FOCUS (2006) “Guidance Document Estimating Persistence Degradation Kinetics Environmental Fate Studies Pesticides EU Registration” Report FOCUS Work Group Degradation Kinetics, EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/max_twa_parent.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Function to calculate maximum time weighted average concentrations from kinetic models fitted with mkinfit — max_twa_parent","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/max_twa_parent.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function to calculate maximum time weighted average concentrations from kinetic models fitted with mkinfit — max_twa_parent","text":"","code":"fit <- mkinfit(\"FOMC\", FOCUS_2006_C, quiet = TRUE)   max_twa_parent(fit, c(7, 21)) #>        7       21  #> 34.71343 18.22124"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mccall81_245T.html","id":null,"dir":"Reference","previous_headings":"","what":"Datasets on aerobic soil metabolism of 2,4,5-T in six soils — mccall81_245T","title":"Datasets on aerobic soil metabolism of 2,4,5-T in six soils — mccall81_245T","text":"Time course 2,4,5-trichlorophenoxyacetic acid, corresponding  2,4,5-trichlorophenol 2,4,5-trichloroanisole recovered diethylether  extracts.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mccall81_245T.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Datasets on aerobic soil metabolism of 2,4,5-T in six soils — mccall81_245T","text":"","code":"mccall81_245T"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mccall81_245T.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Datasets on aerobic soil metabolism of 2,4,5-T in six soils — mccall81_245T","text":"dataframe containing following variables. name name compound observed. Note T245 used                        acronym 2,4,5-T. T245 legitimate object name                        R, necessary specifying models using                        mkinmod. time numeric vector containing sampling times days                        treatment value numeric vector containing concentrations percent applied radioactivity soil factor containing name soil","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mccall81_245T.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Datasets on aerobic soil metabolism of 2,4,5-T in six soils — mccall81_245T","text":"McCall P, Vrona SA, Kelley SS (1981) Fate uniformly carbon-14 ring labelled 2,4,5-Trichlorophenoxyacetic acid 2,4-dichlorophenoxyacetic acid. J Agric Chem 29, 100-107   doi:10.1021/jf00103a026","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mccall81_245T.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Datasets on aerobic soil metabolism of 2,4,5-T in six soils — mccall81_245T","text":"","code":"SFO_SFO_SFO <- mkinmod(T245 = list(type = \"SFO\", to = \"phenol\"),     phenol = list(type = \"SFO\", to = \"anisole\"),     anisole = list(type = \"SFO\")) #> Temporary DLL for differentials generated and loaded   # \\dontrun{     fit.1 <- mkinfit(SFO_SFO_SFO, subset(mccall81_245T, soil == \"Commerce\"), quiet = TRUE) #> Warning: Observations with value of zero were removed from the data     summary(fit.1)$bpar #>                         Estimate   se_notrans   t value       Pr(>t) #> T245_0              1.038550e+02 2.1847074943 47.537272 4.472189e-18 #> k_T245              4.337042e-02 0.0018983965 22.845818 2.276911e-13 #> k_phenol            4.050581e-01 0.2986993738  1.356073 9.756990e-02 #> k_anisole           6.678742e-03 0.0008021439  8.326114 2.623177e-07 #> f_T245_to_phenol    6.227599e-01 0.3985340721  1.562626 6.949414e-02 #> f_phenol_to_anisole 1.000000e+00 0.6718440131  1.488441 7.867790e-02 #> sigma               2.514628e+00 0.4907558973  5.123989 6.233159e-05 #>                            Lower        Upper #> T245_0              99.246061490 1.084640e+02 #> k_T245               0.039631621 4.746194e-02 #> k_phenol             0.218013879 7.525762e-01 #> k_anisole            0.005370739 8.305299e-03 #> f_T245_to_phenol     0.547559080 6.924813e-01 #> f_phenol_to_anisole  0.000000000 1.000000e+00 #> sigma                1.706607296 3.322649e+00     endpoints(fit.1) #> $ff #>    T245_phenol      T245_sink phenol_anisole    phenol_sink  #>   6.227599e-01   3.772401e-01   1.000000e+00   3.072478e-10  #>  #> $distimes #>               DT50      DT90 #> T245     15.982025  53.09114 #> phenol    1.711229   5.68458 #> anisole 103.784093 344.76329 #>      # formation fraction from phenol to anisol is practically 1. As we cannot     # fix formation fractions when using the ilr transformation, we can turn of     # the sink in the model generation     SFO_SFO_SFO_2 <- mkinmod(T245 = list(type = \"SFO\", to = \"phenol\"),       phenol = list(type = \"SFO\", to = \"anisole\", sink = FALSE),       anisole = list(type = \"SFO\")) #> Temporary DLL for differentials generated and loaded     fit.2 <- mkinfit(SFO_SFO_SFO_2, subset(mccall81_245T, soil == \"Commerce\"),       quiet = TRUE) #> Warning: Observations with value of zero were removed from the data     summary(fit.2)$bpar #>                      Estimate   se_notrans   t value       Pr(>t)        Lower #> T245_0           1.038550e+02 2.1623653059 48.028439 4.993108e-19 99.271020328 #> k_T245           4.337042e-02 0.0018343666 23.643268 3.573556e-14  0.039650976 #> k_phenol         4.050582e-01 0.1177237651  3.440752 1.679255e-03  0.218746589 #> k_anisole        6.678742e-03 0.0006829745  9.778903 1.872894e-08  0.005377083 #> f_T245_to_phenol 6.227599e-01 0.0342197873 18.198824 2.039411e-12  0.547975634 #> sigma            2.514628e+00 0.3790944250  6.633250 2.875782e-06  1.710983655 #>                         Upper #> T245_0           108.43904079 #> k_T245             0.04743877 #> k_phenol           0.75005593 #> k_anisole          0.00829550 #> f_T245_to_phenol   0.69212307 #> sigma              3.31827222     endpoints(fit.1) #> $ff #>    T245_phenol      T245_sink phenol_anisole    phenol_sink  #>   6.227599e-01   3.772401e-01   1.000000e+00   3.072478e-10  #>  #> $distimes #>               DT50      DT90 #> T245     15.982025  53.09114 #> phenol    1.711229   5.68458 #> anisole 103.784093 344.76329 #>      plot_sep(fit.2)    # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mean_degparms.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate mean degradation parameters for an mmkin row object — mean_degparms","title":"Calculate mean degradation parameters for an mmkin row object — mean_degparms","text":"Calculate mean degradation parameters mmkin row object","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mean_degparms.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate mean degradation parameters for an mmkin row object — mean_degparms","text":"","code":"mean_degparms(   object,   random = FALSE,   test_log_parms = FALSE,   conf.level = 0.6,   default_log_parms = NA )"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mean_degparms.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate mean degradation parameters for an mmkin row object — mean_degparms","text":"object mmkin row object containing several fits model different datasets random list fixed random effects returned? test_log_parms TRUE, log parameters considered mean calculations untransformed counterparts (likely rate constants) pass t-test significant difference zero. conf.level Possibility adjust required confidence level parameter tested requested 'test_log_parms'. default_log_parms set numeric value, used default value tested log parameters failed t-test.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mean_degparms.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate mean degradation parameters for an mmkin row object — mean_degparms","text":"random FALSE (default), named vector containing mean values fitted degradation model parameters. random TRUE, list fixed random effects, format required start argument nlme case single grouping variable ds.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mhmkin.html","id":null,"dir":"Reference","previous_headings":"","what":"Fit nonlinear mixed-effects models built from one or more kinetic degradation models and one or more error models — mhmkin","title":"Fit nonlinear mixed-effects models built from one or more kinetic degradation models and one or more error models — mhmkin","text":"name methods expresses (multiple) hierarchichal (also known multilevel) multicompartment kinetic models fitted. kinetic models nonlinear, can use various nonlinear mixed-effects model fitting functions.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mhmkin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fit nonlinear mixed-effects models built from one or more kinetic degradation models and one or more error models — mhmkin","text":"","code":"mhmkin(objects, ...)  # S3 method for class 'mmkin' mhmkin(objects, ...)  # S3 method for class 'list' mhmkin(   objects,   backend = \"saemix\",   algorithm = \"saem\",   no_random_effect = NULL,   ...,   cores = if (Sys.info()[\"sysname\"] == \"Windows\") 1 else parallel::detectCores(),   cluster = NULL )  # S3 method for class 'mhmkin' x[i, j, ..., drop = FALSE]  # S3 method for class 'mhmkin' print(x, ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mhmkin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fit nonlinear mixed-effects models built from one or more kinetic degradation models and one or more error models — mhmkin","text":"objects list mmkin objects containing fits degradation models data, using different error models. Alternatively, single mmkin object containing fits several degradation models data ... arguments passed nonlinear mixed-effects model fitting function. backend backend used fitting. Currently, saemix supported algorithm algorithm used fitting (currently used) no_random_effect Default NULL passed saem. character vector supplied, passed calls saem, exclude random effects matching parameters. Alternatively, list character vectors object class illparms.mhmkin can specified. dimensions return object current call , .e. number rows must match number degradation models mmkin object(s), number columns must match number error models used mmkin object(s). cores number cores used multicore processing. used cluster argument NULL. Windows machines, cores > 1 supported, need use cluster argument use multiple logical processors. Per default, cores detected parallel::detectCores() used, except Windows default 1. cluster cluster returned makeCluster used parallel execution. x mhmkin object. Row index selecting fits specific models j Column index selecting fits specific datasets drop FALSE, method always returns mhmkin object, otherwise either list fit objects single fit object.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mhmkin.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fit nonlinear mixed-effects models built from one or more kinetic degradation models and one or more error models — mhmkin","text":"two-dimensional array fit objects /try-errors can indexed using degradation model names first index (row index) error model names second index (column index), class attribute 'mhmkin'. object inheriting mhmkin.","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/reference/mhmkin.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fit nonlinear mixed-effects models built from one or more kinetic degradation models and one or more error models — mhmkin","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mhmkin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fit nonlinear mixed-effects models built from one or more kinetic degradation models and one or more error models — mhmkin","text":"","code":"# \\dontrun{ # We start with separate evaluations of all the first six datasets with two # degradation models and two error models f_sep_const <- mmkin(c(\"SFO\", \"FOMC\"), ds_fomc[1:6], cores = 2, quiet = TRUE) f_sep_tc <- update(f_sep_const, error_model = \"tc\") # The mhmkin function sets up hierarchical degradation models aka # nonlinear mixed-effects models for all four combinations, specifying # uncorrelated random effects for all degradation parameters f_saem_1 <- mhmkin(list(f_sep_const, f_sep_tc), cores = 2) status(f_saem_1) #>            error #> degradation const tc #>        SFO  OK    OK #>        FOMC OK    OK #>  #> OK: Fit terminated successfully # The 'illparms' function shows that in all hierarchical fits, at least # one random effect is ill-defined (the confidence interval for the # random effect expressed as standard deviation includes zero) illparms(f_saem_1) #>            error #> degradation const        tc                         #>        SFO  sd(parent_0) sd(parent_0)               #>        FOMC sd(log_beta) sd(parent_0), sd(log_beta) # Therefore we repeat the fits, excluding the ill-defined random effects f_saem_2 <- update(f_saem_1, no_random_effect = illparms(f_saem_1)) status(f_saem_2) #>            error #> degradation const tc #>        SFO  OK    OK #>        FOMC OK    OK #>  #> OK: Fit terminated successfully illparms(f_saem_2) #>            error #> degradation const tc #>        SFO           #>        FOMC          # Model comparisons show that FOMC with two-component error is preferable, # and confirms our reduction of the default parameter model anova(f_saem_1) #> Data: 95 observations of 1 variable(s) grouped in 6 datasets #>  #>            npar    AIC    BIC     Lik #> SFO const     5 574.40 573.35 -282.20 #> SFO tc        6 543.72 542.47 -265.86 #> FOMC const    7 489.67 488.22 -237.84 #> FOMC tc       8 406.11 404.44 -195.05 anova(f_saem_2) #> Data: 95 observations of 1 variable(s) grouped in 6 datasets #>  #>            npar    AIC    BIC     Lik #> SFO const     4 572.22 571.39 -282.11 #> SFO tc        5 541.63 540.59 -265.81 #> FOMC const    6 487.38 486.13 -237.69 #> FOMC tc       6 402.12 400.88 -195.06 # The convergence plot for the selected model looks fine saemix::plot(f_saem_2[[\"FOMC\", \"tc\"]]$so, plot.type = \"convergence\")  # The plot of predictions versus data shows that we have a pretty data-rich # situation with homogeneous distribution of residuals, because we used the # same degradation model, error model and parameter distribution model that # was used in the data generation. plot(f_saem_2[[\"FOMC\", \"tc\"]])  # We can specify the same parameter model reductions manually no_ranef <- list(\"parent_0\", \"log_beta\", \"parent_0\", c(\"parent_0\", \"log_beta\")) dim(no_ranef) <- c(2, 2) f_saem_2m <- update(f_saem_1, no_random_effect = no_ranef) anova(f_saem_2m) #> Data: 95 observations of 1 variable(s) grouped in 6 datasets #>  #>            npar    AIC    BIC     Lik #> SFO const     4 572.22 571.39 -282.11 #> SFO tc        5 541.63 540.59 -265.81 #> FOMC const    6 487.38 486.13 -237.69 #> FOMC tc       6 402.12 400.88 -195.06 # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mixed.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a mixed effects model from an mmkin row object — mixed","title":"Create a mixed effects model from an mmkin row object — mixed","text":"Create mixed effects model mmkin row object","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mixed.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a mixed effects model from an mmkin row object — mixed","text":"","code":"mixed(object, ...)  # S3 method for class 'mmkin' mixed(object, method = c(\"none\"), ...)  # S3 method for class 'mixed.mmkin' print(x, digits = max(3, getOption(\"digits\") - 3), ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mixed.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a mixed effects model from an mmkin row object — mixed","text":"object mmkin row object ... Currently used method method used x mixed.mmkin object print digits Number digits use printing.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mixed.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a mixed effects model from an mmkin row object — mixed","text":"object class 'mixed.mmkin' observed data single dataframe convenient plotting","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mixed.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a mixed effects model from an mmkin row object — mixed","text":"","code":"sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120) n_biphasic <- 8 err_1 = list(const = 1, prop = 0.07)  DFOP_SFO <- mkinmod(   parent = mkinsub(\"DFOP\", \"m1\"),   m1 = mkinsub(\"SFO\"),   quiet = TRUE)  set.seed(123456) log_sd <- 0.3 syn_biphasic_parms <- as.matrix(data.frame(   k1 = rlnorm(n_biphasic, log(0.05), log_sd),   k2 = rlnorm(n_biphasic, log(0.01), log_sd),   g = plogis(rnorm(n_biphasic, 0, log_sd)),   f_parent_to_m1 = plogis(rnorm(n_biphasic, 0, log_sd)),   k_m1 = rlnorm(n_biphasic, log(0.002), log_sd)))  ds_biphasic_mean <- lapply(1:n_biphasic,   function(i) {     mkinpredict(DFOP_SFO, syn_biphasic_parms[i, ],       c(parent = 100, m1 = 0), sampling_times)   } )  set.seed(123456L) ds_biphasic <- lapply(ds_biphasic_mean, function(ds) {   add_err(ds,     sdfunc = function(value) sqrt(err_1$const^2 + value^2 * err_1$prop^2),     n = 1, secondary = \"m1\")[[1]] })  # \\dontrun{ f_mmkin <- mmkin(list(\"DFOP-SFO\" = DFOP_SFO), ds_biphasic, error_model = \"tc\", quiet = TRUE)  f_mixed <- mixed(f_mmkin) print(f_mixed) #> Kinetic model fitted by nonlinear regression to each dataset #> Structural model: #> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * #>            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) #>            * parent #> d_m1/dt = + f_parent_to_m1 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g) #>            * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * #>            exp(-k2 * time))) * parent - k_m1 * m1 #>  #> Data: #> 271 observations of 2 variable(s) grouped in 8 datasets #>  #> <mmkin> object #> Status of individual fits: #>  #>           dataset #> model      1  2  3  4  5  6 7  8  #>   DFOP-SFO OK OK OK OK OK C OK OK #>  #> C: Optimisation did not converge: #> iteration limit reached without convergence (10) #> OK: No warnings #>  #> Mean fitted parameters: #>        parent_0        log_k_m1 f_parent_qlogis          log_k1          log_k2  #>      100.605312       -8.758664       -0.001917       -3.350887       -3.990017  #>        g_qlogis  #>       -0.091167  plot(f_mixed)  # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkin_long_to_wide.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert a dataframe from long to wide format — mkin_long_to_wide","title":"Convert a dataframe from long to wide format — mkin_long_to_wide","text":"function takes dataframe long form, .e. row observed value, converts dataframe one independent variable several dependent variables columns.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkin_long_to_wide.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert a dataframe from long to wide format — mkin_long_to_wide","text":"","code":"mkin_long_to_wide(long_data, time = \"time\", outtime = \"time\")"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkin_long_to_wide.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert a dataframe from long to wide format — mkin_long_to_wide","text":"long_data dataframe must contain one variable called \"time\" time values specified time argument, one column called \"name\" grouping observed values, finally one column observed values called \"value\". time name time variable long input data. outtime name time variable wide output data.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkin_long_to_wide.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert a dataframe from long to wide format — mkin_long_to_wide","text":"Dataframe wide format.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkin_long_to_wide.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Convert a dataframe from long to wide format — mkin_long_to_wide","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkin_long_to_wide.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert a dataframe from long to wide format — mkin_long_to_wide","text":"","code":"mkin_long_to_wide(FOCUS_2006_D) #>    time parent    m1 #> 1     0  99.46  0.00 #> 2     0 102.04  0.00 #> 3     1  93.50  4.84 #> 4     1  92.50  5.64 #> 5     3  63.23 12.91 #> 6     3  68.99 12.96 #> 7     7  52.32 22.97 #> 8     7  55.13 24.47 #> 9    14  27.27 41.69 #> 10   14  26.64 33.21 #> 11   21  11.50 44.37 #> 12   21  11.64 46.44 #> 13   35   2.85 41.22 #> 14   35   2.91 37.95 #> 15   50   0.69 41.19 #> 16   50   0.63 40.01 #> 17   75   0.05 40.09 #> 18   75   0.06 33.85 #> 19  100     NA 31.04 #> 20  100     NA 33.13 #> 21  120     NA 25.15 #> 22  120     NA 33.31"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkin_wide_to_long.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert a dataframe with observations over time into long format — mkin_wide_to_long","title":"Convert a dataframe with observations over time into long format — mkin_wide_to_long","text":"function simply takes dataframe one independent variable several dependent variable converts long form required mkinfit.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkin_wide_to_long.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert a dataframe with observations over time into long format — mkin_wide_to_long","text":"","code":"mkin_wide_to_long(wide_data, time = \"t\")"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkin_wide_to_long.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert a dataframe with observations over time into long format — mkin_wide_to_long","text":"wide_data dataframe must contain one variable time values specified time argument usually one column observed values. time name time variable.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkin_wide_to_long.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert a dataframe with observations over time into long format — mkin_wide_to_long","text":"Dataframe long format needed mkinfit.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkin_wide_to_long.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Convert a dataframe with observations over time into long format — mkin_wide_to_long","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkin_wide_to_long.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert a dataframe with observations over time into long format — mkin_wide_to_long","text":"","code":"wide <- data.frame(t = c(1,2,3), x = c(1,4,7), y = c(3,4,5)) mkin_wide_to_long(wide) #>   name time value #> 1    x    1     1 #> 2    x    2     4 #> 3    x    3     7 #> 4    y    1     3 #> 5    y    2     4 #> 6    y    3     5"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinds.html","id":null,"dir":"Reference","previous_headings":"","what":"A dataset class for mkin — mkinds","title":"A dataset class for mkin — mkinds","text":"moment dataset class hardly used mkin. example, mkinfit take mkinds datasets argument, works dataframes contained data field mkinds objects. datasets provided package come mkinds objects nevertheless.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinds.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A dataset class for mkin — mkinds","text":"","code":"# S3 method for class 'mkinds' print(x, data = FALSE, ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinds.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A dataset class for mkin — mkinds","text":"x mkinds object. data data printed? ... used.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinds.html","id":"public-fields","dir":"Reference","previous_headings":"","what":"Public fields","title":"A dataset class for mkin — mkinds","text":"title full title dataset sampling_times sampling times time_unit time unit observed Names observed variables unit unit observations replicates maximum number replicates per sampling time data data frame least columns name, time value order compatible mkinfit","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinds.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"A dataset class for mkin — mkinds","text":"mkinds$new() mkinds$clone()","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinds.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"A dataset class for mkin — mkinds","text":"Create new mkinds object","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinds.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A dataset class for mkin — mkinds","text":"","code":"mkinds$new(title = \"\", data, time_unit = NA, unit = NA)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinds.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"A dataset class for mkin — mkinds","text":"title dataset title data data time_unit time unit unit unit observations","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinds.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"A dataset class for mkin — mkinds","text":"objects class cloneable method.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinds.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"A dataset class for mkin — mkinds","text":"","code":"mkinds$clone(deep = FALSE)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinds.html","id":"arguments-2","dir":"Reference","previous_headings":"","what":"Arguments","title":"A dataset class for mkin — mkinds","text":"deep Whether make deep clone.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinds.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A dataset class for mkin — mkinds","text":"","code":"mds <- mkinds$new(\"FOCUS A\", FOCUS_2006_A) print(mds) #> <mkinds> with $title:  FOCUS A  #> Observed compounds $observed:  parent  #> Sampling times $sampling_times: #> 0, 3, 7, 14, 30, 62, 90, 118  #> With a maximum of  1  replicates"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkindsg.html","id":null,"dir":"Reference","previous_headings":"","what":"A class for dataset groups for mkin — mkindsg","title":"A class for dataset groups for mkin — mkindsg","text":"container working datasets share least one compound, combined evaluations desirable. Time normalisation factors initialised value 1 dataset data supplied.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkindsg.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A class for dataset groups for mkin — mkindsg","text":"","code":"# S3 method for class 'mkindsg' print(x, data = FALSE, verbose = data, ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkindsg.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A class for dataset groups for mkin — mkindsg","text":"x mkindsg object. data mkinds objects printed data? verbose mkinds objects printed? ... used.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkindsg.html","id":"public-fields","dir":"Reference","previous_headings":"","what":"Public fields","title":"A class for dataset groups for mkin — mkindsg","text":"title title dataset group ds list mkinds objects observed_n Occurrence counts compounds datasets f_time_norm Time normalisation factors meta data frame row dataset, containing additional information form categorical data (factors) numerical data (e.g. temperature, moisture, covariates like soil pH).","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkindsg.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"A class for dataset groups for mkin — mkindsg","text":"mkindsg$new() mkindsg$clone()","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkindsg.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"A class for dataset groups for mkin — mkindsg","text":"Create new mkindsg object","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkindsg.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A class for dataset groups for mkin — mkindsg","text":"","code":"mkindsg$new(title = \"\", ds, f_time_norm = rep(1, length(ds)), meta)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkindsg.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"A class for dataset groups for mkin — mkindsg","text":"title title ds list mkinds objects f_time_norm Time normalisation factors meta meta data","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkindsg.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"A class for dataset groups for mkin — mkindsg","text":"objects class cloneable method.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkindsg.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"A class for dataset groups for mkin — mkindsg","text":"","code":"mkindsg$clone(deep = FALSE)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkindsg.html","id":"arguments-2","dir":"Reference","previous_headings":"","what":"Arguments","title":"A class for dataset groups for mkin — mkindsg","text":"deep Whether make deep clone.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkindsg.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A class for dataset groups for mkin — mkindsg","text":"","code":"mdsg <- mkindsg$new(\"Experimental X\", experimental_data_for_UBA_2019[6:10]) print(mdsg) #> <mkindsg> holding 5 mkinds objects #> Title $title:  Experimental X  #> Occurrence of observed compounds $observed_n: #> parent     A1  #>      5      5  print(mdsg, verbose = TRUE) #> <mkindsg> holding 5 mkinds objects #> Title $title:  Experimental X  #> Occurrence of observed compounds $observed_n: #> parent     A1  #>      5      5  #>  #> Datasets $ds: #> <mkinds> with $title:  Soil 6  #> Observed compounds $observed:  parent, A1  #> Sampling times $sampling_times: #> 0, 3, 6, 10, 20, 34, 55, 90, 112, 132  #> With a maximum of  2  replicates #> Time unit:  days  #> Observation unit:  \\%AR  #>  #> <mkinds> with $title:  Soil 7  #> Observed compounds $observed:  parent, A1  #> Sampling times $sampling_times: #> 0, 3, 7, 14, 30, 60, 90, 120, 180  #> With a maximum of  2  replicates #> Time unit:  days  #> Observation unit:  \\%AR  #>  #> <mkinds> with $title:  Soil 8  #> Observed compounds $observed:  parent, A1  #> Sampling times $sampling_times: #> 0, 1, 3, 8, 14, 27, 48, 70  #> With a maximum of  2  replicates #> Time unit:  days  #> Observation unit:  \\%AR  #>  #> <mkinds> with $title:  Soil 9  #> Observed compounds $observed:  parent, A1  #> Sampling times $sampling_times: #> 0, 1, 3, 8, 14, 27, 48, 70, 91, 120  #> With a maximum of  2  replicates #> Time unit:  days  #> Observation unit:  \\%AR  #>  #> <mkinds> with $title:  Soil 10  #> Observed compounds $observed:  parent, A1  #> Sampling times $sampling_times: #> 0, 8, 14, 21, 41, 63, 91, 120  #> With a maximum of  2  replicates #> Time unit:  days  #> Observation unit:  \\%AR  print(mdsg, verbose = TRUE, data = TRUE) #> <mkindsg> holding 5 mkinds objects #> Title $title:  Experimental X  #> Occurrence of observed compounds $observed_n: #> parent     A1  #>      5      5  #>  #> Datasets $ds: #> <mkinds> with $title:  Soil 6  #> Observed compounds $observed:  parent, A1  #> Sampling times $sampling_times: #> 0, 3, 6, 10, 20, 34, 55, 90, 112, 132  #> With a maximum of  2  replicates #> Time unit:  days  #> Observation unit:  \\%AR  #>    time parent   A1 #> 1     0   97.2   NA #> 2     0   96.4   NA #> 3     3   71.1  4.3 #> 4     3   69.2  4.6 #> 5     6   58.1  7.0 #> 6     6   56.6  7.2 #> 7    10   44.4  8.2 #> 8    10   43.4  8.0 #> 9    20   33.3 11.0 #> 10   20   29.2 13.7 #> 11   34   17.6 11.5 #> 12   34   18.0 12.7 #> 13   55   10.5 14.9 #> 14   55    9.3 14.5 #> 15   90    4.5 12.1 #> 16   90    4.7 12.3 #> 17  112    3.0  9.9 #> 18  112    3.4 10.2 #> 19  132    2.3  8.8 #> 20  132    2.7  7.8 #>  #> <mkinds> with $title:  Soil 7  #> Observed compounds $observed:  parent, A1  #> Sampling times $sampling_times: #> 0, 3, 7, 14, 30, 60, 90, 120, 180  #> With a maximum of  2  replicates #> Time unit:  days  #> Observation unit:  \\%AR  #>    time parent   A1 #> 1     0   93.6   NA #> 2     0   92.3   NA #> 3     3   87.0  3.9 #> 4     3   82.2  3.1 #> 5     7   74.0  6.9 #> 6     7   73.9  6.6 #> 7    14   64.2 10.4 #> 8    14   69.5  8.3 #> 9    30   54.0 14.4 #> 10   30   54.6 13.7 #> 11   60   41.1 22.1 #> 12   60   38.4 22.3 #> 13   90   32.5 27.5 #> 14   90   35.5 25.4 #> 15  120   28.1 28.0 #> 16  120   29.0 26.6 #> 17  180   26.5 25.8 #> 18  180   27.6 25.3 #>  #> <mkinds> with $title:  Soil 8  #> Observed compounds $observed:  parent, A1  #> Sampling times $sampling_times: #> 0, 1, 3, 8, 14, 27, 48, 70  #> With a maximum of  2  replicates #> Time unit:  days  #> Observation unit:  \\%AR  #>    time parent   A1 #> 1     0   91.9   NA #> 2     0   90.8   NA #> 3     1   64.9  9.6 #> 4     1   66.2  7.7 #> 5     3   43.5 15.0 #> 6     3   44.1 15.1 #> 7     8   18.3 21.2 #> 8     8   18.1 21.1 #> 9    14   10.2 19.7 #> 10   14   10.8 18.9 #> 11   27    4.9 17.5 #> 12   27    3.3 15.9 #> 13   48    1.6  9.5 #> 14   48    1.5  9.8 #> 15   70    1.1  6.2 #> 16   70    0.9  6.1 #>  #> <mkinds> with $title:  Soil 9  #> Observed compounds $observed:  parent, A1  #> Sampling times $sampling_times: #> 0, 1, 3, 8, 14, 27, 48, 70, 91, 120  #> With a maximum of  2  replicates #> Time unit:  days  #> Observation unit:  \\%AR  #>    time parent   A1 #> 1     0   99.8   NA #> 2     0   98.3   NA #> 3     1   77.1  4.2 #> 4     1   77.2  3.9 #> 5     3   59.0  7.4 #> 6     3   58.1  7.9 #> 7     8   27.4 14.5 #> 8     8   29.2 13.7 #> 9    14   19.1 14.2 #> 10   14   29.6 12.2 #> 11   27   10.1 13.7 #> 12   27   18.2 13.2 #> 13   48    4.5 13.6 #> 14   48    9.1 15.4 #> 15   70    2.3 10.4 #> 16   70    2.9 11.6 #> 17   91    2.0 10.0 #> 18   91    1.8  9.5 #> 19  120    2.0  9.1 #> 20  120    2.2  9.0 #>  #> <mkinds> with $title:  Soil 10  #> Observed compounds $observed:  parent, A1  #> Sampling times $sampling_times: #> 0, 8, 14, 21, 41, 63, 91, 120  #> With a maximum of  2  replicates #> Time unit:  days  #> Observation unit:  \\%AR  #>    time parent   A1 #> 1     0   96.1   NA #> 2     0   94.3   NA #> 3     8   73.9  3.3 #> 4     8   73.9  3.4 #> 5    14   69.4  3.9 #> 6    14   73.1  2.9 #> 7    21   65.6  6.4 #> 8    21   65.3  7.2 #> 9    41   55.9  9.1 #> 10   41   54.4  8.5 #> 11   63   47.0 11.7 #> 12   63   49.3 12.0 #> 13   91   44.7 13.3 #> 14   91   46.7 13.2 #> 15  120   42.1 14.3 #> 16  120   41.3 12.1"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinerrmin.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate the minimum error to assume in order to pass the variance test — mkinerrmin","title":"Calculate the minimum error to assume in order to pass the variance test — mkinerrmin","text":"function finds smallest relative error still resulting passing chi-squared test defined FOCUS kinetics report 2006.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinerrmin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate the minimum error to assume in order to pass the variance test — mkinerrmin","text":"","code":"mkinerrmin(fit, alpha = 0.05)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinerrmin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate the minimum error to assume in order to pass the variance test — mkinerrmin","text":"fit object class mkinfit. alpha confidence level chosen chi-squared test.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinerrmin.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate the minimum error to assume in order to pass the variance test — mkinerrmin","text":"dataframe following components: err.min relative error, expressed fraction. n.optim number optimised parameters attributed data series. df number remaining degrees freedom chi2 error level calculations.  Note mean values used chi2 statistic therefore every time point observed values series counts one time. dataframe one row total dataset one row observed state variable model.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinerrmin.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate the minimum error to assume in order to pass the variance test — mkinerrmin","text":"function used internally summary.mkinfit.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinerrmin.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Calculate the minimum error to assume in order to pass the variance test — mkinerrmin","text":"FOCUS (2006) “Guidance Document Estimating Persistence Degradation Kinetics Environmental Fate Studies Pesticides EU Registration” Report FOCUS Work Group Degradation Kinetics, EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinerrmin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate the minimum error to assume in order to pass the variance test — mkinerrmin","text":"","code":"SFO_SFO = mkinmod(parent = mkinsub(\"SFO\", to = \"m1\"),                   m1 = mkinsub(\"SFO\"),                   use_of_ff = \"max\") #> Temporary DLL for differentials generated and loaded  fit_FOCUS_D = mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE) #> Warning: Observations with value of zero were removed from the data round(mkinerrmin(fit_FOCUS_D), 4) #>          err.min n.optim df #> All data  0.0640       4 15 #> parent    0.0646       2  7 #> m1        0.0469       2  8 # \\dontrun{   fit_FOCUS_E = mkinfit(SFO_SFO, FOCUS_2006_E, quiet = TRUE)   round(mkinerrmin(fit_FOCUS_E), 4) #>          err.min n.optim df #> All data  0.1544       4 13 #> parent    0.1659       2  7 #> m1        0.1095       2  6 # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinerrplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to plot squared residuals and the error model for an mkin object — mkinerrplot","title":"Function to plot squared residuals and the error model for an mkin object — mkinerrplot","text":"function plots squared residuals specified subset observed variables mkinfit object. addition, one dashed line(s) show fitted error model.  combined plot fitted model error model plot can obtained plot.mkinfit using argument show_errplot = TRUE.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinerrplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to plot squared residuals and the error model for an mkin object — mkinerrplot","text":"","code":"mkinerrplot(   object,   obs_vars = names(object$mkinmod$map),   xlim = c(0, 1.1 * max(object$data$predicted)),   xlab = \"Predicted\",   ylab = \"Squared residual\",   maxy = \"auto\",   legend = TRUE,   lpos = \"topright\",   col_obs = \"auto\",   pch_obs = \"auto\",   frame = TRUE,   ... )"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinerrplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to plot squared residuals and the error model for an mkin object — mkinerrplot","text":"object fit represented mkinfit object. obs_vars character vector names observed variables residuals plotted. Defaults observed variables model xlim plot range x direction. xlab Label x axis. ylab Label y axis. maxy Maximum value residuals. used scaling y axis defaults \"auto\". legend legend plotted? lpos legend placed? Default \"topright\". passed legend. col_obs Colors observed variables. pch_obs Symbols used observed variables. frame frame drawn around plots? ... arguments passed plot.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinerrplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to plot squared residuals and the error model for an mkin object — mkinerrplot","text":"Nothing returned function, called side effect, namely produce plot.","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinerrplot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Function to plot squared residuals and the error model for an mkin object — mkinerrplot","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinerrplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function to plot squared residuals and the error model for an mkin object — mkinerrplot","text":"","code":"# \\dontrun{ model <- mkinmod(parent = mkinsub(\"SFO\", \"m1\"), m1 = mkinsub(\"SFO\")) #> Temporary DLL for differentials generated and loaded fit <- mkinfit(model, FOCUS_2006_D, error_model = \"tc\", quiet = TRUE) #> Warning: Observations with value of zero were removed from the data mkinerrplot(fit)  # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinfit.html","id":null,"dir":"Reference","previous_headings":"","what":"Fit a kinetic model to data with one or more state variables — mkinfit","title":"Fit a kinetic model to data with one or more state variables — mkinfit","text":"function maximises likelihood observed data using Port algorithm stats::nlminb(), specified initial fixed parameters starting values.  step optimisation, kinetic model solved using function mkinpredict(), except analytical solution implemented, case model solved using degradation function mkinmod object. parameters selected error model fitted simultaneously degradation model parameters, arguments likelihood function.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinfit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fit a kinetic model to data with one or more state variables — mkinfit","text":"","code":"mkinfit(   mkinmod,   observed,   parms.ini = \"auto\",   state.ini = \"auto\",   err.ini = \"auto\",   fixed_parms = NULL,   fixed_initials = names(mkinmod$diffs)[-1],   from_max_mean = FALSE,   solution_type = c(\"auto\", \"analytical\", \"eigen\", \"deSolve\"),   method.ode = \"lsoda\",   use_compiled = \"auto\",   control = list(eval.max = 300, iter.max = 200),   transform_rates = TRUE,   transform_fractions = TRUE,   quiet = FALSE,   atol = 1e-08,   rtol = 1e-10,   error_model = c(\"const\", \"obs\", \"tc\"),   error_model_algorithm = c(\"auto\", \"d_3\", \"direct\", \"twostep\", \"threestep\", \"fourstep\",     \"IRLS\", \"OLS\"),   reweight.tol = 1e-08,   reweight.max.iter = 10,   trace_parms = FALSE,   test_residuals = FALSE,   ... )"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinfit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fit a kinetic model to data with one or more state variables — mkinfit","text":"mkinmod list class mkinmod, containing kinetic model fitted data, one shorthand names (\"SFO\", \"FOMC\", \"DFOP\", \"HS\", \"SFORB\", \"IORE\"). shorthand name given, parent degradation model generated variable highest value observed. observed dataframe object coercible dataframe (e.g. tibble) observed data.  first column called \"name\" must contain name observed variable data point. second column must contain times observation, named \"time\". third column must named \"value\" contain observed values. Zero values \"value\" column removed, warning, order avoid problems fitting two-component error model. expected problem, general, values zero observed degradation data, lower limit detection. parms.ini named vector initial values parameters, including parameters optimised potentially also fixed parameters indicated fixed_parms.  set \"auto\", initial values rate constants set default values.  Using parameter names model gives error. possible specify subset parameters model needs. can use parameter lists \"bparms.ode\" previously fitted model, contains differential equation parameters model.  works nicely models nested. example given . state.ini named vector initial values state variables model. case observed variables represented one model variable, names differ names observed variables (see map component mkinmod). default set initial value first model variable mean time zero values variable maximum observed value, others 0.  variable time zero observations, initial value set 100. err.ini named vector initial values error model parameters optimised.  set \"auto\", initial values set default values.  Otherwise, inital values error model parameters must given. fixed_parms names parameters optimised rather kept values specified parms.ini. Alternatively, named numeric vector parameters fixed, regardless values parms.ini. fixed_initials names model variables initial state time 0 excluded optimisation. Defaults state variables except first one. from_max_mean set TRUE, model one observed variable, data time maximum observed value (averaging sampling time) discarded, time subtracted remaining time values, time maximum observed mean value new time zero. solution_type set \"eigen\", solution system differential equations based spectral decomposition coefficient matrix cases possible. set \"deSolve\", numerical ode solver package deSolve used. set \"analytical\", analytical solution model used. implemented relatively simple degradation models.  default \"auto\", uses \"analytical\" possible, otherwise \"deSolve\" compiler present, \"eigen\" compiler present model can expressed using eigenvalues eigenvectors. method.ode solution method passed via mkinpredict() deSolve::ode() case solution type \"deSolve\". default \"lsoda\" performant, sometimes fails converge. use_compiled set FALSE, compiled version mkinmod model used calls mkinpredict() even compiled version present. control list control arguments passed stats::nlminb(). transform_rates Boolean specifying kinetic rate constants transformed model specification used fitting better compliance assumption normal distribution estimator. TRUE, also alpha beta parameters FOMC model log-transformed, well k1 k2 rate constants DFOP HS models break point tb HS model.  FALSE, zero used lower bound rates optimisation. transform_fractions Boolean specifying formation fractions transformed model specification used fitting better compliance assumption normal distribution estimator. default (TRUE) transformations. TRUE, g parameter DFOP model also transformed. Transformations described transform_odeparms. quiet Suppress printing current value negative log-likelihood improvement? atol Absolute error tolerance, passed deSolve::ode(). Default 1e-8, lower default deSolve::lsoda() function used per default. rtol Absolute error tolerance, passed deSolve::ode(). Default 1e-10, much lower deSolve::lsoda(). error_model error model \"const\", constant standard deviation assumed. error model \"obs\", observed variable assumed variance. error model \"tc\" (two-component error model), two component error model similar one described Rocke Lorenzato (1995) used setting likelihood function.  Note model deviates model Rocke Lorenzato, model implies errors follow lognormal distribution large values, normal distribution assumed method. error_model_algorithm \"auto\", selected algorithm depends error model.  error model \"const\", unweighted nonlinear least squares fitting (\"OLS\") selected. error model \"obs\", \"tc\", \"d_3\" algorithm selected. algorithm \"d_3\" directly minimize negative log-likelihood independently also use three step algorithm described . fit higher likelihood returned. algorithm \"direct\" directly minimize negative log-likelihood. algorithm \"twostep\" minimize negative log-likelihood initial unweighted least squares optimisation step. algorithm \"threestep\" starts unweighted least squares, optimizes error model using degradation model parameters found, minimizes negative log-likelihood free degradation error model parameters. algorithm \"fourstep\" starts unweighted least squares, optimizes error model using degradation model parameters found, optimizes degradation model fixed error model parameters, finally minimizes negative log-likelihood free degradation error model parameters. algorithm \"IRLS\" (Iteratively Reweighted Least Squares) starts unweighted least squares, iterates optimization error model parameters subsequent optimization degradation model using error model parameters, error model parameters converge. reweight.tol Tolerance convergence criterion calculated error model parameters IRLS fits. reweight.max.iter Maximum number iterations IRLS fits. trace_parms trace parameter values listed? test_residuals residuals tested normal distribution? ... arguments passed deSolve::ode().","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinfit.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fit a kinetic model to data with one or more state variables — mkinfit","text":"list \"mkinfit\" class attribute.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinfit.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Fit a kinetic model to data with one or more state variables — mkinfit","text":"Per default, parameters kinetic models internally transformed order better satisfy assumption normal distribution estimators.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinfit.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Fit a kinetic model to data with one or more state variables — mkinfit","text":"using \"IORE\" submodel metabolites, fitting \"transform_rates = TRUE\" (default) often leads failures numerical ODE solver. situation may help switch internal rate transformation.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinfit.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fit a kinetic model to data with one or more state variables — mkinfit","text":"Rocke DM Lorenzato S (1995) two-component model measurement error analytical chemistry. Technometrics 37(2), 176-184. Ranke J Meinecke S (2019) Error Models Kinetic Evaluation Chemical Degradation Data. Environments 6(12) 124 doi:10.3390/environments6120124 .","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinfit.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fit a kinetic model to data with one or more state variables — mkinfit","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinfit.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fit a kinetic model to data with one or more state variables — mkinfit","text":"","code":"# Use shorthand notation for parent only degradation fit <- mkinfit(\"FOMC\", FOCUS_2006_C, quiet = TRUE) summary(fit) #> mkin version used for fitting:    1.2.9  #> R version used for fitting:       4.4.2  #> Date of fit:     Thu Feb 13 14:56:49 2025  #> Date of summary: Thu Feb 13 14:56:49 2025  #>  #> Equations: #> d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent #>  #> Model predictions using solution type analytical  #>  #> Fitted using 222 model solutions performed in 0.014 s #>  #> Error model: Constant variance  #>  #> Error model algorithm: OLS  #>  #> Starting values for parameters to be optimised: #>          value   type #> parent_0  85.1  state #> alpha      1.0 deparm #> beta      10.0 deparm #>  #> Starting values for the transformed parameters actually optimised: #>               value lower upper #> parent_0  85.100000  -Inf   Inf #> log_alpha  0.000000  -Inf   Inf #> log_beta   2.302585  -Inf   Inf #>  #> Fixed parameter values: #> None #>  #> Results: #>  #>        AIC      BIC    logLik #>   44.68652 45.47542 -18.34326 #>  #> Optimised, transformed parameters with symmetric confidence intervals: #>           Estimate Std. Error    Lower   Upper #> parent_0  85.87000     1.8070 81.23000 90.5200 #> log_alpha  0.05192     0.1353 -0.29580  0.3996 #> log_beta   0.65100     0.2287  0.06315  1.2390 #> sigma      1.85700     0.4378  0.73200  2.9830 #>  #> Parameter correlation: #>             parent_0  log_alpha   log_beta     sigma #> parent_0   1.000e+00 -1.565e-01 -3.142e-01 4.681e-08 #> log_alpha -1.565e-01  1.000e+00  9.564e-01 1.013e-07 #> log_beta  -3.142e-01  9.564e-01  1.000e+00 8.637e-08 #> sigma      4.681e-08  1.013e-07  8.637e-08 1.000e+00 #>  #> Backtransformed parameters: #> Confidence intervals for internally transformed parameters are asymmetric. #> t-test (unrealistically) based on the assumption of normal distribution #> for estimators of untransformed parameters. #>          Estimate t value    Pr(>t)   Lower  Upper #> parent_0   85.870  47.530 3.893e-08 81.2300 90.520 #> alpha       1.053   7.393 3.562e-04  0.7439  1.491 #> beta        1.917   4.373 3.601e-03  1.0650  3.451 #> sigma       1.857   4.243 4.074e-03  0.7320  2.983 #>  #> FOCUS Chi2 error levels in percent: #>          err.min n.optim df #> All data   6.657       3  6 #> parent     6.657       3  6 #>  #> Estimated disappearance times: #>         DT50  DT90 DT50back #> parent 1.785 15.15     4.56 #>  #> Data: #>  time variable observed predicted residual #>     0   parent     85.1    85.875  -0.7749 #>     1   parent     57.9    55.191   2.7091 #>     3   parent     29.9    31.845  -1.9452 #>     7   parent     14.6    17.012  -2.4124 #>    14   parent      9.7     9.241   0.4590 #>    28   parent      6.6     4.754   1.8460 #>    63   parent      4.0     2.102   1.8977 #>    91   parent      3.9     1.441   2.4590 #>   119   parent      0.6     1.092  -0.4919  # One parent compound, one metabolite, both single first order. # We remove zero values from FOCUS dataset D in order to avoid warnings FOCUS_D <- subset(FOCUS_2006_D, value != 0) # Use mkinsub for convenience in model formulation. Pathway to sink included per default. SFO_SFO <- mkinmod(   parent = mkinsub(\"SFO\", \"m1\"),   m1 = mkinsub(\"SFO\")) #> Temporary DLL for differentials generated and loaded  # Fit the model quietly to the FOCUS example dataset D using defaults fit <- mkinfit(SFO_SFO, FOCUS_D, quiet = TRUE) plot_sep(fit)  # As lower parent values appear to have lower variance, we try an alternative error model fit.tc <- mkinfit(SFO_SFO, FOCUS_D, quiet = TRUE, error_model = \"tc\") # This avoids the warning, and the likelihood ratio test confirms it is preferable lrtest(fit.tc, fit) #> Likelihood ratio test #>  #> Model 1: SFO_SFO with error model tc and fixed parameter(s) m1_0 #> Model 2: SFO_SFO with error model const and fixed parameter(s) m1_0 #>   #Df  LogLik Df  Chisq Pr(>Chisq)     #> 1   6 -64.983                          #> 2   5 -97.224 -1 64.483  9.737e-16 *** #> --- #> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 # We can also allow for different variances of parent and metabolite as error model fit.obs <- mkinfit(SFO_SFO, FOCUS_D, quiet = TRUE, error_model = \"obs\") # The two-component error model has significantly higher likelihood lrtest(fit.obs, fit.tc) #> Likelihood ratio test #>  #> Model 1: SFO_SFO with error model tc and fixed parameter(s) m1_0 #> Model 2: SFO_SFO with error model obs and fixed parameter(s) m1_0 #>   #Df  LogLik Df  Chisq Pr(>Chisq)     #> 1   6 -64.983                          #> 2   6 -96.936  0 63.907  < 2.2e-16 *** #> --- #> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 parms(fit.tc) #>       parent_0       k_parent           k_m1 f_parent_to_m1      sigma_low  #>   1.007343e+02   1.005562e-01   5.166712e-03   5.083933e-01   3.049883e-03  #>       rsd_high  #>   7.928118e-02  endpoints(fit.tc) #> $ff #>   parent_m1 parent_sink  #>   0.5083933   0.4916067  #>  #> $distimes #>             DT50      DT90 #> parent   6.89313  22.89848 #> m1     134.15634 445.65771 #>   # We can show a quick (only one replication) benchmark for this case, as we # have several alternative solution methods for the model. We skip # uncompiled deSolve, as it is so slow. More benchmarks are found in the # benchmark vignette # \\dontrun{ if(require(rbenchmark)) {   benchmark(replications = 1, order = \"relative\", columns = c(\"test\", \"relative\", \"elapsed\"),     deSolve_compiled = mkinfit(SFO_SFO, FOCUS_D, quiet = TRUE, error_model = \"tc\",       solution_type = \"deSolve\", use_compiled = TRUE),     eigen = mkinfit(SFO_SFO, FOCUS_D, quiet = TRUE, error_model = \"tc\",       solution_type = \"eigen\"),     analytical = mkinfit(SFO_SFO, FOCUS_D, quiet = TRUE, error_model = \"tc\",       solution_type = \"analytical\")) } #>               test relative elapsed #> 3       analytical    1.000   0.227 #> 2            eigen    1.930   0.438 #> 1 deSolve_compiled    1.991   0.452 # }  # Use stepwise fitting, using optimised parameters from parent only fit, FOMC-SFO # \\dontrun{ FOMC_SFO <- mkinmod(   parent = mkinsub(\"FOMC\", \"m1\"),   m1 = mkinsub(\"SFO\")) #> Temporary DLL for differentials generated and loaded fit.FOMC_SFO <- mkinfit(FOMC_SFO, FOCUS_D, quiet = TRUE) # Again, we get a warning and try a more sophisticated error model fit.FOMC_SFO.tc <- mkinfit(FOMC_SFO, FOCUS_D, quiet = TRUE, error_model = \"tc\") # This model has a higher likelihood, but not significantly so lrtest(fit.tc, fit.FOMC_SFO.tc) #> Likelihood ratio test #>  #> Model 1: FOMC_SFO with error model tc and fixed parameter(s) m1_0 #> Model 2: SFO_SFO with error model tc and fixed parameter(s) m1_0 #>   #Df  LogLik Df  Chisq Pr(>Chisq) #> 1   7 -64.829                      #> 2   6 -64.983 -1 0.3075     0.5792 # Also, the missing standard error for log_beta and the t-tests for alpha # and beta indicate overparameterisation summary(fit.FOMC_SFO.tc, data = FALSE) #> Warning: NaNs produced #> Warning: diag(V) had non-positive or NA entries; the non-finite result may be dubious #> mkin version used for fitting:    1.2.9  #> R version used for fitting:       4.4.2  #> Date of fit:     Thu Feb 13 14:56:53 2025  #> Date of summary: Thu Feb 13 14:56:53 2025  #>  #> Equations: #> d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent #> d_m1/dt = + f_parent_to_m1 * (alpha/beta) * 1/((time/beta) + 1) * #>            parent - k_m1 * m1 #>  #> Model predictions using solution type deSolve  #>  #> Fitted using 4062 model solutions performed in 0.751 s #>  #> Error model: Two-component variance function  #>  #> Error model algorithm: d_3  #> Direct fitting and three-step fitting yield approximately the same likelihood  #>  #> Starting values for parameters to be optimised: #>                 value   type #> parent_0       100.75  state #> alpha            1.00 deparm #> beta            10.00 deparm #> k_m1             0.10 deparm #> f_parent_to_m1   0.50 deparm #> sigma_low        0.10  error #> rsd_high         0.10  error #>  #> Starting values for the transformed parameters actually optimised: #>                      value lower upper #> parent_0        100.750000  -Inf   Inf #> log_k_m1         -2.302585  -Inf   Inf #> f_parent_qlogis   0.000000  -Inf   Inf #> log_alpha         0.000000  -Inf   Inf #> log_beta          2.302585  -Inf   Inf #> sigma_low         0.100000     0   Inf #> rsd_high          0.100000     0   Inf #>  #> Fixed parameter values: #>      value  type #> m1_0     0 state #>  #> Results: #>  #>       AIC      BIC    logLik #>   143.658 155.1211 -64.82902 #>  #> Optimised, transformed parameters with symmetric confidence intervals: #>                   Estimate Std. Error     Lower      Upper #> parent_0        101.600000  2.6400000 96.240000 107.000000 #> log_k_m1         -5.284000  0.0929100 -5.474000  -5.095000 #> f_parent_qlogis   0.001426  0.0766900 -0.155000   0.157800 #> log_alpha         5.522000  0.0077320  5.506000   5.538000 #> log_beta          7.806000        NaN       NaN        NaN #> sigma_low         0.002488  0.0002431  0.001992   0.002984 #> rsd_high          0.079210  0.0093280  0.060180   0.098230 #>  #> Parameter correlation: #>                  parent_0  log_k_m1 f_parent_qlogis log_alpha log_beta #> parent_0         1.000000 -0.095161        -0.76675   0.70542      NaN #> log_k_m1        -0.095161  1.000000         0.51429  -0.14382      NaN #> f_parent_qlogis -0.766750  0.514286         1.00000  -0.61393      NaN #> log_alpha        0.705417 -0.143821        -0.61393   1.00000      NaN #> log_beta              NaN       NaN             NaN       NaN        1 #> sigma_low        0.016086  0.001583         0.01547   5.87036      NaN #> rsd_high         0.006618 -0.011695        -0.05356   0.04848      NaN #>                 sigma_low  rsd_high #> parent_0         0.016086  0.006618 #> log_k_m1         0.001583 -0.011695 #> f_parent_qlogis  0.015466 -0.053560 #> log_alpha        5.870361  0.048483 #> log_beta              NaN       NaN #> sigma_low        1.000000 -0.652545 #> rsd_high        -0.652545  1.000000 #>  #> Backtransformed parameters: #> Confidence intervals for internally transformed parameters are asymmetric. #> t-test (unrealistically) based on the assumption of normal distribution #> for estimators of untransformed parameters. #>                 Estimate t value    Pr(>t)     Lower     Upper #> parent_0       1.016e+02 32.7800 6.310e-26 9.624e+01 1.070e+02 #> k_m1           5.072e-03 10.1200 1.216e-11 4.196e-03 6.130e-03 #> f_parent_to_m1 5.004e-01 20.8300 4.316e-20 4.613e-01 5.394e-01 #> alpha          2.502e+02  0.5624 2.889e-01 2.463e+02 2.542e+02 #> beta           2.455e+03  0.5549 2.915e-01        NA        NA #> sigma_low      2.488e-03  0.4843 3.158e-01 1.992e-03 2.984e-03 #> rsd_high       7.921e-02  8.4300 8.001e-10 6.018e-02 9.823e-02 #>  #> FOCUS Chi2 error levels in percent: #>          err.min n.optim df #> All data   6.781       5 14 #> parent     7.141       3  6 #> m1         4.640       2  8 #>  #> Resulting formation fractions: #>                 ff #> parent_m1   0.5004 #> parent_sink 0.4996 #>  #> Estimated disappearance times: #>           DT50  DT90 DT50back #> parent   6.812  22.7    6.834 #> m1     136.661 454.0       NA  # We can easily use starting parameters from the parent only fit (only for illustration) fit.FOMC = mkinfit(\"FOMC\", FOCUS_2006_D, quiet = TRUE, error_model = \"tc\") fit.FOMC_SFO <- mkinfit(FOMC_SFO, FOCUS_D, quiet = TRUE,   parms.ini = fit.FOMC$bparms.ode, error_model = \"tc\") # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinmod.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to set up a kinetic model with one or more state variables — mkinmod","title":"Function to set up a kinetic model with one or more state variables — mkinmod","text":"function usually called using call mkinsub() observed variable, specifying corresponding submodel well outgoing pathways (see examples). Print mkinmod objects way user finds way get components.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinmod.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to set up a kinetic model with one or more state variables — mkinmod","text":"","code":"mkinmod(   ...,   use_of_ff = \"max\",   name = NULL,   speclist = NULL,   quiet = FALSE,   verbose = FALSE,   dll_dir = NULL,   unload = FALSE,   overwrite = FALSE )  # S3 method for class 'mkinmod' print(x, ...)  mkinsub(submodel, to = NULL, sink = TRUE, full_name = NA)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinmod.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to set up a kinetic model with one or more state variables — mkinmod","text":"... observed variable, list obtained mkinsub() specified argument (see examples).  Currently, single first order kinetics \"SFO\", indeterminate order rate equation kinetics \"IORE\", single first order reversible binding \"SFORB\" implemented variables, \"FOMC\", \"DFOP\", \"HS\" \"logistic\" can additionally chosen first variable assumed source compartment. Additionally, mkinsub() argument , specifying names variables transfer assumed model. argument use_of_ff set \"min\" model compartment \"SFO\" \"SFORB\", additional mkinsub() argument can sink = FALSE, effectively fixing flux sink zero. print.mkinmod, argument currently used. use_of_ff Specification use formation fractions model equations , applicable, coefficient matrix.  \"max\", formation fractions always used (default).  \"min\", minimum use formation fractions made, .e. first-order pathway metabolite rate constant. name name model. valid R object name. speclist specification observed variables submodel types pathways can given single list using argument. Default NULL. quiet messages suppressed? verbose TRUE, passed inline::cfunction() applicable give detailed information C function built. dll_dir Directory DLL object, generated internally inline::cfunction(), saved.  DLL stored permanent location use future sessions, 'dll_dir' 'name' specified. helpful fit objects cached e.g. knitr, cache remains functional across sessions DLL stored user defined location. unload DLL target location 'dll_dir' already loaded, unloaded first? overwrite file exists target DLL location 'dll_dir', overwritten? x mkinmod object. submodel Character vector length one specify submodel type. See mkinmod list allowed submodel names. Vector names state variable transformation shall included model. sink pathway sink included model addition pathways state variables? full_name optional name used e.g. plotting fits performed model.  can use non-ASCII characters , R code portable, .e. may produce unintended plot results operating systems system configurations.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinmod.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to set up a kinetic model with one or more state variables — mkinmod","text":"list class mkinmod use mkinfit(), containing, among others, diffs vector string representations differential equations, one modelling variable. map list containing named character vectors observed variable, specifying modelling variables represented. use_of_ff content use_of_ff passed list component. deg_func generated, function containing solution degradation model. coefmat coefficient matrix, system differential equations can represented one. cf generated, compiled function calculating derivatives returned cfunction. list use mkinmod.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinmod.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Function to set up a kinetic model with one or more state variables — mkinmod","text":"definition model types parameters, equations given FOCUS NAFTA guidance documents used. kinetic models one observed variable, symbolic solution system differential equations included resulting mkinmod object cases, speeding solution. C compiler found pkgbuild::has_compiler() one observed variable specification, C code generated evaluating differential equations, compiled using inline::cfunction() added resulting mkinmod object.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinmod.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Function to set up a kinetic model with one or more state variables — mkinmod","text":"IORE submodel well tested metabolites. using model metabolites, may want read note help page mkinfit.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinmod.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Function to set up a kinetic model with one or more state variables — mkinmod","text":"FOCUS (2006) “Guidance Document Estimating Persistence Degradation Kinetics Environmental Fate Studies Pesticides EU Registration” Report FOCUS Work Group Degradation Kinetics, EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics NAFTA Technical Working Group Pesticides (dated) Guidance Evaluating Calculating Degradation Kinetics Environmental Media","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinmod.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Function to set up a kinetic model with one or more state variables — mkinmod","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinmod.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function to set up a kinetic model with one or more state variables — mkinmod","text":"","code":"# Specify the SFO model (this is not needed any more, as we can now mkinfit(\"SFO\", ...) SFO <- mkinmod(parent = mkinsub(\"SFO\"))  # One parent compound, one metabolite, both single first order SFO_SFO <- mkinmod(   parent = mkinsub(\"SFO\", \"m1\"),   m1 = mkinsub(\"SFO\")) #> Temporary DLL for differentials generated and loaded print(SFO_SFO) #> <mkinmod> model generated with #> Use of formation fractions $use_of_ff: max  #> Specification $spec: #> $parent #> $type: SFO; $to: m1; $sink: TRUE #> $m1 #> $type: SFO; $sink: TRUE #> Coefficient matrix $coefmat available #> Compiled model $cf available #> Differential equations: #> d_parent/dt = - k_parent * parent #> d_m1/dt = + f_parent_to_m1 * k_parent * parent - k_m1 * m1  # \\dontrun{  fit_sfo_sfo <- mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE, solution_type = \"deSolve\") #> Warning: Observations with value of zero were removed from the data   # Now supplying compound names used for plotting, and write to user defined location  # We need to choose a path outside the session tempdir because this gets removed  DLL_dir <- \"~/.local/share/mkin\"  if (!dir.exists(DLL_dir)) dir.create(DLL_dir)  SFO_SFO.2 <- mkinmod(    parent = mkinsub(\"SFO\", \"m1\", full_name = \"Test compound\"),    m1 = mkinsub(\"SFO\", full_name = \"Metabolite M1\"),    name = \"SFO_SFO\", dll_dir = DLL_dir, unload = TRUE, overwrite = TRUE) #> Temporary DLL for differentials generated and loaded #> Copied DLL from /tmp/RtmpUBdk0y/file210eb9601bdd53.so to /home/jranke/.local/share/mkin/SFO_SFO.so # Now we can save the model and restore it in a new session saveRDS(SFO_SFO.2, file = \"~/SFO_SFO.rds\") # Terminate the R session here if you would like to check, and then do library(mkin) SFO_SFO.3 <- readRDS(\"~/SFO_SFO.rds\") fit_sfo_sfo <- mkinfit(SFO_SFO.3, FOCUS_2006_D, quiet = TRUE, solution_type = \"deSolve\") #> Warning: Observations with value of zero were removed from the data  # Show details of creating the C function SFO_SFO <- mkinmod(   parent = mkinsub(\"SFO\", \"m1\"),   m1 = mkinsub(\"SFO\"), verbose = TRUE) #> Program source: #>   1: #include <R.h> #>   2:  #>   3:  #>   4: static double parms [3]; #>   5: #define k_parent parms[0] #>   6: #define f_parent_to_m1 parms[1] #>   7: #define k_m1 parms[2] #>   8:  #>   9: void initpar(void (* odeparms)(int *, double *)) { #>  10:     int N = 3; #>  11:     odeparms(&N, parms); #>  12: } #>  13:  #>  14:  #>  15: void diffs ( int * n, double * t, double * y, double * f, double * rpar, int * ipar ) { #>  16:  #>  17: f[0] = - k_parent * y[0]; #>  18: f[1] = + f_parent_to_m1 * k_parent * y[0] - k_m1 * y[1]; #>  19: } #> Temporary DLL for differentials generated and loaded  # The symbolic solution which is available in this case is not # made for human reading but for speed of computation SFO_SFO$deg_func #> function (observed, odeini, odeparms)  #> { #>     predicted <- numeric(0) #>     with(as.list(odeparms), { #>         t <- observed[observed$name == \"parent\", \"time\"] #>         predicted <<- c(predicted, SFO.solution(t, odeini[\"parent\"],  #>             k_parent)) #>         t <- observed[observed$name == \"m1\", \"time\"] #>         predicted <<- c(predicted, (((k_m1 - k_parent) * odeini[\"m1\"] -  #>             f_parent_to_m1 * k_parent * odeini[\"parent\"]) * exp(-k_m1 *  #>             t) + f_parent_to_m1 * k_parent * odeini[\"parent\"] *  #>             exp(-k_parent * t))/(k_m1 - k_parent)) #>     }) #>     return(predicted) #> } #> <environment: 0x55555a37aab8>  # If we have several parallel metabolites # (compare tests/testthat/test_synthetic_data_for_UBA_2014.R) m_synth_DFOP_par <- mkinmod(  parent = mkinsub(\"DFOP\", c(\"M1\", \"M2\")),  M1 = mkinsub(\"SFO\"),  M2 = mkinsub(\"SFO\"),  quiet = TRUE)  fit_DFOP_par_c <- mkinfit(m_synth_DFOP_par,   synthetic_data_for_UBA_2014[[12]]$data,   quiet = TRUE) # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinparplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to plot the confidence intervals obtained using mkinfit — mkinparplot","title":"Function to plot the confidence intervals obtained using mkinfit — mkinparplot","text":"function plots confidence intervals parameters fitted using mkinfit.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinparplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to plot the confidence intervals obtained using mkinfit — mkinparplot","text":"","code":"mkinparplot(object)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinparplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to plot the confidence intervals obtained using mkinfit — mkinparplot","text":"object fit represented mkinfit object.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinparplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to plot the confidence intervals obtained using mkinfit — mkinparplot","text":"Nothing returned function, called side effect, namely produce plot.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinparplot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Function to plot the confidence intervals obtained using mkinfit — mkinparplot","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinparplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function to plot the confidence intervals obtained using mkinfit — mkinparplot","text":"","code":"# \\dontrun{ model <- mkinmod(   T245 = mkinsub(\"SFO\", to = c(\"phenol\"), sink = FALSE),   phenol = mkinsub(\"SFO\", to = c(\"anisole\")),   anisole = mkinsub(\"SFO\"), use_of_ff = \"max\") #> Temporary DLL for differentials generated and loaded fit <- mkinfit(model, subset(mccall81_245T, soil == \"Commerce\"), quiet = TRUE) #> Warning: Observations with value of zero were removed from the data mkinparplot(fit)  # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot the observed data and the fitted model of an mkinfit object — mkinplot","title":"Plot the observed data and the fitted model of an mkinfit object — mkinplot","text":"Deprecated function. now calls plot method plot.mkinfit.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot the observed data and the fitted model of an mkinfit object — mkinplot","text":"","code":"mkinplot(fit, ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot the observed data and the fitted model of an mkinfit object — mkinplot","text":"fit object class mkinfit. ... arguments passed plot.mkinfit.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot the observed data and the fitted model of an mkinfit object — mkinplot","text":"function called side effect.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinplot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot the observed data and the fitted model of an mkinfit object — mkinplot","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinpredict.html","id":null,"dir":"Reference","previous_headings":"","what":"Produce predictions from a kinetic model using specific parameters — mkinpredict","title":"Produce predictions from a kinetic model using specific parameters — mkinpredict","text":"function produces time series observed variables kinetic model specified mkinmod, using specific set kinetic parameters initial values state variables.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinpredict.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Produce predictions from a kinetic model using specific parameters — mkinpredict","text":"","code":"mkinpredict(x, odeparms, odeini, outtimes, ...)  # S3 method for class 'mkinmod' mkinpredict(   x,   odeparms = c(k_parent_sink = 0.1),   odeini = c(parent = 100),   outtimes = seq(0, 120, by = 0.1),   solution_type = \"deSolve\",   use_compiled = \"auto\",   use_symbols = FALSE,   method.ode = \"lsoda\",   atol = 1e-08,   rtol = 1e-10,   maxsteps = 20000L,   map_output = TRUE,   na_stop = TRUE,   ... )  # S3 method for class 'mkinfit' mkinpredict(   x,   odeparms = x$bparms.ode,   odeini = x$bparms.state,   outtimes = seq(0, 120, by = 0.1),   solution_type = \"deSolve\",   use_compiled = \"auto\",   method.ode = \"lsoda\",   atol = 1e-08,   rtol = 1e-10,   map_output = TRUE,   ... )"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinpredict.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Produce predictions from a kinetic model using specific parameters — mkinpredict","text":"x kinetic model produced mkinmod, kinetic fit fitted mkinfit. latter case, fitted parameters used prediction. odeparms numeric vector specifying parameters used kinetic model, generally defined set ordinary differential equations. odeini numeric vector containing initial values state variables model. Note state variables can differ observed variables, example case SFORB model. outtimes numeric vector specifying time points model predictions generated. ... arguments passed ode solver case solver used. solution_type method used producing predictions. generally \"analytical\" one observed variable, usually \"deSolve\" case several observed variables. third possibility \"eigen\" fast comparison uncompiled ODE models, applicable models, e.g. using FOMC parent compound. use_compiled set FALSE, compiled version mkinmod model used, even present. use_symbols set TRUE (default), symbol info present mkinmod object used available accessing compiled code method.ode solution method passed via mkinpredict deSolve::ode() case solution type \"deSolve\" using compiled code. using compiled code, lsoda supported. atol Absolute error tolerance, passed ode solver. rtol Absolute error tolerance, passed ode solver. maxsteps Maximum number steps, passed ode solver. map_output Boolean specify output list values observed variables (default) state variables (set FALSE). Setting FALSE effect analytical solutions, always return mapped output. na_stop error deSolve::ode() returns NaN values","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinpredict.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Produce predictions from a kinetic model using specific parameters — mkinpredict","text":"matrix numeric solution wide format","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinpredict.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Produce predictions from a kinetic model using specific parameters — mkinpredict","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinpredict.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Produce predictions from a kinetic model using specific parameters — mkinpredict","text":"","code":"SFO <- mkinmod(degradinol = mkinsub(\"SFO\")) # Compare solution types mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,       solution_type = \"analytical\") #>    time  degradinol #> 0     0 100.0000000 #> 1     1  74.0818221 #> 2     2  54.8811636 #> 3     3  40.6569660 #> 4     4  30.1194212 #> 5     5  22.3130160 #> 6     6  16.5298888 #> 7     7  12.2456428 #> 8     8   9.0717953 #> 9     9   6.7205513 #> 10   10   4.9787068 #> 11   11   3.6883167 #> 12   12   2.7323722 #> 13   13   2.0241911 #> 14   14   1.4995577 #> 15   15   1.1108997 #> 16   16   0.8229747 #> 17   17   0.6096747 #> 18   18   0.4516581 #> 19   19   0.3345965 #> 20   20   0.2478752 mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,       solution_type = \"deSolve\") #>    time  degradinol #> 0     0 100.0000000 #> 1     1  74.0818221 #> 2     2  54.8811636 #> 3     3  40.6569660 #> 4     4  30.1194212 #> 5     5  22.3130160 #> 6     6  16.5298888 #> 7     7  12.2456428 #> 8     8   9.0717953 #> 9     9   6.7205513 #> 10   10   4.9787068 #> 11   11   3.6883167 #> 12   12   2.7323722 #> 13   13   2.0241911 #> 14   14   1.4995577 #> 15   15   1.1108996 #> 16   16   0.8229747 #> 17   17   0.6096747 #> 18   18   0.4516581 #> 19   19   0.3345965 #> 20   20   0.2478752 mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,       solution_type = \"deSolve\", use_compiled = FALSE) #>    time  degradinol #> 0     0 100.0000000 #> 1     1  74.0818221 #> 2     2  54.8811636 #> 3     3  40.6569660 #> 4     4  30.1194212 #> 5     5  22.3130160 #> 6     6  16.5298888 #> 7     7  12.2456428 #> 8     8   9.0717953 #> 9     9   6.7205513 #> 10   10   4.9787068 #> 11   11   3.6883167 #> 12   12   2.7323722 #> 13   13   2.0241911 #> 14   14   1.4995577 #> 15   15   1.1108996 #> 16   16   0.8229747 #> 17   17   0.6096747 #> 18   18   0.4516581 #> 19   19   0.3345965 #> 20   20   0.2478752 mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,       solution_type = \"eigen\") #>    time  degradinol #> 0     0 100.0000000 #> 1     1  74.0818221 #> 2     2  54.8811636 #> 3     3  40.6569660 #> 4     4  30.1194212 #> 5     5  22.3130160 #> 6     6  16.5298888 #> 7     7  12.2456428 #> 8     8   9.0717953 #> 9     9   6.7205513 #> 10   10   4.9787068 #> 11   11   3.6883167 #> 12   12   2.7323722 #> 13   13   2.0241911 #> 14   14   1.4995577 #> 15   15   1.1108997 #> 16   16   0.8229747 #> 17   17   0.6096747 #> 18   18   0.4516581 #> 19   19   0.3345965 #> 20   20   0.2478752  # Compare integration methods to analytical solution mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,       solution_type = \"analytical\")[21,] #>       time degradinol  #> 20.0000000  0.2478752  mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,       method = \"lsoda\", use_compiled = FALSE)[21,] #>       time degradinol  #> 20.0000000  0.2478752  mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,       method = \"ode45\", use_compiled = FALSE)[21,] #>       time degradinol  #> 20.0000000  0.2478752  mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,       method = \"rk4\", use_compiled = FALSE)[21,] #>       time degradinol  #> 20.0000000  0.2480043  # rk4 is not as precise here  # The number of output times used to make a lot of difference until the # default for atol was adjusted mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100),       seq(0, 20, by = 0.1))[201,] #>       time degradinol  #> 20.0000000  0.2478752  mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100),       seq(0, 20, by = 0.01))[2001,] #>       time degradinol  #> 20.0000000  0.2478752   # Comparison of the performance of solution types SFO_SFO = mkinmod(parent = list(type = \"SFO\", to = \"m1\"),                   m1 = list(type = \"SFO\"), use_of_ff = \"max\") #> Temporary DLL for differentials generated and loaded if(require(rbenchmark)) {   benchmark(replications = 10, order = \"relative\", columns = c(\"test\", \"relative\", \"elapsed\"),     eigen = mkinpredict(SFO_SFO,       c(k_parent = 0.15, f_parent_to_m1 = 0.5, k_m1 = 0.01),       c(parent = 100, m1 = 0), seq(0, 20, by = 0.1),       solution_type = \"eigen\")[201,],     deSolve_compiled = mkinpredict(SFO_SFO,       c(k_parent = 0.15, f_parent_to_m1 = 0.5, k_m1 = 0.01),       c(parent = 100, m1 = 0), seq(0, 20, by = 0.1),       solution_type = \"deSolve\")[201,],     deSolve = mkinpredict(SFO_SFO,       c(k_parent = 0.15, f_parent_to_m1 = 0.5, k_m1 = 0.01),       c(parent = 100, m1 = 0), seq(0, 20, by = 0.1),       solution_type = \"deSolve\", use_compiled = FALSE)[201,],     analytical = mkinpredict(SFO_SFO,       c(k_parent = 0.15, f_parent_to_m1 = 0.5, k_m1 = 0.01),       c(parent = 100, m1 = 0), seq(0, 20, by = 0.1),       solution_type = \"analytical\", use_compiled = FALSE)[201,]) } #>               test relative elapsed #> 2 deSolve_compiled        1   0.002 #> 4       analytical        1   0.002 #> 1            eigen        4   0.008 #> 3          deSolve       32   0.064  # \\dontrun{   # Predict from a fitted model   f <- mkinfit(SFO_SFO, FOCUS_2006_C, quiet = TRUE)   f <- mkinfit(SFO_SFO, FOCUS_2006_C, quiet = TRUE, solution_type = \"deSolve\")   head(mkinpredict(f)) #> Error in !is.null(x$symbols) & use_symbols: operations are possible only for numeric, logical or complex types # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinresplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to plot residuals stored in an mkin object — mkinresplot","title":"Function to plot residuals stored in an mkin object — mkinresplot","text":"function plots residuals specified subset observed variables mkinfit object. combined plot fitted model residuals can obtained using plot.mkinfit using argument show_residuals = TRUE.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinresplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to plot residuals stored in an mkin object — mkinresplot","text":"","code":"mkinresplot(   object,   obs_vars = names(object$mkinmod$map),   xlim = c(0, 1.1 * max(object$data$time)),   standardized = FALSE,   xlab = \"Time\",   ylab = ifelse(standardized, \"Standardized residual\", \"Residual\"),   maxabs = \"auto\",   legend = TRUE,   lpos = \"topright\",   col_obs = \"auto\",   pch_obs = \"auto\",   frame = TRUE,   ... )"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinresplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to plot residuals stored in an mkin object — mkinresplot","text":"object fit represented mkinfit object. obs_vars character vector names observed variables residuals plotted. Defaults observed variables model xlim plot range x direction. standardized residuals standardized dividing standard deviation given error model fit? xlab Label x axis. ylab Label y axis. maxabs Maximum absolute value residuals. used scaling y axis defaults \"auto\". legend legend plotted? lpos legend placed? Default \"topright\". passed legend. col_obs Colors observed variables. pch_obs Symbols used observed variables. frame frame drawn around plots? ... arguments passed plot.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinresplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to plot residuals stored in an mkin object — mkinresplot","text":"Nothing returned function, called side effect, namely produce plot.","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinresplot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Function to plot residuals stored in an mkin object — mkinresplot","text":"Johannes Ranke Katrin Lindenberger","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mkinresplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function to plot residuals stored in an mkin object — mkinresplot","text":"","code":"model <- mkinmod(parent = mkinsub(\"SFO\", \"m1\"), m1 = mkinsub(\"SFO\")) #> Temporary DLL for differentials generated and loaded fit <- mkinfit(model, FOCUS_2006_D, quiet = TRUE) #> Warning: Observations with value of zero were removed from the data mkinresplot(fit, \"m1\")"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mmkin.html","id":null,"dir":"Reference","previous_headings":"","what":"Fit one or more kinetic models with one or more state variables to one or more datasets — mmkin","title":"Fit one or more kinetic models with one or more state variables to one or more datasets — mmkin","text":"function calls mkinfit combinations models datasets specified first two arguments.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mmkin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fit one or more kinetic models with one or more state variables to one or more datasets — mmkin","text":"","code":"mmkin(   models = c(\"SFO\", \"FOMC\", \"DFOP\"),   datasets,   cores = if (Sys.info()[\"sysname\"] == \"Windows\") 1 else parallel::detectCores(),   cluster = NULL,   ... )  # S3 method for class 'mmkin' print(x, ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/mmkin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fit one or more kinetic models with one or more state variables to one or more datasets — mmkin","text":"models Either character vector shorthand names like c(\"SFO\", \"FOMC\", \"DFOP\", \"HS\", \"SFORB\"), optionally named list mkinmod objects. datasets optionally named list datasets suitable observed data mkinfit. cores number cores used multicore processing. used cluster argument NULL. Windows machines, cores > 1 supported, need use cluster argument use multiple logical processors. Per default, cores detected parallel::detectCores() used, except Windows default 1. cluster cluster returned makeCluster used parallel execution. ... used. x mmkin object.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mmkin.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fit one or more kinetic models with one or more state variables to one or more datasets — mmkin","text":"two-dimensional array mkinfit objects /try-errors can indexed using model names first index (row index) dataset names second index (column index).","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/reference/mmkin.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fit one or more kinetic models with one or more state variables to one or more datasets — mmkin","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/mmkin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fit one or more kinetic models with one or more state variables to one or more datasets — mmkin","text":"","code":"# \\dontrun{ m_synth_SFO_lin <- mkinmod(parent = mkinsub(\"SFO\", \"M1\"),                            M1 = mkinsub(\"SFO\", \"M2\"),                            M2 = mkinsub(\"SFO\"), use_of_ff = \"max\") #> Temporary DLL for differentials generated and loaded  m_synth_FOMC_lin <- mkinmod(parent = mkinsub(\"FOMC\", \"M1\"),                             M1 = mkinsub(\"SFO\", \"M2\"),                             M2 = mkinsub(\"SFO\"), use_of_ff = \"max\") #> Temporary DLL for differentials generated and loaded  models <- list(SFO_lin = m_synth_SFO_lin, FOMC_lin = m_synth_FOMC_lin) datasets <- lapply(synthetic_data_for_UBA_2014[1:3], function(x) x$data) names(datasets) <- paste(\"Dataset\", 1:3)  time_default <- system.time(fits.0 <- mmkin(models, datasets, quiet = TRUE)) time_1 <- system.time(fits.4 <- mmkin(models, datasets, cores = 1, quiet = TRUE))  time_default #>    user  system elapsed  #>   1.608   0.866   0.732  time_1 #>    user  system elapsed  #>   1.964   0.016   1.980   endpoints(fits.0[[\"SFO_lin\", 2]]) #> $ff #>   parent_M1 parent_sink       M1_M2     M1_sink  #>   0.7340481   0.2659519   0.7505690   0.2494310  #>  #> $distimes #>              DT50       DT90 #> parent  0.8777689   2.915885 #> M1      2.3257403   7.725942 #> M2     33.7201060 112.015767 #>   # plot.mkinfit handles rows or columns of mmkin result objects plot(fits.0[1, ])  plot(fits.0[1, ], obs_var = c(\"M1\", \"M2\"))  plot(fits.0[, 1])  # Use double brackets to extract a single mkinfit object, which will be plotted # by plot.mkinfit and can be plotted using plot_sep plot(fits.0[[1, 1]], sep_obs = TRUE, show_residuals = TRUE, show_errmin = TRUE)  plot_sep(fits.0[[1, 1]]) # Plotting with mmkin (single brackets, extracting an mmkin object) does not # allow to plot the observed variables separately plot(fits.0[1, 1])   # On Windows, we can use multiple cores by making a cluster first cl <- parallel::makePSOCKcluster(12) f <- mmkin(c(\"SFO\", \"FOMC\", \"DFOP\"),   list(A = FOCUS_2006_A, B = FOCUS_2006_B, C = FOCUS_2006_C, D = FOCUS_2006_D),   cluster = cl, quiet = TRUE) print(f) #> <mmkin> object #> Status of individual fits: #>  #>       dataset #> model  A  B  C  D  #>   SFO  OK OK OK OK #>   FOMC C  OK OK OK #>   DFOP OK OK OK OK #>  #> C: Optimisation did not converge: #> false convergence (8) #> OK: No warnings # We get false convergence for the FOMC fit to FOCUS_2006_A because this # dataset is really SFO, and the FOMC fit is overparameterised parallel::stopCluster(cl) # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/multistart.html","id":null,"dir":"Reference","previous_headings":"","what":"Perform a hierarchical model fit with multiple starting values — multistart","title":"Perform a hierarchical model fit with multiple starting values — multistart","text":"purpose method check certain algorithm fitting nonlinear hierarchical models (also known nonlinear mixed-effects models) reliably yield results sufficiently similar , started certain range reasonable starting parameters. inspired article practical identifiabiliy frame nonlinear mixed-effects models Duchesne et al (2021).","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/multistart.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Perform a hierarchical model fit with multiple starting values — multistart","text":"","code":"multistart(   object,   n = 50,   cores = if (Sys.info()[\"sysname\"] == \"Windows\") 1 else parallel::detectCores(),   cluster = NULL,   ... )  # S3 method for class 'saem.mmkin' multistart(object, n = 50, cores = 1, cluster = NULL, ...)  # S3 method for class 'multistart' print(x, ...)  best(object, ...)  # Default S3 method best(object, ...)  which.best(object, ...)  # Default S3 method which.best(object, ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/multistart.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Perform a hierarchical model fit with multiple starting values — multistart","text":"object fit object work n many different combinations starting parameters used? cores many fits run parallel (posix platforms)? cluster cluster returned parallel::makeCluster used parallel execution. ... Passed update function. x multistart object print","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/multistart.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Perform a hierarchical model fit with multiple starting values — multistart","text":"list saem.mmkin objects, class attributes 'multistart.saem.mmkin' 'multistart'. object highest likelihood index object highest likelihood","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/multistart.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Perform a hierarchical model fit with multiple starting values — multistart","text":"Duchesne R, Guillemin , Gandrillon O, Crauste F. Practical identifiability frame nonlinear mixed effects models: example vitro erythropoiesis. BMC Bioinformatics. 2021 Oct 4;22(1):478. doi: 10.1186/s12859-021-04373-4.","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/reference/multistart.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Perform a hierarchical model fit with multiple starting values — multistart","text":"","code":"# \\dontrun{ library(mkin) dmta_ds <- lapply(1:7, function(i) {   ds_i <- dimethenamid_2018$ds[[i]]$data   ds_i[ds_i$name == \"DMTAP\", \"name\"] <-  \"DMTA\"   ds_i$time <- ds_i$time * dimethenamid_2018$f_time_norm[i]   ds_i }) names(dmta_ds) <- sapply(dimethenamid_2018$ds, function(ds) ds$title) dmta_ds[[\"Elliot\"]] <- rbind(dmta_ds[[\"Elliot 1\"]], dmta_ds[[\"Elliot 2\"]]) dmta_ds[[\"Elliot 1\"]] <- dmta_ds[[\"Elliot 2\"]] <- NULL  f_mmkin <- mmkin(\"DFOP\", dmta_ds, error_model = \"tc\", cores = 7, quiet = TRUE) f_saem_full <- saem(f_mmkin) f_saem_full_multi <- multistart(f_saem_full, n = 16, cores = 16) parplot(f_saem_full_multi, lpos = \"topleft\", las = 2)  illparms(f_saem_full) #> [1] \"sd(log_k2)\"  f_saem_reduced <- update(f_saem_full, no_random_effect = \"log_k2\") illparms(f_saem_reduced) # On Windows, we need to create a PSOCK cluster first and refer to it # in the call to multistart() library(parallel) cl <- makePSOCKcluster(12) f_saem_reduced_multi <- multistart(f_saem_reduced, n = 16, cluster = cl) parplot(f_saem_reduced_multi, lpos = \"topright\", ylim = c(0.5, 2), las = 2)  stopCluster(cl) # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/nafta.html","id":null,"dir":"Reference","previous_headings":"","what":"Evaluate parent kinetics using the NAFTA guidance — nafta","title":"Evaluate parent kinetics using the NAFTA guidance — nafta","text":"function fits SFO, IORE DFOP models using mmkin returns object class nafta methods printing plotting. Print nafta objects. results three models printed order increasing model complexity, .e. SFO, IORE, finally DFOP.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/nafta.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Evaluate parent kinetics using the NAFTA guidance — nafta","text":"","code":"nafta(ds, title = NA, quiet = FALSE, ...)  # S3 method for class 'nafta' print(x, quiet = TRUE, digits = 3, ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/nafta.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Evaluate parent kinetics using the NAFTA guidance — nafta","text":"NAFTA (2011) Guidance evaluating calculating degradation kinetics environmental media. NAFTA Technical Working Group Pesticides https://www.epa.gov/pesticide-science--assessing-pesticide-risks/guidance-evaluating--calculating-degradation accessed 2019-02-22 US EPA (2015) Standard Operating Procedure Using NAFTA Guidance Calculate Representative Half-life Values Characterizing Pesticide Degradation https://www.epa.gov/pesticide-science--assessing-pesticide-risks/standard-operating-procedure-using-nafta-guidance","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/nafta.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Evaluate parent kinetics using the NAFTA guidance — nafta","text":"ds dataframe must contain one variable called \"time\" time values specified time argument, one column called \"name\" grouping observed values, finally one column observed values called \"value\". title Optional title dataset quiet evaluation text shown? ... arguments passed mmkin (printing method). x nafta object. digits Number digits used printing parameters dissipation times.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/nafta.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Evaluate parent kinetics using the NAFTA guidance — nafta","text":"list class nafta. list element named \"mmkin\" mmkin object containing fits three models. list element named \"title\" contains title dataset used. list element \"data\" contains dataset used fits.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/nafta.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Evaluate parent kinetics using the NAFTA guidance — nafta","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/nafta.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Evaluate parent kinetics using the NAFTA guidance — nafta","text":"","code":"nafta_evaluation <- nafta(NAFTA_SOP_Appendix_D, cores = 1) #> The SFO model is rejected as S_SFO is equal or higher than the critical value S_c #> The representative half-life of the IORE model is longer than the one corresponding #> to the terminal degradation rate found with the DFOP model. #> The representative half-life obtained from the DFOP model may be used   print(nafta_evaluation) #> Sums of squares: #>       SFO      IORE      DFOP  #> 1378.6832  615.7730  517.8836  #>  #> Critical sum of squares for checking the SFO model: #> [1] 717.4598 #>  #> Parameters: #> $SFO #>          Estimate   Pr(>t)    Lower   Upper #> parent_0  83.7558 1.80e-14 77.18268 90.3288 #> k_parent   0.0017 7.43e-05  0.00112  0.0026 #> sigma      8.7518 1.22e-05  5.64278 11.8608 #>  #> $IORE #>                Estimate Pr(>t)    Lower    Upper #> parent_0       9.69e+01     NA 8.88e+01 1.05e+02 #> k__iore_parent 8.40e-14     NA 1.79e-18 3.94e-09 #> N_parent       6.68e+00     NA 4.19e+00 9.17e+00 #> sigma          5.85e+00     NA 3.76e+00 7.94e+00 #>  #> $DFOP #>          Estimate   Pr(>t)    Lower    Upper #> parent_0 9.76e+01 1.94e-13 9.02e+01 1.05e+02 #> k1       4.24e-02 5.92e-03 2.03e-02 8.88e-02 #> k2       8.24e-04 6.48e-03 3.89e-04 1.75e-03 #> g        2.88e-01 2.47e-05 1.95e-01 4.03e-01 #> sigma    5.36e+00 2.22e-05 3.43e+00 7.30e+00 #>  #>  #> DTx values: #>      DT50    DT90 DT50_rep #> SFO   407    1350      407 #> IORE  541 5190000  1560000 #> DFOP  429    2380      841 #>  #> Representative half-life: #> [1] 841.41   plot(nafta_evaluation)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/nlme.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper functions to create nlme models from mmkin row objects — nlme_function","title":"Helper functions to create nlme models from mmkin row objects — nlme_function","text":"functions facilitate setting nonlinear mixed effects model mmkin row object. mmkin row object essentially list mkinfit objects obtained fitting model list datasets. used internally nlme.mmkin() method.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/nlme.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper functions to create nlme models from mmkin row objects — nlme_function","text":"","code":"nlme_function(object)  nlme_data(object)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/nlme.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper functions to create nlme models from mmkin row objects — nlme_function","text":"object mmkin row object containing several fits model different datasets","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/nlme.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper functions to create nlme models from mmkin row objects — nlme_function","text":"function can used nlme nlme::groupedData object","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/reference/nlme.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper functions to create nlme models from mmkin row objects — nlme_function","text":"","code":"sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120) m_SFO <- mkinmod(parent = mkinsub(\"SFO\")) d_SFO_1 <- mkinpredict(m_SFO,   c(k_parent = 0.1),   c(parent = 98), sampling_times) d_SFO_1_long <- mkin_wide_to_long(d_SFO_1, time = \"time\") d_SFO_2 <- mkinpredict(m_SFO,   c(k_parent = 0.05),   c(parent = 102), sampling_times) d_SFO_2_long <- mkin_wide_to_long(d_SFO_2, time = \"time\") d_SFO_3 <- mkinpredict(m_SFO,   c(k_parent = 0.02),   c(parent = 103), sampling_times) d_SFO_3_long <- mkin_wide_to_long(d_SFO_3, time = \"time\")  d1 <- add_err(d_SFO_1, function(value) 3, n = 1) d2 <- add_err(d_SFO_2, function(value) 2, n = 1) d3 <- add_err(d_SFO_3, function(value) 4, n = 1) ds <- c(d1 = d1, d2 = d2, d3 = d3)  f <- mmkin(\"SFO\", ds, cores = 1, quiet = TRUE) mean_dp <- mean_degparms(f) grouped_data <- nlme_data(f) nlme_f <- nlme_function(f) # These assignments are necessary for these objects to be # visible to nlme and augPred when evaluation is done by # pkgdown to generate the html docs. assign(\"nlme_f\", nlme_f, globalenv()) assign(\"grouped_data\", grouped_data, globalenv())  library(nlme) m_nlme <- nlme(value ~ nlme_f(name, time, parent_0, log_k_parent_sink),   data = grouped_data,   fixed = parent_0 + log_k_parent_sink ~ 1,   random = pdDiag(parent_0 + log_k_parent_sink ~ 1),   start = mean_dp) summary(m_nlme) #> Nonlinear mixed-effects model fit by maximum likelihood #>   Model: value ~ nlme_f(name, time, parent_0, log_k_parent_sink)  #>   Data: grouped_data  #>        AIC      BIC    logLik #>   266.6428 275.8935 -128.3214 #>  #> Random effects: #>  Formula: list(parent_0 ~ 1, log_k_parent_sink ~ 1) #>  Level: ds #>  Structure: Diagonal #>             parent_0 log_k_parent_sink Residual #> StdDev: 0.0003775775         0.7058039 3.065183 #>  #> Fixed effects:  parent_0 + log_k_parent_sink ~ 1  #>                       Value Std.Error DF   t-value p-value #> parent_0          101.18323 0.7900461 43 128.07257       0 #> log_k_parent_sink  -3.08708 0.4171755 43  -7.39995       0 #>  Correlation:  #>                   prnt_0 #> log_k_parent_sink 0.031  #>  #> Standardized Within-Group Residuals: #>         Min          Q1         Med          Q3         Max  #> -2.38427070 -0.52059848  0.03593021  0.39987268  2.73188969  #>  #> Number of Observations: 47 #> Number of Groups: 3  plot(augPred(m_nlme, level = 0:1), layout = c(3, 1))  # augPred does not work on fits with more than one state # variable # # The procedure is greatly simplified by the nlme.mmkin function f_nlme <- nlme(f) plot(f_nlme)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/nlme.mmkin.html","id":null,"dir":"Reference","previous_headings":"","what":"Create an nlme model for an mmkin row object — nlme.mmkin","title":"Create an nlme model for an mmkin row object — nlme.mmkin","text":"functions sets nonlinear mixed effects model mmkin row object. mmkin row object essentially list mkinfit objects obtained fitting model list datasets.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/nlme.mmkin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create an nlme model for an mmkin row object — nlme.mmkin","text":"","code":"# S3 method for class 'mmkin' nlme(   model,   data = \"auto\",   fixed = lapply(as.list(names(mean_degparms(model))), function(el) eval(parse(text =     paste(el, 1, sep = \"~\")))),   random = pdDiag(fixed),   groups,   start = mean_degparms(model, random = TRUE, test_log_parms = TRUE),   correlation = NULL,   weights = NULL,   subset,   method = c(\"ML\", \"REML\"),   na.action = na.fail,   naPattern,   control = list(),   verbose = FALSE )  # S3 method for class 'nlme.mmkin' print(x, digits = max(3, getOption(\"digits\") - 3), ...)  # S3 method for class 'nlme.mmkin' update(object, ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/nlme.mmkin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create an nlme model for an mmkin row object — nlme.mmkin","text":"model mmkin row object. data Ignored, data taken mmkin model fixed Ignored, degradation parameters fitted mmkin model used fixed parameters random specified, correlations random effects set optimised degradation model parameters. achieved using nlme::pdDiag method. groups See documentation nlme start specified, mean values fitted degradation parameters taken mmkin object used correlation See documentation nlme weights passed nlme subset passed nlme method passed nlme na.action passed nlme naPattern passed nlme control passed nlme verbose passed nlme x nlme.mmkin object print digits Number digits use printing ... Update specifications passed update.nlme object nlme.mmkin object update","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/nlme.mmkin.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create an nlme model for an mmkin row object — nlme.mmkin","text":"Upon success, fitted 'nlme.mmkin' object, nlme object additional elements. also inherits 'mixed.mmkin'.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/nlme.mmkin.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create an nlme model for an mmkin row object — nlme.mmkin","text":"Note convergence nlme algorithms depends quality data. degradation kinetics, often datasets (e.g. data soils) complicated degradation models, may make impossible obtain convergence nlme.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/nlme.mmkin.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Create an nlme model for an mmkin row object — nlme.mmkin","text":"object inherits nlme::nlme, wealth methods automatically work 'nlme.mmkin' objects, nlme::intervals(), nlme::anova.lme() nlme::coef.lme().","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/reference/nlme.mmkin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create an nlme model for an mmkin row object — nlme.mmkin","text":"","code":"ds <- lapply(experimental_data_for_UBA_2019[6:10],  function(x) subset(x$data[c(\"name\", \"time\", \"value\")], name == \"parent\"))  # \\dontrun{   f <- mmkin(c(\"SFO\", \"DFOP\"), ds, quiet = TRUE, cores = 1)   library(nlme)   f_nlme_sfo <- nlme(f[\"SFO\", ])   f_nlme_dfop <- nlme(f[\"DFOP\", ])   anova(f_nlme_sfo, f_nlme_dfop) #>             Model df      AIC      BIC    logLik   Test  L.Ratio p-value #> f_nlme_sfo      1  5 625.0539 637.5529 -307.5269                         #> f_nlme_dfop     2  9 495.1270 517.6253 -238.5635 1 vs 2 137.9269  <.0001   print(f_nlme_dfop) #> Kinetic nonlinear mixed-effects model fit by maximum likelihood #>  #> Structural model: #> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * #>            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) #>            * parent #>  #> Data: #> 90 observations of 1 variable(s) grouped in 5 datasets #>  #> Log-likelihood: -238.6 #>  #> Fixed effects: #>  list(parent_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1)  #> parent_0   log_k1   log_k2 g_qlogis  #>  94.1702  -1.8002  -4.1474   0.0324  #>  #> Random effects: #>  Formula: list(parent_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1) #>  Level: ds #>  Structure: Diagonal #>         parent_0 log_k1 log_k2 g_qlogis Residual #> StdDev:    2.488 0.8447   1.33   0.4652    2.321 #>    plot(f_nlme_dfop)    endpoints(f_nlme_dfop) #> $distimes #>            DT50     DT90 DT50back  DT50_k1  DT50_k2 #> parent 10.79857 100.7937 30.34192 4.193937 43.85442 #>     ds_2 <- lapply(experimental_data_for_UBA_2019[6:10],    function(x) x$data[c(\"name\", \"time\", \"value\")])   m_sfo_sfo <- mkinmod(parent = mkinsub(\"SFO\", \"A1\"),     A1 = mkinsub(\"SFO\"), use_of_ff = \"min\", quiet = TRUE)   m_sfo_sfo_ff <- mkinmod(parent = mkinsub(\"SFO\", \"A1\"),     A1 = mkinsub(\"SFO\"), use_of_ff = \"max\", quiet = TRUE)   m_dfop_sfo <- mkinmod(parent = mkinsub(\"DFOP\", \"A1\"),     A1 = mkinsub(\"SFO\"), quiet = TRUE)    f_2 <- mmkin(list(\"SFO-SFO\" = m_sfo_sfo,    \"SFO-SFO-ff\" = m_sfo_sfo_ff,    \"DFOP-SFO\" = m_dfop_sfo),     ds_2, quiet = TRUE)    f_nlme_sfo_sfo <- nlme(f_2[\"SFO-SFO\", ])   plot(f_nlme_sfo_sfo)     # With formation fractions this does not coverge with defaults   # f_nlme_sfo_sfo_ff <- nlme(f_2[\"SFO-SFO-ff\", ])   #plot(f_nlme_sfo_sfo_ff)    # For the following, we need to increase pnlsMaxIter and the tolerance   # to get convergence   f_nlme_dfop_sfo <- nlme(f_2[\"DFOP-SFO\", ],     control = list(pnlsMaxIter = 120, tolerance = 5e-4))    plot(f_nlme_dfop_sfo)     anova(f_nlme_dfop_sfo, f_nlme_sfo_sfo) #>                 Model df       AIC      BIC    logLik   Test  L.Ratio p-value #> f_nlme_dfop_sfo     1 13  843.8547  884.620 -408.9273                         #> f_nlme_sfo_sfo      2  9 1085.1821 1113.404 -533.5910 1 vs 2 249.3274  <.0001    endpoints(f_nlme_sfo_sfo) #> $ff #> parent_sink   parent_A1     A1_sink  #>   0.5912432   0.4087568   1.0000000  #>  #> $distimes #>            DT50     DT90 #> parent 19.13518  63.5657 #> A1     66.02155 219.3189 #>    endpoints(f_nlme_dfop_sfo) #> $ff #>   parent_A1 parent_sink  #>   0.2768574   0.7231426  #>  #> $distimes #>             DT50     DT90 DT50back  DT50_k1  DT50_k2 #> parent  11.07091 104.6320 31.49737 4.462383 46.20825 #> A1     162.30550 539.1672       NA       NA       NA #>     if (length(findFunction(\"varConstProp\")) > 0) { # tc error model for nlme available     # Attempts to fit metabolite kinetics with the tc error model are possible,     # but need tweeking of control values and sometimes do not converge      f_tc <- mmkin(c(\"SFO\", \"DFOP\"), ds, quiet = TRUE, error_model = \"tc\")     f_nlme_sfo_tc <- nlme(f_tc[\"SFO\", ])     f_nlme_dfop_tc <- nlme(f_tc[\"DFOP\", ])     AIC(f_nlme_sfo, f_nlme_sfo_tc, f_nlme_dfop, f_nlme_dfop_tc)     print(f_nlme_dfop_tc)   } #> Kinetic nonlinear mixed-effects model fit by maximum likelihood #>  #> Structural model: #> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * #>            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) #>            * parent #>  #> Data: #> 90 observations of 1 variable(s) grouped in 5 datasets #>  #> Log-likelihood: -238.4 #>  #> Fixed effects: #>  list(parent_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1)  #> parent_0   log_k1   log_k2 g_qlogis  #> 94.04774 -1.82340 -4.16716  0.05685  #>  #> Random effects: #>  Formula: list(parent_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1) #>  Level: ds #>  Structure: Diagonal #>         parent_0 log_k1 log_k2 g_qlogis Residual #> StdDev:    2.474   0.85  1.337   0.4659        1 #>  #> Variance function: #>  Structure: Constant plus proportion of variance covariate #>  Formula: ~fitted(.)  #>  Parameter estimates: #>      const       prop  #> 2.23222933 0.01262399     f_2_obs <- update(f_2, error_model = \"obs\")   f_nlme_sfo_sfo_obs <- nlme(f_2_obs[\"SFO-SFO\", ])   print(f_nlme_sfo_sfo_obs) #> Kinetic nonlinear mixed-effects model fit by maximum likelihood #>  #> Structural model: #> d_parent/dt = - k_parent_sink * parent - k_parent_A1 * parent #> d_A1/dt = + k_parent_A1 * parent - k_A1_sink * A1 #>  #> Data: #> 170 observations of 2 variable(s) grouped in 5 datasets #>  #> Log-likelihood: -473 #>  #> Fixed effects: #>  list(parent_0 ~ 1, log_k_parent_sink ~ 1, log_k_parent_A1 ~ 1,      log_k_A1_sink ~ 1)  #>          parent_0 log_k_parent_sink   log_k_parent_A1     log_k_A1_sink  #>            87.976            -3.670            -4.164            -4.645  #>  #> Random effects: #>  Formula: list(parent_0 ~ 1, log_k_parent_sink ~ 1, log_k_parent_A1 ~ 1,      log_k_A1_sink ~ 1) #>  Level: ds #>  Structure: Diagonal #>         parent_0 log_k_parent_sink log_k_parent_A1 log_k_A1_sink Residual #> StdDev:    3.992             1.777           1.055        0.4821    6.483 #>  #> Variance function: #>  Structure: Different standard deviations per stratum #>  Formula: ~1 | name  #>  Parameter estimates: #>    parent        A1  #> 1.0000000 0.2050005    f_nlme_dfop_sfo_obs <- nlme(f_2_obs[\"DFOP-SFO\", ],     control = list(pnlsMaxIter = 120, tolerance = 5e-4))    f_2_tc <- update(f_2, error_model = \"tc\")   # f_nlme_sfo_sfo_tc <- nlme(f_2_tc[\"SFO-SFO\", ]) # No convergence with 50 iterations   # f_nlme_dfop_sfo_tc <- nlme(f_2_tc[\"DFOP-SFO\", ],   #  control = list(pnlsMaxIter = 120, tolerance = 5e-4)) # Error in X[, fmap[[nm]]] <- gradnm    anova(f_nlme_dfop_sfo, f_nlme_dfop_sfo_obs) #>                     Model df      AIC     BIC    logLik   Test  L.Ratio p-value #> f_nlme_dfop_sfo         1 13 843.8547 884.620 -408.9273                         #> f_nlme_dfop_sfo_obs     2 14 817.5338 861.435 -394.7669 1 vs 2 28.32084  <.0001  # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/nobs.mkinfit.html","id":null,"dir":"Reference","previous_headings":"","what":"Number of observations on which an mkinfit object was fitted — nobs.mkinfit","title":"Number of observations on which an mkinfit object was fitted — nobs.mkinfit","text":"Number observations mkinfit object fitted","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/nobs.mkinfit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Number of observations on which an mkinfit object was fitted — nobs.mkinfit","text":"","code":"# S3 method for class 'mkinfit' nobs(object, ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/nobs.mkinfit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Number of observations on which an mkinfit object was fitted — nobs.mkinfit","text":"object mkinfit object ... compatibility generic method","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/nobs.mkinfit.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Number of observations on which an mkinfit object was fitted — nobs.mkinfit","text":"number rows data included mkinfit object","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/parms.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract model parameters — parms","title":"Extract model parameters — parms","text":"function returns degradation model parameters well error model parameters per default, order avoid working fitted model without considering error structure assumed fit.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/parms.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract model parameters — parms","text":"","code":"parms(object, ...)  # S3 method for class 'mkinfit' parms(object, transformed = FALSE, errparms = TRUE, ...)  # S3 method for class 'mmkin' parms(object, transformed = FALSE, errparms = TRUE, ...)  # S3 method for class 'multistart' parms(object, exclude_failed = TRUE, ...)  # S3 method for class 'saem.mmkin' parms(object, ci = FALSE, covariates = NULL, ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/parms.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract model parameters — parms","text":"object fitted model object. ... used transformed parameters returned used internally optimisation? errparms error model parameters returned addition degradation parameters? exclude_failed multistart objects, rows failed fits removed returned parameter matrix? ci matrix estimates confidence interval boundaries returned? FALSE (default), vector estimates returned covariates given, otherwise matrix estimates returned, column corresponding row data frame holding covariates covariates data frame holding covariate values return parameter values. effect 'ci' FALSE.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/parms.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract model parameters — parms","text":"Depending object, numeric vector fitted model parameters, matrix (e.g. mmkin row objects), list matrices (e.g. mmkin objects one row).","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/reference/parms.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract model parameters — parms","text":"","code":"# mkinfit objects fit <- mkinfit(\"SFO\", FOCUS_2006_C, quiet = TRUE) parms(fit) #>   parent_0   k_parent      sigma  #> 82.4921598  0.3060633  4.6730124  parms(fit, transformed = TRUE) #>     parent_0 log_k_parent        sigma  #>    82.492160    -1.183963     4.673012   # mmkin objects ds <- lapply(experimental_data_for_UBA_2019[6:10],  function(x) subset(x$data[c(\"name\", \"time\", \"value\")])) names(ds) <- paste(\"Dataset\", 6:10) # \\dontrun{ fits <- mmkin(c(\"SFO\", \"FOMC\", \"DFOP\"), ds, quiet = TRUE, cores = 1) parms(fits[\"SFO\", ]) #>            Dataset 6    Dataset 7  Dataset 8  Dataset 9  Dataset 10 #> parent_0 88.52275400 82.666781678 86.8547308 91.7779306 82.14809450 #> k_parent  0.05794659  0.009647805  0.2102974  0.1232258  0.00720421 #> sigma     5.15274487  7.040168584  3.6769645  6.4669234  6.50457673 parms(fits[, 2]) #> $SFO #>             Dataset 7 #> parent_0 82.666781678 #> k_parent  0.009647805 #> sigma     7.040168584 #>  #> $FOMC #>           Dataset 7 #> parent_0 92.6837649 #> alpha     0.4967832 #> beta     14.1451255 #> sigma     1.9167519 #>  #> $DFOP #>             Dataset 7 #> parent_0 91.058971584 #> k1        0.044946770 #> k2        0.002868336 #> g         0.526942414 #> sigma     2.221302196 #>  parms(fits) #> $SFO #>            Dataset 6    Dataset 7  Dataset 8  Dataset 9  Dataset 10 #> parent_0 88.52275400 82.666781678 86.8547308 91.7779306 82.14809450 #> k_parent  0.05794659  0.009647805  0.2102974  0.1232258  0.00720421 #> sigma     5.15274487  7.040168584  3.6769645  6.4669234  6.50457673 #>  #> $FOMC #>          Dataset 6  Dataset 7 Dataset 8 Dataset 9 Dataset 10 #> parent_0 95.558575 92.6837649 90.719787 98.383939 94.8481458 #> alpha     1.338667  0.4967832  1.639099  1.074460  0.2805272 #> beta     13.033315 14.1451255  5.007077  4.397126  6.9052224 #> sigma     1.847671  1.9167519  1.066063  3.146056  1.6222778 #>  #> $DFOP #>            Dataset 6    Dataset 7   Dataset 8   Dataset 9   Dataset 10 #> parent_0 96.55213663 91.058971584 90.34509493 98.14858820 94.311323735 #> k1        0.21954588  0.044946770  0.41232288  0.31697588  0.080663857 #> k2        0.02957934  0.002868336  0.07581766  0.03260384  0.003425417 #> g         0.44845068  0.526942414  0.66091967  0.65322767  0.342652880 #> sigma     1.35690468  2.221302196  1.34169076  2.87159846  1.942067831 #>  parms(fits, transformed = TRUE) #> $SFO #>              Dataset 6 Dataset 7 Dataset 8 Dataset 9 Dataset 10 #> parent_0     88.522754 82.666782 86.854731 91.777931  82.148095 #> log_k_parent -2.848234 -4.641025 -1.559232 -2.093737  -4.933090 #> sigma         5.152745  7.040169  3.676964  6.466923   6.504577 #>  #> $FOMC #>            Dataset 6  Dataset 7  Dataset 8   Dataset 9 Dataset 10 #> parent_0  95.5585751 92.6837649 90.7197870 98.38393898  94.848146 #> log_alpha  0.2916741 -0.6996015  0.4941466  0.07181816  -1.271085 #> log_beta   2.5675088  2.6493701  1.6108523  1.48095106   1.932278 #> sigma      1.8476712  1.9167519  1.0660627  3.14605557   1.622278 #>  #> $DFOP #>           Dataset 6  Dataset 7  Dataset 8  Dataset 9 Dataset 10 #> parent_0 96.5521366 91.0589716 90.3450949 98.1485882 94.3113237 #> log_k1   -1.5161940 -3.1022764 -0.8859486 -1.1489296 -2.5174647 #> log_k2   -3.5206791 -5.8540232 -2.5794240 -3.4233253 -5.6765322 #> g_qlogis -0.2069326  0.1078741  0.6673953  0.6332573 -0.6514943 #> sigma     1.3569047  2.2213022  1.3416908  2.8715985  1.9420678 #>  # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/parplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot parameter variability of multistart objects — parplot","title":"Plot parameter variability of multistart objects — parplot","text":"Produces boxplot parameters multiple runs, scaled either parameters run highest likelihood, medians proposed paper Duchesne et al. (2021).","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/parplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot parameter variability of multistart objects — parplot","text":"","code":"parplot(object, ...)  # S3 method for class 'multistart.saem.mmkin' parplot(   object,   llmin = -Inf,   llquant = NA,   scale = c(\"best\", \"median\"),   lpos = \"bottomleft\",   main = \"\",   ... )"},{"path":"https://pkgdown.jrwb.de/mkin/reference/parplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot parameter variability of multistart objects — parplot","text":"object multistart object ... Passed boxplot llmin minimum likelihood objects shown llquant Fractional value selecting fits higher likelihoods. Overrides 'llmin'. scale default, scale parameters using best available fit. 'median', parameters scaled using median parameters fits. lpos Positioning legend. main Title plot","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/parplot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plot parameter variability of multistart objects — parplot","text":"Starting values degradation model parameters error model parameters shown green circles. results obtained original run shown red circles.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/parplot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Plot parameter variability of multistart objects — parplot","text":"Duchesne R, Guillemin , Gandrillon O, Crauste F. Practical identifiability frame nonlinear mixed effects models: example vitro erythropoiesis. BMC Bioinformatics. 2021 Oct 4;22(1):478. doi: 10.1186/s12859-021-04373-4.","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.mixed.mmkin.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot predictions from a fitted nonlinear mixed model obtained via an mmkin row object — plot.mixed.mmkin","title":"Plot predictions from a fitted nonlinear mixed model obtained via an mmkin row object — plot.mixed.mmkin","text":"Plot predictions fitted nonlinear mixed model obtained via mmkin row object","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.mixed.mmkin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot predictions from a fitted nonlinear mixed model obtained via an mmkin row object — plot.mixed.mmkin","text":"","code":"# S3 method for class 'mixed.mmkin' plot(   x,   i = 1:ncol(x$mmkin),   obs_vars = names(x$mkinmod$map),   standardized = TRUE,   covariates = NULL,   covariate_quantiles = c(0.5, 0.05, 0.95),   xlab = \"Time\",   xlim = range(x$data$time),   resplot = c(\"predicted\", \"time\"),   pop_curves = \"auto\",   pred_over = NULL,   test_log_parms = FALSE,   conf.level = 0.6,   default_log_parms = NA,   ymax = \"auto\",   maxabs = \"auto\",   ncol.legend = ifelse(length(i) <= 3, length(i) + 1, ifelse(length(i) <= 8, 3, 4)),   nrow.legend = ceiling((length(i) + 1)/ncol.legend),   rel.height.legend = 0.02 + 0.07 * nrow.legend,   rel.height.bottom = 1.1,   pch_ds = c(1:25, 33, 35:38, 40:41, 47:57, 60:90)[1:length(i)],   col_ds = pch_ds + 1,   lty_ds = col_ds,   frame = TRUE,   ... )"},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.mixed.mmkin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot predictions from a fitted nonlinear mixed model obtained via an mmkin row object — plot.mixed.mmkin","text":"x object class mixed.mmkin, saem.mmkin nlme.mmkin numeric index select datasets plot individual predictions, case plots get large obs_vars character vector names observed variables data model plotted. Defauls observed variables model. standardized residuals standardized? takes effect resplot = \"time\". covariates Data frame covariate values variables covariate models object. given, overrides 'covariate_quantiles'. line data frame result line drawn population. Rownames used legend label lines. covariate_quantiles argument effect fitted object covariate models. , default show three population curves, 5th percentile, 50th percentile 95th percentile covariate values used fitting model. xlab Label x axis. xlim Plot range x direction. resplot residuals plotted time predicted values? pop_curves Per default, one population curve drawn case population parameters fitted model, e.g. saem objects. case covariate model, behaviour depends value 'covariates' pred_over Named list alternative predictions obtained mkinpredict compatible mkinmod. test_log_parms Passed mean_degparms case mixed.mmkin object conf.level Passed mean_degparms case mixed.mmkin object default_log_parms Passed mean_degparms case mixed.mmkin object ymax Vector maximum y axis values maxabs Maximum absolute value residuals. used scaling y axis defaults \"auto\". ncol.legend Number columns use legend nrow.legend Number rows use legend rel.height.legend relative height legend shown top rel.height.bottom relative height bottom plot row pch_ds Symbols used plotting data. col_ds Colors used plotting observed data corresponding model prediction lines different datasets. lty_ds Line types used model predictions. frame frame drawn around plots? ... arguments passed plot.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.mixed.mmkin.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot predictions from a fitted nonlinear mixed model obtained via an mmkin row object — plot.mixed.mmkin","text":"function called side effect.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.mixed.mmkin.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Plot predictions from a fitted nonlinear mixed model obtained via an mmkin row object — plot.mixed.mmkin","text":"Covariate models currently supported saem.mmkin objects.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.mixed.mmkin.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot predictions from a fitted nonlinear mixed model obtained via an mmkin row object — plot.mixed.mmkin","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.mixed.mmkin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot predictions from a fitted nonlinear mixed model obtained via an mmkin row object — plot.mixed.mmkin","text":"","code":"ds <- lapply(experimental_data_for_UBA_2019[6:10],  function(x) x$data[c(\"name\", \"time\", \"value\")]) names(ds) <- paste0(\"ds \", 6:10) dfop_sfo <- mkinmod(parent = mkinsub(\"DFOP\", \"A1\"),   A1 = mkinsub(\"SFO\"), quiet = TRUE) # \\dontrun{ f <- mmkin(list(\"DFOP-SFO\" = dfop_sfo), ds, quiet = TRUE) plot(f[, 3:4], standardized = TRUE)   # For this fit we need to increase pnlsMaxiter, and we increase the # tolerance in order to speed up the fit for this example evaluation # It still takes 20 seconds to run f_nlme <- nlme(f, control = list(pnlsMaxIter = 120, tolerance = 1e-3)) plot(f_nlme)   f_saem <- saem(f, transformations = \"saemix\") plot(f_saem)   f_obs <- mmkin(list(\"DFOP-SFO\" = dfop_sfo), ds, quiet = TRUE, error_model = \"obs\") f_nlmix <- nlmix(f_obs) #> Error in nlmix(f_obs): could not find function \"nlmix\" plot(f_nlmix) #> Error: object 'f_nlmix' not found  # We can overlay the two variants if we generate predictions pred_nlme <- mkinpredict(dfop_sfo,   f_nlme$bparms.optim[-1],   c(parent = f_nlme$bparms.optim[[1]], A1 = 0),   seq(0, 180, by = 0.2)) plot(f_saem, pred_over = list(nlme = pred_nlme))  # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.mkinfit.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot the observed data and the fitted model of an mkinfit object — plot.mkinfit","title":"Plot the observed data and the fitted model of an mkinfit object — plot.mkinfit","text":"Solves differential equations optimised fixed parameters previous successful call mkinfit plots observed data together solution fitted model.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.mkinfit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot the observed data and the fitted model of an mkinfit object — plot.mkinfit","text":"","code":"# S3 method for class 'mkinfit' plot(   x,   fit = x,   obs_vars = names(fit$mkinmod$map),   xlab = \"Time\",   ylab = \"Residue\",   xlim = range(fit$data$time),   ylim = \"default\",   col_obs = 1:length(obs_vars),   pch_obs = col_obs,   lty_obs = rep(1, length(obs_vars)),   add = FALSE,   legend = !add,   show_residuals = FALSE,   show_errplot = FALSE,   maxabs = \"auto\",   sep_obs = FALSE,   rel.height.middle = 0.9,   row_layout = FALSE,   lpos = \"topright\",   inset = c(0.05, 0.05),   show_errmin = FALSE,   errmin_digits = 3,   frame = TRUE,   ... )  plot_sep(   fit,   show_errmin = TRUE,   show_residuals = ifelse(identical(fit$err_mod, \"const\"), TRUE, \"standardized\"),   ... )  plot_res(   fit,   sep_obs = FALSE,   show_errmin = sep_obs,   standardized = ifelse(identical(fit$err_mod, \"const\"), FALSE, TRUE),   ... )  plot_err(fit, sep_obs = FALSE, show_errmin = sep_obs, ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.mkinfit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot the observed data and the fitted model of an mkinfit object — plot.mkinfit","text":"x Alias fit introduced compatibility generic S3 method. fit object class mkinfit. obs_vars character vector names observed variables data model plotted. Defauls observed variables model. xlab Label x axis. ylab Label y axis. xlim Plot range x direction. ylim Plot range y direction. given list, plot ranges different plot rows can given row layout. col_obs Colors used plotting observed data corresponding model prediction lines. pch_obs Symbols used plotting data. lty_obs Line types used model predictions. add plot added existing plot? legend legend added plot? show_residuals residuals shown? one plot fits shown, residual plot lower third plot. Otherwise, .e. \"sep_obs\" given, residual plots located right plots fitted curves. set 'standardized', plot residuals divided standard deviation given fitted error model shown. show_errplot squared residuals error model shown? one plot fits shown, plot lower third plot.  Otherwise, .e. \"sep_obs\" given, residual plots located right plots fitted curves. maxabs Maximum absolute value residuals. used scaling y axis defaults \"auto\". sep_obs observed variables shown separate subplots? yes, residual plots requested \"show_residuals\" shown next , plot fits. rel.height.middle relative height middle plot, two rows plots shown. row_layout use row layout residual plot error model plot shown right? lpos Position(s) legend(s). Passed legend first argument.  length one, length obs_var argument. inset Passed legend applicable. show_errmin FOCUS chi2 error value shown upper margin plot? errmin_digits number significant digits rounding FOCUS chi2 error percentage. frame frame drawn around plots? ... arguments passed plot. standardized calling 'plot_res', residuals standardized residual plot?","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.mkinfit.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot the observed data and the fitted model of an mkinfit object — plot.mkinfit","text":"function called side effect.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.mkinfit.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plot the observed data and the fitted model of an mkinfit object — plot.mkinfit","text":"current plot device tikz device, latex used formatting chi2 error level, show_errmin = TRUE.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.mkinfit.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot the observed data and the fitted model of an mkinfit object — plot.mkinfit","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.mkinfit.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot the observed data and the fitted model of an mkinfit object — plot.mkinfit","text":"","code":"# One parent compound, one metabolite, both single first order, path from # parent to sink included # \\dontrun{ SFO_SFO <- mkinmod(parent = mkinsub(\"SFO\", \"m1\", full = \"Parent\"),                    m1 = mkinsub(\"SFO\", full = \"Metabolite M1\" )) #> Temporary DLL for differentials generated and loaded fit <- mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE) #> Warning: Observations with value of zero were removed from the data fit <- mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE, error_model = \"tc\") #> Warning: Observations with value of zero were removed from the data plot(fit)  plot_res(fit)  plot_res(fit, standardized = FALSE)  plot_err(fit)   # Show the observed variables separately, with residuals plot(fit, sep_obs = TRUE, show_residuals = TRUE, lpos = c(\"topright\", \"bottomright\"),      show_errmin = TRUE)   # The same can be obtained with less typing, using the convenience function plot_sep plot_sep(fit, lpos = c(\"topright\", \"bottomright\"))   # Show the observed variables separately, with the error model plot(fit, sep_obs = TRUE, show_errplot = TRUE, lpos = c(\"topright\", \"bottomright\"),      show_errmin = TRUE)  # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.mmkin.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot model fits (observed and fitted) and the residuals for a row or column of an mmkin object — plot.mmkin","title":"Plot model fits (observed and fitted) and the residuals for a row or column of an mmkin object — plot.mmkin","text":"x row selected mmkin object ([.mmkin), model fitted least one dataset shown. column, fit least one model dataset shown.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.mmkin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot model fits (observed and fitted) and the residuals for a row or column of an mmkin object — plot.mmkin","text":"","code":"# S3 method for class 'mmkin' plot(   x,   main = \"auto\",   legends = 1,   resplot = c(\"time\", \"errmod\"),   ylab = \"Residue\",   standardized = FALSE,   show_errmin = TRUE,   errmin_var = \"All data\",   errmin_digits = 3,   cex = 0.7,   rel.height.middle = 0.9,   ymax = \"auto\",   ... )"},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.mmkin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot model fits (observed and fitted) and the residuals for a row or column of an mmkin object — plot.mmkin","text":"x object class mmkin, either one row one column. main main title placed outer margin plot. legends index fits legends shown. resplot residuals plotted time, using mkinresplot, squared residuals predicted values, error model, using mkinerrplot. ylab Label y axis. standardized residuals standardized? option passed mkinresplot, takes effect resplot = \"time\". show_errmin chi2 error level shown top plots left? errmin_var variable FOCUS chi2 error value shown. errmin_digits number significant digits rounding FOCUS chi2 error percentage. cex Passed plot functions mtext. rel.height.middle relative height middle plot, two rows plots shown. ymax Maximum y axis value plot.mkinfit. ... arguments passed plot.mkinfit mkinresplot.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.mmkin.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot model fits (observed and fitted) and the residuals for a row or column of an mmkin object — plot.mmkin","text":"function called side effect.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.mmkin.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plot model fits (observed and fitted) and the residuals for a row or column of an mmkin object — plot.mmkin","text":"current plot device tikz device, latex used formatting chi2 error level.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.mmkin.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot model fits (observed and fitted) and the residuals for a row or column of an mmkin object — plot.mmkin","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.mmkin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot model fits (observed and fitted) and the residuals for a row or column of an mmkin object — plot.mmkin","text":"","code":"# \\dontrun{   # Only use one core not to offend CRAN checks   fits <- mmkin(c(\"FOMC\", \"HS\"),                 list(\"FOCUS B\" = FOCUS_2006_B, \"FOCUS C\" = FOCUS_2006_C), # named list for titles                 cores = 1, quiet = TRUE, error_model = \"tc\") #> Warning: Optimisation did not converge: #> iteration limit reached without convergence (10)   plot(fits[, \"FOCUS C\"])    plot(fits[\"FOMC\", ])    plot(fits[\"FOMC\", ], show_errmin = FALSE)     # We can also plot a single fit, if we like the way plot.mmkin works, but then the plot   # height should be smaller than the plot width (this is not possible for the html pages   # generated by pkgdown, as far as I know).   plot(fits[\"FOMC\", \"FOCUS C\"]) # same as plot(fits[1, 2])     # Show the error models   plot(fits[\"FOMC\", ], resplot = \"errmod\")    # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.nafta.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot the results of the three models used in the NAFTA scheme. — plot.nafta","title":"Plot the results of the three models used in the NAFTA scheme. — plot.nafta","text":"plots ordered increasing complexity model function (SFO, IORE, DFOP).","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.nafta.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot the results of the three models used in the NAFTA scheme. — plot.nafta","text":"","code":"# S3 method for class 'nafta' plot(x, legend = FALSE, main = \"auto\", ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.nafta.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot the results of the three models used in the NAFTA scheme. — plot.nafta","text":"x object class nafta. legend legend added? main Possibility override main title plot. ... arguments passed plot.mmkin.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.nafta.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot the results of the three models used in the NAFTA scheme. — plot.nafta","text":"function called side effect.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.nafta.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plot the results of the three models used in the NAFTA scheme. — plot.nafta","text":"Calls plot.mmkin.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/plot.nafta.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot the results of the three models used in the NAFTA scheme. — plot.nafta","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/read_spreadsheet.html","id":null,"dir":"Reference","previous_headings":"","what":"Read datasets and relevant meta information from a spreadsheet file — read_spreadsheet","title":"Read datasets and relevant meta information from a spreadsheet file — read_spreadsheet","text":"function imports one dataset sheet spreadsheet file. sheets selected based contents sheet 'Datasets', column called 'Dataset Number', containing numbers identifying dataset sheets read . second column must grouping variable, often named 'Soil'. Optionally, time normalization factors can given columns named 'Temperature' 'Moisture'.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/read_spreadsheet.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Read datasets and relevant meta information from a spreadsheet file — read_spreadsheet","text":"","code":"read_spreadsheet(   path,   valid_datasets = \"all\",   parent_only = FALSE,   normalize = TRUE )"},{"path":"https://pkgdown.jrwb.de/mkin/reference/read_spreadsheet.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Read datasets and relevant meta information from a spreadsheet file — read_spreadsheet","text":"path Absolute relative path spreadsheet file valid_datasets Optional numeric index valid datasets, default use datasets parent_only parent data used? normalize time scale normalized using temperature moisture normalisation factors sheet 'Datasets'?","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/read_spreadsheet.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Read datasets and relevant meta information from a spreadsheet file — read_spreadsheet","text":"must sheet 'Compounds', columns 'Name' 'Acronym'. first row read header read sheet assumed contain name acronym parent compound. dataset sheets named using dataset numbers read 'Datasets' sheet, .e. '1', '2', ... . dataset sheet, name observed variable (e.g. acronym parent compound one transformation products) first column, time values second colum, observed value third column. case relevant covariate data available, given sheet 'Covariates', containing one line value grouping variable specified 'Datasets'. values first column column must name second column 'Datasets'. Covariates read columns four higher. names preferably contain special characters like spaces, can easily used specifying covariate models. similar data structure defined R6 class mkindsg, probably complicated use.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/reexports.html","id":null,"dir":"Reference","previous_headings":"","what":"Objects exported from other packages — reexports","title":"Objects exported from other packages — reexports","text":"objects imported packages. Follow links see documentation. lmtest lrtest nlme intervals, nlme","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/residuals.mkinfit.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract residuals from an mkinfit model — residuals.mkinfit","title":"Extract residuals from an mkinfit model — residuals.mkinfit","text":"Extract residuals mkinfit model","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/residuals.mkinfit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract residuals from an mkinfit model — residuals.mkinfit","text":"","code":"# S3 method for class 'mkinfit' residuals(object, standardized = FALSE, ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/residuals.mkinfit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract residuals from an mkinfit model — residuals.mkinfit","text":"object mkinfit object standardized residuals standardized dividing standard deviation obtained fitted error model? ... used","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/residuals.mkinfit.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract residuals from an mkinfit model — residuals.mkinfit","text":"","code":"f <- mkinfit(\"DFOP\", FOCUS_2006_C, quiet = TRUE) residuals(f) #> [1]  0.09726374 -0.13912142 -0.15351210  0.73388322 -0.08657004 -0.93204702 #> [7] -0.03269080  1.45347823 -0.88423697 residuals(f, standardized = TRUE) #> [1]  0.13969917 -0.19981904 -0.22048826  1.05407091 -0.12433989 -1.33869208 #> [7] -0.04695355  2.08761977 -1.27002287"},{"path":"https://pkgdown.jrwb.de/mkin/reference/saem.html","id":null,"dir":"Reference","previous_headings":"","what":"Fit nonlinear mixed models with SAEM — saem","title":"Fit nonlinear mixed models with SAEM — saem","text":"function uses saemix::saemix() backend fitting nonlinear mixed effects models created mmkin row objects using Stochastic Approximation Expectation Maximisation algorithm (SAEM).","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/saem.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fit nonlinear mixed models with SAEM — saem","text":"","code":"saem(object, ...)  # S3 method for class 'mmkin' saem(   object,   transformations = c(\"mkin\", \"saemix\"),   error_model = \"auto\",   degparms_start = numeric(),   test_log_parms = TRUE,   conf.level = 0.6,   solution_type = \"auto\",   covariance.model = \"auto\",   omega.init = \"auto\",   covariates = NULL,   covariate_models = NULL,   no_random_effect = NULL,   error.init = c(1, 1),   nbiter.saemix = c(300, 100),   control = list(displayProgress = FALSE, print = FALSE, nbiter.saemix = nbiter.saemix,     save = FALSE, save.graphs = FALSE),   verbose = FALSE,   quiet = FALSE,   ... )  # S3 method for class 'saem.mmkin' print(x, digits = max(3, getOption(\"digits\") - 3), ...)  saemix_model(   object,   solution_type = \"auto\",   transformations = c(\"mkin\", \"saemix\"),   error_model = \"auto\",   degparms_start = numeric(),   covariance.model = \"auto\",   no_random_effect = NULL,   omega.init = \"auto\",   covariates = NULL,   covariate_models = NULL,   error.init = numeric(),   test_log_parms = FALSE,   conf.level = 0.6,   verbose = FALSE,   ... )  saemix_data(object, covariates = NULL, verbose = FALSE, ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/saem.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fit nonlinear mixed models with SAEM — saem","text":"object mmkin row object containing several fits mkinmod model different datasets ... parameters passed saemix::saemixModel. transformations Per default, parameter transformations done mkin. argument set 'saemix', parameter transformations done 'saemix' supported cases, .e. (version 1.1.2) SFO, FOMC, DFOP HS without fixing parent_0, SFO DFOP one SFO metabolite. error_model Possibility override error model used mmkin object degparms_start Parameter values given named numeric vector used override starting values obtained 'mmkin' object. test_log_parms TRUE, attempt made use robust starting values population parameters fitted log parameters mkin (like rate constants) considering rate constants pass t-test calculating mean degradation parameters using mean_degparms. conf.level Possibility adjust required confidence level parameter tested requested 'test_log_parms'. solution_type Possibility specify solution type case automatic choice desired covariance.model passed saemix::saemixModel(). Per default, uncorrelated random effects specified degradation parameters. omega.init passed saemix::saemixModel(). using mkin transformations default covariance model optionally excluded random effects, variances degradation parameters estimated using mean_degparms, testing untransformed log parameters significant difference zero. using mkin transformations custom covariance model, default initialisation saemix::saemixModel used omega.init. covariates data frame covariate data use 'covariate_models', dataset names row names. covariate_models list containing linear model formulas one explanatory variable, .e. type 'parameter ~ covariate'. Covariates must available 'covariates' data frame. no_random_effect Character vector degradation parameters variability groups. used covariance model explicitly specified. error.init passed saemix::saemixModel(). nbiter.saemix Convenience option increase number iterations control Passed saemix::saemix. verbose print information created objects type saemix::SaemixModel saemix::SaemixData? quiet suppress messages saemix prints beginning end optimisation process? x saem.mmkin object print digits Number digits use printing","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/saem.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fit nonlinear mixed models with SAEM — saem","text":"S3 object class 'saem.mmkin', containing fitted saemix::SaemixObject list component named ''. object also inherits 'mixed.mmkin'. saemix::SaemixModel object. saemix::SaemixData object.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/saem.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Fit nonlinear mixed models with SAEM — saem","text":"mmkin row object essentially list mkinfit objects obtained fitting model list datasets using mkinfit. Starting values fixed effects (population mean parameters, argument psi0 saemix::saemixModel() mean values parameters found using mmkin.","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/reference/saem.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fit nonlinear mixed models with SAEM — saem","text":"","code":"# \\dontrun{ ds <- lapply(experimental_data_for_UBA_2019[6:10],  function(x) subset(x$data[c(\"name\", \"time\", \"value\")])) names(ds) <- paste(\"Dataset\", 6:10) f_mmkin_parent_p0_fixed <- mmkin(\"FOMC\", ds,   state.ini = c(parent = 100), fixed_initials = \"parent\", quiet = TRUE) f_saem_p0_fixed <- saem(f_mmkin_parent_p0_fixed)  f_mmkin_parent <- mmkin(c(\"SFO\", \"FOMC\", \"DFOP\"), ds, quiet = TRUE) f_saem_sfo <- saem(f_mmkin_parent[\"SFO\", ]) f_saem_fomc <- saem(f_mmkin_parent[\"FOMC\", ]) f_saem_dfop <- saem(f_mmkin_parent[\"DFOP\", ]) anova(f_saem_sfo, f_saem_fomc, f_saem_dfop) #> Data: 90 observations of 1 variable(s) grouped in 5 datasets #>  #>             npar    AIC    BIC     Lik #> f_saem_sfo     5 624.33 622.38 -307.17 #> f_saem_fomc    7 467.85 465.11 -226.92 #> f_saem_dfop    9 493.76 490.24 -237.88 anova(f_saem_sfo, f_saem_dfop, test = TRUE) #> Data: 90 observations of 1 variable(s) grouped in 5 datasets #>  #>             npar    AIC    BIC     Lik  Chisq Df Pr(>Chisq)     #> f_saem_sfo     5 624.33 622.38 -307.17                          #> f_saem_dfop    9 493.76 490.24 -237.88 138.57  4  < 2.2e-16 *** #> --- #> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 illparms(f_saem_dfop) #> [1] \"sd(g_qlogis)\" f_saem_dfop_red <- update(f_saem_dfop, no_random_effect = \"g_qlogis\") anova(f_saem_dfop, f_saem_dfop_red, test = TRUE) #> Data: 90 observations of 1 variable(s) grouped in 5 datasets #>  #>                 npar    AIC    BIC     Lik Chisq Df Pr(>Chisq) #> f_saem_dfop_red    8 488.68 485.55 -236.34                     #> f_saem_dfop        9 493.76 490.24 -237.88     0  1          1  anova(f_saem_sfo, f_saem_fomc, f_saem_dfop) #> Data: 90 observations of 1 variable(s) grouped in 5 datasets #>  #>             npar    AIC    BIC     Lik #> f_saem_sfo     5 624.33 622.38 -307.17 #> f_saem_fomc    7 467.85 465.11 -226.92 #> f_saem_dfop    9 493.76 490.24 -237.88 # The returned saem.mmkin object contains an SaemixObject, therefore we can use # functions from saemix library(saemix) #> Loading required package: npde #> Package saemix, version 3.3, March 2024 #>   please direct bugs, questions and feedback to emmanuelle.comets@inserm.fr #>  #> Attaching package: ‘saemix’ #> The following objects are masked from ‘package:npde’: #>  #>     kurtosis, skewness compare.saemix(f_saem_sfo$so, f_saem_fomc$so, f_saem_dfop$so) #> Likelihoods calculated by importance sampling #>        AIC      BIC #> 1 624.3316 622.3788 #> 2 467.8472 465.1132 #> 3 493.7592 490.2441 plot(f_saem_fomc$so, plot.type = \"convergence\")  plot(f_saem_fomc$so, plot.type = \"individual.fit\") #> Simulating data using nsim = 1000 simulated datasets #> Computing WRES and npde .  plot(f_saem_fomc$so, plot.type = \"npde\") #> Simulating data using nsim = 1000 simulated datasets #> Computing WRES and npde . #> Please use npdeSaemix to obtain VPC and npde plot(f_saem_fomc$so, plot.type = \"vpc\")   f_mmkin_parent_tc <- update(f_mmkin_parent, error_model = \"tc\") f_saem_fomc_tc <- saem(f_mmkin_parent_tc[\"FOMC\", ]) anova(f_saem_fomc, f_saem_fomc_tc, test = TRUE) #> Data: 90 observations of 1 variable(s) grouped in 5 datasets #>  #>                npar    AIC    BIC     Lik Chisq Df Pr(>Chisq) #> f_saem_fomc       7 467.85 465.11 -226.92                     #> f_saem_fomc_tc    8 469.90 466.77 -226.95     0  1          1  sfo_sfo <- mkinmod(parent = mkinsub(\"SFO\", \"A1\"),   A1 = mkinsub(\"SFO\")) #> Temporary DLL for differentials generated and loaded fomc_sfo <- mkinmod(parent = mkinsub(\"FOMC\", \"A1\"),   A1 = mkinsub(\"SFO\")) #> Temporary DLL for differentials generated and loaded dfop_sfo <- mkinmod(parent = mkinsub(\"DFOP\", \"A1\"),   A1 = mkinsub(\"SFO\")) #> Temporary DLL for differentials generated and loaded # The following fit uses analytical solutions for SFO-SFO and DFOP-SFO, # and compiled ODEs for FOMC that are much slower f_mmkin <- mmkin(list(     \"SFO-SFO\" = sfo_sfo, \"FOMC-SFO\" = fomc_sfo, \"DFOP-SFO\" = dfop_sfo),   ds, quiet = TRUE) # saem fits of SFO-SFO and DFOP-SFO to these data take about five seconds # each on this system, as we use analytical solutions written for saemix. # When using the analytical solutions written for mkin this took around # four minutes f_saem_sfo_sfo <- saem(f_mmkin[\"SFO-SFO\", ]) f_saem_dfop_sfo <- saem(f_mmkin[\"DFOP-SFO\", ]) # We can use print, plot and summary methods to check the results print(f_saem_dfop_sfo) #> Kinetic nonlinear mixed-effects model fit by SAEM #> Structural model: #> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * #>            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) #>            * parent #> d_A1/dt = + f_parent_to_A1 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g) #>            * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * #>            exp(-k2 * time))) * parent - k_A1 * A1 #>  #> Data: #> 170 observations of 2 variable(s) grouped in 5 datasets #>  #> Likelihood computed by importance sampling #>     AIC   BIC logLik #>   839.2 834.1 -406.6 #>  #> Fitted parameters: #>                    estimate    lower   upper #> parent_0           93.70402 91.04104 96.3670 #> log_k_A1           -5.83760 -7.66452 -4.0107 #> f_parent_qlogis    -0.95718 -1.35955 -0.5548 #> log_k1             -2.35514 -3.39402 -1.3163 #> log_k2             -3.79634 -5.64009 -1.9526 #> g_qlogis           -0.02108 -0.66463  0.6225 #> a.1                 1.88191  1.66491  2.0989 #> SD.parent_0         2.81628  0.78922  4.8433 #> SD.log_k_A1         1.78751  0.42105  3.1540 #> SD.f_parent_qlogis  0.45016  0.16116  0.7391 #> SD.log_k1           1.06923  0.31676  1.8217 #> SD.log_k2           2.03768  0.70938  3.3660 #> SD.g_qlogis         0.44024 -0.09262  0.9731 plot(f_saem_dfop_sfo)  summary(f_saem_dfop_sfo, data = TRUE) #> saemix version used for fitting:      3.3  #> mkin version used for pre-fitting:  1.2.9  #> R version used for fitting:         4.4.2  #> Date of fit:     Thu Feb 13 14:59:07 2025  #> Date of summary: Thu Feb 13 14:59:07 2025  #>  #> Equations: #> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * #>            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) #>            * parent #> d_A1/dt = + f_parent_to_A1 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g) #>            * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * #>            exp(-k2 * time))) * parent - k_A1 * A1 #>  #> Data: #> 170 observations of 2 variable(s) grouped in 5 datasets #>  #> Model predictions using solution type analytical  #>  #> Fitted in 3.96 s #> Using 300, 100 iterations and 10 chains #>  #> Variance model: Constant variance  #>  #> Starting values for degradation parameters: #>        parent_0        log_k_A1 f_parent_qlogis          log_k1          log_k2  #>         93.8102         -5.3734         -0.9711         -1.8799         -4.2708  #>        g_qlogis  #>          0.1356  #>  #> Fixed degradation parameter values: #> None #>  #> Starting values for random effects (square root of initial entries in omega): #>                 parent_0 log_k_A1 f_parent_qlogis log_k1 log_k2 g_qlogis #> parent_0           4.941    0.000          0.0000  0.000  0.000   0.0000 #> log_k_A1           0.000    2.551          0.0000  0.000  0.000   0.0000 #> f_parent_qlogis    0.000    0.000          0.7251  0.000  0.000   0.0000 #> log_k1             0.000    0.000          0.0000  1.449  0.000   0.0000 #> log_k2             0.000    0.000          0.0000  0.000  2.228   0.0000 #> g_qlogis           0.000    0.000          0.0000  0.000  0.000   0.7814 #>  #> Starting values for error model parameters: #> a.1  #>   1  #>  #> Results: #>  #> Likelihood computed by importance sampling #>     AIC   BIC logLik #>   839.2 834.1 -406.6 #>  #> Optimised parameters: #>                        est.    lower   upper #> parent_0           93.70402 91.04104 96.3670 #> log_k_A1           -5.83760 -7.66452 -4.0107 #> f_parent_qlogis    -0.95718 -1.35955 -0.5548 #> log_k1             -2.35514 -3.39402 -1.3163 #> log_k2             -3.79634 -5.64009 -1.9526 #> g_qlogis           -0.02108 -0.66463  0.6225 #> a.1                 1.88191  1.66491  2.0989 #> SD.parent_0         2.81628  0.78922  4.8433 #> SD.log_k_A1         1.78751  0.42105  3.1540 #> SD.f_parent_qlogis  0.45016  0.16116  0.7391 #> SD.log_k1           1.06923  0.31676  1.8217 #> SD.log_k2           2.03768  0.70938  3.3660 #> SD.g_qlogis         0.44024 -0.09262  0.9731 #>  #> Correlation:  #>                 parnt_0 lg_k_A1 f_prnt_ log_k1  log_k2  #> log_k_A1        -0.0147                                 #> f_parent_qlogis -0.0269  0.0573                         #> log_k1           0.0263 -0.0011 -0.0040                 #> log_k2           0.0020  0.0065 -0.0002 -0.0776         #> g_qlogis        -0.0248 -0.0180 -0.0004 -0.0903 -0.0603 #>  #> Random effects: #>                      est.    lower  upper #> SD.parent_0        2.8163  0.78922 4.8433 #> SD.log_k_A1        1.7875  0.42105 3.1540 #> SD.f_parent_qlogis 0.4502  0.16116 0.7391 #> SD.log_k1          1.0692  0.31676 1.8217 #> SD.log_k2          2.0377  0.70938 3.3660 #> SD.g_qlogis        0.4402 -0.09262 0.9731 #>  #> Variance model: #>      est. lower upper #> a.1 1.882 1.665 2.099 #>  #> Backtransformed parameters: #>                     est.     lower    upper #> parent_0       93.704015 9.104e+01 96.36699 #> k_A1            0.002916 4.692e-04  0.01812 #> f_parent_to_A1  0.277443 2.043e-01  0.36475 #> k1              0.094880 3.357e-02  0.26813 #> k2              0.022453 3.553e-03  0.14191 #> g               0.494731 3.397e-01  0.65078 #>  #> Resulting formation fractions: #>                 ff #> parent_A1   0.2774 #> parent_sink 0.7226 #>  #> Estimated disappearance times: #>         DT50   DT90 DT50back DT50_k1 DT50_k2 #> parent  14.0  72.38    21.79   7.306   30.87 #> A1     237.7 789.68       NA      NA      NA #>  #> Data: #>          ds   name time observed predicted residual   std standardized #>   Dataset 6 parent    0     97.2  95.70025  1.49975 1.882      0.79693 #>   Dataset 6 parent    0     96.4  95.70025  0.69975 1.882      0.37183 #>   Dataset 6 parent    3     71.1  71.44670 -0.34670 1.882     -0.18423 #>   Dataset 6 parent    3     69.2  71.44670 -2.24670 1.882     -1.19384 #>   Dataset 6 parent    6     58.1  56.59283  1.50717 1.882      0.80087 #>   Dataset 6 parent    6     56.6  56.59283  0.00717 1.882      0.00381 #>   Dataset 6 parent   10     44.4  44.56648 -0.16648 1.882     -0.08847 #>   Dataset 6 parent   10     43.4  44.56648 -1.16648 1.882     -0.61984 #>   Dataset 6 parent   20     33.3  29.76020  3.53980 1.882      1.88096 #>   Dataset 6 parent   20     29.2  29.76020 -0.56020 1.882     -0.29767 #>   Dataset 6 parent   34     17.6  19.39208 -1.79208 1.882     -0.95226 #>   Dataset 6 parent   34     18.0  19.39208 -1.39208 1.882     -0.73971 #>   Dataset 6 parent   55     10.5  10.55761 -0.05761 1.882     -0.03061 #>   Dataset 6 parent   55      9.3  10.55761 -1.25761 1.882     -0.66826 #>   Dataset 6 parent   90      4.5   3.84742  0.65258 1.882      0.34676 #>   Dataset 6 parent   90      4.7   3.84742  0.85258 1.882      0.45304 #>   Dataset 6 parent  112      3.0   2.03997  0.96003 1.882      0.51013 #>   Dataset 6 parent  112      3.4   2.03997  1.36003 1.882      0.72268 #>   Dataset 6 parent  132      2.3   1.14585  1.15415 1.882      0.61328 #>   Dataset 6 parent  132      2.7   1.14585  1.55415 1.882      0.82583 #>   Dataset 6     A1    3      4.3   4.86054 -0.56054 1.882     -0.29786 #>   Dataset 6     A1    3      4.6   4.86054 -0.26054 1.882     -0.13844 #>   Dataset 6     A1    6      7.0   7.74179 -0.74179 1.882     -0.39417 #>   Dataset 6     A1    6      7.2   7.74179 -0.54179 1.882     -0.28789 #>   Dataset 6     A1   10      8.2   9.94048 -1.74048 1.882     -0.92485 #>   Dataset 6     A1   10      8.0   9.94048 -1.94048 1.882     -1.03112 #>   Dataset 6     A1   20     11.0  12.19109 -1.19109 1.882     -0.63291 #>   Dataset 6     A1   20     13.7  12.19109  1.50891 1.882      0.80180 #>   Dataset 6     A1   34     11.5  13.10706 -1.60706 1.882     -0.85395 #>   Dataset 6     A1   34     12.7  13.10706 -0.40706 1.882     -0.21630 #>   Dataset 6     A1   55     14.9  13.06131  1.83869 1.882      0.97703 #>   Dataset 6     A1   55     14.5  13.06131  1.43869 1.882      0.76448 #>   Dataset 6     A1   90     12.1  11.54495  0.55505 1.882      0.29494 #>   Dataset 6     A1   90     12.3  11.54495  0.75505 1.882      0.40122 #>   Dataset 6     A1  112      9.9  10.31533 -0.41533 1.882     -0.22070 #>   Dataset 6     A1  112     10.2  10.31533 -0.11533 1.882     -0.06128 #>   Dataset 6     A1  132      8.8   9.20222 -0.40222 1.882     -0.21373 #>   Dataset 6     A1  132      7.8   9.20222 -1.40222 1.882     -0.74510 #>   Dataset 7 parent    0     93.6  90.82357  2.77643 1.882      1.47532 #>   Dataset 7 parent    0     92.3  90.82357  1.47643 1.882      0.78453 #>   Dataset 7 parent    3     87.0  84.73448  2.26552 1.882      1.20384 #>   Dataset 7 parent    3     82.2  84.73448 -2.53448 1.882     -1.34675 #>   Dataset 7 parent    7     74.0  77.65013 -3.65013 1.882     -1.93958 #>   Dataset 7 parent    7     73.9  77.65013 -3.75013 1.882     -1.99272 #>   Dataset 7 parent   14     64.2  67.60639 -3.40639 1.882     -1.81007 #>   Dataset 7 parent   14     69.5  67.60639  1.89361 1.882      1.00621 #>   Dataset 7 parent   30     54.0  52.53663  1.46337 1.882      0.77760 #>   Dataset 7 parent   30     54.6  52.53663  2.06337 1.882      1.09642 #>   Dataset 7 parent   60     41.1  39.42728  1.67272 1.882      0.88884 #>   Dataset 7 parent   60     38.4  39.42728 -1.02728 1.882     -0.54587 #>   Dataset 7 parent   90     32.5  33.76360 -1.26360 1.882     -0.67144 #>   Dataset 7 parent   90     35.5  33.76360  1.73640 1.882      0.92268 #>   Dataset 7 parent  120     28.1  30.39975 -2.29975 1.882     -1.22203 #>   Dataset 7 parent  120     29.0  30.39975 -1.39975 1.882     -0.74379 #>   Dataset 7 parent  180     26.5  25.62379  0.87621 1.882      0.46559 #>   Dataset 7 parent  180     27.6  25.62379  1.97621 1.882      1.05010 #>   Dataset 7     A1    3      3.9   2.70005  1.19995 1.882      0.63762 #>   Dataset 7     A1    3      3.1   2.70005  0.39995 1.882      0.21252 #>   Dataset 7     A1    7      6.9   5.83475  1.06525 1.882      0.56605 #>   Dataset 7     A1    7      6.6   5.83475  0.76525 1.882      0.40663 #>   Dataset 7     A1   14     10.4  10.26142  0.13858 1.882      0.07364 #>   Dataset 7     A1   14      8.3  10.26142 -1.96142 1.882     -1.04225 #>   Dataset 7     A1   30     14.4  16.82999 -2.42999 1.882     -1.29123 #>   Dataset 7     A1   30     13.7  16.82999 -3.12999 1.882     -1.66319 #>   Dataset 7     A1   60     22.1  22.32486 -0.22486 1.882     -0.11949 #>   Dataset 7     A1   60     22.3  22.32486 -0.02486 1.882     -0.01321 #>   Dataset 7     A1   90     27.5  24.45927  3.04073 1.882      1.61576 #>   Dataset 7     A1   90     25.4  24.45927  0.94073 1.882      0.49988 #>   Dataset 7     A1  120     28.0  25.54862  2.45138 1.882      1.30260 #>   Dataset 7     A1  120     26.6  25.54862  1.05138 1.882      0.55868 #>   Dataset 7     A1  180     25.8  26.82277 -1.02277 1.882     -0.54347 #>   Dataset 7     A1  180     25.3  26.82277 -1.52277 1.882     -0.80916 #>   Dataset 8 parent    0     91.9  91.16791  0.73209 1.882      0.38901 #>   Dataset 8 parent    0     90.8  91.16791 -0.36791 1.882     -0.19550 #>   Dataset 8 parent    1     64.9  67.58358 -2.68358 1.882     -1.42598 #>   Dataset 8 parent    1     66.2  67.58358 -1.38358 1.882     -0.73520 #>   Dataset 8 parent    3     43.5  41.62086  1.87914 1.882      0.99853 #>   Dataset 8 parent    3     44.1  41.62086  2.47914 1.882      1.31735 #>   Dataset 8 parent    8     18.3  19.60116 -1.30116 1.882     -0.69140 #>   Dataset 8 parent    8     18.1  19.60116 -1.50116 1.882     -0.79768 #>   Dataset 8 parent   14     10.2  10.63101 -0.43101 1.882     -0.22903 #>   Dataset 8 parent   14     10.8  10.63101  0.16899 1.882      0.08980 #>   Dataset 8 parent   27      4.9   3.12435  1.77565 1.882      0.94354 #>   Dataset 8 parent   27      3.3   3.12435  0.17565 1.882      0.09334 #>   Dataset 8 parent   48      1.6   0.43578  1.16422 1.882      0.61864 #>   Dataset 8 parent   48      1.5   0.43578  1.06422 1.882      0.56550 #>   Dataset 8 parent   70      1.1   0.05534  1.04466 1.882      0.55510 #>   Dataset 8 parent   70      0.9   0.05534  0.84466 1.882      0.44883 #>   Dataset 8     A1    1      9.6   7.63450  1.96550 1.882      1.04442 #>   Dataset 8     A1    1      7.7   7.63450  0.06550 1.882      0.03481 #>   Dataset 8     A1    3     15.0  15.52593 -0.52593 1.882     -0.27947 #>   Dataset 8     A1    3     15.1  15.52593 -0.42593 1.882     -0.22633 #>   Dataset 8     A1    8     21.2  20.32192  0.87808 1.882      0.46659 #>   Dataset 8     A1    8     21.1  20.32192  0.77808 1.882      0.41345 #>   Dataset 8     A1   14     19.7  20.09721 -0.39721 1.882     -0.21107 #>   Dataset 8     A1   14     18.9  20.09721 -1.19721 1.882     -0.63617 #>   Dataset 8     A1   27     17.5  16.37477  1.12523 1.882      0.59792 #>   Dataset 8     A1   27     15.9  16.37477 -0.47477 1.882     -0.25228 #>   Dataset 8     A1   48      9.5  10.13141 -0.63141 1.882     -0.33551 #>   Dataset 8     A1   48      9.8  10.13141 -0.33141 1.882     -0.17610 #>   Dataset 8     A1   70      6.2   5.81827  0.38173 1.882      0.20284 #>   Dataset 8     A1   70      6.1   5.81827  0.28173 1.882      0.14970 #>   Dataset 9 parent    0     99.8  97.48728  2.31272 1.882      1.22892 #>   Dataset 9 parent    0     98.3  97.48728  0.81272 1.882      0.43186 #>   Dataset 9 parent    1     77.1  79.29476 -2.19476 1.882     -1.16624 #>   Dataset 9 parent    1     77.2  79.29476 -2.09476 1.882     -1.11310 #>   Dataset 9 parent    3     59.0  55.67060  3.32940 1.882      1.76915 #>   Dataset 9 parent    3     58.1  55.67060  2.42940 1.882      1.29092 #>   Dataset 9 parent    8     27.4  31.57871 -4.17871 1.882     -2.22046 #>   Dataset 9 parent    8     29.2  31.57871 -2.37871 1.882     -1.26398 #>   Dataset 9 parent   14     19.1  22.51546 -3.41546 1.882     -1.81489 #>   Dataset 9 parent   14     29.6  22.51546  7.08454 1.882      3.76454 #>   Dataset 9 parent   27     10.1  14.09074 -3.99074 1.882     -2.12057 #>   Dataset 9 parent   27     18.2  14.09074  4.10926 1.882      2.18355 #>   Dataset 9 parent   48      4.5   6.95747 -2.45747 1.882     -1.30584 #>   Dataset 9 parent   48      9.1   6.95747  2.14253 1.882      1.13848 #>   Dataset 9 parent   70      2.3   3.32472 -1.02472 1.882     -0.54451 #>   Dataset 9 parent   70      2.9   3.32472 -0.42472 1.882     -0.22569 #>   Dataset 9 parent   91      2.0   1.64300  0.35700 1.882      0.18970 #>   Dataset 9 parent   91      1.8   1.64300  0.15700 1.882      0.08343 #>   Dataset 9 parent  120      2.0   0.62073  1.37927 1.882      0.73291 #>   Dataset 9 parent  120      2.2   0.62073  1.57927 1.882      0.83918 #>   Dataset 9     A1    1      4.2   3.64568  0.55432 1.882      0.29455 #>   Dataset 9     A1    1      3.9   3.64568  0.25432 1.882      0.13514 #>   Dataset 9     A1    3      7.4   8.30173 -0.90173 1.882     -0.47916 #>   Dataset 9     A1    3      7.9   8.30173 -0.40173 1.882     -0.21347 #>   Dataset 9     A1    8     14.5  12.71589  1.78411 1.882      0.94803 #>   Dataset 9     A1    8     13.7  12.71589  0.98411 1.882      0.52293 #>   Dataset 9     A1   14     14.2  13.90452  0.29548 1.882      0.15701 #>   Dataset 9     A1   14     12.2  13.90452 -1.70452 1.882     -0.90574 #>   Dataset 9     A1   27     13.7  14.15523 -0.45523 1.882     -0.24190 #>   Dataset 9     A1   27     13.2  14.15523 -0.95523 1.882     -0.50759 #>   Dataset 9     A1   48     13.6  13.31038  0.28962 1.882      0.15389 #>   Dataset 9     A1   48     15.4  13.31038  2.08962 1.882      1.11037 #>   Dataset 9     A1   70     10.4  11.85965 -1.45965 1.882     -0.77562 #>   Dataset 9     A1   70     11.6  11.85965 -0.25965 1.882     -0.13797 #>   Dataset 9     A1   91     10.0  10.36294 -0.36294 1.882     -0.19286 #>   Dataset 9     A1   91      9.5  10.36294 -0.86294 1.882     -0.45855 #>   Dataset 9     A1  120      9.1   8.43003  0.66997 1.882      0.35601 #>   Dataset 9     A1  120      9.0   8.43003  0.56997 1.882      0.30287 #>  Dataset 10 parent    0     96.1  93.95603  2.14397 1.882      1.13925 #>  Dataset 10 parent    0     94.3  93.95603  0.34397 1.882      0.18278 #>  Dataset 10 parent    8     73.9  77.70592 -3.80592 1.882     -2.02237 #>  Dataset 10 parent    8     73.9  77.70592 -3.80592 1.882     -2.02237 #>  Dataset 10 parent   14     69.4  70.04570 -0.64570 1.882     -0.34311 #>  Dataset 10 parent   14     73.1  70.04570  3.05430 1.882      1.62298 #>  Dataset 10 parent   21     65.6  64.01710  1.58290 1.882      0.84111 #>  Dataset 10 parent   21     65.3  64.01710  1.28290 1.882      0.68170 #>  Dataset 10 parent   41     55.9  54.98434  0.91566 1.882      0.48656 #>  Dataset 10 parent   41     54.4  54.98434 -0.58434 1.882     -0.31050 #>  Dataset 10 parent   63     47.0  49.87137 -2.87137 1.882     -1.52577 #>  Dataset 10 parent   63     49.3  49.87137 -0.57137 1.882     -0.30361 #>  Dataset 10 parent   91     44.7  45.06727 -0.36727 1.882     -0.19516 #>  Dataset 10 parent   91     46.7  45.06727  1.63273 1.882      0.86759 #>  Dataset 10 parent  120     42.1  40.76402  1.33598 1.882      0.70991 #>  Dataset 10 parent  120     41.3  40.76402  0.53598 1.882      0.28481 #>  Dataset 10     A1    8      3.3   4.14599 -0.84599 1.882     -0.44954 #>  Dataset 10     A1    8      3.4   4.14599 -0.74599 1.882     -0.39640 #>  Dataset 10     A1   14      3.9   6.08478 -2.18478 1.882     -1.16093 #>  Dataset 10     A1   14      2.9   6.08478 -3.18478 1.882     -1.69231 #>  Dataset 10     A1   21      6.4   7.59411 -1.19411 1.882     -0.63452 #>  Dataset 10     A1   21      7.2   7.59411 -0.39411 1.882     -0.20942 #>  Dataset 10     A1   41      9.1   9.78292 -0.68292 1.882     -0.36289 #>  Dataset 10     A1   41      8.5   9.78292 -1.28292 1.882     -0.68171 #>  Dataset 10     A1   63     11.7  10.93274  0.76726 1.882      0.40770 #>  Dataset 10     A1   63     12.0  10.93274  1.06726 1.882      0.56711 #>  Dataset 10     A1   91     13.3  11.93986  1.36014 1.882      0.72274 #>  Dataset 10     A1   91     13.2  11.93986  1.26014 1.882      0.66961 #>  Dataset 10     A1  120     14.3  12.79238  1.50762 1.882      0.80111 #>  Dataset 10     A1  120     12.1  12.79238 -0.69238 1.882     -0.36791  # The following takes about 6 minutes f_saem_dfop_sfo_deSolve <- saem(f_mmkin[\"DFOP-SFO\", ], solution_type = \"deSolve\",   nbiter.saemix = c(200, 80)) #> DINTDY-  T (=R1) illegal       #> In above message, R1 = 70 #>   #>       T not in interval TCUR - HU (= R1) to TCUR (=R2)       #> In above message, R1 = 53.1042, R2 = 56.6326 #>   #> DINTDY-  T (=R1) illegal       #> In above message, R1 = 91 #>   #>       T not in interval TCUR - HU (= R1) to TCUR (=R2)       #> In above message, R1 = 53.1042, R2 = 56.6326 #>   #> DLSODA-  Trouble in DINTDY.  ITASK = I1, TOUT = R1 #> In above message, I1 = 1 #>   #> In above message, R1 = 91 #>   #> Error in deSolve::lsoda(y = odeini, times = outtimes, func = lsoda_func,  :  #>   illegal input detected before taking any integration steps - see written message  #anova( #  f_saem_dfop_sfo, #  f_saem_dfop_sfo_deSolve))  # If the model supports it, we can also use eigenvalue based solutions, which # take a similar amount of time #f_saem_sfo_sfo_eigen <- saem(f_mmkin[\"SFO-SFO\", ], solution_type = \"eigen\", #  control = list(nbiter.saemix = c(200, 80), nbdisplay = 10)) # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/schaefer07_complex_case.html","id":null,"dir":"Reference","previous_headings":"","what":"Metabolism data set used for checking the software quality of KinGUI — schaefer07_complex_case","title":"Metabolism data set used for checking the software quality of KinGUI — schaefer07_complex_case","text":"dataset used comparison KinGUI ModelMaker check   software quality KinGUI original publication (Schäfer et al., 2007).   results fitting also included.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/schaefer07_complex_case.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Metabolism data set used for checking the software quality of KinGUI — schaefer07_complex_case","text":"","code":"schaefer07_complex_case"},{"path":"https://pkgdown.jrwb.de/mkin/reference/schaefer07_complex_case.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Metabolism data set used for checking the software quality of KinGUI — schaefer07_complex_case","text":"data set data frame 8 observations following 6 variables. time numeric vector parent numeric vector A1 numeric vector B1 numeric vector C1 numeric vector A2 numeric vector results data frame 14 results different parameter values","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/schaefer07_complex_case.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Metabolism data set used for checking the software quality of KinGUI — schaefer07_complex_case","text":"Schäfer D, Mikolasch B, Rainbird P Harvey B (2007). KinGUI: new kinetic   software tool evaluations according FOCUS degradation kinetics. : Del   Re AAM, Capri E, Fragoulis G Trevisan M (Eds.). Proceedings XIII   Symposium Pesticide Chemistry, Piacenza, 2007, p. 916-923.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/schaefer07_complex_case.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Metabolism data set used for checking the software quality of KinGUI — schaefer07_complex_case","text":"","code":"data <- mkin_wide_to_long(schaefer07_complex_case, time = \"time\") model <- mkinmod(   parent = list(type = \"SFO\", to = c(\"A1\", \"B1\", \"C1\"), sink = FALSE),   A1 = list(type = \"SFO\", to = \"A2\"),   B1 = list(type = \"SFO\"),   C1 = list(type = \"SFO\"),   A2 = list(type = \"SFO\"), use_of_ff = \"max\") #> Temporary DLL for differentials generated and loaded   # \\dontrun{     fit <- mkinfit(model, data, quiet = TRUE)     plot(fit)      endpoints(fit) #> $ff #>   parent_A1   parent_B1   parent_C1 parent_sink       A1_A2     A1_sink  #>   0.3809618   0.1954668   0.4235714   0.0000000   0.4479540   0.5520460  #>  #> $distimes #>            DT50      DT90 #> parent 13.95078  46.34349 #> A1     49.75347 165.27745 #> B1     37.26905 123.80511 #> C1     11.23129  37.30955 #> A2     28.50690  94.69789 #>    # }  # Compare with the results obtained in the original publication  print(schaefer07_complex_results) #>         compound          parameter  KinGUI ModelMaker deviation #> 1         parent   degradation rate  0.0496     0.0506       2.0 #> 2         parent               DT50 13.9900    13.6900       2.2 #> 3  metabolite A1 formation fraction  0.3803     0.3696       2.9 #> 4  metabolite A1   degradation rate  0.0139     0.0136       2.2 #> 5  metabolite A1               DT50 49.9600    50.8900       1.8 #> 6  metabolite B1 formation fraction  0.1866     0.1818       2.6 #> 7  metabolite B1   degradation rate  0.0175     0.0172       1.7 #> 8  metabolite B1               DT50 39.6100    40.2400       1.6 #> 9  metabolite C1 formation fraction  0.4331     0.4486       3.5 #> 10 metabolite C1   degradation rate  0.0638     0.0700       8.9 #> 11 metabolite C1               DT50 10.8700     9.9000       9.8 #> 12 metabolite A2 formation fraction  0.4529     0.4559       0.7 #> 13 metabolite A2   degradation rate  0.0245     0.0244       0.4 #> 14 metabolite A2               DT50 28.2400    28.4500       0.7"},{"path":"https://pkgdown.jrwb.de/mkin/reference/set_nd_nq.html","id":null,"dir":"Reference","previous_headings":"","what":"Set non-detects and unquantified values in residue series without replicates — set_nd_nq","title":"Set non-detects and unquantified values in residue series without replicates — set_nd_nq","text":"function automates replacing unquantified values residue time depth series. time series, function performs part residue processing proposed FOCUS kinetics guidance parent compounds metabolites. two-dimensional residue series time depth, automates proposal Boesten et al (2015).","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/set_nd_nq.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set non-detects and unquantified values in residue series without replicates — set_nd_nq","text":"","code":"set_nd_nq(res_raw, lod, loq = NA, time_zero_presence = FALSE)  set_nd_nq_focus(   res_raw,   lod,   loq = NA,   set_first_sample_nd = TRUE,   first_sample_nd_value = 0,   ignore_below_loq_after_first_nd = TRUE )"},{"path":"https://pkgdown.jrwb.de/mkin/reference/set_nd_nq.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set non-detects and unquantified values in residue series without replicates — set_nd_nq","text":"res_raw Character vector residue time series, matrix residue values rows representing depth profiles specific sampling time, columns representing time series residues depth. Values limit detection (lod) coded \"nd\", values limit detection limit quantification, , coded \"nq\". Samples analysed coded \"na\". values \"na\", \"nd\" \"nq\" coercible numeric lod Limit detection (numeric) loq Limit quantification(numeric). Must specified FOCUS rule stop first non-detection applied time_zero_presence assume residues occur time zero? affects samples first sampling time reported \"nd\" (detected). set_first_sample_nd first sample set \"first_sample_nd_value\" case non-detection? first_sample_nd_value Value used first sample non-detection ignore_below_loq_after_first_nd ignore values LOQ first non-detection occurs quantified values?","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/set_nd_nq.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Set non-detects and unquantified values in residue series without replicates — set_nd_nq","text":"numeric vector, vector supplied, numeric matrix otherwise","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/set_nd_nq.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Set non-detects and unquantified values in residue series without replicates — set_nd_nq","text":"set_nd_nq_focus(): Set non-detects residue time series according FOCUS rules","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/set_nd_nq.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Set non-detects and unquantified values in residue series without replicates — set_nd_nq","text":"Boesten, J. J. T. ., van der Linden, . M. ., Beltman, W. H. J. Pol, J. W. (2015). Leaching plant protection products transformation products; Proposals improving assessment leaching groundwater Netherlands — Version 2. Alterra report 2630, Alterra Wageningen UR (University & Research centre) FOCUS (2014) Generic Guidance Estimating Persistence Degradation Kinetics Environmental Fate Studies Pesticides EU Registration, Version 1.1, 18 December 2014, p. 251","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/set_nd_nq.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Set non-detects and unquantified values in residue series without replicates — set_nd_nq","text":"","code":"# FOCUS (2014) p. 75/76 and 131/132 parent_1 <- c(.12, .09, .05, .03, \"nd\", \"nd\", \"nd\", \"nd\", \"nd\", \"nd\") set_nd_nq(parent_1, 0.02) #>  [1] 0.12 0.09 0.05 0.03 0.01   NA   NA   NA   NA   NA parent_2 <- c(.12, .09, .05, .03, \"nd\", \"nd\", .03, \"nd\", \"nd\", \"nd\") set_nd_nq(parent_2, 0.02) #>  [1] 0.12 0.09 0.05 0.03 0.01 0.01 0.03 0.01   NA   NA set_nd_nq_focus(parent_2, 0.02, loq = 0.05) #>  [1] 0.12 0.09 0.05 0.03 0.01   NA   NA   NA   NA   NA parent_3 <- c(.12, .09, .05, .03, \"nd\", \"nd\", .06, \"nd\", \"nd\", \"nd\") set_nd_nq(parent_3, 0.02) #>  [1] 0.12 0.09 0.05 0.03 0.01 0.01 0.06 0.01   NA   NA set_nd_nq_focus(parent_3, 0.02, loq = 0.05) #>  [1] 0.12 0.09 0.05 0.03 0.01 0.01 0.06 0.01   NA   NA metabolite <- c(\"nd\", \"nd\", \"nd\", 0.03, 0.06, 0.10, 0.11, 0.10, 0.09, 0.05, 0.03, \"nd\", \"nd\") set_nd_nq(metabolite, 0.02) #>  [1]   NA   NA 0.01 0.03 0.06 0.10 0.11 0.10 0.09 0.05 0.03 0.01   NA set_nd_nq_focus(metabolite, 0.02, 0.05) #>  [1] 0.00   NA 0.01 0.03 0.06 0.10 0.11 0.10 0.09 0.05 0.03 0.01   NA # # Boesten et al. (2015), p. 57/58 table_8 <- matrix(   c(10, 10, rep(\"nd\", 4),     10, 10, rep(\"nq\", 2), rep(\"nd\", 2),     10, 10, 10, \"nq\", \"nd\", \"nd\",     \"nq\", 10, \"nq\", rep(\"nd\", 3),     \"nd\", \"nq\", \"nq\", rep(\"nd\", 3),     rep(\"nd\", 6), rep(\"nd\", 6)),   ncol = 6, byrow = TRUE) set_nd_nq(table_8, 0.5, 1.5, time_zero_presence = TRUE) #>       [,1]  [,2]  [,3] [,4] [,5] [,6] #> [1,] 10.00 10.00  0.25 0.25   NA   NA #> [2,] 10.00 10.00  1.00 1.00 0.25   NA #> [3,] 10.00 10.00 10.00 1.00 0.25   NA #> [4,]  1.00 10.00  1.00 0.25   NA   NA #> [5,]  0.25  1.00  1.00 0.25   NA   NA #> [6,]    NA  0.25  0.25   NA   NA   NA #> [7,]    NA    NA    NA   NA   NA   NA table_10 <- matrix(   c(10, 10, rep(\"nd\", 4),     10, 10, rep(\"nd\", 4),     10, 10, 10, rep(\"nd\", 3),     \"nd\", 10, rep(\"nd\", 4),     rep(\"nd\", 18)),   ncol = 6, byrow = TRUE) set_nd_nq(table_10, 0.5, time_zero_presence = TRUE) #>       [,1]  [,2]  [,3] [,4] [,5] [,6] #> [1,] 10.00 10.00  0.25   NA   NA   NA #> [2,] 10.00 10.00  0.25   NA   NA   NA #> [3,] 10.00 10.00 10.00 0.25   NA   NA #> [4,]  0.25 10.00  0.25   NA   NA   NA #> [5,]    NA  0.25    NA   NA   NA   NA #> [6,]    NA    NA    NA   NA   NA   NA #> [7,]    NA    NA    NA   NA   NA   NA"},{"path":"https://pkgdown.jrwb.de/mkin/reference/sigma_twocomp.html","id":null,"dir":"Reference","previous_headings":"","what":"Two-component error model — sigma_twocomp","title":"Two-component error model — sigma_twocomp","text":"Function describing standard deviation measurement error dependence measured value \\(y\\):","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/sigma_twocomp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Two-component error model — sigma_twocomp","text":"","code":"sigma_twocomp(y, sigma_low, rsd_high)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/sigma_twocomp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Two-component error model — sigma_twocomp","text":"y magnitude observed value sigma_low asymptotic minimum standard deviation low observed values rsd_high coefficient describing increase standard deviation magnitude observed value","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/sigma_twocomp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Two-component error model — sigma_twocomp","text":"standard deviation response variable.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/sigma_twocomp.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Two-component error model — sigma_twocomp","text":"$$\\sigma = \\sqrt{ \\sigma_{low}^2 + y^2 * {rsd}_{high}^2}$$ sigma = sqrt(sigma_low^2 + y^2 * rsd_high^2) error model used example Werner et al. (1978). model proposed Rocke Lorenzato (1995) can written form well, assumes approximate lognormal distribution errors high values y.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/sigma_twocomp.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Two-component error model — sigma_twocomp","text":"Werner, Mario, Brooks, Samuel H., Knott, Lancaster B. (1978) Additive, Multiplicative, Mixed Analytical Errors. Clinical Chemistry 24(11), 1895-1898. Rocke, David M. Lorenzato, Stefan (1995) two-component model measurement error analytical chemistry. Technometrics 37(2), 176-184. Ranke J Meinecke S (2019) Error Models Kinetic Evaluation Chemical Degradation Data. Environments 6(12) 124 doi:10.3390/environments6120124 .","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/sigma_twocomp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Two-component error model — sigma_twocomp","text":"","code":"times <- c(0, 1, 3, 7, 14, 28, 60, 90, 120) d_pred <- data.frame(time = times, parent = 100 * exp(- 0.03 * times)) set.seed(123456) d_syn <- add_err(d_pred, function(y) sigma_twocomp(y, 1, 0.07),   reps = 2, n = 1)[[1]] f_nls <- nls(value ~ SSasymp(time, 0, parent_0, lrc), data = d_syn,  start = list(parent_0 = 100, lrc = -3)) library(nlme) f_gnls <- gnls(value ~ SSasymp(time, 0, parent_0, lrc),   data = d_syn, na.action = na.omit,   start = list(parent_0 = 100, lrc = -3)) if (length(findFunction(\"varConstProp\")) > 0) {   f_gnls_tc <- update(f_gnls, weights = varConstProp())   f_gnls_tc_sf <- update(f_gnls_tc, control = list(sigma = 1)) } f_mkin <- mkinfit(\"SFO\", d_syn, error_model = \"const\", quiet = TRUE) f_mkin_tc <- mkinfit(\"SFO\", d_syn, error_model = \"tc\", quiet = TRUE) plot_res(f_mkin_tc, standardized = TRUE)  AIC(f_nls, f_gnls, f_gnls_tc, f_gnls_tc_sf, f_mkin, f_mkin_tc) #>              df      AIC #> f_nls         3 114.4817 #> f_gnls        3 114.4817 #> f_gnls_tc     5 103.6447 #> f_gnls_tc_sf  4 101.6447 #> f_mkin        3 114.4817 #> f_mkin_tc     4 101.6446"},{"path":"https://pkgdown.jrwb.de/mkin/reference/status.html","id":null,"dir":"Reference","previous_headings":"","what":"Method to get status information for fit array objects — status","title":"Method to get status information for fit array objects — status","text":"Method get status information fit array objects","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/status.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Method to get status information for fit array objects — status","text":"","code":"status(object, ...)  # S3 method for class 'mmkin' status(object, ...)  # S3 method for class 'status.mmkin' print(x, ...)  # S3 method for class 'mhmkin' status(object, ...)  # S3 method for class 'status.mhmkin' print(x, ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/status.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Method to get status information for fit array objects — status","text":"object object investigate ... potential future extensions x object printed","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/status.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Method to get status information for fit array objects — status","text":"object dimensions fit array suitable printing method.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/status.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Method to get status information for fit array objects — status","text":"","code":"# \\dontrun{ fits <- mmkin(   c(\"SFO\", \"FOMC\"),   list(\"FOCUS A\" = FOCUS_2006_A,        \"FOCUS B\" = FOCUS_2006_C),   quiet = TRUE) status(fits) #>       dataset #> model  FOCUS A FOCUS B #>   SFO  OK      OK      #>   FOMC C       OK      #>  #> C: Optimisation did not converge: #> false convergence (8) #> OK: No warnings # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/summary.mkinfit.html","id":null,"dir":"Reference","previous_headings":"","what":"Summary method for class ","title":"Summary method for class ","text":"Lists model equations, initial parameter values, optimised parameters uncertainty statistics, chi2 error levels calculated according FOCUS guidance (2006) defined therein, formation fractions, DT50 values optionally data, consisting observed, predicted residual values.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/summary.mkinfit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summary method for class ","text":"","code":"# S3 method for class 'mkinfit' summary(object, data = TRUE, distimes = TRUE, alpha = 0.05, ...)  # S3 method for class 'summary.mkinfit' print(x, digits = max(3, getOption(\"digits\") - 3), ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/summary.mkinfit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summary method for class ","text":"object object class mkinfit. data logical, indicating whether data included summary. distimes logical, indicating whether DT50 DT90 values included. alpha error level confidence interval estimation t distribution ... optional arguments passed methods like print. x object class summary.mkinfit. digits Number digits use printing","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/summary.mkinfit.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summary method for class ","text":"summary function returns list components, among others version, Rversion mkin R versions used date.fit, date.summary dates fit summary produced diffs differential equations used model use_of_ff maximum minimum use made formation fractions bpar Optimised backtransformed parameters data data (see Description ). start starting values bounds, applicable, optimised parameters. fixed values fixed parameters. errmin chi2 error levels observed variable. bparms.ode backtransformed ODE parameters, use starting parameters related models. errparms Error model parameters. ff estimated formation fractions derived fitted model. distimes DT50 DT90 values observed variable. SFORB applicable, eigenvalues fractional eigenvector component g SFORB systems model. print method called side effect, .e. printing summary.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/summary.mkinfit.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Summary method for class ","text":"FOCUS (2006) “Guidance Document Estimating Persistence Degradation Kinetics Environmental Fate Studies Pesticides EU Registration” Report FOCUS Work Group Degradation Kinetics, EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/summary.mkinfit.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Summary method for class ","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/summary.mkinfit.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summary method for class ","text":"","code":"summary(mkinfit(\"SFO\", FOCUS_2006_A, quiet = TRUE)) #> mkin version used for fitting:    1.2.9  #> R version used for fitting:       4.4.2  #> Date of fit:     Thu Feb 13 15:01:12 2025  #> Date of summary: Thu Feb 13 15:01:12 2025  #>  #> Equations: #> d_parent/dt = - k_parent * parent #>  #> Model predictions using solution type analytical  #>  #> Fitted using 131 model solutions performed in 0.01 s #>  #> Error model: Constant variance  #>  #> Error model algorithm: OLS  #>  #> Starting values for parameters to be optimised: #>           value   type #> parent_0 101.24  state #> k_parent   0.10 deparm #>  #> Starting values for the transformed parameters actually optimised: #>                   value lower upper #> parent_0     101.240000  -Inf   Inf #> log_k_parent  -2.302585  -Inf   Inf #>  #> Fixed parameter values: #> None #>  #> Results: #>  #>        AIC     BIC    logLik #>   55.28197 55.5203 -24.64099 #>  #> Optimised, transformed parameters with symmetric confidence intervals: #>              Estimate Std. Error  Lower   Upper #> parent_0      109.200    3.70400 99.630 118.700 #> log_k_parent   -3.291    0.09176 -3.527  -3.055 #> sigma           5.266    1.31600  1.882   8.649 #>  #> Parameter correlation: #>               parent_0 log_k_parent     sigma #> parent_0     1.000e+00    5.428e-01 1.642e-07 #> log_k_parent 5.428e-01    1.000e+00 2.507e-07 #> sigma        1.642e-07    2.507e-07 1.000e+00 #>  #> Backtransformed parameters: #> Confidence intervals for internally transformed parameters are asymmetric. #> t-test (unrealistically) based on the assumption of normal distribution #> for estimators of untransformed parameters. #>           Estimate t value    Pr(>t)   Lower     Upper #> parent_0 109.20000   29.47 4.218e-07 99.6300 118.70000 #> k_parent   0.03722   10.90 5.650e-05  0.0294   0.04712 #> sigma      5.26600    4.00 5.162e-03  1.8820   8.64900 #>  #> FOCUS Chi2 error levels in percent: #>          err.min n.optim df #> All data   8.385       2  6 #> parent     8.385       2  6 #>  #> Estimated disappearance times: #>         DT50  DT90 #> parent 18.62 61.87 #>  #> Data: #>  time variable observed predicted residual #>     0   parent   101.24   109.153  -7.9132 #>     3   parent    99.27    97.622   1.6484 #>     7   parent    90.11    84.119   5.9913 #>    14   parent    72.19    64.826   7.3641 #>    30   parent    29.71    35.738  -6.0283 #>    62   parent     5.98    10.862  -4.8818 #>    90   parent     1.54     3.831  -2.2911 #>   118   parent     0.39     1.351  -0.9613"},{"path":"https://pkgdown.jrwb.de/mkin/reference/summary.mmkin.html","id":null,"dir":"Reference","previous_headings":"","what":"Summary method for class ","title":"Summary method for class ","text":"Shows status information mkinfit objects contained object gives overview ill-defined parameters calculated illparms.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/summary.mmkin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summary method for class ","text":"","code":"# S3 method for class 'mmkin' summary(object, conf.level = 0.95, ...)  # S3 method for class 'summary.mmkin' print(x, digits = max(3, getOption(\"digits\") - 3), ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/summary.mmkin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summary method for class ","text":"object object class mmkin conf.level confidence level testing parameters ... optional arguments passed methods like print. x object class summary.mmkin. digits number digits use printing","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/summary.mmkin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summary method for class ","text":"","code":"fits <- mmkin(   c(\"SFO\", \"FOMC\"),   list(\"FOCUS A\" = FOCUS_2006_A,        \"FOCUS C\" = FOCUS_2006_C),   quiet = TRUE, cores = 1) #> Warning: Optimisation did not converge: #> false convergence (8)   summary(fits) #> Error model: Constant variance  #> Fitted in 0.478 s #>  #> Status: #>       dataset #> model  FOCUS A FOCUS C #>   SFO  OK      OK      #>   FOMC C       OK      #>  #> C: Optimisation did not converge: #> false convergence (8) #> OK: No warnings #>  #> Ill-defined parameters: #>       dataset #> model  FOCUS A                      FOCUS C #>   SFO                                       #>   FOMC parent_0, alpha, beta, sigma"},{"path":"https://pkgdown.jrwb.de/mkin/reference/summary.nlme.mmkin.html","id":null,"dir":"Reference","previous_headings":"","what":"Summary method for class ","title":"Summary method for class ","text":"Lists model equations, initial parameter values, optimised parameters fixed effects (population), random effects (deviations population mean) residual error model, well resulting endpoints formation fractions DT50 values. Optionally (default FALSE), data listed full.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/summary.nlme.mmkin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summary method for class ","text":"","code":"# S3 method for class 'nlme.mmkin' summary(   object,   data = FALSE,   verbose = FALSE,   distimes = TRUE,   alpha = 0.05,   ... )  # S3 method for class 'summary.nlme.mmkin' print(x, digits = max(3, getOption(\"digits\") - 3), verbose = x$verbose, ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/summary.nlme.mmkin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summary method for class ","text":"object object class nlme.mmkin data logical, indicating whether full data included summary. verbose summary verbose? distimes logical, indicating whether DT50 DT90 values included. alpha error level confidence interval estimation t distribution ... optional arguments passed methods like print. x object class summary.nlme.mmkin digits Number digits use printing","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/summary.nlme.mmkin.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summary method for class ","text":"summary function returns list based nlme object obtained fit, least following additional components nlmeversion, mkinversion, Rversion nlme, mkin R versions used date.fit, date.summary dates fit summary produced diffs differential equations used degradation model use_of_ff maximum minimum use made formation fractions data data confint_trans Transformed parameters used optimisation, confidence intervals confint_back Backtransformed parameters, confidence intervals available ff estimated formation fractions derived fitted model. distimes DT50 DT90 values observed variable. SFORB applicable, eigenvalues SFORB components model. print method called side effect, .e. printing summary.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/summary.nlme.mmkin.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Summary method for class ","text":"Johannes Ranke mkin specific parts José Pinheiro Douglas Bates components inherited nlme","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/summary.nlme.mmkin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summary method for class ","text":"","code":"# Generate five datasets following SFO kinetics sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120) dt50_sfo_in_pop <- 50 k_in_pop <- log(2) / dt50_sfo_in_pop set.seed(1234) k_in <- rlnorm(5, log(k_in_pop), 0.5) SFO <- mkinmod(parent = mkinsub(\"SFO\"))  pred_sfo <- function(k) {   mkinpredict(SFO,     c(k_parent = k),     c(parent = 100),     sampling_times) }  ds_sfo_mean <- lapply(k_in, pred_sfo) names(ds_sfo_mean) <- paste(\"ds\", 1:5)  set.seed(12345) ds_sfo_syn <- lapply(ds_sfo_mean, function(ds) {   add_err(ds,     sdfunc = function(value) sqrt(1^2 + value^2 * 0.07^2),     n = 1)[[1]] })  # \\dontrun{ # Evaluate using mmkin and nlme library(nlme) f_mmkin <- mmkin(\"SFO\", ds_sfo_syn, quiet = TRUE, error_model = \"tc\", cores = 1) #> Warning: Optimisation did not converge: #> iteration limit reached without convergence (10) f_nlme <- nlme(f_mmkin) summary(f_nlme, data = TRUE) #> nlme version used for fitting:      3.1.166  #> mkin version used for pre-fitting:  1.2.9  #> R version used for fitting:         4.4.2  #> Date of fit:     Thu Feb 13 15:01:14 2025  #> Date of summary: Thu Feb 13 15:01:14 2025  #>  #> Equations: #> d_parent/dt = - k_parent * parent #>  #> Data: #> 90 observations of 1 variable(s) grouped in 5 datasets #>  #> Model predictions using solution type analytical  #>  #> Fitted in 0.185 s using 4 iterations #>  #> Variance model: Two-component variance function  #>  #> Mean of starting values for individual parameters: #>     parent_0 log_k_parent  #>      101.569       -4.454  #>  #> Fixed degradation parameter values: #> None #>  #> Results: #>  #>     AIC   BIC logLik #>   584.5 599.5 -286.2 #>  #> Optimised, transformed parameters with symmetric confidence intervals: #>               lower    est.   upper #> parent_0     99.371 101.592 103.814 #> log_k_parent -4.973  -4.449  -3.926 #>  #> Correlation:  #>              parnt_0 #> log_k_parent 0.0507  #>  #> Random effects: #>  Formula: list(parent_0 ~ 1, log_k_parent ~ 1) #>  Level: ds #>  Structure: Diagonal #>          parent_0 log_k_parent Residual #> StdDev: 6.921e-05       0.5863        1 #>  #> Variance function: #>  Structure: Constant plus proportion of variance covariate #>  Formula: ~fitted(.)  #>  Parameter estimates: #>        const         prop  #> 0.0001208313 0.0789967985  #>  #> Backtransformed parameters with asymmetric confidence intervals: #>              lower      est.     upper #> parent_0 99.370882 101.59243 103.81398 #> k_parent  0.006923   0.01168   0.01972 #>  #> Estimated disappearance times: #>         DT50  DT90 #> parent 59.32 197.1 #>  #> Data: #>    ds   name time observed predicted  residual    std standardized #>  ds 1 parent    0    104.1   101.592   2.50757 8.0255     0.312451 #>  ds 1 parent    0    105.0   101.592   3.40757 8.0255     0.424594 #>  ds 1 parent    1     98.5   100.796  -2.29571 7.9625    -0.288313 #>  ds 1 parent    1     96.1   100.796  -4.69571 7.9625    -0.589725 #>  ds 1 parent    3    101.9    99.221   2.67904 7.8381     0.341796 #>  ds 1 parent    3     85.2    99.221 -14.02096 7.8381    -1.788812 #>  ds 1 parent    7     99.1    96.145   2.95512 7.5951     0.389081 #>  ds 1 parent    7     93.0    96.145  -3.14488 7.5951    -0.414065 #>  ds 1 parent   14     88.1    90.989  -2.88944 7.1879    -0.401987 #>  ds 1 parent   14     84.1    90.989  -6.88944 7.1879    -0.958480 #>  ds 1 parent   28     80.2    81.493  -1.29305 6.4377    -0.200857 #>  ds 1 parent   28     91.3    81.493   9.80695 6.4377     1.523364 #>  ds 1 parent   60     65.1    63.344   1.75642 5.0039     0.351008 #>  ds 1 parent   60     65.8    63.344   2.45642 5.0039     0.490898 #>  ds 1 parent   90     47.8    50.018  -2.21764 3.9512    -0.561252 #>  ds 1 parent   90     53.5    50.018   3.48236 3.9512     0.881335 #>  ds 1 parent  120     37.6    39.495  -1.89515 3.1200    -0.607423 #>  ds 1 parent  120     39.3    39.495  -0.19515 3.1200    -0.062549 #>  ds 2 parent    0    107.9   101.592   6.30757 8.0255     0.785943 #>  ds 2 parent    0    102.1   101.592   0.50757 8.0255     0.063245 #>  ds 2 parent    1    103.8   100.058   3.74159 7.9043     0.473361 #>  ds 2 parent    1    108.6   100.058   8.54159 7.9043     1.080626 #>  ds 2 parent    3     91.0    97.060  -6.05952 7.6674    -0.790297 #>  ds 2 parent    3     84.9    97.060 -12.15952 7.6674    -1.585874 #>  ds 2 parent    7     79.3    91.329 -12.02867 7.2147    -1.667251 #>  ds 2 parent    7    100.9    91.329   9.57133 7.2147     1.326647 #>  ds 2 parent   14     77.3    82.102  -4.80185 6.4858    -0.740366 #>  ds 2 parent   14     83.5    82.102   1.39815 6.4858     0.215571 #>  ds 2 parent   28     66.8    66.351   0.44945 5.2415     0.085748 #>  ds 2 parent   28     63.3    66.351  -3.05055 5.2415    -0.582002 #>  ds 2 parent   60     40.8    40.775   0.02474 3.2211     0.007679 #>  ds 2 parent   60     44.8    40.775   4.02474 3.2211     1.249485 #>  ds 2 parent   90     27.8    25.832   1.96762 2.0407     0.964198 #>  ds 2 parent   90     27.0    25.832   1.16762 2.0407     0.572171 #>  ds 2 parent  120     15.2    16.366  -1.16561 1.2928    -0.901595 #>  ds 2 parent  120     15.5    16.366  -0.86561 1.2928    -0.669547 #>  ds 3 parent    0     97.7   101.592  -3.89243 8.0255    -0.485009 #>  ds 3 parent    0     88.2   101.592 -13.39243 8.0255    -1.668740 #>  ds 3 parent    1    109.9    99.218  10.68196 7.8379     1.362858 #>  ds 3 parent    1     97.8    99.218  -1.41804 7.8379    -0.180921 #>  ds 3 parent    3    100.5    94.634   5.86555 7.4758     0.784603 #>  ds 3 parent    3     77.4    94.634 -17.23445 7.4758    -2.305360 #>  ds 3 parent    7     78.3    86.093  -7.79273 6.8011    -1.145813 #>  ds 3 parent    7     90.3    86.093   4.20727 6.8011     0.618620 #>  ds 3 parent   14     76.0    72.958   3.04222 5.7634     0.527848 #>  ds 3 parent   14     79.1    72.958   6.14222 5.7634     1.065722 #>  ds 3 parent   28     46.0    52.394  -6.39404 4.1390    -1.544842 #>  ds 3 parent   28     53.4    52.394   1.00596 4.1390     0.243046 #>  ds 3 parent   60     25.1    24.582   0.51786 1.9419     0.266676 #>  ds 3 parent   60     21.4    24.582  -3.18214 1.9419    -1.638664 #>  ds 3 parent   90     11.0    12.092  -1.09202 0.9552    -1.143199 #>  ds 3 parent   90     14.2    12.092   2.10798 0.9552     2.206777 #>  ds 3 parent  120      5.8     5.948  -0.14810 0.4699    -0.315178 #>  ds 3 parent  120      6.1     5.948   0.15190 0.4699     0.323282 #>  ds 4 parent    0     95.3   101.592  -6.29243 8.0255    -0.784057 #>  ds 4 parent    0    102.0   101.592   0.40757 8.0255     0.050784 #>  ds 4 parent    1    104.4   101.125   3.27549 7.9885     0.410025 #>  ds 4 parent    1    105.4   101.125   4.27549 7.9885     0.535205 #>  ds 4 parent    3    113.7   100.195  13.50487 7.9151     1.706218 #>  ds 4 parent    3     82.3   100.195 -17.89513 7.9151    -2.260886 #>  ds 4 parent    7     98.1    98.362  -0.26190 7.7703    -0.033706 #>  ds 4 parent    7     87.8    98.362 -10.56190 7.7703    -1.359270 #>  ds 4 parent   14     97.9    95.234   2.66590 7.5232     0.354357 #>  ds 4 parent   14    104.8    95.234   9.56590 7.5232     1.271521 #>  ds 4 parent   28     85.0    89.274  -4.27372 7.0523    -0.606001 #>  ds 4 parent   28     77.2    89.274 -12.07372 7.0523    -1.712017 #>  ds 4 parent   60     82.2    77.013   5.18661 6.0838     0.852526 #>  ds 4 parent   60     86.1    77.013   9.08661 6.0838     1.493571 #>  ds 4 parent   90     70.5    67.053   3.44692 5.2970     0.650733 #>  ds 4 parent   90     61.7    67.053  -5.35308 5.2970    -1.010591 #>  ds 4 parent  120     60.0    58.381   1.61905 4.6119     0.351058 #>  ds 4 parent  120     56.4    58.381  -1.98095 4.6119    -0.429530 #>  ds 5 parent    0     92.6   101.592  -8.99243 8.0255    -1.120485 #>  ds 5 parent    0    116.5   101.592  14.90757 8.0255     1.857531 #>  ds 5 parent    1    108.0    99.914   8.08560 7.8929     1.024413 #>  ds 5 parent    1    104.9    99.914   4.98560 7.8929     0.631655 #>  ds 5 parent    3    100.5    96.641   3.85898 7.6343     0.505477 #>  ds 5 parent    3     89.5    96.641  -7.14102 7.6343    -0.935383 #>  ds 5 parent    7     91.7    90.412   1.28752 7.1423     0.180267 #>  ds 5 parent    7     95.1    90.412   4.68752 7.1423     0.656304 #>  ds 5 parent   14     82.2    80.463   1.73715 6.3563     0.273295 #>  ds 5 parent   14     84.5    80.463   4.03715 6.3563     0.635141 #>  ds 5 parent   28     60.5    63.728  -3.22788 5.0343    -0.641178 #>  ds 5 parent   28     72.8    63.728   9.07212 5.0343     1.802062 #>  ds 5 parent   60     38.3    37.399   0.90061 2.9544     0.304835 #>  ds 5 parent   60     40.7    37.399   3.30061 2.9544     1.117174 #>  ds 5 parent   90     22.5    22.692  -0.19165 1.7926    -0.106913 #>  ds 5 parent   90     20.8    22.692  -1.89165 1.7926    -1.055273 #>  ds 5 parent  120     13.4    13.768  -0.36790 1.0876    -0.338259 #>  ds 5 parent  120     13.8    13.768   0.03210 1.0876     0.029517 # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/summary.saem.mmkin.html","id":null,"dir":"Reference","previous_headings":"","what":"Summary method for class ","title":"Summary method for class ","text":"Lists model equations, initial parameter values, optimised parameters fixed effects (population), random effects (deviations population mean) residual error model, well resulting endpoints formation fractions DT50 values. Optionally (default FALSE), data listed full.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/summary.saem.mmkin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summary method for class ","text":"","code":"# S3 method for class 'saem.mmkin' summary(   object,   data = FALSE,   verbose = FALSE,   covariates = NULL,   covariate_quantile = 0.5,   distimes = TRUE,   ... )  # S3 method for class 'summary.saem.mmkin' print(x, digits = max(3, getOption(\"digits\") - 3), verbose = x$verbose, ...)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/summary.saem.mmkin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summary method for class ","text":"object object class saem.mmkin data logical, indicating whether full data included summary. verbose summary verbose? covariates Numeric vector covariate values variables covariate models object. given, overrides 'covariate_quantile'. covariate_quantile argument effect fitted object covariate models. , default show endpoints median covariate values (50th percentile). distimes logical, indicating whether DT50 DT90 values included. ... optional arguments passed methods like print. x object class summary.saem.mmkin digits Number digits use printing","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/summary.saem.mmkin.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summary method for class ","text":"summary function returns list based saemix::SaemixObject obtained fit, least following additional components saemixversion, mkinversion, Rversion saemix, mkin R versions used date.fit, date.summary dates fit summary produced diffs differential equations used degradation model use_of_ff maximum minimum use made formation fractions data data confint_trans Transformed parameters used optimisation, confidence intervals confint_back Backtransformed parameters, confidence intervals available confint_errmod Error model parameters confidence intervals ff estimated formation fractions derived fitted model. distimes DT50 DT90 values observed variable. SFORB applicable, eigenvalues SFORB components model. print method called side effect, .e. printing summary.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/summary.saem.mmkin.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Summary method for class ","text":"Johannes Ranke mkin specific parts saemix authors parts inherited saemix.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/summary.saem.mmkin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summary method for class ","text":"","code":"# Generate five datasets following DFOP-SFO kinetics sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120) dfop_sfo <- mkinmod(parent = mkinsub(\"DFOP\", \"m1\"),  m1 = mkinsub(\"SFO\"), quiet = TRUE) set.seed(1234) k1_in <- rlnorm(5, log(0.1), 0.3) k2_in <- rlnorm(5, log(0.02), 0.3) g_in <- plogis(rnorm(5, qlogis(0.5), 0.3)) f_parent_to_m1_in <- plogis(rnorm(5, qlogis(0.3), 0.3)) k_m1_in <- rlnorm(5, log(0.02), 0.3)  pred_dfop_sfo <- function(k1, k2, g, f_parent_to_m1, k_m1) {   mkinpredict(dfop_sfo,     c(k1 = k1, k2 = k2, g = g, f_parent_to_m1 = f_parent_to_m1, k_m1 = k_m1),     c(parent = 100, m1 = 0),     sampling_times) }  ds_mean_dfop_sfo <- lapply(1:5, function(i) {   mkinpredict(dfop_sfo,     c(k1 = k1_in[i], k2 = k2_in[i], g = g_in[i],       f_parent_to_m1 = f_parent_to_m1_in[i], k_m1 = k_m1_in[i]),     c(parent = 100, m1 = 0),     sampling_times) }) names(ds_mean_dfop_sfo) <- paste(\"ds\", 1:5)  ds_syn_dfop_sfo <- lapply(ds_mean_dfop_sfo, function(ds) {   add_err(ds,     sdfunc = function(value) sqrt(1^2 + value^2 * 0.07^2),     n = 1)[[1]] })  # \\dontrun{ # Evaluate using mmkin and saem f_mmkin_dfop_sfo <- mmkin(list(dfop_sfo), ds_syn_dfop_sfo,   quiet = TRUE, error_model = \"tc\", cores = 5) f_saem_dfop_sfo <- saem(f_mmkin_dfop_sfo) print(f_saem_dfop_sfo) #> Kinetic nonlinear mixed-effects model fit by SAEM #> Structural model: #> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * #>            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) #>            * parent #> d_m1/dt = + f_parent_to_m1 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g) #>            * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * #>            exp(-k2 * time))) * parent - k_m1 * m1 #>  #> Data: #> 171 observations of 2 variable(s) grouped in 5 datasets #>  #> Likelihood computed by importance sampling #>     AIC   BIC logLik #>   810.8 805.4 -391.4 #>  #> Fitted parameters: #>                      estimate     lower     upper #> parent_0           100.966822  97.90584 104.02780 #> log_k_m1            -4.076164  -4.17485  -3.97748 #> f_parent_qlogis     -0.940902  -1.35358  -0.52823 #> log_k1              -2.363988  -2.71690  -2.01107 #> log_k2              -4.060016  -4.21743  -3.90260 #> g_qlogis            -0.029999  -0.44766   0.38766 #> a.1                  0.876272   0.67790   1.07464 #> b.1                  0.079594   0.06521   0.09398 #> SD.parent_0          0.076322 -76.45825  76.61089 #> SD.log_k_m1          0.005052  -1.08943   1.09953 #> SD.f_parent_qlogis   0.446968   0.16577   0.72816 #> SD.log_k1            0.348786   0.09502   0.60255 #> SD.log_k2            0.147456   0.03111   0.26380 #> SD.g_qlogis          0.348244   0.02794   0.66854 illparms(f_saem_dfop_sfo) #> [1] \"sd(parent_0)\" \"sd(log_k_m1)\" f_saem_dfop_sfo_2 <- update(f_saem_dfop_sfo,   no_random_effect = c(\"parent_0\", \"log_k_m1\")) illparms(f_saem_dfop_sfo_2) intervals(f_saem_dfop_sfo_2) #> Approximate 95% confidence intervals #>  #>  Fixed effects: #>                      lower         est.        upper #> parent_0       98.04247057 101.09950884 104.15654711 #> k_m1            0.01528983   0.01687734   0.01862969 #> f_parent_to_m1  0.20447650   0.27932896   0.36887691 #> k1              0.06779844   0.09638524   0.13702550 #> k2              0.01495629   0.01741775   0.02028431 #> g               0.37669311   0.48368409   0.59219202 #>  #>  Random effects: #>                          lower      est.     upper #> sd(f_parent_qlogis) 0.16515113 0.4448330 0.7245148 #> sd(log_k1)          0.08982399 0.3447403 0.5996565 #> sd(log_k2)          0.02806780 0.1419560 0.2558443 #> sd(g_qlogis)        0.04908644 0.3801993 0.7113121 #>  #>   #>          lower       est.      upper #> a.1 0.67993373 0.87630147 1.07266921 #> b.1 0.06522297 0.07920531 0.09318766 summary(f_saem_dfop_sfo_2, data = TRUE) #> saemix version used for fitting:      3.3  #> mkin version used for pre-fitting:  1.2.9  #> R version used for fitting:         4.4.2  #> Date of fit:     Thu Feb 13 15:01:30 2025  #> Date of summary: Thu Feb 13 15:01:30 2025  #>  #> Equations: #> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * #>            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) #>            * parent #> d_m1/dt = + f_parent_to_m1 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g) #>            * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * #>            exp(-k2 * time))) * parent - k_m1 * m1 #>  #> Data: #> 171 observations of 2 variable(s) grouped in 5 datasets #>  #> Model predictions using solution type analytical  #>  #> Fitted in 9.035 s #> Using 300, 100 iterations and 10 chains #>  #> Variance model: Two-component variance function  #>  #> Starting values for degradation parameters: #>        parent_0        log_k_m1 f_parent_qlogis          log_k1          log_k2  #>       101.65645        -4.05368        -0.94311        -2.35943        -4.07006  #>        g_qlogis  #>        -0.01133  #>  #> Fixed degradation parameter values: #> None #>  #> Starting values for random effects (square root of initial entries in omega): #>                 parent_0 log_k_m1 f_parent_qlogis log_k1 log_k2 g_qlogis #> parent_0           6.742   0.0000          0.0000 0.0000 0.0000    0.000 #> log_k_m1           0.000   0.2236          0.0000 0.0000 0.0000    0.000 #> f_parent_qlogis    0.000   0.0000          0.5572 0.0000 0.0000    0.000 #> log_k1             0.000   0.0000          0.0000 0.8031 0.0000    0.000 #> log_k2             0.000   0.0000          0.0000 0.0000 0.2931    0.000 #> g_qlogis           0.000   0.0000          0.0000 0.0000 0.0000    0.807 #>  #> Starting values for error model parameters: #> a.1 b.1  #>   1   1  #>  #> Results: #>  #> Likelihood computed by importance sampling #>     AIC   BIC logLik #>   806.9 802.2 -391.5 #>  #> Optimised parameters: #>                         est.    lower     upper #> parent_0           101.09951 98.04247 104.15655 #> log_k_m1            -4.08178 -4.18057  -3.98300 #> f_parent_qlogis     -0.94779 -1.35855  -0.53704 #> log_k1              -2.33940 -2.69122  -1.98759 #> log_k2              -4.05027 -4.20262  -3.89791 #> g_qlogis            -0.06529 -0.50361   0.37303 #> a.1                  0.87630  0.67993   1.07267 #> b.1                  0.07921  0.06522   0.09319 #> SD.f_parent_qlogis   0.44483  0.16515   0.72451 #> SD.log_k1            0.34474  0.08982   0.59966 #> SD.log_k2            0.14196  0.02807   0.25584 #> SD.g_qlogis          0.38020  0.04909   0.71131 #>  #> Correlation:  #>                 parnt_0 lg_k_m1 f_prnt_ log_k1  log_k2  #> log_k_m1        -0.4716                                 #> f_parent_qlogis -0.2394  0.2617                         #> log_k1           0.1677 -0.1566 -0.0659                 #> log_k2           0.0165  0.0638  0.0045  0.2013         #> g_qlogis         0.1118 -0.1118 -0.0340 -0.2324 -0.3419 #>  #> Random effects: #>                      est.   lower  upper #> SD.f_parent_qlogis 0.4448 0.16515 0.7245 #> SD.log_k1          0.3447 0.08982 0.5997 #> SD.log_k2          0.1420 0.02807 0.2558 #> SD.g_qlogis        0.3802 0.04909 0.7113 #>  #> Variance model: #>        est.   lower   upper #> a.1 0.87630 0.67993 1.07267 #> b.1 0.07921 0.06522 0.09319 #>  #> Backtransformed parameters: #>                     est.    lower     upper #> parent_0       101.09951 98.04247 104.15655 #> k_m1             0.01688  0.01529   0.01863 #> f_parent_to_m1   0.27933  0.20448   0.36888 #> k1               0.09639  0.06780   0.13703 #> k2               0.01742  0.01496   0.02028 #> g                0.48368  0.37669   0.59219 #>  #> Resulting formation fractions: #>                 ff #> parent_m1   0.2793 #> parent_sink 0.7207 #>  #> Estimated disappearance times: #>         DT50   DT90 DT50back DT50_k1 DT50_k2 #> parent 15.66  94.28    28.38   7.191    39.8 #> m1     41.07 136.43       NA      NA      NA #>  #> Data: #>    ds   name time observed  predicted  residual    std standardized #>  ds 1 parent    0     89.8  1.011e+02 -11.29951 8.0554    -1.402721 #>  ds 1 parent    0    104.1  1.011e+02   3.00049 8.0554     0.372481 #>  ds 1 parent    1     88.7  9.624e+01  -7.53600 7.6726    -0.982195 #>  ds 1 parent    1     95.5  9.624e+01  -0.73600 7.6726    -0.095925 #>  ds 1 parent    3     81.8  8.736e+01  -5.55672 6.9744    -0.796732 #>  ds 1 parent    3     94.5  8.736e+01   7.14328 6.9744     1.024217 #>  ds 1 parent    7     71.5  7.251e+01  -1.00511 5.8093    -0.173019 #>  ds 1 parent    7     70.3  7.251e+01  -2.20511 5.8093    -0.379585 #>  ds 1 parent   14     54.2  5.356e+01   0.63921 4.3319     0.147560 #>  ds 1 parent   14     49.6  5.356e+01  -3.96079 4.3319    -0.914340 #>  ds 1 parent   28     31.5  3.175e+01  -0.25429 2.6634    -0.095475 #>  ds 1 parent   28     28.8  3.175e+01  -2.95429 2.6634    -1.109218 #>  ds 1 parent   60     12.1  1.281e+01  -0.71388 1.3409    -0.532390 #>  ds 1 parent   60     13.6  1.281e+01   0.78612 1.3409     0.586271 #>  ds 1 parent   90      6.2  6.405e+00  -0.20462 1.0125    -0.202083 #>  ds 1 parent   90      8.3  6.405e+00   1.89538 1.0125     1.871910 #>  ds 1 parent  120      2.2  3.329e+00  -1.12941 0.9151    -1.234165 #>  ds 1 parent  120      2.4  3.329e+00  -0.92941 0.9151    -1.015615 #>  ds 1     m1    1      0.3  1.177e+00  -0.87699 0.8812    -0.995168 #>  ds 1     m1    1      0.2  1.177e+00  -0.97699 0.8812    -1.108644 #>  ds 1     m1    3      2.2  3.268e+00  -1.06821 0.9137    -1.169063 #>  ds 1     m1    3      3.0  3.268e+00  -0.26821 0.9137    -0.293536 #>  ds 1     m1    7      6.5  6.555e+00  -0.05539 1.0186    -0.054377 #>  ds 1     m1    7      5.0  6.555e+00  -1.55539 1.0186    -1.527022 #>  ds 1     m1   14     10.2  1.017e+01   0.03108 1.1902     0.026117 #>  ds 1     m1   14      9.5  1.017e+01  -0.66892 1.1902    -0.562010 #>  ds 1     m1   28     12.2  1.270e+01  -0.50262 1.3342    -0.376708 #>  ds 1     m1   28     13.4  1.270e+01   0.69738 1.3342     0.522686 #>  ds 1     m1   60     11.8  1.078e+01   1.01734 1.2236     0.831403 #>  ds 1     m1   60     13.2  1.078e+01   2.41734 1.2236     1.975530 #>  ds 1     m1   90      6.6  7.686e+00  -1.08586 1.0670    -1.017675 #>  ds 1     m1   90      9.3  7.686e+00   1.61414 1.0670     1.512779 #>  ds 1     m1  120      3.5  5.205e+00  -1.70467 0.9684    -1.760250 #>  ds 1     m1  120      5.4  5.205e+00   0.19533 0.9684     0.201701 #>  ds 2 parent    0    118.0  1.011e+02  16.90049 8.0554     2.098026 #>  ds 2 parent    0     99.8  1.011e+02  -1.29951 8.0554    -0.161321 #>  ds 2 parent    1     90.2  9.574e+01  -5.53784 7.6334    -0.725473 #>  ds 2 parent    1     94.6  9.574e+01  -1.13784 7.6334    -0.149060 #>  ds 2 parent    3     96.1  8.638e+01   9.72233 6.8975     1.409551 #>  ds 2 parent    3     78.4  8.638e+01  -7.97767 6.8975    -1.156610 #>  ds 2 parent    7     77.9  7.194e+01   5.95854 5.7651     1.033547 #>  ds 2 parent    7     77.7  7.194e+01   5.75854 5.7651     0.998856 #>  ds 2 parent   14     56.0  5.558e+01   0.42141 4.4885     0.093888 #>  ds 2 parent   14     54.7  5.558e+01  -0.87859 4.4885    -0.195742 #>  ds 2 parent   28     36.6  3.852e+01  -1.92382 3.1746    -0.605999 #>  ds 2 parent   28     36.8  3.852e+01  -1.72382 3.1746    -0.543000 #>  ds 2 parent   60     22.1  2.108e+01   1.02043 1.8856     0.541168 #>  ds 2 parent   60     24.7  2.108e+01   3.62043 1.8856     1.920034 #>  ds 2 parent   90     12.4  1.250e+01  -0.09675 1.3220    -0.073184 #>  ds 2 parent   90     10.8  1.250e+01  -1.69675 1.3220    -1.283492 #>  ds 2 parent  120      6.8  7.426e+00  -0.62587 1.0554    -0.593027 #>  ds 2 parent  120      7.9  7.426e+00   0.47413 1.0554     0.449242 #>  ds 2     m1    1      1.3  1.417e+00  -0.11735 0.8835    -0.132825 #>  ds 2     m1    3      3.7  3.823e+00  -0.12301 0.9271    -0.132673 #>  ds 2     m1    3      4.7  3.823e+00   0.87699 0.9271     0.945909 #>  ds 2     m1    7      8.1  7.288e+00   0.81180 1.0494     0.773619 #>  ds 2     m1    7      7.9  7.288e+00   0.61180 1.0494     0.583025 #>  ds 2     m1   14     10.1  1.057e+01  -0.46957 1.2119    -0.387459 #>  ds 2     m1   14     10.3  1.057e+01  -0.26957 1.2119    -0.222432 #>  ds 2     m1   28     10.7  1.234e+01  -1.63555 1.3124    -1.246185 #>  ds 2     m1   28     12.2  1.234e+01  -0.13555 1.3124    -0.103281 #>  ds 2     m1   60     10.7  1.065e+01   0.04641 1.2165     0.038151 #>  ds 2     m1   60     12.5  1.065e+01   1.84641 1.2165     1.517773 #>  ds 2     m1   90      9.1  8.177e+00   0.92337 1.0896     0.847403 #>  ds 2     m1   90      7.4  8.177e+00  -0.77663 1.0896    -0.712734 #>  ds 2     m1  120      6.1  5.966e+00   0.13404 0.9956     0.134631 #>  ds 2     m1  120      4.5  5.966e+00  -1.46596 0.9956    -1.472460 #>  ds 3 parent    0    106.2  1.011e+02   5.10049 8.0554     0.633175 #>  ds 3 parent    0    106.9  1.011e+02   5.80049 8.0554     0.720073 #>  ds 3 parent    1    107.4  9.365e+01  13.74627 7.4695     1.840332 #>  ds 3 parent    1     96.1  9.365e+01   2.44627 7.4695     0.327504 #>  ds 3 parent    3     79.4  8.139e+01  -1.99118 6.5059    -0.306058 #>  ds 3 parent    3     82.6  8.139e+01   1.20882 6.5059     0.185803 #>  ds 3 parent    7     63.9  6.445e+01  -0.54666 5.1792    -0.105549 #>  ds 3 parent    7     62.4  6.445e+01  -2.04666 5.1792    -0.395170 #>  ds 3 parent   14     51.0  4.830e+01   2.69944 3.9247     0.687800 #>  ds 3 parent   14     47.1  4.830e+01  -1.20056 3.9247    -0.305896 #>  ds 3 parent   28     36.1  3.426e+01   1.83885 2.8516     0.644839 #>  ds 3 parent   28     36.6  3.426e+01   2.33885 2.8516     0.820177 #>  ds 3 parent   60     20.1  1.968e+01   0.42208 1.7881     0.236053 #>  ds 3 parent   60     19.8  1.968e+01   0.12208 1.7881     0.068273 #>  ds 3 parent   90     11.3  1.194e+01  -0.64013 1.2893    -0.496496 #>  ds 3 parent   90     10.7  1.194e+01  -1.24013 1.2893    -0.961865 #>  ds 3 parent  120      8.2  7.247e+00   0.95264 1.0476     0.909381 #>  ds 3 parent  120      7.3  7.247e+00   0.05264 1.0476     0.050254 #>  ds 3     m1    0      0.8 -2.956e-12   0.80000 0.8763     0.912928 #>  ds 3     m1    1      1.8  1.757e+00   0.04318 0.8873     0.048666 #>  ds 3     m1    1      2.3  1.757e+00   0.54318 0.8873     0.612186 #>  ds 3     m1    3      4.2  4.566e+00  -0.36607 0.9480    -0.386149 #>  ds 3     m1    3      4.1  4.566e+00  -0.46607 0.9480    -0.491634 #>  ds 3     m1    7      6.8  8.157e+00  -1.35680 1.0887    -1.246241 #>  ds 3     m1    7     10.1  8.157e+00   1.94320 1.0887     1.784855 #>  ds 3     m1   14     11.4  1.085e+01   0.55367 1.2272     0.451182 #>  ds 3     m1   14     12.8  1.085e+01   1.95367 1.2272     1.592023 #>  ds 3     m1   28     11.5  1.149e+01   0.01098 1.2633     0.008689 #>  ds 3     m1   28     10.6  1.149e+01  -0.88902 1.2633    -0.703717 #>  ds 3     m1   60      7.5  9.295e+00  -1.79500 1.1445    -1.568351 #>  ds 3     m1   60      8.6  9.295e+00  -0.69500 1.1445    -0.607245 #>  ds 3     m1   90      7.3  7.017e+00   0.28305 1.0377     0.272775 #>  ds 3     m1   90      8.1  7.017e+00   1.08305 1.0377     1.043720 #>  ds 3     m1  120      5.3  5.087e+00   0.21272 0.9645     0.220547 #>  ds 3     m1  120      3.8  5.087e+00  -1.28728 0.9645    -1.334660 #>  ds 4 parent    0    104.7  1.011e+02   3.60049 8.0554     0.446965 #>  ds 4 parent    0     88.3  1.011e+02 -12.79951 8.0554    -1.588930 #>  ds 4 parent    1     94.2  9.755e+01  -3.35176 7.7762    -0.431030 #>  ds 4 parent    1     94.6  9.755e+01  -2.95176 7.7762    -0.379591 #>  ds 4 parent    3     78.1  9.095e+01 -12.85198 7.2570    -1.770981 #>  ds 4 parent    3     96.5  9.095e+01   5.54802 7.2570     0.764508 #>  ds 4 parent    7     76.2  7.949e+01  -3.29267 6.3569    -0.517966 #>  ds 4 parent    7     77.8  7.949e+01  -1.69267 6.3569    -0.266272 #>  ds 4 parent   14     70.8  6.384e+01   6.95621 5.1321     1.355423 #>  ds 4 parent   14     67.3  6.384e+01   3.45621 5.1321     0.673445 #>  ds 4 parent   28     43.1  4.345e+01  -0.35291 3.5515    -0.099370 #>  ds 4 parent   28     45.1  4.345e+01   1.64709 3.5515     0.463771 #>  ds 4 parent   60     21.3  2.137e+01  -0.07478 1.9063    -0.039229 #>  ds 4 parent   60     23.5  2.137e+01   2.12522 1.9063     1.114813 #>  ds 4 parent   90     11.8  1.205e+01  -0.24925 1.2957    -0.192375 #>  ds 4 parent   90     12.1  1.205e+01   0.05075 1.2957     0.039168 #>  ds 4 parent  120      7.0  6.967e+00   0.03315 1.0356     0.032013 #>  ds 4 parent  120      6.2  6.967e+00  -0.76685 1.0356    -0.740510 #>  ds 4     m1    0      1.6  1.421e-13   1.60000 0.8763     1.825856 #>  ds 4     m1    1      0.9  7.250e-01   0.17503 0.8782     0.199310 #>  ds 4     m1    3      3.7  2.038e+00   1.66201 0.8910     1.865236 #>  ds 4     m1    3      2.0  2.038e+00  -0.03799 0.8910    -0.042637 #>  ds 4     m1    7      3.6  4.186e+00  -0.58623 0.9369    -0.625692 #>  ds 4     m1    7      3.8  4.186e+00  -0.38623 0.9369    -0.412230 #>  ds 4     m1   14      7.1  6.752e+00   0.34768 1.0266     0.338666 #>  ds 4     m1   14      6.6  6.752e+00  -0.15232 1.0266    -0.148372 #>  ds 4     m1   28      9.5  9.034e+00   0.46628 1.1313     0.412159 #>  ds 4     m1   28      9.3  9.034e+00   0.26628 1.1313     0.235373 #>  ds 4     m1   60      8.3  8.634e+00  -0.33359 1.1115    -0.300112 #>  ds 4     m1   60      9.0  8.634e+00   0.36641 1.1115     0.329645 #>  ds 4     m1   90      6.6  6.671e+00  -0.07091 1.0233    -0.069295 #>  ds 4     m1   90      7.7  6.671e+00   1.02909 1.0233     1.005691 #>  ds 4     m1  120      3.7  4.823e+00  -1.12301 0.9559    -1.174763 #>  ds 4     m1  120      3.5  4.823e+00  -1.32301 0.9559    -1.383979 #>  ds 5 parent    0    110.4  1.011e+02   9.30049 8.0554     1.154563 #>  ds 5 parent    0    112.1  1.011e+02  11.00049 8.0554     1.365601 #>  ds 5 parent    1     93.5  9.440e+01  -0.90098 7.5282    -0.119681 #>  ds 5 parent    1     91.0  9.440e+01  -3.40098 7.5282    -0.451764 #>  ds 5 parent    3     71.0  8.287e+01 -11.86698 6.6217    -1.792122 #>  ds 5 parent    3     89.7  8.287e+01   6.83302 6.6217     1.031907 #>  ds 5 parent    7     60.4  6.562e+01  -5.22329 5.2711    -0.990936 #>  ds 5 parent    7     59.1  6.562e+01  -6.52329 5.2711    -1.237566 #>  ds 5 parent   14     56.5  4.739e+01   9.10588 3.8548     2.362225 #>  ds 5 parent   14     47.0  4.739e+01  -0.39412 3.8548    -0.102240 #>  ds 5 parent   28     30.2  3.118e+01  -0.98128 2.6206    -0.374451 #>  ds 5 parent   28     23.9  3.118e+01  -7.28128 2.6206    -2.778500 #>  ds 5 parent   60     17.0  1.804e+01  -1.03959 1.6761    -0.620224 #>  ds 5 parent   60     18.7  1.804e+01   0.66041 1.6761     0.394008 #>  ds 5 parent   90     11.3  1.165e+01  -0.35248 1.2727    -0.276958 #>  ds 5 parent   90     11.9  1.165e+01   0.24752 1.2727     0.194488 #>  ds 5 parent  120      9.0  7.556e+00   1.44368 1.0612     1.360449 #>  ds 5 parent  120      8.1  7.556e+00   0.54368 1.0612     0.512338 #>  ds 5     m1    0      0.7 -1.421e-14   0.70000 0.8763     0.798812 #>  ds 5     m1    1      3.0  3.160e+00  -0.15979 0.9113    -0.175340 #>  ds 5     m1    1      2.6  3.160e+00  -0.55979 0.9113    -0.614254 #>  ds 5     m1    3      5.1  8.448e+00  -3.34789 1.1026    -3.036487 #>  ds 5     m1    3      7.5  8.448e+00  -0.94789 1.1026    -0.859720 #>  ds 5     m1    7     16.5  1.581e+01   0.68760 1.5286     0.449839 #>  ds 5     m1    7     19.0  1.581e+01   3.18760 1.5286     2.085373 #>  ds 5     m1   14     22.9  2.218e+01   0.71983 1.9632     0.366658 #>  ds 5     m1   14     23.2  2.218e+01   1.01983 1.9632     0.519469 #>  ds 5     m1   28     22.2  2.425e+01  -2.05105 2.1113    -0.971479 #>  ds 5     m1   28     24.4  2.425e+01   0.14895 2.1113     0.070552 #>  ds 5     m1   60     15.5  1.876e+01  -3.25968 1.7250    -1.889646 #>  ds 5     m1   60     19.8  1.876e+01   1.04032 1.7250     0.603074 #>  ds 5     m1   90     14.9  1.365e+01   1.25477 1.3914     0.901806 #>  ds 5     m1   90     14.2  1.365e+01   0.55477 1.3914     0.398714 #>  ds 5     m1  120     10.9  9.726e+00   1.17443 1.1667     1.006587 #>  ds 5     m1  120     10.4  9.726e+00   0.67443 1.1667     0.578044 # Add a correlation between random effects of g and k2 cov_model_3 <- f_saem_dfop_sfo_2$so@model@covariance.model cov_model_3[\"log_k2\", \"g_qlogis\"] <- 1 cov_model_3[\"g_qlogis\", \"log_k2\"] <- 1 f_saem_dfop_sfo_3 <- update(f_saem_dfop_sfo,   covariance.model = cov_model_3) intervals(f_saem_dfop_sfo_3) #> Approximate 95% confidence intervals #>  #>  Fixed effects: #>                      lower         est.        upper #> parent_0       98.42519529 101.51623115 104.60726702 #> k_m1            0.01505059   0.01662123   0.01835577 #> f_parent_to_m1  0.20100222   0.27477835   0.36332008 #> k1              0.07347479   0.10139028   0.13991179 #> k2              0.01469861   0.01771120   0.02134125 #> g               0.35506898   0.46263682   0.57379888 #>  #>  Random effects: #>                             lower       est.     upper #> sd(f_parent_qlogis)    0.16472883  0.4435866 0.7224443 #> sd(log_k1)             0.05323856  0.2981783 0.5431180 #> sd(log_k2)             0.05013379  0.1912531 0.3323723 #> sd(g_qlogis)           0.04710647  0.3997298 0.7523531 #> corr(log_k2,g_qlogis) -1.31087397 -0.5845703 0.1417334 #>  #>   #>          lower       est.      upper #> a.1 0.67769608 0.87421677 1.07073746 #> b.1 0.06525119 0.07925135 0.09325151 # The correlation does not improve the fit judged by AIC and BIC, although # the likelihood is higher with the additional parameter anova(f_saem_dfop_sfo, f_saem_dfop_sfo_2, f_saem_dfop_sfo_3) #> Data: 171 observations of 2 variable(s) grouped in 5 datasets #>  #>                   npar    AIC    BIC     Lik #> f_saem_dfop_sfo_2   12 806.91 802.23 -391.46 #> f_saem_dfop_sfo_3   13 807.96 802.88 -390.98 #> f_saem_dfop_sfo     14 810.83 805.36 -391.41 # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/summary_listing.html","id":null,"dir":"Reference","previous_headings":"","what":"Display the output of a summary function according to the output format — summary_listing","title":"Display the output of a summary function according to the output format — summary_listing","text":"function intended use R markdown code chunk chunk option results = \"asis\".","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/summary_listing.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Display the output of a summary function according to the output format — summary_listing","text":"","code":"summary_listing(object, caption = NULL, label = NULL, clearpage = TRUE)  tex_listing(object, caption = NULL, label = NULL, clearpage = TRUE)  html_listing(object, caption = NULL)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/summary_listing.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Display the output of a summary function according to the output format — summary_listing","text":"object object summary listed caption optional caption label optional label, ignored html output clearpage new page started listing? Ignored html output","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/synthetic_data_for_UBA_2014.html","id":null,"dir":"Reference","previous_headings":"","what":"Synthetic datasets for one parent compound with two metabolites — synthetic_data_for_UBA_2014","title":"Synthetic datasets for one parent compound with two metabolites — synthetic_data_for_UBA_2014","text":"12 datasets generated using four different models three different  variance components. four models either SFO DFOP model either  two sequential two parallel metabolites. Variance component '' based normal distribution standard deviation 3,  Variance component 'b' also based normal distribution, standard deviation 7.  Variance component 'c' based error model Rocke Lorenzato (1995),  minimum standard deviation (small y values) 0.5, proportionality constant 0.07  increase standard deviation y. Note simplified version  error model proposed Rocke Lorenzato (1995), model error  measured values approximates lognormal distribution high values, whereas using  normally distributed error components along. Initial concentrations metabolites values adding variance component resulted  value assumed limit detection 0.1 set NA. example, first dataset title SFO_lin_a based SFO model  two sequential metabolites (linear pathway), added variance component ''. Compare also code example section see degradation models.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/synthetic_data_for_UBA_2014.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Synthetic datasets for one parent compound with two metabolites — synthetic_data_for_UBA_2014","text":"","code":"synthetic_data_for_UBA_2014"},{"path":"https://pkgdown.jrwb.de/mkin/reference/synthetic_data_for_UBA_2014.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Synthetic datasets for one parent compound with two metabolites — synthetic_data_for_UBA_2014","text":"list containing twelve datasets R6 class defined mkinds,   containing, among others, following components title name dataset, e.g. SFO_lin_a data data frame data form expected mkinfit","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/synthetic_data_for_UBA_2014.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Synthetic datasets for one parent compound with two metabolites — synthetic_data_for_UBA_2014","text":"Ranke (2014) Prüfung und Validierung von Modellierungssoftware als Alternative   zu ModelMaker 4.0, Umweltbundesamt Projektnummer 27452 Rocke, David M. und Lorenzato, Stefan (1995) two-component model   measurement error analytical chemistry. Technometrics 37(2), 176-184.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/synthetic_data_for_UBA_2014.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Synthetic datasets for one parent compound with two metabolites — synthetic_data_for_UBA_2014","text":"","code":"# \\dontrun{ # The data have been generated using the following kinetic models m_synth_SFO_lin <- mkinmod(parent = list(type = \"SFO\", to = \"M1\"),                            M1 = list(type = \"SFO\", to = \"M2\"),                            M2 = list(type = \"SFO\"), use_of_ff = \"max\") #> Temporary DLL for differentials generated and loaded   m_synth_SFO_par <- mkinmod(parent = list(type = \"SFO\", to = c(\"M1\", \"M2\"),                                          sink = FALSE),                            M1 = list(type = \"SFO\"),                            M2 = list(type = \"SFO\"), use_of_ff = \"max\") #> Temporary DLL for differentials generated and loaded  m_synth_DFOP_lin <- mkinmod(parent = list(type = \"DFOP\", to = \"M1\"),                             M1 = list(type = \"SFO\", to = \"M2\"),                             M2 = list(type = \"SFO\"), use_of_ff = \"max\") #> Temporary DLL for differentials generated and loaded  m_synth_DFOP_par <- mkinmod(parent = list(type = \"DFOP\", to = c(\"M1\", \"M2\"),                                           sink = FALSE),                             M1 = list(type = \"SFO\"),                             M2 = list(type = \"SFO\"), use_of_ff = \"max\") #> Temporary DLL for differentials generated and loaded  # The model predictions without intentional error were generated as follows sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)  d_synth_SFO_lin <- mkinpredict(m_synth_SFO_lin,                                c(k_parent = 0.7, f_parent_to_M1 = 0.8,                                  k_M1 = 0.3, f_M1_to_M2 = 0.7,                                  k_M2 = 0.02),                                c(parent = 100, M1 = 0, M2 = 0),                                sampling_times)  d_synth_DFOP_lin <- mkinpredict(m_synth_DFOP_lin,                                 c(k1 = 0.2, k2 = 0.02, g = 0.5,                                   f_parent_to_M1 = 0.5, k_M1 = 0.3,                                   f_M1_to_M2 = 0.7, k_M2 = 0.02),                                  c(parent = 100, M1 = 0, M2 = 0),                                  sampling_times)  d_synth_SFO_par <- mkinpredict(m_synth_SFO_par,                                c(k_parent = 0.2,                                  f_parent_to_M1 = 0.8, k_M1 = 0.01,                                  f_parent_to_M2 = 0.2, k_M2 = 0.02),                                  c(parent = 100, M1 = 0, M2 = 0),                                  sampling_times)  d_synth_DFOP_par <- mkinpredict(m_synth_DFOP_par,                                c(k1 = 0.3, k2 = 0.02, g = 0.7,                                  f_parent_to_M1 = 0.6, k_M1 = 0.04,                                  f_parent_to_M2 = 0.4, k_M2 = 0.01),                                  c(parent = 100, M1 = 0, M2 = 0),                                  sampling_times)  # Construct names for datasets with errors d_synth_names = paste0(\"d_synth_\", c(\"SFO_lin\", \"SFO_par\",                                      \"DFOP_lin\", \"DFOP_par\"))  # Original function used or adding errors. The add_err function now published # with this package is a slightly generalised version where the names of # secondary compartments that should have an initial value of zero (M1 and M2 # in this case) are not hardcoded any more. # add_err = function(d, sdfunc, LOD = 0.1, reps = 2, seed = 123456789) # { #   set.seed(seed) #   d_long = mkin_wide_to_long(d, time = \"time\") #   d_rep = data.frame(lapply(d_long, rep, each = 2)) #   d_rep$value = rnorm(length(d_rep$value), d_rep$value, sdfunc(d_rep$value)) # #   d_rep[d_rep$time == 0 & d_rep$name %in% c(\"M1\", \"M2\"), \"value\"] <- 0 #   d_NA <- transform(d_rep, value = ifelse(value < LOD, NA, value)) #   d_NA$value <- round(d_NA$value, 1) #   return(d_NA) # }  # The following is the simplified version of the two-component model of Rocke # and Lorenzato (1995) sdfunc_twocomp = function(value, sd_low, rsd_high) {   sqrt(sd_low^2 + value^2 * rsd_high^2) }  # Add the errors. for (d_synth_name in d_synth_names) {   d_synth = get(d_synth_name)   assign(paste0(d_synth_name, \"_a\"), add_err(d_synth, function(value) 3))   assign(paste0(d_synth_name, \"_b\"), add_err(d_synth, function(value) 7))   assign(paste0(d_synth_name, \"_c\"), add_err(d_synth,                            function(value) sdfunc_twocomp(value, 0.5, 0.07)))  }  d_synth_err_names = c(   paste(rep(d_synth_names, each = 3), letters[1:3], sep = \"_\") )  # This is just one example of an evaluation using the kinetic model used for # the generation of the data   fit <- mkinfit(m_synth_SFO_lin, synthetic_data_for_UBA_2014[[1]]$data,                  quiet = TRUE)   plot_sep(fit)    summary(fit) #> mkin version used for fitting:    1.2.9  #> R version used for fitting:       4.4.2  #> Date of fit:     Thu Feb 13 15:01:40 2025  #> Date of summary: Thu Feb 13 15:01:40 2025  #>  #> Equations: #> d_parent/dt = - k_parent * parent #> d_M1/dt = + f_parent_to_M1 * k_parent * parent - k_M1 * M1 #> d_M2/dt = + f_M1_to_M2 * k_M1 * M1 - k_M2 * M2 #>  #> Model predictions using solution type deSolve  #>  #> Fitted using 848 model solutions performed in 0.165 s #>  #> Error model: Constant variance  #>  #> Error model algorithm: OLS  #>  #> Starting values for parameters to be optimised: #>                   value   type #> parent_0       101.3500  state #> k_parent         0.1000 deparm #> k_M1             0.1001 deparm #> k_M2             0.1002 deparm #> f_parent_to_M1   0.5000 deparm #> f_M1_to_M2       0.5000 deparm #>  #> Starting values for the transformed parameters actually optimised: #>                      value lower upper #> parent_0        101.350000  -Inf   Inf #> log_k_parent     -2.302585  -Inf   Inf #> log_k_M1         -2.301586  -Inf   Inf #> log_k_M2         -2.300587  -Inf   Inf #> f_parent_qlogis   0.000000  -Inf   Inf #> f_M1_qlogis       0.000000  -Inf   Inf #>  #> Fixed parameter values: #>      value  type #> M1_0     0 state #> M2_0     0 state #>  #> Results: #>  #>        AIC      BIC    logLik #>   188.7274 200.3723 -87.36368 #>  #> Optimised, transformed parameters with symmetric confidence intervals: #>                 Estimate Std. Error   Lower    Upper #> parent_0        102.1000    1.57000 98.8600 105.3000 #> log_k_parent     -0.3020    0.03885 -0.3812  -0.2229 #> log_k_M1         -1.2070    0.07123 -1.3520  -1.0620 #> log_k_M2         -3.9010    0.06571 -4.0350  -3.7670 #> f_parent_qlogis   1.2010    0.23530  0.7216   1.6800 #> f_M1_qlogis       0.9589    0.24890  0.4520   1.4660 #> sigma             2.2730    0.25740  1.7490   2.7970 #>  #> Parameter correlation: #>                   parent_0 log_k_parent   log_k_M1   log_k_M2 f_parent_qlogis #> parent_0         1.000e+00    3.933e-01 -1.605e-01  2.819e-02      -4.624e-01 #> log_k_parent     3.933e-01    1.000e+00 -4.082e-01  7.166e-02      -5.682e-01 #> log_k_M1        -1.605e-01   -4.082e-01  1.000e+00 -3.929e-01       7.478e-01 #> log_k_M2         2.819e-02    7.166e-02 -3.929e-01  1.000e+00      -2.658e-01 #> f_parent_qlogis -4.624e-01   -5.682e-01  7.478e-01 -2.658e-01       1.000e+00 #> f_M1_qlogis      1.614e-01    4.102e-01 -8.109e-01  5.419e-01      -8.605e-01 #> sigma           -1.377e-08    7.536e-10  1.089e-08 -4.422e-08       7.124e-09 #>                 f_M1_qlogis      sigma #> parent_0          1.614e-01 -1.377e-08 #> log_k_parent      4.102e-01  7.536e-10 #> log_k_M1         -8.109e-01  1.089e-08 #> log_k_M2          5.419e-01 -4.422e-08 #> f_parent_qlogis  -8.605e-01  7.124e-09 #> f_M1_qlogis       1.000e+00 -2.685e-08 #> sigma            -2.685e-08  1.000e+00 #>  #> Backtransformed parameters: #> Confidence intervals for internally transformed parameters are asymmetric. #> t-test (unrealistically) based on the assumption of normal distribution #> for estimators of untransformed parameters. #>                 Estimate t value    Pr(>t)    Lower     Upper #> parent_0       102.10000  65.000 7.281e-36 98.86000 105.30000 #> k_parent         0.73930  25.740 2.948e-23  0.68310   0.80020 #> k_M1             0.29920  14.040 1.577e-15  0.25880   0.34590 #> k_M2             0.02023  15.220 1.653e-16  0.01769   0.02312 #> f_parent_to_M1   0.76870  18.370 7.295e-19  0.67300   0.84290 #> f_M1_to_M2       0.72290  14.500 6.418e-16  0.61110   0.81240 #> sigma            2.27300   8.832 2.161e-10  1.74900   2.79700 #>  #> FOCUS Chi2 error levels in percent: #>          err.min n.optim df #> All data   8.454       6 17 #> parent     8.660       2  6 #> M1        10.583       2  5 #> M2         3.586       2  6 #>  #> Resulting formation fractions: #>                 ff #> parent_M1   0.7687 #> parent_sink 0.2313 #> M1_M2       0.7229 #> M1_sink     0.2771 #>  #> Estimated disappearance times: #>           DT50    DT90 #> parent  0.9376   3.114 #> M1      2.3170   7.697 #> M2     34.2689 113.839 #>  #> Data: #>  time variable observed  predicted residual #>     0   parent    101.5  1.021e+02 -0.56248 #>     0   parent    101.2  1.021e+02 -0.86248 #>     1   parent     53.9  4.873e+01  5.17118 #>     1   parent     47.5  4.873e+01 -1.22882 #>     3   parent     10.4  1.111e+01 -0.70773 #>     3   parent      7.6  1.111e+01 -3.50773 #>     7   parent      1.1  5.772e-01  0.52283 #>     7   parent      0.3  5.772e-01 -0.27717 #>    14   parent      3.5  3.264e-03  3.49674 #>    28   parent      3.2  1.045e-07  3.20000 #>    90   parent      0.6  9.532e-10  0.60000 #>   120   parent      3.5 -5.940e-10  3.50000 #>     1       M1     36.4  3.479e+01  1.61088 #>     1       M1     37.4  3.479e+01  2.61088 #>     3       M1     34.3  3.937e+01 -5.07027 #>     3       M1     39.8  3.937e+01  0.42973 #>     7       M1     15.1  1.549e+01 -0.38715 #>     7       M1     17.8  1.549e+01  2.31285 #>    14       M1      5.8  1.995e+00  3.80469 #>    14       M1      1.2  1.995e+00 -0.79531 #>    60       M1      0.5  2.111e-06  0.50000 #>    90       M1      3.2 -9.672e-10  3.20000 #>   120       M1      1.5  7.670e-10  1.50000 #>   120       M1      0.6  7.670e-10  0.60000 #>     1       M2      4.8  4.455e+00  0.34517 #>     3       M2     20.9  2.153e+01 -0.62527 #>     3       M2     19.3  2.153e+01 -2.22527 #>     7       M2     42.0  4.192e+01  0.07941 #>     7       M2     43.1  4.192e+01  1.17941 #>    14       M2     49.4  4.557e+01  3.83353 #>    14       M2     44.3  4.557e+01 -1.26647 #>    28       M2     34.6  3.547e+01 -0.87275 #>    28       M2     33.0  3.547e+01 -2.47275 #>    60       M2     18.8  1.858e+01  0.21837 #>    60       M2     17.6  1.858e+01 -0.98163 #>    90       M2     10.6  1.013e+01  0.47130 #>    90       M2     10.8  1.013e+01  0.67130 #>   120       M2      9.8  5.521e+00  4.27893 #>   120       M2      3.3  5.521e+00 -2.22107 # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/test_data_from_UBA_2014.html","id":null,"dir":"Reference","previous_headings":"","what":"Three experimental datasets from two water sediment systems and one soil — test_data_from_UBA_2014","title":"Three experimental datasets from two water sediment systems and one soil — test_data_from_UBA_2014","text":"datasets used comparative validation several kinetic evaluation  software packages (Ranke, 2014).","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/test_data_from_UBA_2014.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Three experimental datasets from two water sediment systems and one soil — test_data_from_UBA_2014","text":"","code":"test_data_from_UBA_2014"},{"path":"https://pkgdown.jrwb.de/mkin/reference/test_data_from_UBA_2014.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Three experimental datasets from two water sediment systems and one soil — test_data_from_UBA_2014","text":"list containing three datasets R6 class defined mkinds.   dataset , among others, following components title name dataset, e.g. UBA_2014_WS_river data data frame data form expected mkinfit","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/test_data_from_UBA_2014.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Three experimental datasets from two water sediment systems and one soil — test_data_from_UBA_2014","text":"Ranke (2014) Prüfung und Validierung von Modellierungssoftware als Alternative   zu ModelMaker 4.0, Umweltbundesamt Projektnummer 27452","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/test_data_from_UBA_2014.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Three experimental datasets from two water sediment systems and one soil — test_data_from_UBA_2014","text":"","code":"# \\dontrun{   # This is a level P-II evaluation of the dataset according to the FOCUS kinetics   # guidance. Due to the strong correlation of the parameter estimates, the   # covariance matrix is not returned. Note that level P-II evaluations are   # generally considered deprecated due to the frequent occurrence of such   # large parameter correlations, among other reasons (e.g. the adequacy of the   # model).   m_ws <- mkinmod(parent_w = mkinsub(\"SFO\", \"parent_s\"),                   parent_s = mkinsub(\"SFO\", \"parent_w\")) #> Temporary DLL for differentials generated and loaded   f_river <- mkinfit(m_ws, test_data_from_UBA_2014[[1]]$data, quiet = TRUE) #> Warning: Observations with value of zero were removed from the data   plot_sep(f_river)     summary(f_river)$bpar #>                           Estimate se_notrans    t value       Pr(>t) #> parent_w_0             95.98567441 2.16285684 44.3791159 1.245593e-17 #> k_parent_w              0.42068803 0.05573864  7.5475120 8.752928e-07 #> k_parent_s              0.07419672 0.10108562  0.7339987 2.371337e-01 #> f_parent_w_to_parent_s  0.14336920 0.05809346  2.4679062 1.305295e-02 #> f_parent_s_to_parent_w  1.00000000 3.13868615  0.3186046 3.772097e-01 #> sigma                   2.98287858 0.68923267  4.3278253 2.987160e-04 #>                              Lower       Upper #> parent_w_0             91.48420501 100.4871438 #> k_parent_w              0.36593946   0.4836276 #> k_parent_s              0.02289956   0.2404043 #> f_parent_w_to_parent_s  0.08180934   0.2391848 #> f_parent_s_to_parent_w  0.00000000   1.0000000 #> sigma                   2.00184022   3.9639169   mkinerrmin(f_river) #>             err.min n.optim df #> All data 0.09246946       5  6 #> parent_w 0.06377096       3  3 #> parent_s 0.20882325       2  3    # This is the evaluation used for the validation of software packages   # in the expertise from 2014   m_soil <- mkinmod(parent = mkinsub(\"SFO\", c(\"M1\", \"M2\")),                     M1 = mkinsub(\"SFO\", \"M3\"),                     M2 = mkinsub(\"SFO\", \"M3\"),                     M3 = mkinsub(\"SFO\"),                     use_of_ff = \"max\") #> Temporary DLL for differentials generated and loaded    f_soil <- mkinfit(m_soil, test_data_from_UBA_2014[[3]]$data, quiet = TRUE) #> Warning: Observations with value of zero were removed from the data   plot_sep(f_soil, lpos = c(\"topright\", \"topright\", \"topright\", \"bottomright\"))    summary(f_soil)$bpar #>                   Estimate  se_notrans    t value       Pr(>t)        Lower #> parent_0       76.55425650 0.859186398 89.1008711 1.113861e-26 74.755959420 #> k_parent        0.12081956 0.004601918 26.2541722 1.077359e-16  0.111561575 #> k_M1            0.84258615 0.806159719  1.0451851 1.545266e-01  0.113779564 #> k_M2            0.04210880 0.017083034  2.4649483 1.170188e-02  0.018013857 #> k_M3            0.01122918 0.007245855  1.5497385 6.885051e-02  0.002909431 #> f_parent_to_M1  0.32240200 0.240783878  1.3389684 9.819070e-02           NA #> f_parent_to_M2  0.16099855 0.033691952  4.7785463 6.531136e-05           NA #> f_M1_to_M3      0.27921507 0.269423709  1.0363419 1.565266e-01  0.022978202 #> f_M2_to_M3      0.55641252 0.595119937  0.9349586 1.807707e-01  0.008002509 #> sigma           1.14005399 0.149696423  7.6157731 1.727024e-07  0.826735778 #>                      Upper #> parent_0       78.35255358 #> k_parent        0.13084582 #> k_M1            6.23970946 #> k_M2            0.09843260 #> k_M3            0.04333992 #> f_parent_to_M1          NA #> f_parent_to_M2          NA #> f_M1_to_M3      0.86450778 #> f_M2_to_M3      0.99489895 #> sigma           1.45337221   mkinerrmin(f_soil) #>             err.min n.optim df #> All data 0.09649963       9 20 #> parent   0.04721283       2  6 #> M1       0.26551208       2  5 #> M2       0.20327575       2  5 #> M3       0.05196550       3  4   # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/transform_odeparms.html","id":null,"dir":"Reference","previous_headings":"","what":"Functions to transform and backtransform kinetic parameters for fitting — transform_odeparms","title":"Functions to transform and backtransform kinetic parameters for fitting — transform_odeparms","text":"transformations intended map parameters take restricted values full scale real numbers. kinetic rate constants parameters can take positive values, simple log transformation used. compositional parameters, formations fractions always sum 1 can negative, ilr transformation used.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/transform_odeparms.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Functions to transform and backtransform kinetic parameters for fitting — transform_odeparms","text":"","code":"transform_odeparms(   parms,   mkinmod,   transform_rates = TRUE,   transform_fractions = TRUE )  backtransform_odeparms(   transparms,   mkinmod,   transform_rates = TRUE,   transform_fractions = TRUE )"},{"path":"https://pkgdown.jrwb.de/mkin/reference/transform_odeparms.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Functions to transform and backtransform kinetic parameters for fitting — transform_odeparms","text":"parms Parameters kinetic models used differential equations. mkinmod kinetic model class mkinmod, containing names model variables needed grouping formation fractions ilr transformation, parameter names information pathway sink included model. transform_rates Boolean specifying kinetic rate constants transformed model specification used fitting better compliance assumption normal distribution estimator. TRUE, also alpha beta parameters FOMC model log-transformed, well k1 k2 rate constants DFOP HS models break point tb HS model. transform_fractions Boolean specifying formation fractions constants transformed model specification used fitting better compliance assumption normal distribution estimator. default (TRUE) transformations. g parameter DFOP model also seen fraction. single fraction transformed (g parameter DFOP single target variable e.g. single metabolite plus pathway sink), logistic transformation used stats::qlogis(). cases, .e. two formation fractions need transformed whose sum exceed one, ilr transformation used. transparms Transformed parameters kinetic models used fitting procedure.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/transform_odeparms.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Functions to transform and backtransform kinetic parameters for fitting — transform_odeparms","text":"vector transformed backtransformed parameters","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/transform_odeparms.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Functions to transform and backtransform kinetic parameters for fitting — transform_odeparms","text":"transformation sets formation fractions fragile, supposes ordering components forward backward transformation. problem internal use mkinfit.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/transform_odeparms.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Functions to transform and backtransform kinetic parameters for fitting — transform_odeparms","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/transform_odeparms.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Functions to transform and backtransform kinetic parameters for fitting — transform_odeparms","text":"","code":"SFO_SFO <- mkinmod(   parent = list(type = \"SFO\", to = \"m1\", sink = TRUE),   m1 = list(type = \"SFO\"), use_of_ff = \"min\") #> Temporary DLL for differentials generated and loaded  # Fit the model to the FOCUS example dataset D using defaults FOCUS_D <- subset(FOCUS_2006_D, value != 0) # remove zero values to avoid warning fit <- mkinfit(SFO_SFO, FOCUS_D, quiet = TRUE) fit.s <- summary(fit) # Transformed and backtransformed parameters print(fit.s$par, 3) #>                   Estimate Std. Error Lower  Upper #> parent_0             99.60     1.5702 96.40 102.79 #> log_k_parent_sink    -3.04     0.0763 -3.19  -2.88 #> log_k_parent_m1      -2.98     0.0403 -3.06  -2.90 #> log_k_m1_sink        -5.25     0.1332 -5.52  -4.98 #> sigma                 3.13     0.3585  2.40   3.85 print(fit.s$bpar, 3) #>               Estimate se_notrans t value   Pr(>t)    Lower    Upper #> parent_0      99.59848    1.57022   63.43 2.30e-36 96.40384 102.7931 #> k_parent_sink  0.04792    0.00365   13.11 6.13e-15  0.04103   0.0560 #> k_parent_m1    0.05078    0.00205   24.80 3.27e-23  0.04678   0.0551 #> k_m1_sink      0.00526    0.00070    7.51 6.16e-09  0.00401   0.0069 #> sigma          3.12550    0.35852    8.72 2.24e-10  2.39609   3.8549  # \\dontrun{ # Compare to the version without transforming rate parameters (does not work # with analytical solution, we get NA values for m1 in predictions) fit.2 <- mkinfit(SFO_SFO, FOCUS_D, transform_rates = FALSE,   solution_type = \"deSolve\", quiet = TRUE) fit.2.s <- summary(fit.2) print(fit.2.s$par, 3) #>               Estimate Std. Error    Lower    Upper #> parent_0      99.59848    1.57022 96.40384 1.03e+02 #> k_parent_sink  0.04792    0.00365  0.04049 5.54e-02 #> k_parent_m1    0.05078    0.00205  0.04661 5.49e-02 #> k_m1_sink      0.00526    0.00070  0.00384 6.69e-03 #> sigma          3.12550    0.35852  2.39609 3.85e+00 print(fit.2.s$bpar, 3) #>               Estimate se_notrans t value   Pr(>t)    Lower    Upper #> parent_0      99.59848    1.57022   63.43 2.30e-36 96.40384 1.03e+02 #> k_parent_sink  0.04792    0.00365   13.11 6.13e-15  0.04049 5.54e-02 #> k_parent_m1    0.05078    0.00205   24.80 3.27e-23  0.04661 5.49e-02 #> k_m1_sink      0.00526    0.00070    7.51 6.16e-09  0.00384 6.69e-03 #> sigma          3.12550    0.35852    8.72 2.24e-10  2.39609 3.85e+00 # }  initials <- fit$start$value names(initials) <- rownames(fit$start) transformed <- fit$start_transformed$value names(transformed) <- rownames(fit$start_transformed) transform_odeparms(initials, SFO_SFO) #>          parent_0 log_k_parent_sink   log_k_parent_m1     log_k_m1_sink  #>        100.750000         -2.302585         -2.301586         -2.300587  backtransform_odeparms(transformed, SFO_SFO) #>      parent_0 k_parent_sink   k_parent_m1     k_m1_sink  #>      100.7500        0.1000        0.1001        0.1002   # \\dontrun{ # The case of formation fractions (this is now the default) SFO_SFO.ff <- mkinmod(   parent = list(type = \"SFO\", to = \"m1\", sink = TRUE),   m1 = list(type = \"SFO\"),   use_of_ff = \"max\") #> Temporary DLL for differentials generated and loaded  fit.ff <- mkinfit(SFO_SFO.ff, FOCUS_D, quiet = TRUE) fit.ff.s <- summary(fit.ff) print(fit.ff.s$par, 3) #>                 Estimate Std. Error  Lower  Upper #> parent_0         99.5985     1.5702 96.404 102.79 #> log_k_parent     -2.3157     0.0409 -2.399  -2.23 #> log_k_m1         -5.2475     0.1332 -5.518  -4.98 #> f_parent_qlogis   0.0579     0.0893 -0.124   0.24 #> sigma             3.1255     0.3585  2.396   3.85 print(fit.ff.s$bpar, 3) #>                Estimate se_notrans t value   Pr(>t)    Lower    Upper #> parent_0       99.59848    1.57022   63.43 2.30e-36 96.40383 102.7931 #> k_parent        0.09870    0.00403   24.47 4.96e-23  0.09082   0.1073 #> k_m1            0.00526    0.00070    7.51 6.16e-09  0.00401   0.0069 #> f_parent_to_m1  0.51448    0.02230   23.07 3.10e-22  0.46912   0.5596 #> sigma           3.12550    0.35852    8.72 2.24e-10  2.39609   3.8549 initials <- c(\"f_parent_to_m1\" = 0.5) transformed <- transform_odeparms(initials, SFO_SFO.ff) backtransform_odeparms(transformed, SFO_SFO.ff) #> f_parent_to_m1  #>            0.5   # And without sink SFO_SFO.ff.2 <- mkinmod(   parent = list(type = \"SFO\", to = \"m1\", sink = FALSE),   m1 = list(type = \"SFO\"),   use_of_ff = \"max\") #> Temporary DLL for differentials generated and loaded   fit.ff.2 <- mkinfit(SFO_SFO.ff.2, FOCUS_D, quiet = TRUE) fit.ff.2.s <- summary(fit.ff.2) print(fit.ff.2.s$par, 3) #>              Estimate Std. Error Lower Upper #> parent_0        84.79      3.012 78.67 90.91 #> log_k_parent    -2.76      0.082 -2.92 -2.59 #> log_k_m1        -4.21      0.123 -4.46 -3.96 #> sigma            8.22      0.943  6.31 10.14 print(fit.ff.2.s$bpar, 3) #>          Estimate se_notrans t value   Pr(>t)   Lower  Upper #> parent_0  84.7916    3.01203   28.15 1.92e-25 78.6704 90.913 #> k_parent   0.0635    0.00521   12.19 2.91e-14  0.0538  0.075 #> k_m1       0.0148    0.00182    8.13 8.81e-10  0.0115  0.019 #> sigma      8.2229    0.94323    8.72 1.73e-10  6.3060 10.140 # }"},{"path":"https://pkgdown.jrwb.de/mkin/reference/update.mkinfit.html","id":null,"dir":"Reference","previous_headings":"","what":"Update an mkinfit model with different arguments — update.mkinfit","title":"Update an mkinfit model with different arguments — update.mkinfit","text":"function return updated mkinfit object. fitted degradation model parameters old fit used starting values updated fit. Values specified 'parms.ini' /'state.ini' override starting values.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/update.mkinfit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Update an mkinfit model with different arguments — update.mkinfit","text":"","code":"# S3 method for class 'mkinfit' update(object, ..., evaluate = TRUE)"},{"path":"https://pkgdown.jrwb.de/mkin/reference/update.mkinfit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Update an mkinfit model with different arguments — update.mkinfit","text":"object mkinfit object updated ... Arguments mkinfit replace arguments original call. Arguments set NULL remove arguments given original call evaluate call evaluated returned call","code":""},{"path":"https://pkgdown.jrwb.de/mkin/reference/update.mkinfit.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Update an mkinfit model with different arguments — update.mkinfit","text":"","code":"# \\dontrun{ fit <- mkinfit(\"SFO\", subset(FOCUS_2006_D, value != 0), quiet = TRUE) parms(fit) #>    parent_0    k_parent       sigma  #> 99.44423885  0.09793574  3.39632469  plot_err(fit)  fit_2 <- update(fit, error_model = \"tc\") parms(fit_2) #>     parent_0     k_parent    sigma_low     rsd_high  #> 1.008549e+02 1.005665e-01 3.752222e-03 6.763434e-02  plot_err(fit_2)  # }"},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-129","dir":"Changelog","previous_headings":"","what":"mkin 1.2.9","title":"mkin 1.2.9","text":"‘R/plot.mixed.R’: Support 25 datasets ‘R/mkinfit.R’: Support passing observed data ‘tibble’ ‘R/parplot.R’: Support multistart objects covariate models filter negative values scaled parameters (warning) plotting. ’R/create_deg_func.R: Make sure reversible reactions specified case two observed variables, supported","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-128-unreleased","dir":"Changelog","previous_headings":"","what":"mkin 1.2.8 (unreleased)","title":"mkin 1.2.8 (unreleased)","text":"‘R/{mhmkin,status}.R’: Deal ‘saem’ fits fail updating ‘mhmkin’ object","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-127-unreleased","dir":"Changelog","previous_headings":"","what":"mkin 1.2.7 (unreleased)","title":"mkin 1.2.7 (unreleased)","text":"‘R/illparms.R’: Fix bug prevented ill-defined random effect found one random effect model. Also add test .","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-126-2023-10-14","dir":"Changelog","previous_headings":"","what":"mkin 1.2.6 (2023-10-14)","title":"mkin 1.2.6 (2023-10-14)","text":"‘inst/rmarkdown/templates/hierarchical_kinetics/skeleton/skeleton.Rmd’: Fix erroneous call ‘endpoints()’ function","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-125-2023-08-09","dir":"Changelog","previous_headings":"","what":"mkin 1.2.5 (2023-08-09)","title":"mkin 1.2.5 (2023-08-09)","text":"‘vignettes/mesotrione_parent_2023.rnw’: Prebuilt vignette showing covariate modelling can done relevant parent degradation models. ‘inst/testdata/mesotrione_soil_efsa_2016}.xlsx’: Another example spreadsheets use ‘read_spreadsheet()’, featuring pH dependent degradation R/illparms.R: Fix detection ill-defined slope error model parameters case estimate negative","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-124-2023-05-19","dir":"Changelog","previous_headings":"","what":"mkin 1.2.4 (2023-05-19)","title":"mkin 1.2.4 (2023-05-19)","text":"R/endpoints.R: Fix calculation endpoints user specified covariate values","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-1231-unreleased","dir":"Changelog","previous_headings":"","what":"mkin 1.2.3.1 (unreleased)","title":"mkin 1.2.3.1 (unreleased)","text":"Small fixes get online docs right (example code R/hierarchical_kinetics, cluster setup cyantraniliprole dmta pathway vignettes, graphics model comparison multistart vignette), rebuild online docs","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-123-2023-04-17","dir":"Changelog","previous_headings":"","what":"mkin 1.2.3 (2023-04-17)","title":"mkin 1.2.3 (2023-04-17)","text":"‘R/{endpoints,parms,plot.mixed.mmkin,summary.saem.mmkin}.R’: Calculate parameters endpoints plot population curves specific covariate values, specific percentiles covariate values used saem fits. Depend current deSolve version possibility avoid resolving symbols shared library (compiled models) , thanks Thomas Petzoldt. ‘inst/rmarkdown/templates/hierarchical_kinetics/skeleton/skeleton.Rmd’: Start new cluster creating model stored user specified location, otherwise symbols found worker processes. ‘tests/testthat/test_compiled_symbols.R’: new tests control problems may introduced possibility use pre-resolved symbols. ‘R/mkinerrmin.R’: Fix typo subset (use = instead ==), thanks Sebastian Meyer spotting work R 4.3.0.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-122-unreleased","dir":"Changelog","previous_headings":"","what":"mkin 1.2.2 (unreleased)","title":"mkin 1.2.2 (unreleased)","text":"‘inst/rmarkdown/templates/hierarchical_kinetics’: R markdown template facilitate application hierarchical kinetic models. ‘inst/testdata/{cyantraniliprole_soil_efsa_2014,lambda-cyhalothrin_soil_efsa_2014}.xlsx’: Example spreadsheets use ‘read_spreadsheet()’. ‘R/mhmkin.R’: Allow ‘illparms.mhmkin’ object list suitable dimensions value argument ‘no_random_effects’, making possible exclude random effects ill-defined simpler variants set degradation models. Remove possibility exclude random effects based separate fits, work well. ‘R/summary.saem.mmkin.R’: List initial parameter values summary, including random effects error model parameters. Avoid redundant warnings occurred calculation correlations fixed effects case Fisher information matrix inverted. List correlations random effects specified user covariance model. ‘R/parplot.R’: Possibility select top ‘llquant’ fraction fits parameter plots, improved legend text. ‘R/illparms.R’: Also check confidence intervals slope parameters covariate models include zero. implemented fits obtained saemix backend. ‘R/parplot.R’: Make function work also case multistart runs failed. ‘R/intervals.R’: Include correlations random effects model case .","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-121-2022-11-19","dir":"Changelog","previous_headings":"","what":"mkin 1.2.1 (2022-11-19)","title":"mkin 1.2.1 (2022-11-19)","text":"‘{data,R}/ds_mixed.rda’: Include test data package instead generating ‘tests/testthat/setup_script.R’. Refactor generating code make consistent update tests. ‘tests/testthat/setup_script.R’: Excluded another ill-defined random effect DFOP fit ‘saem’, attempt avoid platform dependence surfaced Fedora systems CRAN check farm ‘tests/testthat/test_mixed.R’: Round parameters found saemix two significant digits printing, also help avoid platform dependence tests ‘R/saem.R’: Fix bug prevented ‘error.ini’ passed ‘saemix_model’, set default c(1, 1) avoid changing test results ‘R/parplot.R’: Show initial values error model parameters ‘R/loglik.mkinfit.R’: Add ‘nobs’ attribute resulting ‘logLik’ object, order make test_AIC.R succeed current R-devel","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-120-2022-11-17","dir":"Changelog","previous_headings":"","what":"mkin 1.2.0 (2022-11-17)","title":"mkin 1.2.0 (2022-11-17)","text":"‘R/saem.R’: ‘logLik’, ‘update’ ‘anova’ methods ‘saem.mmkin’ objects. ‘R/saem.R’: Automatic estimation start parameters random effects case mkin transformations, nicely improving convergence reducing problems iterative ODE solutions. ‘R/status.R’: New generic show status information fit array objects methods ‘mmkin’, ‘mhmkin’ ‘multistart’ objects. ‘R/mhmkin.R’: New method performing multiple hierarchical mkin fits one function call, optionally parallel. ‘R/mhmkin.R’: ‘anova.mhmkin’ conveniently comparing resulting fits. ‘R/illparms.R’: New generic show ill-defined parameters methods ‘mkinfit’, ‘mmkin’, ‘saem.mmkin’ ‘mhmkin’ objects. ‘R/multistart.R’: New method testing multiple start parameters hierarchical model fits, function ‘llhist’ new generic ‘parplot’ diagnostics, new generics ‘.best’ ‘best’ extracting fit highest likelihood ‘R/summary.mmkin.R’: Summary method mmkin objects. ‘R/saem.R’: Implement test saemix transformations FOMC HS. Also, error saemix transformations requested supported. ‘R/read_spreadsheet.R’: Conveniently read data spreadsheet file. ‘R/tex_listings.R’: Conveniently include summaries fit objects R markdown documents compiled LaTeX.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-111-2022-07-12","dir":"Changelog","previous_headings":"","what":"mkin 1.1.1 (2022-07-12)","title":"mkin 1.1.1 (2022-07-12)","text":"’R/{mkinmod,mkinpredict}.R: Store DLL information mkinmod objects use information mkinpredict avoid performance regression brought bugfix R 4.2.x. Thanks Tomas Kalibera analysis problem r-package-devel list suggestion fix . ‘vignettes/FOCUS_L.rmd’: Remove outdated note referring failure calculate covariance matrix DFOP L2 dataset. Since 0.9.45.5 covariance matrix available ‘vignettes/web_only/benchmarks.rmd’: Add first benchmark data using laptop system, therefore add CPU showing benchmark results. ‘dimethenamid_2018’: Update example code use saemix ‘CAKE_export’: Check validity map argument, updates ‘saem()’: Slightly improve speed case analytical solution saemix implemented, activate test respective code ‘mean_degparms’: New argument ‘default_log_parms’ makes possible supply surrogate value (default) log parameters fail t-test ‘plot.mixed.mmkin’: Pass frame argument also residual plots, take ‘default_log_parms’ argument ‘mean_degparms’ used constructing approximate population curves, plot population curve last avoid covered data ‘plot.mkinfit’: Respect argument ‘maxabs’ residual plots, make possible give ylim list, row layouts","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mixed-effects-models-1-1-0","dir":"Changelog","previous_headings":"","what":"Mixed-effects models","title":"mkin 1.1.0 (2022-03-14)","text":"Reintroduce interface saemix version 3.0 (now CRAN), particular generic function ‘saem’ generator ‘saem.mmkin’, currently using ‘saemix_model’ ‘saemix_data’, summary plot methods ‘mean_degparms’: New argument ‘test_log_parms’ makes function consider log-transformed parameters untransformed parameters pass t-test certain confidence level. can used obtain plausible starting parameters different mixed-effects model backends ‘plot.mixed.mmkin’: Gains arguments ‘test_log_parms’ ‘conf.level’ ‘vignettes/web_only/dimethenamid_2018.rmd’: Example evaluations dimethenamid data. ‘intervals’: Provide method nlme function ‘saem.mmkin’ objects.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-105-2021-09-15","dir":"Changelog","previous_headings":"","what":"mkin 1.0.5 (2021-09-15)","title":"mkin 1.0.5 (2021-09-15)","text":"‘dimethenamid_2018’: Correct data Borstel soil. five observations Staudenmaier (2013) previously stored “Borstel 2” actually just subset 16 observations “Borstel 1” now simply “Borstel”","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-104-2021-04-20","dir":"Changelog","previous_headings":"","what":"mkin 1.0.4 (2021-04-20)","title":"mkin 1.0.4 (2021-04-20)","text":"plotting functions setting graphical parameters: Use .exit() resetting graphical parameters ‘plot.mkinfit’: Use xlab xlim residual plot show_residuals TRUE ‘mmkin’: Use cores = 1 per default Windows make easier first time users","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-103-2021-02-15","dir":"Changelog","previous_headings":"","what":"mkin 1.0.3 (2021-02-15)","title":"mkin 1.0.3 (2021-02-15)","text":"Review update README, ‘Introduction mkin’ vignette help pages","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-102-unreleased","dir":"Changelog","previous_headings":"","what":"mkin 1.0.2 (Unreleased)","title":"mkin 1.0.2 (Unreleased)","text":"‘mkinfit’: Keep model names stored ‘mkinmod’ objects, avoiding loss ‘gmkin’","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-101-2021-02-10","dir":"Changelog","previous_headings":"","what":"mkin 1.0.1 (2021-02-10)","title":"mkin 1.0.1 (2021-02-10)","text":"‘confint.mmkin’, ‘nlme.mmkin’, ‘transform_odeparms’: Fix example code dontrun sections failed current defaults ‘logLik.mkinfit’: Improve example code avoid warnings show convenient syntax ‘mkinresplot’: Re-add Katrin Lindenberger coauthor accidentally removed long ago Remove tests relying non-convergence FOMC fit FOCUS dataset platform dependent (revealed new additional tests CRAN, thanks!) Increase test tolerance parameter comparisons also proved platform dependent","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"general-1-0-0","dir":"Changelog","previous_headings":"","what":"General","title":"mkin 1.0.0 (2021-02-03)","text":"‘mkinmod’ models gain arguments ‘name’ ‘dll_dir’ , conjunction current version ‘inline’ package, make possible still use DLL used fast ODE solutions ‘deSolve’ saving restoring ‘mkinmod’ object. ‘mkindsg’ R6 class groups ‘mkinds’ datasets metadata ‘f_norm_temp_focus’ generic function normalise time intervals using FOCUS method, methods numeric vectors ‘mkindsg’ objects ‘D24_2014’ ‘dimethenamid_2018’ datasets ‘focus_soil_moisture’ FOCUS default soil moisture data ‘update’ method ‘mmkin’ objects ‘transform_odeparms’, ‘backtransform_odeparms’: Use logit transformation solitary fractions like g parameter DFOP model, formation fractions pathway one target variable ‘plot.mmkin’: Add ylab argument, making possible customize y axis label panels left without affecting residual plots. Reduce legend size vertical distance panels ‘plot.mkinfit’: Change default ylab “Observed” “Residue”. Pass xlab residual plot show_residuals TRUE.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mixed-effects-models-1-0-0","dir":"Changelog","previous_headings":"","what":"Mixed-effects models","title":"mkin 1.0.0 (2021-02-03)","text":"‘mixed.mmkin’ New container mmkin objects plotting ‘plot.mixed.mmkin’ method ‘plot.mixed.mmkin’ method used ‘nlme.mmkin’ inheriting ‘mixed.mmkin’ (currently virtual) ‘plot’, ‘summary’ ‘print’ methods ‘nlme.mmkin’ objects","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-09503-2020-10-08","dir":"Changelog","previous_headings":"","what":"mkin 0.9.50.3 (2020-10-08)","title":"mkin 0.9.50.3 (2020-10-08)","text":"‘parms’: Add method mmkin objects ‘mmkin’ ‘confint(method = ’profile’): Use cores detected parallel::detectCores() per default ‘confint(method = ’profile’): Choose accuracy based ‘rel_tol’ argument, relative bounds obtained quadratic approximation ‘mkinfit’: Make ‘use_of_ff’ = “max” also default models specified using short names like “SFO” “FOMC” ‘mkinfit’: Run ‘stats::shapiro.test()’ standardized residuals warn p < 0.05 ‘mkinfit’: ‘error_model_algorithm’ = ‘d_3’ fail direct fitting fails, reports results threestep algorithm returned ‘mmkin’: fail one fits fails, assign try-error respective position mmkin object ‘mkinfit’: Ignore components state.ini correspond state variables model ‘endpoints’: Back-calculate DT50 value DT90 also biphasic models DFOP, HS SFORB","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-09502-2020-05-12","dir":"Changelog","previous_headings":"","what":"mkin 0.9.50.2 (2020-05-12)","title":"mkin 0.9.50.2 (2020-05-12)","text":"Increase tolerance platform specific test results Solaris test machine CRAN Updates corrections (using spelling package) documentation","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-09501-2020-05-11","dir":"Changelog","previous_headings":"","what":"mkin 0.9.50.1 (2020-05-11)","title":"mkin 0.9.50.1 (2020-05-11)","text":"Support SFORB formation fractions ‘mkinmod’: Make ‘use_of_ff’ = “max” default Improve performance ) avoiding expensive calls cost function like merge() data.frame(), b) implementing analytical solutions SFO-SFO DFOP-SFO","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-094911-2020-04-20","dir":"Changelog","previous_headings":"","what":"mkin 0.9.49.11 (2020-04-20)","title":"mkin 0.9.49.11 (2020-04-20)","text":"Increase test tolerance make pass CRAN check machines","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-094910-2020-04-18","dir":"Changelog","previous_headings":"","what":"mkin 0.9.49.10 (2020-04-18)","title":"mkin 0.9.49.10 (2020-04-18)","text":"‘nlme.mmkin’: nlme method mmkin row objects associated S3 class print, plot, anova endpoint methods ‘mean_degparms, nlme_data, nlme_function’: Three new functions facilitate building nlme models mmkin row objects ‘endpoints’: Don’t return SFORB list component ’s empty. reduces distraction complies documentation Article compiled models: Add platform specific code suppress warnings zero values removed FOCUS D dataset ‘plot.mmkin’: Add argument ‘standardized’ avoid warnings occurred passed part additional arguments captured dots (…) ‘summary.mkinfit’: Add AIC, BIC log likelihood summary","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-09499-2020-03-31","dir":"Changelog","previous_headings":"","what":"mkin 0.9.49.9 (2020-03-31)","title":"mkin 0.9.49.9 (2020-03-31)","text":"‘mkinmod’: Use pkgbuild::has_compiler instead Sys.(‘gcc’), latter often fail even Rtools installed ‘mkinds’: Use roxygen documenting fields methods R6 class","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-09498-2020-01-09","dir":"Changelog","previous_headings":"","what":"mkin 0.9.49.8 (2020-01-09)","title":"mkin 0.9.49.8 (2020-01-09)","text":"‘aw’: Generic function calculating Akaike weights, methods mkinfit objects mmkin columns ‘loftest’: Add lack--fit test ‘plot_res’, ‘plot_sep’ ‘mkinerrplot’: Add possibility show standardized residuals make default fits error models ‘const’ ‘lrtest.mkinfit’: Improve naming compared fits case fixed parameters ‘confint.mkinfit’: Make quadratic approximation default, likelihood profiling takes lot time, especially fit three parameters","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-09497-2019-11-01","dir":"Changelog","previous_headings":"","what":"mkin 0.9.49.7 (2019-11-01)","title":"mkin 0.9.49.7 (2019-11-01)","text":"Fix bug introduced 0.9.49.6 occurred direct optimisation yielded higher likelihood three-step optimisation d_3 algorithm, caused fitted parameters three-step optimisation returned instead parameters direct optimisation Add ‘nobs’ method mkinfit objects, enabling default ‘BIC’ method stats package. Also, add ‘BIC’ method mmkin column objects.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-09496-2019-10-31","dir":"Changelog","previous_headings":"","what":"mkin 0.9.49.6 (2019-10-31)","title":"mkin 0.9.49.6 (2019-10-31)","text":"Implement likelihood ratio test method ‘lrtest’ lmtest package Add ‘update’ method mkinfit objects remembers fitted parameters appropriate Add ‘residuals’ method mkinfit objects supports scaling based error model Fix bug ‘mkinfit’ prevented summaries objects fitted fixed parameters generated Add ‘parms’ ‘confint’ methods mkinfit objects. Confidence intervals based quadratic approximation summary, based profile likelihood Move long-running tests tests/testthat/slow separate test log. currently take around 7 minutes system ‘mkinfit’: Clean code return functions calculate log-likelihood sum squared residuals Vignette ‘twa.html’: Add maximum time weighted average formulas hockey stick model Support frameless plots (‘frame = FALSE’) Support suppress chi2 error level (‘show_errmin = FALSE’) ‘plot.mmkin’ Update README introductory vignette Report ‘OLS’ error_model_algorithm summary case default error_model (‘const’) used Support summarizing ‘mkinfit’ objects generated versions < 0.9.49.5","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-09495-2019-07-04","dir":"Changelog","previous_headings":"","what":"mkin 0.9.49.5 (2019-07-04)","title":"mkin 0.9.49.5 (2019-07-04)","text":"Several algorithms minimization negative log-likelihood non-constant error models (two-component variance variable). case error model constant variance, least squares used stable. default algorithm ‘d_3’ tries direct minimization three-step procedure, returns model highest likelihood. argument ‘reweight.method’ mkinfit mmkin now obsolete, use ‘error_model’ ‘error_model_algorithm’ instead Add test checks get best known AIC parent fits 12 test datasets. Add test datasets purpose. New function ‘mkinerrplot’. function also used residual plots ‘plot.mmkin’ argument ‘resplot = “errmod”’ given, ‘plot.mkinfit’ ‘show_errplot’ set TRUE. Remove dependency FME, use nlminb optimisation (‘Port’ algorithm). remember cases one optimisation algorithms preferable, except sometime used Levenberg-Marquardt speed cases expect get trapped local minimum. Use numDeriv package calculate hessians. results slightly different confidence intervals, takes bit longer, apparently robust Add simple benchmark vignette document impact performance. code manual weighting removed. functionality might get added later time. time , please use earlier version, e.g. 0.9.48.1 want manual weighting. fitting time reported summary now includes time used calculation hessians Adapt tests Fix error FOCUS chi2 error level calculations occurred parameters specified parms.ini model. warning already issued, fitting parallel via mmkin go unnoticed. Add example datasets obtained risk assessment reports published European Food Safety Agency.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-09481-2019-03-04","dir":"Changelog","previous_headings":"","what":"mkin 0.9.48.1 (2019-03-04)","title":"mkin 0.9.48.1 (2019-03-04)","text":"Add function ‘logLik.mkinfit’ makes possible calculate AIC mkinfit objects Add function ‘AIC.mmkin’ make easy compare columns mmkin objects ‘add_err’: Respect argument giving number replicates synthetic dataset ‘max_twa_parent’: Support maximum time weighted average concentration calculations hockey stick (HS) model ‘mkinpredict’: Make function generic create method mkinfit objects ‘mkinfit’: Improve correctness fitted two component error model fitting mean absolute deviance observation observed values, weighting current two-component error model ‘tests/testthat/test_irls.R’: Test components error model used generate data can reproduced moderate accuracy Add function ‘CAKE_export’ facilitate cross-checking results Implement logistic model (tested parent fits) ‘nafta’: Add evaluations according NAFTA guidance","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-09475-2018-09-14","dir":"Changelog","previous_headings":"","what":"mkin 0.9.47.5 (2018-09-14)","title":"mkin 0.9.47.5 (2018-09-14)","text":"Make two-component error model stop cases inadequate avoid nls crashes windows Move two vignettes location built CRAN (avoid NOTES long execution times) Exclude example code testing CRAN avoid NOTES long execution times","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-09473","dir":"Changelog","previous_headings":"","what":"mkin 0.9.47.3","title":"mkin 0.9.47.3","text":"‘mkinfit’: Improve fitting error model reweight.method = ‘tc’. Add ‘manual’ possible arguments ‘weight’ Test FOCUS_2006_C can evaluated DFOP reweight.method = ‘tc’","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-09472-2018-07-19","dir":"Changelog","previous_headings":"","what":"mkin 0.9.47.2 (2018-07-19)","title":"mkin 0.9.47.2 (2018-07-19)","text":"‘sigma_twocomp’: Rename ‘sigma_rl’ ‘sigma_twocomp’ Rocke Lorenzato model assumes lognormal distribution large y. Correct references Rocke Lorenzato model accordingly. ‘mkinfit’: Use 1.1 starting value N parameter IORE models obtain convergence difficult cases. Show parameter names ‘trace_parms’ ‘TRUE’.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-09471-2018-02-06","dir":"Changelog","previous_headings":"","what":"mkin 0.9.47.1 (2018-02-06)","title":"mkin 0.9.47.1 (2018-02-06)","text":"Skip tests CRAN winbuilder avoid timeouts ‘test_data_from_UBA_2014’: Added list datasets containing experimental data used expertise 2014 ‘mkinfit’: Added iterative reweighting method ‘tc’ using two-component error model Rocke Lorenzato. NA values data returned . ‘mkinfit’: Work around bug current FME version prevented convergence message returned case non-convergence. ‘summary.mkinfit’: Improved output regarding weighting method. predictions returned NA values model (see ). ‘summary.mkinfit’: Show versions mkin R used fitting (ones used summary) fit generated mkin >= 0.9.47.1","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-09463-2017-11-16","dir":"Changelog","previous_headings":"","what":"mkin 0.9.46.3 (2017-11-16)","title":"mkin 0.9.46.3 (2017-11-16)","text":"README.md, vignettes/mkin.Rmd: URLs updated synthetic_data_for_UBA: Add code used generate data interest reproducibility","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-09462-2017-10-10","dir":"Changelog","previous_headings":"","what":"mkin 0.9.46.2 (2017-10-10)","title":"mkin 0.9.46.2 (2017-10-10)","text":"Converted vignette FOCUS_Z tex/pdf markdown/html DESCRIPTION: Add ORCID","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-09461-2017-09-14","dir":"Changelog","previous_headings":"","what":"mkin 0.9.46.1 (2017-09-14)","title":"mkin 0.9.46.1 (2017-09-14)","text":"plot.mkinfit: Fix scaling residual plots case separate plots observed variable plot.mkinfit: Use data points fitted curve y axis scaling case separate plots observed variable Documentation updates","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-0946-2017-07-24","dir":"Changelog","previous_headings":"","what":"mkin 0.9.46 (2017-07-24)","title":"mkin 0.9.46 (2017-07-24)","text":"Remove test_FOMC_ill-defined.R platform dependent","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-09452-2017-07-24","dir":"Changelog","previous_headings":"","what":"mkin 0.9.45.2 (2017-07-24)","title":"mkin 0.9.45.2 (2017-07-24)","text":"Rename twa max_twa_parent avoid conflict twa pfm package Update URLs documentation Limit test code one core pass windows Switch microbenchmark rbenchmark former supported platforms","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"new-features-0-9-45-1","dir":"Changelog","previous_headings":"","what":"New features","title":"mkin 0.9.45.1 (2016-12-20)","text":"twa function, calculating maximum time weighted average concentrations parent (SFO, FOMC DFOP).","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"minor-changes-0-9-45","dir":"Changelog","previous_headings":"","what":"Minor changes","title":"mkin 0.9.45 (2016-12-08)","text":"plot.mkinfit plot.mmkin: plotting device tikz, LaTeX markup used chi2 error graphs. Use pkgdown, successor staticdocs generating static HTML documentation. Include example output graphs also dontrun sections. plot.mkinfit: Plotting fail compiled model available, e.g. removed temporary directory. case, uncompiled model now used plotting","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"bug-fixes-0-9-44","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"mkin 0.9.44 (2016-06-29)","text":"test test_FOMC_ill-defined failed several architectures, test now skipped","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"major-changes-0-9-43","dir":"Changelog","previous_headings":"","what":"Major changes","title":"mkin 0.9.43 (2016-06-28)","text":"title changed Kinetic evaluations chemical degradation data plot.mkinfit: Add possibility show fits (residual plots requested) separately observed variables plot.mkinfit: Add possibility show chi2 error levels plot, similar way shown plot.mmkin plot_sep: Add function convenience wrapper plotting observed variables mkinfit objects separately, chi2 error values residual plots. Vignettes: main vignette mkin converted R markdown updated. vignettes also updated show current improved functionality. function add_err added package, making easy generate simulated data using error model based normal distribution","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"minor-changes-0-9-43","dir":"Changelog","previous_headings":"","what":"Minor changes","title":"mkin 0.9.43 (2016-06-28)","text":"Remove outdated reference inline package compiled_models vignette mkinfit: error cases fit converges, Jacobian untransformed model cost can estimated. Give warning instead return NA t-test results. summary.mkinfit: Give warning message covariance matrix can obtained. test added containing corresponding edge case check warnings correctly issued fit terminate. plot.mmkin: Round chi2 error value three significant digits, instead two decimal digits. mkinfit: Return err values used weighted fits column named err. Also include inverse weights column value observed data used, returned observed data component mkinfit object.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"bug-fixes-0-9-43","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"mkin 0.9.43 (2016-06-28)","text":"endpoints: name substance degrading metabolite (e.g. parent compound) used model formulation ended letter f, rate parameters listed formation fractions mixed names. also appear summary. mkinfit: Check observed variables checking user tried fix formation fractions fitting using ilr transformation. plot.mmkin: Set plot margins correctly, also case single fit plotted, main title placed reasonable way. plot.mkinfit: Correct default values col_obs, pch_obs lty_obs case obs_vars specified.","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"major-changes-0-9-42","dir":"Changelog","previous_headings":"","what":"Major changes","title":"mkin 0.9.42 (2016-03-25)","text":"Add argument from_max_mean mkinfit, fitting decline maximum observed value models single observed variable","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"minor-changes-0-9-42","dir":"Changelog","previous_headings":"","what":"Minor changes","title":"mkin 0.9.42 (2016-03-25)","text":"Add plots compiled_models vignette Give explanatory error message mkinmod fails due missing definition target variable print.mkinmod(): Improve formatting printing mkinmod model definitions","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"minor-changes-0-9-41","dir":"Changelog","previous_headings":"","what":"Minor changes","title":"mkin 0.9-41 (2015-11-09)","text":"Add R6 class mkinds representing datasets printing method Add printing method mkinmod objects Make possible specify arbitrary strings names compounds mkinmod, show plot Use index.r file group help topics static documentation","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"bug-fixes-0-9-41","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"mkin 0.9-41 (2015-11-09)","text":"print.summary.mkinfit(): Avoid error occurred printing summaries generated mkin versions 0.9-36","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"bug-fixes-0-9-40","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"mkin 0.9-40 (2015-07-21)","text":"endpoints(): DFOP SFORB models, optimize() used, make use fact DT50 must DT50_k1 DT50_k2 (DFOP) DT50_b1 DT50_b2 (SFORB), optimize() sometimes find minimum. Likewise finding DT90 values. Also fit log scale make function efficient.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"internal-changes-0-9-40","dir":"Changelog","previous_headings":"","what":"Internal changes","title":"mkin 0.9-40 (2015-07-21)","text":"DESCRIPTION, NAMESPACE, R/*.R: Import () stats, graphics methods packages, qualify function calls non-base packages installed R avoid NOTES made R CMD check –-cran upcoming R versions.","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"major-changes-0-9-39","dir":"Changelog","previous_headings":"","what":"Major changes","title":"mkin 0.9-39 (2015-06-26)","text":"New function mmkin(): function takes character vector model shorthand names, alternatively list mkinmod models, well list dataset main arguments. returns matrix mkinfit objects, row model column dataset. subsetting method single brackets available. Fitting models parallel using parallel package supported. New function plot.mmkin(): Plots single-row single-column mmkin objects including residual plots.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"bug-fixes-0-9-39","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"mkin 0.9-39 (2015-06-26)","text":"mkinparplot(): Fix x axis scaling rate constants formation fractions got confused introduction t-values transformed parameters.","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"minor-changes-0-9-38","dir":"Changelog","previous_headings":"","what":"Minor changes","title":"mkin 0.9-38 (2015-06-24)","text":"vignettes/compiled_models.html: Show performance improvement factor actually obtained building vignette, well mkin version, system info CPU model used building vignette. GNUMakefile,vignettes/*: Clean vignette generation include table contents HTML vignettes.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"bug-fixes-0-9-38","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"mkin 0.9-38 (2015-06-24)","text":"mkinmod(): generating C code derivatives, declare time variable needed remove ‘-W--unused-variable’ compiler flag C compiler used CRAN checks Solaris know .","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"major-changes-0-9-36","dir":"Changelog","previous_headings":"","what":"Major changes","title":"mkin 0.9-36 (2015-06-21)","text":"summary.mkinfit(): one-sided t-test significant difference untransformed parameters zero now always shown, based assumption normal distribution estimators untransformed parameters. Use caution, assumption unrealistic e.g. rate constants nonlinear kinetic models. compiler (gcc) installed, use version differential equation model compiled C code, huge performance boost models deSolve method works. mkinmod(): Create list component $cf (class CFuncList) list returned mkinmod, version can compiled autogenerated C code (see ). mkinfit(): Set default solution_type deSolve compiled version model present, except analytical solution possible.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"minor-changes-0-9-36","dir":"Changelog","previous_headings":"","what":"Minor changes","title":"mkin 0.9-36 (2015-06-21)","text":"Added simple showcase vignette evaluation FOCUS example dataset D","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"major-changes-0-9-35","dir":"Changelog","previous_headings":"","what":"Major changes","title":"mkin 0.9-35 (2015-05-15)","text":"Switch RUnit testthat testing","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"bug-fixes-0-9-35","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"mkin 0.9-35 (2015-05-15)","text":"mkinparplot(): Avoid warnings occurred confidence intervals available summary fit print.summary.mkinfit(): Fix printing summary case number iterations available NAMESPACE: export S3 methods plot.mkinfit, summary.mkinfit print.summary.mkinfit satisfy R CMD check R-devel mkinparplot(): Avoid warning R CMD check undeclared global variable Lower","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"new-features-0-9-35","dir":"Changelog","previous_headings":"","what":"New features","title":"mkin 0.9-35 (2015-05-15)","text":"mkinfit(): Report successful termination quiet = FALSE. helpful difficult problems fitted reweight.method = obs, progress often indicated reweighting. first test using results established expertise written German Federal Environmental Agency (UBA) added. Add synthetic datasets generated expertise written German Federal Environmental Agency UBA Add tests based datasets","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"new-features-0-9-34","dir":"Changelog","previous_headings":"","what":"New features","title":"mkin 0.9-34 (2014-11-22)","text":"Add convenience function mkinsub() creating lists used mkinmod() Add possibility fit indeterminate order rate equation (IORE) models using analytical solution (parent ) numeric solution. Paths IORE compounds metabolites supported using formation fractions (use_of_ff = ‘max’). Note numerical solution (method.ode = ‘deSolve’) IORE differential equations sometimes fails due numerical problems. Switch using Port algorithm (using model/trust region approach) per default. needing iterations Levenberg-Marquardt algorithm previously used per default, less sensitive starting parameters.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"minor-changes-0-9-34","dir":"Changelog","previous_headings":"","what":"Minor changes","title":"mkin 0.9-34 (2014-11-22)","text":"formatting differential equations summary improved Always include 0 y axis plotting fit","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"new-features-0-9-33","dir":"Changelog","previous_headings":"","what":"New features","title":"mkin 0.9-33 (2014-10-22)","text":"initial value (state.ini) observed variable highest observed residue set 100 case time zero observation state.ini = \"auto\" basic unit test mkinerrmin() written","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"bug-fixes-0-9-33","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"mkin 0.9-33 (2014-10-22)","text":"mkinfit(): internally fitted parameter g named g_ilr even transform_fractions=FALSE mkinfit(): initial value (state.ini) parent compound set parent () variable highest value observed data. mkinerrmin(): checking degrees freedom metabolites, check time zero value fixed instead checking observed value zero. ensures correct calculation degrees freedom also cases metabolite residue time zero greater zero. plot.mkinfit(): Avoid warning message using first component ylim occurred ylim specified explicitly","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"minor-changes-0-9-33","dir":"Changelog","previous_headings":"","what":"Minor changes","title":"mkin 0.9-33 (2014-10-22)","text":"formatting differential equations summary improved wrapping overly long lines FOCUS_Z vignette rebuilt improvement using width 70 avoid output outside grey area print.summary.mkinfit(): Avoid warning occurred gmkin showed summaries initial fits without iterations mkinfit(): Avoid warning occurred summarising fit performed maxitmodFit = 0 done gmkin configuring new fits.","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"new-features-0-9-32","dir":"Changelog","previous_headings":"","what":"New features","title":"mkin 0.9-32 (2014-07-24)","text":"number degrees freedom difficult define case ilr transformation formation fractions. Now source compartment number ilr parameters (=number optimised parameters) divided number pathways metabolites (=number affected data series) leads fractional degrees freedom cases. default initial value first state value now taken mean observations time zero, available. kinetic model can alternatively specified shorthand name parent degradation models, e.g. SFO, DFOP. Optimisation method, number model evaluations time elapsed optimisation given summary mkinfit objects. maximum number iterations optimisation algorithm can specified using argument maxit.modFit mkinfit function. mkinfit gives warning fit converge (apply SANN method). warning included summary.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"bug-fixes-0-9-32","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"mkin 0.9-32 (2014-07-24)","text":"Avoid plotting artificial 0 residual time zero mkinresplot determination degrees freedom mkinerrmin, formation fractions accounted multiple times case parallel formation metabolites. See new feature described solution. transform_rates=FALSE mkinfit now also works FOMC HS models. Initial values formation fractions set cases. warning given fit converge method default Levenberg-Marquardt method Marq used.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"minor-changes-0-9-32","dir":"Changelog","previous_headings":"","what":"Minor changes","title":"mkin 0.9-32 (2014-07-24)","text":"Vignettes rebuilt reflect changes summary method. Algorithm Pseudo excluded needs user-defined parameter limits supported. Algorithm Newton excluded different way specify maximum number iterations appear provide additional benefits.","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"bug-fixes-0-9-31","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"mkin 0.9-31 (2014-07-14)","text":"internal renaming optimised parameters Version 0.9-30 led errors determination degrees freedom chi2 error level calulations mkinerrmin() used summary function.","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"new-features-0-9-30","dir":"Changelog","previous_headings":"","what":"New features","title":"mkin 0.9-30 (2014-07-11)","text":"now possible use formation fractions combination turning sink mkinmod().","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"major-changes-0-9-30","dir":"Changelog","previous_headings":"","what":"Major changes","title":"mkin 0.9-30 (2014-07-11)","text":"original transformed parameters now different names (e.g. k_parent log_k_parent. also differ many formation fractions pathway sink. order information blocks print.summary.mkinfit.R() ordered logical way.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"minor-changes-0-9-30","dir":"Changelog","previous_headings":"","what":"Minor changes","title":"mkin 0.9-30 (2014-07-11)","text":"vignette FOCUS_Z simplified use formation fractions turning sink, slightly amended use new versions DT50 values calculated since mkin 0.9-29. vignettes rebuilt reflect changes. ChangeLog renamed NEWS.md entries converted markdown syntax compatible tools::news() function built R. test suite overhauled. Tests DFOP SFORB models dataset FOCUS_2006_A removed, much dependent optimisation algorithm /starting parameters, dataset SFO (compare kinfit vignette). Also, Schaefer complex case can now fitted using formation fractions, ‘Port’ optimisation method also fit A2 way published Piacenza paper. checks introduced mkinfit(), leading warnings stopping execution unsupported combinations methods parameters requested.","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-09-29-2014-06-27","dir":"Changelog","previous_headings":"","what":"mkin 0.9-29 (2014-06-27)","title":"mkin 0.9-29 (2014-06-27)","text":"R/mkinresplot.R: Make possible specify xlim R/geometric_mean.R, man/geometric_mean.Rd: Add geometric mean function R/endpoints.R, man/endpoints.Rd: Calculate additional (pseudo)-DT50 values FOMC, DFOP, HS SFORB. Avoid calculation formation fractions rate constants directly fitted","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-09-28-2014-05-20","dir":"Changelog","previous_headings":"","what":"mkin 0.9-28 (2014-05-20)","title":"mkin 0.9-28 (2014-05-20)","text":"backtransform confidence intervals formation fractions one compound formed, parameters define pathways set Add historical remarks background main package vignette Correct ‘isotropic’ ‘isometric’ ilr transformation","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-09-27-2014-05-10","dir":"Changelog","previous_headings":"","what":"mkin 0.9-27 (2014-05-10)","title":"mkin 0.9-27 (2014-05-10)","text":"Fork GUI separate package gmkin DESCRIPTION, NAMESPACE, TODO: Adapt add copyright information Remove files belonging GUI Possibility fit without parameter transformations, using bounds implemented FME Add McCall 2,4,5-T dataset Enable selection observed variables plotting Add possibility show residual plot plot.mkinfit R/mkinparplot.R, man/mkinparplot.Rd: plot parameters confidence intervals Change vignette format Sweave knitr Split examples vignette FOCUS_L FOCUS_Z Remove warning constant formation fractions mkinmod based misconception Restrict unit test Schaefer data parent primary metabolites formation fraction DT50 A2 highly correlated passing test platform dependent. example, test fails 1 14 platforms CRAN today. Add Eurofins Regulatory AG copyright notices Import FME deSolve instead depending clean startup Add starter function GUI: gmkin() Change format workspace files gmkin can distributed documented package Add gmkin workspace datasets FOCUS_2006_gmkin FOCUS_2006_Z_gmkin","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-09-24-2013-11-06","dir":"Changelog","previous_headings":"","what":"mkin 0.9-24 (2013-11-06)","title":"mkin 0.9-24 (2013-11-06)","text":"Bugfix re-enabling fixing combination initial values state variables Default values kinetic rate constants 0.1 “salted” small increment avoid numeric artefacts eigenvalue based solutions Backtransform fixed ODE parameters summary","code":""},{"path":"https://pkgdown.jrwb.de/mkin/news/index.html","id":"mkin-09-22-2013-10-26","dir":"Changelog","previous_headings":"","what":"mkin 0.9-22 (2013-10-26)","title":"mkin 0.9-22 (2013-10-26)","text":"Get rid optimisation step mkinerrmin - unnecessary. Thanks KinGUII inspiration - actually equation 6-2 FOCUS kinetics p. 91 overlooked originally Fix plot.mkinfit passed graphical arguments like main solver use plot=TRUE mkinfit() example first successful fits simple GUI Fix iteratively reweighted least squares case many metabolites Unify naming initial values state variables Unify naming dataframes optimised fixed parameters summary Show weighting method residuals summary Correct output data case manual weighting Implement IRLS assuming different variances observed variables use 0 values time zero chi2 error level calculations. way done KinGUII makes sense. impact chi2 error levels output. Generally seem lower metabolites now, presumably mean observed values higher detailed list changes mkin source please consult commit history http://github.com/jranke/mkin","code":""}]
diff --git a/docs/sitemap.xml b/docs/sitemap.xml
index 9a84d4c0..5eca1e1a 100644
--- a/docs/sitemap.xml
+++ b/docs/sitemap.xml
@@ -1,354 +1,110 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<urlset xmlns="http://www.sitemaps.org/schemas/sitemap/0.9">
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/404.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/articles/FOCUS_D.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/articles/FOCUS_L.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/articles/index.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/articles/mkin.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_cyan_pathway.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_dmta_parent.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_dmta_pathway.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/articles/prebuilt/2023_mesotrione_parent.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/articles/twa.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/articles/web_only/FOCUS_Z.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/articles/web_only/NAFTA_examples.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/articles/web_only/benchmarks.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/articles/web_only/compiled_models.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/articles/web_only/dimethenamid_2018.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/articles/web_only/multistart.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/articles/web_only/saem_benchmarks.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/authors.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/index.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/news/index.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/AIC.mmkin.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/CAKE_export.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/D24_2014.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/DFOP.solution.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/Extract.mmkin.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_DFOP_ref_A_to_B.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_FOMC_ref_A_to_F.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_HS_ref_A_to_F.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_SFO_ref_A_to_F.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_datasets.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/FOMC.solution.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/HS.solution.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/IORE.solution.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/NAFTA_SOP_2015.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/NAFTA_SOP_Attachment.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/SFO.solution.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/SFORB.solution.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/add_err.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/anova.saem.mmkin.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/aw.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/confint.mkinfit.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/convergence.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/create_deg_func.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/dimethenamid_2018.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/ds_mixed.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/endpoints.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/experimental_data_for_UBA.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/f_time_norm_focus.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/focus_soil_moisture.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/get_deg_func.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/hierarchical_kinetics.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/illparms.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/ilr.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/index.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/intervals.nlmixr.mmkin.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/intervals.saem.mmkin.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/llhist.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/loftest.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/logLik.mkinfit.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/logLik.saem.mmkin.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/logistic.solution.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/lrtest.mkinfit.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/max_twa_parent.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/mccall81_245T.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/mean_degparms.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/mhmkin.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/mixed.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/mkin_long_to_wide.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/mkin_wide_to_long.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/mkinds.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/mkindsg.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/mkinerrmin.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/mkinerrplot.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/mkinfit.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/mkinmod.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/mkinparplot.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/mkinplot.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/mkinpredict.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/mkinresplot.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/mkinsub.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/mmkin.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/multistart.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/nafta.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/nlme.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/nlme.mmkin.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/nlmixr.mmkin.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/nobs.mkinfit.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/parms.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/parplot.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/plot.mixed.mmkin.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/plot.mkinfit.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/plot.mmkin.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/plot.nafta.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/plot.nlme.mmkin.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/print.mkinds.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/print.mkinmod.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/print.mmkin.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/read_spreadsheet.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/reexports.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/residuals.mkinfit.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/saem.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/schaefer07_complex_case.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/set_nd_nq.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/sigma_twocomp.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/status.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/summary.mkinfit.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/summary.mmkin.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/summary.nlme.mmkin.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/summary.nlmixr.mmkin.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/summary.saem.mmkin.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/summary_listing.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/synthetic_data_for_UBA_2014.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/test_data_from_UBA_2014.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/tex_listing.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/tffm0.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/transform_odeparms.html</loc>
-  </url>
-  <url>
-    <loc>https://pkgdown.jrwb.de/mkin/reference/update.mkinfit.html</loc>
-  </url>
+<urlset xmlns = 'http://www.sitemaps.org/schemas/sitemap/0.9'>
+<url><loc>https://pkgdown.jrwb.de/mkin/404.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/articles/FOCUS_D.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/articles/FOCUS_L.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/articles/index.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/articles/mkin.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_cyan_pathway.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_dmta_parent.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_dmta_pathway.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/articles/prebuilt/2023_mesotrione_parent.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/articles/twa.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/articles/web_only/FOCUS_Z.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/articles/web_only/NAFTA_examples.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/articles/web_only/benchmarks.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/articles/web_only/compiled_models.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/articles/web_only/dimethenamid_2018.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/articles/web_only/multistart.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/articles/web_only/saem_benchmarks.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/authors.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/index.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/news/index.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/AIC.mmkin.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/CAKE_export.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/D24_2014.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/DFOP.solution.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/Extract.mmkin.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_DFOP_ref_A_to_B.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_FOMC_ref_A_to_F.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_HS_ref_A_to_F.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_SFO_ref_A_to_F.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/FOCUS_2006_datasets.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/FOMC.solution.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/HS.solution.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/IORE.solution.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/NAFTA_SOP_2015.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/NAFTA_SOP_Attachment.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/SFO.solution.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/SFORB.solution.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/add_err.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/anova.saem.mmkin.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/aw.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/check_failed.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/confint.mkinfit.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/create_deg_func.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/dimethenamid_2018.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/ds_mixed.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/endpoints.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/experimental_data_for_UBA.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/f_time_norm_focus.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/focus_soil_moisture.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/get_deg_func.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/hierarchical_kinetics.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/illparms.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/ilr.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/index.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/intervals.saem.mmkin.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/llhist.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/loftest.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/logLik.mkinfit.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/logLik.saem.mmkin.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/logistic.solution.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/lrtest.mkinfit.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/max_twa_parent.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/mccall81_245T.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/mean_degparms.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/mhmkin.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/mixed.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/mkin_long_to_wide.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/mkin_wide_to_long.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/mkinds.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/mkindsg.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/mkinerrmin.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/mkinerrplot.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/mkinfit.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/mkinmod.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/mkinparplot.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/mkinplot.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/mkinpredict.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/mkinresplot.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/mmkin.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/multistart.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/nafta.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/nlme.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/nlme.mmkin.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/nobs.mkinfit.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/parms.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/parplot.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/plot.mixed.mmkin.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/plot.mkinfit.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/plot.mmkin.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/plot.nafta.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/read_spreadsheet.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/reexports.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/residuals.mkinfit.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/saem.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/schaefer07_complex_case.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/set_nd_nq.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/sigma_twocomp.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/status.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/summary.mkinfit.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/summary.mmkin.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/summary.nlme.mmkin.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/summary.saem.mmkin.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/summary_listing.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/synthetic_data_for_UBA_2014.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/test_data_from_UBA_2014.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/transform_odeparms.html</loc></url>
+<url><loc>https://pkgdown.jrwb.de/mkin/reference/update.mkinfit.html</loc></url>
 </urlset>
+
diff --git a/inst/WORDLIST b/inst/WORDLIST
index 895550ba..8b3aae37 100644
--- a/inst/WORDLIST
+++ b/inst/WORDLIST
@@ -1,118 +1,241 @@
 AAM
+AR
 Agric
+Alterra
+Anova
+BBA
+BMC
+Backtransform
+Backtransformed
+BayerCropScience
+Beltman
+Berater
+BerliOS
+Boesten
+Borstel
+CFuncList
+CMD
+ChangeLog
+Colors
+Crauste
+DAR
+DCP
+DFOP
+DI
+DM
+DMTA
+DMTAP
+DT
+Defauls
+DegKin
+Dimethenamid
+EFSA
+Ethofumesate
+Eurofins
+FIM
+FME
+FO
+FOMC
+FOrum
+Filzmoser
+Flaach
+Fragoulis
+Fth
+Gandrillon
+Geosci
+Glyphosate
+Grenzach
+Guillemin
+Götz
+HS
+Hellas
+Hierarchichal
+Hron
+IKF
+ILR
+IORE
+IRLS
+Imazamox
+Isofetamid
+JCZ
+JSE
+Janina
+KP
+Kalibera
+Karel
+Karline
+KinGUI
+KinGUII
+KineticEval
+Kronacher
+Kullback
+LME
+LOD
+LOQ
+LR
+Leibler
+Levenberg
+Lorenzato
+MWHC
+Marquardt
+MatLab
+Math
+Meinecke
+Mikolasch
+ModelMaker
+Modellierungssoftware
+Moolgavkar
+Multimodel
+NAFTA
+NLHM
+Nambsheim
+Nonlinear
+Nvim
+ORCID
+OpenBlas
+PEC
+Pawitan
+PestDF
+Petzoldt
+Piacenza
+Pinheiro
+Pol
+Privatdozent
+Projektnummer
+Prüfung
+RAR
+RCC
+RUnit
+Rainbird
+Rmd
+Rocke
+Rprofile
+Rtools
+SAEM
+SFO
+SFORB
+Sanco
+Schäfer
+Soetaert
+Staudenmaier
+Str
+Syngenta
+Sys
+TWAs
+TeX
+Technometrics
+Tessella
+Thifensulfuron
+Topfit
+Trichlorophenoxyacetic
+UBA
+USe
+Umweltbundesamt
+Validierung
+Venzon
+Vrona
+Wageningen
+Wissenschaftlicher
+Workgroup
+Wyhlen
+Wöltjen
 als
 anova
-AR
 autogenerated
 autogeneration
 aw
+backends
 backtransform
-Backtransform
 backtransformation
 backtransformed
-BayerCropScience
-Berater
-BerliOS
+biexponential
 bpar
 bparms
-bremen
 calulations
+centering
 cf
-CFuncList
 cfunction
-ChangeLog
+ci
+clearpage
 cloneable
-CMD
-codecov
-Colors
+coauthor
+colum
+conf
 confint
 const
 cran
 csf
 cutoff
-Defauls
-DegKin
-degparms
+cyantraniliprole
+cyhalothrin
 deSolve
-DFOP
-DI
+degparms
+detectCores
 dichlorophenoxyacetic
 diethylether
+dimethenamid
+dir
 distimes
-DM
+dll
+dmta
 doi
+dontrun
 ds
-DT
-EFSA
+efsa
 eigen
 errmin
 errmod
 errplot
+erythropoiesis
 ethofumesate
-Ethofumesate
 etics
-Eurofins
-Filzmoser
-FME
-FOMC
-FOrum
+eu
+europa
 fourstep
 frac
-Fragoulis
+func
 gcc
-Geosci
 github
 glyphosate
-Glyphosate
 gmkin
-Grenzach
+gq
 hessians
-Hron
-HS
-IKF
+hierarchichal
+identifiabiliy
+illparms
 ilr
-ILR
 imazamox
-Imazamox
 ini
+init
 inital
-IORE
+intersoil
 irls
-IRLS
 isofetamid
-Isofetamid
 jrwb
-Karel
-Karline
-KineticEval
 kinfit
-KinGUI
-KinGUII
 knitr
-KP
-Kronacher
-Kullback
-Leibler
-Levenberg
+lin
 linux
+llhist
+llmin
+llquant
+lme
 lmtest
-LOD
+lod
 loftest
 logLik
+loglik
 logratio
-LOQ
-Lorenzato
-LR
 lrtest
 lsoda
-Marquardt
-Math
-MatLab
+maxabs
 maxitmodFit
 md
-Meinecke
-Mikolasch
+mesotrione
+mhmkin
 mkinds
+mkindsg
+mkinerrmin
 mkinerrplot
 mkinfit
 mkinmod
@@ -120,100 +243,79 @@ mkinparplot
 mkinpredict
 mkinresplot
 mmkin
-Modellierungssoftware
-ModelMaker
-Moolgavkar
-Multimodel
+multicompartment
+multistart
 nafta
-NAFTA
+nd
 nlme
 nlminb
 nls
 nobs
 nonlinear
-Nonlinear
+nq
 numDeriv
-Nvim
 obs
-ODEs
-oftentimes
-ORCID
+odeparms
+optim
 overparameterisation
 overparameterised
+pF
 parms
-Pawitan
-PEC
-PestDF
+parplot
 pfm
 piacenza
-Piacenza
 pkgbuild
-Privatdozent
-Projektnummer
-Prüfung
+posix
+racemic
 radiolabel
 radiolabels
-Rainbird
-RAR
-RCC
+rda
+rel
 reparameterisation
 res
 resplot
 reweight
 reweighting
 rl
-Rocke
+rmarkdown
+rmd
+rnw
 roxygen
-Rprofile
 rsd
-Rtools
-RUnit
-Sanco
-Schäfer
+saem
+saemix
 sep
-SFO
-SFORB
-Soetaert
-speclist
-Str
-Syngenta
-Sys
+shapiro
 tb
 tc
-Technometrics
-Tessella
+testdata
 testthat
 tex
 textrm
 texttt
+th
 thifensulfuron
-Thifensulfuron
 threestep
-Topfit
+tinytex
+tlmgr
+tol
 topright
 trichloroanisole
 trichlorophenol
 trichlorophenoxyacetic
-Trichlorophenoxyacetic
 twa
-TWAs
 twocomp
 twostep
-UBA
 ulticompartment
-Umweltbundesamt
 uncompiled
 und
 unixoid
-USe
-Validierung
 var
-Venzon
-Vrona
+vspace
 winbuilder
-Wissenschaftlicher
 workgroup
-Workgroup
-Wyhlen
+xlab
+xlim
+ylab
 ylim
 zu
diff --git a/inst/testdata/active_substance_medium_source_year.xlsx b/inst/testdata/active_substance_medium_source_year.xlsx
new file mode 100644
index 00000000..a11f0dc5
--- /dev/null
+++ b/inst/testdata/active_substance_medium_source_year.xlsx
diff --git a/log/build.log b/log/build.log
index 6f7ac8ab..de7e4488 100644
--- a/log/build.log
+++ b/log/build.log
@@ -7,5 +7,5 @@
 * checking for empty or unneeded directories
 Removed empty directory ‘mkin/inst/rmarkdown/templates/hierarchical_kinetics_parent’
 Removed empty directory ‘mkin/vignettes/web_only’
-* building ‘mkin_1.2.6.tar.gz’
+* building ‘mkin_1.2.9.tar.gz’
 
diff --git a/log/check.log b/log/check.log
index 64457e57..0c54a49d 100644
--- a/log/check.log
+++ b/log/check.log
@@ -1,17 +1,17 @@
-* using log directory ‘/home/agsad.admin.ch/f80868656/projects/mkin/mkin.Rcheck’
-* using R version 4.3.1 (2023-06-16)
-* using platform: x86_64-pc-linux-gnu (64-bit)
+* using log directory ‘/home/jranke/git/mkin/mkin.Rcheck’
+* using R version 4.4.2 (2024-10-31)
+* using platform: x86_64-pc-linux-gnu
 * R was compiled by
-    gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
-    GNU Fortran (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
-* running under: Ubuntu 22.04.3 LTS
+    gcc (Debian 12.2.0-14) 12.2.0
+    GNU Fortran (Debian 12.2.0-14) 12.2.0
+* running under: Debian GNU/Linux 12 (bookworm)
 * using session charset: UTF-8
 * using options ‘--no-tests --as-cran’
 * checking for file ‘mkin/DESCRIPTION’ ... OK
 * checking extension type ... Package
-* this is package ‘mkin’ version ‘1.2.5’
+* this is package ‘mkin’ version ‘1.2.9’
 * package encoding: UTF-8
-* checking CRAN incoming feasibility ... [5s/26s] Note_to_CRAN_maintainers
+* checking CRAN incoming feasibility ... Note_to_CRAN_maintainers
 Maintainer: ‘Johannes Ranke <johannes.ranke@jrwb.de>’
 * checking package namespace information ... OK
 * checking package dependencies ... OK
@@ -21,18 +21,18 @@ Maintainer: ‘Johannes Ranke <johannes.ranke@jrwb.de>’
 * checking for hidden files and directories ... OK
 * checking for portable file names ... OK
 * checking for sufficient/correct file permissions ... OK
-* checking serialization versions ... OK
 * checking whether package ‘mkin’ can be installed ... OK
 * checking installed package size ... OK
 * checking package directory ... OK
-* checking for future file timestamps ... OK
+* checking for future file timestamps ... NOTE
+unable to verify current time
 * checking ‘build’ directory ... OK
 * checking DESCRIPTION meta-information ... OK
 * checking top-level files ... OK
 * checking for left-over files ... OK
 * checking index information ... OK
 * checking package subdirectories ... OK
-* checking R files for non-ASCII characters ... OK
+* checking code files for non-ASCII characters ... OK
 * checking R files for syntax errors ... OK
 * checking whether the package can be loaded ... OK
 * checking whether the package can be loaded with stated dependencies ... OK
@@ -45,7 +45,7 @@ Maintainer: ‘Johannes Ranke <johannes.ranke@jrwb.de>’
 * checking S3 generic/method consistency ... OK
 * checking replacement functions ... OK
 * checking foreign function calls ... OK
-* checking R code for possible problems ... [14s/15s] OK
+* checking R code for possible problems ... OK
 * checking Rd files ... OK
 * checking Rd metadata ... OK
 * checking Rd line widths ... OK
@@ -62,22 +62,20 @@ Maintainer: ‘Johannes Ranke <johannes.ranke@jrwb.de>’
 * checking sizes of PDF files under ‘inst/doc’ ... OK
 * checking installed files from ‘inst/doc’ ... OK
 * checking files in ‘vignettes’ ... OK
-* checking examples ... [20s/22s] OK
+* checking examples ... [10s/10s] OK
 * checking for unstated dependencies in ‘tests’ ... OK
 * checking tests ... SKIPPED
 * checking for unstated dependencies in vignettes ... OK
-* checking package vignettes in ‘inst/doc’ ... OK
-* checking re-building of vignette outputs ... [17s/18s] OK
+* checking package vignettes ... OK
+* checking re-building of vignette outputs ... [13s/10s] OK
 * checking PDF version of manual ... OK
-* checking HTML version of manual ... NOTE
-Skipping checking HTML validation: no command 'tidy' found
+* checking HTML version of manual ... OK
 * checking for non-standard things in the check directory ... OK
 * checking for detritus in the temp directory ... OK
 * DONE
 
 Status: 1 NOTE
 See
-  ‘/home/agsad.admin.ch/f80868656/projects/mkin/mkin.Rcheck/00check.log’
+  ‘/home/jranke/git/mkin/mkin.Rcheck/00check.log’
 for details.
 
-
diff --git a/log/test.log b/log/test.log
index 6d5bc470..746f0458 100644
--- a/log/test.log
+++ b/log/test.log
@@ -1,117 +1,51 @@
 ℹ Testing mkin
-✔ | F W S  OK | Context
-✔ |         5 | AIC calculation
-✔ |         5 | Analytical solutions for coupled models [1.5s]
-✔ |         5 | Calculation of Akaike weights
-✔ |         3 | Export dataset for reading into CAKE
-✔ |         6 | Use of precompiled symbols in mkinpredict [3.1s]
-✔ |        12 | Confidence intervals and p-values [0.4s]
-✔ |     1  12 | Dimethenamid data from 2018 [13.2s]
-────────────────────────────────────────────────────────────────────────────────
-Skip ('test_dmta.R:88:3'): Different backends get consistent results for SFO-SFO3+, dimethenamid data
-Reason: Fitting this ODE model with saemix takes about 5 minutes on my new system
-────────────────────────────────────────────────────────────────────────────────
-✔ |        14 | Error model fitting [2.5s]
-✔ |         5 | Time step normalisation
-✔ |         4 | Calculation of FOCUS chi2 error levels [0.3s]
-✔ |        14 | Results for FOCUS D established in expertise for UBA (Ranke 2014) [0.4s]
-✔ |         4 | Test fitting the decline of metabolites from their maximum [0.2s]
-✔ |         1 | Fitting the logistic model [0.1s]
-✔ |        10 | Batch fitting and diagnosing hierarchical kinetic models [19.0s]
-✖ | 1   2  15 | Nonlinear mixed-effects models [149.2s]
-────────────────────────────────────────────────────────────────────────────────
-Failure ('test_mixed.R:21:3'): Print methods work
-Results have changed from known value recorded in 'print_dfop_saem_1.txt'.
-
-old[13:23] vs new[13:23]
-  ""
-  "Fitted parameters:"
-  "          estimate lower  upper"
-- "parent_0     99.92 98.77 101.06"
-+ "parent_0     99.96 98.82 101.11"
-- "log_k1       -2.72 -2.95  -2.50"
-+ "log_k1       -2.71 -2.94  -2.49"
-- "log_k2       -4.14 -4.27  -4.01"
-+ "log_k2       -4.14 -4.26  -4.01"
-- "g_qlogis     -0.35 -0.53  -0.16"
-+ "g_qlogis     -0.36 -0.54  -0.17"
-- "a.1           0.92  0.68   1.16"
-+ "a.1           0.93  0.69   1.17"
-  "b.1           0.05  0.04   0.06"
-  "SD.log_k1     0.37  0.23   0.51"
-and 1 more ...
-
-Skip ('test_mixed.R:80:3'): saemix results are reproducible for biphasic fits
-Reason: Fitting with saemix takes around 10 minutes when using deSolve
-
-Skip ('test_mixed.R:133:3'): SFO-SFO saemix specific analytical solution work
-Reason: This is seldom used, so save some time
-────────────────────────────────────────────────────────────────────────────────
-✔ |         3 | Test dataset classes mkinds and mkindsg
-✔ |        10 | Special cases of mkinfit calls [0.3s]
-✔ |         3 | mkinfit features [0.5s]
-✔ |         8 | mkinmod model generation and printing
-✔ |         3 | Model predictions with mkinpredict [0.1s]
-✖ | 3       9 | Multistart method for saem.mmkin models [23.2s]
-────────────────────────────────────────────────────────────────────────────────
-Failure ('test_multistart.R:44:3'): multistart works for saem.mmkin models
-Snapshot of `testcase` to 'multistart/mixed-model-fit-for-saem-object-with-mkin-transformations.svg' has changed
-Run `testthat::snapshot_review('multistart/')` to review changes
-Backtrace:
- 1. vdiffr::expect_doppelganger(...)
-      at test_multistart.R:44:2
- 3. testthat::expect_snapshot_file(...)
-
-Failure ('test_multistart.R:55:3'): multistart works for saem.mmkin models
-Snapshot of `testcase` to 'multistart/llhist-for-dfop-sfo-fit.svg' has changed
-Run `testthat::snapshot_review('multistart/')` to review changes
-Backtrace:
- 1. vdiffr::expect_doppelganger("llhist for dfop sfo fit", llhist_dfop_sfo)
-      at test_multistart.R:55:2
- 3. testthat::expect_snapshot_file(...)
-
-Failure ('test_multistart.R:56:3'): multistart works for saem.mmkin models
-Snapshot of `testcase` to 'multistart/parplot-for-dfop-sfo-fit.svg' has changed
-Run `testthat::snapshot_review('multistart/')` to review changes
-Backtrace:
- 1. vdiffr::expect_doppelganger("parplot for dfop sfo fit", parplot_dfop_sfo)
-      at test_multistart.R:56:2
- 3. testthat::expect_snapshot_file(...)
-────────────────────────────────────────────────────────────────────────────────
-✔ |        16 | Evaluations according to 2015 NAFTA guidance [1.6s]
-✔ |         9 | Nonlinear mixed-effects models with nlme [3.8s]
-✖ | 1      14 | Plotting [4.7s]
-────────────────────────────────────────────────────────────────────────────────
-Failure ('test_plot.R:55:3'): Plotting mkinfit, mmkin and mixed model objects is reproducible
-Snapshot of `testcase` to 'plot/mixed-model-fit-for-nlme-object.svg' has changed
-Run `testthat::snapshot_review('plot/')` to review changes
-Backtrace:
- 1. vdiffr::expect_doppelganger(...)
-      at test_plot.R:55:2
- 3. testthat::expect_snapshot_file(...)
-────────────────────────────────────────────────────────────────────────────────
-✔ |         4 | Residuals extracted from mkinfit models
-✔ |     1  36 | saemix parent models [31.4s]
-────────────────────────────────────────────────────────────────────────────────
-Skip ('test_saemix_parent.R:143:3'): We can also use mkin solution methods for saem
-Reason: This still takes almost 2.5 minutes although we do not solve ODEs
-────────────────────────────────────────────────────────────────────────────────
-✔ |         2 | Complex test case from Schaefer et al. (2007) Piacenza paper [0.5s]
-✔ |        11 | Processing of residue series
-✔ |        10 | Fitting the SFORB model [1.7s]
-✔ |         1 | Summaries of old mkinfit objects
-✔ |         5 | Summary
-✔ |         4 | Results for synthetic data established in expertise for UBA (Ranke 2014) [0.8s]
-✔ |         9 | Hypothesis tests [2.9s]
-✔ |         4 | Calculation of maximum time weighted average concentrations (TWAs) [0.7s]
+✔ | F W  S  OK | Context
+✔ |          5 | AIC calculation
+✔ |          5 | Analytical solutions for coupled models [1.6s]
+✔ |          5 | Calculation of Akaike weights
+✔ |          3 | Export dataset for reading into CAKE
+✔ |          6 | Use of precompiled symbols in mkinpredict [3.3s]
+✔ |         12 | Confidence intervals and p-values
+✔ |      1  12 | Dimethenamid data from 2018 [13.3s]
+✔ |         14 | Error model fitting [2.7s]
+✔ |          5 | Time step normalisation
+✔ |          4 | Calculation of FOCUS chi2 error levels
+✔ |         14 | Results for FOCUS D established in expertise for UBA (Ranke 2014)
+✔ |          4 | Test fitting the decline of metabolites from their maximum
+✔ |          1 | Fitting the logistic model
+✔ |         10 | Batch fitting and diagnosing hierarchical kinetic models [19.7s]
+✔ |      2  16 | Nonlinear mixed-effects models [148.2s]
+✔ |          3 | Test dataset classes mkinds and mkindsg
+✔ |         10 | Special cases of mkinfit calls
+✔ |          3 | mkinfit features
+✔ |          8 | mkinmod model generation and printing
+✔ |          3 | Model predictions with mkinpredict
+✔ |         12 | Multistart method for saem.mmkin models [23.7s]
+✔ |         16 | Evaluations according to 2015 NAFTA guidance [1.6s]
+✔ |          9 | Nonlinear mixed-effects models with nlme [4.2s]
+✔ |         15 | Plotting [4.6s]
+✔ |          4 | Residuals extracted from mkinfit models
+✔ |      1  38 | saemix parent models [35.9s]
+✔ |          2 | Complex test case from Schaefer et al. (2007) Piacenza paper
+✔ |         11 | Processing of residue series
+✔ |         10 | Fitting the SFORB model [1.7s]
+✔ |          1 | Summaries of old mkinfit objects
+✔ |          5 | Summary
+✔ |          4 | Results for synthetic data established in expertise for UBA (Ranke 2014)
+✔ |          9 | Hypothesis tests [2.9s]
+✔ |          4 | Calculation of maximum time weighted average concentrations (TWAs)
+✔ |          2 | water-sediment
 
 ══ Results ═════════════════════════════════════════════════════════════════════
-Duration: 262.6 s
-
-── Skipped tests  ──────────────────────────────────────────────────────────────
-• Fitting this ODE model with saemix takes about 5 minutes on my new system (1)
-• Fitting with saemix takes around 10 minutes when using deSolve (1)
-• This is seldom used, so save some time (1)
-• This still takes almost 2.5 minutes although we do not solve ODEs (1)
-
-[ FAIL 5 | WARN 0 | SKIP 4 | PASS 276 ]
+Duration: 268.8 s
+
+── Skipped tests (4) ───────────────────────────────────────────────────────────
+• Fitting this ODE model with saemix takes about 5 minutes on my new system
+  (1): 'test_dmta.R:88:3'
+• Fitting with saemix takes around 10 minutes when using deSolve (1):
+  'test_mixed.R:80:3'
+• This is seldom used, so save some time (1): 'test_mixed.R:135:3'
+• This still takes almost 2.5 minutes although we do not solve ODEs (1):
+  'test_saemix_parent.R:143:3'
+
+[ FAIL 0 | WARN 0 | SKIP 4 | PASS 285 ]
diff --git a/man/check_failed.Rd b/man/check_failed.Rd
new file mode 100644
index 00000000..85029024
--- /dev/null
+++ b/man/check_failed.Rd
@@ -0,0 +1,14 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/mhmkin.R
+\name{check_failed}
+\alias{check_failed}
+\title{Check if fit within an mhmkin object failed}
+\usage{
+check_failed(x)
+}
+\arguments{
+\item{x}{The object to be checked}
+}
+\description{
+Check if fit within an mhmkin object failed
+}
diff --git a/man/hierarchical_kinetics.Rd b/man/hierarchical_kinetics.Rd
index bcbe1e06..8330fe68 100644
--- a/man/hierarchical_kinetics.Rd
+++ b/man/hierarchical_kinetics.Rd
@@ -9,7 +9,11 @@ hierarchical_kinetics(..., keep_tex = FALSE)
 \arguments{
 \item{...}{Arguments to \code{rmarkdown::pdf_document}}
 
-\item{keep_tex}{Keep the intermediate tex file used in the conversion to PDF}
+\item{keep_tex}{Keep the intermediate tex file used in the conversion to PDF.
+Note that this argument does not control whether to keep the auxiliary
+files (e.g., \file{.aux}) generated by LaTeX when compiling \file{.tex} to
+\file{.pdf}. To keep these files, you may set \code{options(tinytex.clean =
+FALSE)}.}
 }
 \value{
 R Markdown output format to pass to
diff --git a/man/mkinfit.Rd b/man/mkinfit.Rd
index f96b4d22..edec9546 100644
--- a/man/mkinfit.Rd
+++ b/man/mkinfit.Rd
@@ -39,7 +39,8 @@ model to be fitted to the data, or one of the shorthand names ("SFO",
 parent only degradation model is generated for the variable with the
 highest value in \code{observed}.}
 
-\item{observed}{A dataframe with the observed data.  The first column called
+\item{observed}{A dataframe or an object coercible to a dataframe
+(e.g. a \code{tibble}) with the observed data.  The first column called
 "name" must contain the name of the observed variable for each data point.
 The second column must contain the times of observation, named "time".
 The third column must be named "value" and contain the observed values.
diff --git a/man/mkinpredict.Rd b/man/mkinpredict.Rd
index 792d0e47..f68f6c7a 100644
--- a/man/mkinpredict.Rd
+++ b/man/mkinpredict.Rd
@@ -71,7 +71,7 @@ parent compound.}
 \item{use_symbols}{If set to \code{TRUE} (default), symbol info present in
 the \link{mkinmod} object is used if available for accessing compiled code}
 
-\item{method.ode}{The solution method passed via \link{mkinpredict} to \link{ode}] in
+\item{method.ode}{The solution method passed via \link{mkinpredict} to \code{deSolve::ode()} in
 case the solution type is "deSolve" and we are not using compiled code.
 When using compiled code, only lsoda is supported.}
 
@@ -86,7 +86,7 @@ the observed variables (default) or for all state variables (if set to
 FALSE). Setting this to FALSE has no effect for analytical solutions,
 as these always return mapped output.}
 
-\item{na_stop}{Should it be an error if \link{ode} returns NaN values}
+\item{na_stop}{Should it be an error if \code{deSolve::ode()} returns NaN values}
 }
 \value{
 A matrix with the numeric solution in wide format
diff --git a/man/multistart.Rd b/man/multistart.Rd
index 0df29bfa..d3c23bcf 100644
--- a/man/multistart.Rd
+++ b/man/multistart.Rd
@@ -77,7 +77,7 @@ dmta_ds[["Elliot 1"]] <- dmta_ds[["Elliot 2"]] <- NULL
 f_mmkin <- mmkin("DFOP", dmta_ds, error_model = "tc", cores = 7, quiet = TRUE)
 f_saem_full <- saem(f_mmkin)
 f_saem_full_multi <- multistart(f_saem_full, n = 16, cores = 16)
-parplot(f_saem_full_multi, lpos = "topleft")
+parplot(f_saem_full_multi, lpos = "topleft", las = 2)
 illparms(f_saem_full)
 
 f_saem_reduced <- update(f_saem_full, no_random_effect = "log_k2")
@@ -87,7 +87,7 @@ illparms(f_saem_reduced)
 library(parallel)
 cl <- makePSOCKcluster(12)
 f_saem_reduced_multi <- multistart(f_saem_reduced, n = 16, cluster = cl)
-parplot(f_saem_reduced_multi, lpos = "topright", ylim = c(0.5, 2))
+parplot(f_saem_reduced_multi, lpos = "topright", ylim = c(0.5, 2), las = 2)
 stopCluster(cl)
 }
 }
diff --git a/man/nlme.Rd b/man/nlme.Rd
index e87b7a00..2c92f31b 100644
--- a/man/nlme.Rd
+++ b/man/nlme.Rd
@@ -15,7 +15,7 @@ nlme_data(object)
 \value{
 A function that can be used with nlme
 
-A \code{\link{groupedData}} object
+A \code{nlme::groupedData} object
 }
 \description{
 These functions facilitate setting up a nonlinear mixed effects model for
diff --git a/man/plot.mixed.mmkin.Rd b/man/plot.mixed.mmkin.Rd
index 1e264db3..3c44510d 100644
--- a/man/plot.mixed.mmkin.Rd
+++ b/man/plot.mixed.mmkin.Rd
@@ -25,7 +25,7 @@
   nrow.legend = ceiling((length(i) + 1)/ncol.legend),
   rel.height.legend = 0.02 + 0.07 * nrow.legend,
   rel.height.bottom = 1.1,
-  pch_ds = 1:length(i),
+  pch_ds = c(1:25, 33, 35:38, 40:41, 47:57, 60:90)[1:length(i)],
   col_ds = pch_ds + 1,
   lty_ds = col_ds,
   frame = TRUE,
diff --git a/tests/testthat/Rplots.pdf b/tests/testthat/Rplots.pdf
new file mode 100644
index 00000000..2638aff8
--- /dev/null
+++ b/tests/testthat/Rplots.pdf
diff --git a/tests/testthat/_snaps/multistart/parplot-for-dfop-sfo-fit.svg b/tests/testthat/_snaps/multistart/parplot-for-dfop-sfo-fit.svg
index ed9168fb..c733f84f 100644
--- a/tests/testthat/_snaps/multistart/parplot-for-dfop-sfo-fit.svg
+++ b/tests/testthat/_snaps/multistart/parplot-for-dfop-sfo-fit.svg
@@ -20,177 +20,182 @@
 <g clip-path='url(#cpMC4wMHw3MjAuMDB8MC4wMHw1NzYuMDA=)'>
 </g>
 <defs>
-  <clipPath id='cpNTkuMDR8Njg5Ljc2fDU5LjA0fDUwMi41Ng=='>
-    <rect x='59.04' y='59.04' width='630.72' height='443.52' />
+  <clipPath id='cpNTkuMDR8Njg5Ljc2fDMwLjI0fDQzMC41Ng=='>
+    <rect x='59.04' y='30.24' width='630.72' height='400.32' />
   </clipPath>
 </defs>
-<g clip-path='url(#cpNTkuMDR8Njg5Ljc2fDU5LjA0fDUwMi41Ng==)'>
-<polygon points='86.57,280.78 119.94,280.78 119.94,280.26 86.57,280.26 ' style='stroke-width: 0.75; stroke: none; fill: #D3D3D3;' />
-<line x1='86.57' y1='280.70' x2='119.94' y2='280.70' style='stroke-width: 2.25; stroke-linecap: butt;' />
-<line x1='103.26' y1='280.80' x2='103.26' y2='280.78' style='stroke-width: 0.75; stroke-dasharray: 4.00,4.00;' />
-<line x1='103.26' y1='279.89' x2='103.26' y2='280.26' style='stroke-width: 0.75; stroke-dasharray: 4.00,4.00;' />
-<line x1='94.91' y1='280.80' x2='111.60' y2='280.80' style='stroke-width: 0.75;' />
-<line x1='94.91' y1='279.89' x2='111.60' y2='279.89' style='stroke-width: 0.75;' />
-<polygon points='86.57,280.78 119.94,280.78 119.94,280.26 86.57,280.26 ' style='stroke-width: 0.75; fill: none;' />
-<polygon points='128.29,282.92 161.66,282.92 161.66,281.57 128.29,281.57 ' style='stroke-width: 0.75; stroke: none; fill: #D3D3D3;' />
-<line x1='128.29' y1='282.61' x2='161.66' y2='282.61' style='stroke-width: 2.25; stroke-linecap: butt;' />
-<line x1='144.97' y1='282.96' x2='144.97' y2='282.92' style='stroke-width: 0.75; stroke-dasharray: 4.00,4.00;' />
-<line x1='144.97' y1='280.80' x2='144.97' y2='281.57' style='stroke-width: 0.75; stroke-dasharray: 4.00,4.00;' />
-<line x1='136.63' y1='282.96' x2='153.31' y2='282.96' style='stroke-width: 0.75;' />
-<line x1='136.63' y1='280.80' x2='153.31' y2='280.80' style='stroke-width: 0.75;' />
-<polygon points='128.29,282.92 161.66,282.92 161.66,281.57 128.29,281.57 ' style='stroke-width: 0.75; fill: none;' />
-<polygon points='170.00,282.03 203.37,282.03 203.37,281.10 170.00,281.10 ' style='stroke-width: 0.75; stroke: none; fill: #D3D3D3;' />
-<line x1='170.00' y1='281.44' x2='203.37' y2='281.44' style='stroke-width: 2.25; stroke-linecap: butt;' />
-<line x1='186.69' y1='282.58' x2='186.69' y2='282.03' style='stroke-width: 0.75; stroke-dasharray: 4.00,4.00;' />
-<line x1='186.69' y1='280.80' x2='186.69' y2='281.10' style='stroke-width: 0.75; stroke-dasharray: 4.00,4.00;' />
-<line x1='178.34' y1='282.58' x2='195.03' y2='282.58' style='stroke-width: 0.75;' />
-<line x1='178.34' y1='280.80' x2='195.03' y2='280.80' style='stroke-width: 0.75;' />
-<polygon points='170.00,282.03 203.37,282.03 203.37,281.10 170.00,281.10 ' style='stroke-width: 0.75; fill: none;' />
-<polygon points='211.71,281.58 245.09,281.58 245.09,280.01 211.71,280.01 ' style='stroke-width: 0.75; stroke: none; fill: #D3D3D3;' />
-<line x1='211.71' y1='281.14' x2='245.09' y2='281.14' style='stroke-width: 2.25; stroke-linecap: butt;' />
-<line x1='228.40' y1='281.69' x2='228.40' y2='281.58' style='stroke-width: 0.75; stroke-dasharray: 4.00,4.00;' />
-<line x1='228.40' y1='279.21' x2='228.40' y2='280.01' style='stroke-width: 0.75; stroke-dasharray: 4.00,4.00;' />
-<line x1='220.06' y1='281.69' x2='236.74' y2='281.69' style='stroke-width: 0.75;' />
-<line x1='220.06' y1='279.21' x2='236.74' y2='279.21' style='stroke-width: 0.75;' />
-<polygon points='211.71,281.58 245.09,281.58 245.09,280.01 211.71,280.01 ' style='stroke-width: 0.75; fill: none;' />
-<polygon points='253.43,281.87 286.80,281.87 286.80,280.63 253.43,280.63 ' style='stroke-width: 0.75; stroke: none; fill: #D3D3D3;' />
-<line x1='253.43' y1='281.30' x2='286.80' y2='281.30' style='stroke-width: 2.25; stroke-linecap: butt;' />
-<line x1='270.11' y1='281.93' x2='270.11' y2='281.87' style='stroke-width: 0.75; stroke-dasharray: 4.00,4.00;' />
-<line x1='270.11' y1='280.47' x2='270.11' y2='280.63' style='stroke-width: 0.75; stroke-dasharray: 4.00,4.00;' />
-<line x1='261.77' y1='281.93' x2='278.46' y2='281.93' style='stroke-width: 0.75;' />
-<line x1='261.77' y1='280.47' x2='278.46' y2='280.47' style='stroke-width: 0.75;' />
-<polygon points='253.43,281.87 286.80,281.87 286.80,280.63 253.43,280.63 ' style='stroke-width: 0.75; fill: none;' />
-<polygon points='295.14,281.17 328.51,281.17 328.51,278.48 295.14,278.48 ' style='stroke-width: 0.75; stroke: none; fill: #D3D3D3;' />
-<line x1='295.14' y1='280.00' x2='328.51' y2='280.00' style='stroke-width: 2.25; stroke-linecap: butt;' />
-<line x1='311.83' y1='281.54' x2='311.83' y2='281.17' style='stroke-width: 0.75; stroke-dasharray: 4.00,4.00;' />
-<line x1='311.83' y1='277.75' x2='311.83' y2='278.48' style='stroke-width: 0.75; stroke-dasharray: 4.00,4.00;' />
-<line x1='303.49' y1='281.54' x2='320.17' y2='281.54' style='stroke-width: 0.75;' />
-<line x1='303.49' y1='277.75' x2='320.17' y2='277.75' style='stroke-width: 0.75;' />
-<polygon points='295.14,281.17 328.51,281.17 328.51,278.48 295.14,278.48 ' style='stroke-width: 0.75; fill: none;' />
-<polygon points='336.86,282.43 370.23,282.43 370.23,280.88 336.86,280.88 ' style='stroke-width: 0.75; stroke: none; fill: #D3D3D3;' />
-<line x1='336.86' y1='281.56' x2='370.23' y2='281.56' style='stroke-width: 2.25; stroke-linecap: butt;' />
-<line x1='353.54' y1='282.69' x2='353.54' y2='282.43' style='stroke-width: 0.75; stroke-dasharray: 4.00,4.00;' />
-<line x1='353.54' y1='280.80' x2='353.54' y2='280.88' style='stroke-width: 0.75; stroke-dasharray: 4.00,4.00;' />
-<line x1='345.20' y1='282.69' x2='361.89' y2='282.69' style='stroke-width: 0.75;' />
-<line x1='345.20' y1='280.80' x2='361.89' y2='280.80' style='stroke-width: 0.75;' />
-<polygon points='336.86,282.43 370.23,282.43 370.23,280.88 336.86,280.88 ' style='stroke-width: 0.75; fill: none;' />
-<polygon points='378.57,280.97 411.94,280.97 411.94,280.81 378.57,280.81 ' style='stroke-width: 0.75; stroke: none; fill: #D3D3D3;' />
-<line x1='378.57' y1='280.83' x2='411.94' y2='280.83' style='stroke-width: 2.25; stroke-linecap: butt;' />
-<line x1='395.26' y1='281.09' x2='395.26' y2='280.97' style='stroke-width: 0.75; stroke-dasharray: 4.00,4.00;' />
-<line x1='395.26' y1='280.80' x2='395.26' y2='280.81' style='stroke-width: 0.75; stroke-dasharray: 4.00,4.00;' />
-<line x1='386.91' y1='281.09' x2='403.60' y2='281.09' style='stroke-width: 0.75;' />
-<line x1='386.91' y1='280.80' x2='403.60' y2='280.80' style='stroke-width: 0.75;' />
-<polygon points='378.57,280.97 411.94,280.97 411.94,280.81 378.57,280.81 ' style='stroke-width: 0.75; fill: none;' />
-<polygon points='420.29,409.00 453.66,409.00 453.66,-143.74 420.29,-143.74 ' style='stroke-width: 0.75; stroke: none; fill: #D3D3D3;' />
-<line x1='420.29' y1='40.61' x2='453.66' y2='40.61' style='stroke-width: 2.25; stroke-linecap: butt;' />
-<line x1='436.97' y1='640.00' x2='436.97' y2='409.00' style='stroke-width: 0.75; stroke-dasharray: 4.00,4.00;' />
-<line x1='436.97' y1='-189.02' x2='436.97' y2='-143.74' style='stroke-width: 0.75; stroke-dasharray: 4.00,4.00;' />
-<line x1='428.63' y1='640.00' x2='445.31' y2='640.00' style='stroke-width: 0.75;' />
-<line x1='428.63' y1='-189.02' x2='445.31' y2='-189.02' style='stroke-width: 0.75;' />
-<polygon points='420.29,409.00 453.66,409.00 453.66,-143.74 420.29,-143.74 ' style='stroke-width: 0.75; fill: none;' />
-<polygon points='462.00,279.09 495.37,279.09 495.37,274.52 462.00,274.52 ' style='stroke-width: 0.75; stroke: none; fill: #D3D3D3;' />
-<line x1='462.00' y1='277.09' x2='495.37' y2='277.09' style='stroke-width: 2.25; stroke-linecap: butt;' />
-<line x1='478.69' y1='280.80' x2='478.69' y2='279.09' style='stroke-width: 0.75; stroke-dasharray: 4.00,4.00;' />
-<line x1='478.69' y1='272.26' x2='478.69' y2='274.52' style='stroke-width: 0.75; stroke-dasharray: 4.00,4.00;' />
-<line x1='470.34' y1='280.80' x2='487.03' y2='280.80' style='stroke-width: 0.75;' />
-<line x1='470.34' y1='272.26' x2='487.03' y2='272.26' style='stroke-width: 0.75;' />
-<polygon points='462.00,279.09 495.37,279.09 495.37,274.52 462.00,274.52 ' style='stroke-width: 0.75; fill: none;' />
-<polygon points='503.71,282.68 537.09,282.68 537.09,281.44 503.71,281.44 ' style='stroke-width: 0.75; stroke: none; fill: #D3D3D3;' />
-<line x1='503.71' y1='282.32' x2='537.09' y2='282.32' style='stroke-width: 2.25; stroke-linecap: butt;' />
-<line x1='520.40' y1='282.78' x2='520.40' y2='282.68' style='stroke-width: 0.75; stroke-dasharray: 4.00,4.00;' />
-<line x1='520.40' y1='280.80' x2='520.40' y2='281.44' style='stroke-width: 0.75; stroke-dasharray: 4.00,4.00;' />
-<line x1='512.06' y1='282.78' x2='528.74' y2='282.78' style='stroke-width: 0.75;' />
-<line x1='512.06' y1='280.80' x2='528.74' y2='280.80' style='stroke-width: 0.75;' />
-<polygon points='503.71,282.68 537.09,282.68 537.09,281.44 503.71,281.44 ' style='stroke-width: 0.75; fill: none;' />
-<polygon points='545.43,282.11 578.80,282.11 578.80,277.47 545.43,277.47 ' style='stroke-width: 0.75; stroke: none; fill: #D3D3D3;' />
-<line x1='545.43' y1='279.88' x2='578.80' y2='279.88' style='stroke-width: 2.25; stroke-linecap: butt;' />
-<line x1='562.11' y1='283.43' x2='562.11' y2='282.11' style='stroke-width: 0.75; stroke-dasharray: 4.00,4.00;' />
-<line x1='562.11' y1='275.97' x2='562.11' y2='277.47' style='stroke-width: 0.75; stroke-dasharray: 4.00,4.00;' />
-<line x1='553.77' y1='283.43' x2='570.46' y2='283.43' style='stroke-width: 0.75;' />
-<line x1='553.77' y1='275.97' x2='570.46' y2='275.97' style='stroke-width: 0.75;' />
-<polygon points='545.43,282.11 578.80,282.11 578.80,277.47 545.43,277.47 ' style='stroke-width: 0.75; fill: none;' />
-<polygon points='587.14,282.38 620.51,282.38 620.51,279.47 587.14,279.47 ' style='stroke-width: 0.75; stroke: none; fill: #D3D3D3;' />
-<line x1='587.14' y1='281.36' x2='620.51' y2='281.36' style='stroke-width: 2.25; stroke-linecap: butt;' />
-<line x1='603.83' y1='282.85' x2='603.83' y2='282.38' style='stroke-width: 0.75; stroke-dasharray: 4.00,4.00;' />
-<line x1='603.83' y1='278.14' x2='603.83' y2='279.47' style='stroke-width: 0.75; stroke-dasharray: 4.00,4.00;' />
-<line x1='595.49' y1='282.85' x2='612.17' y2='282.85' style='stroke-width: 0.75;' />
-<line x1='595.49' y1='278.14' x2='612.17' y2='278.14' style='stroke-width: 0.75;' />
-<polygon points='587.14,282.38 620.51,282.38 620.51,279.47 587.14,279.47 ' style='stroke-width: 0.75; fill: none;' />
-<polygon points='628.86,296.96 662.23,296.96 662.23,282.03 628.86,282.03 ' style='stroke-width: 0.75; stroke: none; fill: #D3D3D3;' />
-<line x1='628.86' y1='289.88' x2='662.23' y2='289.88' style='stroke-width: 2.25; stroke-linecap: butt;' />
-<line x1='645.54' y1='297.26' x2='645.54' y2='296.96' style='stroke-width: 0.75; stroke-dasharray: 4.00,4.00;' />
-<line x1='645.54' y1='280.80' x2='645.54' y2='282.03' style='stroke-width: 0.75; stroke-dasharray: 4.00,4.00;' />
-<line x1='637.20' y1='297.26' x2='653.89' y2='297.26' style='stroke-width: 0.75;' />
-<line x1='637.20' y1='280.80' x2='653.89' y2='280.80' style='stroke-width: 0.75;' />
-<polygon points='628.86,296.96 662.23,296.96 662.23,282.03 628.86,282.03 ' style='stroke-width: 0.75; fill: none;' />
+<g clip-path='url(#cpNTkuMDR8Njg5Ljc2fDMwLjI0fDQzMC41Ng==)'>
+<polygon points='86.57,230.39 119.94,230.39 119.94,229.91 86.57,229.91 ' style='stroke-width: 0.75; stroke: none; fill: #D3D3D3;' />
+<line x1='86.57' y1='230.31' x2='119.94' y2='230.31' style='stroke-width: 2.25; stroke-linecap: butt;' />
+<line x1='103.26' y1='230.40' x2='103.26' y2='230.39' style='stroke-width: 0.75; stroke-dasharray: 4.00,4.00;' />
+<line x1='103.26' y1='229.58' x2='103.26' y2='229.91' style='stroke-width: 0.75; stroke-dasharray: 4.00,4.00;' />
+<line x1='94.91' y1='230.40' x2='111.60' y2='230.40' style='stroke-width: 0.75;' />
+<line x1='94.91' y1='229.58' x2='111.60' y2='229.58' style='stroke-width: 0.75;' />
+<polygon points='86.57,230.39 119.94,230.39 119.94,229.91 86.57,229.91 ' style='stroke-width: 0.75; fill: none;' />
+<polygon points='128.29,232.31 161.66,232.31 161.66,231.10 128.29,231.10 ' style='stroke-width: 0.75; stroke: none; fill: #D3D3D3;' />
+<line x1='128.29' y1='232.04' x2='161.66' y2='232.04' style='stroke-width: 2.25; stroke-linecap: butt;' />
+<line x1='144.97' y1='232.35' x2='144.97' y2='232.31' style='stroke-width: 0.75; stroke-dasharray: 4.00,4.00;' />
+<line x1='144.97' y1='230.40' x2='144.97' y2='231.10' style='stroke-width: 0.75; stroke-dasharray: 4.00,4.00;' />
+<line x1='136.63' y1='232.35' x2='153.31' y2='232.35' style='stroke-width: 0.75;' />
+<line x1='136.63' y1='230.40' x2='153.31' y2='230.40' style='stroke-width: 0.75;' />
+<polygon points='128.29,232.31 161.66,232.31 161.66,231.10 128.29,231.10 ' style='stroke-width: 0.75; fill: none;' />
+<polygon points='170.00,231.51 203.37,231.51 203.37,230.67 170.00,230.67 ' style='stroke-width: 0.75; stroke: none; fill: #D3D3D3;' />
+<line x1='170.00' y1='230.98' x2='203.37' y2='230.98' style='stroke-width: 2.25; stroke-linecap: butt;' />
+<line x1='186.69' y1='232.01' x2='186.69' y2='231.51' style='stroke-width: 0.75; stroke-dasharray: 4.00,4.00;' />
+<line x1='186.69' y1='230.40' x2='186.69' y2='230.67' style='stroke-width: 0.75; stroke-dasharray: 4.00,4.00;' />
+<line x1='178.34' y1='232.01' x2='195.03' y2='232.01' style='stroke-width: 0.75;' />
+<line x1='178.34' y1='230.40' x2='195.03' y2='230.40' style='stroke-width: 0.75;' />
+<polygon points='170.00,231.51 203.37,231.51 203.37,230.67 170.00,230.67 ' style='stroke-width: 0.75; fill: none;' />
+<polygon points='211.71,231.11 245.09,231.11 245.09,229.68 211.71,229.68 ' style='stroke-width: 0.75; stroke: none; fill: #D3D3D3;' />
+<line x1='211.71' y1='230.71' x2='245.09' y2='230.71' style='stroke-width: 2.25; stroke-linecap: butt;' />
+<line x1='228.40' y1='231.20' x2='228.40' y2='231.11' style='stroke-width: 0.75; stroke-dasharray: 4.00,4.00;' />
+<line x1='228.40' y1='228.97' x2='228.40' y2='229.68' style='stroke-width: 0.75; stroke-dasharray: 4.00,4.00;' />
+<line x1='220.06' y1='231.20' x2='236.74' y2='231.20' style='stroke-width: 0.75;' />
+<line x1='220.06' y1='228.97' x2='236.74' y2='228.97' style='stroke-width: 0.75;' />
+<polygon points='211.71,231.11 245.09,231.11 245.09,229.68 211.71,229.68 ' style='stroke-width: 0.75; fill: none;' />
+<polygon points='253.43,231.37 286.80,231.37 286.80,230.25 253.43,230.25 ' style='stroke-width: 0.75; stroke: none; fill: #D3D3D3;' />
+<line x1='253.43' y1='230.85' x2='286.80' y2='230.85' style='stroke-width: 2.25; stroke-linecap: butt;' />
+<line x1='270.11' y1='231.42' x2='270.11' y2='231.37' style='stroke-width: 0.75; stroke-dasharray: 4.00,4.00;' />
+<line x1='270.11' y1='230.10' x2='270.11' y2='230.25' style='stroke-width: 0.75; stroke-dasharray: 4.00,4.00;' />
+<line x1='261.77' y1='231.42' x2='278.46' y2='231.42' style='stroke-width: 0.75;' />
+<line x1='261.77' y1='230.10' x2='278.46' y2='230.10' style='stroke-width: 0.75;' />
+<polygon points='253.43,231.37 286.80,231.37 286.80,230.25 253.43,230.25 ' style='stroke-width: 0.75; fill: none;' />
+<polygon points='295.14,230.73 328.51,230.73 328.51,228.30 295.14,228.30 ' style='stroke-width: 0.75; stroke: none; fill: #D3D3D3;' />
+<line x1='295.14' y1='229.68' x2='328.51' y2='229.68' style='stroke-width: 2.25; stroke-linecap: butt;' />
+<line x1='311.83' y1='231.07' x2='311.83' y2='230.73' style='stroke-width: 0.75; stroke-dasharray: 4.00,4.00;' />
+<line x1='311.83' y1='227.65' x2='311.83' y2='228.30' style='stroke-width: 0.75; stroke-dasharray: 4.00,4.00;' />
+<line x1='303.49' y1='231.07' x2='320.17' y2='231.07' style='stroke-width: 0.75;' />
+<line x1='303.49' y1='227.65' x2='320.17' y2='227.65' style='stroke-width: 0.75;' />
+<polygon points='295.14,230.73 328.51,230.73 328.51,228.30 295.14,228.30 ' style='stroke-width: 0.75; fill: none;' />
+<polygon points='336.86,231.87 370.23,231.87 370.23,230.47 336.86,230.47 ' style='stroke-width: 0.75; stroke: none; fill: #D3D3D3;' />
+<line x1='336.86' y1='231.08' x2='370.23' y2='231.08' style='stroke-width: 2.25; stroke-linecap: butt;' />
+<line x1='353.54' y1='232.11' x2='353.54' y2='231.87' style='stroke-width: 0.75; stroke-dasharray: 4.00,4.00;' />
+<line x1='353.54' y1='230.40' x2='353.54' y2='230.47' style='stroke-width: 0.75; stroke-dasharray: 4.00,4.00;' />
+<line x1='345.20' y1='232.11' x2='361.89' y2='232.11' style='stroke-width: 0.75;' />
+<line x1='345.20' y1='230.40' x2='361.89' y2='230.40' style='stroke-width: 0.75;' />
+<polygon points='336.86,231.87 370.23,231.87 370.23,230.47 336.86,230.47 ' style='stroke-width: 0.75; fill: none;' />
+<polygon points='378.57,230.55 411.94,230.55 411.94,230.41 378.57,230.41 ' style='stroke-width: 0.75; stroke: none; fill: #D3D3D3;' />
+<line x1='378.57' y1='230.43' x2='411.94' y2='230.43' style='stroke-width: 2.25; stroke-linecap: butt;' />
+<line x1='395.26' y1='230.66' x2='395.26' y2='230.55' style='stroke-width: 0.75; stroke-dasharray: 4.00,4.00;' />
+<line x1='395.26' y1='230.40' x2='395.26' y2='230.41' style='stroke-width: 0.75; stroke-dasharray: 4.00,4.00;' />
+<line x1='386.91' y1='230.66' x2='403.60' y2='230.66' style='stroke-width: 0.75;' />
+<line x1='386.91' y1='230.40' x2='403.60' y2='230.40' style='stroke-width: 0.75;' />
+<polygon points='378.57,230.55 411.94,230.55 411.94,230.41 378.57,230.41 ' style='stroke-width: 0.75; fill: none;' />
+<polygon points='420.29,346.11 453.66,346.11 453.66,-152.79 420.29,-152.79 ' style='stroke-width: 0.75; stroke: none; fill: #D3D3D3;' />
+<line x1='420.29' y1='13.60' x2='453.66' y2='13.60' style='stroke-width: 2.25; stroke-linecap: butt;' />
+<line x1='436.97' y1='554.61' x2='436.97' y2='346.11' style='stroke-width: 0.75; stroke-dasharray: 4.00,4.00;' />
+<line x1='436.97' y1='-193.66' x2='436.97' y2='-152.79' style='stroke-width: 0.75; stroke-dasharray: 4.00,4.00;' />
+<line x1='428.63' y1='554.61' x2='445.31' y2='554.61' style='stroke-width: 0.75;' />
+<line x1='428.63' y1='-193.66' x2='445.31' y2='-193.66' style='stroke-width: 0.75;' />
+<polygon points='420.29,346.11 453.66,346.11 453.66,-152.79 420.29,-152.79 ' style='stroke-width: 0.75; fill: none;' />
+<polygon points='462.00,228.86 495.37,228.86 495.37,224.73 462.00,224.73 ' style='stroke-width: 0.75; stroke: none; fill: #D3D3D3;' />
+<line x1='462.00' y1='227.05' x2='495.37' y2='227.05' style='stroke-width: 2.25; stroke-linecap: butt;' />
+<line x1='478.69' y1='230.40' x2='478.69' y2='228.86' style='stroke-width: 0.75; stroke-dasharray: 4.00,4.00;' />
+<line x1='478.69' y1='222.69' x2='478.69' y2='224.73' style='stroke-width: 0.75; stroke-dasharray: 4.00,4.00;' />
+<line x1='470.34' y1='230.40' x2='487.03' y2='230.40' style='stroke-width: 0.75;' />
+<line x1='470.34' y1='222.69' x2='487.03' y2='222.69' style='stroke-width: 0.75;' />
+<polygon points='462.00,228.86 495.37,228.86 495.37,224.73 462.00,224.73 ' style='stroke-width: 0.75; fill: none;' />
+<polygon points='503.71,232.09 537.09,232.09 537.09,230.98 503.71,230.98 ' style='stroke-width: 0.75; stroke: none; fill: #D3D3D3;' />
+<line x1='503.71' y1='231.78' x2='537.09' y2='231.78' style='stroke-width: 2.25; stroke-linecap: butt;' />
+<line x1='520.40' y1='232.19' x2='520.40' y2='232.09' style='stroke-width: 0.75; stroke-dasharray: 4.00,4.00;' />
+<line x1='520.40' y1='230.40' x2='520.40' y2='230.98' style='stroke-width: 0.75; stroke-dasharray: 4.00,4.00;' />
+<line x1='512.06' y1='232.19' x2='528.74' y2='232.19' style='stroke-width: 0.75;' />
+<line x1='512.06' y1='230.40' x2='528.74' y2='230.40' style='stroke-width: 0.75;' />
+<polygon points='503.71,232.09 537.09,232.09 537.09,230.98 503.71,230.98 ' style='stroke-width: 0.75; fill: none;' />
+<polygon points='545.43,231.58 578.80,231.58 578.80,227.39 545.43,227.39 ' style='stroke-width: 0.75; stroke: none; fill: #D3D3D3;' />
+<line x1='545.43' y1='229.57' x2='578.80' y2='229.57' style='stroke-width: 2.25; stroke-linecap: butt;' />
+<line x1='562.11' y1='232.77' x2='562.11' y2='231.58' style='stroke-width: 0.75; stroke-dasharray: 4.00,4.00;' />
+<line x1='562.11' y1='226.04' x2='562.11' y2='227.39' style='stroke-width: 0.75; stroke-dasharray: 4.00,4.00;' />
+<line x1='553.77' y1='232.77' x2='570.46' y2='232.77' style='stroke-width: 0.75;' />
+<line x1='553.77' y1='226.04' x2='570.46' y2='226.04' style='stroke-width: 0.75;' />
+<polygon points='545.43,231.58 578.80,231.58 578.80,227.39 545.43,227.39 ' style='stroke-width: 0.75; fill: none;' />
+<polygon points='587.14,231.83 620.51,231.83 620.51,229.20 587.14,229.20 ' style='stroke-width: 0.75; stroke: none; fill: #D3D3D3;' />
+<line x1='587.14' y1='230.91' x2='620.51' y2='230.91' style='stroke-width: 2.25; stroke-linecap: butt;' />
+<line x1='603.83' y1='232.25' x2='603.83' y2='231.83' style='stroke-width: 0.75; stroke-dasharray: 4.00,4.00;' />
+<line x1='603.83' y1='228.00' x2='603.83' y2='229.20' style='stroke-width: 0.75; stroke-dasharray: 4.00,4.00;' />
+<line x1='595.49' y1='232.25' x2='612.17' y2='232.25' style='stroke-width: 0.75;' />
+<line x1='595.49' y1='228.00' x2='612.17' y2='228.00' style='stroke-width: 0.75;' />
+<polygon points='587.14,231.83 620.51,231.83 620.51,229.20 587.14,229.20 ' style='stroke-width: 0.75; fill: none;' />
+<polygon points='628.86,244.99 662.23,244.99 662.23,231.51 628.86,231.51 ' style='stroke-width: 0.75; stroke: none; fill: #D3D3D3;' />
+<line x1='628.86' y1='238.60' x2='662.23' y2='238.60' style='stroke-width: 2.25; stroke-linecap: butt;' />
+<line x1='645.54' y1='245.26' x2='645.54' y2='244.99' style='stroke-width: 0.75; stroke-dasharray: 4.00,4.00;' />
+<line x1='645.54' y1='230.40' x2='645.54' y2='231.51' style='stroke-width: 0.75; stroke-dasharray: 4.00,4.00;' />
+<line x1='637.20' y1='245.26' x2='653.89' y2='245.26' style='stroke-width: 0.75;' />
+<line x1='637.20' y1='230.40' x2='653.89' y2='230.40' style='stroke-width: 0.75;' />
+<polygon points='628.86,244.99 662.23,244.99 662.23,231.51 628.86,231.51 ' style='stroke-width: 0.75; fill: none;' />
 </g>
 <g clip-path='url(#cpMC4wMHw3MjAuMDB8MC4wMHw1NzYuMDA=)'>
-<line x1='103.26' y1='502.56' x2='645.54' y2='502.56' style='stroke-width: 0.75;' />
-<line x1='103.26' y1='502.56' x2='103.26' y2='509.76' style='stroke-width: 0.75;' />
-<line x1='144.97' y1='502.56' x2='144.97' y2='509.76' style='stroke-width: 0.75;' />
-<line x1='186.69' y1='502.56' x2='186.69' y2='509.76' style='stroke-width: 0.75;' />
-<line x1='228.40' y1='502.56' x2='228.40' y2='509.76' style='stroke-width: 0.75;' />
-<line x1='270.11' y1='502.56' x2='270.11' y2='509.76' style='stroke-width: 0.75;' />
-<line x1='311.83' y1='502.56' x2='311.83' y2='509.76' style='stroke-width: 0.75;' />
-<line x1='353.54' y1='502.56' x2='353.54' y2='509.76' style='stroke-width: 0.75;' />
-<line x1='395.26' y1='502.56' x2='395.26' y2='509.76' style='stroke-width: 0.75;' />
-<line x1='436.97' y1='502.56' x2='436.97' y2='509.76' style='stroke-width: 0.75;' />
-<line x1='478.69' y1='502.56' x2='478.69' y2='509.76' style='stroke-width: 0.75;' />
-<line x1='520.40' y1='502.56' x2='520.40' y2='509.76' style='stroke-width: 0.75;' />
-<line x1='562.11' y1='502.56' x2='562.11' y2='509.76' style='stroke-width: 0.75;' />
-<line x1='603.83' y1='502.56' x2='603.83' y2='509.76' style='stroke-width: 0.75;' />
-<line x1='645.54' y1='502.56' x2='645.54' y2='509.76' style='stroke-width: 0.75;' />
-<text x='103.26' y='528.48' text-anchor='middle' style='font-size: 12.00px; font-family: sans;' textLength='47.38px' lengthAdjust='spacingAndGlyphs'>parent_0</text>
-<text x='186.69' y='528.48' text-anchor='middle' style='font-size: 12.00px; font-family: sans;' textLength='84.07px' lengthAdjust='spacingAndGlyphs'>f_parent_to_m1</text>
-<text x='270.11' y='528.48' text-anchor='middle' style='font-size: 12.00px; font-family: sans;' textLength='12.68px' lengthAdjust='spacingAndGlyphs'>k2</text>
-<text x='311.83' y='528.48' text-anchor='middle' style='font-size: 12.00px; font-family: sans;' textLength='6.67px' lengthAdjust='spacingAndGlyphs'>g</text>
-<text x='353.54' y='528.48' text-anchor='middle' style='font-size: 12.00px; font-family: sans;' textLength='16.68px' lengthAdjust='spacingAndGlyphs'>a.1</text>
-<text x='395.26' y='528.48' text-anchor='middle' style='font-size: 12.00px; font-family: sans;' textLength='16.68px' lengthAdjust='spacingAndGlyphs'>b.1</text>
-<text x='478.69' y='528.48' text-anchor='middle' style='font-size: 12.00px; font-family: sans;' textLength='72.05px' lengthAdjust='spacingAndGlyphs'>SD.log_k_m1</text>
-<text x='562.11' y='528.48' text-anchor='middle' style='font-size: 12.00px; font-family: sans;' textLength='55.38px' lengthAdjust='spacingAndGlyphs'>SD.log_k1</text>
-<text x='645.54' y='528.48' text-anchor='middle' style='font-size: 12.00px; font-family: sans;' textLength='64.71px' lengthAdjust='spacingAndGlyphs'>SD.g_qlogis</text>
-<line x1='59.04' y1='486.13' x2='59.04' y2='75.47' style='stroke-width: 0.75;' />
-<line x1='59.04' y1='486.13' x2='51.84' y2='486.13' style='stroke-width: 0.75;' />
-<line x1='59.04' y1='280.80' x2='51.84' y2='280.80' style='stroke-width: 0.75;' />
-<line x1='59.04' y1='160.69' x2='51.84' y2='160.69' style='stroke-width: 0.75;' />
-<line x1='59.04' y1='75.47' x2='51.84' y2='75.47' style='stroke-width: 0.75;' />
-<text x='44.64' y='490.26' text-anchor='end' style='font-size: 12.00px; font-family: sans;' textLength='16.68px' lengthAdjust='spacingAndGlyphs'>0.5</text>
-<text x='44.64' y='284.93' text-anchor='end' style='font-size: 12.00px; font-family: sans;' textLength='16.68px' lengthAdjust='spacingAndGlyphs'>1.0</text>
-<text x='44.64' y='164.82' text-anchor='end' style='font-size: 12.00px; font-family: sans;' textLength='16.68px' lengthAdjust='spacingAndGlyphs'>1.5</text>
-<text x='44.64' y='79.60' text-anchor='end' style='font-size: 12.00px; font-family: sans;' textLength='16.68px' lengthAdjust='spacingAndGlyphs'>2.0</text>
-<text transform='translate(12.96,280.80) rotate(-90)' text-anchor='middle' style='font-size: 12.00px; font-family: sans;' textLength='124.73px' lengthAdjust='spacingAndGlyphs'>Normalised parameters</text>
-<polygon points='59.04,502.56 689.76,502.56 689.76,59.04 59.04,59.04 ' style='stroke-width: 0.75; fill: none;' />
+<line x1='103.26' y1='430.56' x2='645.54' y2='430.56' style='stroke-width: 0.75;' />
+<line x1='103.26' y1='430.56' x2='103.26' y2='437.76' style='stroke-width: 0.75;' />
+<line x1='144.97' y1='430.56' x2='144.97' y2='437.76' style='stroke-width: 0.75;' />
+<line x1='186.69' y1='430.56' x2='186.69' y2='437.76' style='stroke-width: 0.75;' />
+<line x1='228.40' y1='430.56' x2='228.40' y2='437.76' style='stroke-width: 0.75;' />
+<line x1='270.11' y1='430.56' x2='270.11' y2='437.76' style='stroke-width: 0.75;' />
+<line x1='311.83' y1='430.56' x2='311.83' y2='437.76' style='stroke-width: 0.75;' />
+<line x1='353.54' y1='430.56' x2='353.54' y2='437.76' style='stroke-width: 0.75;' />
+<line x1='395.26' y1='430.56' x2='395.26' y2='437.76' style='stroke-width: 0.75;' />
+<line x1='436.97' y1='430.56' x2='436.97' y2='437.76' style='stroke-width: 0.75;' />
+<line x1='478.69' y1='430.56' x2='478.69' y2='437.76' style='stroke-width: 0.75;' />
+<line x1='520.40' y1='430.56' x2='520.40' y2='437.76' style='stroke-width: 0.75;' />
+<line x1='562.11' y1='430.56' x2='562.11' y2='437.76' style='stroke-width: 0.75;' />
+<line x1='603.83' y1='430.56' x2='603.83' y2='437.76' style='stroke-width: 0.75;' />
+<line x1='645.54' y1='430.56' x2='645.54' y2='437.76' style='stroke-width: 0.75;' />
+<text transform='translate(107.39,444.96) rotate(-90)' text-anchor='end' style='font-size: 12.00px; font-family: sans;' textLength='47.38px' lengthAdjust='spacingAndGlyphs'>parent_0</text>
+<text transform='translate(149.10,444.96) rotate(-90)' text-anchor='end' style='font-size: 12.00px; font-family: sans;' textLength='29.35px' lengthAdjust='spacingAndGlyphs'>k_m1</text>
+<text transform='translate(190.81,444.96) rotate(-90)' text-anchor='end' style='font-size: 12.00px; font-family: sans;' textLength='84.07px' lengthAdjust='spacingAndGlyphs'>f_parent_to_m1</text>
+<text transform='translate(232.53,444.96) rotate(-90)' text-anchor='end' style='font-size: 12.00px; font-family: sans;' textLength='12.68px' lengthAdjust='spacingAndGlyphs'>k1</text>
+<text transform='translate(274.24,444.96) rotate(-90)' text-anchor='end' style='font-size: 12.00px; font-family: sans;' textLength='12.68px' lengthAdjust='spacingAndGlyphs'>k2</text>
+<text transform='translate(315.96,444.96) rotate(-90)' text-anchor='end' style='font-size: 12.00px; font-family: sans;' textLength='6.67px' lengthAdjust='spacingAndGlyphs'>g</text>
+<text transform='translate(357.67,444.96) rotate(-90)' text-anchor='end' style='font-size: 12.00px; font-family: sans;' textLength='16.68px' lengthAdjust='spacingAndGlyphs'>a.1</text>
+<text transform='translate(399.39,444.96) rotate(-90)' text-anchor='end' style='font-size: 12.00px; font-family: sans;' textLength='16.68px' lengthAdjust='spacingAndGlyphs'>b.1</text>
+<text transform='translate(441.10,444.96) rotate(-90)' text-anchor='end' style='font-size: 12.00px; font-family: sans;' textLength='67.38px' lengthAdjust='spacingAndGlyphs'>SD.parent_0</text>
+<text transform='translate(482.81,444.96) rotate(-90)' text-anchor='end' style='font-size: 12.00px; font-family: sans;' textLength='72.05px' lengthAdjust='spacingAndGlyphs'>SD.log_k_m1</text>
+<text transform='translate(524.53,444.96) rotate(-90)' text-anchor='end' style='font-size: 12.00px; font-family: sans;' textLength='102.07px' lengthAdjust='spacingAndGlyphs'>SD.f_parent_qlogis</text>
+<text transform='translate(566.24,444.96) rotate(-90)' text-anchor='end' style='font-size: 12.00px; font-family: sans;' textLength='55.38px' lengthAdjust='spacingAndGlyphs'>SD.log_k1</text>
+<text transform='translate(607.96,444.96) rotate(-90)' text-anchor='end' style='font-size: 12.00px; font-family: sans;' textLength='55.38px' lengthAdjust='spacingAndGlyphs'>SD.log_k2</text>
+<text transform='translate(649.67,444.96) rotate(-90)' text-anchor='end' style='font-size: 12.00px; font-family: sans;' textLength='64.71px' lengthAdjust='spacingAndGlyphs'>SD.g_qlogis</text>
+<line x1='59.04' y1='415.73' x2='59.04' y2='45.07' style='stroke-width: 0.75;' />
+<line x1='59.04' y1='415.73' x2='51.84' y2='415.73' style='stroke-width: 0.75;' />
+<line x1='59.04' y1='230.40' x2='51.84' y2='230.40' style='stroke-width: 0.75;' />
+<line x1='59.04' y1='121.99' x2='51.84' y2='121.99' style='stroke-width: 0.75;' />
+<line x1='59.04' y1='45.07' x2='51.84' y2='45.07' style='stroke-width: 0.75;' />
+<text x='44.64' y='419.86' text-anchor='end' style='font-size: 12.00px; font-family: sans;' textLength='16.68px' lengthAdjust='spacingAndGlyphs'>0.5</text>
+<text x='44.64' y='234.53' text-anchor='end' style='font-size: 12.00px; font-family: sans;' textLength='16.68px' lengthAdjust='spacingAndGlyphs'>1.0</text>
+<text x='44.64' y='126.12' text-anchor='end' style='font-size: 12.00px; font-family: sans;' textLength='16.68px' lengthAdjust='spacingAndGlyphs'>1.5</text>
+<text x='44.64' y='49.20' text-anchor='end' style='font-size: 12.00px; font-family: sans;' textLength='16.68px' lengthAdjust='spacingAndGlyphs'>2.0</text>
+<text transform='translate(12.96,230.40) rotate(-90)' text-anchor='middle' style='font-size: 12.00px; font-family: sans;' textLength='124.73px' lengthAdjust='spacingAndGlyphs'>Normalised parameters</text>
+<polygon points='59.04,430.56 689.76,430.56 689.76,30.24 59.04,30.24 ' style='stroke-width: 0.75; fill: none;' />
 </g>
-<g clip-path='url(#cpNTkuMDR8Njg5Ljc2fDU5LjA0fDUwMi41Ng==)'>
-<circle cx='103.26' cy='279.64' r='8.10' style='stroke-width: 0.75; stroke: #61D04F;' />
-<circle cx='144.97' cy='286.44' r='8.10' style='stroke-width: 0.75; stroke: #61D04F;' />
-<circle cx='186.69' cy='283.22' r='8.10' style='stroke-width: 0.75; stroke: #61D04F;' />
-<circle cx='228.40' cy='269.36' r='8.10' style='stroke-width: 0.75; stroke: #61D04F;' />
-<circle cx='270.11' cy='283.47' r='8.10' style='stroke-width: 0.75; stroke: #61D04F;' />
-<circle cx='311.83' cy='285.42' r='8.10' style='stroke-width: 0.75; stroke: #61D04F;' />
-<circle cx='353.54' cy='258.25' r='8.10' style='stroke-width: 0.75; stroke: #61D04F;' />
-<circle cx='395.26' cy='-596.36' r='8.10' style='stroke-width: 0.75; stroke: #61D04F;' />
-<circle cx='103.26' cy='280.60' r='5.40' style='stroke-width: 0.75; stroke: #DF536B;' />
-<circle cx='144.97' cy='281.94' r='5.40' style='stroke-width: 0.75; stroke: #DF536B;' />
-<circle cx='186.69' cy='281.42' r='5.40' style='stroke-width: 0.75; stroke: #DF536B;' />
-<circle cx='228.40' cy='283.69' r='5.40' style='stroke-width: 0.75; stroke: #DF536B;' />
-<circle cx='270.11' cy='282.86' r='5.40' style='stroke-width: 0.75; stroke: #DF536B;' />
-<circle cx='311.83' cy='277.19' r='5.40' style='stroke-width: 0.75; stroke: #DF536B;' />
-<circle cx='353.54' cy='280.74' r='5.40' style='stroke-width: 0.75; stroke: #DF536B;' />
-<circle cx='395.26' cy='281.31' r='5.40' style='stroke-width: 0.75; stroke: #DF536B;' />
-<circle cx='436.97' cy='-50.55' r='5.40' style='stroke-width: 0.75; stroke: #DF536B;' />
-<circle cx='478.69' cy='282.91' r='5.40' style='stroke-width: 0.75; stroke: #DF536B;' />
-<circle cx='520.40' cy='281.35' r='5.40' style='stroke-width: 0.75; stroke: #DF536B;' />
-<circle cx='562.11' cy='277.34' r='5.40' style='stroke-width: 0.75; stroke: #DF536B;' />
-<circle cx='603.83' cy='281.94' r='5.40' style='stroke-width: 0.75; stroke: #DF536B;' />
-<circle cx='645.54' cy='296.99' r='5.40' style='stroke-width: 0.75; stroke: #DF536B;' />
-<line x1='59.04' y1='280.80' x2='689.76' y2='280.80' style='stroke-width: 0.75; stroke-dasharray: 4.00,4.00;' />
-<line x1='93.82' y1='465.98' x2='115.42' y2='465.98' style='stroke-width: 0.75;' />
-<circle cx='104.62' cy='437.18' r='2.70' style='stroke-width: 0.75; stroke: #61D04F;' />
-<circle cx='104.62' cy='451.58' r='2.70' style='stroke-width: 0.75; stroke: #DF536B;' />
-<circle cx='104.62' cy='465.98' r='2.70' style='stroke-width: 0.75;' />
-<text x='126.22' y='441.31' style='font-size: 12.00px; font-family: sans;' textLength='68.03px' lengthAdjust='spacingAndGlyphs'>Original start</text>
-<text x='126.22' y='455.71' style='font-size: 12.00px; font-family: sans;' textLength='80.04px' lengthAdjust='spacingAndGlyphs'>Original results</text>
-<text x='126.22' y='470.11' style='font-size: 12.00px; font-family: sans;' textLength='75.36px' lengthAdjust='spacingAndGlyphs'>Multistart runs</text>
+<g clip-path='url(#cpNTkuMDR8Njg5Ljc2fDMwLjI0fDQzMC41Ng==)'>
+<circle cx='103.26' cy='229.36' r='8.10' style='stroke-width: 0.75; stroke: #61D04F;' />
+<circle cx='144.97' cy='235.49' r='8.10' style='stroke-width: 0.75; stroke: #61D04F;' />
+<circle cx='186.69' cy='232.58' r='8.10' style='stroke-width: 0.75; stroke: #61D04F;' />
+<circle cx='228.40' cy='220.07' r='8.10' style='stroke-width: 0.75; stroke: #61D04F;' />
+<circle cx='270.11' cy='232.81' r='8.10' style='stroke-width: 0.75; stroke: #61D04F;' />
+<circle cx='311.83' cy='234.57' r='8.10' style='stroke-width: 0.75; stroke: #61D04F;' />
+<circle cx='353.54' cy='210.04' r='8.10' style='stroke-width: 0.75; stroke: #61D04F;' />
+<circle cx='395.26' cy='-561.33' r='8.10' style='stroke-width: 0.75; stroke: #61D04F;' />
+<circle cx='103.26' cy='230.22' r='5.40' style='stroke-width: 0.75; stroke: #DF536B;' />
+<circle cx='144.97' cy='231.43' r='5.40' style='stroke-width: 0.75; stroke: #DF536B;' />
+<circle cx='186.69' cy='230.96' r='5.40' style='stroke-width: 0.75; stroke: #DF536B;' />
+<circle cx='228.40' cy='233.01' r='5.40' style='stroke-width: 0.75; stroke: #DF536B;' />
+<circle cx='270.11' cy='232.26' r='5.40' style='stroke-width: 0.75; stroke: #DF536B;' />
+<circle cx='311.83' cy='227.14' r='5.40' style='stroke-width: 0.75; stroke: #DF536B;' />
+<circle cx='353.54' cy='230.35' r='5.40' style='stroke-width: 0.75; stroke: #DF536B;' />
+<circle cx='395.26' cy='230.86' r='5.40' style='stroke-width: 0.75; stroke: #DF536B;' />
+<circle cx='436.97' cy='-68.67' r='5.40' style='stroke-width: 0.75; stroke: #DF536B;' />
+<circle cx='478.69' cy='232.30' r='5.40' style='stroke-width: 0.75; stroke: #DF536B;' />
+<circle cx='520.40' cy='230.90' r='5.40' style='stroke-width: 0.75; stroke: #DF536B;' />
+<circle cx='562.11' cy='227.27' r='5.40' style='stroke-width: 0.75; stroke: #DF536B;' />
+<circle cx='603.83' cy='231.42' r='5.40' style='stroke-width: 0.75; stroke: #DF536B;' />
+<circle cx='645.54' cy='245.02' r='5.40' style='stroke-width: 0.75; stroke: #DF536B;' />
+<line x1='59.04' y1='230.40' x2='689.76' y2='230.40' style='stroke-width: 0.75; stroke-dasharray: 4.00,4.00;' />
+<line x1='93.82' y1='396.14' x2='115.42' y2='396.14' style='stroke-width: 0.75;' />
+<circle cx='104.62' cy='367.34' r='2.70' style='stroke-width: 0.75; stroke: #61D04F;' />
+<circle cx='104.62' cy='381.74' r='2.70' style='stroke-width: 0.75; stroke: #DF536B;' />
+<circle cx='104.62' cy='396.14' r='2.70' style='stroke-width: 0.75;' />
+<text x='126.22' y='371.47' style='font-size: 12.00px; font-family: sans;' textLength='68.03px' lengthAdjust='spacingAndGlyphs'>Original start</text>
+<text x='126.22' y='385.87' style='font-size: 12.00px; font-family: sans;' textLength='80.04px' lengthAdjust='spacingAndGlyphs'>Original results</text>
+<text x='126.22' y='400.27' style='font-size: 12.00px; font-family: sans;' textLength='75.36px' lengthAdjust='spacingAndGlyphs'>Multistart runs</text>
 </g>
 </svg>
diff --git a/tests/testthat/print_dfop_saem_1.txt b/tests/testthat/print_dfop_saem_1.txt
index f7354320..3a1f1667 100644
--- a/tests/testthat/print_dfop_saem_1.txt
+++ b/tests/testthat/print_dfop_saem_1.txt
@@ -18,6 +18,6 @@ log_k1       -2.71 -2.94  -2.49
 log_k2       -4.14 -4.26  -4.01
 g_qlogis     -0.36 -0.54  -0.17
 a.1           0.93  0.69   1.17
-b.1           0.05  0.04   0.06
+b.1           0.05  0.04   0.05
 SD.log_k1     0.37  0.23   0.51
 SD.log_k2     0.23  0.14   0.31
diff --git a/tests/testthat/summary_hfit_sfo_tc.txt b/tests/testthat/summary_hfit_sfo_tc.txt
index 0618c715..ba7d1362 100644
--- a/tests/testthat/summary_hfit_sfo_tc.txt
+++ b/tests/testthat/summary_hfit_sfo_tc.txt
@@ -43,7 +43,7 @@ Optimised parameters:
                  est. lower upper
 parent_0        92.52 89.11  95.9
 log_k_parent    -1.66 -2.07  -1.3
-a.1              2.03  1.60   2.5
+a.1              2.03  1.61   2.5
 b.1              0.09  0.07   0.1
 SD.log_k_parent  0.51  0.22   0.8
 
@@ -57,7 +57,7 @@ SD.log_k_parent  0.5   0.2   0.8
 
 Variance model:
     est. lower upper
-a.1 2.03  1.60   2.5
+a.1 2.03  1.61   2.5
 b.1 0.09  0.07   0.1
 
 Backtransformed parameters:
diff --git a/tests/testthat/summary_saem_dfop_sfo_s.txt b/tests/testthat/summary_saem_dfop_sfo_s.txt
index 6468ff17..a19824ce 100644
--- a/tests/testthat/summary_saem_dfop_sfo_s.txt
+++ b/tests/testthat/summary_saem_dfop_sfo_s.txt
@@ -86,7 +86,7 @@ SD.g              0.21   0.06   0.4
 
 Variance model:
     est. lower upper
-a.1 0.93  0.79  1.06
+a.1 0.93  0.80  1.06
 b.1 0.05  0.05  0.06
 
 Resulting formation fractions:
diff --git a/tests/testthat/test_mixed.R b/tests/testthat/test_mixed.R
index d8ad4417..b9715096 100644
--- a/tests/testthat/test_mixed.R
+++ b/tests/testthat/test_mixed.R
@@ -89,6 +89,7 @@ test_that("saemix results are reproducible for biphasic fits", {
 test_that("Reading spreadsheets, finding ill-defined parameters and covariate modelling", {
 
   skip_on_cran()
+  skip_on_travis()
 
   data_path <- system.file(
     "testdata", "lambda-cyhalothrin_soil_efsa_2014.xlsx",
@@ -115,6 +116,7 @@ test_that("Reading spreadsheets, finding ill-defined parameters and covariate mo
 test_that("SFO-SFO saemix specific analytical solution work", {
 
   skip_on_cran()
+  skip_on_travis()
 
   SFO_SFO <- mkinmod(DMTA = mkinsub("SFO", "M23"),
     M23 = mkinsub("SFO"), quiet = TRUE)
diff --git a/tests/testthat/test_multistart.R b/tests/testthat/test_multistart.R
index dda0ea23..36aa63f7 100644
--- a/tests/testthat/test_multistart.R
+++ b/tests/testthat/test_multistart.R
@@ -44,13 +44,16 @@ test_that("multistart works for saem.mmkin models", {
   vdiffr::expect_doppelganger("mixed model fit for saem object with mkin transformations", plot_dfop_sfo_saem_m)
 
   llhist_sfo <- function() llhist(saem_sfo_s_multi)
-  parplot_sfo <- function() parplot(saem_sfo_s_multi, ylim = c(0.5, 2))
+  parplot_sfo <- function() parplot(saem_sfo_s_multi, ylim = c(0.5, 2), las = 1)
   vdiffr::expect_doppelganger("llhist for sfo fit", llhist_sfo)
   vdiffr::expect_doppelganger("parplot for sfo fit", parplot_sfo)
 
   llhist_dfop_sfo <- function() llhist(saem_dfop_sfo_m_multi)
-  parplot_dfop_sfo <- function() parplot(saem_dfop_sfo_m_multi,
-    ylim = c(0.5, 2), llquant = 0.5)
+  parplot_dfop_sfo <- function() {
+    par(mar = c(10.1, 4.1, 2.1, 2.1))
+    parplot(saem_dfop_sfo_m_multi,
+      ylim = c(0.5, 2), llquant = 0.5, las = 2)
+  }
 
   vdiffr::expect_doppelganger("llhist for dfop sfo fit", llhist_dfop_sfo)
   vdiffr::expect_doppelganger("parplot for dfop sfo fit", parplot_dfop_sfo)
diff --git a/tests/testthat/test_saemix_parent.R b/tests/testthat/test_saemix_parent.R
index 7fbecd0c..8ac04614 100644
--- a/tests/testthat/test_saemix_parent.R
+++ b/tests/testthat/test_saemix_parent.R
@@ -148,3 +148,30 @@ test_that("We can also use mkin solution methods for saem", {
   rel_diff <- abs(distimes_dfop_analytical - distimes_dfop) / distimes_dfop
   expect_true(all(rel_diff < 0.01))
 })
+
+test_that("illparms finds a single random effect that is ill-defined", {
+  set.seed(123456)
+  n <- 4
+  SFO <- mkinmod(parent = mkinsub("SFO"))
+  sfo_pop <- list(parent_0 = 100, k_parent = 0.03)
+  sfo_parms <- as.matrix(data.frame(
+      k_parent = rlnorm(n, log(sfo_pop$k_parent), 0.001)))
+  sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)
+  err_1 = list(const = 1, prop = 0.05)
+  tc <- function(value) sigma_twocomp(value, err_1$const, err_1$prop)
+  set.seed(123456)
+  ds_sfo <- lapply(1:n, function(i) {
+    ds_mean <- mkinpredict(SFO, sfo_parms[i, ],
+      c(parent = sfo_pop$parent_0), sampling_times)
+    add_err(ds_mean, tc, n = 1)[[1]]
+  })
+  m_mmkin <- mmkin("SFO", ds_sfo, error_model = "tc", quiet = TRUE, cores = n_cores)
+  m_saem_1 <- saem(m_mmkin)
+  expect_equal(
+    as.character(illparms(m_saem_1)),
+    c("sd(parent_0)", "sd(log_k_parent)"))
+  m_saem_2 <- saem(m_mmkin, no_random_effect = "parent_0")
+  expect_equal(
+    as.character(illparms(m_saem_2)),
+    "sd(log_k_parent)")
+})
diff --git a/tests/testthat/test_water-sediment.R b/tests/testthat/test_water-sediment.R
new file mode 100644
index 00000000..6d5693c9
--- /dev/null
+++ b/tests/testthat/test_water-sediment.R
@@ -0,0 +1,17 @@
+# Issue #13 on github
+water_sed_no_sed_sink <- mkinmod(
+  use_of_ff = "min",
+  water = mkinsub("SFO", "sediment"),
+  sediment = mkinsub("SFO", "water", sink = FALSE))
+
+ws_data <- FOCUS_D
+levels(ws_data$name) <- c("water", "sediment")
+
+test_that("An reversible reaction with the sink turned off in the second compartment works", {
+  # Solution method "analytical" was previously available, but erroneous
+  expect_error(
+    ws_fit_no_sed_sink <- mkinfit(water_sed_no_sed_sink, ws_data, quiet = TRUE, solution_type = "analytical"),
+    "Analytical solution not implemented")
+  ws_fit_no_sed_sink_default <- mkinfit(water_sed_no_sed_sink, ws_data, quiet = TRUE)
+  expect_equal(ws_fit_no_sed_sink_default$solution_type, "deSolve")
+})
diff --git a/vignettes/FOCUS_D.html b/vignettes/FOCUS_D.html
index d27b4767..9cf020ec 100644
--- a/vignettes/FOCUS_D.html
+++ b/vignettes/FOCUS_D.html
@@ -31,7 +31,7 @@ document.addEventListener('DOMContentLoaded', function(e) {
 !function(e,t){"use strict";"object"==typeof module&&"object"==typeof module.exports?module.exports=e.document?t(e,!0):function(e){if(!e.document)throw new Error("jQuery requires a window with a document");return t(e)}:t(e)}("undefined"!=typeof window?window:this,function(C,e){"use strict";var t=[],r=Object.getPrototypeOf,s=t.slice,g=t.flat?function(e){return t.flat.call(e)}:function(e){return t.concat.apply([],e)},u=t.push,i=t.indexOf,n={},o=n.toString,v=n.hasOwnProperty,a=v.toString,l=a.call(Object),y={},m=function(e){return"function"==typeof e&&"number"!=typeof e.nodeType&&"function"!=typeof e.item},x=function(e){return null!=e&&e===e.window},E=C.document,c={type:!0,src:!0,nonce:!0,noModule:!0};function b(e,t,n){var r,i,o=(n=n||E).createElement("script");if(o.text=e,t)for(r in c)(i=t[r]||t.getAttribute&&t.getAttribute(r))&&o.setAttribute(r,i);n.head.appendChild(o).parentNode.removeChild(o)}function w(e){return null==e?e+"":"object"==typeof e||"function"==typeof e?n[o.call(e)]||"object":typeof e}var f="3.6.0",S=function(e,t){return new S.fn.init(e,t)};function p(e){var t=!!e&&"length"in e&&e.length,n=w(e);return!m(e)&&!x(e)&&("array"===n||0===t||"number"==typeof t&&0<t&&t-1 in e)}S.fn=S.prototype={jquery:f,constructor:S,length:0,toArray:function(){return s.call(this)},get:function(e){return null==e?s.call(this):e<0?this[e+this.length]:this[e]},pushStack:function(e){var t=S.merge(this.constructor(),e);return t.prevObject=this,t},each:function(e){return S.each(this,e)},map:function(n){return this.pushStack(S.map(this,function(e,t){return n.call(e,t,e)}))},slice:function(){return this.pushStack(s.apply(this,arguments))},first:function(){return this.eq(0)},last:function(){return this.eq(-1)},even:function(){return this.pushStack(S.grep(this,function(e,t){return(t+1)%2}))},odd:function(){return this.pushStack(S.grep(this,function(e,t){return t%2}))},eq:function(e){var t=this.length,n=+e+(e<0?t:0);return this.pushStack(0<=n&&n<t?[this[n]]:[])},end:function(){return this.prevObject||this.constructor()},push:u,sort:t.sort,splice:t.splice},S.extend=S.fn.extend=function(){var e,t,n,r,i,o,a=arguments[0]||{},s=1,u=arguments.length,l=!1;for("boolean"==typeof a&&(l=a,a=arguments[s]||{},s++),"object"==typeof a||m(a)||(a={}),s===u&&(a=this,s--);s<u;s++)if(null!=(e=arguments[s]))for(t in e)r=e[t],"__proto__"!==t&&a!==r&&(l&&r&&(S.isPlainObject(r)||(i=Array.isArray(r)))?(n=a[t],o=i&&!Array.isArray(n)?[]:i||S.isPlainObject(n)?n:{},i=!1,a[t]=S.extend(l,o,r)):void 0!==r&&(a[t]=r));return a},S.extend({expando:"jQuery"+(f+Math.random()).replace(/\D/g,""),isReady:!0,error:function(e){throw new Error(e)},noop:function(){},isPlainObject:function(e){var t,n;return!(!e||"[object Object]"!==o.call(e))&&(!(t=r(e))||"function"==typeof(n=v.call(t,"constructor")&&t.constructor)&&a.call(n)===l)},isEmptyObject:function(e){var t;for(t in e)return!1;return!0},globalEval:function(e,t,n){b(e,{nonce:t&&t.nonce},n)},each:function(e,t){var n,r=0;if(p(e)){for(n=e.length;r<n;r++)if(!1===t.call(e[r],r,e[r]))break}else for(r in e)if(!1===t.call(e[r],r,e[r]))break;return e},makeArray:function(e,t){var n=t||[];return null!=e&&(p(Object(e))?S.merge(n,"string"==typeof e?[e]:e):u.call(n,e)),n},inArray:function(e,t,n){return null==t?-1:i.call(t,e,n)},merge:function(e,t){for(var n=+t.length,r=0,i=e.length;r<n;r++)e[i++]=t[r];return e.length=i,e},grep:function(e,t,n){for(var r=[],i=0,o=e.length,a=!n;i<o;i++)!t(e[i],i)!==a&&r.push(e[i]);return r},map:function(e,t,n){var r,i,o=0,a=[];if(p(e))for(r=e.length;o<r;o++)null!=(i=t(e[o],o,n))&&a.push(i);else for(o in e)null!=(i=t(e[o],o,n))&&a.push(i);return g(a)},guid:1,support:y}),"function"==typeof Symbol&&(S.fn[Symbol.iterator]=t[Symbol.iterator]),S.each("Boolean Number String Function Array Date RegExp Object Error Symbol".split(" "),function(e,t){n["[object "+t+"]"]=t.toLowerCase()});var d=function(n){var e,d,b,o,i,h,f,g,w,u,l,T,C,a,E,v,s,c,y,S="sizzle"+1*new Date,p=n.document,k=0,r=0,m=ue(),x=ue(),A=ue(),N=ue(),j=function(e,t){return e===t&&(l=!0),0},D={}.hasOwnProperty,t=[],q=t.pop,L=t.push,H=t.push,O=t.slice,P=function(e,t){for(var n=0,r=e.length;n<r;n++)if(e[n]===t)return n;return-1},R="checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|ismap|loop|multiple|open|readonly|required|scoped",M="[\\x20\\t\\r\\n\\f]",I="(?:\\\\[\\da-fA-F]{1,6}"+M+"?|\\\\[^\\r\\n\\f]|[\\w-]|[^\0-\\x7f])+",W="\\["+M+"*("+I+")(?:"+M+"*([*^$|!~]?=)"+M+"*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|("+I+"))|)"+M+"*\\]",F=":("+I+")(?:\\((('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|((?:\\\\.|[^\\\\()[\\]]|"+W+")*)|.*)\\)|)",B=new RegExp(M+"+","g"),$=new RegExp("^"+M+"+|((?:^|[^\\\\])(?:\\\\.)*)"+M+"+$","g"),_=new RegExp("^"+M+"*,"+M+"*"),z=new RegExp("^"+M+"*([>+~]|"+M+")"+M+"*"),U=new RegExp(M+"|>"),X=new RegExp(F),V=new RegExp("^"+I+"$"),G={ID:new RegExp("^#("+I+")"),CLASS:new RegExp("^\\.("+I+")"),TAG:new RegExp("^("+I+"|[*])"),ATTR:new RegExp("^"+W),PSEUDO:new RegExp("^"+F),CHILD:new RegExp("^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\("+M+"*(even|odd|(([+-]|)(\\d*)n|)"+M+"*(?:([+-]|)"+M+"*(\\d+)|))"+M+"*\\)|)","i"),bool:new RegExp("^(?:"+R+")$","i"),needsContext:new RegExp("^"+M+"*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\("+M+"*((?:-\\d)?\\d*)"+M+"*\\)|)(?=[^-]|$)","i")},Y=/HTML$/i,Q=/^(?:input|select|textarea|button)$/i,J=/^h\d$/i,K=/^[^{]+\{\s*\[native \w/,Z=/^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/,ee=/[+~]/,te=new RegExp("\\\\[\\da-fA-F]{1,6}"+M+"?|\\\\([^\\r\\n\\f])","g"),ne=function(e,t){var n="0x"+e.slice(1)-65536;return t||(n<0?String.fromCharCode(n+65536):String.fromCharCode(n>>10|55296,1023&n|56320))},re=/([\0-\x1f\x7f]|^-?\d)|^-$|[^\0-\x1f\x7f-\uFFFF\w-]/g,ie=function(e,t){return t?"\0"===e?"\ufffd":e.slice(0,-1)+"\\"+e.charCodeAt(e.length-1).toString(16)+" ":"\\"+e},oe=function(){T()},ae=be(function(e){return!0===e.disabled&&"fieldset"===e.nodeName.toLowerCase()},{dir:"parentNode",next:"legend"});try{H.apply(t=O.call(p.childNodes),p.childNodes),t[p.childNodes.length].nodeType}catch(e){H={apply:t.length?function(e,t){L.apply(e,O.call(t))}:function(e,t){var n=e.length,r=0;while(e[n++]=t[r++]);e.length=n-1}}}function se(t,e,n,r){var i,o,a,s,u,l,c,f=e&&e.ownerDocument,p=e?e.nodeType:9;if(n=n||[],"string"!=typeof t||!t||1!==p&&9!==p&&11!==p)return n;if(!r&&(T(e),e=e||C,E)){if(11!==p&&(u=Z.exec(t)))if(i=u[1]){if(9===p){if(!(a=e.getElementById(i)))return n;if(a.id===i)return n.push(a),n}else if(f&&(a=f.getElementById(i))&&y(e,a)&&a.id===i)return n.push(a),n}else{if(u[2])return H.apply(n,e.getElementsByTagName(t)),n;if((i=u[3])&&d.getElementsByClassName&&e.getElementsByClassName)return H.apply(n,e.getElementsByClassName(i)),n}if(d.qsa&&!N[t+" "]&&(!v||!v.test(t))&&(1!==p||"object"!==e.nodeName.toLowerCase())){if(c=t,f=e,1===p&&(U.test(t)||z.test(t))){(f=ee.test(t)&&ye(e.parentNode)||e)===e&&d.scope||((s=e.getAttribute("id"))?s=s.replace(re,ie):e.setAttribute("id",s=S)),o=(l=h(t)).length;while(o--)l[o]=(s?"#"+s:":scope")+" "+xe(l[o]);c=l.join(",")}try{return H.apply(n,f.querySelectorAll(c)),n}catch(e){N(t,!0)}finally{s===S&&e.removeAttribute("id")}}}return g(t.replace($,"$1"),e,n,r)}function ue(){var r=[];return function e(t,n){return r.push(t+" ")>b.cacheLength&&delete e[r.shift()],e[t+" "]=n}}function le(e){return e[S]=!0,e}function ce(e){var t=C.createElement("fieldset");try{return!!e(t)}catch(e){return!1}finally{t.parentNode&&t.parentNode.removeChild(t),t=null}}function fe(e,t){var n=e.split("|"),r=n.length;while(r--)b.attrHandle[n[r]]=t}function pe(e,t){var n=t&&e,r=n&&1===e.nodeType&&1===t.nodeType&&e.sourceIndex-t.sourceIndex;if(r)return r;if(n)while(n=n.nextSibling)if(n===t)return-1;return e?1:-1}function de(t){return function(e){return"input"===e.nodeName.toLowerCase()&&e.type===t}}function he(n){return function(e){var t=e.nodeName.toLowerCase();return("input"===t||"button"===t)&&e.type===n}}function ge(t){return function(e){return"form"in e?e.parentNode&&!1===e.disabled?"label"in e?"label"in e.parentNode?e.parentNode.disabled===t:e.disabled===t:e.isDisabled===t||e.isDisabled!==!t&&ae(e)===t:e.disabled===t:"label"in e&&e.disabled===t}}function ve(a){return le(function(o){return o=+o,le(function(e,t){var n,r=a([],e.length,o),i=r.length;while(i--)e[n=r[i]]&&(e[n]=!(t[n]=e[n]))})})}function ye(e){return e&&"undefined"!=typeof e.getElementsByTagName&&e}for(e in d=se.support={},i=se.isXML=function(e){var t=e&&e.namespaceURI,n=e&&(e.ownerDocument||e).documentElement;return!Y.test(t||n&&n.nodeName||"HTML")},T=se.setDocument=function(e){var t,n,r=e?e.ownerDocument||e:p;return r!=C&&9===r.nodeType&&r.documentElement&&(a=(C=r).documentElement,E=!i(C),p!=C&&(n=C.defaultView)&&n.top!==n&&(n.addEventListener?n.addEventListener("unload",oe,!1):n.attachEvent&&n.attachEvent("onunload",oe)),d.scope=ce(function(e){return a.appendChild(e).appendChild(C.createElement("div")),"undefined"!=typeof e.querySelectorAll&&!e.querySelectorAll(":scope fieldset div").length}),d.attributes=ce(function(e){return e.className="i",!e.getAttribute("className")}),d.getElementsByTagName=ce(function(e){return e.appendChild(C.createComment("")),!e.getElementsByTagName("*").length}),d.getElementsByClassName=K.test(C.getElementsByClassName),d.getById=ce(function(e){return a.appendChild(e).id=S,!C.getElementsByName||!C.getElementsByName(S).length}),d.getById?(b.filter.ID=function(e){var t=e.replace(te,ne);return function(e){return e.getAttribute("id")===t}},b.find.ID=function(e,t){if("undefined"!=typeof t.getElementById&&E){var n=t.getElementById(e);return n?[n]:[]}}):(b.filter.ID=function(e){var n=e.replace(te,ne);return function(e){var t="undefined"!=typeof e.getAttributeNode&&e.getAttributeNode("id");return t&&t.value===n}},b.find.ID=function(e,t){if("undefined"!=typeof t.getElementById&&E){var n,r,i,o=t.getElementById(e);if(o){if((n=o.getAttributeNode("id"))&&n.value===e)return[o];i=t.getElementsByName(e),r=0;while(o=i[r++])if((n=o.getAttributeNode("id"))&&n.value===e)return[o]}return[]}}),b.find.TAG=d.getElementsByTagName?function(e,t){return"undefined"!=typeof t.getElementsByTagName?t.getElementsByTagName(e):d.qsa?t.querySelectorAll(e):void 0}:function(e,t){var n,r=[],i=0,o=t.getElementsByTagName(e);if("*"===e){while(n=o[i++])1===n.nodeType&&r.push(n);return r}return o},b.find.CLASS=d.getElementsByClassName&&function(e,t){if("undefined"!=typeof t.getElementsByClassName&&E)return t.getElementsByClassName(e)},s=[],v=[],(d.qsa=K.test(C.querySelectorAll))&&(ce(function(e){var t;a.appendChild(e).innerHTML="<a id='"+S+"'></a><select id='"+S+"-\r\\' msallowcapture=''><option selected=''></option></select>",e.querySelectorAll("[msallowcapture^='']").length&&v.push("[*^$]="+M+"*(?:''|\"\")"),e.querySelectorAll("[selected]").length||v.push("\\["+M+"*(?:value|"+R+")"),e.querySelectorAll("[id~="+S+"-]").length||v.push("~="),(t=C.createElement("input")).setAttribute("name",""),e.appendChild(t),e.querySelectorAll("[name='']").length||v.push("\\["+M+"*name"+M+"*="+M+"*(?:''|\"\")"),e.querySelectorAll(":checked").length||v.push(":checked"),e.querySelectorAll("a#"+S+"+*").length||v.push(".#.+[+~]"),e.querySelectorAll("\\\f"),v.push("[\\r\\n\\f]")}),ce(function(e){e.innerHTML="<a href='' disabled='disabled'></a><select disabled='disabled'><option/></select>";var t=C.createElement("input");t.setAttribute("type","hidden"),e.appendChild(t).setAttribute("name","D"),e.querySelectorAll("[name=d]").length&&v.push("name"+M+"*[*^$|!~]?="),2!==e.querySelectorAll(":enabled").length&&v.push(":enabled",":disabled"),a.appendChild(e).disabled=!0,2!==e.querySelectorAll(":disabled").length&&v.push(":enabled",":disabled"),e.querySelectorAll("*,:x"),v.push(",.*:")})),(d.matchesSelector=K.test(c=a.matches||a.webkitMatchesSelector||a.mozMatchesSelector||a.oMatchesSelector||a.msMatchesSelector))&&ce(function(e){d.disconnectedMatch=c.call(e,"*"),c.call(e,"[s!='']:x"),s.push("!=",F)}),v=v.length&&new RegExp(v.join("|")),s=s.length&&new RegExp(s.join("|")),t=K.test(a.compareDocumentPosition),y=t||K.test(a.contains)?function(e,t){var n=9===e.nodeType?e.documentElement:e,r=t&&t.parentNode;return e===r||!(!r||1!==r.nodeType||!(n.contains?n.contains(r):e.compareDocumentPosition&&16&e.compareDocumentPosition(r)))}:function(e,t){if(t)while(t=t.parentNode)if(t===e)return!0;return!1},j=t?function(e,t){if(e===t)return l=!0,0;var n=!e.compareDocumentPosition-!t.compareDocumentPosition;return n||(1&(n=(e.ownerDocument||e)==(t.ownerDocument||t)?e.compareDocumentPosition(t):1)||!d.sortDetached&&t.compareDocumentPosition(e)===n?e==C||e.ownerDocument==p&&y(p,e)?-1:t==C||t.ownerDocument==p&&y(p,t)?1:u?P(u,e)-P(u,t):0:4&n?-1:1)}:function(e,t){if(e===t)return l=!0,0;var n,r=0,i=e.parentNode,o=t.parentNode,a=[e],s=[t];if(!i||!o)return e==C?-1:t==C?1:i?-1:o?1:u?P(u,e)-P(u,t):0;if(i===o)return pe(e,t);n=e;while(n=n.parentNode)a.unshift(n);n=t;while(n=n.parentNode)s.unshift(n);while(a[r]===s[r])r++;return r?pe(a[r],s[r]):a[r]==p?-1:s[r]==p?1:0}),C},se.matches=function(e,t){return se(e,null,null,t)},se.matchesSelector=function(e,t){if(T(e),d.matchesSelector&&E&&!N[t+" "]&&(!s||!s.test(t))&&(!v||!v.test(t)))try{var n=c.call(e,t);if(n||d.disconnectedMatch||e.document&&11!==e.document.nodeType)return n}catch(e){N(t,!0)}return 0<se(t,C,null,[e]).length},se.contains=function(e,t){return(e.ownerDocument||e)!=C&&T(e),y(e,t)},se.attr=function(e,t){(e.ownerDocument||e)!=C&&T(e);var n=b.attrHandle[t.toLowerCase()],r=n&&D.call(b.attrHandle,t.toLowerCase())?n(e,t,!E):void 0;return void 0!==r?r:d.attributes||!E?e.getAttribute(t):(r=e.getAttributeNode(t))&&r.specified?r.value:null},se.escape=function(e){return(e+"").replace(re,ie)},se.error=function(e){throw new Error("Syntax error, unrecognized expression: "+e)},se.uniqueSort=function(e){var t,n=[],r=0,i=0;if(l=!d.detectDuplicates,u=!d.sortStable&&e.slice(0),e.sort(j),l){while(t=e[i++])t===e[i]&&(r=n.push(i));while(r--)e.splice(n[r],1)}return u=null,e},o=se.getText=function(e){var t,n="",r=0,i=e.nodeType;if(i){if(1===i||9===i||11===i){if("string"==typeof e.textContent)return e.textContent;for(e=e.firstChild;e;e=e.nextSibling)n+=o(e)}else if(3===i||4===i)return e.nodeValue}else while(t=e[r++])n+=o(t);return n},(b=se.selectors={cacheLength:50,createPseudo:le,match:G,attrHandle:{},find:{},relative:{">":{dir:"parentNode",first:!0}," ":{dir:"parentNode"},"+":{dir:"previousSibling",first:!0},"~":{dir:"previousSibling"}},preFilter:{ATTR:function(e){return e[1]=e[1].replace(te,ne),e[3]=(e[3]||e[4]||e[5]||"").replace(te,ne),"~="===e[2]&&(e[3]=" "+e[3]+" "),e.slice(0,4)},CHILD:function(e){return e[1]=e[1].toLowerCase(),"nth"===e[1].slice(0,3)?(e[3]||se.error(e[0]),e[4]=+(e[4]?e[5]+(e[6]||1):2*("even"===e[3]||"odd"===e[3])),e[5]=+(e[7]+e[8]||"odd"===e[3])):e[3]&&se.error(e[0]),e},PSEUDO:function(e){var t,n=!e[6]&&e[2];return G.CHILD.test(e[0])?null:(e[3]?e[2]=e[4]||e[5]||"":n&&X.test(n)&&(t=h(n,!0))&&(t=n.indexOf(")",n.length-t)-n.length)&&(e[0]=e[0].slice(0,t),e[2]=n.slice(0,t)),e.slice(0,3))}},filter:{TAG:function(e){var t=e.replace(te,ne).toLowerCase();return"*"===e?function(){return!0}:function(e){return e.nodeName&&e.nodeName.toLowerCase()===t}},CLASS:function(e){var t=m[e+" "];return t||(t=new RegExp("(^|"+M+")"+e+"("+M+"|$)"))&&m(e,function(e){return t.test("string"==typeof e.className&&e.className||"undefined"!=typeof e.getAttribute&&e.getAttribute("class")||"")})},ATTR:function(n,r,i){return function(e){var t=se.attr(e,n);return null==t?"!="===r:!r||(t+="","="===r?t===i:"!="===r?t!==i:"^="===r?i&&0===t.indexOf(i):"*="===r?i&&-1<t.indexOf(i):"$="===r?i&&t.slice(-i.length)===i:"~="===r?-1<(" "+t.replace(B," ")+" ").indexOf(i):"|="===r&&(t===i||t.slice(0,i.length+1)===i+"-"))}},CHILD:function(h,e,t,g,v){var y="nth"!==h.slice(0,3),m="last"!==h.slice(-4),x="of-type"===e;return 1===g&&0===v?function(e){return!!e.parentNode}:function(e,t,n){var r,i,o,a,s,u,l=y!==m?"nextSibling":"previousSibling",c=e.parentNode,f=x&&e.nodeName.toLowerCase(),p=!n&&!x,d=!1;if(c){if(y){while(l){a=e;while(a=a[l])if(x?a.nodeName.toLowerCase()===f:1===a.nodeType)return!1;u=l="only"===h&&!u&&"nextSibling"}return!0}if(u=[m?c.firstChild:c.lastChild],m&&p){d=(s=(r=(i=(o=(a=c)[S]||(a[S]={}))[a.uniqueID]||(o[a.uniqueID]={}))[h]||[])[0]===k&&r[1])&&r[2],a=s&&c.childNodes[s];while(a=++s&&a&&a[l]||(d=s=0)||u.pop())if(1===a.nodeType&&++d&&a===e){i[h]=[k,s,d];break}}else if(p&&(d=s=(r=(i=(o=(a=e)[S]||(a[S]={}))[a.uniqueID]||(o[a.uniqueID]={}))[h]||[])[0]===k&&r[1]),!1===d)while(a=++s&&a&&a[l]||(d=s=0)||u.pop())if((x?a.nodeName.toLowerCase()===f:1===a.nodeType)&&++d&&(p&&((i=(o=a[S]||(a[S]={}))[a.uniqueID]||(o[a.uniqueID]={}))[h]=[k,d]),a===e))break;return(d-=v)===g||d%g==0&&0<=d/g}}},PSEUDO:function(e,o){var t,a=b.pseudos[e]||b.setFilters[e.toLowerCase()]||se.error("unsupported pseudo: "+e);return a[S]?a(o):1<a.length?(t=[e,e,"",o],b.setFilters.hasOwnProperty(e.toLowerCase())?le(function(e,t){var n,r=a(e,o),i=r.length;while(i--)e[n=P(e,r[i])]=!(t[n]=r[i])}):function(e){return a(e,0,t)}):a}},pseudos:{not:le(function(e){var r=[],i=[],s=f(e.replace($,"$1"));return s[S]?le(function(e,t,n,r){var i,o=s(e,null,r,[]),a=e.length;while(a--)(i=o[a])&&(e[a]=!(t[a]=i))}):function(e,t,n){return r[0]=e,s(r,null,n,i),r[0]=null,!i.pop()}}),has:le(function(t){return function(e){return 0<se(t,e).length}}),contains:le(function(t){return t=t.replace(te,ne),function(e){return-1<(e.textContent||o(e)).indexOf(t)}}),lang:le(function(n){return V.test(n||"")||se.error("unsupported lang: "+n),n=n.replace(te,ne).toLowerCase(),function(e){var t;do{if(t=E?e.lang:e.getAttribute("xml:lang")||e.getAttribute("lang"))return(t=t.toLowerCase())===n||0===t.indexOf(n+"-")}while((e=e.parentNode)&&1===e.nodeType);return!1}}),target:function(e){var t=n.location&&n.location.hash;return t&&t.slice(1)===e.id},root:function(e){return e===a},focus:function(e){return e===C.activeElement&&(!C.hasFocus||C.hasFocus())&&!!(e.type||e.href||~e.tabIndex)},enabled:ge(!1),disabled:ge(!0),checked:function(e){var t=e.nodeName.toLowerCase();return"input"===t&&!!e.checked||"option"===t&&!!e.selected},selected:function(e){return e.parentNode&&e.parentNode.selectedIndex,!0===e.selected},empty:function(e){for(e=e.firstChild;e;e=e.nextSibling)if(e.nodeType<6)return!1;return!0},parent:function(e){return!b.pseudos.empty(e)},header:function(e){return J.test(e.nodeName)},input:function(e){return Q.test(e.nodeName)},button:function(e){var t=e.nodeName.toLowerCase();return"input"===t&&"button"===e.type||"button"===t},text:function(e){var t;return"input"===e.nodeName.toLowerCase()&&"text"===e.type&&(null==(t=e.getAttribute("type"))||"text"===t.toLowerCase())},first:ve(function(){return[0]}),last:ve(function(e,t){return[t-1]}),eq:ve(function(e,t,n){return[n<0?n+t:n]}),even:ve(function(e,t){for(var n=0;n<t;n+=2)e.push(n);return e}),odd:ve(function(e,t){for(var n=1;n<t;n+=2)e.push(n);return e}),lt:ve(function(e,t,n){for(var r=n<0?n+t:t<n?t:n;0<=--r;)e.push(r);return e}),gt:ve(function(e,t,n){for(var r=n<0?n+t:n;++r<t;)e.push(r);return e})}}).pseudos.nth=b.pseudos.eq,{radio:!0,checkbox:!0,file:!0,password:!0,image:!0})b.pseudos[e]=de(e);for(e in{submit:!0,reset:!0})b.pseudos[e]=he(e);function me(){}function xe(e){for(var t=0,n=e.length,r="";t<n;t++)r+=e[t].value;return r}function be(s,e,t){var u=e.dir,l=e.next,c=l||u,f=t&&"parentNode"===c,p=r++;return e.first?function(e,t,n){while(e=e[u])if(1===e.nodeType||f)return s(e,t,n);return!1}:function(e,t,n){var r,i,o,a=[k,p];if(n){while(e=e[u])if((1===e.nodeType||f)&&s(e,t,n))return!0}else while(e=e[u])if(1===e.nodeType||f)if(i=(o=e[S]||(e[S]={}))[e.uniqueID]||(o[e.uniqueID]={}),l&&l===e.nodeName.toLowerCase())e=e[u]||e;else{if((r=i[c])&&r[0]===k&&r[1]===p)return a[2]=r[2];if((i[c]=a)[2]=s(e,t,n))return!0}return!1}}function we(i){return 1<i.length?function(e,t,n){var r=i.length;while(r--)if(!i[r](e,t,n))return!1;return!0}:i[0]}function Te(e,t,n,r,i){for(var o,a=[],s=0,u=e.length,l=null!=t;s<u;s++)(o=e[s])&&(n&&!n(o,r,i)||(a.push(o),l&&t.push(s)));return a}function Ce(d,h,g,v,y,e){return v&&!v[S]&&(v=Ce(v)),y&&!y[S]&&(y=Ce(y,e)),le(function(e,t,n,r){var i,o,a,s=[],u=[],l=t.length,c=e||function(e,t,n){for(var r=0,i=t.length;r<i;r++)se(e,t[r],n);return n}(h||"*",n.nodeType?[n]:n,[]),f=!d||!e&&h?c:Te(c,s,d,n,r),p=g?y||(e?d:l||v)?[]:t:f;if(g&&g(f,p,n,r),v){i=Te(p,u),v(i,[],n,r),o=i.length;while(o--)(a=i[o])&&(p[u[o]]=!(f[u[o]]=a))}if(e){if(y||d){if(y){i=[],o=p.length;while(o--)(a=p[o])&&i.push(f[o]=a);y(null,p=[],i,r)}o=p.length;while(o--)(a=p[o])&&-1<(i=y?P(e,a):s[o])&&(e[i]=!(t[i]=a))}}else p=Te(p===t?p.splice(l,p.length):p),y?y(null,t,p,r):H.apply(t,p)})}function Ee(e){for(var i,t,n,r=e.length,o=b.relative[e[0].type],a=o||b.relative[" "],s=o?1:0,u=be(function(e){return e===i},a,!0),l=be(function(e){return-1<P(i,e)},a,!0),c=[function(e,t,n){var r=!o&&(n||t!==w)||((i=t).nodeType?u(e,t,n):l(e,t,n));return i=null,r}];s<r;s++)if(t=b.relative[e[s].type])c=[be(we(c),t)];else{if((t=b.filter[e[s].type].apply(null,e[s].matches))[S]){for(n=++s;n<r;n++)if(b.relative[e[n].type])break;return Ce(1<s&&we(c),1<s&&xe(e.slice(0,s-1).concat({value:" "===e[s-2].type?"*":""})).replace($,"$1"),t,s<n&&Ee(e.slice(s,n)),n<r&&Ee(e=e.slice(n)),n<r&&xe(e))}c.push(t)}return we(c)}return me.prototype=b.filters=b.pseudos,b.setFilters=new me,h=se.tokenize=function(e,t){var n,r,i,o,a,s,u,l=x[e+" "];if(l)return t?0:l.slice(0);a=e,s=[],u=b.preFilter;while(a){for(o in n&&!(r=_.exec(a))||(r&&(a=a.slice(r[0].length)||a),s.push(i=[])),n=!1,(r=z.exec(a))&&(n=r.shift(),i.push({value:n,type:r[0].replace($," ")}),a=a.slice(n.length)),b.filter)!(r=G[o].exec(a))||u[o]&&!(r=u[o](r))||(n=r.shift(),i.push({value:n,type:o,matches:r}),a=a.slice(n.length));if(!n)break}return t?a.length:a?se.error(e):x(e,s).slice(0)},f=se.compile=function(e,t){var n,v,y,m,x,r,i=[],o=[],a=A[e+" "];if(!a){t||(t=h(e)),n=t.length;while(n--)(a=Ee(t[n]))[S]?i.push(a):o.push(a);(a=A(e,(v=o,m=0<(y=i).length,x=0<v.length,r=function(e,t,n,r,i){var o,a,s,u=0,l="0",c=e&&[],f=[],p=w,d=e||x&&b.find.TAG("*",i),h=k+=null==p?1:Math.random()||.1,g=d.length;for(i&&(w=t==C||t||i);l!==g&&null!=(o=d[l]);l++){if(x&&o){a=0,t||o.ownerDocument==C||(T(o),n=!E);while(s=v[a++])if(s(o,t||C,n)){r.push(o);break}i&&(k=h)}m&&((o=!s&&o)&&u--,e&&c.push(o))}if(u+=l,m&&l!==u){a=0;while(s=y[a++])s(c,f,t,n);if(e){if(0<u)while(l--)c[l]||f[l]||(f[l]=q.call(r));f=Te(f)}H.apply(r,f),i&&!e&&0<f.length&&1<u+y.length&&se.uniqueSort(r)}return i&&(k=h,w=p),c},m?le(r):r))).selector=e}return a},g=se.select=function(e,t,n,r){var i,o,a,s,u,l="function"==typeof e&&e,c=!r&&h(e=l.selector||e);if(n=n||[],1===c.length){if(2<(o=c[0]=c[0].slice(0)).length&&"ID"===(a=o[0]).type&&9===t.nodeType&&E&&b.relative[o[1].type]){if(!(t=(b.find.ID(a.matches[0].replace(te,ne),t)||[])[0]))return n;l&&(t=t.parentNode),e=e.slice(o.shift().value.length)}i=G.needsContext.test(e)?0:o.length;while(i--){if(a=o[i],b.relative[s=a.type])break;if((u=b.find[s])&&(r=u(a.matches[0].replace(te,ne),ee.test(o[0].type)&&ye(t.parentNode)||t))){if(o.splice(i,1),!(e=r.length&&xe(o)))return H.apply(n,r),n;break}}}return(l||f(e,c))(r,t,!E,n,!t||ee.test(e)&&ye(t.parentNode)||t),n},d.sortStable=S.split("").sort(j).join("")===S,d.detectDuplicates=!!l,T(),d.sortDetached=ce(function(e){return 1&e.compareDocumentPosition(C.createElement("fieldset"))}),ce(function(e){return e.innerHTML="<a href='#'></a>","#"===e.firstChild.getAttribute("href")})||fe("type|href|height|width",function(e,t,n){if(!n)return e.getAttribute(t,"type"===t.toLowerCase()?1:2)}),d.attributes&&ce(function(e){return e.innerHTML="<input/>",e.firstChild.setAttribute("value",""),""===e.firstChild.getAttribute("value")})||fe("value",function(e,t,n){if(!n&&"input"===e.nodeName.toLowerCase())return e.defaultValue}),ce(function(e){return null==e.getAttribute("disabled")})||fe(R,function(e,t,n){var r;if(!n)return!0===e[t]?t.toLowerCase():(r=e.getAttributeNode(t))&&r.specified?r.value:null}),se}(C);S.find=d,S.expr=d.selectors,S.expr[":"]=S.expr.pseudos,S.uniqueSort=S.unique=d.uniqueSort,S.text=d.getText,S.isXMLDoc=d.isXML,S.contains=d.contains,S.escapeSelector=d.escape;var h=function(e,t,n){var r=[],i=void 0!==n;while((e=e[t])&&9!==e.nodeType)if(1===e.nodeType){if(i&&S(e).is(n))break;r.push(e)}return r},T=function(e,t){for(var n=[];e;e=e.nextSibling)1===e.nodeType&&e!==t&&n.push(e);return n},k=S.expr.match.needsContext;function A(e,t){return e.nodeName&&e.nodeName.toLowerCase()===t.toLowerCase()}var N=/^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i;function j(e,n,r){return m(n)?S.grep(e,function(e,t){return!!n.call(e,t,e)!==r}):n.nodeType?S.grep(e,function(e){return e===n!==r}):"string"!=typeof n?S.grep(e,function(e){return-1<i.call(n,e)!==r}):S.filter(n,e,r)}S.filter=function(e,t,n){var r=t[0];return n&&(e=":not("+e+")"),1===t.length&&1===r.nodeType?S.find.matchesSelector(r,e)?[r]:[]:S.find.matches(e,S.grep(t,function(e){return 1===e.nodeType}))},S.fn.extend({find:function(e){var t,n,r=this.length,i=this;if("string"!=typeof e)return this.pushStack(S(e).filter(function(){for(t=0;t<r;t++)if(S.contains(i[t],this))return!0}));for(n=this.pushStack([]),t=0;t<r;t++)S.find(e,i[t],n);return 1<r?S.uniqueSort(n):n},filter:function(e){return this.pushStack(j(this,e||[],!1))},not:function(e){return this.pushStack(j(this,e||[],!0))},is:function(e){return!!j(this,"string"==typeof e&&k.test(e)?S(e):e||[],!1).length}});var D,q=/^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]+))$/;(S.fn.init=function(e,t,n){var r,i;if(!e)return this;if(n=n||D,"string"==typeof e){if(!(r="<"===e[0]&&">"===e[e.length-1]&&3<=e.length?[null,e,null]:q.exec(e))||!r[1]&&t)return!t||t.jquery?(t||n).find(e):this.constructor(t).find(e);if(r[1]){if(t=t instanceof S?t[0]:t,S.merge(this,S.parseHTML(r[1],t&&t.nodeType?t.ownerDocument||t:E,!0)),N.test(r[1])&&S.isPlainObject(t))for(r in t)m(this[r])?this[r](t[r]):this.attr(r,t[r]);return this}return(i=E.getElementById(r[2]))&&(this[0]=i,this.length=1),this}return e.nodeType?(this[0]=e,this.length=1,this):m(e)?void 0!==n.ready?n.ready(e):e(S):S.makeArray(e,this)}).prototype=S.fn,D=S(E);var L=/^(?:parents|prev(?:Until|All))/,H={children:!0,contents:!0,next:!0,prev:!0};function O(e,t){while((e=e[t])&&1!==e.nodeType);return e}S.fn.extend({has:function(e){var t=S(e,this),n=t.length;return this.filter(function(){for(var e=0;e<n;e++)if(S.contains(this,t[e]))return!0})},closest:function(e,t){var n,r=0,i=this.length,o=[],a="string"!=typeof e&&S(e);if(!k.test(e))for(;r<i;r++)for(n=this[r];n&&n!==t;n=n.parentNode)if(n.nodeType<11&&(a?-1<a.index(n):1===n.nodeType&&S.find.matchesSelector(n,e))){o.push(n);break}return this.pushStack(1<o.length?S.uniqueSort(o):o)},index:function(e){return e?"string"==typeof e?i.call(S(e),this[0]):i.call(this,e.jquery?e[0]:e):this[0]&&this[0].parentNode?this.first().prevAll().length:-1},add:function(e,t){return this.pushStack(S.uniqueSort(S.merge(this.get(),S(e,t))))},addBack:function(e){return this.add(null==e?this.prevObject:this.prevObject.filter(e))}}),S.each({parent:function(e){var t=e.parentNode;return t&&11!==t.nodeType?t:null},parents:function(e){return h(e,"parentNode")},parentsUntil:function(e,t,n){return h(e,"parentNode",n)},next:function(e){return O(e,"nextSibling")},prev:function(e){return O(e,"previousSibling")},nextAll:function(e){return h(e,"nextSibling")},prevAll:function(e){return h(e,"previousSibling")},nextUntil:function(e,t,n){return h(e,"nextSibling",n)},prevUntil:function(e,t,n){return h(e,"previousSibling",n)},siblings:function(e){return T((e.parentNode||{}).firstChild,e)},children:function(e){return T(e.firstChild)},contents:function(e){return null!=e.contentDocument&&r(e.contentDocument)?e.contentDocument:(A(e,"template")&&(e=e.content||e),S.merge([],e.childNodes))}},function(r,i){S.fn[r]=function(e,t){var n=S.map(this,i,e);return"Until"!==r.slice(-5)&&(t=e),t&&"string"==typeof t&&(n=S.filter(t,n)),1<this.length&&(H[r]||S.uniqueSort(n),L.test(r)&&n.reverse()),this.pushStack(n)}});var P=/[^\x20\t\r\n\f]+/g;function R(e){return e}function M(e){throw e}function I(e,t,n,r){var i;try{e&&m(i=e.promise)?i.call(e).done(t).fail(n):e&&m(i=e.then)?i.call(e,t,n):t.apply(void 0,[e].slice(r))}catch(e){n.apply(void 0,[e])}}S.Callbacks=function(r){var e,n;r="string"==typeof r?(e=r,n={},S.each(e.match(P)||[],function(e,t){n[t]=!0}),n):S.extend({},r);var i,t,o,a,s=[],u=[],l=-1,c=function(){for(a=a||r.once,o=i=!0;u.length;l=-1){t=u.shift();while(++l<s.length)!1===s[l].apply(t[0],t[1])&&r.stopOnFalse&&(l=s.length,t=!1)}r.memory||(t=!1),i=!1,a&&(s=t?[]:"")},f={add:function(){return s&&(t&&!i&&(l=s.length-1,u.push(t)),function n(e){S.each(e,function(e,t){m(t)?r.unique&&f.has(t)||s.push(t):t&&t.length&&"string"!==w(t)&&n(t)})}(arguments),t&&!i&&c()),this},remove:function(){return S.each(arguments,function(e,t){var n;while(-1<(n=S.inArray(t,s,n)))s.splice(n,1),n<=l&&l--}),this},has:function(e){return e?-1<S.inArray(e,s):0<s.length},empty:function(){return s&&(s=[]),this},disable:function(){return a=u=[],s=t="",this},disabled:function(){return!s},lock:function(){return a=u=[],t||i||(s=t=""),this},locked:function(){return!!a},fireWith:function(e,t){return a||(t=[e,(t=t||[]).slice?t.slice():t],u.push(t),i||c()),this},fire:function(){return f.fireWith(this,arguments),this},fired:function(){return!!o}};return f},S.extend({Deferred:function(e){var o=[["notify","progress",S.Callbacks("memory"),S.Callbacks("memory"),2],["resolve","done",S.Callbacks("once memory"),S.Callbacks("once memory"),0,"resolved"],["reject","fail",S.Callbacks("once memory"),S.Callbacks("once memory"),1,"rejected"]],i="pending",a={state:function(){return i},always:function(){return s.done(arguments).fail(arguments),this},"catch":function(e){return a.then(null,e)},pipe:function(){var i=arguments;return S.Deferred(function(r){S.each(o,function(e,t){var n=m(i[t[4]])&&i[t[4]];s[t[1]](function(){var e=n&&n.apply(this,arguments);e&&m(e.promise)?e.promise().progress(r.notify).done(r.resolve).fail(r.reject):r[t[0]+"With"](this,n?[e]:arguments)})}),i=null}).promise()},then:function(t,n,r){var u=0;function l(i,o,a,s){return function(){var n=this,r=arguments,e=function(){var e,t;if(!(i<u)){if((e=a.apply(n,r))===o.promise())throw new TypeError("Thenable self-resolution");t=e&&("object"==typeof e||"function"==typeof e)&&e.then,m(t)?s?t.call(e,l(u,o,R,s),l(u,o,M,s)):(u++,t.call(e,l(u,o,R,s),l(u,o,M,s),l(u,o,R,o.notifyWith))):(a!==R&&(n=void 0,r=[e]),(s||o.resolveWith)(n,r))}},t=s?e:function(){try{e()}catch(e){S.Deferred.exceptionHook&&S.Deferred.exceptionHook(e,t.stackTrace),u<=i+1&&(a!==M&&(n=void 0,r=[e]),o.rejectWith(n,r))}};i?t():(S.Deferred.getStackHook&&(t.stackTrace=S.Deferred.getStackHook()),C.setTimeout(t))}}return S.Deferred(function(e){o[0][3].add(l(0,e,m(r)?r:R,e.notifyWith)),o[1][3].add(l(0,e,m(t)?t:R)),o[2][3].add(l(0,e,m(n)?n:M))}).promise()},promise:function(e){return null!=e?S.extend(e,a):a}},s={};return S.each(o,function(e,t){var n=t[2],r=t[5];a[t[1]]=n.add,r&&n.add(function(){i=r},o[3-e][2].disable,o[3-e][3].disable,o[0][2].lock,o[0][3].lock),n.add(t[3].fire),s[t[0]]=function(){return s[t[0]+"With"](this===s?void 0:this,arguments),this},s[t[0]+"With"]=n.fireWith}),a.promise(s),e&&e.call(s,s),s},when:function(e){var n=arguments.length,t=n,r=Array(t),i=s.call(arguments),o=S.Deferred(),a=function(t){return function(e){r[t]=this,i[t]=1<arguments.length?s.call(arguments):e,--n||o.resolveWith(r,i)}};if(n<=1&&(I(e,o.done(a(t)).resolve,o.reject,!n),"pending"===o.state()||m(i[t]&&i[t].then)))return o.then();while(t--)I(i[t],a(t),o.reject);return o.promise()}});var W=/^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/;S.Deferred.exceptionHook=function(e,t){C.console&&C.console.warn&&e&&W.test(e.name)&&C.console.warn("jQuery.Deferred exception: "+e.message,e.stack,t)},S.readyException=function(e){C.setTimeout(function(){throw e})};var F=S.Deferred();function B(){E.removeEventListener("DOMContentLoaded",B),C.removeEventListener("load",B),S.ready()}S.fn.ready=function(e){return F.then(e)["catch"](function(e){S.readyException(e)}),this},S.extend({isReady:!1,readyWait:1,ready:function(e){(!0===e?--S.readyWait:S.isReady)||(S.isReady=!0)!==e&&0<--S.readyWait||F.resolveWith(E,[S])}}),S.ready.then=F.then,"complete"===E.readyState||"loading"!==E.readyState&&!E.documentElement.doScroll?C.setTimeout(S.ready):(E.addEventListener("DOMContentLoaded",B),C.addEventListener("load",B));var $=function(e,t,n,r,i,o,a){var s=0,u=e.length,l=null==n;if("object"===w(n))for(s in i=!0,n)$(e,t,s,n[s],!0,o,a);else if(void 0!==r&&(i=!0,m(r)||(a=!0),l&&(a?(t.call(e,r),t=null):(l=t,t=function(e,t,n){return l.call(S(e),n)})),t))for(;s<u;s++)t(e[s],n,a?r:r.call(e[s],s,t(e[s],n)));return i?e:l?t.call(e):u?t(e[0],n):o},_=/^-ms-/,z=/-([a-z])/g;function U(e,t){return t.toUpperCase()}function X(e){return e.replace(_,"ms-").replace(z,U)}var V=function(e){return 1===e.nodeType||9===e.nodeType||!+e.nodeType};function G(){this.expando=S.expando+G.uid++}G.uid=1,G.prototype={cache:function(e){var t=e[this.expando];return t||(t={},V(e)&&(e.nodeType?e[this.expando]=t:Object.defineProperty(e,this.expando,{value:t,configurable:!0}))),t},set:function(e,t,n){var r,i=this.cache(e);if("string"==typeof t)i[X(t)]=n;else for(r in t)i[X(r)]=t[r];return i},get:function(e,t){return void 0===t?this.cache(e):e[this.expando]&&e[this.expando][X(t)]},access:function(e,t,n){return void 0===t||t&&"string"==typeof t&&void 0===n?this.get(e,t):(this.set(e,t,n),void 0!==n?n:t)},remove:function(e,t){var n,r=e[this.expando];if(void 0!==r){if(void 0!==t){n=(t=Array.isArray(t)?t.map(X):(t=X(t))in r?[t]:t.match(P)||[]).length;while(n--)delete r[t[n]]}(void 0===t||S.isEmptyObject(r))&&(e.nodeType?e[this.expando]=void 0:delete e[this.expando])}},hasData:function(e){var t=e[this.expando];return void 0!==t&&!S.isEmptyObject(t)}};var Y=new G,Q=new G,J=/^(?:\{[\w\W]*\}|\[[\w\W]*\])$/,K=/[A-Z]/g;function Z(e,t,n){var r,i;if(void 0===n&&1===e.nodeType)if(r="data-"+t.replace(K,"-$&").toLowerCase(),"string"==typeof(n=e.getAttribute(r))){try{n="true"===(i=n)||"false"!==i&&("null"===i?null:i===+i+""?+i:J.test(i)?JSON.parse(i):i)}catch(e){}Q.set(e,t,n)}else n=void 0;return n}S.extend({hasData:function(e){return Q.hasData(e)||Y.hasData(e)},data:function(e,t,n){return Q.access(e,t,n)},removeData:function(e,t){Q.remove(e,t)},_data:function(e,t,n){return Y.access(e,t,n)},_removeData:function(e,t){Y.remove(e,t)}}),S.fn.extend({data:function(n,e){var t,r,i,o=this[0],a=o&&o.attributes;if(void 0===n){if(this.length&&(i=Q.get(o),1===o.nodeType&&!Y.get(o,"hasDataAttrs"))){t=a.length;while(t--)a[t]&&0===(r=a[t].name).indexOf("data-")&&(r=X(r.slice(5)),Z(o,r,i[r]));Y.set(o,"hasDataAttrs",!0)}return i}return"object"==typeof n?this.each(function(){Q.set(this,n)}):$(this,function(e){var t;if(o&&void 0===e)return void 0!==(t=Q.get(o,n))?t:void 0!==(t=Z(o,n))?t:void 0;this.each(function(){Q.set(this,n,e)})},null,e,1<arguments.length,null,!0)},removeData:function(e){return this.each(function(){Q.remove(this,e)})}}),S.extend({queue:function(e,t,n){var r;if(e)return t=(t||"fx")+"queue",r=Y.get(e,t),n&&(!r||Array.isArray(n)?r=Y.access(e,t,S.makeArray(n)):r.push(n)),r||[]},dequeue:function(e,t){t=t||"fx";var n=S.queue(e,t),r=n.length,i=n.shift(),o=S._queueHooks(e,t);"inprogress"===i&&(i=n.shift(),r--),i&&("fx"===t&&n.unshift("inprogress"),delete o.stop,i.call(e,function(){S.dequeue(e,t)},o)),!r&&o&&o.empty.fire()},_queueHooks:function(e,t){var n=t+"queueHooks";return Y.get(e,n)||Y.access(e,n,{empty:S.Callbacks("once memory").add(function(){Y.remove(e,[t+"queue",n])})})}}),S.fn.extend({queue:function(t,n){var e=2;return"string"!=typeof t&&(n=t,t="fx",e--),arguments.length<e?S.queue(this[0],t):void 0===n?this:this.each(function(){var e=S.queue(this,t,n);S._queueHooks(this,t),"fx"===t&&"inprogress"!==e[0]&&S.dequeue(this,t)})},dequeue:function(e){return this.each(function(){S.dequeue(this,e)})},clearQueue:function(e){return this.queue(e||"fx",[])},promise:function(e,t){var n,r=1,i=S.Deferred(),o=this,a=this.length,s=function(){--r||i.resolveWith(o,[o])};"string"!=typeof e&&(t=e,e=void 0),e=e||"fx";while(a--)(n=Y.get(o[a],e+"queueHooks"))&&n.empty&&(r++,n.empty.add(s));return s(),i.promise(t)}});var ee=/[+-]?(?:\d*\.|)\d+(?:[eE][+-]?\d+|)/.source,te=new RegExp("^(?:([+-])=|)("+ee+")([a-z%]*)$","i"),ne=["Top","Right","Bottom","Left"],re=E.documentElement,ie=function(e){return S.contains(e.ownerDocument,e)},oe={composed:!0};re.getRootNode&&(ie=function(e){return S.contains(e.ownerDocument,e)||e.getRootNode(oe)===e.ownerDocument});var ae=function(e,t){return"none"===(e=t||e).style.display||""===e.style.display&&ie(e)&&"none"===S.css(e,"display")};function se(e,t,n,r){var i,o,a=20,s=r?function(){return r.cur()}:function(){return S.css(e,t,"")},u=s(),l=n&&n[3]||(S.cssNumber[t]?"":"px"),c=e.nodeType&&(S.cssNumber[t]||"px"!==l&&+u)&&te.exec(S.css(e,t));if(c&&c[3]!==l){u/=2,l=l||c[3],c=+u||1;while(a--)S.style(e,t,c+l),(1-o)*(1-(o=s()/u||.5))<=0&&(a=0),c/=o;c*=2,S.style(e,t,c+l),n=n||[]}return n&&(c=+c||+u||0,i=n[1]?c+(n[1]+1)*n[2]:+n[2],r&&(r.unit=l,r.start=c,r.end=i)),i}var ue={};function le(e,t){for(var n,r,i,o,a,s,u,l=[],c=0,f=e.length;c<f;c++)(r=e[c]).style&&(n=r.style.display,t?("none"===n&&(l[c]=Y.get(r,"display")||null,l[c]||(r.style.display="")),""===r.style.display&&ae(r)&&(l[c]=(u=a=o=void 0,a=(i=r).ownerDocument,s=i.nodeName,(u=ue[s])||(o=a.body.appendChild(a.createElement(s)),u=S.css(o,"display"),o.parentNode.removeChild(o),"none"===u&&(u="block"),ue[s]=u)))):"none"!==n&&(l[c]="none",Y.set(r,"display",n)));for(c=0;c<f;c++)null!=l[c]&&(e[c].style.display=l[c]);return e}S.fn.extend({show:function(){return le(this,!0)},hide:function(){return le(this)},toggle:function(e){return"boolean"==typeof e?e?this.show():this.hide():this.each(function(){ae(this)?S(this).show():S(this).hide()})}});var ce,fe,pe=/^(?:checkbox|radio)$/i,de=/<([a-z][^\/\0>\x20\t\r\n\f]*)/i,he=/^$|^module$|\/(?:java|ecma)script/i;ce=E.createDocumentFragment().appendChild(E.createElement("div")),(fe=E.createElement("input")).setAttribute("type","radio"),fe.setAttribute("checked","checked"),fe.setAttribute("name","t"),ce.appendChild(fe),y.checkClone=ce.cloneNode(!0).cloneNode(!0).lastChild.checked,ce.innerHTML="<textarea>x</textarea>",y.noCloneChecked=!!ce.cloneNode(!0).lastChild.defaultValue,ce.innerHTML="<option></option>",y.option=!!ce.lastChild;var ge={thead:[1,"<table>","</table>"],col:[2,"<table><colgroup>","</colgroup></table>"],tr:[2,"<table><tbody>","</tbody></table>"],td:[3,"<table><tbody><tr>","</tr></tbody></table>"],_default:[0,"",""]};function ve(e,t){var n;return n="undefined"!=typeof e.getElementsByTagName?e.getElementsByTagName(t||"*"):"undefined"!=typeof e.querySelectorAll?e.querySelectorAll(t||"*"):[],void 0===t||t&&A(e,t)?S.merge([e],n):n}function ye(e,t){for(var n=0,r=e.length;n<r;n++)Y.set(e[n],"globalEval",!t||Y.get(t[n],"globalEval"))}ge.tbody=ge.tfoot=ge.colgroup=ge.caption=ge.thead,ge.th=ge.td,y.option||(ge.optgroup=ge.option=[1,"<select multiple='multiple'>","</select>"]);var me=/<|&#?\w+;/;function xe(e,t,n,r,i){for(var o,a,s,u,l,c,f=t.createDocumentFragment(),p=[],d=0,h=e.length;d<h;d++)if((o=e[d])||0===o)if("object"===w(o))S.merge(p,o.nodeType?[o]:o);else if(me.test(o)){a=a||f.appendChild(t.createElement("div")),s=(de.exec(o)||["",""])[1].toLowerCase(),u=ge[s]||ge._default,a.innerHTML=u[1]+S.htmlPrefilter(o)+u[2],c=u[0];while(c--)a=a.lastChild;S.merge(p,a.childNodes),(a=f.firstChild).textContent=""}else p.push(t.createTextNode(o));f.textContent="",d=0;while(o=p[d++])if(r&&-1<S.inArray(o,r))i&&i.push(o);else if(l=ie(o),a=ve(f.appendChild(o),"script"),l&&ye(a),n){c=0;while(o=a[c++])he.test(o.type||"")&&n.push(o)}return f}var be=/^([^.]*)(?:\.(.+)|)/;function we(){return!0}function Te(){return!1}function Ce(e,t){return e===function(){try{return E.activeElement}catch(e){}}()==("focus"===t)}function Ee(e,t,n,r,i,o){var a,s;if("object"==typeof t){for(s in"string"!=typeof n&&(r=r||n,n=void 0),t)Ee(e,s,n,r,t[s],o);return e}if(null==r&&null==i?(i=n,r=n=void 0):null==i&&("string"==typeof n?(i=r,r=void 0):(i=r,r=n,n=void 0)),!1===i)i=Te;else if(!i)return e;return 1===o&&(a=i,(i=function(e){return S().off(e),a.apply(this,arguments)}).guid=a.guid||(a.guid=S.guid++)),e.each(function(){S.event.add(this,t,i,r,n)})}function Se(e,i,o){o?(Y.set(e,i,!1),S.event.add(e,i,{namespace:!1,handler:function(e){var t,n,r=Y.get(this,i);if(1&e.isTrigger&&this[i]){if(r.length)(S.event.special[i]||{}).delegateType&&e.stopPropagation();else if(r=s.call(arguments),Y.set(this,i,r),t=o(this,i),this[i](),r!==(n=Y.get(this,i))||t?Y.set(this,i,!1):n={},r!==n)return e.stopImmediatePropagation(),e.preventDefault(),n&&n.value}else r.length&&(Y.set(this,i,{value:S.event.trigger(S.extend(r[0],S.Event.prototype),r.slice(1),this)}),e.stopImmediatePropagation())}})):void 0===Y.get(e,i)&&S.event.add(e,i,we)}S.event={global:{},add:function(t,e,n,r,i){var o,a,s,u,l,c,f,p,d,h,g,v=Y.get(t);if(V(t)){n.handler&&(n=(o=n).handler,i=o.selector),i&&S.find.matchesSelector(re,i),n.guid||(n.guid=S.guid++),(u=v.events)||(u=v.events=Object.create(null)),(a=v.handle)||(a=v.handle=function(e){return"undefined"!=typeof S&&S.event.triggered!==e.type?S.event.dispatch.apply(t,arguments):void 0}),l=(e=(e||"").match(P)||[""]).length;while(l--)d=g=(s=be.exec(e[l])||[])[1],h=(s[2]||"").split(".").sort(),d&&(f=S.event.special[d]||{},d=(i?f.delegateType:f.bindType)||d,f=S.event.special[d]||{},c=S.extend({type:d,origType:g,data:r,handler:n,guid:n.guid,selector:i,needsContext:i&&S.expr.match.needsContext.test(i),namespace:h.join(".")},o),(p=u[d])||((p=u[d]=[]).delegateCount=0,f.setup&&!1!==f.setup.call(t,r,h,a)||t.addEventListener&&t.addEventListener(d,a)),f.add&&(f.add.call(t,c),c.handler.guid||(c.handler.guid=n.guid)),i?p.splice(p.delegateCount++,0,c):p.push(c),S.event.global[d]=!0)}},remove:function(e,t,n,r,i){var o,a,s,u,l,c,f,p,d,h,g,v=Y.hasData(e)&&Y.get(e);if(v&&(u=v.events)){l=(t=(t||"").match(P)||[""]).length;while(l--)if(d=g=(s=be.exec(t[l])||[])[1],h=(s[2]||"").split(".").sort(),d){f=S.event.special[d]||{},p=u[d=(r?f.delegateType:f.bindType)||d]||[],s=s[2]&&new RegExp("(^|\\.)"+h.join("\\.(?:.*\\.|)")+"(\\.|$)"),a=o=p.length;while(o--)c=p[o],!i&&g!==c.origType||n&&n.guid!==c.guid||s&&!s.test(c.namespace)||r&&r!==c.selector&&("**"!==r||!c.selector)||(p.splice(o,1),c.selector&&p.delegateCount--,f.remove&&f.remove.call(e,c));a&&!p.length&&(f.teardown&&!1!==f.teardown.call(e,h,v.handle)||S.removeEvent(e,d,v.handle),delete u[d])}else for(d in u)S.event.remove(e,d+t[l],n,r,!0);S.isEmptyObject(u)&&Y.remove(e,"handle events")}},dispatch:function(e){var t,n,r,i,o,a,s=new Array(arguments.length),u=S.event.fix(e),l=(Y.get(this,"events")||Object.create(null))[u.type]||[],c=S.event.special[u.type]||{};for(s[0]=u,t=1;t<arguments.length;t++)s[t]=arguments[t];if(u.delegateTarget=this,!c.preDispatch||!1!==c.preDispatch.call(this,u)){a=S.event.handlers.call(this,u,l),t=0;while((i=a[t++])&&!u.isPropagationStopped()){u.currentTarget=i.elem,n=0;while((o=i.handlers[n++])&&!u.isImmediatePropagationStopped())u.rnamespace&&!1!==o.namespace&&!u.rnamespace.test(o.namespace)||(u.handleObj=o,u.data=o.data,void 0!==(r=((S.event.special[o.origType]||{}).handle||o.handler).apply(i.elem,s))&&!1===(u.result=r)&&(u.preventDefault(),u.stopPropagation()))}return c.postDispatch&&c.postDispatch.call(this,u),u.result}},handlers:function(e,t){var n,r,i,o,a,s=[],u=t.delegateCount,l=e.target;if(u&&l.nodeType&&!("click"===e.type&&1<=e.button))for(;l!==this;l=l.parentNode||this)if(1===l.nodeType&&("click"!==e.type||!0!==l.disabled)){for(o=[],a={},n=0;n<u;n++)void 0===a[i=(r=t[n]).selector+" "]&&(a[i]=r.needsContext?-1<S(i,this).index(l):S.find(i,this,null,[l]).length),a[i]&&o.push(r);o.length&&s.push({elem:l,handlers:o})}return l=this,u<t.length&&s.push({elem:l,handlers:t.slice(u)}),s},addProp:function(t,e){Object.defineProperty(S.Event.prototype,t,{enumerable:!0,configurable:!0,get:m(e)?function(){if(this.originalEvent)return e(this.originalEvent)}:function(){if(this.originalEvent)return this.originalEvent[t]},set:function(e){Object.defineProperty(this,t,{enumerable:!0,configurable:!0,writable:!0,value:e})}})},fix:function(e){return e[S.expando]?e:new S.Event(e)},special:{load:{noBubble:!0},click:{setup:function(e){var t=this||e;return pe.test(t.type)&&t.click&&A(t,"input")&&Se(t,"click",we),!1},trigger:function(e){var t=this||e;return pe.test(t.type)&&t.click&&A(t,"input")&&Se(t,"click"),!0},_default:function(e){var t=e.target;return pe.test(t.type)&&t.click&&A(t,"input")&&Y.get(t,"click")||A(t,"a")}},beforeunload:{postDispatch:function(e){void 0!==e.result&&e.originalEvent&&(e.originalEvent.returnValue=e.result)}}}},S.removeEvent=function(e,t,n){e.removeEventListener&&e.removeEventListener(t,n)},S.Event=function(e,t){if(!(this instanceof S.Event))return new S.Event(e,t);e&&e.type?(this.originalEvent=e,this.type=e.type,this.isDefaultPrevented=e.defaultPrevented||void 0===e.defaultPrevented&&!1===e.returnValue?we:Te,this.target=e.target&&3===e.target.nodeType?e.target.parentNode:e.target,this.currentTarget=e.currentTarget,this.relatedTarget=e.relatedTarget):this.type=e,t&&S.extend(this,t),this.timeStamp=e&&e.timeStamp||Date.now(),this[S.expando]=!0},S.Event.prototype={constructor:S.Event,isDefaultPrevented:Te,isPropagationStopped:Te,isImmediatePropagationStopped:Te,isSimulated:!1,preventDefault:function(){var e=this.originalEvent;this.isDefaultPrevented=we,e&&!this.isSimulated&&e.preventDefault()},stopPropagation:function(){var e=this.originalEvent;this.isPropagationStopped=we,e&&!this.isSimulated&&e.stopPropagation()},stopImmediatePropagation:function(){var e=this.originalEvent;this.isImmediatePropagationStopped=we,e&&!this.isSimulated&&e.stopImmediatePropagation(),this.stopPropagation()}},S.each({altKey:!0,bubbles:!0,cancelable:!0,changedTouches:!0,ctrlKey:!0,detail:!0,eventPhase:!0,metaKey:!0,pageX:!0,pageY:!0,shiftKey:!0,view:!0,"char":!0,code:!0,charCode:!0,key:!0,keyCode:!0,button:!0,buttons:!0,clientX:!0,clientY:!0,offsetX:!0,offsetY:!0,pointerId:!0,pointerType:!0,screenX:!0,screenY:!0,targetTouches:!0,toElement:!0,touches:!0,which:!0},S.event.addProp),S.each({focus:"focusin",blur:"focusout"},function(e,t){S.event.special[e]={setup:function(){return Se(this,e,Ce),!1},trigger:function(){return Se(this,e),!0},_default:function(){return!0},delegateType:t}}),S.each({mouseenter:"mouseover",mouseleave:"mouseout",pointerenter:"pointerover",pointerleave:"pointerout"},function(e,i){S.event.special[e]={delegateType:i,bindType:i,handle:function(e){var t,n=e.relatedTarget,r=e.handleObj;return n&&(n===this||S.contains(this,n))||(e.type=r.origType,t=r.handler.apply(this,arguments),e.type=i),t}}}),S.fn.extend({on:function(e,t,n,r){return Ee(this,e,t,n,r)},one:function(e,t,n,r){return Ee(this,e,t,n,r,1)},off:function(e,t,n){var r,i;if(e&&e.preventDefault&&e.handleObj)return r=e.handleObj,S(e.delegateTarget).off(r.namespace?r.origType+"."+r.namespace:r.origType,r.selector,r.handler),this;if("object"==typeof e){for(i in e)this.off(i,t,e[i]);return this}return!1!==t&&"function"!=typeof t||(n=t,t=void 0),!1===n&&(n=Te),this.each(function(){S.event.remove(this,e,n,t)})}});var ke=/<script|<style|<link/i,Ae=/checked\s*(?:[^=]|=\s*.checked.)/i,Ne=/^\s*<!(?:\[CDATA\[|--)|(?:\]\]|--)>\s*$/g;function je(e,t){return A(e,"table")&&A(11!==t.nodeType?t:t.firstChild,"tr")&&S(e).children("tbody")[0]||e}function De(e){return e.type=(null!==e.getAttribute("type"))+"/"+e.type,e}function qe(e){return"true/"===(e.type||"").slice(0,5)?e.type=e.type.slice(5):e.removeAttribute("type"),e}function Le(e,t){var n,r,i,o,a,s;if(1===t.nodeType){if(Y.hasData(e)&&(s=Y.get(e).events))for(i in Y.remove(t,"handle events"),s)for(n=0,r=s[i].length;n<r;n++)S.event.add(t,i,s[i][n]);Q.hasData(e)&&(o=Q.access(e),a=S.extend({},o),Q.set(t,a))}}function He(n,r,i,o){r=g(r);var e,t,a,s,u,l,c=0,f=n.length,p=f-1,d=r[0],h=m(d);if(h||1<f&&"string"==typeof d&&!y.checkClone&&Ae.test(d))return n.each(function(e){var t=n.eq(e);h&&(r[0]=d.call(this,e,t.html())),He(t,r,i,o)});if(f&&(t=(e=xe(r,n[0].ownerDocument,!1,n,o)).firstChild,1===e.childNodes.length&&(e=t),t||o)){for(s=(a=S.map(ve(e,"script"),De)).length;c<f;c++)u=e,c!==p&&(u=S.clone(u,!0,!0),s&&S.merge(a,ve(u,"script"))),i.call(n[c],u,c);if(s)for(l=a[a.length-1].ownerDocument,S.map(a,qe),c=0;c<s;c++)u=a[c],he.test(u.type||"")&&!Y.access(u,"globalEval")&&S.contains(l,u)&&(u.src&&"module"!==(u.type||"").toLowerCase()?S._evalUrl&&!u.noModule&&S._evalUrl(u.src,{nonce:u.nonce||u.getAttribute("nonce")},l):b(u.textContent.replace(Ne,""),u,l))}return n}function Oe(e,t,n){for(var r,i=t?S.filter(t,e):e,o=0;null!=(r=i[o]);o++)n||1!==r.nodeType||S.cleanData(ve(r)),r.parentNode&&(n&&ie(r)&&ye(ve(r,"script")),r.parentNode.removeChild(r));return e}S.extend({htmlPrefilter:function(e){return e},clone:function(e,t,n){var r,i,o,a,s,u,l,c=e.cloneNode(!0),f=ie(e);if(!(y.noCloneChecked||1!==e.nodeType&&11!==e.nodeType||S.isXMLDoc(e)))for(a=ve(c),r=0,i=(o=ve(e)).length;r<i;r++)s=o[r],u=a[r],void 0,"input"===(l=u.nodeName.toLowerCase())&&pe.test(s.type)?u.checked=s.checked:"input"!==l&&"textarea"!==l||(u.defaultValue=s.defaultValue);if(t)if(n)for(o=o||ve(e),a=a||ve(c),r=0,i=o.length;r<i;r++)Le(o[r],a[r]);else Le(e,c);return 0<(a=ve(c,"script")).length&&ye(a,!f&&ve(e,"script")),c},cleanData:function(e){for(var t,n,r,i=S.event.special,o=0;void 0!==(n=e[o]);o++)if(V(n)){if(t=n[Y.expando]){if(t.events)for(r in t.events)i[r]?S.event.remove(n,r):S.removeEvent(n,r,t.handle);n[Y.expando]=void 0}n[Q.expando]&&(n[Q.expando]=void 0)}}}),S.fn.extend({detach:function(e){return Oe(this,e,!0)},remove:function(e){return Oe(this,e)},text:function(e){return $(this,function(e){return void 0===e?S.text(this):this.empty().each(function(){1!==this.nodeType&&11!==this.nodeType&&9!==this.nodeType||(this.textContent=e)})},null,e,arguments.length)},append:function(){return He(this,arguments,function(e){1!==this.nodeType&&11!==this.nodeType&&9!==this.nodeType||je(this,e).appendChild(e)})},prepend:function(){return He(this,arguments,function(e){if(1===this.nodeType||11===this.nodeType||9===this.nodeType){var t=je(this,e);t.insertBefore(e,t.firstChild)}})},before:function(){return He(this,arguments,function(e){this.parentNode&&this.parentNode.insertBefore(e,this)})},after:function(){return He(this,arguments,function(e){this.parentNode&&this.parentNode.insertBefore(e,this.nextSibling)})},empty:function(){for(var e,t=0;null!=(e=this[t]);t++)1===e.nodeType&&(S.cleanData(ve(e,!1)),e.textContent="");return this},clone:function(e,t){return e=null!=e&&e,t=null==t?e:t,this.map(function(){return S.clone(this,e,t)})},html:function(e){return $(this,function(e){var t=this[0]||{},n=0,r=this.length;if(void 0===e&&1===t.nodeType)return t.innerHTML;if("string"==typeof e&&!ke.test(e)&&!ge[(de.exec(e)||["",""])[1].toLowerCase()]){e=S.htmlPrefilter(e);try{for(;n<r;n++)1===(t=this[n]||{}).nodeType&&(S.cleanData(ve(t,!1)),t.innerHTML=e);t=0}catch(e){}}t&&this.empty().append(e)},null,e,arguments.length)},replaceWith:function(){var n=[];return He(this,arguments,function(e){var t=this.parentNode;S.inArray(this,n)<0&&(S.cleanData(ve(this)),t&&t.replaceChild(e,this))},n)}}),S.each({appendTo:"append",prependTo:"prepend",insertBefore:"before",insertAfter:"after",replaceAll:"replaceWith"},function(e,a){S.fn[e]=function(e){for(var t,n=[],r=S(e),i=r.length-1,o=0;o<=i;o++)t=o===i?this:this.clone(!0),S(r[o])[a](t),u.apply(n,t.get());return this.pushStack(n)}});var Pe=new RegExp("^("+ee+")(?!px)[a-z%]+$","i"),Re=function(e){var t=e.ownerDocument.defaultView;return t&&t.opener||(t=C),t.getComputedStyle(e)},Me=function(e,t,n){var r,i,o={};for(i in t)o[i]=e.style[i],e.style[i]=t[i];for(i in r=n.call(e),t)e.style[i]=o[i];return r},Ie=new RegExp(ne.join("|"),"i");function We(e,t,n){var r,i,o,a,s=e.style;return(n=n||Re(e))&&(""!==(a=n.getPropertyValue(t)||n[t])||ie(e)||(a=S.style(e,t)),!y.pixelBoxStyles()&&Pe.test(a)&&Ie.test(t)&&(r=s.width,i=s.minWidth,o=s.maxWidth,s.minWidth=s.maxWidth=s.width=a,a=n.width,s.width=r,s.minWidth=i,s.maxWidth=o)),void 0!==a?a+"":a}function Fe(e,t){return{get:function(){if(!e())return(this.get=t).apply(this,arguments);delete this.get}}}!function(){function e(){if(l){u.style.cssText="position:absolute;left:-11111px;width:60px;margin-top:1px;padding:0;border:0",l.style.cssText="position:relative;display:block;box-sizing:border-box;overflow:scroll;margin:auto;border:1px;padding:1px;width:60%;top:1%",re.appendChild(u).appendChild(l);var e=C.getComputedStyle(l);n="1%"!==e.top,s=12===t(e.marginLeft),l.style.right="60%",o=36===t(e.right),r=36===t(e.width),l.style.position="absolute",i=12===t(l.offsetWidth/3),re.removeChild(u),l=null}}function t(e){return Math.round(parseFloat(e))}var n,r,i,o,a,s,u=E.createElement("div"),l=E.createElement("div");l.style&&(l.style.backgroundClip="content-box",l.cloneNode(!0).style.backgroundClip="",y.clearCloneStyle="content-box"===l.style.backgroundClip,S.extend(y,{boxSizingReliable:function(){return e(),r},pixelBoxStyles:function(){return e(),o},pixelPosition:function(){return e(),n},reliableMarginLeft:function(){return e(),s},scrollboxSize:function(){return e(),i},reliableTrDimensions:function(){var e,t,n,r;return null==a&&(e=E.createElement("table"),t=E.createElement("tr"),n=E.createElement("div"),e.style.cssText="position:absolute;left:-11111px;border-collapse:separate",t.style.cssText="border:1px solid",t.style.height="1px",n.style.height="9px",n.style.display="block",re.appendChild(e).appendChild(t).appendChild(n),r=C.getComputedStyle(t),a=parseInt(r.height,10)+parseInt(r.borderTopWidth,10)+parseInt(r.borderBottomWidth,10)===t.offsetHeight,re.removeChild(e)),a}}))}();var Be=["Webkit","Moz","ms"],$e=E.createElement("div").style,_e={};function ze(e){var t=S.cssProps[e]||_e[e];return t||(e in $e?e:_e[e]=function(e){var t=e[0].toUpperCase()+e.slice(1),n=Be.length;while(n--)if((e=Be[n]+t)in $e)return e}(e)||e)}var Ue=/^(none|table(?!-c[ea]).+)/,Xe=/^--/,Ve={position:"absolute",visibility:"hidden",display:"block"},Ge={letterSpacing:"0",fontWeight:"400"};function Ye(e,t,n){var r=te.exec(t);return r?Math.max(0,r[2]-(n||0))+(r[3]||"px"):t}function Qe(e,t,n,r,i,o){var a="width"===t?1:0,s=0,u=0;if(n===(r?"border":"content"))return 0;for(;a<4;a+=2)"margin"===n&&(u+=S.css(e,n+ne[a],!0,i)),r?("content"===n&&(u-=S.css(e,"padding"+ne[a],!0,i)),"margin"!==n&&(u-=S.css(e,"border"+ne[a]+"Width",!0,i))):(u+=S.css(e,"padding"+ne[a],!0,i),"padding"!==n?u+=S.css(e,"border"+ne[a]+"Width",!0,i):s+=S.css(e,"border"+ne[a]+"Width",!0,i));return!r&&0<=o&&(u+=Math.max(0,Math.ceil(e["offset"+t[0].toUpperCase()+t.slice(1)]-o-u-s-.5))||0),u}function Je(e,t,n){var r=Re(e),i=(!y.boxSizingReliable()||n)&&"border-box"===S.css(e,"boxSizing",!1,r),o=i,a=We(e,t,r),s="offset"+t[0].toUpperCase()+t.slice(1);if(Pe.test(a)){if(!n)return a;a="auto"}return(!y.boxSizingReliable()&&i||!y.reliableTrDimensions()&&A(e,"tr")||"auto"===a||!parseFloat(a)&&"inline"===S.css(e,"display",!1,r))&&e.getClientRects().length&&(i="border-box"===S.css(e,"boxSizing",!1,r),(o=s in e)&&(a=e[s])),(a=parseFloat(a)||0)+Qe(e,t,n||(i?"border":"content"),o,r,a)+"px"}function Ke(e,t,n,r,i){return new Ke.prototype.init(e,t,n,r,i)}S.extend({cssHooks:{opacity:{get:function(e,t){if(t){var n=We(e,"opacity");return""===n?"1":n}}}},cssNumber:{animationIterationCount:!0,columnCount:!0,fillOpacity:!0,flexGrow:!0,flexShrink:!0,fontWeight:!0,gridArea:!0,gridColumn:!0,gridColumnEnd:!0,gridColumnStart:!0,gridRow:!0,gridRowEnd:!0,gridRowStart:!0,lineHeight:!0,opacity:!0,order:!0,orphans:!0,widows:!0,zIndex:!0,zoom:!0},cssProps:{},style:function(e,t,n,r){if(e&&3!==e.nodeType&&8!==e.nodeType&&e.style){var i,o,a,s=X(t),u=Xe.test(t),l=e.style;if(u||(t=ze(s)),a=S.cssHooks[t]||S.cssHooks[s],void 0===n)return a&&"get"in a&&void 0!==(i=a.get(e,!1,r))?i:l[t];"string"===(o=typeof n)&&(i=te.exec(n))&&i[1]&&(n=se(e,t,i),o="number"),null!=n&&n==n&&("number"!==o||u||(n+=i&&i[3]||(S.cssNumber[s]?"":"px")),y.clearCloneStyle||""!==n||0!==t.indexOf("background")||(l[t]="inherit"),a&&"set"in a&&void 0===(n=a.set(e,n,r))||(u?l.setProperty(t,n):l[t]=n))}},css:function(e,t,n,r){var i,o,a,s=X(t);return Xe.test(t)||(t=ze(s)),(a=S.cssHooks[t]||S.cssHooks[s])&&"get"in a&&(i=a.get(e,!0,n)),void 0===i&&(i=We(e,t,r)),"normal"===i&&t in Ge&&(i=Ge[t]),""===n||n?(o=parseFloat(i),!0===n||isFinite(o)?o||0:i):i}}),S.each(["height","width"],function(e,u){S.cssHooks[u]={get:function(e,t,n){if(t)return!Ue.test(S.css(e,"display"))||e.getClientRects().length&&e.getBoundingClientRect().width?Je(e,u,n):Me(e,Ve,function(){return Je(e,u,n)})},set:function(e,t,n){var r,i=Re(e),o=!y.scrollboxSize()&&"absolute"===i.position,a=(o||n)&&"border-box"===S.css(e,"boxSizing",!1,i),s=n?Qe(e,u,n,a,i):0;return a&&o&&(s-=Math.ceil(e["offset"+u[0].toUpperCase()+u.slice(1)]-parseFloat(i[u])-Qe(e,u,"border",!1,i)-.5)),s&&(r=te.exec(t))&&"px"!==(r[3]||"px")&&(e.style[u]=t,t=S.css(e,u)),Ye(0,t,s)}}}),S.cssHooks.marginLeft=Fe(y.reliableMarginLeft,function(e,t){if(t)return(parseFloat(We(e,"marginLeft"))||e.getBoundingClientRect().left-Me(e,{marginLeft:0},function(){return e.getBoundingClientRect().left}))+"px"}),S.each({margin:"",padding:"",border:"Width"},function(i,o){S.cssHooks[i+o]={expand:function(e){for(var t=0,n={},r="string"==typeof e?e.split(" "):[e];t<4;t++)n[i+ne[t]+o]=r[t]||r[t-2]||r[0];return n}},"margin"!==i&&(S.cssHooks[i+o].set=Ye)}),S.fn.extend({css:function(e,t){return $(this,function(e,t,n){var r,i,o={},a=0;if(Array.isArray(t)){for(r=Re(e),i=t.length;a<i;a++)o[t[a]]=S.css(e,t[a],!1,r);return o}return void 0!==n?S.style(e,t,n):S.css(e,t)},e,t,1<arguments.length)}}),((S.Tween=Ke).prototype={constructor:Ke,init:function(e,t,n,r,i,o){this.elem=e,this.prop=n,this.easing=i||S.easing._default,this.options=t,this.start=this.now=this.cur(),this.end=r,this.unit=o||(S.cssNumber[n]?"":"px")},cur:function(){var e=Ke.propHooks[this.prop];return e&&e.get?e.get(this):Ke.propHooks._default.get(this)},run:function(e){var t,n=Ke.propHooks[this.prop];return this.options.duration?this.pos=t=S.easing[this.easing](e,this.options.duration*e,0,1,this.options.duration):this.pos=t=e,this.now=(this.end-this.start)*t+this.start,this.options.step&&this.options.step.call(this.elem,this.now,this),n&&n.set?n.set(this):Ke.propHooks._default.set(this),this}}).init.prototype=Ke.prototype,(Ke.propHooks={_default:{get:function(e){var t;return 1!==e.elem.nodeType||null!=e.elem[e.prop]&&null==e.elem.style[e.prop]?e.elem[e.prop]:(t=S.css(e.elem,e.prop,""))&&"auto"!==t?t:0},set:function(e){S.fx.step[e.prop]?S.fx.step[e.prop](e):1!==e.elem.nodeType||!S.cssHooks[e.prop]&&null==e.elem.style[ze(e.prop)]?e.elem[e.prop]=e.now:S.style(e.elem,e.prop,e.now+e.unit)}}}).scrollTop=Ke.propHooks.scrollLeft={set:function(e){e.elem.nodeType&&e.elem.parentNode&&(e.elem[e.prop]=e.now)}},S.easing={linear:function(e){return e},swing:function(e){return.5-Math.cos(e*Math.PI)/2},_default:"swing"},S.fx=Ke.prototype.init,S.fx.step={};var Ze,et,tt,nt,rt=/^(?:toggle|show|hide)$/,it=/queueHooks$/;function ot(){et&&(!1===E.hidden&&C.requestAnimationFrame?C.requestAnimationFrame(ot):C.setTimeout(ot,S.fx.interval),S.fx.tick())}function at(){return C.setTimeout(function(){Ze=void 0}),Ze=Date.now()}function st(e,t){var n,r=0,i={height:e};for(t=t?1:0;r<4;r+=2-t)i["margin"+(n=ne[r])]=i["padding"+n]=e;return t&&(i.opacity=i.width=e),i}function ut(e,t,n){for(var r,i=(lt.tweeners[t]||[]).concat(lt.tweeners["*"]),o=0,a=i.length;o<a;o++)if(r=i[o].call(n,t,e))return r}function lt(o,e,t){var n,a,r=0,i=lt.prefilters.length,s=S.Deferred().always(function(){delete u.elem}),u=function(){if(a)return!1;for(var e=Ze||at(),t=Math.max(0,l.startTime+l.duration-e),n=1-(t/l.duration||0),r=0,i=l.tweens.length;r<i;r++)l.tweens[r].run(n);return s.notifyWith(o,[l,n,t]),n<1&&i?t:(i||s.notifyWith(o,[l,1,0]),s.resolveWith(o,[l]),!1)},l=s.promise({elem:o,props:S.extend({},e),opts:S.extend(!0,{specialEasing:{},easing:S.easing._default},t),originalProperties:e,originalOptions:t,startTime:Ze||at(),duration:t.duration,tweens:[],createTween:function(e,t){var n=S.Tween(o,l.opts,e,t,l.opts.specialEasing[e]||l.opts.easing);return l.tweens.push(n),n},stop:function(e){var t=0,n=e?l.tweens.length:0;if(a)return this;for(a=!0;t<n;t++)l.tweens[t].run(1);return e?(s.notifyWith(o,[l,1,0]),s.resolveWith(o,[l,e])):s.rejectWith(o,[l,e]),this}}),c=l.props;for(!function(e,t){var n,r,i,o,a;for(n in e)if(i=t[r=X(n)],o=e[n],Array.isArray(o)&&(i=o[1],o=e[n]=o[0]),n!==r&&(e[r]=o,delete e[n]),(a=S.cssHooks[r])&&"expand"in a)for(n in o=a.expand(o),delete e[r],o)n in e||(e[n]=o[n],t[n]=i);else t[r]=i}(c,l.opts.specialEasing);r<i;r++)if(n=lt.prefilters[r].call(l,o,c,l.opts))return m(n.stop)&&(S._queueHooks(l.elem,l.opts.queue).stop=n.stop.bind(n)),n;return S.map(c,ut,l),m(l.opts.start)&&l.opts.start.call(o,l),l.progress(l.opts.progress).done(l.opts.done,l.opts.complete).fail(l.opts.fail).always(l.opts.always),S.fx.timer(S.extend(u,{elem:o,anim:l,queue:l.opts.queue})),l}S.Animation=S.extend(lt,{tweeners:{"*":[function(e,t){var n=this.createTween(e,t);return se(n.elem,e,te.exec(t),n),n}]},tweener:function(e,t){m(e)?(t=e,e=["*"]):e=e.match(P);for(var n,r=0,i=e.length;r<i;r++)n=e[r],lt.tweeners[n]=lt.tweeners[n]||[],lt.tweeners[n].unshift(t)},prefilters:[function(e,t,n){var r,i,o,a,s,u,l,c,f="width"in t||"height"in t,p=this,d={},h=e.style,g=e.nodeType&&ae(e),v=Y.get(e,"fxshow");for(r in n.queue||(null==(a=S._queueHooks(e,"fx")).unqueued&&(a.unqueued=0,s=a.empty.fire,a.empty.fire=function(){a.unqueued||s()}),a.unqueued++,p.always(function(){p.always(function(){a.unqueued--,S.queue(e,"fx").length||a.empty.fire()})})),t)if(i=t[r],rt.test(i)){if(delete t[r],o=o||"toggle"===i,i===(g?"hide":"show")){if("show"!==i||!v||void 0===v[r])continue;g=!0}d[r]=v&&v[r]||S.style(e,r)}if((u=!S.isEmptyObject(t))||!S.isEmptyObject(d))for(r in f&&1===e.nodeType&&(n.overflow=[h.overflow,h.overflowX,h.overflowY],null==(l=v&&v.display)&&(l=Y.get(e,"display")),"none"===(c=S.css(e,"display"))&&(l?c=l:(le([e],!0),l=e.style.display||l,c=S.css(e,"display"),le([e]))),("inline"===c||"inline-block"===c&&null!=l)&&"none"===S.css(e,"float")&&(u||(p.done(function(){h.display=l}),null==l&&(c=h.display,l="none"===c?"":c)),h.display="inline-block")),n.overflow&&(h.overflow="hidden",p.always(function(){h.overflow=n.overflow[0],h.overflowX=n.overflow[1],h.overflowY=n.overflow[2]})),u=!1,d)u||(v?"hidden"in v&&(g=v.hidden):v=Y.access(e,"fxshow",{display:l}),o&&(v.hidden=!g),g&&le([e],!0),p.done(function(){for(r in g||le([e]),Y.remove(e,"fxshow"),d)S.style(e,r,d[r])})),u=ut(g?v[r]:0,r,p),r in v||(v[r]=u.start,g&&(u.end=u.start,u.start=0))}],prefilter:function(e,t){t?lt.prefilters.unshift(e):lt.prefilters.push(e)}}),S.speed=function(e,t,n){var r=e&&"object"==typeof e?S.extend({},e):{complete:n||!n&&t||m(e)&&e,duration:e,easing:n&&t||t&&!m(t)&&t};return S.fx.off?r.duration=0:"number"!=typeof r.duration&&(r.duration in S.fx.speeds?r.duration=S.fx.speeds[r.duration]:r.duration=S.fx.speeds._default),null!=r.queue&&!0!==r.queue||(r.queue="fx"),r.old=r.complete,r.complete=function(){m(r.old)&&r.old.call(this),r.queue&&S.dequeue(this,r.queue)},r},S.fn.extend({fadeTo:function(e,t,n,r){return this.filter(ae).css("opacity",0).show().end().animate({opacity:t},e,n,r)},animate:function(t,e,n,r){var i=S.isEmptyObject(t),o=S.speed(e,n,r),a=function(){var e=lt(this,S.extend({},t),o);(i||Y.get(this,"finish"))&&e.stop(!0)};return a.finish=a,i||!1===o.queue?this.each(a):this.queue(o.queue,a)},stop:function(i,e,o){var a=function(e){var t=e.stop;delete e.stop,t(o)};return"string"!=typeof i&&(o=e,e=i,i=void 0),e&&this.queue(i||"fx",[]),this.each(function(){var e=!0,t=null!=i&&i+"queueHooks",n=S.timers,r=Y.get(this);if(t)r[t]&&r[t].stop&&a(r[t]);else for(t in r)r[t]&&r[t].stop&&it.test(t)&&a(r[t]);for(t=n.length;t--;)n[t].elem!==this||null!=i&&n[t].queue!==i||(n[t].anim.stop(o),e=!1,n.splice(t,1));!e&&o||S.dequeue(this,i)})},finish:function(a){return!1!==a&&(a=a||"fx"),this.each(function(){var e,t=Y.get(this),n=t[a+"queue"],r=t[a+"queueHooks"],i=S.timers,o=n?n.length:0;for(t.finish=!0,S.queue(this,a,[]),r&&r.stop&&r.stop.call(this,!0),e=i.length;e--;)i[e].elem===this&&i[e].queue===a&&(i[e].anim.stop(!0),i.splice(e,1));for(e=0;e<o;e++)n[e]&&n[e].finish&&n[e].finish.call(this);delete t.finish})}}),S.each(["toggle","show","hide"],function(e,r){var i=S.fn[r];S.fn[r]=function(e,t,n){return null==e||"boolean"==typeof e?i.apply(this,arguments):this.animate(st(r,!0),e,t,n)}}),S.each({slideDown:st("show"),slideUp:st("hide"),slideToggle:st("toggle"),fadeIn:{opacity:"show"},fadeOut:{opacity:"hide"},fadeToggle:{opacity:"toggle"}},function(e,r){S.fn[e]=function(e,t,n){return this.animate(r,e,t,n)}}),S.timers=[],S.fx.tick=function(){var e,t=0,n=S.timers;for(Ze=Date.now();t<n.length;t++)(e=n[t])()||n[t]!==e||n.splice(t--,1);n.length||S.fx.stop(),Ze=void 0},S.fx.timer=function(e){S.timers.push(e),S.fx.start()},S.fx.interval=13,S.fx.start=function(){et||(et=!0,ot())},S.fx.stop=function(){et=null},S.fx.speeds={slow:600,fast:200,_default:400},S.fn.delay=function(r,e){return r=S.fx&&S.fx.speeds[r]||r,e=e||"fx",this.queue(e,function(e,t){var n=C.setTimeout(e,r);t.stop=function(){C.clearTimeout(n)}})},tt=E.createElement("input"),nt=E.createElement("select").appendChild(E.createElement("option")),tt.type="checkbox",y.checkOn=""!==tt.value,y.optSelected=nt.selected,(tt=E.createElement("input")).value="t",tt.type="radio",y.radioValue="t"===tt.value;var ct,ft=S.expr.attrHandle;S.fn.extend({attr:function(e,t){return $(this,S.attr,e,t,1<arguments.length)},removeAttr:function(e){return this.each(function(){S.removeAttr(this,e)})}}),S.extend({attr:function(e,t,n){var r,i,o=e.nodeType;if(3!==o&&8!==o&&2!==o)return"undefined"==typeof e.getAttribute?S.prop(e,t,n):(1===o&&S.isXMLDoc(e)||(i=S.attrHooks[t.toLowerCase()]||(S.expr.match.bool.test(t)?ct:void 0)),void 0!==n?null===n?void S.removeAttr(e,t):i&&"set"in i&&void 0!==(r=i.set(e,n,t))?r:(e.setAttribute(t,n+""),n):i&&"get"in i&&null!==(r=i.get(e,t))?r:null==(r=S.find.attr(e,t))?void 0:r)},attrHooks:{type:{set:function(e,t){if(!y.radioValue&&"radio"===t&&A(e,"input")){var n=e.value;return e.setAttribute("type",t),n&&(e.value=n),t}}}},removeAttr:function(e,t){var n,r=0,i=t&&t.match(P);if(i&&1===e.nodeType)while(n=i[r++])e.removeAttribute(n)}}),ct={set:function(e,t,n){return!1===t?S.removeAttr(e,n):e.setAttribute(n,n),n}},S.each(S.expr.match.bool.source.match(/\w+/g),function(e,t){var a=ft[t]||S.find.attr;ft[t]=function(e,t,n){var r,i,o=t.toLowerCase();return n||(i=ft[o],ft[o]=r,r=null!=a(e,t,n)?o:null,ft[o]=i),r}});var pt=/^(?:input|select|textarea|button)$/i,dt=/^(?:a|area)$/i;function ht(e){return(e.match(P)||[]).join(" ")}function gt(e){return e.getAttribute&&e.getAttribute("class")||""}function vt(e){return Array.isArray(e)?e:"string"==typeof e&&e.match(P)||[]}S.fn.extend({prop:function(e,t){return $(this,S.prop,e,t,1<arguments.length)},removeProp:function(e){return this.each(function(){delete this[S.propFix[e]||e]})}}),S.extend({prop:function(e,t,n){var r,i,o=e.nodeType;if(3!==o&&8!==o&&2!==o)return 1===o&&S.isXMLDoc(e)||(t=S.propFix[t]||t,i=S.propHooks[t]),void 0!==n?i&&"set"in i&&void 0!==(r=i.set(e,n,t))?r:e[t]=n:i&&"get"in i&&null!==(r=i.get(e,t))?r:e[t]},propHooks:{tabIndex:{get:function(e){var t=S.find.attr(e,"tabindex");return t?parseInt(t,10):pt.test(e.nodeName)||dt.test(e.nodeName)&&e.href?0:-1}}},propFix:{"for":"htmlFor","class":"className"}}),y.optSelected||(S.propHooks.selected={get:function(e){var t=e.parentNode;return t&&t.parentNode&&t.parentNode.selectedIndex,null},set:function(e){var t=e.parentNode;t&&(t.selectedIndex,t.parentNode&&t.parentNode.selectedIndex)}}),S.each(["tabIndex","readOnly","maxLength","cellSpacing","cellPadding","rowSpan","colSpan","useMap","frameBorder","contentEditable"],function(){S.propFix[this.toLowerCase()]=this}),S.fn.extend({addClass:function(t){var e,n,r,i,o,a,s,u=0;if(m(t))return this.each(function(e){S(this).addClass(t.call(this,e,gt(this)))});if((e=vt(t)).length)while(n=this[u++])if(i=gt(n),r=1===n.nodeType&&" "+ht(i)+" "){a=0;while(o=e[a++])r.indexOf(" "+o+" ")<0&&(r+=o+" ");i!==(s=ht(r))&&n.setAttribute("class",s)}return this},removeClass:function(t){var e,n,r,i,o,a,s,u=0;if(m(t))return this.each(function(e){S(this).removeClass(t.call(this,e,gt(this)))});if(!arguments.length)return this.attr("class","");if((e=vt(t)).length)while(n=this[u++])if(i=gt(n),r=1===n.nodeType&&" "+ht(i)+" "){a=0;while(o=e[a++])while(-1<r.indexOf(" "+o+" "))r=r.replace(" "+o+" "," ");i!==(s=ht(r))&&n.setAttribute("class",s)}return this},toggleClass:function(i,t){var o=typeof i,a="string"===o||Array.isArray(i);return"boolean"==typeof t&&a?t?this.addClass(i):this.removeClass(i):m(i)?this.each(function(e){S(this).toggleClass(i.call(this,e,gt(this),t),t)}):this.each(function(){var e,t,n,r;if(a){t=0,n=S(this),r=vt(i);while(e=r[t++])n.hasClass(e)?n.removeClass(e):n.addClass(e)}else void 0!==i&&"boolean"!==o||((e=gt(this))&&Y.set(this,"__className__",e),this.setAttribute&&this.setAttribute("class",e||!1===i?"":Y.get(this,"__className__")||""))})},hasClass:function(e){var t,n,r=0;t=" "+e+" ";while(n=this[r++])if(1===n.nodeType&&-1<(" "+ht(gt(n))+" ").indexOf(t))return!0;return!1}});var yt=/\r/g;S.fn.extend({val:function(n){var r,e,i,t=this[0];return arguments.length?(i=m(n),this.each(function(e){var t;1===this.nodeType&&(null==(t=i?n.call(this,e,S(this).val()):n)?t="":"number"==typeof t?t+="":Array.isArray(t)&&(t=S.map(t,function(e){return null==e?"":e+""})),(r=S.valHooks[this.type]||S.valHooks[this.nodeName.toLowerCase()])&&"set"in r&&void 0!==r.set(this,t,"value")||(this.value=t))})):t?(r=S.valHooks[t.type]||S.valHooks[t.nodeName.toLowerCase()])&&"get"in r&&void 0!==(e=r.get(t,"value"))?e:"string"==typeof(e=t.value)?e.replace(yt,""):null==e?"":e:void 0}}),S.extend({valHooks:{option:{get:function(e){var t=S.find.attr(e,"value");return null!=t?t:ht(S.text(e))}},select:{get:function(e){var t,n,r,i=e.options,o=e.selectedIndex,a="select-one"===e.type,s=a?null:[],u=a?o+1:i.length;for(r=o<0?u:a?o:0;r<u;r++)if(((n=i[r]).selected||r===o)&&!n.disabled&&(!n.parentNode.disabled||!A(n.parentNode,"optgroup"))){if(t=S(n).val(),a)return t;s.push(t)}return s},set:function(e,t){var n,r,i=e.options,o=S.makeArray(t),a=i.length;while(a--)((r=i[a]).selected=-1<S.inArray(S.valHooks.option.get(r),o))&&(n=!0);return n||(e.selectedIndex=-1),o}}}}),S.each(["radio","checkbox"],function(){S.valHooks[this]={set:function(e,t){if(Array.isArray(t))return e.checked=-1<S.inArray(S(e).val(),t)}},y.checkOn||(S.valHooks[this].get=function(e){return null===e.getAttribute("value")?"on":e.value})}),y.focusin="onfocusin"in C;var mt=/^(?:focusinfocus|focusoutblur)$/,xt=function(e){e.stopPropagation()};S.extend(S.event,{trigger:function(e,t,n,r){var i,o,a,s,u,l,c,f,p=[n||E],d=v.call(e,"type")?e.type:e,h=v.call(e,"namespace")?e.namespace.split("."):[];if(o=f=a=n=n||E,3!==n.nodeType&&8!==n.nodeType&&!mt.test(d+S.event.triggered)&&(-1<d.indexOf(".")&&(d=(h=d.split(".")).shift(),h.sort()),u=d.indexOf(":")<0&&"on"+d,(e=e[S.expando]?e:new S.Event(d,"object"==typeof e&&e)).isTrigger=r?2:3,e.namespace=h.join("."),e.rnamespace=e.namespace?new RegExp("(^|\\.)"+h.join("\\.(?:.*\\.|)")+"(\\.|$)"):null,e.result=void 0,e.target||(e.target=n),t=null==t?[e]:S.makeArray(t,[e]),c=S.event.special[d]||{},r||!c.trigger||!1!==c.trigger.apply(n,t))){if(!r&&!c.noBubble&&!x(n)){for(s=c.delegateType||d,mt.test(s+d)||(o=o.parentNode);o;o=o.parentNode)p.push(o),a=o;a===(n.ownerDocument||E)&&p.push(a.defaultView||a.parentWindow||C)}i=0;while((o=p[i++])&&!e.isPropagationStopped())f=o,e.type=1<i?s:c.bindType||d,(l=(Y.get(o,"events")||Object.create(null))[e.type]&&Y.get(o,"handle"))&&l.apply(o,t),(l=u&&o[u])&&l.apply&&V(o)&&(e.result=l.apply(o,t),!1===e.result&&e.preventDefault());return e.type=d,r||e.isDefaultPrevented()||c._default&&!1!==c._default.apply(p.pop(),t)||!V(n)||u&&m(n[d])&&!x(n)&&((a=n[u])&&(n[u]=null),S.event.triggered=d,e.isPropagationStopped()&&f.addEventListener(d,xt),n[d](),e.isPropagationStopped()&&f.removeEventListener(d,xt),S.event.triggered=void 0,a&&(n[u]=a)),e.result}},simulate:function(e,t,n){var r=S.extend(new S.Event,n,{type:e,isSimulated:!0});S.event.trigger(r,null,t)}}),S.fn.extend({trigger:function(e,t){return this.each(function(){S.event.trigger(e,t,this)})},triggerHandler:function(e,t){var n=this[0];if(n)return S.event.trigger(e,t,n,!0)}}),y.focusin||S.each({focus:"focusin",blur:"focusout"},function(n,r){var i=function(e){S.event.simulate(r,e.target,S.event.fix(e))};S.event.special[r]={setup:function(){var e=this.ownerDocument||this.document||this,t=Y.access(e,r);t||e.addEventListener(n,i,!0),Y.access(e,r,(t||0)+1)},teardown:function(){var e=this.ownerDocument||this.document||this,t=Y.access(e,r)-1;t?Y.access(e,r,t):(e.removeEventListener(n,i,!0),Y.remove(e,r))}}});var bt=C.location,wt={guid:Date.now()},Tt=/\?/;S.parseXML=function(e){var t,n;if(!e||"string"!=typeof e)return null;try{t=(new C.DOMParser).parseFromString(e,"text/xml")}catch(e){}return n=t&&t.getElementsByTagName("parsererror")[0],t&&!n||S.error("Invalid XML: "+(n?S.map(n.childNodes,function(e){return e.textContent}).join("\n"):e)),t};var Ct=/\[\]$/,Et=/\r?\n/g,St=/^(?:submit|button|image|reset|file)$/i,kt=/^(?:input|select|textarea|keygen)/i;function At(n,e,r,i){var t;if(Array.isArray(e))S.each(e,function(e,t){r||Ct.test(n)?i(n,t):At(n+"["+("object"==typeof t&&null!=t?e:"")+"]",t,r,i)});else if(r||"object"!==w(e))i(n,e);else for(t in e)At(n+"["+t+"]",e[t],r,i)}S.param=function(e,t){var n,r=[],i=function(e,t){var n=m(t)?t():t;r[r.length]=encodeURIComponent(e)+"="+encodeURIComponent(null==n?"":n)};if(null==e)return"";if(Array.isArray(e)||e.jquery&&!S.isPlainObject(e))S.each(e,function(){i(this.name,this.value)});else for(n in e)At(n,e[n],t,i);return r.join("&")},S.fn.extend({serialize:function(){return S.param(this.serializeArray())},serializeArray:function(){return this.map(function(){var e=S.prop(this,"elements");return e?S.makeArray(e):this}).filter(function(){var e=this.type;return this.name&&!S(this).is(":disabled")&&kt.test(this.nodeName)&&!St.test(e)&&(this.checked||!pe.test(e))}).map(function(e,t){var n=S(this).val();return null==n?null:Array.isArray(n)?S.map(n,function(e){return{name:t.name,value:e.replace(Et,"\r\n")}}):{name:t.name,value:n.replace(Et,"\r\n")}}).get()}});var Nt=/%20/g,jt=/#.*$/,Dt=/([?&])_=[^&]*/,qt=/^(.*?):[ \t]*([^\r\n]*)$/gm,Lt=/^(?:GET|HEAD)$/,Ht=/^\/\//,Ot={},Pt={},Rt="*/".concat("*"),Mt=E.createElement("a");function It(o){return function(e,t){"string"!=typeof e&&(t=e,e="*");var n,r=0,i=e.toLowerCase().match(P)||[];if(m(t))while(n=i[r++])"+"===n[0]?(n=n.slice(1)||"*",(o[n]=o[n]||[]).unshift(t)):(o[n]=o[n]||[]).push(t)}}function Wt(t,i,o,a){var s={},u=t===Pt;function l(e){var r;return s[e]=!0,S.each(t[e]||[],function(e,t){var n=t(i,o,a);return"string"!=typeof n||u||s[n]?u?!(r=n):void 0:(i.dataTypes.unshift(n),l(n),!1)}),r}return l(i.dataTypes[0])||!s["*"]&&l("*")}function Ft(e,t){var n,r,i=S.ajaxSettings.flatOptions||{};for(n in t)void 0!==t[n]&&((i[n]?e:r||(r={}))[n]=t[n]);return r&&S.extend(!0,e,r),e}Mt.href=bt.href,S.extend({active:0,lastModified:{},etag:{},ajaxSettings:{url:bt.href,type:"GET",isLocal:/^(?:about|app|app-storage|.+-extension|file|res|widget):$/.test(bt.protocol),global:!0,processData:!0,async:!0,contentType:"application/x-www-form-urlencoded; charset=UTF-8",accepts:{"*":Rt,text:"text/plain",html:"text/html",xml:"application/xml, text/xml",json:"application/json, text/javascript"},contents:{xml:/\bxml\b/,html:/\bhtml/,json:/\bjson\b/},responseFields:{xml:"responseXML",text:"responseText",json:"responseJSON"},converters:{"* text":String,"text html":!0,"text json":JSON.parse,"text xml":S.parseXML},flatOptions:{url:!0,context:!0}},ajaxSetup:function(e,t){return t?Ft(Ft(e,S.ajaxSettings),t):Ft(S.ajaxSettings,e)},ajaxPrefilter:It(Ot),ajaxTransport:It(Pt),ajax:function(e,t){"object"==typeof e&&(t=e,e=void 0),t=t||{};var c,f,p,n,d,r,h,g,i,o,v=S.ajaxSetup({},t),y=v.context||v,m=v.context&&(y.nodeType||y.jquery)?S(y):S.event,x=S.Deferred(),b=S.Callbacks("once memory"),w=v.statusCode||{},a={},s={},u="canceled",T={readyState:0,getResponseHeader:function(e){var t;if(h){if(!n){n={};while(t=qt.exec(p))n[t[1].toLowerCase()+" "]=(n[t[1].toLowerCase()+" "]||[]).concat(t[2])}t=n[e.toLowerCase()+" "]}return null==t?null:t.join(", ")},getAllResponseHeaders:function(){return h?p:null},setRequestHeader:function(e,t){return null==h&&(e=s[e.toLowerCase()]=s[e.toLowerCase()]||e,a[e]=t),this},overrideMimeType:function(e){return null==h&&(v.mimeType=e),this},statusCode:function(e){var t;if(e)if(h)T.always(e[T.status]);else for(t in e)w[t]=[w[t],e[t]];return this},abort:function(e){var t=e||u;return c&&c.abort(t),l(0,t),this}};if(x.promise(T),v.url=((e||v.url||bt.href)+"").replace(Ht,bt.protocol+"//"),v.type=t.method||t.type||v.method||v.type,v.dataTypes=(v.dataType||"*").toLowerCase().match(P)||[""],null==v.crossDomain){r=E.createElement("a");try{r.href=v.url,r.href=r.href,v.crossDomain=Mt.protocol+"//"+Mt.host!=r.protocol+"//"+r.host}catch(e){v.crossDomain=!0}}if(v.data&&v.processData&&"string"!=typeof v.data&&(v.data=S.param(v.data,v.traditional)),Wt(Ot,v,t,T),h)return T;for(i in(g=S.event&&v.global)&&0==S.active++&&S.event.trigger("ajaxStart"),v.type=v.type.toUpperCase(),v.hasContent=!Lt.test(v.type),f=v.url.replace(jt,""),v.hasContent?v.data&&v.processData&&0===(v.contentType||"").indexOf("application/x-www-form-urlencoded")&&(v.data=v.data.replace(Nt,"+")):(o=v.url.slice(f.length),v.data&&(v.processData||"string"==typeof v.data)&&(f+=(Tt.test(f)?"&":"?")+v.data,delete v.data),!1===v.cache&&(f=f.replace(Dt,"$1"),o=(Tt.test(f)?"&":"?")+"_="+wt.guid+++o),v.url=f+o),v.ifModified&&(S.lastModified[f]&&T.setRequestHeader("If-Modified-Since",S.lastModified[f]),S.etag[f]&&T.setRequestHeader("If-None-Match",S.etag[f])),(v.data&&v.hasContent&&!1!==v.contentType||t.contentType)&&T.setRequestHeader("Content-Type",v.contentType),T.setRequestHeader("Accept",v.dataTypes[0]&&v.accepts[v.dataTypes[0]]?v.accepts[v.dataTypes[0]]+("*"!==v.dataTypes[0]?", "+Rt+"; q=0.01":""):v.accepts["*"]),v.headers)T.setRequestHeader(i,v.headers[i]);if(v.beforeSend&&(!1===v.beforeSend.call(y,T,v)||h))return T.abort();if(u="abort",b.add(v.complete),T.done(v.success),T.fail(v.error),c=Wt(Pt,v,t,T)){if(T.readyState=1,g&&m.trigger("ajaxSend",[T,v]),h)return T;v.async&&0<v.timeout&&(d=C.setTimeout(function(){T.abort("timeout")},v.timeout));try{h=!1,c.send(a,l)}catch(e){if(h)throw e;l(-1,e)}}else l(-1,"No Transport");function l(e,t,n,r){var i,o,a,s,u,l=t;h||(h=!0,d&&C.clearTimeout(d),c=void 0,p=r||"",T.readyState=0<e?4:0,i=200<=e&&e<300||304===e,n&&(s=function(e,t,n){var r,i,o,a,s=e.contents,u=e.dataTypes;while("*"===u[0])u.shift(),void 0===r&&(r=e.mimeType||t.getResponseHeader("Content-Type"));if(r)for(i in s)if(s[i]&&s[i].test(r)){u.unshift(i);break}if(u[0]in n)o=u[0];else{for(i in n){if(!u[0]||e.converters[i+" "+u[0]]){o=i;break}a||(a=i)}o=o||a}if(o)return o!==u[0]&&u.unshift(o),n[o]}(v,T,n)),!i&&-1<S.inArray("script",v.dataTypes)&&S.inArray("json",v.dataTypes)<0&&(v.converters["text script"]=function(){}),s=function(e,t,n,r){var i,o,a,s,u,l={},c=e.dataTypes.slice();if(c[1])for(a in e.converters)l[a.toLowerCase()]=e.converters[a];o=c.shift();while(o)if(e.responseFields[o]&&(n[e.responseFields[o]]=t),!u&&r&&e.dataFilter&&(t=e.dataFilter(t,e.dataType)),u=o,o=c.shift())if("*"===o)o=u;else if("*"!==u&&u!==o){if(!(a=l[u+" "+o]||l["* "+o]))for(i in l)if((s=i.split(" "))[1]===o&&(a=l[u+" "+s[0]]||l["* "+s[0]])){!0===a?a=l[i]:!0!==l[i]&&(o=s[0],c.unshift(s[1]));break}if(!0!==a)if(a&&e["throws"])t=a(t);else try{t=a(t)}catch(e){return{state:"parsererror",error:a?e:"No conversion from "+u+" to "+o}}}return{state:"success",data:t}}(v,s,T,i),i?(v.ifModified&&((u=T.getResponseHeader("Last-Modified"))&&(S.lastModified[f]=u),(u=T.getResponseHeader("etag"))&&(S.etag[f]=u)),204===e||"HEAD"===v.type?l="nocontent":304===e?l="notmodified":(l=s.state,o=s.data,i=!(a=s.error))):(a=l,!e&&l||(l="error",e<0&&(e=0))),T.status=e,T.statusText=(t||l)+"",i?x.resolveWith(y,[o,l,T]):x.rejectWith(y,[T,l,a]),T.statusCode(w),w=void 0,g&&m.trigger(i?"ajaxSuccess":"ajaxError",[T,v,i?o:a]),b.fireWith(y,[T,l]),g&&(m.trigger("ajaxComplete",[T,v]),--S.active||S.event.trigger("ajaxStop")))}return T},getJSON:function(e,t,n){return S.get(e,t,n,"json")},getScript:function(e,t){return S.get(e,void 0,t,"script")}}),S.each(["get","post"],function(e,i){S[i]=function(e,t,n,r){return m(t)&&(r=r||n,n=t,t=void 0),S.ajax(S.extend({url:e,type:i,dataType:r,data:t,success:n},S.isPlainObject(e)&&e))}}),S.ajaxPrefilter(function(e){var t;for(t in e.headers)"content-type"===t.toLowerCase()&&(e.contentType=e.headers[t]||"")}),S._evalUrl=function(e,t,n){return S.ajax({url:e,type:"GET",dataType:"script",cache:!0,async:!1,global:!1,converters:{"text script":function(){}},dataFilter:function(e){S.globalEval(e,t,n)}})},S.fn.extend({wrapAll:function(e){var t;return this[0]&&(m(e)&&(e=e.call(this[0])),t=S(e,this[0].ownerDocument).eq(0).clone(!0),this[0].parentNode&&t.insertBefore(this[0]),t.map(function(){var e=this;while(e.firstElementChild)e=e.firstElementChild;return e}).append(this)),this},wrapInner:function(n){return m(n)?this.each(function(e){S(this).wrapInner(n.call(this,e))}):this.each(function(){var e=S(this),t=e.contents();t.length?t.wrapAll(n):e.append(n)})},wrap:function(t){var n=m(t);return this.each(function(e){S(this).wrapAll(n?t.call(this,e):t)})},unwrap:function(e){return this.parent(e).not("body").each(function(){S(this).replaceWith(this.childNodes)}),this}}),S.expr.pseudos.hidden=function(e){return!S.expr.pseudos.visible(e)},S.expr.pseudos.visible=function(e){return!!(e.offsetWidth||e.offsetHeight||e.getClientRects().length)},S.ajaxSettings.xhr=function(){try{return new C.XMLHttpRequest}catch(e){}};var Bt={0:200,1223:204},$t=S.ajaxSettings.xhr();y.cors=!!$t&&"withCredentials"in $t,y.ajax=$t=!!$t,S.ajaxTransport(function(i){var o,a;if(y.cors||$t&&!i.crossDomain)return{send:function(e,t){var n,r=i.xhr();if(r.open(i.type,i.url,i.async,i.username,i.password),i.xhrFields)for(n in i.xhrFields)r[n]=i.xhrFields[n];for(n in i.mimeType&&r.overrideMimeType&&r.overrideMimeType(i.mimeType),i.crossDomain||e["X-Requested-With"]||(e["X-Requested-With"]="XMLHttpRequest"),e)r.setRequestHeader(n,e[n]);o=function(e){return function(){o&&(o=a=r.onload=r.onerror=r.onabort=r.ontimeout=r.onreadystatechange=null,"abort"===e?r.abort():"error"===e?"number"!=typeof r.status?t(0,"error"):t(r.status,r.statusText):t(Bt[r.status]||r.status,r.statusText,"text"!==(r.responseType||"text")||"string"!=typeof r.responseText?{binary:r.response}:{text:r.responseText},r.getAllResponseHeaders()))}},r.onload=o(),a=r.onerror=r.ontimeout=o("error"),void 0!==r.onabort?r.onabort=a:r.onreadystatechange=function(){4===r.readyState&&C.setTimeout(function(){o&&a()})},o=o("abort");try{r.send(i.hasContent&&i.data||null)}catch(e){if(o)throw e}},abort:function(){o&&o()}}}),S.ajaxPrefilter(function(e){e.crossDomain&&(e.contents.script=!1)}),S.ajaxSetup({accepts:{script:"text/javascript, application/javascript, application/ecmascript, application/x-ecmascript"},contents:{script:/\b(?:java|ecma)script\b/},converters:{"text script":function(e){return S.globalEval(e),e}}}),S.ajaxPrefilter("script",function(e){void 0===e.cache&&(e.cache=!1),e.crossDomain&&(e.type="GET")}),S.ajaxTransport("script",function(n){var r,i;if(n.crossDomain||n.scriptAttrs)return{send:function(e,t){r=S("<script>").attr(n.scriptAttrs||{}).prop({charset:n.scriptCharset,src:n.url}).on("load error",i=function(e){r.remove(),i=null,e&&t("error"===e.type?404:200,e.type)}),E.head.appendChild(r[0])},abort:function(){i&&i()}}});var _t,zt=[],Ut=/(=)\?(?=&|$)|\?\?/;S.ajaxSetup({jsonp:"callback",jsonpCallback:function(){var e=zt.pop()||S.expando+"_"+wt.guid++;return this[e]=!0,e}}),S.ajaxPrefilter("json jsonp",function(e,t,n){var r,i,o,a=!1!==e.jsonp&&(Ut.test(e.url)?"url":"string"==typeof e.data&&0===(e.contentType||"").indexOf("application/x-www-form-urlencoded")&&Ut.test(e.data)&&"data");if(a||"jsonp"===e.dataTypes[0])return r=e.jsonpCallback=m(e.jsonpCallback)?e.jsonpCallback():e.jsonpCallback,a?e[a]=e[a].replace(Ut,"$1"+r):!1!==e.jsonp&&(e.url+=(Tt.test(e.url)?"&":"?")+e.jsonp+"="+r),e.converters["script json"]=function(){return o||S.error(r+" was not called"),o[0]},e.dataTypes[0]="json",i=C[r],C[r]=function(){o=arguments},n.always(function(){void 0===i?S(C).removeProp(r):C[r]=i,e[r]&&(e.jsonpCallback=t.jsonpCallback,zt.push(r)),o&&m(i)&&i(o[0]),o=i=void 0}),"script"}),y.createHTMLDocument=((_t=E.implementation.createHTMLDocument("").body).innerHTML="<form></form><form></form>",2===_t.childNodes.length),S.parseHTML=function(e,t,n){return"string"!=typeof e?[]:("boolean"==typeof t&&(n=t,t=!1),t||(y.createHTMLDocument?((r=(t=E.implementation.createHTMLDocument("")).createElement("base")).href=E.location.href,t.head.appendChild(r)):t=E),o=!n&&[],(i=N.exec(e))?[t.createElement(i[1])]:(i=xe([e],t,o),o&&o.length&&S(o).remove(),S.merge([],i.childNodes)));var r,i,o},S.fn.load=function(e,t,n){var r,i,o,a=this,s=e.indexOf(" ");return-1<s&&(r=ht(e.slice(s)),e=e.slice(0,s)),m(t)?(n=t,t=void 0):t&&"object"==typeof t&&(i="POST"),0<a.length&&S.ajax({url:e,type:i||"GET",dataType:"html",data:t}).done(function(e){o=arguments,a.html(r?S("<div>").append(S.parseHTML(e)).find(r):e)}).always(n&&function(e,t){a.each(function(){n.apply(this,o||[e.responseText,t,e])})}),this},S.expr.pseudos.animated=function(t){return S.grep(S.timers,function(e){return t===e.elem}).length},S.offset={setOffset:function(e,t,n){var r,i,o,a,s,u,l=S.css(e,"position"),c=S(e),f={};"static"===l&&(e.style.position="relative"),s=c.offset(),o=S.css(e,"top"),u=S.css(e,"left"),("absolute"===l||"fixed"===l)&&-1<(o+u).indexOf("auto")?(a=(r=c.position()).top,i=r.left):(a=parseFloat(o)||0,i=parseFloat(u)||0),m(t)&&(t=t.call(e,n,S.extend({},s))),null!=t.top&&(f.top=t.top-s.top+a),null!=t.left&&(f.left=t.left-s.left+i),"using"in t?t.using.call(e,f):c.css(f)}},S.fn.extend({offset:function(t){if(arguments.length)return void 0===t?this:this.each(function(e){S.offset.setOffset(this,t,e)});var e,n,r=this[0];return r?r.getClientRects().length?(e=r.getBoundingClientRect(),n=r.ownerDocument.defaultView,{top:e.top+n.pageYOffset,left:e.left+n.pageXOffset}):{top:0,left:0}:void 0},position:function(){if(this[0]){var e,t,n,r=this[0],i={top:0,left:0};if("fixed"===S.css(r,"position"))t=r.getBoundingClientRect();else{t=this.offset(),n=r.ownerDocument,e=r.offsetParent||n.documentElement;while(e&&(e===n.body||e===n.documentElement)&&"static"===S.css(e,"position"))e=e.parentNode;e&&e!==r&&1===e.nodeType&&((i=S(e).offset()).top+=S.css(e,"borderTopWidth",!0),i.left+=S.css(e,"borderLeftWidth",!0))}return{top:t.top-i.top-S.css(r,"marginTop",!0),left:t.left-i.left-S.css(r,"marginLeft",!0)}}},offsetParent:function(){return this.map(function(){var e=this.offsetParent;while(e&&"static"===S.css(e,"position"))e=e.offsetParent;return e||re})}}),S.each({scrollLeft:"pageXOffset",scrollTop:"pageYOffset"},function(t,i){var o="pageYOffset"===i;S.fn[t]=function(e){return $(this,function(e,t,n){var r;if(x(e)?r=e:9===e.nodeType&&(r=e.defaultView),void 0===n)return r?r[i]:e[t];r?r.scrollTo(o?r.pageXOffset:n,o?n:r.pageYOffset):e[t]=n},t,e,arguments.length)}}),S.each(["top","left"],function(e,n){S.cssHooks[n]=Fe(y.pixelPosition,function(e,t){if(t)return t=We(e,n),Pe.test(t)?S(e).position()[n]+"px":t})}),S.each({Height:"height",Width:"width"},function(a,s){S.each({padding:"inner"+a,content:s,"":"outer"+a},function(r,o){S.fn[o]=function(e,t){var n=arguments.length&&(r||"boolean"!=typeof e),i=r||(!0===e||!0===t?"margin":"border");return $(this,function(e,t,n){var r;return x(e)?0===o.indexOf("outer")?e["inner"+a]:e.document.documentElement["client"+a]:9===e.nodeType?(r=e.documentElement,Math.max(e.body["scroll"+a],r["scroll"+a],e.body["offset"+a],r["offset"+a],r["client"+a])):void 0===n?S.css(e,t,i):S.style(e,t,n,i)},s,n?e:void 0,n)}})}),S.each(["ajaxStart","ajaxStop","ajaxComplete","ajaxError","ajaxSuccess","ajaxSend"],function(e,t){S.fn[t]=function(e){return this.on(t,e)}}),S.fn.extend({bind:function(e,t,n){return this.on(e,null,t,n)},unbind:function(e,t){return this.off(e,null,t)},delegate:function(e,t,n,r){return this.on(t,e,n,r)},undelegate:function(e,t,n){return 1===arguments.length?this.off(e,"**"):this.off(t,e||"**",n)},hover:function(e,t){return this.mouseenter(e).mouseleave(t||e)}}),S.each("blur focus focusin focusout resize scroll click dblclick mousedown mouseup mousemove mouseover mouseout mouseenter mouseleave change select submit keydown keypress keyup contextmenu".split(" "),function(e,n){S.fn[n]=function(e,t){return 0<arguments.length?this.on(n,null,e,t):this.trigger(n)}});var Xt=/^[\s\uFEFF\xA0]+|[\s\uFEFF\xA0]+$/g;S.proxy=function(e,t){var n,r,i;if("string"==typeof t&&(n=e[t],t=e,e=n),m(e))return r=s.call(arguments,2),(i=function(){return e.apply(t||this,r.concat(s.call(arguments)))}).guid=e.guid=e.guid||S.guid++,i},S.holdReady=function(e){e?S.readyWait++:S.ready(!0)},S.isArray=Array.isArray,S.parseJSON=JSON.parse,S.nodeName=A,S.isFunction=m,S.isWindow=x,S.camelCase=X,S.type=w,S.now=Date.now,S.isNumeric=function(e){var t=S.type(e);return("number"===t||"string"===t)&&!isNaN(e-parseFloat(e))},S.trim=function(e){return null==e?"":(e+"").replace(Xt,"")},"function"==typeof define&&define.amd&&define("jquery",[],function(){return S});var Vt=C.jQuery,Gt=C.$;return S.noConflict=function(e){return C.$===S&&(C.$=Gt),e&&C.jQuery===S&&(C.jQuery=Vt),S},"undefined"==typeof e&&(C.jQuery=C.$=S),S});
 </script>
 <meta name="viewport" content="width=device-width, initial-scale=1" />
-<style type="text/css">html{font-family:sans-serif;-webkit-text-size-adjust:100%;-ms-text-size-adjust:100%}body{margin:0}article,aside,details,figcaption,figure,footer,header,hgroup,main,menu,nav,section,summary{display:block}audio,canvas,progress,video{display:inline-block;vertical-align:baseline}audio:not([controls]){display:none;height:0}[hidden],template{display:none}a{background-color:transparent}a:active,a:hover{outline:0}abbr[title]{border-bottom:1px dotted}b,strong{font-weight:700}dfn{font-style:italic}h1{margin:.67em 0;font-size:2em}mark{color:#000;background:#ff0}small{font-size:80%}sub,sup{position:relative;font-size:75%;line-height:0;vertical-align:baseline}sup{top:-.5em}sub{bottom:-.25em}img{border:0}svg:not(:root){overflow:hidden}figure{margin:1em 40px}hr{height:0;-webkit-box-sizing:content-box;-moz-box-sizing:content-box;box-sizing:content-box}pre{overflow:auto}code,kbd,pre,samp{font-family:monospace,monospace;font-size:1em}button,input,optgroup,select,textarea{margin:0;font:inherit;color:inherit}button{overflow:visible}button,select{text-transform:none}button,html input[type=button],input[type=reset],input[type=submit]{-webkit-appearance:button;cursor:pointer}button[disabled],html input[disabled]{cursor:default}button::-moz-focus-inner,input::-moz-focus-inner{padding:0;border:0}input{line-height:normal}input[type=checkbox],input[type=radio]{-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;padding:0}input[type=number]::-webkit-inner-spin-button,input[type=number]::-webkit-outer-spin-button{height:auto}input[type=search]{-webkit-box-sizing:content-box;-moz-box-sizing:content-box;box-sizing:content-box;-webkit-appearance:textfield}input[type=search]::-webkit-search-cancel-button,input[type=search]::-webkit-search-decoration{-webkit-appearance:none}fieldset{padding:.35em .625em .75em;margin:0 2px;border:1px solid silver}legend{padding:0;border:0}textarea{overflow:auto}optgroup{font-weight:700}table{border-spacing:0;border-collapse:collapse}td,th{padding:0}@media print{*,:after,:before{color:#000!important;text-shadow:none!important;background:0 0!important;-webkit-box-shadow:none!important;box-shadow:none!important}a,a:visited{text-decoration:underline}a[href]:after{content:" (" attr(href) ")"}abbr[title]:after{content:" (" attr(title) ")"}a[href^="javascript:"]:after,a[href^="#"]:after{content:""}blockquote,pre{border:1px solid #999;page-break-inside:avoid}thead{display:table-header-group}img,tr{page-break-inside:avoid}img{max-width:100%!important}h2,h3,p{orphans:3;widows:3}h2,h3{page-break-after:avoid}.navbar{display:none}.btn>.caret,.dropup>.btn>.caret{border-top-color:#000!important}.label{border:1px solid #000}.table{border-collapse:collapse!important}.table td,.table th{background-color:#fff!important}.table-bordered td,.table-bordered th{border:1px solid #ddd!important}}@font-face{font-family:'Glyphicons Halflings';src:url(data:application/vnd.ms-fontobject;base64,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);src:url(data:application/vnd.ms-fontobject;base64,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) format('embedded-opentype'),url(data:application/font-woff;base64,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) format('woff'),url(data:application/font-sfnt;base64,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) format('truetype'),url(data:image/svg+xml;base64,<?xml version="1.0" standalone="no"?>
<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN" "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd" >
<svg xmlns="http://www.w3.org/2000/svg">
<metadata></metadata>
<defs>
<font id="glyphicons_halflingsregular" horiz-adv-x="1200" >
<font-face units-per-em="1200" ascent="960" descent="-240" />
<missing-glyph horiz-adv-x="500" />
<glyph horiz-adv-x="0" />
<glyph horiz-adv-x="400" />
<glyph unicode=" " />
<glyph unicode="*" d="M600 1100q15 0 34 -1.5t30 -3.5l11 -1q10 -2 17.5 -10.5t7.5 -18.5v-224l158 158q7 7 18 8t19 -6l106 -106q7 -8 6 -19t-8 -18l-158 -158h224q10 0 18.5 -7.5t10.5 -17.5q6 -41 6 -75q0 -15 -1.5 -34t-3.5 -30l-1 -11q-2 -10 -10.5 -17.5t-18.5 -7.5h-224l158 -158 q7 -7 8 -18t-6 -19l-106 -106q-8 -7 -19 -6t-18 8l-158 158v-224q0 -10 -7.5 -18.5t-17.5 -10.5q-41 -6 -75 -6q-15 0 -34 1.5t-30 3.5l-11 1q-10 2 -17.5 10.5t-7.5 18.5v224l-158 -158q-7 -7 -18 -8t-19 6l-106 106q-7 8 -6 19t8 18l158 158h-224q-10 0 -18.5 7.5 t-10.5 17.5q-6 41 -6 75q0 15 1.5 34t3.5 30l1 11q2 10 10.5 17.5t18.5 7.5h224l-158 158q-7 7 -8 18t6 19l106 106q8 7 19 6t18 -8l158 -158v224q0 10 7.5 18.5t17.5 10.5q41 6 75 6z" />
<glyph unicode="+" d="M450 1100h200q21 0 35.5 -14.5t14.5 -35.5v-350h350q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-350v-350q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v350h-350q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5 h350v350q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xa0;" />
<glyph unicode="&#xa5;" d="M825 1100h250q10 0 12.5 -5t-5.5 -13l-364 -364q-6 -6 -11 -18h268q10 0 13 -6t-3 -14l-120 -160q-6 -8 -18 -14t-22 -6h-125v-100h275q10 0 13 -6t-3 -14l-120 -160q-6 -8 -18 -14t-22 -6h-125v-174q0 -11 -7.5 -18.5t-18.5 -7.5h-148q-11 0 -18.5 7.5t-7.5 18.5v174 h-275q-10 0 -13 6t3 14l120 160q6 8 18 14t22 6h125v100h-275q-10 0 -13 6t3 14l120 160q6 8 18 14t22 6h118q-5 12 -11 18l-364 364q-8 8 -5.5 13t12.5 5h250q25 0 43 -18l164 -164q8 -8 18 -8t18 8l164 164q18 18 43 18z" />
<glyph unicode="&#x2000;" horiz-adv-x="650" />
<glyph unicode="&#x2001;" horiz-adv-x="1300" />
<glyph unicode="&#x2002;" horiz-adv-x="650" />
<glyph unicode="&#x2003;" horiz-adv-x="1300" />
<glyph unicode="&#x2004;" horiz-adv-x="433" />
<glyph unicode="&#x2005;" horiz-adv-x="325" />
<glyph unicode="&#x2006;" horiz-adv-x="216" />
<glyph unicode="&#x2007;" horiz-adv-x="216" />
<glyph unicode="&#x2008;" horiz-adv-x="162" />
<glyph unicode="&#x2009;" horiz-adv-x="260" />
<glyph unicode="&#x200a;" horiz-adv-x="72" />
<glyph unicode="&#x202f;" horiz-adv-x="260" />
<glyph unicode="&#x205f;" horiz-adv-x="325" />
<glyph unicode="&#x20ac;" d="M744 1198q242 0 354 -189q60 -104 66 -209h-181q0 45 -17.5 82.5t-43.5 61.5t-58 40.5t-60.5 24t-51.5 7.5q-19 0 -40.5 -5.5t-49.5 -20.5t-53 -38t-49 -62.5t-39 -89.5h379l-100 -100h-300q-6 -50 -6 -100h406l-100 -100h-300q9 -74 33 -132t52.5 -91t61.5 -54.5t59 -29 t47 -7.5q22 0 50.5 7.5t60.5 24.5t58 41t43.5 61t17.5 80h174q-30 -171 -128 -278q-107 -117 -274 -117q-206 0 -324 158q-36 48 -69 133t-45 204h-217l100 100h112q1 47 6 100h-218l100 100h134q20 87 51 153.5t62 103.5q117 141 297 141z" />
<glyph unicode="&#x20bd;" d="M428 1200h350q67 0 120 -13t86 -31t57 -49.5t35 -56.5t17 -64.5t6.5 -60.5t0.5 -57v-16.5v-16.5q0 -36 -0.5 -57t-6.5 -61t-17 -65t-35 -57t-57 -50.5t-86 -31.5t-120 -13h-178l-2 -100h288q10 0 13 -6t-3 -14l-120 -160q-6 -8 -18 -14t-22 -6h-138v-175q0 -11 -5.5 -18 t-15.5 -7h-149q-10 0 -17.5 7.5t-7.5 17.5v175h-267q-10 0 -13 6t3 14l120 160q6 8 18 14t22 6h117v100h-267q-10 0 -13 6t3 14l120 160q6 8 18 14t22 6h117v475q0 10 7.5 17.5t17.5 7.5zM600 1000v-300h203q64 0 86.5 33t22.5 119q0 84 -22.5 116t-86.5 32h-203z" />
<glyph unicode="&#x2212;" d="M250 700h800q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#x231b;" d="M1000 1200v-150q0 -21 -14.5 -35.5t-35.5 -14.5h-50v-100q0 -91 -49.5 -165.5t-130.5 -109.5q81 -35 130.5 -109.5t49.5 -165.5v-150h50q21 0 35.5 -14.5t14.5 -35.5v-150h-800v150q0 21 14.5 35.5t35.5 14.5h50v150q0 91 49.5 165.5t130.5 109.5q-81 35 -130.5 109.5 t-49.5 165.5v100h-50q-21 0 -35.5 14.5t-14.5 35.5v150h800zM400 1000v-100q0 -60 32.5 -109.5t87.5 -73.5q28 -12 44 -37t16 -55t-16 -55t-44 -37q-55 -24 -87.5 -73.5t-32.5 -109.5v-150h400v150q0 60 -32.5 109.5t-87.5 73.5q-28 12 -44 37t-16 55t16 55t44 37 q55 24 87.5 73.5t32.5 109.5v100h-400z" />
<glyph unicode="&#x25fc;" horiz-adv-x="500" d="M0 0z" />
<glyph unicode="&#x2601;" d="M503 1089q110 0 200.5 -59.5t134.5 -156.5q44 14 90 14q120 0 205 -86.5t85 -206.5q0 -121 -85 -207.5t-205 -86.5h-750q-79 0 -135.5 57t-56.5 137q0 69 42.5 122.5t108.5 67.5q-2 12 -2 37q0 153 108 260.5t260 107.5z" />
<glyph unicode="&#x26fa;" d="M774 1193.5q16 -9.5 20.5 -27t-5.5 -33.5l-136 -187l467 -746h30q20 0 35 -18.5t15 -39.5v-42h-1200v42q0 21 15 39.5t35 18.5h30l468 746l-135 183q-10 16 -5.5 34t20.5 28t34 5.5t28 -20.5l111 -148l112 150q9 16 27 20.5t34 -5zM600 200h377l-182 112l-195 534v-646z " />
<glyph unicode="&#x2709;" d="M25 1100h1150q10 0 12.5 -5t-5.5 -13l-564 -567q-8 -8 -18 -8t-18 8l-564 567q-8 8 -5.5 13t12.5 5zM18 882l264 -264q8 -8 8 -18t-8 -18l-264 -264q-8 -8 -13 -5.5t-5 12.5v550q0 10 5 12.5t13 -5.5zM918 618l264 264q8 8 13 5.5t5 -12.5v-550q0 -10 -5 -12.5t-13 5.5 l-264 264q-8 8 -8 18t8 18zM818 482l364 -364q8 -8 5.5 -13t-12.5 -5h-1150q-10 0 -12.5 5t5.5 13l364 364q8 8 18 8t18 -8l164 -164q8 -8 18 -8t18 8l164 164q8 8 18 8t18 -8z" />
<glyph unicode="&#x270f;" d="M1011 1210q19 0 33 -13l153 -153q13 -14 13 -33t-13 -33l-99 -92l-214 214l95 96q13 14 32 14zM1013 800l-615 -614l-214 214l614 614zM317 96l-333 -112l110 335z" />
<glyph unicode="&#xe001;" d="M700 650v-550h250q21 0 35.5 -14.5t14.5 -35.5v-50h-800v50q0 21 14.5 35.5t35.5 14.5h250v550l-500 550h1200z" />
<glyph unicode="&#xe002;" d="M368 1017l645 163q39 15 63 0t24 -49v-831q0 -55 -41.5 -95.5t-111.5 -63.5q-79 -25 -147 -4.5t-86 75t25.5 111.5t122.5 82q72 24 138 8v521l-600 -155v-606q0 -42 -44 -90t-109 -69q-79 -26 -147 -5.5t-86 75.5t25.5 111.5t122.5 82.5q72 24 138 7v639q0 38 14.5 59 t53.5 34z" />
<glyph unicode="&#xe003;" d="M500 1191q100 0 191 -39t156.5 -104.5t104.5 -156.5t39 -191l-1 -2l1 -5q0 -141 -78 -262l275 -274q23 -26 22.5 -44.5t-22.5 -42.5l-59 -58q-26 -20 -46.5 -20t-39.5 20l-275 274q-119 -77 -261 -77l-5 1l-2 -1q-100 0 -191 39t-156.5 104.5t-104.5 156.5t-39 191 t39 191t104.5 156.5t156.5 104.5t191 39zM500 1022q-88 0 -162 -43t-117 -117t-43 -162t43 -162t117 -117t162 -43t162 43t117 117t43 162t-43 162t-117 117t-162 43z" />
<glyph unicode="&#xe005;" d="M649 949q48 68 109.5 104t121.5 38.5t118.5 -20t102.5 -64t71 -100.5t27 -123q0 -57 -33.5 -117.5t-94 -124.5t-126.5 -127.5t-150 -152.5t-146 -174q-62 85 -145.5 174t-150 152.5t-126.5 127.5t-93.5 124.5t-33.5 117.5q0 64 28 123t73 100.5t104 64t119 20 t120.5 -38.5t104.5 -104z" />
<glyph unicode="&#xe006;" d="M407 800l131 353q7 19 17.5 19t17.5 -19l129 -353h421q21 0 24 -8.5t-14 -20.5l-342 -249l130 -401q7 -20 -0.5 -25.5t-24.5 6.5l-343 246l-342 -247q-17 -12 -24.5 -6.5t-0.5 25.5l130 400l-347 251q-17 12 -14 20.5t23 8.5h429z" />
<glyph unicode="&#xe007;" d="M407 800l131 353q7 19 17.5 19t17.5 -19l129 -353h421q21 0 24 -8.5t-14 -20.5l-342 -249l130 -401q7 -20 -0.5 -25.5t-24.5 6.5l-343 246l-342 -247q-17 -12 -24.5 -6.5t-0.5 25.5l130 400l-347 251q-17 12 -14 20.5t23 8.5h429zM477 700h-240l197 -142l-74 -226 l193 139l195 -140l-74 229l192 140h-234l-78 211z" />
<glyph unicode="&#xe008;" d="M600 1200q124 0 212 -88t88 -212v-250q0 -46 -31 -98t-69 -52v-75q0 -10 6 -21.5t15 -17.5l358 -230q9 -5 15 -16.5t6 -21.5v-93q0 -10 -7.5 -17.5t-17.5 -7.5h-1150q-10 0 -17.5 7.5t-7.5 17.5v93q0 10 6 21.5t15 16.5l358 230q9 6 15 17.5t6 21.5v75q-38 0 -69 52 t-31 98v250q0 124 88 212t212 88z" />
<glyph unicode="&#xe009;" d="M25 1100h1150q10 0 17.5 -7.5t7.5 -17.5v-1050q0 -10 -7.5 -17.5t-17.5 -7.5h-1150q-10 0 -17.5 7.5t-7.5 17.5v1050q0 10 7.5 17.5t17.5 7.5zM100 1000v-100h100v100h-100zM875 1000h-550q-10 0 -17.5 -7.5t-7.5 -17.5v-350q0 -10 7.5 -17.5t17.5 -7.5h550 q10 0 17.5 7.5t7.5 17.5v350q0 10 -7.5 17.5t-17.5 7.5zM1000 1000v-100h100v100h-100zM100 800v-100h100v100h-100zM1000 800v-100h100v100h-100zM100 600v-100h100v100h-100zM1000 600v-100h100v100h-100zM875 500h-550q-10 0 -17.5 -7.5t-7.5 -17.5v-350q0 -10 7.5 -17.5 t17.5 -7.5h550q10 0 17.5 7.5t7.5 17.5v350q0 10 -7.5 17.5t-17.5 7.5zM100 400v-100h100v100h-100zM1000 400v-100h100v100h-100zM100 200v-100h100v100h-100zM1000 200v-100h100v100h-100z" />
<glyph unicode="&#xe010;" d="M50 1100h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5zM650 1100h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v400 q0 21 14.5 35.5t35.5 14.5zM50 500h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5zM650 500h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400 q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe011;" d="M50 1100h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM450 1100h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200 q0 21 14.5 35.5t35.5 14.5zM850 1100h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM50 700h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200 q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM450 700h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM850 700h200q21 0 35.5 -14.5t14.5 -35.5v-200 q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM50 300h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM450 300h200 q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM850 300h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5 t35.5 14.5z" />
<glyph unicode="&#xe012;" d="M50 1100h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM450 1100h700q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-700q-21 0 -35.5 14.5t-14.5 35.5v200 q0 21 14.5 35.5t35.5 14.5zM50 700h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM450 700h700q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-700 q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM50 300h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM450 300h700q21 0 35.5 -14.5t14.5 -35.5v-200 q0 -21 -14.5 -35.5t-35.5 -14.5h-700q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe013;" d="M465 477l571 571q8 8 18 8t17 -8l177 -177q8 -7 8 -17t-8 -18l-783 -784q-7 -8 -17.5 -8t-17.5 8l-384 384q-8 8 -8 18t8 17l177 177q7 8 17 8t18 -8l171 -171q7 -7 18 -7t18 7z" />
<glyph unicode="&#xe014;" d="M904 1083l178 -179q8 -8 8 -18.5t-8 -17.5l-267 -268l267 -268q8 -7 8 -17.5t-8 -18.5l-178 -178q-8 -8 -18.5 -8t-17.5 8l-268 267l-268 -267q-7 -8 -17.5 -8t-18.5 8l-178 178q-8 8 -8 18.5t8 17.5l267 268l-267 268q-8 7 -8 17.5t8 18.5l178 178q8 8 18.5 8t17.5 -8 l268 -267l268 268q7 7 17.5 7t18.5 -7z" />
<glyph unicode="&#xe015;" d="M507 1177q98 0 187.5 -38.5t154.5 -103.5t103.5 -154.5t38.5 -187.5q0 -141 -78 -262l300 -299q8 -8 8 -18.5t-8 -18.5l-109 -108q-7 -8 -17.5 -8t-18.5 8l-300 299q-119 -77 -261 -77q-98 0 -188 38.5t-154.5 103t-103 154.5t-38.5 188t38.5 187.5t103 154.5 t154.5 103.5t188 38.5zM506.5 1023q-89.5 0 -165.5 -44t-120 -120.5t-44 -166t44 -165.5t120 -120t165.5 -44t166 44t120.5 120t44 165.5t-44 166t-120.5 120.5t-166 44zM425 900h150q10 0 17.5 -7.5t7.5 -17.5v-75h75q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5 t-17.5 -7.5h-75v-75q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v75h-75q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5h75v75q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe016;" d="M507 1177q98 0 187.5 -38.5t154.5 -103.5t103.5 -154.5t38.5 -187.5q0 -141 -78 -262l300 -299q8 -8 8 -18.5t-8 -18.5l-109 -108q-7 -8 -17.5 -8t-18.5 8l-300 299q-119 -77 -261 -77q-98 0 -188 38.5t-154.5 103t-103 154.5t-38.5 188t38.5 187.5t103 154.5 t154.5 103.5t188 38.5zM506.5 1023q-89.5 0 -165.5 -44t-120 -120.5t-44 -166t44 -165.5t120 -120t165.5 -44t166 44t120.5 120t44 165.5t-44 166t-120.5 120.5t-166 44zM325 800h350q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-350q-10 0 -17.5 7.5 t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe017;" d="M550 1200h100q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5zM800 975v166q167 -62 272 -209.5t105 -331.5q0 -117 -45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5 t-184.5 123t-123 184.5t-45.5 224q0 184 105 331.5t272 209.5v-166q-103 -55 -165 -155t-62 -220q0 -116 57 -214.5t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5q0 120 -62 220t-165 155z" />
<glyph unicode="&#xe018;" d="M1025 1200h150q10 0 17.5 -7.5t7.5 -17.5v-1150q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v1150q0 10 7.5 17.5t17.5 7.5zM725 800h150q10 0 17.5 -7.5t7.5 -17.5v-750q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v750 q0 10 7.5 17.5t17.5 7.5zM425 500h150q10 0 17.5 -7.5t7.5 -17.5v-450q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v450q0 10 7.5 17.5t17.5 7.5zM125 300h150q10 0 17.5 -7.5t7.5 -17.5v-250q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5 v250q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe019;" d="M600 1174q33 0 74 -5l38 -152l5 -1q49 -14 94 -39l5 -2l134 80q61 -48 104 -105l-80 -134l3 -5q25 -44 39 -93l1 -6l152 -38q5 -43 5 -73q0 -34 -5 -74l-152 -38l-1 -6q-15 -49 -39 -93l-3 -5l80 -134q-48 -61 -104 -105l-134 81l-5 -3q-44 -25 -94 -39l-5 -2l-38 -151 q-43 -5 -74 -5q-33 0 -74 5l-38 151l-5 2q-49 14 -94 39l-5 3l-134 -81q-60 48 -104 105l80 134l-3 5q-25 45 -38 93l-2 6l-151 38q-6 42 -6 74q0 33 6 73l151 38l2 6q13 48 38 93l3 5l-80 134q47 61 105 105l133 -80l5 2q45 25 94 39l5 1l38 152q43 5 74 5zM600 815 q-89 0 -152 -63t-63 -151.5t63 -151.5t152 -63t152 63t63 151.5t-63 151.5t-152 63z" />
<glyph unicode="&#xe020;" d="M500 1300h300q41 0 70.5 -29.5t29.5 -70.5v-100h275q10 0 17.5 -7.5t7.5 -17.5v-75h-1100v75q0 10 7.5 17.5t17.5 7.5h275v100q0 41 29.5 70.5t70.5 29.5zM500 1200v-100h300v100h-300zM1100 900v-800q0 -41 -29.5 -70.5t-70.5 -29.5h-700q-41 0 -70.5 29.5t-29.5 70.5 v800h900zM300 800v-700h100v700h-100zM500 800v-700h100v700h-100zM700 800v-700h100v700h-100zM900 800v-700h100v700h-100z" />
<glyph unicode="&#xe021;" d="M18 618l620 608q8 7 18.5 7t17.5 -7l608 -608q8 -8 5.5 -13t-12.5 -5h-175v-575q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v375h-300v-375q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v575h-175q-10 0 -12.5 5t5.5 13z" />
<glyph unicode="&#xe022;" d="M600 1200v-400q0 -41 29.5 -70.5t70.5 -29.5h300v-650q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5v1100q0 21 14.5 35.5t35.5 14.5h450zM1000 800h-250q-21 0 -35.5 14.5t-14.5 35.5v250z" />
<glyph unicode="&#xe023;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 1027q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5 t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5t-57 214.5t-155.5 155.5t-214.5 57zM525 900h50q10 0 17.5 -7.5t7.5 -17.5v-275h175q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v350q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe024;" d="M1300 0h-538l-41 400h-242l-41 -400h-538l431 1200h209l-21 -300h162l-20 300h208zM515 800l-27 -300h224l-27 300h-170z" />
<glyph unicode="&#xe025;" d="M550 1200h200q21 0 35.5 -14.5t14.5 -35.5v-450h191q20 0 25.5 -11.5t-7.5 -27.5l-327 -400q-13 -16 -32 -16t-32 16l-327 400q-13 16 -7.5 27.5t25.5 11.5h191v450q0 21 14.5 35.5t35.5 14.5zM1125 400h50q10 0 17.5 -7.5t7.5 -17.5v-350q0 -10 -7.5 -17.5t-17.5 -7.5 h-1050q-10 0 -17.5 7.5t-7.5 17.5v350q0 10 7.5 17.5t17.5 7.5h50q10 0 17.5 -7.5t7.5 -17.5v-175h900v175q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe026;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 1027q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5 t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5t-57 214.5t-155.5 155.5t-214.5 57zM525 900h150q10 0 17.5 -7.5t7.5 -17.5v-275h137q21 0 26 -11.5t-8 -27.5l-223 -275q-13 -16 -32 -16t-32 16l-223 275q-13 16 -8 27.5t26 11.5h137v275q0 10 7.5 17.5t17.5 7.5z " />
<glyph unicode="&#xe027;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 1027q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5 t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5t-57 214.5t-155.5 155.5t-214.5 57zM632 914l223 -275q13 -16 8 -27.5t-26 -11.5h-137v-275q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v275h-137q-21 0 -26 11.5t8 27.5l223 275q13 16 32 16 t32 -16z" />
<glyph unicode="&#xe028;" d="M225 1200h750q10 0 19.5 -7t12.5 -17l186 -652q7 -24 7 -49v-425q0 -12 -4 -27t-9 -17q-12 -6 -37 -6h-1100q-12 0 -27 4t-17 8q-6 13 -6 38l1 425q0 25 7 49l185 652q3 10 12.5 17t19.5 7zM878 1000h-556q-10 0 -19 -7t-11 -18l-87 -450q-2 -11 4 -18t16 -7h150 q10 0 19.5 -7t11.5 -17l38 -152q2 -10 11.5 -17t19.5 -7h250q10 0 19.5 7t11.5 17l38 152q2 10 11.5 17t19.5 7h150q10 0 16 7t4 18l-87 450q-2 11 -11 18t-19 7z" />
<glyph unicode="&#xe029;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 1027q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5 t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5t-57 214.5t-155.5 155.5t-214.5 57zM540 820l253 -190q17 -12 17 -30t-17 -30l-253 -190q-16 -12 -28 -6.5t-12 26.5v400q0 21 12 26.5t28 -6.5z" />
<glyph unicode="&#xe030;" d="M947 1060l135 135q7 7 12.5 5t5.5 -13v-362q0 -10 -7.5 -17.5t-17.5 -7.5h-362q-11 0 -13 5.5t5 12.5l133 133q-109 76 -238 76q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5h150q0 -117 -45.5 -224 t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5q192 0 347 -117z" />
<glyph unicode="&#xe031;" d="M947 1060l135 135q7 7 12.5 5t5.5 -13v-361q0 -11 -7.5 -18.5t-18.5 -7.5h-361q-11 0 -13 5.5t5 12.5l134 134q-110 75 -239 75q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5h-150q0 117 45.5 224t123 184.5t184.5 123t224 45.5q192 0 347 -117zM1027 600h150 q0 -117 -45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5q-192 0 -348 118l-134 -134q-7 -8 -12.5 -5.5t-5.5 12.5v360q0 11 7.5 18.5t18.5 7.5h360q10 0 12.5 -5.5t-5.5 -12.5l-133 -133q110 -76 240 -76q116 0 214.5 57t155.5 155.5t57 214.5z" />
<glyph unicode="&#xe032;" d="M125 1200h1050q10 0 17.5 -7.5t7.5 -17.5v-1150q0 -10 -7.5 -17.5t-17.5 -7.5h-1050q-10 0 -17.5 7.5t-7.5 17.5v1150q0 10 7.5 17.5t17.5 7.5zM1075 1000h-850q-10 0 -17.5 -7.5t-7.5 -17.5v-850q0 -10 7.5 -17.5t17.5 -7.5h850q10 0 17.5 7.5t7.5 17.5v850 q0 10 -7.5 17.5t-17.5 7.5zM325 900h50q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-50q-10 0 -17.5 7.5t-7.5 17.5v50q0 10 7.5 17.5t17.5 7.5zM525 900h450q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-450q-10 0 -17.5 7.5t-7.5 17.5v50 q0 10 7.5 17.5t17.5 7.5zM325 700h50q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-50q-10 0 -17.5 7.5t-7.5 17.5v50q0 10 7.5 17.5t17.5 7.5zM525 700h450q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-450q-10 0 -17.5 7.5t-7.5 17.5v50 q0 10 7.5 17.5t17.5 7.5zM325 500h50q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-50q-10 0 -17.5 7.5t-7.5 17.5v50q0 10 7.5 17.5t17.5 7.5zM525 500h450q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-450q-10 0 -17.5 7.5t-7.5 17.5v50 q0 10 7.5 17.5t17.5 7.5zM325 300h50q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-50q-10 0 -17.5 7.5t-7.5 17.5v50q0 10 7.5 17.5t17.5 7.5zM525 300h450q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-450q-10 0 -17.5 7.5t-7.5 17.5v50 q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe033;" d="M900 800v200q0 83 -58.5 141.5t-141.5 58.5h-300q-82 0 -141 -59t-59 -141v-200h-100q-41 0 -70.5 -29.5t-29.5 -70.5v-600q0 -41 29.5 -70.5t70.5 -29.5h900q41 0 70.5 29.5t29.5 70.5v600q0 41 -29.5 70.5t-70.5 29.5h-100zM400 800v150q0 21 15 35.5t35 14.5h200 q20 0 35 -14.5t15 -35.5v-150h-300z" />
<glyph unicode="&#xe034;" d="M125 1100h50q10 0 17.5 -7.5t7.5 -17.5v-1075h-100v1075q0 10 7.5 17.5t17.5 7.5zM1075 1052q4 0 9 -2q16 -6 16 -23v-421q0 -6 -3 -12q-33 -59 -66.5 -99t-65.5 -58t-56.5 -24.5t-52.5 -6.5q-26 0 -57.5 6.5t-52.5 13.5t-60 21q-41 15 -63 22.5t-57.5 15t-65.5 7.5 q-85 0 -160 -57q-7 -5 -15 -5q-6 0 -11 3q-14 7 -14 22v438q22 55 82 98.5t119 46.5q23 2 43 0.5t43 -7t32.5 -8.5t38 -13t32.5 -11q41 -14 63.5 -21t57 -14t63.5 -7q103 0 183 87q7 8 18 8z" />
<glyph unicode="&#xe035;" d="M600 1175q116 0 227 -49.5t192.5 -131t131 -192.5t49.5 -227v-300q0 -10 -7.5 -17.5t-17.5 -7.5h-50q-10 0 -17.5 7.5t-7.5 17.5v300q0 127 -70.5 231.5t-184.5 161.5t-245 57t-245 -57t-184.5 -161.5t-70.5 -231.5v-300q0 -10 -7.5 -17.5t-17.5 -7.5h-50 q-10 0 -17.5 7.5t-7.5 17.5v300q0 116 49.5 227t131 192.5t192.5 131t227 49.5zM220 500h160q8 0 14 -6t6 -14v-460q0 -8 -6 -14t-14 -6h-160q-8 0 -14 6t-6 14v460q0 8 6 14t14 6zM820 500h160q8 0 14 -6t6 -14v-460q0 -8 -6 -14t-14 -6h-160q-8 0 -14 6t-6 14v460 q0 8 6 14t14 6z" />
<glyph unicode="&#xe036;" d="M321 814l258 172q9 6 15 2.5t6 -13.5v-750q0 -10 -6 -13.5t-15 2.5l-258 172q-21 14 -46 14h-250q-10 0 -17.5 7.5t-7.5 17.5v350q0 10 7.5 17.5t17.5 7.5h250q25 0 46 14zM900 668l120 120q7 7 17 7t17 -7l34 -34q7 -7 7 -17t-7 -17l-120 -120l120 -120q7 -7 7 -17 t-7 -17l-34 -34q-7 -7 -17 -7t-17 7l-120 119l-120 -119q-7 -7 -17 -7t-17 7l-34 34q-7 7 -7 17t7 17l119 120l-119 120q-7 7 -7 17t7 17l34 34q7 8 17 8t17 -8z" />
<glyph unicode="&#xe037;" d="M321 814l258 172q9 6 15 2.5t6 -13.5v-750q0 -10 -6 -13.5t-15 2.5l-258 172q-21 14 -46 14h-250q-10 0 -17.5 7.5t-7.5 17.5v350q0 10 7.5 17.5t17.5 7.5h250q25 0 46 14zM766 900h4q10 -1 16 -10q96 -129 96 -290q0 -154 -90 -281q-6 -9 -17 -10l-3 -1q-9 0 -16 6 l-29 23q-7 7 -8.5 16.5t4.5 17.5q72 103 72 229q0 132 -78 238q-6 8 -4.5 18t9.5 17l29 22q7 5 15 5z" />
<glyph unicode="&#xe038;" d="M967 1004h3q11 -1 17 -10q135 -179 135 -396q0 -105 -34 -206.5t-98 -185.5q-7 -9 -17 -10h-3q-9 0 -16 6l-42 34q-8 6 -9 16t5 18q111 150 111 328q0 90 -29.5 176t-84.5 157q-6 9 -5 19t10 16l42 33q7 5 15 5zM321 814l258 172q9 6 15 2.5t6 -13.5v-750q0 -10 -6 -13.5 t-15 2.5l-258 172q-21 14 -46 14h-250q-10 0 -17.5 7.5t-7.5 17.5v350q0 10 7.5 17.5t17.5 7.5h250q25 0 46 14zM766 900h4q10 -1 16 -10q96 -129 96 -290q0 -154 -90 -281q-6 -9 -17 -10l-3 -1q-9 0 -16 6l-29 23q-7 7 -8.5 16.5t4.5 17.5q72 103 72 229q0 132 -78 238 q-6 8 -4.5 18.5t9.5 16.5l29 22q7 5 15 5z" />
<glyph unicode="&#xe039;" d="M500 900h100v-100h-100v-100h-400v-100h-100v600h500v-300zM1200 700h-200v-100h200v-200h-300v300h-200v300h-100v200h600v-500zM100 1100v-300h300v300h-300zM800 1100v-300h300v300h-300zM300 900h-100v100h100v-100zM1000 900h-100v100h100v-100zM300 500h200v-500 h-500v500h200v100h100v-100zM800 300h200v-100h-100v-100h-200v100h-100v100h100v200h-200v100h300v-300zM100 400v-300h300v300h-300zM300 200h-100v100h100v-100zM1200 200h-100v100h100v-100zM700 0h-100v100h100v-100zM1200 0h-300v100h300v-100z" />
<glyph unicode="&#xe040;" d="M100 200h-100v1000h100v-1000zM300 200h-100v1000h100v-1000zM700 200h-200v1000h200v-1000zM900 200h-100v1000h100v-1000zM1200 200h-200v1000h200v-1000zM400 0h-300v100h300v-100zM600 0h-100v91h100v-91zM800 0h-100v91h100v-91zM1100 0h-200v91h200v-91z" />
<glyph unicode="&#xe041;" d="M500 1200l682 -682q8 -8 8 -18t-8 -18l-464 -464q-8 -8 -18 -8t-18 8l-682 682l1 475q0 10 7.5 17.5t17.5 7.5h474zM319.5 1024.5q-29.5 29.5 -71 29.5t-71 -29.5t-29.5 -71.5t29.5 -71.5t71 -29.5t71 29.5t29.5 71.5t-29.5 71.5z" />
<glyph unicode="&#xe042;" d="M500 1200l682 -682q8 -8 8 -18t-8 -18l-464 -464q-8 -8 -18 -8t-18 8l-682 682l1 475q0 10 7.5 17.5t17.5 7.5h474zM800 1200l682 -682q8 -8 8 -18t-8 -18l-464 -464q-8 -8 -18 -8t-18 8l-56 56l424 426l-700 700h150zM319.5 1024.5q-29.5 29.5 -71 29.5t-71 -29.5 t-29.5 -71.5t29.5 -71.5t71 -29.5t71 29.5t29.5 71.5t-29.5 71.5z" />
<glyph unicode="&#xe043;" d="M300 1200h825q75 0 75 -75v-900q0 -25 -18 -43l-64 -64q-8 -8 -13 -5.5t-5 12.5v950q0 10 -7.5 17.5t-17.5 7.5h-700q-25 0 -43 -18l-64 -64q-8 -8 -5.5 -13t12.5 -5h700q10 0 17.5 -7.5t7.5 -17.5v-950q0 -10 -7.5 -17.5t-17.5 -7.5h-850q-10 0 -17.5 7.5t-7.5 17.5v975 q0 25 18 43l139 139q18 18 43 18z" />
<glyph unicode="&#xe044;" d="M250 1200h800q21 0 35.5 -14.5t14.5 -35.5v-1150l-450 444l-450 -445v1151q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe045;" d="M822 1200h-444q-11 0 -19 -7.5t-9 -17.5l-78 -301q-7 -24 7 -45l57 -108q6 -9 17.5 -15t21.5 -6h450q10 0 21.5 6t17.5 15l62 108q14 21 7 45l-83 301q-1 10 -9 17.5t-19 7.5zM1175 800h-150q-10 0 -21 -6.5t-15 -15.5l-78 -156q-4 -9 -15 -15.5t-21 -6.5h-550 q-10 0 -21 6.5t-15 15.5l-78 156q-4 9 -15 15.5t-21 6.5h-150q-10 0 -17.5 -7.5t-7.5 -17.5v-650q0 -10 7.5 -17.5t17.5 -7.5h150q10 0 17.5 7.5t7.5 17.5v150q0 10 7.5 17.5t17.5 7.5h750q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 7.5 -17.5t17.5 -7.5h150q10 0 17.5 7.5 t7.5 17.5v650q0 10 -7.5 17.5t-17.5 7.5zM850 200h-500q-10 0 -19.5 -7t-11.5 -17l-38 -152q-2 -10 3.5 -17t15.5 -7h600q10 0 15.5 7t3.5 17l-38 152q-2 10 -11.5 17t-19.5 7z" />
<glyph unicode="&#xe046;" d="M500 1100h200q56 0 102.5 -20.5t72.5 -50t44 -59t25 -50.5l6 -20h150q41 0 70.5 -29.5t29.5 -70.5v-600q0 -41 -29.5 -70.5t-70.5 -29.5h-1000q-41 0 -70.5 29.5t-29.5 70.5v600q0 41 29.5 70.5t70.5 29.5h150q2 8 6.5 21.5t24 48t45 61t72 48t102.5 21.5zM900 800v-100 h100v100h-100zM600 730q-95 0 -162.5 -67.5t-67.5 -162.5t67.5 -162.5t162.5 -67.5t162.5 67.5t67.5 162.5t-67.5 162.5t-162.5 67.5zM600 603q43 0 73 -30t30 -73t-30 -73t-73 -30t-73 30t-30 73t30 73t73 30z" />
<glyph unicode="&#xe047;" d="M681 1199l385 -998q20 -50 60 -92q18 -19 36.5 -29.5t27.5 -11.5l10 -2v-66h-417v66q53 0 75 43.5t5 88.5l-82 222h-391q-58 -145 -92 -234q-11 -34 -6.5 -57t25.5 -37t46 -20t55 -6v-66h-365v66q56 24 84 52q12 12 25 30.5t20 31.5l7 13l399 1006h93zM416 521h340 l-162 457z" />
<glyph unicode="&#xe048;" d="M753 641q5 -1 14.5 -4.5t36 -15.5t50.5 -26.5t53.5 -40t50.5 -54.5t35.5 -70t14.5 -87q0 -67 -27.5 -125.5t-71.5 -97.5t-98.5 -66.5t-108.5 -40.5t-102 -13h-500v89q41 7 70.5 32.5t29.5 65.5v827q0 24 -0.5 34t-3.5 24t-8.5 19.5t-17 13.5t-28 12.5t-42.5 11.5v71 l471 -1q57 0 115.5 -20.5t108 -57t80.5 -94t31 -124.5q0 -51 -15.5 -96.5t-38 -74.5t-45 -50.5t-38.5 -30.5zM400 700h139q78 0 130.5 48.5t52.5 122.5q0 41 -8.5 70.5t-29.5 55.5t-62.5 39.5t-103.5 13.5h-118v-350zM400 200h216q80 0 121 50.5t41 130.5q0 90 -62.5 154.5 t-156.5 64.5h-159v-400z" />
<glyph unicode="&#xe049;" d="M877 1200l2 -57q-83 -19 -116 -45.5t-40 -66.5l-132 -839q-9 -49 13 -69t96 -26v-97h-500v97q186 16 200 98l173 832q3 17 3 30t-1.5 22.5t-9 17.5t-13.5 12.5t-21.5 10t-26 8.5t-33.5 10q-13 3 -19 5v57h425z" />
<glyph unicode="&#xe050;" d="M1300 900h-50q0 21 -4 37t-9.5 26.5t-18 17.5t-22 11t-28.5 5.5t-31 2t-37 0.5h-200v-850q0 -22 25 -34.5t50 -13.5l25 -2v-100h-400v100q4 0 11 0.5t24 3t30 7t24 15t11 24.5v850h-200q-25 0 -37 -0.5t-31 -2t-28.5 -5.5t-22 -11t-18 -17.5t-9.5 -26.5t-4 -37h-50v300 h1000v-300zM175 1000h-75v-800h75l-125 -167l-125 167h75v800h-75l125 167z" />
<glyph unicode="&#xe051;" d="M1100 900h-50q0 21 -4 37t-9.5 26.5t-18 17.5t-22 11t-28.5 5.5t-31 2t-37 0.5h-200v-650q0 -22 25 -34.5t50 -13.5l25 -2v-100h-400v100q4 0 11 0.5t24 3t30 7t24 15t11 24.5v650h-200q-25 0 -37 -0.5t-31 -2t-28.5 -5.5t-22 -11t-18 -17.5t-9.5 -26.5t-4 -37h-50v300 h1000v-300zM1167 50l-167 -125v75h-800v-75l-167 125l167 125v-75h800v75z" />
<glyph unicode="&#xe052;" d="M50 1100h600q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-600q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 800h1000q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1000q-21 0 -35.5 14.5t-14.5 35.5v100 q0 21 14.5 35.5t35.5 14.5zM50 500h800q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 200h1100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100 q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe053;" d="M250 1100h700q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-700q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 800h1100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5v100 q0 21 14.5 35.5t35.5 14.5zM250 500h700q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-700q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 200h1100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100 q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe054;" d="M500 950v100q0 21 14.5 35.5t35.5 14.5h600q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-600q-21 0 -35.5 14.5t-14.5 35.5zM100 650v100q0 21 14.5 35.5t35.5 14.5h1000q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1000 q-21 0 -35.5 14.5t-14.5 35.5zM300 350v100q0 21 14.5 35.5t35.5 14.5h800q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5zM0 50v100q0 21 14.5 35.5t35.5 14.5h1100q21 0 35.5 -14.5t14.5 -35.5v-100 q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5z" />
<glyph unicode="&#xe055;" d="M50 1100h1100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 800h1100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5v100 q0 21 14.5 35.5t35.5 14.5zM50 500h1100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 200h1100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100 q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe056;" d="M50 1100h100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM350 1100h800q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5v100 q0 21 14.5 35.5t35.5 14.5zM50 800h100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM350 800h800q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-800 q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 500h100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM350 500h800q21 0 35.5 -14.5t14.5 -35.5v-100 q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 200h100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM350 200h800 q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe057;" d="M400 0h-100v1100h100v-1100zM550 1100h100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM550 800h500q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-500 q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM267 550l-167 -125v75h-200v100h200v75zM550 500h300q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-300q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM550 200h600 q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-600q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe058;" d="M50 1100h100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM900 0h-100v1100h100v-1100zM50 800h500q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-500 q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM1100 600h200v-100h-200v-75l-167 125l167 125v-75zM50 500h300q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-300q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 200h600 q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-600q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe059;" d="M75 1000h750q31 0 53 -22t22 -53v-650q0 -31 -22 -53t-53 -22h-750q-31 0 -53 22t-22 53v650q0 31 22 53t53 22zM1200 300l-300 300l300 300v-600z" />
<glyph unicode="&#xe060;" d="M44 1100h1112q18 0 31 -13t13 -31v-1012q0 -18 -13 -31t-31 -13h-1112q-18 0 -31 13t-13 31v1012q0 18 13 31t31 13zM100 1000v-737l247 182l298 -131l-74 156l293 318l236 -288v500h-1000zM342 884q56 0 95 -39t39 -94.5t-39 -95t-95 -39.5t-95 39.5t-39 95t39 94.5 t95 39z" />
<glyph unicode="&#xe062;" d="M648 1169q117 0 216 -60t156.5 -161t57.5 -218q0 -115 -70 -258q-69 -109 -158 -225.5t-143 -179.5l-54 -62q-9 8 -25.5 24.5t-63.5 67.5t-91 103t-98.5 128t-95.5 148q-60 132 -60 249q0 88 34 169.5t91.5 142t137 96.5t166.5 36zM652.5 974q-91.5 0 -156.5 -65 t-65 -157t65 -156.5t156.5 -64.5t156.5 64.5t65 156.5t-65 157t-156.5 65z" />
<glyph unicode="&#xe063;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 173v854q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5 t155.5 -155.5t214.5 -57z" />
<glyph unicode="&#xe064;" d="M554 1295q21 -72 57.5 -143.5t76 -130t83 -118t82.5 -117t70 -116t49.5 -126t18.5 -136.5q0 -71 -25.5 -135t-68.5 -111t-99 -82t-118.5 -54t-125.5 -23q-84 5 -161.5 34t-139.5 78.5t-99 125t-37 164.5q0 69 18 136.5t49.5 126.5t69.5 116.5t81.5 117.5t83.5 119 t76.5 131t58.5 143zM344 710q-23 -33 -43.5 -70.5t-40.5 -102.5t-17 -123q1 -37 14.5 -69.5t30 -52t41 -37t38.5 -24.5t33 -15q21 -7 32 -1t13 22l6 34q2 10 -2.5 22t-13.5 19q-5 4 -14 12t-29.5 40.5t-32.5 73.5q-26 89 6 271q2 11 -6 11q-8 1 -15 -10z" />
<glyph unicode="&#xe065;" d="M1000 1013l108 115q2 1 5 2t13 2t20.5 -1t25 -9.5t28.5 -21.5q22 -22 27 -43t0 -32l-6 -10l-108 -115zM350 1100h400q50 0 105 -13l-187 -187h-368q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5v182l200 200v-332 q0 -165 -93.5 -257.5t-256.5 -92.5h-400q-165 0 -257.5 92.5t-92.5 257.5v400q0 165 92.5 257.5t257.5 92.5zM1009 803l-362 -362l-161 -50l55 170l355 355z" />
<glyph unicode="&#xe066;" d="M350 1100h361q-164 -146 -216 -200h-195q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5l200 153v-103q0 -165 -92.5 -257.5t-257.5 -92.5h-400q-165 0 -257.5 92.5t-92.5 257.5v400q0 165 92.5 257.5t257.5 92.5z M824 1073l339 -301q8 -7 8 -17.5t-8 -17.5l-340 -306q-7 -6 -12.5 -4t-6.5 11v203q-26 1 -54.5 0t-78.5 -7.5t-92 -17.5t-86 -35t-70 -57q10 59 33 108t51.5 81.5t65 58.5t68.5 40.5t67 24.5t56 13.5t40 4.5v210q1 10 6.5 12.5t13.5 -4.5z" />
<glyph unicode="&#xe067;" d="M350 1100h350q60 0 127 -23l-178 -177h-349q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5v69l200 200v-219q0 -165 -92.5 -257.5t-257.5 -92.5h-400q-165 0 -257.5 92.5t-92.5 257.5v400q0 165 92.5 257.5t257.5 92.5z M643 639l395 395q7 7 17.5 7t17.5 -7l101 -101q7 -7 7 -17.5t-7 -17.5l-531 -532q-7 -7 -17.5 -7t-17.5 7l-248 248q-7 7 -7 17.5t7 17.5l101 101q7 7 17.5 7t17.5 -7l111 -111q8 -7 18 -7t18 7z" />
<glyph unicode="&#xe068;" d="M318 918l264 264q8 8 18 8t18 -8l260 -264q7 -8 4.5 -13t-12.5 -5h-170v-200h200v173q0 10 5 12t13 -5l264 -260q8 -7 8 -17.5t-8 -17.5l-264 -265q-8 -7 -13 -5t-5 12v173h-200v-200h170q10 0 12.5 -5t-4.5 -13l-260 -264q-8 -8 -18 -8t-18 8l-264 264q-8 8 -5.5 13 t12.5 5h175v200h-200v-173q0 -10 -5 -12t-13 5l-264 265q-8 7 -8 17.5t8 17.5l264 260q8 7 13 5t5 -12v-173h200v200h-175q-10 0 -12.5 5t5.5 13z" />
<glyph unicode="&#xe069;" d="M250 1100h100q21 0 35.5 -14.5t14.5 -35.5v-438l464 453q15 14 25.5 10t10.5 -25v-1000q0 -21 -10.5 -25t-25.5 10l-464 453v-438q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v1000q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe070;" d="M50 1100h100q21 0 35.5 -14.5t14.5 -35.5v-438l464 453q15 14 25.5 10t10.5 -25v-438l464 453q15 14 25.5 10t10.5 -25v-1000q0 -21 -10.5 -25t-25.5 10l-464 453v-438q0 -21 -10.5 -25t-25.5 10l-464 453v-438q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5 t-14.5 35.5v1000q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe071;" d="M1200 1050v-1000q0 -21 -10.5 -25t-25.5 10l-464 453v-438q0 -21 -10.5 -25t-25.5 10l-492 480q-15 14 -15 35t15 35l492 480q15 14 25.5 10t10.5 -25v-438l464 453q15 14 25.5 10t10.5 -25z" />
<glyph unicode="&#xe072;" d="M243 1074l814 -498q18 -11 18 -26t-18 -26l-814 -498q-18 -11 -30.5 -4t-12.5 28v1000q0 21 12.5 28t30.5 -4z" />
<glyph unicode="&#xe073;" d="M250 1000h200q21 0 35.5 -14.5t14.5 -35.5v-800q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v800q0 21 14.5 35.5t35.5 14.5zM650 1000h200q21 0 35.5 -14.5t14.5 -35.5v-800q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v800 q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe074;" d="M1100 950v-800q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5v800q0 21 14.5 35.5t35.5 14.5h800q21 0 35.5 -14.5t14.5 -35.5z" />
<glyph unicode="&#xe075;" d="M500 612v438q0 21 10.5 25t25.5 -10l492 -480q15 -14 15 -35t-15 -35l-492 -480q-15 -14 -25.5 -10t-10.5 25v438l-464 -453q-15 -14 -25.5 -10t-10.5 25v1000q0 21 10.5 25t25.5 -10z" />
<glyph unicode="&#xe076;" d="M1048 1102l100 1q20 0 35 -14.5t15 -35.5l5 -1000q0 -21 -14.5 -35.5t-35.5 -14.5l-100 -1q-21 0 -35.5 14.5t-14.5 35.5l-2 437l-463 -454q-14 -15 -24.5 -10.5t-10.5 25.5l-2 437l-462 -455q-15 -14 -25.5 -9.5t-10.5 24.5l-5 1000q0 21 10.5 25.5t25.5 -10.5l466 -450 l-2 438q0 20 10.5 24.5t25.5 -9.5l466 -451l-2 438q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe077;" d="M850 1100h100q21 0 35.5 -14.5t14.5 -35.5v-1000q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v438l-464 -453q-15 -14 -25.5 -10t-10.5 25v1000q0 21 10.5 25t25.5 -10l464 -453v438q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe078;" d="M686 1081l501 -540q15 -15 10.5 -26t-26.5 -11h-1042q-22 0 -26.5 11t10.5 26l501 540q15 15 36 15t36 -15zM150 400h1000q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1000q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe079;" d="M885 900l-352 -353l352 -353l-197 -198l-552 552l552 550z" />
<glyph unicode="&#xe080;" d="M1064 547l-551 -551l-198 198l353 353l-353 353l198 198z" />
<glyph unicode="&#xe081;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM650 900h-100q-21 0 -35.5 -14.5t-14.5 -35.5v-150h-150 q-21 0 -35.5 -14.5t-14.5 -35.5v-100q0 -21 14.5 -35.5t35.5 -14.5h150v-150q0 -21 14.5 -35.5t35.5 -14.5h100q21 0 35.5 14.5t14.5 35.5v150h150q21 0 35.5 14.5t14.5 35.5v100q0 21 -14.5 35.5t-35.5 14.5h-150v150q0 21 -14.5 35.5t-35.5 14.5z" />
<glyph unicode="&#xe082;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM850 700h-500q-21 0 -35.5 -14.5t-14.5 -35.5v-100q0 -21 14.5 -35.5 t35.5 -14.5h500q21 0 35.5 14.5t14.5 35.5v100q0 21 -14.5 35.5t-35.5 14.5z" />
<glyph unicode="&#xe083;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM741.5 913q-12.5 0 -21.5 -9l-120 -120l-120 120q-9 9 -21.5 9 t-21.5 -9l-141 -141q-9 -9 -9 -21.5t9 -21.5l120 -120l-120 -120q-9 -9 -9 -21.5t9 -21.5l141 -141q9 -9 21.5 -9t21.5 9l120 120l120 -120q9 -9 21.5 -9t21.5 9l141 141q9 9 9 21.5t-9 21.5l-120 120l120 120q9 9 9 21.5t-9 21.5l-141 141q-9 9 -21.5 9z" />
<glyph unicode="&#xe084;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM546 623l-84 85q-7 7 -17.5 7t-18.5 -7l-139 -139q-7 -8 -7 -18t7 -18 l242 -241q7 -8 17.5 -8t17.5 8l375 375q7 7 7 17.5t-7 18.5l-139 139q-7 7 -17.5 7t-17.5 -7z" />
<glyph unicode="&#xe085;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM588 941q-29 0 -59 -5.5t-63 -20.5t-58 -38.5t-41.5 -63t-16.5 -89.5 q0 -25 20 -25h131q30 -5 35 11q6 20 20.5 28t45.5 8q20 0 31.5 -10.5t11.5 -28.5q0 -23 -7 -34t-26 -18q-1 0 -13.5 -4t-19.5 -7.5t-20 -10.5t-22 -17t-18.5 -24t-15.5 -35t-8 -46q-1 -8 5.5 -16.5t20.5 -8.5h173q7 0 22 8t35 28t37.5 48t29.5 74t12 100q0 47 -17 83 t-42.5 57t-59.5 34.5t-64 18t-59 4.5zM675 400h-150q-10 0 -17.5 -7.5t-7.5 -17.5v-150q0 -10 7.5 -17.5t17.5 -7.5h150q10 0 17.5 7.5t7.5 17.5v150q0 10 -7.5 17.5t-17.5 7.5z" />
<glyph unicode="&#xe086;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM675 1000h-150q-10 0 -17.5 -7.5t-7.5 -17.5v-150q0 -10 7.5 -17.5 t17.5 -7.5h150q10 0 17.5 7.5t7.5 17.5v150q0 10 -7.5 17.5t-17.5 7.5zM675 700h-250q-10 0 -17.5 -7.5t-7.5 -17.5v-50q0 -10 7.5 -17.5t17.5 -7.5h75v-200h-75q-10 0 -17.5 -7.5t-7.5 -17.5v-50q0 -10 7.5 -17.5t17.5 -7.5h350q10 0 17.5 7.5t7.5 17.5v50q0 10 -7.5 17.5 t-17.5 7.5h-75v275q0 10 -7.5 17.5t-17.5 7.5z" />
<glyph unicode="&#xe087;" d="M525 1200h150q10 0 17.5 -7.5t7.5 -17.5v-194q103 -27 178.5 -102.5t102.5 -178.5h194q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-194q-27 -103 -102.5 -178.5t-178.5 -102.5v-194q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v194 q-103 27 -178.5 102.5t-102.5 178.5h-194q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5h194q27 103 102.5 178.5t178.5 102.5v194q0 10 7.5 17.5t17.5 7.5zM700 893v-168q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v168q-68 -23 -119 -74 t-74 -119h168q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-168q23 -68 74 -119t119 -74v168q0 10 7.5 17.5t17.5 7.5h150q10 0 17.5 -7.5t7.5 -17.5v-168q68 23 119 74t74 119h-168q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5h168 q-23 68 -74 119t-119 74z" />
<glyph unicode="&#xe088;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 1027q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5 t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5t-57 214.5t-155.5 155.5t-214.5 57zM759 823l64 -64q7 -7 7 -17.5t-7 -17.5l-124 -124l124 -124q7 -7 7 -17.5t-7 -17.5l-64 -64q-7 -7 -17.5 -7t-17.5 7l-124 124l-124 -124q-7 -7 -17.5 -7t-17.5 7l-64 64 q-7 7 -7 17.5t7 17.5l124 124l-124 124q-7 7 -7 17.5t7 17.5l64 64q7 7 17.5 7t17.5 -7l124 -124l124 124q7 7 17.5 7t17.5 -7z" />
<glyph unicode="&#xe089;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 1027q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5 t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5t-57 214.5t-155.5 155.5t-214.5 57zM782 788l106 -106q7 -7 7 -17.5t-7 -17.5l-320 -321q-8 -7 -18 -7t-18 7l-202 203q-8 7 -8 17.5t8 17.5l106 106q7 8 17.5 8t17.5 -8l79 -79l197 197q7 7 17.5 7t17.5 -7z" />
<glyph unicode="&#xe090;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 1027q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5q0 -120 65 -225 l587 587q-105 65 -225 65zM965 819l-584 -584q104 -62 219 -62q116 0 214.5 57t155.5 155.5t57 214.5q0 115 -62 219z" />
<glyph unicode="&#xe091;" d="M39 582l522 427q16 13 27.5 8t11.5 -26v-291h550q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-550v-291q0 -21 -11.5 -26t-27.5 8l-522 427q-16 13 -16 32t16 32z" />
<glyph unicode="&#xe092;" d="M639 1009l522 -427q16 -13 16 -32t-16 -32l-522 -427q-16 -13 -27.5 -8t-11.5 26v291h-550q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5h550v291q0 21 11.5 26t27.5 -8z" />
<glyph unicode="&#xe093;" d="M682 1161l427 -522q13 -16 8 -27.5t-26 -11.5h-291v-550q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v550h-291q-21 0 -26 11.5t8 27.5l427 522q13 16 32 16t32 -16z" />
<glyph unicode="&#xe094;" d="M550 1200h200q21 0 35.5 -14.5t14.5 -35.5v-550h291q21 0 26 -11.5t-8 -27.5l-427 -522q-13 -16 -32 -16t-32 16l-427 522q-13 16 -8 27.5t26 11.5h291v550q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe095;" d="M639 1109l522 -427q16 -13 16 -32t-16 -32l-522 -427q-16 -13 -27.5 -8t-11.5 26v291q-94 -2 -182 -20t-170.5 -52t-147 -92.5t-100.5 -135.5q5 105 27 193.5t67.5 167t113 135t167 91.5t225.5 42v262q0 21 11.5 26t27.5 -8z" />
<glyph unicode="&#xe096;" d="M850 1200h300q21 0 35.5 -14.5t14.5 -35.5v-300q0 -21 -10.5 -25t-24.5 10l-94 94l-249 -249q-8 -7 -18 -7t-18 7l-106 106q-7 8 -7 18t7 18l249 249l-94 94q-14 14 -10 24.5t25 10.5zM350 0h-300q-21 0 -35.5 14.5t-14.5 35.5v300q0 21 10.5 25t24.5 -10l94 -94l249 249 q8 7 18 7t18 -7l106 -106q7 -8 7 -18t-7 -18l-249 -249l94 -94q14 -14 10 -24.5t-25 -10.5z" />
<glyph unicode="&#xe097;" d="M1014 1120l106 -106q7 -8 7 -18t-7 -18l-249 -249l94 -94q14 -14 10 -24.5t-25 -10.5h-300q-21 0 -35.5 14.5t-14.5 35.5v300q0 21 10.5 25t24.5 -10l94 -94l249 249q8 7 18 7t18 -7zM250 600h300q21 0 35.5 -14.5t14.5 -35.5v-300q0 -21 -10.5 -25t-24.5 10l-94 94 l-249 -249q-8 -7 -18 -7t-18 7l-106 106q-7 8 -7 18t7 18l249 249l-94 94q-14 14 -10 24.5t25 10.5z" />
<glyph unicode="&#xe101;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM704 900h-208q-20 0 -32 -14.5t-8 -34.5l58 -302q4 -20 21.5 -34.5 t37.5 -14.5h54q20 0 37.5 14.5t21.5 34.5l58 302q4 20 -8 34.5t-32 14.5zM675 400h-150q-10 0 -17.5 -7.5t-7.5 -17.5v-150q0 -10 7.5 -17.5t17.5 -7.5h150q10 0 17.5 7.5t7.5 17.5v150q0 10 -7.5 17.5t-17.5 7.5z" />
<glyph unicode="&#xe102;" d="M260 1200q9 0 19 -2t15 -4l5 -2q22 -10 44 -23l196 -118q21 -13 36 -24q29 -21 37 -12q11 13 49 35l196 118q22 13 45 23q17 7 38 7q23 0 47 -16.5t37 -33.5l13 -16q14 -21 18 -45l25 -123l8 -44q1 -9 8.5 -14.5t17.5 -5.5h61q10 0 17.5 -7.5t7.5 -17.5v-50 q0 -10 -7.5 -17.5t-17.5 -7.5h-50q-10 0 -17.5 -7.5t-7.5 -17.5v-175h-400v300h-200v-300h-400v175q0 10 -7.5 17.5t-17.5 7.5h-50q-10 0 -17.5 7.5t-7.5 17.5v50q0 10 7.5 17.5t17.5 7.5h61q11 0 18 3t7 8q0 4 9 52l25 128q5 25 19 45q2 3 5 7t13.5 15t21.5 19.5t26.5 15.5 t29.5 7zM915 1079l-166 -162q-7 -7 -5 -12t12 -5h219q10 0 15 7t2 17l-51 149q-3 10 -11 12t-15 -6zM463 917l-177 157q-8 7 -16 5t-11 -12l-51 -143q-3 -10 2 -17t15 -7h231q11 0 12.5 5t-5.5 12zM500 0h-375q-10 0 -17.5 7.5t-7.5 17.5v375h400v-400zM1100 400v-375 q0 -10 -7.5 -17.5t-17.5 -7.5h-375v400h400z" />
<glyph unicode="&#xe103;" d="M1165 1190q8 3 21 -6.5t13 -17.5q-2 -178 -24.5 -323.5t-55.5 -245.5t-87 -174.5t-102.5 -118.5t-118 -68.5t-118.5 -33t-120 -4.5t-105 9.5t-90 16.5q-61 12 -78 11q-4 1 -12.5 0t-34 -14.5t-52.5 -40.5l-153 -153q-26 -24 -37 -14.5t-11 43.5q0 64 42 102q8 8 50.5 45 t66.5 58q19 17 35 47t13 61q-9 55 -10 102.5t7 111t37 130t78 129.5q39 51 80 88t89.5 63.5t94.5 45t113.5 36t129 31t157.5 37t182 47.5zM1116 1098q-8 9 -22.5 -3t-45.5 -50q-38 -47 -119 -103.5t-142 -89.5l-62 -33q-56 -30 -102 -57t-104 -68t-102.5 -80.5t-85.5 -91 t-64 -104.5q-24 -56 -31 -86t2 -32t31.5 17.5t55.5 59.5q25 30 94 75.5t125.5 77.5t147.5 81q70 37 118.5 69t102 79.5t99 111t86.5 148.5q22 50 24 60t-6 19z" />
<glyph unicode="&#xe104;" d="M653 1231q-39 -67 -54.5 -131t-10.5 -114.5t24.5 -96.5t47.5 -80t63.5 -62.5t68.5 -46.5t65 -30q-4 7 -17.5 35t-18.5 39.5t-17 39.5t-17 43t-13 42t-9.5 44.5t-2 42t4 43t13.5 39t23 38.5q96 -42 165 -107.5t105 -138t52 -156t13 -159t-19 -149.5q-13 -55 -44 -106.5 t-68 -87t-78.5 -64.5t-72.5 -45t-53 -22q-72 -22 -127 -11q-31 6 -13 19q6 3 17 7q13 5 32.5 21t41 44t38.5 63.5t21.5 81.5t-6.5 94.5t-50 107t-104 115.5q10 -104 -0.5 -189t-37 -140.5t-65 -93t-84 -52t-93.5 -11t-95 24.5q-80 36 -131.5 114t-53.5 171q-2 23 0 49.5 t4.5 52.5t13.5 56t27.5 60t46 64.5t69.5 68.5q-8 -53 -5 -102.5t17.5 -90t34 -68.5t44.5 -39t49 -2q31 13 38.5 36t-4.5 55t-29 64.5t-36 75t-26 75.5q-15 85 2 161.5t53.5 128.5t85.5 92.5t93.5 61t81.5 25.5z" />
<glyph unicode="&#xe105;" d="M600 1094q82 0 160.5 -22.5t140 -59t116.5 -82.5t94.5 -95t68 -95t42.5 -82.5t14 -57.5t-14 -57.5t-43 -82.5t-68.5 -95t-94.5 -95t-116.5 -82.5t-140 -59t-159.5 -22.5t-159.5 22.5t-140 59t-116.5 82.5t-94.5 95t-68.5 95t-43 82.5t-14 57.5t14 57.5t42.5 82.5t68 95 t94.5 95t116.5 82.5t140 59t160.5 22.5zM888 829q-15 15 -18 12t5 -22q25 -57 25 -119q0 -124 -88 -212t-212 -88t-212 88t-88 212q0 59 23 114q8 19 4.5 22t-17.5 -12q-70 -69 -160 -184q-13 -16 -15 -40.5t9 -42.5q22 -36 47 -71t70 -82t92.5 -81t113 -58.5t133.5 -24.5 t133.5 24t113 58.5t92.5 81.5t70 81.5t47 70.5q11 18 9 42.5t-14 41.5q-90 117 -163 189zM448 727l-35 -36q-15 -15 -19.5 -38.5t4.5 -41.5q37 -68 93 -116q16 -13 38.5 -11t36.5 17l35 34q14 15 12.5 33.5t-16.5 33.5q-44 44 -89 117q-11 18 -28 20t-32 -12z" />
<glyph unicode="&#xe106;" d="M592 0h-148l31 120q-91 20 -175.5 68.5t-143.5 106.5t-103.5 119t-66.5 110t-22 76q0 21 14 57.5t42.5 82.5t68 95t94.5 95t116.5 82.5t140 59t160.5 22.5q61 0 126 -15l32 121h148zM944 770l47 181q108 -85 176.5 -192t68.5 -159q0 -26 -19.5 -71t-59.5 -102t-93 -112 t-129 -104.5t-158 -75.5l46 173q77 49 136 117t97 131q11 18 9 42.5t-14 41.5q-54 70 -107 130zM310 824q-70 -69 -160 -184q-13 -16 -15 -40.5t9 -42.5q18 -30 39 -60t57 -70.5t74 -73t90 -61t105 -41.5l41 154q-107 18 -178.5 101.5t-71.5 193.5q0 59 23 114q8 19 4.5 22 t-17.5 -12zM448 727l-35 -36q-15 -15 -19.5 -38.5t4.5 -41.5q37 -68 93 -116q16 -13 38.5 -11t36.5 17l12 11l22 86l-3 4q-44 44 -89 117q-11 18 -28 20t-32 -12z" />
<glyph unicode="&#xe107;" d="M-90 100l642 1066q20 31 48 28.5t48 -35.5l642 -1056q21 -32 7.5 -67.5t-50.5 -35.5h-1294q-37 0 -50.5 34t7.5 66zM155 200h345v75q0 10 7.5 17.5t17.5 7.5h150q10 0 17.5 -7.5t7.5 -17.5v-75h345l-445 723zM496 700h208q20 0 32 -14.5t8 -34.5l-58 -252 q-4 -20 -21.5 -34.5t-37.5 -14.5h-54q-20 0 -37.5 14.5t-21.5 34.5l-58 252q-4 20 8 34.5t32 14.5z" />
<glyph unicode="&#xe108;" d="M650 1200q62 0 106 -44t44 -106v-339l363 -325q15 -14 26 -38.5t11 -44.5v-41q0 -20 -12 -26.5t-29 5.5l-359 249v-263q100 -93 100 -113v-64q0 -21 -13 -29t-32 1l-205 128l-205 -128q-19 -9 -32 -1t-13 29v64q0 20 100 113v263l-359 -249q-17 -12 -29 -5.5t-12 26.5v41 q0 20 11 44.5t26 38.5l363 325v339q0 62 44 106t106 44z" />
<glyph unicode="&#xe109;" d="M850 1200h100q21 0 35.5 -14.5t14.5 -35.5v-50h50q21 0 35.5 -14.5t14.5 -35.5v-150h-1100v150q0 21 14.5 35.5t35.5 14.5h50v50q0 21 14.5 35.5t35.5 14.5h100q21 0 35.5 -14.5t14.5 -35.5v-50h500v50q0 21 14.5 35.5t35.5 14.5zM1100 800v-750q0 -21 -14.5 -35.5 t-35.5 -14.5h-1000q-21 0 -35.5 14.5t-14.5 35.5v750h1100zM100 600v-100h100v100h-100zM300 600v-100h100v100h-100zM500 600v-100h100v100h-100zM700 600v-100h100v100h-100zM900 600v-100h100v100h-100zM100 400v-100h100v100h-100zM300 400v-100h100v100h-100zM500 400 v-100h100v100h-100zM700 400v-100h100v100h-100zM900 400v-100h100v100h-100zM100 200v-100h100v100h-100zM300 200v-100h100v100h-100zM500 200v-100h100v100h-100zM700 200v-100h100v100h-100zM900 200v-100h100v100h-100z" />
<glyph unicode="&#xe110;" d="M1135 1165l249 -230q15 -14 15 -35t-15 -35l-249 -230q-14 -14 -24.5 -10t-10.5 25v150h-159l-600 -600h-291q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5h209l600 600h241v150q0 21 10.5 25t24.5 -10zM522 819l-141 -141l-122 122h-209q-21 0 -35.5 14.5 t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5h291zM1135 565l249 -230q15 -14 15 -35t-15 -35l-249 -230q-14 -14 -24.5 -10t-10.5 25v150h-241l-181 181l141 141l122 -122h159v150q0 21 10.5 25t24.5 -10z" />
<glyph unicode="&#xe111;" d="M100 1100h1000q41 0 70.5 -29.5t29.5 -70.5v-600q0 -41 -29.5 -70.5t-70.5 -29.5h-596l-304 -300v300h-100q-41 0 -70.5 29.5t-29.5 70.5v600q0 41 29.5 70.5t70.5 29.5z" />
<glyph unicode="&#xe112;" d="M150 1200h200q21 0 35.5 -14.5t14.5 -35.5v-250h-300v250q0 21 14.5 35.5t35.5 14.5zM850 1200h200q21 0 35.5 -14.5t14.5 -35.5v-250h-300v250q0 21 14.5 35.5t35.5 14.5zM1100 800v-300q0 -41 -3 -77.5t-15 -89.5t-32 -96t-58 -89t-89 -77t-129 -51t-174 -20t-174 20 t-129 51t-89 77t-58 89t-32 96t-15 89.5t-3 77.5v300h300v-250v-27v-42.5t1.5 -41t5 -38t10 -35t16.5 -30t25.5 -24.5t35 -19t46.5 -12t60 -4t60 4.5t46.5 12.5t35 19.5t25 25.5t17 30.5t10 35t5 38t2 40.5t-0.5 42v25v250h300z" />
<glyph unicode="&#xe113;" d="M1100 411l-198 -199l-353 353l-353 -353l-197 199l551 551z" />
<glyph unicode="&#xe114;" d="M1101 789l-550 -551l-551 551l198 199l353 -353l353 353z" />
<glyph unicode="&#xe115;" d="M404 1000h746q21 0 35.5 -14.5t14.5 -35.5v-551h150q21 0 25 -10.5t-10 -24.5l-230 -249q-14 -15 -35 -15t-35 15l-230 249q-14 14 -10 24.5t25 10.5h150v401h-381zM135 984l230 -249q14 -14 10 -24.5t-25 -10.5h-150v-400h385l215 -200h-750q-21 0 -35.5 14.5 t-14.5 35.5v550h-150q-21 0 -25 10.5t10 24.5l230 249q14 15 35 15t35 -15z" />
<glyph unicode="&#xe116;" d="M56 1200h94q17 0 31 -11t18 -27l38 -162h896q24 0 39 -18.5t10 -42.5l-100 -475q-5 -21 -27 -42.5t-55 -21.5h-633l48 -200h535q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-50v-50q0 -21 -14.5 -35.5t-35.5 -14.5t-35.5 14.5t-14.5 35.5v50h-300v-50 q0 -21 -14.5 -35.5t-35.5 -14.5t-35.5 14.5t-14.5 35.5v50h-31q-18 0 -32.5 10t-20.5 19l-5 10l-201 961h-54q-20 0 -35 14.5t-15 35.5t15 35.5t35 14.5z" />
<glyph unicode="&#xe117;" d="M1200 1000v-100h-1200v100h200q0 41 29.5 70.5t70.5 29.5h300q41 0 70.5 -29.5t29.5 -70.5h500zM0 800h1200v-800h-1200v800z" />
<glyph unicode="&#xe118;" d="M200 800l-200 -400v600h200q0 41 29.5 70.5t70.5 29.5h300q42 0 71 -29.5t29 -70.5h500v-200h-1000zM1500 700l-300 -700h-1200l300 700h1200z" />
<glyph unicode="&#xe119;" d="M635 1184l230 -249q14 -14 10 -24.5t-25 -10.5h-150v-601h150q21 0 25 -10.5t-10 -24.5l-230 -249q-14 -15 -35 -15t-35 15l-230 249q-14 14 -10 24.5t25 10.5h150v601h-150q-21 0 -25 10.5t10 24.5l230 249q14 15 35 15t35 -15z" />
<glyph unicode="&#xe120;" d="M936 864l249 -229q14 -15 14 -35.5t-14 -35.5l-249 -229q-15 -15 -25.5 -10.5t-10.5 24.5v151h-600v-151q0 -20 -10.5 -24.5t-25.5 10.5l-249 229q-14 15 -14 35.5t14 35.5l249 229q15 15 25.5 10.5t10.5 -25.5v-149h600v149q0 21 10.5 25.5t25.5 -10.5z" />
<glyph unicode="&#xe121;" d="M1169 400l-172 732q-5 23 -23 45.5t-38 22.5h-672q-20 0 -38 -20t-23 -41l-172 -739h1138zM1100 300h-1000q-41 0 -70.5 -29.5t-29.5 -70.5v-100q0 -41 29.5 -70.5t70.5 -29.5h1000q41 0 70.5 29.5t29.5 70.5v100q0 41 -29.5 70.5t-70.5 29.5zM800 100v100h100v-100h-100 zM1000 100v100h100v-100h-100z" />
<glyph unicode="&#xe122;" d="M1150 1100q21 0 35.5 -14.5t14.5 -35.5v-850q0 -21 -14.5 -35.5t-35.5 -14.5t-35.5 14.5t-14.5 35.5v850q0 21 14.5 35.5t35.5 14.5zM1000 200l-675 200h-38l47 -276q3 -16 -5.5 -20t-29.5 -4h-7h-84q-20 0 -34.5 14t-18.5 35q-55 337 -55 351v250v6q0 16 1 23.5t6.5 14 t17.5 6.5h200l675 250v-850zM0 750v-250q-4 0 -11 0.5t-24 6t-30 15t-24 30t-11 48.5v50q0 26 10.5 46t25 30t29 16t25.5 7z" />
<glyph unicode="&#xe123;" d="M553 1200h94q20 0 29 -10.5t3 -29.5l-18 -37q83 -19 144 -82.5t76 -140.5l63 -327l118 -173h17q19 0 33 -14.5t14 -35t-13 -40.5t-31 -27q-8 -4 -23 -9.5t-65 -19.5t-103 -25t-132.5 -20t-158.5 -9q-57 0 -115 5t-104 12t-88.5 15.5t-73.5 17.5t-54.5 16t-35.5 12l-11 4 q-18 8 -31 28t-13 40.5t14 35t33 14.5h17l118 173l63 327q15 77 76 140t144 83l-18 32q-6 19 3.5 32t28.5 13zM498 110q50 -6 102 -6q53 0 102 6q-12 -49 -39.5 -79.5t-62.5 -30.5t-63 30.5t-39 79.5z" />
<glyph unicode="&#xe124;" d="M800 946l224 78l-78 -224l234 -45l-180 -155l180 -155l-234 -45l78 -224l-224 78l-45 -234l-155 180l-155 -180l-45 234l-224 -78l78 224l-234 45l180 155l-180 155l234 45l-78 224l224 -78l45 234l155 -180l155 180z" />
<glyph unicode="&#xe125;" d="M650 1200h50q40 0 70 -40.5t30 -84.5v-150l-28 -125h328q40 0 70 -40.5t30 -84.5v-100q0 -45 -29 -74l-238 -344q-16 -24 -38 -40.5t-45 -16.5h-250q-7 0 -42 25t-66 50l-31 25h-61q-45 0 -72.5 18t-27.5 57v400q0 36 20 63l145 196l96 198q13 28 37.5 48t51.5 20z M650 1100l-100 -212l-150 -213v-375h100l136 -100h214l250 375v125h-450l50 225v175h-50zM50 800h100q21 0 35.5 -14.5t14.5 -35.5v-500q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v500q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe126;" d="M600 1100h250q23 0 45 -16.5t38 -40.5l238 -344q29 -29 29 -74v-100q0 -44 -30 -84.5t-70 -40.5h-328q28 -118 28 -125v-150q0 -44 -30 -84.5t-70 -40.5h-50q-27 0 -51.5 20t-37.5 48l-96 198l-145 196q-20 27 -20 63v400q0 39 27.5 57t72.5 18h61q124 100 139 100z M50 1000h100q21 0 35.5 -14.5t14.5 -35.5v-500q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v500q0 21 14.5 35.5t35.5 14.5zM636 1000l-136 -100h-100v-375l150 -213l100 -212h50v175l-50 225h450v125l-250 375h-214z" />
<glyph unicode="&#xe127;" d="M356 873l363 230q31 16 53 -6l110 -112q13 -13 13.5 -32t-11.5 -34l-84 -121h302q84 0 138 -38t54 -110t-55 -111t-139 -39h-106l-131 -339q-6 -21 -19.5 -41t-28.5 -20h-342q-7 0 -90 81t-83 94v525q0 17 14 35.5t28 28.5zM400 792v-503l100 -89h293l131 339 q6 21 19.5 41t28.5 20h203q21 0 30.5 25t0.5 50t-31 25h-456h-7h-6h-5.5t-6 0.5t-5 1.5t-5 2t-4 2.5t-4 4t-2.5 4.5q-12 25 5 47l146 183l-86 83zM50 800h100q21 0 35.5 -14.5t14.5 -35.5v-500q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v500 q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe128;" d="M475 1103l366 -230q2 -1 6 -3.5t14 -10.5t18 -16.5t14.5 -20t6.5 -22.5v-525q0 -13 -86 -94t-93 -81h-342q-15 0 -28.5 20t-19.5 41l-131 339h-106q-85 0 -139.5 39t-54.5 111t54 110t138 38h302l-85 121q-11 15 -10.5 34t13.5 32l110 112q22 22 53 6zM370 945l146 -183 q17 -22 5 -47q-2 -2 -3.5 -4.5t-4 -4t-4 -2.5t-5 -2t-5 -1.5t-6 -0.5h-6h-6.5h-6h-475v-100h221q15 0 29 -20t20 -41l130 -339h294l106 89v503l-342 236zM1050 800h100q21 0 35.5 -14.5t14.5 -35.5v-500q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5 v500q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe129;" d="M550 1294q72 0 111 -55t39 -139v-106l339 -131q21 -6 41 -19.5t20 -28.5v-342q0 -7 -81 -90t-94 -83h-525q-17 0 -35.5 14t-28.5 28l-9 14l-230 363q-16 31 6 53l112 110q13 13 32 13.5t34 -11.5l121 -84v302q0 84 38 138t110 54zM600 972v203q0 21 -25 30.5t-50 0.5 t-25 -31v-456v-7v-6v-5.5t-0.5 -6t-1.5 -5t-2 -5t-2.5 -4t-4 -4t-4.5 -2.5q-25 -12 -47 5l-183 146l-83 -86l236 -339h503l89 100v293l-339 131q-21 6 -41 19.5t-20 28.5zM450 200h500q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-500 q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe130;" d="M350 1100h500q21 0 35.5 14.5t14.5 35.5v100q0 21 -14.5 35.5t-35.5 14.5h-500q-21 0 -35.5 -14.5t-14.5 -35.5v-100q0 -21 14.5 -35.5t35.5 -14.5zM600 306v-106q0 -84 -39 -139t-111 -55t-110 54t-38 138v302l-121 -84q-15 -12 -34 -11.5t-32 13.5l-112 110 q-22 22 -6 53l230 363q1 2 3.5 6t10.5 13.5t16.5 17t20 13.5t22.5 6h525q13 0 94 -83t81 -90v-342q0 -15 -20 -28.5t-41 -19.5zM308 900l-236 -339l83 -86l183 146q22 17 47 5q2 -1 4.5 -2.5t4 -4t2.5 -4t2 -5t1.5 -5t0.5 -6v-5.5v-6v-7v-456q0 -22 25 -31t50 0.5t25 30.5 v203q0 15 20 28.5t41 19.5l339 131v293l-89 100h-503z" />
<glyph unicode="&#xe131;" d="M600 1178q118 0 225 -45.5t184.5 -123t123 -184.5t45.5 -225t-45.5 -225t-123 -184.5t-184.5 -123t-225 -45.5t-225 45.5t-184.5 123t-123 184.5t-45.5 225t45.5 225t123 184.5t184.5 123t225 45.5zM914 632l-275 223q-16 13 -27.5 8t-11.5 -26v-137h-275 q-10 0 -17.5 -7.5t-7.5 -17.5v-150q0 -10 7.5 -17.5t17.5 -7.5h275v-137q0 -21 11.5 -26t27.5 8l275 223q16 13 16 32t-16 32z" />
<glyph unicode="&#xe132;" d="M600 1178q118 0 225 -45.5t184.5 -123t123 -184.5t45.5 -225t-45.5 -225t-123 -184.5t-184.5 -123t-225 -45.5t-225 45.5t-184.5 123t-123 184.5t-45.5 225t45.5 225t123 184.5t184.5 123t225 45.5zM561 855l-275 -223q-16 -13 -16 -32t16 -32l275 -223q16 -13 27.5 -8 t11.5 26v137h275q10 0 17.5 7.5t7.5 17.5v150q0 10 -7.5 17.5t-17.5 7.5h-275v137q0 21 -11.5 26t-27.5 -8z" />
<glyph unicode="&#xe133;" d="M600 1178q118 0 225 -45.5t184.5 -123t123 -184.5t45.5 -225t-45.5 -225t-123 -184.5t-184.5 -123t-225 -45.5t-225 45.5t-184.5 123t-123 184.5t-45.5 225t45.5 225t123 184.5t184.5 123t225 45.5zM855 639l-223 275q-13 16 -32 16t-32 -16l-223 -275q-13 -16 -8 -27.5 t26 -11.5h137v-275q0 -10 7.5 -17.5t17.5 -7.5h150q10 0 17.5 7.5t7.5 17.5v275h137q21 0 26 11.5t-8 27.5z" />
<glyph unicode="&#xe134;" d="M600 1178q118 0 225 -45.5t184.5 -123t123 -184.5t45.5 -225t-45.5 -225t-123 -184.5t-184.5 -123t-225 -45.5t-225 45.5t-184.5 123t-123 184.5t-45.5 225t45.5 225t123 184.5t184.5 123t225 45.5zM675 900h-150q-10 0 -17.5 -7.5t-7.5 -17.5v-275h-137q-21 0 -26 -11.5 t8 -27.5l223 -275q13 -16 32 -16t32 16l223 275q13 16 8 27.5t-26 11.5h-137v275q0 10 -7.5 17.5t-17.5 7.5z" />
<glyph unicode="&#xe135;" d="M600 1176q116 0 222.5 -46t184 -123.5t123.5 -184t46 -222.5t-46 -222.5t-123.5 -184t-184 -123.5t-222.5 -46t-222.5 46t-184 123.5t-123.5 184t-46 222.5t46 222.5t123.5 184t184 123.5t222.5 46zM627 1101q-15 -12 -36.5 -20.5t-35.5 -12t-43 -8t-39 -6.5 q-15 -3 -45.5 0t-45.5 -2q-20 -7 -51.5 -26.5t-34.5 -34.5q-3 -11 6.5 -22.5t8.5 -18.5q-3 -34 -27.5 -91t-29.5 -79q-9 -34 5 -93t8 -87q0 -9 17 -44.5t16 -59.5q12 0 23 -5t23.5 -15t19.5 -14q16 -8 33 -15t40.5 -15t34.5 -12q21 -9 52.5 -32t60 -38t57.5 -11 q7 -15 -3 -34t-22.5 -40t-9.5 -38q13 -21 23 -34.5t27.5 -27.5t36.5 -18q0 -7 -3.5 -16t-3.5 -14t5 -17q104 -2 221 112q30 29 46.5 47t34.5 49t21 63q-13 8 -37 8.5t-36 7.5q-15 7 -49.5 15t-51.5 19q-18 0 -41 -0.5t-43 -1.5t-42 -6.5t-38 -16.5q-51 -35 -66 -12 q-4 1 -3.5 25.5t0.5 25.5q-6 13 -26.5 17.5t-24.5 6.5q1 15 -0.5 30.5t-7 28t-18.5 11.5t-31 -21q-23 -25 -42 4q-19 28 -8 58q6 16 22 22q6 -1 26 -1.5t33.5 -4t19.5 -13.5q7 -12 18 -24t21.5 -20.5t20 -15t15.5 -10.5l5 -3q2 12 7.5 30.5t8 34.5t-0.5 32q-3 18 3.5 29 t18 22.5t15.5 24.5q6 14 10.5 35t8 31t15.5 22.5t34 22.5q-6 18 10 36q8 0 24 -1.5t24.5 -1.5t20 4.5t20.5 15.5q-10 23 -31 42.5t-37.5 29.5t-49 27t-43.5 23q0 1 2 8t3 11.5t1.5 10.5t-1 9.5t-4.5 4.5q31 -13 58.5 -14.5t38.5 2.5l12 5q5 28 -9.5 46t-36.5 24t-50 15 t-41 20q-18 -4 -37 0zM613 994q0 -17 8 -42t17 -45t9 -23q-8 1 -39.5 5.5t-52.5 10t-37 16.5q3 11 16 29.5t16 25.5q10 -10 19 -10t14 6t13.5 14.5t16.5 12.5z" />
<glyph unicode="&#xe136;" d="M756 1157q164 92 306 -9l-259 -138l145 -232l251 126q6 -89 -34 -156.5t-117 -110.5q-60 -34 -127 -39.5t-126 16.5l-596 -596q-15 -16 -36.5 -16t-36.5 16l-111 110q-15 15 -15 36.5t15 37.5l600 599q-34 101 5.5 201.5t135.5 154.5z" />
<glyph unicode="&#xe137;" horiz-adv-x="1220" d="M100 1196h1000q41 0 70.5 -29.5t29.5 -70.5v-100q0 -41 -29.5 -70.5t-70.5 -29.5h-1000q-41 0 -70.5 29.5t-29.5 70.5v100q0 41 29.5 70.5t70.5 29.5zM1100 1096h-200v-100h200v100zM100 796h1000q41 0 70.5 -29.5t29.5 -70.5v-100q0 -41 -29.5 -70.5t-70.5 -29.5h-1000 q-41 0 -70.5 29.5t-29.5 70.5v100q0 41 29.5 70.5t70.5 29.5zM1100 696h-500v-100h500v100zM100 396h1000q41 0 70.5 -29.5t29.5 -70.5v-100q0 -41 -29.5 -70.5t-70.5 -29.5h-1000q-41 0 -70.5 29.5t-29.5 70.5v100q0 41 29.5 70.5t70.5 29.5zM1100 296h-300v-100h300v100z " />
<glyph unicode="&#xe138;" d="M150 1200h900q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-900q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5zM700 500v-300l-200 -200v500l-350 500h900z" />
<glyph unicode="&#xe139;" d="M500 1200h200q41 0 70.5 -29.5t29.5 -70.5v-100h300q41 0 70.5 -29.5t29.5 -70.5v-400h-500v100h-200v-100h-500v400q0 41 29.5 70.5t70.5 29.5h300v100q0 41 29.5 70.5t70.5 29.5zM500 1100v-100h200v100h-200zM1200 400v-200q0 -41 -29.5 -70.5t-70.5 -29.5h-1000 q-41 0 -70.5 29.5t-29.5 70.5v200h1200z" />
<glyph unicode="&#xe140;" d="M50 1200h300q21 0 25 -10.5t-10 -24.5l-94 -94l199 -199q7 -8 7 -18t-7 -18l-106 -106q-8 -7 -18 -7t-18 7l-199 199l-94 -94q-14 -14 -24.5 -10t-10.5 25v300q0 21 14.5 35.5t35.5 14.5zM850 1200h300q21 0 35.5 -14.5t14.5 -35.5v-300q0 -21 -10.5 -25t-24.5 10l-94 94 l-199 -199q-8 -7 -18 -7t-18 7l-106 106q-7 8 -7 18t7 18l199 199l-94 94q-14 14 -10 24.5t25 10.5zM364 470l106 -106q7 -8 7 -18t-7 -18l-199 -199l94 -94q14 -14 10 -24.5t-25 -10.5h-300q-21 0 -35.5 14.5t-14.5 35.5v300q0 21 10.5 25t24.5 -10l94 -94l199 199 q8 7 18 7t18 -7zM1071 271l94 94q14 14 24.5 10t10.5 -25v-300q0 -21 -14.5 -35.5t-35.5 -14.5h-300q-21 0 -25 10.5t10 24.5l94 94l-199 199q-7 8 -7 18t7 18l106 106q8 7 18 7t18 -7z" />
<glyph unicode="&#xe141;" d="M596 1192q121 0 231.5 -47.5t190 -127t127 -190t47.5 -231.5t-47.5 -231.5t-127 -190.5t-190 -127t-231.5 -47t-231.5 47t-190.5 127t-127 190.5t-47 231.5t47 231.5t127 190t190.5 127t231.5 47.5zM596 1010q-112 0 -207.5 -55.5t-151 -151t-55.5 -207.5t55.5 -207.5 t151 -151t207.5 -55.5t207.5 55.5t151 151t55.5 207.5t-55.5 207.5t-151 151t-207.5 55.5zM454.5 905q22.5 0 38.5 -16t16 -38.5t-16 -39t-38.5 -16.5t-38.5 16.5t-16 39t16 38.5t38.5 16zM754.5 905q22.5 0 38.5 -16t16 -38.5t-16 -39t-38 -16.5q-14 0 -29 10l-55 -145 q17 -23 17 -51q0 -36 -25.5 -61.5t-61.5 -25.5t-61.5 25.5t-25.5 61.5q0 32 20.5 56.5t51.5 29.5l122 126l1 1q-9 14 -9 28q0 23 16 39t38.5 16zM345.5 709q22.5 0 38.5 -16t16 -38.5t-16 -38.5t-38.5 -16t-38.5 16t-16 38.5t16 38.5t38.5 16zM854.5 709q22.5 0 38.5 -16 t16 -38.5t-16 -38.5t-38.5 -16t-38.5 16t-16 38.5t16 38.5t38.5 16z" />
<glyph unicode="&#xe142;" d="M546 173l469 470q91 91 99 192q7 98 -52 175.5t-154 94.5q-22 4 -47 4q-34 0 -66.5 -10t-56.5 -23t-55.5 -38t-48 -41.5t-48.5 -47.5q-376 -375 -391 -390q-30 -27 -45 -41.5t-37.5 -41t-32 -46.5t-16 -47.5t-1.5 -56.5q9 -62 53.5 -95t99.5 -33q74 0 125 51l548 548 q36 36 20 75q-7 16 -21.5 26t-32.5 10q-26 0 -50 -23q-13 -12 -39 -38l-341 -338q-15 -15 -35.5 -15.5t-34.5 13.5t-14 34.5t14 34.5q327 333 361 367q35 35 67.5 51.5t78.5 16.5q14 0 29 -1q44 -8 74.5 -35.5t43.5 -68.5q14 -47 2 -96.5t-47 -84.5q-12 -11 -32 -32 t-79.5 -81t-114.5 -115t-124.5 -123.5t-123 -119.5t-96.5 -89t-57 -45q-56 -27 -120 -27q-70 0 -129 32t-93 89q-48 78 -35 173t81 163l511 511q71 72 111 96q91 55 198 55q80 0 152 -33q78 -36 129.5 -103t66.5 -154q17 -93 -11 -183.5t-94 -156.5l-482 -476 q-15 -15 -36 -16t-37 14t-17.5 34t14.5 35z" />
<glyph unicode="&#xe143;" d="M649 949q48 68 109.5 104t121.5 38.5t118.5 -20t102.5 -64t71 -100.5t27 -123q0 -57 -33.5 -117.5t-94 -124.5t-126.5 -127.5t-150 -152.5t-146 -174q-62 85 -145.5 174t-150 152.5t-126.5 127.5t-93.5 124.5t-33.5 117.5q0 64 28 123t73 100.5t104 64t119 20 t120.5 -38.5t104.5 -104zM896 972q-33 0 -64.5 -19t-56.5 -46t-47.5 -53.5t-43.5 -45.5t-37.5 -19t-36 19t-40 45.5t-43 53.5t-54 46t-65.5 19q-67 0 -122.5 -55.5t-55.5 -132.5q0 -23 13.5 -51t46 -65t57.5 -63t76 -75l22 -22q15 -14 44 -44t50.5 -51t46 -44t41 -35t23 -12 t23.5 12t42.5 36t46 44t52.5 52t44 43q4 4 12 13q43 41 63.5 62t52 55t46 55t26 46t11.5 44q0 79 -53 133.5t-120 54.5z" />
<glyph unicode="&#xe144;" d="M776.5 1214q93.5 0 159.5 -66l141 -141q66 -66 66 -160q0 -42 -28 -95.5t-62 -87.5l-29 -29q-31 53 -77 99l-18 18l95 95l-247 248l-389 -389l212 -212l-105 -106l-19 18l-141 141q-66 66 -66 159t66 159l283 283q65 66 158.5 66zM600 706l105 105q10 -8 19 -17l141 -141 q66 -66 66 -159t-66 -159l-283 -283q-66 -66 -159 -66t-159 66l-141 141q-66 66 -66 159.5t66 159.5l55 55q29 -55 75 -102l18 -17l-95 -95l247 -248l389 389z" />
<glyph unicode="&#xe145;" d="M603 1200q85 0 162 -15t127 -38t79 -48t29 -46v-953q0 -41 -29.5 -70.5t-70.5 -29.5h-600q-41 0 -70.5 29.5t-29.5 70.5v953q0 21 30 46.5t81 48t129 37.5t163 15zM300 1000v-700h600v700h-600zM600 254q-43 0 -73.5 -30.5t-30.5 -73.5t30.5 -73.5t73.5 -30.5t73.5 30.5 t30.5 73.5t-30.5 73.5t-73.5 30.5z" />
<glyph unicode="&#xe146;" d="M902 1185l283 -282q15 -15 15 -36t-14.5 -35.5t-35.5 -14.5t-35 15l-36 35l-279 -267v-300l-212 210l-308 -307l-280 -203l203 280l307 308l-210 212h300l267 279l-35 36q-15 14 -15 35t14.5 35.5t35.5 14.5t35 -15z" />
<glyph unicode="&#xe148;" d="M700 1248v-78q38 -5 72.5 -14.5t75.5 -31.5t71 -53.5t52 -84t24 -118.5h-159q-4 36 -10.5 59t-21 45t-40 35.5t-64.5 20.5v-307l64 -13q34 -7 64 -16.5t70 -32t67.5 -52.5t47.5 -80t20 -112q0 -139 -89 -224t-244 -97v-77h-100v79q-150 16 -237 103q-40 40 -52.5 93.5 t-15.5 139.5h139q5 -77 48.5 -126t117.5 -65v335l-27 8q-46 14 -79 26.5t-72 36t-63 52t-40 72.5t-16 98q0 70 25 126t67.5 92t94.5 57t110 27v77h100zM600 754v274q-29 -4 -50 -11t-42 -21.5t-31.5 -41.5t-10.5 -65q0 -29 7 -50.5t16.5 -34t28.5 -22.5t31.5 -14t37.5 -10 q9 -3 13 -4zM700 547v-310q22 2 42.5 6.5t45 15.5t41.5 27t29 42t12 59.5t-12.5 59.5t-38 44.5t-53 31t-66.5 24.5z" />
<glyph unicode="&#xe149;" d="M561 1197q84 0 160.5 -40t123.5 -109.5t47 -147.5h-153q0 40 -19.5 71.5t-49.5 48.5t-59.5 26t-55.5 9q-37 0 -79 -14.5t-62 -35.5q-41 -44 -41 -101q0 -26 13.5 -63t26.5 -61t37 -66q6 -9 9 -14h241v-100h-197q8 -50 -2.5 -115t-31.5 -95q-45 -62 -99 -112 q34 10 83 17.5t71 7.5q32 1 102 -16t104 -17q83 0 136 30l50 -147q-31 -19 -58 -30.5t-55 -15.5t-42 -4.5t-46 -0.5q-23 0 -76 17t-111 32.5t-96 11.5q-39 -3 -82 -16t-67 -25l-23 -11l-55 145q4 3 16 11t15.5 10.5t13 9t15.5 12t14.5 14t17.5 18.5q48 55 54 126.5 t-30 142.5h-221v100h166q-23 47 -44 104q-7 20 -12 41.5t-6 55.5t6 66.5t29.5 70.5t58.5 71q97 88 263 88z" />
<glyph unicode="&#xe150;" d="M400 300h150q21 0 25 -11t-10 -25l-230 -250q-14 -15 -35 -15t-35 15l-230 250q-14 14 -10 25t25 11h150v900h200v-900zM935 1184l230 -249q14 -14 10 -24.5t-25 -10.5h-150v-900h-200v900h-150q-21 0 -25 10.5t10 24.5l230 249q14 15 35 15t35 -15z" />
<glyph unicode="&#xe151;" d="M1000 700h-100v100h-100v-100h-100v500h300v-500zM400 300h150q21 0 25 -11t-10 -25l-230 -250q-14 -15 -35 -15t-35 15l-230 250q-14 14 -10 25t25 11h150v900h200v-900zM801 1100v-200h100v200h-100zM1000 350l-200 -250h200v-100h-300v150l200 250h-200v100h300v-150z " />
<glyph unicode="&#xe152;" d="M400 300h150q21 0 25 -11t-10 -25l-230 -250q-14 -15 -35 -15t-35 15l-230 250q-14 14 -10 25t25 11h150v900h200v-900zM1000 1050l-200 -250h200v-100h-300v150l200 250h-200v100h300v-150zM1000 0h-100v100h-100v-100h-100v500h300v-500zM801 400v-200h100v200h-100z " />
<glyph unicode="&#xe153;" d="M400 300h150q21 0 25 -11t-10 -25l-230 -250q-14 -15 -35 -15t-35 15l-230 250q-14 14 -10 25t25 11h150v900h200v-900zM1000 700h-100v400h-100v100h200v-500zM1100 0h-100v100h-200v400h300v-500zM901 400v-200h100v200h-100z" />
<glyph unicode="&#xe154;" d="M400 300h150q21 0 25 -11t-10 -25l-230 -250q-14 -15 -35 -15t-35 15l-230 250q-14 14 -10 25t25 11h150v900h200v-900zM1100 700h-100v100h-200v400h300v-500zM901 1100v-200h100v200h-100zM1000 0h-100v400h-100v100h200v-500z" />
<glyph unicode="&#xe155;" d="M400 300h150q21 0 25 -11t-10 -25l-230 -250q-14 -15 -35 -15t-35 15l-230 250q-14 14 -10 25t25 11h150v900h200v-900zM900 1000h-200v200h200v-200zM1000 700h-300v200h300v-200zM1100 400h-400v200h400v-200zM1200 100h-500v200h500v-200z" />
<glyph unicode="&#xe156;" d="M400 300h150q21 0 25 -11t-10 -25l-230 -250q-14 -15 -35 -15t-35 15l-230 250q-14 14 -10 25t25 11h150v900h200v-900zM1200 1000h-500v200h500v-200zM1100 700h-400v200h400v-200zM1000 400h-300v200h300v-200zM900 100h-200v200h200v-200z" />
<glyph unicode="&#xe157;" d="M350 1100h400q162 0 256 -93.5t94 -256.5v-400q0 -165 -93.5 -257.5t-256.5 -92.5h-400q-165 0 -257.5 92.5t-92.5 257.5v400q0 165 92.5 257.5t257.5 92.5zM800 900h-500q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5 v500q0 41 -29.5 70.5t-70.5 29.5z" />
<glyph unicode="&#xe158;" d="M350 1100h400q165 0 257.5 -92.5t92.5 -257.5v-400q0 -165 -92.5 -257.5t-257.5 -92.5h-400q-163 0 -256.5 92.5t-93.5 257.5v400q0 163 94 256.5t256 93.5zM800 900h-500q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5 v500q0 41 -29.5 70.5t-70.5 29.5zM440 770l253 -190q17 -12 17 -30t-17 -30l-253 -190q-16 -12 -28 -6.5t-12 26.5v400q0 21 12 26.5t28 -6.5z" />
<glyph unicode="&#xe159;" d="M350 1100h400q163 0 256.5 -94t93.5 -256v-400q0 -165 -92.5 -257.5t-257.5 -92.5h-400q-165 0 -257.5 92.5t-92.5 257.5v400q0 163 92.5 256.5t257.5 93.5zM800 900h-500q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5 v500q0 41 -29.5 70.5t-70.5 29.5zM350 700h400q21 0 26.5 -12t-6.5 -28l-190 -253q-12 -17 -30 -17t-30 17l-190 253q-12 16 -6.5 28t26.5 12z" />
<glyph unicode="&#xe160;" d="M350 1100h400q165 0 257.5 -92.5t92.5 -257.5v-400q0 -163 -92.5 -256.5t-257.5 -93.5h-400q-163 0 -256.5 94t-93.5 256v400q0 165 92.5 257.5t257.5 92.5zM800 900h-500q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5 v500q0 41 -29.5 70.5t-70.5 29.5zM580 693l190 -253q12 -16 6.5 -28t-26.5 -12h-400q-21 0 -26.5 12t6.5 28l190 253q12 17 30 17t30 -17z" />
<glyph unicode="&#xe161;" d="M550 1100h400q165 0 257.5 -92.5t92.5 -257.5v-400q0 -165 -92.5 -257.5t-257.5 -92.5h-400q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5h450q41 0 70.5 29.5t29.5 70.5v500q0 41 -29.5 70.5t-70.5 29.5h-450q-21 0 -35.5 14.5t-14.5 35.5v100 q0 21 14.5 35.5t35.5 14.5zM338 867l324 -284q16 -14 16 -33t-16 -33l-324 -284q-16 -14 -27 -9t-11 26v150h-250q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5h250v150q0 21 11 26t27 -9z" />
<glyph unicode="&#xe162;" d="M793 1182l9 -9q8 -10 5 -27q-3 -11 -79 -225.5t-78 -221.5l300 1q24 0 32.5 -17.5t-5.5 -35.5q-1 0 -133.5 -155t-267 -312.5t-138.5 -162.5q-12 -15 -26 -15h-9l-9 8q-9 11 -4 32q2 9 42 123.5t79 224.5l39 110h-302q-23 0 -31 19q-10 21 6 41q75 86 209.5 237.5 t228 257t98.5 111.5q9 16 25 16h9z" />
<glyph unicode="&#xe163;" d="M350 1100h400q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-450q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h450q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-165 0 -257.5 92.5t-92.5 257.5v400 q0 165 92.5 257.5t257.5 92.5zM938 867l324 -284q16 -14 16 -33t-16 -33l-324 -284q-16 -14 -27 -9t-11 26v150h-250q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5h250v150q0 21 11 26t27 -9z" />
<glyph unicode="&#xe164;" d="M750 1200h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -10.5 -25t-24.5 10l-109 109l-312 -312q-15 -15 -35.5 -15t-35.5 15l-141 141q-15 15 -15 35.5t15 35.5l312 312l-109 109q-14 14 -10 24.5t25 10.5zM456 900h-156q-41 0 -70.5 -29.5t-29.5 -70.5v-500 q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5v148l200 200v-298q0 -165 -93.5 -257.5t-256.5 -92.5h-400q-165 0 -257.5 92.5t-92.5 257.5v400q0 165 92.5 257.5t257.5 92.5h300z" />
<glyph unicode="&#xe165;" d="M600 1186q119 0 227.5 -46.5t187 -125t125 -187t46.5 -227.5t-46.5 -227.5t-125 -187t-187 -125t-227.5 -46.5t-227.5 46.5t-187 125t-125 187t-46.5 227.5t46.5 227.5t125 187t187 125t227.5 46.5zM600 1022q-115 0 -212 -56.5t-153.5 -153.5t-56.5 -212t56.5 -212 t153.5 -153.5t212 -56.5t212 56.5t153.5 153.5t56.5 212t-56.5 212t-153.5 153.5t-212 56.5zM600 794q80 0 137 -57t57 -137t-57 -137t-137 -57t-137 57t-57 137t57 137t137 57z" />
<glyph unicode="&#xe166;" d="M450 1200h200q21 0 35.5 -14.5t14.5 -35.5v-350h245q20 0 25 -11t-9 -26l-383 -426q-14 -15 -33.5 -15t-32.5 15l-379 426q-13 15 -8.5 26t25.5 11h250v350q0 21 14.5 35.5t35.5 14.5zM50 300h1000q21 0 35.5 -14.5t14.5 -35.5v-250h-1100v250q0 21 14.5 35.5t35.5 14.5z M900 200v-50h100v50h-100z" />
<glyph unicode="&#xe167;" d="M583 1182l378 -435q14 -15 9 -31t-26 -16h-244v-250q0 -20 -17 -35t-39 -15h-200q-20 0 -32 14.5t-12 35.5v250h-250q-20 0 -25.5 16.5t8.5 31.5l383 431q14 16 33.5 17t33.5 -14zM50 300h1000q21 0 35.5 -14.5t14.5 -35.5v-250h-1100v250q0 21 14.5 35.5t35.5 14.5z M900 200v-50h100v50h-100z" />
<glyph unicode="&#xe168;" d="M396 723l369 369q7 7 17.5 7t17.5 -7l139 -139q7 -8 7 -18.5t-7 -17.5l-525 -525q-7 -8 -17.5 -8t-17.5 8l-292 291q-7 8 -7 18t7 18l139 139q8 7 18.5 7t17.5 -7zM50 300h1000q21 0 35.5 -14.5t14.5 -35.5v-250h-1100v250q0 21 14.5 35.5t35.5 14.5zM900 200v-50h100v50 h-100z" />
<glyph unicode="&#xe169;" d="M135 1023l142 142q14 14 35 14t35 -14l77 -77l-212 -212l-77 76q-14 15 -14 36t14 35zM655 855l210 210q14 14 24.5 10t10.5 -25l-2 -599q-1 -20 -15.5 -35t-35.5 -15l-597 -1q-21 0 -25 10.5t10 24.5l208 208l-154 155l212 212zM50 300h1000q21 0 35.5 -14.5t14.5 -35.5 v-250h-1100v250q0 21 14.5 35.5t35.5 14.5zM900 200v-50h100v50h-100z" />
<glyph unicode="&#xe170;" d="M350 1200l599 -2q20 -1 35 -15.5t15 -35.5l1 -597q0 -21 -10.5 -25t-24.5 10l-208 208l-155 -154l-212 212l155 154l-210 210q-14 14 -10 24.5t25 10.5zM524 512l-76 -77q-15 -14 -36 -14t-35 14l-142 142q-14 14 -14 35t14 35l77 77zM50 300h1000q21 0 35.5 -14.5 t14.5 -35.5v-250h-1100v250q0 21 14.5 35.5t35.5 14.5zM900 200v-50h100v50h-100z" />
<glyph unicode="&#xe171;" d="M1200 103l-483 276l-314 -399v423h-399l1196 796v-1096zM483 424v-230l683 953z" />
<glyph unicode="&#xe172;" d="M1100 1000v-850q0 -21 -14.5 -35.5t-35.5 -14.5h-150v400h-700v-400h-150q-21 0 -35.5 14.5t-14.5 35.5v1000q0 20 14.5 35t35.5 15h250v-300h500v300h100zM700 1000h-100v200h100v-200z" />
<glyph unicode="&#xe173;" d="M1100 1000l-2 -149l-299 -299l-95 95q-9 9 -21.5 9t-21.5 -9l-149 -147h-312v-400h-150q-21 0 -35.5 14.5t-14.5 35.5v1000q0 20 14.5 35t35.5 15h250v-300h500v300h100zM700 1000h-100v200h100v-200zM1132 638l106 -106q7 -7 7 -17.5t-7 -17.5l-420 -421q-8 -7 -18 -7 t-18 7l-202 203q-8 7 -8 17.5t8 17.5l106 106q7 8 17.5 8t17.5 -8l79 -79l297 297q7 7 17.5 7t17.5 -7z" />
<glyph unicode="&#xe174;" d="M1100 1000v-269l-103 -103l-134 134q-15 15 -33.5 16.5t-34.5 -12.5l-266 -266h-329v-400h-150q-21 0 -35.5 14.5t-14.5 35.5v1000q0 20 14.5 35t35.5 15h250v-300h500v300h100zM700 1000h-100v200h100v-200zM1202 572l70 -70q15 -15 15 -35.5t-15 -35.5l-131 -131 l131 -131q15 -15 15 -35.5t-15 -35.5l-70 -70q-15 -15 -35.5 -15t-35.5 15l-131 131l-131 -131q-15 -15 -35.5 -15t-35.5 15l-70 70q-15 15 -15 35.5t15 35.5l131 131l-131 131q-15 15 -15 35.5t15 35.5l70 70q15 15 35.5 15t35.5 -15l131 -131l131 131q15 15 35.5 15 t35.5 -15z" />
<glyph unicode="&#xe175;" d="M1100 1000v-300h-350q-21 0 -35.5 -14.5t-14.5 -35.5v-150h-500v-400h-150q-21 0 -35.5 14.5t-14.5 35.5v1000q0 20 14.5 35t35.5 15h250v-300h500v300h100zM700 1000h-100v200h100v-200zM850 600h100q21 0 35.5 -14.5t14.5 -35.5v-250h150q21 0 25 -10.5t-10 -24.5 l-230 -230q-14 -14 -35 -14t-35 14l-230 230q-14 14 -10 24.5t25 10.5h150v250q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe176;" d="M1100 1000v-400l-165 165q-14 15 -35 15t-35 -15l-263 -265h-402v-400h-150q-21 0 -35.5 14.5t-14.5 35.5v1000q0 20 14.5 35t35.5 15h250v-300h500v300h100zM700 1000h-100v200h100v-200zM935 565l230 -229q14 -15 10 -25.5t-25 -10.5h-150v-250q0 -20 -14.5 -35 t-35.5 -15h-100q-21 0 -35.5 15t-14.5 35v250h-150q-21 0 -25 10.5t10 25.5l230 229q14 15 35 15t35 -15z" />
<glyph unicode="&#xe177;" d="M50 1100h1100q21 0 35.5 -14.5t14.5 -35.5v-150h-1200v150q0 21 14.5 35.5t35.5 14.5zM1200 800v-550q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5v550h1200zM100 500v-200h400v200h-400z" />
<glyph unicode="&#xe178;" d="M935 1165l248 -230q14 -14 14 -35t-14 -35l-248 -230q-14 -14 -24.5 -10t-10.5 25v150h-400v200h400v150q0 21 10.5 25t24.5 -10zM200 800h-50q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5h50v-200zM400 800h-100v200h100v-200zM18 435l247 230 q14 14 24.5 10t10.5 -25v-150h400v-200h-400v-150q0 -21 -10.5 -25t-24.5 10l-247 230q-15 14 -15 35t15 35zM900 300h-100v200h100v-200zM1000 500h51q20 0 34.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-34.5 -14.5h-51v200z" />
<glyph unicode="&#xe179;" d="M862 1073l276 116q25 18 43.5 8t18.5 -41v-1106q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v397q-4 1 -11 5t-24 17.5t-30 29t-24 42t-11 56.5v359q0 31 18.5 65t43.5 52zM550 1200q22 0 34.5 -12.5t14.5 -24.5l1 -13v-450q0 -28 -10.5 -59.5 t-25 -56t-29 -45t-25.5 -31.5l-10 -11v-447q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v447q-4 4 -11 11.5t-24 30.5t-30 46t-24 55t-11 60v450q0 2 0.5 5.5t4 12t8.5 15t14.5 12t22.5 5.5q20 0 32.5 -12.5t14.5 -24.5l3 -13v-350h100v350v5.5t2.5 12 t7 15t15 12t25.5 5.5q23 0 35.5 -12.5t13.5 -24.5l1 -13v-350h100v350q0 2 0.5 5.5t3 12t7 15t15 12t24.5 5.5z" />
<glyph unicode="&#xe180;" d="M1200 1100v-56q-4 0 -11 -0.5t-24 -3t-30 -7.5t-24 -15t-11 -24v-888q0 -22 25 -34.5t50 -13.5l25 -2v-56h-400v56q75 0 87.5 6.5t12.5 43.5v394h-500v-394q0 -37 12.5 -43.5t87.5 -6.5v-56h-400v56q4 0 11 0.5t24 3t30 7.5t24 15t11 24v888q0 22 -25 34.5t-50 13.5 l-25 2v56h400v-56q-75 0 -87.5 -6.5t-12.5 -43.5v-394h500v394q0 37 -12.5 43.5t-87.5 6.5v56h400z" />
<glyph unicode="&#xe181;" d="M675 1000h375q21 0 35.5 -14.5t14.5 -35.5v-150h-105l-295 -98v98l-200 200h-400l100 100h375zM100 900h300q41 0 70.5 -29.5t29.5 -70.5v-500q0 -41 -29.5 -70.5t-70.5 -29.5h-300q-41 0 -70.5 29.5t-29.5 70.5v500q0 41 29.5 70.5t70.5 29.5zM100 800v-200h300v200 h-300zM1100 535l-400 -133v163l400 133v-163zM100 500v-200h300v200h-300zM1100 398v-248q0 -21 -14.5 -35.5t-35.5 -14.5h-375l-100 -100h-375l-100 100h400l200 200h105z" />
<glyph unicode="&#xe182;" d="M17 1007l162 162q17 17 40 14t37 -22l139 -194q14 -20 11 -44.5t-20 -41.5l-119 -118q102 -142 228 -268t267 -227l119 118q17 17 42.5 19t44.5 -12l192 -136q19 -14 22.5 -37.5t-13.5 -40.5l-163 -162q-3 -1 -9.5 -1t-29.5 2t-47.5 6t-62.5 14.5t-77.5 26.5t-90 42.5 t-101.5 60t-111 83t-119 108.5q-74 74 -133.5 150.5t-94.5 138.5t-60 119.5t-34.5 100t-15 74.5t-4.5 48z" />
<glyph unicode="&#xe183;" d="M600 1100q92 0 175 -10.5t141.5 -27t108.5 -36.5t81.5 -40t53.5 -37t31 -27l9 -10v-200q0 -21 -14.5 -33t-34.5 -9l-202 34q-20 3 -34.5 20t-14.5 38v146q-141 24 -300 24t-300 -24v-146q0 -21 -14.5 -38t-34.5 -20l-202 -34q-20 -3 -34.5 9t-14.5 33v200q3 4 9.5 10.5 t31 26t54 37.5t80.5 39.5t109 37.5t141 26.5t175 10.5zM600 795q56 0 97 -9.5t60 -23.5t30 -28t12 -24l1 -10v-50l365 -303q14 -15 24.5 -40t10.5 -45v-212q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5v212q0 20 10.5 45t24.5 40l365 303v50 q0 4 1 10.5t12 23t30 29t60 22.5t97 10z" />
<glyph unicode="&#xe184;" d="M1100 700l-200 -200h-600l-200 200v500h200v-200h200v200h200v-200h200v200h200v-500zM250 400h700q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-12l137 -100h-950l137 100h-12q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5zM50 100h1100q21 0 35.5 -14.5 t14.5 -35.5v-50h-1200v50q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe185;" d="M700 1100h-100q-41 0 -70.5 -29.5t-29.5 -70.5v-1000h300v1000q0 41 -29.5 70.5t-70.5 29.5zM1100 800h-100q-41 0 -70.5 -29.5t-29.5 -70.5v-700h300v700q0 41 -29.5 70.5t-70.5 29.5zM400 0h-300v400q0 41 29.5 70.5t70.5 29.5h100q41 0 70.5 -29.5t29.5 -70.5v-400z " />
<glyph unicode="&#xe186;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM500 700h-200v-100h200v-300h-300v100h200v100h-200v300h300v-100zM900 700v-300l-100 -100h-200v500h200z M700 700v-300h100v300h-100z" />
<glyph unicode="&#xe187;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM500 300h-100v200h-100v-200h-100v500h100v-200h100v200h100v-500zM900 700v-300l-100 -100h-200v500h200z M700 700v-300h100v300h-100z" />
<glyph unicode="&#xe188;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM500 700h-200v-300h200v-100h-300v500h300v-100zM900 700h-200v-300h200v-100h-300v500h300v-100z" />
<glyph unicode="&#xe189;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM500 400l-300 150l300 150v-300zM900 550l-300 -150v300z" />
<glyph unicode="&#xe190;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM900 300h-700v500h700v-500zM800 700h-130q-38 0 -66.5 -43t-28.5 -108t27 -107t68 -42h130v300zM300 700v-300 h130q41 0 68 42t27 107t-28.5 108t-66.5 43h-130z" />
<glyph unicode="&#xe191;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM500 700h-200v-100h200v-300h-300v100h200v100h-200v300h300v-100zM900 300h-100v400h-100v100h200v-500z M700 300h-100v100h100v-100z" />
<glyph unicode="&#xe192;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM300 700h200v-400h-300v500h100v-100zM900 300h-100v400h-100v100h200v-500zM300 600v-200h100v200h-100z M700 300h-100v100h100v-100z" />
<glyph unicode="&#xe193;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM500 500l-199 -200h-100v50l199 200v150h-200v100h300v-300zM900 300h-100v400h-100v100h200v-500zM701 300h-100 v100h100v-100z" />
<glyph unicode="&#xe194;" d="M600 1191q120 0 229.5 -47t188.5 -126t126 -188.5t47 -229.5t-47 -229.5t-126 -188.5t-188.5 -126t-229.5 -47t-229.5 47t-188.5 126t-126 188.5t-47 229.5t47 229.5t126 188.5t188.5 126t229.5 47zM600 1021q-114 0 -211 -56.5t-153.5 -153.5t-56.5 -211t56.5 -211 t153.5 -153.5t211 -56.5t211 56.5t153.5 153.5t56.5 211t-56.5 211t-153.5 153.5t-211 56.5zM800 700h-300v-200h300v-100h-300l-100 100v200l100 100h300v-100z" />
<glyph unicode="&#xe195;" d="M600 1191q120 0 229.5 -47t188.5 -126t126 -188.5t47 -229.5t-47 -229.5t-126 -188.5t-188.5 -126t-229.5 -47t-229.5 47t-188.5 126t-126 188.5t-47 229.5t47 229.5t126 188.5t188.5 126t229.5 47zM600 1021q-114 0 -211 -56.5t-153.5 -153.5t-56.5 -211t56.5 -211 t153.5 -153.5t211 -56.5t211 56.5t153.5 153.5t56.5 211t-56.5 211t-153.5 153.5t-211 56.5zM800 700v-100l-50 -50l100 -100v-50h-100l-100 100h-150v-100h-100v400h300zM500 700v-100h200v100h-200z" />
<glyph unicode="&#xe197;" d="M503 1089q110 0 200.5 -59.5t134.5 -156.5q44 14 90 14q120 0 205 -86.5t85 -207t-85 -207t-205 -86.5h-128v250q0 21 -14.5 35.5t-35.5 14.5h-300q-21 0 -35.5 -14.5t-14.5 -35.5v-250h-222q-80 0 -136 57.5t-56 136.5q0 69 43 122.5t108 67.5q-2 19 -2 37q0 100 49 185 t134 134t185 49zM525 500h150q10 0 17.5 -7.5t7.5 -17.5v-275h137q21 0 26 -11.5t-8 -27.5l-223 -244q-13 -16 -32 -16t-32 16l-223 244q-13 16 -8 27.5t26 11.5h137v275q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe198;" d="M502 1089q110 0 201 -59.5t135 -156.5q43 15 89 15q121 0 206 -86.5t86 -206.5q0 -99 -60 -181t-150 -110l-378 360q-13 16 -31.5 16t-31.5 -16l-381 -365h-9q-79 0 -135.5 57.5t-56.5 136.5q0 69 43 122.5t108 67.5q-2 19 -2 38q0 100 49 184.5t133.5 134t184.5 49.5z M632 467l223 -228q13 -16 8 -27.5t-26 -11.5h-137v-275q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v275h-137q-21 0 -26 11.5t8 27.5q199 204 223 228q19 19 31.5 19t32.5 -19z" />
<glyph unicode="&#xe199;" d="M700 100v100h400l-270 300h170l-270 300h170l-300 333l-300 -333h170l-270 -300h170l-270 -300h400v-100h-50q-21 0 -35.5 -14.5t-14.5 -35.5v-50h400v50q0 21 -14.5 35.5t-35.5 14.5h-50z" />
<glyph unicode="&#xe200;" d="M600 1179q94 0 167.5 -56.5t99.5 -145.5q89 -6 150.5 -71.5t61.5 -155.5q0 -61 -29.5 -112.5t-79.5 -82.5q9 -29 9 -55q0 -74 -52.5 -126.5t-126.5 -52.5q-55 0 -100 30v-251q21 0 35.5 -14.5t14.5 -35.5v-50h-300v50q0 21 14.5 35.5t35.5 14.5v251q-45 -30 -100 -30 q-74 0 -126.5 52.5t-52.5 126.5q0 18 4 38q-47 21 -75.5 65t-28.5 97q0 74 52.5 126.5t126.5 52.5q5 0 23 -2q0 2 -1 10t-1 13q0 116 81.5 197.5t197.5 81.5z" />
<glyph unicode="&#xe201;" d="M1010 1010q111 -111 150.5 -260.5t0 -299t-150.5 -260.5q-83 -83 -191.5 -126.5t-218.5 -43.5t-218.5 43.5t-191.5 126.5q-111 111 -150.5 260.5t0 299t150.5 260.5q83 83 191.5 126.5t218.5 43.5t218.5 -43.5t191.5 -126.5zM476 1065q-4 0 -8 -1q-121 -34 -209.5 -122.5 t-122.5 -209.5q-4 -12 2.5 -23t18.5 -14l36 -9q3 -1 7 -1q23 0 29 22q27 96 98 166q70 71 166 98q11 3 17.5 13.5t3.5 22.5l-9 35q-3 13 -14 19q-7 4 -15 4zM512 920q-4 0 -9 -2q-80 -24 -138.5 -82.5t-82.5 -138.5q-4 -13 2 -24t19 -14l34 -9q4 -1 8 -1q22 0 28 21 q18 58 58.5 98.5t97.5 58.5q12 3 18 13.5t3 21.5l-9 35q-3 12 -14 19q-7 4 -15 4zM719.5 719.5q-49.5 49.5 -119.5 49.5t-119.5 -49.5t-49.5 -119.5t49.5 -119.5t119.5 -49.5t119.5 49.5t49.5 119.5t-49.5 119.5zM855 551q-22 0 -28 -21q-18 -58 -58.5 -98.5t-98.5 -57.5 q-11 -4 -17 -14.5t-3 -21.5l9 -35q3 -12 14 -19q7 -4 15 -4q4 0 9 2q80 24 138.5 82.5t82.5 138.5q4 13 -2.5 24t-18.5 14l-34 9q-4 1 -8 1zM1000 515q-23 0 -29 -22q-27 -96 -98 -166q-70 -71 -166 -98q-11 -3 -17.5 -13.5t-3.5 -22.5l9 -35q3 -13 14 -19q7 -4 15 -4 q4 0 8 1q121 34 209.5 122.5t122.5 209.5q4 12 -2.5 23t-18.5 14l-36 9q-3 1 -7 1z" />
<glyph unicode="&#xe202;" d="M700 800h300v-380h-180v200h-340v-200h-380v755q0 10 7.5 17.5t17.5 7.5h575v-400zM1000 900h-200v200zM700 300h162l-212 -212l-212 212h162v200h100v-200zM520 0h-395q-10 0 -17.5 7.5t-7.5 17.5v395zM1000 220v-195q0 -10 -7.5 -17.5t-17.5 -7.5h-195z" />
<glyph unicode="&#xe203;" d="M700 800h300v-520l-350 350l-550 -550v1095q0 10 7.5 17.5t17.5 7.5h575v-400zM1000 900h-200v200zM862 200h-162v-200h-100v200h-162l212 212zM480 0h-355q-10 0 -17.5 7.5t-7.5 17.5v55h380v-80zM1000 80v-55q0 -10 -7.5 -17.5t-17.5 -7.5h-155v80h180z" />
<glyph unicode="&#xe204;" d="M1162 800h-162v-200h100l100 -100h-300v300h-162l212 212zM200 800h200q27 0 40 -2t29.5 -10.5t23.5 -30t7 -57.5h300v-100h-600l-200 -350v450h100q0 36 7 57.5t23.5 30t29.5 10.5t40 2zM800 400h240l-240 -400h-800l300 500h500v-100z" />
<glyph unicode="&#xe205;" d="M650 1100h100q21 0 35.5 -14.5t14.5 -35.5v-50h50q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-300q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5h50v50q0 21 14.5 35.5t35.5 14.5zM1000 850v150q41 0 70.5 -29.5t29.5 -70.5v-800 q0 -41 -29.5 -70.5t-70.5 -29.5h-600q-1 0 -20 4l246 246l-326 326v324q0 41 29.5 70.5t70.5 29.5v-150q0 -62 44 -106t106 -44h300q62 0 106 44t44 106zM412 250l-212 -212v162h-200v100h200v162z" />
<glyph unicode="&#xe206;" d="M450 1100h100q21 0 35.5 -14.5t14.5 -35.5v-50h50q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-300q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5h50v50q0 21 14.5 35.5t35.5 14.5zM800 850v150q41 0 70.5 -29.5t29.5 -70.5v-500 h-200v-300h200q0 -36 -7 -57.5t-23.5 -30t-29.5 -10.5t-40 -2h-600q-41 0 -70.5 29.5t-29.5 70.5v800q0 41 29.5 70.5t70.5 29.5v-150q0 -62 44 -106t106 -44h300q62 0 106 44t44 106zM1212 250l-212 -212v162h-200v100h200v162z" />
<glyph unicode="&#xe209;" d="M658 1197l637 -1104q23 -38 7 -65.5t-60 -27.5h-1276q-44 0 -60 27.5t7 65.5l637 1104q22 39 54 39t54 -39zM704 800h-208q-20 0 -32 -14.5t-8 -34.5l58 -302q4 -20 21.5 -34.5t37.5 -14.5h54q20 0 37.5 14.5t21.5 34.5l58 302q4 20 -8 34.5t-32 14.5zM500 300v-100h200 v100h-200z" />
<glyph unicode="&#xe210;" d="M425 1100h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5zM425 800h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5 t17.5 7.5zM825 800h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5zM25 500h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v150 q0 10 7.5 17.5t17.5 7.5zM425 500h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5zM825 500h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5 v150q0 10 7.5 17.5t17.5 7.5zM25 200h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5zM425 200h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5 t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5zM825 200h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe211;" d="M700 1200h100v-200h-100v-100h350q62 0 86.5 -39.5t-3.5 -94.5l-66 -132q-41 -83 -81 -134h-772q-40 51 -81 134l-66 132q-28 55 -3.5 94.5t86.5 39.5h350v100h-100v200h100v100h200v-100zM250 400h700q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-12l137 -100 h-950l138 100h-13q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5zM50 100h1100q21 0 35.5 -14.5t14.5 -35.5v-50h-1200v50q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe212;" d="M600 1300q40 0 68.5 -29.5t28.5 -70.5h-194q0 41 28.5 70.5t68.5 29.5zM443 1100h314q18 -37 18 -75q0 -8 -3 -25h328q41 0 44.5 -16.5t-30.5 -38.5l-175 -145h-678l-178 145q-34 22 -29 38.5t46 16.5h328q-3 17 -3 25q0 38 18 75zM250 700h700q21 0 35.5 -14.5 t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-150v-200l275 -200h-950l275 200v200h-150q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5zM50 100h1100q21 0 35.5 -14.5t14.5 -35.5v-50h-1200v50q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe213;" d="M600 1181q75 0 128 -53t53 -128t-53 -128t-128 -53t-128 53t-53 128t53 128t128 53zM602 798h46q34 0 55.5 -28.5t21.5 -86.5q0 -76 39 -183h-324q39 107 39 183q0 58 21.5 86.5t56.5 28.5h45zM250 400h700q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-13 l138 -100h-950l137 100h-12q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5zM50 100h1100q21 0 35.5 -14.5t14.5 -35.5v-50h-1200v50q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe214;" d="M600 1300q47 0 92.5 -53.5t71 -123t25.5 -123.5q0 -78 -55.5 -133.5t-133.5 -55.5t-133.5 55.5t-55.5 133.5q0 62 34 143l144 -143l111 111l-163 163q34 26 63 26zM602 798h46q34 0 55.5 -28.5t21.5 -86.5q0 -76 39 -183h-324q39 107 39 183q0 58 21.5 86.5t56.5 28.5h45 zM250 400h700q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-13l138 -100h-950l137 100h-12q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5zM50 100h1100q21 0 35.5 -14.5t14.5 -35.5v-50h-1200v50q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe215;" d="M600 1200l300 -161v-139h-300q0 -57 18.5 -108t50 -91.5t63 -72t70 -67.5t57.5 -61h-530q-60 83 -90.5 177.5t-30.5 178.5t33 164.5t87.5 139.5t126 96.5t145.5 41.5v-98zM250 400h700q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-13l138 -100h-950l137 100 h-12q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5zM50 100h1100q21 0 35.5 -14.5t14.5 -35.5v-50h-1200v50q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe216;" d="M600 1300q41 0 70.5 -29.5t29.5 -70.5v-78q46 -26 73 -72t27 -100v-50h-400v50q0 54 27 100t73 72v78q0 41 29.5 70.5t70.5 29.5zM400 800h400q54 0 100 -27t72 -73h-172v-100h200v-100h-200v-100h200v-100h-200v-100h200q0 -83 -58.5 -141.5t-141.5 -58.5h-400 q-83 0 -141.5 58.5t-58.5 141.5v400q0 83 58.5 141.5t141.5 58.5z" />
<glyph unicode="&#xe218;" d="M150 1100h900q21 0 35.5 -14.5t14.5 -35.5v-500q0 -21 -14.5 -35.5t-35.5 -14.5h-900q-21 0 -35.5 14.5t-14.5 35.5v500q0 21 14.5 35.5t35.5 14.5zM125 400h950q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-283l224 -224q13 -13 13 -31.5t-13 -32 t-31.5 -13.5t-31.5 13l-88 88h-524l-87 -88q-13 -13 -32 -13t-32 13.5t-13 32t13 31.5l224 224h-289q-10 0 -17.5 7.5t-7.5 17.5v50q0 10 7.5 17.5t17.5 7.5zM541 300l-100 -100h324l-100 100h-124z" />
<glyph unicode="&#xe219;" d="M200 1100h800q83 0 141.5 -58.5t58.5 -141.5v-200h-100q0 41 -29.5 70.5t-70.5 29.5h-250q-41 0 -70.5 -29.5t-29.5 -70.5h-100q0 41 -29.5 70.5t-70.5 29.5h-250q-41 0 -70.5 -29.5t-29.5 -70.5h-100v200q0 83 58.5 141.5t141.5 58.5zM100 600h1000q41 0 70.5 -29.5 t29.5 -70.5v-300h-1200v300q0 41 29.5 70.5t70.5 29.5zM300 100v-50q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v50h200zM1100 100v-50q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v50h200z" />
<glyph unicode="&#xe221;" d="M480 1165l682 -683q31 -31 31 -75.5t-31 -75.5l-131 -131h-481l-517 518q-32 31 -32 75.5t32 75.5l295 296q31 31 75.5 31t76.5 -31zM108 794l342 -342l303 304l-341 341zM250 100h800q21 0 35.5 -14.5t14.5 -35.5v-50h-900v50q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe223;" d="M1057 647l-189 506q-8 19 -27.5 33t-40.5 14h-400q-21 0 -40.5 -14t-27.5 -33l-189 -506q-8 -19 1.5 -33t30.5 -14h625v-150q0 -21 14.5 -35.5t35.5 -14.5t35.5 14.5t14.5 35.5v150h125q21 0 30.5 14t1.5 33zM897 0h-595v50q0 21 14.5 35.5t35.5 14.5h50v50 q0 21 14.5 35.5t35.5 14.5h48v300h200v-300h47q21 0 35.5 -14.5t14.5 -35.5v-50h50q21 0 35.5 -14.5t14.5 -35.5v-50z" />
<glyph unicode="&#xe224;" d="M900 800h300v-575q0 -10 -7.5 -17.5t-17.5 -7.5h-375v591l-300 300v84q0 10 7.5 17.5t17.5 7.5h375v-400zM1200 900h-200v200zM400 600h300v-575q0 -10 -7.5 -17.5t-17.5 -7.5h-650q-10 0 -17.5 7.5t-7.5 17.5v950q0 10 7.5 17.5t17.5 7.5h375v-400zM700 700h-200v200z " />
<glyph unicode="&#xe225;" d="M484 1095h195q75 0 146 -32.5t124 -86t89.5 -122.5t48.5 -142q18 -14 35 -20q31 -10 64.5 6.5t43.5 48.5q10 34 -15 71q-19 27 -9 43q5 8 12.5 11t19 -1t23.5 -16q41 -44 39 -105q-3 -63 -46 -106.5t-104 -43.5h-62q-7 -55 -35 -117t-56 -100l-39 -234q-3 -20 -20 -34.5 t-38 -14.5h-100q-21 0 -33 14.5t-9 34.5l12 70q-49 -14 -91 -14h-195q-24 0 -65 8l-11 -64q-3 -20 -20 -34.5t-38 -14.5h-100q-21 0 -33 14.5t-9 34.5l26 157q-84 74 -128 175l-159 53q-19 7 -33 26t-14 40v50q0 21 14.5 35.5t35.5 14.5h124q11 87 56 166l-111 95 q-16 14 -12.5 23.5t24.5 9.5h203q116 101 250 101zM675 1000h-250q-10 0 -17.5 -7.5t-7.5 -17.5v-50q0 -10 7.5 -17.5t17.5 -7.5h250q10 0 17.5 7.5t7.5 17.5v50q0 10 -7.5 17.5t-17.5 7.5z" />
<glyph unicode="&#xe226;" d="M641 900l423 247q19 8 42 2.5t37 -21.5l32 -38q14 -15 12.5 -36t-17.5 -34l-139 -120h-390zM50 1100h106q67 0 103 -17t66 -71l102 -212h823q21 0 35.5 -14.5t14.5 -35.5v-50q0 -21 -14 -40t-33 -26l-737 -132q-23 -4 -40 6t-26 25q-42 67 -100 67h-300q-62 0 -106 44 t-44 106v200q0 62 44 106t106 44zM173 928h-80q-19 0 -28 -14t-9 -35v-56q0 -51 42 -51h134q16 0 21.5 8t5.5 24q0 11 -16 45t-27 51q-18 28 -43 28zM550 727q-32 0 -54.5 -22.5t-22.5 -54.5t22.5 -54.5t54.5 -22.5t54.5 22.5t22.5 54.5t-22.5 54.5t-54.5 22.5zM130 389 l152 130q18 19 34 24t31 -3.5t24.5 -17.5t25.5 -28q28 -35 50.5 -51t48.5 -13l63 5l48 -179q13 -61 -3.5 -97.5t-67.5 -79.5l-80 -69q-47 -40 -109 -35.5t-103 51.5l-130 151q-40 47 -35.5 109.5t51.5 102.5zM380 377l-102 -88q-31 -27 2 -65l37 -43q13 -15 27.5 -19.5 t31.5 6.5l61 53q19 16 14 49q-2 20 -12 56t-17 45q-11 12 -19 14t-23 -8z" />
<glyph unicode="&#xe227;" d="M625 1200h150q10 0 17.5 -7.5t7.5 -17.5v-109q79 -33 131 -87.5t53 -128.5q1 -46 -15 -84.5t-39 -61t-46 -38t-39 -21.5l-17 -6q6 0 15 -1.5t35 -9t50 -17.5t53 -30t50 -45t35.5 -64t14.5 -84q0 -59 -11.5 -105.5t-28.5 -76.5t-44 -51t-49.5 -31.5t-54.5 -16t-49.5 -6.5 t-43.5 -1v-75q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v75h-100v-75q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v75h-175q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5h75v600h-75q-10 0 -17.5 7.5t-7.5 17.5v150 q0 10 7.5 17.5t17.5 7.5h175v75q0 10 7.5 17.5t17.5 7.5h150q10 0 17.5 -7.5t7.5 -17.5v-75h100v75q0 10 7.5 17.5t17.5 7.5zM400 900v-200h263q28 0 48.5 10.5t30 25t15 29t5.5 25.5l1 10q0 4 -0.5 11t-6 24t-15 30t-30 24t-48.5 11h-263zM400 500v-200h363q28 0 48.5 10.5 t30 25t15 29t5.5 25.5l1 10q0 4 -0.5 11t-6 24t-15 30t-30 24t-48.5 11h-363z" />
<glyph unicode="&#xe230;" d="M212 1198h780q86 0 147 -61t61 -147v-416q0 -51 -18 -142.5t-36 -157.5l-18 -66q-29 -87 -93.5 -146.5t-146.5 -59.5h-572q-82 0 -147 59t-93 147q-8 28 -20 73t-32 143.5t-20 149.5v416q0 86 61 147t147 61zM600 1045q-70 0 -132.5 -11.5t-105.5 -30.5t-78.5 -41.5 t-57 -45t-36 -41t-20.5 -30.5l-6 -12l156 -243h560l156 243q-2 5 -6 12.5t-20 29.5t-36.5 42t-57 44.5t-79 42t-105 29.5t-132.5 12zM762 703h-157l195 261z" />
<glyph unicode="&#xe231;" d="M475 1300h150q103 0 189 -86t86 -189v-500q0 -41 -42 -83t-83 -42h-450q-41 0 -83 42t-42 83v500q0 103 86 189t189 86zM700 300v-225q0 -21 -27 -48t-48 -27h-150q-21 0 -48 27t-27 48v225h300z" />
<glyph unicode="&#xe232;" d="M475 1300h96q0 -150 89.5 -239.5t239.5 -89.5v-446q0 -41 -42 -83t-83 -42h-450q-41 0 -83 42t-42 83v500q0 103 86 189t189 86zM700 300v-225q0 -21 -27 -48t-48 -27h-150q-21 0 -48 27t-27 48v225h300z" />
<glyph unicode="&#xe233;" d="M1294 767l-638 -283l-378 170l-78 -60v-224l100 -150v-199l-150 148l-150 -149v200l100 150v250q0 4 -0.5 10.5t0 9.5t1 8t3 8t6.5 6l47 40l-147 65l642 283zM1000 380l-350 -166l-350 166v147l350 -165l350 165v-147z" />
<glyph unicode="&#xe234;" d="M250 800q62 0 106 -44t44 -106t-44 -106t-106 -44t-106 44t-44 106t44 106t106 44zM650 800q62 0 106 -44t44 -106t-44 -106t-106 -44t-106 44t-44 106t44 106t106 44zM1050 800q62 0 106 -44t44 -106t-44 -106t-106 -44t-106 44t-44 106t44 106t106 44z" />
<glyph unicode="&#xe235;" d="M550 1100q62 0 106 -44t44 -106t-44 -106t-106 -44t-106 44t-44 106t44 106t106 44zM550 700q62 0 106 -44t44 -106t-44 -106t-106 -44t-106 44t-44 106t44 106t106 44zM550 300q62 0 106 -44t44 -106t-44 -106t-106 -44t-106 44t-44 106t44 106t106 44z" />
<glyph unicode="&#xe236;" d="M125 1100h950q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-950q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5zM125 700h950q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-950q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5 t17.5 7.5zM125 300h950q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-950q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe237;" d="M350 1200h500q162 0 256 -93.5t94 -256.5v-500q0 -165 -93.5 -257.5t-256.5 -92.5h-500q-165 0 -257.5 92.5t-92.5 257.5v500q0 165 92.5 257.5t257.5 92.5zM900 1000h-600q-41 0 -70.5 -29.5t-29.5 -70.5v-600q0 -41 29.5 -70.5t70.5 -29.5h600q41 0 70.5 29.5 t29.5 70.5v600q0 41 -29.5 70.5t-70.5 29.5zM350 900h500q21 0 35.5 -14.5t14.5 -35.5v-300q0 -21 -14.5 -35.5t-35.5 -14.5h-500q-21 0 -35.5 14.5t-14.5 35.5v300q0 21 14.5 35.5t35.5 14.5zM400 800v-200h400v200h-400z" />
<glyph unicode="&#xe238;" d="M150 1100h1000q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-50v-200h50q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-50v-200h50q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-50v-200h50q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5 t-35.5 -14.5h-1000q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5h50v200h-50q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5h50v200h-50q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5h50v200h-50q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe239;" d="M650 1187q87 -67 118.5 -156t0 -178t-118.5 -155q-87 66 -118.5 155t0 178t118.5 156zM300 800q124 0 212 -88t88 -212q-124 0 -212 88t-88 212zM1000 800q0 -124 -88 -212t-212 -88q0 124 88 212t212 88zM300 500q124 0 212 -88t88 -212q-124 0 -212 88t-88 212z M1000 500q0 -124 -88 -212t-212 -88q0 124 88 212t212 88zM700 199v-144q0 -21 -14.5 -35.5t-35.5 -14.5t-35.5 14.5t-14.5 35.5v142q40 -4 43 -4q17 0 57 6z" />
<glyph unicode="&#xe240;" d="M745 878l69 19q25 6 45 -12l298 -295q11 -11 15 -26.5t-2 -30.5q-5 -14 -18 -23.5t-28 -9.5h-8q1 0 1 -13q0 -29 -2 -56t-8.5 -62t-20 -63t-33 -53t-51 -39t-72.5 -14h-146q-184 0 -184 288q0 24 10 47q-20 4 -62 4t-63 -4q11 -24 11 -47q0 -288 -184 -288h-142 q-48 0 -84.5 21t-56 51t-32 71.5t-16 75t-3.5 68.5q0 13 2 13h-7q-15 0 -27.5 9.5t-18.5 23.5q-6 15 -2 30.5t15 25.5l298 296q20 18 46 11l76 -19q20 -5 30.5 -22.5t5.5 -37.5t-22.5 -31t-37.5 -5l-51 12l-182 -193h891l-182 193l-44 -12q-20 -5 -37.5 6t-22.5 31t6 37.5 t31 22.5z" />
<glyph unicode="&#xe241;" d="M1200 900h-50q0 21 -4 37t-9.5 26.5t-18 17.5t-22 11t-28.5 5.5t-31 2t-37 0.5h-200v-850q0 -22 25 -34.5t50 -13.5l25 -2v-100h-400v100q4 0 11 0.5t24 3t30 7t24 15t11 24.5v850h-200q-25 0 -37 -0.5t-31 -2t-28.5 -5.5t-22 -11t-18 -17.5t-9.5 -26.5t-4 -37h-50v300 h1000v-300zM500 450h-25q0 15 -4 24.5t-9 14.5t-17 7.5t-20 3t-25 0.5h-100v-425q0 -11 12.5 -17.5t25.5 -7.5h12v-50h-200v50q50 0 50 25v425h-100q-17 0 -25 -0.5t-20 -3t-17 -7.5t-9 -14.5t-4 -24.5h-25v150h500v-150z" />
<glyph unicode="&#xe242;" d="M1000 300v50q-25 0 -55 32q-14 14 -25 31t-16 27l-4 11l-289 747h-69l-300 -754q-18 -35 -39 -56q-9 -9 -24.5 -18.5t-26.5 -14.5l-11 -5v-50h273v50q-49 0 -78.5 21.5t-11.5 67.5l69 176h293l61 -166q13 -34 -3.5 -66.5t-55.5 -32.5v-50h312zM412 691l134 342l121 -342 h-255zM1100 150v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1000q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5h1000q21 0 35.5 -14.5t14.5 -35.5z" />
<glyph unicode="&#xe243;" d="M50 1200h1100q21 0 35.5 -14.5t14.5 -35.5v-1100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5v1100q0 21 14.5 35.5t35.5 14.5zM611 1118h-70q-13 0 -18 -12l-299 -753q-17 -32 -35 -51q-18 -18 -56 -34q-12 -5 -12 -18v-50q0 -8 5.5 -14t14.5 -6 h273q8 0 14 6t6 14v50q0 8 -6 14t-14 6q-55 0 -71 23q-10 14 0 39l63 163h266l57 -153q11 -31 -6 -55q-12 -17 -36 -17q-8 0 -14 -6t-6 -14v-50q0 -8 6 -14t14 -6h313q8 0 14 6t6 14v50q0 7 -5.5 13t-13.5 7q-17 0 -42 25q-25 27 -40 63h-1l-288 748q-5 12 -19 12zM639 611 h-197l103 264z" />
<glyph unicode="&#xe244;" d="M1200 1100h-1200v100h1200v-100zM50 1000h400q21 0 35.5 -14.5t14.5 -35.5v-900q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v900q0 21 14.5 35.5t35.5 14.5zM650 1000h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400 q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5zM700 900v-300h300v300h-300z" />
<glyph unicode="&#xe245;" d="M50 1200h400q21 0 35.5 -14.5t14.5 -35.5v-900q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v900q0 21 14.5 35.5t35.5 14.5zM650 700h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v400 q0 21 14.5 35.5t35.5 14.5zM700 600v-300h300v300h-300zM1200 0h-1200v100h1200v-100z" />
<glyph unicode="&#xe246;" d="M50 1000h400q21 0 35.5 -14.5t14.5 -35.5v-350h100v150q0 21 14.5 35.5t35.5 14.5h400q21 0 35.5 -14.5t14.5 -35.5v-150h100v-100h-100v-150q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v150h-100v-350q0 -21 -14.5 -35.5t-35.5 -14.5h-400 q-21 0 -35.5 14.5t-14.5 35.5v800q0 21 14.5 35.5t35.5 14.5zM700 700v-300h300v300h-300z" />
<glyph unicode="&#xe247;" d="M100 0h-100v1200h100v-1200zM250 1100h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5zM300 1000v-300h300v300h-300zM250 500h900q21 0 35.5 -14.5t14.5 -35.5v-400 q0 -21 -14.5 -35.5t-35.5 -14.5h-900q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe248;" d="M600 1100h150q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-150v-100h450q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-900q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5h350v100h-150q-21 0 -35.5 14.5 t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5h150v100h100v-100zM400 1000v-300h300v300h-300z" />
<glyph unicode="&#xe249;" d="M1200 0h-100v1200h100v-1200zM550 1100h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5zM600 1000v-300h300v300h-300zM50 500h900q21 0 35.5 -14.5t14.5 -35.5v-400 q0 -21 -14.5 -35.5t-35.5 -14.5h-900q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe250;" d="M865 565l-494 -494q-23 -23 -41 -23q-14 0 -22 13.5t-8 38.5v1000q0 25 8 38.5t22 13.5q18 0 41 -23l494 -494q14 -14 14 -35t-14 -35z" />
<glyph unicode="&#xe251;" d="M335 635l494 494q29 29 50 20.5t21 -49.5v-1000q0 -41 -21 -49.5t-50 20.5l-494 494q-14 14 -14 35t14 35z" />
<glyph unicode="&#xe252;" d="M100 900h1000q41 0 49.5 -21t-20.5 -50l-494 -494q-14 -14 -35 -14t-35 14l-494 494q-29 29 -20.5 50t49.5 21z" />
<glyph unicode="&#xe253;" d="M635 865l494 -494q29 -29 20.5 -50t-49.5 -21h-1000q-41 0 -49.5 21t20.5 50l494 494q14 14 35 14t35 -14z" />
<glyph unicode="&#xe254;" d="M700 741v-182l-692 -323v221l413 193l-413 193v221zM1200 0h-800v200h800v-200z" />
<glyph unicode="&#xe255;" d="M1200 900h-200v-100h200v-100h-300v300h200v100h-200v100h300v-300zM0 700h50q0 21 4 37t9.5 26.5t18 17.5t22 11t28.5 5.5t31 2t37 0.5h100v-550q0 -22 -25 -34.5t-50 -13.5l-25 -2v-100h400v100q-4 0 -11 0.5t-24 3t-30 7t-24 15t-11 24.5v550h100q25 0 37 -0.5t31 -2 t28.5 -5.5t22 -11t18 -17.5t9.5 -26.5t4 -37h50v300h-800v-300z" />
<glyph unicode="&#xe256;" d="M800 700h-50q0 21 -4 37t-9.5 26.5t-18 17.5t-22 11t-28.5 5.5t-31 2t-37 0.5h-100v-550q0 -22 25 -34.5t50 -14.5l25 -1v-100h-400v100q4 0 11 0.5t24 3t30 7t24 15t11 24.5v550h-100q-25 0 -37 -0.5t-31 -2t-28.5 -5.5t-22 -11t-18 -17.5t-9.5 -26.5t-4 -37h-50v300 h800v-300zM1100 200h-200v-100h200v-100h-300v300h200v100h-200v100h300v-300z" />
<glyph unicode="&#xe257;" d="M701 1098h160q16 0 21 -11t-7 -23l-464 -464l464 -464q12 -12 7 -23t-21 -11h-160q-13 0 -23 9l-471 471q-7 8 -7 18t7 18l471 471q10 9 23 9z" />
<glyph unicode="&#xe258;" d="M339 1098h160q13 0 23 -9l471 -471q7 -8 7 -18t-7 -18l-471 -471q-10 -9 -23 -9h-160q-16 0 -21 11t7 23l464 464l-464 464q-12 12 -7 23t21 11z" />
<glyph unicode="&#xe259;" d="M1087 882q11 -5 11 -21v-160q0 -13 -9 -23l-471 -471q-8 -7 -18 -7t-18 7l-471 471q-9 10 -9 23v160q0 16 11 21t23 -7l464 -464l464 464q12 12 23 7z" />
<glyph unicode="&#xe260;" d="M618 993l471 -471q9 -10 9 -23v-160q0 -16 -11 -21t-23 7l-464 464l-464 -464q-12 -12 -23 -7t-11 21v160q0 13 9 23l471 471q8 7 18 7t18 -7z" />
<glyph unicode="&#xf8ff;" d="M1000 1200q0 -124 -88 -212t-212 -88q0 124 88 212t212 88zM450 1000h100q21 0 40 -14t26 -33l79 -194q5 1 16 3q34 6 54 9.5t60 7t65.5 1t61 -10t56.5 -23t42.5 -42t29 -64t5 -92t-19.5 -121.5q-1 -7 -3 -19.5t-11 -50t-20.5 -73t-32.5 -81.5t-46.5 -83t-64 -70 t-82.5 -50q-13 -5 -42 -5t-65.5 2.5t-47.5 2.5q-14 0 -49.5 -3.5t-63 -3.5t-43.5 7q-57 25 -104.5 78.5t-75 111.5t-46.5 112t-26 90l-7 35q-15 63 -18 115t4.5 88.5t26 64t39.5 43.5t52 25.5t58.5 13t62.5 2t59.5 -4.5t55.5 -8l-147 192q-12 18 -5.5 30t27.5 12z" />
<glyph unicode="&#x1f511;" d="M250 1200h600q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-150v-500l-255 -178q-19 -9 -32 -1t-13 29v650h-150q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5zM400 1100v-100h300v100h-300z" />
<glyph unicode="&#x1f6aa;" d="M250 1200h750q39 0 69.5 -40.5t30.5 -84.5v-933l-700 -117v950l600 125h-700v-1000h-100v1025q0 23 15.5 49t34.5 26zM500 525v-100l100 20v100z" />
</font>
</defs></svg> ) format('svg')}.glyphicon{position:relative;top:1px;display:inline-block;font-family:'Glyphicons Halflings';font-style:normal;font-weight:400;line-height:1;-webkit-font-smoothing:antialiased;-moz-osx-font-smoothing:grayscale}.glyphicon-asterisk:before{content:"\2a"}.glyphicon-plus:before{content:"\2b"}.glyphicon-eur:before,.glyphicon-euro:before{content:"\20ac"}.glyphicon-minus:before{content:"\2212"}.glyphicon-cloud:before{content:"\2601"}.glyphicon-envelope:before{content:"\2709"}.glyphicon-pencil:before{content:"\270f"}.glyphicon-glass:before{content:"\e001"}.glyphicon-music:before{content:"\e002"}.glyphicon-search:before{content:"\e003"}.glyphicon-heart:before{content:"\e005"}.glyphicon-star:before{content:"\e006"}.glyphicon-star-empty:before{content:"\e007"}.glyphicon-user:before{content:"\e008"}.glyphicon-film:before{content:"\e009"}.glyphicon-th-large:before{content:"\e010"}.glyphicon-th:before{content:"\e011"}.glyphicon-th-list:before{content:"\e012"}.glyphicon-ok:before{content:"\e013"}.glyphicon-remove:before{content:"\e014"}.glyphicon-zoom-in:before{content:"\e015"}.glyphicon-zoom-out:before{content:"\e016"}.glyphicon-off:before{content:"\e017"}.glyphicon-signal:before{content:"\e018"}.glyphicon-cog:before{content:"\e019"}.glyphicon-trash:before{content:"\e020"}.glyphicon-home:before{content:"\e021"}.glyphicon-file:before{content:"\e022"}.glyphicon-time:before{content:"\e023"}.glyphicon-road:before{content:"\e024"}.glyphicon-download-alt:before{content:"\e025"}.glyphicon-download:before{content:"\e026"}.glyphicon-upload:before{content:"\e027"}.glyphicon-inbox:before{content:"\e028"}.glyphicon-play-circle:before{content:"\e029"}.glyphicon-repeat:before{content:"\e030"}.glyphicon-refresh:before{content:"\e031"}.glyphicon-list-alt:before{content:"\e032"}.glyphicon-lock:before{content:"\e033"}.glyphicon-flag:before{content:"\e034"}.glyphicon-headphones:before{content:"\e035"}.glyphicon-volume-off:before{content:"\e036"}.glyphicon-volume-down:before{content:"\e037"}.glyphicon-volume-up:before{content:"\e038"}.glyphicon-qrcode:before{content:"\e039"}.glyphicon-barcode:before{content:"\e040"}.glyphicon-tag:before{content:"\e041"}.glyphicon-tags:before{content:"\e042"}.glyphicon-book:before{content:"\e043"}.glyphicon-bookmark:before{content:"\e044"}.glyphicon-print:before{content:"\e045"}.glyphicon-camera:before{content:"\e046"}.glyphicon-font:before{content:"\e047"}.glyphicon-bold:before{content:"\e048"}.glyphicon-italic:before{content:"\e049"}.glyphicon-text-height:before{content:"\e050"}.glyphicon-text-width:before{content:"\e051"}.glyphicon-align-left:before{content:"\e052"}.glyphicon-align-center:before{content:"\e053"}.glyphicon-align-right:before{content:"\e054"}.glyphicon-align-justify:before{content:"\e055"}.glyphicon-list:before{content:"\e056"}.glyphicon-indent-left:before{content:"\e057"}.glyphicon-indent-right:before{content:"\e058"}.glyphicon-facetime-video:before{content:"\e059"}.glyphicon-picture:before{content:"\e060"}.glyphicon-map-marker:before{content:"\e062"}.glyphicon-adjust:before{content:"\e063"}.glyphicon-tint:before{content:"\e064"}.glyphicon-edit:before{content:"\e065"}.glyphicon-share:before{content:"\e066"}.glyphicon-check:before{content:"\e067"}.glyphicon-move:before{content:"\e068"}.glyphicon-step-backward:before{content:"\e069"}.glyphicon-fast-backward:before{content:"\e070"}.glyphicon-backward:before{content:"\e071"}.glyphicon-play:before{content:"\e072"}.glyphicon-pause:before{content:"\e073"}.glyphicon-stop:before{content:"\e074"}.glyphicon-forward:before{content:"\e075"}.glyphicon-fast-forward:before{content:"\e076"}.glyphicon-step-forward:before{content:"\e077"}.glyphicon-eject:before{content:"\e078"}.glyphicon-chevron-left:before{content:"\e079"}.glyphicon-chevron-right:before{content:"\e080"}.glyphicon-plus-sign:before{content:"\e081"}.glyphicon-minus-sign:before{content:"\e082"}.glyphicon-remove-sign:before{content:"\e083"}.glyphicon-ok-sign:before{content:"\e084"}.glyphicon-question-sign:before{content:"\e085"}.glyphicon-info-sign:before{content:"\e086"}.glyphicon-screenshot:before{content:"\e087"}.glyphicon-remove-circle:before{content:"\e088"}.glyphicon-ok-circle:before{content:"\e089"}.glyphicon-ban-circle:before{content:"\e090"}.glyphicon-arrow-left:before{content:"\e091"}.glyphicon-arrow-right:before{content:"\e092"}.glyphicon-arrow-up:before{content:"\e093"}.glyphicon-arrow-down:before{content:"\e094"}.glyphicon-share-alt:before{content:"\e095"}.glyphicon-resize-full:before{content:"\e096"}.glyphicon-resize-small:before{content:"\e097"}.glyphicon-exclamation-sign:before{content:"\e101"}.glyphicon-gift:before{content:"\e102"}.glyphicon-leaf:before{content:"\e103"}.glyphicon-fire:before{content:"\e104"}.glyphicon-eye-open:before{content:"\e105"}.glyphicon-eye-close:before{content:"\e106"}.glyphicon-warning-sign:before{content:"\e107"}.glyphicon-plane:before{content:"\e108"}.glyphicon-calendar:before{content:"\e109"}.glyphicon-random:before{content:"\e110"}.glyphicon-comment:before{content:"\e111"}.glyphicon-magnet:before{content:"\e112"}.glyphicon-chevron-up:before{content:"\e113"}.glyphicon-chevron-down:before{content:"\e114"}.glyphicon-retweet:before{content:"\e115"}.glyphicon-shopping-cart:before{content:"\e116"}.glyphicon-folder-close:before{content:"\e117"}.glyphicon-folder-open:before{content:"\e118"}.glyphicon-resize-vertical:before{content:"\e119"}.glyphicon-resize-horizontal:before{content:"\e120"}.glyphicon-hdd:before{content:"\e121"}.glyphicon-bullhorn:before{content:"\e122"}.glyphicon-bell:before{content:"\e123"}.glyphicon-certificate:before{content:"\e124"}.glyphicon-thumbs-up:before{content:"\e125"}.glyphicon-thumbs-down:before{content:"\e126"}.glyphicon-hand-right:before{content:"\e127"}.glyphicon-hand-left:before{content:"\e128"}.glyphicon-hand-up:before{content:"\e129"}.glyphicon-hand-down:before{content:"\e130"}.glyphicon-circle-arrow-right:before{content:"\e131"}.glyphicon-circle-arrow-left:before{content:"\e132"}.glyphicon-circle-arrow-up:before{content:"\e133"}.glyphicon-circle-arrow-down:before{content:"\e134"}.glyphicon-globe:before{content:"\e135"}.glyphicon-wrench:before{content:"\e136"}.glyphicon-tasks:before{content:"\e137"}.glyphicon-filter:before{content:"\e138"}.glyphicon-briefcase:before{content:"\e139"}.glyphicon-fullscreen:before{content:"\e140"}.glyphicon-dashboard:before{content:"\e141"}.glyphicon-paperclip:before{content:"\e142"}.glyphicon-heart-empty:before{content:"\e143"}.glyphicon-link:before{content:"\e144"}.glyphicon-phone:before{content:"\e145"}.glyphicon-pushpin:before{content:"\e146"}.glyphicon-usd:before{content:"\e148"}.glyphicon-gbp:before{content:"\e149"}.glyphicon-sort:before{content:"\e150"}.glyphicon-sort-by-alphabet:before{content:"\e151"}.glyphicon-sort-by-alphabet-alt:before{content:"\e152"}.glyphicon-sort-by-order:before{content:"\e153"}.glyphicon-sort-by-order-alt:before{content:"\e154"}.glyphicon-sort-by-attributes:before{content:"\e155"}.glyphicon-sort-by-attributes-alt:before{content:"\e156"}.glyphicon-unchecked:before{content:"\e157"}.glyphicon-expand:before{content:"\e158"}.glyphicon-collapse-down:before{content:"\e159"}.glyphicon-collapse-up:before{content:"\e160"}.glyphicon-log-in:before{content:"\e161"}.glyphicon-flash:before{content:"\e162"}.glyphicon-log-out:before{content:"\e163"}.glyphicon-new-window:before{content:"\e164"}.glyphicon-record:before{content:"\e165"}.glyphicon-save:before{content:"\e166"}.glyphicon-open:before{content:"\e167"}.glyphicon-saved:before{content:"\e168"}.glyphicon-import:before{content:"\e169"}.glyphicon-export:before{content:"\e170"}.glyphicon-send:before{content:"\e171"}.glyphicon-floppy-disk:before{content:"\e172"}.glyphicon-floppy-saved:before{content:"\e173"}.glyphicon-floppy-remove:before{content:"\e174"}.glyphicon-floppy-save:before{content:"\e175"}.glyphicon-floppy-open:before{content:"\e176"}.glyphicon-credit-card:before{content:"\e177"}.glyphicon-transfer:before{content:"\e178"}.glyphicon-cutlery:before{content:"\e179"}.glyphicon-header:before{content:"\e180"}.glyphicon-compressed:before{content:"\e181"}.glyphicon-earphone:before{content:"\e182"}.glyphicon-phone-alt:before{content:"\e183"}.glyphicon-tower:before{content:"\e184"}.glyphicon-stats:before{content:"\e185"}.glyphicon-sd-video:before{content:"\e186"}.glyphicon-hd-video:before{content:"\e187"}.glyphicon-subtitles:before{content:"\e188"}.glyphicon-sound-stereo:before{content:"\e189"}.glyphicon-sound-dolby:before{content:"\e190"}.glyphicon-sound-5-1:before{content:"\e191"}.glyphicon-sound-6-1:before{content:"\e192"}.glyphicon-sound-7-1:before{content:"\e193"}.glyphicon-copyright-mark:before{content:"\e194"}.glyphicon-registration-mark:before{content:"\e195"}.glyphicon-cloud-download:before{content:"\e197"}.glyphicon-cloud-upload:before{content:"\e198"}.glyphicon-tree-conifer:before{content:"\e199"}.glyphicon-tree-deciduous:before{content:"\e200"}.glyphicon-cd:before{content:"\e201"}.glyphicon-save-file:before{content:"\e202"}.glyphicon-open-file:before{content:"\e203"}.glyphicon-level-up:before{content:"\e204"}.glyphicon-copy:before{content:"\e205"}.glyphicon-paste:before{content:"\e206"}.glyphicon-alert:before{content:"\e209"}.glyphicon-equalizer:before{content:"\e210"}.glyphicon-king:before{content:"\e211"}.glyphicon-queen:before{content:"\e212"}.glyphicon-pawn:before{content:"\e213"}.glyphicon-bishop:before{content:"\e214"}.glyphicon-knight:before{content:"\e215"}.glyphicon-baby-formula:before{content:"\e216"}.glyphicon-tent:before{content:"\26fa"}.glyphicon-blackboard:before{content:"\e218"}.glyphicon-bed:before{content:"\e219"}.glyphicon-apple:before{content:"\f8ff"}.glyphicon-erase:before{content:"\e221"}.glyphicon-hourglass:before{content:"\231b"}.glyphicon-lamp:before{content:"\e223"}.glyphicon-duplicate:before{content:"\e224"}.glyphicon-piggy-bank:before{content:"\e225"}.glyphicon-scissors:before{content:"\e226"}.glyphicon-bitcoin:before{content:"\e227"}.glyphicon-btc:before{content:"\e227"}.glyphicon-xbt:before{content:"\e227"}.glyphicon-yen:before{content:"\00a5"}.glyphicon-jpy:before{content:"\00a5"}.glyphicon-ruble:before{content:"\20bd"}.glyphicon-rub:before{content:"\20bd"}.glyphicon-scale:before{content:"\e230"}.glyphicon-ice-lolly:before{content:"\e231"}.glyphicon-ice-lolly-tasted:before{content:"\e232"}.glyphicon-education:before{content:"\e233"}.glyphicon-option-horizontal:before{content:"\e234"}.glyphicon-option-vertical:before{content:"\e235"}.glyphicon-menu-hamburger:before{content:"\e236"}.glyphicon-modal-window:before{content:"\e237"}.glyphicon-oil:before{content:"\e238"}.glyphicon-grain:before{content:"\e239"}.glyphicon-sunglasses:before{content:"\e240"}.glyphicon-text-size:before{content:"\e241"}.glyphicon-text-color:before{content:"\e242"}.glyphicon-text-background:before{content:"\e243"}.glyphicon-object-align-top:before{content:"\e244"}.glyphicon-object-align-bottom:before{content:"\e245"}.glyphicon-object-align-horizontal:before{content:"\e246"}.glyphicon-object-align-left:before{content:"\e247"}.glyphicon-object-align-vertical:before{content:"\e248"}.glyphicon-object-align-right:before{content:"\e249"}.glyphicon-triangle-right:before{content:"\e250"}.glyphicon-triangle-left:before{content:"\e251"}.glyphicon-triangle-bottom:before{content:"\e252"}.glyphicon-triangle-top:before{content:"\e253"}.glyphicon-console:before{content:"\e254"}.glyphicon-superscript:before{content:"\e255"}.glyphicon-subscript:before{content:"\e256"}.glyphicon-menu-left:before{content:"\e257"}.glyphicon-menu-right:before{content:"\e258"}.glyphicon-menu-down:before{content:"\e259"}.glyphicon-menu-up:before{content:"\e260"}*{-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}:after,:before{-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}html{font-size:10px;-webkit-tap-highlight-color:rgba(0,0,0,0)}body{font-family:"Helvetica Neue",Helvetica,Arial,sans-serif;font-size:14px;line-height:1.42857143;color:#333;background-color:#fff}button,input,select,textarea{font-family:inherit;font-size:inherit;line-height:inherit}a{color:#337ab7;text-decoration:none}a:focus,a:hover{color:#23527c;text-decoration:underline}a:focus{outline:thin dotted;outline:5px auto -webkit-focus-ring-color;outline-offset:-2px}figure{margin:0}img{vertical-align:middle}.carousel-inner>.item>a>img,.carousel-inner>.item>img,.img-responsive,.thumbnail a>img,.thumbnail>img{display:block;max-width:100%;height:auto}.img-rounded{border-radius:6px}.img-thumbnail{display:inline-block;max-width:100%;height:auto;padding:4px;line-height:1.42857143;background-color:#fff;border:1px solid #ddd;border-radius:4px;-webkit-transition:all .2s ease-in-out;-o-transition:all .2s ease-in-out;transition:all .2s ease-in-out}.img-circle{border-radius:50%}hr{margin-top:20px;margin-bottom:20px;border:0;border-top:1px solid #eee}.sr-only{position:absolute;width:1px;height:1px;padding:0;margin:-1px;overflow:hidden;clip:rect(0,0,0,0);border:0}.sr-only-focusable:active,.sr-only-focusable:focus{position:static;width:auto;height:auto;margin:0;overflow:visible;clip:auto}[role=button]{cursor:pointer}.h1,.h2,.h3,.h4,.h5,.h6,h1,h2,h3,h4,h5,h6{font-family:inherit;font-weight:500;line-height:1.1;color:inherit}.h1 .small,.h1 small,.h2 .small,.h2 small,.h3 .small,.h3 small,.h4 .small,.h4 small,.h5 .small,.h5 small,.h6 .small,.h6 small,h1 .small,h1 small,h2 .small,h2 small,h3 .small,h3 small,h4 .small,h4 small,h5 .small,h5 small,h6 .small,h6 small{font-weight:400;line-height:1;color:#777}.h1,.h2,.h3,h1,h2,h3{margin-top:20px;margin-bottom:10px}.h1 .small,.h1 small,.h2 .small,.h2 small,.h3 .small,.h3 small,h1 .small,h1 small,h2 .small,h2 small,h3 .small,h3 small{font-size:65%}.h4,.h5,.h6,h4,h5,h6{margin-top:10px;margin-bottom:10px}.h4 .small,.h4 small,.h5 .small,.h5 small,.h6 .small,.h6 small,h4 .small,h4 small,h5 .small,h5 small,h6 .small,h6 small{font-size:75%}.h1,h1{font-size:36px}.h2,h2{font-size:30px}.h3,h3{font-size:24px}.h4,h4{font-size:18px}.h5,h5{font-size:14px}.h6,h6{font-size:12px}p{margin:0 0 10px}.lead{margin-bottom:20px;font-size:16px;font-weight:300;line-height:1.4}@media (min-width:768px){.lead{font-size:21px}}.small,small{font-size:85%}.mark,mark{padding:.2em;background-color:#fcf8e3}.text-left{text-align:left}.text-right{text-align:right}.text-center{text-align:center}.text-justify{text-align:justify}.text-nowrap{white-space:nowrap}.text-lowercase{text-transform:lowercase}.text-uppercase{text-transform:uppercase}.text-capitalize{text-transform:capitalize}.text-muted{color:#777}.text-primary{color:#337ab7}a.text-primary:focus,a.text-primary:hover{color:#286090}.text-success{color:#3c763d}a.text-success:focus,a.text-success:hover{color:#2b542c}.text-info{color:#31708f}a.text-info:focus,a.text-info:hover{color:#245269}.text-warning{color:#8a6d3b}a.text-warning:focus,a.text-warning:hover{color:#66512c}.text-danger{color:#a94442}a.text-danger:focus,a.text-danger:hover{color:#843534}.bg-primary{color:#fff;background-color:#337ab7}a.bg-primary:focus,a.bg-primary:hover{background-color:#286090}.bg-success{background-color:#dff0d8}a.bg-success:focus,a.bg-success:hover{background-color:#c1e2b3}.bg-info{background-color:#d9edf7}a.bg-info:focus,a.bg-info:hover{background-color:#afd9ee}.bg-warning{background-color:#fcf8e3}a.bg-warning:focus,a.bg-warning:hover{background-color:#f7ecb5}.bg-danger{background-color:#f2dede}a.bg-danger:focus,a.bg-danger:hover{background-color:#e4b9b9}.page-header{padding-bottom:9px;margin:40px 0 20px;border-bottom:1px solid #eee}ol,ul{margin-top:0;margin-bottom:10px}ol ol,ol ul,ul ol,ul ul{margin-bottom:0}.list-unstyled{padding-left:0;list-style:none}.list-inline{padding-left:0;margin-left:-5px;list-style:none}.list-inline>li{display:inline-block;padding-right:5px;padding-left:5px}dl{margin-top:0;margin-bottom:20px}dd,dt{line-height:1.42857143}dt{font-weight:700}dd{margin-left:0}@media (min-width:768px){.dl-horizontal dt{float:left;width:160px;overflow:hidden;clear:left;text-align:right;text-overflow:ellipsis;white-space:nowrap}.dl-horizontal dd{margin-left:180px}}abbr[data-original-title],abbr[title]{cursor:help;border-bottom:1px dotted #777}.initialism{font-size:90%;text-transform:uppercase}blockquote{padding:10px 20px;margin:0 0 20px;font-size:17.5px;border-left:5px solid #eee}blockquote ol:last-child,blockquote p:last-child,blockquote ul:last-child{margin-bottom:0}blockquote .small,blockquote footer,blockquote small{display:block;font-size:80%;line-height:1.42857143;color:#777}blockquote .small:before,blockquote footer:before,blockquote small:before{content:'\2014 \00A0'}.blockquote-reverse,blockquote.pull-right{padding-right:15px;padding-left:0;text-align:right;border-right:5px solid #eee;border-left:0}.blockquote-reverse .small:before,.blockquote-reverse footer:before,.blockquote-reverse small:before,blockquote.pull-right .small:before,blockquote.pull-right footer:before,blockquote.pull-right small:before{content:''}.blockquote-reverse .small:after,.blockquote-reverse footer:after,.blockquote-reverse small:after,blockquote.pull-right .small:after,blockquote.pull-right footer:after,blockquote.pull-right small:after{content:'\00A0 \2014'}address{margin-bottom:20px;font-style:normal;line-height:1.42857143}code,kbd,pre,samp{font-family:monospace}code{padding:2px 4px;font-size:90%;color:#c7254e;background-color:#f9f2f4;border-radius:4px}kbd{padding:2px 4px;font-size:90%;color:#fff;background-color:#333;border-radius:3px;-webkit-box-shadow:inset 0 -1px 0 rgba(0,0,0,.25);box-shadow:inset 0 -1px 0 rgba(0,0,0,.25)}kbd kbd{padding:0;font-size:100%;font-weight:700;-webkit-box-shadow:none;box-shadow:none}pre{display:block;padding:9.5px;margin:0 0 10px;font-size:13px;line-height:1.42857143;color:#333;word-break:break-all;word-wrap:break-word;background-color:#f5f5f5;border:1px solid #ccc;border-radius:4px}pre code{padding:0;font-size:inherit;color:inherit;white-space:pre-wrap;background-color:transparent;border-radius:0}.pre-scrollable{max-height:340px;overflow-y:scroll}.container{padding-right:15px;padding-left:15px;margin-right:auto;margin-left:auto}@media (min-width:768px){.container{width:750px}}@media (min-width:992px){.container{width:970px}}@media (min-width:1200px){.container{width:1170px}}.container-fluid{padding-right:15px;padding-left:15px;margin-right:auto;margin-left:auto}.row{margin-right:-15px;margin-left:-15px}.col-lg-1,.col-lg-10,.col-lg-11,.col-lg-12,.col-lg-2,.col-lg-3,.col-lg-4,.col-lg-5,.col-lg-6,.col-lg-7,.col-lg-8,.col-lg-9,.col-md-1,.col-md-10,.col-md-11,.col-md-12,.col-md-2,.col-md-3,.col-md-4,.col-md-5,.col-md-6,.col-md-7,.col-md-8,.col-md-9,.col-sm-1,.col-sm-10,.col-sm-11,.col-sm-12,.col-sm-2,.col-sm-3,.col-sm-4,.col-sm-5,.col-sm-6,.col-sm-7,.col-sm-8,.col-sm-9,.col-xs-1,.col-xs-10,.col-xs-11,.col-xs-12,.col-xs-2,.col-xs-3,.col-xs-4,.col-xs-5,.col-xs-6,.col-xs-7,.col-xs-8,.col-xs-9{position:relative;min-height:1px;padding-right:15px;padding-left:15px}.col-xs-1,.col-xs-10,.col-xs-11,.col-xs-12,.col-xs-2,.col-xs-3,.col-xs-4,.col-xs-5,.col-xs-6,.col-xs-7,.col-xs-8,.col-xs-9{float:left}.col-xs-12{width:100%}.col-xs-11{width:91.66666667%}.col-xs-10{width:83.33333333%}.col-xs-9{width:75%}.col-xs-8{width:66.66666667%}.col-xs-7{width:58.33333333%}.col-xs-6{width:50%}.col-xs-5{width:41.66666667%}.col-xs-4{width:33.33333333%}.col-xs-3{width:25%}.col-xs-2{width:16.66666667%}.col-xs-1{width:8.33333333%}.col-xs-pull-12{right:100%}.col-xs-pull-11{right:91.66666667%}.col-xs-pull-10{right:83.33333333%}.col-xs-pull-9{right:75%}.col-xs-pull-8{right:66.66666667%}.col-xs-pull-7{right:58.33333333%}.col-xs-pull-6{right:50%}.col-xs-pull-5{right:41.66666667%}.col-xs-pull-4{right:33.33333333%}.col-xs-pull-3{right:25%}.col-xs-pull-2{right:16.66666667%}.col-xs-pull-1{right:8.33333333%}.col-xs-pull-0{right:auto}.col-xs-push-12{left:100%}.col-xs-push-11{left:91.66666667%}.col-xs-push-10{left:83.33333333%}.col-xs-push-9{left:75%}.col-xs-push-8{left:66.66666667%}.col-xs-push-7{left:58.33333333%}.col-xs-push-6{left:50%}.col-xs-push-5{left:41.66666667%}.col-xs-push-4{left:33.33333333%}.col-xs-push-3{left:25%}.col-xs-push-2{left:16.66666667%}.col-xs-push-1{left:8.33333333%}.col-xs-push-0{left:auto}.col-xs-offset-12{margin-left:100%}.col-xs-offset-11{margin-left:91.66666667%}.col-xs-offset-10{margin-left:83.33333333%}.col-xs-offset-9{margin-left:75%}.col-xs-offset-8{margin-left:66.66666667%}.col-xs-offset-7{margin-left:58.33333333%}.col-xs-offset-6{margin-left:50%}.col-xs-offset-5{margin-left:41.66666667%}.col-xs-offset-4{margin-left:33.33333333%}.col-xs-offset-3{margin-left:25%}.col-xs-offset-2{margin-left:16.66666667%}.col-xs-offset-1{margin-left:8.33333333%}.col-xs-offset-0{margin-left:0}@media (min-width:768px){.col-sm-1,.col-sm-10,.col-sm-11,.col-sm-12,.col-sm-2,.col-sm-3,.col-sm-4,.col-sm-5,.col-sm-6,.col-sm-7,.col-sm-8,.col-sm-9{float:left}.col-sm-12{width:100%}.col-sm-11{width:91.66666667%}.col-sm-10{width:83.33333333%}.col-sm-9{width:75%}.col-sm-8{width:66.66666667%}.col-sm-7{width:58.33333333%}.col-sm-6{width:50%}.col-sm-5{width:41.66666667%}.col-sm-4{width:33.33333333%}.col-sm-3{width:25%}.col-sm-2{width:16.66666667%}.col-sm-1{width:8.33333333%}.col-sm-pull-12{right:100%}.col-sm-pull-11{right:91.66666667%}.col-sm-pull-10{right:83.33333333%}.col-sm-pull-9{right:75%}.col-sm-pull-8{right:66.66666667%}.col-sm-pull-7{right:58.33333333%}.col-sm-pull-6{right:50%}.col-sm-pull-5{right:41.66666667%}.col-sm-pull-4{right:33.33333333%}.col-sm-pull-3{right:25%}.col-sm-pull-2{right:16.66666667%}.col-sm-pull-1{right:8.33333333%}.col-sm-pull-0{right:auto}.col-sm-push-12{left:100%}.col-sm-push-11{left:91.66666667%}.col-sm-push-10{left:83.33333333%}.col-sm-push-9{left:75%}.col-sm-push-8{left:66.66666667%}.col-sm-push-7{left:58.33333333%}.col-sm-push-6{left:50%}.col-sm-push-5{left:41.66666667%}.col-sm-push-4{left:33.33333333%}.col-sm-push-3{left:25%}.col-sm-push-2{left:16.66666667%}.col-sm-push-1{left:8.33333333%}.col-sm-push-0{left:auto}.col-sm-offset-12{margin-left:100%}.col-sm-offset-11{margin-left:91.66666667%}.col-sm-offset-10{margin-left:83.33333333%}.col-sm-offset-9{margin-left:75%}.col-sm-offset-8{margin-left:66.66666667%}.col-sm-offset-7{margin-left:58.33333333%}.col-sm-offset-6{margin-left:50%}.col-sm-offset-5{margin-left:41.66666667%}.col-sm-offset-4{margin-left:33.33333333%}.col-sm-offset-3{margin-left:25%}.col-sm-offset-2{margin-left:16.66666667%}.col-sm-offset-1{margin-left:8.33333333%}.col-sm-offset-0{margin-left:0}}@media (min-width:992px){.col-md-1,.col-md-10,.col-md-11,.col-md-12,.col-md-2,.col-md-3,.col-md-4,.col-md-5,.col-md-6,.col-md-7,.col-md-8,.col-md-9{float:left}.col-md-12{width:100%}.col-md-11{width:91.66666667%}.col-md-10{width:83.33333333%}.col-md-9{width:75%}.col-md-8{width:66.66666667%}.col-md-7{width:58.33333333%}.col-md-6{width:50%}.col-md-5{width:41.66666667%}.col-md-4{width:33.33333333%}.col-md-3{width:25%}.col-md-2{width:16.66666667%}.col-md-1{width:8.33333333%}.col-md-pull-12{right:100%}.col-md-pull-11{right:91.66666667%}.col-md-pull-10{right:83.33333333%}.col-md-pull-9{right:75%}.col-md-pull-8{right:66.66666667%}.col-md-pull-7{right:58.33333333%}.col-md-pull-6{right:50%}.col-md-pull-5{right:41.66666667%}.col-md-pull-4{right:33.33333333%}.col-md-pull-3{right:25%}.col-md-pull-2{right:16.66666667%}.col-md-pull-1{right:8.33333333%}.col-md-pull-0{right:auto}.col-md-push-12{left:100%}.col-md-push-11{left:91.66666667%}.col-md-push-10{left:83.33333333%}.col-md-push-9{left:75%}.col-md-push-8{left:66.66666667%}.col-md-push-7{left:58.33333333%}.col-md-push-6{left:50%}.col-md-push-5{left:41.66666667%}.col-md-push-4{left:33.33333333%}.col-md-push-3{left:25%}.col-md-push-2{left:16.66666667%}.col-md-push-1{left:8.33333333%}.col-md-push-0{left:auto}.col-md-offset-12{margin-left:100%}.col-md-offset-11{margin-left:91.66666667%}.col-md-offset-10{margin-left:83.33333333%}.col-md-offset-9{margin-left:75%}.col-md-offset-8{margin-left:66.66666667%}.col-md-offset-7{margin-left:58.33333333%}.col-md-offset-6{margin-left:50%}.col-md-offset-5{margin-left:41.66666667%}.col-md-offset-4{margin-left:33.33333333%}.col-md-offset-3{margin-left:25%}.col-md-offset-2{margin-left:16.66666667%}.col-md-offset-1{margin-left:8.33333333%}.col-md-offset-0{margin-left:0}}@media (min-width:1200px){.col-lg-1,.col-lg-10,.col-lg-11,.col-lg-12,.col-lg-2,.col-lg-3,.col-lg-4,.col-lg-5,.col-lg-6,.col-lg-7,.col-lg-8,.col-lg-9{float:left}.col-lg-12{width:100%}.col-lg-11{width:91.66666667%}.col-lg-10{width:83.33333333%}.col-lg-9{width:75%}.col-lg-8{width:66.66666667%}.col-lg-7{width:58.33333333%}.col-lg-6{width:50%}.col-lg-5{width:41.66666667%}.col-lg-4{width:33.33333333%}.col-lg-3{width:25%}.col-lg-2{width:16.66666667%}.col-lg-1{width:8.33333333%}.col-lg-pull-12{right:100%}.col-lg-pull-11{right:91.66666667%}.col-lg-pull-10{right:83.33333333%}.col-lg-pull-9{right:75%}.col-lg-pull-8{right:66.66666667%}.col-lg-pull-7{right:58.33333333%}.col-lg-pull-6{right:50%}.col-lg-pull-5{right:41.66666667%}.col-lg-pull-4{right:33.33333333%}.col-lg-pull-3{right:25%}.col-lg-pull-2{right:16.66666667%}.col-lg-pull-1{right:8.33333333%}.col-lg-pull-0{right:auto}.col-lg-push-12{left:100%}.col-lg-push-11{left:91.66666667%}.col-lg-push-10{left:83.33333333%}.col-lg-push-9{left:75%}.col-lg-push-8{left:66.66666667%}.col-lg-push-7{left:58.33333333%}.col-lg-push-6{left:50%}.col-lg-push-5{left:41.66666667%}.col-lg-push-4{left:33.33333333%}.col-lg-push-3{left:25%}.col-lg-push-2{left:16.66666667%}.col-lg-push-1{left:8.33333333%}.col-lg-push-0{left:auto}.col-lg-offset-12{margin-left:100%}.col-lg-offset-11{margin-left:91.66666667%}.col-lg-offset-10{margin-left:83.33333333%}.col-lg-offset-9{margin-left:75%}.col-lg-offset-8{margin-left:66.66666667%}.col-lg-offset-7{margin-left:58.33333333%}.col-lg-offset-6{margin-left:50%}.col-lg-offset-5{margin-left:41.66666667%}.col-lg-offset-4{margin-left:33.33333333%}.col-lg-offset-3{margin-left:25%}.col-lg-offset-2{margin-left:16.66666667%}.col-lg-offset-1{margin-left:8.33333333%}.col-lg-offset-0{margin-left:0}}table{background-color:transparent}caption{padding-top:8px;padding-bottom:8px;color:#777;text-align:left}th{}.table{width:100%;max-width:100%;margin-bottom:20px}.table>tbody>tr>td,.table>tbody>tr>th,.table>tfoot>tr>td,.table>tfoot>tr>th,.table>thead>tr>td,.table>thead>tr>th{padding:8px;line-height:1.42857143;vertical-align:top;border-top:1px solid #ddd}.table>thead>tr>th{vertical-align:bottom;border-bottom:2px solid #ddd}.table>caption+thead>tr:first-child>td,.table>caption+thead>tr:first-child>th,.table>colgroup+thead>tr:first-child>td,.table>colgroup+thead>tr:first-child>th,.table>thead:first-child>tr:first-child>td,.table>thead:first-child>tr:first-child>th{border-top:0}.table>tbody+tbody{border-top:2px solid #ddd}.table .table{background-color:#fff}.table-condensed>tbody>tr>td,.table-condensed>tbody>tr>th,.table-condensed>tfoot>tr>td,.table-condensed>tfoot>tr>th,.table-condensed>thead>tr>td,.table-condensed>thead>tr>th{padding:5px}.table-bordered{border:1px solid #ddd}.table-bordered>tbody>tr>td,.table-bordered>tbody>tr>th,.table-bordered>tfoot>tr>td,.table-bordered>tfoot>tr>th,.table-bordered>thead>tr>td,.table-bordered>thead>tr>th{border:1px solid #ddd}.table-bordered>thead>tr>td,.table-bordered>thead>tr>th{border-bottom-width:2px}.table-striped>tbody>tr:nth-of-type(odd){background-color:#f9f9f9}.table-hover>tbody>tr:hover{background-color:#f5f5f5}table col[class*=col-]{position:static;display:table-column;float:none}table td[class*=col-],table th[class*=col-]{position:static;display:table-cell;float:none}.table>tbody>tr.active>td,.table>tbody>tr.active>th,.table>tbody>tr>td.active,.table>tbody>tr>th.active,.table>tfoot>tr.active>td,.table>tfoot>tr.active>th,.table>tfoot>tr>td.active,.table>tfoot>tr>th.active,.table>thead>tr.active>td,.table>thead>tr.active>th,.table>thead>tr>td.active,.table>thead>tr>th.active{background-color:#f5f5f5}.table-hover>tbody>tr.active:hover>td,.table-hover>tbody>tr.active:hover>th,.table-hover>tbody>tr:hover>.active,.table-hover>tbody>tr>td.active:hover,.table-hover>tbody>tr>th.active:hover{background-color:#e8e8e8}.table>tbody>tr.success>td,.table>tbody>tr.success>th,.table>tbody>tr>td.success,.table>tbody>tr>th.success,.table>tfoot>tr.success>td,.table>tfoot>tr.success>th,.table>tfoot>tr>td.success,.table>tfoot>tr>th.success,.table>thead>tr.success>td,.table>thead>tr.success>th,.table>thead>tr>td.success,.table>thead>tr>th.success{background-color:#dff0d8}.table-hover>tbody>tr.success:hover>td,.table-hover>tbody>tr.success:hover>th,.table-hover>tbody>tr:hover>.success,.table-hover>tbody>tr>td.success:hover,.table-hover>tbody>tr>th.success:hover{background-color:#d0e9c6}.table>tbody>tr.info>td,.table>tbody>tr.info>th,.table>tbody>tr>td.info,.table>tbody>tr>th.info,.table>tfoot>tr.info>td,.table>tfoot>tr.info>th,.table>tfoot>tr>td.info,.table>tfoot>tr>th.info,.table>thead>tr.info>td,.table>thead>tr.info>th,.table>thead>tr>td.info,.table>thead>tr>th.info{background-color:#d9edf7}.table-hover>tbody>tr.info:hover>td,.table-hover>tbody>tr.info:hover>th,.table-hover>tbody>tr:hover>.info,.table-hover>tbody>tr>td.info:hover,.table-hover>tbody>tr>th.info:hover{background-color:#c4e3f3}.table>tbody>tr.warning>td,.table>tbody>tr.warning>th,.table>tbody>tr>td.warning,.table>tbody>tr>th.warning,.table>tfoot>tr.warning>td,.table>tfoot>tr.warning>th,.table>tfoot>tr>td.warning,.table>tfoot>tr>th.warning,.table>thead>tr.warning>td,.table>thead>tr.warning>th,.table>thead>tr>td.warning,.table>thead>tr>th.warning{background-color:#fcf8e3}.table-hover>tbody>tr.warning:hover>td,.table-hover>tbody>tr.warning:hover>th,.table-hover>tbody>tr:hover>.warning,.table-hover>tbody>tr>td.warning:hover,.table-hover>tbody>tr>th.warning:hover{background-color:#faf2cc}.table>tbody>tr.danger>td,.table>tbody>tr.danger>th,.table>tbody>tr>td.danger,.table>tbody>tr>th.danger,.table>tfoot>tr.danger>td,.table>tfoot>tr.danger>th,.table>tfoot>tr>td.danger,.table>tfoot>tr>th.danger,.table>thead>tr.danger>td,.table>thead>tr.danger>th,.table>thead>tr>td.danger,.table>thead>tr>th.danger{background-color:#f2dede}.table-hover>tbody>tr.danger:hover>td,.table-hover>tbody>tr.danger:hover>th,.table-hover>tbody>tr:hover>.danger,.table-hover>tbody>tr>td.danger:hover,.table-hover>tbody>tr>th.danger:hover{background-color:#ebcccc}.table-responsive{min-height:.01%;overflow-x:auto}@media screen and (max-width:767px){.table-responsive{width:100%;margin-bottom:15px;overflow-y:hidden;-ms-overflow-style:-ms-autohiding-scrollbar;border:1px solid #ddd}.table-responsive>.table{margin-bottom:0}.table-responsive>.table>tbody>tr>td,.table-responsive>.table>tbody>tr>th,.table-responsive>.table>tfoot>tr>td,.table-responsive>.table>tfoot>tr>th,.table-responsive>.table>thead>tr>td,.table-responsive>.table>thead>tr>th{white-space:nowrap}.table-responsive>.table-bordered{border:0}.table-responsive>.table-bordered>tbody>tr>td:first-child,.table-responsive>.table-bordered>tbody>tr>th:first-child,.table-responsive>.table-bordered>tfoot>tr>td:first-child,.table-responsive>.table-bordered>tfoot>tr>th:first-child,.table-responsive>.table-bordered>thead>tr>td:first-child,.table-responsive>.table-bordered>thead>tr>th:first-child{border-left:0}.table-responsive>.table-bordered>tbody>tr>td:last-child,.table-responsive>.table-bordered>tbody>tr>th:last-child,.table-responsive>.table-bordered>tfoot>tr>td:last-child,.table-responsive>.table-bordered>tfoot>tr>th:last-child,.table-responsive>.table-bordered>thead>tr>td:last-child,.table-responsive>.table-bordered>thead>tr>th:last-child{border-right:0}.table-responsive>.table-bordered>tbody>tr:last-child>td,.table-responsive>.table-bordered>tbody>tr:last-child>th,.table-responsive>.table-bordered>tfoot>tr:last-child>td,.table-responsive>.table-bordered>tfoot>tr:last-child>th{border-bottom:0}}fieldset{min-width:0;padding:0;margin:0;border:0}legend{display:block;width:100%;padding:0;margin-bottom:20px;font-size:21px;line-height:inherit;color:#333;border:0;border-bottom:1px solid #e5e5e5}label{display:inline-block;max-width:100%;margin-bottom:5px;font-weight:700}input[type=search]{-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}input[type=checkbox],input[type=radio]{margin:4px 0 0;margin-top:1px\9;line-height:normal}input[type=file]{display:block}input[type=range]{display:block;width:100%}select[multiple],select[size]{height:auto}input[type=file]:focus,input[type=checkbox]:focus,input[type=radio]:focus{outline:thin dotted;outline:5px auto -webkit-focus-ring-color;outline-offset:-2px}output{display:block;padding-top:7px;font-size:14px;line-height:1.42857143;color:#555}.form-control{display:block;width:100%;height:34px;padding:6px 12px;font-size:14px;line-height:1.42857143;color:#555;background-color:#fff;background-image:none;border:1px solid #ccc;border-radius:4px;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075);box-shadow:inset 0 1px 1px rgba(0,0,0,.075);-webkit-transition:border-color ease-in-out .15s,-webkit-box-shadow ease-in-out .15s;-o-transition:border-color ease-in-out .15s,box-shadow ease-in-out .15s;transition:border-color ease-in-out .15s,box-shadow ease-in-out .15s}.form-control:focus{border-color:#66afe9;outline:0;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 8px rgba(102,175,233,.6);box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 8px rgba(102,175,233,.6)}.form-control::-moz-placeholder{color:#999;opacity:1}.form-control:-ms-input-placeholder{color:#999}.form-control::-webkit-input-placeholder{color:#999}.form-control[disabled],.form-control[readonly],fieldset[disabled] .form-control{background-color:#eee;opacity:1}.form-control[disabled],fieldset[disabled] .form-control{cursor:not-allowed}textarea.form-control{height:auto}input[type=search]{-webkit-appearance:none}@media screen and (-webkit-min-device-pixel-ratio:0){input[type=date].form-control,input[type=time].form-control,input[type=datetime-local].form-control,input[type=month].form-control{line-height:34px}.input-group-sm input[type=date],.input-group-sm input[type=time],.input-group-sm input[type=datetime-local],.input-group-sm input[type=month],input[type=date].input-sm,input[type=time].input-sm,input[type=datetime-local].input-sm,input[type=month].input-sm{line-height:30px}.input-group-lg input[type=date],.input-group-lg input[type=time],.input-group-lg input[type=datetime-local],.input-group-lg input[type=month],input[type=date].input-lg,input[type=time].input-lg,input[type=datetime-local].input-lg,input[type=month].input-lg{line-height:46px}}.form-group{margin-bottom:15px}.checkbox,.radio{position:relative;display:block;margin-top:10px;margin-bottom:10px}.checkbox label,.radio label{min-height:20px;padding-left:20px;margin-bottom:0;font-weight:400;cursor:pointer}.checkbox input[type=checkbox],.checkbox-inline input[type=checkbox],.radio input[type=radio],.radio-inline input[type=radio]{position:absolute;margin-top:4px\9;margin-left:-20px}.checkbox+.checkbox,.radio+.radio{margin-top:-5px}.checkbox-inline,.radio-inline{position:relative;display:inline-block;padding-left:20px;margin-bottom:0;font-weight:400;vertical-align:middle;cursor:pointer}.checkbox-inline+.checkbox-inline,.radio-inline+.radio-inline{margin-top:0;margin-left:10px}fieldset[disabled] input[type=checkbox],fieldset[disabled] input[type=radio],input[type=checkbox].disabled,input[type=checkbox][disabled],input[type=radio].disabled,input[type=radio][disabled]{cursor:not-allowed}.checkbox-inline.disabled,.radio-inline.disabled,fieldset[disabled] .checkbox-inline,fieldset[disabled] .radio-inline{cursor:not-allowed}.checkbox.disabled label,.radio.disabled label,fieldset[disabled] .checkbox label,fieldset[disabled] .radio label{cursor:not-allowed}.form-control-static{min-height:34px;padding-top:7px;padding-bottom:7px;margin-bottom:0}.form-control-static.input-lg,.form-control-static.input-sm{padding-right:0;padding-left:0}.input-sm{height:30px;padding:5px 10px;font-size:12px;line-height:1.5;border-radius:3px}select.input-sm{height:30px;line-height:30px}select[multiple].input-sm,textarea.input-sm{height:auto}.form-group-sm .form-control{height:30px;padding:5px 10px;font-size:12px;line-height:1.5;border-radius:3px}.form-group-sm select.form-control{height:30px;line-height:30px}.form-group-sm select[multiple].form-control,.form-group-sm textarea.form-control{height:auto}.form-group-sm .form-control-static{height:30px;min-height:32px;padding:6px 10px;font-size:12px;line-height:1.5}.input-lg{height:46px;padding:10px 16px;font-size:18px;line-height:1.3333333;border-radius:6px}select.input-lg{height:46px;line-height:46px}select[multiple].input-lg,textarea.input-lg{height:auto}.form-group-lg .form-control{height:46px;padding:10px 16px;font-size:18px;line-height:1.3333333;border-radius:6px}.form-group-lg select.form-control{height:46px;line-height:46px}.form-group-lg select[multiple].form-control,.form-group-lg textarea.form-control{height:auto}.form-group-lg .form-control-static{height:46px;min-height:38px;padding:11px 16px;font-size:18px;line-height:1.3333333}.has-feedback{position:relative}.has-feedback .form-control{padding-right:42.5px}.form-control-feedback{position:absolute;top:0;right:0;z-index:2;display:block;width:34px;height:34px;line-height:34px;text-align:center;pointer-events:none}.form-group-lg .form-control+.form-control-feedback,.input-group-lg+.form-control-feedback,.input-lg+.form-control-feedback{width:46px;height:46px;line-height:46px}.form-group-sm .form-control+.form-control-feedback,.input-group-sm+.form-control-feedback,.input-sm+.form-control-feedback{width:30px;height:30px;line-height:30px}.has-success .checkbox,.has-success .checkbox-inline,.has-success .control-label,.has-success .help-block,.has-success .radio,.has-success .radio-inline,.has-success.checkbox label,.has-success.checkbox-inline label,.has-success.radio label,.has-success.radio-inline label{color:#3c763d}.has-success .form-control{border-color:#3c763d;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075);box-shadow:inset 0 1px 1px rgba(0,0,0,.075)}.has-success .form-control:focus{border-color:#2b542c;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 6px #67b168;box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 6px #67b168}.has-success .input-group-addon{color:#3c763d;background-color:#dff0d8;border-color:#3c763d}.has-success .form-control-feedback{color:#3c763d}.has-warning .checkbox,.has-warning .checkbox-inline,.has-warning .control-label,.has-warning .help-block,.has-warning .radio,.has-warning .radio-inline,.has-warning.checkbox label,.has-warning.checkbox-inline label,.has-warning.radio label,.has-warning.radio-inline label{color:#8a6d3b}.has-warning .form-control{border-color:#8a6d3b;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075);box-shadow:inset 0 1px 1px rgba(0,0,0,.075)}.has-warning .form-control:focus{border-color:#66512c;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 6px #c0a16b;box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 6px #c0a16b}.has-warning .input-group-addon{color:#8a6d3b;background-color:#fcf8e3;border-color:#8a6d3b}.has-warning .form-control-feedback{color:#8a6d3b}.has-error .checkbox,.has-error .checkbox-inline,.has-error .control-label,.has-error .help-block,.has-error .radio,.has-error .radio-inline,.has-error.checkbox label,.has-error.checkbox-inline label,.has-error.radio label,.has-error.radio-inline label{color:#a94442}.has-error .form-control{border-color:#a94442;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075);box-shadow:inset 0 1px 1px rgba(0,0,0,.075)}.has-error .form-control:focus{border-color:#843534;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 6px #ce8483;box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 6px #ce8483}.has-error .input-group-addon{color:#a94442;background-color:#f2dede;border-color:#a94442}.has-error .form-control-feedback{color:#a94442}.has-feedback label~.form-control-feedback{top:25px}.has-feedback label.sr-only~.form-control-feedback{top:0}.help-block{display:block;margin-top:5px;margin-bottom:10px;color:#737373}@media (min-width:768px){.form-inline .form-group{display:inline-block;margin-bottom:0;vertical-align:middle}.form-inline .form-control{display:inline-block;width:auto;vertical-align:middle}.form-inline .form-control-static{display:inline-block}.form-inline .input-group{display:inline-table;vertical-align:middle}.form-inline .input-group .form-control,.form-inline .input-group .input-group-addon,.form-inline .input-group .input-group-btn{width:auto}.form-inline .input-group>.form-control{width:100%}.form-inline .control-label{margin-bottom:0;vertical-align:middle}.form-inline .checkbox,.form-inline .radio{display:inline-block;margin-top:0;margin-bottom:0;vertical-align:middle}.form-inline .checkbox label,.form-inline .radio label{padding-left:0}.form-inline .checkbox input[type=checkbox],.form-inline .radio input[type=radio]{position:relative;margin-left:0}.form-inline .has-feedback .form-control-feedback{top:0}}.form-horizontal .checkbox,.form-horizontal .checkbox-inline,.form-horizontal .radio,.form-horizontal .radio-inline{padding-top:7px;margin-top:0;margin-bottom:0}.form-horizontal .checkbox,.form-horizontal .radio{min-height:27px}.form-horizontal .form-group{margin-right:-15px;margin-left:-15px}@media (min-width:768px){.form-horizontal .control-label{padding-top:7px;margin-bottom:0;text-align:right}}.form-horizontal .has-feedback .form-control-feedback{right:15px}@media (min-width:768px){.form-horizontal .form-group-lg .control-label{padding-top:14.33px;font-size:18px}}@media (min-width:768px){.form-horizontal .form-group-sm .control-label{padding-top:6px;font-size:12px}}.btn{display:inline-block;padding:6px 12px;margin-bottom:0;font-size:14px;font-weight:400;line-height:1.42857143;text-align:center;white-space:nowrap;vertical-align:middle;-ms-touch-action:manipulation;touch-action:manipulation;cursor:pointer;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;background-image:none;border:1px solid transparent;border-radius:4px}.btn.active.focus,.btn.active:focus,.btn.focus,.btn:active.focus,.btn:active:focus,.btn:focus{outline:thin dotted;outline:5px auto -webkit-focus-ring-color;outline-offset:-2px}.btn.focus,.btn:focus,.btn:hover{color:#333;text-decoration:none}.btn.active,.btn:active{background-image:none;outline:0;-webkit-box-shadow:inset 0 3px 5px rgba(0,0,0,.125);box-shadow:inset 0 3px 5px rgba(0,0,0,.125)}.btn.disabled,.btn[disabled],fieldset[disabled] .btn{cursor:not-allowed;filter:alpha(opacity=65);-webkit-box-shadow:none;box-shadow:none;opacity:.65}a.btn.disabled,fieldset[disabled] a.btn{pointer-events:none}.btn-default{color:#333;background-color:#fff;border-color:#ccc}.btn-default.focus,.btn-default:focus{color:#333;background-color:#e6e6e6;border-color:#8c8c8c}.btn-default:hover{color:#333;background-color:#e6e6e6;border-color:#adadad}.btn-default.active,.btn-default:active,.open>.dropdown-toggle.btn-default{color:#333;background-color:#e6e6e6;border-color:#adadad}.btn-default.active.focus,.btn-default.active:focus,.btn-default.active:hover,.btn-default:active.focus,.btn-default:active:focus,.btn-default:active:hover,.open>.dropdown-toggle.btn-default.focus,.open>.dropdown-toggle.btn-default:focus,.open>.dropdown-toggle.btn-default:hover{color:#333;background-color:#d4d4d4;border-color:#8c8c8c}.btn-default.active,.btn-default:active,.open>.dropdown-toggle.btn-default{background-image:none}.btn-default.disabled,.btn-default.disabled.active,.btn-default.disabled.focus,.btn-default.disabled:active,.btn-default.disabled:focus,.btn-default.disabled:hover,.btn-default[disabled],.btn-default[disabled].active,.btn-default[disabled].focus,.btn-default[disabled]:active,.btn-default[disabled]:focus,.btn-default[disabled]:hover,fieldset[disabled] .btn-default,fieldset[disabled] .btn-default.active,fieldset[disabled] .btn-default.focus,fieldset[disabled] .btn-default:active,fieldset[disabled] .btn-default:focus,fieldset[disabled] .btn-default:hover{background-color:#fff;border-color:#ccc}.btn-default .badge{color:#fff;background-color:#333}.btn-primary{color:#fff;background-color:#337ab7;border-color:#2e6da4}.btn-primary.focus,.btn-primary:focus{color:#fff;background-color:#286090;border-color:#122b40}.btn-primary:hover{color:#fff;background-color:#286090;border-color:#204d74}.btn-primary.active,.btn-primary:active,.open>.dropdown-toggle.btn-primary{color:#fff;background-color:#286090;border-color:#204d74}.btn-primary.active.focus,.btn-primary.active:focus,.btn-primary.active:hover,.btn-primary:active.focus,.btn-primary:active:focus,.btn-primary:active:hover,.open>.dropdown-toggle.btn-primary.focus,.open>.dropdown-toggle.btn-primary:focus,.open>.dropdown-toggle.btn-primary:hover{color:#fff;background-color:#204d74;border-color:#122b40}.btn-primary.active,.btn-primary:active,.open>.dropdown-toggle.btn-primary{background-image:none}.btn-primary.disabled,.btn-primary.disabled.active,.btn-primary.disabled.focus,.btn-primary.disabled:active,.btn-primary.disabled:focus,.btn-primary.disabled:hover,.btn-primary[disabled],.btn-primary[disabled].active,.btn-primary[disabled].focus,.btn-primary[disabled]:active,.btn-primary[disabled]:focus,.btn-primary[disabled]:hover,fieldset[disabled] .btn-primary,fieldset[disabled] .btn-primary.active,fieldset[disabled] .btn-primary.focus,fieldset[disabled] .btn-primary:active,fieldset[disabled] .btn-primary:focus,fieldset[disabled] .btn-primary:hover{background-color:#337ab7;border-color:#2e6da4}.btn-primary .badge{color:#337ab7;background-color:#fff}.btn-success{color:#fff;background-color:#5cb85c;border-color:#4cae4c}.btn-success.focus,.btn-success:focus{color:#fff;background-color:#449d44;border-color:#255625}.btn-success:hover{color:#fff;background-color:#449d44;border-color:#398439}.btn-success.active,.btn-success:active,.open>.dropdown-toggle.btn-success{color:#fff;background-color:#449d44;border-color:#398439}.btn-success.active.focus,.btn-success.active:focus,.btn-success.active:hover,.btn-success:active.focus,.btn-success:active:focus,.btn-success:active:hover,.open>.dropdown-toggle.btn-success.focus,.open>.dropdown-toggle.btn-success:focus,.open>.dropdown-toggle.btn-success:hover{color:#fff;background-color:#398439;border-color:#255625}.btn-success.active,.btn-success:active,.open>.dropdown-toggle.btn-success{background-image:none}.btn-success.disabled,.btn-success.disabled.active,.btn-success.disabled.focus,.btn-success.disabled:active,.btn-success.disabled:focus,.btn-success.disabled:hover,.btn-success[disabled],.btn-success[disabled].active,.btn-success[disabled].focus,.btn-success[disabled]:active,.btn-success[disabled]:focus,.btn-success[disabled]:hover,fieldset[disabled] .btn-success,fieldset[disabled] .btn-success.active,fieldset[disabled] .btn-success.focus,fieldset[disabled] .btn-success:active,fieldset[disabled] .btn-success:focus,fieldset[disabled] .btn-success:hover{background-color:#5cb85c;border-color:#4cae4c}.btn-success .badge{color:#5cb85c;background-color:#fff}.btn-info{color:#fff;background-color:#5bc0de;border-color:#46b8da}.btn-info.focus,.btn-info:focus{color:#fff;background-color:#31b0d5;border-color:#1b6d85}.btn-info:hover{color:#fff;background-color:#31b0d5;border-color:#269abc}.btn-info.active,.btn-info:active,.open>.dropdown-toggle.btn-info{color:#fff;background-color:#31b0d5;border-color:#269abc}.btn-info.active.focus,.btn-info.active:focus,.btn-info.active:hover,.btn-info:active.focus,.btn-info:active:focus,.btn-info:active:hover,.open>.dropdown-toggle.btn-info.focus,.open>.dropdown-toggle.btn-info:focus,.open>.dropdown-toggle.btn-info:hover{color:#fff;background-color:#269abc;border-color:#1b6d85}.btn-info.active,.btn-info:active,.open>.dropdown-toggle.btn-info{background-image:none}.btn-info.disabled,.btn-info.disabled.active,.btn-info.disabled.focus,.btn-info.disabled:active,.btn-info.disabled:focus,.btn-info.disabled:hover,.btn-info[disabled],.btn-info[disabled].active,.btn-info[disabled].focus,.btn-info[disabled]:active,.btn-info[disabled]:focus,.btn-info[disabled]:hover,fieldset[disabled] .btn-info,fieldset[disabled] .btn-info.active,fieldset[disabled] .btn-info.focus,fieldset[disabled] .btn-info:active,fieldset[disabled] .btn-info:focus,fieldset[disabled] .btn-info:hover{background-color:#5bc0de;border-color:#46b8da}.btn-info .badge{color:#5bc0de;background-color:#fff}.btn-warning{color:#fff;background-color:#f0ad4e;border-color:#eea236}.btn-warning.focus,.btn-warning:focus{color:#fff;background-color:#ec971f;border-color:#985f0d}.btn-warning:hover{color:#fff;background-color:#ec971f;border-color:#d58512}.btn-warning.active,.btn-warning:active,.open>.dropdown-toggle.btn-warning{color:#fff;background-color:#ec971f;border-color:#d58512}.btn-warning.active.focus,.btn-warning.active:focus,.btn-warning.active:hover,.btn-warning:active.focus,.btn-warning:active:focus,.btn-warning:active:hover,.open>.dropdown-toggle.btn-warning.focus,.open>.dropdown-toggle.btn-warning:focus,.open>.dropdown-toggle.btn-warning:hover{color:#fff;background-color:#d58512;border-color:#985f0d}.btn-warning.active,.btn-warning:active,.open>.dropdown-toggle.btn-warning{background-image:none}.btn-warning.disabled,.btn-warning.disabled.active,.btn-warning.disabled.focus,.btn-warning.disabled:active,.btn-warning.disabled:focus,.btn-warning.disabled:hover,.btn-warning[disabled],.btn-warning[disabled].active,.btn-warning[disabled].focus,.btn-warning[disabled]:active,.btn-warning[disabled]:focus,.btn-warning[disabled]:hover,fieldset[disabled] .btn-warning,fieldset[disabled] .btn-warning.active,fieldset[disabled] .btn-warning.focus,fieldset[disabled] .btn-warning:active,fieldset[disabled] .btn-warning:focus,fieldset[disabled] .btn-warning:hover{background-color:#f0ad4e;border-color:#eea236}.btn-warning .badge{color:#f0ad4e;background-color:#fff}.btn-danger{color:#fff;background-color:#d9534f;border-color:#d43f3a}.btn-danger.focus,.btn-danger:focus{color:#fff;background-color:#c9302c;border-color:#761c19}.btn-danger:hover{color:#fff;background-color:#c9302c;border-color:#ac2925}.btn-danger.active,.btn-danger:active,.open>.dropdown-toggle.btn-danger{color:#fff;background-color:#c9302c;border-color:#ac2925}.btn-danger.active.focus,.btn-danger.active:focus,.btn-danger.active:hover,.btn-danger:active.focus,.btn-danger:active:focus,.btn-danger:active:hover,.open>.dropdown-toggle.btn-danger.focus,.open>.dropdown-toggle.btn-danger:focus,.open>.dropdown-toggle.btn-danger:hover{color:#fff;background-color:#ac2925;border-color:#761c19}.btn-danger.active,.btn-danger:active,.open>.dropdown-toggle.btn-danger{background-image:none}.btn-danger.disabled,.btn-danger.disabled.active,.btn-danger.disabled.focus,.btn-danger.disabled:active,.btn-danger.disabled:focus,.btn-danger.disabled:hover,.btn-danger[disabled],.btn-danger[disabled].active,.btn-danger[disabled].focus,.btn-danger[disabled]:active,.btn-danger[disabled]:focus,.btn-danger[disabled]:hover,fieldset[disabled] .btn-danger,fieldset[disabled] .btn-danger.active,fieldset[disabled] .btn-danger.focus,fieldset[disabled] .btn-danger:active,fieldset[disabled] .btn-danger:focus,fieldset[disabled] .btn-danger:hover{background-color:#d9534f;border-color:#d43f3a}.btn-danger .badge{color:#d9534f;background-color:#fff}.btn-link{font-weight:400;color:#337ab7;border-radius:0}.btn-link,.btn-link.active,.btn-link:active,.btn-link[disabled],fieldset[disabled] .btn-link{background-color:transparent;-webkit-box-shadow:none;box-shadow:none}.btn-link,.btn-link:active,.btn-link:focus,.btn-link:hover{border-color:transparent}.btn-link:focus,.btn-link:hover{color:#23527c;text-decoration:underline;background-color:transparent}.btn-link[disabled]:focus,.btn-link[disabled]:hover,fieldset[disabled] .btn-link:focus,fieldset[disabled] .btn-link:hover{color:#777;text-decoration:none}.btn-group-lg>.btn,.btn-lg{padding:10px 16px;font-size:18px;line-height:1.3333333;border-radius:6px}.btn-group-sm>.btn,.btn-sm{padding:5px 10px;font-size:12px;line-height:1.5;border-radius:3px}.btn-group-xs>.btn,.btn-xs{padding:1px 5px;font-size:12px;line-height:1.5;border-radius:3px}.btn-block{display:block;width:100%}.btn-block+.btn-block{margin-top:5px}input[type=button].btn-block,input[type=reset].btn-block,input[type=submit].btn-block{width:100%}.fade{opacity:0;-webkit-transition:opacity .15s linear;-o-transition:opacity .15s linear;transition:opacity .15s linear}.fade.in{opacity:1}.collapse{display:none}.collapse.in{display:block}tr.collapse.in{display:table-row}tbody.collapse.in{display:table-row-group}.collapsing{position:relative;height:0;overflow:hidden;-webkit-transition-timing-function:ease;-o-transition-timing-function:ease;transition-timing-function:ease;-webkit-transition-duration:.35s;-o-transition-duration:.35s;transition-duration:.35s;-webkit-transition-property:height,visibility;-o-transition-property:height,visibility;transition-property:height,visibility}.caret{display:inline-block;width:0;height:0;margin-left:2px;vertical-align:middle;border-top:4px dashed;border-top:4px solid\9;border-right:4px solid transparent;border-left:4px solid transparent}.dropdown,.dropup{position:relative}.dropdown-toggle:focus{outline:0}.dropdown-menu{position:absolute;top:100%;left:0;z-index:1000;display:none;float:left;min-width:160px;padding:5px 0;margin:2px 0 0;font-size:14px;text-align:left;list-style:none;background-color:#fff;-webkit-background-clip:padding-box;background-clip:padding-box;border:1px solid #ccc;border:1px solid rgba(0,0,0,.15);border-radius:4px;-webkit-box-shadow:0 6px 12px rgba(0,0,0,.175);box-shadow:0 6px 12px rgba(0,0,0,.175)}.dropdown-menu.pull-right{right:0;left:auto}.dropdown-menu .divider{height:1px;margin:9px 0;overflow:hidden;background-color:#e5e5e5}.dropdown-menu>li>a{display:block;padding:3px 20px;clear:both;font-weight:400;line-height:1.42857143;color:#333;white-space:nowrap}.dropdown-menu>li>a:focus,.dropdown-menu>li>a:hover{color:#262626;text-decoration:none;background-color:#f5f5f5}.dropdown-menu>.active>a,.dropdown-menu>.active>a:focus,.dropdown-menu>.active>a:hover{color:#fff;text-decoration:none;background-color:#337ab7;outline:0}.dropdown-menu>.disabled>a,.dropdown-menu>.disabled>a:focus,.dropdown-menu>.disabled>a:hover{color:#777}.dropdown-menu>.disabled>a:focus,.dropdown-menu>.disabled>a:hover{text-decoration:none;cursor:not-allowed;background-color:transparent;background-image:none;filter:progid:DXImageTransform.Microsoft.gradient(enabled=false)}.open>.dropdown-menu{display:block}.open>a{outline:0}.dropdown-menu-right{right:0;left:auto}.dropdown-menu-left{right:auto;left:0}.dropdown-header{display:block;padding:3px 20px;font-size:12px;line-height:1.42857143;color:#777;white-space:nowrap}.dropdown-backdrop{position:fixed;top:0;right:0;bottom:0;left:0;z-index:990}.pull-right>.dropdown-menu{right:0;left:auto}.dropup .caret,.navbar-fixed-bottom .dropdown .caret{content:"";border-top:0;border-bottom:4px dashed;border-bottom:4px solid\9}.dropup .dropdown-menu,.navbar-fixed-bottom .dropdown .dropdown-menu{top:auto;bottom:100%;margin-bottom:2px}@media (min-width:768px){.navbar-right .dropdown-menu{right:0;left:auto}.navbar-right .dropdown-menu-left{right:auto;left:0}}.btn-group,.btn-group-vertical{position:relative;display:inline-block;vertical-align:middle}.btn-group-vertical>.btn,.btn-group>.btn{position:relative;float:left}.btn-group-vertical>.btn.active,.btn-group-vertical>.btn:active,.btn-group-vertical>.btn:focus,.btn-group-vertical>.btn:hover,.btn-group>.btn.active,.btn-group>.btn:active,.btn-group>.btn:focus,.btn-group>.btn:hover{z-index:2}.btn-group .btn+.btn,.btn-group .btn+.btn-group,.btn-group .btn-group+.btn,.btn-group .btn-group+.btn-group{margin-left:-1px}.btn-toolbar{margin-left:-5px}.btn-toolbar .btn,.btn-toolbar .btn-group,.btn-toolbar .input-group{float:left}.btn-toolbar>.btn,.btn-toolbar>.btn-group,.btn-toolbar>.input-group{margin-left:5px}.btn-group>.btn:not(:first-child):not(:last-child):not(.dropdown-toggle){border-radius:0}.btn-group>.btn:first-child{margin-left:0}.btn-group>.btn:first-child:not(:last-child):not(.dropdown-toggle){border-top-right-radius:0;border-bottom-right-radius:0}.btn-group>.btn:last-child:not(:first-child),.btn-group>.dropdown-toggle:not(:first-child){border-top-left-radius:0;border-bottom-left-radius:0}.btn-group>.btn-group{float:left}.btn-group>.btn-group:not(:first-child):not(:last-child)>.btn{border-radius:0}.btn-group>.btn-group:first-child:not(:last-child)>.btn:last-child,.btn-group>.btn-group:first-child:not(:last-child)>.dropdown-toggle{border-top-right-radius:0;border-bottom-right-radius:0}.btn-group>.btn-group:last-child:not(:first-child)>.btn:first-child{border-top-left-radius:0;border-bottom-left-radius:0}.btn-group .dropdown-toggle:active,.btn-group.open .dropdown-toggle{outline:0}.btn-group>.btn+.dropdown-toggle{padding-right:8px;padding-left:8px}.btn-group>.btn-lg+.dropdown-toggle{padding-right:12px;padding-left:12px}.btn-group.open .dropdown-toggle{-webkit-box-shadow:inset 0 3px 5px rgba(0,0,0,.125);box-shadow:inset 0 3px 5px rgba(0,0,0,.125)}.btn-group.open .dropdown-toggle.btn-link{-webkit-box-shadow:none;box-shadow:none}.btn .caret{margin-left:0}.btn-lg .caret{border-width:5px 5px 0;border-bottom-width:0}.dropup .btn-lg .caret{border-width:0 5px 5px}.btn-group-vertical>.btn,.btn-group-vertical>.btn-group,.btn-group-vertical>.btn-group>.btn{display:block;float:none;width:100%;max-width:100%}.btn-group-vertical>.btn-group>.btn{float:none}.btn-group-vertical>.btn+.btn,.btn-group-vertical>.btn+.btn-group,.btn-group-vertical>.btn-group+.btn,.btn-group-vertical>.btn-group+.btn-group{margin-top:-1px;margin-left:0}.btn-group-vertical>.btn:not(:first-child):not(:last-child){border-radius:0}.btn-group-vertical>.btn:first-child:not(:last-child){border-top-right-radius:4px;border-bottom-right-radius:0;border-bottom-left-radius:0}.btn-group-vertical>.btn:last-child:not(:first-child){border-top-left-radius:0;border-top-right-radius:0;border-bottom-left-radius:4px}.btn-group-vertical>.btn-group:not(:first-child):not(:last-child)>.btn{border-radius:0}.btn-group-vertical>.btn-group:first-child:not(:last-child)>.btn:last-child,.btn-group-vertical>.btn-group:first-child:not(:last-child)>.dropdown-toggle{border-bottom-right-radius:0;border-bottom-left-radius:0}.btn-group-vertical>.btn-group:last-child:not(:first-child)>.btn:first-child{border-top-left-radius:0;border-top-right-radius:0}.btn-group-justified{display:table;width:100%;table-layout:fixed;border-collapse:separate}.btn-group-justified>.btn,.btn-group-justified>.btn-group{display:table-cell;float:none;width:1%}.btn-group-justified>.btn-group .btn{width:100%}.btn-group-justified>.btn-group .dropdown-menu{left:auto}[data-toggle=buttons]>.btn input[type=checkbox],[data-toggle=buttons]>.btn input[type=radio],[data-toggle=buttons]>.btn-group>.btn input[type=checkbox],[data-toggle=buttons]>.btn-group>.btn input[type=radio]{position:absolute;clip:rect(0,0,0,0);pointer-events:none}.input-group{position:relative;display:table;border-collapse:separate}.input-group[class*=col-]{float:none;padding-right:0;padding-left:0}.input-group .form-control{position:relative;z-index:2;float:left;width:100%;margin-bottom:0}.input-group-lg>.form-control,.input-group-lg>.input-group-addon,.input-group-lg>.input-group-btn>.btn{height:46px;padding:10px 16px;font-size:18px;line-height:1.3333333;border-radius:6px}select.input-group-lg>.form-control,select.input-group-lg>.input-group-addon,select.input-group-lg>.input-group-btn>.btn{height:46px;line-height:46px}select[multiple].input-group-lg>.form-control,select[multiple].input-group-lg>.input-group-addon,select[multiple].input-group-lg>.input-group-btn>.btn,textarea.input-group-lg>.form-control,textarea.input-group-lg>.input-group-addon,textarea.input-group-lg>.input-group-btn>.btn{height:auto}.input-group-sm>.form-control,.input-group-sm>.input-group-addon,.input-group-sm>.input-group-btn>.btn{height:30px;padding:5px 10px;font-size:12px;line-height:1.5;border-radius:3px}select.input-group-sm>.form-control,select.input-group-sm>.input-group-addon,select.input-group-sm>.input-group-btn>.btn{height:30px;line-height:30px}select[multiple].input-group-sm>.form-control,select[multiple].input-group-sm>.input-group-addon,select[multiple].input-group-sm>.input-group-btn>.btn,textarea.input-group-sm>.form-control,textarea.input-group-sm>.input-group-addon,textarea.input-group-sm>.input-group-btn>.btn{height:auto}.input-group .form-control,.input-group-addon,.input-group-btn{display:table-cell}.input-group .form-control:not(:first-child):not(:last-child),.input-group-addon:not(:first-child):not(:last-child),.input-group-btn:not(:first-child):not(:last-child){border-radius:0}.input-group-addon,.input-group-btn{width:1%;white-space:nowrap;vertical-align:middle}.input-group-addon{padding:6px 12px;font-size:14px;font-weight:400;line-height:1;color:#555;text-align:center;background-color:#eee;border:1px solid #ccc;border-radius:4px}.input-group-addon.input-sm{padding:5px 10px;font-size:12px;border-radius:3px}.input-group-addon.input-lg{padding:10px 16px;font-size:18px;border-radius:6px}.input-group-addon input[type=checkbox],.input-group-addon input[type=radio]{margin-top:0}.input-group .form-control:first-child,.input-group-addon:first-child,.input-group-btn:first-child>.btn,.input-group-btn:first-child>.btn-group>.btn,.input-group-btn:first-child>.dropdown-toggle,.input-group-btn:last-child>.btn-group:not(:last-child)>.btn,.input-group-btn:last-child>.btn:not(:last-child):not(.dropdown-toggle){border-top-right-radius:0;border-bottom-right-radius:0}.input-group-addon:first-child{border-right:0}.input-group .form-control:last-child,.input-group-addon:last-child,.input-group-btn:first-child>.btn-group:not(:first-child)>.btn,.input-group-btn:first-child>.btn:not(:first-child),.input-group-btn:last-child>.btn,.input-group-btn:last-child>.btn-group>.btn,.input-group-btn:last-child>.dropdown-toggle{border-top-left-radius:0;border-bottom-left-radius:0}.input-group-addon:last-child{border-left:0}.input-group-btn{position:relative;font-size:0;white-space:nowrap}.input-group-btn>.btn{position:relative}.input-group-btn>.btn+.btn{margin-left:-1px}.input-group-btn>.btn:active,.input-group-btn>.btn:focus,.input-group-btn>.btn:hover{z-index:2}.input-group-btn:first-child>.btn,.input-group-btn:first-child>.btn-group{margin-right:-1px}.input-group-btn:last-child>.btn,.input-group-btn:last-child>.btn-group{z-index:2;margin-left:-1px}.nav{padding-left:0;margin-bottom:0;list-style:none}.nav>li{position:relative;display:block}.nav>li>a{position:relative;display:block;padding:10px 15px}.nav>li>a:focus,.nav>li>a:hover{text-decoration:none;background-color:#eee}.nav>li.disabled>a{color:#777}.nav>li.disabled>a:focus,.nav>li.disabled>a:hover{color:#777;text-decoration:none;cursor:not-allowed;background-color:transparent}.nav .open>a,.nav .open>a:focus,.nav .open>a:hover{background-color:#eee;border-color:#337ab7}.nav .nav-divider{height:1px;margin:9px 0;overflow:hidden;background-color:#e5e5e5}.nav>li>a>img{max-width:none}.nav-tabs{border-bottom:1px solid #ddd}.nav-tabs>li{float:left;margin-bottom:-1px}.nav-tabs>li>a{margin-right:2px;line-height:1.42857143;border:1px solid transparent;border-radius:4px 4px 0 0}.nav-tabs>li>a:hover{border-color:#eee #eee #ddd}.nav-tabs>li.active>a,.nav-tabs>li.active>a:focus,.nav-tabs>li.active>a:hover{color:#555;cursor:default;background-color:#fff;border:1px solid #ddd;border-bottom-color:transparent}.nav-tabs.nav-justified{width:100%;border-bottom:0}.nav-tabs.nav-justified>li{float:none}.nav-tabs.nav-justified>li>a{margin-bottom:5px;text-align:center}.nav-tabs.nav-justified>.dropdown .dropdown-menu{top:auto;left:auto}@media (min-width:768px){.nav-tabs.nav-justified>li{display:table-cell;width:1%}.nav-tabs.nav-justified>li>a{margin-bottom:0}}.nav-tabs.nav-justified>li>a{margin-right:0;border-radius:4px}.nav-tabs.nav-justified>.active>a,.nav-tabs.nav-justified>.active>a:focus,.nav-tabs.nav-justified>.active>a:hover{border:1px solid #ddd}@media (min-width:768px){.nav-tabs.nav-justified>li>a{border-bottom:1px solid #ddd;border-radius:4px 4px 0 0}.nav-tabs.nav-justified>.active>a,.nav-tabs.nav-justified>.active>a:focus,.nav-tabs.nav-justified>.active>a:hover{border-bottom-color:#fff}}.nav-pills>li{float:left}.nav-pills>li>a{border-radius:4px}.nav-pills>li+li{margin-left:2px}.nav-pills>li.active>a,.nav-pills>li.active>a:focus,.nav-pills>li.active>a:hover{color:#fff;background-color:#337ab7}.nav-stacked>li{float:none}.nav-stacked>li+li{margin-top:2px;margin-left:0}.nav-justified{width:100%}.nav-justified>li{float:none}.nav-justified>li>a{margin-bottom:5px;text-align:center}.nav-justified>.dropdown .dropdown-menu{top:auto;left:auto}@media (min-width:768px){.nav-justified>li{display:table-cell;width:1%}.nav-justified>li>a{margin-bottom:0}}.nav-tabs-justified{border-bottom:0}.nav-tabs-justified>li>a{margin-right:0;border-radius:4px}.nav-tabs-justified>.active>a,.nav-tabs-justified>.active>a:focus,.nav-tabs-justified>.active>a:hover{border:1px solid #ddd}@media (min-width:768px){.nav-tabs-justified>li>a{border-bottom:1px solid #ddd;border-radius:4px 4px 0 0}.nav-tabs-justified>.active>a,.nav-tabs-justified>.active>a:focus,.nav-tabs-justified>.active>a:hover{border-bottom-color:#fff}}.tab-content>.tab-pane{display:none}.tab-content>.active{display:block}.nav-tabs .dropdown-menu{margin-top:-1px;border-top-left-radius:0;border-top-right-radius:0}.navbar{position:relative;min-height:50px;margin-bottom:20px;border:1px solid transparent}@media (min-width:768px){.navbar{border-radius:4px}}@media (min-width:768px){.navbar-header{float:left}}.navbar-collapse{padding-right:15px;padding-left:15px;overflow-x:visible;-webkit-overflow-scrolling:touch;border-top:1px solid transparent;-webkit-box-shadow:inset 0 1px 0 rgba(255,255,255,.1);box-shadow:inset 0 1px 0 rgba(255,255,255,.1)}.navbar-collapse.in{overflow-y:auto}@media (min-width:768px){.navbar-collapse{width:auto;border-top:0;-webkit-box-shadow:none;box-shadow:none}.navbar-collapse.collapse{display:block!important;height:auto!important;padding-bottom:0;overflow:visible!important}.navbar-collapse.in{overflow-y:visible}.navbar-fixed-bottom .navbar-collapse,.navbar-fixed-top .navbar-collapse,.navbar-static-top .navbar-collapse{padding-right:0;padding-left:0}}.navbar-fixed-bottom .navbar-collapse,.navbar-fixed-top .navbar-collapse{max-height:340px}@media (max-device-width:480px) and (orientation:landscape){.navbar-fixed-bottom .navbar-collapse,.navbar-fixed-top .navbar-collapse{max-height:200px}}.container-fluid>.navbar-collapse,.container-fluid>.navbar-header,.container>.navbar-collapse,.container>.navbar-header{margin-right:-15px;margin-left:-15px}@media (min-width:768px){.container-fluid>.navbar-collapse,.container-fluid>.navbar-header,.container>.navbar-collapse,.container>.navbar-header{margin-right:0;margin-left:0}}.navbar-static-top{z-index:1000;border-width:0 0 1px}@media (min-width:768px){.navbar-static-top{border-radius:0}}.navbar-fixed-bottom,.navbar-fixed-top{position:fixed;right:0;left:0;z-index:1030}@media (min-width:768px){.navbar-fixed-bottom,.navbar-fixed-top{border-radius:0}}.navbar-fixed-top{top:0;border-width:0 0 1px}.navbar-fixed-bottom{bottom:0;margin-bottom:0;border-width:1px 0 0}.navbar-brand{float:left;height:50px;padding:15px 15px;font-size:18px;line-height:20px}.navbar-brand:focus,.navbar-brand:hover{text-decoration:none}.navbar-brand>img{display:block}@media (min-width:768px){.navbar>.container .navbar-brand,.navbar>.container-fluid .navbar-brand{margin-left:-15px}}.navbar-toggle{position:relative;float:right;padding:9px 10px;margin-top:8px;margin-right:15px;margin-bottom:8px;background-color:transparent;background-image:none;border:1px solid transparent;border-radius:4px}.navbar-toggle:focus{outline:0}.navbar-toggle .icon-bar{display:block;width:22px;height:2px;border-radius:1px}.navbar-toggle .icon-bar+.icon-bar{margin-top:4px}@media (min-width:768px){.navbar-toggle{display:none}}.navbar-nav{margin:7.5px -15px}.navbar-nav>li>a{padding-top:10px;padding-bottom:10px;line-height:20px}@media (max-width:767px){.navbar-nav .open .dropdown-menu{position:static;float:none;width:auto;margin-top:0;background-color:transparent;border:0;-webkit-box-shadow:none;box-shadow:none}.navbar-nav .open .dropdown-menu .dropdown-header,.navbar-nav .open .dropdown-menu>li>a{padding:5px 15px 5px 25px}.navbar-nav .open .dropdown-menu>li>a{line-height:20px}.navbar-nav .open .dropdown-menu>li>a:focus,.navbar-nav .open .dropdown-menu>li>a:hover{background-image:none}}@media (min-width:768px){.navbar-nav{float:left;margin:0}.navbar-nav>li{float:left}.navbar-nav>li>a{padding-top:15px;padding-bottom:15px}}.navbar-form{padding:10px 15px;margin-top:8px;margin-right:-15px;margin-bottom:8px;margin-left:-15px;border-top:1px solid transparent;border-bottom:1px solid transparent;-webkit-box-shadow:inset 0 1px 0 rgba(255,255,255,.1),0 1px 0 rgba(255,255,255,.1);box-shadow:inset 0 1px 0 rgba(255,255,255,.1),0 1px 0 rgba(255,255,255,.1)}@media (min-width:768px){.navbar-form .form-group{display:inline-block;margin-bottom:0;vertical-align:middle}.navbar-form .form-control{display:inline-block;width:auto;vertical-align:middle}.navbar-form .form-control-static{display:inline-block}.navbar-form .input-group{display:inline-table;vertical-align:middle}.navbar-form .input-group .form-control,.navbar-form .input-group .input-group-addon,.navbar-form .input-group .input-group-btn{width:auto}.navbar-form .input-group>.form-control{width:100%}.navbar-form .control-label{margin-bottom:0;vertical-align:middle}.navbar-form .checkbox,.navbar-form .radio{display:inline-block;margin-top:0;margin-bottom:0;vertical-align:middle}.navbar-form .checkbox label,.navbar-form .radio label{padding-left:0}.navbar-form .checkbox input[type=checkbox],.navbar-form .radio input[type=radio]{position:relative;margin-left:0}.navbar-form .has-feedback .form-control-feedback{top:0}}@media (max-width:767px){.navbar-form .form-group{margin-bottom:5px}.navbar-form .form-group:last-child{margin-bottom:0}}@media (min-width:768px){.navbar-form{width:auto;padding-top:0;padding-bottom:0;margin-right:0;margin-left:0;border:0;-webkit-box-shadow:none;box-shadow:none}}.navbar-nav>li>.dropdown-menu{margin-top:0;border-top-left-radius:0;border-top-right-radius:0}.navbar-fixed-bottom .navbar-nav>li>.dropdown-menu{margin-bottom:0;border-top-left-radius:4px;border-top-right-radius:4px;border-bottom-right-radius:0;border-bottom-left-radius:0}.navbar-btn{margin-top:8px;margin-bottom:8px}.navbar-btn.btn-sm{margin-top:10px;margin-bottom:10px}.navbar-btn.btn-xs{margin-top:14px;margin-bottom:14px}.navbar-text{margin-top:15px;margin-bottom:15px}@media (min-width:768px){.navbar-text{float:left;margin-right:15px;margin-left:15px}}@media (min-width:768px){.navbar-left{float:left!important}.navbar-right{float:right!important;margin-right:-15px}.navbar-right~.navbar-right{margin-right:0}}.navbar-default{background-color:#f8f8f8;border-color:#e7e7e7}.navbar-default .navbar-brand{color:#777}.navbar-default .navbar-brand:focus,.navbar-default .navbar-brand:hover{color:#5e5e5e;background-color:transparent}.navbar-default .navbar-text{color:#777}.navbar-default .navbar-nav>li>a{color:#777}.navbar-default .navbar-nav>li>a:focus,.navbar-default .navbar-nav>li>a:hover{color:#333;background-color:transparent}.navbar-default .navbar-nav>.active>a,.navbar-default .navbar-nav>.active>a:focus,.navbar-default .navbar-nav>.active>a:hover{color:#555;background-color:#e7e7e7}.navbar-default .navbar-nav>.disabled>a,.navbar-default .navbar-nav>.disabled>a:focus,.navbar-default .navbar-nav>.disabled>a:hover{color:#ccc;background-color:transparent}.navbar-default .navbar-toggle{border-color:#ddd}.navbar-default .navbar-toggle:focus,.navbar-default .navbar-toggle:hover{background-color:#ddd}.navbar-default .navbar-toggle .icon-bar{background-color:#888}.navbar-default .navbar-collapse,.navbar-default .navbar-form{border-color:#e7e7e7}.navbar-default .navbar-nav>.open>a,.navbar-default .navbar-nav>.open>a:focus,.navbar-default .navbar-nav>.open>a:hover{color:#555;background-color:#e7e7e7}@media (max-width:767px){.navbar-default .navbar-nav .open .dropdown-menu>li>a{color:#777}.navbar-default .navbar-nav .open .dropdown-menu>li>a:focus,.navbar-default .navbar-nav .open .dropdown-menu>li>a:hover{color:#333;background-color:transparent}.navbar-default .navbar-nav .open .dropdown-menu>.active>a,.navbar-default .navbar-nav .open .dropdown-menu>.active>a:focus,.navbar-default .navbar-nav .open .dropdown-menu>.active>a:hover{color:#555;background-color:#e7e7e7}.navbar-default .navbar-nav .open .dropdown-menu>.disabled>a,.navbar-default .navbar-nav .open .dropdown-menu>.disabled>a:focus,.navbar-default .navbar-nav .open .dropdown-menu>.disabled>a:hover{color:#ccc;background-color:transparent}}.navbar-default .navbar-link{color:#777}.navbar-default .navbar-link:hover{color:#333}.navbar-default .btn-link{color:#777}.navbar-default .btn-link:focus,.navbar-default .btn-link:hover{color:#333}.navbar-default .btn-link[disabled]:focus,.navbar-default .btn-link[disabled]:hover,fieldset[disabled] .navbar-default .btn-link:focus,fieldset[disabled] .navbar-default .btn-link:hover{color:#ccc}.navbar-inverse{background-color:#222;border-color:#080808}.navbar-inverse .navbar-brand{color:#9d9d9d}.navbar-inverse .navbar-brand:focus,.navbar-inverse .navbar-brand:hover{color:#fff;background-color:transparent}.navbar-inverse .navbar-text{color:#9d9d9d}.navbar-inverse .navbar-nav>li>a{color:#9d9d9d}.navbar-inverse .navbar-nav>li>a:focus,.navbar-inverse .navbar-nav>li>a:hover{color:#fff;background-color:transparent}.navbar-inverse .navbar-nav>.active>a,.navbar-inverse .navbar-nav>.active>a:focus,.navbar-inverse .navbar-nav>.active>a:hover{color:#fff;background-color:#080808}.navbar-inverse .navbar-nav>.disabled>a,.navbar-inverse .navbar-nav>.disabled>a:focus,.navbar-inverse .navbar-nav>.disabled>a:hover{color:#444;background-color:transparent}.navbar-inverse .navbar-toggle{border-color:#333}.navbar-inverse .navbar-toggle:focus,.navbar-inverse .navbar-toggle:hover{background-color:#333}.navbar-inverse .navbar-toggle .icon-bar{background-color:#fff}.navbar-inverse .navbar-collapse,.navbar-inverse .navbar-form{border-color:#101010}.navbar-inverse .navbar-nav>.open>a,.navbar-inverse .navbar-nav>.open>a:focus,.navbar-inverse .navbar-nav>.open>a:hover{color:#fff;background-color:#080808}@media (max-width:767px){.navbar-inverse .navbar-nav .open .dropdown-menu>.dropdown-header{border-color:#080808}.navbar-inverse .navbar-nav .open .dropdown-menu .divider{background-color:#080808}.navbar-inverse .navbar-nav .open .dropdown-menu>li>a{color:#9d9d9d}.navbar-inverse .navbar-nav .open .dropdown-menu>li>a:focus,.navbar-inverse .navbar-nav .open .dropdown-menu>li>a:hover{color:#fff;background-color:transparent}.navbar-inverse .navbar-nav .open .dropdown-menu>.active>a,.navbar-inverse .navbar-nav .open .dropdown-menu>.active>a:focus,.navbar-inverse .navbar-nav .open .dropdown-menu>.active>a:hover{color:#fff;background-color:#080808}.navbar-inverse .navbar-nav .open .dropdown-menu>.disabled>a,.navbar-inverse .navbar-nav .open .dropdown-menu>.disabled>a:focus,.navbar-inverse .navbar-nav .open .dropdown-menu>.disabled>a:hover{color:#444;background-color:transparent}}.navbar-inverse .navbar-link{color:#9d9d9d}.navbar-inverse .navbar-link:hover{color:#fff}.navbar-inverse .btn-link{color:#9d9d9d}.navbar-inverse .btn-link:focus,.navbar-inverse .btn-link:hover{color:#fff}.navbar-inverse .btn-link[disabled]:focus,.navbar-inverse .btn-link[disabled]:hover,fieldset[disabled] .navbar-inverse .btn-link:focus,fieldset[disabled] .navbar-inverse .btn-link:hover{color:#444}.breadcrumb{padding:8px 15px;margin-bottom:20px;list-style:none;background-color:#f5f5f5;border-radius:4px}.breadcrumb>li{display:inline-block}.breadcrumb>li+li:before{padding:0 5px;color:#ccc;content:"/\00a0"}.breadcrumb>.active{color:#777}.pagination{display:inline-block;padding-left:0;margin:20px 0;border-radius:4px}.pagination>li{display:inline}.pagination>li>a,.pagination>li>span{position:relative;float:left;padding:6px 12px;margin-left:-1px;line-height:1.42857143;color:#337ab7;text-decoration:none;background-color:#fff;border:1px solid #ddd}.pagination>li:first-child>a,.pagination>li:first-child>span{margin-left:0;border-top-left-radius:4px;border-bottom-left-radius:4px}.pagination>li:last-child>a,.pagination>li:last-child>span{border-top-right-radius:4px;border-bottom-right-radius:4px}.pagination>li>a:focus,.pagination>li>a:hover,.pagination>li>span:focus,.pagination>li>span:hover{z-index:3;color:#23527c;background-color:#eee;border-color:#ddd}.pagination>.active>a,.pagination>.active>a:focus,.pagination>.active>a:hover,.pagination>.active>span,.pagination>.active>span:focus,.pagination>.active>span:hover{z-index:2;color:#fff;cursor:default;background-color:#337ab7;border-color:#337ab7}.pagination>.disabled>a,.pagination>.disabled>a:focus,.pagination>.disabled>a:hover,.pagination>.disabled>span,.pagination>.disabled>span:focus,.pagination>.disabled>span:hover{color:#777;cursor:not-allowed;background-color:#fff;border-color:#ddd}.pagination-lg>li>a,.pagination-lg>li>span{padding:10px 16px;font-size:18px;line-height:1.3333333}.pagination-lg>li:first-child>a,.pagination-lg>li:first-child>span{border-top-left-radius:6px;border-bottom-left-radius:6px}.pagination-lg>li:last-child>a,.pagination-lg>li:last-child>span{border-top-right-radius:6px;border-bottom-right-radius:6px}.pagination-sm>li>a,.pagination-sm>li>span{padding:5px 10px;font-size:12px;line-height:1.5}.pagination-sm>li:first-child>a,.pagination-sm>li:first-child>span{border-top-left-radius:3px;border-bottom-left-radius:3px}.pagination-sm>li:last-child>a,.pagination-sm>li:last-child>span{border-top-right-radius:3px;border-bottom-right-radius:3px}.pager{padding-left:0;margin:20px 0;text-align:center;list-style:none}.pager li{display:inline}.pager li>a,.pager li>span{display:inline-block;padding:5px 14px;background-color:#fff;border:1px solid #ddd;border-radius:15px}.pager li>a:focus,.pager li>a:hover{text-decoration:none;background-color:#eee}.pager .next>a,.pager .next>span{float:right}.pager .previous>a,.pager .previous>span{float:left}.pager .disabled>a,.pager .disabled>a:focus,.pager .disabled>a:hover,.pager .disabled>span{color:#777;cursor:not-allowed;background-color:#fff}.label{display:inline;padding:.2em .6em .3em;font-size:75%;font-weight:700;line-height:1;color:#fff;text-align:center;white-space:nowrap;vertical-align:baseline;border-radius:.25em}a.label:focus,a.label:hover{color:#fff;text-decoration:none;cursor:pointer}.label:empty{display:none}.btn .label{position:relative;top:-1px}.label-default{background-color:#777}.label-default[href]:focus,.label-default[href]:hover{background-color:#5e5e5e}.label-primary{background-color:#337ab7}.label-primary[href]:focus,.label-primary[href]:hover{background-color:#286090}.label-success{background-color:#5cb85c}.label-success[href]:focus,.label-success[href]:hover{background-color:#449d44}.label-info{background-color:#5bc0de}.label-info[href]:focus,.label-info[href]:hover{background-color:#31b0d5}.label-warning{background-color:#f0ad4e}.label-warning[href]:focus,.label-warning[href]:hover{background-color:#ec971f}.label-danger{background-color:#d9534f}.label-danger[href]:focus,.label-danger[href]:hover{background-color:#c9302c}.badge{display:inline-block;min-width:10px;padding:3px 7px;font-size:12px;font-weight:700;line-height:1;color:#fff;text-align:center;white-space:nowrap;vertical-align:middle;background-color:#777;border-radius:10px}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.btn-group-xs>.btn .badge,.btn-xs .badge{top:0;padding:1px 5px}a.badge:focus,a.badge:hover{color:#fff;text-decoration:none;cursor:pointer}.list-group-item.active>.badge,.nav-pills>.active>a>.badge{color:#337ab7;background-color:#fff}.list-group-item>.badge{float:right}.list-group-item>.badge+.badge{margin-right:5px}.nav-pills>li>a>.badge{margin-left:3px}.jumbotron{padding-top:30px;padding-bottom:30px;margin-bottom:30px;color:inherit;background-color:#eee}.jumbotron .h1,.jumbotron h1{color:inherit}.jumbotron p{margin-bottom:15px;font-size:21px;font-weight:200}.jumbotron>hr{border-top-color:#d5d5d5}.container .jumbotron,.container-fluid .jumbotron{border-radius:6px}.jumbotron .container{max-width:100%}@media screen and (min-width:768px){.jumbotron{padding-top:48px;padding-bottom:48px}.container .jumbotron,.container-fluid .jumbotron{padding-right:60px;padding-left:60px}.jumbotron .h1,.jumbotron h1{font-size:63px}}.thumbnail{display:block;padding:4px;margin-bottom:20px;line-height:1.42857143;background-color:#fff;border:1px solid #ddd;border-radius:4px;-webkit-transition:border .2s ease-in-out;-o-transition:border .2s ease-in-out;transition:border .2s ease-in-out}.thumbnail a>img,.thumbnail>img{margin-right:auto;margin-left:auto}a.thumbnail.active,a.thumbnail:focus,a.thumbnail:hover{border-color:#337ab7}.thumbnail .caption{padding:9px;color:#333}.alert{padding:15px;margin-bottom:20px;border:1px solid transparent;border-radius:4px}.alert h4{margin-top:0;color:inherit}.alert .alert-link{font-weight:700}.alert>p,.alert>ul{margin-bottom:0}.alert>p+p{margin-top:5px}.alert-dismissable,.alert-dismissible{padding-right:35px}.alert-dismissable .close,.alert-dismissible .close{position:relative;top:-2px;right:-21px;color:inherit}.alert-success{color:#3c763d;background-color:#dff0d8;border-color:#d6e9c6}.alert-success hr{border-top-color:#c9e2b3}.alert-success .alert-link{color:#2b542c}.alert-info{color:#31708f;background-color:#d9edf7;border-color:#bce8f1}.alert-info hr{border-top-color:#a6e1ec}.alert-info .alert-link{color:#245269}.alert-warning{color:#8a6d3b;background-color:#fcf8e3;border-color:#faebcc}.alert-warning hr{border-top-color:#f7e1b5}.alert-warning .alert-link{color:#66512c}.alert-danger{color:#a94442;background-color:#f2dede;border-color:#ebccd1}.alert-danger hr{border-top-color:#e4b9c0}.alert-danger .alert-link{color:#843534}@-webkit-keyframes progress-bar-stripes{from{background-position:40px 0}to{background-position:0 0}}@-o-keyframes progress-bar-stripes{from{background-position:40px 0}to{background-position:0 0}}@keyframes progress-bar-stripes{from{background-position:40px 0}to{background-position:0 0}}.progress{height:20px;margin-bottom:20px;overflow:hidden;background-color:#f5f5f5;border-radius:4px;-webkit-box-shadow:inset 0 1px 2px rgba(0,0,0,.1);box-shadow:inset 0 1px 2px rgba(0,0,0,.1)}.progress-bar{float:left;width:0;height:100%;font-size:12px;line-height:20px;color:#fff;text-align:center;background-color:#337ab7;-webkit-box-shadow:inset 0 -1px 0 rgba(0,0,0,.15);box-shadow:inset 0 -1px 0 rgba(0,0,0,.15);-webkit-transition:width .6s ease;-o-transition:width .6s ease;transition:width .6s ease}.progress-bar-striped,.progress-striped .progress-bar{background-image:-webkit-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:-o-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);-webkit-background-size:40px 40px;background-size:40px 40px}.progress-bar.active,.progress.active .progress-bar{-webkit-animation:progress-bar-stripes 2s linear infinite;-o-animation:progress-bar-stripes 2s linear infinite;animation:progress-bar-stripes 2s linear infinite}.progress-bar-success{background-color:#5cb85c}.progress-striped .progress-bar-success{background-image:-webkit-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:-o-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent)}.progress-bar-info{background-color:#5bc0de}.progress-striped .progress-bar-info{background-image:-webkit-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:-o-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent)}.progress-bar-warning{background-color:#f0ad4e}.progress-striped .progress-bar-warning{background-image:-webkit-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:-o-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent)}.progress-bar-danger{background-color:#d9534f}.progress-striped .progress-bar-danger{background-image:-webkit-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:-o-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent)}.media{margin-top:15px}.media:first-child{margin-top:0}.media,.media-body{overflow:hidden;zoom:1}.media-body{width:10000px}.media-object{display:block}.media-object.img-thumbnail{max-width:none}.media-right,.media>.pull-right{padding-left:10px}.media-left,.media>.pull-left{padding-right:10px}.media-body,.media-left,.media-right{display:table-cell;vertical-align:top}.media-middle{vertical-align:middle}.media-bottom{vertical-align:bottom}.media-heading{margin-top:0;margin-bottom:5px}.media-list{padding-left:0;list-style:none}.list-group{padding-left:0;margin-bottom:20px}.list-group-item{position:relative;display:block;padding:10px 15px;margin-bottom:-1px;background-color:#fff;border:1px solid #ddd}.list-group-item:first-child{border-top-left-radius:4px;border-top-right-radius:4px}.list-group-item:last-child{margin-bottom:0;border-bottom-right-radius:4px;border-bottom-left-radius:4px}a.list-group-item,button.list-group-item{color:#555}a.list-group-item .list-group-item-heading,button.list-group-item .list-group-item-heading{color:#333}a.list-group-item:focus,a.list-group-item:hover,button.list-group-item:focus,button.list-group-item:hover{color:#555;text-decoration:none;background-color:#f5f5f5}button.list-group-item{width:100%;text-align:left}.list-group-item.disabled,.list-group-item.disabled:focus,.list-group-item.disabled:hover{color:#777;cursor:not-allowed;background-color:#eee}.list-group-item.disabled .list-group-item-heading,.list-group-item.disabled:focus .list-group-item-heading,.list-group-item.disabled:hover .list-group-item-heading{color:inherit}.list-group-item.disabled .list-group-item-text,.list-group-item.disabled:focus .list-group-item-text,.list-group-item.disabled:hover .list-group-item-text{color:#777}.list-group-item.active,.list-group-item.active:focus,.list-group-item.active:hover{z-index:2;color:#fff;background-color:#337ab7;border-color:#337ab7}.list-group-item.active .list-group-item-heading,.list-group-item.active .list-group-item-heading>.small,.list-group-item.active .list-group-item-heading>small,.list-group-item.active:focus .list-group-item-heading,.list-group-item.active:focus .list-group-item-heading>.small,.list-group-item.active:focus .list-group-item-heading>small,.list-group-item.active:hover .list-group-item-heading,.list-group-item.active:hover .list-group-item-heading>.small,.list-group-item.active:hover .list-group-item-heading>small{color:inherit}.list-group-item.active .list-group-item-text,.list-group-item.active:focus .list-group-item-text,.list-group-item.active:hover .list-group-item-text{color:#c7ddef}.list-group-item-success{color:#3c763d;background-color:#dff0d8}a.list-group-item-success,button.list-group-item-success{color:#3c763d}a.list-group-item-success .list-group-item-heading,button.list-group-item-success .list-group-item-heading{color:inherit}a.list-group-item-success:focus,a.list-group-item-success:hover,button.list-group-item-success:focus,button.list-group-item-success:hover{color:#3c763d;background-color:#d0e9c6}a.list-group-item-success.active,a.list-group-item-success.active:focus,a.list-group-item-success.active:hover,button.list-group-item-success.active,button.list-group-item-success.active:focus,button.list-group-item-success.active:hover{color:#fff;background-color:#3c763d;border-color:#3c763d}.list-group-item-info{color:#31708f;background-color:#d9edf7}a.list-group-item-info,button.list-group-item-info{color:#31708f}a.list-group-item-info .list-group-item-heading,button.list-group-item-info .list-group-item-heading{color:inherit}a.list-group-item-info:focus,a.list-group-item-info:hover,button.list-group-item-info:focus,button.list-group-item-info:hover{color:#31708f;background-color:#c4e3f3}a.list-group-item-info.active,a.list-group-item-info.active:focus,a.list-group-item-info.active:hover,button.list-group-item-info.active,button.list-group-item-info.active:focus,button.list-group-item-info.active:hover{color:#fff;background-color:#31708f;border-color:#31708f}.list-group-item-warning{color:#8a6d3b;background-color:#fcf8e3}a.list-group-item-warning,button.list-group-item-warning{color:#8a6d3b}a.list-group-item-warning .list-group-item-heading,button.list-group-item-warning .list-group-item-heading{color:inherit}a.list-group-item-warning:focus,a.list-group-item-warning:hover,button.list-group-item-warning:focus,button.list-group-item-warning:hover{color:#8a6d3b;background-color:#faf2cc}a.list-group-item-warning.active,a.list-group-item-warning.active:focus,a.list-group-item-warning.active:hover,button.list-group-item-warning.active,button.list-group-item-warning.active:focus,button.list-group-item-warning.active:hover{color:#fff;background-color:#8a6d3b;border-color:#8a6d3b}.list-group-item-danger{color:#a94442;background-color:#f2dede}a.list-group-item-danger,button.list-group-item-danger{color:#a94442}a.list-group-item-danger .list-group-item-heading,button.list-group-item-danger .list-group-item-heading{color:inherit}a.list-group-item-danger:focus,a.list-group-item-danger:hover,button.list-group-item-danger:focus,button.list-group-item-danger:hover{color:#a94442;background-color:#ebcccc}a.list-group-item-danger.active,a.list-group-item-danger.active:focus,a.list-group-item-danger.active:hover,button.list-group-item-danger.active,button.list-group-item-danger.active:focus,button.list-group-item-danger.active:hover{color:#fff;background-color:#a94442;border-color:#a94442}.list-group-item-heading{margin-top:0;margin-bottom:5px}.list-group-item-text{margin-bottom:0;line-height:1.3}.panel{margin-bottom:20px;background-color:#fff;border:1px solid transparent;border-radius:4px;-webkit-box-shadow:0 1px 1px rgba(0,0,0,.05);box-shadow:0 1px 1px rgba(0,0,0,.05)}.panel-body{padding:15px}.panel-heading{padding:10px 15px;border-bottom:1px solid transparent;border-top-left-radius:3px;border-top-right-radius:3px}.panel-heading>.dropdown .dropdown-toggle{color:inherit}.panel-title{margin-top:0;margin-bottom:0;font-size:16px;color:inherit}.panel-title>.small,.panel-title>.small>a,.panel-title>a,.panel-title>small,.panel-title>small>a{color:inherit}.panel-footer{padding:10px 15px;background-color:#f5f5f5;border-top:1px solid #ddd;border-bottom-right-radius:3px;border-bottom-left-radius:3px}.panel>.list-group,.panel>.panel-collapse>.list-group{margin-bottom:0}.panel>.list-group .list-group-item,.panel>.panel-collapse>.list-group .list-group-item{border-width:1px 0;border-radius:0}.panel>.list-group:first-child .list-group-item:first-child,.panel>.panel-collapse>.list-group:first-child .list-group-item:first-child{border-top:0;border-top-left-radius:3px;border-top-right-radius:3px}.panel>.list-group:last-child .list-group-item:last-child,.panel>.panel-collapse>.list-group:last-child .list-group-item:last-child{border-bottom:0;border-bottom-right-radius:3px;border-bottom-left-radius:3px}.panel>.panel-heading+.panel-collapse>.list-group .list-group-item:first-child{border-top-left-radius:0;border-top-right-radius:0}.panel-heading+.list-group .list-group-item:first-child{border-top-width:0}.list-group+.panel-footer{border-top-width:0}.panel>.panel-collapse>.table,.panel>.table,.panel>.table-responsive>.table{margin-bottom:0}.panel>.panel-collapse>.table caption,.panel>.table caption,.panel>.table-responsive>.table caption{padding-right:15px;padding-left:15px}.panel>.table-responsive:first-child>.table:first-child,.panel>.table:first-child{border-top-left-radius:3px;border-top-right-radius:3px}.panel>.table-responsive:first-child>.table:first-child>tbody:first-child>tr:first-child,.panel>.table-responsive:first-child>.table:first-child>thead:first-child>tr:first-child,.panel>.table:first-child>tbody:first-child>tr:first-child,.panel>.table:first-child>thead:first-child>tr:first-child{border-top-left-radius:3px;border-top-right-radius:3px}.panel>.table-responsive:first-child>.table:first-child>tbody:first-child>tr:first-child td:first-child,.panel>.table-responsive:first-child>.table:first-child>tbody:first-child>tr:first-child th:first-child,.panel>.table-responsive:first-child>.table:first-child>thead:first-child>tr:first-child td:first-child,.panel>.table-responsive:first-child>.table:first-child>thead:first-child>tr:first-child th:first-child,.panel>.table:first-child>tbody:first-child>tr:first-child td:first-child,.panel>.table:first-child>tbody:first-child>tr:first-child th:first-child,.panel>.table:first-child>thead:first-child>tr:first-child td:first-child,.panel>.table:first-child>thead:first-child>tr:first-child th:first-child{border-top-left-radius:3px}.panel>.table-responsive:first-child>.table:first-child>tbody:first-child>tr:first-child td:last-child,.panel>.table-responsive:first-child>.table:first-child>tbody:first-child>tr:first-child th:last-child,.panel>.table-responsive:first-child>.table:first-child>thead:first-child>tr:first-child td:last-child,.panel>.table-responsive:first-child>.table:first-child>thead:first-child>tr:first-child th:last-child,.panel>.table:first-child>tbody:first-child>tr:first-child td:last-child,.panel>.table:first-child>tbody:first-child>tr:first-child th:last-child,.panel>.table:first-child>thead:first-child>tr:first-child td:last-child,.panel>.table:first-child>thead:first-child>tr:first-child th:last-child{border-top-right-radius:3px}.panel>.table-responsive:last-child>.table:last-child,.panel>.table:last-child{border-bottom-right-radius:3px;border-bottom-left-radius:3px}.panel>.table-responsive:last-child>.table:last-child>tbody:last-child>tr:last-child,.panel>.table-responsive:last-child>.table:last-child>tfoot:last-child>tr:last-child,.panel>.table:last-child>tbody:last-child>tr:last-child,.panel>.table:last-child>tfoot:last-child>tr:last-child{border-bottom-right-radius:3px;border-bottom-left-radius:3px}.panel>.table-responsive:last-child>.table:last-child>tbody:last-child>tr:last-child td:first-child,.panel>.table-responsive:last-child>.table:last-child>tbody:last-child>tr:last-child th:first-child,.panel>.table-responsive:last-child>.table:last-child>tfoot:last-child>tr:last-child td:first-child,.panel>.table-responsive:last-child>.table:last-child>tfoot:last-child>tr:last-child th:first-child,.panel>.table:last-child>tbody:last-child>tr:last-child td:first-child,.panel>.table:last-child>tbody:last-child>tr:last-child th:first-child,.panel>.table:last-child>tfoot:last-child>tr:last-child td:first-child,.panel>.table:last-child>tfoot:last-child>tr:last-child th:first-child{border-bottom-left-radius:3px}.panel>.table-responsive:last-child>.table:last-child>tbody:last-child>tr:last-child td:last-child,.panel>.table-responsive:last-child>.table:last-child>tbody:last-child>tr:last-child th:last-child,.panel>.table-responsive:last-child>.table:last-child>tfoot:last-child>tr:last-child td:last-child,.panel>.table-responsive:last-child>.table:last-child>tfoot:last-child>tr:last-child th:last-child,.panel>.table:last-child>tbody:last-child>tr:last-child td:last-child,.panel>.table:last-child>tbody:last-child>tr:last-child th:last-child,.panel>.table:last-child>tfoot:last-child>tr:last-child td:last-child,.panel>.table:last-child>tfoot:last-child>tr:last-child th:last-child{border-bottom-right-radius:3px}.panel>.panel-body+.table,.panel>.panel-body+.table-responsive,.panel>.table+.panel-body,.panel>.table-responsive+.panel-body{border-top:1px solid #ddd}.panel>.table>tbody:first-child>tr:first-child td,.panel>.table>tbody:first-child>tr:first-child th{border-top:0}.panel>.table-bordered,.panel>.table-responsive>.table-bordered{border:0}.panel>.table-bordered>tbody>tr>td:first-child,.panel>.table-bordered>tbody>tr>th:first-child,.panel>.table-bordered>tfoot>tr>td:first-child,.panel>.table-bordered>tfoot>tr>th:first-child,.panel>.table-bordered>thead>tr>td:first-child,.panel>.table-bordered>thead>tr>th:first-child,.panel>.table-responsive>.table-bordered>tbody>tr>td:first-child,.panel>.table-responsive>.table-bordered>tbody>tr>th:first-child,.panel>.table-responsive>.table-bordered>tfoot>tr>td:first-child,.panel>.table-responsive>.table-bordered>tfoot>tr>th:first-child,.panel>.table-responsive>.table-bordered>thead>tr>td:first-child,.panel>.table-responsive>.table-bordered>thead>tr>th:first-child{border-left:0}.panel>.table-bordered>tbody>tr>td:last-child,.panel>.table-bordered>tbody>tr>th:last-child,.panel>.table-bordered>tfoot>tr>td:last-child,.panel>.table-bordered>tfoot>tr>th:last-child,.panel>.table-bordered>thead>tr>td:last-child,.panel>.table-bordered>thead>tr>th:last-child,.panel>.table-responsive>.table-bordered>tbody>tr>td:last-child,.panel>.table-responsive>.table-bordered>tbody>tr>th:last-child,.panel>.table-responsive>.table-bordered>tfoot>tr>td:last-child,.panel>.table-responsive>.table-bordered>tfoot>tr>th:last-child,.panel>.table-responsive>.table-bordered>thead>tr>td:last-child,.panel>.table-responsive>.table-bordered>thead>tr>th:last-child{border-right:0}.panel>.table-bordered>tbody>tr:first-child>td,.panel>.table-bordered>tbody>tr:first-child>th,.panel>.table-bordered>thead>tr:first-child>td,.panel>.table-bordered>thead>tr:first-child>th,.panel>.table-responsive>.table-bordered>tbody>tr:first-child>td,.panel>.table-responsive>.table-bordered>tbody>tr:first-child>th,.panel>.table-responsive>.table-bordered>thead>tr:first-child>td,.panel>.table-responsive>.table-bordered>thead>tr:first-child>th{border-bottom:0}.panel>.table-bordered>tbody>tr:last-child>td,.panel>.table-bordered>tbody>tr:last-child>th,.panel>.table-bordered>tfoot>tr:last-child>td,.panel>.table-bordered>tfoot>tr:last-child>th,.panel>.table-responsive>.table-bordered>tbody>tr:last-child>td,.panel>.table-responsive>.table-bordered>tbody>tr:last-child>th,.panel>.table-responsive>.table-bordered>tfoot>tr:last-child>td,.panel>.table-responsive>.table-bordered>tfoot>tr:last-child>th{border-bottom:0}.panel>.table-responsive{margin-bottom:0;border:0}.panel-group{margin-bottom:20px}.panel-group .panel{margin-bottom:0;border-radius:4px}.panel-group .panel+.panel{margin-top:5px}.panel-group .panel-heading{border-bottom:0}.panel-group .panel-heading+.panel-collapse>.list-group,.panel-group .panel-heading+.panel-collapse>.panel-body{border-top:1px solid #ddd}.panel-group .panel-footer{border-top:0}.panel-group .panel-footer+.panel-collapse .panel-body{border-bottom:1px solid #ddd}.panel-default{border-color:#ddd}.panel-default>.panel-heading{color:#333;background-color:#f5f5f5;border-color:#ddd}.panel-default>.panel-heading+.panel-collapse>.panel-body{border-top-color:#ddd}.panel-default>.panel-heading .badge{color:#f5f5f5;background-color:#333}.panel-default>.panel-footer+.panel-collapse>.panel-body{border-bottom-color:#ddd}.panel-primary{border-color:#337ab7}.panel-primary>.panel-heading{color:#fff;background-color:#337ab7;border-color:#337ab7}.panel-primary>.panel-heading+.panel-collapse>.panel-body{border-top-color:#337ab7}.panel-primary>.panel-heading .badge{color:#337ab7;background-color:#fff}.panel-primary>.panel-footer+.panel-collapse>.panel-body{border-bottom-color:#337ab7}.panel-success{border-color:#d6e9c6}.panel-success>.panel-heading{color:#3c763d;background-color:#dff0d8;border-color:#d6e9c6}.panel-success>.panel-heading+.panel-collapse>.panel-body{border-top-color:#d6e9c6}.panel-success>.panel-heading .badge{color:#dff0d8;background-color:#3c763d}.panel-success>.panel-footer+.panel-collapse>.panel-body{border-bottom-color:#d6e9c6}.panel-info{border-color:#bce8f1}.panel-info>.panel-heading{color:#31708f;background-color:#d9edf7;border-color:#bce8f1}.panel-info>.panel-heading+.panel-collapse>.panel-body{border-top-color:#bce8f1}.panel-info>.panel-heading .badge{color:#d9edf7;background-color:#31708f}.panel-info>.panel-footer+.panel-collapse>.panel-body{border-bottom-color:#bce8f1}.panel-warning{border-color:#faebcc}.panel-warning>.panel-heading{color:#8a6d3b;background-color:#fcf8e3;border-color:#faebcc}.panel-warning>.panel-heading+.panel-collapse>.panel-body{border-top-color:#faebcc}.panel-warning>.panel-heading .badge{color:#fcf8e3;background-color:#8a6d3b}.panel-warning>.panel-footer+.panel-collapse>.panel-body{border-bottom-color:#faebcc}.panel-danger{border-color:#ebccd1}.panel-danger>.panel-heading{color:#a94442;background-color:#f2dede;border-color:#ebccd1}.panel-danger>.panel-heading+.panel-collapse>.panel-body{border-top-color:#ebccd1}.panel-danger>.panel-heading .badge{color:#f2dede;background-color:#a94442}.panel-danger>.panel-footer+.panel-collapse>.panel-body{border-bottom-color:#ebccd1}.embed-responsive{position:relative;display:block;height:0;padding:0;overflow:hidden}.embed-responsive .embed-responsive-item,.embed-responsive embed,.embed-responsive iframe,.embed-responsive object,.embed-responsive video{position:absolute;top:0;bottom:0;left:0;width:100%;height:100%;border:0}.embed-responsive-16by9{padding-bottom:56.25%}.embed-responsive-4by3{padding-bottom:75%}.well{min-height:20px;padding:19px;margin-bottom:20px;background-color:#f5f5f5;border:1px solid #e3e3e3;border-radius:4px;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.05);box-shadow:inset 0 1px 1px rgba(0,0,0,.05)}.well blockquote{border-color:#ddd;border-color:rgba(0,0,0,.15)}.well-lg{padding:24px;border-radius:6px}.well-sm{padding:9px;border-radius:3px}.close{float:right;font-size:21px;font-weight:700;line-height:1;color:#000;text-shadow:0 1px 0 #fff;filter:alpha(opacity=20);opacity:.2}.close:focus,.close:hover{color:#000;text-decoration:none;cursor:pointer;filter:alpha(opacity=50);opacity:.5}button.close{-webkit-appearance:none;padding:0;cursor:pointer;background:0 0;border:0}.modal-open{overflow:hidden}.modal{position:fixed;top:0;right:0;bottom:0;left:0;z-index:1050;display:none;overflow:hidden;-webkit-overflow-scrolling:touch;outline:0}.modal.fade .modal-dialog{-webkit-transition:-webkit-transform .3s ease-out;-o-transition:-o-transform .3s ease-out;transition:transform .3s ease-out;-webkit-transform:translate(0,-25%);-ms-transform:translate(0,-25%);-o-transform:translate(0,-25%);transform:translate(0,-25%)}.modal.in .modal-dialog{-webkit-transform:translate(0,0);-ms-transform:translate(0,0);-o-transform:translate(0,0);transform:translate(0,0)}.modal-open .modal{overflow-x:hidden;overflow-y:auto}.modal-dialog{position:relative;width:auto;margin:10px}.modal-content{position:relative;background-color:#fff;-webkit-background-clip:padding-box;background-clip:padding-box;border:1px solid #999;border:1px solid rgba(0,0,0,.2);border-radius:6px;outline:0;-webkit-box-shadow:0 3px 9px rgba(0,0,0,.5);box-shadow:0 3px 9px rgba(0,0,0,.5)}.modal-backdrop{position:fixed;top:0;right:0;bottom:0;left:0;z-index:1040;background-color:#000}.modal-backdrop.fade{filter:alpha(opacity=0);opacity:0}.modal-backdrop.in{filter:alpha(opacity=50);opacity:.5}.modal-header{min-height:16.43px;padding:15px;border-bottom:1px solid #e5e5e5}.modal-header .close{margin-top:-2px}.modal-title{margin:0;line-height:1.42857143}.modal-body{position:relative;padding:15px}.modal-footer{padding:15px;text-align:right;border-top:1px solid #e5e5e5}.modal-footer .btn+.btn{margin-bottom:0;margin-left:5px}.modal-footer .btn-group .btn+.btn{margin-left:-1px}.modal-footer .btn-block+.btn-block{margin-left:0}.modal-scrollbar-measure{position:absolute;top:-9999px;width:50px;height:50px;overflow:scroll}@media (min-width:768px){.modal-dialog{width:600px;margin:30px auto}.modal-content{-webkit-box-shadow:0 5px 15px rgba(0,0,0,.5);box-shadow:0 5px 15px rgba(0,0,0,.5)}.modal-sm{width:300px}}@media (min-width:992px){.modal-lg{width:900px}}.tooltip{position:absolute;z-index:1070;display:block;font-family:"Helvetica Neue",Helvetica,Arial,sans-serif;font-size:12px;font-style:normal;font-weight:400;line-height:1.42857143;text-align:left;text-align:start;text-decoration:none;text-shadow:none;text-transform:none;letter-spacing:normal;word-break:normal;word-spacing:normal;word-wrap:normal;white-space:normal;filter:alpha(opacity=0);opacity:0;line-break:auto}.tooltip.in{filter:alpha(opacity=90);opacity:.9}.tooltip.top{padding:5px 0;margin-top:-3px}.tooltip.right{padding:0 5px;margin-left:3px}.tooltip.bottom{padding:5px 0;margin-top:3px}.tooltip.left{padding:0 5px;margin-left:-3px}.tooltip-inner{max-width:200px;padding:3px 8px;color:#fff;text-align:center;background-color:#000;border-radius:4px}.tooltip-arrow{position:absolute;width:0;height:0;border-color:transparent;border-style:solid}.tooltip.top .tooltip-arrow{bottom:0;left:50%;margin-left:-5px;border-width:5px 5px 0;border-top-color:#000}.tooltip.top-left .tooltip-arrow{right:5px;bottom:0;margin-bottom:-5px;border-width:5px 5px 0;border-top-color:#000}.tooltip.top-right .tooltip-arrow{bottom:0;left:5px;margin-bottom:-5px;border-width:5px 5px 0;border-top-color:#000}.tooltip.right .tooltip-arrow{top:50%;left:0;margin-top:-5px;border-width:5px 5px 5px 0;border-right-color:#000}.tooltip.left .tooltip-arrow{top:50%;right:0;margin-top:-5px;border-width:5px 0 5px 5px;border-left-color:#000}.tooltip.bottom .tooltip-arrow{top:0;left:50%;margin-left:-5px;border-width:0 5px 5px;border-bottom-color:#000}.tooltip.bottom-left .tooltip-arrow{top:0;right:5px;margin-top:-5px;border-width:0 5px 5px;border-bottom-color:#000}.tooltip.bottom-right .tooltip-arrow{top:0;left:5px;margin-top:-5px;border-width:0 5px 5px;border-bottom-color:#000}.popover{position:absolute;top:0;left:0;z-index:1060;display:none;max-width:276px;padding:1px;font-family:"Helvetica Neue",Helvetica,Arial,sans-serif;font-size:14px;font-style:normal;font-weight:400;line-height:1.42857143;text-align:left;text-align:start;text-decoration:none;text-shadow:none;text-transform:none;letter-spacing:normal;word-break:normal;word-spacing:normal;word-wrap:normal;white-space:normal;background-color:#fff;-webkit-background-clip:padding-box;background-clip:padding-box;border:1px solid #ccc;border:1px solid rgba(0,0,0,.2);border-radius:6px;-webkit-box-shadow:0 5px 10px rgba(0,0,0,.2);box-shadow:0 5px 10px rgba(0,0,0,.2);line-break:auto}.popover.top{margin-top:-10px}.popover.right{margin-left:10px}.popover.bottom{margin-top:10px}.popover.left{margin-left:-10px}.popover-title{padding:8px 14px;margin:0;font-size:14px;background-color:#f7f7f7;border-bottom:1px solid #ebebeb;border-radius:5px 5px 0 0}.popover-content{padding:9px 14px}.popover>.arrow,.popover>.arrow:after{position:absolute;display:block;width:0;height:0;border-color:transparent;border-style:solid}.popover>.arrow{border-width:11px}.popover>.arrow:after{content:"";border-width:10px}.popover.top>.arrow{bottom:-11px;left:50%;margin-left:-11px;border-top-color:#999;border-top-color:rgba(0,0,0,.25);border-bottom-width:0}.popover.top>.arrow:after{bottom:1px;margin-left:-10px;content:" ";border-top-color:#fff;border-bottom-width:0}.popover.right>.arrow{top:50%;left:-11px;margin-top:-11px;border-right-color:#999;border-right-color:rgba(0,0,0,.25);border-left-width:0}.popover.right>.arrow:after{bottom:-10px;left:1px;content:" ";border-right-color:#fff;border-left-width:0}.popover.bottom>.arrow{top:-11px;left:50%;margin-left:-11px;border-top-width:0;border-bottom-color:#999;border-bottom-color:rgba(0,0,0,.25)}.popover.bottom>.arrow:after{top:1px;margin-left:-10px;content:" ";border-top-width:0;border-bottom-color:#fff}.popover.left>.arrow{top:50%;right:-11px;margin-top:-11px;border-right-width:0;border-left-color:#999;border-left-color:rgba(0,0,0,.25)}.popover.left>.arrow:after{right:1px;bottom:-10px;content:" ";border-right-width:0;border-left-color:#fff}.carousel{position:relative}.carousel-inner{position:relative;width:100%;overflow:hidden}.carousel-inner>.item{position:relative;display:none;-webkit-transition:.6s ease-in-out left;-o-transition:.6s ease-in-out left;transition:.6s ease-in-out left}.carousel-inner>.item>a>img,.carousel-inner>.item>img{line-height:1}@media all and (transform-3d),(-webkit-transform-3d){.carousel-inner>.item{-webkit-transition:-webkit-transform .6s ease-in-out;-o-transition:-o-transform .6s ease-in-out;transition:transform .6s ease-in-out;-webkit-backface-visibility:hidden;backface-visibility:hidden;-webkit-perspective:1000px;perspective:1000px}.carousel-inner>.item.active.right,.carousel-inner>.item.next{left:0;-webkit-transform:translate3d(100%,0,0);transform:translate3d(100%,0,0)}.carousel-inner>.item.active.left,.carousel-inner>.item.prev{left:0;-webkit-transform:translate3d(-100%,0,0);transform:translate3d(-100%,0,0)}.carousel-inner>.item.active,.carousel-inner>.item.next.left,.carousel-inner>.item.prev.right{left:0;-webkit-transform:translate3d(0,0,0);transform:translate3d(0,0,0)}}.carousel-inner>.active,.carousel-inner>.next,.carousel-inner>.prev{display:block}.carousel-inner>.active{left:0}.carousel-inner>.next,.carousel-inner>.prev{position:absolute;top:0;width:100%}.carousel-inner>.next{left:100%}.carousel-inner>.prev{left:-100%}.carousel-inner>.next.left,.carousel-inner>.prev.right{left:0}.carousel-inner>.active.left{left:-100%}.carousel-inner>.active.right{left:100%}.carousel-control{position:absolute;top:0;bottom:0;left:0;width:15%;font-size:20px;color:#fff;text-align:center;text-shadow:0 1px 2px rgba(0,0,0,.6);filter:alpha(opacity=50);opacity:.5}.carousel-control.left{background-image:-webkit-linear-gradient(left,rgba(0,0,0,.5) 0,rgba(0,0,0,.0001) 100%);background-image:-o-linear-gradient(left,rgba(0,0,0,.5) 0,rgba(0,0,0,.0001) 100%);background-image:-webkit-gradient(linear,left top,right top,from(rgba(0,0,0,.5)),to(rgba(0,0,0,.0001)));background-image:linear-gradient(to right,rgba(0,0,0,.5) 0,rgba(0,0,0,.0001) 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1);background-repeat:repeat-x}.carousel-control.right{right:0;left:auto;background-image:-webkit-linear-gradient(left,rgba(0,0,0,.0001) 0,rgba(0,0,0,.5) 100%);background-image:-o-linear-gradient(left,rgba(0,0,0,.0001) 0,rgba(0,0,0,.5) 100%);background-image:-webkit-gradient(linear,left top,right top,from(rgba(0,0,0,.0001)),to(rgba(0,0,0,.5)));background-image:linear-gradient(to right,rgba(0,0,0,.0001) 0,rgba(0,0,0,.5) 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1);background-repeat:repeat-x}.carousel-control:focus,.carousel-control:hover{color:#fff;text-decoration:none;filter:alpha(opacity=90);outline:0;opacity:.9}.carousel-control .glyphicon-chevron-left,.carousel-control .glyphicon-chevron-right,.carousel-control .icon-next,.carousel-control .icon-prev{position:absolute;top:50%;z-index:5;display:inline-block;margin-top:-10px}.carousel-control .glyphicon-chevron-left,.carousel-control .icon-prev{left:50%;margin-left:-10px}.carousel-control .glyphicon-chevron-right,.carousel-control .icon-next{right:50%;margin-right:-10px}.carousel-control .icon-next,.carousel-control .icon-prev{width:20px;height:20px;font-family:serif;line-height:1}.carousel-control .icon-prev:before{content:'\2039'}.carousel-control .icon-next:before{content:'\203a'}.carousel-indicators{position:absolute;bottom:10px;left:50%;z-index:15;width:60%;padding-left:0;margin-left:-30%;text-align:center;list-style:none}.carousel-indicators li{display:inline-block;width:10px;height:10px;margin:1px;text-indent:-999px;cursor:pointer;background-color:#000\9;background-color:rgba(0,0,0,0);border:1px solid #fff;border-radius:10px}.carousel-indicators .active{width:12px;height:12px;margin:0;background-color:#fff}.carousel-caption{position:absolute;right:15%;bottom:20px;left:15%;z-index:10;padding-top:20px;padding-bottom:20px;color:#fff;text-align:center;text-shadow:0 1px 2px rgba(0,0,0,.6)}.carousel-caption .btn{text-shadow:none}@media screen and (min-width:768px){.carousel-control .glyphicon-chevron-left,.carousel-control .glyphicon-chevron-right,.carousel-control .icon-next,.carousel-control .icon-prev{width:30px;height:30px;margin-top:-15px;font-size:30px}.carousel-control .glyphicon-chevron-left,.carousel-control .icon-prev{margin-left:-15px}.carousel-control .glyphicon-chevron-right,.carousel-control .icon-next{margin-right:-15px}.carousel-caption{right:20%;left:20%;padding-bottom:30px}.carousel-indicators{bottom:20px}}.btn-group-vertical>.btn-group:after,.btn-group-vertical>.btn-group:before,.btn-toolbar:after,.btn-toolbar:before,.clearfix:after,.clearfix:before,.container-fluid:after,.container-fluid:before,.container:after,.container:before,.dl-horizontal dd:after,.dl-horizontal dd:before,.form-horizontal .form-group:after,.form-horizontal .form-group:before,.modal-footer:after,.modal-footer:before,.nav:after,.nav:before,.navbar-collapse:after,.navbar-collapse:before,.navbar-header:after,.navbar-header:before,.navbar:after,.navbar:before,.pager:after,.pager:before,.panel-body:after,.panel-body:before,.row:after,.row:before{display:table;content:" "}.btn-group-vertical>.btn-group:after,.btn-toolbar:after,.clearfix:after,.container-fluid:after,.container:after,.dl-horizontal dd:after,.form-horizontal .form-group:after,.modal-footer:after,.nav:after,.navbar-collapse:after,.navbar-header:after,.navbar:after,.pager:after,.panel-body:after,.row:after{clear:both}.center-block{display:block;margin-right:auto;margin-left:auto}.pull-right{float:right!important}.pull-left{float:left!important}.hide{display:none!important}.show{display:block!important}.invisible{visibility:hidden}.text-hide{font:0/0 a;color:transparent;text-shadow:none;background-color:transparent;border:0}.hidden{display:none!important}.affix{position:fixed}@-ms-viewport{width:device-width}.visible-lg,.visible-md,.visible-sm,.visible-xs{display:none!important}.visible-lg-block,.visible-lg-inline,.visible-lg-inline-block,.visible-md-block,.visible-md-inline,.visible-md-inline-block,.visible-sm-block,.visible-sm-inline,.visible-sm-inline-block,.visible-xs-block,.visible-xs-inline,.visible-xs-inline-block{display:none!important}@media (max-width:767px){.visible-xs{display:block!important}table.visible-xs{display:table!important}tr.visible-xs{display:table-row!important}td.visible-xs,th.visible-xs{display:table-cell!important}}@media (max-width:767px){.visible-xs-block{display:block!important}}@media (max-width:767px){.visible-xs-inline{display:inline!important}}@media (max-width:767px){.visible-xs-inline-block{display:inline-block!important}}@media (min-width:768px) and (max-width:991px){.visible-sm{display:block!important}table.visible-sm{display:table!important}tr.visible-sm{display:table-row!important}td.visible-sm,th.visible-sm{display:table-cell!important}}@media (min-width:768px) and (max-width:991px){.visible-sm-block{display:block!important}}@media (min-width:768px) and (max-width:991px){.visible-sm-inline{display:inline!important}}@media (min-width:768px) and (max-width:991px){.visible-sm-inline-block{display:inline-block!important}}@media (min-width:992px) and (max-width:1199px){.visible-md{display:block!important}table.visible-md{display:table!important}tr.visible-md{display:table-row!important}td.visible-md,th.visible-md{display:table-cell!important}}@media (min-width:992px) and (max-width:1199px){.visible-md-block{display:block!important}}@media (min-width:992px) and (max-width:1199px){.visible-md-inline{display:inline!important}}@media (min-width:992px) and (max-width:1199px){.visible-md-inline-block{display:inline-block!important}}@media (min-width:1200px){.visible-lg{display:block!important}table.visible-lg{display:table!important}tr.visible-lg{display:table-row!important}td.visible-lg,th.visible-lg{display:table-cell!important}}@media (min-width:1200px){.visible-lg-block{display:block!important}}@media (min-width:1200px){.visible-lg-inline{display:inline!important}}@media (min-width:1200px){.visible-lg-inline-block{display:inline-block!important}}@media (max-width:767px){.hidden-xs{display:none!important}}@media (min-width:768px) and (max-width:991px){.hidden-sm{display:none!important}}@media (min-width:992px) and (max-width:1199px){.hidden-md{display:none!important}}@media (min-width:1200px){.hidden-lg{display:none!important}}.visible-print{display:none!important}@media print{.visible-print{display:block!important}table.visible-print{display:table!important}tr.visible-print{display:table-row!important}td.visible-print,th.visible-print{display:table-cell!important}}.visible-print-block{display:none!important}@media print{.visible-print-block{display:block!important}}.visible-print-inline{display:none!important}@media print{.visible-print-inline{display:inline!important}}.visible-print-inline-block{display:none!important}@media print{.visible-print-inline-block{display:inline-block!important}}@media print{.hidden-print{display:none!important}}
+<style type="text/css">html{font-family:sans-serif;-webkit-text-size-adjust:100%;-ms-text-size-adjust:100%}body{margin:0}article,aside,details,figcaption,figure,footer,header,hgroup,main,menu,nav,section,summary{display:block}audio,canvas,progress,video{display:inline-block;vertical-align:baseline}audio:not([controls]){display:none;height:0}[hidden],template{display:none}a{background-color:transparent}a:active,a:hover{outline:0}abbr[title]{border-bottom:1px dotted}b,strong{font-weight:700}dfn{font-style:italic}h1{margin:.67em 0;font-size:2em}mark{color:#000;background:#ff0}small{font-size:80%}sub,sup{position:relative;font-size:75%;line-height:0;vertical-align:baseline}sup{top:-.5em}sub{bottom:-.25em}img{border:0}svg:not(:root){overflow:hidden}figure{margin:1em 40px}hr{height:0;-webkit-box-sizing:content-box;-moz-box-sizing:content-box;box-sizing:content-box}pre{overflow:auto}code,kbd,pre,samp{font-family:monospace,monospace;font-size:1em}button,input,optgroup,select,textarea{margin:0;font:inherit;color:inherit}button{overflow:visible}button,select{text-transform:none}button,html input[type=button],input[type=reset],input[type=submit]{-webkit-appearance:button;cursor:pointer}button[disabled],html input[disabled]{cursor:default}button::-moz-focus-inner,input::-moz-focus-inner{padding:0;border:0}input{line-height:normal}input[type=checkbox],input[type=radio]{-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;padding:0}input[type=number]::-webkit-inner-spin-button,input[type=number]::-webkit-outer-spin-button{height:auto}input[type=search]{-webkit-box-sizing:content-box;-moz-box-sizing:content-box;box-sizing:content-box;-webkit-appearance:textfield}input[type=search]::-webkit-search-cancel-button,input[type=search]::-webkit-search-decoration{-webkit-appearance:none}fieldset{padding:.35em .625em .75em;margin:0 2px;border:1px solid silver}legend{padding:0;border:0}textarea{overflow:auto}optgroup{font-weight:700}table{border-spacing:0;border-collapse:collapse}td,th{padding:0}@media print{*,:after,:before{color:#000!important;text-shadow:none!important;background:0 0!important;-webkit-box-shadow:none!important;box-shadow:none!important}a,a:visited{text-decoration:underline}a[href]:after{content:" (" attr(href) ")"}abbr[title]:after{content:" (" attr(title) ")"}a[href^="javascript:"]:after,a[href^="#"]:after{content:""}blockquote,pre{border:1px solid #999;page-break-inside:avoid}thead{display:table-header-group}img,tr{page-break-inside:avoid}img{max-width:100%!important}h2,h3,p{orphans:3;widows:3}h2,h3{page-break-after:avoid}.navbar{display:none}.btn>.caret,.dropup>.btn>.caret{border-top-color:#000!important}.label{border:1px solid #000}.table{border-collapse:collapse!important}.table td,.table th{background-color:#fff!important}.table-bordered td,.table-bordered th{border:1px solid #ddd!important}}@font-face{font-family:'Glyphicons Halflings';src:url(data:application/vnd.ms-fontobject;base64,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);src:url(data:application/vnd.ms-fontobject;base64,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) format('embedded-opentype'),url(data:font/woff;base64,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) format('woff'),url(data:font/ttf;base64,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) format('truetype'),url(data:image/svg+xml;base64,<?xml version="1.0" standalone="no"?>
<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN" "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd" >
<svg xmlns="http://www.w3.org/2000/svg">
<metadata></metadata>
<defs>
<font id="glyphicons_halflingsregular" horiz-adv-x="1200" >
<font-face units-per-em="1200" ascent="960" descent="-240" />
<missing-glyph horiz-adv-x="500" />
<glyph horiz-adv-x="0" />
<glyph horiz-adv-x="400" />
<glyph unicode=" " />
<glyph unicode="*" d="M600 1100q15 0 34 -1.5t30 -3.5l11 -1q10 -2 17.5 -10.5t7.5 -18.5v-224l158 158q7 7 18 8t19 -6l106 -106q7 -8 6 -19t-8 -18l-158 -158h224q10 0 18.5 -7.5t10.5 -17.5q6 -41 6 -75q0 -15 -1.5 -34t-3.5 -30l-1 -11q-2 -10 -10.5 -17.5t-18.5 -7.5h-224l158 -158 q7 -7 8 -18t-6 -19l-106 -106q-8 -7 -19 -6t-18 8l-158 158v-224q0 -10 -7.5 -18.5t-17.5 -10.5q-41 -6 -75 -6q-15 0 -34 1.5t-30 3.5l-11 1q-10 2 -17.5 10.5t-7.5 18.5v224l-158 -158q-7 -7 -18 -8t-19 6l-106 106q-7 8 -6 19t8 18l158 158h-224q-10 0 -18.5 7.5 t-10.5 17.5q-6 41 -6 75q0 15 1.5 34t3.5 30l1 11q2 10 10.5 17.5t18.5 7.5h224l-158 158q-7 7 -8 18t6 19l106 106q8 7 19 6t18 -8l158 -158v224q0 10 7.5 18.5t17.5 10.5q41 6 75 6z" />
<glyph unicode="+" d="M450 1100h200q21 0 35.5 -14.5t14.5 -35.5v-350h350q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-350v-350q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v350h-350q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5 h350v350q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xa0;" />
<glyph unicode="&#xa5;" d="M825 1100h250q10 0 12.5 -5t-5.5 -13l-364 -364q-6 -6 -11 -18h268q10 0 13 -6t-3 -14l-120 -160q-6 -8 -18 -14t-22 -6h-125v-100h275q10 0 13 -6t-3 -14l-120 -160q-6 -8 -18 -14t-22 -6h-125v-174q0 -11 -7.5 -18.5t-18.5 -7.5h-148q-11 0 -18.5 7.5t-7.5 18.5v174 h-275q-10 0 -13 6t3 14l120 160q6 8 18 14t22 6h125v100h-275q-10 0 -13 6t3 14l120 160q6 8 18 14t22 6h118q-5 12 -11 18l-364 364q-8 8 -5.5 13t12.5 5h250q25 0 43 -18l164 -164q8 -8 18 -8t18 8l164 164q18 18 43 18z" />
<glyph unicode="&#x2000;" horiz-adv-x="650" />
<glyph unicode="&#x2001;" horiz-adv-x="1300" />
<glyph unicode="&#x2002;" horiz-adv-x="650" />
<glyph unicode="&#x2003;" horiz-adv-x="1300" />
<glyph unicode="&#x2004;" horiz-adv-x="433" />
<glyph unicode="&#x2005;" horiz-adv-x="325" />
<glyph unicode="&#x2006;" horiz-adv-x="216" />
<glyph unicode="&#x2007;" horiz-adv-x="216" />
<glyph unicode="&#x2008;" horiz-adv-x="162" />
<glyph unicode="&#x2009;" horiz-adv-x="260" />
<glyph unicode="&#x200a;" horiz-adv-x="72" />
<glyph unicode="&#x202f;" horiz-adv-x="260" />
<glyph unicode="&#x205f;" horiz-adv-x="325" />
<glyph unicode="&#x20ac;" d="M744 1198q242 0 354 -189q60 -104 66 -209h-181q0 45 -17.5 82.5t-43.5 61.5t-58 40.5t-60.5 24t-51.5 7.5q-19 0 -40.5 -5.5t-49.5 -20.5t-53 -38t-49 -62.5t-39 -89.5h379l-100 -100h-300q-6 -50 -6 -100h406l-100 -100h-300q9 -74 33 -132t52.5 -91t61.5 -54.5t59 -29 t47 -7.5q22 0 50.5 7.5t60.5 24.5t58 41t43.5 61t17.5 80h174q-30 -171 -128 -278q-107 -117 -274 -117q-206 0 -324 158q-36 48 -69 133t-45 204h-217l100 100h112q1 47 6 100h-218l100 100h134q20 87 51 153.5t62 103.5q117 141 297 141z" />
<glyph unicode="&#x20bd;" d="M428 1200h350q67 0 120 -13t86 -31t57 -49.5t35 -56.5t17 -64.5t6.5 -60.5t0.5 -57v-16.5v-16.5q0 -36 -0.5 -57t-6.5 -61t-17 -65t-35 -57t-57 -50.5t-86 -31.5t-120 -13h-178l-2 -100h288q10 0 13 -6t-3 -14l-120 -160q-6 -8 -18 -14t-22 -6h-138v-175q0 -11 -5.5 -18 t-15.5 -7h-149q-10 0 -17.5 7.5t-7.5 17.5v175h-267q-10 0 -13 6t3 14l120 160q6 8 18 14t22 6h117v100h-267q-10 0 -13 6t3 14l120 160q6 8 18 14t22 6h117v475q0 10 7.5 17.5t17.5 7.5zM600 1000v-300h203q64 0 86.5 33t22.5 119q0 84 -22.5 116t-86.5 32h-203z" />
<glyph unicode="&#x2212;" d="M250 700h800q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#x231b;" d="M1000 1200v-150q0 -21 -14.5 -35.5t-35.5 -14.5h-50v-100q0 -91 -49.5 -165.5t-130.5 -109.5q81 -35 130.5 -109.5t49.5 -165.5v-150h50q21 0 35.5 -14.5t14.5 -35.5v-150h-800v150q0 21 14.5 35.5t35.5 14.5h50v150q0 91 49.5 165.5t130.5 109.5q-81 35 -130.5 109.5 t-49.5 165.5v100h-50q-21 0 -35.5 14.5t-14.5 35.5v150h800zM400 1000v-100q0 -60 32.5 -109.5t87.5 -73.5q28 -12 44 -37t16 -55t-16 -55t-44 -37q-55 -24 -87.5 -73.5t-32.5 -109.5v-150h400v150q0 60 -32.5 109.5t-87.5 73.5q-28 12 -44 37t-16 55t16 55t44 37 q55 24 87.5 73.5t32.5 109.5v100h-400z" />
<glyph unicode="&#x25fc;" horiz-adv-x="500" d="M0 0z" />
<glyph unicode="&#x2601;" d="M503 1089q110 0 200.5 -59.5t134.5 -156.5q44 14 90 14q120 0 205 -86.5t85 -206.5q0 -121 -85 -207.5t-205 -86.5h-750q-79 0 -135.5 57t-56.5 137q0 69 42.5 122.5t108.5 67.5q-2 12 -2 37q0 153 108 260.5t260 107.5z" />
<glyph unicode="&#x26fa;" d="M774 1193.5q16 -9.5 20.5 -27t-5.5 -33.5l-136 -187l467 -746h30q20 0 35 -18.5t15 -39.5v-42h-1200v42q0 21 15 39.5t35 18.5h30l468 746l-135 183q-10 16 -5.5 34t20.5 28t34 5.5t28 -20.5l111 -148l112 150q9 16 27 20.5t34 -5zM600 200h377l-182 112l-195 534v-646z " />
<glyph unicode="&#x2709;" d="M25 1100h1150q10 0 12.5 -5t-5.5 -13l-564 -567q-8 -8 -18 -8t-18 8l-564 567q-8 8 -5.5 13t12.5 5zM18 882l264 -264q8 -8 8 -18t-8 -18l-264 -264q-8 -8 -13 -5.5t-5 12.5v550q0 10 5 12.5t13 -5.5zM918 618l264 264q8 8 13 5.5t5 -12.5v-550q0 -10 -5 -12.5t-13 5.5 l-264 264q-8 8 -8 18t8 18zM818 482l364 -364q8 -8 5.5 -13t-12.5 -5h-1150q-10 0 -12.5 5t5.5 13l364 364q8 8 18 8t18 -8l164 -164q8 -8 18 -8t18 8l164 164q8 8 18 8t18 -8z" />
<glyph unicode="&#x270f;" d="M1011 1210q19 0 33 -13l153 -153q13 -14 13 -33t-13 -33l-99 -92l-214 214l95 96q13 14 32 14zM1013 800l-615 -614l-214 214l614 614zM317 96l-333 -112l110 335z" />
<glyph unicode="&#xe001;" d="M700 650v-550h250q21 0 35.5 -14.5t14.5 -35.5v-50h-800v50q0 21 14.5 35.5t35.5 14.5h250v550l-500 550h1200z" />
<glyph unicode="&#xe002;" d="M368 1017l645 163q39 15 63 0t24 -49v-831q0 -55 -41.5 -95.5t-111.5 -63.5q-79 -25 -147 -4.5t-86 75t25.5 111.5t122.5 82q72 24 138 8v521l-600 -155v-606q0 -42 -44 -90t-109 -69q-79 -26 -147 -5.5t-86 75.5t25.5 111.5t122.5 82.5q72 24 138 7v639q0 38 14.5 59 t53.5 34z" />
<glyph unicode="&#xe003;" d="M500 1191q100 0 191 -39t156.5 -104.5t104.5 -156.5t39 -191l-1 -2l1 -5q0 -141 -78 -262l275 -274q23 -26 22.5 -44.5t-22.5 -42.5l-59 -58q-26 -20 -46.5 -20t-39.5 20l-275 274q-119 -77 -261 -77l-5 1l-2 -1q-100 0 -191 39t-156.5 104.5t-104.5 156.5t-39 191 t39 191t104.5 156.5t156.5 104.5t191 39zM500 1022q-88 0 -162 -43t-117 -117t-43 -162t43 -162t117 -117t162 -43t162 43t117 117t43 162t-43 162t-117 117t-162 43z" />
<glyph unicode="&#xe005;" d="M649 949q48 68 109.5 104t121.5 38.5t118.5 -20t102.5 -64t71 -100.5t27 -123q0 -57 -33.5 -117.5t-94 -124.5t-126.5 -127.5t-150 -152.5t-146 -174q-62 85 -145.5 174t-150 152.5t-126.5 127.5t-93.5 124.5t-33.5 117.5q0 64 28 123t73 100.5t104 64t119 20 t120.5 -38.5t104.5 -104z" />
<glyph unicode="&#xe006;" d="M407 800l131 353q7 19 17.5 19t17.5 -19l129 -353h421q21 0 24 -8.5t-14 -20.5l-342 -249l130 -401q7 -20 -0.5 -25.5t-24.5 6.5l-343 246l-342 -247q-17 -12 -24.5 -6.5t-0.5 25.5l130 400l-347 251q-17 12 -14 20.5t23 8.5h429z" />
<glyph unicode="&#xe007;" d="M407 800l131 353q7 19 17.5 19t17.5 -19l129 -353h421q21 0 24 -8.5t-14 -20.5l-342 -249l130 -401q7 -20 -0.5 -25.5t-24.5 6.5l-343 246l-342 -247q-17 -12 -24.5 -6.5t-0.5 25.5l130 400l-347 251q-17 12 -14 20.5t23 8.5h429zM477 700h-240l197 -142l-74 -226 l193 139l195 -140l-74 229l192 140h-234l-78 211z" />
<glyph unicode="&#xe008;" d="M600 1200q124 0 212 -88t88 -212v-250q0 -46 -31 -98t-69 -52v-75q0 -10 6 -21.5t15 -17.5l358 -230q9 -5 15 -16.5t6 -21.5v-93q0 -10 -7.5 -17.5t-17.5 -7.5h-1150q-10 0 -17.5 7.5t-7.5 17.5v93q0 10 6 21.5t15 16.5l358 230q9 6 15 17.5t6 21.5v75q-38 0 -69 52 t-31 98v250q0 124 88 212t212 88z" />
<glyph unicode="&#xe009;" d="M25 1100h1150q10 0 17.5 -7.5t7.5 -17.5v-1050q0 -10 -7.5 -17.5t-17.5 -7.5h-1150q-10 0 -17.5 7.5t-7.5 17.5v1050q0 10 7.5 17.5t17.5 7.5zM100 1000v-100h100v100h-100zM875 1000h-550q-10 0 -17.5 -7.5t-7.5 -17.5v-350q0 -10 7.5 -17.5t17.5 -7.5h550 q10 0 17.5 7.5t7.5 17.5v350q0 10 -7.5 17.5t-17.5 7.5zM1000 1000v-100h100v100h-100zM100 800v-100h100v100h-100zM1000 800v-100h100v100h-100zM100 600v-100h100v100h-100zM1000 600v-100h100v100h-100zM875 500h-550q-10 0 -17.5 -7.5t-7.5 -17.5v-350q0 -10 7.5 -17.5 t17.5 -7.5h550q10 0 17.5 7.5t7.5 17.5v350q0 10 -7.5 17.5t-17.5 7.5zM100 400v-100h100v100h-100zM1000 400v-100h100v100h-100zM100 200v-100h100v100h-100zM1000 200v-100h100v100h-100z" />
<glyph unicode="&#xe010;" d="M50 1100h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5zM650 1100h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v400 q0 21 14.5 35.5t35.5 14.5zM50 500h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5zM650 500h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400 q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe011;" d="M50 1100h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM450 1100h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200 q0 21 14.5 35.5t35.5 14.5zM850 1100h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM50 700h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200 q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM450 700h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM850 700h200q21 0 35.5 -14.5t14.5 -35.5v-200 q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM50 300h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM450 300h200 q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM850 300h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5 t35.5 14.5z" />
<glyph unicode="&#xe012;" d="M50 1100h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM450 1100h700q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-700q-21 0 -35.5 14.5t-14.5 35.5v200 q0 21 14.5 35.5t35.5 14.5zM50 700h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM450 700h700q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-700 q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM50 300h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM450 300h700q21 0 35.5 -14.5t14.5 -35.5v-200 q0 -21 -14.5 -35.5t-35.5 -14.5h-700q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe013;" d="M465 477l571 571q8 8 18 8t17 -8l177 -177q8 -7 8 -17t-8 -18l-783 -784q-7 -8 -17.5 -8t-17.5 8l-384 384q-8 8 -8 18t8 17l177 177q7 8 17 8t18 -8l171 -171q7 -7 18 -7t18 7z" />
<glyph unicode="&#xe014;" d="M904 1083l178 -179q8 -8 8 -18.5t-8 -17.5l-267 -268l267 -268q8 -7 8 -17.5t-8 -18.5l-178 -178q-8 -8 -18.5 -8t-17.5 8l-268 267l-268 -267q-7 -8 -17.5 -8t-18.5 8l-178 178q-8 8 -8 18.5t8 17.5l267 268l-267 268q-8 7 -8 17.5t8 18.5l178 178q8 8 18.5 8t17.5 -8 l268 -267l268 268q7 7 17.5 7t18.5 -7z" />
<glyph unicode="&#xe015;" d="M507 1177q98 0 187.5 -38.5t154.5 -103.5t103.5 -154.5t38.5 -187.5q0 -141 -78 -262l300 -299q8 -8 8 -18.5t-8 -18.5l-109 -108q-7 -8 -17.5 -8t-18.5 8l-300 299q-119 -77 -261 -77q-98 0 -188 38.5t-154.5 103t-103 154.5t-38.5 188t38.5 187.5t103 154.5 t154.5 103.5t188 38.5zM506.5 1023q-89.5 0 -165.5 -44t-120 -120.5t-44 -166t44 -165.5t120 -120t165.5 -44t166 44t120.5 120t44 165.5t-44 166t-120.5 120.5t-166 44zM425 900h150q10 0 17.5 -7.5t7.5 -17.5v-75h75q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5 t-17.5 -7.5h-75v-75q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v75h-75q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5h75v75q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe016;" d="M507 1177q98 0 187.5 -38.5t154.5 -103.5t103.5 -154.5t38.5 -187.5q0 -141 -78 -262l300 -299q8 -8 8 -18.5t-8 -18.5l-109 -108q-7 -8 -17.5 -8t-18.5 8l-300 299q-119 -77 -261 -77q-98 0 -188 38.5t-154.5 103t-103 154.5t-38.5 188t38.5 187.5t103 154.5 t154.5 103.5t188 38.5zM506.5 1023q-89.5 0 -165.5 -44t-120 -120.5t-44 -166t44 -165.5t120 -120t165.5 -44t166 44t120.5 120t44 165.5t-44 166t-120.5 120.5t-166 44zM325 800h350q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-350q-10 0 -17.5 7.5 t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe017;" d="M550 1200h100q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5zM800 975v166q167 -62 272 -209.5t105 -331.5q0 -117 -45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5 t-184.5 123t-123 184.5t-45.5 224q0 184 105 331.5t272 209.5v-166q-103 -55 -165 -155t-62 -220q0 -116 57 -214.5t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5q0 120 -62 220t-165 155z" />
<glyph unicode="&#xe018;" d="M1025 1200h150q10 0 17.5 -7.5t7.5 -17.5v-1150q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v1150q0 10 7.5 17.5t17.5 7.5zM725 800h150q10 0 17.5 -7.5t7.5 -17.5v-750q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v750 q0 10 7.5 17.5t17.5 7.5zM425 500h150q10 0 17.5 -7.5t7.5 -17.5v-450q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v450q0 10 7.5 17.5t17.5 7.5zM125 300h150q10 0 17.5 -7.5t7.5 -17.5v-250q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5 v250q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe019;" d="M600 1174q33 0 74 -5l38 -152l5 -1q49 -14 94 -39l5 -2l134 80q61 -48 104 -105l-80 -134l3 -5q25 -44 39 -93l1 -6l152 -38q5 -43 5 -73q0 -34 -5 -74l-152 -38l-1 -6q-15 -49 -39 -93l-3 -5l80 -134q-48 -61 -104 -105l-134 81l-5 -3q-44 -25 -94 -39l-5 -2l-38 -151 q-43 -5 -74 -5q-33 0 -74 5l-38 151l-5 2q-49 14 -94 39l-5 3l-134 -81q-60 48 -104 105l80 134l-3 5q-25 45 -38 93l-2 6l-151 38q-6 42 -6 74q0 33 6 73l151 38l2 6q13 48 38 93l3 5l-80 134q47 61 105 105l133 -80l5 2q45 25 94 39l5 1l38 152q43 5 74 5zM600 815 q-89 0 -152 -63t-63 -151.5t63 -151.5t152 -63t152 63t63 151.5t-63 151.5t-152 63z" />
<glyph unicode="&#xe020;" d="M500 1300h300q41 0 70.5 -29.5t29.5 -70.5v-100h275q10 0 17.5 -7.5t7.5 -17.5v-75h-1100v75q0 10 7.5 17.5t17.5 7.5h275v100q0 41 29.5 70.5t70.5 29.5zM500 1200v-100h300v100h-300zM1100 900v-800q0 -41 -29.5 -70.5t-70.5 -29.5h-700q-41 0 -70.5 29.5t-29.5 70.5 v800h900zM300 800v-700h100v700h-100zM500 800v-700h100v700h-100zM700 800v-700h100v700h-100zM900 800v-700h100v700h-100z" />
<glyph unicode="&#xe021;" d="M18 618l620 608q8 7 18.5 7t17.5 -7l608 -608q8 -8 5.5 -13t-12.5 -5h-175v-575q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v375h-300v-375q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v575h-175q-10 0 -12.5 5t5.5 13z" />
<glyph unicode="&#xe022;" d="M600 1200v-400q0 -41 29.5 -70.5t70.5 -29.5h300v-650q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5v1100q0 21 14.5 35.5t35.5 14.5h450zM1000 800h-250q-21 0 -35.5 14.5t-14.5 35.5v250z" />
<glyph unicode="&#xe023;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 1027q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5 t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5t-57 214.5t-155.5 155.5t-214.5 57zM525 900h50q10 0 17.5 -7.5t7.5 -17.5v-275h175q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v350q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe024;" d="M1300 0h-538l-41 400h-242l-41 -400h-538l431 1200h209l-21 -300h162l-20 300h208zM515 800l-27 -300h224l-27 300h-170z" />
<glyph unicode="&#xe025;" d="M550 1200h200q21 0 35.5 -14.5t14.5 -35.5v-450h191q20 0 25.5 -11.5t-7.5 -27.5l-327 -400q-13 -16 -32 -16t-32 16l-327 400q-13 16 -7.5 27.5t25.5 11.5h191v450q0 21 14.5 35.5t35.5 14.5zM1125 400h50q10 0 17.5 -7.5t7.5 -17.5v-350q0 -10 -7.5 -17.5t-17.5 -7.5 h-1050q-10 0 -17.5 7.5t-7.5 17.5v350q0 10 7.5 17.5t17.5 7.5h50q10 0 17.5 -7.5t7.5 -17.5v-175h900v175q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe026;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 1027q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5 t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5t-57 214.5t-155.5 155.5t-214.5 57zM525 900h150q10 0 17.5 -7.5t7.5 -17.5v-275h137q21 0 26 -11.5t-8 -27.5l-223 -275q-13 -16 -32 -16t-32 16l-223 275q-13 16 -8 27.5t26 11.5h137v275q0 10 7.5 17.5t17.5 7.5z " />
<glyph unicode="&#xe027;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 1027q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5 t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5t-57 214.5t-155.5 155.5t-214.5 57zM632 914l223 -275q13 -16 8 -27.5t-26 -11.5h-137v-275q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v275h-137q-21 0 -26 11.5t8 27.5l223 275q13 16 32 16 t32 -16z" />
<glyph unicode="&#xe028;" d="M225 1200h750q10 0 19.5 -7t12.5 -17l186 -652q7 -24 7 -49v-425q0 -12 -4 -27t-9 -17q-12 -6 -37 -6h-1100q-12 0 -27 4t-17 8q-6 13 -6 38l1 425q0 25 7 49l185 652q3 10 12.5 17t19.5 7zM878 1000h-556q-10 0 -19 -7t-11 -18l-87 -450q-2 -11 4 -18t16 -7h150 q10 0 19.5 -7t11.5 -17l38 -152q2 -10 11.5 -17t19.5 -7h250q10 0 19.5 7t11.5 17l38 152q2 10 11.5 17t19.5 7h150q10 0 16 7t4 18l-87 450q-2 11 -11 18t-19 7z" />
<glyph unicode="&#xe029;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 1027q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5 t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5t-57 214.5t-155.5 155.5t-214.5 57zM540 820l253 -190q17 -12 17 -30t-17 -30l-253 -190q-16 -12 -28 -6.5t-12 26.5v400q0 21 12 26.5t28 -6.5z" />
<glyph unicode="&#xe030;" d="M947 1060l135 135q7 7 12.5 5t5.5 -13v-362q0 -10 -7.5 -17.5t-17.5 -7.5h-362q-11 0 -13 5.5t5 12.5l133 133q-109 76 -238 76q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5h150q0 -117 -45.5 -224 t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5q192 0 347 -117z" />
<glyph unicode="&#xe031;" d="M947 1060l135 135q7 7 12.5 5t5.5 -13v-361q0 -11 -7.5 -18.5t-18.5 -7.5h-361q-11 0 -13 5.5t5 12.5l134 134q-110 75 -239 75q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5h-150q0 117 45.5 224t123 184.5t184.5 123t224 45.5q192 0 347 -117zM1027 600h150 q0 -117 -45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5q-192 0 -348 118l-134 -134q-7 -8 -12.5 -5.5t-5.5 12.5v360q0 11 7.5 18.5t18.5 7.5h360q10 0 12.5 -5.5t-5.5 -12.5l-133 -133q110 -76 240 -76q116 0 214.5 57t155.5 155.5t57 214.5z" />
<glyph unicode="&#xe032;" d="M125 1200h1050q10 0 17.5 -7.5t7.5 -17.5v-1150q0 -10 -7.5 -17.5t-17.5 -7.5h-1050q-10 0 -17.5 7.5t-7.5 17.5v1150q0 10 7.5 17.5t17.5 7.5zM1075 1000h-850q-10 0 -17.5 -7.5t-7.5 -17.5v-850q0 -10 7.5 -17.5t17.5 -7.5h850q10 0 17.5 7.5t7.5 17.5v850 q0 10 -7.5 17.5t-17.5 7.5zM325 900h50q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-50q-10 0 -17.5 7.5t-7.5 17.5v50q0 10 7.5 17.5t17.5 7.5zM525 900h450q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-450q-10 0 -17.5 7.5t-7.5 17.5v50 q0 10 7.5 17.5t17.5 7.5zM325 700h50q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-50q-10 0 -17.5 7.5t-7.5 17.5v50q0 10 7.5 17.5t17.5 7.5zM525 700h450q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-450q-10 0 -17.5 7.5t-7.5 17.5v50 q0 10 7.5 17.5t17.5 7.5zM325 500h50q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-50q-10 0 -17.5 7.5t-7.5 17.5v50q0 10 7.5 17.5t17.5 7.5zM525 500h450q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-450q-10 0 -17.5 7.5t-7.5 17.5v50 q0 10 7.5 17.5t17.5 7.5zM325 300h50q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-50q-10 0 -17.5 7.5t-7.5 17.5v50q0 10 7.5 17.5t17.5 7.5zM525 300h450q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-450q-10 0 -17.5 7.5t-7.5 17.5v50 q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe033;" d="M900 800v200q0 83 -58.5 141.5t-141.5 58.5h-300q-82 0 -141 -59t-59 -141v-200h-100q-41 0 -70.5 -29.5t-29.5 -70.5v-600q0 -41 29.5 -70.5t70.5 -29.5h900q41 0 70.5 29.5t29.5 70.5v600q0 41 -29.5 70.5t-70.5 29.5h-100zM400 800v150q0 21 15 35.5t35 14.5h200 q20 0 35 -14.5t15 -35.5v-150h-300z" />
<glyph unicode="&#xe034;" d="M125 1100h50q10 0 17.5 -7.5t7.5 -17.5v-1075h-100v1075q0 10 7.5 17.5t17.5 7.5zM1075 1052q4 0 9 -2q16 -6 16 -23v-421q0 -6 -3 -12q-33 -59 -66.5 -99t-65.5 -58t-56.5 -24.5t-52.5 -6.5q-26 0 -57.5 6.5t-52.5 13.5t-60 21q-41 15 -63 22.5t-57.5 15t-65.5 7.5 q-85 0 -160 -57q-7 -5 -15 -5q-6 0 -11 3q-14 7 -14 22v438q22 55 82 98.5t119 46.5q23 2 43 0.5t43 -7t32.5 -8.5t38 -13t32.5 -11q41 -14 63.5 -21t57 -14t63.5 -7q103 0 183 87q7 8 18 8z" />
<glyph unicode="&#xe035;" d="M600 1175q116 0 227 -49.5t192.5 -131t131 -192.5t49.5 -227v-300q0 -10 -7.5 -17.5t-17.5 -7.5h-50q-10 0 -17.5 7.5t-7.5 17.5v300q0 127 -70.5 231.5t-184.5 161.5t-245 57t-245 -57t-184.5 -161.5t-70.5 -231.5v-300q0 -10 -7.5 -17.5t-17.5 -7.5h-50 q-10 0 -17.5 7.5t-7.5 17.5v300q0 116 49.5 227t131 192.5t192.5 131t227 49.5zM220 500h160q8 0 14 -6t6 -14v-460q0 -8 -6 -14t-14 -6h-160q-8 0 -14 6t-6 14v460q0 8 6 14t14 6zM820 500h160q8 0 14 -6t6 -14v-460q0 -8 -6 -14t-14 -6h-160q-8 0 -14 6t-6 14v460 q0 8 6 14t14 6z" />
<glyph unicode="&#xe036;" d="M321 814l258 172q9 6 15 2.5t6 -13.5v-750q0 -10 -6 -13.5t-15 2.5l-258 172q-21 14 -46 14h-250q-10 0 -17.5 7.5t-7.5 17.5v350q0 10 7.5 17.5t17.5 7.5h250q25 0 46 14zM900 668l120 120q7 7 17 7t17 -7l34 -34q7 -7 7 -17t-7 -17l-120 -120l120 -120q7 -7 7 -17 t-7 -17l-34 -34q-7 -7 -17 -7t-17 7l-120 119l-120 -119q-7 -7 -17 -7t-17 7l-34 34q-7 7 -7 17t7 17l119 120l-119 120q-7 7 -7 17t7 17l34 34q7 8 17 8t17 -8z" />
<glyph unicode="&#xe037;" d="M321 814l258 172q9 6 15 2.5t6 -13.5v-750q0 -10 -6 -13.5t-15 2.5l-258 172q-21 14 -46 14h-250q-10 0 -17.5 7.5t-7.5 17.5v350q0 10 7.5 17.5t17.5 7.5h250q25 0 46 14zM766 900h4q10 -1 16 -10q96 -129 96 -290q0 -154 -90 -281q-6 -9 -17 -10l-3 -1q-9 0 -16 6 l-29 23q-7 7 -8.5 16.5t4.5 17.5q72 103 72 229q0 132 -78 238q-6 8 -4.5 18t9.5 17l29 22q7 5 15 5z" />
<glyph unicode="&#xe038;" d="M967 1004h3q11 -1 17 -10q135 -179 135 -396q0 -105 -34 -206.5t-98 -185.5q-7 -9 -17 -10h-3q-9 0 -16 6l-42 34q-8 6 -9 16t5 18q111 150 111 328q0 90 -29.5 176t-84.5 157q-6 9 -5 19t10 16l42 33q7 5 15 5zM321 814l258 172q9 6 15 2.5t6 -13.5v-750q0 -10 -6 -13.5 t-15 2.5l-258 172q-21 14 -46 14h-250q-10 0 -17.5 7.5t-7.5 17.5v350q0 10 7.5 17.5t17.5 7.5h250q25 0 46 14zM766 900h4q10 -1 16 -10q96 -129 96 -290q0 -154 -90 -281q-6 -9 -17 -10l-3 -1q-9 0 -16 6l-29 23q-7 7 -8.5 16.5t4.5 17.5q72 103 72 229q0 132 -78 238 q-6 8 -4.5 18.5t9.5 16.5l29 22q7 5 15 5z" />
<glyph unicode="&#xe039;" d="M500 900h100v-100h-100v-100h-400v-100h-100v600h500v-300zM1200 700h-200v-100h200v-200h-300v300h-200v300h-100v200h600v-500zM100 1100v-300h300v300h-300zM800 1100v-300h300v300h-300zM300 900h-100v100h100v-100zM1000 900h-100v100h100v-100zM300 500h200v-500 h-500v500h200v100h100v-100zM800 300h200v-100h-100v-100h-200v100h-100v100h100v200h-200v100h300v-300zM100 400v-300h300v300h-300zM300 200h-100v100h100v-100zM1200 200h-100v100h100v-100zM700 0h-100v100h100v-100zM1200 0h-300v100h300v-100z" />
<glyph unicode="&#xe040;" d="M100 200h-100v1000h100v-1000zM300 200h-100v1000h100v-1000zM700 200h-200v1000h200v-1000zM900 200h-100v1000h100v-1000zM1200 200h-200v1000h200v-1000zM400 0h-300v100h300v-100zM600 0h-100v91h100v-91zM800 0h-100v91h100v-91zM1100 0h-200v91h200v-91z" />
<glyph unicode="&#xe041;" d="M500 1200l682 -682q8 -8 8 -18t-8 -18l-464 -464q-8 -8 -18 -8t-18 8l-682 682l1 475q0 10 7.5 17.5t17.5 7.5h474zM319.5 1024.5q-29.5 29.5 -71 29.5t-71 -29.5t-29.5 -71.5t29.5 -71.5t71 -29.5t71 29.5t29.5 71.5t-29.5 71.5z" />
<glyph unicode="&#xe042;" d="M500 1200l682 -682q8 -8 8 -18t-8 -18l-464 -464q-8 -8 -18 -8t-18 8l-682 682l1 475q0 10 7.5 17.5t17.5 7.5h474zM800 1200l682 -682q8 -8 8 -18t-8 -18l-464 -464q-8 -8 -18 -8t-18 8l-56 56l424 426l-700 700h150zM319.5 1024.5q-29.5 29.5 -71 29.5t-71 -29.5 t-29.5 -71.5t29.5 -71.5t71 -29.5t71 29.5t29.5 71.5t-29.5 71.5z" />
<glyph unicode="&#xe043;" d="M300 1200h825q75 0 75 -75v-900q0 -25 -18 -43l-64 -64q-8 -8 -13 -5.5t-5 12.5v950q0 10 -7.5 17.5t-17.5 7.5h-700q-25 0 -43 -18l-64 -64q-8 -8 -5.5 -13t12.5 -5h700q10 0 17.5 -7.5t7.5 -17.5v-950q0 -10 -7.5 -17.5t-17.5 -7.5h-850q-10 0 -17.5 7.5t-7.5 17.5v975 q0 25 18 43l139 139q18 18 43 18z" />
<glyph unicode="&#xe044;" d="M250 1200h800q21 0 35.5 -14.5t14.5 -35.5v-1150l-450 444l-450 -445v1151q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe045;" d="M822 1200h-444q-11 0 -19 -7.5t-9 -17.5l-78 -301q-7 -24 7 -45l57 -108q6 -9 17.5 -15t21.5 -6h450q10 0 21.5 6t17.5 15l62 108q14 21 7 45l-83 301q-1 10 -9 17.5t-19 7.5zM1175 800h-150q-10 0 -21 -6.5t-15 -15.5l-78 -156q-4 -9 -15 -15.5t-21 -6.5h-550 q-10 0 -21 6.5t-15 15.5l-78 156q-4 9 -15 15.5t-21 6.5h-150q-10 0 -17.5 -7.5t-7.5 -17.5v-650q0 -10 7.5 -17.5t17.5 -7.5h150q10 0 17.5 7.5t7.5 17.5v150q0 10 7.5 17.5t17.5 7.5h750q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 7.5 -17.5t17.5 -7.5h150q10 0 17.5 7.5 t7.5 17.5v650q0 10 -7.5 17.5t-17.5 7.5zM850 200h-500q-10 0 -19.5 -7t-11.5 -17l-38 -152q-2 -10 3.5 -17t15.5 -7h600q10 0 15.5 7t3.5 17l-38 152q-2 10 -11.5 17t-19.5 7z" />
<glyph unicode="&#xe046;" d="M500 1100h200q56 0 102.5 -20.5t72.5 -50t44 -59t25 -50.5l6 -20h150q41 0 70.5 -29.5t29.5 -70.5v-600q0 -41 -29.5 -70.5t-70.5 -29.5h-1000q-41 0 -70.5 29.5t-29.5 70.5v600q0 41 29.5 70.5t70.5 29.5h150q2 8 6.5 21.5t24 48t45 61t72 48t102.5 21.5zM900 800v-100 h100v100h-100zM600 730q-95 0 -162.5 -67.5t-67.5 -162.5t67.5 -162.5t162.5 -67.5t162.5 67.5t67.5 162.5t-67.5 162.5t-162.5 67.5zM600 603q43 0 73 -30t30 -73t-30 -73t-73 -30t-73 30t-30 73t30 73t73 30z" />
<glyph unicode="&#xe047;" d="M681 1199l385 -998q20 -50 60 -92q18 -19 36.5 -29.5t27.5 -11.5l10 -2v-66h-417v66q53 0 75 43.5t5 88.5l-82 222h-391q-58 -145 -92 -234q-11 -34 -6.5 -57t25.5 -37t46 -20t55 -6v-66h-365v66q56 24 84 52q12 12 25 30.5t20 31.5l7 13l399 1006h93zM416 521h340 l-162 457z" />
<glyph unicode="&#xe048;" d="M753 641q5 -1 14.5 -4.5t36 -15.5t50.5 -26.5t53.5 -40t50.5 -54.5t35.5 -70t14.5 -87q0 -67 -27.5 -125.5t-71.5 -97.5t-98.5 -66.5t-108.5 -40.5t-102 -13h-500v89q41 7 70.5 32.5t29.5 65.5v827q0 24 -0.5 34t-3.5 24t-8.5 19.5t-17 13.5t-28 12.5t-42.5 11.5v71 l471 -1q57 0 115.5 -20.5t108 -57t80.5 -94t31 -124.5q0 -51 -15.5 -96.5t-38 -74.5t-45 -50.5t-38.5 -30.5zM400 700h139q78 0 130.5 48.5t52.5 122.5q0 41 -8.5 70.5t-29.5 55.5t-62.5 39.5t-103.5 13.5h-118v-350zM400 200h216q80 0 121 50.5t41 130.5q0 90 -62.5 154.5 t-156.5 64.5h-159v-400z" />
<glyph unicode="&#xe049;" d="M877 1200l2 -57q-83 -19 -116 -45.5t-40 -66.5l-132 -839q-9 -49 13 -69t96 -26v-97h-500v97q186 16 200 98l173 832q3 17 3 30t-1.5 22.5t-9 17.5t-13.5 12.5t-21.5 10t-26 8.5t-33.5 10q-13 3 -19 5v57h425z" />
<glyph unicode="&#xe050;" d="M1300 900h-50q0 21 -4 37t-9.5 26.5t-18 17.5t-22 11t-28.5 5.5t-31 2t-37 0.5h-200v-850q0 -22 25 -34.5t50 -13.5l25 -2v-100h-400v100q4 0 11 0.5t24 3t30 7t24 15t11 24.5v850h-200q-25 0 -37 -0.5t-31 -2t-28.5 -5.5t-22 -11t-18 -17.5t-9.5 -26.5t-4 -37h-50v300 h1000v-300zM175 1000h-75v-800h75l-125 -167l-125 167h75v800h-75l125 167z" />
<glyph unicode="&#xe051;" d="M1100 900h-50q0 21 -4 37t-9.5 26.5t-18 17.5t-22 11t-28.5 5.5t-31 2t-37 0.5h-200v-650q0 -22 25 -34.5t50 -13.5l25 -2v-100h-400v100q4 0 11 0.5t24 3t30 7t24 15t11 24.5v650h-200q-25 0 -37 -0.5t-31 -2t-28.5 -5.5t-22 -11t-18 -17.5t-9.5 -26.5t-4 -37h-50v300 h1000v-300zM1167 50l-167 -125v75h-800v-75l-167 125l167 125v-75h800v75z" />
<glyph unicode="&#xe052;" d="M50 1100h600q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-600q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 800h1000q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1000q-21 0 -35.5 14.5t-14.5 35.5v100 q0 21 14.5 35.5t35.5 14.5zM50 500h800q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 200h1100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100 q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe053;" d="M250 1100h700q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-700q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 800h1100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5v100 q0 21 14.5 35.5t35.5 14.5zM250 500h700q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-700q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 200h1100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100 q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe054;" d="M500 950v100q0 21 14.5 35.5t35.5 14.5h600q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-600q-21 0 -35.5 14.5t-14.5 35.5zM100 650v100q0 21 14.5 35.5t35.5 14.5h1000q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1000 q-21 0 -35.5 14.5t-14.5 35.5zM300 350v100q0 21 14.5 35.5t35.5 14.5h800q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5zM0 50v100q0 21 14.5 35.5t35.5 14.5h1100q21 0 35.5 -14.5t14.5 -35.5v-100 q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5z" />
<glyph unicode="&#xe055;" d="M50 1100h1100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 800h1100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5v100 q0 21 14.5 35.5t35.5 14.5zM50 500h1100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 200h1100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100 q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe056;" d="M50 1100h100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM350 1100h800q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5v100 q0 21 14.5 35.5t35.5 14.5zM50 800h100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM350 800h800q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-800 q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 500h100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM350 500h800q21 0 35.5 -14.5t14.5 -35.5v-100 q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 200h100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM350 200h800 q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe057;" d="M400 0h-100v1100h100v-1100zM550 1100h100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM550 800h500q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-500 q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM267 550l-167 -125v75h-200v100h200v75zM550 500h300q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-300q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM550 200h600 q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-600q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe058;" d="M50 1100h100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM900 0h-100v1100h100v-1100zM50 800h500q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-500 q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM1100 600h200v-100h-200v-75l-167 125l167 125v-75zM50 500h300q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-300q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 200h600 q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-600q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe059;" d="M75 1000h750q31 0 53 -22t22 -53v-650q0 -31 -22 -53t-53 -22h-750q-31 0 -53 22t-22 53v650q0 31 22 53t53 22zM1200 300l-300 300l300 300v-600z" />
<glyph unicode="&#xe060;" d="M44 1100h1112q18 0 31 -13t13 -31v-1012q0 -18 -13 -31t-31 -13h-1112q-18 0 -31 13t-13 31v1012q0 18 13 31t31 13zM100 1000v-737l247 182l298 -131l-74 156l293 318l236 -288v500h-1000zM342 884q56 0 95 -39t39 -94.5t-39 -95t-95 -39.5t-95 39.5t-39 95t39 94.5 t95 39z" />
<glyph unicode="&#xe062;" d="M648 1169q117 0 216 -60t156.5 -161t57.5 -218q0 -115 -70 -258q-69 -109 -158 -225.5t-143 -179.5l-54 -62q-9 8 -25.5 24.5t-63.5 67.5t-91 103t-98.5 128t-95.5 148q-60 132 -60 249q0 88 34 169.5t91.5 142t137 96.5t166.5 36zM652.5 974q-91.5 0 -156.5 -65 t-65 -157t65 -156.5t156.5 -64.5t156.5 64.5t65 156.5t-65 157t-156.5 65z" />
<glyph unicode="&#xe063;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 173v854q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5 t155.5 -155.5t214.5 -57z" />
<glyph unicode="&#xe064;" d="M554 1295q21 -72 57.5 -143.5t76 -130t83 -118t82.5 -117t70 -116t49.5 -126t18.5 -136.5q0 -71 -25.5 -135t-68.5 -111t-99 -82t-118.5 -54t-125.5 -23q-84 5 -161.5 34t-139.5 78.5t-99 125t-37 164.5q0 69 18 136.5t49.5 126.5t69.5 116.5t81.5 117.5t83.5 119 t76.5 131t58.5 143zM344 710q-23 -33 -43.5 -70.5t-40.5 -102.5t-17 -123q1 -37 14.5 -69.5t30 -52t41 -37t38.5 -24.5t33 -15q21 -7 32 -1t13 22l6 34q2 10 -2.5 22t-13.5 19q-5 4 -14 12t-29.5 40.5t-32.5 73.5q-26 89 6 271q2 11 -6 11q-8 1 -15 -10z" />
<glyph unicode="&#xe065;" d="M1000 1013l108 115q2 1 5 2t13 2t20.5 -1t25 -9.5t28.5 -21.5q22 -22 27 -43t0 -32l-6 -10l-108 -115zM350 1100h400q50 0 105 -13l-187 -187h-368q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5v182l200 200v-332 q0 -165 -93.5 -257.5t-256.5 -92.5h-400q-165 0 -257.5 92.5t-92.5 257.5v400q0 165 92.5 257.5t257.5 92.5zM1009 803l-362 -362l-161 -50l55 170l355 355z" />
<glyph unicode="&#xe066;" d="M350 1100h361q-164 -146 -216 -200h-195q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5l200 153v-103q0 -165 -92.5 -257.5t-257.5 -92.5h-400q-165 0 -257.5 92.5t-92.5 257.5v400q0 165 92.5 257.5t257.5 92.5z M824 1073l339 -301q8 -7 8 -17.5t-8 -17.5l-340 -306q-7 -6 -12.5 -4t-6.5 11v203q-26 1 -54.5 0t-78.5 -7.5t-92 -17.5t-86 -35t-70 -57q10 59 33 108t51.5 81.5t65 58.5t68.5 40.5t67 24.5t56 13.5t40 4.5v210q1 10 6.5 12.5t13.5 -4.5z" />
<glyph unicode="&#xe067;" d="M350 1100h350q60 0 127 -23l-178 -177h-349q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5v69l200 200v-219q0 -165 -92.5 -257.5t-257.5 -92.5h-400q-165 0 -257.5 92.5t-92.5 257.5v400q0 165 92.5 257.5t257.5 92.5z M643 639l395 395q7 7 17.5 7t17.5 -7l101 -101q7 -7 7 -17.5t-7 -17.5l-531 -532q-7 -7 -17.5 -7t-17.5 7l-248 248q-7 7 -7 17.5t7 17.5l101 101q7 7 17.5 7t17.5 -7l111 -111q8 -7 18 -7t18 7z" />
<glyph unicode="&#xe068;" d="M318 918l264 264q8 8 18 8t18 -8l260 -264q7 -8 4.5 -13t-12.5 -5h-170v-200h200v173q0 10 5 12t13 -5l264 -260q8 -7 8 -17.5t-8 -17.5l-264 -265q-8 -7 -13 -5t-5 12v173h-200v-200h170q10 0 12.5 -5t-4.5 -13l-260 -264q-8 -8 -18 -8t-18 8l-264 264q-8 8 -5.5 13 t12.5 5h175v200h-200v-173q0 -10 -5 -12t-13 5l-264 265q-8 7 -8 17.5t8 17.5l264 260q8 7 13 5t5 -12v-173h200v200h-175q-10 0 -12.5 5t5.5 13z" />
<glyph unicode="&#xe069;" d="M250 1100h100q21 0 35.5 -14.5t14.5 -35.5v-438l464 453q15 14 25.5 10t10.5 -25v-1000q0 -21 -10.5 -25t-25.5 10l-464 453v-438q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v1000q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe070;" d="M50 1100h100q21 0 35.5 -14.5t14.5 -35.5v-438l464 453q15 14 25.5 10t10.5 -25v-438l464 453q15 14 25.5 10t10.5 -25v-1000q0 -21 -10.5 -25t-25.5 10l-464 453v-438q0 -21 -10.5 -25t-25.5 10l-464 453v-438q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5 t-14.5 35.5v1000q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe071;" d="M1200 1050v-1000q0 -21 -10.5 -25t-25.5 10l-464 453v-438q0 -21 -10.5 -25t-25.5 10l-492 480q-15 14 -15 35t15 35l492 480q15 14 25.5 10t10.5 -25v-438l464 453q15 14 25.5 10t10.5 -25z" />
<glyph unicode="&#xe072;" d="M243 1074l814 -498q18 -11 18 -26t-18 -26l-814 -498q-18 -11 -30.5 -4t-12.5 28v1000q0 21 12.5 28t30.5 -4z" />
<glyph unicode="&#xe073;" d="M250 1000h200q21 0 35.5 -14.5t14.5 -35.5v-800q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v800q0 21 14.5 35.5t35.5 14.5zM650 1000h200q21 0 35.5 -14.5t14.5 -35.5v-800q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v800 q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe074;" d="M1100 950v-800q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5v800q0 21 14.5 35.5t35.5 14.5h800q21 0 35.5 -14.5t14.5 -35.5z" />
<glyph unicode="&#xe075;" d="M500 612v438q0 21 10.5 25t25.5 -10l492 -480q15 -14 15 -35t-15 -35l-492 -480q-15 -14 -25.5 -10t-10.5 25v438l-464 -453q-15 -14 -25.5 -10t-10.5 25v1000q0 21 10.5 25t25.5 -10z" />
<glyph unicode="&#xe076;" d="M1048 1102l100 1q20 0 35 -14.5t15 -35.5l5 -1000q0 -21 -14.5 -35.5t-35.5 -14.5l-100 -1q-21 0 -35.5 14.5t-14.5 35.5l-2 437l-463 -454q-14 -15 -24.5 -10.5t-10.5 25.5l-2 437l-462 -455q-15 -14 -25.5 -9.5t-10.5 24.5l-5 1000q0 21 10.5 25.5t25.5 -10.5l466 -450 l-2 438q0 20 10.5 24.5t25.5 -9.5l466 -451l-2 438q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe077;" d="M850 1100h100q21 0 35.5 -14.5t14.5 -35.5v-1000q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v438l-464 -453q-15 -14 -25.5 -10t-10.5 25v1000q0 21 10.5 25t25.5 -10l464 -453v438q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe078;" d="M686 1081l501 -540q15 -15 10.5 -26t-26.5 -11h-1042q-22 0 -26.5 11t10.5 26l501 540q15 15 36 15t36 -15zM150 400h1000q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1000q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe079;" d="M885 900l-352 -353l352 -353l-197 -198l-552 552l552 550z" />
<glyph unicode="&#xe080;" d="M1064 547l-551 -551l-198 198l353 353l-353 353l198 198z" />
<glyph unicode="&#xe081;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM650 900h-100q-21 0 -35.5 -14.5t-14.5 -35.5v-150h-150 q-21 0 -35.5 -14.5t-14.5 -35.5v-100q0 -21 14.5 -35.5t35.5 -14.5h150v-150q0 -21 14.5 -35.5t35.5 -14.5h100q21 0 35.5 14.5t14.5 35.5v150h150q21 0 35.5 14.5t14.5 35.5v100q0 21 -14.5 35.5t-35.5 14.5h-150v150q0 21 -14.5 35.5t-35.5 14.5z" />
<glyph unicode="&#xe082;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM850 700h-500q-21 0 -35.5 -14.5t-14.5 -35.5v-100q0 -21 14.5 -35.5 t35.5 -14.5h500q21 0 35.5 14.5t14.5 35.5v100q0 21 -14.5 35.5t-35.5 14.5z" />
<glyph unicode="&#xe083;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM741.5 913q-12.5 0 -21.5 -9l-120 -120l-120 120q-9 9 -21.5 9 t-21.5 -9l-141 -141q-9 -9 -9 -21.5t9 -21.5l120 -120l-120 -120q-9 -9 -9 -21.5t9 -21.5l141 -141q9 -9 21.5 -9t21.5 9l120 120l120 -120q9 -9 21.5 -9t21.5 9l141 141q9 9 9 21.5t-9 21.5l-120 120l120 120q9 9 9 21.5t-9 21.5l-141 141q-9 9 -21.5 9z" />
<glyph unicode="&#xe084;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM546 623l-84 85q-7 7 -17.5 7t-18.5 -7l-139 -139q-7 -8 -7 -18t7 -18 l242 -241q7 -8 17.5 -8t17.5 8l375 375q7 7 7 17.5t-7 18.5l-139 139q-7 7 -17.5 7t-17.5 -7z" />
<glyph unicode="&#xe085;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM588 941q-29 0 -59 -5.5t-63 -20.5t-58 -38.5t-41.5 -63t-16.5 -89.5 q0 -25 20 -25h131q30 -5 35 11q6 20 20.5 28t45.5 8q20 0 31.5 -10.5t11.5 -28.5q0 -23 -7 -34t-26 -18q-1 0 -13.5 -4t-19.5 -7.5t-20 -10.5t-22 -17t-18.5 -24t-15.5 -35t-8 -46q-1 -8 5.5 -16.5t20.5 -8.5h173q7 0 22 8t35 28t37.5 48t29.5 74t12 100q0 47 -17 83 t-42.5 57t-59.5 34.5t-64 18t-59 4.5zM675 400h-150q-10 0 -17.5 -7.5t-7.5 -17.5v-150q0 -10 7.5 -17.5t17.5 -7.5h150q10 0 17.5 7.5t7.5 17.5v150q0 10 -7.5 17.5t-17.5 7.5z" />
<glyph unicode="&#xe086;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM675 1000h-150q-10 0 -17.5 -7.5t-7.5 -17.5v-150q0 -10 7.5 -17.5 t17.5 -7.5h150q10 0 17.5 7.5t7.5 17.5v150q0 10 -7.5 17.5t-17.5 7.5zM675 700h-250q-10 0 -17.5 -7.5t-7.5 -17.5v-50q0 -10 7.5 -17.5t17.5 -7.5h75v-200h-75q-10 0 -17.5 -7.5t-7.5 -17.5v-50q0 -10 7.5 -17.5t17.5 -7.5h350q10 0 17.5 7.5t7.5 17.5v50q0 10 -7.5 17.5 t-17.5 7.5h-75v275q0 10 -7.5 17.5t-17.5 7.5z" />
<glyph unicode="&#xe087;" d="M525 1200h150q10 0 17.5 -7.5t7.5 -17.5v-194q103 -27 178.5 -102.5t102.5 -178.5h194q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-194q-27 -103 -102.5 -178.5t-178.5 -102.5v-194q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v194 q-103 27 -178.5 102.5t-102.5 178.5h-194q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5h194q27 103 102.5 178.5t178.5 102.5v194q0 10 7.5 17.5t17.5 7.5zM700 893v-168q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v168q-68 -23 -119 -74 t-74 -119h168q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-168q23 -68 74 -119t119 -74v168q0 10 7.5 17.5t17.5 7.5h150q10 0 17.5 -7.5t7.5 -17.5v-168q68 23 119 74t74 119h-168q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5h168 q-23 68 -74 119t-119 74z" />
<glyph unicode="&#xe088;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 1027q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5 t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5t-57 214.5t-155.5 155.5t-214.5 57zM759 823l64 -64q7 -7 7 -17.5t-7 -17.5l-124 -124l124 -124q7 -7 7 -17.5t-7 -17.5l-64 -64q-7 -7 -17.5 -7t-17.5 7l-124 124l-124 -124q-7 -7 -17.5 -7t-17.5 7l-64 64 q-7 7 -7 17.5t7 17.5l124 124l-124 124q-7 7 -7 17.5t7 17.5l64 64q7 7 17.5 7t17.5 -7l124 -124l124 124q7 7 17.5 7t17.5 -7z" />
<glyph unicode="&#xe089;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 1027q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5 t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5t-57 214.5t-155.5 155.5t-214.5 57zM782 788l106 -106q7 -7 7 -17.5t-7 -17.5l-320 -321q-8 -7 -18 -7t-18 7l-202 203q-8 7 -8 17.5t8 17.5l106 106q7 8 17.5 8t17.5 -8l79 -79l197 197q7 7 17.5 7t17.5 -7z" />
<glyph unicode="&#xe090;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 1027q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5q0 -120 65 -225 l587 587q-105 65 -225 65zM965 819l-584 -584q104 -62 219 -62q116 0 214.5 57t155.5 155.5t57 214.5q0 115 -62 219z" />
<glyph unicode="&#xe091;" d="M39 582l522 427q16 13 27.5 8t11.5 -26v-291h550q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-550v-291q0 -21 -11.5 -26t-27.5 8l-522 427q-16 13 -16 32t16 32z" />
<glyph unicode="&#xe092;" d="M639 1009l522 -427q16 -13 16 -32t-16 -32l-522 -427q-16 -13 -27.5 -8t-11.5 26v291h-550q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5h550v291q0 21 11.5 26t27.5 -8z" />
<glyph unicode="&#xe093;" d="M682 1161l427 -522q13 -16 8 -27.5t-26 -11.5h-291v-550q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v550h-291q-21 0 -26 11.5t8 27.5l427 522q13 16 32 16t32 -16z" />
<glyph unicode="&#xe094;" d="M550 1200h200q21 0 35.5 -14.5t14.5 -35.5v-550h291q21 0 26 -11.5t-8 -27.5l-427 -522q-13 -16 -32 -16t-32 16l-427 522q-13 16 -8 27.5t26 11.5h291v550q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe095;" d="M639 1109l522 -427q16 -13 16 -32t-16 -32l-522 -427q-16 -13 -27.5 -8t-11.5 26v291q-94 -2 -182 -20t-170.5 -52t-147 -92.5t-100.5 -135.5q5 105 27 193.5t67.5 167t113 135t167 91.5t225.5 42v262q0 21 11.5 26t27.5 -8z" />
<glyph unicode="&#xe096;" d="M850 1200h300q21 0 35.5 -14.5t14.5 -35.5v-300q0 -21 -10.5 -25t-24.5 10l-94 94l-249 -249q-8 -7 -18 -7t-18 7l-106 106q-7 8 -7 18t7 18l249 249l-94 94q-14 14 -10 24.5t25 10.5zM350 0h-300q-21 0 -35.5 14.5t-14.5 35.5v300q0 21 10.5 25t24.5 -10l94 -94l249 249 q8 7 18 7t18 -7l106 -106q7 -8 7 -18t-7 -18l-249 -249l94 -94q14 -14 10 -24.5t-25 -10.5z" />
<glyph unicode="&#xe097;" d="M1014 1120l106 -106q7 -8 7 -18t-7 -18l-249 -249l94 -94q14 -14 10 -24.5t-25 -10.5h-300q-21 0 -35.5 14.5t-14.5 35.5v300q0 21 10.5 25t24.5 -10l94 -94l249 249q8 7 18 7t18 -7zM250 600h300q21 0 35.5 -14.5t14.5 -35.5v-300q0 -21 -10.5 -25t-24.5 10l-94 94 l-249 -249q-8 -7 -18 -7t-18 7l-106 106q-7 8 -7 18t7 18l249 249l-94 94q-14 14 -10 24.5t25 10.5z" />
<glyph unicode="&#xe101;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM704 900h-208q-20 0 -32 -14.5t-8 -34.5l58 -302q4 -20 21.5 -34.5 t37.5 -14.5h54q20 0 37.5 14.5t21.5 34.5l58 302q4 20 -8 34.5t-32 14.5zM675 400h-150q-10 0 -17.5 -7.5t-7.5 -17.5v-150q0 -10 7.5 -17.5t17.5 -7.5h150q10 0 17.5 7.5t7.5 17.5v150q0 10 -7.5 17.5t-17.5 7.5z" />
<glyph unicode="&#xe102;" d="M260 1200q9 0 19 -2t15 -4l5 -2q22 -10 44 -23l196 -118q21 -13 36 -24q29 -21 37 -12q11 13 49 35l196 118q22 13 45 23q17 7 38 7q23 0 47 -16.5t37 -33.5l13 -16q14 -21 18 -45l25 -123l8 -44q1 -9 8.5 -14.5t17.5 -5.5h61q10 0 17.5 -7.5t7.5 -17.5v-50 q0 -10 -7.5 -17.5t-17.5 -7.5h-50q-10 0 -17.5 -7.5t-7.5 -17.5v-175h-400v300h-200v-300h-400v175q0 10 -7.5 17.5t-17.5 7.5h-50q-10 0 -17.5 7.5t-7.5 17.5v50q0 10 7.5 17.5t17.5 7.5h61q11 0 18 3t7 8q0 4 9 52l25 128q5 25 19 45q2 3 5 7t13.5 15t21.5 19.5t26.5 15.5 t29.5 7zM915 1079l-166 -162q-7 -7 -5 -12t12 -5h219q10 0 15 7t2 17l-51 149q-3 10 -11 12t-15 -6zM463 917l-177 157q-8 7 -16 5t-11 -12l-51 -143q-3 -10 2 -17t15 -7h231q11 0 12.5 5t-5.5 12zM500 0h-375q-10 0 -17.5 7.5t-7.5 17.5v375h400v-400zM1100 400v-375 q0 -10 -7.5 -17.5t-17.5 -7.5h-375v400h400z" />
<glyph unicode="&#xe103;" d="M1165 1190q8 3 21 -6.5t13 -17.5q-2 -178 -24.5 -323.5t-55.5 -245.5t-87 -174.5t-102.5 -118.5t-118 -68.5t-118.5 -33t-120 -4.5t-105 9.5t-90 16.5q-61 12 -78 11q-4 1 -12.5 0t-34 -14.5t-52.5 -40.5l-153 -153q-26 -24 -37 -14.5t-11 43.5q0 64 42 102q8 8 50.5 45 t66.5 58q19 17 35 47t13 61q-9 55 -10 102.5t7 111t37 130t78 129.5q39 51 80 88t89.5 63.5t94.5 45t113.5 36t129 31t157.5 37t182 47.5zM1116 1098q-8 9 -22.5 -3t-45.5 -50q-38 -47 -119 -103.5t-142 -89.5l-62 -33q-56 -30 -102 -57t-104 -68t-102.5 -80.5t-85.5 -91 t-64 -104.5q-24 -56 -31 -86t2 -32t31.5 17.5t55.5 59.5q25 30 94 75.5t125.5 77.5t147.5 81q70 37 118.5 69t102 79.5t99 111t86.5 148.5q22 50 24 60t-6 19z" />
<glyph unicode="&#xe104;" d="M653 1231q-39 -67 -54.5 -131t-10.5 -114.5t24.5 -96.5t47.5 -80t63.5 -62.5t68.5 -46.5t65 -30q-4 7 -17.5 35t-18.5 39.5t-17 39.5t-17 43t-13 42t-9.5 44.5t-2 42t4 43t13.5 39t23 38.5q96 -42 165 -107.5t105 -138t52 -156t13 -159t-19 -149.5q-13 -55 -44 -106.5 t-68 -87t-78.5 -64.5t-72.5 -45t-53 -22q-72 -22 -127 -11q-31 6 -13 19q6 3 17 7q13 5 32.5 21t41 44t38.5 63.5t21.5 81.5t-6.5 94.5t-50 107t-104 115.5q10 -104 -0.5 -189t-37 -140.5t-65 -93t-84 -52t-93.5 -11t-95 24.5q-80 36 -131.5 114t-53.5 171q-2 23 0 49.5 t4.5 52.5t13.5 56t27.5 60t46 64.5t69.5 68.5q-8 -53 -5 -102.5t17.5 -90t34 -68.5t44.5 -39t49 -2q31 13 38.5 36t-4.5 55t-29 64.5t-36 75t-26 75.5q-15 85 2 161.5t53.5 128.5t85.5 92.5t93.5 61t81.5 25.5z" />
<glyph unicode="&#xe105;" d="M600 1094q82 0 160.5 -22.5t140 -59t116.5 -82.5t94.5 -95t68 -95t42.5 -82.5t14 -57.5t-14 -57.5t-43 -82.5t-68.5 -95t-94.5 -95t-116.5 -82.5t-140 -59t-159.5 -22.5t-159.5 22.5t-140 59t-116.5 82.5t-94.5 95t-68.5 95t-43 82.5t-14 57.5t14 57.5t42.5 82.5t68 95 t94.5 95t116.5 82.5t140 59t160.5 22.5zM888 829q-15 15 -18 12t5 -22q25 -57 25 -119q0 -124 -88 -212t-212 -88t-212 88t-88 212q0 59 23 114q8 19 4.5 22t-17.5 -12q-70 -69 -160 -184q-13 -16 -15 -40.5t9 -42.5q22 -36 47 -71t70 -82t92.5 -81t113 -58.5t133.5 -24.5 t133.5 24t113 58.5t92.5 81.5t70 81.5t47 70.5q11 18 9 42.5t-14 41.5q-90 117 -163 189zM448 727l-35 -36q-15 -15 -19.5 -38.5t4.5 -41.5q37 -68 93 -116q16 -13 38.5 -11t36.5 17l35 34q14 15 12.5 33.5t-16.5 33.5q-44 44 -89 117q-11 18 -28 20t-32 -12z" />
<glyph unicode="&#xe106;" d="M592 0h-148l31 120q-91 20 -175.5 68.5t-143.5 106.5t-103.5 119t-66.5 110t-22 76q0 21 14 57.5t42.5 82.5t68 95t94.5 95t116.5 82.5t140 59t160.5 22.5q61 0 126 -15l32 121h148zM944 770l47 181q108 -85 176.5 -192t68.5 -159q0 -26 -19.5 -71t-59.5 -102t-93 -112 t-129 -104.5t-158 -75.5l46 173q77 49 136 117t97 131q11 18 9 42.5t-14 41.5q-54 70 -107 130zM310 824q-70 -69 -160 -184q-13 -16 -15 -40.5t9 -42.5q18 -30 39 -60t57 -70.5t74 -73t90 -61t105 -41.5l41 154q-107 18 -178.5 101.5t-71.5 193.5q0 59 23 114q8 19 4.5 22 t-17.5 -12zM448 727l-35 -36q-15 -15 -19.5 -38.5t4.5 -41.5q37 -68 93 -116q16 -13 38.5 -11t36.5 17l12 11l22 86l-3 4q-44 44 -89 117q-11 18 -28 20t-32 -12z" />
<glyph unicode="&#xe107;" d="M-90 100l642 1066q20 31 48 28.5t48 -35.5l642 -1056q21 -32 7.5 -67.5t-50.5 -35.5h-1294q-37 0 -50.5 34t7.5 66zM155 200h345v75q0 10 7.5 17.5t17.5 7.5h150q10 0 17.5 -7.5t7.5 -17.5v-75h345l-445 723zM496 700h208q20 0 32 -14.5t8 -34.5l-58 -252 q-4 -20 -21.5 -34.5t-37.5 -14.5h-54q-20 0 -37.5 14.5t-21.5 34.5l-58 252q-4 20 8 34.5t32 14.5z" />
<glyph unicode="&#xe108;" d="M650 1200q62 0 106 -44t44 -106v-339l363 -325q15 -14 26 -38.5t11 -44.5v-41q0 -20 -12 -26.5t-29 5.5l-359 249v-263q100 -93 100 -113v-64q0 -21 -13 -29t-32 1l-205 128l-205 -128q-19 -9 -32 -1t-13 29v64q0 20 100 113v263l-359 -249q-17 -12 -29 -5.5t-12 26.5v41 q0 20 11 44.5t26 38.5l363 325v339q0 62 44 106t106 44z" />
<glyph unicode="&#xe109;" d="M850 1200h100q21 0 35.5 -14.5t14.5 -35.5v-50h50q21 0 35.5 -14.5t14.5 -35.5v-150h-1100v150q0 21 14.5 35.5t35.5 14.5h50v50q0 21 14.5 35.5t35.5 14.5h100q21 0 35.5 -14.5t14.5 -35.5v-50h500v50q0 21 14.5 35.5t35.5 14.5zM1100 800v-750q0 -21 -14.5 -35.5 t-35.5 -14.5h-1000q-21 0 -35.5 14.5t-14.5 35.5v750h1100zM100 600v-100h100v100h-100zM300 600v-100h100v100h-100zM500 600v-100h100v100h-100zM700 600v-100h100v100h-100zM900 600v-100h100v100h-100zM100 400v-100h100v100h-100zM300 400v-100h100v100h-100zM500 400 v-100h100v100h-100zM700 400v-100h100v100h-100zM900 400v-100h100v100h-100zM100 200v-100h100v100h-100zM300 200v-100h100v100h-100zM500 200v-100h100v100h-100zM700 200v-100h100v100h-100zM900 200v-100h100v100h-100z" />
<glyph unicode="&#xe110;" d="M1135 1165l249 -230q15 -14 15 -35t-15 -35l-249 -230q-14 -14 -24.5 -10t-10.5 25v150h-159l-600 -600h-291q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5h209l600 600h241v150q0 21 10.5 25t24.5 -10zM522 819l-141 -141l-122 122h-209q-21 0 -35.5 14.5 t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5h291zM1135 565l249 -230q15 -14 15 -35t-15 -35l-249 -230q-14 -14 -24.5 -10t-10.5 25v150h-241l-181 181l141 141l122 -122h159v150q0 21 10.5 25t24.5 -10z" />
<glyph unicode="&#xe111;" d="M100 1100h1000q41 0 70.5 -29.5t29.5 -70.5v-600q0 -41 -29.5 -70.5t-70.5 -29.5h-596l-304 -300v300h-100q-41 0 -70.5 29.5t-29.5 70.5v600q0 41 29.5 70.5t70.5 29.5z" />
<glyph unicode="&#xe112;" d="M150 1200h200q21 0 35.5 -14.5t14.5 -35.5v-250h-300v250q0 21 14.5 35.5t35.5 14.5zM850 1200h200q21 0 35.5 -14.5t14.5 -35.5v-250h-300v250q0 21 14.5 35.5t35.5 14.5zM1100 800v-300q0 -41 -3 -77.5t-15 -89.5t-32 -96t-58 -89t-89 -77t-129 -51t-174 -20t-174 20 t-129 51t-89 77t-58 89t-32 96t-15 89.5t-3 77.5v300h300v-250v-27v-42.5t1.5 -41t5 -38t10 -35t16.5 -30t25.5 -24.5t35 -19t46.5 -12t60 -4t60 4.5t46.5 12.5t35 19.5t25 25.5t17 30.5t10 35t5 38t2 40.5t-0.5 42v25v250h300z" />
<glyph unicode="&#xe113;" d="M1100 411l-198 -199l-353 353l-353 -353l-197 199l551 551z" />
<glyph unicode="&#xe114;" d="M1101 789l-550 -551l-551 551l198 199l353 -353l353 353z" />
<glyph unicode="&#xe115;" d="M404 1000h746q21 0 35.5 -14.5t14.5 -35.5v-551h150q21 0 25 -10.5t-10 -24.5l-230 -249q-14 -15 -35 -15t-35 15l-230 249q-14 14 -10 24.5t25 10.5h150v401h-381zM135 984l230 -249q14 -14 10 -24.5t-25 -10.5h-150v-400h385l215 -200h-750q-21 0 -35.5 14.5 t-14.5 35.5v550h-150q-21 0 -25 10.5t10 24.5l230 249q14 15 35 15t35 -15z" />
<glyph unicode="&#xe116;" d="M56 1200h94q17 0 31 -11t18 -27l38 -162h896q24 0 39 -18.5t10 -42.5l-100 -475q-5 -21 -27 -42.5t-55 -21.5h-633l48 -200h535q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-50v-50q0 -21 -14.5 -35.5t-35.5 -14.5t-35.5 14.5t-14.5 35.5v50h-300v-50 q0 -21 -14.5 -35.5t-35.5 -14.5t-35.5 14.5t-14.5 35.5v50h-31q-18 0 -32.5 10t-20.5 19l-5 10l-201 961h-54q-20 0 -35 14.5t-15 35.5t15 35.5t35 14.5z" />
<glyph unicode="&#xe117;" d="M1200 1000v-100h-1200v100h200q0 41 29.5 70.5t70.5 29.5h300q41 0 70.5 -29.5t29.5 -70.5h500zM0 800h1200v-800h-1200v800z" />
<glyph unicode="&#xe118;" d="M200 800l-200 -400v600h200q0 41 29.5 70.5t70.5 29.5h300q42 0 71 -29.5t29 -70.5h500v-200h-1000zM1500 700l-300 -700h-1200l300 700h1200z" />
<glyph unicode="&#xe119;" d="M635 1184l230 -249q14 -14 10 -24.5t-25 -10.5h-150v-601h150q21 0 25 -10.5t-10 -24.5l-230 -249q-14 -15 -35 -15t-35 15l-230 249q-14 14 -10 24.5t25 10.5h150v601h-150q-21 0 -25 10.5t10 24.5l230 249q14 15 35 15t35 -15z" />
<glyph unicode="&#xe120;" d="M936 864l249 -229q14 -15 14 -35.5t-14 -35.5l-249 -229q-15 -15 -25.5 -10.5t-10.5 24.5v151h-600v-151q0 -20 -10.5 -24.5t-25.5 10.5l-249 229q-14 15 -14 35.5t14 35.5l249 229q15 15 25.5 10.5t10.5 -25.5v-149h600v149q0 21 10.5 25.5t25.5 -10.5z" />
<glyph unicode="&#xe121;" d="M1169 400l-172 732q-5 23 -23 45.5t-38 22.5h-672q-20 0 -38 -20t-23 -41l-172 -739h1138zM1100 300h-1000q-41 0 -70.5 -29.5t-29.5 -70.5v-100q0 -41 29.5 -70.5t70.5 -29.5h1000q41 0 70.5 29.5t29.5 70.5v100q0 41 -29.5 70.5t-70.5 29.5zM800 100v100h100v-100h-100 zM1000 100v100h100v-100h-100z" />
<glyph unicode="&#xe122;" d="M1150 1100q21 0 35.5 -14.5t14.5 -35.5v-850q0 -21 -14.5 -35.5t-35.5 -14.5t-35.5 14.5t-14.5 35.5v850q0 21 14.5 35.5t35.5 14.5zM1000 200l-675 200h-38l47 -276q3 -16 -5.5 -20t-29.5 -4h-7h-84q-20 0 -34.5 14t-18.5 35q-55 337 -55 351v250v6q0 16 1 23.5t6.5 14 t17.5 6.5h200l675 250v-850zM0 750v-250q-4 0 -11 0.5t-24 6t-30 15t-24 30t-11 48.5v50q0 26 10.5 46t25 30t29 16t25.5 7z" />
<glyph unicode="&#xe123;" d="M553 1200h94q20 0 29 -10.5t3 -29.5l-18 -37q83 -19 144 -82.5t76 -140.5l63 -327l118 -173h17q19 0 33 -14.5t14 -35t-13 -40.5t-31 -27q-8 -4 -23 -9.5t-65 -19.5t-103 -25t-132.5 -20t-158.5 -9q-57 0 -115 5t-104 12t-88.5 15.5t-73.5 17.5t-54.5 16t-35.5 12l-11 4 q-18 8 -31 28t-13 40.5t14 35t33 14.5h17l118 173l63 327q15 77 76 140t144 83l-18 32q-6 19 3.5 32t28.5 13zM498 110q50 -6 102 -6q53 0 102 6q-12 -49 -39.5 -79.5t-62.5 -30.5t-63 30.5t-39 79.5z" />
<glyph unicode="&#xe124;" d="M800 946l224 78l-78 -224l234 -45l-180 -155l180 -155l-234 -45l78 -224l-224 78l-45 -234l-155 180l-155 -180l-45 234l-224 -78l78 224l-234 45l180 155l-180 155l234 45l-78 224l224 -78l45 234l155 -180l155 180z" />
<glyph unicode="&#xe125;" d="M650 1200h50q40 0 70 -40.5t30 -84.5v-150l-28 -125h328q40 0 70 -40.5t30 -84.5v-100q0 -45 -29 -74l-238 -344q-16 -24 -38 -40.5t-45 -16.5h-250q-7 0 -42 25t-66 50l-31 25h-61q-45 0 -72.5 18t-27.5 57v400q0 36 20 63l145 196l96 198q13 28 37.5 48t51.5 20z M650 1100l-100 -212l-150 -213v-375h100l136 -100h214l250 375v125h-450l50 225v175h-50zM50 800h100q21 0 35.5 -14.5t14.5 -35.5v-500q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v500q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe126;" d="M600 1100h250q23 0 45 -16.5t38 -40.5l238 -344q29 -29 29 -74v-100q0 -44 -30 -84.5t-70 -40.5h-328q28 -118 28 -125v-150q0 -44 -30 -84.5t-70 -40.5h-50q-27 0 -51.5 20t-37.5 48l-96 198l-145 196q-20 27 -20 63v400q0 39 27.5 57t72.5 18h61q124 100 139 100z M50 1000h100q21 0 35.5 -14.5t14.5 -35.5v-500q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v500q0 21 14.5 35.5t35.5 14.5zM636 1000l-136 -100h-100v-375l150 -213l100 -212h50v175l-50 225h450v125l-250 375h-214z" />
<glyph unicode="&#xe127;" d="M356 873l363 230q31 16 53 -6l110 -112q13 -13 13.5 -32t-11.5 -34l-84 -121h302q84 0 138 -38t54 -110t-55 -111t-139 -39h-106l-131 -339q-6 -21 -19.5 -41t-28.5 -20h-342q-7 0 -90 81t-83 94v525q0 17 14 35.5t28 28.5zM400 792v-503l100 -89h293l131 339 q6 21 19.5 41t28.5 20h203q21 0 30.5 25t0.5 50t-31 25h-456h-7h-6h-5.5t-6 0.5t-5 1.5t-5 2t-4 2.5t-4 4t-2.5 4.5q-12 25 5 47l146 183l-86 83zM50 800h100q21 0 35.5 -14.5t14.5 -35.5v-500q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v500 q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe128;" d="M475 1103l366 -230q2 -1 6 -3.5t14 -10.5t18 -16.5t14.5 -20t6.5 -22.5v-525q0 -13 -86 -94t-93 -81h-342q-15 0 -28.5 20t-19.5 41l-131 339h-106q-85 0 -139.5 39t-54.5 111t54 110t138 38h302l-85 121q-11 15 -10.5 34t13.5 32l110 112q22 22 53 6zM370 945l146 -183 q17 -22 5 -47q-2 -2 -3.5 -4.5t-4 -4t-4 -2.5t-5 -2t-5 -1.5t-6 -0.5h-6h-6.5h-6h-475v-100h221q15 0 29 -20t20 -41l130 -339h294l106 89v503l-342 236zM1050 800h100q21 0 35.5 -14.5t14.5 -35.5v-500q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5 v500q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe129;" d="M550 1294q72 0 111 -55t39 -139v-106l339 -131q21 -6 41 -19.5t20 -28.5v-342q0 -7 -81 -90t-94 -83h-525q-17 0 -35.5 14t-28.5 28l-9 14l-230 363q-16 31 6 53l112 110q13 13 32 13.5t34 -11.5l121 -84v302q0 84 38 138t110 54zM600 972v203q0 21 -25 30.5t-50 0.5 t-25 -31v-456v-7v-6v-5.5t-0.5 -6t-1.5 -5t-2 -5t-2.5 -4t-4 -4t-4.5 -2.5q-25 -12 -47 5l-183 146l-83 -86l236 -339h503l89 100v293l-339 131q-21 6 -41 19.5t-20 28.5zM450 200h500q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-500 q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe130;" d="M350 1100h500q21 0 35.5 14.5t14.5 35.5v100q0 21 -14.5 35.5t-35.5 14.5h-500q-21 0 -35.5 -14.5t-14.5 -35.5v-100q0 -21 14.5 -35.5t35.5 -14.5zM600 306v-106q0 -84 -39 -139t-111 -55t-110 54t-38 138v302l-121 -84q-15 -12 -34 -11.5t-32 13.5l-112 110 q-22 22 -6 53l230 363q1 2 3.5 6t10.5 13.5t16.5 17t20 13.5t22.5 6h525q13 0 94 -83t81 -90v-342q0 -15 -20 -28.5t-41 -19.5zM308 900l-236 -339l83 -86l183 146q22 17 47 5q2 -1 4.5 -2.5t4 -4t2.5 -4t2 -5t1.5 -5t0.5 -6v-5.5v-6v-7v-456q0 -22 25 -31t50 0.5t25 30.5 v203q0 15 20 28.5t41 19.5l339 131v293l-89 100h-503z" />
<glyph unicode="&#xe131;" d="M600 1178q118 0 225 -45.5t184.5 -123t123 -184.5t45.5 -225t-45.5 -225t-123 -184.5t-184.5 -123t-225 -45.5t-225 45.5t-184.5 123t-123 184.5t-45.5 225t45.5 225t123 184.5t184.5 123t225 45.5zM914 632l-275 223q-16 13 -27.5 8t-11.5 -26v-137h-275 q-10 0 -17.5 -7.5t-7.5 -17.5v-150q0 -10 7.5 -17.5t17.5 -7.5h275v-137q0 -21 11.5 -26t27.5 8l275 223q16 13 16 32t-16 32z" />
<glyph unicode="&#xe132;" d="M600 1178q118 0 225 -45.5t184.5 -123t123 -184.5t45.5 -225t-45.5 -225t-123 -184.5t-184.5 -123t-225 -45.5t-225 45.5t-184.5 123t-123 184.5t-45.5 225t45.5 225t123 184.5t184.5 123t225 45.5zM561 855l-275 -223q-16 -13 -16 -32t16 -32l275 -223q16 -13 27.5 -8 t11.5 26v137h275q10 0 17.5 7.5t7.5 17.5v150q0 10 -7.5 17.5t-17.5 7.5h-275v137q0 21 -11.5 26t-27.5 -8z" />
<glyph unicode="&#xe133;" d="M600 1178q118 0 225 -45.5t184.5 -123t123 -184.5t45.5 -225t-45.5 -225t-123 -184.5t-184.5 -123t-225 -45.5t-225 45.5t-184.5 123t-123 184.5t-45.5 225t45.5 225t123 184.5t184.5 123t225 45.5zM855 639l-223 275q-13 16 -32 16t-32 -16l-223 -275q-13 -16 -8 -27.5 t26 -11.5h137v-275q0 -10 7.5 -17.5t17.5 -7.5h150q10 0 17.5 7.5t7.5 17.5v275h137q21 0 26 11.5t-8 27.5z" />
<glyph unicode="&#xe134;" d="M600 1178q118 0 225 -45.5t184.5 -123t123 -184.5t45.5 -225t-45.5 -225t-123 -184.5t-184.5 -123t-225 -45.5t-225 45.5t-184.5 123t-123 184.5t-45.5 225t45.5 225t123 184.5t184.5 123t225 45.5zM675 900h-150q-10 0 -17.5 -7.5t-7.5 -17.5v-275h-137q-21 0 -26 -11.5 t8 -27.5l223 -275q13 -16 32 -16t32 16l223 275q13 16 8 27.5t-26 11.5h-137v275q0 10 -7.5 17.5t-17.5 7.5z" />
<glyph unicode="&#xe135;" d="M600 1176q116 0 222.5 -46t184 -123.5t123.5 -184t46 -222.5t-46 -222.5t-123.5 -184t-184 -123.5t-222.5 -46t-222.5 46t-184 123.5t-123.5 184t-46 222.5t46 222.5t123.5 184t184 123.5t222.5 46zM627 1101q-15 -12 -36.5 -20.5t-35.5 -12t-43 -8t-39 -6.5 q-15 -3 -45.5 0t-45.5 -2q-20 -7 -51.5 -26.5t-34.5 -34.5q-3 -11 6.5 -22.5t8.5 -18.5q-3 -34 -27.5 -91t-29.5 -79q-9 -34 5 -93t8 -87q0 -9 17 -44.5t16 -59.5q12 0 23 -5t23.5 -15t19.5 -14q16 -8 33 -15t40.5 -15t34.5 -12q21 -9 52.5 -32t60 -38t57.5 -11 q7 -15 -3 -34t-22.5 -40t-9.5 -38q13 -21 23 -34.5t27.5 -27.5t36.5 -18q0 -7 -3.5 -16t-3.5 -14t5 -17q104 -2 221 112q30 29 46.5 47t34.5 49t21 63q-13 8 -37 8.5t-36 7.5q-15 7 -49.5 15t-51.5 19q-18 0 -41 -0.5t-43 -1.5t-42 -6.5t-38 -16.5q-51 -35 -66 -12 q-4 1 -3.5 25.5t0.5 25.5q-6 13 -26.5 17.5t-24.5 6.5q1 15 -0.5 30.5t-7 28t-18.5 11.5t-31 -21q-23 -25 -42 4q-19 28 -8 58q6 16 22 22q6 -1 26 -1.5t33.5 -4t19.5 -13.5q7 -12 18 -24t21.5 -20.5t20 -15t15.5 -10.5l5 -3q2 12 7.5 30.5t8 34.5t-0.5 32q-3 18 3.5 29 t18 22.5t15.5 24.5q6 14 10.5 35t8 31t15.5 22.5t34 22.5q-6 18 10 36q8 0 24 -1.5t24.5 -1.5t20 4.5t20.5 15.5q-10 23 -31 42.5t-37.5 29.5t-49 27t-43.5 23q0 1 2 8t3 11.5t1.5 10.5t-1 9.5t-4.5 4.5q31 -13 58.5 -14.5t38.5 2.5l12 5q5 28 -9.5 46t-36.5 24t-50 15 t-41 20q-18 -4 -37 0zM613 994q0 -17 8 -42t17 -45t9 -23q-8 1 -39.5 5.5t-52.5 10t-37 16.5q3 11 16 29.5t16 25.5q10 -10 19 -10t14 6t13.5 14.5t16.5 12.5z" />
<glyph unicode="&#xe136;" d="M756 1157q164 92 306 -9l-259 -138l145 -232l251 126q6 -89 -34 -156.5t-117 -110.5q-60 -34 -127 -39.5t-126 16.5l-596 -596q-15 -16 -36.5 -16t-36.5 16l-111 110q-15 15 -15 36.5t15 37.5l600 599q-34 101 5.5 201.5t135.5 154.5z" />
<glyph unicode="&#xe137;" horiz-adv-x="1220" d="M100 1196h1000q41 0 70.5 -29.5t29.5 -70.5v-100q0 -41 -29.5 -70.5t-70.5 -29.5h-1000q-41 0 -70.5 29.5t-29.5 70.5v100q0 41 29.5 70.5t70.5 29.5zM1100 1096h-200v-100h200v100zM100 796h1000q41 0 70.5 -29.5t29.5 -70.5v-100q0 -41 -29.5 -70.5t-70.5 -29.5h-1000 q-41 0 -70.5 29.5t-29.5 70.5v100q0 41 29.5 70.5t70.5 29.5zM1100 696h-500v-100h500v100zM100 396h1000q41 0 70.5 -29.5t29.5 -70.5v-100q0 -41 -29.5 -70.5t-70.5 -29.5h-1000q-41 0 -70.5 29.5t-29.5 70.5v100q0 41 29.5 70.5t70.5 29.5zM1100 296h-300v-100h300v100z " />
<glyph unicode="&#xe138;" d="M150 1200h900q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-900q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5zM700 500v-300l-200 -200v500l-350 500h900z" />
<glyph unicode="&#xe139;" d="M500 1200h200q41 0 70.5 -29.5t29.5 -70.5v-100h300q41 0 70.5 -29.5t29.5 -70.5v-400h-500v100h-200v-100h-500v400q0 41 29.5 70.5t70.5 29.5h300v100q0 41 29.5 70.5t70.5 29.5zM500 1100v-100h200v100h-200zM1200 400v-200q0 -41 -29.5 -70.5t-70.5 -29.5h-1000 q-41 0 -70.5 29.5t-29.5 70.5v200h1200z" />
<glyph unicode="&#xe140;" d="M50 1200h300q21 0 25 -10.5t-10 -24.5l-94 -94l199 -199q7 -8 7 -18t-7 -18l-106 -106q-8 -7 -18 -7t-18 7l-199 199l-94 -94q-14 -14 -24.5 -10t-10.5 25v300q0 21 14.5 35.5t35.5 14.5zM850 1200h300q21 0 35.5 -14.5t14.5 -35.5v-300q0 -21 -10.5 -25t-24.5 10l-94 94 l-199 -199q-8 -7 -18 -7t-18 7l-106 106q-7 8 -7 18t7 18l199 199l-94 94q-14 14 -10 24.5t25 10.5zM364 470l106 -106q7 -8 7 -18t-7 -18l-199 -199l94 -94q14 -14 10 -24.5t-25 -10.5h-300q-21 0 -35.5 14.5t-14.5 35.5v300q0 21 10.5 25t24.5 -10l94 -94l199 199 q8 7 18 7t18 -7zM1071 271l94 94q14 14 24.5 10t10.5 -25v-300q0 -21 -14.5 -35.5t-35.5 -14.5h-300q-21 0 -25 10.5t10 24.5l94 94l-199 199q-7 8 -7 18t7 18l106 106q8 7 18 7t18 -7z" />
<glyph unicode="&#xe141;" d="M596 1192q121 0 231.5 -47.5t190 -127t127 -190t47.5 -231.5t-47.5 -231.5t-127 -190.5t-190 -127t-231.5 -47t-231.5 47t-190.5 127t-127 190.5t-47 231.5t47 231.5t127 190t190.5 127t231.5 47.5zM596 1010q-112 0 -207.5 -55.5t-151 -151t-55.5 -207.5t55.5 -207.5 t151 -151t207.5 -55.5t207.5 55.5t151 151t55.5 207.5t-55.5 207.5t-151 151t-207.5 55.5zM454.5 905q22.5 0 38.5 -16t16 -38.5t-16 -39t-38.5 -16.5t-38.5 16.5t-16 39t16 38.5t38.5 16zM754.5 905q22.5 0 38.5 -16t16 -38.5t-16 -39t-38 -16.5q-14 0 -29 10l-55 -145 q17 -23 17 -51q0 -36 -25.5 -61.5t-61.5 -25.5t-61.5 25.5t-25.5 61.5q0 32 20.5 56.5t51.5 29.5l122 126l1 1q-9 14 -9 28q0 23 16 39t38.5 16zM345.5 709q22.5 0 38.5 -16t16 -38.5t-16 -38.5t-38.5 -16t-38.5 16t-16 38.5t16 38.5t38.5 16zM854.5 709q22.5 0 38.5 -16 t16 -38.5t-16 -38.5t-38.5 -16t-38.5 16t-16 38.5t16 38.5t38.5 16z" />
<glyph unicode="&#xe142;" d="M546 173l469 470q91 91 99 192q7 98 -52 175.5t-154 94.5q-22 4 -47 4q-34 0 -66.5 -10t-56.5 -23t-55.5 -38t-48 -41.5t-48.5 -47.5q-376 -375 -391 -390q-30 -27 -45 -41.5t-37.5 -41t-32 -46.5t-16 -47.5t-1.5 -56.5q9 -62 53.5 -95t99.5 -33q74 0 125 51l548 548 q36 36 20 75q-7 16 -21.5 26t-32.5 10q-26 0 -50 -23q-13 -12 -39 -38l-341 -338q-15 -15 -35.5 -15.5t-34.5 13.5t-14 34.5t14 34.5q327 333 361 367q35 35 67.5 51.5t78.5 16.5q14 0 29 -1q44 -8 74.5 -35.5t43.5 -68.5q14 -47 2 -96.5t-47 -84.5q-12 -11 -32 -32 t-79.5 -81t-114.5 -115t-124.5 -123.5t-123 -119.5t-96.5 -89t-57 -45q-56 -27 -120 -27q-70 0 -129 32t-93 89q-48 78 -35 173t81 163l511 511q71 72 111 96q91 55 198 55q80 0 152 -33q78 -36 129.5 -103t66.5 -154q17 -93 -11 -183.5t-94 -156.5l-482 -476 q-15 -15 -36 -16t-37 14t-17.5 34t14.5 35z" />
<glyph unicode="&#xe143;" d="M649 949q48 68 109.5 104t121.5 38.5t118.5 -20t102.5 -64t71 -100.5t27 -123q0 -57 -33.5 -117.5t-94 -124.5t-126.5 -127.5t-150 -152.5t-146 -174q-62 85 -145.5 174t-150 152.5t-126.5 127.5t-93.5 124.5t-33.5 117.5q0 64 28 123t73 100.5t104 64t119 20 t120.5 -38.5t104.5 -104zM896 972q-33 0 -64.5 -19t-56.5 -46t-47.5 -53.5t-43.5 -45.5t-37.5 -19t-36 19t-40 45.5t-43 53.5t-54 46t-65.5 19q-67 0 -122.5 -55.5t-55.5 -132.5q0 -23 13.5 -51t46 -65t57.5 -63t76 -75l22 -22q15 -14 44 -44t50.5 -51t46 -44t41 -35t23 -12 t23.5 12t42.5 36t46 44t52.5 52t44 43q4 4 12 13q43 41 63.5 62t52 55t46 55t26 46t11.5 44q0 79 -53 133.5t-120 54.5z" />
<glyph unicode="&#xe144;" d="M776.5 1214q93.5 0 159.5 -66l141 -141q66 -66 66 -160q0 -42 -28 -95.5t-62 -87.5l-29 -29q-31 53 -77 99l-18 18l95 95l-247 248l-389 -389l212 -212l-105 -106l-19 18l-141 141q-66 66 -66 159t66 159l283 283q65 66 158.5 66zM600 706l105 105q10 -8 19 -17l141 -141 q66 -66 66 -159t-66 -159l-283 -283q-66 -66 -159 -66t-159 66l-141 141q-66 66 -66 159.5t66 159.5l55 55q29 -55 75 -102l18 -17l-95 -95l247 -248l389 389z" />
<glyph unicode="&#xe145;" d="M603 1200q85 0 162 -15t127 -38t79 -48t29 -46v-953q0 -41 -29.5 -70.5t-70.5 -29.5h-600q-41 0 -70.5 29.5t-29.5 70.5v953q0 21 30 46.5t81 48t129 37.5t163 15zM300 1000v-700h600v700h-600zM600 254q-43 0 -73.5 -30.5t-30.5 -73.5t30.5 -73.5t73.5 -30.5t73.5 30.5 t30.5 73.5t-30.5 73.5t-73.5 30.5z" />
<glyph unicode="&#xe146;" d="M902 1185l283 -282q15 -15 15 -36t-14.5 -35.5t-35.5 -14.5t-35 15l-36 35l-279 -267v-300l-212 210l-308 -307l-280 -203l203 280l307 308l-210 212h300l267 279l-35 36q-15 14 -15 35t14.5 35.5t35.5 14.5t35 -15z" />
<glyph unicode="&#xe148;" d="M700 1248v-78q38 -5 72.5 -14.5t75.5 -31.5t71 -53.5t52 -84t24 -118.5h-159q-4 36 -10.5 59t-21 45t-40 35.5t-64.5 20.5v-307l64 -13q34 -7 64 -16.5t70 -32t67.5 -52.5t47.5 -80t20 -112q0 -139 -89 -224t-244 -97v-77h-100v79q-150 16 -237 103q-40 40 -52.5 93.5 t-15.5 139.5h139q5 -77 48.5 -126t117.5 -65v335l-27 8q-46 14 -79 26.5t-72 36t-63 52t-40 72.5t-16 98q0 70 25 126t67.5 92t94.5 57t110 27v77h100zM600 754v274q-29 -4 -50 -11t-42 -21.5t-31.5 -41.5t-10.5 -65q0 -29 7 -50.5t16.5 -34t28.5 -22.5t31.5 -14t37.5 -10 q9 -3 13 -4zM700 547v-310q22 2 42.5 6.5t45 15.5t41.5 27t29 42t12 59.5t-12.5 59.5t-38 44.5t-53 31t-66.5 24.5z" />
<glyph unicode="&#xe149;" d="M561 1197q84 0 160.5 -40t123.5 -109.5t47 -147.5h-153q0 40 -19.5 71.5t-49.5 48.5t-59.5 26t-55.5 9q-37 0 -79 -14.5t-62 -35.5q-41 -44 -41 -101q0 -26 13.5 -63t26.5 -61t37 -66q6 -9 9 -14h241v-100h-197q8 -50 -2.5 -115t-31.5 -95q-45 -62 -99 -112 q34 10 83 17.5t71 7.5q32 1 102 -16t104 -17q83 0 136 30l50 -147q-31 -19 -58 -30.5t-55 -15.5t-42 -4.5t-46 -0.5q-23 0 -76 17t-111 32.5t-96 11.5q-39 -3 -82 -16t-67 -25l-23 -11l-55 145q4 3 16 11t15.5 10.5t13 9t15.5 12t14.5 14t17.5 18.5q48 55 54 126.5 t-30 142.5h-221v100h166q-23 47 -44 104q-7 20 -12 41.5t-6 55.5t6 66.5t29.5 70.5t58.5 71q97 88 263 88z" />
<glyph unicode="&#xe150;" d="M400 300h150q21 0 25 -11t-10 -25l-230 -250q-14 -15 -35 -15t-35 15l-230 250q-14 14 -10 25t25 11h150v900h200v-900zM935 1184l230 -249q14 -14 10 -24.5t-25 -10.5h-150v-900h-200v900h-150q-21 0 -25 10.5t10 24.5l230 249q14 15 35 15t35 -15z" />
<glyph unicode="&#xe151;" d="M1000 700h-100v100h-100v-100h-100v500h300v-500zM400 300h150q21 0 25 -11t-10 -25l-230 -250q-14 -15 -35 -15t-35 15l-230 250q-14 14 -10 25t25 11h150v900h200v-900zM801 1100v-200h100v200h-100zM1000 350l-200 -250h200v-100h-300v150l200 250h-200v100h300v-150z " />
<glyph unicode="&#xe152;" d="M400 300h150q21 0 25 -11t-10 -25l-230 -250q-14 -15 -35 -15t-35 15l-230 250q-14 14 -10 25t25 11h150v900h200v-900zM1000 1050l-200 -250h200v-100h-300v150l200 250h-200v100h300v-150zM1000 0h-100v100h-100v-100h-100v500h300v-500zM801 400v-200h100v200h-100z " />
<glyph unicode="&#xe153;" d="M400 300h150q21 0 25 -11t-10 -25l-230 -250q-14 -15 -35 -15t-35 15l-230 250q-14 14 -10 25t25 11h150v900h200v-900zM1000 700h-100v400h-100v100h200v-500zM1100 0h-100v100h-200v400h300v-500zM901 400v-200h100v200h-100z" />
<glyph unicode="&#xe154;" d="M400 300h150q21 0 25 -11t-10 -25l-230 -250q-14 -15 -35 -15t-35 15l-230 250q-14 14 -10 25t25 11h150v900h200v-900zM1100 700h-100v100h-200v400h300v-500zM901 1100v-200h100v200h-100zM1000 0h-100v400h-100v100h200v-500z" />
<glyph unicode="&#xe155;" d="M400 300h150q21 0 25 -11t-10 -25l-230 -250q-14 -15 -35 -15t-35 15l-230 250q-14 14 -10 25t25 11h150v900h200v-900zM900 1000h-200v200h200v-200zM1000 700h-300v200h300v-200zM1100 400h-400v200h400v-200zM1200 100h-500v200h500v-200z" />
<glyph unicode="&#xe156;" d="M400 300h150q21 0 25 -11t-10 -25l-230 -250q-14 -15 -35 -15t-35 15l-230 250q-14 14 -10 25t25 11h150v900h200v-900zM1200 1000h-500v200h500v-200zM1100 700h-400v200h400v-200zM1000 400h-300v200h300v-200zM900 100h-200v200h200v-200z" />
<glyph unicode="&#xe157;" d="M350 1100h400q162 0 256 -93.5t94 -256.5v-400q0 -165 -93.5 -257.5t-256.5 -92.5h-400q-165 0 -257.5 92.5t-92.5 257.5v400q0 165 92.5 257.5t257.5 92.5zM800 900h-500q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5 v500q0 41 -29.5 70.5t-70.5 29.5z" />
<glyph unicode="&#xe158;" d="M350 1100h400q165 0 257.5 -92.5t92.5 -257.5v-400q0 -165 -92.5 -257.5t-257.5 -92.5h-400q-163 0 -256.5 92.5t-93.5 257.5v400q0 163 94 256.5t256 93.5zM800 900h-500q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5 v500q0 41 -29.5 70.5t-70.5 29.5zM440 770l253 -190q17 -12 17 -30t-17 -30l-253 -190q-16 -12 -28 -6.5t-12 26.5v400q0 21 12 26.5t28 -6.5z" />
<glyph unicode="&#xe159;" d="M350 1100h400q163 0 256.5 -94t93.5 -256v-400q0 -165 -92.5 -257.5t-257.5 -92.5h-400q-165 0 -257.5 92.5t-92.5 257.5v400q0 163 92.5 256.5t257.5 93.5zM800 900h-500q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5 v500q0 41 -29.5 70.5t-70.5 29.5zM350 700h400q21 0 26.5 -12t-6.5 -28l-190 -253q-12 -17 -30 -17t-30 17l-190 253q-12 16 -6.5 28t26.5 12z" />
<glyph unicode="&#xe160;" d="M350 1100h400q165 0 257.5 -92.5t92.5 -257.5v-400q0 -163 -92.5 -256.5t-257.5 -93.5h-400q-163 0 -256.5 94t-93.5 256v400q0 165 92.5 257.5t257.5 92.5zM800 900h-500q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5 v500q0 41 -29.5 70.5t-70.5 29.5zM580 693l190 -253q12 -16 6.5 -28t-26.5 -12h-400q-21 0 -26.5 12t6.5 28l190 253q12 17 30 17t30 -17z" />
<glyph unicode="&#xe161;" d="M550 1100h400q165 0 257.5 -92.5t92.5 -257.5v-400q0 -165 -92.5 -257.5t-257.5 -92.5h-400q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5h450q41 0 70.5 29.5t29.5 70.5v500q0 41 -29.5 70.5t-70.5 29.5h-450q-21 0 -35.5 14.5t-14.5 35.5v100 q0 21 14.5 35.5t35.5 14.5zM338 867l324 -284q16 -14 16 -33t-16 -33l-324 -284q-16 -14 -27 -9t-11 26v150h-250q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5h250v150q0 21 11 26t27 -9z" />
<glyph unicode="&#xe162;" d="M793 1182l9 -9q8 -10 5 -27q-3 -11 -79 -225.5t-78 -221.5l300 1q24 0 32.5 -17.5t-5.5 -35.5q-1 0 -133.5 -155t-267 -312.5t-138.5 -162.5q-12 -15 -26 -15h-9l-9 8q-9 11 -4 32q2 9 42 123.5t79 224.5l39 110h-302q-23 0 -31 19q-10 21 6 41q75 86 209.5 237.5 t228 257t98.5 111.5q9 16 25 16h9z" />
<glyph unicode="&#xe163;" d="M350 1100h400q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-450q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h450q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-165 0 -257.5 92.5t-92.5 257.5v400 q0 165 92.5 257.5t257.5 92.5zM938 867l324 -284q16 -14 16 -33t-16 -33l-324 -284q-16 -14 -27 -9t-11 26v150h-250q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5h250v150q0 21 11 26t27 -9z" />
<glyph unicode="&#xe164;" d="M750 1200h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -10.5 -25t-24.5 10l-109 109l-312 -312q-15 -15 -35.5 -15t-35.5 15l-141 141q-15 15 -15 35.5t15 35.5l312 312l-109 109q-14 14 -10 24.5t25 10.5zM456 900h-156q-41 0 -70.5 -29.5t-29.5 -70.5v-500 q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5v148l200 200v-298q0 -165 -93.5 -257.5t-256.5 -92.5h-400q-165 0 -257.5 92.5t-92.5 257.5v400q0 165 92.5 257.5t257.5 92.5h300z" />
<glyph unicode="&#xe165;" d="M600 1186q119 0 227.5 -46.5t187 -125t125 -187t46.5 -227.5t-46.5 -227.5t-125 -187t-187 -125t-227.5 -46.5t-227.5 46.5t-187 125t-125 187t-46.5 227.5t46.5 227.5t125 187t187 125t227.5 46.5zM600 1022q-115 0 -212 -56.5t-153.5 -153.5t-56.5 -212t56.5 -212 t153.5 -153.5t212 -56.5t212 56.5t153.5 153.5t56.5 212t-56.5 212t-153.5 153.5t-212 56.5zM600 794q80 0 137 -57t57 -137t-57 -137t-137 -57t-137 57t-57 137t57 137t137 57z" />
<glyph unicode="&#xe166;" d="M450 1200h200q21 0 35.5 -14.5t14.5 -35.5v-350h245q20 0 25 -11t-9 -26l-383 -426q-14 -15 -33.5 -15t-32.5 15l-379 426q-13 15 -8.5 26t25.5 11h250v350q0 21 14.5 35.5t35.5 14.5zM50 300h1000q21 0 35.5 -14.5t14.5 -35.5v-250h-1100v250q0 21 14.5 35.5t35.5 14.5z M900 200v-50h100v50h-100z" />
<glyph unicode="&#xe167;" d="M583 1182l378 -435q14 -15 9 -31t-26 -16h-244v-250q0 -20 -17 -35t-39 -15h-200q-20 0 -32 14.5t-12 35.5v250h-250q-20 0 -25.5 16.5t8.5 31.5l383 431q14 16 33.5 17t33.5 -14zM50 300h1000q21 0 35.5 -14.5t14.5 -35.5v-250h-1100v250q0 21 14.5 35.5t35.5 14.5z M900 200v-50h100v50h-100z" />
<glyph unicode="&#xe168;" d="M396 723l369 369q7 7 17.5 7t17.5 -7l139 -139q7 -8 7 -18.5t-7 -17.5l-525 -525q-7 -8 -17.5 -8t-17.5 8l-292 291q-7 8 -7 18t7 18l139 139q8 7 18.5 7t17.5 -7zM50 300h1000q21 0 35.5 -14.5t14.5 -35.5v-250h-1100v250q0 21 14.5 35.5t35.5 14.5zM900 200v-50h100v50 h-100z" />
<glyph unicode="&#xe169;" d="M135 1023l142 142q14 14 35 14t35 -14l77 -77l-212 -212l-77 76q-14 15 -14 36t14 35zM655 855l210 210q14 14 24.5 10t10.5 -25l-2 -599q-1 -20 -15.5 -35t-35.5 -15l-597 -1q-21 0 -25 10.5t10 24.5l208 208l-154 155l212 212zM50 300h1000q21 0 35.5 -14.5t14.5 -35.5 v-250h-1100v250q0 21 14.5 35.5t35.5 14.5zM900 200v-50h100v50h-100z" />
<glyph unicode="&#xe170;" d="M350 1200l599 -2q20 -1 35 -15.5t15 -35.5l1 -597q0 -21 -10.5 -25t-24.5 10l-208 208l-155 -154l-212 212l155 154l-210 210q-14 14 -10 24.5t25 10.5zM524 512l-76 -77q-15 -14 -36 -14t-35 14l-142 142q-14 14 -14 35t14 35l77 77zM50 300h1000q21 0 35.5 -14.5 t14.5 -35.5v-250h-1100v250q0 21 14.5 35.5t35.5 14.5zM900 200v-50h100v50h-100z" />
<glyph unicode="&#xe171;" d="M1200 103l-483 276l-314 -399v423h-399l1196 796v-1096zM483 424v-230l683 953z" />
<glyph unicode="&#xe172;" d="M1100 1000v-850q0 -21 -14.5 -35.5t-35.5 -14.5h-150v400h-700v-400h-150q-21 0 -35.5 14.5t-14.5 35.5v1000q0 20 14.5 35t35.5 15h250v-300h500v300h100zM700 1000h-100v200h100v-200z" />
<glyph unicode="&#xe173;" d="M1100 1000l-2 -149l-299 -299l-95 95q-9 9 -21.5 9t-21.5 -9l-149 -147h-312v-400h-150q-21 0 -35.5 14.5t-14.5 35.5v1000q0 20 14.5 35t35.5 15h250v-300h500v300h100zM700 1000h-100v200h100v-200zM1132 638l106 -106q7 -7 7 -17.5t-7 -17.5l-420 -421q-8 -7 -18 -7 t-18 7l-202 203q-8 7 -8 17.5t8 17.5l106 106q7 8 17.5 8t17.5 -8l79 -79l297 297q7 7 17.5 7t17.5 -7z" />
<glyph unicode="&#xe174;" d="M1100 1000v-269l-103 -103l-134 134q-15 15 -33.5 16.5t-34.5 -12.5l-266 -266h-329v-400h-150q-21 0 -35.5 14.5t-14.5 35.5v1000q0 20 14.5 35t35.5 15h250v-300h500v300h100zM700 1000h-100v200h100v-200zM1202 572l70 -70q15 -15 15 -35.5t-15 -35.5l-131 -131 l131 -131q15 -15 15 -35.5t-15 -35.5l-70 -70q-15 -15 -35.5 -15t-35.5 15l-131 131l-131 -131q-15 -15 -35.5 -15t-35.5 15l-70 70q-15 15 -15 35.5t15 35.5l131 131l-131 131q-15 15 -15 35.5t15 35.5l70 70q15 15 35.5 15t35.5 -15l131 -131l131 131q15 15 35.5 15 t35.5 -15z" />
<glyph unicode="&#xe175;" d="M1100 1000v-300h-350q-21 0 -35.5 -14.5t-14.5 -35.5v-150h-500v-400h-150q-21 0 -35.5 14.5t-14.5 35.5v1000q0 20 14.5 35t35.5 15h250v-300h500v300h100zM700 1000h-100v200h100v-200zM850 600h100q21 0 35.5 -14.5t14.5 -35.5v-250h150q21 0 25 -10.5t-10 -24.5 l-230 -230q-14 -14 -35 -14t-35 14l-230 230q-14 14 -10 24.5t25 10.5h150v250q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe176;" d="M1100 1000v-400l-165 165q-14 15 -35 15t-35 -15l-263 -265h-402v-400h-150q-21 0 -35.5 14.5t-14.5 35.5v1000q0 20 14.5 35t35.5 15h250v-300h500v300h100zM700 1000h-100v200h100v-200zM935 565l230 -229q14 -15 10 -25.5t-25 -10.5h-150v-250q0 -20 -14.5 -35 t-35.5 -15h-100q-21 0 -35.5 15t-14.5 35v250h-150q-21 0 -25 10.5t10 25.5l230 229q14 15 35 15t35 -15z" />
<glyph unicode="&#xe177;" d="M50 1100h1100q21 0 35.5 -14.5t14.5 -35.5v-150h-1200v150q0 21 14.5 35.5t35.5 14.5zM1200 800v-550q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5v550h1200zM100 500v-200h400v200h-400z" />
<glyph unicode="&#xe178;" d="M935 1165l248 -230q14 -14 14 -35t-14 -35l-248 -230q-14 -14 -24.5 -10t-10.5 25v150h-400v200h400v150q0 21 10.5 25t24.5 -10zM200 800h-50q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5h50v-200zM400 800h-100v200h100v-200zM18 435l247 230 q14 14 24.5 10t10.5 -25v-150h400v-200h-400v-150q0 -21 -10.5 -25t-24.5 10l-247 230q-15 14 -15 35t15 35zM900 300h-100v200h100v-200zM1000 500h51q20 0 34.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-34.5 -14.5h-51v200z" />
<glyph unicode="&#xe179;" d="M862 1073l276 116q25 18 43.5 8t18.5 -41v-1106q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v397q-4 1 -11 5t-24 17.5t-30 29t-24 42t-11 56.5v359q0 31 18.5 65t43.5 52zM550 1200q22 0 34.5 -12.5t14.5 -24.5l1 -13v-450q0 -28 -10.5 -59.5 t-25 -56t-29 -45t-25.5 -31.5l-10 -11v-447q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v447q-4 4 -11 11.5t-24 30.5t-30 46t-24 55t-11 60v450q0 2 0.5 5.5t4 12t8.5 15t14.5 12t22.5 5.5q20 0 32.5 -12.5t14.5 -24.5l3 -13v-350h100v350v5.5t2.5 12 t7 15t15 12t25.5 5.5q23 0 35.5 -12.5t13.5 -24.5l1 -13v-350h100v350q0 2 0.5 5.5t3 12t7 15t15 12t24.5 5.5z" />
<glyph unicode="&#xe180;" d="M1200 1100v-56q-4 0 -11 -0.5t-24 -3t-30 -7.5t-24 -15t-11 -24v-888q0 -22 25 -34.5t50 -13.5l25 -2v-56h-400v56q75 0 87.5 6.5t12.5 43.5v394h-500v-394q0 -37 12.5 -43.5t87.5 -6.5v-56h-400v56q4 0 11 0.5t24 3t30 7.5t24 15t11 24v888q0 22 -25 34.5t-50 13.5 l-25 2v56h400v-56q-75 0 -87.5 -6.5t-12.5 -43.5v-394h500v394q0 37 -12.5 43.5t-87.5 6.5v56h400z" />
<glyph unicode="&#xe181;" d="M675 1000h375q21 0 35.5 -14.5t14.5 -35.5v-150h-105l-295 -98v98l-200 200h-400l100 100h375zM100 900h300q41 0 70.5 -29.5t29.5 -70.5v-500q0 -41 -29.5 -70.5t-70.5 -29.5h-300q-41 0 -70.5 29.5t-29.5 70.5v500q0 41 29.5 70.5t70.5 29.5zM100 800v-200h300v200 h-300zM1100 535l-400 -133v163l400 133v-163zM100 500v-200h300v200h-300zM1100 398v-248q0 -21 -14.5 -35.5t-35.5 -14.5h-375l-100 -100h-375l-100 100h400l200 200h105z" />
<glyph unicode="&#xe182;" d="M17 1007l162 162q17 17 40 14t37 -22l139 -194q14 -20 11 -44.5t-20 -41.5l-119 -118q102 -142 228 -268t267 -227l119 118q17 17 42.5 19t44.5 -12l192 -136q19 -14 22.5 -37.5t-13.5 -40.5l-163 -162q-3 -1 -9.5 -1t-29.5 2t-47.5 6t-62.5 14.5t-77.5 26.5t-90 42.5 t-101.5 60t-111 83t-119 108.5q-74 74 -133.5 150.5t-94.5 138.5t-60 119.5t-34.5 100t-15 74.5t-4.5 48z" />
<glyph unicode="&#xe183;" d="M600 1100q92 0 175 -10.5t141.5 -27t108.5 -36.5t81.5 -40t53.5 -37t31 -27l9 -10v-200q0 -21 -14.5 -33t-34.5 -9l-202 34q-20 3 -34.5 20t-14.5 38v146q-141 24 -300 24t-300 -24v-146q0 -21 -14.5 -38t-34.5 -20l-202 -34q-20 -3 -34.5 9t-14.5 33v200q3 4 9.5 10.5 t31 26t54 37.5t80.5 39.5t109 37.5t141 26.5t175 10.5zM600 795q56 0 97 -9.5t60 -23.5t30 -28t12 -24l1 -10v-50l365 -303q14 -15 24.5 -40t10.5 -45v-212q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5v212q0 20 10.5 45t24.5 40l365 303v50 q0 4 1 10.5t12 23t30 29t60 22.5t97 10z" />
<glyph unicode="&#xe184;" d="M1100 700l-200 -200h-600l-200 200v500h200v-200h200v200h200v-200h200v200h200v-500zM250 400h700q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-12l137 -100h-950l137 100h-12q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5zM50 100h1100q21 0 35.5 -14.5 t14.5 -35.5v-50h-1200v50q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe185;" d="M700 1100h-100q-41 0 -70.5 -29.5t-29.5 -70.5v-1000h300v1000q0 41 -29.5 70.5t-70.5 29.5zM1100 800h-100q-41 0 -70.5 -29.5t-29.5 -70.5v-700h300v700q0 41 -29.5 70.5t-70.5 29.5zM400 0h-300v400q0 41 29.5 70.5t70.5 29.5h100q41 0 70.5 -29.5t29.5 -70.5v-400z " />
<glyph unicode="&#xe186;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM500 700h-200v-100h200v-300h-300v100h200v100h-200v300h300v-100zM900 700v-300l-100 -100h-200v500h200z M700 700v-300h100v300h-100z" />
<glyph unicode="&#xe187;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM500 300h-100v200h-100v-200h-100v500h100v-200h100v200h100v-500zM900 700v-300l-100 -100h-200v500h200z M700 700v-300h100v300h-100z" />
<glyph unicode="&#xe188;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM500 700h-200v-300h200v-100h-300v500h300v-100zM900 700h-200v-300h200v-100h-300v500h300v-100z" />
<glyph unicode="&#xe189;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM500 400l-300 150l300 150v-300zM900 550l-300 -150v300z" />
<glyph unicode="&#xe190;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM900 300h-700v500h700v-500zM800 700h-130q-38 0 -66.5 -43t-28.5 -108t27 -107t68 -42h130v300zM300 700v-300 h130q41 0 68 42t27 107t-28.5 108t-66.5 43h-130z" />
<glyph unicode="&#xe191;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM500 700h-200v-100h200v-300h-300v100h200v100h-200v300h300v-100zM900 300h-100v400h-100v100h200v-500z M700 300h-100v100h100v-100z" />
<glyph unicode="&#xe192;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM300 700h200v-400h-300v500h100v-100zM900 300h-100v400h-100v100h200v-500zM300 600v-200h100v200h-100z M700 300h-100v100h100v-100z" />
<glyph unicode="&#xe193;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM500 500l-199 -200h-100v50l199 200v150h-200v100h300v-300zM900 300h-100v400h-100v100h200v-500zM701 300h-100 v100h100v-100z" />
<glyph unicode="&#xe194;" d="M600 1191q120 0 229.5 -47t188.5 -126t126 -188.5t47 -229.5t-47 -229.5t-126 -188.5t-188.5 -126t-229.5 -47t-229.5 47t-188.5 126t-126 188.5t-47 229.5t47 229.5t126 188.5t188.5 126t229.5 47zM600 1021q-114 0 -211 -56.5t-153.5 -153.5t-56.5 -211t56.5 -211 t153.5 -153.5t211 -56.5t211 56.5t153.5 153.5t56.5 211t-56.5 211t-153.5 153.5t-211 56.5zM800 700h-300v-200h300v-100h-300l-100 100v200l100 100h300v-100z" />
<glyph unicode="&#xe195;" d="M600 1191q120 0 229.5 -47t188.5 -126t126 -188.5t47 -229.5t-47 -229.5t-126 -188.5t-188.5 -126t-229.5 -47t-229.5 47t-188.5 126t-126 188.5t-47 229.5t47 229.5t126 188.5t188.5 126t229.5 47zM600 1021q-114 0 -211 -56.5t-153.5 -153.5t-56.5 -211t56.5 -211 t153.5 -153.5t211 -56.5t211 56.5t153.5 153.5t56.5 211t-56.5 211t-153.5 153.5t-211 56.5zM800 700v-100l-50 -50l100 -100v-50h-100l-100 100h-150v-100h-100v400h300zM500 700v-100h200v100h-200z" />
<glyph unicode="&#xe197;" d="M503 1089q110 0 200.5 -59.5t134.5 -156.5q44 14 90 14q120 0 205 -86.5t85 -207t-85 -207t-205 -86.5h-128v250q0 21 -14.5 35.5t-35.5 14.5h-300q-21 0 -35.5 -14.5t-14.5 -35.5v-250h-222q-80 0 -136 57.5t-56 136.5q0 69 43 122.5t108 67.5q-2 19 -2 37q0 100 49 185 t134 134t185 49zM525 500h150q10 0 17.5 -7.5t7.5 -17.5v-275h137q21 0 26 -11.5t-8 -27.5l-223 -244q-13 -16 -32 -16t-32 16l-223 244q-13 16 -8 27.5t26 11.5h137v275q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe198;" d="M502 1089q110 0 201 -59.5t135 -156.5q43 15 89 15q121 0 206 -86.5t86 -206.5q0 -99 -60 -181t-150 -110l-378 360q-13 16 -31.5 16t-31.5 -16l-381 -365h-9q-79 0 -135.5 57.5t-56.5 136.5q0 69 43 122.5t108 67.5q-2 19 -2 38q0 100 49 184.5t133.5 134t184.5 49.5z M632 467l223 -228q13 -16 8 -27.5t-26 -11.5h-137v-275q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v275h-137q-21 0 -26 11.5t8 27.5q199 204 223 228q19 19 31.5 19t32.5 -19z" />
<glyph unicode="&#xe199;" d="M700 100v100h400l-270 300h170l-270 300h170l-300 333l-300 -333h170l-270 -300h170l-270 -300h400v-100h-50q-21 0 -35.5 -14.5t-14.5 -35.5v-50h400v50q0 21 -14.5 35.5t-35.5 14.5h-50z" />
<glyph unicode="&#xe200;" d="M600 1179q94 0 167.5 -56.5t99.5 -145.5q89 -6 150.5 -71.5t61.5 -155.5q0 -61 -29.5 -112.5t-79.5 -82.5q9 -29 9 -55q0 -74 -52.5 -126.5t-126.5 -52.5q-55 0 -100 30v-251q21 0 35.5 -14.5t14.5 -35.5v-50h-300v50q0 21 14.5 35.5t35.5 14.5v251q-45 -30 -100 -30 q-74 0 -126.5 52.5t-52.5 126.5q0 18 4 38q-47 21 -75.5 65t-28.5 97q0 74 52.5 126.5t126.5 52.5q5 0 23 -2q0 2 -1 10t-1 13q0 116 81.5 197.5t197.5 81.5z" />
<glyph unicode="&#xe201;" d="M1010 1010q111 -111 150.5 -260.5t0 -299t-150.5 -260.5q-83 -83 -191.5 -126.5t-218.5 -43.5t-218.5 43.5t-191.5 126.5q-111 111 -150.5 260.5t0 299t150.5 260.5q83 83 191.5 126.5t218.5 43.5t218.5 -43.5t191.5 -126.5zM476 1065q-4 0 -8 -1q-121 -34 -209.5 -122.5 t-122.5 -209.5q-4 -12 2.5 -23t18.5 -14l36 -9q3 -1 7 -1q23 0 29 22q27 96 98 166q70 71 166 98q11 3 17.5 13.5t3.5 22.5l-9 35q-3 13 -14 19q-7 4 -15 4zM512 920q-4 0 -9 -2q-80 -24 -138.5 -82.5t-82.5 -138.5q-4 -13 2 -24t19 -14l34 -9q4 -1 8 -1q22 0 28 21 q18 58 58.5 98.5t97.5 58.5q12 3 18 13.5t3 21.5l-9 35q-3 12 -14 19q-7 4 -15 4zM719.5 719.5q-49.5 49.5 -119.5 49.5t-119.5 -49.5t-49.5 -119.5t49.5 -119.5t119.5 -49.5t119.5 49.5t49.5 119.5t-49.5 119.5zM855 551q-22 0 -28 -21q-18 -58 -58.5 -98.5t-98.5 -57.5 q-11 -4 -17 -14.5t-3 -21.5l9 -35q3 -12 14 -19q7 -4 15 -4q4 0 9 2q80 24 138.5 82.5t82.5 138.5q4 13 -2.5 24t-18.5 14l-34 9q-4 1 -8 1zM1000 515q-23 0 -29 -22q-27 -96 -98 -166q-70 -71 -166 -98q-11 -3 -17.5 -13.5t-3.5 -22.5l9 -35q3 -13 14 -19q7 -4 15 -4 q4 0 8 1q121 34 209.5 122.5t122.5 209.5q4 12 -2.5 23t-18.5 14l-36 9q-3 1 -7 1z" />
<glyph unicode="&#xe202;" d="M700 800h300v-380h-180v200h-340v-200h-380v755q0 10 7.5 17.5t17.5 7.5h575v-400zM1000 900h-200v200zM700 300h162l-212 -212l-212 212h162v200h100v-200zM520 0h-395q-10 0 -17.5 7.5t-7.5 17.5v395zM1000 220v-195q0 -10 -7.5 -17.5t-17.5 -7.5h-195z" />
<glyph unicode="&#xe203;" d="M700 800h300v-520l-350 350l-550 -550v1095q0 10 7.5 17.5t17.5 7.5h575v-400zM1000 900h-200v200zM862 200h-162v-200h-100v200h-162l212 212zM480 0h-355q-10 0 -17.5 7.5t-7.5 17.5v55h380v-80zM1000 80v-55q0 -10 -7.5 -17.5t-17.5 -7.5h-155v80h180z" />
<glyph unicode="&#xe204;" d="M1162 800h-162v-200h100l100 -100h-300v300h-162l212 212zM200 800h200q27 0 40 -2t29.5 -10.5t23.5 -30t7 -57.5h300v-100h-600l-200 -350v450h100q0 36 7 57.5t23.5 30t29.5 10.5t40 2zM800 400h240l-240 -400h-800l300 500h500v-100z" />
<glyph unicode="&#xe205;" d="M650 1100h100q21 0 35.5 -14.5t14.5 -35.5v-50h50q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-300q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5h50v50q0 21 14.5 35.5t35.5 14.5zM1000 850v150q41 0 70.5 -29.5t29.5 -70.5v-800 q0 -41 -29.5 -70.5t-70.5 -29.5h-600q-1 0 -20 4l246 246l-326 326v324q0 41 29.5 70.5t70.5 29.5v-150q0 -62 44 -106t106 -44h300q62 0 106 44t44 106zM412 250l-212 -212v162h-200v100h200v162z" />
<glyph unicode="&#xe206;" d="M450 1100h100q21 0 35.5 -14.5t14.5 -35.5v-50h50q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-300q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5h50v50q0 21 14.5 35.5t35.5 14.5zM800 850v150q41 0 70.5 -29.5t29.5 -70.5v-500 h-200v-300h200q0 -36 -7 -57.5t-23.5 -30t-29.5 -10.5t-40 -2h-600q-41 0 -70.5 29.5t-29.5 70.5v800q0 41 29.5 70.5t70.5 29.5v-150q0 -62 44 -106t106 -44h300q62 0 106 44t44 106zM1212 250l-212 -212v162h-200v100h200v162z" />
<glyph unicode="&#xe209;" d="M658 1197l637 -1104q23 -38 7 -65.5t-60 -27.5h-1276q-44 0 -60 27.5t7 65.5l637 1104q22 39 54 39t54 -39zM704 800h-208q-20 0 -32 -14.5t-8 -34.5l58 -302q4 -20 21.5 -34.5t37.5 -14.5h54q20 0 37.5 14.5t21.5 34.5l58 302q4 20 -8 34.5t-32 14.5zM500 300v-100h200 v100h-200z" />
<glyph unicode="&#xe210;" d="M425 1100h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5zM425 800h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5 t17.5 7.5zM825 800h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5zM25 500h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v150 q0 10 7.5 17.5t17.5 7.5zM425 500h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5zM825 500h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5 v150q0 10 7.5 17.5t17.5 7.5zM25 200h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5zM425 200h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5 t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5zM825 200h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe211;" d="M700 1200h100v-200h-100v-100h350q62 0 86.5 -39.5t-3.5 -94.5l-66 -132q-41 -83 -81 -134h-772q-40 51 -81 134l-66 132q-28 55 -3.5 94.5t86.5 39.5h350v100h-100v200h100v100h200v-100zM250 400h700q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-12l137 -100 h-950l138 100h-13q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5zM50 100h1100q21 0 35.5 -14.5t14.5 -35.5v-50h-1200v50q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe212;" d="M600 1300q40 0 68.5 -29.5t28.5 -70.5h-194q0 41 28.5 70.5t68.5 29.5zM443 1100h314q18 -37 18 -75q0 -8 -3 -25h328q41 0 44.5 -16.5t-30.5 -38.5l-175 -145h-678l-178 145q-34 22 -29 38.5t46 16.5h328q-3 17 -3 25q0 38 18 75zM250 700h700q21 0 35.5 -14.5 t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-150v-200l275 -200h-950l275 200v200h-150q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5zM50 100h1100q21 0 35.5 -14.5t14.5 -35.5v-50h-1200v50q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe213;" d="M600 1181q75 0 128 -53t53 -128t-53 -128t-128 -53t-128 53t-53 128t53 128t128 53zM602 798h46q34 0 55.5 -28.5t21.5 -86.5q0 -76 39 -183h-324q39 107 39 183q0 58 21.5 86.5t56.5 28.5h45zM250 400h700q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-13 l138 -100h-950l137 100h-12q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5zM50 100h1100q21 0 35.5 -14.5t14.5 -35.5v-50h-1200v50q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe214;" d="M600 1300q47 0 92.5 -53.5t71 -123t25.5 -123.5q0 -78 -55.5 -133.5t-133.5 -55.5t-133.5 55.5t-55.5 133.5q0 62 34 143l144 -143l111 111l-163 163q34 26 63 26zM602 798h46q34 0 55.5 -28.5t21.5 -86.5q0 -76 39 -183h-324q39 107 39 183q0 58 21.5 86.5t56.5 28.5h45 zM250 400h700q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-13l138 -100h-950l137 100h-12q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5zM50 100h1100q21 0 35.5 -14.5t14.5 -35.5v-50h-1200v50q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe215;" d="M600 1200l300 -161v-139h-300q0 -57 18.5 -108t50 -91.5t63 -72t70 -67.5t57.5 -61h-530q-60 83 -90.5 177.5t-30.5 178.5t33 164.5t87.5 139.5t126 96.5t145.5 41.5v-98zM250 400h700q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-13l138 -100h-950l137 100 h-12q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5zM50 100h1100q21 0 35.5 -14.5t14.5 -35.5v-50h-1200v50q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe216;" d="M600 1300q41 0 70.5 -29.5t29.5 -70.5v-78q46 -26 73 -72t27 -100v-50h-400v50q0 54 27 100t73 72v78q0 41 29.5 70.5t70.5 29.5zM400 800h400q54 0 100 -27t72 -73h-172v-100h200v-100h-200v-100h200v-100h-200v-100h200q0 -83 -58.5 -141.5t-141.5 -58.5h-400 q-83 0 -141.5 58.5t-58.5 141.5v400q0 83 58.5 141.5t141.5 58.5z" />
<glyph unicode="&#xe218;" d="M150 1100h900q21 0 35.5 -14.5t14.5 -35.5v-500q0 -21 -14.5 -35.5t-35.5 -14.5h-900q-21 0 -35.5 14.5t-14.5 35.5v500q0 21 14.5 35.5t35.5 14.5zM125 400h950q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-283l224 -224q13 -13 13 -31.5t-13 -32 t-31.5 -13.5t-31.5 13l-88 88h-524l-87 -88q-13 -13 -32 -13t-32 13.5t-13 32t13 31.5l224 224h-289q-10 0 -17.5 7.5t-7.5 17.5v50q0 10 7.5 17.5t17.5 7.5zM541 300l-100 -100h324l-100 100h-124z" />
<glyph unicode="&#xe219;" d="M200 1100h800q83 0 141.5 -58.5t58.5 -141.5v-200h-100q0 41 -29.5 70.5t-70.5 29.5h-250q-41 0 -70.5 -29.5t-29.5 -70.5h-100q0 41 -29.5 70.5t-70.5 29.5h-250q-41 0 -70.5 -29.5t-29.5 -70.5h-100v200q0 83 58.5 141.5t141.5 58.5zM100 600h1000q41 0 70.5 -29.5 t29.5 -70.5v-300h-1200v300q0 41 29.5 70.5t70.5 29.5zM300 100v-50q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v50h200zM1100 100v-50q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v50h200z" />
<glyph unicode="&#xe221;" d="M480 1165l682 -683q31 -31 31 -75.5t-31 -75.5l-131 -131h-481l-517 518q-32 31 -32 75.5t32 75.5l295 296q31 31 75.5 31t76.5 -31zM108 794l342 -342l303 304l-341 341zM250 100h800q21 0 35.5 -14.5t14.5 -35.5v-50h-900v50q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe223;" d="M1057 647l-189 506q-8 19 -27.5 33t-40.5 14h-400q-21 0 -40.5 -14t-27.5 -33l-189 -506q-8 -19 1.5 -33t30.5 -14h625v-150q0 -21 14.5 -35.5t35.5 -14.5t35.5 14.5t14.5 35.5v150h125q21 0 30.5 14t1.5 33zM897 0h-595v50q0 21 14.5 35.5t35.5 14.5h50v50 q0 21 14.5 35.5t35.5 14.5h48v300h200v-300h47q21 0 35.5 -14.5t14.5 -35.5v-50h50q21 0 35.5 -14.5t14.5 -35.5v-50z" />
<glyph unicode="&#xe224;" d="M900 800h300v-575q0 -10 -7.5 -17.5t-17.5 -7.5h-375v591l-300 300v84q0 10 7.5 17.5t17.5 7.5h375v-400zM1200 900h-200v200zM400 600h300v-575q0 -10 -7.5 -17.5t-17.5 -7.5h-650q-10 0 -17.5 7.5t-7.5 17.5v950q0 10 7.5 17.5t17.5 7.5h375v-400zM700 700h-200v200z " />
<glyph unicode="&#xe225;" d="M484 1095h195q75 0 146 -32.5t124 -86t89.5 -122.5t48.5 -142q18 -14 35 -20q31 -10 64.5 6.5t43.5 48.5q10 34 -15 71q-19 27 -9 43q5 8 12.5 11t19 -1t23.5 -16q41 -44 39 -105q-3 -63 -46 -106.5t-104 -43.5h-62q-7 -55 -35 -117t-56 -100l-39 -234q-3 -20 -20 -34.5 t-38 -14.5h-100q-21 0 -33 14.5t-9 34.5l12 70q-49 -14 -91 -14h-195q-24 0 -65 8l-11 -64q-3 -20 -20 -34.5t-38 -14.5h-100q-21 0 -33 14.5t-9 34.5l26 157q-84 74 -128 175l-159 53q-19 7 -33 26t-14 40v50q0 21 14.5 35.5t35.5 14.5h124q11 87 56 166l-111 95 q-16 14 -12.5 23.5t24.5 9.5h203q116 101 250 101zM675 1000h-250q-10 0 -17.5 -7.5t-7.5 -17.5v-50q0 -10 7.5 -17.5t17.5 -7.5h250q10 0 17.5 7.5t7.5 17.5v50q0 10 -7.5 17.5t-17.5 7.5z" />
<glyph unicode="&#xe226;" d="M641 900l423 247q19 8 42 2.5t37 -21.5l32 -38q14 -15 12.5 -36t-17.5 -34l-139 -120h-390zM50 1100h106q67 0 103 -17t66 -71l102 -212h823q21 0 35.5 -14.5t14.5 -35.5v-50q0 -21 -14 -40t-33 -26l-737 -132q-23 -4 -40 6t-26 25q-42 67 -100 67h-300q-62 0 -106 44 t-44 106v200q0 62 44 106t106 44zM173 928h-80q-19 0 -28 -14t-9 -35v-56q0 -51 42 -51h134q16 0 21.5 8t5.5 24q0 11 -16 45t-27 51q-18 28 -43 28zM550 727q-32 0 -54.5 -22.5t-22.5 -54.5t22.5 -54.5t54.5 -22.5t54.5 22.5t22.5 54.5t-22.5 54.5t-54.5 22.5zM130 389 l152 130q18 19 34 24t31 -3.5t24.5 -17.5t25.5 -28q28 -35 50.5 -51t48.5 -13l63 5l48 -179q13 -61 -3.5 -97.5t-67.5 -79.5l-80 -69q-47 -40 -109 -35.5t-103 51.5l-130 151q-40 47 -35.5 109.5t51.5 102.5zM380 377l-102 -88q-31 -27 2 -65l37 -43q13 -15 27.5 -19.5 t31.5 6.5l61 53q19 16 14 49q-2 20 -12 56t-17 45q-11 12 -19 14t-23 -8z" />
<glyph unicode="&#xe227;" d="M625 1200h150q10 0 17.5 -7.5t7.5 -17.5v-109q79 -33 131 -87.5t53 -128.5q1 -46 -15 -84.5t-39 -61t-46 -38t-39 -21.5l-17 -6q6 0 15 -1.5t35 -9t50 -17.5t53 -30t50 -45t35.5 -64t14.5 -84q0 -59 -11.5 -105.5t-28.5 -76.5t-44 -51t-49.5 -31.5t-54.5 -16t-49.5 -6.5 t-43.5 -1v-75q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v75h-100v-75q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v75h-175q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5h75v600h-75q-10 0 -17.5 7.5t-7.5 17.5v150 q0 10 7.5 17.5t17.5 7.5h175v75q0 10 7.5 17.5t17.5 7.5h150q10 0 17.5 -7.5t7.5 -17.5v-75h100v75q0 10 7.5 17.5t17.5 7.5zM400 900v-200h263q28 0 48.5 10.5t30 25t15 29t5.5 25.5l1 10q0 4 -0.5 11t-6 24t-15 30t-30 24t-48.5 11h-263zM400 500v-200h363q28 0 48.5 10.5 t30 25t15 29t5.5 25.5l1 10q0 4 -0.5 11t-6 24t-15 30t-30 24t-48.5 11h-363z" />
<glyph unicode="&#xe230;" d="M212 1198h780q86 0 147 -61t61 -147v-416q0 -51 -18 -142.5t-36 -157.5l-18 -66q-29 -87 -93.5 -146.5t-146.5 -59.5h-572q-82 0 -147 59t-93 147q-8 28 -20 73t-32 143.5t-20 149.5v416q0 86 61 147t147 61zM600 1045q-70 0 -132.5 -11.5t-105.5 -30.5t-78.5 -41.5 t-57 -45t-36 -41t-20.5 -30.5l-6 -12l156 -243h560l156 243q-2 5 -6 12.5t-20 29.5t-36.5 42t-57 44.5t-79 42t-105 29.5t-132.5 12zM762 703h-157l195 261z" />
<glyph unicode="&#xe231;" d="M475 1300h150q103 0 189 -86t86 -189v-500q0 -41 -42 -83t-83 -42h-450q-41 0 -83 42t-42 83v500q0 103 86 189t189 86zM700 300v-225q0 -21 -27 -48t-48 -27h-150q-21 0 -48 27t-27 48v225h300z" />
<glyph unicode="&#xe232;" d="M475 1300h96q0 -150 89.5 -239.5t239.5 -89.5v-446q0 -41 -42 -83t-83 -42h-450q-41 0 -83 42t-42 83v500q0 103 86 189t189 86zM700 300v-225q0 -21 -27 -48t-48 -27h-150q-21 0 -48 27t-27 48v225h300z" />
<glyph unicode="&#xe233;" d="M1294 767l-638 -283l-378 170l-78 -60v-224l100 -150v-199l-150 148l-150 -149v200l100 150v250q0 4 -0.5 10.5t0 9.5t1 8t3 8t6.5 6l47 40l-147 65l642 283zM1000 380l-350 -166l-350 166v147l350 -165l350 165v-147z" />
<glyph unicode="&#xe234;" d="M250 800q62 0 106 -44t44 -106t-44 -106t-106 -44t-106 44t-44 106t44 106t106 44zM650 800q62 0 106 -44t44 -106t-44 -106t-106 -44t-106 44t-44 106t44 106t106 44zM1050 800q62 0 106 -44t44 -106t-44 -106t-106 -44t-106 44t-44 106t44 106t106 44z" />
<glyph unicode="&#xe235;" d="M550 1100q62 0 106 -44t44 -106t-44 -106t-106 -44t-106 44t-44 106t44 106t106 44zM550 700q62 0 106 -44t44 -106t-44 -106t-106 -44t-106 44t-44 106t44 106t106 44zM550 300q62 0 106 -44t44 -106t-44 -106t-106 -44t-106 44t-44 106t44 106t106 44z" />
<glyph unicode="&#xe236;" d="M125 1100h950q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-950q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5zM125 700h950q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-950q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5 t17.5 7.5zM125 300h950q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-950q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe237;" d="M350 1200h500q162 0 256 -93.5t94 -256.5v-500q0 -165 -93.5 -257.5t-256.5 -92.5h-500q-165 0 -257.5 92.5t-92.5 257.5v500q0 165 92.5 257.5t257.5 92.5zM900 1000h-600q-41 0 -70.5 -29.5t-29.5 -70.5v-600q0 -41 29.5 -70.5t70.5 -29.5h600q41 0 70.5 29.5 t29.5 70.5v600q0 41 -29.5 70.5t-70.5 29.5zM350 900h500q21 0 35.5 -14.5t14.5 -35.5v-300q0 -21 -14.5 -35.5t-35.5 -14.5h-500q-21 0 -35.5 14.5t-14.5 35.5v300q0 21 14.5 35.5t35.5 14.5zM400 800v-200h400v200h-400z" />
<glyph unicode="&#xe238;" d="M150 1100h1000q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-50v-200h50q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-50v-200h50q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-50v-200h50q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5 t-35.5 -14.5h-1000q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5h50v200h-50q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5h50v200h-50q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5h50v200h-50q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe239;" d="M650 1187q87 -67 118.5 -156t0 -178t-118.5 -155q-87 66 -118.5 155t0 178t118.5 156zM300 800q124 0 212 -88t88 -212q-124 0 -212 88t-88 212zM1000 800q0 -124 -88 -212t-212 -88q0 124 88 212t212 88zM300 500q124 0 212 -88t88 -212q-124 0 -212 88t-88 212z M1000 500q0 -124 -88 -212t-212 -88q0 124 88 212t212 88zM700 199v-144q0 -21 -14.5 -35.5t-35.5 -14.5t-35.5 14.5t-14.5 35.5v142q40 -4 43 -4q17 0 57 6z" />
<glyph unicode="&#xe240;" d="M745 878l69 19q25 6 45 -12l298 -295q11 -11 15 -26.5t-2 -30.5q-5 -14 -18 -23.5t-28 -9.5h-8q1 0 1 -13q0 -29 -2 -56t-8.5 -62t-20 -63t-33 -53t-51 -39t-72.5 -14h-146q-184 0 -184 288q0 24 10 47q-20 4 -62 4t-63 -4q11 -24 11 -47q0 -288 -184 -288h-142 q-48 0 -84.5 21t-56 51t-32 71.5t-16 75t-3.5 68.5q0 13 2 13h-7q-15 0 -27.5 9.5t-18.5 23.5q-6 15 -2 30.5t15 25.5l298 296q20 18 46 11l76 -19q20 -5 30.5 -22.5t5.5 -37.5t-22.5 -31t-37.5 -5l-51 12l-182 -193h891l-182 193l-44 -12q-20 -5 -37.5 6t-22.5 31t6 37.5 t31 22.5z" />
<glyph unicode="&#xe241;" d="M1200 900h-50q0 21 -4 37t-9.5 26.5t-18 17.5t-22 11t-28.5 5.5t-31 2t-37 0.5h-200v-850q0 -22 25 -34.5t50 -13.5l25 -2v-100h-400v100q4 0 11 0.5t24 3t30 7t24 15t11 24.5v850h-200q-25 0 -37 -0.5t-31 -2t-28.5 -5.5t-22 -11t-18 -17.5t-9.5 -26.5t-4 -37h-50v300 h1000v-300zM500 450h-25q0 15 -4 24.5t-9 14.5t-17 7.5t-20 3t-25 0.5h-100v-425q0 -11 12.5 -17.5t25.5 -7.5h12v-50h-200v50q50 0 50 25v425h-100q-17 0 -25 -0.5t-20 -3t-17 -7.5t-9 -14.5t-4 -24.5h-25v150h500v-150z" />
<glyph unicode="&#xe242;" d="M1000 300v50q-25 0 -55 32q-14 14 -25 31t-16 27l-4 11l-289 747h-69l-300 -754q-18 -35 -39 -56q-9 -9 -24.5 -18.5t-26.5 -14.5l-11 -5v-50h273v50q-49 0 -78.5 21.5t-11.5 67.5l69 176h293l61 -166q13 -34 -3.5 -66.5t-55.5 -32.5v-50h312zM412 691l134 342l121 -342 h-255zM1100 150v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1000q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5h1000q21 0 35.5 -14.5t14.5 -35.5z" />
<glyph unicode="&#xe243;" d="M50 1200h1100q21 0 35.5 -14.5t14.5 -35.5v-1100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5v1100q0 21 14.5 35.5t35.5 14.5zM611 1118h-70q-13 0 -18 -12l-299 -753q-17 -32 -35 -51q-18 -18 -56 -34q-12 -5 -12 -18v-50q0 -8 5.5 -14t14.5 -6 h273q8 0 14 6t6 14v50q0 8 -6 14t-14 6q-55 0 -71 23q-10 14 0 39l63 163h266l57 -153q11 -31 -6 -55q-12 -17 -36 -17q-8 0 -14 -6t-6 -14v-50q0 -8 6 -14t14 -6h313q8 0 14 6t6 14v50q0 7 -5.5 13t-13.5 7q-17 0 -42 25q-25 27 -40 63h-1l-288 748q-5 12 -19 12zM639 611 h-197l103 264z" />
<glyph unicode="&#xe244;" d="M1200 1100h-1200v100h1200v-100zM50 1000h400q21 0 35.5 -14.5t14.5 -35.5v-900q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v900q0 21 14.5 35.5t35.5 14.5zM650 1000h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400 q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5zM700 900v-300h300v300h-300z" />
<glyph unicode="&#xe245;" d="M50 1200h400q21 0 35.5 -14.5t14.5 -35.5v-900q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v900q0 21 14.5 35.5t35.5 14.5zM650 700h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v400 q0 21 14.5 35.5t35.5 14.5zM700 600v-300h300v300h-300zM1200 0h-1200v100h1200v-100z" />
<glyph unicode="&#xe246;" d="M50 1000h400q21 0 35.5 -14.5t14.5 -35.5v-350h100v150q0 21 14.5 35.5t35.5 14.5h400q21 0 35.5 -14.5t14.5 -35.5v-150h100v-100h-100v-150q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v150h-100v-350q0 -21 -14.5 -35.5t-35.5 -14.5h-400 q-21 0 -35.5 14.5t-14.5 35.5v800q0 21 14.5 35.5t35.5 14.5zM700 700v-300h300v300h-300z" />
<glyph unicode="&#xe247;" d="M100 0h-100v1200h100v-1200zM250 1100h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5zM300 1000v-300h300v300h-300zM250 500h900q21 0 35.5 -14.5t14.5 -35.5v-400 q0 -21 -14.5 -35.5t-35.5 -14.5h-900q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe248;" d="M600 1100h150q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-150v-100h450q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-900q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5h350v100h-150q-21 0 -35.5 14.5 t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5h150v100h100v-100zM400 1000v-300h300v300h-300z" />
<glyph unicode="&#xe249;" d="M1200 0h-100v1200h100v-1200zM550 1100h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5zM600 1000v-300h300v300h-300zM50 500h900q21 0 35.5 -14.5t14.5 -35.5v-400 q0 -21 -14.5 -35.5t-35.5 -14.5h-900q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe250;" d="M865 565l-494 -494q-23 -23 -41 -23q-14 0 -22 13.5t-8 38.5v1000q0 25 8 38.5t22 13.5q18 0 41 -23l494 -494q14 -14 14 -35t-14 -35z" />
<glyph unicode="&#xe251;" d="M335 635l494 494q29 29 50 20.5t21 -49.5v-1000q0 -41 -21 -49.5t-50 20.5l-494 494q-14 14 -14 35t14 35z" />
<glyph unicode="&#xe252;" d="M100 900h1000q41 0 49.5 -21t-20.5 -50l-494 -494q-14 -14 -35 -14t-35 14l-494 494q-29 29 -20.5 50t49.5 21z" />
<glyph unicode="&#xe253;" d="M635 865l494 -494q29 -29 20.5 -50t-49.5 -21h-1000q-41 0 -49.5 21t20.5 50l494 494q14 14 35 14t35 -14z" />
<glyph unicode="&#xe254;" d="M700 741v-182l-692 -323v221l413 193l-413 193v221zM1200 0h-800v200h800v-200z" />
<glyph unicode="&#xe255;" d="M1200 900h-200v-100h200v-100h-300v300h200v100h-200v100h300v-300zM0 700h50q0 21 4 37t9.5 26.5t18 17.5t22 11t28.5 5.5t31 2t37 0.5h100v-550q0 -22 -25 -34.5t-50 -13.5l-25 -2v-100h400v100q-4 0 -11 0.5t-24 3t-30 7t-24 15t-11 24.5v550h100q25 0 37 -0.5t31 -2 t28.5 -5.5t22 -11t18 -17.5t9.5 -26.5t4 -37h50v300h-800v-300z" />
<glyph unicode="&#xe256;" d="M800 700h-50q0 21 -4 37t-9.5 26.5t-18 17.5t-22 11t-28.5 5.5t-31 2t-37 0.5h-100v-550q0 -22 25 -34.5t50 -14.5l25 -1v-100h-400v100q4 0 11 0.5t24 3t30 7t24 15t11 24.5v550h-100q-25 0 -37 -0.5t-31 -2t-28.5 -5.5t-22 -11t-18 -17.5t-9.5 -26.5t-4 -37h-50v300 h800v-300zM1100 200h-200v-100h200v-100h-300v300h200v100h-200v100h300v-300z" />
<glyph unicode="&#xe257;" d="M701 1098h160q16 0 21 -11t-7 -23l-464 -464l464 -464q12 -12 7 -23t-21 -11h-160q-13 0 -23 9l-471 471q-7 8 -7 18t7 18l471 471q10 9 23 9z" />
<glyph unicode="&#xe258;" d="M339 1098h160q13 0 23 -9l471 -471q7 -8 7 -18t-7 -18l-471 -471q-10 -9 -23 -9h-160q-16 0 -21 11t7 23l464 464l-464 464q-12 12 -7 23t21 11z" />
<glyph unicode="&#xe259;" d="M1087 882q11 -5 11 -21v-160q0 -13 -9 -23l-471 -471q-8 -7 -18 -7t-18 7l-471 471q-9 10 -9 23v160q0 16 11 21t23 -7l464 -464l464 464q12 12 23 7z" />
<glyph unicode="&#xe260;" d="M618 993l471 -471q9 -10 9 -23v-160q0 -16 -11 -21t-23 7l-464 464l-464 -464q-12 -12 -23 -7t-11 21v160q0 13 9 23l471 471q8 7 18 7t18 -7z" />
<glyph unicode="&#xf8ff;" d="M1000 1200q0 -124 -88 -212t-212 -88q0 124 88 212t212 88zM450 1000h100q21 0 40 -14t26 -33l79 -194q5 1 16 3q34 6 54 9.5t60 7t65.5 1t61 -10t56.5 -23t42.5 -42t29 -64t5 -92t-19.5 -121.5q-1 -7 -3 -19.5t-11 -50t-20.5 -73t-32.5 -81.5t-46.5 -83t-64 -70 t-82.5 -50q-13 -5 -42 -5t-65.5 2.5t-47.5 2.5q-14 0 -49.5 -3.5t-63 -3.5t-43.5 7q-57 25 -104.5 78.5t-75 111.5t-46.5 112t-26 90l-7 35q-15 63 -18 115t4.5 88.5t26 64t39.5 43.5t52 25.5t58.5 13t62.5 2t59.5 -4.5t55.5 -8l-147 192q-12 18 -5.5 30t27.5 12z" />
<glyph unicode="&#x1f511;" d="M250 1200h600q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-150v-500l-255 -178q-19 -9 -32 -1t-13 29v650h-150q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5zM400 1100v-100h300v100h-300z" />
<glyph unicode="&#x1f6aa;" d="M250 1200h750q39 0 69.5 -40.5t30.5 -84.5v-933l-700 -117v950l600 125h-700v-1000h-100v1025q0 23 15.5 49t34.5 26zM500 525v-100l100 20v100z" />
</font>
</defs></svg> ) format('svg')}.glyphicon{position:relative;top:1px;display:inline-block;font-family:'Glyphicons Halflings';font-style:normal;font-weight:400;line-height:1;-webkit-font-smoothing:antialiased;-moz-osx-font-smoothing:grayscale}.glyphicon-asterisk:before{content:"\2a"}.glyphicon-plus:before{content:"\2b"}.glyphicon-eur:before,.glyphicon-euro:before{content:"\20ac"}.glyphicon-minus:before{content:"\2212"}.glyphicon-cloud:before{content:"\2601"}.glyphicon-envelope:before{content:"\2709"}.glyphicon-pencil:before{content:"\270f"}.glyphicon-glass:before{content:"\e001"}.glyphicon-music:before{content:"\e002"}.glyphicon-search:before{content:"\e003"}.glyphicon-heart:before{content:"\e005"}.glyphicon-star:before{content:"\e006"}.glyphicon-star-empty:before{content:"\e007"}.glyphicon-user:before{content:"\e008"}.glyphicon-film:before{content:"\e009"}.glyphicon-th-large:before{content:"\e010"}.glyphicon-th:before{content:"\e011"}.glyphicon-th-list:before{content:"\e012"}.glyphicon-ok:before{content:"\e013"}.glyphicon-remove:before{content:"\e014"}.glyphicon-zoom-in:before{content:"\e015"}.glyphicon-zoom-out:before{content:"\e016"}.glyphicon-off:before{content:"\e017"}.glyphicon-signal:before{content:"\e018"}.glyphicon-cog:before{content:"\e019"}.glyphicon-trash:before{content:"\e020"}.glyphicon-home:before{content:"\e021"}.glyphicon-file:before{content:"\e022"}.glyphicon-time:before{content:"\e023"}.glyphicon-road:before{content:"\e024"}.glyphicon-download-alt:before{content:"\e025"}.glyphicon-download:before{content:"\e026"}.glyphicon-upload:before{content:"\e027"}.glyphicon-inbox:before{content:"\e028"}.glyphicon-play-circle:before{content:"\e029"}.glyphicon-repeat:before{content:"\e030"}.glyphicon-refresh:before{content:"\e031"}.glyphicon-list-alt:before{content:"\e032"}.glyphicon-lock:before{content:"\e033"}.glyphicon-flag:before{content:"\e034"}.glyphicon-headphones:before{content:"\e035"}.glyphicon-volume-off:before{content:"\e036"}.glyphicon-volume-down:before{content:"\e037"}.glyphicon-volume-up:before{content:"\e038"}.glyphicon-qrcode:before{content:"\e039"}.glyphicon-barcode:before{content:"\e040"}.glyphicon-tag:before{content:"\e041"}.glyphicon-tags:before{content:"\e042"}.glyphicon-book:before{content:"\e043"}.glyphicon-bookmark:before{content:"\e044"}.glyphicon-print:before{content:"\e045"}.glyphicon-camera:before{content:"\e046"}.glyphicon-font:before{content:"\e047"}.glyphicon-bold:before{content:"\e048"}.glyphicon-italic:before{content:"\e049"}.glyphicon-text-height:before{content:"\e050"}.glyphicon-text-width:before{content:"\e051"}.glyphicon-align-left:before{content:"\e052"}.glyphicon-align-center:before{content:"\e053"}.glyphicon-align-right:before{content:"\e054"}.glyphicon-align-justify:before{content:"\e055"}.glyphicon-list:before{content:"\e056"}.glyphicon-indent-left:before{content:"\e057"}.glyphicon-indent-right:before{content:"\e058"}.glyphicon-facetime-video:before{content:"\e059"}.glyphicon-picture:before{content:"\e060"}.glyphicon-map-marker:before{content:"\e062"}.glyphicon-adjust:before{content:"\e063"}.glyphicon-tint:before{content:"\e064"}.glyphicon-edit:before{content:"\e065"}.glyphicon-share:before{content:"\e066"}.glyphicon-check:before{content:"\e067"}.glyphicon-move:before{content:"\e068"}.glyphicon-step-backward:before{content:"\e069"}.glyphicon-fast-backward:before{content:"\e070"}.glyphicon-backward:before{content:"\e071"}.glyphicon-play:before{content:"\e072"}.glyphicon-pause:before{content:"\e073"}.glyphicon-stop:before{content:"\e074"}.glyphicon-forward:before{content:"\e075"}.glyphicon-fast-forward:before{content:"\e076"}.glyphicon-step-forward:before{content:"\e077"}.glyphicon-eject:before{content:"\e078"}.glyphicon-chevron-left:before{content:"\e079"}.glyphicon-chevron-right:before{content:"\e080"}.glyphicon-plus-sign:before{content:"\e081"}.glyphicon-minus-sign:before{content:"\e082"}.glyphicon-remove-sign:before{content:"\e083"}.glyphicon-ok-sign:before{content:"\e084"}.glyphicon-question-sign:before{content:"\e085"}.glyphicon-info-sign:before{content:"\e086"}.glyphicon-screenshot:before{content:"\e087"}.glyphicon-remove-circle:before{content:"\e088"}.glyphicon-ok-circle:before{content:"\e089"}.glyphicon-ban-circle:before{content:"\e090"}.glyphicon-arrow-left:before{content:"\e091"}.glyphicon-arrow-right:before{content:"\e092"}.glyphicon-arrow-up:before{content:"\e093"}.glyphicon-arrow-down:before{content:"\e094"}.glyphicon-share-alt:before{content:"\e095"}.glyphicon-resize-full:before{content:"\e096"}.glyphicon-resize-small:before{content:"\e097"}.glyphicon-exclamation-sign:before{content:"\e101"}.glyphicon-gift:before{content:"\e102"}.glyphicon-leaf:before{content:"\e103"}.glyphicon-fire:before{content:"\e104"}.glyphicon-eye-open:before{content:"\e105"}.glyphicon-eye-close:before{content:"\e106"}.glyphicon-warning-sign:before{content:"\e107"}.glyphicon-plane:before{content:"\e108"}.glyphicon-calendar:before{content:"\e109"}.glyphicon-random:before{content:"\e110"}.glyphicon-comment:before{content:"\e111"}.glyphicon-magnet:before{content:"\e112"}.glyphicon-chevron-up:before{content:"\e113"}.glyphicon-chevron-down:before{content:"\e114"}.glyphicon-retweet:before{content:"\e115"}.glyphicon-shopping-cart:before{content:"\e116"}.glyphicon-folder-close:before{content:"\e117"}.glyphicon-folder-open:before{content:"\e118"}.glyphicon-resize-vertical:before{content:"\e119"}.glyphicon-resize-horizontal:before{content:"\e120"}.glyphicon-hdd:before{content:"\e121"}.glyphicon-bullhorn:before{content:"\e122"}.glyphicon-bell:before{content:"\e123"}.glyphicon-certificate:before{content:"\e124"}.glyphicon-thumbs-up:before{content:"\e125"}.glyphicon-thumbs-down:before{content:"\e126"}.glyphicon-hand-right:before{content:"\e127"}.glyphicon-hand-left:before{content:"\e128"}.glyphicon-hand-up:before{content:"\e129"}.glyphicon-hand-down:before{content:"\e130"}.glyphicon-circle-arrow-right:before{content:"\e131"}.glyphicon-circle-arrow-left:before{content:"\e132"}.glyphicon-circle-arrow-up:before{content:"\e133"}.glyphicon-circle-arrow-down:before{content:"\e134"}.glyphicon-globe:before{content:"\e135"}.glyphicon-wrench:before{content:"\e136"}.glyphicon-tasks:before{content:"\e137"}.glyphicon-filter:before{content:"\e138"}.glyphicon-briefcase:before{content:"\e139"}.glyphicon-fullscreen:before{content:"\e140"}.glyphicon-dashboard:before{content:"\e141"}.glyphicon-paperclip:before{content:"\e142"}.glyphicon-heart-empty:before{content:"\e143"}.glyphicon-link:before{content:"\e144"}.glyphicon-phone:before{content:"\e145"}.glyphicon-pushpin:before{content:"\e146"}.glyphicon-usd:before{content:"\e148"}.glyphicon-gbp:before{content:"\e149"}.glyphicon-sort:before{content:"\e150"}.glyphicon-sort-by-alphabet:before{content:"\e151"}.glyphicon-sort-by-alphabet-alt:before{content:"\e152"}.glyphicon-sort-by-order:before{content:"\e153"}.glyphicon-sort-by-order-alt:before{content:"\e154"}.glyphicon-sort-by-attributes:before{content:"\e155"}.glyphicon-sort-by-attributes-alt:before{content:"\e156"}.glyphicon-unchecked:before{content:"\e157"}.glyphicon-expand:before{content:"\e158"}.glyphicon-collapse-down:before{content:"\e159"}.glyphicon-collapse-up:before{content:"\e160"}.glyphicon-log-in:before{content:"\e161"}.glyphicon-flash:before{content:"\e162"}.glyphicon-log-out:before{content:"\e163"}.glyphicon-new-window:before{content:"\e164"}.glyphicon-record:before{content:"\e165"}.glyphicon-save:before{content:"\e166"}.glyphicon-open:before{content:"\e167"}.glyphicon-saved:before{content:"\e168"}.glyphicon-import:before{content:"\e169"}.glyphicon-export:before{content:"\e170"}.glyphicon-send:before{content:"\e171"}.glyphicon-floppy-disk:before{content:"\e172"}.glyphicon-floppy-saved:before{content:"\e173"}.glyphicon-floppy-remove:before{content:"\e174"}.glyphicon-floppy-save:before{content:"\e175"}.glyphicon-floppy-open:before{content:"\e176"}.glyphicon-credit-card:before{content:"\e177"}.glyphicon-transfer:before{content:"\e178"}.glyphicon-cutlery:before{content:"\e179"}.glyphicon-header:before{content:"\e180"}.glyphicon-compressed:before{content:"\e181"}.glyphicon-earphone:before{content:"\e182"}.glyphicon-phone-alt:before{content:"\e183"}.glyphicon-tower:before{content:"\e184"}.glyphicon-stats:before{content:"\e185"}.glyphicon-sd-video:before{content:"\e186"}.glyphicon-hd-video:before{content:"\e187"}.glyphicon-subtitles:before{content:"\e188"}.glyphicon-sound-stereo:before{content:"\e189"}.glyphicon-sound-dolby:before{content:"\e190"}.glyphicon-sound-5-1:before{content:"\e191"}.glyphicon-sound-6-1:before{content:"\e192"}.glyphicon-sound-7-1:before{content:"\e193"}.glyphicon-copyright-mark:before{content:"\e194"}.glyphicon-registration-mark:before{content:"\e195"}.glyphicon-cloud-download:before{content:"\e197"}.glyphicon-cloud-upload:before{content:"\e198"}.glyphicon-tree-conifer:before{content:"\e199"}.glyphicon-tree-deciduous:before{content:"\e200"}.glyphicon-cd:before{content:"\e201"}.glyphicon-save-file:before{content:"\e202"}.glyphicon-open-file:before{content:"\e203"}.glyphicon-level-up:before{content:"\e204"}.glyphicon-copy:before{content:"\e205"}.glyphicon-paste:before{content:"\e206"}.glyphicon-alert:before{content:"\e209"}.glyphicon-equalizer:before{content:"\e210"}.glyphicon-king:before{content:"\e211"}.glyphicon-queen:before{content:"\e212"}.glyphicon-pawn:before{content:"\e213"}.glyphicon-bishop:before{content:"\e214"}.glyphicon-knight:before{content:"\e215"}.glyphicon-baby-formula:before{content:"\e216"}.glyphicon-tent:before{content:"\26fa"}.glyphicon-blackboard:before{content:"\e218"}.glyphicon-bed:before{content:"\e219"}.glyphicon-apple:before{content:"\f8ff"}.glyphicon-erase:before{content:"\e221"}.glyphicon-hourglass:before{content:"\231b"}.glyphicon-lamp:before{content:"\e223"}.glyphicon-duplicate:before{content:"\e224"}.glyphicon-piggy-bank:before{content:"\e225"}.glyphicon-scissors:before{content:"\e226"}.glyphicon-bitcoin:before{content:"\e227"}.glyphicon-btc:before{content:"\e227"}.glyphicon-xbt:before{content:"\e227"}.glyphicon-yen:before{content:"\00a5"}.glyphicon-jpy:before{content:"\00a5"}.glyphicon-ruble:before{content:"\20bd"}.glyphicon-rub:before{content:"\20bd"}.glyphicon-scale:before{content:"\e230"}.glyphicon-ice-lolly:before{content:"\e231"}.glyphicon-ice-lolly-tasted:before{content:"\e232"}.glyphicon-education:before{content:"\e233"}.glyphicon-option-horizontal:before{content:"\e234"}.glyphicon-option-vertical:before{content:"\e235"}.glyphicon-menu-hamburger:before{content:"\e236"}.glyphicon-modal-window:before{content:"\e237"}.glyphicon-oil:before{content:"\e238"}.glyphicon-grain:before{content:"\e239"}.glyphicon-sunglasses:before{content:"\e240"}.glyphicon-text-size:before{content:"\e241"}.glyphicon-text-color:before{content:"\e242"}.glyphicon-text-background:before{content:"\e243"}.glyphicon-object-align-top:before{content:"\e244"}.glyphicon-object-align-bottom:before{content:"\e245"}.glyphicon-object-align-horizontal:before{content:"\e246"}.glyphicon-object-align-left:before{content:"\e247"}.glyphicon-object-align-vertical:before{content:"\e248"}.glyphicon-object-align-right:before{content:"\e249"}.glyphicon-triangle-right:before{content:"\e250"}.glyphicon-triangle-left:before{content:"\e251"}.glyphicon-triangle-bottom:before{content:"\e252"}.glyphicon-triangle-top:before{content:"\e253"}.glyphicon-console:before{content:"\e254"}.glyphicon-superscript:before{content:"\e255"}.glyphicon-subscript:before{content:"\e256"}.glyphicon-menu-left:before{content:"\e257"}.glyphicon-menu-right:before{content:"\e258"}.glyphicon-menu-down:before{content:"\e259"}.glyphicon-menu-up:before{content:"\e260"}*{-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}:after,:before{-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}html{font-size:10px;-webkit-tap-highlight-color:rgba(0,0,0,0)}body{font-family:"Helvetica Neue",Helvetica,Arial,sans-serif;font-size:14px;line-height:1.42857143;color:#333;background-color:#fff}button,input,select,textarea{font-family:inherit;font-size:inherit;line-height:inherit}a{color:#337ab7;text-decoration:none}a:focus,a:hover{color:#23527c;text-decoration:underline}a:focus{outline:thin dotted;outline:5px auto -webkit-focus-ring-color;outline-offset:-2px}figure{margin:0}img{vertical-align:middle}.carousel-inner>.item>a>img,.carousel-inner>.item>img,.img-responsive,.thumbnail a>img,.thumbnail>img{display:block;max-width:100%;height:auto}.img-rounded{border-radius:6px}.img-thumbnail{display:inline-block;max-width:100%;height:auto;padding:4px;line-height:1.42857143;background-color:#fff;border:1px solid #ddd;border-radius:4px;-webkit-transition:all .2s ease-in-out;-o-transition:all .2s ease-in-out;transition:all .2s ease-in-out}.img-circle{border-radius:50%}hr{margin-top:20px;margin-bottom:20px;border:0;border-top:1px solid #eee}.sr-only{position:absolute;width:1px;height:1px;padding:0;margin:-1px;overflow:hidden;clip:rect(0,0,0,0);border:0}.sr-only-focusable:active,.sr-only-focusable:focus{position:static;width:auto;height:auto;margin:0;overflow:visible;clip:auto}[role=button]{cursor:pointer}.h1,.h2,.h3,.h4,.h5,.h6,h1,h2,h3,h4,h5,h6{font-family:inherit;font-weight:500;line-height:1.1;color:inherit}.h1 .small,.h1 small,.h2 .small,.h2 small,.h3 .small,.h3 small,.h4 .small,.h4 small,.h5 .small,.h5 small,.h6 .small,.h6 small,h1 .small,h1 small,h2 .small,h2 small,h3 .small,h3 small,h4 .small,h4 small,h5 .small,h5 small,h6 .small,h6 small{font-weight:400;line-height:1;color:#777}.h1,.h2,.h3,h1,h2,h3{margin-top:20px;margin-bottom:10px}.h1 .small,.h1 small,.h2 .small,.h2 small,.h3 .small,.h3 small,h1 .small,h1 small,h2 .small,h2 small,h3 .small,h3 small{font-size:65%}.h4,.h5,.h6,h4,h5,h6{margin-top:10px;margin-bottom:10px}.h4 .small,.h4 small,.h5 .small,.h5 small,.h6 .small,.h6 small,h4 .small,h4 small,h5 .small,h5 small,h6 .small,h6 small{font-size:75%}.h1,h1{font-size:36px}.h2,h2{font-size:30px}.h3,h3{font-size:24px}.h4,h4{font-size:18px}.h5,h5{font-size:14px}.h6,h6{font-size:12px}p{margin:0 0 10px}.lead{margin-bottom:20px;font-size:16px;font-weight:300;line-height:1.4}@media (min-width:768px){.lead{font-size:21px}}.small,small{font-size:85%}.mark,mark{padding:.2em;background-color:#fcf8e3}.text-left{text-align:left}.text-right{text-align:right}.text-center{text-align:center}.text-justify{text-align:justify}.text-nowrap{white-space:nowrap}.text-lowercase{text-transform:lowercase}.text-uppercase{text-transform:uppercase}.text-capitalize{text-transform:capitalize}.text-muted{color:#777}.text-primary{color:#337ab7}a.text-primary:focus,a.text-primary:hover{color:#286090}.text-success{color:#3c763d}a.text-success:focus,a.text-success:hover{color:#2b542c}.text-info{color:#31708f}a.text-info:focus,a.text-info:hover{color:#245269}.text-warning{color:#8a6d3b}a.text-warning:focus,a.text-warning:hover{color:#66512c}.text-danger{color:#a94442}a.text-danger:focus,a.text-danger:hover{color:#843534}.bg-primary{color:#fff;background-color:#337ab7}a.bg-primary:focus,a.bg-primary:hover{background-color:#286090}.bg-success{background-color:#dff0d8}a.bg-success:focus,a.bg-success:hover{background-color:#c1e2b3}.bg-info{background-color:#d9edf7}a.bg-info:focus,a.bg-info:hover{background-color:#afd9ee}.bg-warning{background-color:#fcf8e3}a.bg-warning:focus,a.bg-warning:hover{background-color:#f7ecb5}.bg-danger{background-color:#f2dede}a.bg-danger:focus,a.bg-danger:hover{background-color:#e4b9b9}.page-header{padding-bottom:9px;margin:40px 0 20px;border-bottom:1px solid #eee}ol,ul{margin-top:0;margin-bottom:10px}ol ol,ol ul,ul ol,ul ul{margin-bottom:0}.list-unstyled{padding-left:0;list-style:none}.list-inline{padding-left:0;margin-left:-5px;list-style:none}.list-inline>li{display:inline-block;padding-right:5px;padding-left:5px}dl{margin-top:0;margin-bottom:20px}dd,dt{line-height:1.42857143}dt{font-weight:700}dd{margin-left:0}@media (min-width:768px){.dl-horizontal dt{float:left;width:160px;overflow:hidden;clear:left;text-align:right;text-overflow:ellipsis;white-space:nowrap}.dl-horizontal dd{margin-left:180px}}abbr[data-original-title],abbr[title]{cursor:help;border-bottom:1px dotted #777}.initialism{font-size:90%;text-transform:uppercase}blockquote{padding:10px 20px;margin:0 0 20px;font-size:17.5px;border-left:5px solid #eee}blockquote ol:last-child,blockquote p:last-child,blockquote ul:last-child{margin-bottom:0}blockquote .small,blockquote footer,blockquote small{display:block;font-size:80%;line-height:1.42857143;color:#777}blockquote .small:before,blockquote footer:before,blockquote small:before{content:'\2014 \00A0'}.blockquote-reverse,blockquote.pull-right{padding-right:15px;padding-left:0;text-align:right;border-right:5px solid #eee;border-left:0}.blockquote-reverse .small:before,.blockquote-reverse footer:before,.blockquote-reverse small:before,blockquote.pull-right .small:before,blockquote.pull-right footer:before,blockquote.pull-right small:before{content:''}.blockquote-reverse .small:after,.blockquote-reverse footer:after,.blockquote-reverse small:after,blockquote.pull-right .small:after,blockquote.pull-right footer:after,blockquote.pull-right small:after{content:'\00A0 \2014'}address{margin-bottom:20px;font-style:normal;line-height:1.42857143}code,kbd,pre,samp{font-family:monospace}code{padding:2px 4px;font-size:90%;color:#c7254e;background-color:#f9f2f4;border-radius:4px}kbd{padding:2px 4px;font-size:90%;color:#fff;background-color:#333;border-radius:3px;-webkit-box-shadow:inset 0 -1px 0 rgba(0,0,0,.25);box-shadow:inset 0 -1px 0 rgba(0,0,0,.25)}kbd kbd{padding:0;font-size:100%;font-weight:700;-webkit-box-shadow:none;box-shadow:none}pre{display:block;padding:9.5px;margin:0 0 10px;font-size:13px;line-height:1.42857143;color:#333;word-break:break-all;word-wrap:break-word;background-color:#f5f5f5;border:1px solid #ccc;border-radius:4px}pre code{padding:0;font-size:inherit;color:inherit;white-space:pre-wrap;background-color:transparent;border-radius:0}.pre-scrollable{max-height:340px;overflow-y:scroll}.container{padding-right:15px;padding-left:15px;margin-right:auto;margin-left:auto}@media (min-width:768px){.container{width:750px}}@media (min-width:992px){.container{width:970px}}@media (min-width:1200px){.container{width:1170px}}.container-fluid{padding-right:15px;padding-left:15px;margin-right:auto;margin-left:auto}.row{margin-right:-15px;margin-left:-15px}.col-lg-1,.col-lg-10,.col-lg-11,.col-lg-12,.col-lg-2,.col-lg-3,.col-lg-4,.col-lg-5,.col-lg-6,.col-lg-7,.col-lg-8,.col-lg-9,.col-md-1,.col-md-10,.col-md-11,.col-md-12,.col-md-2,.col-md-3,.col-md-4,.col-md-5,.col-md-6,.col-md-7,.col-md-8,.col-md-9,.col-sm-1,.col-sm-10,.col-sm-11,.col-sm-12,.col-sm-2,.col-sm-3,.col-sm-4,.col-sm-5,.col-sm-6,.col-sm-7,.col-sm-8,.col-sm-9,.col-xs-1,.col-xs-10,.col-xs-11,.col-xs-12,.col-xs-2,.col-xs-3,.col-xs-4,.col-xs-5,.col-xs-6,.col-xs-7,.col-xs-8,.col-xs-9{position:relative;min-height:1px;padding-right:15px;padding-left:15px}.col-xs-1,.col-xs-10,.col-xs-11,.col-xs-12,.col-xs-2,.col-xs-3,.col-xs-4,.col-xs-5,.col-xs-6,.col-xs-7,.col-xs-8,.col-xs-9{float:left}.col-xs-12{width:100%}.col-xs-11{width:91.66666667%}.col-xs-10{width:83.33333333%}.col-xs-9{width:75%}.col-xs-8{width:66.66666667%}.col-xs-7{width:58.33333333%}.col-xs-6{width:50%}.col-xs-5{width:41.66666667%}.col-xs-4{width:33.33333333%}.col-xs-3{width:25%}.col-xs-2{width:16.66666667%}.col-xs-1{width:8.33333333%}.col-xs-pull-12{right:100%}.col-xs-pull-11{right:91.66666667%}.col-xs-pull-10{right:83.33333333%}.col-xs-pull-9{right:75%}.col-xs-pull-8{right:66.66666667%}.col-xs-pull-7{right:58.33333333%}.col-xs-pull-6{right:50%}.col-xs-pull-5{right:41.66666667%}.col-xs-pull-4{right:33.33333333%}.col-xs-pull-3{right:25%}.col-xs-pull-2{right:16.66666667%}.col-xs-pull-1{right:8.33333333%}.col-xs-pull-0{right:auto}.col-xs-push-12{left:100%}.col-xs-push-11{left:91.66666667%}.col-xs-push-10{left:83.33333333%}.col-xs-push-9{left:75%}.col-xs-push-8{left:66.66666667%}.col-xs-push-7{left:58.33333333%}.col-xs-push-6{left:50%}.col-xs-push-5{left:41.66666667%}.col-xs-push-4{left:33.33333333%}.col-xs-push-3{left:25%}.col-xs-push-2{left:16.66666667%}.col-xs-push-1{left:8.33333333%}.col-xs-push-0{left:auto}.col-xs-offset-12{margin-left:100%}.col-xs-offset-11{margin-left:91.66666667%}.col-xs-offset-10{margin-left:83.33333333%}.col-xs-offset-9{margin-left:75%}.col-xs-offset-8{margin-left:66.66666667%}.col-xs-offset-7{margin-left:58.33333333%}.col-xs-offset-6{margin-left:50%}.col-xs-offset-5{margin-left:41.66666667%}.col-xs-offset-4{margin-left:33.33333333%}.col-xs-offset-3{margin-left:25%}.col-xs-offset-2{margin-left:16.66666667%}.col-xs-offset-1{margin-left:8.33333333%}.col-xs-offset-0{margin-left:0}@media (min-width:768px){.col-sm-1,.col-sm-10,.col-sm-11,.col-sm-12,.col-sm-2,.col-sm-3,.col-sm-4,.col-sm-5,.col-sm-6,.col-sm-7,.col-sm-8,.col-sm-9{float:left}.col-sm-12{width:100%}.col-sm-11{width:91.66666667%}.col-sm-10{width:83.33333333%}.col-sm-9{width:75%}.col-sm-8{width:66.66666667%}.col-sm-7{width:58.33333333%}.col-sm-6{width:50%}.col-sm-5{width:41.66666667%}.col-sm-4{width:33.33333333%}.col-sm-3{width:25%}.col-sm-2{width:16.66666667%}.col-sm-1{width:8.33333333%}.col-sm-pull-12{right:100%}.col-sm-pull-11{right:91.66666667%}.col-sm-pull-10{right:83.33333333%}.col-sm-pull-9{right:75%}.col-sm-pull-8{right:66.66666667%}.col-sm-pull-7{right:58.33333333%}.col-sm-pull-6{right:50%}.col-sm-pull-5{right:41.66666667%}.col-sm-pull-4{right:33.33333333%}.col-sm-pull-3{right:25%}.col-sm-pull-2{right:16.66666667%}.col-sm-pull-1{right:8.33333333%}.col-sm-pull-0{right:auto}.col-sm-push-12{left:100%}.col-sm-push-11{left:91.66666667%}.col-sm-push-10{left:83.33333333%}.col-sm-push-9{left:75%}.col-sm-push-8{left:66.66666667%}.col-sm-push-7{left:58.33333333%}.col-sm-push-6{left:50%}.col-sm-push-5{left:41.66666667%}.col-sm-push-4{left:33.33333333%}.col-sm-push-3{left:25%}.col-sm-push-2{left:16.66666667%}.col-sm-push-1{left:8.33333333%}.col-sm-push-0{left:auto}.col-sm-offset-12{margin-left:100%}.col-sm-offset-11{margin-left:91.66666667%}.col-sm-offset-10{margin-left:83.33333333%}.col-sm-offset-9{margin-left:75%}.col-sm-offset-8{margin-left:66.66666667%}.col-sm-offset-7{margin-left:58.33333333%}.col-sm-offset-6{margin-left:50%}.col-sm-offset-5{margin-left:41.66666667%}.col-sm-offset-4{margin-left:33.33333333%}.col-sm-offset-3{margin-left:25%}.col-sm-offset-2{margin-left:16.66666667%}.col-sm-offset-1{margin-left:8.33333333%}.col-sm-offset-0{margin-left:0}}@media (min-width:992px){.col-md-1,.col-md-10,.col-md-11,.col-md-12,.col-md-2,.col-md-3,.col-md-4,.col-md-5,.col-md-6,.col-md-7,.col-md-8,.col-md-9{float:left}.col-md-12{width:100%}.col-md-11{width:91.66666667%}.col-md-10{width:83.33333333%}.col-md-9{width:75%}.col-md-8{width:66.66666667%}.col-md-7{width:58.33333333%}.col-md-6{width:50%}.col-md-5{width:41.66666667%}.col-md-4{width:33.33333333%}.col-md-3{width:25%}.col-md-2{width:16.66666667%}.col-md-1{width:8.33333333%}.col-md-pull-12{right:100%}.col-md-pull-11{right:91.66666667%}.col-md-pull-10{right:83.33333333%}.col-md-pull-9{right:75%}.col-md-pull-8{right:66.66666667%}.col-md-pull-7{right:58.33333333%}.col-md-pull-6{right:50%}.col-md-pull-5{right:41.66666667%}.col-md-pull-4{right:33.33333333%}.col-md-pull-3{right:25%}.col-md-pull-2{right:16.66666667%}.col-md-pull-1{right:8.33333333%}.col-md-pull-0{right:auto}.col-md-push-12{left:100%}.col-md-push-11{left:91.66666667%}.col-md-push-10{left:83.33333333%}.col-md-push-9{left:75%}.col-md-push-8{left:66.66666667%}.col-md-push-7{left:58.33333333%}.col-md-push-6{left:50%}.col-md-push-5{left:41.66666667%}.col-md-push-4{left:33.33333333%}.col-md-push-3{left:25%}.col-md-push-2{left:16.66666667%}.col-md-push-1{left:8.33333333%}.col-md-push-0{left:auto}.col-md-offset-12{margin-left:100%}.col-md-offset-11{margin-left:91.66666667%}.col-md-offset-10{margin-left:83.33333333%}.col-md-offset-9{margin-left:75%}.col-md-offset-8{margin-left:66.66666667%}.col-md-offset-7{margin-left:58.33333333%}.col-md-offset-6{margin-left:50%}.col-md-offset-5{margin-left:41.66666667%}.col-md-offset-4{margin-left:33.33333333%}.col-md-offset-3{margin-left:25%}.col-md-offset-2{margin-left:16.66666667%}.col-md-offset-1{margin-left:8.33333333%}.col-md-offset-0{margin-left:0}}@media (min-width:1200px){.col-lg-1,.col-lg-10,.col-lg-11,.col-lg-12,.col-lg-2,.col-lg-3,.col-lg-4,.col-lg-5,.col-lg-6,.col-lg-7,.col-lg-8,.col-lg-9{float:left}.col-lg-12{width:100%}.col-lg-11{width:91.66666667%}.col-lg-10{width:83.33333333%}.col-lg-9{width:75%}.col-lg-8{width:66.66666667%}.col-lg-7{width:58.33333333%}.col-lg-6{width:50%}.col-lg-5{width:41.66666667%}.col-lg-4{width:33.33333333%}.col-lg-3{width:25%}.col-lg-2{width:16.66666667%}.col-lg-1{width:8.33333333%}.col-lg-pull-12{right:100%}.col-lg-pull-11{right:91.66666667%}.col-lg-pull-10{right:83.33333333%}.col-lg-pull-9{right:75%}.col-lg-pull-8{right:66.66666667%}.col-lg-pull-7{right:58.33333333%}.col-lg-pull-6{right:50%}.col-lg-pull-5{right:41.66666667%}.col-lg-pull-4{right:33.33333333%}.col-lg-pull-3{right:25%}.col-lg-pull-2{right:16.66666667%}.col-lg-pull-1{right:8.33333333%}.col-lg-pull-0{right:auto}.col-lg-push-12{left:100%}.col-lg-push-11{left:91.66666667%}.col-lg-push-10{left:83.33333333%}.col-lg-push-9{left:75%}.col-lg-push-8{left:66.66666667%}.col-lg-push-7{left:58.33333333%}.col-lg-push-6{left:50%}.col-lg-push-5{left:41.66666667%}.col-lg-push-4{left:33.33333333%}.col-lg-push-3{left:25%}.col-lg-push-2{left:16.66666667%}.col-lg-push-1{left:8.33333333%}.col-lg-push-0{left:auto}.col-lg-offset-12{margin-left:100%}.col-lg-offset-11{margin-left:91.66666667%}.col-lg-offset-10{margin-left:83.33333333%}.col-lg-offset-9{margin-left:75%}.col-lg-offset-8{margin-left:66.66666667%}.col-lg-offset-7{margin-left:58.33333333%}.col-lg-offset-6{margin-left:50%}.col-lg-offset-5{margin-left:41.66666667%}.col-lg-offset-4{margin-left:33.33333333%}.col-lg-offset-3{margin-left:25%}.col-lg-offset-2{margin-left:16.66666667%}.col-lg-offset-1{margin-left:8.33333333%}.col-lg-offset-0{margin-left:0}}table{background-color:transparent}caption{padding-top:8px;padding-bottom:8px;color:#777;text-align:left}th{}.table{width:100%;max-width:100%;margin-bottom:20px}.table>tbody>tr>td,.table>tbody>tr>th,.table>tfoot>tr>td,.table>tfoot>tr>th,.table>thead>tr>td,.table>thead>tr>th{padding:8px;line-height:1.42857143;vertical-align:top;border-top:1px solid #ddd}.table>thead>tr>th{vertical-align:bottom;border-bottom:2px solid #ddd}.table>caption+thead>tr:first-child>td,.table>caption+thead>tr:first-child>th,.table>colgroup+thead>tr:first-child>td,.table>colgroup+thead>tr:first-child>th,.table>thead:first-child>tr:first-child>td,.table>thead:first-child>tr:first-child>th{border-top:0}.table>tbody+tbody{border-top:2px solid #ddd}.table .table{background-color:#fff}.table-condensed>tbody>tr>td,.table-condensed>tbody>tr>th,.table-condensed>tfoot>tr>td,.table-condensed>tfoot>tr>th,.table-condensed>thead>tr>td,.table-condensed>thead>tr>th{padding:5px}.table-bordered{border:1px solid #ddd}.table-bordered>tbody>tr>td,.table-bordered>tbody>tr>th,.table-bordered>tfoot>tr>td,.table-bordered>tfoot>tr>th,.table-bordered>thead>tr>td,.table-bordered>thead>tr>th{border:1px solid #ddd}.table-bordered>thead>tr>td,.table-bordered>thead>tr>th{border-bottom-width:2px}.table-striped>tbody>tr:nth-of-type(odd){background-color:#f9f9f9}.table-hover>tbody>tr:hover{background-color:#f5f5f5}table col[class*=col-]{position:static;display:table-column;float:none}table td[class*=col-],table th[class*=col-]{position:static;display:table-cell;float:none}.table>tbody>tr.active>td,.table>tbody>tr.active>th,.table>tbody>tr>td.active,.table>tbody>tr>th.active,.table>tfoot>tr.active>td,.table>tfoot>tr.active>th,.table>tfoot>tr>td.active,.table>tfoot>tr>th.active,.table>thead>tr.active>td,.table>thead>tr.active>th,.table>thead>tr>td.active,.table>thead>tr>th.active{background-color:#f5f5f5}.table-hover>tbody>tr.active:hover>td,.table-hover>tbody>tr.active:hover>th,.table-hover>tbody>tr:hover>.active,.table-hover>tbody>tr>td.active:hover,.table-hover>tbody>tr>th.active:hover{background-color:#e8e8e8}.table>tbody>tr.success>td,.table>tbody>tr.success>th,.table>tbody>tr>td.success,.table>tbody>tr>th.success,.table>tfoot>tr.success>td,.table>tfoot>tr.success>th,.table>tfoot>tr>td.success,.table>tfoot>tr>th.success,.table>thead>tr.success>td,.table>thead>tr.success>th,.table>thead>tr>td.success,.table>thead>tr>th.success{background-color:#dff0d8}.table-hover>tbody>tr.success:hover>td,.table-hover>tbody>tr.success:hover>th,.table-hover>tbody>tr:hover>.success,.table-hover>tbody>tr>td.success:hover,.table-hover>tbody>tr>th.success:hover{background-color:#d0e9c6}.table>tbody>tr.info>td,.table>tbody>tr.info>th,.table>tbody>tr>td.info,.table>tbody>tr>th.info,.table>tfoot>tr.info>td,.table>tfoot>tr.info>th,.table>tfoot>tr>td.info,.table>tfoot>tr>th.info,.table>thead>tr.info>td,.table>thead>tr.info>th,.table>thead>tr>td.info,.table>thead>tr>th.info{background-color:#d9edf7}.table-hover>tbody>tr.info:hover>td,.table-hover>tbody>tr.info:hover>th,.table-hover>tbody>tr:hover>.info,.table-hover>tbody>tr>td.info:hover,.table-hover>tbody>tr>th.info:hover{background-color:#c4e3f3}.table>tbody>tr.warning>td,.table>tbody>tr.warning>th,.table>tbody>tr>td.warning,.table>tbody>tr>th.warning,.table>tfoot>tr.warning>td,.table>tfoot>tr.warning>th,.table>tfoot>tr>td.warning,.table>tfoot>tr>th.warning,.table>thead>tr.warning>td,.table>thead>tr.warning>th,.table>thead>tr>td.warning,.table>thead>tr>th.warning{background-color:#fcf8e3}.table-hover>tbody>tr.warning:hover>td,.table-hover>tbody>tr.warning:hover>th,.table-hover>tbody>tr:hover>.warning,.table-hover>tbody>tr>td.warning:hover,.table-hover>tbody>tr>th.warning:hover{background-color:#faf2cc}.table>tbody>tr.danger>td,.table>tbody>tr.danger>th,.table>tbody>tr>td.danger,.table>tbody>tr>th.danger,.table>tfoot>tr.danger>td,.table>tfoot>tr.danger>th,.table>tfoot>tr>td.danger,.table>tfoot>tr>th.danger,.table>thead>tr.danger>td,.table>thead>tr.danger>th,.table>thead>tr>td.danger,.table>thead>tr>th.danger{background-color:#f2dede}.table-hover>tbody>tr.danger:hover>td,.table-hover>tbody>tr.danger:hover>th,.table-hover>tbody>tr:hover>.danger,.table-hover>tbody>tr>td.danger:hover,.table-hover>tbody>tr>th.danger:hover{background-color:#ebcccc}.table-responsive{min-height:.01%;overflow-x:auto}@media screen and (max-width:767px){.table-responsive{width:100%;margin-bottom:15px;overflow-y:hidden;-ms-overflow-style:-ms-autohiding-scrollbar;border:1px solid #ddd}.table-responsive>.table{margin-bottom:0}.table-responsive>.table>tbody>tr>td,.table-responsive>.table>tbody>tr>th,.table-responsive>.table>tfoot>tr>td,.table-responsive>.table>tfoot>tr>th,.table-responsive>.table>thead>tr>td,.table-responsive>.table>thead>tr>th{white-space:nowrap}.table-responsive>.table-bordered{border:0}.table-responsive>.table-bordered>tbody>tr>td:first-child,.table-responsive>.table-bordered>tbody>tr>th:first-child,.table-responsive>.table-bordered>tfoot>tr>td:first-child,.table-responsive>.table-bordered>tfoot>tr>th:first-child,.table-responsive>.table-bordered>thead>tr>td:first-child,.table-responsive>.table-bordered>thead>tr>th:first-child{border-left:0}.table-responsive>.table-bordered>tbody>tr>td:last-child,.table-responsive>.table-bordered>tbody>tr>th:last-child,.table-responsive>.table-bordered>tfoot>tr>td:last-child,.table-responsive>.table-bordered>tfoot>tr>th:last-child,.table-responsive>.table-bordered>thead>tr>td:last-child,.table-responsive>.table-bordered>thead>tr>th:last-child{border-right:0}.table-responsive>.table-bordered>tbody>tr:last-child>td,.table-responsive>.table-bordered>tbody>tr:last-child>th,.table-responsive>.table-bordered>tfoot>tr:last-child>td,.table-responsive>.table-bordered>tfoot>tr:last-child>th{border-bottom:0}}fieldset{min-width:0;padding:0;margin:0;border:0}legend{display:block;width:100%;padding:0;margin-bottom:20px;font-size:21px;line-height:inherit;color:#333;border:0;border-bottom:1px solid #e5e5e5}label{display:inline-block;max-width:100%;margin-bottom:5px;font-weight:700}input[type=search]{-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}input[type=checkbox],input[type=radio]{margin:4px 0 0;margin-top:1px\9;line-height:normal}input[type=file]{display:block}input[type=range]{display:block;width:100%}select[multiple],select[size]{height:auto}input[type=file]:focus,input[type=checkbox]:focus,input[type=radio]:focus{outline:thin dotted;outline:5px auto -webkit-focus-ring-color;outline-offset:-2px}output{display:block;padding-top:7px;font-size:14px;line-height:1.42857143;color:#555}.form-control{display:block;width:100%;height:34px;padding:6px 12px;font-size:14px;line-height:1.42857143;color:#555;background-color:#fff;background-image:none;border:1px solid #ccc;border-radius:4px;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075);box-shadow:inset 0 1px 1px rgba(0,0,0,.075);-webkit-transition:border-color ease-in-out .15s,-webkit-box-shadow ease-in-out .15s;-o-transition:border-color ease-in-out .15s,box-shadow ease-in-out .15s;transition:border-color ease-in-out .15s,box-shadow ease-in-out .15s}.form-control:focus{border-color:#66afe9;outline:0;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 8px rgba(102,175,233,.6);box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 8px rgba(102,175,233,.6)}.form-control::-moz-placeholder{color:#999;opacity:1}.form-control:-ms-input-placeholder{color:#999}.form-control::-webkit-input-placeholder{color:#999}.form-control[disabled],.form-control[readonly],fieldset[disabled] .form-control{background-color:#eee;opacity:1}.form-control[disabled],fieldset[disabled] .form-control{cursor:not-allowed}textarea.form-control{height:auto}input[type=search]{-webkit-appearance:none}@media screen and (-webkit-min-device-pixel-ratio:0){input[type=date].form-control,input[type=time].form-control,input[type=datetime-local].form-control,input[type=month].form-control{line-height:34px}.input-group-sm input[type=date],.input-group-sm input[type=time],.input-group-sm input[type=datetime-local],.input-group-sm input[type=month],input[type=date].input-sm,input[type=time].input-sm,input[type=datetime-local].input-sm,input[type=month].input-sm{line-height:30px}.input-group-lg input[type=date],.input-group-lg input[type=time],.input-group-lg input[type=datetime-local],.input-group-lg input[type=month],input[type=date].input-lg,input[type=time].input-lg,input[type=datetime-local].input-lg,input[type=month].input-lg{line-height:46px}}.form-group{margin-bottom:15px}.checkbox,.radio{position:relative;display:block;margin-top:10px;margin-bottom:10px}.checkbox label,.radio label{min-height:20px;padding-left:20px;margin-bottom:0;font-weight:400;cursor:pointer}.checkbox input[type=checkbox],.checkbox-inline input[type=checkbox],.radio input[type=radio],.radio-inline input[type=radio]{position:absolute;margin-top:4px\9;margin-left:-20px}.checkbox+.checkbox,.radio+.radio{margin-top:-5px}.checkbox-inline,.radio-inline{position:relative;display:inline-block;padding-left:20px;margin-bottom:0;font-weight:400;vertical-align:middle;cursor:pointer}.checkbox-inline+.checkbox-inline,.radio-inline+.radio-inline{margin-top:0;margin-left:10px}fieldset[disabled] input[type=checkbox],fieldset[disabled] input[type=radio],input[type=checkbox].disabled,input[type=checkbox][disabled],input[type=radio].disabled,input[type=radio][disabled]{cursor:not-allowed}.checkbox-inline.disabled,.radio-inline.disabled,fieldset[disabled] .checkbox-inline,fieldset[disabled] .radio-inline{cursor:not-allowed}.checkbox.disabled label,.radio.disabled label,fieldset[disabled] .checkbox label,fieldset[disabled] .radio label{cursor:not-allowed}.form-control-static{min-height:34px;padding-top:7px;padding-bottom:7px;margin-bottom:0}.form-control-static.input-lg,.form-control-static.input-sm{padding-right:0;padding-left:0}.input-sm{height:30px;padding:5px 10px;font-size:12px;line-height:1.5;border-radius:3px}select.input-sm{height:30px;line-height:30px}select[multiple].input-sm,textarea.input-sm{height:auto}.form-group-sm .form-control{height:30px;padding:5px 10px;font-size:12px;line-height:1.5;border-radius:3px}.form-group-sm select.form-control{height:30px;line-height:30px}.form-group-sm select[multiple].form-control,.form-group-sm textarea.form-control{height:auto}.form-group-sm .form-control-static{height:30px;min-height:32px;padding:6px 10px;font-size:12px;line-height:1.5}.input-lg{height:46px;padding:10px 16px;font-size:18px;line-height:1.3333333;border-radius:6px}select.input-lg{height:46px;line-height:46px}select[multiple].input-lg,textarea.input-lg{height:auto}.form-group-lg .form-control{height:46px;padding:10px 16px;font-size:18px;line-height:1.3333333;border-radius:6px}.form-group-lg select.form-control{height:46px;line-height:46px}.form-group-lg select[multiple].form-control,.form-group-lg textarea.form-control{height:auto}.form-group-lg .form-control-static{height:46px;min-height:38px;padding:11px 16px;font-size:18px;line-height:1.3333333}.has-feedback{position:relative}.has-feedback .form-control{padding-right:42.5px}.form-control-feedback{position:absolute;top:0;right:0;z-index:2;display:block;width:34px;height:34px;line-height:34px;text-align:center;pointer-events:none}.form-group-lg .form-control+.form-control-feedback,.input-group-lg+.form-control-feedback,.input-lg+.form-control-feedback{width:46px;height:46px;line-height:46px}.form-group-sm .form-control+.form-control-feedback,.input-group-sm+.form-control-feedback,.input-sm+.form-control-feedback{width:30px;height:30px;line-height:30px}.has-success .checkbox,.has-success .checkbox-inline,.has-success .control-label,.has-success .help-block,.has-success .radio,.has-success .radio-inline,.has-success.checkbox label,.has-success.checkbox-inline label,.has-success.radio label,.has-success.radio-inline label{color:#3c763d}.has-success .form-control{border-color:#3c763d;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075);box-shadow:inset 0 1px 1px rgba(0,0,0,.075)}.has-success .form-control:focus{border-color:#2b542c;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 6px #67b168;box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 6px #67b168}.has-success .input-group-addon{color:#3c763d;background-color:#dff0d8;border-color:#3c763d}.has-success .form-control-feedback{color:#3c763d}.has-warning .checkbox,.has-warning .checkbox-inline,.has-warning .control-label,.has-warning .help-block,.has-warning .radio,.has-warning .radio-inline,.has-warning.checkbox label,.has-warning.checkbox-inline label,.has-warning.radio label,.has-warning.radio-inline label{color:#8a6d3b}.has-warning .form-control{border-color:#8a6d3b;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075);box-shadow:inset 0 1px 1px rgba(0,0,0,.075)}.has-warning .form-control:focus{border-color:#66512c;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 6px #c0a16b;box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 6px #c0a16b}.has-warning .input-group-addon{color:#8a6d3b;background-color:#fcf8e3;border-color:#8a6d3b}.has-warning .form-control-feedback{color:#8a6d3b}.has-error .checkbox,.has-error .checkbox-inline,.has-error .control-label,.has-error .help-block,.has-error .radio,.has-error .radio-inline,.has-error.checkbox label,.has-error.checkbox-inline label,.has-error.radio label,.has-error.radio-inline label{color:#a94442}.has-error .form-control{border-color:#a94442;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075);box-shadow:inset 0 1px 1px rgba(0,0,0,.075)}.has-error .form-control:focus{border-color:#843534;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 6px #ce8483;box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 6px #ce8483}.has-error .input-group-addon{color:#a94442;background-color:#f2dede;border-color:#a94442}.has-error .form-control-feedback{color:#a94442}.has-feedback label~.form-control-feedback{top:25px}.has-feedback label.sr-only~.form-control-feedback{top:0}.help-block{display:block;margin-top:5px;margin-bottom:10px;color:#737373}@media (min-width:768px){.form-inline .form-group{display:inline-block;margin-bottom:0;vertical-align:middle}.form-inline .form-control{display:inline-block;width:auto;vertical-align:middle}.form-inline .form-control-static{display:inline-block}.form-inline .input-group{display:inline-table;vertical-align:middle}.form-inline .input-group .form-control,.form-inline .input-group .input-group-addon,.form-inline .input-group .input-group-btn{width:auto}.form-inline .input-group>.form-control{width:100%}.form-inline .control-label{margin-bottom:0;vertical-align:middle}.form-inline .checkbox,.form-inline .radio{display:inline-block;margin-top:0;margin-bottom:0;vertical-align:middle}.form-inline .checkbox label,.form-inline .radio label{padding-left:0}.form-inline .checkbox input[type=checkbox],.form-inline .radio input[type=radio]{position:relative;margin-left:0}.form-inline .has-feedback .form-control-feedback{top:0}}.form-horizontal .checkbox,.form-horizontal .checkbox-inline,.form-horizontal .radio,.form-horizontal .radio-inline{padding-top:7px;margin-top:0;margin-bottom:0}.form-horizontal .checkbox,.form-horizontal .radio{min-height:27px}.form-horizontal .form-group{margin-right:-15px;margin-left:-15px}@media (min-width:768px){.form-horizontal .control-label{padding-top:7px;margin-bottom:0;text-align:right}}.form-horizontal .has-feedback .form-control-feedback{right:15px}@media (min-width:768px){.form-horizontal .form-group-lg .control-label{padding-top:14.33px;font-size:18px}}@media (min-width:768px){.form-horizontal .form-group-sm .control-label{padding-top:6px;font-size:12px}}.btn{display:inline-block;padding:6px 12px;margin-bottom:0;font-size:14px;font-weight:400;line-height:1.42857143;text-align:center;white-space:nowrap;vertical-align:middle;-ms-touch-action:manipulation;touch-action:manipulation;cursor:pointer;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;background-image:none;border:1px solid transparent;border-radius:4px}.btn.active.focus,.btn.active:focus,.btn.focus,.btn:active.focus,.btn:active:focus,.btn:focus{outline:thin dotted;outline:5px auto -webkit-focus-ring-color;outline-offset:-2px}.btn.focus,.btn:focus,.btn:hover{color:#333;text-decoration:none}.btn.active,.btn:active{background-image:none;outline:0;-webkit-box-shadow:inset 0 3px 5px rgba(0,0,0,.125);box-shadow:inset 0 3px 5px rgba(0,0,0,.125)}.btn.disabled,.btn[disabled],fieldset[disabled] .btn{cursor:not-allowed;filter:alpha(opacity=65);-webkit-box-shadow:none;box-shadow:none;opacity:.65}a.btn.disabled,fieldset[disabled] a.btn{pointer-events:none}.btn-default{color:#333;background-color:#fff;border-color:#ccc}.btn-default.focus,.btn-default:focus{color:#333;background-color:#e6e6e6;border-color:#8c8c8c}.btn-default:hover{color:#333;background-color:#e6e6e6;border-color:#adadad}.btn-default.active,.btn-default:active,.open>.dropdown-toggle.btn-default{color:#333;background-color:#e6e6e6;border-color:#adadad}.btn-default.active.focus,.btn-default.active:focus,.btn-default.active:hover,.btn-default:active.focus,.btn-default:active:focus,.btn-default:active:hover,.open>.dropdown-toggle.btn-default.focus,.open>.dropdown-toggle.btn-default:focus,.open>.dropdown-toggle.btn-default:hover{color:#333;background-color:#d4d4d4;border-color:#8c8c8c}.btn-default.active,.btn-default:active,.open>.dropdown-toggle.btn-default{background-image:none}.btn-default.disabled,.btn-default.disabled.active,.btn-default.disabled.focus,.btn-default.disabled:active,.btn-default.disabled:focus,.btn-default.disabled:hover,.btn-default[disabled],.btn-default[disabled].active,.btn-default[disabled].focus,.btn-default[disabled]:active,.btn-default[disabled]:focus,.btn-default[disabled]:hover,fieldset[disabled] .btn-default,fieldset[disabled] .btn-default.active,fieldset[disabled] .btn-default.focus,fieldset[disabled] .btn-default:active,fieldset[disabled] .btn-default:focus,fieldset[disabled] .btn-default:hover{background-color:#fff;border-color:#ccc}.btn-default .badge{color:#fff;background-color:#333}.btn-primary{color:#fff;background-color:#337ab7;border-color:#2e6da4}.btn-primary.focus,.btn-primary:focus{color:#fff;background-color:#286090;border-color:#122b40}.btn-primary:hover{color:#fff;background-color:#286090;border-color:#204d74}.btn-primary.active,.btn-primary:active,.open>.dropdown-toggle.btn-primary{color:#fff;background-color:#286090;border-color:#204d74}.btn-primary.active.focus,.btn-primary.active:focus,.btn-primary.active:hover,.btn-primary:active.focus,.btn-primary:active:focus,.btn-primary:active:hover,.open>.dropdown-toggle.btn-primary.focus,.open>.dropdown-toggle.btn-primary:focus,.open>.dropdown-toggle.btn-primary:hover{color:#fff;background-color:#204d74;border-color:#122b40}.btn-primary.active,.btn-primary:active,.open>.dropdown-toggle.btn-primary{background-image:none}.btn-primary.disabled,.btn-primary.disabled.active,.btn-primary.disabled.focus,.btn-primary.disabled:active,.btn-primary.disabled:focus,.btn-primary.disabled:hover,.btn-primary[disabled],.btn-primary[disabled].active,.btn-primary[disabled].focus,.btn-primary[disabled]:active,.btn-primary[disabled]:focus,.btn-primary[disabled]:hover,fieldset[disabled] .btn-primary,fieldset[disabled] .btn-primary.active,fieldset[disabled] .btn-primary.focus,fieldset[disabled] .btn-primary:active,fieldset[disabled] .btn-primary:focus,fieldset[disabled] .btn-primary:hover{background-color:#337ab7;border-color:#2e6da4}.btn-primary .badge{color:#337ab7;background-color:#fff}.btn-success{color:#fff;background-color:#5cb85c;border-color:#4cae4c}.btn-success.focus,.btn-success:focus{color:#fff;background-color:#449d44;border-color:#255625}.btn-success:hover{color:#fff;background-color:#449d44;border-color:#398439}.btn-success.active,.btn-success:active,.open>.dropdown-toggle.btn-success{color:#fff;background-color:#449d44;border-color:#398439}.btn-success.active.focus,.btn-success.active:focus,.btn-success.active:hover,.btn-success:active.focus,.btn-success:active:focus,.btn-success:active:hover,.open>.dropdown-toggle.btn-success.focus,.open>.dropdown-toggle.btn-success:focus,.open>.dropdown-toggle.btn-success:hover{color:#fff;background-color:#398439;border-color:#255625}.btn-success.active,.btn-success:active,.open>.dropdown-toggle.btn-success{background-image:none}.btn-success.disabled,.btn-success.disabled.active,.btn-success.disabled.focus,.btn-success.disabled:active,.btn-success.disabled:focus,.btn-success.disabled:hover,.btn-success[disabled],.btn-success[disabled].active,.btn-success[disabled].focus,.btn-success[disabled]:active,.btn-success[disabled]:focus,.btn-success[disabled]:hover,fieldset[disabled] .btn-success,fieldset[disabled] .btn-success.active,fieldset[disabled] .btn-success.focus,fieldset[disabled] .btn-success:active,fieldset[disabled] .btn-success:focus,fieldset[disabled] .btn-success:hover{background-color:#5cb85c;border-color:#4cae4c}.btn-success .badge{color:#5cb85c;background-color:#fff}.btn-info{color:#fff;background-color:#5bc0de;border-color:#46b8da}.btn-info.focus,.btn-info:focus{color:#fff;background-color:#31b0d5;border-color:#1b6d85}.btn-info:hover{color:#fff;background-color:#31b0d5;border-color:#269abc}.btn-info.active,.btn-info:active,.open>.dropdown-toggle.btn-info{color:#fff;background-color:#31b0d5;border-color:#269abc}.btn-info.active.focus,.btn-info.active:focus,.btn-info.active:hover,.btn-info:active.focus,.btn-info:active:focus,.btn-info:active:hover,.open>.dropdown-toggle.btn-info.focus,.open>.dropdown-toggle.btn-info:focus,.open>.dropdown-toggle.btn-info:hover{color:#fff;background-color:#269abc;border-color:#1b6d85}.btn-info.active,.btn-info:active,.open>.dropdown-toggle.btn-info{background-image:none}.btn-info.disabled,.btn-info.disabled.active,.btn-info.disabled.focus,.btn-info.disabled:active,.btn-info.disabled:focus,.btn-info.disabled:hover,.btn-info[disabled],.btn-info[disabled].active,.btn-info[disabled].focus,.btn-info[disabled]:active,.btn-info[disabled]:focus,.btn-info[disabled]:hover,fieldset[disabled] .btn-info,fieldset[disabled] .btn-info.active,fieldset[disabled] .btn-info.focus,fieldset[disabled] .btn-info:active,fieldset[disabled] .btn-info:focus,fieldset[disabled] .btn-info:hover{background-color:#5bc0de;border-color:#46b8da}.btn-info .badge{color:#5bc0de;background-color:#fff}.btn-warning{color:#fff;background-color:#f0ad4e;border-color:#eea236}.btn-warning.focus,.btn-warning:focus{color:#fff;background-color:#ec971f;border-color:#985f0d}.btn-warning:hover{color:#fff;background-color:#ec971f;border-color:#d58512}.btn-warning.active,.btn-warning:active,.open>.dropdown-toggle.btn-warning{color:#fff;background-color:#ec971f;border-color:#d58512}.btn-warning.active.focus,.btn-warning.active:focus,.btn-warning.active:hover,.btn-warning:active.focus,.btn-warning:active:focus,.btn-warning:active:hover,.open>.dropdown-toggle.btn-warning.focus,.open>.dropdown-toggle.btn-warning:focus,.open>.dropdown-toggle.btn-warning:hover{color:#fff;background-color:#d58512;border-color:#985f0d}.btn-warning.active,.btn-warning:active,.open>.dropdown-toggle.btn-warning{background-image:none}.btn-warning.disabled,.btn-warning.disabled.active,.btn-warning.disabled.focus,.btn-warning.disabled:active,.btn-warning.disabled:focus,.btn-warning.disabled:hover,.btn-warning[disabled],.btn-warning[disabled].active,.btn-warning[disabled].focus,.btn-warning[disabled]:active,.btn-warning[disabled]:focus,.btn-warning[disabled]:hover,fieldset[disabled] .btn-warning,fieldset[disabled] .btn-warning.active,fieldset[disabled] .btn-warning.focus,fieldset[disabled] .btn-warning:active,fieldset[disabled] .btn-warning:focus,fieldset[disabled] .btn-warning:hover{background-color:#f0ad4e;border-color:#eea236}.btn-warning .badge{color:#f0ad4e;background-color:#fff}.btn-danger{color:#fff;background-color:#d9534f;border-color:#d43f3a}.btn-danger.focus,.btn-danger:focus{color:#fff;background-color:#c9302c;border-color:#761c19}.btn-danger:hover{color:#fff;background-color:#c9302c;border-color:#ac2925}.btn-danger.active,.btn-danger:active,.open>.dropdown-toggle.btn-danger{color:#fff;background-color:#c9302c;border-color:#ac2925}.btn-danger.active.focus,.btn-danger.active:focus,.btn-danger.active:hover,.btn-danger:active.focus,.btn-danger:active:focus,.btn-danger:active:hover,.open>.dropdown-toggle.btn-danger.focus,.open>.dropdown-toggle.btn-danger:focus,.open>.dropdown-toggle.btn-danger:hover{color:#fff;background-color:#ac2925;border-color:#761c19}.btn-danger.active,.btn-danger:active,.open>.dropdown-toggle.btn-danger{background-image:none}.btn-danger.disabled,.btn-danger.disabled.active,.btn-danger.disabled.focus,.btn-danger.disabled:active,.btn-danger.disabled:focus,.btn-danger.disabled:hover,.btn-danger[disabled],.btn-danger[disabled].active,.btn-danger[disabled].focus,.btn-danger[disabled]:active,.btn-danger[disabled]:focus,.btn-danger[disabled]:hover,fieldset[disabled] .btn-danger,fieldset[disabled] .btn-danger.active,fieldset[disabled] .btn-danger.focus,fieldset[disabled] .btn-danger:active,fieldset[disabled] .btn-danger:focus,fieldset[disabled] .btn-danger:hover{background-color:#d9534f;border-color:#d43f3a}.btn-danger .badge{color:#d9534f;background-color:#fff}.btn-link{font-weight:400;color:#337ab7;border-radius:0}.btn-link,.btn-link.active,.btn-link:active,.btn-link[disabled],fieldset[disabled] .btn-link{background-color:transparent;-webkit-box-shadow:none;box-shadow:none}.btn-link,.btn-link:active,.btn-link:focus,.btn-link:hover{border-color:transparent}.btn-link:focus,.btn-link:hover{color:#23527c;text-decoration:underline;background-color:transparent}.btn-link[disabled]:focus,.btn-link[disabled]:hover,fieldset[disabled] .btn-link:focus,fieldset[disabled] .btn-link:hover{color:#777;text-decoration:none}.btn-group-lg>.btn,.btn-lg{padding:10px 16px;font-size:18px;line-height:1.3333333;border-radius:6px}.btn-group-sm>.btn,.btn-sm{padding:5px 10px;font-size:12px;line-height:1.5;border-radius:3px}.btn-group-xs>.btn,.btn-xs{padding:1px 5px;font-size:12px;line-height:1.5;border-radius:3px}.btn-block{display:block;width:100%}.btn-block+.btn-block{margin-top:5px}input[type=button].btn-block,input[type=reset].btn-block,input[type=submit].btn-block{width:100%}.fade{opacity:0;-webkit-transition:opacity .15s linear;-o-transition:opacity .15s linear;transition:opacity .15s linear}.fade.in{opacity:1}.collapse{display:none}.collapse.in{display:block}tr.collapse.in{display:table-row}tbody.collapse.in{display:table-row-group}.collapsing{position:relative;height:0;overflow:hidden;-webkit-transition-timing-function:ease;-o-transition-timing-function:ease;transition-timing-function:ease;-webkit-transition-duration:.35s;-o-transition-duration:.35s;transition-duration:.35s;-webkit-transition-property:height,visibility;-o-transition-property:height,visibility;transition-property:height,visibility}.caret{display:inline-block;width:0;height:0;margin-left:2px;vertical-align:middle;border-top:4px dashed;border-top:4px solid\9;border-right:4px solid transparent;border-left:4px solid transparent}.dropdown,.dropup{position:relative}.dropdown-toggle:focus{outline:0}.dropdown-menu{position:absolute;top:100%;left:0;z-index:1000;display:none;float:left;min-width:160px;padding:5px 0;margin:2px 0 0;font-size:14px;text-align:left;list-style:none;background-color:#fff;-webkit-background-clip:padding-box;background-clip:padding-box;border:1px solid #ccc;border:1px solid rgba(0,0,0,.15);border-radius:4px;-webkit-box-shadow:0 6px 12px rgba(0,0,0,.175);box-shadow:0 6px 12px rgba(0,0,0,.175)}.dropdown-menu.pull-right{right:0;left:auto}.dropdown-menu .divider{height:1px;margin:9px 0;overflow:hidden;background-color:#e5e5e5}.dropdown-menu>li>a{display:block;padding:3px 20px;clear:both;font-weight:400;line-height:1.42857143;color:#333;white-space:nowrap}.dropdown-menu>li>a:focus,.dropdown-menu>li>a:hover{color:#262626;text-decoration:none;background-color:#f5f5f5}.dropdown-menu>.active>a,.dropdown-menu>.active>a:focus,.dropdown-menu>.active>a:hover{color:#fff;text-decoration:none;background-color:#337ab7;outline:0}.dropdown-menu>.disabled>a,.dropdown-menu>.disabled>a:focus,.dropdown-menu>.disabled>a:hover{color:#777}.dropdown-menu>.disabled>a:focus,.dropdown-menu>.disabled>a:hover{text-decoration:none;cursor:not-allowed;background-color:transparent;background-image:none;filter:progid:DXImageTransform.Microsoft.gradient(enabled=false)}.open>.dropdown-menu{display:block}.open>a{outline:0}.dropdown-menu-right{right:0;left:auto}.dropdown-menu-left{right:auto;left:0}.dropdown-header{display:block;padding:3px 20px;font-size:12px;line-height:1.42857143;color:#777;white-space:nowrap}.dropdown-backdrop{position:fixed;top:0;right:0;bottom:0;left:0;z-index:990}.pull-right>.dropdown-menu{right:0;left:auto}.dropup .caret,.navbar-fixed-bottom .dropdown .caret{content:"";border-top:0;border-bottom:4px dashed;border-bottom:4px solid\9}.dropup .dropdown-menu,.navbar-fixed-bottom .dropdown .dropdown-menu{top:auto;bottom:100%;margin-bottom:2px}@media (min-width:768px){.navbar-right .dropdown-menu{right:0;left:auto}.navbar-right .dropdown-menu-left{right:auto;left:0}}.btn-group,.btn-group-vertical{position:relative;display:inline-block;vertical-align:middle}.btn-group-vertical>.btn,.btn-group>.btn{position:relative;float:left}.btn-group-vertical>.btn.active,.btn-group-vertical>.btn:active,.btn-group-vertical>.btn:focus,.btn-group-vertical>.btn:hover,.btn-group>.btn.active,.btn-group>.btn:active,.btn-group>.btn:focus,.btn-group>.btn:hover{z-index:2}.btn-group .btn+.btn,.btn-group .btn+.btn-group,.btn-group .btn-group+.btn,.btn-group .btn-group+.btn-group{margin-left:-1px}.btn-toolbar{margin-left:-5px}.btn-toolbar .btn,.btn-toolbar .btn-group,.btn-toolbar .input-group{float:left}.btn-toolbar>.btn,.btn-toolbar>.btn-group,.btn-toolbar>.input-group{margin-left:5px}.btn-group>.btn:not(:first-child):not(:last-child):not(.dropdown-toggle){border-radius:0}.btn-group>.btn:first-child{margin-left:0}.btn-group>.btn:first-child:not(:last-child):not(.dropdown-toggle){border-top-right-radius:0;border-bottom-right-radius:0}.btn-group>.btn:last-child:not(:first-child),.btn-group>.dropdown-toggle:not(:first-child){border-top-left-radius:0;border-bottom-left-radius:0}.btn-group>.btn-group{float:left}.btn-group>.btn-group:not(:first-child):not(:last-child)>.btn{border-radius:0}.btn-group>.btn-group:first-child:not(:last-child)>.btn:last-child,.btn-group>.btn-group:first-child:not(:last-child)>.dropdown-toggle{border-top-right-radius:0;border-bottom-right-radius:0}.btn-group>.btn-group:last-child:not(:first-child)>.btn:first-child{border-top-left-radius:0;border-bottom-left-radius:0}.btn-group .dropdown-toggle:active,.btn-group.open .dropdown-toggle{outline:0}.btn-group>.btn+.dropdown-toggle{padding-right:8px;padding-left:8px}.btn-group>.btn-lg+.dropdown-toggle{padding-right:12px;padding-left:12px}.btn-group.open .dropdown-toggle{-webkit-box-shadow:inset 0 3px 5px rgba(0,0,0,.125);box-shadow:inset 0 3px 5px rgba(0,0,0,.125)}.btn-group.open .dropdown-toggle.btn-link{-webkit-box-shadow:none;box-shadow:none}.btn .caret{margin-left:0}.btn-lg .caret{border-width:5px 5px 0;border-bottom-width:0}.dropup .btn-lg .caret{border-width:0 5px 5px}.btn-group-vertical>.btn,.btn-group-vertical>.btn-group,.btn-group-vertical>.btn-group>.btn{display:block;float:none;width:100%;max-width:100%}.btn-group-vertical>.btn-group>.btn{float:none}.btn-group-vertical>.btn+.btn,.btn-group-vertical>.btn+.btn-group,.btn-group-vertical>.btn-group+.btn,.btn-group-vertical>.btn-group+.btn-group{margin-top:-1px;margin-left:0}.btn-group-vertical>.btn:not(:first-child):not(:last-child){border-radius:0}.btn-group-vertical>.btn:first-child:not(:last-child){border-top-right-radius:4px;border-bottom-right-radius:0;border-bottom-left-radius:0}.btn-group-vertical>.btn:last-child:not(:first-child){border-top-left-radius:0;border-top-right-radius:0;border-bottom-left-radius:4px}.btn-group-vertical>.btn-group:not(:first-child):not(:last-child)>.btn{border-radius:0}.btn-group-vertical>.btn-group:first-child:not(:last-child)>.btn:last-child,.btn-group-vertical>.btn-group:first-child:not(:last-child)>.dropdown-toggle{border-bottom-right-radius:0;border-bottom-left-radius:0}.btn-group-vertical>.btn-group:last-child:not(:first-child)>.btn:first-child{border-top-left-radius:0;border-top-right-radius:0}.btn-group-justified{display:table;width:100%;table-layout:fixed;border-collapse:separate}.btn-group-justified>.btn,.btn-group-justified>.btn-group{display:table-cell;float:none;width:1%}.btn-group-justified>.btn-group .btn{width:100%}.btn-group-justified>.btn-group .dropdown-menu{left:auto}[data-toggle=buttons]>.btn input[type=checkbox],[data-toggle=buttons]>.btn input[type=radio],[data-toggle=buttons]>.btn-group>.btn input[type=checkbox],[data-toggle=buttons]>.btn-group>.btn input[type=radio]{position:absolute;clip:rect(0,0,0,0);pointer-events:none}.input-group{position:relative;display:table;border-collapse:separate}.input-group[class*=col-]{float:none;padding-right:0;padding-left:0}.input-group .form-control{position:relative;z-index:2;float:left;width:100%;margin-bottom:0}.input-group-lg>.form-control,.input-group-lg>.input-group-addon,.input-group-lg>.input-group-btn>.btn{height:46px;padding:10px 16px;font-size:18px;line-height:1.3333333;border-radius:6px}select.input-group-lg>.form-control,select.input-group-lg>.input-group-addon,select.input-group-lg>.input-group-btn>.btn{height:46px;line-height:46px}select[multiple].input-group-lg>.form-control,select[multiple].input-group-lg>.input-group-addon,select[multiple].input-group-lg>.input-group-btn>.btn,textarea.input-group-lg>.form-control,textarea.input-group-lg>.input-group-addon,textarea.input-group-lg>.input-group-btn>.btn{height:auto}.input-group-sm>.form-control,.input-group-sm>.input-group-addon,.input-group-sm>.input-group-btn>.btn{height:30px;padding:5px 10px;font-size:12px;line-height:1.5;border-radius:3px}select.input-group-sm>.form-control,select.input-group-sm>.input-group-addon,select.input-group-sm>.input-group-btn>.btn{height:30px;line-height:30px}select[multiple].input-group-sm>.form-control,select[multiple].input-group-sm>.input-group-addon,select[multiple].input-group-sm>.input-group-btn>.btn,textarea.input-group-sm>.form-control,textarea.input-group-sm>.input-group-addon,textarea.input-group-sm>.input-group-btn>.btn{height:auto}.input-group .form-control,.input-group-addon,.input-group-btn{display:table-cell}.input-group .form-control:not(:first-child):not(:last-child),.input-group-addon:not(:first-child):not(:last-child),.input-group-btn:not(:first-child):not(:last-child){border-radius:0}.input-group-addon,.input-group-btn{width:1%;white-space:nowrap;vertical-align:middle}.input-group-addon{padding:6px 12px;font-size:14px;font-weight:400;line-height:1;color:#555;text-align:center;background-color:#eee;border:1px solid #ccc;border-radius:4px}.input-group-addon.input-sm{padding:5px 10px;font-size:12px;border-radius:3px}.input-group-addon.input-lg{padding:10px 16px;font-size:18px;border-radius:6px}.input-group-addon input[type=checkbox],.input-group-addon input[type=radio]{margin-top:0}.input-group .form-control:first-child,.input-group-addon:first-child,.input-group-btn:first-child>.btn,.input-group-btn:first-child>.btn-group>.btn,.input-group-btn:first-child>.dropdown-toggle,.input-group-btn:last-child>.btn-group:not(:last-child)>.btn,.input-group-btn:last-child>.btn:not(:last-child):not(.dropdown-toggle){border-top-right-radius:0;border-bottom-right-radius:0}.input-group-addon:first-child{border-right:0}.input-group .form-control:last-child,.input-group-addon:last-child,.input-group-btn:first-child>.btn-group:not(:first-child)>.btn,.input-group-btn:first-child>.btn:not(:first-child),.input-group-btn:last-child>.btn,.input-group-btn:last-child>.btn-group>.btn,.input-group-btn:last-child>.dropdown-toggle{border-top-left-radius:0;border-bottom-left-radius:0}.input-group-addon:last-child{border-left:0}.input-group-btn{position:relative;font-size:0;white-space:nowrap}.input-group-btn>.btn{position:relative}.input-group-btn>.btn+.btn{margin-left:-1px}.input-group-btn>.btn:active,.input-group-btn>.btn:focus,.input-group-btn>.btn:hover{z-index:2}.input-group-btn:first-child>.btn,.input-group-btn:first-child>.btn-group{margin-right:-1px}.input-group-btn:last-child>.btn,.input-group-btn:last-child>.btn-group{z-index:2;margin-left:-1px}.nav{padding-left:0;margin-bottom:0;list-style:none}.nav>li{position:relative;display:block}.nav>li>a{position:relative;display:block;padding:10px 15px}.nav>li>a:focus,.nav>li>a:hover{text-decoration:none;background-color:#eee}.nav>li.disabled>a{color:#777}.nav>li.disabled>a:focus,.nav>li.disabled>a:hover{color:#777;text-decoration:none;cursor:not-allowed;background-color:transparent}.nav .open>a,.nav .open>a:focus,.nav .open>a:hover{background-color:#eee;border-color:#337ab7}.nav .nav-divider{height:1px;margin:9px 0;overflow:hidden;background-color:#e5e5e5}.nav>li>a>img{max-width:none}.nav-tabs{border-bottom:1px solid #ddd}.nav-tabs>li{float:left;margin-bottom:-1px}.nav-tabs>li>a{margin-right:2px;line-height:1.42857143;border:1px solid transparent;border-radius:4px 4px 0 0}.nav-tabs>li>a:hover{border-color:#eee #eee #ddd}.nav-tabs>li.active>a,.nav-tabs>li.active>a:focus,.nav-tabs>li.active>a:hover{color:#555;cursor:default;background-color:#fff;border:1px solid #ddd;border-bottom-color:transparent}.nav-tabs.nav-justified{width:100%;border-bottom:0}.nav-tabs.nav-justified>li{float:none}.nav-tabs.nav-justified>li>a{margin-bottom:5px;text-align:center}.nav-tabs.nav-justified>.dropdown .dropdown-menu{top:auto;left:auto}@media (min-width:768px){.nav-tabs.nav-justified>li{display:table-cell;width:1%}.nav-tabs.nav-justified>li>a{margin-bottom:0}}.nav-tabs.nav-justified>li>a{margin-right:0;border-radius:4px}.nav-tabs.nav-justified>.active>a,.nav-tabs.nav-justified>.active>a:focus,.nav-tabs.nav-justified>.active>a:hover{border:1px solid #ddd}@media (min-width:768px){.nav-tabs.nav-justified>li>a{border-bottom:1px solid #ddd;border-radius:4px 4px 0 0}.nav-tabs.nav-justified>.active>a,.nav-tabs.nav-justified>.active>a:focus,.nav-tabs.nav-justified>.active>a:hover{border-bottom-color:#fff}}.nav-pills>li{float:left}.nav-pills>li>a{border-radius:4px}.nav-pills>li+li{margin-left:2px}.nav-pills>li.active>a,.nav-pills>li.active>a:focus,.nav-pills>li.active>a:hover{color:#fff;background-color:#337ab7}.nav-stacked>li{float:none}.nav-stacked>li+li{margin-top:2px;margin-left:0}.nav-justified{width:100%}.nav-justified>li{float:none}.nav-justified>li>a{margin-bottom:5px;text-align:center}.nav-justified>.dropdown .dropdown-menu{top:auto;left:auto}@media (min-width:768px){.nav-justified>li{display:table-cell;width:1%}.nav-justified>li>a{margin-bottom:0}}.nav-tabs-justified{border-bottom:0}.nav-tabs-justified>li>a{margin-right:0;border-radius:4px}.nav-tabs-justified>.active>a,.nav-tabs-justified>.active>a:focus,.nav-tabs-justified>.active>a:hover{border:1px solid #ddd}@media (min-width:768px){.nav-tabs-justified>li>a{border-bottom:1px solid #ddd;border-radius:4px 4px 0 0}.nav-tabs-justified>.active>a,.nav-tabs-justified>.active>a:focus,.nav-tabs-justified>.active>a:hover{border-bottom-color:#fff}}.tab-content>.tab-pane{display:none}.tab-content>.active{display:block}.nav-tabs .dropdown-menu{margin-top:-1px;border-top-left-radius:0;border-top-right-radius:0}.navbar{position:relative;min-height:50px;margin-bottom:20px;border:1px solid transparent}@media (min-width:768px){.navbar{border-radius:4px}}@media (min-width:768px){.navbar-header{float:left}}.navbar-collapse{padding-right:15px;padding-left:15px;overflow-x:visible;-webkit-overflow-scrolling:touch;border-top:1px solid transparent;-webkit-box-shadow:inset 0 1px 0 rgba(255,255,255,.1);box-shadow:inset 0 1px 0 rgba(255,255,255,.1)}.navbar-collapse.in{overflow-y:auto}@media (min-width:768px){.navbar-collapse{width:auto;border-top:0;-webkit-box-shadow:none;box-shadow:none}.navbar-collapse.collapse{display:block!important;height:auto!important;padding-bottom:0;overflow:visible!important}.navbar-collapse.in{overflow-y:visible}.navbar-fixed-bottom .navbar-collapse,.navbar-fixed-top .navbar-collapse,.navbar-static-top .navbar-collapse{padding-right:0;padding-left:0}}.navbar-fixed-bottom .navbar-collapse,.navbar-fixed-top .navbar-collapse{max-height:340px}@media (max-device-width:480px) and (orientation:landscape){.navbar-fixed-bottom .navbar-collapse,.navbar-fixed-top .navbar-collapse{max-height:200px}}.container-fluid>.navbar-collapse,.container-fluid>.navbar-header,.container>.navbar-collapse,.container>.navbar-header{margin-right:-15px;margin-left:-15px}@media (min-width:768px){.container-fluid>.navbar-collapse,.container-fluid>.navbar-header,.container>.navbar-collapse,.container>.navbar-header{margin-right:0;margin-left:0}}.navbar-static-top{z-index:1000;border-width:0 0 1px}@media (min-width:768px){.navbar-static-top{border-radius:0}}.navbar-fixed-bottom,.navbar-fixed-top{position:fixed;right:0;left:0;z-index:1030}@media (min-width:768px){.navbar-fixed-bottom,.navbar-fixed-top{border-radius:0}}.navbar-fixed-top{top:0;border-width:0 0 1px}.navbar-fixed-bottom{bottom:0;margin-bottom:0;border-width:1px 0 0}.navbar-brand{float:left;height:50px;padding:15px 15px;font-size:18px;line-height:20px}.navbar-brand:focus,.navbar-brand:hover{text-decoration:none}.navbar-brand>img{display:block}@media (min-width:768px){.navbar>.container .navbar-brand,.navbar>.container-fluid .navbar-brand{margin-left:-15px}}.navbar-toggle{position:relative;float:right;padding:9px 10px;margin-top:8px;margin-right:15px;margin-bottom:8px;background-color:transparent;background-image:none;border:1px solid transparent;border-radius:4px}.navbar-toggle:focus{outline:0}.navbar-toggle .icon-bar{display:block;width:22px;height:2px;border-radius:1px}.navbar-toggle .icon-bar+.icon-bar{margin-top:4px}@media (min-width:768px){.navbar-toggle{display:none}}.navbar-nav{margin:7.5px -15px}.navbar-nav>li>a{padding-top:10px;padding-bottom:10px;line-height:20px}@media (max-width:767px){.navbar-nav .open .dropdown-menu{position:static;float:none;width:auto;margin-top:0;background-color:transparent;border:0;-webkit-box-shadow:none;box-shadow:none}.navbar-nav .open .dropdown-menu .dropdown-header,.navbar-nav .open .dropdown-menu>li>a{padding:5px 15px 5px 25px}.navbar-nav .open .dropdown-menu>li>a{line-height:20px}.navbar-nav .open .dropdown-menu>li>a:focus,.navbar-nav .open .dropdown-menu>li>a:hover{background-image:none}}@media (min-width:768px){.navbar-nav{float:left;margin:0}.navbar-nav>li{float:left}.navbar-nav>li>a{padding-top:15px;padding-bottom:15px}}.navbar-form{padding:10px 15px;margin-top:8px;margin-right:-15px;margin-bottom:8px;margin-left:-15px;border-top:1px solid transparent;border-bottom:1px solid transparent;-webkit-box-shadow:inset 0 1px 0 rgba(255,255,255,.1),0 1px 0 rgba(255,255,255,.1);box-shadow:inset 0 1px 0 rgba(255,255,255,.1),0 1px 0 rgba(255,255,255,.1)}@media (min-width:768px){.navbar-form .form-group{display:inline-block;margin-bottom:0;vertical-align:middle}.navbar-form .form-control{display:inline-block;width:auto;vertical-align:middle}.navbar-form .form-control-static{display:inline-block}.navbar-form .input-group{display:inline-table;vertical-align:middle}.navbar-form .input-group .form-control,.navbar-form .input-group .input-group-addon,.navbar-form .input-group .input-group-btn{width:auto}.navbar-form .input-group>.form-control{width:100%}.navbar-form .control-label{margin-bottom:0;vertical-align:middle}.navbar-form .checkbox,.navbar-form .radio{display:inline-block;margin-top:0;margin-bottom:0;vertical-align:middle}.navbar-form .checkbox label,.navbar-form .radio label{padding-left:0}.navbar-form .checkbox input[type=checkbox],.navbar-form .radio input[type=radio]{position:relative;margin-left:0}.navbar-form .has-feedback .form-control-feedback{top:0}}@media (max-width:767px){.navbar-form .form-group{margin-bottom:5px}.navbar-form .form-group:last-child{margin-bottom:0}}@media (min-width:768px){.navbar-form{width:auto;padding-top:0;padding-bottom:0;margin-right:0;margin-left:0;border:0;-webkit-box-shadow:none;box-shadow:none}}.navbar-nav>li>.dropdown-menu{margin-top:0;border-top-left-radius:0;border-top-right-radius:0}.navbar-fixed-bottom .navbar-nav>li>.dropdown-menu{margin-bottom:0;border-top-left-radius:4px;border-top-right-radius:4px;border-bottom-right-radius:0;border-bottom-left-radius:0}.navbar-btn{margin-top:8px;margin-bottom:8px}.navbar-btn.btn-sm{margin-top:10px;margin-bottom:10px}.navbar-btn.btn-xs{margin-top:14px;margin-bottom:14px}.navbar-text{margin-top:15px;margin-bottom:15px}@media (min-width:768px){.navbar-text{float:left;margin-right:15px;margin-left:15px}}@media (min-width:768px){.navbar-left{float:left!important}.navbar-right{float:right!important;margin-right:-15px}.navbar-right~.navbar-right{margin-right:0}}.navbar-default{background-color:#f8f8f8;border-color:#e7e7e7}.navbar-default .navbar-brand{color:#777}.navbar-default .navbar-brand:focus,.navbar-default .navbar-brand:hover{color:#5e5e5e;background-color:transparent}.navbar-default .navbar-text{color:#777}.navbar-default .navbar-nav>li>a{color:#777}.navbar-default .navbar-nav>li>a:focus,.navbar-default .navbar-nav>li>a:hover{color:#333;background-color:transparent}.navbar-default .navbar-nav>.active>a,.navbar-default .navbar-nav>.active>a:focus,.navbar-default .navbar-nav>.active>a:hover{color:#555;background-color:#e7e7e7}.navbar-default .navbar-nav>.disabled>a,.navbar-default .navbar-nav>.disabled>a:focus,.navbar-default .navbar-nav>.disabled>a:hover{color:#ccc;background-color:transparent}.navbar-default .navbar-toggle{border-color:#ddd}.navbar-default .navbar-toggle:focus,.navbar-default .navbar-toggle:hover{background-color:#ddd}.navbar-default .navbar-toggle .icon-bar{background-color:#888}.navbar-default .navbar-collapse,.navbar-default .navbar-form{border-color:#e7e7e7}.navbar-default .navbar-nav>.open>a,.navbar-default .navbar-nav>.open>a:focus,.navbar-default .navbar-nav>.open>a:hover{color:#555;background-color:#e7e7e7}@media (max-width:767px){.navbar-default .navbar-nav .open .dropdown-menu>li>a{color:#777}.navbar-default .navbar-nav .open .dropdown-menu>li>a:focus,.navbar-default .navbar-nav .open .dropdown-menu>li>a:hover{color:#333;background-color:transparent}.navbar-default .navbar-nav .open .dropdown-menu>.active>a,.navbar-default .navbar-nav .open .dropdown-menu>.active>a:focus,.navbar-default .navbar-nav .open .dropdown-menu>.active>a:hover{color:#555;background-color:#e7e7e7}.navbar-default .navbar-nav .open .dropdown-menu>.disabled>a,.navbar-default .navbar-nav .open .dropdown-menu>.disabled>a:focus,.navbar-default .navbar-nav .open .dropdown-menu>.disabled>a:hover{color:#ccc;background-color:transparent}}.navbar-default .navbar-link{color:#777}.navbar-default .navbar-link:hover{color:#333}.navbar-default .btn-link{color:#777}.navbar-default .btn-link:focus,.navbar-default .btn-link:hover{color:#333}.navbar-default .btn-link[disabled]:focus,.navbar-default .btn-link[disabled]:hover,fieldset[disabled] .navbar-default .btn-link:focus,fieldset[disabled] .navbar-default .btn-link:hover{color:#ccc}.navbar-inverse{background-color:#222;border-color:#080808}.navbar-inverse .navbar-brand{color:#9d9d9d}.navbar-inverse .navbar-brand:focus,.navbar-inverse .navbar-brand:hover{color:#fff;background-color:transparent}.navbar-inverse .navbar-text{color:#9d9d9d}.navbar-inverse .navbar-nav>li>a{color:#9d9d9d}.navbar-inverse .navbar-nav>li>a:focus,.navbar-inverse .navbar-nav>li>a:hover{color:#fff;background-color:transparent}.navbar-inverse .navbar-nav>.active>a,.navbar-inverse .navbar-nav>.active>a:focus,.navbar-inverse .navbar-nav>.active>a:hover{color:#fff;background-color:#080808}.navbar-inverse .navbar-nav>.disabled>a,.navbar-inverse .navbar-nav>.disabled>a:focus,.navbar-inverse .navbar-nav>.disabled>a:hover{color:#444;background-color:transparent}.navbar-inverse .navbar-toggle{border-color:#333}.navbar-inverse .navbar-toggle:focus,.navbar-inverse .navbar-toggle:hover{background-color:#333}.navbar-inverse .navbar-toggle .icon-bar{background-color:#fff}.navbar-inverse .navbar-collapse,.navbar-inverse .navbar-form{border-color:#101010}.navbar-inverse .navbar-nav>.open>a,.navbar-inverse .navbar-nav>.open>a:focus,.navbar-inverse .navbar-nav>.open>a:hover{color:#fff;background-color:#080808}@media (max-width:767px){.navbar-inverse .navbar-nav .open .dropdown-menu>.dropdown-header{border-color:#080808}.navbar-inverse .navbar-nav .open .dropdown-menu .divider{background-color:#080808}.navbar-inverse .navbar-nav .open .dropdown-menu>li>a{color:#9d9d9d}.navbar-inverse .navbar-nav .open .dropdown-menu>li>a:focus,.navbar-inverse .navbar-nav .open .dropdown-menu>li>a:hover{color:#fff;background-color:transparent}.navbar-inverse .navbar-nav .open .dropdown-menu>.active>a,.navbar-inverse .navbar-nav .open .dropdown-menu>.active>a:focus,.navbar-inverse .navbar-nav .open .dropdown-menu>.active>a:hover{color:#fff;background-color:#080808}.navbar-inverse .navbar-nav .open .dropdown-menu>.disabled>a,.navbar-inverse .navbar-nav .open .dropdown-menu>.disabled>a:focus,.navbar-inverse .navbar-nav .open .dropdown-menu>.disabled>a:hover{color:#444;background-color:transparent}}.navbar-inverse .navbar-link{color:#9d9d9d}.navbar-inverse .navbar-link:hover{color:#fff}.navbar-inverse .btn-link{color:#9d9d9d}.navbar-inverse .btn-link:focus,.navbar-inverse .btn-link:hover{color:#fff}.navbar-inverse .btn-link[disabled]:focus,.navbar-inverse .btn-link[disabled]:hover,fieldset[disabled] .navbar-inverse .btn-link:focus,fieldset[disabled] .navbar-inverse .btn-link:hover{color:#444}.breadcrumb{padding:8px 15px;margin-bottom:20px;list-style:none;background-color:#f5f5f5;border-radius:4px}.breadcrumb>li{display:inline-block}.breadcrumb>li+li:before{padding:0 5px;color:#ccc;content:"/\00a0"}.breadcrumb>.active{color:#777}.pagination{display:inline-block;padding-left:0;margin:20px 0;border-radius:4px}.pagination>li{display:inline}.pagination>li>a,.pagination>li>span{position:relative;float:left;padding:6px 12px;margin-left:-1px;line-height:1.42857143;color:#337ab7;text-decoration:none;background-color:#fff;border:1px solid #ddd}.pagination>li:first-child>a,.pagination>li:first-child>span{margin-left:0;border-top-left-radius:4px;border-bottom-left-radius:4px}.pagination>li:last-child>a,.pagination>li:last-child>span{border-top-right-radius:4px;border-bottom-right-radius:4px}.pagination>li>a:focus,.pagination>li>a:hover,.pagination>li>span:focus,.pagination>li>span:hover{z-index:3;color:#23527c;background-color:#eee;border-color:#ddd}.pagination>.active>a,.pagination>.active>a:focus,.pagination>.active>a:hover,.pagination>.active>span,.pagination>.active>span:focus,.pagination>.active>span:hover{z-index:2;color:#fff;cursor:default;background-color:#337ab7;border-color:#337ab7}.pagination>.disabled>a,.pagination>.disabled>a:focus,.pagination>.disabled>a:hover,.pagination>.disabled>span,.pagination>.disabled>span:focus,.pagination>.disabled>span:hover{color:#777;cursor:not-allowed;background-color:#fff;border-color:#ddd}.pagination-lg>li>a,.pagination-lg>li>span{padding:10px 16px;font-size:18px;line-height:1.3333333}.pagination-lg>li:first-child>a,.pagination-lg>li:first-child>span{border-top-left-radius:6px;border-bottom-left-radius:6px}.pagination-lg>li:last-child>a,.pagination-lg>li:last-child>span{border-top-right-radius:6px;border-bottom-right-radius:6px}.pagination-sm>li>a,.pagination-sm>li>span{padding:5px 10px;font-size:12px;line-height:1.5}.pagination-sm>li:first-child>a,.pagination-sm>li:first-child>span{border-top-left-radius:3px;border-bottom-left-radius:3px}.pagination-sm>li:last-child>a,.pagination-sm>li:last-child>span{border-top-right-radius:3px;border-bottom-right-radius:3px}.pager{padding-left:0;margin:20px 0;text-align:center;list-style:none}.pager li{display:inline}.pager li>a,.pager li>span{display:inline-block;padding:5px 14px;background-color:#fff;border:1px solid #ddd;border-radius:15px}.pager li>a:focus,.pager li>a:hover{text-decoration:none;background-color:#eee}.pager .next>a,.pager .next>span{float:right}.pager .previous>a,.pager .previous>span{float:left}.pager .disabled>a,.pager .disabled>a:focus,.pager .disabled>a:hover,.pager .disabled>span{color:#777;cursor:not-allowed;background-color:#fff}.label{display:inline;padding:.2em .6em .3em;font-size:75%;font-weight:700;line-height:1;color:#fff;text-align:center;white-space:nowrap;vertical-align:baseline;border-radius:.25em}a.label:focus,a.label:hover{color:#fff;text-decoration:none;cursor:pointer}.label:empty{display:none}.btn .label{position:relative;top:-1px}.label-default{background-color:#777}.label-default[href]:focus,.label-default[href]:hover{background-color:#5e5e5e}.label-primary{background-color:#337ab7}.label-primary[href]:focus,.label-primary[href]:hover{background-color:#286090}.label-success{background-color:#5cb85c}.label-success[href]:focus,.label-success[href]:hover{background-color:#449d44}.label-info{background-color:#5bc0de}.label-info[href]:focus,.label-info[href]:hover{background-color:#31b0d5}.label-warning{background-color:#f0ad4e}.label-warning[href]:focus,.label-warning[href]:hover{background-color:#ec971f}.label-danger{background-color:#d9534f}.label-danger[href]:focus,.label-danger[href]:hover{background-color:#c9302c}.badge{display:inline-block;min-width:10px;padding:3px 7px;font-size:12px;font-weight:700;line-height:1;color:#fff;text-align:center;white-space:nowrap;vertical-align:middle;background-color:#777;border-radius:10px}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.btn-group-xs>.btn .badge,.btn-xs .badge{top:0;padding:1px 5px}a.badge:focus,a.badge:hover{color:#fff;text-decoration:none;cursor:pointer}.list-group-item.active>.badge,.nav-pills>.active>a>.badge{color:#337ab7;background-color:#fff}.list-group-item>.badge{float:right}.list-group-item>.badge+.badge{margin-right:5px}.nav-pills>li>a>.badge{margin-left:3px}.jumbotron{padding-top:30px;padding-bottom:30px;margin-bottom:30px;color:inherit;background-color:#eee}.jumbotron .h1,.jumbotron h1{color:inherit}.jumbotron p{margin-bottom:15px;font-size:21px;font-weight:200}.jumbotron>hr{border-top-color:#d5d5d5}.container .jumbotron,.container-fluid .jumbotron{border-radius:6px}.jumbotron .container{max-width:100%}@media screen and (min-width:768px){.jumbotron{padding-top:48px;padding-bottom:48px}.container .jumbotron,.container-fluid .jumbotron{padding-right:60px;padding-left:60px}.jumbotron .h1,.jumbotron h1{font-size:63px}}.thumbnail{display:block;padding:4px;margin-bottom:20px;line-height:1.42857143;background-color:#fff;border:1px solid #ddd;border-radius:4px;-webkit-transition:border .2s ease-in-out;-o-transition:border .2s ease-in-out;transition:border .2s ease-in-out}.thumbnail a>img,.thumbnail>img{margin-right:auto;margin-left:auto}a.thumbnail.active,a.thumbnail:focus,a.thumbnail:hover{border-color:#337ab7}.thumbnail .caption{padding:9px;color:#333}.alert{padding:15px;margin-bottom:20px;border:1px solid transparent;border-radius:4px}.alert h4{margin-top:0;color:inherit}.alert .alert-link{font-weight:700}.alert>p,.alert>ul{margin-bottom:0}.alert>p+p{margin-top:5px}.alert-dismissable,.alert-dismissible{padding-right:35px}.alert-dismissable .close,.alert-dismissible .close{position:relative;top:-2px;right:-21px;color:inherit}.alert-success{color:#3c763d;background-color:#dff0d8;border-color:#d6e9c6}.alert-success hr{border-top-color:#c9e2b3}.alert-success .alert-link{color:#2b542c}.alert-info{color:#31708f;background-color:#d9edf7;border-color:#bce8f1}.alert-info hr{border-top-color:#a6e1ec}.alert-info .alert-link{color:#245269}.alert-warning{color:#8a6d3b;background-color:#fcf8e3;border-color:#faebcc}.alert-warning hr{border-top-color:#f7e1b5}.alert-warning .alert-link{color:#66512c}.alert-danger{color:#a94442;background-color:#f2dede;border-color:#ebccd1}.alert-danger hr{border-top-color:#e4b9c0}.alert-danger .alert-link{color:#843534}@-webkit-keyframes progress-bar-stripes{from{background-position:40px 0}to{background-position:0 0}}@-o-keyframes progress-bar-stripes{from{background-position:40px 0}to{background-position:0 0}}@keyframes progress-bar-stripes{from{background-position:40px 0}to{background-position:0 0}}.progress{height:20px;margin-bottom:20px;overflow:hidden;background-color:#f5f5f5;border-radius:4px;-webkit-box-shadow:inset 0 1px 2px rgba(0,0,0,.1);box-shadow:inset 0 1px 2px rgba(0,0,0,.1)}.progress-bar{float:left;width:0;height:100%;font-size:12px;line-height:20px;color:#fff;text-align:center;background-color:#337ab7;-webkit-box-shadow:inset 0 -1px 0 rgba(0,0,0,.15);box-shadow:inset 0 -1px 0 rgba(0,0,0,.15);-webkit-transition:width .6s ease;-o-transition:width .6s ease;transition:width .6s ease}.progress-bar-striped,.progress-striped .progress-bar{background-image:-webkit-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:-o-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);-webkit-background-size:40px 40px;background-size:40px 40px}.progress-bar.active,.progress.active .progress-bar{-webkit-animation:progress-bar-stripes 2s linear infinite;-o-animation:progress-bar-stripes 2s linear infinite;animation:progress-bar-stripes 2s linear infinite}.progress-bar-success{background-color:#5cb85c}.progress-striped .progress-bar-success{background-image:-webkit-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:-o-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent)}.progress-bar-info{background-color:#5bc0de}.progress-striped .progress-bar-info{background-image:-webkit-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:-o-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent)}.progress-bar-warning{background-color:#f0ad4e}.progress-striped .progress-bar-warning{background-image:-webkit-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:-o-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent)}.progress-bar-danger{background-color:#d9534f}.progress-striped .progress-bar-danger{background-image:-webkit-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:-o-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent)}.media{margin-top:15px}.media:first-child{margin-top:0}.media,.media-body{overflow:hidden;zoom:1}.media-body{width:10000px}.media-object{display:block}.media-object.img-thumbnail{max-width:none}.media-right,.media>.pull-right{padding-left:10px}.media-left,.media>.pull-left{padding-right:10px}.media-body,.media-left,.media-right{display:table-cell;vertical-align:top}.media-middle{vertical-align:middle}.media-bottom{vertical-align:bottom}.media-heading{margin-top:0;margin-bottom:5px}.media-list{padding-left:0;list-style:none}.list-group{padding-left:0;margin-bottom:20px}.list-group-item{position:relative;display:block;padding:10px 15px;margin-bottom:-1px;background-color:#fff;border:1px solid #ddd}.list-group-item:first-child{border-top-left-radius:4px;border-top-right-radius:4px}.list-group-item:last-child{margin-bottom:0;border-bottom-right-radius:4px;border-bottom-left-radius:4px}a.list-group-item,button.list-group-item{color:#555}a.list-group-item .list-group-item-heading,button.list-group-item .list-group-item-heading{color:#333}a.list-group-item:focus,a.list-group-item:hover,button.list-group-item:focus,button.list-group-item:hover{color:#555;text-decoration:none;background-color:#f5f5f5}button.list-group-item{width:100%;text-align:left}.list-group-item.disabled,.list-group-item.disabled:focus,.list-group-item.disabled:hover{color:#777;cursor:not-allowed;background-color:#eee}.list-group-item.disabled .list-group-item-heading,.list-group-item.disabled:focus .list-group-item-heading,.list-group-item.disabled:hover .list-group-item-heading{color:inherit}.list-group-item.disabled .list-group-item-text,.list-group-item.disabled:focus .list-group-item-text,.list-group-item.disabled:hover .list-group-item-text{color:#777}.list-group-item.active,.list-group-item.active:focus,.list-group-item.active:hover{z-index:2;color:#fff;background-color:#337ab7;border-color:#337ab7}.list-group-item.active .list-group-item-heading,.list-group-item.active .list-group-item-heading>.small,.list-group-item.active .list-group-item-heading>small,.list-group-item.active:focus .list-group-item-heading,.list-group-item.active:focus .list-group-item-heading>.small,.list-group-item.active:focus .list-group-item-heading>small,.list-group-item.active:hover .list-group-item-heading,.list-group-item.active:hover .list-group-item-heading>.small,.list-group-item.active:hover .list-group-item-heading>small{color:inherit}.list-group-item.active .list-group-item-text,.list-group-item.active:focus .list-group-item-text,.list-group-item.active:hover .list-group-item-text{color:#c7ddef}.list-group-item-success{color:#3c763d;background-color:#dff0d8}a.list-group-item-success,button.list-group-item-success{color:#3c763d}a.list-group-item-success .list-group-item-heading,button.list-group-item-success .list-group-item-heading{color:inherit}a.list-group-item-success:focus,a.list-group-item-success:hover,button.list-group-item-success:focus,button.list-group-item-success:hover{color:#3c763d;background-color:#d0e9c6}a.list-group-item-success.active,a.list-group-item-success.active:focus,a.list-group-item-success.active:hover,button.list-group-item-success.active,button.list-group-item-success.active:focus,button.list-group-item-success.active:hover{color:#fff;background-color:#3c763d;border-color:#3c763d}.list-group-item-info{color:#31708f;background-color:#d9edf7}a.list-group-item-info,button.list-group-item-info{color:#31708f}a.list-group-item-info .list-group-item-heading,button.list-group-item-info .list-group-item-heading{color:inherit}a.list-group-item-info:focus,a.list-group-item-info:hover,button.list-group-item-info:focus,button.list-group-item-info:hover{color:#31708f;background-color:#c4e3f3}a.list-group-item-info.active,a.list-group-item-info.active:focus,a.list-group-item-info.active:hover,button.list-group-item-info.active,button.list-group-item-info.active:focus,button.list-group-item-info.active:hover{color:#fff;background-color:#31708f;border-color:#31708f}.list-group-item-warning{color:#8a6d3b;background-color:#fcf8e3}a.list-group-item-warning,button.list-group-item-warning{color:#8a6d3b}a.list-group-item-warning .list-group-item-heading,button.list-group-item-warning .list-group-item-heading{color:inherit}a.list-group-item-warning:focus,a.list-group-item-warning:hover,button.list-group-item-warning:focus,button.list-group-item-warning:hover{color:#8a6d3b;background-color:#faf2cc}a.list-group-item-warning.active,a.list-group-item-warning.active:focus,a.list-group-item-warning.active:hover,button.list-group-item-warning.active,button.list-group-item-warning.active:focus,button.list-group-item-warning.active:hover{color:#fff;background-color:#8a6d3b;border-color:#8a6d3b}.list-group-item-danger{color:#a94442;background-color:#f2dede}a.list-group-item-danger,button.list-group-item-danger{color:#a94442}a.list-group-item-danger .list-group-item-heading,button.list-group-item-danger .list-group-item-heading{color:inherit}a.list-group-item-danger:focus,a.list-group-item-danger:hover,button.list-group-item-danger:focus,button.list-group-item-danger:hover{color:#a94442;background-color:#ebcccc}a.list-group-item-danger.active,a.list-group-item-danger.active:focus,a.list-group-item-danger.active:hover,button.list-group-item-danger.active,button.list-group-item-danger.active:focus,button.list-group-item-danger.active:hover{color:#fff;background-color:#a94442;border-color:#a94442}.list-group-item-heading{margin-top:0;margin-bottom:5px}.list-group-item-text{margin-bottom:0;line-height:1.3}.panel{margin-bottom:20px;background-color:#fff;border:1px solid transparent;border-radius:4px;-webkit-box-shadow:0 1px 1px rgba(0,0,0,.05);box-shadow:0 1px 1px rgba(0,0,0,.05)}.panel-body{padding:15px}.panel-heading{padding:10px 15px;border-bottom:1px solid transparent;border-top-left-radius:3px;border-top-right-radius:3px}.panel-heading>.dropdown .dropdown-toggle{color:inherit}.panel-title{margin-top:0;margin-bottom:0;font-size:16px;color:inherit}.panel-title>.small,.panel-title>.small>a,.panel-title>a,.panel-title>small,.panel-title>small>a{color:inherit}.panel-footer{padding:10px 15px;background-color:#f5f5f5;border-top:1px solid #ddd;border-bottom-right-radius:3px;border-bottom-left-radius:3px}.panel>.list-group,.panel>.panel-collapse>.list-group{margin-bottom:0}.panel>.list-group .list-group-item,.panel>.panel-collapse>.list-group .list-group-item{border-width:1px 0;border-radius:0}.panel>.list-group:first-child .list-group-item:first-child,.panel>.panel-collapse>.list-group:first-child .list-group-item:first-child{border-top:0;border-top-left-radius:3px;border-top-right-radius:3px}.panel>.list-group:last-child .list-group-item:last-child,.panel>.panel-collapse>.list-group:last-child .list-group-item:last-child{border-bottom:0;border-bottom-right-radius:3px;border-bottom-left-radius:3px}.panel>.panel-heading+.panel-collapse>.list-group .list-group-item:first-child{border-top-left-radius:0;border-top-right-radius:0}.panel-heading+.list-group .list-group-item:first-child{border-top-width:0}.list-group+.panel-footer{border-top-width:0}.panel>.panel-collapse>.table,.panel>.table,.panel>.table-responsive>.table{margin-bottom:0}.panel>.panel-collapse>.table caption,.panel>.table caption,.panel>.table-responsive>.table caption{padding-right:15px;padding-left:15px}.panel>.table-responsive:first-child>.table:first-child,.panel>.table:first-child{border-top-left-radius:3px;border-top-right-radius:3px}.panel>.table-responsive:first-child>.table:first-child>tbody:first-child>tr:first-child,.panel>.table-responsive:first-child>.table:first-child>thead:first-child>tr:first-child,.panel>.table:first-child>tbody:first-child>tr:first-child,.panel>.table:first-child>thead:first-child>tr:first-child{border-top-left-radius:3px;border-top-right-radius:3px}.panel>.table-responsive:first-child>.table:first-child>tbody:first-child>tr:first-child td:first-child,.panel>.table-responsive:first-child>.table:first-child>tbody:first-child>tr:first-child th:first-child,.panel>.table-responsive:first-child>.table:first-child>thead:first-child>tr:first-child td:first-child,.panel>.table-responsive:first-child>.table:first-child>thead:first-child>tr:first-child th:first-child,.panel>.table:first-child>tbody:first-child>tr:first-child td:first-child,.panel>.table:first-child>tbody:first-child>tr:first-child th:first-child,.panel>.table:first-child>thead:first-child>tr:first-child td:first-child,.panel>.table:first-child>thead:first-child>tr:first-child th:first-child{border-top-left-radius:3px}.panel>.table-responsive:first-child>.table:first-child>tbody:first-child>tr:first-child td:last-child,.panel>.table-responsive:first-child>.table:first-child>tbody:first-child>tr:first-child th:last-child,.panel>.table-responsive:first-child>.table:first-child>thead:first-child>tr:first-child td:last-child,.panel>.table-responsive:first-child>.table:first-child>thead:first-child>tr:first-child th:last-child,.panel>.table:first-child>tbody:first-child>tr:first-child td:last-child,.panel>.table:first-child>tbody:first-child>tr:first-child th:last-child,.panel>.table:first-child>thead:first-child>tr:first-child td:last-child,.panel>.table:first-child>thead:first-child>tr:first-child th:last-child{border-top-right-radius:3px}.panel>.table-responsive:last-child>.table:last-child,.panel>.table:last-child{border-bottom-right-radius:3px;border-bottom-left-radius:3px}.panel>.table-responsive:last-child>.table:last-child>tbody:last-child>tr:last-child,.panel>.table-responsive:last-child>.table:last-child>tfoot:last-child>tr:last-child,.panel>.table:last-child>tbody:last-child>tr:last-child,.panel>.table:last-child>tfoot:last-child>tr:last-child{border-bottom-right-radius:3px;border-bottom-left-radius:3px}.panel>.table-responsive:last-child>.table:last-child>tbody:last-child>tr:last-child td:first-child,.panel>.table-responsive:last-child>.table:last-child>tbody:last-child>tr:last-child th:first-child,.panel>.table-responsive:last-child>.table:last-child>tfoot:last-child>tr:last-child td:first-child,.panel>.table-responsive:last-child>.table:last-child>tfoot:last-child>tr:last-child th:first-child,.panel>.table:last-child>tbody:last-child>tr:last-child td:first-child,.panel>.table:last-child>tbody:last-child>tr:last-child th:first-child,.panel>.table:last-child>tfoot:last-child>tr:last-child td:first-child,.panel>.table:last-child>tfoot:last-child>tr:last-child th:first-child{border-bottom-left-radius:3px}.panel>.table-responsive:last-child>.table:last-child>tbody:last-child>tr:last-child td:last-child,.panel>.table-responsive:last-child>.table:last-child>tbody:last-child>tr:last-child th:last-child,.panel>.table-responsive:last-child>.table:last-child>tfoot:last-child>tr:last-child td:last-child,.panel>.table-responsive:last-child>.table:last-child>tfoot:last-child>tr:last-child th:last-child,.panel>.table:last-child>tbody:last-child>tr:last-child td:last-child,.panel>.table:last-child>tbody:last-child>tr:last-child th:last-child,.panel>.table:last-child>tfoot:last-child>tr:last-child td:last-child,.panel>.table:last-child>tfoot:last-child>tr:last-child th:last-child{border-bottom-right-radius:3px}.panel>.panel-body+.table,.panel>.panel-body+.table-responsive,.panel>.table+.panel-body,.panel>.table-responsive+.panel-body{border-top:1px solid #ddd}.panel>.table>tbody:first-child>tr:first-child td,.panel>.table>tbody:first-child>tr:first-child th{border-top:0}.panel>.table-bordered,.panel>.table-responsive>.table-bordered{border:0}.panel>.table-bordered>tbody>tr>td:first-child,.panel>.table-bordered>tbody>tr>th:first-child,.panel>.table-bordered>tfoot>tr>td:first-child,.panel>.table-bordered>tfoot>tr>th:first-child,.panel>.table-bordered>thead>tr>td:first-child,.panel>.table-bordered>thead>tr>th:first-child,.panel>.table-responsive>.table-bordered>tbody>tr>td:first-child,.panel>.table-responsive>.table-bordered>tbody>tr>th:first-child,.panel>.table-responsive>.table-bordered>tfoot>tr>td:first-child,.panel>.table-responsive>.table-bordered>tfoot>tr>th:first-child,.panel>.table-responsive>.table-bordered>thead>tr>td:first-child,.panel>.table-responsive>.table-bordered>thead>tr>th:first-child{border-left:0}.panel>.table-bordered>tbody>tr>td:last-child,.panel>.table-bordered>tbody>tr>th:last-child,.panel>.table-bordered>tfoot>tr>td:last-child,.panel>.table-bordered>tfoot>tr>th:last-child,.panel>.table-bordered>thead>tr>td:last-child,.panel>.table-bordered>thead>tr>th:last-child,.panel>.table-responsive>.table-bordered>tbody>tr>td:last-child,.panel>.table-responsive>.table-bordered>tbody>tr>th:last-child,.panel>.table-responsive>.table-bordered>tfoot>tr>td:last-child,.panel>.table-responsive>.table-bordered>tfoot>tr>th:last-child,.panel>.table-responsive>.table-bordered>thead>tr>td:last-child,.panel>.table-responsive>.table-bordered>thead>tr>th:last-child{border-right:0}.panel>.table-bordered>tbody>tr:first-child>td,.panel>.table-bordered>tbody>tr:first-child>th,.panel>.table-bordered>thead>tr:first-child>td,.panel>.table-bordered>thead>tr:first-child>th,.panel>.table-responsive>.table-bordered>tbody>tr:first-child>td,.panel>.table-responsive>.table-bordered>tbody>tr:first-child>th,.panel>.table-responsive>.table-bordered>thead>tr:first-child>td,.panel>.table-responsive>.table-bordered>thead>tr:first-child>th{border-bottom:0}.panel>.table-bordered>tbody>tr:last-child>td,.panel>.table-bordered>tbody>tr:last-child>th,.panel>.table-bordered>tfoot>tr:last-child>td,.panel>.table-bordered>tfoot>tr:last-child>th,.panel>.table-responsive>.table-bordered>tbody>tr:last-child>td,.panel>.table-responsive>.table-bordered>tbody>tr:last-child>th,.panel>.table-responsive>.table-bordered>tfoot>tr:last-child>td,.panel>.table-responsive>.table-bordered>tfoot>tr:last-child>th{border-bottom:0}.panel>.table-responsive{margin-bottom:0;border:0}.panel-group{margin-bottom:20px}.panel-group .panel{margin-bottom:0;border-radius:4px}.panel-group .panel+.panel{margin-top:5px}.panel-group .panel-heading{border-bottom:0}.panel-group .panel-heading+.panel-collapse>.list-group,.panel-group .panel-heading+.panel-collapse>.panel-body{border-top:1px solid #ddd}.panel-group .panel-footer{border-top:0}.panel-group .panel-footer+.panel-collapse .panel-body{border-bottom:1px solid #ddd}.panel-default{border-color:#ddd}.panel-default>.panel-heading{color:#333;background-color:#f5f5f5;border-color:#ddd}.panel-default>.panel-heading+.panel-collapse>.panel-body{border-top-color:#ddd}.panel-default>.panel-heading .badge{color:#f5f5f5;background-color:#333}.panel-default>.panel-footer+.panel-collapse>.panel-body{border-bottom-color:#ddd}.panel-primary{border-color:#337ab7}.panel-primary>.panel-heading{color:#fff;background-color:#337ab7;border-color:#337ab7}.panel-primary>.panel-heading+.panel-collapse>.panel-body{border-top-color:#337ab7}.panel-primary>.panel-heading .badge{color:#337ab7;background-color:#fff}.panel-primary>.panel-footer+.panel-collapse>.panel-body{border-bottom-color:#337ab7}.panel-success{border-color:#d6e9c6}.panel-success>.panel-heading{color:#3c763d;background-color:#dff0d8;border-color:#d6e9c6}.panel-success>.panel-heading+.panel-collapse>.panel-body{border-top-color:#d6e9c6}.panel-success>.panel-heading .badge{color:#dff0d8;background-color:#3c763d}.panel-success>.panel-footer+.panel-collapse>.panel-body{border-bottom-color:#d6e9c6}.panel-info{border-color:#bce8f1}.panel-info>.panel-heading{color:#31708f;background-color:#d9edf7;border-color:#bce8f1}.panel-info>.panel-heading+.panel-collapse>.panel-body{border-top-color:#bce8f1}.panel-info>.panel-heading .badge{color:#d9edf7;background-color:#31708f}.panel-info>.panel-footer+.panel-collapse>.panel-body{border-bottom-color:#bce8f1}.panel-warning{border-color:#faebcc}.panel-warning>.panel-heading{color:#8a6d3b;background-color:#fcf8e3;border-color:#faebcc}.panel-warning>.panel-heading+.panel-collapse>.panel-body{border-top-color:#faebcc}.panel-warning>.panel-heading .badge{color:#fcf8e3;background-color:#8a6d3b}.panel-warning>.panel-footer+.panel-collapse>.panel-body{border-bottom-color:#faebcc}.panel-danger{border-color:#ebccd1}.panel-danger>.panel-heading{color:#a94442;background-color:#f2dede;border-color:#ebccd1}.panel-danger>.panel-heading+.panel-collapse>.panel-body{border-top-color:#ebccd1}.panel-danger>.panel-heading .badge{color:#f2dede;background-color:#a94442}.panel-danger>.panel-footer+.panel-collapse>.panel-body{border-bottom-color:#ebccd1}.embed-responsive{position:relative;display:block;height:0;padding:0;overflow:hidden}.embed-responsive .embed-responsive-item,.embed-responsive embed,.embed-responsive iframe,.embed-responsive object,.embed-responsive video{position:absolute;top:0;bottom:0;left:0;width:100%;height:100%;border:0}.embed-responsive-16by9{padding-bottom:56.25%}.embed-responsive-4by3{padding-bottom:75%}.well{min-height:20px;padding:19px;margin-bottom:20px;background-color:#f5f5f5;border:1px solid #e3e3e3;border-radius:4px;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.05);box-shadow:inset 0 1px 1px rgba(0,0,0,.05)}.well blockquote{border-color:#ddd;border-color:rgba(0,0,0,.15)}.well-lg{padding:24px;border-radius:6px}.well-sm{padding:9px;border-radius:3px}.close{float:right;font-size:21px;font-weight:700;line-height:1;color:#000;text-shadow:0 1px 0 #fff;filter:alpha(opacity=20);opacity:.2}.close:focus,.close:hover{color:#000;text-decoration:none;cursor:pointer;filter:alpha(opacity=50);opacity:.5}button.close{-webkit-appearance:none;padding:0;cursor:pointer;background:0 0;border:0}.modal-open{overflow:hidden}.modal{position:fixed;top:0;right:0;bottom:0;left:0;z-index:1050;display:none;overflow:hidden;-webkit-overflow-scrolling:touch;outline:0}.modal.fade .modal-dialog{-webkit-transition:-webkit-transform .3s ease-out;-o-transition:-o-transform .3s ease-out;transition:transform .3s ease-out;-webkit-transform:translate(0,-25%);-ms-transform:translate(0,-25%);-o-transform:translate(0,-25%);transform:translate(0,-25%)}.modal.in .modal-dialog{-webkit-transform:translate(0,0);-ms-transform:translate(0,0);-o-transform:translate(0,0);transform:translate(0,0)}.modal-open .modal{overflow-x:hidden;overflow-y:auto}.modal-dialog{position:relative;width:auto;margin:10px}.modal-content{position:relative;background-color:#fff;-webkit-background-clip:padding-box;background-clip:padding-box;border:1px solid #999;border:1px solid rgba(0,0,0,.2);border-radius:6px;outline:0;-webkit-box-shadow:0 3px 9px rgba(0,0,0,.5);box-shadow:0 3px 9px rgba(0,0,0,.5)}.modal-backdrop{position:fixed;top:0;right:0;bottom:0;left:0;z-index:1040;background-color:#000}.modal-backdrop.fade{filter:alpha(opacity=0);opacity:0}.modal-backdrop.in{filter:alpha(opacity=50);opacity:.5}.modal-header{min-height:16.43px;padding:15px;border-bottom:1px solid #e5e5e5}.modal-header .close{margin-top:-2px}.modal-title{margin:0;line-height:1.42857143}.modal-body{position:relative;padding:15px}.modal-footer{padding:15px;text-align:right;border-top:1px solid #e5e5e5}.modal-footer .btn+.btn{margin-bottom:0;margin-left:5px}.modal-footer .btn-group .btn+.btn{margin-left:-1px}.modal-footer .btn-block+.btn-block{margin-left:0}.modal-scrollbar-measure{position:absolute;top:-9999px;width:50px;height:50px;overflow:scroll}@media (min-width:768px){.modal-dialog{width:600px;margin:30px auto}.modal-content{-webkit-box-shadow:0 5px 15px rgba(0,0,0,.5);box-shadow:0 5px 15px rgba(0,0,0,.5)}.modal-sm{width:300px}}@media (min-width:992px){.modal-lg{width:900px}}.tooltip{position:absolute;z-index:1070;display:block;font-family:"Helvetica Neue",Helvetica,Arial,sans-serif;font-size:12px;font-style:normal;font-weight:400;line-height:1.42857143;text-align:left;text-align:start;text-decoration:none;text-shadow:none;text-transform:none;letter-spacing:normal;word-break:normal;word-spacing:normal;word-wrap:normal;white-space:normal;filter:alpha(opacity=0);opacity:0;line-break:auto}.tooltip.in{filter:alpha(opacity=90);opacity:.9}.tooltip.top{padding:5px 0;margin-top:-3px}.tooltip.right{padding:0 5px;margin-left:3px}.tooltip.bottom{padding:5px 0;margin-top:3px}.tooltip.left{padding:0 5px;margin-left:-3px}.tooltip-inner{max-width:200px;padding:3px 8px;color:#fff;text-align:center;background-color:#000;border-radius:4px}.tooltip-arrow{position:absolute;width:0;height:0;border-color:transparent;border-style:solid}.tooltip.top .tooltip-arrow{bottom:0;left:50%;margin-left:-5px;border-width:5px 5px 0;border-top-color:#000}.tooltip.top-left .tooltip-arrow{right:5px;bottom:0;margin-bottom:-5px;border-width:5px 5px 0;border-top-color:#000}.tooltip.top-right .tooltip-arrow{bottom:0;left:5px;margin-bottom:-5px;border-width:5px 5px 0;border-top-color:#000}.tooltip.right .tooltip-arrow{top:50%;left:0;margin-top:-5px;border-width:5px 5px 5px 0;border-right-color:#000}.tooltip.left .tooltip-arrow{top:50%;right:0;margin-top:-5px;border-width:5px 0 5px 5px;border-left-color:#000}.tooltip.bottom .tooltip-arrow{top:0;left:50%;margin-left:-5px;border-width:0 5px 5px;border-bottom-color:#000}.tooltip.bottom-left .tooltip-arrow{top:0;right:5px;margin-top:-5px;border-width:0 5px 5px;border-bottom-color:#000}.tooltip.bottom-right .tooltip-arrow{top:0;left:5px;margin-top:-5px;border-width:0 5px 5px;border-bottom-color:#000}.popover{position:absolute;top:0;left:0;z-index:1060;display:none;max-width:276px;padding:1px;font-family:"Helvetica Neue",Helvetica,Arial,sans-serif;font-size:14px;font-style:normal;font-weight:400;line-height:1.42857143;text-align:left;text-align:start;text-decoration:none;text-shadow:none;text-transform:none;letter-spacing:normal;word-break:normal;word-spacing:normal;word-wrap:normal;white-space:normal;background-color:#fff;-webkit-background-clip:padding-box;background-clip:padding-box;border:1px solid #ccc;border:1px solid rgba(0,0,0,.2);border-radius:6px;-webkit-box-shadow:0 5px 10px rgba(0,0,0,.2);box-shadow:0 5px 10px rgba(0,0,0,.2);line-break:auto}.popover.top{margin-top:-10px}.popover.right{margin-left:10px}.popover.bottom{margin-top:10px}.popover.left{margin-left:-10px}.popover-title{padding:8px 14px;margin:0;font-size:14px;background-color:#f7f7f7;border-bottom:1px solid #ebebeb;border-radius:5px 5px 0 0}.popover-content{padding:9px 14px}.popover>.arrow,.popover>.arrow:after{position:absolute;display:block;width:0;height:0;border-color:transparent;border-style:solid}.popover>.arrow{border-width:11px}.popover>.arrow:after{content:"";border-width:10px}.popover.top>.arrow{bottom:-11px;left:50%;margin-left:-11px;border-top-color:#999;border-top-color:rgba(0,0,0,.25);border-bottom-width:0}.popover.top>.arrow:after{bottom:1px;margin-left:-10px;content:" ";border-top-color:#fff;border-bottom-width:0}.popover.right>.arrow{top:50%;left:-11px;margin-top:-11px;border-right-color:#999;border-right-color:rgba(0,0,0,.25);border-left-width:0}.popover.right>.arrow:after{bottom:-10px;left:1px;content:" ";border-right-color:#fff;border-left-width:0}.popover.bottom>.arrow{top:-11px;left:50%;margin-left:-11px;border-top-width:0;border-bottom-color:#999;border-bottom-color:rgba(0,0,0,.25)}.popover.bottom>.arrow:after{top:1px;margin-left:-10px;content:" ";border-top-width:0;border-bottom-color:#fff}.popover.left>.arrow{top:50%;right:-11px;margin-top:-11px;border-right-width:0;border-left-color:#999;border-left-color:rgba(0,0,0,.25)}.popover.left>.arrow:after{right:1px;bottom:-10px;content:" ";border-right-width:0;border-left-color:#fff}.carousel{position:relative}.carousel-inner{position:relative;width:100%;overflow:hidden}.carousel-inner>.item{position:relative;display:none;-webkit-transition:.6s ease-in-out left;-o-transition:.6s ease-in-out left;transition:.6s ease-in-out left}.carousel-inner>.item>a>img,.carousel-inner>.item>img{line-height:1}@media all and (transform-3d),(-webkit-transform-3d){.carousel-inner>.item{-webkit-transition:-webkit-transform .6s ease-in-out;-o-transition:-o-transform .6s ease-in-out;transition:transform .6s ease-in-out;-webkit-backface-visibility:hidden;backface-visibility:hidden;-webkit-perspective:1000px;perspective:1000px}.carousel-inner>.item.active.right,.carousel-inner>.item.next{left:0;-webkit-transform:translate3d(100%,0,0);transform:translate3d(100%,0,0)}.carousel-inner>.item.active.left,.carousel-inner>.item.prev{left:0;-webkit-transform:translate3d(-100%,0,0);transform:translate3d(-100%,0,0)}.carousel-inner>.item.active,.carousel-inner>.item.next.left,.carousel-inner>.item.prev.right{left:0;-webkit-transform:translate3d(0,0,0);transform:translate3d(0,0,0)}}.carousel-inner>.active,.carousel-inner>.next,.carousel-inner>.prev{display:block}.carousel-inner>.active{left:0}.carousel-inner>.next,.carousel-inner>.prev{position:absolute;top:0;width:100%}.carousel-inner>.next{left:100%}.carousel-inner>.prev{left:-100%}.carousel-inner>.next.left,.carousel-inner>.prev.right{left:0}.carousel-inner>.active.left{left:-100%}.carousel-inner>.active.right{left:100%}.carousel-control{position:absolute;top:0;bottom:0;left:0;width:15%;font-size:20px;color:#fff;text-align:center;text-shadow:0 1px 2px rgba(0,0,0,.6);filter:alpha(opacity=50);opacity:.5}.carousel-control.left{background-image:-webkit-linear-gradient(left,rgba(0,0,0,.5) 0,rgba(0,0,0,.0001) 100%);background-image:-o-linear-gradient(left,rgba(0,0,0,.5) 0,rgba(0,0,0,.0001) 100%);background-image:-webkit-gradient(linear,left top,right top,from(rgba(0,0,0,.5)),to(rgba(0,0,0,.0001)));background-image:linear-gradient(to right,rgba(0,0,0,.5) 0,rgba(0,0,0,.0001) 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1);background-repeat:repeat-x}.carousel-control.right{right:0;left:auto;background-image:-webkit-linear-gradient(left,rgba(0,0,0,.0001) 0,rgba(0,0,0,.5) 100%);background-image:-o-linear-gradient(left,rgba(0,0,0,.0001) 0,rgba(0,0,0,.5) 100%);background-image:-webkit-gradient(linear,left top,right top,from(rgba(0,0,0,.0001)),to(rgba(0,0,0,.5)));background-image:linear-gradient(to right,rgba(0,0,0,.0001) 0,rgba(0,0,0,.5) 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1);background-repeat:repeat-x}.carousel-control:focus,.carousel-control:hover{color:#fff;text-decoration:none;filter:alpha(opacity=90);outline:0;opacity:.9}.carousel-control .glyphicon-chevron-left,.carousel-control .glyphicon-chevron-right,.carousel-control .icon-next,.carousel-control .icon-prev{position:absolute;top:50%;z-index:5;display:inline-block;margin-top:-10px}.carousel-control .glyphicon-chevron-left,.carousel-control .icon-prev{left:50%;margin-left:-10px}.carousel-control .glyphicon-chevron-right,.carousel-control .icon-next{right:50%;margin-right:-10px}.carousel-control .icon-next,.carousel-control .icon-prev{width:20px;height:20px;font-family:serif;line-height:1}.carousel-control .icon-prev:before{content:'\2039'}.carousel-control .icon-next:before{content:'\203a'}.carousel-indicators{position:absolute;bottom:10px;left:50%;z-index:15;width:60%;padding-left:0;margin-left:-30%;text-align:center;list-style:none}.carousel-indicators li{display:inline-block;width:10px;height:10px;margin:1px;text-indent:-999px;cursor:pointer;background-color:#000\9;background-color:rgba(0,0,0,0);border:1px solid #fff;border-radius:10px}.carousel-indicators .active{width:12px;height:12px;margin:0;background-color:#fff}.carousel-caption{position:absolute;right:15%;bottom:20px;left:15%;z-index:10;padding-top:20px;padding-bottom:20px;color:#fff;text-align:center;text-shadow:0 1px 2px rgba(0,0,0,.6)}.carousel-caption .btn{text-shadow:none}@media screen and (min-width:768px){.carousel-control .glyphicon-chevron-left,.carousel-control .glyphicon-chevron-right,.carousel-control .icon-next,.carousel-control .icon-prev{width:30px;height:30px;margin-top:-15px;font-size:30px}.carousel-control .glyphicon-chevron-left,.carousel-control .icon-prev{margin-left:-15px}.carousel-control .glyphicon-chevron-right,.carousel-control .icon-next{margin-right:-15px}.carousel-caption{right:20%;left:20%;padding-bottom:30px}.carousel-indicators{bottom:20px}}.btn-group-vertical>.btn-group:after,.btn-group-vertical>.btn-group:before,.btn-toolbar:after,.btn-toolbar:before,.clearfix:after,.clearfix:before,.container-fluid:after,.container-fluid:before,.container:after,.container:before,.dl-horizontal dd:after,.dl-horizontal dd:before,.form-horizontal .form-group:after,.form-horizontal .form-group:before,.modal-footer:after,.modal-footer:before,.nav:after,.nav:before,.navbar-collapse:after,.navbar-collapse:before,.navbar-header:after,.navbar-header:before,.navbar:after,.navbar:before,.pager:after,.pager:before,.panel-body:after,.panel-body:before,.row:after,.row:before{display:table;content:" "}.btn-group-vertical>.btn-group:after,.btn-toolbar:after,.clearfix:after,.container-fluid:after,.container:after,.dl-horizontal dd:after,.form-horizontal .form-group:after,.modal-footer:after,.nav:after,.navbar-collapse:after,.navbar-header:after,.navbar:after,.pager:after,.panel-body:after,.row:after{clear:both}.center-block{display:block;margin-right:auto;margin-left:auto}.pull-right{float:right!important}.pull-left{float:left!important}.hide{display:none!important}.show{display:block!important}.invisible{visibility:hidden}.text-hide{font:0/0 a;color:transparent;text-shadow:none;background-color:transparent;border:0}.hidden{display:none!important}.affix{position:fixed}@-ms-viewport{width:device-width}.visible-lg,.visible-md,.visible-sm,.visible-xs{display:none!important}.visible-lg-block,.visible-lg-inline,.visible-lg-inline-block,.visible-md-block,.visible-md-inline,.visible-md-inline-block,.visible-sm-block,.visible-sm-inline,.visible-sm-inline-block,.visible-xs-block,.visible-xs-inline,.visible-xs-inline-block{display:none!important}@media (max-width:767px){.visible-xs{display:block!important}table.visible-xs{display:table!important}tr.visible-xs{display:table-row!important}td.visible-xs,th.visible-xs{display:table-cell!important}}@media (max-width:767px){.visible-xs-block{display:block!important}}@media (max-width:767px){.visible-xs-inline{display:inline!important}}@media (max-width:767px){.visible-xs-inline-block{display:inline-block!important}}@media (min-width:768px) and (max-width:991px){.visible-sm{display:block!important}table.visible-sm{display:table!important}tr.visible-sm{display:table-row!important}td.visible-sm,th.visible-sm{display:table-cell!important}}@media (min-width:768px) and (max-width:991px){.visible-sm-block{display:block!important}}@media (min-width:768px) and (max-width:991px){.visible-sm-inline{display:inline!important}}@media (min-width:768px) and (max-width:991px){.visible-sm-inline-block{display:inline-block!important}}@media (min-width:992px) and (max-width:1199px){.visible-md{display:block!important}table.visible-md{display:table!important}tr.visible-md{display:table-row!important}td.visible-md,th.visible-md{display:table-cell!important}}@media (min-width:992px) and (max-width:1199px){.visible-md-block{display:block!important}}@media (min-width:992px) and (max-width:1199px){.visible-md-inline{display:inline!important}}@media (min-width:992px) and (max-width:1199px){.visible-md-inline-block{display:inline-block!important}}@media (min-width:1200px){.visible-lg{display:block!important}table.visible-lg{display:table!important}tr.visible-lg{display:table-row!important}td.visible-lg,th.visible-lg{display:table-cell!important}}@media (min-width:1200px){.visible-lg-block{display:block!important}}@media (min-width:1200px){.visible-lg-inline{display:inline!important}}@media (min-width:1200px){.visible-lg-inline-block{display:inline-block!important}}@media (max-width:767px){.hidden-xs{display:none!important}}@media (min-width:768px) and (max-width:991px){.hidden-sm{display:none!important}}@media (min-width:992px) and (max-width:1199px){.hidden-md{display:none!important}}@media (min-width:1200px){.hidden-lg{display:none!important}}.visible-print{display:none!important}@media print{.visible-print{display:block!important}table.visible-print{display:table!important}tr.visible-print{display:table-row!important}td.visible-print,th.visible-print{display:table-cell!important}}.visible-print-block{display:none!important}@media print{.visible-print-block{display:block!important}}.visible-print-inline{display:none!important}@media print{.visible-print-inline{display:inline!important}}.visible-print-inline-block{display:none!important}@media print{.visible-print-inline-block{display:inline-block!important}}@media print{.hidden-print{display:none!important}}
 </style>
 <script>/*!
  * Bootstrap v3.3.5 (http://getbootstrap.com)
@@ -58,14 +58,14 @@ if (!!window.navigator.userAgent.match("MSIE 8")) {
 };
 </script>
 <style>h1 {font-size: 34px;}
-       h1.title {font-size: 38px;}
-       h2 {font-size: 30px;}
-       h3 {font-size: 24px;}
-       h4 {font-size: 18px;}
-       h5 {font-size: 16px;}
-       h6 {font-size: 12px;}
-       code {color: inherit; background-color: rgba(0, 0, 0, 0.04);}
-       pre:not([class]) { background-color: white }</style>
+h1.title {font-size: 38px;}
+h2 {font-size: 30px;}
+h3 {font-size: 24px;}
+h4 {font-size: 18px;}
+h5 {font-size: 16px;}
+h6 {font-size: 12px;}
+code {color: inherit; background-color: rgba(0, 0, 0, 0.04);}
+pre:not([class]) { background-color: white }</style>
 <script>
 
 /**
@@ -229,13 +229,13 @@ color: #d14;
 <script src="data:application/javascript;base64,/*! highlight.js v9.12.0 | BSD3 License | git.io/hljslicense */
!function(e){var n="object"==typeof window&&window||"object"==typeof self&&self;"undefined"!=typeof exports?e(exports):n&&(n.hljs=e({}),"function"==typeof define&&define.amd&&define([],function(){return n.hljs}))}(function(e){function n(e){return e.replace(/&/g,"&amp;").replace(/</g,"&lt;").replace(/>/g,"&gt;")}function t(e){return e.nodeName.toLowerCase()}function r(e,n){var t=e&&e.exec(n);return t&&0===t.index}function a(e){return k.test(e)}function i(e){var n,t,r,i,o=e.className+" ";if(o+=e.parentNode?e.parentNode.className:"",t=B.exec(o))return w(t[1])?t[1]:"no-highlight";for(o=o.split(/\s+/),n=0,r=o.length;r>n;n++)if(i=o[n],a(i)||w(i))return i}function o(e){var n,t={},r=Array.prototype.slice.call(arguments,1);for(n in e)t[n]=e[n];return r.forEach(function(e){for(n in e)t[n]=e[n]}),t}function u(e){var n=[];return function r(e,a){for(var i=e.firstChild;i;i=i.nextSibling)3===i.nodeType?a+=i.nodeValue.length:1===i.nodeType&&(n.push({event:"start",offset:a,node:i}),a=r(i,a),t(i).match(/br|hr|img|input/)||n.push({event:"stop",offset:a,node:i}));return a}(e,0),n}function c(e,r,a){function i(){return e.length&&r.length?e[0].offset!==r[0].offset?e[0].offset<r[0].offset?e:r:"start"===r[0].event?e:r:e.length?e:r}function o(e){function r(e){return" "+e.nodeName+'="'+n(e.value).replace('"',"&quot;")+'"'}s+="<"+t(e)+E.map.call(e.attributes,r).join("")+">"}function u(e){s+="</"+t(e)+">"}function c(e){("start"===e.event?o:u)(e.node)}for(var l=0,s="",f=[];e.length||r.length;){var g=i();if(s+=n(a.substring(l,g[0].offset)),l=g[0].offset,g===e){f.reverse().forEach(u);do c(g.splice(0,1)[0]),g=i();while(g===e&&g.length&&g[0].offset===l);f.reverse().forEach(o)}else"start"===g[0].event?f.push(g[0].node):f.pop(),c(g.splice(0,1)[0])}return s+n(a.substr(l))}function l(e){return e.v&&!e.cached_variants&&(e.cached_variants=e.v.map(function(n){return o(e,{v:null},n)})),e.cached_variants||e.eW&&[o(e)]||[e]}function s(e){function n(e){return e&&e.source||e}function t(t,r){return new RegExp(n(t),"m"+(e.cI?"i":"")+(r?"g":""))}function r(a,i){if(!a.compiled){if(a.compiled=!0,a.k=a.k||a.bK,a.k){var o={},u=function(n,t){e.cI&&(t=t.toLowerCase()),t.split(" ").forEach(function(e){var t=e.split("|");o[t[0]]=[n,t[1]?Number(t[1]):1]})};"string"==typeof a.k?u("keyword",a.k):x(a.k).forEach(function(e){u(e,a.k[e])}),a.k=o}a.lR=t(a.l||/\w+/,!0),i&&(a.bK&&(a.b="\\b("+a.bK.split(" ").join("|")+")\\b"),a.b||(a.b=/\B|\b/),a.bR=t(a.b),a.e||a.eW||(a.e=/\B|\b/),a.e&&(a.eR=t(a.e)),a.tE=n(a.e)||"",a.eW&&i.tE&&(a.tE+=(a.e?"|":"")+i.tE)),a.i&&(a.iR=t(a.i)),null==a.r&&(a.r=1),a.c||(a.c=[]),a.c=Array.prototype.concat.apply([],a.c.map(function(e){return l("self"===e?a:e)})),a.c.forEach(function(e){r(e,a)}),a.starts&&r(a.starts,i);var c=a.c.map(function(e){return e.bK?"\\.?("+e.b+")\\.?":e.b}).concat([a.tE,a.i]).map(n).filter(Boolean);a.t=c.length?t(c.join("|"),!0):{exec:function(){return null}}}}r(e)}function f(e,t,a,i){function o(e,n){var t,a;for(t=0,a=n.c.length;a>t;t++)if(r(n.c[t].bR,e))return n.c[t]}function u(e,n){if(r(e.eR,n)){for(;e.endsParent&&e.parent;)e=e.parent;return e}return e.eW?u(e.parent,n):void 0}function c(e,n){return!a&&r(n.iR,e)}function l(e,n){var t=N.cI?n[0].toLowerCase():n[0];return e.k.hasOwnProperty(t)&&e.k[t]}function p(e,n,t,r){var a=r?"":I.classPrefix,i='<span class="'+a,o=t?"":C;return i+=e+'">',i+n+o}function h(){var e,t,r,a;if(!E.k)return n(k);for(a="",t=0,E.lR.lastIndex=0,r=E.lR.exec(k);r;)a+=n(k.substring(t,r.index)),e=l(E,r),e?(B+=e[1],a+=p(e[0],n(r[0]))):a+=n(r[0]),t=E.lR.lastIndex,r=E.lR.exec(k);return a+n(k.substr(t))}function d(){var e="string"==typeof E.sL;if(e&&!y[E.sL])return n(k);var t=e?f(E.sL,k,!0,x[E.sL]):g(k,E.sL.length?E.sL:void 0);return E.r>0&&(B+=t.r),e&&(x[E.sL]=t.top),p(t.language,t.value,!1,!0)}function b(){L+=null!=E.sL?d():h(),k=""}function v(e){L+=e.cN?p(e.cN,"",!0):"",E=Object.create(e,{parent:{value:E}})}function m(e,n){if(k+=e,null==n)return b(),0;var t=o(n,E);if(t)return t.skip?k+=n:(t.eB&&(k+=n),b(),t.rB||t.eB||(k=n)),v(t,n),t.rB?0:n.length;var r=u(E,n);if(r){var a=E;a.skip?k+=n:(a.rE||a.eE||(k+=n),b(),a.eE&&(k=n));do E.cN&&(L+=C),E.skip||(B+=E.r),E=E.parent;while(E!==r.parent);return r.starts&&v(r.starts,""),a.rE?0:n.length}if(c(n,E))throw new Error('Illegal lexeme "'+n+'" for mode "'+(E.cN||"<unnamed>")+'"');return k+=n,n.length||1}var N=w(e);if(!N)throw new Error('Unknown language: "'+e+'"');s(N);var R,E=i||N,x={},L="";for(R=E;R!==N;R=R.parent)R.cN&&(L=p(R.cN,"",!0)+L);var k="",B=0;try{for(var M,j,O=0;;){if(E.t.lastIndex=O,M=E.t.exec(t),!M)break;j=m(t.substring(O,M.index),M[0]),O=M.index+j}for(m(t.substr(O)),R=E;R.parent;R=R.parent)R.cN&&(L+=C);return{r:B,value:L,language:e,top:E}}catch(T){if(T.message&&-1!==T.message.indexOf("Illegal"))return{r:0,value:n(t)};throw T}}function g(e,t){t=t||I.languages||x(y);var r={r:0,value:n(e)},a=r;return t.filter(w).forEach(function(n){var t=f(n,e,!1);t.language=n,t.r>a.r&&(a=t),t.r>r.r&&(a=r,r=t)}),a.language&&(r.second_best=a),r}function p(e){return I.tabReplace||I.useBR?e.replace(M,function(e,n){return I.useBR&&"\n"===e?"<br>":I.tabReplace?n.replace(/\t/g,I.tabReplace):""}):e}function h(e,n,t){var r=n?L[n]:t,a=[e.trim()];return e.match(/\bhljs\b/)||a.push("hljs"),-1===e.indexOf(r)&&a.push(r),a.join(" ").trim()}function d(e){var n,t,r,o,l,s=i(e);a(s)||(I.useBR?(n=document.createElementNS("http://www.w3.org/1999/xhtml","div"),n.innerHTML=e.innerHTML.replace(/\n/g,"").replace(/<br[ \/]*>/g,"\n")):n=e,l=n.textContent,r=s?f(s,l,!0):g(l),t=u(n),t.length&&(o=document.createElementNS("http://www.w3.org/1999/xhtml","div"),o.innerHTML=r.value,r.value=c(t,u(o),l)),r.value=p(r.value),e.innerHTML=r.value,e.className=h(e.className,s,r.language),e.result={language:r.language,re:r.r},r.second_best&&(e.second_best={language:r.second_best.language,re:r.second_best.r}))}function b(e){I=o(I,e)}function v(){if(!v.called){v.called=!0;var e=document.querySelectorAll("pre code");E.forEach.call(e,d)}}function m(){addEventListener("DOMContentLoaded",v,!1),addEventListener("load",v,!1)}function N(n,t){var r=y[n]=t(e);r.aliases&&r.aliases.forEach(function(e){L[e]=n})}function R(){return x(y)}function w(e){return e=(e||"").toLowerCase(),y[e]||y[L[e]]}var E=[],x=Object.keys,y={},L={},k=/^(no-?highlight|plain|text)$/i,B=/\blang(?:uage)?-([\w-]+)\b/i,M=/((^(<[^>]+>|\t|)+|(?:\n)))/gm,C="</span>",I={classPrefix:"hljs-",tabReplace:null,useBR:!1,languages:void 0};return e.highlight=f,e.highlightAuto=g,e.fixMarkup=p,e.highlightBlock=d,e.configure=b,e.initHighlighting=v,e.initHighlightingOnLoad=m,e.registerLanguage=N,e.listLanguages=R,e.getLanguage=w,e.inherit=o,e.IR="[a-zA-Z]\\w*",e.UIR="[a-zA-Z_]\\w*",e.NR="\\b\\d+(\\.\\d+)?",e.CNR="(-?)(\\b0[xX][a-fA-F0-9]+|(\\b\\d+(\\.\\d*)?|\\.\\d+)([eE][-+]?\\d+)?)",e.BNR="\\b(0b[01]+)",e.RSR="!|!=|!==|%|%=|&|&&|&=|\\*|\\*=|\\+|\\+=|,|-|-=|/=|/|:|;|<<|<<=|<=|<|===|==|=|>>>=|>>=|>=|>>>|>>|>|\\?|\\[|\\{|\\(|\\^|\\^=|\\||\\|=|\\|\\||~",e.BE={b:"\\\\[\\s\\S]",r:0},e.ASM={cN:"string",b:"'",e:"'",i:"\\n",c:[e.BE]},e.QSM={cN:"string",b:'"',e:'"',i:"\\n",c:[e.BE]},e.PWM={b:/\b(a|an|the|are|I'm|isn't|don't|doesn't|won't|but|just|should|pretty|simply|enough|gonna|going|wtf|so|such|will|you|your|they|like|more)\b/},e.C=function(n,t,r){var a=e.inherit({cN:"comment",b:n,e:t,c:[]},r||{});return a.c.push(e.PWM),a.c.push({cN:"doctag",b:"(?:TODO|FIXME|NOTE|BUG|XXX):",r:0}),a},e.CLCM=e.C("//","$"),e.CBCM=e.C("/\\*","\\*/"),e.HCM=e.C("#","$"),e.NM={cN:"number",b:e.NR,r:0},e.CNM={cN:"number",b:e.CNR,r:0},e.BNM={cN:"number",b:e.BNR,r:0},e.CSSNM={cN:"number",b:e.NR+"(%|em|ex|ch|rem|vw|vh|vmin|vmax|cm|mm|in|pt|pc|px|deg|grad|rad|turn|s|ms|Hz|kHz|dpi|dpcm|dppx)?",r:0},e.RM={cN:"regexp",b:/\//,e:/\/[gimuy]*/,i:/\n/,c:[e.BE,{b:/\[/,e:/\]/,r:0,c:[e.BE]}]},e.TM={cN:"title",b:e.IR,r:0},e.UTM={cN:"title",b:e.UIR,r:0},e.METHOD_GUARD={b:"\\.\\s*"+e.UIR,r:0},e});hljs.registerLanguage("sql",function(e){var t=e.C("--","$");return{cI:!0,i:/[<>{}*#]/,c:[{bK:"begin end start commit rollback savepoint lock alter create drop rename call delete do handler insert load replace select truncate update set show pragma grant merge describe use explain help declare prepare execute deallocate release unlock purge reset change stop analyze cache flush optimize repair kill install uninstall checksum restore check backup revoke comment",e:/;/,eW:!0,l:/[\w\.]+/,k:{keyword:"abort abs absolute acc acce accep accept access accessed accessible account acos action activate add addtime admin administer advanced advise aes_decrypt aes_encrypt after agent aggregate ali alia alias allocate allow alter always analyze ancillary and any anydata anydataset anyschema anytype apply archive archived archivelog are as asc ascii asin assembly assertion associate asynchronous at atan atn2 attr attri attrib attribu attribut attribute attributes audit authenticated authentication authid authors auto autoallocate autodblink autoextend automatic availability avg backup badfile basicfile before begin beginning benchmark between bfile bfile_base big bigfile bin binary_double binary_float binlog bit_and bit_count bit_length bit_or bit_xor bitmap blob_base block blocksize body both bound buffer_cache buffer_pool build bulk by byte byteordermark bytes cache caching call calling cancel capacity cascade cascaded case cast catalog category ceil ceiling chain change changed char_base char_length character_length characters characterset charindex charset charsetform charsetid check checksum checksum_agg child choose chr chunk class cleanup clear client clob clob_base clone close cluster_id cluster_probability cluster_set clustering coalesce coercibility col collate collation collect colu colum column column_value columns columns_updated comment commit compact compatibility compiled complete composite_limit compound compress compute concat concat_ws concurrent confirm conn connec connect connect_by_iscycle connect_by_isleaf connect_by_root connect_time connection consider consistent constant constraint constraints constructor container content contents context contributors controlfile conv convert convert_tz corr corr_k corr_s corresponding corruption cos cost count count_big counted covar_pop covar_samp cpu_per_call cpu_per_session crc32 create creation critical cross cube cume_dist curdate current current_date current_time current_timestamp current_user cursor curtime customdatum cycle data database databases datafile datafiles datalength date_add date_cache date_format date_sub dateadd datediff datefromparts datename datepart datetime2fromparts day day_to_second dayname dayofmonth dayofweek dayofyear days db_role_change dbtimezone ddl deallocate declare decode decompose decrement decrypt deduplicate def defa defau defaul default defaults deferred defi defin define degrees delayed delegate delete delete_all delimited demand dense_rank depth dequeue des_decrypt des_encrypt des_key_file desc descr descri describ describe descriptor deterministic diagnostics difference dimension direct_load directory disable disable_all disallow disassociate discardfile disconnect diskgroup distinct distinctrow distribute distributed div do document domain dotnet double downgrade drop dumpfile duplicate duration each edition editionable editions element ellipsis else elsif elt empty enable enable_all enclosed encode encoding encrypt end end-exec endian enforced engine engines enqueue enterprise entityescaping eomonth error errors escaped evalname evaluate event eventdata events except exception exceptions exchange exclude excluding execu execut execute exempt exists exit exp expire explain export export_set extended extent external external_1 external_2 externally extract failed failed_login_attempts failover failure far fast feature_set feature_value fetch field fields file file_name_convert filesystem_like_logging final finish first first_value fixed flash_cache flashback floor flush following follows for forall force form forma format found found_rows freelist freelists freepools fresh from from_base64 from_days ftp full function general generated get get_format get_lock getdate getutcdate global global_name globally go goto grant grants greatest group group_concat group_id grouping grouping_id groups gtid_subtract guarantee guard handler hash hashkeys having hea head headi headin heading heap help hex hierarchy high high_priority hosts hour http id ident_current ident_incr ident_seed identified identity idle_time if ifnull ignore iif ilike ilm immediate import in include including increment index indexes indexing indextype indicator indices inet6_aton inet6_ntoa inet_aton inet_ntoa infile initial initialized initially initrans inmemory inner innodb input insert install instance instantiable instr interface interleaved intersect into invalidate invisible is is_free_lock is_ipv4 is_ipv4_compat is_not is_not_null is_used_lock isdate isnull isolation iterate java join json json_exists keep keep_duplicates key keys kill language large last last_day last_insert_id last_value lax lcase lead leading least leaves left len lenght length less level levels library like like2 like4 likec limit lines link list listagg little ln load load_file lob lobs local localtime localtimestamp locate locator lock locked log log10 log2 logfile logfiles logging logical logical_reads_per_call logoff logon logs long loop low low_priority lower lpad lrtrim ltrim main make_set makedate maketime managed management manual map mapping mask master master_pos_wait match matched materialized max maxextents maximize maxinstances maxlen maxlogfiles maxloghistory maxlogmembers maxsize maxtrans md5 measures median medium member memcompress memory merge microsecond mid migration min minextents minimum mining minus minute minvalue missing mod mode model modification modify module monitoring month months mount move movement multiset mutex name name_const names nan national native natural nav nchar nclob nested never new newline next nextval no no_write_to_binlog noarchivelog noaudit nobadfile nocheck nocompress nocopy nocycle nodelay nodiscardfile noentityescaping noguarantee nokeep nologfile nomapping nomaxvalue nominimize nominvalue nomonitoring none noneditionable nonschema noorder nopr nopro noprom nopromp noprompt norely noresetlogs noreverse normal norowdependencies noschemacheck noswitch not nothing notice notrim novalidate now nowait nth_value nullif nulls num numb numbe nvarchar nvarchar2 object ocicoll ocidate ocidatetime ociduration ociinterval ociloblocator ocinumber ociref ocirefcursor ocirowid ocistring ocitype oct octet_length of off offline offset oid oidindex old on online only opaque open operations operator optimal optimize option optionally or oracle oracle_date oradata ord ordaudio orddicom orddoc order ordimage ordinality ordvideo organization orlany orlvary out outer outfile outline output over overflow overriding package pad parallel parallel_enable parameters parent parse partial partition partitions pascal passing password password_grace_time password_lock_time password_reuse_max password_reuse_time password_verify_function patch path patindex pctincrease pctthreshold pctused pctversion percent percent_rank percentile_cont percentile_disc performance period period_add period_diff permanent physical pi pipe pipelined pivot pluggable plugin policy position post_transaction pow power pragma prebuilt precedes preceding precision prediction prediction_cost prediction_details prediction_probability prediction_set prepare present preserve prior priority private private_sga privileges procedural procedure procedure_analyze processlist profiles project prompt protection public publishingservername purge quarter query quick quiesce quota quotename radians raise rand range rank raw read reads readsize rebuild record records recover recovery recursive recycle redo reduced ref reference referenced references referencing refresh regexp_like register regr_avgx regr_avgy regr_count regr_intercept regr_r2 regr_slope regr_sxx regr_sxy reject rekey relational relative relaylog release release_lock relies_on relocate rely rem remainder rename repair repeat replace replicate replication required reset resetlogs resize resource respect restore restricted result result_cache resumable resume retention return returning returns reuse reverse revoke right rlike role roles rollback rolling rollup round row row_count rowdependencies rowid rownum rows rtrim rules safe salt sample save savepoint sb1 sb2 sb4 scan schema schemacheck scn scope scroll sdo_georaster sdo_topo_geometry search sec_to_time second section securefile security seed segment select self sequence sequential serializable server servererror session session_user sessions_per_user set sets settings sha sha1 sha2 share shared shared_pool short show shrink shutdown si_averagecolor si_colorhistogram si_featurelist si_positionalcolor si_stillimage si_texture siblings sid sign sin size size_t sizes skip slave sleep smalldatetimefromparts smallfile snapshot some soname sort soundex source space sparse spfile split sql sql_big_result sql_buffer_result sql_cache sql_calc_found_rows sql_small_result sql_variant_property sqlcode sqldata sqlerror sqlname sqlstate sqrt square standalone standby start starting startup statement static statistics stats_binomial_test stats_crosstab stats_ks_test stats_mode stats_mw_test stats_one_way_anova stats_t_test_ stats_t_test_indep stats_t_test_one stats_t_test_paired stats_wsr_test status std stddev stddev_pop stddev_samp stdev stop storage store stored str str_to_date straight_join strcmp strict string struct stuff style subdate subpartition subpartitions substitutable substr substring subtime subtring_index subtype success sum suspend switch switchoffset switchover sync synchronous synonym sys sys_xmlagg sysasm sysaux sysdate sysdatetimeoffset sysdba sysoper system system_user sysutcdatetime table tables tablespace tan tdo template temporary terminated tertiary_weights test than then thread through tier ties time time_format time_zone timediff timefromparts timeout timestamp timestampadd timestampdiff timezone_abbr timezone_minute timezone_region to to_base64 to_date to_days to_seconds todatetimeoffset trace tracking transaction transactional translate translation treat trigger trigger_nestlevel triggers trim truncate try_cast try_convert try_parse type ub1 ub2 ub4 ucase unarchived unbounded uncompress under undo unhex unicode uniform uninstall union unique unix_timestamp unknown unlimited unlock unpivot unrecoverable unsafe unsigned until untrusted unusable unused update updated upgrade upped upper upsert url urowid usable usage use use_stored_outlines user user_data user_resources users using utc_date utc_timestamp uuid uuid_short validate validate_password_strength validation valist value values var var_samp varcharc vari varia variab variabl variable variables variance varp varraw varrawc varray verify version versions view virtual visible void wait wallet warning warnings week weekday weekofyear wellformed when whene whenev wheneve whenever where while whitespace with within without work wrapped xdb xml xmlagg xmlattributes xmlcast xmlcolattval xmlelement xmlexists xmlforest xmlindex xmlnamespaces xmlpi xmlquery xmlroot xmlschema xmlserialize xmltable xmltype xor year year_to_month years yearweek",literal:"true false null",built_in:"array bigint binary bit blob boolean char character date dec decimal float int int8 integer interval number numeric real record serial serial8 smallint text varchar varying void"},c:[{cN:"string",b:"'",e:"'",c:[e.BE,{b:"''"}]},{cN:"string",b:'"',e:'"',c:[e.BE,{b:'""'}]},{cN:"string",b:"`",e:"`",c:[e.BE]},e.CNM,e.CBCM,t]},e.CBCM,t]}});hljs.registerLanguage("r",function(e){var r="([a-zA-Z]|\\.[a-zA-Z.])[a-zA-Z0-9._]*";return{c:[e.HCM,{b:r,l:r,k:{keyword:"function if in break next repeat else for return switch while try tryCatch stop warning require library attach detach source setMethod setGeneric setGroupGeneric setClass ...",literal:"NULL NA TRUE FALSE T F Inf NaN NA_integer_|10 NA_real_|10 NA_character_|10 NA_complex_|10"},r:0},{cN:"number",b:"0[xX][0-9a-fA-F]+[Li]?\\b",r:0},{cN:"number",b:"\\d+(?:[eE][+\\-]?\\d*)?L\\b",r:0},{cN:"number",b:"\\d+\\.(?!\\d)(?:i\\b)?",r:0},{cN:"number",b:"\\d+(?:\\.\\d*)?(?:[eE][+\\-]?\\d*)?i?\\b",r:0},{cN:"number",b:"\\.\\d+(?:[eE][+\\-]?\\d*)?i?\\b",r:0},{b:"`",e:"`",r:0},{cN:"string",c:[e.BE],v:[{b:'"',e:'"'},{b:"'",e:"'"}]}]}});hljs.registerLanguage("perl",function(e){var t="getpwent getservent quotemeta msgrcv scalar kill dbmclose undef lc ma syswrite tr send umask sysopen shmwrite vec qx utime local oct semctl localtime readpipe do return format read sprintf dbmopen pop getpgrp not getpwnam rewinddir qqfileno qw endprotoent wait sethostent bless s|0 opendir continue each sleep endgrent shutdown dump chomp connect getsockname die socketpair close flock exists index shmgetsub for endpwent redo lstat msgctl setpgrp abs exit select print ref gethostbyaddr unshift fcntl syscall goto getnetbyaddr join gmtime symlink semget splice x|0 getpeername recv log setsockopt cos last reverse gethostbyname getgrnam study formline endhostent times chop length gethostent getnetent pack getprotoent getservbyname rand mkdir pos chmod y|0 substr endnetent printf next open msgsnd readdir use unlink getsockopt getpriority rindex wantarray hex system getservbyport endservent int chr untie rmdir prototype tell listen fork shmread ucfirst setprotoent else sysseek link getgrgid shmctl waitpid unpack getnetbyname reset chdir grep split require caller lcfirst until warn while values shift telldir getpwuid my getprotobynumber delete and sort uc defined srand accept package seekdir getprotobyname semop our rename seek if q|0 chroot sysread setpwent no crypt getc chown sqrt write setnetent setpriority foreach tie sin msgget map stat getlogin unless elsif truncate exec keys glob tied closedirioctl socket readlink eval xor readline binmode setservent eof ord bind alarm pipe atan2 getgrent exp time push setgrent gt lt or ne m|0 break given say state when",r={cN:"subst",b:"[$@]\\{",e:"\\}",k:t},s={b:"->{",e:"}"},n={v:[{b:/\$\d/},{b:/[\$%@](\^\w\b|#\w+(::\w+)*|{\w+}|\w+(::\w*)*)/},{b:/[\$%@][^\s\w{]/,r:0}]},i=[e.BE,r,n],o=[n,e.HCM,e.C("^\\=\\w","\\=cut",{eW:!0}),s,{cN:"string",c:i,v:[{b:"q[qwxr]?\\s*\\(",e:"\\)",r:5},{b:"q[qwxr]?\\s*\\[",e:"\\]",r:5},{b:"q[qwxr]?\\s*\\{",e:"\\}",r:5},{b:"q[qwxr]?\\s*\\|",e:"\\|",r:5},{b:"q[qwxr]?\\s*\\<",e:"\\>",r:5},{b:"qw\\s+q",e:"q",r:5},{b:"'",e:"'",c:[e.BE]},{b:'"',e:'"'},{b:"`",e:"`",c:[e.BE]},{b:"{\\w+}",c:[],r:0},{b:"-?\\w+\\s*\\=\\>",c:[],r:0}]},{cN:"number",b:"(\\b0[0-7_]+)|(\\b0x[0-9a-fA-F_]+)|(\\b[1-9][0-9_]*(\\.[0-9_]+)?)|[0_]\\b",r:0},{b:"(\\/\\/|"+e.RSR+"|\\b(split|return|print|reverse|grep)\\b)\\s*",k:"split return print reverse grep",r:0,c:[e.HCM,{cN:"regexp",b:"(s|tr|y)/(\\\\.|[^/])*/(\\\\.|[^/])*/[a-z]*",r:10},{cN:"regexp",b:"(m|qr)?/",e:"/[a-z]*",c:[e.BE],r:0}]},{cN:"function",bK:"sub",e:"(\\s*\\(.*?\\))?[;{]",eE:!0,r:5,c:[e.TM]},{b:"-\\w\\b",r:0},{b:"^__DATA__$",e:"^__END__$",sL:"mojolicious",c:[{b:"^@@.*",e:"$",cN:"comment"}]}];return r.c=o,s.c=o,{aliases:["pl","pm"],l:/[\w\.]+/,k:t,c:o}});hljs.registerLanguage("ini",function(e){var b={cN:"string",c:[e.BE],v:[{b:"'''",e:"'''",r:10},{b:'"""',e:'"""',r:10},{b:'"',e:'"'},{b:"'",e:"'"}]};return{aliases:["toml"],cI:!0,i:/\S/,c:[e.C(";","$"),e.HCM,{cN:"section",b:/^\s*\[+/,e:/\]+/},{b:/^[a-z0-9\[\]_-]+\s*=\s*/,e:"$",rB:!0,c:[{cN:"attr",b:/[a-z0-9\[\]_-]+/},{b:/=/,eW:!0,r:0,c:[{cN:"literal",b:/\bon|off|true|false|yes|no\b/},{cN:"variable",v:[{b:/\$[\w\d"][\w\d_]*/},{b:/\$\{(.*?)}/}]},b,{cN:"number",b:/([\+\-]+)?[\d]+_[\d_]+/},e.NM]}]}]}});hljs.registerLanguage("diff",function(e){return{aliases:["patch"],c:[{cN:"meta",r:10,v:[{b:/^@@ +\-\d+,\d+ +\+\d+,\d+ +@@$/},{b:/^\*\*\* +\d+,\d+ +\*\*\*\*$/},{b:/^\-\-\- +\d+,\d+ +\-\-\-\-$/}]},{cN:"comment",v:[{b:/Index: /,e:/$/},{b:/={3,}/,e:/$/},{b:/^\-{3}/,e:/$/},{b:/^\*{3} /,e:/$/},{b:/^\+{3}/,e:/$/},{b:/\*{5}/,e:/\*{5}$/}]},{cN:"addition",b:"^\\+",e:"$"},{cN:"deletion",b:"^\\-",e:"$"},{cN:"addition",b:"^\\!",e:"$"}]}});hljs.registerLanguage("go",function(e){var t={keyword:"break default func interface select case map struct chan else goto package switch const fallthrough if range type continue for import return var go defer bool byte complex64 complex128 float32 float64 int8 int16 int32 int64 string uint8 uint16 uint32 uint64 int uint uintptr rune",literal:"true false iota nil",built_in:"append cap close complex copy imag len make new panic print println real recover delete"};return{aliases:["golang"],k:t,i:"</",c:[e.CLCM,e.CBCM,{cN:"string",v:[e.QSM,{b:"'",e:"[^\\\\]'"},{b:"`",e:"`"}]},{cN:"number",v:[{b:e.CNR+"[dflsi]",r:1},e.CNM]},{b:/:=/},{cN:"function",bK:"func",e:/\s*\{/,eE:!0,c:[e.TM,{cN:"params",b:/\(/,e:/\)/,k:t,i:/["']/}]}]}});hljs.registerLanguage("bash",function(e){var t={cN:"variable",v:[{b:/\$[\w\d#@][\w\d_]*/},{b:/\$\{(.*?)}/}]},s={cN:"string",b:/"/,e:/"/,c:[e.BE,t,{cN:"variable",b:/\$\(/,e:/\)/,c:[e.BE]}]},a={cN:"string",b:/'/,e:/'/};return{aliases:["sh","zsh"],l:/\b-?[a-z\._]+\b/,k:{keyword:"if then else elif fi for while in do done case esac function",literal:"true false",built_in:"break cd continue eval exec exit export getopts hash pwd readonly return shift test times trap umask unset alias bind builtin caller command declare echo enable help let local logout mapfile printf read readarray source type typeset ulimit unalias set shopt autoload bg bindkey bye cap chdir clone comparguments compcall compctl compdescribe compfiles compgroups compquote comptags comptry compvalues dirs disable disown echotc echoti emulate fc fg float functions getcap getln history integer jobs kill limit log noglob popd print pushd pushln rehash sched setcap setopt stat suspend ttyctl unfunction unhash unlimit unsetopt vared wait whence where which zcompile zformat zftp zle zmodload zparseopts zprof zpty zregexparse zsocket zstyle ztcp",_:"-ne -eq -lt -gt -f -d -e -s -l -a"},c:[{cN:"meta",b:/^#![^\n]+sh\s*$/,r:10},{cN:"function",b:/\w[\w\d_]*\s*\(\s*\)\s*\{/,rB:!0,c:[e.inherit(e.TM,{b:/\w[\w\d_]*/})],r:0},e.HCM,s,a,t]}});hljs.registerLanguage("python",function(e){var r={keyword:"and elif is global as in if from raise for except finally print import pass return exec else break not with class assert yield try while continue del or def lambda async await nonlocal|10 None True False",built_in:"Ellipsis NotImplemented"},b={cN:"meta",b:/^(>>>|\.\.\.) /},c={cN:"subst",b:/\{/,e:/\}/,k:r,i:/#/},a={cN:"string",c:[e.BE],v:[{b:/(u|b)?r?'''/,e:/'''/,c:[b],r:10},{b:/(u|b)?r?"""/,e:/"""/,c:[b],r:10},{b:/(fr|rf|f)'''/,e:/'''/,c:[b,c]},{b:/(fr|rf|f)"""/,e:/"""/,c:[b,c]},{b:/(u|r|ur)'/,e:/'/,r:10},{b:/(u|r|ur)"/,e:/"/,r:10},{b:/(b|br)'/,e:/'/},{b:/(b|br)"/,e:/"/},{b:/(fr|rf|f)'/,e:/'/,c:[c]},{b:/(fr|rf|f)"/,e:/"/,c:[c]},e.ASM,e.QSM]},s={cN:"number",r:0,v:[{b:e.BNR+"[lLjJ]?"},{b:"\\b(0o[0-7]+)[lLjJ]?"},{b:e.CNR+"[lLjJ]?"}]},i={cN:"params",b:/\(/,e:/\)/,c:["self",b,s,a]};return c.c=[a,s,b],{aliases:["py","gyp"],k:r,i:/(<\/|->|\?)|=>/,c:[b,s,a,e.HCM,{v:[{cN:"function",bK:"def"},{cN:"class",bK:"class"}],e:/:/,i:/[${=;\n,]/,c:[e.UTM,i,{b:/->/,eW:!0,k:"None"}]},{cN:"meta",b:/^[\t ]*@/,e:/$/},{b:/\b(print|exec)\(/}]}});hljs.registerLanguage("julia",function(e){var r={keyword:"in isa where baremodule begin break catch ccall const continue do else elseif end export false finally for function global if import importall let local macro module quote return true try using while type immutable abstract bitstype typealias ",literal:"true false ARGS C_NULL DevNull ENDIAN_BOM ENV I Inf Inf16 Inf32 Inf64 InsertionSort JULIA_HOME LOAD_PATH MergeSort NaN NaN16 NaN32 NaN64 PROGRAM_FILE QuickSort RoundDown RoundFromZero RoundNearest RoundNearestTiesAway RoundNearestTiesUp RoundToZero RoundUp STDERR STDIN STDOUT VERSION catalan e|0 eu|0 eulergamma golden im nothing pi γ π φ ",built_in:"ANY AbstractArray AbstractChannel AbstractFloat AbstractMatrix AbstractRNG AbstractSerializer AbstractSet AbstractSparseArray AbstractSparseMatrix AbstractSparseVector AbstractString AbstractUnitRange AbstractVecOrMat AbstractVector Any ArgumentError Array AssertionError Associative Base64DecodePipe Base64EncodePipe Bidiagonal BigFloat BigInt BitArray BitMatrix BitVector Bool BoundsError BufferStream CachingPool CapturedException CartesianIndex CartesianRange Cchar Cdouble Cfloat Channel Char Cint Cintmax_t Clong Clonglong ClusterManager Cmd CodeInfo Colon Complex Complex128 Complex32 Complex64 CompositeException Condition ConjArray ConjMatrix ConjVector Cptrdiff_t Cshort Csize_t Cssize_t Cstring Cuchar Cuint Cuintmax_t Culong Culonglong Cushort Cwchar_t Cwstring DataType Date DateFormat DateTime DenseArray DenseMatrix DenseVecOrMat DenseVector Diagonal Dict DimensionMismatch Dims DirectIndexString Display DivideError DomainError EOFError EachLine Enum Enumerate ErrorException Exception ExponentialBackOff Expr Factorization FileMonitor Float16 Float32 Float64 Function Future GlobalRef GotoNode HTML Hermitian IO IOBuffer IOContext IOStream IPAddr IPv4 IPv6 IndexCartesian IndexLinear IndexStyle InexactError InitError Int Int128 Int16 Int32 Int64 Int8 IntSet Integer InterruptException InvalidStateException Irrational KeyError LabelNode LinSpace LineNumberNode LoadError LowerTriangular MIME Matrix MersenneTwister Method MethodError MethodTable Module NTuple NewvarNode NullException Nullable Number ObjectIdDict OrdinalRange OutOfMemoryError OverflowError Pair ParseError PartialQuickSort PermutedDimsArray Pipe PollingFileWatcher ProcessExitedException Ptr QuoteNode RandomDevice Range RangeIndex Rational RawFD ReadOnlyMemoryError Real ReentrantLock Ref Regex RegexMatch RemoteChannel RemoteException RevString RoundingMode RowVector SSAValue SegmentationFault SerializationState Set SharedArray SharedMatrix SharedVector Signed SimpleVector Slot SlotNumber SparseMatrixCSC SparseVector StackFrame StackOverflowError StackTrace StepRange StepRangeLen StridedArray StridedMatrix StridedVecOrMat StridedVector String SubArray SubString SymTridiagonal Symbol Symmetric SystemError TCPSocket Task Text TextDisplay Timer Tridiagonal Tuple Type TypeError TypeMapEntry TypeMapLevel TypeName TypeVar TypedSlot UDPSocket UInt UInt128 UInt16 UInt32 UInt64 UInt8 UndefRefError UndefVarError UnicodeError UniformScaling Union UnionAll UnitRange Unsigned UpperTriangular Val Vararg VecElement VecOrMat Vector VersionNumber Void WeakKeyDict WeakRef WorkerConfig WorkerPool "},t="[A-Za-z_\\u00A1-\\uFFFF][A-Za-z_0-9\\u00A1-\\uFFFF]*",a={l:t,k:r,i:/<\//},n={cN:"number",b:/(\b0x[\d_]*(\.[\d_]*)?|0x\.\d[\d_]*)p[-+]?\d+|\b0[box][a-fA-F0-9][a-fA-F0-9_]*|(\b\d[\d_]*(\.[\d_]*)?|\.\d[\d_]*)([eEfF][-+]?\d+)?/,r:0},o={cN:"string",b:/'(.|\\[xXuU][a-zA-Z0-9]+)'/},i={cN:"subst",b:/\$\(/,e:/\)/,k:r},l={cN:"variable",b:"\\$"+t},c={cN:"string",c:[e.BE,i,l],v:[{b:/\w*"""/,e:/"""\w*/,r:10},{b:/\w*"/,e:/"\w*/}]},s={cN:"string",c:[e.BE,i,l],b:"`",e:"`"},d={cN:"meta",b:"@"+t},u={cN:"comment",v:[{b:"#=",e:"=#",r:10},{b:"#",e:"$"}]};return a.c=[n,o,c,s,d,u,e.HCM,{cN:"keyword",b:"\\b(((abstract|primitive)\\s+)type|(mutable\\s+)?struct)\\b"},{b:/<:/}],i.c=a.c,a});hljs.registerLanguage("coffeescript",function(e){var c={keyword:"in if for while finally new do return else break catch instanceof throw try this switch continue typeof delete debugger super yield import export from as default await then unless until loop of by when and or is isnt not",literal:"true false null undefined yes no on off",built_in:"npm require console print module global window document"},n="[A-Za-z$_][0-9A-Za-z$_]*",r={cN:"subst",b:/#\{/,e:/}/,k:c},i=[e.BNM,e.inherit(e.CNM,{starts:{e:"(\\s*/)?",r:0}}),{cN:"string",v:[{b:/'''/,e:/'''/,c:[e.BE]},{b:/'/,e:/'/,c:[e.BE]},{b:/"""/,e:/"""/,c:[e.BE,r]},{b:/"/,e:/"/,c:[e.BE,r]}]},{cN:"regexp",v:[{b:"///",e:"///",c:[r,e.HCM]},{b:"//[gim]*",r:0},{b:/\/(?![ *])(\\\/|.)*?\/[gim]*(?=\W|$)/}]},{b:"@"+n},{sL:"javascript",eB:!0,eE:!0,v:[{b:"```",e:"```"},{b:"`",e:"`"}]}];r.c=i;var s=e.inherit(e.TM,{b:n}),t="(\\(.*\\))?\\s*\\B[-=]>",o={cN:"params",b:"\\([^\\(]",rB:!0,c:[{b:/\(/,e:/\)/,k:c,c:["self"].concat(i)}]};return{aliases:["coffee","cson","iced"],k:c,i:/\/\*/,c:i.concat([e.C("###","###"),e.HCM,{cN:"function",b:"^\\s*"+n+"\\s*=\\s*"+t,e:"[-=]>",rB:!0,c:[s,o]},{b:/[:\(,=]\s*/,r:0,c:[{cN:"function",b:t,e:"[-=]>",rB:!0,c:[o]}]},{cN:"class",bK:"class",e:"$",i:/[:="\[\]]/,c:[{bK:"extends",eW:!0,i:/[:="\[\]]/,c:[s]},s]},{b:n+":",e:":",rB:!0,rE:!0,r:0}])}});hljs.registerLanguage("cpp",function(t){var e={cN:"keyword",b:"\\b[a-z\\d_]*_t\\b"},r={cN:"string",v:[{b:'(u8?|U)?L?"',e:'"',i:"\\n",c:[t.BE]},{b:'(u8?|U)?R"',e:'"',c:[t.BE]},{b:"'\\\\?.",e:"'",i:"."}]},s={cN:"number",v:[{b:"\\b(0b[01']+)"},{b:"(-?)\\b([\\d']+(\\.[\\d']*)?|\\.[\\d']+)(u|U|l|L|ul|UL|f|F|b|B)"},{b:"(-?)(\\b0[xX][a-fA-F0-9']+|(\\b[\\d']+(\\.[\\d']*)?|\\.[\\d']+)([eE][-+]?[\\d']+)?)"}],r:0},i={cN:"meta",b:/#\s*[a-z]+\b/,e:/$/,k:{"meta-keyword":"if else elif endif define undef warning error line pragma ifdef ifndef include"},c:[{b:/\\\n/,r:0},t.inherit(r,{cN:"meta-string"}),{cN:"meta-string",b:/<[^\n>]*>/,e:/$/,i:"\\n"},t.CLCM,t.CBCM]},a=t.IR+"\\s*\\(",c={keyword:"int float while private char catch import module export virtual operator sizeof dynamic_cast|10 typedef const_cast|10 const for static_cast|10 union namespace unsigned long volatile static protected bool template mutable if public friend do goto auto void enum else break extern using asm case typeid short reinterpret_cast|10 default double register explicit signed typename try this switch continue inline delete alignof constexpr decltype noexcept static_assert thread_local restrict _Bool complex _Complex _Imaginary atomic_bool atomic_char atomic_schar atomic_uchar atomic_short atomic_ushort atomic_int atomic_uint atomic_long atomic_ulong atomic_llong atomic_ullong new throw return and or not",built_in:"std string cin cout cerr clog stdin stdout stderr stringstream istringstream ostringstream auto_ptr deque list queue stack vector map set bitset multiset multimap unordered_set unordered_map unordered_multiset unordered_multimap array shared_ptr abort abs acos asin atan2 atan calloc ceil cosh cos exit exp fabs floor fmod fprintf fputs free frexp fscanf isalnum isalpha iscntrl isdigit isgraph islower isprint ispunct isspace isupper isxdigit tolower toupper labs ldexp log10 log malloc realloc memchr memcmp memcpy memset modf pow printf putchar puts scanf sinh sin snprintf sprintf sqrt sscanf strcat strchr strcmp strcpy strcspn strlen strncat strncmp strncpy strpbrk strrchr strspn strstr tanh tan vfprintf vprintf vsprintf endl initializer_list unique_ptr",literal:"true false nullptr NULL"},n=[e,t.CLCM,t.CBCM,s,r];return{aliases:["c","cc","h","c++","h++","hpp"],k:c,i:"</",c:n.concat([i,{b:"\\b(deque|list|queue|stack|vector|map|set|bitset|multiset|multimap|unordered_map|unordered_set|unordered_multiset|unordered_multimap|array)\\s*<",e:">",k:c,c:["self",e]},{b:t.IR+"::",k:c},{v:[{b:/=/,e:/;/},{b:/\(/,e:/\)/},{bK:"new throw return else",e:/;/}],k:c,c:n.concat([{b:/\(/,e:/\)/,k:c,c:n.concat(["self"]),r:0}]),r:0},{cN:"function",b:"("+t.IR+"[\\*&\\s]+)+"+a,rB:!0,e:/[{;=]/,eE:!0,k:c,i:/[^\w\s\*&]/,c:[{b:a,rB:!0,c:[t.TM],r:0},{cN:"params",b:/\(/,e:/\)/,k:c,r:0,c:[t.CLCM,t.CBCM,r,s,e]},t.CLCM,t.CBCM,i]},{cN:"class",bK:"class struct",e:/[{;:]/,c:[{b:/</,e:/>/,c:["self"]},t.TM]}]),exports:{preprocessor:i,strings:r,k:c}}});hljs.registerLanguage("ruby",function(e){var b="[a-zA-Z_]\\w*[!?=]?|[-+~]\\@|<<|>>|=~|===?|<=>|[<>]=?|\\*\\*|[-/+%^&*~`|]|\\[\\]=?",r={keyword:"and then defined module in return redo if BEGIN retry end for self when next until do begin unless END rescue else break undef not super class case require yield alias while ensure elsif or include attr_reader attr_writer attr_accessor",literal:"true false nil"},c={cN:"doctag",b:"@[A-Za-z]+"},a={b:"#<",e:">"},s=[e.C("#","$",{c:[c]}),e.C("^\\=begin","^\\=end",{c:[c],r:10}),e.C("^__END__","\\n$")],n={cN:"subst",b:"#\\{",e:"}",k:r},t={cN:"string",c:[e.BE,n],v:[{b:/'/,e:/'/},{b:/"/,e:/"/},{b:/`/,e:/`/},{b:"%[qQwWx]?\\(",e:"\\)"},{b:"%[qQwWx]?\\[",e:"\\]"},{b:"%[qQwWx]?{",e:"}"},{b:"%[qQwWx]?<",e:">"},{b:"%[qQwWx]?/",e:"/"},{b:"%[qQwWx]?%",e:"%"},{b:"%[qQwWx]?-",e:"-"},{b:"%[qQwWx]?\\|",e:"\\|"},{b:/\B\?(\\\d{1,3}|\\x[A-Fa-f0-9]{1,2}|\\u[A-Fa-f0-9]{4}|\\?\S)\b/},{b:/<<(-?)\w+$/,e:/^\s*\w+$/}]},i={cN:"params",b:"\\(",e:"\\)",endsParent:!0,k:r},d=[t,a,{cN:"class",bK:"class module",e:"$|;",i:/=/,c:[e.inherit(e.TM,{b:"[A-Za-z_]\\w*(::\\w+)*(\\?|\\!)?"}),{b:"<\\s*",c:[{b:"("+e.IR+"::)?"+e.IR}]}].concat(s)},{cN:"function",bK:"def",e:"$|;",c:[e.inherit(e.TM,{b:b}),i].concat(s)},{b:e.IR+"::"},{cN:"symbol",b:e.UIR+"(\\!|\\?)?:",r:0},{cN:"symbol",b:":(?!\\s)",c:[t,{b:b}],r:0},{cN:"number",b:"(\\b0[0-7_]+)|(\\b0x[0-9a-fA-F_]+)|(\\b[1-9][0-9_]*(\\.[0-9_]+)?)|[0_]\\b",r:0},{b:"(\\$\\W)|((\\$|\\@\\@?)(\\w+))"},{cN:"params",b:/\|/,e:/\|/,k:r},{b:"("+e.RSR+"|unless)\\s*",k:"unless",c:[a,{cN:"regexp",c:[e.BE,n],i:/\n/,v:[{b:"/",e:"/[a-z]*"},{b:"%r{",e:"}[a-z]*"},{b:"%r\\(",e:"\\)[a-z]*"},{b:"%r!",e:"![a-z]*"},{b:"%r\\[",e:"\\][a-z]*"}]}].concat(s),r:0}].concat(s);n.c=d,i.c=d;var l="[>?]>",o="[\\w#]+\\(\\w+\\):\\d+:\\d+>",u="(\\w+-)?\\d+\\.\\d+\\.\\d(p\\d+)?[^>]+>",w=[{b:/^\s*=>/,starts:{e:"$",c:d}},{cN:"meta",b:"^("+l+"|"+o+"|"+u+")",starts:{e:"$",c:d}}];return{aliases:["rb","gemspec","podspec","thor","irb"],k:r,i:/\/\*/,c:s.concat(w).concat(d)}});hljs.registerLanguage("yaml",function(e){var b="true false yes no null",a="^[ \\-]*",r="[a-zA-Z_][\\w\\-]*",t={cN:"attr",v:[{b:a+r+":"},{b:a+'"'+r+'":'},{b:a+"'"+r+"':"}]},c={cN:"template-variable",v:[{b:"{{",e:"}}"},{b:"%{",e:"}"}]},l={cN:"string",r:0,v:[{b:/'/,e:/'/},{b:/"/,e:/"/},{b:/\S+/}],c:[e.BE,c]};return{cI:!0,aliases:["yml","YAML","yaml"],c:[t,{cN:"meta",b:"^---s*$",r:10},{cN:"string",b:"[\\|>] *$",rE:!0,c:l.c,e:t.v[0].b},{b:"<%[%=-]?",e:"[%-]?%>",sL:"ruby",eB:!0,eE:!0,r:0},{cN:"type",b:"!!"+e.UIR},{cN:"meta",b:"&"+e.UIR+"$"},{cN:"meta",b:"\\*"+e.UIR+"$"},{cN:"bullet",b:"^ *-",r:0},e.HCM,{bK:b,k:{literal:b}},e.CNM,l]}});hljs.registerLanguage("css",function(e){var c="[a-zA-Z-][a-zA-Z0-9_-]*",t={b:/[A-Z\_\.\-]+\s*:/,rB:!0,e:";",eW:!0,c:[{cN:"attribute",b:/\S/,e:":",eE:!0,starts:{eW:!0,eE:!0,c:[{b:/[\w-]+\(/,rB:!0,c:[{cN:"built_in",b:/[\w-]+/},{b:/\(/,e:/\)/,c:[e.ASM,e.QSM]}]},e.CSSNM,e.QSM,e.ASM,e.CBCM,{cN:"number",b:"#[0-9A-Fa-f]+"},{cN:"meta",b:"!important"}]}}]};return{cI:!0,i:/[=\/|'\$]/,c:[e.CBCM,{cN:"selector-id",b:/#[A-Za-z0-9_-]+/},{cN:"selector-class",b:/\.[A-Za-z0-9_-]+/},{cN:"selector-attr",b:/\[/,e:/\]/,i:"$"},{cN:"selector-pseudo",b:/:(:)?[a-zA-Z0-9\_\-\+\(\)"'.]+/},{b:"@(font-face|page)",l:"[a-z-]+",k:"font-face page"},{b:"@",e:"[{;]",i:/:/,c:[{cN:"keyword",b:/\w+/},{b:/\s/,eW:!0,eE:!0,r:0,c:[e.ASM,e.QSM,e.CSSNM]}]},{cN:"selector-tag",b:c,r:0},{b:"{",e:"}",i:/\S/,c:[e.CBCM,t]}]}});hljs.registerLanguage("fortran",function(e){var t={cN:"params",b:"\\(",e:"\\)"},n={literal:".False. .True.",keyword:"kind do while private call intrinsic where elsewhere type endtype endmodule endselect endinterface end enddo endif if forall endforall only contains default return stop then public subroutine|10 function program .and. .or. .not. .le. .eq. .ge. .gt. .lt. goto save else use module select case access blank direct exist file fmt form formatted iostat name named nextrec number opened rec recl sequential status unformatted unit continue format pause cycle exit c_null_char c_alert c_backspace c_form_feed flush wait decimal round iomsg synchronous nopass non_overridable pass protected volatile abstract extends import non_intrinsic value deferred generic final enumerator class associate bind enum c_int c_short c_long c_long_long c_signed_char c_size_t c_int8_t c_int16_t c_int32_t c_int64_t c_int_least8_t c_int_least16_t c_int_least32_t c_int_least64_t c_int_fast8_t c_int_fast16_t c_int_fast32_t c_int_fast64_t c_intmax_t C_intptr_t c_float c_double c_long_double c_float_complex c_double_complex c_long_double_complex c_bool c_char c_null_ptr c_null_funptr c_new_line c_carriage_return c_horizontal_tab c_vertical_tab iso_c_binding c_loc c_funloc c_associated  c_f_pointer c_ptr c_funptr iso_fortran_env character_storage_size error_unit file_storage_size input_unit iostat_end iostat_eor numeric_storage_size output_unit c_f_procpointer ieee_arithmetic ieee_support_underflow_control ieee_get_underflow_mode ieee_set_underflow_mode newunit contiguous recursive pad position action delim readwrite eor advance nml interface procedure namelist include sequence elemental pure integer real character complex logical dimension allocatable|10 parameter external implicit|10 none double precision assign intent optional pointer target in out common equivalence data",built_in:"alog alog10 amax0 amax1 amin0 amin1 amod cabs ccos cexp clog csin csqrt dabs dacos dasin datan datan2 dcos dcosh ddim dexp dint dlog dlog10 dmax1 dmin1 dmod dnint dsign dsin dsinh dsqrt dtan dtanh float iabs idim idint idnint ifix isign max0 max1 min0 min1 sngl algama cdabs cdcos cdexp cdlog cdsin cdsqrt cqabs cqcos cqexp cqlog cqsin cqsqrt dcmplx dconjg derf derfc dfloat dgamma dimag dlgama iqint qabs qacos qasin qatan qatan2 qcmplx qconjg qcos qcosh qdim qerf qerfc qexp qgamma qimag qlgama qlog qlog10 qmax1 qmin1 qmod qnint qsign qsin qsinh qsqrt qtan qtanh abs acos aimag aint anint asin atan atan2 char cmplx conjg cos cosh exp ichar index int log log10 max min nint sign sin sinh sqrt tan tanh print write dim lge lgt lle llt mod nullify allocate deallocate adjustl adjustr all allocated any associated bit_size btest ceiling count cshift date_and_time digits dot_product eoshift epsilon exponent floor fraction huge iand ibclr ibits ibset ieor ior ishft ishftc lbound len_trim matmul maxexponent maxloc maxval merge minexponent minloc minval modulo mvbits nearest pack present product radix random_number random_seed range repeat reshape rrspacing scale scan selected_int_kind selected_real_kind set_exponent shape size spacing spread sum system_clock tiny transpose trim ubound unpack verify achar iachar transfer dble entry dprod cpu_time command_argument_count get_command get_command_argument get_environment_variable is_iostat_end ieee_arithmetic ieee_support_underflow_control ieee_get_underflow_mode ieee_set_underflow_mode is_iostat_eor move_alloc new_line selected_char_kind same_type_as extends_type_ofacosh asinh atanh bessel_j0 bessel_j1 bessel_jn bessel_y0 bessel_y1 bessel_yn erf erfc erfc_scaled gamma log_gamma hypot norm2 atomic_define atomic_ref execute_command_line leadz trailz storage_size merge_bits bge bgt ble blt dshiftl dshiftr findloc iall iany iparity image_index lcobound ucobound maskl maskr num_images parity popcnt poppar shifta shiftl shiftr this_image"};return{cI:!0,aliases:["f90","f95"],k:n,i:/\/\*/,c:[e.inherit(e.ASM,{cN:"string",r:0}),e.inherit(e.QSM,{cN:"string",r:0}),{cN:"function",bK:"subroutine function program",i:"[${=\\n]",c:[e.UTM,t]},e.C("!","$",{r:0}),{cN:"number",b:"(?=\\b|\\+|\\-|\\.)(?=\\.\\d|\\d)(?:\\d+)?(?:\\.?\\d*)(?:[de][+-]?\\d+)?\\b\\.?",r:0}]}});hljs.registerLanguage("awk",function(e){var r={cN:"variable",v:[{b:/\$[\w\d#@][\w\d_]*/},{b:/\$\{(.*?)}/}]},b="BEGIN END if else while do for in break continue delete next nextfile function func exit|10",n={cN:"string",c:[e.BE],v:[{b:/(u|b)?r?'''/,e:/'''/,r:10},{b:/(u|b)?r?"""/,e:/"""/,r:10},{b:/(u|r|ur)'/,e:/'/,r:10},{b:/(u|r|ur)"/,e:/"/,r:10},{b:/(b|br)'/,e:/'/},{b:/(b|br)"/,e:/"/},e.ASM,e.QSM]};return{k:{keyword:b},c:[r,n,e.RM,e.HCM,e.NM]}});hljs.registerLanguage("makefile",function(e){var i={cN:"variable",v:[{b:"\\$\\("+e.UIR+"\\)",c:[e.BE]},{b:/\$[@%<?\^\+\*]/}]},r={cN:"string",b:/"/,e:/"/,c:[e.BE,i]},a={cN:"variable",b:/\$\([\w-]+\s/,e:/\)/,k:{built_in:"subst patsubst strip findstring filter filter-out sort word wordlist firstword lastword dir notdir suffix basename addsuffix addprefix join wildcard realpath abspath error warning shell origin flavor foreach if or and call eval file value"},c:[i]},n={b:"^"+e.UIR+"\\s*[:+?]?=",i:"\\n",rB:!0,c:[{b:"^"+e.UIR,e:"[:+?]?=",eE:!0}]},t={cN:"meta",b:/^\.PHONY:/,e:/$/,k:{"meta-keyword":".PHONY"},l:/[\.\w]+/},l={cN:"section",b:/^[^\s]+:/,e:/$/,c:[i]};return{aliases:["mk","mak"],k:"define endef undefine ifdef ifndef ifeq ifneq else endif include -include sinclude override export unexport private vpath",l:/[\w-]+/,c:[e.HCM,i,r,a,n,t,l]}});hljs.registerLanguage("java",function(e){var a="[À-ʸa-zA-Z_$][À-ʸa-zA-Z_$0-9]*",t=a+"(<"+a+"(\\s*,\\s*"+a+")*>)?",r="false synchronized int abstract float private char boolean static null if const for true while long strictfp finally protected import native final void enum else break transient catch instanceof byte super volatile case assert short package default double public try this switch continue throws protected public private module requires exports do",s="\\b(0[bB]([01]+[01_]+[01]+|[01]+)|0[xX]([a-fA-F0-9]+[a-fA-F0-9_]+[a-fA-F0-9]+|[a-fA-F0-9]+)|(([\\d]+[\\d_]+[\\d]+|[\\d]+)(\\.([\\d]+[\\d_]+[\\d]+|[\\d]+))?|\\.([\\d]+[\\d_]+[\\d]+|[\\d]+))([eE][-+]?\\d+)?)[lLfF]?",c={cN:"number",b:s,r:0};return{aliases:["jsp"],k:r,i:/<\/|#/,c:[e.C("/\\*\\*","\\*/",{r:0,c:[{b:/\w+@/,r:0},{cN:"doctag",b:"@[A-Za-z]+"}]}),e.CLCM,e.CBCM,e.ASM,e.QSM,{cN:"class",bK:"class interface",e:/[{;=]/,eE:!0,k:"class interface",i:/[:"\[\]]/,c:[{bK:"extends implements"},e.UTM]},{bK:"new throw return else",r:0},{cN:"function",b:"("+t+"\\s+)+"+e.UIR+"\\s*\\(",rB:!0,e:/[{;=]/,eE:!0,k:r,c:[{b:e.UIR+"\\s*\\(",rB:!0,r:0,c:[e.UTM]},{cN:"params",b:/\(/,e:/\)/,k:r,r:0,c:[e.ASM,e.QSM,e.CNM,e.CBCM]},e.CLCM,e.CBCM]},c,{cN:"meta",b:"@[A-Za-z]+"}]}});hljs.registerLanguage("stan",function(e){return{c:[e.HCM,e.CLCM,e.CBCM,{b:e.UIR,l:e.UIR,k:{name:"for in while repeat until if then else",symbol:"bernoulli bernoulli_logit binomial binomial_logit beta_binomial hypergeometric categorical categorical_logit ordered_logistic neg_binomial neg_binomial_2 neg_binomial_2_log poisson poisson_log multinomial normal exp_mod_normal skew_normal student_t cauchy double_exponential logistic gumbel lognormal chi_square inv_chi_square scaled_inv_chi_square exponential inv_gamma weibull frechet rayleigh wiener pareto pareto_type_2 von_mises uniform multi_normal multi_normal_prec multi_normal_cholesky multi_gp multi_gp_cholesky multi_student_t gaussian_dlm_obs dirichlet lkj_corr lkj_corr_cholesky wishart inv_wishart","selector-tag":"int real vector simplex unit_vector ordered positive_ordered row_vector matrix cholesky_factor_corr cholesky_factor_cov corr_matrix cov_matrix",title:"functions model data parameters quantities transformed generated",literal:"true false"},r:0},{cN:"number",b:"0[xX][0-9a-fA-F]+[Li]?\\b",r:0},{cN:"number",b:"0[xX][0-9a-fA-F]+[Li]?\\b",r:0},{cN:"number",b:"\\d+(?:[eE][+\\-]?\\d*)?L\\b",r:0},{cN:"number",b:"\\d+\\.(?!\\d)(?:i\\b)?",r:0},{cN:"number",b:"\\d+(?:\\.\\d*)?(?:[eE][+\\-]?\\d*)?i?\\b",r:0},{cN:"number",b:"\\.\\d+(?:[eE][+\\-]?\\d*)?i?\\b",r:0}]}});hljs.registerLanguage("javascript",function(e){var r="[A-Za-z$_][0-9A-Za-z$_]*",t={keyword:"in of if for while finally var new function do return void else break catch instanceof with throw case default try this switch continue typeof delete let yield const export super debugger as async await static import from as",literal:"true false null undefined NaN Infinity",built_in:"eval isFinite isNaN parseFloat parseInt decodeURI decodeURIComponent encodeURI encodeURIComponent escape unescape Object Function Boolean Error EvalError InternalError RangeError ReferenceError StopIteration SyntaxError TypeError URIError Number Math Date String RegExp Array Float32Array Float64Array Int16Array Int32Array Int8Array Uint16Array Uint32Array Uint8Array Uint8ClampedArray ArrayBuffer DataView JSON Intl arguments require module console window document Symbol Set Map WeakSet WeakMap Proxy Reflect Promise"},a={cN:"number",v:[{b:"\\b(0[bB][01]+)"},{b:"\\b(0[oO][0-7]+)"},{b:e.CNR}],r:0},n={cN:"subst",b:"\\$\\{",e:"\\}",k:t,c:[]},c={cN:"string",b:"`",e:"`",c:[e.BE,n]};n.c=[e.ASM,e.QSM,c,a,e.RM];var s=n.c.concat([e.CBCM,e.CLCM]);return{aliases:["js","jsx"],k:t,c:[{cN:"meta",r:10,b:/^\s*['"]use (strict|asm)['"]/},{cN:"meta",b:/^#!/,e:/$/},e.ASM,e.QSM,c,e.CLCM,e.CBCM,a,{b:/[{,]\s*/,r:0,c:[{b:r+"\\s*:",rB:!0,r:0,c:[{cN:"attr",b:r,r:0}]}]},{b:"("+e.RSR+"|\\b(case|return|throw)\\b)\\s*",k:"return throw case",c:[e.CLCM,e.CBCM,e.RM,{cN:"function",b:"(\\(.*?\\)|"+r+")\\s*=>",rB:!0,e:"\\s*=>",c:[{cN:"params",v:[{b:r},{b:/\(\s*\)/},{b:/\(/,e:/\)/,eB:!0,eE:!0,k:t,c:s}]}]},{b:/</,e:/(\/\w+|\w+\/)>/,sL:"xml",c:[{b:/<\w+\s*\/>/,skip:!0},{b:/<\w+/,e:/(\/\w+|\w+\/)>/,skip:!0,c:[{b:/<\w+\s*\/>/,skip:!0},"self"]}]}],r:0},{cN:"function",bK:"function",e:/\{/,eE:!0,c:[e.inherit(e.TM,{b:r}),{cN:"params",b:/\(/,e:/\)/,eB:!0,eE:!0,c:s}],i:/\[|%/},{b:/\$[(.]/},e.METHOD_GUARD,{cN:"class",bK:"class",e:/[{;=]/,eE:!0,i:/[:"\[\]]/,c:[{bK:"extends"},e.UTM]},{bK:"constructor",e:/\{/,eE:!0}],i:/#(?!!)/}});hljs.registerLanguage("tex",function(c){var e={cN:"tag",b:/\\/,r:0,c:[{cN:"name",v:[{b:/[a-zA-Zа-яА-я]+[*]?/},{b:/[^a-zA-Zа-яА-я0-9]/}],starts:{eW:!0,r:0,c:[{cN:"string",v:[{b:/\[/,e:/\]/},{b:/\{/,e:/\}/}]},{b:/\s*=\s*/,eW:!0,r:0,c:[{cN:"number",b:/-?\d*\.?\d+(pt|pc|mm|cm|in|dd|cc|ex|em)?/}]}]}}]};return{c:[e,{cN:"formula",c:[e],r:0,v:[{b:/\$\$/,e:/\$\$/},{b:/\$/,e:/\$/}]},c.C("%","$",{r:0})]}});hljs.registerLanguage("xml",function(s){var e="[A-Za-z0-9\\._:-]+",t={eW:!0,i:/</,r:0,c:[{cN:"attr",b:e,r:0},{b:/=\s*/,r:0,c:[{cN:"string",endsParent:!0,v:[{b:/"/,e:/"/},{b:/'/,e:/'/},{b:/[^\s"'=<>`]+/}]}]}]};return{aliases:["html","xhtml","rss","atom","xjb","xsd","xsl","plist"],cI:!0,c:[{cN:"meta",b:"<!DOCTYPE",e:">",r:10,c:[{b:"\\[",e:"\\]"}]},s.C("<!--","-->",{r:10}),{b:"<\\!\\[CDATA\\[",e:"\\]\\]>",r:10},{b:/<\?(php)?/,e:/\?>/,sL:"php",c:[{b:"/\\*",e:"\\*/",skip:!0}]},{cN:"tag",b:"<style(?=\\s|>|$)",e:">",k:{name:"style"},c:[t],starts:{e:"</style>",rE:!0,sL:["css","xml"]}},{cN:"tag",b:"<script(?=\\s|>|$)",e:">",k:{name:"script"},c:[t],starts:{e:"</script>",rE:!0,sL:["actionscript","javascript","handlebars","xml"]}},{cN:"meta",v:[{b:/<\?xml/,e:/\?>/,r:10},{b:/<\?\w+/,e:/\?>/}]},{cN:"tag",b:"</?",e:"/?>",c:[{cN:"name",b:/[^\/><\s]+/,r:0},t]}]}});hljs.registerLanguage("markdown",function(e){return{aliases:["md","mkdown","mkd"],c:[{cN:"section",v:[{b:"^#{1,6}",e:"$"},{b:"^.+?\\n[=-]{2,}$"}]},{b:"<",e:">",sL:"xml",r:0},{cN:"bullet",b:"^([*+-]|(\\d+\\.))\\s+"},{cN:"strong",b:"[*_]{2}.+?[*_]{2}"},{cN:"emphasis",v:[{b:"\\*.+?\\*"},{b:"_.+?_",r:0}]},{cN:"quote",b:"^>\\s+",e:"$"},{cN:"code",v:[{b:"^```w*s*$",e:"^```s*$"},{b:"`.+?`"},{b:"^( {4}|	)",e:"$",r:0}]},{b:"^[-\\*]{3,}",e:"$"},{b:"\\[.+?\\][\\(\\[].*?[\\)\\]]",rB:!0,c:[{cN:"string",b:"\\[",e:"\\]",eB:!0,rE:!0,r:0},{cN:"link",b:"\\]\\(",e:"\\)",eB:!0,eE:!0},{cN:"symbol",b:"\\]\\[",e:"\\]",eB:!0,eE:!0}],r:10},{b:/^\[[^\n]+\]:/,rB:!0,c:[{cN:"symbol",b:/\[/,e:/\]/,eB:!0,eE:!0},{cN:"link",b:/:\s*/,e:/$/,eB:!0}]}]}});hljs.registerLanguage("json",function(e){var i={literal:"true false null"},n=[e.QSM,e.CNM],r={e:",",eW:!0,eE:!0,c:n,k:i},t={b:"{",e:"}",c:[{cN:"attr",b:/"/,e:/"/,c:[e.BE],i:"\\n"},e.inherit(r,{b:/:/})],i:"\\S"},c={b:"\\[",e:"\\]",c:[e.inherit(r)],i:"\\S"};return n.splice(n.length,0,t,c),{c:n,k:i,i:"\\S"}});"></script>
 
 <style type="text/css">
-  code{white-space: pre-wrap;}
-  span.smallcaps{font-variant: small-caps;}
-  span.underline{text-decoration: underline;}
-  div.column{display: inline-block; vertical-align: top; width: 50%;}
-  div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;}
-  ul.task-list{list-style: none;}
-    </style>
+code{white-space: pre-wrap;}
+span.smallcaps{font-variant: small-caps;}
+span.underline{text-decoration: underline;}
+div.column{display: inline-block; vertical-align: top; width: 50%;}
+div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;}
+ul.task-list{list-style: none;}
+</style>
 
 <style type="text/css">code{white-space: pre;}</style>
 <script type="text/javascript">
@@ -258,30 +258,30 @@ if (window.hljs) {
 
 <style type="text/css">
 .main-container {
-  max-width: 940px;
-  margin-left: auto;
-  margin-right: auto;
+max-width: 940px;
+margin-left: auto;
+margin-right: auto;
 }
 img {
-  max-width:100%;
+max-width:100%;
 }
 .tabbed-pane {
-  padding-top: 12px;
+padding-top: 12px;
 }
 .html-widget {
-  margin-bottom: 20px;
+margin-bottom: 20px;
 }
 button.code-folding-btn:focus {
-  outline: none;
+outline: none;
 }
 summary {
-  display: list-item;
+display: list-item;
 }
 details > summary > p:only-child {
-  display: inline;
+display: inline;
 }
 pre code {
-  padding: 0;
+padding: 0;
 }
 </style>
 
@@ -291,48 +291,42 @@ pre code {
 
 <style type="text/css">
 .tabset-dropdown > .nav-tabs {
-  display: inline-table;
-  max-height: 500px;
-  min-height: 44px;
-  overflow-y: auto;
-  border: 1px solid #ddd;
-  border-radius: 4px;
+display: inline-table;
+max-height: 500px;
+min-height: 44px;
+overflow-y: auto;
+border: 1px solid #ddd;
+border-radius: 4px;
 }
-
 .tabset-dropdown > .nav-tabs > li.active:before, .tabset-dropdown > .nav-tabs.nav-tabs-open:before {
-  content: "\e259";
-  font-family: 'Glyphicons Halflings';
-  display: inline-block;
-  padding: 10px;
-  border-right: 1px solid #ddd;
+content: "\e259";
+font-family: 'Glyphicons Halflings';
+display: inline-block;
+padding: 10px;
+border-right: 1px solid #ddd;
 }
-
 .tabset-dropdown > .nav-tabs.nav-tabs-open > li.active:before {
-  content: "\e258";
-  font-family: 'Glyphicons Halflings';
-  border: none;
+content: "\e258";
+font-family: 'Glyphicons Halflings';
+border: none;
 }
-
 .tabset-dropdown > .nav-tabs > li.active {
-  display: block;
+display: block;
 }
-
 .tabset-dropdown > .nav-tabs > li > a,
 .tabset-dropdown > .nav-tabs > li > a:focus,
 .tabset-dropdown > .nav-tabs > li > a:hover {
-  border: none;
-  display: inline-block;
-  border-radius: 4px;
-  background-color: transparent;
+border: none;
+display: inline-block;
+border-radius: 4px;
+background-color: transparent;
 }
-
 .tabset-dropdown > .nav-tabs.nav-tabs-open > li {
-  display: block;
-  float: none;
+display: block;
+float: none;
 }
-
 .tabset-dropdown > .nav-tabs > li {
-  display: none;
+display: none;
 }
 </style>
 
@@ -358,7 +352,7 @@ pre code {
 <h1 class="title toc-ignore">Example evaluation of FOCUS Example Dataset
 D</h1>
 <h4 class="author">Johannes Ranke</h4>
-<h4 class="date">Last change 31 January 2019 (rebuilt 2023-05-18)</h4>
+<h4 class="date">Last change 31 January 2019 (rebuilt 2024-12-19)</h4>
 
 </div>
 
@@ -441,18 +435,18 @@ variables is obtained using the <code>plot_sep</code> method for
 <code>mkinfit</code> objects, which shows separate graphs for all
 compounds and their residuals.</p>
 <pre class="r"><code>plot_sep(fit, lpos = c(&quot;topright&quot;, &quot;bottomright&quot;))</code></pre>
-<p><img src="data:image/png;base64,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" width="768" /></p>
+<p><img role="img" src="data:image/png;base64,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" width="768" /></p>
 <p>Confidence intervals for the parameter estimates are obtained using
 the <code>mkinparplot</code> function.</p>
 <pre class="r"><code>mkinparplot(fit)</code></pre>
-<p><img src="data:image/png;base64,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" width="768" /></p>
+<p><img role="img" src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABgAAAAMACAMAAADVCFz+AAAC/VBMVEUAAAABAQECAgIDAwMEBAQFBQUGBgYHBwcICAgJCQkKCgoLCwsMDAwNDQ0ODg4PDw8QEBARERESEhITExMUFBQVFRUWFhYXFxcYGBgZGRkaGhobGxscHBwdHR0eHh4fHx8gICAhISEiIiIjIyMkJCQlJSUmJiYnJycoKCgpKSkqKiorKyssLCwtLS0uLi4vLy8wMDAxMTEyMjIzMzM0NDQ1NTU2NjY3Nzc4ODg5OTk6Ojo7Ozs8PDw9PT0+Pj4/Pz9AQEBBQUFCQkJDQ0NERERFRUVGRkZHR0dISEhJSUlKSkpLS0tMTExNTU1OTk5PT09QUFBRUVFSUlJTU1NUVFRVVVVWVlZXV1dYWFhZWVlaWlpbW1tcXFxdXV1eXl5fX19gYGBhYWFiYmJjY2NkZGRlZWVmZmZnZ2doaGhpaWlqampra2tsbGxtbW1ubm5vb29wcHBxcXFycnJzc3N0dHR1dXV2dnZ3d3d4eHh5eXl6enp7e3t8fHx9fX1+fn5/f3+AgICBgYGCgoKDg4OEhISFhYWGhoaHh4eIiIiJiYmKioqLi4uMjIyNjY2Ojo6Pj4+QkJCRkZGSkpKTk5OUlJSVlZWWlpaXl5eYmJiZmZmampqbm5ucnJydnZ2enp6fn5+goKChoaGioqKjo6OkpKSlpaWnp6eoqKipqamqqqqrq6usrKytra2urq6vr6+wsLCxsbGysrKzs7O0tLS1tbW2tra3t7e4uLi5ubm6urq7u7u8vLy9vb2+vr6/v7/AwMDBwcHCwsLDw8PExMTFxcXGxsbHx8fIyMjJycnKysrLy8vMzMzNzc3Ozs7Pz8/Q0NDR0dHS0tLT09PU1NTV1dXW1tbX19fY2NjZ2dna2trb29vc3Nzd3d3e3t7f39/g4ODh4eHi4uLj4+Pk5OTl5eXm5ubn5+fo6Ojp6enq6urr6+vs7Ozt7e3u7u7v7+/w8PDx8fHy8vLz8/P09PT19fX29vb39/f4+Pj5+fn6+vr7+/v8/Pz9/f3+/v7////JahlgAAAACXBIWXMAAB2HAAAdhwGP5fFlAAAgAElEQVR4nO3deZwU9Z3/8fdwDPeNB+AJUQE1AVRQAolrdFFRVzEkmpgs8QCDoIggrEFDjGv8SYzRRGIIuooa4iZeBInHz+gaE+9FXaP5GYIKIWxAjggoINOPX3VX9UzPTB9TUF3f7vq8nv90dVX1zPc7PdRr6KuUAgCYJNcDAAC4EXkA1qwAAJTBmqiP11EH4NkaAQDKoObZiA/YUQfgHnXuDwCIXGfdE/EBO/oAnBfxVwQAeM4jAABgEwEAAKMIAAAYRQAAwCgCAABGEQAAMIoAAIBRBAAAjCIAAGAUAQAAowgAABhFAADAKAIAAEYRAAAwigAAgFEEAACMIgAAYBQBAACjCAAAGEUAAMAoAgAARhEAADCKAACAUQQAAIwiAABgFAEAAKMIAAAYRQAAwCgCAABGEQAAMIoAAIBRBAAAjCIAAGAUAQAAowgAABhFAADAKAIAAEYRAAAwigAAgFEEAACMIgAAYBQBAACjCAAAGEUAAMAoAgAARhEAADCKAACAUQQAAIwiAABgFAEAAKMIQPmsmD6wY+/Rt213PQ4AyIsA7Ik3L774DwU3LuigjMP/EuOIAKDFCMCe+I10V6Ft99VIn5sxaV+p/8Y4xwQALUQA9kSRAGzordaLvcttY6XL4hwTALQQAdgTRQJwrTQjs7BpX7VZG9+QAFSLLS9E+uV2PLcr7E3MBWDnlroW7LV1feFNHzUsFwnA4Wqzxl/6tjS/xcMDYMYkPRPll7tei8LexFAAvKP18g0XdFPrvqfcWx+BTd8/qU9t7yHjHw+uH61zUlvnHFjzZPrK7yce0qnX8Okrg22jdFrqpdGt1fEzF2ZWTfOf5NW7eb7Ze9LIYPFVaWw0MwCQJGfrl1F+uem6KexNbAVg2YHBMfuz6/x1y3oEK3SC/2pNLwBbP+9d9QLw8YRgU+tr/J29ADzazl9V+0CqeACWSpcHi5900EHRzABAkpQKwKoh/Rs5tfhDPASgGC8A+0mf+vqkIa2kIZ+kV73jHc/7Xzz3ilPaSJdkdvICMEEDLpm3JpU6Q2o7ZsbUUd4h/qLMtlEa0lODr1h4tfdluns7/HX5bdK1y5fvyPPN5kk/yi4fppqt0UwBQIKUCsCDauLvRXcnAMV4AVCrG9JLy7pLd6YXvL/xp2Wa+lo39cvsdLT6aEbmUf5HpKPeTC882U9aml5Ip2By+j8KW4bK/7EVfg5gthru2uOlVdFMAUCClArAA/rnFTl6EoA9kA7ANf7i76QDPvYuh6pz8D7d86QP0pdHS8dlVmz/lHqu9rc9VaNh6UsvAMf4Tx4sla4IvmSBAEyRlmWXT5XeimYKABKkdADOyr26FwHYA97Rulv2LVneMflB7+KV598IVkySMq/V9ALwaGbFw9Lc7C0/p5r0cwajgv8JpFKrpCnBlywQgPOlp7LLZ0kvRTMFAAlCAEqLMgBnZ5fvk67M2bRzWe+GAPiv3pwpPZ3dPE16LJUJwP/6K9aXDMBk/yYZY6XXo5kCgAQhAKVFGYD6g/7z0rmZhY+fumXK2CNq08+vBAFo5z/K8+XGT77cm0oHoHNw89IBuNL/L0bGP0kro5kCgAQhAKVFGYCbs8trpFO8i+3XdfNf6HncsPoA+E8Gp05uHIA7UukA9A5uXjoA86SfZJcHSZujmQKABPmcjhtfzEj1y73aTmcU3f1QzQk7AlsBmJVdfkX6YipVl36l5xn/vuTNj9OP8gQB2M/fY7z02PIG6XcGhwnAIw3frK6T+kYzAwBJ0r/pyzz31FfCjsBWAL6YXV4sXZpKLZGODF6g2SwA3ooXm3yBMAFYKZ0QLL4pjYlkAgAS5QxdcX8xMzQ892pXLSy6+2maF3YEtgLQI/tQzDjp56nU5VL2IyAmNA3AL6TvZW9505QZ6ScGwgQgNVDtg5cc3STdEs0MACQJzwGUFun7AL7jL74g7eW1YKL0ir9iXb+mAdjSR3sH/zt4uUbnpC9DBeBq6cbMwo5D1eb9aGYAIEkIQGmRBqD1D9JLj/eSfuxdzg+O46kXhnjbVqSX6gOQulPaL/Pqzdf6Zj4aqEAAbi/w3dZ3UeeXvcu6qdLEaCYAIFEIQGlRBmB/adCFU45pJR2e/gCfVT2kE364aO7pNTpMGvfoR7kB2DVCanfmNd8a6+19VWZN8wA8I31m0cL8L/HxAtJx6t23jpQOXhfNBAAkCgEoLcoAzP9m8GT5CP8ULQ8Fp+3tfveL6YvVuQFIbRoT7Fw7039rQPMAbMy8ijTfp4F65rXxbz6Qz4EAkEfpABx+Q45OBGAPZB6vefLsPrX7jFmU/VTVVRcP69Jt2Nz1qdSNfdoesq5RAFKpR796UIe9R01ZGVxtHoDUf322S+1Bawp8w+UTB7TvNeJmPgkUQD6lArC0yas8azYV3Z0AFFPkAXsAiF2pAOyYNyvX7F8U/3IEoBgCAKCSnK1fRfnlpusHYW9CAADAiYVHrIzyyy0d1PTNqyURAAAwigDsuSafG5d2XVm+EQBEiQDsuZsvbObXZflGABAlQwHY8PTThV6wCQAGGQoAACAXAQAAowgAABhFAADAKAIAAEYRAAAwigAAgFEEAACMIgAAYBQBAACjCAAAGEUAAMAoAgAARhEAADCKAACAUQQAAIwiAABgFAEAAKMIAAAYRQAAwCgCAABGEQAAMIoAAIBRBAAAjCIAAGAUAQAAowgAABhFAADAKAIAAEYRAAAwigAAgFEEAACMIgAAYBQBAACjCAAAGEUAAMAoAgAARhEAADCKAACAUQQAAIwiAABgFAEAAKMIAAAYRQAAwCgCAABGEQAAMIoAAIBRBAAAjCIAAGAUAQAAowgAABhVDQE45eWwfrjoHovuuNn1CNy4dYHrEbjxf2z+mi/8oesRuHHLb0IfCEs7peIDcJ8AAGVxX8QH7KgDsPqEo8LaV206WtRK7V0PwYUOqnE9BCfaqq3rIThh9te8TegjYQucsjriA3bUAdgNN2m66yE4MULPux6CCyt1kOshOHGDZrkeghPD9IrrIbjw/3SI6yG0CAFwhgCYQgBMIQAtRgBMIQC2EICKRgCcIQCmEABTCECLEQBTCIAtBKCiEQBnCIApBMAUAtBiBMAUAmALAahoBMAZAmAKATCFALQYATCFANhCACoaAXCGAJhCAEwhAC1GAEwhALYQgIpGAJwhAKYQAFMIQIsRAFMIgC0EoKIRAGcIgCkEwBQC0GIEwBQCYAsBqGgEwBkCYAoBMIUAtBgBMIUA2EIAKhoBcIYAmEIATCEALfbMPve7HoITMwescz0EFz46YoLrITjxf/d50PUQnJh2yAbXQ3Bhy+ALXQ+hRSogAAAAFwgAABhFAADAKAIAAEYRAAAwigAAgFEEAACMIgAAYBQBAACjCAAAGEUAAMAoAgAARhEAADCKAACAUQQAAIwiAABgFAEAAKMIAAAYRQAAwCgCAABGEQAAMIoAAIBRBAAAjCIAAGAUAQAAowgAAOyhNe/ucj2E3eI8ACumD+zYe/Rt212PIx5PfOOwrj2O/ddnG9YYmv+rtXqy/oqNee964IyDOhx57h8a1piY97MTRnQ94OQ5HzSsSfq0d3bX2pyrzadbqT8A1wFY0EEZh//F8UDisHGcAl9bH6wyNP9tg9QQABvzXvV5f5Y1k7J/HlqY94Zzg9/yXndkVyV+2kuUG4Dm063YH4DjANxXI31uxqR9pf4b3Y4kBjtP9A4FJ8y6bLT3izCmLrPK0vwnqyEANua9boDU96IrxniT/Y6/xsS8T5fanDr9gsHeb/tSf03ip71lcG4Amk+3cn8AbgOwobdaL/Yut42VLnM6kjgskGoz/yKe8H4RfpResDT/pWoIgJF5e7ObtMO7fK6zWr2WXmFi3j+RBvzRu9z1A2mvTek1iZ/2b9N/09UHoPl0K/gH4DYA10ozMgub9lWbtSV2rnpHSL/0lx6rUc9PUqbm//d91Lo+ADbm/ZR0kv8fvdulq9OXJuY9VHrGXzpbWpa+TPa0bz2tb+bRnfqJNZ9uBf8A3AbgcLVZ4y99W5rvdCjl9yfp09nlY6QXU6bmf5r6nV8fABvzHie94i99OOSIL6cvTcy7nToFz3jcIX03fZnsaR8fPONRf2BvPt0K/gE4DcB70shg8VVprMuhxOBh6ZLs8kTpIVPzn6+ax2dkA2Bj3ts6alDjNSbmXddW3YPFxdK0VOKnvWC25+CGADSfbiX/AJwGYKl0ebD4SQcd5HIoMVjUvv1N2eWvSb+3NP+3O+rSVH0AbMz7OWlS4zU25j1YesNfmuz/uWth2ic3BKD5dCv5B+A0APOC50LTDlPNVpdjidWO/VSz3tD8dwzTwG0NAbAxb/8BkJcX3rjw9WCNjXn/VBq+Ib2wrLV6pV/zYmHaOQFoPt1K/gE4DcDs+mdFM4+krXI5llhdL52dMjT/WWr7cqohADbmPUf62e8Ozzw8fOxbmTU25p2aJvW6bP4Np0s9nk9ftzDtnAA0n24l/wCcBmBK8CqBtFOlt1yOJU4/rlG79MsCrcz/6Va6LpUTABvz9o6Dk9sGTxC2vyu9xsa8U6mFwawHrc5ctTDtnAA0n24l/wCcBuB86ans8lnSSy7HEp8V3u9Aq/9MLxmZ/8YDdGz6Ra/1AbAx74mZv/1/vXr9b707vOOKlJV5p5YdmH3D+1czb3i3MO2cADSfbiX/AJwGYLL0WHZ5rPR6sX2TYsucdlLPX2WWjcz/XHV6J31ZHwAb877AOwJe5L8g8jLppJSVeS9urfZzXtr8/tJR0qfTBbAw7ZwANJ9uJf8AnAbgSunB7PI/SSsdDiUuD+zvHRbO/Jt/xcb875EWZBbqA2Bj3lOl/bf4ix/um3nk18S8369Vh+AQ53XvSykb084JQPPpVvIPwPWrgH6SXR4kbXY5llhs/pJ3+B9W/99BE/Pf3DX7yufcVwElf96pf5MuyC6Pz7zvw8S8vXv5e8Hi9v5qtdLGtBu/CqjJdCv5B+A0AI9Is4LFuk7q63Iosdh4pNTzrrr66ybmv1qNTTAy7/RHP83LLl8l3Wlk3if6b3LP+Ib0sI1p5wSg+XQr+QfgNAArpROCxTelMS6HEoePRnuT/FvOChPzzxMAE/NOPeu/DzbjosyDACbmfbT0bnZ5lrTIxrRzAtB8upX8A3D7WUAD1T74dNSbpFucDiUGM6Wv7Gy0xsL8N8/OGi6dM3t2+vVPFuad2t5dx2aXh0rp58EtzPuszF/9Pu+w+HTKxLRzApBnuhX8A3AbgKulGzMLOw5Vm/edDqX8tvbQgG2NV5maf8NzAEbmfYF0v790vzQ0fWlh3guk0cGHwb3SVt3Sp8AyMO3cADSfbgX/ANwGYH0XdX7Zu6ybKk10OpIY3JnzmHDA1PxzAmBj3u+1V/dMAR7sqVaZ98RamPf2IdLXMyeDfOZg6fb0goFp5wag+XQr+Afg+Ixg3kGx49S7bx0pHbzO7UjK75vSkcc3+HN6naX55wTAyLznSzpq8iXDlf04eBPzfquj1PPMWROP86Y9zl+V/GnnBiDPdCv3B+D6nMDz2vhPDQ6sqPdHl8WYxk+GLs+sNDT/3AAYmfcNwUdBdJ6ffemXhXm/OjL4FW93VfZzzxI/7UYByDPdiv0BuA5AavnEAe17jbi5oj4hrzwOyRcAQ/NvFAAj835j6mGdOw+aurJhjYV51917et/aroMnv9OwKunTbhyAPNOt1B+A8wAAANwgAABgFAEAAKMIAAAYRQAAwCgCAABGEQAAMIoAAIBRBAAAjCIAAGAUAQAAowgAABhFAADAKAIAAEZFHYC1404EAJTBuLWlj8GhRB2A+wQAKIv7Ij5gRx2Ae/SFJ0KapC+GvQkAmPMF3RPxATv6AJwX9iY3aXrEgwCA5DmPAACATYkMwA8IAACUlMgAPH/gkogHAQDJk8gAAABKIwAAYBQBAACjCAAAGEUAAMCoZAZgW8RjAIAESmQAftPmPyIeBAAkTyIDwDuBAaA0AgAARhEAADCKAACAUQQAAIwiAABgFAEAAKMSGQDOBwAApSUyAJwPAABKS2QAAAClEQAAMIoAAIBRBAAAjCIAAGBUMgPA+QAAoKREBoDzAQBAaYkMAO8EBoDSCAAAGEUAAMAoAgAARhEAADCKAACAUQQAAIxKZAA4HwAAlJbIAHA+AAAoLZEBAACURgAAwCgCAABGEQAAMIoAAIBRyQwA5wMAgJISGQDOBwAApSUyALwTGABKIwAAYBQBAACjCAAAGEUAAMAoAgAARhkKwNvS3AjGAwAJkcgA5D8fgJMAvHnxxX+I/ZsCQEskMgD5zwfgJAC/ke6K/ZsCQEskMgD5EQAAyEUAyosAAFXhT5f+1fUQ9tSfLl0T9iYEoJmdW+pasNfW9YU3fdSwTACAqnClfuh6CHtqpm4JexOLAfhgiHRlvj28o/XyDRd0U+u+p9xbH4FN3z+pT23vIeMfD64frXNSW+ccWPNk+srvJx7Sqdfw6SuDbaN0Wuql0a3V8TMXZlZNk+/daGYAoFyu0PfL8WXfeKKR98vxPbKm66awNzEYgA3DpBl59/ACsOzA4Jj92XX+umU9ghU6YXtmhReArZ/3rnoB+HhCsKn1Nf7OXgAebeevqn0gRQCAqlGeALxYo0Y6bynDN8kiAIG85wMIArDxaBV6n5gXgP2kT3190pBW0pBP0qve8Y7n/S+ee8UpbaRLMjt5AZigAZfMW5NKnSG1HTNj6ijvfr0os22UhvTU4CsWXu19me7eDn9dfpt07fLlO0LPAECsyhOAh7XXiTlq9bcyfJMsAuDLfz4APwCbh0vTCt3OO5K3uiG9tKy7dGd6wfsbf9qu9MJr3dQvs9PR6qMZmUf5H5GOejO98GQ/aWl6IZ2Cyen/KGwZKv8Hy3MAQFUoVwDOyL26LwEIK9p3Av/jWOmyQrdLB+Aaf/F30gEfe5dD1dl/5Mf7SemD9KX3H4jjMiu2f0o9V/vbnqrRsPSlF4Bj/CcPlkpXBF+SAACVjwBEo6ID8OFI6ZsFb+cdrbttDJZPlR70Ll55/o1gxSRpbfrSC8CjmRUP57yq6HOqST9nMCr4n0AqtUqaEnxJAgBUPgIQjUoOwIzR6cdoCt7OO1qfnV2+r/ErhXYu690QAP/FtjOlp7Obp0mPpTIB+F9/xXoCAFQTAhCNSg5AW9XUqtULhW73m5yD/vPSuZmFj5+6ZcrYI2rTz+AHAWjnP8rz5cZP79+bSgegc3BzAgBUlSv03Q3Ru0+n5F7dW2+X4ZtkTSYAGQUDoJoF10tDPylwO+9ofXN2eY10inex/bpu/gs9jxtWHwD/yeDUyY0DcEcqHYDewc0JAFBVTlUCXBx21rYCULMgtWOwCr7jzztaz8ouvyJ9MZWqS7/S84x/X/Lmx+lHeYIA7OfvMV56bHmD9DuDCQBQpf5FHXpEr7Pa5l5tpW5l+CZZ7f2DThi2ApD+8TwjdSnwoR+/yRz0fYulS1OpJdKRq/wVzQLgrXixyRcgAECV4jmAaFRAAIqfD2CCND7/7byjdY/NwfI46eep1OVS9iMgJjQNwC+k72VvedOUGeknBggAUKUIQDQqIADFzwewrmf2hZxNpd8H8B1/8QVpL68FE6VX/BXr+jUNwJY+2jv438HLNTonfUkAgCpFAKJRAQHIr/7D4H4m9c/7YRHpALT+QXrp8V7Sj73L+cFxPPXCEG/bivRSfQBSd0r7vZ5eeK1v5qOBCgTg9miGD6CMCEA0Kj8AdSOlb+Xbwzta7y8NunDKMa2kw9Mf4LOqh3TCDxfNPb1Gh0njHv0oNwC7RkjtzrzmW2O9va/KrGkegGekzyxauLn59wJQSQhANCo/AKnX26j2j3n28AIw/5vB66lGZB7vST3Uwb/a/e4X0xercwOQ2jQm2Ll2pv/WgOYB2Jh5FSmfBgpUuHIFoGP/HK0JQFjlOCHMTOn4PHtkHq958uw+tfuMWbQrWLfq4mFdug2buz6VurFP20PWNQpAKvXoVw/qsPeoKSuDq80DkPqvz3apPSj0eXoAxKs8Afjv1o1fqN8j74PPESEAe4YH7AGrynRCmLUrGtlY+ha7jwAEdrOyBACwarZudT2EPTVLPwp7k0QGIP/5AFpwOwIAGPXetwuf5rtK7MYUEhmA/O8ELo0AALDEbACafJRb2nUEAIAlZgNw84XN/JoAALDEbADy2fD007xgE4AZBAAAjCIAAGAUAQAAoxIZgPznAwAA5EpkAPKfDwAAkCuRAQAAlEYAAMAoAgAARhEAADCKAACAUckMQDnPugMACZHIAOzu+QAAwJJEBoB3AgNAaQQAAIwiAABgFAEAAKMIAAAYRQAAwCgCAABGJTIAnA8AAEpLZAA4HwAAlJbIAAAASiMAAGAUAQAAowgAABhFAADAqGQGgPMBAEBJiQwA5wMAgNISGQDeCQwApREAADCKAACAUQQAAIwiAABgFAEAAKMIAAAYlcgAcD4AACgtkQHgfAAAUFoiAwAAKI0AAIBRBAAAjCIAAGAUAQAAo5IZAM4HAAAlJTIAnA8AAEpLZAB4JzAAlEYAAMAoAgAARhEAADCKAACAUQQAAIwiAABgVCIDwPkAAKC0RAaA8wEAQGmJDAAAoDQCAABGEQAAMIoAAIBRBAAAjEpmADgfAACUlMgAcD4AACgtkQHgncAAUBoBAACjCAAAGEUAAMAoAgAARhEAADCKAACAUYkMAOcDAIDSEhkAzgcAAKUlMgAAgNIIAAAYRQAAwCgCAABGEQAAMCqZAeB8AABQUiIDwPkAAKC0RAaAdwIDQGkEAACMIgAAYBQBAACjCAAAGEUAAMAoAgAAVWPNu7si/GqJDADnAwBQcd6W5u7p19jZXWujGEsgkQHgfAAAKk4UAVgiAgAAVSeCAGwZTAAAoPrseQB+O1oEIHLvPltqj38s+aTcgwCQbHsYgFtP6ysRgOiNrHmvxB6zdW+5BwEg2VocgHXb8609XiIA5XCE3iixx2TdVu5BAEi2+gB8MES6Mu8uo3Raav5+Us9R6T85l52+T7tDx/9PsG3BbM/BBKC0kOcDCALwwaE9Ghm0qX4PAgBgD2UDsGGYNCP/Ll4ApgV/6F9eN8VfaJP7osaTCUBJYc8HEATgBTXx3/V7EAAAeygIwMajVfCdSqPUTTVfvPX7x3nHny+oy+QF1+wr9fu4YQcCUFrYdwLXB2DYhhxHEgAA0fEDsHm4NK3QLqOkDk94l7vO8ApwyJ+9pXV7S8sbdiAApe12AIbnrh1CAABEJxOAfxwrXVZwFy8AN2YWnvMC8Exm6dvS4oYdCEBpBABAxUkH4MOR0jcL7zIqe3xfJx3qr/q5dFfDDgSgNAIAoOJ4AZiRfifX5MK7jFIPf2GLNM5feogAhEQAAFQcLwBtVVOrVi8U3GWUevsLXgCCAx8BCHuTsAHoq2NP9AxX1xNzdNGI+uX9dG3YQQBALi8AqllwvTS04AcLEICmYghA56av/8xjUthBAECut9PH/9SOwdIPC+1CAJqK4XwAg/WzJzw/0sAncgzQ7fXLZ/AQEIA94wVginfxjNTlrwV2IQBNxXA+AJ4DAFB22XcCT5DGF9iFADQV32cBEQAA5ZMNwLqe0qP5dyEATREAAElQ/2FwP5P65//AMgLQFAEAkAT1AagbKX0r7y4EoKk4A9BnVo69CQCA6DScD+D1Nqr9Y75dCEBT8QXgf5q+9PP/1e9BAADsoZwTwsyUjs+3CwFoKr7zAaTuv6GRXzXsQQAAJE8iAxD2fABH6n9K7DFZ88MOAgAqXCIDEPadwDee9lGJPR7/7FthBwEAFY4AAIBRBAAA4ndy808cuy72QRAAAIjfzRc28+vYB0EAAMAoAgAARhEAADAqkQEIez4AALAokQEIez4AALAokQEAAJRGAADAKAIAAEYRAAAwigAAgFHJDEDI8wEAgEWJDEDY8wEAgEWJDADvBAaA0ggAABhFAADAKAIAAEYRAAAwigAAgFEEAACMSmQAOB8AAJSWyABwPgAAKC2RAQAAlEYAAMAoAgAARhEAADCKAACAUckMAOcDAICSEhkAzgcAAKUlMgC8ExgASiMAAGAUAQAAowgAABhFAADAKAIAAEYRAAAwKpEB4HwAAFBaIgPA+QAAoLREBgAAUBoBAACjCAAAGEUAAMAoAgAARiUzAJwPAABKSmQAOB8AAJSWyADwTmAAKI0AAIBRBAAAjCIAAGAUAQAAowgAABhVDQH40oaQrtMFf7HjT++4HkGc/uh6AHEyNdl3TP0erwh7UCuTL1VBAAAAZVHpAXj7wB5htZNq7GCyiWVqtsYm2yH0Ya08Dnw74gN21AHYDaaeA/hf7e16CDH6Zz3megjxuVtfdz2E+OxSK9dDiNFXdJ/rIZQLAYgXAUgsApBYBKCcCEBiEYCkIgAJQQDiRQASiwAkFgEoJwKQWAQgqQhAQhCAeBGAxCIAiUUAyokAJBYBSCoCkBAEIF4EILEIQGIRgHIiAIlFAJKKACQEAYgXAUgsApBYBKCcCEBiEYCkIgAJQQDiRQASiwAkFgEoJwKQWAQgqQhAQhCAeBGAxCIAiUUAyokAJBYBSCoCkBAEIF4EILEIQGIRgHIiAIlFAJKKACQEAYgXAUgsApBYBKCcHq6Z73oI8fl475GuhxCjybVRn8Gugv2u1bWuhxCjAYe6HkGMrmn1B9dDKJcKCEDq764HEKd/fOR6BDHatd71COJk6vd4y1bXI4hTcu/aSggAAMABAgAARhEAADCKAACAUQQAAIwiAABgFAEAAKMIAAAYRQAAwCgCAABGEQAAMIoAAIBRBAAAjCIAAGAUAQAAowgAABhFAADAKAIAAEYRAAAwigAAgFEEAACMIgAAYBQBAACjCAAAGEUAAKC0Ne/ucj2E6MUXgBXTB3bsPfq27S3aVGTnqhBqsk9847CuPY7912djG13UQjo2S5EAABtFSURBVM0249VaPRnHyMog1GR3PXDGQR2OPPcPsY0uYqEm++yEEV0POHnOB7GNLmrFjzo7u2tti3euGrEFYEEHZRz+lxZsKrJzVQgz2Y3jFPja+nhHGZVQd23GtkGq1gCEmuyqz/traiZV59+OYSa74dzg17jXHfEOMjIljjpLlBuAaj9EZcUVgPtqpM/NmLSv1H9jyU1Fdq4KYSa780Tv+HDCrMtGe79MY+riH+ueC3XX+iarWgMQarLrBkh9L7pijLfhO7GPNAKhJnu61ObU6RcM9n6dl8Y+0iiUOOpsGZwbgGo/RNWLKQAbeqv1Yu9y21jpslKbiuxcFUJNdoFUm/kH84T3y/SjmEcahVCz9S1VtQYg3GS9pUk7vMvnOqvVazGPNAKhJvsTacAfvctdP5D22hT3UCNQ4qjz2/RfaGtbuHMViSkA10ozMgub9lWbtSU2Fdm5KoSa7BHSL/1tj9Wo5ycxDjMioWab8fd91LpKAxBqsk9JJ/n/p7tdujrOYUYj1GSHSs/4286WlsU4yqgUO+rcelrfzOM9a1uyc3WJKQCHq80af+nb0vwSm4rsXBXCTPZP0qezG4+RXoxrjNEJdddmnKZ+51dpAEJNdpz0ir/mwyFHfDm2MUYm1GTbqVPwPMcd0ndjG2N0ih11jg+e3ljbkp2rSzwBeE8aGSy+Ko0tvqnIzlUh1GQfli7Jbp0oPRTTGKMTarYZ81Xz+IzqDECoyW7rqEGxji5ioSZb11bdgzWLpWlxjTE6RY86C2Z7Dm4IQLUfonLEE4Cl0uXB4icddFDxTUV2rgqhJruoffubslu/Jv0+pjFGJ9Rs097uqEtTVRqAUJN9TpoU6+giFu6eHSy94a+ZXJV/E5c+6pzcEIBqP0TliCcA83Ke3zxMNVuLbiqyc1UINdkcO/ZTTfW9EDTsbHcM08Bt1RqAUJP1Hwp5eeGNC1+PdZBRCXfP/lQaviF9fVlr9arCF8aUPurkBKDaD1E54gnA7PpnOjOPp60quqnIzlUh1GRzXC+dXf7RRS3sbGep7cupag1AqMnOkX72u8Mzjx0f+1asw4xGyHt2mtTrsvk3nC71eD7GUUal9FEnJwDVfojKEU8ApuS8MOBU6a2im4rsXBVCTbbBj2vUrgpfKxhytk+30nWpqg1AqMl6R8TJbYNnD9vfFes4IxH293hhMNdBq+MbY3RKH3VyAlDth6gc8QTgfOmp7PJZ0ktFNxXZuSqEmmzWCu/3qNV/xjK+aIWb7cYDdGz6pa5VGoBQk52Y+dv/16vX/9a7bzuuiHWgUQj5e7zswOw72r9afQ9ktuSokxOAaj9E5YgnAJOlx7LLY6XXi24qsnNVCDVZ35Y57aSev4ppgJEKN9tz1emd9NUqDUCoyV7gHQsv8l8aeZl0UnyjjEi4e3Zxa7Wf89Lm95eOkj5dhQUofdTJCUC1H6JyxBOAK6UHs8v/JK0suqnIzlUh1GQzHtjfO1ac+bd4hhexULO9R1qQuVqlAQg12anS/lv8FR/uW4UPFIea7Pu16hAcB73afSmmIUao9FEnJwDVfojKEdurgH6SXR4kbS66qcjOVSHUZD2bv+Qd/oc9lapOYWa7uWv2NdNVGoBQd+2/SRdk14yvwrd4hJqsd4d+L1ixvb9arYxpjNEpfdRp/Cqgqj5E5YgnAI9Is4LFuk7qW3xTkZ2rQqjJplIbj5R63lWVHwOXFma2q9XYhHiHuudC3bULpHnZrVdJd8YzxOiEmuyJOe9i/4b0cExjjE7po05OAKr9EJUjngCslE4IFt+UxhTfVGTnqhBqsqmPRnsL1fnoT0aY2VZ9AELdtc/mvCP2opzHDKpFqMkeLb2b3TpLWhTTGKNT+qiTE4BqP0TliOmzgAaqffDmkJukW0psKrJzVQg12ZnSV3bGOryIhZjt5tlZw6VzZs+uvlc9hblrt3fXsdmNQ6V34hpjZMJM9qycv/q9I+XTcY0xOiWPOjkBqPpDVIOYAnC1dGNmYcehavN+iU1Fdq4KYSa7tYcGbIt7gJEKdddmVelzAOEme4F0v7/tfmlojKOMSJjJLpBGBx8G90pbdavC82SVPOrkBqDaD1ENYgrA+i7q/LJ3WTdVmphZs+Ghhx76KP+m5muqS5jJ3pnzQHF1CnXXZlVrAEJN9r326p4pwIM91aoK3x0bZrLbh0hfz5wM8pmDpdsdjXhPFJmtLzcA1X6IahDXGcG8A13HqXffOlI6eF1mxfOSVufflGdNdQkx2W9KRx7f4M/uBr3bQt21gWoNQLjJzve2HTX5kuHKfnp8lQkz2bc6Sj3PnDXxOG+Xcc5GvCeKzDYjNwBVf4iqF9s5gee18Z/4Gxi8bzrnp9t0U7411aXlkx3T+GnR5Y4GvEdC3bW+qg1AuMneEHwUROf51fkqrzCTfXVk8Dvc7qoq/XC0IrNNaxSAqj9EZcUWgNTyiQPa9xpxc/aXI/en22RT3jXVpcWTPSQBAQh312ZUbwDCTfaNqYd17jxo6sqYxxiZMJOtu/f0vrVdB0+uvme7s4rMNtU0AFV/iArEFwAAQEUhAABgFAEAAKMIAAAYRQAAwCgCAABGEQAAMIoAAIBRBAAAjCIAAGAUAQAAowgAABhFAADAKAIAAEZFHYB/zJwIACiDOf+I+IAddQAWCwBQFosjPmBHHYB7NOKnIY3XiWFvAgDV7USND3uTEbon4gN29AE4L+xNbtL0iAcBABVuum4Ke5PzCAAAJAAB8BEAAOYQAB8BAGAOAfARAADmEADfQs2NeBAAUOHmamHYmyQyADuWbI14EABQ4bYu2RH2JokMAACgNAIAAEYRAAAwigAAgFEEAACMSmQA/jr17YgHAQAV7u2pfw17k0QG4BbNjHgQAFDhZuqWsDdJZAB4JzAAc3gnsI8AADCHAPgIAABzCICPAAAwhwD4CAAAcwiAjwAAMIcA+AgAAHMIgI/zAQAwh/MB+DgfAABzOB8AAKClCAAAGEUAAMAoAgAARhEAADAqkQHgfAAAzOF8AD7OBwDAHM4H4OOdwADM4Z3APgIAwBwC4CMAAMwhAD4CAMAcAuAjAADMIQA+AgDAHALgIwAAzCEAPs4HAMAczgfg43wAAMzhfAAAgJYiAABgFAEAAKMIAAAYRQAAwKhEBoDzAQAwh/MB+DgfAABzOB+Aj3cCAzCHdwL7CAAAcwiAjwAAMIcA+AgAAHMIgM9WANa8u8v1EAC4RwB8hQKw62fDOu/94h6PqLLs7K61rscAwD0C4CsUgLnyPL3HI2qRNy+++A/hN+2GJSIAAAhAVoHzAexsp46X3xPT0fI30l3hN4W3ZTABAJDifABZBc4H8LY0cc/H00IxBeC3o0UAAKQ4H0AJy6UbIvpSpcURgFtP65t+UIsAxGHjhMfi/pafXHZH3N8SxhCABju31LXgq2xdX3jTRw3L0QZg3fZ8a4+XCEBcluisuL/lX3Ro3N8SxtgJwA+Do2W+J4G9Q/LyDRd0U+u+p9xbH4FN3z+pT23vIeMfD64frXNSW+ccWPNk+srvJx7Sqdfw6SuDbaN0Wuql0a3V8TMXZlZNC77Zu82/V6NNn9xzxn61PYdeubL5fjlfef5+Us9R93rXlp2+T7tDx/9PsG3BbM/BBCAWD+lfwt5k3S/vz7VkS8jbv6NPhf2WQCgEIM0LwLIDg+2fXeevW9YjWKET/D+/vQBs/bx31QvAxxOCTa2v8Xf2DtOPtvNX1T6QanEAVn4muNLuB4WG7X3l7C0ur5viL7RZkrPDyQQgFrsRgNPUWNh3pxAAlJudAKxb/gvp0uXL8/0Z5gXA+yP7U1+fNKSVNOST9Kp3vON5/4vnXnFKG+mSzE5eACZowCXz1qRSZ0htx8yYOsr7R31RZtsoDempwVcsvNr7Mt29Hf66/Dbp2uXL8zwnk7Nps7d355Nmff1Q7+vcVmDYo9RNNV+89fvHeft8QV0mL7hmX6nfxw07EIB47EYARuqE8Q2O0YSQtycAKLdEBqDA+QAKPwfgBUCtMtuWdZfuTC94f+NPy7zD9rVu6pfZ6Wj10YzMo/yPSEe9mV54sp+0NL2QTsHk9H8UtgyV/xNtyXMA/+bV5i/eZd13W6nXpvw7e1+5wxPe5S4vOjrkz97Sur2l5Q07EIB47FYAnsu59h8EAOXF+QB8Bc4HUDwA1/iLv5MOSP99PVSdgydez5M+SF8eLR2XWbH9U+q52t/2VI2GpS+9w/Qx/pMHS6Urgi9ZKgDvt1f3jf6qGdLs/Dt7X/nGzMJz3hifySx9W1rcsAMBiAcBQKXjfAC+Au8ELhqAbsGhOHWq9KB38crzbwQrJgVHWC8Aj2ZWPKyGN5p9TjXp5wxGBf8TSKVWSVOCL1kqADc0fJ21HbR3/p1HZY/v65R9TcjPG31pAhAPAoBKxzuBfbsTgLOzy/dJV+Zs2rmsd0MA1mRWzcx5JnmalH55uHeY/l9/xfqWB+BM6bXsui9If8m78yj18Be2SOP8pYcIgAMEAJWOAPh2JwD1B/3npXMzCx8/dcuUsUfU1r/Q/mi18x/l+XLj13akX545Sp2Dm4cIwDHSP7LrLpR+l3fnUertL3gBCH4QBMCFh3TiipCG6T9zrt2os0Pe/ikCgDAIgG93AnBzdnmNdIp3sf26bv4LPY8bVh8A/8ng9CE3V/rdmvWH6TABGKhO9eu+Lf06784EoELcKgc6lR4XkEUAfLsTgFnZ5VekL6ZSdelXep7x70ve/Dj9KE8QgP38PcZLjy1vkH5n8G4FwPsfwIfZdZMKfUwpAagQd6hT/5Daq2/Otb3UJeTt988+/Ae0BAHw7U4AvphdXixdmvmQ5SNX+SuaBcBb0fSkArsVAK8xr2fXnSj9Ke/OBKBC8BwAKh0B8O1OAHpsDpbHST9PpS6Xsh8BMaFpAH4hfa/+W02ZkX5iYLcCcL10XfZWndUz/wcREYAKQQBQ6QiAr8D5AIq/D+A7/uIL0l5eCyZKr/gr1vVrGoAtfbR38L+Dl2t0TvpytwKwsl19dq6ULs+/MwGoEAQAlY7zAfgKnA+geABaZz6O5/Fe0o+9y/nBcTz1whBv24r0Un0AUndK+2UevHmtb+ajgQoE4PYCw6vfNFM66j3vsu57rdRrY/6dCUCFIACodJwPoKiiAdhfGnThlGNaSYenf4irekgn/HDR3NNrdJg07tGPcgOwa4TU7sxrvjXW2/uqzJrmAXhG+syihZubf6+cTZv6SF3HzrlwsNeYnxYYNAGoEAQACUQA0rwAzP9m8Mq7Ef7h9KEO/tXud7+YvlidG4DUpjHBzrUz/UfumwdgY+ZVpHk+DTR304ojgq/T/tZCgyYAFWK3AtClR4NOBAAVhwCkZR6UefLsPrX7jFm0K1i36uJhXboNm7s+lbqxT9tD1jUKQCr16FcP6rD3qCkrg6vNA5D6r892qT1oTd5v17Dpk7tP71fbfcis9woOmgBUiN0IwEVNXtU/L+TtCQDKzVIACivygD2QsRsBqNvQSIHPey2MAKDcCEAaAUApv9GX4/6Wq3RE3N8SxiQyAAXOB1AYAUApn/zkz7F/z5+/EPu3RBXjfAC+AucDKIwAAKh2nA/AV+CdwL4mH+WWdl3ZApDvm0WzMwA0wjuBfUUDcPOFzfy6bAHI982i2RkAGiEAvqIByGfD00/nf8EmAFQJAuALHQAAqHYEwEcAAJhDAHwEAIA5BMBHAACYQwB8Bc4HAADJxfkAfAXOBwAAycX5AAAALUUAAMAoAgAARhEAADCKAACAUYkMQOjzAQBAteN8AL7Q5wMAgGrH+QB8vBMYgDm8E9hHAACYQwB8BACAOQTARwAAmEMAfAQAgDkEwEcAAJhDAHwEAIA5BMDH+QAAmMP5AHycDwCAOZwPAADQUgQAAIwiAABgFAEAAKMIAAAYlcgAcD4AAOZwPgAf5wMAYA7nA/DxTmAA5vBOYB8BAGAOAfARAADmEAAfAQBgDgHwEQAA5hAAHwEAYA4B8BEAAOYQAB/nAwBgDucD8HE+AADmcD4AAEBLEQAAMIoAAIBRBAAAjCIAAGBUIgPA+QAAmMP5AHycDwCAOZwPwMc7gQGYwzuBfQQAgDkEwEcAAJhDAHwEAIA5BMBHAACYQwB8BACAOQkNwIHjQzpUnfYzrFdX1yNwqV+XvVwPwaW9uvRzPQSXuvZyPQKXOunQsMfKAys+AI8IAFAWj0R8wI46AHVL7w/rPB11uWHddL7rITh0pg52PQSXDtaZrofg0Pnq5noILh2l80IfLJfWRXzAjjoAu8H4cwD9tcL1EBxaqlNdD8GlU7XU9RAcWqH+rofg0m48BxA9AuAaATCMABhGAHwEwPUQHCIArofgEAFwPQQC4B4BMIwAGEYAfATA9RAcIgCuh+AQAXA9BALgHgEwjAAYRgB8BMD1EBwiAK6H4BABcD0EAuAeATCMABhGAHwEwPUQHCIArofgEAFwPQQC4B4BMIwAGEYAfATA9RAcIgCuh+AQAXA9BALgHgEwjAAYRgB8BMD1EBwiAK6H4BABcD0EAuAeATCMABhGAHwEwPUQHCIArofgEAFwPQQC4B4BMIwAGEYAfATA9RAcIgCuh+AQAXA9hIoIwI80y/UQXDpMq1wPwaEn9C+uh+DSv+gJ10NwaJUOcz0El2bpR66HUBEB+GDu+66H4NLj812PwKUd1/+36yG49N/X73A9BJfmP+56BC69P/cD10OoiAAAAFwgAABgFAEAAKMIAAAYRQAAwCgCAABGEQAAMIoAAIBRBAAAjCIAAGAUAQAAowgAABhFAADAKAIAAEYRAAAwigAAgFEEAACMIgAAYBQBAACjCAAAGEUAAMAoAgAARhEAADCKAACAUQQAAOKx5t1drofQmIMArJg+sGPv0bdtD7cpMYrM8YlvHNa1x7H/+mzsY4pPqXv41Vo9Ged4YlZk+rseOOOgDkee+4fYxxSfIrN/dsKIrgecPOeD2McUr53dtTbfencHvvgDsKCDMg7/S5hNiVF4jhvHKfC19S5GFodS9/C2QUpyAIpMf9Xn/U01kyrsT8ToFJ79hnOD3/xed7gYWHyWKG8AHB74Yg/AfTXS52ZM2lfqv7HlmxKj8Bx3nuj96z9h1mWjvV+EMXVuRlduJe/hyUpyAIpMf90Aqe9FV4zx9viOk7GVX5HZny61OXX6BYO9fwFLnYwtJlsG5w2AywNf3AHY0FutF3uX28ZKl7V4U2IUmeMCqTbz2/+E94vwIwdjK7+S9/BSJTkAxabvrZq0w7t8rrNaveZgbOVXZPY/kQb80bvc9QNpr00uBheP36b/uGseAKcHvrgDcK00I7OwaV+1WdvSTYlRZI5HSL/0lx6rUc9P4h5ZHErdw3/fR60THIAi039KOsn/X9/t0tWxjywORWY/VHrGXzpbWhb3wGJy62l9Mw/zNP+9d3rgizsAh6vNGn/p29L8lm5KjMJz/JP06ezyMdKL8Y4rHqXu4dPU7/wEB6DI9MdJr/hLHw454ssxjyseRWbfTp2CJz7ukL4b87jicnzwNEfzI7zTA1/MAXhPGhksviqNbeGmxCgyx4elS7LLE6WHYh1XPErdw/NV8/iM5AagyPS3ddQgByOKU5HZ17VV92BxsTQt3nHFZsFsz8F5AuD2wBdzAJZKlweLn3TQQS3clBhF5rioffubsstfk34f67jiUeIefrujLk0lOABFpv+cNMnBiOJU7M4fLL3hL01O7P/9fSfnCYDbA1/MAZiX8/zmYarZ2rJNidGyOe7YTzVJfCFo8dnvGKaB25IcgCLT9x/5eHnhjQtfdzCwWBS7838qDd+QXljWWr0S+wLAtHwBcHvgizkAs+uf6cw8KLaqZZsSo2VzvF46O64Rxan47Gep7cupJAegyPTnSD/73eGZh4iPfcvB0GJQ9M6fJvW6bP4Np0s9no99ZHHKFwC3B76YAzAl51n+U6W3WrYpMVo0xx/XqF0iXwlYdPZPt9J1qUQHoMj0vQPg5LbBk4Tt73IwtvIr/qu/MJj8oNVxjyte+QLg9sAXcwDOl57KLp8lvdSyTYnRgjmu8H4HWv1njGOKT7HZbzxAx6Zf+prgABSZ/sTM3/6/Xr3+t96933GFg8GVXdFf/WUHZt8E/9UkPvbZIF8A3B74Yg7AZOmx7PJY6fWWbUqMknPcMqed1PNXsQ4qNsVmf646vZO+THAAikz/Au/Qd5H/SsjLpJPiHlkcit35i1ur/ZyXNr+/dJT06UQXIF8A3B74Yg7AldKD2eV/kla2bFNilJrjA/t7R4Iz/xbrmOJTZPb3SAsyCwkOQJHpT5X23+IvfrhvMp//KjL792vVITjsefn7Urzjile+ALg98MX/KqCfZJcHSZtbtikxis9x85e8w/+wp5reKDEKz35z1+zrnxMcgCJ3/r9JF2SXxyfzTSBFZu/d598LFrf3V6uVsY4rXgVeBeTwwBdzAB6RZgWLdZ3Ut4WbEqPoHDceKfW8K6EfA5dWePar1dgEF8MrtyJ3/gJpXnb5KunOOIcVkyKzPzHnje/fkB6OdVzxyhcAtwe+mAOwUjohWHxTGtPCTYlRbI4fjfbWJPXRn4zCszcRgCJ3/rM5b4C9KOcRgQQpMvujpXezy7OkRbGOK175AuD2wBf3ZwENVPvgnR43Sbe0dFNiFJnjTOkrOx0MKUYFZ795dtZw6ZzZs5P5KqjCd/727jo2uzxUeifeccWj8OzPyvmr3ztCPh3vuGKVLwBuD3xxB+Bq6cbMwo5D1eb9lm5KjMJz3NpDA7Y5GVN8WnAPJ/g5gGLTv0C631+6Xxoa98BiUXj2C6TRwYfBvdJW3ZJ8PsC8AXB64Is7AOu7qPPL3mXdVGliZs2Ghx566KP8m5Kn8PTvzHkYOKmK3PlZSQ5Akem/117dMwV4sKdaJfPNsIVnv32I9PXMySCfOVi63eEYyy43AJVx4Iv9jGDega7j1LtvHSkdvC6z4nlJq/NvSqCC0/+mdOTxDf7sdphlUuTODyQ5AMWmP99bOmryJcOV/Wz45Ck8+7c6Sj3PnDXxOG/FOKdjLLfcAFTGgS/+cwLPa+M/0TcweM9zzjGg6aYkKjT9MY2fBl3udJBlU+TO9yU6AMWmf0PwURCd5yf2dWCFZ//qyODXvt1VifwQyHr5A+DywBd/AFLLJw5o32vEzdl7OvcY0GRTIhWY/iEmAlDszs9IdgCKTf+NqYd17jxo6kpHI4tD4dnX3Xt639qugycn8unvHAUC4PDA5yAAAIBKQAAAwCgCAABGEQAAMIoAAIBRBAAAjCIAAGAUAQAAowgAABhFAADAKAIAAEYRAAAw6v8D9326WQ+aolwAAAAASUVORK5CYII=" width="768" /></p>
 <p>A comprehensive report of the results is obtained using the
 <code>summary</code> method for <code>mkinfit</code> objects.</p>
 <pre class="r"><code>summary(fit)</code></pre>
-<pre><code>## mkin version used for fitting:    1.2.4 
-## R version used for fitting:       4.3.0 
-## Date of fit:     Thu May 18 11:39:03 2023 
-## Date of summary: Thu May 18 11:39:03 2023 
+<pre><code>## mkin version used for fitting:    1.2.6 
+## R version used for fitting:       4.4.2 
+## Date of fit:     Thu Dec 19 10:28:45 2024 
+## Date of summary: Thu Dec 19 10:28:45 2024 
 ## 
 ## Equations:
 ## d_parent/dt = - k_parent * parent
@@ -460,7 +454,7 @@ the <code>mkinparplot</code> function.</p>
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted using 401 model solutions performed in 0.053 s
+## Fitted using 401 model solutions performed in 0.101 s
 ## 
 ## Error model: Constant variance 
 ## 
@@ -504,10 +498,10 @@ the <code>mkinparplot</code> function.</p>
 ## Parameter correlation:
 ##                   parent_0 log_k_parent   log_k_m1 f_parent_qlogis      sigma
 ## parent_0         1.000e+00    5.174e-01 -1.688e-01      -5.471e-01 -1.172e-06
-## log_k_parent     5.174e-01    1.000e+00 -3.263e-01      -5.426e-01 -8.483e-07
-## log_k_m1        -1.688e-01   -3.263e-01  1.000e+00       7.478e-01  8.205e-07
-## f_parent_qlogis -5.471e-01   -5.426e-01  7.478e-01       1.000e+00  1.305e-06
-## sigma           -1.172e-06   -8.483e-07  8.205e-07       1.305e-06  1.000e+00
+## log_k_parent     5.174e-01    1.000e+00 -3.263e-01      -5.426e-01 -8.487e-07
+## log_k_m1        -1.688e-01   -3.263e-01  1.000e+00       7.478e-01  8.203e-07
+## f_parent_qlogis -5.471e-01   -5.426e-01  7.478e-01       1.000e+00  1.304e-06
+## sigma           -1.172e-06   -8.487e-07  8.203e-07       1.304e-06  1.000e+00
 ## 
 ## Backtransformed parameters:
 ## Confidence intervals for internally transformed parameters are asymmetric.
diff --git a/vignettes/FOCUS_L.html b/vignettes/FOCUS_L.html
index bd1fa16e..c341f104 100644
--- a/vignettes/FOCUS_L.html
+++ b/vignettes/FOCUS_L.html
@@ -30,28 +30,28 @@ document.addEventListener('DOMContentLoaded', function(e) {
 </script>
 
 <style type="text/css">
-  code{white-space: pre-wrap;}
-  span.smallcaps{font-variant: small-caps;}
-  span.underline{text-decoration: underline;}
-  div.column{display: inline-block; vertical-align: top; width: 50%;}
-  div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;}
-  ul.task-list{list-style: none;}
-      .display.math{display: block; text-align: center; margin: 0.5rem auto;}
-  </style>
+code{white-space: pre-wrap;}
+span.smallcaps{font-variant: small-caps;}
+span.underline{text-decoration: underline;}
+div.column{display: inline-block; vertical-align: top; width: 50%;}
+div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;}
+ul.task-list{list-style: none;}
+.display.math{display: block; text-align: center; margin: 0.5rem auto;}
+</style>
 
 
 
 <style type="text/css">
-  code {
-    white-space: pre;
-  }
-  .sourceCode {
-    overflow: visible;
-  }
+code {
+white-space: pre;
+}
+.sourceCode {
+overflow: visible;
+}
 </style>
 <style type="text/css" data-origin="pandoc">
 pre > code.sourceCode { white-space: pre; position: relative; }
-pre > code.sourceCode > span { display: inline-block; line-height: 1.25; }
+pre > code.sourceCode > span { line-height: 1.25; }
 pre > code.sourceCode > span:empty { height: 1.2em; }
 .sourceCode { overflow: visible; }
 code.sourceCode > span { color: inherit; text-decoration: inherit; }
@@ -62,58 +62,57 @@ div.sourceCode { overflow: auto; }
 }
 @media print {
 pre > code.sourceCode { white-space: pre-wrap; }
-pre > code.sourceCode > span { text-indent: -5em; padding-left: 5em; }
+pre > code.sourceCode > span { display: inline-block; text-indent: -5em; padding-left: 5em; }
 }
 pre.numberSource code
-  { counter-reset: source-line 0; }
+{ counter-reset: source-line 0; }
 pre.numberSource code > span
-  { position: relative; left: -4em; counter-increment: source-line; }
+{ position: relative; left: -4em; counter-increment: source-line; }
 pre.numberSource code > span > a:first-child::before
-  { content: counter(source-line);
-    position: relative; left: -1em; text-align: right; vertical-align: baseline;
-    border: none; display: inline-block;
-    -webkit-touch-callout: none; -webkit-user-select: none;
-    -khtml-user-select: none; -moz-user-select: none;
-    -ms-user-select: none; user-select: none;
-    padding: 0 4px; width: 4em;
-    color: #aaaaaa;
-  }
-pre.numberSource { margin-left: 3em; border-left: 1px solid #aaaaaa;  padding-left: 4px; }
+{ content: counter(source-line);
+position: relative; left: -1em; text-align: right; vertical-align: baseline;
+border: none; display: inline-block;
+-webkit-touch-callout: none; -webkit-user-select: none;
+-khtml-user-select: none; -moz-user-select: none;
+-ms-user-select: none; user-select: none;
+padding: 0 4px; width: 4em;
+color: #aaaaaa;
+}
+pre.numberSource { margin-left: 3em; border-left: 1px solid #aaaaaa; padding-left: 4px; }
 div.sourceCode
-  {   }
+{ }
 @media screen {
 pre > code.sourceCode > span > a:first-child::before { text-decoration: underline; }
 }
-code span.al { color: #ff0000; font-weight: bold; } /* Alert */
-code span.an { color: #60a0b0; font-weight: bold; font-style: italic; } /* Annotation */
-code span.at { color: #7d9029; } /* Attribute */
-code span.bn { color: #40a070; } /* BaseN */
-code span.bu { color: #008000; } /* BuiltIn */
-code span.cf { color: #007020; font-weight: bold; } /* ControlFlow */
-code span.ch { color: #4070a0; } /* Char */
-code span.cn { color: #880000; } /* Constant */
-code span.co { color: #60a0b0; font-style: italic; } /* Comment */
-code span.cv { color: #60a0b0; font-weight: bold; font-style: italic; } /* CommentVar */
-code span.do { color: #ba2121; font-style: italic; } /* Documentation */
-code span.dt { color: #902000; } /* DataType */
-code span.dv { color: #40a070; } /* DecVal */
-code span.er { color: #ff0000; font-weight: bold; } /* Error */
-code span.ex { } /* Extension */
-code span.fl { color: #40a070; } /* Float */
-code span.fu { color: #06287e; } /* Function */
-code span.im { color: #008000; font-weight: bold; } /* Import */
-code span.in { color: #60a0b0; font-weight: bold; font-style: italic; } /* Information */
-code span.kw { color: #007020; font-weight: bold; } /* Keyword */
-code span.op { color: #666666; } /* Operator */
-code span.ot { color: #007020; } /* Other */
-code span.pp { color: #bc7a00; } /* Preprocessor */
-code span.sc { color: #4070a0; } /* SpecialChar */
-code span.ss { color: #bb6688; } /* SpecialString */
-code span.st { color: #4070a0; } /* String */
-code span.va { color: #19177c; } /* Variable */
-code span.vs { color: #4070a0; } /* VerbatimString */
-code span.wa { color: #60a0b0; font-weight: bold; font-style: italic; } /* Warning */
-
+code span.al { color: #ff0000; font-weight: bold; } 
+code span.an { color: #60a0b0; font-weight: bold; font-style: italic; } 
+code span.at { color: #7d9029; } 
+code span.bn { color: #40a070; } 
+code span.bu { color: #008000; } 
+code span.cf { color: #007020; font-weight: bold; } 
+code span.ch { color: #4070a0; } 
+code span.cn { color: #880000; } 
+code span.co { color: #60a0b0; font-style: italic; } 
+code span.cv { color: #60a0b0; font-weight: bold; font-style: italic; } 
+code span.do { color: #ba2121; font-style: italic; } 
+code span.dt { color: #902000; } 
+code span.dv { color: #40a070; } 
+code span.er { color: #ff0000; font-weight: bold; } 
+code span.ex { } 
+code span.fl { color: #40a070; } 
+code span.fu { color: #06287e; } 
+code span.im { color: #008000; font-weight: bold; } 
+code span.in { color: #60a0b0; font-weight: bold; font-style: italic; } 
+code span.kw { color: #007020; font-weight: bold; } 
+code span.op { color: #666666; } 
+code span.ot { color: #007020; } 
+code span.pp { color: #bc7a00; } 
+code span.sc { color: #4070a0; } 
+code span.ss { color: #bb6688; } 
+code span.st { color: #4070a0; } 
+code span.va { color: #19177c; } 
+code span.vs { color: #4070a0; } 
+code span.wa { color: #60a0b0; font-weight: bold; font-style: italic; } 
 </style>
 <script>
 // apply pandoc div.sourceCode style to pre.sourceCode instead
@@ -147,25 +146,26 @@ code span.wa { color: #60a0b0; font-weight: bold; font-style: italic; } /* Warni
 
 
 <style type="text/css">
-/* for pandoc --citeproc since 2.11 */
+
 div.csl-bib-body { }
 div.csl-entry {
-  clear: both;
+clear: both;
+margin-bottom: 0em;
 }
 .hanging div.csl-entry {
-  margin-left:2em;
-  text-indent:-2em;
+margin-left:2em;
+text-indent:-2em;
 }
 div.csl-left-margin {
-  min-width:2em;
-  float:left;
+min-width:2em;
+float:left;
 }
 div.csl-right-inline {
-  margin-left:2em;
-  padding-left:1em;
+margin-left:2em;
+padding-left:1em;
 }
 div.csl-indent {
-  margin-left: 2em;
+margin-left: 2em;
 }
 </style>
 
@@ -364,25 +364,33 @@ code > span.er { color: #a61717; background-color: #e3d2d2; }
 <h1 class="title toc-ignore">Example evaluation of FOCUS Laboratory Data
 L1 to L3</h1>
 <h4 class="author">Johannes Ranke</h4>
-<h4 class="date">Last change 18 May 2023 (rebuilt 2023-05-18)</h4>
+<h4 class="date">Last change 18 May 2023 (rebuilt 2024-12-19)</h4>
 
 
 <div id="TOC">
 <ul>
-<li><a href="#laboratory-data-l1">Laboratory Data L1</a></li>
-<li><a href="#laboratory-data-l2">Laboratory Data L2</a>
+<li><a href="#laboratory-data-l1" id="toc-laboratory-data-l1">Laboratory
+Data L1</a></li>
+<li><a href="#laboratory-data-l2" id="toc-laboratory-data-l2">Laboratory
+Data L2</a>
 <ul>
-<li><a href="#sfo-fit-for-l2">SFO fit for L2</a></li>
-<li><a href="#fomc-fit-for-l2">FOMC fit for L2</a></li>
-<li><a href="#dfop-fit-for-l2">DFOP fit for L2</a></li>
+<li><a href="#sfo-fit-for-l2" id="toc-sfo-fit-for-l2">SFO fit for
+L2</a></li>
+<li><a href="#fomc-fit-for-l2" id="toc-fomc-fit-for-l2">FOMC fit for
+L2</a></li>
+<li><a href="#dfop-fit-for-l2" id="toc-dfop-fit-for-l2">DFOP fit for
+L2</a></li>
 </ul></li>
-<li><a href="#laboratory-data-l3">Laboratory Data L3</a>
+<li><a href="#laboratory-data-l3" id="toc-laboratory-data-l3">Laboratory
+Data L3</a>
 <ul>
-<li><a href="#fit-multiple-models">Fit multiple models</a></li>
-<li><a href="#accessing-mmkin-objects">Accessing mmkin objects</a></li>
+<li><a href="#fit-multiple-models" id="toc-fit-multiple-models">Fit
+multiple models</a></li>
+<li><a href="#accessing-mmkin-objects" id="toc-accessing-mmkin-objects">Accessing mmkin objects</a></li>
 </ul></li>
-<li><a href="#laboratory-data-l4">Laboratory Data L4</a></li>
-<li><a href="#references">References</a></li>
+<li><a href="#laboratory-data-l4" id="toc-laboratory-data-l4">Laboratory
+Data L4</a></li>
+<li><a href="#references" id="toc-references">References</a></li>
 </ul>
 </div>
 
@@ -390,13 +398,13 @@ L1 to L3</h1>
 <h1>Laboratory Data L1</h1>
 <p>The following code defines example dataset L1 from the FOCUS kinetics
 report, p. 284:</p>
-<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(<span class="st">&quot;mkin&quot;</span>, <span class="at">quietly =</span> <span class="cn">TRUE</span>)</span>
-<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a>FOCUS_2006_L1 <span class="ot">=</span> <span class="fu">data.frame</span>(</span>
-<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a>  <span class="at">t =</span> <span class="fu">rep</span>(<span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">1</span>, <span class="dv">2</span>, <span class="dv">3</span>, <span class="dv">5</span>, <span class="dv">7</span>, <span class="dv">14</span>, <span class="dv">21</span>, <span class="dv">30</span>), <span class="at">each =</span> <span class="dv">2</span>),</span>
-<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a>  <span class="at">parent =</span> <span class="fu">c</span>(<span class="fl">88.3</span>, <span class="fl">91.4</span>, <span class="fl">85.6</span>, <span class="fl">84.5</span>, <span class="fl">78.9</span>, <span class="fl">77.6</span>,</span>
-<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a>             <span class="fl">72.0</span>, <span class="fl">71.9</span>, <span class="fl">50.3</span>, <span class="fl">59.4</span>, <span class="fl">47.0</span>, <span class="fl">45.1</span>,</span>
-<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a>             <span class="fl">27.7</span>, <span class="fl">27.3</span>, <span class="fl">10.0</span>, <span class="fl">10.4</span>, <span class="fl">2.9</span>, <span class="fl">4.0</span>))</span>
-<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a>FOCUS_2006_L1_mkin <span class="ot">&lt;-</span> <span class="fu">mkin_wide_to_long</span>(FOCUS_2006_L1)</span></code></pre></div>
+<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" tabindex="-1"></a><span class="fu">library</span>(<span class="st">&quot;mkin&quot;</span>, <span class="at">quietly =</span> <span class="cn">TRUE</span>)</span>
+<span id="cb1-2"><a href="#cb1-2" tabindex="-1"></a>FOCUS_2006_L1 <span class="ot">=</span> <span class="fu">data.frame</span>(</span>
+<span id="cb1-3"><a href="#cb1-3" tabindex="-1"></a>  <span class="at">t =</span> <span class="fu">rep</span>(<span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">1</span>, <span class="dv">2</span>, <span class="dv">3</span>, <span class="dv">5</span>, <span class="dv">7</span>, <span class="dv">14</span>, <span class="dv">21</span>, <span class="dv">30</span>), <span class="at">each =</span> <span class="dv">2</span>),</span>
+<span id="cb1-4"><a href="#cb1-4" tabindex="-1"></a>  <span class="at">parent =</span> <span class="fu">c</span>(<span class="fl">88.3</span>, <span class="fl">91.4</span>, <span class="fl">85.6</span>, <span class="fl">84.5</span>, <span class="fl">78.9</span>, <span class="fl">77.6</span>,</span>
+<span id="cb1-5"><a href="#cb1-5" tabindex="-1"></a>             <span class="fl">72.0</span>, <span class="fl">71.9</span>, <span class="fl">50.3</span>, <span class="fl">59.4</span>, <span class="fl">47.0</span>, <span class="fl">45.1</span>,</span>
+<span id="cb1-6"><a href="#cb1-6" tabindex="-1"></a>             <span class="fl">27.7</span>, <span class="fl">27.3</span>, <span class="fl">10.0</span>, <span class="fl">10.4</span>, <span class="fl">2.9</span>, <span class="fl">4.0</span>))</span>
+<span id="cb1-7"><a href="#cb1-7" tabindex="-1"></a>FOCUS_2006_L1_mkin <span class="ot">&lt;-</span> <span class="fu">mkin_wide_to_long</span>(FOCUS_2006_L1)</span></code></pre></div>
 <p>Here we use the assumptions of simple first order (SFO), the case of
 declining rate constant over time (FOMC) and the case of two different
 phases of the kinetics (DFOP). For a more detailed discussion of the
@@ -406,19 +414,19 @@ like <code>&quot;SFO&quot;</code> for parent only degradation models. The
 following two lines fit the model and produce the summary report of the
 model fit. This covers the numerical analysis given in the FOCUS
 report.</p>
-<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a>m.L1.SFO <span class="ot">&lt;-</span> <span class="fu">mkinfit</span>(<span class="st">&quot;SFO&quot;</span>, FOCUS_2006_L1_mkin, <span class="at">quiet =</span> <span class="cn">TRUE</span>)</span>
-<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a><span class="fu">summary</span>(m.L1.SFO)</span></code></pre></div>
-<pre><code>## mkin version used for fitting:    1.2.4 
-## R version used for fitting:       4.3.0 
-## Date of fit:     Thu May 18 11:38:11 2023 
-## Date of summary: Thu May 18 11:38:11 2023 
+<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" tabindex="-1"></a>m.L1.SFO <span class="ot">&lt;-</span> <span class="fu">mkinfit</span>(<span class="st">&quot;SFO&quot;</span>, FOCUS_2006_L1_mkin, <span class="at">quiet =</span> <span class="cn">TRUE</span>)</span>
+<span id="cb2-2"><a href="#cb2-2" tabindex="-1"></a><span class="fu">summary</span>(m.L1.SFO)</span></code></pre></div>
+<pre><code>## mkin version used for fitting:    1.2.6 
+## R version used for fitting:       4.4.2 
+## Date of fit:     Thu Dec 19 10:28:47 2024 
+## Date of summary: Thu Dec 19 10:28:47 2024 
 ## 
 ## Equations:
 ## d_parent/dt = - k_parent * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted using 133 model solutions performed in 0.011 s
+## Fitted using 133 model solutions performed in 0.02 s
 ## 
 ## Error model: Constant variance 
 ## 
@@ -494,34 +502,33 @@ report.</p>
 ##    30   parent      4.0     5.251  -1.2513</code></pre>
 <p>A plot of the fit is obtained with the plot function for mkinfit
 objects.</p>
-<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(m.L1.SFO, <span class="at">show_errmin =</span> <span class="cn">TRUE</span>, <span class="at">main =</span> <span class="st">&quot;FOCUS L1 - SFO&quot;</span>)</span></code></pre></div>
-<p><img src="data:image/png;base64,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" /><!-- --></p>
+<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" tabindex="-1"></a><span class="fu">plot</span>(m.L1.SFO, <span class="at">show_errmin =</span> <span class="cn">TRUE</span>, <span class="at">main =</span> <span class="st">&quot;FOCUS L1 - SFO&quot;</span>)</span></code></pre></div>
+<p><img role="img" src="data:image/png;base64,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" /><!-- --></p>
 <p>The residual plot can be easily obtained by</p>
-<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a><span class="fu">mkinresplot</span>(m.L1.SFO, <span class="at">ylab =</span> <span class="st">&quot;Observed&quot;</span>, <span class="at">xlab =</span> <span class="st">&quot;Time&quot;</span>)</span></code></pre></div>
-<p><img src="data:image/png;base64,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" /><!-- --></p>
+<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" tabindex="-1"></a><span class="fu">mkinresplot</span>(m.L1.SFO, <span class="at">ylab =</span> <span class="st">&quot;Observed&quot;</span>, <span class="at">xlab =</span> <span class="st">&quot;Time&quot;</span>)</span></code></pre></div>
+<p><img role="img" src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAkAAAAHgCAMAAAB6sCJ3AAAC/VBMVEUAAAABAQECAgIDAwMEBAQFBQUGBgYHBwcICAgJCQkKCgoLCwsMDAwNDQ0ODg4PDw8QEBARERESEhITExMUFBQVFRUWFhYXFxcYGBgZGRkaGhobGxscHBwdHR0eHh4fHx8gICAhISEiIiIjIyMkJCQlJSUmJiYnJycoKCgpKSkqKiorKyssLCwtLS0uLi4vLy8wMDAxMTEyMjIzMzM0NDQ1NTU2NjY3Nzc4ODg5OTk6Ojo7Ozs8PDw9PT0+Pj4/Pz9AQEBBQUFCQkJDQ0NERERFRUVGRkZHR0dISEhJSUlKSkpLS0tMTExNTU1OTk5PT09QUFBRUVFSUlJTU1NUVFRVVVVWVlZXV1dYWFhZWVlaWlpbW1tcXFxdXV1eXl5gYGBhYWFiYmJjY2NkZGRlZWVmZmZnZ2doaGhpaWlqampra2tsbGxtbW1ubm5vb29wcHBxcXFycnJzc3N0dHR1dXV2dnZ3d3d4eHh5eXl6enp7e3t8fHx9fX1+fn5/f3+AgICBgYGCgoKDg4OEhISFhYWGhoaHh4eIiIiJiYmKioqLi4uMjIyNjY2Ojo6Pj4+QkJCRkZGSkpKTk5OUlJSVlZWWlpaXl5eYmJiZmZmampqbm5ucnJydnZ2enp6fn5+goKChoaGioqKjo6OkpKSlpaWmpqanp6eoqKipqamqqqqrq6usrKytra2urq6vr6+wsLCxsbGysrKzs7O0tLS1tbW2tra3t7e4uLi5ubm6urq7u7u8vLy9vb2+vr6/v7/AwMDBwcHCwsLDw8PExMTFxcXGxsbHx8fIyMjJycnKysrLy8vMzMzNzc3Ozs7Pz8/Q0NDR0dHS0tLT09PU1NTV1dXW1tbX19fY2NjZ2dna2trb29vc3Nzd3d3e3t7f39/g4ODh4eHi4uLj4+Pk5OTl5eXm5ubn5+fo6Ojp6enq6urr6+vs7Ozt7e3u7u7v7+/w8PDx8fHy8vLz8/P09PT19fX29vb39/f4+Pj5+fn6+vr7+/v8/Pz9/f3+/v7///+7nL8pAAAACXBIWXMAAA7DAAAOwwHHb6hkAAAW6UlEQVR4nO3deXjU5KLH8ZTdspRSabEsSgHZKoIglMWjCAhVuCi1goKgQhWUTcoiCEeFqyA9IFU5RRaRCrIUZClFqSLniCxaEUGFoy1y8YhQ2Xe6TJ6bZFoYsNOZvL9kMk1/nz+StjNv3jzM96GdTCYjyUQAyeodoJKNARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARGEARHE4IBebE220OmoNQFFvZ9BdtDoe4sC2mHs9sgiLRgQIRgQQRgQQRgQQRgQQRgQQRgQQRgQQRgQQRgQQRgQQRgQQRiQTX0UZ7RfipyHAdlUbNw8Y7VeWeQ8DMimYot+vA3fIAOyKQZEEAZEEAZEEAZEEAZEEAZEEAZEEAZEEAZEEAZEEAZEEKMC+u6b4jfIgGzKqIAGPFz8BhmQTbk+3kcH1w2MWn/DHRx5fxlzJcf1u1xtyYBKKZfH+0jdSYcvbGqS4HJrprSpXkCTGcpXZ0c2rBQx1SHLtVbEBx+SF7cObL5I+XGdd7tKQbEn5ChJkk7fuEFXDMimXB7vERPU5W/Bp6/dmindNGRtfMDfZblPcML6kdIyJaA2vVddSiw/JW1UwFwloOD+W98s97x8IqbHUceNG3TFgGzK5fFumaGtumy+dmumFKMsx1Y9K8ckKV80HqcE1MIhnw+Zqnw3JFQJqKWSTa+O/BVWark83pF7tVX0xmu3ZkopynKftEtZnvtuQfl4JaDxsrxL2n78+PFk6bBc5yXllvgoBlRquTzeA/+hLs+E/HHt1kxpm7I8pWS0rUVAeHS4GtAsWV4hOe2V68yUGVCp5vJ4/xi6zCEf6vqCy63O/4F+kr46WX7YMeXRUAN6S5a3SNkFd6ij/snNgEox18c7o0P1iOA3cl1uzZQeU5YTA09tljJl+dItBQFlV1yo/HhyFwZE1z/epw7mX3drplRp2MaXykyUD5WP/XJdu8oPHtcCksdXmJY2NmCOS0BPNc/IK2KDVzEgmyr+SHSm9PFD1W9/XXmmtbxx5bYbPgie4gzIkRAZ2Ex9XnY1oC/qVz1T3AYZkE15CijDoA0yIJtiQARhQAQpPqDcQ1cM2iADsimeUEYQBkQQBkQQBkQQBkQQBkQQBkQQBkSQ2OkGfzhqVwZUqkw3+uOZ2xT94gcDIggDIggDIggDIggDIggDIggDIggDIggDIggDIoiZAeVnHnV7GwOyCXMCGrxVWcysJkn1Vrm5BwOyCXMCkpJkOUkauGZ9XJlNRd+DAdmEeQHdEad+9UJH15+fWFmo3hY92yO/ZV5AgevUr1Kruf58T2yhcrP1bI/8lnkBtUpUv3qtadH3qDJfz/bIb5kUUGiXuAdCsmTH0tBRRd+DAdmEOQGtSRjW4/YKy+UMqd3Fou/BgGzCxONA+VfkY+n5bm5kQDZh1ZFoBmQTDIggDIggDIggDIggDIggDIggDIggDIggDIggDIggDIggDIggDIggDIggDIggDEjc8UMOq3fBegxI1NY7Q26t+XapT4gBCdp2S6osH4h61er9sBoDEtQjWV0eCTpv9Y5YjAEJCnVeOKL1Nxbvh9UYkKDww9oqco/F+2E1BiSon/rB6vIPYTlW74jFGJCgn8NGvfHaK7cusXo/rMaABOU/FRhYoWoL95fQKiUYkKA5fzsry46J0Vbvh9UYkKCWX6nL3JpHrN4RizEgQTVOaKtSf6U1BiSoifb83VH7oNU7YjEGJGjKo+qFR+a3s3o/rMaABF2Mbj17wWP191u9H1YrUQFdntW728Q/jd0RcetHPjPXzeWzShG/DyjtuUenFDzTyW7Wd+Wa0WFFf3YnWcPPA8rrd8fcVWNC07RvhvSPqly94ciWxu4JQfw8oPn3qq817bjlgvpNjdCUfHlH86DDxu4KIfw8oOi12ur+T9RlmQ/U5S/l+DvMj/h5QG13aasB2tlbZT5Vl2fKfG3srhDCzwPqu1Bb3bldXd7U8ndZPv94UKl/6uxP/Dygz247KMuO2S21qwVHx4T2fCz84bA8Y3eFEH4ekJwUGju8ddss7etvw2atXLaw/gdF3vG/F4zaNdLD3wOSj65I/KLwvTP7oqtUvruoz4/KnRFcu8r9ew3aN9LB7wO6Xn7RZ5AO7XZIzl8YlonsEQkpYQEV7fDN59TV/8YZuE3yji0CWtdLW317l4HbJO/oDOhmF9C8hgaU6jyxdOfdBm6TvKMzoKSkpLeq1B41a1LjuqnQvIYGlF1DO7c9foyB2yTv6P8V9ty9l5VlXo9noXmNPR/otRZfXDn6ct1S/xYJC+gPqHaKtlobDs1r8AllK1pVrPXMH4ZukrwiENBb2iqxHjRvcQHte6n/y7/o3WCpv86KRfQHNCRog7JMDTLtV9iM8Fc/nBQ2D9o8+Yr+gM7dJ9WIrCHdj13XxH1AGXWOKctDoTwqWCKIHAfaMn3UzH+D87oP6KVXtNXwBHAG8gmhA4mH98Hzug9osPOWN8fCc5APCAT0SS1JkrskYvO6D2jqi9pq0NzCH/CdD/5Mf0DJ5eKSJXlyAPZXrvuADtZUT1n9IjRb++63J6oFNpjn7sPDyXL6A2o2Wj6ufDM2Epq3mGdh68L7TOhVb4v29R91pp127O4wGpqLTKQ/oMA0LaC0ytC8xR0HOrX8jVUFz/HGaq9OnK75f9BkZB79Ad31dy2g11pA83p5JLrTv7RVzEpoMjKP/oDeLz91u5S9sMIsaF4vA+roPFwQuwKajMwj8CwsMUSSpIoTsNcOvAzoxfHq8lzYIWgyMo/IcaDzu1ZsQS9x4GVAv4cnXJL33/c8OBuZRn9Aw7YbMa+3r8Zn9QmsEf5WrhFTkhkEXo2X6k8+AM/r/ekcuSfhycg8+gNy7IivL7WZDZ58U+IvMEVOYifV757UpGx3aF4GZBNiAZ1dNaBSWWheBmQTAgEdfrd7hfIPvJcNzVtcQLm7Vmbw1a8SQn9AraSKPd+H/7AtJqBdzVvFRrbBzxghX9Af0CNLzxgwr/uAjoZ9rCwX1z1rwCxkOt0B5Ry6ZMS87gOa6XyDcswiI6Yhs+kO6HLwaiPmdR/QkPe01YxxRkxDZtP/K2xMbyPeQeM+oDFvaKuxrxswC5lOf0Ar2tw5ftZbCmhe9wFtaayeC3Syrrc7RpbSH1CtQtC8xTwLG9o8efvCBi9DmydfMe/yLlf+LO65fnHHgdL6tu+/Ve90ZA2T3tZzeGL9AEmq2HCCu09D4pFomzDnbT27A+sOnZO8JPGFiGA3H4vNgGzCnLf13Bdd8Gau3Me7uv48/9esApVfz8r6TfnJ2SyuS/S6mSlv66mWUvjVl9Vdf741olCZGhERDQ7L8rMRXJfodYgpb+tpM6zwq1fcfKQff4XZhDlv60kJiF64ff+BnUv6lE0p+h6+CsjxYa+7+n7pm7lKJZPe1pPaWVIF3J/m5g4+Cij/kQ4ff7uoPo9qm8a0t/Wc/CE9fe8Jtzf7KKAV7dXz8Y/xEuSm8fO39aAGOacZ8k+fzFYaCR6JPrDJ/X8uXvFRQDHOP8HGTffJbKWR/oAOd4uXV5eVbnZzhNBLPgpokvMyVfeu98lspZH+gB4OXyu37H6wW09oXh8FdCg0VZbzZjQv+jNaCKc/oBrvyUekdHlZTWheXz2N39b0zv+5rRvfWm8a/QFVXy4vqnRJ3lAFmtdnBxJzv12Hv4+W3NIf0AMdPrujt3z+oTbQvDwSbRP6A9oTJlXbLd9eYQM0LwOyCYGn8RczlKfwK3/G5mVANiFyHOjEttXfYNep/0tA/4qJ7DaPn8ZcAukPKH9MJUmSqoOH5q4PaHrE4n2bunTjZYBKHv0BvRIwbt+pH18qg11p/LqADtVUP+krv8tCaJNkBf0BNXIe3B1n4HWiFz2prZY+Bm2SxOXM6dri8QyRkQIHEtdpq9Qgkemuui6gxOHaKi0a2iQJu9i+1+bvk2qLPLEReCnjOW31fGeB2a65LqB05zGll+OhTZKwhBh1mXmzwEVXdAa0Z8+ezSH91u5c+3iAu1PFvHNdQHltJuco/6eFunsPEJms6yZt9dA6/UN1BiS50D+Zi+ufhR3pHd6jeTOeeWqVKOeVd/t/qH+ozoAyXeifzMWNBxJ/3fQ9DwNZ5ul/qMu8ht/pH6r7b6C8959u37zXfPTR5pFof7Kn1lblL+nh3QSG6g1ob0spIrpXQ6kjeIYEA/IrnzZo2T3sCZGzTHUGdL5B08/V9dYmodiF7hiQf8nZ/clvQgN1BjQx6Ffnt78GTRSarxADsgmdAd3zQuH3I1pD8zIgm9AZUNCCwu/frwrNy4BsQmdAEVdfhH+zITQvAyrOyQtW74HXdAbUp0vh910fheZlQO4taxBcJeorq/fCSzoD+jRgjvPbxIDN0LwMyK23I3fJ+Sm1SkhBeo8DjZCiV//4w+oe0ghsXgbkTk7IL+rqo66e7ugfdB+J/rCW+jpYWDI4r+8COrmv5PxBofqpibY6gz1J8Rn9p3Pk7k9ZvR8++dRXAf2nW3BklSHgG/l96kAjbXUSO9/KZ8y7zG/xfBTQn7XfyZPPDetUgj49Kq/WXnW1AHvruF4/vzNlxRWRgTYPaJp29pujVbpPZjPGkvqz306aGirwyri4aWHPvxrdSOTDAWweUOxKbRU/0yezGeN0+0rlylcc5Mv/NNc3Va/3tKyRwDUobB5Q/w+01fNzfDKbMfoNzclxnLpntg+njHE+J4oS+HwAmwe0oId6Jb7zdff6ZDZDnArSnjV+08yHc7bbqa2eXKJ/qM0DyomK/e7klrbDPN/Tb3zvvPztpUo+nLP3Cm11j8CfijYPSL407Y7gdslGfMKZr/weqv31k1XXh3Muv+ucstxU77L+oXYPqARqr53xMGS0D6d0jKz3ytwna4u8esKA/M5P9QZ+tLBzlBEfbey9b6YMfUdoRgbkf87P6vv0kpJy6JMBEYQBEYQBEYQBEYQBEYQBEYQBEYQBEYQBEYQBEYQBEYQBEYQBEYQBEYQBEYQBEYQBEYQBEYQBEYQBEcRfAjqxk5/tXiL5R0DHBwa3q9N2t7FTkC/4RUCOTiMvyI6lYWLXSicr+UVAW1to7z0eO8HYOcgH/CKgt53Xv9/4oLFzkA/4RUDzB2mr5THGzkE+4BcBZYapV8F0PJRk7BzkA34RkDyp+dr/7nikg9BVHslS/hGQvKFr7bYz2U8J5CcBUUnFgAjCgAhiZkD5mUfd3saAbMKcgAarFxyeWU2S6q1ycw8GZBPmBCQlyXKSNHDN+rgym4q+BwOyCfMCuiNO/eqFjkXfgwHZhHkBBa5Tv0qtVvQ9GJBNmBdQq0T1q9eaFn0PBmQTJgUU2iXugZAs2bE0dJTrz/8dUajMLD3bI79lTkBrEob1uL3CcjlDanfR9ee5WYVa7NCzPfJbJh4Hyr8iH0t3d73sKAZkD6YeiZ6Q7fYmBmQTpgYkHXB7EwOyCQZEEAZEEFMD+uyC25sYkE1YdToHA7IJBkQQBkQQBkQQBkQQBkQQBkQQBkQQBkQQBkQQBkQQBkQQBkQQBkQQBkQQBkQQBkQQBkQQBkQQBkQQBkQQBkQQmwR0evYz47YaukXyjj0C+ir8qQXTGw1ydyUHMo8tAsq5LU1ZXu7Ei1b5ni0C+vJubZXa1cBtkndsEVCK83Oi9kUauE3yji0C+rq5tlrZ08BtkndsEVB+i7nKMrvZGgO3Sd6xRUDyf1rcOykubKqRmyTv2CMgOW/dtKQsQ7dI3rFJQGQVBkQQBkQQBkQQBkQQBkQQBkQQBkQQBkQQBkQQBkQQBkQQBkQQBkQQBkQQBkQQBkQQBkQQBkQQBkQQBkQQBkQQBkQQBkQQBkQQBkQQBkQQBkQQBkQQywKaMO96vZ8YAHmgLzY+OgYb3+thbPwjPbHxsd2x8Y8/Ok9IbYsCmh93g0oNmkIC62Hjg8Kx8SGh2PhaNbDxdapi42+reuMj4p0RZ6wJ6C8a/YyN77wFGz8gGRs/4Q1sfOJwbPyaR7Dxu9pi4z1iQMVjQB4woOIxIA8YUPEYkAcMqHgMyAMGVDwG5AEDKh4D8oABFY8BeWB2QE0PYuO7fYmNH7QcG/9yAjb+n6Ox8RtisfHfdsDGe2R2QMfB8Scc2PgzOdj485ew8ZfPYePzTmHjHSew8R6ZHRDZHAMiCAMiCAMiCAMiCAMiCAMiCAMiCAMiCAMiCAMiCAMiiLkBpdwd1Pk78eFrJNVg0eHp67CdcI4X3Ym321VpPDNXfP7C8YLznxtVv3LrVbL4/F4yNaDUgKGrelQ+LDw+ITRJsVVwdH7beGgnCsYL7sRU6cXUCeUmC89/dbzg/AOqzk57SkrHHwQPTA2ocw9Zvlh3ovD4YV3E5/7t3b9J8cBOXB0vthNXqo1QlmNuyhOc/9p4sflPByyRZUfjQfiD4IGZAZ2UFirL5+oLb6BHnPjkaZ06VYoHdqJwvOBOZEmblWWKdFBw/qvjBef/+b5MZfm3x/AHwQMzA/pB2q4s5wRcEd3A7d3vqnznPOH5G8ZjO6GNF9yJy5mXleXomy4Jzn91PPCP4Ei7KRl/EDwwM6DPpP3KMln6U3B8foWQOWsHS8InlWoBADuhjUd24sNyY6F/BHW8+PxzKkmj8AfBEzMDSpcOKMsl0knB8VeWZynLgdXyBcdrAQA7oY0X34nsJ6VBucD8zvHi8x/8eGz5BPhB8MTMgPZKO5VlYkVsK2ukTMGRWgDATjh/hYnuxMbQ+muR+QvGC8+vGt3AoAfBPTMDOqE+E5CHNxAdfyxDPaN+vXRUcLwWALAT2njRndhYdph2Pr7o/IXjBedf9aA6bIF0AX0QPDH1afz9fWQ5N2K86PB0aamyfLae6Hjn/yDiO1HwK1BoJ3LDnyz4Smz+q+MF50+TvlaWz9SBHwRPTA0oreyr254IFn5nWF670KkbR5RZJTreGZD4TmjjBXfic2ncYtUlwfmvjhecP6dDxOJP4sskwQ+CJ+a+lLGqbVAX4Cj6xVFNqnbYJDy84G8Y4Z1wjhfbiSTJ6ajg/NfGC/4jnB3cuEob9T8v9EHwgC+mEoQBEYQBEYQBEYQBEYQBEYQBEYQBEYQBEYQBEYQBEYQBEYQBEYQBEYQBEYQBEYQBEYQBEYQBEYQBEYQBEYQBEYQBEYQBEYQBEYQBEYQBefJowZuMpWlylQVW74z/YUCe7EhJSanbUVnsl/ulW70z/ocBeSOyn9V74LcYkDcKAqqu/AqrM7t9YMTcIz2r36pe+mJx68Dmi6zdN4sxIG+4BlR+yuc9A+rMTmtb6ZycWH5K2qiAuRbvnaUYkDdcA4qV5QPSSFlOlfadD5mq/HRIqLU7Zy0G5A3XgGbIcp60TK1ozy5p+/Hjx5Ml0z5HoARgQN5wDShBDShFC2hFwRP8vRbvnpUYkDfcBLRFyrZ4x6zHgLzhJqDsiurnUEwGPhKm5GNA3nATkDy+wrS0sQFzLN47SzEgb7gLyJEQGdgsyeKdsxYDIggDIggDIggDIggDIggDIggDIggDIggDIggDIggDIggDIggDIggDIggDIggDIggDIggDIggDIggDIggDIggDIggDIggDIggDIsj/A4NB6COn/MNOAAAAAElFTkSuQmCC" /><!-- --></p>
 <p>For comparison, the FOMC model is fitted as well, and the <span class="math inline"><em>χ</em><sup>2</sup></span> error level is
 checked.</p>
-<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a>m.L1.FOMC <span class="ot">&lt;-</span> <span class="fu">mkinfit</span>(<span class="st">&quot;FOMC&quot;</span>, FOCUS_2006_L1_mkin, <span class="at">quiet=</span><span class="cn">TRUE</span>)</span></code></pre></div>
+<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" tabindex="-1"></a>m.L1.FOMC <span class="ot">&lt;-</span> <span class="fu">mkinfit</span>(<span class="st">&quot;FOMC&quot;</span>, FOCUS_2006_L1_mkin, <span class="at">quiet=</span><span class="cn">TRUE</span>)</span></code></pre></div>
 <pre><code>## Warning in mkinfit(&quot;FOMC&quot;, FOCUS_2006_L1_mkin, quiet = TRUE): Optimisation did not converge:
 ## false convergence (8)</code></pre>
-<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(m.L1.FOMC, <span class="at">show_errmin =</span> <span class="cn">TRUE</span>, <span class="at">main =</span> <span class="st">&quot;FOCUS L1 - FOMC&quot;</span>)</span></code></pre></div>
-<p><img src="data:image/png;base64,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" /><!-- --></p>
-<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a><span class="fu">summary</span>(m.L1.FOMC, <span class="at">data =</span> <span class="cn">FALSE</span>)</span></code></pre></div>
+<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1" tabindex="-1"></a><span class="fu">plot</span>(m.L1.FOMC, <span class="at">show_errmin =</span> <span class="cn">TRUE</span>, <span class="at">main =</span> <span class="st">&quot;FOCUS L1 - FOMC&quot;</span>)</span></code></pre></div>
+<p><img role="img" src="data:image/png;base64,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" /><!-- --></p>
+<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1" tabindex="-1"></a><span class="fu">summary</span>(m.L1.FOMC, <span class="at">data =</span> <span class="cn">FALSE</span>)</span></code></pre></div>
 <pre><code>## Warning in sqrt(diag(covar)): NaNs produced</code></pre>
-<pre><code>## Warning in sqrt(1/diag(V)): NaNs produced</code></pre>
-<pre><code>## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result
-## is doubtful</code></pre>
-<pre><code>## mkin version used for fitting:    1.2.4 
-## R version used for fitting:       4.3.0 
-## Date of fit:     Thu May 18 11:38:11 2023 
-## Date of summary: Thu May 18 11:38:11 2023 
+<pre><code>## Warning in cov2cor(ans$covar): diag(V) had non-positive or NA entries; the
+## non-finite result may be dubious</code></pre>
+<pre><code>## mkin version used for fitting:    1.2.6 
+## R version used for fitting:       4.4.2 
+## Date of fit:     Thu Dec 19 10:28:47 2024 
+## Date of summary: Thu Dec 19 10:28:47 2024 
 ## 
 ## Equations:
 ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted using 369 model solutions performed in 0.025 s
+## Fitted using 342 model solutions performed in 0.045 s
 ## 
 ## Error model: Constant variance 
 ## 
@@ -549,32 +556,32 @@ checked.</p>
 ## 
 ## Results:
 ## 
-##        AIC      BIC   logLik
-##   95.88781 99.44929 -43.9439
+##        AIC      BIC    logLik
+##   95.88782 99.44931 -43.94391
 ## 
 ## Optimised, transformed parameters with symmetric confidence intervals:
-##           Estimate Std. Error  Lower  Upper
-## parent_0     92.47     1.2820 89.720 95.220
-## log_alpha    13.78        NaN    NaN    NaN
-## log_beta     16.13        NaN    NaN    NaN
-## sigma         2.78     0.4598  1.794  3.766
+##           Estimate Std. Error Lower Upper
+## parent_0     92.46     1.2820 89.71 95.21
+## log_alpha    15.08        NaN   NaN   NaN
+## log_beta     17.43        NaN   NaN   NaN
+## sigma         2.78     0.4569  1.80  3.76
 ## 
 ## Parameter correlation:
 ##            parent_0 log_alpha log_beta     sigma
-## parent_0  1.0000000       NaN      NaN 0.0001671
+## parent_0   1.000000       NaN      NaN -0.000772
 ## log_alpha       NaN         1      NaN       NaN
 ## log_beta        NaN       NaN        1       NaN
-## sigma     0.0001671       NaN      NaN 1.0000000
+## sigma     -0.000772       NaN      NaN  1.000000
 ## 
 ## Backtransformed parameters:
 ## Confidence intervals for internally transformed parameters are asymmetric.
 ## t-test (unrealistically) based on the assumption of normal distribution
 ## for estimators of untransformed parameters.
-##           Estimate t value Pr(&gt;t)  Lower  Upper
-## parent_0 9.247e+01      NA     NA 89.720 95.220
-## alpha    9.658e+05      NA     NA     NA     NA
-## beta     1.010e+07      NA     NA     NA     NA
-## sigma    2.780e+00      NA     NA  1.794  3.766
+##           Estimate t value Pr(&gt;t) Lower Upper
+## parent_0 9.246e+01      NA     NA 89.71 95.21
+## alpha    3.555e+06      NA     NA    NA    NA
+## beta     3.719e+07      NA     NA    NA    NA
+## sigma    2.780e+00      NA     NA  1.80  3.76
 ## 
 ## FOCUS Chi2 error levels in percent:
 ##          err.min n.optim df
@@ -583,7 +590,7 @@ checked.</p>
 ## 
 ## Estimated disappearance times:
 ##        DT50  DT90 DT50back
-## parent 7.25 24.08     7.25</code></pre>
+## parent 7.25 24.09     7.25</code></pre>
 <p>We get a warning that the default optimisation algorithm
 <code>Port</code> did not converge, which is an indication that the
 model is overparameterised, <em>i.e.</em> contains too many parameters
@@ -618,21 +625,21 @@ sponsored by the German Umweltbundesamt <span class="citation">(Ranke
 <h1>Laboratory Data L2</h1>
 <p>The following code defines example dataset L2 from the FOCUS kinetics
 report, p. 287:</p>
-<div class="sourceCode" id="cb14"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb14-1"><a href="#cb14-1" aria-hidden="true" tabindex="-1"></a>FOCUS_2006_L2 <span class="ot">=</span> <span class="fu">data.frame</span>(</span>
-<span id="cb14-2"><a href="#cb14-2" aria-hidden="true" tabindex="-1"></a>  <span class="at">t =</span> <span class="fu">rep</span>(<span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">1</span>, <span class="dv">3</span>, <span class="dv">7</span>, <span class="dv">14</span>, <span class="dv">28</span>), <span class="at">each =</span> <span class="dv">2</span>),</span>
-<span id="cb14-3"><a href="#cb14-3" aria-hidden="true" tabindex="-1"></a>  <span class="at">parent =</span> <span class="fu">c</span>(<span class="fl">96.1</span>, <span class="fl">91.8</span>, <span class="fl">41.4</span>, <span class="fl">38.7</span>,</span>
-<span id="cb14-4"><a href="#cb14-4" aria-hidden="true" tabindex="-1"></a>             <span class="fl">19.3</span>, <span class="fl">22.3</span>, <span class="fl">4.6</span>, <span class="fl">4.6</span>,</span>
-<span id="cb14-5"><a href="#cb14-5" aria-hidden="true" tabindex="-1"></a>             <span class="fl">2.6</span>, <span class="fl">1.2</span>, <span class="fl">0.3</span>, <span class="fl">0.6</span>))</span>
-<span id="cb14-6"><a href="#cb14-6" aria-hidden="true" tabindex="-1"></a>FOCUS_2006_L2_mkin <span class="ot">&lt;-</span> <span class="fu">mkin_wide_to_long</span>(FOCUS_2006_L2)</span></code></pre></div>
+<div class="sourceCode" id="cb13"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb13-1"><a href="#cb13-1" tabindex="-1"></a>FOCUS_2006_L2 <span class="ot">=</span> <span class="fu">data.frame</span>(</span>
+<span id="cb13-2"><a href="#cb13-2" tabindex="-1"></a>  <span class="at">t =</span> <span class="fu">rep</span>(<span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">1</span>, <span class="dv">3</span>, <span class="dv">7</span>, <span class="dv">14</span>, <span class="dv">28</span>), <span class="at">each =</span> <span class="dv">2</span>),</span>
+<span id="cb13-3"><a href="#cb13-3" tabindex="-1"></a>  <span class="at">parent =</span> <span class="fu">c</span>(<span class="fl">96.1</span>, <span class="fl">91.8</span>, <span class="fl">41.4</span>, <span class="fl">38.7</span>,</span>
+<span id="cb13-4"><a href="#cb13-4" tabindex="-1"></a>             <span class="fl">19.3</span>, <span class="fl">22.3</span>, <span class="fl">4.6</span>, <span class="fl">4.6</span>,</span>
+<span id="cb13-5"><a href="#cb13-5" tabindex="-1"></a>             <span class="fl">2.6</span>, <span class="fl">1.2</span>, <span class="fl">0.3</span>, <span class="fl">0.6</span>))</span>
+<span id="cb13-6"><a href="#cb13-6" tabindex="-1"></a>FOCUS_2006_L2_mkin <span class="ot">&lt;-</span> <span class="fu">mkin_wide_to_long</span>(FOCUS_2006_L2)</span></code></pre></div>
 <div id="sfo-fit-for-l2" class="section level2">
 <h2>SFO fit for L2</h2>
 <p>Again, the SFO model is fitted and the result is plotted. The
 residual plot can be obtained simply by adding the argument
 <code>show_residuals</code> to the plot command.</p>
-<div class="sourceCode" id="cb15"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb15-1"><a href="#cb15-1" aria-hidden="true" tabindex="-1"></a>m.L2.SFO <span class="ot">&lt;-</span> <span class="fu">mkinfit</span>(<span class="st">&quot;SFO&quot;</span>, FOCUS_2006_L2_mkin, <span class="at">quiet=</span><span class="cn">TRUE</span>)</span>
-<span id="cb15-2"><a href="#cb15-2" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(m.L2.SFO, <span class="at">show_residuals =</span> <span class="cn">TRUE</span>, <span class="at">show_errmin =</span> <span class="cn">TRUE</span>,</span>
-<span id="cb15-3"><a href="#cb15-3" aria-hidden="true" tabindex="-1"></a>     <span class="at">main =</span> <span class="st">&quot;FOCUS L2 - SFO&quot;</span>)</span></code></pre></div>
-<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqAAAAJACAMAAABlpiR1AAADAFBMVEUAAAABAQECAgIDAwMEBAQFBQUGBgYHBwcICAgJCQkKCgoLCwsMDAwNDQ0ODg4PDw8QEBARERESEhITExMUFBQVFRUWFhYXFxcYGBgZGRkaGhobGxscHBwdHR0eHh4fHx8gICAhISEiIiIjIyMkJCQlJSUmJiYnJycoKCgpKSkqKiorKyssLCwtLS0uLi4vLy8wMDAxMTEyMjIzMzM0NDQ1NTU2NjY3Nzc4ODg5OTk6Ojo7Ozs8PDw9PT0+Pj4/Pz9AQEBBQUFCQkJDQ0NERERFRUVGRkZHR0dISEhJSUlKSkpLS0tMTExNTU1OTk5PT09QUFBRUVFSUlJTU1NUVFRVVVVWVlZXV1dYWFhZWVlaWlpbW1tcXFxdXV1eXl5fX19gYGBhYWFiYmJjY2NkZGRlZWVmZmZnZ2doaGhpaWlqampra2tsbGxtbW1ubm5vb29wcHBxcXFycnJzc3N0dHR1dXV2dnZ3d3d4eHh5eXl6enp7e3t8fHx9fX1+fn5/f3+AgICBgYGCgoKDg4OEhISFhYWGhoaHh4eIiIiJiYmKioqLi4uMjIyNjY2Ojo6Pj4+QkJCRkZGSkpKTk5OUlJSVlZWWlpaXl5eYmJiZmZmampqbm5ucnJydnZ2enp6fn5+goKChoaGioqKjo6OkpKSlpaWmpqanp6eoqKipqamqqqqrq6usrKytra2urq6vr6+wsLCxsbGysrKzs7O0tLS1tbW2tra3t7e4uLi5ubm6urq7u7u8vLy9vb2+vr6/v7/AwMDBwcHCwsLDw8PExMTFxcXGxsbHx8fIyMjJycnKysrLy8vMzMzNzc3Ozs7Pz8/Q0NDR0dHS0tLT09PU1NTV1dXW1tbX19fY2NjZ2dna2trb29vc3Nzd3d3e3t7f39/g4ODh4eHi4uLj4+Pk5OTl5eXm5ubn5+fo6Ojp6enq6urr6+vs7Ozt7e3u7u7v7+/w8PDx8fHy8vLz8/P09PT19fX29vb39/f4+Pj5+fn6+vr7+/v8/Pz9/f3+/v7////isF19AAAACXBIWXMAAA7DAAAOwwHHb6hkAAAgAElEQVR4nO2dCXwM5//HnyBEEiFC3Cqh4r6PUFr3UVqq9FC0/RWttK46Qp0tbSnVimpDKRVVJdQZJaqUUq2/qmppG5TeRBxRufP8Z2Z3c+zubGZ25sk+M/t5v15mZmef+X6/Ju/Xzs6zM/MQCgDHEE8XAIArICjgGggKuAaCAq6BoIBrICjgGggKuAaCAq6BoIBrICjgGggKuAaCAq6BoIBrICjgGggKuAaCAq6BoIBrICjgGggKuAaCAq6BoIBrICjgGggKuAaCAq6BoIBrICjgGggKuAaCAq6BoIBrICjgGggKuAaCyvEcsXFQeLVnRPOAZsM25lrey13RKcQvbOT34vJkQuLFeTwhE4RZyuz2oYFNHvsmL84CQmLzXpx6sHa5Nq+mO8tXcENXub0MCCpHQUn+G2Nd7JsqvvVXL8urkm9Re0EvVLK2nG+LU1DQxFLSe20yHdMV2tBFbm8DgsohSBIVK/EXHUFI4MMvDQ4k5Enxrc6EVBw6a3BJQrbbC3ofIYPXfji2LClx3BqnoKD1if87+x8m5HXHdIU2dJHb24CgcgiS7LIuJhJS/2dh/vPdhCRS+gkhjS4JL3cS0sJO0HRf0k188TEhr1o3LiDoX4RMpfROEOnpkK3whvK5vQ4IKkcBSdoQckBa2CccnintRMhe6eWjLVplFhb0CiHtsoUX6evXH7NuXEDQLwn5SJg1Jnc7ZCu8oXxurwOCyiFIMnaVyI/XfEhd68o6xCeZhpBKBdoVPsSHE1JvyuakAic0BQTNTE7OoPRWIOnhmK7QhvK5vQ4IKkfeico73wknKNaVwtnRyWThw65Au8KCHq0hbVNh8AHb+4XO4gVyhhOyyTFdoQ1lc+v5HzQGEFSOfElOEnK/deX9hHwjHI07Fmhn1810e8PIZr7iZsus79sJemMgIeOtyztfEFhufVFwQ9ncLP6jfANB5cj/Hpicf5itS8i/NJhUt7zKTE/PtRdUJOOrZ4RT7xzLi8KC/l84KfmG7cVM0cE+BXLaNpTP7XVAUDkKnKi0tnSYU3qQkJaUdiDE0ofUkvj+R+cRslp8sZqQufTsggWWkyPhePyHZeNCgq7zI1XyDv4FBS28oXxurwOCylFAkr2ENDgvzH9tQMgeSj8kpMN14eUuH3IPpVsIeUBs9AAhm+nPhNwnnozntiMB2ZaNCwq6w4dE/O40W+EN5XN7HRBUjgKS0GGEBD0+5/FyhAyjkkSkzsTFT5QkJQRlbtQSfJ01U/hYrXld/FAlHd7ZsqIbIY9YtxUEHSadka/6L6c2IRPFpS2O6QptKJ/b64CgchSU5FZP60lLz1viy0udLa983xRfHQqxvKp0SHjxWzVryyY3rNsuyPvZ8o8jtqUmjukKbegit7cBQeUoKAnN3TC0aUDToRus/Zs5ywbWKdfqmV8tr65HdwgJ6RB9XXpxe1GnmmXCu6/Jsm1aQNC1LgQttKGr3F4GBAVcA0EB10BQwDUQFHANBAVcA0EB10BQwDUQ1C3ODxgRW3QroB0I6hZTTtLOnq7BO4CgbnEn9/eHXLc4OLDIVZYfQ7OuWl/mLaRc1FSbuYCg7rFpwN+uGxQt6M1m0myq7RoQYeG3yIillEad06NCkwBB3WLXi4V/Gc+0/st/VaSgrzaoI62sZBVUXHj2s+xWN3+O0rlYQwNBVTK1VRqNGfVMv8GSVzFh9V7MOTL0sZniPzo/PGyK9IpKNkpv9v6U0jaHpMXCgp7YKgp6vW2MRVBpYeqSi43ujPiz2P9THANB1fLkqrS61ovl6cHmV24/Nv9I+XNU/PdZxM30TuvEJfGtgZY31wyn58O/kBZFQXe1FxgtbfyHKOjjOzZaBJUWbj7bY9+30z3xv+IWCKqWYx2XRNuWZ9Xp0qVxvyP3USr+mzqD0uVPiUtUFNTy5o0qma+9bFm0O+qLgq4fRS2C5i3Qwdd39J7s9OlNXgkEVU1EWIptceHrlKbdPNJfEFT4N0U4tMcOF5eoKKjlTTpgb4sLlkUnn6ADq9WpHCDetpm3sPeN5FaXZr3pif8Yl0BQ1fR+Im/xuwZX0u9PsAm6p2FqRuc1eYJa3qQfd+lqbefkE1RA+ODMPZdlWaA098G0S91zN71UXP8Z7oGgajnSplr+ATi2bo2x1CYonVcvfFJOnqCWN+lt/7XWdrKCppPfqFXQjasondOw8z/F9J/hHwiqlo5fPuzkwSCAERBUJZ/2oQndPF2EFwFB1ZHd8ATNfbT5FU/X4TVAUMA1EBRwDQQFXANBAddAUMA1EBRwDQQFXANBAddAUMA1EBRwDQQFXKOzoC+2BkABnZReUKizoJFrTgBQNHd/7yFBjxXdBgDaDIICnuFI0FOLZsVnOWkKvBluBM0ZW2vyK92bJOmbBhgdbgRdcU+qMF3WVt80wOhwI+g9ieI0N/wnffMAg8ONoHUuSrNee/XNAwwON4J2+EKa3f2DvnmAweFG0GVd04Tp6hbeOY4fv+QcSmRPsnx+bgTNfrru3LcH1MVXUM44Xa4Hc+q5eIAPN4JSemT2+DV4WBtvfNeCfY7Xp8m/x4+gGUt6d3gO3aC8AUGt3GgxeM/R10J3OW0NPAYEtTJtpDg9Vj3TSWPgOSColeYnpFmr4/rmARqBoFbCLtA/tlLac5++eYBGIKiVvhvpsbY0PfR3ffMAjUBQK5/VOXMz8Pb/huibBmgFgtqIq3avX+Unb+mbBmgFguaReqjFh/omAdqBoAUYj0FVuAOCFiB2pL5JgHYgaAEO3aNvEqAdhoJ+9611wTCCJlfQNwnQDkNBh9mGgzKMoLRyEWOrg2LHQdDcrZMnbsxxuU1utsOqjEI/YVtu3jWgoPd9rm8WoBl7Qa937rhoSZc2/8pukET21PZpsFBYujW+nl/4vFxKq34yOfg3ura1f+MPhNU1l/cg5Ydco5GEkBvSJsYRdMwyfbMAzdgLOipKvOch+lHZDZJI2VHbJvvMoXRQ8OId48kGQdA2AzanxfjOTpjg864gaPATB98o9Ty99nCffyz3TxhH0JgofbMAzUiCpqfYuFbuF3H2R+DfeavsLjJPIg8L0ynlbtGHY4WFiKmCoM1y6e2QecKrUaGCoOJtPQ/cY8hD/P6u+mYBmpEEvT/YRgUfy7xE+bxVHQtvkETihekPRLwsLfW7Vb6TBUGjKT1OjiYnJ8eRy7TmdOGdyZGGFPTPKvpmAZqxP8RX/EOcXg+SvTkniRwRGwiaHmnmU71vdVHQJZR+QiycpjUXUS4Ezbia4uJdmYeHBbu4vw94AntBJz4unI9nj35GdgPLJ+hP5KsU3yjhVCpSFPRtSg8Q27ClNRdTzwt6+aUwH0LK1Jt2QaaBjKAdDqtKA5hjL+idQQ2ipzftc1N2gyTyiDB9yf/6PpJEaVo1q6BXyqwWVs/qzoegJ/1rjVkaty7mhfDgU85byAj6zAo1aQB7HDvqDy947YCLDZKIX9Tu6SVeor/5Djm8vX3A/cmSoDS69PyEKT5LCwj6dOMTlg7T4he0S987loWsx3s4byEj6OIJatIA9qj+JSmJfNqvQv3XhDP1jREB7XZ+GDzbImju4ib+jcTz+jxBvwgrZ/kgLn5Bg+JtS4dlfr2UETSht5o0gD1uCHpCbY7iF7RNXn/m3PbOW8gIerGWmjSAPeYUNN6n7+qjZ899vW5QyXjnLWQEzS0n/+0beAJzCkp3dZU6vXy6Jcg0kHtGfWvcdswXqgXN+i1DbQ6P9IOmnElMPH3NbmVq3vPM6n3pfLPha1TmAWwx9QXLOb/aPSfkm7wHmpWSubvjtalu5AHsMKmguwb3X0NXhBC/+TLP+wx83/n6bf1V5QGsMaegm0jb+32jAuftmem72nkLOUF/rqsmD2COOQVtOYbS98jrwtL0ls5byAma7f+fmkSANeYU1H83pVelq1r2BDhvIScobfqdmkSANeYU9O6FlH5N1gpLSxs4byEr6KMfqUkEWGNOQV/3m/ByjbbV9yVvD5nhvIWsoHNnqkkEWGNOQbNmVq/8XOZIQkg/ma+UsoJuGqQmEWCNOQW1cnad7O9CsoKeiXAjEWDGmdLhzKk4Sz6/p275kBU0KwAPuOOKi+fZ46LnhjtBaTtcVA/y4U/Q55bqmwkYGv4EXfmkvpmAoeFP0BNN9c0EDA1/gmbgx06QD3+C0pZf65sKGBkOBR35rr6pgJHhUNDleBA4yINDQY+10jcVMDIcCprmj1HjgQ0OBaVNVd+3CkwLj4I+uVLfXMDA8Cjo0jH65gIGhkdBD7fTNxcwMDwKmuqfKf8m8C54FJQ2UFoUMD1cCjp0jb7JgHHhUtDFY/VNBowLl4IewKCywAqXgt4IdBzsEXgnXApK6/2kbzZgWPgU9JE4fbMBw+KpgbxcC/r6i2qzAZPiqYG8XAu6r4uqbMC8eGogL9eCXi0v89xb4G14aiAv14LSumfUpAPmxS1BL/9QROOiB/IqQtARsQrLAibHDUE/q0oI7R7jqnHRA3kVIej7wxSWBUyOekHjSo2OI3SWj6thX4seyKsIQc/epbAsYHLUC9poIk0WXkxp4qp1kQN5FSFobuhlhXUBc6NeUP8ESdAEmYfP23A6kNcPo234vuV684c2KKwLmBv1graaIwn6SrOit7n+S07hFVffX2HF7z3Xm74Z5fp94CWoF3SN77yj5Mrq0ktctY7rcJr+05+Qcm/LNCjiEE+/wSPEgIgbZ/ExIcK3yzLTXHWlLyWd/6G9ary3Z0qpdc5bFCVoVpD91wPglbjTD3r7+CcHrrpsHDad0n+IOF7sNJUDeeXRfafCwoCpYfNLUsgW4YSI3BaWEso5b1GkoHOj1SQEZkW9oP1tuGg88P4MmlVuv7A03b2Oekr3d1RYGDA16gV9SmRgaIkXXDQ+G9rkjS/mV//gi+m+HztvUaSg/wXgObbA/UP87V5tXbX+ZUxFqae+8ScyDYoUlLY7pLAyYGbc/g56kFxx2T7zwrHdR+WuBlUi6KT5iuoC5sZtQdeW1XTJZtGCftpHS3xgEtQLul5ibjVtF70XLejV8ri1E7ghqJ+Ef+RZTXmLFpQ2PKkpAzAFfN7VKTHK5SWnwDvgWNAPh+ibEhgRlYJWKoCmvAoEvVBVUwZgClQKGhsb+3ZgjQlLZkTU2qUprwJBac1fNaUAZkD9If65+8RBOLL7PKsprxJBn8SIXkC9oDUsdxltq64prxJBNz6gKQUwA24IarkGOaa2prxKBE0pjwGTvB71go4qL16puas8+0M87ZioKQcwAeoFTe1CKjapSLrd1pRXkaDz8Awxr8edftADCyYs+lJjXkWCnmioMQswPBx31FOaW+28vlmB4VAp6KpEusqGpryKBEVHE1ApaOBjtIINTXmVCYqOJq+H60M8OpqAm4Ke26PxtnVlgqKjyetRL+jlnpPplpKkksyjkxWiUFB0NHk76gUdWH0bbdH7Qk9Xtx0XjUJB0dHk7agXtOJK+hdJpBsqa8qrUFB0NHk76gWtsJF+4JdGdwZqyqtQUHQ0eTvqBe3VcX/TAfR2vzaa8ioVFB1NXo56QU9VIUEnaf3SRT3cS8tAXvmgo8nLcaOb6c6Ja5Ru+sVla40DedkyfRa7r8M+hQUCU8JmGBqtA3lZOBje9dn7QgYqLBCYEjbD0GgeyEvkr9DPhenaEn8qrBCYETbD0GgeyEtk0XPSLBRPq/dm2AxDo3kgL5Exlh6mhxoprBCYETbD0GgeyEtkxmxp9nzZOwpLBCaE0TA0WgfyEvm29httQtsurt5+i8ISgQlhNAyNzEBeB0geC4vMmFvfb/z658s0W/mIwhKBCWEzDI2NbbIPuVXwCbqr9dGofi983XhbhVRFFQIzwmYYmryN9su9o0DQSZYP2Zdn9PtIUTJgRtQKmrr3k0vC0ffPHw/2c9F46zALpPswmXG1FQj6/DJptmhSHH6P915UCvpTLeHwPub72uIJkIvGu/1J60gB0jAy0nkLBYKuHCTN+q6/VR6jznktKgV9ICTu9MeVa3TYuO+4ywc3nGvd7meq8RD/X73XMmn6nEbpdLC2O0iBgVEpaGXxe+FC8luRzTOjA9/TKCi9NCC4ZYXBf1C6uafCIoHpUCkoEcc92qLoDs+Dtfr+rU1QSlNOXhdnaRX/UdIamBC1goo/DG1Tdgvy9UcraRXUxrBlaloDE8FQUEo/nvCz3FvqBE1soaY1MBFMBXWBOkFzI77SnhIYEbWC+vr5+fkSaagkTXnVCUrffkJTNmBYVAo6swCa8qoU9EYwTpO8E76fzZTPqFf1zQ8MglEEPVUbI3d6JUYRlHbcpm8BwBgYRtD1vfUtABgDwwiaUVW2TxWYGMMISqfjSYzeiHEEvVRJ28A3wJAYR1D6IC6680IMJOjepkpugwLmwkCC0vYy99gDE2MkQXc3ztG3CMA/RhIUH6FeiKEETcBHqNdhKEHpPZv1rQJwj7EE3YOPUG/DWILSTpv0LQPwjsEExUeot2EwQfER6m0YTdC9jbL0LQTwDTtB9RknyYG+ds8lzd06adx6XG1vWhgJqs84Sc74tfIfBV/evK/D4qU9m//lZjTAO2wE1WecJOfMeKzgq6jR4hUkczCYkllhI6gu4yTJcCe84PN0QqRRlNKC8BBmk8JGUF3GSZJj+935w3dm+lrmYXJfJYDBYSOoLuMkyfLA6/nLVSUzU4P+cz8c4Bk2guoyTpIslypdzFuOfvjLRa8nRg13PxrgGkZn8XqMkyTPy4PyFm/U9m3boWxI0U/UBcaEWT+o03GSrry/worfeyrjFSS9wUbbYvTgL994bf/z+AQ1K6wEzb6QJs3TCj3064fRNnzfUhevMKer2k6K8B3U5LARNGtWWVJ2qvj7zlqZ7TQd4ild2jZDmuMs3uywEfSNUpPiJ5Z6mjITNHfANMsC+kFNDhtB608XJuvJdmaC0pQ6+6T58yPFX5JmP6QtGuAWNoIG7Banw8LSmAlKD1X7W5zd7BK5OKZHC/wWb1bYCNpWeo7Sv6HPsxOUzuwtPcch99NJ4z7C1UymhY2gMWTcvnRKd5ccEc1M0KzOM7SGAPzDqJtpXhBJEmY7qxNmgtLkiHc0xwC8w6ofNOO8dEVH1uexzt/XQVB6vjqeumx6jHbLRyG+rXxMhyiAZwwtKN1V9Rc9wgB+MbagdHlEsi5xAK8YXFD6UnOM8GVqjC4oXRB+XqdIgEcMLyh9pw6G/zAxxheUxtWQuXMUmAATCEo3V8Fg3abFDILSz0Jlfg4AhscUgtJfmw3DJfXmxByC0rSnW+Bk3pSYRFBKl1bZpXNEwAOmEZQeCXvK1eP0gDExj6D0v+jqH+oeFHgYEwlK6dFG/X9nEBZ4EFMJStNnVXoVp/OmwlyCUvrb6CoLMtiEBp7AbIJS+m33+psxFIhpMJ+glO5rG/GeGwf6OS3qDsDty7xhRkEpPTG84jiVp0up1co+NLJeyTg2BQF3MaeglP76QsUhO9WMWNMvVPzqOrk0RrnhC7MKSumNlZ2qTPhOcfOA5dKsDAYK4wvzCiqQNDssYuoRZY8dKXVYmoW8xrIglxyZPf6D9KKbeRmmFpTS3P+b07Ly05uvFN2ywiviNKvUPtYlyZD9VL2X3xpY90cPpecWkwsqcumd/hWajvu0iNs/n/X/hdKcHuWLpyZHYrqJn55rmud6qgBO8QJBBbK/eaNv0N1PvH00Tb5N5xIRbQPKfVN8RRWmw+erH+kT/efdP3iqAE7xDkFFcn5cG9WmbMNH5m1Ncv6t9MDw/gs918N/V5ueG/dMC22z12MVeJrTS+ZudfzLGG4wWW1knt4wrX+YX0T/ie8m/MjVr/a1uonT/aW89RM0d0xgSFBIPYfLzg03mKwepP/46aLRvRuWrdTqwTEvv7/zm0scqFqrmfgU8xj/bz1diIdYXCbqzO/rK4TbrzfeYLI68u+329+d/Uy/1rX8/Gu36fXYmBmLVmz67OiZiymZxV9L2JO1Js/v3qSTp3oRPE21R8TpTyXtfTHeYLJMSL1w/LMNy+dNGjWkV2Sju4J9fYNr120d2eOBIcNHj42etmDB0hUrPtq0aU9i4oETJ06cPC/wZ4qIfufcPbeeemPG5jvVLuoW0Vj47pFm5ZfYrS/ewWS/6WGj1Jtq4hU7mSm/JZ04mrhj04crli54PTp63OjRjw8Z0rtHj66tW7duES5QPVjERxpQj5QLtlItvBDNWxdBx7wd0tz/nh49uteu1MNLKdGkRw9BTn/7D67iHUw2NdFGvS/VxOOdWylW/jpfiFMniuCrvB2SOLZCZPeq92xN9FLCa+9JPE9XlLR/FpynBpONxKNn7bm6I85bT+EFvipb89V3+vk/ar/eU4PJQlBQmMTaFSr7RzvcDVG8g8nmA0GBHbkXTjr5oc9TvyRBUKAICAq4BoICrvGUoN3yug7zCSA+zGAYmiUGLbuE4x/XXUopHcdFZ0HTUhzZ0P0CM2odYhZ65v+Yhf6lFLPQF4a9wiz0/npO/rpuckOpUToL6owdD7CLHSZ31Yp2lkxkFjqrFLPQNGo5s9BnGzALLQ8ElQOC2gNB1QNB7YGgqoGg9kBQ5UBQOSCoPRBUPRDUHgiqGghqDwRVDgSVA4LaY1ZBf2U4HNecVGahD3/KLHTuZGah6eavmYW+MY9ZaHmKQVAA3AeCAq6BoIBrICjgGggKuAaCAq6BoIBrICjgGggKuAaCAq6BoIBrICjgGvaCxrct31X5wFtq2Co9KGokg8iJ26UZi9ItoVmUvqx9YMQicUQ9/cu2hWa3x2VhLugunzGb+wRcZhF6cWiswEH9A+e0ky43YlG6NTSD0ueRF3dNKzWLRdl5oZntcXmYC9q1D6V3ar3EInRUdxZR6e/L7yWSRfqXnhda/9IzgsYJ00lls/UvOz80oz3uCtaCppDVwvS5MBax+4xmEZUmdOrkJ1rEoHRbaAalnyfiY/DjyQX9y84LzWqPu4K1oGfIUWG61MfhAZE6UL93q4DmKxgEpvVEi9iULoVmUHp6kjik3cSyafqXnRea4R6XhbWg+8lZYRpHruofOqd0yNJtI8li/SNbLGJTuhSaVenrS01htcfF0Az3uCysBU0k54TpOuJq9C83ydgoDg81IojBMHKSRWxKl0KzKf3KcPJUFpuyLaEZ7nFZWAt6moj3yMSUYZZgK0nSP6hkEZvS6+Xfj6Rz6btDw7ZRNmVbQ1tgssdlYS3oNZ91wnRsXQah/z0hDme0g9gPIKEDkkVsSpdCsyh9d8ko6VHbDMq2hWa4x2Vh3s3UbRClWeHRDCInko+E6bO1GYS2fMwxKd367UH30rOqD7cu6V52XmiGe1wW5oImlHz5yNBgFrevZ7cPnbd7XInNDEJbBGVSuhSaQemfk6lrRdL0LzsvNMM9Lgv7nzo3tyvfnc1PnXcmNCjXcQ+LyNYviixKt4TWv/RYy+B44uFX77LzQ7Pb47LgYhHANRAUcA0EBVwDQQHXQFDANRAUcA0EBVwDQQHXQFDANRAUcA0EBVwDQQHXQFDANRAUcA0EBVwDQQHXQFDANRAUcA0EBVwDQQHXQFDANRAUcA0EBVwDQQHXQFDANRAUcI3Ogm5bAIACFv3nGUEjh0cDUDSVv/eQoMf0jQdMSjMICngGggKugaCAayAo4BoICrgGggKugaCAayAo4BrjC/rF/Dm7c/UKBnjD6ILeGdB4xty2nZP1iQa4o3gFzU2x0VonQScNzRLCTnxMn2iAO4pX0IPBNnze1iMepaGXxOntIKXXvACD4alDfOD7uoTJ9LXMw1gMoQg4wOCC0kq/i9M7Qan6hAO8YXRBxz+ZI0ynDdEnGuAOowua2qvla2/c2/ZffaIB7jC6oJQmTJ8Sn6NXMMAbxhcUmBoICrgGggKugaCAayAo4BoICrjG+IIef3PhF3rFAtxhdEHTh9Z9cWrT3jf0iQa4w+iCTns4ndLsMSP0iQa4w+iCVpUuY7oVdEefcIA3DC4oLrczOwYXlFb8S5ymB93SJxzgDaMLGvWseMPc3IH6RAPcYXRBb3Tu+NbyXk3/1Cca4A6Vgr5TAE15desHzY0fHxWXpVMwwB0qBa1UgCI3yUn6R/Y9/JIEFMHmED/yoDBZFERI7c0yLSAoUITbgibPdtU4ltJYMmLrjtEl9jhv4Sho9srBPSb9obAa4C2oFzT3o6mTBe6v6KqxIGjT0eLSC/c4b+EgaGqHPpsTZ4TuV1gO8BLUC/oyifCtHlktYJ2rxoKg/tvFpV1BBdd/QfJYaLfJ7GFSg9o43wEFUS9o2AT6/v30TrtdrhoLgraMEZdeaei8hcMnaIvj0qz5twrrAd6BekHL7KQna1C6rb2rxqHdR/cKOS98HQid4LyFg6B1LD9W9tynsB4GJB3Cxzd3qBe07pv0ls8V+mWgi8ZbF0f1qV96Iz1B2stcxeEgaK8t4jSz6m8K69Gd9wN8Svncn+Gp9MA56gUdFxxLm0UlDWlQ1BY5GfTfRLk71h0E3X53EqUZLzyosBzd2VBiCqWHg1t7Kj9wjnpBbw56mB72Jb6bNOV17GZ6L7Tv0LseTtEUVQM1pSc4XvD5xVMFAKe42Q+ask/jodhJR/21hI9+1BZUC76WL7/Biz1XAnCC0S8W0Y3S26RZOW2XGAC9US9ofxua8nInaKN7xelBn6seq+BMzCvbsz2WnVfUC/qUyMDQEi9oysudoN+UvPfrf2b6euxZ4rmTaoyd1bnlRU/l5xV3D/G3e7XVlJc7Qel39Uv6BL3qsfSr2t8Upm9GeqwATnH7O+hBckVLXv4E9TCd94rT3DpnPV0IZ7gt6NqymgYngqB21LkozXrt9WwZ3KFe0PUSc6t10ZQXgtrR/pA0izjt4Tp4Q72gfhL+kdqORRDUjrd6ZgrTuKYYNK8w6AflhKyh4QMHdr4LH6B2QFBOyJ0W0rJleOtLnq6DN1jeNOcKCEznwTUAAApTSURBVGrHmjbXhekCmRsQvBeVgsbGxr4dWGPCkhkRtVxdsFw0ENSOez8Tp7l3nfN0IR7j+zfnbHG8IFf9If65+9KFaXafZzVV40zQy2e8+GpMb+9myh1f88VZXZuet1+vXtAa8dJsW3VN9TgKeqBhrcbl52Rqimpg2n0pzRoo/XuYjZWtHipfttnT7ezXuyGoZZzimNpFbJBx1dW1nQ6CHq8mHOP+7BulsBzT8WYf8fC2sZG3djO1qbj4RtbBxhV/sluvXtBR5XcK013lXR7iL78U5kNImXrT5B6L6CDoIGnFzWBNP6AamMwhTRatfqLWd56uw1METhGnv/tus1uvXtDULqRik4qk220XjU/61xqzNG5dzAvhwadk6rEXtG6SNOvmvTfG7530v2Wudqq5Kbu24Cwfd/pBDyyYsOhLl4279LXeK5f1eA/nLRwEbWTpoo48rLAeYC4qR4rn3msDPrdbz6ajPijetnS4QsH1XwTb8CkbHBxymdIng63zMn7i/FyZCnbrMfeOeelSJfxfGXSXr8PfX52gqxLpKhsuGrfJO9eZW/j2+espVgKXpqSIF0BmpFjnZ2s+f+Lvj2uvTLFbj7l3zI9Vnvji2JfbvmS/vok6QQMfoxVsuGgc79N39dGz575eN6hkvPMWjt1MyePqV+2DA7zXcqprQOUasQ63qTP6LX5XV+kBTD7dEmQa4JckYE+asx4cNwU9t+daEe1TziQmnpZvBEGBItQLernnZLqlJKkk03+kEAgKFKFe0IHVt9EWvS/0NNltx4BP1AtacSX9iyTSDZU15YWgQBHqBa2wkX7gl0Z3unq6XdFAUKAI9YL26ri/6QB6u18bTXkhKFCEekFPVSFBJ2n90js15YWgQBFudDPdOXGN0k0aH1MIQYEi3OoHvfyD5rwQFCjCDUE/q0oI7R6jLS8EBYpQL2hcqdFxhM7yWaEpLwQFilAvaKOJNFl4MaWJprwQFChCvaD+CZKgCQGa8kJQoAj1graaIwn6SjNNeSEoUIR6Qdf4zjtKrqwuvURTXggKFOHGWXxMCCGkzDRt98dCUKAId/pBbx//5IDWsQYgKFCEWkFT935yidLcP3882E9TXggKFKFS0J9qCYf3Md/XFm/n0JQXggJFqBT0gZC40x9XrtFh477j2p4xAEGBIlQKWnmhMFlItA9JDEGBIlQKSj4RJlvcftrywXAbJRYqaH7omW4jj7qbC5gCtYKKt7lvc1vQnAvnrQQo+ASNrvve5+/UmeNuMmAGilfQfBQc4o+F3RCm12ppu30UGBuOBZ0+V5pNe0V7NmBY1Arq6+fn50ukoZI05VUg6Jh3pdmSiZoSAWOjUtCZBdCUV4GgS0ZKs2HvaUoEjA3H4yT9W0W8Ly++mqsniQOzw7Gg9GhE5Ii2jb/VNzEwFjwLSjMPfnjYceQc4E1wLSgAEBRwDQQFXANBAdfwLejVb5L1TQuMBjtBVQ6F6ISL/ULaBg+8rDYxMBOMBFU/FKIjt8MXZdGMeRFpqjIDc8FGUDeGQnRkxWBp1i9OTWZgMtgI6sZQiI48v0yaLZ6kJjMwGcU7FGI+CgSdtECazdV2VQowNmwElR0KMQ8Fgia0zBSm6Y0OqskMTAYbQd0ZCtGB3Ie6Hvr7QKehahIDs8HzUIg5KyKrdvzAYfRG4E0w6wfFUIhAD/j+JQl4PRAUcE3xCnqktY2Sb+oRD5geNoJun5xPwfWZ/3fCSuOv1MQDXgsbQRPbEb96Vpy3iDymJh7wWhgd4rM7d3LdAIICRbD6DvoOBAV6wErQ3+V66K1AUKAIT3UzQVCgCKaCTrsi+xYEBYpgKig5J/sWBAWKgKCAayAo4Bqmgu7/T/YtCAoUgbN4wDUeE3TaCgde7T2MGd2eYBZ6YH9moYd1ZRe63yBmoR8f6PjHdZcaHhL0/dGOdAlsyAzfesxCV6nILHQDH2ahGwZXZRY6vKyTv66bjLvpGUGdseMBdrHD5J4goR2GD9HPKsUsNI1aziz02QbMQssDQeWAoPZAUPVAUHsgqGogqD0QVDkQVA4Iag8EVQ8EtQeCqgaC2gNBlQNB5YCg9phV0D0PsYt9N7sHNS+bwix0dgCz0HTcSmahf23CLLQ8xSBo9nV2sRk+6z7tNrvYDMtOTWcX2xMjCxSDoAC4DwQFXANBAddAUMA1EBRwDQQFXANBAddAUMA1EBRwDQQFXANBAddAUMA17AWNb1u+63dMIm+VRhUbySBy4nZpxqJ0S2gWpS9rHxixKIuyKNsWmt0el4W5oLt8xmzuE8DkqrjFobECDMb8zGknjRHBonRraAalzyMv7ppWahaLsvNCM9vj8jAXtGsfSu/UeolF6KjuLKLS35ffSySL9C89L7T+pWcEjROmk8pm6192fmhGe9wVrAVNIauF6XNhLGL3Gc0iKk3o1MlPtIhB6bbQDEo/T/YJ03hyQf+y80Kz2uOuYC3oGXJUmC71yWAQu37vVgHNVzAITOuJFrEpXQrNoPT0JPFK5Yll0/QvOy80wz0uC2tB95OzwjSOXNU/dE7pkKXbRpLF+ke2WMSmdCk0q9LXl5rCao+LoRnucVlYC5ooPe12HUnRP3TGxvPCdEQQg4G9JYvYlC6FZlP6leHkqSw2ZVtCM9zjsrAW9DT5WpjGlGGWYCtJ0j+oZBGb0uvlDyKpc+m7Q8O2UTZlW0NbYLLHZWEt6DWfdcJ0bF0Gof89kStMd5B/9A8tWcSmdCk0i9J3l4xKE+cMyraFZrjHZWHezdRtEKVZ4dEMIieSj4Tps7UZhLZ8zDEp3frtQffSs6oPty7pXnZeaIZ7XBbmgiaUfPnI0GAWz1fIbh86b/e4EpsZhLYIyqR0KTSD0j8nU9eKpOlfdl5ohntcFvY/dW5uV747m58670xoUK7jHhaRrV8UWZRuCa1/6bHEwj/6l50fmt0elwUXiwCugaCAayAo4BoICrgGggKugaCAayAo4BoICrgGggKugaCAayAo4BoICrgGggKugaCAayAo4BoICrgGggKugaCAayAo4BoICrgGggKugaCAayAo4BoICrgGggKugaCAayCo3gy2PiiGzKeBqzxdjPGBoHpzLD4+vtY9wuQsfSzR08UYHwjKgiaPeboC0wBBWWAVtIJwiK/5Vgf/8Hf/6l/hLvHZmmtb+zf+wLO1GQwIyoKCgvrO/ry/T823Etr5pdIY39kJE3ze9XB1hgKCsqCgoEMoPUfGU7qL/HA7ZJ6wdlSoZ4szFhCUBQUFXUhpNtkgWnrqODmanJwcR5gMDGlSICgLCgq6WBQ0XhL0E2sH1GkPl2ckICgLZAQ9QK54uDDjAUFZICPolTLiMJqzin9EVgMDQVkgIyiNLj0/YYrPUg9XZyggKAvkBM1d3MS/UayHizMWEBRwDQQFXANBAddAUMA1EBRwDQQFXANBAddAUMA1EBRwDQQFXANBAddAUMA1EBRwDQQFXANBAddAUMA1EBRwDQQFXANBAddAUMA1EBRwDQQFXANBAddAUMA1EBRwzf8DxNPXAPOajDkAAAAASUVORK5CYII=" /><!-- --></p>
+<div class="sourceCode" id="cb14"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb14-1"><a href="#cb14-1" tabindex="-1"></a>m.L2.SFO <span class="ot">&lt;-</span> <span class="fu">mkinfit</span>(<span class="st">&quot;SFO&quot;</span>, FOCUS_2006_L2_mkin, <span class="at">quiet=</span><span class="cn">TRUE</span>)</span>
+<span id="cb14-2"><a href="#cb14-2" tabindex="-1"></a><span class="fu">plot</span>(m.L2.SFO, <span class="at">show_residuals =</span> <span class="cn">TRUE</span>, <span class="at">show_errmin =</span> <span class="cn">TRUE</span>,</span>
+<span id="cb14-3"><a href="#cb14-3" tabindex="-1"></a>     <span class="at">main =</span> <span class="st">&quot;FOCUS L2 - SFO&quot;</span>)</span></code></pre></div>
+<p><img role="img" src="data:image/png;base64,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" /><!-- --></p>
 <p>The <span class="math inline"><em>χ</em><sup>2</sup></span> error
 level of 14% suggests that the model does not fit very well. This is
 also obvious from the plots of the fit, in which we have included the
@@ -652,22 +659,22 @@ kinetics.</p>
 <h2>FOMC fit for L2</h2>
 <p>For comparison, the FOMC model is fitted as well, and the <span class="math inline"><em>χ</em><sup>2</sup></span> error level is
 checked.</p>
-<div class="sourceCode" id="cb16"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb16-1"><a href="#cb16-1" aria-hidden="true" tabindex="-1"></a>m.L2.FOMC <span class="ot">&lt;-</span> <span class="fu">mkinfit</span>(<span class="st">&quot;FOMC&quot;</span>, FOCUS_2006_L2_mkin, <span class="at">quiet =</span> <span class="cn">TRUE</span>)</span>
-<span id="cb16-2"><a href="#cb16-2" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(m.L2.FOMC, <span class="at">show_residuals =</span> <span class="cn">TRUE</span>,</span>
-<span id="cb16-3"><a href="#cb16-3" aria-hidden="true" tabindex="-1"></a>     <span class="at">main =</span> <span class="st">&quot;FOCUS L2 - FOMC&quot;</span>)</span></code></pre></div>
-<p><img src="data:image/png;base64,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" /><!-- --></p>
-<div class="sourceCode" id="cb17"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb17-1"><a href="#cb17-1" aria-hidden="true" tabindex="-1"></a><span class="fu">summary</span>(m.L2.FOMC, <span class="at">data =</span> <span class="cn">FALSE</span>)</span></code></pre></div>
-<pre><code>## mkin version used for fitting:    1.2.4 
-## R version used for fitting:       4.3.0 
-## Date of fit:     Thu May 18 11:38:11 2023 
-## Date of summary: Thu May 18 11:38:11 2023 
+<div class="sourceCode" id="cb15"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb15-1"><a href="#cb15-1" tabindex="-1"></a>m.L2.FOMC <span class="ot">&lt;-</span> <span class="fu">mkinfit</span>(<span class="st">&quot;FOMC&quot;</span>, FOCUS_2006_L2_mkin, <span class="at">quiet =</span> <span class="cn">TRUE</span>)</span>
+<span id="cb15-2"><a href="#cb15-2" tabindex="-1"></a><span class="fu">plot</span>(m.L2.FOMC, <span class="at">show_residuals =</span> <span class="cn">TRUE</span>,</span>
+<span id="cb15-3"><a href="#cb15-3" tabindex="-1"></a>     <span class="at">main =</span> <span class="st">&quot;FOCUS L2 - FOMC&quot;</span>)</span></code></pre></div>
+<p><img role="img" src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqAAAAJACAMAAABlpiR1AAADAFBMVEUAAAABAQECAgIDAwMEBAQFBQUGBgYHBwcICAgJCQkKCgoLCwsMDAwNDQ0ODg4PDw8QEBARERESEhITExMUFBQVFRUWFhYXFxcYGBgZGRkaGhobGxscHBwdHR0eHh4fHx8gICAhISEiIiIjIyMkJCQlJSUmJiYnJycoKCgpKSkqKiorKyssLCwtLS0uLi4vLy8wMDAxMTEyMjIzMzM0NDQ1NTU2NjY3Nzc4ODg5OTk6Ojo7Ozs8PDw9PT0+Pj4/Pz9AQEBBQUFCQkJDQ0NERERFRUVGRkZHR0dISEhJSUlKSkpLS0tMTExNTU1OTk5PT09QUFBRUVFSUlJTU1NUVFRVVVVWVlZXV1dYWFhZWVlaWlpbW1tcXFxdXV1eXl5fX19gYGBhYWFiYmJjY2NkZGRlZWVmZmZnZ2doaGhpaWlqampra2tsbGxtbW1ubm5vb29wcHBxcXFycnJzc3N0dHR1dXV2dnZ3d3d4eHh5eXl6enp7e3t8fHx9fX1+fn5/f3+AgICBgYGCgoKDg4OEhISFhYWGhoaHh4eIiIiJiYmKioqLi4uMjIyNjY2Ojo6Pj4+QkJCRkZGSkpKTk5OUlJSVlZWWlpaXl5eYmJiZmZmampqbm5ucnJydnZ2enp6fn5+goKChoaGioqKjo6OkpKSlpaWmpqanp6eoqKipqamqqqqrq6usrKytra2urq6vr6+wsLCxsbGysrKzs7O0tLS1tbW2tra3t7e4uLi5ubm6urq7u7u8vLy9vb2+vr6/v7/AwMDBwcHCwsLDw8PExMTFxcXGxsbHx8fIyMjJycnKysrLy8vMzMzNzc3Ozs7Pz8/Q0NDR0dHS0tLT09PU1NTV1dXW1tbX19fY2NjZ2dna2trb29vc3Nzd3d3e3t7f39/g4ODh4eHi4uLj4+Pk5OTl5eXm5ubn5+fo6Ojp6enq6urr6+vs7Ozt7e3u7u7v7+/w8PDx8fHy8vLz8/P09PT19fX29vb39/f4+Pj5+fn6+vr7+/v8/Pz9/f3+/v7////isF19AAAACXBIWXMAAA7DAAAOwwHHb6hkAAAgAElEQVR4nO2deWAM5//Hn0hC5BARouJoE3GUVBH31ZZQZ6mjLdXqgRLfOuqIuHpQTVF1taSt4yvqiKPqiFYULV+qXz+Uto4Kpa2vEqGiiBzPb2Z2E8lmZzOzM0/2mdn364+Z2dlnPp+PzcvMzuzM8xAKAMcQVxcAgCMgKOAaCAq4BoICroGggGsgKOAaCAq4BoICroGggGsgKOAaCAq4BoICroGggGsgKOAaCAq4BoICroGggGsgKOAaCAq4BoICroGggGsgKOAaCAq4BoICroGggGsgKOAaCAq4BoICroGggGsgKOAaCAq4BoLaMozksVd4tePFR/0aDFyba3kvN6FNsE/Y4B/F5XGEbBDnGwgZLczSpzUP8Y987of8OPGELMl/ceypGgFN3r2rKl20sK6PuDBWWHjANr27AEFtKWjMP8Oti10yxLcudbK88vyQ2gp6rqK15Yy8OAUFTfGS3mtyT0266DwvW1kXCqV3FyCoLYIxMUskLtEXCfHvM6mvPyGDxLfaElJhwNS+noR8aSvoY4T0XfHv18uSUoescQoKWpv4LtrVh5D31KQTBSXnKc30sQpaKL27AEFtEYzZZl1MIaT2aWF+uhYhKZSuI6TeBeHlVkIa2gh615u0F1+sIeRd68YFBL1EyARKb5cjHdWkEwR9gHxO6SFS0VMUtHB6dwGC2lLAmCaE7JYWdgqHZ0rbEPK19PLZho3vFRb0CiHNsoUXd1etOmjduICg3xFRNFqf1FKTThC0NxlB6TzylJcoaOH07gIEtUUw5vXPRH6+5kFqWlc+RDzSaDCpWKBd4UN8OCER49efzb3/fgFB76WlZVJ6059Eq0knCPoBaST4SOIlQQundxcgqC35Zy2LjgpnK9aVwunJkTRhL1mgXWFBD1SVtinfd3fe+4XO4gVyXiAkSUU6UdCDxPMmrUG+EwW1Se8uQFBb7htzhJCu1pVdCflBOIy3KtDO5jLTrdWDG3iLmy20vm8j6I1ehIyyLm/9l8BHxaQTBT1bh+y6RLxvi4LapHcXIKgt978Upt0/5tYk5C8aREItr+7dvZtrK6hI5n9eFc7DcywvCgv6f+HEc1beiymikJ2LSycK+gp5Z6Ow45QO8YXTuwsQ1JYCZy1RlqvnlO4l4pfBloRYriE1It7/0OmELBVfLCXkLXoyPt5yciQcnP+wbFxI0JU+pHL+wV9GUNt0oqBLSedxgv+SoIXTuwsQ1JYCxnxNSN1UYf5rXUJ2UPpvQlpeF15u8yCtKd1ISA+xUQ9C1tPThDwmnsXnNiN+2ZaNCwq6xYPU+V1tOlHQUySwpfDNVRK0cHp3AYLaUsAYOpCQcv3f7B9AyEAq2UceGjPneU9SSvDnRnVBmKlThP1ateviXo20XLQxoT0hz1i3FQQdKJ2ef/ZPTg1CxohLG9WkEwWlFYkH+d0iaOH07gIEtaWgMTc7Ws9gOt4UX15oa3nl/YH46ttgy6uK3wovfqtibRl5w7ptfP5vmH/sz1uKVJNOEvQpQmpQi6A26d0ECGpLQWNo7uoBj/g9MmC19aQkZ2GvhwIav/qr5dX12JbBwS1jr0svbs1uU61MeIflWXmbFhB0hUJBC6eTBJ1FyLN5gtqkdw8gKOAaCAq4BoICroGggGsgKOAaCAq4BoICroGggGsgKOAaCAq4BoICroGggGsgKOAaCAq4BoICroGggGsgKOAaCAq4BoICroGggGsgKOAaCAq4BoICroGggGsgKOAaCAq4BoICroGggGsgKOAaCAq4BoICroGggGt0FvSNKAAU0OayawRtsfwwAMVTS+mY4noLerD4NgDQBhAU8AxHgh6bPXVDlp2mwJ3hRtCc16uPe6dD5Fl90wCjw42gCa0zhOnCpvqmAUaHG0Fbp4jT3PBf9M0DDA43gj50Xpp1+lrfPMDgcCNoyz3SrNYJffMAg8ONoAufuCNMlzZ0nwHOjUHOtynsSZPPz42g2S/XfGtez5r4CsoZxwOimRMxST4/N4JSun/K4OV39c0CNHO0Ifsc702Uf48fQTPnNik/DJdBeQOCWrnRsO+SsJkh2+y2Bi4DglqZOJjeCKAHQ+/pmwdoBIJaefQwpQF/08aH9M0DNAJBrYSdo/Thn2jHnfrmARqBoFa6rKW001d3Q37XNw/QCAS18tVDP9FXPn6ln75pgFYgaB6JVdrV9h90U980QCsQNJ+Mb0f01zcJ0A4ELcD2LvomAdqBoAX4MVLfJEA7ELQA6eX1TQK0w1DQo/+1LhhGUOqPcyTeYCjowF7WBeMIWuekvlmAZooImrtp3Ji1OQ63yc0usiqz0E/Ylod3DShohxR9swDN2Ap6vW2r2XMfb/KX7AZnyY4aHnXfF5ZujorwCZ+eS+kD68YF/UZXRPnWXyasrvZRNAnsd422IITckDYxjqCDluubBWjGVtAhMeIzD7HPym5wlpQdsnmcx5uU9g6as2UUWS0I2qTn+jsLvKclj/b4WBA06Pm9s7xG0Gt9Ol+2PD9hHEEnT9c3C9CMJOjd9DyuBZwRZ3/4/y9/lc1N5mdJH2E6PuAm7bNEWKgzQRC0QS69FSz+bYeECIKKj/X0aG3IQ/zHr+mbBWhGErRrUB7lPSzzUoH5q1oV3uAs2SBMTxDxtrSMo595jxMEjaX0EDmQlpaWSC7SanHCO+NaGFLQrd30zQI0Y3uIr/CHOL1eTvbhnLNkv9hA0HR/A4/QLqGioHMpXUcsHKfVZlMuBM28mu7gXfuCHn1UbRbAGFtBx/QXzsezh74qu4FlD/oL+U+6d4xwKtVCFHQepbvJFWuDanOo6wW9OCnMg5AyERPPyTSwL+jVYFVZAHtsBb3du25s3COd/5bd4Cx5RphO8r2+k5yl9E4Vq6BXyiwVVk/twIegR3yrD5+fuHLBv8KDjtlvYV/Q3LL/qEkD2FP0Qv2++Jm7HWxwlvjEbI8rNYn+5t1v35fN/bqmSYLS2NIzksd7zC8g6Mv1D1sumJa8oI93uW1ZyOofbb+FTP+gEWfUpAHsUf1L0lnyRbfytWcKZ+pr6/g12/rvoGkWQXPnRPrWE8/r8wXdExZg2RGXvKDlNuQt7ZP5eV1G0Mcd/d8ELsAJQQ+rzVHygjaJyVt6q7n9FjKCDlypJg1gjzkF3eDRZemBk6e+X9nbc4P9FjKCTnxXTRrAHnMKSrc9IV308mifLNNARtBFMXZXA5ehWtCs3zLV5nDJddD0n1JSjl+zWZmR359ZxHd2t9r8lMo0gDGmvmE551ebfkJ+yO/QzOsDu1scbuxEGsAQkwq6rW/35TQhmPjMkOnv0/9Tu6svh6hKA5hjTkGTSNOu3jH+03dM8V5qv4WMoLk+d9TkAcwxp6CNhlO6mLwnLMU1st9CRlAalqomD2COOQX13U7pVemulh1+9lvICdp2r5o8gDnmFLTW+5R+T1YIS/Pr2m8hJ+iAVWryAOaYU9D3fEa/XbVp6M60L4Mn228hJ+iEeDV5AHPMKWjWlNBKw+4NJoR0k7k9SU7Q+a+ryQOYY05BrZxcKdsdrZygG592Ig9gx0+lw5lTYap8flc98iEn6CEM1skZ51PZ4+AuYN4E/bOKvnmAweFN0Jwyqm81AGaGN0Hpg+f1TQSMDXeCtt6nbyJgbLgT9Nk1+iYCxoY7QcfO1jcRMDbcCTp3tL6JgLHhTtCkvvomAsaGO0EPyjwGCtwT7gT9s7K+iYCx4U5QWuGK3DvADeFP0LZ79M0EDA1/gg5foG8mYGj4E3QRelkG9+FP0L2t9c0EDA1/gl4LlHmUHrgj/AlKq1zUNxUwMhwK2lGuwzHghnAo6JhZ+qYCRoZDQZcO0jcVMDIcCnooSt9UwMhwKOgtv6KD5QJ3xVUDeTkQlIZjqA+Qh6sG8nIk6FObVOUCZsZVA3k5EnTSO2pyAVPjqoG8HAn6+TNqcgFT45SgF08U07j4gbwcCfpjPYU1AfPjhKBfPUAI7eDwprjiB/JyJGhmWdmxnoG7oV7QRK+hiYRO9Uhw0Lj4gbwcCUrrKS0KmB71gtYbQ9OEF+MjHbUudiAvh4I+87nCooDpUS+ob7IkaLJM5/N52B3I68TQPLw/dLDt25MUFgVMj3pBG78pCfpOg+K3uX4mp/CKq58mWPFZ7GC7jRhvDlhRL+hy7+kHyJWlpec6ap3Y8ji93J2QgHkyDRwe4k+HKywKmB4nzuIXBAvfLstMdHTf+3zS9jLtVHXxjvFeMuNrOxQ02++mwqqA2XHmOuitQ+t2X3XYOCyO0stEHC92osqBvCxEyfZtD9wMNr8kBW8UTojILWEpOcB+C8eCDpIZQBG4HeoF7Z6Hg8a9umbSrIBdwlKcExfqKZ31hsKqgNlRL+hLIr1CSv3LQeOTIZGz9swIXbYnzlumP1rHgiZ3UlgVMDvOHuJvdXI4XsyZ4RWkK/X118k0cCzoxVCFVQGz4/R30L3EcSdf984d3H5A7m7Q4gSlwZcUlgVMjtOCriirqX+FYgTttVZLcGAe1Au6SuKtKo9ryluMoB8O0xQdmAb1gvpI+LY4qSlvMYIekxnFG7gbHD7VKZJb8U998wGDwqmg9GkMlwREVApasQCa8hYn6Dz0EgpEVAq6ZMmSef5VR8+dXKf6Nk15ixP0WB1N4YFZUH+IH/aY+MRQdmdtu7jiBMWXUCChXtCqlqeMNmv7sac4QWnv1ZriA5PghKCWe5AX1NCUt1hB5w/VFB+YBPWCDgncKky3BbI9xNMfa2uKD0yCekEzHicVIiuQ9rc05S1W0NxKf2hKAMyBM9dBd8ePnv2dxrzFCkr74NljwO+FeuFL7hB9MwJDolLQz1LoZ3loylu8oMdraUoAzIFKQf2fo+Xz0JS3eEFzK+NLKOD4EE/7rtI3JTAiTgp6aodtrzYqUSDowsHaUgAzoF7Qix3H0Y2epKJM18kKUSDoiQhNGYApUC9or9DNtOGT5zo6euy4eBQImhuCMRGBekErfEIvkRS6upKmvAoEpYMwcjxQL2j5tXSZzx261V9TXiWC7mipKQUwA+oF7dRq1yM96a1uTTTlVSJoVmX5x5aBm6Be0GOVSbkjtHbprcVs4PxAXvkMe09RacDEOHGZ6fbha5QmOR4NTtNAXvnsDF+yE+MpuDdshqHRNpBXHnvDfZ55LOI/CgsEpoTNMDTaBvKycinkm7FT6fbKjrsiBeaGzTA02gbysjJ7GD1cM5cO+khhhcCMsBmGRttAXlaGf0xp7f/SuWMUVgjMCJthaDQO5GVh8jRKp46lE2YorBCYEUbD0GgbyMvCf2vMahLsPTu0uDMyYGYYDUMjM5DXbpLP+8VmzK3tM2pVRW8F4zEB88JmGJo8Nst2cqtgD7ot6kBMt1a963+jqD5gTtgMQ5O/0S65dxQIOlbcyV6oNG2yolTAnKgVNOPrdReEo++fP+/t5qDxpoEWSIeBA+23UCDoiIXitNWrYxVWCMyISkF/qS4c3of/WEM8AXLQeLsviWohQB5u0cJ+CwWCftJbnC6viCc/3BmVgvYITjy+plLVlmt3HnLYccOpqGanqcZD/D8RM+/Ru1O9DyqsEJgRlYJWEr8Xvk9+K7b5vVj/xRoFpRd6BjUq33fqAIUVAjOiUlAijnu0UdETnnurd/mfNkEpTT9ynd6seFZJU2BO1Aoq/jC0WdkjyNefrahVUIm41xU3BaaDoaCUrhl9Wu4tFYJeroD7mdwXpoI6QIWgdMhb2vMBg6JWUG8fHx9vIg2VpCmvGkFPV9LW1SMwMCoFnVIATXnVCEp7L9KUCxgYjvtmus8PYVn6ZgeGwRCC0nYY1stdMYagKTXv6JseGAVjCEp7v6NvemAUDCLoxYroZMQ9MYigdPrT+uYHBsEogmbWlnu6CZgaowhKv45AJzjuiGEEpT3Qk5g7YhxBUysUfxcqMB3GEZROw3mSG2IgQe82Ri9N7oeBBKVnQ/5P3yIA/xhJUJoUcUPfKgD3GEpQGtNP3yoA9xhL0LuNP9a3DMA7xhIUX0PdDoMJStfVvKxvIYBvjCYofesRR6PbALPBTlAdxkmyyxstCz1Cl7tp7MhV2c4GA7zDSFB9xkmyS+7L3Qs8ofT3Yy3nzO/46CVnowHOYSOoPuMkyZDd+/mc/BcxQ8WOdN/s5XQ0wDdsBNVlnCRZbrcZlb8c/Kc4vVMuw/lwgGfYCKrLOEnyXI96zfqt8563ZR6GJ0JMChtBdRknyQG3ujz1j2XpAcnMjHL/aAkH+IWNoLqMk+SIrMHNLOMzxPb5bvZ7KTEvaIoG+IXRWbwe4yQ5JPfNmr+K8xs1vJu2LBuMe5nNCrProHbHSbryaYIVn8Uq4xVhYVWxb/DYvt/NmrlrBPagZoWVoNnnLH2B3Cn0y+SJoXl4f6gunh22hMzOxXdQs8NG0KypZUnZCeKZ9gqZ7bQe4kUutu7+P5zFmxw2gs7yGrthjNfLlK2gNOvNGoG4Dmpu2AhaO06YrCJfMhaU0q2+jcXH5fE8nWlhI6jfdnE6MOwOa0HpqYplRyyIbojf4s0KG0GbviFO/woZwVxQmjupXN0E3M1kWtgIuoCM3Ckcerd7vhjLWlBK/x4RukrJ0MvAiDC6zDS9HBGH39oaStgLSunB5o1TdAwHOILVddDMVKmvr6xvlth/X1dBKd1SKxoPK5kSwz3yIcO9j6oMOK5vSMADZhGU0oz3q3Tfr3dQ4GrMIyild5dEtNmaU3w7YCDMJCil2WubhsdfYREZuAhzCSpweHCF/t/iqpNpMJ2glN5YGBk+9RSz8KBEMaGgAkfHhjad/wfLDKCEMKegwrfRlJeCm79/hm0SwB6zCipwL2V4aGTctxiH1tCYWFCBnINTmgT1+eRCSeQCTDC3oCJ/JQ6oHP5qIr6RGhPzCyry86K+lWoNSjiBq/iGwz0EFcj96ZNBtQM7T/0S9zYbCrcRVOLKlmldKlXt+eYXeMbOKLiXoBK/rZ/cvVpguxGLv7N9bB/whxsKKpG2a/6QVoGhHWIW7ryAH0Y5xl0FtXBx54KYDtXKPvL0hE92n8eDTTzi3oJayDi6/r1XHqtepmb0kBmJ+y7gyj5PQNB8pjxc5dHXn29drUy1Vs++MW/9/t8yXV0RgKD5ZFQp+/TgCM9ESu9d2L9mzqg+rWqUDnmky6C4+Wv3/pLm6urcFwhqpVuIuMMcV7rgAf7yj8krZo589rGHg0uHNuj4/KgZCZu+/fkyvgKUJBDUip9lrO8ySXbfzfzj6I7EuXGv9mr7cIhnYHjTzv1HTJm7fPO3P164WZJFuiEQ1IrXPmkWPLP4ptd/PZS8auHbo198qu0j1f09K4Q3bv/0SyOnzEpYm7zvWGr6PScr2D9t1LK7Tm5rXiColfLviNMsr51qN8xKO3t418Zl894ZN/SZLq0bhAV5lQkOf7RVdL+XYmJnfJCw5ouU74+l/nW9uDDZL0W8/WGvmj87U7uZgaBWXvM9Q2lOdKAOoe5cTT26f2fSskXxk8YMfbZndLMG4SGBxCfowYio1tG9+g0ZFjslfl7CqqTklO8Pn069YtnlLmgv7j2XP4pfDQoDQfNoW6pOU7+AH5jFv51+/szh/Smbkj75OP6d2JFDn+/XObp5VK3wSkHexDeoik/1B4OCH+ri32PosNjYGfEfJCSsTtqYkvKfw4dTUy+mp7vtsR+C5rP7he7vu+h+vFvpl6pGNp8587nAmhMSPo6Pnxz7xtChz/XrHR3dKioqPLxaUFAZQvyDgmqEhzeMimofLeyH+704dGhMbGzszHhhb5zwSVJS0paUFGGXfPhIamrq+fT09Buu+ac4z/G5b20q+mue8QaTNSnV24vTXV4nZFtkpKdfSE09evjwNykpXyQl/TshYVF8fHxcrLA3Hjq4X79+PaKjo5tFRTUKDw9/KCgoKFAcZkWYBz0orIiMiopqLrwf3VNo2E8cJECUO3ZSvIg0qsUqQfGkDSkiwl5b4EyqyOV0kRLovzp3uH9wueCIVNv1xhtM1qRUbyBasMD3v3oGzRXt+k3w7Lhg3PeifJtFD0UhPxLVfFe0NFYa1eJ50dw+osPiXlugVrhIZVHxID9pUCESKL2oIL0RXjvKQntpm+ge/Sy8Yh0lY1SshcnxVj7MG+BF3Ntb2JGSx77Do0v3Szq6qny47T/BgIPJmpOwQdXHzegQ2Ub1VYQS5Ia0O70m7VpTTx+28I1FsS1W6ZZaNZxn9XKGVdTY0XkDvAzpl0fn6DzaRHkHRUVNp7942vpixMFkTUnHTcdmTV5/u8p5VxfiIrx3SLPAuTbrS3Yw2R/y/8t4faAmnhuwubbwbejeqO6ursNV+KyWZr62O66SHUw2I/9LR8R3auK5A4tCug8M6+W2N/k3eFi8FyLB87LtehcNJtvioJp4bsHVLYnyp/Cm5z9lq727qJvvs7brXTWYLAQFhUmpUb6Sb2yRe3BLdjDZ+0BQYEPuuSN3iq511S9JEBQoAoICroGggGtcJWj7gKAi+BEPZjAMzRKDll2q6B/XWbyU9vGqs6B30ouyusM5ZlT/llnoKa8wC33Gi1nocwPfYRZ6V4Sdv66TKL4pS2dB7bGlB7vYYex6Y5o7hlnoLC9moWnMR8xCn6zLLLQ8EFQOCGoLBFUPBLUFgqoGgtoCQZUDQeWAoLZAUPVAUFsgqGogqC0QVDkQVA4IaotZBf11CbvYb7J7MHHfF8xC545jFpqu/55Z6BvTmYWWpwQEBcB5ICjgGggKuAaCAq6BoIBrICjgGggKuAaCAq6BoIBrICjgGggKuAaCAq5hL+iGpoFPHGUSeZPUUdRgBpFTvpRmLEq3hGZR+sLm/nVmi+Pl6V92Xmh2n7gszAXd5jF8fWe/iyxCzwlZIrBX/8A5zaTbjViUbg3NoPTp5I1tE72msig7PzSzT1we5oI+0ZnS29UnsQgd04FFVPr7R+2IZJH+peeH1r/0zHIjhenYstn6l30/NKNP3BGsBU0nS4XpsDAWsTsPZRGVJrdp4yNaxKD0vNAMSk8lYjf4G8g5/cvOD83qE3cEa0F/IgeE6XwPFoO0136ysd+jCQwC0wjRIjalS6EZlH73rDgo2Jiyd/QvOz80w09cFtaC7iInhWkiuap/6JzSwfM3DyZz9I9ssYhN6VJoVqWv8hrP6hMXQzP8xGVhLWgKOSVMVxJHo385SeZacXioF8sxGEZOsohN6VJoNqVfeYG8lMWmbEtohp+4LKwFPU7EZ2QWlGGWYBM5q39QySI2pUfcfx5J59K3h4RtpmzKtoa2wOQTl4W1oNc8VgrT12syCP3XYXEI4S3EdgAJHZAsYlO6FJpF6ds9Y6SuthmUnRea4ScuC/PLTO17U5oVHssgcgr5XJi+VoNBaMtujknp1m8PupeeFfqCdUn3svNDM/zEZWEuaLLn2/sHBLF4fD27ecj07SNLrWcQ2iIok9Kl0AxK/4ZMWCFyR/+y80Mz/MRlYf9T5/pmgR3Y/NR5e3TdgFY7WES2flFkUboltP6lL7EMDCsefvUu+35odp+4LLhZBHANBAVcA0EB10BQwDUQFHANBAVcA0EB10BQwDUQFHANBAVcA0EB10BQwDUQFHANBAVcA0EB10BQwDUQFHANBAVcA0EB10BQwDUQFHANBAVcA0EB10BQwDUQFHANBAVco7Ogm+MBUMDsf1wjaIsXYgEonko/MhT0+hnZLnZbHHQiHnA/GrARNLHlcXq5OyEB82QaQFCgCDaCzidtL9NOVRfvGO+10n4LCAoUwUbQsDhKL5PvhKWJjey3gKBAEWwEDd5I6QlyS1hKDii4/tvwPDyXqIkH3BY2gvbqmkmzAnYJS3HNC67PSc3D71M18YDbwkbQkyGRs/bMCF22J857jf0W/hAUKIHRWfyZ4RWk3srrr5NpAEGBIhgJSum9cwe3H5AfQQKCAkUwE7QY9BN0z4w3t+fqFQzwhtEFvd2z/uS3mrZN0yca4A6jCzp2QBaluWOe0yca4A6jCxpyQZzeKqf0nhdgMAwu6D1vyzyMxRCKgAMMLiit+Ls4vV0uQ59wgDeMLuioQeIdfRP76RMNcIfRBc3o1GjmrHZN/9InGuAOowtKaXLc+A2y90UDo2N8QYGpgaCAayAo4BoICrgGggKuKVlBTw0basVb7nFPAApSsoL+mZBHmUV6xAOmB4d4wDUQFHANBAVcA0EB10BQwDUQFHANBAVco1LQRQXQlBeCAkWoFLRiATTlhaBAETjEA65xWtC0aZry2hM057amkMCMqBc09/MJ4wS6VtCUt6igp3v4+dZeiU5sQCHUC/o2qeMd2qKKn0zf3gopIuiZyotu537fcIamqMB0qBc0bDT9tCu93WybprxFBH1xlji9VP5vtZG2x41PwkNzpkW9oGW20iNVKd3c3FFrmn3ujjS/c9n++0UErX1SmrXbq7AeKxmdGuOxYzOjXtCaH9CbHlfod/4OGmdNLUvKTsgWllbInP0XFfSUNGu3R2E9Vka+LO494/qo2woYBvWCjgxaQhvEnO1X10HjWV5jN4zxepmqEPSF2eL0f0E3FNZjpeIf4vR2uVvqNgNGQb2gf/fuQ/d5E+8kB41rxwmTVeRLW0EzUvIo+4nNJqcrL75Lf2g8XWE5VtB5mNlx8jpo+s7fHDX22y5OB4bdsRH0h+g8POfYbvNLV9+AiOVqLzOFSHWg+0XTwuaXpKZviNO/QkYoP8QLZN1Uk8PCuOfEDmxHD1C/JTAE6gXtnoeDxgvIyJ13Kd3u+WKsCkGd4XavepPfbtLumj7RAHeoF/QlkV4hpf7lqPX0cuSsMNsaShgLSuneGW8m4/cn0+LsIf5Wp6YOm2em3hVnWd/IDHmIm0WAIpz+DrqXXNGSF4ICRTgt6Iqymo6rEBQoQr2gqyTeqvK4prwQFChCvaA+Er4tTmrKC0GBInBHPeAaCAq4Bg/NAa5RKeiSJUvm+VcdPXdyneo63xEoeD8AAAsSSURBVLAMgD3UH+KHPSZegs/u/JqmvBAUKEK9oFU3SLPNoZryQlCgCCcEtXTevaCGprwQFChCvaBDArcK022BJXCIT+pav/sXmtIAo6Ne0IzHSYXICqS9tocslAj6UpPNP214dLimPMDgOHMddHf86NnfacyrQNBv6okPht6KOKAxlXHIdnUBHFKyF+r3BOXhUTYoKPgipYOCZOc+vtLcx6eYdiaZB5b2KNN4o+vr4G2uTtDPUuhneThj6PV0K/7z09PFPhoy02Xnr34gzWfEFNPOHPOjld/6LXNXvVmuroO3eaQ6Qf2fo+XzULihfRQc4hc9L816L9OUyCi8InX6czEow9WFuIyvxr6yoOi/nuPf4m9U+zSX5iwMc49H3uv8Is3afOviOlzFvb6Rcz4bWP2I7XonBT21Q+NjakrO4n9uHdb5wcdPa0tkFCIs/87Hd7u4Dlcxp0uWMF33sO1t8OoFvdhxHN3oSSoe01SPouuguad3/Kopi4HoP1+cXg1Kd3UhLqLZ3uXPdY37X11bH9UL2it0M2345LmOjh47Lh78kmTDTyErc+gvLSe7ug5X8WCTDmu2jw9p8rXNevWCVviEXiIpdHUlTfVAUFuOtPN/4IFFbtuR5IPSI0QpXj/brFcvaPm1dJnPHbrVUe92xQNBi/KPTFeVbsGDdcUvN+/5HbZZr17QTq12PdKT3urWRFM9EBQU4qHhVYbFtWjaVvsh/lhlUu4IrV16q6Z6ICgoRPSm04tn7rhb5bzNeicuM90+fI3SpDPa6oGgoBBf1D5P6b1RRU69nboOevGE5nogKCjMopAeL4T3TLNd7YSgXz1ACO2wwHFz1X3Ug5Mfv7vdbU/iBa58ufJ40bXqBU30GppI6FSPBAeNneij3t3Jja0yfFKrJg77BXZH1AtabwxNE16Mj3TQ2Ik+6ik9OmtKUpbCaszHsqbXhen7rVxdB2+oF9Q3WRI02c9BY7k+6u9TRNCcf9UYPz26vtv8tGlLu6/Eae6Dp1xdCGeoF7Txm5Kg7zRw0Fiuj/r7FBF0SWvxrqVF2q6uGpiHzkuzTrbXAd0d9YIu955+gFxZWnqug8ZO9FHfepc4zQ3/RWE9+vN34hzb39lKkGaWh2iK3Czh7jhxFr8gmBBSZqKj7kHl+qg//kw/K162frt8DzLO0zeoVL2rrko/90nx+/ea+ujNvDDOXAe9dWjd7mL+kDJ91Kcl5eGz2GaLlnukWS3tl1idY45XIqV/PBTmovT03jP1Z302oLq2mxhNiFpBM75ed0E4Ev/5895uDpur7qN+4RPihdOlDV21BwmRHm++VqrILd0lxs5xry5yj6cH1KBS0F+qC4f34T/WEKYexW8zUb4b+yKCZr9c8615PWu67Cuol2UU2wrvu6oAYBeVgvYITjy+plLVlmt3HlLwn53IXzOxcx10/7RRy+8WWpPyfOuBexTWpxWf1dLMDz8g8IVKQSuJO5j3icLfO9QJWoTRdZfv+zQiTlkurTSVfnhILOW+T1XyiUpByTphslHpE57aBN0fIcpyvcb/KcymjXNlwhasf7pUbIkkA4pRK6jY9+JmpYLukh/iVYGgE9+RZpPeVphNI9eiA31qauuVF+gPU0EdoEDQ4R9Js7ljtGcDhoVjQee9Is36O7ptCpgdtYJ6+/j4eBNpqCRNeRUIevUB8X/Dqqo3NCUCxkaloFMKoCmvkrP4/z7SqH+DRvhtxa3huG8mSrMOrf7Bne8xB5wLCgAEBVwDQQHXQFDANRAUcA0EBVxTsoJek7+jHgB7lKygx/rJPpMEgD1wiAdcA0EB10BQwDUQFHANBAVcA0EB10BQwDUQFHANBAVcA0EB10BQwDVMBb1+RvaJIggKFMFI0MSWx+nl7oQEzJNpAEGBItgIOp+0vUw7VV28Y7zXSvstIChQBBtBw+IovUzEXtcnNrLfAoICRbARNHgjpSeI2INocoD9FhAUKIKNoL26ZtKsAHHcjrjm9ltAUKAINoKeDImctWdG6LI9cd5r7LeAoEARjM7izwyvQETqryu0eo8HseKxSFU84K4wuw5679zB7QfOyb7d4qDKeMA9YXqh3sEoHxAUKIKpoA76qIegQBEQFHCNywSdmFCEd58cyIz2zzML3as7s9ADn2AXultvZqH79yr6x3WWqiwFdTDKx6dDi/K4/8PM8I5gFrpyBWah63owC/1w0APMQoeXtfPXdZKRfzMUVCVberCLHSZ/QUErDIchyfJiFprGfMQs9Mm6zELLA0HlgKC2QFD1QFBbIKhqIKgtEFQ5EFQOCGoLBFUPBLUFgqoGgtoCQZUDQeWAoLaYVdAdT7OLXesis9ALxzMLne3HLDQd+Qmz0L9GMgstTwkImn2dXew0dqHv3GIXm2HZGXfZxWZYtiwlICgAzgNBAddAUMA1EBRwDQQFXANBAddAUMA1EBRwDQQFXANBAddAUMA1EBRwDXtBNzQNfOIok8ibpP7KBjOInPKlNGNRuiU0i9IXNvevMzuLsig7LzS7T1wW5oJu8xi+vrMfk7vi5oQsEdirf+CcZuPEGYvSraEZlD6dvLFtotdUFmXnh2b2icvDXNAnOlN6u/okFqFjOrCISn//qB2RLNK/9PzQ+peeWW6kMB1bNlv/su+HZvSJO4K1oOlkqTAdFsYiduehLKLS5DZtfESLGJSeF5pB6alkpzDdQM7pX3Z+aFafuCNYC/oTOSBM53tkMohd+8nGfo8mMAhMI0SL2JQuhWZQ+t2z4p3KY8re0b/s/NAMP3FZWAu6i5wUponkqv6hc0oHz988mMzRP7LFIjalS6FZlb7KazyrT1wMzfATl4W1oClSV3grSbr+oTPXpgrTF8vJDnvnPJJFbEqXQrMp/coL5KUsNmVbQjP8xGVhLehx8r0wXVCGWYJN5Kz+QSWL2JRuOcRL6Fz69pCwzZRN2dbQFph84rKwFvSahzgm3es1GYT+63CuMN1CLusfWrKITelSaBalb/eMuSPOGZSdF5rhJy4L88tM7XtTmhUeyyByCvlcmL5Wg0Foy26OSenWbw+6l54V+oJ1Sfey80Mz/MRlYS5osufb+wcEsehfIbt5yPTtI0utZxDaIiiT0qXQDEr/hkxYIXJH/7LzQzP8xGVh/1Pn+maBHdj81Hl7dN2AVjtYRLZ+UWRRuiW0/qUvsY5UdVn/su+HZveJy4KbRQDXQFDANRAUcA0EBVwDQQHXQFDANRAUcA0EBVwDQQHXQFDANRAUcA0EBVwDQQHXQFDANRAUcA0EBVwDQQHXQFDANRAUcA0EBVwDQQHXQFDANRAUcA0EBVwDQQHXQFDANRBUb/paO4ohM6j/Z64uxvhAUL05uGHDhuqthclJ+lyKq4sxPhCUBZHPuboC0wBBWWAVtLxwiK/2YUvf8I8vdS//oNi35ooo3/rLXFubwYCgLCgoqPe0b7p7VPswuZlPBl3gPS15tMfHLq7OUEBQFhQUtB+lp8goSreRE7eCpwtrh4S4tjhjAUFZUFDQ9ynNJqtFS48dIgfS0tISCZOBIU0KBGVBQUHniIJukARdZ70AddzF5RkJCMoCGUF3kysuLsx4QFAWyAh6pYw4jObUkh+R1cBAUBbICEpjS89IHu8x38XVGQoIygI5QXPnRPrWW+Li4owFBAVcA0EB10BQwDUQFHANBAVcA0EB10BQwDUQFHANBAVcA0EB10BQwDUQFHANBAVcA0EB10BQwDUQFHANBAVcA0EB10BQwDUQFHANBAVcA0EB10BQwDUQFHANBAVc8/9HUPmXE6PYSgAAAABJRU5ErkJggg==" /><!-- --></p>
+<div class="sourceCode" id="cb16"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb16-1"><a href="#cb16-1" tabindex="-1"></a><span class="fu">summary</span>(m.L2.FOMC, <span class="at">data =</span> <span class="cn">FALSE</span>)</span></code></pre></div>
+<pre><code>## mkin version used for fitting:    1.2.6 
+## R version used for fitting:       4.4.2 
+## Date of fit:     Thu Dec 19 10:28:48 2024 
+## Date of summary: Thu Dec 19 10:28:48 2024 
 ## 
 ## Equations:
 ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted using 239 model solutions performed in 0.015 s
+## Fitted using 239 model solutions performed in 0.029 s
 ## 
 ## Error model: Constant variance 
 ## 
@@ -702,10 +709,10 @@ checked.</p>
 ## 
 ## Parameter correlation:
 ##             parent_0  log_alpha   log_beta      sigma
-## parent_0   1.000e+00 -1.151e-01 -2.085e-01 -7.828e-09
-## log_alpha -1.151e-01  1.000e+00  9.741e-01 -1.602e-07
-## log_beta  -2.085e-01  9.741e-01  1.000e+00 -1.372e-07
-## sigma     -7.828e-09 -1.602e-07 -1.372e-07  1.000e+00
+## parent_0   1.000e+00 -1.151e-01 -2.085e-01 -7.436e-09
+## log_alpha -1.151e-01  1.000e+00  9.741e-01 -1.617e-07
+## log_beta  -2.085e-01  9.741e-01  1.000e+00 -1.386e-07
+## sigma     -7.436e-09 -1.617e-07 -1.386e-07  1.000e+00
 ## 
 ## Backtransformed parameters:
 ## Confidence intervals for internally transformed parameters are asymmetric.
@@ -733,15 +740,15 @@ order to explain the data.</p>
 <div id="dfop-fit-for-l2" class="section level2">
 <h2>DFOP fit for L2</h2>
 <p>Fitting the four parameter DFOP model further reduces the <span class="math inline"><em>χ</em><sup>2</sup></span> error level.</p>
-<div class="sourceCode" id="cb19"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb19-1"><a href="#cb19-1" aria-hidden="true" tabindex="-1"></a>m.L2.DFOP <span class="ot">&lt;-</span> <span class="fu">mkinfit</span>(<span class="st">&quot;DFOP&quot;</span>, FOCUS_2006_L2_mkin, <span class="at">quiet =</span> <span class="cn">TRUE</span>)</span>
-<span id="cb19-2"><a href="#cb19-2" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(m.L2.DFOP, <span class="at">show_residuals =</span> <span class="cn">TRUE</span>, <span class="at">show_errmin =</span> <span class="cn">TRUE</span>,</span>
-<span id="cb19-3"><a href="#cb19-3" aria-hidden="true" tabindex="-1"></a>     <span class="at">main =</span> <span class="st">&quot;FOCUS L2 - DFOP&quot;</span>)</span></code></pre></div>
-<p><img src="data:image/png;base64,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" /><!-- --></p>
-<div class="sourceCode" id="cb20"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb20-1"><a href="#cb20-1" aria-hidden="true" tabindex="-1"></a><span class="fu">summary</span>(m.L2.DFOP, <span class="at">data =</span> <span class="cn">FALSE</span>)</span></code></pre></div>
-<pre><code>## mkin version used for fitting:    1.2.4 
-## R version used for fitting:       4.3.0 
-## Date of fit:     Thu May 18 11:38:11 2023 
-## Date of summary: Thu May 18 11:38:11 2023 
+<div class="sourceCode" id="cb18"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb18-1"><a href="#cb18-1" tabindex="-1"></a>m.L2.DFOP <span class="ot">&lt;-</span> <span class="fu">mkinfit</span>(<span class="st">&quot;DFOP&quot;</span>, FOCUS_2006_L2_mkin, <span class="at">quiet =</span> <span class="cn">TRUE</span>)</span>
+<span id="cb18-2"><a href="#cb18-2" tabindex="-1"></a><span class="fu">plot</span>(m.L2.DFOP, <span class="at">show_residuals =</span> <span class="cn">TRUE</span>, <span class="at">show_errmin =</span> <span class="cn">TRUE</span>,</span>
+<span id="cb18-3"><a href="#cb18-3" tabindex="-1"></a>     <span class="at">main =</span> <span class="st">&quot;FOCUS L2 - DFOP&quot;</span>)</span></code></pre></div>
+<p><img role="img" src="data:image/png;base64,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" /><!-- --></p>
+<div class="sourceCode" id="cb19"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb19-1"><a href="#cb19-1" tabindex="-1"></a><span class="fu">summary</span>(m.L2.DFOP, <span class="at">data =</span> <span class="cn">FALSE</span>)</span></code></pre></div>
+<pre><code>## mkin version used for fitting:    1.2.6 
+## R version used for fitting:       4.4.2 
+## Date of fit:     Thu Dec 19 10:28:48 2024 
+## Date of summary: Thu Dec 19 10:28:48 2024 
 ## 
 ## Equations:
 ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
@@ -750,7 +757,7 @@ order to explain the data.</p>
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted using 581 model solutions performed in 0.041 s
+## Fitted using 581 model solutions performed in 0.08 s
 ## 
 ## Error model: Constant variance 
 ## 
@@ -781,18 +788,18 @@ order to explain the data.</p>
 ## Optimised, transformed parameters with symmetric confidence intervals:
 ##          Estimate Std. Error      Lower     Upper
 ## parent_0   93.950  9.998e-01    91.5900   96.3100
-## log_k1      3.112  1.842e+03 -4353.0000 4359.0000
+## log_k1      3.113  1.849e+03 -4369.0000 4375.0000
 ## log_k2     -1.088  6.285e-02    -1.2370   -0.9394
 ## g_qlogis   -0.399  9.946e-02    -0.6342   -0.1638
 ## sigma       1.414  2.886e-01     0.7314    2.0960
 ## 
 ## Parameter correlation:
 ##            parent_0     log_k1     log_k2   g_qlogis      sigma
-## parent_0  1.000e+00  6.783e-07 -3.390e-10  2.665e-01 -2.967e-10
-## log_k1    6.783e-07  1.000e+00  1.116e-04 -2.196e-04 -1.031e-05
-## log_k2   -3.390e-10  1.116e-04  1.000e+00 -7.903e-01  2.917e-09
-## g_qlogis  2.665e-01 -2.196e-04 -7.903e-01  1.000e+00 -4.408e-09
-## sigma    -2.967e-10 -1.031e-05  2.917e-09 -4.408e-09  1.000e+00
+## parent_0  1.000e+00  6.765e-07 -9.004e-10  2.665e-01 -1.109e-09
+## log_k1    6.765e-07  1.000e+00  1.112e-04 -2.187e-04 -1.027e-05
+## log_k2   -9.004e-10  1.112e-04  1.000e+00 -7.903e-01  9.553e-09
+## g_qlogis  2.665e-01 -2.187e-04 -7.903e-01  1.000e+00 -1.545e-08
+## sigma    -1.109e-09 -1.027e-05  9.553e-09 -1.545e-08  1.000e+00
 ## 
 ## Backtransformed parameters:
 ## Confidence intervals for internally transformed parameters are asymmetric.
@@ -800,7 +807,7 @@ order to explain the data.</p>
 ## for estimators of untransformed parameters.
 ##          Estimate   t value    Pr(&gt;t)   Lower   Upper
 ## parent_0  93.9500 9.397e+01 2.036e-12 91.5900 96.3100
-## k1        22.4800 5.553e-04 4.998e-01  0.0000     Inf
+## k1        22.4900 5.533e-04 4.998e-01  0.0000     Inf
 ## k2         0.3369 1.591e+01 4.697e-07  0.2904  0.3909
 ## g          0.4016 1.680e+01 3.238e-07  0.3466  0.4591
 ## sigma      1.4140 4.899e+00 8.776e-04  0.7314  2.0960
@@ -812,7 +819,7 @@ order to explain the data.</p>
 ## 
 ## Estimated disappearance times:
 ##          DT50  DT90 DT50back DT50_k1 DT50_k2
-## parent 0.5335 5.311    1.599 0.03084   2.058</code></pre>
+## parent 0.5335 5.311    1.599 0.03083   2.058</code></pre>
 <p>Here, the DFOP model is clearly the best-fit model for dataset L2
 based on the chi^2 error level criterion.</p>
 </div>
@@ -821,21 +828,21 @@ based on the chi^2 error level criterion.</p>
 <h1>Laboratory Data L3</h1>
 <p>The following code defines example dataset L3 from the FOCUS kinetics
 report, p. 290.</p>
-<div class="sourceCode" id="cb22"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb22-1"><a href="#cb22-1" aria-hidden="true" tabindex="-1"></a>FOCUS_2006_L3 <span class="ot">=</span> <span class="fu">data.frame</span>(</span>
-<span id="cb22-2"><a href="#cb22-2" aria-hidden="true" tabindex="-1"></a>  <span class="at">t =</span> <span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">3</span>, <span class="dv">7</span>, <span class="dv">14</span>, <span class="dv">30</span>, <span class="dv">60</span>, <span class="dv">91</span>, <span class="dv">120</span>),</span>
-<span id="cb22-3"><a href="#cb22-3" aria-hidden="true" tabindex="-1"></a>  <span class="at">parent =</span> <span class="fu">c</span>(<span class="fl">97.8</span>, <span class="dv">60</span>, <span class="dv">51</span>, <span class="dv">43</span>, <span class="dv">35</span>, <span class="dv">22</span>, <span class="dv">15</span>, <span class="dv">12</span>))</span>
-<span id="cb22-4"><a href="#cb22-4" aria-hidden="true" tabindex="-1"></a>FOCUS_2006_L3_mkin <span class="ot">&lt;-</span> <span class="fu">mkin_wide_to_long</span>(FOCUS_2006_L3)</span></code></pre></div>
+<div class="sourceCode" id="cb21"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb21-1"><a href="#cb21-1" tabindex="-1"></a>FOCUS_2006_L3 <span class="ot">=</span> <span class="fu">data.frame</span>(</span>
+<span id="cb21-2"><a href="#cb21-2" tabindex="-1"></a>  <span class="at">t =</span> <span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">3</span>, <span class="dv">7</span>, <span class="dv">14</span>, <span class="dv">30</span>, <span class="dv">60</span>, <span class="dv">91</span>, <span class="dv">120</span>),</span>
+<span id="cb21-3"><a href="#cb21-3" tabindex="-1"></a>  <span class="at">parent =</span> <span class="fu">c</span>(<span class="fl">97.8</span>, <span class="dv">60</span>, <span class="dv">51</span>, <span class="dv">43</span>, <span class="dv">35</span>, <span class="dv">22</span>, <span class="dv">15</span>, <span class="dv">12</span>))</span>
+<span id="cb21-4"><a href="#cb21-4" tabindex="-1"></a>FOCUS_2006_L3_mkin <span class="ot">&lt;-</span> <span class="fu">mkin_wide_to_long</span>(FOCUS_2006_L3)</span></code></pre></div>
 <div id="fit-multiple-models" class="section level2">
 <h2>Fit multiple models</h2>
 <p>As of mkin version 0.9-39 (June 2015), we can fit several models to
 one or more datasets in one call to the function <code>mmkin</code>. The
 datasets have to be passed in a list, in this case a named list holding
 only the L3 dataset prepared above.</p>
-<div class="sourceCode" id="cb23"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb23-1"><a href="#cb23-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Only use one core here, not to offend the CRAN checks</span></span>
-<span id="cb23-2"><a href="#cb23-2" aria-hidden="true" tabindex="-1"></a>mm.L3 <span class="ot">&lt;-</span> <span class="fu">mmkin</span>(<span class="fu">c</span>(<span class="st">&quot;SFO&quot;</span>, <span class="st">&quot;FOMC&quot;</span>, <span class="st">&quot;DFOP&quot;</span>), <span class="at">cores =</span> <span class="dv">1</span>,</span>
-<span id="cb23-3"><a href="#cb23-3" aria-hidden="true" tabindex="-1"></a>               <span class="fu">list</span>(<span class="st">&quot;FOCUS L3&quot;</span> <span class="ot">=</span> FOCUS_2006_L3_mkin), <span class="at">quiet =</span> <span class="cn">TRUE</span>)</span>
-<span id="cb23-4"><a href="#cb23-4" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(mm.L3)</span></code></pre></div>
-<p><img src="data:image/png;base64,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" /><!-- --></p>
+<div class="sourceCode" id="cb22"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb22-1"><a href="#cb22-1" tabindex="-1"></a><span class="co"># Only use one core here, not to offend the CRAN checks</span></span>
+<span id="cb22-2"><a href="#cb22-2" tabindex="-1"></a>mm.L3 <span class="ot">&lt;-</span> <span class="fu">mmkin</span>(<span class="fu">c</span>(<span class="st">&quot;SFO&quot;</span>, <span class="st">&quot;FOMC&quot;</span>, <span class="st">&quot;DFOP&quot;</span>), <span class="at">cores =</span> <span class="dv">1</span>,</span>
+<span id="cb22-3"><a href="#cb22-3" tabindex="-1"></a>               <span class="fu">list</span>(<span class="st">&quot;FOCUS L3&quot;</span> <span class="ot">=</span> FOCUS_2006_L3_mkin), <span class="at">quiet =</span> <span class="cn">TRUE</span>)</span>
+<span id="cb22-4"><a href="#cb22-4" tabindex="-1"></a><span class="fu">plot</span>(mm.L3)</span></code></pre></div>
+<p><img role="img" src="data:image/png;base64,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" /><!-- --></p>
 <p>The <span class="math inline"><em>χ</em><sup>2</sup></span> error
 level of 21% as well as the plot suggest that the SFO model does not fit
 very well. The FOMC model performs better, with an error level at which
@@ -850,11 +857,11 @@ as a row index and datasets as a column index.</p>
 <p>We can extract the summary and plot for <em>e.g.</em> the DFOP fit,
 using square brackets for indexing which will result in the use of the
 summary and plot functions working on mkinfit objects.</p>
-<div class="sourceCode" id="cb24"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb24-1"><a href="#cb24-1" aria-hidden="true" tabindex="-1"></a><span class="fu">summary</span>(mm.L3[[<span class="st">&quot;DFOP&quot;</span>, <span class="dv">1</span>]])</span></code></pre></div>
-<pre><code>## mkin version used for fitting:    1.2.4 
-## R version used for fitting:       4.3.0 
-## Date of fit:     Thu May 18 11:38:12 2023 
-## Date of summary: Thu May 18 11:38:12 2023 
+<div class="sourceCode" id="cb23"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb23-1"><a href="#cb23-1" tabindex="-1"></a><span class="fu">summary</span>(mm.L3[[<span class="st">&quot;DFOP&quot;</span>, <span class="dv">1</span>]])</span></code></pre></div>
+<pre><code>## mkin version used for fitting:    1.2.6 
+## R version used for fitting:       4.4.2 
+## Date of fit:     Thu Dec 19 10:28:48 2024 
+## Date of summary: Thu Dec 19 10:28:48 2024 
 ## 
 ## Equations:
 ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
@@ -863,7 +870,7 @@ summary and plot functions working on mkinfit objects.</p>
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted using 376 model solutions performed in 0.024 s
+## Fitted using 376 model solutions performed in 0.047 s
 ## 
 ## Error model: Constant variance 
 ## 
@@ -901,11 +908,11 @@ summary and plot functions working on mkinfit objects.</p>
 ## 
 ## Parameter correlation:
 ##            parent_0     log_k1     log_k2   g_qlogis      sigma
-## parent_0  1.000e+00  1.732e-01  2.282e-02  4.009e-01 -9.664e-08
-## log_k1    1.732e-01  1.000e+00  4.945e-01 -5.809e-01  7.147e-07
+## parent_0  1.000e+00  1.732e-01  2.282e-02  4.009e-01 -9.671e-08
+## log_k1    1.732e-01  1.000e+00  4.945e-01 -5.809e-01  7.148e-07
 ## log_k2    2.282e-02  4.945e-01  1.000e+00 -6.812e-01  1.022e-06
-## g_qlogis  4.009e-01 -5.809e-01 -6.812e-01  1.000e+00 -7.926e-07
-## sigma    -9.664e-08  7.147e-07  1.022e-06 -7.926e-07  1.000e+00
+## g_qlogis  4.009e-01 -5.809e-01 -6.812e-01  1.000e+00 -7.929e-07
+## sigma    -9.671e-08  7.148e-07  1.022e-06 -7.929e-07  1.000e+00
 ## 
 ## Backtransformed parameters:
 ## Confidence intervals for internally transformed parameters are asymmetric.
@@ -937,8 +944,8 @@ summary and plot functions working on mkinfit objects.</p>
 ##    60   parent     22.0     23.26 -1.25919
 ##    91   parent     15.0     15.18 -0.18181
 ##   120   parent     12.0     10.19  1.81395</code></pre>
-<div class="sourceCode" id="cb26"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb26-1"><a href="#cb26-1" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(mm.L3[[<span class="st">&quot;DFOP&quot;</span>, <span class="dv">1</span>]], <span class="at">show_errmin =</span> <span class="cn">TRUE</span>)</span></code></pre></div>
-<p><img src="data:image/png;base64,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" /><!-- --></p>
+<div class="sourceCode" id="cb25"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb25-1"><a href="#cb25-1" tabindex="-1"></a><span class="fu">plot</span>(mm.L3[[<span class="st">&quot;DFOP&quot;</span>, <span class="dv">1</span>]], <span class="at">show_errmin =</span> <span class="cn">TRUE</span>)</span></code></pre></div>
+<p><img role="img" src="data:image/png;base64,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" /><!-- --></p>
 <p>Here, a look to the model plot, the confidence intervals of the
 parameters and the correlation matrix suggest that the parameter
 estimates are reliable, and the DFOP model can be used as the best-fit
@@ -955,35 +962,35 @@ parameter <code>g</code> is quite narrow.</p>
 <h1>Laboratory Data L4</h1>
 <p>The following code defines example dataset L4 from the FOCUS kinetics
 report, p. 293:</p>
-<div class="sourceCode" id="cb27"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb27-1"><a href="#cb27-1" aria-hidden="true" tabindex="-1"></a>FOCUS_2006_L4 <span class="ot">=</span> <span class="fu">data.frame</span>(</span>
-<span id="cb27-2"><a href="#cb27-2" aria-hidden="true" tabindex="-1"></a>  <span class="at">t =</span> <span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">3</span>, <span class="dv">7</span>, <span class="dv">14</span>, <span class="dv">30</span>, <span class="dv">60</span>, <span class="dv">91</span>, <span class="dv">120</span>),</span>
-<span id="cb27-3"><a href="#cb27-3" aria-hidden="true" tabindex="-1"></a>  <span class="at">parent =</span> <span class="fu">c</span>(<span class="fl">96.6</span>, <span class="fl">96.3</span>, <span class="fl">94.3</span>, <span class="fl">88.8</span>, <span class="fl">74.9</span>, <span class="fl">59.9</span>, <span class="fl">53.5</span>, <span class="fl">49.0</span>))</span>
-<span id="cb27-4"><a href="#cb27-4" aria-hidden="true" tabindex="-1"></a>FOCUS_2006_L4_mkin <span class="ot">&lt;-</span> <span class="fu">mkin_wide_to_long</span>(FOCUS_2006_L4)</span></code></pre></div>
+<div class="sourceCode" id="cb26"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb26-1"><a href="#cb26-1" tabindex="-1"></a>FOCUS_2006_L4 <span class="ot">=</span> <span class="fu">data.frame</span>(</span>
+<span id="cb26-2"><a href="#cb26-2" tabindex="-1"></a>  <span class="at">t =</span> <span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">3</span>, <span class="dv">7</span>, <span class="dv">14</span>, <span class="dv">30</span>, <span class="dv">60</span>, <span class="dv">91</span>, <span class="dv">120</span>),</span>
+<span id="cb26-3"><a href="#cb26-3" tabindex="-1"></a>  <span class="at">parent =</span> <span class="fu">c</span>(<span class="fl">96.6</span>, <span class="fl">96.3</span>, <span class="fl">94.3</span>, <span class="fl">88.8</span>, <span class="fl">74.9</span>, <span class="fl">59.9</span>, <span class="fl">53.5</span>, <span class="fl">49.0</span>))</span>
+<span id="cb26-4"><a href="#cb26-4" tabindex="-1"></a>FOCUS_2006_L4_mkin <span class="ot">&lt;-</span> <span class="fu">mkin_wide_to_long</span>(FOCUS_2006_L4)</span></code></pre></div>
 <p>Fits of the SFO and FOMC models, plots and summaries are produced
 below:</p>
-<div class="sourceCode" id="cb28"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb28-1"><a href="#cb28-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Only use one core here, not to offend the CRAN checks</span></span>
-<span id="cb28-2"><a href="#cb28-2" aria-hidden="true" tabindex="-1"></a>mm.L4 <span class="ot">&lt;-</span> <span class="fu">mmkin</span>(<span class="fu">c</span>(<span class="st">&quot;SFO&quot;</span>, <span class="st">&quot;FOMC&quot;</span>), <span class="at">cores =</span> <span class="dv">1</span>,</span>
-<span id="cb28-3"><a href="#cb28-3" aria-hidden="true" tabindex="-1"></a>               <span class="fu">list</span>(<span class="st">&quot;FOCUS L4&quot;</span> <span class="ot">=</span> FOCUS_2006_L4_mkin),</span>
-<span id="cb28-4"><a href="#cb28-4" aria-hidden="true" tabindex="-1"></a>               <span class="at">quiet =</span> <span class="cn">TRUE</span>)</span>
-<span id="cb28-5"><a href="#cb28-5" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(mm.L4)</span></code></pre></div>
-<p><img src="data:image/png;base64,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" /><!-- --></p>
+<div class="sourceCode" id="cb27"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb27-1"><a href="#cb27-1" tabindex="-1"></a><span class="co"># Only use one core here, not to offend the CRAN checks</span></span>
+<span id="cb27-2"><a href="#cb27-2" tabindex="-1"></a>mm.L4 <span class="ot">&lt;-</span> <span class="fu">mmkin</span>(<span class="fu">c</span>(<span class="st">&quot;SFO&quot;</span>, <span class="st">&quot;FOMC&quot;</span>), <span class="at">cores =</span> <span class="dv">1</span>,</span>
+<span id="cb27-3"><a href="#cb27-3" tabindex="-1"></a>               <span class="fu">list</span>(<span class="st">&quot;FOCUS L4&quot;</span> <span class="ot">=</span> FOCUS_2006_L4_mkin),</span>
+<span id="cb27-4"><a href="#cb27-4" tabindex="-1"></a>               <span class="at">quiet =</span> <span class="cn">TRUE</span>)</span>
+<span id="cb27-5"><a href="#cb27-5" tabindex="-1"></a><span class="fu">plot</span>(mm.L4)</span></code></pre></div>
+<p><img role="img" src="data:image/png;base64,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" /><!-- --></p>
 <p>The <span class="math inline"><em>χ</em><sup>2</sup></span> error
 level of 3.3% as well as the plot suggest that the SFO model fits very
 well. The error level at which the <span class="math inline"><em>χ</em><sup>2</sup></span> test passes is
 slightly lower for the FOMC model. However, the difference appears
 negligible.</p>
-<div class="sourceCode" id="cb29"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb29-1"><a href="#cb29-1" aria-hidden="true" tabindex="-1"></a><span class="fu">summary</span>(mm.L4[[<span class="st">&quot;SFO&quot;</span>, <span class="dv">1</span>]], <span class="at">data =</span> <span class="cn">FALSE</span>)</span></code></pre></div>
-<pre><code>## mkin version used for fitting:    1.2.4 
-## R version used for fitting:       4.3.0 
-## Date of fit:     Thu May 18 11:38:12 2023 
-## Date of summary: Thu May 18 11:38:12 2023 
+<div class="sourceCode" id="cb28"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb28-1"><a href="#cb28-1" tabindex="-1"></a><span class="fu">summary</span>(mm.L4[[<span class="st">&quot;SFO&quot;</span>, <span class="dv">1</span>]], <span class="at">data =</span> <span class="cn">FALSE</span>)</span></code></pre></div>
+<pre><code>## mkin version used for fitting:    1.2.6 
+## R version used for fitting:       4.4.2 
+## Date of fit:     Thu Dec 19 10:28:49 2024 
+## Date of summary: Thu Dec 19 10:28:49 2024 
 ## 
 ## Equations:
 ## d_parent/dt = - k_parent * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted using 142 model solutions performed in 0.01 s
+## Fitted using 142 model solutions performed in 0.018 s
 ## 
 ## Error model: Constant variance 
 ## 
@@ -1036,18 +1043,18 @@ negligible.</p>
 ## Estimated disappearance times:
 ##        DT50 DT90
 ## parent  106  352</code></pre>
-<div class="sourceCode" id="cb31"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb31-1"><a href="#cb31-1" aria-hidden="true" tabindex="-1"></a><span class="fu">summary</span>(mm.L4[[<span class="st">&quot;FOMC&quot;</span>, <span class="dv">1</span>]], <span class="at">data =</span> <span class="cn">FALSE</span>)</span></code></pre></div>
-<pre><code>## mkin version used for fitting:    1.2.4 
-## R version used for fitting:       4.3.0 
-## Date of fit:     Thu May 18 11:38:12 2023 
-## Date of summary: Thu May 18 11:38:12 2023 
+<div class="sourceCode" id="cb30"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb30-1"><a href="#cb30-1" tabindex="-1"></a><span class="fu">summary</span>(mm.L4[[<span class="st">&quot;FOMC&quot;</span>, <span class="dv">1</span>]], <span class="at">data =</span> <span class="cn">FALSE</span>)</span></code></pre></div>
+<pre><code>## mkin version used for fitting:    1.2.6 
+## R version used for fitting:       4.4.2 
+## Date of fit:     Thu Dec 19 10:28:49 2024 
+## Date of summary: Thu Dec 19 10:28:49 2024 
 ## 
 ## Equations:
 ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted using 224 model solutions performed in 0.013 s
+## Fitted using 224 model solutions performed in 0.027 s
 ## 
 ## Error model: Constant variance 
 ## 
@@ -1082,10 +1089,10 @@ negligible.</p>
 ## 
 ## Parameter correlation:
 ##             parent_0  log_alpha   log_beta      sigma
-## parent_0   1.000e+00 -4.696e-01 -5.543e-01 -2.468e-07
-## log_alpha -4.696e-01  1.000e+00  9.889e-01  2.478e-08
-## log_beta  -5.543e-01  9.889e-01  1.000e+00  5.211e-08
-## sigma     -2.468e-07  2.478e-08  5.211e-08  1.000e+00
+## parent_0   1.000e+00 -4.696e-01 -5.543e-01 -2.456e-07
+## log_alpha -4.696e-01  1.000e+00  9.889e-01  2.169e-08
+## log_beta  -5.543e-01  9.889e-01  1.000e+00  4.910e-08
+## sigma     -2.456e-07  2.169e-08  4.910e-08  1.000e+00
 ## 
 ## Backtransformed parameters:
 ## Confidence intervals for internally transformed parameters are asymmetric.
@@ -1108,7 +1115,7 @@ negligible.</p>
 </div>
 <div id="references" class="section level1 unnumbered">
 <h1 class="unnumbered">References</h1>
-<div id="refs" class="references csl-bib-body hanging-indent">
+<div id="refs" class="references csl-bib-body hanging-indent" entry-spacing="0">
 <div id="ref-ranke2014" class="csl-entry">
 Ranke, Johannes. 2014. <span>“<span class="nocase">Prüfung und
 Validierung von Modellierungssoftware als Alternative zu ModelMaker
diff --git a/vignettes/cyan_pathway_2022_prebuilt.rnw b/vignettes/cyan_pathway_2022_prebuilt.rnw
new file mode 100644
index 00000000..955160ea
--- /dev/null
+++ b/vignettes/cyan_pathway_2022_prebuilt.rnw
@@ -0,0 +1,7 @@
+\documentclass{article}
+\usepackage{pdfpages}
+%\VignetteIndexEntry{Testing hierarchical pathway kinetics with residue data on cyantraniliprole}
+
+\begin{document}
+\includepdf[pages=-, fitpaper=true]{prebuilt/2022_cyan_pathway.pdf}
+\end{document}
diff --git a/vignettes/mkin.html b/vignettes/mkin.html
index 12b8671e..bda45abb 100644
--- a/vignettes/mkin.html
+++ b/vignettes/mkin.html
@@ -40,27 +40,27 @@ display: none;
 </style>
 
 <style type="text/css">
-  code{white-space: pre-wrap;}
-  span.smallcaps{font-variant: small-caps;}
-  span.underline{text-decoration: underline;}
-  div.column{display: inline-block; vertical-align: top; width: 50%;}
-  div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;}
-  ul.task-list{list-style: none;}
-    </style>
+code{white-space: pre-wrap;}
+span.smallcaps{font-variant: small-caps;}
+span.underline{text-decoration: underline;}
+div.column{display: inline-block; vertical-align: top; width: 50%;}
+div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;}
+ul.task-list{list-style: none;}
+</style>
 
 
 
 <style type="text/css">
-  code {
-    white-space: pre;
-  }
-  .sourceCode {
-    overflow: visible;
-  }
+code {
+white-space: pre;
+}
+.sourceCode {
+overflow: visible;
+}
 </style>
 <style type="text/css" data-origin="pandoc">
 pre > code.sourceCode { white-space: pre; position: relative; }
-pre > code.sourceCode > span { display: inline-block; line-height: 1.25; }
+pre > code.sourceCode > span { line-height: 1.25; }
 pre > code.sourceCode > span:empty { height: 1.2em; }
 .sourceCode { overflow: visible; }
 code.sourceCode > span { color: inherit; text-decoration: inherit; }
@@ -71,58 +71,57 @@ div.sourceCode { overflow: auto; }
 }
 @media print {
 pre > code.sourceCode { white-space: pre-wrap; }
-pre > code.sourceCode > span { text-indent: -5em; padding-left: 5em; }
+pre > code.sourceCode > span { display: inline-block; text-indent: -5em; padding-left: 5em; }
 }
 pre.numberSource code
-  { counter-reset: source-line 0; }
+{ counter-reset: source-line 0; }
 pre.numberSource code > span
-  { position: relative; left: -4em; counter-increment: source-line; }
+{ position: relative; left: -4em; counter-increment: source-line; }
 pre.numberSource code > span > a:first-child::before
-  { content: counter(source-line);
-    position: relative; left: -1em; text-align: right; vertical-align: baseline;
-    border: none; display: inline-block;
-    -webkit-touch-callout: none; -webkit-user-select: none;
-    -khtml-user-select: none; -moz-user-select: none;
-    -ms-user-select: none; user-select: none;
-    padding: 0 4px; width: 4em;
-    color: #aaaaaa;
-  }
-pre.numberSource { margin-left: 3em; border-left: 1px solid #aaaaaa;  padding-left: 4px; }
+{ content: counter(source-line);
+position: relative; left: -1em; text-align: right; vertical-align: baseline;
+border: none; display: inline-block;
+-webkit-touch-callout: none; -webkit-user-select: none;
+-khtml-user-select: none; -moz-user-select: none;
+-ms-user-select: none; user-select: none;
+padding: 0 4px; width: 4em;
+color: #aaaaaa;
+}
+pre.numberSource { margin-left: 3em; border-left: 1px solid #aaaaaa; padding-left: 4px; }
 div.sourceCode
-  {   }
+{ }
 @media screen {
 pre > code.sourceCode > span > a:first-child::before { text-decoration: underline; }
 }
-code span.al { color: #ff0000; font-weight: bold; } /* Alert */
-code span.an { color: #60a0b0; font-weight: bold; font-style: italic; } /* Annotation */
-code span.at { color: #7d9029; } /* Attribute */
-code span.bn { color: #40a070; } /* BaseN */
-code span.bu { color: #008000; } /* BuiltIn */
-code span.cf { color: #007020; font-weight: bold; } /* ControlFlow */
-code span.ch { color: #4070a0; } /* Char */
-code span.cn { color: #880000; } /* Constant */
-code span.co { color: #60a0b0; font-style: italic; } /* Comment */
-code span.cv { color: #60a0b0; font-weight: bold; font-style: italic; } /* CommentVar */
-code span.do { color: #ba2121; font-style: italic; } /* Documentation */
-code span.dt { color: #902000; } /* DataType */
-code span.dv { color: #40a070; } /* DecVal */
-code span.er { color: #ff0000; font-weight: bold; } /* Error */
-code span.ex { } /* Extension */
-code span.fl { color: #40a070; } /* Float */
-code span.fu { color: #06287e; } /* Function */
-code span.im { color: #008000; font-weight: bold; } /* Import */
-code span.in { color: #60a0b0; font-weight: bold; font-style: italic; } /* Information */
-code span.kw { color: #007020; font-weight: bold; } /* Keyword */
-code span.op { color: #666666; } /* Operator */
-code span.ot { color: #007020; } /* Other */
-code span.pp { color: #bc7a00; } /* Preprocessor */
-code span.sc { color: #4070a0; } /* SpecialChar */
-code span.ss { color: #bb6688; } /* SpecialString */
-code span.st { color: #4070a0; } /* String */
-code span.va { color: #19177c; } /* Variable */
-code span.vs { color: #4070a0; } /* VerbatimString */
-code span.wa { color: #60a0b0; font-weight: bold; font-style: italic; } /* Warning */
-
+code span.al { color: #ff0000; font-weight: bold; } 
+code span.an { color: #60a0b0; font-weight: bold; font-style: italic; } 
+code span.at { color: #7d9029; } 
+code span.bn { color: #40a070; } 
+code span.bu { color: #008000; } 
+code span.cf { color: #007020; font-weight: bold; } 
+code span.ch { color: #4070a0; } 
+code span.cn { color: #880000; } 
+code span.co { color: #60a0b0; font-style: italic; } 
+code span.cv { color: #60a0b0; font-weight: bold; font-style: italic; } 
+code span.do { color: #ba2121; font-style: italic; } 
+code span.dt { color: #902000; } 
+code span.dv { color: #40a070; } 
+code span.er { color: #ff0000; font-weight: bold; } 
+code span.ex { } 
+code span.fl { color: #40a070; } 
+code span.fu { color: #06287e; } 
+code span.im { color: #008000; font-weight: bold; } 
+code span.in { color: #60a0b0; font-weight: bold; font-style: italic; } 
+code span.kw { color: #007020; font-weight: bold; } 
+code span.op { color: #666666; } 
+code span.ot { color: #007020; } 
+code span.pp { color: #bc7a00; } 
+code span.sc { color: #4070a0; } 
+code span.ss { color: #bb6688; } 
+code span.st { color: #4070a0; } 
+code span.va { color: #19177c; } 
+code span.vs { color: #4070a0; } 
+code span.wa { color: #60a0b0; font-weight: bold; font-style: italic; } 
 </style>
 <script>
 // apply pandoc div.sourceCode style to pre.sourceCode instead
@@ -156,25 +155,26 @@ code span.wa { color: #60a0b0; font-weight: bold; font-style: italic; } /* Warni
 
 
 <style type="text/css">
-/* for pandoc --citeproc since 2.11 */
+
 div.csl-bib-body { }
 div.csl-entry {
-  clear: both;
+clear: both;
+margin-bottom: 0em;
 }
 .hanging div.csl-entry {
-  margin-left:2em;
-  text-indent:-2em;
+margin-left:2em;
+text-indent:-2em;
 }
 div.csl-left-margin {
-  min-width:2em;
-  float:left;
+min-width:2em;
+float:left;
 }
 div.csl-right-inline {
-  margin-left:2em;
-  padding-left:1em;
+margin-left:2em;
+padding-left:1em;
 }
 div.csl-indent {
-  margin-left: 2em;
+margin-left: 2em;
 }
 </style>
 
@@ -372,26 +372,27 @@ code > span.er { color: #a61717; background-color: #e3d2d2; }
 
 <h1 class="title toc-ignore">Short introduction to mkin</h1>
 <h4 class="author">Johannes Ranke</h4>
-<h4 class="date">Last change 18 May 2023 (rebuilt 2023-05-19)</h4>
+<h4 class="date">Last change 18 May 2023 (rebuilt 2024-12-19)</h4>
 
 
 <div id="TOC">
 <ul>
-<li><a href="#abstract">Abstract</a></li>
-<li><a href="#background">Background</a>
+<li><a href="#abstract" id="toc-abstract">Abstract</a></li>
+<li><a href="#background" id="toc-background">Background</a>
 <ul>
-<li><a href="#derived-software-tools">Derived software tools</a></li>
+<li><a href="#derived-software-tools" id="toc-derived-software-tools">Derived software tools</a></li>
 </ul></li>
-<li><a href="#unique-features">Unique features</a></li>
-<li><a href="#internal-parameter-transformations">Internal parameter
+<li><a href="#unique-features" id="toc-unique-features">Unique
+features</a></li>
+<li><a href="#internal-parameter-transformations" id="toc-internal-parameter-transformations">Internal parameter
 transformations</a>
 <ul>
-<li><a href="#confidence-intervals-based-on-transformed-parameters">Confidence
+<li><a href="#confidence-intervals-based-on-transformed-parameters" id="toc-confidence-intervals-based-on-transformed-parameters">Confidence
 intervals based on transformed parameters</a></li>
-<li><a href="#parameter-t-test-based-on-untransformed-parameters">Parameter
+<li><a href="#parameter-t-test-based-on-untransformed-parameters" id="toc-parameter-t-test-based-on-untransformed-parameters">Parameter
 t-test based on untransformed parameters</a></li>
 </ul></li>
-<li><a href="#references">References</a></li>
+<li><a href="#references" id="toc-references">References</a></li>
 </ul>
 </div>
 
@@ -410,36 +411,36 @@ this guidance from within R and calculates some statistical measures for
 data series within one or more compartments, for parent and
 metabolites.</p>
 <details class="chunk-details"><summary class="chunk-summary"><span class="chunk-summary-text">Code</span></summary>
-<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(<span class="st">&quot;mkin&quot;</span>, <span class="at">quietly =</span> <span class="cn">TRUE</span>)</span>
-<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="co"># Define the kinetic model</span></span>
-<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a>m_SFO_SFO_SFO <span class="ot">&lt;-</span> <span class="fu">mkinmod</span>(<span class="at">parent =</span> <span class="fu">mkinsub</span>(<span class="st">&quot;SFO&quot;</span>, <span class="st">&quot;M1&quot;</span>),</span>
-<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a>                         <span class="at">M1 =</span> <span class="fu">mkinsub</span>(<span class="st">&quot;SFO&quot;</span>, <span class="st">&quot;M2&quot;</span>),</span>
-<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a>                         <span class="at">M2 =</span> <span class="fu">mkinsub</span>(<span class="st">&quot;SFO&quot;</span>),</span>
-<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a>                         <span class="at">use_of_ff =</span> <span class="st">&quot;max&quot;</span>, <span class="at">quiet =</span> <span class="cn">TRUE</span>)</span>
-<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1-8"><a href="#cb1-8" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1-9"><a href="#cb1-9" aria-hidden="true" tabindex="-1"></a><span class="co"># Produce model predictions using some arbitrary parameters</span></span>
-<span id="cb1-10"><a href="#cb1-10" aria-hidden="true" tabindex="-1"></a>sampling_times <span class="ot">=</span> <span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">1</span>, <span class="dv">3</span>, <span class="dv">7</span>, <span class="dv">14</span>, <span class="dv">28</span>, <span class="dv">60</span>, <span class="dv">90</span>, <span class="dv">120</span>)</span>
-<span id="cb1-11"><a href="#cb1-11" aria-hidden="true" tabindex="-1"></a>d_SFO_SFO_SFO <span class="ot">&lt;-</span> <span class="fu">mkinpredict</span>(m_SFO_SFO_SFO,</span>
-<span id="cb1-12"><a href="#cb1-12" aria-hidden="true" tabindex="-1"></a>  <span class="fu">c</span>(<span class="at">k_parent =</span> <span class="fl">0.03</span>,</span>
-<span id="cb1-13"><a href="#cb1-13" aria-hidden="true" tabindex="-1"></a>    <span class="at">f_parent_to_M1 =</span> <span class="fl">0.5</span>, <span class="at">k_M1 =</span> <span class="fu">log</span>(<span class="dv">2</span>)<span class="sc">/</span><span class="dv">100</span>,</span>
-<span id="cb1-14"><a href="#cb1-14" aria-hidden="true" tabindex="-1"></a>    <span class="at">f_M1_to_M2 =</span> <span class="fl">0.9</span>, <span class="at">k_M2 =</span> <span class="fu">log</span>(<span class="dv">2</span>)<span class="sc">/</span><span class="dv">50</span>),</span>
-<span id="cb1-15"><a href="#cb1-15" aria-hidden="true" tabindex="-1"></a>  <span class="fu">c</span>(<span class="at">parent =</span> <span class="dv">100</span>, <span class="at">M1 =</span> <span class="dv">0</span>, <span class="at">M2 =</span> <span class="dv">0</span>),</span>
-<span id="cb1-16"><a href="#cb1-16" aria-hidden="true" tabindex="-1"></a>  sampling_times)</span>
-<span id="cb1-17"><a href="#cb1-17" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1-18"><a href="#cb1-18" aria-hidden="true" tabindex="-1"></a><span class="co"># Generate a dataset by adding normally distributed errors with</span></span>
-<span id="cb1-19"><a href="#cb1-19" aria-hidden="true" tabindex="-1"></a><span class="co"># standard deviation 3, for two replicates at each sampling time</span></span>
-<span id="cb1-20"><a href="#cb1-20" aria-hidden="true" tabindex="-1"></a>d_SFO_SFO_SFO_err <span class="ot">&lt;-</span> <span class="fu">add_err</span>(d_SFO_SFO_SFO, <span class="at">reps =</span> <span class="dv">2</span>,</span>
-<span id="cb1-21"><a href="#cb1-21" aria-hidden="true" tabindex="-1"></a>                             <span class="at">sdfunc =</span> <span class="cf">function</span>(x) <span class="dv">3</span>,</span>
-<span id="cb1-22"><a href="#cb1-22" aria-hidden="true" tabindex="-1"></a>                             <span class="at">n =</span> <span class="dv">1</span>, <span class="at">seed =</span> <span class="dv">123456789</span> )</span>
-<span id="cb1-23"><a href="#cb1-23" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1-24"><a href="#cb1-24" aria-hidden="true" tabindex="-1"></a><span class="co"># Fit the model to the dataset</span></span>
-<span id="cb1-25"><a href="#cb1-25" aria-hidden="true" tabindex="-1"></a>f_SFO_SFO_SFO <span class="ot">&lt;-</span> <span class="fu">mkinfit</span>(m_SFO_SFO_SFO, d_SFO_SFO_SFO_err[[<span class="dv">1</span>]], <span class="at">quiet =</span> <span class="cn">TRUE</span>)</span>
-<span id="cb1-26"><a href="#cb1-26" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1-27"><a href="#cb1-27" aria-hidden="true" tabindex="-1"></a><span class="co"># Plot the results separately for parent and metabolites</span></span>
-<span id="cb1-28"><a href="#cb1-28" aria-hidden="true" tabindex="-1"></a><span class="fu">plot_sep</span>(f_SFO_SFO_SFO, <span class="at">lpos =</span> <span class="fu">c</span>(<span class="st">&quot;topright&quot;</span>, <span class="st">&quot;bottomright&quot;</span>, <span class="st">&quot;bottomright&quot;</span>))</span></code></pre></div>
+<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" tabindex="-1"></a><span class="fu">library</span>(<span class="st">&quot;mkin&quot;</span>, <span class="at">quietly =</span> <span class="cn">TRUE</span>)</span>
+<span id="cb1-2"><a href="#cb1-2" tabindex="-1"></a><span class="co"># Define the kinetic model</span></span>
+<span id="cb1-3"><a href="#cb1-3" tabindex="-1"></a>m_SFO_SFO_SFO <span class="ot">&lt;-</span> <span class="fu">mkinmod</span>(<span class="at">parent =</span> <span class="fu">mkinsub</span>(<span class="st">&quot;SFO&quot;</span>, <span class="st">&quot;M1&quot;</span>),</span>
+<span id="cb1-4"><a href="#cb1-4" tabindex="-1"></a>                         <span class="at">M1 =</span> <span class="fu">mkinsub</span>(<span class="st">&quot;SFO&quot;</span>, <span class="st">&quot;M2&quot;</span>),</span>
+<span id="cb1-5"><a href="#cb1-5" tabindex="-1"></a>                         <span class="at">M2 =</span> <span class="fu">mkinsub</span>(<span class="st">&quot;SFO&quot;</span>),</span>
+<span id="cb1-6"><a href="#cb1-6" tabindex="-1"></a>                         <span class="at">use_of_ff =</span> <span class="st">&quot;max&quot;</span>, <span class="at">quiet =</span> <span class="cn">TRUE</span>)</span>
+<span id="cb1-7"><a href="#cb1-7" tabindex="-1"></a></span>
+<span id="cb1-8"><a href="#cb1-8" tabindex="-1"></a></span>
+<span id="cb1-9"><a href="#cb1-9" tabindex="-1"></a><span class="co"># Produce model predictions using some arbitrary parameters</span></span>
+<span id="cb1-10"><a href="#cb1-10" tabindex="-1"></a>sampling_times <span class="ot">=</span> <span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">1</span>, <span class="dv">3</span>, <span class="dv">7</span>, <span class="dv">14</span>, <span class="dv">28</span>, <span class="dv">60</span>, <span class="dv">90</span>, <span class="dv">120</span>)</span>
+<span id="cb1-11"><a href="#cb1-11" tabindex="-1"></a>d_SFO_SFO_SFO <span class="ot">&lt;-</span> <span class="fu">mkinpredict</span>(m_SFO_SFO_SFO,</span>
+<span id="cb1-12"><a href="#cb1-12" tabindex="-1"></a>  <span class="fu">c</span>(<span class="at">k_parent =</span> <span class="fl">0.03</span>,</span>
+<span id="cb1-13"><a href="#cb1-13" tabindex="-1"></a>    <span class="at">f_parent_to_M1 =</span> <span class="fl">0.5</span>, <span class="at">k_M1 =</span> <span class="fu">log</span>(<span class="dv">2</span>)<span class="sc">/</span><span class="dv">100</span>,</span>
+<span id="cb1-14"><a href="#cb1-14" tabindex="-1"></a>    <span class="at">f_M1_to_M2 =</span> <span class="fl">0.9</span>, <span class="at">k_M2 =</span> <span class="fu">log</span>(<span class="dv">2</span>)<span class="sc">/</span><span class="dv">50</span>),</span>
+<span id="cb1-15"><a href="#cb1-15" tabindex="-1"></a>  <span class="fu">c</span>(<span class="at">parent =</span> <span class="dv">100</span>, <span class="at">M1 =</span> <span class="dv">0</span>, <span class="at">M2 =</span> <span class="dv">0</span>),</span>
+<span id="cb1-16"><a href="#cb1-16" tabindex="-1"></a>  sampling_times)</span>
+<span id="cb1-17"><a href="#cb1-17" tabindex="-1"></a></span>
+<span id="cb1-18"><a href="#cb1-18" tabindex="-1"></a><span class="co"># Generate a dataset by adding normally distributed errors with</span></span>
+<span id="cb1-19"><a href="#cb1-19" tabindex="-1"></a><span class="co"># standard deviation 3, for two replicates at each sampling time</span></span>
+<span id="cb1-20"><a href="#cb1-20" tabindex="-1"></a>d_SFO_SFO_SFO_err <span class="ot">&lt;-</span> <span class="fu">add_err</span>(d_SFO_SFO_SFO, <span class="at">reps =</span> <span class="dv">2</span>,</span>
+<span id="cb1-21"><a href="#cb1-21" tabindex="-1"></a>                             <span class="at">sdfunc =</span> <span class="cf">function</span>(x) <span class="dv">3</span>,</span>
+<span id="cb1-22"><a href="#cb1-22" tabindex="-1"></a>                             <span class="at">n =</span> <span class="dv">1</span>, <span class="at">seed =</span> <span class="dv">123456789</span> )</span>
+<span id="cb1-23"><a href="#cb1-23" tabindex="-1"></a></span>
+<span id="cb1-24"><a href="#cb1-24" tabindex="-1"></a><span class="co"># Fit the model to the dataset</span></span>
+<span id="cb1-25"><a href="#cb1-25" tabindex="-1"></a>f_SFO_SFO_SFO <span class="ot">&lt;-</span> <span class="fu">mkinfit</span>(m_SFO_SFO_SFO, d_SFO_SFO_SFO_err[[<span class="dv">1</span>]], <span class="at">quiet =</span> <span class="cn">TRUE</span>)</span>
+<span id="cb1-26"><a href="#cb1-26" tabindex="-1"></a></span>
+<span id="cb1-27"><a href="#cb1-27" tabindex="-1"></a><span class="co"># Plot the results separately for parent and metabolites</span></span>
+<span id="cb1-28"><a href="#cb1-28" tabindex="-1"></a><span class="fu">plot_sep</span>(f_SFO_SFO_SFO, <span class="at">lpos =</span> <span class="fu">c</span>(<span class="st">&quot;topright&quot;</span>, <span class="st">&quot;bottomright&quot;</span>, <span class="st">&quot;bottomright&quot;</span>))</span></code></pre></div>
 </details>
-<p><img src="data:image/png;base64,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" /><!-- --></p>
+<p><img role="img" src="data:image/png;base64,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" /><!-- --></p>
 </div>
 <div id="background" class="section level1">
 <h1>Background</h1>
@@ -617,7 +618,7 @@ parameter estimators.</p>
 <div id="references" class="section level1">
 <h1>References</h1>
 <!-- vim: set foldmethod=syntax: -->
-<div id="refs" class="references csl-bib-body hanging-indent">
+<div id="refs" class="references csl-bib-body hanging-indent" entry-spacing="0">
 <div id="ref-bates1988" class="csl-entry">
 Bates, D., and D. Watts. 1988. <em>Nonlinear Regression and Its
 Applications</em>. Wiley-Interscience.
diff --git a/vignettes/prebuilt/2022_cyan_pathway.pdf b/vignettes/prebuilt/2022_cyan_pathway.pdf
index ec37706f..78e1964c 100644
--- a/vignettes/prebuilt/2022_cyan_pathway.pdf
+++ b/vignettes/prebuilt/2022_cyan_pathway.pdf
diff --git a/vignettes/prebuilt/2022_cyan_pathway.rmd b/vignettes/prebuilt/2022_cyan_pathway.rmd
index 8463c854..e7401f3e 100644
--- a/vignettes/prebuilt/2022_cyan_pathway.rmd
+++ b/vignettes/prebuilt/2022_cyan_pathway.rmd
@@ -1,7 +1,7 @@
 ---
 title: "Testing hierarchical pathway kinetics with residue data on cyantraniliprole"
 author: Johannes Ranke
-date: Last change on 20 April 2023, last compiled on `r format(Sys.time(), "%e
+date: Last change on 13 February 2023, last compiled on `r format(Sys.time(), "%e
   %B %Y")`
 output:
   pdf_document:
@@ -240,11 +240,12 @@ because it relies on the Fisher Information Matrix.
 illparms(f_saem_1) |> kable()
 ```
 
-The model comparison below suggests that the pathway fits using
+The model comparisons below suggest that the pathway fits using
 DFOP or SFORB for the parent compound provide the best fit.
 
 ```{r, dependson = "f-saem-1"}
-anova(f_saem_1) |> kable(digits = 1)
+anova(f_saem_1[, "const"]) |> kable(digits = 1)
+anova(f_saem_1[1:4, ]) |> kable(digits = 1)
 ```
 
 For these two parent model, successful fits are shown below. Plots of the fits
@@ -344,18 +345,21 @@ f_saem_2 <- mhmkin(list(f_sep_2_const, f_sep_2_tc),
 status(f_saem_2) |> kable()
 ```
 
-The hierarchical fits for the alternative pathway completed successfully.
+The hierarchical fits for the alternative pathway completed successfully, with
+the exception of the model using FOMC for the parent compound and constant
+variance as the error model.
 
 ```{r dependson = "f-saem-2"}
 illparms(f_saem_2) |> kable()
 ```
 
-In both fits, the random effects for the formation fractions for the
-pathways from JCZ38 to JSE76, and for the reverse pathway from JSE76
-to JCZ38 are ill-defined.
+In all biphasic fits (DFOP or SFORB for the parent compound), the random
+effects for the formation fractions for the pathways from JCZ38 to JSE76, and
+for the reverse pathway from JSE76 to JCZ38 are ill-defined.
 
 ```{r dependson = "f-saem-2"}
-anova(f_saem_2) |> kable(digits = 1)
+anova(f_saem_2[, "tc"]) |> kable(digits = 1)
+anova(f_saem_2[2:3,]) |> kable(digits = 1)
 ```
 
 The variants using the biexponential models DFOP and SFORB for the parent
@@ -423,7 +427,8 @@ illparms(f_saem_3) |> kable()
 ```
 
 ```{r dependson = "f-saem-3"}
-anova(f_saem_3) |> kable(digits = 1)
+anova(f_saem_3[, "tc"]) |> kable(digits = 1)
+anova(f_saem_3[2:3,]) |> kable(digits = 1)
 ```
 
 While the AIC and BIC values of the best fit (DFOP pathway fit with
diff --git a/vignettes/prebuilt/2022_dmta_parent.pdf b/vignettes/prebuilt/2022_dmta_parent.pdf
index 6e05e6a6..a48626df 100644
--- a/vignettes/prebuilt/2022_dmta_parent.pdf
+++ b/vignettes/prebuilt/2022_dmta_parent.pdf
diff --git a/vignettes/prebuilt/2022_dmta_pathway.pdf b/vignettes/prebuilt/2022_dmta_pathway.pdf
index 95d0964d..def37fc7 100644
--- a/vignettes/prebuilt/2022_dmta_pathway.pdf
+++ b/vignettes/prebuilt/2022_dmta_pathway.pdf
diff --git a/vignettes/web_only/dimethenamid_2018.html b/vignettes/web_only/dimethenamid_2018.html
index daa0bb8f..18f9c9de 100644
--- a/vignettes/web_only/dimethenamid_2018.html
+++ b/vignettes/web_only/dimethenamid_2018.html
@@ -66,18 +66,12 @@ if (!!window.navigator.userAgent.match("MSIE 8")) {
        h6 {font-size: 12px;}
        code {color: inherit; background-color: rgba(0, 0, 0, 0.04);}
        pre:not([class]) { background-color: white }</style>
-<script>/*! jQuery UI - v1.11.4 - 2016-01-05
+<script>/*! jQuery UI - v1.13.2 - 2022-07-14
 * http://jqueryui.com
-* Includes: core.js, widget.js, mouse.js, position.js, draggable.js, droppable.js, resizable.js, selectable.js, sortable.js, accordion.js, autocomplete.js, button.js, dialog.js, menu.js, progressbar.js, selectmenu.js, slider.js, spinner.js, tabs.js, tooltip.js, effect.js, effect-blind.js, effect-bounce.js, effect-clip.js, effect-drop.js, effect-explode.js, effect-fade.js, effect-fold.js, effect-highlight.js, effect-puff.js, effect-pulsate.js, effect-scale.js, effect-shake.js, effect-size.js, effect-slide.js, effect-transfer.js
+* Includes: widget.js, position.js, data.js, disable-selection.js, effect.js, effects/effect-blind.js, effects/effect-bounce.js, effects/effect-clip.js, effects/effect-drop.js, effects/effect-explode.js, effects/effect-fade.js, effects/effect-fold.js, effects/effect-highlight.js, effects/effect-puff.js, effects/effect-pulsate.js, effects/effect-scale.js, effects/effect-shake.js, effects/effect-size.js, effects/effect-slide.js, effects/effect-transfer.js, focusable.js, form-reset-mixin.js, jquery-patch.js, keycode.js, labels.js, scroll-parent.js, tabbable.js, unique-id.js, widgets/accordion.js, widgets/autocomplete.js, widgets/button.js, widgets/checkboxradio.js, widgets/controlgroup.js, widgets/datepicker.js, widgets/dialog.js, widgets/draggable.js, widgets/droppable.js, widgets/menu.js, widgets/mouse.js, widgets/progressbar.js, widgets/resizable.js, widgets/selectable.js, widgets/selectmenu.js, widgets/slider.js, widgets/sortable.js, widgets/spinner.js, widgets/tabs.js, widgets/tooltip.js
 * Copyright jQuery Foundation and other contributors; Licensed MIT */
 
-(function(e){"function"==typeof define&&define.amd?define(["jquery"],e):e(jQuery)})(function(e){function t(t,s){var n,a,o,r=t.nodeName.toLowerCase();return"area"===r?(n=t.parentNode,a=n.name,t.href&&a&&"map"===n.nodeName.toLowerCase()?(o=e("img[usemap='#"+a+"']")[0],!!o&&i(o)):!1):(/^(input|select|textarea|button|object)$/.test(r)?!t.disabled:"a"===r?t.href||s:s)&&i(t)}function i(t){return e.expr.filters.visible(t)&&!e(t).parents().addBack().filter(function(){return"hidden"===e.css(this,"visibility")}).length}function s(e){return function(){var t=this.element.val();e.apply(this,arguments),this._refresh(),t!==this.element.val()&&this._trigger("change")}}e.ui=e.ui||{},e.extend(e.ui,{version:"1.11.4",keyCode:{BACKSPACE:8,COMMA:188,DELETE:46,DOWN:40,END:35,ENTER:13,ESCAPE:27,HOME:36,LEFT:37,PAGE_DOWN:34,PAGE_UP:33,PERIOD:190,RIGHT:39,SPACE:32,TAB:9,UP:38}}),e.fn.extend({scrollParent:function(t){var i=this.css("position"),s="absolute"===i,n=t?/(auto|scroll|hidden)/:/(auto|scroll)/,a=this.parents().filter(function(){var t=e(this);return s&&"static"===t.css("position")?!1:n.test(t.css("overflow")+t.css("overflow-y")+t.css("overflow-x"))}).eq(0);return"fixed"!==i&&a.length?a:e(this[0].ownerDocument||document)},uniqueId:function(){var e=0;return function(){return this.each(function(){this.id||(this.id="ui-id-"+ ++e)})}}(),removeUniqueId:function(){return this.each(function(){/^ui-id-\d+$/.test(this.id)&&e(this).removeAttr("id")})}}),e.extend(e.expr[":"],{data:e.expr.createPseudo?e.expr.createPseudo(function(t){return function(i){return!!e.data(i,t)}}):function(t,i,s){return!!e.data(t,s[3])},focusable:function(i){return t(i,!isNaN(e.attr(i,"tabindex")))},tabbable:function(i){var s=e.attr(i,"tabindex"),n=isNaN(s);return(n||s>=0)&&t(i,!n)}}),e("<a>").outerWidth(1).jquery||e.each(["Width","Height"],function(t,i){function s(t,i,s,a){return e.each(n,function(){i-=parseFloat(e.css(t,"padding"+this))||0,s&&(i-=parseFloat(e.css(t,"border"+this+"Width"))||0),a&&(i-=parseFloat(e.css(t,"margin"+this))||0)}),i}var n="Width"===i?["Left","Right"]:["Top","Bottom"],a=i.toLowerCase(),o={innerWidth:e.fn.innerWidth,innerHeight:e.fn.innerHeight,outerWidth:e.fn.outerWidth,outerHeight:e.fn.outerHeight};e.fn["inner"+i]=function(t){return void 0===t?o["inner"+i].call(this):this.each(function(){e(this).css(a,s(this,t)+"px")})},e.fn["outer"+i]=function(t,n){return"number"!=typeof t?o["outer"+i].call(this,t):this.each(function(){e(this).css(a,s(this,t,!0,n)+"px")})}}),e.fn.addBack||(e.fn.addBack=function(e){return this.add(null==e?this.prevObject:this.prevObject.filter(e))}),e("<a>").data("a-b","a").removeData("a-b").data("a-b")&&(e.fn.removeData=function(t){return function(i){return arguments.length?t.call(this,e.camelCase(i)):t.call(this)}}(e.fn.removeData)),e.ui.ie=!!/msie [\w.]+/.exec(navigator.userAgent.toLowerCase()),e.fn.extend({focus:function(t){return function(i,s){return"number"==typeof i?this.each(function(){var t=this;setTimeout(function(){e(t).focus(),s&&s.call(t)},i)}):t.apply(this,arguments)}}(e.fn.focus),disableSelection:function(){var e="onselectstart"in document.createElement("div")?"selectstart":"mousedown";return function(){return this.bind(e+".ui-disableSelection",function(e){e.preventDefault()})}}(),enableSelection:function(){return this.unbind(".ui-disableSelection")},zIndex:function(t){if(void 0!==t)return this.css("zIndex",t);if(this.length)for(var i,s,n=e(this[0]);n.length&&n[0]!==document;){if(i=n.css("position"),("absolute"===i||"relative"===i||"fixed"===i)&&(s=parseInt(n.css("zIndex"),10),!isNaN(s)&&0!==s))return s;n=n.parent()}return 0}}),e.ui.plugin={add:function(t,i,s){var n,a=e.ui[t].prototype;for(n in s)a.plugins[n]=a.plugins[n]||[],a.plugins[n].push([i,s[n]])},call:function(e,t,i,s){var n,a=e.plugins[t];if(a&&(s||e.element[0].parentNode&&11!==e.element[0].parentNode.nodeType))for(n=0;a.length>n;n++)e.options[a[n][0]]&&a[n][1].apply(e.element,i)}};var n=0,a=Array.prototype.slice;e.cleanData=function(t){return function(i){var s,n,a;for(a=0;null!=(n=i[a]);a++)try{s=e._data(n,"events"),s&&s.remove&&e(n).triggerHandler("remove")}catch(o){}t(i)}}(e.cleanData),e.widget=function(t,i,s){var n,a,o,r,h={},l=t.split(".")[0];return t=t.split(".")[1],n=l+"-"+t,s||(s=i,i=e.Widget),e.expr[":"][n.toLowerCase()]=function(t){return!!e.data(t,n)},e[l]=e[l]||{},a=e[l][t],o=e[l][t]=function(e,t){return this._createWidget?(arguments.length&&this._createWidget(e,t),void 0):new o(e,t)},e.extend(o,a,{version:s.version,_proto:e.extend({},s),_childConstructors:[]}),r=new i,r.options=e.widget.extend({},r.options),e.each(s,function(t,s){return e.isFunction(s)?(h[t]=function(){var e=function(){return i.prototype[t].apply(this,arguments)},n=function(e){return i.prototype[t].apply(this,e)};return function(){var t,i=this._super,a=this._superApply;return this._super=e,this._superApply=n,t=s.apply(this,arguments),this._super=i,this._superApply=a,t}}(),void 0):(h[t]=s,void 0)}),o.prototype=e.widget.extend(r,{widgetEventPrefix:a?r.widgetEventPrefix||t:t},h,{constructor:o,namespace:l,widgetName:t,widgetFullName:n}),a?(e.each(a._childConstructors,function(t,i){var s=i.prototype;e.widget(s.namespace+"."+s.widgetName,o,i._proto)}),delete a._childConstructors):i._childConstructors.push(o),e.widget.bridge(t,o),o},e.widget.extend=function(t){for(var i,s,n=a.call(arguments,1),o=0,r=n.length;r>o;o++)for(i in n[o])s=n[o][i],n[o].hasOwnProperty(i)&&void 0!==s&&(t[i]=e.isPlainObject(s)?e.isPlainObject(t[i])?e.widget.extend({},t[i],s):e.widget.extend({},s):s);return t},e.widget.bridge=function(t,i){var s=i.prototype.widgetFullName||t;e.fn[t]=function(n){var o="string"==typeof n,r=a.call(arguments,1),h=this;return o?this.each(function(){var i,a=e.data(this,s);return"instance"===n?(h=a,!1):a?e.isFunction(a[n])&&"_"!==n.charAt(0)?(i=a[n].apply(a,r),i!==a&&void 0!==i?(h=i&&i.jquery?h.pushStack(i.get()):i,!1):void 0):e.error("no such method '"+n+"' for "+t+" widget instance"):e.error("cannot call methods on "+t+" prior to initialization; "+"attempted to call method '"+n+"'")}):(r.length&&(n=e.widget.extend.apply(null,[n].concat(r))),this.each(function(){var t=e.data(this,s);t?(t.option(n||{}),t._init&&t._init()):e.data(this,s,new i(n,this))})),h}},e.Widget=function(){},e.Widget._childConstructors=[],e.Widget.prototype={widgetName:"widget",widgetEventPrefix:"",defaultElement:"<div>",options:{disabled:!1,create:null},_createWidget:function(t,i){i=e(i||this.defaultElement||this)[0],this.element=e(i),this.uuid=n++,this.eventNamespace="."+this.widgetName+this.uuid,this.bindings=e(),this.hoverable=e(),this.focusable=e(),i!==this&&(e.data(i,this.widgetFullName,this),this._on(!0,this.element,{remove:function(e){e.target===i&&this.destroy()}}),this.document=e(i.style?i.ownerDocument:i.document||i),this.window=e(this.document[0].defaultView||this.document[0].parentWindow)),this.options=e.widget.extend({},this.options,this._getCreateOptions(),t),this._create(),this._trigger("create",null,this._getCreateEventData()),this._init()},_getCreateOptions:e.noop,_getCreateEventData:e.noop,_create:e.noop,_init:e.noop,destroy:function(){this._destroy(),this.element.unbind(this.eventNamespace).removeData(this.widgetFullName).removeData(e.camelCase(this.widgetFullName)),this.widget().unbind(this.eventNamespace).removeAttr("aria-disabled").removeClass(this.widgetFullName+"-disabled "+"ui-state-disabled"),this.bindings.unbind(this.eventNamespace),this.hoverable.removeClass("ui-state-hover"),this.focusable.removeClass("ui-state-focus")},_destroy:e.noop,widget:function(){return this.element},option:function(t,i){var s,n,a,o=t;if(0===arguments.length)return e.widget.extend({},this.options);if("string"==typeof t)if(o={},s=t.split("."),t=s.shift(),s.length){for(n=o[t]=e.widget.extend({},this.options[t]),a=0;s.length-1>a;a++)n[s[a]]=n[s[a]]||{},n=n[s[a]];if(t=s.pop(),1===arguments.length)return void 0===n[t]?null:n[t];n[t]=i}else{if(1===arguments.length)return void 0===this.options[t]?null:this.options[t];o[t]=i}return this._setOptions(o),this},_setOptions:function(e){var t;for(t in e)this._setOption(t,e[t]);return this},_setOption:function(e,t){return this.options[e]=t,"disabled"===e&&(this.widget().toggleClass(this.widgetFullName+"-disabled",!!t),t&&(this.hoverable.removeClass("ui-state-hover"),this.focusable.removeClass("ui-state-focus"))),this},enable:function(){return this._setOptions({disabled:!1})},disable:function(){return this._setOptions({disabled:!0})},_on:function(t,i,s){var n,a=this;"boolean"!=typeof t&&(s=i,i=t,t=!1),s?(i=n=e(i),this.bindings=this.bindings.add(i)):(s=i,i=this.element,n=this.widget()),e.each(s,function(s,o){function r(){return t||a.options.disabled!==!0&&!e(this).hasClass("ui-state-disabled")?("string"==typeof o?a[o]:o).apply(a,arguments):void 0}"string"!=typeof o&&(r.guid=o.guid=o.guid||r.guid||e.guid++);var h=s.match(/^([\w:-]*)\s*(.*)$/),l=h[1]+a.eventNamespace,u=h[2];u?n.delegate(u,l,r):i.bind(l,r)})},_off:function(t,i){i=(i||"").split(" ").join(this.eventNamespace+" ")+this.eventNamespace,t.unbind(i).undelegate(i),this.bindings=e(this.bindings.not(t).get()),this.focusable=e(this.focusable.not(t).get()),this.hoverable=e(this.hoverable.not(t).get())},_delay:function(e,t){function i(){return("string"==typeof e?s[e]:e).apply(s,arguments)}var s=this;return setTimeout(i,t||0)},_hoverable:function(t){this.hoverable=this.hoverable.add(t),this._on(t,{mouseenter:function(t){e(t.currentTarget).addClass("ui-state-hover")},mouseleave:function(t){e(t.currentTarget).removeClass("ui-state-hover")}})},_focusable:function(t){this.focusable=this.focusable.add(t),this._on(t,{focusin:function(t){e(t.currentTarget).addClass("ui-state-focus")},focusout:function(t){e(t.currentTarget).removeClass("ui-state-focus")}})},_trigger:function(t,i,s){var n,a,o=this.options[t];if(s=s||{},i=e.Event(i),i.type=(t===this.widgetEventPrefix?t:this.widgetEventPrefix+t).toLowerCase(),i.target=this.element[0],a=i.originalEvent)for(n in a)n in i||(i[n]=a[n]);return this.element.trigger(i,s),!(e.isFunction(o)&&o.apply(this.element[0],[i].concat(s))===!1||i.isDefaultPrevented())}},e.each({show:"fadeIn",hide:"fadeOut"},function(t,i){e.Widget.prototype["_"+t]=function(s,n,a){"string"==typeof n&&(n={effect:n});var o,r=n?n===!0||"number"==typeof n?i:n.effect||i:t;n=n||{},"number"==typeof n&&(n={duration:n}),o=!e.isEmptyObject(n),n.complete=a,n.delay&&s.delay(n.delay),o&&e.effects&&e.effects.effect[r]?s[t](n):r!==t&&s[r]?s[r](n.duration,n.easing,a):s.queue(function(i){e(this)[t](),a&&a.call(s[0]),i()})}}),e.widget;var o=!1;e(document).mouseup(function(){o=!1}),e.widget("ui.mouse",{version:"1.11.4",options:{cancel:"input,textarea,button,select,option",distance:1,delay:0},_mouseInit:function(){var t=this;this.element.bind("mousedown."+this.widgetName,function(e){return t._mouseDown(e)}).bind("click."+this.widgetName,function(i){return!0===e.data(i.target,t.widgetName+".preventClickEvent")?(e.removeData(i.target,t.widgetName+".preventClickEvent"),i.stopImmediatePropagation(),!1):void 0}),this.started=!1},_mouseDestroy:function(){this.element.unbind("."+this.widgetName),this._mouseMoveDelegate&&this.document.unbind("mousemove."+this.widgetName,this._mouseMoveDelegate).unbind("mouseup."+this.widgetName,this._mouseUpDelegate)},_mouseDown:function(t){if(!o){this._mouseMoved=!1,this._mouseStarted&&this._mouseUp(t),this._mouseDownEvent=t;var i=this,s=1===t.which,n="string"==typeof this.options.cancel&&t.target.nodeName?e(t.target).closest(this.options.cancel).length:!1;return s&&!n&&this._mouseCapture(t)?(this.mouseDelayMet=!this.options.delay,this.mouseDelayMet||(this._mouseDelayTimer=setTimeout(function(){i.mouseDelayMet=!0},this.options.delay)),this._mouseDistanceMet(t)&&this._mouseDelayMet(t)&&(this._mouseStarted=this._mouseStart(t)!==!1,!this._mouseStarted)?(t.preventDefault(),!0):(!0===e.data(t.target,this.widgetName+".preventClickEvent")&&e.removeData(t.target,this.widgetName+".preventClickEvent"),this._mouseMoveDelegate=function(e){return i._mouseMove(e)},this._mouseUpDelegate=function(e){return i._mouseUp(e)},this.document.bind("mousemove."+this.widgetName,this._mouseMoveDelegate).bind("mouseup."+this.widgetName,this._mouseUpDelegate),t.preventDefault(),o=!0,!0)):!0}},_mouseMove:function(t){if(this._mouseMoved){if(e.ui.ie&&(!document.documentMode||9>document.documentMode)&&!t.button)return this._mouseUp(t);if(!t.which)return this._mouseUp(t)}return(t.which||t.button)&&(this._mouseMoved=!0),this._mouseStarted?(this._mouseDrag(t),t.preventDefault()):(this._mouseDistanceMet(t)&&this._mouseDelayMet(t)&&(this._mouseStarted=this._mouseStart(this._mouseDownEvent,t)!==!1,this._mouseStarted?this._mouseDrag(t):this._mouseUp(t)),!this._mouseStarted)},_mouseUp:function(t){return this.document.unbind("mousemove."+this.widgetName,this._mouseMoveDelegate).unbind("mouseup."+this.widgetName,this._mouseUpDelegate),this._mouseStarted&&(this._mouseStarted=!1,t.target===this._mouseDownEvent.target&&e.data(t.target,this.widgetName+".preventClickEvent",!0),this._mouseStop(t)),o=!1,!1},_mouseDistanceMet:function(e){return Math.max(Math.abs(this._mouseDownEvent.pageX-e.pageX),Math.abs(this._mouseDownEvent.pageY-e.pageY))>=this.options.distance},_mouseDelayMet:function(){return this.mouseDelayMet},_mouseStart:function(){},_mouseDrag:function(){},_mouseStop:function(){},_mouseCapture:function(){return!0}}),function(){function t(e,t,i){return[parseFloat(e[0])*(p.test(e[0])?t/100:1),parseFloat(e[1])*(p.test(e[1])?i/100:1)]}function i(t,i){return parseInt(e.css(t,i),10)||0}function s(t){var i=t[0];return 9===i.nodeType?{width:t.width(),height:t.height(),offset:{top:0,left:0}}:e.isWindow(i)?{width:t.width(),height:t.height(),offset:{top:t.scrollTop(),left:t.scrollLeft()}}:i.preventDefault?{width:0,height:0,offset:{top:i.pageY,left:i.pageX}}:{width:t.outerWidth(),height:t.outerHeight(),offset:t.offset()}}e.ui=e.ui||{};var n,a,o=Math.max,r=Math.abs,h=Math.round,l=/left|center|right/,u=/top|center|bottom/,d=/[\+\-]\d+(\.[\d]+)?%?/,c=/^\w+/,p=/%$/,f=e.fn.position;e.position={scrollbarWidth:function(){if(void 0!==n)return n;var t,i,s=e("<div style='display:block;position:absolute;width:50px;height:50px;overflow:hidden;'><div style='height:100px;width:auto;'></div></div>"),a=s.children()[0];return e("body").append(s),t=a.offsetWidth,s.css("overflow","scroll"),i=a.offsetWidth,t===i&&(i=s[0].clientWidth),s.remove(),n=t-i},getScrollInfo:function(t){var i=t.isWindow||t.isDocument?"":t.element.css("overflow-x"),s=t.isWindow||t.isDocument?"":t.element.css("overflow-y"),n="scroll"===i||"auto"===i&&t.width<t.element[0].scrollWidth,a="scroll"===s||"auto"===s&&t.height<t.element[0].scrollHeight;return{width:a?e.position.scrollbarWidth():0,height:n?e.position.scrollbarWidth():0}},getWithinInfo:function(t){var i=e(t||window),s=e.isWindow(i[0]),n=!!i[0]&&9===i[0].nodeType;return{element:i,isWindow:s,isDocument:n,offset:i.offset()||{left:0,top:0},scrollLeft:i.scrollLeft(),scrollTop:i.scrollTop(),width:s||n?i.width():i.outerWidth(),height:s||n?i.height():i.outerHeight()}}},e.fn.position=function(n){if(!n||!n.of)return f.apply(this,arguments);n=e.extend({},n);var p,m,g,v,y,b,_=e(n.of),x=e.position.getWithinInfo(n.within),w=e.position.getScrollInfo(x),k=(n.collision||"flip").split(" "),T={};return b=s(_),_[0].preventDefault&&(n.at="left top"),m=b.width,g=b.height,v=b.offset,y=e.extend({},v),e.each(["my","at"],function(){var e,t,i=(n[this]||"").split(" ");1===i.length&&(i=l.test(i[0])?i.concat(["center"]):u.test(i[0])?["center"].concat(i):["center","center"]),i[0]=l.test(i[0])?i[0]:"center",i[1]=u.test(i[1])?i[1]:"center",e=d.exec(i[0]),t=d.exec(i[1]),T[this]=[e?e[0]:0,t?t[0]:0],n[this]=[c.exec(i[0])[0],c.exec(i[1])[0]]}),1===k.length&&(k[1]=k[0]),"right"===n.at[0]?y.left+=m:"center"===n.at[0]&&(y.left+=m/2),"bottom"===n.at[1]?y.top+=g:"center"===n.at[1]&&(y.top+=g/2),p=t(T.at,m,g),y.left+=p[0],y.top+=p[1],this.each(function(){var s,l,u=e(this),d=u.outerWidth(),c=u.outerHeight(),f=i(this,"marginLeft"),b=i(this,"marginTop"),D=d+f+i(this,"marginRight")+w.width,S=c+b+i(this,"marginBottom")+w.height,N=e.extend({},y),M=t(T.my,u.outerWidth(),u.outerHeight());"right"===n.my[0]?N.left-=d:"center"===n.my[0]&&(N.left-=d/2),"bottom"===n.my[1]?N.top-=c:"center"===n.my[1]&&(N.top-=c/2),N.left+=M[0],N.top+=M[1],a||(N.left=h(N.left),N.top=h(N.top)),s={marginLeft:f,marginTop:b},e.each(["left","top"],function(t,i){e.ui.position[k[t]]&&e.ui.position[k[t]][i](N,{targetWidth:m,targetHeight:g,elemWidth:d,elemHeight:c,collisionPosition:s,collisionWidth:D,collisionHeight:S,offset:[p[0]+M[0],p[1]+M[1]],my:n.my,at:n.at,within:x,elem:u})}),n.using&&(l=function(e){var t=v.left-N.left,i=t+m-d,s=v.top-N.top,a=s+g-c,h={target:{element:_,left:v.left,top:v.top,width:m,height:g},element:{element:u,left:N.left,top:N.top,width:d,height:c},horizontal:0>i?"left":t>0?"right":"center",vertical:0>a?"top":s>0?"bottom":"middle"};d>m&&m>r(t+i)&&(h.horizontal="center"),c>g&&g>r(s+a)&&(h.vertical="middle"),h.important=o(r(t),r(i))>o(r(s),r(a))?"horizontal":"vertical",n.using.call(this,e,h)}),u.offset(e.extend(N,{using:l}))})},e.ui.position={fit:{left:function(e,t){var i,s=t.within,n=s.isWindow?s.scrollLeft:s.offset.left,a=s.width,r=e.left-t.collisionPosition.marginLeft,h=n-r,l=r+t.collisionWidth-a-n;t.collisionWidth>a?h>0&&0>=l?(i=e.left+h+t.collisionWidth-a-n,e.left+=h-i):e.left=l>0&&0>=h?n:h>l?n+a-t.collisionWidth:n:h>0?e.left+=h:l>0?e.left-=l:e.left=o(e.left-r,e.left)},top:function(e,t){var i,s=t.within,n=s.isWindow?s.scrollTop:s.offset.top,a=t.within.height,r=e.top-t.collisionPosition.marginTop,h=n-r,l=r+t.collisionHeight-a-n;t.collisionHeight>a?h>0&&0>=l?(i=e.top+h+t.collisionHeight-a-n,e.top+=h-i):e.top=l>0&&0>=h?n:h>l?n+a-t.collisionHeight:n:h>0?e.top+=h:l>0?e.top-=l:e.top=o(e.top-r,e.top)}},flip:{left:function(e,t){var i,s,n=t.within,a=n.offset.left+n.scrollLeft,o=n.width,h=n.isWindow?n.scrollLeft:n.offset.left,l=e.left-t.collisionPosition.marginLeft,u=l-h,d=l+t.collisionWidth-o-h,c="left"===t.my[0]?-t.elemWidth:"right"===t.my[0]?t.elemWidth:0,p="left"===t.at[0]?t.targetWidth:"right"===t.at[0]?-t.targetWidth:0,f=-2*t.offset[0];0>u?(i=e.left+c+p+f+t.collisionWidth-o-a,(0>i||r(u)>i)&&(e.left+=c+p+f)):d>0&&(s=e.left-t.collisionPosition.marginLeft+c+p+f-h,(s>0||d>r(s))&&(e.left+=c+p+f))},top:function(e,t){var i,s,n=t.within,a=n.offset.top+n.scrollTop,o=n.height,h=n.isWindow?n.scrollTop:n.offset.top,l=e.top-t.collisionPosition.marginTop,u=l-h,d=l+t.collisionHeight-o-h,c="top"===t.my[1],p=c?-t.elemHeight:"bottom"===t.my[1]?t.elemHeight:0,f="top"===t.at[1]?t.targetHeight:"bottom"===t.at[1]?-t.targetHeight:0,m=-2*t.offset[1];0>u?(s=e.top+p+f+m+t.collisionHeight-o-a,(0>s||r(u)>s)&&(e.top+=p+f+m)):d>0&&(i=e.top-t.collisionPosition.marginTop+p+f+m-h,(i>0||d>r(i))&&(e.top+=p+f+m))}},flipfit:{left:function(){e.ui.position.flip.left.apply(this,arguments),e.ui.position.fit.left.apply(this,arguments)},top:function(){e.ui.position.flip.top.apply(this,arguments),e.ui.position.fit.top.apply(this,arguments)}}},function(){var t,i,s,n,o,r=document.getElementsByTagName("body")[0],h=document.createElement("div");t=document.createElement(r?"div":"body"),s={visibility:"hidden",width:0,height:0,border:0,margin:0,background:"none"},r&&e.extend(s,{position:"absolute",left:"-1000px",top:"-1000px"});for(o in s)t.style[o]=s[o];t.appendChild(h),i=r||document.documentElement,i.insertBefore(t,i.firstChild),h.style.cssText="position: absolute; left: 10.7432222px;",n=e(h).offset().left,a=n>10&&11>n,t.innerHTML="",i.removeChild(t)}()}(),e.ui.position,e.widget("ui.draggable",e.ui.mouse,{version:"1.11.4",widgetEventPrefix:"drag",options:{addClasses:!0,appendTo:"parent",axis:!1,connectToSortable:!1,containment:!1,cursor:"auto",cursorAt:!1,grid:!1,handle:!1,helper:"original",iframeFix:!1,opacity:!1,refreshPositions:!1,revert:!1,revertDuration:500,scope:"default",scroll:!0,scrollSensitivity:20,scrollSpeed:20,snap:!1,snapMode:"both",snapTolerance:20,stack:!1,zIndex:!1,drag:null,start:null,stop:null},_create:function(){"original"===this.options.helper&&this._setPositionRelative(),this.options.addClasses&&this.element.addClass("ui-draggable"),this.options.disabled&&this.element.addClass("ui-draggable-disabled"),this._setHandleClassName(),this._mouseInit()},_setOption:function(e,t){this._super(e,t),"handle"===e&&(this._removeHandleClassName(),this._setHandleClassName())},_destroy:function(){return(this.helper||this.element).is(".ui-draggable-dragging")?(this.destroyOnClear=!0,void 0):(this.element.removeClass("ui-draggable ui-draggable-dragging ui-draggable-disabled"),this._removeHandleClassName(),this._mouseDestroy(),void 0)},_mouseCapture:function(t){var i=this.options;return this._blurActiveElement(t),this.helper||i.disabled||e(t.target).closest(".ui-resizable-handle").length>0?!1:(this.handle=this._getHandle(t),this.handle?(this._blockFrames(i.iframeFix===!0?"iframe":i.iframeFix),!0):!1)},_blockFrames:function(t){this.iframeBlocks=this.document.find(t).map(function(){var t=e(this);return e("<div>").css("position","absolute").appendTo(t.parent()).outerWidth(t.outerWidth()).outerHeight(t.outerHeight()).offset(t.offset())[0]})},_unblockFrames:function(){this.iframeBlocks&&(this.iframeBlocks.remove(),delete this.iframeBlocks)},_blurActiveElement:function(t){var i=this.document[0];if(this.handleElement.is(t.target))try{i.activeElement&&"body"!==i.activeElement.nodeName.toLowerCase()&&e(i.activeElement).blur()}catch(s){}},_mouseStart:function(t){var i=this.options;return this.helper=this._createHelper(t),this.helper.addClass("ui-draggable-dragging"),this._cacheHelperProportions(),e.ui.ddmanager&&(e.ui.ddmanager.current=this),this._cacheMargins(),this.cssPosition=this.helper.css("position"),this.scrollParent=this.helper.scrollParent(!0),this.offsetParent=this.helper.offsetParent(),this.hasFixedAncestor=this.helper.parents().filter(function(){return"fixed"===e(this).css("position")}).length>0,this.positionAbs=this.element.offset(),this._refreshOffsets(t),this.originalPosition=this.position=this._generatePosition(t,!1),this.originalPageX=t.pageX,this.originalPageY=t.pageY,i.cursorAt&&this._adjustOffsetFromHelper(i.cursorAt),this._setContainment(),this._trigger("start",t)===!1?(this._clear(),!1):(this._cacheHelperProportions(),e.ui.ddmanager&&!i.dropBehaviour&&e.ui.ddmanager.prepareOffsets(this,t),this._normalizeRightBottom(),this._mouseDrag(t,!0),e.ui.ddmanager&&e.ui.ddmanager.dragStart(this,t),!0)},_refreshOffsets:function(e){this.offset={top:this.positionAbs.top-this.margins.top,left:this.positionAbs.left-this.margins.left,scroll:!1,parent:this._getParentOffset(),relative:this._getRelativeOffset()},this.offset.click={left:e.pageX-this.offset.left,top:e.pageY-this.offset.top}},_mouseDrag:function(t,i){if(this.hasFixedAncestor&&(this.offset.parent=this._getParentOffset()),this.position=this._generatePosition(t,!0),this.positionAbs=this._convertPositionTo("absolute"),!i){var s=this._uiHash();if(this._trigger("drag",t,s)===!1)return this._mouseUp({}),!1;this.position=s.position}return this.helper[0].style.left=this.position.left+"px",this.helper[0].style.top=this.position.top+"px",e.ui.ddmanager&&e.ui.ddmanager.drag(this,t),!1},_mouseStop:function(t){var i=this,s=!1;return e.ui.ddmanager&&!this.options.dropBehaviour&&(s=e.ui.ddmanager.drop(this,t)),this.dropped&&(s=this.dropped,this.dropped=!1),"invalid"===this.options.revert&&!s||"valid"===this.options.revert&&s||this.options.revert===!0||e.isFunction(this.options.revert)&&this.options.revert.call(this.element,s)?e(this.helper).animate(this.originalPosition,parseInt(this.options.revertDuration,10),function(){i._trigger("stop",t)!==!1&&i._clear()}):this._trigger("stop",t)!==!1&&this._clear(),!1},_mouseUp:function(t){return this._unblockFrames(),e.ui.ddmanager&&e.ui.ddmanager.dragStop(this,t),this.handleElement.is(t.target)&&this.element.focus(),e.ui.mouse.prototype._mouseUp.call(this,t)},cancel:function(){return this.helper.is(".ui-draggable-dragging")?this._mouseUp({}):this._clear(),this},_getHandle:function(t){return this.options.handle?!!e(t.target).closest(this.element.find(this.options.handle)).length:!0},_setHandleClassName:function(){this.handleElement=this.options.handle?this.element.find(this.options.handle):this.element,this.handleElement.addClass("ui-draggable-handle")},_removeHandleClassName:function(){this.handleElement.removeClass("ui-draggable-handle")},_createHelper:function(t){var i=this.options,s=e.isFunction(i.helper),n=s?e(i.helper.apply(this.element[0],[t])):"clone"===i.helper?this.element.clone().removeAttr("id"):this.element;return n.parents("body").length||n.appendTo("parent"===i.appendTo?this.element[0].parentNode:i.appendTo),s&&n[0]===this.element[0]&&this._setPositionRelative(),n[0]===this.element[0]||/(fixed|absolute)/.test(n.css("position"))||n.css("position","absolute"),n},_setPositionRelative:function(){/^(?:r|a|f)/.test(this.element.css("position"))||(this.element[0].style.position="relative")},_adjustOffsetFromHelper:function(t){"string"==typeof t&&(t=t.split(" ")),e.isArray(t)&&(t={left:+t[0],top:+t[1]||0}),"left"in t&&(this.offset.click.left=t.left+this.margins.left),"right"in t&&(this.offset.click.left=this.helperProportions.width-t.right+this.margins.left),"top"in t&&(this.offset.click.top=t.top+this.margins.top),"bottom"in t&&(this.offset.click.top=this.helperProportions.height-t.bottom+this.margins.top)},_isRootNode:function(e){return/(html|body)/i.test(e.tagName)||e===this.document[0]},_getParentOffset:function(){var t=this.offsetParent.offset(),i=this.document[0];return"absolute"===this.cssPosition&&this.scrollParent[0]!==i&&e.contains(this.scrollParent[0],this.offsetParent[0])&&(t.left+=this.scrollParent.scrollLeft(),t.top+=this.scrollParent.scrollTop()),this._isRootNode(this.offsetParent[0])&&(t={top:0,left:0}),{top:t.top+(parseInt(this.offsetParent.css("borderTopWidth"),10)||0),left:t.left+(parseInt(this.offsetParent.css("borderLeftWidth"),10)||0)}},_getRelativeOffset:function(){if("relative"!==this.cssPosition)return{top:0,left:0};var e=this.element.position(),t=this._isRootNode(this.scrollParent[0]);return{top:e.top-(parseInt(this.helper.css("top"),10)||0)+(t?0:this.scrollParent.scrollTop()),left:e.left-(parseInt(this.helper.css("left"),10)||0)+(t?0:this.scrollParent.scrollLeft())}},_cacheMargins:function(){this.margins={left:parseInt(this.element.css("marginLeft"),10)||0,top:parseInt(this.element.css("marginTop"),10)||0,right:parseInt(this.element.css("marginRight"),10)||0,bottom:parseInt(this.element.css("marginBottom"),10)||0}},_cacheHelperProportions:function(){this.helperProportions={width:this.helper.outerWidth(),height:this.helper.outerHeight()}},_setContainment:function(){var t,i,s,n=this.options,a=this.document[0];return this.relativeContainer=null,n.containment?"window"===n.containment?(this.containment=[e(window).scrollLeft()-this.offset.relative.left-this.offset.parent.left,e(window).scrollTop()-this.offset.relative.top-this.offset.parent.top,e(window).scrollLeft()+e(window).width()-this.helperProportions.width-this.margins.left,e(window).scrollTop()+(e(window).height()||a.body.parentNode.scrollHeight)-this.helperProportions.height-this.margins.top],void 0):"document"===n.containment?(this.containment=[0,0,e(a).width()-this.helperProportions.width-this.margins.left,(e(a).height()||a.body.parentNode.scrollHeight)-this.helperProportions.height-this.margins.top],void 0):n.containment.constructor===Array?(this.containment=n.containment,void 0):("parent"===n.containment&&(n.containment=this.helper[0].parentNode),i=e(n.containment),s=i[0],s&&(t=/(scroll|auto)/.test(i.css("overflow")),this.containment=[(parseInt(i.css("borderLeftWidth"),10)||0)+(parseInt(i.css("paddingLeft"),10)||0),(parseInt(i.css("borderTopWidth"),10)||0)+(parseInt(i.css("paddingTop"),10)||0),(t?Math.max(s.scrollWidth,s.offsetWidth):s.offsetWidth)-(parseInt(i.css("borderRightWidth"),10)||0)-(parseInt(i.css("paddingRight"),10)||0)-this.helperProportions.width-this.margins.left-this.margins.right,(t?Math.max(s.scrollHeight,s.offsetHeight):s.offsetHeight)-(parseInt(i.css("borderBottomWidth"),10)||0)-(parseInt(i.css("paddingBottom"),10)||0)-this.helperProportions.height-this.margins.top-this.margins.bottom],this.relativeContainer=i),void 0):(this.containment=null,void 0)},_convertPositionTo:function(e,t){t||(t=this.position);var i="absolute"===e?1:-1,s=this._isRootNode(this.scrollParent[0]);return{top:t.top+this.offset.relative.top*i+this.offset.parent.top*i-("fixed"===this.cssPosition?-this.offset.scroll.top:s?0:this.offset.scroll.top)*i,left:t.left+this.offset.relative.left*i+this.offset.parent.left*i-("fixed"===this.cssPosition?-this.offset.scroll.left:s?0:this.offset.scroll.left)*i}},_generatePosition:function(e,t){var i,s,n,a,o=this.options,r=this._isRootNode(this.scrollParent[0]),h=e.pageX,l=e.pageY;return r&&this.offset.scroll||(this.offset.scroll={top:this.scrollParent.scrollTop(),left:this.scrollParent.scrollLeft()}),t&&(this.containment&&(this.relativeContainer?(s=this.relativeContainer.offset(),i=[this.containment[0]+s.left,this.containment[1]+s.top,this.containment[2]+s.left,this.containment[3]+s.top]):i=this.containment,e.pageX-this.offset.click.left<i[0]&&(h=i[0]+this.offset.click.left),e.pageY-this.offset.click.top<i[1]&&(l=i[1]+this.offset.click.top),e.pageX-this.offset.click.left>i[2]&&(h=i[2]+this.offset.click.left),e.pageY-this.offset.click.top>i[3]&&(l=i[3]+this.offset.click.top)),o.grid&&(n=o.grid[1]?this.originalPageY+Math.round((l-this.originalPageY)/o.grid[1])*o.grid[1]:this.originalPageY,l=i?n-this.offset.click.top>=i[1]||n-this.offset.click.top>i[3]?n:n-this.offset.click.top>=i[1]?n-o.grid[1]:n+o.grid[1]:n,a=o.grid[0]?this.originalPageX+Math.round((h-this.originalPageX)/o.grid[0])*o.grid[0]:this.originalPageX,h=i?a-this.offset.click.left>=i[0]||a-this.offset.click.left>i[2]?a:a-this.offset.click.left>=i[0]?a-o.grid[0]:a+o.grid[0]:a),"y"===o.axis&&(h=this.originalPageX),"x"===o.axis&&(l=this.originalPageY)),{top:l-this.offset.click.top-this.offset.relative.top-this.offset.parent.top+("fixed"===this.cssPosition?-this.offset.scroll.top:r?0:this.offset.scroll.top),left:h-this.offset.click.left-this.offset.relative.left-this.offset.parent.left+("fixed"===this.cssPosition?-this.offset.scroll.left:r?0:this.offset.scroll.left)}},_clear:function(){this.helper.removeClass("ui-draggable-dragging"),this.helper[0]===this.element[0]||this.cancelHelperRemoval||this.helper.remove(),this.helper=null,this.cancelHelperRemoval=!1,this.destroyOnClear&&this.destroy()},_normalizeRightBottom:function(){"y"!==this.options.axis&&"auto"!==this.helper.css("right")&&(this.helper.width(this.helper.width()),this.helper.css("right","auto")),"x"!==this.options.axis&&"auto"!==this.helper.css("bottom")&&(this.helper.height(this.helper.height()),this.helper.css("bottom","auto"))},_trigger:function(t,i,s){return s=s||this._uiHash(),e.ui.plugin.call(this,t,[i,s,this],!0),/^(drag|start|stop)/.test(t)&&(this.positionAbs=this._convertPositionTo("absolute"),s.offset=this.positionAbs),e.Widget.prototype._trigger.call(this,t,i,s)},plugins:{},_uiHash:function(){return{helper:this.helper,position:this.position,originalPosition:this.originalPosition,offset:this.positionAbs}}}),e.ui.plugin.add("draggable","connectToSortable",{start:function(t,i,s){var n=e.extend({},i,{item:s.element});s.sortables=[],e(s.options.connectToSortable).each(function(){var i=e(this).sortable("instance");i&&!i.options.disabled&&(s.sortables.push(i),i.refreshPositions(),i._trigger("activate",t,n))})},stop:function(t,i,s){var n=e.extend({},i,{item:s.element});s.cancelHelperRemoval=!1,e.each(s.sortables,function(){var e=this;e.isOver?(e.isOver=0,s.cancelHelperRemoval=!0,e.cancelHelperRemoval=!1,e._storedCSS={position:e.placeholder.css("position"),top:e.placeholder.css("top"),left:e.placeholder.css("left")},e._mouseStop(t),e.options.helper=e.options._helper):(e.cancelHelperRemoval=!0,e._trigger("deactivate",t,n))})},drag:function(t,i,s){e.each(s.sortables,function(){var n=!1,a=this;a.positionAbs=s.positionAbs,a.helperProportions=s.helperProportions,a.offset.click=s.offset.click,a._intersectsWith(a.containerCache)&&(n=!0,e.each(s.sortables,function(){return this.positionAbs=s.positionAbs,this.helperProportions=s.helperProportions,this.offset.click=s.offset.click,this!==a&&this._intersectsWith(this.containerCache)&&e.contains(a.element[0],this.element[0])&&(n=!1),n
-})),n?(a.isOver||(a.isOver=1,s._parent=i.helper.parent(),a.currentItem=i.helper.appendTo(a.element).data("ui-sortable-item",!0),a.options._helper=a.options.helper,a.options.helper=function(){return i.helper[0]},t.target=a.currentItem[0],a._mouseCapture(t,!0),a._mouseStart(t,!0,!0),a.offset.click.top=s.offset.click.top,a.offset.click.left=s.offset.click.left,a.offset.parent.left-=s.offset.parent.left-a.offset.parent.left,a.offset.parent.top-=s.offset.parent.top-a.offset.parent.top,s._trigger("toSortable",t),s.dropped=a.element,e.each(s.sortables,function(){this.refreshPositions()}),s.currentItem=s.element,a.fromOutside=s),a.currentItem&&(a._mouseDrag(t),i.position=a.position)):a.isOver&&(a.isOver=0,a.cancelHelperRemoval=!0,a.options._revert=a.options.revert,a.options.revert=!1,a._trigger("out",t,a._uiHash(a)),a._mouseStop(t,!0),a.options.revert=a.options._revert,a.options.helper=a.options._helper,a.placeholder&&a.placeholder.remove(),i.helper.appendTo(s._parent),s._refreshOffsets(t),i.position=s._generatePosition(t,!0),s._trigger("fromSortable",t),s.dropped=!1,e.each(s.sortables,function(){this.refreshPositions()}))})}}),e.ui.plugin.add("draggable","cursor",{start:function(t,i,s){var n=e("body"),a=s.options;n.css("cursor")&&(a._cursor=n.css("cursor")),n.css("cursor",a.cursor)},stop:function(t,i,s){var n=s.options;n._cursor&&e("body").css("cursor",n._cursor)}}),e.ui.plugin.add("draggable","opacity",{start:function(t,i,s){var n=e(i.helper),a=s.options;n.css("opacity")&&(a._opacity=n.css("opacity")),n.css("opacity",a.opacity)},stop:function(t,i,s){var n=s.options;n._opacity&&e(i.helper).css("opacity",n._opacity)}}),e.ui.plugin.add("draggable","scroll",{start:function(e,t,i){i.scrollParentNotHidden||(i.scrollParentNotHidden=i.helper.scrollParent(!1)),i.scrollParentNotHidden[0]!==i.document[0]&&"HTML"!==i.scrollParentNotHidden[0].tagName&&(i.overflowOffset=i.scrollParentNotHidden.offset())},drag:function(t,i,s){var n=s.options,a=!1,o=s.scrollParentNotHidden[0],r=s.document[0];o!==r&&"HTML"!==o.tagName?(n.axis&&"x"===n.axis||(s.overflowOffset.top+o.offsetHeight-t.pageY<n.scrollSensitivity?o.scrollTop=a=o.scrollTop+n.scrollSpeed:t.pageY-s.overflowOffset.top<n.scrollSensitivity&&(o.scrollTop=a=o.scrollTop-n.scrollSpeed)),n.axis&&"y"===n.axis||(s.overflowOffset.left+o.offsetWidth-t.pageX<n.scrollSensitivity?o.scrollLeft=a=o.scrollLeft+n.scrollSpeed:t.pageX-s.overflowOffset.left<n.scrollSensitivity&&(o.scrollLeft=a=o.scrollLeft-n.scrollSpeed))):(n.axis&&"x"===n.axis||(t.pageY-e(r).scrollTop()<n.scrollSensitivity?a=e(r).scrollTop(e(r).scrollTop()-n.scrollSpeed):e(window).height()-(t.pageY-e(r).scrollTop())<n.scrollSensitivity&&(a=e(r).scrollTop(e(r).scrollTop()+n.scrollSpeed))),n.axis&&"y"===n.axis||(t.pageX-e(r).scrollLeft()<n.scrollSensitivity?a=e(r).scrollLeft(e(r).scrollLeft()-n.scrollSpeed):e(window).width()-(t.pageX-e(r).scrollLeft())<n.scrollSensitivity&&(a=e(r).scrollLeft(e(r).scrollLeft()+n.scrollSpeed)))),a!==!1&&e.ui.ddmanager&&!n.dropBehaviour&&e.ui.ddmanager.prepareOffsets(s,t)}}),e.ui.plugin.add("draggable","snap",{start:function(t,i,s){var n=s.options;s.snapElements=[],e(n.snap.constructor!==String?n.snap.items||":data(ui-draggable)":n.snap).each(function(){var t=e(this),i=t.offset();this!==s.element[0]&&s.snapElements.push({item:this,width:t.outerWidth(),height:t.outerHeight(),top:i.top,left:i.left})})},drag:function(t,i,s){var n,a,o,r,h,l,u,d,c,p,f=s.options,m=f.snapTolerance,g=i.offset.left,v=g+s.helperProportions.width,y=i.offset.top,b=y+s.helperProportions.height;for(c=s.snapElements.length-1;c>=0;c--)h=s.snapElements[c].left-s.margins.left,l=h+s.snapElements[c].width,u=s.snapElements[c].top-s.margins.top,d=u+s.snapElements[c].height,h-m>v||g>l+m||u-m>b||y>d+m||!e.contains(s.snapElements[c].item.ownerDocument,s.snapElements[c].item)?(s.snapElements[c].snapping&&s.options.snap.release&&s.options.snap.release.call(s.element,t,e.extend(s._uiHash(),{snapItem:s.snapElements[c].item})),s.snapElements[c].snapping=!1):("inner"!==f.snapMode&&(n=m>=Math.abs(u-b),a=m>=Math.abs(d-y),o=m>=Math.abs(h-v),r=m>=Math.abs(l-g),n&&(i.position.top=s._convertPositionTo("relative",{top:u-s.helperProportions.height,left:0}).top),a&&(i.position.top=s._convertPositionTo("relative",{top:d,left:0}).top),o&&(i.position.left=s._convertPositionTo("relative",{top:0,left:h-s.helperProportions.width}).left),r&&(i.position.left=s._convertPositionTo("relative",{top:0,left:l}).left)),p=n||a||o||r,"outer"!==f.snapMode&&(n=m>=Math.abs(u-y),a=m>=Math.abs(d-b),o=m>=Math.abs(h-g),r=m>=Math.abs(l-v),n&&(i.position.top=s._convertPositionTo("relative",{top:u,left:0}).top),a&&(i.position.top=s._convertPositionTo("relative",{top:d-s.helperProportions.height,left:0}).top),o&&(i.position.left=s._convertPositionTo("relative",{top:0,left:h}).left),r&&(i.position.left=s._convertPositionTo("relative",{top:0,left:l-s.helperProportions.width}).left)),!s.snapElements[c].snapping&&(n||a||o||r||p)&&s.options.snap.snap&&s.options.snap.snap.call(s.element,t,e.extend(s._uiHash(),{snapItem:s.snapElements[c].item})),s.snapElements[c].snapping=n||a||o||r||p)}}),e.ui.plugin.add("draggable","stack",{start:function(t,i,s){var n,a=s.options,o=e.makeArray(e(a.stack)).sort(function(t,i){return(parseInt(e(t).css("zIndex"),10)||0)-(parseInt(e(i).css("zIndex"),10)||0)});o.length&&(n=parseInt(e(o[0]).css("zIndex"),10)||0,e(o).each(function(t){e(this).css("zIndex",n+t)}),this.css("zIndex",n+o.length))}}),e.ui.plugin.add("draggable","zIndex",{start:function(t,i,s){var n=e(i.helper),a=s.options;n.css("zIndex")&&(a._zIndex=n.css("zIndex")),n.css("zIndex",a.zIndex)},stop:function(t,i,s){var n=s.options;n._zIndex&&e(i.helper).css("zIndex",n._zIndex)}}),e.ui.draggable,e.widget("ui.droppable",{version:"1.11.4",widgetEventPrefix:"drop",options:{accept:"*",activeClass:!1,addClasses:!0,greedy:!1,hoverClass:!1,scope:"default",tolerance:"intersect",activate:null,deactivate:null,drop:null,out:null,over:null},_create:function(){var t,i=this.options,s=i.accept;this.isover=!1,this.isout=!0,this.accept=e.isFunction(s)?s:function(e){return e.is(s)},this.proportions=function(){return arguments.length?(t=arguments[0],void 0):t?t:t={width:this.element[0].offsetWidth,height:this.element[0].offsetHeight}},this._addToManager(i.scope),i.addClasses&&this.element.addClass("ui-droppable")},_addToManager:function(t){e.ui.ddmanager.droppables[t]=e.ui.ddmanager.droppables[t]||[],e.ui.ddmanager.droppables[t].push(this)},_splice:function(e){for(var t=0;e.length>t;t++)e[t]===this&&e.splice(t,1)},_destroy:function(){var t=e.ui.ddmanager.droppables[this.options.scope];this._splice(t),this.element.removeClass("ui-droppable ui-droppable-disabled")},_setOption:function(t,i){if("accept"===t)this.accept=e.isFunction(i)?i:function(e){return e.is(i)};else if("scope"===t){var s=e.ui.ddmanager.droppables[this.options.scope];this._splice(s),this._addToManager(i)}this._super(t,i)},_activate:function(t){var i=e.ui.ddmanager.current;this.options.activeClass&&this.element.addClass(this.options.activeClass),i&&this._trigger("activate",t,this.ui(i))},_deactivate:function(t){var i=e.ui.ddmanager.current;this.options.activeClass&&this.element.removeClass(this.options.activeClass),i&&this._trigger("deactivate",t,this.ui(i))},_over:function(t){var i=e.ui.ddmanager.current;i&&(i.currentItem||i.element)[0]!==this.element[0]&&this.accept.call(this.element[0],i.currentItem||i.element)&&(this.options.hoverClass&&this.element.addClass(this.options.hoverClass),this._trigger("over",t,this.ui(i)))},_out:function(t){var i=e.ui.ddmanager.current;i&&(i.currentItem||i.element)[0]!==this.element[0]&&this.accept.call(this.element[0],i.currentItem||i.element)&&(this.options.hoverClass&&this.element.removeClass(this.options.hoverClass),this._trigger("out",t,this.ui(i)))},_drop:function(t,i){var s=i||e.ui.ddmanager.current,n=!1;return s&&(s.currentItem||s.element)[0]!==this.element[0]?(this.element.find(":data(ui-droppable)").not(".ui-draggable-dragging").each(function(){var i=e(this).droppable("instance");return i.options.greedy&&!i.options.disabled&&i.options.scope===s.options.scope&&i.accept.call(i.element[0],s.currentItem||s.element)&&e.ui.intersect(s,e.extend(i,{offset:i.element.offset()}),i.options.tolerance,t)?(n=!0,!1):void 0}),n?!1:this.accept.call(this.element[0],s.currentItem||s.element)?(this.options.activeClass&&this.element.removeClass(this.options.activeClass),this.options.hoverClass&&this.element.removeClass(this.options.hoverClass),this._trigger("drop",t,this.ui(s)),this.element):!1):!1},ui:function(e){return{draggable:e.currentItem||e.element,helper:e.helper,position:e.position,offset:e.positionAbs}}}),e.ui.intersect=function(){function e(e,t,i){return e>=t&&t+i>e}return function(t,i,s,n){if(!i.offset)return!1;var a=(t.positionAbs||t.position.absolute).left+t.margins.left,o=(t.positionAbs||t.position.absolute).top+t.margins.top,r=a+t.helperProportions.width,h=o+t.helperProportions.height,l=i.offset.left,u=i.offset.top,d=l+i.proportions().width,c=u+i.proportions().height;switch(s){case"fit":return a>=l&&d>=r&&o>=u&&c>=h;case"intersect":return a+t.helperProportions.width/2>l&&d>r-t.helperProportions.width/2&&o+t.helperProportions.height/2>u&&c>h-t.helperProportions.height/2;case"pointer":return e(n.pageY,u,i.proportions().height)&&e(n.pageX,l,i.proportions().width);case"touch":return(o>=u&&c>=o||h>=u&&c>=h||u>o&&h>c)&&(a>=l&&d>=a||r>=l&&d>=r||l>a&&r>d);default:return!1}}}(),e.ui.ddmanager={current:null,droppables:{"default":[]},prepareOffsets:function(t,i){var s,n,a=e.ui.ddmanager.droppables[t.options.scope]||[],o=i?i.type:null,r=(t.currentItem||t.element).find(":data(ui-droppable)").addBack();e:for(s=0;a.length>s;s++)if(!(a[s].options.disabled||t&&!a[s].accept.call(a[s].element[0],t.currentItem||t.element))){for(n=0;r.length>n;n++)if(r[n]===a[s].element[0]){a[s].proportions().height=0;continue e}a[s].visible="none"!==a[s].element.css("display"),a[s].visible&&("mousedown"===o&&a[s]._activate.call(a[s],i),a[s].offset=a[s].element.offset(),a[s].proportions({width:a[s].element[0].offsetWidth,height:a[s].element[0].offsetHeight}))}},drop:function(t,i){var s=!1;return e.each((e.ui.ddmanager.droppables[t.options.scope]||[]).slice(),function(){this.options&&(!this.options.disabled&&this.visible&&e.ui.intersect(t,this,this.options.tolerance,i)&&(s=this._drop.call(this,i)||s),!this.options.disabled&&this.visible&&this.accept.call(this.element[0],t.currentItem||t.element)&&(this.isout=!0,this.isover=!1,this._deactivate.call(this,i)))}),s},dragStart:function(t,i){t.element.parentsUntil("body").bind("scroll.droppable",function(){t.options.refreshPositions||e.ui.ddmanager.prepareOffsets(t,i)})},drag:function(t,i){t.options.refreshPositions&&e.ui.ddmanager.prepareOffsets(t,i),e.each(e.ui.ddmanager.droppables[t.options.scope]||[],function(){if(!this.options.disabled&&!this.greedyChild&&this.visible){var s,n,a,o=e.ui.intersect(t,this,this.options.tolerance,i),r=!o&&this.isover?"isout":o&&!this.isover?"isover":null;r&&(this.options.greedy&&(n=this.options.scope,a=this.element.parents(":data(ui-droppable)").filter(function(){return e(this).droppable("instance").options.scope===n}),a.length&&(s=e(a[0]).droppable("instance"),s.greedyChild="isover"===r)),s&&"isover"===r&&(s.isover=!1,s.isout=!0,s._out.call(s,i)),this[r]=!0,this["isout"===r?"isover":"isout"]=!1,this["isover"===r?"_over":"_out"].call(this,i),s&&"isout"===r&&(s.isout=!1,s.isover=!0,s._over.call(s,i)))}})},dragStop:function(t,i){t.element.parentsUntil("body").unbind("scroll.droppable"),t.options.refreshPositions||e.ui.ddmanager.prepareOffsets(t,i)}},e.ui.droppable,e.widget("ui.resizable",e.ui.mouse,{version:"1.11.4",widgetEventPrefix:"resize",options:{alsoResize:!1,animate:!1,animateDuration:"slow",animateEasing:"swing",aspectRatio:!1,autoHide:!1,containment:!1,ghost:!1,grid:!1,handles:"e,s,se",helper:!1,maxHeight:null,maxWidth:null,minHeight:10,minWidth:10,zIndex:90,resize:null,start:null,stop:null},_num:function(e){return parseInt(e,10)||0},_isNumber:function(e){return!isNaN(parseInt(e,10))},_hasScroll:function(t,i){if("hidden"===e(t).css("overflow"))return!1;var s=i&&"left"===i?"scrollLeft":"scrollTop",n=!1;return t[s]>0?!0:(t[s]=1,n=t[s]>0,t[s]=0,n)},_create:function(){var t,i,s,n,a,o=this,r=this.options;if(this.element.addClass("ui-resizable"),e.extend(this,{_aspectRatio:!!r.aspectRatio,aspectRatio:r.aspectRatio,originalElement:this.element,_proportionallyResizeElements:[],_helper:r.helper||r.ghost||r.animate?r.helper||"ui-resizable-helper":null}),this.element[0].nodeName.match(/^(canvas|textarea|input|select|button|img)$/i)&&(this.element.wrap(e("<div class='ui-wrapper' style='overflow: hidden;'></div>").css({position:this.element.css("position"),width:this.element.outerWidth(),height:this.element.outerHeight(),top:this.element.css("top"),left:this.element.css("left")})),this.element=this.element.parent().data("ui-resizable",this.element.resizable("instance")),this.elementIsWrapper=!0,this.element.css({marginLeft:this.originalElement.css("marginLeft"),marginTop:this.originalElement.css("marginTop"),marginRight:this.originalElement.css("marginRight"),marginBottom:this.originalElement.css("marginBottom")}),this.originalElement.css({marginLeft:0,marginTop:0,marginRight:0,marginBottom:0}),this.originalResizeStyle=this.originalElement.css("resize"),this.originalElement.css("resize","none"),this._proportionallyResizeElements.push(this.originalElement.css({position:"static",zoom:1,display:"block"})),this.originalElement.css({margin:this.originalElement.css("margin")}),this._proportionallyResize()),this.handles=r.handles||(e(".ui-resizable-handle",this.element).length?{n:".ui-resizable-n",e:".ui-resizable-e",s:".ui-resizable-s",w:".ui-resizable-w",se:".ui-resizable-se",sw:".ui-resizable-sw",ne:".ui-resizable-ne",nw:".ui-resizable-nw"}:"e,s,se"),this._handles=e(),this.handles.constructor===String)for("all"===this.handles&&(this.handles="n,e,s,w,se,sw,ne,nw"),t=this.handles.split(","),this.handles={},i=0;t.length>i;i++)s=e.trim(t[i]),a="ui-resizable-"+s,n=e("<div class='ui-resizable-handle "+a+"'></div>"),n.css({zIndex:r.zIndex}),"se"===s&&n.addClass("ui-icon ui-icon-gripsmall-diagonal-se"),this.handles[s]=".ui-resizable-"+s,this.element.append(n);this._renderAxis=function(t){var i,s,n,a;t=t||this.element;for(i in this.handles)this.handles[i].constructor===String?this.handles[i]=this.element.children(this.handles[i]).first().show():(this.handles[i].jquery||this.handles[i].nodeType)&&(this.handles[i]=e(this.handles[i]),this._on(this.handles[i],{mousedown:o._mouseDown})),this.elementIsWrapper&&this.originalElement[0].nodeName.match(/^(textarea|input|select|button)$/i)&&(s=e(this.handles[i],this.element),a=/sw|ne|nw|se|n|s/.test(i)?s.outerHeight():s.outerWidth(),n=["padding",/ne|nw|n/.test(i)?"Top":/se|sw|s/.test(i)?"Bottom":/^e$/.test(i)?"Right":"Left"].join(""),t.css(n,a),this._proportionallyResize()),this._handles=this._handles.add(this.handles[i])},this._renderAxis(this.element),this._handles=this._handles.add(this.element.find(".ui-resizable-handle")),this._handles.disableSelection(),this._handles.mouseover(function(){o.resizing||(this.className&&(n=this.className.match(/ui-resizable-(se|sw|ne|nw|n|e|s|w)/i)),o.axis=n&&n[1]?n[1]:"se")}),r.autoHide&&(this._handles.hide(),e(this.element).addClass("ui-resizable-autohide").mouseenter(function(){r.disabled||(e(this).removeClass("ui-resizable-autohide"),o._handles.show())}).mouseleave(function(){r.disabled||o.resizing||(e(this).addClass("ui-resizable-autohide"),o._handles.hide())})),this._mouseInit()},_destroy:function(){this._mouseDestroy();var t,i=function(t){e(t).removeClass("ui-resizable ui-resizable-disabled ui-resizable-resizing").removeData("resizable").removeData("ui-resizable").unbind(".resizable").find(".ui-resizable-handle").remove()};return this.elementIsWrapper&&(i(this.element),t=this.element,this.originalElement.css({position:t.css("position"),width:t.outerWidth(),height:t.outerHeight(),top:t.css("top"),left:t.css("left")}).insertAfter(t),t.remove()),this.originalElement.css("resize",this.originalResizeStyle),i(this.originalElement),this},_mouseCapture:function(t){var i,s,n=!1;for(i in this.handles)s=e(this.handles[i])[0],(s===t.target||e.contains(s,t.target))&&(n=!0);return!this.options.disabled&&n},_mouseStart:function(t){var i,s,n,a=this.options,o=this.element;return this.resizing=!0,this._renderProxy(),i=this._num(this.helper.css("left")),s=this._num(this.helper.css("top")),a.containment&&(i+=e(a.containment).scrollLeft()||0,s+=e(a.containment).scrollTop()||0),this.offset=this.helper.offset(),this.position={left:i,top:s},this.size=this._helper?{width:this.helper.width(),height:this.helper.height()}:{width:o.width(),height:o.height()},this.originalSize=this._helper?{width:o.outerWidth(),height:o.outerHeight()}:{width:o.width(),height:o.height()},this.sizeDiff={width:o.outerWidth()-o.width(),height:o.outerHeight()-o.height()},this.originalPosition={left:i,top:s},this.originalMousePosition={left:t.pageX,top:t.pageY},this.aspectRatio="number"==typeof a.aspectRatio?a.aspectRatio:this.originalSize.width/this.originalSize.height||1,n=e(".ui-resizable-"+this.axis).css("cursor"),e("body").css("cursor","auto"===n?this.axis+"-resize":n),o.addClass("ui-resizable-resizing"),this._propagate("start",t),!0},_mouseDrag:function(t){var i,s,n=this.originalMousePosition,a=this.axis,o=t.pageX-n.left||0,r=t.pageY-n.top||0,h=this._change[a];return this._updatePrevProperties(),h?(i=h.apply(this,[t,o,r]),this._updateVirtualBoundaries(t.shiftKey),(this._aspectRatio||t.shiftKey)&&(i=this._updateRatio(i,t)),i=this._respectSize(i,t),this._updateCache(i),this._propagate("resize",t),s=this._applyChanges(),!this._helper&&this._proportionallyResizeElements.length&&this._proportionallyResize(),e.isEmptyObject(s)||(this._updatePrevProperties(),this._trigger("resize",t,this.ui()),this._applyChanges()),!1):!1},_mouseStop:function(t){this.resizing=!1;var i,s,n,a,o,r,h,l=this.options,u=this;return this._helper&&(i=this._proportionallyResizeElements,s=i.length&&/textarea/i.test(i[0].nodeName),n=s&&this._hasScroll(i[0],"left")?0:u.sizeDiff.height,a=s?0:u.sizeDiff.width,o={width:u.helper.width()-a,height:u.helper.height()-n},r=parseInt(u.element.css("left"),10)+(u.position.left-u.originalPosition.left)||null,h=parseInt(u.element.css("top"),10)+(u.position.top-u.originalPosition.top)||null,l.animate||this.element.css(e.extend(o,{top:h,left:r})),u.helper.height(u.size.height),u.helper.width(u.size.width),this._helper&&!l.animate&&this._proportionallyResize()),e("body").css("cursor","auto"),this.element.removeClass("ui-resizable-resizing"),this._propagate("stop",t),this._helper&&this.helper.remove(),!1},_updatePrevProperties:function(){this.prevPosition={top:this.position.top,left:this.position.left},this.prevSize={width:this.size.width,height:this.size.height}},_applyChanges:function(){var e={};return this.position.top!==this.prevPosition.top&&(e.top=this.position.top+"px"),this.position.left!==this.prevPosition.left&&(e.left=this.position.left+"px"),this.size.width!==this.prevSize.width&&(e.width=this.size.width+"px"),this.size.height!==this.prevSize.height&&(e.height=this.size.height+"px"),this.helper.css(e),e},_updateVirtualBoundaries:function(e){var t,i,s,n,a,o=this.options;a={minWidth:this._isNumber(o.minWidth)?o.minWidth:0,maxWidth:this._isNumber(o.maxWidth)?o.maxWidth:1/0,minHeight:this._isNumber(o.minHeight)?o.minHeight:0,maxHeight:this._isNumber(o.maxHeight)?o.maxHeight:1/0},(this._aspectRatio||e)&&(t=a.minHeight*this.aspectRatio,s=a.minWidth/this.aspectRatio,i=a.maxHeight*this.aspectRatio,n=a.maxWidth/this.aspectRatio,t>a.minWidth&&(a.minWidth=t),s>a.minHeight&&(a.minHeight=s),a.maxWidth>i&&(a.maxWidth=i),a.maxHeight>n&&(a.maxHeight=n)),this._vBoundaries=a},_updateCache:function(e){this.offset=this.helper.offset(),this._isNumber(e.left)&&(this.position.left=e.left),this._isNumber(e.top)&&(this.position.top=e.top),this._isNumber(e.height)&&(this.size.height=e.height),this._isNumber(e.width)&&(this.size.width=e.width)},_updateRatio:function(e){var t=this.position,i=this.size,s=this.axis;return this._isNumber(e.height)?e.width=e.height*this.aspectRatio:this._isNumber(e.width)&&(e.height=e.width/this.aspectRatio),"sw"===s&&(e.left=t.left+(i.width-e.width),e.top=null),"nw"===s&&(e.top=t.top+(i.height-e.height),e.left=t.left+(i.width-e.width)),e},_respectSize:function(e){var t=this._vBoundaries,i=this.axis,s=this._isNumber(e.width)&&t.maxWidth&&t.maxWidth<e.width,n=this._isNumber(e.height)&&t.maxHeight&&t.maxHeight<e.height,a=this._isNumber(e.width)&&t.minWidth&&t.minWidth>e.width,o=this._isNumber(e.height)&&t.minHeight&&t.minHeight>e.height,r=this.originalPosition.left+this.originalSize.width,h=this.position.top+this.size.height,l=/sw|nw|w/.test(i),u=/nw|ne|n/.test(i);return a&&(e.width=t.minWidth),o&&(e.height=t.minHeight),s&&(e.width=t.maxWidth),n&&(e.height=t.maxHeight),a&&l&&(e.left=r-t.minWidth),s&&l&&(e.left=r-t.maxWidth),o&&u&&(e.top=h-t.minHeight),n&&u&&(e.top=h-t.maxHeight),e.width||e.height||e.left||!e.top?e.width||e.height||e.top||!e.left||(e.left=null):e.top=null,e},_getPaddingPlusBorderDimensions:function(e){for(var t=0,i=[],s=[e.css("borderTopWidth"),e.css("borderRightWidth"),e.css("borderBottomWidth"),e.css("borderLeftWidth")],n=[e.css("paddingTop"),e.css("paddingRight"),e.css("paddingBottom"),e.css("paddingLeft")];4>t;t++)i[t]=parseInt(s[t],10)||0,i[t]+=parseInt(n[t],10)||0;return{height:i[0]+i[2],width:i[1]+i[3]}},_proportionallyResize:function(){if(this._proportionallyResizeElements.length)for(var e,t=0,i=this.helper||this.element;this._proportionallyResizeElements.length>t;t++)e=this._proportionallyResizeElements[t],this.outerDimensions||(this.outerDimensions=this._getPaddingPlusBorderDimensions(e)),e.css({height:i.height()-this.outerDimensions.height||0,width:i.width()-this.outerDimensions.width||0})},_renderProxy:function(){var t=this.element,i=this.options;this.elementOffset=t.offset(),this._helper?(this.helper=this.helper||e("<div style='overflow:hidden;'></div>"),this.helper.addClass(this._helper).css({width:this.element.outerWidth()-1,height:this.element.outerHeight()-1,position:"absolute",left:this.elementOffset.left+"px",top:this.elementOffset.top+"px",zIndex:++i.zIndex}),this.helper.appendTo("body").disableSelection()):this.helper=this.element},_change:{e:function(e,t){return{width:this.originalSize.width+t}},w:function(e,t){var i=this.originalSize,s=this.originalPosition;return{left:s.left+t,width:i.width-t}},n:function(e,t,i){var s=this.originalSize,n=this.originalPosition;return{top:n.top+i,height:s.height-i}},s:function(e,t,i){return{height:this.originalSize.height+i}},se:function(t,i,s){return e.extend(this._change.s.apply(this,arguments),this._change.e.apply(this,[t,i,s]))},sw:function(t,i,s){return e.extend(this._change.s.apply(this,arguments),this._change.w.apply(this,[t,i,s]))},ne:function(t,i,s){return e.extend(this._change.n.apply(this,arguments),this._change.e.apply(this,[t,i,s]))},nw:function(t,i,s){return e.extend(this._change.n.apply(this,arguments),this._change.w.apply(this,[t,i,s]))}},_propagate:function(t,i){e.ui.plugin.call(this,t,[i,this.ui()]),"resize"!==t&&this._trigger(t,i,this.ui())},plugins:{},ui:function(){return{originalElement:this.originalElement,element:this.element,helper:this.helper,position:this.position,size:this.size,originalSize:this.originalSize,originalPosition:this.originalPosition}}}),e.ui.plugin.add("resizable","animate",{stop:function(t){var i=e(this).resizable("instance"),s=i.options,n=i._proportionallyResizeElements,a=n.length&&/textarea/i.test(n[0].nodeName),o=a&&i._hasScroll(n[0],"left")?0:i.sizeDiff.height,r=a?0:i.sizeDiff.width,h={width:i.size.width-r,height:i.size.height-o},l=parseInt(i.element.css("left"),10)+(i.position.left-i.originalPosition.left)||null,u=parseInt(i.element.css("top"),10)+(i.position.top-i.originalPosition.top)||null;i.element.animate(e.extend(h,u&&l?{top:u,left:l}:{}),{duration:s.animateDuration,easing:s.animateEasing,step:function(){var s={width:parseInt(i.element.css("width"),10),height:parseInt(i.element.css("height"),10),top:parseInt(i.element.css("top"),10),left:parseInt(i.element.css("left"),10)};n&&n.length&&e(n[0]).css({width:s.width,height:s.height}),i._updateCache(s),i._propagate("resize",t)}})}}),e.ui.plugin.add("resizable","containment",{start:function(){var t,i,s,n,a,o,r,h=e(this).resizable("instance"),l=h.options,u=h.element,d=l.containment,c=d instanceof e?d.get(0):/parent/.test(d)?u.parent().get(0):d;c&&(h.containerElement=e(c),/document/.test(d)||d===document?(h.containerOffset={left:0,top:0},h.containerPosition={left:0,top:0},h.parentData={element:e(document),left:0,top:0,width:e(document).width(),height:e(document).height()||document.body.parentNode.scrollHeight}):(t=e(c),i=[],e(["Top","Right","Left","Bottom"]).each(function(e,s){i[e]=h._num(t.css("padding"+s))}),h.containerOffset=t.offset(),h.containerPosition=t.position(),h.containerSize={height:t.innerHeight()-i[3],width:t.innerWidth()-i[1]},s=h.containerOffset,n=h.containerSize.height,a=h.containerSize.width,o=h._hasScroll(c,"left")?c.scrollWidth:a,r=h._hasScroll(c)?c.scrollHeight:n,h.parentData={element:c,left:s.left,top:s.top,width:o,height:r}))},resize:function(t){var i,s,n,a,o=e(this).resizable("instance"),r=o.options,h=o.containerOffset,l=o.position,u=o._aspectRatio||t.shiftKey,d={top:0,left:0},c=o.containerElement,p=!0;c[0]!==document&&/static/.test(c.css("position"))&&(d=h),l.left<(o._helper?h.left:0)&&(o.size.width=o.size.width+(o._helper?o.position.left-h.left:o.position.left-d.left),u&&(o.size.height=o.size.width/o.aspectRatio,p=!1),o.position.left=r.helper?h.left:0),l.top<(o._helper?h.top:0)&&(o.size.height=o.size.height+(o._helper?o.position.top-h.top:o.position.top),u&&(o.size.width=o.size.height*o.aspectRatio,p=!1),o.position.top=o._helper?h.top:0),n=o.containerElement.get(0)===o.element.parent().get(0),a=/relative|absolute/.test(o.containerElement.css("position")),n&&a?(o.offset.left=o.parentData.left+o.position.left,o.offset.top=o.parentData.top+o.position.top):(o.offset.left=o.element.offset().left,o.offset.top=o.element.offset().top),i=Math.abs(o.sizeDiff.width+(o._helper?o.offset.left-d.left:o.offset.left-h.left)),s=Math.abs(o.sizeDiff.height+(o._helper?o.offset.top-d.top:o.offset.top-h.top)),i+o.size.width>=o.parentData.width&&(o.size.width=o.parentData.width-i,u&&(o.size.height=o.size.width/o.aspectRatio,p=!1)),s+o.size.height>=o.parentData.height&&(o.size.height=o.parentData.height-s,u&&(o.size.width=o.size.height*o.aspectRatio,p=!1)),p||(o.position.left=o.prevPosition.left,o.position.top=o.prevPosition.top,o.size.width=o.prevSize.width,o.size.height=o.prevSize.height)},stop:function(){var t=e(this).resizable("instance"),i=t.options,s=t.containerOffset,n=t.containerPosition,a=t.containerElement,o=e(t.helper),r=o.offset(),h=o.outerWidth()-t.sizeDiff.width,l=o.outerHeight()-t.sizeDiff.height;t._helper&&!i.animate&&/relative/.test(a.css("position"))&&e(this).css({left:r.left-n.left-s.left,width:h,height:l}),t._helper&&!i.animate&&/static/.test(a.css("position"))&&e(this).css({left:r.left-n.left-s.left,width:h,height:l})}}),e.ui.plugin.add("resizable","alsoResize",{start:function(){var t=e(this).resizable("instance"),i=t.options;e(i.alsoResize).each(function(){var t=e(this);t.data("ui-resizable-alsoresize",{width:parseInt(t.width(),10),height:parseInt(t.height(),10),left:parseInt(t.css("left"),10),top:parseInt(t.css("top"),10)})})},resize:function(t,i){var s=e(this).resizable("instance"),n=s.options,a=s.originalSize,o=s.originalPosition,r={height:s.size.height-a.height||0,width:s.size.width-a.width||0,top:s.position.top-o.top||0,left:s.position.left-o.left||0};e(n.alsoResize).each(function(){var t=e(this),s=e(this).data("ui-resizable-alsoresize"),n={},a=t.parents(i.originalElement[0]).length?["width","height"]:["width","height","top","left"];e.each(a,function(e,t){var i=(s[t]||0)+(r[t]||0);i&&i>=0&&(n[t]=i||null)}),t.css(n)})},stop:function(){e(this).removeData("resizable-alsoresize")}}),e.ui.plugin.add("resizable","ghost",{start:function(){var t=e(this).resizable("instance"),i=t.options,s=t.size;t.ghost=t.originalElement.clone(),t.ghost.css({opacity:.25,display:"block",position:"relative",height:s.height,width:s.width,margin:0,left:0,top:0}).addClass("ui-resizable-ghost").addClass("string"==typeof i.ghost?i.ghost:""),t.ghost.appendTo(t.helper)},resize:function(){var t=e(this).resizable("instance");t.ghost&&t.ghost.css({position:"relative",height:t.size.height,width:t.size.width})},stop:function(){var t=e(this).resizable("instance");t.ghost&&t.helper&&t.helper.get(0).removeChild(t.ghost.get(0))}}),e.ui.plugin.add("resizable","grid",{resize:function(){var t,i=e(this).resizable("instance"),s=i.options,n=i.size,a=i.originalSize,o=i.originalPosition,r=i.axis,h="number"==typeof s.grid?[s.grid,s.grid]:s.grid,l=h[0]||1,u=h[1]||1,d=Math.round((n.width-a.width)/l)*l,c=Math.round((n.height-a.height)/u)*u,p=a.width+d,f=a.height+c,m=s.maxWidth&&p>s.maxWidth,g=s.maxHeight&&f>s.maxHeight,v=s.minWidth&&s.minWidth>p,y=s.minHeight&&s.minHeight>f;s.grid=h,v&&(p+=l),y&&(f+=u),m&&(p-=l),g&&(f-=u),/^(se|s|e)$/.test(r)?(i.size.width=p,i.size.height=f):/^(ne)$/.test(r)?(i.size.width=p,i.size.height=f,i.position.top=o.top-c):/^(sw)$/.test(r)?(i.size.width=p,i.size.height=f,i.position.left=o.left-d):((0>=f-u||0>=p-l)&&(t=i._getPaddingPlusBorderDimensions(this)),f-u>0?(i.size.height=f,i.position.top=o.top-c):(f=u-t.height,i.size.height=f,i.position.top=o.top+a.height-f),p-l>0?(i.size.width=p,i.position.left=o.left-d):(p=l-t.width,i.size.width=p,i.position.left=o.left+a.width-p))}}),e.ui.resizable,e.widget("ui.selectable",e.ui.mouse,{version:"1.11.4",options:{appendTo:"body",autoRefresh:!0,distance:0,filter:"*",tolerance:"touch",selected:null,selecting:null,start:null,stop:null,unselected:null,unselecting:null},_create:function(){var t,i=this;this.element.addClass("ui-selectable"),this.dragged=!1,this.refresh=function(){t=e(i.options.filter,i.element[0]),t.addClass("ui-selectee"),t.each(function(){var t=e(this),i=t.offset();e.data(this,"selectable-item",{element:this,$element:t,left:i.left,top:i.top,right:i.left+t.outerWidth(),bottom:i.top+t.outerHeight(),startselected:!1,selected:t.hasClass("ui-selected"),selecting:t.hasClass("ui-selecting"),unselecting:t.hasClass("ui-unselecting")})})},this.refresh(),this.selectees=t.addClass("ui-selectee"),this._mouseInit(),this.helper=e("<div class='ui-selectable-helper'></div>")},_destroy:function(){this.selectees.removeClass("ui-selectee").removeData("selectable-item"),this.element.removeClass("ui-selectable ui-selectable-disabled"),this._mouseDestroy()},_mouseStart:function(t){var i=this,s=this.options;this.opos=[t.pageX,t.pageY],this.options.disabled||(this.selectees=e(s.filter,this.element[0]),this._trigger("start",t),e(s.appendTo).append(this.helper),this.helper.css({left:t.pageX,top:t.pageY,width:0,height:0}),s.autoRefresh&&this.refresh(),this.selectees.filter(".ui-selected").each(function(){var s=e.data(this,"selectable-item");s.startselected=!0,t.metaKey||t.ctrlKey||(s.$element.removeClass("ui-selected"),s.selected=!1,s.$element.addClass("ui-unselecting"),s.unselecting=!0,i._trigger("unselecting",t,{unselecting:s.element}))}),e(t.target).parents().addBack().each(function(){var s,n=e.data(this,"selectable-item");return n?(s=!t.metaKey&&!t.ctrlKey||!n.$element.hasClass("ui-selected"),n.$element.removeClass(s?"ui-unselecting":"ui-selected").addClass(s?"ui-selecting":"ui-unselecting"),n.unselecting=!s,n.selecting=s,n.selected=s,s?i._trigger("selecting",t,{selecting:n.element}):i._trigger("unselecting",t,{unselecting:n.element}),!1):void 0}))},_mouseDrag:function(t){if(this.dragged=!0,!this.options.disabled){var i,s=this,n=this.options,a=this.opos[0],o=this.opos[1],r=t.pageX,h=t.pageY;return a>r&&(i=r,r=a,a=i),o>h&&(i=h,h=o,o=i),this.helper.css({left:a,top:o,width:r-a,height:h-o}),this.selectees.each(function(){var i=e.data(this,"selectable-item"),l=!1;
-i&&i.element!==s.element[0]&&("touch"===n.tolerance?l=!(i.left>r||a>i.right||i.top>h||o>i.bottom):"fit"===n.tolerance&&(l=i.left>a&&r>i.right&&i.top>o&&h>i.bottom),l?(i.selected&&(i.$element.removeClass("ui-selected"),i.selected=!1),i.unselecting&&(i.$element.removeClass("ui-unselecting"),i.unselecting=!1),i.selecting||(i.$element.addClass("ui-selecting"),i.selecting=!0,s._trigger("selecting",t,{selecting:i.element}))):(i.selecting&&((t.metaKey||t.ctrlKey)&&i.startselected?(i.$element.removeClass("ui-selecting"),i.selecting=!1,i.$element.addClass("ui-selected"),i.selected=!0):(i.$element.removeClass("ui-selecting"),i.selecting=!1,i.startselected&&(i.$element.addClass("ui-unselecting"),i.unselecting=!0),s._trigger("unselecting",t,{unselecting:i.element}))),i.selected&&(t.metaKey||t.ctrlKey||i.startselected||(i.$element.removeClass("ui-selected"),i.selected=!1,i.$element.addClass("ui-unselecting"),i.unselecting=!0,s._trigger("unselecting",t,{unselecting:i.element})))))}),!1}},_mouseStop:function(t){var i=this;return this.dragged=!1,e(".ui-unselecting",this.element[0]).each(function(){var s=e.data(this,"selectable-item");s.$element.removeClass("ui-unselecting"),s.unselecting=!1,s.startselected=!1,i._trigger("unselected",t,{unselected:s.element})}),e(".ui-selecting",this.element[0]).each(function(){var s=e.data(this,"selectable-item");s.$element.removeClass("ui-selecting").addClass("ui-selected"),s.selecting=!1,s.selected=!0,s.startselected=!0,i._trigger("selected",t,{selected:s.element})}),this._trigger("stop",t),this.helper.remove(),!1}}),e.widget("ui.sortable",e.ui.mouse,{version:"1.11.4",widgetEventPrefix:"sort",ready:!1,options:{appendTo:"parent",axis:!1,connectWith:!1,containment:!1,cursor:"auto",cursorAt:!1,dropOnEmpty:!0,forcePlaceholderSize:!1,forceHelperSize:!1,grid:!1,handle:!1,helper:"original",items:"> *",opacity:!1,placeholder:!1,revert:!1,scroll:!0,scrollSensitivity:20,scrollSpeed:20,scope:"default",tolerance:"intersect",zIndex:1e3,activate:null,beforeStop:null,change:null,deactivate:null,out:null,over:null,receive:null,remove:null,sort:null,start:null,stop:null,update:null},_isOverAxis:function(e,t,i){return e>=t&&t+i>e},_isFloating:function(e){return/left|right/.test(e.css("float"))||/inline|table-cell/.test(e.css("display"))},_create:function(){this.containerCache={},this.element.addClass("ui-sortable"),this.refresh(),this.offset=this.element.offset(),this._mouseInit(),this._setHandleClassName(),this.ready=!0},_setOption:function(e,t){this._super(e,t),"handle"===e&&this._setHandleClassName()},_setHandleClassName:function(){this.element.find(".ui-sortable-handle").removeClass("ui-sortable-handle"),e.each(this.items,function(){(this.instance.options.handle?this.item.find(this.instance.options.handle):this.item).addClass("ui-sortable-handle")})},_destroy:function(){this.element.removeClass("ui-sortable ui-sortable-disabled").find(".ui-sortable-handle").removeClass("ui-sortable-handle"),this._mouseDestroy();for(var e=this.items.length-1;e>=0;e--)this.items[e].item.removeData(this.widgetName+"-item");return this},_mouseCapture:function(t,i){var s=null,n=!1,a=this;return this.reverting?!1:this.options.disabled||"static"===this.options.type?!1:(this._refreshItems(t),e(t.target).parents().each(function(){return e.data(this,a.widgetName+"-item")===a?(s=e(this),!1):void 0}),e.data(t.target,a.widgetName+"-item")===a&&(s=e(t.target)),s?!this.options.handle||i||(e(this.options.handle,s).find("*").addBack().each(function(){this===t.target&&(n=!0)}),n)?(this.currentItem=s,this._removeCurrentsFromItems(),!0):!1:!1)},_mouseStart:function(t,i,s){var n,a,o=this.options;if(this.currentContainer=this,this.refreshPositions(),this.helper=this._createHelper(t),this._cacheHelperProportions(),this._cacheMargins(),this.scrollParent=this.helper.scrollParent(),this.offset=this.currentItem.offset(),this.offset={top:this.offset.top-this.margins.top,left:this.offset.left-this.margins.left},e.extend(this.offset,{click:{left:t.pageX-this.offset.left,top:t.pageY-this.offset.top},parent:this._getParentOffset(),relative:this._getRelativeOffset()}),this.helper.css("position","absolute"),this.cssPosition=this.helper.css("position"),this.originalPosition=this._generatePosition(t),this.originalPageX=t.pageX,this.originalPageY=t.pageY,o.cursorAt&&this._adjustOffsetFromHelper(o.cursorAt),this.domPosition={prev:this.currentItem.prev()[0],parent:this.currentItem.parent()[0]},this.helper[0]!==this.currentItem[0]&&this.currentItem.hide(),this._createPlaceholder(),o.containment&&this._setContainment(),o.cursor&&"auto"!==o.cursor&&(a=this.document.find("body"),this.storedCursor=a.css("cursor"),a.css("cursor",o.cursor),this.storedStylesheet=e("<style>*{ cursor: "+o.cursor+" !important; }</style>").appendTo(a)),o.opacity&&(this.helper.css("opacity")&&(this._storedOpacity=this.helper.css("opacity")),this.helper.css("opacity",o.opacity)),o.zIndex&&(this.helper.css("zIndex")&&(this._storedZIndex=this.helper.css("zIndex")),this.helper.css("zIndex",o.zIndex)),this.scrollParent[0]!==this.document[0]&&"HTML"!==this.scrollParent[0].tagName&&(this.overflowOffset=this.scrollParent.offset()),this._trigger("start",t,this._uiHash()),this._preserveHelperProportions||this._cacheHelperProportions(),!s)for(n=this.containers.length-1;n>=0;n--)this.containers[n]._trigger("activate",t,this._uiHash(this));return e.ui.ddmanager&&(e.ui.ddmanager.current=this),e.ui.ddmanager&&!o.dropBehaviour&&e.ui.ddmanager.prepareOffsets(this,t),this.dragging=!0,this.helper.addClass("ui-sortable-helper"),this._mouseDrag(t),!0},_mouseDrag:function(t){var i,s,n,a,o=this.options,r=!1;for(this.position=this._generatePosition(t),this.positionAbs=this._convertPositionTo("absolute"),this.lastPositionAbs||(this.lastPositionAbs=this.positionAbs),this.options.scroll&&(this.scrollParent[0]!==this.document[0]&&"HTML"!==this.scrollParent[0].tagName?(this.overflowOffset.top+this.scrollParent[0].offsetHeight-t.pageY<o.scrollSensitivity?this.scrollParent[0].scrollTop=r=this.scrollParent[0].scrollTop+o.scrollSpeed:t.pageY-this.overflowOffset.top<o.scrollSensitivity&&(this.scrollParent[0].scrollTop=r=this.scrollParent[0].scrollTop-o.scrollSpeed),this.overflowOffset.left+this.scrollParent[0].offsetWidth-t.pageX<o.scrollSensitivity?this.scrollParent[0].scrollLeft=r=this.scrollParent[0].scrollLeft+o.scrollSpeed:t.pageX-this.overflowOffset.left<o.scrollSensitivity&&(this.scrollParent[0].scrollLeft=r=this.scrollParent[0].scrollLeft-o.scrollSpeed)):(t.pageY-this.document.scrollTop()<o.scrollSensitivity?r=this.document.scrollTop(this.document.scrollTop()-o.scrollSpeed):this.window.height()-(t.pageY-this.document.scrollTop())<o.scrollSensitivity&&(r=this.document.scrollTop(this.document.scrollTop()+o.scrollSpeed)),t.pageX-this.document.scrollLeft()<o.scrollSensitivity?r=this.document.scrollLeft(this.document.scrollLeft()-o.scrollSpeed):this.window.width()-(t.pageX-this.document.scrollLeft())<o.scrollSensitivity&&(r=this.document.scrollLeft(this.document.scrollLeft()+o.scrollSpeed))),r!==!1&&e.ui.ddmanager&&!o.dropBehaviour&&e.ui.ddmanager.prepareOffsets(this,t)),this.positionAbs=this._convertPositionTo("absolute"),this.options.axis&&"y"===this.options.axis||(this.helper[0].style.left=this.position.left+"px"),this.options.axis&&"x"===this.options.axis||(this.helper[0].style.top=this.position.top+"px"),i=this.items.length-1;i>=0;i--)if(s=this.items[i],n=s.item[0],a=this._intersectsWithPointer(s),a&&s.instance===this.currentContainer&&n!==this.currentItem[0]&&this.placeholder[1===a?"next":"prev"]()[0]!==n&&!e.contains(this.placeholder[0],n)&&("semi-dynamic"===this.options.type?!e.contains(this.element[0],n):!0)){if(this.direction=1===a?"down":"up","pointer"!==this.options.tolerance&&!this._intersectsWithSides(s))break;this._rearrange(t,s),this._trigger("change",t,this._uiHash());break}return this._contactContainers(t),e.ui.ddmanager&&e.ui.ddmanager.drag(this,t),this._trigger("sort",t,this._uiHash()),this.lastPositionAbs=this.positionAbs,!1},_mouseStop:function(t,i){if(t){if(e.ui.ddmanager&&!this.options.dropBehaviour&&e.ui.ddmanager.drop(this,t),this.options.revert){var s=this,n=this.placeholder.offset(),a=this.options.axis,o={};a&&"x"!==a||(o.left=n.left-this.offset.parent.left-this.margins.left+(this.offsetParent[0]===this.document[0].body?0:this.offsetParent[0].scrollLeft)),a&&"y"!==a||(o.top=n.top-this.offset.parent.top-this.margins.top+(this.offsetParent[0]===this.document[0].body?0:this.offsetParent[0].scrollTop)),this.reverting=!0,e(this.helper).animate(o,parseInt(this.options.revert,10)||500,function(){s._clear(t)})}else this._clear(t,i);return!1}},cancel:function(){if(this.dragging){this._mouseUp({target:null}),"original"===this.options.helper?this.currentItem.css(this._storedCSS).removeClass("ui-sortable-helper"):this.currentItem.show();for(var t=this.containers.length-1;t>=0;t--)this.containers[t]._trigger("deactivate",null,this._uiHash(this)),this.containers[t].containerCache.over&&(this.containers[t]._trigger("out",null,this._uiHash(this)),this.containers[t].containerCache.over=0)}return this.placeholder&&(this.placeholder[0].parentNode&&this.placeholder[0].parentNode.removeChild(this.placeholder[0]),"original"!==this.options.helper&&this.helper&&this.helper[0].parentNode&&this.helper.remove(),e.extend(this,{helper:null,dragging:!1,reverting:!1,_noFinalSort:null}),this.domPosition.prev?e(this.domPosition.prev).after(this.currentItem):e(this.domPosition.parent).prepend(this.currentItem)),this},serialize:function(t){var i=this._getItemsAsjQuery(t&&t.connected),s=[];return t=t||{},e(i).each(function(){var i=(e(t.item||this).attr(t.attribute||"id")||"").match(t.expression||/(.+)[\-=_](.+)/);i&&s.push((t.key||i[1]+"[]")+"="+(t.key&&t.expression?i[1]:i[2]))}),!s.length&&t.key&&s.push(t.key+"="),s.join("&")},toArray:function(t){var i=this._getItemsAsjQuery(t&&t.connected),s=[];return t=t||{},i.each(function(){s.push(e(t.item||this).attr(t.attribute||"id")||"")}),s},_intersectsWith:function(e){var t=this.positionAbs.left,i=t+this.helperProportions.width,s=this.positionAbs.top,n=s+this.helperProportions.height,a=e.left,o=a+e.width,r=e.top,h=r+e.height,l=this.offset.click.top,u=this.offset.click.left,d="x"===this.options.axis||s+l>r&&h>s+l,c="y"===this.options.axis||t+u>a&&o>t+u,p=d&&c;return"pointer"===this.options.tolerance||this.options.forcePointerForContainers||"pointer"!==this.options.tolerance&&this.helperProportions[this.floating?"width":"height"]>e[this.floating?"width":"height"]?p:t+this.helperProportions.width/2>a&&o>i-this.helperProportions.width/2&&s+this.helperProportions.height/2>r&&h>n-this.helperProportions.height/2},_intersectsWithPointer:function(e){var t="x"===this.options.axis||this._isOverAxis(this.positionAbs.top+this.offset.click.top,e.top,e.height),i="y"===this.options.axis||this._isOverAxis(this.positionAbs.left+this.offset.click.left,e.left,e.width),s=t&&i,n=this._getDragVerticalDirection(),a=this._getDragHorizontalDirection();return s?this.floating?a&&"right"===a||"down"===n?2:1:n&&("down"===n?2:1):!1},_intersectsWithSides:function(e){var t=this._isOverAxis(this.positionAbs.top+this.offset.click.top,e.top+e.height/2,e.height),i=this._isOverAxis(this.positionAbs.left+this.offset.click.left,e.left+e.width/2,e.width),s=this._getDragVerticalDirection(),n=this._getDragHorizontalDirection();return this.floating&&n?"right"===n&&i||"left"===n&&!i:s&&("down"===s&&t||"up"===s&&!t)},_getDragVerticalDirection:function(){var e=this.positionAbs.top-this.lastPositionAbs.top;return 0!==e&&(e>0?"down":"up")},_getDragHorizontalDirection:function(){var e=this.positionAbs.left-this.lastPositionAbs.left;return 0!==e&&(e>0?"right":"left")},refresh:function(e){return this._refreshItems(e),this._setHandleClassName(),this.refreshPositions(),this},_connectWith:function(){var e=this.options;return e.connectWith.constructor===String?[e.connectWith]:e.connectWith},_getItemsAsjQuery:function(t){function i(){r.push(this)}var s,n,a,o,r=[],h=[],l=this._connectWith();if(l&&t)for(s=l.length-1;s>=0;s--)for(a=e(l[s],this.document[0]),n=a.length-1;n>=0;n--)o=e.data(a[n],this.widgetFullName),o&&o!==this&&!o.options.disabled&&h.push([e.isFunction(o.options.items)?o.options.items.call(o.element):e(o.options.items,o.element).not(".ui-sortable-helper").not(".ui-sortable-placeholder"),o]);for(h.push([e.isFunction(this.options.items)?this.options.items.call(this.element,null,{options:this.options,item:this.currentItem}):e(this.options.items,this.element).not(".ui-sortable-helper").not(".ui-sortable-placeholder"),this]),s=h.length-1;s>=0;s--)h[s][0].each(i);return e(r)},_removeCurrentsFromItems:function(){var t=this.currentItem.find(":data("+this.widgetName+"-item)");this.items=e.grep(this.items,function(e){for(var i=0;t.length>i;i++)if(t[i]===e.item[0])return!1;return!0})},_refreshItems:function(t){this.items=[],this.containers=[this];var i,s,n,a,o,r,h,l,u=this.items,d=[[e.isFunction(this.options.items)?this.options.items.call(this.element[0],t,{item:this.currentItem}):e(this.options.items,this.element),this]],c=this._connectWith();if(c&&this.ready)for(i=c.length-1;i>=0;i--)for(n=e(c[i],this.document[0]),s=n.length-1;s>=0;s--)a=e.data(n[s],this.widgetFullName),a&&a!==this&&!a.options.disabled&&(d.push([e.isFunction(a.options.items)?a.options.items.call(a.element[0],t,{item:this.currentItem}):e(a.options.items,a.element),a]),this.containers.push(a));for(i=d.length-1;i>=0;i--)for(o=d[i][1],r=d[i][0],s=0,l=r.length;l>s;s++)h=e(r[s]),h.data(this.widgetName+"-item",o),u.push({item:h,instance:o,width:0,height:0,left:0,top:0})},refreshPositions:function(t){this.floating=this.items.length?"x"===this.options.axis||this._isFloating(this.items[0].item):!1,this.offsetParent&&this.helper&&(this.offset.parent=this._getParentOffset());var i,s,n,a;for(i=this.items.length-1;i>=0;i--)s=this.items[i],s.instance!==this.currentContainer&&this.currentContainer&&s.item[0]!==this.currentItem[0]||(n=this.options.toleranceElement?e(this.options.toleranceElement,s.item):s.item,t||(s.width=n.outerWidth(),s.height=n.outerHeight()),a=n.offset(),s.left=a.left,s.top=a.top);if(this.options.custom&&this.options.custom.refreshContainers)this.options.custom.refreshContainers.call(this);else for(i=this.containers.length-1;i>=0;i--)a=this.containers[i].element.offset(),this.containers[i].containerCache.left=a.left,this.containers[i].containerCache.top=a.top,this.containers[i].containerCache.width=this.containers[i].element.outerWidth(),this.containers[i].containerCache.height=this.containers[i].element.outerHeight();return this},_createPlaceholder:function(t){t=t||this;var i,s=t.options;s.placeholder&&s.placeholder.constructor!==String||(i=s.placeholder,s.placeholder={element:function(){var s=t.currentItem[0].nodeName.toLowerCase(),n=e("<"+s+">",t.document[0]).addClass(i||t.currentItem[0].className+" ui-sortable-placeholder").removeClass("ui-sortable-helper");return"tbody"===s?t._createTrPlaceholder(t.currentItem.find("tr").eq(0),e("<tr>",t.document[0]).appendTo(n)):"tr"===s?t._createTrPlaceholder(t.currentItem,n):"img"===s&&n.attr("src",t.currentItem.attr("src")),i||n.css("visibility","hidden"),n},update:function(e,n){(!i||s.forcePlaceholderSize)&&(n.height()||n.height(t.currentItem.innerHeight()-parseInt(t.currentItem.css("paddingTop")||0,10)-parseInt(t.currentItem.css("paddingBottom")||0,10)),n.width()||n.width(t.currentItem.innerWidth()-parseInt(t.currentItem.css("paddingLeft")||0,10)-parseInt(t.currentItem.css("paddingRight")||0,10)))}}),t.placeholder=e(s.placeholder.element.call(t.element,t.currentItem)),t.currentItem.after(t.placeholder),s.placeholder.update(t,t.placeholder)},_createTrPlaceholder:function(t,i){var s=this;t.children().each(function(){e("<td>&#160;</td>",s.document[0]).attr("colspan",e(this).attr("colspan")||1).appendTo(i)})},_contactContainers:function(t){var i,s,n,a,o,r,h,l,u,d,c=null,p=null;for(i=this.containers.length-1;i>=0;i--)if(!e.contains(this.currentItem[0],this.containers[i].element[0]))if(this._intersectsWith(this.containers[i].containerCache)){if(c&&e.contains(this.containers[i].element[0],c.element[0]))continue;c=this.containers[i],p=i}else this.containers[i].containerCache.over&&(this.containers[i]._trigger("out",t,this._uiHash(this)),this.containers[i].containerCache.over=0);if(c)if(1===this.containers.length)this.containers[p].containerCache.over||(this.containers[p]._trigger("over",t,this._uiHash(this)),this.containers[p].containerCache.over=1);else{for(n=1e4,a=null,u=c.floating||this._isFloating(this.currentItem),o=u?"left":"top",r=u?"width":"height",d=u?"clientX":"clientY",s=this.items.length-1;s>=0;s--)e.contains(this.containers[p].element[0],this.items[s].item[0])&&this.items[s].item[0]!==this.currentItem[0]&&(h=this.items[s].item.offset()[o],l=!1,t[d]-h>this.items[s][r]/2&&(l=!0),n>Math.abs(t[d]-h)&&(n=Math.abs(t[d]-h),a=this.items[s],this.direction=l?"up":"down"));if(!a&&!this.options.dropOnEmpty)return;if(this.currentContainer===this.containers[p])return this.currentContainer.containerCache.over||(this.containers[p]._trigger("over",t,this._uiHash()),this.currentContainer.containerCache.over=1),void 0;a?this._rearrange(t,a,null,!0):this._rearrange(t,null,this.containers[p].element,!0),this._trigger("change",t,this._uiHash()),this.containers[p]._trigger("change",t,this._uiHash(this)),this.currentContainer=this.containers[p],this.options.placeholder.update(this.currentContainer,this.placeholder),this.containers[p]._trigger("over",t,this._uiHash(this)),this.containers[p].containerCache.over=1}},_createHelper:function(t){var i=this.options,s=e.isFunction(i.helper)?e(i.helper.apply(this.element[0],[t,this.currentItem])):"clone"===i.helper?this.currentItem.clone():this.currentItem;return s.parents("body").length||e("parent"!==i.appendTo?i.appendTo:this.currentItem[0].parentNode)[0].appendChild(s[0]),s[0]===this.currentItem[0]&&(this._storedCSS={width:this.currentItem[0].style.width,height:this.currentItem[0].style.height,position:this.currentItem.css("position"),top:this.currentItem.css("top"),left:this.currentItem.css("left")}),(!s[0].style.width||i.forceHelperSize)&&s.width(this.currentItem.width()),(!s[0].style.height||i.forceHelperSize)&&s.height(this.currentItem.height()),s},_adjustOffsetFromHelper:function(t){"string"==typeof t&&(t=t.split(" ")),e.isArray(t)&&(t={left:+t[0],top:+t[1]||0}),"left"in t&&(this.offset.click.left=t.left+this.margins.left),"right"in t&&(this.offset.click.left=this.helperProportions.width-t.right+this.margins.left),"top"in t&&(this.offset.click.top=t.top+this.margins.top),"bottom"in t&&(this.offset.click.top=this.helperProportions.height-t.bottom+this.margins.top)},_getParentOffset:function(){this.offsetParent=this.helper.offsetParent();var t=this.offsetParent.offset();return"absolute"===this.cssPosition&&this.scrollParent[0]!==this.document[0]&&e.contains(this.scrollParent[0],this.offsetParent[0])&&(t.left+=this.scrollParent.scrollLeft(),t.top+=this.scrollParent.scrollTop()),(this.offsetParent[0]===this.document[0].body||this.offsetParent[0].tagName&&"html"===this.offsetParent[0].tagName.toLowerCase()&&e.ui.ie)&&(t={top:0,left:0}),{top:t.top+(parseInt(this.offsetParent.css("borderTopWidth"),10)||0),left:t.left+(parseInt(this.offsetParent.css("borderLeftWidth"),10)||0)}},_getRelativeOffset:function(){if("relative"===this.cssPosition){var e=this.currentItem.position();return{top:e.top-(parseInt(this.helper.css("top"),10)||0)+this.scrollParent.scrollTop(),left:e.left-(parseInt(this.helper.css("left"),10)||0)+this.scrollParent.scrollLeft()}}return{top:0,left:0}},_cacheMargins:function(){this.margins={left:parseInt(this.currentItem.css("marginLeft"),10)||0,top:parseInt(this.currentItem.css("marginTop"),10)||0}},_cacheHelperProportions:function(){this.helperProportions={width:this.helper.outerWidth(),height:this.helper.outerHeight()}},_setContainment:function(){var t,i,s,n=this.options;"parent"===n.containment&&(n.containment=this.helper[0].parentNode),("document"===n.containment||"window"===n.containment)&&(this.containment=[0-this.offset.relative.left-this.offset.parent.left,0-this.offset.relative.top-this.offset.parent.top,"document"===n.containment?this.document.width():this.window.width()-this.helperProportions.width-this.margins.left,("document"===n.containment?this.document.width():this.window.height()||this.document[0].body.parentNode.scrollHeight)-this.helperProportions.height-this.margins.top]),/^(document|window|parent)$/.test(n.containment)||(t=e(n.containment)[0],i=e(n.containment).offset(),s="hidden"!==e(t).css("overflow"),this.containment=[i.left+(parseInt(e(t).css("borderLeftWidth"),10)||0)+(parseInt(e(t).css("paddingLeft"),10)||0)-this.margins.left,i.top+(parseInt(e(t).css("borderTopWidth"),10)||0)+(parseInt(e(t).css("paddingTop"),10)||0)-this.margins.top,i.left+(s?Math.max(t.scrollWidth,t.offsetWidth):t.offsetWidth)-(parseInt(e(t).css("borderLeftWidth"),10)||0)-(parseInt(e(t).css("paddingRight"),10)||0)-this.helperProportions.width-this.margins.left,i.top+(s?Math.max(t.scrollHeight,t.offsetHeight):t.offsetHeight)-(parseInt(e(t).css("borderTopWidth"),10)||0)-(parseInt(e(t).css("paddingBottom"),10)||0)-this.helperProportions.height-this.margins.top])},_convertPositionTo:function(t,i){i||(i=this.position);var s="absolute"===t?1:-1,n="absolute"!==this.cssPosition||this.scrollParent[0]!==this.document[0]&&e.contains(this.scrollParent[0],this.offsetParent[0])?this.scrollParent:this.offsetParent,a=/(html|body)/i.test(n[0].tagName);return{top:i.top+this.offset.relative.top*s+this.offset.parent.top*s-("fixed"===this.cssPosition?-this.scrollParent.scrollTop():a?0:n.scrollTop())*s,left:i.left+this.offset.relative.left*s+this.offset.parent.left*s-("fixed"===this.cssPosition?-this.scrollParent.scrollLeft():a?0:n.scrollLeft())*s}},_generatePosition:function(t){var i,s,n=this.options,a=t.pageX,o=t.pageY,r="absolute"!==this.cssPosition||this.scrollParent[0]!==this.document[0]&&e.contains(this.scrollParent[0],this.offsetParent[0])?this.scrollParent:this.offsetParent,h=/(html|body)/i.test(r[0].tagName);return"relative"!==this.cssPosition||this.scrollParent[0]!==this.document[0]&&this.scrollParent[0]!==this.offsetParent[0]||(this.offset.relative=this._getRelativeOffset()),this.originalPosition&&(this.containment&&(t.pageX-this.offset.click.left<this.containment[0]&&(a=this.containment[0]+this.offset.click.left),t.pageY-this.offset.click.top<this.containment[1]&&(o=this.containment[1]+this.offset.click.top),t.pageX-this.offset.click.left>this.containment[2]&&(a=this.containment[2]+this.offset.click.left),t.pageY-this.offset.click.top>this.containment[3]&&(o=this.containment[3]+this.offset.click.top)),n.grid&&(i=this.originalPageY+Math.round((o-this.originalPageY)/n.grid[1])*n.grid[1],o=this.containment?i-this.offset.click.top>=this.containment[1]&&i-this.offset.click.top<=this.containment[3]?i:i-this.offset.click.top>=this.containment[1]?i-n.grid[1]:i+n.grid[1]:i,s=this.originalPageX+Math.round((a-this.originalPageX)/n.grid[0])*n.grid[0],a=this.containment?s-this.offset.click.left>=this.containment[0]&&s-this.offset.click.left<=this.containment[2]?s:s-this.offset.click.left>=this.containment[0]?s-n.grid[0]:s+n.grid[0]:s)),{top:o-this.offset.click.top-this.offset.relative.top-this.offset.parent.top+("fixed"===this.cssPosition?-this.scrollParent.scrollTop():h?0:r.scrollTop()),left:a-this.offset.click.left-this.offset.relative.left-this.offset.parent.left+("fixed"===this.cssPosition?-this.scrollParent.scrollLeft():h?0:r.scrollLeft())}},_rearrange:function(e,t,i,s){i?i[0].appendChild(this.placeholder[0]):t.item[0].parentNode.insertBefore(this.placeholder[0],"down"===this.direction?t.item[0]:t.item[0].nextSibling),this.counter=this.counter?++this.counter:1;var n=this.counter;this._delay(function(){n===this.counter&&this.refreshPositions(!s)})},_clear:function(e,t){function i(e,t,i){return function(s){i._trigger(e,s,t._uiHash(t))}}this.reverting=!1;var s,n=[];if(!this._noFinalSort&&this.currentItem.parent().length&&this.placeholder.before(this.currentItem),this._noFinalSort=null,this.helper[0]===this.currentItem[0]){for(s in this._storedCSS)("auto"===this._storedCSS[s]||"static"===this._storedCSS[s])&&(this._storedCSS[s]="");this.currentItem.css(this._storedCSS).removeClass("ui-sortable-helper")}else this.currentItem.show();for(this.fromOutside&&!t&&n.push(function(e){this._trigger("receive",e,this._uiHash(this.fromOutside))}),!this.fromOutside&&this.domPosition.prev===this.currentItem.prev().not(".ui-sortable-helper")[0]&&this.domPosition.parent===this.currentItem.parent()[0]||t||n.push(function(e){this._trigger("update",e,this._uiHash())}),this!==this.currentContainer&&(t||(n.push(function(e){this._trigger("remove",e,this._uiHash())}),n.push(function(e){return function(t){e._trigger("receive",t,this._uiHash(this))}}.call(this,this.currentContainer)),n.push(function(e){return function(t){e._trigger("update",t,this._uiHash(this))}}.call(this,this.currentContainer)))),s=this.containers.length-1;s>=0;s--)t||n.push(i("deactivate",this,this.containers[s])),this.containers[s].containerCache.over&&(n.push(i("out",this,this.containers[s])),this.containers[s].containerCache.over=0);if(this.storedCursor&&(this.document.find("body").css("cursor",this.storedCursor),this.storedStylesheet.remove()),this._storedOpacity&&this.helper.css("opacity",this._storedOpacity),this._storedZIndex&&this.helper.css("zIndex","auto"===this._storedZIndex?"":this._storedZIndex),this.dragging=!1,t||this._trigger("beforeStop",e,this._uiHash()),this.placeholder[0].parentNode.removeChild(this.placeholder[0]),this.cancelHelperRemoval||(this.helper[0]!==this.currentItem[0]&&this.helper.remove(),this.helper=null),!t){for(s=0;n.length>s;s++)n[s].call(this,e);this._trigger("stop",e,this._uiHash())}return this.fromOutside=!1,!this.cancelHelperRemoval},_trigger:function(){e.Widget.prototype._trigger.apply(this,arguments)===!1&&this.cancel()},_uiHash:function(t){var i=t||this;return{helper:i.helper,placeholder:i.placeholder||e([]),position:i.position,originalPosition:i.originalPosition,offset:i.positionAbs,item:i.currentItem,sender:t?t.element:null}}}),e.widget("ui.accordion",{version:"1.11.4",options:{active:0,animate:{},collapsible:!1,event:"click",header:"> li > :first-child,> :not(li):even",heightStyle:"auto",icons:{activeHeader:"ui-icon-triangle-1-s",header:"ui-icon-triangle-1-e"},activate:null,beforeActivate:null},hideProps:{borderTopWidth:"hide",borderBottomWidth:"hide",paddingTop:"hide",paddingBottom:"hide",height:"hide"},showProps:{borderTopWidth:"show",borderBottomWidth:"show",paddingTop:"show",paddingBottom:"show",height:"show"},_create:function(){var t=this.options;this.prevShow=this.prevHide=e(),this.element.addClass("ui-accordion ui-widget ui-helper-reset").attr("role","tablist"),t.collapsible||t.active!==!1&&null!=t.active||(t.active=0),this._processPanels(),0>t.active&&(t.active+=this.headers.length),this._refresh()},_getCreateEventData:function(){return{header:this.active,panel:this.active.length?this.active.next():e()}},_createIcons:function(){var t=this.options.icons;t&&(e("<span>").addClass("ui-accordion-header-icon ui-icon "+t.header).prependTo(this.headers),this.active.children(".ui-accordion-header-icon").removeClass(t.header).addClass(t.activeHeader),this.headers.addClass("ui-accordion-icons"))},_destroyIcons:function(){this.headers.removeClass("ui-accordion-icons").children(".ui-accordion-header-icon").remove()},_destroy:function(){var e;this.element.removeClass("ui-accordion ui-widget ui-helper-reset").removeAttr("role"),this.headers.removeClass("ui-accordion-header ui-accordion-header-active ui-state-default ui-corner-all ui-state-active ui-state-disabled ui-corner-top").removeAttr("role").removeAttr("aria-expanded").removeAttr("aria-selected").removeAttr("aria-controls").removeAttr("tabIndex").removeUniqueId(),this._destroyIcons(),e=this.headers.next().removeClass("ui-helper-reset ui-widget-content ui-corner-bottom ui-accordion-content ui-accordion-content-active ui-state-disabled").css("display","").removeAttr("role").removeAttr("aria-hidden").removeAttr("aria-labelledby").removeUniqueId(),"content"!==this.options.heightStyle&&e.css("height","")},_setOption:function(e,t){return"active"===e?(this._activate(t),void 0):("event"===e&&(this.options.event&&this._off(this.headers,this.options.event),this._setupEvents(t)),this._super(e,t),"collapsible"!==e||t||this.options.active!==!1||this._activate(0),"icons"===e&&(this._destroyIcons(),t&&this._createIcons()),"disabled"===e&&(this.element.toggleClass("ui-state-disabled",!!t).attr("aria-disabled",t),this.headers.add(this.headers.next()).toggleClass("ui-state-disabled",!!t)),void 0)},_keydown:function(t){if(!t.altKey&&!t.ctrlKey){var i=e.ui.keyCode,s=this.headers.length,n=this.headers.index(t.target),a=!1;switch(t.keyCode){case i.RIGHT:case i.DOWN:a=this.headers[(n+1)%s];break;case i.LEFT:case i.UP:a=this.headers[(n-1+s)%s];break;case i.SPACE:case i.ENTER:this._eventHandler(t);break;case i.HOME:a=this.headers[0];break;case i.END:a=this.headers[s-1]}a&&(e(t.target).attr("tabIndex",-1),e(a).attr("tabIndex",0),a.focus(),t.preventDefault())}},_panelKeyDown:function(t){t.keyCode===e.ui.keyCode.UP&&t.ctrlKey&&e(t.currentTarget).prev().focus()},refresh:function(){var t=this.options;this._processPanels(),t.active===!1&&t.collapsible===!0||!this.headers.length?(t.active=!1,this.active=e()):t.active===!1?this._activate(0):this.active.length&&!e.contains(this.element[0],this.active[0])?this.headers.length===this.headers.find(".ui-state-disabled").length?(t.active=!1,this.active=e()):this._activate(Math.max(0,t.active-1)):t.active=this.headers.index(this.active),this._destroyIcons(),this._refresh()},_processPanels:function(){var e=this.headers,t=this.panels;this.headers=this.element.find(this.options.header).addClass("ui-accordion-header ui-state-default ui-corner-all"),this.panels=this.headers.next().addClass("ui-accordion-content ui-helper-reset ui-widget-content ui-corner-bottom").filter(":not(.ui-accordion-content-active)").hide(),t&&(this._off(e.not(this.headers)),this._off(t.not(this.panels)))},_refresh:function(){var t,i=this.options,s=i.heightStyle,n=this.element.parent();this.active=this._findActive(i.active).addClass("ui-accordion-header-active ui-state-active ui-corner-top").removeClass("ui-corner-all"),this.active.next().addClass("ui-accordion-content-active").show(),this.headers.attr("role","tab").each(function(){var t=e(this),i=t.uniqueId().attr("id"),s=t.next(),n=s.uniqueId().attr("id");t.attr("aria-controls",n),s.attr("aria-labelledby",i)}).next().attr("role","tabpanel"),this.headers.not(this.active).attr({"aria-selected":"false","aria-expanded":"false",tabIndex:-1}).next().attr({"aria-hidden":"true"}).hide(),this.active.length?this.active.attr({"aria-selected":"true","aria-expanded":"true",tabIndex:0}).next().attr({"aria-hidden":"false"}):this.headers.eq(0).attr("tabIndex",0),this._createIcons(),this._setupEvents(i.event),"fill"===s?(t=n.height(),this.element.siblings(":visible").each(function(){var i=e(this),s=i.css("position");"absolute"!==s&&"fixed"!==s&&(t-=i.outerHeight(!0))}),this.headers.each(function(){t-=e(this).outerHeight(!0)}),this.headers.next().each(function(){e(this).height(Math.max(0,t-e(this).innerHeight()+e(this).height()))}).css("overflow","auto")):"auto"===s&&(t=0,this.headers.next().each(function(){t=Math.max(t,e(this).css("height","").height())}).height(t))},_activate:function(t){var i=this._findActive(t)[0];i!==this.active[0]&&(i=i||this.active[0],this._eventHandler({target:i,currentTarget:i,preventDefault:e.noop}))},_findActive:function(t){return"number"==typeof t?this.headers.eq(t):e()},_setupEvents:function(t){var i={keydown:"_keydown"};t&&e.each(t.split(" "),function(e,t){i[t]="_eventHandler"}),this._off(this.headers.add(this.headers.next())),this._on(this.headers,i),this._on(this.headers.next(),{keydown:"_panelKeyDown"}),this._hoverable(this.headers),this._focusable(this.headers)},_eventHandler:function(t){var i=this.options,s=this.active,n=e(t.currentTarget),a=n[0]===s[0],o=a&&i.collapsible,r=o?e():n.next(),h=s.next(),l={oldHeader:s,oldPanel:h,newHeader:o?e():n,newPanel:r};
-t.preventDefault(),a&&!i.collapsible||this._trigger("beforeActivate",t,l)===!1||(i.active=o?!1:this.headers.index(n),this.active=a?e():n,this._toggle(l),s.removeClass("ui-accordion-header-active ui-state-active"),i.icons&&s.children(".ui-accordion-header-icon").removeClass(i.icons.activeHeader).addClass(i.icons.header),a||(n.removeClass("ui-corner-all").addClass("ui-accordion-header-active ui-state-active ui-corner-top"),i.icons&&n.children(".ui-accordion-header-icon").removeClass(i.icons.header).addClass(i.icons.activeHeader),n.next().addClass("ui-accordion-content-active")))},_toggle:function(t){var i=t.newPanel,s=this.prevShow.length?this.prevShow:t.oldPanel;this.prevShow.add(this.prevHide).stop(!0,!0),this.prevShow=i,this.prevHide=s,this.options.animate?this._animate(i,s,t):(s.hide(),i.show(),this._toggleComplete(t)),s.attr({"aria-hidden":"true"}),s.prev().attr({"aria-selected":"false","aria-expanded":"false"}),i.length&&s.length?s.prev().attr({tabIndex:-1,"aria-expanded":"false"}):i.length&&this.headers.filter(function(){return 0===parseInt(e(this).attr("tabIndex"),10)}).attr("tabIndex",-1),i.attr("aria-hidden","false").prev().attr({"aria-selected":"true","aria-expanded":"true",tabIndex:0})},_animate:function(e,t,i){var s,n,a,o=this,r=0,h=e.css("box-sizing"),l=e.length&&(!t.length||e.index()<t.index()),u=this.options.animate||{},d=l&&u.down||u,c=function(){o._toggleComplete(i)};return"number"==typeof d&&(a=d),"string"==typeof d&&(n=d),n=n||d.easing||u.easing,a=a||d.duration||u.duration,t.length?e.length?(s=e.show().outerHeight(),t.animate(this.hideProps,{duration:a,easing:n,step:function(e,t){t.now=Math.round(e)}}),e.hide().animate(this.showProps,{duration:a,easing:n,complete:c,step:function(e,i){i.now=Math.round(e),"height"!==i.prop?"content-box"===h&&(r+=i.now):"content"!==o.options.heightStyle&&(i.now=Math.round(s-t.outerHeight()-r),r=0)}}),void 0):t.animate(this.hideProps,a,n,c):e.animate(this.showProps,a,n,c)},_toggleComplete:function(e){var t=e.oldPanel;t.removeClass("ui-accordion-content-active").prev().removeClass("ui-corner-top").addClass("ui-corner-all"),t.length&&(t.parent()[0].className=t.parent()[0].className),this._trigger("activate",null,e)}}),e.widget("ui.menu",{version:"1.11.4",defaultElement:"<ul>",delay:300,options:{icons:{submenu:"ui-icon-carat-1-e"},items:"> *",menus:"ul",position:{my:"left-1 top",at:"right top"},role:"menu",blur:null,focus:null,select:null},_create:function(){this.activeMenu=this.element,this.mouseHandled=!1,this.element.uniqueId().addClass("ui-menu ui-widget ui-widget-content").toggleClass("ui-menu-icons",!!this.element.find(".ui-icon").length).attr({role:this.options.role,tabIndex:0}),this.options.disabled&&this.element.addClass("ui-state-disabled").attr("aria-disabled","true"),this._on({"mousedown .ui-menu-item":function(e){e.preventDefault()},"click .ui-menu-item":function(t){var i=e(t.target);!this.mouseHandled&&i.not(".ui-state-disabled").length&&(this.select(t),t.isPropagationStopped()||(this.mouseHandled=!0),i.has(".ui-menu").length?this.expand(t):!this.element.is(":focus")&&e(this.document[0].activeElement).closest(".ui-menu").length&&(this.element.trigger("focus",[!0]),this.active&&1===this.active.parents(".ui-menu").length&&clearTimeout(this.timer)))},"mouseenter .ui-menu-item":function(t){if(!this.previousFilter){var i=e(t.currentTarget);i.siblings(".ui-state-active").removeClass("ui-state-active"),this.focus(t,i)}},mouseleave:"collapseAll","mouseleave .ui-menu":"collapseAll",focus:function(e,t){var i=this.active||this.element.find(this.options.items).eq(0);t||this.focus(e,i)},blur:function(t){this._delay(function(){e.contains(this.element[0],this.document[0].activeElement)||this.collapseAll(t)})},keydown:"_keydown"}),this.refresh(),this._on(this.document,{click:function(e){this._closeOnDocumentClick(e)&&this.collapseAll(e),this.mouseHandled=!1}})},_destroy:function(){this.element.removeAttr("aria-activedescendant").find(".ui-menu").addBack().removeClass("ui-menu ui-widget ui-widget-content ui-menu-icons ui-front").removeAttr("role").removeAttr("tabIndex").removeAttr("aria-labelledby").removeAttr("aria-expanded").removeAttr("aria-hidden").removeAttr("aria-disabled").removeUniqueId().show(),this.element.find(".ui-menu-item").removeClass("ui-menu-item").removeAttr("role").removeAttr("aria-disabled").removeUniqueId().removeClass("ui-state-hover").removeAttr("tabIndex").removeAttr("role").removeAttr("aria-haspopup").children().each(function(){var t=e(this);t.data("ui-menu-submenu-carat")&&t.remove()}),this.element.find(".ui-menu-divider").removeClass("ui-menu-divider ui-widget-content")},_keydown:function(t){var i,s,n,a,o=!0;switch(t.keyCode){case e.ui.keyCode.PAGE_UP:this.previousPage(t);break;case e.ui.keyCode.PAGE_DOWN:this.nextPage(t);break;case e.ui.keyCode.HOME:this._move("first","first",t);break;case e.ui.keyCode.END:this._move("last","last",t);break;case e.ui.keyCode.UP:this.previous(t);break;case e.ui.keyCode.DOWN:this.next(t);break;case e.ui.keyCode.LEFT:this.collapse(t);break;case e.ui.keyCode.RIGHT:this.active&&!this.active.is(".ui-state-disabled")&&this.expand(t);break;case e.ui.keyCode.ENTER:case e.ui.keyCode.SPACE:this._activate(t);break;case e.ui.keyCode.ESCAPE:this.collapse(t);break;default:o=!1,s=this.previousFilter||"",n=String.fromCharCode(t.keyCode),a=!1,clearTimeout(this.filterTimer),n===s?a=!0:n=s+n,i=this._filterMenuItems(n),i=a&&-1!==i.index(this.active.next())?this.active.nextAll(".ui-menu-item"):i,i.length||(n=String.fromCharCode(t.keyCode),i=this._filterMenuItems(n)),i.length?(this.focus(t,i),this.previousFilter=n,this.filterTimer=this._delay(function(){delete this.previousFilter},1e3)):delete this.previousFilter}o&&t.preventDefault()},_activate:function(e){this.active.is(".ui-state-disabled")||(this.active.is("[aria-haspopup='true']")?this.expand(e):this.select(e))},refresh:function(){var t,i,s=this,n=this.options.icons.submenu,a=this.element.find(this.options.menus);this.element.toggleClass("ui-menu-icons",!!this.element.find(".ui-icon").length),a.filter(":not(.ui-menu)").addClass("ui-menu ui-widget ui-widget-content ui-front").hide().attr({role:this.options.role,"aria-hidden":"true","aria-expanded":"false"}).each(function(){var t=e(this),i=t.parent(),s=e("<span>").addClass("ui-menu-icon ui-icon "+n).data("ui-menu-submenu-carat",!0);i.attr("aria-haspopup","true").prepend(s),t.attr("aria-labelledby",i.attr("id"))}),t=a.add(this.element),i=t.find(this.options.items),i.not(".ui-menu-item").each(function(){var t=e(this);s._isDivider(t)&&t.addClass("ui-widget-content ui-menu-divider")}),i.not(".ui-menu-item, .ui-menu-divider").addClass("ui-menu-item").uniqueId().attr({tabIndex:-1,role:this._itemRole()}),i.filter(".ui-state-disabled").attr("aria-disabled","true"),this.active&&!e.contains(this.element[0],this.active[0])&&this.blur()},_itemRole:function(){return{menu:"menuitem",listbox:"option"}[this.options.role]},_setOption:function(e,t){"icons"===e&&this.element.find(".ui-menu-icon").removeClass(this.options.icons.submenu).addClass(t.submenu),"disabled"===e&&this.element.toggleClass("ui-state-disabled",!!t).attr("aria-disabled",t),this._super(e,t)},focus:function(e,t){var i,s;this.blur(e,e&&"focus"===e.type),this._scrollIntoView(t),this.active=t.first(),s=this.active.addClass("ui-state-focus").removeClass("ui-state-active"),this.options.role&&this.element.attr("aria-activedescendant",s.attr("id")),this.active.parent().closest(".ui-menu-item").addClass("ui-state-active"),e&&"keydown"===e.type?this._close():this.timer=this._delay(function(){this._close()},this.delay),i=t.children(".ui-menu"),i.length&&e&&/^mouse/.test(e.type)&&this._startOpening(i),this.activeMenu=t.parent(),this._trigger("focus",e,{item:t})},_scrollIntoView:function(t){var i,s,n,a,o,r;this._hasScroll()&&(i=parseFloat(e.css(this.activeMenu[0],"borderTopWidth"))||0,s=parseFloat(e.css(this.activeMenu[0],"paddingTop"))||0,n=t.offset().top-this.activeMenu.offset().top-i-s,a=this.activeMenu.scrollTop(),o=this.activeMenu.height(),r=t.outerHeight(),0>n?this.activeMenu.scrollTop(a+n):n+r>o&&this.activeMenu.scrollTop(a+n-o+r))},blur:function(e,t){t||clearTimeout(this.timer),this.active&&(this.active.removeClass("ui-state-focus"),this.active=null,this._trigger("blur",e,{item:this.active}))},_startOpening:function(e){clearTimeout(this.timer),"true"===e.attr("aria-hidden")&&(this.timer=this._delay(function(){this._close(),this._open(e)},this.delay))},_open:function(t){var i=e.extend({of:this.active},this.options.position);clearTimeout(this.timer),this.element.find(".ui-menu").not(t.parents(".ui-menu")).hide().attr("aria-hidden","true"),t.show().removeAttr("aria-hidden").attr("aria-expanded","true").position(i)},collapseAll:function(t,i){clearTimeout(this.timer),this.timer=this._delay(function(){var s=i?this.element:e(t&&t.target).closest(this.element.find(".ui-menu"));s.length||(s=this.element),this._close(s),this.blur(t),this.activeMenu=s},this.delay)},_close:function(e){e||(e=this.active?this.active.parent():this.element),e.find(".ui-menu").hide().attr("aria-hidden","true").attr("aria-expanded","false").end().find(".ui-state-active").not(".ui-state-focus").removeClass("ui-state-active")},_closeOnDocumentClick:function(t){return!e(t.target).closest(".ui-menu").length},_isDivider:function(e){return!/[^\-\u2014\u2013\s]/.test(e.text())},collapse:function(e){var t=this.active&&this.active.parent().closest(".ui-menu-item",this.element);t&&t.length&&(this._close(),this.focus(e,t))},expand:function(e){var t=this.active&&this.active.children(".ui-menu ").find(this.options.items).first();t&&t.length&&(this._open(t.parent()),this._delay(function(){this.focus(e,t)}))},next:function(e){this._move("next","first",e)},previous:function(e){this._move("prev","last",e)},isFirstItem:function(){return this.active&&!this.active.prevAll(".ui-menu-item").length},isLastItem:function(){return this.active&&!this.active.nextAll(".ui-menu-item").length},_move:function(e,t,i){var s;this.active&&(s="first"===e||"last"===e?this.active["first"===e?"prevAll":"nextAll"](".ui-menu-item").eq(-1):this.active[e+"All"](".ui-menu-item").eq(0)),s&&s.length&&this.active||(s=this.activeMenu.find(this.options.items)[t]()),this.focus(i,s)},nextPage:function(t){var i,s,n;return this.active?(this.isLastItem()||(this._hasScroll()?(s=this.active.offset().top,n=this.element.height(),this.active.nextAll(".ui-menu-item").each(function(){return i=e(this),0>i.offset().top-s-n}),this.focus(t,i)):this.focus(t,this.activeMenu.find(this.options.items)[this.active?"last":"first"]())),void 0):(this.next(t),void 0)},previousPage:function(t){var i,s,n;return this.active?(this.isFirstItem()||(this._hasScroll()?(s=this.active.offset().top,n=this.element.height(),this.active.prevAll(".ui-menu-item").each(function(){return i=e(this),i.offset().top-s+n>0}),this.focus(t,i)):this.focus(t,this.activeMenu.find(this.options.items).first())),void 0):(this.next(t),void 0)},_hasScroll:function(){return this.element.outerHeight()<this.element.prop("scrollHeight")},select:function(t){this.active=this.active||e(t.target).closest(".ui-menu-item");var i={item:this.active};this.active.has(".ui-menu").length||this.collapseAll(t,!0),this._trigger("select",t,i)},_filterMenuItems:function(t){var i=t.replace(/[\-\[\]{}()*+?.,\\\^$|#\s]/g,"\\$&"),s=RegExp("^"+i,"i");return this.activeMenu.find(this.options.items).filter(".ui-menu-item").filter(function(){return s.test(e.trim(e(this).text()))})}}),e.widget("ui.autocomplete",{version:"1.11.4",defaultElement:"<input>",options:{appendTo:null,autoFocus:!1,delay:300,minLength:1,position:{my:"left top",at:"left bottom",collision:"none"},source:null,change:null,close:null,focus:null,open:null,response:null,search:null,select:null},requestIndex:0,pending:0,_create:function(){var t,i,s,n=this.element[0].nodeName.toLowerCase(),a="textarea"===n,o="input"===n;this.isMultiLine=a?!0:o?!1:this.element.prop("isContentEditable"),this.valueMethod=this.element[a||o?"val":"text"],this.isNewMenu=!0,this.element.addClass("ui-autocomplete-input").attr("autocomplete","off"),this._on(this.element,{keydown:function(n){if(this.element.prop("readOnly"))return t=!0,s=!0,i=!0,void 0;t=!1,s=!1,i=!1;var a=e.ui.keyCode;switch(n.keyCode){case a.PAGE_UP:t=!0,this._move("previousPage",n);break;case a.PAGE_DOWN:t=!0,this._move("nextPage",n);break;case a.UP:t=!0,this._keyEvent("previous",n);break;case a.DOWN:t=!0,this._keyEvent("next",n);break;case a.ENTER:this.menu.active&&(t=!0,n.preventDefault(),this.menu.select(n));break;case a.TAB:this.menu.active&&this.menu.select(n);break;case a.ESCAPE:this.menu.element.is(":visible")&&(this.isMultiLine||this._value(this.term),this.close(n),n.preventDefault());break;default:i=!0,this._searchTimeout(n)}},keypress:function(s){if(t)return t=!1,(!this.isMultiLine||this.menu.element.is(":visible"))&&s.preventDefault(),void 0;if(!i){var n=e.ui.keyCode;switch(s.keyCode){case n.PAGE_UP:this._move("previousPage",s);break;case n.PAGE_DOWN:this._move("nextPage",s);break;case n.UP:this._keyEvent("previous",s);break;case n.DOWN:this._keyEvent("next",s)}}},input:function(e){return s?(s=!1,e.preventDefault(),void 0):(this._searchTimeout(e),void 0)},focus:function(){this.selectedItem=null,this.previous=this._value()},blur:function(e){return this.cancelBlur?(delete this.cancelBlur,void 0):(clearTimeout(this.searching),this.close(e),this._change(e),void 0)}}),this._initSource(),this.menu=e("<ul>").addClass("ui-autocomplete ui-front").appendTo(this._appendTo()).menu({role:null}).hide().menu("instance"),this._on(this.menu.element,{mousedown:function(t){t.preventDefault(),this.cancelBlur=!0,this._delay(function(){delete this.cancelBlur});var i=this.menu.element[0];e(t.target).closest(".ui-menu-item").length||this._delay(function(){var t=this;this.document.one("mousedown",function(s){s.target===t.element[0]||s.target===i||e.contains(i,s.target)||t.close()})})},menufocus:function(t,i){var s,n;return this.isNewMenu&&(this.isNewMenu=!1,t.originalEvent&&/^mouse/.test(t.originalEvent.type))?(this.menu.blur(),this.document.one("mousemove",function(){e(t.target).trigger(t.originalEvent)}),void 0):(n=i.item.data("ui-autocomplete-item"),!1!==this._trigger("focus",t,{item:n})&&t.originalEvent&&/^key/.test(t.originalEvent.type)&&this._value(n.value),s=i.item.attr("aria-label")||n.value,s&&e.trim(s).length&&(this.liveRegion.children().hide(),e("<div>").text(s).appendTo(this.liveRegion)),void 0)},menuselect:function(e,t){var i=t.item.data("ui-autocomplete-item"),s=this.previous;this.element[0]!==this.document[0].activeElement&&(this.element.focus(),this.previous=s,this._delay(function(){this.previous=s,this.selectedItem=i})),!1!==this._trigger("select",e,{item:i})&&this._value(i.value),this.term=this._value(),this.close(e),this.selectedItem=i}}),this.liveRegion=e("<span>",{role:"status","aria-live":"assertive","aria-relevant":"additions"}).addClass("ui-helper-hidden-accessible").appendTo(this.document[0].body),this._on(this.window,{beforeunload:function(){this.element.removeAttr("autocomplete")}})},_destroy:function(){clearTimeout(this.searching),this.element.removeClass("ui-autocomplete-input").removeAttr("autocomplete"),this.menu.element.remove(),this.liveRegion.remove()},_setOption:function(e,t){this._super(e,t),"source"===e&&this._initSource(),"appendTo"===e&&this.menu.element.appendTo(this._appendTo()),"disabled"===e&&t&&this.xhr&&this.xhr.abort()},_appendTo:function(){var t=this.options.appendTo;return t&&(t=t.jquery||t.nodeType?e(t):this.document.find(t).eq(0)),t&&t[0]||(t=this.element.closest(".ui-front")),t.length||(t=this.document[0].body),t},_initSource:function(){var t,i,s=this;e.isArray(this.options.source)?(t=this.options.source,this.source=function(i,s){s(e.ui.autocomplete.filter(t,i.term))}):"string"==typeof this.options.source?(i=this.options.source,this.source=function(t,n){s.xhr&&s.xhr.abort(),s.xhr=e.ajax({url:i,data:t,dataType:"json",success:function(e){n(e)},error:function(){n([])}})}):this.source=this.options.source},_searchTimeout:function(e){clearTimeout(this.searching),this.searching=this._delay(function(){var t=this.term===this._value(),i=this.menu.element.is(":visible"),s=e.altKey||e.ctrlKey||e.metaKey||e.shiftKey;(!t||t&&!i&&!s)&&(this.selectedItem=null,this.search(null,e))},this.options.delay)},search:function(e,t){return e=null!=e?e:this._value(),this.term=this._value(),e.length<this.options.minLength?this.close(t):this._trigger("search",t)!==!1?this._search(e):void 0},_search:function(e){this.pending++,this.element.addClass("ui-autocomplete-loading"),this.cancelSearch=!1,this.source({term:e},this._response())},_response:function(){var t=++this.requestIndex;return e.proxy(function(e){t===this.requestIndex&&this.__response(e),this.pending--,this.pending||this.element.removeClass("ui-autocomplete-loading")},this)},__response:function(e){e&&(e=this._normalize(e)),this._trigger("response",null,{content:e}),!this.options.disabled&&e&&e.length&&!this.cancelSearch?(this._suggest(e),this._trigger("open")):this._close()},close:function(e){this.cancelSearch=!0,this._close(e)},_close:function(e){this.menu.element.is(":visible")&&(this.menu.element.hide(),this.menu.blur(),this.isNewMenu=!0,this._trigger("close",e))},_change:function(e){this.previous!==this._value()&&this._trigger("change",e,{item:this.selectedItem})},_normalize:function(t){return t.length&&t[0].label&&t[0].value?t:e.map(t,function(t){return"string"==typeof t?{label:t,value:t}:e.extend({},t,{label:t.label||t.value,value:t.value||t.label})})},_suggest:function(t){var i=this.menu.element.empty();this._renderMenu(i,t),this.isNewMenu=!0,this.menu.refresh(),i.show(),this._resizeMenu(),i.position(e.extend({of:this.element},this.options.position)),this.options.autoFocus&&this.menu.next()},_resizeMenu:function(){var e=this.menu.element;e.outerWidth(Math.max(e.width("").outerWidth()+1,this.element.outerWidth()))},_renderMenu:function(t,i){var s=this;e.each(i,function(e,i){s._renderItemData(t,i)})},_renderItemData:function(e,t){return this._renderItem(e,t).data("ui-autocomplete-item",t)},_renderItem:function(t,i){return e("<li>").text(i.label).appendTo(t)},_move:function(e,t){return this.menu.element.is(":visible")?this.menu.isFirstItem()&&/^previous/.test(e)||this.menu.isLastItem()&&/^next/.test(e)?(this.isMultiLine||this._value(this.term),this.menu.blur(),void 0):(this.menu[e](t),void 0):(this.search(null,t),void 0)},widget:function(){return this.menu.element},_value:function(){return this.valueMethod.apply(this.element,arguments)},_keyEvent:function(e,t){(!this.isMultiLine||this.menu.element.is(":visible"))&&(this._move(e,t),t.preventDefault())}}),e.extend(e.ui.autocomplete,{escapeRegex:function(e){return e.replace(/[\-\[\]{}()*+?.,\\\^$|#\s]/g,"\\$&")},filter:function(t,i){var s=RegExp(e.ui.autocomplete.escapeRegex(i),"i");return e.grep(t,function(e){return s.test(e.label||e.value||e)})}}),e.widget("ui.autocomplete",e.ui.autocomplete,{options:{messages:{noResults:"No search results.",results:function(e){return e+(e>1?" results are":" result is")+" available, use up and down arrow keys to navigate."}}},__response:function(t){var i;this._superApply(arguments),this.options.disabled||this.cancelSearch||(i=t&&t.length?this.options.messages.results(t.length):this.options.messages.noResults,this.liveRegion.children().hide(),e("<div>").text(i).appendTo(this.liveRegion))}}),e.ui.autocomplete;var r,h="ui-button ui-widget ui-state-default ui-corner-all",l="ui-button-icons-only ui-button-icon-only ui-button-text-icons ui-button-text-icon-primary ui-button-text-icon-secondary ui-button-text-only",u=function(){var t=e(this);setTimeout(function(){t.find(":ui-button").button("refresh")},1)},d=function(t){var i=t.name,s=t.form,n=e([]);return i&&(i=i.replace(/'/g,"\\'"),n=s?e(s).find("[name='"+i+"'][type=radio]"):e("[name='"+i+"'][type=radio]",t.ownerDocument).filter(function(){return!this.form})),n};e.widget("ui.button",{version:"1.11.4",defaultElement:"<button>",options:{disabled:null,text:!0,label:null,icons:{primary:null,secondary:null}},_create:function(){this.element.closest("form").unbind("reset"+this.eventNamespace).bind("reset"+this.eventNamespace,u),"boolean"!=typeof this.options.disabled?this.options.disabled=!!this.element.prop("disabled"):this.element.prop("disabled",this.options.disabled),this._determineButtonType(),this.hasTitle=!!this.buttonElement.attr("title");var t=this,i=this.options,s="checkbox"===this.type||"radio"===this.type,n=s?"":"ui-state-active";null===i.label&&(i.label="input"===this.type?this.buttonElement.val():this.buttonElement.html()),this._hoverable(this.buttonElement),this.buttonElement.addClass(h).attr("role","button").bind("mouseenter"+this.eventNamespace,function(){i.disabled||this===r&&e(this).addClass("ui-state-active")}).bind("mouseleave"+this.eventNamespace,function(){i.disabled||e(this).removeClass(n)}).bind("click"+this.eventNamespace,function(e){i.disabled&&(e.preventDefault(),e.stopImmediatePropagation())}),this._on({focus:function(){this.buttonElement.addClass("ui-state-focus")},blur:function(){this.buttonElement.removeClass("ui-state-focus")}}),s&&this.element.bind("change"+this.eventNamespace,function(){t.refresh()}),"checkbox"===this.type?this.buttonElement.bind("click"+this.eventNamespace,function(){return i.disabled?!1:void 0}):"radio"===this.type?this.buttonElement.bind("click"+this.eventNamespace,function(){if(i.disabled)return!1;e(this).addClass("ui-state-active"),t.buttonElement.attr("aria-pressed","true");var s=t.element[0];d(s).not(s).map(function(){return e(this).button("widget")[0]}).removeClass("ui-state-active").attr("aria-pressed","false")}):(this.buttonElement.bind("mousedown"+this.eventNamespace,function(){return i.disabled?!1:(e(this).addClass("ui-state-active"),r=this,t.document.one("mouseup",function(){r=null}),void 0)}).bind("mouseup"+this.eventNamespace,function(){return i.disabled?!1:(e(this).removeClass("ui-state-active"),void 0)}).bind("keydown"+this.eventNamespace,function(t){return i.disabled?!1:((t.keyCode===e.ui.keyCode.SPACE||t.keyCode===e.ui.keyCode.ENTER)&&e(this).addClass("ui-state-active"),void 0)}).bind("keyup"+this.eventNamespace+" blur"+this.eventNamespace,function(){e(this).removeClass("ui-state-active")}),this.buttonElement.is("a")&&this.buttonElement.keyup(function(t){t.keyCode===e.ui.keyCode.SPACE&&e(this).click()})),this._setOption("disabled",i.disabled),this._resetButton()},_determineButtonType:function(){var e,t,i;this.type=this.element.is("[type=checkbox]")?"checkbox":this.element.is("[type=radio]")?"radio":this.element.is("input")?"input":"button","checkbox"===this.type||"radio"===this.type?(e=this.element.parents().last(),t="label[for='"+this.element.attr("id")+"']",this.buttonElement=e.find(t),this.buttonElement.length||(e=e.length?e.siblings():this.element.siblings(),this.buttonElement=e.filter(t),this.buttonElement.length||(this.buttonElement=e.find(t))),this.element.addClass("ui-helper-hidden-accessible"),i=this.element.is(":checked"),i&&this.buttonElement.addClass("ui-state-active"),this.buttonElement.prop("aria-pressed",i)):this.buttonElement=this.element},widget:function(){return this.buttonElement},_destroy:function(){this.element.removeClass("ui-helper-hidden-accessible"),this.buttonElement.removeClass(h+" ui-state-active "+l).removeAttr("role").removeAttr("aria-pressed").html(this.buttonElement.find(".ui-button-text").html()),this.hasTitle||this.buttonElement.removeAttr("title")},_setOption:function(e,t){return this._super(e,t),"disabled"===e?(this.widget().toggleClass("ui-state-disabled",!!t),this.element.prop("disabled",!!t),t&&("checkbox"===this.type||"radio"===this.type?this.buttonElement.removeClass("ui-state-focus"):this.buttonElement.removeClass("ui-state-focus ui-state-active")),void 0):(this._resetButton(),void 0)},refresh:function(){var t=this.element.is("input, button")?this.element.is(":disabled"):this.element.hasClass("ui-button-disabled");t!==this.options.disabled&&this._setOption("disabled",t),"radio"===this.type?d(this.element[0]).each(function(){e(this).is(":checked")?e(this).button("widget").addClass("ui-state-active").attr("aria-pressed","true"):e(this).button("widget").removeClass("ui-state-active").attr("aria-pressed","false")}):"checkbox"===this.type&&(this.element.is(":checked")?this.buttonElement.addClass("ui-state-active").attr("aria-pressed","true"):this.buttonElement.removeClass("ui-state-active").attr("aria-pressed","false"))},_resetButton:function(){if("input"===this.type)return this.options.label&&this.element.val(this.options.label),void 0;var t=this.buttonElement.removeClass(l),i=e("<span></span>",this.document[0]).addClass("ui-button-text").html(this.options.label).appendTo(t.empty()).text(),s=this.options.icons,n=s.primary&&s.secondary,a=[];s.primary||s.secondary?(this.options.text&&a.push("ui-button-text-icon"+(n?"s":s.primary?"-primary":"-secondary")),s.primary&&t.prepend("<span class='ui-button-icon-primary ui-icon "+s.primary+"'></span>"),s.secondary&&t.append("<span class='ui-button-icon-secondary ui-icon "+s.secondary+"'></span>"),this.options.text||(a.push(n?"ui-button-icons-only":"ui-button-icon-only"),this.hasTitle||t.attr("title",e.trim(i)))):a.push("ui-button-text-only"),t.addClass(a.join(" "))}}),e.widget("ui.buttonset",{version:"1.11.4",options:{items:"button, input[type=button], input[type=submit], input[type=reset], input[type=checkbox], input[type=radio], a, :data(ui-button)"},_create:function(){this.element.addClass("ui-buttonset")},_init:function(){this.refresh()},_setOption:function(e,t){"disabled"===e&&this.buttons.button("option",e,t),this._super(e,t)},refresh:function(){var t="rtl"===this.element.css("direction"),i=this.element.find(this.options.items),s=i.filter(":ui-button");i.not(":ui-button").button(),s.button("refresh"),this.buttons=i.map(function(){return e(this).button("widget")[0]}).removeClass("ui-corner-all ui-corner-left ui-corner-right").filter(":first").addClass(t?"ui-corner-right":"ui-corner-left").end().filter(":last").addClass(t?"ui-corner-left":"ui-corner-right").end().end()},_destroy:function(){this.element.removeClass("ui-buttonset"),this.buttons.map(function(){return e(this).button("widget")[0]}).removeClass("ui-corner-left ui-corner-right").end().button("destroy")}}),e.ui.button,e.widget("ui.dialog",{version:"1.11.4",options:{appendTo:"body",autoOpen:!0,buttons:[],closeOnEscape:!0,closeText:"Close",dialogClass:"",draggable:!0,hide:null,height:"auto",maxHeight:null,maxWidth:null,minHeight:150,minWidth:150,modal:!1,position:{my:"center",at:"center",of:window,collision:"fit",using:function(t){var i=e(this).css(t).offset().top;0>i&&e(this).css("top",t.top-i)}},resizable:!0,show:null,title:null,width:300,beforeClose:null,close:null,drag:null,dragStart:null,dragStop:null,focus:null,open:null,resize:null,resizeStart:null,resizeStop:null},sizeRelatedOptions:{buttons:!0,height:!0,maxHeight:!0,maxWidth:!0,minHeight:!0,minWidth:!0,width:!0},resizableRelatedOptions:{maxHeight:!0,maxWidth:!0,minHeight:!0,minWidth:!0},_create:function(){this.originalCss={display:this.element[0].style.display,width:this.element[0].style.width,minHeight:this.element[0].style.minHeight,maxHeight:this.element[0].style.maxHeight,height:this.element[0].style.height},this.originalPosition={parent:this.element.parent(),index:this.element.parent().children().index(this.element)},this.originalTitle=this.element.attr("title"),this.options.title=this.options.title||this.originalTitle,this._createWrapper(),this.element.show().removeAttr("title").addClass("ui-dialog-content ui-widget-content").appendTo(this.uiDialog),this._createTitlebar(),this._createButtonPane(),this.options.draggable&&e.fn.draggable&&this._makeDraggable(),this.options.resizable&&e.fn.resizable&&this._makeResizable(),this._isOpen=!1,this._trackFocus()},_init:function(){this.options.autoOpen&&this.open()},_appendTo:function(){var t=this.options.appendTo;return t&&(t.jquery||t.nodeType)?e(t):this.document.find(t||"body").eq(0)},_destroy:function(){var e,t=this.originalPosition;this._untrackInstance(),this._destroyOverlay(),this.element.removeUniqueId().removeClass("ui-dialog-content ui-widget-content").css(this.originalCss).detach(),this.uiDialog.stop(!0,!0).remove(),this.originalTitle&&this.element.attr("title",this.originalTitle),e=t.parent.children().eq(t.index),e.length&&e[0]!==this.element[0]?e.before(this.element):t.parent.append(this.element)},widget:function(){return this.uiDialog},disable:e.noop,enable:e.noop,close:function(t){var i,s=this;if(this._isOpen&&this._trigger("beforeClose",t)!==!1){if(this._isOpen=!1,this._focusedElement=null,this._destroyOverlay(),this._untrackInstance(),!this.opener.filter(":focusable").focus().length)try{i=this.document[0].activeElement,i&&"body"!==i.nodeName.toLowerCase()&&e(i).blur()}catch(n){}this._hide(this.uiDialog,this.options.hide,function(){s._trigger("close",t)})}},isOpen:function(){return this._isOpen},moveToTop:function(){this._moveToTop()},_moveToTop:function(t,i){var s=!1,n=this.uiDialog.siblings(".ui-front:visible").map(function(){return+e(this).css("z-index")}).get(),a=Math.max.apply(null,n);return a>=+this.uiDialog.css("z-index")&&(this.uiDialog.css("z-index",a+1),s=!0),s&&!i&&this._trigger("focus",t),s},open:function(){var t=this;return this._isOpen?(this._moveToTop()&&this._focusTabbable(),void 0):(this._isOpen=!0,this.opener=e(this.document[0].activeElement),this._size(),this._position(),this._createOverlay(),this._moveToTop(null,!0),this.overlay&&this.overlay.css("z-index",this.uiDialog.css("z-index")-1),this._show(this.uiDialog,this.options.show,function(){t._focusTabbable(),t._trigger("focus")}),this._makeFocusTarget(),this._trigger("open"),void 0)},_focusTabbable:function(){var e=this._focusedElement;e||(e=this.element.find("[autofocus]")),e.length||(e=this.element.find(":tabbable")),e.length||(e=this.uiDialogButtonPane.find(":tabbable")),e.length||(e=this.uiDialogTitlebarClose.filter(":tabbable")),e.length||(e=this.uiDialog),e.eq(0).focus()},_keepFocus:function(t){function i(){var t=this.document[0].activeElement,i=this.uiDialog[0]===t||e.contains(this.uiDialog[0],t);i||this._focusTabbable()}t.preventDefault(),i.call(this),this._delay(i)},_createWrapper:function(){this.uiDialog=e("<div>").addClass("ui-dialog ui-widget ui-widget-content ui-corner-all ui-front "+this.options.dialogClass).hide().attr({tabIndex:-1,role:"dialog"}).appendTo(this._appendTo()),this._on(this.uiDialog,{keydown:function(t){if(this.options.closeOnEscape&&!t.isDefaultPrevented()&&t.keyCode&&t.keyCode===e.ui.keyCode.ESCAPE)return t.preventDefault(),this.close(t),void 0;if(t.keyCode===e.ui.keyCode.TAB&&!t.isDefaultPrevented()){var i=this.uiDialog.find(":tabbable"),s=i.filter(":first"),n=i.filter(":last");t.target!==n[0]&&t.target!==this.uiDialog[0]||t.shiftKey?t.target!==s[0]&&t.target!==this.uiDialog[0]||!t.shiftKey||(this._delay(function(){n.focus()}),t.preventDefault()):(this._delay(function(){s.focus()}),t.preventDefault())}},mousedown:function(e){this._moveToTop(e)&&this._focusTabbable()}}),this.element.find("[aria-describedby]").length||this.uiDialog.attr({"aria-describedby":this.element.uniqueId().attr("id")})},_createTitlebar:function(){var t;this.uiDialogTitlebar=e("<div>").addClass("ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix").prependTo(this.uiDialog),this._on(this.uiDialogTitlebar,{mousedown:function(t){e(t.target).closest(".ui-dialog-titlebar-close")||this.uiDialog.focus()}}),this.uiDialogTitlebarClose=e("<button type='button'></button>").button({label:this.options.closeText,icons:{primary:"ui-icon-closethick"},text:!1}).addClass("ui-dialog-titlebar-close").appendTo(this.uiDialogTitlebar),this._on(this.uiDialogTitlebarClose,{click:function(e){e.preventDefault(),this.close(e)}}),t=e("<span>").uniqueId().addClass("ui-dialog-title").prependTo(this.uiDialogTitlebar),this._title(t),this.uiDialog.attr({"aria-labelledby":t.attr("id")})},_title:function(e){this.options.title||e.html("&#160;"),e.text(this.options.title)
-},_createButtonPane:function(){this.uiDialogButtonPane=e("<div>").addClass("ui-dialog-buttonpane ui-widget-content ui-helper-clearfix"),this.uiButtonSet=e("<div>").addClass("ui-dialog-buttonset").appendTo(this.uiDialogButtonPane),this._createButtons()},_createButtons:function(){var t=this,i=this.options.buttons;return this.uiDialogButtonPane.remove(),this.uiButtonSet.empty(),e.isEmptyObject(i)||e.isArray(i)&&!i.length?(this.uiDialog.removeClass("ui-dialog-buttons"),void 0):(e.each(i,function(i,s){var n,a;s=e.isFunction(s)?{click:s,text:i}:s,s=e.extend({type:"button"},s),n=s.click,s.click=function(){n.apply(t.element[0],arguments)},a={icons:s.icons,text:s.showText},delete s.icons,delete s.showText,e("<button></button>",s).button(a).appendTo(t.uiButtonSet)}),this.uiDialog.addClass("ui-dialog-buttons"),this.uiDialogButtonPane.appendTo(this.uiDialog),void 0)},_makeDraggable:function(){function t(e){return{position:e.position,offset:e.offset}}var i=this,s=this.options;this.uiDialog.draggable({cancel:".ui-dialog-content, .ui-dialog-titlebar-close",handle:".ui-dialog-titlebar",containment:"document",start:function(s,n){e(this).addClass("ui-dialog-dragging"),i._blockFrames(),i._trigger("dragStart",s,t(n))},drag:function(e,s){i._trigger("drag",e,t(s))},stop:function(n,a){var o=a.offset.left-i.document.scrollLeft(),r=a.offset.top-i.document.scrollTop();s.position={my:"left top",at:"left"+(o>=0?"+":"")+o+" "+"top"+(r>=0?"+":"")+r,of:i.window},e(this).removeClass("ui-dialog-dragging"),i._unblockFrames(),i._trigger("dragStop",n,t(a))}})},_makeResizable:function(){function t(e){return{originalPosition:e.originalPosition,originalSize:e.originalSize,position:e.position,size:e.size}}var i=this,s=this.options,n=s.resizable,a=this.uiDialog.css("position"),o="string"==typeof n?n:"n,e,s,w,se,sw,ne,nw";this.uiDialog.resizable({cancel:".ui-dialog-content",containment:"document",alsoResize:this.element,maxWidth:s.maxWidth,maxHeight:s.maxHeight,minWidth:s.minWidth,minHeight:this._minHeight(),handles:o,start:function(s,n){e(this).addClass("ui-dialog-resizing"),i._blockFrames(),i._trigger("resizeStart",s,t(n))},resize:function(e,s){i._trigger("resize",e,t(s))},stop:function(n,a){var o=i.uiDialog.offset(),r=o.left-i.document.scrollLeft(),h=o.top-i.document.scrollTop();s.height=i.uiDialog.height(),s.width=i.uiDialog.width(),s.position={my:"left top",at:"left"+(r>=0?"+":"")+r+" "+"top"+(h>=0?"+":"")+h,of:i.window},e(this).removeClass("ui-dialog-resizing"),i._unblockFrames(),i._trigger("resizeStop",n,t(a))}}).css("position",a)},_trackFocus:function(){this._on(this.widget(),{focusin:function(t){this._makeFocusTarget(),this._focusedElement=e(t.target)}})},_makeFocusTarget:function(){this._untrackInstance(),this._trackingInstances().unshift(this)},_untrackInstance:function(){var t=this._trackingInstances(),i=e.inArray(this,t);-1!==i&&t.splice(i,1)},_trackingInstances:function(){var e=this.document.data("ui-dialog-instances");return e||(e=[],this.document.data("ui-dialog-instances",e)),e},_minHeight:function(){var e=this.options;return"auto"===e.height?e.minHeight:Math.min(e.minHeight,e.height)},_position:function(){var e=this.uiDialog.is(":visible");e||this.uiDialog.show(),this.uiDialog.position(this.options.position),e||this.uiDialog.hide()},_setOptions:function(t){var i=this,s=!1,n={};e.each(t,function(e,t){i._setOption(e,t),e in i.sizeRelatedOptions&&(s=!0),e in i.resizableRelatedOptions&&(n[e]=t)}),s&&(this._size(),this._position()),this.uiDialog.is(":data(ui-resizable)")&&this.uiDialog.resizable("option",n)},_setOption:function(e,t){var i,s,n=this.uiDialog;"dialogClass"===e&&n.removeClass(this.options.dialogClass).addClass(t),"disabled"!==e&&(this._super(e,t),"appendTo"===e&&this.uiDialog.appendTo(this._appendTo()),"buttons"===e&&this._createButtons(),"closeText"===e&&this.uiDialogTitlebarClose.button({label:""+t}),"draggable"===e&&(i=n.is(":data(ui-draggable)"),i&&!t&&n.draggable("destroy"),!i&&t&&this._makeDraggable()),"position"===e&&this._position(),"resizable"===e&&(s=n.is(":data(ui-resizable)"),s&&!t&&n.resizable("destroy"),s&&"string"==typeof t&&n.resizable("option","handles",t),s||t===!1||this._makeResizable()),"title"===e&&this._title(this.uiDialogTitlebar.find(".ui-dialog-title")))},_size:function(){var e,t,i,s=this.options;this.element.show().css({width:"auto",minHeight:0,maxHeight:"none",height:0}),s.minWidth>s.width&&(s.width=s.minWidth),e=this.uiDialog.css({height:"auto",width:s.width}).outerHeight(),t=Math.max(0,s.minHeight-e),i="number"==typeof s.maxHeight?Math.max(0,s.maxHeight-e):"none","auto"===s.height?this.element.css({minHeight:t,maxHeight:i,height:"auto"}):this.element.height(Math.max(0,s.height-e)),this.uiDialog.is(":data(ui-resizable)")&&this.uiDialog.resizable("option","minHeight",this._minHeight())},_blockFrames:function(){this.iframeBlocks=this.document.find("iframe").map(function(){var t=e(this);return e("<div>").css({position:"absolute",width:t.outerWidth(),height:t.outerHeight()}).appendTo(t.parent()).offset(t.offset())[0]})},_unblockFrames:function(){this.iframeBlocks&&(this.iframeBlocks.remove(),delete this.iframeBlocks)},_allowInteraction:function(t){return e(t.target).closest(".ui-dialog").length?!0:!!e(t.target).closest(".ui-datepicker").length},_createOverlay:function(){if(this.options.modal){var t=!0;this._delay(function(){t=!1}),this.document.data("ui-dialog-overlays")||this._on(this.document,{focusin:function(e){t||this._allowInteraction(e)||(e.preventDefault(),this._trackingInstances()[0]._focusTabbable())}}),this.overlay=e("<div>").addClass("ui-widget-overlay ui-front").appendTo(this._appendTo()),this._on(this.overlay,{mousedown:"_keepFocus"}),this.document.data("ui-dialog-overlays",(this.document.data("ui-dialog-overlays")||0)+1)}},_destroyOverlay:function(){if(this.options.modal&&this.overlay){var e=this.document.data("ui-dialog-overlays")-1;e?this.document.data("ui-dialog-overlays",e):this.document.unbind("focusin").removeData("ui-dialog-overlays"),this.overlay.remove(),this.overlay=null}}}),e.widget("ui.progressbar",{version:"1.11.4",options:{max:100,value:0,change:null,complete:null},min:0,_create:function(){this.oldValue=this.options.value=this._constrainedValue(),this.element.addClass("ui-progressbar ui-widget ui-widget-content ui-corner-all").attr({role:"progressbar","aria-valuemin":this.min}),this.valueDiv=e("<div class='ui-progressbar-value ui-widget-header ui-corner-left'></div>").appendTo(this.element),this._refreshValue()},_destroy:function(){this.element.removeClass("ui-progressbar ui-widget ui-widget-content ui-corner-all").removeAttr("role").removeAttr("aria-valuemin").removeAttr("aria-valuemax").removeAttr("aria-valuenow"),this.valueDiv.remove()},value:function(e){return void 0===e?this.options.value:(this.options.value=this._constrainedValue(e),this._refreshValue(),void 0)},_constrainedValue:function(e){return void 0===e&&(e=this.options.value),this.indeterminate=e===!1,"number"!=typeof e&&(e=0),this.indeterminate?!1:Math.min(this.options.max,Math.max(this.min,e))},_setOptions:function(e){var t=e.value;delete e.value,this._super(e),this.options.value=this._constrainedValue(t),this._refreshValue()},_setOption:function(e,t){"max"===e&&(t=Math.max(this.min,t)),"disabled"===e&&this.element.toggleClass("ui-state-disabled",!!t).attr("aria-disabled",t),this._super(e,t)},_percentage:function(){return this.indeterminate?100:100*(this.options.value-this.min)/(this.options.max-this.min)},_refreshValue:function(){var t=this.options.value,i=this._percentage();this.valueDiv.toggle(this.indeterminate||t>this.min).toggleClass("ui-corner-right",t===this.options.max).width(i.toFixed(0)+"%"),this.element.toggleClass("ui-progressbar-indeterminate",this.indeterminate),this.indeterminate?(this.element.removeAttr("aria-valuenow"),this.overlayDiv||(this.overlayDiv=e("<div class='ui-progressbar-overlay'></div>").appendTo(this.valueDiv))):(this.element.attr({"aria-valuemax":this.options.max,"aria-valuenow":t}),this.overlayDiv&&(this.overlayDiv.remove(),this.overlayDiv=null)),this.oldValue!==t&&(this.oldValue=t,this._trigger("change")),t===this.options.max&&this._trigger("complete")}}),e.widget("ui.selectmenu",{version:"1.11.4",defaultElement:"<select>",options:{appendTo:null,disabled:null,icons:{button:"ui-icon-triangle-1-s"},position:{my:"left top",at:"left bottom",collision:"none"},width:null,change:null,close:null,focus:null,open:null,select:null},_create:function(){var e=this.element.uniqueId().attr("id");this.ids={element:e,button:e+"-button",menu:e+"-menu"},this._drawButton(),this._drawMenu(),this.options.disabled&&this.disable()},_drawButton:function(){var t=this;this.label=e("label[for='"+this.ids.element+"']").attr("for",this.ids.button),this._on(this.label,{click:function(e){this.button.focus(),e.preventDefault()}}),this.element.hide(),this.button=e("<span>",{"class":"ui-selectmenu-button ui-widget ui-state-default ui-corner-all",tabindex:this.options.disabled?-1:0,id:this.ids.button,role:"combobox","aria-expanded":"false","aria-autocomplete":"list","aria-owns":this.ids.menu,"aria-haspopup":"true"}).insertAfter(this.element),e("<span>",{"class":"ui-icon "+this.options.icons.button}).prependTo(this.button),this.buttonText=e("<span>",{"class":"ui-selectmenu-text"}).appendTo(this.button),this._setText(this.buttonText,this.element.find("option:selected").text()),this._resizeButton(),this._on(this.button,this._buttonEvents),this.button.one("focusin",function(){t.menuItems||t._refreshMenu()}),this._hoverable(this.button),this._focusable(this.button)},_drawMenu:function(){var t=this;this.menu=e("<ul>",{"aria-hidden":"true","aria-labelledby":this.ids.button,id:this.ids.menu}),this.menuWrap=e("<div>",{"class":"ui-selectmenu-menu ui-front"}).append(this.menu).appendTo(this._appendTo()),this.menuInstance=this.menu.menu({role:"listbox",select:function(e,i){e.preventDefault(),t._setSelection(),t._select(i.item.data("ui-selectmenu-item"),e)},focus:function(e,i){var s=i.item.data("ui-selectmenu-item");null!=t.focusIndex&&s.index!==t.focusIndex&&(t._trigger("focus",e,{item:s}),t.isOpen||t._select(s,e)),t.focusIndex=s.index,t.button.attr("aria-activedescendant",t.menuItems.eq(s.index).attr("id"))}}).menu("instance"),this.menu.addClass("ui-corner-bottom").removeClass("ui-corner-all"),this.menuInstance._off(this.menu,"mouseleave"),this.menuInstance._closeOnDocumentClick=function(){return!1},this.menuInstance._isDivider=function(){return!1}},refresh:function(){this._refreshMenu(),this._setText(this.buttonText,this._getSelectedItem().text()),this.options.width||this._resizeButton()},_refreshMenu:function(){this.menu.empty();var e,t=this.element.find("option");t.length&&(this._parseOptions(t),this._renderMenu(this.menu,this.items),this.menuInstance.refresh(),this.menuItems=this.menu.find("li").not(".ui-selectmenu-optgroup"),e=this._getSelectedItem(),this.menuInstance.focus(null,e),this._setAria(e.data("ui-selectmenu-item")),this._setOption("disabled",this.element.prop("disabled")))},open:function(e){this.options.disabled||(this.menuItems?(this.menu.find(".ui-state-focus").removeClass("ui-state-focus"),this.menuInstance.focus(null,this._getSelectedItem())):this._refreshMenu(),this.isOpen=!0,this._toggleAttr(),this._resizeMenu(),this._position(),this._on(this.document,this._documentClick),this._trigger("open",e))},_position:function(){this.menuWrap.position(e.extend({of:this.button},this.options.position))},close:function(e){this.isOpen&&(this.isOpen=!1,this._toggleAttr(),this.range=null,this._off(this.document),this._trigger("close",e))},widget:function(){return this.button},menuWidget:function(){return this.menu},_renderMenu:function(t,i){var s=this,n="";e.each(i,function(i,a){a.optgroup!==n&&(e("<li>",{"class":"ui-selectmenu-optgroup ui-menu-divider"+(a.element.parent("optgroup").prop("disabled")?" ui-state-disabled":""),text:a.optgroup}).appendTo(t),n=a.optgroup),s._renderItemData(t,a)})},_renderItemData:function(e,t){return this._renderItem(e,t).data("ui-selectmenu-item",t)},_renderItem:function(t,i){var s=e("<li>");return i.disabled&&s.addClass("ui-state-disabled"),this._setText(s,i.label),s.appendTo(t)},_setText:function(e,t){t?e.text(t):e.html("&#160;")},_move:function(e,t){var i,s,n=".ui-menu-item";this.isOpen?i=this.menuItems.eq(this.focusIndex):(i=this.menuItems.eq(this.element[0].selectedIndex),n+=":not(.ui-state-disabled)"),s="first"===e||"last"===e?i["first"===e?"prevAll":"nextAll"](n).eq(-1):i[e+"All"](n).eq(0),s.length&&this.menuInstance.focus(t,s)},_getSelectedItem:function(){return this.menuItems.eq(this.element[0].selectedIndex)},_toggle:function(e){this[this.isOpen?"close":"open"](e)},_setSelection:function(){var e;this.range&&(window.getSelection?(e=window.getSelection(),e.removeAllRanges(),e.addRange(this.range)):this.range.select(),this.button.focus())},_documentClick:{mousedown:function(t){this.isOpen&&(e(t.target).closest(".ui-selectmenu-menu, #"+this.ids.button).length||this.close(t))}},_buttonEvents:{mousedown:function(){var e;window.getSelection?(e=window.getSelection(),e.rangeCount&&(this.range=e.getRangeAt(0))):this.range=document.selection.createRange()},click:function(e){this._setSelection(),this._toggle(e)},keydown:function(t){var i=!0;switch(t.keyCode){case e.ui.keyCode.TAB:case e.ui.keyCode.ESCAPE:this.close(t),i=!1;break;case e.ui.keyCode.ENTER:this.isOpen&&this._selectFocusedItem(t);break;case e.ui.keyCode.UP:t.altKey?this._toggle(t):this._move("prev",t);break;case e.ui.keyCode.DOWN:t.altKey?this._toggle(t):this._move("next",t);break;case e.ui.keyCode.SPACE:this.isOpen?this._selectFocusedItem(t):this._toggle(t);break;case e.ui.keyCode.LEFT:this._move("prev",t);break;case e.ui.keyCode.RIGHT:this._move("next",t);break;case e.ui.keyCode.HOME:case e.ui.keyCode.PAGE_UP:this._move("first",t);break;case e.ui.keyCode.END:case e.ui.keyCode.PAGE_DOWN:this._move("last",t);break;default:this.menu.trigger(t),i=!1}i&&t.preventDefault()}},_selectFocusedItem:function(e){var t=this.menuItems.eq(this.focusIndex);t.hasClass("ui-state-disabled")||this._select(t.data("ui-selectmenu-item"),e)},_select:function(e,t){var i=this.element[0].selectedIndex;this.element[0].selectedIndex=e.index,this._setText(this.buttonText,e.label),this._setAria(e),this._trigger("select",t,{item:e}),e.index!==i&&this._trigger("change",t,{item:e}),this.close(t)},_setAria:function(e){var t=this.menuItems.eq(e.index).attr("id");this.button.attr({"aria-labelledby":t,"aria-activedescendant":t}),this.menu.attr("aria-activedescendant",t)},_setOption:function(e,t){"icons"===e&&this.button.find("span.ui-icon").removeClass(this.options.icons.button).addClass(t.button),this._super(e,t),"appendTo"===e&&this.menuWrap.appendTo(this._appendTo()),"disabled"===e&&(this.menuInstance.option("disabled",t),this.button.toggleClass("ui-state-disabled",t).attr("aria-disabled",t),this.element.prop("disabled",t),t?(this.button.attr("tabindex",-1),this.close()):this.button.attr("tabindex",0)),"width"===e&&this._resizeButton()},_appendTo:function(){var t=this.options.appendTo;return t&&(t=t.jquery||t.nodeType?e(t):this.document.find(t).eq(0)),t&&t[0]||(t=this.element.closest(".ui-front")),t.length||(t=this.document[0].body),t},_toggleAttr:function(){this.button.toggleClass("ui-corner-top",this.isOpen).toggleClass("ui-corner-all",!this.isOpen).attr("aria-expanded",this.isOpen),this.menuWrap.toggleClass("ui-selectmenu-open",this.isOpen),this.menu.attr("aria-hidden",!this.isOpen)},_resizeButton:function(){var e=this.options.width;e||(e=this.element.show().outerWidth(),this.element.hide()),this.button.outerWidth(e)},_resizeMenu:function(){this.menu.outerWidth(Math.max(this.button.outerWidth(),this.menu.width("").outerWidth()+1))},_getCreateOptions:function(){return{disabled:this.element.prop("disabled")}},_parseOptions:function(t){var i=[];t.each(function(t,s){var n=e(s),a=n.parent("optgroup");i.push({element:n,index:t,value:n.val(),label:n.text(),optgroup:a.attr("label")||"",disabled:a.prop("disabled")||n.prop("disabled")})}),this.items=i},_destroy:function(){this.menuWrap.remove(),this.button.remove(),this.element.show(),this.element.removeUniqueId(),this.label.attr("for",this.ids.element)}}),e.widget("ui.slider",e.ui.mouse,{version:"1.11.4",widgetEventPrefix:"slide",options:{animate:!1,distance:0,max:100,min:0,orientation:"horizontal",range:!1,step:1,value:0,values:null,change:null,slide:null,start:null,stop:null},numPages:5,_create:function(){this._keySliding=!1,this._mouseSliding=!1,this._animateOff=!0,this._handleIndex=null,this._detectOrientation(),this._mouseInit(),this._calculateNewMax(),this.element.addClass("ui-slider ui-slider-"+this.orientation+" ui-widget"+" ui-widget-content"+" ui-corner-all"),this._refresh(),this._setOption("disabled",this.options.disabled),this._animateOff=!1},_refresh:function(){this._createRange(),this._createHandles(),this._setupEvents(),this._refreshValue()},_createHandles:function(){var t,i,s=this.options,n=this.element.find(".ui-slider-handle").addClass("ui-state-default ui-corner-all"),a="<span class='ui-slider-handle ui-state-default ui-corner-all' tabindex='0'></span>",o=[];for(i=s.values&&s.values.length||1,n.length>i&&(n.slice(i).remove(),n=n.slice(0,i)),t=n.length;i>t;t++)o.push(a);this.handles=n.add(e(o.join("")).appendTo(this.element)),this.handle=this.handles.eq(0),this.handles.each(function(t){e(this).data("ui-slider-handle-index",t)})},_createRange:function(){var t=this.options,i="";t.range?(t.range===!0&&(t.values?t.values.length&&2!==t.values.length?t.values=[t.values[0],t.values[0]]:e.isArray(t.values)&&(t.values=t.values.slice(0)):t.values=[this._valueMin(),this._valueMin()]),this.range&&this.range.length?this.range.removeClass("ui-slider-range-min ui-slider-range-max").css({left:"",bottom:""}):(this.range=e("<div></div>").appendTo(this.element),i="ui-slider-range ui-widget-header ui-corner-all"),this.range.addClass(i+("min"===t.range||"max"===t.range?" ui-slider-range-"+t.range:""))):(this.range&&this.range.remove(),this.range=null)},_setupEvents:function(){this._off(this.handles),this._on(this.handles,this._handleEvents),this._hoverable(this.handles),this._focusable(this.handles)},_destroy:function(){this.handles.remove(),this.range&&this.range.remove(),this.element.removeClass("ui-slider ui-slider-horizontal ui-slider-vertical ui-widget ui-widget-content ui-corner-all"),this._mouseDestroy()},_mouseCapture:function(t){var i,s,n,a,o,r,h,l,u=this,d=this.options;return d.disabled?!1:(this.elementSize={width:this.element.outerWidth(),height:this.element.outerHeight()},this.elementOffset=this.element.offset(),i={x:t.pageX,y:t.pageY},s=this._normValueFromMouse(i),n=this._valueMax()-this._valueMin()+1,this.handles.each(function(t){var i=Math.abs(s-u.values(t));(n>i||n===i&&(t===u._lastChangedValue||u.values(t)===d.min))&&(n=i,a=e(this),o=t)}),r=this._start(t,o),r===!1?!1:(this._mouseSliding=!0,this._handleIndex=o,a.addClass("ui-state-active").focus(),h=a.offset(),l=!e(t.target).parents().addBack().is(".ui-slider-handle"),this._clickOffset=l?{left:0,top:0}:{left:t.pageX-h.left-a.width()/2,top:t.pageY-h.top-a.height()/2-(parseInt(a.css("borderTopWidth"),10)||0)-(parseInt(a.css("borderBottomWidth"),10)||0)+(parseInt(a.css("marginTop"),10)||0)},this.handles.hasClass("ui-state-hover")||this._slide(t,o,s),this._animateOff=!0,!0))},_mouseStart:function(){return!0},_mouseDrag:function(e){var t={x:e.pageX,y:e.pageY},i=this._normValueFromMouse(t);return this._slide(e,this._handleIndex,i),!1},_mouseStop:function(e){return this.handles.removeClass("ui-state-active"),this._mouseSliding=!1,this._stop(e,this._handleIndex),this._change(e,this._handleIndex),this._handleIndex=null,this._clickOffset=null,this._animateOff=!1,!1},_detectOrientation:function(){this.orientation="vertical"===this.options.orientation?"vertical":"horizontal"},_normValueFromMouse:function(e){var t,i,s,n,a;return"horizontal"===this.orientation?(t=this.elementSize.width,i=e.x-this.elementOffset.left-(this._clickOffset?this._clickOffset.left:0)):(t=this.elementSize.height,i=e.y-this.elementOffset.top-(this._clickOffset?this._clickOffset.top:0)),s=i/t,s>1&&(s=1),0>s&&(s=0),"vertical"===this.orientation&&(s=1-s),n=this._valueMax()-this._valueMin(),a=this._valueMin()+s*n,this._trimAlignValue(a)},_start:function(e,t){var i={handle:this.handles[t],value:this.value()};return this.options.values&&this.options.values.length&&(i.value=this.values(t),i.values=this.values()),this._trigger("start",e,i)},_slide:function(e,t,i){var s,n,a;this.options.values&&this.options.values.length?(s=this.values(t?0:1),2===this.options.values.length&&this.options.range===!0&&(0===t&&i>s||1===t&&s>i)&&(i=s),i!==this.values(t)&&(n=this.values(),n[t]=i,a=this._trigger("slide",e,{handle:this.handles[t],value:i,values:n}),s=this.values(t?0:1),a!==!1&&this.values(t,i))):i!==this.value()&&(a=this._trigger("slide",e,{handle:this.handles[t],value:i}),a!==!1&&this.value(i))},_stop:function(e,t){var i={handle:this.handles[t],value:this.value()};this.options.values&&this.options.values.length&&(i.value=this.values(t),i.values=this.values()),this._trigger("stop",e,i)},_change:function(e,t){if(!this._keySliding&&!this._mouseSliding){var i={handle:this.handles[t],value:this.value()};this.options.values&&this.options.values.length&&(i.value=this.values(t),i.values=this.values()),this._lastChangedValue=t,this._trigger("change",e,i)}},value:function(e){return arguments.length?(this.options.value=this._trimAlignValue(e),this._refreshValue(),this._change(null,0),void 0):this._value()},values:function(t,i){var s,n,a;if(arguments.length>1)return this.options.values[t]=this._trimAlignValue(i),this._refreshValue(),this._change(null,t),void 0;if(!arguments.length)return this._values();if(!e.isArray(arguments[0]))return this.options.values&&this.options.values.length?this._values(t):this.value();for(s=this.options.values,n=arguments[0],a=0;s.length>a;a+=1)s[a]=this._trimAlignValue(n[a]),this._change(null,a);this._refreshValue()},_setOption:function(t,i){var s,n=0;switch("range"===t&&this.options.range===!0&&("min"===i?(this.options.value=this._values(0),this.options.values=null):"max"===i&&(this.options.value=this._values(this.options.values.length-1),this.options.values=null)),e.isArray(this.options.values)&&(n=this.options.values.length),"disabled"===t&&this.element.toggleClass("ui-state-disabled",!!i),this._super(t,i),t){case"orientation":this._detectOrientation(),this.element.removeClass("ui-slider-horizontal ui-slider-vertical").addClass("ui-slider-"+this.orientation),this._refreshValue(),this.handles.css("horizontal"===i?"bottom":"left","");break;case"value":this._animateOff=!0,this._refreshValue(),this._change(null,0),this._animateOff=!1;break;case"values":for(this._animateOff=!0,this._refreshValue(),s=0;n>s;s+=1)this._change(null,s);this._animateOff=!1;break;case"step":case"min":case"max":this._animateOff=!0,this._calculateNewMax(),this._refreshValue(),this._animateOff=!1;break;case"range":this._animateOff=!0,this._refresh(),this._animateOff=!1}},_value:function(){var e=this.options.value;return e=this._trimAlignValue(e)},_values:function(e){var t,i,s;if(arguments.length)return t=this.options.values[e],t=this._trimAlignValue(t);if(this.options.values&&this.options.values.length){for(i=this.options.values.slice(),s=0;i.length>s;s+=1)i[s]=this._trimAlignValue(i[s]);return i}return[]},_trimAlignValue:function(e){if(this._valueMin()>=e)return this._valueMin();if(e>=this._valueMax())return this._valueMax();var t=this.options.step>0?this.options.step:1,i=(e-this._valueMin())%t,s=e-i;return 2*Math.abs(i)>=t&&(s+=i>0?t:-t),parseFloat(s.toFixed(5))},_calculateNewMax:function(){var e=this.options.max,t=this._valueMin(),i=this.options.step,s=Math.floor(+(e-t).toFixed(this._precision())/i)*i;e=s+t,this.max=parseFloat(e.toFixed(this._precision()))},_precision:function(){var e=this._precisionOf(this.options.step);return null!==this.options.min&&(e=Math.max(e,this._precisionOf(this.options.min))),e},_precisionOf:function(e){var t=""+e,i=t.indexOf(".");return-1===i?0:t.length-i-1},_valueMin:function(){return this.options.min},_valueMax:function(){return this.max},_refreshValue:function(){var t,i,s,n,a,o=this.options.range,r=this.options,h=this,l=this._animateOff?!1:r.animate,u={};this.options.values&&this.options.values.length?this.handles.each(function(s){i=100*((h.values(s)-h._valueMin())/(h._valueMax()-h._valueMin())),u["horizontal"===h.orientation?"left":"bottom"]=i+"%",e(this).stop(1,1)[l?"animate":"css"](u,r.animate),h.options.range===!0&&("horizontal"===h.orientation?(0===s&&h.range.stop(1,1)[l?"animate":"css"]({left:i+"%"},r.animate),1===s&&h.range[l?"animate":"css"]({width:i-t+"%"},{queue:!1,duration:r.animate})):(0===s&&h.range.stop(1,1)[l?"animate":"css"]({bottom:i+"%"},r.animate),1===s&&h.range[l?"animate":"css"]({height:i-t+"%"},{queue:!1,duration:r.animate}))),t=i}):(s=this.value(),n=this._valueMin(),a=this._valueMax(),i=a!==n?100*((s-n)/(a-n)):0,u["horizontal"===this.orientation?"left":"bottom"]=i+"%",this.handle.stop(1,1)[l?"animate":"css"](u,r.animate),"min"===o&&"horizontal"===this.orientation&&this.range.stop(1,1)[l?"animate":"css"]({width:i+"%"},r.animate),"max"===o&&"horizontal"===this.orientation&&this.range[l?"animate":"css"]({width:100-i+"%"},{queue:!1,duration:r.animate}),"min"===o&&"vertical"===this.orientation&&this.range.stop(1,1)[l?"animate":"css"]({height:i+"%"},r.animate),"max"===o&&"vertical"===this.orientation&&this.range[l?"animate":"css"]({height:100-i+"%"},{queue:!1,duration:r.animate}))},_handleEvents:{keydown:function(t){var i,s,n,a,o=e(t.target).data("ui-slider-handle-index");switch(t.keyCode){case e.ui.keyCode.HOME:case e.ui.keyCode.END:case e.ui.keyCode.PAGE_UP:case e.ui.keyCode.PAGE_DOWN:case e.ui.keyCode.UP:case e.ui.keyCode.RIGHT:case e.ui.keyCode.DOWN:case e.ui.keyCode.LEFT:if(t.preventDefault(),!this._keySliding&&(this._keySliding=!0,e(t.target).addClass("ui-state-active"),i=this._start(t,o),i===!1))return}switch(a=this.options.step,s=n=this.options.values&&this.options.values.length?this.values(o):this.value(),t.keyCode){case e.ui.keyCode.HOME:n=this._valueMin();break;case e.ui.keyCode.END:n=this._valueMax();break;case e.ui.keyCode.PAGE_UP:n=this._trimAlignValue(s+(this._valueMax()-this._valueMin())/this.numPages);break;case e.ui.keyCode.PAGE_DOWN:n=this._trimAlignValue(s-(this._valueMax()-this._valueMin())/this.numPages);break;case e.ui.keyCode.UP:case e.ui.keyCode.RIGHT:if(s===this._valueMax())return;n=this._trimAlignValue(s+a);break;case e.ui.keyCode.DOWN:case e.ui.keyCode.LEFT:if(s===this._valueMin())return;n=this._trimAlignValue(s-a)}this._slide(t,o,n)},keyup:function(t){var i=e(t.target).data("ui-slider-handle-index");this._keySliding&&(this._keySliding=!1,this._stop(t,i),this._change(t,i),e(t.target).removeClass("ui-state-active"))}}}),e.widget("ui.spinner",{version:"1.11.4",defaultElement:"<input>",widgetEventPrefix:"spin",options:{culture:null,icons:{down:"ui-icon-triangle-1-s",up:"ui-icon-triangle-1-n"},incremental:!0,max:null,min:null,numberFormat:null,page:10,step:1,change:null,spin:null,start:null,stop:null},_create:function(){this._setOption("max",this.options.max),this._setOption("min",this.options.min),this._setOption("step",this.options.step),""!==this.value()&&this._value(this.element.val(),!0),this._draw(),this._on(this._events),this._refresh(),this._on(this.window,{beforeunload:function(){this.element.removeAttr("autocomplete")}})},_getCreateOptions:function(){var t={},i=this.element;return e.each(["min","max","step"],function(e,s){var n=i.attr(s);void 0!==n&&n.length&&(t[s]=n)}),t},_events:{keydown:function(e){this._start(e)&&this._keydown(e)&&e.preventDefault()},keyup:"_stop",focus:function(){this.previous=this.element.val()},blur:function(e){return this.cancelBlur?(delete this.cancelBlur,void 0):(this._stop(),this._refresh(),this.previous!==this.element.val()&&this._trigger("change",e),void 0)},mousewheel:function(e,t){if(t){if(!this.spinning&&!this._start(e))return!1;this._spin((t>0?1:-1)*this.options.step,e),clearTimeout(this.mousewheelTimer),this.mousewheelTimer=this._delay(function(){this.spinning&&this._stop(e)},100),e.preventDefault()}},"mousedown .ui-spinner-button":function(t){function i(){var e=this.element[0]===this.document[0].activeElement;e||(this.element.focus(),this.previous=s,this._delay(function(){this.previous=s}))}var s;s=this.element[0]===this.document[0].activeElement?this.previous:this.element.val(),t.preventDefault(),i.call(this),this.cancelBlur=!0,this._delay(function(){delete this.cancelBlur,i.call(this)}),this._start(t)!==!1&&this._repeat(null,e(t.currentTarget).hasClass("ui-spinner-up")?1:-1,t)},"mouseup .ui-spinner-button":"_stop","mouseenter .ui-spinner-button":function(t){return e(t.currentTarget).hasClass("ui-state-active")?this._start(t)===!1?!1:(this._repeat(null,e(t.currentTarget).hasClass("ui-spinner-up")?1:-1,t),void 0):void 0},"mouseleave .ui-spinner-button":"_stop"},_draw:function(){var e=this.uiSpinner=this.element.addClass("ui-spinner-input").attr("autocomplete","off").wrap(this._uiSpinnerHtml()).parent().append(this._buttonHtml());this.element.attr("role","spinbutton"),this.buttons=e.find(".ui-spinner-button").attr("tabIndex",-1).button().removeClass("ui-corner-all"),this.buttons.height()>Math.ceil(.5*e.height())&&e.height()>0&&e.height(e.height()),this.options.disabled&&this.disable()},_keydown:function(t){var i=this.options,s=e.ui.keyCode;switch(t.keyCode){case s.UP:return this._repeat(null,1,t),!0;case s.DOWN:return this._repeat(null,-1,t),!0;case s.PAGE_UP:return this._repeat(null,i.page,t),!0;case s.PAGE_DOWN:return this._repeat(null,-i.page,t),!0}return!1},_uiSpinnerHtml:function(){return"<span class='ui-spinner ui-widget ui-widget-content ui-corner-all'></span>"},_buttonHtml:function(){return"<a class='ui-spinner-button ui-spinner-up ui-corner-tr'><span class='ui-icon "+this.options.icons.up+"'>&#9650;</span>"+"</a>"+"<a class='ui-spinner-button ui-spinner-down ui-corner-br'>"+"<span class='ui-icon "+this.options.icons.down+"'>&#9660;</span>"+"</a>"},_start:function(e){return this.spinning||this._trigger("start",e)!==!1?(this.counter||(this.counter=1),this.spinning=!0,!0):!1},_repeat:function(e,t,i){e=e||500,clearTimeout(this.timer),this.timer=this._delay(function(){this._repeat(40,t,i)},e),this._spin(t*this.options.step,i)},_spin:function(e,t){var i=this.value()||0;this.counter||(this.counter=1),i=this._adjustValue(i+e*this._increment(this.counter)),this.spinning&&this._trigger("spin",t,{value:i})===!1||(this._value(i),this.counter++)},_increment:function(t){var i=this.options.incremental;return i?e.isFunction(i)?i(t):Math.floor(t*t*t/5e4-t*t/500+17*t/200+1):1},_precision:function(){var e=this._precisionOf(this.options.step);return null!==this.options.min&&(e=Math.max(e,this._precisionOf(this.options.min))),e},_precisionOf:function(e){var t=""+e,i=t.indexOf(".");return-1===i?0:t.length-i-1},_adjustValue:function(e){var t,i,s=this.options;return t=null!==s.min?s.min:0,i=e-t,i=Math.round(i/s.step)*s.step,e=t+i,e=parseFloat(e.toFixed(this._precision())),null!==s.max&&e>s.max?s.max:null!==s.min&&s.min>e?s.min:e},_stop:function(e){this.spinning&&(clearTimeout(this.timer),clearTimeout(this.mousewheelTimer),this.counter=0,this.spinning=!1,this._trigger("stop",e))},_setOption:function(e,t){if("culture"===e||"numberFormat"===e){var i=this._parse(this.element.val());return this.options[e]=t,this.element.val(this._format(i)),void 0}("max"===e||"min"===e||"step"===e)&&"string"==typeof t&&(t=this._parse(t)),"icons"===e&&(this.buttons.first().find(".ui-icon").removeClass(this.options.icons.up).addClass(t.up),this.buttons.last().find(".ui-icon").removeClass(this.options.icons.down).addClass(t.down)),this._super(e,t),"disabled"===e&&(this.widget().toggleClass("ui-state-disabled",!!t),this.element.prop("disabled",!!t),this.buttons.button(t?"disable":"enable"))},_setOptions:s(function(e){this._super(e)}),_parse:function(e){return"string"==typeof e&&""!==e&&(e=window.Globalize&&this.options.numberFormat?Globalize.parseFloat(e,10,this.options.culture):+e),""===e||isNaN(e)?null:e
-},_format:function(e){return""===e?"":window.Globalize&&this.options.numberFormat?Globalize.format(e,this.options.numberFormat,this.options.culture):e},_refresh:function(){this.element.attr({"aria-valuemin":this.options.min,"aria-valuemax":this.options.max,"aria-valuenow":this._parse(this.element.val())})},isValid:function(){var e=this.value();return null===e?!1:e===this._adjustValue(e)},_value:function(e,t){var i;""!==e&&(i=this._parse(e),null!==i&&(t||(i=this._adjustValue(i)),e=this._format(i))),this.element.val(e),this._refresh()},_destroy:function(){this.element.removeClass("ui-spinner-input").prop("disabled",!1).removeAttr("autocomplete").removeAttr("role").removeAttr("aria-valuemin").removeAttr("aria-valuemax").removeAttr("aria-valuenow"),this.uiSpinner.replaceWith(this.element)},stepUp:s(function(e){this._stepUp(e)}),_stepUp:function(e){this._start()&&(this._spin((e||1)*this.options.step),this._stop())},stepDown:s(function(e){this._stepDown(e)}),_stepDown:function(e){this._start()&&(this._spin((e||1)*-this.options.step),this._stop())},pageUp:s(function(e){this._stepUp((e||1)*this.options.page)}),pageDown:s(function(e){this._stepDown((e||1)*this.options.page)}),value:function(e){return arguments.length?(s(this._value).call(this,e),void 0):this._parse(this.element.val())},widget:function(){return this.uiSpinner}}),e.widget("ui.tabs",{version:"1.11.4",delay:300,options:{active:null,collapsible:!1,event:"click",heightStyle:"content",hide:null,show:null,activate:null,beforeActivate:null,beforeLoad:null,load:null},_isLocal:function(){var e=/#.*$/;return function(t){var i,s;t=t.cloneNode(!1),i=t.href.replace(e,""),s=location.href.replace(e,"");try{i=decodeURIComponent(i)}catch(n){}try{s=decodeURIComponent(s)}catch(n){}return t.hash.length>1&&i===s}}(),_create:function(){var t=this,i=this.options;this.running=!1,this.element.addClass("ui-tabs ui-widget ui-widget-content ui-corner-all").toggleClass("ui-tabs-collapsible",i.collapsible),this._processTabs(),i.active=this._initialActive(),e.isArray(i.disabled)&&(i.disabled=e.unique(i.disabled.concat(e.map(this.tabs.filter(".ui-state-disabled"),function(e){return t.tabs.index(e)}))).sort()),this.active=this.options.active!==!1&&this.anchors.length?this._findActive(i.active):e(),this._refresh(),this.active.length&&this.load(i.active)},_initialActive:function(){var t=this.options.active,i=this.options.collapsible,s=location.hash.substring(1);return null===t&&(s&&this.tabs.each(function(i,n){return e(n).attr("aria-controls")===s?(t=i,!1):void 0}),null===t&&(t=this.tabs.index(this.tabs.filter(".ui-tabs-active"))),(null===t||-1===t)&&(t=this.tabs.length?0:!1)),t!==!1&&(t=this.tabs.index(this.tabs.eq(t)),-1===t&&(t=i?!1:0)),!i&&t===!1&&this.anchors.length&&(t=0),t},_getCreateEventData:function(){return{tab:this.active,panel:this.active.length?this._getPanelForTab(this.active):e()}},_tabKeydown:function(t){var i=e(this.document[0].activeElement).closest("li"),s=this.tabs.index(i),n=!0;if(!this._handlePageNav(t)){switch(t.keyCode){case e.ui.keyCode.RIGHT:case e.ui.keyCode.DOWN:s++;break;case e.ui.keyCode.UP:case e.ui.keyCode.LEFT:n=!1,s--;break;case e.ui.keyCode.END:s=this.anchors.length-1;break;case e.ui.keyCode.HOME:s=0;break;case e.ui.keyCode.SPACE:return t.preventDefault(),clearTimeout(this.activating),this._activate(s),void 0;case e.ui.keyCode.ENTER:return t.preventDefault(),clearTimeout(this.activating),this._activate(s===this.options.active?!1:s),void 0;default:return}t.preventDefault(),clearTimeout(this.activating),s=this._focusNextTab(s,n),t.ctrlKey||t.metaKey||(i.attr("aria-selected","false"),this.tabs.eq(s).attr("aria-selected","true"),this.activating=this._delay(function(){this.option("active",s)},this.delay))}},_panelKeydown:function(t){this._handlePageNav(t)||t.ctrlKey&&t.keyCode===e.ui.keyCode.UP&&(t.preventDefault(),this.active.focus())},_handlePageNav:function(t){return t.altKey&&t.keyCode===e.ui.keyCode.PAGE_UP?(this._activate(this._focusNextTab(this.options.active-1,!1)),!0):t.altKey&&t.keyCode===e.ui.keyCode.PAGE_DOWN?(this._activate(this._focusNextTab(this.options.active+1,!0)),!0):void 0},_findNextTab:function(t,i){function s(){return t>n&&(t=0),0>t&&(t=n),t}for(var n=this.tabs.length-1;-1!==e.inArray(s(),this.options.disabled);)t=i?t+1:t-1;return t},_focusNextTab:function(e,t){return e=this._findNextTab(e,t),this.tabs.eq(e).focus(),e},_setOption:function(e,t){return"active"===e?(this._activate(t),void 0):"disabled"===e?(this._setupDisabled(t),void 0):(this._super(e,t),"collapsible"===e&&(this.element.toggleClass("ui-tabs-collapsible",t),t||this.options.active!==!1||this._activate(0)),"event"===e&&this._setupEvents(t),"heightStyle"===e&&this._setupHeightStyle(t),void 0)},_sanitizeSelector:function(e){return e?e.replace(/[!"$%&'()*+,.\/:;<=>?@\[\]\^`{|}~]/g,"\\$&"):""},refresh:function(){var t=this.options,i=this.tablist.children(":has(a[href])");t.disabled=e.map(i.filter(".ui-state-disabled"),function(e){return i.index(e)}),this._processTabs(),t.active!==!1&&this.anchors.length?this.active.length&&!e.contains(this.tablist[0],this.active[0])?this.tabs.length===t.disabled.length?(t.active=!1,this.active=e()):this._activate(this._findNextTab(Math.max(0,t.active-1),!1)):t.active=this.tabs.index(this.active):(t.active=!1,this.active=e()),this._refresh()},_refresh:function(){this._setupDisabled(this.options.disabled),this._setupEvents(this.options.event),this._setupHeightStyle(this.options.heightStyle),this.tabs.not(this.active).attr({"aria-selected":"false","aria-expanded":"false",tabIndex:-1}),this.panels.not(this._getPanelForTab(this.active)).hide().attr({"aria-hidden":"true"}),this.active.length?(this.active.addClass("ui-tabs-active ui-state-active").attr({"aria-selected":"true","aria-expanded":"true",tabIndex:0}),this._getPanelForTab(this.active).show().attr({"aria-hidden":"false"})):this.tabs.eq(0).attr("tabIndex",0)},_processTabs:function(){var t=this,i=this.tabs,s=this.anchors,n=this.panels;this.tablist=this._getList().addClass("ui-tabs-nav ui-helper-reset ui-helper-clearfix ui-widget-header ui-corner-all").attr("role","tablist").delegate("> li","mousedown"+this.eventNamespace,function(t){e(this).is(".ui-state-disabled")&&t.preventDefault()}).delegate(".ui-tabs-anchor","focus"+this.eventNamespace,function(){e(this).closest("li").is(".ui-state-disabled")&&this.blur()}),this.tabs=this.tablist.find("> li:has(a[href])").addClass("ui-state-default ui-corner-top").attr({role:"tab",tabIndex:-1}),this.anchors=this.tabs.map(function(){return e("a",this)[0]}).addClass("ui-tabs-anchor").attr({role:"presentation",tabIndex:-1}),this.panels=e(),this.anchors.each(function(i,s){var n,a,o,r=e(s).uniqueId().attr("id"),h=e(s).closest("li"),l=h.attr("aria-controls");t._isLocal(s)?(n=s.hash,o=n.substring(1),a=t.element.find(t._sanitizeSelector(n))):(o=h.attr("aria-controls")||e({}).uniqueId()[0].id,n="#"+o,a=t.element.find(n),a.length||(a=t._createPanel(o),a.insertAfter(t.panels[i-1]||t.tablist)),a.attr("aria-live","polite")),a.length&&(t.panels=t.panels.add(a)),l&&h.data("ui-tabs-aria-controls",l),h.attr({"aria-controls":o,"aria-labelledby":r}),a.attr("aria-labelledby",r)}),this.panels.addClass("ui-tabs-panel ui-widget-content ui-corner-bottom").attr("role","tabpanel"),i&&(this._off(i.not(this.tabs)),this._off(s.not(this.anchors)),this._off(n.not(this.panels)))},_getList:function(){return this.tablist||this.element.find("ol,ul").eq(0)},_createPanel:function(t){return e("<div>").attr("id",t).addClass("ui-tabs-panel ui-widget-content ui-corner-bottom").data("ui-tabs-destroy",!0)},_setupDisabled:function(t){e.isArray(t)&&(t.length?t.length===this.anchors.length&&(t=!0):t=!1);for(var i,s=0;i=this.tabs[s];s++)t===!0||-1!==e.inArray(s,t)?e(i).addClass("ui-state-disabled").attr("aria-disabled","true"):e(i).removeClass("ui-state-disabled").removeAttr("aria-disabled");this.options.disabled=t},_setupEvents:function(t){var i={};t&&e.each(t.split(" "),function(e,t){i[t]="_eventHandler"}),this._off(this.anchors.add(this.tabs).add(this.panels)),this._on(!0,this.anchors,{click:function(e){e.preventDefault()}}),this._on(this.anchors,i),this._on(this.tabs,{keydown:"_tabKeydown"}),this._on(this.panels,{keydown:"_panelKeydown"}),this._focusable(this.tabs),this._hoverable(this.tabs)},_setupHeightStyle:function(t){var i,s=this.element.parent();"fill"===t?(i=s.height(),i-=this.element.outerHeight()-this.element.height(),this.element.siblings(":visible").each(function(){var t=e(this),s=t.css("position");"absolute"!==s&&"fixed"!==s&&(i-=t.outerHeight(!0))}),this.element.children().not(this.panels).each(function(){i-=e(this).outerHeight(!0)}),this.panels.each(function(){e(this).height(Math.max(0,i-e(this).innerHeight()+e(this).height()))}).css("overflow","auto")):"auto"===t&&(i=0,this.panels.each(function(){i=Math.max(i,e(this).height("").height())}).height(i))},_eventHandler:function(t){var i=this.options,s=this.active,n=e(t.currentTarget),a=n.closest("li"),o=a[0]===s[0],r=o&&i.collapsible,h=r?e():this._getPanelForTab(a),l=s.length?this._getPanelForTab(s):e(),u={oldTab:s,oldPanel:l,newTab:r?e():a,newPanel:h};t.preventDefault(),a.hasClass("ui-state-disabled")||a.hasClass("ui-tabs-loading")||this.running||o&&!i.collapsible||this._trigger("beforeActivate",t,u)===!1||(i.active=r?!1:this.tabs.index(a),this.active=o?e():a,this.xhr&&this.xhr.abort(),l.length||h.length||e.error("jQuery UI Tabs: Mismatching fragment identifier."),h.length&&this.load(this.tabs.index(a),t),this._toggle(t,u))},_toggle:function(t,i){function s(){a.running=!1,a._trigger("activate",t,i)}function n(){i.newTab.closest("li").addClass("ui-tabs-active ui-state-active"),o.length&&a.options.show?a._show(o,a.options.show,s):(o.show(),s())}var a=this,o=i.newPanel,r=i.oldPanel;this.running=!0,r.length&&this.options.hide?this._hide(r,this.options.hide,function(){i.oldTab.closest("li").removeClass("ui-tabs-active ui-state-active"),n()}):(i.oldTab.closest("li").removeClass("ui-tabs-active ui-state-active"),r.hide(),n()),r.attr("aria-hidden","true"),i.oldTab.attr({"aria-selected":"false","aria-expanded":"false"}),o.length&&r.length?i.oldTab.attr("tabIndex",-1):o.length&&this.tabs.filter(function(){return 0===e(this).attr("tabIndex")}).attr("tabIndex",-1),o.attr("aria-hidden","false"),i.newTab.attr({"aria-selected":"true","aria-expanded":"true",tabIndex:0})},_activate:function(t){var i,s=this._findActive(t);s[0]!==this.active[0]&&(s.length||(s=this.active),i=s.find(".ui-tabs-anchor")[0],this._eventHandler({target:i,currentTarget:i,preventDefault:e.noop}))},_findActive:function(t){return t===!1?e():this.tabs.eq(t)},_getIndex:function(e){return"string"==typeof e&&(e=this.anchors.index(this.anchors.filter("[href$='"+e+"']"))),e},_destroy:function(){this.xhr&&this.xhr.abort(),this.element.removeClass("ui-tabs ui-widget ui-widget-content ui-corner-all ui-tabs-collapsible"),this.tablist.removeClass("ui-tabs-nav ui-helper-reset ui-helper-clearfix ui-widget-header ui-corner-all").removeAttr("role"),this.anchors.removeClass("ui-tabs-anchor").removeAttr("role").removeAttr("tabIndex").removeUniqueId(),this.tablist.unbind(this.eventNamespace),this.tabs.add(this.panels).each(function(){e.data(this,"ui-tabs-destroy")?e(this).remove():e(this).removeClass("ui-state-default ui-state-active ui-state-disabled ui-corner-top ui-corner-bottom ui-widget-content ui-tabs-active ui-tabs-panel").removeAttr("tabIndex").removeAttr("aria-live").removeAttr("aria-busy").removeAttr("aria-selected").removeAttr("aria-labelledby").removeAttr("aria-hidden").removeAttr("aria-expanded").removeAttr("role")}),this.tabs.each(function(){var t=e(this),i=t.data("ui-tabs-aria-controls");i?t.attr("aria-controls",i).removeData("ui-tabs-aria-controls"):t.removeAttr("aria-controls")}),this.panels.show(),"content"!==this.options.heightStyle&&this.panels.css("height","")},enable:function(t){var i=this.options.disabled;i!==!1&&(void 0===t?i=!1:(t=this._getIndex(t),i=e.isArray(i)?e.map(i,function(e){return e!==t?e:null}):e.map(this.tabs,function(e,i){return i!==t?i:null})),this._setupDisabled(i))},disable:function(t){var i=this.options.disabled;if(i!==!0){if(void 0===t)i=!0;else{if(t=this._getIndex(t),-1!==e.inArray(t,i))return;i=e.isArray(i)?e.merge([t],i).sort():[t]}this._setupDisabled(i)}},load:function(t,i){t=this._getIndex(t);var s=this,n=this.tabs.eq(t),a=n.find(".ui-tabs-anchor"),o=this._getPanelForTab(n),r={tab:n,panel:o},h=function(e,t){"abort"===t&&s.panels.stop(!1,!0),n.removeClass("ui-tabs-loading"),o.removeAttr("aria-busy"),e===s.xhr&&delete s.xhr};this._isLocal(a[0])||(this.xhr=e.ajax(this._ajaxSettings(a,i,r)),this.xhr&&"canceled"!==this.xhr.statusText&&(n.addClass("ui-tabs-loading"),o.attr("aria-busy","true"),this.xhr.done(function(e,t,n){setTimeout(function(){o.html(e),s._trigger("load",i,r),h(n,t)},1)}).fail(function(e,t){setTimeout(function(){h(e,t)},1)})))},_ajaxSettings:function(t,i,s){var n=this;return{url:t.attr("href"),beforeSend:function(t,a){return n._trigger("beforeLoad",i,e.extend({jqXHR:t,ajaxSettings:a},s))}}},_getPanelForTab:function(t){var i=e(t).attr("aria-controls");return this.element.find(this._sanitizeSelector("#"+i))}}),e.widget("ui.tooltip",{version:"1.11.4",options:{content:function(){var t=e(this).attr("title")||"";return e("<a>").text(t).html()},hide:!0,items:"[title]:not([disabled])",position:{my:"left top+15",at:"left bottom",collision:"flipfit flip"},show:!0,tooltipClass:null,track:!1,close:null,open:null},_addDescribedBy:function(t,i){var s=(t.attr("aria-describedby")||"").split(/\s+/);s.push(i),t.data("ui-tooltip-id",i).attr("aria-describedby",e.trim(s.join(" ")))},_removeDescribedBy:function(t){var i=t.data("ui-tooltip-id"),s=(t.attr("aria-describedby")||"").split(/\s+/),n=e.inArray(i,s);-1!==n&&s.splice(n,1),t.removeData("ui-tooltip-id"),s=e.trim(s.join(" ")),s?t.attr("aria-describedby",s):t.removeAttr("aria-describedby")},_create:function(){this._on({mouseover:"open",focusin:"open"}),this.tooltips={},this.parents={},this.options.disabled&&this._disable(),this.liveRegion=e("<div>").attr({role:"log","aria-live":"assertive","aria-relevant":"additions"}).addClass("ui-helper-hidden-accessible").appendTo(this.document[0].body)},_setOption:function(t,i){var s=this;return"disabled"===t?(this[i?"_disable":"_enable"](),this.options[t]=i,void 0):(this._super(t,i),"content"===t&&e.each(this.tooltips,function(e,t){s._updateContent(t.element)}),void 0)},_disable:function(){var t=this;e.each(this.tooltips,function(i,s){var n=e.Event("blur");n.target=n.currentTarget=s.element[0],t.close(n,!0)}),this.element.find(this.options.items).addBack().each(function(){var t=e(this);t.is("[title]")&&t.data("ui-tooltip-title",t.attr("title")).removeAttr("title")})},_enable:function(){this.element.find(this.options.items).addBack().each(function(){var t=e(this);t.data("ui-tooltip-title")&&t.attr("title",t.data("ui-tooltip-title"))})},open:function(t){var i=this,s=e(t?t.target:this.element).closest(this.options.items);s.length&&!s.data("ui-tooltip-id")&&(s.attr("title")&&s.data("ui-tooltip-title",s.attr("title")),s.data("ui-tooltip-open",!0),t&&"mouseover"===t.type&&s.parents().each(function(){var t,s=e(this);s.data("ui-tooltip-open")&&(t=e.Event("blur"),t.target=t.currentTarget=this,i.close(t,!0)),s.attr("title")&&(s.uniqueId(),i.parents[this.id]={element:this,title:s.attr("title")},s.attr("title",""))}),this._registerCloseHandlers(t,s),this._updateContent(s,t))},_updateContent:function(e,t){var i,s=this.options.content,n=this,a=t?t.type:null;return"string"==typeof s?this._open(t,e,s):(i=s.call(e[0],function(i){n._delay(function(){e.data("ui-tooltip-open")&&(t&&(t.type=a),this._open(t,e,i))})}),i&&this._open(t,e,i),void 0)},_open:function(t,i,s){function n(e){l.of=e,o.is(":hidden")||o.position(l)}var a,o,r,h,l=e.extend({},this.options.position);if(s){if(a=this._find(i))return a.tooltip.find(".ui-tooltip-content").html(s),void 0;i.is("[title]")&&(t&&"mouseover"===t.type?i.attr("title",""):i.removeAttr("title")),a=this._tooltip(i),o=a.tooltip,this._addDescribedBy(i,o.attr("id")),o.find(".ui-tooltip-content").html(s),this.liveRegion.children().hide(),s.clone?(h=s.clone(),h.removeAttr("id").find("[id]").removeAttr("id")):h=s,e("<div>").html(h).appendTo(this.liveRegion),this.options.track&&t&&/^mouse/.test(t.type)?(this._on(this.document,{mousemove:n}),n(t)):o.position(e.extend({of:i},this.options.position)),o.hide(),this._show(o,this.options.show),this.options.show&&this.options.show.delay&&(r=this.delayedShow=setInterval(function(){o.is(":visible")&&(n(l.of),clearInterval(r))},e.fx.interval)),this._trigger("open",t,{tooltip:o})}},_registerCloseHandlers:function(t,i){var s={keyup:function(t){if(t.keyCode===e.ui.keyCode.ESCAPE){var s=e.Event(t);s.currentTarget=i[0],this.close(s,!0)}}};i[0]!==this.element[0]&&(s.remove=function(){this._removeTooltip(this._find(i).tooltip)}),t&&"mouseover"!==t.type||(s.mouseleave="close"),t&&"focusin"!==t.type||(s.focusout="close"),this._on(!0,i,s)},close:function(t){var i,s=this,n=e(t?t.currentTarget:this.element),a=this._find(n);return a?(i=a.tooltip,a.closing||(clearInterval(this.delayedShow),n.data("ui-tooltip-title")&&!n.attr("title")&&n.attr("title",n.data("ui-tooltip-title")),this._removeDescribedBy(n),a.hiding=!0,i.stop(!0),this._hide(i,this.options.hide,function(){s._removeTooltip(e(this))}),n.removeData("ui-tooltip-open"),this._off(n,"mouseleave focusout keyup"),n[0]!==this.element[0]&&this._off(n,"remove"),this._off(this.document,"mousemove"),t&&"mouseleave"===t.type&&e.each(this.parents,function(t,i){e(i.element).attr("title",i.title),delete s.parents[t]}),a.closing=!0,this._trigger("close",t,{tooltip:i}),a.hiding||(a.closing=!1)),void 0):(n.removeData("ui-tooltip-open"),void 0)},_tooltip:function(t){var i=e("<div>").attr("role","tooltip").addClass("ui-tooltip ui-widget ui-corner-all ui-widget-content "+(this.options.tooltipClass||"")),s=i.uniqueId().attr("id");return e("<div>").addClass("ui-tooltip-content").appendTo(i),i.appendTo(this.document[0].body),this.tooltips[s]={element:t,tooltip:i}},_find:function(e){var t=e.data("ui-tooltip-id");return t?this.tooltips[t]:null},_removeTooltip:function(e){e.remove(),delete this.tooltips[e.attr("id")]},_destroy:function(){var t=this;e.each(this.tooltips,function(i,s){var n=e.Event("blur"),a=s.element;n.target=n.currentTarget=a[0],t.close(n,!0),e("#"+i).remove(),a.data("ui-tooltip-title")&&(a.attr("title")||a.attr("title",a.data("ui-tooltip-title")),a.removeData("ui-tooltip-title"))}),this.liveRegion.remove()}});var c="ui-effects-",p=e;e.effects={effect:{}},function(e,t){function i(e,t,i){var s=d[t.type]||{};return null==e?i||!t.def?null:t.def:(e=s.floor?~~e:parseFloat(e),isNaN(e)?t.def:s.mod?(e+s.mod)%s.mod:0>e?0:e>s.max?s.max:e)}function s(i){var s=l(),n=s._rgba=[];return i=i.toLowerCase(),f(h,function(e,a){var o,r=a.re.exec(i),h=r&&a.parse(r),l=a.space||"rgba";return h?(o=s[l](h),s[u[l].cache]=o[u[l].cache],n=s._rgba=o._rgba,!1):t}),n.length?("0,0,0,0"===n.join()&&e.extend(n,a.transparent),s):a[i]}function n(e,t,i){return i=(i+1)%1,1>6*i?e+6*(t-e)*i:1>2*i?t:2>3*i?e+6*(t-e)*(2/3-i):e}var a,o="backgroundColor borderBottomColor borderLeftColor borderRightColor borderTopColor color columnRuleColor outlineColor textDecorationColor textEmphasisColor",r=/^([\-+])=\s*(\d+\.?\d*)/,h=[{re:/rgba?\(\s*(\d{1,3})\s*,\s*(\d{1,3})\s*,\s*(\d{1,3})\s*(?:,\s*(\d?(?:\.\d+)?)\s*)?\)/,parse:function(e){return[e[1],e[2],e[3],e[4]]}},{re:/rgba?\(\s*(\d+(?:\.\d+)?)\%\s*,\s*(\d+(?:\.\d+)?)\%\s*,\s*(\d+(?:\.\d+)?)\%\s*(?:,\s*(\d?(?:\.\d+)?)\s*)?\)/,parse:function(e){return[2.55*e[1],2.55*e[2],2.55*e[3],e[4]]}},{re:/#([a-f0-9]{2})([a-f0-9]{2})([a-f0-9]{2})/,parse:function(e){return[parseInt(e[1],16),parseInt(e[2],16),parseInt(e[3],16)]}},{re:/#([a-f0-9])([a-f0-9])([a-f0-9])/,parse:function(e){return[parseInt(e[1]+e[1],16),parseInt(e[2]+e[2],16),parseInt(e[3]+e[3],16)]}},{re:/hsla?\(\s*(\d+(?:\.\d+)?)\s*,\s*(\d+(?:\.\d+)?)\%\s*,\s*(\d+(?:\.\d+)?)\%\s*(?:,\s*(\d?(?:\.\d+)?)\s*)?\)/,space:"hsla",parse:function(e){return[e[1],e[2]/100,e[3]/100,e[4]]}}],l=e.Color=function(t,i,s,n){return new e.Color.fn.parse(t,i,s,n)},u={rgba:{props:{red:{idx:0,type:"byte"},green:{idx:1,type:"byte"},blue:{idx:2,type:"byte"}}},hsla:{props:{hue:{idx:0,type:"degrees"},saturation:{idx:1,type:"percent"},lightness:{idx:2,type:"percent"}}}},d={"byte":{floor:!0,max:255},percent:{max:1},degrees:{mod:360,floor:!0}},c=l.support={},p=e("<p>")[0],f=e.each;p.style.cssText="background-color:rgba(1,1,1,.5)",c.rgba=p.style.backgroundColor.indexOf("rgba")>-1,f(u,function(e,t){t.cache="_"+e,t.props.alpha={idx:3,type:"percent",def:1}}),l.fn=e.extend(l.prototype,{parse:function(n,o,r,h){if(n===t)return this._rgba=[null,null,null,null],this;(n.jquery||n.nodeType)&&(n=e(n).css(o),o=t);var d=this,c=e.type(n),p=this._rgba=[];return o!==t&&(n=[n,o,r,h],c="array"),"string"===c?this.parse(s(n)||a._default):"array"===c?(f(u.rgba.props,function(e,t){p[t.idx]=i(n[t.idx],t)}),this):"object"===c?(n instanceof l?f(u,function(e,t){n[t.cache]&&(d[t.cache]=n[t.cache].slice())}):f(u,function(t,s){var a=s.cache;f(s.props,function(e,t){if(!d[a]&&s.to){if("alpha"===e||null==n[e])return;d[a]=s.to(d._rgba)}d[a][t.idx]=i(n[e],t,!0)}),d[a]&&0>e.inArray(null,d[a].slice(0,3))&&(d[a][3]=1,s.from&&(d._rgba=s.from(d[a])))}),this):t},is:function(e){var i=l(e),s=!0,n=this;return f(u,function(e,a){var o,r=i[a.cache];return r&&(o=n[a.cache]||a.to&&a.to(n._rgba)||[],f(a.props,function(e,i){return null!=r[i.idx]?s=r[i.idx]===o[i.idx]:t})),s}),s},_space:function(){var e=[],t=this;return f(u,function(i,s){t[s.cache]&&e.push(i)}),e.pop()},transition:function(e,t){var s=l(e),n=s._space(),a=u[n],o=0===this.alpha()?l("transparent"):this,r=o[a.cache]||a.to(o._rgba),h=r.slice();return s=s[a.cache],f(a.props,function(e,n){var a=n.idx,o=r[a],l=s[a],u=d[n.type]||{};null!==l&&(null===o?h[a]=l:(u.mod&&(l-o>u.mod/2?o+=u.mod:o-l>u.mod/2&&(o-=u.mod)),h[a]=i((l-o)*t+o,n)))}),this[n](h)},blend:function(t){if(1===this._rgba[3])return this;var i=this._rgba.slice(),s=i.pop(),n=l(t)._rgba;return l(e.map(i,function(e,t){return(1-s)*n[t]+s*e}))},toRgbaString:function(){var t="rgba(",i=e.map(this._rgba,function(e,t){return null==e?t>2?1:0:e});return 1===i[3]&&(i.pop(),t="rgb("),t+i.join()+")"},toHslaString:function(){var t="hsla(",i=e.map(this.hsla(),function(e,t){return null==e&&(e=t>2?1:0),t&&3>t&&(e=Math.round(100*e)+"%"),e});return 1===i[3]&&(i.pop(),t="hsl("),t+i.join()+")"},toHexString:function(t){var i=this._rgba.slice(),s=i.pop();return t&&i.push(~~(255*s)),"#"+e.map(i,function(e){return e=(e||0).toString(16),1===e.length?"0"+e:e}).join("")},toString:function(){return 0===this._rgba[3]?"transparent":this.toRgbaString()}}),l.fn.parse.prototype=l.fn,u.hsla.to=function(e){if(null==e[0]||null==e[1]||null==e[2])return[null,null,null,e[3]];var t,i,s=e[0]/255,n=e[1]/255,a=e[2]/255,o=e[3],r=Math.max(s,n,a),h=Math.min(s,n,a),l=r-h,u=r+h,d=.5*u;return t=h===r?0:s===r?60*(n-a)/l+360:n===r?60*(a-s)/l+120:60*(s-n)/l+240,i=0===l?0:.5>=d?l/u:l/(2-u),[Math.round(t)%360,i,d,null==o?1:o]},u.hsla.from=function(e){if(null==e[0]||null==e[1]||null==e[2])return[null,null,null,e[3]];var t=e[0]/360,i=e[1],s=e[2],a=e[3],o=.5>=s?s*(1+i):s+i-s*i,r=2*s-o;return[Math.round(255*n(r,o,t+1/3)),Math.round(255*n(r,o,t)),Math.round(255*n(r,o,t-1/3)),a]},f(u,function(s,n){var a=n.props,o=n.cache,h=n.to,u=n.from;l.fn[s]=function(s){if(h&&!this[o]&&(this[o]=h(this._rgba)),s===t)return this[o].slice();var n,r=e.type(s),d="array"===r||"object"===r?s:arguments,c=this[o].slice();return f(a,function(e,t){var s=d["object"===r?e:t.idx];null==s&&(s=c[t.idx]),c[t.idx]=i(s,t)}),u?(n=l(u(c)),n[o]=c,n):l(c)},f(a,function(t,i){l.fn[t]||(l.fn[t]=function(n){var a,o=e.type(n),h="alpha"===t?this._hsla?"hsla":"rgba":s,l=this[h](),u=l[i.idx];return"undefined"===o?u:("function"===o&&(n=n.call(this,u),o=e.type(n)),null==n&&i.empty?this:("string"===o&&(a=r.exec(n),a&&(n=u+parseFloat(a[2])*("+"===a[1]?1:-1))),l[i.idx]=n,this[h](l)))})})}),l.hook=function(t){var i=t.split(" ");f(i,function(t,i){e.cssHooks[i]={set:function(t,n){var a,o,r="";if("transparent"!==n&&("string"!==e.type(n)||(a=s(n)))){if(n=l(a||n),!c.rgba&&1!==n._rgba[3]){for(o="backgroundColor"===i?t.parentNode:t;(""===r||"transparent"===r)&&o&&o.style;)try{r=e.css(o,"backgroundColor"),o=o.parentNode}catch(h){}n=n.blend(r&&"transparent"!==r?r:"_default")}n=n.toRgbaString()}try{t.style[i]=n}catch(h){}}},e.fx.step[i]=function(t){t.colorInit||(t.start=l(t.elem,i),t.end=l(t.end),t.colorInit=!0),e.cssHooks[i].set(t.elem,t.start.transition(t.end,t.pos))}})},l.hook(o),e.cssHooks.borderColor={expand:function(e){var t={};return f(["Top","Right","Bottom","Left"],function(i,s){t["border"+s+"Color"]=e}),t}},a=e.Color.names={aqua:"#00ffff",black:"#000000",blue:"#0000ff",fuchsia:"#ff00ff",gray:"#808080",green:"#008000",lime:"#00ff00",maroon:"#800000",navy:"#000080",olive:"#808000",purple:"#800080",red:"#ff0000",silver:"#c0c0c0",teal:"#008080",white:"#ffffff",yellow:"#ffff00",transparent:[null,null,null,0],_default:"#ffffff"}}(p),function(){function t(t){var i,s,n=t.ownerDocument.defaultView?t.ownerDocument.defaultView.getComputedStyle(t,null):t.currentStyle,a={};if(n&&n.length&&n[0]&&n[n[0]])for(s=n.length;s--;)i=n[s],"string"==typeof n[i]&&(a[e.camelCase(i)]=n[i]);else for(i in n)"string"==typeof n[i]&&(a[i]=n[i]);return a}function i(t,i){var s,a,o={};for(s in i)a=i[s],t[s]!==a&&(n[s]||(e.fx.step[s]||!isNaN(parseFloat(a)))&&(o[s]=a));return o}var s=["add","remove","toggle"],n={border:1,borderBottom:1,borderColor:1,borderLeft:1,borderRight:1,borderTop:1,borderWidth:1,margin:1,padding:1};e.each(["borderLeftStyle","borderRightStyle","borderBottomStyle","borderTopStyle"],function(t,i){e.fx.step[i]=function(e){("none"!==e.end&&!e.setAttr||1===e.pos&&!e.setAttr)&&(p.style(e.elem,i,e.end),e.setAttr=!0)}}),e.fn.addBack||(e.fn.addBack=function(e){return this.add(null==e?this.prevObject:this.prevObject.filter(e))}),e.effects.animateClass=function(n,a,o,r){var h=e.speed(a,o,r);return this.queue(function(){var a,o=e(this),r=o.attr("class")||"",l=h.children?o.find("*").addBack():o;l=l.map(function(){var i=e(this);return{el:i,start:t(this)}}),a=function(){e.each(s,function(e,t){n[t]&&o[t+"Class"](n[t])})},a(),l=l.map(function(){return this.end=t(this.el[0]),this.diff=i(this.start,this.end),this}),o.attr("class",r),l=l.map(function(){var t=this,i=e.Deferred(),s=e.extend({},h,{queue:!1,complete:function(){i.resolve(t)}});return this.el.animate(this.diff,s),i.promise()}),e.when.apply(e,l.get()).done(function(){a(),e.each(arguments,function(){var t=this.el;e.each(this.diff,function(e){t.css(e,"")})}),h.complete.call(o[0])})})},e.fn.extend({addClass:function(t){return function(i,s,n,a){return s?e.effects.animateClass.call(this,{add:i},s,n,a):t.apply(this,arguments)}}(e.fn.addClass),removeClass:function(t){return function(i,s,n,a){return arguments.length>1?e.effects.animateClass.call(this,{remove:i},s,n,a):t.apply(this,arguments)}}(e.fn.removeClass),toggleClass:function(t){return function(i,s,n,a,o){return"boolean"==typeof s||void 0===s?n?e.effects.animateClass.call(this,s?{add:i}:{remove:i},n,a,o):t.apply(this,arguments):e.effects.animateClass.call(this,{toggle:i},s,n,a)}}(e.fn.toggleClass),switchClass:function(t,i,s,n,a){return e.effects.animateClass.call(this,{add:i,remove:t},s,n,a)}})}(),function(){function t(t,i,s,n){return e.isPlainObject(t)&&(i=t,t=t.effect),t={effect:t},null==i&&(i={}),e.isFunction(i)&&(n=i,s=null,i={}),("number"==typeof i||e.fx.speeds[i])&&(n=s,s=i,i={}),e.isFunction(s)&&(n=s,s=null),i&&e.extend(t,i),s=s||i.duration,t.duration=e.fx.off?0:"number"==typeof s?s:s in e.fx.speeds?e.fx.speeds[s]:e.fx.speeds._default,t.complete=n||i.complete,t}function i(t){return!t||"number"==typeof t||e.fx.speeds[t]?!0:"string"!=typeof t||e.effects.effect[t]?e.isFunction(t)?!0:"object"!=typeof t||t.effect?!1:!0:!0}e.extend(e.effects,{version:"1.11.4",save:function(e,t){for(var i=0;t.length>i;i++)null!==t[i]&&e.data(c+t[i],e[0].style[t[i]])},restore:function(e,t){var i,s;for(s=0;t.length>s;s++)null!==t[s]&&(i=e.data(c+t[s]),void 0===i&&(i=""),e.css(t[s],i))},setMode:function(e,t){return"toggle"===t&&(t=e.is(":hidden")?"show":"hide"),t},getBaseline:function(e,t){var i,s;switch(e[0]){case"top":i=0;break;case"middle":i=.5;break;case"bottom":i=1;break;default:i=e[0]/t.height}switch(e[1]){case"left":s=0;break;case"center":s=.5;break;case"right":s=1;break;default:s=e[1]/t.width}return{x:s,y:i}},createWrapper:function(t){if(t.parent().is(".ui-effects-wrapper"))return t.parent();var i={width:t.outerWidth(!0),height:t.outerHeight(!0),"float":t.css("float")},s=e("<div></div>").addClass("ui-effects-wrapper").css({fontSize:"100%",background:"transparent",border:"none",margin:0,padding:0}),n={width:t.width(),height:t.height()},a=document.activeElement;try{a.id}catch(o){a=document.body}return t.wrap(s),(t[0]===a||e.contains(t[0],a))&&e(a).focus(),s=t.parent(),"static"===t.css("position")?(s.css({position:"relative"}),t.css({position:"relative"})):(e.extend(i,{position:t.css("position"),zIndex:t.css("z-index")}),e.each(["top","left","bottom","right"],function(e,s){i[s]=t.css(s),isNaN(parseInt(i[s],10))&&(i[s]="auto")}),t.css({position:"relative",top:0,left:0,right:"auto",bottom:"auto"})),t.css(n),s.css(i).show()},removeWrapper:function(t){var i=document.activeElement;return t.parent().is(".ui-effects-wrapper")&&(t.parent().replaceWith(t),(t[0]===i||e.contains(t[0],i))&&e(i).focus()),t},setTransition:function(t,i,s,n){return n=n||{},e.each(i,function(e,i){var a=t.cssUnit(i);a[0]>0&&(n[i]=a[0]*s+a[1])}),n}}),e.fn.extend({effect:function(){function i(t){function i(){e.isFunction(a)&&a.call(n[0]),e.isFunction(t)&&t()}var n=e(this),a=s.complete,r=s.mode;(n.is(":hidden")?"hide"===r:"show"===r)?(n[r](),i()):o.call(n[0],s,i)}var s=t.apply(this,arguments),n=s.mode,a=s.queue,o=e.effects.effect[s.effect];return e.fx.off||!o?n?this[n](s.duration,s.complete):this.each(function(){s.complete&&s.complete.call(this)}):a===!1?this.each(i):this.queue(a||"fx",i)},show:function(e){return function(s){if(i(s))return e.apply(this,arguments);var n=t.apply(this,arguments);return n.mode="show",this.effect.call(this,n)}}(e.fn.show),hide:function(e){return function(s){if(i(s))return e.apply(this,arguments);var n=t.apply(this,arguments);return n.mode="hide",this.effect.call(this,n)}}(e.fn.hide),toggle:function(e){return function(s){if(i(s)||"boolean"==typeof s)return e.apply(this,arguments);var n=t.apply(this,arguments);return n.mode="toggle",this.effect.call(this,n)}}(e.fn.toggle),cssUnit:function(t){var i=this.css(t),s=[];return e.each(["em","px","%","pt"],function(e,t){i.indexOf(t)>0&&(s=[parseFloat(i),t])}),s}})}(),function(){var t={};e.each(["Quad","Cubic","Quart","Quint","Expo"],function(e,i){t[i]=function(t){return Math.pow(t,e+2)}}),e.extend(t,{Sine:function(e){return 1-Math.cos(e*Math.PI/2)},Circ:function(e){return 1-Math.sqrt(1-e*e)},Elastic:function(e){return 0===e||1===e?e:-Math.pow(2,8*(e-1))*Math.sin((80*(e-1)-7.5)*Math.PI/15)},Back:function(e){return e*e*(3*e-2)},Bounce:function(e){for(var t,i=4;((t=Math.pow(2,--i))-1)/11>e;);return 1/Math.pow(4,3-i)-7.5625*Math.pow((3*t-2)/22-e,2)}}),e.each(t,function(t,i){e.easing["easeIn"+t]=i,e.easing["easeOut"+t]=function(e){return 1-i(1-e)},e.easing["easeInOut"+t]=function(e){return.5>e?i(2*e)/2:1-i(-2*e+2)/2}})}(),e.effects,e.effects.effect.blind=function(t,i){var s,n,a,o=e(this),r=/up|down|vertical/,h=/up|left|vertical|horizontal/,l=["position","top","bottom","left","right","height","width"],u=e.effects.setMode(o,t.mode||"hide"),d=t.direction||"up",c=r.test(d),p=c?"height":"width",f=c?"top":"left",m=h.test(d),g={},v="show"===u;o.parent().is(".ui-effects-wrapper")?e.effects.save(o.parent(),l):e.effects.save(o,l),o.show(),s=e.effects.createWrapper(o).css({overflow:"hidden"}),n=s[p](),a=parseFloat(s.css(f))||0,g[p]=v?n:0,m||(o.css(c?"bottom":"right",0).css(c?"top":"left","auto").css({position:"absolute"}),g[f]=v?a:n+a),v&&(s.css(p,0),m||s.css(f,a+n)),s.animate(g,{duration:t.duration,easing:t.easing,queue:!1,complete:function(){"hide"===u&&o.hide(),e.effects.restore(o,l),e.effects.removeWrapper(o),i()
-}})},e.effects.effect.bounce=function(t,i){var s,n,a,o=e(this),r=["position","top","bottom","left","right","height","width"],h=e.effects.setMode(o,t.mode||"effect"),l="hide"===h,u="show"===h,d=t.direction||"up",c=t.distance,p=t.times||5,f=2*p+(u||l?1:0),m=t.duration/f,g=t.easing,v="up"===d||"down"===d?"top":"left",y="up"===d||"left"===d,b=o.queue(),_=b.length;for((u||l)&&r.push("opacity"),e.effects.save(o,r),o.show(),e.effects.createWrapper(o),c||(c=o["top"===v?"outerHeight":"outerWidth"]()/3),u&&(a={opacity:1},a[v]=0,o.css("opacity",0).css(v,y?2*-c:2*c).animate(a,m,g)),l&&(c/=Math.pow(2,p-1)),a={},a[v]=0,s=0;p>s;s++)n={},n[v]=(y?"-=":"+=")+c,o.animate(n,m,g).animate(a,m,g),c=l?2*c:c/2;l&&(n={opacity:0},n[v]=(y?"-=":"+=")+c,o.animate(n,m,g)),o.queue(function(){l&&o.hide(),e.effects.restore(o,r),e.effects.removeWrapper(o),i()}),_>1&&b.splice.apply(b,[1,0].concat(b.splice(_,f+1))),o.dequeue()},e.effects.effect.clip=function(t,i){var s,n,a,o=e(this),r=["position","top","bottom","left","right","height","width"],h=e.effects.setMode(o,t.mode||"hide"),l="show"===h,u=t.direction||"vertical",d="vertical"===u,c=d?"height":"width",p=d?"top":"left",f={};e.effects.save(o,r),o.show(),s=e.effects.createWrapper(o).css({overflow:"hidden"}),n="IMG"===o[0].tagName?s:o,a=n[c](),l&&(n.css(c,0),n.css(p,a/2)),f[c]=l?a:0,f[p]=l?0:a/2,n.animate(f,{queue:!1,duration:t.duration,easing:t.easing,complete:function(){l||o.hide(),e.effects.restore(o,r),e.effects.removeWrapper(o),i()}})},e.effects.effect.drop=function(t,i){var s,n=e(this),a=["position","top","bottom","left","right","opacity","height","width"],o=e.effects.setMode(n,t.mode||"hide"),r="show"===o,h=t.direction||"left",l="up"===h||"down"===h?"top":"left",u="up"===h||"left"===h?"pos":"neg",d={opacity:r?1:0};e.effects.save(n,a),n.show(),e.effects.createWrapper(n),s=t.distance||n["top"===l?"outerHeight":"outerWidth"](!0)/2,r&&n.css("opacity",0).css(l,"pos"===u?-s:s),d[l]=(r?"pos"===u?"+=":"-=":"pos"===u?"-=":"+=")+s,n.animate(d,{queue:!1,duration:t.duration,easing:t.easing,complete:function(){"hide"===o&&n.hide(),e.effects.restore(n,a),e.effects.removeWrapper(n),i()}})},e.effects.effect.explode=function(t,i){function s(){b.push(this),b.length===d*c&&n()}function n(){p.css({visibility:"visible"}),e(b).remove(),m||p.hide(),i()}var a,o,r,h,l,u,d=t.pieces?Math.round(Math.sqrt(t.pieces)):3,c=d,p=e(this),f=e.effects.setMode(p,t.mode||"hide"),m="show"===f,g=p.show().css("visibility","hidden").offset(),v=Math.ceil(p.outerWidth()/c),y=Math.ceil(p.outerHeight()/d),b=[];for(a=0;d>a;a++)for(h=g.top+a*y,u=a-(d-1)/2,o=0;c>o;o++)r=g.left+o*v,l=o-(c-1)/2,p.clone().appendTo("body").wrap("<div></div>").css({position:"absolute",visibility:"visible",left:-o*v,top:-a*y}).parent().addClass("ui-effects-explode").css({position:"absolute",overflow:"hidden",width:v,height:y,left:r+(m?l*v:0),top:h+(m?u*y:0),opacity:m?0:1}).animate({left:r+(m?0:l*v),top:h+(m?0:u*y),opacity:m?1:0},t.duration||500,t.easing,s)},e.effects.effect.fade=function(t,i){var s=e(this),n=e.effects.setMode(s,t.mode||"toggle");s.animate({opacity:n},{queue:!1,duration:t.duration,easing:t.easing,complete:i})},e.effects.effect.fold=function(t,i){var s,n,a=e(this),o=["position","top","bottom","left","right","height","width"],r=e.effects.setMode(a,t.mode||"hide"),h="show"===r,l="hide"===r,u=t.size||15,d=/([0-9]+)%/.exec(u),c=!!t.horizFirst,p=h!==c,f=p?["width","height"]:["height","width"],m=t.duration/2,g={},v={};e.effects.save(a,o),a.show(),s=e.effects.createWrapper(a).css({overflow:"hidden"}),n=p?[s.width(),s.height()]:[s.height(),s.width()],d&&(u=parseInt(d[1],10)/100*n[l?0:1]),h&&s.css(c?{height:0,width:u}:{height:u,width:0}),g[f[0]]=h?n[0]:u,v[f[1]]=h?n[1]:0,s.animate(g,m,t.easing).animate(v,m,t.easing,function(){l&&a.hide(),e.effects.restore(a,o),e.effects.removeWrapper(a),i()})},e.effects.effect.highlight=function(t,i){var s=e(this),n=["backgroundImage","backgroundColor","opacity"],a=e.effects.setMode(s,t.mode||"show"),o={backgroundColor:s.css("backgroundColor")};"hide"===a&&(o.opacity=0),e.effects.save(s,n),s.show().css({backgroundImage:"none",backgroundColor:t.color||"#ffff99"}).animate(o,{queue:!1,duration:t.duration,easing:t.easing,complete:function(){"hide"===a&&s.hide(),e.effects.restore(s,n),i()}})},e.effects.effect.size=function(t,i){var s,n,a,o=e(this),r=["position","top","bottom","left","right","width","height","overflow","opacity"],h=["position","top","bottom","left","right","overflow","opacity"],l=["width","height","overflow"],u=["fontSize"],d=["borderTopWidth","borderBottomWidth","paddingTop","paddingBottom"],c=["borderLeftWidth","borderRightWidth","paddingLeft","paddingRight"],p=e.effects.setMode(o,t.mode||"effect"),f=t.restore||"effect"!==p,m=t.scale||"both",g=t.origin||["middle","center"],v=o.css("position"),y=f?r:h,b={height:0,width:0,outerHeight:0,outerWidth:0};"show"===p&&o.show(),s={height:o.height(),width:o.width(),outerHeight:o.outerHeight(),outerWidth:o.outerWidth()},"toggle"===t.mode&&"show"===p?(o.from=t.to||b,o.to=t.from||s):(o.from=t.from||("show"===p?b:s),o.to=t.to||("hide"===p?b:s)),a={from:{y:o.from.height/s.height,x:o.from.width/s.width},to:{y:o.to.height/s.height,x:o.to.width/s.width}},("box"===m||"both"===m)&&(a.from.y!==a.to.y&&(y=y.concat(d),o.from=e.effects.setTransition(o,d,a.from.y,o.from),o.to=e.effects.setTransition(o,d,a.to.y,o.to)),a.from.x!==a.to.x&&(y=y.concat(c),o.from=e.effects.setTransition(o,c,a.from.x,o.from),o.to=e.effects.setTransition(o,c,a.to.x,o.to))),("content"===m||"both"===m)&&a.from.y!==a.to.y&&(y=y.concat(u).concat(l),o.from=e.effects.setTransition(o,u,a.from.y,o.from),o.to=e.effects.setTransition(o,u,a.to.y,o.to)),e.effects.save(o,y),o.show(),e.effects.createWrapper(o),o.css("overflow","hidden").css(o.from),g&&(n=e.effects.getBaseline(g,s),o.from.top=(s.outerHeight-o.outerHeight())*n.y,o.from.left=(s.outerWidth-o.outerWidth())*n.x,o.to.top=(s.outerHeight-o.to.outerHeight)*n.y,o.to.left=(s.outerWidth-o.to.outerWidth)*n.x),o.css(o.from),("content"===m||"both"===m)&&(d=d.concat(["marginTop","marginBottom"]).concat(u),c=c.concat(["marginLeft","marginRight"]),l=r.concat(d).concat(c),o.find("*[width]").each(function(){var i=e(this),s={height:i.height(),width:i.width(),outerHeight:i.outerHeight(),outerWidth:i.outerWidth()};f&&e.effects.save(i,l),i.from={height:s.height*a.from.y,width:s.width*a.from.x,outerHeight:s.outerHeight*a.from.y,outerWidth:s.outerWidth*a.from.x},i.to={height:s.height*a.to.y,width:s.width*a.to.x,outerHeight:s.height*a.to.y,outerWidth:s.width*a.to.x},a.from.y!==a.to.y&&(i.from=e.effects.setTransition(i,d,a.from.y,i.from),i.to=e.effects.setTransition(i,d,a.to.y,i.to)),a.from.x!==a.to.x&&(i.from=e.effects.setTransition(i,c,a.from.x,i.from),i.to=e.effects.setTransition(i,c,a.to.x,i.to)),i.css(i.from),i.animate(i.to,t.duration,t.easing,function(){f&&e.effects.restore(i,l)})})),o.animate(o.to,{queue:!1,duration:t.duration,easing:t.easing,complete:function(){0===o.to.opacity&&o.css("opacity",o.from.opacity),"hide"===p&&o.hide(),e.effects.restore(o,y),f||("static"===v?o.css({position:"relative",top:o.to.top,left:o.to.left}):e.each(["top","left"],function(e,t){o.css(t,function(t,i){var s=parseInt(i,10),n=e?o.to.left:o.to.top;return"auto"===i?n+"px":s+n+"px"})})),e.effects.removeWrapper(o),i()}})},e.effects.effect.scale=function(t,i){var s=e(this),n=e.extend(!0,{},t),a=e.effects.setMode(s,t.mode||"effect"),o=parseInt(t.percent,10)||(0===parseInt(t.percent,10)?0:"hide"===a?0:100),r=t.direction||"both",h=t.origin,l={height:s.height(),width:s.width(),outerHeight:s.outerHeight(),outerWidth:s.outerWidth()},u={y:"horizontal"!==r?o/100:1,x:"vertical"!==r?o/100:1};n.effect="size",n.queue=!1,n.complete=i,"effect"!==a&&(n.origin=h||["middle","center"],n.restore=!0),n.from=t.from||("show"===a?{height:0,width:0,outerHeight:0,outerWidth:0}:l),n.to={height:l.height*u.y,width:l.width*u.x,outerHeight:l.outerHeight*u.y,outerWidth:l.outerWidth*u.x},n.fade&&("show"===a&&(n.from.opacity=0,n.to.opacity=1),"hide"===a&&(n.from.opacity=1,n.to.opacity=0)),s.effect(n)},e.effects.effect.puff=function(t,i){var s=e(this),n=e.effects.setMode(s,t.mode||"hide"),a="hide"===n,o=parseInt(t.percent,10)||150,r=o/100,h={height:s.height(),width:s.width(),outerHeight:s.outerHeight(),outerWidth:s.outerWidth()};e.extend(t,{effect:"scale",queue:!1,fade:!0,mode:n,complete:i,percent:a?o:100,from:a?h:{height:h.height*r,width:h.width*r,outerHeight:h.outerHeight*r,outerWidth:h.outerWidth*r}}),s.effect(t)},e.effects.effect.pulsate=function(t,i){var s,n=e(this),a=e.effects.setMode(n,t.mode||"show"),o="show"===a,r="hide"===a,h=o||"hide"===a,l=2*(t.times||5)+(h?1:0),u=t.duration/l,d=0,c=n.queue(),p=c.length;for((o||!n.is(":visible"))&&(n.css("opacity",0).show(),d=1),s=1;l>s;s++)n.animate({opacity:d},u,t.easing),d=1-d;n.animate({opacity:d},u,t.easing),n.queue(function(){r&&n.hide(),i()}),p>1&&c.splice.apply(c,[1,0].concat(c.splice(p,l+1))),n.dequeue()},e.effects.effect.shake=function(t,i){var s,n=e(this),a=["position","top","bottom","left","right","height","width"],o=e.effects.setMode(n,t.mode||"effect"),r=t.direction||"left",h=t.distance||20,l=t.times||3,u=2*l+1,d=Math.round(t.duration/u),c="up"===r||"down"===r?"top":"left",p="up"===r||"left"===r,f={},m={},g={},v=n.queue(),y=v.length;for(e.effects.save(n,a),n.show(),e.effects.createWrapper(n),f[c]=(p?"-=":"+=")+h,m[c]=(p?"+=":"-=")+2*h,g[c]=(p?"-=":"+=")+2*h,n.animate(f,d,t.easing),s=1;l>s;s++)n.animate(m,d,t.easing).animate(g,d,t.easing);n.animate(m,d,t.easing).animate(f,d/2,t.easing).queue(function(){"hide"===o&&n.hide(),e.effects.restore(n,a),e.effects.removeWrapper(n),i()}),y>1&&v.splice.apply(v,[1,0].concat(v.splice(y,u+1))),n.dequeue()},e.effects.effect.slide=function(t,i){var s,n=e(this),a=["position","top","bottom","left","right","width","height"],o=e.effects.setMode(n,t.mode||"show"),r="show"===o,h=t.direction||"left",l="up"===h||"down"===h?"top":"left",u="up"===h||"left"===h,d={};e.effects.save(n,a),n.show(),s=t.distance||n["top"===l?"outerHeight":"outerWidth"](!0),e.effects.createWrapper(n).css({overflow:"hidden"}),r&&n.css(l,u?isNaN(s)?"-"+s:-s:s),d[l]=(r?u?"+=":"-=":u?"-=":"+=")+s,n.animate(d,{queue:!1,duration:t.duration,easing:t.easing,complete:function(){"hide"===o&&n.hide(),e.effects.restore(n,a),e.effects.removeWrapper(n),i()}})},e.effects.effect.transfer=function(t,i){var s=e(this),n=e(t.to),a="fixed"===n.css("position"),o=e("body"),r=a?o.scrollTop():0,h=a?o.scrollLeft():0,l=n.offset(),u={top:l.top-r,left:l.left-h,height:n.innerHeight(),width:n.innerWidth()},d=s.offset(),c=e("<div class='ui-effects-transfer'></div>").appendTo(document.body).addClass(t.className).css({top:d.top-r,left:d.left-h,height:s.innerHeight(),width:s.innerWidth(),position:a?"fixed":"absolute"}).animate(u,t.duration,t.easing,function(){c.remove(),i()})}});</script>
+!function(t){"use strict";"function"==typeof define&&define.amd?define(["jquery"],t):t(jQuery)}(function(V){"use strict";V.ui=V.ui||{};V.ui.version="1.13.2";var n,i=0,a=Array.prototype.hasOwnProperty,r=Array.prototype.slice;V.cleanData=(n=V.cleanData,function(t){for(var e,i,s=0;null!=(i=t[s]);s++)(e=V._data(i,"events"))&&e.remove&&V(i).triggerHandler("remove");n(t)}),V.widget=function(t,i,e){var s,n,o,a={},r=t.split(".")[0],l=r+"-"+(t=t.split(".")[1]);return e||(e=i,i=V.Widget),Array.isArray(e)&&(e=V.extend.apply(null,[{}].concat(e))),V.expr.pseudos[l.toLowerCase()]=function(t){return!!V.data(t,l)},V[r]=V[r]||{},s=V[r][t],n=V[r][t]=function(t,e){if(!this||!this._createWidget)return new n(t,e);arguments.length&&this._createWidget(t,e)},V.extend(n,s,{version:e.version,_proto:V.extend({},e),_childConstructors:[]}),(o=new i).options=V.widget.extend({},o.options),V.each(e,function(e,s){function n(){return i.prototype[e].apply(this,arguments)}function o(t){return i.prototype[e].apply(this,t)}a[e]="function"==typeof s?function(){var t,e=this._super,i=this._superApply;return this._super=n,this._superApply=o,t=s.apply(this,arguments),this._super=e,this._superApply=i,t}:s}),n.prototype=V.widget.extend(o,{widgetEventPrefix:s&&o.widgetEventPrefix||t},a,{constructor:n,namespace:r,widgetName:t,widgetFullName:l}),s?(V.each(s._childConstructors,function(t,e){var i=e.prototype;V.widget(i.namespace+"."+i.widgetName,n,e._proto)}),delete s._childConstructors):i._childConstructors.push(n),V.widget.bridge(t,n),n},V.widget.extend=function(t){for(var e,i,s=r.call(arguments,1),n=0,o=s.length;n<o;n++)for(e in s[n])i=s[n][e],a.call(s[n],e)&&void 0!==i&&(V.isPlainObject(i)?t[e]=V.isPlainObject(t[e])?V.widget.extend({},t[e],i):V.widget.extend({},i):t[e]=i);return t},V.widget.bridge=function(o,e){var a=e.prototype.widgetFullName||o;V.fn[o]=function(i){var t="string"==typeof i,s=r.call(arguments,1),n=this;return t?this.length||"instance"!==i?this.each(function(){var t,e=V.data(this,a);return"instance"===i?(n=e,!1):e?"function"!=typeof e[i]||"_"===i.charAt(0)?V.error("no such method '"+i+"' for "+o+" widget instance"):(t=e[i].apply(e,s))!==e&&void 0!==t?(n=t&&t.jquery?n.pushStack(t.get()):t,!1):void 0:V.error("cannot call methods on "+o+" prior to initialization; attempted to call method '"+i+"'")}):n=void 0:(s.length&&(i=V.widget.extend.apply(null,[i].concat(s))),this.each(function(){var t=V.data(this,a);t?(t.option(i||{}),t._init&&t._init()):V.data(this,a,new e(i,this))})),n}},V.Widget=function(){},V.Widget._childConstructors=[],V.Widget.prototype={widgetName:"widget",widgetEventPrefix:"",defaultElement:"<div>",options:{classes:{},disabled:!1,create:null},_createWidget:function(t,e){e=V(e||this.defaultElement||this)[0],this.element=V(e),this.uuid=i++,this.eventNamespace="."+this.widgetName+this.uuid,this.bindings=V(),this.hoverable=V(),this.focusable=V(),this.classesElementLookup={},e!==this&&(V.data(e,this.widgetFullName,this),this._on(!0,this.element,{remove:function(t){t.target===e&&this.destroy()}}),this.document=V(e.style?e.ownerDocument:e.document||e),this.window=V(this.document[0].defaultView||this.document[0].parentWindow)),this.options=V.widget.extend({},this.options,this._getCreateOptions(),t),this._create(),this.options.disabled&&this._setOptionDisabled(this.options.disabled),this._trigger("create",null,this._getCreateEventData()),this._init()},_getCreateOptions:function(){return{}},_getCreateEventData:V.noop,_create:V.noop,_init:V.noop,destroy:function(){var i=this;this._destroy(),V.each(this.classesElementLookup,function(t,e){i._removeClass(e,t)}),this.element.off(this.eventNamespace).removeData(this.widgetFullName),this.widget().off(this.eventNamespace).removeAttr("aria-disabled"),this.bindings.off(this.eventNamespace)},_destroy:V.noop,widget:function(){return this.element},option:function(t,e){var i,s,n,o=t;if(0===arguments.length)return V.widget.extend({},this.options);if("string"==typeof t)if(o={},t=(i=t.split(".")).shift(),i.length){for(s=o[t]=V.widget.extend({},this.options[t]),n=0;n<i.length-1;n++)s[i[n]]=s[i[n]]||{},s=s[i[n]];if(t=i.pop(),1===arguments.length)return void 0===s[t]?null:s[t];s[t]=e}else{if(1===arguments.length)return void 0===this.options[t]?null:this.options[t];o[t]=e}return this._setOptions(o),this},_setOptions:function(t){for(var e in t)this._setOption(e,t[e]);return this},_setOption:function(t,e){return"classes"===t&&this._setOptionClasses(e),this.options[t]=e,"disabled"===t&&this._setOptionDisabled(e),this},_setOptionClasses:function(t){var e,i,s;for(e in t)s=this.classesElementLookup[e],t[e]!==this.options.classes[e]&&s&&s.length&&(i=V(s.get()),this._removeClass(s,e),i.addClass(this._classes({element:i,keys:e,classes:t,add:!0})))},_setOptionDisabled:function(t){this._toggleClass(this.widget(),this.widgetFullName+"-disabled",null,!!t),t&&(this._removeClass(this.hoverable,null,"ui-state-hover"),this._removeClass(this.focusable,null,"ui-state-focus"))},enable:function(){return this._setOptions({disabled:!1})},disable:function(){return this._setOptions({disabled:!0})},_classes:function(n){var o=[],a=this;function t(t,e){for(var i,s=0;s<t.length;s++)i=a.classesElementLookup[t[s]]||V(),i=n.add?(function(){var i=[];n.element.each(function(t,e){V.map(a.classesElementLookup,function(t){return t}).some(function(t){return t.is(e)})||i.push(e)}),a._on(V(i),{remove:"_untrackClassesElement"})}(),V(V.uniqueSort(i.get().concat(n.element.get())))):V(i.not(n.element).get()),a.classesElementLookup[t[s]]=i,o.push(t[s]),e&&n.classes[t[s]]&&o.push(n.classes[t[s]])}return(n=V.extend({element:this.element,classes:this.options.classes||{}},n)).keys&&t(n.keys.match(/\S+/g)||[],!0),n.extra&&t(n.extra.match(/\S+/g)||[]),o.join(" ")},_untrackClassesElement:function(i){var s=this;V.each(s.classesElementLookup,function(t,e){-1!==V.inArray(i.target,e)&&(s.classesElementLookup[t]=V(e.not(i.target).get()))}),this._off(V(i.target))},_removeClass:function(t,e,i){return this._toggleClass(t,e,i,!1)},_addClass:function(t,e,i){return this._toggleClass(t,e,i,!0)},_toggleClass:function(t,e,i,s){var n="string"==typeof t||null===t,i={extra:n?e:i,keys:n?t:e,element:n?this.element:t,add:s="boolean"==typeof s?s:i};return i.element.toggleClass(this._classes(i),s),this},_on:function(n,o,t){var a,r=this;"boolean"!=typeof n&&(t=o,o=n,n=!1),t?(o=a=V(o),this.bindings=this.bindings.add(o)):(t=o,o=this.element,a=this.widget()),V.each(t,function(t,e){function i(){if(n||!0!==r.options.disabled&&!V(this).hasClass("ui-state-disabled"))return("string"==typeof e?r[e]:e).apply(r,arguments)}"string"!=typeof e&&(i.guid=e.guid=e.guid||i.guid||V.guid++);var s=t.match(/^([\w:-]*)\s*(.*)$/),t=s[1]+r.eventNamespace,s=s[2];s?a.on(t,s,i):o.on(t,i)})},_off:function(t,e){e=(e||"").split(" ").join(this.eventNamespace+" ")+this.eventNamespace,t.off(e),this.bindings=V(this.bindings.not(t).get()),this.focusable=V(this.focusable.not(t).get()),this.hoverable=V(this.hoverable.not(t).get())},_delay:function(t,e){var i=this;return setTimeout(function(){return("string"==typeof t?i[t]:t).apply(i,arguments)},e||0)},_hoverable:function(t){this.hoverable=this.hoverable.add(t),this._on(t,{mouseenter:function(t){this._addClass(V(t.currentTarget),null,"ui-state-hover")},mouseleave:function(t){this._removeClass(V(t.currentTarget),null,"ui-state-hover")}})},_focusable:function(t){this.focusable=this.focusable.add(t),this._on(t,{focusin:function(t){this._addClass(V(t.currentTarget),null,"ui-state-focus")},focusout:function(t){this._removeClass(V(t.currentTarget),null,"ui-state-focus")}})},_trigger:function(t,e,i){var s,n,o=this.options[t];if(i=i||{},(e=V.Event(e)).type=(t===this.widgetEventPrefix?t:this.widgetEventPrefix+t).toLowerCase(),e.target=this.element[0],n=e.originalEvent)for(s in n)s in e||(e[s]=n[s]);return this.element.trigger(e,i),!("function"==typeof o&&!1===o.apply(this.element[0],[e].concat(i))||e.isDefaultPrevented())}},V.each({show:"fadeIn",hide:"fadeOut"},function(o,a){V.Widget.prototype["_"+o]=function(e,t,i){var s,n=(t="string"==typeof t?{effect:t}:t)?!0!==t&&"number"!=typeof t&&t.effect||a:o;"number"==typeof(t=t||{})?t={duration:t}:!0===t&&(t={}),s=!V.isEmptyObject(t),t.complete=i,t.delay&&e.delay(t.delay),s&&V.effects&&V.effects.effect[n]?e[o](t):n!==o&&e[n]?e[n](t.duration,t.easing,i):e.queue(function(t){V(this)[o](),i&&i.call(e[0]),t()})}});var s,x,k,o,l,h,c,u,C;V.widget;function D(t,e,i){return[parseFloat(t[0])*(u.test(t[0])?e/100:1),parseFloat(t[1])*(u.test(t[1])?i/100:1)]}function I(t,e){return parseInt(V.css(t,e),10)||0}function T(t){return null!=t&&t===t.window}x=Math.max,k=Math.abs,o=/left|center|right/,l=/top|center|bottom/,h=/[\+\-]\d+(\.[\d]+)?%?/,c=/^\w+/,u=/%$/,C=V.fn.position,V.position={scrollbarWidth:function(){if(void 0!==s)return s;var t,e=V("<div style='display:block;position:absolute;width:200px;height:200px;overflow:hidden;'><div style='height:300px;width:auto;'></div></div>"),i=e.children()[0];return V("body").append(e),t=i.offsetWidth,e.css("overflow","scroll"),t===(i=i.offsetWidth)&&(i=e[0].clientWidth),e.remove(),s=t-i},getScrollInfo:function(t){var e=t.isWindow||t.isDocument?"":t.element.css("overflow-x"),i=t.isWindow||t.isDocument?"":t.element.css("overflow-y"),e="scroll"===e||"auto"===e&&t.width<t.element[0].scrollWidth;return{width:"scroll"===i||"auto"===i&&t.height<t.element[0].scrollHeight?V.position.scrollbarWidth():0,height:e?V.position.scrollbarWidth():0}},getWithinInfo:function(t){var e=V(t||window),i=T(e[0]),s=!!e[0]&&9===e[0].nodeType;return{element:e,isWindow:i,isDocument:s,offset:!i&&!s?V(t).offset():{left:0,top:0},scrollLeft:e.scrollLeft(),scrollTop:e.scrollTop(),width:e.outerWidth(),height:e.outerHeight()}}},V.fn.position=function(u){if(!u||!u.of)return C.apply(this,arguments);var d,p,f,g,m,t,_="string"==typeof(u=V.extend({},u)).of?V(document).find(u.of):V(u.of),v=V.position.getWithinInfo(u.within),b=V.position.getScrollInfo(v),y=(u.collision||"flip").split(" "),w={},e=9===(t=(e=_)[0]).nodeType?{width:e.width(),height:e.height(),offset:{top:0,left:0}}:T(t)?{width:e.width(),height:e.height(),offset:{top:e.scrollTop(),left:e.scrollLeft()}}:t.preventDefault?{width:0,height:0,offset:{top:t.pageY,left:t.pageX}}:{width:e.outerWidth(),height:e.outerHeight(),offset:e.offset()};return _[0].preventDefault&&(u.at="left top"),p=e.width,f=e.height,m=V.extend({},g=e.offset),V.each(["my","at"],function(){var t,e,i=(u[this]||"").split(" ");(i=1===i.length?o.test(i[0])?i.concat(["center"]):l.test(i[0])?["center"].concat(i):["center","center"]:i)[0]=o.test(i[0])?i[0]:"center",i[1]=l.test(i[1])?i[1]:"center",t=h.exec(i[0]),e=h.exec(i[1]),w[this]=[t?t[0]:0,e?e[0]:0],u[this]=[c.exec(i[0])[0],c.exec(i[1])[0]]}),1===y.length&&(y[1]=y[0]),"right"===u.at[0]?m.left+=p:"center"===u.at[0]&&(m.left+=p/2),"bottom"===u.at[1]?m.top+=f:"center"===u.at[1]&&(m.top+=f/2),d=D(w.at,p,f),m.left+=d[0],m.top+=d[1],this.each(function(){var i,t,a=V(this),r=a.outerWidth(),l=a.outerHeight(),e=I(this,"marginLeft"),s=I(this,"marginTop"),n=r+e+I(this,"marginRight")+b.width,o=l+s+I(this,"marginBottom")+b.height,h=V.extend({},m),c=D(w.my,a.outerWidth(),a.outerHeight());"right"===u.my[0]?h.left-=r:"center"===u.my[0]&&(h.left-=r/2),"bottom"===u.my[1]?h.top-=l:"center"===u.my[1]&&(h.top-=l/2),h.left+=c[0],h.top+=c[1],i={marginLeft:e,marginTop:s},V.each(["left","top"],function(t,e){V.ui.position[y[t]]&&V.ui.position[y[t]][e](h,{targetWidth:p,targetHeight:f,elemWidth:r,elemHeight:l,collisionPosition:i,collisionWidth:n,collisionHeight:o,offset:[d[0]+c[0],d[1]+c[1]],my:u.my,at:u.at,within:v,elem:a})}),u.using&&(t=function(t){var e=g.left-h.left,i=e+p-r,s=g.top-h.top,n=s+f-l,o={target:{element:_,left:g.left,top:g.top,width:p,height:f},element:{element:a,left:h.left,top:h.top,width:r,height:l},horizontal:i<0?"left":0<e?"right":"center",vertical:n<0?"top":0<s?"bottom":"middle"};p<r&&k(e+i)<p&&(o.horizontal="center"),f<l&&k(s+n)<f&&(o.vertical="middle"),x(k(e),k(i))>x(k(s),k(n))?o.important="horizontal":o.important="vertical",u.using.call(this,t,o)}),a.offset(V.extend(h,{using:t}))})},V.ui.position={fit:{left:function(t,e){var i=e.within,s=i.isWindow?i.scrollLeft:i.offset.left,n=i.width,o=t.left-e.collisionPosition.marginLeft,a=s-o,r=o+e.collisionWidth-n-s;e.collisionWidth>n?0<a&&r<=0?(i=t.left+a+e.collisionWidth-n-s,t.left+=a-i):t.left=!(0<r&&a<=0)&&r<a?s+n-e.collisionWidth:s:0<a?t.left+=a:0<r?t.left-=r:t.left=x(t.left-o,t.left)},top:function(t,e){var i=e.within,s=i.isWindow?i.scrollTop:i.offset.top,n=e.within.height,o=t.top-e.collisionPosition.marginTop,a=s-o,r=o+e.collisionHeight-n-s;e.collisionHeight>n?0<a&&r<=0?(i=t.top+a+e.collisionHeight-n-s,t.top+=a-i):t.top=!(0<r&&a<=0)&&r<a?s+n-e.collisionHeight:s:0<a?t.top+=a:0<r?t.top-=r:t.top=x(t.top-o,t.top)}},flip:{left:function(t,e){var i=e.within,s=i.offset.left+i.scrollLeft,n=i.width,o=i.isWindow?i.scrollLeft:i.offset.left,a=t.left-e.collisionPosition.marginLeft,r=a-o,l=a+e.collisionWidth-n-o,h="left"===e.my[0]?-e.elemWidth:"right"===e.my[0]?e.elemWidth:0,i="left"===e.at[0]?e.targetWidth:"right"===e.at[0]?-e.targetWidth:0,a=-2*e.offset[0];r<0?((s=t.left+h+i+a+e.collisionWidth-n-s)<0||s<k(r))&&(t.left+=h+i+a):0<l&&(0<(o=t.left-e.collisionPosition.marginLeft+h+i+a-o)||k(o)<l)&&(t.left+=h+i+a)},top:function(t,e){var i=e.within,s=i.offset.top+i.scrollTop,n=i.height,o=i.isWindow?i.scrollTop:i.offset.top,a=t.top-e.collisionPosition.marginTop,r=a-o,l=a+e.collisionHeight-n-o,h="top"===e.my[1]?-e.elemHeight:"bottom"===e.my[1]?e.elemHeight:0,i="top"===e.at[1]?e.targetHeight:"bottom"===e.at[1]?-e.targetHeight:0,a=-2*e.offset[1];r<0?((s=t.top+h+i+a+e.collisionHeight-n-s)<0||s<k(r))&&(t.top+=h+i+a):0<l&&(0<(o=t.top-e.collisionPosition.marginTop+h+i+a-o)||k(o)<l)&&(t.top+=h+i+a)}},flipfit:{left:function(){V.ui.position.flip.left.apply(this,arguments),V.ui.position.fit.left.apply(this,arguments)},top:function(){V.ui.position.flip.top.apply(this,arguments),V.ui.position.fit.top.apply(this,arguments)}}};V.ui.position,V.extend(V.expr.pseudos,{data:V.expr.createPseudo?V.expr.createPseudo(function(e){return function(t){return!!V.data(t,e)}}):function(t,e,i){return!!V.data(t,i[3])}}),V.fn.extend({disableSelection:(t="onselectstart"in document.createElement("div")?"selectstart":"mousedown",function(){return this.on(t+".ui-disableSelection",function(t){t.preventDefault()})}),enableSelection:function(){return this.off(".ui-disableSelection")}});var t,d=V,p={},e=p.toString,f=/^([\-+])=\s*(\d+\.?\d*)/,g=[{re:/rgba?\(\s*(\d{1,3})\s*,\s*(\d{1,3})\s*,\s*(\d{1,3})\s*(?:,\s*(\d?(?:\.\d+)?)\s*)?\)/,parse:function(t){return[t[1],t[2],t[3],t[4]]}},{re:/rgba?\(\s*(\d+(?:\.\d+)?)\%\s*,\s*(\d+(?:\.\d+)?)\%\s*,\s*(\d+(?:\.\d+)?)\%\s*(?:,\s*(\d?(?:\.\d+)?)\s*)?\)/,parse:function(t){return[2.55*t[1],2.55*t[2],2.55*t[3],t[4]]}},{re:/#([a-f0-9]{2})([a-f0-9]{2})([a-f0-9]{2})([a-f0-9]{2})?/,parse:function(t){return[parseInt(t[1],16),parseInt(t[2],16),parseInt(t[3],16),t[4]?(parseInt(t[4],16)/255).toFixed(2):1]}},{re:/#([a-f0-9])([a-f0-9])([a-f0-9])([a-f0-9])?/,parse:function(t){return[parseInt(t[1]+t[1],16),parseInt(t[2]+t[2],16),parseInt(t[3]+t[3],16),t[4]?(parseInt(t[4]+t[4],16)/255).toFixed(2):1]}},{re:/hsla?\(\s*(\d+(?:\.\d+)?)\s*,\s*(\d+(?:\.\d+)?)\%\s*,\s*(\d+(?:\.\d+)?)\%\s*(?:,\s*(\d?(?:\.\d+)?)\s*)?\)/,space:"hsla",parse:function(t){return[t[1],t[2]/100,t[3]/100,t[4]]}}],m=d.Color=function(t,e,i,s){return new d.Color.fn.parse(t,e,i,s)},_={rgba:{props:{red:{idx:0,type:"byte"},green:{idx:1,type:"byte"},blue:{idx:2,type:"byte"}}},hsla:{props:{hue:{idx:0,type:"degrees"},saturation:{idx:1,type:"percent"},lightness:{idx:2,type:"percent"}}}},v={byte:{floor:!0,max:255},percent:{max:1},degrees:{mod:360,floor:!0}},b=m.support={},y=d("<p>")[0],w=d.each;function P(t){return null==t?t+"":"object"==typeof t?p[e.call(t)]||"object":typeof t}function M(t,e,i){var s=v[e.type]||{};return null==t?i||!e.def?null:e.def:(t=s.floor?~~t:parseFloat(t),isNaN(t)?e.def:s.mod?(t+s.mod)%s.mod:Math.min(s.max,Math.max(0,t)))}function S(s){var n=m(),o=n._rgba=[];return s=s.toLowerCase(),w(g,function(t,e){var i=e.re.exec(s),i=i&&e.parse(i),e=e.space||"rgba";if(i)return i=n[e](i),n[_[e].cache]=i[_[e].cache],o=n._rgba=i._rgba,!1}),o.length?("0,0,0,0"===o.join()&&d.extend(o,B.transparent),n):B[s]}function H(t,e,i){return 6*(i=(i+1)%1)<1?t+(e-t)*i*6:2*i<1?e:3*i<2?t+(e-t)*(2/3-i)*6:t}y.style.cssText="background-color:rgba(1,1,1,.5)",b.rgba=-1<y.style.backgroundColor.indexOf("rgba"),w(_,function(t,e){e.cache="_"+t,e.props.alpha={idx:3,type:"percent",def:1}}),d.each("Boolean Number String Function Array Date RegExp Object Error Symbol".split(" "),function(t,e){p["[object "+e+"]"]=e.toLowerCase()}),(m.fn=d.extend(m.prototype,{parse:function(n,t,e,i){if(void 0===n)return this._rgba=[null,null,null,null],this;(n.jquery||n.nodeType)&&(n=d(n).css(t),t=void 0);var o=this,s=P(n),a=this._rgba=[];return void 0!==t&&(n=[n,t,e,i],s="array"),"string"===s?this.parse(S(n)||B._default):"array"===s?(w(_.rgba.props,function(t,e){a[e.idx]=M(n[e.idx],e)}),this):"object"===s?(w(_,n instanceof m?function(t,e){n[e.cache]&&(o[e.cache]=n[e.cache].slice())}:function(t,i){var s=i.cache;w(i.props,function(t,e){if(!o[s]&&i.to){if("alpha"===t||null==n[t])return;o[s]=i.to(o._rgba)}o[s][e.idx]=M(n[t],e,!0)}),o[s]&&d.inArray(null,o[s].slice(0,3))<0&&(null==o[s][3]&&(o[s][3]=1),i.from&&(o._rgba=i.from(o[s])))}),this):void 0},is:function(t){var n=m(t),o=!0,a=this;return w(_,function(t,e){var i,s=n[e.cache];return s&&(i=a[e.cache]||e.to&&e.to(a._rgba)||[],w(e.props,function(t,e){if(null!=s[e.idx])return o=s[e.idx]===i[e.idx]})),o}),o},_space:function(){var i=[],s=this;return w(_,function(t,e){s[e.cache]&&i.push(t)}),i.pop()},transition:function(t,a){var e=(h=m(t))._space(),i=_[e],t=0===this.alpha()?m("transparent"):this,r=t[i.cache]||i.to(t._rgba),l=r.slice(),h=h[i.cache];return w(i.props,function(t,e){var i=e.idx,s=r[i],n=h[i],o=v[e.type]||{};null!==n&&(null===s?l[i]=n:(o.mod&&(n-s>o.mod/2?s+=o.mod:s-n>o.mod/2&&(s-=o.mod)),l[i]=M((n-s)*a+s,e)))}),this[e](l)},blend:function(t){if(1===this._rgba[3])return this;var e=this._rgba.slice(),i=e.pop(),s=m(t)._rgba;return m(d.map(e,function(t,e){return(1-i)*s[e]+i*t}))},toRgbaString:function(){var t="rgba(",e=d.map(this._rgba,function(t,e){return null!=t?t:2<e?1:0});return 1===e[3]&&(e.pop(),t="rgb("),t+e.join()+")"},toHslaString:function(){var t="hsla(",e=d.map(this.hsla(),function(t,e){return null==t&&(t=2<e?1:0),t=e&&e<3?Math.round(100*t)+"%":t});return 1===e[3]&&(e.pop(),t="hsl("),t+e.join()+")"},toHexString:function(t){var e=this._rgba.slice(),i=e.pop();return t&&e.push(~~(255*i)),"#"+d.map(e,function(t){return 1===(t=(t||0).toString(16)).length?"0"+t:t}).join("")},toString:function(){return 0===this._rgba[3]?"transparent":this.toRgbaString()}})).parse.prototype=m.fn,_.hsla.to=function(t){if(null==t[0]||null==t[1]||null==t[2])return[null,null,null,t[3]];var e=t[0]/255,i=t[1]/255,s=t[2]/255,n=t[3],o=Math.max(e,i,s),a=Math.min(e,i,s),r=o-a,l=o+a,t=.5*l,i=a===o?0:e===o?60*(i-s)/r+360:i===o?60*(s-e)/r+120:60*(e-i)/r+240,l=0==r?0:t<=.5?r/l:r/(2-l);return[Math.round(i)%360,l,t,null==n?1:n]},_.hsla.from=function(t){if(null==t[0]||null==t[1]||null==t[2])return[null,null,null,t[3]];var e=t[0]/360,i=t[1],s=t[2],t=t[3],i=s<=.5?s*(1+i):s+i-s*i,s=2*s-i;return[Math.round(255*H(s,i,e+1/3)),Math.round(255*H(s,i,e)),Math.round(255*H(s,i,e-1/3)),t]},w(_,function(l,t){var e=t.props,o=t.cache,a=t.to,r=t.from;m.fn[l]=function(t){if(a&&!this[o]&&(this[o]=a(this._rgba)),void 0===t)return this[o].slice();var i=P(t),s="array"===i||"object"===i?t:arguments,n=this[o].slice();return w(e,function(t,e){t=s["object"===i?t:e.idx];null==t&&(t=n[e.idx]),n[e.idx]=M(t,e)}),r?((t=m(r(n)))[o]=n,t):m(n)},w(e,function(a,r){m.fn[a]||(m.fn[a]=function(t){var e,i=P(t),s="alpha"===a?this._hsla?"hsla":"rgba":l,n=this[s](),o=n[r.idx];return"undefined"===i?o:("function"===i&&(i=P(t=t.call(this,o))),null==t&&r.empty?this:("string"===i&&(e=f.exec(t))&&(t=o+parseFloat(e[2])*("+"===e[1]?1:-1)),n[r.idx]=t,this[s](n)))})})}),(m.hook=function(t){t=t.split(" ");w(t,function(t,o){d.cssHooks[o]={set:function(t,e){var i,s,n="";if("transparent"!==e&&("string"!==P(e)||(i=S(e)))){if(e=m(i||e),!b.rgba&&1!==e._rgba[3]){for(s="backgroundColor"===o?t.parentNode:t;(""===n||"transparent"===n)&&s&&s.style;)try{n=d.css(s,"backgroundColor"),s=s.parentNode}catch(t){}e=e.blend(n&&"transparent"!==n?n:"_default")}e=e.toRgbaString()}try{t.style[o]=e}catch(t){}}},d.fx.step[o]=function(t){t.colorInit||(t.start=m(t.elem,o),t.end=m(t.end),t.colorInit=!0),d.cssHooks[o].set(t.elem,t.start.transition(t.end,t.pos))}})})("backgroundColor borderBottomColor borderLeftColor borderRightColor borderTopColor color columnRuleColor outlineColor textDecorationColor textEmphasisColor"),d.cssHooks.borderColor={expand:function(i){var s={};return w(["Top","Right","Bottom","Left"],function(t,e){s["border"+e+"Color"]=i}),s}};var z,A,O,N,E,W,F,L,R,Y,B=d.Color.names={aqua:"#00ffff",black:"#000000",blue:"#0000ff",fuchsia:"#ff00ff",gray:"#808080",green:"#008000",lime:"#00ff00",maroon:"#800000",navy:"#000080",olive:"#808000",purple:"#800080",red:"#ff0000",silver:"#c0c0c0",teal:"#008080",white:"#ffffff",yellow:"#ffff00",transparent:[null,null,null,0],_default:"#ffffff"},j="ui-effects-",q="ui-effects-style",K="ui-effects-animated";function U(t){var e,i,s=t.ownerDocument.defaultView?t.ownerDocument.defaultView.getComputedStyle(t,null):t.currentStyle,n={};if(s&&s.length&&s[0]&&s[s[0]])for(i=s.length;i--;)"string"==typeof s[e=s[i]]&&(n[e.replace(/-([\da-z])/gi,function(t,e){return e.toUpperCase()})]=s[e]);else for(e in s)"string"==typeof s[e]&&(n[e]=s[e]);return n}function X(t,e,i,s){return t={effect:t=V.isPlainObject(t)?(e=t).effect:t},"function"==typeof(e=null==e?{}:e)&&(s=e,i=null,e={}),"number"!=typeof e&&!V.fx.speeds[e]||(s=i,i=e,e={}),"function"==typeof i&&(s=i,i=null),e&&V.extend(t,e),i=i||e.duration,t.duration=V.fx.off?0:"number"==typeof i?i:i in V.fx.speeds?V.fx.speeds[i]:V.fx.speeds._default,t.complete=s||e.complete,t}function $(t){return!t||"number"==typeof t||V.fx.speeds[t]||("string"==typeof t&&!V.effects.effect[t]||("function"==typeof t||"object"==typeof t&&!t.effect))}function G(t,e){var i=e.outerWidth(),e=e.outerHeight(),t=/^rect\((-?\d*\.?\d*px|-?\d+%|auto),?\s*(-?\d*\.?\d*px|-?\d+%|auto),?\s*(-?\d*\.?\d*px|-?\d+%|auto),?\s*(-?\d*\.?\d*px|-?\d+%|auto)\)$/.exec(t)||["",0,i,e,0];return{top:parseFloat(t[1])||0,right:"auto"===t[2]?i:parseFloat(t[2]),bottom:"auto"===t[3]?e:parseFloat(t[3]),left:parseFloat(t[4])||0}}V.effects={effect:{}},N=["add","remove","toggle"],E={border:1,borderBottom:1,borderColor:1,borderLeft:1,borderRight:1,borderTop:1,borderWidth:1,margin:1,padding:1},V.each(["borderLeftStyle","borderRightStyle","borderBottomStyle","borderTopStyle"],function(t,e){V.fx.step[e]=function(t){("none"!==t.end&&!t.setAttr||1===t.pos&&!t.setAttr)&&(d.style(t.elem,e,t.end),t.setAttr=!0)}}),V.fn.addBack||(V.fn.addBack=function(t){return this.add(null==t?this.prevObject:this.prevObject.filter(t))}),V.effects.animateClass=function(n,t,e,i){var o=V.speed(t,e,i);return this.queue(function(){var i=V(this),t=i.attr("class")||"",e=(e=o.children?i.find("*").addBack():i).map(function(){return{el:V(this),start:U(this)}}),s=function(){V.each(N,function(t,e){n[e]&&i[e+"Class"](n[e])})};s(),e=e.map(function(){return this.end=U(this.el[0]),this.diff=function(t,e){var i,s,n={};for(i in e)s=e[i],t[i]!==s&&(E[i]||!V.fx.step[i]&&isNaN(parseFloat(s))||(n[i]=s));return n}(this.start,this.end),this}),i.attr("class",t),e=e.map(function(){var t=this,e=V.Deferred(),i=V.extend({},o,{queue:!1,complete:function(){e.resolve(t)}});return this.el.animate(this.diff,i),e.promise()}),V.when.apply(V,e.get()).done(function(){s(),V.each(arguments,function(){var e=this.el;V.each(this.diff,function(t){e.css(t,"")})}),o.complete.call(i[0])})})},V.fn.extend({addClass:(O=V.fn.addClass,function(t,e,i,s){return e?V.effects.animateClass.call(this,{add:t},e,i,s):O.apply(this,arguments)}),removeClass:(A=V.fn.removeClass,function(t,e,i,s){return 1<arguments.length?V.effects.animateClass.call(this,{remove:t},e,i,s):A.apply(this,arguments)}),toggleClass:(z=V.fn.toggleClass,function(t,e,i,s,n){return"boolean"==typeof e||void 0===e?i?V.effects.animateClass.call(this,e?{add:t}:{remove:t},i,s,n):z.apply(this,arguments):V.effects.animateClass.call(this,{toggle:t},e,i,s)}),switchClass:function(t,e,i,s,n){return V.effects.animateClass.call(this,{add:e,remove:t},i,s,n)}}),V.expr&&V.expr.pseudos&&V.expr.pseudos.animated&&(V.expr.pseudos.animated=(W=V.expr.pseudos.animated,function(t){return!!V(t).data(K)||W(t)})),!1!==V.uiBackCompat&&V.extend(V.effects,{save:function(t,e){for(var i=0,s=e.length;i<s;i++)null!==e[i]&&t.data(j+e[i],t[0].style[e[i]])},restore:function(t,e){for(var i,s=0,n=e.length;s<n;s++)null!==e[s]&&(i=t.data(j+e[s]),t.css(e[s],i))},setMode:function(t,e){return e="toggle"===e?t.is(":hidden")?"show":"hide":e},createWrapper:function(i){if(i.parent().is(".ui-effects-wrapper"))return i.parent();var s={width:i.outerWidth(!0),height:i.outerHeight(!0),float:i.css("float")},t=V("<div></div>").addClass("ui-effects-wrapper").css({fontSize:"100%",background:"transparent",border:"none",margin:0,padding:0}),e={width:i.width(),height:i.height()},n=document.activeElement;try{n.id}catch(t){n=document.body}return i.wrap(t),i[0]!==n&&!V.contains(i[0],n)||V(n).trigger("focus"),t=i.parent(),"static"===i.css("position")?(t.css({position:"relative"}),i.css({position:"relative"})):(V.extend(s,{position:i.css("position"),zIndex:i.css("z-index")}),V.each(["top","left","bottom","right"],function(t,e){s[e]=i.css(e),isNaN(parseInt(s[e],10))&&(s[e]="auto")}),i.css({position:"relative",top:0,left:0,right:"auto",bottom:"auto"})),i.css(e),t.css(s).show()},removeWrapper:function(t){var e=document.activeElement;return t.parent().is(".ui-effects-wrapper")&&(t.parent().replaceWith(t),t[0]!==e&&!V.contains(t[0],e)||V(e).trigger("focus")),t}}),V.extend(V.effects,{version:"1.13.2",define:function(t,e,i){return i||(i=e,e="effect"),V.effects.effect[t]=i,V.effects.effect[t].mode=e,i},scaledDimensions:function(t,e,i){if(0===e)return{height:0,width:0,outerHeight:0,outerWidth:0};var s="horizontal"!==i?(e||100)/100:1,e="vertical"!==i?(e||100)/100:1;return{height:t.height()*e,width:t.width()*s,outerHeight:t.outerHeight()*e,outerWidth:t.outerWidth()*s}},clipToBox:function(t){return{width:t.clip.right-t.clip.left,height:t.clip.bottom-t.clip.top,left:t.clip.left,top:t.clip.top}},unshift:function(t,e,i){var s=t.queue();1<e&&s.splice.apply(s,[1,0].concat(s.splice(e,i))),t.dequeue()},saveStyle:function(t){t.data(q,t[0].style.cssText)},restoreStyle:function(t){t[0].style.cssText=t.data(q)||"",t.removeData(q)},mode:function(t,e){t=t.is(":hidden");return"toggle"===e&&(e=t?"show":"hide"),e=(t?"hide"===e:"show"===e)?"none":e},getBaseline:function(t,e){var i,s;switch(t[0]){case"top":i=0;break;case"middle":i=.5;break;case"bottom":i=1;break;default:i=t[0]/e.height}switch(t[1]){case"left":s=0;break;case"center":s=.5;break;case"right":s=1;break;default:s=t[1]/e.width}return{x:s,y:i}},createPlaceholder:function(t){var e,i=t.css("position"),s=t.position();return t.css({marginTop:t.css("marginTop"),marginBottom:t.css("marginBottom"),marginLeft:t.css("marginLeft"),marginRight:t.css("marginRight")}).outerWidth(t.outerWidth()).outerHeight(t.outerHeight()),/^(static|relative)/.test(i)&&(i="absolute",e=V("<"+t[0].nodeName+">").insertAfter(t).css({display:/^(inline|ruby)/.test(t.css("display"))?"inline-block":"block",visibility:"hidden",marginTop:t.css("marginTop"),marginBottom:t.css("marginBottom"),marginLeft:t.css("marginLeft"),marginRight:t.css("marginRight"),float:t.css("float")}).outerWidth(t.outerWidth()).outerHeight(t.outerHeight()).addClass("ui-effects-placeholder"),t.data(j+"placeholder",e)),t.css({position:i,left:s.left,top:s.top}),e},removePlaceholder:function(t){var e=j+"placeholder",i=t.data(e);i&&(i.remove(),t.removeData(e))},cleanUp:function(t){V.effects.restoreStyle(t),V.effects.removePlaceholder(t)},setTransition:function(s,t,n,o){return o=o||{},V.each(t,function(t,e){var i=s.cssUnit(e);0<i[0]&&(o[e]=i[0]*n+i[1])}),o}}),V.fn.extend({effect:function(){function t(t){var e=V(this),i=V.effects.mode(e,r)||o;e.data(K,!0),l.push(i),o&&("show"===i||i===o&&"hide"===i)&&e.show(),o&&"none"===i||V.effects.saveStyle(e),"function"==typeof t&&t()}var s=X.apply(this,arguments),n=V.effects.effect[s.effect],o=n.mode,e=s.queue,i=e||"fx",a=s.complete,r=s.mode,l=[];return V.fx.off||!n?r?this[r](s.duration,a):this.each(function(){a&&a.call(this)}):!1===e?this.each(t).each(h):this.queue(i,t).queue(i,h);function h(t){var e=V(this);function i(){"function"==typeof a&&a.call(e[0]),"function"==typeof t&&t()}s.mode=l.shift(),!1===V.uiBackCompat||o?"none"===s.mode?(e[r](),i()):n.call(e[0],s,function(){e.removeData(K),V.effects.cleanUp(e),"hide"===s.mode&&e.hide(),i()}):(e.is(":hidden")?"hide"===r:"show"===r)?(e[r](),i()):n.call(e[0],s,i)}},show:(R=V.fn.show,function(t){if($(t))return R.apply(this,arguments);t=X.apply(this,arguments);return t.mode="show",this.effect.call(this,t)}),hide:(L=V.fn.hide,function(t){if($(t))return L.apply(this,arguments);t=X.apply(this,arguments);return t.mode="hide",this.effect.call(this,t)}),toggle:(F=V.fn.toggle,function(t){if($(t)||"boolean"==typeof t)return F.apply(this,arguments);t=X.apply(this,arguments);return t.mode="toggle",this.effect.call(this,t)}),cssUnit:function(t){var i=this.css(t),s=[];return V.each(["em","px","%","pt"],function(t,e){0<i.indexOf(e)&&(s=[parseFloat(i),e])}),s},cssClip:function(t){return t?this.css("clip","rect("+t.top+"px "+t.right+"px "+t.bottom+"px "+t.left+"px)"):G(this.css("clip"),this)},transfer:function(t,e){var i=V(this),s=V(t.to),n="fixed"===s.css("position"),o=V("body"),a=n?o.scrollTop():0,r=n?o.scrollLeft():0,o=s.offset(),o={top:o.top-a,left:o.left-r,height:s.innerHeight(),width:s.innerWidth()},s=i.offset(),l=V("<div class='ui-effects-transfer'></div>");l.appendTo("body").addClass(t.className).css({top:s.top-a,left:s.left-r,height:i.innerHeight(),width:i.innerWidth(),position:n?"fixed":"absolute"}).animate(o,t.duration,t.easing,function(){l.remove(),"function"==typeof e&&e()})}}),V.fx.step.clip=function(t){t.clipInit||(t.start=V(t.elem).cssClip(),"string"==typeof t.end&&(t.end=G(t.end,t.elem)),t.clipInit=!0),V(t.elem).cssClip({top:t.pos*(t.end.top-t.start.top)+t.start.top,right:t.pos*(t.end.right-t.start.right)+t.start.right,bottom:t.pos*(t.end.bottom-t.start.bottom)+t.start.bottom,left:t.pos*(t.end.left-t.start.left)+t.start.left})},Y={},V.each(["Quad","Cubic","Quart","Quint","Expo"],function(e,t){Y[t]=function(t){return Math.pow(t,e+2)}}),V.extend(Y,{Sine:function(t){return 1-Math.cos(t*Math.PI/2)},Circ:function(t){return 1-Math.sqrt(1-t*t)},Elastic:function(t){return 0===t||1===t?t:-Math.pow(2,8*(t-1))*Math.sin((80*(t-1)-7.5)*Math.PI/15)},Back:function(t){return t*t*(3*t-2)},Bounce:function(t){for(var e,i=4;t<((e=Math.pow(2,--i))-1)/11;);return 1/Math.pow(4,3-i)-7.5625*Math.pow((3*e-2)/22-t,2)}}),V.each(Y,function(t,e){V.easing["easeIn"+t]=e,V.easing["easeOut"+t]=function(t){return 1-e(1-t)},V.easing["easeInOut"+t]=function(t){return t<.5?e(2*t)/2:1-e(-2*t+2)/2}});y=V.effects,V.effects.define("blind","hide",function(t,e){var i={up:["bottom","top"],vertical:["bottom","top"],down:["top","bottom"],left:["right","left"],horizontal:["right","left"],right:["left","right"]},s=V(this),n=t.direction||"up",o=s.cssClip(),a={clip:V.extend({},o)},r=V.effects.createPlaceholder(s);a.clip[i[n][0]]=a.clip[i[n][1]],"show"===t.mode&&(s.cssClip(a.clip),r&&r.css(V.effects.clipToBox(a)),a.clip=o),r&&r.animate(V.effects.clipToBox(a),t.duration,t.easing),s.animate(a,{queue:!1,duration:t.duration,easing:t.easing,complete:e})}),V.effects.define("bounce",function(t,e){var i,s,n=V(this),o=t.mode,a="hide"===o,r="show"===o,l=t.direction||"up",h=t.distance,c=t.times||5,o=2*c+(r||a?1:0),u=t.duration/o,d=t.easing,p="up"===l||"down"===l?"top":"left",f="up"===l||"left"===l,g=0,t=n.queue().length;for(V.effects.createPlaceholder(n),l=n.css(p),h=h||n["top"==p?"outerHeight":"outerWidth"]()/3,r&&((s={opacity:1})[p]=l,n.css("opacity",0).css(p,f?2*-h:2*h).animate(s,u,d)),a&&(h/=Math.pow(2,c-1)),(s={})[p]=l;g<c;g++)(i={})[p]=(f?"-=":"+=")+h,n.animate(i,u,d).animate(s,u,d),h=a?2*h:h/2;a&&((i={opacity:0})[p]=(f?"-=":"+=")+h,n.animate(i,u,d)),n.queue(e),V.effects.unshift(n,t,1+o)}),V.effects.define("clip","hide",function(t,e){var i={},s=V(this),n=t.direction||"vertical",o="both"===n,a=o||"horizontal"===n,o=o||"vertical"===n,n=s.cssClip();i.clip={top:o?(n.bottom-n.top)/2:n.top,right:a?(n.right-n.left)/2:n.right,bottom:o?(n.bottom-n.top)/2:n.bottom,left:a?(n.right-n.left)/2:n.left},V.effects.createPlaceholder(s),"show"===t.mode&&(s.cssClip(i.clip),i.clip=n),s.animate(i,{queue:!1,duration:t.duration,easing:t.easing,complete:e})}),V.effects.define("drop","hide",function(t,e){var i=V(this),s="show"===t.mode,n=t.direction||"left",o="up"===n||"down"===n?"top":"left",a="up"===n||"left"===n?"-=":"+=",r="+="==a?"-=":"+=",l={opacity:0};V.effects.createPlaceholder(i),n=t.distance||i["top"==o?"outerHeight":"outerWidth"](!0)/2,l[o]=a+n,s&&(i.css(l),l[o]=r+n,l.opacity=1),i.animate(l,{queue:!1,duration:t.duration,easing:t.easing,complete:e})}),V.effects.define("explode","hide",function(t,e){var i,s,n,o,a,r,l=t.pieces?Math.round(Math.sqrt(t.pieces)):3,h=l,c=V(this),u="show"===t.mode,d=c.show().css("visibility","hidden").offset(),p=Math.ceil(c.outerWidth()/h),f=Math.ceil(c.outerHeight()/l),g=[];function m(){g.push(this),g.length===l*h&&(c.css({visibility:"visible"}),V(g).remove(),e())}for(i=0;i<l;i++)for(o=d.top+i*f,r=i-(l-1)/2,s=0;s<h;s++)n=d.left+s*p,a=s-(h-1)/2,c.clone().appendTo("body").wrap("<div></div>").css({position:"absolute",visibility:"visible",left:-s*p,top:-i*f}).parent().addClass("ui-effects-explode").css({position:"absolute",overflow:"hidden",width:p,height:f,left:n+(u?a*p:0),top:o+(u?r*f:0),opacity:u?0:1}).animate({left:n+(u?0:a*p),top:o+(u?0:r*f),opacity:u?1:0},t.duration||500,t.easing,m)}),V.effects.define("fade","toggle",function(t,e){var i="show"===t.mode;V(this).css("opacity",i?0:1).animate({opacity:i?1:0},{queue:!1,duration:t.duration,easing:t.easing,complete:e})}),V.effects.define("fold","hide",function(e,t){var i=V(this),s=e.mode,n="show"===s,o="hide"===s,a=e.size||15,r=/([0-9]+)%/.exec(a),l=!!e.horizFirst?["right","bottom"]:["bottom","right"],h=e.duration/2,c=V.effects.createPlaceholder(i),u=i.cssClip(),d={clip:V.extend({},u)},p={clip:V.extend({},u)},f=[u[l[0]],u[l[1]]],s=i.queue().length;r&&(a=parseInt(r[1],10)/100*f[o?0:1]),d.clip[l[0]]=a,p.clip[l[0]]=a,p.clip[l[1]]=0,n&&(i.cssClip(p.clip),c&&c.css(V.effects.clipToBox(p)),p.clip=u),i.queue(function(t){c&&c.animate(V.effects.clipToBox(d),h,e.easing).animate(V.effects.clipToBox(p),h,e.easing),t()}).animate(d,h,e.easing).animate(p,h,e.easing).queue(t),V.effects.unshift(i,s,4)}),V.effects.define("highlight","show",function(t,e){var i=V(this),s={backgroundColor:i.css("backgroundColor")};"hide"===t.mode&&(s.opacity=0),V.effects.saveStyle(i),i.css({backgroundImage:"none",backgroundColor:t.color||"#ffff99"}).animate(s,{queue:!1,duration:t.duration,easing:t.easing,complete:e})}),V.effects.define("size",function(s,e){var n,i=V(this),t=["fontSize"],o=["borderTopWidth","borderBottomWidth","paddingTop","paddingBottom"],a=["borderLeftWidth","borderRightWidth","paddingLeft","paddingRight"],r=s.mode,l="effect"!==r,h=s.scale||"both",c=s.origin||["middle","center"],u=i.css("position"),d=i.position(),p=V.effects.scaledDimensions(i),f=s.from||p,g=s.to||V.effects.scaledDimensions(i,0);V.effects.createPlaceholder(i),"show"===r&&(r=f,f=g,g=r),n={from:{y:f.height/p.height,x:f.width/p.width},to:{y:g.height/p.height,x:g.width/p.width}},"box"!==h&&"both"!==h||(n.from.y!==n.to.y&&(f=V.effects.setTransition(i,o,n.from.y,f),g=V.effects.setTransition(i,o,n.to.y,g)),n.from.x!==n.to.x&&(f=V.effects.setTransition(i,a,n.from.x,f),g=V.effects.setTransition(i,a,n.to.x,g))),"content"!==h&&"both"!==h||n.from.y!==n.to.y&&(f=V.effects.setTransition(i,t,n.from.y,f),g=V.effects.setTransition(i,t,n.to.y,g)),c&&(c=V.effects.getBaseline(c,p),f.top=(p.outerHeight-f.outerHeight)*c.y+d.top,f.left=(p.outerWidth-f.outerWidth)*c.x+d.left,g.top=(p.outerHeight-g.outerHeight)*c.y+d.top,g.left=(p.outerWidth-g.outerWidth)*c.x+d.left),delete f.outerHeight,delete f.outerWidth,i.css(f),"content"!==h&&"both"!==h||(o=o.concat(["marginTop","marginBottom"]).concat(t),a=a.concat(["marginLeft","marginRight"]),i.find("*[width]").each(function(){var t=V(this),e=V.effects.scaledDimensions(t),i={height:e.height*n.from.y,width:e.width*n.from.x,outerHeight:e.outerHeight*n.from.y,outerWidth:e.outerWidth*n.from.x},e={height:e.height*n.to.y,width:e.width*n.to.x,outerHeight:e.height*n.to.y,outerWidth:e.width*n.to.x};n.from.y!==n.to.y&&(i=V.effects.setTransition(t,o,n.from.y,i),e=V.effects.setTransition(t,o,n.to.y,e)),n.from.x!==n.to.x&&(i=V.effects.setTransition(t,a,n.from.x,i),e=V.effects.setTransition(t,a,n.to.x,e)),l&&V.effects.saveStyle(t),t.css(i),t.animate(e,s.duration,s.easing,function(){l&&V.effects.restoreStyle(t)})})),i.animate(g,{queue:!1,duration:s.duration,easing:s.easing,complete:function(){var t=i.offset();0===g.opacity&&i.css("opacity",f.opacity),l||(i.css("position","static"===u?"relative":u).offset(t),V.effects.saveStyle(i)),e()}})}),V.effects.define("scale",function(t,e){var i=V(this),s=t.mode,s=parseInt(t.percent,10)||(0===parseInt(t.percent,10)||"effect"!==s?0:100),s=V.extend(!0,{from:V.effects.scaledDimensions(i),to:V.effects.scaledDimensions(i,s,t.direction||"both"),origin:t.origin||["middle","center"]},t);t.fade&&(s.from.opacity=1,s.to.opacity=0),V.effects.effect.size.call(this,s,e)}),V.effects.define("puff","hide",function(t,e){t=V.extend(!0,{},t,{fade:!0,percent:parseInt(t.percent,10)||150});V.effects.effect.scale.call(this,t,e)}),V.effects.define("pulsate","show",function(t,e){var i=V(this),s=t.mode,n="show"===s,o=2*(t.times||5)+(n||"hide"===s?1:0),a=t.duration/o,r=0,l=1,s=i.queue().length;for(!n&&i.is(":visible")||(i.css("opacity",0).show(),r=1);l<o;l++)i.animate({opacity:r},a,t.easing),r=1-r;i.animate({opacity:r},a,t.easing),i.queue(e),V.effects.unshift(i,s,1+o)}),V.effects.define("shake",function(t,e){var i=1,s=V(this),n=t.direction||"left",o=t.distance||20,a=t.times||3,r=2*a+1,l=Math.round(t.duration/r),h="up"===n||"down"===n?"top":"left",c="up"===n||"left"===n,u={},d={},p={},n=s.queue().length;for(V.effects.createPlaceholder(s),u[h]=(c?"-=":"+=")+o,d[h]=(c?"+=":"-=")+2*o,p[h]=(c?"-=":"+=")+2*o,s.animate(u,l,t.easing);i<a;i++)s.animate(d,l,t.easing).animate(p,l,t.easing);s.animate(d,l,t.easing).animate(u,l/2,t.easing).queue(e),V.effects.unshift(s,n,1+r)}),V.effects.define("slide","show",function(t,e){var i,s,n=V(this),o={up:["bottom","top"],down:["top","bottom"],left:["right","left"],right:["left","right"]},a=t.mode,r=t.direction||"left",l="up"===r||"down"===r?"top":"left",h="up"===r||"left"===r,c=t.distance||n["top"==l?"outerHeight":"outerWidth"](!0),u={};V.effects.createPlaceholder(n),i=n.cssClip(),s=n.position()[l],u[l]=(h?-1:1)*c+s,u.clip=n.cssClip(),u.clip[o[r][1]]=u.clip[o[r][0]],"show"===a&&(n.cssClip(u.clip),n.css(l,u[l]),u.clip=i,u[l]=s),n.animate(u,{queue:!1,duration:t.duration,easing:t.easing,complete:e})}),y=!1!==V.uiBackCompat?V.effects.define("transfer",function(t,e){V(this).transfer(t,e)}):y;V.ui.focusable=function(t,e){var i,s,n,o,a=t.nodeName.toLowerCase();return"area"===a?(s=(i=t.parentNode).name,!(!t.href||!s||"map"!==i.nodeName.toLowerCase())&&(0<(s=V("img[usemap='#"+s+"']")).length&&s.is(":visible"))):(/^(input|select|textarea|button|object)$/.test(a)?(n=!t.disabled)&&(o=V(t).closest("fieldset")[0])&&(n=!o.disabled):n="a"===a&&t.href||e,n&&V(t).is(":visible")&&function(t){var e=t.css("visibility");for(;"inherit"===e;)t=t.parent(),e=t.css("visibility");return"visible"===e}(V(t)))},V.extend(V.expr.pseudos,{focusable:function(t){return V.ui.focusable(t,null!=V.attr(t,"tabindex"))}});var Q,J;V.ui.focusable,V.fn._form=function(){return"string"==typeof this[0].form?this.closest("form"):V(this[0].form)},V.ui.formResetMixin={_formResetHandler:function(){var e=V(this);setTimeout(function(){var t=e.data("ui-form-reset-instances");V.each(t,function(){this.refresh()})})},_bindFormResetHandler:function(){var t;this.form=this.element._form(),this.form.length&&((t=this.form.data("ui-form-reset-instances")||[]).length||this.form.on("reset.ui-form-reset",this._formResetHandler),t.push(this),this.form.data("ui-form-reset-instances",t))},_unbindFormResetHandler:function(){var t;this.form.length&&((t=this.form.data("ui-form-reset-instances")).splice(V.inArray(this,t),1),t.length?this.form.data("ui-form-reset-instances",t):this.form.removeData("ui-form-reset-instances").off("reset.ui-form-reset"))}};V.expr.pseudos||(V.expr.pseudos=V.expr[":"]),V.uniqueSort||(V.uniqueSort=V.unique),V.escapeSelector||(Q=/([\0-\x1f\x7f]|^-?\d)|^-$|[^\x80-\uFFFF\w-]/g,J=function(t,e){return e?"\0"===t?"�":t.slice(0,-1)+"\\"+t.charCodeAt(t.length-1).toString(16)+" ":"\\"+t},V.escapeSelector=function(t){return(t+"").replace(Q,J)}),V.fn.even&&V.fn.odd||V.fn.extend({even:function(){return this.filter(function(t){return t%2==0})},odd:function(){return this.filter(function(t){return t%2==1})}});var Z;V.ui.keyCode={BACKSPACE:8,COMMA:188,DELETE:46,DOWN:40,END:35,ENTER:13,ESCAPE:27,HOME:36,LEFT:37,PAGE_DOWN:34,PAGE_UP:33,PERIOD:190,RIGHT:39,SPACE:32,TAB:9,UP:38},V.fn.labels=function(){var t,e,i;return this.length?this[0].labels&&this[0].labels.length?this.pushStack(this[0].labels):(e=this.eq(0).parents("label"),(t=this.attr("id"))&&(i=(i=this.eq(0).parents().last()).add((i.length?i:this).siblings()),t="label[for='"+V.escapeSelector(t)+"']",e=e.add(i.find(t).addBack(t))),this.pushStack(e)):this.pushStack([])},V.fn.scrollParent=function(t){var e=this.css("position"),i="absolute"===e,s=t?/(auto|scroll|hidden)/:/(auto|scroll)/,t=this.parents().filter(function(){var t=V(this);return(!i||"static"!==t.css("position"))&&s.test(t.css("overflow")+t.css("overflow-y")+t.css("overflow-x"))}).eq(0);return"fixed"!==e&&t.length?t:V(this[0].ownerDocument||document)},V.extend(V.expr.pseudos,{tabbable:function(t){var e=V.attr(t,"tabindex"),i=null!=e;return(!i||0<=e)&&V.ui.focusable(t,i)}}),V.fn.extend({uniqueId:(Z=0,function(){return this.each(function(){this.id||(this.id="ui-id-"+ ++Z)})}),removeUniqueId:function(){return this.each(function(){/^ui-id-\d+$/.test(this.id)&&V(this).removeAttr("id")})}}),V.widget("ui.accordion",{version:"1.13.2",options:{active:0,animate:{},classes:{"ui-accordion-header":"ui-corner-top","ui-accordion-header-collapsed":"ui-corner-all","ui-accordion-content":"ui-corner-bottom"},collapsible:!1,event:"click",header:function(t){return t.find("> li > :first-child").add(t.find("> :not(li)").even())},heightStyle:"auto",icons:{activeHeader:"ui-icon-triangle-1-s",header:"ui-icon-triangle-1-e"},activate:null,beforeActivate:null},hideProps:{borderTopWidth:"hide",borderBottomWidth:"hide",paddingTop:"hide",paddingBottom:"hide",height:"hide"},showProps:{borderTopWidth:"show",borderBottomWidth:"show",paddingTop:"show",paddingBottom:"show",height:"show"},_create:function(){var t=this.options;this.prevShow=this.prevHide=V(),this._addClass("ui-accordion","ui-widget ui-helper-reset"),this.element.attr("role","tablist"),t.collapsible||!1!==t.active&&null!=t.active||(t.active=0),this._processPanels(),t.active<0&&(t.active+=this.headers.length),this._refresh()},_getCreateEventData:function(){return{header:this.active,panel:this.active.length?this.active.next():V()}},_createIcons:function(){var t,e=this.options.icons;e&&(t=V("<span>"),this._addClass(t,"ui-accordion-header-icon","ui-icon "+e.header),t.prependTo(this.headers),t=this.active.children(".ui-accordion-header-icon"),this._removeClass(t,e.header)._addClass(t,null,e.activeHeader)._addClass(this.headers,"ui-accordion-icons"))},_destroyIcons:function(){this._removeClass(this.headers,"ui-accordion-icons"),this.headers.children(".ui-accordion-header-icon").remove()},_destroy:function(){var t;this.element.removeAttr("role"),this.headers.removeAttr("role aria-expanded aria-selected aria-controls tabIndex").removeUniqueId(),this._destroyIcons(),t=this.headers.next().css("display","").removeAttr("role aria-hidden aria-labelledby").removeUniqueId(),"content"!==this.options.heightStyle&&t.css("height","")},_setOption:function(t,e){"active"!==t?("event"===t&&(this.options.event&&this._off(this.headers,this.options.event),this._setupEvents(e)),this._super(t,e),"collapsible"!==t||e||!1!==this.options.active||this._activate(0),"icons"===t&&(this._destroyIcons(),e&&this._createIcons())):this._activate(e)},_setOptionDisabled:function(t){this._super(t),this.element.attr("aria-disabled",t),this._toggleClass(null,"ui-state-disabled",!!t),this._toggleClass(this.headers.add(this.headers.next()),null,"ui-state-disabled",!!t)},_keydown:function(t){if(!t.altKey&&!t.ctrlKey){var e=V.ui.keyCode,i=this.headers.length,s=this.headers.index(t.target),n=!1;switch(t.keyCode){case e.RIGHT:case e.DOWN:n=this.headers[(s+1)%i];break;case e.LEFT:case e.UP:n=this.headers[(s-1+i)%i];break;case e.SPACE:case e.ENTER:this._eventHandler(t);break;case e.HOME:n=this.headers[0];break;case e.END:n=this.headers[i-1]}n&&(V(t.target).attr("tabIndex",-1),V(n).attr("tabIndex",0),V(n).trigger("focus"),t.preventDefault())}},_panelKeyDown:function(t){t.keyCode===V.ui.keyCode.UP&&t.ctrlKey&&V(t.currentTarget).prev().trigger("focus")},refresh:function(){var t=this.options;this._processPanels(),!1===t.active&&!0===t.collapsible||!this.headers.length?(t.active=!1,this.active=V()):!1===t.active?this._activate(0):this.active.length&&!V.contains(this.element[0],this.active[0])?this.headers.length===this.headers.find(".ui-state-disabled").length?(t.active=!1,this.active=V()):this._activate(Math.max(0,t.active-1)):t.active=this.headers.index(this.active),this._destroyIcons(),this._refresh()},_processPanels:function(){var t=this.headers,e=this.panels;"function"==typeof this.options.header?this.headers=this.options.header(this.element):this.headers=this.element.find(this.options.header),this._addClass(this.headers,"ui-accordion-header ui-accordion-header-collapsed","ui-state-default"),this.panels=this.headers.next().filter(":not(.ui-accordion-content-active)").hide(),this._addClass(this.panels,"ui-accordion-content","ui-helper-reset ui-widget-content"),e&&(this._off(t.not(this.headers)),this._off(e.not(this.panels)))},_refresh:function(){var i,t=this.options,e=t.heightStyle,s=this.element.parent();this.active=this._findActive(t.active),this._addClass(this.active,"ui-accordion-header-active","ui-state-active")._removeClass(this.active,"ui-accordion-header-collapsed"),this._addClass(this.active.next(),"ui-accordion-content-active"),this.active.next().show(),this.headers.attr("role","tab").each(function(){var t=V(this),e=t.uniqueId().attr("id"),i=t.next(),s=i.uniqueId().attr("id");t.attr("aria-controls",s),i.attr("aria-labelledby",e)}).next().attr("role","tabpanel"),this.headers.not(this.active).attr({"aria-selected":"false","aria-expanded":"false",tabIndex:-1}).next().attr({"aria-hidden":"true"}).hide(),this.active.length?this.active.attr({"aria-selected":"true","aria-expanded":"true",tabIndex:0}).next().attr({"aria-hidden":"false"}):this.headers.eq(0).attr("tabIndex",0),this._createIcons(),this._setupEvents(t.event),"fill"===e?(i=s.height(),this.element.siblings(":visible").each(function(){var t=V(this),e=t.css("position");"absolute"!==e&&"fixed"!==e&&(i-=t.outerHeight(!0))}),this.headers.each(function(){i-=V(this).outerHeight(!0)}),this.headers.next().each(function(){V(this).height(Math.max(0,i-V(this).innerHeight()+V(this).height()))}).css("overflow","auto")):"auto"===e&&(i=0,this.headers.next().each(function(){var t=V(this).is(":visible");t||V(this).show(),i=Math.max(i,V(this).css("height","").height()),t||V(this).hide()}).height(i))},_activate:function(t){t=this._findActive(t)[0];t!==this.active[0]&&(t=t||this.active[0],this._eventHandler({target:t,currentTarget:t,preventDefault:V.noop}))},_findActive:function(t){return"number"==typeof t?this.headers.eq(t):V()},_setupEvents:function(t){var i={keydown:"_keydown"};t&&V.each(t.split(" "),function(t,e){i[e]="_eventHandler"}),this._off(this.headers.add(this.headers.next())),this._on(this.headers,i),this._on(this.headers.next(),{keydown:"_panelKeyDown"}),this._hoverable(this.headers),this._focusable(this.headers)},_eventHandler:function(t){var e=this.options,i=this.active,s=V(t.currentTarget),n=s[0]===i[0],o=n&&e.collapsible,a=o?V():s.next(),r=i.next(),a={oldHeader:i,oldPanel:r,newHeader:o?V():s,newPanel:a};t.preventDefault(),n&&!e.collapsible||!1===this._trigger("beforeActivate",t,a)||(e.active=!o&&this.headers.index(s),this.active=n?V():s,this._toggle(a),this._removeClass(i,"ui-accordion-header-active","ui-state-active"),e.icons&&(i=i.children(".ui-accordion-header-icon"),this._removeClass(i,null,e.icons.activeHeader)._addClass(i,null,e.icons.header)),n||(this._removeClass(s,"ui-accordion-header-collapsed")._addClass(s,"ui-accordion-header-active","ui-state-active"),e.icons&&(n=s.children(".ui-accordion-header-icon"),this._removeClass(n,null,e.icons.header)._addClass(n,null,e.icons.activeHeader)),this._addClass(s.next(),"ui-accordion-content-active")))},_toggle:function(t){var e=t.newPanel,i=this.prevShow.length?this.prevShow:t.oldPanel;this.prevShow.add(this.prevHide).stop(!0,!0),this.prevShow=e,this.prevHide=i,this.options.animate?this._animate(e,i,t):(i.hide(),e.show(),this._toggleComplete(t)),i.attr({"aria-hidden":"true"}),i.prev().attr({"aria-selected":"false","aria-expanded":"false"}),e.length&&i.length?i.prev().attr({tabIndex:-1,"aria-expanded":"false"}):e.length&&this.headers.filter(function(){return 0===parseInt(V(this).attr("tabIndex"),10)}).attr("tabIndex",-1),e.attr("aria-hidden","false").prev().attr({"aria-selected":"true","aria-expanded":"true",tabIndex:0})},_animate:function(t,i,e){var s,n,o,a=this,r=0,l=t.css("box-sizing"),h=t.length&&(!i.length||t.index()<i.index()),c=this.options.animate||{},u=h&&c.down||c,h=function(){a._toggleComplete(e)};return n=(n="string"==typeof u?u:n)||u.easing||c.easing,o=(o="number"==typeof u?u:o)||u.duration||c.duration,i.length?t.length?(s=t.show().outerHeight(),i.animate(this.hideProps,{duration:o,easing:n,step:function(t,e){e.now=Math.round(t)}}),void t.hide().animate(this.showProps,{duration:o,easing:n,complete:h,step:function(t,e){e.now=Math.round(t),"height"!==e.prop?"content-box"===l&&(r+=e.now):"content"!==a.options.heightStyle&&(e.now=Math.round(s-i.outerHeight()-r),r=0)}})):i.animate(this.hideProps,o,n,h):t.animate(this.showProps,o,n,h)},_toggleComplete:function(t){var e=t.oldPanel,i=e.prev();this._removeClass(e,"ui-accordion-content-active"),this._removeClass(i,"ui-accordion-header-active")._addClass(i,"ui-accordion-header-collapsed"),e.length&&(e.parent()[0].className=e.parent()[0].className),this._trigger("activate",null,t)}}),V.ui.safeActiveElement=function(e){var i;try{i=e.activeElement}catch(t){i=e.body}return i=!(i=i||e.body).nodeName?e.body:i},V.widget("ui.menu",{version:"1.13.2",defaultElement:"<ul>",delay:300,options:{icons:{submenu:"ui-icon-caret-1-e"},items:"> *",menus:"ul",position:{my:"left top",at:"right top"},role:"menu",blur:null,focus:null,select:null},_create:function(){this.activeMenu=this.element,this.mouseHandled=!1,this.lastMousePosition={x:null,y:null},this.element.uniqueId().attr({role:this.options.role,tabIndex:0}),this._addClass("ui-menu","ui-widget ui-widget-content"),this._on({"mousedown .ui-menu-item":function(t){t.preventDefault(),this._activateItem(t)},"click .ui-menu-item":function(t){var e=V(t.target),i=V(V.ui.safeActiveElement(this.document[0]));!this.mouseHandled&&e.not(".ui-state-disabled").length&&(this.select(t),t.isPropagationStopped()||(this.mouseHandled=!0),e.has(".ui-menu").length?this.expand(t):!this.element.is(":focus")&&i.closest(".ui-menu").length&&(this.element.trigger("focus",[!0]),this.active&&1===this.active.parents(".ui-menu").length&&clearTimeout(this.timer)))},"mouseenter .ui-menu-item":"_activateItem","mousemove .ui-menu-item":"_activateItem",mouseleave:"collapseAll","mouseleave .ui-menu":"collapseAll",focus:function(t,e){var i=this.active||this._menuItems().first();e||this.focus(t,i)},blur:function(t){this._delay(function(){V.contains(this.element[0],V.ui.safeActiveElement(this.document[0]))||this.collapseAll(t)})},keydown:"_keydown"}),this.refresh(),this._on(this.document,{click:function(t){this._closeOnDocumentClick(t)&&this.collapseAll(t,!0),this.mouseHandled=!1}})},_activateItem:function(t){var e,i;this.previousFilter||t.clientX===this.lastMousePosition.x&&t.clientY===this.lastMousePosition.y||(this.lastMousePosition={x:t.clientX,y:t.clientY},e=V(t.target).closest(".ui-menu-item"),i=V(t.currentTarget),e[0]===i[0]&&(i.is(".ui-state-active")||(this._removeClass(i.siblings().children(".ui-state-active"),null,"ui-state-active"),this.focus(t,i))))},_destroy:function(){var t=this.element.find(".ui-menu-item").removeAttr("role aria-disabled").children(".ui-menu-item-wrapper").removeUniqueId().removeAttr("tabIndex role aria-haspopup");this.element.removeAttr("aria-activedescendant").find(".ui-menu").addBack().removeAttr("role aria-labelledby aria-expanded aria-hidden aria-disabled tabIndex").removeUniqueId().show(),t.children().each(function(){var t=V(this);t.data("ui-menu-submenu-caret")&&t.remove()})},_keydown:function(t){var e,i,s,n=!0;switch(t.keyCode){case V.ui.keyCode.PAGE_UP:this.previousPage(t);break;case V.ui.keyCode.PAGE_DOWN:this.nextPage(t);break;case V.ui.keyCode.HOME:this._move("first","first",t);break;case V.ui.keyCode.END:this._move("last","last",t);break;case V.ui.keyCode.UP:this.previous(t);break;case V.ui.keyCode.DOWN:this.next(t);break;case V.ui.keyCode.LEFT:this.collapse(t);break;case V.ui.keyCode.RIGHT:this.active&&!this.active.is(".ui-state-disabled")&&this.expand(t);break;case V.ui.keyCode.ENTER:case V.ui.keyCode.SPACE:this._activate(t);break;case V.ui.keyCode.ESCAPE:this.collapse(t);break;default:e=this.previousFilter||"",s=n=!1,i=96<=t.keyCode&&t.keyCode<=105?(t.keyCode-96).toString():String.fromCharCode(t.keyCode),clearTimeout(this.filterTimer),i===e?s=!0:i=e+i,e=this._filterMenuItems(i),(e=s&&-1!==e.index(this.active.next())?this.active.nextAll(".ui-menu-item"):e).length||(i=String.fromCharCode(t.keyCode),e=this._filterMenuItems(i)),e.length?(this.focus(t,e),this.previousFilter=i,this.filterTimer=this._delay(function(){delete this.previousFilter},1e3)):delete this.previousFilter}n&&t.preventDefault()},_activate:function(t){this.active&&!this.active.is(".ui-state-disabled")&&(this.active.children("[aria-haspopup='true']").length?this.expand(t):this.select(t))},refresh:function(){var t,e,s=this,n=this.options.icons.submenu,i=this.element.find(this.options.menus);this._toggleClass("ui-menu-icons",null,!!this.element.find(".ui-icon").length),e=i.filter(":not(.ui-menu)").hide().attr({role:this.options.role,"aria-hidden":"true","aria-expanded":"false"}).each(function(){var t=V(this),e=t.prev(),i=V("<span>").data("ui-menu-submenu-caret",!0);s._addClass(i,"ui-menu-icon","ui-icon "+n),e.attr("aria-haspopup","true").prepend(i),t.attr("aria-labelledby",e.attr("id"))}),this._addClass(e,"ui-menu","ui-widget ui-widget-content ui-front"),(t=i.add(this.element).find(this.options.items)).not(".ui-menu-item").each(function(){var t=V(this);s._isDivider(t)&&s._addClass(t,"ui-menu-divider","ui-widget-content")}),i=(e=t.not(".ui-menu-item, .ui-menu-divider")).children().not(".ui-menu").uniqueId().attr({tabIndex:-1,role:this._itemRole()}),this._addClass(e,"ui-menu-item")._addClass(i,"ui-menu-item-wrapper"),t.filter(".ui-state-disabled").attr("aria-disabled","true"),this.active&&!V.contains(this.element[0],this.active[0])&&this.blur()},_itemRole:function(){return{menu:"menuitem",listbox:"option"}[this.options.role]},_setOption:function(t,e){var i;"icons"===t&&(i=this.element.find(".ui-menu-icon"),this._removeClass(i,null,this.options.icons.submenu)._addClass(i,null,e.submenu)),this._super(t,e)},_setOptionDisabled:function(t){this._super(t),this.element.attr("aria-disabled",String(t)),this._toggleClass(null,"ui-state-disabled",!!t)},focus:function(t,e){var i;this.blur(t,t&&"focus"===t.type),this._scrollIntoView(e),this.active=e.first(),i=this.active.children(".ui-menu-item-wrapper"),this._addClass(i,null,"ui-state-active"),this.options.role&&this.element.attr("aria-activedescendant",i.attr("id")),i=this.active.parent().closest(".ui-menu-item").children(".ui-menu-item-wrapper"),this._addClass(i,null,"ui-state-active"),t&&"keydown"===t.type?this._close():this.timer=this._delay(function(){this._close()},this.delay),(i=e.children(".ui-menu")).length&&t&&/^mouse/.test(t.type)&&this._startOpening(i),this.activeMenu=e.parent(),this._trigger("focus",t,{item:e})},_scrollIntoView:function(t){var e,i,s;this._hasScroll()&&(i=parseFloat(V.css(this.activeMenu[0],"borderTopWidth"))||0,s=parseFloat(V.css(this.activeMenu[0],"paddingTop"))||0,e=t.offset().top-this.activeMenu.offset().top-i-s,i=this.activeMenu.scrollTop(),s=this.activeMenu.height(),t=t.outerHeight(),e<0?this.activeMenu.scrollTop(i+e):s<e+t&&this.activeMenu.scrollTop(i+e-s+t))},blur:function(t,e){e||clearTimeout(this.timer),this.active&&(this._removeClass(this.active.children(".ui-menu-item-wrapper"),null,"ui-state-active"),this._trigger("blur",t,{item:this.active}),this.active=null)},_startOpening:function(t){clearTimeout(this.timer),"true"===t.attr("aria-hidden")&&(this.timer=this._delay(function(){this._close(),this._open(t)},this.delay))},_open:function(t){var e=V.extend({of:this.active},this.options.position);clearTimeout(this.timer),this.element.find(".ui-menu").not(t.parents(".ui-menu")).hide().attr("aria-hidden","true"),t.show().removeAttr("aria-hidden").attr("aria-expanded","true").position(e)},collapseAll:function(e,i){clearTimeout(this.timer),this.timer=this._delay(function(){var t=i?this.element:V(e&&e.target).closest(this.element.find(".ui-menu"));t.length||(t=this.element),this._close(t),this.blur(e),this._removeClass(t.find(".ui-state-active"),null,"ui-state-active"),this.activeMenu=t},i?0:this.delay)},_close:function(t){(t=t||(this.active?this.active.parent():this.element)).find(".ui-menu").hide().attr("aria-hidden","true").attr("aria-expanded","false")},_closeOnDocumentClick:function(t){return!V(t.target).closest(".ui-menu").length},_isDivider:function(t){return!/[^\-\u2014\u2013\s]/.test(t.text())},collapse:function(t){var e=this.active&&this.active.parent().closest(".ui-menu-item",this.element);e&&e.length&&(this._close(),this.focus(t,e))},expand:function(t){var e=this.active&&this._menuItems(this.active.children(".ui-menu")).first();e&&e.length&&(this._open(e.parent()),this._delay(function(){this.focus(t,e)}))},next:function(t){this._move("next","first",t)},previous:function(t){this._move("prev","last",t)},isFirstItem:function(){return this.active&&!this.active.prevAll(".ui-menu-item").length},isLastItem:function(){return this.active&&!this.active.nextAll(".ui-menu-item").length},_menuItems:function(t){return(t||this.element).find(this.options.items).filter(".ui-menu-item")},_move:function(t,e,i){var s;(s=this.active?"first"===t||"last"===t?this.active["first"===t?"prevAll":"nextAll"](".ui-menu-item").last():this.active[t+"All"](".ui-menu-item").first():s)&&s.length&&this.active||(s=this._menuItems(this.activeMenu)[e]()),this.focus(i,s)},nextPage:function(t){var e,i,s;this.active?this.isLastItem()||(this._hasScroll()?(i=this.active.offset().top,s=this.element.innerHeight(),0===V.fn.jquery.indexOf("3.2.")&&(s+=this.element[0].offsetHeight-this.element.outerHeight()),this.active.nextAll(".ui-menu-item").each(function(){return(e=V(this)).offset().top-i-s<0}),this.focus(t,e)):this.focus(t,this._menuItems(this.activeMenu)[this.active?"last":"first"]())):this.next(t)},previousPage:function(t){var e,i,s;this.active?this.isFirstItem()||(this._hasScroll()?(i=this.active.offset().top,s=this.element.innerHeight(),0===V.fn.jquery.indexOf("3.2.")&&(s+=this.element[0].offsetHeight-this.element.outerHeight()),this.active.prevAll(".ui-menu-item").each(function(){return 0<(e=V(this)).offset().top-i+s}),this.focus(t,e)):this.focus(t,this._menuItems(this.activeMenu).first())):this.next(t)},_hasScroll:function(){return this.element.outerHeight()<this.element.prop("scrollHeight")},select:function(t){this.active=this.active||V(t.target).closest(".ui-menu-item");var e={item:this.active};this.active.has(".ui-menu").length||this.collapseAll(t,!0),this._trigger("select",t,e)},_filterMenuItems:function(t){var t=t.replace(/[\-\[\]{}()*+?.,\\\^$|#\s]/g,"\\$&"),e=new RegExp("^"+t,"i");return this.activeMenu.find(this.options.items).filter(".ui-menu-item").filter(function(){return e.test(String.prototype.trim.call(V(this).children(".ui-menu-item-wrapper").text()))})}});V.widget("ui.autocomplete",{version:"1.13.2",defaultElement:"<input>",options:{appendTo:null,autoFocus:!1,delay:300,minLength:1,position:{my:"left top",at:"left bottom",collision:"none"},source:null,change:null,close:null,focus:null,open:null,response:null,search:null,select:null},requestIndex:0,pending:0,liveRegionTimer:null,_create:function(){var i,s,n,t=this.element[0].nodeName.toLowerCase(),e="textarea"===t,t="input"===t;this.isMultiLine=e||!t&&this._isContentEditable(this.element),this.valueMethod=this.element[e||t?"val":"text"],this.isNewMenu=!0,this._addClass("ui-autocomplete-input"),this.element.attr("autocomplete","off"),this._on(this.element,{keydown:function(t){if(this.element.prop("readOnly"))s=n=i=!0;else{s=n=i=!1;var e=V.ui.keyCode;switch(t.keyCode){case e.PAGE_UP:i=!0,this._move("previousPage",t);break;case e.PAGE_DOWN:i=!0,this._move("nextPage",t);break;case e.UP:i=!0,this._keyEvent("previous",t);break;case e.DOWN:i=!0,this._keyEvent("next",t);break;case e.ENTER:this.menu.active&&(i=!0,t.preventDefault(),this.menu.select(t));break;case e.TAB:this.menu.active&&this.menu.select(t);break;case e.ESCAPE:this.menu.element.is(":visible")&&(this.isMultiLine||this._value(this.term),this.close(t),t.preventDefault());break;default:s=!0,this._searchTimeout(t)}}},keypress:function(t){if(i)return i=!1,void(this.isMultiLine&&!this.menu.element.is(":visible")||t.preventDefault());if(!s){var e=V.ui.keyCode;switch(t.keyCode){case e.PAGE_UP:this._move("previousPage",t);break;case e.PAGE_DOWN:this._move("nextPage",t);break;case e.UP:this._keyEvent("previous",t);break;case e.DOWN:this._keyEvent("next",t)}}},input:function(t){if(n)return n=!1,void t.preventDefault();this._searchTimeout(t)},focus:function(){this.selectedItem=null,this.previous=this._value()},blur:function(t){clearTimeout(this.searching),this.close(t),this._change(t)}}),this._initSource(),this.menu=V("<ul>").appendTo(this._appendTo()).menu({role:null}).hide().attr({unselectable:"on"}).menu("instance"),this._addClass(this.menu.element,"ui-autocomplete","ui-front"),this._on(this.menu.element,{mousedown:function(t){t.preventDefault()},menufocus:function(t,e){var i,s;if(this.isNewMenu&&(this.isNewMenu=!1,t.originalEvent&&/^mouse/.test(t.originalEvent.type)))return this.menu.blur(),void this.document.one("mousemove",function(){V(t.target).trigger(t.originalEvent)});s=e.item.data("ui-autocomplete-item"),!1!==this._trigger("focus",t,{item:s})&&t.originalEvent&&/^key/.test(t.originalEvent.type)&&this._value(s.value),(i=e.item.attr("aria-label")||s.value)&&String.prototype.trim.call(i).length&&(clearTimeout(this.liveRegionTimer),this.liveRegionTimer=this._delay(function(){this.liveRegion.html(V("<div>").text(i))},100))},menuselect:function(t,e){var i=e.item.data("ui-autocomplete-item"),s=this.previous;this.element[0]!==V.ui.safeActiveElement(this.document[0])&&(this.element.trigger("focus"),this.previous=s,this._delay(function(){this.previous=s,this.selectedItem=i})),!1!==this._trigger("select",t,{item:i})&&this._value(i.value),this.term=this._value(),this.close(t),this.selectedItem=i}}),this.liveRegion=V("<div>",{role:"status","aria-live":"assertive","aria-relevant":"additions"}).appendTo(this.document[0].body),this._addClass(this.liveRegion,null,"ui-helper-hidden-accessible"),this._on(this.window,{beforeunload:function(){this.element.removeAttr("autocomplete")}})},_destroy:function(){clearTimeout(this.searching),this.element.removeAttr("autocomplete"),this.menu.element.remove(),this.liveRegion.remove()},_setOption:function(t,e){this._super(t,e),"source"===t&&this._initSource(),"appendTo"===t&&this.menu.element.appendTo(this._appendTo()),"disabled"===t&&e&&this.xhr&&this.xhr.abort()},_isEventTargetInWidget:function(t){var e=this.menu.element[0];return t.target===this.element[0]||t.target===e||V.contains(e,t.target)},_closeOnClickOutside:function(t){this._isEventTargetInWidget(t)||this.close()},_appendTo:function(){var t=this.options.appendTo;return t=!(t=!(t=t&&(t.jquery||t.nodeType?V(t):this.document.find(t).eq(0)))||!t[0]?this.element.closest(".ui-front, dialog"):t).length?this.document[0].body:t},_initSource:function(){var i,s,n=this;Array.isArray(this.options.source)?(i=this.options.source,this.source=function(t,e){e(V.ui.autocomplete.filter(i,t.term))}):"string"==typeof this.options.source?(s=this.options.source,this.source=function(t,e){n.xhr&&n.xhr.abort(),n.xhr=V.ajax({url:s,data:t,dataType:"json",success:function(t){e(t)},error:function(){e([])}})}):this.source=this.options.source},_searchTimeout:function(s){clearTimeout(this.searching),this.searching=this._delay(function(){var t=this.term===this._value(),e=this.menu.element.is(":visible"),i=s.altKey||s.ctrlKey||s.metaKey||s.shiftKey;t&&(e||i)||(this.selectedItem=null,this.search(null,s))},this.options.delay)},search:function(t,e){return t=null!=t?t:this._value(),this.term=this._value(),t.length<this.options.minLength?this.close(e):!1!==this._trigger("search",e)?this._search(t):void 0},_search:function(t){this.pending++,this._addClass("ui-autocomplete-loading"),this.cancelSearch=!1,this.source({term:t},this._response())},_response:function(){var e=++this.requestIndex;return function(t){e===this.requestIndex&&this.__response(t),this.pending--,this.pending||this._removeClass("ui-autocomplete-loading")}.bind(this)},__response:function(t){t=t&&this._normalize(t),this._trigger("response",null,{content:t}),!this.options.disabled&&t&&t.length&&!this.cancelSearch?(this._suggest(t),this._trigger("open")):this._close()},close:function(t){this.cancelSearch=!0,this._close(t)},_close:function(t){this._off(this.document,"mousedown"),this.menu.element.is(":visible")&&(this.menu.element.hide(),this.menu.blur(),this.isNewMenu=!0,this._trigger("close",t))},_change:function(t){this.previous!==this._value()&&this._trigger("change",t,{item:this.selectedItem})},_normalize:function(t){return t.length&&t[0].label&&t[0].value?t:V.map(t,function(t){return"string"==typeof t?{label:t,value:t}:V.extend({},t,{label:t.label||t.value,value:t.value||t.label})})},_suggest:function(t){var e=this.menu.element.empty();this._renderMenu(e,t),this.isNewMenu=!0,this.menu.refresh(),e.show(),this._resizeMenu(),e.position(V.extend({of:this.element},this.options.position)),this.options.autoFocus&&this.menu.next(),this._on(this.document,{mousedown:"_closeOnClickOutside"})},_resizeMenu:function(){var t=this.menu.element;t.outerWidth(Math.max(t.width("").outerWidth()+1,this.element.outerWidth()))},_renderMenu:function(i,t){var s=this;V.each(t,function(t,e){s._renderItemData(i,e)})},_renderItemData:function(t,e){return this._renderItem(t,e).data("ui-autocomplete-item",e)},_renderItem:function(t,e){return V("<li>").append(V("<div>").text(e.label)).appendTo(t)},_move:function(t,e){if(this.menu.element.is(":visible"))return this.menu.isFirstItem()&&/^previous/.test(t)||this.menu.isLastItem()&&/^next/.test(t)?(this.isMultiLine||this._value(this.term),void this.menu.blur()):void this.menu[t](e);this.search(null,e)},widget:function(){return this.menu.element},_value:function(){return this.valueMethod.apply(this.element,arguments)},_keyEvent:function(t,e){this.isMultiLine&&!this.menu.element.is(":visible")||(this._move(t,e),e.preventDefault())},_isContentEditable:function(t){if(!t.length)return!1;var e=t.prop("contentEditable");return"inherit"===e?this._isContentEditable(t.parent()):"true"===e}}),V.extend(V.ui.autocomplete,{escapeRegex:function(t){return t.replace(/[\-\[\]{}()*+?.,\\\^$|#\s]/g,"\\$&")},filter:function(t,e){var i=new RegExp(V.ui.autocomplete.escapeRegex(e),"i");return V.grep(t,function(t){return i.test(t.label||t.value||t)})}}),V.widget("ui.autocomplete",V.ui.autocomplete,{options:{messages:{noResults:"No search results.",results:function(t){return t+(1<t?" results are":" result is")+" available, use up and down arrow keys to navigate."}}},__response:function(t){var e;this._superApply(arguments),this.options.disabled||this.cancelSearch||(e=t&&t.length?this.options.messages.results(t.length):this.options.messages.noResults,clearTimeout(this.liveRegionTimer),this.liveRegionTimer=this._delay(function(){this.liveRegion.html(V("<div>").text(e))},100))}});V.ui.autocomplete;var tt=/ui-corner-([a-z]){2,6}/g;V.widget("ui.controlgroup",{version:"1.13.2",defaultElement:"<div>",options:{direction:"horizontal",disabled:null,onlyVisible:!0,items:{button:"input[type=button], input[type=submit], input[type=reset], button, a",controlgroupLabel:".ui-controlgroup-label",checkboxradio:"input[type='checkbox'], input[type='radio']",selectmenu:"select",spinner:".ui-spinner-input"}},_create:function(){this._enhance()},_enhance:function(){this.element.attr("role","toolbar"),this.refresh()},_destroy:function(){this._callChildMethod("destroy"),this.childWidgets.removeData("ui-controlgroup-data"),this.element.removeAttr("role"),this.options.items.controlgroupLabel&&this.element.find(this.options.items.controlgroupLabel).find(".ui-controlgroup-label-contents").contents().unwrap()},_initWidgets:function(){var o=this,a=[];V.each(this.options.items,function(s,t){var e,n={};if(t)return"controlgroupLabel"===s?((e=o.element.find(t)).each(function(){var t=V(this);t.children(".ui-controlgroup-label-contents").length||t.contents().wrapAll("<span class='ui-controlgroup-label-contents'></span>")}),o._addClass(e,null,"ui-widget ui-widget-content ui-state-default"),void(a=a.concat(e.get()))):void(V.fn[s]&&(n=o["_"+s+"Options"]?o["_"+s+"Options"]("middle"):{classes:{}},o.element.find(t).each(function(){var t=V(this),e=t[s]("instance"),i=V.widget.extend({},n);"button"===s&&t.parent(".ui-spinner").length||((e=e||t[s]()[s]("instance"))&&(i.classes=o._resolveClassesValues(i.classes,e)),t[s](i),i=t[s]("widget"),V.data(i[0],"ui-controlgroup-data",e||t[s]("instance")),a.push(i[0]))})))}),this.childWidgets=V(V.uniqueSort(a)),this._addClass(this.childWidgets,"ui-controlgroup-item")},_callChildMethod:function(e){this.childWidgets.each(function(){var t=V(this).data("ui-controlgroup-data");t&&t[e]&&t[e]()})},_updateCornerClass:function(t,e){e=this._buildSimpleOptions(e,"label").classes.label;this._removeClass(t,null,"ui-corner-top ui-corner-bottom ui-corner-left ui-corner-right ui-corner-all"),this._addClass(t,null,e)},_buildSimpleOptions:function(t,e){var i="vertical"===this.options.direction,s={classes:{}};return s.classes[e]={middle:"",first:"ui-corner-"+(i?"top":"left"),last:"ui-corner-"+(i?"bottom":"right"),only:"ui-corner-all"}[t],s},_spinnerOptions:function(t){t=this._buildSimpleOptions(t,"ui-spinner");return t.classes["ui-spinner-up"]="",t.classes["ui-spinner-down"]="",t},_buttonOptions:function(t){return this._buildSimpleOptions(t,"ui-button")},_checkboxradioOptions:function(t){return this._buildSimpleOptions(t,"ui-checkboxradio-label")},_selectmenuOptions:function(t){var e="vertical"===this.options.direction;return{width:e&&"auto",classes:{middle:{"ui-selectmenu-button-open":"","ui-selectmenu-button-closed":""},first:{"ui-selectmenu-button-open":"ui-corner-"+(e?"top":"tl"),"ui-selectmenu-button-closed":"ui-corner-"+(e?"top":"left")},last:{"ui-selectmenu-button-open":e?"":"ui-corner-tr","ui-selectmenu-button-closed":"ui-corner-"+(e?"bottom":"right")},only:{"ui-selectmenu-button-open":"ui-corner-top","ui-selectmenu-button-closed":"ui-corner-all"}}[t]}},_resolveClassesValues:function(i,s){var n={};return V.each(i,function(t){var e=s.options.classes[t]||"",e=String.prototype.trim.call(e.replace(tt,""));n[t]=(e+" "+i[t]).replace(/\s+/g," ")}),n},_setOption:function(t,e){"direction"===t&&this._removeClass("ui-controlgroup-"+this.options.direction),this._super(t,e),"disabled"!==t?this.refresh():this._callChildMethod(e?"disable":"enable")},refresh:function(){var n,o=this;this._addClass("ui-controlgroup ui-controlgroup-"+this.options.direction),"horizontal"===this.options.direction&&this._addClass(null,"ui-helper-clearfix"),this._initWidgets(),n=this.childWidgets,(n=this.options.onlyVisible?n.filter(":visible"):n).length&&(V.each(["first","last"],function(t,e){var i,s=n[e]().data("ui-controlgroup-data");s&&o["_"+s.widgetName+"Options"]?((i=o["_"+s.widgetName+"Options"](1===n.length?"only":e)).classes=o._resolveClassesValues(i.classes,s),s.element[s.widgetName](i)):o._updateCornerClass(n[e](),e)}),this._callChildMethod("refresh"))}});V.widget("ui.checkboxradio",[V.ui.formResetMixin,{version:"1.13.2",options:{disabled:null,label:null,icon:!0,classes:{"ui-checkboxradio-label":"ui-corner-all","ui-checkboxradio-icon":"ui-corner-all"}},_getCreateOptions:function(){var t,e=this._super()||{};return this._readType(),t=this.element.labels(),this.label=V(t[t.length-1]),this.label.length||V.error("No label found for checkboxradio widget"),this.originalLabel="",(t=this.label.contents().not(this.element[0])).length&&(this.originalLabel+=t.clone().wrapAll("<div></div>").parent().html()),this.originalLabel&&(e.label=this.originalLabel),null!=(t=this.element[0].disabled)&&(e.disabled=t),e},_create:function(){var t=this.element[0].checked;this._bindFormResetHandler(),null==this.options.disabled&&(this.options.disabled=this.element[0].disabled),this._setOption("disabled",this.options.disabled),this._addClass("ui-checkboxradio","ui-helper-hidden-accessible"),this._addClass(this.label,"ui-checkboxradio-label","ui-button ui-widget"),"radio"===this.type&&this._addClass(this.label,"ui-checkboxradio-radio-label"),this.options.label&&this.options.label!==this.originalLabel?this._updateLabel():this.originalLabel&&(this.options.label=this.originalLabel),this._enhance(),t&&this._addClass(this.label,"ui-checkboxradio-checked","ui-state-active"),this._on({change:"_toggleClasses",focus:function(){this._addClass(this.label,null,"ui-state-focus ui-visual-focus")},blur:function(){this._removeClass(this.label,null,"ui-state-focus ui-visual-focus")}})},_readType:function(){var t=this.element[0].nodeName.toLowerCase();this.type=this.element[0].type,"input"===t&&/radio|checkbox/.test(this.type)||V.error("Can't create checkboxradio on element.nodeName="+t+" and element.type="+this.type)},_enhance:function(){this._updateIcon(this.element[0].checked)},widget:function(){return this.label},_getRadioGroup:function(){var t=this.element[0].name,e="input[name='"+V.escapeSelector(t)+"']";return t?(this.form.length?V(this.form[0].elements).filter(e):V(e).filter(function(){return 0===V(this)._form().length})).not(this.element):V([])},_toggleClasses:function(){var t=this.element[0].checked;this._toggleClass(this.label,"ui-checkboxradio-checked","ui-state-active",t),this.options.icon&&"checkbox"===this.type&&this._toggleClass(this.icon,null,"ui-icon-check ui-state-checked",t)._toggleClass(this.icon,null,"ui-icon-blank",!t),"radio"===this.type&&this._getRadioGroup().each(function(){var t=V(this).checkboxradio("instance");t&&t._removeClass(t.label,"ui-checkboxradio-checked","ui-state-active")})},_destroy:function(){this._unbindFormResetHandler(),this.icon&&(this.icon.remove(),this.iconSpace.remove())},_setOption:function(t,e){if("label"!==t||e){if(this._super(t,e),"disabled"===t)return this._toggleClass(this.label,null,"ui-state-disabled",e),void(this.element[0].disabled=e);this.refresh()}},_updateIcon:function(t){var e="ui-icon ui-icon-background ";this.options.icon?(this.icon||(this.icon=V("<span>"),this.iconSpace=V("<span> </span>"),this._addClass(this.iconSpace,"ui-checkboxradio-icon-space")),"checkbox"===this.type?(e+=t?"ui-icon-check ui-state-checked":"ui-icon-blank",this._removeClass(this.icon,null,t?"ui-icon-blank":"ui-icon-check")):e+="ui-icon-blank",this._addClass(this.icon,"ui-checkboxradio-icon",e),t||this._removeClass(this.icon,null,"ui-icon-check ui-state-checked"),this.icon.prependTo(this.label).after(this.iconSpace)):void 0!==this.icon&&(this.icon.remove(),this.iconSpace.remove(),delete this.icon)},_updateLabel:function(){var t=this.label.contents().not(this.element[0]);this.icon&&(t=t.not(this.icon[0])),(t=this.iconSpace?t.not(this.iconSpace[0]):t).remove(),this.label.append(this.options.label)},refresh:function(){var t=this.element[0].checked,e=this.element[0].disabled;this._updateIcon(t),this._toggleClass(this.label,"ui-checkboxradio-checked","ui-state-active",t),null!==this.options.label&&this._updateLabel(),e!==this.options.disabled&&this._setOptions({disabled:e})}}]);var et;V.ui.checkboxradio;V.widget("ui.button",{version:"1.13.2",defaultElement:"<button>",options:{classes:{"ui-button":"ui-corner-all"},disabled:null,icon:null,iconPosition:"beginning",label:null,showLabel:!0},_getCreateOptions:function(){var t,e=this._super()||{};return this.isInput=this.element.is("input"),null!=(t=this.element[0].disabled)&&(e.disabled=t),this.originalLabel=this.isInput?this.element.val():this.element.html(),this.originalLabel&&(e.label=this.originalLabel),e},_create:function(){!this.option.showLabel&!this.options.icon&&(this.options.showLabel=!0),null==this.options.disabled&&(this.options.disabled=this.element[0].disabled||!1),this.hasTitle=!!this.element.attr("title"),this.options.label&&this.options.label!==this.originalLabel&&(this.isInput?this.element.val(this.options.label):this.element.html(this.options.label)),this._addClass("ui-button","ui-widget"),this._setOption("disabled",this.options.disabled),this._enhance(),this.element.is("a")&&this._on({keyup:function(t){t.keyCode===V.ui.keyCode.SPACE&&(t.preventDefault(),this.element[0].click?this.element[0].click():this.element.trigger("click"))}})},_enhance:function(){this.element.is("button")||this.element.attr("role","button"),this.options.icon&&(this._updateIcon("icon",this.options.icon),this._updateTooltip())},_updateTooltip:function(){this.title=this.element.attr("title"),this.options.showLabel||this.title||this.element.attr("title",this.options.label)},_updateIcon:function(t,e){var i="iconPosition"!==t,s=i?this.options.iconPosition:e,t="top"===s||"bottom"===s;this.icon?i&&this._removeClass(this.icon,null,this.options.icon):(this.icon=V("<span>"),this._addClass(this.icon,"ui-button-icon","ui-icon"),this.options.showLabel||this._addClass("ui-button-icon-only")),i&&this._addClass(this.icon,null,e),this._attachIcon(s),t?(this._addClass(this.icon,null,"ui-widget-icon-block"),this.iconSpace&&this.iconSpace.remove()):(this.iconSpace||(this.iconSpace=V("<span> </span>"),this._addClass(this.iconSpace,"ui-button-icon-space")),this._removeClass(this.icon,null,"ui-wiget-icon-block"),this._attachIconSpace(s))},_destroy:function(){this.element.removeAttr("role"),this.icon&&this.icon.remove(),this.iconSpace&&this.iconSpace.remove(),this.hasTitle||this.element.removeAttr("title")},_attachIconSpace:function(t){this.icon[/^(?:end|bottom)/.test(t)?"before":"after"](this.iconSpace)},_attachIcon:function(t){this.element[/^(?:end|bottom)/.test(t)?"append":"prepend"](this.icon)},_setOptions:function(t){var e=(void 0===t.showLabel?this.options:t).showLabel,i=(void 0===t.icon?this.options:t).icon;e||i||(t.showLabel=!0),this._super(t)},_setOption:function(t,e){"icon"===t&&(e?this._updateIcon(t,e):this.icon&&(this.icon.remove(),this.iconSpace&&this.iconSpace.remove())),"iconPosition"===t&&this._updateIcon(t,e),"showLabel"===t&&(this._toggleClass("ui-button-icon-only",null,!e),this._updateTooltip()),"label"===t&&(this.isInput?this.element.val(e):(this.element.html(e),this.icon&&(this._attachIcon(this.options.iconPosition),this._attachIconSpace(this.options.iconPosition)))),this._super(t,e),"disabled"===t&&(this._toggleClass(null,"ui-state-disabled",e),(this.element[0].disabled=e)&&this.element.trigger("blur"))},refresh:function(){var t=this.element.is("input, button")?this.element[0].disabled:this.element.hasClass("ui-button-disabled");t!==this.options.disabled&&this._setOptions({disabled:t}),this._updateTooltip()}}),!1!==V.uiBackCompat&&(V.widget("ui.button",V.ui.button,{options:{text:!0,icons:{primary:null,secondary:null}},_create:function(){this.options.showLabel&&!this.options.text&&(this.options.showLabel=this.options.text),!this.options.showLabel&&this.options.text&&(this.options.text=this.options.showLabel),this.options.icon||!this.options.icons.primary&&!this.options.icons.secondary?this.options.icon&&(this.options.icons.primary=this.options.icon):this.options.icons.primary?this.options.icon=this.options.icons.primary:(this.options.icon=this.options.icons.secondary,this.options.iconPosition="end"),this._super()},_setOption:function(t,e){"text"!==t?("showLabel"===t&&(this.options.text=e),"icon"===t&&(this.options.icons.primary=e),"icons"===t&&(e.primary?(this._super("icon",e.primary),this._super("iconPosition","beginning")):e.secondary&&(this._super("icon",e.secondary),this._super("iconPosition","end"))),this._superApply(arguments)):this._super("showLabel",e)}}),V.fn.button=(et=V.fn.button,function(i){var t="string"==typeof i,s=Array.prototype.slice.call(arguments,1),n=this;return t?this.length||"instance"!==i?this.each(function(){var t=V(this).attr("type"),e=V.data(this,"ui-"+("checkbox"!==t&&"radio"!==t?"button":"checkboxradio"));return"instance"===i?(n=e,!1):e?"function"!=typeof e[i]||"_"===i.charAt(0)?V.error("no such method '"+i+"' for button widget instance"):(t=e[i].apply(e,s))!==e&&void 0!==t?(n=t&&t.jquery?n.pushStack(t.get()):t,!1):void 0:V.error("cannot call methods on button prior to initialization; attempted to call method '"+i+"'")}):n=void 0:(s.length&&(i=V.widget.extend.apply(null,[i].concat(s))),this.each(function(){var t=V(this).attr("type"),e="checkbox"!==t&&"radio"!==t?"button":"checkboxradio",t=V.data(this,"ui-"+e);t?(t.option(i||{}),t._init&&t._init()):"button"!=e?V(this).checkboxradio(V.extend({icon:!1},i)):et.call(V(this),i)})),n}),V.fn.buttonset=function(){return V.ui.controlgroup||V.error("Controlgroup widget missing"),"option"===arguments[0]&&"items"===arguments[1]&&arguments[2]?this.controlgroup.apply(this,[arguments[0],"items.button",arguments[2]]):"option"===arguments[0]&&"items"===arguments[1]?this.controlgroup.apply(this,[arguments[0],"items.button"]):("object"==typeof arguments[0]&&arguments[0].items&&(arguments[0].items={button:arguments[0].items}),this.controlgroup.apply(this,arguments))});var it;V.ui.button;function st(){this._curInst=null,this._keyEvent=!1,this._disabledInputs=[],this._datepickerShowing=!1,this._inDialog=!1,this._mainDivId="ui-datepicker-div",this._inlineClass="ui-datepicker-inline",this._appendClass="ui-datepicker-append",this._triggerClass="ui-datepicker-trigger",this._dialogClass="ui-datepicker-dialog",this._disableClass="ui-datepicker-disabled",this._unselectableClass="ui-datepicker-unselectable",this._currentClass="ui-datepicker-current-day",this._dayOverClass="ui-datepicker-days-cell-over",this.regional=[],this.regional[""]={closeText:"Done",prevText:"Prev",nextText:"Next",currentText:"Today",monthNames:["January","February","March","April","May","June","July","August","September","October","November","December"],monthNamesShort:["Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec"],dayNames:["Sunday","Monday","Tuesday","Wednesday","Thursday","Friday","Saturday"],dayNamesShort:["Sun","Mon","Tue","Wed","Thu","Fri","Sat"],dayNamesMin:["Su","Mo","Tu","We","Th","Fr","Sa"],weekHeader:"Wk",dateFormat:"mm/dd/yy",firstDay:0,isRTL:!1,showMonthAfterYear:!1,yearSuffix:"",selectMonthLabel:"Select month",selectYearLabel:"Select year"},this._defaults={showOn:"focus",showAnim:"fadeIn",showOptions:{},defaultDate:null,appendText:"",buttonText:"...",buttonImage:"",buttonImageOnly:!1,hideIfNoPrevNext:!1,navigationAsDateFormat:!1,gotoCurrent:!1,changeMonth:!1,changeYear:!1,yearRange:"c-10:c+10",showOtherMonths:!1,selectOtherMonths:!1,showWeek:!1,calculateWeek:this.iso8601Week,shortYearCutoff:"+10",minDate:null,maxDate:null,duration:"fast",beforeShowDay:null,beforeShow:null,onSelect:null,onChangeMonthYear:null,onClose:null,onUpdateDatepicker:null,numberOfMonths:1,showCurrentAtPos:0,stepMonths:1,stepBigMonths:12,altField:"",altFormat:"",constrainInput:!0,showButtonPanel:!1,autoSize:!1,disabled:!1},V.extend(this._defaults,this.regional[""]),this.regional.en=V.extend(!0,{},this.regional[""]),this.regional["en-US"]=V.extend(!0,{},this.regional.en),this.dpDiv=nt(V("<div id='"+this._mainDivId+"' class='ui-datepicker ui-widget ui-widget-content ui-helper-clearfix ui-corner-all'></div>"))}function nt(t){var e="button, .ui-datepicker-prev, .ui-datepicker-next, .ui-datepicker-calendar td a";return t.on("mouseout",e,function(){V(this).removeClass("ui-state-hover"),-1!==this.className.indexOf("ui-datepicker-prev")&&V(this).removeClass("ui-datepicker-prev-hover"),-1!==this.className.indexOf("ui-datepicker-next")&&V(this).removeClass("ui-datepicker-next-hover")}).on("mouseover",e,ot)}function ot(){V.datepicker._isDisabledDatepicker((it.inline?it.dpDiv.parent():it.input)[0])||(V(this).parents(".ui-datepicker-calendar").find("a").removeClass("ui-state-hover"),V(this).addClass("ui-state-hover"),-1!==this.className.indexOf("ui-datepicker-prev")&&V(this).addClass("ui-datepicker-prev-hover"),-1!==this.className.indexOf("ui-datepicker-next")&&V(this).addClass("ui-datepicker-next-hover"))}function at(t,e){for(var i in V.extend(t,e),e)null==e[i]&&(t[i]=e[i]);return t}V.extend(V.ui,{datepicker:{version:"1.13.2"}}),V.extend(st.prototype,{markerClassName:"hasDatepicker",maxRows:4,_widgetDatepicker:function(){return this.dpDiv},setDefaults:function(t){return at(this._defaults,t||{}),this},_attachDatepicker:function(t,e){var i,s=t.nodeName.toLowerCase(),n="div"===s||"span"===s;t.id||(this.uuid+=1,t.id="dp"+this.uuid),(i=this._newInst(V(t),n)).settings=V.extend({},e||{}),"input"===s?this._connectDatepicker(t,i):n&&this._inlineDatepicker(t,i)},_newInst:function(t,e){return{id:t[0].id.replace(/([^A-Za-z0-9_\-])/g,"\\\\$1"),input:t,selectedDay:0,selectedMonth:0,selectedYear:0,drawMonth:0,drawYear:0,inline:e,dpDiv:e?nt(V("<div class='"+this._inlineClass+" ui-datepicker ui-widget ui-widget-content ui-helper-clearfix ui-corner-all'></div>")):this.dpDiv}},_connectDatepicker:function(t,e){var i=V(t);e.append=V([]),e.trigger=V([]),i.hasClass(this.markerClassName)||(this._attachments(i,e),i.addClass(this.markerClassName).on("keydown",this._doKeyDown).on("keypress",this._doKeyPress).on("keyup",this._doKeyUp),this._autoSize(e),V.data(t,"datepicker",e),e.settings.disabled&&this._disableDatepicker(t))},_attachments:function(t,e){var i,s=this._get(e,"appendText"),n=this._get(e,"isRTL");e.append&&e.append.remove(),s&&(e.append=V("<span>").addClass(this._appendClass).text(s),t[n?"before":"after"](e.append)),t.off("focus",this._showDatepicker),e.trigger&&e.trigger.remove(),"focus"!==(i=this._get(e,"showOn"))&&"both"!==i||t.on("focus",this._showDatepicker),"button"!==i&&"both"!==i||(s=this._get(e,"buttonText"),i=this._get(e,"buttonImage"),this._get(e,"buttonImageOnly")?e.trigger=V("<img>").addClass(this._triggerClass).attr({src:i,alt:s,title:s}):(e.trigger=V("<button type='button'>").addClass(this._triggerClass),i?e.trigger.html(V("<img>").attr({src:i,alt:s,title:s})):e.trigger.text(s)),t[n?"before":"after"](e.trigger),e.trigger.on("click",function(){return V.datepicker._datepickerShowing&&V.datepicker._lastInput===t[0]?V.datepicker._hideDatepicker():(V.datepicker._datepickerShowing&&V.datepicker._lastInput!==t[0]&&V.datepicker._hideDatepicker(),V.datepicker._showDatepicker(t[0])),!1}))},_autoSize:function(t){var e,i,s,n,o,a;this._get(t,"autoSize")&&!t.inline&&(o=new Date(2009,11,20),(a=this._get(t,"dateFormat")).match(/[DM]/)&&(e=function(t){for(n=s=i=0;n<t.length;n++)t[n].length>i&&(i=t[n].length,s=n);return s},o.setMonth(e(this._get(t,a.match(/MM/)?"monthNames":"monthNamesShort"))),o.setDate(e(this._get(t,a.match(/DD/)?"dayNames":"dayNamesShort"))+20-o.getDay())),t.input.attr("size",this._formatDate(t,o).length))},_inlineDatepicker:function(t,e){var i=V(t);i.hasClass(this.markerClassName)||(i.addClass(this.markerClassName).append(e.dpDiv),V.data(t,"datepicker",e),this._setDate(e,this._getDefaultDate(e),!0),this._updateDatepicker(e),this._updateAlternate(e),e.settings.disabled&&this._disableDatepicker(t),e.dpDiv.css("display","block"))},_dialogDatepicker:function(t,e,i,s,n){var o,a=this._dialogInst;return a||(this.uuid+=1,o="dp"+this.uuid,this._dialogInput=V("<input type='text' id='"+o+"' style='position: absolute; top: -100px; width: 0px;'/>"),this._dialogInput.on("keydown",this._doKeyDown),V("body").append(this._dialogInput),(a=this._dialogInst=this._newInst(this._dialogInput,!1)).settings={},V.data(this._dialogInput[0],"datepicker",a)),at(a.settings,s||{}),e=e&&e.constructor===Date?this._formatDate(a,e):e,this._dialogInput.val(e),this._pos=n?n.length?n:[n.pageX,n.pageY]:null,this._pos||(o=document.documentElement.clientWidth,s=document.documentElement.clientHeight,e=document.documentElement.scrollLeft||document.body.scrollLeft,n=document.documentElement.scrollTop||document.body.scrollTop,this._pos=[o/2-100+e,s/2-150+n]),this._dialogInput.css("left",this._pos[0]+20+"px").css("top",this._pos[1]+"px"),a.settings.onSelect=i,this._inDialog=!0,this.dpDiv.addClass(this._dialogClass),this._showDatepicker(this._dialogInput[0]),V.blockUI&&V.blockUI(this.dpDiv),V.data(this._dialogInput[0],"datepicker",a),this},_destroyDatepicker:function(t){var e,i=V(t),s=V.data(t,"datepicker");i.hasClass(this.markerClassName)&&(e=t.nodeName.toLowerCase(),V.removeData(t,"datepicker"),"input"===e?(s.append.remove(),s.trigger.remove(),i.removeClass(this.markerClassName).off("focus",this._showDatepicker).off("keydown",this._doKeyDown).off("keypress",this._doKeyPress).off("keyup",this._doKeyUp)):"div"!==e&&"span"!==e||i.removeClass(this.markerClassName).empty(),it===s&&(it=null,this._curInst=null))},_enableDatepicker:function(e){var t,i=V(e),s=V.data(e,"datepicker");i.hasClass(this.markerClassName)&&("input"===(t=e.nodeName.toLowerCase())?(e.disabled=!1,s.trigger.filter("button").each(function(){this.disabled=!1}).end().filter("img").css({opacity:"1.0",cursor:""})):"div"!==t&&"span"!==t||((i=i.children("."+this._inlineClass)).children().removeClass("ui-state-disabled"),i.find("select.ui-datepicker-month, select.ui-datepicker-year").prop("disabled",!1)),this._disabledInputs=V.map(this._disabledInputs,function(t){return t===e?null:t}))},_disableDatepicker:function(e){var t,i=V(e),s=V.data(e,"datepicker");i.hasClass(this.markerClassName)&&("input"===(t=e.nodeName.toLowerCase())?(e.disabled=!0,s.trigger.filter("button").each(function(){this.disabled=!0}).end().filter("img").css({opacity:"0.5",cursor:"default"})):"div"!==t&&"span"!==t||((i=i.children("."+this._inlineClass)).children().addClass("ui-state-disabled"),i.find("select.ui-datepicker-month, select.ui-datepicker-year").prop("disabled",!0)),this._disabledInputs=V.map(this._disabledInputs,function(t){return t===e?null:t}),this._disabledInputs[this._disabledInputs.length]=e)},_isDisabledDatepicker:function(t){if(!t)return!1;for(var e=0;e<this._disabledInputs.length;e++)if(this._disabledInputs[e]===t)return!0;return!1},_getInst:function(t){try{return V.data(t,"datepicker")}catch(t){throw"Missing instance data for this datepicker"}},_optionDatepicker:function(t,e,i){var s,n,o=this._getInst(t);if(2===arguments.length&&"string"==typeof e)return"defaults"===e?V.extend({},V.datepicker._defaults):o?"all"===e?V.extend({},o.settings):this._get(o,e):null;s=e||{},"string"==typeof e&&((s={})[e]=i),o&&(this._curInst===o&&this._hideDatepicker(),n=this._getDateDatepicker(t,!0),e=this._getMinMaxDate(o,"min"),i=this._getMinMaxDate(o,"max"),at(o.settings,s),null!==e&&void 0!==s.dateFormat&&void 0===s.minDate&&(o.settings.minDate=this._formatDate(o,e)),null!==i&&void 0!==s.dateFormat&&void 0===s.maxDate&&(o.settings.maxDate=this._formatDate(o,i)),"disabled"in s&&(s.disabled?this._disableDatepicker(t):this._enableDatepicker(t)),this._attachments(V(t),o),this._autoSize(o),this._setDate(o,n),this._updateAlternate(o),this._updateDatepicker(o))},_changeDatepicker:function(t,e,i){this._optionDatepicker(t,e,i)},_refreshDatepicker:function(t){t=this._getInst(t);t&&this._updateDatepicker(t)},_setDateDatepicker:function(t,e){t=this._getInst(t);t&&(this._setDate(t,e),this._updateDatepicker(t),this._updateAlternate(t))},_getDateDatepicker:function(t,e){t=this._getInst(t);return t&&!t.inline&&this._setDateFromField(t,e),t?this._getDate(t):null},_doKeyDown:function(t){var e,i,s=V.datepicker._getInst(t.target),n=!0,o=s.dpDiv.is(".ui-datepicker-rtl");if(s._keyEvent=!0,V.datepicker._datepickerShowing)switch(t.keyCode){case 9:V.datepicker._hideDatepicker(),n=!1;break;case 13:return(i=V("td."+V.datepicker._dayOverClass+":not(."+V.datepicker._currentClass+")",s.dpDiv))[0]&&V.datepicker._selectDay(t.target,s.selectedMonth,s.selectedYear,i[0]),(e=V.datepicker._get(s,"onSelect"))?(i=V.datepicker._formatDate(s),e.apply(s.input?s.input[0]:null,[i,s])):V.datepicker._hideDatepicker(),!1;case 27:V.datepicker._hideDatepicker();break;case 33:V.datepicker._adjustDate(t.target,t.ctrlKey?-V.datepicker._get(s,"stepBigMonths"):-V.datepicker._get(s,"stepMonths"),"M");break;case 34:V.datepicker._adjustDate(t.target,t.ctrlKey?+V.datepicker._get(s,"stepBigMonths"):+V.datepicker._get(s,"stepMonths"),"M");break;case 35:(t.ctrlKey||t.metaKey)&&V.datepicker._clearDate(t.target),n=t.ctrlKey||t.metaKey;break;case 36:(t.ctrlKey||t.metaKey)&&V.datepicker._gotoToday(t.target),n=t.ctrlKey||t.metaKey;break;case 37:(t.ctrlKey||t.metaKey)&&V.datepicker._adjustDate(t.target,o?1:-1,"D"),n=t.ctrlKey||t.metaKey,t.originalEvent.altKey&&V.datepicker._adjustDate(t.target,t.ctrlKey?-V.datepicker._get(s,"stepBigMonths"):-V.datepicker._get(s,"stepMonths"),"M");break;case 38:(t.ctrlKey||t.metaKey)&&V.datepicker._adjustDate(t.target,-7,"D"),n=t.ctrlKey||t.metaKey;break;case 39:(t.ctrlKey||t.metaKey)&&V.datepicker._adjustDate(t.target,o?-1:1,"D"),n=t.ctrlKey||t.metaKey,t.originalEvent.altKey&&V.datepicker._adjustDate(t.target,t.ctrlKey?+V.datepicker._get(s,"stepBigMonths"):+V.datepicker._get(s,"stepMonths"),"M");break;case 40:(t.ctrlKey||t.metaKey)&&V.datepicker._adjustDate(t.target,7,"D"),n=t.ctrlKey||t.metaKey;break;default:n=!1}else 36===t.keyCode&&t.ctrlKey?V.datepicker._showDatepicker(this):n=!1;n&&(t.preventDefault(),t.stopPropagation())},_doKeyPress:function(t){var e,i=V.datepicker._getInst(t.target);if(V.datepicker._get(i,"constrainInput"))return e=V.datepicker._possibleChars(V.datepicker._get(i,"dateFormat")),i=String.fromCharCode(null==t.charCode?t.keyCode:t.charCode),t.ctrlKey||t.metaKey||i<" "||!e||-1<e.indexOf(i)},_doKeyUp:function(t){t=V.datepicker._getInst(t.target);if(t.input.val()!==t.lastVal)try{V.datepicker.parseDate(V.datepicker._get(t,"dateFormat"),t.input?t.input.val():null,V.datepicker._getFormatConfig(t))&&(V.datepicker._setDateFromField(t),V.datepicker._updateAlternate(t),V.datepicker._updateDatepicker(t))}catch(t){}return!0},_showDatepicker:function(t){var e,i,s,n;"input"!==(t=t.target||t).nodeName.toLowerCase()&&(t=V("input",t.parentNode)[0]),V.datepicker._isDisabledDatepicker(t)||V.datepicker._lastInput===t||(n=V.datepicker._getInst(t),V.datepicker._curInst&&V.datepicker._curInst!==n&&(V.datepicker._curInst.dpDiv.stop(!0,!0),n&&V.datepicker._datepickerShowing&&V.datepicker._hideDatepicker(V.datepicker._curInst.input[0])),!1!==(i=(s=V.datepicker._get(n,"beforeShow"))?s.apply(t,[t,n]):{})&&(at(n.settings,i),n.lastVal=null,V.datepicker._lastInput=t,V.datepicker._setDateFromField(n),V.datepicker._inDialog&&(t.value=""),V.datepicker._pos||(V.datepicker._pos=V.datepicker._findPos(t),V.datepicker._pos[1]+=t.offsetHeight),e=!1,V(t).parents().each(function(){return!(e|="fixed"===V(this).css("position"))}),s={left:V.datepicker._pos[0],top:V.datepicker._pos[1]},V.datepicker._pos=null,n.dpDiv.empty(),n.dpDiv.css({position:"absolute",display:"block",top:"-1000px"}),V.datepicker._updateDatepicker(n),s=V.datepicker._checkOffset(n,s,e),n.dpDiv.css({position:V.datepicker._inDialog&&V.blockUI?"static":e?"fixed":"absolute",display:"none",left:s.left+"px",top:s.top+"px"}),n.inline||(i=V.datepicker._get(n,"showAnim"),s=V.datepicker._get(n,"duration"),n.dpDiv.css("z-index",function(t){for(var e,i;t.length&&t[0]!==document;){if(("absolute"===(e=t.css("position"))||"relative"===e||"fixed"===e)&&(i=parseInt(t.css("zIndex"),10),!isNaN(i)&&0!==i))return i;t=t.parent()}return 0}(V(t))+1),V.datepicker._datepickerShowing=!0,V.effects&&V.effects.effect[i]?n.dpDiv.show(i,V.datepicker._get(n,"showOptions"),s):n.dpDiv[i||"show"](i?s:null),V.datepicker._shouldFocusInput(n)&&n.input.trigger("focus"),V.datepicker._curInst=n)))},_updateDatepicker:function(t){this.maxRows=4,(it=t).dpDiv.empty().append(this._generateHTML(t)),this._attachHandlers(t);var e,i=this._getNumberOfMonths(t),s=i[1],n=t.dpDiv.find("."+this._dayOverClass+" a"),o=V.datepicker._get(t,"onUpdateDatepicker");0<n.length&&ot.apply(n.get(0)),t.dpDiv.removeClass("ui-datepicker-multi-2 ui-datepicker-multi-3 ui-datepicker-multi-4").width(""),1<s&&t.dpDiv.addClass("ui-datepicker-multi-"+s).css("width",17*s+"em"),t.dpDiv[(1!==i[0]||1!==i[1]?"add":"remove")+"Class"]("ui-datepicker-multi"),t.dpDiv[(this._get(t,"isRTL")?"add":"remove")+"Class"]("ui-datepicker-rtl"),t===V.datepicker._curInst&&V.datepicker._datepickerShowing&&V.datepicker._shouldFocusInput(t)&&t.input.trigger("focus"),t.yearshtml&&(e=t.yearshtml,setTimeout(function(){e===t.yearshtml&&t.yearshtml&&t.dpDiv.find("select.ui-datepicker-year").first().replaceWith(t.yearshtml),e=t.yearshtml=null},0)),o&&o.apply(t.input?t.input[0]:null,[t])},_shouldFocusInput:function(t){return t.input&&t.input.is(":visible")&&!t.input.is(":disabled")&&!t.input.is(":focus")},_checkOffset:function(t,e,i){var s=t.dpDiv.outerWidth(),n=t.dpDiv.outerHeight(),o=t.input?t.input.outerWidth():0,a=t.input?t.input.outerHeight():0,r=document.documentElement.clientWidth+(i?0:V(document).scrollLeft()),l=document.documentElement.clientHeight+(i?0:V(document).scrollTop());return e.left-=this._get(t,"isRTL")?s-o:0,e.left-=i&&e.left===t.input.offset().left?V(document).scrollLeft():0,e.top-=i&&e.top===t.input.offset().top+a?V(document).scrollTop():0,e.left-=Math.min(e.left,e.left+s>r&&s<r?Math.abs(e.left+s-r):0),e.top-=Math.min(e.top,e.top+n>l&&n<l?Math.abs(n+a):0),e},_findPos:function(t){for(var e=this._getInst(t),i=this._get(e,"isRTL");t&&("hidden"===t.type||1!==t.nodeType||V.expr.pseudos.hidden(t));)t=t[i?"previousSibling":"nextSibling"];return[(e=V(t).offset()).left,e.top]},_hideDatepicker:function(t){var e,i,s=this._curInst;!s||t&&s!==V.data(t,"datepicker")||this._datepickerShowing&&(e=this._get(s,"showAnim"),i=this._get(s,"duration"),t=function(){V.datepicker._tidyDialog(s)},V.effects&&(V.effects.effect[e]||V.effects[e])?s.dpDiv.hide(e,V.datepicker._get(s,"showOptions"),i,t):s.dpDiv["slideDown"===e?"slideUp":"fadeIn"===e?"fadeOut":"hide"](e?i:null,t),e||t(),this._datepickerShowing=!1,(t=this._get(s,"onClose"))&&t.apply(s.input?s.input[0]:null,[s.input?s.input.val():"",s]),this._lastInput=null,this._inDialog&&(this._dialogInput.css({position:"absolute",left:"0",top:"-100px"}),V.blockUI&&(V.unblockUI(),V("body").append(this.dpDiv))),this._inDialog=!1)},_tidyDialog:function(t){t.dpDiv.removeClass(this._dialogClass).off(".ui-datepicker-calendar")},_checkExternalClick:function(t){var e;V.datepicker._curInst&&(e=V(t.target),t=V.datepicker._getInst(e[0]),(e[0].id===V.datepicker._mainDivId||0!==e.parents("#"+V.datepicker._mainDivId).length||e.hasClass(V.datepicker.markerClassName)||e.closest("."+V.datepicker._triggerClass).length||!V.datepicker._datepickerShowing||V.datepicker._inDialog&&V.blockUI)&&(!e.hasClass(V.datepicker.markerClassName)||V.datepicker._curInst===t)||V.datepicker._hideDatepicker())},_adjustDate:function(t,e,i){var s=V(t),t=this._getInst(s[0]);this._isDisabledDatepicker(s[0])||(this._adjustInstDate(t,e,i),this._updateDatepicker(t))},_gotoToday:function(t){var e=V(t),i=this._getInst(e[0]);this._get(i,"gotoCurrent")&&i.currentDay?(i.selectedDay=i.currentDay,i.drawMonth=i.selectedMonth=i.currentMonth,i.drawYear=i.selectedYear=i.currentYear):(t=new Date,i.selectedDay=t.getDate(),i.drawMonth=i.selectedMonth=t.getMonth(),i.drawYear=i.selectedYear=t.getFullYear()),this._notifyChange(i),this._adjustDate(e)},_selectMonthYear:function(t,e,i){var s=V(t),t=this._getInst(s[0]);t["selected"+("M"===i?"Month":"Year")]=t["draw"+("M"===i?"Month":"Year")]=parseInt(e.options[e.selectedIndex].value,10),this._notifyChange(t),this._adjustDate(s)},_selectDay:function(t,e,i,s){var n=V(t);V(s).hasClass(this._unselectableClass)||this._isDisabledDatepicker(n[0])||((n=this._getInst(n[0])).selectedDay=n.currentDay=parseInt(V("a",s).attr("data-date")),n.selectedMonth=n.currentMonth=e,n.selectedYear=n.currentYear=i,this._selectDate(t,this._formatDate(n,n.currentDay,n.currentMonth,n.currentYear)))},_clearDate:function(t){t=V(t);this._selectDate(t,"")},_selectDate:function(t,e){var i=V(t),t=this._getInst(i[0]);e=null!=e?e:this._formatDate(t),t.input&&t.input.val(e),this._updateAlternate(t),(i=this._get(t,"onSelect"))?i.apply(t.input?t.input[0]:null,[e,t]):t.input&&t.input.trigger("change"),t.inline?this._updateDatepicker(t):(this._hideDatepicker(),this._lastInput=t.input[0],"object"!=typeof t.input[0]&&t.input.trigger("focus"),this._lastInput=null)},_updateAlternate:function(t){var e,i,s=this._get(t,"altField");s&&(e=this._get(t,"altFormat")||this._get(t,"dateFormat"),i=this._getDate(t),t=this.formatDate(e,i,this._getFormatConfig(t)),V(document).find(s).val(t))},noWeekends:function(t){t=t.getDay();return[0<t&&t<6,""]},iso8601Week:function(t){var e=new Date(t.getTime());return e.setDate(e.getDate()+4-(e.getDay()||7)),t=e.getTime(),e.setMonth(0),e.setDate(1),Math.floor(Math.round((t-e)/864e5)/7)+1},parseDate:function(e,n,t){if(null==e||null==n)throw"Invalid arguments";if(""===(n="object"==typeof n?n.toString():n+""))return null;for(var i,s,o,a=0,r=(t?t.shortYearCutoff:null)||this._defaults.shortYearCutoff,r="string"!=typeof r?r:(new Date).getFullYear()%100+parseInt(r,10),l=(t?t.dayNamesShort:null)||this._defaults.dayNamesShort,h=(t?t.dayNames:null)||this._defaults.dayNames,c=(t?t.monthNamesShort:null)||this._defaults.monthNamesShort,u=(t?t.monthNames:null)||this._defaults.monthNames,d=-1,p=-1,f=-1,g=-1,m=!1,_=function(t){t=w+1<e.length&&e.charAt(w+1)===t;return t&&w++,t},v=function(t){var e=_(t),e="@"===t?14:"!"===t?20:"y"===t&&e?4:"o"===t?3:2,e=new RegExp("^\\d{"+("y"===t?e:1)+","+e+"}"),e=n.substring(a).match(e);if(!e)throw"Missing number at position "+a;return a+=e[0].length,parseInt(e[0],10)},b=function(t,e,i){var s=-1,e=V.map(_(t)?i:e,function(t,e){return[[e,t]]}).sort(function(t,e){return-(t[1].length-e[1].length)});if(V.each(e,function(t,e){var i=e[1];if(n.substr(a,i.length).toLowerCase()===i.toLowerCase())return s=e[0],a+=i.length,!1}),-1!==s)return s+1;throw"Unknown name at position "+a},y=function(){if(n.charAt(a)!==e.charAt(w))throw"Unexpected literal at position "+a;a++},w=0;w<e.length;w++)if(m)"'"!==e.charAt(w)||_("'")?y():m=!1;else switch(e.charAt(w)){case"d":f=v("d");break;case"D":b("D",l,h);break;case"o":g=v("o");break;case"m":p=v("m");break;case"M":p=b("M",c,u);break;case"y":d=v("y");break;case"@":d=(o=new Date(v("@"))).getFullYear(),p=o.getMonth()+1,f=o.getDate();break;case"!":d=(o=new Date((v("!")-this._ticksTo1970)/1e4)).getFullYear(),p=o.getMonth()+1,f=o.getDate();break;case"'":_("'")?y():m=!0;break;default:y()}if(a<n.length&&(s=n.substr(a),!/^\s+/.test(s)))throw"Extra/unparsed characters found in date: "+s;if(-1===d?d=(new Date).getFullYear():d<100&&(d+=(new Date).getFullYear()-(new Date).getFullYear()%100+(d<=r?0:-100)),-1<g)for(p=1,f=g;;){if(f<=(i=this._getDaysInMonth(d,p-1)))break;p++,f-=i}if((o=this._daylightSavingAdjust(new Date(d,p-1,f))).getFullYear()!==d||o.getMonth()+1!==p||o.getDate()!==f)throw"Invalid date";return o},ATOM:"yy-mm-dd",COOKIE:"D, dd M yy",ISO_8601:"yy-mm-dd",RFC_822:"D, d M y",RFC_850:"DD, dd-M-y",RFC_1036:"D, d M y",RFC_1123:"D, d M yy",RFC_2822:"D, d M yy",RSS:"D, d M y",TICKS:"!",TIMESTAMP:"@",W3C:"yy-mm-dd",_ticksTo1970:24*(718685+Math.floor(492.5)-Math.floor(19.7)+Math.floor(4.925))*60*60*1e7,formatDate:function(e,t,i){if(!t)return"";function s(t,e,i){var s=""+e;if(c(t))for(;s.length<i;)s="0"+s;return s}function n(t,e,i,s){return(c(t)?s:i)[e]}var o,a=(i?i.dayNamesShort:null)||this._defaults.dayNamesShort,r=(i?i.dayNames:null)||this._defaults.dayNames,l=(i?i.monthNamesShort:null)||this._defaults.monthNamesShort,h=(i?i.monthNames:null)||this._defaults.monthNames,c=function(t){t=o+1<e.length&&e.charAt(o+1)===t;return t&&o++,t},u="",d=!1;if(t)for(o=0;o<e.length;o++)if(d)"'"!==e.charAt(o)||c("'")?u+=e.charAt(o):d=!1;else switch(e.charAt(o)){case"d":u+=s("d",t.getDate(),2);break;case"D":u+=n("D",t.getDay(),a,r);break;case"o":u+=s("o",Math.round((new Date(t.getFullYear(),t.getMonth(),t.getDate()).getTime()-new Date(t.getFullYear(),0,0).getTime())/864e5),3);break;case"m":u+=s("m",t.getMonth()+1,2);break;case"M":u+=n("M",t.getMonth(),l,h);break;case"y":u+=c("y")?t.getFullYear():(t.getFullYear()%100<10?"0":"")+t.getFullYear()%100;break;case"@":u+=t.getTime();break;case"!":u+=1e4*t.getTime()+this._ticksTo1970;break;case"'":c("'")?u+="'":d=!0;break;default:u+=e.charAt(o)}return u},_possibleChars:function(e){for(var t="",i=!1,s=function(t){t=n+1<e.length&&e.charAt(n+1)===t;return t&&n++,t},n=0;n<e.length;n++)if(i)"'"!==e.charAt(n)||s("'")?t+=e.charAt(n):i=!1;else switch(e.charAt(n)){case"d":case"m":case"y":case"@":t+="0123456789";break;case"D":case"M":return null;case"'":s("'")?t+="'":i=!0;break;default:t+=e.charAt(n)}return t},_get:function(t,e){return(void 0!==t.settings[e]?t.settings:this._defaults)[e]},_setDateFromField:function(t,e){if(t.input.val()!==t.lastVal){var i=this._get(t,"dateFormat"),s=t.lastVal=t.input?t.input.val():null,n=this._getDefaultDate(t),o=n,a=this._getFormatConfig(t);try{o=this.parseDate(i,s,a)||n}catch(t){s=e?"":s}t.selectedDay=o.getDate(),t.drawMonth=t.selectedMonth=o.getMonth(),t.drawYear=t.selectedYear=o.getFullYear(),t.currentDay=s?o.getDate():0,t.currentMonth=s?o.getMonth():0,t.currentYear=s?o.getFullYear():0,this._adjustInstDate(t)}},_getDefaultDate:function(t){return this._restrictMinMax(t,this._determineDate(t,this._get(t,"defaultDate"),new Date))},_determineDate:function(r,t,e){var i,s,t=null==t||""===t?e:"string"==typeof t?function(t){try{return V.datepicker.parseDate(V.datepicker._get(r,"dateFormat"),t,V.datepicker._getFormatConfig(r))}catch(t){}for(var e=(t.toLowerCase().match(/^c/)?V.datepicker._getDate(r):null)||new Date,i=e.getFullYear(),s=e.getMonth(),n=e.getDate(),o=/([+\-]?[0-9]+)\s*(d|D|w|W|m|M|y|Y)?/g,a=o.exec(t);a;){switch(a[2]||"d"){case"d":case"D":n+=parseInt(a[1],10);break;case"w":case"W":n+=7*parseInt(a[1],10);break;case"m":case"M":s+=parseInt(a[1],10),n=Math.min(n,V.datepicker._getDaysInMonth(i,s));break;case"y":case"Y":i+=parseInt(a[1],10),n=Math.min(n,V.datepicker._getDaysInMonth(i,s))}a=o.exec(t)}return new Date(i,s,n)}(t):"number"==typeof t?isNaN(t)?e:(i=t,(s=new Date).setDate(s.getDate()+i),s):new Date(t.getTime());return(t=t&&"Invalid Date"===t.toString()?e:t)&&(t.setHours(0),t.setMinutes(0),t.setSeconds(0),t.setMilliseconds(0)),this._daylightSavingAdjust(t)},_daylightSavingAdjust:function(t){return t?(t.setHours(12<t.getHours()?t.getHours()+2:0),t):null},_setDate:function(t,e,i){var s=!e,n=t.selectedMonth,o=t.selectedYear,e=this._restrictMinMax(t,this._determineDate(t,e,new Date));t.selectedDay=t.currentDay=e.getDate(),t.drawMonth=t.selectedMonth=t.currentMonth=e.getMonth(),t.drawYear=t.selectedYear=t.currentYear=e.getFullYear(),n===t.selectedMonth&&o===t.selectedYear||i||this._notifyChange(t),this._adjustInstDate(t),t.input&&t.input.val(s?"":this._formatDate(t))},_getDate:function(t){return!t.currentYear||t.input&&""===t.input.val()?null:this._daylightSavingAdjust(new Date(t.currentYear,t.currentMonth,t.currentDay))},_attachHandlers:function(t){var e=this._get(t,"stepMonths"),i="#"+t.id.replace(/\\\\/g,"\\");t.dpDiv.find("[data-handler]").map(function(){var t={prev:function(){V.datepicker._adjustDate(i,-e,"M")},next:function(){V.datepicker._adjustDate(i,+e,"M")},hide:function(){V.datepicker._hideDatepicker()},today:function(){V.datepicker._gotoToday(i)},selectDay:function(){return V.datepicker._selectDay(i,+this.getAttribute("data-month"),+this.getAttribute("data-year"),this),!1},selectMonth:function(){return V.datepicker._selectMonthYear(i,this,"M"),!1},selectYear:function(){return V.datepicker._selectMonthYear(i,this,"Y"),!1}};V(this).on(this.getAttribute("data-event"),t[this.getAttribute("data-handler")])})},_generateHTML:function(t){var e,i,s,n,o,a,r,l,h,c,u,d,p,f,g,m,_,v,b,y,w,x,k,C,D,I,T,P,M,S,H,z,A=new Date,O=this._daylightSavingAdjust(new Date(A.getFullYear(),A.getMonth(),A.getDate())),N=this._get(t,"isRTL"),E=this._get(t,"showButtonPanel"),W=this._get(t,"hideIfNoPrevNext"),F=this._get(t,"navigationAsDateFormat"),L=this._getNumberOfMonths(t),R=this._get(t,"showCurrentAtPos"),A=this._get(t,"stepMonths"),Y=1!==L[0]||1!==L[1],B=this._daylightSavingAdjust(t.currentDay?new Date(t.currentYear,t.currentMonth,t.currentDay):new Date(9999,9,9)),j=this._getMinMaxDate(t,"min"),q=this._getMinMaxDate(t,"max"),K=t.drawMonth-R,U=t.drawYear;if(K<0&&(K+=12,U--),q)for(e=this._daylightSavingAdjust(new Date(q.getFullYear(),q.getMonth()-L[0]*L[1]+1,q.getDate())),e=j&&e<j?j:e;this._daylightSavingAdjust(new Date(U,K,1))>e;)--K<0&&(K=11,U--);for(t.drawMonth=K,t.drawYear=U,R=this._get(t,"prevText"),R=F?this.formatDate(R,this._daylightSavingAdjust(new Date(U,K-A,1)),this._getFormatConfig(t)):R,i=this._canAdjustMonth(t,-1,U,K)?V("<a>").attr({class:"ui-datepicker-prev ui-corner-all","data-handler":"prev","data-event":"click",title:R}).append(V("<span>").addClass("ui-icon ui-icon-circle-triangle-"+(N?"e":"w")).text(R))[0].outerHTML:W?"":V("<a>").attr({class:"ui-datepicker-prev ui-corner-all ui-state-disabled",title:R}).append(V("<span>").addClass("ui-icon ui-icon-circle-triangle-"+(N?"e":"w")).text(R))[0].outerHTML,R=this._get(t,"nextText"),R=F?this.formatDate(R,this._daylightSavingAdjust(new Date(U,K+A,1)),this._getFormatConfig(t)):R,s=this._canAdjustMonth(t,1,U,K)?V("<a>").attr({class:"ui-datepicker-next ui-corner-all","data-handler":"next","data-event":"click",title:R}).append(V("<span>").addClass("ui-icon ui-icon-circle-triangle-"+(N?"w":"e")).text(R))[0].outerHTML:W?"":V("<a>").attr({class:"ui-datepicker-next ui-corner-all ui-state-disabled",title:R}).append(V("<span>").attr("class","ui-icon ui-icon-circle-triangle-"+(N?"w":"e")).text(R))[0].outerHTML,A=this._get(t,"currentText"),W=this._get(t,"gotoCurrent")&&t.currentDay?B:O,A=F?this.formatDate(A,W,this._getFormatConfig(t)):A,R="",t.inline||(R=V("<button>").attr({type:"button",class:"ui-datepicker-close ui-state-default ui-priority-primary ui-corner-all","data-handler":"hide","data-event":"click"}).text(this._get(t,"closeText"))[0].outerHTML),F="",E&&(F=V("<div class='ui-datepicker-buttonpane ui-widget-content'>").append(N?R:"").append(this._isInRange(t,W)?V("<button>").attr({type:"button",class:"ui-datepicker-current ui-state-default ui-priority-secondary ui-corner-all","data-handler":"today","data-event":"click"}).text(A):"").append(N?"":R)[0].outerHTML),n=parseInt(this._get(t,"firstDay"),10),n=isNaN(n)?0:n,o=this._get(t,"showWeek"),a=this._get(t,"dayNames"),r=this._get(t,"dayNamesMin"),l=this._get(t,"monthNames"),h=this._get(t,"monthNamesShort"),c=this._get(t,"beforeShowDay"),u=this._get(t,"showOtherMonths"),d=this._get(t,"selectOtherMonths"),p=this._getDefaultDate(t),f="",m=0;m<L[0];m++){for(_="",this.maxRows=4,v=0;v<L[1];v++){if(b=this._daylightSavingAdjust(new Date(U,K,t.selectedDay)),y=" ui-corner-all",w="",Y){if(w+="<div class='ui-datepicker-group",1<L[1])switch(v){case 0:w+=" ui-datepicker-group-first",y=" ui-corner-"+(N?"right":"left");break;case L[1]-1:w+=" ui-datepicker-group-last",y=" ui-corner-"+(N?"left":"right");break;default:w+=" ui-datepicker-group-middle",y=""}w+="'>"}for(w+="<div class='ui-datepicker-header ui-widget-header ui-helper-clearfix"+y+"'>"+(/all|left/.test(y)&&0===m?N?s:i:"")+(/all|right/.test(y)&&0===m?N?i:s:"")+this._generateMonthYearHeader(t,K,U,j,q,0<m||0<v,l,h)+"</div><table class='ui-datepicker-calendar'><thead><tr>",x=o?"<th class='ui-datepicker-week-col'>"+this._get(t,"weekHeader")+"</th>":"",g=0;g<7;g++)x+="<th scope='col'"+(5<=(g+n+6)%7?" class='ui-datepicker-week-end'":"")+"><span title='"+a[k=(g+n)%7]+"'>"+r[k]+"</span></th>";for(w+=x+"</tr></thead><tbody>",D=this._getDaysInMonth(U,K),U===t.selectedYear&&K===t.selectedMonth&&(t.selectedDay=Math.min(t.selectedDay,D)),C=(this._getFirstDayOfMonth(U,K)-n+7)%7,D=Math.ceil((C+D)/7),I=Y&&this.maxRows>D?this.maxRows:D,this.maxRows=I,T=this._daylightSavingAdjust(new Date(U,K,1-C)),P=0;P<I;P++){for(w+="<tr>",M=o?"<td class='ui-datepicker-week-col'>"+this._get(t,"calculateWeek")(T)+"</td>":"",g=0;g<7;g++)S=c?c.apply(t.input?t.input[0]:null,[T]):[!0,""],z=(H=T.getMonth()!==K)&&!d||!S[0]||j&&T<j||q&&q<T,M+="<td class='"+(5<=(g+n+6)%7?" ui-datepicker-week-end":"")+(H?" ui-datepicker-other-month":"")+(T.getTime()===b.getTime()&&K===t.selectedMonth&&t._keyEvent||p.getTime()===T.getTime()&&p.getTime()===b.getTime()?" "+this._dayOverClass:"")+(z?" "+this._unselectableClass+" ui-state-disabled":"")+(H&&!u?"":" "+S[1]+(T.getTime()===B.getTime()?" "+this._currentClass:"")+(T.getTime()===O.getTime()?" ui-datepicker-today":""))+"'"+(H&&!u||!S[2]?"":" title='"+S[2].replace(/'/g,"&#39;")+"'")+(z?"":" data-handler='selectDay' data-event='click' data-month='"+T.getMonth()+"' data-year='"+T.getFullYear()+"'")+">"+(H&&!u?"&#xa0;":z?"<span class='ui-state-default'>"+T.getDate()+"</span>":"<a class='ui-state-default"+(T.getTime()===O.getTime()?" ui-state-highlight":"")+(T.getTime()===B.getTime()?" ui-state-active":"")+(H?" ui-priority-secondary":"")+"' href='#' aria-current='"+(T.getTime()===B.getTime()?"true":"false")+"' data-date='"+T.getDate()+"'>"+T.getDate()+"</a>")+"</td>",T.setDate(T.getDate()+1),T=this._daylightSavingAdjust(T);w+=M+"</tr>"}11<++K&&(K=0,U++),_+=w+="</tbody></table>"+(Y?"</div>"+(0<L[0]&&v===L[1]-1?"<div class='ui-datepicker-row-break'></div>":""):"")}f+=_}return f+=F,t._keyEvent=!1,f},_generateMonthYearHeader:function(t,e,i,s,n,o,a,r){var l,h,c,u,d,p,f=this._get(t,"changeMonth"),g=this._get(t,"changeYear"),m=this._get(t,"showMonthAfterYear"),_=this._get(t,"selectMonthLabel"),v=this._get(t,"selectYearLabel"),b="<div class='ui-datepicker-title'>",y="";if(o||!f)y+="<span class='ui-datepicker-month'>"+a[e]+"</span>";else{for(l=s&&s.getFullYear()===i,h=n&&n.getFullYear()===i,y+="<select class='ui-datepicker-month' aria-label='"+_+"' data-handler='selectMonth' data-event='change'>",c=0;c<12;c++)(!l||c>=s.getMonth())&&(!h||c<=n.getMonth())&&(y+="<option value='"+c+"'"+(c===e?" selected='selected'":"")+">"+r[c]+"</option>");y+="</select>"}if(m||(b+=y+(!o&&f&&g?"":"&#xa0;")),!t.yearshtml)if(t.yearshtml="",o||!g)b+="<span class='ui-datepicker-year'>"+i+"</span>";else{for(a=this._get(t,"yearRange").split(":"),u=(new Date).getFullYear(),d=(_=function(t){t=t.match(/c[+\-].*/)?i+parseInt(t.substring(1),10):t.match(/[+\-].*/)?u+parseInt(t,10):parseInt(t,10);return isNaN(t)?u:t})(a[0]),p=Math.max(d,_(a[1]||"")),d=s?Math.max(d,s.getFullYear()):d,p=n?Math.min(p,n.getFullYear()):p,t.yearshtml+="<select class='ui-datepicker-year' aria-label='"+v+"' data-handler='selectYear' data-event='change'>";d<=p;d++)t.yearshtml+="<option value='"+d+"'"+(d===i?" selected='selected'":"")+">"+d+"</option>";t.yearshtml+="</select>",b+=t.yearshtml,t.yearshtml=null}return b+=this._get(t,"yearSuffix"),m&&(b+=(!o&&f&&g?"":"&#xa0;")+y),b+="</div>"},_adjustInstDate:function(t,e,i){var s=t.selectedYear+("Y"===i?e:0),n=t.selectedMonth+("M"===i?e:0),e=Math.min(t.selectedDay,this._getDaysInMonth(s,n))+("D"===i?e:0),e=this._restrictMinMax(t,this._daylightSavingAdjust(new Date(s,n,e)));t.selectedDay=e.getDate(),t.drawMonth=t.selectedMonth=e.getMonth(),t.drawYear=t.selectedYear=e.getFullYear(),"M"!==i&&"Y"!==i||this._notifyChange(t)},_restrictMinMax:function(t,e){var i=this._getMinMaxDate(t,"min"),t=this._getMinMaxDate(t,"max"),e=i&&e<i?i:e;return t&&t<e?t:e},_notifyChange:function(t){var e=this._get(t,"onChangeMonthYear");e&&e.apply(t.input?t.input[0]:null,[t.selectedYear,t.selectedMonth+1,t])},_getNumberOfMonths:function(t){t=this._get(t,"numberOfMonths");return null==t?[1,1]:"number"==typeof t?[1,t]:t},_getMinMaxDate:function(t,e){return this._determineDate(t,this._get(t,e+"Date"),null)},_getDaysInMonth:function(t,e){return 32-this._daylightSavingAdjust(new Date(t,e,32)).getDate()},_getFirstDayOfMonth:function(t,e){return new Date(t,e,1).getDay()},_canAdjustMonth:function(t,e,i,s){var n=this._getNumberOfMonths(t),n=this._daylightSavingAdjust(new Date(i,s+(e<0?e:n[0]*n[1]),1));return e<0&&n.setDate(this._getDaysInMonth(n.getFullYear(),n.getMonth())),this._isInRange(t,n)},_isInRange:function(t,e){var i=this._getMinMaxDate(t,"min"),s=this._getMinMaxDate(t,"max"),n=null,o=null,a=this._get(t,"yearRange");return a&&(t=a.split(":"),a=(new Date).getFullYear(),n=parseInt(t[0],10),o=parseInt(t[1],10),t[0].match(/[+\-].*/)&&(n+=a),t[1].match(/[+\-].*/)&&(o+=a)),(!i||e.getTime()>=i.getTime())&&(!s||e.getTime()<=s.getTime())&&(!n||e.getFullYear()>=n)&&(!o||e.getFullYear()<=o)},_getFormatConfig:function(t){var e=this._get(t,"shortYearCutoff");return{shortYearCutoff:e="string"!=typeof e?e:(new Date).getFullYear()%100+parseInt(e,10),dayNamesShort:this._get(t,"dayNamesShort"),dayNames:this._get(t,"dayNames"),monthNamesShort:this._get(t,"monthNamesShort"),monthNames:this._get(t,"monthNames")}},_formatDate:function(t,e,i,s){e||(t.currentDay=t.selectedDay,t.currentMonth=t.selectedMonth,t.currentYear=t.selectedYear);e=e?"object"==typeof e?e:this._daylightSavingAdjust(new Date(s,i,e)):this._daylightSavingAdjust(new Date(t.currentYear,t.currentMonth,t.currentDay));return this.formatDate(this._get(t,"dateFormat"),e,this._getFormatConfig(t))}}),V.fn.datepicker=function(t){if(!this.length)return this;V.datepicker.initialized||(V(document).on("mousedown",V.datepicker._checkExternalClick),V.datepicker.initialized=!0),0===V("#"+V.datepicker._mainDivId).length&&V("body").append(V.datepicker.dpDiv);var e=Array.prototype.slice.call(arguments,1);return"string"==typeof t&&("isDisabled"===t||"getDate"===t||"widget"===t)||"option"===t&&2===arguments.length&&"string"==typeof arguments[1]?V.datepicker["_"+t+"Datepicker"].apply(V.datepicker,[this[0]].concat(e)):this.each(function(){"string"==typeof t?V.datepicker["_"+t+"Datepicker"].apply(V.datepicker,[this].concat(e)):V.datepicker._attachDatepicker(this,t)})},V.datepicker=new st,V.datepicker.initialized=!1,V.datepicker.uuid=(new Date).getTime(),V.datepicker.version="1.13.2";V.datepicker,V.ui.ie=!!/msie [\w.]+/.exec(navigator.userAgent.toLowerCase());var rt=!1;V(document).on("mouseup",function(){rt=!1});V.widget("ui.mouse",{version:"1.13.2",options:{cancel:"input, textarea, button, select, option",distance:1,delay:0},_mouseInit:function(){var e=this;this.element.on("mousedown."+this.widgetName,function(t){return e._mouseDown(t)}).on("click."+this.widgetName,function(t){if(!0===V.data(t.target,e.widgetName+".preventClickEvent"))return V.removeData(t.target,e.widgetName+".preventClickEvent"),t.stopImmediatePropagation(),!1}),this.started=!1},_mouseDestroy:function(){this.element.off("."+this.widgetName),this._mouseMoveDelegate&&this.document.off("mousemove."+this.widgetName,this._mouseMoveDelegate).off("mouseup."+this.widgetName,this._mouseUpDelegate)},_mouseDown:function(t){if(!rt){this._mouseMoved=!1,this._mouseStarted&&this._mouseUp(t),this._mouseDownEvent=t;var e=this,i=1===t.which,s=!("string"!=typeof this.options.cancel||!t.target.nodeName)&&V(t.target).closest(this.options.cancel).length;return i&&!s&&this._mouseCapture(t)?(this.mouseDelayMet=!this.options.delay,this.mouseDelayMet||(this._mouseDelayTimer=setTimeout(function(){e.mouseDelayMet=!0},this.options.delay)),this._mouseDistanceMet(t)&&this._mouseDelayMet(t)&&(this._mouseStarted=!1!==this._mouseStart(t),!this._mouseStarted)?(t.preventDefault(),!0):(!0===V.data(t.target,this.widgetName+".preventClickEvent")&&V.removeData(t.target,this.widgetName+".preventClickEvent"),this._mouseMoveDelegate=function(t){return e._mouseMove(t)},this._mouseUpDelegate=function(t){return e._mouseUp(t)},this.document.on("mousemove."+this.widgetName,this._mouseMoveDelegate).on("mouseup."+this.widgetName,this._mouseUpDelegate),t.preventDefault(),rt=!0)):!0}},_mouseMove:function(t){if(this._mouseMoved){if(V.ui.ie&&(!document.documentMode||document.documentMode<9)&&!t.button)return this._mouseUp(t);if(!t.which)if(t.originalEvent.altKey||t.originalEvent.ctrlKey||t.originalEvent.metaKey||t.originalEvent.shiftKey)this.ignoreMissingWhich=!0;else if(!this.ignoreMissingWhich)return this._mouseUp(t)}return(t.which||t.button)&&(this._mouseMoved=!0),this._mouseStarted?(this._mouseDrag(t),t.preventDefault()):(this._mouseDistanceMet(t)&&this._mouseDelayMet(t)&&(this._mouseStarted=!1!==this._mouseStart(this._mouseDownEvent,t),this._mouseStarted?this._mouseDrag(t):this._mouseUp(t)),!this._mouseStarted)},_mouseUp:function(t){this.document.off("mousemove."+this.widgetName,this._mouseMoveDelegate).off("mouseup."+this.widgetName,this._mouseUpDelegate),this._mouseStarted&&(this._mouseStarted=!1,t.target===this._mouseDownEvent.target&&V.data(t.target,this.widgetName+".preventClickEvent",!0),this._mouseStop(t)),this._mouseDelayTimer&&(clearTimeout(this._mouseDelayTimer),delete this._mouseDelayTimer),this.ignoreMissingWhich=!1,rt=!1,t.preventDefault()},_mouseDistanceMet:function(t){return Math.max(Math.abs(this._mouseDownEvent.pageX-t.pageX),Math.abs(this._mouseDownEvent.pageY-t.pageY))>=this.options.distance},_mouseDelayMet:function(){return this.mouseDelayMet},_mouseStart:function(){},_mouseDrag:function(){},_mouseStop:function(){},_mouseCapture:function(){return!0}}),V.ui.plugin={add:function(t,e,i){var s,n=V.ui[t].prototype;for(s in i)n.plugins[s]=n.plugins[s]||[],n.plugins[s].push([e,i[s]])},call:function(t,e,i,s){var n,o=t.plugins[e];if(o&&(s||t.element[0].parentNode&&11!==t.element[0].parentNode.nodeType))for(n=0;n<o.length;n++)t.options[o[n][0]]&&o[n][1].apply(t.element,i)}},V.ui.safeBlur=function(t){t&&"body"!==t.nodeName.toLowerCase()&&V(t).trigger("blur")};V.widget("ui.draggable",V.ui.mouse,{version:"1.13.2",widgetEventPrefix:"drag",options:{addClasses:!0,appendTo:"parent",axis:!1,connectToSortable:!1,containment:!1,cursor:"auto",cursorAt:!1,grid:!1,handle:!1,helper:"original",iframeFix:!1,opacity:!1,refreshPositions:!1,revert:!1,revertDuration:500,scope:"default",scroll:!0,scrollSensitivity:20,scrollSpeed:20,snap:!1,snapMode:"both",snapTolerance:20,stack:!1,zIndex:!1,drag:null,start:null,stop:null},_create:function(){"original"===this.options.helper&&this._setPositionRelative(),this.options.addClasses&&this._addClass("ui-draggable"),this._setHandleClassName(),this._mouseInit()},_setOption:function(t,e){this._super(t,e),"handle"===t&&(this._removeHandleClassName(),this._setHandleClassName())},_destroy:function(){(this.helper||this.element).is(".ui-draggable-dragging")?this.destroyOnClear=!0:(this._removeHandleClassName(),this._mouseDestroy())},_mouseCapture:function(t){var e=this.options;return!(this.helper||e.disabled||0<V(t.target).closest(".ui-resizable-handle").length)&&(this.handle=this._getHandle(t),!!this.handle&&(this._blurActiveElement(t),this._blockFrames(!0===e.iframeFix?"iframe":e.iframeFix),!0))},_blockFrames:function(t){this.iframeBlocks=this.document.find(t).map(function(){var t=V(this);return V("<div>").css("position","absolute").appendTo(t.parent()).outerWidth(t.outerWidth()).outerHeight(t.outerHeight()).offset(t.offset())[0]})},_unblockFrames:function(){this.iframeBlocks&&(this.iframeBlocks.remove(),delete this.iframeBlocks)},_blurActiveElement:function(t){var e=V.ui.safeActiveElement(this.document[0]);V(t.target).closest(e).length||V.ui.safeBlur(e)},_mouseStart:function(t){var e=this.options;return this.helper=this._createHelper(t),this._addClass(this.helper,"ui-draggable-dragging"),this._cacheHelperProportions(),V.ui.ddmanager&&(V.ui.ddmanager.current=this),this._cacheMargins(),this.cssPosition=this.helper.css("position"),this.scrollParent=this.helper.scrollParent(!0),this.offsetParent=this.helper.offsetParent(),this.hasFixedAncestor=0<this.helper.parents().filter(function(){return"fixed"===V(this).css("position")}).length,this.positionAbs=this.element.offset(),this._refreshOffsets(t),this.originalPosition=this.position=this._generatePosition(t,!1),this.originalPageX=t.pageX,this.originalPageY=t.pageY,e.cursorAt&&this._adjustOffsetFromHelper(e.cursorAt),this._setContainment(),!1===this._trigger("start",t)?(this._clear(),!1):(this._cacheHelperProportions(),V.ui.ddmanager&&!e.dropBehaviour&&V.ui.ddmanager.prepareOffsets(this,t),this._mouseDrag(t,!0),V.ui.ddmanager&&V.ui.ddmanager.dragStart(this,t),!0)},_refreshOffsets:function(t){this.offset={top:this.positionAbs.top-this.margins.top,left:this.positionAbs.left-this.margins.left,scroll:!1,parent:this._getParentOffset(),relative:this._getRelativeOffset()},this.offset.click={left:t.pageX-this.offset.left,top:t.pageY-this.offset.top}},_mouseDrag:function(t,e){if(this.hasFixedAncestor&&(this.offset.parent=this._getParentOffset()),this.position=this._generatePosition(t,!0),this.positionAbs=this._convertPositionTo("absolute"),!e){e=this._uiHash();if(!1===this._trigger("drag",t,e))return this._mouseUp(new V.Event("mouseup",t)),!1;this.position=e.position}return this.helper[0].style.left=this.position.left+"px",this.helper[0].style.top=this.position.top+"px",V.ui.ddmanager&&V.ui.ddmanager.drag(this,t),!1},_mouseStop:function(t){var e=this,i=!1;return V.ui.ddmanager&&!this.options.dropBehaviour&&(i=V.ui.ddmanager.drop(this,t)),this.dropped&&(i=this.dropped,this.dropped=!1),"invalid"===this.options.revert&&!i||"valid"===this.options.revert&&i||!0===this.options.revert||"function"==typeof this.options.revert&&this.options.revert.call(this.element,i)?V(this.helper).animate(this.originalPosition,parseInt(this.options.revertDuration,10),function(){!1!==e._trigger("stop",t)&&e._clear()}):!1!==this._trigger("stop",t)&&this._clear(),!1},_mouseUp:function(t){return this._unblockFrames(),V.ui.ddmanager&&V.ui.ddmanager.dragStop(this,t),this.handleElement.is(t.target)&&this.element.trigger("focus"),V.ui.mouse.prototype._mouseUp.call(this,t)},cancel:function(){return this.helper.is(".ui-draggable-dragging")?this._mouseUp(new V.Event("mouseup",{target:this.element[0]})):this._clear(),this},_getHandle:function(t){return!this.options.handle||!!V(t.target).closest(this.element.find(this.options.handle)).length},_setHandleClassName:function(){this.handleElement=this.options.handle?this.element.find(this.options.handle):this.element,this._addClass(this.handleElement,"ui-draggable-handle")},_removeHandleClassName:function(){this._removeClass(this.handleElement,"ui-draggable-handle")},_createHelper:function(t){var e=this.options,i="function"==typeof e.helper,t=i?V(e.helper.apply(this.element[0],[t])):"clone"===e.helper?this.element.clone().removeAttr("id"):this.element;return t.parents("body").length||t.appendTo("parent"===e.appendTo?this.element[0].parentNode:e.appendTo),i&&t[0]===this.element[0]&&this._setPositionRelative(),t[0]===this.element[0]||/(fixed|absolute)/.test(t.css("position"))||t.css("position","absolute"),t},_setPositionRelative:function(){/^(?:r|a|f)/.test(this.element.css("position"))||(this.element[0].style.position="relative")},_adjustOffsetFromHelper:function(t){"string"==typeof t&&(t=t.split(" ")),"left"in(t=Array.isArray(t)?{left:+t[0],top:+t[1]||0}:t)&&(this.offset.click.left=t.left+this.margins.left),"right"in t&&(this.offset.click.left=this.helperProportions.width-t.right+this.margins.left),"top"in t&&(this.offset.click.top=t.top+this.margins.top),"bottom"in t&&(this.offset.click.top=this.helperProportions.height-t.bottom+this.margins.top)},_isRootNode:function(t){return/(html|body)/i.test(t.tagName)||t===this.document[0]},_getParentOffset:function(){var t=this.offsetParent.offset(),e=this.document[0];return"absolute"===this.cssPosition&&this.scrollParent[0]!==e&&V.contains(this.scrollParent[0],this.offsetParent[0])&&(t.left+=this.scrollParent.scrollLeft(),t.top+=this.scrollParent.scrollTop()),{top:(t=this._isRootNode(this.offsetParent[0])?{top:0,left:0}:t).top+(parseInt(this.offsetParent.css("borderTopWidth"),10)||0),left:t.left+(parseInt(this.offsetParent.css("borderLeftWidth"),10)||0)}},_getRelativeOffset:function(){if("relative"!==this.cssPosition)return{top:0,left:0};var t=this.element.position(),e=this._isRootNode(this.scrollParent[0]);return{top:t.top-(parseInt(this.helper.css("top"),10)||0)+(e?0:this.scrollParent.scrollTop()),left:t.left-(parseInt(this.helper.css("left"),10)||0)+(e?0:this.scrollParent.scrollLeft())}},_cacheMargins:function(){this.margins={left:parseInt(this.element.css("marginLeft"),10)||0,top:parseInt(this.element.css("marginTop"),10)||0,right:parseInt(this.element.css("marginRight"),10)||0,bottom:parseInt(this.element.css("marginBottom"),10)||0}},_cacheHelperProportions:function(){this.helperProportions={width:this.helper.outerWidth(),height:this.helper.outerHeight()}},_setContainment:function(){var t,e,i,s=this.options,n=this.document[0];this.relativeContainer=null,s.containment?"window"!==s.containment?"document"!==s.containment?s.containment.constructor!==Array?("parent"===s.containment&&(s.containment=this.helper[0].parentNode),(i=(e=V(s.containment))[0])&&(t=/(scroll|auto)/.test(e.css("overflow")),this.containment=[(parseInt(e.css("borderLeftWidth"),10)||0)+(parseInt(e.css("paddingLeft"),10)||0),(parseInt(e.css("borderTopWidth"),10)||0)+(parseInt(e.css("paddingTop"),10)||0),(t?Math.max(i.scrollWidth,i.offsetWidth):i.offsetWidth)-(parseInt(e.css("borderRightWidth"),10)||0)-(parseInt(e.css("paddingRight"),10)||0)-this.helperProportions.width-this.margins.left-this.margins.right,(t?Math.max(i.scrollHeight,i.offsetHeight):i.offsetHeight)-(parseInt(e.css("borderBottomWidth"),10)||0)-(parseInt(e.css("paddingBottom"),10)||0)-this.helperProportions.height-this.margins.top-this.margins.bottom],this.relativeContainer=e)):this.containment=s.containment:this.containment=[0,0,V(n).width()-this.helperProportions.width-this.margins.left,(V(n).height()||n.body.parentNode.scrollHeight)-this.helperProportions.height-this.margins.top]:this.containment=[V(window).scrollLeft()-this.offset.relative.left-this.offset.parent.left,V(window).scrollTop()-this.offset.relative.top-this.offset.parent.top,V(window).scrollLeft()+V(window).width()-this.helperProportions.width-this.margins.left,V(window).scrollTop()+(V(window).height()||n.body.parentNode.scrollHeight)-this.helperProportions.height-this.margins.top]:this.containment=null},_convertPositionTo:function(t,e){e=e||this.position;var i="absolute"===t?1:-1,t=this._isRootNode(this.scrollParent[0]);return{top:e.top+this.offset.relative.top*i+this.offset.parent.top*i-("fixed"===this.cssPosition?-this.offset.scroll.top:t?0:this.offset.scroll.top)*i,left:e.left+this.offset.relative.left*i+this.offset.parent.left*i-("fixed"===this.cssPosition?-this.offset.scroll.left:t?0:this.offset.scroll.left)*i}},_generatePosition:function(t,e){var i,s=this.options,n=this._isRootNode(this.scrollParent[0]),o=t.pageX,a=t.pageY;return n&&this.offset.scroll||(this.offset.scroll={top:this.scrollParent.scrollTop(),left:this.scrollParent.scrollLeft()}),e&&(this.containment&&(i=this.relativeContainer?(i=this.relativeContainer.offset(),[this.containment[0]+i.left,this.containment[1]+i.top,this.containment[2]+i.left,this.containment[3]+i.top]):this.containment,t.pageX-this.offset.click.left<i[0]&&(o=i[0]+this.offset.click.left),t.pageY-this.offset.click.top<i[1]&&(a=i[1]+this.offset.click.top),t.pageX-this.offset.click.left>i[2]&&(o=i[2]+this.offset.click.left),t.pageY-this.offset.click.top>i[3]&&(a=i[3]+this.offset.click.top)),s.grid&&(t=s.grid[1]?this.originalPageY+Math.round((a-this.originalPageY)/s.grid[1])*s.grid[1]:this.originalPageY,a=!i||t-this.offset.click.top>=i[1]||t-this.offset.click.top>i[3]?t:t-this.offset.click.top>=i[1]?t-s.grid[1]:t+s.grid[1],t=s.grid[0]?this.originalPageX+Math.round((o-this.originalPageX)/s.grid[0])*s.grid[0]:this.originalPageX,o=!i||t-this.offset.click.left>=i[0]||t-this.offset.click.left>i[2]?t:t-this.offset.click.left>=i[0]?t-s.grid[0]:t+s.grid[0]),"y"===s.axis&&(o=this.originalPageX),"x"===s.axis&&(a=this.originalPageY)),{top:a-this.offset.click.top-this.offset.relative.top-this.offset.parent.top+("fixed"===this.cssPosition?-this.offset.scroll.top:n?0:this.offset.scroll.top),left:o-this.offset.click.left-this.offset.relative.left-this.offset.parent.left+("fixed"===this.cssPosition?-this.offset.scroll.left:n?0:this.offset.scroll.left)}},_clear:function(){this._removeClass(this.helper,"ui-draggable-dragging"),this.helper[0]===this.element[0]||this.cancelHelperRemoval||this.helper.remove(),this.helper=null,this.cancelHelperRemoval=!1,this.destroyOnClear&&this.destroy()},_trigger:function(t,e,i){return i=i||this._uiHash(),V.ui.plugin.call(this,t,[e,i,this],!0),/^(drag|start|stop)/.test(t)&&(this.positionAbs=this._convertPositionTo("absolute"),i.offset=this.positionAbs),V.Widget.prototype._trigger.call(this,t,e,i)},plugins:{},_uiHash:function(){return{helper:this.helper,position:this.position,originalPosition:this.originalPosition,offset:this.positionAbs}}}),V.ui.plugin.add("draggable","connectToSortable",{start:function(e,t,i){var s=V.extend({},t,{item:i.element});i.sortables=[],V(i.options.connectToSortable).each(function(){var t=V(this).sortable("instance");t&&!t.options.disabled&&(i.sortables.push(t),t.refreshPositions(),t._trigger("activate",e,s))})},stop:function(e,t,i){var s=V.extend({},t,{item:i.element});i.cancelHelperRemoval=!1,V.each(i.sortables,function(){var t=this;t.isOver?(t.isOver=0,i.cancelHelperRemoval=!0,t.cancelHelperRemoval=!1,t._storedCSS={position:t.placeholder.css("position"),top:t.placeholder.css("top"),left:t.placeholder.css("left")},t._mouseStop(e),t.options.helper=t.options._helper):(t.cancelHelperRemoval=!0,t._trigger("deactivate",e,s))})},drag:function(i,s,n){V.each(n.sortables,function(){var t=!1,e=this;e.positionAbs=n.positionAbs,e.helperProportions=n.helperProportions,e.offset.click=n.offset.click,e._intersectsWith(e.containerCache)&&(t=!0,V.each(n.sortables,function(){return this.positionAbs=n.positionAbs,this.helperProportions=n.helperProportions,this.offset.click=n.offset.click,t=this!==e&&this._intersectsWith(this.containerCache)&&V.contains(e.element[0],this.element[0])?!1:t})),t?(e.isOver||(e.isOver=1,n._parent=s.helper.parent(),e.currentItem=s.helper.appendTo(e.element).data("ui-sortable-item",!0),e.options._helper=e.options.helper,e.options.helper=function(){return s.helper[0]},i.target=e.currentItem[0],e._mouseCapture(i,!0),e._mouseStart(i,!0,!0),e.offset.click.top=n.offset.click.top,e.offset.click.left=n.offset.click.left,e.offset.parent.left-=n.offset.parent.left-e.offset.parent.left,e.offset.parent.top-=n.offset.parent.top-e.offset.parent.top,n._trigger("toSortable",i),n.dropped=e.element,V.each(n.sortables,function(){this.refreshPositions()}),n.currentItem=n.element,e.fromOutside=n),e.currentItem&&(e._mouseDrag(i),s.position=e.position)):e.isOver&&(e.isOver=0,e.cancelHelperRemoval=!0,e.options._revert=e.options.revert,e.options.revert=!1,e._trigger("out",i,e._uiHash(e)),e._mouseStop(i,!0),e.options.revert=e.options._revert,e.options.helper=e.options._helper,e.placeholder&&e.placeholder.remove(),s.helper.appendTo(n._parent),n._refreshOffsets(i),s.position=n._generatePosition(i,!0),n._trigger("fromSortable",i),n.dropped=!1,V.each(n.sortables,function(){this.refreshPositions()}))})}}),V.ui.plugin.add("draggable","cursor",{start:function(t,e,i){var s=V("body"),i=i.options;s.css("cursor")&&(i._cursor=s.css("cursor")),s.css("cursor",i.cursor)},stop:function(t,e,i){i=i.options;i._cursor&&V("body").css("cursor",i._cursor)}}),V.ui.plugin.add("draggable","opacity",{start:function(t,e,i){e=V(e.helper),i=i.options;e.css("opacity")&&(i._opacity=e.css("opacity")),e.css("opacity",i.opacity)},stop:function(t,e,i){i=i.options;i._opacity&&V(e.helper).css("opacity",i._opacity)}}),V.ui.plugin.add("draggable","scroll",{start:function(t,e,i){i.scrollParentNotHidden||(i.scrollParentNotHidden=i.helper.scrollParent(!1)),i.scrollParentNotHidden[0]!==i.document[0]&&"HTML"!==i.scrollParentNotHidden[0].tagName&&(i.overflowOffset=i.scrollParentNotHidden.offset())},drag:function(t,e,i){var s=i.options,n=!1,o=i.scrollParentNotHidden[0],a=i.document[0];o!==a&&"HTML"!==o.tagName?(s.axis&&"x"===s.axis||(i.overflowOffset.top+o.offsetHeight-t.pageY<s.scrollSensitivity?o.scrollTop=n=o.scrollTop+s.scrollSpeed:t.pageY-i.overflowOffset.top<s.scrollSensitivity&&(o.scrollTop=n=o.scrollTop-s.scrollSpeed)),s.axis&&"y"===s.axis||(i.overflowOffset.left+o.offsetWidth-t.pageX<s.scrollSensitivity?o.scrollLeft=n=o.scrollLeft+s.scrollSpeed:t.pageX-i.overflowOffset.left<s.scrollSensitivity&&(o.scrollLeft=n=o.scrollLeft-s.scrollSpeed))):(s.axis&&"x"===s.axis||(t.pageY-V(a).scrollTop()<s.scrollSensitivity?n=V(a).scrollTop(V(a).scrollTop()-s.scrollSpeed):V(window).height()-(t.pageY-V(a).scrollTop())<s.scrollSensitivity&&(n=V(a).scrollTop(V(a).scrollTop()+s.scrollSpeed))),s.axis&&"y"===s.axis||(t.pageX-V(a).scrollLeft()<s.scrollSensitivity?n=V(a).scrollLeft(V(a).scrollLeft()-s.scrollSpeed):V(window).width()-(t.pageX-V(a).scrollLeft())<s.scrollSensitivity&&(n=V(a).scrollLeft(V(a).scrollLeft()+s.scrollSpeed)))),!1!==n&&V.ui.ddmanager&&!s.dropBehaviour&&V.ui.ddmanager.prepareOffsets(i,t)}}),V.ui.plugin.add("draggable","snap",{start:function(t,e,i){var s=i.options;i.snapElements=[],V(s.snap.constructor!==String?s.snap.items||":data(ui-draggable)":s.snap).each(function(){var t=V(this),e=t.offset();this!==i.element[0]&&i.snapElements.push({item:this,width:t.outerWidth(),height:t.outerHeight(),top:e.top,left:e.left})})},drag:function(t,e,i){for(var s,n,o,a,r,l,h,c,u,d=i.options,p=d.snapTolerance,f=e.offset.left,g=f+i.helperProportions.width,m=e.offset.top,_=m+i.helperProportions.height,v=i.snapElements.length-1;0<=v;v--)l=(r=i.snapElements[v].left-i.margins.left)+i.snapElements[v].width,c=(h=i.snapElements[v].top-i.margins.top)+i.snapElements[v].height,g<r-p||l+p<f||_<h-p||c+p<m||!V.contains(i.snapElements[v].item.ownerDocument,i.snapElements[v].item)?(i.snapElements[v].snapping&&i.options.snap.release&&i.options.snap.release.call(i.element,t,V.extend(i._uiHash(),{snapItem:i.snapElements[v].item})),i.snapElements[v].snapping=!1):("inner"!==d.snapMode&&(s=Math.abs(h-_)<=p,n=Math.abs(c-m)<=p,o=Math.abs(r-g)<=p,a=Math.abs(l-f)<=p,s&&(e.position.top=i._convertPositionTo("relative",{top:h-i.helperProportions.height,left:0}).top),n&&(e.position.top=i._convertPositionTo("relative",{top:c,left:0}).top),o&&(e.position.left=i._convertPositionTo("relative",{top:0,left:r-i.helperProportions.width}).left),a&&(e.position.left=i._convertPositionTo("relative",{top:0,left:l}).left)),u=s||n||o||a,"outer"!==d.snapMode&&(s=Math.abs(h-m)<=p,n=Math.abs(c-_)<=p,o=Math.abs(r-f)<=p,a=Math.abs(l-g)<=p,s&&(e.position.top=i._convertPositionTo("relative",{top:h,left:0}).top),n&&(e.position.top=i._convertPositionTo("relative",{top:c-i.helperProportions.height,left:0}).top),o&&(e.position.left=i._convertPositionTo("relative",{top:0,left:r}).left),a&&(e.position.left=i._convertPositionTo("relative",{top:0,left:l-i.helperProportions.width}).left)),!i.snapElements[v].snapping&&(s||n||o||a||u)&&i.options.snap.snap&&i.options.snap.snap.call(i.element,t,V.extend(i._uiHash(),{snapItem:i.snapElements[v].item})),i.snapElements[v].snapping=s||n||o||a||u)}}),V.ui.plugin.add("draggable","stack",{start:function(t,e,i){var s,i=i.options,i=V.makeArray(V(i.stack)).sort(function(t,e){return(parseInt(V(t).css("zIndex"),10)||0)-(parseInt(V(e).css("zIndex"),10)||0)});i.length&&(s=parseInt(V(i[0]).css("zIndex"),10)||0,V(i).each(function(t){V(this).css("zIndex",s+t)}),this.css("zIndex",s+i.length))}}),V.ui.plugin.add("draggable","zIndex",{start:function(t,e,i){e=V(e.helper),i=i.options;e.css("zIndex")&&(i._zIndex=e.css("zIndex")),e.css("zIndex",i.zIndex)},stop:function(t,e,i){i=i.options;i._zIndex&&V(e.helper).css("zIndex",i._zIndex)}});V.ui.draggable;V.widget("ui.resizable",V.ui.mouse,{version:"1.13.2",widgetEventPrefix:"resize",options:{alsoResize:!1,animate:!1,animateDuration:"slow",animateEasing:"swing",aspectRatio:!1,autoHide:!1,classes:{"ui-resizable-se":"ui-icon ui-icon-gripsmall-diagonal-se"},containment:!1,ghost:!1,grid:!1,handles:"e,s,se",helper:!1,maxHeight:null,maxWidth:null,minHeight:10,minWidth:10,zIndex:90,resize:null,start:null,stop:null},_num:function(t){return parseFloat(t)||0},_isNumber:function(t){return!isNaN(parseFloat(t))},_hasScroll:function(t,e){if("hidden"===V(t).css("overflow"))return!1;var i=e&&"left"===e?"scrollLeft":"scrollTop",e=!1;if(0<t[i])return!0;try{t[i]=1,e=0<t[i],t[i]=0}catch(t){}return e},_create:function(){var t,e=this.options,i=this;this._addClass("ui-resizable"),V.extend(this,{_aspectRatio:!!e.aspectRatio,aspectRatio:e.aspectRatio,originalElement:this.element,_proportionallyResizeElements:[],_helper:e.helper||e.ghost||e.animate?e.helper||"ui-resizable-helper":null}),this.element[0].nodeName.match(/^(canvas|textarea|input|select|button|img)$/i)&&(this.element.wrap(V("<div class='ui-wrapper'></div>").css({overflow:"hidden",position:this.element.css("position"),width:this.element.outerWidth(),height:this.element.outerHeight(),top:this.element.css("top"),left:this.element.css("left")})),this.element=this.element.parent().data("ui-resizable",this.element.resizable("instance")),this.elementIsWrapper=!0,t={marginTop:this.originalElement.css("marginTop"),marginRight:this.originalElement.css("marginRight"),marginBottom:this.originalElement.css("marginBottom"),marginLeft:this.originalElement.css("marginLeft")},this.element.css(t),this.originalElement.css("margin",0),this.originalResizeStyle=this.originalElement.css("resize"),this.originalElement.css("resize","none"),this._proportionallyResizeElements.push(this.originalElement.css({position:"static",zoom:1,display:"block"})),this.originalElement.css(t),this._proportionallyResize()),this._setupHandles(),e.autoHide&&V(this.element).on("mouseenter",function(){e.disabled||(i._removeClass("ui-resizable-autohide"),i._handles.show())}).on("mouseleave",function(){e.disabled||i.resizing||(i._addClass("ui-resizable-autohide"),i._handles.hide())}),this._mouseInit()},_destroy:function(){this._mouseDestroy(),this._addedHandles.remove();function t(t){V(t).removeData("resizable").removeData("ui-resizable").off(".resizable")}var e;return this.elementIsWrapper&&(t(this.element),e=this.element,this.originalElement.css({position:e.css("position"),width:e.outerWidth(),height:e.outerHeight(),top:e.css("top"),left:e.css("left")}).insertAfter(e),e.remove()),this.originalElement.css("resize",this.originalResizeStyle),t(this.originalElement),this},_setOption:function(t,e){switch(this._super(t,e),t){case"handles":this._removeHandles(),this._setupHandles();break;case"aspectRatio":this._aspectRatio=!!e}},_setupHandles:function(){var t,e,i,s,n,o=this.options,a=this;if(this.handles=o.handles||(V(".ui-resizable-handle",this.element).length?{n:".ui-resizable-n",e:".ui-resizable-e",s:".ui-resizable-s",w:".ui-resizable-w",se:".ui-resizable-se",sw:".ui-resizable-sw",ne:".ui-resizable-ne",nw:".ui-resizable-nw"}:"e,s,se"),this._handles=V(),this._addedHandles=V(),this.handles.constructor===String)for("all"===this.handles&&(this.handles="n,e,s,w,se,sw,ne,nw"),i=this.handles.split(","),this.handles={},e=0;e<i.length;e++)s="ui-resizable-"+(t=String.prototype.trim.call(i[e])),n=V("<div>"),this._addClass(n,"ui-resizable-handle "+s),n.css({zIndex:o.zIndex}),this.handles[t]=".ui-resizable-"+t,this.element.children(this.handles[t]).length||(this.element.append(n),this._addedHandles=this._addedHandles.add(n));this._renderAxis=function(t){var e,i,s;for(e in t=t||this.element,this.handles)this.handles[e].constructor===String?this.handles[e]=this.element.children(this.handles[e]).first().show():(this.handles[e].jquery||this.handles[e].nodeType)&&(this.handles[e]=V(this.handles[e]),this._on(this.handles[e],{mousedown:a._mouseDown})),this.elementIsWrapper&&this.originalElement[0].nodeName.match(/^(textarea|input|select|button)$/i)&&(i=V(this.handles[e],this.element),s=/sw|ne|nw|se|n|s/.test(e)?i.outerHeight():i.outerWidth(),i=["padding",/ne|nw|n/.test(e)?"Top":/se|sw|s/.test(e)?"Bottom":/^e$/.test(e)?"Right":"Left"].join(""),t.css(i,s),this._proportionallyResize()),this._handles=this._handles.add(this.handles[e])},this._renderAxis(this.element),this._handles=this._handles.add(this.element.find(".ui-resizable-handle")),this._handles.disableSelection(),this._handles.on("mouseover",function(){a.resizing||(this.className&&(n=this.className.match(/ui-resizable-(se|sw|ne|nw|n|e|s|w)/i)),a.axis=n&&n[1]?n[1]:"se")}),o.autoHide&&(this._handles.hide(),this._addClass("ui-resizable-autohide"))},_removeHandles:function(){this._addedHandles.remove()},_mouseCapture:function(t){var e,i,s=!1;for(e in this.handles)(i=V(this.handles[e])[0])!==t.target&&!V.contains(i,t.target)||(s=!0);return!this.options.disabled&&s},_mouseStart:function(t){var e,i,s=this.options,n=this.element;return this.resizing=!0,this._renderProxy(),e=this._num(this.helper.css("left")),i=this._num(this.helper.css("top")),s.containment&&(e+=V(s.containment).scrollLeft()||0,i+=V(s.containment).scrollTop()||0),this.offset=this.helper.offset(),this.position={left:e,top:i},this.size=this._helper?{width:this.helper.width(),height:this.helper.height()}:{width:n.width(),height:n.height()},this.originalSize=this._helper?{width:n.outerWidth(),height:n.outerHeight()}:{width:n.width(),height:n.height()},this.sizeDiff={width:n.outerWidth()-n.width(),height:n.outerHeight()-n.height()},this.originalPosition={left:e,top:i},this.originalMousePosition={left:t.pageX,top:t.pageY},this.aspectRatio="number"==typeof s.aspectRatio?s.aspectRatio:this.originalSize.width/this.originalSize.height||1,s=V(".ui-resizable-"+this.axis).css("cursor"),V("body").css("cursor","auto"===s?this.axis+"-resize":s),this._addClass("ui-resizable-resizing"),this._propagate("start",t),!0},_mouseDrag:function(t){var e=this.originalMousePosition,i=this.axis,s=t.pageX-e.left||0,e=t.pageY-e.top||0,i=this._change[i];return this._updatePrevProperties(),i&&(e=i.apply(this,[t,s,e]),this._updateVirtualBoundaries(t.shiftKey),(this._aspectRatio||t.shiftKey)&&(e=this._updateRatio(e,t)),e=this._respectSize(e,t),this._updateCache(e),this._propagate("resize",t),e=this._applyChanges(),!this._helper&&this._proportionallyResizeElements.length&&this._proportionallyResize(),V.isEmptyObject(e)||(this._updatePrevProperties(),this._trigger("resize",t,this.ui()),this._applyChanges())),!1},_mouseStop:function(t){this.resizing=!1;var e,i,s,n=this.options,o=this;return this._helper&&(s=(e=(i=this._proportionallyResizeElements).length&&/textarea/i.test(i[0].nodeName))&&this._hasScroll(i[0],"left")?0:o.sizeDiff.height,i=e?0:o.sizeDiff.width,e={width:o.helper.width()-i,height:o.helper.height()-s},i=parseFloat(o.element.css("left"))+(o.position.left-o.originalPosition.left)||null,s=parseFloat(o.element.css("top"))+(o.position.top-o.originalPosition.top)||null,n.animate||this.element.css(V.extend(e,{top:s,left:i})),o.helper.height(o.size.height),o.helper.width(o.size.width),this._helper&&!n.animate&&this._proportionallyResize()),V("body").css("cursor","auto"),this._removeClass("ui-resizable-resizing"),this._propagate("stop",t),this._helper&&this.helper.remove(),!1},_updatePrevProperties:function(){this.prevPosition={top:this.position.top,left:this.position.left},this.prevSize={width:this.size.width,height:this.size.height}},_applyChanges:function(){var t={};return this.position.top!==this.prevPosition.top&&(t.top=this.position.top+"px"),this.position.left!==this.prevPosition.left&&(t.left=this.position.left+"px"),this.size.width!==this.prevSize.width&&(t.width=this.size.width+"px"),this.size.height!==this.prevSize.height&&(t.height=this.size.height+"px"),this.helper.css(t),t},_updateVirtualBoundaries:function(t){var e,i,s=this.options,n={minWidth:this._isNumber(s.minWidth)?s.minWidth:0,maxWidth:this._isNumber(s.maxWidth)?s.maxWidth:1/0,minHeight:this._isNumber(s.minHeight)?s.minHeight:0,maxHeight:this._isNumber(s.maxHeight)?s.maxHeight:1/0};(this._aspectRatio||t)&&(e=n.minHeight*this.aspectRatio,i=n.minWidth/this.aspectRatio,s=n.maxHeight*this.aspectRatio,t=n.maxWidth/this.aspectRatio,e>n.minWidth&&(n.minWidth=e),i>n.minHeight&&(n.minHeight=i),s<n.maxWidth&&(n.maxWidth=s),t<n.maxHeight&&(n.maxHeight=t)),this._vBoundaries=n},_updateCache:function(t){this.offset=this.helper.offset(),this._isNumber(t.left)&&(this.position.left=t.left),this._isNumber(t.top)&&(this.position.top=t.top),this._isNumber(t.height)&&(this.size.height=t.height),this._isNumber(t.width)&&(this.size.width=t.width)},_updateRatio:function(t){var e=this.position,i=this.size,s=this.axis;return this._isNumber(t.height)?t.width=t.height*this.aspectRatio:this._isNumber(t.width)&&(t.height=t.width/this.aspectRatio),"sw"===s&&(t.left=e.left+(i.width-t.width),t.top=null),"nw"===s&&(t.top=e.top+(i.height-t.height),t.left=e.left+(i.width-t.width)),t},_respectSize:function(t){var e=this._vBoundaries,i=this.axis,s=this._isNumber(t.width)&&e.maxWidth&&e.maxWidth<t.width,n=this._isNumber(t.height)&&e.maxHeight&&e.maxHeight<t.height,o=this._isNumber(t.width)&&e.minWidth&&e.minWidth>t.width,a=this._isNumber(t.height)&&e.minHeight&&e.minHeight>t.height,r=this.originalPosition.left+this.originalSize.width,l=this.originalPosition.top+this.originalSize.height,h=/sw|nw|w/.test(i),i=/nw|ne|n/.test(i);return o&&(t.width=e.minWidth),a&&(t.height=e.minHeight),s&&(t.width=e.maxWidth),n&&(t.height=e.maxHeight),o&&h&&(t.left=r-e.minWidth),s&&h&&(t.left=r-e.maxWidth),a&&i&&(t.top=l-e.minHeight),n&&i&&(t.top=l-e.maxHeight),t.width||t.height||t.left||!t.top?t.width||t.height||t.top||!t.left||(t.left=null):t.top=null,t},_getPaddingPlusBorderDimensions:function(t){for(var e=0,i=[],s=[t.css("borderTopWidth"),t.css("borderRightWidth"),t.css("borderBottomWidth"),t.css("borderLeftWidth")],n=[t.css("paddingTop"),t.css("paddingRight"),t.css("paddingBottom"),t.css("paddingLeft")];e<4;e++)i[e]=parseFloat(s[e])||0,i[e]+=parseFloat(n[e])||0;return{height:i[0]+i[2],width:i[1]+i[3]}},_proportionallyResize:function(){if(this._proportionallyResizeElements.length)for(var t,e=0,i=this.helper||this.element;e<this._proportionallyResizeElements.length;e++)t=this._proportionallyResizeElements[e],this.outerDimensions||(this.outerDimensions=this._getPaddingPlusBorderDimensions(t)),t.css({height:i.height()-this.outerDimensions.height||0,width:i.width()-this.outerDimensions.width||0})},_renderProxy:function(){var t=this.element,e=this.options;this.elementOffset=t.offset(),this._helper?(this.helper=this.helper||V("<div></div>").css({overflow:"hidden"}),this._addClass(this.helper,this._helper),this.helper.css({width:this.element.outerWidth(),height:this.element.outerHeight(),position:"absolute",left:this.elementOffset.left+"px",top:this.elementOffset.top+"px",zIndex:++e.zIndex}),this.helper.appendTo("body").disableSelection()):this.helper=this.element},_change:{e:function(t,e){return{width:this.originalSize.width+e}},w:function(t,e){var i=this.originalSize;return{left:this.originalPosition.left+e,width:i.width-e}},n:function(t,e,i){var s=this.originalSize;return{top:this.originalPosition.top+i,height:s.height-i}},s:function(t,e,i){return{height:this.originalSize.height+i}},se:function(t,e,i){return V.extend(this._change.s.apply(this,arguments),this._change.e.apply(this,[t,e,i]))},sw:function(t,e,i){return V.extend(this._change.s.apply(this,arguments),this._change.w.apply(this,[t,e,i]))},ne:function(t,e,i){return V.extend(this._change.n.apply(this,arguments),this._change.e.apply(this,[t,e,i]))},nw:function(t,e,i){return V.extend(this._change.n.apply(this,arguments),this._change.w.apply(this,[t,e,i]))}},_propagate:function(t,e){V.ui.plugin.call(this,t,[e,this.ui()]),"resize"!==t&&this._trigger(t,e,this.ui())},plugins:{},ui:function(){return{originalElement:this.originalElement,element:this.element,helper:this.helper,position:this.position,size:this.size,originalSize:this.originalSize,originalPosition:this.originalPosition}}}),V.ui.plugin.add("resizable","animate",{stop:function(e){var i=V(this).resizable("instance"),t=i.options,s=i._proportionallyResizeElements,n=s.length&&/textarea/i.test(s[0].nodeName),o=n&&i._hasScroll(s[0],"left")?0:i.sizeDiff.height,a=n?0:i.sizeDiff.width,n={width:i.size.width-a,height:i.size.height-o},a=parseFloat(i.element.css("left"))+(i.position.left-i.originalPosition.left)||null,o=parseFloat(i.element.css("top"))+(i.position.top-i.originalPosition.top)||null;i.element.animate(V.extend(n,o&&a?{top:o,left:a}:{}),{duration:t.animateDuration,easing:t.animateEasing,step:function(){var t={width:parseFloat(i.element.css("width")),height:parseFloat(i.element.css("height")),top:parseFloat(i.element.css("top")),left:parseFloat(i.element.css("left"))};s&&s.length&&V(s[0]).css({width:t.width,height:t.height}),i._updateCache(t),i._propagate("resize",e)}})}}),V.ui.plugin.add("resizable","containment",{start:function(){var i,s,n=V(this).resizable("instance"),t=n.options,e=n.element,o=t.containment,a=o instanceof V?o.get(0):/parent/.test(o)?e.parent().get(0):o;a&&(n.containerElement=V(a),/document/.test(o)||o===document?(n.containerOffset={left:0,top:0},n.containerPosition={left:0,top:0},n.parentData={element:V(document),left:0,top:0,width:V(document).width(),height:V(document).height()||document.body.parentNode.scrollHeight}):(i=V(a),s=[],V(["Top","Right","Left","Bottom"]).each(function(t,e){s[t]=n._num(i.css("padding"+e))}),n.containerOffset=i.offset(),n.containerPosition=i.position(),n.containerSize={height:i.innerHeight()-s[3],width:i.innerWidth()-s[1]},t=n.containerOffset,e=n.containerSize.height,o=n.containerSize.width,o=n._hasScroll(a,"left")?a.scrollWidth:o,e=n._hasScroll(a)?a.scrollHeight:e,n.parentData={element:a,left:t.left,top:t.top,width:o,height:e}))},resize:function(t){var e=V(this).resizable("instance"),i=e.options,s=e.containerOffset,n=e.position,o=e._aspectRatio||t.shiftKey,a={top:0,left:0},r=e.containerElement,t=!0;r[0]!==document&&/static/.test(r.css("position"))&&(a=s),n.left<(e._helper?s.left:0)&&(e.size.width=e.size.width+(e._helper?e.position.left-s.left:e.position.left-a.left),o&&(e.size.height=e.size.width/e.aspectRatio,t=!1),e.position.left=i.helper?s.left:0),n.top<(e._helper?s.top:0)&&(e.size.height=e.size.height+(e._helper?e.position.top-s.top:e.position.top),o&&(e.size.width=e.size.height*e.aspectRatio,t=!1),e.position.top=e._helper?s.top:0),i=e.containerElement.get(0)===e.element.parent().get(0),n=/relative|absolute/.test(e.containerElement.css("position")),i&&n?(e.offset.left=e.parentData.left+e.position.left,e.offset.top=e.parentData.top+e.position.top):(e.offset.left=e.element.offset().left,e.offset.top=e.element.offset().top),n=Math.abs(e.sizeDiff.width+(e._helper?e.offset.left-a.left:e.offset.left-s.left)),s=Math.abs(e.sizeDiff.height+(e._helper?e.offset.top-a.top:e.offset.top-s.top)),n+e.size.width>=e.parentData.width&&(e.size.width=e.parentData.width-n,o&&(e.size.height=e.size.width/e.aspectRatio,t=!1)),s+e.size.height>=e.parentData.height&&(e.size.height=e.parentData.height-s,o&&(e.size.width=e.size.height*e.aspectRatio,t=!1)),t||(e.position.left=e.prevPosition.left,e.position.top=e.prevPosition.top,e.size.width=e.prevSize.width,e.size.height=e.prevSize.height)},stop:function(){var t=V(this).resizable("instance"),e=t.options,i=t.containerOffset,s=t.containerPosition,n=t.containerElement,o=V(t.helper),a=o.offset(),r=o.outerWidth()-t.sizeDiff.width,o=o.outerHeight()-t.sizeDiff.height;t._helper&&!e.animate&&/relative/.test(n.css("position"))&&V(this).css({left:a.left-s.left-i.left,width:r,height:o}),t._helper&&!e.animate&&/static/.test(n.css("position"))&&V(this).css({left:a.left-s.left-i.left,width:r,height:o})}}),V.ui.plugin.add("resizable","alsoResize",{start:function(){var t=V(this).resizable("instance").options;V(t.alsoResize).each(function(){var t=V(this);t.data("ui-resizable-alsoresize",{width:parseFloat(t.width()),height:parseFloat(t.height()),left:parseFloat(t.css("left")),top:parseFloat(t.css("top"))})})},resize:function(t,i){var e=V(this).resizable("instance"),s=e.options,n=e.originalSize,o=e.originalPosition,a={height:e.size.height-n.height||0,width:e.size.width-n.width||0,top:e.position.top-o.top||0,left:e.position.left-o.left||0};V(s.alsoResize).each(function(){var t=V(this),s=V(this).data("ui-resizable-alsoresize"),n={},e=t.parents(i.originalElement[0]).length?["width","height"]:["width","height","top","left"];V.each(e,function(t,e){var i=(s[e]||0)+(a[e]||0);i&&0<=i&&(n[e]=i||null)}),t.css(n)})},stop:function(){V(this).removeData("ui-resizable-alsoresize")}}),V.ui.plugin.add("resizable","ghost",{start:function(){var t=V(this).resizable("instance"),e=t.size;t.ghost=t.originalElement.clone(),t.ghost.css({opacity:.25,display:"block",position:"relative",height:e.height,width:e.width,margin:0,left:0,top:0}),t._addClass(t.ghost,"ui-resizable-ghost"),!1!==V.uiBackCompat&&"string"==typeof t.options.ghost&&t.ghost.addClass(this.options.ghost),t.ghost.appendTo(t.helper)},resize:function(){var t=V(this).resizable("instance");t.ghost&&t.ghost.css({position:"relative",height:t.size.height,width:t.size.width})},stop:function(){var t=V(this).resizable("instance");t.ghost&&t.helper&&t.helper.get(0).removeChild(t.ghost.get(0))}}),V.ui.plugin.add("resizable","grid",{resize:function(){var t,e=V(this).resizable("instance"),i=e.options,s=e.size,n=e.originalSize,o=e.originalPosition,a=e.axis,r="number"==typeof i.grid?[i.grid,i.grid]:i.grid,l=r[0]||1,h=r[1]||1,c=Math.round((s.width-n.width)/l)*l,u=Math.round((s.height-n.height)/h)*h,d=n.width+c,p=n.height+u,f=i.maxWidth&&i.maxWidth<d,g=i.maxHeight&&i.maxHeight<p,m=i.minWidth&&i.minWidth>d,s=i.minHeight&&i.minHeight>p;i.grid=r,m&&(d+=l),s&&(p+=h),f&&(d-=l),g&&(p-=h),/^(se|s|e)$/.test(a)?(e.size.width=d,e.size.height=p):/^(ne)$/.test(a)?(e.size.width=d,e.size.height=p,e.position.top=o.top-u):/^(sw)$/.test(a)?(e.size.width=d,e.size.height=p,e.position.left=o.left-c):((p-h<=0||d-l<=0)&&(t=e._getPaddingPlusBorderDimensions(this)),0<p-h?(e.size.height=p,e.position.top=o.top-u):(p=h-t.height,e.size.height=p,e.position.top=o.top+n.height-p),0<d-l?(e.size.width=d,e.position.left=o.left-c):(d=l-t.width,e.size.width=d,e.position.left=o.left+n.width-d))}});V.ui.resizable;V.widget("ui.dialog",{version:"1.13.2",options:{appendTo:"body",autoOpen:!0,buttons:[],classes:{"ui-dialog":"ui-corner-all","ui-dialog-titlebar":"ui-corner-all"},closeOnEscape:!0,closeText:"Close",draggable:!0,hide:null,height:"auto",maxHeight:null,maxWidth:null,minHeight:150,minWidth:150,modal:!1,position:{my:"center",at:"center",of:window,collision:"fit",using:function(t){var e=V(this).css(t).offset().top;e<0&&V(this).css("top",t.top-e)}},resizable:!0,show:null,title:null,width:300,beforeClose:null,close:null,drag:null,dragStart:null,dragStop:null,focus:null,open:null,resize:null,resizeStart:null,resizeStop:null},sizeRelatedOptions:{buttons:!0,height:!0,maxHeight:!0,maxWidth:!0,minHeight:!0,minWidth:!0,width:!0},resizableRelatedOptions:{maxHeight:!0,maxWidth:!0,minHeight:!0,minWidth:!0},_create:function(){this.originalCss={display:this.element[0].style.display,width:this.element[0].style.width,minHeight:this.element[0].style.minHeight,maxHeight:this.element[0].style.maxHeight,height:this.element[0].style.height},this.originalPosition={parent:this.element.parent(),index:this.element.parent().children().index(this.element)},this.originalTitle=this.element.attr("title"),null==this.options.title&&null!=this.originalTitle&&(this.options.title=this.originalTitle),this.options.disabled&&(this.options.disabled=!1),this._createWrapper(),this.element.show().removeAttr("title").appendTo(this.uiDialog),this._addClass("ui-dialog-content","ui-widget-content"),this._createTitlebar(),this._createButtonPane(),this.options.draggable&&V.fn.draggable&&this._makeDraggable(),this.options.resizable&&V.fn.resizable&&this._makeResizable(),this._isOpen=!1,this._trackFocus()},_init:function(){this.options.autoOpen&&this.open()},_appendTo:function(){var t=this.options.appendTo;return t&&(t.jquery||t.nodeType)?V(t):this.document.find(t||"body").eq(0)},_destroy:function(){var t,e=this.originalPosition;this._untrackInstance(),this._destroyOverlay(),this.element.removeUniqueId().css(this.originalCss).detach(),this.uiDialog.remove(),this.originalTitle&&this.element.attr("title",this.originalTitle),(t=e.parent.children().eq(e.index)).length&&t[0]!==this.element[0]?t.before(this.element):e.parent.append(this.element)},widget:function(){return this.uiDialog},disable:V.noop,enable:V.noop,close:function(t){var e=this;this._isOpen&&!1!==this._trigger("beforeClose",t)&&(this._isOpen=!1,this._focusedElement=null,this._destroyOverlay(),this._untrackInstance(),this.opener.filter(":focusable").trigger("focus").length||V.ui.safeBlur(V.ui.safeActiveElement(this.document[0])),this._hide(this.uiDialog,this.options.hide,function(){e._trigger("close",t)}))},isOpen:function(){return this._isOpen},moveToTop:function(){this._moveToTop()},_moveToTop:function(t,e){var i=!1,s=this.uiDialog.siblings(".ui-front:visible").map(function(){return+V(this).css("z-index")}).get(),s=Math.max.apply(null,s);return s>=+this.uiDialog.css("z-index")&&(this.uiDialog.css("z-index",s+1),i=!0),i&&!e&&this._trigger("focus",t),i},open:function(){var t=this;this._isOpen?this._moveToTop()&&this._focusTabbable():(this._isOpen=!0,this.opener=V(V.ui.safeActiveElement(this.document[0])),this._size(),this._position(),this._createOverlay(),this._moveToTop(null,!0),this.overlay&&this.overlay.css("z-index",this.uiDialog.css("z-index")-1),this._show(this.uiDialog,this.options.show,function(){t._focusTabbable(),t._trigger("focus")}),this._makeFocusTarget(),this._trigger("open"))},_focusTabbable:function(){var t=this._focusedElement;(t=!(t=!(t=!(t=!(t=t||this.element.find("[autofocus]")).length?this.element.find(":tabbable"):t).length?this.uiDialogButtonPane.find(":tabbable"):t).length?this.uiDialogTitlebarClose.filter(":tabbable"):t).length?this.uiDialog:t).eq(0).trigger("focus")},_restoreTabbableFocus:function(){var t=V.ui.safeActiveElement(this.document[0]);this.uiDialog[0]===t||V.contains(this.uiDialog[0],t)||this._focusTabbable()},_keepFocus:function(t){t.preventDefault(),this._restoreTabbableFocus(),this._delay(this._restoreTabbableFocus)},_createWrapper:function(){this.uiDialog=V("<div>").hide().attr({tabIndex:-1,role:"dialog"}).appendTo(this._appendTo()),this._addClass(this.uiDialog,"ui-dialog","ui-widget ui-widget-content ui-front"),this._on(this.uiDialog,{keydown:function(t){if(this.options.closeOnEscape&&!t.isDefaultPrevented()&&t.keyCode&&t.keyCode===V.ui.keyCode.ESCAPE)return t.preventDefault(),void this.close(t);var e,i,s;t.keyCode!==V.ui.keyCode.TAB||t.isDefaultPrevented()||(e=this.uiDialog.find(":tabbable"),i=e.first(),s=e.last(),t.target!==s[0]&&t.target!==this.uiDialog[0]||t.shiftKey?t.target!==i[0]&&t.target!==this.uiDialog[0]||!t.shiftKey||(this._delay(function(){s.trigger("focus")}),t.preventDefault()):(this._delay(function(){i.trigger("focus")}),t.preventDefault()))},mousedown:function(t){this._moveToTop(t)&&this._focusTabbable()}}),this.element.find("[aria-describedby]").length||this.uiDialog.attr({"aria-describedby":this.element.uniqueId().attr("id")})},_createTitlebar:function(){var t;this.uiDialogTitlebar=V("<div>"),this._addClass(this.uiDialogTitlebar,"ui-dialog-titlebar","ui-widget-header ui-helper-clearfix"),this._on(this.uiDialogTitlebar,{mousedown:function(t){V(t.target).closest(".ui-dialog-titlebar-close")||this.uiDialog.trigger("focus")}}),this.uiDialogTitlebarClose=V("<button type='button'></button>").button({label:V("<a>").text(this.options.closeText).html(),icon:"ui-icon-closethick",showLabel:!1}).appendTo(this.uiDialogTitlebar),this._addClass(this.uiDialogTitlebarClose,"ui-dialog-titlebar-close"),this._on(this.uiDialogTitlebarClose,{click:function(t){t.preventDefault(),this.close(t)}}),t=V("<span>").uniqueId().prependTo(this.uiDialogTitlebar),this._addClass(t,"ui-dialog-title"),this._title(t),this.uiDialogTitlebar.prependTo(this.uiDialog),this.uiDialog.attr({"aria-labelledby":t.attr("id")})},_title:function(t){this.options.title?t.text(this.options.title):t.html("&#160;")},_createButtonPane:function(){this.uiDialogButtonPane=V("<div>"),this._addClass(this.uiDialogButtonPane,"ui-dialog-buttonpane","ui-widget-content ui-helper-clearfix"),this.uiButtonSet=V("<div>").appendTo(this.uiDialogButtonPane),this._addClass(this.uiButtonSet,"ui-dialog-buttonset"),this._createButtons()},_createButtons:function(){var s=this,t=this.options.buttons;this.uiDialogButtonPane.remove(),this.uiButtonSet.empty(),V.isEmptyObject(t)||Array.isArray(t)&&!t.length?this._removeClass(this.uiDialog,"ui-dialog-buttons"):(V.each(t,function(t,e){var i;e=V.extend({type:"button"},e="function"==typeof e?{click:e,text:t}:e),i=e.click,t={icon:e.icon,iconPosition:e.iconPosition,showLabel:e.showLabel,icons:e.icons,text:e.text},delete e.click,delete e.icon,delete e.iconPosition,delete e.showLabel,delete e.icons,"boolean"==typeof e.text&&delete e.text,V("<button></button>",e).button(t).appendTo(s.uiButtonSet).on("click",function(){i.apply(s.element[0],arguments)})}),this._addClass(this.uiDialog,"ui-dialog-buttons"),this.uiDialogButtonPane.appendTo(this.uiDialog))},_makeDraggable:function(){var n=this,o=this.options;function a(t){return{position:t.position,offset:t.offset}}this.uiDialog.draggable({cancel:".ui-dialog-content, .ui-dialog-titlebar-close",handle:".ui-dialog-titlebar",containment:"document",start:function(t,e){n._addClass(V(this),"ui-dialog-dragging"),n._blockFrames(),n._trigger("dragStart",t,a(e))},drag:function(t,e){n._trigger("drag",t,a(e))},stop:function(t,e){var i=e.offset.left-n.document.scrollLeft(),s=e.offset.top-n.document.scrollTop();o.position={my:"left top",at:"left"+(0<=i?"+":"")+i+" top"+(0<=s?"+":"")+s,of:n.window},n._removeClass(V(this),"ui-dialog-dragging"),n._unblockFrames(),n._trigger("dragStop",t,a(e))}})},_makeResizable:function(){var n=this,o=this.options,t=o.resizable,e=this.uiDialog.css("position"),t="string"==typeof t?t:"n,e,s,w,se,sw,ne,nw";function a(t){return{originalPosition:t.originalPosition,originalSize:t.originalSize,position:t.position,size:t.size}}this.uiDialog.resizable({cancel:".ui-dialog-content",containment:"document",alsoResize:this.element,maxWidth:o.maxWidth,maxHeight:o.maxHeight,minWidth:o.minWidth,minHeight:this._minHeight(),handles:t,start:function(t,e){n._addClass(V(this),"ui-dialog-resizing"),n._blockFrames(),n._trigger("resizeStart",t,a(e))},resize:function(t,e){n._trigger("resize",t,a(e))},stop:function(t,e){var i=n.uiDialog.offset(),s=i.left-n.document.scrollLeft(),i=i.top-n.document.scrollTop();o.height=n.uiDialog.height(),o.width=n.uiDialog.width(),o.position={my:"left top",at:"left"+(0<=s?"+":"")+s+" top"+(0<=i?"+":"")+i,of:n.window},n._removeClass(V(this),"ui-dialog-resizing"),n._unblockFrames(),n._trigger("resizeStop",t,a(e))}}).css("position",e)},_trackFocus:function(){this._on(this.widget(),{focusin:function(t){this._makeFocusTarget(),this._focusedElement=V(t.target)}})},_makeFocusTarget:function(){this._untrackInstance(),this._trackingInstances().unshift(this)},_untrackInstance:function(){var t=this._trackingInstances(),e=V.inArray(this,t);-1!==e&&t.splice(e,1)},_trackingInstances:function(){var t=this.document.data("ui-dialog-instances");return t||this.document.data("ui-dialog-instances",t=[]),t},_minHeight:function(){var t=this.options;return"auto"===t.height?t.minHeight:Math.min(t.minHeight,t.height)},_position:function(){var t=this.uiDialog.is(":visible");t||this.uiDialog.show(),this.uiDialog.position(this.options.position),t||this.uiDialog.hide()},_setOptions:function(t){var i=this,s=!1,n={};V.each(t,function(t,e){i._setOption(t,e),t in i.sizeRelatedOptions&&(s=!0),t in i.resizableRelatedOptions&&(n[t]=e)}),s&&(this._size(),this._position()),this.uiDialog.is(":data(ui-resizable)")&&this.uiDialog.resizable("option",n)},_setOption:function(t,e){var i,s=this.uiDialog;"disabled"!==t&&(this._super(t,e),"appendTo"===t&&this.uiDialog.appendTo(this._appendTo()),"buttons"===t&&this._createButtons(),"closeText"===t&&this.uiDialogTitlebarClose.button({label:V("<a>").text(""+this.options.closeText).html()}),"draggable"===t&&((i=s.is(":data(ui-draggable)"))&&!e&&s.draggable("destroy"),!i&&e&&this._makeDraggable()),"position"===t&&this._position(),"resizable"===t&&((i=s.is(":data(ui-resizable)"))&&!e&&s.resizable("destroy"),i&&"string"==typeof e&&s.resizable("option","handles",e),i||!1===e||this._makeResizable()),"title"===t&&this._title(this.uiDialogTitlebar.find(".ui-dialog-title")))},_size:function(){var t,e,i,s=this.options;this.element.show().css({width:"auto",minHeight:0,maxHeight:"none",height:0}),s.minWidth>s.width&&(s.width=s.minWidth),t=this.uiDialog.css({height:"auto",width:s.width}).outerHeight(),e=Math.max(0,s.minHeight-t),i="number"==typeof s.maxHeight?Math.max(0,s.maxHeight-t):"none","auto"===s.height?this.element.css({minHeight:e,maxHeight:i,height:"auto"}):this.element.height(Math.max(0,s.height-t)),this.uiDialog.is(":data(ui-resizable)")&&this.uiDialog.resizable("option","minHeight",this._minHeight())},_blockFrames:function(){this.iframeBlocks=this.document.find("iframe").map(function(){var t=V(this);return V("<div>").css({position:"absolute",width:t.outerWidth(),height:t.outerHeight()}).appendTo(t.parent()).offset(t.offset())[0]})},_unblockFrames:function(){this.iframeBlocks&&(this.iframeBlocks.remove(),delete this.iframeBlocks)},_allowInteraction:function(t){return!!V(t.target).closest(".ui-dialog").length||!!V(t.target).closest(".ui-datepicker").length},_createOverlay:function(){var i,s;this.options.modal&&(i=V.fn.jquery.substring(0,4),s=!0,this._delay(function(){s=!1}),this.document.data("ui-dialog-overlays")||this.document.on("focusin.ui-dialog",function(t){var e;s||((e=this._trackingInstances()[0])._allowInteraction(t)||(t.preventDefault(),e._focusTabbable(),"3.4."!==i&&"3.5."!==i||e._delay(e._restoreTabbableFocus)))}.bind(this)),this.overlay=V("<div>").appendTo(this._appendTo()),this._addClass(this.overlay,null,"ui-widget-overlay ui-front"),this._on(this.overlay,{mousedown:"_keepFocus"}),this.document.data("ui-dialog-overlays",(this.document.data("ui-dialog-overlays")||0)+1))},_destroyOverlay:function(){var t;this.options.modal&&this.overlay&&((t=this.document.data("ui-dialog-overlays")-1)?this.document.data("ui-dialog-overlays",t):(this.document.off("focusin.ui-dialog"),this.document.removeData("ui-dialog-overlays")),this.overlay.remove(),this.overlay=null)}}),!1!==V.uiBackCompat&&V.widget("ui.dialog",V.ui.dialog,{options:{dialogClass:""},_createWrapper:function(){this._super(),this.uiDialog.addClass(this.options.dialogClass)},_setOption:function(t,e){"dialogClass"===t&&this.uiDialog.removeClass(this.options.dialogClass).addClass(e),this._superApply(arguments)}});V.ui.dialog;function lt(t,e,i){return e<=t&&t<e+i}V.widget("ui.droppable",{version:"1.13.2",widgetEventPrefix:"drop",options:{accept:"*",addClasses:!0,greedy:!1,scope:"default",tolerance:"intersect",activate:null,deactivate:null,drop:null,out:null,over:null},_create:function(){var t,e=this.options,i=e.accept;this.isover=!1,this.isout=!0,this.accept="function"==typeof i?i:function(t){return t.is(i)},this.proportions=function(){if(!arguments.length)return t=t||{width:this.element[0].offsetWidth,height:this.element[0].offsetHeight};t=arguments[0]},this._addToManager(e.scope),e.addClasses&&this._addClass("ui-droppable")},_addToManager:function(t){V.ui.ddmanager.droppables[t]=V.ui.ddmanager.droppables[t]||[],V.ui.ddmanager.droppables[t].push(this)},_splice:function(t){for(var e=0;e<t.length;e++)t[e]===this&&t.splice(e,1)},_destroy:function(){var t=V.ui.ddmanager.droppables[this.options.scope];this._splice(t)},_setOption:function(t,e){var i;"accept"===t?this.accept="function"==typeof e?e:function(t){return t.is(e)}:"scope"===t&&(i=V.ui.ddmanager.droppables[this.options.scope],this._splice(i),this._addToManager(e)),this._super(t,e)},_activate:function(t){var e=V.ui.ddmanager.current;this._addActiveClass(),e&&this._trigger("activate",t,this.ui(e))},_deactivate:function(t){var e=V.ui.ddmanager.current;this._removeActiveClass(),e&&this._trigger("deactivate",t,this.ui(e))},_over:function(t){var e=V.ui.ddmanager.current;e&&(e.currentItem||e.element)[0]!==this.element[0]&&this.accept.call(this.element[0],e.currentItem||e.element)&&(this._addHoverClass(),this._trigger("over",t,this.ui(e)))},_out:function(t){var e=V.ui.ddmanager.current;e&&(e.currentItem||e.element)[0]!==this.element[0]&&this.accept.call(this.element[0],e.currentItem||e.element)&&(this._removeHoverClass(),this._trigger("out",t,this.ui(e)))},_drop:function(e,t){var i=t||V.ui.ddmanager.current,s=!1;return!(!i||(i.currentItem||i.element)[0]===this.element[0])&&(this.element.find(":data(ui-droppable)").not(".ui-draggable-dragging").each(function(){var t=V(this).droppable("instance");if(t.options.greedy&&!t.options.disabled&&t.options.scope===i.options.scope&&t.accept.call(t.element[0],i.currentItem||i.element)&&V.ui.intersect(i,V.extend(t,{offset:t.element.offset()}),t.options.tolerance,e))return!(s=!0)}),!s&&(!!this.accept.call(this.element[0],i.currentItem||i.element)&&(this._removeActiveClass(),this._removeHoverClass(),this._trigger("drop",e,this.ui(i)),this.element)))},ui:function(t){return{draggable:t.currentItem||t.element,helper:t.helper,position:t.position,offset:t.positionAbs}},_addHoverClass:function(){this._addClass("ui-droppable-hover")},_removeHoverClass:function(){this._removeClass("ui-droppable-hover")},_addActiveClass:function(){this._addClass("ui-droppable-active")},_removeActiveClass:function(){this._removeClass("ui-droppable-active")}}),V.ui.intersect=function(t,e,i,s){if(!e.offset)return!1;var n=(t.positionAbs||t.position.absolute).left+t.margins.left,o=(t.positionAbs||t.position.absolute).top+t.margins.top,a=n+t.helperProportions.width,r=o+t.helperProportions.height,l=e.offset.left,h=e.offset.top,c=l+e.proportions().width,u=h+e.proportions().height;switch(i){case"fit":return l<=n&&a<=c&&h<=o&&r<=u;case"intersect":return l<n+t.helperProportions.width/2&&a-t.helperProportions.width/2<c&&h<o+t.helperProportions.height/2&&r-t.helperProportions.height/2<u;case"pointer":return lt(s.pageY,h,e.proportions().height)&&lt(s.pageX,l,e.proportions().width);case"touch":return(h<=o&&o<=u||h<=r&&r<=u||o<h&&u<r)&&(l<=n&&n<=c||l<=a&&a<=c||n<l&&c<a);default:return!1}},!(V.ui.ddmanager={current:null,droppables:{default:[]},prepareOffsets:function(t,e){var i,s,n=V.ui.ddmanager.droppables[t.options.scope]||[],o=e?e.type:null,a=(t.currentItem||t.element).find(":data(ui-droppable)").addBack();t:for(i=0;i<n.length;i++)if(!(n[i].options.disabled||t&&!n[i].accept.call(n[i].element[0],t.currentItem||t.element))){for(s=0;s<a.length;s++)if(a[s]===n[i].element[0]){n[i].proportions().height=0;continue t}n[i].visible="none"!==n[i].element.css("display"),n[i].visible&&("mousedown"===o&&n[i]._activate.call(n[i],e),n[i].offset=n[i].element.offset(),n[i].proportions({width:n[i].element[0].offsetWidth,height:n[i].element[0].offsetHeight}))}},drop:function(t,e){var i=!1;return V.each((V.ui.ddmanager.droppables[t.options.scope]||[]).slice(),function(){this.options&&(!this.options.disabled&&this.visible&&V.ui.intersect(t,this,this.options.tolerance,e)&&(i=this._drop.call(this,e)||i),!this.options.disabled&&this.visible&&this.accept.call(this.element[0],t.currentItem||t.element)&&(this.isout=!0,this.isover=!1,this._deactivate.call(this,e)))}),i},dragStart:function(t,e){t.element.parentsUntil("body").on("scroll.droppable",function(){t.options.refreshPositions||V.ui.ddmanager.prepareOffsets(t,e)})},drag:function(n,o){n.options.refreshPositions&&V.ui.ddmanager.prepareOffsets(n,o),V.each(V.ui.ddmanager.droppables[n.options.scope]||[],function(){var t,e,i,s;this.options.disabled||this.greedyChild||!this.visible||(s=!(i=V.ui.intersect(n,this,this.options.tolerance,o))&&this.isover?"isout":i&&!this.isover?"isover":null)&&(this.options.greedy&&(e=this.options.scope,(i=this.element.parents(":data(ui-droppable)").filter(function(){return V(this).droppable("instance").options.scope===e})).length&&((t=V(i[0]).droppable("instance")).greedyChild="isover"===s)),t&&"isover"===s&&(t.isover=!1,t.isout=!0,t._out.call(t,o)),this[s]=!0,this["isout"===s?"isover":"isout"]=!1,this["isover"===s?"_over":"_out"].call(this,o),t&&"isout"===s&&(t.isout=!1,t.isover=!0,t._over.call(t,o)))})},dragStop:function(t,e){t.element.parentsUntil("body").off("scroll.droppable"),t.options.refreshPositions||V.ui.ddmanager.prepareOffsets(t,e)}})!==V.uiBackCompat&&V.widget("ui.droppable",V.ui.droppable,{options:{hoverClass:!1,activeClass:!1},_addActiveClass:function(){this._super(),this.options.activeClass&&this.element.addClass(this.options.activeClass)},_removeActiveClass:function(){this._super(),this.options.activeClass&&this.element.removeClass(this.options.activeClass)},_addHoverClass:function(){this._super(),this.options.hoverClass&&this.element.addClass(this.options.hoverClass)},_removeHoverClass:function(){this._super(),this.options.hoverClass&&this.element.removeClass(this.options.hoverClass)}});V.ui.droppable,V.widget("ui.progressbar",{version:"1.13.2",options:{classes:{"ui-progressbar":"ui-corner-all","ui-progressbar-value":"ui-corner-left","ui-progressbar-complete":"ui-corner-right"},max:100,value:0,change:null,complete:null},min:0,_create:function(){this.oldValue=this.options.value=this._constrainedValue(),this.element.attr({role:"progressbar","aria-valuemin":this.min}),this._addClass("ui-progressbar","ui-widget ui-widget-content"),this.valueDiv=V("<div>").appendTo(this.element),this._addClass(this.valueDiv,"ui-progressbar-value","ui-widget-header"),this._refreshValue()},_destroy:function(){this.element.removeAttr("role aria-valuemin aria-valuemax aria-valuenow"),this.valueDiv.remove()},value:function(t){if(void 0===t)return this.options.value;this.options.value=this._constrainedValue(t),this._refreshValue()},_constrainedValue:function(t){return void 0===t&&(t=this.options.value),this.indeterminate=!1===t,"number"!=typeof t&&(t=0),!this.indeterminate&&Math.min(this.options.max,Math.max(this.min,t))},_setOptions:function(t){var e=t.value;delete t.value,this._super(t),this.options.value=this._constrainedValue(e),this._refreshValue()},_setOption:function(t,e){"max"===t&&(e=Math.max(this.min,e)),this._super(t,e)},_setOptionDisabled:function(t){this._super(t),this.element.attr("aria-disabled",t),this._toggleClass(null,"ui-state-disabled",!!t)},_percentage:function(){return this.indeterminate?100:100*(this.options.value-this.min)/(this.options.max-this.min)},_refreshValue:function(){var t=this.options.value,e=this._percentage();this.valueDiv.toggle(this.indeterminate||t>this.min).width(e.toFixed(0)+"%"),this._toggleClass(this.valueDiv,"ui-progressbar-complete",null,t===this.options.max)._toggleClass("ui-progressbar-indeterminate",null,this.indeterminate),this.indeterminate?(this.element.removeAttr("aria-valuenow"),this.overlayDiv||(this.overlayDiv=V("<div>").appendTo(this.valueDiv),this._addClass(this.overlayDiv,"ui-progressbar-overlay"))):(this.element.attr({"aria-valuemax":this.options.max,"aria-valuenow":t}),this.overlayDiv&&(this.overlayDiv.remove(),this.overlayDiv=null)),this.oldValue!==t&&(this.oldValue=t,this._trigger("change")),t===this.options.max&&this._trigger("complete")}}),V.widget("ui.selectable",V.ui.mouse,{version:"1.13.2",options:{appendTo:"body",autoRefresh:!0,distance:0,filter:"*",tolerance:"touch",selected:null,selecting:null,start:null,stop:null,unselected:null,unselecting:null},_create:function(){var i=this;this._addClass("ui-selectable"),this.dragged=!1,this.refresh=function(){i.elementPos=V(i.element[0]).offset(),i.selectees=V(i.options.filter,i.element[0]),i._addClass(i.selectees,"ui-selectee"),i.selectees.each(function(){var t=V(this),e=t.offset(),e={left:e.left-i.elementPos.left,top:e.top-i.elementPos.top};V.data(this,"selectable-item",{element:this,$element:t,left:e.left,top:e.top,right:e.left+t.outerWidth(),bottom:e.top+t.outerHeight(),startselected:!1,selected:t.hasClass("ui-selected"),selecting:t.hasClass("ui-selecting"),unselecting:t.hasClass("ui-unselecting")})})},this.refresh(),this._mouseInit(),this.helper=V("<div>"),this._addClass(this.helper,"ui-selectable-helper")},_destroy:function(){this.selectees.removeData("selectable-item"),this._mouseDestroy()},_mouseStart:function(i){var s=this,t=this.options;this.opos=[i.pageX,i.pageY],this.elementPos=V(this.element[0]).offset(),this.options.disabled||(this.selectees=V(t.filter,this.element[0]),this._trigger("start",i),V(t.appendTo).append(this.helper),this.helper.css({left:i.pageX,top:i.pageY,width:0,height:0}),t.autoRefresh&&this.refresh(),this.selectees.filter(".ui-selected").each(function(){var t=V.data(this,"selectable-item");t.startselected=!0,i.metaKey||i.ctrlKey||(s._removeClass(t.$element,"ui-selected"),t.selected=!1,s._addClass(t.$element,"ui-unselecting"),t.unselecting=!0,s._trigger("unselecting",i,{unselecting:t.element}))}),V(i.target).parents().addBack().each(function(){var t,e=V.data(this,"selectable-item");if(e)return t=!i.metaKey&&!i.ctrlKey||!e.$element.hasClass("ui-selected"),s._removeClass(e.$element,t?"ui-unselecting":"ui-selected")._addClass(e.$element,t?"ui-selecting":"ui-unselecting"),e.unselecting=!t,e.selecting=t,(e.selected=t)?s._trigger("selecting",i,{selecting:e.element}):s._trigger("unselecting",i,{unselecting:e.element}),!1}))},_mouseDrag:function(s){if(this.dragged=!0,!this.options.disabled){var t,n=this,o=this.options,a=this.opos[0],r=this.opos[1],l=s.pageX,h=s.pageY;return l<a&&(t=l,l=a,a=t),h<r&&(t=h,h=r,r=t),this.helper.css({left:a,top:r,width:l-a,height:h-r}),this.selectees.each(function(){var t=V.data(this,"selectable-item"),e=!1,i={};t&&t.element!==n.element[0]&&(i.left=t.left+n.elementPos.left,i.right=t.right+n.elementPos.left,i.top=t.top+n.elementPos.top,i.bottom=t.bottom+n.elementPos.top,"touch"===o.tolerance?e=!(i.left>l||i.right<a||i.top>h||i.bottom<r):"fit"===o.tolerance&&(e=i.left>a&&i.right<l&&i.top>r&&i.bottom<h),e?(t.selected&&(n._removeClass(t.$element,"ui-selected"),t.selected=!1),t.unselecting&&(n._removeClass(t.$element,"ui-unselecting"),t.unselecting=!1),t.selecting||(n._addClass(t.$element,"ui-selecting"),t.selecting=!0,n._trigger("selecting",s,{selecting:t.element}))):(t.selecting&&((s.metaKey||s.ctrlKey)&&t.startselected?(n._removeClass(t.$element,"ui-selecting"),t.selecting=!1,n._addClass(t.$element,"ui-selected"),t.selected=!0):(n._removeClass(t.$element,"ui-selecting"),t.selecting=!1,t.startselected&&(n._addClass(t.$element,"ui-unselecting"),t.unselecting=!0),n._trigger("unselecting",s,{unselecting:t.element}))),t.selected&&(s.metaKey||s.ctrlKey||t.startselected||(n._removeClass(t.$element,"ui-selected"),t.selected=!1,n._addClass(t.$element,"ui-unselecting"),t.unselecting=!0,n._trigger("unselecting",s,{unselecting:t.element})))))}),!1}},_mouseStop:function(e){var i=this;return this.dragged=!1,V(".ui-unselecting",this.element[0]).each(function(){var t=V.data(this,"selectable-item");i._removeClass(t.$element,"ui-unselecting"),t.unselecting=!1,t.startselected=!1,i._trigger("unselected",e,{unselected:t.element})}),V(".ui-selecting",this.element[0]).each(function(){var t=V.data(this,"selectable-item");i._removeClass(t.$element,"ui-selecting")._addClass(t.$element,"ui-selected"),t.selecting=!1,t.selected=!0,t.startselected=!0,i._trigger("selected",e,{selected:t.element})}),this._trigger("stop",e),this.helper.remove(),!1}}),V.widget("ui.selectmenu",[V.ui.formResetMixin,{version:"1.13.2",defaultElement:"<select>",options:{appendTo:null,classes:{"ui-selectmenu-button-open":"ui-corner-top","ui-selectmenu-button-closed":"ui-corner-all"},disabled:null,icons:{button:"ui-icon-triangle-1-s"},position:{my:"left top",at:"left bottom",collision:"none"},width:!1,change:null,close:null,focus:null,open:null,select:null},_create:function(){var t=this.element.uniqueId().attr("id");this.ids={element:t,button:t+"-button",menu:t+"-menu"},this._drawButton(),this._drawMenu(),this._bindFormResetHandler(),this._rendered=!1,this.menuItems=V()},_drawButton:function(){var t,e=this,i=this._parseOption(this.element.find("option:selected"),this.element[0].selectedIndex);this.labels=this.element.labels().attr("for",this.ids.button),this._on(this.labels,{click:function(t){this.button.trigger("focus"),t.preventDefault()}}),this.element.hide(),this.button=V("<span>",{tabindex:this.options.disabled?-1:0,id:this.ids.button,role:"combobox","aria-expanded":"false","aria-autocomplete":"list","aria-owns":this.ids.menu,"aria-haspopup":"true",title:this.element.attr("title")}).insertAfter(this.element),this._addClass(this.button,"ui-selectmenu-button ui-selectmenu-button-closed","ui-button ui-widget"),t=V("<span>").appendTo(this.button),this._addClass(t,"ui-selectmenu-icon","ui-icon "+this.options.icons.button),this.buttonItem=this._renderButtonItem(i).appendTo(this.button),!1!==this.options.width&&this._resizeButton(),this._on(this.button,this._buttonEvents),this.button.one("focusin",function(){e._rendered||e._refreshMenu()})},_drawMenu:function(){var i=this;this.menu=V("<ul>",{"aria-hidden":"true","aria-labelledby":this.ids.button,id:this.ids.menu}),this.menuWrap=V("<div>").append(this.menu),this._addClass(this.menuWrap,"ui-selectmenu-menu","ui-front"),this.menuWrap.appendTo(this._appendTo()),this.menuInstance=this.menu.menu({classes:{"ui-menu":"ui-corner-bottom"},role:"listbox",select:function(t,e){t.preventDefault(),i._setSelection(),i._select(e.item.data("ui-selectmenu-item"),t)},focus:function(t,e){e=e.item.data("ui-selectmenu-item");null!=i.focusIndex&&e.index!==i.focusIndex&&(i._trigger("focus",t,{item:e}),i.isOpen||i._select(e,t)),i.focusIndex=e.index,i.button.attr("aria-activedescendant",i.menuItems.eq(e.index).attr("id"))}}).menu("instance"),this.menuInstance._off(this.menu,"mouseleave"),this.menuInstance._closeOnDocumentClick=function(){return!1},this.menuInstance._isDivider=function(){return!1}},refresh:function(){this._refreshMenu(),this.buttonItem.replaceWith(this.buttonItem=this._renderButtonItem(this._getSelectedItem().data("ui-selectmenu-item")||{})),null===this.options.width&&this._resizeButton()},_refreshMenu:function(){var t=this.element.find("option");this.menu.empty(),this._parseOptions(t),this._renderMenu(this.menu,this.items),this.menuInstance.refresh(),this.menuItems=this.menu.find("li").not(".ui-selectmenu-optgroup").find(".ui-menu-item-wrapper"),this._rendered=!0,t.length&&(t=this._getSelectedItem(),this.menuInstance.focus(null,t),this._setAria(t.data("ui-selectmenu-item")),this._setOption("disabled",this.element.prop("disabled")))},open:function(t){this.options.disabled||(this._rendered?(this._removeClass(this.menu.find(".ui-state-active"),null,"ui-state-active"),this.menuInstance.focus(null,this._getSelectedItem())):this._refreshMenu(),this.menuItems.length&&(this.isOpen=!0,this._toggleAttr(),this._resizeMenu(),this._position(),this._on(this.document,this._documentClick),this._trigger("open",t)))},_position:function(){this.menuWrap.position(V.extend({of:this.button},this.options.position))},close:function(t){this.isOpen&&(this.isOpen=!1,this._toggleAttr(),this.range=null,this._off(this.document),this._trigger("close",t))},widget:function(){return this.button},menuWidget:function(){return this.menu},_renderButtonItem:function(t){var e=V("<span>");return this._setText(e,t.label),this._addClass(e,"ui-selectmenu-text"),e},_renderMenu:function(s,t){var n=this,o="";V.each(t,function(t,e){var i;e.optgroup!==o&&(i=V("<li>",{text:e.optgroup}),n._addClass(i,"ui-selectmenu-optgroup","ui-menu-divider"+(e.element.parent("optgroup").prop("disabled")?" ui-state-disabled":"")),i.appendTo(s),o=e.optgroup),n._renderItemData(s,e)})},_renderItemData:function(t,e){return this._renderItem(t,e).data("ui-selectmenu-item",e)},_renderItem:function(t,e){var i=V("<li>"),s=V("<div>",{title:e.element.attr("title")});return e.disabled&&this._addClass(i,null,"ui-state-disabled"),this._setText(s,e.label),i.append(s).appendTo(t)},_setText:function(t,e){e?t.text(e):t.html("&#160;")},_move:function(t,e){var i,s=".ui-menu-item";this.isOpen?i=this.menuItems.eq(this.focusIndex).parent("li"):(i=this.menuItems.eq(this.element[0].selectedIndex).parent("li"),s+=":not(.ui-state-disabled)"),(s="first"===t||"last"===t?i["first"===t?"prevAll":"nextAll"](s).eq(-1):i[t+"All"](s).eq(0)).length&&this.menuInstance.focus(e,s)},_getSelectedItem:function(){return this.menuItems.eq(this.element[0].selectedIndex).parent("li")},_toggle:function(t){this[this.isOpen?"close":"open"](t)},_setSelection:function(){var t;this.range&&(window.getSelection?((t=window.getSelection()).removeAllRanges(),t.addRange(this.range)):this.range.select(),this.button.trigger("focus"))},_documentClick:{mousedown:function(t){this.isOpen&&(V(t.target).closest(".ui-selectmenu-menu, #"+V.escapeSelector(this.ids.button)).length||this.close(t))}},_buttonEvents:{mousedown:function(){var t;window.getSelection?(t=window.getSelection()).rangeCount&&(this.range=t.getRangeAt(0)):this.range=document.selection.createRange()},click:function(t){this._setSelection(),this._toggle(t)},keydown:function(t){var e=!0;switch(t.keyCode){case V.ui.keyCode.TAB:case V.ui.keyCode.ESCAPE:this.close(t),e=!1;break;case V.ui.keyCode.ENTER:this.isOpen&&this._selectFocusedItem(t);break;case V.ui.keyCode.UP:t.altKey?this._toggle(t):this._move("prev",t);break;case V.ui.keyCode.DOWN:t.altKey?this._toggle(t):this._move("next",t);break;case V.ui.keyCode.SPACE:this.isOpen?this._selectFocusedItem(t):this._toggle(t);break;case V.ui.keyCode.LEFT:this._move("prev",t);break;case V.ui.keyCode.RIGHT:this._move("next",t);break;case V.ui.keyCode.HOME:case V.ui.keyCode.PAGE_UP:this._move("first",t);break;case V.ui.keyCode.END:case V.ui.keyCode.PAGE_DOWN:this._move("last",t);break;default:this.menu.trigger(t),e=!1}e&&t.preventDefault()}},_selectFocusedItem:function(t){var e=this.menuItems.eq(this.focusIndex).parent("li");e.hasClass("ui-state-disabled")||this._select(e.data("ui-selectmenu-item"),t)},_select:function(t,e){var i=this.element[0].selectedIndex;this.element[0].selectedIndex=t.index,this.buttonItem.replaceWith(this.buttonItem=this._renderButtonItem(t)),this._setAria(t),this._trigger("select",e,{item:t}),t.index!==i&&this._trigger("change",e,{item:t}),this.close(e)},_setAria:function(t){t=this.menuItems.eq(t.index).attr("id");this.button.attr({"aria-labelledby":t,"aria-activedescendant":t}),this.menu.attr("aria-activedescendant",t)},_setOption:function(t,e){var i;"icons"===t&&(i=this.button.find("span.ui-icon"),this._removeClass(i,null,this.options.icons.button)._addClass(i,null,e.button)),this._super(t,e),"appendTo"===t&&this.menuWrap.appendTo(this._appendTo()),"width"===t&&this._resizeButton()},_setOptionDisabled:function(t){this._super(t),this.menuInstance.option("disabled",t),this.button.attr("aria-disabled",t),this._toggleClass(this.button,null,"ui-state-disabled",t),this.element.prop("disabled",t),t?(this.button.attr("tabindex",-1),this.close()):this.button.attr("tabindex",0)},_appendTo:function(){var t=this.options.appendTo;return t=!(t=!(t=t&&(t.jquery||t.nodeType?V(t):this.document.find(t).eq(0)))||!t[0]?this.element.closest(".ui-front, dialog"):t).length?this.document[0].body:t},_toggleAttr:function(){this.button.attr("aria-expanded",this.isOpen),this._removeClass(this.button,"ui-selectmenu-button-"+(this.isOpen?"closed":"open"))._addClass(this.button,"ui-selectmenu-button-"+(this.isOpen?"open":"closed"))._toggleClass(this.menuWrap,"ui-selectmenu-open",null,this.isOpen),this.menu.attr("aria-hidden",!this.isOpen)},_resizeButton:function(){var t=this.options.width;!1!==t?(null===t&&(t=this.element.show().outerWidth(),this.element.hide()),this.button.outerWidth(t)):this.button.css("width","")},_resizeMenu:function(){this.menu.outerWidth(Math.max(this.button.outerWidth(),this.menu.width("").outerWidth()+1))},_getCreateOptions:function(){var t=this._super();return t.disabled=this.element.prop("disabled"),t},_parseOptions:function(t){var i=this,s=[];t.each(function(t,e){e.hidden||s.push(i._parseOption(V(e),t))}),this.items=s},_parseOption:function(t,e){var i=t.parent("optgroup");return{element:t,index:e,value:t.val(),label:t.text(),optgroup:i.attr("label")||"",disabled:i.prop("disabled")||t.prop("disabled")}},_destroy:function(){this._unbindFormResetHandler(),this.menuWrap.remove(),this.button.remove(),this.element.show(),this.element.removeUniqueId(),this.labels.attr("for",this.ids.element)}}]),V.widget("ui.slider",V.ui.mouse,{version:"1.13.2",widgetEventPrefix:"slide",options:{animate:!1,classes:{"ui-slider":"ui-corner-all","ui-slider-handle":"ui-corner-all","ui-slider-range":"ui-corner-all ui-widget-header"},distance:0,max:100,min:0,orientation:"horizontal",range:!1,step:1,value:0,values:null,change:null,slide:null,start:null,stop:null},numPages:5,_create:function(){this._keySliding=!1,this._mouseSliding=!1,this._animateOff=!0,this._handleIndex=null,this._detectOrientation(),this._mouseInit(),this._calculateNewMax(),this._addClass("ui-slider ui-slider-"+this.orientation,"ui-widget ui-widget-content"),this._refresh(),this._animateOff=!1},_refresh:function(){this._createRange(),this._createHandles(),this._setupEvents(),this._refreshValue()},_createHandles:function(){var t,e=this.options,i=this.element.find(".ui-slider-handle"),s=[],n=e.values&&e.values.length||1;for(i.length>n&&(i.slice(n).remove(),i=i.slice(0,n)),t=i.length;t<n;t++)s.push("<span tabindex='0'></span>");this.handles=i.add(V(s.join("")).appendTo(this.element)),this._addClass(this.handles,"ui-slider-handle","ui-state-default"),this.handle=this.handles.eq(0),this.handles.each(function(t){V(this).data("ui-slider-handle-index",t).attr("tabIndex",0)})},_createRange:function(){var t=this.options;t.range?(!0===t.range&&(t.values?t.values.length&&2!==t.values.length?t.values=[t.values[0],t.values[0]]:Array.isArray(t.values)&&(t.values=t.values.slice(0)):t.values=[this._valueMin(),this._valueMin()]),this.range&&this.range.length?(this._removeClass(this.range,"ui-slider-range-min ui-slider-range-max"),this.range.css({left:"",bottom:""})):(this.range=V("<div>").appendTo(this.element),this._addClass(this.range,"ui-slider-range")),"min"!==t.range&&"max"!==t.range||this._addClass(this.range,"ui-slider-range-"+t.range)):(this.range&&this.range.remove(),this.range=null)},_setupEvents:function(){this._off(this.handles),this._on(this.handles,this._handleEvents),this._hoverable(this.handles),this._focusable(this.handles)},_destroy:function(){this.handles.remove(),this.range&&this.range.remove(),this._mouseDestroy()},_mouseCapture:function(t){var i,s,n,o,e,a,r=this,l=this.options;return!l.disabled&&(this.elementSize={width:this.element.outerWidth(),height:this.element.outerHeight()},this.elementOffset=this.element.offset(),a={x:t.pageX,y:t.pageY},i=this._normValueFromMouse(a),s=this._valueMax()-this._valueMin()+1,this.handles.each(function(t){var e=Math.abs(i-r.values(t));(e<s||s===e&&(t===r._lastChangedValue||r.values(t)===l.min))&&(s=e,n=V(this),o=t)}),!1!==this._start(t,o)&&(this._mouseSliding=!0,this._handleIndex=o,this._addClass(n,null,"ui-state-active"),n.trigger("focus"),e=n.offset(),a=!V(t.target).parents().addBack().is(".ui-slider-handle"),this._clickOffset=a?{left:0,top:0}:{left:t.pageX-e.left-n.width()/2,top:t.pageY-e.top-n.height()/2-(parseInt(n.css("borderTopWidth"),10)||0)-(parseInt(n.css("borderBottomWidth"),10)||0)+(parseInt(n.css("marginTop"),10)||0)},this.handles.hasClass("ui-state-hover")||this._slide(t,o,i),this._animateOff=!0))},_mouseStart:function(){return!0},_mouseDrag:function(t){var e={x:t.pageX,y:t.pageY},e=this._normValueFromMouse(e);return this._slide(t,this._handleIndex,e),!1},_mouseStop:function(t){return this._removeClass(this.handles,null,"ui-state-active"),this._mouseSliding=!1,this._stop(t,this._handleIndex),this._change(t,this._handleIndex),this._handleIndex=null,this._clickOffset=null,this._animateOff=!1},_detectOrientation:function(){this.orientation="vertical"===this.options.orientation?"vertical":"horizontal"},_normValueFromMouse:function(t){var e,t="horizontal"===this.orientation?(e=this.elementSize.width,t.x-this.elementOffset.left-(this._clickOffset?this._clickOffset.left:0)):(e=this.elementSize.height,t.y-this.elementOffset.top-(this._clickOffset?this._clickOffset.top:0)),t=t/e;return(t=1<t?1:t)<0&&(t=0),"vertical"===this.orientation&&(t=1-t),e=this._valueMax()-this._valueMin(),e=this._valueMin()+t*e,this._trimAlignValue(e)},_uiHash:function(t,e,i){var s={handle:this.handles[t],handleIndex:t,value:void 0!==e?e:this.value()};return this._hasMultipleValues()&&(s.value=void 0!==e?e:this.values(t),s.values=i||this.values()),s},_hasMultipleValues:function(){return this.options.values&&this.options.values.length},_start:function(t,e){return this._trigger("start",t,this._uiHash(e))},_slide:function(t,e,i){var s,n=this.value(),o=this.values();this._hasMultipleValues()&&(s=this.values(e?0:1),n=this.values(e),2===this.options.values.length&&!0===this.options.range&&(i=0===e?Math.min(s,i):Math.max(s,i)),o[e]=i),i!==n&&!1!==this._trigger("slide",t,this._uiHash(e,i,o))&&(this._hasMultipleValues()?this.values(e,i):this.value(i))},_stop:function(t,e){this._trigger("stop",t,this._uiHash(e))},_change:function(t,e){this._keySliding||this._mouseSliding||(this._lastChangedValue=e,this._trigger("change",t,this._uiHash(e)))},value:function(t){return arguments.length?(this.options.value=this._trimAlignValue(t),this._refreshValue(),void this._change(null,0)):this._value()},values:function(t,e){var i,s,n;if(1<arguments.length)return this.options.values[t]=this._trimAlignValue(e),this._refreshValue(),void this._change(null,t);if(!arguments.length)return this._values();if(!Array.isArray(t))return this._hasMultipleValues()?this._values(t):this.value();for(i=this.options.values,s=t,n=0;n<i.length;n+=1)i[n]=this._trimAlignValue(s[n]),this._change(null,n);this._refreshValue()},_setOption:function(t,e){var i,s=0;switch("range"===t&&!0===this.options.range&&("min"===e?(this.options.value=this._values(0),this.options.values=null):"max"===e&&(this.options.value=this._values(this.options.values.length-1),this.options.values=null)),Array.isArray(this.options.values)&&(s=this.options.values.length),this._super(t,e),t){case"orientation":this._detectOrientation(),this._removeClass("ui-slider-horizontal ui-slider-vertical")._addClass("ui-slider-"+this.orientation),this._refreshValue(),this.options.range&&this._refreshRange(e),this.handles.css("horizontal"===e?"bottom":"left","");break;case"value":this._animateOff=!0,this._refreshValue(),this._change(null,0),this._animateOff=!1;break;case"values":for(this._animateOff=!0,this._refreshValue(),i=s-1;0<=i;i--)this._change(null,i);this._animateOff=!1;break;case"step":case"min":case"max":this._animateOff=!0,this._calculateNewMax(),this._refreshValue(),this._animateOff=!1;break;case"range":this._animateOff=!0,this._refresh(),this._animateOff=!1}},_setOptionDisabled:function(t){this._super(t),this._toggleClass(null,"ui-state-disabled",!!t)},_value:function(){var t=this.options.value;return t=this._trimAlignValue(t)},_values:function(t){var e,i;if(arguments.length)return t=this.options.values[t],t=this._trimAlignValue(t);if(this._hasMultipleValues()){for(e=this.options.values.slice(),i=0;i<e.length;i+=1)e[i]=this._trimAlignValue(e[i]);return e}return[]},_trimAlignValue:function(t){if(t<=this._valueMin())return this._valueMin();if(t>=this._valueMax())return this._valueMax();var e=0<this.options.step?this.options.step:1,i=(t-this._valueMin())%e,t=t-i;return 2*Math.abs(i)>=e&&(t+=0<i?e:-e),parseFloat(t.toFixed(5))},_calculateNewMax:function(){var t=this.options.max,e=this._valueMin(),i=this.options.step;(t=Math.round((t-e)/i)*i+e)>this.options.max&&(t-=i),this.max=parseFloat(t.toFixed(this._precision()))},_precision:function(){var t=this._precisionOf(this.options.step);return t=null!==this.options.min?Math.max(t,this._precisionOf(this.options.min)):t},_precisionOf:function(t){var e=t.toString(),t=e.indexOf(".");return-1===t?0:e.length-t-1},_valueMin:function(){return this.options.min},_valueMax:function(){return this.max},_refreshRange:function(t){"vertical"===t&&this.range.css({width:"",left:""}),"horizontal"===t&&this.range.css({height:"",bottom:""})},_refreshValue:function(){var e,i,t,s,n,o=this.options.range,a=this.options,r=this,l=!this._animateOff&&a.animate,h={};this._hasMultipleValues()?this.handles.each(function(t){i=(r.values(t)-r._valueMin())/(r._valueMax()-r._valueMin())*100,h["horizontal"===r.orientation?"left":"bottom"]=i+"%",V(this).stop(1,1)[l?"animate":"css"](h,a.animate),!0===r.options.range&&("horizontal"===r.orientation?(0===t&&r.range.stop(1,1)[l?"animate":"css"]({left:i+"%"},a.animate),1===t&&r.range[l?"animate":"css"]({width:i-e+"%"},{queue:!1,duration:a.animate})):(0===t&&r.range.stop(1,1)[l?"animate":"css"]({bottom:i+"%"},a.animate),1===t&&r.range[l?"animate":"css"]({height:i-e+"%"},{queue:!1,duration:a.animate}))),e=i}):(t=this.value(),s=this._valueMin(),n=this._valueMax(),i=n!==s?(t-s)/(n-s)*100:0,h["horizontal"===this.orientation?"left":"bottom"]=i+"%",this.handle.stop(1,1)[l?"animate":"css"](h,a.animate),"min"===o&&"horizontal"===this.orientation&&this.range.stop(1,1)[l?"animate":"css"]({width:i+"%"},a.animate),"max"===o&&"horizontal"===this.orientation&&this.range.stop(1,1)[l?"animate":"css"]({width:100-i+"%"},a.animate),"min"===o&&"vertical"===this.orientation&&this.range.stop(1,1)[l?"animate":"css"]({height:i+"%"},a.animate),"max"===o&&"vertical"===this.orientation&&this.range.stop(1,1)[l?"animate":"css"]({height:100-i+"%"},a.animate))},_handleEvents:{keydown:function(t){var e,i,s,n=V(t.target).data("ui-slider-handle-index");switch(t.keyCode){case V.ui.keyCode.HOME:case V.ui.keyCode.END:case V.ui.keyCode.PAGE_UP:case V.ui.keyCode.PAGE_DOWN:case V.ui.keyCode.UP:case V.ui.keyCode.RIGHT:case V.ui.keyCode.DOWN:case V.ui.keyCode.LEFT:if(t.preventDefault(),!this._keySliding&&(this._keySliding=!0,this._addClass(V(t.target),null,"ui-state-active"),!1===this._start(t,n)))return}switch(s=this.options.step,e=i=this._hasMultipleValues()?this.values(n):this.value(),t.keyCode){case V.ui.keyCode.HOME:i=this._valueMin();break;case V.ui.keyCode.END:i=this._valueMax();break;case V.ui.keyCode.PAGE_UP:i=this._trimAlignValue(e+(this._valueMax()-this._valueMin())/this.numPages);break;case V.ui.keyCode.PAGE_DOWN:i=this._trimAlignValue(e-(this._valueMax()-this._valueMin())/this.numPages);break;case V.ui.keyCode.UP:case V.ui.keyCode.RIGHT:if(e===this._valueMax())return;i=this._trimAlignValue(e+s);break;case V.ui.keyCode.DOWN:case V.ui.keyCode.LEFT:if(e===this._valueMin())return;i=this._trimAlignValue(e-s)}this._slide(t,n,i)},keyup:function(t){var e=V(t.target).data("ui-slider-handle-index");this._keySliding&&(this._keySliding=!1,this._stop(t,e),this._change(t,e),this._removeClass(V(t.target),null,"ui-state-active"))}}}),V.widget("ui.sortable",V.ui.mouse,{version:"1.13.2",widgetEventPrefix:"sort",ready:!1,options:{appendTo:"parent",axis:!1,connectWith:!1,containment:!1,cursor:"auto",cursorAt:!1,dropOnEmpty:!0,forcePlaceholderSize:!1,forceHelperSize:!1,grid:!1,handle:!1,helper:"original",items:"> *",opacity:!1,placeholder:!1,revert:!1,scroll:!0,scrollSensitivity:20,scrollSpeed:20,scope:"default",tolerance:"intersect",zIndex:1e3,activate:null,beforeStop:null,change:null,deactivate:null,out:null,over:null,receive:null,remove:null,sort:null,start:null,stop:null,update:null},_isOverAxis:function(t,e,i){return e<=t&&t<e+i},_isFloating:function(t){return/left|right/.test(t.css("float"))||/inline|table-cell/.test(t.css("display"))},_create:function(){this.containerCache={},this._addClass("ui-sortable"),this.refresh(),this.offset=this.element.offset(),this._mouseInit(),this._setHandleClassName(),this.ready=!0},_setOption:function(t,e){this._super(t,e),"handle"===t&&this._setHandleClassName()},_setHandleClassName:function(){var t=this;this._removeClass(this.element.find(".ui-sortable-handle"),"ui-sortable-handle"),V.each(this.items,function(){t._addClass(this.instance.options.handle?this.item.find(this.instance.options.handle):this.item,"ui-sortable-handle")})},_destroy:function(){this._mouseDestroy();for(var t=this.items.length-1;0<=t;t--)this.items[t].item.removeData(this.widgetName+"-item");return this},_mouseCapture:function(t,e){var i=null,s=!1,n=this;return!this.reverting&&(!this.options.disabled&&"static"!==this.options.type&&(this._refreshItems(t),V(t.target).parents().each(function(){if(V.data(this,n.widgetName+"-item")===n)return i=V(this),!1}),!!(i=V.data(t.target,n.widgetName+"-item")===n?V(t.target):i)&&(!(this.options.handle&&!e&&(V(this.options.handle,i).find("*").addBack().each(function(){this===t.target&&(s=!0)}),!s))&&(this.currentItem=i,this._removeCurrentsFromItems(),!0))))},_mouseStart:function(t,e,i){var s,n,o=this.options;if((this.currentContainer=this).refreshPositions(),this.appendTo=V("parent"!==o.appendTo?o.appendTo:this.currentItem.parent()),this.helper=this._createHelper(t),this._cacheHelperProportions(),this._cacheMargins(),this.offset=this.currentItem.offset(),this.offset={top:this.offset.top-this.margins.top,left:this.offset.left-this.margins.left},V.extend(this.offset,{click:{left:t.pageX-this.offset.left,top:t.pageY-this.offset.top},relative:this._getRelativeOffset()}),this.helper.css("position","absolute"),this.cssPosition=this.helper.css("position"),o.cursorAt&&this._adjustOffsetFromHelper(o.cursorAt),this.domPosition={prev:this.currentItem.prev()[0],parent:this.currentItem.parent()[0]},this.helper[0]!==this.currentItem[0]&&this.currentItem.hide(),this._createPlaceholder(),this.scrollParent=this.placeholder.scrollParent(),V.extend(this.offset,{parent:this._getParentOffset()}),o.containment&&this._setContainment(),o.cursor&&"auto"!==o.cursor&&(n=this.document.find("body"),this.storedCursor=n.css("cursor"),n.css("cursor",o.cursor),this.storedStylesheet=V("<style>*{ cursor: "+o.cursor+" !important; }</style>").appendTo(n)),o.zIndex&&(this.helper.css("zIndex")&&(this._storedZIndex=this.helper.css("zIndex")),this.helper.css("zIndex",o.zIndex)),o.opacity&&(this.helper.css("opacity")&&(this._storedOpacity=this.helper.css("opacity")),this.helper.css("opacity",o.opacity)),this.scrollParent[0]!==this.document[0]&&"HTML"!==this.scrollParent[0].tagName&&(this.overflowOffset=this.scrollParent.offset()),this._trigger("start",t,this._uiHash()),this._preserveHelperProportions||this._cacheHelperProportions(),!i)for(s=this.containers.length-1;0<=s;s--)this.containers[s]._trigger("activate",t,this._uiHash(this));return V.ui.ddmanager&&(V.ui.ddmanager.current=this),V.ui.ddmanager&&!o.dropBehaviour&&V.ui.ddmanager.prepareOffsets(this,t),this.dragging=!0,this._addClass(this.helper,"ui-sortable-helper"),this.helper.parent().is(this.appendTo)||(this.helper.detach().appendTo(this.appendTo),this.offset.parent=this._getParentOffset()),this.position=this.originalPosition=this._generatePosition(t),this.originalPageX=t.pageX,this.originalPageY=t.pageY,this.lastPositionAbs=this.positionAbs=this._convertPositionTo("absolute"),this._mouseDrag(t),!0},_scroll:function(t){var e=this.options,i=!1;return this.scrollParent[0]!==this.document[0]&&"HTML"!==this.scrollParent[0].tagName?(this.overflowOffset.top+this.scrollParent[0].offsetHeight-t.pageY<e.scrollSensitivity?this.scrollParent[0].scrollTop=i=this.scrollParent[0].scrollTop+e.scrollSpeed:t.pageY-this.overflowOffset.top<e.scrollSensitivity&&(this.scrollParent[0].scrollTop=i=this.scrollParent[0].scrollTop-e.scrollSpeed),this.overflowOffset.left+this.scrollParent[0].offsetWidth-t.pageX<e.scrollSensitivity?this.scrollParent[0].scrollLeft=i=this.scrollParent[0].scrollLeft+e.scrollSpeed:t.pageX-this.overflowOffset.left<e.scrollSensitivity&&(this.scrollParent[0].scrollLeft=i=this.scrollParent[0].scrollLeft-e.scrollSpeed)):(t.pageY-this.document.scrollTop()<e.scrollSensitivity?i=this.document.scrollTop(this.document.scrollTop()-e.scrollSpeed):this.window.height()-(t.pageY-this.document.scrollTop())<e.scrollSensitivity&&(i=this.document.scrollTop(this.document.scrollTop()+e.scrollSpeed)),t.pageX-this.document.scrollLeft()<e.scrollSensitivity?i=this.document.scrollLeft(this.document.scrollLeft()-e.scrollSpeed):this.window.width()-(t.pageX-this.document.scrollLeft())<e.scrollSensitivity&&(i=this.document.scrollLeft(this.document.scrollLeft()+e.scrollSpeed))),i},_mouseDrag:function(t){var e,i,s,n,o=this.options;for(this.position=this._generatePosition(t),this.positionAbs=this._convertPositionTo("absolute"),this.options.axis&&"y"===this.options.axis||(this.helper[0].style.left=this.position.left+"px"),this.options.axis&&"x"===this.options.axis||(this.helper[0].style.top=this.position.top+"px"),o.scroll&&!1!==this._scroll(t)&&(this._refreshItemPositions(!0),V.ui.ddmanager&&!o.dropBehaviour&&V.ui.ddmanager.prepareOffsets(this,t)),this.dragDirection={vertical:this._getDragVerticalDirection(),horizontal:this._getDragHorizontalDirection()},e=this.items.length-1;0<=e;e--)if(s=(i=this.items[e]).item[0],(n=this._intersectsWithPointer(i))&&i.instance===this.currentContainer&&!(s===this.currentItem[0]||this.placeholder[1===n?"next":"prev"]()[0]===s||V.contains(this.placeholder[0],s)||"semi-dynamic"===this.options.type&&V.contains(this.element[0],s))){if(this.direction=1===n?"down":"up","pointer"!==this.options.tolerance&&!this._intersectsWithSides(i))break;this._rearrange(t,i),this._trigger("change",t,this._uiHash());break}return this._contactContainers(t),V.ui.ddmanager&&V.ui.ddmanager.drag(this,t),this._trigger("sort",t,this._uiHash()),this.lastPositionAbs=this.positionAbs,!1},_mouseStop:function(t,e){var i,s,n,o;if(t)return V.ui.ddmanager&&!this.options.dropBehaviour&&V.ui.ddmanager.drop(this,t),this.options.revert?(s=(i=this).placeholder.offset(),o={},(n=this.options.axis)&&"x"!==n||(o.left=s.left-this.offset.parent.left-this.margins.left+(this.offsetParent[0]===this.document[0].body?0:this.offsetParent[0].scrollLeft)),n&&"y"!==n||(o.top=s.top-this.offset.parent.top-this.margins.top+(this.offsetParent[0]===this.document[0].body?0:this.offsetParent[0].scrollTop)),this.reverting=!0,V(this.helper).animate(o,parseInt(this.options.revert,10)||500,function(){i._clear(t)})):this._clear(t,e),!1},cancel:function(){if(this.dragging){this._mouseUp(new V.Event("mouseup",{target:null})),"original"===this.options.helper?(this.currentItem.css(this._storedCSS),this._removeClass(this.currentItem,"ui-sortable-helper")):this.currentItem.show();for(var t=this.containers.length-1;0<=t;t--)this.containers[t]._trigger("deactivate",null,this._uiHash(this)),this.containers[t].containerCache.over&&(this.containers[t]._trigger("out",null,this._uiHash(this)),this.containers[t].containerCache.over=0)}return this.placeholder&&(this.placeholder[0].parentNode&&this.placeholder[0].parentNode.removeChild(this.placeholder[0]),"original"!==this.options.helper&&this.helper&&this.helper[0].parentNode&&this.helper.remove(),V.extend(this,{helper:null,dragging:!1,reverting:!1,_noFinalSort:null}),this.domPosition.prev?V(this.domPosition.prev).after(this.currentItem):V(this.domPosition.parent).prepend(this.currentItem)),this},serialize:function(e){var t=this._getItemsAsjQuery(e&&e.connected),i=[];return e=e||{},V(t).each(function(){var t=(V(e.item||this).attr(e.attribute||"id")||"").match(e.expression||/(.+)[\-=_](.+)/);t&&i.push((e.key||t[1]+"[]")+"="+(e.key&&e.expression?t[1]:t[2]))}),!i.length&&e.key&&i.push(e.key+"="),i.join("&")},toArray:function(t){var e=this._getItemsAsjQuery(t&&t.connected),i=[];return t=t||{},e.each(function(){i.push(V(t.item||this).attr(t.attribute||"id")||"")}),i},_intersectsWith:function(t){var e=this.positionAbs.left,i=e+this.helperProportions.width,s=this.positionAbs.top,n=s+this.helperProportions.height,o=t.left,a=o+t.width,r=t.top,l=r+t.height,h=this.offset.click.top,c=this.offset.click.left,h="x"===this.options.axis||r<s+h&&s+h<l,c="y"===this.options.axis||o<e+c&&e+c<a;return"pointer"===this.options.tolerance||this.options.forcePointerForContainers||"pointer"!==this.options.tolerance&&this.helperProportions[this.floating?"width":"height"]>t[this.floating?"width":"height"]?h&&c:o<e+this.helperProportions.width/2&&i-this.helperProportions.width/2<a&&r<s+this.helperProportions.height/2&&n-this.helperProportions.height/2<l},_intersectsWithPointer:function(t){var e="x"===this.options.axis||this._isOverAxis(this.positionAbs.top+this.offset.click.top,t.top,t.height),t="y"===this.options.axis||this._isOverAxis(this.positionAbs.left+this.offset.click.left,t.left,t.width);return!(!e||!t)&&(e=this.dragDirection.vertical,t=this.dragDirection.horizontal,this.floating?"right"===t||"down"===e?2:1:e&&("down"===e?2:1))},_intersectsWithSides:function(t){var e=this._isOverAxis(this.positionAbs.top+this.offset.click.top,t.top+t.height/2,t.height),i=this._isOverAxis(this.positionAbs.left+this.offset.click.left,t.left+t.width/2,t.width),s=this.dragDirection.vertical,t=this.dragDirection.horizontal;return this.floating&&t?"right"===t&&i||"left"===t&&!i:s&&("down"===s&&e||"up"===s&&!e)},_getDragVerticalDirection:function(){var t=this.positionAbs.top-this.lastPositionAbs.top;return 0!=t&&(0<t?"down":"up")},_getDragHorizontalDirection:function(){var t=this.positionAbs.left-this.lastPositionAbs.left;return 0!=t&&(0<t?"right":"left")},refresh:function(t){return this._refreshItems(t),this._setHandleClassName(),this.refreshPositions(),this},_connectWith:function(){var t=this.options;return t.connectWith.constructor===String?[t.connectWith]:t.connectWith},_getItemsAsjQuery:function(t){var e,i,s,n,o=[],a=[],r=this._connectWith();if(r&&t)for(e=r.length-1;0<=e;e--)for(i=(s=V(r[e],this.document[0])).length-1;0<=i;i--)(n=V.data(s[i],this.widgetFullName))&&n!==this&&!n.options.disabled&&a.push(["function"==typeof n.options.items?n.options.items.call(n.element):V(n.options.items,n.element).not(".ui-sortable-helper").not(".ui-sortable-placeholder"),n]);function l(){o.push(this)}for(a.push(["function"==typeof this.options.items?this.options.items.call(this.element,null,{options:this.options,item:this.currentItem}):V(this.options.items,this.element).not(".ui-sortable-helper").not(".ui-sortable-placeholder"),this]),e=a.length-1;0<=e;e--)a[e][0].each(l);return V(o)},_removeCurrentsFromItems:function(){var i=this.currentItem.find(":data("+this.widgetName+"-item)");this.items=V.grep(this.items,function(t){for(var e=0;e<i.length;e++)if(i[e]===t.item[0])return!1;return!0})},_refreshItems:function(t){this.items=[],this.containers=[this];var e,i,s,n,o,a,r,l,h=this.items,c=[["function"==typeof this.options.items?this.options.items.call(this.element[0],t,{item:this.currentItem}):V(this.options.items,this.element),this]],u=this._connectWith();if(u&&this.ready)for(e=u.length-1;0<=e;e--)for(i=(s=V(u[e],this.document[0])).length-1;0<=i;i--)(n=V.data(s[i],this.widgetFullName))&&n!==this&&!n.options.disabled&&(c.push(["function"==typeof n.options.items?n.options.items.call(n.element[0],t,{item:this.currentItem}):V(n.options.items,n.element),n]),this.containers.push(n));for(e=c.length-1;0<=e;e--)for(o=c[e][1],l=(a=c[e][i=0]).length;i<l;i++)(r=V(a[i])).data(this.widgetName+"-item",o),h.push({item:r,instance:o,width:0,height:0,left:0,top:0})},_refreshItemPositions:function(t){for(var e,i,s=this.items.length-1;0<=s;s--)e=this.items[s],this.currentContainer&&e.instance!==this.currentContainer&&e.item[0]!==this.currentItem[0]||(i=this.options.toleranceElement?V(this.options.toleranceElement,e.item):e.item,t||(e.width=i.outerWidth(),e.height=i.outerHeight()),i=i.offset(),e.left=i.left,e.top=i.top)},refreshPositions:function(t){var e,i;if(this.floating=!!this.items.length&&("x"===this.options.axis||this._isFloating(this.items[0].item)),this.offsetParent&&this.helper&&(this.offset.parent=this._getParentOffset()),this._refreshItemPositions(t),this.options.custom&&this.options.custom.refreshContainers)this.options.custom.refreshContainers.call(this);else for(e=this.containers.length-1;0<=e;e--)i=this.containers[e].element.offset(),this.containers[e].containerCache.left=i.left,this.containers[e].containerCache.top=i.top,this.containers[e].containerCache.width=this.containers[e].element.outerWidth(),this.containers[e].containerCache.height=this.containers[e].element.outerHeight();return this},_createPlaceholder:function(i){var s,n,o=(i=i||this).options;o.placeholder&&o.placeholder.constructor!==String||(s=o.placeholder,n=i.currentItem[0].nodeName.toLowerCase(),o.placeholder={element:function(){var t=V("<"+n+">",i.document[0]);return i._addClass(t,"ui-sortable-placeholder",s||i.currentItem[0].className)._removeClass(t,"ui-sortable-helper"),"tbody"===n?i._createTrPlaceholder(i.currentItem.find("tr").eq(0),V("<tr>",i.document[0]).appendTo(t)):"tr"===n?i._createTrPlaceholder(i.currentItem,t):"img"===n&&t.attr("src",i.currentItem.attr("src")),s||t.css("visibility","hidden"),t},update:function(t,e){s&&!o.forcePlaceholderSize||(e.height()&&(!o.forcePlaceholderSize||"tbody"!==n&&"tr"!==n)||e.height(i.currentItem.innerHeight()-parseInt(i.currentItem.css("paddingTop")||0,10)-parseInt(i.currentItem.css("paddingBottom")||0,10)),e.width()||e.width(i.currentItem.innerWidth()-parseInt(i.currentItem.css("paddingLeft")||0,10)-parseInt(i.currentItem.css("paddingRight")||0,10)))}}),i.placeholder=V(o.placeholder.element.call(i.element,i.currentItem)),i.currentItem.after(i.placeholder),o.placeholder.update(i,i.placeholder)},_createTrPlaceholder:function(t,e){var i=this;t.children().each(function(){V("<td>&#160;</td>",i.document[0]).attr("colspan",V(this).attr("colspan")||1).appendTo(e)})},_contactContainers:function(t){for(var e,i,s,n,o,a,r,l,h,c=null,u=null,d=this.containers.length-1;0<=d;d--)V.contains(this.currentItem[0],this.containers[d].element[0])||(this._intersectsWith(this.containers[d].containerCache)?c&&V.contains(this.containers[d].element[0],c.element[0])||(c=this.containers[d],u=d):this.containers[d].containerCache.over&&(this.containers[d]._trigger("out",t,this._uiHash(this)),this.containers[d].containerCache.over=0));if(c)if(1===this.containers.length)this.containers[u].containerCache.over||(this.containers[u]._trigger("over",t,this._uiHash(this)),this.containers[u].containerCache.over=1);else{for(i=1e4,s=null,n=(l=c.floating||this._isFloating(this.currentItem))?"left":"top",o=l?"width":"height",h=l?"pageX":"pageY",e=this.items.length-1;0<=e;e--)V.contains(this.containers[u].element[0],this.items[e].item[0])&&this.items[e].item[0]!==this.currentItem[0]&&(a=this.items[e].item.offset()[n],r=!1,t[h]-a>this.items[e][o]/2&&(r=!0),Math.abs(t[h]-a)<i&&(i=Math.abs(t[h]-a),s=this.items[e],this.direction=r?"up":"down"));(s||this.options.dropOnEmpty)&&(this.currentContainer!==this.containers[u]?(s?this._rearrange(t,s,null,!0):this._rearrange(t,null,this.containers[u].element,!0),this._trigger("change",t,this._uiHash()),this.containers[u]._trigger("change",t,this._uiHash(this)),this.currentContainer=this.containers[u],this.options.placeholder.update(this.currentContainer,this.placeholder),this.scrollParent=this.placeholder.scrollParent(),this.scrollParent[0]!==this.document[0]&&"HTML"!==this.scrollParent[0].tagName&&(this.overflowOffset=this.scrollParent.offset()),this.containers[u]._trigger("over",t,this._uiHash(this)),this.containers[u].containerCache.over=1):this.currentContainer.containerCache.over||(this.containers[u]._trigger("over",t,this._uiHash()),this.currentContainer.containerCache.over=1))}},_createHelper:function(t){var e=this.options,t="function"==typeof e.helper?V(e.helper.apply(this.element[0],[t,this.currentItem])):"clone"===e.helper?this.currentItem.clone():this.currentItem;return t.parents("body").length||this.appendTo[0].appendChild(t[0]),t[0]===this.currentItem[0]&&(this._storedCSS={width:this.currentItem[0].style.width,height:this.currentItem[0].style.height,position:this.currentItem.css("position"),top:this.currentItem.css("top"),left:this.currentItem.css("left")}),t[0].style.width&&!e.forceHelperSize||t.width(this.currentItem.width()),t[0].style.height&&!e.forceHelperSize||t.height(this.currentItem.height()),t},_adjustOffsetFromHelper:function(t){"string"==typeof t&&(t=t.split(" ")),"left"in(t=Array.isArray(t)?{left:+t[0],top:+t[1]||0}:t)&&(this.offset.click.left=t.left+this.margins.left),"right"in t&&(this.offset.click.left=this.helperProportions.width-t.right+this.margins.left),"top"in t&&(this.offset.click.top=t.top+this.margins.top),"bottom"in t&&(this.offset.click.top=this.helperProportions.height-t.bottom+this.margins.top)},_getParentOffset:function(){this.offsetParent=this.helper.offsetParent();var t=this.offsetParent.offset();return"absolute"===this.cssPosition&&this.scrollParent[0]!==this.document[0]&&V.contains(this.scrollParent[0],this.offsetParent[0])&&(t.left+=this.scrollParent.scrollLeft(),t.top+=this.scrollParent.scrollTop()),{top:(t=this.offsetParent[0]===this.document[0].body||this.offsetParent[0].tagName&&"html"===this.offsetParent[0].tagName.toLowerCase()&&V.ui.ie?{top:0,left:0}:t).top+(parseInt(this.offsetParent.css("borderTopWidth"),10)||0),left:t.left+(parseInt(this.offsetParent.css("borderLeftWidth"),10)||0)}},_getRelativeOffset:function(){if("relative"!==this.cssPosition)return{top:0,left:0};var t=this.currentItem.position();return{top:t.top-(parseInt(this.helper.css("top"),10)||0)+this.scrollParent.scrollTop(),left:t.left-(parseInt(this.helper.css("left"),10)||0)+this.scrollParent.scrollLeft()}},_cacheMargins:function(){this.margins={left:parseInt(this.currentItem.css("marginLeft"),10)||0,top:parseInt(this.currentItem.css("marginTop"),10)||0}},_cacheHelperProportions:function(){this.helperProportions={width:this.helper.outerWidth(),height:this.helper.outerHeight()}},_setContainment:function(){var t,e,i=this.options;"parent"===i.containment&&(i.containment=this.helper[0].parentNode),"document"!==i.containment&&"window"!==i.containment||(this.containment=[0-this.offset.relative.left-this.offset.parent.left,0-this.offset.relative.top-this.offset.parent.top,"document"===i.containment?this.document.width():this.window.width()-this.helperProportions.width-this.margins.left,("document"===i.containment?this.document.height()||document.body.parentNode.scrollHeight:this.window.height()||this.document[0].body.parentNode.scrollHeight)-this.helperProportions.height-this.margins.top]),/^(document|window|parent)$/.test(i.containment)||(t=V(i.containment)[0],e=V(i.containment).offset(),i="hidden"!==V(t).css("overflow"),this.containment=[e.left+(parseInt(V(t).css("borderLeftWidth"),10)||0)+(parseInt(V(t).css("paddingLeft"),10)||0)-this.margins.left,e.top+(parseInt(V(t).css("borderTopWidth"),10)||0)+(parseInt(V(t).css("paddingTop"),10)||0)-this.margins.top,e.left+(i?Math.max(t.scrollWidth,t.offsetWidth):t.offsetWidth)-(parseInt(V(t).css("borderLeftWidth"),10)||0)-(parseInt(V(t).css("paddingRight"),10)||0)-this.helperProportions.width-this.margins.left,e.top+(i?Math.max(t.scrollHeight,t.offsetHeight):t.offsetHeight)-(parseInt(V(t).css("borderTopWidth"),10)||0)-(parseInt(V(t).css("paddingBottom"),10)||0)-this.helperProportions.height-this.margins.top])},_convertPositionTo:function(t,e){e=e||this.position;var i="absolute"===t?1:-1,s="absolute"!==this.cssPosition||this.scrollParent[0]!==this.document[0]&&V.contains(this.scrollParent[0],this.offsetParent[0])?this.scrollParent:this.offsetParent,t=/(html|body)/i.test(s[0].tagName);return{top:e.top+this.offset.relative.top*i+this.offset.parent.top*i-("fixed"===this.cssPosition?-this.scrollParent.scrollTop():t?0:s.scrollTop())*i,left:e.left+this.offset.relative.left*i+this.offset.parent.left*i-("fixed"===this.cssPosition?-this.scrollParent.scrollLeft():t?0:s.scrollLeft())*i}},_generatePosition:function(t){var e=this.options,i=t.pageX,s=t.pageY,n="absolute"!==this.cssPosition||this.scrollParent[0]!==this.document[0]&&V.contains(this.scrollParent[0],this.offsetParent[0])?this.scrollParent:this.offsetParent,o=/(html|body)/i.test(n[0].tagName);return"relative"!==this.cssPosition||this.scrollParent[0]!==this.document[0]&&this.scrollParent[0]!==this.offsetParent[0]||(this.offset.relative=this._getRelativeOffset()),this.originalPosition&&(this.containment&&(t.pageX-this.offset.click.left<this.containment[0]&&(i=this.containment[0]+this.offset.click.left),t.pageY-this.offset.click.top<this.containment[1]&&(s=this.containment[1]+this.offset.click.top),t.pageX-this.offset.click.left>this.containment[2]&&(i=this.containment[2]+this.offset.click.left),t.pageY-this.offset.click.top>this.containment[3]&&(s=this.containment[3]+this.offset.click.top)),e.grid&&(t=this.originalPageY+Math.round((s-this.originalPageY)/e.grid[1])*e.grid[1],s=!this.containment||t-this.offset.click.top>=this.containment[1]&&t-this.offset.click.top<=this.containment[3]?t:t-this.offset.click.top>=this.containment[1]?t-e.grid[1]:t+e.grid[1],t=this.originalPageX+Math.round((i-this.originalPageX)/e.grid[0])*e.grid[0],i=!this.containment||t-this.offset.click.left>=this.containment[0]&&t-this.offset.click.left<=this.containment[2]?t:t-this.offset.click.left>=this.containment[0]?t-e.grid[0]:t+e.grid[0])),{top:s-this.offset.click.top-this.offset.relative.top-this.offset.parent.top+("fixed"===this.cssPosition?-this.scrollParent.scrollTop():o?0:n.scrollTop()),left:i-this.offset.click.left-this.offset.relative.left-this.offset.parent.left+("fixed"===this.cssPosition?-this.scrollParent.scrollLeft():o?0:n.scrollLeft())}},_rearrange:function(t,e,i,s){i?i[0].appendChild(this.placeholder[0]):e.item[0].parentNode.insertBefore(this.placeholder[0],"down"===this.direction?e.item[0]:e.item[0].nextSibling),this.counter=this.counter?++this.counter:1;var n=this.counter;this._delay(function(){n===this.counter&&this.refreshPositions(!s)})},_clear:function(t,e){this.reverting=!1;var i,s=[];if(!this._noFinalSort&&this.currentItem.parent().length&&this.placeholder.before(this.currentItem),this._noFinalSort=null,this.helper[0]===this.currentItem[0]){for(i in this._storedCSS)"auto"!==this._storedCSS[i]&&"static"!==this._storedCSS[i]||(this._storedCSS[i]="");this.currentItem.css(this._storedCSS),this._removeClass(this.currentItem,"ui-sortable-helper")}else this.currentItem.show();function n(e,i,s){return function(t){s._trigger(e,t,i._uiHash(i))}}for(this.fromOutside&&!e&&s.push(function(t){this._trigger("receive",t,this._uiHash(this.fromOutside))}),!this.fromOutside&&this.domPosition.prev===this.currentItem.prev().not(".ui-sortable-helper")[0]&&this.domPosition.parent===this.currentItem.parent()[0]||e||s.push(function(t){this._trigger("update",t,this._uiHash())}),this!==this.currentContainer&&(e||(s.push(function(t){this._trigger("remove",t,this._uiHash())}),s.push(function(e){return function(t){e._trigger("receive",t,this._uiHash(this))}}.call(this,this.currentContainer)),s.push(function(e){return function(t){e._trigger("update",t,this._uiHash(this))}}.call(this,this.currentContainer)))),i=this.containers.length-1;0<=i;i--)e||s.push(n("deactivate",this,this.containers[i])),this.containers[i].containerCache.over&&(s.push(n("out",this,this.containers[i])),this.containers[i].containerCache.over=0);if(this.storedCursor&&(this.document.find("body").css("cursor",this.storedCursor),this.storedStylesheet.remove()),this._storedOpacity&&this.helper.css("opacity",this._storedOpacity),this._storedZIndex&&this.helper.css("zIndex","auto"===this._storedZIndex?"":this._storedZIndex),this.dragging=!1,e||this._trigger("beforeStop",t,this._uiHash()),this.placeholder[0].parentNode.removeChild(this.placeholder[0]),this.cancelHelperRemoval||(this.helper[0]!==this.currentItem[0]&&this.helper.remove(),this.helper=null),!e){for(i=0;i<s.length;i++)s[i].call(this,t);this._trigger("stop",t,this._uiHash())}return this.fromOutside=!1,!this.cancelHelperRemoval},_trigger:function(){!1===V.Widget.prototype._trigger.apply(this,arguments)&&this.cancel()},_uiHash:function(t){var e=t||this;return{helper:e.helper,placeholder:e.placeholder||V([]),position:e.position,originalPosition:e.originalPosition,offset:e.positionAbs,item:e.currentItem,sender:t?t.element:null}}});function ht(e){return function(){var t=this.element.val();e.apply(this,arguments),this._refresh(),t!==this.element.val()&&this._trigger("change")}}V.widget("ui.spinner",{version:"1.13.2",defaultElement:"<input>",widgetEventPrefix:"spin",options:{classes:{"ui-spinner":"ui-corner-all","ui-spinner-down":"ui-corner-br","ui-spinner-up":"ui-corner-tr"},culture:null,icons:{down:"ui-icon-triangle-1-s",up:"ui-icon-triangle-1-n"},incremental:!0,max:null,min:null,numberFormat:null,page:10,step:1,change:null,spin:null,start:null,stop:null},_create:function(){this._setOption("max",this.options.max),this._setOption("min",this.options.min),this._setOption("step",this.options.step),""!==this.value()&&this._value(this.element.val(),!0),this._draw(),this._on(this._events),this._refresh(),this._on(this.window,{beforeunload:function(){this.element.removeAttr("autocomplete")}})},_getCreateOptions:function(){var s=this._super(),n=this.element;return V.each(["min","max","step"],function(t,e){var i=n.attr(e);null!=i&&i.length&&(s[e]=i)}),s},_events:{keydown:function(t){this._start(t)&&this._keydown(t)&&t.preventDefault()},keyup:"_stop",focus:function(){this.previous=this.element.val()},blur:function(t){this.cancelBlur?delete this.cancelBlur:(this._stop(),this._refresh(),this.previous!==this.element.val()&&this._trigger("change",t))},mousewheel:function(t,e){var i=V.ui.safeActiveElement(this.document[0]);if(this.element[0]===i&&e){if(!this.spinning&&!this._start(t))return!1;this._spin((0<e?1:-1)*this.options.step,t),clearTimeout(this.mousewheelTimer),this.mousewheelTimer=this._delay(function(){this.spinning&&this._stop(t)},100),t.preventDefault()}},"mousedown .ui-spinner-button":function(t){var e;function i(){this.element[0]===V.ui.safeActiveElement(this.document[0])||(this.element.trigger("focus"),this.previous=e,this._delay(function(){this.previous=e}))}e=this.element[0]===V.ui.safeActiveElement(this.document[0])?this.previous:this.element.val(),t.preventDefault(),i.call(this),this.cancelBlur=!0,this._delay(function(){delete this.cancelBlur,i.call(this)}),!1!==this._start(t)&&this._repeat(null,V(t.currentTarget).hasClass("ui-spinner-up")?1:-1,t)},"mouseup .ui-spinner-button":"_stop","mouseenter .ui-spinner-button":function(t){if(V(t.currentTarget).hasClass("ui-state-active"))return!1!==this._start(t)&&void this._repeat(null,V(t.currentTarget).hasClass("ui-spinner-up")?1:-1,t)},"mouseleave .ui-spinner-button":"_stop"},_enhance:function(){this.uiSpinner=this.element.attr("autocomplete","off").wrap("<span>").parent().append("<a></a><a></a>")},_draw:function(){this._enhance(),this._addClass(this.uiSpinner,"ui-spinner","ui-widget ui-widget-content"),this._addClass("ui-spinner-input"),this.element.attr("role","spinbutton"),this.buttons=this.uiSpinner.children("a").attr("tabIndex",-1).attr("aria-hidden",!0).button({classes:{"ui-button":""}}),this._removeClass(this.buttons,"ui-corner-all"),this._addClass(this.buttons.first(),"ui-spinner-button ui-spinner-up"),this._addClass(this.buttons.last(),"ui-spinner-button ui-spinner-down"),this.buttons.first().button({icon:this.options.icons.up,showLabel:!1}),this.buttons.last().button({icon:this.options.icons.down,showLabel:!1}),this.buttons.height()>Math.ceil(.5*this.uiSpinner.height())&&0<this.uiSpinner.height()&&this.uiSpinner.height(this.uiSpinner.height())},_keydown:function(t){var e=this.options,i=V.ui.keyCode;switch(t.keyCode){case i.UP:return this._repeat(null,1,t),!0;case i.DOWN:return this._repeat(null,-1,t),!0;case i.PAGE_UP:return this._repeat(null,e.page,t),!0;case i.PAGE_DOWN:return this._repeat(null,-e.page,t),!0}return!1},_start:function(t){return!(!this.spinning&&!1===this._trigger("start",t))&&(this.counter||(this.counter=1),this.spinning=!0)},_repeat:function(t,e,i){t=t||500,clearTimeout(this.timer),this.timer=this._delay(function(){this._repeat(40,e,i)},t),this._spin(e*this.options.step,i)},_spin:function(t,e){var i=this.value()||0;this.counter||(this.counter=1),i=this._adjustValue(i+t*this._increment(this.counter)),this.spinning&&!1===this._trigger("spin",e,{value:i})||(this._value(i),this.counter++)},_increment:function(t){var e=this.options.incremental;return e?"function"==typeof e?e(t):Math.floor(t*t*t/5e4-t*t/500+17*t/200+1):1},_precision:function(){var t=this._precisionOf(this.options.step);return t=null!==this.options.min?Math.max(t,this._precisionOf(this.options.min)):t},_precisionOf:function(t){var e=t.toString(),t=e.indexOf(".");return-1===t?0:e.length-t-1},_adjustValue:function(t){var e=this.options,i=null!==e.min?e.min:0,s=t-i;return t=i+Math.round(s/e.step)*e.step,t=parseFloat(t.toFixed(this._precision())),null!==e.max&&t>e.max?e.max:null!==e.min&&t<e.min?e.min:t},_stop:function(t){this.spinning&&(clearTimeout(this.timer),clearTimeout(this.mousewheelTimer),this.counter=0,this.spinning=!1,this._trigger("stop",t))},_setOption:function(t,e){var i;if("culture"===t||"numberFormat"===t)return i=this._parse(this.element.val()),this.options[t]=e,void this.element.val(this._format(i));"max"!==t&&"min"!==t&&"step"!==t||"string"==typeof e&&(e=this._parse(e)),"icons"===t&&(i=this.buttons.first().find(".ui-icon"),this._removeClass(i,null,this.options.icons.up),this._addClass(i,null,e.up),i=this.buttons.last().find(".ui-icon"),this._removeClass(i,null,this.options.icons.down),this._addClass(i,null,e.down)),this._super(t,e)},_setOptionDisabled:function(t){this._super(t),this._toggleClass(this.uiSpinner,null,"ui-state-disabled",!!t),this.element.prop("disabled",!!t),this.buttons.button(t?"disable":"enable")},_setOptions:ht(function(t){this._super(t)}),_parse:function(t){return""===(t="string"==typeof t&&""!==t?window.Globalize&&this.options.numberFormat?Globalize.parseFloat(t,10,this.options.culture):+t:t)||isNaN(t)?null:t},_format:function(t){return""===t?"":window.Globalize&&this.options.numberFormat?Globalize.format(t,this.options.numberFormat,this.options.culture):t},_refresh:function(){this.element.attr({"aria-valuemin":this.options.min,"aria-valuemax":this.options.max,"aria-valuenow":this._parse(this.element.val())})},isValid:function(){var t=this.value();return null!==t&&t===this._adjustValue(t)},_value:function(t,e){var i;""!==t&&null!==(i=this._parse(t))&&(e||(i=this._adjustValue(i)),t=this._format(i)),this.element.val(t),this._refresh()},_destroy:function(){this.element.prop("disabled",!1).removeAttr("autocomplete role aria-valuemin aria-valuemax aria-valuenow"),this.uiSpinner.replaceWith(this.element)},stepUp:ht(function(t){this._stepUp(t)}),_stepUp:function(t){this._start()&&(this._spin((t||1)*this.options.step),this._stop())},stepDown:ht(function(t){this._stepDown(t)}),_stepDown:function(t){this._start()&&(this._spin((t||1)*-this.options.step),this._stop())},pageUp:ht(function(t){this._stepUp((t||1)*this.options.page)}),pageDown:ht(function(t){this._stepDown((t||1)*this.options.page)}),value:function(t){if(!arguments.length)return this._parse(this.element.val());ht(this._value).call(this,t)},widget:function(){return this.uiSpinner}}),!1!==V.uiBackCompat&&V.widget("ui.spinner",V.ui.spinner,{_enhance:function(){this.uiSpinner=this.element.attr("autocomplete","off").wrap(this._uiSpinnerHtml()).parent().append(this._buttonHtml())},_uiSpinnerHtml:function(){return"<span>"},_buttonHtml:function(){return"<a></a><a></a>"}});var ct;V.ui.spinner;V.widget("ui.tabs",{version:"1.13.2",delay:300,options:{active:null,classes:{"ui-tabs":"ui-corner-all","ui-tabs-nav":"ui-corner-all","ui-tabs-panel":"ui-corner-bottom","ui-tabs-tab":"ui-corner-top"},collapsible:!1,event:"click",heightStyle:"content",hide:null,show:null,activate:null,beforeActivate:null,beforeLoad:null,load:null},_isLocal:(ct=/#.*$/,function(t){var e=t.href.replace(ct,""),i=location.href.replace(ct,"");try{e=decodeURIComponent(e)}catch(t){}try{i=decodeURIComponent(i)}catch(t){}return 1<t.hash.length&&e===i}),_create:function(){var e=this,t=this.options;this.running=!1,this._addClass("ui-tabs","ui-widget ui-widget-content"),this._toggleClass("ui-tabs-collapsible",null,t.collapsible),this._processTabs(),t.active=this._initialActive(),Array.isArray(t.disabled)&&(t.disabled=V.uniqueSort(t.disabled.concat(V.map(this.tabs.filter(".ui-state-disabled"),function(t){return e.tabs.index(t)}))).sort()),!1!==this.options.active&&this.anchors.length?this.active=this._findActive(t.active):this.active=V(),this._refresh(),this.active.length&&this.load(t.active)},_initialActive:function(){var i=this.options.active,t=this.options.collapsible,s=location.hash.substring(1);return null===i&&(s&&this.tabs.each(function(t,e){if(V(e).attr("aria-controls")===s)return i=t,!1}),null!==(i=null===i?this.tabs.index(this.tabs.filter(".ui-tabs-active")):i)&&-1!==i||(i=!!this.tabs.length&&0)),!1!==i&&-1===(i=this.tabs.index(this.tabs.eq(i)))&&(i=!t&&0),i=!t&&!1===i&&this.anchors.length?0:i},_getCreateEventData:function(){return{tab:this.active,panel:this.active.length?this._getPanelForTab(this.active):V()}},_tabKeydown:function(t){var e=V(V.ui.safeActiveElement(this.document[0])).closest("li"),i=this.tabs.index(e),s=!0;if(!this._handlePageNav(t)){switch(t.keyCode){case V.ui.keyCode.RIGHT:case V.ui.keyCode.DOWN:i++;break;case V.ui.keyCode.UP:case V.ui.keyCode.LEFT:s=!1,i--;break;case V.ui.keyCode.END:i=this.anchors.length-1;break;case V.ui.keyCode.HOME:i=0;break;case V.ui.keyCode.SPACE:return t.preventDefault(),clearTimeout(this.activating),void this._activate(i);case V.ui.keyCode.ENTER:return t.preventDefault(),clearTimeout(this.activating),void this._activate(i!==this.options.active&&i);default:return}t.preventDefault(),clearTimeout(this.activating),i=this._focusNextTab(i,s),t.ctrlKey||t.metaKey||(e.attr("aria-selected","false"),this.tabs.eq(i).attr("aria-selected","true"),this.activating=this._delay(function(){this.option("active",i)},this.delay))}},_panelKeydown:function(t){this._handlePageNav(t)||t.ctrlKey&&t.keyCode===V.ui.keyCode.UP&&(t.preventDefault(),this.active.trigger("focus"))},_handlePageNav:function(t){return t.altKey&&t.keyCode===V.ui.keyCode.PAGE_UP?(this._activate(this._focusNextTab(this.options.active-1,!1)),!0):t.altKey&&t.keyCode===V.ui.keyCode.PAGE_DOWN?(this._activate(this._focusNextTab(this.options.active+1,!0)),!0):void 0},_findNextTab:function(t,e){var i=this.tabs.length-1;for(;-1!==V.inArray(t=(t=i<t?0:t)<0?i:t,this.options.disabled);)t=e?t+1:t-1;return t},_focusNextTab:function(t,e){return t=this._findNextTab(t,e),this.tabs.eq(t).trigger("focus"),t},_setOption:function(t,e){"active"!==t?(this._super(t,e),"collapsible"===t&&(this._toggleClass("ui-tabs-collapsible",null,e),e||!1!==this.options.active||this._activate(0)),"event"===t&&this._setupEvents(e),"heightStyle"===t&&this._setupHeightStyle(e)):this._activate(e)},_sanitizeSelector:function(t){return t?t.replace(/[!"$%&'()*+,.\/:;<=>?@\[\]\^`{|}~]/g,"\\$&"):""},refresh:function(){var t=this.options,e=this.tablist.children(":has(a[href])");t.disabled=V.map(e.filter(".ui-state-disabled"),function(t){return e.index(t)}),this._processTabs(),!1!==t.active&&this.anchors.length?this.active.length&&!V.contains(this.tablist[0],this.active[0])?this.tabs.length===t.disabled.length?(t.active=!1,this.active=V()):this._activate(this._findNextTab(Math.max(0,t.active-1),!1)):t.active=this.tabs.index(this.active):(t.active=!1,this.active=V()),this._refresh()},_refresh:function(){this._setOptionDisabled(this.options.disabled),this._setupEvents(this.options.event),this._setupHeightStyle(this.options.heightStyle),this.tabs.not(this.active).attr({"aria-selected":"false","aria-expanded":"false",tabIndex:-1}),this.panels.not(this._getPanelForTab(this.active)).hide().attr({"aria-hidden":"true"}),this.active.length?(this.active.attr({"aria-selected":"true","aria-expanded":"true",tabIndex:0}),this._addClass(this.active,"ui-tabs-active","ui-state-active"),this._getPanelForTab(this.active).show().attr({"aria-hidden":"false"})):this.tabs.eq(0).attr("tabIndex",0)},_processTabs:function(){var l=this,t=this.tabs,e=this.anchors,i=this.panels;this.tablist=this._getList().attr("role","tablist"),this._addClass(this.tablist,"ui-tabs-nav","ui-helper-reset ui-helper-clearfix ui-widget-header"),this.tablist.on("mousedown"+this.eventNamespace,"> li",function(t){V(this).is(".ui-state-disabled")&&t.preventDefault()}).on("focus"+this.eventNamespace,".ui-tabs-anchor",function(){V(this).closest("li").is(".ui-state-disabled")&&this.blur()}),this.tabs=this.tablist.find("> li:has(a[href])").attr({role:"tab",tabIndex:-1}),this._addClass(this.tabs,"ui-tabs-tab","ui-state-default"),this.anchors=this.tabs.map(function(){return V("a",this)[0]}).attr({tabIndex:-1}),this._addClass(this.anchors,"ui-tabs-anchor"),this.panels=V(),this.anchors.each(function(t,e){var i,s,n,o=V(e).uniqueId().attr("id"),a=V(e).closest("li"),r=a.attr("aria-controls");l._isLocal(e)?(n=(i=e.hash).substring(1),s=l.element.find(l._sanitizeSelector(i))):(n=a.attr("aria-controls")||V({}).uniqueId()[0].id,(s=l.element.find(i="#"+n)).length||(s=l._createPanel(n)).insertAfter(l.panels[t-1]||l.tablist),s.attr("aria-live","polite")),s.length&&(l.panels=l.panels.add(s)),r&&a.data("ui-tabs-aria-controls",r),a.attr({"aria-controls":n,"aria-labelledby":o}),s.attr("aria-labelledby",o)}),this.panels.attr("role","tabpanel"),this._addClass(this.panels,"ui-tabs-panel","ui-widget-content"),t&&(this._off(t.not(this.tabs)),this._off(e.not(this.anchors)),this._off(i.not(this.panels)))},_getList:function(){return this.tablist||this.element.find("ol, ul").eq(0)},_createPanel:function(t){return V("<div>").attr("id",t).data("ui-tabs-destroy",!0)},_setOptionDisabled:function(t){var e,i;for(Array.isArray(t)&&(t.length?t.length===this.anchors.length&&(t=!0):t=!1),i=0;e=this.tabs[i];i++)e=V(e),!0===t||-1!==V.inArray(i,t)?(e.attr("aria-disabled","true"),this._addClass(e,null,"ui-state-disabled")):(e.removeAttr("aria-disabled"),this._removeClass(e,null,"ui-state-disabled"));this.options.disabled=t,this._toggleClass(this.widget(),this.widgetFullName+"-disabled",null,!0===t)},_setupEvents:function(t){var i={};t&&V.each(t.split(" "),function(t,e){i[e]="_eventHandler"}),this._off(this.anchors.add(this.tabs).add(this.panels)),this._on(!0,this.anchors,{click:function(t){t.preventDefault()}}),this._on(this.anchors,i),this._on(this.tabs,{keydown:"_tabKeydown"}),this._on(this.panels,{keydown:"_panelKeydown"}),this._focusable(this.tabs),this._hoverable(this.tabs)},_setupHeightStyle:function(t){var i,e=this.element.parent();"fill"===t?(i=e.height(),i-=this.element.outerHeight()-this.element.height(),this.element.siblings(":visible").each(function(){var t=V(this),e=t.css("position");"absolute"!==e&&"fixed"!==e&&(i-=t.outerHeight(!0))}),this.element.children().not(this.panels).each(function(){i-=V(this).outerHeight(!0)}),this.panels.each(function(){V(this).height(Math.max(0,i-V(this).innerHeight()+V(this).height()))}).css("overflow","auto")):"auto"===t&&(i=0,this.panels.each(function(){i=Math.max(i,V(this).height("").height())}).height(i))},_eventHandler:function(t){var e=this.options,i=this.active,s=V(t.currentTarget).closest("li"),n=s[0]===i[0],o=n&&e.collapsible,a=o?V():this._getPanelForTab(s),r=i.length?this._getPanelForTab(i):V(),i={oldTab:i,oldPanel:r,newTab:o?V():s,newPanel:a};t.preventDefault(),s.hasClass("ui-state-disabled")||s.hasClass("ui-tabs-loading")||this.running||n&&!e.collapsible||!1===this._trigger("beforeActivate",t,i)||(e.active=!o&&this.tabs.index(s),this.active=n?V():s,this.xhr&&this.xhr.abort(),r.length||a.length||V.error("jQuery UI Tabs: Mismatching fragment identifier."),a.length&&this.load(this.tabs.index(s),t),this._toggle(t,i))},_toggle:function(t,e){var i=this,s=e.newPanel,n=e.oldPanel;function o(){i.running=!1,i._trigger("activate",t,e)}function a(){i._addClass(e.newTab.closest("li"),"ui-tabs-active","ui-state-active"),s.length&&i.options.show?i._show(s,i.options.show,o):(s.show(),o())}this.running=!0,n.length&&this.options.hide?this._hide(n,this.options.hide,function(){i._removeClass(e.oldTab.closest("li"),"ui-tabs-active","ui-state-active"),a()}):(this._removeClass(e.oldTab.closest("li"),"ui-tabs-active","ui-state-active"),n.hide(),a()),n.attr("aria-hidden","true"),e.oldTab.attr({"aria-selected":"false","aria-expanded":"false"}),s.length&&n.length?e.oldTab.attr("tabIndex",-1):s.length&&this.tabs.filter(function(){return 0===V(this).attr("tabIndex")}).attr("tabIndex",-1),s.attr("aria-hidden","false"),e.newTab.attr({"aria-selected":"true","aria-expanded":"true",tabIndex:0})},_activate:function(t){var t=this._findActive(t);t[0]!==this.active[0]&&(t=(t=!t.length?this.active:t).find(".ui-tabs-anchor")[0],this._eventHandler({target:t,currentTarget:t,preventDefault:V.noop}))},_findActive:function(t){return!1===t?V():this.tabs.eq(t)},_getIndex:function(t){return t="string"==typeof t?this.anchors.index(this.anchors.filter("[href$='"+V.escapeSelector(t)+"']")):t},_destroy:function(){this.xhr&&this.xhr.abort(),this.tablist.removeAttr("role").off(this.eventNamespace),this.anchors.removeAttr("role tabIndex").removeUniqueId(),this.tabs.add(this.panels).each(function(){V.data(this,"ui-tabs-destroy")?V(this).remove():V(this).removeAttr("role tabIndex aria-live aria-busy aria-selected aria-labelledby aria-hidden aria-expanded")}),this.tabs.each(function(){var t=V(this),e=t.data("ui-tabs-aria-controls");e?t.attr("aria-controls",e).removeData("ui-tabs-aria-controls"):t.removeAttr("aria-controls")}),this.panels.show(),"content"!==this.options.heightStyle&&this.panels.css("height","")},enable:function(i){var t=this.options.disabled;!1!==t&&(t=void 0!==i&&(i=this._getIndex(i),Array.isArray(t)?V.map(t,function(t){return t!==i?t:null}):V.map(this.tabs,function(t,e){return e!==i?e:null})),this._setOptionDisabled(t))},disable:function(t){var e=this.options.disabled;if(!0!==e){if(void 0===t)e=!0;else{if(t=this._getIndex(t),-1!==V.inArray(t,e))return;e=Array.isArray(e)?V.merge([t],e).sort():[t]}this._setOptionDisabled(e)}},load:function(t,s){t=this._getIndex(t);function n(t,e){"abort"===e&&o.panels.stop(!1,!0),o._removeClass(i,"ui-tabs-loading"),a.removeAttr("aria-busy"),t===o.xhr&&delete o.xhr}var o=this,i=this.tabs.eq(t),t=i.find(".ui-tabs-anchor"),a=this._getPanelForTab(i),r={tab:i,panel:a};this._isLocal(t[0])||(this.xhr=V.ajax(this._ajaxSettings(t,s,r)),this.xhr&&"canceled"!==this.xhr.statusText&&(this._addClass(i,"ui-tabs-loading"),a.attr("aria-busy","true"),this.xhr.done(function(t,e,i){setTimeout(function(){a.html(t),o._trigger("load",s,r),n(i,e)},1)}).fail(function(t,e){setTimeout(function(){n(t,e)},1)})))},_ajaxSettings:function(t,i,s){var n=this;return{url:t.attr("href").replace(/#.*$/,""),beforeSend:function(t,e){return n._trigger("beforeLoad",i,V.extend({jqXHR:t,ajaxSettings:e},s))}}},_getPanelForTab:function(t){t=V(t).attr("aria-controls");return this.element.find(this._sanitizeSelector("#"+t))}}),!1!==V.uiBackCompat&&V.widget("ui.tabs",V.ui.tabs,{_processTabs:function(){this._superApply(arguments),this._addClass(this.tabs,"ui-tab")}});V.ui.tabs;V.widget("ui.tooltip",{version:"1.13.2",options:{classes:{"ui-tooltip":"ui-corner-all ui-widget-shadow"},content:function(){var t=V(this).attr("title");return V("<a>").text(t).html()},hide:!0,items:"[title]:not([disabled])",position:{my:"left top+15",at:"left bottom",collision:"flipfit flip"},show:!0,track:!1,close:null,open:null},_addDescribedBy:function(t,e){var i=(t.attr("aria-describedby")||"").split(/\s+/);i.push(e),t.data("ui-tooltip-id",e).attr("aria-describedby",String.prototype.trim.call(i.join(" ")))},_removeDescribedBy:function(t){var e=t.data("ui-tooltip-id"),i=(t.attr("aria-describedby")||"").split(/\s+/),e=V.inArray(e,i);-1!==e&&i.splice(e,1),t.removeData("ui-tooltip-id"),(i=String.prototype.trim.call(i.join(" ")))?t.attr("aria-describedby",i):t.removeAttr("aria-describedby")},_create:function(){this._on({mouseover:"open",focusin:"open"}),this.tooltips={},this.parents={},this.liveRegion=V("<div>").attr({role:"log","aria-live":"assertive","aria-relevant":"additions"}).appendTo(this.document[0].body),this._addClass(this.liveRegion,null,"ui-helper-hidden-accessible"),this.disabledTitles=V([])},_setOption:function(t,e){var i=this;this._super(t,e),"content"===t&&V.each(this.tooltips,function(t,e){i._updateContent(e.element)})},_setOptionDisabled:function(t){this[t?"_disable":"_enable"]()},_disable:function(){var s=this;V.each(this.tooltips,function(t,e){var i=V.Event("blur");i.target=i.currentTarget=e.element[0],s.close(i,!0)}),this.disabledTitles=this.disabledTitles.add(this.element.find(this.options.items).addBack().filter(function(){var t=V(this);if(t.is("[title]"))return t.data("ui-tooltip-title",t.attr("title")).removeAttr("title")}))},_enable:function(){this.disabledTitles.each(function(){var t=V(this);t.data("ui-tooltip-title")&&t.attr("title",t.data("ui-tooltip-title"))}),this.disabledTitles=V([])},open:function(t){var i=this,e=V(t?t.target:this.element).closest(this.options.items);e.length&&!e.data("ui-tooltip-id")&&(e.attr("title")&&e.data("ui-tooltip-title",e.attr("title")),e.data("ui-tooltip-open",!0),t&&"mouseover"===t.type&&e.parents().each(function(){var t,e=V(this);e.data("ui-tooltip-open")&&((t=V.Event("blur")).target=t.currentTarget=this,i.close(t,!0)),e.attr("title")&&(e.uniqueId(),i.parents[this.id]={element:this,title:e.attr("title")},e.attr("title",""))}),this._registerCloseHandlers(t,e),this._updateContent(e,t))},_updateContent:function(e,i){var t=this.options.content,s=this,n=i?i.type:null;if("string"==typeof t||t.nodeType||t.jquery)return this._open(i,e,t);(t=t.call(e[0],function(t){s._delay(function(){e.data("ui-tooltip-open")&&(i&&(i.type=n),this._open(i,e,t))})}))&&this._open(i,e,t)},_open:function(t,e,i){var s,n,o,a=V.extend({},this.options.position);function r(t){a.of=t,n.is(":hidden")||n.position(a)}i&&((s=this._find(e))?s.tooltip.find(".ui-tooltip-content").html(i):(e.is("[title]")&&(t&&"mouseover"===t.type?e.attr("title",""):e.removeAttr("title")),s=this._tooltip(e),n=s.tooltip,this._addDescribedBy(e,n.attr("id")),n.find(".ui-tooltip-content").html(i),this.liveRegion.children().hide(),(i=V("<div>").html(n.find(".ui-tooltip-content").html())).removeAttr("name").find("[name]").removeAttr("name"),i.removeAttr("id").find("[id]").removeAttr("id"),i.appendTo(this.liveRegion),this.options.track&&t&&/^mouse/.test(t.type)?(this._on(this.document,{mousemove:r}),r(t)):n.position(V.extend({of:e},this.options.position)),n.hide(),this._show(n,this.options.show),this.options.track&&this.options.show&&this.options.show.delay&&(o=this.delayedShow=setInterval(function(){n.is(":visible")&&(r(a.of),clearInterval(o))},13)),this._trigger("open",t,{tooltip:n})))},_registerCloseHandlers:function(t,e){var i={keyup:function(t){t.keyCode===V.ui.keyCode.ESCAPE&&((t=V.Event(t)).currentTarget=e[0],this.close(t,!0))}};e[0]!==this.element[0]&&(i.remove=function(){var t=this._find(e);t&&this._removeTooltip(t.tooltip)}),t&&"mouseover"!==t.type||(i.mouseleave="close"),t&&"focusin"!==t.type||(i.focusout="close"),this._on(!0,e,i)},close:function(t){var e,i=this,s=V(t?t.currentTarget:this.element),n=this._find(s);n?(e=n.tooltip,n.closing||(clearInterval(this.delayedShow),s.data("ui-tooltip-title")&&!s.attr("title")&&s.attr("title",s.data("ui-tooltip-title")),this._removeDescribedBy(s),n.hiding=!0,e.stop(!0),this._hide(e,this.options.hide,function(){i._removeTooltip(V(this))}),s.removeData("ui-tooltip-open"),this._off(s,"mouseleave focusout keyup"),s[0]!==this.element[0]&&this._off(s,"remove"),this._off(this.document,"mousemove"),t&&"mouseleave"===t.type&&V.each(this.parents,function(t,e){V(e.element).attr("title",e.title),delete i.parents[t]}),n.closing=!0,this._trigger("close",t,{tooltip:e}),n.hiding||(n.closing=!1))):s.removeData("ui-tooltip-open")},_tooltip:function(t){var e=V("<div>").attr("role","tooltip"),i=V("<div>").appendTo(e),s=e.uniqueId().attr("id");return this._addClass(i,"ui-tooltip-content"),this._addClass(e,"ui-tooltip","ui-widget ui-widget-content"),e.appendTo(this._appendTo(t)),this.tooltips[s]={element:t,tooltip:e}},_find:function(t){t=t.data("ui-tooltip-id");return t?this.tooltips[t]:null},_removeTooltip:function(t){clearInterval(this.delayedShow),t.remove(),delete this.tooltips[t.attr("id")]},_appendTo:function(t){t=t.closest(".ui-front, dialog");return t=!t.length?this.document[0].body:t},_destroy:function(){var s=this;V.each(this.tooltips,function(t,e){var i=V.Event("blur"),e=e.element;i.target=i.currentTarget=e[0],s.close(i,!0),V("#"+t).remove(),e.data("ui-tooltip-title")&&(e.attr("title")||e.attr("title",e.data("ui-tooltip-title")),e.removeData("ui-tooltip-title"))}),this.liveRegion.remove()}}),!1!==V.uiBackCompat&&V.widget("ui.tooltip",V.ui.tooltip,{options:{tooltipClass:null},_tooltip:function(){var t=this._superApply(arguments);return this.options.tooltipClass&&t.tooltip.addClass(this.options.tooltipClass),t}});V.ui.tooltip});</script>
 <style type="text/css">
 
 .tocify {
@@ -1315,7 +1309,7 @@ window.initializeCodeFolding = function(show) {
     $(this).detach().appendTo(div);
 
     // add a show code button right above
-    var showCodeText = $('<span>' + (showThis ? 'Hide' : 'Code') + '</span>');
+    var showCodeText = $('<span>' + (showThis ? 'Hide' : 'Show') + '</span>');
     var showCodeButton = $('<button type="button" class="btn btn-default btn-xs btn-secondary btn-sm code-folding-btn pull-right float-right"></button>');
     showCodeButton.append(showCodeText);
     showCodeButton
@@ -1341,7 +1335,7 @@ window.initializeCodeFolding = function(show) {
     //   * Change text
     //   * add a class for intermediate states styling
     div.on('hide.bs.collapse', function () {
-      showCodeText.text('Code');
+      showCodeText.text('Show');
       showCodeButton.addClass('btn-collapsing');
     });
     div.on('hidden.bs.collapse', function () {
@@ -1615,7 +1609,7 @@ div.tocify {
 <h1 class="title toc-ignore">Example evaluations of the dimethenamid
 data from 2018</h1>
 <h4 class="author">Johannes Ranke</h4>
-<h4 class="date">Last change 1 July 2022, built on 05 Jan 2023</h4>
+<h4 class="date">Last change 1 July 2022, built on 13 Feb 2025</h4>
 
 </div>
 
@@ -1696,7 +1690,7 @@ smaller values (panel to the right):</p>
 <p>Using biexponential decline (DFOP) results in a slightly more random
 scatter of the residuals:</p>
 <pre class="r"><code>plot(mixed(f_parent_mkin_const[&quot;DFOP&quot;, ]))</code></pre>
-<p><img src="data:image/png;base64,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" /><!-- --></p>
+<p><img src="data:image/png;base64,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" /><!-- --></p>
 <p>The population curve (bold line) in the above plot results from
 taking the mean of the individual transformed parameters, i.e. of log k1
 and log k2, as well as of the logit of the g parameter of the DFOP
@@ -1707,7 +1701,7 @@ dominates the average. This is alleviated if only rate constants that
 pass the t-test for significant difference from zero (on the
 untransformed scale) are considered in the averaging:</p>
 <pre class="r"><code>plot(mixed(f_parent_mkin_const[&quot;DFOP&quot;, ]), test_log_parms = TRUE)</code></pre>
-<p><img src="data:image/png;base64,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" /><!-- --></p>
+<p><img src="data:image/png;base64,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" /><!-- --></p>
 <p>While this is visually much more satisfactory, such an average
 procedure could introduce a bias, as not all results from the individual
 fits enter the population curve with the same weight. This is where
@@ -1718,7 +1712,7 @@ degradation model and the error model (see below).</p>
 predicted residues is reduced by using the two-component error
 model:</p>
 <pre class="r"><code>plot(mixed(f_parent_mkin_tc[&quot;DFOP&quot;, ]), test_log_parms = TRUE)</code></pre>
-<p><img src="data:image/png;base64,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" /><!-- --></p>
+<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqAAAAHgCAIAAAD17khjAAAACXBIWXMAAA7DAAAOwwHHb6hkAAAgAElEQVR4nOzdd1gU1xYA8DttF7bALksHQRBQJKLYSzRG7JXoM0ZNbDEmJrYXTSxJjIldo1Fj1NhiFzVRo6jR2FKMYsOuiCC9bWNZ2Drl/bE+goBY2GVxcn6fn9/snZl7zzbOzsydezGO4xAAAAAA+AV3dgAAAAAAsD9I8AAAAAAPQYIHAAAAeAgSPAAAAMBDkOABAAAAHoIEDwAAAPAQJHgAAACAhyDBAwAAADwECR4AAADgIUjwAAAAAA9BggcAAAB4CBI8AAAAwEOQ4AEAAAAeggQPAAAA8BAkeAAAAICHIMEDAAAAPAQJHgAAAOAhSPAAAAAAD0GCBwAAAHgIEjwAAADAQ5DgAQAAAB6CBA8AAADwECR4AAAAgIcgwQMAAAA8BAkeAAAA4CFI8AAAAAAPQYIHAAAAeAgSPAAAAMBDkOABAAAAHoIEDwAAAPAQJHgAAACAhyDBAx66fft2v379/P395XJ5bGzs+fPnn7RlTk4OhmEMw5w7dy48PLw2g6xMaWC/+ENnr9piYmKwxw0ePLjs+dawcrVabZd6qsSa2PQxt4sOK+1VoVQqLXsRPD09R4wYUVxc/NRVNnPmzMEwbNWqVU+qPC0trWfPnnK5PCgoaN68eZU3OH36dPPmzcVicaNGjXbt2mWvJwXAU0GCB05mvH6vKD5Bt/+EJTPXLhVevXq1devWzZo1+/XXX3///ffmzZt37949OTnZLpU7lNbEXs632LHCBQsW5JezYcMGO1buIKyJTRtyndHTWZPvFR2yW47ftWtXfn5+bm7u0aNHr1+//umnnz7LKoRQfHx8SEjInj17qqzWYrG89tprwcHBN2/e3Lp164oVKzZv3lx+A41GM2jQoBEjRqSkpEycOHHEiBF37tyx15MCoHqQ4IEzqdbs1G7dj1EUazQVzP1ef/JczeucMGHCtGnT5s6dGx0dHR0dvXTp0s6dO69Zs6bmNb903NzcfMqRyWTOjugpODP78O2bhDsVujs67GCzrP/aLcd7eHj4+Pj4+fm1bt161KhRN27ceJZVSUlJqamp69evP3/+fFZWVuVqExMTdTrd6tWrAwMDX3/99fHjxx88eLD8BhcuXPD29p4yZYq/v/9HH30UHBycmJhol2cEwFNBggdOY0y6Y0nL8lsy3X1QD/nw/n4LphbtTmB0+prUmZmZef78+YkTJ5Yv3Lp1q+2w7Pjx4y1bthSLxQEBAXPmzKmyBpqmBw8e3LdvX7PZnJqa2rNnT5lM1qFDhx07dtQksDqoylejysILFy60bdvWzc2tXbt2ly5dKqshISEhMjJSKpW++eabZrO5hvFwZjZt+E1cRNTfHIWRmGu0NOyAPXO8TU5Ozv79+wcOHPgsq+Lj47t169a1a9fQ0NC9e/dW3sXX13fFihUURdkeajQaHH/sj2qnTp3Onj1rW87Ly1Or1VFRUfZ6LgBUj3R2AIDnjNfvGS4klT2Udu8oCAm0lWt3HcJIQrNpb1m5ICy45OTftEpTeftndPv2bYVC4enpWb7Qw8MDIWQwGN54441FixYNGTLkxo0bffv2jYuL8/LyKr8lTdPDhw83Go0///wzQqhbt27Dhw/fvn377du3hw0bplAoevXq9SKvAkImmlt8Qe8vId5rJrYtW1gOIaQzc3klTCMFybDoYp6loJT57A8dw6JrhRYCw5p6UxiG6ruRZXvZanjGRidMmDBhwoSyhyUlJWXLVb4aERERlQv9/f179OixfPny3r17b9q0qU+fPrm5jy6mbNu27c8//8zLy2vfvv1PP/00fPjwZ4lK+3OB/nctQsh3av3SyzrbMkKo+KQaIzBJZ7k1x1xWHrS6cfqYW+rtMspPWL4S36n1BcEuz/g6IIR69uxZthwVFTVixIinruI4bs+ePXPnzkUIDRo0aO/evVOnTq1QbXh4eFnXjTNnzuzYsWP37t3lN5BIJBKJRKfTde3a9d69ezNmzGjduvWzhw1ATUCCB45FergLQoPKHuJScVk54emBMaxtra2cs1gIuRvuJqm8/TNiGAbDsCpXURSVlJTUsGFDlmV9fX1FIpFarS6f4G3Z/ciRI/n5+UKh8JdffjGbzbNnz6YoqnPnzuPHj//hhx9eOMFTBBblSSpcibJlmkUIoWIz6+WCN1SQLIeKjKzOxDbxpFgOsSyHYVi0F4UQ8hETFWp4RgsWLBgzZkzZQ5FIVFRUVM2rUWXh77//3qpVq3fffRchNGvWLE9PT53uUU/AxYsXe3p6enp6du/eXaVSPWNUgiBXUTMaIYS7EWXLCCHzQ6PlocE1UlK+nNZaMBITxbhRvoLyleBuz/E6IIR27drVpUsXhJBer58zZ063bt2uX79e/arz58/n5+f36NHDbDb36dNnyZIlDx8+DAkJqVy50Wj88ssv16xZs3Xr1t69e1feQCwWL1q06NSpU999911sbGzbtm2fK3gAXgwkeOBYVD0/qp5fleWyQT2U32xUjB9KyN0RQqa7qdacQlG7GNz1OY7MKmjcuLFKpVIqleUz9/79+7dt23bgwIGEhISBAwcSBBEeHl7hVCpCKD09vX379n5+fqtWrZo1a1ZaWpparQ4K+ufXScuWLV84MAJD/2kkqrxcXks/weST2rcaixBCw6IqbvCkvaphuwZf5SqSJCu/GlUWZmRklB2k4jj+wQcfIITUajVCKDg42FYuEAiqbKVK4lZu4lZuj8JoRZUte44OyP70vmZXnsdQX9s2RYcKMyclh/3STNTc7bmeeGW2C+0IIR8fn6+++iosLCwtLS00NLSaVfHx8WazufwLuHfv3unTp1eoOTU1tX///n5+fleuXGnYsGGFtbZfVDKZLDY2NjY2Njk5eefOnZDgQe2ABA+cRhgW7Na3S+4ni1wiw1iT2ZKe4zVpRE2yO0IoJCQkOjr622+/XbBgQVnhxo0b3d3djx07tnjx4kuXLtnSUuWb4gIDA7dv337q1KkBAwa89dZbfn5+LVu2/Ouvv2xrMzMzaZquSWx1SpWvRpWFAQEBp06dsu3FcdycOXNGjBhh669X+UdSjWAocElE9qf3H/RLCjscU/KnNnNSctj+pjXP7hXYwi5/waLyKoZh9u7du2XLlpEjR9pWTZ06dc+ePRUSvNVq7dWrV79+/ZYuXVrlq7Fq1aorV6788ssvtocBAQFGo9G+TweAJ4FOdsCZ3Pq+7rd4uqh9c7eenQJWfu7SpOIB0PPCMGzdunXffPPN9OnTb9y4kZSUNGHChNOnT3/++edarZYgCISQwWBYtWpVampqaWlp+X1dXFxwHO/WrVtcXNyHH37Ys2fPlJSU5cuXq1Sqv/76q3Xr1hcvXqxheHVHla9GlYWDBw/+448/du7cqVKpli1btnLlSoVC4aiwMBS4JELcxv1+1ytZU5LDD9nh2N1Gp9Op1Wq1Wp2RkfHVV18FBQU1atSomlVnz57VarUDBgwoq2Hw4MFJSUkpKSnlq01ISFCr1ePGjXv48GFqampqampeXh5CaP/+/VevXkUI9e/f/+TJk/v379dqtb/99tu2bdvi4uLs8owAeDoOAN65ePFibGysp6enQqGIjY29cOECx3EWi2X48OFSqTQ0NHTp0qUzZ86Uy+XXrl1DCNE0/ddff4WFhdl2z8/Pl8lk8fHxSUlJnTp1kkgkwcHBy5cvd3TYOhOz+XqJvWpr1qzZ6tWrKxRmZ2fbnm+Vr0Zubm7lQqVSaRuqRSQSxcTEnD17luM42xV3mqZt1b711lsrVqywV+Qcy+V/m264Xmyv+iSSf3p1CIXC1q1bX7p0qfpVY8eO7dev32NBsWxQUNDcuXPLF1a+F6Nv374cx4WFhc2YMcO2zaFDh1q3bm0b6GbTpk32elIAPBXGcVwt/IwAAAAAQG2CU/QAAAAAD0GCBwAAAHgIEjwAAADAQ5DgAQAAAB6CBA+cT21RGhmDs6MAAABegQQPnIzhmOVpX+3I+cFeFT51hm/7stcM63bn3EnQn7pBbYqJicEeV69ePVTuvStbyMjIKJs5BoCXHSR44GSnVEfklMe9klupBrtN2V79DN/Pq3Xr1rZBSxykhOVW6/R0uRtWM2l6u760ml2ekbMmQX/qBrVvwYIF+eU86Q1VKBTff/99NfU4+sMAgB3BULXAmfR08ZHCn2aGLUwzpOzMXv9FxDcYqnqqmOdSNrq4n5/fqFGj9u3b99RdOI6zWq1VjqmuVCqtVmvNo3oSVxy7ZbVOVWuXKeQkhmXS9MhC9QR3ac1rruZ1qGaVbRL0Y8eOde/ePSsry3awW17ZJOgURQUGBtomQS8/q81TN6iS5cH7tKqKKVkRRrm8cgoXN3nu519ONcPylyeRSMaNG1fNBo7+MABgR3AED5xpf/6O9vLXfYUB7eSvUbjgb80Z+9ZfYYbvy5cvv/rqq1KpNCoqyja/e0FBgVwuP3HiREhIyPnz5xcvXhwUFCQWi1977TXboKSdO3fOysrq3bu3bQJZR8wQTyD0nUJOI/SxWptmtY4sVI9zkwwSP9+8MtWr5UnQn7pBlQjv4Ziwvqh1vqittuyfIGwDLorExbU0h3r5U/SVPy0VPgwA1HEwkh1wrGvFF89rf+/hNSBUFFFh+ZTqaIYhdVHkuvult89rf492a/lT3rah/mOu6C5U3v7ZW5RKpeXnEYmKijp9+rS3t7dGowkLC/vss8/eeeedxMTEYcOGHTlypGHDhkFBQcOHD1+4cOGdO3cGDBjw66+/BgUFTZ48mWGYgwcPIoRCQkLi4+PbtGljNpsjIyOHDx8+adIk2wzxmzZtio6ODgwMpGnaNor7k5g47ktNkaVSeQhJTnKXmjjuC7X2vMWiZdiGFBn8/xxTtrb8vk0F1CippFJNz/o6VL+K47iQkJC5c+e+884706dPP3v2bGJiYjWtnDlzJi4ubvfu3VVOk1rlBrRyJ1N0ssqNWX0iIuW46//fbo5lik4S0haIrGL0ewwTUMHzMMqr8qoKYmJibGMSl4mLiztw4EBOTo7tvcvPz7ctZGdnh4WFWa3WKj8tnTp1KvswPLVRAJwOTtEDx/J3CWrp3t5T4F15WWtV9fEZJCLEtvKGkqi7+hvJJXeq3P65VDnD98GDBxs0aDB16lSEUL9+/UaPHr1t27b58+dbLJaZM2f6+PgkJSWxLKtSqVq0aLFnz57KZ2J//fXXyjPEV3/JtowAw7qLXK2Vfk97EYRtbQsX4V9miy9BUBjW1UVIYFj5teX3DSCf9Wvr3EnQn7QBLm6O4eIqA+bc2lsz5xK+4zDCHSHEaA4Q4mjS+wnn9jEcI551KpoFCxaUv0YgFAqr377KT0unTp2esTkA6gJI8MCxvAW+3gLfysuZxrQiq1ZGKS4VnbOVJJfcDhaF/pS7vZd3nBspq7D9c6lyhm/bwVnZNuHh4YcPH7Yt22ZH7dmz59KlS+fPnz906NCWLVvOnj07Nja2fLU1mSEeRyj2yTPhZtH0D8Ulk9yl/xGLpqi1x42m5Qo5iWHPsm81nDUJevUb4KJIJIp8UsycOZfVXxI0WM1ZciwPp7k0OYMJK3YCeAHPeA2+TDWfFgBeFnANHjiHlbVESZtd1V24rPu77N+D0nvN3FvpaXve1VY2w3dAQEBaWlpZeVpaWmBgoG3ZdnY9LS3t9ddfT0xMLCws7N+//4gRIyrc/GabIT7v/xITE1euXFnzCPUsO6pQ/ZG7dKhETGHYtwq5BaGvtLqa11zec02CXjYb1ccff1y5L71tEvSePXueOHGiyuz+1A2qQQZMZXSn2ZIrlowvSN9xdsnuL6CaTwsALw0nzWIHgKNIJJI9e/aoVCqVSpWenj5y5MigoCCz2axUKt3d3VeuXKnRaI4dOyaVSs+ePZufn4/+P+3pxo0bQ0JCbty4oVKpFi5c6O3tzbIsx3H169c/fvw4x3Fardbb23vZsmVKpfLPP//08fHZvXt32QSsLxwwzXHXzebyJRaWvWm21OxleOLrUM2qkydPCgQCrVZbVsn58+cRQvfv3y9f8/79+z08PO7du/fg/3JzczmO+/nnn69cuVLNBs/IWrjDkBRjuNqEYww1fBFsmjVrtmjRItXjGIYpe+/KFtLT00mS5Diuyk8LV+7DAEDdBwke8E01k39fuHChXbt2EokkMjJyx44dHMeVT/BWq/XDDz/08vISiUTt2rX7+++/bXtNnz5dIpHs3buX47jKM8TXPME7iBMnQX/SBs+MMd7qTqsP1uj5l9OsWbPKxzbp6enVJHiuqk8L9/iHAYA6DnrRAwAAADwE1+ABAAAAHoIEDwAAAPAQJHgAAACAhyDBAwAAADwECR4AAADgIUjwAAAAAA9BggcAAAB4CBI8AAAAwEOQ4AEAAAAeggQPAAAA8BAkeAAAAICHIMEDAAAAPAQJHgAAAOAhSPAAAAAAD0GCBwAAAHgIEjwAAADAQ5DgAQAAAB6CBA8AAADwECR4AAAAgIcgwQMAAAA8BAkeAAAA4CFI8AAAAAAPQYIHAAAAeAgSPAAAAMBDkOABAAAAHoIEDwAAAPAQJHgAAACAhyDBAwAAADwECR4AAADgIUjwAAAAAA9BggcAAAB4CBI8AAAAwEOQ4AEAAAAeggQPAAAA8BAkeAAAAICHIMEDAAAAPAQJHgAAAOAhSPAAAAAAD0GCBwAAAHgIEjwAAADAQ5DgAQAAAB6CBA8AAADwECR4AAAAgIcgwQMAAAA8BAkeAAAA4CFI8AAAAAAPQYIHAAAAeIh0dgA1xXHc9evXGYZxdiAA1Iirq2vjxo2dHUVdB993wA+1833HOI5zdBsOlZyc3KxZs6ioKGcHAkCNXLt2rbS0VCgUOjuQOg2+74Afauf7/tIfwTMMExoaevnyZWcHAkCNiEQilmWdHUVdB993wA+1832Ha/AAAAAAD0GCBwAAAHgIEjwAAADAQ5DgAQAAAB6CBA8AAADwECR4AAAAgIde+tvkKmD1F63ZC6tcRXj0J31G13I8AAAAgFPUdoLX6/W7d+++detWQUEBTdN+fn5NmzYdMmSIm5ubXerHXILZkstU8FxMWP+fUo62PHiP9P3ALk0AAAAAdV+tnqI/d+5cQEDAunXrWJaNjIxs0qQJjuMbN24MDg6+ePGiXZrAKB/S/2Om6BTh3rnsH2e8j4ubEfIedmkCAAAAqPtq9Qh+0qRJ8+bNmzRpUoXy7du3T5gwwV45nvL/yHi9PaM7Tbh3QQhxtNaau0LY+JBdKgcAAABeCrV6BJ+amjpgwIDK5XFxcQ8ePLBbM5hAEPSlNX0W4miEkDVrLuE5GHdtZLf6AQAAgDqvVhN8586dZ8yYUVBQUL5QrVZPnTq1U6dOdmyI8OiHCQJupk3d8WAqo0mgAqbZsXIAAACg7qvVU/Tr168fPXp0QEBASEiIQqHAMEyj0aSlpcXGxu7atcs+bbAm1ngPIWRWxPk9nCbGXTUeffzMGZw5AxMEYpSnfVoBAAAA6rZaTfDe3t5HjhzJyMi4fft2Xl4ex3E+Pj5NmzYNCgqyVxNM8V/me4NxcXSxReWKUZ6sUan51Wy4zRlTKN9xVNCX9moIAAAAqMuccJvc8ePHy98ml5+fb8fb5AhZLC5tq5P1WlaUJMhqh/LcFZ1utnet3yJ3Eek33i5NAAAAAHUf326TQwij6i/Asxe3yWkaN7Z+3CyvegmB4tylrN94jPK2UxMAAABAXfcy3San1+tpmq5QWFxczHFc+ZJLFr3QFNzm9NXsga8WPGQbnyryb8oeZIXDa/4EAAAAgJdErSb4am6Tmzx5cvX7lpSUNGzY0GQyVSinadpgMJQ9tLDmA39djPtsqvz7yaoLr9NpDQKnb81e8W5KO1nWR+n1XOrX+EkAAAAAL4GX5jY5iUSSm5urqWTnzp0k+c/PlLuXrvef0Gj7RLdd7n2ZTit1nxxLFYpmTOjZZbXs7tarDnlWAAAAQN1Tqwl+/fr1xcXFAQEB4eHhbdu2bdeuXcOGDX19fTMzMzdv3myXJgJuBTCuZFak/IbnrHpW61to+/c+q70pQ3YUFnq+gV2aAAAAAOo+vt0mpxyivnPj6uRxLX+agav2vGv2ywhrwPpeY6QG9e05+jDU1F4NAQAAAHWZE6aLDQ4ODg4OdlDlQXT+kOFbUY8fW5qJkmmEqJidwB3HojhOQXM4zBULAADg34Jv88GLJK0w1pqxbtxN30hFmsWrwFwYIXZTFUfNneMq6+Ls6AAAAIBaUqsJfteuXX/++eeT1q5du7bmTRiuEYWb37T+99RJpmPslcRGJYmzes8aS+8MTGpivB/sObbmLQAAntXt27cvXrwYFRXVunXrCxcu7Nq1y2AwDBw4sHfv3s4ODQD+q9VOdjExMVqtdt26dSUlJS6V2KUJza2Sor96S0XF9YMzksPcCLmuqYJuJ/ipMPED5YUiuzQBAHgWO3fubNq06bx587p37z5//vy4uLicnByRSDRs2LAff/zR2dEBwH+1egQfGRm5Y8eOs2fPzpgxIyoqyhFNrA0R0x0VcYtHDJm0YL7pM0xqbJ+2NP1qn+uFXsfe8z7giCYBAFX5+uuvV61a9eGHH+bl5YWHh0+ePHn+/PkIob59+06ZMmX0aOgTA4Bj1fY1eJIkJ06c6O7u7qD6Fa74llaezKmOk28ldPP/o7heSRvD1UPHN64ZFviKa62ergDgXy4rKysuLg4h5Ofn16hRoy5dHnWCadmyZVZWVvX7Go3GTz75xGq1VigvLCx86r4AABsndLL77LPPHFf55JbSEXKd+t5xHJvVx30ojlmSz3z0+hBl90DMs8erjmsXAFBBw4YN9+7dO2XKFITQqVOnxGKxrfz06dNPPYEnEAhatWplNpsrlN+4caPycJYAgCrxrRc9iSOxRs35yLw/6vj35p5NIw6e7djmPSSklUoXEnN2dAD8iyxbtmzQoEFr1649e/asn58fQqioqGjEiBGnTp2Kj4+vfl+CIEaOHFm5/PDhwxs3bnzhkDhLHmtMrmIFRuLiZhgheeGaAaiD+JbgEUJUgI/lYTZHMz9ap33LHG67wsPc55ZLdCNnxwXAv0uXLl1SU1MvXLgglUrLClu2bPn11183a9bMKSHRhdvpvO9wScvHSjmWKf5T2HA3Ie/hlKgAcBA+JvhAXyK8fu6c7e+vDcYOsa5JRcpcSYMrzTmE4BAegNrk4eFR/o44mUw2e/ZsJ8ZD+o2nC3+kgmbj4piyQlq5k+OMhLy7EwMDwBF41++MYzhz+h+xTZS/CF3D0lmauvXlMZZjjr9//IpR7+zgAADOhBFSKnC6JX0mQo/mmOaYEmvWfEHwQvj9D/iHb0fwTNFv5uShPfN9mIVYiQ+FWS29mxwwbvytC6M0bnwDTVzn7AABAM5Eeo+gC7Yx6oOE4g2EEJ2zjHDvgktaODsuAOyPb0fwhKybJaO+9tBYaa/bM/1Oa4uC91xaciBsEy6QFKzsnPP5A2cHCABwLlxQf6El4wvEGjlzOl24nQr6wtkhAeAQfEvwCCP0l6fI+28svaIcQ0lKWYkQlbbJHqe71N+i9Jb19XR2fAAAJ8OlbXBpK2veakv6Z6T/BIzycXZEADgE7xI8QvWWjtSXRBRtnrv7cp6JFgdL7oRqSvPmdcd+biJuK3N2dAAA5xMEfU3nfc8a71K+450dCwCOwsMEjxASDVzp/mbChIlXWZ2weVCCdvXIb1fJ/Ttyzo4LAFAnYMJ6VNBXgpBvES50diwAOAo/E/whxis1+G3RzJ1BpkKC5n4d0yWvdfHaguPOjgsAUFeQ3iMJ99ecHQUADsTPBD9KKgnwfUfSNFEY9eAC2fGdbtHtLAla/cmU0rvODg0AAACoDfxM8AihtQVHl7nNuo+auGWTS1Yk9fZ7ByFuZ856DsGJegAAAPzHzwT/a1HSL0T3DJx88PdrQTmaLtvMvy9hhaQ8x5R5Qfu7s6MDAAAAHI6fCT6dkzUw/ToiSRqTxCBFqXbCyVYJhrbbehIY5U7KnR0dAAAA4HB8G8nOJsZy3nThlv9q2bXZf7/i/vCym9WlSXL3Od1b0WFZ09IbS5s6O0AAAADAsXiY4DlrQZO0iZFecvK7q4GsWcIVT9ffZ0SYdHFS4+Rg6+5xaJKzQwQAAAAcjIen6DFSwVrbWDLCTwk+3q4ayWrccizTUuR9Gdy18N6r1lyzswMEAAAAHI6PR/C0Fne5IorhWl2xetV3xQXF3knHggVKjtO5x1z1Gzjf2QECAAAADsfHI3jKi/KbiIsjXf2lgnc+4Dj8cuDXuDGfs7r49FiIcJgUEgAAAP/xMMEjhKiAqaxRhdDt1DcyccraKGehvtSzJLHt6V+9nR0aAAAAUBv4meBNyWzBmrcMUmHUyI2sRahomoj7q5a3/a/k09Siw0pnRwcAAAA4HA8TvMZgVibu4XCBi0ggwg24i5EhhReoTi6Bt/3isrU/Fzg7QAAAAMDheNjJ7qtTSfMbTaMbuOAlmKeLCWHIhVK3s/wRqzxu7SmTvQrD0QMAAOA/Hh7Bj2kTwxA+rCjwzp+r1debYxw6Y/zP1T3TccQaguNwMeHsAAEAAACH42GC33zDuFqzXGBNO9MG/+XmDGNugO+2iIgmCQbCNUP8ubOjAwAAAGoDD0/Rr+wqQ6iX6XbLUeQnPd8688aVxl4+Gd5Rl370XqyWcB2cHR4AAABQC3iY4G3I8B+lSdHDVJtTmwc0Fh7CqOB+4s63hTw8YwEAcBxWf4FjTZXLMcoHF0XWfjwAPDv+JniBn8DrnfeZ7/MMbSWkhjF8dbzw+4iWPZCou7NDAwC8JFij+d5gTBiMUV6PFZvSMdLNpQnMPQ3qNN4meISQoP6CkoI9wZI/9MW+7n4tI/66L2+ncHZQAICXB+5KBkxlSy4LI3b8U8hZTTc6UPW+cF5YADwTXp+yxl0Ols5iWFKTE0H6eDa4zka7tXB2TACAlwnl9/VIct0AACAASURBVCFruMfoTpeV0PnrMWEIIevqxKgAeBa8TvAI/Zg/+M+H8wS4iVHIGF1Jhv6BsyMCALxUMIEg6Ctr+izE0Qghzqqy5q6gguc5OywAno7nCd5XTNTzDnah9HqEuQ+IXZIxe03mkhvFV5wdFwDgpUF49MEEAXTBFoSQNWs+4TkEdw13dlAAPB3PEzyOISSSC11K9Bwre6tvM2nLq9rETZkrac7q7NAAAC8Nqv4Ca84SRvc7oz1KBXzi7HAAeCY87GT3Z5ZZb+FsyzklzF6LyzRpya0M6y3OfEX/0NPVW2tVJhTsi/Md5tw4AQAvC9y1IeHR35w8RBC8ECPda16hkeYeFtGNPamaVwXAk/DwCP7EQ9OxNKPtX7GZPW+VYbKSE2mmLbcfaEpcB/uNaCN77ZjyoM6qdXakAPAZx3GbN2/u379/XFzcsWPHysrz8vL69evnxMBeDFVvFuk9gvQeYZfaruZbliTq7VIVAE9S20fwer1+9+7dt27dKigooGnaz8+vadOmQ4YMcXNzs1cTczv98/v6zYPqSS3leAYa19a0Om1ufZXsrPr4+OBPLhWd25azbmL9mfZqFABQwfz581euXPnBBx9oNJq33npr8+bNgwYNQggZDIaEhARnR/fcMNJDUH+JvWrjEOI4e1UGQNVq9Qj+3LlzAQEB69atY1k2MjKySZMmOI5v3LgxODj44sWLjmgRQ8iFxNhi8U+pxzGE9z3jG+PWWkSIB/oNv1V89aEhxRGNAgAQQuvXr9+3b9/cuXO///77AwcOjBw5Mjk52dlBAfAvUqtH8JMmTZo3b96kSZMqlG/fvn3ChAmOyPEGK7fnrmGmUWLQ5Q6IGOKW9lcTj54IoVhFn+OFv2zI+nZ+w+8xhNm9XQCAXq8PD3/U27xLly7jx4//6KOPTpw48Sz7Go3GmTNnGo3GCuUZGRkMw9g50NpiYbiCUta2rDSwJprLKn70XFwpzNP1OQ63WJPZcPEGU1QsCPBxbR6FMAf+EeMs+YirYrBehIswyttx7YKaw7haPE8kk8muX78eHBxcoVyv1wcHB2s0mheo8/Dhw4MHDzaZqvr8ITTmqMZg5WZ79V4v62Zl0zijmROSOI5jCKM5xsKapzeY11Dyygu0C4B9iUQitVrt6urq7EDspnv37qGhod999x1FUQgho9HYokWLXr16jRs3rlGjRtX/5aFpetu2bTRNVyi/cePG+vXrLRaLA+N2mIQHxm8uPrrubqI5vYXzEj1K6gFSYme/Zx1n05pTUDB3taBBMOWjMN1LxQjCZ/YEjHJQfz3WeDkUYQKEix8rZvQIp1yb33VMo/xXO9/3Wk3wcXFxrq6uK1as8PHxKStUq9UzZ84sLCw8ePDgC9RZfYJPzLWsvKz/Cg094NFBqchmjWYDZSEx0oV49LJOqD8j2LXBC7QLgH3xL8GnpqZ27txZp9OtWLFizJgxCKEHDx707t1brVZrNJoX+8tT/ff9JfJXtnnDtdKtfT1eYN+8z5dLY9tLXm+LEEIcp1q9g/TykL3Vx84h/p81Zzlbek0Ysa18ofnefwhZd9J3nIMa5b3a+b7X6in69evXjx49OiAgICQkRKFQYBim0WjS0tJiY2N37drliBaFJGamOVetvAku6/Xqlwgh23X3EBGMUwGAYzVo0CA1NTUxMdHX19dWEhYWduvWrSNHjty4ccO5sb28OIvVmpErea31o8cYJu3WQbPjoAw5KsFT/hOM19szutOEexdbCaM9ypmzSJ/RDmoR2EutJnhvb+8jR45kZGTcvn07Ly+P4zgfH5+mTZsGBQU9dd+SkpLBgwdXPmWnUqkqF5aRUBiLEMvKKKvOVgKpHYBaIxAIOnbsWKHkjTfeeOONN5wV0ksPQwghjvun6xCHOMyR1+ARJhAEfWlNn0VE/4UwEnEWS8aXgvqLEAY38dd1tX2b3NWrVxMTEzt06NC7d+8jR45s2bLFarUOGTJk6NCh1e8oFos/++yzyqfmzp8/f+fOnSftFSYnt/bxKNrmLqSLEEKsyWxJSb8WmN9G3gn61gEAnEVIYELiRf4EYRQlCK1XcvKctEdHhBDiOP2x312aNrJzfI8jPPrRBVvowq2kz7vWvDW4awQhi3Voi8AuajXB79ixY9SoUY0bN546dernn3++du3a999/n+O4KVOmFBUVjR8/vpp9MQx79dVXK5cbjcYn/XpldKe50iQRQpxvcpRQQ+cuY3R6w4UrBa1LC4Q/+MKhPADASVr5CZp6v+ARsGL8sIK53xsu36T8vE23UwiZm3tcd/uGVxlVf4H5Tj/crSOdu9rllV8d3Rywi1pN8HPnzt28efOIESOOHDnSt2/fc+fOtW/fHiHUoUOHDz/8sPoE/wIsaVM4c7aJEws9rCROW7IzEELCSGM3I43MaQgSPADAeQQvdASPEKJ8vQJWfGa8eptWF3m0aeoSVRt/ynDXhoRHP/PtHqT3CMwlrBZadBQOFazIkHaSi1rYbXS1OqtWB7rJycnp0KEDQsj2f9OmTW3l0dHROTk5dm9OUH8JQtj7D+PNOauK8lqKWmeKWmcyWg+MChLJe9i9OQAAqB0YRYnaNHPr3bl2srsNVW8WLmlBBkyrtRbtj0NZU5M1u/Mf/Od66eViZ0fjcLWa4Js3b758+fKsrKxFixZhGBYfH28rj4+Pj46Otn97HIMJvLZGjBLHJDIBGaacby1pk0kvpRU1T1Qdsn9zAADAXxipEDb6CSOkdqntgslc8PiYRb8bTTqWtUvlVeNQ1if3Sy/pIk60qL++cdqb10sv8TzH12qCX7Vq1cGDB4OCgn766afExMTZs2d37NixQ4cOM2fOXLx4sd2bY/TnEMIRreaK70sspVZax6h/RrgHwRw/WrDH7s0BAHiM5qx/a884Owr+yKDpEYXq/P/n+H2lhjlancVx47LYsntiUfjh5oSMdOumCP6hcdoQnuf4Wr0G36xZs4yMjKysrODgYBzHr1y5kpCQYLFYdu3aVXl4u5qjAj5lVD/fozs2REmEFZlwqZg14+4dCWmrQdLX7N4cAIDHTigP/5S3TUq6N5E2d3YsfDBEIjZz6O1C1VYvzwtm81qdfqu3wosgHNRc9swUwxVd+JHmhNujrOfWTRG0JjL1zevhvzRzjbbPaYm6prZvkyNJMiQkxLbs6+s7duxYx7WFkTIq4BPT7X2ItJCuFiJvKenRjym5SkX8GI27OK5dAADPFNNFx5UHh/q/uzNnw7yG35FYbf/l5KURUjFCaHChkuKw7T6KINKBr6o110z5CXHXx35AUL5ChBBd9MSRVF52PJwPvjzSZ7Q7qbO6D8AwjsOErPG+IOhr/anLqYbkm/qrzo4OAPBy2Je3raNHt25e/XwEfqdVR50dDn+IcYxhEYZxhIMHJqm/sTHi0MORtzjro6sAxhv61EHXglY1knaSO7RpJ+J5gkcYsUk7CzcksgylkcZipAch76vZ/LPSkPeX5pSzgwMAvAQyjKm39El9vAchhIYHvJdQ+JOO1jo7KAe6mGcxWGtjjpKfSw3f6/T7fD3HSKUjlaoc2oHzBGICPGTrK4jj0obe4Mys8Yb+wRvX6q1oJOvn5bhGnY7/J5rSmPa0MIos1LkLz1CNDyMcJ30Ur5TWD/Cu7+zQAAB1HYe43Tmb/uP7jishQgh5C/3ayzv/kh8/ItDO43bUHSsu6ae0krb2Ezi0lV9KDat1+q3eiiCSHCElWcSNUap3+3h64I467LTl+LR3bqa+ed14syRoVSP3vi+e3a25K+jclVWuEoSuIDwGvHDNdsT3I3iEtvX1kIfNI2XFuEsrXPQKQojy88bziuq51nd2aAAAJ+AYLnv6fe3PBc+ycaL2DzNrbu/xelnJAJ+3rhVfSjemOixA56uFSUYVBLHN27PsuvsoqWSqzM3R7WICPHR7E8pfGPRdZE2yO0KI9OiHECeMOuHSLKnsnyBiK8IEuPvrT9+/VvD/CN5NiCMUYrwfKW7d01ZCBXhbcwv/1JwMdg0Ncg11bngAOAvDVHdGlHBYf2bn4hgu4/07lkyT9udCxCL5YJ9qNqY56768bfVc6/+ct718uZxS7Mvd+kmDrx0cLJ+96iKsUNLdtTb6PmMCPHhtYzvU49KA9BpB560WhP5zHG/NXkLVm4URdWWMPP4n+CUX9N1Dhd4FIQUlOtu7Ku32KmellZZTxXQRJHjwr0VW22n5xeZrr+M4hssYf9eSZQo70MySY0rpm8QxnMdbvk/aHkd4T+84K2utUN7CvZ2M4lXPrHQdXWJ59I6XWrmHRbSYetTrLVROikjH9oBjiujSyzq3rgqHtmJ3ZOCnpuut2JKruKQ5QohR70dMMen1trPj+gf/E/ypDHORmR3HyPJLlbYET/p4IoR6MAMYjrd3RwDwVNnZ2U9apVKpajOS2vEou2cYw/Y3w8WES4Q4PCEmpW8SQuhJOR7HiG6e/Wo3TOeYe65YZXw0itzDInrjjdKyBD+ppSQ22IHH1kwRndL3qvmBwX9umNd7gY5ryO4wQkIFzrRkzHSJ+hWxZkvmV8IGaxBWh0598T/BdwwUeokwFskEtLJ8uZiQOCskAOqCgICAsuVr166p1Wrbcmlp6ahRozQajZPicpSsj5OtueawgzG466O+Ry4R4rCDzR70SyLcSfdens4Nz7k29fYoWx52SD25pbSNv2M72dkwOjplQJLkVXno7uiU3lcRi7zef5lyPOn9Nl24lVEfYI0puKQl7tbB2RE9hv8JPr2Y1lmwHgMjLOaHZYXFh09LunXYpd7Sz+dNd5JXp9oAeF5ffvnlokWLXF1dSZKUy+WpqakzZ850dlAv7q7FuqWkZK5cJvj/RNJ7SwwlHNvTU1B6qZg1MmUJHiHEFNEcgwh3/v8lrIMYHZ3SP0nSTha4MJzWWsOPNk/pfRWhlyvH44LgeeaUsYg1uzSpcyMZ878XfYqGvq+x4hfIqL9zsj9JthWWXrhmSc/WWYseGh44NzwAnG7Dhg1Hjhz5448/OnfunJKSsmLFCrn8Jf7VGyGgWA5NUGnMHIcQ2ldq+EGv7+rq4v9FqHtPRUqfq7T60TX1kgtFacNvhvwYJWkvc2rI/0ZMEZ3S56q0gyxwQVjGhLu3o/625prDE5oXrMpQbnzixaM6CJe2I9w6kL7jMGE9Z8dSET8T/MyzuuY/Ftj+5ZTQqvvGojV6UqzPW59z6J1bCCEqwMeaUzCm3kQYVhqAoqKi6OjoV155JSUlBSE0duzYdevWOTuoF0cgtEghd8fxiSrN7pLSdcX6H70UH6o0F0xm/9kN3Ht5pvS9ej63dOLR9LShN0M2R0lf93h6pf8mYgoruwDvOCXni8xpRu8Pg7I+vm++bwheF5n65nVLtkkx3E+59mVK8AghQdh6KvBTZ0dRBX6emJrT0W1Gu0eTBwxdm/fZyjTCR2b1K/WcE0rOTcuYdC+jf8grOQVSQuTcOAGoCyIjI+Pj4ydNmmS1WjMzM1mWVSqVT9+tDrPl+GEFyiVFxft8PINI8mu5bJJa842HvO3sBtlGWtM7abSWC9kaJe0M2b2iDb1q4zVx7+XpNyPkTssLLhGi8GPNcTGBS8gHA68RUqLhyZa1EMC/AT+P4A+nGJck6pck6lcfVs9akGIRYOsWN6WkxWtay4onByu35+Zup+jcQoTQN6mzLazF2fEC4EwLFiyYMWNGcnLymDFjWrZs2a5duwED6sQ4XDWxv9SgZNl2LsIlRcVmjmsuFKxSeEzTaDcVl0yZ4O473D98+yuQ3Z2JQ6Y0A6mgrCoLrbIihCyZRlxMcCbWkmN2RINfa3U7SkrLHqoZdkiBKpPm871U/DyCD5SSNIsQQg2GXzfg6MuZDfwpRsDQLWTstrGyFoSl7dJcNvJVhJCFs6gsBf4ude7aCQC1pkePHiqViqKojz/+uHHjxlqt9s0333R2UDWyr9Swrli/xUsRQJIz1NqJKs13nh7NhYL3pNIlRbpPZe4tPnnive+gdmgPFGj3Frxyp4NqS05K/ySPob7qHXmNzrTS7i9IH3v7lbv2744+zk0yslDNcmiEVKxh2TFKdXeRi0OnsHM6fj63tgGCtgEChNAdD4rJMgWYuW8IKV0qOR6cQwn8e+WiIgwTNvRECH3aYB7M/Aj+5TZt2lShZNu2be+++65Tgqm5WxbrumL9Fi/PeiSBEFqkkE9Ta78v1rcVCn8o1s+Su6/V6RtSZNtKI6mB2iTr5639qeDhmFsNdkcjDFNtzI440tyaby5YkRH8gx1GmqvMlyC2eitGFqoNHHfMYOwucvnIjZ/TwJfhc25jiv8MO5xyf27a15dPSKL8WQZrpdvV/riYKyoNmD9S/oYPQojESJ1V+33G4skhn8Od8eDf6aeffrItcByXn59//fr1/v37v6QJvtjMRgmpQ77eYgxDCLEGJu0/1z9yI7N/CJum1q709GglFERS1CSV5huFHHK8E2EUFrK1ycORN1OH3miwO9pnclBpoi71zevBPzR27+moMQl8CWKFQv5WoaqlUOCI7H635Eam8WEPr7pyhYvXCV71M6M79XBQ+8bXivXn0qRtuAF3rhOIEU+8Nrf+8Nm/XyS9FXjDwFnJHwkw4S/5u4cFvOfskAFwgmPHjpV/uGHDhvXr1zsrmJq4Vmhdmli8oaeHrRM4a2BSB10XBLkwerpg5O3V25tE6/aY1fujEDrAshkqxiygyvalAqbZfZSSvHlpuJT0mRxk32p5o3yO95kS/HDELYdmd4SQmmGna7TDpKLTBvPG4pKxbvY8qKM5env2Oh1dFC1t4edSJ27l52cnu1tK69FU0x/MVIvVuN8wdljnb+7u+sh4o5FhR7uSUO/t7iMlLvVopcaYdDvXnG1hLZ80+Ppi0blsU4azAwfA+d5+++2kpCRnR/EirAynMrBfnStGtuw++Log2CV4bWTI1iatREK3kfcQHsiWXCb9xnsE/jem/jTSfzLpP5lQDGT0FzGXEPsGk/NZiu6YSr0lJ39pun1r5hNbjsdd8NRB1+pvdHh2H61U9RC5Tnd33+qt2Fdq2FhcYsf6TyoP+woD4nyG7szdYMdqa4KfCf5SnuVYmvGXdMlJ47vvyJfgHLF9TUOmyC1zSIHQL7FZ8Kf1SEJQz9eSlbc7Z+NA32HfpH3ZxbNXfE7FK5EA/NtwHLd7926F4iWb9qMMyyErwz3K7vVcgtdEIhwryyLp4+S4pCNruEO4dy77x+hOUwFTMIG/HcPInZNafEYbdjgm/HgLzZ78vEUPn77Pv5Xt3Yk838bRk818qyvuJXK1nZm3XY//udRgr170xbTumPLAm/6jYz1766zaG8WX7VJtDT3TKfqUlJS9e/d+9tlnjo7GXkZHi0cjMUIIcVNzE9vOElzp165P2jH30AanCgI+ay/2bI+QNdDvsusdK0umlN51xV3zzDk6uuha8aVmbq2cHT4AtUoq/ediJMMwJpNp+fLlToyn5rKm3cco3JbdbSW2LHK/xxXtgXHu3ceSXkMxygchxOoTWf0lYYM1dmw9d06q7oQ6PCGG9KAQQmUjsPrNsPNJAt7AKEwYZudRSdIND6Sku0Lwz6Tv8zweG6/QlyCO+Xnbq7n9eTs6yLv4Cv0RQkP9392Ws66xtCmJUU/d0aGqO4J/+PDh4sWLmzdvHhER8ZJek0OY4DQ9o1HpnD8MJXffNIqQZRrWPdlqRQix3m4nm+e0k72WZXxIYNSD0rudFN3jczbRXMWpIQHgt2vl3Lx5U6VSTZkyxdlBPatjaaayYSvfParJ0NHH0kwfe7lmJ+qmffmQs7Bpb9/MGH+HY7iig4WWTJN7XEvS+x1r5lyEEEKsJX2mIHguwl3tFU/eooe6E+rwI4+yO0KI8haEJ8Ro4vOV61+yAdpeXmbWtCp9wY/Zq2unuUxj2vXiy/18Ht1c2lja1FcYcEp1tHZar0YVR/CZmZn79u3bu3fvxYsXEUIff/zxmjVr2rRpU+ux2ccdpkcgtu1a1qox5t8zXRrNlMneU2o2eHncVu9v7NPqD+3JYQHvnVQd6eURd0J52N+l3kllQk/vN5wdNQAO99///reatd9++22tRVIT3UNcMovpnbdLEUJmBpkZROBIGeG6flL9979LP5FQGB4psmSbU3pfMaeawg54CQIuc2xrOnUCnb+hqDhNxBoRKWN0Z3FxM4y0w4j0ggAXRmtlimhS/s/RG622siUM5Qud9mvJkYKfGoqjsk0ZSbrEGHeHJ69dOZve8BvmWm5o1GH+7857ML2tvJNzJzN7LMGvWrVqz54958+fDw0NHTRo0NKlS1977bVly5Y5Kzi7CHTDl7t8srvoP8bcaMKP6SFyZRGar9VJVUesnIVjuc1Z3yGEduWkGVlDjikz3ZgKCR78G7i4PJrkW6vVrl+/vnPnzk2aNMnIyDh+/Pjnn3/u3NieHYGh0U3EvUNdEUI3Cq3fXCxu6EF91t4NWdiisyr6crHHVw1YI5P54V1JRzkhO2xO/paQtsYE3taseQLaWCJoQuR8yxT/KWy0l5B1q3k8inf8EMul9L4afqS5MNQVIWRKLn3wxrWA+WGy/l5P3R3UnMpScFZz4uuIFXnm7C1Z3zdxa+7QU+W39dful942scazql/Ll1tZywnlocF+Ix3X9FM9luAnT54cEhLy22+/xcbGIoTMZoeMF1jL3m4s3rCrkbDvd9rDVv+AVQihXiLXXiJXjfy7r+5/PLHBTD9hIMuxJXSxjtGuy1g2O/wbZ4cMQG1YuHChbWHgwIHffffdRx99ZHu4ZcuWQ4cOOS+u5yYgsHpuBEIov5QhcUxEYYEuWNq4u2KFwDuhedqwGxiBRZxqkTfvYd5Xr3l/uJmq9zkuiTHd7HpFG2H1XdFFHM9xFkLWtZomzAxnpDmZ8Jl6JStG+iOEUvpcDT/SnLOyKf2SAuaGeQyBsfNqyZ7cLd29+ssoDxnlEeha/4TycG/vgWVraaUlpV+S/A1v3+n26RIRLo6cHVH1YbAH5eTOqo99Xr/88kuBQNCzZ8/evXtv375dp9M5Kyw7ElE4hhDp9ZbPxC4C1+Ky8iMF+6LJV1wOXEMIZZsz1mcur+cSEiFqfFJ1xHnBAuAEp0+fHjjwn7+AvXv3Pn36tBPjeWE4hjCEWIxL+fQ+W8KEbH1F0s49PCGm/rEYYxNJyPYmHHbZmhtqvv+ONXMuLm1t5qSh+i+tGV/gLqGs4V41NR9KMS6/qH/2SBQj/X2n1U/pfTWlT1LgwvC6kN2vmC3M4yWXzDychuNeyc0MY1oPr/62h2/5j/lVeVBn1doe0krL/d5X3bp4aPYW2OvWBgEurO/aoMp/bva46FMTjx3Bz5kzZ86cOUlJSfHx8V988cV7772HENqyZUtcXJxM9rLOl0zi6Pfh3gihknOI8tMwHENgBMux14svm2nj1fAL+N0EDiELa55x9wOEUI458z9+7zg7agBqT7169RISEmzfd4RQQkJCUJAdxmbZtWvXn3/++aS1a9eurXkTFUR7U4MjXS+VWBZ1oiYeVJZe1Ek6yLDG4glKTetS9u2/zZy5kPS8xiKZ2ZjBuTYpYo2ult9ZQQin2mtSfCB2QcQTDtEZFjHc8wXj+W4ALiFwESHrVyfOzG/Tl+wswZYq5ARCCKHVOv1xo/Ggrzfh5LjsieXYXTmbhviPojCBrcRL4NPRI3Z//s7R9SbQSktKnyT3Xp4BX4f5/Df4fu+riEN+M/l8a0MVnexiYmJiYmIWLVp0/vz5+Pj4GTNmvP/++z169Hi5ztqVJxVgCCFLNsN5iA1mpbeLL47h3zTeiDguc8Qngevm4mK79aEF4KWzcOHCQYMGHT16tGHDhsnJyUePHv35559rXm1MTMyhQ4f27Nnz9ttve3o+3wAmZrN5/vz5FkvFQ8wHDx4wDFPlLgghCsfeihR3MbikNRd9S2BTht8M3ho1rTEXQBKW3zTpn2SG7Zny0HzmjLJjX/m+wYLPZRnKTmEbf1H2wFHjJQd8ZrQzvNnInjdr1YUD9zLfKOST1dqpau0yhXytTn/MaNzipeBTdkcI/aH5TWtVFZjzjhbuLyskEHFOe/p11N04SOvWUxHwdRhCiPQSRBxtft92+yJ/c/xjCT48PPz8+fO2ryKGYe3bt2/fvv2333579uzZ3bt3OylCOxh/XLu4szspIy0GWalF6e3y/28dhlGBvtasPGGjUAtrVloKAlxgUEnwr9O3b9/r16/v3LkzJycnOjp68eLFERERNa82MjJyx44dZ8+enTFjRlRU1HPti+O4XC6vnOAlEgmGYdXsKBVgUgHZAJGou/+3K9G4ETej14SOEYjuT8tYtSZobowk0rg0OGXU31SLDaLtTINrJe7vvOGy06Xp329Rdrslum6iMGylQj5ZrR2Qr+QQt8VL4UXwLL8jb4Hva4ruBqbi+HQ9veJKhqncO3nasrsN6SUIT2h+r12iS7hI/h+f2o20ljyW4Kv8dUwQRGxsrK3b3UvqoY4usXIiOWXQy43mAoSalK0ShATSSo2wUWiOKXN79ron9ZUAgN8aNWo0d+5cu1dLkuTEiRPd3d2fd0eKoqq8i+/w4cPx8fHPUkNHF+HqttI1ywI++Sg9A0ON9zXr0YgcoVQt9miqpFoEuHiHaTYX4ArKilEB/8Wqyu7fXSlZcemx6+7xdwy2ha71XX7o6czbn14MhWGNSDLRbG4tFHq8bNmdNSYzyqrfetytve0OiMbSpo2lTavcRj0qN2/RQ+8P69lubbDR7MrD3UhJh5f1AvRT8XmymTI/xXlKBFiJB8XkuZstyvKrPN4djOE4QijINbS5e1snBQiAE1AU9f33348bN46iqriJyGq1z4hPThkB08Rx41WaKIrq3CdwuQs+3U8ubuH2JkI6lh2lVE31/+K1h73IwGkXko3dBPGkz9gqK5nYQjKxxaPJSOLv5i+LUgAAIABJREFUGBILitwClsxosKD87c4vTGdmbyqtrwbW6p3xq3X64ybTYR+veUXFtnP1TkzyP+pLgkgy1vXRvZpWjltQVDxSKq7/pAnaWbM17zvS8030+LSfjHIXKfAm0FNucVSM9EcYut/jSnhCjEtDMUKoYFWmamtuxNHmlN8LvQuclWNKq1yDkW51ZBj4ii/lqFGjyu6OreDAgQOOj8chJAIMIUTISU7tzlgLy6/C/v8zlsCIvj6DnRAcAE7y4MED25jzDx48cHYs9mTiuPeVmgCSmOchwxHCYv3Ha4vWPsySlBj2ebj1cHXdYSH6+bwn0V9sJ7iT6T5XgT/T3/fU0uSGTGlC4T673Nl8U2ndcK20NhP82mL9caNxm5engsC/VcgnqjSfqLXLFPLqLng4UhuhcJxKzSFZV1cXK8f9V61FCAU8+bwCZ81HHMMU/YYJ/pmojaOLOMaILAXP0qJihD9CKKVvUnhCjO64WrU5J+Joc8r/Bd8Ca85ya843WKVJxjlaJwzbQHgOerFq7atigg8JCRGLxU4JxXHm/V08qKFrmELAatxYWvmkzUro4jxzTrg4sjZjA8BZgoODKywghBiGwXG8+uvcdVw2zbwioKbK3GzHUK/rS4sOnf5V7vZrk4YDE84Oc5P81i92RPHQPaV9vWQRQRF9nqlOU4aJNc4KWzTn/n87enSzDTn+chFi2BYvTwWB25a/8/TYqC9hEXLWQXxjAbXRS/GeUs1w7ocNRiuHvvOUU0/+4BGybrg4hjWlCht8j4saI4QQazJea4EREtLvw8rbm1kThVE49s/zW1RUHD5I1pkLvd/1CulJ+R1pNo4q+Yomgp50zqBapM9oOn+DyyvHMZcGZYVM8Z+W1A8Jj94vUKFDcOUghPLz87mXyqFDh4RCYfXbjDum+e2hkeO4c3fXXrz7foW1eXNW0dpijuPSSu9/cW+yg+IEoHqurq4Gg8EpTScnJw8YMMBgMPz222/+/v4+Pj7nz593SiRP9Szf9wpyZy3THTrVK7dgl76E0ZfmTF1Y8vfV7fqSCRlnWXPOs9RgZa1Dzqz/8OQDjuOOFPy86uGCFwn9cX9mmUYcVte8npfdLbM5OitnUL7SzLJP3ZjRXzJcDDbd6mN7aMlaZLjcyJq7uvKWLMcuTJkVn7O5fGG2le6am79LX1L0q0qTYxicX7hIq6tJ8JbsZabkd8q1Shuvv0qrf3mWfWvn+14nrhM4miuJGWkOIVRvN2rIaitvYMnIRggFuzbo6R1X28EB4Gxjx44lSZKiqK+//nrq1KlTpkyZNm2as4OyD0ZfQucVuvXrssVbMVQixiUi9wFdDRdvvC0RfxHw6jNOEXtSldAyUDO7bX2EUHev/nmmrFv6pBcIptjMnss22/7dVlmLTP88vKv+N85xRXPc2uKSJgJBPkP/YXr6wKm4pCUh786akhntMc6Sa81bg3BBlV0oLhWdK2X0f2vP5JtzywoDSGKLl+dmfcn+9sL3iZIWQuF0mVtN4qf8J7CG24zuzKOnU7gNERLCo19N6rSvx05NdO3aVSAQOCsUxxFRmMHKIYTMVwVU/4qn6AXB/paMXNemkTiGt5d3dkJ8ADjV5cuXU1JSGIa5cuXKsWPHjEbjvHnznB2UfXAGE+bqghDy/v/FXUzkwhmM5UuqV0zrjhXunxm20EdIIIRIjBzsN3J37qavI1YS2POd3r6Ub9l681E//CIzm6Nnfrj2qJeWlwhf1oW3fbmrRHPcFLXWyqHNXoo0mn5PqUZI1tW16h5gZaigr5jrxy3pM3BxMwwTCEKWokpdKCys5ae8bWODJj80PIjP3Twl5J+JFQJI4nuFx+BCZfMaZ3eEEMIEgqAvremziOg/OabUmr1IGPkzQnXo8tZjCX7Hjh0Wi6WgoOoOCz4+L+udgm5CzDYKFUt5lVgLKgxqIwgOMN26b1susmrul95pLXu1tkMEwHkUCkVGRsaVK1eaNGkiFotTU1N50xGH9FZwZos5NVPY4NEQF4a/rwobhj57DQfyd7aXv+4rDCgriXFvc0b96xn1r109n+n6fZnYYJfY4EcJ7K9s84ZrpVv7ejxXDXyyTKdHCK32lFMY1oiifvBUjFOpQ0jPBlR1V8QxgR/pP5HO38TqfsfFTQlZ98rbHCvc30DUMEIc1UDU6Hf1iZv6q02kzW2r9Cz7ubaov0h8wWzaXVI6VFLTzznh0Z8u2EIXbmNNDwiPPrjolRpWaF+PvZS+vtWNu8RxzzlUY50xpaWUxDGEkOSHV8k0dYW1wrBgw+WbtmUTa/opbzskePCv8tFHH/Xs2ZNl2W+++ebevXtxcXGDB/PljhIMU3wwtHDhWmm3VwkPd+PVO7Raq3h/6DPubWZNf2vOWjnLceUvFVZprKoKCX5xUXFXV5cWwkcnQUtY7gtt0Zdydxn+nFdCWRPHmqp4KrjQjvPWO91YqURG4GXnQBoLqF98vGVPGiu4HMpvAl2wBXE0FbK08lqtVX1KffTL8GUIIQIjhviPjs/Z3LhhUwIj9Cz7rlJtOzOfQ0tGKVUI/Y+9845vqvwa+Lkzq2lm994UOtgFRECQKcgWBwIqoggvIKCAbEQB9SdDQSxDhoKIIrJBxYkMQSirhe49kzRNm3Hn+0dqaUtoS5t0YL6f/NHnyTNO2uaee89zBjRdxxMB71oSRwGAMOZ8E5eyOzUUfPfu3RMSEgYMGDB27NhBgwaJRPb/ZzIYDPv3779582ZhYSHDMF5eXrGxsRMmTHB1bbK15MEI8UqbCf9VGcQhPFtePbaB8PNyf6syC7enwPsVv1mOk8SJk1bIwoUL4+LiLBbLkCFD0tLSFi9ePGXKlJYWym6Iu0YTq+aU/3aJSssRde7g8kQPBG+oaV2ACj+P+aaBg/sKBbNKtJvUyi4CspzjpxZr2pOE7GG1O4D5Zn/OnI6gNY3VPMPztLhrGqD2TKbbgqju0+X399gGFZLB6znjbVRkI9/iN3m7BqiGqcjK/P+xrl3Plpw4W3JioNuIdaVl3QWC+XJXAPDBsR1uqpeKNY8JBY3zor8njrgD7jYREfgjxMPlY24GavxCL168mJycPHjw4N27d0dFRc2YMePs2bNCoVClUlnjZZvIuXPnfHx8tm7dynFcZGRkdHQ0iqLbt28PCAi4dOlS09d/ED+mm3fdqAAALttcYVaaqaI6Bke4PFxOTSdOHgFMJtPWrVtHjBgBABKJBGtrac6sJGuZWt5qnOmOJfklrmKhuOshl/6nycDtVPpUS/JLluSXmGI7p9/uIRRsVCtnl2h/NZunFmvCCXypQvag81gxjljNiveDe7yKSjqKuqaJuqZXvTC3ibjbC4+Mdm8imHwg4T37/v7kisRbhmtd5b2KqcKq1wD1sCOFB8qZsuUK2fxq5+7+OH7K072J2t0K4b8c93ip6evYndqfzc/Pb9asWbNmzSoqKjp8+PD27dunTp3av3//sWPHTpw4sYmbzZo1a/Xq1bNm1X5E3rt378yZMx2n48soPlPPAgCuIkwmucFSJBJVHsLxNG04+bv5dgqQpEuvTuIeHUtp7a+a06M8G2rEc+KkrbN3794FCxZMnz59y5YtOI7PmTOnsLBw9mwbF9BWzpl0s4XjI1X3EvMhmIwt/ZHweQsVBFQbyFMZizDZE/WvyDNMQTzP2yirihAeuFvtq0RXAblGqZhRrOklEqxUyuvwtjKzPPeAQ0/cfRJTuJPVHsWUlTVPOVMyq/lWGHuhfoH/26QZ74ow8ab092r1SzCXTFNaB2nHWv11hN0/GiD1nqz/9ddf06ZNu3XrVtPP4OVyeUJCQvWsGlYMBkNAQIBWq23EmkePHh0/frzZbOPIqgqOB5bnCRQp3pqtVU4n+00Lcn8aAHiWLVi2EVfJUZkLU6hhdXpRbKTguScXJL72SdSXjRDGiZNGIxaLNRqNI87F6qVnz55r167t27evl5dXfn7+H3/8MWnSpPR0+1TLti91f9/nny39NctyeUoNd2A6939cxXVB+O6qHlZziM7bIIz6Bep1g+cp07UuqKhdLecpVv8rgisFkbVr7lkt80oUvU5R/1Mr4gQPzJJWt5OdNV+KKPaS9dDdkjQek/XHvabXI62dWPK7fkq0JFTxn0hk3lI0z/f9gWceN2/eXLFiRVRU1NixY/v27fvzzz83fbN+/fotXLiwlpe+RqOZN29enz59mr7+g0ARIFAEAHAVyZYqKSrf2l9x7h9UJHCb+7Kke0feZPZcMbv81wukjloe/rHjhHHipLWRmJjYvn37qmZkZKRGU9sXtU2gN3MGilt3oUaFGMJrJme8WRWsDJyZylpJBqypX7uDNQ5qJU8XEX5LCP/l1hfuPZun8oiAVbXGWrV7OIFvdlNuUCvnleguWuqP7b4fOmctW3IIADPfGk6lvWlJHM+V/82Zkqj0t8CW853dSStlNCauGTZy4mhqK/hr164tWbIkIiJiyJAhOp3us88+y83N3bx5c//+/Zu+WXx8fFlZmY+PT1hYWI8ePXr27BkREeHp6ZmVlbVz586mr/8g7miZlX+WAQCmJECj5KgCaz+Vni3qGAkAZLAvlZGLioWCsEAqI0dNPuKFI504qU5cXNymTZuqqsvs2bOnS5cuLStSw8k2sFXpYnRmjgO4WUxZm7kGFgAAFViDlYFnAIDO24i6dEVdH2vg+phqDGAuTPFXVT10zhpM+TQqru2ss0RX2p4krJb5rgLyI5Xi8x+yC/66Z5jkeNBbOOurguYZjq9qMtX0KW9O40y3CY8XeVMSKvDhTNcx9QSeNXCGvwB9BPOUOHEcNYwwISEhOTk5Tz755MKFC3v06IGiKAAkJydb342IiGjiZu7u7sePH8/MzLx161Z+fj7P8x4eHrGxsf7+9Vdhp2n6008/vb8+dGJiIsfVc7NpoLhbJTRvySLccmQ8o6DvchXXAABXF3PGLM7kh0rCUKmEzi9idWWY3NXCmbdkfPBm8LJGf1InTtoQmzdvHjhw4M6dO3U6Xe/evZOTk8+cOdPSQjWUU2nmP7IrH5Qzy1ieh2RdZQKZHt7kG51d4F6w8l5MMZgp2CaMPvtQW5ABayxJ4zHVKARz5Ux3Wc33wlgbAVErakbERf5smL0gv4jPl+yJkvZRAMCRZNOqc2XWdxmOt7DwxL7KvFsBMuz7MZU+2IT/SvP1x7HQ8TxrpAt3ouJYMuBdU0JPMuijVlKjrEXgzWlM0V6bb6Hy/pjr480sz77bxgmRYqx1H+LXUPBpaWkAcOLEiRMnTtw/1C5x8H/++WdeXt4zzzzDcdyWLVs2b94sFosnTpw4ZsyYuidyHKfVau8vYVleXl6vYGIcMTG8JWkcsBXKIUIoK6bS8gCA8KIZTY75+jpxtwxJ7y7lv13izBYy0BdQNN+SW84YXHBpUz6sEyetH4vFkpubm5SUdPLkyfT0dC8vr6FDhzaigntLQWKQVlp5WTDSPA9QTnPWnqIK1qrgoTJYeQxb+iPu+QoiqP+JojqoJAZTDGZyPiICVtGZ7xA+8xHcRlRRde2uP63J+r/E0IOxPM2lPX8j6IsO0n7KUeGiUeGVZ651nMEjpBfuOY3KWikI3sSW/kwEvEfnfYqKOzTIK7CxLPtDrzNXPiml6JiNlw0qUeXHmdhBEuftcMvBiaLv0ozJMwMXPmgAzxnp/E8I79mA1bgsM3mbCIEfNLuC/99Fw7AQoVzQqm+5aih4hmEcutmmTZvmz5+/atUqAFi8ePGuXbuee+45uVz++uuvl5SUTJs2rY65AoHg3Xffvb//6NGjx44dq3tfEY6YaB73eYvJ35yx5Q3Z65u8oisP5IwXx5r+qdDt+4A3WzCFzP3tV60xsu+324wjTh8TJ48+JElOnjz50qVLo0a1yUIM3bwEv2dRd7XWaxcLAMAjCKAAQHH8mvNli3q6AgAq7oApn2J1ZwRhOxqxC+G3xJzQCyE9eEtOvQFR+tOarOm3g7+JlXR1BYDgfdFVOr6h23nPMiXEcRXXhNG/8nQRk79FGHW6EWI3nMFBQr2l8kkpWcvEeQvC/nWyC5I7/EqoozWnin8QosKbhqtR0k42x6DiKNzteeAZwntOVSerPY4Q7rj7JEdL2Eap8ZdzdPDr2rVrDxw4MHr0aADYvn37zz//HBMTAwB9+vR57bXX6lbwTcFVgHIAuHocU7hTYLmIWSrP4DnDJYS4o5x6gSmuQEgSVyuqpji1u5P/CAiCxMfHL1q0aNGiRf7+/lUXgbYSCt9ehb/X1zVVxwDAx5fKr5dQnTzw1ztVPrgrhPcesAj/VYTXzMYlg0MId9xnNpW5RNDuW0CIOkbqT5RkzkgMPdRR3KnyQdOlpzxoT1T6izeDv4xy6a2oY+49UCHpv5zKXCSM+oXOWo57TK5ek9QRPFatMv2Xtyrauxm7eGFKUgXAma/3MbEV909BCLUw6ke77H4wf3d/1dBAcWjdSf4Jv6Xm671w9xcRYSgAAE9RWcvJwHXQEpdrI81TbG3jMc1TJtbkircWA1jt3wvDMPv27Tt9+rQ1SCYoKGjQoEETJ060y7cdw7DIyEgA4HmepunAwEBrf7t27YqK6ko+00RUIvTkM2oAhAxcq5owGvgK4GlAMCpjIRmwCiFcCG8XAGCKtbjbvVvstxNfWxWxQfgI5YZ04sQmEyZMMBqNtRxd21Bqak8J5inBAOBgkvFWCbiJserqqgoEc4FqKSwfFsLzNYRwx+QD6h5m+F0nDBML29VIgCruKCW8BeXnSqsUPIEidedtw1Sj6YLtVMZbrP43YawD84DZ5IvczcfLszZ0+AIARV06AUcTfjWM51TmcoT0ssteqRV37pTfmtzuDQEq/KXk5C+ak0+qh9sciRBuuNf/UZlLBRH7AYDO34KK29X7F7EXLA9nM8z0vx5fNMefTLO4iVAAQBDo5y8Q4ciBvC/ulN9aGb4efchCRA6ixr9YWVlZ586d33rrLaVSOW7cuPHjx6tUqgULFnTu3LmsrKzpm02YMOGVV165desWgiDPP//8hx9+CAAsy65Zs8ahYXIAIMAQAEAlsbz/IBrBebqYKfoSUAJTja4aU7BiE1N8z+U1wqWDlipxqFROnLQGkpKSsrKyCmrS0kI1hi6eJPaA9HB2ACFx9YR6R/m8F0oGCNMmJHD/RppxFWzquARJF1fPt4OqhnX3JtcPsF0+znz9MeMFhfGCkjOcZwq/4Kl8099+xgsK4yVP4Ix2+Sh1U0wV0ixVwRpOFH0HAITfMlb/M/AMIgi0vnjOzBnOE75vN30vHvh9edvHe00SoEIAeNb75WOFB8uZB6obwut1zpTClv7M00VM/mbSf2XTZWggJpo/nmo+mWayvgDg9+zKn4/fKS/WU7nmrMulf0kwl7OaU80mVd3UeIJ/8803ZTLZxYsXq0fff/DBB4MHD543b962bduauNm6devefPPNHj16yGQytVqdkJCwbds2mqYjIiIOHz7cxMXr5oWjmj3DVRgC5V9PcR36Hav/nc5ZI4jYV720H+nnRaVlVT3EO5PSO/mP0HYLRdait5+gW2ZzRIrXAYIhAVvbZ05PTJuQEHwgFjg+Zcw1YYTEf2O76nVEEQDZA/yzcPcXWd0pQeRhAOAMl1BpVwCUzlrBM5pmSFVr5swi5eHxgT0JIupw4f4n1SNIwg33mkllLhNE7LOOoTPeIXwXIrgdSuGd055FETROUfmA5yX07S5//HDh/ok+r9megJBkwLt05mJU0hF3e8HRJxfVcSGRDU/euycLS81f9pjMsyBbE3+AKdbwX7J7XtYPDxnRwbPHutTFPeSPu+AOLLDSQGoo+J9++mn37t21cusIhcLVq1dPmmQHLwYMwzZt2rR27drz588XFhaWlpYqlcrw8PDOnTs3ffG6SdUxNMtjOEJluGjMvu7pb+Lq8aikhjcHGexnScsWx91LZ6ijNQrCDkn4nThx0gyIcGRWVynlWF/h+kEwJOCzyMxpt9MmJLAVrDhW6ve/iIZXCcc9pjJFe1jdKUwxBJV2BwDenM4UfSmMPedAof/ls4x1oZ4Zz/i9AwC/lpzelr1+RsACwmu6qehLtvRnTD6A1R7lqTy7+LWZOdN3BV++4jfLWO2Mf6DbiFV35/VTDfEV1s55agVTDGEKtrH6X4QdrzRdhoZjZPjP/imvOnbnAbZe0mLnbot6P0N4uXXySzTkftbusxTP90d3l/c+XPj1RB9HeZU1nBoKPisrKzzcRn2eiIiIjIwMe20pFosHDGimU5MqfnvB3Wqlx93IPG2MQiEV+S2pNUYQ4l928rfqPVsyP3jW++UQcVMTADhx4qQZ+O6OycLyc7u1fHQrgiEB8e2zZyehLrjvmrCGa3cAAAQnAtZQ6W+KZE8AKgAAKnMx7v1/COFwQ0uuOfOm4drc4OXW5it+szZlvFfokesh9CEDVtKZ72Cuv1BZK8mgD+zi13an/BbD0Z9n/u/+t67ozz9IwQMAGbyBp/KQJnhUNAICBZUItTCVGp4HQAuLVZ4KcYA7gvCntAdebjcbgzNUWvbI4OcWJ83spxpcx0doHmr/kWw607UVf9o6EGAIV3GVZ/TC8AIJQxmFES7G2wC3AQBBBai0JwAI2gUL0rKrz+qrHMTyLf044MSJkwbSML9AnuVz30mWdJMpxjlQZSIY4v9pZOPmYrK+qCiCLownvP6PLfudMyUJwr6wr3g2+SRjbZA4vKooS4ysq48wcHPWulXhmzDFMKZgu/nWU6g4EpPZIbEpAMS6dm1c1Q9E4IcI/OwiQ8MhUGRK9D3fyQ8vGkazOWEemKyTy/GibymjWwdpx2L3K0yJThIWOMJj/P7cHW+F1M5n3MzUVvC7d+++P8dFeXl5c8njKOadLV2hnInzRlGIlz+TT1RYmLwSAODpQp7KF3VOAlSASsTy8UOrz+qtbG5LgxMnzcabb75Zx7vr169vNknsSH45+/nV8tc6PfDZjmf5zNduU9lm3XdFPMcrn/FsyLI3iumsMvapEGH9Q+0EGfC++eZAXDWWzlhEBrxnfZR3KOe1vxRb8ru69zxZ+H1VZ6RL9JmSo1f057vIehKB71tuDhKEN8etRusHAZB4u5n/vgRPdTle+N0z3lNu6a6UmG4pvEJwQ4K7wCvblP6P/mJnWVwLCllDwUdFRe3dazsXYFRUlM3+tkKmns0PWuxftoIxbS8/d9Rt8llBuwMAYEkah7tPruPLk2FM8RB4izBnGWYnjxpCYaW60ul08fHx/fr1i46OzszMPH369JIltQ+wWi3lFG9NwYZwFrNFV25ikigkR2cBAJkAdSERAEDwSt8onuUzX79NZZtDD3Wkcs3JI64CC8rn6tfxt4rpGyV0cyp4RBiEuz1vvjUMFQZhiqH1T2gyWZYMMS79TVs7tF2Cu2Sa0rvIeqKidqIuyc1wq9E6oTn++7umqsToPMDPslCczjB8dd7k+9Qf9B+YLg95HCe4X6AYAMBfHKxndC0oMNRS8Ddu3GgpORyNmECKiAEBgi9IjwO6Ak/CkucBwOpO8ZZs3OPlqmFcuZEpKiGD72WyPK/7TYq7DvcY3xJSO3HiQNasWWP9YcyYMZ988smMGTOszV27dh05cqTl5Ho4PrtafjzVBABz3BZPdfnBLBMBAJKIAACDghkHntELO5xkyzsxWrpwQyaVZQ491BGVYMJwSdjRTskjrgI0SMc3P7jvfLb0DBG4pnm2m+D10gSvepL0/We1OwDQHCSWMBRXeQ6EoUi6gRP07ktl5KqvSkaZTQFdnpT261NPfoPm5b+Sr01MIG66tSCQIvwH4rlPiowpVNosRnMEk3anMheTAasBIQGASs8pPXjSc9XsqokD3J4qtOS1nOBOnDics2fPbt68uao5bNiwOXPm1DG+VfFWnPStOCkAcKZ5pdd/XWc4DZjy439DzJnir5mCz5nSmORhV+g8i7CDJPx0V/TfLOvCcEnY4U7Jw//BZLhsmLrFPsMDQDBXYeyFlpbCSSViHFne+17k26lU89LHXOUCFMAaadW3pQSrg/+KglcIUJJO4TEt4tLd9UYmGkDzjA4V+AIq4gwXq3JPkiF+VFoWz3JVd2HupKc72Rrv7p04sRd+fn7Hjh179dVXrc1jx441pMBj6+HXLMuPGWYAr+7Uk535TV8ULV78ux4ACDDPEKx2dduSPOya2xt+xitluoOFZb9o5MPcquYyWppnAVPYuBImFNEXci1VPxdUsJ9frfRGClXiAwKaz1zvxEnj+K8o+HlxLq8fmb83ZKwg8qgga5gRlUoNfwkjj5jvPC8I+awq3Q0qFmFqBZ2TTwb4VM3NMqUb2fJ2LtEtJLsTJ45lzZo1Y8eOPXHiRERExJ07d06cOPHdd9+1tFAPgY8LFq0mAGD79bmf+w392/JMtLojAERaPjZb4opG4m5v+LlP9wMAnuPTn7sRtC9a/pQbAJSfL02beCNoVweXnjaSyuktnJ6qtMeaGJ7ioKqpMdVTotqJk9bAf0XBAyBRgnOIpD2V9ro5I1Iiv4AIO1gylwAm5SoSUGkc/Js6WBAaYEnOqK7gDYz+SOGBRaHNdBLmxEkzM3z48ISEhK+++io3NzcmJmbdunU282G0WsKUeJgSB4BDd5VHDK9NVa2Jbn+Ut+SYE77MWvBxlXYHgKCdUelwM/35G0H7onElUXeRtz5+gj5+lUfOX9823iih345r+Qh7J62EPv4CMd66q8E3RMGzLIuiKIK09k9SNydTzQNcjgJr5s0Z0icGlemlckLFGS6ikk50QTzu+WrVSNdh/aCml0R7aawQc5accfIok5aWduPGDZZllyxZcunSpbal4K2w2qPT3c8zLOeF3rbcncIZb9PaQNnAE4ohV3nLK1Vh00E7o25fu5A59RYiwIJ2R0n7Nqy8m5NHlztaJkL50M+6Dyol0Kqw7e939+7dUaNGmUymn35eAy7OAAAgAElEQVT6yd/f38vL68KFtu3rYWT49fkLeEse7reCSb91sWIoy5biXjN5Vk8GvFs9KxMZ7Ff98R0AEECsyex2ZW++brjc3KI7ceJg9u7dO3Xq1C5duly+fBnH8Tlz5mzcuLGlhWooFpbXWzi9hTOWp7RHvzPwslvMALb0Z44uJtsPwT05Km8LZ7lnUc9bmYoIUfeZ/kF7o37zEWYb2BYU3klD+DbJeDGPctDiLAejvntki4rZvm2ZOnWqu7s7QRCrVq2aN28eRVHz58//888/m1m4JnK7hH7jTGUYIsVBYUXECc0T5pIbvcshFLnNGzMQlRzBVZjSdmnC6pTS2v252//Wn7+sP/dp1D4HC+7ESbOyZcuW/fv39+3bd8uWLf7+/gcPHpw0adLs2bPrn9kKWPir/rcsCwCQyLjdwbuP5oVfrnhppS/9q2FQMAx/o+9b+h+fy/9IF7zfDxWieStT9ac1Ycc64UoCAI6f1pEY4ietP1NnkBx/uHSzTuzH9WLawkKcN+mIxXkAru1URn5YbCv4y5cvJycnsyx75cqVkydPmkym1atXN7NkTSdSTXw1QmWNWkzXM68c18Z1XalI6625OzfQdzEi6kTnbRREHrp/ounqbdxDRXjfS2Ppgksvl13oII1NKU/8Onfnsz4v3z/LiZM2SmJiYvv27auakZGRGo3GLisbDIb9+/ffvHmzsLCQYRgvL6/Y2NgJEya4utqt0FaVpZTO/V9uOr8hYKbAJQIAGQbxwG1gtOnS3lGmu2npkxBhmLjsF12Vdrfyj/5iRyqi3kiZOG/SQQrGiRPHYdtEr1KprAmtoqOjJRJJXl6eRCKxObI1gwD4SDE/V8zPFSuneQ7AV+kh8JmjCD9hvPYW0LmY8mlU3OH+ieak1Irfa5jifyw5jvDIjMC3n/GZ8rPmeB3lip04aXPExcVt2rSJpmlrc8+ePV26dGn6sufOnfPx8dm6dSvHcZGRkdHR0SiKbt++PSAg4NKlS01fvxaoMAhD6Fw2ApP3B4Tg6ULOnA6ogDNdV09a6fbK89JeIwM3v4nL75nrK9jyC6W/7cvdbndhnLRyyiyc/t8XWCMmLJzewhmoR+pp3vYT/IwZM4YMGcJx3EcffZSUlDRq1Kjx49t2KjcjzVsNbLjnq2jqDj2bJ6KLhX4LbQ4WtgvRH/mpqslwzOH8r57yGCNExU+ohh4v+u6zzI9avIqAEyf2YvPmzQMHDty5c6dOp+vdu3dycvKZM2eavuysWbNWr149a9asWv179+6dOXNm3TqeoqhPPvmEYWqXekpMTGQYZt26ddZmUFDQM888AwAmk2nzF2m9O7pfL+vowccXmrsFe7ghuILjifOJyuSiCISHWJ/L7jo6Owfyy400Tf/yyy/X5X4ebLefDclJfx/tFzXgMV+BmqA2b97MsqyN9Z39zdh/lwzVoa5yubxdu3ZXC+l0Hf392fMcxwGAD1v4uC/ZlPVLefEhl2EACIqiJEkyHPT7qshisQAAAtxYw9GoQM9619n/xQoPaZa1X6VSde3aFQBomj579rcMTVC5xaVueaBZsK3gFy5cGBcXZ7FYhgwZkpaWtnjx4ilTpjSPQHbkVgk96rsSrtoNWcjWfACYZJj91uNzOMARrHZZHSuC8CAq5V66m8+zPyZRwWjPF95LXjA98K03At9+L3lBSkVSqKRds3wOJ04cS2hoaFJS0smTJ9PT0728vIYOtU/m89TU1JEjR97fP2rUqHoP+HmeLy8vN5lMtfqtha90ukrfGi8vr6rxBoPh0/Q31kYsTMrzUynLOdNNXP0cZ/jn79Qws9ksEZhC3W/+lT8rS0bnV7AMw2RKBDQrNBiUQktokjulLKF8XTGVkjcYDNZr/f3r19E/te9hpUSPYpjp8kIA4Hl4Oc4EPHCA7D03wmxu6DrOfms/LdZyOEe4CGQkIsBAiIHJVMYxDACYLBqzWtGU9RlLydOarQDQvn375ydO6rA9/9yzruvWrbOOpwDMnvJ611EL7zwW9NfNnDAAcEHLuHICAHjG3C/0xL7ioTqdX0PkcTQI/wD/ghMnTsTHx7Msu3HjxkuXLj377LPNI9DDcvTo0fHjx5vN5rqHXSmgnv1Bm/yaJwCYk40ZWzfKn9vjEX3mQTUH8+a+r541mQz0KaYKFyZOfyPwrS6yngfzd4dJ2nd07bY25R0dXbIuMt7+n8fJfxWxWKzRaESiFgjIDAoKmjlz5rx586p6RCLR/cr1YRk1apRIJNqwYYOHxz13Fo1Gs2jRoqKiosOHDzdizfu/78v+0H91y1jVXOG3jObJZ9RfIzyPIDyCuVhjZHjGCDwHGC7umgGowMKZ30maUZKx4oVIt0FBwvVpq6KknQa6jWj0hzUnjuLKLxI+NYyCnPY4W5Eg7pYOqLNaVeNZ9oc+Qkm80MEhv0OGgw7b8+9Me3iNy1Pm672JgPcx+ZP3VivYyupOCSLr/99unu+77Sf4vXv3LliwYPr06Vu2bLGGzRQWFrYVr1qbyIVolQ+sMEysWz6HTjvrTuU8SMG7zX0Z91ABwGcZHwDC78/bsT9vBwBcLP3jy9zPaY42MPpLpX90lz/eTB/AiROHkZWVtWfPnoSEhPj4+KoSc00nPj7+pZde8vHxCQoKUqlUCIJotdq0tLQBAwbs22e3UJRVj8tWPV5pitt8pbyce2cKOgiTP8+V/cKxRjLoI0w1puzUrwj/sjGpi8DXW9xdAADHCr9tL429jFVWlX3W5+W1Ke/0UPSV4o30/hOG7TJeCQOUJDynV3ZxZlP2+4TvvFai3Q+UVwwVi1zRSr8rhuf3lRtfkErqDyF41MGAZfK38LyNSDwEV+HuL9qehpCE/7t05juYrK812TnP6OjcjwWRPzhU2ofCtpOdNWxm6dKlAGANm9mwYUPzCmZnSBTB0Hu2CvGQ6wWoJ2fJftB4wtcTIQgAGOoxdqBqRFfXXtZXrEvXzq5xPeV9B6mfDhE7TfROHgVQFP3rr78MBkPfvn3z8uxWWsnd3f348eOpqakbN2589dVXX3nllY8++ig1NfXUqVNKpe3kcU3EyICBU+LeszhzMkcVEG4vUpnLys7k0IWLMfUU194J+Z88nb82vZgq/E17ZqznxKqJXgLfHoq+3xc04bYDlxMe05isVcBV6gkq+RXAXAiftxu4wDGjKZu5F5TPA+wvr9BzdsuJm82wLxdryjgOABien6fRnbdYWlHhsxYCQUBIAF24ndP/Doy++ovVHmU1deVsxhSDEYE/U7jD2qSz38NUY1BxZLMI3iBsP8E7LmympaA4nqn+Tcmj8hhP/sEKvopusl7dZL2qmj8UfM0CO8bzBQfI6MRJiyGRSA4dOrR06dLu3bsfOmQjdrTRBAQEBAQE2HHBOvj+jlEmRN7u/hpTuAdXP8NoD7EV0UzBM6J2BCq5gKuXhH77ePJT/6TpU4kXif15O8rxmD8NeamZJQBgZCsSy68PcRvpLmjk+SgR+B5d8hWVPpMMiefM6UzpKTLiy4ZPp3l+clHJLneVP47zAGtL9dcs9CiJ3Z7+58tdP9aXTSnWbHNTrdHpTTz/iVrZgNh+znz9cZ413v8GQqiEUT/d32934rwFPg3IVdA4MAR+fd6brFhFZ68m2n1TlfSMZ/RM8T4ysp6iDETA+5bbT2Hq8TxdwmqPtLbqf7YVvDVsZtmyZdamvcJmWhCZAEURMDO8EEcAQBgkLKE8aUsO8eAp2j3fy4b3x5Q1HPF6Kvtd1V90sLBOnLQACIKsXr06KipqyJAhVd5AbQhWf/YZxV8iAqHzJZhqFIKKuMIrltRsUbtkwusNVv8r7vYCIFjY0U70UPo52WT+daxrFwAIB6jMy/uEaoiCVDVFBjLwYyrlVdxnKXX3eUQcjcsfwl1xtETMAUwp0nzhrtpXXnHZQu10U4nsmiN8rsyVh7Jh+UXtSXyrWiVo0OIo6tIVOBPh9071Xipz6YPON+3OUyEOLNzHA6y7aHivz3Cm8AumcBfuOdXaT+euw5RPoeKouqejonBMNY7Ofp83ZxC+CxDcIaapRmNbwTsobKZl4XmkSsFH/NLty/RMxvxXHePZYq3p5l2XPt2qd7qTnoPdbDgGO3HSdqleO+7ZZ58NDQ3dsWNHC8rTODjDpfGKPVfZscAwCEC2RVeoj44JPQIATMlBPGTbm9qyKJKYIhYKFSI3k8JbHmJ3GXD1GCbnA8utJ3m6RNjx2sNOHysRA8DYgmIvHPvSXS1D7WxBZwHyGVaGIqUcb+L5hil4IPyWmK/3wn3eQkVh1h7OeJMzXBIGf2pf8VoEloNvk4zv9ZERge9bbo/A1GMQXMmZktmSg8KY8w1ZgfBdYE7ohhBuuPtkR0v7sNhW8PeHzchktiPKWjOZeuZ/lwzWg3cLCzjKL/1Db30rbn/e62N9SN+6TPSCiGBLUmotBQ8AZs6UXJEYLe3sEKGdOGl2nn766erNrl27WoN62xaE92w2c+8dtv9w/4EAEAzwg6rM68pNNZeBCrstYtobOe45TpAy+pooysV7hf21uxUyfJf5ei9cNQ4VNuYBN4NhXDHUwPF6jrOvgmcBFmh0ZRx31NN9c5nh5WLNTjeVvAFbIIQb7j2bzloqiPja2kNnLCL8FiF421MKdYCKIjDl03TOOjJwHZ35DuE9FyHUDZmI4HIyZCtCKKvXNGkl2Bao6v7d1dW1oqLi22+/BYBXXnml+eSyByoRNiS4MgjBQPGX8ixD/23KXDAsWcG55dQxXdg+pPysjTs4C2eOz/z44w47CcSZutJJG4YgiM2bN0+bNo0gbBxVVSW2a+X0/aoot7zSN224YtZE9crw+PYcjwLAGL9UV/fiAn2Y5o2n0U3m9WHq7NEJomgX/w3tHJdYHhW3E3Y4jUo7NWLu//Rlf5kt33u4/WQyTynSWM/j7SXYOxpdOc9/qlaSCDJX5krxZa8Wa772cGvI4TbhOc1ctIct/QmTP8lqDvNMKe72AN/ytgzh9445oQct8OUtmVW2+oaAyQc4TqqmYPu/x6rRAYDn+YKCgoSEhKeffrrNKXgXEhn27+FNiYlbdY4PkGEd1MR8jW5YEJGUZen0mJBnNPkgn1Gi3emuUtS8mSUDfdnSMp5mEKLGb0mGK6b4zUDBGV3ipG2TkpKiUqmsP7S0LI3n6Hi13lzpQDv1xGgB+e2fQ36xyJ+9xFAhWatJz8Vv+g4PHV8yZWLaeVUm00U62JHa3QoqrW32awif6A2XzNQuN5UURcdKxCwP8/7J36xSuofYJ034UxJxDwFJ/muWXyh3PWsyN9RE8G9IGCqNo7JWkCGfAtKGL4ClFm5nQgXDAwBwPHA8fHDRAAAARGdkZq/MZYJ231gj39o6thX8yZMnqze3bdsWH9/mk7pwPJJjYDuoiZky6UZpcd98s5bwtRjTX6rwn+giUdxvqkJR38/fraXdrXSR9WwOiZ04cSR1x76uX7++2SRpCq4k6kpWfnlxFD1OLZ1dPM3NZ6SX/mwepx3KDIoQYN4v++7C0ZBc5uX321fX7pak8Vy5zQLQmDD2L4Rwb44P8C+9hIIpUon03wvR0zl8yPMZBZApPdJR1N6l6ev3EQpq9fQXPYTzGqYYzBRus9x+CpV0xFx7N12eFgQBcCER/l8FDwBqsgLhWQDIQ0aBhxR16cYzpQCAoGQrSWPQOBpk/5k4ceL06dPrH9e6QYAvp3gACMTxV+/yxuOl+TM8fy6+OdE98kWp7XtkxJbp0srRwm/6KAfKCIWjxHXixMFU5bTR6XTx8fH9+vWLjo62VplasmRJy8rWaHLYaEzen85Zw2pPHHV/n0QJI88Xsdy1cYp0FJ0IvLiahseUw3lGK2hXIxSKKdzGGS42s3YHgC6Ce0d+5mRj8sirQe+GIUI05elroT90FHWwg45vIkTA+5ZbgwXhDxH7ZxeuFFBKIRokt9tphUyATutY+ftkONhyRfMs3QMQDLE+teeBOW8VAPCcGSF9RB1t3gLWA51nMV43yIY06BTfcdT/K+N5fv/+/VZTXttFLkA7eggMFAcAxdtyLNvyUOB9Drn0HJ3+ZKMsYHqm9ELp706PeidtlzVr1lh/GDNmzCeffDJjxgxrc9euXUeOHGk5uRoPggLCI4T/cvO1rjqXfmminseV8jGFxckm00kvjz2G8hNG0/Mu977vuPuLTOFuTv8Lphpt7eEZHVOwrTmTkZXSWjlRI7bKnGxMHv6Pz4pQ5XOVRWxTRrYKHY+KwkVd7kCz+x79kGyKUBJ2VPC1oHiS8HyNZzRk8CfV+823h+PqxlRZo3Mtd4deYXWM97uh6inedhKzMdg+gpFWQyKRTJ06ddGiRc0smX3BUejtS7I8aPbkZ76dbB6lLvEjC3m/yF9vHvm1YKG2tPQBGaPy5q9hy8rv7x/vNelx5ZP39ztx0uY4e/bsmDFjqprDhg07e/ZsC8rTaNb0dZ3fwwUhPMiwXV4hH36oUqzU6ZUoOlQsnl2ifc1VWl27AwAAqvOaRWUuAa4y8T6d/S6mHtdsycjSjcnzE1/NNKVW9VRq95X3tLtitLvvh+EpI6+Zbtm4EDU3j6hnMe4zny39mSu/UtXDag4BW4a7TbQ5nqcemGHQqt1Vk73b/dm94KOM4m11uXI7GtsK/lo1bty4UVJSMmfOnGaWzO681snl6atlWbOTzOPUr65SX/4zNv71ToLQYo8xifzf+gdFpOBqpfnG3fv7BahQjNnH+cWJk5bFz8/v2LFjVc1jx475+/u3oDyNJtqNDHDFAQAhlKwpaXvOMRfDb/HkjWXo1UH0xfVZR4z6c9XHlzH69woO5hNedN4nAMCZkljtMcJnfvNIywO/P29HB5fY/bk7eKhMpF28JVsQJFY+41F9pGKkmzBC3LKq4lEFQ+H59hIEcyF836EyF4H1D8GZqayVZMAam76EJbvyErx/M/ypu/+tKu3uOS+QDBCGH+9cuDGrBf9wNYweb775Zh1D24rTzYMw/aHLnpmonuS1c6XHBAwLnJCYutKbCi0yBpDjnk3V5fooxTZMQMKYCPP1JMljNqLebxquXtGfn+z7huNld+LEgaxZs2bs2LEnTpyIiIi4c+fOiRMnqqe+aYtQqf9nYsq7Y74RJIEagQWYAFBkzqOLCqHLHUAr3c2+L/gqxrXLrrLzCwq24m7P0RnvEL6Lmi0Z2QXdbzRHLwxZvDplwd+l57rLewOAz3uhqc9cT3/5VuD2DgiOAADwkDX3Dk/zvu+FNY9grYG7WqbYWBn9WFDO4ihyLqcyu2KADPe1X+ZaBGB5b1cAwN2fZ4p2sSWHMPVYOm8T6tIVdX3s/vElu/Ky595BRaj13ETa+54bFp1nuTvsH/VUX49ZlffHZIAw7Ein5GH/IATaIrb6Go+twn8xmUwbN25MSEjgOC49PX3r1q1qdQs7CzSdRIrnUbBkmDbJFUsVslAhYUqXSbhSd4tJIMEUmO0neFFMO9P1JJtvBYvDs00ZDpTYiRPHY7FYpFJpQkJCVFRUUVFRTEzMjRs3hg8f3tJyNQnCf5kQxTrGHCI8pnLmZM6czJuT1XQmiYApoYvpapTpapTu7sRr+r8n+rzWzW38dWEHS9I4ni7E3Sc1j4QUZzlU8NXzPlNRBHvee+o3ebssnBkAUDEW8k0MU0JnTL3FMzzwkPVmkvlWRej3HdH/UuG3Y6mmz69VWF+3NMyf2Zaq5sU8G2Xf7AFKBqyhspbz5hSmIJ70X37/CO2+/Oy5d2TDVNGpj4s7S1NGXiv7RVv1bvlfpVw5o5zgWX2KIFgkG6rWfpXvGJnrocYz66PndFMdrr3LmeWhw1anJj911fdYx4RQorMBZXPceNf8difGIQLbCp7w9SR8PHiGRfDa3y4xJlkS9oHjBXfixIGQJDl58uRLly69++67LS2L3cDkg1DhNkvyVu3+UPngXET+JIqQrOE8rp4AgAJwdEF8GkhH+a8UY5Ih7qNWaM90oNIloZ81W3j38aLvwiXtwySRABAqaRcqaXeq+PBIj2fhXx2fOj4h841EBEeodFPo4f+WdgeAud2kVT87tB58dVBpN9S1h/nmENzjZURQ+5RKuy8/c2aSbJgqeE80oEj48c53h/2TOi4h5NtY1yeUAKAY50HlmJOHXAk70ZnwqrQS5a9OM/yuCzvRMplPbWu1R8bppjpx3uTbc/xCv4+t+Fv/8+C/C6d5Dv6wkMn3PLSBu+ZVV74HjyUz7tfuVRRYch0grBMnzQSCIPHx8YsWLUpJSaEoiv2XlparqSCSFXT2/yquEsa7XbnyG6zpLhm6jQhcQwS+x/EsjxLHJd17lB4233iCvTl4riWhhDXTmUvNN54w33jCfHMATxc5TjYtXfJLyckx1erVTvB+6efi4xqq2NpExVjIwVg610ylm0K+jf2vafcWhPRfiYojCZ/aPmcVV8oyXrstG6YO3hsDKAIACImGHessjBCnjL7GljHWYR5zAlSTvZOH/UPnWwAgf3Wa7vuisBOdCY+WcU60rdgeGaebWhAoIu2r/Gl7iNdV41NxN2gdndWrw3M+pW+W6HSNrbscn7X+Tvkt+8rpxElzMmHChC+//DIsLEwgEOD/0tJCNQm6iEoZbaB1I/zXn6QzP+apYuCQynyiTClbtPOUsMME72moKAKAJ4PXK8K2/e7a/4ZqAhm8HpP1AUARws1x4h3I+yLatbOBLcswpVpfeqa0g7Tjt/l7qsagYiz0SKfQY50cp90t6SYHrdx2QUgfQfuj9ye3waQYgiFUlpmz3NMUXDnDlNCoEEWwe8kVPOYEKJ/zSh5+NWfB3dKjxeGnW0y7w4Pi4B89pxsAKLNwr5/W7XtaNXmkj0DqkjUz0RwkSHINmMBkH/J3s5HJrhp0TgHh4wG2ii+N8ni2ygPWiZO2SFJSbS+TkpKSFpHELtBFVOnXz/u+W0764UzB3/KRVzgj8JYU8/WnEVIAbIUJl/1DRvozpQmi2BB6W3rpH+WSzjJZvy81p6LU46DkoCB8Lzgsny0PvJGtqGAMe7K3VO8vQv180Roa9y+a6iIgHVQqNXdpStGmrICt7avi8Vot7mJMLbZzYb2HRRguCT3eKWXktbuDroSf6YIKUaaESnrsEqtn2v3WrdZNmOfbgQCgP1EcdrwTrm7JwELbCn748OEJCQlfffVVbm5uTEzMunXrwsPDm1kyu4MiyM1iGgDcMQwGKkO/jb079eZdzJe3nPPA6rlHLt60W/Xac4IQG2aMGNe2V3fLiZPqeHh4XLt2TaPRWJsVFRVTpkzRarV1z2qdsKVM8pB/vJcqSH+66PMRuFIpG3QCV41gin6ic+4QPr6c6VqKz2Jfjr2s/wsACiS9uuRv/N1tKgdYe5dYJv9jgawf6tLFcRIigMwLXnF//2dlhp9M5qoicvvKK74wlH/r4Sa0a0l4K3krU8t+1oaf6ZI26QbP8aoXvOy+hR2Z2aXls/gBgPQxRegPHVNGXrs78HLw/pi7Ay4zpUy737oJ29mIl/Z8O9Cq5luWBxri2rVr9yg53QCAhEQsLM9wgKMAAIS3APKp64gvZ06vd64wMsR8PcmmggeAAktekSU/xtWBFwUnThzH8uXL165dKxKJcBxXKBSpqaltN7EVT3OcmaWLZuOypxFsqudb20t2Tdd+PUgcLVRP+obRlSFEVJzf3LhqUyxJ414RKHDP13hLhvnGk0TMny0i+XRXKcXDlCLNF+6qM0bzTkP5Ljf7l4QHgLyVqfrTmrBjnXAlEXasc/LwfwCglev4VoL0MUXod7EpYxJuRf2FiNB2f3YXholZnd6SkomQhCA8CH2Y9P7NQA0F3wzlIw0Gw/79+2/evFlYWMgwjJeXV2xs7IQJE1xdXZu+eN0gAC4kaqA4hRAFAFxJ8Ca2gg3kqFzg6bprBwmjI8qO/yobPcjmuyzP7M7Z8r/2OxwitxMnDmbbtm3Hjx93d3dftWrVt99+u2nTJopyUCSSw8HdyNAfOiX1uug+dYL7qztRyQgo7U4XKPB+ZYDKUVFZ6Tc9Mdc7wuiIqilEwPuW209h6nFU5lLcawZCtpjJerZMCgAT8nMEYNmtUnqxZRwLVee7CCoAVNTELfJW/avd5Xj5hVKXHnJH6Hg6p0C7+5D5dgpC4OK4WMXEkZi0VTyFNx1pX2Xo97HZs5KCD3YUhokNp34vPXhSEB7EWyxUToHb/00WRrcia3cNBe/o8pHnzp0bOnRoaGhor169IiMjAUCr1W7fvv3tt98+ffp09+7dHbFpdbxdMIr997wcAcJTEKTFGNydp3IRQWAdE4Udwko27uYtFCKwcaDiI/R/I+BtB8jrxElzUFpaGhMTo1ark5OTAWDq1KkxMTHz5zdTQjf7wpm57Ll3XJ9UlZ4Y5jr4J0x1VHeUE4bqXJ+4oNn7ifcqqeswovyPy9UVPCoKx1RjLHcm8lSBIHRbo7eeWaLtIRRM/DcbbirNTCvRfOtRj39PLbxx7OOCcd5MtiBPaKpgAQCVYIAA8AzP0+KuaU0pbma8aij8X2bk5R64HM+cnqg9UOC9MtRjtr/fB+HpL91SjHK36dDHlp4B1mhjOUxqsw46aygvXL1ZNmaw+/ypHEXpD54s/miH54pZNn2Y2iLSPsr213oBAJWWrT/8o9cHC3CVHADMt1OKN+zy2bik9TzH11DwAQEBtX4AAJZlURRF7PG3mTVr1urVq2fNmlWrf+/evTNnzrx06VLTt6ibo+NqpOsRd3H1YpEKUaCLOQ2rU8GjIqHixVF1/IOGSCIe9JYTJ62cyMjIr7/+etasWTRNZ2VlcRxXXFzc0kI1EqaIMv6tD9obLQwLy54/xW3qQip/ddjne6jcmbojUq9VPTFlMqvT15pF+C40X+tGBq8HtJGXZirl9bWGi0UsW4ygLijC8DzCcQdQVFiI8+2PIGSDspgdrDBuLTN87T1NW3jo2kt1Q8YAACAASURBVMLl0d4SAYFSuZaQb2KY4qXAmZtYulTcSer5dmDaswmiDlK60BJ5KS51XAKdZ9Z9XxSwJfIB7voclTYXIVSIMLh6L2+6CzyNyW1ctI0XrgmjI6SDegMAJiCVL43Lm7eGSs8hg/2aInwrxJSQKOnTzardAUDYPpT09bAkZ4piWos6sH1reffu3VGjRplMpp9++snf39/Ly+vChQtN3yw1NXXkSBvl10aNGuUgm0HdBO7o4B7uUi4I5M0Z9Q6WDuqNkHWZ8b/I/jTHnGk34Zw4aS7ef//9hQsX3rlz5+WXX+7atWvPnj1tfk/bBKS/MPRwp4ypt0xJFX4fvczqA/zWrWBLszL/r3Po4U64gjAlJN6vaRBcIez0D6Yc0eh9UWk3HHeVtP9+vvuO/X5fTlNvTw05IPOYhOCyBtr8D1YY48sMe9zUKtlkdWZRQLcLi991d9vSjvQRZEw9yhYfJHzfabR4VXgtCkJJVH+yJGBre2G4JGBzZPHWHJc4mfLZBwmJEv4rAEAQtkMQ9kXlK3QbAE/4r7I5gSkoIXyrrYYghK8nXdBWbxmrY+H5+RpdVWUy3kKlyF3fL713v4gIBLylFR1v2VbwU6dOxXGcIIhVq1bNmzdvzpw5drHX9evXb+HChYWFhdU7NRrNvHnz+vTp0/T162XN+bI/si1VzeIt2eN2aINcwjlzah2zGoivKOCK/nzT13HipJkZPHhwSUlJcHDw3Llzd+3a9dFHH+3Y0YYdSiQ9ZCEHYjNfu21KqpCP3ET45+e++6zXQhJBcrS7vjNeui4bYcOwjGBNcgPC3acAz6rNt2e4R603Kf0kIQNlgUzRF2TAew+6zNYWG0F2u6m9aCR1wk3DhVkdRn/xrBRnEAj4LFI55vPSo+N5SlH/KnXD8ZnTE1Ep7jbdL+2564Y/dOkv3fL7MMKYYCj4MOOBH009FlAJU7Svqocp3I4Q7phiiM3xhLcHlZZV1eRZjsrIqaHy2ywCBPHCsZeKNFYdn9khbE6IX2es0hDOaEotd9MFoa0oZ4xtL/rLly8nJyezLHvlypWTJ0+aTKbVq1c3fbP4+PiXXnrJx8cnKChIpVIhCKLVatPS0gYMGLBv37765zcZjged+V6aAkxOmP7UocIwRv97Q6ZXnL8qbBeMKWQ23x2obvztvxMnzU8dxaUuX77cpotLSXrIgg/EpD17Qz3ZW3883uudMDr7ctnRcjLYz3vd26iLA5KeIhgRuMaYMnOpR+x8uXJfufGf9A1R0sdQaY8GLjBMLAKA7AV3eIrzWf0ilfrT04ZdhOtctvSMMExTsmdU7uIUvw1Nsv0ab5RrDxREXogTtpMgBJIy/Grgjg6KcR6YGs987bb7G34PsNIjZOBaS9IzmOppBHPlGR2du14QefhBu4h7ddIfPlN68KRLvzjeQpV+e4rw8SD96vHg43iW5mlBY49Imo15MlccDC8VaRYpXOeqZP/3982Y+G/0j3flzRbD2Qvy8UMfpCBaBNsKXqVSZWZmXrlyJTo6WiKRpKamSiR2KI3q7u5+/PjxzMzMW7du5efn8zzv4eERGxvbwDR5WVlZDMPU6iwsLOT5huaZWdyrxk26KNql5B/93Ar1WlNyQ6abb9yh07LZCiOdU4CpFK5P9ROEBlQfwAOvpUpUpANzYDlxYi+EwsqLqU6ni4+P79evX3R0dGZm5unTp5csWdKysjUdlx7y4K+js2YmBX3xpCjaBaCDo3fMEvdMxSM+oL6Olb49BNfy2XuPhp4a+5CLuL3mmzz8qu5goWL0u+YbA3D1ODpzCZX/tvGGxXtlU8uRiWOlfusjUsZcCzve2XtpsHqKN+knLL9Qmj3vbsg3dSXERSUxmPxJJnc94b+czn4PU41GxZEPHCwSeqyYXbr/aMGyDQhBiHvEysYMrtfD7pv83enG5IWh7yMOSzFkL2bLpBqOe6VIu1Qpe2bkk6ZAH/PNZIQkPN55nQz0bWnpamBbwc+YMWPIkCEcx3300UdJSUmjRo0aP368vbYMCAiocuIzGo0RERHZ2dn1zjIYDMOGDTOZaudWrKioaHTebHFHqXdE2PmSfJ4uBM5cr38NGeSr3f6tfMIwSe+udG5h0Qfx6tefF3W+d+GoYAwr785dF/m5CHN4XQQnTprIo11cCgBcesjb/QKWO9Gmyzbexd2nELYqhjWazXrDYL9V4anDeZ9J8uxlFV6vf0kp+nPcQ3nRC8MlYcc6JQ+/ChAqfWyy+dYQ1hCeMd0j9FBHcSdp/fPrQ/2SDwAkP/VP2PHOgiBR+YXStOduBO3sIH2iniK5hP8y8/XHUJeurPaIMLYelyxcJVfPfLHhUuVbcs7rfpXhykulf8bJH2/4xBYhiabPmkz9RML9hoqBIqEiNlIUW+N2J41mvHGseoaiFJrxxzGy2eMIbCv4hQsXxsXFWSyWIUOGpKWlLV68eMqUKU3fLD09fc+ePdV7KIrKyclZuXIlACxfXteXTSqV3rx58/7+o0ePNvzm46cMc0IRPa/7ve9JesQ59I8wXhDAmVNRcT33+ObrdwBFpAMfQ6USYftQwsdDs+2ATzUF74K7PuUxzsyZnAreSRvi7NmzmzdvrmoOGzZszpzaxTbaCp+VlQdg2DBJZbw4KulYikj1visjVE/cG8SZzTf6YfKB9t36I5UCQEF7TLLcncjTxcrQrd83yuBcpeN9332B9DmVvWiCvbS7FfVLPjzNJ4+46rM8JPutu4EN0O4AgBDuuNdMy91JZNAHCF7/+Ifi69ydw93HB4vDP8v8sKNrt9ZsqE+i6anFmkVy2VNi0Ua94eUizU53Va17uEMVxhsUvdVNKUIQAPjZZF6uK/3Ow63elKl254GZ7EwmU3x8/ObNmzdu3CiRSDB7SIZh2JdffpmdnT106FBrNQuryd2m5nYEFAvppTWM/KgYa1fCG13ChaY7UJ+Cp7LzBRFB5tsp4rhYABBGhjBFGp6mkWp5gQa7tVX3Yyf/WazFpV599VVrs60Xl3pLq7MAjJaIAGC2zgSuc1flr0PcRwBSebmjc9ahro+hrr3suCmr/4W3ZAIAEB68ORlTjWFKvgYAABRXP/Ow0XfCcEno9x1TRl4Dfm3o4U6iaDtniXGb5gsAmTMSg7+Oce3fUG1NeL4ObAXuPtm+wlwvu1xCFfVXD8MQLEwSearo8EjPZ+27hb2w8PyrxZqlctlgsQgAZsukDPDzNLqdbqrqw+bJXVfp9K8Wa+LdVH+bLct1pZ+pVc2v3eFBCn7v3r0LFiyYPn36li1bcByfM2dOYWHh7Nmzm7iZv7//tWvXZs+enZiYuGfPnpCQkIqKChcXl4MHDzZx5QYiEyB6S40De9JfGFZAazuEyY1JmOpB8yrBFDLpgF7Cf2McWX05KhQg92X9u1txCwDCJQ4/83PixC48SsWlpru68ACLdToeuPNmy88m8y7/54VpR5iiPbjHywDAU3lMwTZhdI3611ctVIyArH4BvmKhutjKavUgmKIvuYprmGsfAMAUTwMPXHkCT+WxZX9iimHIwz+Sijq4hJ/qzPMgDHOIOdBtmq/qRW9U9DCpcFEB4WeHUL3qsDz7dd4Xz/m8giEYADzjPWX53Td7Kwc0rycTz+pOAWex8Q6uwGR9q1oCBPnOw829mqqeJ3Mtuu+MGAFYppCt0uknFBZrOW6rWhVdZ4i147D9192yZcv+/fuXLl0KAP7+/gcPHtywYYNd9pNIJNu3b587d27//v23bdvWcP84uyAToHpLjbKwZIDQN4/NIcI40+16p7v07a4//CNXYQIAzmjSfL7fpa+N7HsWznIgb5edRHbixOFYi0tFRUUVFRXFxMTcuHFj+PDhLS1U43nD1WWmq+tirf6EybzLTd1VQBKB79M5a3lGCwBU1jLccyoiqGGi2GEoX6TRVV2nP9UbVuhKH8q1h/RfBoye8F1ABq8nQz4hg9db0+YQPnMRQl3/fFsIQsUO0u5WHk67O4afSo65CzyjpZ2tTQWhGqAaVr1sbnPA01TabDr/E0b7Q/UXnbOGzlpZa6z7fQ/i9/cAAALQVyjIYVh3DAshWqz4su2NExMT27dvX9WMjIysqjRlF8aOHdujR4/Jkyd/8803dly2XtRiFKv5Ly0IEHnk0HfwsF7GBij4fnFsqSFv3vuoVMLpyyV9u8ufsxEaFyXtpCDqswY4cdJqMJvNFy9elMvlcrkcAI4ePQoA8+bNa2m5Gk86TSEI8ABZDN1VQKKidphyOJ3zIa4aw5VdEARvsg774KJhUJCwozvxsUoxS6N7S6P7UKX4TG84aTLtclM9lEUVEQTgHpPo7JVkyGfWHs5wgav4RxC61d4f7tHBwJQdLfxmos+0DNO9TCSR0pgzJUeSKxLDJA901LczCEn4LWaK9wnCdt4rE8xZTNd7kf5LG7fkn9e0x38r3D099PsK47RiTbybStwSmXptK/i4uLhNmzYtW7bM2tyzZ0+XLnYulebj4/Pjjz9+8sknnp7NlwDBU4J9P6bG3TTpJ3Q/Z7xJBvFUIbAVgNUTDSgb9SQR6Gu88I/qlfH3G+etIID4CgNsvuXESStk0qRJP/3008CBA0nSnrWr9+3b98cffzzo3c8++8yOe1XnLY32pMm8x019yUIt0ekBYIxEQvgtNif04PQ/E/4rqhK+ppcyRRUsAEEiyCaVYpZGN7qgmAV+l5vK7eFPTAnveaaEblz5ZdSlK/Aslf4WGbC66eVhHmGyTenupOeZ4tohGx6kV4YppfkUPGehs9fwtMb0ty8glV8BnrMAz9BZ72LRT9Q9+35+vaqBMbeeF2NuFdlL3wleqi2dXqz93E3piMq/dWNbwW/evHngwIE7d+7U6XS9e/dOTk4+c+aM3fdGEOT+vPTNjLiT1DPdNF+hMIvCOVMi6lJ/cXfS201z+QYyrR43kK2ZH/VXDwuXtK97mBMnLc6JEyf++uuvmJgY+y7bqVOnI0eOHDhwYOLEiWp1I83UD8sybekpk3mvWt1JQHYVkMz/s3ff0VFUXQDA75u2vadX0hMICSC9FxGpgoDw2UCaBQS72OiIYkNQUURAURAQUAELTSwgIr1DCKT3bLaXae/7IzGEEEISQgJkfmePJzs78+buGnJ33rx3HxbfMFt9KaqbzEQHv8gXf0/5VD01nUEogaL+9bIdZYyxbuOhSBUd+gab9oo8cTtf8BWQatI45IbezJ2uuSZ5hua9xo4CgJCR+j5YsIuOg/LE3UDIMVfkPdWPUMYTut61bcxz3ql84LRyblT8vb4pA4+IHnHunOhlNrtdFOW3yCj66Ojos2fP/vzzz5cuXQoMDOzfv79OdwtV57kRQzcWrbnPpKTKvkkpkjRMmMKLMaFsLrpO1yTBU34m0mjwnEmVt4ipZre7dJ3OO05JCV5y61OpVMHBwfXebEJCwtdff71nz57p06e3aFG7Maccx3388cdeb+VxT2fOnBFFscpDSrWXyUaqlS3/64qYqtMGUmQQSQIAFTCJCph0rQM/stq3ezxb/H3mWGylffWlf4yx4ABcub4WXGPxVsp3NJ+/gs/7nMt+Txa/Dm75mi2SUnTYTM/xLoSqNV/wFR3yEpc2ndTfLdj/kQVOrlU7nvPOlEFHoufGlNb2j9nWOmXgEQB4Yk70TYn7eq5I8BjjtWvX7t27NygoaPjw4UOHDt23b9+pU6c+++yzNWvWHD16tFFCrF+8CF4elyd4wHAy/q83DiWsksera3AbvpSqUyvnviPVJ/h2+i43GKpE0jBmz549ceLEhQsXhoSElK8bKZPJbrxliqKefvrpOlweCIJQXFzMcVyl7Q6Ho/qRuYNUlZPuyCurcC78x55pK0vYxwq4Yrew5YL7og+XrxVmM4YgilpkMkwuMr9SXLLQZADR7T4UDYhCiAYAzPOYFwADUDwpj5C3/ueq8yOm2QLPyX6U36OEqlVt37WksSDajwqcLNr+4vOWEYoYwboHaBMTPqd8fuNR278aShulrK5UsOecM2XgkZC3YthhPgIACUD5MDFbW6cMPOKSo8hXoxp+ntwVCX7GjBnz5s1LTEwMDAz85JNPJk6cOHv27ObNm/v7+3fuXJ9zRhvR1pFXdhUiILRUeJGYHRQXa91Rw0ZUXduWrNlSkz1P2o8kalrXNkiJpCG98sorNptt8+bNFTfW1wyX1157rQ5HyeXyuXPnXr19y5YtW7duvZF4eoTKit1lo2ey7EIbf1mSH/0bgcaIsmQ9DQByhD72MX5ucwgAJKGg/MYAFpmId8wrv/NeyNDdfw+hkHG5D7kPRchacFevMEmo2zKR75PG23gaQtNEBz7lLvia1PXypkwgfUYBm1V+h8UpOFZmLlGT2jlxH5ZO56uSN80DIlYkaz6yOfIFYZHJQCFEmWhXc+Wpv4tVfLMgqlG76FeuXLl69eqHH34YANatWzd69OjNmzcPHTq0gWNqYPrBviEUeZ6OjXadquEhlK/Rd9r1qz1gwKuzPp0Y9my0Kv7GYpRIbqKrF2suKiq6GSfq06fPrl27bkbLNdch6PJAwi0X3G0C6Hsi5APginnqcoSe1pVVjqNDXvEc68DLhzv3HgpeMpNQyIXiTSAoPEf7OH4/oOlbRUddvZeCkTQExDBhs7nMOVTgFKHoW1nC5VIQP+StbafvWujN2130U1/fay4qputnClkYmzLg8HM/tnrZV5hcVLLEqD/5+OmLaY64jckNn92hUoLPycnp2bNn6c+l67cOHDiw4WO6qWb/ZesdLusWern7MWhmVJDbdQ5kAxCJuXxE+9fXuRCgSeHPqcl6qzEpkdwMJpPp6NGj5VNhnU7n2LFjzWZzvZ9o37599d7mjWhN/xjiLORyrh6KRNABE0oH2yNKTwe/yKW/zkTcTyjkIHrYjNmyqKXyRIFNy274mCU3D2kcyOd/Llp+Jo0DCWVi6cZcT9YBy1/z4pbYBduClFc6GLppKf21WjCM8AeAi0OOvv1jq+km77ePHVNkeMI2JncwNU7x8itmhWOM6f+mfpVOmKGvMRPs9sWLON12RQWLgk8yY5bnX+J4QpEg1vgiHgC8qRlwvW7MKGWcv+xG14CSSG6qmTNndujQYfjw4aNGjXriiSeGDh365JNPNnZQNyrLLjz443WqdzSn9vh71gNvrfgQ7f/yuUswvjyUj/J/DCg7Yg4AAJfzIaFuS2g784VmynTNP/SS2xQdvgALDjrkcsG+tTlfDPJ/QE1pA2UhnYw9v8/7tvoWDCP8g2ZFXRx6dMzULDLH88OqqNbGRpsq2fiVjBqYUUGY3VeMwlUmLUvq0/7D1GjB9of3zHDXfkP5g0uvbtFM84rv3MfPXveMJVzx5xm38dLakjve559/vm3btj/++KNnz54pKSmLFi0yGAw340Q3b+771VwcLvFUN94eAITgGXJspvwfo8Nmlj+wYKVDX0dkhfLviGSiFyrb/G79fhWf9zkTNstz8rxjzz/KztLwmjsNoUxQtDldXnzwiPWfYq6wl+ne0qf3+Y8+bN1fsSxPlUwPBXKvhZ/yeputS9KrqaeLStiGrdlarnLf1JIlSzQaDQA4nU4AePvtt8tfevnllxsyspvEpCAuXrneDGkgAFAx6WtCAmA3QfkAAGBeZHMxsNU0perSxvnnwUoLBV5NTxtzPFk5nswgeegNhy+R1D+LxZKUlOTj45OSkgIAEyZMSEpKeuGFF+r9RPWyKGU96hEVwckmshmzZTFflG4RijeDYKN8H660J6nrQfm3R/Qbzr9jij79hJDLfaaOoQMasl66pKHxmN+Q++WDwRPLB9YpSdXQgNFrs5dPj35TNP/IF39f1XHEBdP4p/tEvj8quYNM1h4rnykuea64pHTMXUPGD5USfNeuXX///feKTysOWL0zEnyohrpQckWCZ4KH89mfyBHwoW/Q2QvpyA8BgEudCgCUqbqFaFVd21rWbRM9XkJe3YQiBGhm7C1QzEEiuYaEhIRvv/126tSpHMdlZGSIolhYWNjYQTUQOmia+1gH0baP0HYG0cNmzJJFfQJVjZSWRb/lTX3KOG4N5khSr234UBvMtJ2WJ1qrEkx32v3Z2tpT/KuZK/rTvPNP887yjSIWLzjPHrMdbEkoBMsOJmIRQpfTKOZL2LTpJ/VPf+BjaC+TAQCN0CKTYYHFViSKAY1b6KaaupJ3jF7hsl7hV+RjJqxNyfrOmq5/MwTDClZC3Uaw/SVyWaS+D6FuV01TpFYti49yHzyh6nr98jhe0WPlSvxkgTf6BiSS+vbmm28OGzasX79+48aNa9u2LUmS9913W656fCiPnbvXVtoZ6uVxhl24b2PZdAAtQ3w5yEhcfQVFyJmwmWz6K/LE3VzOYkJ9F6GtuoIFkkfIW/x802K/hRS6BJu3cbqUq3TOzOtlyF/V0NkxUdNad+V4Og6jlV6fR3XdwxWRJG0iNZ2KPPlfqh95+b8vfOyl5yn/caN8ryiBQCM0w9A4leIabZWbW0fJxvz8jyaoO/7rTXmB1CVgx1Hu4mQAmolcct1jjWOGAVGjcQyFbP4HF+csiF/KEPVZ7lsiuXH9+vUrKiqiafq5555r3rx5SUnJAw880NhB1UVzH3pe97K/pBk24Z1/bPP/eyojURXZHQAASNMwLm85l/3u1cvISm4F35xyxpvoB5s39ED0AFlQwFVDpH+3WD/xGpeTBgDIDZ4rOzuwdfxQAC0AiO5zgnmLPHl/A8dZjSY3yM7J4X7rLnc/lmwuyHoxBQifwq0PIHDzRQ4uZxHmbFTgFMRcfxUcOsi/hvfhQuTh9wWMFqFWS1BKJA1h48aNSqWSpmmCIAYMGPDQQw99/32VNxdvdQoKJfrSpY9oAyWv8DTGWM3FDGKaLeCyFlL+j1VaRlZyi2ikMWpVeEWv6yBnxhYWH2fZx+wGq+H+nkXvl77Epb1Kh0xHlLFxI6yoyV3By8nLX+NLNhdkPX9e0+OQYehPPBIxJgnmvGA9B4jCjn+9Z4ZR/mNJY731VXY39q2vpiSSenH48GGO4yZNmhQSElK+0Wq1jhs3buTI6gag3GEIVbIsdiWpb7r/Qp/ZaTH/N+ngbDH/5t82nazs8m9Csqp7aD3ULb5jPKfTWkTLwwVFrxn0cX6veY51EP0fw55LmM2m/B5t7Oiu0OQSPEnAr6N8AcDyY0HWC+cpX4qLiqb8jjp3vHY4JKlLwkoAsFjG0ZGdVZeeoMNmXLdB0eUu/nx9TQrbAUCK88xpx7H7/K+zEp1E0jCeeOKJwsJCq9U6evQVv5OTJ9dujY07QBNf+W1CssrqLUvwb/5tGxKtiDdRAEAglOjbCKPt8pxCkassHrNbzHEIJwvLFiYI11EapjFX8bnE83+6vV3k8vUO570KkyrkZTZtOvZmM5GLAN1aKfXWiqYhuc+4eBunG2i68Hyrc4eGtmYOL+z/9vrDh2SGE+v/GDA8dofOZyShuv48V0KpYC9meFPSZDHNrrtzkDz00/R3+/sOYwjpG7Gk8R04cAAAevbsuWfPnsaOpZ6F68hXOtVxrLv995KsF89HfNNSHtM4BcgaXsUsrpMRib50xZq+De/TI44j+WUZPcchHCvg/soqW1pwWKxibEvVtQ+9uS7x/LiC4md12iEqxXtW27jC4hU+j8jyVxCqRFLXo7GiupammOAf/6Vkyl1qXa6H8WMcf1lted5vQp7r2HFAR2vK2cWvRYye21Lzh65oLd2qpmU11b062XfurUmCV5HqhQnLqlmuQCJpeOXZPS0tTRTFyMjIRg2nfshIVLeOZfvvJZfGnDQ9Epgy6HDM1jZNJ8ffUmZ1vTzsfMaf1jgj/VCLxv8fwWI8tqD4Bb12sFIBAM/ptCy2vmS2fRq/7uq1g28FTW6QHQCQCLLtQsALzYBAdBATO/xML87nZ2H8pANvXMAa2/nmib02KYKn1bwovbpnB9f+Y6LbU7Ozkw7enueVqlhLGt+iRYvKV2qfO3duREREVFRUjx49btJiMw3MztZ6aJZjr+XS2JMRXyUGz40OmR+TMuig+/hqvmDV1Q/BUtPFJyV3DAahtX4+pdkdABDAK3rdTIMeMcG31Ni6ck0xwRsVxJkingySx/zchk33UIFMr4cvxI3tIFNbEox/ZQ0P0aI8OuDxmjdI6jWqbm25rLwa7p/vzXn/4hweV17rWiJpSJ988snixYtfffVVALhw4cK8efO++OKLvLw8jUYza9asxo7uRl2y8A9drxZ9JY69losPn4j4MlHT3QAAhhH+oW824/NmcBnfiY5jFR98/gou+46tXvV4K3Vzn6Ze5eZarl4UrlGWiauhpthFb1QQ68+6+kXKmofIY35uk9L/MBsus9hlucvH6GesVCMKAwCq3Vcf04RazBuOUsXdH/Bg7YKWSOrbt99+u2zZsrvvvhsA1q5d26pVq3HjxgHArFmzRo4c+dFHHzV2gDeEE4ETa3EF70lxXbj/aPTGZHXXy3X49cPDLJuncXnr9cPfIVT//bUU3e5j7WXRC+s34FtHj7Bba4SQkkYqujFH1d2+muIVfISOQv/Nq2RC5OKPSYWpLoWDL1J059SxZjLwEhmeaT1yU2PoaOhBIRoAMODzzlosYSeR1JejR48mJSWV/rxz587777+/9OeAgID8/PzGi6txMEEyZZLGvCEfKnwrEN1i4YpupAFj95byjVz2e6SmC6Hp2AhRNknTO2qHxt6Kd7hvfU0xwQ+LVQgilM8JKQ6gP94QmbMkOjifR1Gf/h366UlFR8H2R22bxYLoOXG+5vunu1M/Sluwz/zb2xdev+C8/qp0Ekn90uv1x44dAwCr1fr333/36dOndPulS5eioqIaNbRGQKjI6O9bec+70qecAREDgOgWUx84Rhll6m4fsBlvgOgGAOzN4vNX0aHVrTMpkdwimlCCd3HY6hVLH8Vu8d9cb9nPrHBfmG7k3YGeFNfnCv1tlAAAIABJREFUDtRcYZLrusvtf9W2fcxxBe9/wRdbarh/qLyZR3B/l7u6k6HnmpzlGG6ZWk2SpmHIkCFvvfXWvn37pk6dGh0d3aZNGwBwOp2zZs3q3bt3Y0dXF/uyvW1W5pc+Hvi++EIJX/707rWF1+2wJ1Rk1HfJ7CV3+tNnRaeQ+sAxykg3+6IFqe9IaNpzuR8BAJv+GhX4JJKFXKctieQW0FTuwWOAId8VlZZqEgEwwLKj7i9PuAEAIXi/t54IIykT3TxPPBfGTQu425MzDUQv1Ga2OiGXqXu0t//8u+HhGhW/IxDZTBmNAe+3/B4qj9hr3t3V2Kdu704iqYN58+aNGTOmR48ekZGRn332GUEQv/zyy//+97/o6OgFCxY0dnR10SlY9tuDZaWjU0v46b9bNww1lT6liGvWoq+IUJFRG5IvDDt6Kmmfppcx/LPmiEQAwITP9RzvgWQRovOwLPrTm/YOJJL61FQSPALY+b+yf/kWj9h2Vf7ku1RPtVZX3CfwtcgIk3wdyyLSiBRxouPgtdaVuhbtwF65Ly3Ujbi3+gVkSxWy+X+Yd8iQLEIZLWBhY97Xd+k6KcjGn+spaSL0ev0PP/zAsizDlJU0SUhI2LRpU/fu3ckGX9eyXiCA8gKraoYg0eWnNUeoyOjNrczr8kxjgtB/la33FvjI0SPNL0ySxa68NWc8S26qVNc5JakKvN16bppKggeAr0463VxZJx0GOJDjLf8b1j9KEaYlNV0N0cXsKRMLAKSuu2D7s7YJnvI1yhNjXHsPq/t0uu7O63JWNFNE0YQsy51GISpQHrKt4LsRgbdWKWPJHa88uwNAeHh4eHh4IwZziyBUpM+44IpbsmzCOdfjyVG6elycQnKLOJDLtg+srmwfK3qXpr2jow2vxyxEcDuN528qCV4E+PCgw81fvgv3dw53ILdsJrqaIR5qoXTss4g7isW5pkJBMGq7cVkL6ZDptT2R6YkazX874zie7k71iuzr0W+TiMpjs1dkLsl2p3Uz3u1/1QKFEomktoj6/kvMYgUd9Ey9NilpfLwIj24xn51U3dqh2wo2xqqbF7OF+8y/dTHeTsNTmkqCJwD+fNiPE8oSfJuV+Q+3UE69Sw0AJEJqBgGAIlGd935aAhNwhuO6azp6XSdAcAJZu6LHhOr63XciFtdkL0dARCpjzjhOlG5UEmoWPBtyv5zS7JXavTeJRHKVCD216G7D9ferim1HcdZL5yPXJMkTGq3muaTBVD/A2cwV/Vb8y8yY9xyCbdGleW10HW+jG6lNJcEDgJJCQJV9pycQKKjK9+fkCSq/yWEJNH2a5brLNYSqlWDfT+rrMvANczyir/nZiiAGyUOP2Q7GUYlp7gulG81cIYe5Vkz7OpxOIpFUQiKIrW4N+Guy7ShOm3jad0LwyYGHd82OtYXLASC1hDd7xNf+sJbuE62nHkuScn+TsC5n5d0+A02Mrwl8EzWttxVsHBH4SGMHVVNNKMFXhBDYvJW/tSEKMSGyoU7K6UsBAKntLtr+rFuCz5+9RP/wEHl81ZOJSUQWs4VtdB2TNHeVb/Sh/f6x/uXk7XU4nUQiqRe2Xea0Caci1ySpu+iLwxS9ZpzPWpzARik9HAaAlv8VcI3QN9G/nHeMTLtQet0uiIABMm1C6XaEIERzeYRpivNMqvPc+NCppU9HBj76xrmp3Yx9bpcbqU3015QhEV/VrNiS7/I1CWpxa6Ht5WbqDj3ZS8/TYbPq0L66d0frhp/lb0yp8lUec36yQAHzB61XLFgXKAtWkuoqD5FIJDfPNw7nQKWC+M2SNu5k5JokprPuC7tj7KOBNg2peO5s9A+tIJCRF3Gjm982fbOSamTahDHbzGJZOVMQRHhka9myBTISfTXI6K8iAQADXpvzxaigx8pX99ZS+n6+991GN1KbaIKf1lZNVzUrlglX5L19yXq/yT3hdMTyONqQh70ZSBZW2/ZVPdpbvvvFe/aiLL6KlTdpxEwKe7bKAzfnrdld9HNvn/61PaNEIqmzYkF8YX/25P+lxvzQiumom1pcIkeIADAM8xNt/IVBR4gNrRo7Rkm9CdWSu/+bNc2L0GJ57p4H/a7eba95d44nM819Id2dWr6Rw9wR64GzjpPx6sQGCvcGNKFKdhVNTFaPbVn5FhqXzxavziG11IG2CvNnMWnjzwrObnVbFBKRpG5YX+vm7bU9sIux9ynH0TqcUSKR1NlUnaZVlDatpSJ7VfZzhWYZwLtGPQIQnULx2jxtfx9RfrMKA5yyS//eb1FB8tAh/qNUpEZJqssfOsowPPARHa1v7OhqpIlewQMAK2CGvHwRz+WzKQMOmx4KzHs3rccrmfZ4ZcSXLQs+jvGf8gPlP74O7at7dSKUte7Q82MCnr5NOn8kkjvJFH/d4m/iPQ+f6THNM3R1awoh0SWkjjwmi1SEL4l3l/C+6vq/HDpi/WdJ2oJnIl5P0rat98YlNyhSGRupjG3sKG5IE72CL3aLAzcUlT/FPD7f75BhpD8WAREE+XXCpVhZztxU3ycewewh1/HcOpwCUaSqS5u6hZfnzdlW8F3djpVIJHXAYXyOEtYta0YU8RfHnxIcQurIY0wzRfhH8UCgeBPdJ1xev2fkMbcuZ+W9vkO/zVnBY75+G5fUEKpTucPbRRO9gtfKkJ/q8v9URCF1J13xVzmkmtQN8VFm8RNf9+v9vi39ybSAZ5M0UbsAHm7I8PS04S/z7lba9sHyWt/+l0gkNcSmTcfucwCAAc5x3OMAsTSd9S4vO+4+0+kZTfeo8CXxUJMS9nWyvfDHEEX4A0Fjc7xZu4q29fOVauQ1AhLBXw9XcQP+znDHfnOpHk2gbwabrtjiLxMcAqGiAl5sZrzbJ5ahiw2EYBfdp3pj5+Y6n4jLLcybtfi/1edrSk4o5sZ9KGV3yW0NY7xixYohQ4YMHTr0559/Lt+em5s7ePDgRgysHKL0mC+hgqZ9rh7/h+GJhPAX6KBpYbpEaOHaNCky5GZmdxtv+bXwh5GBYwDgwaDx2wo2WvmSm3QuSfUq3qu9wzTRBA8ArHA562IeF3+TG/RKhCJZnfH0ucJlWSM+LmQ3FUata1nyXZJoP4y5grqdhQ7wwR6vc9/h2h5IIRoA/jTvdAuuup1aImlc8+fPf/nll5OTk4ODg0ePHr1x48bS7S6Xa+vWrY0bWyk66BnMmxFiOvnd81ToIFrXk9R0FEp+DopeeM/EKPKmZXcA2JD7VTdj39Lp1H6ywC6GXt/nrb15p5M0TU03wb/xp+2Xi57SnxGFYn9uk/9hhqqtVoiU5b6fFrTFvHNh6KXxp0LeS76g6/1DZl3/7SFkeHRYyTc/Yo6rw9FZnvR1OSvreGqJpFEtW7Zsw4YNc+fO/fjjjzdv3jxmzJhz5841dlBXIuRM6Ew2/ZW2Mqp0lDyXu4RQtya1XTvWYEHIOkt3p560HxnoN7x8y30Bo4/ZDqa5Lty8k0qaoIa+B2+329euXXvy5Mn8/Hye5wMDA5OTk0eNGqXVahs4EoMMZdouD2yRRStjtrZOGXQk8PWIC/utPz1oGDolLXh2zNcDVIXm/i87lwFMq9uJ5M2jmchQ27bfdUPvru2xowLHWqSOO8ntyW63x8TElP7cu3fvJ598cvLkydu313ru6E1F+gznClbwBd9Qfo9gNpfP/UzecvfNPuk32Z9HKKIPWP6quDFMEfltzsrp0fNv9tklTUeDJvi9e/f2798/Ojq6c+fOCQkJAGA2m5cvX/7SSy/9+uuv7ds3aBn2IA15ySJU3CKLVkZvaZ0y6HDIML/Bn+atm24a33xcj4vuKJoknMe9x7sDUTaMFlNaeXwtRrkbHxvBXsysQ5AEIo20j5UvMbNFEcqYOrQgkTSWdu3azZ07d8mSJTRNA8CcOXPuuuuuF198cdKkSdc9luf51atXc1f1ex0/flwUxfqNkwlf4D37AGm6j8uYRfk/VofCVrWCAUerEtyCs3wdilIG2qiSS4Usr0l0Hufzq+7OJPU9pWV8q9SgCX7q1Knz5s2bOnVqpe2rV6+eMmXKgQMHGjKYIDX5VyZbaaM8Rqn+ITllyJGARE3qYB+/jCMUoSxikuSkr050IcYPAETHQYRrd3OOMukpU90LIzh4+4eX5r0R866J8a1zIxJJA1u6dGnPnj3XrFmzaNGicePGKRSKH3/8ccCAAatWrbrusRzHHTlyxOv1Vtqenp6Oazlk9boIVRJpuIdNGS+6TiuSb/pfIQTogcAxN/ssdx5EqvnCNXTIC4iu8GcQYy5jJqnt2nhx3dIaNMGnpqbed18V37OGDh06bVodO8DrLM5IB2kq//kAgIh4zba/kmUtD1vt/msMrzxqnjNV/9ZnwWaU/qw8/jvRk+k52oqOXtqQoQbLw56NnKGhGvouhkRyI6KiolJTU//555+AgLLFtqOjo0+ePLlt27bjx49Xf6xCoVi8ePHV27ds2bJnz556D5UOfd1ztC0d8U5tl4eWNBgkj6QDn8DeTDr4xfKNfMHXSBlP+tzfiIHdyhp0kF3Pnj2nT5+en59fcWNxcfHzzz/fvXv3howEAEK15KyuuipfEmVERqIi8ZBzkOUTAotrsrpoU0aL7hT3vyGeY60BAZ/9bh3OyBcUZ0+dW7fRduGKKIaQpTjPSIPqJbcRhmG6detWfie+T58+DMMMGzZs5syZjRtYJYj2kyfvp3weaOxAJNWhgl8ULDtER9mkJCw4uKw3mfAFAHfsPLcb1KAJftmyZTabLTg4OCYmpmPHjp06dYqLiwsICMjIyFixYkVDRlLK4q3iZl4Gz+/zeNv19mt1xGMQzIAYAgtOkAOlxSIPWABEYjavDqej/ExMeJB1U90HGZ2wH/48Y1GdD5dIGte+ffuuv1MjQUyQlCducYhU0yGvsunTATAA8Nnvkvo+hPqu6x7YZDVoF72fn9+2bdvS09NPnTqVm5uLMfb3909OTg4Lu/6oFofD0a5dO5atfNfc6XRePRKnhh7dYn6vtz7GeMWHEEZR3/r7bO3A6WbnbxkzfpB9BaVM1goWWcsD7kMRpKad4DhKhdaxXLzxsRE5LyxQdmrNhNVlOeFhAQ9ecJ6t26klEonkdkf5PcQXrBKKNhHqNnzB1/LkvY0d0S2tQRM8xnjlypXff/89QRCPP/54//5li6Lm5uZOmjRpy5Yt1RyrVqu3b99+dS7fvXv35MmT6xZP99Cqp7pucLqWx6C3UrwlPi95HOuKeb4Zl8WefxCAENwXEe1DB0ys2xlJo07/4GDz5+sC5la9XGz1EKAYVQIGfMJ2OEkrfW+V3GaWLm3QwSuSOxHBhM/3XphIKOKpoCmI9m/seG5pDZrg58+f/+GHHz7xxBNms3n06NErVqwYPnw41LiyVWho6NUbT506hVAdO9Ze6KC5emMGzy+z2VcE+3o75oVmEQsNr88wv3JB2T3a9huh6yFaf2fi1oqOw4K96s5GyjgYyaOqOammT2emWUjdAi6Fsfhd7morX9LNWOuJ9RJJIxo7dmxjhyC57RGajoS6veg8Iov9urFjudU1aIIvrWzVs2dPABg+fPiQIUMSExPj4uIaMoaKBAxFLsFfdcVKz2EUtSPQHwDg+1YA4C4aKrpXxbAnyaApfMFXiDJiLg9zeXz2e5T/hIoHYtHF531OarpUn+ABIVl0+I2ETSDyxag5blEabSeRSJoiJuIdzBUDcROrDd4ZGnSQXZWVreq9bEXNnS7iJm+/Zp0470X3+b6HlvgY1THLMG8Vi7chwcNEfchlv0/5jEKycELdhg6bWf5AlIH0uZ/QtKvh2T2nL2Bv5SEFNaShtH5MgIUzH7b+U7cWJBKJ5DaFKCOhkAp/XV+DJvjSylbl99HnzJmTk5Pz4osv8nzjrIUcqiUvlFzz1Ey43HPeyeV4i5kY0jhIZNMp/7GkYRChiOWL1tPNFrDpb4BYVs0eszl83nImtBaTf5x/HSxeseFG4kcIrc1Zft55+kYakUgkEskdqUET/NKlS7dt22YymUonxZVWttqyZUvnzp0bMoxyehlBEajQVXUXAiKRcXQAm+V5yWw5HjSP1N1Nh84AADpkOpf9PqnpQKhacrllg4bY9DeogIlIVsUogWsxPDrMe+ai889/6xy/jjLMjHkv8holbO28zSk46ty4RCKRSG5rDXoP/kYqW90kkXrqooX3VTJVvhrydixv5jpT3l9Yql38utKNhLotoWzB5S1jwud6TtxN+f4PezNE+z+yqCW1OjUhl/m+MD5/9hI6PLhus+YAQE1pAeCs40SuN7uX6d6KL32c9hZDMs9FzKpbyxKJRCK5rTX0crGVKluVbmnEylaDoxUa5pqD8DErnkra1xcxu9yeivWv6ZAX+ax3Rc9F0tiPvTiVvfg07T9OdJ8XnUdBdNf87ExYkHHCA97TN7pGZIAs+PfiXwvZyyUCD1r3ZXguXnCcPW47dIONSyQSieR21HTXgy/1SKISAM6bq74TjxhC1Uar21GiIdDxCjV2hJLtWHSyKZMEx0nB+pvIZvPmLezFZzwneguWXbUKQNWptebe7pjjvKkZ3nMXRU8V5fGvS08bZ8V+4MuUTQnlMPt11jIVqZUTilXZn/C4joWAJBKJRHL7auj14G81AoZPjzj8VeRrnateysUwKsC8Lq9vn8gdLk8yU9aTTwU8zuctBxBlUR+C6EW0CcmjheJNXM6HpHFAbWPwnr1YuPhLUq0Emubzi4zjR6o6ta7b29lT/OsZx/FAWQgreieETWNFdnnmBzsLt97rN6xuDUokEonkNtXUr+Bz7MLONM+5a1zBA4D+Pj/Hn5Y+bnKn21O+EVF6OuRloP3Y1CmEujWSR4PoYTNmM+ELavuRii534QcrTBNHETqNultb/xlTzF9s4HIL6/Z2uhr7aCjdL4U/RCpjEzWt2+g6hMojfshfZ+WvORtQIpFIJHekpp7gg9SkQV7dh0CqSe3dJv9tJQjgFHu5r5vyfwwhEggll/UOAHA5iwh1W0Jb6+kA3vNpdGigonVz08RRlk3bucxcVec27iOn6vBeAIBClI23AOCB/iMK2DwAGBPyFAb8Xc7qujUokUgkkttUE03waVZ+b5Z3b5Z3f44XAUq3lT3dm+XNtAmVdjaODjCvz5tn1PuRFT4uRNLN3sRsHl/wpVDyK5+3nAmbVYdIRKeLUCkBgPIzBcx62vzlJt5iEx11rFKX6jp3xPpPC03rNFfKnPMvHCj508KZY9XN95fsyXBfqlubEolEIrkdNdF78BvOuv7OLrscL3YLgNB7B8qmjPcIl027S11xZ+09JsyKsbLKU+lIbXdCnYQIpffCBMp/Qq0mwZeTRYWbv9xUmubpIH+/lyYVzP9E07tTHZoCgFTnWRHjQjbXzBboacNPhZsRgACCCHDRdT5MEVG3ZiUSiURy22miCZ4mkIDLJr4JuPS/ZU+v7rBHFGJC5dZfiv7orjIRRGe5TCjexJu3AACIrGDZDhgLRWu93ksAiA54qubVagGACvDR9O6U++p7mnu7I4py7PlHnhSnaJVQt/dloH0SNC0rbnEJDjtnjVO30NOGurUpkUgkkttRE03wz7TTPNOubCm5u1bmsyL+YbhPNfuLXjFnRqry9+SPbPbOchkgSrRsp0PfAEQiUoEoPW/9C9hCwXmQCan1UvH60YNkCdHugycwx2sH9FR1bg0AotsjWh1UQHVRXa2dvks7fZeKW3jMbcz9ur2+a8Q1Ct5JJBKJ5I7URBP8hrOuowVlXfReAXsE/Nof1tKnbQOYYbGKSvurO+lFVux4kZ9vEM6wXLTzBBY9bNY7iPxvch0WBPt+AFF0nSYVsbWNR5Ecr0iOr7iFLyjOn/ORz9QxlbbXFoXoUUGPAYBX9KQ6zzXXJN9IaxKJRCK5XTTRQXYReqqlD136kFMIMMT8t6WZrqovPQhif2mjjFM9pFGtcjiogImI0CIAecuditZHFK2PyOK+BkoFiMSCvV4iZMKD/V6eVLTkK/vOvfXSoFf0rs35YmfRtnppTSKRSCS3uCZ6Bd82gGkbUDZobluqx+wWH2iuVFLXrFkLAHSALGXQkeGfJgxAjlytr2/Q03zh11zmW0zEOwDApk9HSEVHzOWy3gLMU/6P3XiQstiIgFlT899cighCXddhd+W0lG5W7AelVe0ELJCIvPEIJRKJRHLLaqIJvqLW/rSMQtVn91KK5irnZ1n3P+/3pcPxSuBkvmA1X/wd5T8Wu8+BJ51QxlF+Y0htF8/poUAwlO9DNxgYl7VQdJ/xmcJhfrE3ZVmFVxDT7C1E+9W2QRKRpXl95vlnOht6DfC7/wYjlEhuWRaP+NVJ19S26uvvKpHcoZpoF30lPMa/XPRcdzf/aeHFq3IeZpkfnO4SoJnw2QjJuLSX2fQZWHTR4QsAAMmj5c1/4LLe5rLfBcDXbbNaGLvO0P4jmOBRlPE+yngfqRuEaH/RcQSRuhtp96WoeYGy4BuLTSK5pRW4xJ8v1mLlJ4nkziMleOgcLGvlR7/yu1W8Xjqmg2WqDlp2ftoLeq1TxKRxCKFMED0XgWBI3/8RyrK5bUgeJU/cKZT8yl54vFaLy1U+XdA0LLoR7UeahpKmoaK3c+7rh/iCXUz4XCBkdW4WALSUrrWuAwBsK/huTfZyHl+zUq9EIpFIblNSgoeOwUyPUPnsrlriep301p+KHHutxSuy+/3rCaFIAKDD54LoAcFBB79UcU9E+8mbbwFEe070El0n6xgZIWfCZrLprwAWAIAO8vN5SsNleWw/CSCKdWzzSj1N98oJuUeQLnQkEonkTiMl+DJDYipPjavEtr04fdIpQCAw6NyIYxePW7+wOwhloug75hvDCylYVfkAQs5EfUwFP+c9M4zLXVKapGuLNN0PpJov/AYAMG8BYZWq83LvhfSChZ/XobWrqUj1/YEPqylNEVuwLOODIragXpqVSBpFll347Iij9LHhjMvsEcuffnnCeYM3zCSS246U4MtYvOKa09esAG/bXpw27iQgFP19K+H9aJ6CwiHHth8qPuhhx8snn9CNCKYovpB1Ha08R47yeUCeuFO07Pac7Cva/61DYEz4Ai5zPhZsXNYC0jiECugQMPNp/YOD69BUNYy0KUwRcc5R184GieQWIIjYypY97BwWMZQ/dfNSfpc0OdIo+jKiCO/8Yx+VoCSv6qi3/lyUNuEUIlDUxmRVe92JeDK3yHnvvNxpY9JmfYUDm2sOediiXI9z8DEu19tsZaKun6ni4UgWLkvYxGUt8J59ACkiSOMQROkr7kAaByPKBFcRLLswmwkASBHhPT0Yey5SodP5glUAiAkZWbqP+9BJys9Ehwbe4NsnEHmv79DSnzfnrdFR+t4+VS9sf8p+tLkmGcH1Jx1IJA0sXEe91KGsQuV5M3+sgC1/KpE0QVKCL2NUEH5KYsdFz7aL7iV9ryjbXrA4Q/SIsdtaq9rrAGCCVv3huCDrF0X6LLbzJuvGaNlKpHXed8ww0l/Xzyd1+NGwJQm6gZVKzCLRcRhjD3gucRnzgFQhSgdAAnDYmytjQkl9n6tDYlMmYMEKQABgABGA4NJmAogAGMnCSV1PABA9bN7MD1Xd2+tH9idU17nLUEPt9V13Fm3lMUchutJLZ+0n3784a3jgo9IUO4lEIrnFSV30l3UIYrane/Zns5W2k0aa1FAFS7OwUNbLN45SOdWkQCGrnlQV8XDfCcNw/8DpEcrWmqiNrTKePmPdVlSpEULbEzBLB01lIt8ntZ2ANxPycKAMQJCEKqnKeEq302EzmcjFTOgMJmIRE/khoo0AFKlpX7qPqkuboEWvY683e9pc577D9fI5BMvDxoQ8RSG6wJs7/8LLKc4zpdsx4M8zP6AI5of8b91CHRe0lUgkEknDkBL8ZT3D5MfyWTtb+V5ds8+by+OUjn2WtHGnsIDtdv63wYdMxfz++3RaBC+NydjWV+U3PUKw896L7mvleMpwDyL1XP4KJAulAp6goz7BpBo7TwKlFy07QaxiFr4s7mtAiC/6lvJ7iAp+lvJ/BBgfzJmZ4OeBUJbvRmrVpsf/5//6ZEJ+Q3PnruYnCxzoN8IjugGAw+z2wh+tvGVm7HskkF9mfVK/55JI6pePgugVLm/sKCSSxiQl+Mt6hMnaBzFXz4YnlGT05laySIVjryX98dP/DjxozPQe76butTSx+1abKZ/98THT0sySlEFHznY94NxvVbbW6Ab75i/JqNgIl7cMMIe9OVzaa3zOh0L+Smz9ExAFbCFXsMp9uDl7capg+/OKwfakhg58GrvOiJbdAABY4FImIFJNhb58dfBMs2BFmxalP9t//dO+Yy/m6zJuv5JW2nYtNW0AYHPe2vU5qzrougbJQkcHjTto3Zfjybzx9iV3tlOnTq1cufLAgQMAsH///qlTp06YMOGnn35qgFMbFYR0A17SxEkJHvZmeR/dan50q/mxbea9WayIcenTR7eap+4oKd2HUJLRm1ox4XL7HyW6HO5Yd3XXT1skyWWdtrcjMcy9P7XHQynKVpqI1S1TRx3LmHbW/ntJxMoWFc9Ch7wEiCFVSSKbLYtbQ/mPA+whjUNI0/3yFr/Kk/YhRSyXPtN9KI5NfUowby2tkEOHzUCEynthHIDIZS3AgpOJ+QKuN8BNFhvh+vd49uSZth93iY766Uu3cSUUQT0YMhEAZKTcQJs+TX+3XlqW3Km++eab5OTkefPm3XPPPfPnzx86dGh2drZSqXzwwQdXrlzZ2NFJJHc+KcGDV8AWj1j6cLIiBih/6uIuX84TKjLmx9aySKX7HkOPpYlJchkAUEa6+cbWhnROvOQueiM8t7tGPdi3cGWOYUYEHVjWYX6G5RZabNhzidB2xKIHRJf33P+8qU8BaRRLfiVULTBvRkwAHThF3nK3POkPQn0Xn7/CfSjOe3Ykn7uUDnkd81Y2YzaX8yFSxpETDDVkAAAgAElEQVT6vtd9R0xEiP+rT/pNf4JNzyl4+7Mb/4gK2Lz9lj9HB41TkWoM+KzjhIH2yfVmHrTuu/HGJXeqOXPmLF68ODU19cyZMwsWLBg/fvzGjRsXL168fv36d955p7Gjk0jufNIoekAIVEzZNbGcQuDFSgahsqdXfAEiVGTsL20qLvYu2Pjs11P095psu0vwoGObeir77XUuezf4kWlnSRPt38N0huUeKSwKoSjB+7NoP4BFL6IDBOsfgAig9IgO5TLfJA2DEGUsC4YJovzHU/7jsWATrXsE6x+i7Q8Aks9ZDICYkBmAObhqcHuVmIgQn6cfKX/KFxS7j51Vtksi9bXut1ya9o6MYBAQvxdvB4BmiuhmimgbV7Iic8nOwm3DAx+OUSXUtk3JHS8zM3Po0KEAEBgYGB8f37t379Ltbdu2zcyU7u9IJDedlOChhQ/zyH+96d+nuApc3kdblJWlUzPX6Qwv/jqXy2djf7nLttucMeVM31+dr68KGRVl/Puws+iN8yE72jxZVCJDaInJSKHn+cJvmZDn+fxvgNIQ6o6i6xSh6UpoOiMi+uqWEakljUNI4xAAEO3/eE71R/JmXNZ8b+pEQpVMqtsS6jaEKhnJmtX0fZKk92xqydffy2Kaqbq1VXVug2gaAHPpM6tcwx7RRjr0jdKfzVyhV/Suyb6ifB4GLALu5dOvdIW6NHeqgPkoZVxN45Hc6eLi4tavX//MM88AwK5du1Sqsn9Wu3fvbtGiRbWHgiAIa9ascbsrF1E+fvy4WE91miWSO56U4MFPSQyIKhtte7yQ3ZPOdguVaa6X2kv5Ph7iOmhLHX08cm1Sy9Rulh8K3/jbtem3jBF7XK+vDM0pLNYRxLd+vqEk6T6J6JCX+aL1SBZMmoYIll1UwON87mJM7Tzd/g+f8UEhb8Ve6yyEpoM8cTuhSgZEY8EhOg6KjkN84Xox7VUQXYQqmVC2RKpEQtmCUMQCYqpshDLpfZ5+FLOc698Tzr0HKZNBnhgLgATHP4gykoZ+FXcWzD9hz4XypwsTllW5IA0BhIIsG89vZot+yF87IfSZUEWzmnx0kjvee++9N3z48KVLl+7ZsycwMBAALBbLo48+umvXrm+//bb6Y71e7/79+3m+8m9dQUGBXC6NjZdIagRhfHtXcNyyZcvIkSM9nusv9loTK4873/3H0dKPmtxG3S20RrPOsIDTJ57mS7jItUl8AXuu70HOws38ISojlGGxGEKS3/n52l5KKfw8O2RhlLbLWMpnOJs5j1AlI1IjOLud75dE+dBcHuv7eEjI29fM8dc8O1coOo+LrhOi8wR2nRa9aYQsHCkTCEU8oYhDijhCHln90nPmL+YzkUu57IXyFq3kzaORjMG81XOsvSx+HaFqdb2zcwCA6Mq3DBamviFg/uWoeQQia/uOmiylUllcXKxQ1E+1oluE2Wzev39/9+7d1Wo1AFgslsWLFw8ZMqRVq+v8al3L6dOnR44ceerUqXoNUyJpaA3z7126gr9CpIEM0hCPJqrePWDvGiqryVU8IlH4suaXxp689MgJbR8jAqCfDTMbSN9Ud9eWxh+drm+mHO/ykw0B5M1PJ+c/q2z5GhPyChByNmN5ypDHZTHKuN/amdfkZj53DvM49L3adXEj2pfU97lcCA+zojsFu8+JrjN88SbsPit6MxATRMijCUUckkcieSQhj0Ky4PKh+MZxr7qPnsfcr9bNBUUfrQ75dC6Xs5A0DkRMYjXn5bLyCj9cxWXkAgI6PNh36hg62L/81RciZ2V60ghEClj4OO3tu3Qduxh71+p9Se4MRqNxwIDLNY+HDx++a9euRoxHImlSpAR/hSRfZspd6v5R8qVHHDsvefpG1KgzEFEoYlXipbEn8xdlhPzcpq/M1izF8+q4LO0w9l4X791myQ6T9dnd9sKwo9mv6ZstS6B8XVzmV9kzHmIitHG/tSPkhM+4YADIfO4cANQ2x18ZCkMoW4CyBVle2x5zoicNe1JEd4roPIaLN4meVMybCVkEkkcgeYToOIpkIqk7bHjIgygde+kR0bKbMNxr+aav82APWUyELDJU3iKaiQwrP4no9uS+/j4QhG54P8DY9sufea+9F7x0DqEo+7gIRIYrogCAROQ9voML2XwAsHIlO4u2dTR0D5aHgaRJ2rdPmnYhkTQcKcFfwSAn7otRAMCz7TTvH7DXMMFDaY7/MlF0CV41GZrv/KBraMC/wSkDDstOOzx+9OpFYT0jFbE72p7vezD79ZFhS552HUrkizvF722HZGUD9X3GBYsuMevV82QrddAjwRUbt4uihqjrhEZEE4oYUMSQFevri27RcxF7LoneS6J9PyACMBYsO0rr3iOgBfPP8gRR2XUyly1yl3Ltu4pMkWEAgL2s9cddXE6+6GFDPplJGQ0AoB3UO3Piq47f9msH9Lz6/PHqlvHQEgDkpIIm6BTnmWB5WL4356zjZBtdRw2lreP7kkgkEkm1pARftd7hsquXlaseIhGpoZQAmwJ8AcCd7facdsrjVIp22mn3pSwbUqhbGD18+12nuwk5b04RHS28WV42yyuLUjxdZO4llw9TKKzbixCBXjZ573e6hqnKBq+tc7o+tdl/C/Sv9uS1RCjKLvQB85lvYtGDKC0WHAiRGAuYIEBgMaKAW0vpM4nmmYhQek58hJhgRAeTejebVigLofPfeEme0NE0ZTyhUtD+Ps6/DjLNQigfA2nSI7KKW+8yQj7Ef1TZZ4WIC66zClLZXt81zZ2a48loo+soJ+6o28+Sqy1durSxQ5BImhApwV9Tj7AbK+1OACDgbVzc9EhiXoz2j+LVZs/fA48Yzbzlz867DrQcMvrCmc7/rNoS+0cQTFOrU4cfc/xZErUhGSfjuSVWDHC/SrnB6XqrxNpaVvXA+PqAqODnuex3SW0v0mek9/zDsvj1XP4XovU3OuQ1Omhq6U6YK8JsNmazsTdLHpeN4KLgSGOayTG3xX3oLUT7KTvZQdC7D/3JF5N8IVK06Kgb8TCifQSrx7F7H6nXEho1qVPTIQGEUgEAfkzA+NCpAOA5O9LHcUQjejlChhHFY07EIk0wiKBksWuvtQyP5DY1duzYxg5BImlCpARfnfVnXUfyuAU9dXU4VpGoDv0wLntG6rl7DsZtb9t8WMCM4/ZzJSIPsH+I7rc8R1I4rS9iR/c/N3FrEkw5bf/dHLU+WdvXtBzjcYXFc0us/3rZX13ulgy91Md43dPlzElVd9Jr+1axrnz16MApXN7nfMkWKuw1Wfx3iAkWLTuA8qUDHi/fB9E+iPYBVXLZU3lx9tS58uQ4/Yj+IBTbtv/I554yTeiDZDbM5WNvDmZ/8pxcibkiROopH7noUfPZCu85GZUapWjXHdF+fCE2r9qJSB911/OE2qIgupM6PQB4WUuRKztI8FJc5h+5f5oVRwcEjpSu7CUSiaQOpARfnUHRit/SvbwIFAEjNxcNjlE8mqiq+eE+jwUDQM6MC+f7HQp9Ly79ydMIIGZ1y9YDfJLtnmX3sS0CiN4brN6+R70IKZI0mMVcjte9z/JxsOyRQP4Hp6sZRX7ua5Kh69wtSJt4uuTb3DwSRa9L0vbzqX7nygi5LGKh98Lj3nNjFMn7PMfaIcQwkYuqmVxH+ZlMTzxoXr4u/+wnAACCYJz4EhPV/qodMeYKMVeI2VzMFWEuH3P5omUH5gowV6AbUghiaal/AfDvgpkERCgxChNEIFgACNqz2x4VL/pzQCiWnX7TfO7UiF3hcsx49ZRP1y66/r0AgMvKs2z4qbRXgFDI5cnxiuQEABAsdte/x8ven0LGRIbSQf4AgL2s59wlACBUCoSA8vMh1EoAAFHkC82l+yOFnFApEVk26EF0ugmlHK73v0AikUhuNVKCr46SQkv7lQ1OS7UIR/PZKhN8gUv0U1Y9CM7nsWAs4NzZqRcfOk6oyIiVidp7TACw2uM82ldzuK8mY3ro0MfTfhuhy9ESJj829lJJ6835QrY34n5toJZ45I2co6ZUg6+MNNGkliLVpPF/gZSJtu0qRhTS3m0iFET6pNO2nebA2dGOvZbUkcdDPojzeSQQEOJyvaSWIrUkAAg2AQAQjQhVFXfHSeN9hPIj0XHYe/EZ0Z1KaNqThnur/2TUPdsr7mruPXsJAGTxEaRGXdVeCNF+iPYD5TXKlmGBvfgsX/Q1YET5jQHRDYJNcBzGbA4ACmuXGcofxwc/dVO6h0iDN1KmjPYgpD/sTT2BfhqQN56kjeeEDLoTBDsMGNSii0BM2b0MzLJsWhaIIgCILg9gXJrgubxC2w87AGPR6cYY1N3baQf1AgDvxczCRSsBAwBgl0czsKd+xL0A4D1/Kf/NpaLTDRgDgH5Ef/3ogaXb82YuRkxZAQDt4F76Ef3L9p9fdpuZUCk093TVDe2LeSt74bh51SYAQAThM/lh0scAAIAYxARV/zlLJBJJnUkJvkYe22Z2cdjKVlEjM98l9F5TeGpCwNUveQUMAL4TQgAga3pKeXafVFi838uu9/MtEsUni4r9vo1ZYNCXiOIJljvPcqs/ZPZ5vAk0bcV4dhslWcJ1d5Ht7YTahTUuUSEDpZ33nHZiHsuaKQo+yrDuKJa3UFt/LMACJvVk5rSz3vMudRdd1vQUwcbvfT0Y1GSX6RkAgDksOsvWkA3/OIFQk5fGnASA8E8SSMPrtO4BoeBLzDKXJj7IZvwOACELYgkVkTH1LACEvBVLKMt+rijkrVhvpjNj6iEACHkzhlCRGdOu2uftWEJGZDxzFgBC5scQKrL0ZwBA1JDIFRuRzOs5XyDYF2bP/Cfy85+AIISS11NGtAYAQJjU2AmNndLY/Z7UIKUtaIs2TGP39D6AaGvAhWxC6xDUTkrlIEwsl6niC40AOvcpQnCqlYnBiNSVfO9xHruQt3in6FSJTpXoMvlPa4kU+oxpqYVrIGR+DqEiM55JA+hVHrB5BwjO0u0ZAAPLY7buh/QXfv9vr6HBM8IJFZH50sWSXejy/vjytO/iH1GINUcWNYgg0rX9KUQjIBCbtQayABAG0SuL30Dq777eb59EIpHUhVTJrkZSLfw93xZF6Ijto/2IKztrD+ayo34oTn0i8Oqjxm8roUj47F4DAAh2ntRQAPCa2bLV5XoD6UcEKwFgl9s7tbj4Nb3uQXVZ30DPnPxwivrc15gviBc4boHFZhXFngqZWRCLBDGeoRcY9cWC+FBB0QPTs5O+N+/4OLKgv16DkAyhJBnTcmqqeUNB1tdxnl4GAMgXhE9t9lYM/ZROG0CRkwvNJYK42MegrjDvjgBQE4Qx/RlUvJ4yPVyoe7P0WwDSUYAAW3gAMBloFUkIFt4hihV/Y5CGRASibQKDEKEhHVgs7S2oSKmlGAIJNsEhiqAhEQKxwj5y21ox7zkAEJIOEimPI89RzPgLLY+INhEAGIRK71BUOhZpSISAsovlrxZ4s5xsVoTRDwmWbalfKIDrF9iFEEpOF/yhIZgAWoVEG8cXUdhFYDsINgASEVqgtZjUYqzGhA5IDSbUgNSYUDNqPU1pRa/Sg1VYo0G0VnAqgVABkgOADKHS9+vEmLfx5fGUxlb6KgA4RYw1BH28BXD5mPIt7ecnSnv7OQvGXln8JlJf9sXijqxkV++kSnaSO4NUya7xjfi+OPNyKsJpNiFheZ6OIRCCkXGKFzpcZ1m28yUc9d/XgdLsDgAPqVWPa9W9vyjq8pAsUE32UcjW+Pn4VZhXNkmrHqFSMgiFUmQoRXaSyzY7Xf9TX3FrwEQS9r8g+Xc7CpJ3HuzvppFNxF6MlQj5PxNu3lCAfiw+1VUFADYRt5fJDrDel80lIgYO8Df+vrNLLN4KX+xEAIco3q19dqonxRryyqQSK1f6atHlM3a0yuYY9WYNuj/fzFbM8G4AgJ4K2dsGQ6Eg3F9QyF71jbEnyN42GswaNCzfzFVePQR6aofMtnwkejPMpwb5CHkiwDO6t07kl527h1y20GQoEISheWah4rHuq14FJVCxYAMAPQTMBYB9MtlCk8GmuftJpy8GGgB7RA8CJCPkGHA4d2qSLKOVpvPLhRm0YNNhu0p0qrBTIThVOCuKTWlPe1jeftpTIi9wqQSHCjsU2EmB4EIqN6k2UGoeKY+KSjdSeAi5G1QuQu1BMg+hCGKM92uMNpDNtGGPRT6CTu7Gbc8B5Q7lQAHRo9VaHXbz2e8BIghVy+p/hSQSiaTOpARfnXAdSfyXCM0uQcsgo4LU0kASSE4CLi/3WhvNGRoAMMYOrmxLMnPFLLgHr8zlcoQqZfdSecWC67tW2sFHfHocS9jfgVASAOA+aT/b46D6Lu3gz1oOrtDVcMjDPlJYRCL4LSjAhyC+8bvGQDzfX+QA26q42/Df6yT5Z9A1X77uq39d41VRvtJzoqevkIMQQ2g7LY8cWGkHP5LcF3zNlqt/tYe+zX595Y0Y8DlHEYmCfVXxHylCV2Z+5Mv4PxA0xcZbZp5/tpWuXbeQp5yCY2v+uujA+GR9FxGLF1xnlbJANalVCXYQ7Fh0MaKrE28FwYlFN4gOzNtA9IDoxkKqaDulEt3v8lYsegCcIqBgPv0xx9cAorzZH97UJwGACn4e0bUcESmRSCQ11tAJ3m63r1279uTJk/n5+TzPBwYGJicnjxo1Squ9FSuatQtg/JVl19bHCnitnCitbff/9u48KIqzzwP40z0H1zDMcA2HL4g4orKEZMW7ojEkJRiNJOK1ulJoRU1ZHqyJEaVqXYMx2ay8GBREXYIXvonHC/GoxUJNGXxFiSYYCMQIEQ0wjAPRV0CY69k/OhlHDkUSuifN9/NXd09Pz5enn+7f9NBHXYs5/euWv37dYpszdFcDNyCXkH/8u5/amY+TrqmvfMTVcZWjSirHXR5RMrbjZmvV5K/dIt2HFY0idtXdSsime/edCWMidOsv97d5qZ+wTEGwrv8i8XjZcu8sJVQ+dDcPn8gQZrji16NnV4nbisHvccNKqSo1LIMblrNyjTyAexjuA/P9At3fglxC5gYkNltN/3Vz4xjVxIWBy34xNf2tPmeE4rmXfOMfWtou3/vqLy6DQ13DKKF3Hv7kI/fjnrZn1n9qrFnLyHycn//a2nLV+uAKI1HIA9by8JcCwIDFa4G/ePFibGzs0KFDJ0yYMGLECEJIc3Pz3r17161bV1hYOGZM1+usBDZvhKtteN/1tiEqybqx7oSQi3UdJXUmK6UswxjN5J9Gi5RlpCzDstRkoRP2NXKH9iYrJYQZsVvHLeEFP+lHLz0qrroWizP35YEhg9yf9b55v5IHOI24PPb70Zcrx5SY6jrcXlAOO/Ovnar7TN1dncV8JlBzx2heZDCsbSIOWOPlQ7Par4VLNEsYma+wSdwkv14RIGPkL3vHcsMeMvW7oZu5YS+5z3+PyGYIQwhxl3qMVU3ykKkIIWZqrm+/zTJsqGtYk1G/7+esYW4j5wYk6jsa/rPx9BaJ0rm92tz8hanmHYYQeWjWk5/yBwDwO/F6kt2oUaMSEhJWrVrVafqBAwcyMjKuXLnSh2Xyc5IdISR8T+O4QbL/je18zxnuJLtjb3r/0GSq/aflakOHoZ0aLbShxcJQwrKMrxtraLMSQo0WQhhCKGEY7qqrRxdXj/aXD1H9+mVrSpDTK4OdG1osByraRvvJpwQ76Vot+8vbjBZy4uZDKyUuUsZNxtxoNrk7se5yRiFj3fXG/9h8o22I69W/DqcsM8pPFh3szL3r64D2WsZywMMr//t2QkiD3Fro3TrHqtgUrORmiPKTv/zbR9j+KPuJvM2gb67a/6OvlcgEzNBPM/zc0nG2/B/xJJ5lWEotrIvWOfJyp46Ek+x6AyfZgTiI8CS76urqmTNndp0eFxe3evXqJ7+3vb09NTXVbDZ3mn7z5k2LpfNp2/1hkJJ9zqfHW8Y+7yt73perTI/OvHvxoF7KMmfn+1Q1mR6ayd9vtJotxN2J9XRm/+fKgxcHOQ/2YDVuEjMlSjlju/e9pzNLCHGWMmpn1lXGEEKcJIzaiW0xWtuMViulRjPTYiSEkA6TlVqZVqNV7yY5uj9C6+ukZAghxE3KEkJkLOMhZxaz7pP85e0dRO3EWin1IJJBzcrR3lJCiJRlPOSMq5SxDduS20/kbQbWZZi7vK1fP0KoGZwlsnan0a1kgqKjmBBWPmw/AQDoZ7wewcfFxbm4uKSnp2s0jx6d0tTUlJycrNfr8/Pzn/Bes9mclZXV1tbWaXp9ff3hw4f1en2/JLazv7w1yk8+0lvWafpVnXFOfveXyXEF/vy/+XR9KXRXw//N9dWqu7ntTC+F7mr4dJp6UlBvn3cHgqPG+ofXnpd6vyEfmt31VRzB9waO4EEcRHgEv3v37sTExMDAwJCQEC8vL4Zhmpuba2pqoqOj8/LynvxeqVS6cuXKrtO///77oqKi/sn7mJ5uUhuklCrsjtgAesLIA5xH/p3BE3QAgBe8FnhfX99Tp07V1tZWVFQ0NDRQSjUaTWRkZFBQEJ8x/lg+rmzZ4u6v0RoX6CTv6eQ5hsj6+oR3+PNilROFjgAAA4UA18EHBwcHBwfz/7n8+3hKj4+hmzfc5S/Kvv8+Twhxk7EhHp3/XwAAAMDBjW6EsWVyl3uvPKPrSzRPnwkAAAYq/EwMAAAgQijwAAAAIoQCDwAAIEIo8AAAACKEAg8AACBCf/qz6CUSSU1NTVRUVNeXDAZDQ0ODVOpAfyOl1Gw2y2QOdHkbpdRkMsnlPd6FVxBGo9HRIplMpvDwcInkd13c+ARGo5Fl8YX7KRx8exe835rNZpZlBexIVqvVarViLfj7+3t7P+lh0Pxs77zeqrY/UErLysq6vR398ePHi4uL33rrLf5T9aSiouLEiRPr168XOsgjOp1u27ZtH3/8sdBBHjGZTMuWLcvJyRE6yGNWrFhx7Ngxtbq/HsTn4uIycuTIflq4aDjy9u4I/faTTz4ZP3786NGjhQpQWlpaUlLS7V1HeZOYmLhnzx4Bv2Ts3r170qRJb7zxxhPm4Wl7p+KVk5OTmJgodIrHFBUVRUdHC53iMT/88MOwYcOETvGYhw8fOjs7C52iMx8fH71eL3QK6JHg23t7e7uTk5OAASils2bNOnr0qIABjhw5Eh8fL2AASqlcLu/o6BAwQEJCQm5uroABbPCTIAAAgAihwAMAAIgQCjwAAIAIocADAACIEAo8AACACIm5wEul0v67arlvHDOSQ90qgBDCsqxD3SqA44ANBfYE37hYlhX85g0SiUTYXir4WiCEyOVyYW8pIfhasPnTXwf/BB0dHS0tLV5eXkIHecRqter1ej8/P6GDPKa+vj4gIEDoFI9BJHhWjrC9C95J9Hq9p6engNXFbDY3Nzf7+voKFYA4wFowGAxKpVLwb3tE3AUeAABgwBLzT/QAAAADFgo8AACACKHAAwAAiBAKPAAAgAihwAMAAIgQCjwAAIAIocADAACIEAo8AACACIm2wOv1+jfffFOtVo8aNerixYtCxVi8eDFjp6CgQNhslZWVeXl5ttFuk/Acr1MkYVuspqYmJiZGrVYHBQWlpqZyEx2hleDJBFkdve8tPLh9+/ZPP/3EfwaLxfLuu+8GBQUFBgZmZGTwH4AQ8u23306ePFmhUAwfPvzQoUN8ZnDAPao90Rb4xMREuVz+zTffLFq0KDY29sGDB4LEqK6u3r59e9VvoqOjBcxmsVg2b9781Vdf2aZ0m4TPeF0jCdhiRqNx8uTJwcHB33333b59+9LT03Nycnr6dAfpYMDhf3U8U2/pb62trdHR0fv37+dG+cyQlJR08eLFwsLCjIyMtWvXFhcX8xzAbDZPnz49PDy8rKxsw4YNCQkJ165d4yeDA+5RO6NidOvWLYlEotPpuNEXXnghOztbkCQBAQHXr1+3nyJUti1btnD3wF++fPkTkvAZr2skKmiLXbhwwd3d3Wg0cqMpKSkzZswQvJXgqQRZHb3vLf2dhFK6ZMkSJyenTZs2UX5bo7W1VaFQlJeXc6Nbt2797LPPeG6EmpoaQsjt27e50YiIiMzMTB4yOOAetStxHsFXVFSEhIRoNBpudMKECeXl5fzHaGtrq6+vT05OViqVWq02OztbwGyLFi0qKipauHChbUq3SfiM1zWSsC3m5+eXnp5ue5Bdc3Mzy7KCtxI8lSCro/e9pb+THD9+vLKyMjY2lhvlM8OlS5c8PDzCw8MppYSQ9evXz5kzh+dGCA4ODg0NzczMvHfv3unTp2/evDlx4kQeMjjgHrUrcRb4hoYG+4dKeXt7NzY28h+jurqaZdmpU6dWVVVt2bIlKSnp9OnTQmUbNGhQeHi4t7e3bUq3SfiM1zWSsC2m1WoXL17MDZ8/f/7gwYNLly4VvJXgqQRZHb3vLf0ao66uLikpaf/+/bYnyPGZobGx0dvbe8WKFZ6enj4+PsnJyVarledGYFn28OHDH330kVqtfu2111JSUp577jkeMjjgHrUrcRZ4SinDMPZTzGYz/zEiIiKMRuPKlSsDAgLmzJmTmJh44MABB8lGemglYeM5Qos9fPhw3bp1M2bMyMnJmTZtmgO2EnQi4OroTW/pv0+nlCYkJKSkpISGhtpP5C3DvXv3ysrK1Gr1rVu3ioqKcnNzMzMzeW4EnU4XFxe3Z8+eBw8eFBcXZ2VlnTp1SpAu4YD7CnEWeI1G09zcbBttbm729/cXJIlEIrENDx8+vKGhwXGydZtE8HjCtlh1dXVUVNS1a9euXr06a9Ys4qitBPaEWh297C39FyAjI4NhmPnz57e2tprNZqPR2NbWxmcGT09PjUaTmprq4eERGRk5f/78kydP8twIJ0+eHDJkyJIlSxQKxcSJE5ctW5abmytIl3DAfYU4C3xkZGRNTU1TUxM3evny5cjISP5j5OXlLViwwDZaXV0dGhrqINlID60kbDxhW8xkMsXGxsbExJw5c0YaN8IAAAZoSURBVCYsLIyb6ICtBJ0Isjp631v6L0NpaWlRUZG7u7tCocjPz//ggw/CwsL4zDBs2DCTyWSxWLhRlUrl6urKcyMYjUar1WobpZSaTCZBuoQj7it4O52PZ1OnTl26dOmDBw8OHjyoUqnu3bvHf4aKigqJRLJ169a6urqCggKlUllaWipstjVr1tifst5tEp7j2UcStsWOHz/u6elZVVV18zf19fU9fbojdDCw4X91PFNv4UF8fDx3Fj3PGcaPH79q1SqdTvfll1/6+PgcPXqU5wC3bt1SKBQ7duwwGAznzp3z9fXNy8vjLYMD7lHtibbANzU1vf766yqVKioqqqSkRKgY586dGzt2rEKhiIyM/OKLLwTP1qk7dpuE53idIgnYYps2ber09Xf69Ok9fbqDdDDg8L86nqm38MC+wPOZQafTTZ8+3cPDQ6vV7tq1i/8AlNILFy6MGzfOzc1Nq9Xu3LmTzwwOuEe1x1BK+fmpAAAAAHgjzv/BAwAADHAo8AAAACKEAg8AACBCKPAAAAAihAIPAAAgQijwAAAAIoQCDwAAIEIo8AAAACKEAg8AACBCKPAAAAAihAIPAAAgQijwAAAAIoQCDwAAIEIo8AAAACKEAg8AACBCKPAAAAAihAIPAAAgQijwAAAAIoQCDwAAIEIo8AAAACKEAg8AACBCKPAD16uvvsp0odVqa2trZTKZ0OkAoLdGjx5t24Td3d1feeWVysrKPiwnOTk5KSnpj90DcMv8o5YGzwQFfuD6/PPPdTqdTqd7++23Y2JiuOFLly55eXnt3LlT6HQA8Aw2b97MbcLFxcXt7e2zZ8/u86KeugcYM2bMtWvX+rx84I1U6AAgGLVazQ24ubk5OztrNBrbS0uXLhUoFAD0hVKp5DZhjUazcePGadOmNTQ0+Pv7c69SSk0mk1wu782iFArFk/cAd+/eNZlMvz8z9DccwUNnth/ompqaVCrVhg0bPDw8AgICsrKysrOzBw8erFarP/zwQ27m6urqmJgYlUo1ceLEgwcPChocAB4xmUyNjY1qtfrMmTMhISGXLl3qdms9efJkRESEWq2Oj4//5ZdfiN0egBBSUlIybtw4pVI5fvz40tJSQshLL710586dadOmHTt2jPSwB+i6TBAGhQHvnXfeiYuLs43eunVLKpVSSg0GAyFk4cKFdXV1aWlphJB58+bdv38/MzOTZdmWlpb29vaQkJCUlBS9Xn/+/Hl/f//Tp08L93cADFBRUVGpqakGg8FgMFRUVLz44otardZqtep0OrlcnpiYqNPput1aq6qq5HJ5dnZ2XV3djh07GIZZs2aNbQ/Q2NioVCr37t1bX1///vvv+/j4mEwmSungwYNLSkoopb1fpsANNFChwMNTCvydO3copdzX8LKyMkop9+vczz//nJ+fHxAQYDQauTdu3rx55syZQvwFAANaVFSU7ZhNJpONHz/++vXrlFKdTkcIuXHjBqW02601JSXFftufMmWKfYFPT0+Pjo7mXrJYLFlZWQaDgdoV+N4vk4dGgK7wP3h4Ch8fH0II95NdYGAgIUQq/bXb1NTUNDU1BQUF2Wa239EAAG/S09NXr17d7UvBwcGkh621rq4uLCzMNiUiIsL+jbW1tVqtlhtmWXb58uWdltyHZQKfUOCh7/z9/aOiooqLi7nR27dvm81mYSMBQCcSiYT0sLXm5uaWl5fb5qytrQ0JCbGNBgYGnj17lhumlG7atGnRokWhoaG2GfqwTOATTrKDvouJifnxxx/T0tIMBkNxcfGYMWOuXLkidCgA6Ea3W+uCBQtOnTr16aef3r17Nzc3t7Cw0P4ts2fPvnDhwqFDhwwGw7Zt27Zv3+7l5cW9dP/+/b4tE/iEAg99p1KpCgsLCwoKQkJCFi5c+N57782bN0/oUADQjW631rCwsKNHj6alpQ0dOjQ/P3/jxo32bwkKCjpx4kRaWlpwcHBeXl5BQYFKpSKEzJ07d9asWUeOHOnDMoFPDKVU6AwAAADwB8MRPAAAgAihwAMAAIgQCjwAAIAIocADAACIEAo8AACACKHAAwAAiBAKPAAAgAihwAMAAIgQCjwAAIAIocADAACIEAo8AACACKHAAwAAiBAKPAAAgAihwAMAAIgQCjwAAIAIocADAACIEAo8AACACKHAAwAAiBAKPAAAgAihwAMAAIjQ/wMO8QAD62mMDwAAAABJRU5ErkJggg==" /><!-- --></p>
 <p>However, note that in the case of using this error model, the fits to
 the Flaach and BBA 2.3 datasets appear to be ill-defined, indicated by
 the fact that they did not converge:</p>
@@ -1728,7 +1722,7 @@ Status of individual fits:
 
       dataset
 model  Calke Borstel Flaach BBA 2.2 BBA 2.3 Elliot
-  DFOP OK    OK      C      OK      C       OK    
+  DFOP OK    OK      OK     OK      C       OK    
 
 C: Optimisation did not converge:
 iteration limit reached without convergence (10)
@@ -1780,7 +1774,7 @@ indicates that this difference is significant as the p-value is below
 <pre><code>                        Model df    AIC    BIC  logLik   Test L.Ratio p-value
 f_parent_nlme_sfo_const     1  5 796.60 811.82 -393.30                       
 f_parent_nlme_sfo_tc        2  6 798.60 816.86 -393.30 1 vs 2    0.00   0.998
-f_parent_nlme_dfop_tc       3 10 671.91 702.34 -325.96 2 vs 3  134.69  &lt;.0001</code></pre>
+f_parent_nlme_dfop_tc       3 10 671.91 702.34 -325.95 2 vs 3  134.69  &lt;.0001</code></pre>
 <p>In addition to these fits, attempts were also made to include
 correlations between random effects by using the log Cholesky
 parameterisation of the matrix specifying them. The code used for these
@@ -1801,7 +1795,7 @@ effects does not improve the fits.</p>
 <p>The selected model (DFOP with two-component error) fitted to the data
 assuming no correlations between random effects is shown below.</p>
 <pre class="r"><code>plot(f_parent_nlme_dfop_tc)</code></pre>
-<p><img src="data:image/png;base64,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" /><!-- --></p>
+<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqAAAAHgCAIAAAD17khjAAAACXBIWXMAAA7DAAAOwwHHb6hkAAAgAElEQVR4nOzdd3wT5RsA8Ocue7VJkzZddFJKGS0gskRAyp4FRECULQ6WikoVflJliQgyZIiALKGAqIzKUBEFZI9Cy+reaZukadM28+5+f0RrgVIqTZoSnu8nHz/X9+7e97mY8OTmQzAMAwghhBByLaSzA0AIIYSQ/WGCRwghhFwQJniEEELIBWGCRwghhFwQJniEEELIBWGCRwghhFwQJniEEELIBWGCRwghhFwQJniEEELIBWGCRwghhFwQJniEEELIBWGCRwghhFwQJniEEELIBWGCRwghhFwQJniEEELIBWGCRwghhFwQJniEEELIBWGCRwghhFwQJniEEELIBWGCRwghhFwQJniEEELIBWGCRwghhFwQJniEEELIBWGCR43Xs88+S/xDIpH06tXr1q1b9e82Ly+PIAiKoh65ZFZWFofDqf+INdIY6EV/ldmrt7Zt2xL3GjlyZN23tHYajcYu/TQeD75dTZo0gWqfjaoJh34GEHIoTPCoUfv0009VKpVKpTp9+rTRaBw5cmQDDNqhQ4crV64AgFwuX7t2rYNG0Rjo07kmO3a4ePFiVTXffPONHTt3Pfe9Xbb/4w965Geg6tOCUGPDdnYACNXGzc1NqVQCgFKpnDt37oABAwoKCnx8fBw6aHFxscViAQCxWDx16lSHjmVHVe9VlYqKCmcFYxfmzDlUyfEaZ/HCd5HCiPp0/uDbVaNHfgaqPi0INTa4B4/qymKxlNgJwzD1CQMALl261LVrV4lE0rJly507dwJAQkJCjx49xo8f7+7uHhAQ8Mknn9A0nZaWJpFIqtbt37//5s2bq/d27Nix9u3bi0QiPz+/uLg4AOjRo0dOTs6AAQP2799f/fDsgyNqNBqZTLZhwwZvb2+xWPzSSy+ZTPbcI7evB7f0YY3nzp3r1KmTm5tb586dL168WNXD4cOHIyIiJBJJg20py60rAM2P+J7f4seqF8fnLYLkkIKmDRAA3Hua5sHPQPVPS8PEg1Dd4R48qhOKoiIiItLS0uzS25gxY3bt2lWXJcvLyzUaDQAUFhYuWbIkLCysSZMmWq22T58+c+fO/eGHH86fP//yyy8HBAQAwB9//DF+/PgbN26kpaWNHTtWqVT27t27ls4rKyuHDRv22WefjRo16vr164MGDYqJiTl58mRwcHB8fHzHjh2zsrJsS9Y4YsuWLcvKyo4dO5aUlJSXl/fCCy/Ex8ePHz8eAEwUs/ZyuVLEGttSaJu2MgAAJUa6oJxqoeBQDPyVa8rVU5+f11MMXFGZCQKeUXIJAvzE/65l66GO7+r06dOnT59e/a2rfUubNWv2YKOvr2/fvn1XrFgxYMCAzZs3Dxw4MD8/39bJ9u3bT506VVBQ0KVLl++//37s2LF1DOyRqJKfaf3FGmcRQJgzPiBFkVXLWou+I927WXIW17g823cmwZbVZdD73q6YmJgff/yxluVr/AxU/7TUZVCEGhImeFQnBEH4+vpqtVq79Obt7V3HJefNmzdv3jwA4HA47du3379/P0EQP/30U2ho6OzZswFg8ODBEydO3L59+7Bhw4RC4dq1a0UiUUBAwLx587Zu3Vp7gudwOFevXg0PD6dp2tvbWygU2n5MPKjGEZcuXUrT9LJlyxQKhUKh6N27t1qtti3PJgi5gJTxyappo5UBABIIk5V05xI0A25cgkWAbVrGI1gkIeURAFB9Ldt0HS1evHjSpElVfwqFQp1OV8uW1tj4xx9/PPvss5MnTwaAjz76SKFQlJaW2jpZunSpbUv79OlTtaV2QbBlwHavcRbpMZAq2kZIniFIIQBQ+r8IjoIURdXcD8EjCG4dB73v7eLxeLUvX+NnoFu3bnUcDqGGhwke1QlJkn/++WfDj7ty5cpZs2bd15ibm9u06b9HaMPCwg4dOgQAvr6+IpHI1hgREZGdnX3fivedGmCz2YcPHx4+fDiLxQoLCyPJh2bTh40IAP7+/rYJLvff1MIiYXxr0YPT1UUH8Wf9WvJ6W3GNIz5srVrUclK5xi2tsTErKyssLMy2FkmSb7zxBgDYfvcEBgY+uKV2QUo6k5LOD5trpg1g1XOCPmIsxdaCtbwWP5OCsPoPWsdz8FVq+Qwg1DhhgkdPHj8/v4SEhKo/09PTbVk2Pz+/srJSKBQCwN27d22NFEUxDEMQBACoVKrq/Rw5cmTp0qUXL160pa6qxFb3EQHA1nMjV+OW1tjo5+f322+/2dZiGCYuLm7cuHFSqRQAavkB5FCcJv8zJnakvcZZVV+zFaPtkt0fQy2fAYQaJ7zIDj15hg4deufOndWrV5eUlBw9enTz5s22M9+VlZUzZ84sKCg4ffr0ggULxo8fL5PJjEbj1q1bjUbjpk2bEhMTq/dTUlLCYrFsK65evTotLa3qsvOq49K1j/ikqHFLa2wcOXLkn3/++d1336nV6uXLl69atUoulzs3eIIt4/i9a057nSo5wvZ/317d2i7vqI6m6VqWr+UzcN+nBaFGAhM8evIoFIpjx47Fx8cHBAS8++6769ev7969OwC0aNHC19e3Xbt2o0ePnjx58htvvOHh4bF8+fI5c+aIRKINGzYMGTKkej8vvfRSdHR069atW7dubTabY2NjJ0yYoFarR40aNWLEiH379j1yxPpQisghYYJ6dlJHNW5pz549H2wUCoWHDh1asWJFYGDgrl27Dhw4YNt9dy62cjLQZk6TuQTLzV59xsbGKu6Vk5NTy/IP+ww8+GlBqJEg6nPDEkKNR0JCQmxs7I0bN5wdCHIM2gTkI66DQwhVh3vwCKEnAWZ3hP4jTPDIRQQEBLz00kvOjgIhhBoLPESPEEIIuSDcg0cIIYRcECZ4hBBCyAVhgkeNWnJy8uDBg319fWUyWXR09NmzZx+2ZFUB7zNnztTyyBpXJZFIqkqbKxSKcePGlZWVPXKWTVxcHEEQq1evfljn6enp/fr1k8lkAQEBCxcufIwFEEINDxM8spvKSzdKtv+oi08wp9d2P3HdXblypUOHDm3atDl69Ogff/zRrl27Pn363Llzxy6dO0sFw3xdVk5Va8mzUrvL7VDXddeuXSqVKj8//+eff05MTPzggw/qMgsA4uPjg4OD9+zZU2O3ZrO5e/fugYGBN27c2LZt28qVK7ds2fKfFkAIOQUmeGQfxau26vYeIaVuQBJFS7/WH7XDg+unT5/+3nvvLViwIDIyMjIyctmyZT169Fi3bl39e3YiHsAFkylWU2LL8XlWanyx2i5Pu/Xw8FAqlT4+Ph06dJgwYcL169frMuvq1atpaWkbN248e/ZsjU96OX/+fGlp6VdffeXv7//CCy+8+eabP/30039aACHkFJjgkR1UXrphzS/yWTLbfUi09KUB3ovf0+07Qun09ekzOzv77NmzM2bMqN64bds2295njYXM72O1WkeOHDlo0CCTyZSWltavXz+pVPrcc8/ZKnk7C5sg1is89AzzvqYkl6ImFqsnSMSjxf+trkzt8vLyfvjhh+HDh9dlVnx8fO/evXv16hUSErJ3794HV/H29l65cmVVTXStVnvfQ+kfuQBCyCmw2AyqK8PVm5UX/n2Wu7jXc7zQAFu7bk8CwWJpN+2tauc1DdT/cprS6h5cvo6Sk5PlcrlCoaje6OHhAQ+pbu7p6Vl9SavVOnbsWIPBsH//fgDo3bv32LFjd+zYkZyc/PLLL8vl8v79+z/Ou1A3Rob5XFdGwf33oDZhsae4iWkAJUmeNJkGFBS14nJSLJb5Jbqqufet25zDGVO39N+vX7+q6ZYtW44bN+6RsxiG2bNnz4IFCwBgxIgRe/futZVDrS4sLKzqmobff/99586du3fv/k8LIIScAhM8qiu2Us4N+TdDs2XuVe1shQdDUba5tnbaZGYr5Syp24PL1xFFUQ8r1FZjIfPqCd6W3RMSElQqFY/HO3DggMlk+vjjjzkcTo8ePd58882vv/7aoQmeQxBRXI7pgWdM+LJZtrkhHPYvBqOMJC0MRHC45L1zq68byqnrl3TXrl09e/YEAL1eHxcX17t376riOg+bdfbsWZVK1bdvX5PJNHDgwM8//zwjIyM4OPjBzg0Gw/z589etW7dt27YBAwY8xgIIoQaGCR7VFcdXyfGtoX42x1cpHdmvaOlGwdTRbIUMAAzX71gLioQdokj+4z9etEWLFmq1uri4uHrm/uGHH7Zv3/7jjz/WXsc9MzOzS5cuPj4+q1ev/uijj9LT0zUaTUDAv79O2rdv/9iB1QULYKhI+LC5RRT1XXnFW+6Sl0TCmZqSCybTMrmMVbd1a2E70Q4ASqXyk08+adq0aXp6ekhISC2z4uPjTSZT9bLoe/funTNnzn09p6WlDRkyxMfH5/Lly+Hh4Q8O/cgFEEINDxM8sgNuSID78L4Fcz7nNQ2kTWZrQZFi1oT6ZHcACA4OjoyM/PLLLxcvXlzVuGnTJnd390fWcff399+xY8dvv/02dOjQ0aNH+/j4tG/f/vTp07a52dnZVqu1PrHVRznNvFqkniQRvywWAcBKuWyaWruopPTj/3iEo3a2Hz3l5eW1zKIoau/evVu3bq0qezp79uw9e/bcl+AtFkv//v0HDx68bNmyGk+uP3IBhJBTYIJH9iHp+7ywYxtzWhbB5fCaBRM8bj07JAhiw4YN3bt3pyhq7NixFEVt3rz5xIkTly9fvnbtWlUh802bNlWv427D5/NJkuzdu3dMTMxbb70VHx8/a9asFStWjBs37vbt2y+++OLKlSttu7YNT0ASn3vI2v3z/vAJYp3C447FUv+eS0tLNRoNAJSXl3/yyScBAQHNmzevZdbJkydLSkqGDh1a1cPIkSNXrFiRkpJS/TfT4cOHNRrN1KlTMzIybC1CodDHx+eHH34ICgpq167dwxao/xYhhOqFQagRu3DhQnR0tEKhkMvl0dHR586dYxjGbDaPHTtWIpGEhIQsW7bsww8/lMlk165dAwCr1Xr69OmmTZvaVlepVFKpND4+/urVq926dROLxYGBgStWrHDqNjmEWCyu+lLzeLwOHTpcvHix9llTpkwZPHhw9U5omg4ICFiwYEH1xgdvUhg0aBDDME2bNo2Nja1lAYSQc2GxGYQQQsgF4QkzhBBCyAVhgkcIIYRcECZ4hBBCyAVhgkcIIYRcECZ4ZE+FpvwKqoZ7rxFCCDUwTPDIbqyM9cuMBTtzv7ZXh48sZG5fVRXlHTdEdaWHi+/2umRRmezSm3PrwZ84caJdu3Yikah58+a7du2yyxYhhOoJEzyym1+LDym5PqmVt1Mqbtmrz9oLmf9XHTp0uHLlir1iqw/doeLsWbcFUZKU/lfsleOdVQ9eq9WOGDFi3LhxKSkpM2bMGDdu3M2bN+2yRQih+sAEj+yjzKo7UvzjGL8pL/qM+y7vG5qh7dJtLYXMH4ZhGLPZXOOs4uJiiz2eGVdPpUfVObNuh+6NarI8XD7R726/K5Z8O+R4Z9WDP3funJeX19tvv+3r6ztt2rTAwMDz58/Xf3MQQvWECR7Zx/cFO7p6RHvzfDtKn+eTgjMlJ+zb/32FzC9dutS1a1eJRNKyZUtbfffCwkKZTHb8+PHg4OCzZ88uXbo0ICBAJBJ17949JSUFAHr06JGTkzNgwABbAVlnVYgvParOfutW6L4o4TNuAKCcGaCY5Hd3gH1yvE0D14Pv1q3byZMnbdMFBQUajaZly5b22haE0ONz9qP00BPjsu7suszPU8pvPTj9RdrH05NeqbRW2NpPa357O3nCWe3JGpevu+rPWAWAli1bFhYWMgyj0WhkMtkXX3xRWFh48OBBsVj8xx9/qFQqLpc7ceJElUp14sQJiURy5syZnJyc4cOHDx061NZhUFCQ7WG3RqMxODh43rx5RUVFv//+u4+Pz88//5ybmwsAVqu1/u8VVWFNn3Aj/ZXreXGpVdPpr1y/O+DKFfff7g66Ur09Ly41Ly71mvfJ9LGJtsVsr7y41Pq/V7XPomk6MDBw+/btDMN88MEHHTp0qH2UEydOuLm5JSQkPDhLp9O1b99eLBYvXLiw7mEjhBwH9+BRXQUKQtu7d/Hm+d03HSAI0Zo1Q5WjBCyhrT3K7dlIyTN3Km7WuPx/Yjt5rFKpUlJS2rRp07t3bwD46aefQkNDZ8+e7eXlNXjw4IkTJ27fvh0AzGbzhx9+qFQqTSYTTdNqtdrT03PPnj27d+++r9ujR4/aKsR7enpWVYiv/1tUhRSwPEZ6S4cr3fspqqalw5WyUUq2ksuWsau3S56X6U9qJV2l0hF/L2Z7ufdT/KdBa3yvap91Xz34CxcuVBWMuY/BYPjggw8GDx68ZcuWGsu9i0Sizz77bMaMGWvWrDl37tx/ihwh5AhYTQ7VlZzrKed6PjidVnmnzKpzY0sv6s7YWm6VXw8QBO/J39rfM0bMlty3/H9SYyHz3Nzcpk2bVi0TFhZ26NAh27StgGy/fv2WLVu2aNGiMWPGtG/f/uOPP46Ojq7ercMrxBPgPuDf9Fx9WtpfkTLwqi6hWNTR3X2Agiqzpg69KmgtCVjVHIh6jemsevA6nQ4ApFJpdHR0dHT0nTt3vvvuu06dOtVrYxBC9YZ78Ki+CCBaSKIul569VPpX1etuxc227h0MdKUdB6oqZO7n55eenl7Vnp6e7u/vb5u2lZFNT09/4YUXzp8/X1RUNGTIkHHjxt1385utQnzBP86fP79q1So7hloLtic3LKFt2VFN3sepdszu9/lP9eCrDui9++67D15Lbyv33q9fv+PHjz+Y3QFg9erVVRXlAcDPz89oNNpzYxBCj8eJpwcQqp1YLN6zZ49arVar1ZmZmePHjw8ICDCZTMXFxe7u7qtWrdJqtUeOHJFIJCdPnlSpVPDPGfRNmzYFBwdfv35drVYvWbLEy8uLpmmGYYKCgo4dO8YwTElJiZeX1/Lly4uLi0+dOqVUKnfv3m3Hc/CPZC40JT9z9nrYqex3bzO0HTp82HtVy6xff/2Vy+WWlJRUdXL27FkAuHv3bvWef/jhBw8Pj9u3b6f+Iz8/n2GY/fv3X758mWGYq1evCoXC/fv3a7Xa48ePu7u7Hz582A6bhBCqH0zwqPGqpcb5uXPnOnfuLBaLIyIidu7cyTBM9QRvsVjeeustT09PoVDYuXPnv/76y7bWnDlzxGLx3r17GYZ5sEJ8QyZ4hmHMhaaib3Lskt0Zp9aDZxjm4MGDHTp0sD3oZvPmzfbZJIRQ/WA9eIQQQsgF4Tl4hBBCyAVhgkcIIYRcECZ4hBBCyAVhgkcIIYRcECZ4hBBCyAVhgkcIIYRcECZ4hBBCyAVhgkcIIYRcECZ4hBBCyAVhgkcIIYRcECZ4hBBCyAVhgkcIIYRcECZ4hBBCyAVhgkcIIYRcECZ4hBBCyAVhgkcIIYRcECZ4hBBCyAVhgkcIIYRcECZ4hBBCyAVhgkcIIYRcECZ4hBBCyAVhgkcIIYRcECZ4hBBCyAVhgkcIIYRcECZ4hBBCyAVhgkcIIYRcECZ4hBBCyAVhgkcIIYRcECZ4hBBCyAVhgkcIIYRcECZ4hBBCyAVhgkcIIYRcECZ4hBBCyAVhgkcIIYRcECZ4hBBCyAVhgkcIIYRcECZ4hBBCyAVhgkcIIYRcECZ4hBBCyAWxnR1AfTEMk5iYSFGUswNBqF4EAkGLFi2cHUVjh9935Boa5vtOMAzj6DEc6s6dO23atGnZsqWzA0GoXq5du1ZRUcHj8ZwdSKOG33fkGhrm+/7E78FTFBUSEnLp0iVnB4JQvQiFQpqmnR1FY4ffd+QaGub7jufgEUIIIReECR4hhBByQZjgEUIIIReECR4hhBByQZjgEUIIIReECR4hhBByQU/8bXL3ofUXLLlLapzF8hjCVk5s4HgQQgghp2joBK/X63fv3p2UlFRYWGi1Wn18fKKiokaNGuXm5maX/gl+IF1+iRO4gOAF/dvKWM2pr7G937DLEAghhFDj16CH6M+cOePn57dhwwaapiMiIlq3bk2S5KZNmwIDAy9cuGCXIQiOku37LqX7jeXeo+rFGO6SojYsWV+7DIEQQgg1fg26Bz9z5syFCxfOnDnzvvYdO3ZMnz7dXjme4zvNkNiFKj3Bcu8JAIy1xJK/ktfioF06RwghhJ4IDboHn5aWNnTo0AfbY2JiUlNT7TYMweUGzLdkfgSMFQAsOQtYipGkoLnd+kcIIYQavQZN8D169IiNjS0sLKzeqNFoZs+e3a1bNzsOxPIYTHD9bqTP3pk6m9Ie5vi9Z8fOEUIIocavQQ/Rb9y4ceLEiX5+fsHBwXK5nCAIrVabnp4eHR29a9cu+4xBG2nDbQAwyWN8Mt4TkQKtx0AfUxZjyiK4/gRHYZ9REEIIocatQRO8l5dXQkJCVlZWcnJyQUEBwzBKpTIqKiogIMBeQ1Blp023R5KiyDKzWkiwPenKIu1RU2UyY0jheE/lBMy310AIIYRQY+aE2+SOHTtW/TY5lUplx9vkWNJoUtKpVNp/ue5q4F1fbqa5fAiviyDomfzP2D5v2mUIhBBCqPFztdvkAAhO0GIyd2nHvKjOb3XrsKhvk8P+ovxltM+bBMfLTkMghBBCjd2TdJucXq+3Wq33NZaVlTEMU73lolnPMwZ2PHElZfTzljt0i990vlH0TzRvbP03ACGEEHpCNGiCr+U2uVmzZtW+bnl5eXh4uNFovK/darVWVlZW/WmmTT+evhAzd7Zs7Swio3c2GdJp2LbclZNTOktzpmU24QfVeyMQQgihJ8ATc5ucWCzOz8/XPuC7775js//9mXLrYuKQ6c13zHDb5T5I2GIVvPhDGk8YO71fz6+kt7ZdcchWIYQQQo1Pgyb4jRs3lpWV+fn5hYWFderUqXPnzuHh4d7e3tnZ2Vu2bLHLEH5JfpSAnRMhu674qEmpYahm91rlV16cytyWRMjZULsMgRBCCDV+rnabXPEozc3rV2ZNbf99LPnSbOsdfcn0DysCbnIllZrkOH1TiLLXQAghhFBj5oRysYGBgYGBgQ7qPMCqGjV2G/T9tr2J1V6QqVObByumNhvLYeRWhsRasQg5U2VlZXh4eE5OjrMDQeip4Gr14IXiZwnakrVh6g3vCJ5xGkDehf1vAKVsuSBOIO3p7OgQeopkZGRs3769eovZbM7Nzf3kk08AYP58fOoUQo7VoAl+165dp06detjc9evX13+Iymusoi0vWd757VfqefJ3IRTD7iHu/s8k+19tbbgbqJhS/xEQQnXCYrF27tyZk5PTv39/25Wwtttck5KSnB0aQk+FBk3wbdu2PXjw4J49e1555RWFwiGPhdcmletOD5BO/j3II+sPuRRSIYQo78z9vuD8VzyBTjHFzxGDIoQeFBAQcO3atVmzZt26dWv79u2hoaEVFRVisXjfvn2PXFev13fq1OnB22KNRqNarXZMvAi5mgZN8BERETt37jx58mRsbGzLli0dMcT6YJH1eXnM0nGjZi7ezPECAI/MvZkVAxOLPI+85vWjI4ZECD2ESCTatGnT/v37e/bsOW/evDFjxtRxRYlEcuTIkQcfbHXixIlp06bZO0yEXFNDn4Nns9kzZsxwd3d3UP9yAbn1WQX12/Ozkg6HuhWkAygNGcnHlq172b+VoEHvCUQI2YwYMaJTp07jx4/fu3dv3deq8eaa5ORkgiDsFxpCrswJF9nNnTvXcZ3Pai8ZJyvV3D5GEh+1lQ36BaAoud2YMcV9/AlF366OGxchVAs/P79ffvllzZo13t7ezo4FoaeFq11FzyZBpNUwSqnXtOfLfo8AOJ/hpZAHeFiLi/ls/OGPkNMQBPFgHYoGxlh1dMW1muYQpLAlwXHIhUEIOYurJXgA4PgpzRm5jJW6LRwKcN50QW3qmsGPbO7suBBCTkaXnjClTmW5PX9fO6U/z2nyIcdnhlOiQshBXDHB+3uzwoLy43b0+rHyJACoDcXfiEMvt2MAcBceoacZSx5D5q9he41nyWOqGmn9RTplAkc52YmBIeQILnfdGUMxpsw/o1sXH+BJ/SoAQOddRDPUsdePXTbonR0cQsi5SG7QZ+bs/wFt+KeFNmd9yAmIA1LozLgQcgBX24OndL+Y7ozpp1JSS4jUAjNMBVqU6f71op5UsWHTMJixwdkBIoSciZR0JMXtLQVfcfzeBwBr8W4gSLbiRWfHhZD9udoePEva25wVVHJwiqR/8raQrQCQq5FvDFhEcsWFq3rkzUt1doAIISfjBi60FnzNmHIZqtySs4gbuARP3yGX5GoJHgiW/tLbsiGbKi4Xj/PwBgCToWJg0f9KLw4xF3tJB+FVsgg97QiuH1s5yZLzqTVvOcv9BVL8jLMjQsghXC7BAzRZNl5f3ky3ZcGhFAMAGE2VoVpDwcI+xP7Wok5SZ0eHEHI+jt/bVNlf1qLtnICPnR0LQo7iaufgbYTDVxG+L7w9pcd3BGEwmTVrXv1yteyL5xlnx4UQahxIITfkS8aiIThKZ4eCkKO44B48ABykPNMCXxF/uEvIIxkGVg51L+hQtr7wmLPjQgg1Fixpb7bnaGdHgZADueYe/ASJuIB8lYnq6SaBciP0YVGe5sMl1tKUivZhoghnR4cQQgg5nGvuwQPA+sKfl7vN5QsFAHDqnO9wv9cAmO/yNjKAB+oRQgi5PtdM8Ed1Vw+w+uSyQMiWAkDgzwVHlpTz2LI8Y/a5kj+cHR1CCCHkcK6Z4DMZaajx6NgrUo8KIQAkvfRL+8MVnbb3YxEcd7bM2dEhhBBCDueaCb6t+Wy3c0m+XxgqgwEAVPL806u/8vor49mdTXOMmU4ODiGEEHI8F7zIjrEUtk6fEeEpY6+5Ev5Z0SWA3mWaqW4ELF3a4k6gZfdUcHLJSoQQQsjhXHAPnmDLaUtHc1bYb9x389htAaBE0+WS5DkrKSi63dWSb3J2gAghhJDDueIevLWE5F8WtmWevWxp7q46CaBOuxyUzX2OmOUAACAASURBVAZ2qXvbKz7DFzk7QIQQQsjhXHEPnuPJ8ZlBiiIEvhLZnt4AkFEZJrUaGAtf2XcJkFhVAiGEkOtzwQQPABy/2bRBDZBs7lwBAIQ5yaD3qjjf+cRRL2eHhhBCCDUE10zwxjt04brRlRJe2IA/AMDIUjN+RUs6vCn+IE13qNjZ0SGEEEIO54IJXltpKj6/hyG5fCHX080CALpKOMvtxvVP8onJLdlf6OwAEUIIIYdzwYvsPvnt6qLm71lD+WQ5ESCsAAB9uaWz+c9owzFLP6m06y1nB4gQQgg5nAvuwU/q2JZiKWmh/81TX9HZkQCgLuNe/zGOBKYyMIYUsZwdIEIIIeRwLpjgt1w3fKVdwbWk/96R/DMjFgAq9NaQ8H2VLP5F9mRnR4cQQgg1BBc8RL+qlxSgvzG5/QT2+33G/s7ZRJqsFo9mF9ZLY7NE5mHODg8hhBBqAC64B2/DDvtWQpWMVW8RSvgAUKqVv3IjbLh7sLPjQgghhBqC6yZ4rg/X89U3lGtlQhIAdOWvC/JL+grFzo4LIYQQaggueIi+CjdocXnhHk+ZMTMfytlBlqL0L9I+fi/0U2fHhRB6YphuDacrrtYwg+TzI88RbPcGjwihunLlBA8k/4Bhrrv4HQDQs4BSaVSm4kqqQsgSOTsyhNCTgZS+AEBzw7ZWb7Sq1tPl1zC7o0bOZQ/R2xwoeYnktAWAUrPJfVifJRHrMLsjhOqO4/06bcqjyy8TbKntBYzFWriFE7jQ2aEh9AgunuB5LELq7gkAWp3ObdALHIL7dfby62WXnR0XQugJQXC5gQssWR8BY7E1WHIWshVjSEGYc+NC6JFcPMFXWBhaoACAQq0WAA4V7r1Qcnpz9irrP99VhBCqHUvWj+A1sRZuBgC64jpVcpTt/56zg0Lo0VzwHPypHJPezNim1QYauJ4AcDmz+KdLBQnECQ6Xa2QqDxfui/F+2alhIoSeGJzAxaabA1nyF81ZH3KazCVYbs6OCKFHc8EEfzzDqDPRtmkLxZBSbwC4W6DelpLJ8XP7osWcX9WHjxT/9IK8vztH5tRIEUJPBlLQjCUfYbo1DAiS7TnW2eEgVCcNneD1ev3u3buTkpIKCwutVquPj09UVNSoUaPc3Oz2i3hBt38vbR31k6Z9O/9LAGEsjVeL5a1yvQIEwaN9J13Undmet2FG0If2GhQh5No4/rHGxI68sG+BwHoW6MnQoOfgz5w54+fnt2HDBpqmIyIiWrduTZLkpk2bAgMDL1y44IgRTRSTaREDQEbBLYIg+51U7Mz7OtOQNtxnbFLZlYzKFEcMihByPQRbKmh3g3Tr4uxAEKqrBt2Dnzlz5sKFC2fOnHlf+44dO6ZPn+6IHG+0wh2DCAA0urKhnqO5uafcWVK1uTBaPvBY0YFvcr5cFL6WAMLu4yKEXBDBdXYECP0HDZrg09LShg4d+mB7TEzMrFmzHDGiv4SVL5YCgEFvPaz5/vAcilAfAIC9+d9aGcpsMd0tTw4Xt3LE0AghhJATNegh+h49esTGxhYWFlZv1Gg0s2fP7tatmyNGfKudiCeRAYC51CRkiYQ8NwFLZHtJ2G5yriefJXDEuAghhJBzNege/MaNGydOnOjn5xccHCyXywmC0Gq16enp0dHRu3btcsSIfDZBsYUcNmkymBeFruVyuQDw/q3XFoV/xSV5jhgRIYQQagwaNMF7eXklJCRkZWUlJycXFBQwDKNUKqOiogICAh65bnl5+ciRI61W633tarX6wcYqYR7sT553/0vA0+gNOp1ORhPWIq2fJLCCKscEjxBCyIU19G1yV65cOX/+/HPPPTdgwICEhIStW7daLJZRo0aNGTOm9hVFItHcuXONRuN97WfPnr158+bD1uKQRFslRyoUavQGrVYrLjWVnzz39px5dtgShBBCqBFr0AS/c+fOCRMmtGjRYvbs2fPmzVu/fv3rr7/OMMzbb7+t0+nefPPNWtYlCKJr164PthsMBoKo+TJ4qvQEVX71QoFZJqWhENQp64JCZGyPK5YCC3i9wmFJ7bNVCCGEUOPToAl+wYIFW7ZsGTduXEJCwqBBg86cOdOlSxcAeO655956663aE/xjMKe/zZhywymR3KMSAIpSd9NKAS/CYMk6fUB3ZUzEFvsOhxBCrsdavNuq2ljTHILT5COWtFdDB4TqrEETfF5e3nPPPQcAtv9GRUXZ2iMjI/Py8uw+HDfoc9OdsSr/I1LJDICTlZ5fCju8UnYonOXL0wrC7T4cQgi5HlIQxhhTeeHxUK3WNmNWmVImk4KmTgwMPVKD3ibXrl27FStW5OTkfPbZZwRBxMfH29rj4+MjIyPtPx5DEVzPpurhcmUZAKgzfzSnz2J7FrNlfd/yf83+wyGEkMshxe1ZHoOo0hOkqE3Vi9Ie4Pi8QfCCnB0dqk2D7sGvXr164MCB69atCw0NPX/+fExMzNatW2mavn79+pEjR+w+HKU/A0AyliJ3EgBAq9VSmvME14vS7Db7zSRZYg4+lwohhB6FE/CJMbEL23MswQ8BALriGqX7nd/morPjQo/QoAm+TZs2WVlZOTk5gYGBJElevnz58OHDZrN5165dgYGBdh+O4/cBpd5/vvw5qew0AGjKaIY2kZLn2ZJnj+rOAsBg5Ut2HxQhhFwMwfFi+7xlzv6E12wbAGPOjOUExBEsibPjQo/Q0LfJsdns4OBg27S3t/eUKVMcNxbBlnL83heU7pO5MQCgK7jA9hhDlV0y5k5t1skr25juuKERQsiVcHymGa53oUp/Z8yFQJvYnrh39ARo0HPwDY+tnOjGLhUrOwGAtoygDXdZbu+XHTwVLm7ZWzHY2dEhhNATguRxA+ZbsuZZchZwg5e5fO5wDa7+P4lgbdV9JOekAYCmQkKwPTjBL1uLSxiK0piLaYZ2dnwIIfRkYHkMIdhylltXUtze2bGgOnH1BA/wcqf+Xr7NAUCnLeEELSbYLJaHu7VQszV37a3y686ODiGEnhjcZju4ISudHQWqK9dP8G28OL5RcQCgKSNIYSsA4Ph6WVXFHaXP80ksJYeQozAMs2XLliFDhsTExFS/TaagoGDwYDxB9kQi2O6A/2w+OVw/wX9/u9IgakUQoCuz0jQNAB7jh/Oah3T1iA4V4eNu0NOLqlX9+1+0aNGcOXOioqL8/PxGjx69f/9+W3tlZeXhw4fr3z9CqHYNfRV9wzuaYZLw+BIet8xoLisrk0qlHH9v26z0yrshwmbODQ8hZ2Gza/v6MwxTz/43bty4b9++Hj16AMCIESOGDBnSqlWr8PC6/qq+efOmwWC4rzE1NbX+gSH0lHD9BK8UkRk62kMoKDOaNRqNVPpvjZnl6XGfR2wUscRODA8hZ8nNzX3YLLVaXf/+9Xp9WFiYbbpnz55vvvnmtGnTjh8/Xsd1J02a9GAl6NLSUrscXUDoaeD6Cd6NS5IEI5VKQFuq0WhCQ0OBprU7fvIYP3yIchQBNVeiQ8jl+fn5VU1fu3ZNo9HYpisqKiZMmKDVauvZ/7PPPrtgwYI1a9ZwOBwA+PTTT5955pn3339/6tSpj1xXIpGcO3fuwfZDhw6NHDmynoEh9JRw/XPwZ/PNFwrMYh8PAPj7nzCC0B8/TRuMfT2HCquVT0Do6TR//vyOHTuOGDFi1KhRb7zxRkxMjF1KO65fvz4hIUEul2/ZsgUABALBwYMHDx06ZKshiRByNNdP8KpyKqeMkumEAJDyVRIAAEFwfDytBUUWxny7/IaT40PI2b755puEhIQ///yzR48eKSkpK1eulMlk9e82NDQ0LS0tISHh+eeft7U0bdo0KSlp06ZNcXFx9e8focdDV1CFX2bRRtd/DoprHqL/8GTpsQyjbVpvZsg8s6JYBABZJ7IOvpo0ZEcrjp/Skldk9Bd8k71yeYvNTg0WISfT6XSRkZEKhSIlJQUApkyZEhkZ+d5779W/Zy6XW5Xdq1qGDRs2bNiw+neO0GOgK6jU4dfMeSb9HyUh8ZEk35X3cl1z2+Ked/v9ZU/bq5mRWrYqXUYqAMDajWySUJQ18/at5qGWQrWc69nfC/+hQU+7iIiI+Ph4kiQtFkt2dnZRUVFxcbGzg0LI/uhKKm1kIq+psOXVzmwFJ31UIm1w5f1419yDP5RiuFpkAQBhkfnDRSlmPpE0uDmsg9OCyuGzAqmVWQUGj64r2gNAL8UgZweLkJMtXrx42LBhffv2nTRpUvv27Vks1tChQ50dFHJxX+jK+gj5kdy/a3aX08wCnW6e1F1COmq3k66k0l5M5AYJAtc0B5II/LpF1us300cnhsRHkYLHHJQxpjNUWQ0zCLbtuWrO5ZoJ3l/CttIAAKFjEytJ+PzjMNHZKwDANmm2T5E+wzJ3Wpav6eDp+ZpIYy7WWIqbiVo4OWKEnKdv375qtZrD4bz77rstWrQoKSl56SWsFYYcqwOf+1axdp2nRySXW04zrxVrWnI5YsdldwOdOuwaP0wYsLo5kAQAECwi8OsWmZOT08dcD90XRXAe544qU8pkxlxAcH2qNzJUGWPOFzyTQjj7HmzXTPCd/Lid/LgAcNODQ+UYZRXUkJDAnwGy9flRXOifDzqCYCvYAJBvyjlefHB2SJyTI0bIeTZvvv8ylO3bt0+ePNkpwaCnRDc+/zO57C21drmH7PC+3Ik7NC/sbuu4u5YpncV4q8LrzSa27G5DsAi3XvL8+alUuZUt4zxGt5wmH1qy5vFbHQfi39VNqVNI/kinZ3dw1QRvQ5Wdanoo5c6CtLiLx4s60QDAKs54a+d6Rlfht2g8U3KK0vZuJm1RailZnBo7K3gePvEGPZ2+//572wTDMCqVKjExcciQIZjgkaN15fPmy6Tr4jOnfqLyH+CVOvBKWEI7tvxxEu0jcXx4YQntUodeZSyMbKTS1ljyfWH+J2lhh9s9XnYHAJa0j1X1jVX1DdvnLVsLrb9Il53jhay2T9z149IJXr2fKv0tfXiXVoll/IwKAKDUem84JZpxbUHQ2Pe2qk1pWcJnI+9UJBebCg+odr/s95qzQ0bICapXggGAb775ZuPGjc4KxrHoStpwt8Y5BC+IYEtrnIUcpJxmfj9YMDVOtXGV/ztSN6UP726fy2E/t+MouY4YTtBa3PRA29ShVwFANlJZ8n1h7ocpYQfb8iPq9TQUTuBiU3I/luJFguMFQJuzPuQExAEptFPU9eKaCT6p2JJdRnHp2c9aDh+zTF0e7fPh6JsAhzQ6iz7Y6wf38WJ+E04Tb0uOKrOV6FrpxdcCZm3J+aqbvI8/P9DZsSPkZK+88opdHnTTCFm1CebUN0hR5H3ttOEWxz+W4/u2U6J6OpXTzOJ9mYPn5LT6LmrGp6mmy1mFi0KVw7xSBlxxbI7/qU1qzLXyszrd4eKwhLb8ZvV91hkpCGN5jrbkfsYNXmEt2gUEyVaMsEu09eeat8ldLDAfSTccyBT/apj8ouQzq5X8fnVLMY9joSmL29k2gR80YbM4ft7mvILdeZuaisLXZ33RU9E/Pg9viEdPO4Zhdu/eLZfLnR2IQ7Dlw0hhOMd/Dr/171UvbsgqgiVlKyc5O7qny5e/5A59N6tVfJRqcbpnqMhy5pnildlWT457X3na8GuOG1cQKWn6U5vKRH3YYTtkdxuOfyxVcoQqO23JXcwNXAKN5gnoddqDT0lJ2bt379y5cx0djb1MjBRNBBEAADM771ynERXnZw8YesxNWF5cepf/eoxI0QXAHOh7KvUnC22hSb6E7VZgyiu16q6VXWzj9qyzw0eoQUkkkqppiqKMRuOKFSucGI8DEWxO4BJzxjsC9xcMtyz85iKCRZizPuQ0mUuw3Jwd3NNlSiu5Vpad/XqyuIs08KvmgSRR8HM7zcBrQIB0gKdDhxZESsJ/a2/HDgmWhOP3gen2SLZ8OCl+xo4911Nte/AZGRlLly5t165ds2bNntRzcgT3F0vsAM6CPw3lYgUHAD4taXHHYgEACFKeeE7d2eOFQlPB/GYrUitudZP3ic/bbGUsTo4ZoYZ1rZobN26o1eq333bZg9Us9+6kILz06Be3n7uQOSXZWrwfqHK258vOjuupo5TxON48a5GZFyqwXdnuJeexpWyr1sprav8T2EaG2V1eUb3SsJamf6qotFf/bK9xbMWLnICP7dWhXdSwB5+dnb1v3769e/deuHABAN59991169Z17NixwWOzj1ToF0Rsv5azOlBScQcgxmh6rVj7jadHsub7cFHLPzXHR/tNvqT7K1ox4C/t7778Jr8WH+6Hj7dDT4F33nmnlrlffvllg0XSwAy33mWJXgw/OVq1VGu4Nk/YfhMQLGcH9dQpP6OrvFEefuSZ9Ak3gCA8X/NPHXpV2N5dNtK78Mssz9f97TscCyCh0nDLbPnEQ0oAaCh6YrF6gFBgtwEIFjdkjd16s5N79uBXr1793HPPBQUFrV+/vkePHn/88QcALF++vFOnTgTRWE4q/Ff+7uQKz/df030p5boDQIC+7EOp26KS0t/UCRfKzqiMeZuzV+3K++Yn1e4cY0aS/tpx9SFnh4xQQ+D/w2AwrFq1KjExkabpjIyMDRs2KBQKZ0dnN4yZtqhMtmmq7JT+9zHm7E9ZUk8CRvjGzmCALv15uenuREp7wO5Dl/+lU2/Nt3u3rsGtt9xnbkj6pKSQba012/JvdT4vbOcm6S4rWpcT+kOU3YfjEMQ3nvIsK/U/rU5N05OKNd0F/DfcJI9e80l2zx78rFmzgoODf/nll+joaAAwmUxOisqexrcUrd0WzhuyRiLZDXCwqKhoglDQXyjQytbEJc14rWx4SNd+5VY9DVSptWRD1vKPw75wdsgINYQlS5bYJoYPH75mzZpp06bZ/ty6devBgwedF5c9MSY6/eXrFRfLwg63FURK9CdZHI/fJN0/5fi6WzI/ZMAqiPik+HQJL3AZy3OCfYfWnyrJeDWJFLMorUX5Lt6eUwPlzAAASJ+YFLKtdfnFUrackxub0vRAG0ELhzySREAQGzw9JhdpBhUUvSgWznZ3/asu7tmDnz9/PpfL7dev34ABA3bs2FFaWuqssOxIwCa2DfRge472bBkBAFVVNBIK97WyhLkllQCAmC1xY0ub8IObCVv8qk5wZrgINbgTJ04MHz686s8BAwacOHHCifHYC2Om01+9QQhYTb4MTxlyreJSadYbFqt+FMstle05hhu6ihPwCcd/ssdouuJCO8O11nYcuvysLmNcUvC2VuEn2mt2Fag+z7Rj5zaG63e0W3/QbtlXefEJrnmtnBkgf9UnY3IyQzG5c1LCDrV1UHa3MTBMOcMICUJH0a5cZOYf9+zBx8XFxcXFXb16NT4+/n//+99rr70GAFu3bo2JiZFKn+BHQLTz5gKAIskNAIqKCgGAZujEsksmfmVih2Ty1h8AYGbMHIJNAJlnyn7R51XnBoxQQ2rSpMnhw4dt33cAOHz4cEBAgHNDqj/GTKe/coPgksHftiI4BMEh0l68Hrg2hcX70VpYSan32s67W7I+ZSi9qIOIG7oLYLpdhi4/q0t/+Ubw1laS7jIACPu5XcqAKwDg/UGQXfoHgJJdByvPJ0p6dwWS1O1NMFy6IX/TntcJmimGy2qg07Les4MAoHBZZtjP7fjN7XPfWo20ND2xSNNdwHvLTfJGsfZjre5TD6lr3ilehXkImqbPnDkzY8YMpVLJ5XIHDx78sCWd6+DBgzwer/ZlvrqsN1rpHa9uBoB+fXtWtdMmc+bL79BWimGYzdmrz2h/d2ioCNVCIBBUVlY6ZehDhw5xudyYmJg5c+bExMRwudxDhw45JZJHqsv3nWEY2kSljryWNvY6baarGksOFN5olVB+OjjvfxMqzvSmLSVmVWHRVzHa+ImVFwNpQ6pdItSfLUkM/LPsD231RnO+MSnyr8LVWdUbv76qv6k2P8YQ5pyCnNfnUeV/f1poszl31gLDLfvEb9MnvqiogrJjh49Emxw7nIGmBxcUrtaV2f6soOmxhcWLS3QOHbQWDfN9v+fnS1hYmFqttk0TBNGlS5fVq1fn5eX9/PPPXl5eTvj1YSf7blWqK2kvby8AUBWqqtoJLofj40WX6QFgtO+k9u6dnRYiQs4zaNCgxMTEVq1aFRUVRUZG3rhxY9CgJ7uMsvrb/MpEffDmlgSHKD2myf0whaEY6RAv2ZAw3aEx0n6F5txi/a+Hs6cfF7RKErXnsr0mEPxQuwxtyTGRfJIXwK/eyJJxeIF84917bsq6rLLk6anHGMKUmsVv1YwU/X0FOMHhCNu3Mt3JeOyYH2SwMmaKefRy9kNwHbsvzQZ4291thvvfV9UJCWKjp/x5Pr/2tZ509xyiT01Npaj7P3AsFis6Otp22d0TamVvmY+YpfbzBgBVUXH1Wb5fxNomhCwHHhpCqJFr3rz5ggULnB2F3Sgm+Jb9qsl87aZslDJ7+m1eiCDrjZuyEUrtblXovvdIziC2/zCqaKHnZE9e6BRr4bf8NhfsNbRspJIqt94dcCUsoR0vWAAAjInOePUGy53d5MtwuwxBCgV0paF6C11hYCsd+3CYJx2bIHoK7knnQoLoyuc5K56G4eInIGzaeHFIArwDfABAU1zCMDX/Ml2X+XmptaRhQ0PIaTgcju0BVpyaODu6eiF4ZMjO1uZsQ+a4pJDdkWGH2xmSKzLG3giJjxQ+I+MELWEJfuQ1bcv151Klf3CazLPvY+wUE/283w9KGXjFlGFgTHT6KzdIPhn0bSuCbZ+z2ryIEFNKlvFWmu1Pc2Ze5cUbgqjmdukcuZL7H3QzYcIE/kOOWvz444+Oj8ch4m9Wdg/gSZQiMZ9TbrSUlpbWeM0gA0yuIctdImv4CBFqeKmpqbZnzqempjo7FvvT/1liyjIKIiXqjbnSGE9rgYnfWlK0NieonRvLrRspjCBEkQRbQRXvYHvZ/zF2iol+jJVJGXiFFyJkKzhBm1sSLAIAvr1eUWz4+/LtlBLrj3cNVwr/fnRmz0Bee+86VVhhScSKGePUK79le8qBRVryCuWvjWJ71at8gNHKDN2vNv1zWL6wghp1QMP+ZwdwZS9ZG68n+zff0+n+BB8cHCwSudrB6uOZRh8x67nmIplUXK4qKSoqqkrwVKm+cOFa32WxAPB64GwWPtAKPTUCAwPvmwAAiqJIknxyH2xlo/+zJHPqzdB9UYJW4vTR17PeuhV2uB2/mTBtZGLOO3cC1jTnBi40JvUBks8L2+SgA5mer/kTJFF5Xd9kRTjxzxXpCiFZdW6bQ4KQTbhz/57l/l9OQgsiw/1Wf2zKyAGK5oY0IQX1PZfMZxPbBnlY/jlDO+qAZmW0VCn6+99DP4lL/cNoSC7PeftO4PoIRzwTt1G5P8HPnz9fqVQ6JRTHEbKJSivD9uC4K6Q5qpLi4uJmzZrZZrEkIkt+EWMyEzwum3DN4rkI1e7u3bsffPDB7t27z5w5M378eIqifvrpp06dOjk7rsdHcAhggDHTJJ8M3RNp1Vo4PjyqnGJMNMEjAYDgh7CV4xhTLilx4GYqJvvd1zK46b/PRr1SaOkbwu8V9Ji5meBx+c3tc2Ggjbfo3yzOJsFHzHKxvG5jSC5PHXrNfaDi7sArzRLauXaOfypSGp9NGCwMY2G8i4VJAEVFRf/OI0mOj5c5V8ULDQCAuLvvvB+6QMRy4JMWEGpspkyZ4uXlxeFwPv3009mzZ5vN5vfee+/06dPOjusxVTIMv5N7yHet08feCN7WStJNZvHmQgWVOTKRFy5q8nmYbTFOk8ZVFwQ9El1xzVq4rcZZLFlvlmxA7asXUpR7qjF12DW/xU09XvLW7Ci43e9K+OG2Dr353rnuOSjUq1cvLrdOJ4GeLGIuYbAyBIeQER5wX4IH4Ab6WrL/fl60D89fYy6qoQuEXNelS5dWrVpFUdTly5dff/31KVOmXLvmwILcjrZTX/GGWsvu4h6ys3XG+KTMk5qX0lXXh1/lhggC1zS3FS5DtSMAHH2W5jeDUX/v0+QSKg3mh1wBbcNQZdbi7YxVDUzFvy+6glLvZgwptQ9XTjPT/spNGnTFb2FTj5e8GYANg4QnZ3unDLpqvF1hh+1plO7Zg9+5c6fZbC4sLKxx0Sf30P3kSLGQQwCAb1QwqE7dl+D5rZox1r9PPb0eONsJ8SHkVHK5PCsr6/Lly61btxaJRGlpaU/0hTiT3cRpWss0tXZtFw/3TRHZ45I+8uN5PisNWNUcGlNyfyGQ11TWSI+hfhktrX7E3hGum80byvRbPOUSkgSAL3RlZ4ym6EdcTEAAQzOGu4SkS1UTbbwLjBWIR1wDyCsyz301e+s8r6GDpS8ALNGVXjWbt7weYhYIUwZfjbjQkS1zwasI7/l4eXt717Low+4ua/yauP39SfVvHQbH/n0cvY34hXtOwlkYM4dwwcMYCD3MtGnT+vXrR9P0F198cfv27ZiYmJEjRzo7qMfHAljsIftIW/JasaawBf3GhrDOV40+H4U0quwOAKMjaj77y1gKGXNBDTMILikIf+zKtllWqztJSsl/j9qmWaxeLFJC1nBxX7u6Xc9fH++4uzFQNr5Y862n/Ft9+Rmj6VsvOb/W4wYst+dZ0p6U/gLX+zVS2AIAGKrccDWS4CjZysm1D8dyYwtDBZOuUzP66/bxuMUUtcVTLmGIrDMl/OYiloN/zTjLPQm+Q4cOiYmJ0dHRI0aM6NOnj0Bgv1q5/9Dr9bt3705KSiosLLRarT4+PlFRUaNGjXJzc2Bhn0sqs85I9wris09yAKCosLYCjh/dnjY/bIWY7eJlBBGqEhsb27FjR5PJ1K9fv/T09Llz506YMMHZQdULC2Cmu9tQVdEL5sv9vGYQfcFw+f5l2MrXOE0+ckZ0j2DJjqNKjhK8oHtaGTNdeZPf+iQpesxSqn8ZTbvKK7Z6KuQsEgCumszT1drNXvLmNSX4hvGuuxtA2UBVkQdJlBcKywAAIABJREFUbvdSSOsQCTfoC+P15y0Zs3ktjwCANWcJAcAN/gLIRzyyhhSywg62TR2ROGO+auHHngsVHhKGyHrzlrXQHLo36rGfo2dM7scY7tQwg+Dw21whnH051z1bdf78+ZSUlL59+27btq1Vq1bTpk07ceIEn8+Xy+W2+2Xr6cyZM35+fhs2bKBpOiIionXr1iRJbtq0KTAw8MIFuz1J6kG31NYzuWYA8FR4AkDhAwmerjTAP8cn2rp1LKfKHBcMQo2QwWDYsGHD4MGDAUAkErFYT/YOjYqiJhVppru58dyeKQEBhKzjt7n67yvyDBBclrS3s8OsGcfvfQAWr/kefuvfq14sxUssWf/Hzu4AMEYs6isQjCtWF1PUVZN5mlq7yEPa3NlPNLJlIKLO92US/GC2ciJtuEOV/MwYM6xF20hhc5asf53WFbF+2BzMSTd9u6TkC7Xu3NQkq8oUsieKFDz+Txy2x1BC2PKeT1ebq2zPV0i3552e3QEeXmymsLDw66+/7tOnj5ubW0xMzI4dO+r/4Pt27dqtWrXqwfbt27c/++yzj9dnXYpPaA1Ups7CMMwv4w8DQHh4UNUs2mwuPfBr9pSPCv73ZcXZq48XA0L158RiM9u3b/fx8fn000+9vb2zsrKUSuXKlSudEskj1eX7XmC19skv3FZWzjCMlWG2Z26/c6WLgbZWLWDOXmhKmerYQOvHlPmRKf2dqj9pc1Hlpaa0IaX+Pa/RlfXMV3XMzf+90lD/3urpS11pTEFRCUUt15UOUxXpqDrVm6Gt+sqLwZWXWxpuDq284E9VJNdpLYZZoNWNUhWXllpu97x4pdXpXX3O/a6pqN8WMAxtMVzrZC059m+DKa/yYihtzK59vYb5vhPMo86s//XXX1OnTk1OTn7kko8klUoTExOrP1XDRq/XBwYGarXax+jz0KFDI0eONBqNdVn45rwrLRc94+YuKtWVAwBDUaqPV7HlUoaigQSrSi2IipC9MlRtLlJwn+DiOuhJJBQKNRqNI86LPVLnzp0/++yz7t27+/j4FBQUnDp1aty4cRkZ9ixeYi91+b7vLq9gAF4W/32dIAWQer0vWzE61HciADDmPOP17vzWJwmef11GtOQtZ0y5NcwgWNyAT8AxNSwYSm9M7MAL302K2gCAOX0GwZZzAuLq3/NVk3lSsUbKIvd6KTydepxmTan+hMH4rZfcdmR+ia70ism800vBq8POvLVouzl7PjA023MkN+iLugxXTjMLdLr/SaVikqDKqeKvc9RTfI4ypvek9T07TJWeNGfMFkT+ZTtNYEqZTAqacfzn1L5Ww3zfH3poIikpKS4urlWrViNGjOjevftvv/1W/8F69OgRGxt731X6Go1m9uzZ3bp1q3//D5Orp3YmVwKAooknl80qK62orKwEgIozV0gBz/PdSaIu7YBmvONmlZ88Z1WXLE6N1Vke59cGQk+iW7dutWjRourPiIgIjUbjxHjqKYDNbsH998gzC6DQb6GyYClDlQGAOXMu2+eNOmZ3AGBMmXTldVIcVf0FdCVd9ieQDrkSzZz6huFiAGNWGW+8UHlOVnlOZi3aaclfVXlORlfU6/ZF25H5L+WyEULhhGJN8QOlxRpSOJez9Z/sDgAfSt1fFdf1mDbb6xWS60cAwfGfW8dVxCSx1EMmJgkAYIlZ3rODWrnz6p/dAYDl3oPkN7UUfgMAtP4CrT/P8Z1R/27t4v4Ef+3atXnz5oWHh/fr16+kpGT9+vV5eXlr167t2bNn/QfbuHFjWVmZn59fWFhYp06dOnfuHB4e7u3tnZ2dvWXLlvr3/zC5ZdSRdAMAcORcD4kQAGw/MswZOYI2EQDADWliTs8hRQJeWJA5MzdaMcDKWBwXD0KNSseOHVevXm2x/P2Z3759+zPPPOPckOqDRxDTirWXTGbbnzvLKxaYAyhpL2veclp/nq64zPGZVvfeOE3+x5iyWW5d2V4T/n55vkJXXOcELnrkrVmPh62cRHD9hB1ySFEbXrOtpFsXbsgabtOvSVEUKYp87G4Tzebpau3nclkPAX+6u6S/QDCpWFNy753oDamPgO9+71V1Q0SCuuy+AwAAyW26gRu2mWA3itIh3KAl1ryVjFllyoplBXwMZGN5Ot49V9GHhobm5ub26tUrNja2U6dOJEkCQErK3w8QCA+vb61DLy+vhISErKys5OTkgoIChmGUSmVUVFRAQMAj17VYLF999ZXZbL6v/datW/SjPqN8DmG0MIwpW9hWJfcUq0r0eel/BnqVshXFdGU2bWjC8W7KjwgFAKqkjCV1G+j14mNvI0JPnLVr1/bu3XvLli0lJSVdu3ZNSUk5fvy4s4N6fO153NUKj1lq7XKFLNNi/VZfvs1T4cZ8bEzsQpUc4QYsAPI/HBclOF5sn2nmrPm88O9sLVbVRoLr47hr9EhJB/L/7J13fJNVF8fPs7LT7O69aAtt2SAiQ/beS5GNishQkCEqikxFZQhKZSNDlqBQRAEHAoKsAqW7pbtNM9pm51nvHymllEJbusA330/+6HNz731O0iTnee4953fEHci8TYTfp7aksSgvGFcOs8R15AZ/VxfZfDXNfKGQdbxfIPVtiViBoVqakTVdFH1dQAUtQNCiqa0oA+EFYqqx1ruD8mnzGXP2601tTzkPOfj09HQAiI2NjY2NfbRr3ffgAeDvv//Oy8sbPXo0wzCbN2/etGmTQCAYP3788OHDnzyQYRidTld+k1GO0Wis1jCHFr0tcSRLm3zdTPHJkHPrc7urivAgKW2O9dYaQbt7ylkTDKfPM1Ybx98bAJJN8SHCCORZy5x14qS+sdlsubm5iYmJp06dysjI8PDw6Nevn0QiaWq76kQbLme9Uv5GkZaHIgdcVd44lkbKEbdZXoazmGKoo88tux0DpDmn+rtwwuMti7oTXXwWk/ZgKT2Zt44b8VNNzLBlWOxZVnHXWt9lcvw+td7qwlO9QrhNxeSDyNx1mLhTHWXzez2iITNOVHUAwby7U5uLo6f4zK7L6f7fILwXWDWHT/JaZRoTUk2JwcJnonrvQw6eoqgGPdmGDRvmz5+/bNkyAFiyZMnOnTvHjRsnlUrffPNNjUbz+utPuu7hcrmffvrpo+0///zziRMnnnxePoFYKRb3eo/M2ijNdAP4XcuZwYucCQDmyyMs1036fZ+xVhsmk7gumI7gGADsydky3XeuLz/w6V+tEyfPAxwOZ+LEiVeuXBk6dGhT21KfpJIkH0VoFnJpyhvHaGCnYGOWB7zRDQAArtrsczW6DUp5jeZCOBzfT8jMJZikC5n9KaYchfKr//m2pZmT+19nLYzv12HSwbUL2kU4nrj7VDJ7GSf4O9aeSxXu4EX+UasZnpqjBftKKP0F3R+D3MaoOM+remnjw6DC1cIe47ymmijj97kxS0O/eBbuDx9y8A2d/Lp69eoffvhh2LBhALB169azZ89GRUUBQJcuXd54440nO/i64C3G1veU4cqRVN42d6UBsiA/Px8AGMMVhEiST/uHKjIBhgNJEp5lH+hh7q8KnoUsRidOGhgEQWJiYhYvXrx48WJfX9/yH4HnOhX+B6MpxmDc76oqpGnHWn0HLvc7peJ1jfZjkMpQdK5Gt1Yha82taZQcJh9AFW6131tI607yoi9X29/h3T3eCxC2d0kdepO1s7KRtXOWhOc7lrj2jOESWRCDe7xR86jAumBlrL+ojw5xH3tFd35TxpqPm30JAMCSLF21WjuCiZ9aWe8pYEkNU1p1DSRUGInw6rO2Xm05oznhynWPFLdmgf1D+8sF3bnO8h5NaI+DykrIFEXt27fv9OnTjiSZgICA3r17jx8/vl6+7RiGhYeHAwDLsiRJ+vv7O9rDwsIq6cPXLwhAlCsBAJygNQEv94brkJ+fC8DY7y3i+C1DCBHhKSLz1OrPYrw2lBWYai3p0HD2OHHyTDFmzBiz2Vwp0LVetuSahJ9Mlq0G4x6V0hPHvHFsrUL2rka/3VURziFilIrJRRqGhQ1KefludA0h/FZabr/0u7DTALya/Yty766c5gUAwcdapg69CQA19PFk7ldM6V8AgGBiW8JwAABSZyu9CACE/2coP6RWZteKLfc+F2KiQa6jO0q7Lk5480bJ5VaSDmTeRjJnJYJVFvdkqRJO0CZcNa7h7KkEY7ppS5mCyQdWuqpgis/iXu8SnnMbzZJKGKjSWPWRxcGrAAABZJzX1HUZy9tIXuBjTRxt91B4RWlpaevWrd977z25XD5y5MhRo0YpFIqFCxe2bt26tLQexN3GjBkzderU+Ph4BEFeeeWVzz//HABoml61alWDpskBwOpLpSwAKoyWK6MBID83i1J/DyiBKYY5OhDuSlpXwpgt5UMuF58n2coxfU6c/PdITEzMysoqeJimNurpacEh9roqPfEyH9CRx93uqnDkfJtZFmERFAFT7S9fSgjXb/kv/Iv7/aP/8wndHN7dc0mgw7sDAD9KHPRjy5wFycU/PVQFI6uUtlJVmIEQCsaainvOJvyWI4Jw3HMO7vUOKunKmBMQjkdtza45udbsW4brU33nAoCK49bSpcP2nK8BAHedgGBSbvNT/LYZ5Q9uswMIxxNXNOq2DibtiUm6YZJu3JAd5Q/C/S3AXAj3poxsO1rwfSdZd3du2X/cjx8UKW59Un24CU0qo6LqzZQpUzp37lxJXsdisXTp0mXatGl1V9WhKGrWrFkikcjLyys6OhoA3Nzc5HL5Cy+8UFhY+HRz1kTZimXZiO/ybRTDsuyhjhsBILqFv/laGG28XrFP3uK1lvjk8sM1qR/cLr1eeSInThqGJlSye46o4fe9Sq5abS/m5F+yWO/a7J1z88/UUsptS+YXP+bvSzUlvhs/xUo/dmzh5qw7URepEvKhVoZNn3g7bWxcxbY3f9GduVflPLTlVndKc6TieMudvqR6X60Mri2LEmYsS5lffkjStulxI3/M38eyLJn3tfXusEcsPNqg9lQJbU4wXw1hSO0DS26/TBUdbnxLyskyZ7x5a0y8IS7DnFr+iCv5941bowtt+Y8b1Tjf94eW6M+cObNr165K2jo8Hm/58uUTJkyo+8UEhmEbNmxYvXr1pUuXCgsLi4uL5XJ5aGho69at6z75k9k7WMHBEADwEIQAQEF+FiadiQpbVezDCfSxp+fwIspWwEZ7TpLg0oY2zIkTJ43AHTtZvhkPAN8oFW9qtEJEVsOF+jRTUpIxfmLYW1yUFyZqEas+Osz9lSp7ur7pY8+wpPS/Hvxzq7IKpCxkz0+yZ1uDj7Ws2JNh4THrCCjHf7UtZQpf1teRUU1pDgFjwVVjavOKa8eV4vNqe/7awK2Ow/VXDdNbioa5vXK04Pu+rkN47q9T6l108RlM2hMAKPVuQDlY496+O0D5YZh8EJnzOcd/FQBQ6n2AYJiymiSsBiXbmuHB9Tqct6tSuyfXO8dyz5XzpBqtDc1DDj4rKys0NPTRTs2aNbt37159nVIgEPTo0djRBy1dy5Jh3LzcUQQp0jOIx6JKfURd2jHmBxKY/vymDNlw4sRJPSJGkc0qeRSnLKquBYfYrlLUcJmeBXZf3tahbhO5KA8ARnpM+Chpzouy7q7cqhbMEfBeE5qzMDl10I3gn1vhUiJ7fpL5piH4WEtMXNPq7/dT4TcS3guBsZDZn9YxCb5aYguPsCw7L6Gs6Orl2ysuM1/wuDoA+FP7ax/VUMJvhf3eQn70JZaxkTlruM32QRNFiRM+H1jjOjKuExCuD5mzkhu6p6ksKbYxV/PtPf27d5J1d7TcM6fiKOHNqyzH3lRU/sBVGUz3XMfTOth83fhqQJIINYha5EhduLoSa2HWJQ93BQAgKBcVvwAA3NCASqNOFx3vIH1JStQsl8aJEydNh+3uEMZ8q4onUD4v+p+7duIHo/kblZyPIABAsuyGEsOLPG6zGpRTu6A7hyLo4lNhp0azYg4iIxS9VYMP5+95y39B1QPu+/i0YTf5kWLLXWO5d7dQrJ0uu64gGdZMsiW2Mp0uEQfFKvgpRyo8rnqFUu+uexJ8tXzcbF3Fwy531J+Hf+clfvDLj0l7obwYqmArY8/DpH0rLX82JgguI7zeITMXo8JoTNoDFTWZ6mKyjtoWZ+rpXyYwQLHkN5mfc1H+x6FfosgzIR9U2cHv2rXrUY0Lo9HYWPY0FL+kW1+B2XbELIh081AguhLIufO1ipGyZCFrz+e3TqyynHCeNTuu9GpXRe/GN9iJk4bmnXfeecKzX331VaNZUi+g0pcZQKcmrd076EFtayp/E2OORzCXvgK4YrNPL9JuUSk4AO9o9RTACGH1Qc5WxnKk4PuxnlOOUHShVe1Y4msjeeGM5kSi8U6Y6DFiag4fvzjFcssQfLwVJirzlFNO6pJ0ZXIjBjt7Jc/Oue/VF3QUjw1/YI8jFd6ePocx3eJF/VX796P+IfxW2uL7AYLwoi42rSW421SqcAdlusmLbsA649VisDM6ywMd1V+KjvvyA62M5U/dr90VfZvQsHIecvAtWrTYs2dPlf1atHhWRAGfDiGB5EuXBBg+tudvEWH9Ae5qefO54QNsiSNx14nl3t2WmonLpZi87BJnhMdrz4JYgRMnDQGPV3bnodfrY2JiunXrFhkZmZmZefr06Q8++KBpbXsKCI8Z9sLvxeSNA8n9x0UIAIAl1ZR6J+G2r+iLbZY7yZM5nG8mDptO0xKCYAE2KGQoi2QZaF+XJ61QZlnSOQjnaP73FnrBpntfcwizo52H8pMedvAMwNclhtddRDyHoDoCgpVBsUZziOjB/PuHPLj46LhbPSqMN6/9Y+udEJ5zLTfb4R5vIBzPp3xTagzNwvd3TOVR/QY7uy/e5MJFAQBDkSEhfJUARfkhuPs0hHBDCFVD21MNCMEJ3MjacxGiKct+5hjoQnNZwZ5SqvjXop+WBK+mWOrztA/bSV4U4ZUTCxufhxz87du3m8qOhoZPIIV4j0DuDlH74/4BvpdT7ubn59P6X1hbNu42pbyb6eJ1zEUkGVqmMu1SXcKrEyfPL6tWrXL8MXz48I0bN86cWVaCZefOnT/9VCMp1mcLhAPen8wp/XDyvx0dDp7MWoZJxxasOinu85Li9bGMnZx99PSrMpd0F9EpD1cOglwpsG+8atwz6El7cKHC5mvCtwDA2cuFH4V+IeM9dukVBSig6TeKdFtUch6C6Bhmslrb8xGB2FqA8nktziCE8ulnqDEMy2qtDHX/dpRl2VISWMTh71nr/W0Fwrty6FJTgYrbN8l59VbmrqZMMT29mKYZuJBjA4BT6l8CeKPcuJ4A0Fryws+FB8d5TW0SCytS06CP5x0RgboVrwaemDWv8nFTAEBuwjZbaj4mbm/PXMLxWw4IBwC4Qb6mizcqDjxddNyHHxAhevoiTk6cPOOcO3du06ZN5Yf9+/efO7fJNEPqAivpV0h9M1T6A8A8xnSLLj5DGzZxQ7Ilg3sAAM2yK4b18r2b6i3gz9LotqgUDAMMPDbSrtTGDD+qpe5HupfYmCFHNOj9Fb0tfeXN5JV/P1fIpZ/oi6cVadcoZG8V6V7icWdJnnQbZ2OsJMsjkMcK6iGNFYNNoMi77R6YeiLV8mZLYcU9eCcOErXUlptlun45BtpKs1tumiyMJdvi3UkVBhEAACM8xi9JnNlF0cuLV30dtQbl/8XBv9dBLM1NZykNS7RTsPEAkJevQ7negPIZw+Xyso/cEH/97h8rDuSi3LjSf50O3sl/GB8fnxMnTkyfPt1xeOLEiZoUeHxGyCyhPjpfygL44vGUTUfZ+77n+dlXv3kM5G9IIAe11V8ThXAAGJJFHPvuK+MSXRD0ixYhbxRpp8GTvK8LF/1+sJy8XzN9yBHNwpf0ejq9q6I3joK7qArnhwAslUnf1+kH5atHiYSPlhvfedukNpfdJptI5nhGdrIxOUIcDQA9/Lht3BukwLyTeiRYhpeHShxOtmSXUmPC+ceyD7dUE5F/XMnaVSDoGC0fP3SA68j9edvmB37StNb+vzh4Fy4y+da7e4JGEH7HvP17AEBeXi43+Kg16RVu0DflWRa4q4KlaFpXUr4N31XRh2YbtgaPEydNy6pVq0aMGBEbG9usWbOkpKTY2NgjR440tVE1xVOMTYoSxBWSg5llBJ2XYvYzM4Ip4gUACEIkEsTvOJLD0tP+sOMcBFknk6gzciy9u80WiD83lJynbRTzII6dhyNc7KGYG3fhAy+OIvB76TYzm93do7nH4+/M9Axz1075EVi83W5mWcHDBc7lPNR2f59bzLVIBCYNlY6gAS64xIXzTMRdO3ky6cXUqfQywdPMEoplYW98ZobVq1AsvzsYs0XctibEM6ev8Dq1TDTevm24HilucJWXJ/D/4uDPZ9vDib8RYQRdMFOljABQ52kQW+YHgIkZUxwq7lAubqyY8QoqqhDLCgiOVJ9I48TJ88vAgQPj4uL27t2bm5sbFRW1Zs2aKvUwnk0IFAmU4EeTLEexRSOIt+ekf83HyZ/DB+61bM6g207kve51x8uWcbajm6IjyxYnZvzuHvHFRS5cLAIAmgELzXTfV6Yg6yXGfh752A1vkrFLCVk/Rdd9uVvfC1pWZR/HvvtLPO48qcsn+uLXi7QxKkVFHz84pExGrJQq2ZWaPiUiXCozxJVseeMxEzYV3X25Eu5/5IKjhNK74NL6Cpfu4Mnp4Fm20LLsYun3JdQS/q0ENF7QPhoAAGTCVm1Kfz4nMKHD3cfLGyV+4glU7+BpmkZRFEGe72DyZB3Zy+UE0FbWes/Hpz0A5Kkp1nAZFbYiC2Jw9+nlPQVtIyuNvaA7l2/LGelRD1p+Tpw8m6Snp9++fZum6Q8++ODKlSvPkYMHAD8JvrGXjCoqSrgrWRfw9hXjC3LfN2ch8YztBK29Rvn1Lt53hqVwQAEAGfrW+FdfLCv68k+ufeN1Q8W0usdhoo121jbC/dVmLt5/an91VGGp1IcBmKzW9uDzZkvEAPCRTPqBrvgdjX6LqoogviP5exREFxkhf1nR/y/tbzdL/23p0q6ub0T98clLTRBfzAL7U8EP/VyHcapKWn46bIx1WfL83qrBfVRD6mXCQhN9raAsyC7fwCAIxGe6YtIgMDQXc5CXfLgAoNZqRcWtBCFNv7Fb9TVacnLy0KFDLRbLmTNnfH19PTw8/vnnn0a2rH4ZHMLfrF7I2vJwn49dyXsoiqi1VlY1g6VLOH6fAvKkC51AQWiRvRAAdmZvumW42lgmO3HSSOzZs2fatGlt2rS5evUqjuNz585dv359UxtVUw4lmiec0E04obuScBQVkK1FNzw4eX+mJJ9Pvm1SH88EGcr/yfOz4T5bJ/rETPT4eLhux2HGaK7tWY7kfz+5461wiS+KoOO8ph7I206xZKU+KMBHMsns+1F1KMByufQNlyqqTmdZ0m8ZrjkirlEEG+c17UDutkcn/H/jb93Zn9UHY9VH63HOWPVRN65nrPpIKVVSLxNmG+hT6RbHI8dIIQC/cz1Pq9FT6ZY/smwAwFK0PTOX8GpKhdpyqnbw06ZNw3GcIIhly5bNmzdv7ty58+fPb2TL6s5dDdltn9rxmHhS929ps1ht94Nxt61AKGUEw0ChDkVwBSYfWGlgyZHTLP1AvsCD5z3D770Ew62/dGdiMr9s3BfhxEmDs3nz5v3793/44YcA4Ovre+jQoXXr1lU76hmhjTungyengwcnTbBURukPuYyViPXt+CeieH8wQHlQ+QgmojTL7Onv2JLGMeb3OEG+tuQMALDcMkBVxdweJdeadb3kElnalWRYAAgXRXnz/X8t+rkKYx4uMI8CVFlyfl/u1uHur/b0FwTLcACIEEV58LzPaE4+xcv/z2BlLD8W7HvLb+E57SmNvX6qh+tIze+aU1N9ZneSdf+xYG+9zNnWnbOxl8zxmBIplPGwTSN8Poo//mnJ5cUhpD0zt+irHdwQP8KzKRP0y6n6zvXq1aspKSk0TV+7du3UqVMWi2X58uWNbFndCVcSewcpGBYA4FKu7f0/Szq0/USW3rno6iw/93/VWsi8FeM3JvbRgaYL13gtw7lBD+JoGJb5NuuLSHGrZGP8gdztY72mPDrKiZPnlISEhIiIiPLD8PBwrVZbLzMbDIb9+/ffuXOnsLCQoigPD4/o6OgxY8a4uDxW3aW2zDitT9M7HLXc4DlMRuuj+Ld/I3v0xH7dyk6fYvuVzP9Q+NJAYGnT350J34UIXsRStHZPftbsBKKngjPeu9pTHMjbPtR93Px/qbdasTIeAgBjPad8mvLei7LuEkJWW4MvF58vsOXJCaXCO8UMcNcAABApbnOkYE8nWTeX/9cCVz8V/BApbt1a0iHbmnE4f/ebfg/dUtIsWClWSNRup/iHvB29VINi1UfaSV+MyfrqnjnVXxBcZc8iM4MgoOTXLuzAS4z5SzBUwHf/ZE7xgRMFH29AeVxBx5blSipNTtWvR6FQOAStIiMjhUJhXl6eUChsZMvqDgLgJcZ8XDAfF8yR6uItd+N6zZVF/crnhQBAgaUlKmj+6EBueJAtIa1iy4H8HQaqdIb//NFek85qTxqp0sZ4AU6cNAodOnTYsGEDSZYtEe/evbtNm3rQ975w4YKXl9e3337LMEx4eHhkZCSKolu3bvXz87typd4URoeG8kUcFEcRHEW+KZjxAvv3j/SIvuhJloXJzPbztpdM2r1k3taSn1+jigz5y1Itt7JN10V5n6SF/dlOgsKiA7ks+aT7+ATjrXjDzT+0p820aW3aR8uS5y1LnvfNvc9Ixv5L0bGnMNhEGbz5fqeKfowtOlr+uF76T6AgtJjUPe3bUD0ZFKVjmIotqSRV+nBLU6G2F/ytPzfc/VUA6Kcanm5OTjLGV+zwW4Z16fnarbGnmBLSzcnefL9L+j8P5e8e7DZmf9429jGyB/vvmg/crfXGTTm4UqZxeTCPAAAgAElEQVR8+zXvzZ94fvm+dHR/hPOsxGVXfQc/c+bMvn37Mgyzdu3axMTEoUOHjho1qpEtq18EeJkmE+4+HRdvbabg/gWQa25bZWdeWKD5yi0YWFYgiGKoPzW/+PL9eaigu6LfSfWRbzLXPi6M1omT545Nmzb16tVr+/bter2+c+fOKSkpv/76a92nnT179vLly2fPnl2pfc+ePW+//faTfbzdbt+4cSNFVc5QTUhIoChqzZo1jsOAgADPloMmtOBnF9uTkhKTiMBvTLOGuR6xkTw7QojA0tLVbCvM1h7eK4q+a7MKxC/vzVn0RcmFPN+j4Zt+3UK3hOaHQ9JfSDbOY0eNGwUAFotl06ZNNE2Xzz905NCPQr+w2WwHSYQ9L2ABBQBPT88Jvd6SEfLy/gHKNG/JPYmLxBGfSFFUXNxNhmUB4HZ+S5lb29GjRzvmv77zLk0Lyucvb9+0adN++lCV7RXteer2Fb+cvhParO+Z3/hWS0BAQNCQobM1um8kol+//bZe5q9Lu75zVv+QYRJC5mjnuErXa1f6XogMDAh09DdZbXfuJq75988azm+2mNddXyFL94gJXq9K8i8O1+AKgmTJi+o/Lu2+9mh/kiL/vfKv6cyNWtmvZYUGTviaNZef4n2ARqFqB79o0aIOHTrYbLa+ffump6cvWbJk0qRJjWNQPRKvIYce0TAVrtiCvs0HgBlFswObzYRfISe36nVIbrNA/b4HG2xbsr/koNyPQ8tqb7zlv2BFysJUU2KwMKwBrXfipLEIDg5OTEw8depURkaGh4dHv3796mXatLS0IUOqCF0eOnTonDlznjyWZVmj0WixWCq1Owpf6fV6x6GHh8eWa0adlWFZ1ob7oCrkRMSw0XkHLhS/1F1y9havJSppnrq/1UutzxjSrQq/rMRbo4UkhktRVIobDIYcRHVinPuCXenKTWJ2JIsQCMuyBoPBZrMBAIOg94xu+jg7gBtJkjRrvF0SgTEkAOQRriORAAmOmu1mR/9BzS/4K/PMlIzSJzjs91cYUKBFPHOBnmu0tih/XeXzO+xvtHavmzf0dvvJl3u+eOwIzeVt0OjWKmTBDH2kiewpb7e7m21EcU/VwPJ2SoPSHmy+/J6n1bO8P0mSFf/vT57/QvE5CiX1PC1tZ00ai5vG76DLzr7KIT9q9grMnnaz/dF5bDZbzed3tLM2TQvI1NfhfWhoEJateskiNjY2JiaGpun169dfuXJl7NixjWNQbfn5559HjRpltVqf3O1WETniqCblDQ8AIPNs326ePXtFzPChA478eKLK/mROAeHtDgBF9sJFCTPe8n+vjeSFu4Y4h+bU6tT39aRmTXhMfb8aJ/+/CAQCrVbL5/Mb/9QBAQFvv/32vHnzylv4fP6jzrW2DB06lM/nr1u3zs3NrbxRq9UuXrxYrVYfO/Y069uPft+/u2naecdYZGYAAOvN0vlIy7RrO4InFNg9Em8PbN/iUPHGD7wXrsRUI4EqTeg6xmt5sD3bWnJSExLb+qqV3XjdsKOVMD76UvCRaHG3h/LZrBS7+bqxPA5v123TmHABDy/bBp7YQuBWQQYH7EXmG2GcwA246tUHMySOYk23+G2SnuKVNhDbDMa9BpOVZb5QyF/g1Vs2Wl1YmjyXYRlPnk/FRo1drbWr10ZswxEcAH5OtZy9Z1vXs6YBCkfyv79dei3XmuXO9SRQjmNCDMHkHNV033fcuZ4AYKdZy/3/7pabRmCRN1qV7UTzcYSDNWxmeON836u+g9+zZ8/ChQtnzJixefNmR9pMYWFhtRfdzzIeIsyFc1+uTkkQ/IEAMdnZGY/r7/DuAPDNvc8AYffnbdufu01P6SS4FEVQkiENVMmV4vPtpS81hvVOnDQkWVlZu3fvjouLi4mJKS8xV3diYmImT57s5eUVEBCgUCgQBNHpdOnp6T169Ni3b199nWV6S+H0lsJcA80yluHHcjmAppIhJvkX9Bpr59GfWf6J8nxnCe4+kyrcmR/xR/ZeHJmc4r0yuCjbarllgFCRoJRKGx7nOtOnkncHAB6OvNv+gZbtwQTzrDaixxab4ahw1Sv2ewtx1ThHbBNjTmRKzvHCDj7lC2Mh79M0YWsXycD6LN3WisPZzBhcUDSIqKnKGWNOYIyXq3wKk3RHuH51NGmM5xQTZXi0/bcUefudZYusJMOSNLTeUeg4bOvOien3pPDGER7jTbQhTBw51rMsILqUKv4wafbrvu84shMBYNUlw/GUsqtYK80iLBxIKNuGH9mM/36nGsWBWu8OYM13q3gCFfCiLyNYFUmSjUnV/2NH2kzXrl03b97sSJuZMGHCc+3grSRrvn+xRunIwC0kAGRl5VU7sJ/biDRjouPvQnueDFeUizAECZxL9E7+C6AoevHixfHjx3ft2vXHH3/09Kyf4qSurq4nT57MzMyMj4/Pz89nWdbNzS06OrohhO69xJj19oDToUkUy2NYVpDLCCciNNcqfukigtDmW5fNkW9OKiZmtRN7f9Xs3sQ7kqleTCepJd486tM07giVaElgiY0hMESAP/19GyfwK0r7oy1jATdgLQDYU17DhK1RSY8aDtczzLQi7Sal3B3DgIXsd5PyruiZ7TnhTIR0cP34+Os2+2yN7mul/C5JTlRrd7kqXLHqy8kwxsv2e4txZaVFXIpS7+OG7cfq7OAfV+mjVRuYGVUWA/hLuvWvbNvKrmXyO9XeXudas66X/LMi7EEJJRdc2kc15FD+rrf9FztalnZ2Wdq5zIuvv2rAgXyrZfnVG8NSxeVjkcenNmDSXgzC44Rsq9hI5W9gLClN7t3hcQ6+4dJmmgo7w5I00CxgCOBKjsIgwQlMrSmxWq1V3rIwZot2ywHVO5PbSTq1k3RqfIOdOGlMhELh0aNHP/zww/bt2x89Wp9KI35+fn5+dfUBNQH3eCvzztdf5B8cuiKjXU85vOl18dOLfWdNNOVGiPCUXTFLXWeh2Wmm1IUp57rKX9hXsNaO9P9FfSnaZVGwC+wrguqkamtiAsd3pf3eu+CziCr+k7Gk81rfqvlgGYoOEQjGqzW7lEpmUWreFf3H2/zWmfnZo24DQN19/HWbfZZG94VC1pHHfYHHtbPs5CLtbpVSgVWTG4arXqMKvsOkPTH5gPJGquA7zCULk/auo1VPOi8K5XK5AgIhUKTm6rk/Ff5As/QXaUsrNlIslWPNzLFmevOq+EB2ZJZarv2AYJXzxViqmBtxHHPpUuWJCI+3LOrvGeM1TFp2JceSaqpwN6/F6Rqa2qBU7eAdaTMfffSR47C+0maaEMcnw2hnJFwUwRHfryPEiyT6HF1OTk5wcBWZkSifZ7l5ly41Yg8LUf2l+62lS7v/21xVJ/9hEARZvnx5ixYt+vbtWx4N9ByBK0erBFvfO7/9tP+AgTN9kwdeb97TjttB6nXrwo8fdt9ucDMXtj1V4rE4oP0kT+PFYsmwm+QUr8xO8us1kKoFgD4BPEF1edi42wQqb60taQJticeVY1GOV61ewgSxEFg4OutWm1u2T7f7bQhShRC45ceWqcNuQp19vI5h1inl7e4L78xwEctRtJhhqnXwgGCE/yp72hy+tAegPABgqWIydy03/MdqBjYdr3pN15FV3JRigFVZwjVZR5VgEyKkp3nR/yD4g80aWnuMzP0CE7/42DMhHI7vJ2TmEkzSxVGVlMxairtNQHhB9fAy6kzVDr6B0maaFgn3wQUgR0q4uLvqc3RZWVlVOnhAEG6Ivy0pXdDuoeWjTHOamTL2dR3WCAY7cdI4VKwdN3bs2ODg4G3btj2h/7MKolCtEA8a2zthWPLA6x7vBYg7r8+4+br9kvUvl149xfr2F4xuCwOUkzwBQNRJGpnc+V8jA9er2P2tkhVdayTPzgndZbv9MqB8TvDTyP32WJ13965tzg6vdX7yEAIHAH6UOOhIy7ThN1EB6tKzRtciVdKTX3mpcoyopuommEsXVBBBFnxLeM4FADJnJaYYggoiqh1YX6AI1KocigsurdVtWImNKUWCsMAhZM4ajn9ZEiYwVnvWx5ygr8tLkVUJJh9AqXdQhTtx9+mM6SZd/Duv5b+1sLUhqdrBP5o2I5E0Qe2BOpJZQn1xxeDYeLfRYKHYWb+VZUH0X5WhFHllQmJWVtbjhnPDAm2JlR38UPdXrExdo4udOHmmGDx4cMXDtm3btm1btUTEMw4voD2V3U2R+QX98nzZyHhjRtqMLl9OOlo4ZFfR7n1BrqHCJUEPVuAxCQ5Ge73bgApboW7TMXG7p6vVmc7QRpoZLxAv1RfvUim98AqupUkrfnH8V1hv98RV41iqmNYe40Vfasyzd/PlRamqEP2tL1q5EwgghM8Sa1wHxnWi49qFzN+IilphLp2rHU74rbDdHYQph9vvLSZ8lyKYuNohjUPVH8Hy63cXFxeTyXT48GEAmDp1auPZVR8o+FjfwLIkBIOd/TvH1sGDK+ejACDx5bmW+gFAdnb244bzWzQrOf5bpUYx7iKGelPZdOKkCSEIYtOmTa+//jpBVCG8VS5s9xxBFtqz5g3z+2J29pKe5jufL/N4v+dhc0CcBWZ4DZ2dKTTQvwxT9v8msrxwKI4B3gBekxvwWQ175lqzpIRceD8a65TZsmaW7GsSwl9JVu5rNrFIs0ullMWb00bc9Fkf5tKjiqp0T03JiSJbptV1pk/1XQEAAOH6466vkVmfsmQB4fUegj/9WsITSDcnBwqqqGQoJBAhUX084FODIwgCCIJLCa955L3F3IjjrD2fyt/CizxXk+EovxkmH2K7OwRQLq4a03B21paqHbzDowMAy7IFBQVxcXGDBw9+7hy8iIP0DypbldJYmKV/s94uWDdf7nytfqAv/lJq4CmA7Ky0PIqeqdFtd1XI0If2orhhga5hbzw67W3D9UJbXk9l5RI1Tpw8X6SmpioUCscfTW1L/YDyUQRxtWW/6rt2qfmmb/SOYP+/1W1i2xR+kIZpqF2rPcccNWS+neC3MQxQBADauHM29pL+VPiDG9ezQ6OnvNoY6xfpH0eIoqf5zgEAPcN8Vly6zVURtNYtZ2Fym3FJ7P7QLRfyR03J8FkXJh1Un8lyxcfV2e8mYVKCLiE93g+s4Sjca571ZlsEl+Fuk+vRmHLSzckrUhbO8H+v7SNxzRdybH9m2WqYulZDjiZZrhWWLeHcKSIBoNBMozBiGrE9J/54O+5J3G0Kwq1pxgfhs9ga9wI39PuKAvCabbni7nJuYBOIWzio2sGfOnWq4uF3330XE/Pci7oggBjtDAC8LRHvcMkXMkoASE5PnlykHS8SVvLuT0BBqI7k73E6eCfPO08uGffVV181miX1BeaCh8S2Sh1i91hy0d7+c/cj9jaxbQo/Sis5VRSwo/kr/aXxQ22qyamZsxIdPp6k2TvFub8V/YyjRJS4DR8TNKa1JwsPBwvCEoy3HLetMhT93bNMEch7TWjOguS245Kj82y+G8MlA+oS21+Z4uPqrLlJwUdbcny4KQNuMFbGa1nVJVgqgWAiTvAWBHd5cn3tp4MFdl/u1i6KXgfzdkW7tCWQhxbkdVZGba5n2Xw/CWany9au1CYaASRSSQAQqfTSduY5tI3gt3zoC8IC+37umS6S1v1ECkpjTxl602tZ8O42RF8BP5jAEVzOb33HEWfnIPejVP2RwoLP74XEtm4qH18jrzZ+/PgbN240tCkNDQpgtLMA4I/jo7PYwN+4AHArNX28SPia+LHBJixFV2rx5Pl8EFLTJTgnTp5ZePexWCzr16+Pi4tjGCYjI+Pbb79VKuvTozQmuJITfLxD3ier2KPiHW8FpS9KKTlZ5L+juWyYWwcud4q7S9DhaHu6JXNWIjDs3ZyE2H8PThMFDcR51zLep7XHyh8sqWlQOzX2wj90v77iNW24+6v7cx8pgoKA92eh0kEq300N5d0ZK116ThdyslXpaW3uRzVdwsEkXVFhq5r0ZKw289Xbxj+vkLmFNel/Sf8Hw9ITvGf484N+UT+NymFtaePOGRshcDxaqIixyt3DkFeHIa92xmMIDENwqS3pVVvCMFvCMMZ0CwAu689nFsd+qCv9PceQ0O6y9Y4xeURc3lmNZ3mcRAXvnrcsrfQ3bdhf7T3eD0juc82aZGqEV/Qo1V+IsSy7f/9+x1Le84uUi7b2IEwUCwBF3+XYD6i9UHcUQUpzi8bxH6vXSOYUaL7d57H83UrtOEJQLIU3wGWsEyeNxqpVqxx/DB8+fOPGjTNnznQc7ty586effmo6u+oKrsSDDiQWbkh/7zdGxCElm1TiF7MoNQAAgssx+eCAvZHxERckfRSk34658n0islcQUPElfxnpAh7KB5akdSe5EccxourU53rhh7ydvVWDpYS8k7z779pfLun/7CTr9lAPBNwX1nNJkpJfNNnvJoX81IoqJjNeu4NwEaY0IOREq5QBNzAJ7j7Pv75OZEvNLPr8O8LXE3MR6ff+JOzcRj7hSZlHNsZ6JP/7N/3mI4CM9ZrycfK7nWTdFZz63JWoFguoGPNdTvA3Fe97WUsymbMK4XrZGfuRgj2LfN7cGr8bHdvNxiBnL0cGLMocOfUeJXKB3g/5x7xlaSWnNCEnWuMKQjHBEwBSBt4IOdGK16yxi7JW7aLE4gdBgDRNW63WL7/8srFMahBwFL58WYajoN2dn7kghRymONhFoJwtUhsNOw/fSO0dskjqIn1klR53U5KZeYzFij6cYcKwzIKE11eFbeai9abr6cRJU3Hu3LlNmx5ofvXv33/u3LlNaE9dYSla/5lykp/6sornx7NfzbRnyHEVhzH+C7gEFQ3MnHFX3EMh7Cvbfk22GFzk7m/yxB316h93me7ODlhC5W8G2vQ4YZN6IdF4O9OS/rrfOwCAADLOa+rX91YHiNq9p7d8pZCp7qvL/Wqx/mWxLpfXm+oGY2EAQyyJxpwFKQG7WnAD+CkDrtMmGuGhjKnyUuXTw7JFX+2QTx8jaBsJAIzFWvDhOvOVW4L2VSvWAcDJwsPhoqgQYTgAyAlld0XfowXfe9jfPJhYlrKksdBaMzvhRFk53XAFvviFeg52TkMG9+DvZW25uOtr5Y22/K8J74UIrjhZsC9EGB5hj5w4rthO2eadDBdJ2LePtCl45XbamFtBP0S53Pfxd5amlJzWdohtg8vLbugTRytsVhIZcjP0l9Yc/0Zdq696if5mBW7fvq3RaJ7vLzwAAKgEKHOwMGtOonWkcvoyVetSwJp5AYDt9fPsvyWSqvbgEQLnhvpb76RUakcRtLWko8aubgy7nThpYHx8fE6ceFB16cSJEw2hJtt4IBzC92MENU/GVl0NWido/3XauLGkdgFL6QiPj9Neu02hiHxLxHF1LB/1/dE4z5A2P8ekbecyIMeiji/+k8xbR/ivajjrGJbZl7ttjOek8m3mIEGz5qLoS9pjL/N5E9RaNU0DwG9FJv3Am6O/KqrHU8uGuSone96betdrWbC4q4zjywv8ISp/WRomwjw/qjdhFjJPjRC4w7sDAMrnift0ttysSrAdAAA09sLftb8M93hQpGeA64hk012lJPONlkLHo4cfN1iGlx8ODa1nN9nRk9vBk8PxW0Vmr2DpUkcjrf+FtWXjblN1pOZ37S/DleMT2l3mAGfDCZqroksY5h+bLWhfpLirNG10nPlGmaAC+k9pQgveRrTs0uQvq22hVq/qKGUstC2jsVOsH7qDf+edd57Q9XkMuqlI/M9F1pkJqgke2z9xG4NhAZ8l8dsGwLXEDKl6zNg0fa6XXFDFegYvKsx6K1HQLrJS+3iv1xvFaidOGpxVq1aNGDEiNja2WbNmSUlJsbGxFaVvniNYgBFHNTorg0CXzzy39BIf23ctElVsdfmctN2LRz0J8/HP+b3Zf735dy/CldLWv5WMsdNsW2zv8dT9Zwyj3ETv6m2jUOVIlN+AZSb+0v1WQumMlOFP7QP1MCXH7ZT6xxVhvQAEE9XaqZgARtxu1tyFOKvPRVJrGARXLcZLxUVbct3f889bliZs50J4crPeSpCNcjP8qdfszHNIANUdlqKQhyXuERxnSepx/Q/m7QoUhKaZktLgQdm9UGHEb8XbPgj5DAEEAHRWJruUedG7ocrfdfB0XGxFYbJeVO6XhO/HwNrtmR9y/FcDQvyQt6OncoCCUOWUxpf6cWh584jSNZMDPntbVwwyaVhzUelZPVlUFpDf7HA0DLn5z/z0zz8PfIHPfV+n31TMg5G3vdeEirvXZ6JjTXjIpZWrsuv1+piYmG7dukVGRmZmZp4+ffqDDz5oZMvqnUIMdUHBds+yQSpDcORfLx4uDQaAUlM2V4jJHqPXKGgXZTx3scqn8q05Mo6ChzZZCoQTJ3XHZrOJxeK4uLi9e/fm5uZGRUWtWbMmNLSKXORnHwTgmz4yOw0AgFs/n4ONyRAtaGf7iZV7IDY1medGuKYIPbl9ARhrcoD8gsZuvZP14u9K7rvCT1HRP64IGWrMvCvuWqMosqeFjwlaSzres1SOa+sk70Yy9ili1/QSu/2VO8EtxC03RdAGOmXQjdz3U7xWhtT91NZEEybGVNO8uAH8lME3CFeO6EWp5yfBaSNumm+WAtSPgye83ekSgy01kxvsBwDAMMY/r4i6dXhcfxmhKKZ0V0sq/8x68nwYlsGeqCJX7xA+H1lvdcJdx1O6WJQfjEl7pJgS0kxJU31mIyhy7Div/1Dr0tH5F/YE3yv++RvliNhFd2Xbtd6rQyT3l+gxMd7seEsYcvPv+WlzPnbfXspHh93xWhYsH+vemC/EQdX14IcPH96jR49KQTf1W4KivqhhPXgAsNFszq/a0ldvC9tJvE+0XHky61Tcsesfv9NP9sKx62efYmvkUP4uDMGHu79afVcnTqqjqerBsywbEBBw5coVV1fXRj71U1Dt991KsTa67Dft9LlpYe6ewewJoEsZ91m45XVuoADBEapwtz3jXUvU+b8KuKuu5O4aKBFlfZRgV7+AiyjZABfPt5/OqTAAM4p0S2WS8rDqRJLcWmpcq3hSYdNK/KYx6UbdFoTwv1nqvtNd6YphdAmVMuiGuLO0Xnx84foszY7c0NjWhr/0tnSz+zz/9PG3UR7qv6MFUodKepWwXLuj2bxX2Kk1Khaar97GVQrX+VProsRX23rwdYHMW88Un2UsCbzmpxBe8KrUxTpS48b1NFAlafbS4GzVwNd6Gn1Mh777YcHxj7Qb892XB3u+XXlL62+1qWhYXIkfN+wfc+jqEPnoyt69cb7vVd+2njt3bvjw4eWH/fv3P3euRoI+zzJcDAnqpwz+Mdr0b8nZPv96WuDNbwgAiPNQ3/SoaRJ8RXooB7hza1dMwomTZw0EQWJiYhYvXpyammq32+n7NLVdTwMLMOCQps/+nOSLL6df6hLKuxtg3aI1m4HW52XsMhgG2NNfA6DsWe9jblMUgvAbJVekhDxcFOUd/G17W7rVXih7Wu8OAChADz7vtSJNNkUDQCJJTivSdn9EAf4J/Gqx5k2Nb+YrGLQleqSLcKJaq6ZpTIIHH2up/6lIsy336QyriNscX+Vkr+T+18VdZA3k3QGA36aFx5oFuLsKAGSvDHZ9b1oddXbbunPGhjeSSgHhMYOx5+LKsQgvGADGe70x2fvtPsohRsowTPGyxifXchwk2eJpI1/Xbsj3WRnyqHc/b7UtogxBx1t2NKO/vO++rXej6itUpOooekfQzfTp0x2Hz33QDQAAGO3stzeM87vKz2wN6j41zfuNNC3uBgCWgsJ3NPoTHq6P07phacaekV223FQBOaGsnNnixMlzyJgxY8xm8/bt2ys2Vrm294yDAJwdpwIAW5IHQqheuTRkRYuTPpYdKC/IhyhFBf4oL8ie/g4AcANWZ1rStLYb7YVujIkEANbjzVjdnyMM/wgwIQCg/DCofY7MaJEAACYVaT6UST/Q6RdLJQMENb1F0zPMCn3J5nHezAfp1kTTlHARxcIyfcnXSnnJSQ1LMuKutVgJeAJuc3xZkkkZcJ3w5uEKwn9bc6S68upPAa6UuQzoVl+zeYgwD1FjrdUjHF7zWAQrWy3w4fsDQKz6SIAgeITHeAkh+4d//u0/FyT3uua5Kkj1ljcAUBp98f6frQlpKI+b3uOFhdFh37oqojgcONZqLsNMVGslBuPr4iYoD1+1g//PBN1UxEyxhxLN8zuIJw7x4opFmW/GB77UTHCQo9cZd/I5T1CyY4ymwhWbfbevfvQi1EKbL+jPOVXtnDzXJCYmVmrRaBpW5qWh4fittN7phXDetLp/wmEjcOVo87UgxnAN919ru9WBE7iBBWR/7rb5co0rOt6eHgoAfIBejKY09Q2cUDKmW9xm+zBZ36c49WiRoICmZ2q0CyW18O4AIEPRMx6uxFhEz6HTxp4P3Bs5PUxI8hHt3qSCtRkhx6I4/vVWHcB9vj+CI9Zks+/GsIbw7s87COFW8bCUKj5ddPz94NUA8LKi31/aXxO941vldXU8y5gtBUvXi19+QTpmAGOx3v7rCmu1CZH7lUsRRICAka5nGb4aUrWDHzhw4H8j6KYiUi5SamcBwBXDoJfc/5uInM1ZEh9Xc3KOKSsL5I+Nb8QkYkwitqVnc4MqL2NgCP5TwQ/tpC9K8Pq5uHbipPFxc3O7efOmVltWP9tkMk2aNEmn0zWtVXUB4QXgrq9OKlmHY5tw5TQAQAgVay+iUl9HuQG46tXLxeezrfdyXYcqSs7cUU60cjwBwEQbjxbsXaXsKQbAZL2f7tSJJHnQZBouEO4yGrvzeT54Le47CQQBAGHUpwHfHqMLuRYTBjTLdWcCYjDWBJarJbzIczUUkqsWt7mVlyQfB6XeQxVur/IpwnsBJutXL/Y8yxzK3/2SvJcb1xMAUAQd5zV1Z/amSJfWOEIAgOn8VV5YkGREH0fn3q8MLtq8d/LQXtvcVd44Nk7aGSAAACAASURBVKNIF0QQc6VNU6LssVpsYWFhn376aWOa0tBwMARFwEKxfBwBAMKLy+bZER9fSM5JT09v2bLlE8byW4Zb4xIfdfAclLMoeKUYc9aXc/Ics3Tp0tWrV/P5fBzHZTJZWlra4sWLm9qouoJ7vdcyp43NdgOgPa05ghAqoM2M6QY36jwAcFFue2nnNLsGE7/oWvjN38qJWlasQAzdZV14+Rs4ITtYQO9RVAD+4Bcy5qZxSpQIf2K4jmPf3bEyf9BITCrS7FQpa+XjAQD3eIsu+cueeyJnSh7CQ0NOtOaFCCj1bqpoLyp80s9UA4EKoxlLMjdsP1Lhh4615drS3kAFlfOH/3tkWtLuGG6sbPZACSpcFOXF8/ut6EQ/12EAQOYWcoIeFOVDMKy32SoqLp2CoR4YFkrgy+TSpwnyqg8ecvCNUD7SYDDs37//zp07hYWFFEV5eHhER0ePGTPGxaUxfKSEixrt9x28J5fKsyJRzQAupqenPXkgPyqs5KezkuFVXNR78mpab9GJk2eT77777uTJk66ursuWLTt8+PCGDRvs9vovlN7IIJhI4Pu+ovhD8Dhmz/6EG/QtsBa69CIqaAEALV3at3RpDwDATrfeenG0MKSfJepdiUs/3ZesSxdE3HGprljHMF8rHyzsxdw0jQoTyHiP+a1m7TRtnl9YtEzq0p1jYynbKB6wNL2kKGO3RxBALZbBUWFLTPqyuMM+/20zuYECbiCfpY1kzipu6N5azVNfoMIoXDmS0Z8h/JaVN9ryNhIesxCud+Pb08gczNvFRXk7czZVbDTRhhPqQ90UffiYAHdTktkFD55jWTK34GWpy1YESSHJT5vOu0MlB9/Q5SMvXLjQr1+/4ODgTp06hYeHA4BOp9u6deuCBQtOnz7dvn37hjhpRXYPlCsFZe82LiNYO6v0CsoFyEh7rMqSA15kKG0wPu7ZYwX7AwQh0S5t69NWJ04ai+Li4qioKKVSmZKSAgDTpk2LioqaP39+U9tVV2Ter1n1O6wJw1BRO9SlEwCgkh6VOyEE4bfcfm/Rrubn3ytI7lqwXRz156f6kjSKilHVogCHPWsZVRBzEBMiBVCewzcYYBBVTPN/wKS1W/AnfD+2xnUSd34V4SkAgMr5DJP2RkWtazVJPUL4fGC91QlzfQ3lhwAAY7jCGC5zgzY2lT2NyRD3sSWk/tF2DMEcUuXCF1vnLfiM+/s/ws5tWatNf+AE5eE6i8tpiWNv8bjTi7TbVIoQomkKlzx0Vj8/v0p/AABN0yiKInVLcnAwe/bs5cuXz549u1L7nj173n777StXrtT9FE8mWPbQ6w3cF+ldkBcHkJZaOcioEghBiLo+VqghUBDyb8lFp4N38pwSHh5+4MCB2bNnkySZlZXFMExRUX0qpDYV447rP2u/QpE9mh+89QndMGlvlPedl2bnt+bLB8Sv/VPCtwMZo1IIH/+jx9IM8rA0FuE+jS46wIu+UDE+i9afIrM+xiTda2s5QrjiHjPtWZ9wQ3ez1gyqaB8v+kJtJ6lHEEKFe84msz7kNjsAwNgzF3P8lgHaZNlfjUmoMKL87+0GoxBBxojKasbYWfZDnX6iSBT8wVv6nUe0Ww8iOI52brNk0vBAHPtELkUAaICpRdodKkVQU/j4qk+ZnJy8YMGC/fv3X7hwYeLEiTRNHzt2rGPHjnU8WVpa2pAhQx5tHzp06Jw5c+o4eU3Yedv0ghe3mbzsVdPFVAuBz0mA9Ix7dZk2yqVtlNO7O3luWbly5bBhw/r06TNlypS2bdtiGFbl9/S5w0SypUR77zYJSHVRMoTfSlt8Lz4myfRdc81s/0DmIkQQmoFjKRbyvmyOjWZ/TLIQarXpnxuITt+l9J5bl1aysQMRLgcAEK4/7voamfUpJ+jrsklZuz3zI4fW6VMYT3jOtMR1okvOUQXf4Z6zK8V1Nz6E+xuWwt108VnWngsIjimeVB3uv0o/AX+iWkMCjBcJSZZ9R6vHAUIJHPf1dPtoFrAsIMhdO9naYnlH4uK4PBwk4OMAN+z2Z8jBT5s2zdXVlSCIZcuWzZs3z263z58//++//67jybp167Zo0aJ169a5uT34sGq12sWLF3fp0oC1m8q5U0TKeWi5gy/9XdeumQJBkMysIpqmMayaWJjig7HSUf0ep9igIzVy4nmtou3k/5k+ffpoNBqCIN59992IiAi9Xj969OimNqreqNa7AwDKD8G85h2kfTJp/Ad3yVtFOi6C9OLwb6nt1H05AJqB+OwS6mYqr0UbXC4RBiP0gcParQeVM8c7OuBe86xx7RnjNVTUBgDI/G9QfggmfWRToKZ2czi+S+2pMwATcUN3PeUk9QjC4fgtJzMXs1QpN+xAk0QDNDkeGLbLVTlRraFZ9orNjgN8oZDh5R4BQQAggkNEcAgAYGk2f1m6ZJCqX9smi8Ku2sFfvXo1JSWFpulr166dOnXKYrEsX7687ieLiYmZPHmyl5dXQECAQqFAEESn06Wnp/fo0WPfvn11n79a5ncQu3AeLKxxfXneWkruLtfmazMzMwMDA5883PT3VUHbSE5gFVF1JGtfmjR3ZdhmMe6MqHfyfPCE4lJXr1593otL1QoWYAXvtTSS+k6lECDINpViSpEWkSKfvCQp73M63TpT/a8ySuQyuGwHk501IXv6+8yUkY5y0ggmInyW2DMX85qfZkkNlf81r/mpuliFyQeh2qO46lW4X3euacFkfajC71CxZ5ME8z8jeGDYNpViSEGRH4EfclXij7nfY2k284271mSzZnde0KFoYRP5+KodvEKhyMzMvHbtWmRkpFAoTEtLEwrroVK9q6vryZMnMzMz4+Pj8/PzWZZ1c3OLjo6uoUxeVlYWRVUuSVRYWFhzyS134UP36IoJnjILE/1XyLl8bXJycrUOnt+mhfHPK+yZC2ROAaaQuQzoVi5vRyCcoe7jzLTR6eCdPC/894pLsQCv/qQ1kWU/CGnF1Jo/7/R3+cFx2MGT4y1+8AuASl7CJC87/raxLAZIjEohQBAACCTw7SrFCXPl4p60WotHPyirinAIXCGjinQc37IyLbhqHFW4g9IcYUr/wlWvOLRO6wI3ZEcdZ3g67DTLqUoAhxu653EC5/8nkCy7uri0HZeTTlEHTObxoio8I0uzmW/etWdbQ0+1Nl4sTh8dF/hDtLBdE7iGqh38zJkz+/btyzDM2rVrExMThw4dOmrUqPo6pZ+fX3kQn9lsbtasWXZ2drWjDAZD//79LZbKXzmTyVRz3ezLefY8Iz3sfi1hlI/pf1Q3C4s698c/yUmJfftWI1zFCfDRbPpeNmaAsHNbMrdQ/VmM8s1X+K2bO57toRxQQzOcOHkWWLWqrOr58OHDN27cWKm4VNPZ9fQgACu7Soz2Mgf/ztniic3RjiXbSiUzGEyiEKDl+2tU/jcEoYT7Dp6HIB/JJBWnCiTw2RJxpflxD5U9JVPQpoXjkDGYqCId4aGq0AXl+K20JY8HQHkt/63/V9hYDDqs+X6QQiV4xJf/fxfPJFl2rlaPA6xXyosYZqJaAwCVfHyZd8+yBh9tiQoxl14Kvy0R6WOaxsdX7eAXLVrUoUMHm83Wt2/f9PT0JUuWTJo0qe4ny8jI2L17d8UWu92ek5PzySefAMDSpUufMFYsFt+5c+fRdkd1qRoakGOg/8mzlTt42kgVrsv8+zUfAEhKuFrtcMvV2yiBi/u8hAr5vIhgwstN+90PXvcdPABkWtIstDlM9N8Xf3DyX+LcuXObNj1I8+3fv//cuXOb0J664C958JvGxxF3ZQTBnaRAbBz/eeXtdMkfgAlxt2m1mrmjJ0cV3Vm/5HNMKua3bk7rSnR7jon7d0UIAgCo/M2U5lBZV5YFjGe7OxgAAME5fstR8WNzcJ5NLBRrp5+/YgR1gcxZRRUdrOIJBOcGbUbF7QDg61IDB0HWKmQYgAeGbVcpJxVpojmcSM6DOMqsmQlkgT34WCuUX3Z55NJL4bs5PG10XMjPrfgtGlWR/rFxfRaLJSYmZtOmTevXrxcKhdUGoNUEDMO+//777Ozsfv364TgOAI4l9yo9d0Mg4SKltgefWo4nl9aRLr5BAJCYGF/tcHtWnuv7b6LCsusDXngQpdayJInc1wWyM/Y9uVtWNPv68XM4cfLM8Z8sLlUO4bPEGteBcZ2ICiIAAFiKzHyf47cKUG6t5vm6twwAuJ/MKT5wouSns5iLSNS9o7jXi45nEUEYa8viNjsA6IPfesaSQt5bgPCCqp7RybMEKmoPhbu4ET8jFf+Dhmv2rA/KPjkAb7iIeQhSvqzhjWPH3V0FD2/D06U0JsYrVefDxDiwwFgaW5G+age/Z8+ehQsXzpgxY/PmzTiOz507t7CwsO6ZbL6+vjdv3pwzZ05CQsLu3buDgoJMJpNIJDp06FD1g+sDMQcttVV4i1GE8OBGSQMuACQl36t2OCaTsOSD7QC6xIjyuEgF1b8QYfg0n8bI93PipB75TxaXcoAAILiU8JpH3lvMjTgOAFThVoRwxWR9nm5CwstNNW/qo+2Y5GVU3J42XCI8H+h8kNkrCO+FiDO5pj6gGHiyTnAdYS1JAJQteRzKeRBGTRuvoxwvxpriiCsUPBJS96hYQsDO5ukT7qSPuxW4NxLhogBgvFScPv52wM7mjb9EX/Ubtnnz5v3793/44YcA4Ovre+h/7N13eFRF1wDwM7dt7+k9JCHUhN4FDAKC0kTEV1QURVQ+UUFeRFRAENTXAqKi0i1YEFCqIiiCVOkJJKQR0vtms31vme+PjaEFCCEkgczv2cdn9+69M2cjydl7Z+6cdesWLVpUL/2pVKrly5dPnTo1ISFh2bJlDVySUi+nbPwlPXLhingIlMm5/MKKysrKax+u7tfN/PXPQqkZACSHs+yL79T9Ll99L1IZU78xE8St5i0u1a5du+Li4ri4uMTExPvvvxMKJM7qpfWubcX4T8BCqWjejgUzn/cRG77wVnTHhS8Q8j/GfJH3pWj+DbvPM/41fBtoglwCHvxDSf+1xd5HkV0c+0tZ9csTxfVWyK5udma53thjuaVdIC4IMAZPCe33OBP0IhP0IlLFI1qD+UIkq21hHgBAHNXiq3bAoMxxidgt2Q5UZD6SGLmqrab/VeuZ3To1n8EnJye3aXNh+Z7WrVtXV5qqF6NHj+7Ro8f48eN//LGmMY9bpqWRWTr4krJvXLg8KF/0i47ISUpNTk7u3v1aQ2Xq/t3FCmveC3ORSgFuXtWvm/4/w67cbXn24n6mQTGq1vUcPUHcGi6X69ChQ3q9Xq/XA8DmzZsBYNq0adc7rqnrHvTv3WWIYcPnec5Np7W9adMoSnlLfje9Jez4nLe5Fh8D9vDZb7DhC+q2yk3DkzNozf3G6guUY38pWzRA7//vbUfBmoaqxX4VTgE7hVt7NkibRqKi5QhxkmU312IxiHZPxmRKFkSbxqIbLBbqzfGZjyamP3DCedoeuaadpl/jlButOcF37979448/fvPNN70vv/rqq86dO9dvx8HBwb///vuSJUsCAgLqt+VrQAAhl/5LlYUrfIs8ilbtISk1KfHEtRM8AOhG3kMbdfY/DvjNeg7VVJIHANprOyVaj5EET9wuHn/88Z07dw4cOJDjmsT91rcCrUugFLFi+VZ5h6O3rhcmeLrrZFfJdky07kPyFrT+nlvXV727+C5ihoJANd3oeb2BceELXSmjkfO0ZD8ulm+h5C0wX8L4j69DU4ijWnzTPmd6asB/Ixsru8PVEvynn346cODAlStXms3mPn36pKWl7dixo977RghduS79rTZzt2V+X131MtI+TwU7bB78ZSsASDq1H2DSdVtQdm1fvvxHLIhXS/Dd9XfVX7wEcctt27Zt//79cXFxjR3IrcVFvo9d5xCjv3VdIFrNhrzmyZqO3dmyNttuXUfErUCp2jOG+yT3eU/mS9idB7SGi3y/ztdgEEeFLW5VvxHeqJoTfHR0dEpKyvbt28+dOxcYGDhkyBCdTlfjnredXeddM3iNXnbRenbfFfm2apUGcDrpZG1aoBRyedsYx+FTVw7AXyzDfjZKFXuz4RLEradSqYKDgxs7ilsOcUGIC7raux6MuUsnTF25pTYYv3FC0QraZ6y38NptCsHVluRuON8nOz4/XlXD08Fjp4D7ry32vozz5T4eeEu+qLGhs1yneiLGB6k6IFpe95WGm4ZLEjzG2FtgJigoaPTo0SNHjty/f//p06e/+OKLtWvXnjhxorGirEcbHvC5OLtjl1S+Iv/r33pFAZxOPlfLRjT39hUKiq+xAwb82fn3Xox8PUwReVPhEsStN3fu3IkTJ7733nshISHVdSNlshu7i+y25sJ4YEHRIpOxs6xqkGKV1bbH5V7la/Kcm4ZdmTUcgxguZiWiL18MB4CStdmMKPmtjfgWW3SP/rJ1Pxve8BhF7+Cqf4S7zrv253re6F01C13J3qpvH4j1YYNeEsq3YPsJWbvfb1EvDeaSBP/mm2/Onz+/Xbt2gYGBn3322cSJE+fOndumTRt/f/9evXo1Voj167IxeDaQEyv44KAwlVKeX1hpNpsNhuuPlyjiW0H8ta69IEATQl9gqdtjfg3RzM2cObOysnLjxo0Xb2zgO1walxyhRSbji6XlH/gYustka6y2H2yONX4mAABKgSUXGzLj4v3F8q2S7Siia17Au6asf5vp6N/4szGUDFJqq/5cmxSUkkWh2ob4zsEETBSK1zB+jyH5bX96dkmCX7Vq1ddff/3oo48CwA8//PDwww9v3Lhx5MiRjRTbLfHlCdvdYfKYfwvKAYVa7em21O0IjI1OP5505syZ3r1710tHbTXNtx4DcXtJT0+/bEtpaWmjRNKIOsu4xT7GF0vLBynl+12eNX4mf5oGADZkhutkN8ToKFXHql0llydziiz6i2a+KvsdC3GyNltqOVcD8yUg2WtqhEVc4w97XZLg8/Pz+/fv733urd9633132vrqyWWCn5K/kOABKv8sD+kmV7dqD8eTkhKP1DbBY+w8laKIv9ZU+TJPyc7SLWODnrzJmAniljKZTCdOnKi+FdZutz/xxBPl5eWNG1XD6yzjhigV39ns7/sY/P9duxPRGjZkhidrprztdm+NVD7vQ1rTg9L0bNRgm5cGng+AWL9a7ulOfgB7coG+9NuA5MSCWdElA9ENujDtlS4fg2f/nRnuvWGGvcpE8duXUU6Vuy5ZL9CVbA+nJDomFgBOn9wHUKul6LAglny0KnjR67T+qosT6VjDMcvBAT5DfTj/mwybIG6d2bNnv/POOwqFgmEYg8GQkZExc+bMxg6qEayx2va63B/5GOeWVxh8qO7/zkJg/B4Xir4Sy36hTSOxJ08oWilvv7tRI212+oTIIvVXXVi9EbHBL/P5i+Tt/gR0YfjAk/kSojWNnt3hGmvR36mMCqr80gWBNXet9AlbNsvBPwCQeGSj4+CFMXg2cDIbPr/GdhDLKLvG2fce0Q5LuFpfDGLmt1rCNo1azgRxNcuWLdu6daufn99bb731008/ffzxxx6Pp7GDaghC0SqxvKpuXoEoxgriOo7lKtBGSXrT/QwE3/Nvjqe4iIXu9KcVhsGe828wgZOQLKQRw26G9HJKL2+KAyK06QG+aKVQ8g3jV3W7vGRPFM3b5fGHGzcwr8sT/JIlSzQaDQDY7XYAePfdd6vfmjFjBtz+fJVU4qXLLtImCgAFxvgDZJ7JxJQsDAAAC5KnAMO1/syp+3cvX7HuGgkeAFjEeSS3W3KTOvFEk1VRUREXF+fj45OWlgYATz/9dFxc3CuvvNLYcd1ySBYm2Y5yMSs8wKy1VE7UapQUhd1ZqvOvPx3W/gebo/okntJ0p9Rd3GlPS45EWdRnjRs2Ue/sPEZ1nZzPhS90pzxIG0ciRgcAnvMz2dDXvM8b3SUJvk+fPn/99dfFL7ds2VL98s5I8CNiFPeEX3IHC2McCa4vwvxpjUZRXO4sU80O8DfyGVMAgDFdqxCtvE205HJ7svO5sKveXAsA6Y6zP+avmt3yQ9TQA0kEUSutW7f+/vvvp0yZwvN8dna2JEklJSWNHVRDoPUDKG1vyZGsCJry2r+jqO7Ux9igaZ01YZct3smFzXWe7C6L+hRu81vgiCutOmVHCCZ3qstFdUrVnjYMEfLeZ8PniWUbQaxkfB+t9wjr5pIEv3fv3saKo8HIaCRTXJJo5S27lnzWS3fXgeg2occPpSZmMP6+DonPpfUDKHXXa7WFkN+rk1j/65SKaqOO66TrIWKBuU1WpSaamwULFowaNWrw4METJkzo0qULTdMjRoxo7KAaCBe+wJU0kPEdi1h/ABAr90j2JFn0siv3RLIwRfxhJAu98i3ididijHDdT8DY0FmuU71o37Ge7DmyqM8uHo9vXE1xVOOWsnrwZVWJLL+WFi55GgSmR1geABw/vIPPnAzAci2WXLc1LiwIya4/xD7cfyzJ7kSTNXjw4NLS0hYtWkydOnX16tXvv//+ihUrGjuoBoLkkYzvI3zOAgAALPJZr3Hh8652jk6yO1EjxPoyQS+6zwyjVB0pbf3caF0vml2Cxxj/es5V/dK8sTh3ehprCKj49eH4GACA44c2Yb6SCfw/xNVnFZwUW9JfZfW/nj9B3Lz169crlUqWZSmKGjp06Lhx437++efGDqrhMCH/FSt2SLZjQtEqxJho451QKpdoYGzAM5SmJxf+VmMHcolmN4teK6O2PFh1Ud28sTh3Wqqm31Hd4M2IhbhKFgBOnM4HZMC2f9zJoxj/J2jjda5VejKzrb/vM036z7V38+X8Pz///l3GAVSTuXpDEMeOHeN5/plnngkJuTAt3GKxTJgwYcyYa01AuZMgWs2GzPRkzcDubFnrjdc/gLgjzPnbsimt6mTPJWKEYXVi1ZI1I2IUs/vc4LRoxMli19ZvhDev2SV4APAWOa7YVJz7Sirjy/BR0Vx4ov3w7AplO45dnJEr5hU8qm/RX3XuWTbszeu2xgYH2A8c148ZQhuvtfKRifNd2Oozkt2JJuXZZ58tKSmxWCwPP/zwxdsnT57cWCE1CsbvUaFoJW28n1K28W6xH7bwBW79iNoueELcdt7opXu5a9Wiwl+csAFGkzpWrT2sZu+Qa9vNMcHP3185IU6Fkh1CJa+7z5Q+rUPmvuHt2aNLhy1s9e6KUxm2xetbzYj/R+cz5sLilFeHZJyqdxfrzv36h4Zee08FrbSLNkHidWyjlQcmiIsdPnwYAPr377979+7GjqVxUbLW6xFV9ffddqAi85FExCDBLPg8ca17ZIjbF02B7t/CYzIaIUA62R2S16vdaZ+nNlLLhcwKgS9wcX6c7W9LZaH7y1bTofOerpbUSHdfABDKt+hKv2NDZ9WyQc2g3rZd+0GSrrtnmv3Mx1kLMDSjMh5E01ed3bOysjIza6qc1gwgxuSdW2c/aDk3LjFyZdvYnV0K388qWZbb2KERRB01xwRvlFPlTinglQigEBvEtRydPMBpyvltzJOHXjdp4wCgWNivCH7Re9tMbXDhwYqObcXKmkoOXCpe27Wbvk+zqtNFNGWLFi1q27at9/m8efMiIyOjoqL69etXL8VmMMYrV64cPnz4yJEjt2/fXr29oKBg2LBhN9/+rWA/aMl85FTEiraau41cuLzl1k6lK0+VrTmI3VlXPHIaO1ii3lAI0XfiMiXNMsErqJQygQ6Sx2zv5DnvYgK5ux9Np5cmsBrLgFA3ACQmV7IBk26oTdOz/6H1168RiQAN9h1Boeb4Yyeams8+++zjjz9+7bXXACA9PX3+/PkrVqwoLCzUaDRz5sy5+fbffvvtGTNmxMfHBwcHP/zww+vXr/dudzgcF6+g1XTYD1sy/lOV3b1buHB5xCf/k4U+aD883HVm1IVH0r3OE52xYLl2g8Tt4tG2ykfaKhs7ivrXHDONj4LamOo8W85zIfKY7Z34bJfLl7GouYIV4zu+vlMuozJyBbPFdusCSLMnrytYc+vaJ4ja+P7777/88stx48YBwHfffdehQ4cJEyb4+/vPmTNn69atN9/+l19+uW7dunnz5n366acbN24cP3782bNnb77ZW8eZZKPVtLzNJcuZsaHTJYfJvGGtouPx6gdtuJcJeKaJLEdK3DyDnDI0ybXub9Id+JGua0KcyqSkvFfJuRC5tCmuJMMhc0hFAXfnqKNattZhDFv+Wn+jzYpWm+NIYm32DFNEnrD8YxdtAIABp9pP32hfBHHzTpw4ERcX532+c+fOBx54wPs8ICCgqKjo5tu3Wq0xMTHe5wkJCc8999zkyZOlWkxVaSw+E4J9ngpOHXyUL3B7t2CPdP6FYMkREDB9X/Vu3moibPD0RgqTIGqrOSZ4OYNK7KLFXfWHpiyA/XRdi8IFkVHFUlbEp/peYwDg6N5NN9yuKJV++o1kc1x3Rxklf7vVJypaDQD7y/98N/31dHvKDXdHEDdHr9efPHkSACwWy4EDBwYMGODdfu7cuaioqJtvv2vXrvPmzeP5qtpOb731Vn5+/vTp0wVBuPnGbxH/l8J9nghKHXKML3Bjj5T5aCJiKc3Qj4WCD7BQ5t3Hc34mGzqTnL4TTV8zuk3OwWNewgBQYBPLnNI/Be62PiwAlHnEEWG64Trm7Ns5z+iDTAmD9nz25YlDR2+0fVqvVXaLs/62Vzd6cG32d0nOk5VHNhR+29PQf23+8jdi/keq0RANafjw4e+8845Kpfriiy+io6M7deoEAHa7fc6cOQkJ16qRWEtLly7t37//2rVrFy1aNGHCBIVCsWnTpqFDh65evfq6x9pstoEDB1Z/OahmsVhu9fcD/5fCsVtKu+84Fy6nNUzEyraIQbRpFJ/zDhf5v3+riTx2S2MgiHrRXBI8Bhj+U2m5SwIACWMM8OUJ55pEJwAgBB8m6LlQGWCweyT/bt0QQkePF/MeF8vd5CE9lQAAIABJREFUWNko3Yh7Ct9crB2WgLjrrzwvYnF1zqfB8rCDFX+FyiP3lf/Rxzigbp+OIOpg/vz548eP79evX4sWLb744guKon799df//Oc/0dHRCxcuvPn2o6KiMjIyDh06FBBQtepzdHR0UlLS1q1bT506de1j1Wr1smXL3G73Zdv37Nnz6quv3nxs1xYwIxIY5E5zhH3SGjEIANiQma6T3SXfh5taNRGCuAZ0u9+ytXnz5jFjxrhcruvvepHozwumdtc83/Hy4oAnPJ6F5sqT/fqcPXv2wO6vevS74e/pxe99qe7XXdk9/rp7lniK5qZNlYMiVtO2wJ1r5ssXxH6qoO/AmZxEbSiVyrKyMoVC0cD9ejwejqsqmHT+/PnMzMy+ffvS9C1JYAMGDNi1a9fNtFC33/c6wG5J8ki05sIpkFC4nM95i9IlyFquvtW9E3e8hvl9by5n8ADwVZLdyVd9m8EAh/Pd1X/DhkQpwrR0wYJzATGK1J64T9++Z8+e3b1zXR0SvO+0pxBVq5kNP+SvbKGIkVGKR4InfpAxO1AesrX4pwcDH7/RHgniZlRndwAIDw8PDw+/dX3t37//1jVejySHmDH6pDvH1XJ7Jy606jIe4/+EWPl3U6smQhDX0FwSvASw+IjNKVy4XLE/nz9cUDXCp+aocW2VtI5xH7AE32WI7d0bli3b+/c/dbgUiGp36pNsO3XemeGWPK9Hvzs3depQ39GbS37Mc2bdZbzHX0aWxiSIRiM5xIwxJ7kIuX60X+q9R1tu7cRFKAAAEEPO3YnbS3NJ8BTA3kf9eLEqwXdaVfRQrGJ6dw0A0AipOQQAqm46+yFLG5bV9+wFAH8fKRZ5B83W5Zq5UFzG+Jmu9q6EpbV5yxFQLZQxybbEjtruOa4sDa1zgn1dwZr/i5hZl09IEE3e0qVLGzuE66jK7mHy8E9bA4UQQqn3HbuQ4wnittKMbpNTMkgno7wPCoGKq3ruze4AoOqqjfyqXVuOLfL1i4iIqLRJJw+vq1tfxe8vd5666poeEkhB8tBKwaJhdFnOdA92SyAGyoIrBLOJI9WriDvWE0880dghXMtl2R0AfJ4KDpgakXrfMU+Ws7GjI4gb1owS/MUQQpXuGhbcOJtwpH2BlOjh+/XrBwC7d/5Ut/Z1wwdU/HDVtcBoRJd5SjrpesRpOrdRx3sfvMT7cYF2wVq3HgmCuElFH54XSvnq7O7l81Swuqc+Z1qTXoOPIGrUTBO8nEaoppJubAAXsLdy9Ji0Lj6dAGDP3//UrX1V787Y6XKerHn5GgHzfrJAEQtHLPurHy7JYROtSvryif0EQTQMvxfCkJzKez394o3mDUXW3eagedGNFRVB1FlzGYO/zLqRxlBdDZ+di1SUzskMGGFUfu8HAH8fKZU8RRRX27JyFyCkG32vZcNvivhWV77JIu6ZsJdvPGqCIG4hWsfEbOqYNvx43mtpwQtiAMC8oSj3v2nRmzoo2pBv3k0A9mBPfo3vIMYXaFUDh9P0NdMEH2uqYSEavshTsb6YMTDt7/ZlHhzsd49PcUVp0uE1cX3+W4cuVL06MgE+N3pUiafon4q/h/qNrkOPBEHcpKocP+x43mtpyi5akt2bFNG83Z36JJJdficn5gvYwBfY0FmNElVT1kwv0RfZxZd3VVy8hS/ypA09Znw4wJ3nPv9Cyum30hMGDACA7T9+X8c+EJJFhd3oQRpG+3f5H+ccaXXslCCIm0PrmOifO1TuNue+khqzuSPJ7k0HbbifUrbmIt6+uLKfvPV6RKmZgImNHV1T1EwTPEej40We6pdYwKmDjxrG+AONAINpQ/vD0Uzn3HYAsPNgsvNUyc30JdmvX36mmpxSvBW7KEJJBvwIotEwRrblr51i93SVtyZXfZsSRLMRCz1Zs0C6sIaxJ2smE/wSYsn9RzVopgneIKd+G+tb/RIxSN1TV/ZVvmVzse+kEB8l+8vsoJ69Eyig9p7wuLnf69yRUGrOe3kBdnuuv+u/GMQiQIcq9rokcmcOQTQOWstwITdWioJoALS2L6VswxdWLaggWnZLrnTWn5y+16yZJngAkNGXlG5j/WWiTaRUTNBrkYwf11nOuYNMbbhYNy/99deyOvfC+BjkbaMtv+y80QPT7Sk/5K+qc78EQRB3JC7ibSH/E8wXARb4rJlcxEKgZI0dVBPVTCfZAcD8/ZXDoxVxfiwAYAGXfVsQNDPSedZ+dvAxRMPAATp6Y+mwccOTViVv3XFk+FPliDHWrSPDoyMLXnlH3b/7Nda2u9LDQRNKPUV165EgiNrg8xcJRWtqfIuLfJ/Wk+qOTRGSRTB+j/HZ8yhlW8QF0PpBjR1R09V8z+BtHpxSXrUWPWJQy+2dihZnq7pocRuF/aTVsKns24VBHf+IBYANfwnrsr6rc0eMSa+5r1/Fj9tu6Cga0f6yILtoS7WfrnPXBEFcA63tC6JZFvu1vM3G6gcX/hZINkrdpbGjI66KCZ4mWnbxef9jI95t7FiatIY+g7dard99911SUlJRUZEgCIGBgfHx8WPHjtVqtQ0cSaCayreK1S9l0cqYLR3T7j8e+HpkSo7r26cMY6dkR7892OfNFqWZmbp930L05Dr3pRs5SCisy0w9l+j8LOu9V6LeCpHfwhpfBNE8UepOtOE+sXQ9Gza7eiOf8X9s6CzE6BoxMOLaEK3mwhdKrgxK0bKxY2nSGvQMft++fcHBwZ9//rkkSa1bt27fvj1FUcuXLw8PDz98+HBDRgIAwWo63yZevEUWrYze3DF/XmaLPsYnZxRs+K/J0vap4f0EANi07YT7VH930iDvw5Xy4A31hRiaDQmoQ5Amzve/UfNNrO/1dyUI4saxYbOFkm+wK8P7UizbCGIl43vDdaKJBkabRrLB0xo7iqauQc/gp0yZMn/+/ClTply2/euvv/6///u/Bs7xEXrm9yz3ZRvlMUr1L/FpQ48ZtKxnZEBo1onxd9MrV8H6P/j3ZthppQ8ASLYjCKOamrwejD05BVzYjVWDDZKHAkCmI9WPC1Qzmrr0SxDEVSDWjwmc7MmeLWv5DUguT/YcWdRngGpV9JkgmrgGPYPPyMgYMWLEldtHjhyZnp5+5fZbqksA98kg/ZXbI1tpTh7oYCt0l1TySf5zOrZ0KcNCi0qFw0keeaufuIiPQLCykXUZ+MG8UPTWEk9WXh2OPWs7vSRrQR0OJAji2tjA5yVHimj5g8//mFJ3prS9GzsigqgfDZrg+/fv/+qrrxYVXTI5vKysbNq0aX379m3ISLxYquYTcSynstspov+xtSj5iMLSxLtKAWDj9lTnPyGukx0BgZD3fh26QxxrGDei7PO1INVQyO7ahviNGhM4vg6dEgRxHYjjwubwWa8KhV9yYXMaOxqCqDcNmuC//PLLysrK4ODgmJiYHj169OzZMzY2NiAgIDs7e+XKlQ0Zide4zWVm1+W5NlsQ9rvcXe/xa3/EoRHKMeJG9qcBYMMfgiSJgEVANPYU1q1Hdf/ulFJeh9viASBa1QoADpr3SFi87s4EQdQebbwfccFMwFNIdsPLSxNEk9WgY/B+fn5bt249f/786dOnCwoKMMb+/v7x8fFhYdf/pbLZbF27dvV4Ll8Szm638zxft3gwhjKnZJBf8i0njGG+9/fZ3FMwzij4feLT91hWdOnaOSzwQHaB58hpe4/uPUXbcSZ0Zt16BIRMzz9aOOsD7fABiK7LON8/ln25rvMPBpJJQARRn2StfgTUfNcFIe5IDfoPGmO8atWqn3/+maKoSZMmDRkyxLu9oKDgmWee2bx58zWOVavVO3bsuDKX//HHH5Mn1/EGtrXDa155Zp3dsawFvJst+JpmOiq/N4vC8H7UJ9/DT7/zXeMzEevD3kRhA8bHEPLF/Dof/mz4NAtvrvPhBEHUDNVQYZIgbmsNmuDffvvtxYsXP/vss+Xl5Q8//PDKlStHjx4NAA6HY8uWLdc9PDQ09MqNp0+fRqhOc9qvIlsQvqy0rgzy1SyltBrVOz5vvlAyo/fInp98/+f3O6R5z5dq2n0n2Y6J1v01Hs4YhyF5VD3GcxkWcT6cv120HTD/NcBnKIL6/OwEQRDEHaOhx+DXrVs3b968Tz/9dOPGjePHjz979mxDBnCZI4WeqZcWjQWAMIb5PdA/lKGVnbSVv5cpTQ+YZTEjonPj2/iXlnu27ecwXyQ5zwh5H4BgufiBPQV89luYL69l7/b9x5wnkusWOUdxhyv27iv/o26HEwRBEHe8Bk3wVqs1JibG+zwhIeG5556bPHmydONTyuuLlqOSSq46fi9W8HlvpA9UKOYZ38OCZfwwFgDW7Ajm8z5gfMYiWTil7sSGza5+IMZA+zxAabrWsnfGz1T6yVdCcVkdImcR92rU270M/etwLEEQBNEcNGiC79q167x586rH0d966638/Pzp06cLgtCQYVSL0NG5VlG4yhcMRXsNdktReYJc2QqM94+9x66QMzt3n8wsDhJKf2AjFnrOvwGSy7sz9uQLhcu50Nk1t1UTWXS4bvS9xf9bhj11mSRIIZpCdI4za03uZwJunB8gQRAE0WQ1aIJfunTp1q1bTSaT96Y4hUKxadOmzZs39+rVqyHDqMbRyF9F5VReJTsiaPFdHBcoW+JjVES+ZwoZ+Mgj4yRJWrbZyOd9SKu7Uar2fEFVWWLP+TeYgIlIVsMsgWvQDunHhQWVr1hX548QKA92S+40+5kr37IKlXbRVueWCYIgiNtagyb4qKiojIyMrVu33nXXXd4t0dHRSUlJy5cvnzNnTkNGUq1LIGfj8dXelUUoCt7KAIBMrJO1+uH/XngJAFZ/s80qtuILP+fC5wkFn2JPoWQ9LFkPsUEv1CEA06T/KDq1rXP8DGKfCXu5tToOA77sPP7TrHe+yK7LgjwEQRDEHaChy8VyHHfXXXdVj8R7t4waNWr27Bu4uF2P/ne3vr3vVW+PoZRU2TcFfKH76ZKybEHo0KFDQkKCxWJZ9WuQkPeR5MqkjYM9mVM8mS+w/hMkZ6pkPwGS84YCQByr7B5/058DUm1n3jg7xcxXjegfsezPdmWm21JOVR69+cYJgiCI207zrQdf7VSxJ7W85qv0iKN0w3zLfyy6WyH/3ekCgBkzZgDAR4tXOJ02T9ozoi1JtPwpefKE8s2ezJdciQlixa46xIB53vrXYduew5Lr8vo3tRSrbvtAwDiP5AYAHnu+yf1SRWvllGJ13mcCruNCQARBEMTtq7kn+DOl/KTfzOtSHFfbwfSfgPJvCgYq5DscLgAYOHBghw4dikptP/xOA0KyFh/I22xRtN8tb/8nGzSFUrWnjUNvNAZ3Smbei/Mrf/q19LNvc5+bbT9wvG6fpau+t78syMyXLc9e7JHc40OeezTkWZdo31ly/TUGCIIgiDtMc0/wQWraLeCzVzmDBwB1b4PoEOPS+TxByBFEhJD3JP7d1aIH+3gynqdU8UgeDZLLkz2XC194oz9SyeEs+WilaeLY4CVv+jz7CKKp8mU/8AUldf5EVt5ypOKAr8y/naZjJ133UHnkL0U/WASy+B1BEETz0twTvF5OxRiuuUQlAuND/hXfFdynUvxidwDAQw89FB8ffz63bMX6YmBNnpx5AMDnL6LUXSjtDd8O4E7NYkMDFR3bAIC6f3f9mCFYEB2HTtTx8wBsKfmJpdhJYa94X44PeR4D/in/6zo3SBAEQdyOmmmCz7II+3Ld3kdupXi+8sLLnMrLa7WZHgvy5LtHq5Tr7Q4RgKKouXPnAsA7y8sry3PFsg1i2S9C4fK6FZqU7A5Kpax+qRl8l6JLe8nlqtvnynCcPW451FbTsYIvO2Te80ry02n2My3VbQ6ad2c7z9WtTYIgCOJ21EyrJ61LcRzIq5p6VuIUEUIfHK66ZbxfuOzFzuqLd5ZFKsKWtAYn9qWpI253d5lsxIgRPXr0OHjw4CfrwmZNuced8RwT8NSN3gRf1XhUePmaDdVpHosin1ug7lvb5fAuk2FPkTAu8RSsK1gDADTQ20s2cpRMAsh0pIYpIuvWLEEQBHHbaaYJnqWQiC/c/o4xrn4pr+miRsXPxdbd5Ys+b6WlKAAQyzbMf8Fwz0F4/8vTD/fPjQiihOJvsTsbgGIDnq/9arUAwAT4aBJ6Frz2gebevohhbLsPsYG+ig6tAaBs2Q+6BwYzJn3tWzOwPq017atfqhiN94lb5pbTitq3QxAEQdzummmCf6mr5qWuVcmv6+oiu4B/Ge1zjf11Q31yX01tZ8e0DgEAIKZXzMlHHui2dsPhlz6Sb10zRrQexu5syXGGC7nhUvH6h++XtY52HknEvKAd2l/Vq6N3OxvgWzjrA/9Zz7OhgbVsqqu+d1d97yu3/1TwVWLl0R76vjcaG0EQBHGbaqYJfl2K40Rx1SV6F489Ip61x+J92SWAG9Xy8pNdxsgGzY6S7OKvLH+OFybZE7HkentS0bad9O9789f99MvIATrgUwFLkuMMrWh5o/Eo4lsp4ltdtlE7LIHxNRbOXeI79Ul5m5gaD6ylBwMf9z4pdhcYOBOLuJtpjSAIgmj6mukku0g9096H9T7kLOJo1MZUtSVCV/OXHt+JIda/zJ0p9mub3e73FKK0/kZ64TvvAcD0RR6H3xKg1YAYyZVej3Eqe3TwffEJ89rN9dXg7vIdc1OnSbjRKvgRBEEQDaOZnsF3CeC6BFSdxW7NcMkZNK6t6rpHlX2Tb0JwzyD59276qaAXhJJvnhiU/22fPn///ffECQ+tXahno94T8j4CLLEh/62vUOXtWwa2n1pfrT0UOL6PIYFCFAaMANVXswRBEERT00zP4C/W0Z+N82Ozr7g77kr+0yIK/5c1QaX61mYXA54HjCXz+tVfvKHTKn/+vXDVFhnj85CszS9i2QY+972bD4zPfc+d9uTFD/vfw1zJj7nTJmC+uM7NBslDAeCE5fDMlOfzXTk3HydBEATRBJEEDwBQ5hQn77j+Wm/aBCOtZ4ybyrvKZN/YBS58LkKyEPh4yUwjALzy7tljx44h1k/WZpNo3urJnALYc3NxYexIZowjqh9iRSfHwRzR/A+idTfXMnTUdX8s5FmOIoPxBEEQdyaS4KFXsGxoC0WPoFqlOuODfrmvp78oV6202mz6+ylla8mV+eC9vhMf6eh0ukaNGlVQUIBYP3nb7Vgwu5JH3cypNhv0IpaciPWjTSO9D+19b8pb26y/ta389QDgq1a5raU26jgfzh8Dnp360gHz7ptsjSAIgmhSSIKHHsFctyBuVi/tdfe0bCvNn39OKPUw75wfqJR/bbWz4fNAcoFoW7Ls1z59+mRnZw8ePNhsNgOllLVcQ+v6u071FSt21DEySs6Fzfacnwm4avhAKFpFa4NNz3ziPHaaL6j7V4eLIUBPh71Ept0RBEHcYUiCr2L14DOl1yqrWrmj7PwzpwEDL0OFS3NfyKWGKhWUsp3kO/5bwyvnWcPPP//cpk2bxMTEIUOG2Gw2AIoNni5rudpzbrrn3FQsWusQFW16AGi1UPItAGChgs/7HxuxgPH38X99MhvkX8ePeoVQeURvYwIA/F6y+ZOsd2xCZX21TBAEQTQWkuCrZFQIM/+yXO3dyh1lWROSAKHonzvgj6JdHMoadCww1VUuSk/JJyfqHgxmGJ2k+fnD9eHh4YcOHXrooYd4ngcAStNDHvc3ALhO9hBKfwC44evqXPhCPudtLFbyuQtp43BK2fbid50nk4vf+YIvrHv1uYvd7XNvO01HEV9/viFBEATRxJEEX6WdD3uuQqh013Cl2rK99NyTSYBQ1Pp4VTdd4hD91jmByC4mDjo64e9sN5aOujylBa7Ue485nixa/+aPfn5+27dvHzlypMPhAABEa7jID9mQ6XzOQufxjnz2HKF49cUPLJTVGJJYsUsoXi3ZjyFFpPvMMLFkLVJECMWrheI1IFUVsJe3i5W3jSl87YOKH7dh/lpXIGqDQWx/02Ada8CAp515anPRj1fuc9p6At/41xSCIAiigZEEX4WhIN6P25zmeuH3y6fTF3+cLbmkqHVxqm46AHhaq/YbG3jwMR+mUui7sTKVF9/HKvuIk4Yx/jFbO3Hz3T/N+t7X13fbtm0DBw4sK6tK3mL5FswXYdHBF3zqyXqNz32Pz1vM573vyZwm2WouDutJe9qTOdWT+YpU+Y9kP4VFB58125M51ZP5kmg97N0H0ZR2WELQ+6/yuYX5r7xTXz8NBGhuy49iVG0AwCHaq8vJp1iTPsycs714Y311RBAEQdwiJMFf0CuE25XtOph3+b1ttJGlNUzx0lwsVp25PkUp/ZOcDjVtpSG+EpwjEg2j/QNfjVR21ESt76BfxGybtyk8PHz//v3du3c/ceIEAFDa/oA9XNBzsqiljM9DgHmgZAgpgKIpVVyN8Xi3s2GzuRYfc6FvcpGLuBaLEWsEYGhNt0sj1PtOnRAw/+V6/GmoGW0rdTsASLenvJ7ygvfEfVnORwzF/VL0vVN01GNfBEEQRL0jCf6CB2OVJjmyei6//hyxrI08VmnbX5E14TQWsdUq/DnsaECu5+i9mrbH7ZOGpe0brDnwgr9oFdyZTm+Ol73L/zZvS5cuXTIyMnr16rV69WrGMAjRer5oJbAm2jSSi/qc1vaU3BkArFC0DLtzr4xHFvsNICSUfs/4jWOCX2b8HwPOB/PlXPA0oJRX7k9rqqrc2g8cL5y7xJWcUS8/ljht5/dafxmrbrujZFOFYH467CUa6DW5n9VL4wRBEMQtQhL8Bb5KyqSkpSvGlyklHb2xg6yFwrav4vykM//cd8SY4z51l/rupe3ouwzqMj63o+qTnIrE+46l9DlsP2hRdtTohvmibz179+596qmnnE7nk08++dCY4WVmN3bn81mzhPzFQsEnYukGABokh+RIcyX2d58ZLhR/fcl4PK1hA1/AjmSp4g8AACzyaU8jWs2Ezrj2B1F1j1f361a65KvCuUucJ1Nu/iejoJUCFjYWrm2pbL2rZOvYoAlHLPvJKngEQRBNGcI3vV5K49q8efOYMWNcLledW9iX6/7ihN37PM3Ml9ilXiEy70u9DH080OB9LtnFtOHHPTkuj4QP3qW6a2nbOLkMMOx44Khxt0WMVhzrIB81Nqz4qdP6kX7Wv8wtt3diA2UAsHr16ilTplitVqOOXvBy9KNDnKouKWLFTk/605ThfgCQxawA7BHNO4SyDZLlD6Rsxxjuo433IVkYgOQ8HA4UreiSyecs4PM+lLX6gdYPrM3nwqJo33vE9tchvxmTKLmszj8fr+XZi45YDixuu0ZGyQFgevJEC1/xROjkXob+N9ky4aVUKsvKyhSKyysZEhe7+d93gmgKGub3nZzBg1vEFS7J+/AIgAGqXzr4C99+KBUds6mjrIXSOcjQb2m7OG/KRDBgdTzIKTbVET4xjLpbrx7mW7Iq3/BmpDe7A0D3R8Y9v+fvgQndyy3is3PODpxYsG/9EHfG80AbJfNvlKotFsoBcbTxflnMSkWnFDZoiuRMcSXd4zrV23P+DSbweSxYPNlz+fzFSBlby+wOAIim1f27B8ye4s3ukt1RufkPsfyqtwJeQ7Gn8GDF3rFBT3izOwA8Hz5DAqGULwaAQndeovUYmVpPXJfD4QgNDW3sKAiiuWim1eQuhhCouKq6agoWLO4LL+XMJV+AKBXd8tdOFxd7FyuF9JHH/R4KcKfY0ZCTwgth1n0VX74f/NiLKbSJ9e9nSvbwj5WUhgQGbl117/ffnp/xUcmBU/xdY/8aeTf32rPydrGhfM4C2nA/Yoz/9iGn9YNo/SCAjyTbcdHyl2TZDQgJ+R8DABv4AhYqEKOvy+ekKL6gOG/q2/I20ZqBfRQdWgOqbTW5pVn/k1EcAuqvsgur8vmw/r+XbLrf90Gn6NhStE7EYgdt17oERtyhzp0799VXX128xePx5Obmzp07FwBmz57dSHERRHNBEjy09eEe+3fxmJ/THEV292P/lo5Vc9dJgWXfFPBFnpbvtUQcdW58knlDSfqsoNy+igNH7aVvpIb83um5UrMMoSUmI4OmPTjo+/tGv7zwnUWfri3c+Ifn5z/ThybgFyck3NMhuqa2KUrdmVJ3huCp2JHsPNWLUrYSS3/ks2YgWSil7kapO1PqTpQiFlCt/idSCrnpmYcNj49y7DtW8eM2yWZX3dUVAPPnZ9e4yh5ijWzoG97n5XyJW3KvzVt28Q4YsATYJTkilTEzoxd6N67K+eS8M3Naizka5vpL/xJ3Npqmv/nmm5ycnCFDhjAMAwCCIABAUlJSY4dGEM0CGYO/xDsHK1eedHQLYpcONmqul90BAIv4/MQzgplv8V0cJadcqfb04SdyWsvlp+0L1oTnh7I6ivrBzzeUpp1JNtZvnVD6I6IURfaW/1u8duX6IofTAwAxTIvH+j8y+YeXjUbj1TqSbEcoVTwgFrAgOZIk6xHJflSyHZc8uZSiNaWKo1RxlLI9pWwD/15FryX73z2woGaDR9A6TfVGsXwbSE5Zm83el27JJWDhymMpoBT0JfP5MeAsR3q4IopC1KqcT6JUsX2NtR1TaObuyDF4u93+4osvJicnf/XVV1FRUXa7Xa1W38zfHDIGT9wZyBh8I/BX0iyFAtX0qlP22uyPaBS+rA1jYDP/c0pySfKWKt/JoYF/W48tbZEfwkkAKoS0COW8cja59+HyjQkguWh9go+0+n+vtk7dOej1SUN8KFOakPnmzvmBfoEPPvjgDz/8YLXWcD5NqbsAYgEAEEOpOjABT3NRS+XxB5WdznLhb1GKlpL1sOfcy44jkc4TXd2pT/C574jlv0jONMDXWd6OCZiH6PSSRdnF7+Tb9vgzvuNp4yjJfpINn1e9j4ySq2j1lQ+5xF62fB4CFKmMoRAFAD0Mfb3L2nskzx+l20s99VMdh7iNqFSq5cuXT506NSEhYdmyZTdRMdkZAAAgAElEQVSU2nNycjKvUFRUdLufkxBEgyFn8Jf4K8c1f5911X3GURtKfx/rq5fX6gsQFvC5J5KwW9IOMBYtzqY2xT0vWN+8L33Ti767H9A/s6Co97ZKySzQOib47Qpl+1ms/9NAyT3Zy9OGL0ARirMvFnz5zud/nNotgQQAcrn8nnvuGTVq1LBhw3x9fW/sA2BecmVgR7LkOCM5UyRHMvbkUbIwpIilFDFIHkMpopE8CjGGiw/yZExGrJ/ketyVnKEbPsCT/TpITtEyURYVRmlUNfbD5xaWLF7NZxcAAjY82HfKeDb4qsVvPJJ7XcFXDGLGBj3pFB3nnZnRqlZM7UYWmo878gy+Wl5e3vjx4xFCO3furM3fHJvN1rVrV4/n8lWnXC5XSUnJldsJ4vbSML/vJMFfwuyS9uS4R8QoXt9j0cqo/3bXXP8YAPg3xzuOVIZs6zhQVskh+KZEaXnpbGWmg/JIlVHyAWvi00edEM1CxJefKNp35PN+yntznFDZO/bPrpScAoCkD4+tmbVij+nw0cLjoigCAEVRcXFxCQkJCQkJffv21WhqG8ylkXkkZwZ2pUrONOw8KzkzsCsdEIcUUZS8BZK3ECsPAvDYeojSD0C0GotWqeIPSjfIfdpc8XNrNshP3j5W1bMD1yKsuknJ6cp97k2gKO29fQHjyl/3IiwFL32LUlx/dKDAnbsye0m8tsv9/mMcot0h2ny4eiuLd1u7sxM8AGCMlyxZ8s8//3z99dd1buTMmTNjxow5ffp0PQZGEA2PJPhauUVjcsUO6edUxzMd1LU/BItYcohuNf1oUenHPsYQms6Znlq+Nl9w45J2irhXo4yddBkDj1BsXtiSFxxH25WumddqX3cku3CRoPiTnNzXUpmFPoe4wxs3bty9e7fT6fS+xTBMXFxcz549u3fv3rNnz+joGufl1TpUvhg70yX3OezK4PMWAwAgDBiA4gCLCFgMEgKJa7ebPy+6knIopVx7fwIASDaHO/WcOzPbsmFH6PIFlFIBAJLdmTPxNcOjI7RD+99QGInWY6tzPh0TOL6Hoa+FN4sgGlmfm/lct7U7PsEDwIABA3bt2nUzLZAET9wZSIKvlSY76cZ5ypbS+7CivSp6e2frzrJDq3K+/Djk5W0u9sNsU5s9kq2tM8XQel93WZTihdLyu+XyUQpF+qgTtj3mD9dGPNAvYJRK6XK5Dhw48Mlvv+3a9Yf9xHHvDGQvo9HYqVOnjh07xsfHx8fHx8bGsixbpzCx83AQllyI0WLRhhCNJREoFiQ3IJqSR0nu84jWIC4EyUIQF4JdRtvec+7TFsGmUrTvpR3ST9YyEgDypy5gQwJ8p06o849rb/nO9QVfT454NUbVOseVRQEVLA+7/mF3kOaQ4BUKRfV31rohCZ64MzTM7zsZB72WlafsHf3Zjv5cXQ6mABDwpR6pQjCM9r93tL/a5c7an9SmhLfs6fn74bgRD6Ul9zq0enPLPUHwolqdMfqkba85al08jsfzzBYM8IBKWdqte2ZMqwemTf9EpThy5MjBgwcPHDhw8ODBwsLCnTt37ty509sVy7KtWrVq/a+YmJiWLVuq1bW5/ICY4Gl83vu09m7aZ4w79VFZqx/5ohWS5U82ZBYbNAW8p/vuXOzJxe5cCXLU/ayymGQQCynlz6LF13U6DHFBspapgH2sv+Yyfi3Z4HaMbwxQtVo7z5UyBtsTAaALQBcASBnlBNBKbif2lLX82qRPOFF5mEZMO01HBLW9a58gCIIAkuCvLVLHvHfQ+t0IUx2OVbRThy6OzXsz4+ygI7E7unDh8s6pHu3fdl7DbJsRsKvCcVcFL+PxuEHJTy+IxluSbAcqon6M1w40Lcd4QknZPLPlH7fnN4ezPccu9THKEOrbt2/fvn29jefm5h47duzEiRMnT548depUZmZmYmJiYmLixQEEBwdHR0e3+FdUVFRERIS//+UD3mzg//GFywTzZiZslqzVT4gLlip+B8aXDZjk3QGxfoj1A+hUfQitL8ubMk/RMUo3shOIxbY9v4lWubKzCot/C8XrhTIzrXUjWoM4PxCNWNTR2jBKG45YP8QGAOuLuABEV80nQGIFFsop4/0Xh8Q5k1lHqkIVCwBWofK45XALZUsVrf658DsZJb/XbyRJ9reppUuXNnYIBNGMkEv01+ERMUcjABizsXRYjOLxdjXPKr+a0lV5+W+mUyom9IPY88+dwSKOWNZWN9TnR5tjbpm59RHnrJfycIUAGNggeciCaHUfvfO0nWqjHI0sObwYwdAbA/xk11tyLuXJY8d+PJSOz1cMd53nc9LS0lJTU91u95V7yuXyiIiI8PDwsLCwkJCQ8PDwkJCQAHWKn+sttT5KEb/fdbIrdudyMatow73X6NG2+3D58h+ApgEARNE4cay63yUVbLFQhj3FzuN/uU4fwu4CxFlpE5a1UCK6EnsKseRBjAnJgiTnGRAdgBhALCDvdAQEkg0wkrfdRml6XNzmaeuJc460oX6jKUS9mzFLSatfiJgJAEXufD1rlN3gAgBNTXO4RH/zyCV64s5ALtE3CRyN3CJeeKAyvUI8UeSpMcEXOyQ/Zc031Pk8GYxFXDA3I3PcKUpFR65qpx1kAoCdTidFo+Tuyq0H2g+cmLnmKb3Zl2vnS3VIqQj9MM+daI2d5hcYwHT4zfp3rLVza62shZILl9OaGv5/ZU9Odm6oGLJ2tGVLifnHoshv2+vv95UkKTs7Oz09/eJ7iLOyssrKylJSUlJSaigxp1GVhASa/PWVQYF+wa3+CApK8fPzCwkJ8fX1DQgIMBguubNO3b+bonMbd8o5AJC1iqyuVFsNMSbEmFS9W6t6PwsAksMpFJayoYGIZQCgYt3P9r93Idqs6lPOBmUCYFrTE0t2cJuxkIcxAGD32bHA+CHW4G0KMcaWrCmWMeGKXyXG+Jz/qApJBMxjxKzK+UTAwusx70lY3Fy8LkQe3lnXsxb/bxsIFiwgmmt4A3GIC2rwcAiCaC5Igr8+GY2cAtg9uNwlXflukUNMWFty+umAK99yixgAfJ8OAYDcV9Oqs/szJWUH3Z4f/XxLJem50jJhbfRnBn2WIBxwuf/y9/y9LKhClHQU9Y2oWZ+f+0+mjTtiNWW5PdkuxKCIle2Alyp3lbMBnHFcYP6bGeafikKXtDIM8zWM8AOAc+MSvTk+IiIiIiJielk5i6gfjVXL11fYbA8eO96tpDi0uDgvLy8nJyc7Ozs3Nzc3N8dqdyenlycDAOTD5v9d/kOQybyZ3t/f38fHx8fHx9/f39fX18fHxyTYfHx8fH199fqrLpJPKRVciwtVRvRjRurHjMRuD1+YLxT0AXAD6yePXlr+zWp51DQAynGwr2jvapx0Hy13YaGczz2L+TKKywYmGVAlFso5wewrmB2CGVHyFxkDYgzu5JHAGNsLFh7JefthgVZ9X7jOVxk7NGiCRKmP2lOC1W1D5JE39v++PrhPD8TOTIwunQWJMEhuWat1tP6ehg+JIIjmgFyirxURQ+tlhTIavh9hautzyV/qIwWesb+UZTwbeOVRT201MzR8ca8BAESr4D3/nlVescXheAPpHwxWAsAup3tKWdksve4RddW1gf75RUE0PUGj6qOQA8CA/CKzJLXi2DCGaWGHQD0Xmi/47LYoSwTXGXvF1mIuWI49klDG0waW8WHFMp4v5YPeiMRuTBvZrJGG6bllQyuoFyNNYGAecJrLEP4tyE9PXX7JoeDYxLyzGwrdd5ehYfn5+YWFhYWFhQUFBcXFxQUFBZWVldf9KdE0bfqX0Wg0Go0Gg6H6STWdTqfT6eTyqivqQsm3nowpAKDoeNydPlGyH0esPxv6l1hhlUWFeSviWDb85kw8K5orxYpKkHDI0rcolQIALL/slOyljB6QRqA1mAtRYrESCxUgVmChQuBLsWihRbskmHm+mJVciFZJlKZUctCMyVcRLdHKYtGu5kK08jBEa4HWIFrt/S9i9ECpEa2+0aV/r+Q82hrzhcD6InTRz5yvwNgta7WB1t/t3UAu0dcGuURP3BnIJfrG9+DPZTmVove5KIEH4Qc3lmllFACMiVW8cr1lcFLNPENVDZ9XX10fp1ZN0qoTVpT2HicLVNMDFLK1fj5+3sFsAAB4Rqt+UKXk/h133xXk/6Pd3pmTZQtCDiueFPgtRtE8Ul7+J/pkRa4jiFu5K1YvZ/yA8jVLxgrRcMLGTM1wHLUq4jWec844u2nRKZS+OudgRTZj4edVSrSCyjVmcF+3tx+vrNxRRuuYwJktPDku+uTk2HYZUZYFlNJEx9C0hlG0VWEBAwDiKJfL5c30JSUlpaWlJSUlRUVFpaWlpaWlZWVl3i0Wi6W4uLi4uFZL0srlcr1e7032GkrQqdx6bQe13KlRUYbIBJ3fBp1Op8s8q1arNRqNumNL/d3d1Go1y7KAcXUdPC4i2JMh8sV2KcMjOVy+L41FMg4AiuZ94skRaLU/pVbRJp3P848qWQYAHP8cBFeFTGmmZTxSqES5I7dil160aQWL3ZmSaN4TxBqCWIMoWByeAgUWKOwCLGJKiRg9RauBViFKBYweUUqglIhWA61FtAIoBaJ1QKuAkiNaA5QKUXKgNYhWAuIY4xC+aBXFmGjfsd6wsWgX8j4ARFGq9rX5WREEQdQBSfDXEq6jqX+vcJQ7RDWDovQMANh4/E+BJ6dSDNXS12ygBm04FgAwxrZ/F3GP5y65Da/6VN5LjtDjajUAtOUuuXIQZSmw/tRBPezYE0NSs/+IK2JxhhGlF7oH/zejIF4Rv7Y9TaFxxaV5glV7v5y6P+Ysz1MAI1TKFi70mCCThchOGZDHRMltkk2FOV7AxarKjZ9SlR7JmiNZRckphn3auuKX4qKPzgNA7J9d2W2C9mfQq/xj1cG0hqGUFK1n/OeHu9McjuNWWs9oRpqKkgsKkwvM7opK3lrhqjDbKiwuS4XTYr6UxWJxuVzeiwQXfabqWYEfX+2nx3GcSqUyGAxKpVKpVGq1Wo1GI5fLNRqNRqORz3tLo9Go1WrWl9WFxHASyDDScJw68ZROr+c4TkzOZwpLGbdHsDtLBSHw3f/29n8MAIrmfyoUozD146CW2zQm5aRRJyr/9GH9Yo7hYlzwne6X9lLwIEX/Cl/n1sKvO9CBcYpYp1CW6TgVyGgMtEqS7A5PoRxLNOaxaAXJjiUniDYs2gGLiNEAgORMgZLvgdbIYla4058BACZ4GmrGC/sQBHGrNXSCt1qt3333XVJSUlFRkSAIgYGB8fHxY8eO1WqbYnXRrgGcv7IqhZ8sFrRyqkuQDACyK4VfM1z91144VY36vMD7hKNh/2MBBnlD3MeF/bi2R3smdz4YOyBx5MHu7nR7yvCzqnhtt52dgUIAsNLXZJakCkl6odTMARIQTuP5Nlolp5YDwB4jzr5LbsfYKtkdbbGrjdaOsQqhXUH+CODx4tJC0aGdpGUmxakwyBle+ZguIkE5nlJgu/hnsV2wi4xL8oBHbXOpSpxMhqjqpw1Kkxm3KBVOWaDdhB2Y4iWpQojY1tH6dUHpjjxKRcds61SxsdiyvdRFue1ypw3b7azDhu1Uwgpz+WmbDUrOPixGKc15ZkthhcVpsfMOm8tmdzusDqvVYfV4PB6Px2yuac7ajdBqtTRN6zcsQwgZDAaEsU6lRhhrlSoKwJj0NwAYDAZ3RraKlyhJSqIy0+jzxvsHCLKEJEqRvumArbzwfCt3gMvRotJH+/rDPxV+08m3W4/tQi5Xsq73qX5FrQZautofiNtc+H1XbXzrtN0IljmdRRb6iVBnvmQ9jGg1FzTt5v8NEARBXE2DjsHv27dvyJAh0dHRvXr18vHxAYDy8vJDhw6lpqb+9ttv3bp1u24LV2qwlezaLivqEcKsGGICgH157qm7LN7tTh7beYlCgAEYhIDCSETeyXgixgCI/XfgtWMA827/qrno/dcWr77PFKGjAQAQhGjoG/1GEPV5waqhhr5hck+260zXQ6w/y+e5lR21LXd0AupCYxLAiMKSQlHYHuif4xEeLy0dpFB8YDJco2WvCkmySrhSknjATgk7MOYxxgBDlAoA+M5mzxdFHoMTSzwGJ8Y8xjTAYh8jADxbWp7J8wBgk7AI4MbYjXG4QG01+NB65slzxTjDyYqgt0uUgNUOzErA3y1NK7rLUzBmR9L/pT9s9Dlo9d9nYwRgbAIlIYVN9KfpsI9i936Tgdfn2VzW0gUB+Jd8YXuuw25zg9vFuO2M04U87gc09pMlnv9v7/6joirzP4A/996ZAWR+8kMEdIAQcWONOqIreVrrS53wRxtrZba6etCT2umUebISZb/HNcw6fuVgKsZuEanh2VZczB/f1SjNME2/oZgklSCgwIADMgwDM3Nn7vP9Y3AkfihS3Dt7fb/+mnvnzjNvnnnu/cwMc59b025jO10JCnt9h/Vam0tw2QSb3e1wuB2d7i7efZsr7P0qWJbV6bRu6lYqVBpOSTizUiEolfrl8xR/ns6rxu3kgmb23B7/gx8M/A8e5EGGU9VOnDhx4cKFL7/8cq/1O3fu3LJly+nTp4fQprgFXvnB9N6XbPf+yM7cJVxuc+3+3lZ+zdXhEGw8dQqUIUyAgrE6BZZhBEoJ6f73MaWE9JiuZVK46h69ghCiUTJL7lcHBbCHL9t/anX9+bcjdH7s8SuOi2ZeIGR3RaeLEo0fyxLyYysf5M8GBXAsQ0Y0Od/I+lGIC7QU/FYbwE0KVylYUmNxNdmEfGXHOYdzpy7YaSeEkArBtY6/vlqvm6cObOkSLA7B87ztDsHi6B4JCpaEqzlCiMNNnW6iUTGEEKebdrm8GzCByqF/ReF56+AitFOghJBOSl2UBrDMfe5a1j/6S7u7yS2QG28LCCF2SlUMs0SrJoS8a7G2CG43JbYb49YqCGqWzR6how5hZcv1K2qisgmKBidrFxrH+2maXKNrnOsMerfF9U6b5ZrD7Wdzn5ypM/5fc/wFe2dXe81M/WuVNtt5yxFLi2Dl7XwHpYLb1vHDYxrNuWsjLzsC+U51iqHsnMmvuoOnTqfgYATS4WwnhLTEMprmLr7FYaW2rjClcN0mdDkIIVbaQcmAu9Wfpqs+2HC/f+I3vdajwA8GCjzIgwx/ZFdVVfXkk0/2XZ+WlrZ8+fJbP9Zut2dlZfWcj93j0qVLnmuvDbfRWva+0FvNWRsSwIYEqCaF39zmoV3NCpY5+qdQSkhrl7DjQofDxWj9GIaQ/zltTY32/02IgmOYFrswWs35KxhCiP3GvDoBCoYh3VXCxlOLk1odgtkuUEo7ecHzEb3dSXmBZxmGD+Cyt947Uqd0V9s7eRql00WouSOX7SeuOl+dphkbzL1f1vl5bfd7oBilasrvVYSQj76zldTY30sNMmq53LKOf1/u3oAh5KNZwUYt9/ZJ6/5LXXtnhxi13Fsn2z/96cYGDPnX7BCjllv9paXoh87P5o40armMY5ZPKjs9GyhZ5sjcUKOW+8txy/9W2/81O2SMlvvLccvBqu4WOJbs/WNIglbx319ZDlfb9/wxZIyWe/NE2Oe13d+9cwxTMDNojJbL+rr9WK3jw5lBhJD1X7d/VtPdgoplPpgZNEbDbfzG+nW9o/4xOkaviPzBv/qcgyccIQEKhnygM4wezW1yWxfVO3IeDX5bMzL7jPXLOoexgtARxp8eJG8/rBujUXwYbDsT63z99xpNALuzovPcNaebIaMI4f6LvDBRnazz21bWUdvmWPDAiOAAtqiqq7KV95z1byTMtgcM4Wpu5fdtF9v5tLgAnR9bcrWrxuJWOqiyiw82db3+qOGzdsuZc9fa7HRZ0Crt9bMJsUrVuB1DH4gAAIMj6if4tLS0gICAnJycnhOmtrS0ZGRkNDc3FxcX3+KxLpdr+/btnZ2dvdY3NDTs3r17kL/c/iV2XLAljVLdG9L7mi7fmpxzivs/Tc5b4PveFfte47+fHRlnuOPf6PVswfMV/ZBbEIFbIF0uqlYxhBCXQGx890QCDCGekxF4gXbx1HO700VbOm9swJDRGo4QYuNpq10Yo+EIIR1Oet07FcGN/2tctwsmm3t8sJIhpLVLaOhwe1v4TbCSZYi5S2iyuT23mzuFZtvNDcYHKzmG1FvdV63upFEqjiVXre5aS/ebSJZhJoerOJZcaXdfsbomh/spWFLX7r7S7vK24FlZa3HVd7gnhauULFNjcdVbbz6FZ+Wl667qNtfDo1pc5Q8oQv6oGpvXt6/wCX4w8Ake5EGGn+D/9re/paenR0ZGxsTEBAcHMwzT2tpaXV2dkpJSWFh468cqFIqXXnqp7/rvv//ee82VYTXQJLVGrcJTwKAvjiXezlGwROfX++R7Jcso/bo3GKFgRvQ5KyFQyQQqu1eqVYxa1XsDgz9r8O9uNiiADQro/RQhAWzIjZUjR7B95xyM1HCRmu5mR2u40ZreTzFGy3lPlzBqOWOfkFE6RZSue1eK1imidb13q7EGxViDgpBIxb3/YgLvIwAAw0/UAj9y5MiDBw/W1tZWVFQ0NjZSSsPCwhITE43G/+ALg4aOYMsX9TONHSFkSqSfaqAfzzFE2f/ktiBnrHaq1BEA4G4hwXnwUVFRUVFR4j+v+DY+ohvorrnjA4ZwDn1PgUo2Rje0a8ADAID8YaIbaayfNuC07YN0fnHvC78CAAB44WtiAAAAGUKBBwAAkCEUeAAAABlCgQcAAJAhFHgAAAAZ+o//FT3HcdXV1UlJSX3vMpvNjY2NCoUP/Y2UUpfLpVT60OltlFKe51WqW83CKz6n0+lrkXieT0hI4LhfdHLjLTidTpbFG+7b8PH9ned5hULBMJLNfOULRxiXy8WyrLSDWfIDiNvtDgkJGTWq/ylSiFj7u6hT1Q4HSml5eXm/09Hv3bu3tLT0+eefFz/VQCoqKvbv379q1Sqpg9xkMpk2bdq0ceNGqYPcxPP80qVL8/PzpQ7yMy+++GJRUZHBcPsL8Q1NQEDAvffeO0yNy4aP7+9r1qxZunSphDN3VVZWFhUVrVmzRqoAhJB33303OTl50qRJUgVobW3961//unnzZqkCEEJ2794dHR29aNGigTYQaX+n8pWfn5+eni51ip8pKSlJSUmROsXP/PDDD+PGjZM6xc90dXX5+/tLnaK30NDQ5uZmqVPAgHxhf7///vvPnj0rYYBjx45NmzZNwgCU0tmzZxcVFUkYoK6ubsyYMRIGoJSuXLly48aN0maglOIrQQAAABlCgQcAAJAhFHgAAAAZQoEHAACQIRR4AAAAGZJzgVcoFMN31vLQ+GYkn5oqgBDCsqxPTRXg4YMdBT35ws4l+SDxhU7gOE7yTpB8V5W8Ezz+48+DvwWHw9HR0REcHCx1kJsEQWhubr7F7AeSaGhoiIiIkDrFzyAS3Clf2N8bGxtHjRol4UQ3vnCEaW5uDgoKkra8Sb63WiwWhUIRGBgoYQYi7wIPAABw15LzV/QAAAB3LRR4AAAAGUKBBwAAkCEUeAAAABlCgQcAAJAhFHgAAAAZQoEHAACQIRR4AAAAGZJtgW9ubp49e7bBYJg4ceKJEyekirFo0SKmh3379kmb7eLFi4WFhd7FfpOIHK9XJGl7rLq6OjU11WAwGI3GrKwsz0pf6CW4NalejsEPmOFWV1d3+fJlSQK43e7XXnvNaDRGRkZu2bJF/Aznzp2bNm2aWq0eP378xx9/LHIAHzyoesm2wKenp6tUqrNnzy5YsGD69OlWq1WSGFVVVZs3b668ISUlRcJsbrd73bp1X331lXdNv0nEjNc3koQ95nQ6p02bFhUV9d1333300Uc5OTn5+fkDPbuPDDDwkOTluKMBM6xsNltKSsqOHTs8iyIHWLFixYkTJw4fPrxly5ZXX321tLRUzAwul2vWrFkJCQnl5eWrV69euHBhWVmZaAF88KD6M1SOampqOI4zmUyexQceeCAvL0+SJBEREefPn++5Rqps69ev98xQvWzZslskETNe30hU0h47fvy4RqNxOp2exczMzCeeeELyXoLbkurlGPyAGe4kixcv9vPzW7t2LRW9N2w2m1qtvnDhgmdxw4YN//jHP8TMUF1dTQipq6vzLE6YMCE3N1ecAD54UO1Fnp/gKyoqYmJiwsLCPIsPPvjghQsXxI/R2dnZ0NCQkZGh1Wrj4uLy8vIkzLZgwYKSkpL58+d71/SbRMx4fSNJ22OjRo3KycnxXsiutbWVZVnJewluS6qXY/ADZlhj7N279+LFi9OnT/csihzg5MmTOp0uISGBUkoIWbVq1Zw5c8TMEBUVFRsbm5ub29bWdujQoUuXLk2dOlWcAD54UO1FngW+sbGx50WlQkJCmpqaxI9RVVXFsuzjjz9eWVm5fv36FStWHDp0SKpso0ePTkhICAkJ8a7pN4mY8fpGkrbH4uLiFi1a5Ll99OjRXbt2LVmyRPJegtuS6uUY/IAZvgz19fUrVqzYsWOH9+ptIgdoamoKCQl58cUXg4KCQkNDMzIyBEEQMwPLsrt3737nnXcMBsPMmTMzMzPvu+8+cQL44EG1F3kWeEpprys2ulwu8WNMmDDB6XS+9NJLERERc+bMSU9P37lzp49kIwP0krTxfKHHurq6Xn/99SeeeCI/P3/GjBk+2EvQi7Qvx2AGzDA9NaV04cKFmZmZsbGxPVeK2RttbW3l5eUGg6GmpqakpKSgoCA3N1fMDCaTKS0t7e9//7vVai0tLd2+ffvBgwelGhK+driQZ4EPCwtrbW31Lra2toaHh0uShOM47+3x48c3Njb6TrZ+k0geT9oeq6qqSkpKKisr+/bbb5966iniq70EPUn4cgxywAzTs2/ZsoVhmOeee85ms7lcLqfT2dnZKXJvBAUFhYWFZWVl6XS6xMTE55577sCBA2JmOHDgwD333LN48WK1Wj116tSlS5cWFBRINSR87XAhzwKfmGOzf7IAAAanSURBVJhYXV3d0tLiWfzmm28SExPFj1FYWDhv3jzvYlVVVWxsrI9kIwP0krTxpO0xnuenT5+empp65MiR+Ph4z0of7CXoRaqXY/ADZpgCnDlzpqSkRKPRqNXq4uLit956Kz4+XuTeGDduHM/zbrfbs6jX60eMGCFmBqfTKQiCd5FSyvO8VEPC5w4X4vyWT3yPP/74kiVLrFbrrl279Hp9W1ub+BkqKio4jtuwYUN9ff2+ffu0Wu2ZM2ekzfbKK6/0/Ml6v0lEjtczkrQ9tnfv3qCgoMrKyks3NDQ0DPTsvjDAwEuSl+OOBsxwe/rppz2/ohc/QHJy8ssvv2wymY4dOxYaGrpnzx4xM9TU1KjV6q1bt5rN5i+++GLkyJGFhYViBvDBg6qXbAt8S0vLH/7wB71en5SUdOrUKalifPHFF7/73e/UanViYuKnn34qebZeY7HfJCLH6xVJwh5bu3Ztr7e/s2bNGujZfWSAgYckL8cdDZjh1rPAixzAZDLNmjVLp9PFxcW999574mc4fvz4lClTAgMD4+Litm3bJnIAHzyoejGUUnG+KgAAAADRyPN/8AAAAHc5FHgAAAAZQoEHAACQIRR4AAAAGUKBBwAAkCEUeAAAABlCgQcAAJAhFHgAAAAZQoEHAACQIRR4AAAAGUKBBwAAkCEUeAAAABlCgQcAAJAhFHgAAAAZQoEHAACQIRR4AAAAGUKBBwAAkCEUeAAAABlCgQcAAJAhFHgAAAAZQoEHAACQIRT4u9djjz3G9BEXF1dbW6tUKqVOBwCDNWnSJO8urNFoHn300YsXLw6hnYyMjBUrVvy6RwBPm79Wa3BHUODvXp988onJZDKZTC+88EJqaqrn9smTJ4ODg7dt2yZ1OgC4A+vWrfPswqWlpXa7/ZlnnhlyU7c9AkyePLmsrGzI7YNoFFIHAMkYDAbPjcDAQH9//7CwMO9dS5YskSgUAAyFVqv17MJhYWFr1qyZMWNGY2NjeHi4515KKc/zKpVqME2p1epbHwGuXbvG8/wvzwzDDZ/goTfvF3QtLS16vX716tU6nS4iImL79u15eXnR0dEGg+Htt9/2bFxVVZWamqrX66dOnbpr1y5JgwPATTzPNzU1GQyGI0eOxMTEnDx5st+99cCBAxMmTDAYDE8//fT169dJjyMAIeTUqVNTpkzRarXJyclnzpwhhDz88MNXrlyZMWNGUVERGeAI0LdNkAaFu97KlSvT0tK8izU1NQqFglJqNpsJIfPnz6+vr8/OziaEzJ0712Kx5Obmsizb0dFht9tjYmIyMzObm5uPHj0aHh5+6NAh6f4OgLtUUlJSVlaW2Ww2m80VFRUPPfRQXFycIAgmk0mlUqWnp5tMpn731srKSpVKlZeXV19fv3XrVoZhXnnlFe8RoKmpSavVvv/++w0NDW+++WZoaCjP85TS6OjoU6dOUUoH36bEHXS3QoGH2xT4K1euUEo9b8PLy8sppZ5v565evVpcXBwREeF0Oj0PXLdu3ZNPPinFXwBwV0tKSvJ+ZlMqlcnJyefPn6eUmkwmQsiPP/5IKe13b83MzOy57z/yyCM9C3xOTk5KSornLrfbvX37drPZTHsU+MG3KUInQF/4HzzcRmhoKCHE85VdZGQkIUSh6B421dXVLS0tRqPRu3HPAw0AiCYnJ2f58uX93hUVFUUG2Fvr6+vj4+O9ayZMmNDzgbW1tXFxcZ7bLMsuW7asV8tDaBPEhAIPQxceHp6UlFRaWupZrKurc7lc0kYCgF44jiMD7K0FBQUXLlzwbllbWxsTE+NdjIyM/Pzzzz23KaVr165dsGBBbGysd4MhtAliwo/sYOhSU1N/+umn7Oxss9lcWlo6efLk06dPSx0KAPrR7946b968gwcPfvjhh9euXSsoKDh8+HDPhzzzzDPHjx//+OOPzWbzpk2bNm/eHBwc7LnLYrEMrU0QEwo8DJ1erz98+PC+fftiYmLmz5//xhtvzJ07V+pQANCPfvfW+Pj4PXv2ZGdnjx07tri4eM2aNT0fYjQa9+/fn52dHRUVVVhYuG/fPr1eTwh59tlnn3rqqX/+859DaBPExFBKpc4AAAAAvzJ8ggcAAJAhFHgAAAAZQoEHAACQIRR4AAAAGUKBBwAAkCEUeAAAABlCgQcAAJAhFHgAAAAZQoEHAACQIRR4AAAAGUKBBwAAkCEUeAAAABlCgQcAAJAhFHgAAAAZQoEHAACQIRR4AAAAGUKBBwAAkCEUeAAAABlCgQcAAJAhFHgAAAAZ+n+mJkibnLudsgAAAABJRU5ErkJggg==" /><!-- --></p>
 </div>
 <div id="saemix" class="section level3">
 <h3>saemix</h3>
@@ -1861,11 +1855,11 @@ DMTA_0    97.99583 96.50079 99.4909
 k1         0.06377  0.03432  0.0932
 k2         0.00848  0.00444  0.0125
 g          0.95701  0.91313  1.0009
-a.1        1.82141  1.65122  1.9916
-SD.DMTA_0  1.64787  0.45772  2.8380
+a.1        1.82141  1.60516  2.0377
+SD.DMTA_0  1.64787  0.45729  2.8384
 SD.k1      0.57439  0.24731  0.9015
-SD.k2      0.03296 -2.50195  2.5679
-SD.g       1.10266  0.32369  1.8816</code></pre>
+SD.k2      0.03296 -2.50524  2.5712
+SD.g       1.10266  0.32354  1.8818</code></pre>
 <p>While the other parameters converge to credible values, the variance
 of k2 (<code>omega2.k2</code>) converges to a very small value. The
 printout of the <code>saem.mmkin</code> model shows that the estimated
@@ -1881,7 +1875,7 @@ of <code>SD.k2</code>.</p>
 f_parent_saemix_dfop_tc_moreiter &lt;- mkin::saem(f_parent_mkin_tc[&quot;DFOP&quot;, ], quiet = TRUE,
   control = saemix_control_moreiter, transformations = &quot;saemix&quot;)
 plot(f_parent_saemix_dfop_tc$so, plot.type = &quot;convergence&quot;)</code></pre>
-<p><img src="data:image/png;base64,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" /><!-- --></p>
+<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqAAAAHgCAIAAAD17khjAAAACXBIWXMAAA7DAAAOwwHHb6hkAAAgAElEQVR4nOzdZ1xT198A8JOEFcJKIMgGUWQpDlRkqKCiYq0o4qpWsW4Uq+KqWAdWW+tAqxaqIirUgThwoaLiKCoCIlUUxMEMYcoOI+N5cf7Nk4ZRRmLW7/vCTzjee3KSk+R375kEHo+HAAAAACBfiJIuAAAAAABEDwI8AAAAIIcgwAMAAAByCAI8AAAAIIcgwAMAAAByCAI8AAAAIIcgwAMAAAByCAI8AAAAIIcgwAMAAAByCAI8AAAAIIcgwAMAAAByCAI8AAAAIIcgwAMAAAByCAI8AAAAIIcgwAMAAAByCAI8AAAAIIcgwAMAAAByCAI8AAAAIIcgwAMAAAByCAI8AAAAIIcgwAMAAABySP4DfHp6OuEfRkZGCxcurKmpQQjFxsbixKysLME/09PTraysCP8WGxuLEHr9+jWBQDA0NORyue08Y3BwsIGBga2t7dWrV7/MawTdFx4eTiAQiouLhdJLSkp27dolkSKBL6BlvbPZ7IULF1KpVBMTk/3790uwbAB0k/wHeGzp0qXR0dHffvttRETE1q1b+ekEAuHZs2cIoeTkZAKBgBMPHToUExMzefJkhNCpU6diYmKGDBmCEDp//jyBQGAymY8fP27riW7fvr1169YdO3Z4eHhMmzatoqJCvC8MiE1NTU1sbKyvr6/gBwbIvcjIyPDw8ODg4MmTJwcGBiYlJUm6RAB0kaIE+MGDB0+bNm337t3Tpk37448/+LfgNjY2T58+RQg9f/7c1tYWJ44fP37q1Kk2NjYIIW9v76lTpxoZGSGEzp8/7+vrq62tfeHChbae6MaNGxYWFosWLVq3bl1TU1NCQoLYXxsQqd9++01VVfXx48epqakbN258+/atpEsEvgR+vefn5zs7OwcEBKxbtw4h9ObNG0kXDYhXSEiInp7ehAkT+vbtO3/+fEkXR5QUJcDzOTo61tfXFxYW4j+dnZ2fPXvG4/GSk5OHDRvWzokvXrzIzs6eOXPm119/HRMT01YrfXFxsa6uLkII/1taWirqVwDE6MGDB2vXrg0PDx8+fLi7u/vbt29XrFgh6UIBsROs9y1btjx58gQhFBcXhxBq/2cByLrk5OTAwEAfHx8XF5eMjAxJF0fEFC7A89vhMWdn59evX7948aK+vn7QoEHtnHju3DkymTx+/HgfH5/i4uK2Wun5gZ9IJCKEeDyeiAoOvoSFCxcaGxt/8803ki4I+KJa1vuJEye+//777du38xv2gFy6ffs2kUjcv39/UFCQvr6+pIsjYgoX4F+8eEEmk42NjfGfFhYWdDr9yJEjAwcOVFNTa+ssHo8XHR3NYrEoFIqPjw9CKDo6utUj6XR6SUkJQqisrAwhZGBgIPrXAMTGzc0tJycnIiJC0gUBX5RQva9Zs8bf3//YsWNbtmyRbMGAuLFYLCKRSCKRCASCioqKpIsjYooS4NPS0q5cufLjjz9GR0cvWbIE314jhAgEgrOz85kzZ5ydnds5/dmzZ7m5uRs3boyJiYmJiRk1atTFixdbbaUfN25cfn5+SEjI9u3b1dTURo4cKZbXA8QjIiJi7NixP/74Y11dnaTLAr4cwXq/fv16SEjI9OnTtbW1Y2Njc3JyJF06IEYjRoxobm7euHHj5s2bCwoKJF0cEVOUAH/kyJEpU6aEh4f7+fkFBwcL/peLi0tjY2P7PW3nz59XUVH54Ycfpk6dOnXq1O+++664uPjRo0ctj/T29t6yZcvu3bsfPnwYGRlJo9FE/EqAOBEIhB07dhQVFe3Zs0fSZQFfjmC9x8fHI4QiIyMnT548efLk27dvS7p0QIzGjRu3YcOGyMjIxMREa2trSRdHxAjQSQwAAEAxPX369MyZM8uWLVNXV+/Xr9+6devkqV9GSdIFkGEZGRlr164VSjxz5gyVSpVIeQAAAHSKvb19WVmZq6srkUicOHHimjVrJF0iUYI7eAAAAEAOKUofPAAAAKBQIMADAAAAcggCPAAAACCHIMADAAAAcggCPAAAACCHIMADAAAAcggCPAAAACCHIMADAESgqqoKL6qRmpoaFRX19u1bSZcIAEUHAR4A0F1Hjx51cnJis9m7du2aNm1afHz8+PHjjx07JulyAaDQYCU7AEB3mZubv3r1SktLy9TUND09nUajlZaWuri4ZGdnS7poACguuIMHAHQXnU5vampCCOno6OC9mEkkkqQLBYCig81mAADdtXPnThcXl0mTJvXp08fd3X3s2LE3btwICAiQdLkAUGjQRA8AEIHKysrY2Nj8/HwWi0Wn08ePH29jYyPpQgGg0CDAAwAAAHII+uABAAAAOQR98ACA7goLC3v48GHL9LNnz375wgAAMAjwAIDu+vbbb5OSkqqrq/39/SVdFgDA/0AfPABABP7+++9z587t2rVL0gUBAPyPtAT4P/7449OnT4IpL1++JJPJ1tbWkipStzQ3EzgcpKzME9FsYDMzMwW5N0pMTLx27ZqkS9FVUO9dBfUuCOpdNkh9vUtLgD979mxeXp5gyuHDh1VUVBYvXiypInUHMTaW8PQp19ub5+zc/dyqqqoiIyPz8/O7n5X0W79+fUZGxogRIyRdkK5Q5Hrncrnp6elMJpPNZhsbGw8YMACveNNBUO98slXv3QT1zieOepeWPvhZs2YJpVy6dIlOp2/YsKG4uDg4OHj37t0aGhr4vx4/fuzn5+fm5ubl5UWn0/v3719SUkIgEFRVVblcbmVlZV5eXv/+/fX19TU1NZlMZm1t7Y0bNywsLAoLC52dnfX09NTU1HJzc1+/fk2hUHr37s1isfr378/hcI4ePerp6WlqaspisZqampqamlJTU62trd+9e2doaNijRw9dXd2ioqKampr379+PHDnSxMSkrKwsJSXl48ePo0ePfvr06ahRo8rKyvLS03OfPvX09By4fHn335yCgoLIyMju5yMr3N3d161bl5ub6+TklJ6e3qNHD0mXqKPu5eenKWS9JyQkLFu2zNDQ0NjYGCHEYDDy8/PDw8Pb+uH28vJKSkoSTKmtrR02bNiGDRvwnwsWLPD393d0dBR3yUVCYeudw+HwlyxMS0t78+bN4MGDO9vsir/v7R+zcuVKPT29LVu2IISam5uVlZVxelxcHP7JxR+8L0z6611aAnw7tLS0rly5smrVKisrK5yyZcuWjx8/fvz48fTp0wghCoVSV1dHIBB4PB6JROJyufxmCR0dHR6PV11d3VZDhYqKCpvN5nK5QulKSkpsNhvnhhDi8Xg4fzKZzGKxtLS06uvr2Ww2/0hlZeXm5mYKhcLhcBoaGqYg5ILQ+vXrP+7fjxCqra0lk8nV1dWdfe1bt279/vvvO3uWfDA3N9fS0qqqqpKhAK+wVq5ceePGjV69evFT8vPzfX19haI438WLFxsbGwVTxowZo6mpyf8zJyenrKxMTKUFomJgYFBaWooQCgkJ2bt376hRo4KCgoKDg+fOnSvaJ/rw4UNBQQFCKD4+fsaMGR8/ftTR0UEIBQQERERETJs27dmzZxYWFqJ9UjkgAwGeTCZraGjgCM1isRITE1+/fm1sbOzg4ODr60uj0QgEgrGxsYqKSmpqan5+PpVK9fDwKCsrU1VVLS4uLioqMjQ0dHd3ZzAYNTU1bDa7uLiYxWINHz5cS0tLS0uroKCgoaHh9evXRkZGVCrV1NQ0Nze3urq6ubmZyWSOHDmyqqpKSUmJw+F8+PChubmZTCYbGBjY2tq+ePGCw+E0NzdzudxevXrV1NSYm5tfuXLFwcEhfdu2wgsXtmzZYjxtGpFIVFdXb25uVldXb/UF8i8jiEQivozgcDj4vwR/8hQQgUCQdBFAh3A4HENDQ8GUHj16tNP9p66uLvR1UFJSEqxuDofD/xYA6bdv376UlBRDQ8PS0lJXV9e2Ary7u3vL6ZTDhg1r/w5+4cKFnz59Kisr4/F4z58///z5819//RUYGBgWFlZbW5uYmFhcXHzr1q2lS5eK7PXICxkI8IKuX78+ffp0hND79+8FbxcwBweHds7FV3wtmZmZIYT69OnDT7G1tRU8gE6n4wdCz9hq++HMmTMRQkX6+oUIaWhoWFpatlMk0LKptqamZujQofgLjy93JFQ00AkrVqxwdHT09vY2MTEhEAgMBuPq1asrVqzocoYcDqdluxqQWnp6evgKT09Pr519hh48eCCU4uTkpKur29bxp0+fZjAYERER+MNw+vTpU6dOkUikZcuWFRQUjBkzhsvlvn//HiG0bNkyAwODyZMni+TlyA3ZWMmO/0P/5s0bhBCdTm/nMwFkyOXLlz/8m4ODA5VKlXS5QOf4+/vfuXPH0tKyrKystLTUzMzs5s2bS5Ys6XKGXC5369atcBMv5ZSVle3t7b28vKqrq0+dOsXj8RYuXOjm5iaq/FNSUnbv3o2ju7q6+t69e7Ozs62trXFzPU6/ePGio6MjkUj08fER6vdpH97/sFVHjhwpKSnpdvElT1ru4EtKSmprawVTGhoa+Jfw/ACPu+WuXLnS1u04kC1qampqamqCKUpK//+ZhDt4GWJqaio050VwBFZnsdnsFy9eNDY2ttWxBaQBg8FgsVi5ubk5OTkGBgY8Hs/S0nLNmjUiyTw3N7empqayshKPwunXr9/du3cRQra2tm/evPHw8EhOTtbV1c3Nzf3mm2/MzMwuX76clJSUmJiYkZFx+vTp9idxPHr0KDAw0MPDo0+fPlpaWrhhGCHU0NBAIpF+++03S0tLLy+vdnLA3bgieaXiIy0BPiAgICUlRTClsLBQRUVFMCU+Pv7w4cPjxo0bOnToly0dkAwI8LKruLgY/+J37XQWi4UQggAv/YhEYmlpaW1tbWFhIZFIDAoKEkm26enpkyZNGjx4MEJIX18/NTU1KCjo77//vnTpkoGBwcePHwMDA2/fvv3VV18tWrRo8eLFXC738uXLp0+f1tLS+vPPP7/++uu//vpr7dq15ubmCKEpU6b4+PhMnz6dy+UmJycbGRk9ffo0JSXl5cuXpqamhYWF/fv3//z5s4qKiru7u6Wl5bt37xISEoQCfH19fWJioqenJ/5z9+7dtXFxpgi9ePHilxkzFi9e7ODgoKur26nZoeImLQH+/PnzQilOTk78zm/8Q19XV4cQ2rRpk+BNHpBjEOBlF51OZzKZXTv3zp07mZmZCCHpv0NScPHx8QsWLLC2tn727Ble+aCioiI6OlpoGFMXJCcnNzY2lpeXI4SmTJmirKy8bdu2devW4aDw4sULhNBXX31VUlIybdo0DQ2NKVOmREVFxcbG4lb6HTt2MJnM0NDQjx8/mpmZ/fXXX0lJSWlpaTdv3qypqVm1atWHDx8QQmw2+9OnTyQSKTg4+MyZM/gHJz09XU1NLSEhobGxcf78+Y6Ojnv27Jk5cyaHwzl9+nRVVVVDQ8Pu3buvXr1qlZ9vilBUVNSYLVv8/Pxqa2stLS0DAgIqKysNDAwoFEpZWZm1tTWbza6pqamvr0cINTc3l5aWstnsqqqqpqYmFotVUFAwZcqUOXPmdPMda5UYI2U3F75oCXfIQQet4oAAL0Naft+7PL/x1KlTOLTj+3ggtQICAu7du2dlZZWXlzdz5szHjx///fffixcvfvz4cTdzLi8vLysre/Hihaam5ujRo1FrMy8QQvr6+vv27UMI9erV688//3R2dq6pqVFWVs7IyMAHREVFqaurf/78mcPhhISE4MScnJz4+Hh+JoGBgREREQgh/q+NtbV1SkrKnDlzkpOT8YZJly5dKigo4PF4gYGBf//9N+4swPO2N2/ePDooKCgoiMfjXbp06eLFizo6OsnJyfX19RQK5cSJEwQCAc/YYrPZampqdDqdSCRqa2tTKBRjY2M3NzfxrfMjrgDf2YUv2od/6NlstqGhoawuXgs6DwK8rOjs9720tLSmpkYwRXDMTW5uLn7QhaUjwJfU2NiIa9zAwKCoqAghZG9vjx+0isViNTQ0CKaw2exWv+M4JLNYrLdv3/bs2bMjhbG3t3dxcXn58uWZM2c8PT2JRKKlpeWBAwdKS0vxMmiNjY14wZJjx441NzevXr3azMzs119/HTNmzK+//qqnpzdz5kxLS8uKigoPD4/Ro0fHxMTgnP38/E6ePIkf79+/Hz/w9vZWj49H9fU0Gg2nEAiEqVOnTp06tSOl/TLEFeA7u/BFR7DZ7FGjRgl1zAM5BgFeVnT2+758+fLU1FTBFAaDwf9q82O/UDAA0sbHx2fSpEne3t537twZOXIkj8cbN27c+PHj2zm+5bTYVo+sqKhACPXp06d3796dKo+Njc2oUaO2b99+9OjRU6dOOTs7I4SGDh2Kx+LhVfOysrJws7yGhoa/vz+LxVJVVf3111/nz5+P8+FyudbW1tnZ2ZMmTXr48GFQUFBsbOznz5+9vb2vXr2KEJo2bdrRo0fvLFuWK90bIosrwHd24Yv24R/6R48eQe+7nDl//nxOTo5gSlFREYyrkjmd/b5HR0cLpfDH3OTk5GRlZeHETs16Al/e3r17z5w5k5yc/NVXXy1YsAAhtGvXrnYGQcfFxQmlCI61ElRZWUmn0/X09DpVHv48joULF9bU1Njb26urq/fp0+f27dunTp2aP3++pqamsbHx9OnTv/vuO7z2uYqKioqKSmho6PDhw/n5EIlEBweHuXPnrlu3jsvlqqqqOjo69uvXb+HChcbGxosXL+7fvz9CiEaj5XaqfF+cuOKlaBe+wAH+9OnTCrLDkuKoqKj4/PmzYAqbzebPfoY7eFkhwu+7hYXFwoULjxw5gtqdqQykAYFAmD179uzZs/kpQ4cO7c70SL6cnBxPT0+8HHgXGBgY7N69GyE0ZsyYpUuXamtrr1y5Ev/XoEGDHj58SKFQBI/n37vzbd++3djYmL/o/bx583r37m1nZ4c/mbJCXAHe39//66+/jouLYzAYCCG88IWpqWlbx/v5+b1+/VowJTMz087OTjClqalJ6C4ByLply5YJpSQkJGhpaeHHEOBlRWe/7+0zMTHBDyDAy5xuTo/ka2ho8PPz6/6Q6ujoaFVVVaHEjuxMIzQRQEyj3MVNjC3e+vr6tra2NBqNTCabmpq2/23funUr7nThmzdvnuDgBbx0ZZd/MoAsggAvQ1oudNOdrGxtbdlsNgR4mdOd6ZGCGhoazM3NBVcQ75qW0V2hiCvAd3Z+ZM+ePYWGSlIoFMEedzxthh/ygSKAAK+YTExMPD09GQxGVlbW06dPnUWx2TYQExFOjxTU3NwM46m7T1wBXrTzIwkEAg7wQh0nQL5BgFdMI0eOHDlypJ+f34ULF16/fg0BXmqJdjq0oIaGBgW/+RYJcQX4zs6PbB+BQMALD8E1HQAKgkwmv337lj8gA0ihzk6PLCsrE1rbQHD9A0FNTU0Q4LtPXAG+s/Mj/1NZWRmJRIImeoUCd/CKjEQi1dbWiqRDF4hJZ6dHLl26NC0tTTClsLCQP1JdUGNjIwT47hNXgO/s/Mj/VFdXN378eNheXaFAgJdXN2/exDt+8pWUlAh1wOEhOHhYPpBOnZ0eyV8bjq/VefB+fn4sFkton0nQBeIK8K3Oj2zn+IsXL75//14wpaioiP+FJxAIDQ0NUN/yx8vLq+XKVg4ODvgxkUiEHcHl0qtXrz5+/CiYUl9fLzRmHs+lhtVqpZlop0diBQUFUVFRJBKp+5PpgbQsDFdcXNxywRP+XlIEAqGxsRE64OXP5cuXhTYU8fT0NDAwwI8JBEJSUpKTk5MkigbEaMOGDUIpL1++1NHREUzBd/AcDkckC6cAMRHh9MiSkhJ9fX28Cj2ZTBZJngpOWgJ8yyXqEhIStLW18WM8ir7Vrhog09TU1IQaZgR/yvGF3RcvFJAKurq6+EFTUxP83Mu9nJwcGxubd+/e4fHU0AEvElK0NX37UlJSYCF6RTN48OAur1UJZN3EiRMRQkQi8dy5c5IuCxA7CwsLCwsLFot1/fp1hBDeIhZ0k2wEeDxNDprppJ9gl3laWtqff/7J3zikC3R0dFJTU0tLS0VRNCBjevfuHRMTY2dnV1xcLOmygC+BSCRyudz6+vr58+eHh4dLujjyQDYCPEKorq4OmuilH7/7PCQkZOLEibdu3Ro3btzp06e7lhuJRLp48eKdO3dEV0AgM1RUVKZOnWpqapqfny/psgDR+PTpU+q/1dXV8Vvp+AF+0qRJeKs30E2y0eiN++BhF1EZsm/fvpSUFENDw9LSUldX17lz53YhE9wpA93wMqGurg4hRKFQGAzG48ePbWxs8Jaa3eTh4REUFHTo0CEiUWbuRkBbgoODX716JZiSk5MjOFuKx+PV1dXBiqWiIi3fGRaL9fnf2Gy24Bzo5ubmcePGSbCEoFP09PTwChh6enpd7lvBJ16+fFmUJQNiEBkZaWxsbG5ufvjw4eHDh1+7dm3SpEmHDx/ufs7q6urNzc3QTSMfIiIiUv7N3t6ePw+eSCQWFxfjsfSSLafckJY7eB8fn5bzofmPCQQCm83W09P74uUCnaOsrGxvb29mZlZdXX3q1Km5c+cuXLjQzc2treOjo6M/ffokmFJUVMRvqoGlTmTFtm3bMjMz1dTUTE1Nb9265erqWlZWNnTo0K5tCS+od+/eJBIpLy9PJFuYAGlGJBKjoqJYLBY01oqKtAT4uLg4oRTBFY4IBAJCqHfv3l+6WKCTGAwGi8XKzc3NycnB20JbWlquWbOmrePLy8tbrn/AH6mHA/yLFy/q6+vhOy/NuFwujUZTUlKiUCj4QlxDQwN/bVv1888/5+TkCKbk5OS0Ok1m3Lhxjo6Op06dGjJkiKhLDaQLkUg8ffo0lUqFJU9ERVwBXnBtirS0tDdv3gwePNja2ro7ecLMSJnQ1NRkbW1tY2OTmpp65swZHx+fdiYxL1u2TCglISGBv78I/yP05MkTExMTGxsbMZUZdNP06dOdnJxUVFQ8PT39/Pxmz54dFxfXzt4Tjo6O/GnuWHx8fFufE01NzfLychGXGEgfPMiuoqICfupFRVx98KIdTY3BPHjpd/ToUScnJzabvWvXrmnTpsXHx48fP/7YsWNdy41f435+fleuXBFdMYGI7d69++DBg/v374+MjPzpp59ycnImT57822+/tXX82LFjF/8bnU5vay3qcePG1dfXp6enC930AzmDx1HyeDyYMCUqYg+ZIhlNTSAQlJSU2mnxA1Ji586dr169UlZWDg0NTU9Pp9FopaWlLi4uixYt6kJuvXv31tbW1tHRyc3NbWhoEHlpgQi5ubmlp6fHxcWx2exvvvlmwIABohr3PnTo0IiIiG+//XbGjBlBQUEiyRNIIf4HBproRUXsAV4ko6mJRCI02sgEOp2OtwzR0dHBX9fuLE9Eo9HU1dUNDAxyc3Ozs7NFVkogagkJCcuWLTM0NDQ2NkYIMRiM/Pz88PDwESNGdD9zW1vbd+/eEQgEoeEaneLl5eXl5bVy5crulwd0WXp6utCEiKqqKv6S5LgJR11dHfYVExVxBfjOjqZun4qKCsyMlAk7d+50cXGZNGlSnz593N3dx44de+PGjYCAgK7lRiKRyGQynjPz5MkTkZYUiNLKlStv3LjRq1cvfkp+fr6vr6/Q1Jiu0dTUxOMu09PThf4rNzfX3Ny8nXPfvHlz8uTJnTt33rlzp7CwcPny5bAgpgiFhYU9fPiwZfrZs2dbPT4iIiIjI0MwhclkUqlU/BgPwrC3t4cmelERV4Dv7Gjq1atXv379WjAlKyuL/1VUUVGBazqZMG7cuOfPn8fGxuro6NjY2NDp9IsXL3Z5cJyqqqqmpuaKFSuuXbtWVVVVV1f34cMH/mayQHpwOBzcUMfXo0cPwXUsukNNTQ0vgXL37t1Vq1YdOHAAIZSVlTV58uSCgoL09HRLS8uWZxUVFdnY2BgaGubl5dXW1uro6JSXl1+6dGnatGn8YyIiItauXXvx4kULC4uwsLDk5GRvb29bW9vKykobGxsrK6vi4mJ9fX0ikVhfX6+qqoqncnA4nJqaGsFXh9diqq+v704bA0JIU1NTtkYaffvtt0lJSdXV1S13C2sVrjtBTk5O/OGW+O0V+iCB7hDXh+mXX36ZN2+ejY0N/8e9/c6zefPmlZWVCaYcOnSIfy6ZTIZrOlmhpaXl4OCgr6/PZrONjY379OnT5azs7Ozu3bv3/v17hFBNTc3KlStv374dHx9Po9E6OCuawWDQaLQuXB02Nja27BWqqKhoaGgwMjLCfzY1NUFnIbZixQpHR0dvb28TExMCgcBgMK5evdr9SfAYgUCIiooKCgqqrKw8fvx4fHx8YmLi/fv3MzMzEUJ79+79/fffW57l7+9fXV1dW1urrKz8xx9/bNq0qbi42N/ff9KkSUVFRceOHauvr4+MjJw2bZqPjw+LxRo+fPiMGTPOnDlTUFBgaWn5/Pnz+vp6AwODkpISEomkqqra3NxcW1uLEFJSUsKTAAkEgqqqKpvNHlNTMwihbdu2pf78c3deZnBw8PLly7ucw5dHoVBWr1597tw5kewNg/cL5s+OBt0nrgAfHBx88+bNmTNnLlq0qCOxecCAAUIpd+7c4a9so6OjAwFeJnS2L9bLy6vlAkeDBg3i/6mrq0ulUlevXn3hwoUTJ04ghPr16/fzzz+vW7fuPwvDZrO9vb2//fZbMzMzGxsbS0vLlJSUPXv2MBgMQ0NDGxsbNpvt5uZ29erVDx8+DBgwYMeOHe/evRs8eHBJSUn//v0XLFjg7+9PoVBevXpla2t7+vTpTZs2kUikESNG/PDDDwcPHrx58+bmzZvJZPLhw4etra3nzJlD53K79fbJLH9//6+//jouLg6vSmRmZnbz5k1TU1NR5f/NN99888038+fPP3ny5Js3b/bu3XvhwgVXV1dTU9OLFy/GxMSYmJjU19cHBQVpamqWlZUpKSnFx8dPmDBh9uzZe/fuHTVq1I4dO/+Lx4gAACAASURBVA4ePHjs2DFdXV0Oh6OqqlpfXx8VFTV9+vQlS5bU19e7uroihAS3NufxeB0c2HtvxYq0I0d+/fXXgTIVnkXCwcFBVI1qBw4cSE5Olq02DCknrreSQqHcv3//0KFDw4YNmz179sSJE7tzJ6etrQ17D8iEzvbFXrp0SWh4/JYtW0xMTARTiETi/v37P3z4UFZWNnDgwKdPn8bFxY0cOVJLS+vRo0cvX7785ptvzMzMPn78ePXqVT8/v+jo6C1btpw8efLs2bN///336tWruVwuQohOpyspKTGZTGdn54SEhNjYWGVl5X379uGnePz48ZEjR7S0tHr06IFbjHbu3HnkyBEikVhRUWFkZMRgMIYMGZKSkhIXFxcXF0cgEHr27Inbpezs7N69ezd79uwFNJoNQjdv3mRaWtbU1FRWVubn579//15ZWZnL5TY1NeElmRsaGvBdYFVVFYVCwRevysrK+EOuqak5bdq0diaRSydjY+MhQ4YwmUzccoOv8ERrx44dCQkJubm5O3fuRAgFBgbOnTtXXV2dx+PhoVsLFizAk6zs7e3r6upCQ0PNzMzodDpeJGfBggUZGRkJCQlDhgwJCwvjcDi493fgwIGtPh1M2+mIxsbG58+fFxcXk8lkU1PT7gR7DQ0NCoUCgyRESIzXSkpKSqtXr164cOHRo0dxC/z48eMPHTrUhaz69+//4cMHkZcQiFxn+2LJZLLQ8iZkMrnV6VVaWlr9+/c/ePDg9u3bb9y44erq2rdv35cvXxIIhNDQUBUVFRKJxGKxDhw4wOPxcOCvr68vKCgICwvDv/4NDQ1nz54tKCjQ19fPz88vLS21srI6cOBAamrqypUrP336tG/fvoqKivr6+tLS0tTUVH9/fxMTk6NHj+7YsePq1asbN25csmQJQqiysjIxMbGoqMjOzi4rK2vw4MF9+/YlEonHjh3L+OknhFBzc/OBAwc0NDTwFJIpU6ZwOBwul6uqqqqlpaWhoYHbe9lstqqqKn83raamJrxfS01NjQjvfb+Mzrbc+Pr6pqWlCaYUFhb+5+Y0JiYm69ev37NnT05OjqOjo5ubm5qamoODQ0ZGBpfLXbp0KW6rZ7PZKSkpBw8exG+jp6cnPl1DQ+Po0aPdf7GALz4+fsGCBdbW1s+ePcMTIysqKqKjo21tbbuWobW1NTTRi5DYG0M0NTUDAwMDAwOZTOa1a9e6lom2tjasVCoTxNcXu2/fPm1tbVVV1evXr+/bty8uLi4rKystLa2srCw4OFhbW/vz58/e3t5nz551dXX9448/li9f/v333xsYGGzbtg3nwOFw1q9fj3vQe/bs2bNnT4TQ9u3b+U+B4zdCiMFgGBkZ3bhxAyH0ww8/WFhYbN68mX+Yvr7+lClT8GN3d3d+ur+//703b9KOHPH29t6mYE21nW25CQsLq66uFkzZuXNnq2PlhPj7+3t5eS1YsGDWrFl2dnYIoQEDBpibm8+ZM2fKlClTp0718fHZvXs3mUzu2pIboFMCAgLu3btnZWWVl5c3c+bMx48f//3334sXL378+HHXMtTQ0BBa4hB0h7gCfMsB8wYGBp1d7aSiouLjx49cLldJSWnq1KmtzsdoqVO7DYrpYNwTmZ2dXd2xMnO53HZm+zCZzA6WUOJE0hfLr/fs7OxWL+wGDx48cODAoqKiqqoqZWXlHTt2NDc3NzQ0aGpqDh06FCHk5eVFoVByc3Nzc3OFzsVv5n9WpeC0+zdv3kC9t6+zLTd6enpCe0fp6urW1ta2X+98W7duRQjhH4QxY8ZQqVQNDY3ExEQSiRQbG4uPafXnAr7votXY2IjbbAwMDIqKihBC9vb2+EGrHj16VFxcLJhSUVGhpKTEr3d3d3c1NbWO/NTD73xHEEQ1lUXkQkND9+7dixCqrq4uLy/v4MgLLpeLLwg6cjCPx+NwOOI42IzL1edy80ikkg504+Gc8Q1lW2xsbPANpSwS3JjgP0G9C5KVev/9998PHTrUsuWG3yjyn6DeBclKvQcGBqanp3t7e9+5c0dXVzciIsLT09PGxqatnYK3bt369u1bwZSXL19+/vxZS0sL6h2Jo955Uu/KlSve3t4dPDghIcHd3b2DB6empg4aNKiDB2dnZ/fu3buDB5eUlNDp9A4e3NDQoKqq2sGDZQ6+LO3CiVDvsiUvL++PP/7YunXr1q1bw8LC8vLyupYP1LsM4XK5UVFR33//fWhoaFNTE5fLTUpK6lpWUO/iABMSgHjR6XQZanIEXWZqaio4xwx1suUGyCICgTB79uzZs2fzU4YOHQr1Lj0gwAMR43K56enp/OlSAwYM6OCiNECeFBcX4yUsJV0Q8EVBvUsVCPBAlMS66QiQIdByo5ig3qUKBHggSmLddARIM2i5UUxQ79JMBgK8kpJSx3t0ZPFgIpEoNwvxinDTESmpHaj3jhBhy42U1A7Ue0dAvUt7vX/hQX1d0NzczGQyO3gwh8NhMBgdPJjL5RYUFHS8JFJysDQ7cuSIjY3Nhg0bDh06dPjw4U2bNvXt2zcsLKwLWUG9y5C+ffu+f/9eMCUvL2/o0KFdyArqXYZAvUt5vUvvPHggo/Lz8/kL3RgaGk6YMEHmFl4FnWVnZ5eSkiK4Ok1TU5Obm9vz588lWCogblDvUk4GmuiBbGk5XQrIPbFuFwukFtS7lIM7eACACEDLjWKCepdmEOABAAAAOdTKvpwAAAAAkHUQ4AEAAAA5BAEeAAAAkEPSHuB///13Ozs7BweHhISE7ue2Z8+emzdvtpVz155r48aNPXv27NWrV1RUlGhzVmRQ74oJ6l0xQb2Lyxeed98peXl5NjY2dXV12dnZ1tbWHA6nO7n99ddfKioqJ0+ebDXnrj3Xw4cPBwwY0NDQUFRUpK2tXVtbK6qcFRnUu2KCeldMUO/iI9V38HFxcd7e3urq6r179zYwMEhPT+9yVtXV1T/88MPMmTPbyrlrz0UikVRUVJSVldXU1JSUlESYsyKDeldMUO+KCepdfKQ6wBcVFZmYmODHJiYm3dmkKCAgYOvWrTQara2cu/Zcrq6uffr0MTIyMjU13bZtG4VCEVXOigzqXTFBvSsmqHfxkeoAz+VyCQQC/082m921fM6fP0+j0UaPHt1Ozl17rvv372dnZz948ODmzZshISEMBkNUOSsyqHfFBPWumKDexUeql6o1MjLKy8vDjxkMhpGRUdfyOXny5IcPH+7evVtUVHThwoXGxsaWOXftuW7evDlnzhwbGxsbG5thw4Y9evRIVDkrMqh3xQT1rpig3sVI0oMA2pObm9u3b9/Gxsb8/PzevXt3f9jCqlWr8OCLljl37bmOHj06YcIEFotVWlpqZmaWlpYmqpwVGdS7YoJ6V0xQ7+Ij1XfwZmZmy5cvd3NzQwgdP36cSBRZh0LLnLv2XN99911aWpqdnR1CaN26dQMGDEAIiSRnRQb1rpig3hUT1Lv4wFr0AAAAgBySiqsMAAAAAIgWBHgAAABADkGABwAAAOQQBHgAAABADkGABwAAAOQQBHgAAABADkGABwAAAOQQBHgAAABADkGABwAAAOQQBHgAAABADkGABwAAAOQQBHgAAABADkGABwAAAOQQBHgAAABADkGABwAAAOQQBHgAAABADkGABwAAAOSQYgV4TU3Nurq66urqY8eOdfZc/lmxsbEBAQFiKB0QF6h3xQT1rpig3v8fT5FoaGjU1tYWFBS4ubm1f2RTU5NQCv8sFov1+fNncRURiAHUu2KCeldMUO98inUHj61fv/7169c7d+5ECG3btq13794DBgw4efIkQuj27dtLly719PS8du3awoULrayszM3N9+zZI3jWvXv3fvzxRx6Pt3btWjs7O3t7+zNnziCE7t275+Pj4+rqamdnt2nTJom+RNAKqHfFBPWumKDeEVLsO/gbN26MHj26rq6uoqKiT58+aWlpt27dMjIyKikpyczM9PPz43K5lZWVhoaGPIEru+vXr69YseLSpUvu7u5sNrukpMTQ0LCoqOju3bsaGhoVFRVNTU3m5uYlJSUSfrXgH1DvignqXTFBvfMpSfoCQ5IeP37MZDJnzJiBEGKxWGlpaUZGRsOHD6fT6XQ6fdOmTSdOnEhLS/v8+XOr5/r4+JBIJDqd7uTklJycrK6u7uHhQaVSEUJWVlbV1dV0Ov1LvyTQAVDvignqXTEpcr0rYhM9H5lMDgwMvHbt2rVr1968eTNnzhyEkKamJkLo/v37vr6+BAJh6dKlPXr0aHkuj8cjEAj4MYlE4nK5CCFdXd0vWHzQRVDvignqXTEpcr0raIDncDgIIQ8Pj4iIiKampqqqqr59+zKZTP4BKSkpM2bM+O6772pqakpKSnC94rMwNze3y5cvc7nc8vLyxMTEIUOGfPlXAToL6l0xQb0rJqh3RQzwenp6tbW1W7ZsGT58uKenp4ODw6BBg4KCgkxNTfnHzJ49OyEhwcnJ6cSJEx4eHps3b+afhQ/w8fHp37+/g4ODu7v7vn37jIyMJPRqQEdBvSsmqHfFBPWOECLweDxJlwEAAAAAIqaId/AAAACA3IMADwAAAMghCPAAAACAHIIADwAAAMghCPAAAACAHIIADwAAAMghCPAAAACAHIIADwAAAMghCPAAAACAHIIADwAAAMghCPAAAACAHIIADwAAAMghCPAAAACAHIIADwAAAMghCPAAAACAHIIADwAAAMghCPAAAACAHIIADwAAAMghCPAAAACAHIIADwAAAMghCPAAAACAHIIADwAAAMghCPAAAACAHIIADwAAAMghCPAAAACAHIIADwAAAMghCPAAAACAHIIADwAAAMghCPAAAACAHIIADwAAAMghCPAAAACAHIIADwAAAMghCPAAAACAHIIADwAAAMghCPAAAACAHIIADwAAAMghCPAAAACAHIIAjxBCbDZ74cKFVCrVxMRk//79gv+Vnp5O+IeRkdHChQtramoQQrGxsTgxKytL8M/09HQrKyvCv8XGxiKEXr9+TSAQDA0NuVxuO4UJDg42MDCwtbW9evWqOF806LR2Pifh4eEEAqG4uFjolJKSkl27dn3BMgLR61S9t3MwkFEyXKc8wOOdOHECIfTbb78tX74cIfTs2TP+f718+RIhtHTp0ujo6PXr1xOJxNWrV/N4vCtXriCECATCyZMneTxeUFAQgUBACL18+TIuLi4mJmby5MkIoVOnTsXExBQWFvJ4vM2bN+NjHjx40FZJbt26hRA6evTosmXLVFRUysvLxf/qQUe18zk5fvw4QojJZPJTqqurr1y5Mnz4cCUlJUkUFohMp+q9nYOBjJLdOpWNAL9v3z5zc3MdHZ3p06eXlpaePHkSIeTt7a2uru7p6bl//359fX1nZ+eMjAwWi7V48WIajebg4BAeHo5P379/v66urpeXl729vZ+fX1VV1cSJEykUiq6u7vr163k83vbt252dnXk8Xk5ODkLoxIkT/KfGAf748eP4zxkzZqirq3M4HBzgbW1tlyxZwuPxPD097ezscIDHR27cuBEhVFlZyc/Kyspq2rRp2tray5cvb+uVBgQEWFhY8Hi8jx8/IoRiYmJE+07KNwl+Tvg/9AcPHlRRUXn06FFCQoKNjY2enh4EeHGTqnpv52AgJhL8ALQ8/cu//HbIQICPi4vD99Dh4eE6OjqzZ8/G9Tdv3rzt27cjhAYMGHD69GlNTc2AgIDg4GAqlfrgwYODBw8SicS//vrr+fPnBAJh0aJFO3bsQAj5+fnt27dPS0vr6tWrgYGBCKGPHz/ynys0NBQh9ObNG36KUID/9ddfEUJ5eXk4wH/33Xf9+/fncrk6OjrfffddOwE+NTUVIXTx4sU5c+b06NGDw+G0+mKnT5/u6OjI4/GqqqoQQqGhoeJ4S+WSZD8n+If+3LlzysrKkZGR/PRt27ZBgBcr6az3Vg8G4iDZD0DL0yXxHrRJBgL8999/r66u3tDQwOPx5s6dS6fTcf19+PChtLQUIXTgwAEej+fk5DR58uTRo0cTiUQ1NTVVVVWE0E8//bRjxw4SiVRTU8PlcvX19XEFJCQkfP/993369BEMyeHh4SoqKtu3bxd8dqEAv2fPHsEAf+zYMRKJlJKSoqKicvjw4XYC/Lp168hkcl1d3aVLl9pppff19cUBHvf0//777yJ/P+WVZD8n+IdeQ0PDwsJC8OoNAry4SWe9t3owEAfJfgBaPV16yMAgOx6Ph4eqIYRIJBJ/hJqqqipOVFNTQwjhx1paWiNHjmSxWA0NDWw2e9OmTSwWi0gkkkgkAoGgoqKCEDp06NDEiRNtbGwWL17Mf5Y1a9b4+/sfO3Zsy5Yt7RTmxYsXZDLZ2NgY/2lhYUGn048cOTJw4EBcjLZeQnR0NIvFolAoPj4+CKHo6OhWj6TT6SUlJQihsrIyhJCBgUGH3ydFJw2fEzc3t5ycnIiICPG/XPA/UljvHfwxASIh2Q9Ay9OligwE+LFjx9bV1QUGBkZGRl6+fHns2LHtHDxmzJikpKQ7d+789ttv6urqmZmZI0aMaG5u3rhx4+bNmwsKChBC6enpGhoalpaWt2/fRgjxeLzr16+HhIRMnz5dW1s7NjY2Jyfn9evXP/zww6dPn3C2aWlpV65c+fHHH6Ojo5csWUIk/u99IxAIzs7OZ86ccXZ2bqdUz549y83N3bhxY0xMTExMzKhRoy5evNjqWPpx48bl5+eHhIRs375dTU1t5MiRXXvTFJA0fE4iIiLGjh37448/1tXVfYGXDJD01XvLg8X/Hig0yX4AWp4uXSTWdtAZu3fvNjMz09bW9vX1LSkpwS0wBQUF+DY3LCyMx+MNGzZs8uTJjY2NS5cupVKpxsbG/KEQGzZsoFKp7u7u1tbWfn5+qampAwYMMDY2XrlyJUJo3759+AFfWFhYTEwMQujBgwe4iR4zNDRcsGBBdXU1759R9Hfv3sWN9ufOncONda020X///fcqKipVVVX4v6KiohBCCQkJrb7YLVu29OjRo2fPnhcuXBDz+ypvJPg54Q+2SkpKQght3boV5wlN9F+AVNV7y4Ml+t4oBAl+AFqeLsk3ogXZCPDd8eTJkxUrVmRkZHz69ElDQwN6xUCr4HOimKDeFVw3PwBS/vlR6szdvkyyt7cvKytzdXUlEokTJ05cs2aNpEv0PxkZGWvXrhVKPHPmDJVKlUh5FJzUfk6AWEG9K7hufgCk/PND4PF4ki4DAAAAAERMBgbZAQAAAKCzIMADAAAAcggCPAAAACCHIMADAAAAcggCPAAAACCHIMADAAAAcggCPAAAACCHIMADAAAAcggCPAAAACCHIMADAAAAcggCPAAAACCHIMADAAAAcggCPAAAACCHIMADAAAAcggCPAAAACCHIMADAAAAcggCPAAAACCHIMADAAAAckhJ0gVoU2Ji4rVr1yRdiq5qbiZwOEhZmUciiSQ/MzMzf39/kWQl5aDeBUG9ywao966Cehck8non8Hg8EWbXZe7u7g8fPhRKlN1POTE2lvD0Kdfbm+fs3P3cqqqqIiMj8/Pzu5+V9Fu/fn1GRsaIESMkXZCugHrvMqh3Pqh3WSED9c6TVkOHDv3qq694PF5OTk6PHj0KCwslXaJOuLt8+R6EXhw+LJLc8vPzTUxMRJKVtBk/fjz135SUlFxdXXn/1DuDwZB0GTsB6r2D/rPe4fsukqykTX19fcW/LV++/Jdffmnr+AMHDpw4ceJLlrBTpL/epbeJns/c3NzY2JjBYBgZGUm6LEDELl261NDQIJgyZswYbW1thJC5ubm+vn5JSYmhoaGESgfEpf16NzY2PnLkyM6dOyVUOiAuPj4+SUlJgik1NTWDBg3asGFDq8cXFBRQKJQvUjT5JAMBHiFEpVIrKyslXQogemQymUwmC6YoKSkRCAT8WENDo66uThLlAuLVfr1TqdRz585BgJc/cXFxQilOTk50Or2t47lcbn19vZgLJc9kYxQ9lUr9/PmzpEsBvjR1dXX4eisg+L4DjMPhCP0C3Lp1Kz4+XlLlkTlivIPncrnp6elMJpPNZhsbGw8YMIBI7OL1BJVKraioEG3xgPTT0NCora2VdCnAl0alUquqqrhcbpd/MYB84HK5Qm14jx8/bmho8PT0lFSRZIu4AnxCQsKyZcsMDQ2NjY0RQgwGIz8/Pzw8vGujJWk0GlzRywr8haRQKAwG4/HjxzY2Nv379+9aVtBEr5hoNBqXy62qqqJSqZIuC5Cklnfwzc3Nz58/l1R5ZI64LpBXrlx548aNhISEqKioqKio+/fv379/f926dV3LjUajwR28TIiMjDQ2NjY3Nz98+PDw4cOvXbs2adKkw4cPt3W8l5cX7d9evHhRVFSE/5dCocAdvAKi0WgIIfjKAw6HI3SJz2azP3z40P5Zvr6+hYWF4iyXzBDXHTyHwxEa/NyjRw9e23PuAwICMjMzBVMyMzP5g25oNNq7d+/EUU4gWtu2bcvMzFRTUzM1Nb1165arq2tZWdnQoUNXrFjR6vGtjqY2MDDAj6GJXjHhAF9eXt6rVy9JlwVIUqsB/j9b9VJTU5lMJm48VnDiCvArVqxwdHT09vY2MTEhEAgMBuPq1att/cojhBYtWlRSUiKUA542gxDS09MrKysTU1GBCHG5XBqNpqSkRKFQ9PT0EEIaGhr8C7WWYBS9Ypo6derLly8FUwoLC/v27YsfDxw4kE6nl5eXS6JoQIqw2WyhJvqWKS2xWCyxDs5tbm4+duyYTCzCJq4A7+/v//XXX8fFxTEYDISQmZnZzZs3TU1N2zrewcFBKEVbW1tZWRk/1tXVhQAvE6ZPn+7k5KSiouLp6enn5zd79uy4uLjx48d3LTcNDY3S0lLRlhCISVVVlZaWFoFASE1Nffv2raOjo62tbVsHHz9+XGhUjY+PT48ePfBjR0fHsWPHwlde/nh4eDx48EAocdCgQW0dz+FwqqqqBFO4XC6bzW5oaFBTU2vrrKamJsEbg+zsbB6P16dPn8+fP4tkVMenT5+2b98uEwFejINUjY2NhwwZ4uTk5OjoOGTIkO40mNDpdPihlwm7d+8+ePDg/v37IyMjf/rpp5ycnMmTJ//2229dy01DQ6Ompka0JQTicPToUScnJzabvWvXrmnTpsXHx48fP/7YsWNtHU+lUi3/TVVVVbClh06nQ4CXCampqbGxsYJh+Pr1620dnJCQILTU2tChQ9tZyYrNZjOZTMEUDoeD/hnJ26r6+vqamhrBrr2oqKhTp04hhJydnXNycjr0qlpz/vx5/CAvLw8XQ/rJxih6CPAyhF/Fo0ePHj16NPrnO9kFmpqa0AcvE3bu3Pnq1StlZeXQ0ND09HQajVZaWuri4rJo0aKuZUin04X67IAU+vnnn8PCwtzc3FatWnXp0qWBAwcihBYtWsQfJ9tNHA6nurr6l19+2bhxI07hcrkIodraWl1dXaGDKyoqlJWVWSyWUD99U1MTbrGvr6+vrq5u+Sy//vpr7969fXx82ilJfX39rFmzZsyYgRBqaGgoLy+/du2aevde3RcgrgCPR9ELjpHJz8/39fUVWqewg6hUam1tbXNzM7/RHsiK4uJiAwODtsZXenl5tVy6kt9fo6mp2eoXEkgbOp3e1NSEENLR0cGT10nd219LX1//PwdLA4k7cuRIenq6rq7uq1evpk6dmpqaqqmpKcL8y8rKeDzemzdv+Ck4wFdWVpqbmwseWVNTM2PGjPHjx8+cORMhJHhj0NTUhBsY2Gw2i8XCiUVFRdnZ2fhuJDY21s3NTSjAP3nyJDU1ddGiRbgvoLa2lsfjcTgcEonU2NiIEPr8+bP0B3hxNdF3dhR9+4hEItzEyyg6nS7UyCbo0qVLH/7NwcGBP4peU1MTmuhlws6dO11cXNauXdunTx93d/f169cPHz48ICCgyxkaGBjAHbz009TUxGvF9+vXb9myZatWrRJt/niKjWC0xs2B/M9GeXk5/nnp27fv3bt3q6ur8YWm4CnV1dU5OTlMJrOysrK5uRkn3rt378iRIwihS5cuJSUlXbhwQeipk5KSVq5cefPmTfwnPvGnn35CCOGnqK+v73JE+2KkZRT9fzIwMGAymbDfjPRruYIhf/BUS+2PotfS0oI7eJkwbty458+fx8bG6ujo2NjY0On0ixcv2tjYdDnDHj16FBcXi7CEQBxmzZrl6uq6ZMmSxYsXr1q1ysfHZ+bMmSIcwY7v1wsLC0tLS/GS9fwmenxAWFhYeXn56NGj8ZPW1tay2Wz+AXl5edra2jk5ObW1te/evWOxWPwAX19fj48sLCzkcDj4Md/GjRvxLw+O5QghfMDt27fXrFkTERGBEFq5cuX+4cNF9UrFRFpG0Z85c0ZoH1wGgyH4029gYCCqfh0gPqIdewF38DJER0dn3rx5gim4PbNrucH3XSZs2bLF1dUVT2gkEAgxMTHR0dEiXH8Q368/f/780KFDwcHBOEVZWZn/s/Dp0ycGg/Hnn3/iI2tqavgPEELBwcGDBw9uamqqra3F6fwFcHCfL/oncldUVGRlZVlbW+P/unLlCr6eKCwsPHHiRGZmpp+fH/6vy5cv48Xwm5ub+eFfaolxLXo8ip5/J9f+KPq6ujqhaTNcLlewAcTQ0BC+8NJPtGMvtLW1hSbJAFnR/tgLX1/ftLQ0wRQGg8GfB48QMjAwKC0theXopR8eSIuRSKRZs2ZNnz69+9mmpKQcOnSIw+Goqqo2NjbyW/K4XK6Ojg4/wBcXF3/+/LmmpgZ/Ts6dO+fu7o4Qqq2tff36dW1tLe5K19XVxa39r1+/RgidP38+KCjIw8MDIYR75evq6u7du2dtbc3lcufOnZuVlfX+/XuE0I0bN7KysoqLi3GPc15eXkZGBr+QHA6nzSU+pIO0jKJvOdo2ISFBcLyGsbExrD4o/UQ79kJbWxua6GVU+2MvQkNDdP8TjAAAIABJREFUhdpmpkyZIrhtqLKyso6OTmZmpp2dnRhLCUSt/Qu7js+Df/v2bVZWFoPBoFKpTCaT/2lhs9k4wD969Khfv35NTU2fP3/mD52rqanJzs7GDyZMmECj0dLT0/F/TZ48GSHU0NBw4MCBt2/fNjQ04D4g/n0jXiw1MzPz8uXL6J/Gg0ePHuEHb9++xdni+348JozBYEj5anmyMYoeIWRsbJycnCyi0gFx6ezYi/ZH0ZPJZDab3dTUpKKiIt5yg27r1NgLOp0utAu4mpqa0M26iYnJkydPIMDLlvYv7BISEoRS2toPvrKysrS0tLi4ODQ0dOnSpfww3NzcTKPRHj9+fPLkyT179qSnp1dWVgqeiAN8ZmYmi8XiL6XAnzVXWVn54MEDHNrz8vJu3LjB3/Lg+vXrBw4c+PTpk2Bu/Cm+eE4Hl8vFFxOOjo63bt0qKCgw/mdYgHSSjVH0CCFTU1OhTnoghfz9/e/cuWNpaVlWVlZaWorHXixZsqSt49sfRY+glV5GJCQk2NnZrVmz5s8//zx//vzatWutra0fPXrUnTxHjRoFX3mZQyQS27mw67impqaPHz+qqqrOmTMHIRQfH497vpOTk6lU6s2bN7OysvLy8pqamvCkNYTQyJEjiUTi2bNn1dXVy8vL8Wx1oWzxKDx8O15WVjZ79mz+gHw8Lk9otB0f/9IEXyssWLDA2NgYh3/BWXzSRmZG0Zubm+fl5YmwhEBMTE1NFy9e3MGD2x9FjxDS0dGprKxs9RofSA+Rt9ghhGxtbR8+fCiK0gHZg8evGRoaUigUNTW1hoaGgoKC6upqJpPJX+JmxYoV/N8KVVXVzZs3379//+effzY1NWUwGM3NzTj2u7q6JiYm4sM+ffqkrq7Ob/Bvbm5msVgEAsHQ0JC/GE6r5eHfneJz9fX1e/fujQoLkWIG+M6Oov9P5ubmubm5PB6vnZ1LgPzBAV7SpQD/QeQtdgghCwsLvMIokFphYWGtXoSdPXu2mznjAK+qqooQ0tbWbmhoSE5OdnBwoFKpEyZM+PPPP/Fh/M/Y7Nmzx4wZgxvYDQwM3r9/z285nzVrFg7wqqqqGRkZysrK/P/CI+EXLFgwbty4+fPnI4QKCgr4ZcDNxgQCQfCT/ODBgyFDhlhaWlpZWZU/fIgQ+uuvvxYsWHDkyBGh5fE5HM7169fLysoYDEZjY6OqqmpqaqqKioqGhkZjYyObzVZVVaXRaDY2Nq6urv369evmO9YqMY6i19LSWrRoEX/ziW6uOUqhUDQ1NZlMZjsLFwP5AwFeJoi8xQ4h1LNnz48fP4qqhEAcvv3226SkpOrqapHvvNLc3Gxubo6XLg0MDFy/fv3ly5ednJzs7e1HjRoldDCJRDIxMUH/bDQ8ceLEly9f4q49IpGop6dHJpNZLNaECRMuX77Mb9InEAjNzc1v374NCwurrKxksVhC29DhxkUymaynp8dvP66srFy/fr2JicmWLVu+P34cIWRmZpaUlDR//vxt27Y9ffoUH6aionLo0CE2m21tbW1qaqqurl5XVzdr1iwikVhTU6OsrKysrNzc3FxUVPTmzRsrKyvRvnt84grwR48e3b9//6tXr/bs2XP8+PHhw4cHBQVt3ry5rbWpP3z4IPQ7XldXJzSf0tLS8v379xDgFQqVShWaPwmkkMhb7BBCpqampaWl7e8bBiSLQqGsXr363LlzgpPluiwkJGTixIlWVlYPHjw4ffq0paUlnkSzbt26TZs2MZnMEydOUKlUvI345MmTr1y5ghB6+PChpqamuro6QsjCwgIhpKqqqqOjgwP84sWLzc3Np0yZcubMGRz+MQ8PDyaTie88lZSURo4cyeFwHj58+OLFC4TQyZMnHz58iEfg0+l0ExMTwQ5i3BWlpPS/6Ll27VraxIl79+51dXU1NDS0tbVVUVGpqalZuXLl9OnTu7lmczeJK8B3dvOJn3/+WWh/6JycHLwIIl+fPn2ys7OHS/3iQaDj2h9FjyDAy45Ojb2YN2+e4HxihFBmZqbQ9rIkEqlnz57v378XnB8PpI2Dg0PLzb675vTp08bGxlZWVm/fvs3Pzx83bhx/arS2tnZ5eXlKSoqXlxeZTFZTU3NxcXn48OGKFSsEp147OjpOmDBBSUlp1apVq1evxgEIIdSrV69Hjx4JBvgJEybcu3cPj7YjkUgkEklPT2/RokW4ib5nz56+vr5//vlnUFDQ8OHDzczMnjx5oqSkhOfI4cDED/AIIXNz80OHDuGFbPH1h5QQV4Dv7OYTx48fF0ppOX3C1tY2MzNT1CUFknTp0iW8AAXfmDFjBEfR02g0/jwWIDeCg4OFdoP18/NrOZTSxsYmMzMTArzcaOeCPicnJyMjo7KyMjY2Fq8SP3DgQP6Ow2FhYStXriwqKurZsydCKCQkpH///urq6nh5O0GbN2/u0aMHnn/Bb/uh0+kaGhpubm7h4eH498TIyKh37974f3Go1tHRwbfpu3btcnFxUVJSmjRp0tu3b7W0tHg8Xs+ePSdMmBATE8NkMnHTfcuIJlWhHRPjHbyLi8ukSZPw5hNjx469ceNGdzafQAjZ2dmFhYWJqoRAGvznKHoajQabjsgfc3Nzod3A1NXVWy5aZ29vn5GR4evr+wWLBsSonQt6CwsLf3//qqqqqqoq3LqDR9hhvr6+ly9fPnPmDJ6Dt3Tp0tLS0gEDBrR8CmdnZ/TPbDfBzp3o6Gg7OztTU1Mc4MlkMr6hV1JS0tDQQAiZm5vj1escHR1xyDcwMAgJCXn16hWXy7106dLSpUtTU1Nra2txNzHuFJBy4poHjzef6Nev38CBA7/66isjI6OLFy+uXLmyO3n269ePvywRUBC6urotJ7MCBQFfeTlDJpOp/yZ4QU+n0wU3fBNa3iokJMTc3NzMzIx/8PXr19t6InNz84CAAMFLhH79+pFIpNmzZ5uamhKJRENDQzyCLzQ0FC+3yl84VWjH2379+vXv3z86OtrW1tbb21tTUxOHfzU1Ncn2r3eEGEfRt9x8opvMzc1ZLFZJSYm+vr4IswXSTFdXV6gtFyiOAQMGbNiwQdKlAF8IlUotLCzkz0nDAZhPX18/Jyeng1mpqqru2LGjZQAODAycMGHC119/PWzYsIcPHwYEBCxcuJB/CkLIysqq1YYBvKKis7Mz7tTHVFRU0D+r5EonWdrIgUAgDB48uDtLZ4Av7OjRo93MQU9PDwK8wrKysqquroZ9YxUEHov9/Plz/KdQgO8sbW3tkJAQoUQikWhvb4/HcikrKwteAeAAb2pqKtRpKKhv375bt27l/9nNEn4BYryDFwcXF5fExMSvv/5a0gUBrbt582ZpaSn/z+DgYPy1aast5z9H0UOAV2QEAmHYsGGJiYk+Pj6SLgsQO3Nz84KCAv58afHtQIFznjVrluCs91WrVhkaGra/1y2NRsPr4WC9evVC/94UUdrIWIB3d3dfv369pEsB2pSRkbF58+aZM2fiASwNDQ149mNbAf4/R9Hr6+sLXjEARePu7p6QkAABXhHQaLTy8nK8zJyNjY2RkZFYn05oSZWBAwcOHDiwUzm4uLikQYDviA5uI+js7JydnQ3d8FJr3bp1Li4uP/zww9y5c0ePHn337t2WrWSCOrIWPYvFamxszMrKotPpsMyRfFiwYIHQ6LnMzEx7e/uWR06YMGHixIm//fYbrFEt93R1dT99+qSpqUkmk0NDQx0dHSVdIpknLQG+g9sIqqiojB8//sqVKx1fUgN8Ya6urtevXw8ICLh+/Tp/VcguIxAIdDr91KlTp06dmjRpEgy5klott4ttOe2NLygoSGj9onnz5rXaOmpvb0+hUBYtWhQaGir9XZ6gfRMnTnzy5IlgSk1NTf/+/fFjGo2Gp8Xa2tpqaWlJooDyRloCfMd98803u3btggAvzbS0tE6dOnXu3DmRLEzk4eGxYcOGysrK58+fz507F27ipVBCQsKyZcsMDQ3xjCMGg5Gfnx8eHi64ypggS0tLoRQKhSK4NJig33//3cPDY+TIkd9++61oiw26r6qqSktLi7/niKOjo9CKhIKio6OFLvrHjBnD316WSCSamZmVlJTcuXMHry0PukmWRtFjXl5eTCaTv6Y/kFozZ86Mi4tDCOFdk7vsq6++qqyspFAoGhoa586d42/1CKQH3i42ISEhKioqKirq/v379+/fX7dunUgyHzFixOXLl3/55Rf+JmBAShw9etTJyYnNZu/atWvatGnx8fHjx4/nLz/Xkrq6ejvz4BFCenp66J9dXkD3yV6AJ5FImzdvXr9+PXzbZUJxcXFbd2YIIS8vL9q/vXjxQmhUnYeHh42NzQ8//DB//vw1a9YsWLDg8ePHgqvfVFRUdHNnUtBN4tguVtCkSZN0dXUPHz4sqgyBSOzcufP58+d4yfeUlJRTp06lpKT8+uuvXc4Qd8tK/wIyskL2mugRQn5+fidPnvzpp5+2bNki6bKA/0Cn05lMZlv/23IU/Y8//ii0C5m+vj7eE4LBYERERNy6devixYvq6uo9evQIDAwkk8nr16+fPHly3759vb29//rrrylTpuzevXv79u1iekWgJXFsFyskPDx82LBhf//9d21tbUhICPTUSIPO7jnynwYPHhwZGQkBXlTEGOA7NeimU4hE4vnz50eMGMFisbZv3y6+6ZKgC1rWO7+PraWWo+hbXZMcMzIyYjKZmzdvZjKZOOofOXIkIyNj4sSJMTExx44dCwwMZLPZhoaGRUVFiYmJk/6vvXuPa7re/wD+Gdtg3K/jMm4SFwehomZoYI2DpGbGOUZewsc5ZmamYD5KzdSUc3rYTT310NRSNDp2OWqZioIeFcRCs6RFGYio6MABm6AOBgy2735/fH9n7YDQGBsb3+/r+UeP8fWz9z7znbz23ffyeeqppUuXlpWVlZaWXr58mc/nX7p06eTJkw4ODnfv3hUIBAKBoKamprW1VSqVikSiAwcO3Llzh8PhZGZmTp8+fc6cOTdu3Lh27dq///3vYcOGeXl5+fj4aLVapVIpFArpE746OzsN6xrX1dWVlZU5EHLlypWagwednJycnJw8PDzobx15PJ67u7uXl1e3s8G7urpaW1sJIVqtlj76oFarQ0JC+r4e195YY7nYbqKjo8+dO/fcc88FBQU9/PDDn3/+eWJiYlVVVXl5uUwm6+zslEgkFy5cmDdvnqenJ77jHRwWX3OEvpICAW8p1gr4/p50019BQUHnz59fuHBhTEzMvHnzJk2aFB8f7+XlZZHiYDZr993JyWnjxo3GW06cOJGSktLc3KxUKq9du3b9+vWGhoann3568eLFL7/8cnl5eX5+vlKp5HA4Pj4+AoEgPDzc1dX19u3bzs7OsbGxJ0+eNK72+OOPV1RU7N+/Pz09vbS0dOzYsQ899NCkSZMIIcHBwc8888yHH37o5OTk6+sbGxvr6el55MgRjUYTGxu7dOnSzZs3j6urG03I0aNHW2UyjUbT1dV19+5denELOsjv3Lnj7Ozc1dWl1WodHR11Oh2fz6fTSCAQ0A/c3d2zs7Off/55i/yNDZqey8XqdDrL/qYePnw4fQ52YWFhZmamUql0d3d3c3OLiopydHR8++23p0yZsm7dOq1W6+TkNGzYsISEhI6ODk9Pz5iYGD6fX15eXl5eXlJSYrxsKAwEvebI4cOHvby8xGKxUCj8+uuvxWKx2QXpnQFcLmEp1gp4+qSbyMhIw5ba2tqMjIzebjT78ssvV1RUGG+pqqrq+7eDn5/fwYMHy8rKvvzyy+XLl1dWVlIUJRQKPTw8eDye4delTcRVVYUSsnXr1tpDhwZSZ968eZmZmZaa1SDob98HbvLkyYSQwMDAwMDAESNGGLYXFhaWlJSsXr169uzZixcvdnFxCQkJKS8v379/f2lpaUxMTEZGxpYtWz744AMPDw9HR8e6urrHH3+cvtPFuXPnnnnmmTfffHPRokVtbW3Hjx8PCQlZt25dXl7ewoULdTrdlStXlErliRMndu/eHR4efv78+aysrAULFjyl0/22a9crr7wyesmS3ibc1tbm5OTE+H2UxsbGwMDA3g7DL126lD7sYlBVVWX6N3xTp069efNmRUWFWCw2nOHR0dEhEAiUSqWjo6Ner79+/fpvv/2m1Wo7Ozurqqp0Ol1AQMD48eODg4P9/f0pimpra3N3d6e/X3F3d9fr9WbniqX+vT/33HPPPvvsQCoMvp5rjgzkg51IJHJ1dUXAW4q1Ar6/J93Mnz+/26lVW7duFYlE169fpyiqurq6j7X5pk+fTt+8tq2t7e7du7dv33ZyctJoNPTBob5pNBrjFYcsNri9XV9bGxcX92BSkonF73vrnsjISHqX1MQiNmeRk62am5tN6Xs3arXa1dXVeIu/v39ubi4hhL6ZeU1NDSFkypQpEyZMUKvVIpFo8+bNhsEhISEqlaqkpIT+cdeuXS4uLvSPAoGgvb19xYoVWq3WeAFKuVxO32xr4sSJhYWFPB6vdssWQkh1dbXqv3X6RlFUt1VTjQ2hvvfU97kXCxYs6LYKsOn/3o2Vlpb27LuBYeUxwz6lWq2eOXMmffNj+rcEfVikq6uLoigHBweNRmP433Xw/71HREQMrX/vPfX9wS4jI6Pbrd9u3bolFouN+75t27YSE/759NH3QRtMH5Cy53/vHCudfrx9+/atW7f2POnmxRdfNLHCjh07Nm3aRAhRqVRNTU19nIltjKIoiqJMHKzX63U6nTUGh1GUP0XJuFyFCbffoitHRET0MUYsFh87dsyUl7Yt9J2dfScDPucGfTc2hPreDUVRSqWyt9NulEplt8tc8/Ly9u7d6+DggL4Ta/RdbzUymezjjz9ev379+vXrP/roI5lMZl6dQ4cOpaenmzi4uLhYIpGYOLisrGzMmDEmDq6uro6KijJxsEKhEAqFJg7u6OhwcnIycbD9Q99NHMykvhcVFQ0fPlwikWRmZmZmZqakpERFRZWUlJhRCn0fWnQ63U8//VRQUHDkyJGysjKdTmdeHfTdGqx4Fn3Pk26ADdB3Fhr8cy/AHlj7pFoYoCF5HTwA2BVr3+gG7BM+2Nk5BDwADNQg3OgG7BA+2Nk5BDwADNQg3OgG7BA+2Nm5IRDwPB7P9Ksqh+JgBwcHXPfZk510B303kaXOvbCT7qDvprDgBzs76Q7D+m6ty+QsSKvVNjU19XG7U2MURTU2Npp4n2q9Xi+Xy+nTQ0xx69YtexjMEug7O6Hv7IS+W8MQCHgAAADor6G3XCwAAAD8IQQ8AAAAAyHgAQAAGAgBDwAAwEAIeAAAAAZCwAMAADCQvQf89u3b4+LiRo4cWVxcPPBqGzduLCgo6K2yea+1atWqiIiIyMjIzz77zLKV2Qx9Zyf0nZ3Qd2sZ5NXr+kUmk4nFYrVaXV1dPXz4cLMXIqR99913jo6OeXl5961s3muVlJQkJCR0dHTU19d7enq2trZaqjKboe/shL6zE/puPXa9B19YWJienu7i4hIVFRUYGFheXm52KZVK9frrr8+ePbu3yua9FpfLdXR05PP5AoGAx+NZsDKboe/shL6zE/puPXYd8PX19SEhIfTjkJCQhoYGs0tlZ2evX7/ex8ent8rmvVZSUlJMTIxIJAoNDc3JyXF1dbVUZTZD39kJfWcn9N167DrgKYricDiGH7VarXl19u3b5+Pjk5qa2kdl816rqKiourr6zJkzBQUF77//vlwut1RlNkPf2Ql9Zyf03XrsejU5kUgkk8nox3K5XCQSmVcnLy/v2rVrp06dqq+vP3DggEaj6VnZvNcqKCiYO3euWCwWi8Xjx48/e/aspSqzGfrOTug7O6HvVmTrkwD6cvPmzfj4eI1GU1tbGxUVNfDTFpYtW0affNGzsnmvtXPnzieeeKK9vV2pVIaFhUmlUktVZjP0nZ3Qd3ZC363Hrvfgw8LClixZkpycTAjJzc11cLDYAYWelc17rfnz50ul0ri4OELIihUrEhISCCEWqcxm6Ds7oe/shL5bD5aLBQAAYCC7+JQBAAAAloWABwAAYCAEPAAAAAMh4AEAABgIAQ8AAMBACHgAAAAGQsADAAAwEAIeAACAgRDwAAAADISABwAAYCAEPAAAAAMh4AEAABgIAQ8AAMBACHgAAAAGQsADAAAwEAIeAACAgRDwAAAADMSugHd3d1er1SqVateuXf19ruFZhw8fzs7OtsLswFrQd3ZC39kJff+dnk3c3NxaW1vr6uqSk5P7HtnZ2dlti+FZ7e3td+7csdYUwQrQd3ZC39kJfTdg1x48beXKlZcuXdqwYQMhJCcnJyoqKiEhIS8vjxBy4sSJRYsWpaWl5efnL1iwIDo6Ojw8fOPGjcbPOn369BtvvKHX65cvXx4XF/fggw9+8cUXhJDTp0/PmDEjKSkpLi5u9erVNn2LcB/oOzuh7+yEvhPC7j34Y8eOpaamqtXq5ubmmJgYqVR6/PhxkUikUCguX748b948iqLu3r0bFBSkN/pkd/To0aysrIMHD0okEq1Wq1AogoKC6uvrT5065ebm1tzc3NnZGR4erlAobPxu4b/Qd3ZC39kJfTfg2foDhi19++23DQ0Ns2bNIoS0t7dLpVKRSDRx4kShUCgUClevXr1nzx6pVHrnzp37PnfGjBlcLlcoFCYmJv74448uLi4pKSne3t6EkOjoaJVKJRQKB/stgQnQd3ZC39mJzX1n41f0Bs7Ozq+++mp+fn5+fn5FRcXcuXMJIe7u7oSQoqKijIwMDoezaNGigICAns/V6/UcDod+zOVyKYoihPj6+g7i9MFM6Ds7oe/sxOa+szTgdTodISQlJeWTTz7p7Oy8d+9efHx8Q0ODYcDFixdnzZo1f/78lpYWhUJB95V+Fi05Ofmbb76hKKqpqam0tHTcuHGD/y6gv9B3dkLf2Ql9Z2PA+/n5tba2rlu3buLEiWlpaSNHjhwzZsyaNWtCQ0MNYzIzM4uLixMTE/fs2ZOSkrJ27VrDs+gBM2bMGDVq1MiRIyUSyebNm0UikY3eDZgKfWcn9J2d0HdCCEev19t6DgAAAGBhbNyDBwAAYDwEPAAAAAMh4AEAABgIAQ8AAMBACHgAAAAGQsADAAAwEAIeAACAgRDwAAAADISABwAAYCAEPAAAAAMh4AEAABgIAQ8AAMBACHgAAAAGQsADAAAwEAIeAACAgRDwAAAADISABwAAYCAEPAAAAAMh4AEAABgIAQ8AAMBACHgAAAAGQsADAAAwEAIeAACAgRDwAAAADISABwAAYCAEPAAAAAMh4AEAABgIAQ8AAMBACHgAAAAGQsADAAAwEAIeAACAgRDwAAAADISABwAAYCAEPAAAAAMh4AEAABgIAQ8AAMBACHgAAAAGQsD3g0KheOutt2w9C7Cw3bt3czicxsbG3gag7wAwFCHgTdLS0nL48OGMjIz169fbei4weNB3ABi6EPC/U6lU06dPd3Nz8/Pze+2114z/qKysbNWqVZWVlbaaG1jbmjVrvL29H3300Zs3bxo2ou8AMHQh4H+Xm5t79uzZL7/8ct68ee+9915NTY3hjyQSSWVlZVZWlg2nB1ZVXV397rvvXr58edGiRYaN6DsADF08W0/Ajrzyyitjxow5dOhQYWEhIUSlUtl6RjB43nnnnQkTJlRUVOzYsUOn03G5XFvPCABgQLAH/7utW7c++eSTYrF44cKFtp4LDDa9Xk8IQa4DAGMg4H9XXl7u5ub2wAMPnDhxghCi1+svXbr0+uuvG39XD0y1du3aPXv2fP7555MmTaqsrETfAWCoQ8D/bvHixUFBQfPnz4+NjSWEFBUVVVVVvfPOOzKZzNZTA6tzcnJatmxZTEzMjh070HcAYAAO/c0kAAAAMAn24AEAABgIAQ8AAMBACHgAAAAGQsADAAAwEAIeAACAgRDwAAAADISABwAAYCAEPAAAAAMh4AEAABgIAQ8AAMBACHgAAAAGQsADAAAwEAIeAACAgRDwAAAADISABwAAYCAEPAAAAAMh4AEAABgIAQ8AAMBACHgAAAAGQsADAAAwEM/WE+hVaWlpfn6+rWdhrq4ujk5H+Hw9l2uRemFhYYsXL7ZIKTuHvhtjT98BwOI4er3e1nO4v5UrV/7222+PPvqorSdiDofDhznnz1Pp6foJEwZe7d69e3v37q2trR14KfuHvhuwqu8AYHH2uwdPCJFIJCtWrOjvsyiKampqOnv2bGpqql6v37dv36OPPsrlcocPH65Sqerq6qKjo9va2jw9PQkhSqVSKBS2trZSFOXh4fGHxS9dunTnzh1vb+/4+PiysrIPPvjg008/raysfPDBBw1j1Gp1bmFhFyFpaWmjlyzp7/x7qqur27t378Dr2KGpU6deuHDBeEtLS0tiYuJrr7128+bNxMTEn376SSQS2Wp6/XW6tlZ6/jz6DgD2wK4D/g/duHHjs88+0+l0MplMJBKNGjVqy5YtV65caWxsJIRkZGRcvHixtraWz+d3dnaGhobeunXLyckpLCysoaFh9OjRFEWdPXt2+vTp33//vb+//9ixY0eNGnX+/PkrV674+PhER0ffvn3b3d1do9H4+/t///33dXV1TU1Ner2ez+c/++yz9fX1+fn53333nVKpnDZtWlhY2NixY//5z38KhULnb78dT8jt27fPnj3b1tbW0NDQ2dlJCNHr9R0dHR0dHfT8PTw8uPf7Ltfd3Z3L5XI4nDFjxkRGRg7mX+kgO3jwoOFvgzZp0iT6s1d4eHhwcLBcLh9CAQ8AYD+GcMAXFhYuXbr06tWrxhtdXV27uroEAsEDDzzw1VdfcbncqKiopqamlpaW2tpaZ2fntra2qqqq8PDwoqKiyMjIgICAw4cP8/n8pqamqqqqvLw8Ho+n1WoJIb/88ktzczOXy+XxeO3t7YQQgUAwderUc+fONTY27ty509nZ+ZFHHqntmXO7AAASLUlEQVSrq+vq6jpy5AgdVEKhUC6XP+LgQChq06ZNmuJigUAQGBjo5ORECOFwOM7OzvRjQsiNGzfoDx/dDpS0trbSW5ydnZkd8M7Ozs7OzsZbeDweh8OhH3t5ed27d88W8wIAGPKGasD//e9/z8nJ4XA4//jHPzIzM3ft2qVQKIqLi1euXBkdHR0ZGTls2DCZTObq6urr61tRUUF/b//YY499/PHHsbGxSUlJMpksIiKipqbm6tWrEonk6tWrvr6+K1aseOedd06fPr1p06aysrJly5ZFRkZOmDBh0aJFM2fOTElJGT9+fFdX17Vr1wICAjo6OgIDAzs7O5ubm7u6uq5fv15TUzNt2jR/f//TWVnSbdteeeWVyX//u63/qoYwb2/vO3fu2HoWAABDkr0EfEFBQV1dnfGWc+fOJSQkEELa2toOHDiQmZnJ4/3/bOvr67/44gs3N7fc3NxZs2YRQt5++21CiFwuDwwMdHD4/2v/wsLC6AdxcXGGsi+++CL9ICIigv4v/SA2NpYQ8umnnxJC5s6dm5GRweVyt27dSg+WSqWGCnw+XywWG350cnIKCgqiX04ikRi/BX9//4H8nQACHgDAbPYS8L/++uv169eNt1RXV9Nf1ba2ti5fvnzcuHGGnP7++++vXLnyn//8Jy0tzfgpFjxYKxAILFUKzObl5XX37l1bzwIAYEiyl4B/7bXXum35+eef6ZOt/P39o6KijI/Fbt++3c3NrdvuMjCPt7d3c3OzrWcBADAkWfFOdmq1Wq1WE0Lkcvm+ffvKy8vNLuXh4aFSqQw/ajSaZ599ls/nW2CWYMd8fHwQ8AAA5rHWHvzevXuzs7N5PF5OTs77778/YcKElStXrlixIisry4xqnp6exnvwWq3Wy8vLcpMFm1m7du2VK1eMt1y9etVw6aCvr29TU5Mt5gUAMORZK+BzcnIuX74sEAhCQ0OPHz+elJR0+/bthx9+2LyA73Ystqam5r333rPcZMFmpkyZMmrUKOMtUqnUxcWFfoyABwAwm7UCnqIoHx8fHo/n6urq5+dHCHFzczNc39xfxnvwSqWyoaHB29vbYnMFi7p3756HhweHwykrK6usrBw7dix9hcJ9JScnd9uyadMmwxmOfn5+t2/ftuJcAQCYy1rH4GfOnJmYmDhhwoS0tLR58+Z9+OGHTz/99JQpU8yrZhzwUql02LBhxle+gf3YuXNnYmKiVqt96623nnnmmZMnT06ZMmXXrl3mVRMKhQh4AADzWGsP/t133502bRqXy01KSjp9+nRhYeGf//zn+fPnm1fNy8vr8uXL9OO2trbY2FizvwwAq9qwYcOvv/7K5/N37NhRXl7u4+OjVCofeeSRF154wYxqfn5+9L2B0W4AgP6y4mVyhgXBUlNTU1NTCSE6na63weXl5Uql0njLvXv36MvkyP9eLtXe3m7YDvZGKBTSd9338vKi7zh035vtm4jP57u5uTU3N/v6+lpsigAA7DB418E3NjYGBgb2tjrt7t27KysrjbfU19cbTpX38fEx3NGsubkZd6GxWxs2bHjkkUeeeuqpmJgYiUTy+OOPHzt2LDs72+yCAQEBCoUCAQ8A0F+DF/BCobChoaG3P92yZUu3LYmJifTZeeR/b1na1NSEgLdbkydP/uGHHw4fPuzl5SUWi4VC4ddff218Z9/+CgwMbGxs7OM0PQAAuC8rBjxFUeXl5Q0NDVqtNjg4OCEhISAgwLxSPj4+hsul9Hq92XVgEHh5ef3tb38z3qLT6Xr7or6wsLC2ttZ4i1KpdHd3N/wYGBjYx+dCAADojbUCvri4+KWXXgoKCgoODiaEyOXy2tra3bt3Gw7M94vx9dA45Wpo6fvQzC+//NJtDQK1Wk0vzksLCgqSy+XWnSIAABNZK+CXLl167Ngx47XMa2trMzIyLly4YEY1b2/vlpYWekeQoijcpHYI6fvQzH3XIDC+yUFwcDACHgDADNYKeJ1ORy+iahAQENDbbtwfcnBw8PLyam5uFgqF2IO3cxY8NEMICQ4O/vHHHy04PQAAlrBWwGdlZY0dOzY9PT0kJITD4cjl8iNHjph3n1qan5+fUqkUCoUURRlWfAd7Y9lDM4SQ0NDQbgfpAQDAFNYK+MWLF0+fPr2wsJD+fjUsLKygoCA0NNTsgv7+/gqFIi4uDnvw9syyh2YIIaGhoTKZzEKzAwBgESueRR8aGrpw4UITByuVypaWFuMtHR0dFEUZfhQKhfSdcBDw9syyh2YIIcHBwUqlsrOz09HRccCzAwBgkcG7Dr5vS5YsKSsrM94il8uNf6cHBAQ0NjYSQvAVvT2z+KEZLpcbEhJy8+bN6OhoC84TAIDx7CXg9+/f321LYmKiUCg0/BgUFEQHPPbg7Vl/D800NzcbLwRMCNFoNN32+CMjI69evYqABwDoF3sJ+D8UGBhIH8fFHryd69ehmRdeeOHnn3823nLr1i0e73/+t4yOjq6urp46darFpggAwAJDJuBFIhG9U4g9eCb5+uuvu23p9s0NIUQsFldUVAzipAAAmGDI7AqLRKJbt24RBDz7xMXFIeABAPpryAR8SEhIXV0dIUSpVOIrelYZMWLEL7/8YutZAAAMMUMmKX19fdvb29va2pRKpUgksvV0YPAIhUIXF5cbN27YeiIAAEPJkAl4DodD3/OEoijDOvHAEg899BBuWAsA0C9DJuAJIcOGDaupqelj7VFgqgkTJpSWltp6FgAAQ4m9BPyf/vQnzv/64Ycf6uvrjcdERERcv34dl8mx0GOPPXbmzBlbzwIAYCixl8vkioqKum3peblUVFTU1atXEfBMMmvWrIsXLxpvuXXr1ogRI7oNe+ihh2pra+VyOU6/AAAwkRUDvqysrK6uTiKReHp60luOHj365JNPml0wOjq6uLgYX9HbuZ7LxfbxgWzr1q2tra3GW/7yl790+2BHCOHxeNOmTTt06NDixYutMmkAAMaxVsC//fbbH330UXJy8rJlyw4ePDh69GhCyAsvvNDtW/d+iY2NvXz5sr+/P/bg7VZ/l4v19/f39/c33iIQCO7b39mzZ7/55psIeAAAE1kr4Ldt21ZeXu7r6/vrr78+/fTTZWVl7u7uA6z5wAMP1NfXOzs7I+DtlsWXizWYPHnykiVLLly4kJiYOMBSAABsYK2kdHd3d3V1JYSMGDHipZdeWrZs2cBrcrlceieez+cPvBpYg8WXizXgcrmrVq1avXr1wEsBALCBtQJ+zpw5SUlJO3fuJIQsW7asubl59uzZbW1tAyw7evRorVaLpcHtFr1c7KpVqz788MNt27atWbNm7Nixzz//vEWKP//88yqVasuWLRapBgDAbNb6in7dunVJSUlNTU2EEA6H89VXX+3fv9/b23uAZceNG5ebm4uAt1v9XS62X3g83v79+ydOnMjlcpcsWWKRmgAATGXFs+hTU1MNj7lc7pw5c2bOnDnAmsnJya6urj3Psgb70XO5WAte+BAREVFSUjJnzpwtW7bQZ3VotVq9Xt/zqI2LiwshpL29ncvlarVaeiOXy9XpdPRjBwcHiqIIIRwOhz6IQJ/b0W2jgfFzXV1dOzo6OByOoTJtrEwWTch7771X/cknZr9HDoeTnZ3917/+1ewKAABkMK+Db2xsDAwM7O1w7LRp086fP2+8paWlJT4+nr6zTXV1Nf37mhCyffv2vpceUavV9OF/U1hpML3/Wl1drSopMWU8RVHh4eG9/WlDQ4OJM7RDffd9+/btMpnMeEttba1er+/Zd2PvvvsufU9Del1BPp+v0Wg6OjoEAoFhjFar5XK5dAZ3W2CeENJtcN/owcbLGGo0Gicnp54LG3Z8+WXnmTNJSUkpEomJxQMDA3tuDAkJuX79+pDuOwDYXPfdFOuhKEqpVAYEBNz3T9va2jQajfGW3NzcHTt2cDgclUrV1NTU83d0b69CUZSJg/V6vU6ns8bgMIrypygZl6swYWVbunJEREQfY8Ri8bFjx0x5aXvTd9+/+uqra9euGW85efJkZWWlQCBA38lQ7jsA2J7eanQ63U8//VRQUHDkyJGysjKdTmdenUOHDqWnp5s4uLi4WCKRmDi4rKxszJgxJg6urq6OiooycbBCoRAKhSYO7ujooHcHmQF9N3Eww/oOAPbGWl/R9/eGJ8AM6DsAgJ2wVsBb74YnYM/QdwAAO2Gt6+Ctd8MTsGfoOwCAnbDWHjx9w5P09PSQkBAOhyOXy48cOZKVlWWllwM7gb4DANgJawW8BW94wuPxTL+KeigOdnBwYMzNd9F3dvYdAOzQ4F0mZzatVtvU1NTbdVbdUBTV2NjY7Vvi3uj1erlcTp8OZopbt27Zw2CWQN8BAAZiCAQ8AAAA9BfWXQUAAGAgBDwAAAADIeABAAAYCAEPAADAQAh4AAAABkLAAwAAMJC9B/z27dvj4uJGjhxZXFw88GobN24sKCjorbJ5r7Vq1aqIiIjIyMjPPvvMspXZDH0HABgoWy5l90dkMplYLFar1dXV1cOHDzd74VHad9995+jomJeXd9/K5r1WSUlJQkJCR0dHfX29p6dna2urpSqzGfoOADBwdr0HX1hYmJ6e7uLiEhUVFRgYWF5ebnYplUr1+uuvz549u7fK5r0Wl8t1dHTk8/kCgYDH41mwMpuh7wAAA2fXAV9fXx8SEkI/DgkJaWhoMLtUdnb2+vXrfXx8eqts3mslJSXFxMSIRKLQ0NCcnBxXV1dLVWYz9B0AYODsOuApiuJwOIYftVqteXX27dvn4+OTmpraR2XzXquoqKi6uvrMmTMFBQXvv/++XC63VGU2Q98BAAbOWqvJWYRIJJLJZPRjuVwuEonMq5OXl3ft2rVTp07V19cfOHBAo9H0rGzeaxUUFMydO1csFovF4vHjx589e9ZSldkMfQcAsABbnwTQl5s3b8bHx2s0mtra2qioqIGfprRs2TL6ZKuelc17rZ07dz7xxBPt7e1KpTIsLEwqlVqqMpuh7wAAA2fXe/BhYWFLlixJTk4mhOTm5jo4WOyAQs/K5r3W/PnzpVJpXFwcIWTFihUJCQmEEItUZjP0HQBg4LBcLAAAAANhrwIAAICBEPAAAAAMhIAHAABgIAQ8AAAAAyHgAQAAGAgBDwAAwEAIeAAAAAZCwAMAADAQAh4AAICBEPAAAAAMhIAHAABgIAQ8AAAAAyHgAQAAGAgBDwAAwEAIeAAAAAZCwAMAADAQAh4AAICB2BXw7u7uarVapVLt2rWrv881POvw4cPZ2dlWmB1YC/oOACzEroCntbS0/Otf/+p7TFdXV2/Pmjx58ptvvmmtyYHVoO8AwCpsDPiVK1deunRpw4YNhJCcnJyoqKiEhIS8vDxCyIkTJxYtWpSWlpafn79gwYLo6Ojw8PCNGzcaP+v06dNvvPGGXq9fvnx5XFzcgw8++MUXXxBCTp8+PWPGjKSkpLi4uNWrV9v0LcJ9oO8AwC56NnFzc2ttba2rq0tOTtbr9ceOHUtNTVWr1c3NzTExMVKp9Pjx4yKRSKFQXL58ed68eRRF3b17NygoSK/XG5519OjRrKysgwcPSiQSrVarUCiCgoLq6+tPnTrl5ubW3Nzc2dkZHh6uUChs/G7hv9B3AGAhnq0/YNjSt99+29DQMGvWLEJIe3u7VCoViUQTJ04UCoVCoXD16tV79uyRSqV37ty573NnzJjB5XKFQmFiYuKPP/7o4uKSkpLi7e1NCImOjlapVEKhcLDfEpgAfQcANmDjV/QGzs7Or776an5+fn5+fkVFxdy5cwkh7u7uhJCioqKMjAwOh7No0aKAgICez9Xr9RwOh37M5XIpiiKE+Pr6DuL0wUzoOwCwAUsDXqfTEUJSUlI++eSTzs7Oe/fuxcfHNzQ0GAZcvHhx1qxZ8+fPb2lpUSgU9O9x+lm05OTkb775hqKopqam0tLScePGDf67gP5C3wGAPdgY8H5+fq2trevWrZs4cWJaWtrIkSPHjBmzZs2a0NBQw5jMzMzi4uLExMQ9e/akpKSsXbvW8Cx6wIwZM0aNGjVy5EiJRLJ582aRSGSjdwOmQt8BgFU4er3e1nMAAAAAC2PjHjwAAADjIeABAAAYCAEPAADAQAh4AAAABkLAAwAAMBACHgAAgIEQ8AAAAAyEgAcAAGAgBDwAAAADIeABAAAYCAEPAADAQAh4AAAABkLAAwAAMBACHgAAgIEQ8AAAAAz0f+taS7MGKGG5AAAAAElFTkSuQmCC" /><!-- --></p>
 <pre class="r"><code>print(f_parent_saemix_dfop_tc)</code></pre>
 <pre><code>Kinetic nonlinear mixed-effects model fit by SAEM
 Structural model:
@@ -1897,17 +1891,17 @@ Likelihood computed by importance sampling
   666 664   -323
 
 Fitted parameters:
-          estimate    lower    upper
-DMTA_0    98.27617  96.3088 100.2436
-k1         0.06437   0.0337   0.0950
-k2         0.00880   0.0063   0.0113
-g          0.95249   0.9100   0.9949
-a.1        1.06161   0.8625   1.2607
-b.1        0.02967   0.0226   0.0367
-SD.DMTA_0  2.06075   0.4187   3.7028
-SD.k1      0.59357   0.2561   0.9310
-SD.k2      0.00292 -10.2960  10.3019
-SD.g       1.05725   0.3808   1.7337</code></pre>
+          estimate     lower    upper
+DMTA_0    98.24165  96.29190 100.1914
+k1         0.06421   0.03352   0.0949
+k2         0.00866   0.00617   0.0111
+g          0.95340   0.91218   0.9946
+a.1        1.06463   0.87979   1.2495
+b.1        0.02964   0.02266   0.0366
+SD.DMTA_0  2.03611   0.40361   3.6686
+SD.k1      0.59534   0.25692   0.9338
+SD.k2      0.00042 -73.00540  73.0062
+SD.g       1.04234   0.37189   1.7128</code></pre>
 <p>Doubling the number of iterations in the first phase of the algorithm
 leads to a slightly lower likelihood, and therefore to slightly higher
 AIC and BIC values. With even more iterations, the algorithm stops with
@@ -1937,8 +1931,8 @@ print(AIC_parent_saemix)</code></pre>
 SFO const               796.38 795.34
 SFO tc                  798.38 797.13
 DFOP const              705.75 703.88
-DFOP tc                 665.65 663.57
-DFOP tc more iterations 665.88 663.80</code></pre>
+DFOP tc                 665.67 663.59
+DFOP tc more iterations 665.85 663.76</code></pre>
 <p>In order to check the influence of the likelihood calculation
 algorithms implemented in saemix, the likelihood from Gaussian
 quadrature is added to the best fit, and the AIC values obtained from
@@ -1952,7 +1946,7 @@ AIC_parent_saemix_methods &lt;- c(
 )
 print(AIC_parent_saemix_methods)</code></pre>
 <pre><code>    is     gq    lin 
-665.65 665.68 665.11 </code></pre>
+665.67 665.74 665.13 </code></pre>
 <p>The AIC values based on importance sampling and Gaussian quadrature
 are very similar. Using linearisation is known to be less accurate, but
 still gives a similar value.</p>
@@ -1976,7 +1970,7 @@ AIC_parent_saemix_methods_defaults &lt;- c(
 )
 print(AIC_parent_saemix_methods_defaults)</code></pre>
 <pre><code>    is     gq    lin 
-669.77 669.36 670.95 </code></pre>
+670.09 669.37 671.29 </code></pre>
 </div>
 </div>
 <div id="comparison" class="section level2">
@@ -2024,15 +2018,15 @@ kable(AIC_all)</code></pre>
 <td align="left">DFOP</td>
 <td align="left">const</td>
 <td align="right">NA</td>
-<td align="right">709.26</td>
+<td align="right">704.95</td>
 <td align="right">705.75</td>
 </tr>
 <tr class="even">
 <td align="left">DFOP</td>
 <td align="left">tc</td>
 <td align="right">671.91</td>
-<td align="right">665.11</td>
-<td align="right">665.65</td>
+<td align="right">665.13</td>
+<td align="right">665.67</td>
 </tr>
 </tbody>
 </table>
@@ -2058,13 +2052,13 @@ satisfactory precision.</p>
 <div id="session-info" class="section level1">
 <h1>Session Info</h1>
 <pre class="r"><code>sessionInfo()</code></pre>
-<pre><code>R version 4.2.2 Patched (2022-11-10 r83330)
-Platform: x86_64-pc-linux-gnu (64-bit)
-Running under: Debian GNU/Linux bookworm/sid
+<pre><code>R version 4.4.2 (2024-10-31)
+Platform: x86_64-pc-linux-gnu
+Running under: Debian GNU/Linux 12 (bookworm)
 
 Matrix products: default
-BLAS:   /usr/lib/x86_64-linux-gnu/openblas-serial/libblas.so.3
-LAPACK: /usr/lib/x86_64-linux-gnu/openblas-serial/libopenblas-r0.3.21.so
+BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.11.0 
+LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.11.0
 
 locale:
  [1] LC_CTYPE=de_DE.UTF-8       LC_NUMERIC=C              
@@ -2074,25 +2068,28 @@ locale:
  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
 [11] LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=C       
 
+time zone: Europe/Berlin
+tzcode source: system (glibc)
+
 attached base packages:
 [1] stats     graphics  grDevices utils     datasets  methods   base     
 
 other attached packages:
-[1] saemix_3.2   npde_3.3     nlme_3.1-161 mkin_1.2.2   knitr_1.41  
+[1] saemix_3.3      npde_3.5        nlme_3.1-166    mkin_1.2.9     
+[5] knitr_1.49      rmarkdown_2.29  nvimcom_0.9-167
 
 loaded via a namespace (and not attached):
- [1] highr_0.9        bslib_0.4.2      compiler_4.2.2   pillar_1.8.1    
- [5] jquerylib_0.1.4  tools_4.2.2      mclust_6.0.0     digest_0.6.31   
- [9] jsonlite_1.8.4   evaluate_0.19    lifecycle_1.0.3  tibble_3.1.8    
-[13] gtable_0.3.1     lattice_0.20-45  pkgconfig_2.0.3  rlang_1.0.6     
-[17] DBI_1.1.3        cli_3.5.0        yaml_2.3.6       parallel_4.2.2  
-[21] xfun_0.35        fastmap_1.1.0    gridExtra_2.3    dplyr_1.0.10    
-[25] stringr_1.5.0    generics_0.1.3   vctrs_0.5.1      sass_0.4.4      
-[29] tidyselect_1.2.0 lmtest_0.9-40    grid_4.2.2       deSolve_1.34    
-[33] glue_1.6.2       R6_2.5.1         fansi_1.0.3      rmarkdown_2.19  
-[37] ggplot2_3.4.0    magrittr_2.0.3   codetools_0.2-18 scales_1.2.1    
-[41] htmltools_0.5.4  assertthat_0.2.1 colorspace_2.0-3 utf8_1.2.2      
-[45] stringi_1.7.8    munsell_0.5.0    cachem_1.0.6     zoo_1.8-11      </code></pre>
+ [1] gtable_0.3.6      jsonlite_1.8.9    dplyr_1.1.4       compiler_4.4.2   
+ [5] tidyselect_1.2.1  colorout_1.3-2    parallel_4.4.2    gridExtra_2.3    
+ [9] jquerylib_0.1.4   scales_1.3.0      yaml_2.3.10       fastmap_1.2.0    
+[13] lattice_0.22-6    ggplot2_3.5.1     R6_2.5.1          generics_0.1.3   
+[17] lmtest_0.9-40     MASS_7.3-61       tibble_3.2.1      munsell_0.5.1    
+[21] bslib_0.8.0       pillar_1.9.0      rlang_1.1.4       utf8_1.2.4       
+[25] cachem_1.1.0      xfun_0.49         sass_0.4.9        cli_3.6.3        
+[29] magrittr_2.0.3    digest_0.6.37     grid_4.4.2        mclust_6.1.1     
+[33] lifecycle_1.0.4   vctrs_0.6.5       evaluate_1.0.1    glue_1.8.0       
+[37] codetools_0.2-20  zoo_1.8-12        fansi_1.0.6       colorspace_2.1-1 
+[41] tools_4.4.2       pkgconfig_2.0.3   htmltools_0.5.8.1</code></pre>
 </div>
 <div id="references" class="section level1">
 <h1>References</h1>
diff --git a/vignettes/web_only/dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_const-1.png b/vignettes/web_only/dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_const-1.png
index 505072ce..627e5c95 100644
--- a/vignettes/web_only/dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_const-1.png
+++ b/vignettes/web_only/dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_const-1.png
diff --git a/vignettes/web_only/dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_const_test-1.png b/vignettes/web_only/dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_const_test-1.png
index 505072ce..627e5c95 100644
--- a/vignettes/web_only/dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_const_test-1.png
+++ b/vignettes/web_only/dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_const_test-1.png
diff --git a/vignettes/web_only/dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_tc_test-1.png b/vignettes/web_only/dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_tc_test-1.png
index 0dd4da39..9f40fc35 100644
--- a/vignettes/web_only/dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_tc_test-1.png
+++ b/vignettes/web_only/dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_tc_test-1.png
diff --git a/vignettes/web_only/dimethenamid_2018_files/figure-html/f_parent_saemix_dfop_tc-1.png b/vignettes/web_only/dimethenamid_2018_files/figure-html/f_parent_saemix_dfop_tc-1.png
index d154dc9b..fa5d34f0 100644
--- a/vignettes/web_only/dimethenamid_2018_files/figure-html/f_parent_saemix_dfop_tc-1.png
+++ b/vignettes/web_only/dimethenamid_2018_files/figure-html/f_parent_saemix_dfop_tc-1.png
diff --git a/vignettes/web_only/dimethenamid_2018_files/figure-html/plot_parent_nlme-1.png b/vignettes/web_only/dimethenamid_2018_files/figure-html/plot_parent_nlme-1.png
index a799b14c..6bcf3434 100644
--- a/vignettes/web_only/dimethenamid_2018_files/figure-html/plot_parent_nlme-1.png
+++ b/vignettes/web_only/dimethenamid_2018_files/figure-html/plot_parent_nlme-1.png
diff --git a/vignettes/web_only/mkin_benchmarks.rda b/vignettes/web_only/mkin_benchmarks.rda
index 662a5ddb..c8d33f1f 100644
--- a/vignettes/web_only/mkin_benchmarks.rda
+++ b/vignettes/web_only/mkin_benchmarks.rda
diff --git a/vignettes/web_only/saem_benchmarks.rda b/vignettes/web_only/saem_benchmarks.rda
index a319c941..c66d165d 100644
--- a/vignettes/web_only/saem_benchmarks.rda
+++ b/vignettes/web_only/saem_benchmarks.rda